Removed gray background from 538 style.

white background renders better on cheap LCDs, and it will print better as well.

05_Kalman_Filters/Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:156f79b0088cce4bbd51d29b0e44f749e54646e15cb6b070627a4348cde0149f"
+  "signature": "sha256:6eb77832f33b4b1052cec915e1daa15523a5741a051041ac2c0713ab9b1889b1"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,7 +259,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fc586ba4b38>"
+        "<IPython.core.display.HTML at 0x7f402958b9b0>"
        ]
       }
      ],
@@ -353,10 +353,10 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "-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"
+        " 0.0388\t 0.3728\t 0.8481\t 0.9898\t-0.9675\t\n",
+        " 1.8975\t-0.0372\t-0.6346\t-0.8059\t-0.1991\t\n",
+        "-1.0612\t 0.4985\t 0.3849\t-0.6252\t 1.3204\t\n",
+        "-0.8348\t-0.3407\t 1.2853\t-0.1293\t 0.8606\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 0x7fc56a6f4f98>"
+        "<matplotlib.figure.Figure at 0x7f400d0e6828>"
        ]
       }
      ],
@@ -447,9 +447,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc56a24ba90>"
+        "<matplotlib.figure.Figure at 0x7f400cc2c710>"
        ]
       }
      ],
@@ -481,9 +481,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc56a6f4fd0>"
+        "<matplotlib.figure.Figure at 0x7f400d0e6518>"
        ]
       }
      ],
@@ -501,9 +501,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc56a3921d0>"
+        "<matplotlib.figure.Figure at 0x7f400cd7b8d0>"
        ]
       }
      ],
@@ -537,9 +537,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc56a3e7390>"
+        "<matplotlib.figure.Figure at 0x7f400cca54e0>"
        ]
       }
      ],
@@ -576,9 +576,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc56a2cbc88>"
+        "<matplotlib.figure.Figure at 0x7f400d0e68d0>"
        ]
       }
      ],
@@ -679,9 +679,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc568016080>"
+        "<matplotlib.figure.Figure at 0x7f4004266e10>"
        ]
       }
      ],
@@ -727,9 +727,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc568016b70>"
+        "<matplotlib.figure.Figure at 0x7f4004269080>"
        ]
       }
      ],
@@ -777,9 +777,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc56013b7f0>"
+        "<matplotlib.figure.Figure at 0x7f40040395f8>"
        ]
       }
      ],
@@ -895,26 +895,26 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "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"
+        "time: 0 \tposition = 1.667 \tvariance = 2.500\n",
+        "time: 1 \tposition = 0.841 \tvariance = 1.667\n",
+        "time: 2 \tposition = 0.697 \tvariance = 1.250\n",
+        "time: 3 \tposition = 0.475 \tvariance = 1.000\n",
+        "time: 4 \tposition = -0.027 \tvariance = 0.833\n",
+        "time: 5 \tposition = -0.241 \tvariance = 0.714\n",
+        "time: 6 \tposition = -0.283 \tvariance = 0.625\n",
+        "time: 7 \tposition = -0.195 \tvariance = 0.556\n",
+        "time: 8 \tposition = -0.219 \tvariance = 0.500\n",
+        "time: 9 \tposition = -0.457 \tvariance = 0.455\n",
+        "time: 10 \tposition = -0.564 \tvariance = 0.417\n",
+        "time: 11 \tposition = -0.589 \tvariance = 0.385\n",
+        "time: 12 \tposition = -0.467 \tvariance = 0.357\n",
+        "time: 13 \tposition = -0.485 \tvariance = 0.333\n",
+        "time: 14 \tposition = -0.439 \tvariance = 0.312\n",
+        "time: 15 \tposition = -0.344 \tvariance = 0.294\n",
+        "time: 16 \tposition = -0.354 \tvariance = 0.278\n",
+        "time: 17 \tposition = -0.366 \tvariance = 0.263\n",
+        "time: 18 \tposition = -0.315 \tvariance = 0.250\n",
+        "time: 19 \tposition = -0.330 \tvariance = 0.238\n"
        ]
       }
      ],
@@ -1034,24 +1034,24 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "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"
