Reran for 538 plot formatting.

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

02_Discrete_Bayes/discrete_bayes.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:e81fff935ccbcaa4a91a285963630db8aa78e1a28f862ed30e1fd33af76d4504"
+  "signature": "sha256:6a36862b345fff6ec9ecb2e3c5f3a2b4c5027e92f54547c6785bf7854d842d2d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fd93d8320f0>"
+        "<IPython.core.display.HTML at 0x7f8174d31128>"
        ]
       }
      ],
@@ -359,9 +359,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGNCAYAAACMgOcOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d8601d0>"
+        "<matplotlib.figure.Figure at 0x7f81588865c0>"
        ]
       }
      ],
@@ -502,9 +502,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d4fdba8>"
+        "<matplotlib.figure.Figure at 0x7f81585b7a90>"
        ]
       }
      ],
@@ -554,9 +554,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d4b70f0>"
+        "<matplotlib.figure.Figure at 0x7f81583af320>"
        ]
       }
      ],
@@ -634,9 +634,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d47ad30>"
+        "<matplotlib.figure.Figure at 0x7f81584fc048>"
        ]
       }
      ],
@@ -700,9 +700,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d405160>"
+        "<matplotlib.figure.Figure at 0x7f8158455588>"
        ]
       }
      ],
@@ -737,9 +737,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d431d68>"
+        "<matplotlib.figure.Figure at 0x7f8158483780>"
        ]
       }
      ],
@@ -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 0x7fd93d419cc0>"
+        "<matplotlib.figure.Figure at 0x7f815847dc88>"
        ]
       }
      ],
@@ -804,9 +804,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d4be198>"
+        "<matplotlib.figure.Figure at 0x7f81585013c8>"
        ]
       }
      ],
@@ -891,9 +891,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d4be898>"
+        "<matplotlib.figure.Figure at 0x7f8158501898>"
        ]
       }
      ],
@@ -927,9 +927,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d254048>"
+        "<matplotlib.figure.Figure at 0x7f81582a7390>"
        ]
       }
      ],
@@ -956,9 +956,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d2c1240>"
+        "<matplotlib.figure.Figure at 0x7f815841f390>"
        ]
       }
      ],
@@ -985,9 +985,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d30e160>"
+        "<matplotlib.figure.Figure at 0x7f81582cdb38>"
        ]
       }
      ],
@@ -1052,9 +1052,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d5a0710>"
+        "<matplotlib.figure.Figure at 0x7f8158373fd0>"
        ]
       }
      ],
@@ -1081,9 +1081,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d405cf8>"
+        "<matplotlib.figure.Figure at 0x7f81584555c0>"
        ]
       }
      ],
@@ -1114,9 +1114,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd93d4cbb70>"
+        "<matplotlib.figure.Figure at 0x7f8158472ef0>"
        ]
       }
      ],
@@ -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 0x7fd93d3f3eb8>"
+        "<matplotlib.figure.Figure at 0x7f8158254ef0>"
        ]
       }
      ],

02_Discrete_Bayes/discrete_bayes_animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:47d64bacc2f597ef2e39688a14b58dc93224617c01ee343a7bb71c0cc97e6efa"
+  "signature": "sha256:11f30a7baee4cbae7a0eecac272e77e8b4d31b1f2536db69aebf87e33b7a7458"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -34,6 +34,8 @@
        "html": [
         "<style>\n",
         "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
+        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
+        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
         "\n",
         "    div.cell{\n",
         "        width: 850px;\n",
@@ -120,9 +122,9 @@
         "        white-space: nowrap;\n",
         "    }\n",
         "    div.text_cell_render{\n",
-        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
+        "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
         "        line-height: 135%;\n",
-        "        font-size: 110%;\n",
+        "        font-size: 125%;\n",
         "        width:750px;\n",
         "        margin-left:auto;\n",
         "        margin-right:auto;\n",
@@ -132,12 +134,12 @@
         "    div.output_subarea.output_text.output_pyout {\n",
         "        overflow-x: auto;\n",
         "        overflow-y: scroll;\n",
-        "        max-height: 300px;\n",
+        "        max-height: 50000px;\n",
         "    }\n",
         "    div.output_subarea.output_stream.output_stdout.output_text {\n",
         "        overflow-x: auto;\n",
         "        overflow-y: scroll;\n",
-        "        max-height: 300px;\n",
+        "        max-height: 50000px;\n",
         "    }\n",
         "    code{\n",
         "      font-size: 70%;\n",
@@ -250,7 +252,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f77b8e02fd0>"
+        "<IPython.core.display.HTML at 0x7f7d7f3b0f60>"
        ]
       }
      ],
@@ -349,9 +351,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f77b8e02b38>"
+        "<matplotlib.figure.Figure at 0x7f7d5eabb898>"
        ]
       }
      ],
@@ -387,22 +389,12 @@
       "\n",
       "fig = plt.figure(figsize=(6.5, 2.5))\n",
       "anim = animation.FuncAnimation(fig, bar_animate,\n",
-      "                               frames=60, interval=200)\n",
+      "                               frames=60, interval=75)\n",
       "anim.save('simulate.gif', writer='imagemagick')"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f779abe8c18>"
-       ]
-      }
-     ],
-     "prompt_number": 5
+     "outputs": []
     },
     {
      "cell_type": "markdown",