pep8 fix multidim_gaussian.py

examples/other_examples/multidim_gaussian.py

@@ -4,9 +4,6 @@ Testing the recovery of a multi-dimensional
 Gaussian with zero mean and unit variance
 """
 from __future__ import division
-import matplotlib
-
-matplotlib.use("PS")
 import bilby
 import numpy as np
 
@@ -44,19 +41,21 @@ class MultidimGaussianLikelihood(bilby.Likelihood):
     #      self.parameters=dict(zip(self.keys, self.values))
 
     def log_likelihood(self):
-        mu = np.array([self.parameters["mu_{0}".format(i)] for i in range(self.dim)])
+        mu = np.array(
+            [self.parameters["mu_{0}".format(i)] for i in range(self.dim)])
         sigma = np.array(
-            [self.parameters["sigma_{0}".format(i)] for i in range(self.dim)]
-        )
-        # print('here' + str(self.data ))
+            [self.parameters["sigma_{0}".format(i)] for i in range(self.dim)])
         p = np.array([(self.data[n, :] - mu) / sigma for n in range(self.N)])
-        return np.sum(-0.5 * (np.sum(p ** 2) + self.N * np.log(2 * np.pi * sigma ** 2)))
-
+        return np.sum(-0.5 * (np.sum(p ** 2) + 
+                     self.N * np.log(2 * np.pi * sigma ** 2)))
 
 likelihood = MultidimGaussianLikelihood(data, dim)
 priors = bilby.core.prior.PriorDict()
 priors.update(
-    {"mu_{0}".format(i): bilby.core.prior.Uniform(-5, 5, "mu") for i in range(dim)}
+    {
+        "mu_{0}".format(i): bilby.core.prior.Uniform(-5, 5, "mu")
+        for i in range(dim)
+    }
 )
 priors.update(
     {
@@ -64,8 +63,6 @@ priors.update(
         for i in range(dim)
     }
 )
-# likelihood.parameters.update(priors.sample())
-# likelihood.log_likelihood()
 # And run sampler
 result = bilby.run_sampler(
     likelihood=likelihood,