Altered colors of plots.

Maybe not perfect yet, but I have signicantly changed the colors
and various other look-and-fell aspects of the plots.

Designing_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:24de067146358d57b36cdcab824894570734d754a6af4be472de6883fdaf3f22"
+  "signature": "sha256:d6b001b78dc6570e35ed9c44483a5418f73da8c384f456475479b75494374801"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -250,7 +250,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fc890a2ad50>"
+        "<IPython.core.display.HTML at 0x7f3d781ac748>"
        ]
       }
      ],
@@ -343,9 +343,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAFyCAYAAAD/MLwxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X18VPWZ9/HvmackM3mccUIggiKkBAggvMQn3GRx1WBU\nsFhbsN5ot9ZtubVWcduVvfug7GqtpXVdty/relv3pavZZaV1tVCJilC2br2rFUSFoiIIhCTkOZNk\nHs/9R5YgGgiZzGRycj7vv3KSmTNXezmTb35c53eM3bt3mwIAAABsxpHpAgAAAIBMIAgDAADAlgjC\nAAAAsCWCMAAAAGyJIAwAAABbIggDAADAlgYNwtu3b9dVV12lmpoa3X777ZKkDRs2qLq6WtXV1dq8\neXPaiwQAAABSzTjZPsKJREKXX3657rvvPs2bN0+tra3y+Xy6/PLLtW7dOoXDYa1YsUJ1dXUjWTMA\nAAAwbCddEd65c6f8fr/mzZsnSSoqKtKOHTtUVlYmv9+v8ePHq6SkRLt27RqRYgEAAIBUcZ3sh/X1\n9crLy9NNN92k5uZmXXvttfL7/QoGg6qtrVVBQYGCwaAaGxtVXl4+UjUDAAAAw3bSIBwOh/Xmm2/q\nhRdeUG5urq655hp94QtfkCQtW7ZMklRXVyfDMNJfKQAAAJBCJw3CwWBQU6dOVUlJiSSpoqJCkUhE\nTU1N/Y9pampSMBj8zHP37dsnh4NNKQAAAJBenZ2dmjFjxpCfd9IgXFFRoUOHDqm9vV05OTn605/+\npJtvvlnr169XS0uLwuGwGhoaBhyLcDgcmj59+pALQuYFAgGtX79eVVVVmS4FQ0TvrI3+WRe9szb6\nZ22BQEDbtm1L6rknDcJ5eXlavXq1brjhBsViMV111VWaNm2aVq1apeXLl0uSVq9endQLAwAAAJl0\n0iAsSYsWLdKiRYuO+15NTY1qamrSVhQAAACQbgzxYkCMtVgXvbM2+mdd9M7a6J89EYQxID4QrIve\nWRv9sy56Z230z54IwgAAALAlgjAAAABsiSAMAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsiSAMAAAA\nWyIIAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyII\nAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyIIAwAA\nwJYIwgAAALAlgjAAAABsiSAMAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyIIAwAAwJYI\nwgAAALAlgjAAAABsyZXpAgAAAMaid9/16o9/dGny5ITOPrtbXm8i0yXhUwjCAAAAKfb++zlasqRA\n3d2GJOnpp6Wqqq4MV4VPYzQCAAAgxQ4fdvSHYEl6+21nBqvBiRCEAQAATkEo5NLevdlqbPQM+thJ\nk+IqKekbhXA6TV14YSzd5SEJjEYAAAAMor3dpQceyNcvfpGtceMS+rd/a1dZWc8JHz9pUq+efbZN\ne/c6VVxsqrz8xI9F5rAiDAAAMIgPPvDoF7/IliQ1NDj0r/+aLcMwTvqcM8/s1cKFIc2c2S2n0xyJ\nMjFEBGEAAIBB5OSYMoxjYTYYTMg0CbdWRxAGAAAYRFlZrx59tEsVFTFdd12vli7tzXRJSAFmhAEA\nAAbhcpmqqenUxRd3y+Mx5XCwJ/BYQBAGAAA4RdnZ8UyXgBQiCAMAAFvr7o7r4MGEDMOjoqJIpsvB\nCBp0Rnj69Om6+uqrdfXVV+vv//7vJUkbNmxQdXW1qqurtXnz5rQXCQAAkA49PU49/LChs8/26Yor\nCvX++zmZLgkjaNAV4ezsbP3qV7/qP45EIlq7dq3WrVuncDisFStWaOHChWktEgAAYLjq67O0f79T\ngYCpqVP79vXdu9ej730vW5Khfftc+tnPvPrpT3vZEcImhjwasWPHDpWVlcnv90uSSkpKtGvXLpWX\nl6e8OAAAgFQ4eDBLX/5ygfbscSknx9Qvf2lo1qxuuVySYUhHc6/XSwC2k0FHIyKRiJYuXarly5fr\nD3/4g44cOaJgMKja2lpt3LhRwWBQjY2NI1ErAABAUvbudWnPnr71v54eQ1u2uCVJkyf36pFHelRa\nmlBlZVRf+1o3q8E2MuiK8NatWxUIBPT222/rlltu0R133CFJWrZsmSSprq7uhHdWCQQCKSwVI8Xt\n7vtwoH/WQ++sjf5ZF70b/caPj8rtNhWN9mWW8nLJ7/fLMAzdeKNTixZFlJ0dV36+V4bhy3C1GIqj\n779kDBqEj76pZ82apeLiYpWWlmrjxo39P29qalIwGBzwuWvWrOn/urKyUlVVVUkXCgAA7OPw4Zje\nfltyuaS5cw0VFjqHdb5585x67rmQNm50ac6chC6+WP0LeS6XQ6ef7lE0Gk1F6RgBW7Zs0datWyVJ\nTqdTlZWVSZ3npEG4vb1dWVlZys7O1oEDB9TY2Kjy8nLt2bNHLS0tCofDamhoOOF88MqVK487bm5u\nTqpIjKyjf/zQL+uhd9ZG/6yL3qVWd7dTP/pRvv75n/t2cLjnnpBuvLFFTufwRhbmzJHOPtvoH304\n2i76Zz0VFRWqqKiQ1Ne/bdu2JXWekwbhDz/8UHfddZc8Ho+cTqf+7u/+Trm5uVq1apWWL18uSVq9\nenVSLwwAADCQtjaXHnssu//45z/P0TXXdKuwcPgrtsz/4pNOGoTnzp2r3/zmN5/5fk1NjWpqatJW\nFAAAsC+vN6HZs+Pavr0vpsyfH5XXyy2NkXrcWQ4AAIwqhYVRPfJIh158MUs5Oab+4i/C8ni4tTFS\njyAMAABGnUmTevW1r/VmugyMcYPuIwwAAACMRawIAwCAtEskDO3fn6V4XJo4MSyPh4vWkHmsCAMA\ngLTbssWnqqoiVVYWaf36vP4bWwCZRBAGAABp1dHh1t/+rU+xmCHJ0Le/7dORI55MlwUQhAEAQHq5\n3QmVlBzb/szvN+V2MxqBzCMIAwCAtMrJiWvt2i7V1ERUWRnRM8906LTTIpkuC+BiOQAAMHyHDmUp\nHjc0YUJ4wFshT57co0cfDcs0JYeDm2NgdGBFGAAADMtrr/n0Z3/m14UXFunXv85VPD7whXCGkSAE\nY1QhCAMAgKR1dLj07W/nqrfXUCJh6JvfzFVDAxfCwRoIwgAAIGkul1RYeGyV1+fjQjhYB0EYAAAk\nzeuN6ac/7dKFF0Y1e3ZMTz/dqWCQC+FgDVwsBwAAhmXq1B79679GlEhI2dnxTJcDnDKCMAAAGDaP\nhwAM62E0AgAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA\n2BI31AAAIAOiUUP19VlyOqXS0rAkM9MlAbbDijAAACMsFjP0n/+ZpwsvLFJlZZF+9ztvpksCbIkg\nDADACGto8Oj2230yTUO9vYbuuitXnZ2p/0fa9na3WlrcKT8vMFYQhAEAGGFut6m8vGOjEH5/Qm53\nakcj3nnHqyuu8OuyywL63e9yU3puYKwgCAMAMMKKiyN65plOzZsXU1VVRD/+cZeys+MpO39Xl0u3\n3ZanvXudqq936MYb83T4sCdl5z/KMAwRJWBlXCwHAEAGzJ4d0vr1vTIMUy5XIuXnTyQ+/bUhSWpv\nd+ngQbd8PlNnnNGb9PkPH/boiSe82rnTpZUre3XBBSEZBhf8wVr4Mw4AgAxxu+NpCcG5uTH99Kdd\nKilJKD8/occe61RJSUTt7W7dc0++Lr20SJddVqTt231Jnd8wDP3Hf+ToH//Rq82bPbruujy9/352\niv9XAOnHijAAAGPQnDkhbdoUVSIhBYNRSab27XOptrYvsHZ1GXrssWw9/HC3THPoK7kffXQsQkSj\nhrq7U1U5MHJYEQYAYIwKBCIKBiM6ukexzyd5PMdC75Qpyc0lm6apG27o6b/g74tf7NUZZ8SGXS8w\n0lgRBgDAJs46q1e1tR36p3/K0YwZMX3pS8mtBkvSrFndqqtLqKvL0IQJURUUEIRhPQRhAABswjBM\nnXdeSOef3yPJTDoEHzVxYvIX2wGjAUEYAACbMc3UX6AHWBEzwgAAALAlgjAAAABsiSAMAAAAWyII\nAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsiVssAwAwDJGIoVdf\njaqx0dBZZ2Vr8uTeTJcE4BQRhAEAGIY//MGnL37RJ9M0VFyco+eea9WkSeHjHtPd7VR2timHI5Gh\nKgEM5JRGI7q6unTRRRfp8ccflyRt2LBB1dXVqq6u1ubNm9NaIAAAo5VhGNq61S3TNCRJjY0OHTrk\n7P95NGpow4Y8LV58mu68s0iHDmVlqlQAAzilIPzII4+ooqJChmEoEolo7dq1euaZZ/TEE0/o3nvv\nTXeNAACMSqZp6vzzo5JMSVJBQULjxh1b9X3//WzdfHOu3nvPqX/7tyz98pfZGaoUwEAGHY348MMP\n1dLSooqKCpmmqR07dqisrEx+v1+SVFJSol27dqm8vDztxQIAMFyG0bd6a5pmSs53/vndeu45p+rr\nDU2bFj5uRri31+hfLZakpiaHDMNI2WsDGJ5BV4R/8pOf6NZbb+0/PnLkiILBoGpra7Vx40YFg0E1\nNjamtUgAAFJh926v7rjDr/vuK9DBg6kZU8jOTqi62qUbbnDoc5/rOe5nU6ZEdNNNfcG4pCShL3+5\nlxAMjCInXRF+5ZVXdOaZZ2r8+PGfeeMuW7ZMklRXV9f/1/WnBQKBFJWJkeR2uyXRPyuid9ZG/9Lr\nww+j+uIXvTpypG8NqL7eqf/7f3OUleUc5JmDO1HvAgHpnnvi+vrXu5SbK51xRo4Mwzvs10Line truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc8769721d0>"
+        "<matplotlib.figure.Figure at 0x7f3d61ee4240>"
        ]
       }
      ],
@@ -696,9 +696,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc8768d9350>"
+        "<matplotlib.figure.Figure at 0x7f3d61de56a0>"
        ]
       }
      ],
@@ -777,9 +777,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc86f6226d0>"
+        "<matplotlib.figure.Figure at 0x7f3d61c1b908>"
        ]
       },
       {
@@ -862,9 +862,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc876942910>"
+        "<matplotlib.figure.Figure at 0x7f3d61e36c88>"
        ]
       }
      ],
@@ -1012,17 +1012,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc86f622b90>"
+        "<matplotlib.figure.Figure at 0x7f3d61c2cb38>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc86edf0c10>"
+        "<matplotlib.figure.Figure at 0x7f3d5659ada0>"
        ]
       }
      ],
@@ -1202,9 +1202,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc86eddce50>"
+        "<matplotlib.figure.Figure at 0x7f3d5655afd0>"
        ]
       }
      ],
@@ -1398,9 +1398,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc86f090f50>"
+        "<matplotlib.figure.Figure at 0x7f3d565eaef0>"
        ]
       }
      ],
@@ -1539,9 +1539,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc8768954d0>"
+        "<matplotlib.figure.Figure at 0x7f3d61e24b00>"
        ]
       }
      ],
@@ -1659,9 +1659,11 @@
       "    \n",
       "    p1 = plt.scatter(x, y, color='green', marker='o', s=75, alpha=0.5)\n",
       "\n",
-      "p2, = plt.plot (xs, ys,lw=2)\n",
-      "p3, = plt.plot (xs2, ys2,lw=4, c='r')\n",
-      "plt.legend([p1,p2, p3], ['Measurements', 'Kalman filter(R=0.5)', 'Kalman filter(R=10)'])\n",
+      "p2, = plt.plot (xs, ys, lw=2)\n",
+      "p3, = plt.plot (xs2, ys2, lw=4, c='#e24a33')\n",
+      "plt.legend([p1,p2, p3], \n",
+      "           ['Measurements', 'Kalman filter(R=0.5)', 'Kalman filter(R=10)'],\n",
+      "           loc='best')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -1670,13 +1672,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc86ef61210>"
+        "<matplotlib.figure.Figure at 0x7f3d56437320>"
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 32
     },
     {
      "cell_type": "markdown",
@@ -1738,17 +1740,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc86ed4dcd0>"
+        "<matplotlib.figure.Figure at 0x7f3d564b6f28>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc86efec210>"
+        "<matplotlib.figure.Figure at 0x7f3d566bfdd8>"
        ]
       }
      ],
@@ -1776,17 +1778,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc86ed789d0>"
+        "<matplotlib.figure.Figure at 0x7f3d56627c50>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAFyCAYAAAD/MLwxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc87690d610>"
+        "<matplotlib.figure.Figure at 0x7f3d566719e8>"
        ]
       }
      ],

