Layout changes to make PDF read better.

Mostly just shortening lines of code. There was a SymPy font render issue as well.

01_gh_filter/g-h_filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ccf1289e0126a8e295c808727490b0379358aa10f7b39bee76c2752b020a4d54"
+  "signature": "sha256:b8b47f06752bcd373881f57282ed37ec8aee5434acd6a5476d94e6454106c397"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,13 +259,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 4,
+       "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f72b8c41518>"
+        "<IPython.core.display.HTML at 0x7f85acbb8320>"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 1
     },
     {
      "cell_type": "heading",
@@ -354,9 +354,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f55a9f28>"
+        "<matplotlib.figure.Figure at 0x7f859071e7b8>"
        ]
       }
      ],
@@ -390,9 +390,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlclOX+//HXsKOCCqiZKOZCgihuuFSiJG5pqLiglp1O\nR81Kv9liHsss035ZHfO06KnjqbSOJZqo5FJmi2iK+26ioSbhjoCKss3M7w+OFCkGCnPPwPv5ePSI\nue+57/vNXKN+5prrvi5TUlKSFRERERERKRdORgcQEREREanIVHCLiIiIiJQjFdwiIiIiIuVIBbeI\niIiISDlSwS0iIiIiUo5UcIuIiIiIlCMV3CIiIiIi5chmBffatWuJiYmhRYsWTJo0qXB7nz59aN26\ndeF/zZs3Z/r06dcc//DDD9OlSxdbxRURERERKRMutrqQt7c3I0eOZOPGjWRnZxduX7lyZeHPVquV\ne++9l549exY5dtWqVWRlZWEymWwVV0RERESkTNish7t9+/Z0796d6tWrF/ucH3/8EZPJRFhYWOG2\nrKws5s6dy5gxY7BatSimiIiIiDgWm4/hvlHRHBcXR79+/Ypsmz17NjExMVSrVq28o4mIiIiIlDmb\nF9zFDQu5cOEC3377Lf379y/clpycTGJiIjExMbaKJyIiIiJSpmw2hvuq4nq4V6xYQVBQEAEBAYXb\npk+fzlNPPVWisdspKSllllFEREREpDj169cv1fNtXnAXVzzHxcUxcODAItv27dvHqFGjimwLCgpi\n69at1x1iEhQUVHZBxSZ8fX2Ji4vTDDQOSu3n2NR+jktt59jUfo7L19eXDRs2lPo4mxXcFouFvLw8\nzGYzZrOZ3NxcnJ2dcXZ25vDhwyQlJdGnT58ix2zdurXw5y1btjBhwgTWrVtnq8giIiIiIrfMZmO4\nly1bRmhoKHPnziU+Pp6WLVvyr3/9Cyjo3Y6IiMDb27vY461Wq6YFFBERERGHY7Me7ujoaKKjo6+7\nb+LEiX96fIcOHfjhhx/KOJXYAw0FcmxqP8em9nNcajvHpvarXLS0uxhOf+k4NrWfY1P7OS61nWNT\n+1UuKrhFRERERMqRzWcpMUpeXh7p6ekaB25n0tPTAcjPz7fpda1WKzVr1sTV1dWm1xUREZHKp1IU\n3Hl5eaSlpVG7dm2cnNSpLwWz5pw5cwZfX18V3SIiIlKuKkX1mZ6ermJbinBycqJ27dqFPewiIiIi\n5aVSVKAmk0nFtlzDyclJQ4xERESk3KkKFREREREpRyq4RURERETKkQpuEREREZFypIK7kvH392fx\n4sWlPi4lJQV/f39SU1PLIZWIiIhIxaWCu5LZtWsX999/f7le42aLehEREZGKqFLMwy2/8fPzK9fz\nW63WIv8XERERqezUw23HwsLCiI2Nve6+oUOH8uqrrwJw4cIFnnvuOUJDQwkKCmLYsGEcPny4yPMb\nNWqEv78//v7+LFq06JrzZWZmMmrUKJo0acJdd93FJ598gr+/P4mJiUWet23bNiIjI2natCkxMTGc\nPXu2cJ+/vz/169cH4Omnny683u97uy9cuMC4ceMIDQ2lSZMm3HvvvSxduvTmXiARERERB6Ae7v+p\n98Dccjlv6oJRN31s+/bt2blzJzExMUW2W61W9u7dyyOPPILVamXEiBG4uroyf/58qlevzty5cxk6\ndCjr16+nSpUqAGzduhWLxULr1q2vO/f0lClTOHDgAJ9//jmurq489dRT133exx9/zKxZs8jNzWXk\nyJG89dZbvPbaa0DBcBWr1Urr1q155ZVXiIqKAqBatWqFx//jH/9g69atfPzxx9SuXZukpCQuX758\n06+RiIiIiL1TwW3HwsLCWLhwIQA7d+7kk08+YdasWSQnJ5OZmUm7du1Yv349O3bsYMeOHdSqVQuA\nadOm8cUXX7B27drCotfX17fY61y4cIHly5fz9ttvExYWBsD48eN54oknrnnuk08+SYsWLQDo27cv\n27ZtK9z3++EqXl5e1x2+cvz4cZo0aUKbNm2Agl5xERERkYpMBff/3EpPdHlp164dL7/8Mjk5OcTH\nx7NixQr+9re/cfDgQRo3boyPjw/79u3DarVy9913Fzk2Ozub48ePl+g6x48fJz8/n6CgoMJtd955\n53Wf26hRo8Kfa9SoQUZGRql+p+HDh/PYY4/Ro0cPOnTowF133UWvXr204qOIiIhUWCq47VhQUBDu\n7u7s27ePDRs2MGbMGNatW8fp06cLe6IBvL29Wb169TXHV69evcwzubgUfcuU9ubIHj16sHXrVtav\nX8+PP/7I2LFjiYqKYtasWWUZU0RERMRu6KZJO+bs7EybNm1Ys2YNHh4eDBo0iB9++IGdO3cWFtwh\nISFcuHCBvLw8AgICivxXo0aNEl0nICAAFxcX9u/fX7jt4MGDN53b1dUVs9lc7H4fHx/69evHG2+8\nwTPPPMOqVatu+loiIiIi9k4Ft50LCwtj/vz5REREEBAQQFpaGgcOHKBdu3YAdO7cmTZt2vDoo4+S\nkJDAL7/8QkJCAhMmTODo0aMAZGVlcebMGc6cOQMUjNk+c+YMaWlpQMF46/79+xfe0Lhr1y5mz559\n05kbNWrEmjVrOH/+PNnZ2VgslsJ9M2fOZPXq1Rw9epR9+/axZs0aQkJCbvpaIiIiIvZOBbeda9eu\nHVlZWURERABwzz33ULVqVRo3bgyAyWTiv//9L+3bt2f8+PFEREQwYcIEoKAnGeD999+nTZs2tGnT\nBpPJxEsvvUSbNm3o27dv4XWmTp1KcHAwQ4cO5YknnuDhhx8GwM3NrfA5fxxnbTKZrjv2etq0aaSk\npBAWFkaTJk1YsmRJ4T53d3fefPNNunfvzrBhw6hVqxbvvPNOGbxSIiIiIvbJlJSUVCFWKElJSSly\n09/vnT17tnAGDymZH3/8kZiYGHbt2lXui+UYSe+NW3N19pur35aIY1H7OS61nWNT+zkuX19fNmzY\nULjuSEnppkkBCha0OXLkCG3btiUrK4vXX3+dLl26VOhiW0RERMQWVHALUDDbyL///W+OHj2Kl5cX\nnTt3ZurUqUbHEhEREQdzdQYzTfn7GxXcAhTcnLl27VqjY4iIiIiDslqtrN+XyoxFW3lmYFu6tWpg\ndCS7oYJbRERERG7JtsOnmRG7lU0/nQTgo6/3q+D+HRXcIiIiInJTDhxP443F2/hmR8Hq1jWquvP4\n/S15pIem/P09FdwiIiIiUipHT2Uyc8l2lm1KxmqFKu4ujOrdgkfva0H1qu5Gx7M7KrhFREREpERO\nns9i1tIdLPwhCbPFipuLEyMigxkXFUqt6lWMjme3VHCLiIiIyA2dv5jNe/G7mP/NAbLzzDiZTAzt\nEshTA9rgX8vL6Hh2TwW3iIiIiFzXxcu5zF29lw9W7eVSdh4AfTvcwYRB7Whyew2D0zkOFdx2bPz4\n8ZhMJmbNmmV0lBuaOXMmmzZt4osvvrDJ9TZu3MiQIUPYvHkz9erVs8k1RUREKpMrufl8svYA7y7f\nRfqlHAAiWvozcUgYLe7QonilpYLbjplMJruZNH78+PH8+uuvNiuqbyQsLIxdu3bh4+NjdBQREZEK\nJS/fwqKEQ7wVt4NT6VkAhAXW4e9DwugYVNfgdI7LpgX32rVrmTt3LgcOHKBv37689tprAPTp04cT\nJ04UPi83N5dhw4YxefJk1q1bxzvvvMOxY8fw8vIiJiaGxx57zJaxxc64urpqyXkREZEyZLFYiU9M\n5s0vtnPs9AUAghv48PeYMO4NrW83HYCOysmWF/P29mbkyJEMGjSoyPaVK1eyc+dOdu7cyY4dO6hd\nuzY9e/YE4PLlyzz77LMkJiYSGxtLfHw88fHxtoxtqPz8fKZMmUJwcDBt27blk08+AWDWrFncdddd\nRZ5rtVrp1KkT7777LgCxsbH4+/uzcOFCWrduTbNmzXjuuefIzc0tPCY3N5eXXnqJVq1a0bhxY+6/\n/362b99euH/8+PH4+/vzxRdfkJiYiL+/P/7+/gwePPiarO+88w6hoaG0bNmSN998s8i+3Nxcpk+f\nTtu2bWnatClRUVFs27atyHMSExPp27cvgYGBNG/enJiYmCIfxLZv3154fX9/f1JTU6/JkJqayl//\n+leaN29OYGAgvXr1IiEhoaQvt4iISKVitVr5Zscv9Hghjidmf8+x0xe44zZv5oy9l69fjaZbqwYq\ntsuATXu427dvD8D+/fvJzs6+7nN+/PFHTCYTYWFhAPTu3btwX506dejcuTO7du0iKiqqTLPdXk5j\ngU9cpygsKavVyurVq5kwYQKrV6/m448/ZsqUKfTs2ZMhQ4Ywa9YsNm/eTIcOHQDYvHkzqamp1xTD\nixYt4rPPPiMtLY1x48ZRq1YtJkyYABQUyXFxccyaNYuGDRvy7rvv8tBDD5GYmIiXlxfTpk3jhRde\nYMqUKZw8eZL//Oc/QEEv8+/t3r2bVq1asXz5cr766iumT59OZGQkrVu3BuCpp54iKSmJ2bNnc/vt\ntxMXF8ewYcNISEigbt26mM1m/va3v9G7d29mz55NTk4OiYmJ5OfnF14jNDSUXbt2sXXrVkaNGnXd\n12zy5MmcOXOGRYsW4eXlxd69e2/69RcREanINv10khmxW9l2+DQAdX2q8szANgzuHIiLs037ZCs8\nQ15Nq9Va7L64uDj69etX7P5du3bRrFmz8ohll+68805Gjx5NQEAATz75JPn5+ezdu5d69eoRHh7O\nokWLCp/7xRdf0KVLF2677bYi55g8eTJBQUHcc889jBw5kk8//bRw3/z58xk9ejSRkZE0adKEGTNm\nYDabiYuLA8DLy4tatWrh7u6Oi4sLfn5++Pn5Ub169SLX8Pb25oUXXqBhw4aMGTMGLy8vduLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5376fd0>"
+        "<matplotlib.figure.Figure at 0x7f8590477780>"
        ]
       }
      ],
@@ -426,9 +426,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f55d4240>"
+        "<matplotlib.figure.Figure at 0x7f85904062e8>"
        ]
       }
      ],
@@ -451,9 +451,11 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# a lot of the plotting code is not particularly useful to read, so for each chapter\n",
-      "# I have placed the uninteresting code in a file named xxx_internal. I import this\n",
-      "# file and call whatever function I need.\n",
+      "# a lot of the plotting code is not particularly useful to read, \n",
+      "# so for each chapter I have placed the uninteresting code in a \n",
+      "# file named xxx_internal. I import this  file and call whatever \n",
+      "# function I need.\n",
+      "\n",
       "import gh_internal\n",
       "gh_internal.plot_estimate_chart_1()"
      ],
@@ -463,9 +465,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAu4AAAGJCAYAAADVBLa1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAu4AAAGJCAYAAADVBLa1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5584e10>"
+        "<matplotlib.figure.Figure at 0x7f85acbbdba8>"
        ]
       }
      ],
@@ -490,9 +492,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAGJCAYAAADLzYYGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAGJCAYAAADLzYYGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f55d4940>"
+        "<matplotlib.figure.Figure at 0x7f85902e8be0>"
        ]
       }
      ],
@@ -525,9 +527,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAGJCAYAAADLzYYGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAGJCAYAAADLzYYGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f61100622e8>"
+        "<matplotlib.figure.Figure at 0x7f85903ea898>"
        ]
       }
      ],
@@ -593,9 +595,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f546fdd8>"
+        "<matplotlib.figure.Figure at 0x7f85901085c0>"
        ]
       }
      ],
@@ -622,9 +624,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f550ed30>"
+        "<matplotlib.figure.Figure at 0x7f85900efa58>"
        ]
       }
      ],
@@ -694,9 +696,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f55385c0>"
+        "<matplotlib.figure.Figure at 0x7f85901f1a90>"
        ]
       }
      ],
@@ -759,9 +761,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAq0AAAH8CAYAAAD7ZpZkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAq0AAAH8CAYAAAD7ZpZkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5466828>"
+        "<matplotlib.figure.Figure at 0x7f85901bba20>"
        ]
       }
      ],
@@ -872,9 +874,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAF2CAYAAACcQQXQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAF2CAYAAACcQQXQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5438ac8>"
+        "<matplotlib.figure.Figure at 0x7f85900fc9e8>"
        ]
       }
      ],
@@ -951,9 +953,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAF2CAYAAACLY5UZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAF2CAYAAACLY5UZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f553d978>"
+        "<matplotlib.figure.Figure at 0x7f8590129ef0>"
        ]
       }
      ],
@@ -1007,9 +1009,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAF2CAYAAAB+nR6pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAs0AAAF2CAYAAACVqqWqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5576d30>"
+        "<matplotlib.figure.Figure at 0x7f859027e2b0>"
        ]
       }
      ],
@@ -1070,9 +1072,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAF2CAYAAACh02S2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAF2CAYAAACh02S2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f54a0278>"
+        "<matplotlib.figure.Figure at 0x7f85902e5da0>"
        ]
       }
      ],
@@ -1125,18 +1127,18 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "def gen_data (x0, dx, count, noise_factor, accel=0):\n",
+      "def gen_data(x0, dx, count, noise_factor, accel=0):\n",
       "    zs = []\n",
-      "    for i in range (count):\n",
-      "        zs.append (x0 + dx*i + random.randn()*noise_factor)\n",
+      "    for i in range(count):\n",
+      "        zs.append(x0 + dx*i + random.randn()*noise_factor)\n",
       "        dx += accel\n",
       "    return zs\n",
       "   \n",
       "predictions = []\n",
-      "zs = gen_data (x0=10, dx=0, count=20, noise_factor=0, accel = 2)\n",
-      "data = g_h_filter (data=zs, x0=10, dx=0, g=0.2, h=0.02, pred=predictions)\n",
+      "zs = gen_data(x0=10, dx=0, count=20, noise_factor=0, accel = 2)\n",
+      "data = g_h_filter(data=zs, x0=10, dx=0, g=0.2, h=0.02, pred=predictions)\n",
       "plt.xlim([0,20])\n",
-      "plot_g_h_results  (measurements=zs, filtered_data=data)"
+      "plot_g_h_results(measurements=zs, filtered_data=data)"
      ],
      "language": "python",
      "metadata": {},
@@ -1144,9 +1146,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAF2CAYAAAB3dr7TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAF2CAYAAAB3dr7TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3X9c1fX9///bOYdfB1BUEPAHCkImiSSamGmQOi36tVpZ\nvt00t1X21q1tzfXDubamb1db2dZmvffjvfxRrr5lzTK1sB+SlZolZmnI4YeiCCjIz3OA8+P1/cPJ\nJxMVTHwd4H69XLpc4Lw4h/sLn+ad13m9Hi9LXl6egYiIiIiInMRqdgAREREREX+koiwiIiIi0goV\nZRERERGRVqgoi4iIiIi0QkVZRERERKQVKsoiIiIiIq1QURYRERERacVZi3JeXh7Tp09n9OjRXHPN\nNWzatAkAt9vNggULGDVqFBMnTmTDhg0nPW/lypWMHz+e9PR0li5d2jHpRUREREQ6SMDZvuD+++8n\nKyuLF154gQ8//JC5c+fy7rvv8vLLL+NwOMjJyWHPnj3MmTOHtLQ0YmNj2bVrF8uWLWP16tWEh4cz\nY8YMkpOTycrKuhD7JCIiIiLyjZ31iHJhYSFXX301AFdccQXBwcEcPHiQN998k5kzZxIeHk56ejpp\naWlkZ2cDsHHjRqZOnUpiYiIxMTFMmzaN9evXd+yeiIiIiIicR2ctyldeeSUbN27E6/Xy/vvvEx4e\nztChQykqKiIhIYH58+ezfv16EhMTKSoqAqC4uJiEhARWrFjBY489RlJSUss2EREREZHO4KxF+cEH\nH+Tll18mNTWVe++9l0WLFhEcHIzL5SI0NJT8/HwqKioICwvD6XQCtGwrKSlh//79J20TEREREekM\nzniOcmNjI7Nnz+ahhx7iW9/6Fp9++ilz587l1VdfxW6343K5WLt2LQCLFy8mLCwMALvdjtPpZOHC\nhQBkZ2cTGhra6vcoKSk5n/sjIiIiItKquLi4dn39GYvyvn37aGhoYMqUKQCMHj2auLg4du7cSXx8\nPAUFBQwfPhyAgoICJk+eDEB8fDyFhYUtr+NwOBgyZMhpv09ycnK7Qot0tMjISF555RUyMzPNjiJy\nEq1N8Wdan+KvIiMj2bJlS7ufd8ZTLwYOHEhjYyObNm3CMAw+++wzCgoKSExMJCsri1WrVlFXV8e2\nbdvIzc1tKdRZWVlkZ2fjcDgoLy9nzZo1mnghIiIiIp3KGY8o9+nThyeffJInn3yS+++/n8jISBYs\nWMCwYcNISkqisLCQzMxMIiIiWLJkCTExMQCkpqYyb948Zs2ahcfjYfr06SrKIiIiItKpWPLy8gwz\nA5SUlOjUC/E7kZGR7N27l+joaLOjiJxEa1P8mdan+KsTp1609xxl3cJa5DT0C5z4K61N8Wdan9KV\nqCiLiIiIiLRCRVlEREREpBUqyiIiIiIirVBRFhERERFphYqyiIiIiEgrVJRFRERERFqhoiwiIiIi\n0goVZRERERGRVqgoi4iIiIi0QkVZRERERKQVKsoiIiIiIq1QURYRERERaYWKsoiIiIhIK1SURURE\nRERaoaIsIiIiItIKFWURERERkVaoKIuIiIiItEJFWURERESkFSrKIiIiIiKtUFEWEREREWmFirKI\niIiISCtUlEVERESkQ7i9LrYc+CPHXPvNjnJOVJRFRERE5Lxr9NTybvHvOFT3CdtL/45hGGZHarcA\nswOIiIiISNfS0HyUzft/T13zYcICoxkfdy8Wi8XsWO2moiwiIiIi501tUynvFT+Gy1NFRHAcmYPv\nxx7Yy+xY50RFWURERETOiypXITn7H6fJW0dU6FCuHHQfQbYws2OdMxVlEREREfnGyhv2sOXAk3h8\njfQLT+WKuHsJsAabHesbUVEWERERkW/kYO0OPjq4DJ/hYVDEONL7343N2vlrZuffAxERERExTeGx\nHHaU/gMDg6TekxnVbxYWS9cYrKaiLCIiIiLnJO/oBnLLVwMwvO9NDO/7nU453eJ0VJRFREREpF0M\nw2B3xUvsPfo6AGmx32No5NUmpzr/znpcvLS0lLS0tJP+GzZsGNnZ2Xg8HhYsWMCoUaOYOHEiGzZs\nOOm5K1euZPz48aSnp7N06dIO2wkRERERuTB8ho8dh59l79HXsWBl7IA5XbIkQxuOKPfv35+dO3e2\nfL5//35uvvlmrrzySp599lkcDgc5OTns2bOHOXPmkJaWRmxsLLt27WLZsmWsXr2a8PBwZsyYQXJy\nMllZWR26QyIiIiLSMbw+D9sOPUNJ7XZslkDGxf2IAT1GmR2rw7T7TOs1a9YwdepUQkJC2LhxIzNn\nziQ8PJz09HTS0tLIzs4GYOPGjUydOpXExERiYmKYNm0a69evP+87ICIiIiIdz+1tZMuBpZTUbifQ\naidj8P1duiRDO4uyz+dj7dq1fPvb3waguLiYhIQE5s+fz/r160lMTKSoqOikbStWrOCxxx4jKSmp\nZZuIiIiIdB5Nnno273+MsobdBNt6MjF+AdFhw8yO1eHadTHfli1bsFgsjBs3DgCXy0VoaCj5+fmk\npKQQFhZGWVnZSdscDgelpaVkZGTgdDpbfd3IyMhvuBsi51dgYCCgtSn+R2tT/JnWZ9dU33SU7N2P\nUuXaT4/gaG5I/R962QeYHatdTqzN9mpXUX7llVe48cYbWz632+24XC7Wrl0LwOLFiwkLC2vZ5nQ6\nWbhwIQDZ2dmEhoa2+rqLFi1q+TgjI4PMzMz27YWIiIiInHc1rlJe++yX1DWV0zt0EDeMWEx4cJTZ\nsdpk8+bN5OTkAGCz2cjIyGj3a7S5KNfU1PDOO+/w6quvtjwWHx9PQUEBw4cPB6CgoIDJkye3bCss\nLGz5WofDwZAhQ1p97blz5570eWVlZdv3QKQDnDgaorUo/kZrU/yZ1mfXcqxxPzn7/0Cjp4Y+9iFk\nxM2nqd5CU33n+PNNSUkhJSUFOL42t2zZ0u7XaPM5yuvWrWPo0KEkJia2PJaVlcWqVauoq6tj27Zt\n5ObmMmXKlJZt2dnZOBwOysvLWbNmjSZeiIiIiHQCRxryeLdoCY2eGmLChnPV4IcIDuhhdqwLrs1H\nlF955RVuvvnmkx6bPXs2hYWFZGZmEhERwZIlS4iJiQEgNTWVefPmMWvWLDweD9OnT1dRFhEREfFz\npXW5fFjyFF7DzcCeY7h8wH9js57bOb6dnSUvL88wM0BJSQnJyclmRhA5hd4+FH+ltSn+TOuz8yuu\n/oDth/6OgZchva5idP/vY7W0e5qw3zlx6kVcXFy7nqdbWIuIiIgI+ZXZfFq2EoBhUdeTGn0bFovF\n5FTmUlEWERER6cYMw+CLI6/yxZHjAxsujZnOsKjrTE7lH1SURURERLopw/Cxs+w58quysWDhsv4/\nYEjvq8yO5TdUlEVERES6IZ/hYfuhv7O/5kOslgDGDZzLwJ5jzI7lV1SURURERLoZj6+JD0v+wuH6\nXAKsIUyI+ykx4cPNjuV3VJRFREREupFmbwPvH1jKUec+gmzhZAz+BZH21m8K192pKIuIiIh0E42e\nGjbv/z3VjQewB/TmqvgH6Bk8wOxYfktFWURERKQbaGg+wnv7H6O+uZweQbFkDn6AsKAos2P5NRVl\nERERkS6uurGEnP1/wOU5Ru+QeDIGzyckIMLsWH5PRVlERESkCzvqdPD+gcdp9jbQN3QYVw66j0Cb\n3exYnYKKsoiIiEgXVVa/my0H/ojXaKZ/j1GMGziPAGuQ2bE6DRVlERERkS6opGYbWw89g8/wEh8x\ngTED7sRqsZkdq1NRURYRERHpQgzDYF/lRnLL/wUYDO1zNSNjZ2CxWM2O1umoKIuIiIh0ET7DR+5/\nbkkNkBp9O8OirsNisZicrHNSURYRERHpAjy+JrYefJpDdZ9itQSQPuBuBkeMMztWp6aiLCIiItLJ\nNXpqeP/AUqpchQRaQ5kw6GdEhw0zO1anp6IsIiIi0onVNR1m8/7HaXBXEBoYRebg+brb3nmioiwi\nIiLSSR1x7mPLgSdp9tbTOySBKwfdhz2wl9mxugwVZREREZFOqKRmO1sP/S8+w03/8JFcPnAegbYQ\ns2N1KSrKIiIiIp3I18e/JfaexKh+szQjuQOoKIuIiIh0EqeMf4u5nWGRGv/WUVSURURERDoBjX+7\n8FSURURERPycxr+ZQ0VZRERExI9p/Jt5VJRFRERE/JTGv5lLRVlERETED2n8m/lUlEVERET8iMa/\n+Q8VZRERERE/ofFv/kVFWURERMQPaPyb/1FRFhERETGZxr/5JxVlERERERNp/Jv/UlEWERERMYnG\nv/k369m+oLGxkYcffpixY8cyZswYHnnkEQDcbjcLFixg1KhRTJw4kQ0bNpz0vJUrVzJ+/HjS09NZ\nunRpx6QXERER6aRKarbzXvGjNHvr6R8+konxC1SS/cxZjygvWbKEkpIS3njjDSIjIykoKABg+fLl\nOBwOcnJy2LNnD3PmzCEtLY3Y2Fh27drFsmXLWL16NeHh4cyYMYPk5GSysrI6fIdERERE/JnGv3Ue\nZzyi3NjYyNq1a1m4cCFRUVFYLBaSkpIA2LhxIzNnziQ8PJz09HTS0tLIzs5u2TZ16lQSExOJiYlh\n2rRprF+/vuP3RkRERMSP+QwfO8tWkVu+GjBIjbmd0f1mqyT7qTMW5eLiYiwWC5s2bWL8+PFcd911\nbNq0qWVbQkIC8+fPZ/369SQmJlJUVHTSthUrVvDYY4+RlJTUsk1ERESkO/L4mviw5E/kV2VjtQRw\n+cC5JEddrxnJfuyMp17U19fjdrs5ePAg7777Ljt37uSee+7hrbfewuVyERoaSn5+PikpKYSFhVFW\nVgbQss3hcFBaWkpGRgZOp/O03ycyMvL87pXINxQYGAhobYr/0doUf6b1eXrO5mrWf/F7KuLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f54dcb00>"
+        "<matplotlib.figure.Figure at 0x7f859034b128>"
        ]
       }
      ],
@@ -1202,42 +1204,26 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "zs = gen_data (x0=5, dx=5, count=100, noise_factor=50)\n",
+      "zs = gen_data(x0=5, dx=5, count=100, noise_factor=50)\n",
       "\n",
-      "data = g_h_filter (data=zs, x0=0., dx=5., dt=1.,g=0.1, h=0.01)\n",
-      "plot_g_h_results  (zs, data, 'g = 0.1')\n",
+      "data = g_h_filter(data=zs, x0=0., dx=5., dt=1.,g=0.1, h=0.01)\n",
+      "plot_g_h_results (zs, data, 'g = 0.1')\n",
       "\n",
-      "data = g_h_filter (data=zs, x0=0., dx=5., dt=1.,g=0.5, h=0.01)\n",
-      "plot_g_h_results  (zs, data, 'g = 0.5')\n",
+      "data = g_h_filter(data=zs, x0=0., dx=5., dt=1.,g=0.5, h=0.01)\n",
+      "plot_g_h_results (zs, data, 'g = 0.5')\n",
       "\n",
-      "data = g_h_filter (data=zs, x0=0., dx=5., dt=1.,g=0.9, h=0.01)\n",
-      "plot_g_h_results  (zs, data, 'g = 0.9')"
+      "data = g_h_filter(data=zs, x0=0., dx=5., dt=1.,g=0.9, h=0.01)\n",
