matplotlib 非结构化四边形而不是三角形

Question

我有两个包含非结构化网格的 netcdf 文件。第一个网格每个面有 3 个顶点，第二个网格每个面有 4 个顶点。

对于每个面包含 3 个顶点的网格，我可以使用 matplotlib.tri 进行可视化（例如 triplot_demo.py：

import matplotlib.pyplot as plt
import matplotlib.tri as tri
import numpy as np
xy = np.asarray([
    [-0.101, 0.872], [-0.080, 0.883], [-0.069, 0.888], [-0.054, 0.890],
    [-0.045, 0.897], [-0.057, 0.895], [-0.073, 0.900], [-0.087, 0.898],
    [-0.090, 0.904], [-0.069, 0.907], [-0.069, 0.921], [-0.080, 0.919],
    [-0.073, 0.928], [-0.052, 0.930], [-0.048, 0.942], [-0.062, 0.949],
    [-0.054, 0.958], [-0.069, 0.954], [-0.087, 0.952], [-0.087, 0.959],
    [-0.080, 0.966], [-0.085, 0.973], [-0.087, 0.965], [-0.097, 0.965],
    [-0.097, 0.975], [-0.092, 0.984], [-0.101, 0.980], [-0.108, 0.980],
    [-0.104, 0.987], [-0.102, 0.993], [-0.115, 1.001], [-0.099, 0.996],
    [-0.101, 1.007], [-0.090, 1.010], [-0.087, 1.021], [-0.069, 1.021],
    [-0.052, 1.022], [-0.052, 1.017], [-0.069, 1.010], [-0.064, 1.005],
    [-0.048, 1.005], [-0.031, 1.005], [-0.031, 0.996], [-0.040, 0.987],
    [-0.045, 0.980], [-0.052, 0.975], [-0.040, 0.973], [-0.026, 0.968],
    [-0.020, 0.954], [-0.006, 0.947], [ 0.003, 0.935], [ 0.006, 0.926],
    [ 0.005, 0.921], [ 0.022, 0.923], [ 0.033, 0.912], [ 0.029, 0.905],
    [ 0.017, 0.900], [ 0.012, 0.895], [ 0.027, 0.893], [ 0.019, 0.886],
    [ 0.001, 0.883], [-0.012, 0.884], [-0.029, 0.883], [-0.038, 0.879],
    [-0.057, 0.881], [-0.062, 0.876], [-0.078, 0.876], [-0.087, 0.872],
    [-0.030, 0.907], [-0.007, 0.905], [-0.057, 0.916], [-0.025, 0.933],
    [-0.077, 0.990], [-0.059, 0.993]])
x = np.degrees(xy[:, 0])
y = np.degrees(xy[:, 1])

triangles = np.asarray([
    [65, 44, 20],
    [65, 60, 44]])

triang = tri.Triangulation(x, y, triangles)

plt.figure()
plt.gca().set_aspect('equal')
plt.triplot(triang, 'go-', lw=1.0)
plt.title('triplot of user-specified triangulation')
plt.xlabel('Longitude (degrees)')
plt.ylabel('Latitude (degrees)')

plt.show()

-- 之后标注的相关点的索引

但是如何可视化每个面包含 4 个顶点（四边形）的非结构化网格？按照前面的例子，我的脸看起来像：

quatrang = np.asarray([
    [65, 60, 44, 20]])

显然尝试tri.Triangulation 不起作用：

quatr = tri.Triangulation(x, y, quatrang)

ValueError: triangles must be a (?,3) array

我在 matplotlib 库中找不到关于每个面 4 个顶点的任何内容。非常感谢任何帮助..

编辑：根据一个最小、完整且可验证的示例更改了问题

Answer 1

正如已经评论的那样，由于没有 Quatrangulation 或 simiar，因此没有标准方法来绘制与 matplotlib 中每个形状四个点的 triplot 类似的图。 当然，您可以再次对网格进行三角剖分，以获得每个四边形 2 个三角形。或者，您可以绘制形状的 PolyCollection，给定它们在空间中的坐标。下面显示了后者，定义了一个 quatplot 函数，该函数将顶点的坐标和索引作为输入，并将这些顶点的 PolyCollection 绘制到轴上。

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.collections

xy = np.asarray([
    [-0.101, 0.872], [-0.080, 0.883], [-0.069, 0.888], [-0.054, 0.890],
    [-0.090, 0.904], [-0.069, 0.907], [-0.069, 0.921], [-0.080, 0.919],
    [-0.080, 0.966], [-0.085, 0.973], [-0.087, 0.965], [-0.097, 0.965],
    [-0.104, 0.987], [-0.102, 0.993], [-0.115, 1.001], [-0.099, 0.996],
    [-0.052, 1.022], [-0.052, 1.017], [-0.069, 1.010], [-0.064, 1.005],
    [-0.045, 0.980], [-0.052, 0.975], [-0.040, 0.973], [-0.026, 0.968],
    [ 0.017, 0.900], [ 0.012, 0.895], [ 0.027, 0.893], [ 0.019, 0.886],
    [ 0.001, 0.883], [-0.012, 0.884], [-0.029, 0.883], [-0.038, 0.879],
    [-0.030, 0.907], [-0.007, 0.905], [-0.057, 0.916], [-0.025, 0.933],
    [-0.077, 0.990], [-0.059, 0.993]])
x = np.degrees(xy[:, 0])
y = np.degrees(xy[:, 1])

quatrang = np.asarray([
    [19,13,10,22], [35,7,3,28]])

def quatplot(x,y, quatrangles, ax=None, **kwargs):
    if not ax: ax=plt.gca()
    xy = np.c_[x,y]
    verts=xy[quatrangles]
    pc = matplotlib.collections.PolyCollection(verts, **kwargs)
    ax.add_collection(pc)
    ax.autoscale()


plt.figure()
plt.gca().set_aspect('equal')

quatplot(x,y, quatrang, ax=None, color="crimson", facecolor="None")
plt.plot(x,y, marker="o", ls="", color="crimson")

plt.title('quatplot of user-specified quatrangulation')
plt.xlabel('Longitude (degrees)')
plt.ylabel('Latitude (degrees)')

for i, (xi,yi) in enumerate(np.degrees(xy)):
    plt.text(xi,yi,i, size=8)

plt.show()