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+# 基于网格采样数据的二维网格脊线加密
+
+[返回示例目录](index.md)
+
+以下保留原笔记本的代码和已保存输出；转换过程中未重新执行代码。
+
+首先导入所需的库。
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+from meshkernel import (
+    GeometryList,
+    GriddedSamples,
+    MakeGridParameters,
+    MeshKernel,
+    MeshRefinementParameters,
+    RefinementType,
+    GriddedSamples,
+)
+```
+
+`meshkernel` 提供了一组便捷方法，用于创建常见网格。
+
+这里使用 `curvilinear_compute_rectangular_grid` 方法创建一个简单的曲线网格。该方法的完整参数请参阅相应文档。
+
+```python
+mk = MeshKernel()
+
+num_rows = 21
+num_columns = 41
+
+make_grid_parameters = MakeGridParameters()
+make_grid_parameters.num_columns = num_columns
+make_grid_parameters.num_rows = num_rows
+make_grid_parameters.angle = 0.0
+make_grid_parameters.origin_x = 0.0
+make_grid_parameters.origin_y = 0.0
+make_grid_parameters.block_size_x = 10.0
+make_grid_parameters.block_size_y = 10.0
+
+mk.curvilinear_compute_rectangular_grid(make_grid_parameters)
+```
+
+将曲线网格转换为非结构网格，并获取生成的 `mesh2d`。
+
+```python
+mk.curvilinear_convert_to_mesh2d()
+mesh2d_input = mk.mesh2d_get()
+```
+
+生成的网格可以按如下方式可视化。
+
+```python
+fig, ax = plt.subplots()
+mesh2d_input.plot_edges(ax, color="black")
+```
+
+![单元格 8 的绘图输出](images/11_mesh2d_refine_ridges_gridded_samples_8_0.png)
+
+定义均匀间距的网格采样数据。
+
+```python
+def read_asc_file(file_path, dtype=np.float32):
+    """读取 ASC 文件，返回文件头和 NumPy 数组形式的数据。
+    参数：
+            file_path (str)：文件路径。
+    返回值：
+            header：ASCII 文件头。
+            data：以双精度 NumPy 数组表示的 ASCII 数据。
+    """
+
+    header = {}
+    data = []
+
+    with open(file_path, "r") as file:
+        # 读取文件头信息
+        for _ in range(6):
+            line = file.readline().strip().split()
+            header[line[0]] = float(line[1])
+
+        # 读取数据值
+        for line in file:
+            data_row = [float(value) for value in line.strip().split()]
+            data.insert(0, data_row)  # 将该行插入到开头
+
+        # 将数据展平
+        data = np.array(data).flatten().astype(dtype)
+
+    return header, data
+```
+
+```python
+header, values_np = read_asc_file("./data_examples/gaussian_bump.asc", dtype=np.float32)
+```
+
+绘制从 ASC 文件读取的数据。
+
+```python
+values_np_matrix = np.reshape(values_np, (int(header["nrows"]), int(header["ncols"])))
+plt.imshow(values_np_matrix, cmap="viridis", interpolation="nearest")
+plt.title("gaussian bump")
+plt.show()
+```
+
+![单元格 13 的绘图输出](images/11_mesh2d_refine_ridges_gridded_samples_13_0.png)
+
+假设间距均匀，将 ASCII 数据存入 `GriddedSamples` 实例。
+
+```python
+num_sample_x_coordinates = (num_columns - 1) * 2 + 1
+num_sample_y_coordinates = (num_rows - 1) * 2 + 1
+gridded_samples = GriddedSamples(
+    num_x=num_sample_x_coordinates,
+    num_y=num_sample_y_coordinates,
+    x_origin=0.0,
+    y_origin=-0.0,
+    cell_size=5.0,
+    values=values_np,
+)
+```
+
+设置网格加密算法的参数。注意，必须正确设置脊线加密类型。
+
+```python
+refinement_params = MeshRefinementParameters(
+    refine_intersected=False,
+    use_mass_center_when_refining=False,
+    min_edge_size=2.0,
+    refinement_type=RefinementType.RIDGE_DETECTION,
+    connect_hanging_nodes=True,
+    account_for_samples_outside_face=False,
+    max_refinement_iterations=1,
+)
+```
+
+现在可以执行加密。
+
+```python
+relative_search_radius = 1.01
+minimum_num_samples = 1
+number_of_smoothing_iterations = 0
+
+mk.mesh2d_refine_ridges_based_on_gridded_samples(
+    gridded_samples=gridded_samples,
+    relative_search_radius=relative_search_radius,
+    minimum_num_samples=minimum_num_samples,
+    number_of_smoothing_iterations=number_of_smoothing_iterations,
+    mesh_refinement_params=refinement_params,
+)
+```
+
+绘制加密后的网格。
+
+```python
+mesh2d_output = mk.mesh2d_get()
+fig, ax = plt.subplots()
+mesh2d_output.plot_edges(ax, color="black")
+```
+
+![单元格 21 的绘图输出](images/11_mesh2d_refine_ridges_gridded_samples_21_0.png)