+      "plot_g_h_results  zs, data, 'g = 0.9')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAGDCAYAAADkjOwcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f54f9da0>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAGDCAYAAADkjOwcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8VPW5+PHPObMv2RNCwhYIyCqyCEoVEHe07nprbUHs\ncvVqq71Wb2+trb/bWq7aar331rbeel1rW2tdq4jiClg3qIDImpCwZSN7Zl/O+f1x5kwSMlknQCLP\n+x/JzJnvfCcZX69nnnm+z6Ps3LlTRwghhBBCCDFg6rHegBBCCCGEEMOdBNVCCCGEEEKkSYJqIYQQ\nQggh0iRBtRBCCCGEEGmSoFoIIYQQQog0SVAthBBCCCFEmiSoFkIIIYQQIk3WY70BIYQQR9e6det4\n8MEHKSsrIycnhyuuuIIbb7wRi8UyoPWmTJmS8va7776bK6+8Mp2tCiHEsCFBtRBCHEc2btzIDTfc\nwIwZM/jRj37Ejh07eOihh2hpaeHOO+8c8Lrnnnsu55xzTqfbZs2ale52hRBi2JCgWgghjiMPPvgg\nubm5PP7447hcLgDi8Th/+tOfuO666xg1atSA1j3hhBO46KKLBnOrQggxrEhNtRBCDJLPPvuMq6++\nmpkzZ3L22Wfz4osvsmzZMpYtW3astwZAc3MzGzdu5LzzzksG1ABXXnkl8Xicd955J631Q6EQ4XA4\n3W0KIcSwJJlqIYQYBIcOHWLFihW4XC6+853voOs69913H4qiUFpa2u/1QqEQgUCgx2u8Xi92u73P\na5aVlaFpGtOmTet0++TJk1EUhZ07d/Z7n6bHHnuMhx56CF3XmTRpEjfffHOXchAhhPgik6BaCCEG\nweOPP47f7+fpp59OHtw78cQT+cY3vjGgoPr3v/89Dz30UI/X3HPPPVx66aV9XrOurg6AvLw8ysrK\nWLZsGUuXLuUnP/kJmZmZHDp0qN/7BJg7dy4XXHABxcXFVFVV8cQTT/Dd736X+++/nwsvvHBAawoh\nxHAjQbUQQgyCdevWMW3atE6dMBYsWEB+fv6A1rv00ks5+eSTe7xm4sSJ/VozEokAYLfbaWlpoaWl\nhQMHDgBgs9kGXLrx9NNPd/r5kksu4fzzz+cXv/iFBNVCiOOGBNVCCDEIqqqqWLBgQafbFEWhsLBw\nQOuNGTOGMWPGDMbWksxSkUgkwoIFC1i3bh3Z2dkARKNRHA7HoDyP1+vl0ksv5ZFHHqGiooLx48cP\nyrpCCDGUSVAthBCDQFEUdF3vcnuq2/oiEAjg9/t7vCYzM7NfgfCIESMAaGhoAIwyEDCC7NbWVgoK\nCga011TMDxMtLS2DtqYQQgxlElQLIcQgKC4uTpZSmHRdp6ampt9lGgD/93//N+g11RMnTkRVVbZt\n28bll1+evH3Hjh3ous7kyZP7vc/uVFVVAe2BuxBCfNFJUC2EEINg4cKFPPLII+zYsSNZV71+/Xoa\nGxsHtN6RqKnOzs5m7ty5rF69mltvvRW32w3AX//6V6xWK0uWLEn5uPPPPx9FUXj88ce7lLM0NTWR\nk5PT6bbGxkZeeOEFSkpKBr2ERQghhioJqoUQYhCsWLGCP//5z3zrW99i+fLlaJrGU089xciRIwe0\n3pGoqQa45ZZbuPbaa1mxYgVXXHEFO3fu5C9/+Qtf+9rXuh38UllZiaIoxOPxLvf99re/ZcuWLSxY\nsICioiJqa2t55pln8Pv9PPDAA4O+fyGEGKqUnTt3DqzgTwghRCefffYZP//5z9m2bRtFRUV873vf\n4+mnn8Zut/Poo48e6+0lrV27lv/6r/9i9+7d5Obmcvnll3PTTTdhsVhSXj9lyhQUReGtt96iuLi4\n031vvvkmjz32GBUVFbS2tuJyuZgzZw433ngjJ5100tF4OUIIMSSkHVSHQiFWrlzJ66+/jqZpfPnL\nX+auu+4iGo1y1113sXr1arKysvi3f/s3li5dmnzck08+ycMPP0w0GuXqq6/m1ltvTfvFCCHEUHPe\neecxffp0ydoKIcQXXNrlHytXrmT//v28+uqr5OXlUV5eDhiDEMrKyli7di3btm3j+uuvZ/bs2Ywc\nOZLNmzfz0EMP8cc//hGv18s111zD1KlTOwXdQggx3ASDwU7jv8vLy9m3bx9f//rXj+GuhBBCHA1p\nBdWhUIiXXnqJ559/PjngwDw4s3r1alasWIHX62X+/PnMnj2bNWvWsGzZMlavXs25556bnDJ21VVX\nsWrVKgmqhRDD2vnnn8/ChQuZOnUqPp+Pp59+msLCQi677LJjvTUhhBBHWFpBtXl45c0332T58uVk\nZ2fzr//6r5x99tlUVlYyfvx4brvtNs4880xKS0upqKhIPm7evHk88cQT1NTUMHfuXF555ZVBeUFC\nCHGsnH322axbt46XX34Zh8PBggULuP322/F6vcd6a0IIIY6wtIJqn89HNBrlwIEDvPPOO3z66afc\ncMMNvPHGGwSDQdxuN7t372bGjBl4PB5qamoAkveVlZVRVVXFokWLCAQCg/KChBDiWPnxj398rLcg\nhBDiGEkrqHY6ncTjca677jrsdjunnHIK48ePZ/PmzbhcLoLBIC+99BIAd999Nx6PBwCXy0UgEODO\nO+8EYM2aNcl+qR3t3bsXVVXT2aIQQgghhBA9amtrY9q0aWmtkVZQPWbMGBRF6XK7ruuUlJRQXl7O\n9OnTAePAzllnnQVASUkJe/bsSV5fVlbGhAkTuqyjqipTp05NZ4viCyYvL4/nn3+exYsXH+utiCFE\n3hciFXlfiFTkfSEOl5eXx/r169NeJ600cFZWFvPmzePxxx8nFovxySefUFFRwaxZs1i6dClPPfUU\nbW1tfPTRR2zatIlzzjkHgKVLl7JmzRrKysqora3lueeek0OKQgghhBBi2BqUlno//OEPmTdvHoWF\nhfziF7+goKCAFStWsGfPHhYvXkxWVhYrV65MjredOXMmN910E8uXLycWi3H11VdLUC2EEEIIIYat\nIT1Rcf/+/VL+ITrJy8tj+/btjBgx4lhvRQwh8r4Qqcj7QqQi7wtxOLP8Y8yYMWmtI6cAxbAjH7RE\nKvK+EKnI+0KkIu8LcSRIUC2EEEIIIUSaJKgWQgghhBBDTjjmQ9eHbJVyFxJUCyGEEEKIIaUhuIeX\ndt7I1kPPH+ut9JkE1UIIIYQQYkhpCe1HR6cxUH6st9JnElQLIYQQQoghJaaFAAjFW47xTvpOgmoh\nhBBCCDGkRLUgAKGYBNVCCCGEEEIMSCxuZKrDsVY0XUt/PS3MrobXaQxWpL1WdySoFkIIIYQQQ0o0\nUf6hoxOOtaa9XmNwD5/W/IENVY+mvVZ3JKgWQgghhBBDillTDYNTV10f2A1AnntS2mt1R4JqIYQQ\nQggxpHQKqgehrtoMqvMlqBZCCCGEEMeLwQyqdV2jIVgGQL5LgmohhBBCCHGcMLt/AISi6QXVbZEa\nInEfLmsOblteulvrlgTVQgghhBBiSBnMmur6wC7AKP1QFCWttXoiQbUQQgghhBhSovHBK/84GocU\nQYJqIYQQQggxxHSuqW5Oa636wJGvpwYJqoUQQgghxBCi6zqxjjXVaWSqw7E22iJVWBQb2c5xg7G9\nbklQLYQQQgghhoy4HkFHT/4cSmP4S0OwHIBc1wQsqjXtvfVEgmohhBBCCDFkmKUfdosHBZVI3Edc\niw5orY6HFI80CaqFEEIIIcSQYQbVNtWF05oFQDg+sGz10TqkCBJUCyGEEEKIIcTs/GFVXTismQAE\nB1BXrekxGoN7gCN/SBEkqBZCCCGEEEOImam2qk5cZqZ6AEF1c2gfcT1Chn0kDmvGoO4xFQmqhRBC\nCCHEkGFOU7RZnDit2cDAMtVHs/QDJKgWQgghhBBDSMdMtVlTPZBe1WZQne8+YfA21wMJqoUQQggh\nxJARTXFQcSC9qpNB9VGopwYJqoUQQgghxBCSOlPdv6A6EG0gGGvEprrJdBQN+h5TkaBaCCGEEEIM\nGbF4+kF1e+nHRBRFpbYpQFlVeuPOeyNBtRBCCCGEGDLMEeU2tf2gYv+DamPoi3lI8em3t7P49me5\n5y+fDOJOO5OgWgghhBBCDBnRTuUfRp/q/gfVZUD7IcXCHA9TRucw/4SRg7jTzo7sEHQhhBBCCCH6\nIVlTbXFhU92oio2YFiKmhbCqzj49vjm0FwWVXOcEAL525hS+duYUdF0/YvuWTLUQQgghhBgy2rt/\nOFEUpUNddd9GlTcGK9DRyHaOxWbpHIQrijK4m+1AgmohhBBCCDFkmDXVZla6v72qj/bQF5ME1UII\nIYQQYsiIxtsz1UC/O4CYhxTzJagWQgghhBBfFJqu9eugYUwLA6ky1b2voesaDcHEIcWjNPTFJEG1\nEEIIIYQ4IuJajPf23sPLO79LW7i2T485vPzD1Y+gui1STSTux2XNwW3L45bfvct/PvMJjW2hAb6C\nvpOgWgghhBBCDDpd1/lHzZPU+bejo9MS3t+nx5ndP2wWF9CeqQ72IahuH/oyibrmIM+t383vX/sM\nu/XIh7wSVAshhBBCiEFX1vQWe5reSf4cjQd6fYyu653GlEP/yj86HlJ8bUMlug6LTxyN12Xv9/77\nS4JqIYQQQggxqGr92/i0+ikAMuxFAETi/l4fF9fD6OhYFBuqYgEGFlTnuyax6pMKAC6YX9Lv/Q9E\n2kH1smXLmDlzJrNnz2b27Nn84Ac/ACAajXLHHXcwZ84clixZwmuvvdbpcU8++SSnnXYa8+fP54EH\nHkh3G0IIIYQQYgjwRer4+/7/QUdjSt6FjM06FYCI1num2uz80XHIS19HlUfjQdoi1aiKBT1WyIfb\nq7FaFM6ZM26gL6VfBmWi4k9+8hOuvPLKTrc9/vjjlJWVsXbtWrZt28b111/P7NmzGTlyJJLine truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f55c7d68>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAGDCAYAAADkjOwcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5496f28>"
+       "ename": "SyntaxError",
+       "evalue": "invalid syntax (<ipython-input-22-786b45432a0a>, line 10)",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-22-786b45432a0a>\"\u001b[1;36m, line \u001b[1;32m10\u001b[0m\n\u001b[1;33m    plot_g_h_results  zs, data, 'g = 0.9')\u001b[0m\n\u001b[1;37m                       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
        ]
       }
      ],
@@ -1259,44 +1245,18 @@
       "for i in range(100):\n",
       "    zs.append(14)\n",
       "\n",
-      "data = g_h_filter (data=zs, x0=4., dx=1., dt=1.,g=0.1, h=0.01)\n",
-      "plot_g_h_results  (zs, data, 'g = 0.1')\n",
+      "data = g_h_filter(data=zs, x0=4., dx=1., dt=1.,g=0.1, h=0.01)\n",
+      "plot_g_h_results (zs, data, 'g = 0.1')\n",
       "\n",
-      "data = g_h_filter (data=zs, x0=4., dx=1., dt=1.,g=0.5, h=0.01)\n",
-      "plot_g_h_results  (zs, data, 'g = 0.5')\n",
+      "data = g_h_filter(data=zs, x0=4., dx=1., dt=1.,g=0.5, h=0.01)\n",
+      "plot_g_h_results (zs, data, 'g = 0.5')\n",
       "\n",
-      "data = g_h_filter (data=zs, x0=4., dx=1., dt=1.,g=0.9, h=0.01)\n",
-      "plot_g_h_results  (zs, data, 'g = 0.9')"
+      "data = g_h_filter(data=zs, x0=4., dx=1., dt=1.,g=0.9, h=0.01)\n",
+      "plot_g_h_results (zs, data, 'g = 0.9')"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAGDCAYAAAAoFdb3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f53c9d68>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAGDCAYAAAAoFdb3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5466a58>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAGDCAYAAAAoFdb3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5446a20>"
-       ]
-      }
-     ],
-     "prompt_number": 23
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -1313,33 +1273,15 @@
      "input": [
       "zs = [5,6,7,8,9,9,9,9,9,10,11,12,13,14,15,16,16,16,16,16,16,16,16,16,16,16]\n",
       "    \n",
-      "data = g_h_filter (data=zs, x0=4., dx=1., dt=1.,g=.302, h=0.054)\n",
-      "plot_g_h_results  (zs, data, 'g = 0.302, h = 0.054')\n",
+      "data = g_h_filter(data=zs, x0=4., dx=1., dt=1.,g=.302, h=0.054)\n",
+      "plot_g_h_results (zs, data, 'g = 0.302, h = 0.054')\n",
       "\n",
-      "data = g_h_filter (data=zs, x0=4., dx=1., dt=1.,g=.546, h=0.205)\n",
-      "plot_g_h_results  (zs, data, 'g = 0.546, h = 0.205')"
+      "data = g_h_filter(data=zs, x0=4., dx=1., dt=1.,g=.546, h=0.205)\n",
+      "plot_g_h_results (zs, data, 'g = 0.546, h = 0.205')"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAGDCAYAAAAh/naNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f55969b0>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAGDCAYAAAAh/naNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4lFXe//H3zGTSCyGBkEASei9SBBFIRFBAZUWUxceK\nK5Zl8bGBK6y6v0fWirC66qqLBWVVQEXsBVC6AqIoRSItjRgICekzmXb//kCiMRNIIMmEzOd1Xbkg\n95z7nu8wh8knZ86cY0pLSzMQEREREfEzZl8XICIiIiLiCwrCIiIiIuKXFIRFRERExC8pCIuIiIiI\nX1IQFhERERG/pCAsIiIiIn5JQVhE5Ay3adMmunfvzpYtW3xdiojIGUVBWETOCOvWrePyyy+nX79+\nnHfeeTz99NO43e5Tvl737t29fr399ts1nuPxeJgwYQLdu3fnrbfe8trmgw8+YNKkSfTv35+zzz6b\nq6++mh9//PGU62xutm/fzjXXXEP//v0ZNmwY//jHP7DZbKd0raKiIp5//nkmTZrEkCFDGDJkCFdf\nfTVr1qzx2j4jI4NbbrmFAQMGMHjwYO655x4KCgqqtMnOzq6xb3z11VenVKeINF0Bvi5ARORktm7d\nyq233krv3r3529/+xu7du3n22WcpKirivvvuO+XrXnjhhVxwwQVVjp111lk1tl+yZAn79u3DZDJh\nMpmq3f7888/z5JNPMnz4cO655x4Mw2D37t0cOXLklGtsTjIyMrj++utp06YNM2fO5NChQ7z00ktk\nZ2fz/PPP1/l633//Pc8++yyjRo1iwoQJeDweli9fzi233MIDDzzAVVddVdm2sLCQa6+9FoDbb78d\nm83GggUL2Lt3L2+99RYWi6XKtSdPnsygQYOqHOvSpcspPGoRacoUhEWkyXvyySdp2bIlCxcuJCQk\nBAC3282bb77JDTfcQNu2bU/pul27dmX8+PG1altUVMS//vUvrrnmGl555ZVqt2dlZfHMM8/wxz/+\nkQcffPCU6mnunnvuOTweD6+88gpxcXEAhIWFMX/+fLZu3crAgQPrdL3OnTuzYsUK2rRpU3ls0qRJ\njBkzhqeeeoorr7wSs/nYG5+LFi3i8OHDvPPOO/Tq1QuApKQk7rrrLj766CP+8Ic/VLl2//79a903\nROTMpakRIn5u+/btXHnllfTt25fRo0ezfPlyrr322srRM18rLCxk69atjBkzpjIEA1xxxRW43W6+\n/PLL07q+3W6noqLipO2efvppunTpwsiRI73e/v777+PxeLj99tsBKCsrwzAadwf78vJy7rvvPgYP\nHszw4cN57LHH8Hg8jVpDTQzD4IsvvmDo0KGVIRiOPY8AK1eurPM1ExISqoRggODgYPr160dRUVGV\naQ+rVq2ia9eulSEYYMyYMYSHh3u9b8MwKC8vx+Fw1LkuETlzKAiL+LG8vDymTJlCdnY206dPZ9Kk\nSTz++OPs37/f61v/J2O32ykoKDjhV12Dxd69e/F4PPTs2bPK8W7dumEymUhLS6tznce98sor9O/f\nn379+jF+/HhWrFjhtd2ePXtYvHhx5XQHb7Zt20ZiYiI7duzg/PPPZ+DAgQwZMoR//vOfjRaIH374\nYcrLy7nrrrsYMGAAr7zyCq+//nqdr+NwOE76PNrt9jpdMzc3l+Li4mrPY8uWLYmLizut5/H38vPz\nCQoKIioqCjj27sHevXur3bfFYqFLly7s3r272jUeeughBgwYQL9+/bjyyiv55ptv6q0+EWk6NDVC\nxI8tXLiQsrIyXn/9dbp37w5Anz59+NOf/kSnTp3qfL0FCxbw7LPPnrDNo48+yoQJE2p9zcOHDwMQ\nExPD3r17ufbaaxk3bhwPPPAAkZGR5OXl1blOgIEDB3LRRReRkJBATk4Or776Krfddhvz5s3j4osv\nrtL2oYceYsyYMfTu3ZtNmzZ5vV5ubi4VFRXcddddXH/99fTs2ZPPPvuMF154gYiICKZOnXpKddZF\n165dmT9/PgBXXnklF154IatWrarz6P6HH37I7NmzT9hm+vTpTJ8+vdbXPP48xcTEUFBQwBVXXEHX\nrl15/vnniYmJOeXn8fd+/PFHvv32WyZMmIDVagWOvavgcrmIiYnB6XRyxRVXYLFYWLJkCbGxsVWC\nsMViISUlhdGjR9OqVSsOHDjASy+9xJQpU1i4cGG1ecMicmZTEBbxY+vWraNnz56VIRhg6NChxMbG\nntL1JkyYcNKg0Llz5zpd8/gIcmBgIEVFRRQVFZGdnQ2A1Wqt1bQGb34/UnrppZcyduxY5s6dWyUI\nr1ixgq1bt/LJJ5+c8Ho2m43c3NwqAXH06NFkZWXx4osvcuONN57SKHtdjBkzpsr33bt3P6WR1hEj\nRnidB/1biYmJdbrm8efRarVSXl7OkSNHKqe6BAYGUlpaWuc6f89ms3HPPffQsmVL7r77bq/37XQ6\nyc3NxWKx4HK5CAwMrPIuRXx8PP/5z38qvx85ciRjxozh4osvZt68ebz55punXaeINB0KwiJ+LCcn\nh6FDh1Y5ZjKZqszhrIvExMQ6B6STCQwMBI6FmaFDh7Ju3TpatGgBgNPpJCgoqF7uJzw8nAkTJvDi\niy9y4MABOnToQEVFBY8++ihXX3017dq1O2mdJpOJsWPHVjk+YsQItm3bRm5uLvHx8fVSa01atWpV\n5fvQ0FCcTucpXef31zpdx59Hp9NJu3btWLt2LaGhocCx5/Z0n0ePx8Nf//pXMjIyePnll6v8Mvfb\n+w4NDWXVqlWYTCZCQkJqdd9t27blvPPO4/PPP6eioqLe+pyI+J6CsIgfM5lMXuevnuqc1vLycsrK\nyk7YJjIysk5BonXr1sCxeZ9w7K11OBaeiouL6zWwHf8FoKioCIA333yT/Px8JkyYQG5uLkDlB7CK\niorIzc2t/LBWTEwMBw4coGXLllWueTy0Hz58uMGD8PEVEk5XRUUFxcXFJ2wTFhZWGWRr4/jzdPx5\nPP7vAnDkyJE6v1Pwe48++igrVqxg7ty51d6VaNGiBQEBAZX3HR4eXuW+a9OH2rRpg8fjqfc+JyK+\npSAs4scSEhIqpxkcZxgGubm5pxRMXnrppXqfI9y5c2fMZjO7du1i4sSJlcd3796NYRh069atznXW\nJCcnB/g1bOfk5GC3273WO3fuXObOncs333xDeHg4nTp1YsuWLRw+fLhKGD4eviIiIuqtzob20Ucf\n1fsc4TZt2hAVFcXOnTurHM/Pz+fw4cPV5mXXxcsvv8xrr73GrFmzuOSSS6rdbrFY6Ny5c7X7drlc\n/PTTTwwbNuyk95GTk0NAQEDlB/BEpHlQEBbxYyNGjODFF19k9+7dlfOE169fX223rdpqiDnCLVq0\nYODAgXz66afcddddlaOQb7/9NgEBATUuZzZ27FhMJhMLFy6sNtXj6NGjREdHVzlWUFDAu+++S/v2\n7Sund0yePLlaSEpLS2P+/Plce+21DB8+vHKe68iRI1m8eDHvvfde5b+lx+Nh5cqVxMTE0KFDh2o1\nLlu2jNmzZ3P22WezaNGiOv271NapzEtuiDnCJpOJkSNH8sknn3Do0KHK5+Sdd94BYNSoUV7Pu/76\n6zl8+DCPPfYYffv2rXb7hx9+yNy5c7n11lu5/vrra7z/UaNG8eyzz7Jjxw569+4NwGeffUZZWVmV\n+/bWN/bt28cXX3zBkCFDKqdZiEjzoCAs4semTJnC4sWLmTp1Ktdddx0ej4dFixZVW5u1thpijjAc\n2wns+uuvZ8qUKVx++eWkpaWxdOlSrr766ho300hPT8dkMnndhvm5557jhx9+YOjQocTHx3Po0CGW\nLFlCWVlZ5aoLAJ06daq2ekZwcDBwbPm21NTUyuOpqamVS5aVlZXRrVs3Vq1axU8//cT999/vNZAe\nX+O3IcPVqUxzaYg5wgC33norn3/+OVOmTOHaa6+t3FnuvPPOq/EXqKysLHJycrx+KPKHH37g3nvv\npX379nTs2JH33nuvyu3Dhw+vHN2/5pprWLp0KdOmTeNPf/oTdrudBQsW0KNHjyqjyPfffz82m43+\n/fvTunVrMjMzWbx4MUFBQfz1r3+tx38NEWkKFIRF/FhsbCyvvPIKDz30EM888wzx8fHcd999vP76\n6wQENJ2Xh0GDBvHcc8/x1FNP8dBDD9GyZUumTZvGX/7yl1O63uDBg9m5cydLliyhuLiYkJAQBgwY\nwLRp0+jXr99Jz69plPX5559n3rx5rFq1imXLlpGcnMycOXOYNGmS1/bHl+269NJLT+lxnKymmraC\n9pX27duzcOFCHn/8cR5//HHCwsKYPHkyM2bMOOF5NT2Gffv24XK5SE9P55577ql2zmuvvVYZhKOj\no1m0aBGPPPIITz31FFarlfPPP59Zs2ZV2V45NTWVJUuW8N///pfS0lIiIiIYMWIEt912Gx07djzN\nfwERaWpMaWlpNQ4XrFy5kgULFrBr1y4uueQSHnnkEQCKi4t54IEH+OqrrwgMDGTy5Ml1mismIk3b\nmDFj6NWrV5XRUal/48ePx+1289FHHzWpwCoi4i9O+BHjyMhIpk6dWrkF5nFPP/00druddevWsXTp\nUpYsWcKaNWsatFARaRg2m63K9/v27SMzM5P+/fv7qCL/UFBQwN69e5k+fbpCsIiIj5zwvc/BgwcD\nsHPnzirbae7fv58LL7yQwMBA4uPj6d+/P/v27asyX05Ezgxjx45lxIgR9OjRg9LSUl5//XXi4uK4\n7LLLfF1as9ayZUt+/PFHX5chIuLXarXo5O8/bDFixAi+/PJLbDYbWVlZ7Nixg3PPPbdBChSRhjV6\n9Gg2b97MY489xosvvshZZ53FokWLqqy1KiIi0hzV6tMwv3/b7qqrrmLNmjUMGjQIt9vNHXLine truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f55d4630>"
-       ]
-      }
-     ],
-     "prompt_number": 24
+     "outputs": []
     },
     {
      "cell_type": "heading",
@@ -1364,46 +1306,20 @@
      "input": [
       "zs = np.linspace(0,1,50)\n",
       "\n",
-      "data = g_h_filter (data=zs, x0=0, dx=0., dt=1.,g=.2, h=0.05)\n",
-      "plot_g_h_results  (zs, data, 'dx=0, h = 0.05')\n",
+      "data = g_h_filter(data=zs, x0=0, dx=0., dt=1.,g=.2, h=0.05)\n",
+      "plot_g_h_results (zs, data, 'dx=0, h = 0.05')\n",
       "\n",
-      "data = g_h_filter (data=zs, x0=0, dx=2., dt=1.,g=.2, h=0.05)\n",
+      "data = g_h_filter(data=zs, x0=0, dx=2., dt=1.,g=.2, h=0.05)\n",
       "plt.ylim([-1,5])\n",
-      "plot_g_h_results  (zs, data, 'dx=2, h = 0.05')\n",
+      "plot_g_h_results(zs, data, 'dx=2, h = 0.05')\n",
       "\n",
-      "data = g_h_filter (data=zs, x0=0, dx=2., dt=1.,g=.2, h=0.5)\n",
+      "data = g_h_filter(data=zs, x0=0, dx=2., dt=1.,g=.2, h=0.5)\n",
       "plt.ylim([-1,5])\n",
-      "plot_g_h_results  (zs, data, 'dx=2, h = 0.5')"
+      "plot_g_h_results(zs, data, 'dx=2, h = 0.5')"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8VPWh///XzGQmmclkmwQSCHsyQGQThCCLYQ2CC+7a\n2lJsf9Wv19p6bYVvtdprFX9Xr/1d9fah3kq5lVq1VuuuVXYQEVAQQZYshCUsIZDJNpNltvP7IyVX\n6hASCEyW9/Px6OORnDnnc95DP4Z3Dp85x1RQUGAgIiIiIiKnMEc7gIiIiIhIR6SiLCIiIiISgYqy\niIiIiEgEKsoiIiIiIhGoKIuIiIiIRKCiLCIiIiISgYqyiHQq8+bNY968edGOcd4cOnSIoUOH8tZb\nb0U7iohIt6eiLCKdjslkOu/nCAQCPPXUU+Tl5TFq1ChuuukmNmzYcN7Pe9KFeI/n6sCBA/yf//N/\nGDNmDLm5uSxcuBCPx3NOY37yySfccMMNjBo1iqlTp/K73/2OUCh0yj5vvvkmQ4cOjfi/cDh8TucX\nEfmmmGgHEBHpiBYtWsRf//pXvve97zF48GBef/117rjjDl5++WVGjRoV7XhRV1VV1Xxl/5577qG+\nvp7FixdTXFzM66+/jsViafOYW7Zs4c4772T48OH86le/Ys+ePTz77LNUV1fz4IMPfmv/Bx54gJSU\nlFO2mc26/iMi7UdFWUTkn5SWljaX5JMF7YorrmDWrFk8/fTT/PGPf4xywuh76aWXKC8v529/+xvD\nhg0DoF+/fvz85z/ngw8+YO7cuW0e8+mnn8blcvHiiy9it9sBCIVCvPrqq/zwhz8kMzPzlP1nzpxJ\n7969z/3NiIichn71FpEOa/v27XznO99h1KhRzJgxgzfeeONb+6xcuZKhQ4fyl7/85ZTtq1evZujQ\noSxdurTN5121ahWGYXD99dc3b3M6ncyePZvNmzdTW1vb9jfTRoFAgN/+9rdMnDiR8ePHc//999PQ\n0HDez9taK1euZPDgwc0lGeDyyy/H6XSyYsWKNo9XVVXFli1buPzyy5tLMsCNN95IKBRi9erV3zrG\nMAy8Xq+WW4jIeaMryiLSIR08eJD58+eTkpLCz372MxoaGnjyyScxmUwMHjy4eb8ZM2Zw880388QT\nTzBx4kT69euHx+PhwQcfZNKkScyfPx+AhoYG6urqWjyn0+nEZrNRUFCAxWJhyJAhp7yek5NDKBSi\nqKiIMWPGtP+b/oY//OEP9OnTh5/+9Kfs2rWL119/naSkJH75y1+2aZxgMEhNTU2L+8TFxeFwOFo9\nZigUori4mKuuuuqU7RaLBbfbzZ49e9qUEaC4uJhwOMxFF110yvYhQ4ZgMpkoKCj41jHXXHMNXq8X\nm83G1KlT+eUvf6krzCLSrlSURaRDWrx4MY2NjSxevJisrCwARowYwR133PGtfe+//342b97MwoUL\neeWVV3j44YcJhUI8/vjjp4z37LPPtnjOxx9/nGuvvZbjx4+TlJSExWLhv//7v/nDH/7Ac889R2pq\nKgAnTpxox3camdPpZMmSJc3fl5aWsnLlyjYX5S1btjT/snA61113Hf/+7//e6jGrqqoIBoOkpqYS\nCAS48cYbsVgsvPbaa6SlpZ1VUS4vLwcgNTWV4uJi5s2bx5w5c/j1r39NYmIix48fb97X4XBw0003\nMXbsWOLj49m6dSsvvfQS27dv56233vrWumURkbOloiwiHdJnn31GTk5Oc0kGyMvLay6r32S323ny\nySf57ne/y2233cbmzZv53e9+R48ePZr3ufbaaxk7dmyL53S73QD4/X6sVisAR44cwefzUVFRQUJC\nAsAFWQIxa9asU77Pycnhiy++aPM4OTk5Z1xT3bNnzzaN6ff7AbBarQQCAcrKyrBYLASDQWw2W/Pr\nZzOmzWajurqa6upqDh061HyexsbG5n1nz57N7Nmzm7+fOXMmI0aM4N577+XFF1/k3nvvbfP5RUQi\nUVEWkQ6prKyMnJycb21PT0+PuP+IESO4/fbbef7557nyyivJz88/5fW+ffvSt2/fVp37m2Xv4Ycf\n5qc//Sk9evRg1apVQNNShfPtmyUfmq6iBoPBNo+TmJjIhAkT2isW0PTnA03rqB0OBytXrsRkMmG3\n2/H7/cTGxp71mH6/nwkTJvDJJ5+QnJzcfJ4zjTlnzhwWLVrEpk2b2nxuEZHTUVEWkQ7pmx/oao1Q\nKMRnn30GwLZt2/B6vTidzubX6+rq8Pl8LY6RmJhIbGwsPXr0oLq6mlAohMViaS6tFRUVAKSlpbUp\n29lor9ucBQIBqqqqWtzHbref8md1JsnJycTExDT/eXzz2BMnTnyr5LfGyavaJ8c8+S8Hfr+fmpqa\nVo2Znp5+xvcqItIWKsoi0iH16tWLw4cPn7LNMAyOHj3avETim5599ll27NjBb3/7W37961/zm9/8\nhieffLL59SVLlrR6jfLgwYMJh8Ps2bPnlLs67Nq1C7PZHPH8HdXWrVvbfY2yxWIhOzubnTt3nrI9\nGAxSWFjIpEmT2pwzOzsbs9nMrl27TrnbyJ49ezAM41sfrPxnhmFw5MiRU5bqiIicKxVlEemQJk2a\nxIsvvsjevXuby8+6deuorKz81r5bt27l97//PbfffjtXXXUV9fX1PPTQQ0ydOpUrr7wSaN0a5ezs\nbKDpThr/8R//wZtvvtlclL1eLx999BG5ubnNa5W/adOmTcyfP5/evXs3L9HoCM7HGmVo+jN69tln\n+frrrxk+fDgAH3/8MT6fjxkzZkQ8ZuHChezYsYNf/OIXzJw585TXkpOTueSSS/joo4/4+c9/3nwX\njjfeeIOYmBimTZvWvK/H48Hlcp1y/JtvvklVVRV5eXltfi8iIqejoiwiHdJtt93Ga6+9xo9//GO+\n//3v09DQwJ/+9CeSk5MxDKN5P6/Xy8KFCxk8eDB33303ADfddBMrV67k4YcfZsyYMfTq1atNa5T7\n9u3LjTfeyCuvvILJZMLtdvPGG29QW1vLv/7rv0Y85uS9fE9+CLCjOB9rlAG+//3v89e//pW77rqL\nH/3oRzQ0NLB48WJycnK+ddu4k44ePcq+ffvwer0RX7/nnnuYP38+t912GzfccAMFBQXND3755sNG\n5s6dy6WXXsqQIUNwOp1s376dd955B7fbzQ9+8IN2f68i0n3pgSMi0iGlp6fz4osvkp6ezjPPPMNb\nb73F/fffj9vtxmQyNe/36KOPUl5ezhNPPEFMzP/+7r9o0SIsFgsLFy48pVi31kMPPcQdd9zBsmXL\neOyxxwB44YUXuPjiiyPuf/I+v9dee22bz9UaJpPplPcdbSkpKbz00kvk5OTwzDPP8D//8z9Mnz6d\nJUuWtPj46pbew9ixY3n++ecJhUI89thjrFq1irvuuosHHnjglP1mz57Nrl27+P3vf89jjz3Gpk2b\nmDdvHq+++mqb17aLiLTEVFBQ0OLfICtWrGDx4sXs2rWLq666qlXr2NauXct//dd/sX//fhISErjl\nllv4l3/5l3YLLSLS0dx5551s27aNVatWtenhHSIi0nGdcelFYmIiP/7xj9mwYUOr7x1aV1fHfffd\nx9ixY/F4PNx2221kZmYyd+7ccw4sItLRhEIhtmzZwu23366SLCLShZyxKOfm5gKwc+fOVhflOXPm\nNH+dnp7OZZddxrZt21SURaRLslgsfP7559GOISIi7azVa5TPZo3fSdu2bWPo0KFnfbyIiIiIyIXW\n6qJ8th8iefnllwkEAlx33XVndbyIiIiISDS0+vZwZ3NFee3atSxZsoRXXnnltLdMOnDgQLs9gUpE\nREREJJLa2louuuiiNh3T6qLc1ivKW7du5de//jVLliwhIyPjtPuZzWZycnLaNLZ0Xampqbz55ptM\nmTIl2lGkA9G8kEg0LyQSzQuJJDU1lfXr17f5uDNeyg2HwzQ2NhIKhQiFQvj9fkKhUPPr8+bN47e/\n/e0px+zZs4d77rmHp59+uvlJVyIiIiIinckZi/Lbb7/NqFGjWLx4Me+++y4jR47k+eefb3798OHD\nVFRUnHLM0qVLqays5Ec/+hGjR49m9OjR3HHHHe2fXkRERETkPDnjA0fOt9LSUi29kGapqans3r2b\nnj17RjuKdCCaFxKJ5oVEonkhkZxcetG3b982HadP0UmHo1+cJBLNC4lE80Ii0byQ9qKiLCIiIiIS\ngYqyiIiIiEgEKsoiIiIiIhGoKIuIiIiIRKCiLCIiIiISgYqyiIiIiEgEKsoiIiIiIhGoKIuIiIiI\nRKCiLCIiIiISgYqyiIiIiEgEKsoiIiIiIhGoKIuIiIiIRKCiLCIiIiISgYqyiIiIiEgEKsoiIiIi\nIhGoKIuIiIiIRKCiLCIiIiISgYqyiIiIiEgEKsoiIiIiIhGoKIuIiIiIRKCiLCIiIiISgYqyiIiI\niEgEKsoiIiIiIhGoKIuIiIiIRKCiLCIiIiISgYqyiIiIiEgEKsoiIiIiIhGoKIuIiIiIRKCiLCIi\nIiISgYqyiIiIiEgEKsoiIiIiIhGoKIuIiIiIRKCiLCIiIiISgYqyiIiIiEgEKsoiIiIiIhGoKIuI\niIiIRKCiLCIiIiISgYqyiIiIiEgEKsoiIiIiIhGoKIuIiIiIRKCiLCIiIiISgYqyiIiIiEgEKsoi\nIiIiIhGoKIuIiIiIRKCiLCIiIiISwRmL8ooVK7jlllsYMWIE999/f6sH/tOf/sSkSZPIzc3lP//z\nP88ppIiIiIjIhRZzph0SExP58Y9/zIYNG2hoaGjVoF999RXPPvssr7zyCk6nk1tvvZWcnBzmzJlz\nzoFFRERERC6EM15Rzs3NJT8/n6SkpFYP+tFHHzFr1iyysrJIT0/npptu4sMPPzynoCIiIiLine truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5441ac8>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAGDCAYAAADOPB2zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5584198>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAGDCAYAAADOPB2zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5494748>"
