Switched to ggplot style plotting.

01_gh_filter/g-h_filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:59d697bb03f25b3d85c02ebd74747fc150ea9b244db398c0be39d839a761ca74"
+  "signature": "sha256:9a9b36cd7ddaddea122ea8a28f164ea9dd83caabcf16ae22f4a6e58f7a9d8c82"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fc76a0444e0>"
+        "<IPython.core.display.HTML at 0x7f8a3abce6d8>"
        ]
       }
      ],
@@ -354,9 +354,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGGCAYAAABIVFZlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3WlgVNXBxvH/uZkkQMgQgyBECggIhrihssiiBhWKIApq\n0IpU61Kpy6vSWpVaXIoiSomW4q4VpQoiIm4UpSgiKCJgTYgLKGsMSFgSEpKQuef9MJhKIRogM3du\n8vw+JXcmc5/xmPDk5txzjLXWIiIiIiIiEeF4HUBEREREpC5T4RYRERERiSAVbhERERGRCFLhFhER\nERGJIBVuEREREZEIUuEWEREREYmgQDROMnnyZJYvX04wGGTChAkAZGdnk5+fD0BJSQlJSUmMHz++\n6mu2bNnCzTffTFZWFueee240YoqIiIiI1LqoXOHOzMzkjjvu2OvYTTfdxPjx4xk/fjzdu3ene/fu\nez3+3HPPcdJJJx3QeXJzcw85q3hDY+dvGj//0tj5m8bP3zR+/nWgYxeVwp2enk5SUtJ+H7PWsnjx\nYnr16lV1bMmSJRxxxBG0atXqgM6j/3H9S2Pnbxo//9LY+ZvGz980fv4Vk4X7p+Tl5ZGSkkKLFi0A\nKCsrY/bs2Vx00UUeJxMREREROXSeF+4PP/xwr6vb06dPZ+DAgSQmJqJd50VERETE74yNUqvdvHkz\nDzzwQNVNkwChUIhrr72WBx54gNTUVADGjBnDli1bACgtLcUYw7Bhw+jfv/8+r5mbm7vXJf2srKwI\nvwsRERERkfBF4h9kZGSQkZFR7XOjskpJdT7//HNatWpVVbYB7r777qqPX375ZRo2bLjfsg37f3M/\nrHwi/pKcnExxcbHXMeQgafz8S2Pnbxo/f9P4+VdaWtoBXeiNSuHOzs4mLy+P4uJiRo4cSVZWFpmZ\nmSxatGiv6SQiIiIiInVN1KaURIuucPuTfsv3N42ff2ns/E3j528aP/9KS0s7oOd7ftOkiIiIiEhd\npsItIiIiIhJBKtwiIiIiIhGkwi0iIiIiEkEq3CIiIiIiEaTCLSIiIiISQSrcIiIiIiIRpMItIiIi\nIhJBKtwiIiIiIhGkwi0iIiIiEkEq3CIiIiIiEaTCLSIiIiISQSrcIiIiIiIRpMItIiIiIhJBKtwi\nIiIiIhGkwi0iIiIiEkEq3CIiIiIiEaTCLSIiIiISQSrcIiIiIiIRpMItIiIiIhJBKtwiIiIiIhGk\nwi0iIiIiEkEq3CIiIiIiEaTCLSIiIiK1ylrrdYSYosItIiIiIrXChkK4H76Le+fvsIXfex0nZgS8\nDiAiIiIi/mathWWLcGdNhYIN4WML/oUZMtzjZLFBhVtEREREDoq1FnKX4856AdauCh9s1gIz+FeY\nbn28DRdDVLhFRERE5IDZVStxX30evsoNH2iSihk0DNP7LEwg3ttwMUaFW0RERERqzK7/Nly0P18a\nPpCUjBlwAeaMgZjERG/DxSgVbhERERH5WXZTPva1qdhPPggfSGyAOfs8zNnnYxoleRsuxqlwi4iI\niEi17NbvsW9Mw374LrguBOIxZ5wTvqodTPE6ni+ocIuIiIjIPmzxDuxbM7DvvQWVu8FxMH36hedp\npzbzOp6vqHCLiIiISBW7qxQ7dxb2ndegfBcApmsfzOBLMC1aeZzOn6JWuCdPnszy5csJBoNMmDAB\ngOzsbPLz8wEoKSkhKSmJ8ePH85///Id//vOfVFZWEggEGD58OMcee2y0ooqIiIjUO7aiHDv/Lezb\nM6CkOHzwuFNwzr8U07q9t+F8LmqFOzMzkwEDBjBp0qSqYzfddFPVx1OmTCEpKTzhPhgMctttt5GS\nksL69esZO3Ysjz32WLSiioiIiNQbtrIS++G72Ddegu1bwweP7owzZATm6M7ehqsjola409PT2bx5\n834fs9ayePFixowZA0Dbtm2rHmvVqhUVFRVVV7tFRERE5NBZ18V+8gH2tanwfUH4YOt2OEMug4yT\nMMZ4G7AOiYkGm5eXR0pKCi1atNjnsY8//ph27dqpbIuIiIjUAmstfLYkvDvkxrXhgy2OxDnvUjip\nJ8ZxvA1YB8VEi/3www/p1avXPsfXr1/P1KlTufPOOz1IJSIiIlK32C/+E9605psvwwdSD8ecewnm\n1L6YuDhvw9VhnhfuUCjEkiVLeOCBB/Y6XlhYyEMPPcQNN9xA8+bN9/u1ubm55ObmVn2elZVFcnJy\nRPNKZCQkJGjsfEzj518aO3/T+PlbNMevcvUXlL30NKE9u0OaYAoNzr+UhLMHY+ITopKhrpk+fXrV\nxxkZGWRkZFT7XM8L9+eff06rVq1ITU2tOlZSUsK4ceO49NJL6dixY7Vfu783V1xcHLGsEjnJycka\nOx/T+PmXxs7fNH7+Fo3xs/nrwlNHln8UPtCwEab/UMyZ51LRoCEVZeVQVh7RDHVRcnIyWVlZNX5+\n1Ap3dnY2eXl5FBcXM3LkSLKyssjMzGTRokX7TCeZM2cOBQUFzJgxgxkzZgDwpz/9iWAwGK24IiIi\nIr5lvy/Avv4i9qP3wbqQkIDpey7ml0MxSfqrSLQZa631OkRt+mFdb/EXXaXxN42ff2ns/E3j52+R\nGD+7Yxv2zWnYBXMhVAlxcZg+/TEDszApqT//AlIjaWlpB/R8z6eUiIiIiMihsSU7sf96BTvvdaio\nAGMwPTLDu0M223cVOIkuFW4RERERn7Jlu7DzXsf+61XYVRI+eGIPnPOHY45s7W04qaLCLSIiIuIz\ndvdu7II52DenQ/GO8MH0E3CGXIY5qvoFJ8QbKtwiIiIiPmFDIexH87GzX4St34cPHtUxXLTTT/A2\nnFRLhVtEREQkxlnXheWLcWdNhYIN4YNprXGGDIcTumsb9hinwi0iIiISo6y1kLs8vDvkutXhg81a\nYAb/CtOtD8bR7pB+oMItIiIiEoPsqpXhov3Vnl21m6RiBg3D9D4LE4j3NpwcEBVuERERkRhi138b\nLtp7tmEnKRkz4ALMGQMxiYnehpODosItIiIiEgPspnzsa1Oxn3wQPpDYAHP2eZizz8c0SvI2nBwS\nFW4RERERD9mt32PfmIb98F1wXQgEMGecgxlwISaY4nU8qQUq3CIiIiIecIu2407/B/a9t6ByNzgO\npk8/zMBhmKbNvI4ntUiFW0RERCSCbEU57CyGkmLYWYTdWQzrv6Fo/ptQtgsA07VPeBv2Fq08TiuR\noMItIiIiUgPWWthVCjuL9pTnYmxJUbhM73Vsz+c7i6GkCCoqqn/R407BOf9STOv20XsjEnUq3CIi\nIlLv2FAoXJD3lOTwlef/lmZKisNXoquOFUHpTgiFDvxkgQAkBaFxMjQOhlcdCTYh6fRfsqvVUbX/\n5iTmqHCLiIiIr1VN2dhTju0PV5b3OfajK8+7Sg7uZIkN9y7OP/qYxsl7jgXDx/Z8TmKD/e4EGUhO\nhuLiQ3z34gcq3CIiIhJz7OZ82PTdnikbRT+aA71nykbxnivPPzdlozrGQFLj/155rirKyVXl2ST9\nT3FOSsbEa8MZOXAq3CIiIhJT7Ko83PG3g3Vr9gU/nrKRFL7ibH5UnEkK7lOmaZSkbdElalS4RURE\nJGZY18V96clw2W7dHtPiyL2KM433vRJNYsP9TtkQiRUq3CIiIhIz7KJ5sHYVpDTFufV+TGIDryOJ\nHDLH6wAiIiIiALa0BDtzCgDmwstVtqXOUOEWERGRmGDfnA7FO6BDOqbbaV7HEak1KtwiIiLiOVuw\nETvvdTAG5+KrNSdb6hQVbhEREfGcO/1pCFViep2FadPB6zgitUqFW0RERDxlP/8UPl8KDRpihgz3\nOo5IrVPhFhEREc/Yyt24058CwAy6GBM8zONEIrVPhVtEREQ8Y+e/BQUboXka5sxBXscRiQgVbhER\nEfGELdqOff0lAJxhV2IC2jZd6iYVbhEREfGEnfUC7CqBY0/GHN/V6zgiEaPCLSIiIlFn163GLnwH\n4uJwsq70Oo5IRKlwi4iISFRZa3FffBKsxWQOwrRs5XUkkYhS4RYREZGosksXwqqVkNwEc+4wr+OI\nRJwKt4iIiESNLS/HzngWAHP+cEyjxh4nEok8FW4RERGJGvuvV2DrFmjdDtP7LK/jiERFIBonmTx5\nMsuXLycYDDJhwgQAsrOzyc/PB6CkpISkpCTGjx8PwKuvvsr8+fNxHIcrrriCE044IRoxRUREJIJs\n4ffYOTMBcIZdjXHiPE4kEh1RKdyZmZkMGDCASZMmVR276aabqj6eMmUKSUlJAGzYsIFFixbx17/+\nla1bt3Lvvffy8MMP4zi6GC8iIuJndsazsLsC07UPpmOG13FEoiYqLTY9Pb2qUP8vay2LFy+mV69e\nAHzyySf06tWLQCBA8+bNadGiBatWrYpGTBEREYkQ+1VO+GbJhATMBZd7HUckqjy/bJyXl0dKSgot\nWrQAYNu2bTRt2rTq8aZNm7J161av4omIiMghsm4I96UnATD9L8A0beZxIpHo8rxwf/jhh1VXt6tj\njIlSGhEREaltduE7sP5bSG2G6T/U6zgiUReVOdzVCYVCLFmyhAceeKDqWGpqKoWFhVWfFxYWkpqa\nut+vz83NJTc3t+rzrKwskpOTIxdYIiYhIUFj52MaP//S2PmbH8bPLdlJ8aypADS6bCQJhx/ucaLY\n4Yfxk+pNnz696uOMjAwyMqq/L8HTwv3555/TqlWrvQr1KaecwsMPP8ygQYPYunUrBQUFdOjQYb9f\nv783V1xcHNHMEhnJyckaOx/T+PmXxs7f/DB+7rSnsMU7oGMGZRknUx7jeaPJD+Mn+5ecnExWVlaN\nnx+Vwp2dnU1eXh7FxcWMHDmSrKwsMjMzWbRo0T7TSVq1asWpp57KzTffTFxcHFdeeaWmlILine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc751b90a90>"
+        "<matplotlib.figure.Figure at 0x7f8a2273e3c8>"
        ]
       }
      ],
@@ -390,9 +390,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc7518df2b0>"
+        "<matplotlib.figure.Figure at 0x7f8a22495f28>"
        ]
       }
      ],
@@ -426,9 +426,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc751596160>"
+        "<matplotlib.figure.Figure at 0x7f8a223fbc18>"
        ]
       }
      ],
@@ -465,9 +465,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc7518c8da0>"
+        "<matplotlib.figure.Figure at 0x7f8a3abd36a0>"
        ]
       }
      ],
@@ -492,9 +492,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc751770c88>"
+        "<matplotlib.figure.Figure at 0x7f8a22322518>"
        ]
       }
      ],
@@ -527,9 +527,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc751841208>"
+        "<matplotlib.figure.Figure at 0x7f8a22148f60>"
        ]
       }
      ],
@@ -595,9 +595,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc751547748>"
+        "<matplotlib.figure.Figure at 0x7f8a22161c18>"
        ]
       }
      ],
@@ -624,9 +624,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc751574b38>"
+        "<matplotlib.figure.Figure at 0x7f8a22215d30>"