Designing_Nonlinear_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ff55ea156e51d203e3d4a98256614289bbe209beec4a681fe1334120c13e55b2"
+  "signature": "sha256:e015960709e549768dfdf9073da3f63db4cfe4874e8d964fd362f7902638377f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -250,7 +250,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f1ea414e748>"
+        "<IPython.core.display.HTML at 0x7fe4e4e1d1d0>"
        ]
       }
      ],
@@ -367,8 +367,8 @@
      "collapsed": false,
      "input": [
       "\n",
-      "circle1=plt.Circle((-4,0),5,color='r',fill=False,linewidth=10, alpha=.7)\n",
-      "circle2=plt.Circle((4,0),5,color='b', fill=False, linewidth=5, alpha=.7)\n",
+      "circle1=plt.Circle((-4,0),5,color='#004080',fill=False,linewidth=10, alpha=.7)\n",
+      "circle2=plt.Circle((4,0),5,color='#E24A33', fill=False, linewidth=5, alpha=.7)\n",
       "\n",
       "fig = plt.gcf()\n",
       "ax = fig.gca()\n",
@@ -377,8 +377,8 @@
       "plt.xlim((-10,10))\n",
       "plt.ylim((-10,10))\n",
       "\n",
-      "plt.plot ([-4,0], [0,3], 'r')\n",
-      "plt.plot ([4,0], [0,3], 'b')\n",
+      "plt.plot ([-4,0], [0,3], c='#004080')\n",
+      "plt.plot ([4,0], [0,3], c='#E24A33')\n",
       "plt.text(-4, -.5, \"A\", fontsize=16, horizontalalignment='center')\n",
       "plt.text(4, -.5, \"B\", fontsize=16, horizontalalignment='center')\n",
       "#plt.scatter ([-4],[0], 'r')\n",
@@ -393,13 +393,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1e8acc7908>"
+        "<matplotlib.figure.Figure at 0x7fe4c7c10240>"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -508,13 +508,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 15,
+       "prompt_number": 3,
        "text": [
         "\\phi"
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -538,7 +538,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 17,
+       "prompt_number": 4,
        "text": [
         "   1   \n",
         "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
@@ -547,7 +547,7 @@
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -571,14 +571,14 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 26,
+       "prompt_number": 5,
        "text": [
         "        \u239b 2    \u239e\n",
         "(\u03c6 - 1)\u22c5\u239d\u03c6  + 1\u23a0"
        ]
       }
      ],
-     "prompt_number": 26
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -611,7 +611,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 28,
+       "prompt_number": 6,
        "text": [
         "\u23a1           x - x\u2090                           y - y\u2090              \u23a4\n",
         "\u23a2 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500   0   \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500   0\u23a5\n",
@@ -627,7 +627,7 @@
        ]
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -697,7 +697,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 35
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -732,7 +732,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 36
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -813,13 +813,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAFyCAYAAAD8h7IZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1e87783be0>"
+        "<matplotlib.figure.Figure at 0x7fe4ca0c29b0>"
        ]
       }
      ],
-     "prompt_number": 37
+     "prompt_number": 9
     },
     {
      "cell_type": "heading",

Extended_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:8cbcb6e649c23718ffea2f8ea7b974233a106f2a4079bd1aea11f0c7aa86e976"
+  "signature": "sha256:ab67c51059a4e13a6e530ab9ac6055815a6e0856a34dcefe748088741a95b950"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -248,13 +248,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 2,
+       "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x12acb10>"
+        "<IPython.core.display.HTML at 0x7f2d25427748>"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -341,13 +341,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x12085d0>"
+        "<matplotlib.figure.Figure at 0x7f2d0c3b6f60>"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -373,13 +373,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3f70990>"
+        "<matplotlib.figure.Figure at 0x7f2d0bfbeb70>"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -407,13 +407,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x226bad0>"
+        "<matplotlib.figure.Figure at 0x7f2d0bf56320>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -443,13 +443,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x226a910>"
+        "<matplotlib.figure.Figure at 0x7f2d0af77cf8>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -472,12 +472,12 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "input  mean, variance: 0.0009, 1.0005\n",
-        "output mean, variance: -0.0293, 2.2432\n"
+        "input  mean, variance: -0.0016, 0.9985\n",
+        "output mean, variance: -0.0249, 2.2395\n"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -502,20 +502,20 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3f5d090>"
+        "<matplotlib.figure.Figure at 0x7f2d0afb9d30>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "output mean, variance: -0.0014, 2.2510\n"
+        "output mean, variance: 0.0025, 2.2465\n"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -544,12 +544,12 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "linear    output mean, variance: -0.0794, 7485.2514\n",
-        "nonlinear output mean, variance: -1.9762, 26213.4745\n"
+        "linear    output mean, variance: 0.1420, 7470.2773\n",
+        "nonlinear output mean, variance: -1.4736, 26191.9586\n"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -592,13 +592,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3f4ac50>"
+        "<matplotlib.figure.Figure at 0x7f2d0afbe5c0>"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -627,13 +627,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x45b0d90>"
+        "<matplotlib.figure.Figure at 0x7f2d0afd9978>"
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -668,13 +668,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3f6c750>"
+        "<matplotlib.figure.Figure at 0x7f2d0ae82c50>"
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -739,13 +739,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff3e5813750>"
+        "<matplotlib.figure.Figure at 0x7f2d0c3d2f60>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f2d25427550>"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -809,13 +817,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x239d790>"
+        "<matplotlib.figure.Figure at 0x7f2d0afc6eb8>"
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -847,13 +855,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x29f0c90>"
+        "<matplotlib.figure.Figure at 0x7f2d0bfb07f0>"
        ]
       }
      ],
-     "prompt_number": 71
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -879,13 +887,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x23a8c90>"
+        "<matplotlib.figure.Figure at 0x7f2d0afd5198>"
        ]
       }
      ],
-     "prompt_number": 73
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -933,7 +941,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 8
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -987,13 +995,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAFyCAYAAAD/MLwxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xdc1dX/wPHX5242iCCyFJHtwIl74NYySRv2VbNhmalp\nllb+6puZNq1v2fJb+rWknU2z3HsPXCjDiaAyBBmXy52f3x/ozRtDNHDgefbgAXw+55zP+ZxA3vfc\n9+ccKTU1VUYQBEEQBEEQbjOKG90BQRAEQRAEQbgRRCAsCIIgCIIg3JZEICwIgiAIgiDclkQgLAiC\nIAiCINyWRCAsCIIgCIIg3JZEICwIgiAIgiDclmoUCJeUlNCtWzcWLVoEwPLlyxkwYAADBgxg3bp1\nddpBQRAEQRAEQagLqpoU+uSTT2jRogWSJGEymZg3bx7ff/89RqORMWPG0Lt377rupyAIgiAIgiDU\nqivOCB8/fpz8/HxatGiBLMscOHCAsLAwGjRoQOPGjfHz8yMlJeV69FUQBEEQBEEQas0VA+F33nmH\nSZMm2b/Py8vDx8eHb775hj/++AMfHx9ycnLqtJOCIAiCIAiCUNuqTY1Yu3YtTZs2pXHjxsiy407M\n999/PwCrVq1CkqS666EgCIIgCIIg1IFqA+EDBw6wcuVK1qxZQ0FBAQqFggceeIDc3Fx7mdzcXHx8\nfCrUPXXqFAqFWJRCEARBEARBqFvFxcVER0dfdb1qA+EpU6YwZcoUAD744ANcXFwYNWoUAwcOJD8/\nH6PRSHZ2NpGRkRXqKhQKoqKirrpDQkXe3t78+OOP9OzZ80Z35ZYnxrJ2ifGsXWI8a5cYz9ojxrJ2\nifGsXd7e3mzevPma6tZo1YjLqdVqpk2bxsiRIwF44YUXrunCgiAIgiAIgnAj1TgQnjhxov3rwYMH\nM3jw4DrpkCAIgiAIgiBcDyKJ9xYh0kxqjxjL2iXGs3aJ8axdYjxrjxjL2iXG8+YgAuFbhPiFqT1i\nLGuXGM/aJcazdonxrD1iLGuXGM+bw1XnCAuCIAiCcPORZZn8/HxsNlutt11QUACAxWKp9bZvR2I8\nr54sy7i6uuLs7Fyr7YpAWBAEQRDqgfz8fFxcXNDpdDe6K4JQ62RZprCwEJPJhKenZ621K1IjBEEQ\nBKEesNlsIggW6i1JkvD09MRsNtdquyIQFgRBEARBEG5LIhAWBEEQBEEQbksiEBYEQRAEQRBuSyIQ\nFgRBEITblCzL7M3ey8f7P+b1Xa/z9u63+S7tO84bztfaNebNm0dgYCDr1q0DoLCwkJCQEEaMGFFr\n16gPAgMDOXXq1I3uxm1HrBohCIIgCPVUmaWMDZkbSC1IxWQ14aJ2oV2jdrRv1B4JiV+O/cLBvIM4\nq51RK9QAnCw6yYKDCxgbPRZ/V38ADBYDR/KPUGYpI9QjlEYujWrcB0mSCAkJ4ffff6d3796sXLmS\nwMBAJEmqk3u+Fcmy7PBZuH7EjLAgCIIg1EOl5lI+2v8Re3P2YraZkSSJUkspK06u4KsjX3H0wlH2\n5+7HWe24LqtSUqJRaFh6dCk2m41lx5fx7t53+f3476w7vY5PDnzCgoMLKDIW1bgvsbGxJCcnY7Va\nWb58OYMHD0aWZWw2G++88w6dOnUiNjaWl156yb62bkZGBvfccw8xMTFERkby+OOPU1T01zVXrVpF\n9+7diYiIoFu3bmzYsMF+Li4ujk2bNtm///ts65QpU3jppZcYN24c4eHhxMXFodfrAVi+fDnx8fHE\nxMQwevRocnJy7HVGjBhBq1atmD17Nj179mTQoEEYDAagfG3gSZMmERsbS+fOnfnqq68crjdt2jQS\nEhKIiIhg2rRp9nOjRo0iIiICgH79+hEeHs7LL79co/sU/jkRCAuCIAhCPfTTsZ+w2CxolBqH485q\nZ04Un+DLlC9xUbtUWleSJAoMBXyZ8iX7c/ejVWpxVjujU+lw1bhSaCxkYfJCzLaaL2XVqVMnVqxY\nQV5eHk2bNgVgwYIFrFy5kl9++YUtW7aQlpbGwoULATCZTIwaNYrdu3eze/duCgoKeOedd+ztTZ8+\nnenTp5Oamso333yDn5+fQ/+vNOO8dOlS7rvvPlJTU1m0aBFKpZKkpCSeffZZ3n33XQ4ePEiLFi2Y\nPn26vc327dszf/58/ve///Hrr7+i0+nYvXs3AJMnT0aj0bBjxw6++eYb5s2bx4EDB+zX27hxIx9+\n+CFr165l2bJl7Nu3D4DExETS0tIAWL16NWlpaQ6BcHX3KfxzIhAWBEEQhHrGYDFwqvAUSoWy0vPO\nKmeOXThWbbBola3szt6NTlVxbWKlpERv1rM3Z2+N+zR48GBeeukl+vbtaz/29ddf8/TTT9OoUSNc\nXFx48MEH+eOPPwBo3rw5d911F05OTri6ujJkyBAOHz5sr6tQKDh58iTFxcUEBgbaZ1VrqmvXrvTt\n2xdJkoiJiUGn0/H1119zzz330Lp1axQKBY8//jhr1qzBZDIBEBISQnBwMN7e3nh4eBAYGMj58+fJ\nzs5m/fr1vPTSS2i1Wpo0acKQIUPs9wLQv39//P39CQgIIDIykuPHj9eon//0PoXqiRxhQRAEQahn\nikxFmGwmtGirLGOVrdW2kVuai5PaqcrzTionkvOSifOLq7YdWZaRJIkOHToQERHBHXfcwc6dOwE4\nc+YMkydPRqEon5ez2Wz4+voCkJeXx4svvsjOnTsxGAyYTCZat25tb3fBggV88MEHfPzxx4SGhvL2\n22/XOEi8lLf8d2fPnmXbtm1899139mMajcaeHqFQKFCpVCiV5S8wVCoVZrOZs2fPAtC5c2d7PYvF\nwrBhw+zX8/DwsJ9Tq9X24PpK/sl9ClcmAmFBEARBqGd0Sh0Kqfo3fQNdA9Gb9VWmR2iVWry0XtW2\nUZOHuyRJspf78ssvAeyBcEBAAO+++y5t27atUO+1115DqVSyceNGXFxcWLhwIcuWLbOfb9++PYsX\nL8ZkMjFjxgzefPNNe1qFVqvFai0P9IuLiyvt16Vg9nL+/v5MmTKFiRMnXvG+/l5Pq9WSnJx8zQ8B\nVlWvuvsU/jmRGiEIgiAI9YyH1gMfZ58qA1WzzUz3wO40dmmM2Voxz9dgMXBn6J3VzhpbbBZ8nH2u\n2JfK+nDp2H333cdbb71FdnY2sixz7NgxNm7cCIBer8fFxQUnJycyMjJITEx0qL906VL0er090HZz\nc7OfDwkJYe/e8rSN33//vUZ9Arj33ntZsmQJhw4dQpZl8vLy+PXXX6usd+l7X19fOnfuzJw5cygt\nLcVsNrNr1y57Kkd1Y3CJr68vKSkpFcpUd5/CPycCYUEQBEGohwY2GYjBaqgQcFllK0pJSd/gvoyN\nGUtUgygsNgt6sx69WY9OqWN42HDig+Pxc/WrMhg2WU30DOx5xX5U9uDapWOPP/44HTt2JCEhgaio\nKB577DHOny9fw/jpp5/mwIEDREZG8sQTT9C/f397O7Is89NPP9GhQwdatWpFbm4uM2bMsLc/depU\nli5dypAhQ8jOzq7y+n/Xrl07/v3vfzN16lSioqIYPHgwhw4dqlDvUt3L23j//ffJy8ujW7dutG7d\nmtdffx2bzVbtGFxuxowZzJw5k3bt2vH666/bj1d3n8I/J6WmptbJonWnT58mKiqqLpq+7Xh7ewPY\n/3EQrp0Yy9olxrN2ifGsXbfbeObm5uLj4zhDe7zwOCtOriDHkINNtqFWqAlyCyIhNAF3rbu9nNlm\npshYhFqpxl3z1/EiYxELkxeiN+txUpXnC1tsFkxWE0ObDaW1b2sE4Xqq7Ofc29ubzZs3ExQUdNXt\niRxhQRAEQainmnk044nWT1BQVoDBYsBT61lh3WAAtUKNt5N3hePuWncmxk5kT/YeDp8/jCzL+Dj7\n0DOwJx5ajwrlBeFWIwJhQRAEQajnvHReeFH9g29VUSvUdGrciU6NO9VyrwThxhM5woIgCIIgCMJt\nSQTCgiAIgiAIwm1JBMKCIAiCIAjCbUkEwoIgCIIgCMJtSQTCgiAIgiAIwm1JBMKCIAiCIAjCbUkE\nwoIgCIIg3JRmzpxJeHg4QUFBbNq06UZ3x06WZZ566imioqKIiIjAbHbcpjo+Pp7t27c7HOvbty9h\nYWEEBgbad5y7noYNG0ZycvJ1v+7VMBqN9OzZ87pugiMCYUEQBEEQ6szWrVtp3769/fuioiL69+/P\nm2++ecW6c+bMIS0tjYCAgEq3RL5Rdu7cyZYtW0hKSiI1NRW1Wu1wfu3atXTq5Lju8urVq1m3bl21\n7QYGBnLq1Kla7++aNWtwc3MjJibGfmzKlCmEhIQQHh5OmzZtePbZZzEYDDVq78yZM4wYMYLmzZsz\ncOBAUlNTa1Rv69atBAYGEh4ebv84evSo/bxWq2XkyJHMnz//6m7wH7hiIFxQUMDw4cO56667GDp0\nKMuXLwcgKiqKYcOGMWzYMObMmVPnHRUEQRAE4dZmMBgYO3Ys7du3Z/r06Te6O9csMzOToKAgdDrd\nVdWTZfmK56orc60SExMZPny4wzFJkpgwYQJpaWmsWLGCpKQk3nvvvRq1N2PGDKKiokhOTmbo0KE8\n8cQTNe6Ln58faWlp9o/mzZs7nB86dCg//PBDhVn2unLFQNjNzY3ExER++eUXPv/8c2bPno3VakWn\n0/Hzzz/z888/M3PmzOvRV0EQBEEQblFWq5Xx48fj7+/P3Llz7cfXrl1Lv379iIiIIDY2ljfeeKNG\n7U2ZMoURI0bQqlUrZs+eTc+ePRk0aJB9VvOjjz6iS5cuNG/enK5du/Lbb7851J02bRoJCQlEREQw\nbdq0Gl0zKyuL8PBwpk+fzp49ewgPD3dIjZgzZ841pXKMGjWKiIgIAPr160d4eDgvv/yy/fLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x61d0890>"
+        "<matplotlib.figure.Figure at 0x7f2d0bfc6160>"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -1021,7 +1029,8 @@
      "input": [],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 17
     }
    ],
    "metadata": {}

Gaussians.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:f65e8525f0597e76c675123aaa3560c37c9571b274ee7611f78fa3538041edc9"
+  "signature": "sha256:4ced6bfb1e08c63d260e43842dcb48d678646bfb82a467f54245e8e1f7d1dabb"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -250,7 +250,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fc0e923dd50>"
+        "<IPython.core.display.HTML at 0x7f4270036748>"
        ]
       }
      ],
@@ -289,9 +289,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc0c7a8cd10>"
+        "<matplotlib.figure.Figure at 0x7f425801cba8>"
        ]
       }
      ],
@@ -395,9 +395,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc0c7a77850>"
+        "<matplotlib.figure.Figure at 0x7f424d69f978>"
        ]
       }
      ],
@@ -436,10 +436,10 @@
       "import matplotlib.pyplot as plt\n",
       "\n",
       "xs = np.arange(15,30,0.05)\n",
-      "p1, = plt.plot (xs,[gaussian(x, 23, .2) for x in xs],'b')\n",
-      "p2, = plt.plot (xs,[gaussian(x, 23, 1) for x in xs],'g')\n",
-      "p3, = plt.plot (xs,[gaussian(x, 23, 5) for x in xs],'r')\n",
-      "plt.legend([p1,p2,p3], ['var = .2', 'var = 1', 'var = 5'])\n",
+      "p1, = plt.plot (xs,[gaussian(x, 23, .2) for x in xs], label='var=0.2')\n",
+      "p2, = plt.plot (xs,[gaussian(x, 23, 1) for x in xs], label='var=1')\n",
+      "p3, = plt.plot (xs,[gaussian(x, 23, 5) for x in xs], label='var=5')\n",
+      "plt.legend()\n",
       "plt.show()"
      ],
      "language": "python",
@@ -448,13 +448,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc0c7a37f10>"
+        "<matplotlib.figure.Figure at 0x7f424d668080>"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -490,9 +490,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc0c7a37850>"
+        "<matplotlib.figure.Figure at 0x7f424d5dcbe0>"
        ]
       }
      ],
@@ -545,9 +545,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc0c79846d0>"
+        "<matplotlib.figure.Figure at 0x7f424d60aeb8>"
        ]
       },
       {
@@ -613,7 +613,7 @@
        "stream": "stdout",
        "text": [
         "0.131146572034\n",
-        "0.131146572034\n"
+        "0.13114657203397997\n"
        ]
       }
      ],
@@ -672,9 +672,9 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[ 1.33487857  2.32419504  1.07523853 -0.27928953  1.98992013 -0.76257228\n",
-        "  2.07271785 -1.6604275  -0.96593885  0.0456679   1.8342477   1.64217285\n",
-        " -3.3709959   3.87590067  1.86209169]\n"
+        "[ 7.34214043  4.01197645  2.72519952  6.59756375  1.23150108  5.35084192\n",
+        "  5.13531457 -1.17205809  3.62692992 -2.15541009  1.50868156  6.60801968\n",
+        "  4.56818095  0.22491041 -2.23888555]\n"
        ]
       }
      ],

Kalman_Filter_Math.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:9e4bdd9bceb17f11ad4da5080027fc103a05a64cc3d3ab4a8006c45d273b46c4"
+  "signature": "sha256:8de0a063add9b29133c1733f57bd0169991ed4e5c592b8968aa06a208803b3c2"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -41,10 +41,10 @@
         "        margin-right: auto;\n",
         "    }\n",
         "    div.text_cell code {\n",
-        "        background: #FFFFFF;\n",
+        "        background: transparent;\n",
         "        color: #000000;\n",
         "        font-weight: 600;\n",
-        "        font-size: 13pt;\n",
+        "        font-size: 11pt;\n",
         "        font-style: bold;\n",
         "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
         "   }\n",
@@ -250,7 +250,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x2d55b90>"
+        "<IPython.core.display.HTML at 0x7f677177b748>"
        ]
       }
      ],
@@ -375,13 +375,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAGDCAYAAAAs4AbKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2896ad0>"
+        "<matplotlib.figure.Figure at 0x7f677177b4e0>"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -462,7 +462,8 @@
      "input": [],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 2
     }
    ],
    "metadata": {}

Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:372f9fab3c373729afc7c41657597b3a0effd62b311982a1c72c927fd5d0206e"
+  "signature": "sha256:a0b2a1f17afd632455ef42c5fbf5e58dcb19dae1c24d5fc069bfb071867ceb33"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -248,7 +248,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fc174020c90>"
+        "<IPython.core.display.HTML at 0x7ff5e808b5f8>"
        ]
       }
      ],
@@ -340,10 +340,10 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        " 0.5427\t 0.3376\t-0.2458\t-1.0022\t-0.3602\t\n",
-        "-0.4171\t 0.8655\t 0.1328\t-1.3316\t-1.4808\t\n",
-        "-1.0374\t 0.0335\t 0.3618\t-0.0386\t 0.7771\t\n",
-        " 1.4292\t 1.6454\t-1.0276\t 0.2587\t 0.4679\t\n"
+        "-0.7733\t-0.9997\t 0.0225\t 0.2023\t 0.0525\t\n",
+        " 0.4358\t 2.5341\t 1.9472\t 0.4157\t-0.6211\t\n",
+        " 1.3083\t-0.8065\t-0.1134\t-0.3974\t-0.6619\t\n",
+        "-0.9567\t-0.9989\t 0.5416\t 0.7023\t-0.9270\t\n"
        ]
       }
      ],
@@ -371,7 +371,8 @@
       "    x = dog.sense()\n",
       "    xs.append(x)\n",
       "    print(\"%.4f\" % x, end=' '),\n",
-      "plt.plot(xs)\n",
+      "plt.plot(xs, label='dog position')\n",
+      "plt.legend(loc='best')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -387,9 +388,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc159fb92d0>"
+        "<matplotlib.figure.Figure at 0x7ff5ce024f28>"
        ]
       }
      ],
@@ -415,13 +416,13 @@
       "    for i in range(100):\n",
       "        x = dog.sense()\n",
       "        xs.append(x)\n",
-      "    p1, = plt.plot(xs, c='b')\n",
-      "    p2, = plt.plot([0,99],[1,100], 'r--')\n",
+      "    plt.plot(xs, label='sensor')\n",
+      "    plt.plot([0,99],[1,100], 'r--', label='actual')\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.legend(loc='best')\n",
       "    plt.show()\n",
       "    \n",
       "test_sensor(4.0)"
@@ -432,9 +433,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc159fcc4d0>"
+        "<matplotlib.figure.Figure at 0x7ff5cdff8b00>"
        ]
       }
      ],
@@ -466,9 +467,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc159fb9b50>"
+        "<matplotlib.figure.Figure at 0x7ff5e808b550>"
        ]
       }
      ],
@@ -486,9 +487,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc159f8d8d0>"
+        "<matplotlib.figure.Figure at 0x7ff5cdff85f8>"
        ]
       }
      ],
@@ -529,9 +530,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc152f8d890>"
+        "<matplotlib.figure.Figure at 0x7ff5ce028b70>"
        ]
       }
      ],
@@ -558,7 +559,8 @@
       "for i in xs:\n",
       "    ys.append(dog.sense())\n",
       "               \n",
-      "plt.plot(xs,ys)\n",
+      "plt.plot(xs,ys, label='dog position')\n",
+      "plt.legend(loc='best')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -567,9 +569,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc159fb9310>"
+        "<matplotlib.figure.Figure at 0x7ff5ce024f60>"
        ]
       }
      ],
@@ -650,12 +652,12 @@
       "m, v = multiply(m1,v1,m1,v1)\n",
       "\n",
       "ys = [stats.gaussian(x,m1,v1) for x in xs]\n",
-      "p1, = plt.plot (xs,ys)\n",
+      "plt.plot (xs, ys, label='original')\n",
       "\n",
       "ys = [stats.gaussian(x,m,v) for x in xs]\n",
-      "p2, = plt.plot (xs,ys)\n",
+      "plt.plot (xs, ys, label='multiply')\n",
       "\n",
-      "plt.legend([p1,p2],['original', 'multiply'])\n",
+      "plt.legend(loc='best')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -664,9 +666,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1526d0450>"
+        "<matplotlib.figure.Figure at 0x7ff5c6814f28>"
        ]
       }
      ],
@@ -696,14 +698,14 @@
       "m, s = multiply(m1,v1,m2,v2)\n",
       "\n",
       "ys = [stats.gaussian(x,m1,v1) for x in xs]\n",
-      "p1, = plt.plot (xs,ys)\n",
+      "plt.plot (xs, ys, label='measure 1')\n",
       "\n",
       "ys = [stats.gaussian(x,m2,v2) for x in xs]\n",
-      "p2, = plt.plot (xs,ys)\n",
+      "plt.plot (xs, ys, label='measure 2')\n",
       "\n",
       "ys = [stats.gaussian(x,m,v) for x in xs]\n",
-      "p3, = plt.plot(xs,ys)\n",
-      "plt.legend([p1,p2,p3],['measure 1', 'measure 2', 'multiply'])\n",
+      "plt.plot(xs, ys,label='multiply')\n",
+      "plt.legend()\n",
       "plt.show()"
      ],
      "language": "python",
@@ -712,9 +714,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1526ab190>"
+        "<matplotlib.figure.Figure at 0x7ff5c688f4a8>"
        ]
       }
      ],
@@ -746,14 +748,14 @@
       "m, v = multiply(m1,v1,m2,v2)\n",
       "\n",
       "ys = [stats.gaussian(x,m1,v1) for x in xs]\n",
-      "p1, = plt.plot (xs,ys)\n",
+      "plt.plot (xs, ys, label='measure 1')\n",
       "\n",
       "ys = [stats.gaussian(x,m2,v2) for x in xs]\n",
-      "p2, = plt.plot (xs,ys)\n",
+      "plt.plot (xs, ys, label='measure 2')\n",
       "\n",
       "ys = [stats.gaussian(x,m,v) for x in xs]\n",
-      "p3, = plt.plot(xs,ys)\n",
-      "plt.legend([p1,p2,p3],['measure 1', 'measure 2', 'multiply'])\n",
+      "plt.plot(xs, ys, label='multiply')\n",
+      "plt.legend()\n",
       "plt.show()"
      ],
      "language": "python",
@@ -762,9 +764,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc15275c2d0>"
+        "<matplotlib.figure.Figure at 0x7ff5cdfb3fd0>"
        ]
       }
      ],
@@ -887,26 +889,26 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "time: 0 \tposition = 0.323 \tvariance = 2.500\n",
-        "time: 1 \tposition = 0.536 \tvariance = 1.667\n",
-        "time: 2 \tposition = 0.524 \tvariance = 1.250\n",
-        "time: 3 \tposition = 0.068 \tvariance = 1.000\n",
-        "time: 4 \tposition = 0.165 \tvariance = 0.833\n",
-        "time: 5 \tposition = 0.316 \tvariance = 0.714\n",
-        "time: 6 \tposition = 0.292 \tvariance = 0.625\n",
-        "time: 7 \tposition = 0.223 \tvariance = 0.556\n",
-        "time: 8 \tposition = 0.235 \tvariance = 0.500\n",
-        "time: 9 \tposition = 0.226 \tvariance = 0.455\n",
-        "time: 10 \tposition = 0.302 \tvariance = 0.417\n",
-        "time: 11 \tposition = 0.413 \tvariance = 0.385\n",
-        "time: 12 \tposition = 0.400 \tvariance = 0.357\n",
-        "time: 13 \tposition = 0.443 \tvariance = 0.333\n",
-        "time: 14 \tposition = 0.348 \tvariance = 0.312\n",
-        "time: 15 \tposition = 0.327 \tvariance = 0.294\n",
-        "time: 16 \tposition = 0.254 \tvariance = 0.278\n",
-        "time: 17 \tposition = 0.289 \tvariance = 0.263\n",
-        "time: 18 \tposition = 0.254 \tvariance = 0.250\n",
-        "time: 19 \tposition = 0.191 \tvariance = 0.238\n"
+        "time: 0 \tposition = 1.635 \tvariance = 2.500\n",
+        "time: 1 \tposition = 1.179 \tvariance = 1.667\n",
+        "time: 2 \tposition = 0.914 \tvariance = 1.250\n",
+        "time: 3 \tposition = 0.575 \tvariance = 1.000\n",
+        "time: 4 \tposition = 0.675 \tvariance = 0.833\n",
+        "time: 5 \tposition = 0.386 \tvariance = 0.714\n",
+        "time: 6 \tposition = 0.014 \tvariance = 0.625\n",
+        "time: 7 \tposition = -0.306 \tvariance = 0.556\n",
+        "time: 8 \tposition = -0.229 \tvariance = 0.500\n",
+        "time: 9 \tposition = -0.277 \tvariance = 0.455\n",
+        "time: 10 \tposition = -0.323 \tvariance = 0.417\n",
+        "time: 11 \tposition = -0.248 \tvariance = 0.385\n",
+        "time: 12 \tposition = -0.262 \tvariance = 0.357\n",
+        "time: 13 \tposition = -0.159 \tvariance = 0.333\n",
+        "time: 14 \tposition = -0.082 \tvariance = 0.312\n",
+        "time: 15 \tposition = -0.098 \tvariance = 0.294\n",
+        "time: 16 \tposition = 0.002 \tvariance = 0.278\n",
+        "time: 17 \tposition = 0.063 \tvariance = 0.263\n",
+        "time: 18 \tposition = 0.062 \tvariance = 0.250\n",
+        "time: 19 \tposition = -0.011 \tvariance = 0.238\n"
        ]
       }
      ],
@@ -1003,7 +1005,7 @@
       "\n",
       "for i in range(10):\n",
       "    pos = predict(pos[0], pos[1], movement, movement_error)\n",
-      "    print('PREDICT: %.4f,\\t%.4f' % (pos[0], pos[1]))\n",
+      "    print('PREDICT: {: 10.4f} {: 10.4f}'.format(pos[0], pos[1]),end='\\t')\n",
       "    \n",
       "    Z = dog.sense()\n",
       "    zs.append(Z)\n",
@@ -1011,12 +1013,12 @@
       "    pos = update(pos[0], pos[1], Z, sensor_error)\n",
       "    ps.append(pos[0])\n",
       "    \n",
-      "    print('UPDATE:  %.4f,\\t%.4f' % (pos[0], pos[1]))\n",
-      "    print()\n",
+      "    print('UPDATE: {: 10.4f} {: 10.4f}'.format(pos[0], pos[1]))\n",
+      "\n",
       "    \n",
-      "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.plot(ps, label='filter')\n",
+      "plt.plot(zs, c='r', linestyle='dashed', label='measurement')\n",
+      "plt.legend(loc='best')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -1026,44 +1028,24 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "PREDICT: 1.0000,\t502.0000\n",
-        "UPDATE:  7.0253,\t9.8047\n",
-        "\n",
-        "PREDICT: 8.0253,\t11.8047\n",
-        "UPDATE:  1.5049,\t5.4138\n",
-        "\n",
-        "PREDICT: 2.5049,\t7.4138\n",
-        "UPDATE:  3.9463,\t4.2574\n",
-        "\n",
-        "PREDICT: 4.9463,\t6.2574\n",
-        "UPDATE:  4.9988,\t3.8490\n",
-        "\n",
-        "PREDICT: 5.9988,\t5.8490\n",
-        "UPDATE:  4.2774,\t3.6904\n",
-        "\n",
-        "PREDICT: 5.2774,\t5.6904\n",
-        "UPDATE:  6.6767,\t3.6267\n",
-        "\n",
-        "PREDICT: 7.6767,\t5.6267\n",
-        "UPDATE:  7.3807,\t3.6007\n",
-        "\n",
-        "PREDICT: 8.3807,\t5.6007\n",
-        "UPDATE:  7.6023,\t3.5900\n",
-        "\n",
-        "PREDICT: 8.6023,\t5.5900\n",
-        "UPDATE:  9.3074,\t3.5856\n",
-        "\n",
-        "PREDICT: 10.3074,\t5.5856\n",
-        "UPDATE:  9.8777,\t3.5838\n",
-        "\n"
+        "PREDICT:     1.0000   502.0000\tUPDATE:     1.2790     9.8047\n",
+        "PREDICT:     2.2790    11.8047\tUPDATE:    -2.8806     5.4138\n",
+        "PREDICT:    -1.8806     7.4138\tUPDATE:     1.1053     4.2574\n",
+        "PREDICT:     2.1053     6.2574\tUPDATE:     3.3238     3.8490\n",
+        "PREDICT:     4.3238     5.8490\tUPDATE:     3.4857     3.6904\n",
+        "PREDICT:     4.4857     5.6904\tUPDATE:     4.0837     3.6267\n",
+        "PREDICT:     5.0837     5.6267\tUPDATE:     4.1329     3.6007\n",
+        "PREDICT:     5.1329     5.6007\tUPDATE:     7.5553     3.5900\n",
+        "PREDICT:     8.5553     5.5900\tUPDATE:     7.5774     3.5856\n",
+        "PREDICT:     8.5774     5.5856\tUPDATE:     8.1573     3.5838\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1527e9050>"
+        "<matplotlib.figure.Figure at 0x7ff5c68b0080>"
        ]
       }
      ],
@@ -1184,7 +1166,7 @@
       "    \n",
       "#plt.subplot(121)    \n",
       "p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
-      "p2, = plt.plot(ps, c='b')\n",
+      "p2, = plt.plot(ps,)\n",
       "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
       "plt.show()\n",
       "\n",
@@ -1199,17 +1181,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1526e4190>"
+        "<matplotlib.figure.Figure at 0x7ff5c687d160>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc152582d90>"
+        "<matplotlib.figure.Figure at 0x7ff5cde06358>"
        ]
       },
       {
@@ -1404,9 +1386,9 @@
       "\n",
       "    voltage = predict(voltage[0], voltage[1], movement, movement_error)\n",
       "\n",
-      "plt.scatter(range(N), zs, marker='+')\n",
-      "p1, = plt.plot(ps, c='g')\n",
-      "plt.legend([p1], ['filter'], 3)\n",
+      "plt.scatter(range(N), zs, marker='+', s=64, color='r', label='measurements')\n",
+      "p1, = plt.plot(ps, label='filter')\n",
+      "plt.legend(loc='best')\n",
       "plt.xlim((0,N));plt.ylim((0,30))\n",
       "plt.show()\n",
       "plt.plot(vs)\n",
@@ -1421,25 +1403,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1526c4850>"
+        "<matplotlib.figure.Figure at 0x7ff5c6887b00>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAroAAAF9CAYAAADr+MuIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1527a89d0>"
+        "<matplotlib.figure.Figure at 0x7ff5c683da90>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Variance converges to 0.0907297673624\n",
-        "Last voltage is 16.2828743816\n"
+        "Variance converges to 0.09072976736236911\n",
+        "Last voltage is 16.78247179353804\n"
        ]
       }
      ],
@@ -1493,7 +1475,7 @@
       "vs = []\n",
       "for i in range (10000):\n",
       "    vs.append (VKF())\n",
-      "plt.hist(vs, bins=100)   \n",
+      "plt.hist(vs, bins=100, color='#e24a33')   \n",
       "plt.show()"
      ],
      "language": "python",
@@ -1502,9 +1484,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAFyCAYAAAAd7KdIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1527e9dd0>"
+        "<matplotlib.figure.Figure at 0x7ff5ce0280b8>"
        ]
       }
      ],
@@ -1557,9 +1539,9 @@
       "\n",
       "    pos = predict(pos[0], pos[1], movement+ random.randn(), movement_error)\n",
       "\n",
-      "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.plot(ps, label='filter')\n",
+      "plt.plot(zs, linestyle='dashed', c='r', label='measurement')\n",
+      "plt.legend()\n",
       "plt.show()\n",
       "plt.plot(vs)\n",
       "plt.title('Variance')\n",
@@ -1571,17 +1553,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc15259eb90>"
+        "<matplotlib.figure.Figure at 0x7ff5c687d048>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc152513250>"
+        "<matplotlib.figure.Figure at 0x7ff5c4e05908>"
        ]
       }
      ],
@@ -1646,9 +1628,9 @@
       "    ps.append(pos[0])\n",
       "\n",
       "\n",
-      "p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
-      "p2, = plt.plot(ps, c='b')\n",
-      "plt.legend([p1,p2], ['measurement', 'filter'], 2)\n",
+      "p1, = plt.plot(zs,c='r', linestyle='dashed', label='measurement')\n",
+      "p2, = plt.plot(ps, c='#004080', label='filter')\n",
+      "plt.legend(loc='best')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -1657,9 +1639,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAFyCAYAAAAzqYbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc152776bd0>"
+        "<matplotlib.figure.Figure at 0x7ff5c4fab278>"
        ]
       }
      ],
@@ -1710,10 +1692,9 @@
       "    pos = update(pos[0], pos[1], Z, sensor_error)\n",
       "    ps.append(pos[0])\n",
       "\n",
-      "\n",
-      "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.plot(ps, label='filter')\n",
+      "plt.plot(zs,c='r', linestyle='dashed', label='measurement')\n",
+      "plt.legend(loc='best')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -1722,9 +1703,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc159d578d0>"
+        "<matplotlib.figure.Figure at 0x7ff5c67c84e0>"
        ]
       }
      ],
@@ -1774,10 +1755,9 @@
       "    pos = update(pos[0], pos[1], Z, sensor_error)\n",
       "    ps.append(pos[0])\n",
       "\n",
-      "\n",
-      "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.plot(zs,c='r', linestyle='dashed', label='measurement')\n",
+      "plt.plot(ps, c='#004080', label='filter')\n",
+      "plt.legend(loc='best')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -1786,9 +1766,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc159d57a90>"
+        "<matplotlib.figure.Figure at 0x7ff5c67c1208>"
        ]
       }
      ],
@@ -1827,9 +1807,9 @@
       "\n",
       "    pos = predict(pos[0], pos[1], movement, movement_error)\n",
       "\n",
-      "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.plot(zs,c='r', linestyle='dashed', label='measurement')\n",
+      "plt.plot(ps, c='#004080', label='filter')\n",
+      "plt.legend(loc='best')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -1838,9 +1818,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc152743690>"
+        "<matplotlib.figure.Figure at 0x7ff5c686a940>"
        ]
       }
      ],
@@ -1929,9 +1909,9 @@
       "\n",
       "        pos = predict(pos[0], pos[1], movement + random.randn()*movement_noise, movement_noise)\n",
       "\n",
-      "    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.plot(zs,c='r', linestyle='dashed', label='measurement')\n",
+      "    plt.plot(ps, c='#004080', label='filter')\n",
+      "    plt.legend(loc='best')\n",
       "    plt.show()\n",
       "\n",
       "interact(plot_kalman_filter,\n",
@@ -1947,9 +1927,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc1527a68d0>"
+        "<matplotlib.figure.Figure at 0x7ff5c6843748>"
        ]
       },
       {
@@ -2027,10 +2007,9 @@
       "    pos = update(pos[0], pos[1], Z, sensor_error)\n",
       "    ps.append(pos[0])\n",
       "\n",
-      "\n",
-      "p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
-      "p2, = plt.plot(ps, c='b')\n",
-      "plt.legend([p1,p2], ['input', 'filter'], 2)\n",
+      "plt.plot(zs,c='r', linestyle='dashed', label='input')\n",
+      "plt.plot(ps, c='#004080', label='filter')\n",
+      "plt.legend(loc='best')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -2039,9 +2018,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc15250cd90>"
+        "<matplotlib.figure.Figure at 0x7ff5c680c940>"
        ]
       }
      ],
@@ -2113,9 +2092,9 @@
       "    pos = update(pos[0], pos[1], Z, sensor_error)\n",
       "    ps.append(pos[0])\n",
       "\n",
-      "p1, = plt.plot(zs,c='r', linestyle='dashed')\n",
-      "p2, = plt.plot(ps, c='b')\n",
-      "plt.legend([p1,p2], ['measurement', 'filter'], 3)\n",
+      "p1, = plt.plot(zs,c='r', linestyle='dashed', label='measurement')\n",
+      "p2, = plt.plot(ps, c='#004080', label='filter')\n",
+      "plt.legend(loc='best')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -2124,9 +2103,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc180583f10>"
+        "<matplotlib.figure.Figure at 0x7ff5c6805390>"
        ]
       }
      ],

Multidimensional_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:4ee83971412602de6dab7c1f7b6fc956af7e0299b0236d72b70618cdd0155023"
+  "signature": "sha256:5bc3ba112fa5a4987d924ba5871d1b199b24cc99ad6aa892817b9a4b93336875"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -250,7 +250,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f81546a1d50>"
+        "<IPython.core.display.HTML at 0x7f6e14cc5748>"
        ]
       }
      ],
@@ -359,9 +359,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8133cc8110>"
+        "<matplotlib.figure.Figure at 0x7f6e0c132b70>"
        ]
       }
      ],
@@ -494,7 +494,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "0.0174395374407\n"
+        "0.017439537440741816\n"
        ]
       }
      ],
@@ -554,9 +554,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f813109f9d0>"
+        "<matplotlib.figure.Figure at 0x7f6df3ff0c50>"
        ]
       }
      ],
@@ -598,9 +598,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8130f59810>"
+        "<matplotlib.figure.Figure at 0x7f6df3ff0b38>"
        ]
       }
      ],
@@ -686,9 +686,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f81312c3390>"
+        "<matplotlib.figure.Figure at 0x7f6df41520f0>"
        ]
       }
      ],
@@ -713,9 +713,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f81310d29d0>"
+        "<matplotlib.figure.Figure at 0x7f6df41c0908>"
        ]
       }
      ],
@@ -740,9 +740,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f813124c190>"
+        "<matplotlib.figure.Figure at 0x7f6df3fa2908>"
        ]
       }
      ],
@@ -771,9 +771,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f81311ac950>"
+        "<matplotlib.figure.Figure at 0x7f6df41669e8>"
        ]
       }
      ],
@@ -838,9 +838,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f81312c30d0>"
+        "<matplotlib.figure.Figure at 0x7f6df3fd98d0>"
        ]
       }
      ],
@@ -971,7 +971,7 @@
        "stream": "stdout",
        "text": [
         "[1 2 3]\n",
-        "<type 'numpy.ndarray'>\n"
+        "<class 'numpy.ndarray'>\n"
        ]
       }
      ],
@@ -1789,9 +1789,9 @@
       "    ps, zs, cov = filter_dog(noise=noise, data=data, count=count,\n",
       "                             R=R, Q=Q, P=P, initial_x=initial_x)\n",
       "    \n",
-      "    p0, = plt.plot([0,count],[0,count],'g')\n",
-      "    p1, = plt.plot(range(1,count+1),zs,c='r', linestyle='dashed')\n",
-      "    p2, = plt.plot(range(1,count+1),ps, c='b')\n",
+      "    p0, = plt.plot([0,count],[0,count])\n",
+      "    p1, = plt.plot(range(1,count+1),zs, linestyle='dashed')\n",
+      "    p2, = plt.plot(range(1,count+1),ps)\n",
       "    plt.legend([p0,p1,p2], ['actual','measurement', 'filter'], 2)\n",
       "    plt.ylim((0-10,count+10))\n",
       "    plt.title(title)\n",
@@ -1834,17 +1834,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8131154ed0>"
+        "<matplotlib.figure.Figure at 0x7f6df4166eb8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8133cc8110>"
+        "<matplotlib.figure.Figure at 0x7f6df4117240>"
        ]
       }
      ],
@@ -1989,9 +1989,9 @@
       "        pos = predict(pos[0], pos[1], u, Q)\n",
       "        f.predict(u=u)\n",
       "\n",
-      "    plt.plot(x1, 'b')\n",
       "    plt.scatter(range(len(x2)), x2, c='r')\n",
       "    plt.title('State')\n",
+      "    plt.plot(x1)\n",
       "    plt.figure()\n",
       "    plt.plot(p1)\n",
       "    plt.scatter(range(len(x2)), p2, c='r')\n",
@@ -2006,17 +2006,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8131414910>"
+        "<matplotlib.figure.Figure at 0x7f6df3fd9f28>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f81311547d0>"
+        "<matplotlib.figure.Figure at 0x7f6df42a8cc0>"
        ]
       }
      ],
@@ -2214,13 +2214,13 @@
       "\n",
       "    # initialize the filters\n",
       "    f1 = mkf_filter(R, Q)\n",
-      "    f.x[0,0] = 0.\n",
-      "    f.P[0,0] = p0\n",
+      "    f1.x[0,0] = 0.\n",
+      "    f1.P[0,0] = p0\n",
       "    \n",
       "    f2 = pos_vel_filter(R, Q)\n",
       "    f2.x[0,0] = 0.\n",
       "    f2.x[1,0] = 1.\n",
-      "    f.P *= p0\n",
+      "    f2.P *= p0\n",
       "    \n",
       "    for i in range(50):\n",
       "        u = move + randn()\n",
@@ -2234,10 +2234,10 @@
       "        x1.append(f1.x[0,0])\n",
       "        x2.append(f2.x[0,0])\n",
       "\n",
-      "    p1, = plt.plot(x1, 'b')\n",
-      "    p2 = plt.scatter(range(len(x2)), x2, c='r')\n",
+      "    plt.plot(x1, label='1D Filter')\n",
+      "    plt.scatter(range(len(x2)), x2, c='r', label='2D Filter')\n",
       "    plt.title('State')\n",
-      "    plt.legend([p1,p2], ['1D filter', '2D filter'], 4)\n",
+      "    plt.legend(loc=4)\n",
       "    plt.show()\n",
       "    \n",
       "plot_compare_pos_vel(x0=0., p0=500., R=5., Q=.2, move=1.)    "
@@ -2246,14 +2246,11 @@
      "metadata": {},
      "outputs": [
       {
-       "ename": "NameError",
-       "evalue": "global name 'f' is not defined",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-33-b2a3166033b0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m \u001b[0mplot_compare_pos_vel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m500.\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mR\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5.\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mQ\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m.2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmove\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1.\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[1;32m<ipython-input-33-b2a3166033b0>\u001b[0m in \u001b[0;36mplot_compare_pos_vel\u001b[1;34m(x0, p0, R, Q, move)\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[1;31m# initialize the filters\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[0mf1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmkf_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mR\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mQ\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m     \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m     \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mP\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;31mNameError\u001b[0m: global name 'f' is not defined"
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6df420d6a0>"
        ]
       }
      ],
@@ -2307,7 +2304,25 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6df4176358>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6df3fde2b0>"
+       ]
+      }
+     ],
+     "prompt_number": 34
     },
     {
      "cell_type": "markdown",
@@ -2412,7 +2427,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6df41feba8>"
+       ]
+      }
+     ],
+     "prompt_number": 35
     },
     {
      "cell_type": "markdown",
@@ -2471,7 +2496,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6df4207a90>"
+       ]
+      }
+     ],
+     "prompt_number": 37
     },
     {
      "cell_type": "markdown",
@@ -2489,7 +2524,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAGXCAYAAAC9TOY9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6df3ff57f0>"
+       ]
+      }
+     ],
+     "prompt_number": 38
     },
     {
      "cell_type": "markdown",
@@ -2549,7 +2594,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6df4271940>"
+       ]
+      }
+     ],
+     "prompt_number": 39
     },
     {
      "cell_type": "markdown",
@@ -2568,7 +2623,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6df41a7a90>"
+       ]
+      }
+     ],
+     "prompt_number": 40
     },
     {
      "cell_type": "markdown",
@@ -2592,7 +2657,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6df4194ba8>"
+       ]
+      }
+     ],
+     "prompt_number": 41
     },
     {
      "cell_type": "markdown",
@@ -2613,7 +2688,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6df42685f8>"
+       ]
+      }
+     ],
+     "prompt_number": 42
     },
     {
      "cell_type": "markdown",
@@ -2676,7 +2761,25 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6df420dbe0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6df41adeb8>"
+       ]
+      }
+     ],
+     "prompt_number": 43
     },
     {
      "cell_type": "markdown",

Unscented_Kalman_Filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a9ba6c3ba612925867d9e360943975b2d0084c56ca20db167d224fb4e3909bf9"
+  "signature": "sha256:9cb6305654844522aebd6ab807e2b68b865a7b57f69523ba3d4e85317e6035ad"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -250,7 +250,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x2154c50>"
+        "<IPython.core.display.HTML at 0x7f3d30aa40f0>"
        ]
       }
      ],
@@ -289,9 +289,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x30ccf90>"
+        "<matplotlib.figure.Figure at 0x7f3d16d504e0>"
        ]
       }
      ],
@@ -323,9 +323,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2154890>"
+        "<matplotlib.figure.Figure at 0x7f3d3002f7f0>"
        ]
       }
      ],
@@ -375,9 +375,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x38c0f50>"
+        "<matplotlib.figure.Figure at 0x7f3d0f9e4198>"
        ]
       }
      ],
@@ -415,9 +415,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x38e1650>"
+        "<matplotlib.figure.Figure at 0x7f3d0f22a1d0>"
        ]
       }
      ],
@@ -485,9 +485,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3bd4c90>"
+        "<matplotlib.figure.Figure at 0x7f3d16914a20>"
        ]
       }
      ],
@@ -674,7 +674,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -742,7 +742,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 11
+     "prompt_number": 8
     },
     {
      "cell_type": "heading",