-       ]
-      }
-     ],
-     "prompt_number": 25
+     "outputs": []
     },
     {
      "cell_type": "heading",
@@ -1470,7 +1386,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "-  [1] [NASA Kalman Filtering Presentation](http://nescacademy.nasa.gov/review/downloadfile.php?file=FundamentalsofKalmanFiltering_Presentation.pdf&amp;id=199&amp;distr=Public)"
+      "*  [1] NASA Kalman Filtering Presentation<p> http://nescacademy.nasa.gov/review/downloadfile.php?file=FundamentalsofKalmanFiltering_Presentation.pdf&amp;id=199&amp;distr=Public\n",
+      "\n",
+      "\n"
      ]
     }
    ],

02_Discrete_Bayes/discrete_bayes.ipynb

@@ -602,8 +602,8 @@
      "input": [
       "import numpy\n",
       "def perfect_predict(pos, move):\n",
-      "    \"\"\" move the position by 'move' spaces, where positive is to the right, and negative\n",
-      "    is to the left\n",
+      "    \"\"\" move the position by 'move' spaces, where positive is to the right, \n",
+      "    and negative is to the left\n",
       "    \"\"\"\n",
       "    n = len(pos)\n",
       "    result = np.array(pos, dtype=float)\n",

04_Gaussians/Gaussians.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:00a931decd323bc4f252540ee5dd36a369b885268e890c562b4a210ea2fff34b"
+  "signature": "sha256:71e5314e7d6889b41a67a2adfc7f9a248d967c2d98a94f399a428d8a11833da2"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -607,7 +607,8 @@
       "    plt.ylim((0,1))\n",
       "    plt.show()\n",
       "\n",
-      "interact (plt_g, mu=(0,10), variance=widgets.FloatSliderWidget(value=0.6,min=0.2,max=4.5))"
+      "interact (plt_g, mu=(0,10), \n",
+      "          variance=widgets.FloatSliderWidget(value=0.6,min=0.2,max=4.5))"
      ],
      "language": "python",
      "metadata": {},

05_Kalman_Filters/Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:40612f6bc9924dbda2c3d14cf0077f63a2ba9109782c950a0523fc7bd1192850"
+  "signature": "sha256:c5e57a44310de3ce74648ad1f5a4bf5751547875bc0c61e507e3c7704eebb9d0"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -41,6 +41,8 @@
        "html": [
         "<style>\n",
         "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
+        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
+        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
         "\n",
         "    div.cell{\n",
         "        width: 850px;\n",
@@ -127,9 +129,9 @@
         "        white-space: nowrap;\n",
         "    }\n",
         "    div.text_cell_render{\n",
-        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+        "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
         "        line-height: 135%;\n",
-        "        font-size: 110%;\n",
+        "        font-size: 125%;\n",
         "        width:750px;\n",
         "        margin-left:auto;\n",
         "        margin-right:auto;\n",
@@ -139,12 +141,12 @@
         "    div.output_subarea.output_text.output_pyout {\n",
         "        overflow-x: auto;\n",
         "        overflow-y: scroll;\n",
-        "        max-height: 300px;\n",
+        "        max-height: 50000px;\n",
         "    }\n",
         "    div.output_subarea.output_stream.output_stdout.output_text {\n",
         "        overflow-x: auto;\n",
         "        overflow-y: scroll;\n",
-        "        max-height: 300px;\n",
+        "        max-height: 50000px;\n",
         "    }\n",
         "    code{\n",
         "      font-size: 70%;\n",
@@ -257,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 2,
        "text": [
-        "<IPython.core.display.HTML at 0x7f634c5fa588>"
+        "<IPython.core.display.HTML at 0x7f00445d3898>"
        ]
       }
      ],
@@ -349,10 +351,10 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        " 0.7405\t 0.7229\t-0.5326\t 1.2238\t-0.1799\t\n",
-        " 0.0760\t-0.5359\t 1.0663\t 0.5362\t-0.6931\t\n",
-        " 1.9148\t 0.5687\t 1.1368\t-0.7581\t 1.7235\t\n",
-        "-1.1808\t 0.9561\t 0.4169\t 0.1081\t-1.5491\t\n"
+        "-0.4981\t-0.0555\t-1.0808\t 0.3023\t 2.7174\t\n",
+        " 0.7017\t 0.1831\t 0.3057\t-0.5140\t 0.0210\t\n",
+        " 0.0418\t-0.1166\t-0.5818\t 1.1765\t 0.9700\t\n",
+        " 1.1401\t-1.0132\t-0.4607\t 1.7878\t 1.6513\t\n"
        ]
       }
      ],
@@ -399,7 +401,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAr4AAAF2CAYAAACI1JsxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8VPW9//H3ZLKApJBkElAhskOCcBEXZDGTsiqrF8Fr\ny+8itlqqqBd9iAtUFhvLIi5tb6/WKwhewIJswi0UMPfBTYzgfSiLjwciqewDli0kKEjMZDK/P+hM\nCWQ5mSXnzMzr+Vcyc2bOlw+jvHJy5oytuLjYKwAAACDKxZm9AAAAAKAxEL4AAACICYQvAAAAYgLh\nCwAAgJhA+AIAACAmEL4AAACICYQvAAAAYkKd4Zufn68HHnhAPXr00LRp0/y3u91uTZ8+XbfeeqsG\nDBigv/zlL2FfKAAAABCM+LrubN68uR555BFt375d5eXl/tuXLFmiAwcOqLCwUPv27dMvf/lL9erV\nS9dff33YFwwAAAAEos4jvr1799aQIUPUokWLardv3rxZEyZMUHJysnr37q1evXrpo48+CutCAQAA\ngGDUecTXx+ut/qnGR44cUfv27TV16lQNHDhQHTt21OHDh8OyQAAAACAUDL25zWazVfv+0qVLuu66\n6/T111/r9OnTatasmb7//vuwLBAAAAAIhYCO+DZt2lSXLl3S+vXrJUkvv/yymjVrds3jXC5XCJYI\nAAAA1C8zM7PO+w2F79VHfNu1a6eDBw/q5ptvliQdPHhQgwYNqvGx2dnZRnYR0xwOh9auXavc3Fyz\nl2JpzMk4ZmUMczKGORnHrIxhTsYxq+oqKj1a/fHX+v363XKduSBJ6nxjil6c4FTrhHP1Pr7O8K2q\nqpLb7ZbH45HH41FFRYXi4uI0bNgwLV26VAMGDNC+ffu0Z88ezZs3LzR/IgAAAOAKtQXv0/fdqpF3\ntlfLjAwVFRXV+zx1hu+HH36o6dOn+7/fsGGDnnjiCT366KM6dOiQcnNz1aJFC82ZM0etWrUK8o8E\nAAAA/EN9wWuPa9hnsdUZvvfdd5/uu+++Gu+bM2eO5syZ06CdoXacEmIMczKOWRnDnIxhTsYxK2OY\nk3GxOKtQB6+PoXN8EX7Z2dkqKSkxexmWx5yMY1bGMCdjmJNxzMoY5mRcLM0qXMHrQ/gCAADAVOEO\nXh/Tw7esrExut9vsZZiqtLRUklRZWWnySqytoXPyer1KTU1VQkJCOJcFAAAC1FjB62Nq+Po+9CIj\nI8PMZSBKVVVV6fTp03I4HMQvAAAW0tjB62Nq+F64cIHoRdjExcWpZcuWOnv2rFq2bGn2cgAAiHlm\nBa+PqeFrs9mu+XAMIJTi4uJ4jQEAYDKzg9fH9HN8AQAAEJ2sErw+hC8AAABCymrB60P4AgAAICSs\nGrw+5u49yrRp00arVq0yexlh16dPH73xxhtBPcdTTz2lcePGhWhFAADATBWVHr2/bb+cUz/Qsws/\nluvMBXW+MUVvPjFQ/zN/rO7t29H06JU44osAbNq0SU2bNq3xPpfLpb59+2r16tXq06dPrc/x8ssv\ny+PxhGuJAACgEVj9CO/VCF80WFpaWr3beL3eOu9PTk4O1XIAAEAji7Tg9bHmqiJAeXm5pk6dqq5d\nu+rWW2/Vn/70p2u2+dvf/qaHH35YXbt2VdeuXfWLX/xCp0+frrbNokWL1KtXL3Xt2lWzZs3S2LFj\n9fTTTxteh8vlUps2bfTmm29qwIAB6tixo37yk5/oxIkT1bZbvny5+vfvr/bt2+uuu+7SypUrq93/\n7bff6sknn1TPnj3VqVMnDRw4UOvWrau2jdPpVJs2bdSmTRu9/vrrNa6jb9++kqT777/fv+2nn37q\n3+7555/3317bqQ5btmzRoEGD1KFDB/Xu3Vv/8R//Ue3+cePG6eWXX9aTTz6pLl26qG/fvtqyZYvh\nmQEAgMBEyikNtbH8Ed/W/++dsDzvieW/COrxv/3tb/U///M/evfdd5WSkqLp06dfs80TTzyh8vJy\nrV69Wl6vV88995yeeOIJffDBB5KkTz75RC+99JJ+/etfy+l06p133tHOnTvVtm3bBq9n0aJFeuON\nN5SRkaFp06bpySef1Nq1a/37eeGFFzR79mwNHDhQ+fn5mjp1qtq1a6c777xTkvTqq6/qs88+0+LF\ni9WyZUsVFxf7P1nPZ/369aqsrNTw4cOvuTZu69attWfPHp04cUIjRozQwoULdfvtt0uSWrRo4d9u\nxowZmjp1qmbOnKmzZ89e8+c4dOiQJk2apMcee0wPPPCAdu7cqeeff16tWrWqFsrvv/++8vLy9Mwz\nz+iVV17RM888o9zcXDVp0qTBswMAAHWL1CO8V7N8+FrVsmXLNHnyZPXv31+S9OKLL2rMmDH++4uL\ni/V///d/+vDDD9WjRw9J0ksvvaSxY8fqwIED6tSpk/7rv/5LOTk5euihhyRJs2fP1vr16wNaz89/\n/nM5nU5J0syZMzV69Gh9/fXX6ty5s9577z3l5OTo4YcfliT94he/0EcffaTFixf7w/fYsWPq1KmT\nbr31VkmX36h3tdTUVEmS3W6/5r64uDilp6fr0qVLkqSUlBSlp6dfs11ycrKSk5OVlJRU4+kQy5cv\nV9u2bfXCCy9Iktq3b68dO3ZoyZIl1cI3NzdXY8eOlSQ99thj2rBhg44ePaquXbsanBgAAKhPtASv\nj+XDN9gjs+FQVlamsrIyZWVl+W/r1q1btW0OHz4sSbr55pv9t/m+PnTokDp16qRDhw4pNzfXf39S\nUpLatWsX0JquXIsv/g4fPqzOnTvr8OHD1fbjW8v27dv9348fP16PPfaYhg4dqjvvvFP9+vXTPffc\n0+ifenb48GFlZ2dfs9bNmzdXu61Dhw7+r1NSUiRd/nsBAADBi7bg9bF8+FpRqGKwpuep701hDXku\nI/f5DB06VJ999pk+/vhjffLJJ3riiSc0evTooC9b1lA2m83QDOLjr33pBjo7AABwWbQGr09kr94k\nLVq0UFpamr766iv/bfv27au2je/I7d69e/23+b5u3769pMtHLb/88kv//eXl5f4jxQ115Vr2799f\nbQ3t2rWrth/fWnzr8ElLS9O9997rP2d206ZNDV5HQkKCJKmysrLebWsK8nbt2lX7s9S2VgAAEDqR\n/qY1ozjiG6AJEyZo4cKF6tmzp1JTU/Wb3/ym2v1ZWVnq3bu3Zs2apXnz5snr9WrWrFnq27evOnfu\nLEl68MEH9cADD2jx4sXKycnRokWL5Ha7A1rP4sWL1aNHD6WnpysvL0933HGHunTp4t/PT3/6Uy1a\ntEgDBgxQfn6+duzYodWrV/sf/9prr6lbt27KysrSxYsXtXXrVnXv3t1/f3l5ub799ltJl6P2woUL\n/itUtGzZ0r9dy5YtlZycrI0bN6pHjx5KSkpSUlKSbDabqqqq/G9oKy8vV0VFhc6cOSOv16vU1FQl\nJCRo/PjxWrhwoebOnat/+Zd/0a5du7R+/XrNmzev2p+Xo7sAAAQv2o/wXo3wDdCUKVN0+vRpPfzw\nw2rWrJmeffZZ7dy5s9o2f/jDH/Tiiy/635TldDqrBXK/fv300ksv6fe//73mzZunCRMmqGfPnkpM\nTGzweh566CHNmDFDx48f1+23317tcmN33XWX5s6dq7feekt5eXlq3bq1FixYUO0DJpKSkrRgwQId\nO3ZMTZs2VZ8+fTR79mz//evXr9czzzwj6fKR2rfffltvv/22bDabXC6Xf7u4uDgtWLBAr7zyim65\n5Ra53W7/h1mcOHHCf7kz3/P06tVLNptNq1atUp8+fdSxY0f98Y9/1IIFC/Sf//mfSk9P15QpU3T/\n/fdX+/NefbS4sc9FBgAgksVa8PrYiouLw3bozOVyXfNGpSudOXNGGRkZ4dp9ROrdu7ceeughTZ48\n2dD2Rj8pLZbF4uvM4XBIkkpKSkxeibUxJ2OYk3HMyhjmZFyoZxWtwetwOFRUVKTMzMw6t+OIr8le\nffVVDRkyRC1atNCaNWtUUlKi0aNHm70sAAAQRaI1eBuK8DXZF198ocWLF6uiokJdu3bVe++9V+M1\ndOvCr/kBAEBNCN7qCF+TLV26NKjHZ2ZmVjvHFgAAgOCtGeELAAAQJQjeuhG+AAAAEY7gNcbU8PV6\nvfJ6vZyjirCpqqrimr8AgKhF8DZMUOH7+eef6+WXX9bRo0fVvn17zZkzR1lZWYYfn5ycrPPnzysl\nJSWYZQA1qqqq0unTp/2XggEAIFoQvIEJOHwrKir01FNP6bnnntOoUaP0xz/+UVOmTNGWLVsMP8d1\n113n//SuWBYff/mvwcjH/Mayhs7J6/XK4XD4P0YZAIBIR/AGJ+DwPXTokC5duuS/5uzPf/5z/e53\nv9Nf//pX/0flGsHRXi7kbRRzAgDEKoI3NEJ2jq/vXN1jx441KHwBAABQswq3R8vy92ru+x8TvCEQ\ncPh26NBBTZs21YcffqgRI0Zo0aJFstvtunTpUrXtOL+yfr5fxTOrujEn45iVMczJGOZkHLMyhjnV\nzxe8r6xcpSMnyyRJWTc5NH18f43NyZLdTvBeyehpjQGHb2Jion73u9/pN7/5jebNm6cRI0aoTZs2\nSk5OrrZdXl6e/2un06nc3NxAdwkAABDVfME7f8V2HT11XpKUfVO6po3vR/BepaCgQIWFhZIku90u\np9NZ72NsxcXFIbnWU2lpqXJzc7V582bdeOONkiSXy6Xs7OxQPH1U49xVY5iTcczKGOZkDHMyjlkZ\nw5yuVds5vDMedGpsTpbKykpNXqG1ORwOFRUVKTMzs87tgjrH99ChQ2rTpo0uXbqkvLw89enTxx+9\nAAAAqFt9b1prmZFh8gqjS1Dhm5+fr4ULF6qyslJOp1OvvPJKqNYFAAAQtbhKgzmCCt9JkyLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f633596c3c8>"
+        "<matplotlib.figure.Figure at 0x7f002d8fe208>"
        ]
       }
      ],
@@ -442,9 +444,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAGWCAYAAAA5TTZIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAGWCAYAAAA5TTZIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f63356150b8>"
+        "<matplotlib.figure.Figure at 0x7f002d3dca90>"
        ]
       }
      ],
@@ -476,9 +478,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAGWCAYAAAA5TTZIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAGWCAYAAAA5TTZIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6335619d68>"
+        "<matplotlib.figure.Figure at 0x7f002d3e49b0>"
        ]
       }
      ],
@@ -496,9 +498,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAGWCAYAAAA5TTZIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAGWCAYAAAA5TTZIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f63355a0630>"
+        "<matplotlib.figure.Figure at 0x7f002d4c66d8>"
        ]
       }
      ],
@@ -541,7 +543,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAssAAAF2CAYAAACYtNXtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f63354cd8d0>"
+        "<matplotlib.figure.Figure at 0x7f002d4bae48>"
        ]
       }
      ],
@@ -578,9 +580,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f633596c550>"
+        "<matplotlib.figure.Figure at 0x7f002d8fe080>"
        ]
       }
      ],
@@ -677,7 +679,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAF2CAYAAACoOMTMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6335563630>"
+        "<matplotlib.figure.Figure at 0x7f00254d8710>"
        ]
       }
      ],
@@ -725,7 +727,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAF2CAYAAACoOMTMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f63205d3d30>"
+        "<matplotlib.figure.Figure at 0x7f00254313c8>"
        ]
       }
      ],
@@ -775,7 +777,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAF2CAYAAACoOMTMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f632057f208>"
+        "<matplotlib.figure.Figure at 0x7f00202cdbe0>"
        ]
       }
      ],
@@ -889,7 +891,9 @@
       "pos,s = 2, 5\n",
       "for i in range(20):\n",
       "    pos,s = update(pos, s, dog.sense(), 5)\n",
-      "    print('time:', i, '\\tposition =', \"%.3f\" % pos, '\\tvariance =', \"%.3f\" % s)"
+      "    print('time:', i, \n",
+      "          '\\tposition =', \"%.3f\" % pos, \n",
+      "          '\\tvariance =', \"%.3f\" % s)"
      ],
      "language": "python",
      "metadata": {},
@@ -898,26 +902,26 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "time: 0 \tposition = 1.616 \tvariance = 2.500\n",
-        "time: 1 \tposition = 0.501 \tvariance = 1.667\n",
-        "time: 2 \tposition = 0.388 \tvariance = 1.250\n",
-        "time: 3 \tposition = 0.105 \tvariance = 1.000\n",
-        "time: 4 \tposition = 0.222 \tvariance = 0.833\n",
-        "time: 5 \tposition = 0.154 \tvariance = 0.714\n",
-        "time: 6 \tposition = 0.194 \tvariance = 0.625\n",
-        "time: 7 \tposition = 0.024 \tvariance = 0.556\n",
-        "time: 8 \tposition = -0.019 \tvariance = 0.500\n",
-        "time: 9 \tposition = -0.079 \tvariance = 0.455\n",
-        "time: 10 \tposition = -0.064 \tvariance = 0.417\n",
-        "time: 11 \tposition = -0.046 \tvariance = 0.385\n",
-        "time: 12 \tposition = -0.108 \tvariance = 0.357\n",
-        "time: 13 \tposition = -0.138 \tvariance = 0.333\n",
-        "time: 14 \tposition = -0.159 \tvariance = 0.312\n",
-        "time: 15 \tposition = -0.151 \tvariance = 0.294\n",
-        "time: 16 \tposition = -0.212 \tvariance = 0.278\n",
-        "time: 17 \tposition = -0.212 \tvariance = 0.263\n",
-        "time: 18 \tposition = -0.266 \tvariance = 0.250\n",
-        "time: 19 \tposition = -0.241 \tvariance = 0.238\n"
+        "time: 0 \tposition = 1.137 \tvariance = 2.500\n",
+        "time: 1 \tposition = 1.010 \tvariance = 1.667\n",
+        "time: 2 \tposition = 0.593 \tvariance = 1.250\n",
+        "time: 3 \tposition = -0.018 \tvariance = 1.000\n",
+        "time: 4 \tposition = -0.040 \tvariance = 0.833\n",
+        "time: 5 \tposition = -0.093 \tvariance = 0.714\n",
+        "time: 6 \tposition = -0.078 \tvariance = 0.625\n",
+        "time: 7 \tposition = -0.180 \tvariance = 0.556\n",
+        "time: 8 \tposition = -0.200 \tvariance = 0.500\n",
+        "time: 9 \tposition = -0.165 \tvariance = 0.455\n",
+        "time: 10 \tposition = -0.142 \tvariance = 0.417\n",
+        "time: 11 \tposition = -0.220 \tvariance = 0.385\n",
+        "time: 12 \tposition = -0.174 \tvariance = 0.357\n",
+        "time: 13 \tposition = -0.094 \tvariance = 0.333\n",
+        "time: 14 \tposition = -0.187 \tvariance = 0.312\n",
+        "time: 15 \tposition = -0.147 \tvariance = 0.294\n",
+        "time: 16 \tposition = -0.169 \tvariance = 0.278\n",
+        "time: 17 \tposition = -0.082 \tvariance = 0.263\n",
+        "time: 18 \tposition = -0.083 \tvariance = 0.250\n",
+        "time: 19 \tposition = -0.060 \tvariance = 0.238\n"
        ]
       }
      ],
@@ -1037,24 +1041,24 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "PREDICT:     1.0000   502.0000\tUPDATE:     0.0324     9.8047\n",
-        "PREDICT:     1.0324    11.8047\tUPDATE:     1.4467     5.4138\n",
-        "PREDICT:     2.4467     7.4138\tUPDATE:     1.8519     4.2574\n",
-        "PREDICT:     2.8519     6.2574\tUPDATE:     3.6222     3.8490\n",
-        "PREDICT:     4.6222     5.8490\tUPDATE:     3.9355     3.6904\n",
-        "PREDICT:     4.9355     5.6904\tUPDATE:     6.1234     3.6267\n",
-        "PREDICT:     7.1234     5.6267\tUPDATE:     8.3287     3.6007\n",
-        "PREDICT:     9.3287     5.6007\tUPDATE:     8.6857     3.5900\n",
-        "PREDICT:     9.6857     5.5900\tUPDATE:     9.6664     3.5856\n",
-        "PREDICT:    10.6664     5.5856\tUPDATE:     9.7450     3.5838\n"
+        "PREDICT:     1.0000   502.0000\tUPDATE:     0.8389     9.8047\n",
+        "PREDICT:     1.8389    11.8047\tUPDATE:     1.2876     5.4138\n",