        ]
       }
      ],
@@ -696,9 +696,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc7518e43c8>"
+        "<matplotlib.figure.Figure at 0x7f8a224a8400>"
        ]
       }
      ],
@@ -761,9 +761,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc751679198>"
+        "<matplotlib.figure.Figure at 0x7f8a2222f358>"
        ]
       }
      ],
@@ -874,9 +874,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc75183cb38>"
+        "<matplotlib.figure.Figure at 0x7f8a223f5c50>"
        ]
       }
      ],
@@ -953,9 +953,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc7518fd3c8>"
+        "<matplotlib.figure.Figure at 0x7f8a22102ef0>"
        ]
       }
      ],
@@ -1009,9 +1009,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc7516167f0>"
+        "<matplotlib.figure.Figure at 0x7f8a221c4d30>"
        ]
       }
      ],
@@ -1072,9 +1072,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc75178d9e8>"
+        "<matplotlib.figure.Figure at 0x7f8a2218e908>"
        ]
       }
      ],
@@ -1146,9 +1146,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc7518411d0>"
+        "<matplotlib.figure.Figure at 0x7f8a2239ecc0>"
        ]
       }
      ],
@@ -1221,25 +1221,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc751684438>"
+        "<matplotlib.figure.Figure at 0x7f8a221f1128>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc75179e6a0>"
+        "<matplotlib.figure.Figure at 0x7f8a224a8828>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc751b83b38>"
+        "<matplotlib.figure.Figure at 0x7f8a2231ae80>"
        ]
       }
      ],
@@ -1276,25 +1276,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fc75161f1d0>"
+        "<matplotlib.figure.Figure at 0x7f8a22334c88>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc75175b7b8>"
+        "<matplotlib.figure.Figure at 0x7f8a22349828>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc751900eb8>"
+        "<matplotlib.figure.Figure at 0x7f8a22334b38>"
        ]
       }
      ],
@@ -1327,17 +1327,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc7517fb6d8>"
+        "<matplotlib.figure.Figure at 0x7f8a208d1d68>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc75174fb70>"
+        "<matplotlib.figure.Figure at 0x7f8a223143c8>"
        ]
       }
      ],
@@ -1383,29 +1383,29 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc7504ae6d8>"
+        "<matplotlib.figure.Figure at 0x7f8a207b7b00>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc7504436d8>"
+        "<matplotlib.figure.Figure at 0x7f8a20841be0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fc751658588>"
+        "<matplotlib.figure.Figure at 0x7f8a208c7c18>"
        ]
       }
      ],
-     "prompt_number": 26
+     "prompt_number": 25
     },
     {
      "cell_type": "heading",

06_Multivariate_Kalman_filter/Multivariate_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:695690e0838cd280b9573bc1d9660038786445bd10ff74192e04d06aeeaa60e3"
+  "signature": "sha256:c30d0973a8ffc930ac738e35ed5561d4752baf9745fc3e2222e13ec56f8df25a"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fd068a9b128>"
+        "<IPython.core.display.HTML at 0x7f275dfa0128>"
        ]
       }
      ],
@@ -393,13 +393,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd0447b74e0>"