Untitled0.ipynb

@@ -0,0 +1,79 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:075f2495bd19fb0e39255f7c5bce564369366c88b7175dc06b2c25d8816f3ee9"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#format the book\n",
+      "%matplotlib inline\n",
+      "import book_format\n",
+      "book_format.load_style()\n",
+      "import matplotlib.pylab as pylab\n",
+      "import matplotlib.pyplot as plt\n",
+      "pylab.rcParams['figure.figsize'] = 12,6\n",
+      "pylab.rcParams['axes.facecolor'] = 'e5e5e5'\n",
+      "#pylab.rcParams['axes.facecolor'] = 'f8f8f8'\n",
+      "pylab.rcParams['grid.color'] = 'ffffff'\n",
+      "pylab.rcParams['grid.linestyle'] = 'solid'\n",
+      "pylab.rcParams['grid.linewidth'] = 1.5\n",
+      "\n",
+      "\n",
+      "plt.plot([1,2], label='1', color='#004080')\n",
+      "plt.plot([2,3], label='2', color='#8eba42')\n",
+      "plt.plot([3,4], label='3', color = '#e24a33')\n",
+      "plt.plot([4,5], label='4', color = '#348abd')\n",
+      "plt.plot([5,6], label='5', color = '#777760')\n",
+      "plt.plot([6,7], label='6', color = '#988ed5')\n",
+      "plt.plot([7,8], label='7', color = '#FDC15E')\n",
+      "\n",
+      "#plt.plot([7,5,7,6,3,9,0,1,4,6], label='four')\n",
+      "#plt.plot([1,8,5,3,7,6,4,5], label='five')\n",
+      "#plt.plot([2,3,4,5,6,7,8,9], label='siz')\n",
+      "#plt.plot([9,8,7,6,5,4,3], label='7')\n",
+      "plt.legend(loc='best')\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 36,
+       "text": [
+        "<matplotlib.legend.Legend at 0x7f13cb97bcf8>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAFyCAYAAAD4cmIkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f13d08b6710>"
+       ]
+      }
+     ],
+     "prompt_number": 36
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}

book_format.py

@@ -6,18 +6,16 @@ def load_style():
     styles = open("./styles/custom2.css", "r").read()
     return HTML(styles)
 
-pylab.rcParams['axes.color_cycle'] = '348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33'
-pylab.rcParams['lines.linewidth'] = 1
-
+pylab.rcParams['lines.linewidth'] = 2
 pylab.rcParams['lines.antialiased'] = True
 pylab.rcParams['patch.linewidth'] = 0.5
 pylab.rcParams['patch.facecolor'] = '348ABD' #blue
 pylab.rcParams['patch.edgecolor'] = 'eeeeee'
 pylab.rcParams['patch.antialiased'] = True
 pylab.rcParams['font.family'] = 'monospace'
-pylab.rcParams['font.size'] = 10.0
+pylab.rcParams['font.size'] = 12.0
 #pylab.rcParams['font.monospace'] = 'Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace'
-pylab.rcParams['axes.facecolor'] = 'eeeeee'
+pylab.rcParams['axes.facecolor'] = 'E5E5E5'
 pylab.rcParams['axes.edgecolor'] = 'bcbcbc'
 pylab.rcParams['axes.linewidth'] = 1
 pylab.rcParams['axes.grid'] = True
@@ -25,7 +23,7 @@ pylab.rcParams['axes.titlesize'] = 'x-large'
 pylab.rcParams['axes.labelsize'] = 'large'
 pylab.rcParams['axes.labelcolor'] = '555555'
 pylab.rcParams['axes.axisbelow'] = True
-pylab.rcParams['axes.color_cycle'] = '348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33'
+pylab.rcParams['axes.color_cycle'] = '004080, 8EBA42, E24A33, 348ABD, 777760, 988ED5, FDC15E'
 pylab.rcParams['xtick.major.pad'] = 6
 pylab.rcParams['xtick.minor.size'] = 0
 pylab.rcParams['xtick.minor.pad'] = 6
@@ -36,4 +34,8 @@ pylab.rcParams['figure.facecolor'] = '0.85'
 pylab.rcParams['figure.edgecolor'] = '0.50'
 pylab.rcParams['figure.subplot.hspace'] = 0.5
 pylab.rcParams['figure.figsize'] = 12,6
+pylab.rcParams['grid.color'] = 'ffffff'
+pylab.rcParams['grid.linestyle'] = 'solid'
+pylab.rcParams['grid.linewidth'] = 1.5
+
 

discrete_bayes.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:65531b21c1e02194222568498f24cf6992c1fc1a78ff905904e612aa9b45fb5e"
+  "signature": "sha256:cf20f6778d5d707bd2a58daba24397fb6be3a0b09ed847da3a8bc357a6bcef81"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -250,7 +250,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f9944148f90>"
+        "<IPython.core.display.HTML at 0x7f33e0f02748>"
        ]
       }
      ],
@@ -344,9 +344,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f9944148950>"
+        "<matplotlib.figure.Figure at 0x7f33c7e8ff98>"
        ]
       }
      ],
@@ -530,12 +530,12 @@
        "stream": "stdout",
        "text": [
         "sum = 1.0\n",
-        "probability of door = 0.166666666667\n",
-        "probability of wall = 0.166666666667\n"
+        "probability of door = 0.1875\n",
+        "probability of wall = 0.0625\n"
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -597,7 +597,7 @@
        ]
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -654,7 +654,7 @@
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -682,7 +682,7 @@
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -705,13 +705,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f9928afeed0>"
+        "<matplotlib.figure.Figure at 0x7f33c7e5bdd8>"
        ]
       }
      ],
-     "prompt_number": 26
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -743,13 +743,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f992877ae10>"
+        "<matplotlib.figure.Figure at 0x7f33c7e316a0>"
        ]
       }
      ],
-     "prompt_number": 27
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -782,13 +782,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f99287ab050>"
+        "<matplotlib.figure.Figure at 0x7f33c7d82b70>"
        ]
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 14
     },
     {
      "cell_type": "heading",
@@ -829,22 +829,22 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[ 0.16666667  0.05555556  0.16666667  0.05555556  0.05555556  0.16666667\n",
-        "  0.05555556  0.16666667  0.05555556  0.05555556]\n",
-        "[ 0.06666667  0.14444444  0.07777778  0.14444444  0.06666667  0.06666667\n",
-        "  0.14444444  0.07777778  0.14444444  0.06666667]\n"
+        "[ 0.1875  0.1875  0.0625  0.0625  0.0625  0.0625  0.0625  0.0625  0.1875\n",
+        "  0.0625]\n",
+        "[ 0.0875  0.175   0.175   0.075   0.0625  0.0625  0.0625  0.0625  0.075\n",
+        "  0.1625]\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f992883c110>"
+        "<matplotlib.figure.Figure at 0x7f33c7e79be0>"
        ]
       }
      ],
-     "prompt_number": 29
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -868,20 +868,20 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[ 0.12676056  0.0915493   0.14788732  0.0915493   0.04225352  0.12676056\n",
-        "  0.0915493   0.14788732  0.0915493   0.04225352]\n"
+        "[ 0.15671642  0.31343284  0.10447761  0.04477612  0.03731343  0.03731343\n",
+        "  0.03731343  0.03731343  0.13432836  0.09701493]\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f9928afedd0>"
+        "<matplotlib.figure.Figure at 0x7f33c7e39f98>"
        ]
       }
      ],
-     "prompt_number": 30
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -904,13 +904,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f9945e1af50>"
+        "<matplotlib.figure.Figure at 0x7f33c7e08550>"
        ]
       }
      ],
-     "prompt_number": 31
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -933,13 +933,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f99287e4d50>"
+        "<matplotlib.figure.Figure at 0x7f33c7dc08d0>"
        ]
       }
      ],
-     "prompt_number": 32
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -985,9 +985,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f992877a550>"
+        "<matplotlib.figure.Figure at 0x7f33c7e3c6a0>"
        ]
       },
       {
@@ -999,7 +999,7 @@
        ]
       }
      ],
-     "prompt_number": 33
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -1022,13 +1022,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f9945e1af50>"
+        "<matplotlib.figure.Figure at 0x7f33e0f02518>"
        ]
       }
      ],
-     "prompt_number": 35
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -1054,13 +1054,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f99287b9e90>"
+        "<matplotlib.figure.Figure at 0x7f33c7eacba8>"
        ]
       }
      ],
-     "prompt_number": 36
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -1104,7 +1104,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f33c7dfcb70>"
+       ]
+      }
+     ],
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",