+        "PREDICT:     2.2876     7.4138\tUPDATE:     2.3493     4.2574\n",
+        "PREDICT:     3.3493     6.2574\tUPDATE:     1.9995     3.8490\n",
+        "PREDICT:     2.9995     5.8490\tUPDATE:     2.7867     3.6904\n",
+        "PREDICT:     3.7867     5.6904\tUPDATE:     3.2452     3.6267\n",
+        "PREDICT:     4.2452     5.6267\tUPDATE:     4.6676     3.6007\n",
+        "PREDICT:     5.6676     5.6007\tUPDATE:     7.8293     3.5900\n",
+        "PREDICT:     8.8293     5.5900\tUPDATE:     8.5649     3.5856\n",
+        "PREDICT:     9.5649     5.5856\tUPDATE:    10.8447     3.5838\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAr4AAAF2CAYAAACI1JsxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAF2CAYAAABnFvAPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6328076a20>"
+        "<matplotlib.figure.Figure at 0x7f002543f2e8>"
        ]
       }
      ],
@@ -1182,7 +1186,10 @@
       "plt.plot(vs)\n",
       "plt.title('Variance')\n",
       "plt.show()\n",
-      "print ([float(\"%0.4f\" % v) for v in vs])"
+      "\n",
+      "\n",
+      "for i in range(0,len(vs), 5):\n",
+      "   print ('{:.4f} {:.4f} {:.4f} {:.4f} {:.4f}'.format(*[v for v in vs[i:i+5]]))"
      ],
      "language": "python",
      "metadata": {},
@@ -1190,9 +1197,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAF2CAYAAACLY5UZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAF2CAYAAACLY5UZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f63205351d0>"
+        "<matplotlib.figure.Figure at 0x7f0020276d68>"
        ]
       },
       {
@@ -1200,18 +1207,27 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAGDCAYAAADZHo16AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f63280d25f8>"
+        "<matplotlib.figure.Figure at 0x7f00201e3278>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[102.0, 6.3099, 4.6267, 4.2812, 4.1939, 4.1708, 4.1646, 4.1629, 4.1624, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623, 4.1623]\n"
+        "102.0000 6.3099 4.6267 4.2812 4.1939\n",
+        "4.1708 4.1646 4.1629 4.1624 4.1623\n",
+        "4.1623 4.1623 4.1623 4.1623 4.1623\n",
+        "4.1623 4.1623 4.1623 4.1623 4.1623\n",
+        "4.1623 4.1623 4.1623 4.1623 4.1623\n",
+        "4.1623 4.1623 4.1623 4.1623 4.1623\n",
+        "4.1623 4.1623 4.1623 4.1623 4.1623\n",
+        "4.1623 4.1623 4.1623 4.1623 4.1623\n",
+        "4.1623 4.1623 4.1623 4.1623 4.1623\n",
+        "4.1623 4.1623 4.1623 4.1623 4.1623\n"
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -1326,7 +1342,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 29
+     "prompt_number": 19
     },
     {
      "cell_type": "heading",
@@ -1447,7 +1463,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 32
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -1477,41 +1493,6 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "'''\n",
-      "temp_variance = 2.13**2\n",
-      "sensor_error = temp_variance\n",
-      "movement_error = .2\n",
-      "movement = 0\n",
-      "voltage = (25,1000) #who knows what the first value is?\n",
-      "\n",
-      "zs = []\n",
-      "ps = []\n",
-      "vs = []\n",
-      "N=50\n",
-      "\n",
-      "for i in range(N):\n",
-      "    Z = volt()\n",
-      "    zs.append(Z)\n",
-      "    \n",
-      "    voltage = update(voltage[0], voltage[1], Z, sensor_error)\n",
-      "    ps.append(voltage[0])\n",
-      "    vs.append(voltage[1])\n",
-      "\n",
-      "    voltage = predict(voltage[0], voltage[1], movement, movement_error)\n",
-      "\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",
-      "plt.title('Variance')\n",
-      "plt.show()\n",
-      "print('Variance converges to',vs[-1])\n",
-      "print('Last voltage is',voltage[0])\n",
-      "\n",
-      "'''\n",
-      "\n",
       "temp_variance = 2.13**2\n",
       "movement_error = .2\n",
       "\n",
@@ -1521,7 +1502,8 @@
       "estimates = []\n",
       "\n",
       "kf = KalmanFilter1D(x0=25,            # initial state\n",
-      "                    P = 1000,         # initial variance - large says 'who knows?'\n",
+      "                    P = 1000,         # initial variance \n",
+      "                                      # large says 'who knows?'\n",
       "                    R=temp_variance,  # sensor noise\n",
       "                    Q=movement_error) # movement noise\n",
       "\n",
@@ -1542,8 +1524,7 @@
       "plt.plot(ps)\n",
       "plt.title('Variance')\n",
       "plt.show()\n",
-      "print('Variance converges to',vs[-1])\n",
-      "print('Last voltage is',voltage[0])"
+      "print('Variance converges to {:.3f}'.format(ps[-1]))"
      ],
      "language": "python",
      "metadata": {},
@@ -1551,9 +1532,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAF2CAYAAACLY5UZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAF2CAYAAACLY5UZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6320161438>"
+        "<matplotlib.figure.Figure at 0x7f5ff2721198>"
        ]
       },
       {
@@ -1561,19 +1542,18 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6320126ef0>"
+        "<matplotlib.figure.Figure at 0x7f5ffc053cc0>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Variance converges to 16.920051100338714\n",
-        "Last voltage is 25\n"
+        "Variance converges to 0.858\n"
        ]
       }
      ],
-     "prompt_number": 45
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -1642,7 +1622,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 21
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -1676,13 +1656,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAF2CAYAAAB3dr7TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAF2CAYAAAB3dr7TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtwlNdh9/HfSlqBLrYwIl4Ib+wVwhfMQqPIyJkxXdUo\norMhvQwNE4qN6um4Qyv5NQnG3F5icGE04CQYx4UOmXQGcMKkdVWbGIg660skY1LTxBi3NWBWQsYD\n1rpCWFy0sLvSvn+4Ui32PBIr7VX6fmYy0Z5n95zz4KPdn86e5zy2U6dORQQAAABggKxUdwAAAABI\nRwRlAAAAwICgDAAAABgQlAEAAAADgjIAAABgQFAGAAAADAjKAAAAgMGQQXnlypWaO3euysvL9cd/\n/Md6/fXXJUkvvPCCZs6cqbKyMpWVlamqqmrA6/bu3asHH3xQFRUV2rZtW2J6DwAAACRIzlBPeOyx\nx1RfX6/c3Fy9/fbbWrZsmY4ePSpJWrBggZ599tmo1xw/flw7duzQvn37VFhYqCVLlmjGjBnyeDzx\nPwMAAAAgAYacUb733nuVm5urSCSiUCikgoIC2Ww2SVIkYr6pX2Njo+bPn6/S0lI5HA4tWrRIhw4d\nim/PAQAAgAQackZZkjZu3KiGhgaNHz9eu3btUl5enmw2m95880098MADmjJlipYvX66HHnpIktTW\n1qY5c+Zoz549am9vV3l5uQ4cOJDQEwEAAADi6aYu5tu4caOOHTum7373u3rqqad0/fp1eTwevfba\nazpy5Ijq6uq0YsUKtbW1SZICgYDy8/P18ccf66OPPlJBQYG6u7sTeR4AAABAXN3UjLIk5eTk6OGH\nH9bPfvYz/eY3v9Ef/MEf9B+rrq5WRUWF3nrrLTmdTuXl5am7u1vr16+XJHm9XuXn50fV+fHHH4/8\nDAAAAICb8JWvfCWm5990UO5jtS75i5xOp1pbW/sf+3w+TZs2zfjcGTNmxNoFjGLFxcX6l3/5F1VW\nVqa6K0gjjAuYMC5gwriASXFxsQ4fPhzz6wZdetHR0aGXXnpJV65cUTgc1i9+8Qt1dnbqq1/9qrxe\nry5duqTe3l79+te/1tGjRzV37lxJksfjkdfrlc/nk9/vV0NDAzteAAAAIKMMOqOclZWlAwcO6Ec/\n+pFCoZCmT5+unTt3asKECTp48KDWrl2rnp4eOZ1Obd++XSUlJZKk2bNnq66uTjU1NQqHw1q8eDFB\nGQAAABnFdurUqaHXUiTIxx9/zNILDFBcXKwTJ07o9ttvT3VXkEYYFzBhXMCEcQGTvqUXsa5R5hbW\nSDv88QQTxgVMGBcwYVwgXgjKAAAAgAFBGQAAADAgKAMAAAAGBGUAAADAgKAMAAAAGBCUAQAAAAOC\nMgAAAGBAUAYAAAAMCMoAAACAAUEZAAAAMCAoAwAAAAYEZQAAAMCAoAwAAAAYEJQBAAAAg5xUdwAA\nUiGnq1ORDn9UuW2SQ+GiiSnoEQAg3RCUAYxJkQ6/gltWR5XnrtkqEZQBAGLpBQAAAGBEUAYAAAAM\nCMoAAACAAUEZAAAAMCAoAwAAAAYEZQAAAMCAoAwAAAAYEJQBAAAAA4IyAAAAYEBQBgAAAAwIygAA\nAIABQRkAAAAwICgDAAAABgRlAAAAwICgDAAAABgQlAEAAAADgjIAAABgQFAGAAAADAjKAAAAgAFB\nGQAAADDISXUHACDT5XR1KtLhjyq3TXIoXDQxBT0CAMTDkEF55cqV+rd/+zcFAgFNnTpVy5cvV1VV\nlUKhkDZs2KDGxkYVFRVp1apV8ng8/a/bu3evdu3apVAopMWLF2vFihUJPREASJVIh1/BLaujynPX\nbJUIygCQsYYMyo899pjq6+uVm5urt99+W8uWLdPRo0f185//XD6fT83Nzfrggw+0bNkylZWVafLk\nyTp+/Lh27Nihffv2qbCwUEuWLNGMGTMGBGkAAAAgnQ25Rvnee+9Vbm6uIpGIQqGQCgoKZLPZ1NjY\nqKVLl6qwsFAVFRUqKyuT1+uVJDU2Nmr+/PkqLS2Vw+HQokWLdOjQoYSfDAAAABAvN7VGeePGjWpo\naND48eO1a9cu5eXlqa2tTSUlJVq5cqXmzZun0tJSnTlzRpLU1tamOXPmaM+ePWpvb1d5ebkOHDiQ\n0BMBAAAA4ummg/L69ev1j//4j3rqqad06NAhBQIB5efn6/Tp03K5XCooKFB7e7sk9R/z+Xw6f/68\n3G63uru7jXUXFxfH72yQ8ex2uyTGBQZKxLi4etauoEVbE2JsJ5514ebxfgETxgVM+sZFrG5614uc\nnBw9/PDD+tnPfqbf/OY3ysvLUyAQ0P79+yVJmzdvVkFBgSQpLy9P3d3dWr9+vSTJ6/UqPz/fWO+m\nTZv6f3a73aqsrBzWiQAAAAB9mpqa1NzcLEnKzs6W2+2OuY6Yt4eLRCKKRCJyOp1qaWnRzJkzJUkt\nLS2qqqqSJDmdTrW2tva/xufzadq0acb6amtrBzy+cOFCrF3CKNI3A8A4wBclYlxkh0LG8lAoFHM7\n8awLN4/3C5gwLtDH5XLJ5XJJ+nxcHD58OOY6Br2Yr6OjQy+99JKuXLmicDisX/ziF+rs7FRZWZk8\nHo9efPFFXb58We+8847ee+89VVdXS5I8Ho+8Xq98Pp/8fr8aGhrY8QIAAAAZZdAZ5aysLB04cEA/\n+tGPFAqFNH36dO3cuVMTJkzQo48+qtbWVlVWVqqoqEj19fVyOBySpNmzZ6uurk41NTUKh8NavHgx\nQRkAAAAZZdCgPHHiRO3Zs8f8wpwc1dfXq76+3ni8pqZGNTU1I+8hAAAAkAJD7qMMAAAAjEUEZQAA\nAMAg5l0vACAd5XR1KtLhjyq3TXIoXDTxpuvJyh0ntZwwHou1LgBAZiMoAxgVIh1+BbesjirPXbNV\niiHcRrouKvj8M8ZjsdYFAMhsLL0AAAAADAjKAAAAgAFBGQAAADAgKAMAAAAGBGUAAADAgKAMAAAA\nGBCUAQAAAAOCMgAAAGBAUAYAAAAMuDMfACSI1e2wuRU2AGQGgjKAUc0yrIbDCW/b6nbY3AobADID\nQRnAqGYVVsct35CC3gAAMglBGQDSSE5XpyId/qhylmsAQPIRlAEgjUQ6/ApuWR1VznINAEg+dr0A\nAAAADAjKAAAAgAFBGQAAADAgKAMAAAAGBGUAAADAgKAMAAAAGLA9HAAkmdXdAqXk3DEQAHBzCMoA\nkGRWdwuUuGMgAKQTgjIAZACrWWju2AcAiUNQBoAMYDULzR37ACBxuJgPAAAAMGBGGUDayenqVKTD\n3//46lm7JCk7FGKpAQAgaQjKANJOpMOv4JbV/Y+DXzjGUgMAQLKw9AIAAAAwYEYZADIYu2EAQOIQ\nlAEgg7EbBgAkDksvAAAAAANmlAFkFMulBkm49XMq2wYAJB9BGUBGsVpqkIxbP6eybQBA8rH0AgAA\nADAYNCiHw2GtWrVKc+fO1f3336+amhr5fD5J0gsvvKCZM2eqrKxMZWVlqqqqGvDavXv36sEHH1RF\nRYW2bduWuDMAAAAAEmDQpRe9vb2688479eSTT8rhcGj37t2qq6vTv/7rv0qSFixYoGeffTbqdceP\nH9eOHTu0b98+FRYWasmSJZoxY4Y8Hk9izgIAcFNuvOvhF7GlHAAMNGhQzs3NVV1dXf/jhQsXasuW\nLers7JQkRSIR4+saGxs1f/58lZaWSpIWLVqkQ4cOEZQBIMVuvOvhF7GlHAAMFNMa5WPHjsnhcOi2\n226TJL355pt64IEH9Kd/+qd68803+5/X1tamkpIS7dmzR1u3btX06dN15syZ+PYcAAAASKCb3vXi\n8uXLqq+v15o1a2Sz2fTNb35TS5cu1S233KI33nhDK1as0Msvvyyn06lAIKD8/Hz5fD6dP39ebrdb\n3d3dxnqLi4vjdjLIfHa7XRLjYqy7etauoMUxW5YtJeWZ1obdbtcEw+/RYP+2OePzZD/riyrPun2K\n8qbeYfGq1OH9AiaMC5j0jYtY3VRQDgaDqqur04IFC/qXT/Qtq5Ck6upqVVRU6K233pLT6VReXp66\nu7u1fv16SZLX61V+fr6x7k2bNvX/7Ha7VVlZOawTAQCMTG9Xp649tzGqvOD726Q0DMoAMJimpiY1\nNzdLkrKzs+V2u2OuY8ig3NPToxUrVsjpdOqJJ564qUqdTqdaW1v7H/t8Pk2bNs343Nra2gGPL1y4\ncFNtYHTqmwFgHIxt2aGQ5bFIr/naiESXZ1oboVDI+Hs0nH9bq7pSjfcLmDAu0Mflcsnlckn6fFwc\nPnw45jqGXKP89NNPKysrSxs3bhxQ7vV6denSJfX29urXv/61jh49qrlz50qSPB6PvF6vfD6f/H6/\nGhoauJAPAAAAGWXQGeVz586poaFBeXl5Ki8vlyTZbDb95Cc/0cGDB7V27Vr19PTI6XRq+/btKikp\nkSTNnj1bdXV1qqmpUTgc1uLFiwnKAAAAyCiDBuWpU6fq5MmTxmP333//oBXX1NSopqZm+D0DAAAA\nUohbWAMAAAAGBGUAAADAgKAMAAAAGBCUAQAAAAOCMgAAAGBAUAYAAAAMCMoAAACAAUEZAAAAMCAo\nAwAAAAYEZQAAAMCAoAwAAAAY5KS6AwBGv5yuTkU6/FHltkkOhYsmpqBHAAAMjaAMIOEiHX4Ft6yO\nKs9ds1UiKCdEVu44qeVEVLktHE5BbwAgMxGUAcSN5cyxRTgjzCVOpOuigs8/E1U+bvmGFPQGADIT\nQRlA3FjNHFuFM8IcACCdcTEfAAAAYEBQBgAAAAwIygAAAIABQRkAAAAwICgDAAAABux6AQAYFm4k\nA2C0IygDAIaFG8kAGO1YegEAAAAYEJQBAAAAA4IyAAAAYEBQBgAAAAwIygAAAIABQRkAAALine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f63205bf898>"
+        "<matplotlib.figure.Figure at 0x7f5ff26b3a20>"
        ]
       }
      ],
-     "prompt_number": 42
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
@@ -1747,21 +1727,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAF2CAYAAAB+nR6pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAF2CAYAAAB+nR6pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f207ab57e80>"
+        "<matplotlib.figure.Figure at 0x7f5ff2759c18>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAGDCAYAAAAoFdb3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl4lPW9///XPVsymWwkIWEnYRMkWCOIohAKiIpL9WCr\nPbVore2hP/FrWy/1CN9Ta6sHtV+PerSXPbW11VKXiohYLRyjIhTZVMCFfU8MkEAWsmfW3x+TmSQQ\nAmaZ+w55Pq4r18zc6zvwKX3Nx8/ncxs7d+4MCQAAAOjlbGYXAAAAAFgBwRgAAAAQwRgAAACQRDAG\nAAAAJBGMAQAAAEkEYwAAAEASwRgATLNhwwaNHj1aH3/8sdmlAABEMAbQC73xxhsaPXq03nrrrZP2\neb1efeMb39DNN98ck1oMw4jJfQAAp0cwBtDrTJkyRZK0fv36k/Zt2bJFjY2Nmjp1arfXMXHiRH32\n2WeaMGFCt98LAHB6DrMLAIBY69u3r0aPHq0NGzactC+yLRKeu5NhGHK5XN1+HwDAmaHHGECvlJ+f\nr+LiYn311Vettq9fv14ZGRkaM2aMJGnp0qW6/fbbNWXKFI0bN07Tp0/Xf/7nf6qqquqka0bGDG/c\nuFEvvPCCZsyYofPOO09XXHGFVq9eHT1u7ty5Gj16dPRn48aNbdb4wQcfaN68eZo2bZrGjRunyZMn\na/78+SopKWl13DPPPKPRo0ersLBQt912m/Ly8nTFFVfozTffbPO6JSUleuCBBzR16lSdd955uvzy\ny/Wb3/xGtbW1rY77/PPPdfvtt2vChAk6//zzdcstt+jzzz8//R8uAPRQ9BgD6JWmTJmi5557TuvX\nr9e3v/1tSVJ9fb0+++wzXXvttdHj/vSnPykzM1O33367EhMTtWPHDr366qv64osv9Oqrr7Z57Rdf\nfFHbt2/X7NmzlZGRoa1bt6q4uDi6//bbb9c111yjPXv26Pe///0pxxm/9tprqqys1L/+678qIyND\nBw4c0Msvv6yNGzdq+fLlJ/U2z5s3T/n5+Zo5c6Zef/11zZ8/Pxq+I0pKSvSd73xHx48f14033qhR\no0bp6NGjWrp0qb71rW9Fj123bp1+/OMfa/jw4brzzjtlGIaWLFmiW265RYsXL9bIkSM79gcPABZG\nMAbQK51//vlKTEzUhg0bosF406ZN8vv9rYZRPP/888rMzGx1bmpqqn7729/q888/13nnnXfStbdu\n3aq///3vSkpKim4LBoPR9xMnTpQU7mH+/e9/f8oaf/WrXykrK6vVtpycHM2fP1/vv/++Zs2a1Wrf\njTfeqDlz5kiSvvnNb2rGjBn64IMPWgXjJ554QqWlpfrrX//aamzz7bffLp/PF631l7/8pYYNG6bX\nX39dTqdTknTDDTdo5syZevbZZ/Xkk0+esm4A6KkYSgGgV3I6nZo0aVKrccbr16+XzWbT5MmTo9ta\nhuKamhqVl5dr2LBhkqTCwsI2r/29732vVSiWJJvt6/9z2zIU19XVqby8XNnZ2ae89xVXXBF9P2DA\nAKWmpurIkSPRbcFgUAUFBbr44otPmvAXFxenxMRESdK2bdtUWFioK6+8UtXV1SovL1d5ebm8Xq/G\njBnD8nIAzlr0GAPotaZMmaKCggLt379fOTk5Wr9+vc477zwlJydHj9m6daueeeYZffLJJ6qpqWl1\nfmNjY5vXHT58eJfUd/DgQT3zzDP65z//qePHj5/23if2bLvd7mgvsCSVl5errq5OI0aMaPe+kdD9\n9NNP6+mnnz5pv91uP+PfAQB6EoIxgF4r0jO8YcMG9e3bV9u2bdO8efOi+4uLi3XzzTdrwIAB+vnP\nf67BgwfL6XRq69atevzxxxUKhdq8bstg3VE1NTW6+eabZbfbNXfuXA0bNkxxcXEqKSnR/ffff8p7\nd6W77rpLeXl53X4fALAKgjGAXmvAgAEaMWKE1q1bp6ysLAUCAeXn50f3v/fee2poaNBzzz2nQYMG\nRbefuJJFd1i/fr2OHTumF198URdddFF0+7p16zp8zbS0NHk8Hu3evbvd4wYPHixJSkhI0KRJkzp8\nPwDoaRhjDKBXmzJlijZu3Kj169crLS1Nubm50X2RIQMthw74fD698sor3V5XW/cOhUL6y1/+0uFr\n2mw2zZw5Uxs2bNAnn3zSal9jY2N0qEhubq4GDRqkRYsWnbSEmyQdPny4wzUAgJXRYwygV5syZYr+\n/Oc/68033zzpaXf5+fmKi4vTT37yE914441qbGzU3//+d/n9/g7fr6ioSJs2bZIk7du3T5K0Zs0a\nHTp0SJJ0wQUXaPDgwRo/frzS0tJ0//336/vf/75sNpsKCgpUWVnZ4XtL0t13361169bphz/8oW66\n6SaNGjVKx44d05tvvqn//u//1ujRo2UYhn79619r7ty5uvbaazV79mz17dtXRUVFWrVqlXJzc/XI\nI490qg4AsCKCMYBebcKECXK73aqqqjrpaXdDhgzR//zP/+iJJ57Q448/rqSkJF1zzTWaMmWKbrvt\ntjavd6o1iSM+/vhjLViwoNXxkSXbDMPQI488osGDBys5OVl//OMf9eijj+rpp5+Wy+XSZZddpgce\neKDVOsuR805334jMzEwtXrxYv/3tb/Xuu+/q1VdfVf/+/XXZZZdFh1BI0iWXXKKXX35Zzz77rBYt\nWqT6+nr1799f48eP13e/+90zuhcA9DTGzp07253Bcc8992j9+vWqr6/XwIED9dOf/lQzZsyQz+fT\nL3/5S61YsUIpKSm67777TlpTEwAAAOgpThuMd+zYoWHDhsnlcumjjz7S3LlztXHjRr300ksqKCjQ\nn/70J23btk1z587V8uXL1a9fv1jVDgAAAHSZ006+Gz16tFwul0KhkHw+nzwejwzD0IoVKzRnzhwl\nJiZq4sSJysvLU0FBQSxqBgAAALrcGY0xfvDBB7VkyRLFx8fr97//vdxutw4cOKCcnBzdc889mj59\nuoYPH679+/d3d70AAABAtzij5doefPBBbd68WT/72c907733qrGxUfX19UpISNDu3btVWloqj8ej\nurq67q4XAAAA6BZnvCqFw+HQzTffrL/+9a9at26d3G636uvrtWzZMknSww8/LI/Hc9J5RUVFXVct\nAAAA0I6WK+x8XV97ubZQKKRQKKTs7Gzt3btXY8eOlSTt3btXM2bMaPOcMWPGdLhAnH3S09P1xhtv\nnLRmLHo32gXaQrtAW2gXaEt6errWrFnTqWu0O5Ti2LFjWrx4sWpqauT3+/Xqq6+qvLxceXl5mjVr\nlhYtWqTq6mpt2LBBW7Zs0cyZMztVDAAAAGCWdnuMbTab3n77bf3Xf/2XfD6fRowYoWeffVapqan6\nwQ9+oH379mnq1KlKSUnRwoULlZWVFau6AQAAgC7VbjBOS0vTiy++2PaJDocWLlyohQsXdkthOLsx\nvAZtoV2gLbQLtIV2ge5wRqtSAF2Nf9DQFtoF2kK7QFtoF+gOBGMAAABABGMAAABAEsEYAAAAkEQw\nBgAAACQRjAEAAABJBGMAAABAEsEYAAAAkEQwBgAAACQRjAEAAABJBGMAAABAEsEYAAAAkEQwBgAA\nACQRjAEAAABJMQrGgWAwFrcBAAAAOiwmwbi+0R+L2wAAAAAdFpNgXEcwBgAAgMURjAEAAADFLBj7\nYnEbAAAAoMNiEoxrG+gxBgAAgLXFaPIdPcYAAACwNsYYAwAAACIYAwAAAJKYfAcAAABIoscYAAAA\nkBSzVSnoMQYAAIC18UhoAAAAQIwxBgAAACQxxhgAAACQFKtgzJPvAAAAYHGxmXzHUAoAAABYHJPv\nAAAAADH5DgAAAJDEGGMAAABAEqtSAAAAAJIYSgEAAABIoscYAAAAkBSjYNzoCygQDMbiVgAAAECH\nxCQYS0zAAwAAgLXFLhgznAIAAAAWFsNgzAQ8AAAAWFfMgnEtQykAAABgYTELxvX0GAMAAMDCGGMM\nAAAAiDHGAAAAgCR6jAEAAABJpwnGfr9f9913nyZPnqwJEybolltu0Z49eyRJzzzzjMaOHau8vDzl\n5eVpxowZ7d6IYAwAAAArazcYB4NBDR06VEuWLNEnn3yi6dOna968edH9V199tTZv3qzNmzfr/fff\nb/dGtQ0MpQAAAIB1tRuMXS6X5s2bp6ysLEnS7NmzdfDgQZWXl0uSQqHQGd+onh5jAAAAWNjXGmO8\nefNmZWVlqU+fPpKklStX6qKLLtL111+vlStXtnsuk+8AAABgZY4zPbC6uloLFy7U/fffL8MwdNVV\nV2nOnDlKSkrSBx98oLvvvltvvPGGcnJy2jw/aDiUnp7eZYWj53I6nZJEe0ArtAu0hXaBttAu0JZI\nu+iMMwrGXq9X8+bN09VXX61Zs2ZJkoYPHx7dP3PmTE2cOFFr1qw5ZTBeu+ETPVS+Qfn5+Zo6dWqn\nCwcAAEDvtmrVKq1evVqSZLfblZ+f36nrnTYYBwIB3X333crOztZdd93V4RsNzh6uO+64TJJUVlbW\n4eug54t8w6cdoCXaBdpCu0BbaBeIyM3NVW5urqRwu1izZk2nrnfaMcYPPPCAbDabHnzwwVbbCwoK\nVFVVpWAwqA8//FAbN27U5MmTT3kdJt8BAADAytrtMS4uLtaSJUvkdrs1fvx4SZJhGHruuef0zjvv\naP78+QoEAsrOztZTTz11ymEUEpPvAAAAYG3tBuOBAwdqx44dbe6bMGHC17oRD/gAAACAlfFIaAAA\nAEAxDcYMpQAAAIB1xS4YN9BjDAAAAOuixxgAAABQjIKxYUhef1D+QDAWtwMAAAC+tpgEY09c+BF9\nTMADAACAVcUkGCfEh1eFYzgFAAAArCo2wbipx7i2gWAMAAAAa4pJMHbHhXuMeSw0AAAArCLine truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAGDCAYAAAAoFdb3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f207c3d1eb8>"
+        "<matplotlib.figure.Figure at 0x7f5ff25b72b0>"
        ]
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -1833,13 +1813,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAF2CAYAAABULe8GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAF2CAYAAABULe8GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f207c47c4e0>"
+        "<matplotlib.figure.Figure at 0x7f5ffe138780>"
