Updated to work with Python3

3DAnimationTest.ipynb

@@ -2,17 +2,27 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# First import everthing you need\n",
     "import numpy as np\n",
     "from matplotlib import pyplot as plt\n",
     "from matplotlib import animation\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
     "\n",
     "# Create some random data, I took this piece from here:\n",
     "# http://matplotlib.org/mpl_examples/mplot3d/scatter3d_demo.py\n",
@@ -24,10 +34,10 @@
     "zz = randrange(n, -50, -25)\n",
     "\n",
     "# Create a figure and a 3D Axes\n",
-    "fig = plt.figure()\n",
-    "ax = Axes3D(fig)\n",
+    "fig = plt.figure(facecolor=(0,0,0))\n",
+    "ax = fig.add_subplot(projection='3d')\n",
     "ax.set_axis_off()\n",
-    "ax.set_axis_bgcolor((0, 0, 0))\n",
+    "ax.set_facecolor((0, 0, 0))\n",
     "\n",
     "# Create an init function and the animate functions.\n",
     "# Both are explained in the tutorial. Since we are changing\n",
@@ -53,18 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -86,7 +85,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,