diff --git a/Gaussians.ipynb b/Gaussians.ipynb
index 7bb2dfc..38c379b 100644
--- a/Gaussians.ipynb
+++ b/Gaussians.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:7c1d5ebfcacf654b0862f976d6da64ba34c3743b23833736a6b3e7efb53ba119"
+  "signature": "sha256:167e76515c3a54031a76bf820666d9a7a52a0f919f127a68ea60c5e5e7821e63"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -48,24 +48,24 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%matplotlib inline\n",
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
       "\n",
-      "xs = arange(0,10,0.1)\n",
+      "xs = np.arange(0,10,0.1)\n",
       "ys = [gaussian (x, 5, 3) for x in xs]\n",
-      "plot (xs, ys)\n",
-      "show()"
+      "plt.plot (xs, ys)\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -103,7 +103,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "xs = arange(16,30,0.1)\n",
+      "xs = np.arange(16,30,0.1)\n",
       "ys = [gaussian (x,23,1) for x in xs]\n",
       "plt.plot (xs,ys, 'r')\n",
       "plt.axvline(23); plt.axvline(24) \n",
@@ -111,8 +111,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -140,8 +139,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -177,18 +175,17 @@
       "    return math.exp (-0.5 * (x-mu)**2 / sigma) / math.sqrt(2.*math.pi*sigma)\n",
       "\n",
       "def plt_g (mu,variance):\n",
-      "    xs = arange(0,10,0.15)\n",
+      "    xs = np.arange(0,10,0.15)\n",
       "    ys = [gaussian (x, mu,variance) for x in xs]\n",
-      "    plot (xs, ys)\n",
-      "    ylim((0,1))\n",
-      "    show()\n",
+      "    plt.plot (xs, ys)\n",
+      "    plt.ylim((0,1))\n",
+      "    plt.show()\n",
       "\n",
       "interact (plt_g, mu=(0,10), variance=(0.2,4.5))"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -219,8 +216,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     }
    ],
    "metadata": {}
diff --git a/Kalman Filters.ipynb b/Kalman Filters.ipynb
index 692ef17..a670b9c 100644
--- a/Kalman Filters.ipynb	
+++ b/Kalman Filters.ipynb	
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:62f7253d6ad9a6031738dd1335cf418d484588da5b86c0e2b98b648ae1f20f38"
+  "signature": "sha256:6ea7b0e562518e41af4e09c3d07bfdc49bd67a20f9d28c17a5b5a053444de18c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -50,8 +50,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -65,13 +64,13 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "for i in range(20):\n",
-      "    print (\"%.4f\" % random.randn()),"
+      "    print(\"%.4f\" % random.randn(),end='\\t')"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -87,20 +86,20 @@
      "collapsed": false,
      "input": [
       "%matplotlib inline\n",
+      "import matplotlib.pyplot as plt\n",
       "\n",
       "dog = DogSensor (noise=0.0)\n",
       "xs = []\n",
       "for i in range(10):\n",
       "    x = dog.sense()\n",
       "    xs.append(x)\n",
-      "    print(\"%.4f\" % x),\n",
-      "plot(xs)\n",
-      "show()"
+      "    print(\"%.4f\" % x, end=' '),\n",
+      "plt.plot(xs)\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -122,21 +121,20 @@
       "    for i in range(100):\n",
       "        x = dog.sense()\n",
       "        xs.append(x)\n",
-      "    p1, = plot(xs, c='b')\n",
-      "    p2, = plot([0,99],[1,100], 'r--')\n",
-      "    xlabel('time')\n",
-      "    ylabel('pos')\n",
-      "    ylim([0,100])\n",
-      "    title('noise = ' + str(noise_scale))\n",
-      "    legend([p1, p2], ['sensor', 'actual'], loc=2)\n",
-      "    show()\n",
+      "    p1, = plt.plot(xs, c='b')\n",
+      "    p2, = plt.plot([0,99],[1,100], 'r--')\n",
+      "    plt.xlabel('time')\n",
+      "    plt.ylabel('pos')\n",
+      "    plt.ylim([0,100])\n",
+      "    plt.title('noise = ' + str(noise_scale))\n",
+      "    plt.legend([p1, p2], ['sensor', 'actual'], loc=2)\n",
+      "    plt.show()\n",
       "    \n",
       "test_sensor(4.0)"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -159,8 +157,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -170,8 +167,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -198,8 +194,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -222,13 +217,12 @@
       "for i in xs:\n",
       "    ys.append(dog.sense())\n",
       "               \n",
-      "plot(xs,ys)\n",
-      "show()"
+      "plt.plot(xs,ys)\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -277,12 +271,13 @@
      "collapsed": false,
      "input": [
       "import gaussian\n",
+      "import numpy as np\n",
+      "\n",
       "def multiply(mu1, sig1, mu2, sig2):\n",
       "    m = (sig1*mu2 + sig2*mu1) / (sig1+sig2)\n",
       "    s = 1. / (1./sig1 + 1./ sig2)\n",
       "    return (m,s)\n",
       "\n",
-      "\n",
       "xs = np.arange(16, 30, 0.1)\n",
       "\n",
       "\n",
@@ -290,18 +285,17 @@
       "m, s = multiply(m1,s1,m1,s1)\n",
       "\n",
       "ys = [gaussian.gaussian(x,m1,s1) for x in xs]\n",
-      "p1, =plot (xs,ys)\n",
+      "p1, = plt.plot (xs,ys)\n",
       "\n",
       "ys = [gaussian.gaussian(x,m,s) for x in xs]\n",
-      "p2, = plot (xs,ys)\n",
+      "p2, = plt.plot (xs,ys)\n",
       "\n",
-      "legend([p1,p2],['original', 'multiply'])\n",
-      "show()"
+      "plt.legend([p1,p2],['original', 'multiply'])\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -318,26 +312,24 @@
      "input": [
       "xs = np.arange(16, 30, 0.1)\n",
       "\n",
-      "\n",
       "m1,s1 = 23, 5\n",
       "m2,s2 = 25, 5\n",
       "m, s = multiply(m1,s1,m2,s2)\n",
       "\n",
       "ys = [gaussian.gaussian(x,m1,s1) for x in xs]\n",
-      "p1, = plot (xs,ys)\n",
+      "p1, = plt.plot (xs,ys)\n",
       "\n",
       "ys = [gaussian.gaussian(x,m2,s2) for x in xs]\n",
-      "p2, = plot (xs,ys)\n",
+      "p2, = plt.plot (xs,ys)\n",
       "\n",
       "ys = [gaussian.gaussian(x,m,s) for x in xs]\n",
-      "p3, = plot(xs,ys)\n",
-      "legend([p1,p2,p3],['measure 1', 'measure 2', 'multiply'])\n",
-      "show()"
+      "p3, = plt.plot(xs,ys)\n",
+      "plt.legend([p1,p2,p3],['measure 1', 'measure 2', 'multiply'])\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -372,8 +364,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -391,8 +382,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -412,13 +402,11 @@
       "pos,s = 2, 5\n",
       "for i in range(20):\n",
       "    pos,s = sense(pos, s, dog.sense(), 5)\n",
-      "    print 'time:', i, 'position = ', \"%.3f\" % pos, 'variance = ', \"%.3f\" % s\n",
-      "\n"
+      "    print('time:', i, '\\tposition =', \"%.3f\" % pos, '\\tvariance =', \"%.3f\" % s)"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -476,8 +464,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -503,7 +490,7 @@
       "\n",
       "for i in range(10):\n",
       "    pos = update(pos[0], pos[1], movement, movement_error)\n",
-      "    print 'UPDATE:', \"%.4f\" %pos[0], \", %.4f\" %pos[1]\n",
+      "    print('UPDATE: %.4f,\\t%.4f' % (pos[0], pos[1]))\n",
       "    \n",
       "    Z = dog.sense()\n",
       "    zs.append(Z)\n",
@@ -511,18 +498,17 @@
       "    pos = sense(pos[0], pos[1], Z, sensor_error)\n",
       "    ps.append(pos[0])\n",
       "    \n",
-      "    print 'SENSE:', \"%.4f\" %pos[0], \", %.4f\" %pos[1]\n",
-      "    print\n",
+      "    print('SENSE: %.4f,\\t%.4f' % (pos[0], pos[1]))\n",
+      "    print()\n",
       "    \n",
-      "p1, = plot(zs,c='r', linestyle='dashed')\n",
-      "p2, = plot(ps, c='b')\n",
-      "legend([p1,p2], ['measurement', 'filter'], 2)\n",
-      "show()"
+      "p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
+      "p2, = plt.plot(ps, c='b')\n",
+      "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -623,15 +609,14 @@
       "    ps.append(pos[0])\n",
       "\n",
       "\n",
-      "p1, = plot(zs,c='r', linestyle='dashed')\n",
-      "p2, = plot(ps, c='b')\n",
-      "legend([p1,p2], ['measurement', 'filter'], 2)\n",
-      "show()"
+      "p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
+      "p2, = plt.plot(ps, c='b')\n",
+      "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -665,15 +650,14 @@
       "    ps.append(pos[0])\n",
       "\n",
       "\n",
