matplotlib unstructered quadrilaterals instead of triangles

Question

I've two netcdf files containing both unstructured grids. The first grid has 3 vertices per face and the second has 4 vertices per face.

For the grid containing 3 vertices per face I can use matplotlib.tri for visualization (like 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()

-- indices of the related point annotated afterwards

BUT how to visualize the unstructured grid containing 4 vertices per face (quadrilaterals)? Following the previous exapmle, my faces looks like:

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

Obviously trying tri.Triangulation doesn't work:

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

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

I cannot find anything in the matplotlib libraries regarding 4 vertices per face. Any help is greatly appreciated..

EDIT: Changed the question based upon a minimal, complete and verifiable example

Electrocardiogram

Since there is no Quatrangulation or simiar, there is no standard way to plot those in matplotlib. Of course you could triangulate your mesh again to obtain 2 triangles per quadrilateral. Or, you can plot a PolyCollection of the shapes, given their coordinates in space. For help with that, you would need to provide a runnable example (minimal reproducible example). – Unclog

Thanks @Unclog I've modified my question based on your comments to include a runnable example. – Electrocardiogram

Answer 1

As commented already, since there is no Quatrangulation or simiar, there is no standard way to plot a a similar plot as triplot with four points per shape in matplotlib. Of course you could triangulate your mesh again to obtain 2 triangles per quadrilateral. Or, you can plot a PolyCollection of the shapes, given their coordinates in space. The following shows the latter, defining a quatplot function which takes the coordinates and the indices of the vertices as input and draws a PolyCollection of those to the axes.

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()

Unclog

See this new question for the case you want to have a colormapping involved. – Unclog