Layout changes to make PDF read better.

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

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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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f633596c3c8>"
+        "<matplotlib.figure.Figure at 0x7f002d8fe208>"
        ]
       }
      ],
@@ -442,9 +444,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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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": 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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",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f63355a0630>"
+        "<matplotlib.figure.Figure at 0x7f002d4c66d8>"
        ]
       }
      ],
@@ -541,7 +543,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f63354cd8d0>"
+        "<matplotlib.figure.Figure at 0x7f002d4bae48>"
        ]
       }
      ],
@@ -578,9 +580,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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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": 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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": 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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": 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truncated
+       "png": 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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": 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truncated
+       "png": 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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": 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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": 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+       "png": 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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": 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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": 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truncated
+       "png": 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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": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f207ab57e80>"
+        "<matplotlib.figure.Figure at 0x7f5ff2759c18>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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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": 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truncated
+       "png": 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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": 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truncated
+       "png": 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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": 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truncated
+       "png": 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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": 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truncated
+       "png": 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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": 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-       "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": 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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": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f207aa3ada0>"
-       ]
-      }
-     ],
-     "prompt_number": 33
+     "outputs": []
     },
     {
      "cell_type": "markdown",