-      "p1, = plot(zs,c='r', linestyle='dashed')\n",
-      "p2, = plot(ps, c='b')\n",
-      "legend([p1,p2], ['measurement', 'filter'], 2)\n",
-      "show()"
+      "p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
+      "p2, = plt.plot(ps, c='b')\n",
+      "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -707,15 +691,14 @@
       "    ps.append(pos[0])\n",
       "\n",
       "\n",
-      "p1, = plot(zs,c='r', linestyle='dashed')\n",
-      "p2, = plot(ps, c='b')\n",
-      "legend([p1,p2], ['measurement', 'filter'], 2)\n",
-      "show()"
+      "p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
+      "p2, = plt.plot(ps, c='b')\n",
+      "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -750,15 +733,14 @@
       "\n",
       "    pos = update (pos[0], pos[1], movement, movement_error)\n",
       "\n",
-      "p1, = plot(zs,c='r', linestyle='dashed')\n",
-      "p2, = plot(ps, c='b')\n",
-      "legend([p1,p2], ['measurement', 'filter'], 2)\n",
-      "show()"
+      "p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
+      "p2, = plt.plot(ps, c='b')\n",
+      "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -801,15 +783,14 @@
       "\n",
       "    pos = update(pos[0], pos[1], movement, movement_error)\n",
       "\n",
-      "p1, = plot(zs,c='r', linestyle='dashed')\n",
-      "p2, = plot(ps, c='b')\n",
-      "legend([p1,p2], ['measurement', 'filter'], 2)\n",
-      "show()"
+      "p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
+      "p2, = plt.plot(ps, c='b')\n",
+      "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -829,18 +810,14 @@
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
       "author notes:\n",
       "    clean up the code - same stuff duplicated over and over - write a 'clean implemntation' at the end.\n",
       "    \n",
       "    "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     ]
     },
     {
      "cell_type": "code",
@@ -864,10 +841,10 @@
       "    ps.append(pos[0])\n",
       "\n",
       "\n",
-      "p1, = plot(zs,c='r', linestyle='dashed')\n",
-      "p2, = plot(ps, c='b')\n",
-      "legend([p1,p2], ['measurement', 'filter'], 2)\n",
-      "show()"
+      "p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
+      "p2, = plt.plot(ps, c='b')\n",
+      "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
@@ -886,8 +863,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     }
    ],
    "metadata": {}
diff --git a/Multidimensional Kalman Filters.ipynb b/Multidimensional Kalman Filters.ipynb
index 906a70c..b38824a 100644
--- a/Multidimensional Kalman Filters.ipynb	
+++ b/Multidimensional Kalman Filters.ipynb	
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:43e6b1c0a6a87cad97442c35e4be82ce79c07ba2a6b41e95e576e4371959d448"
+  "signature": "sha256:3b71546948dd21caccb388a6147d443538b451fdb64f56494861aa34958fb04a"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -85,8 +85,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -107,8 +106,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -125,8 +123,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -143,8 +140,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -157,12 +153,11 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print multivariate_gaussian(x,mu,cov)"
+      "print(multivariate_gaussian(x,mu,cov))"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -175,13 +170,14 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
+      "\n",
       "x = np.array([2,7])\n",
-      "print multivariate_gaussian(x,mu,cov)"
+      "print(multivariate_gaussian(x,mu,cov))"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -195,12 +191,16 @@
      "collapsed": false,
      "input": [
       "%matplotlib inline\n",
+      "\n",
+      "import matplotlib.pyplot as plt\n",
+      "import matplotlib.pylab as pylab\n",
+      "from mpl_toolkits.mplot3d import Axes3D\n",
+      "import numpy as np\n",
+      "\n",
       "pylab.rcParams['figure.figsize'] = 12,6\n",
       "\n",
-      "from mpl_toolkits.mplot3d import Axes3D\n",
-      "\n",
