No Description

example-plot.ipynb 25KB

    { "cells": [ { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1062c4780>]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x106050240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "a = np.array([0, 1, 1])\n", "plt.plot(a)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x105edd358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "soa = np.array([[0, 0, 6, -2], [0, 0, -4, 4], [0, 0, 2, 2]])\n", "X, Y, U, V = zip(*soa)\n", "plt.figure()\n", "ax = plt.gca()\n", "ax.quiver(X, Y, U, V, angles='xy', scale_units='xy', scale=1)\n", "ax.set_xlim([-5, 7])\n", "ax.set_ylim([-5, 5])\n", "plt.grid()\n", "plt.draw()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }