From d434ac9e035fdac7d54371b7cd25779a85a81f46 Mon Sep 17 00:00:00 2001
From: Reid Johnson <reidj@zillowgroup.com>
Date: Wed, 13 Dec 2023 03:16:04 -0800
Subject: [PATCH] Update black linting

---
 docs/conf.py                                  | 16 +++++++---------
 quantile_forest/tests/test_quantile_forest.py | 12 ++++++------
 2 files changed, 13 insertions(+), 15 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index c1284a1..7fe4bc1 100755
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -184,15 +184,13 @@
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [
-    (
-        "index",
-        "quantile-forest",
-        "quantile-forest Documentation",
-        ["Zillow Group"],
-        1,
-    )
-]
+man_pages = [(
+    "index",
+    "quantile-forest",
+    "quantile-forest Documentation",
+    ["Zillow Group"],
+    1,
+)]
 
 # If true, show URL addresses after external links.
 # man_show_urls = False
diff --git a/quantile_forest/tests/test_quantile_forest.py b/quantile_forest/tests/test_quantile_forest.py
index c2d7054..9d878ea 100755
--- a/quantile_forest/tests/test_quantile_forest.py
+++ b/quantile_forest/tests/test_quantile_forest.py
@@ -609,9 +609,9 @@ def check_proximity_counts(name):
     n_samples = len(X_california)
     proximities = est.proximity_counts(X_california)
     X_leaves = est.apply(X_california)
-    bootstrap_indices = np.array([
-        _generate_sample_indices(e.random_state, n_samples, n_samples) for e in est.estimators_
-    ])
+    bootstrap_indices = np.array(
+        [_generate_sample_indices(e.random_state, n_samples, n_samples) for e in est.estimators_]
+    )
     for train_idx, train_idx_prox in enumerate(proximities):
         for proximity_idx, proximity_count in train_idx_prox:
             bootstrap_count = 0
@@ -1029,9 +1029,9 @@ def check_proximity_counts_oob(name):
     # Check that OOB proximity counts match OOB bootstrap counts.
     X_leaves = est.apply(X)
     n_samples = len(X)
-    bootstrap_indices = np.array([
-        _generate_sample_indices(e.random_state, n_samples, n_samples) for e in est.estimators_
-    ])
+    bootstrap_indices = np.array(
+        [_generate_sample_indices(e.random_state, n_samples, n_samples) for e in est.estimators_]
+    )
     for train_idx, train_idx_prox in enumerate(proximities):
         for proximity_idx, proximity_count in train_idx_prox:
             bootstrap_count = 0