[pre-commit.ci] pre-commit autoupdate (#8)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/psf/black: 23.11.0 → 24.2.0](psf/black@23.11.0...24.2.0)
- [github.com/pycqa/isort: 5.12.0 → 5.13.2](PyCQA/isort@5.12.0...5.13.2)
- [github.com/astral-sh/ruff-pre-commit: v0.1.6 → v0.2.2](astral-sh/ruff-pre-commit@v0.1.6...v0.2.2)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -2,19 +2,19 @@ fail_fast: false
 
 repos:
 - repo: https://github.com/psf/black
-  rev: 23.11.0
+  rev: 24.2.0
   hooks:
     - id: black
       args: [--config, "./pyproject.toml"]
 
 - repo: https://github.com/pycqa/isort
-  rev: 5.12.0
+  rev: 5.13.2
   hooks:
     - id: isort
       args: [--settings-path, "./pyproject.toml"]
 
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.1.6
+  rev: v0.2.2
   hooks:
     - id: ruff
 

src/LocalPathPlanning/FSAE_PathPlanning/FSAE_local_path_planning.py

@@ -235,7 +235,9 @@ def get_translation(
     for i in range(len(test_data)):
         red = get_traffic_cone_matrix(np.array(test_data[i]), 2)  # 左侧锥桶位置矩阵
         blue = get_traffic_cone_matrix(np.array(test_data[i]), 11)  # 右侧锥桶位置矩阵
-        local_path = local_path_fitting(red, blue, translation_dis=1.5)  # 规划出的局部路径矩阵
+        local_path = local_path_fitting(
+            red, blue, translation_dis=1.5
+        )  # 规划出的局部路径矩阵
 
         row = i // 4  # 绘图布局用
         col = i % 4

src/utilities/discrete_curve_funcs.py

@@ -26,9 +26,13 @@ def find_match_point(
 
     x, y = xy_coordinate
     reference_line_length = reference_line_nodes.shape[0]
-    increase_count = 0  # 用 increase_count 记录 distance 连续增大的次数，避免多个局部最小值的干扰
+    increase_count = (
+        0  # 用 increase_count 记录 distance 连续增大的次数，避免多个局部最小值的干扰
+    )
     min_distance_square = float("inf")
-    match_point_index = start_index  # 若最后仍没有找到匹配索引，则说明起始索引已经是最佳匹配，直接返回
+    match_point_index = (
+        start_index  # 若最后仍没有找到匹配索引，则说明起始索引已经是最佳匹配，直接返回
+    )
 
     if start_index == 0:
         # 首次运行情况
@@ -130,7 +134,9 @@ def get_projection_point(
 
     # 计算投影点的位置信息
     x_r, y_r = r_m_v + ds * tau  # 计算投影点坐标
-    theta_r = normalize_angle(theta_match + kappa_match * ds)  # 计算投影点在参考线上切线与 x 轴的夹角
+    theta_r = normalize_angle(
+        theta_match + kappa_match * ds
+    )  # 计算投影点在参考线上切线与 x 轴的夹角
     kappa_r = kappa_match  # 投影点在参考线处的曲率
 
     return x_r, y_r, theta_r, kappa_r
@@ -155,15 +161,19 @@ def calculate_heading_kappa(path_points: npt.NDArray[np.float_]):
     # TODO 将填补差分的功能提取至单独的函数中
 
     points_array = np.array(path_points)
-    d_xy_ = points_array[1:, :] - points_array[:-1, :]  # 一阶差分，此种写法比 np.diff() 性能高得多
+    d_xy_ = (
+        points_array[1:, :] - points_array[:-1, :]
+    )  # 一阶差分，此种写法比 np.diff() 性能高得多
     d_xy = np.empty_like(points_array)  # 定义变量，预分配内存
     # 由于 n 个点差分得到的只有 n-1 个差分结果，所以要在首尾添加重复单元来近似求每个节点的 dx、dy
     d_xy[0, :] = d_xy_[:, :][0]
     d_xy[-1, :] = d_xy_[:, :][-1]
     d_xy[1:-1, :] = (d_xy_[1:, :] + d_xy_[:-1, :]) / 2
 
     # 计算切线方向角 theta
-    theta = np.arctan2(d_xy[:, 1], d_xy[:, 0])  # np.arctan2 会将角度限制在 (-pi, pi)之间
+    theta = np.arctan2(
+        d_xy[:, 1], d_xy[:, 0]
+    )  # np.arctan2 会将角度限制在 (-pi, pi)之间
 
     d_theta_ = theta[1:] - theta[:-1]  # 差分，这种写法比np.diff()性能高得多
     d_theta = np.empty_like(theta)