Pymc3 works with GW likelihood (although not with the NUTS sampler as no grad...

Pymc3 works with GW likelihood (although not with the NUTS sampler as no grad function is defined in the theano Op yet)

examples/open_data_examples/GW150914_minimal_test_pymc3.py

@@ -18,7 +18,8 @@ prior['tilt_1'] = Sine(name='tilt_1')
 prior['tilt_2'] = Sine(name='tilt_2')
 prior['phi_12'] =  Uniform(name='phi_12', minimum=0, maximum=2 * np.pi)
 prior['phi_jl'] =  Uniform(name='phi_jl', minimum=0, maximum=2 * np.pi)
-prior['luminosity_distance'] = DeltaFunction(1e3, name='luminosity_distance')
+#prior['luminosity_distance'] = DeltaFunction(1e3, name='luminosity_distance')
+prior['luminosity_distance'] = tupak.gw.prior.UniformComovingVolume(name='luminosity_distance', minimum=1e2, maximum=1e3)
 prior['dec'] = Cosine(name='dec')
 prior['ra'] = Uniform(name='ra', minimum=0., maximum=2.*np.pi)
 prior['iota'] = Sine(name='iota')

tupak/core/sampler.py

@@ -1070,6 +1070,9 @@ class Pymc3(Sampler):
         prior_map['Lorentzian'] = prior_map['Cauchy']
         prior_map['FromFile'] = prior_map['Interped']
 
+        # GW specific priors
+        prior_map['UniformComovingVolume'] = prior_map['Interped']
+
         # internally defined mappings for tupak priors
         prior_map['DeltaFunction'] = {'internal': self._deltafunction_prior}
         prior_map['Sine'] =          {'internal': self._sine_prior}
@@ -1291,7 +1294,7 @@ class Pymc3(Sampler):
 
         self.setup_prior_mapping()
 
-        self.pymc3_priors = dict()
+        self.pymc3_priors = OrderedDict()
 
         pymc3 = self.external_sampler
 
@@ -1370,17 +1373,23 @@ class Pymc3(Sampler):
             itypes = [tt.dvector]
             otypes = [tt.dscalar]
 
-            def __init__(self, parameters, loglike):
+            def __init__(self, parameters, loglike, priors):
                 self.parameters = parameters
                 self.Nparams = len(parameters)
                 self.likelihood = loglike
+                self.priors = priors
+
+                # get indexes of fixed variables from priors
+                self.fixedidx = []
+                for i, key in enumerate(self.priors.keys()):
+                    if isinstance(self.priors[key], float):
+                        self.fixedidx.append(i)
 
             def perform(self, node, inputs, outputs):
                 theta, = inputs
-                print(theta)
                 for i, k in enumerate(self.parameters):
                     self.likelihood.parameters[k] = theta[i]
-                    
+
                 outputs[0][0] = np.array(self.likelihood.log_likelihood())
 
             #def grad(self, inputs, g):
@@ -1476,8 +1485,8 @@ class Pymc3(Sampler):
                 # set the distribution
                 pymc3.StudentT('likelihood', nu=self.likelihood.nu, mu=model, sd=self.likelihood.sigma, observed=self.likelihood.y)
             elif isinstance(self.likelihood, (GravitationalWaveTransient, BasicGravitationalWaveTransient)):
-                logl = LogLike(self.__search_parameter_keys, self.likelihood)
-                
+                logl = LogLike(self.pymc3_priors.keys(), self.likelihood, self.pymc3_priors)
+
                 # cast prior distributions into float64 values
                 parameters = OrderedDict()
                 for key in self.pymc3_priors: