From a4c52faf2dd5b29e0a3067e84816f86340d60939 Mon Sep 17 00:00:00 2001
From: Matteo Pagin <mattpagg@gmail.com>
Date: Mon, 15 Jul 2024 13:26:43 +0200
Subject: [PATCH] Reflect numpy dtype change from float to float16/64

---
 src/tikzplotlib/_legend.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/src/tikzplotlib/_legend.py b/src/tikzplotlib/_legend.py
index 4609f80c..8e6e4c70 100644
--- a/src/tikzplotlib/_legend.py
+++ b/src/tikzplotlib/_legend.py
@@ -122,40 +122,40 @@ def _get_location_from_best(obj):
     # (or center) of the axes box.
     # 1. Key points of the legend
     lower_left_legend = x0_legend
-    lower_right_legend = np.array([x1_legend[0], x0_legend[1]], dtype=np.float_)
-    upper_left_legend = np.array([x0_legend[0], x1_legend[1]], dtype=np.float_)
+    lower_right_legend = np.array([x1_legend[0], x0_legend[1]], dtype=np.float64_)
+    upper_left_legend = np.array([x0_legend[0], x1_legend[1]], dtype=np.float64_)
     upper_right_legend = x1_legend
     center_legend = x0_legend + dimension_legend / 2.0
     center_left_legend = np.array(
-        [x0_legend[0], x0_legend[1] + dimension_legend[1] / 2.0], dtype=np.float_
+        [x0_legend[0], x0_legend[1] + dimension_legend[1] / 2.0], dtype=np.float64_
     )
     center_right_legend = np.array(
-        [x1_legend[0], x0_legend[1] + dimension_legend[1] / 2.0], dtype=np.float_
+        [x1_legend[0], x0_legend[1] + dimension_legend[1] / 2.0], dtype=np.float64_
     )
     lower_center_legend = np.array(
-        [x0_legend[0] + dimension_legend[0] / 2.0, x0_legend[1]], dtype=np.float_
+        [x0_legend[0] + dimension_legend[0] / 2.0, x0_legend[1]], dtype=np.float64_
     )
     upper_center_legend = np.array(
-        [x0_legend[0] + dimension_legend[0] / 2.0, x1_legend[1]], dtype=np.float_
+        [x0_legend[0] + dimension_legend[0] / 2.0, x1_legend[1]], dtype=np.float64_
     )
 
     # 2. Key points of the axes
     lower_left_axes = x0_axes
-    lower_right_axes = np.array([x1_axes[0], x0_axes[1]], dtype=np.float_)
-    upper_left_axes = np.array([x0_axes[0], x1_axes[1]], dtype=np.float_)
+    lower_right_axes = np.array([x1_axes[0], x0_axes[1]], dtype=np.float64_)
+    upper_left_axes = np.array([x0_axes[0], x1_axes[1]], dtype=np.float64_)
     upper_right_axes = x1_axes
     center_axes = x0_axes + dimension_axes / 2.0
     center_left_axes = np.array(
-        [x0_axes[0], x0_axes[1] + dimension_axes[1] / 2.0], dtype=np.float_
+        [x0_axes[0], x0_axes[1] + dimension_axes[1] / 2.0], dtype=np.float64_
     )
     center_right_axes = np.array(
-        [x1_axes[0], x0_axes[1] + dimension_axes[1] / 2.0], dtype=np.float_
+        [x1_axes[0], x0_axes[1] + dimension_axes[1] / 2.0], dtype=np.float64_
     )
     lower_center_axes = np.array(
-        [x0_axes[0] + dimension_axes[0] / 2.0, x0_axes[1]], dtype=np.float_
+        [x0_axes[0] + dimension_axes[0] / 2.0, x0_axes[1]], dtype=np.float64_
     )
     upper_center_axes = np.array(
-        [x0_axes[0] + dimension_axes[0] / 2.0, x1_axes[1]], dtype=np.float_
+        [x0_axes[0] + dimension_axes[0] / 2.0, x1_axes[1]], dtype=np.float64_
     )
 
     # 3. Compute the distances between comparable points.