black fromatting

arm61 · Apr 23, 2020 · c67d591 · c67d591
1 parent 10dc169
commit c67d591
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Showing 7 changed files with 44 additions and 154 deletions.
diff --git a/uravu/distribution.py b/uravu/distribution.py
@@ -34,11 +34,7 @@ class Distribution:
     """
 
     def __init__(
-        self,
-        samples,
-        name="Distribution",
-        ci_points=None,
-        unit=UREG.dimensionless,
+        self, samples, name="Distribution", ci_points=None, unit=UREG.dimensionless,
     ):
         """
         Initialisation function for a :py:class:`~uravu.distribution.Distribution` object.
@@ -132,18 +128,12 @@ def __str__(self):
         Returns:
             :py:attr:`str`: Description of the distribution.
         """
-        representation = "Distribution: {}\nSize: {}\n".format(
-            self.name, self.size
-        )
+        representation = "Distribution: {}\nSize: {}\n".format(self.name, self.size)
         representation += "Samples: "
         representation += "["
-        representation += " ".join(
-            ["{:.2e}".format(i) for i in self.samples[:2]]
-        )
+        representation += " ".join(["{:.2e}".format(i) for i in self.samples[:2]])
         representation += " ... "
-        representation += " ".join(
-            ["{:.2e}".format(i) for i in self.samples[-2:]]
-        )
+        representation += " ".join(["{:.2e}".format(i) for i in self.samples[-2:]])
         representation += "]\n"
         representation += "Median: {:.2e}\n".format(self.n)
         if self.normal:

diff --git a/uravu/plotting.py b/uravu/plotting.py
@@ -24,9 +24,7 @@
     )
 
 
-def plot_relationship(
-    relationship, axes=None, figsize=(10, 6)
-):  # pragma: no cover
+def plot_relationship(relationship, axes=None, figsize=(10, 6)):  # pragma: no cover
     """
     Plot the relationship. Additional plots will be included on this if the MCMC sampling has been used to find distributions.
 
@@ -43,9 +41,7 @@ def plot_relationship(
     variables = relationship.variables
     if relationship.unaccounted_uncertainty:
         variables = relationship.variables[:-1]
-    axes.plot(
-        relationship.x_n, relationship.y_n, c=list(_fig_params.TABLEAU)[0]
-    )
+    axes.plot(relationship.x_n, relationship.y_n, c=list(_fig_params.TABLEAU)[0])
     x_label = "{}".format(relationship.abscissa_name)
     if relationship.x_u != UREG.dimensionless:
         x_label += "/${:~L}$".format(relationship.x_u)
@@ -54,9 +50,7 @@ def plot_relationship(
     if relationship.y_u != UREG.dimensionless:
         y_label += "/${:~L}$".format(relationship.y_u)
     axes.set_ylabel(y_label)
-    if isinstance(
-        relationship.ordinate.m.any(), uncertainties.core.AffineScalarFunc
-    ):
+    if isinstance(relationship.ordinate.m.any(), uncertainties.core.AffineScalarFunc):
         axes.fill_between(
             relationship.x_n,
             relationship.y_n - relationship.y_s,
@@ -72,9 +66,7 @@ def plot_relationship(
             color=list(_fig_params.TABLEAU)[1],
         )
     else:
-        plot_samples = np.random.randint(
-            0, variables[0].samples.size, size=100
-        )
+        plot_samples = np.random.randint(0, variables[0].samples.size, size=100)
         for i in plot_samples:
             float_variables = [var.samples[i] for var in variables]
             axes.plot(
@@ -161,9 +153,7 @@ def plot_corner(relationship, figsize=(8, 8)):  # pragma: no cover
             - :py:class:`matplotlib.axes.Axes`: The axes with new plots.
     """
     n = len(relationship.variables)
-    if not all(
-        [isinstance(relationship.variables[i], Distribution) for i in range(n)]
-    ):
+    if not all([isinstance(relationship.variables[i], Distribution) for i in range(n)]):
         raise ValueError(
             "In order to use the corner plot functionality, all relationship "
             "variables must be Distributions. Please run MCMC before "
@@ -177,8 +167,7 @@ def plot_corner(relationship, figsize=(8, 8)):  # pragma: no cover
         else:
             var_labels.append(
                 "{}/${:L}$".format(
-                    relationship.variable_names[i],
-                    relationship.variable_units[i],
+                    relationship.variable_names[i], relationship.variable_units[i],
                 )
             )
     corner(
@@ -199,12 +188,7 @@ def plot_corner(relationship, figsize=(8, 8)):  # pragma: no cover
             ]
         )
         ax[n - 1, j].set_xlim(
-            [
-                i
-                for i in np.percentile(
-                    relationship.variables[j].samples, [0.5, 99.5]
-                )
-            ]
+            [i for i in np.percentile(relationship.variables[j].samples, [0.5, 99.5])]
         )
     for j in range(n - 1):
         ax[j + 1, 0].set_yticks(

