Removed need for matplotlibs 538 style.

I got the original code from the author, Cameron Davidson-Pilon,
modified it for the minor changes I make for the book, and put it
in 538.json. The main gain is that somebody doesn't need bleeding
edge matplotlib installed to get the 538 look.

01_gh_filter/g-h_filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:eb9d7a1739e4d5ad6d5727662f733d2138a26ece0b70575d410a7013f6d76aa3"
+  "signature": "sha256:85d8ee85b8e35e1acf65336dd0a3251dbd8b99ae7eedc5ea9b1326ea4e46f798"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fa5dc7fa6d8>"
+        "<IPython.core.display.HTML at 0x7f2e031746d8>"
        ]
       }
      ],
@@ -356,7 +356,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAGkCAYAAAD9t4CTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5c0358390>"
+        "<matplotlib.figure.Figure at 0x7f2de6c93e48>"
        ]
       }
      ],
@@ -392,7 +392,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAGkCAYAAAD9t4CTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5c00b4518>"
+        "<matplotlib.figure.Figure at 0x7f2de69ef780>"
        ]
       }
      ],
@@ -428,7 +428,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bfd23d68>"
+        "<matplotlib.figure.Figure at 0x7f2de665be48>"
        ]
       }
      ],
@@ -467,7 +467,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5dc7ffcf8>"
+        "<matplotlib.figure.Figure at 0x7f2e03179320>"
        ]
       }
      ],
@@ -494,7 +494,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bff35dd8>"
+        "<matplotlib.figure.Figure at 0x7f2de68ea240>"
        ]
       }
      ],
@@ -529,7 +529,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bff661d0>"
+        "<matplotlib.figure.Figure at 0x7f2de6662898>"
        ]
       }
      ],
@@ -597,7 +597,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bffb0b00>"
+        "<matplotlib.figure.Figure at 0x7f2de660f3c8>"
        ]
       }
      ],
@@ -626,7 +626,7 @@
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bfcee5f8>"
+        "<matplotlib.figure.Figure at 0x7f2de6638240>"
        ]
       }
      ],
@@ -698,7 +698,7 @@
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        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bfe330b8>"
+        "<matplotlib.figure.Figure at 0x7f2de6737e10>"
        ]
       }
      ],
@@ -763,7 +763,7 @@
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5c003bb70>"
+        "<matplotlib.figure.Figure at 0x7f2de69c0828>"
        ]
       }
      ],
@@ -876,7 +876,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bfdfd4a8>"
+        "<matplotlib.figure.Figure at 0x7f2de69474e0>"
        ]
       }
      ],
@@ -953,9 +953,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5c0011ef0>"
+        "<matplotlib.figure.Figure at 0x7f2de67c6b00>"
        ]
       }
      ],
@@ -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 0x7fa5bfe3fe48>"
+        "<matplotlib.figure.Figure at 0x7f2de6790240>"
        ]
       }
      ],
@@ -1072,9 +1072,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bff70b70>"
+        "<matplotlib.figure.Figure at 0x7f2de6c86e10>"
        ]
       }
      ],
@@ -1148,7 +1148,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bffd1dd8>"
+        "<matplotlib.figure.Figure at 0x7f2de69dcd30>"
        ]
       }
      ],
@@ -1221,25 +1221,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bfcd6780>"
+        "<matplotlib.figure.Figure at 0x7f2de6783710>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bfd9ef60>"
+        "<matplotlib.figure.Figure at 0x7f2de6637eb8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5c0081550>"
+        "<matplotlib.figure.Figure at 0x7f2de663ac88>"
        ]
       }
      ],
@@ -1278,7 +1278,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bff3cef0>"
+        "<matplotlib.figure.Figure at 0x7f2de6982b38>"
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bff3ceb8>"
+        "<matplotlib.figure.Figure at 0x7f2de673af60>"
        ]
       },
       {
@@ -1294,7 +1294,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bfe45e10>"
+        "<matplotlib.figure.Figure at 0x7f2de69825c0>"
        ]
       }
      ],
@@ -1329,7 +1329,7 @@
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5c006aef0>"
+        "<matplotlib.figure.Figure at 0x7f2de69574a8>"
        ]
       },
       {
@@ -1337,7 +1337,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bfe90198>"
+        "<matplotlib.figure.Figure at 0x7f2de4da4eb8>"
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5be485978>"
+        "<matplotlib.figure.Figure at 0x7f2de4dc0048>"
        ]
       },
       {
@@ -1393,7 +1393,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5be46c048>"
+        "<matplotlib.figure.Figure at 0x7f2de4dc0b70>"
        ]
       },
       {
@@ -1401,7 +1401,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa5bfd6fa20>"
+        "<matplotlib.figure.Figure at 0x7f2de66a90b8>"
        ]
       }
      ],