+        "<matplotlib.figure.Figure at 0x7f275df90f60>"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -469,7 +469,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 52
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -487,7 +487,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 12
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -506,7 +506,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 13
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -532,7 +532,7 @@
        ]
       }
      ],
-     "prompt_number": 55
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -561,13 +561,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd044897518>"
+        "<matplotlib.figure.Figure at 0x7f275dfa5dd8>"
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -595,13 +595,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fd044897550>"
+        "<matplotlib.figure.Figure at 0x7f275dfa5cc0>"
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -653,13 +653,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22fbf7b8>"
+        "<matplotlib.figure.Figure at 0x7f2739e7f4e0>"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -756,13 +756,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a23b46b38>"
+        "<matplotlib.figure.Figure at 0x7f2739da5630>"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -783,13 +783,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a23058668>"
+        "<matplotlib.figure.Figure at 0x7f2739a96470>"
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -810,13 +810,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a23076550>"
+        "<matplotlib.figure.Figure at 0x7f2739d1b6d8>"
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -841,13 +841,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a232bfba8>"
+        "<matplotlib.figure.Figure at 0x7f2739b71c50>"
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -906,13 +906,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a232cab70>"
+        "<matplotlib.figure.Figure at 0x7f2739d33208>"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 15
     },
     {
      "cell_type": "heading",
@@ -1038,7 +1038,7 @@
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -1067,7 +1067,7 @@
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -1096,7 +1096,7 @@
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -1141,7 +1141,7 @@
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -1174,7 +1174,7 @@
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -1209,7 +1209,7 @@
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -1383,7 +1383,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 21
+     "prompt_number": 22
     },
     {
      "cell_type": "heading",
@@ -1759,7 +1759,7 @@
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 23
     },
     {
      "cell_type": "code",
@@ -1774,14 +1774,14 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 23,
+       "prompt_number": 24,
        "text": [
         "array([[  5.87500000e-05,   1.17500000e-03],\n",
         "       [  1.17500000e-03,   2.35000000e-02]])"
        ]
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 24
     },
     {
      "cell_type": "heading",
@@ -1842,7 +1842,7 @@
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -1867,7 +1867,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 25
