Note
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Tricontour Demo#
Contour plots of unstructured triangular grids.
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import numpy as np
Creating a Triangulation without specifying the triangles results in the Delaunay triangulation of the points.
# First create the x and y coordinates of the points.
n_angles = 48
n_radii = 8
min_radius = 0.25
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += np.pi / n_angles
x = (radii * np.cos(angles)).flatten()
y = (radii * np.sin(angles)).flatten()
z = (np.cos(radii) * np.cos(3 * angles)).flatten()
# Create the Triangulation; no triangles so Delaunay triangulation created.
triang = tri.Triangulation(x, y)
# Mask off unwanted triangles.
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
y[triang.triangles].mean(axis=1))
< min_radius)
pcolor plot.
fig1, ax1 = plt.subplots()
ax1.set_aspect('equal')
tcf = ax1.tricontourf(triang, z)
fig1.colorbar(tcf)
ax1.tricontour(triang, z, colors='k')
ax1.set_title('Contour plot of Delaunay triangulation')
Text(0.5, 1.0, 'Contour plot of Delaunay triangulation')
You could also specify hatching patterns along with different cmaps.
fig2, ax2 = plt.subplots()
ax2.set_aspect("equal")
tcf = ax2.tricontourf(
triang,
z,
hatches=["*", "-", "/", "//", "\\", None],
cmap="cividis"
)
fig2.colorbar(tcf)
ax2.tricontour(triang, z, linestyles="solid", colors="k", linewidths=2.0)
ax2.set_title("Hatched Contour plot of Delaunay triangulation")
Text(0.5, 1.0, 'Hatched Contour plot of Delaunay triangulation')
You could also generate hatching patterns labeled with no color.
fig3, ax3 = plt.subplots()
n_levels = 7
tcf = ax3.tricontourf(
triang,
z,
n_levels,
colors="none",
hatches=[".", "/", "\\", None, "\\\\", "*"],
)
ax3.tricontour(triang, z, n_levels, colors="black", linestyles="-")
# create a legend for the contour set
artists, labels = tcf.legend_elements(str_format="{:2.1f}".format)
ax3.legend(artists, labels, handleheight=2, framealpha=1)
<matplotlib.legend.Legend object at 0x7f2cfaeb8c10>
You can specify your own triangulation rather than perform a Delaunay triangulation of the points, where each triangle is given by the indices of the three points that make up the triangle, ordered in either a clockwise or anticlockwise manner.
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])
x0 = -5
y0 = 52
z = np.exp(-0.01 * ((x - x0) ** 2 + (y - y0) ** 2))
triangles = np.asarray([
[67, 66, 1], [65, 2, 66], [ 1, 66, 2], [64, 2, 65], [63, 3, 64],
[60, 59, 57], [ 2, 64, 3], [ 3, 63, 4], [ 0, 67, 1], [62, 4, 63],
[57, 59, 56], [59, 58, 56], [61, 60, 69], [57, 69, 60], [ 4, 62, 68],
[ 6, 5, 9], [61, 68, 62], [69, 68, 61], [ 9, 5, 70], [ 6, 8, 7],
[ 4, 70, 5], [ 8, 6, 9], [56, 69, 57], [69, 56, 52], [70, 10, 9],
[54, 53, 55], [56, 55, 53], [68, 70, 4], [52, 56, 53], [11, 10, 12],
[69, 71, 68], [68, 13, 70], [10, 70, 13], [51, 50, 52], [13, 68, 71],
[52, 71, 69], [12, 10, 13], [71, 52, 50], [71, 14, 13], [50, 49, 71],
[49, 48, 71], [14, 16, 15], [14, 71, 48], [17, 19, 18], [17, 20, 19],
[48, 16, 14], [48, 47, 16], [47, 46, 16], [16, 46, 45], [23, 22, 24],
[21, 24, 22], [17, 16, 45], [20, 17, 45], [21, 25, 24], [27, 26, 28],
[20, 72, 21], [25, 21, 72], [45, 72, 20], [25, 28, 26], [44, 73, 45],
[72, 45, 73], [28, 25, 29], [29, 25, 31], [43, 73, 44], [73, 43, 40],
[72, 73, 39], [72, 31, 25], [42, 40, 43], [31, 30, 29], [39, 73, 40],
[42, 41, 40], [72, 33, 31], [32, 31, 33], [39, 38, 72], [33, 72, 38],
[33, 38, 34], [37, 35, 38], [34, 38, 35], [35, 37, 36]])
Rather than create a Triangulation object, can simply pass x, y and triangles arrays to tripcolor directly. It would be better to use a Triangulation object if the same triangulation was to be used more than once to save duplicated calculations.
fig4, ax4 = plt.subplots()
ax4.set_aspect('equal')
tcf = ax4.tricontourf(x, y, triangles, z)
fig4.colorbar(tcf)
ax4.set_title('Contour plot of user-specified triangulation')
ax4.set_xlabel('Longitude (degrees)')
ax4.set_ylabel('Latitude (degrees)')
plt.show()
References
The use of the following functions, methods, classes and modules is shown in this example:
Total running time of the script: ( 0 minutes 2.280 seconds)