g-h_filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:fa72b967f25a704cc124b844dfac255ea10010e1efeba510c441f83968d39fb1"
+  "signature": "sha256:a19258e631c2ec61dbe30715e5859eb2e636ae447095dfe15105ade7e1065d91"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -250,7 +250,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f1c305f5d50>"
+        "<IPython.core.display.HTML at 0x7fd0302db780>"
        ]
       }
      ],
@@ -343,9 +343,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c15536fd0>"
+        "<matplotlib.figure.Figure at 0x7fd01aa7c048>"
        ]
       }
      ],
@@ -366,10 +366,11 @@
      "input": [
       "import numpy as np\n",
       "ave = np.sum(weights) / len(weights)\n",
-      "plt.plot([0,12], [ave,ave], c='r')\n",
-      "plt.plot(weights, c='b')\n",
+      "plt.plot(weights,label='weights')\n",
+      "plt.plot([0,12], [ave,ave], c='r', label='hypothesis')\n",
       "plt.xlabel('day')\n",
       "plt.ylabel('weight (lbs)')\n",
+      "plt.legend(loc='best')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -378,13 +379,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c154bebd0>"
+        "<matplotlib.figure.Figure at 0x7fd01a7f3f98>"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 36
     },
     {
      "cell_type": "markdown",
@@ -401,10 +402,11 @@
      "input": [
       "xs = range(len(weights))\n",
       "line = np.poly1d(np.polyfit(xs, weights, 1))\n",
-      "plt.plot (xs, line(xs), c='r')\n",
-      "plt.plot(weights, c='b')\n",
+      "plt.plot(weights, label='weights')\n",
+      "plt.plot (xs, line(xs), c='r', label='hypothesis')\n",
       "plt.xlabel('day')\n",
       "plt.ylabel('weight (lbs)')\n",
+      "plt.legend(loc='best')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -413,13 +415,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c154e8590>"
+        "<matplotlib.figure.Figure at 0x7fd01a95e978>"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 37
     },
     {
      "cell_type": "markdown",
@@ -450,9 +452,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c154b2290>"
+        "<matplotlib.figure.Figure at 0x7fd0302db4e0>"
        ]
       }
      ],
@@ -477,13 +479,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c154e7d50>"
+        "<matplotlib.figure.Figure at 0x7fd01a8635c0>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 38
     },
     {
      "cell_type": "markdown",
@@ -512,9 +514,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c15536990>"
+        "<matplotlib.figure.Figure at 0x7fd01aa71e80>"
        ]
       }
      ],
@@ -564,10 +566,10 @@
       "    n = len(weights)\n",
       "    plt.xlim([1, n])\n",
       "\n",
-      "    p1, = plt.plot (range(1, n+1), estimates, 'b--')\n",
-      "    p2, = plt.plot(range(1, n+1), weights, c='r')\n",
-      "    p3, = plt.plot([1, n],[160, 160+n],c='g')\n",
-      "    plt.legend([p1, p2, p3], ['filter', 'measurements', 'actual'], 2)\n",
+      "    plt.plot (range(1, n+1), estimates, '--', label='filter')\n",
+      "    plt.plot(range(1, n+1), weights, c='r', label='measurements')\n",
+      "    plt.plot([1, n],[160, 160+n],c='g', label='actual')\n",
+      "    plt.legend(loc=2)\n",
       "    plt.xlabel('day')\n",
       "    plt.ylabel('weight (lbs)')\n",
       "    plt.show()\n",
@@ -580,13 +582,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAGECAYAAAD9fAyZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c15180f10>"
+        "<matplotlib.figure.Figure at 0x7fd01a825e10>"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 40
     },
     {
      "cell_type": "markdown",
@@ -609,13 +611,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c154b2490>"
+        "<matplotlib.figure.Figure at 0x7fd01a843748>"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 41
     },
     {
      "cell_type": "markdown",
@@ -667,10 +669,10 @@
       "n = len(weights)\n",
       "plt.xlim([1, n])\n",
       "    \n",
-      "p1, = plt.plot (range(1, n+1), estimates, 'b--')\n",
-      "p2, = plt.plot(range(1, n+1), weights, c='r')\n",
-      "p3, = plt.plot([1 ,n], [160,160+n], c='g')\n",
-      "plt.legend([p1, p2, p3], ['filter', 'measurements', 'actual'], 2)\n",
+      "plt.plot (range(1, n+1), estimates, '--', label='filter')\n",
+      "plt.plot(range(1, n+1), weights, label='measurements')\n",
+      "plt.plot([1 ,n], [160,160+n], label='actual')\n",
+      "plt.legend(loc=2)\n",
       "plt.xlabel('day')\n",
       "plt.ylabel('weight (lbs)')\n",
       "plt.show()"
@@ -681,13 +683,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAGECAYAAAD9fAyZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c15490750>"
+        "<matplotlib.figure.Figure at 0x7fd01a7fe048>"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 43
     },
     {
      "cell_type": "markdown",
@@ -748,7 +750,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c151736d0>"
+        "<matplotlib.figure.Figure at 0x7fd01a9d3ac8>"
        ]
       }
      ],
@@ -841,15 +843,15 @@
       "    return results\n",
       "\n",
       "def plot_g_h_results (measurements, filtered_data, title=''):    \n",
-      "    p1, = plt.plot (filtered_data, 'b--')\n",
-      "    p2, = plt.plot(measurements,c='r')\n",
-      "    plt.legend((p1,p2), ('filter', 'measurement'), 4)\n",
+      "    plt.plot (filtered_data, '--',label='filter')\n",
+      "    plt.plot(measurements,label='measurement')\n",
+      "    plt.legend(loc=4)\n",
       "    plt.title(title)\n",
       "    plt.show()\n",
       "\n",
       "\n",
       "plt.xlim([0,10])\n",
-      "plt.plot([0,9],[160,170],c='g')\n",
+      "plt.plot([0,9],[160,170],label='actual')\n",
       "data = g_h_filter (data=weights, x0=160, dx=1, g=6./10, h = 2./3, dt=1.)\n",
       "plot_g_h_results (weights, data)"
      ],
@@ -859,13 +861,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c15173510>"
+        "<matplotlib.figure.Figure at 0x7fd01a8705c0>"
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 47
     },
     {
      "cell_type": "heading",
@@ -938,13 +940,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c154e84d0>"
+        "<matplotlib.figure.Figure at 0x7fd01a974828>"
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 48
     },
     {
      "cell_type": "heading",
@@ -969,7 +971,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 15
     },
     {
      "cell_type": "heading",
@@ -993,13 +996,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c15433610>"
+        "<matplotlib.figure.Figure at 0x7fd01a815668>"
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 49
     },
     {
      "cell_type": "markdown",
@@ -1031,7 +1034,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 17
     },
     {
      "cell_type": "heading",
@@ -1055,13 +1059,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c1551d9d0>"
+        "<matplotlib.figure.Figure at 0x7fd018f74898>"
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 50
     },
     {
      "cell_type": "markdown",
@@ -1095,7 +1099,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 19
     },
     {
      "cell_type": "heading",
@@ -1128,13 +1133,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c152ace90>"
+        "<matplotlib.figure.Figure at 0x7fd01a7b7128>"
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 51
     },
     {
      "cell_type": "markdown",
@@ -1171,7 +1176,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 21
     },
     {
      "cell_type": "heading",
@@ -1202,29 +1208,29 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c15400850>"
+        "<matplotlib.figure.Figure at 0x7fd01a831c18>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c15392150>"
+        "<matplotlib.figure.Figure at 0x7fd01aa1e080>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c15225650>"
+        "<matplotlib.figure.Figure at 0x7fd01a8d47f0>"
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 52
     },
     {
      "cell_type": "markdown",
@@ -1257,29 +1263,29 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c15394710>"
+        "<matplotlib.figure.Figure at 0x7fd01aa2ac88>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c1529ce90>"
+        "<matplotlib.figure.Figure at 0x7fd01a963780>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c1525aad0>"
+        "<matplotlib.figure.Figure at 0x7fd01a7d6828>"
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 53
     },
     {
      "cell_type": "markdown",
@@ -1308,21 +1314,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c15178210>"
+        "<matplotlib.figure.Figure at 0x7fd01a838978>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c15400990>"
+        "<matplotlib.figure.Figure at 0x7fd01a7d0908>"
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 54
     },
     {
      "cell_type": "heading",
@@ -1364,29 +1370,29 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c151f5b90>"
+        "<matplotlib.figure.Figure at 0x7fd01a8d4860>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c1529c7d0>"
+        "<matplotlib.figure.Figure at 0x7fd01a910dd8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAF9CAYAAAAKk97ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1c152a2490>"
+        "<matplotlib.figure.Figure at 0x7fd01a7b3320>"
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 55
     },
     {
      "cell_type": "heading",

mkf_internal.py

@@ -20,16 +20,19 @@ def show_residual_chart():
     plt.scatter ([2],[2.8],marker='o')
     ax = plt.axes()
     ax.annotate('', xy=(2,3), xytext=(1,2),
-                arrowprops=dict(arrowstyle='->', ec='b',shrinkA=3, shrinkB=4))
-    ax.annotate('prediction', xy=(2.04,3.), color='b')
+                arrowprops=dict(arrowstyle='->', ec='#004080',
+                                lw=2,
+                                shrinkA=3, shrinkB=4))
+    ax.annotate('prediction', xy=(2.04,3.), color='#004080')
     ax.annotate('measurement', xy=(2.05, 2.28))
     ax.annotate('prior estimate', xy=(1, 1.9))
-    ax.annotate('residual', xy=(2.04,2.6), color='r')
+    ax.annotate('residual', xy=(2.04,2.6), color='#e24a33')
     ax.annotate('new estimate', xy=(2,2.8),xytext=(2.1,2.8),
                 arrowprops=dict(arrowstyle='->', ec="k", shrinkA=3, shrinkB=4))
     ax.annotate('', xy=(2,3), xytext=(2,2.3),
                 arrowprops=dict(arrowstyle="-",
-                                ec="r",
+                                ec="#e24a33",
+                                lw=2,
                                 shrinkA=5, shrinkB=5))
     plt.title("Kalman Filter Prediction Update Step")
     plt.axis('equal')
@@ -39,7 +42,7 @@ def show_residual_chart():
 def show_position_chart():
     """ Displays 3 measurements at t=1,2,3, with x=1,2,3"""
 
-    plt.scatter ([1,2,3], [1,2,3], s=128)
+    plt.scatter ([1,2,3], [1,2,3], s=128, color='#004080')
     plt.xlim([0,4]);
     plt.ylim([0,4])
 
@@ -54,7 +57,7 @@ def show_position_chart():
 def show_position_prediction_chart():
     """ displays 3 measurements, with the next position predicted"""
 
-    plt.scatter ([1,2,3], [1,2,3], s=128)
+    plt.scatter ([1,2,3], [1,2,3], s=128, color='#004080')
 
     plt.xlim([0,5])
     plt.ylim([0,5])
@@ -65,7 +68,7 @@ def show_position_prediction_chart():
     plt.xticks(np.arange(1,5,1))
     plt.yticks(np.arange(1,5,1))
 
-    plt.scatter ([4], [4], c='g',s=128)
+    plt.scatter ([4], [4], c='g',s=128, color='#8EBA42')
     ax = plt.axes()
     ax.annotate('', xy=(4,4), xytext=(3,3),
                 arrowprops=dict(arrowstyle='->',
@@ -181,6 +184,6 @@ def plot_3d_covariance(mean, cov):
 
 
 if __name__ == "__main__":
-
-    plot_3d_covariance((2,7), np.array([[8.,0],[0,4.]]))
-    #show_residual_chart()
+    #show_position_chart()
+    #plot_3d_covariance((2,7), np.array([[8.,0],[0,4.]]))
+    show_residual_chart()

stats.py

@@ -226,7 +226,7 @@ def is_inside_ellipse(x,y, ex, ey, orientation, width, height):
 
 def plot_covariance_ellipse(mean, cov=None, variance = 1.0,
              ellipse=None, title=None, axis_equal=True,
-             facecolor='none', edgecolor='blue'):
+             facecolor='none', edgecolor='#004080'):
     """ plots the covariance ellipse where
 
     mean is a (x,y) tuple for the mean of the covariance (center of ellipse)
@@ -273,7 +273,7 @@ def plot_covariance_ellipse(mean, cov=None, variance = 1.0,
         e = Ellipse(xy=mean, width=sd*width, height=sd*height, angle=angle,
                     facecolor=facecolor,
                     edgecolor=edgecolor,
-                    lw=1)
+                    lw=2)
         ax.add_patch(e)
     plt.scatter(mean[0], mean[1], marker='+') # mark the center
 
@@ -330,7 +330,7 @@ if __name__ == '__main__':
 
     do_plot_test()
 
-    test_gaussian()
+    #test_gaussian()
 
     # test conversion of scalar to covariance matrix
     x  = multivariate_gaussian(np.array([1,1]), np.array([3,4]), np.eye(2)*1.4)