Reran for 538 plot formatting.

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

14_Smoothing/Smoothing.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:492530ff0ddd97db5e24c23a8bdc358b1994c47f912d2c7ad0fe4b85f11ec641"
+  "signature": "sha256:d490a5cbf987e71d27897004107aa17320363f945ffb43cf718c4bc0c015216c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f37de3cbf98>"
+        "<IPython.core.display.HTML at 0x7f4973273128>"
        ]
       }
      ],
@@ -298,9 +298,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAyYAAAGNCAYAAADkc2U2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f37de3d54e0>"
+        "<matplotlib.figure.Figure at 0x7f49732732b0>"
        ]
       }
      ],
@@ -339,9 +339,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f37c155d208>"
+        "<matplotlib.figure.Figure at 0x7f4956aa4ba8>"
        ]
       }
      ],
@@ -375,9 +375,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f37c14ba5f8>"
+        "<matplotlib.figure.Figure at 0x7f4956a35d68>"
        ]
       }
      ],
@@ -556,13 +556,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f37c1286320>"
+        "<matplotlib.figure.Figure at 0x7f495699bf98>"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -585,13 +585,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f37ba2b7080>"
+        "<matplotlib.figure.Figure at 0x7f49569cee10>"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 6
     },
     {
      "cell_type": "heading",