-      "xs, ys = arange(-8, 13, .75), arange(-8, 20, .75)\n",
-      "xv, yv = meshgrid (xs, ys)\n",
+      "xs, ys = np.arange(-8, 13, .75), np.arange(-8, 20, .75)\n",
+      "xv, yv = np.meshgrid (xs, ys)\n",
       "\n",
       "zs = np.array([multivariate_gaussian(np.array([x,y]),mu,cov) \n",
       "               for x,y in zip(np.ravel(xv), np.ravel(yv))])\n",
@@ -208,13 +208,12 @@
       "\n",
       "ax = plt.figure().add_subplot(111, projection='3d')\n",
       "ax.plot_wireframe(xv, yv, zv)\n",
-      "show()\n",
+      "plt.show()\n",
       "pylab.rcParams['figure.figsize'] = 6,4"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -232,28 +231,28 @@
       "import gaussian as g\n",
       "pylab.rcParams['figure.figsize'] = 12,4\n",
       "\n",
-      "cov = array([[2,0],[0,2]])\n",
+      "cov = np.array([[2,0],[0,2]])\n",
       "e = g.sigma_ellipse (cov, 2, 7)\n",
-      "subplot(131)\n",
+      "plt.subplot(131)\n",
       "g.plot_sigma_ellipse(e, '|2 0|\\n|0 2|')\n",
       "\n",
       "\n",
-      "cov = array([[2,0],[0,9]])\n",
+      "cov = np.array([[2,0],[0,9]])\n",
       "e = g.sigma_ellipse (cov, 2, 7)\n",
-      "subplot(132)\n",
+      "plt.subplot(132)\n",
       "g.plot_sigma_ellipse(e, '|2 0|\\n|0 9|')\n",
       "\n",
-      "subplot(133)\n",
-      "cov = array([[2,1.2],[1.2,3]])\n",
+      "plt.subplot(133)\n",
+      "cov = np.array([[2,1.2],[1.2,3]])\n",
       "e = g.sigma_ellipse (cov, 2, 7)\n",
       "g.plot_sigma_ellipse(e,'|2 1.2|\\n|1.2 2|')\n",
-      "show()\n",
+      "plt.show()\n",
+      "\n",
       "pylab.rcParams['figure.figsize'] = 6,4"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -299,12 +298,14 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "scatter ([1,2,3],[1,2,3]);xlim([0,4]);ylim([0,4]);show()"
+      "plt.scatter ([1,2,3],[1,2,3])\n",
+      "plt.xlim([0,4])\n",
+      "plt.ylim([0,4])\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -317,15 +318,15 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "scatter ([1,2,3],[1,2,3]);xlim([0,5]);ylim([0,5])\n",
-      "plot([0,5],[0,5],'r')\n",
-      "scatter ([4], [4], c='g', marker='s',s=200)\n",
-      "show()"
+      "plt.scatter ([1,2,3],[1,2,3])\n",
+      "plt.xlim([0,5]); plt.ylim([0,5])\n",
+      "plt.plot([0,5],[0,5],'r')\n",
+      "plt.scatter ([4], [4], c='g', marker='s',s=200)\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -338,19 +339,18 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "cov = array([[0.003,0], [0,12]])\n",
-      "sigma = sigma=[0.5,1.,1.5,2]\n",
+      "cov = np.array([[0.003,0], [0,12]])\n",
+      "sigma=[0.5,1.,1.5,2]\n",
       "e1 = g.sigma_ellipses(cov, x=1, y=1, sigma=sigma)\n",
       "e2 = g.sigma_ellipses(cov, x=2, y=2, sigma=sigma)\n",
       "e3 = g.sigma_ellipses(cov, x=3, y=3, sigma=sigma)\n",
       "g.plot_sigma_ellipses([e1, e2, e3], axis_equal=True,x_lim=[0,4],y_lim=[0,15])\n",
       "plt.ylim([0,11])\n",
-      "show()"
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -369,7 +369,7 @@
      "input": [
       "from matplotlib.patches import Ellipse\n",
       "\n",
-      "cov = array([[1,1],[1,1.1]])\n",
+      "cov = np.array([[1,1],[1,1.1]])\n",
       "ev = g.sigma_ellipses(cov, x=2, y=2, sigma=sigma)\n",
       "\n",
       "isct = Ellipse(xy=(2,2), width=.2, height=1.2, edgecolor='r', fc='None', lw=4)\n",
@@ -380,8 +380,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -413,22 +412,20 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "cov = array([[7,4],[4,7.]])\n",
-      "mu = array([0,0])\n",
-      "x = array([0,0])\n",
-      "print multivariate_gaussian(x,mu,cov)"
+      "cov = np.array([[7,4],[4,7.]])\n",
+      "mu = np.array([0,0])\n",
+      "x = np.array([0,0])\n",
+      "print(multivariate_gaussian(x,mu,cov))"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -443,8 +440,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     }
    ],
    "metadata": {}
diff --git a/histogram_filter.ipynb b/histogram_filter.ipynb
index 9cdcf45..f04f6b2 100644
--- a/histogram_filter.ipynb
