Removed gray background from 538 style.

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

01_gh_filter/g-h_filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:85d8ee85b8e35e1acf65336dd0a3251dbd8b99ae7eedc5ea9b1326ea4e46f798"
+  "signature": "sha256:458869d05b26778173e3f1909b6504175360c0f4cf206780b3e2b6d3294557ed"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f2e031746d8>"
+        "<IPython.core.display.HTML at 0x7fc8e9e846d8>"
        ]
       }
      ],
@@ -354,9 +354,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAGkCAYAAAD9t4CTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAGkCAYAAAD9t4CTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de6c93e48>"
+        "<matplotlib.figure.Figure at 0x7fc8cd9cfa90>"
        ]
       }
      ],
@@ -390,9 +390,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de69ef780>"
+        "<matplotlib.figure.Figure at 0x7fc8cd724358>"
        ]
       }
      ],
@@ -426,9 +426,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de665be48>"
+        "<matplotlib.figure.Figure at 0x7fc8e9e8c5f8>"
        ]
       }
      ],
@@ -465,9 +465,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2e03179320>"
+        "<matplotlib.figure.Figure at 0x7fc8e9e89320>"
        ]
       }
      ],
@@ -492,9 +492,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de68ea240>"
+        "<matplotlib.figure.Figure at 0x7fc8cd5adc18>"
        ]
       }
      ],
@@ -527,9 +527,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de6662898>"
+        "<matplotlib.figure.Figure at 0x7fc8cd5b7978>"
        ]
       }
      ],
@@ -595,9 +595,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de660f3c8>"
+        "<matplotlib.figure.Figure at 0x7fc8cd532828>"
        ]
       }
      ],
@@ -624,9 +624,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de6638240>"
+        "<matplotlib.figure.Figure at 0x7fc8cd34b518>"
        ]
       }
      ],
@@ -696,9 +696,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de6737e10>"
+        "<matplotlib.figure.Figure at 0x7fc8cd4ef048>"
        ]
       }
      ],
@@ -763,7 +763,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de69c0828>"
+        "<matplotlib.figure.Figure at 0x7fc8ce50de10>"
        ]
       }
      ],
@@ -874,9 +874,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de69474e0>"
+        "<matplotlib.figure.Figure at 0x7fc8cd67a6a0>"
        ]
       }
      ],
@@ -953,9 +953,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de67c6b00>"
+        "<matplotlib.figure.Figure at 0x7fc8cd68ae80>"
        ]
       }
      ],
@@ -1009,9 +1009,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de6790240>"
+        "<matplotlib.figure.Figure at 0x7fc8cd461dd8>"
        ]
       }
      ],
@@ -1072,9 +1072,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de6c86e10>"
+        "<matplotlib.figure.Figure at 0x7fc8cd693940>"
        ]
       }
      ],
@@ -1146,9 +1146,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de69dcd30>"
+        "<matplotlib.figure.Figure at 0x7fc8cd718080>"
        ]
       }
      ],
@@ -1221,25 +1221,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de6783710>"
+        "<matplotlib.figure.Figure at 0x7fc8cd366550>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de6637eb8>"
+        "<matplotlib.figure.Figure at 0x7fc8cd6fc710>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de663ac88>"
+        "<matplotlib.figure.Figure at 0x7fc8cd61cf28>"
        ]
       }
      ],
@@ -1276,25 +1276,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de6982b38>"
+        "<matplotlib.figure.Figure at 0x7fc8cd39bb70>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de673af60>"
+        "<matplotlib.figure.Figure at 0x7fc8cd59f630>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de69825c0>"
+        "<matplotlib.figure.Figure at 0x7fc8cd4be908>"
        ]
       }
      ],
@@ -1327,17 +1327,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de69574a8>"
+        "<matplotlib.figure.Figure at 0x7fc8cd39b518>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de4da4eb8>"
+        "<matplotlib.figure.Figure at 0x7fc8cc337908>"
        ]
       }
      ],
@@ -1383,25 +1383,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de4dc0048>"
+        "<matplotlib.figure.Figure at 0x7fc8cc28d470>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de4dc0b70>"
+        "<matplotlib.figure.Figure at 0x7fc8cc2537f0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2de66a90b8>"
+        "<matplotlib.figure.Figure at 0x7fc8cd3f77f0>"
        ]
       }
      ],

