{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Triplot Demo\n\nCreating and plotting unstructured triangular grids.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\nimport numpy as np\n\nimport matplotlib.tri as tri" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a Triangulation without specifying the triangles results in the\nDelaunay triangulation of the points.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# First create the x and y coordinates of the points.\nn_angles = 36\nn_radii = 8\nmin_radius = 0.25\nradii = np.linspace(min_radius, 0.95, n_radii)\n\nangles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)\nangles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)\nangles[:, 1::2] += np.pi / n_angles\n\nx = (radii * np.cos(angles)).flatten()\ny = (radii * np.sin(angles)).flatten()\n\n# Create the Triangulation; no triangles so Delaunay triangulation created.\ntriang = tri.Triangulation(x, y)\n\n# Mask off unwanted triangles.\ntriang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),\n y[triang.triangles].mean(axis=1))\n < min_radius)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the triangulation.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig1, ax1 = plt.subplots()\nax1.set_aspect('equal')\nax1.triplot(triang, 'bo-', lw=1)\nax1.set_title('triplot of Delaunay triangulation')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can specify your own triangulation rather than perform a Delaunay\ntriangulation of the points, where each triangle is given by the indices of\nthe three points that make up the triangle, ordered in either a clockwise or\nanticlockwise manner.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xy = np.asarray([\n [-0.101, 0.872], [-0.080, 0.883], [-0.069, 0.888], [-0.054, 0.890],\n [-0.045, 0.897], [-0.057, 0.895], [-0.073, 0.900], [-0.087, 0.898],\n [-0.090, 0.904], [-0.069, 0.907], [-0.069, 0.921], [-0.080, 0.919],\n [-0.073, 0.928], [-0.052, 0.930], [-0.048, 0.942], [-0.062, 0.949],\n [-0.054, 0.958], [-0.069, 0.954], [-0.087, 0.952], [-0.087, 0.959],\n [-0.080, 0.966], [-0.085, 0.973], [-0.087, 0.965], [-0.097, 0.965],\n [-0.097, 0.975], [-0.092, 0.984], [-0.101, 0.980], [-0.108, 0.980],\n [-0.104, 0.987], [-0.102, 0.993], [-0.115, 1.001], [-0.099, 0.996],\n [-0.101, 1.007], [-0.090, 1.010], [-0.087, 1.021], [-0.069, 1.021],\n [-0.052, 1.022], [-0.052, 1.017], [-0.069, 1.010], [-0.064, 1.005],\n [-0.048, 1.005], [-0.031, 1.005], [-0.031, 0.996], [-0.040, 0.987],\n [-0.045, 0.980], [-0.052, 0.975], [-0.040, 0.973], [-0.026, 0.968],\n [-0.020, 0.954], [-0.006, 0.947], [ 0.003, 0.935], [ 0.006, 0.926],\n [ 0.005, 0.921], [ 0.022, 0.923], [ 0.033, 0.912], [ 0.029, 0.905],\n [ 0.017, 0.900], [ 0.012, 0.895], [ 0.027, 0.893], [ 0.019, 0.886],\n [ 0.001, 0.883], [-0.012, 0.884], [-0.029, 0.883], [-0.038, 0.879],\n [-0.057, 0.881], [-0.062, 0.876], [-0.078, 0.876], [-0.087, 0.872],\n [-0.030, 0.907], [-0.007, 0.905], [-0.057, 0.916], [-0.025, 0.933],\n [-0.077, 0.990], [-0.059, 0.993]])\nx = np.degrees(xy[:, 0])\ny = np.degrees(xy[:, 1])\n\ntriangles = np.asarray([\n [67, 66, 1], [65, 2, 66], [ 1, 66, 2], [64, 2, 65], [63, 3, 64],\n [60, 59, 57], [ 2, 64, 3], [ 3, 63, 4], [ 0, 67, 1], [62, 4, 63],\n [57, 59, 56], [59, 58, 56], [61, 60, 69], [57, 69, 60], [ 4, 62, 68],\n [ 6, 5, 9], [61, 68, 62], [69, 68, 61], [ 9, 5, 70], [ 6, 8, 7],\n [ 4, 70, 5], [ 8, 6, 9], [56, 69, 57], [69, 56, 52], [70, 10, 9],\n [54, 53, 55], [56, 55, 53], [68, 70, 4], [52, 56, 53], [11, 10, 12],\n [69, 71, 68], [68, 13, 70], [10, 70, 13], [51, 50, 52], [13, 68, 71],\n [52, 71, 69], [12, 10, 13], [71, 52, 50], [71, 14, 13], [50, 49, 71],\n [49, 48, 71], [14, 16, 15], [14, 71, 48], [17, 19, 18], [17, 20, 19],\n [48, 16, 14], [48, 47, 16], [47, 46, 16], [16, 46, 45], [23, 22, 24],\n [21, 24, 22], [17, 16, 45], [20, 17, 45], [21, 25, 24], [27, 26, 28],\n [20, 72, 21], [25, 21, 72], [45, 72, 20], [25, 28, 26], [44, 73, 45],\n [72, 45, 73], [28, 25, 29], [29, 25, 31], [43, 73, 44], [73, 43, 40],\n [72, 73, 39], [72, 31, 25], [42, 40, 43], [31, 30, 29], [39, 73, 40],\n [42, 41, 40], [72, 33, 31], [32, 31, 33], [39, 38, 72], [33, 72, 38],\n [33, 38, 34], [37, 35, 38], [34, 38, 35], [35, 37, 36]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rather than create a Triangulation object, can simply pass x, y and triangles\narrays to triplot directly. It would be better to use a Triangulation object\nif the same triangulation was to be used more than once to save duplicated\ncalculations.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig2, ax2 = plt.subplots()\nax2.set_aspect('equal')\nax2.triplot(x, y, triangles, 'go-', lw=1.0)\nax2.set_title('triplot of user-specified triangulation')\nax2.set_xlabel('Longitude (degrees)')\nax2.set_ylabel('Latitude (degrees)')\n\nplt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ".. admonition:: References\n\n The use of the following functions, methods, classes and modules is shown\n in this example:\n\n - `matplotlib.axes.Axes.triplot` / `matplotlib.pyplot.triplot`\n - `matplotlib.tri`\n - `matplotlib.tri.Triangulation`\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.2" } }, "nbformat": 4, "nbformat_minor": 0 }