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -1897,13 +1877,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAF2CAYAAAB+nR6pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAF2CAYAAAB+nR6pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcV1X+x/HXd2VfFBQTNA03lFJE0dzX0twry2qyrDT3\n0V+7lWWW7bY6M9U0aWbZ5J6muZVkmksuqSiKW2iKiqBsX77r7w+SCUVFAQF9Px+PHvK9y7mfmzd6\nczj3HENiYqIHERERERG5IGNZFyAiIiIiUhEoOIuIiIiIFIGCs4iIiIhIESg4i4iIiIgUgYKziIiI\niEgRKDiLiIiIiBSBgrOIiIiISBFcNDgvX76cu+++mxtvvJFnnnkmf/snn3zCrbfeStOmTenVqxcr\nVqwocN7nn39O69atiYuLY/LkySVfuYiIiIjIFWS+2AGBgYE88sgjrFmzBpvNlr/dYrHw4YcfUrdu\nXTZt2sTgwYOZN28eNWrUYOvWrUyZMoUvv/wSf39/7r33XqKioujevXup3oyIiIiISGm5aI9zXFwc\nXbt2JSgoqMD2Bx98kLp16wLQtGlTatSoQUJCAgBLlizhlltuITIykrCwMPr37893331XCuWLiIiI\niFwZF+1xPsPjOf/K3KdOneLAgQP5QfrAgQM0b96cadOmcfToUWJjY1m4cGHxqxURERERKSNFfjnQ\nYDCcd9/48ePp168fN9xwAwA5OTn4+vqSnJzMwYMH8fPzIzs7u/jVioiIiIiUkWL3OE+ePJnTp0/z\n9ttv52/z8fEhOzub5557DoBly5bh6+tb6PnJycmXUq+IiIiIyGWpUaNGsc4vcnAurMd56tSp/Pzz\nz0yfPh2z+X9N1apVi3379uV/TkpKyu+NLkxUVFRRy5BrQEhICHPmzKF9+/ZlXYqUE3ompDB6LqQw\nei6kMCEhIaxevbrY7Vx0qIbb7SY3NxeXy4XL5cJut+N0Opk7dy4zZ87kk08+Oac3uXv37ixbtoyk\npCRSUlKYPXu2ZtQQERERkQrtoj3O8+bNY9y4cfmfFyxYwMiRI5k7dy7Hjx+nc+fO+fuGDRvGkCFD\nuOmmmxgxYgQDBw7E6XQyYMAABWcRERERqdAMiYmJ558u4wpITk7WUA0pICQkhJ07d1K1atWyLkXK\nCT0TUhg9F1IYPRdSmDNDNYo7xllLbku5pB+m5Gx6JqQwei6kMHoupLQoOIuIiIiIFEGRZ9UoKw6H\ng7S0tAvOIy1Xl7S0NACcTudlt2GxWAgODi6pkkRERETKd3B2OBykpqZStWpVjEZ1jkvRpaenk52d\nfd75w0VEREQuVblOo2lpaQrNclmCgoLIzMws6zJERETkKlKuE6nBYFBolstiMBg0vEdERERKlFKp\niIiIiEgRKDiLiIiIiBSBgrOIiIiISBEoOJeijIwMHn74YaKiooiIiOCdd94psH/MmDHceeed55w3\nfvx4IiIiiIiIKHS/iIiIiFx5Cs6laPr06WzYsIE5c+awZcsWhg4dWmD/yy+/zKeffnrOeU899RSb\nN2/m0UcfveALbmvWrCEiIoLDhw+XeO0iIiIiUlC5nse5otu3bx/16tU779Kf/v7+hW738/PDz88P\nX19fPB7PRa9TlGNEREREpHjU41wKxowZQ0REBDNnzuSXX37JH3ZxZqjGU089VayhGGd6mu+66y4A\nWrZsmd/eX3ufjxw5wtChQ2nUqBHR0dEMHjyYI0eOFGjr66+/JiIigu3bt9OzZ08iIyOJi4tjx44d\nxfg3ICIiInL1qfA9zuH3fVIq7R6eMfiyz504cSLPPvss48eP58iRI/z73/8GyF/F7vnnn+fxxx9n\n/PjxnDhx4pLbb968OVu2bGHDhg0MHjyY7777jurVqwNQuXJlALKzs7nzzjuJjo5m9uzZmM1mXn/9\ndQYNGsTixYvPGQLy7LPPMmrUKKKioti2bRsWi+Wy719ERETkalThg3N5FBAQQEBAAF5eXpjNZkJD\nQwvs9/f3x9/fHy8vr8saZmGxWAgNDSUoKAiAkJCQc64xb9480tPT+fDDD/ND8CuvvELTpk3ZsmUL\nMTExBY4fOXIkXbt2BaBmzZqXXJOIiIjI1a7CB+fi9AxfzbZt20ZGRgaNGjUqsN1gMPD777+fE5xb\ntGhxJcsTERERqXAqfHCW86tTpw6fffbZOdvP7p0GCAwMvBIliYiIiFRYCs6l7ELTyV1sv5+fHzab\n7bz7zwzBcDqd5+y78cYbmTVrFgEBAfnjnkVERETk8mlWjVJ29hhmt9vNsWPHOHbsGDabDbvdzvHj\nxzl27BgOh6PAsU2aNGHHjh3Ex8dz7NgxsrKyCuyvVasWJpOJ+fPnk5GRUSBk9+vXj6pVqzJ48GA2\nbNjAgQMHWLZsGUOHDiUnJ6f0blhERETkKqXgXIoMBsM5PcqHDx+madOmNG3alIULF7Jp0yZiYmKI\njY3l119/LXBsy5YtGTp0KCNGjKBp06a8+eabBfZXqVKFF198kRkzZhAdHU3dunXzp6Pz8fFh9uzZ\nVK9enYceeojOnTszYcIEqlatitVqPadOEREREbkwQ2JiYpmunpGcnHzeBUKOHz9OlSpVrnBFcrXQ\n83P1CAkJASA1NbWMK5HyRM+FFEbPhRQmJCSE1atXU6NGjWK1ox5nEREREZEiUHAWERERESkCBWcR\nERERkSJQcBYRERERKQIFZxERERGRIlBwFhEREREpAgVnEREREbmiDFlZmA4dKusyLpmCs4iIiIhc\nUV4rVhDWogXBo0aBxwN2e1mXVCQKziIiIiJyRXmtXZv3548/Eta4Mb5ff13GFRWNgrOIiIiIXFHW\nP4NzbocOmFJT8z+XdwrOUuG0aNGCyZMnl3UZIiIichmMJ05g2bMHt7c3mUOHAuC1Zk3ekI1yTsFZ\nKhyDwVDWJYiIiMhlOtO77GjeHGfDhrhCQzEdP455794yruziFJxLwZo1a2jatCkPPfQQUVFRTJ06\nla5du3LTTTfx008/AXD69GmefPJJGjduTFRUFPfccw979uzJbyMpKYlBgwYRExPDDTfcQPv27fny\nyy8LXMfhcDB+/HhiY2OJjIykTZs2fPzxx/n7k5OTiYiI4PDhw/nb3n77bVq2bFmgna+//pqIiAi2\nb99Oz549iYyMJC4ujh07dgBgt9t5+eWXiY2NpW7duvTu3ZuNGzfmn9+iRQtGjRpFo0aNGDJkCBMn\nTqRevXqMGzfunOu0b9+eyMhIOnTowFdffVVgf0REBDNmzKBv377UqVOHnj17kpSUVOA6ERERHDp0\niMmTJxMREUFERATvvPPOJf39iIiISNlyREWR26oVGAzYb74ZAOuaNWVc1cWZy7qAklA9PLzQ7X/8\nJTAW5/jLcfz4cYYOHcr111/P888/zzfffMPy5cv5+OOPadu2Lffffz8Wi4Vp06YRFBTEJ598woAB\nA/jpp5/w9fXl5MmTxMTEMGbMGEJCQoiPj+epp54iPDyc9u3bA/DZZ58xa9YsPv74Y2rXrs2BAwdI\nTk6+aG3n67F99tlnGTVqFFFRUWzbtg2LxQLA2LFjSUxMZMqUKVSvXp05c+Zwzz338NNPP1GtWjUM\nBgOVKlVi2rRp9OvXj0GDBjFz5kz69u3LY489RkhICF988QWTJk3i1VdfJTY2lq1btzJ27FgqVapE\nt27d8mv497//zWuvvUZgYCDDhw9nwoQJTJ8+HYDFixfjcrm47bbb6NOnD0P//PWOr69vsf6uRERE\n5Mqx9eqFrVev/KEZuTffjPfixZhSUsq4sou7KoJzeRQSEkJcXByZmZnMnTuXli1bkpOTw4oVK/jp\np5/YtGkTmzZtokqVKgBMnDiRWbNmsXz5cnr37k1cXBxxcXH57d17771Mnz6dFStW5Afn5ORkwsLC\naNOmDQDh5/mB4Gye84whGjlyJF27dgWgZs2aAOzbt4/58+ezaNEiGjduDMCYMWOYO3cuc+bMYfjw\n4QB06tSJZs2aERISQqdOnWjatCkhISEcOnSIkJAQ3nnnHUaPHk2fPn2AvN7l+Ph4ZsyYUSA4Dxo0\niBYtWgAwYMAAPvjgg/x9lStXBsBkMuHn50doaGiR7ldERETKoT878nLuvJOc/v3xVICOsKsiOF9q\nT3FJ9iyfj7e3d/6fZ7728vLCZrOxfft2PB4PrVu3LnCOzWbj999/ByAnJ4d3332XZcuWkZKSgsPh\nwGaz0ahRo/zj77jjDmbPnk3btm1p1aoVLVq0oGfPnlit1suq+Uxg/avt27cD0L9//3NqPXjwYJHu\n98SJE6SkpPDmm28WeKnP4XBQo0aNAu3ecMMN+V8HBweTnp5+WfciIiIiFYPHz6+sSyiyqyI4VyRn\nensDAwNZvHjxOfuDg4OBvB7o1atX8/zzz3PDDTdgMpkYMmQIbrc7/9gmTZqwfv16Vq9ezdq1a3nu\nuef48ssvmTVrFlD4kIy/nn+2wMDA8+6bN28efmc92AEBARe40zx/7d1+6aWX8nvHzzCbzRf8LCIi\nIlJeXDSlLF++nE8++YSEhAR69uzJq6++CuT1Fr7wwgssWbKEoKAgnnzySbp3755/3ueff85HH32E\nw+FgwIAB/N///V/p3UUF06hRI06fPo3D4aBOnTqFHrN+/Xoeeuih/KETOTk5HDp0iOjo6ALH+fv7\n061bN7p160ZsbCzDhw8nPT2d4ODg/CCcmZmZf/yhQ4cuaVaKMz3cKSkpdOzY8ZLu84zQ0FCqVavG\nwYMHue+++y6rjb+yWCw4HI5ityMiIiJyKS46q0ZgYCCPPPIId955Z4HtU6dOJSkpifj4eF5//XXG\njRvH0aNHAdi6dStTpkzh888/59tvv2XRokWF9q5eq9q2bUvTpk159NFHiY+P5+DBg8THx/Line truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f207ab21b70>"
+        "<matplotlib.figure.Figure at 0x7f5ff60bacc0>"
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -1960,13 +1940,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAF2CAYAAABULe8GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8U/X++PHXyegetAVKoUChVakUtCAFQbZMEfEqLoQr\nTgQHfkVR1kXxoqCCi59eXAwVUEBwAAquiiICshWkLcWWPQpt6Uqa/P7IIN2ZTdO+n48Hj9Kc5HPe\nSU/OeZ/PVA4ePGhECCGEEEII4RYqbwcghBBCCCFEfSIJthBCCCGEEG4kCbYQQgghhBBuJAm2EEII\nIYQQbiQJthBCCCGEEG4kCbYQQgghhBBuJAm2EEIIIYQQblRjgr1p0yZuv/12OnTowLPPPmt9XKfT\nMWXKFDp16kTfvn1Zv359mdctWbKEHj16kJKSwrx588ps27p1K4MGDSI5OZkJEyaQn5/vprcjhBBC\nCCGEd9WYYIeFhXH//fdz6623lnl80aJFpKWlkZqaypw5c5gyZQonTpwAYPfu3SxYsIAlS5bw5Zdf\n8vXXX1sT8MLCQh5//HEee+wxtmzZgqIovPrqqx54a0IIIYQQQtS+GhPslJQUBgwYQHh4eJnHN2zY\nwOjRowkJCSElJYXk5GQ2btxo3TZw4EDi4+OJjo5m5MiRrFu3DjDVXoeFhXHDDTcQEBDAvffea90m\nhBBCCCGEr7O7D7bRWHZF9czMTNq0acOkSZNYt24d8fHxHD58uMy2xYsXM2fOHBISEqzbDh8+TNu2\nbdmxYwf33XcfrVu35sKFC+Tk5LjxbQkhhBBCCOEddifYiqKU+b2wsJCgoCAOHTrEqVOnCA4OpqCg\noMy2rKwsjhw5UmZbQUEBQUFBnDlzhvT0dPz8/KyPCyGEEEII4es09j6xfA12YGAghYWFrF27FoAX\nXniB4OBg67aCggKmTZsGwMaNGwkKCgIgKCiIgoICBg0axKBBg7hw4YL18fKysrKceEtCCCGEEEI4\npmXLlm4ry+4Eu3wNdlxcHOnp6bRv3x6A9PR0+vfvb92WkZFhfW5aWhpt27a1blu2bFmZbeHh4URE\nRFS638TERHtDFA1AVFQUq1evpnfv3t4ORdQhclyIyshxISojx4UoLyoqis2bN7u1zBq7iBgMBoqL\niyktLaW0tJSSkhL0ej1Dhgxh6dKl5OXlsXXrVnbt2sWAAQMAGDJkCBs3biQtLY2TJ0+yatUqhgwZ\nAkC3bt3Iy8vjq6++oqCggA8++IChQ4e69U0JIYQQQgjhLTXWYK9Zs4YpU6ZYf//iiy945JFHGDdu\nHBkZGfTu3Zvw8HBmz55NdHQ0AB07dmTChAmMGTMGvV7PHXfcYU2wAwMDef3115k+fTrTpk2jR48e\nPPnkkx56e0IIIYQQQtQu5eDBg8aan+YdWVlZXHb5FWjUsuCkMImKiuKvv/6iadOm3g5F1CFyXIjK\nyHEhKiPHhSjP0kXEnX2w63zmmnH8grdDEHWM9MsXlZHjQlRGjgtRGTkuhKfV+QR7/5Gz3g5BCCGE\nEEIIu9k9i4i37Dtylpt7JHg7DCGEEELUATqdjpycnAqzm9nLsrCdXq93Z1iiDjMajURERKDVamtt\nn3U+wd6dcdrbIQghhBCiDtDpdJw9e5amTZuiUtX5RnhRRxgMBk6dOkVUVFStJdl1/ujcnXGaUoPB\n22EIIYQQwstycnIkuRYOU6lUNG3a1Np6USv7rLU9OamgWM/B7Nr7QIQQQghRNymKIsm1cIpKpXK6\nW5FT+6u1Pbngt7+OezsEIYQQQggh7OITCfavf0qCLYQQQgghfINPJNi//30Co7HOrocjhBBCCCGE\nVZ1PsBuF+HM2t4hj5y56OxQhhBBCCCFqVOcT7KvaNAZkuj4hhBBCCFFR165dmTdvnrfDKKPOJ9gd\n2zYBYHfGGS9HIoQQQggh6pranB3EXnU+wbbUYO+RGmwhhBBC+KBff/2VTp06ce+995KYmMiiRYsY\nMGAAHTt25OeffwYgNzeXp59+mquuuorExETuvPNODh06ZC0jLS2NsWPHkpycTNu2benduzeffPJJ\nmf3odDpmzJhB586diY+P57rrrmPhwoXW7VlZWcTGxnL06FHrY6+++irdunUrU86KFSuIjY1l3759\nDBs2jPj4eFJSUti/fz8AJSUlvPDCC3Tu3JnLLruM4cOHs337duvru3btyqOPPkr79u158MEHmTVr\nFpdffjlTpkypsJ/evXsTHx9Pnz59WLZsWZntsbGxfPzxx4wYMYKEhASGDRtGWlpamf3ExsaSnZ3N\nvHnziI2NJTY2lvnz5zv09/GEOr+S41XmGuw9h89gNBrr5F2KEEIIIeqO5i1aVPr4MZvE0pXnO+P0\n6dOMGzeO1q1bM336dD777DM2bdrEwoUL6dmzJ6NHj0ar1bJ48WLCw8N59913ueOOO/j5558JCgri\n3LlzJCcnM3HiRKKiokhNTWXy5Mm0aNGC3r17A/Dhhx+ycuVKFi5cSJs2bcjMzCQrK6vG2KrKraZO\nncqjjz5KYmIie/futa6C+MQTT3Dw4EEWLFhA8+bNWb16NXfeeSc///wzzZo1Q1EUIiIiWLx4MTff\nfDNjx45l+fLljBgxgieffJKoqCg++ugjZs+ezYsvvkjnzp3ZvXs3TzzxBBEREQwePNgaw3vvvcdL\nL71EWFgY48eP57nnnmPp0qUArF+/ntLSUoYOHcpNN93EuHHjAAgKCnLpb+UOdT7BjokMpkl4IKcv\nFHLkVB5x0WHeDkkIIYQQwiFRUVGkpKSQn5/P559/Trdu3SgsLOS7777j559/5o8//uCPP/6gSRNT\nxeKsWbNYuXIlmzZtYvjw4aSkpJCSkmIt76677mLp0qV899131gQ7KyuL6OhorrvuOgBaVHHjUF5V\nM7U98sgjDBgwAIBWrVoBkJGRwdq1a/n666+56qqrAJg4cSKff/45q1evZvz48QD069ePa665hqio\nKPr160enTp2IiooiOzubqKgo5s+fz2OPPcZNN90EmGqrU1NT+fjjj8sk2GPHjqVr164A3HHHHbz5\n5pvWbZGRkQCo1WqCg4Np3LixXe+3NtT5BFtRFDq2acx3u7LYnXFaEmwhhBBCVMvRmmd31lRXJSAg\nwPrT8n9/f3+KiorYt28fRqORHj16lHlNUVER//zzDwCFhYW89tprbNy4kZMnT6LT6SgqKqJ9+/bW\n599yyy2sWrWKnj170r17d7p27cqwYcPw8/NzKmZLYmtr3759AIwcObJCrEeOHLHr/Z45c4aTJ0/y\n8ssvlxmcqNPpaNmyZZly27Zta/1/o0aNOH/+vFPvpbbV+QQbTN1EvtuVxZ7DZ7jp2nhvhyOEEEII\n4RaW2uOwsDDWr19fYXujRo0AU4325s2bmT59Om3btkWtVvPggw9iMBisz7366qv5/fff2bx5M1u2\nbGHatGl88sknrFy5Eqi8K4jt68sLC6u6UnPNmjUEBweXeSw0NLSad2piW1v+/PPPW2vbLTQaTbW/\n+wqfiLqjTNUnhBBCiHqqffv25ObmotPpSEhIqPQ5v//+O/fee6+1y0ZhYSHZ2dkkJSWVeV5ISAiD\nBw9m8ODBdO7cmfHjx3P+/HkaNWpkTZjz8/Otz8/OznZofJulxvzkyZP07dvXofdp0bhxY5o1a8aR\nI0cYNWqUU2XY0mq16HQ6l8txpzo/iwiUHehoMMiKjkIIIYSoP3r27EmnTp146KGHSE1N5ciRI6Sm\npvLUU09x+PBhwNRV4osvvuDgwYP8+eefPPbYY5SWlpYp591332X16tWkpaVx6NAh1q5dS4sWLay1\n4GFhYbRp04ZVq1YBsH//fr799luHYo2Pj+fGG29k8uTJrF+/niNHjrB161b+85//sHXrVrvLeeyx\nx3j//fd5//33SU9PZ8+ePbz99tssWrTIoXjA9NmkpqZy/PhxioqKKnwu3uATCXbTRkHERAZzsUhH\nxokL3g5HCCGEEMIhtrXE5f+vKAofffQRKSkpTJw4kb59+/LUU08BEBERAcDMmTMJCgrixhtvZMyY\nMVxzzTUkJyeX2UdoaCgLFy7khhtu4KabbqKwsJDFixeXec7cuXNZv349HTt25OWXX+Zf//pXjfGW\n9/rrr3PzzTczc+ZM+vTpw/jx4zlz5ozdgyoBxowZw4svvsiyZcsYMGAAd911F6mpqVx22WXVvq6y\nuJ555hlUKhW9evUiISGBN954w+44PEU5ePBgna0SzsrKIjExEYD75n/Lhu1HeH1cH27tWf2HL+qv\nqKgoAM6ePevlSERdIseFqIwcF/XP6dOnrbNsCOGoqo6fqKgoNm/eXGGApSt8ogYbbLuJSD9sIYQQ\nQghRd/lOgt3GlGDvkoGOQgghhBCiDvOZBLtjW9NMIvszz6IvrXpKGSGEEEIIIbzJZxLsiJAAWjcN\npUhXysHsHG+HI4QQQgghRKV8JsEG6YcthBBCCCHqPp9MsHdnnPFyJEIIIYQQQlTORxNsqcEWQggh\nhBB1k08l2EmtTXOaHsg6R4ne+6v0CCGEEEIIUZ5PJdihQX60jQmnRG/gYJYMdBRCCCGEEHWPTyXY\nAB3jTNP17c2UfthCCCGE8B15eXncd999JCYmEhsby/z588tsnzhxIrfeemuF182YMYPY2FhiY2Mr\n3S7qHp9LsK+KN/XD3n7opJcjEUIIIYSw39KlS9m2bRurV69m165djBs3rsz2F154gffff7/C6yZP\nnszOnTt56KGHUBSlyvJ//fVXYmNjOXr0qNtjF45xOcE+ePAgd9xxB507d2bw4MFs2rQJAJ1Ox5Qp\nU+jUqRN9+/Zl/fr1ZV63ZMkSevToQUpKCvPmzbN7f90TYwD49c9jroYuhBBCCFFrMjIyuPLine truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAF2CAYAAABULe8GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f207ab21cc0>"
+        "<matplotlib.figure.Figure at 0x7f5ff24ffbe0>"
        ]
       }
      ],
-     "prompt_number": 26
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -2012,13 +1992,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAF2CAYAAABULe8GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAF2CAYAAABULe8GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f207a953588>"
+        "<matplotlib.figure.Figure at 0x7f5ff2569438>"
        ]
       }
      ],
-     "prompt_number": 27
+     "prompt_number": 28
     },
     {
      "cell_type": "markdown",
@@ -2050,7 +2030,13 @@
      "input": [
       "from IPython.html.widgets import interact, interactive, fixed\n",
       "import IPython.html.widgets as widgets\n",
-      "def plot_kalman_filter(start_pos, sensor_noise, movement, movement_noise, noise_scale):\n",
+      "\n",
+      "\n",
+      "def plot_kalman_filter(start_pos, \n",
+      "                       sensor_noise, \n",
+      "                       movement, \n",
+      "                       movement_noise, n\n",
+      "                       oise_scale):\n",
       "    # your code goes here\n",
       "    pass\n",
       "\n",
@@ -2065,15 +2051,15 @@
      "metadata": {},
      "outputs": [
       {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 28,
-       "text": [
-        "<function __main__.plot_kalman_filter>"
+       "ename": "SyntaxError",
+       "evalue": "invalid syntax (<ipython-input-29-83e872123bef>, line 9)",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-29-83e872123bef>\"\u001b[1;36m, line \u001b[1;32m9\u001b[0m\n\u001b[1;33m    oise_scale):\u001b[0m\n\u001b[1;37m             ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
        ]
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 29
     },
     {
      "cell_type": "markdown",
@@ -2089,7 +2075,12 @@
      "input": [
       "zs = np.zeros(100)\n",
       "ps = np.zeros(100)\n",
-      "def plot_kalman_filter(start_pos, sensor_noise, movement, movement_noise,noise_scale):\n",
+      "\n",
+      "def plot_kalman_filter(start_pos, \n",
+      "                       sensor_noise, \n",
+      "                       movement,\n",
+      "                       movement_noise,\n",
+      "                       noise_scale):\n",
       "    dog = DogSensor(start_pos, velocity=movement, noise=sensor_noise)\n",
       "    random.seed(303)\n",
       "    pos = (0,100)\n",
@@ -2101,7 +2092,8 @@
       "        pos = update(pos[0], pos[1], Z, sensor_error)\n",
       "        ps[i] = pos[0]\n",
       "\n",
-      "        pos = predict(pos[0], pos[1], movement + random.randn()*movement_noise, movement_noise)\n",
+      "        noise = random.randn()*movement_noise\n",
+      "        pos = predict(pos[0], pos[1], movement + noise, movement_noise)\n",
       "\n",
       "    plt.plot(zs,c='r', linestyle='dashed', label='measurement')\n",
       "    plt.plot(ps, c='#004080', label='filter')\n",
@@ -2117,25 +2109,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAF2CAYAAAB+nR6pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f207a8827b8>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 29,
-       "text": [
-        "<function __main__.plot_kalman_filter>"
-       ]
-      }
-     ],
-     "prompt_number": 29
+     "outputs": []
     },
     {
      "cell_type": "heading",
@@ -2170,8 +2144,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 30
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -2208,17 +2181,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f207a935f60>"
-       ]
-      }
-     ],
-     "prompt_number": 31
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -2256,8 +2219,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 32
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -2293,17 +2255,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAscAAAF2CAYAAACCiDVjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f207aa3ada0>"
-       ]
-      }
-     ],
-     "prompt_number": 33
+     "outputs": []
     },
     {
      "cell_type": "markdown",

06_Multivariate_Kalman_filter/Multivariate_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:fed7ba512164fc952a0f0fbfb2a3ecd19c0cd7a80c987230271039a7ae2a51a6"
+  "signature": "sha256:d1b0c8df5adc3f6828e00de679f540ececc73c594852c8bb356e1f41477cbe6f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f0c1f6225f8>"
+        "<IPython.core.display.HTML at 0x7fbc822637b8>"
        ]
       }
      ],
@@ -275,6 +275,14 @@
       "Introduction"
      ]
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -395,7 +403,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAFdCAYAAAAkOCRoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0c1f611d68>"
+        "<matplotlib.figure.Figure at 0x7fbc82252f28>"
        ]
       }
      ],
@@ -563,7 +571,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAFdCAYAAAAkOCRoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb44d550>"
+        "<matplotlib.figure.Figure at 0x7fbc5dd79d68>"
        ]
       }
      ],
@@ -617,7 +625,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAA1QAAAG3CAYAAACzLhRYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb4962b0>"
+        "<matplotlib.figure.Figure at 0x7fbc5e0ed278>"
        ]
       }
      ],
@@ -715,7 +723,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAGDCAYAAAA23OZEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb1aa1d0>"
+        "<matplotlib.figure.Figure at 0x7fbc5ddf37f0>"
        ]
       }
      ],
@@ -742,7 +750,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAGDCAYAAADZHo16AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb1a78d0>"
+        "<matplotlib.figure.Figure at 0x7fbc5de04128>"
        ]
       }
      ],
@@ -769,7 +777,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAs8AAAGDCAYAAADUAP09AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfaf8deb8>"
+        "<matplotlib.figure.Figure at 0x7fbc5e06fba8>"
        ]
       }
      ],
@@ -800,7 +808,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAs8AAAGDCAYAAADUAP09AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb41c390>"
+        "<matplotlib.figure.Figure at 0x7fbc5dc3d1d0>"
        ]
       }
      ],
@@ -865,7 +873,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAGDCAYAAAAs4AbKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb3689e8>"
+        "<matplotlib.figure.Figure at 0x7fbc5e00c2b0>"
        ]
       }
      ],
@@ -1226,16 +1234,16 @@
       "class KalmanFilter:\n",
       "\n",
       "    def __init__(self, dim_x, dim_z, dim_u=0):\n",
-      "        \"\"\" Create a Kalman filter. You are responsible for setting the\n",
-      "        various state variables to reasonable values; the defaults below will\n",
-      "        not give you a functional filter.\n",
+      "        \"\"\" Create a Kalman filter. You are responsible for setting \n",
+      "        the various state variables to reasonable values; the defaults \n",
+      "        below will not give you a functional filter.\n",
       "\n",
       "        Parameters\n",
       "        ----------\n",
       "        dim_x : int\n",
-      "            Number of state variables for the Kalman filter. For example, if\n",
-      "            you are tracking the position and velocity of an object in two\n",
-      "            dimensions, dim_x would be 4.\n",
