bug fix and some cosmetics

src/minimization/hmc.py

@@ -172,9 +172,8 @@ class HMC_chain:
         tmp = self._sseq.spawn(2)[1]
         with Context(tmp):
             momentum = self._M.draw_sample_with_dtype(dtype=np.float64)
-
-        new_position, new_momentum = self._integrate(momentum)
-        self._accepting(momentum, new_position, new_momentum)
+            new_position, new_momentum = self._integrate(momentum)
+            self._accepting(momentum, new_position, new_momentum)
         self._update_acceptance()
 
     def _integrate(self, momentum):
@@ -235,8 +234,7 @@ class HMC_chain:
             rate = np.exp(energy - new_energy)
             if np.isnan(rate):
                 return
-            rng = current_rng()
-            accept = rng.binomial(1, rate)
+            accept = current_rng().binomial(1, rate)
             if accept:
                 self._position = new_position
         self._accepted.append(accept)
@@ -245,10 +243,7 @@ class HMC_chain:
 
     def _update_acceptance(self):
         """Calculates the current acceptance rate based on the last ten samples."""
-        current_accepted = self._accepted[-10:]
-        current_accepted = np.array(current_accepted)
-        current_acceptance = np.mean(current_accepted)
-        self._current_acceptance.append(current_acceptance)
+        self._current_acceptance.append(np.mean(self._accepted[-10:]))
 
     def _tune_parameters(self, preferred_acceptance):
         """Increases or decreases the steplength in the leapfrog integration
@@ -393,13 +388,11 @@ class HMC_Sampler:
         The mean and variance over the samples.
 
         """
-        locmeanvar = [
+        lmv = [
             chain.estimate_quantity(function) for chain in self._local_chains
         ]
-        locmean = [x[0] for x in locmeanvar]
-        locvar = [x[1] for x in locmeanvar]
-        mean = allreduce_sum(locmean, self._comm)
-        var = allreduce_sum(locvar, self._comm)
+        mean = allreduce_sum([x[0] for x in lmv], self._comm)
+        var = allreduce_sum([x[1] for x in lmv], self._comm)
         return mean/self._N_chains, var/self._N_chains
 
     @property
@@ -433,8 +426,8 @@ class HMC_Sampler:
         dom = self._dom
         locfld = [_sample_field(chain.samples) for chain in self._local_chains]
         locmeanmean = [_mean(fld, dom) for fld in locfld]
-        locW = [_var(fld, dom) for fld in locfld]
         mean_mean = allreduce_sum(locmeanmean, self._comm)/M
+        locW = [_var(fld, dom) for fld in locfld]
         W = allreduce_sum(locW, self._comm)/M
         locB = [(mean_mean - _mean(fld, dom))**2 for fld in locfld]
         B = allreduce_sum(locB, self._comm)*N/(M - 1)