如何更改应用于 matplotlib 中的补丁的阴影平铺？

Question

如何更改 matplotlib 在补丁中平铺自定义阴影线的方式？我想以网格图案（就像方形瓷砖）平铺舱口，而不是交替行（就像砌砖）。

┌───┐┌───┐┌───┐
│   ││   ││   │
└───┘└───┘└───┘
┌───┐┌───┐┌───┐
│   ││   ││   │
└───┘└───┘└───┘

对

┌───┐┌───┐┌───┐
│   ││   ││   │
└───┘└───┘└───┘
──┐┌───┐┌───┐┌─
  ││   ││   ││
──┘└───┘└───┘└─

这是在 matplotlib 中生成简单的自定义填充的示例代码：

import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.hatch

square_path = patches.Polygon(
    [[-0.4, -0.4], [4.0, -0.4], [0.4, 0.4], [-0.4, 0.4]],
    closed=True, fill=False).get_path()

class SquareHatch(matplotlib.hatch.Shapes):
    """Custom hatches defined by a path drawn inside [-0.5, 0.5] square.

    Identifier 's'.
    """
    filled = True
    size = 1.0
    path = square_path

    def __init__(self, hatch, density):
        """Initialize the custom hatch."""
        self.num_rows = int((hatch.count('s')) * density)
        self.shape_vertices = self.path.vertices
        self.shape_codes = self.path.codes
        matplotlib.hatch.Shapes.__init__(self, hatch, density)

matplotlib.hatch._hatch_types.append(SquareHatch)

# Create a figure and axis
self.fig, self.ax = plt.subplots()
# Create a square patch with hatching
square = patches.Rectangle(xy=(0.25, 0.25), width=0.5, height=0.5, hatch='s', fill=False)
# Add the square patch to the axis
self.ax.add_patch(square)
# Display the plot
plt.show()

我一直在寻找，我认为这是一个选择，但我没有找到。 AI 说这是可能的，但给了我一个不起作用的选项......我会祭坛我的瓷砖，但我无法找到一种方法让我的图案与偏移第二行一起工作。

Answer 1

问题来自

Shapes 超类

中的 set_vertices_and_codes 函数：

def set_vertices_and_codes(self, vertices, codes):
    offset = 1.0 / self.num_rows
    shape_vertices = self.shape_vertices * offset * self.size
    shape_codes = self.shape_codes
    if not self.filled:
        shape_vertices = np.concatenate(  # Forward, then backward.
            [shape_vertices, shape_vertices[::-1] * 0.9])
        shape_codes = np.concatenate([shape_codes, shape_codes])
    vertices_parts = []
    codes_parts = []
    for row in range(self.num_rows + 1):
        ####### Offsetting occurs here #############
        if row % 2 == 0:
            cols = np.linspace(0, 1, self.num_rows + 1)
        else:
            cols = np.linspace(offset / 2, 1 - offset / 2, self.num_rows)
        ####### Offsetting occurs here #############
        row_pos = row * offset
        for col_pos in cols:
            vertices_parts.append(shape_vertices + [col_pos, row_pos])
            codes_parts.append(shape_codes)
    np.concatenate(vertices_parts, out=vertices)
    np.concatenate(codes_parts, out=codes)

无论行号如何，将列更改为

np.linspace(0, 1, self.num_rows+1)

都会删除偏移量，但现在之前在 Shape 类的

self.num_shapes

中设置的

__init__

已关闭。最终，我发现自定义填充更容易复制粘贴 Shape 类，并进行一些小的编辑来删除偏移。

class SquareHatch(matplotlib.hatch.HatchPatternBase):
    """Custom hatches defined by a path drawn inside [-0.5, 0.5] square.

    Identifier 's'.
    """
    filled = True
    size = 1.0
    path = square_path

    def __init__(self, hatch, density):
        """Initialize the custom hatch."""
        self.num_rows = int((hatch.count('s')) * density)
        self.shape_vertices = self.path.vertices
        self.shape_codes = self.path.codes

        self.num_shapes = (self.num_rows+1)**2
        self.num_vertices = (self.num_shapes *
                                 len(self.shape_vertices) *
                                 (1 if self.filled else 2))

    def set_vertices_and_codes(self, vertices, codes):
        offset = 1.0 / self.num_rows
        shape_vertices = self.shape_vertices * offset * self.size
        shape_codes = self.shape_codes
        if not self.filled:
            shape_vertices = np.concatenate(  # Forward, then backward.
                [shape_vertices, shape_vertices[::-1] * 0.9])
            shape_codes = np.concatenate([shape_codes, shape_codes])
        vertices_parts = []
        codes_parts = []
        for row in range(self.num_rows + 1):

            cols = np.linspace(0, 1, self.num_rows + 1)

            row_pos = row * offset
            for col_pos in cols:
                vertices_parts.append(shape_vertices + [col_pos, row_pos])
                codes_parts.append(shape_codes)
        np.concatenate(vertices_parts, out=vertices)
        np.concatenate(codes_parts, out=codes)