black formatting

uravu/distribution.py

@@ -160,7 +160,7 @@ def __str__(self):
                 representation += "{}\n".format(ufloat(self.n, self.s))
             else:
                 representation += "{:.2e}+{:.2e}-{:.2e}\n".format(
-                    self.n, self.con_int[1]-self.n, self.n-self.con_int[0]
+                    self.n, self.con_int[1] - self.n, self.n - self.con_int[0]
                 )
         representation += "Unit: {}\n".format(self.unit)
         representation += "Normal: {}\n".format(self.normal)

uravu/optimize.py

@@ -94,4 +94,3 @@ def ln_likelihood(
     else:
         sigma2 = dy_data ** 2 + model ** 2 * np.exp(2 * log_f)
     return -0.5 * np.sum((model - y_data) ** 2 / sigma2 + np.log(sigma2))
-

uravu/plotting.py

@@ -13,12 +13,15 @@
 from scipy.stats import gaussian_kde
 from uravu import UREG, _fig_params
 from uravu.distribution import Distribution
+
 try:
     import matplotlib.pyplot as plt
     from corner import corner
 except ModuleNotFoundError:
-    print("The matplotlib and corner packages are necessary for the use of "
-          "the plotting module, please install these.")
+    print(
+        "The matplotlib and corner packages are necessary for the use of "
+        "the plotting module, please install these."
+    )
 
 
 def plot_relationship(

uravu/relationship.py

@@ -75,15 +75,15 @@ def __init__(
         if ordinate_uncertainty is None:
             self.unaccounted_uncertainty = True
             self.ordinate = ordinate
-        else:    
+        else:
             if ordinate_uncertainty.shape != ordinate.shape:
                 raise ValueError(
-                        "The number of data points in the ordinate does not "
-                        "match that in the ordinate uncertainty."
-                    )
+                    "The number of data points in the ordinate does not "
+                    "match that in the ordinate uncertainty."
+                )
             else:
                 ordinate_uncertainty = np.array(ordinate_uncertainty)
-                self.ordinate = unp.uarray(ordinate, ordinate_uncertainty) 
+                self.ordinate = unp.uarray(ordinate, ordinate_uncertainty)
         self.ordinate *= ordinate_unit
 
         self.abscissa = abscissa
@@ -480,7 +480,7 @@ def mcmc(
         n_samples=500,
         n_burn=500,
         progress=True,
-        seed=None
+        seed=None,
     ):
         """
         Perform MCMC to get the posterior probability distributions for the variables of the relationship. *Note*, running this method will populate the :py:attr:`~uravu.relationship.Relationship.variables` attribute with :py:class:`~uravu.distribution.Distribution` objects.

uravu/tests/test_optimize.py

@@ -44,7 +44,11 @@ def test_ln_likelihood_b(self):
         )
         expected_lnl = -45.21054955719477
         actual_lnl = optimize.ln_likelihood(
-            test_rel.variables, test_rel.function, test_rel.x, test_rel.y, unaccounted_uncertainty=test_rel.unaccounted_uncertainty
+            test_rel.variables,
+            test_rel.function,
+            test_rel.x,
+            test_rel.y,
+            unaccounted_uncertainty=test_rel.unaccounted_uncertainty,
         )
         assert_almost_equal(actual_lnl, expected_lnl)
 

uravu/utils.py

@@ -1,6 +1,5 @@
 """
-A few additional utility functions to improve the usability of
-``uravu``.
+A few additional utility functions to improve the usability of :py:mod`uravu`.
 """
 
 # Copyright (c) Andrew R. McCluskey