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -1951,7 +1951,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 26
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -2015,7 +2015,6 @@
       "    plt.title(title)\n",
       "    plt.show()\n",
       "    \n",
-      "    \n",
       "    if plot_P:\n",
       "        ax = plt.subplot(121)\n",
       "        ax.set_title(\"$\\sigma^2_x$\")\n",
@@ -2034,7 +2033,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 27
+     "prompt_number": 28
     },
     {
      "cell_type": "markdown",
@@ -2055,21 +2054,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22f8df28>"
+        "<matplotlib.figure.Figure at 0x7f2739e6bb38>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22fa6828>"
+        "<matplotlib.figure.Figure at 0x7f2739d19438>"
        ]
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 29
     },
     {
      "cell_type": "markdown",
@@ -2145,7 +2144,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 29
+     "prompt_number": 30
     },
     {
      "cell_type": "markdown",
@@ -2174,7 +2173,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 30
+     "prompt_number": 31
     },
     {
      "cell_type": "markdown",
@@ -2232,21 +2231,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a2338e898>"
+        "<matplotlib.figure.Figure at 0x7f2739ba5358>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a2312a0b8>"
+        "<matplotlib.figure.Figure at 0x7f27398f7fd0>"
        ]
       }
      ],
-     "prompt_number": 31
+     "prompt_number": 32
     },
     {
      "cell_type": "markdown",
@@ -2410,7 +2409,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 32
+     "prompt_number": 33
     },
     {
      "cell_type": "markdown",
@@ -2476,13 +2475,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a233b4ba8>"
+        "<matplotlib.figure.Figure at 0x7f27398fde80>"
        ]
       }
      ],
-     "prompt_number": 33
+     "prompt_number": 34
     },
     {
      "cell_type": "markdown",
@@ -2546,21 +2545,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a23381630>"
+        "<matplotlib.figure.Figure at 0x7f2739a84ac8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a23381898>"
+        "<matplotlib.figure.Figure at 0x7f2739d16710>"
        ]
       }
      ],
-     "prompt_number": 34
+     "prompt_number": 35
     },
     {
      "cell_type": "markdown",
@@ -2597,13 +2596,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22f8d2b0>"
+        "<matplotlib.figure.Figure at 0x7f2739e5b550>"
        ]
       }
      ],
-     "prompt_number": 35
+     "prompt_number": 36
     },
     {
      "cell_type": "markdown",
@@ -2626,13 +2625,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a230653c8>"
+        "<matplotlib.figure.Figure at 0x7f2739e228d0>"
        ]
       }
      ],
-     "prompt_number": 36
+     "prompt_number": 37
     },
     {
      "cell_type": "markdown",
@@ -2655,13 +2654,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22f86390>"
+        "<matplotlib.figure.Figure at 0x7f2739906240>"
        ]
       }
      ],
-     "prompt_number": 37
+     "prompt_number": 38
     },
     {
      "cell_type": "markdown",
@@ -2724,13 +2723,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a230e2c18>"
+        "<matplotlib.figure.Figure at 0x7f2739adc6d8>"
        ]
       }
      ],
