Reran for 538 plot formatting.

Ran all notebooks so that all images are properly plotted using
the 538 matplotlib style.

05_Kalman_Filters/Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:25e5773098f32686e5a284ee397bd2ea9060a96f46a38df75833442ebd3db7d6"
+  "signature": "sha256:156f79b0088cce4bbd51d29b0e44f749e54646e15cb6b070627a4348cde0149f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fce9a598978>"
+        "<IPython.core.display.HTML at 0x7fc586ba4b38>"
        ]
       }
      ],
@@ -353,10 +353,10 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        " 1.2921\t 0.1444\t 0.7473\t 0.3516\t 0.4305\t\n",
-        " 1.8350\t-0.1108\t-0.1800\t 0.3818\t-0.2313\t\n",
-        "-0.2557\t 1.3211\t 0.1475\t 0.9038\t-0.1875\t\n",
-        "-0.3380\t-1.1683\t 0.6501\t-1.3499\t-0.9251\t\n"
+        "-0.6848\t-0.8225\t 0.5694\t-0.6209\t-0.4933\t\n",
+        "-0.1442\t 0.5606\t 0.8722\t 0.5783\t-1.0106\t\n",
+        " 0.4380\t 0.6382\t-0.0738\t 0.4447\t 1.1688\t\n",
+        " 0.2339\t-0.7171\t-1.2632\t-2.2643\t 0.5534\t\n"
        ]
       }
      ],
@@ -401,9 +401,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxQAAAGNCAYAAACIdTczAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce7e0e9978>"
+        "<matplotlib.figure.Figure at 0x7fc56a6f4f98>"
        ]
       }
      ],
@@ -447,9 +447,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce7dc2d3c8>"
+        "<matplotlib.figure.Figure at 0x7fc56a24ba90>"
        ]
       }
      ],
@@ -481,9 +481,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce7e0e9278>"
+        "<matplotlib.figure.Figure at 0x7fc56a6f4fd0>"
        ]
       }
      ],
@@ -501,9 +501,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce7dd79470>"
+        "<matplotlib.figure.Figure at 0x7fc56a3921d0>"
        ]
       }
      ],
@@ -537,9 +537,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fce7dae7518>"
+        "<matplotlib.figure.Figure at 0x7fc56a3e7390>"
        ]
       }
      ],
@@ -576,9 +576,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79cf12b0>"
+        "<matplotlib.figure.Figure at 0x7fc56a2cbc88>"
        ]
       }
      ],
@@ -679,9 +679,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79d0dc18>"
+        "<matplotlib.figure.Figure at 0x7fc568016080>"
        ]
       }
      ],
@@ -727,9 +727,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79c932e8>"
+        "<matplotlib.figure.Figure at 0x7fc568016b70>"
        ]
       }
      ],
@@ -777,9 +777,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74a743c8>"
+        "<matplotlib.figure.Figure at 0x7fc56013b7f0>"
        ]
       }
      ],
@@ -895,26 +895,26 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "time: 0 \tposition = 1.848 \tvariance = 2.500\n",
-        "time: 1 \tposition = 1.440 \tvariance = 1.667\n",
-        "time: 2 \tposition = 1.032 \tvariance = 1.250\n",
-        "time: 3 \tposition = 0.551 \tvariance = 1.000\n",
-        "time: 4 \tposition = 0.504 \tvariance = 0.833\n",
-        "time: 5 \tposition = 0.379 \tvariance = 0.714\n",
-        "time: 6 \tposition = 0.244 \tvariance = 0.625\n",
-        "time: 7 \tposition = 0.321 \tvariance = 0.556\n",
-        "time: 8 \tposition = 0.381 \tvariance = 0.500\n",
-        "time: 9 \tposition = 0.305 \tvariance = 0.455\n",
-        "time: 10 \tposition = 0.435 \tvariance = 0.417\n",
-        "time: 11 \tposition = 0.423 \tvariance = 0.385\n",
-        "time: 12 \tposition = 0.445 \tvariance = 0.357\n",
-        "time: 13 \tposition = 0.404 \tvariance = 0.333\n",
-        "time: 14 \tposition = 0.397 \tvariance = 0.312\n",
-        "time: 15 \tposition = 0.282 \tvariance = 0.294\n",
-        "time: 16 \tposition = 0.239 \tvariance = 0.278\n",
-        "time: 17 \tposition = 0.161 \tvariance = 0.263\n",
-        "time: 18 \tposition = 0.204 \tvariance = 0.250\n",
-        "time: 19 \tposition = 0.160 \tvariance = 0.238\n"
+        "time: 0 \tposition = 0.257 \tvariance = 2.500\n",
+        "time: 1 \tposition = 0.290 \tvariance = 1.667\n",
+        "time: 2 \tposition = 0.092 \tvariance = 1.250\n",
+        "time: 3 \tposition = 0.276 \tvariance = 1.000\n",
+        "time: 4 \tposition = 0.247 \tvariance = 0.833\n",
+        "time: 5 \tposition = 0.510 \tvariance = 0.714\n",
+        "time: 6 \tposition = 0.516 \tvariance = 0.625\n",
+        "time: 7 \tposition = 0.620 \tvariance = 0.556\n",
+        "time: 8 \tposition = 0.454 \tvariance = 0.500\n",
+        "time: 9 \tposition = 0.478 \tvariance = 0.455\n",
+        "time: 10 \tposition = 0.478 \tvariance = 0.417\n",
+        "time: 11 \tposition = 0.374 \tvariance = 0.385\n",
+        "time: 12 \tposition = 0.394 \tvariance = 0.357\n",
+        "time: 13 \tposition = 0.225 \tvariance = 0.333\n",
+        "time: 14 \tposition = 0.296 \tvariance = 0.312\n",
+        "time: 15 \tposition = 0.302 \tvariance = 0.294\n",
+        "time: 16 \tposition = 0.186 \tvariance = 0.278\n",
+        "time: 17 \tposition = 0.156 \tvariance = 0.263\n",