02_Discrete_Bayes/discrete_bayes.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:6a36862b345fff6ec9ecb2e3c5f3a2b4c5027e92f54547c6785bf7854d842d2d"
+  "signature": "sha256:ddf0e480bb79058d902cbf898ce123b461e76d9f9ed6f40d87c00a2d973cb721"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f8174d31128>"
+        "<IPython.core.display.HTML at 0x7f20287b0160>"
        ]
       }
      ],
@@ -359,9 +359,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f81588865c0>"
+        "<matplotlib.figure.Figure at 0x7f200c318f28>"
        ]
       }
      ],
@@ -502,9 +502,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f81585b7a90>"
+        "<matplotlib.figure.Figure at 0x7f200bfd3128>"
        ]
       }
      ],
@@ -554,9 +554,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f81583af320>"
+        "<matplotlib.figure.Figure at 0x7f200bf8d080>"
        ]
       }
      ],
@@ -634,9 +634,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f81584fc048>"
+        "<matplotlib.figure.Figure at 0x7f200be5ac50>"
        ]
       }
      ],
@@ -700,9 +700,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8158455588>"
+        "<matplotlib.figure.Figure at 0x7f200bf89550>"
        ]
       }
      ],
@@ -737,9 +737,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8158483780>"
+        "<matplotlib.figure.Figure at 0x7f200befbba8>"
        ]
       }
      ],
@@ -766,9 +766,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f815847dc88>"
+        "<matplotlib.figure.Figure at 0x7f200bf9b390>"
        ]
       }
      ],
@@ -804,9 +804,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f81585013c8>"
+        "<matplotlib.figure.Figure at 0x7f200bfbae80>"
        ]
       }
      ],
@@ -891,9 +891,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8158501898>"
+        "<matplotlib.figure.Figure at 0x7f200bf1bb70>"
        ]
       }
      ],
@@ -927,9 +927,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f81582a7390>"
+        "<matplotlib.figure.Figure at 0x7f200bd6ae10>"
        ]
       }
      ],
@@ -956,9 +956,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f815841f390>"
+        "<matplotlib.figure.Figure at 0x7f200bfa16a0>"
        ]
       }
      ],
@@ -985,9 +985,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f81582cdb38>"
+        "<matplotlib.figure.Figure at 0x7f200c321dd8>"
        ]
       }
      ],
@@ -1052,9 +1052,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8158373fd0>"
+        "<matplotlib.figure.Figure at 0x7f200be067f0>"
        ]
       }
      ],
@@ -1081,9 +1081,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f81584555c0>"
+        "<matplotlib.figure.Figure at 0x7f200bef7fd0>"
        ]
       }
      ],
@@ -1114,9 +1114,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8158472ef0>"
+        "<matplotlib.figure.Figure at 0x7f200bd075f8>"
        ]
       }
      ],
@@ -1168,9 +1168,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8158254ef0>"
+        "<matplotlib.figure.Figure at 0x7f200c30dfd0>"
        ]
       }
      ],

02_Discrete_Bayes/discrete_bayes_animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:11f30a7baee4cbae7a0eecac272e77e8b4d31b1f2536db69aebf87e33b7a7458"
+  "signature": "sha256:f7617d896ab55b01e8388aebbb8383939bbad41d33a202d0a073d2fca46d8ec8"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -252,7 +252,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f7d7f3b0f60>"
+        "<IPython.core.display.HTML at 0x7fcd7be3ff60>"
        ]
       }
      ],
@@ -351,9 +351,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOElSkyw4SVKTLDhJUpMsOElSk/4Po5u82b9IDysAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d5eabb898>"
+        "<matplotlib.figure.Figure at 0x7fcd7be57518>"
        ]
       }
      ],
@@ -394,7 +394,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fcd5b4e56d8>"
+       ]
+      }
+     ],
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",