+++ b/histogram_filter.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:d0bfbbe322bb5a6c6b2c484f5f8da49814c5c8d8ec1120813d02a9511ddf94a1"
+  "signature": "sha256:35f19121bea1b617984e84b0fa7dca20a612d6302af68a65f9d7d24120786c2d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -38,12 +38,13 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "pos = array([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1])"
+      "import numpy as np\n",
+      "\n",
+      "pos = np.array([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1])"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -56,12 +57,11 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "hallway = array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])"
+      "hallway = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -75,8 +75,9 @@
      "collapsed": false,
      "input": [
       "%matplotlib inline\n",
+      "import matplotlib.pyplot as plt\n",
       "\n",
-      "pos = array([0.333, 0.333, 0., 0., 0., 0., 0., 0., 0.333, 0.])\n",
+      "pos = np.array([0.333, 0.333, 0., 0., 0., 0., 0., 0., 0.333, 0.])\n",
       "\n",
       "def bar_plot(pos):\n",
       "    ax = plt.figure().gca()\n",
@@ -89,8 +90,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -110,8 +110,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -132,13 +131,12 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "pos = array([0,1,0,0,0,0,0,0,0,0])\n",
+      "pos = np.array([0,1,0,0,0,0,0,0,0,0])\n",
       "print pos"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -168,7 +166,7 @@
      "collapsed": false,
      "input": [
       "def measure (pos, measure, p_hit, p_miss):\n",
-      "    q = array(pos, dtype=float)\n",
+      "    q = np.array(pos, dtype=float)\n",
       "    for i in range(len(hallway)):\n",
       "        if hallway[i] == measure:\n",
       "            q[i] = pos[i] * p_hit\n",
@@ -176,7 +174,7 @@
       "            q[i] = pos[i] * p_miss\n",
       "    return q\n",
       "\n",
-      "pos = array([0.2]*10)\n",
+      "pos = np.array([0.2]*10)\n",
       "reading = 1 # 1 is 'door'\n",
       "pos = measure (pos, 1, .6, .2)\n",
       "\n",
@@ -185,8 +183,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -205,7 +202,7 @@
       "        p[i] = p[i] / s\n",
       "        \n",
       "def sense (pos, measure, p_hit, p_miss):\n",
-      "    q = array(pos, dtype=float)\n",
+      "    q = np.array(pos, dtype=float)\n",
       "    for i in range(len(hallway)):\n",
       "        if hallway[i] == measure:\n",
       "            q[i] = pos[i] * p_hit\n",
@@ -215,7 +212,7 @@
       "    return q\n",
       "\n",
       "\n",
-      "pos = array([0.2]*10)\n",
+      "pos = np.array([0.2]*10)\n",
       "reading = 1 # 1 is 'door'\n",
       "pos = sense (pos, 1, .6, .2)\n",
       "\n",
@@ -225,8 +222,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -260,20 +256,19 @@
       "    is to the left\n",
       "    \"\"\"\n",
       "    n = len(pos)\n",
-      "    result = array(pos, dtype=float)\n",
+      "    result = np.array(pos, dtype=float)\n",
       "    for i in range(n):\n",
       "        result[i] = pos[(i-move) % n]\n",
       "    return result\n",
       "        \n",
-      "pos = array([.4, .1, .2, .3])\n",
+      "pos = np.array([.4, .1, .2, .3])\n",
       "print 'pos before update =', pos\n",
       "pos = perfect_update(pos, 1)\n",
       "print 'pos after update =', pos\n"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -296,7 +291,7 @@
      "input": [
       "def update (pos, move, p_correct, p_under, p_over):\n",
       "    n = len(pos)\n",
-      "    result = array(pos, dtype=float)\n",
+      "    result = np.array(pos, dtype=float)\n",
       "    for i in range(n):\n",
       "                result[i] =  \\\n",
       "            pos[(i-move) % n]   * p_correct + \\\n",
@@ -304,14 +299,13 @@
       "            pos[(i-move+1) % n] * p_under          \n",
       "    return result\n",
       "\n",
-      "p = array([0,0,0,1,0,0,0,0])\n",
+      "p = np.array([0,0,0,1,0,0,0,0])\n",
       "res = update (p, 2, .8, .1, .1)\n",