+      "            Number of state variables for the Kalman filter. For \n",
+      "            example, if you are tracking the position and velocity \n",
+      "            of an object in two dimensions, dim_x would be 4.\n",
       "\n",
       "            This is used to set the default size of P, Q, and u\n",
       "\n",
@@ -1266,8 +1274,8 @@
       "\n",
       "    def update(self, Z, R=None):\n",
       "        \"\"\"\n",
-      "        Add a new measurement (Z) to the kalman filter. If Z is None, nothing\n",
-      "        is changed.\n",
+      "        Add a new measurement (Z) to the kalman filter. If Z is\n",
+      "        None, nothing is changed.\n",
       "\n",
       "        Parameters\n",
       "        ----------\n",
@@ -1275,8 +1283,8 @@
       "            measurement for this update.\n",
       "\n",
       "        R : np.array, scalar, or None\n",
-      "            Optionally provide R to override the measurement noise for this\n",
-      "            one call, otherwise  self.R will be used.\n",
+      "            Optionally provide R to override the measurement noise \n",
+      "            for this one call, otherwise  self.R will be used.\n",
       "        \"\"\"\n",
       "\n",
       "        if Z is None:\n",
@@ -1308,8 +1316,8 @@
       "        Parameters\n",
       "        ----------\n",
       "        u : np.array\n",
-      "            Optional control vector. If non-zero, it is multiplied by B\n",
-      "            to create the control input into the system.\n",
+      "            Optional control vector. If non-zero, it is multiplied\n",
+      "            by B to create the control input into the system.\n",
       "        \"\"\"\n",
       "  \n",
       "        self.x = dot(self.F, self.x) + dot(self.B, u)\n",
@@ -1824,12 +1832,12 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "dog_filter.x = np.array([[0], [0]])          # state (location and velocity)\n",
-      "dog_filter.F = np.array([[1,1], [0,1]])      # state transition matrix\n",
-      "dog_filter.H = np.array([[1,0]])             # Measurement function\n",
-      "dog_filter.R *= 5                            # measurement noise\n",
-      "dog_filter.Q = Q_discrete_white_noise(dim=2) # process noise\n",
-      "dog_filter.P *= 500.                         # covariance matrix "
+      "dog_filter.x = np.array([[0], [0]])      # state (location and velocity)\n",
+      "dog_filter.F = np.array([[1,1], [0,1]])  # state transition matrix\n",
+      "dog_filter.H = np.array([[1,0]])         # Measurement function\n",
+      "dog_filter.R *= 5                        # measurement noise\n",
+      "dog_filter.Q = Q_discrete_white_noise(2) # process noise\n",
+      "dog_filter.P *= 500.                     # covariance matrix "
      ],
      "language": "python",
      "metadata": {},
@@ -1881,7 +1889,7 @@
       "def dog_tracking_filter(R,Q=0,cov=1.):\n",
       "    dog_filter = KalmanFilter (dim_x=2, dim_z=1)\n",
       "    dog_filter.x = np.array([[0], \n",
-      "                             [0]])   # initial state (location and velocity)\n",
+      "                             [0]])    # (location and velocity)\n",
       "    dog_filter.F = np.array([[1,1],\n",
       "                             [0,1]])  # state transition matrix\n",
       "    dog_filter.H = np.array([[1,0]])  # Measurement function\n",
@@ -1894,7 +1902,8 @@
       "    return dog_filter\n",
       "\n",
       "\n",
-      "def filter_dog(noise=0, count=0, R=0, Q=0, P=500., data=None, initial_x=None):\n",
+      "def filter_dog(noise=0, count=0, R=0, Q=0, P=500., data=None, \n",
+      "               initial_x=None):\n",
       "    \"\"\" Kalman filter 'count' readings from the DogSensor.\n",
       "    'noise' is the noise scaling factor for the DogSensor.\n",
       "    'data' provides the measurements. If set, noise will\n",
@@ -2022,9 +2031,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAs0AAAGDCAYAAADQ9S0AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAs0AAAGDCAYAAADQ9S0AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb1c7ac8>"
+        "<matplotlib.figure.Figure at 0x7fbc5de45cc0>"
        ]
       },
       {
@@ -2032,7 +2041,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAGHCAYAAACgU9QVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfbc61b00>"
+        "<matplotlib.figure.Figure at 0x7fbc5dc1a898>"
        ]
       }
      ],
@@ -2199,9 +2208,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb236080>"
+        "<matplotlib.figure.Figure at 0x7fbc5dc2da20>"
        ]
       },
       {
@@ -2209,7 +2218,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAGDCAYAAADJfsOmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb1659b0>"
+        "<matplotlib.figure.Figure at 0x7fbc5df0f9b0>"
        ]
       }
      ],
@@ -2441,9 +2450,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb3f8978>"
+        "<matplotlib.figure.Figure at 0x7fbc5dc2db38>"
        ]
       }
      ],
@@ -2492,8 +2501,11 @@
       "dog = DogSensor(velocity=1, noise=30)\n",
       "zs = [dog.sense() for t in range(30)]\n",
       "\n",
-      "plot_track (data=zs, R=5, Q=10,count=30, plot_P=False, title='R = 5, Q = 10')\n",
-      "plot_track (data=zs, R=5, Q=.02,count=30, plot_P=False, title='R = 5, Q = 0.02')"
+      "plot_track(data=zs, R=5, Q=10,count=30, plot_P=False, \n",
+      "           title='R = 5, Q = 10')\n",
+      "\n",
+      "plot_track(data=zs, R=5, Q=.02,count=30, plot_P=False, \n",
+      "           title='R = 5, Q = 0.02')"
      ],
      "language": "python",
      "metadata": {},
@@ -2501,17 +2513,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb22ae48>"
+        "<matplotlib.figure.Figure at 0x7fbc5dd376a0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb3f34e0>"
+        "<matplotlib.figure.Figure at 0x7fbc5de69320>"
        ]
       }
      ],
@@ -2616,7 +2628,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "plot_track (data=zs, R=1000, Q=0.1,count=30, plot_P=False, title='R = 1000, Q = 0.1')"
+      "plot_track(data=zs, R=1000, Q=0.1,count=30, plot_P=False, \n",
+      "           title='R = 1000, Q = 0.1')"
      ],
      "language": "python",
      "metadata": {},
@@ -2624,9 +2637,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb0fa828>"
+        "<matplotlib.figure.Figure at 0x7fbc5e127e80>"
        ]
       }
      ],
@@ -2685,8 +2698,8 @@
      "collapsed": false,
      "input": [
       "P = np.array([[2,0],[0,6]])\n",
-      "stats.plot_covariance_ellipse ((0,0), P,  facecolor='g', alpha=0.2,\n",
-      "                               title='|2 0|\\n|0 6|')"
+      "stats.plot_covariance_ellipse((0,0), P,  facecolor='g', alpha=0.2,\n",
+      "                              title='|2 0|\\n|0 6|')"
      ],
      "language": "python",
      "metadata": {},
@@ -2696,7 +2709,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAGXCAYAAAC9TOY9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb500470>"
+        "<matplotlib.figure.Figure at 0x7fbc5dc017f0>"
        ]
       }
      ],
@@ -2714,8 +2727,8 @@
      "collapsed": false,
      "input": [
       "P = np.array([[2,2.4],[2.4,6]])\n",
-      "stats.plot_covariance_ellipse ((0,0), P,  facecolor='g', alpha=0.2, \n",
-      "                               title ='|2.0  2.4|\\n|2.4  6.0|')"
+      "stats.plot_covariance_ellipse((0,0), P,  facecolor='g', alpha=0.2, \n",
+      "                              title='|2.0  2.4|\\n|2.4  6.0|')"
      ],
      "language": "python",
      "metadata": {},
@@ -2725,7 +2738,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAGXCAYAAAC9TOY9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb25de48>"
+        "<matplotlib.figure.Figure at 0x7fbc5dd7cda0>"
        ]
       }
      ],
@@ -2785,7 +2798,8 @@
       "      54.235, 62.974, 61.742, 54.863, 52.831, 61.122, 61.187, 58.441, \n",
       "      47.769, 56.855, 53.693, 61.534, 70.665, 60.355, 65.095, 63.386]\n",
       "\n",
-      "plot_track (data=zs, R=5., Q=.02, P=500., count=len(zs), plot_P=False, title='P=500')"
+      "plot_track(data=zs, R=5., Q=.02, P=500., count=len(zs), \n",
+      "           plot_P=False, title='P=500')"
      ],
      "language": "python",
      "metadata": {},
@@ -2795,7 +2809,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb15f198>"
+        "<matplotlib.figure.Figure at 0x7fbc5dd86240>"
        ]
       }
      ],
@@ -2814,7 +2828,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "plot_track (data=zs, R=5., Q=.02, P=1., count=len(zs), plot_P=False, title='P=1')"
+      "plot_track(data=zs, R=5., Q=.02, P=1., count=len(zs), \n",
+      "           plot_P=False, title='P=1')"
      ],
      "language": "python",
      "metadata": {},
@@ -2824,7 +2839,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb148160>"
+        "<matplotlib.figure.Figure at 0x7fbc5ddbe048>"
        ]
       }
      ],
@@ -2858,7 +2873,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb2e5668>"
+        "<matplotlib.figure.Figure at 0x7fbc5df62470>"
        ]
       }
      ],
@@ -2889,7 +2904,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb1ee080>"
+        "<matplotlib.figure.Figure at 0x7fbc5ddc4278>"
        ]
       }
      ],
@@ -2915,7 +2930,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "def plot_track_ellipses(noise, count, R, Q=0, P=20., plot_P=True, title='Kalman Filter'):\n",
+      "def plot_track_ellipses(noise, count, R, Q=0, P=20., plot_P=True, \n",
+      "                        title='Kalman Filter'):\n",
       "    dog = DogSensor(velocity=1, noise=noise)\n",
       "    f = dog_tracking_filter(R=R, Q=Q, cov=P)\n",
       "\n",
@@ -2931,14 +2947,16 @@
       "        f.predict()\n",
       "\n",
       "    p0, = plt.plot([0,count],[0,count],'g', label='actual')\n",
-      "    p1, = plt.plot(range(1,count+1),zs,c='r', linestyle='dashed', label='measurement')\n",
+      "    p1, = plt.plot(range(1,count+1),zs,c='r', linestyle='dashed', \n",
+      "                   label='measurement')\n",
       "    p2, = plt.plot(range(1,count+1),ps, c='b', label='filter')\n",
       "    plt.legend(loc='best')\n",
       "    plt.title(title)\n",
       "\n",
       "    for i,p in enumerate(cov):\n",
-      "        stats.plot_covariance_ellipse ((i+1, ps[i]), cov=p, axis_equal=False, \n",
-      "                                       facecolor='g', edgecolor=None, alpha=0.2)\n",
+      "        stats.plot_covariance_ellipse(\n",
+      "              (i+1, ps[i]), cov=p, axis_equal=False, \n",
+      "               facecolor='g', edgecolor=None, alpha=0.2)\n",
       "\n",
       "        if i == len(cov)-1:\n",
       "            s = ('$\\sigma^2_{pos} = %.2f$' % p[0,0])\n",
@@ -2950,8 +2968,8 @@
       "    plt.show()\n",
       "\n",
       "\n",
-      "plot_track_ellipses (noise=5, R=5, Q=.2, count=20, title='R = 5')\n",
-      "plot_track_ellipses (noise=5, R=.5, Q=.2, count=20, title='R = 0.5')"
+      "plot_track_ellipses(noise=5, R=5, Q=.2, count=20, title='R = 5')\n",
+      "plot_track_ellipses(noise=5, R=.5, Q=.2, count=20, title='R = 0.5')"
      ],
      "language": "python",
      "metadata": {},
@@ -2959,17 +2977,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAGDCAYAAADOPB2zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAGDCAYAAADOPB2zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb411898>"
+        "<matplotlib.figure.Figure at 0x7fbc5e09b9b0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAGDCAYAAADOPB2zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb4114a8>"
+        "<matplotlib.figure.Figure at 0x7fbc5dedf1d0>"
        ]
       }
      ],
@@ -3044,7 +3062,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAGDCAYAAADOPB2zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f0bfb3a2278>"
+        "<matplotlib.figure.Figure at 0x7fbc5e026240>"
        ]
       }
      ],

07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a223b7e436fc202bc6f77b7834cdaa6338449361293f2c25146fc75469ca4718"
+  "signature": "sha256:26a72ead7479bc0a3d34d1516bcea91514ebd0335f33730b27a5299b7d7a8f0b"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -726,9 +726,7 @@
       "\n",
       "$$\\mathbf{Q} = \\int_0^{\\Delta t} \\Phi(t)\\mathbf{Q_c}\\Phi^T(t) dt$$\n",
       "\n",
-      "where $\\mathbf{Q_c}$ is the continuous noise.\n",
-      "\n",
-      "This gives us\n",
+      "where $\\mathbf{Q_c}$ is the continuous noise. This gives us\n",
       "\n",
       "$$\\Phi = \\begin{bmatrix}1 & \\Delta t & {\\Delta t}^2/2 \\\\ 0 & 1 & \\Delta t\\\\ 0& 0& 1\\end{bmatrix}$$\n",
       "\n",

08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:0ef850590dea64bc14dae4522330405274d565dbfaca3167ffa776ae92b11960"
+  "signature": "sha256:a78f0d79d16739c5c601c8274800d5e2241197cf7747eca727508403262be931"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7ffbea79f7b8>"
+        "<IPython.core.display.HTML at 0x7fc18a3cc7b8>"
        ]
       }
      ],
@@ -354,9 +354,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X1s1fX5//HXKe2B9rQUTgs9Cmg7mAocNgqlzmF6Urrj\nVrxbdMuSGQT9shwDbtkMm7NR4wY5mdltlsXlbJo1XyTeIGqNUOLBbHQFRY/SbYpiOdxYmIe7g+PQ\n74Getuf3B7+eUej9uf30PB+JSc+nPZ9zkWvtXn33Ou+3af/+/VEBAAAAWS4n3QUAAAAAmYBgDAAA\nAIhgDAAAAEgiGAMAAACSCMYAAACAJIIxAAAAIIlgDAAAAEgaQTA+f/68Hn/8cd14441asmSJfvaz\nn0mSIpGIGhoatGjRItXW1qq5uTnpxQIAAADJkjvcF7jdbnV0dGjr1q0qKSmR3++XJDU2NurAgQNq\naWnRvn375HK5VFlZKZvNlvSiAQAAgEQbcsX4/Pnzampq0qOPPqrS0lKZTCbNmTNHkrR9+3atWLFC\nhYWFqq6uVmVlpbxeb0qKBgAAABJtyGB8+PBhmUwm7dixQ0uXLtWtt96qHTt2xD5XUVGhdevWadu2\nbZo9e7YOHTqUkqIBAACARBtylOLcuXOKRCI6evSo/vrXv2rv3r164IEH9MYbbygcDqugoEDt7e2y\n2+2yWCwKBAKpqhsAAABIqCGD8aRJk9TT06P77rtPZrNZN954oyoqKvSPf/xD+fn5CofDampqkiRt\n2LBBFovlint0dHQkp3IAAADgMrNmzRrzc4cMxrNmzZLJZLriejQaVXl5ufx+v+bPny9J8vv9qqur\nG/A+c+fOHXOBSJ+SkhK9/PLLcjgc6S4Fo0TvjI3+GRv9My56Z2wlJSVqbW2N6x5DzhgXFxdryZIl\namxsVHd3t959910dOnRICxcuVH19vTZu3KhQKKQ9e/aora1NTqczrmIAAACAdBnRdm2PPPKIlixZ\norKyMv3yl7/UtGnTtGrVKh08eFAOh0PFxcVyu90qKytLRc0AAABAwg0bjGfOnKmNGzde+cTcXLnd\nbrnd7qQUhszAGIxx0Ttjo3/GRv+Mi95lN46ExpD4AWFc9M7Y6J+x0T/jonfZjWAMAAAAiGAMAAAA\nSCIYAwAAAJIIxgAAAIAkgjEAAAAgiWAMAAAASCIYAwAAAJIIxgAAAIAkgjEAAAAgiWAMAAAASCIY\nAwAAAJIIxgAAAIAkgjEAAAAgiWAMAAAASCIYAwAAAJIIxgAAAIAkgjEAAAAgiWAMAAAASCIYAwAA\nAJIIxgAAAIAkgjEAAAAgiWAMAAAASCIYAwAAAJIIxgAAAIAkgjEAAAAgiWAMAAAASCIYAwAAAJII\nxgAAAIAkgjEAAAAgiWAMAAAASCIYAwAAAJIIxgAAACkTDJoVDJrTXQYGQTAGAABIAZ/PIqfTKqfT\nKp/Pku5yMACCMQAAQJIFg2a5XEUKBHIUCOTI5Spi5TgDEYwBAAAAEYwBAACSzmrtkscTks3WK5ut\nVx5PSFZrV7rLwmVy010AAABANqiq6pTXG5EkQnGGIhgDAADEqW9eeLjASyDObIxSAAAAxIHdJsYP\ngjEAAMAYsdvE+EIwBgAAAEQwBgAAGDN2mxhfePMdAABAHNhtYvwgGAMAAMSJQDw+MEoBAADGnWDQ\nzJvgMGojCsYrVqzQl770JVVWVqqyslIPP/ywJCkSiaihoUGLFi1SbW2tmpubk1osAADAcNg+DWM1\n4lGKxx9/XN/61rf6XWtsbNSBAwfU0tKiffv2yeVyqbKyUjabLeGFAgAADOfS7dMkyeUqktcbYdQB\nIzLiUYpoNHrFte3bt2vFihUqLCxUdXW1Kisr5fV6E1ogAAAAkAojDsa/+c1v9JWvfEX333+//H6/\nJOnw4cOqqKjQunXrtG3bNs2ePVuHDh1KWrEAAABDYfs0xGNEoxQPP/ywrrvuOvX09Oipp57SmjVr\ntHXrVoXDYRUUFKi9vV12u10Wi0WBQOCK55eUlCS8cCRfXl6eJPpnRPTO2OifsdG/9LvllqhaW8OS\npBkzJspkmjSi59E7Y+vrXzxGFIztdnvs44ceekibNm2S3+9Xfn6+wuGwmpqaJEkbNmyQxXLlkPv6\n9etjH9fU1MjhcMRbNwAAwIBMJpNmzmRH2mywc+dOtbS0SJImTJigmpqauO43pv/VmEwmRaNRlZeX\ny+/3a/78+ZIkv9+vurq6K75+zZo1/R6fPn16LC+LFOv7jZl+GQ+9Mzb6Z2z0z7iG6l3f1m+MZWQW\nu90eW8AtKSlRa2trXPcbdsY4FApp586d6urqUldXl/7whz+otLRUc+bMUX19vTZu3KhQKKQ9e/ao\nra1NTqczroIAAAAyCdu/ZY9hV4wjkYh+97vf6Yc//KHy8vK0YMEC/fGPf1Rubq5WrVqlgwcPyuFw\nqLi4WG63W2VlZamoGwAAGISRV1vZ/i27DBuMrVarXnnllYGfnJsrt9stt9ud8MIAAIDx+XwWuVxF\nkiSPJ6Sqqs40VwQMjiOhAQBAUly62hoI5MjlKjLcMc1s/5ZdeMsmAADAEKqqOuX1RiQZcxwEI8eK\nMQAASIpErrYGg+a0rjZbrV2E4izAijEAAEiawVZbR/OGPOaUkSqsGAMAgKS6fLV1NNufjYc5ZRgH\nwRgAAKQMQReZjGAMAAAyFrtCIJWYMQYAACnTF3QvnRkeLuiyKwRShWAMAABSaixBl0CMVCAYAwCA\nlCPoIhMxYwwAAACIYAwAAABIIhgDAAAAkgjGAAAAgCSCMQAAACCJYAwAAEYgGDRzQh3GPYIxAAAY\nks9nkdNpldNplc9nSXc5QNIQjAEAwKCCQbNcriIFAjkKBHLkchWxcoxxi2AMAAAAiGAMAACGYLV2\nyeMJyWbrlc3WK48nxKl1GLc4EhoAAAypqqpTXm9EEkc5Y3wjGAMAAEmKzQ4PFH4JxMgGjFIAAAB2\nngBEMAYAIOux8wRwEcEYAAAAEMEYAICsx84TwEW8+Q4AALDzBCCCMQAA48ZQu0qMBIEY2Y5RCgAA\nxgF2lQDiRzAGAMDg2FUCSAyCMQAAACCCMQAAhseuEkBi8OY7AADGAXaVAOJHMAYAYJwgEAPxYZQC\nAAADCgbNvMEOSDCCMQAABsPWbEByEIwBADAQtmYDkodgDAAAAIhgDABAWox1Rpit2YDkYVcKAADi\n1BdwRxpQfT6LXK4iSZLHE1JVVeeoXo+t2YDkYMUYAIA4XP5GuGg0OuTXJ2pG2GrtIhQDCUYwBgBg\nACMZdRgo5B471pOiCgEkGsEYAIDLJHM7NGaEgczFjDEAAJe4dBVYklyuInm9kQHDa1/IvXReeMaM\nicO+BjPCQGYiGAMAEIfLQ67JNGlEzyMQA5mHUQoAAC4xllEH3ggHjA8jDsY+n0833HCDNm/eLEmK\nRCJqaGjQokWLVFtbq+bm5qQVCQBAKl1cBQ7K6w2Oeis1AMY1olGK7u5u/epXv9Ls2bNlMpkkSY2N\njTpw4IBaWlq0b98+uVwuVVZWymazJbVgAABSgRVgIPuMaMX42WefVW1traxWa+za9u3btWLFChUW\nFqq6ulqVlZXyer1JKxQAAABIpmGD8cmTJ/Xyyy/rvvvu63f98OHDqqio0Lp167Rt2zbNnj1bhw4d\nSlqhAAAAQDINO0rx5JNP6oEHHpDZ3H+T83A4rIKCArW3t8tut8tisSgQCAx4j5KSksRUi5TKy8uT\nRP+MiN4ZG/0zNvpnXPTO2Pr6F48hg/F7772no0ePavny5bFrfUdd5ufnKxwOq6mpSZK0YcMGWSwD\nb4K+fv362Mc1NTVyOBxxFw4AAIDstnPnTrW0tEiSJkyYoJqamrjuN2Qw/uCDD9TW1qYbbrghdu3d\nd99Ve3u7ysvL5ff7NX/+fEmS3+9XXV3dgPdZs2ZNv8enT5+Oq2ikRt9vzPTLeOidsdE/Y6N/xkXv\njMdut8tut0u62L/W1ta47jfkjPHKlSv18ccfx/5bsmSJNmzYoIaGBtXX12vjxo0KhULas2eP2tra\n5HQ64yoGAAAASJcxn3y3atUqHTx4UA6HQ8XFxXK73SorK0tkbQAAAEDKjCoYb9y48b9PzM2V2+2W\n2+1OeFEAAKRSMHjxDebsXQxkN46EBgAYXjBojoXb0fL5LHI6rXI6rfL5Bn4TOYDsQDAGABhaPME2\nGDTL5SpSIJCjQCBHLlfRmAM2AOMjGAMADItgCyCRCMYAgKxltXbJ4wnJZuuVzdYrjyfEnDGQxca8\nKwUAAOnWF2xdriJJGlOwrarqlNcbid0PQPYiGAMADC0RwZZADEAiGAMAxgGCLYBEYMYYAAAAEMEY\nAAAAkEQwBgAAACQRjAEAAABJBGMAQArFc3QzACQbwRgAkBLxHN0MAKlAMAYAJB1HNwMwAoIxAAAA\nIIIxACAFcnKkp546J5utVzZb75iObgaAZOPkOwBAUvl8FrlcRZoypVf/+78hXXVVhFAMICOxYgwA\nSJpLZ4s//jhX995blO6SAGBQBGMAAABABGMAQBJZrV3yeELMFgMwBGaMAQBJVVXVKa83IkmEYgAZ\njWAMAEg6AjEAI2CUAgAAABDBGAAAAJBEMAYADCMYNHN8M4CsQDAGAAzK57PI6bTK6bTK57OkuxwA\nSCqCMQBgQJcezhEI5MjlKmLlGMC4RjAGAAAARDAGAAyCwzkAZBv2MQYADIrDOQBkE1aMAQAAABGM\nAQBDYFcKANmEYAwAGBC7UgDINgRjAAAAQARjAMgKYzm9jl0pAGQbdqUAgHHO57PI5SqSJHk8IVVV\ndY74uexKASCbsGIMABlsLCu9lz8/3jlhq7WLUAwgKxCMASADRaNRdoQAgBQjGANABjp2rCLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbd2314470>"