diff --git a/uravu/relationship.py b/uravu/relationship.py
@@ -143,29 +143,19 @@ def __str__(self):
             for i in self.y_n:
                 string += "{:.2e} ".format(i)
             string += "] \n"
-            if isinstance(
-                self.ordinate.m.any(), uncertainties.core.AffineScalarFunc
-            ):
+            if isinstance(self.ordinate.m.any(), uncertainties.core.AffineScalarFunc):
                 string += "Ordinate uncertainty: [ "
                 for i in self.y_s:
                     string += "{:.2e} ".format(i)
                 string += "] \n"
         else:
-            string += (
-                "Abscissa: "
-                "[ {:.2e} {:.2e} ... {:.2e} {:.2e} ] \n".format(
-                    *self.x_n[:2], *self.x_n[-2:]
-                )
+            string += "Abscissa: " "[ {:.2e} {:.2e} ... {:.2e} {:.2e} ] \n".format(
+                *self.x_n[:2], *self.x_n[-2:]
             )
-            string += (
-                "Ordinate: "
-                "[ {:.2e} {:.2e} ... {:.2e} {:.2e} ] \n".format(
-                    *self.y_n[:2], *self.y_n[-2:]
-                )
+            string += "Ordinate: " "[ {:.2e} {:.2e} ... {:.2e} {:.2e} ] \n".format(
+                *self.y_n[:2], *self.y_n[-2:]
             )
-            if isinstance(
-                self.ordinate.m.any(), uncertainties.core.AffineScalarFunc
-            ):
+            if isinstance(self.ordinate.m.any(), uncertainties.core.AffineScalarFunc):
                 string += (
                     "Ordinate uncertainty: "
                     "[ {:.2e} {:.2e} ... {:.2e} {:.2e} ]\n".format(
@@ -180,9 +170,7 @@ def __str__(self):
         for var in self.variables:
             if isinstance(var, Distribution):
                 if var.normal:
-                    string += "{:.2e}+/-{:.2e} ".format(
-                        var.n, var.n - var.con_int[0]
-                    )
+                    string += "{:.2e}+/-{:.2e} ".format(var.n, var.n - var.con_int[0])
                 else:
                     string += "{:.2e}+{:.2e}-{:.2e} ".format(
                         var.n, var.con_int[1] - var.n, var.n - var.con_int[0]
@@ -192,13 +180,9 @@ def __str__(self):
         string += "] \n"
         if self.nested_sampling_done:
             string += "ln(evidence): {:.2e} \n".format(self.ln_evidence)
-        string += "Unaccounted uncertainty: {} \n".format(
-            self.unaccounted_uncertainty
-        )
+        string += "Unaccounted uncertainty: {} \n".format(self.unaccounted_uncertainty)
         string += "MCMC performed: {} \n".format(self.mcmc_done)
-        string += "Nested sampling performed: {} \n".format(
-            self.nested_sampling_done
-        )
+        string += "Nested sampling performed: {} \n".format(self.nested_sampling_done)
         return string
 
     def __repr__(self):
@@ -298,9 +282,7 @@ def y_s(self):
         Returns:
             :py:attr:`array_like`: Ordinate uncertainties.
         """
-        if isinstance(
-            self.ordinate.m.any(), uncertainties.core.AffineScalarFunc
-        ):
+        if isinstance(self.ordinate.m.any(), uncertainties.core.AffineScalarFunc):
             return unp.std_devs(self.ordinate.m)
         else:
             return None

diff --git a/uravu/sampling.py b/uravu/sampling.py
@@ -107,9 +107,7 @@ def ln_probability(
     )
 
 
-def nested_sampling(
-    relationship, prior_function=None, progress=True, **kwargs
-):
+def nested_sampling(relationship, prior_function=None, progress=True, **kwargs):
     """
     Perform the nested sampling in order to determine the Bayesian natural log evidence. See the :py:func:`dynesty.NestedSampler.run_nested()` documentation.
 

diff --git a/uravu/tests/test_optimize.py b/uravu/tests/test_optimize.py
@@ -39,9 +39,7 @@ def test_ln_likelihood_b(self):
         """
         test_x = np.linspace(0, 99, 10)
         test_y = np.ones(10)
-        test_rel = relationship.Relationship(
-            utils.straight_line, test_x, test_y
-        )
+        test_rel = relationship.Relationship(utils.straight_line, test_x, test_y)
         expected_lnl = -45.21054955719477
         actual_lnl = optimize.ln_likelihood(
             test_rel.variables,
@@ -76,11 +74,7 @@ def test_negative_lnl_b(self):
         test_y = np.ones(10)
         test_y_e = np.ones(10) * 0.1
         test_rel = relationship.Relationship(
-            utils.straight_line,
-            test_x,
-            test_y,
-            test_y_e,
-            unaccounted_uncertainty=True,
+            utils.straight_line, test_x, test_y, test_y_e, unaccounted_uncertainty=True,
         )
         expected_negtive_lnl = 45.21123241122563
         actual_negative_lnl = optimize.negative_lnl(