make marginalised likelihood (with no marginalisation) default

tupak/likelihood.py

@@ -4,52 +4,20 @@ try:
     from scipy.special import logsumexp
 except ImportError:
     from scipy.misc import logsumexp
-from scipy.special import i0, i0e
+from scipy.special import i0e
 from scipy.interpolate import interp1d
 import tupak
 import logging
 
 
 class Likelihood(object):
-    def __init__(self, interferometers, waveform_generator):
+    def __init__(self, interferometers, waveform_generator, distance_marginalization=False, phase_marginalization=False,
+                 prior=None):
+        # Likelihood.__init__(self, interferometers, waveform_generator)
         self.interferometers = interferometers
         self.waveform_generator = waveform_generator
-
-    def noise_log_likelihood(self):
-        log_l = 0
-        for interferometer in self.interferometers:
-            log_l -= 2. / self.waveform_generator.time_duration * np.sum(
-                abs(interferometer.data) ** 2 / interferometer.power_spectral_density_array)
-        return log_l.real
-
-    def log_likelihood(self):
-        log_l = 0
-        waveform_polarizations = self.waveform_generator.frequency_domain_strain()
-        if waveform_polarizations is None:
-            return np.nan_to_num(-np.inf)
-        for interferometer in self.interferometers:
-            log_l += self.log_likelihood_interferometer(waveform_polarizations, interferometer)
-        return log_l.real
-
-    def log_likelihood_interferometer(self, waveform_polarizations, interferometer):
-        signal_ifo = interferometer.get_detector_response(waveform_polarizations, self.waveform_generator.parameters)
-
-        log_l = - 2. / self.waveform_generator.time_duration * np.vdot(interferometer.data - signal_ifo,
-                                                                       (interferometer.data - signal_ifo)
-                                                                       / interferometer.power_spectral_density_array)
-        return log_l.real
-
-    def log_likelihood_ratio(self):
-        return self.log_likelihood() - self.noise_log_likelihood()
-
-
-class MarginalizedLikelihood(Likelihood):
-    def __init__(self, interferometers, waveform_generator, distance_marginalization=False, phase_marginalization=False,
-                 time_marginalization=False, prior=None):
-        Likelihood.__init__(self, interferometers, waveform_generator)
         self.distance_marginalization = distance_marginalization
         self.phase_marginalization = phase_marginalization
-        self.time_marginalization = time_marginalization
         self.prior = prior
 
         if self.distance_marginalization:
@@ -64,29 +32,6 @@ class MarginalizedLikelihood(Likelihood):
             self.setup_phase_marginalization()
             prior['psi'] = 0
 
-        if self.time_marginalization:
-            logging.warning('Time marginalisation not yet implemented.')
-            self.time_marginalization = False
-
-    def setup_distance_marginalization(self):
-        if 'luminosity_distance' not in self.prior.keys():
-            logging.info('No prior provided for distance, using default prior.')
-            self.prior['luminosity_distance'] = tupak.prior.create_default_prior('luminosity_distance')
-        self.distance_array = np.linspace(self.prior['luminosity_distance'].minimum,
-                                          self.prior['luminosity_distance'].maximum, 1e4)
-        self.delta_distance = self.distance_array[1] - self.distance_array[0]
-        self.distance_prior_array = np.array([self.prior['luminosity_distance'].prob(distance)
-                                              for distance in self.distance_array])
-
-    def setup_phase_marginalization(self):
-        if 'psi' not in self.prior.keys() or not isinstance(self.prior['psi'], tupak.prior.Prior):
-            logging.info('No prior provided for polarization, using default prior.')
-            self.prior['psi'] = tupak.prior.create_default_prior('psi')
-        self.bessel_function_interped = interp1d(np.linspace(0, 1e6, 1e5),
-                                                 np.log([i0e(snr) for snr in np.linspace(0, 1e6, 1e5)])
-                                                 + np.linspace(0, 1e6, 1e5),
-                                                 bounds_error=False, fill_value=-np.inf)
-
     def noise_log_likelihood(self):
         log_l = 0
         for interferometer in self.interferometers:
@@ -101,10 +46,7 @@ class MarginalizedLikelihood(Likelihood):
         if waveform_polarizations is None:
             return np.nan_to_num(-np.inf)
 
-        if self.time_marginalization:
-            signal_times_data = 0 * 1j
-        else:
-            matched_filter_snr_squared = 0
+        matched_filter_snr_squared = 0
         optimal_snr_squared = 0
 
         for interferometer in self.interferometers:
@@ -142,4 +84,54 @@ class MarginalizedLikelihood(Likelihood):
     def log_likelihood(self):
         return self.log_likelihood() + self.noise_log_likelihood()
 
+    def setup_distance_marginalization(self):
+        if 'luminosity_distance' not in self.prior.keys():
+            logging.info('No prior provided for distance, using default prior.')
+            self.prior['luminosity_distance'] = tupak.prior.create_default_prior('luminosity_distance')
+        self.distance_array = np.linspace(self.prior['luminosity_distance'].minimum,
+                                          self.prior['luminosity_distance'].maximum, 1e4)
+        self.delta_distance = self.distance_array[1] - self.distance_array[0]
+        self.distance_prior_array = np.array([self.prior['luminosity_distance'].prob(distance)
+                                              for distance in self.distance_array])
+
+    def setup_phase_marginalization(self):
+        if 'psi' not in self.prior.keys() or not isinstance(self.prior['psi'], tupak.prior.Prior):
+            logging.info('No prior provided for polarization, using default prior.')
+            self.prior['psi'] = tupak.prior.create_default_prior('psi')
+        self.bessel_function_interped = interp1d(np.linspace(0, 1e6, 1e5),
+                                                 np.log([i0e(snr) for snr in np.linspace(0, 1e6, 1e5)])
+                                                 + np.linspace(0, 1e6, 1e5),
+                                                 bounds_error=False, fill_value=-np.inf)
+
+
+class BasicLikelihood(object):
+    def __init__(self, interferometers, waveform_generator):
+        self.interferometers = interferometers
+        self.waveform_generator = waveform_generator
 
+    def noise_log_likelihood(self):
+        log_l = 0
+        for interferometer in self.interferometers:
+            log_l -= 2. / self.waveform_generator.time_duration * np.sum(
+                abs(interferometer.data) ** 2 / interferometer.power_spectral_density_array)
+        return log_l.real
+
+    def log_likelihood(self):
+        log_l = 0
+        waveform_polarizations = self.waveform_generator.frequency_domain_strain()
+        if waveform_polarizations is None:
+            return np.nan_to_num(-np.inf)
+        for interferometer in self.interferometers:
+            log_l += self.log_likelihood_interferometer(waveform_polarizations, interferometer)
+        return log_l.real
+
+    def log_likelihood_interferometer(self, waveform_polarizations, interferometer):
+        signal_ifo = interferometer.get_detector_response(waveform_polarizations, self.waveform_generator.parameters)
+
+        log_l = - 2. / self.waveform_generator.time_duration * np.vdot(interferometer.data - signal_ifo,
+                                                                       (interferometer.data - signal_ifo)
+                                                                       / interferometer.power_spectral_density_array)
+        return log_l.real
+
+    def log_likelihood_ratio(self):
+        return self.log_likelihood() - self.noise_log_likelihood()