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>"
        ]
       }
      ],