Switched to 538 plot style.

I like this one the best.

01_gh_filter/g-h_filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:9a9b36cd7ddaddea122ea8a28f164ea9dd83caabcf16ae22f4a6e58f7a9d8c82"
+  "signature": "sha256:9b4d23397adb86da71441f61890f0e812c9ed1da66095c62d80570114907ac90"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f8a3abce6d8>"
+        "<IPython.core.display.HTML at 0x7fbe7f3b06d8>"
        ]
       }
      ],
@@ -354,9 +354,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAGkCAYAAAD9t4CTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl4lOXZ/vHvTGayh6zsIWELawDBBWSLFSOvxZeK1IIB\npai44YILrdafWqttbH2LWkVFqxaruKDgjgsuyCKKIATZsrDvSxKSyWSSWZ7fH4OpaQgOkGS283Mc\nOTTzPDNzDTdDcs5z39dtKi8vNxAREREREQkBZn8XICIiIiIi0lQUcEREREREJGQo4IiIiIiISMhQ\nwBERERERkZChgCMiIiIiIiFDAUdEREREREKGAo6IiIiIiIQMvwWc5cuXM3HiRPr06UNycjLz5s2r\ndzw5Ofm4XzNnzmzwWIZh8Otf/5rk5GTeeeedlnoJIiIiIiISYPwWcOx2O9nZ2eTn5xMTE4PJZKp3\nvLCwsN7Xa6+9BsC4ceMaPNaTTz5JREQEQIPHERERERGR8GHx1xPn5uaSm5sLwPTp0xscb926db3v\nP/jgA7Kyshg6dGi929esWcOcOXP48ssvycrKar6CRUREREQk4AXFGhybzcaCBQu48sor691eWVnJ\nNddcw+OPP05aWpqfqhMRERERkUARFAHnzTffxOl0kpeXV+/222+/ndzcXEaNGuWnykREREREJJD4\nbYrayZg7dy5jxowhJSWl7rbXXnuNDRs28MUXXwDeRgM//a+IiIiIiISfgL+CU1BQwNq1a5kyZUq9\n27/66is2b95Mx44dSUtLq1uzc9VVV3HRRRf5o1QREREREfGzgA84c+fOpXPnzuTk5NS7/d5772XF\nihUsW7aMZcuWsXTpUgAeeughnnnmGX+UKgGoqKjI3yVIC9OYhx+NeXjSuIcfjbn4ym9T1Kqqqigp\nKQHA4/Gwa9cuCgoKSElJIT09HfC2kp4/fz4zZsxocP/27dvTvn37Brd37NiRzMzM5i1eREREREQC\nkt+u4KxZs4acnBxycnJwOBzk5+eTk5NDfn5+3TkLFiygurqaSZMm+atMEREREREJIn67gjNixAjK\nyspOeM7kyZOZPHmyz4/5c48nIiIiIiKhLeDX4IiIiIiIiPhKAUdEREREREKGAo6IiIiIiIQMBRwR\nEREREQkZCjgiIiIiIhIyFHBERERERCRkKOCIiIiIiEjIUMAREREREZGQoYAjIiIiIiIhQwFHRERE\nRERChgKOiIiIiIiEDAUcEREREREJGQo4IiIiIiISMhRwREREREQkZCjgiIiIiIhIyFDAERERERGR\nkKGAIyIiIiIiIUMBR0REREREQoYCjoiIiIiIhAwFHBERERERCRkKOCIiIiIiEjIUcEREREREJGQo\n4IiIiIiISMhQwBERERERkZChgCMiIiIiEoDKazw4PYa/ywg6CjgiIiIiIgGkvMbDsxttTFx8hEU7\nHf4uJ+hY/F2AiIiIiIh4g838rXYWbK2m2u29clN41AnE+LewIKOAIyIiIiLiR8cLNoPbRDKlZxx9\nkq1+ri74KOCIiIiIiPjB0VoPb5Qo2DQ1BRwRERERkRZ0tNbD/BI7b/0k2JzTJpIpPeLom6Jgc7oU\ncEREREREWoCCTctQwBERERERaUYVP05F21aN3eUNNme3juS3PRVsmoMCjoiIiIhIM1Cw8Q8FHBER\nERGRJlRR6+2K9tZWBRt/8OtGn8uXL2fixIn06dOH5ORk5s2bV+94cnLycb9mzpwJQFlZGTNnzuSc\nc86hffv2ZGdnc8cdd1BWVuaPlyMiIiIiYayi1sPzm70bdP670I7dZXB260ieHJ7MI+cmKdy0EL9e\nwbHb7WRnZ3P55Zdzww03YDKZ6h0vLCys9/2aNWuYOHEi48aNA2D//v3s37+fP/3pT/Tq1Ys9e/Zw\n5513cvXVV7NgwYIWex0iIiIiEr4qf3LFpuonV2ym9IwjW6Gmxfk14OTm5pKbmwvA9OnTGxxv3bp1\nve8/+OADsrKyGDp0KAC9e/fm3//+d93xzp0786c//YkJEyZgs9mIj49vxupFREREJJwdL9ic1drK\nlB5x9EuN9HN14Sto1uDYbDYWLFjAXXfddcLzKioqiIqKIjY2toUqExEREZFwomAT2IIm4Lz55ps4\nnU7y8vIaPae8vJw///nPTJkyBbPZr8uLRERERCTEVNZ6eHOrnTd/EmzOTLMypWcc/RVsAoapvLzc\n8HcRAOnp6TzyyCNcfvnlxz3+i1/8gi5duvDCCy8c97jNZuOyyy7DYrHw1ltvERl5/L9kRUVFTVaz\niIiIiIS+KjcsLo3is9Ioqj3eNeO945z8b1oNWbFuP1cXmrKysk75vkFxBaegoIC1a9fyxz/+8bjH\nfww3JpOJ119/vdFwA6f3hyXBp6ioSGMeZjTm4UdjHp407uHHH2Ne6fTwZkn9KzaD0qz8VldsAlpQ\nBJy5c+fSuXNncnJyGhyrrKysCzfz58/X2hsREREROS0KNsHNrwGnqqqKkpISADweD7t27aKgoICU\nlBTS09MBbyvp+fPnM2PGjAb3r6ys5NJLL8Vms/HKK69gs9mw2WwApKSkYLWqLZ+IiIiI+KbS6eGt\nrdXML7Er2AQxvwacNWvWMHbsWABMJhP5+fnk5+eTl5fH7NmzAViwYAHV1dVMmjSpwf3Xrl3Ld999\nh8lk4swzz6y73WQy8d577zFs2LCWeSEiIiIiErQaCzZTesYxQMEm6Pg14IwYMYKysrITnjN58mQm\nT558yvcXERERETke24/BZqsdm1PBJlQExRocEREREZGmcrxgMzDNu4/NGWkKNsFOAUdEREREwoLN\n6WHB1mreULAJaQo4IiIiIhLSjhdszkj1TkUbqGATchRwRERERCQkHS/YDEj1dkVTsAldCjgiIiIi\nElKqnB7e2ubtilapYBN2FHBEREREJCQo2Ago4IiIiIhIkKtyeliwrZo3FGwEBRwRERERCVLHCzb9\nU6xM7RXHGalWTCaTnysUf1DAEREREZGgUuX0sPBYsKlQsJH/ooAjIiIiIkHB7jrWFe0nwaZfipWp\nPeMYmKZgI14KOCIiIiIS0OwuDx8ejuKz4iMKNvKzFHBEREREpEV4DIMqp0GF00NlrcHRY/+tcHqo\nqDWorPUc95it1sBDNGCQfSzYDFKwkUYo4IiIiIjISfEYBlUuwxtAGgklFbX1w8t/gsqpyYpxcf0Z\naQo28rMUcERERETClGEY2H4MKo2Ekv8OMT/+91SDSpzFREKkiVZWM60iTSRYzSRGmhvc1irS+/+t\nrGbirSa2lRST1bpDk75+CU0KOCIiIiJBzvjJFRXvVRQPFU6jkcDyk/93GniMU3vO4wWVn4aSBKvp\n2Pf/+f8EqwmLWVdfpHkp4IiIiIgEgR9KnXy513H8EHMaQSXWYjphKGn1k3Dy02MKKhKoFHBERERE\nAlxZjYe7vymv28zyeGItpuOEkmNXVI6FkkQFFQkDCjgiIiIiAW7ORhuVTm8HsTEZ0f8VWLzrV6wK\nKiKAAo6IiIhIQCs4UstHuxxYzXDXGQmkx+vXN5ETMfu7ABERERE5PpfH4NGCSgAu7x6rcCPiAwUc\nERERkQC1YFs12yrdtI81Mykrzt/liAQFBRwRERGRAHSo2s2Lm6sAuKVfAlERWmMj4gsFHBEREZEA\n9NQGG9Vug+HtIjm3bZS/yxEJGgo4IiIiIgHmu4O1fLG3hugIuCk7wd/liAQVBRwRERGRAFLrNnhs\nvbexwBU94mgXG+HnikSCiwKOiIiISAB5vcTO7io3GfER/KZbrL/LEQk6CjgiIiIiAWJflZt/F3ob\nC9zWP0Gbd4qcAgUcERERkQDxxA+V1Hrggo5RDEyL9Hc5IkFJAUdEREQkACzfX8OKA7XEWUzc0Dfe\n3+WIBC0FHBERERE/c7gM/nGsscBVveJIjVZjAZFTpYAjIiIi4mcvF1VxoNpD91YWftU5xt/liAQ1\nBRwRERERP9pR6eK1YjvgbSxgUWMBkdOigCMiIiLiJ4Zh8Pj6SlwGjMmIpm+K1d8liQQ9BRwRERER\nP/l8bw1rDjtpZTUxrbcaC4g0Bb8FnOXLlzNx4kT69OlDcnIy8+bNq3c8OTn5uF8zZ86sO6empoaZ\nM2fSrVs3OnbsyOWXX87evXtb+qWIiIiInLQqp4enfrABcG2feJKi9LmzSFPw2zvJbreTnZ1Nfn4+\nMTExmEz155sWFhbW+3rttdcAGDduXN05d999N++//z4vvPACH374IZWVlUyYMAGPx9Oir0VERETk\nZL24pYojNR76JFv4ZUa0v8sRCRkWfz1xbm4uubm5AEyfPr3B8datW9f7/oMPPiArK4uhQ4cCcPTo\nUV5++WWeeuopcnJyAJgzZw79+vXjyy+/5Pzzz2/mVyAiIiJyaoqPOlmwrRoz3sYCZpMaC4g0laC4\nFmqz2ViwYAFXXnll3W1r167F6XTWCzIdO3akZ8+efPPNN/4oU0RERORneQyDxwpseAy4pEsMWYlq\nLCDSlIIi4Lz55ps4nU7y8vLqbjt48CARERGkpKTUO7d169YcOnSopUsUERER8clHuxz8UOYkOcrM\nVb3i/F2OSMjx2xS1kzF37lzGjBnTIMyciqKioiaoSIKJxjz8aMzDj8Y8PAXjuNtcJp7aGg+YuTTV\nxr7tZf4uKagE45jLqcnKyjrl+wZ8wCkoKGDt2rX88Y9/rHd7mzZtcLvdlJaW1gs+Bw8erFunczyn\n84clwaeoqEhjHmY05uFHYx6egnXc/76uApvbwRmpViafmdmgyZI0LljHXFpewE9Rmzt3Lp07d65r\nJPCjM844A6vVyueff1532549eygsLGTw4MEtXaaIiIjICW0sc/L+DgcRJm9jAYUbkebhtyLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a2273e3c8>"
+        "<matplotlib.figure.Figure at 0x7fbe62ed9390>"
        ]
       }
      ],
@@ -390,9 +390,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a22495f28>"
+        "<matplotlib.figure.Figure at 0x7fbe62c2b240>"
        ]
       }
      ],
@@ -426,9 +426,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a223fbc18>"
+        "<matplotlib.figure.Figure at 0x7fbe62bc2c88>"
        ]
       }
      ],
@@ -465,9 +465,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a3abd36a0>"
+        "<matplotlib.figure.Figure at 0x7fbe7f3b5d68>"
        ]
       }
      ],
@@ -492,9 +492,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a22322518>"
