Standardize methods ordering

bilby/core/sampler/emcee.py

@@ -45,11 +45,11 @@ class Emcee(MCMCSampler):
 
     """
 
-    default_kwargs = dict(nwalkers=500, a=2, args=[], kwargs={},
-                          postargs=None, pool=None, live_dangerously=False,
-                          runtime_sortingfn=None, lnprob0=None, rstate0=None,
-                          blobs0=None, iterations=100, thin=1, storechain=True,
-                          mh_proposal=None)
+    default_kwargs = dict(
+        nwalkers=500, a=2, args=[], kwargs={}, postargs=None, pool=None,
+        live_dangerously=False, runtime_sortingfn=None, lnprob0=None,
+        rstate0=None, blobs0=None, iterations=100, thin=1, storechain=True,
+        mh_proposal=None)
 
     def __init__(self, likelihood, priors, outdir='outdir', label='label',
                  use_ratio=False, plot=False, skip_import_verification=False,
@@ -142,6 +142,14 @@ class Emcee(MCMCSampler):
 
         return init_kwargs
 
+    def lnpostfn(self, theta):
+        log_prior = self.log_prior(theta)
+        if np.isinf(log_prior):
+            return -np.inf, [np.nan, np.nan]
+        else:
+            log_likelihood = self.log_likelihood(theta)
+            return log_likelihood + log_prior, [log_likelihood, log_prior]
+
     @property
     def nburn(self):
         if type(self.__nburn) in [float, int]:
@@ -206,7 +214,8 @@ class Emcee(MCMCSampler):
 
         """
         out_dir = os.path.join(
-            self.outdir, '{}_{}'.format(self.__class__.__name__, self.label))
+            self.outdir, '{}_{}'.format(self.__class__.__name__.lower(),
+                                        self.label))
         check_directory_exists_and_if_not_mkdir(out_dir)
 
         sampler_file = os.path.join(out_dir, 'sampler.pickle')
@@ -253,9 +262,6 @@ class Emcee(MCMCSampler):
         import emcee
         self._sampler = emcee.EnsembleSampler(**self.sampler_init_kwargs)
 
-    def _set_pos0_for_resume(self):
-        self.pos0 = self.sampler.chain[:, -1, :]
-
     @property
     def sampler(self):
         """ Returns the ptemcee sampler object
@@ -285,42 +291,6 @@ class Emcee(MCMCSampler):
             for ii, point in enumerate(points):
                 ff.write(self.checkpoint_info.chain_template.format(ii, *point))
 
-    def run_sampler(self):
-        tqdm = get_progress_bar()
-        sampler_function_kwargs = self.sampler_function_kwargs
-        iterations = sampler_function_kwargs.pop('iterations')
-        iterations -= self._previous_iterations
-
-        print('pos0', self.pos0)
-        sampler_function_kwargs['p0'] = self.pos0
-
-        for sample in tqdm(
-                self.sampler.sample(iterations=iterations, **sampler_function_kwargs),
-                total=iterations):
-            self.write_chains_to_file(sample)
-
-        self.result.sampler_output = np.nan
-        blobs_flat = np.array(self.sampler.blobs).reshape((-1, 2))
-        log_likelihoods, log_priors = blobs_flat.T
-        chain = self.sampler.chain.reshape((-1, self.ndim))
-        log_ls = log_likelihoods
-        log_ps = log_priors
-        self.calculate_autocorrelation(chain)
-        self.print_nburn_logging_info()
-        self.result.nburn = self.nburn
-        n_samples = self.nwalkers * self.nburn
-        if self.result.nburn > self.nsteps:
-            raise SamplerError(
-                "The run has finished, but the chain is not burned in: "
-                "`nburn < nsteps`. Try increasing the number of steps.")
-        self.result.samples = chain[n_samples:, :]
-        self.result.log_likelihood_evaluations = log_ls[n_samples:]
-        self.result.log_prior_evaluations = log_ps[n_samples:]
-        self.result.walkers = self.sampler.chain
-        self.result.log_evidence = np.nan
-        self.result.log_evidence_err = np.nan
-        return self.result
-
     @property
     def _previous_iterations(self):
         """ Returns the number of iterations that the sampler has saved
@@ -356,10 +326,41 @@ class Emcee(MCMCSampler):
             logger.debug("Generating initial walker positions from prior")
             self.pos0 = self._draw_pos0_from_prior()
 