+        "time: 18 \tposition = 0.114 \tvariance = 0.250\n",
+        "time: 19 \tposition = 0.030 \tvariance = 0.238\n"
        ]
       }
      ],
@@ -1034,24 +1034,24 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "PREDICT:     1.0000   502.0000\tUPDATE:    -2.4457     9.8047\n",
-        "PREDICT:    -1.4457    11.8047\tUPDATE:     0.7765     5.4138\n",
-        "PREDICT:     1.7765     7.4138\tUPDATE:     1.4225     4.2574\n",
-        "PREDICT:     2.4225     6.2574\tUPDATE:     2.3347     3.8490\n",
-        "PREDICT:     3.3347     5.8490\tUPDATE:     4.8032     3.6904\n",
-        "PREDICT:     5.8032     5.6904\tUPDATE:     5.4486     3.6267\n",
-        "PREDICT:     6.4486     5.6267\tUPDATE:     6.5119     3.6007\n",
-        "PREDICT:     7.5119     5.6007\tUPDATE:     8.9309     3.5900\n",
-        "PREDICT:     9.9309     5.5900\tUPDATE:     9.5451     3.5856\n",
-        "PREDICT:    10.5451     5.5856\tUPDATE:     9.8194     3.5838\n"
+        "PREDICT:     1.0000   502.0000\tUPDATE:     6.3512     9.8047\n",
+        "PREDICT:     7.3512    11.8047\tUPDATE:     3.3566     5.4138\n",
+        "PREDICT:     4.3566     7.4138\tUPDATE:     5.1701     4.2574\n",
+        "PREDICT:     6.1701     6.2574\tUPDATE:     5.6607     3.8490\n",
+        "PREDICT:     6.6607     5.8490\tUPDATE:     6.0900     3.6904\n",
+        "PREDICT:     7.0900     5.6904\tUPDATE:     3.6639     3.6267\n",
+        "PREDICT:     4.6639     5.6267\tUPDATE:     3.5041     3.6007\n",
+        "PREDICT:     4.5041     5.6007\tUPDATE:     4.4744     3.5900\n",
+        "PREDICT:     5.4744     5.5900\tUPDATE:     7.0620     3.5856\n",
+        "PREDICT:     8.0620     5.5856\tUPDATE:     9.5519     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 0x7fce79c8bfd0>"
+        "<matplotlib.figure.Figure at 0x7fc586ba7400>"
        ]
       }
      ],
@@ -1186,17 +1186,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74a4a710>"
+        "<matplotlib.figure.Figure at 0x7fc5601688d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79d0d320>"
+        "<matplotlib.figure.Figure at 0x7fc560133588>"
        ]
       },
       {
@@ -1522,17 +1522,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fce7dd40780>"
+        "<matplotlib.figure.Figure at 0x7fc560a51ac8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74adaa90>"
+        "<matplotlib.figure.Figure at 0x7fc56010e5f8>"
        ]
       },
       {
@@ -1648,9 +1648,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74aac3c8>"
+        "<matplotlib.figure.Figure at 0x7fc5601a7780>"
        ]
       }
      ],
@@ -1719,17 +1719,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74aa2358>"
+        "<matplotlib.figure.Figure at 0x7fc560168358>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79d25080>"
+        "<matplotlib.figure.Figure at 0x7fc5680c42e8>"
        ]
       }
      ],
@@ -1805,9 +1805,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74a748d0>"
+        "<matplotlib.figure.Figure at 0x7fc5600bc940>"
        ]
       }
      ],
@@ -1869,9 +1869,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce749750b8>"
+        "<matplotlib.figure.Figure at 0x7fc558ee9d30>"
        ]
       }
      ],
@@ -1932,9 +1932,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce74943a90>"
+        "<matplotlib.figure.Figure at 0x7fc558f14358>"
        ]
       }
      ],
@@ -1984,9 +1984,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce748d07b8>"
+        "<matplotlib.figure.Figure at 0x7fc558e757f0>"
        ]
       }
      ],
@@ -2106,9 +2106,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79c68c88>"
+        "<matplotlib.figure.Figure at 0x7fc558e32400>"
        ]
       },
       {
@@ -2195,9 +2195,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce79c28ba8>"
+        "<matplotlib.figure.Figure at 0x7fc56003a3c8>"
        ]
       }
      ],
@@ -2279,9 +2279,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAGNCAYAAADpbRVxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fce749f6128>"
+        "<matplotlib.figure.Figure at 0x7fc568032470>"
        ]
       }
      ],

05_Kalman_Filters/Kalman_Filters_Animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:08cdb1e902d069b45293bbf627273884dedc0356c9048d8247276cd852dee4c4"
+  "signature": "sha256:be92c78bd7810e38db1917d19689572306a78961e048308a9322a3f068c68208"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -36,7 +36,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -102,7 +102,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -167,7 +168,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAcYAAAHGCAYAAADuYispAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7ffa64af50b8>"
+       ]
+      }
+     ],
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -237,13 +248,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f3d805b4d68>"
+        "<matplotlib.figure.Figure at 0x7ffa60799358>"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",