+        "<matplotlib.figure.Figure at 0x7fc16df405f8>"
        ]
       }
      ],
@@ -707,9 +707,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF2CAYAAABkof4nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF2CAYAAABkof4nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbd23144e0>"
+        "<matplotlib.figure.Figure at 0x7fc16df40668>"
        ]
       }
      ],
@@ -789,9 +789,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAFwCAYAAACy+B06AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAFwCAYAAACy+B06AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbd2314588>"
+        "<matplotlib.figure.Figure at 0x7fc16dbcb7b8>"
        ]
       },
       {
@@ -876,7 +876,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAssAAAF2CAYAAACYtNXtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3WuMXPddP/73ucy5z312Zi/eddz0l4uIUJKqLtBg142K\nZIlWkIoCoSZtIFwaURTRCqkiPEiittCIFFUR+oVfpbQRoEpA1QKtpbQB+0HUpKIExL9OnNixvbfZ\ny9znzLmf83+wsRs3Xnt3x96ZnXm/JCu7M3N2P9ueM+c93/M536/w2muvJSAiIiIioncQB10AERER\nEdGwYlgmIiIiItoEwzIRERER0SYYlomIiIiINsGwTERERES0CYZlIiIiIqJNMCwTEREREW2i77Bc\nrVZx7Ngx3Hnnnbjvvvvw+uuvb2m7F198ER/5yEdw11134UMf+hBeffXVfkshIiIiIrqu+g7Ljz76\nKG699Va8/PLLOHr0KB555JFrbrOwsIA/+qM/wh/+4R/iP//zP/EP//APKJfL/ZZCRERERHRd9RWW\nu90uXnzxRTz00ENQFAUPPPAAFhcXcfr06atu981vfhOHDh3C0aNHIYoiSqUSCoVCP6UQEREREV13\nfYXl8+fPQ1EUGIaB+++/HwsLC5ibm8PZs2evut1rr72GTCaDj33sY3j/+9+PP/mTP0G32+2nFCIi\nIiKi666vsOw4DkzThG3bOHPmDNrtNkzThOM4V92u0+ng+PHjeOyxx/C9730Ptm3jr//6r/sphYiI\niIjoupP72VjXddi2jcnJSbz00ksAANu2YRjGNbd7//vfj9tuuw0A8LGPfeyKYXl+fr6f8oiIiIiI\ntmx2dvYdj/UVlvfv3w/P87CysoJKpQLf93HhwgUcOHDgqtvNzc1hfX390vdJkiBJkiu+9vbbb++n\nxKFVLBbxz//8zzh8+PCgS6Ehx32FtoP7C20V9xXajnHYX06dOnXFx/tqw7AsC/fccw+eeeYZeJ6H\nZ599FjMzM7jlllsuvebYsWN48sknL9vuQx/6EE6cOIHTp0/D8zz84z/+I37u536un1KIiIiIiK67\nvqeOe+yxx3D69GkcPHgQx48fx1NPPXXZ84uLi6jVapc99t73vhcPP/wwHnzwQRw6dAiGYeDTn/50\nv6UQEREREV1XfbVhAMDk5CSee+65TZ9/4YUXrvj4gw8+iAcffLDfX7+njWqLCV1/3FdoO7i/0FZx\nX6HtGNf9hctdD9C47nS0fdxXaDu4v9BWcV+h7RjX/YVhmYiIiIhoEwzLRERERESbYFgmIiIiItoE\nwzIRERER0SYYlomIiIiINsGwTERERES0CYZlIiIiIqJNMCwTEREREW2CYZmIiIiIaBMMy0RERERE\nm2BYJiIiIiLaBMMyEREREdEmGJaJiIiIiDbBsExEREREtAmGZSIiIiKiTTAsExERERFtgmGZiIiI\niGgTDMtERERERJtgWCYiIiIi2gTDMhERERHRJhiWiYiIiIg2wbBMRERERLQJedAFEBER7VSSJEgS\nIE4SxG/7GhcfixMkwFvPbTx/8b+SJEASRciiAFEUIEsiJFGAIAiD/rOIaIgwLBMR0VBKkgR+GCMI\nI/hhjDCKEYQx/DBCGMXwwxhRHL8VhGMkSC59DQAxEsRJDFx6PEGCjecFCBAEEZIoQhIkCBAhixIk\nUYIobIRmWRIgvRWgZVGELInQFAmaIkNNSYP9H4eIdg3DMhERDZQXRHD9EH4YwQ9ieGEEP4g2gnEU\nIIg3/kVJhDAOL/sXI4aABIIACIIAUQAgAKIgQAAgiMLGc9h4DAIgvtWAGMdAFCaI4o0R6I3/bvwc\nSZAgChIkUYSIjRAtiRI0SYUiqlDlFDRFhq7I0BTprf/KEEWOShONGoZlIiLaNUEYw/FD9LwAPS+E\n44VwAx9+7CGIAvhvBeMwChAkISQRSMkCUpIISRagyAIMUYAsC0hJKiTp+ofTy8NzgigOEMc+gjBB\nK2jBC2LEMaBIKtRL/xQokgotJV8K0boqw9JSDNBEexzDMhER3RBRHMPxwo1Q7IfouSHcIIAbuXAj\nF17owQ1dCGIMNSUiJQtQVBGmLCAly0jJqYH0D0uiAEkUkLrKa6IogRfE8AMHbmCj5cYIogQy5I0Q\nLavQJA1mSoelK8gYCtKGAkVm+wbRXsOwTEREfUuSBM7FUOxtjBw7fggv8uCFLtxwIyBHSQhNlaCm\nRGQMEWVVgSztvYmZJEmAIUkwtJ+E3yRJEIQJvCCA53uoew0s9xIYXRNGyoCVMmFpKtKGgqyhQld5\nCibaC3ikEhHRjoRRjHbPR8fx0ekFcAIHbrQxWuxFHoLEhyIL0BQRpi6hoKSgpNRBl33DCIIAJSVA\nSYlIGxuPRVEC2/VhOw5qnTWkbBVW24IhGzBVDVlTRSGtQVd4OiYaVjw6iYhoy3pugI4TbARkx0Mv\n7KEX2LCDHiQp3hg1NkVkFRFqSh/7adgkSUDGTCFjppAkCXpehJ7TxFKvBtGWYdlpZJsZZHQdeUtF\nzlIhiXtvpJ1olDEsExHRpqI43gjHPR/tno+e76IX9mAHXXixB10RYZgSCpqClMyQdzWCIMDUZJia\njIk84PoROnYb890GlJ6OTCeDjGIhb2koZXVoHG0mGgo8EomI6DKOH14Kx7brvxWOe7ADG4IYwdQk\n5C0JhsqR435szNksoZRL0O1FaPfWsNpcRc7JYq2dQ94yUM7qMLSr3WpIRDcawzIREcHxQzQ6Llq2\nD9t3N1orwh7c0IGmijB0CTP50e45HhRBEJA2ZaRNGX4Yo9np4HynhZqbRq2TR87UUckZsHRl0KUS\njSWGZSKiMRWEMZq2i0bHQ8d10Q7a6HodxEIIQ5OQzUiY0nTOE7yLFFlEOa+imEnQ7PYw322j7lpo\n2AUULRNTBZPtGUS7jEccEdEYSZIErZ6PRsdFu+eh7XfQ8dsIEg+WLqEyIUNTOII5aJIkoJhVkEun\n0Oq6WOhcQNPLoNUrYiJjopI39uSUe0R7EcMyEdEYsN0AjY6Lpu2j43fR8dqwQxuGJiKfk2Fo7D8e\nRpIooJBRkDVTqLdtnGt30PIKaHTzqOQNTGSNQZdINPIYlomIRpQXRGh2PTS6Ljqug07QRsfvQpZj\npC0ZE4YOiS0We4IkCZjIq8haMdZbDZxtNtH1K2jZGewrWWzNILqBeHQREY2QOE5Q7zg4s9REx3HR\n8Tto+x1E8JExZczkUlBSvHy/VykpEdMlDbYbYqW+hG7QRc8rYTJvoZzjKDPRjcCwTEQ0AvwwwnrL\nwUIzRNNpY6G2ADfqwdQklAoyDI1BapSYmoy5SQm1lo03WzbsoIx2L4vZiTTUlHTtH0BEW8awTES0\nhzleiLVWD/Wug5bXRpQKkVITpNMhJnXOZDHKJFFAOa/C1kOs1pdhBzZcv4y5cgYZgzdpEl0vDMtE\nRHtQy/aw1nLQ6jloek20gxYsTcLcZBGaKqHVDAddIu2Si6PMq/Uuzrc9+NEkpgsZTObNQZdGNBIY\nlomI9og4TtDoulhrOWi7NhpuE25sI23I2F/UIEsiNJWX4MeRJAqYKmlodALMt+fhR5PoeSH2l9OQ\nRPaoE/WDYZmIaMgFYYz1toNa20XLa6PpNRHBQy6dQsVgqwX9RD6dgpoSsVJfgh95CMMYByazSMkM\nzEQ7xbBMRDSkLvYjN7ouWl4LTa8JWY6Rz6ZgGbxhj67M0CTMVjQsrtUQtkNEcYJ3TWV54x/RDjEs\nExENmU7Px2qrh1bPQcPd6Ec2NRGTEyloCgMPXZssidhX1rG81sZCO0acJHjXZBa6ytM+0Xb1fV2m\nWq3i2LFjuPPOO3Hffffh9ddf39b2n/jEJ3D48OF+yyAi2vNsN8CZ5SZeXVzDmfV5XOicB5Qu5ioa\nJosagzJtiyQKmJ7QEIk25tuLOLPcRM8NBl0W0Z7Td1h+9NFHceutt+Lll1/G0aNH8cgjj2x52+98\n5zuwbZtLrBLRWHP8EG9WW3h1YR1nawtY6s1DNX3cNKVjIqey35R2TBQFTJVUiCkX850FnK224Pic\nKYVoO/p6B+52u3jxxRfx0EMPQVEUPPDAA1hcXMTp06evua1t2/jbv/1b/MEf/AGSJOmnDCKiPckL\nIpxfbePV+XWcqS1ioXsBKc3F/kkd+XSKN+7RdSEIAiaLGkTZx2JnGW9WW/CCaNBlEe0ZfYXl8+fP\nQ1EUGIaB+++/HwsLC5ibm8PZs2evue3TTz+NX//1X4dlWf2UQES05/hhhIW1Dk7Nr+PM+iLOd85D\nUGzMTWooZBWGZLohJosqYsnBUmcFb1ZbCEIGZqKt6KvT33EcmKYJ27Zx5swZtNttmKYJx3Guut2Z\nM2fwgx/8AJ/97Gfx8ssvX/W1xWKxnxKHViqVAjC6fx9dP9xXRkcYxajWu1ht+6h5XdTjOtIFCfuz\nZcjXqdVCljfe1rO57HX5eTRaMtkEF6o9dOHi3EoHt82V+N5CWzLO56K+wrKu67BtG5OTk3jppZcA\nbLRXGNeY0uiJJ57AI488sqVe5ccff/zS14cOHeLNgES050RRjJWmjdWGjfVeHQ23Dl0H9k8bUFLs\nR6bdI4oC9lV0XKg2sNzWoVVlzE7wCi+NpxMnTuDkyZOXvj9y5MgVXye89tprO24Y7na7OHjwIP79\n3/8dlUoFvu/jfe97H77xjW/glltu2XS79773veh0OpcXIgj44Q9/eFlbxvz8PG6//fadljfULn4y\nq9VqA66Ehh33lb0rjhOstx2stRzUnSbqbh2qmqCYUaAqNyYkXxxRbjVbN+Tn02jwwxgtO4WZ9CwK\nqoxyjvN209WNw7no1KlTmJ2dfcfjfb1bW5aFe+65B8888ww8z8Ozzz6LmZmZy4LysWPH8OLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbd1f39978>"
+        "<matplotlib.figure.Figure at 0x7fc16db65550>"
        ]
       }
      ],
@@ -1009,7 +1009,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAs0AAAF2CAYAAACVqqWqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbc6425908>"
+        "<matplotlib.figure.Figure at 0x7fc166081978>"
        ]
       }
      ],
@@ -1128,7 +1128,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAs0AAAF2CAYAAACVqqWqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbc641b080>"
+        "<matplotlib.figure.Figure at 0x7fc1660760f0>"
        ]
       }
      ],
@@ -1191,8 +1191,8 @@
       "    y is the initial value for y\n",
       "    x is the initial value for x\n",
       "    dx is the difference in x (e.g. the time step)\n",
-      "    f is a callable function (y, x) that you supply to compute dy/dx for\n",
-      "      the specified values.\n",
+      "    f is a callable function (y, x) that you supply to \n",
+      "      compute dy/dx for the specified values.\n",
       "    \"\"\"\n",
       "    \n",
       "    k1 = dx * f(y, x)\n",
@@ -1210,7 +1210,10 @@
       "\n",
       "\n",
       "class BallTrajectory2D(object):\n",
-      "    def __init__(self, x0, y0, velocity, theta_deg=0., g=9.8, noise=[0.0,0.0]):\n",
+      "    def __init__(self, x0, y0, velocity, \n",
+      "                 theta_deg=0., \n",
+      "                 g=9.8, \n",
+      "                 noise=[0.0,0.0]):\n",
       "        self.x = x0\n",
       "        self.y = y0\n",
       "        self.t = 0\n",
@@ -1228,7 +1231,8 @@
       "        self.x = rk4 (self.x, self.t, dt, fx)\n",
       "        self.y = rk4 (self.y, self.t, dt, fy)\n",
       "        self.t += dt \n",
-      "        return (self.x +random.randn()*self.noise[0], self.y+random.randn()*self.noise[1])"
+      "        return (self.x +random.randn()*self.noise[0], \n",
+      "                self.y+random.randn()*self.noise[1])"
      ],
      "language": "python",
      "metadata": {},
@@ -1253,7 +1257,9 @@
       "def test_ball_vacuum(noise):\n",
       "    y = 15\n",
       "    x = 0\n",
-      "    ball = BallTrajectory2D(x0=x, y0=y, theta_deg=60., velocity=100., noise=noise)\n",
+      "    ball = BallTrajectory2D(x0=x, y0=y, \n",
+      "                            theta_deg=60., velocity=100., \n",
+      "                            noise=noise)\n",
       "    t = 0\n",
       "    dt = 0.25\n",
       "    while y >= 0:\n",
@@ -1276,7 +1282,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAF2CAYAAABULe8GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X9w0/d9x/GXwDZIMhBsgp2AE/9gSxwEi/nhNTNnHziG\niTRt15Qbxw6SJezUw0235Vi7+UgvNzhu3C10t5Vt7tINTJO0Td3WKbF9U3MrnpMU6rSwJqQU+Qex\noTY/5AaDBZZs7Q/XKg62MdZX1vcrPR93vWJ98dcf542klz7f9+fztZ0+fTosAAAAAIaYEe8BAAAA\nAImEgA0AAAAYiIANAAAAGIiADQAAABiIgA0AAAAYiIANAAAAGIiADQAAABgo6oC9detWLV++XEVF\nRSoqKtKXv/xlSVIwGFRVVZVWrFihtWvXqqGhYdT31dTUqKSkRMXFxdq/f3+0wwAAAABMIcWIk3zl\nK1/R5z73uVGPHTx4UD6fT01NTTp16pQ8Ho+KioqUnZ2tkydP6sCBA3rllVeUnp6uLVu2qLCwUG63\n24jhAAAAAHFjSItIOHzrzSAbGxu1detWpaenq7i4WEVFRfJ6vZFj69evV0FBgbKysrRp0ybV19cb\nMRQAAAAgrgwJ2Pv379cnPvEJPf3002ptbZUkdXR0KC8vTzt37lR9fb0KCgrU3t4+6tihQ4e0b98+\nLVmyJHIMAAAAsLKoA/aXv/xlHT16VP/zP/+jpUuXaseOHQqFQgoEAnI4HDpz5owuXLggp9Op/v5+\nSYoc6+zs1NmzZ0cdAwAAAKws6h5sl8sV+fNzzz2nl19+Wa2trbLb7QoEAqqrq5Mk7dmzR06nU5Jk\nt9vV39+vXbt2SZK8Xq8cDsct5+7s7Ix2eAAAAMBt5eTkGHYuQxY53sxmsykcDis3N1etra1aunSp\nJKm1tVXl5eWSpNzcXLW1tUW+x+fzKT8/f8zzFRYWGj1ExFhmZqa+973vqaysLN5DwRRQP2ujftZF\n7ayN+llXZmammpubDT1nVC0ifX19Onr0qAYGBjQwMKCvfe1rWrBggZYsWSK3263Dhw+rr69Px44d\n04kTJ1RRUSFJcrvd8nq98vl86unpUW1tLTuIAAAAICFENYMdDAb1T//0T/qrv/orpaamatmyZfq3\nf/s3paSk6KmnnlJbW5vKyso0b9487d27V1lZWZKk5cuXq7KyUtu2bVMoFNLmzZsJ2AAAAEgIttOn\nT9+6x55JdHZ20iJiQZmZmfrggw+0cOHCeA8FU0D9rI36WRe1szbqZ10jLSJG9mBzq3TEBB+MrI36\nWRv1sy5qZ23UDyMI2AAAAICBCNgAAACAgQjYAAAAgIEI2AAAAICBCNgAAACAgQjYAAAAgIEI2AAA\nAICBCNgAAACAgQjYAAAAgIEI2AAAAICBCNgAAACAgQjYAAAAgIEI2AAAAICBCNgAAACAgQjYAAAA\ngIEI2AAAAICBCNgAAACAgQjYAAAAgIEI2AAAAICBCNgAAACAgQjYAAAAgIEI2AAAAICBCNgAAACA\ngQjYAAAAgIEI2AAAAICBCNgAAACAgQjYAAAAgIEI2AAAAICBCNgAAACAgQjYAAAAgIEI2AAAAICB\nCNgAAACAgQjYAAAAgIEI2AAAAICBCNgAAACAgQjYAAAAgIEI2AAAAICBCNgAAACAgQjYAAAAgIEI\n2AAAAICBCNgAAACAgQjYAAAAgIEI2AAAAICBCNgAAACAgQjYAAAAgIEMC9gtLS168MEH9dprr0mS\ngsGgqqqqtGLFCq1du1YNDQ2j/n5NTY1KSkpUXFys/fv3GzUMAAAAIK5SjDhJKBTSP/7jP6qgoEA2\nm02SdPDgQfl8PjU1NenUqVPyeDwqKipSdna2Tp48qQMHDuiVV15Renq6tmzZosLCQrndbiOGAwAA\nAMSNITPY3/zmN7V27VplZGREHmtsbNTWrVuVnp6u4uJiFRUVyev1Ro6tX79eBQUFysrK0qZNm1Rf\nX2/EUAAAAIC4ijpgX7x4Ud/73vf053/+56Me7+joUF5ennbu3Kn6+noVFBSovb191LFDhw5p3759\nWrJkSeQYAGD6+f1p8vvTDD0GAMkq6haRffv26fOf/7zS0ka/wAYCATkcDp05c0Yul0tOp1Pd3d2j\njvl8Pp0/f16lpaXq7+8f8/yZmZnRDhHTLDU1VRK1syrqZ23j1S8cDuvcuUFJ0qJFMyPtfOFwWG++\nGdL27Q5J0ksv9au8PEU2m23Kx27388Z6HDz3rI76WddI7YwUVcB+99131dXVpY0bN0YeC4fDkiS7\n3a5AIKC6ujpJ0p49e+R0OiPH+vv7tWvXLkmS1+uVw+EY82fs3r078ufS0lKVlZVFM2QASFjhcFhn\nz96QJN1zj21SIfrcuUFt3+5Qd/fwBc3t2x1qbg5o8eKUKR8b7+dJmlIoBwCjHT16VE1NTZKkmTNn\nqrS01NDzRxWw33vvPZ04cUIPPvhg5LGf/vSnOnPmjHJzc9Xa2qqlS5dKklpbW1VeXi5Jys3NVVtb\nW+R7fD6f8vPzx/wZO3bsGPX15cuXoxkypsHIp3dqZU3Uz/xGWjIyMgZGPd7S4pTHM0eSVF3dp1Wr\nrkX+/vbtGaPCsNfrV0bGgAKBNEn2UecJBAK6fHnqx8b7ecN/HnscE40/WfDcszbqZy0ul0sul0vS\ncO2am5sNPX9UPdhPPvmkfvnLX0b+t3r1au3Zs0dVVVVyu906fPiw+vr6dOzYMZ04cUIVFRWSJLfb\nLa/XK5/Pp56eHtXW1rKDCABMQkuLUxUVGaqoyFBLizPyuN+fJo9njrq7Z6i7e4Y8njmT6o3OyBhQ\ndXWfsrOHlJ09pOrqvkjgneqxqZjM+On3BmAVhmzTN5annnpKbW1tKisr07x587R3715lZWVJkpYv\nX67Kykpt27ZNoVBImzdvJmADwE3GmqW+OYRKksczR15v8LbBdiQM3zw7fPP3rFp1TV5v8JafN9Vj\nE/28icYxkWSf3QZgLbbTp0+H4z2I8XR2dqqwsDDew8Ad4jKZtVG/+Juo1aOi4nctFtnZQ3fUYjFe\na0msjPfzptLiMtHvPdE5rYTnnrVRP+saaRHJyckx7Jwxm8EGAEzsTmepJzMT3dw8/LJut986wzvd\n4XO8nzfe4xPNlk+E2W0AZmPYrdIBAJM3Xi/17QyHUL+8Xv+YQXLx4hQtXmzduZORDxIff2y8fm96\ntwGYEQEbAGJorHA3USiczOLBsUJoorvdB4vxTPWDDABEg4ANADESq1nqZMXsNgCrIGADQAwwSz19\nmN0GYDbWbdQDAJOYyg4WU13Qh7GN9d9wokWhU93yEAAmg4ANAFEYbweL2+34MfJ3EFvRfJBJhK3/\nAMQHLSIAMEW36/Gll9oc7rR3W6J9BEB0mMEGgEmY6mwms5/mNd7sNu0jAKLFDDYA3MZ4s5mTWawI\nc5vqYlJ2HwEwEQI2AEyANpDkQ/sIgGjRIgIAv0UbCEbQPgIgGsxgA4BoA8GtaB8BMFUEbABJjzYQ\nTBbtIwAmgxYRAJgEZq0xgvYRALfDDDaApDLW5XvaQHCnuJU9gIkQsAEkjYku39MGgmhN5oMa/dlA\ncqBFBEBSmMzle2YkEa2Jbs3e0uKUxzNHklRd3ccHOSCBMYMNAICBxmofud1CWgCJhYANIOHQZw0A\niCdaRAAklIkuw090+R6IpZEPeDf/2/z4v8GurpAkyW6f9uEBMBgBG0DCoM8aZnb7/uzhZF1dHaI/\nG7A4WkQAAJgm9GcDyYGADSBh0GcNADADWkQAWNLIDN/HAzR91rCayfRnA7AWAjYAy7ndfsKEE1jN\nqlXX1Nw8/JZst9/afz3eB0oA5kSLCABLoV8ViWrx4hQtXnzrvNdEdyAFYE4EbAAATIoPlIA1EbAB\nmBY3jAEAWBEBG4ApTXRZfHgho19er5/9gpHQ+EAJWBOLHAGYDjeMAX7ndjvjsAASMB9msAEAMLmx\nblAjsQASMCsCNgDT4bI4cHssgATMixYRAHHFDWMAAImGGWwAcXO7y9vjXRYHwJUewMyYwQYQF5NZ\nyAhgYiyABMyJGWwAACyMBZCA+RCwAcQFl7eB2GEBJBBftIgAiBsWMgIAEhEz2ABibqxbno9gISNg\nPK4QAfHFDDaAmGppccrjmSNJqq7u49bmwDRhASQQP8xgA4gZ+kCB+GIBJBAfBGwAAJIIH3yB2CNg\nA4gZ+kABAMmIHmwAMcVOIYC5jHzwvXltBM9NwFgEbACGmGjBFG/egLnwwReIrahbRHbu3Kk1a9Zo\n5cqV+tSnPqU333xTkhQMBlVVVaUVK1Zo7dq1amhoGPV9NTU1KikpUXFxsfbv3x/tMADEEQumAOuZ\naIvMibbWBHB7Uc9gb9++XXv37lVaWpreeusteTweHT9+XC+//LJ8Pp+ampp06tQpeTweFRUVKTs7\nWydPntSBAwf0yiuvKD09XVu2bFFhYaHcbrcRvxOAaXTzgilJ8njmyOsNMisGWBRbawLRi3oG+8EH\nH1RaWprC4bCCwaCcTqdsNpsaGxu1detWpaenq7i4WEVFRfJ6vZKkxsZGrV+/XgUFBcrKytKmTZtU\nX18f9S8DAACmjh1GAGMY0oP9wgsvqLa2VrNnz1Z1dbXsdrs6OjqUl5ennTt3at26dSooKFB7e7sk\nqaOjQ6tXr9ahQ4fU3d2tlStX6siRI0YMBcA0Y8EUAACjGRawd+3apW9/+9v6m7/5G9XX1ysQCMjh\ncOjMmTNyuVxyOp3q7u6WpMgxn8+n8+fPq7S0VP39/WOeOzMz04ghYhqlpqZKonZWNZX6rV8fVnNz\nQJK0aNEs2WyzYzI23B7PP+syQ+0yMsJ66aV+bd/ukCS99FK/lixJl81mi9uYrMIM9cPUjNLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbc6428940>"
+        "<matplotlib.figure.Figure at 0x7fc166065cf8>"
        ]
       },
       {
@@ -1284,7 +1290,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAF2CAYAAABULe8GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbc6428748>"
+        "<matplotlib.figure.Figure at 0x7fc166065208>"
        ]
       }
      ],
@@ -1445,15 +1451,19 @@
       "v0 = 100.\n",
       "dt = 0.1   # time step\n",
       "\n",
-      "ball = BallTrajectory2D(x0=x, y0=y, theta_deg=theta, velocity=v0, noise=[0,0])\n",
+      "ball = BallTrajectory2D(x0=x, y0=y, theta_deg=theta, \n",
+      "                        velocity=v0, noise=[0,0])\n",
       "f1 = ball_kf(x,y,theta,v0,dt)\n",
       "t = 0\n",
       "while f1.x[2,0] > 0:\n",
       "    t += dt\n",
       "    f1.predict() \n",
       "    x,y = ball.step(dt)\n",
-      "    p1 = plt.scatter(f1.x[0,0], f1.x[2,0], color='black', marker='+', s=75)\n",
-      "    p2 = plt.scatter(x, y,                 color='green', marker='o', s=75, alpha=0.5)\n",
+      "    p1 = plt.scatter(f1.x[0,0], f1.x[2,0], \n",
+      "                     color='black', marker='+', s=75)\n",
+      "    p2 = plt.scatter(x, y,                 \n",
+      "                     color='green', marker='o', s=75, \n",
+      "                     alpha=0.5)\n",
       "\n",
       "plt.legend([p1,p2], ['Kalman filter', 'idealized'])\n",
       "plt.show()"
@@ -1466,7 +1476,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAF2CAYAAAB+nR6pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbc6272128>"
+        "<matplotlib.figure.Figure at 0x7fc165ecdc18>"
        ]
       }
      ],
@@ -1630,7 +1640,8 @@
       "v0 = 80.\n",
       "dt = 1/10.   # time step\n",
       "\n",
-      "ball = BallTrajectory2D(x0=x, y0=y, theta_deg=theta, velocity=v0, noise=[.2,.2])\n",
+      "ball = BallTrajectory2D(x0=x, y0=y, theta_deg=theta, velocity=v0, \n",
+      "                        noise=[.2,.2])\n",
       "f1 = ball_kf(x,y,theta,v0,dt)\n",
       "\n",
       "t = 0\n",
@@ -1661,7 +1672,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAF2CAYAAAB+nR6pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbd1fe0438>"
+        "<matplotlib.figure.Figure at 0x7fc16dc0b7f0>"
        ]
       }
      ],
@@ -1802,7 +1813,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbc6473400>"
+        "<matplotlib.figure.Figure at 0x7fc166042208>"
        ]
       }
      ],
@@ -1824,14 +1835,18 @@
       "from math import radians, sin, cos, sqrt, exp\n",
       "\n",
       "class BaseballPath(object):\n",
-      "    def __init__(self, x0, y0, launch_angle_deg, velocity_ms, noise=(1.0,1.0)): \n",
+      "    def __init__(self, x0, y0, launch_angle_deg, velocity_ms, \n",
+      "                 noise=(1.0,1.0)): \n",
       "        \"\"\" Create 2D baseball path object  \n",
-      "           (x = distance from start point in ground plane, y=height above ground)\n",
+      "           (x = distance from start point in ground plane, \n",
+      "            y=height above ground)\n",
       "        \n",
       "        x0,y0            initial position\n",
-      "        launch_angle_deg angle ball is travelling respective to ground plane\n",
+      "        launch_angle_deg angle ball is travelling respective to \n",
