如何在python中可视化分形

Question

这是生成分形的代码。

import matplotlib.pyplot as plt

def makes(self, fractal):
    if (fractal == "SierpinskiTriangle"):
        SierpinskiTriangle(self.dimensions)
        for i in range(len(SierpinskiTriangle.verticies)):
            plotPoint(i, self.vertexColor, self.vertexRadius)
        for i in range(SierpinskiTriangle.numPoints):
            listVertices = SierpinskiTriangle.verticies
            randVert = randint(0, len(listVertices)-1)
            newVertexPoint = listVertices[randVert]
            m1 = Point.midpt(m1, newVertexPoint)
            self.plot(m1)

    elif (fractal == "SierpinskiCarpet"):
        SierpinskiCarpet(self.dimensions)
        for i in range(len(SierpinskiCarpet.verticies)):
            plotPoint(i, self.vertexColor, self.vertexRadius)
        for i in range(SierpinskiCarpet.numPoints):
            listVertices = SierpinskiCarpet
            randVert = randint(0, len(listVertices)-1)
            newVertexPoint = listVertices[randVert]
            m1 = Point.midpt(m1, newVertexPoint)
            self.plot(m1)

    else:
        Pentagon(self.dimensions)
        for i in range(len(Pentagon.verticies)):
            plotPoint(i, self.vertexColor, self.vertexRadius)
        for i in range(Pentagon.numPoints):
            listVertices = SierpinskiCarpet
            randVert = randint(0, len(listVertices)-1)
            newVertexPoint = listVertices[randVert]
            m1 = Point.midpt(m1, newVertexPoint)
            self.plot(m1)
            

最后我不知道如何可视化分形。
我认为它与 matplot.lib 有关，但我不确定如何

Answer 1

尽管matplotplib主要适用于绘制图形，但如果您愿意，也可以使用它绘制点和多边形； 另请参阅： 如何使用基于 2D 中的 3 个点 (x,y) 的 matplotlib.pyplot 绘制三角形？

例如，要从多边形组成谢尔宾斯基三角形，并将这些多边形绘制到图形上：

import numpy as np
import matplotlib.pyplot as plt

MAX_LEVEL = 6


def sierpinski(p1, p2, p3, level=0):
    if level >= MAX_LEVEL:
        yield plt.Polygon([p1, p2, p3], color='red')
        return

    yield from sierpinski(p1, (p1+p2) / 2, (p1+p3) / 2, level+1)
    yield from sierpinski((p1+p2) / 2, p2, (p2+p3) / 2, level+1)
    yield from sierpinski((p1+p3) / 2, (p2+p3) / 2, p3, level+1)


plt.figure()
plt.scatter([0, 0, 10, 10], [0, 10, 0, 10], color='blue')

for patch in sierpinski(
        np.array([1.0, 1.0]), np.array([9.0, 1.0]), np.array([5.0, 9.0])):
    plt.gca().add_patch(patch)

plt.show()

上面的代码为我生成了以下图像输出：