-    def lnpostfn(self, theta):
-        log_prior = self.log_prior(theta)
-        if np.isinf(log_prior):
-            return -np.inf, [np.nan, np.nan]
-        else:
-            log_likelihood = self.log_likelihood(theta)
-            return log_likelihood + log_prior, [log_likelihood, log_prior]
+    def _set_pos0_for_resume(self):
+        self.pos0 = self.sampler.chain[:, -1, :]
+
+    def run_sampler(self):
+        tqdm = get_progress_bar()
+        sampler_function_kwargs = self.sampler_function_kwargs
+        iterations = sampler_function_kwargs.pop('iterations')
+        iterations -= self._previous_iterations
+
+        print('pos0', self.pos0)
+        sampler_function_kwargs['p0'] = self.pos0
+
+        for sample in tqdm(
+                self.sampler.sample(iterations=iterations, **sampler_function_kwargs),
+                total=iterations):
+            self.write_chains_to_file(sample)
+
+        self.result.sampler_output = np.nan
+        blobs_flat = np.array(self.sampler.blobs).reshape((-1, 2))
+        log_likelihoods, log_priors = blobs_flat.T
+        chain = self.sampler.chain.reshape((-1, self.ndim))
+        log_ls = log_likelihoods
+        log_ps = log_priors
+        self.calculate_autocorrelation(chain)
+        self.print_nburn_logging_info()
+        self.result.nburn = self.nburn
+        n_samples = self.nwalkers * self.nburn
+        if self.result.nburn > self.nsteps:
+            raise SamplerError(
+                "The run has finished, but the chain is not burned in: "
+                "`nburn < nsteps`. Try increasing the number of steps.")
+        self.result.samples = chain[n_samples:, :]
+        self.result.log_likelihood_evaluations = log_ls[n_samples:]
+        self.result.log_prior_evaluations = log_ps[n_samples:]
+        self.result.walkers = self.sampler.chain
+        self.result.log_evidence = np.nan
+        self.result.log_evidence_err = np.nan
+        return self.result

bilby/core/sampler/ptemcee.py

@@ -59,33 +59,11 @@ class Ptemcee(Emcee):
     def ntemps(self):
         return self.kwargs['ntemps']
 
-    def _draw_pos0_from_prior(self):
-        # for ptemcee, the pos0 has the shape ntemps, nwalkers, ndim
-        return [[self.get_random_draw_from_prior()
-                 for _ in range(self.nwalkers)]
-                for _ in range(self.kwargs['ntemps'])]
-
-    def _set_pos0_for_resume(self):
-        self.pos0 = None
-
-    @property
-    def _previous_iterations(self):
-        """ Returns the number of iterations that the sampler has saved
-
-        This is used when loading in a sampler from a pickle file to figure out
-        how much of the run has already been completed
-        """
-        return self.sampler.time
-
     @property
     def sampler_chain(self):
         nsteps = self._previous_iterations
         return self.sampler.chain[:, :, :nsteps, :]
 
-    @property
-    def _pos0_shape(self):
-        return (self.ntemps, self.nwalkers, self.ndim)
-
     def _initialise_sampler(self):
         import ptemcee
         self._sampler = ptemcee.Sampler(
@@ -104,6 +82,28 @@ class Ptemcee(Emcee):
                 line = np.concatenate((point, [logl, logp]))
                 ff.write(self.checkpoint_info.chain_template.format(ii, *line))
 
+    @property
+    def _previous_iterations(self):
+        """ Returns the number of iterations that the sampler has saved
+
+        This is used when loading in a sampler from a pickle file to figure out
+        how much of the run has already been completed
+        """
+        return self.sampler.time
+
+    def _draw_pos0_from_prior(self):
+        # for ptemcee, the pos0 has the shape ntemps, nwalkers, ndim
+        return [[self.get_random_draw_from_prior()
+                 for _ in range(self.nwalkers)]
+                for _ in range(self.kwargs['ntemps'])]
+
+    @property
+    def _pos0_shape(self):
+        return (self.ntemps, self.nwalkers, self.ndim)
+
+    def _set_pos0_for_resume(self):
+        self.pos0 = None
+
     def run_sampler(self):
         tqdm = get_progress_bar()
         sampler_function_kwargs = self.sampler_function_kwargs