04_Gaussians/Gaussians.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:f315c4edb8acd4ca7bed4bf2ad1a08de3002a20feab253ac00f882073b882911"
+  "signature": "sha256:402152422ec6a095e6eb2472fe34e23b3492cab93a8573e7060446a06e2e7a07"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f7f2a69e128>"
+        "<IPython.core.display.HTML at 0x7fbf8631b128>"
        ]
       }
      ],
@@ -300,9 +300,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7f117465f8>"
+        "<matplotlib.figure.Figure at 0x7fbf65c837b8>"
        ]
       }
      ],
@@ -346,9 +346,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7f09ead5c0>"
+        "<matplotlib.figure.Figure at 0x7fbf65b2a400>"
        ]
       }
      ],
@@ -423,9 +423,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7f06501748>"
+        "<matplotlib.figure.Figure at 0x7fbf65a83748>"
        ]
       }
      ],
@@ -548,9 +548,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7f06403668>"
+        "<matplotlib.figure.Figure at 0x7fbf62071978>"
        ]
       }
      ],
@@ -590,9 +590,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7f09e03e80>"
+        "<matplotlib.figure.Figure at 0x7fbf65a8b780>"
        ]
       }
      ],
@@ -646,9 +646,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7f09e32f98>"
+        "<matplotlib.figure.Figure at 0x7fbf65a7beb8>"
        ]
       },
       {
@@ -782,10 +782,9 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[  2.07782254e+00   2.92284291e+00   3.41939607e+00  -2.74017280e+00\n",
-        "  -9.35326550e-01   2.12233068e+00  -2.40918322e-01  -1.43619992e+00\n",
-        "   3.98720795e+00   6.65972692e-02   4.31909653e+00  -3.55998844e+00\n",
-        "   1.53055228e+00   2.65472646e+00   1.13045111e-03]\n"
+        "[ 3.39543572  3.92455822 -1.47839831 -1.33968944  4.64280739  3.57337785\n",
+        "  2.4334426  -2.65214049  0.09098526  0.59452311  3.72292568  4.64627724\n",
+        "  2.35639579  2.19228319  1.02386714]\n"
        ]
       }
      ],

04_Gaussians/Gaussians_Animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a64f35e7df2ba17ebd70c1debdf2b30922efaea2cea89784b53ec3b424b2eadc"
+  "signature": "sha256:c9bc1ab3d4c4d98f1a764e1c04e56097cb0fd31b74ccc69ddffcc67d4d96ef4d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -247,7 +247,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fba31a08f98>"
+        "<IPython.core.display.HTML at 0x7f1f0bb83f60>"
        ]
       }
      ],
@@ -294,9 +294,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAcEAAAGrCAYAAAC41CzUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fba1557def0>"
+        "<matplotlib.figure.Figure at 0x7f1ee8230438>"
        ]
       }
      ],

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": 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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
+       "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",
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+       "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",
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truncated
+       "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",
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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",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc56a2cbc88>"
+        "<matplotlib.figure.Figure at 0x7f400d0e68d0>"
        ]
       }
      ],
@@ -679,9 +679,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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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",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc568016b70>"
+        "<matplotlib.figure.Figure at 0x7f4004269080>"
        ]
       }
      ],
@@ -777,9 +777,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "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
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc586ba7400>"
+        "<matplotlib.figure.Figure at 0x7f40041f5dd8>"
        ]
       }
      ],
@@ -1186,17 +1186,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc5601688d0>"
+        "<matplotlib.figure.Figure at 0x7f4004056198>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc560133588>"
+        "<matplotlib.figure.Figure at 0x7f400cd3b5f8>"
        ]
       },
       {
@@ -1522,17 +1522,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc560a51ac8>"
+        "<matplotlib.figure.Figure at 0x7f3ffb979550>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc56010e5f8>"
+        "<matplotlib.figure.Figure at 0x7f400407d898>"
        ]
       },
       {
@@ -1648,9 +1648,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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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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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc560168358>"
+        "<matplotlib.figure.Figure at 0x7f4004047128>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc5680c42e8>"
+        "<matplotlib.figure.Figure at 0x7f400429ec50>"
        ]
       }
      ],
@@ -1805,9 +1805,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "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",
-       "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
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc558f14358>"
+        "<matplotlib.figure.Figure at 0x7f3ffb819198>"
        ]
       }
      ],
@@ -1984,9 +1984,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "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
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc558e32400>"
+        "<matplotlib.figure.Figure at 0x7f400414bbe0>"
        ]
       },
       {
@@ -2195,9 +2195,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "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": 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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
        "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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+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7ffa60799358>"
+        "<matplotlib.figure.Figure at 0x7f7aebdb8320>"
        ]
       }
      ],

