From 61ccb22ea71f25c9b6687161cbd121aff7d609ca Mon Sep 17 00:00:00 2001 From: Jason Gross Date: Tue, 6 Aug 2024 16:21:22 -0700 Subject: [PATCH] Replace `np.float_` with `np.float64` for np 2.0 This avoids ``` AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead. ``` --- 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 29d8e635..12f5b698 100644 --- a/src/tikzplotlib/_legend.py +++ b/src/tikzplotlib/_legend.py @@ -117,40 +117,40 @@ 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.