+      "                         ground plane\n",
       "        velocity_ms      speeed of ball in meters/second\n",
-      "        noise            amount of noise to add to each reported position in (x,y)\n",
+      "        noise            amount of noise to add to each position\n",
+      "                         in (x,y)\n",
       "        \"\"\"\n",
       "        \n",
       "        omega = radians(launch_angle_deg)\n",
@@ -1853,8 +1868,8 @@
       "\n",
       "\n",
       "    def update(self, dt, vel_wind=0.):\n",
-      "        \"\"\" compute the ball position based on the specified time step and\n",
-      "        wind velocity. Returns (x,y) position tuple.\n",
+      "        \"\"\" compute the ball position based on the specified time \n",
+      "        step and wind velocity. Returns (x,y) position tuple.\n",
       "        \"\"\"\n",
       "\n",
       "        # Euler equations for x and y\n",
@@ -1895,7 +1910,8 @@
       "v0 = 50.\n",
       "dt = 1/10.   # time step\n",
       "\n",
-      "ball = BaseballPath(x0=x, y0=y, launch_angle_deg=theta, velocity_ms=v0, noise=[.3,.3])\n",
+      "ball = BaseballPath(x0=x, y0=y, launch_angle_deg=theta,\n",
+      "                    velocity_ms=v0, noise=[.3,.3])\n",
       "f1 = ball_kf(x,y,theta,v0,dt,r=1.)\n",
       "f2 = ball_kf(x,y,theta,v0,dt,r=10.)\n",
       "t = 0\n",
@@ -1935,7 +1951,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbc647a908>"
+        "<matplotlib.figure.Figure at 0x7fc16603e978>"
        ]
       }
      ],
@@ -2003,7 +2019,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbc6484828>"
+        "<matplotlib.figure.Figure at 0x7fc16d9f6b70>"
        ]
       },
       {
@@ -2011,7 +2027,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbc63b4b38>"
+        "<matplotlib.figure.Figure at 0x7fc166012dd8>"
        ]
       }
      ],
@@ -2041,7 +2057,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbc6459358>"
+        "<matplotlib.figure.Figure at 0x7fc16606d390>"
        ]
       },
       {
@@ -2049,7 +2065,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffbc615dfd0>"
+        "<matplotlib.figure.Figure at 0x7fc165f6de10>"
        ]
       }
      ],

09_Extended_Kalman_Filters/Extended_Kalman_Filters.ipynb

@@ -259,13 +259,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 1,
+       "prompt_number": 2,
        "text": [
-        "<IPython.core.display.HTML at 0x7f4ddac347b8>"
+        "<IPython.core.display.HTML at 0x7f7bfc0eda20>"
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -547,8 +547,10 @@
       "for i in range(10):\n",
       "    out = h(out)\n",
       "    out2 = g(out2)\n",
-      "print ('linear    output mean, variance: %.4f, %.4f'% (np.average(out), np.std(out)**2))\n",
-      "print ('nonlinear output mean, variance: %.4f, %.4f'% (np.average(out2), np.std(out2)**2))"
+      "print ('linear    output mean, variance: %.4f, %.4f' % \n",
+      "       (np.average(out), np.std(out)**2))\n",
+      "print ('nonlinear output mean, variance: %.4f, %.4f' % \n",
+      "       (np.average(out2), np.std(out2)**2))"
      ],
      "language": "python",
      "metadata": {},
@@ -815,7 +817,7 @@
       "\n",
       "So we want to track the position of an aircraft assuming a constant velocity and altitude, and measurements of the slant distance to the aircraft. That means we need 3 state variables - horizontal distance, velocity, and altitude.\n",
       "\n",
-      "$$ $\\mathbf{x} = \\begin{bmatrix}distance \\\\velocity\\\\ altitude\\end{bmatrix}=  \\begin{bmatrix}x_{pos} \\\\x_{vel}\\\\ x_{alt}\\end{bmatrix}$$"
+      "$$\\mathbf{x} = \\begin{bmatrix}distance \\\\velocity\\\\ altitude\\end{bmatrix}=  \\begin{bmatrix}x_{pos} \\\\x_{vel}\\\\ x_{alt}\\end{bmatrix}$$"
      ]
     },
     {
@@ -973,7 +975,10 @@
      "collapsed": false,
      "input": [
       "def hx(x):\n",
-      "    \"\"\" compute measurement for slant range that would correspond to state x\"\"\"\n",
+      "    \"\"\" compute measurement for slant range that would correspond \n",
+      "    to state x.\n",
+      "    \"\"\"\n",
+      "    \n",
       "    return (x[0,0]**2 + x[2,0]**2) ** 0.5"
      ],
      "language": "python",
@@ -1150,13 +1155,16 @@
       "\n",
       "Depending on your experience with derivatives you may have found the computation of the Jacobian above either fairly straightforward, or quite difficult. Even if you found it easy, a slightly more difficult problem easily leads to very difficult computations.\n",
       "\n",
-      "As explained in Appendix C, we can use the SymPy package to compute the Jacobian for us. "
+      "As explained in Appendix A, we can use the SymPy package to compute the Jacobian for us. "
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "import sympy\n",
+      "sympy.init_printing(use_latex='mathjax')\n",
+      "\n",
       "x_pos, x_vel, x_alt = sympy.symbols('x_pos, x_vel x_alt')\n",
       "\n",
       "H = sympy.Matrix([sympy.sqrt(x_pos**2 + x_alt**2)])\n",
@@ -1173,7 +1181,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 48,
+       "prompt_number": 4,
        "text": [
         "\u23a1       x_pos                    x_alt        \u23a4\n",
         "\u23a2\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\u23a5\n",
@@ -1183,7 +1191,7 @@
        ]
       }
      ],
-     "prompt_number": 48
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -1229,6 +1237,7 @@
      "collapsed": false,
      "input": [
       "import sympy\n",
+      "sympy.init_printing(use_latex='mathjax')\n",
       "w_vel, w_alt, dt = sympy.symbols('w_vel w_alt \\Delta{t}')\n",
       "w = sympy.Matrix([[0, w_vel, w_alt]]).T\n",
       "phi = sympy.Matrix([[1, dt, 0], [0, 1, 0], [0,0,1]])\n",
@@ -1246,7 +1255,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 15,
+       "prompt_number": 5,
        "text": [
         "\u23a1           3      2                2      2             2            \u23a4\n",
         "\u23a2  \\Delta{t} \u22c5w_vel        \\Delta{t} \u22c5w_vel     \\Delta{t} \u22c5w_alt\u22c5w_vel\u23a5\n",
@@ -1265,156 +1274,7 @@
        ]
       }
      ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "f21, f22, t, phi = sympy.symbols('f_21 f_22 t \\Phi_S')\n",
-      "Q_t = sympy.Matrix([[0,0],[0,phi]])\n",
-      "f = sympy.Matrix([[1,dt],[f21, 1+f22*dt]])\n",
-      "term = f*Q_t*f.T\n",
-      "term\n",
-      "sympy.integrate(term,(dt, 0, dt))\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\left[\\begin{matrix}\\frac{\\Delta{t}^{3} \\Phi_{S}}{3} & \\frac{\\Phi_{S} f_{22}}{3} \\Delta{t}^{3} + \\frac{\\Delta{t}^{2} \\Phi_{S}}{2}\\\\\\frac{\\Phi_{S} f_{22}}{3} \\Delta{t}^{3} + \\frac{\\Delta{t}^{2} \\Phi_{S}}{2} & \\frac{\\Delta{t}^{3} \\Phi_{S}}{3} f_{22}^{2} + \\Delta{t}^{2} \\Phi_{S} f_{22} + \\Delta{t} \\Phi_{S}\\end{matrix}\\right]$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 40,
-       "text": [
-        "\u23a1                     3                                          3            \n",
-        "\u23a2            \\Delta{t} \u22c5\\Phi_S                          \\Delta{t} \u22c5\\Phi_S\u22c5f\u2082\u2082 \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\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 \n",
-        "\u23a2                    3                                            3           \n",
-        "\u23a2                                                                             \n",
-        "\u23a2         3                       2                  3           2            \n",
-        "\u23a2\\Delta{t} \u22c5\\Phi_S\u22c5f\u2082\u2082   \\Delta{t} \u22c5\\Phi_S  \\Delta{t} \u22c5\\Phi_S\u22c5f\u2082\u2082             \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\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\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \\Delta{t}\n",
-        "\u23a3          3                     2                    3                       \n",
-        "\n",
-        "           2                   \u23a4\n",
-        "  \\Delta{t} \u22c5\\Phi_S            \u23a5\n",
-        "+ \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500            \u23a5\n",
-        "          2                    \u23a5\n",
-        "                               \u23a5\n",
-        "                               \u23a5\n",
-        "2                              \u23a5\n",
-        " \u22c5\\Phi_S\u22c5f\u2082\u2082 + \\Delta{t}\u22c5\\Phi_S\u23a5\n",
-        "                               \u23a6"
-       ]
-      }
-     ],
-     "prompt_number": 40
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#Bar-Shalom P270\n",
-      "T,t,dt = sympy.symbols('T t \\Delta{t}')\n",
-      "v_k = sympy.Matrix([[T-t],[1]])\n",
-      "term = v_k*v_k.T\n",
-      "term\n",
-      "sympy.integrate(v_k*v_k.T,(t, 0, T))\n",
-      "\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\left[\\begin{matrix}\\frac{T^{3}}{3} & \\frac{T^{2}}{2}\\\\\\frac{T^{2}}{2} & T\\end{matrix}\\right]$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 51,
-       "text": [
-        "\u23a1 3   2\u23a4\n",
-        "\u23a2T   T \u23a5\n",
-        "\u23a2\u2500\u2500  \u2500\u2500\u23a5\n",
-        "\u23a23   2 \u23a5\n",
-        "\u23a2      \u23a5\n",
-        "\u23a2 2    \u23a5\n",
-        "\u23a2T     \u23a5\n",
-        "\u23a2\u2500\u2500  T \u23a5\n",
-        "\u23a32     \u23a6"
-       ]
-      }
-     ],
-     "prompt_number": 51
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#Bar-Shalom P272\n",
-      "T,t,dt = sympy.symbols('T t \\Delta{t}')\n",
-      "v_k = sympy.Matrix([[(T-t)*(T-t)/2], [T-t], [1]])\n",
-      "term = v_k*v_k.T\n",
-      "term\n",
-      "print(sympy.pretty(sympy.integrate(v_k*v_k.T,(t, 0, T))))\n",
-      "((T-t)*(T-t)/2).expand()\n",
-      "\n",
-      "wk=sympy.Matrix([[T**2/2,T,1]]).T\n",
-      "wk*wk.T\n",
-      "sympy.integrate(wk*wk.T, T)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\u23a1 5   4   3\u23a4\n",
-        "\u23a2T   T   T \u23a5\n",
-        "\u23a2\u2500\u2500  \u2500\u2500  \u2500\u2500\u23a5\n",
-        "\u23a220  8   6 \u23a5\n",
-        "\u23a2          \u23a5\n",
-        "\u23a2 4   3   2\u23a5\n",
-        "\u23a2T   T   T \u23a5\n",
-        "\u23a2\u2500\u2500  \u2500\u2500  \u2500\u2500\u23a5\n",
-        "\u23a28   3   2 \u23a5\n",
-        "\u23a2          \u23a5\n",
-        "\u23a2 3   2    \u23a5\n",
-        "\u23a2T   T     \u23a5\n",
-        "\u23a2\u2500\u2500  \u2500\u2500  T \u23a5\n",
-        "\u23a36   2     \u23a6\n"
-       ]
-      },
-      {
-       "latex": [
-        "$$\\left[\\begin{matrix}\\frac{T^{5}}{20} & \\frac{T^{4}}{8} & \\frac{T^{3}}{6}\\\\\\frac{T^{4}}{8} & \\frac{T^{3}}{3} & \\frac{T^{2}}{2}\\\\\\frac{T^{3}}{6} & \\frac{T^{2}}{2} & T\\end{matrix}\\right]$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 72,
-       "text": [
-        "\u23a1 5   4   3\u23a4\n",
-        "\u23a2T   T   T \u23a5\n",
-        "\u23a2\u2500\u2500  \u2500\u2500  \u2500\u2500\u23a5\n",
-        "\u23a220  8   6 \u23a5\n",
-        "\u23a2          \u23a5\n",
-        "\u23a2 4   3   2\u23a5\n",
-        "\u23a2T   T   T \u23a5\n",
-        "\u23a2\u2500\u2500  \u2500\u2500  \u2500\u2500\u23a5\n",
-        "\u23a28   3   2 \u23a5\n",
-        "\u23a2          \u23a5\n",
-        "\u23a2 3   2    \u23a5\n",
-        "\u23a2T   T     \u23a5\n",
-        "\u23a2\u2500\u2500  \u2500\u2500  T \u23a5\n",
-        "\u23a36   2     \u23a6"
-       ]
-      }
-     ],
-     "prompt_number": 72
+     "prompt_number": 5
     },
     {
      "cell_type": "heading",

10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ac5d16f13341646cfbb0fb366e162a56628ec5cb0c19001da7a7fb23105214b0"
+  "signature": "sha256:a2e52b61eda730d642e07ed11f2d1d9c5d578591c7ac587dc7afee8425ce3e4d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -625,10 +625,12 @@
      "collapsed": false,
      "input": [
       "def sigma_points (X, P, kappa):\n",
-      "    \"\"\" Computes the sigma points and weights for an unscented Kalman filter\n",
-      "    given the mean and covariance of the filter.\n",
-      "    kappa is an arbitrary constant\n",
-      "    constant. Returns tuple of the sigma points and weights.\n",
+      "    \"\"\" Computes the sigma points and weights for an unscented \n",
+      "    Kalman filter given the mean and covariance of the filter.\n",
+      "    \n",
+      "    kappa is an arbitraryconstant. \n",
+      "    \n",
+      "    Returns tuple of the sigma points and weights.\n",
       "\n",
       "    Works with both scalar and array inputs:\n",
       "    sigma_points (5, 9, 2) # mean 5, covariance 9\n",
@@ -645,8 +647,8 @@
       "    Returns\n",
       "    -------\n",
       "    sigmas : np.array, of size (n, 2n+1)\n",
-      "        Two dimensional array of sigma points. Each column contains all of\n",
-      "        the sigmas for one dimension in the problem space.\n",
+      "        Two dimensional array of sigma points. Each column contains\n",
+      "        all of the sigmas for one dimension in the problem space.\n",
       "\n",
       "        Ordered by Xi_0, Xi_{1..n}, Xi_{n+1..2n}\n",
       "\n",
@@ -733,7 +735,9 @@
      "collapsed": false,
      "input": [
       "def unscented_transform (Xi, W, NoiseCov=None):\n",
-      "    \"\"\" computes the unscented transform of a set of signma points and weights.\n",
+      "    \"\"\" computes the unscented transform of a set of signma \n",
+      "    points and weights.\n",
+      "    \n",
       "    returns the mean and covariance in a tuple\n",
       "    \"\"\"\n",
       "    kmax,n = Xi.shape\n",

11_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:928cf8eeb269cc5e75d780bd7c29a146c7558c45f138798038288577c813816c"
+  "signature": "sha256:cd758a1ca3157c15f8cb0a9161fe3c9e5fe8a7522243d185ae67fa44bdbd4dc6"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f4ed5fb65f8>"
+        "<IPython.core.display.HTML at 0x7fa6499e67b8>"
        ]
       }
      ],
@@ -405,11 +405,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAFwCAYAAABdOnYEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4eb98327f0>"
+        "<matplotlib.figure.Figure at 0x7fa62d55b7b8>"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -601,15 +601,16 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "phi = sympy.symbols('\\phi')\n",
+      "from sympy import symbols, Matrix\n",
+      "phi = symbols('\\phi')\n",
       "phi\n",
       "\n",
-      "x, y, xa, xb, ya, yb, dx, dy = sympy.symbols('x, y, x_a, x_b, y_a, y_b, dx, dy')\n",
+      "x, y, xa, xb, ya, yb, dx, dy = symbols('x y x_a x_b y_a y_b dx dy')\n",
       "\n",
-      "H = sympy.Matrix([[sympy.sqrt((x-xa)**2 + (y-ya)**2)], \n",
-      "                  [sympy.sqrt((x-xb)**2 + (y-yb)**2)]])\n",
+      "H = Matrix([[sympy.sqrt((x-xa)**2 + (y-ya)**2)], \n",
+      "            [sympy.sqrt((x-xb)**2 + (y-yb)**2)]])\n",
       "\n",
-      "state = sympy.Matrix([x, dx, y, dy])\n",
+      "state = Matrix([x, dx, y, dy])\n",
       "H.jacobian(state)"
      ],
      "language": "python",
@@ -693,9 +694,9 @@
       "from math import sin, cos, atan2\n",
       "\n",
       "def H_of (pos, pos_A, pos_B):\n",
-      "    \"\"\" Given the position of our object at 'pos' in 2D, and two transmitters\n",
-      "    A and B at positions 'pos_A' and 'pos_B', return the partial derivative\n",
-      "    of H\n",
+      "    \"\"\" Given the position of our object at 'pos' in 2D, and two \n",
+      "    transmitters A and B at positions 'pos_A' and 'pos_B', return \n",
+      "    the partial derivative of H\n",
       "    \"\"\"\n",
       "\n",
       "    theta_a = atan2(pos_a[1]-pos[1], pos_a[0] - pos[0])\n",
@@ -792,8 +793,8 @@
       "    # move (1,1) each step, so just use i\n",
       "    pos = [i,i]\n",
       "    \n",
-      "    # compute the difference in range between the nominal track and measured \n",
-      "    # ranges\n",
+      "    # compute the difference in range between the nominal track\n",
+      "    # and measured ranges\n",
       "    ra,rb = d.range_of(pos)\n",
       "    rx,ry = d.range_of((pos[0]+f1.x[0,0], pos[1]+f1.x[2,0]))\n",
       "    z = np.array([[ra-rx],[rb-ry]])\n",
@@ -823,9 +824,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAF2CAYAAACLY5UZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAF2CAYAAACLY5UZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f56ffe59fd0>"
+        "<matplotlib.figure.Figure at 0x7fa62c29bf28>"
        ]
       }
      ],

Appendix_A_Installation/Appendix_Installation.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:aa9b4df3b3595ff9a790185720fa062f01b70b83d6266449e2fba5baecb5af02"
+  "signature": "sha256:5ca766a2f349f8f9ea63fc2504c164a60dfe8bddd92c84cf7387db12cd8f1c4a"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -302,9 +302,7 @@
      "collapsed": false,
      "input": [
       "import sympy\n",
-      "from sympy import  init_printing\n",
-      "#from sympy.interactive import printing\n",
-      "init_printing(use_latex='mathjax')\n",
+      "sympy.init_printing(use_latex='mathjax')\n",
       "\n",
       "phi, x = sympy.symbols('\\phi, x')\n",
       "phi"