+        "<matplotlib.figure.Figure at 0x7fbe62abc7f0>"
        ]
       }
      ],
@@ -527,9 +527,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a22148f60>"
+        "<matplotlib.figure.Figure at 0x7fbe62bc2cc0>"
        ]
       }
      ],
@@ -595,9 +595,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a22161c18>"
+        "<matplotlib.figure.Figure at 0x7fbe629ed400>"
        ]
       }
      ],
@@ -624,9 +624,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a22215d30>"
+        "<matplotlib.figure.Figure at 0x7fbe629c31d0>"
        ]
       }
      ],
@@ -696,9 +696,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a224a8400>"
+        "<matplotlib.figure.Figure at 0x7fbe62927f60>"
        ]
       }
      ],
@@ -761,9 +761,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a2222f358>"
+        "<matplotlib.figure.Figure at 0x7fbe62920f98>"
        ]
       }
      ],
@@ -874,9 +874,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a223f5c50>"
+        "<matplotlib.figure.Figure at 0x7fbe6288afd0>"
        ]
       }
      ],
@@ -953,9 +953,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a22102ef0>"
+        "<matplotlib.figure.Figure at 0x7fbe62b87080>"
        ]
       }
      ],
@@ -1009,9 +1009,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a221c4d30>"
+        "<matplotlib.figure.Figure at 0x7fbe62aba438>"
        ]
       }
      ],
@@ -1072,9 +1072,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a2218e908>"
+        "<matplotlib.figure.Figure at 0x7fbe62b03320>"
        ]
       }
      ],
@@ -1146,9 +1146,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a2239ecc0>"
+        "<matplotlib.figure.Figure at 0x7fbe62b62400>"
        ]
       }
      ],
@@ -1221,25 +1221,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a221f1128>"
+        "<matplotlib.figure.Figure at 0x7fbe62858d68>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a224a8828>"
+        "<matplotlib.figure.Figure at 0x7fbe629979b0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a2231ae80>"
+        "<matplotlib.figure.Figure at 0x7fbe62c04da0>"
        ]
       }
      ],
@@ -1276,25 +1276,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a22334c88>"
+        "<matplotlib.figure.Figure at 0x7fbe62b2b7f0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a22349828>"
+        "<matplotlib.figure.Figure at 0x7fbe6297de48>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a22334b38>"
+        "<matplotlib.figure.Figure at 0x7fbe62b2be80>"
        ]
       }
      ],
@@ -1327,17 +1327,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a208d1d68>"
+        "<matplotlib.figure.Figure at 0x7fbe6296aa90>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a223143c8>"
+        "<matplotlib.figure.Figure at 0x7fbe6296a9b0>"
        ]
       }
      ],
@@ -1383,25 +1383,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a207b7b00>"
+        "<matplotlib.figure.Figure at 0x7fbe60f5a0b8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a20841be0>"
+        "<matplotlib.figure.Figure at 0x7fbe60ff2c18>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f8a208c7c18>"
