From ca34f8d7decc162f18a45fb1b0ab8b2a77fe9d73 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 4 Dec 2023 07:30:34 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

---
 hierarc/Likelihood/hierarchy_likelihood.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/hierarc/Likelihood/hierarchy_likelihood.py b/hierarc/Likelihood/hierarchy_likelihood.py
index ecd5d8a..48fab51 100644
--- a/hierarc/Likelihood/hierarchy_likelihood.py
+++ b/hierarc/Likelihood/hierarchy_likelihood.py
@@ -247,8 +247,9 @@ def log_likelihood_single(
             kappa_ext=kappa_ext,
             mag_source=mag_source,
         )
-        scaling_param_array = self.draw_scaling_params(kwargs_lens=kwargs_lens,
-                                                       **kwargs_kin)
+        scaling_param_array = self.draw_scaling_params(
+            kwargs_lens=kwargs_lens, **kwargs_kin
+        )
         kin_scaling = self.param_scaling(scaling_param_array)
 
         lnlikelihood = self.log_likelihood(
@@ -274,7 +275,8 @@ def draw_scaling_params(self, kwargs_lens=None, **kwargs_kin):
         else:
             return ani_param
 
-    def draw_lens_scaling_params(self,
+    def draw_lens_scaling_params(
+        self,
         lambda_mst=1,
         lambda_mst_sigma=0,
         kappa_ext=0,
@@ -289,7 +291,8 @@ def draw_lens_scaling_params(self,
         alpha_gamma_in=0,
         m2l=1,
         m2l_sigma=0,
-        alpha_m2l=0,):
+        alpha_m2l=0,
+    ):
         """Draws a realization of the anisotropy parameter scaling from the
         distribution.
 
@@ -319,10 +322,11 @@ def draw_lens_scaling_params(self,
         if self._gamma_in_array is not None and self._m2l_array is not None:
             gamma_in_draw = np.random.normal(
                 gamma_in + alpha_gamma_in * self._lambda_scaling_property,
-                gamma_in_sigma)
+                gamma_in_sigma,
+            )
             m2l_draw = np.random.normal(
-                m2l + alpha_m2l * self._lambda_scaling_property,
-                m2l_sigma)
+                m2l + alpha_m2l * self._lambda_scaling_property, m2l_sigma
+            )
             return gamma_in_draw, m2l_draw
         else:
             return None, None