06_Multivariate_Kalman_filter/Multivariate_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:6963206ba9864c263a74cd00e4aab88e4003372150b23f85176ac2f51a3f6698"
+  "signature": "sha256:67cbcc3ff20f4f9dc812d8eeb49af53f8f5d9f0e0570d2a8bbdc7abddb40b6d8"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f1a46e5e128>"
+        "<IPython.core.display.HTML at 0x7f84cdb64128>"
        ]
       }
      ],
@@ -393,9 +393,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a46e4cf60>"
+        "<matplotlib.figure.Figure at 0x7f84cdb54f60>"
        ]
       }
      ],
@@ -561,9 +561,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a46e62320>"
+        "<matplotlib.figure.Figure at 0x7f84cdb69320>"
        ]
       }
      ],
@@ -595,9 +595,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22cdfa20>"
+        "<matplotlib.figure.Figure at 0x7f84cdb69cc0>"
        ]
       }
      ],
@@ -653,9 +653,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22d6f048>"
+        "<matplotlib.figure.Figure at 0x7f84a9a580f0>"
        ]
       }
      ],
@@ -756,9 +756,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22799048>"
+        "<matplotlib.figure.Figure at 0x7f84a973e518>"
        ]
       }
      ],
@@ -783,9 +783,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22947f60>"
+        "<matplotlib.figure.Figure at 0x7f84a98d7fd0>"
        ]
       }
      ],
@@ -810,9 +810,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a229885f8>"
+        "<matplotlib.figure.Figure at 0x7f84a9682b00>"
        ]
       }
      ],
@@ -841,9 +841,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22ae3ac8>"
+        "<matplotlib.figure.Figure at 0x7f84a94d0908>"
        ]
       }
      ],
@@ -906,9 +906,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22c34940>"
+        "<matplotlib.figure.Figure at 0x7f84a99296a0>"
        ]
       }
      ],
@@ -2054,17 +2054,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a229577b8>"
+        "<matplotlib.figure.Figure at 0x7f84a962ee80>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22d68a58>"
+        "<matplotlib.figure.Figure at 0x7f84a94a9898>"
        ]
       }
      ],
@@ -2231,17 +2231,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22990c50>"
+        "<matplotlib.figure.Figure at 0x7f84a9482630>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a227e2080>"
+        "<matplotlib.figure.Figure at 0x7f84a99a2400>"
        ]
       }
      ],
@@ -2475,9 +2475,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22af4c88>"
+        "<matplotlib.figure.Figure at 0x7f84a99ff358>"
        ]
       }
      ],
@@ -2545,17 +2545,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22adfd30>"
+        "<matplotlib.figure.Figure at 0x7f84a962e2e8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22b01fd0>"
+        "<matplotlib.figure.Figure at 0x7f84a97ddf28>"
        ]
       }
      ],
@@ -2596,9 +2596,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22caddd8>"
+        "<matplotlib.figure.Figure at 0x7f84a9486940>"
        ]
       }
      ],
@@ -2625,9 +2625,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a227d6438>"
+        "<matplotlib.figure.Figure at 0x7f84a9666b70>"
        ]
       }
      ],
@@ -2654,9 +2654,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22d36518>"
+        "<matplotlib.figure.Figure at 0x7f84a94a8e48>"
        ]
       }
      ],
@@ -2723,9 +2723,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a229cf908>"
+        "<matplotlib.figure.Figure at 0x7f84a96f3908>"
        ]
       }
      ],
@@ -2753,9 +2753,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22a8f128>"
+        "<matplotlib.figure.Figure at 0x7f84a9777748>"
        ]
       }
      ],
@@ -2825,9 +2825,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22b768d0>"
+        "<matplotlib.figure.Figure at 0x7f84a98473c8>"
        ]
       }
      ],
@@ -2855,9 +2855,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22bad6d8>"
+        "<matplotlib.figure.Figure at 0x7f84a96cb208>"
        ]
       }
      ],
@@ -2888,9 +2888,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a227ce588>"
+        "<matplotlib.figure.Figure at 0x7f84a9720f98>"
        ]
       }
      ],
@@ -2919,9 +2919,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22c34a58>"
+        "<matplotlib.figure.Figure at 0x7f84a96820b8>"
        ]
       }
      ],
@@ -2992,17 +2992,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22acc208>"
+        "<matplotlib.figure.Figure at 0x7f84a9770390>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a2296b278>"
+        "<matplotlib.figure.Figure at 0x7f84a980a748>"
        ]
       }
      ],
@@ -3075,9 +3075,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a229beba8>"
+        "<matplotlib.figure.Figure at 0x7f84a981dda0>"
        ]
       }
      ],
@@ -3149,9 +3149,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22a9da90>"
+        "<matplotlib.figure.Figure at 0x7f84a97e86a0>"
        ]
       }
      ],
@@ -3201,9 +3201,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22a9d710>"
+        "<matplotlib.figure.Figure at 0x7f84a958ef98>"
        ]
       }
      ],
@@ -3245,9 +3245,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a227d75f8>"
+        "<matplotlib.figure.Figure at 0x7f84a94898d0>"
        ]
       }
      ],
@@ -3279,9 +3279,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a228aef28>"
+        "<matplotlib.figure.Figure at 0x7f84a98b45c0>"
        ]
       }
      ],