-     "prompt_number": 38
+     "prompt_number": 39
     },
     {
      "cell_type": "markdown",
@@ -2754,13 +2753,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a2328f630>"
+        "<matplotlib.figure.Figure at 0x7f2739a8b5f8>"
        ]
       }
      ],
-     "prompt_number": 39
+     "prompt_number": 40
     },
     {
      "cell_type": "markdown",
@@ -2826,13 +2825,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a23270a90>"
+        "<matplotlib.figure.Figure at 0x7f2739bed550>"
        ]
       }
      ],
-     "prompt_number": 40
+     "prompt_number": 41
     },
     {
      "cell_type": "markdown",
@@ -2856,13 +2855,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a2326c588>"
+        "<matplotlib.figure.Figure at 0x7f2739c87438>"
        ]
       }
      ],
-     "prompt_number": 41
+     "prompt_number": 42
     },
     {
      "cell_type": "markdown",
@@ -2889,13 +2888,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a232d2ac8>"
+        "<matplotlib.figure.Figure at 0x7f2739d8f0f0>"
        ]
       }
      ],
-     "prompt_number": 42
+     "prompt_number": 43
     },
     {
      "cell_type": "markdown",
@@ -2920,13 +2919,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a230032e8>"
+        "<matplotlib.figure.Figure at 0x7f2739b59dd8>"
        ]
       }
      ],
-     "prompt_number": 43
+     "prompt_number": 44
     },
     {
      "cell_type": "markdown",
@@ -2993,21 +2992,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a23299b38>"
+        "<matplotlib.figure.Figure at 0x7f2739cc6ef0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a2329eac8>"
+        "<matplotlib.figure.Figure at 0x7f2739c5aa20>"
        ]
       }
      ],
-     "prompt_number": 44
+     "prompt_number": 45
     },
     {
      "cell_type": "markdown",
@@ -3076,13 +3075,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a232e3b70>"
+        "<matplotlib.figure.Figure at 0x7f2739c677f0>"
        ]
       }
      ],
-     "prompt_number": 45
+     "prompt_number": 46
     },
     {
      "cell_type": "markdown",
@@ -3150,13 +3149,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a23298a90>"
+        "<matplotlib.figure.Figure at 0x7f27399c3400>"
        ]
       }
      ],
-     "prompt_number": 46
+     "prompt_number": 47
     },
     {
      "cell_type": "markdown",
@@ -3202,13 +3201,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a23298518>"
+        "<matplotlib.figure.Figure at 0x7f2739be9be0>"
        ]
       }
      ],
-     "prompt_number": 47
+     "prompt_number": 48
     },
     {
      "cell_type": "markdown",
@@ -3246,13 +3245,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22ef1f98>"
+        "<matplotlib.figure.Figure at 0x7f2739be9da0>"
        ]
       }
      ],
-     "prompt_number": 48
+     "prompt_number": 49
     },
     {
      "cell_type": "markdown",
@@ -3280,13 +3279,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAF2CAYAAAB3dr7TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAF0CAYAAAA3oG+fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f1a22fd5898>"
+        "<matplotlib.figure.Figure at 0x7f2739ccf400>"
        ]
       }
      ],
-     "prompt_number": 49
+     "prompt_number": 50
     },
     {
      "cell_type": "heading",

code/book_format.py

@@ -30,39 +30,9 @@ def load_style(name='../styles/custom2.css'):