+        "<matplotlib.figure.Figure at 0x7fbe628f66d8>"
        ]
       }
      ],

06_Multivariate_Kalman_filter/Multivariate_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:c30d0973a8ffc930ac738e35ed5561d4752baf9745fc3e2222e13ec56f8df25a"
+  "signature": "sha256:6963206ba9864c263a74cd00e4aab88e4003372150b23f85176ac2f51a3f6698"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f275dfa0128>"
+        "<IPython.core.display.HTML at 0x7f1a46e5e128>"
        ]
       }
      ],
@@ -393,9 +393,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f275df90f60>"
+        "<matplotlib.figure.Figure at 0x7f1a46e4cf60>"
        ]
       }
      ],
@@ -561,9 +561,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f275dfa5dd8>"
+        "<matplotlib.figure.Figure at 0x7f1a46e62320>"
        ]
       }
      ],
@@ -595,9 +595,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f275dfa5cc0>"
+        "<matplotlib.figure.Figure at 0x7f1a22cdfa20>"
        ]
       }
      ],
@@ -653,9 +653,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739e7f4e0>"
+        "<matplotlib.figure.Figure at 0x7f1a22d6f048>"
        ]
       }
      ],
@@ -756,9 +756,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739da5630>"
+        "<matplotlib.figure.Figure at 0x7f1a22799048>"
        ]
       }
      ],
@@ -783,9 +783,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739a96470>"
+        "<matplotlib.figure.Figure at 0x7f1a22947f60>"
        ]
       }
      ],
@@ -810,9 +810,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739d1b6d8>"
+        "<matplotlib.figure.Figure at 0x7f1a229885f8>"
        ]
       }
      ],
@@ -841,9 +841,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739b71c50>"
+        "<matplotlib.figure.Figure at 0x7f1a22ae3ac8>"
        ]
       }
      ],
@@ -906,9 +906,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739d33208>"
+        "<matplotlib.figure.Figure at 0x7f1a22c34940>"
        ]
       }
      ],
@@ -2054,17 +2054,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739e6bb38>"
+        "<matplotlib.figure.Figure at 0x7f1a229577b8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739d19438>"
+        "<matplotlib.figure.Figure at 0x7f1a22d68a58>"
        ]
       }
      ],
@@ -2231,17 +2231,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739ba5358>"
+        "<matplotlib.figure.Figure at 0x7f1a22990c50>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f27398f7fd0>"
+        "<matplotlib.figure.Figure at 0x7f1a227e2080>"
        ]
       }
      ],
@@ -2475,9 +2475,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f27398fde80>"
+        "<matplotlib.figure.Figure at 0x7f1a22af4c88>"
        ]
       }
      ],
@@ -2545,17 +2545,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739a84ac8>"
+        "<matplotlib.figure.Figure at 0x7f1a22adfd30>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739d16710>"
+        "<matplotlib.figure.Figure at 0x7f1a22b01fd0>"
        ]
       }
      ],
@@ -2596,9 +2596,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739e5b550>"