06_Multivariate_Kalman_filter/animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:8eb49d934e443793dcd73543ad252888cd7577420d8e703ec46142d3362c7099"
+  "signature": "sha256:df0ee24d44b70c6bc4ea7fc3b5ad7e572ad8bc3f6f2176128a5165d9cdb182b5"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -247,7 +247,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7ff1356b1f60>"
+        "<IPython.core.display.HTML at 0x7f5bb0483f60>"
        ]
       }
      ],
@@ -372,15 +372,15 @@
        "metadata": {},
        "output_type": "display_data",
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff11d2473c8>"
+        "<matplotlib.figure.Figure at 0x7f5b93fff5f8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff11d2474e0>"
+        "<matplotlib.figure.Figure at 0x7f5b93fff6d8>"
        ]
       }
      ],
@@ -463,9 +463,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff1113c7080>"
+        "<matplotlib.figure.Figure at 0x7f5b8c272208>"
        ]
       }
      ],

07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:134ea54474b96f29362399ed17bc3f4d1772dfef4d9762d9ebebd642331d972d"
+  "signature": "sha256:f6eb3441648c1a743a0155aaa4a53c9703d22947e978a0eb7a6bce0eadc6a698"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f98fbb38128>"
+        "<IPython.core.display.HTML at 0x7f44082d1128>"
        ]
       }
      ],
@@ -737,9 +737,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f98dc7bdb00>"
+        "<matplotlib.figure.Figure at 0x7f43e8fb7e48>"
        ]
       }
      ],

08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:402b3ada55be4953c3dcb978c1f15af1b8d8cb4b33be31ce0955749f1bdff3ce"
+  "signature": "sha256:94fc7a6878c4e0d59e4a1f38278285a034bc499fafe124ac9caa33b209cf1ce5"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f01f6738128>"
+        "<IPython.core.display.HTML at 0x7f8959eac128>"
        ]
       }
      ],
@@ -354,9 +354,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f01de29a278>"
+        "<matplotlib.figure.Figure at 0x7f89419fa5f8>"
        ]
       }
      ],
@@ -707,9 +707,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f01de29a2e8>"
+        "<matplotlib.figure.Figure at 0x7f89419fa668>"
        ]
       }
      ],
@@ -788,9 +788,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f01d5d19438>"
+        "<matplotlib.figure.Figure at 0x7f8941662358>"
        ]
       },
       {
@@ -873,9 +873,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f01ddd604a8>"
+        "<matplotlib.figure.Figure at 0x7f893949a7f0>"
        ]
       }
      ],
@@ -1006,9 +1006,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f01d5ccada0>"
+        "<matplotlib.figure.Figure at 0x7f89393fcd68>"
        ]
       }
      ],
@@ -1125,9 +1125,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f01d5cc3b38>"
+        "<matplotlib.figure.Figure at 0x7f89393f7f60>"
        ]
       }
      ],
@@ -1279,17 +1279,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f01ddfbf780>"
+        "<matplotlib.figure.Figure at 0x7f89416816a0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f01ddd60240>"
+        "<matplotlib.figure.Figure at 0x7f89414b2908>"
        ]
       }
      ],
@@ -1472,9 +1472,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f01d2254e10>"
+        "<matplotlib.figure.Figure at 0x7f89341bbfd0>"
        ]
       }
      ],
@@ -1668,9 +1668,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f01d1fe3e80>"
+        "<matplotlib.figure.Figure at 0x7f892ff4ed30>"
        ]
       }
      ],
@@ -1809,9 +1809,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f01de262f28>"
+        "<matplotlib.figure.Figure at 0x7f8941432c18>"
        ]
       }
      ],
@@ -1947,9 +1947,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f01d2245f28>"
+        "<matplotlib.figure.Figure at 0x7f89419fab00>"
        ]
       }
      ],
@@ -2015,17 +2015,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f01d2254cc0>"
+        "<matplotlib.figure.Figure at 0x7f8941432630>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f01d5ca0ef0>"
+        "<matplotlib.figure.Figure at 0x7f894167f898>"
        ]
       }
      ],
@@ -2053,17 +2053,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f01d5c64278>"
+        "<matplotlib.figure.Figure at 0x7f8941681e80>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f01ddcb52e8>"
+        "<matplotlib.figure.Figure at 0x7f892ff8d320>"
        ]
       }
      ],