+        "PREDICT:     1.0000   502.0000\tUPDATE:     1.2637     9.8047\n",
+        "PREDICT:     2.2637    11.8047\tUPDATE:     3.2532     5.4138\n",
+        "PREDICT:     4.2532     7.4138\tUPDATE:     3.0486     4.2574\n",
+        "PREDICT:     4.0486     6.2574\tUPDATE:     5.5987     3.8490\n",
+        "PREDICT:     6.5987     5.8490\tUPDATE:     7.3142     3.6904\n",
+        "PREDICT:     8.3142     5.6904\tUPDATE:     6.3658     3.6267\n",
+        "PREDICT:     7.3658     5.6267\tUPDATE:     5.6677     3.6007\n",
+        "PREDICT:     6.6677     5.6007\tUPDATE:     5.7674     3.5900\n",
+        "PREDICT:     6.7674     5.5900\tUPDATE:     7.8817     3.5856\n",
+        "PREDICT:     8.8817     5.5856\tUPDATE:     9.8480     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 0x7fc586ba7400>"
+        "<matplotlib.figure.Figure at 0x7f40041f5dd8>"
        ]
       }
      ],
@@ -1186,17 +1186,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc5601688d0>"
+        "<matplotlib.figure.Figure at 0x7f4004056198>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc560133588>"
+        "<matplotlib.figure.Figure at 0x7f400cd3b5f8>"
        ]
       },
       {
@@ -1522,17 +1522,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc560a51ac8>"
+        "<matplotlib.figure.Figure at 0x7f3ffb979550>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc56010e5f8>"
+        "<matplotlib.figure.Figure at 0x7f400407d898>"
        ]
       },
       {
@@ -1648,9 +1648,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc5601a7780>"
+        "<matplotlib.figure.Figure at 0x7f3ffb9730b8>"
        ]
       }
      ],
@@ -1719,17 +1719,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc560168358>"
+        "<matplotlib.figure.Figure at 0x7f4004047128>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc5680c42e8>"
+        "<matplotlib.figure.Figure at 0x7f400429ec50>"
        ]
       }
      ],
@@ -1805,9 +1805,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc5600bc940>"
+        "<matplotlib.figure.Figure at 0x7f4004189780>"
        ]
       }
      ],
@@ -1869,9 +1869,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc558ee9d30>"
+        "<matplotlib.figure.Figure at 0x7f3ffb82a390>"
        ]
       }
      ],
@@ -1932,9 +1932,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc558f14358>"
+        "<matplotlib.figure.Figure at 0x7f3ffb819198>"
        ]
       }
      ],
@@ -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 0x7fc558e757f0>"
+        "<matplotlib.figure.Figure at 0x7f3ffb8a3a58>"
        ]
       }
      ],
@@ -2106,9 +2106,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc558e32400>"
+        "<matplotlib.figure.Figure at 0x7f400414bbe0>"
        ]
       },
       {
@@ -2195,9 +2195,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc56003a3c8>"
+        "<matplotlib.figure.Figure at 0x7f3ffb98f470>"
        ]
       }
      ],
@@ -2279,9 +2279,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAGNCAYAAADpbRVxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc568032470>"
+        "<matplotlib.figure.Figure at 0x7f40041389b0>"
        ]
       }
      ],

05_Kalman_Filters/Kalman_Filters_Animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:be92c78bd7810e38db1917d19689572306a78961e048308a9322a3f068c68208"
+  "signature": "sha256:2864cb6517423288a4854d36160a6a6e45be57d8b3f05f6f01db178492881e70"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -172,9 +172,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffa64af50b8>"
+        "<matplotlib.figure.Figure at 0x7f7af30e5208>"
        ]
       }
      ],
@@ -248,9 +248,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ffa60799358>"
+        "<matplotlib.figure.Figure at 0x7f7aebdb8320>"
        ]
       }
      ],