remove references to wg

tupak/sampler.py

@@ -152,14 +152,14 @@ class Sampler(object):
     def verify_parameters(self):
         for key in self.priors:
             try:
-                self.likelihood.waveform_generator.parameters[key] = self.priors[key].sample()
+                self.likelihood.parameters[key] = self.priors[key].sample()
             except AttributeError as e:
                 logging.warning('Cannot sample from {}, {}'.format(key, e))
         try:
-            self.likelihood.waveform_generator.frequency_domain_strain()
+            self.likelihood.log_likelihood_ratio()
         except TypeError:
-            raise TypeError('Waveform generation failed. Have you definitely specified all the parameters?\n{}'.format(
-                self.likelihood.waveform_generator.parameters))
+            raise TypeError('Likelihood evaluation failed. Have you definitely specified all the parameters?\n{}'.format(
+                self.likelihood.parameters))
 
     def prior_transform(self, theta):
         return [self.priors[key].rescale(t) for key, t in zip(self.__search_parameter_keys, theta)]
@@ -410,7 +410,7 @@ class Ptemcee(Sampler):
 
 def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
                 sampler='nestle', use_ratio=True, injection_parameters=None,
-                sampling_parameters=None, **kwargs):
+                conversion_function=None, **kwargs):
     """
     The primary interface to easy parameter estimation
 
@@ -431,12 +431,15 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
         samplers
     use_ratio: bool (False)
         If True, use the likelihood's loglikelihood_ratio, rather than just
-        the loglikelhood.
+        the log likelhood.
     injection_parameters: dict
         A dictionary of injection parameters used in creating the data (if
         using simulated data). Appended to the result object and saved.
+
+    conversion_function: function, optional
+        Function to apply to posterior to generate additional parameters.
     **kwargs:
-        All kwargs are passed directly to the samplers `run` functino
+        All kwargs are passed directly to the samplers `run` function
 
     Returns
     ------
@@ -449,7 +452,7 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
 
     if priors is None:
         priors = dict()
-    priors = fill_priors(priors, likelihood, sampling_parameters)
+    priors = fill_priors(priors, likelihood, parameters=likelihood.sampling_parameter_keys)
     tupak.prior.write_priors_to_file(priors, outdir)
 
     if implemented_samplers.__contains__(sampler.title()):
@@ -458,8 +461,6 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
                                 label=label, use_ratio=use_ratio,
                                 **kwargs)
 
-        likelihood.waveform_generator.search_parameter_keys = [
-            key for key in priors if not isinstance(priors[key], tupak.prior.DeltaFunction)]
         if sampler.cached_result:
             logging.info("Using cached result")
             return sampler.cached_result
@@ -476,8 +477,7 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
             tupak.conversion.generate_all_bbh_parameters(result.injection_parameters)
         result.fixed_parameter_keys = [key for key in priors if isinstance(key, prior.DeltaFunction)]
         # result.prior = prior  # Removed as this breaks the saving of the data
-        result.samples_to_data_frame(waveform_generator=likelihood.waveform_generator,
-                                     interferometers=likelihood.interferometers, priors=priors)
+        result.samples_to_data_frame(likelihood=likelihood, priors=priors, conversion_function=conversion_function)
         result.kwargs = sampler.kwargs
         result.save_to_file(outdir=outdir, label=label)
         return result