09_Extended_Kalman_Filters/Extended_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:6abee3512debdaedc327c17465301250dd3151d001f8cfc9c2668cfa689b79ef"
+  "signature": "sha256:d69b1ac3d24dfc4df1491bae0c4a10492ca95005dd877635c72cc4d707ff96f5"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f6156366128>"
+        "<IPython.core.display.HTML at 0x7f6bf4f16128>"
        ]
       }
      ],
@@ -354,9 +354,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6139e9d240>"
+        "<matplotlib.figure.Figure at 0x7f6bd8a87400>"
        ]
       }
      ],
@@ -386,9 +386,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6139e90f28>"
+        "<matplotlib.figure.Figure at 0x7f6bd8aeceb8>"
        ]
       }
      ],
@@ -420,9 +420,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f61396da358>"
+        "<matplotlib.figure.Figure at 0x7f6bd83223c8>"
        ]
       }
      ],
@@ -456,9 +456,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f61563666d8>"
+        "<matplotlib.figure.Figure at 0x7f6bf4f05dd8>"
        ]
       }
      ],
@@ -485,8 +485,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "input  mean, variance: -0.0016, 0.9987\n",
-        "output mean, variance: -0.0250, 2.2411\n"
+        "input  mean, variance: 0.0003, 0.9969\n",
+        "output mean, variance: -0.0283, 2.2320\n"
        ]
       }
      ],
@@ -515,16 +515,16 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f615636af28>"
+        "<matplotlib.figure.Figure at 0x7f6bd8250128>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "output mean, variance: 0.0024, 2.2470\n"
+        "output mean, variance: -0.0005, 2.2430\n"
        ]
       }
      ],
@@ -559,8 +559,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "linear    output mean, variance: 0.1369, 7471.9216\n",
-        "nonlinear output mean, variance: -1.4870, 26209.4562\n"
+        "linear    output mean, variance: -0.0296, 7458.6412\n",
+        "nonlinear output mean, variance: -1.9540, 26056.8570\n"
        ]
       }
      ],
@@ -607,9 +607,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f61395a9f60>"
+        "<matplotlib.figure.Figure at 0x7f6bd82813c8>"
        ]
       }
      ],
@@ -642,9 +642,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f61395c8dd8>"
+        "<matplotlib.figure.Figure at 0x7f6bd8254588>"
        ]
       }
      ],
@@ -683,9 +683,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f61396ed9b0>"
+        "<matplotlib.figure.Figure at 0x7f6bd827fd68>"
        ]
       }
      ],
@@ -754,9 +754,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6139592a20>"
+        "<matplotlib.figure.Figure at 0x7f6bd83bd2e8>"
        ]
       }
      ],
@@ -1116,25 +1116,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f61354f0128>"
+        "<matplotlib.figure.Figure at 0x7f6bd409a7f0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f613873c668>"
+        "<matplotlib.figure.Figure at 0x7f6bd41698d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6138607b00>"
+        "<matplotlib.figure.Figure at 0x7f6bd7a55d68>"
        ]
       }
      ],
@@ -1297,9 +1297,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f6139572828>"
+        "<matplotlib.figure.Figure at 0x7f6bd8316d68>"
        ]
       }
      ],