 # chapter so the reader can see that they need to update FilterPy.
 test_filterpy_version()
 
+'''
 
-pylab.rcParams['lines.linewidth'] = 2
-pylab.rcParams['lines.antialiased'] = True
-pylab.rcParams['patch.linewidth'] = 0.5
-pylab.rcParams['patch.facecolor'] = '348ABD' #blue
-pylab.rcParams['patch.edgecolor'] = 'eeeeee'
-pylab.rcParams['patch.antialiased'] = True
-pylab.rcParams['font.family'] = 'monospace'
-pylab.rcParams['font.size'] = 12.0
-#pylab.rcParams['font.monospace'] = 'Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace'
-pylab.rcParams['axes.facecolor'] = 'E5E5E5'
-pylab.rcParams['axes.edgecolor'] = 'bcbcbc'
-pylab.rcParams['axes.linewidth'] = 1
-pylab.rcParams['axes.grid'] = True
-pylab.rcParams['axes.titlesize'] = 'x-large'
-pylab.rcParams['axes.labelsize'] = 'large'
-pylab.rcParams['axes.labelcolor'] = '555555'
-pylab.rcParams['axes.axisbelow'] = True
-pylab.rcParams['axes.color_cycle'] = '004080, 8EBA42, E24A33, 348ABD, 777760, 988ED5, FDC15E'
-pylab.rcParams['xtick.major.pad'] = 6
-pylab.rcParams['xtick.minor.size'] = 0
-pylab.rcParams['xtick.minor.pad'] = 6
-pylab.rcParams['xtick.color'] = '555555'
-pylab.rcParams['ytick.direction'] = 'in'
-pylab.rcParams['legend.fancybox'] = True
-pylab.rcParams['figure.facecolor'] = '1.0'
-pylab.rcParams['figure.edgecolor'] = '0.50'
-pylab.rcParams['figure.subplot.hspace'] = 0.5
-pylab.rcParams['figure.figsize'] = 12,6
-pylab.rcParams['grid.color'] = 'ffffff'
-pylab.rcParams['grid.linestyle'] = 'solid'
-pylab.rcParams['grid.linewidth'] = 1.5
-
+'''
 
 def equal_axis():
     pylab.rcParams['figure.figsize'] = 10,10
@@ -71,3 +41,43 @@ def equal_axis():
 def reset_axis():
     pylab.rcParams['figure.figsize'] = 12, 6
 
+try:
+    plt.style.use('ggplot')
+    reset_axis ()
+    pylab.rcParams['lines.linewidth'] = 2
+    pylab.rcParams['figure.subplot.hspace'] = 0.5
+
+except:
+    pylab.rcParams['lines.linewidth'] = 2
+    pylab.rcParams['lines.antialiased'] = True
+    pylab.rcParams['patch.linewidth'] = 0.5
+    pylab.rcParams['patch.facecolor'] = '348ABD' #blue
+    pylab.rcParams['patch.edgecolor'] = 'eeeeee'
+    pylab.rcParams['patch.antialiased'] = True
+    pylab.rcParams['font.family'] = 'monospace'
+    pylab.rcParams['font.size'] = 12.0
+    #pylab.rcParams['font.monospace'] = 'Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace'
+    pylab.rcParams['axes.facecolor'] = 'E5E5E5'
+    pylab.rcParams['axes.edgecolor'] = 'bcbcbc'
+    pylab.rcParams['axes.linewidth'] = 1
+    pylab.rcParams['axes.grid'] = True
+    pylab.rcParams['axes.titlesize'] = 'x-large'
+    pylab.rcParams['axes.labelsize'] = 'large'
+    pylab.rcParams['axes.labelcolor'] = '555555'
+    pylab.rcParams['axes.axisbelow'] = True
+    pylab.rcParams['axes.color_cycle'] = '004080, 8EBA42, E24A33, 348ABD, 777760, 988ED5, FDC15E'
+    pylab.rcParams['xtick.major.pad'] = 6
+    pylab.rcParams['xtick.minor.size'] = 0
+    pylab.rcParams['xtick.minor.pad'] = 6
+    pylab.rcParams['xtick.color'] = '555555'
+    pylab.rcParams['ytick.direction'] = 'in'
+    pylab.rcParams['legend.fancybox'] = True
+    pylab.rcParams['figure.facecolor'] = '1.0'
+    pylab.rcParams['figure.edgecolor'] = '0.50'
+    pylab.rcParams['figure.subplot.hspace'] = 0.5
+    pylab.rcParams['figure.figsize'] = 12,6
+    pylab.rcParams['grid.color'] = 'ffffff'
+    pylab.rcParams['grid.linestyle'] = 'solid'
+    pylab.rcParams['grid.linewidth'] = 1.5
+
+