|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "code", |
| 5 | + "execution_count": 1, |
| 6 | + "metadata": { |
| 7 | + "collapsed": true |
| 8 | + }, |
| 9 | + "outputs": [], |
| 10 | + "source": [ |
| 11 | + "import matplotlib.pyplot as plt\n", |
| 12 | + "from boid_flockers.model import BoidFlockers\n", |
| 13 | + "\n", |
| 14 | + "%matplotlib inline" |
| 15 | + ] |
| 16 | + }, |
| 17 | + { |
| 18 | + "cell_type": "code", |
| 19 | + "execution_count": 2, |
| 20 | + "metadata": { |
| 21 | + "collapsed": true |
| 22 | + }, |
| 23 | + "outputs": [], |
| 24 | + "source": [ |
| 25 | + "def draw_boids(model):\n", |
| 26 | + " x_vals = []\n", |
| 27 | + " y_vals = []\n", |
| 28 | + " for boid in model.agents:\n", |
| 29 | + " x, y = boid.pos\n", |
| 30 | + " x_vals.append(x)\n", |
| 31 | + " y_vals.append(y)\n", |
| 32 | + " fig = plt.figure(figsize=(10, 10))\n", |
| 33 | + " ax = fig.add_subplot(111)\n", |
| 34 | + " ax.scatter(x_vals, y_vals)" |
| 35 | + ] |
| 36 | + }, |
| 37 | + { |
| 38 | + "cell_type": "code", |
| 39 | + "execution_count": 3, |
| 40 | + "metadata": { |
| 41 | + "collapsed": false |
| 42 | + }, |
| 43 | + "outputs": [], |
| 44 | + "source": [ |
| 45 | + "model = BoidFlockers(100, 100, 100, speed=5, vision=5, separation=1)" |
| 46 | + ] |
| 47 | + }, |
| 48 | + { |
| 49 | + "cell_type": "code", |
| 50 | + "execution_count": 6, |
| 51 | + "metadata": { |
| 52 | + "collapsed": false |
| 53 | + }, |
| 54 | + "outputs": [], |
| 55 | + "source": [ |
| 56 | + "for i in range(50):\n", |
| 57 | + " model.step()" |
| 58 | + ] |
| 59 | + }, |
| 60 | + { |
| 61 | + "cell_type": "code", |
| 62 | + "execution_count": 7, |
| 63 | + "metadata": { |
| 64 | + "collapsed": false |
| 65 | + }, |
| 66 | + "outputs": [ |
| 67 | + { |
| 68 | + "data": { |
| 69 | + "image/png": 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zzie5ZcbXAWC0Lp5M1o4k2TikeHJyf+8TavSMMSazBq4bktye5H9qrf1WVb03m3qyWmut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\narmHiCxjAMBsBC6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|
| 70 | + "text/plain": [ |
| 71 | + "<matplotlib.figure.Figure at 0x108938a58>" |
| 72 | + ] |
| 73 | + }, |
| 74 | + "metadata": {}, |
| 75 | + "output_type": "display_data" |
| 76 | + } |
| 77 | + ], |
| 78 | + "source": [ |
| 79 | + "draw_boids(model)" |
| 80 | + ] |
| 81 | + }, |
| 82 | + { |
| 83 | + "cell_type": "code", |
| 84 | + "execution_count": null, |
| 85 | + "metadata": { |
| 86 | + "collapsed": true |
| 87 | + }, |
| 88 | + "outputs": [], |
| 89 | + "source": [] |
| 90 | + } |
| 91 | + ], |
| 92 | + "metadata": { |
| 93 | + "kernelspec": { |
| 94 | + "display_name": "Python 3", |
| 95 | + "language": "python", |
| 96 | + "name": "python3" |
| 97 | + }, |
| 98 | + "language_info": { |
| 99 | + "codemirror_mode": { |
| 100 | + "name": "ipython", |
| 101 | + "version": 3 |
| 102 | + }, |
| 103 | + "file_extension": ".py", |
| 104 | + "mimetype": "text/x-python", |
| 105 | + "name": "python", |
| 106 | + "nbconvert_exporter": "python", |
| 107 | + "pygments_lexer": "ipython3", |
| 108 | + "version": "3.4.2" |
| 109 | + } |
| 110 | + }, |
| 111 | + "nbformat": 4, |
| 112 | + "nbformat_minor": 0 |
| 113 | +} |
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