10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:b10e3461aef2e9945cab08b3c6d4ed07cf3bf5cd256f4859d14ae941048e9e0d"
+  "signature": "sha256:d27738d4ebd41dd3efed105a08061fdfd276fd46d733ea481d2547b3fae667b0"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fa649670128>"
+        "<IPython.core.display.HTML at 0x7f59ed6af128>"
        ]
       }
      ],
@@ -309,9 +309,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62d1e2e48>"
+        "<matplotlib.figure.Figure at 0x7f59d1249208>"
        ]
       }
      ],
@@ -347,9 +347,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62d1d0da0>"
+        "<matplotlib.figure.Figure at 0x7f59d1223128>"
        ]
       }
      ],
@@ -394,9 +394,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa6496756a0>"
+        "<matplotlib.figure.Figure at 0x7f59d121cc88>"
        ]
       }
      ],
@@ -494,16 +494,16 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62cd9d4e0>"
+        "<matplotlib.figure.Figure at 0x7f59cc9ef940>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Difference in mean x=0.001, y=43.287\n"
+        "Difference in mean x=0.089, y=43.503\n"
        ]
       }
      ],
@@ -540,9 +540,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62cd08358>"
+        "<matplotlib.figure.Figure at 0x7f59cc779780>"
        ]
       }
      ],
@@ -580,9 +580,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62cc6cda0>"
+        "<matplotlib.figure.Figure at 0x7f59cc997828>"
        ]
       }
      ],
@@ -635,9 +635,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62cc61860>"
+        "<matplotlib.figure.Figure at 0x7f59cc918240>"
        ]
       }
      ],
@@ -978,9 +978,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62cdcf908>"
+        "<matplotlib.figure.Figure at 0x7f59cc9bc240>"
        ]
       }
      ],
@@ -1010,9 +1010,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62cc610f0>"
+        "<matplotlib.figure.Figure at 0x7f59cc987be0>"
        ]
       }
      ],
@@ -1057,9 +1057,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62ce960f0>"
+        "<matplotlib.figure.Figure at 0x7f59d0c870f0>"
        ]
       }
      ],
@@ -1180,9 +1180,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62887edd8>"
+        "<matplotlib.figure.Figure at 0x7f59cc93f588>"
        ]
       }
      ],
@@ -1212,9 +1212,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62ccaecc0>"
+        "<matplotlib.figure.Figure at 0x7f59d0c577b8>"
        ]
       }
      ],
@@ -1300,9 +1300,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62cbf9e48>"
+        "<matplotlib.figure.Figure at 0x7f59d0cd53c8>"
        ]
       }
      ],
@@ -1345,9 +1345,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62ce810b8>"
+        "<matplotlib.figure.Figure at 0x7f59cc8b94a8>"
        ]
       }
      ],
@@ -1403,9 +1403,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62ce81f98>"
+        "<matplotlib.figure.Figure at 0x7f59cc890a20>"
        ]
       }
      ],
@@ -1502,9 +1502,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62cc61860>"
+        "<matplotlib.figure.Figure at 0x7f59cc8abac8>"
        ]
       }
      ],
@@ -1561,9 +1561,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62cc6c6a0>"
+        "<matplotlib.figure.Figure at 0x7f59d0c2df60>"
        ]
       }
      ],
@@ -1661,9 +1661,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa6287c5be0>"
+        "<matplotlib.figure.Figure at 0x7f59cc919dd8>"
        ]
       }
      ],
@@ -1696,9 +1696,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa6287d1908>"
+        "<matplotlib.figure.Figure at 0x7f59cc9ef668>"
        ]
       }
      ],
@@ -2403,9 +2403,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62ce78b70>"
+        "<matplotlib.figure.Figure at 0x7f59cc89dbe0>"
        ]
       }
      ],
@@ -2521,9 +2521,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62ccd37b8>"
+        "<matplotlib.figure.Figure at 0x7f59d0cbdf28>"
        ]
       }
      ],
@@ -2619,9 +2619,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa62cdbda90>"
+        "<matplotlib.figure.Figure at 0x7f59d0ca8240>"
        ]
       }
      ],
@@ -2677,9 +2677,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa6286e63c8>"
+        "<matplotlib.figure.Figure at 0x7f59cc720630>"
        ]
       }
      ],