       "print res"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -324,14 +318,13 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "p = array([0, 0, .4, .6, 0, 0, 0, 0])\n",
+      "p = np.array([0, 0, .4, .6, 0, 0, 0, 0])\n",
       "res = update (p, 2, .8, .1, .1)\n",
       "print res"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -350,8 +343,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -372,8 +364,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -394,8 +385,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -416,19 +406,16 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "p = array([.1]*10)\n",
-      "p = sense (p, 1, .6, .2)\n",
-      "print p\n",
-      "p = update (p, 1, .8, .1, .1)\n",
-      "print p\n",
-      "bar_plot(p)\n",
-      "\n",
-      "          "
+      "p = np.array([.1]*10)\n",
+      "p = sense(p, 1, .6, .2)\n",
+      "print(p)\n",
+      "p = update(p, 1, .8, .1, .1)\n",
+      "print(p)\n",
+      "bar_plot(p)"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -441,14 +428,13 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "p = sense (p, 1, .6, .2)\n",
-      "print p\n",
+      "p = sense(p, 1, .6, .2)\n",
+      "print(p)\n",
       "bar_plot(p)"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -461,14 +447,13 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "p = update (p, 1, .8, .1, .1)\n",
-      "p = sense (p, 0, .6, .2)\n",
-      "bar_plot (p)"
+      "p = update(p, 1, .8, .1, .1)\n",
+      "p = sense(p, 0, .6, .2)\n",
+      "bar_plot(p)"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -481,14 +466,13 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "p = update (p, 1, .8, .1, .1)\n",
-      "p = sense (p, 0, .6, .2)\n",
-      "bar_plot (p)"
+      "p = update(p, 1, .8, .1, .1)\n",
+      "p = sense(p, 0, .6, .2)\n",
+      "bar_plot(p)"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -513,19 +497,18 @@
      "collapsed": false,
      "input": [
       "hallway = [1,0,1,0,0,1,0,1,0,0]\n",
-      "pos = array([.1]*10)\n",
+      "pos = np.array([.1]*10)\n",
       "measurements = [1,0,1,0,0]\n",
       "\n",
       "for m in measurements:\n",
-      "    pos = sense (pos, m, .6, .2)\n",
-      "    pos = update (pos, 1, .8, .1, .1)\n",
+      "    pos = sense(pos, m, .6, .2)\n",
+      "    pos = update(pos, 1, .8, .1, .1)\n",
       "bar_plot(pos)\n",
       "print pos"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -538,14 +521,13 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "pos = sense (pos, m, .6, .2)\n",
-      "pos = update (pos, 1, .8, .1, .1)\n",
+      "pos = sense(pos, m, .6, .2)\n",
+      "pos = update(pos, 1, .8, .1, .1)\n",
       "bar_plot(pos)"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -561,14 +543,13 @@
       "measurements = [0,1,0,1,0,0]\n",
       "\n",
       "for m in measurements:\n",
-      "    pos = sense (pos, m, .6, .2)\n",
-      "    pos = update (pos, 1, .8, .1, .1)\n",
+      "    pos = sense(pos, m, .6, .2)\n",
+      "    pos = update(pos, 1, .8, .1, .1)\n",
       "bar_plot(pos)"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -606,11 +587,6 @@
       "**If you can understand this chapter you will be able to understand and implement Kalman filters** I cannot stress this enough. If anything is murky, go back and reread this chapter and play with the code. the rest of this book will build on the algorithms that we use here. If you don't intuitively understand why this histogram filter works, and can at least work through the math, you will have little success with the rest of the material. However, if you grasp the fundamental insight - multiplying probabilities when we measure, and shifting probabilities when we update leads to a converging solution - then you understand everything important you need to grasp the Kalman filter. "
      ]
     },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": []
-    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -624,8 +600,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": ""
+     "outputs": []
     },
     {
      "cell_type": "markdown",