+        "<matplotlib.figure.Figure at 0x7f1a22caddd8>"
        ]
       }
      ],
@@ -2625,9 +2625,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739e228d0>"
+        "<matplotlib.figure.Figure at 0x7f1a227d6438>"
        ]
       }
      ],
@@ -2654,9 +2654,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739906240>"
+        "<matplotlib.figure.Figure at 0x7f1a22d36518>"
        ]
       }
      ],
@@ -2723,9 +2723,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739adc6d8>"
+        "<matplotlib.figure.Figure at 0x7f1a229cf908>"
        ]
       }
      ],
@@ -2753,9 +2753,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739a8b5f8>"
+        "<matplotlib.figure.Figure at 0x7f1a22a8f128>"
        ]
       }
      ],
@@ -2825,9 +2825,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739bed550>"
+        "<matplotlib.figure.Figure at 0x7f1a22b768d0>"
        ]
       }
      ],
@@ -2855,9 +2855,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739c87438>"
+        "<matplotlib.figure.Figure at 0x7f1a22bad6d8>"
        ]
       }
      ],
@@ -2888,9 +2888,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739d8f0f0>"
+        "<matplotlib.figure.Figure at 0x7f1a227ce588>"
        ]
       }
      ],
@@ -2919,9 +2919,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739b59dd8>"
+        "<matplotlib.figure.Figure at 0x7f1a22c34a58>"
        ]
       }
      ],
@@ -2992,17 +2992,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739cc6ef0>"
+        "<matplotlib.figure.Figure at 0x7f1a22acc208>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739c5aa20>"
+        "<matplotlib.figure.Figure at 0x7f1a2296b278>"
        ]
       }
      ],
@@ -3075,9 +3075,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739c677f0>"
+        "<matplotlib.figure.Figure at 0x7f1a229beba8>"
        ]
       }
      ],
@@ -3149,9 +3149,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f27399c3400>"
+        "<matplotlib.figure.Figure at 0x7f1a22a9da90>"
        ]
       }
      ],
@@ -3201,9 +3201,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739be9be0>"
+        "<matplotlib.figure.Figure at 0x7f1a22a9d710>"
        ]
       }
      ],
@@ -3245,9 +3245,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739be9da0>"
+        "<matplotlib.figure.Figure at 0x7f1a227d75f8>"
        ]
       }
      ],
@@ -3279,9 +3279,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2739ccf400>"
+        "<matplotlib.figure.Figure at 0x7f1a228aef28>"
        ]
       }
      ],

code/book_format.py

@@ -30,9 +30,6 @@ def load_style(name='../styles/custom2.css'):
 # chapter so the reader can see that they need to update FilterPy.
 test_filterpy_version()
 
-'''
-
-'''
 
 def equal_axis():
     pylab.rcParams['figure.figsize'] = 10,10
@@ -42,7 +39,7 @@ def reset_axis():
     pylab.rcParams['figure.figsize'] = 12, 6
 
 try:
-    plt.style.use('ggplot')
+    plt.style.use('fivethirtyeight')
     reset_axis ()
     pylab.rcParams['lines.linewidth'] = 2
     pylab.rcParams['figure.subplot.hspace'] = 0.5