11_Ensemble_Kalman_Filter/Ensemble_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:0d7649d8c899af47bfd8eb588286dcfbc6c6ecdfe43eb8952badfd052abd4b2c"
+  "signature": "sha256:ba71339d843e48eaf0b87bcbb0951c5d65908f2ad5a0268b9f700c0d487dfecf"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fabe005f128>"
+        "<IPython.core.display.HTML at 0x7f92d3e98128>"
        ]
       }
      ],
@@ -312,9 +312,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fabc3ba8e48>"
+        "<matplotlib.figure.Figure at 0x7f92b6f5a5f8>"
        ]
       }
      ],
@@ -610,9 +610,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fabbf800400>"
+        "<matplotlib.figure.Figure at 0x7f92b365a908>"
        ]
       }
      ],

12_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:2a0bbfa4cad443230799503aa8753ecf0321463983d5923db288ae86321f739e"
+  "signature": "sha256:791761b5754db825adff7c53fa4f7ad4db30eca96fd08a08c78aa79f84242d39"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f190149d160>"
+        "<IPython.core.display.HTML at 0x7fbcef879128>"
        ]
       }
      ],
@@ -404,9 +404,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f18e5012438>"
+        "<matplotlib.figure.Figure at 0x7fbcd33ea7b8>"
        ]
       }
      ],
@@ -825,9 +825,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f18e3d18080>"
+        "<matplotlib.figure.Figure at 0x7fbcd2103320>"
        ]
       }
      ],

13_HInfinity_Filters/HInfinity_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:cf83c4f12a24f5198d1dfcb28bc9d51c92ce9cb54569170bbd9bb0747a26493e"
+  "signature": "sha256:95a94456205f2facb47cd98f348f7a1b8b8ef4380e7764a6a75ae5445091db01"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -260,7 +260,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f8ab7baaf60>"
+        "<IPython.core.display.HTML at 0x7fdddc15bf60>"
        ]
       }
      ],
@@ -334,9 +334,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8aa4074a58>"
+        "<matplotlib.figure.Figure at 0x7fddbfce7e48>"
        ]
       }
      ],

14_Smoothing/Smoothing.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:d490a5cbf987e71d27897004107aa17320363f945ffb43cf718c4bc0c015216c"
+  "signature": "sha256:4d079ba596720ac4462749b002a8e25677c1666a2db55f444e3770f2e640e242"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f4973273128>"
+        "<IPython.core.display.HTML at 0x7ff9bcc59128>"
        ]
       }
      ],
@@ -298,9 +298,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f49732732b0>"
+        "<matplotlib.figure.Figure at 0x7ff9bcc592b0>"
        ]
       }
      ],
@@ -339,9 +339,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4956aa4ba8>"
+        "<matplotlib.figure.Figure at 0x7ff9a04a4e48>"
        ]
       }
      ],
@@ -375,9 +375,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4956a35d68>"
+        "<matplotlib.figure.Figure at 0x7ff9a0437e80>"
        ]
       }
      ],
@@ -556,9 +556,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f495699bf98>"
+        "<matplotlib.figure.Figure at 0x7ff9a03ad358>"
        ]
       }
      ],
@@ -585,9 +585,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f49569cee10>"
+        "<matplotlib.figure.Figure at 0x7ff9a03d6550>"
        ]
       }
      ],

code/538.json

@@ -10,7 +10,7 @@
     "#6d904f",
     "#8b8b8b"
   ],
-  "axes.facecolor": "#f0f0f0",
+  "axes.facecolor": "#ffffff",
   "axes.labelsize": "large",
   "axes.axisbelow": true,
   "axes.grid": true,
@@ -18,7 +18,7 @@
   "axes.titlesize": "x-large",
   "svg.embed_char_paths": "path",
   "examples.directory": "",
-  "figure.facecolor": "#f0f0f0",
+  "figure.facecolor": "#ffffff",
   "grid.linestyle": "-",
   "grid.linewidth": 1.0,
   "grid.color": "#cbcbcb",