Module 3.ipynb

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   "source": [
    "---\n",
    "\n",
    "_You are currently looking at **version 1.0** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Applied Machine Learning: Module 3 (Evaluation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation for Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preamble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 178\n",
      "1 182\n",
      "2 177\n",
      "3 183\n",
      "4 181\n",
      "5 182\n",
      "6 181\n",
      "7 179\n",
      "8 174\n",
      "9 180\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import load_digits\n",
    "\n",
    "dataset = load_digits()\n",
    "X, y = dataset.data, dataset.target\n",
    "\n",
    "for class_name, class_count in zip(dataset.target_names, np.bincount(dataset.target)):\n",
    "    print(class_name,class_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original labels:\t [1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9]\n",
      "New binary labels:\t [1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Creating a dataset with imbalanced binary classes:  \n",
    "# Negative class (0) is 'not digit 1' \n",
    "# Positive class (1) is 'digit 1'\n",
    "y_binary_imbalanced = y.copy()\n",
    "y_binary_imbalanced[y_binary_imbalanced != 1] = 0\n",
    "\n",
    "print('Original labels:\\t', y[1:30])\n",
    "print('New binary labels:\\t', y_binary_imbalanced[1:30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1615,  182])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(y_binary_imbalanced)    # Negative class (0) is the most frequent class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0.90888888888888886"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y_binary_imbalanced, random_state=0)\n",
    "\n",
    "# Accuracy of Support Vector Machine classifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(kernel='rbf', C=1).fit(X_train, y_train)\n",
    "svm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "DummyClassifier is a classifier that makes predictions using simple rules, which can be useful as a baseline for comparison against actual classifiers, especially with imbalanced classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "# Negative class (0) is most frequent\n",
    "dummy_majority = DummyClassifier(strategy = 'most_frequent').fit(X_train, y_train)\n",
    "# Therefore the dummy 'most_frequent' classifier always predicts class 0\n",
    "y_dummy_predictions = dummy_majority.predict(X_test)\n",
    "\n",
    "y_dummy_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    {
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dummy_majority.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0.97777777777777775"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "svm = SVC(kernel='linear', C=1).fit(X_train, y_train)\n",
    "svm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Binary (two-class) confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most frequent class (dummy classifier)\n",
      " [[407   0]\n",
      " [ 43   0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Negative class (0) is most frequent\n",
    "dummy_majority = DummyClassifier(strategy = 'most_frequent').fit(X_train, y_train)\n",
    "y_majority_predicted = dummy_majority.predict(X_test)\n",
    "confusion = confusion_matrix(y_test, y_majority_predicted)\n",
    "\n",
    "print('Most frequent class (dummy classifier)\\n', confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random class-proportional prediction (dummy classifier)\n",
      " [[371  36]\n",
      " [ 42   1]]\n"
     ]
    }
   ],
   "source": [
    "# produces random predictions w/ same class proportion as training set\n",
    "dummy_classprop = DummyClassifier(strategy='stratified').fit(X_train, y_train)\n",
    "y_classprop_predicted = dummy_classprop.predict(X_test)\n",
    "confusion = confusion_matrix(y_test, y_classprop_predicted)\n",
    "\n",
    "print('Random class-proportional prediction (dummy classifier)\\n', confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support vector machine classifier (linear kernel, C=1)\n",
      " [[402   5]\n",
      " [  5  38]]\n"
     ]
    }
   ],
   "source": [
    "svm = SVC(kernel='linear', C=1).fit(X_train, y_train)\n",
    "svm_predicted = svm.predict(X_test)\n",
    "confusion = confusion_matrix(y_test, svm_predicted)\n",
    "\n",
    "print('Support vector machine classifier (linear kernel, C=1)\\n', confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression classifier (default settings)\n",
      " [[401   6]\n",
      " [  6  37]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression().fit(X_train, y_train)\n",
    "lr_predicted = lr.predict(X_test)\n",
    "confusion = confusion_matrix(y_test, lr_predicted)\n",
    "\n",
    "print('Logistic regression classifier (default settings)\\n', confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree classifier (max_depth = 2)\n",
      " [[400   7]\n",
      " [ 17  26]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt = DecisionTreeClassifier(max_depth=2).fit(X_train, y_train)\n",
    "tree_predicted = dt.predict(X_test)\n",
    "confusion = confusion_matrix(y_test, tree_predicted)\n",
    "\n",
    "print('Decision tree classifier (max_depth = 2)\\n', confusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation metrics for binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.95\n",
      "Precision: 0.79\n",
      "Recall: 0.60\n",
      "F1: 0.68\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "# Accuracy = TP + TN / (TP + TN + FP + FN)\n",
    "# Precision = TP / (TP + FP)\n",
    "# Recall = TP / (TP + FN)  Also known as sensitivity, or True Positive Rate\n",
    "# F1 = 2 * Precision * Recall / (Precision + Recall) \n",
    "print('Accuracy: {:.2f}'.format(accuracy_score(y_test, tree_predicted)))\n",
    "print('Precision: {:.2f}'.format(precision_score(y_test, tree_predicted)))\n",
    "print('Recall: {:.2f}'.format(recall_score(y_test, tree_predicted)))\n",
    "print('F1: {:.2f}'.format(f1_score(y_test, tree_predicted)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "      not 1       0.96      0.98      0.97       407\n",
      "          1       0.79      0.60      0.68        43\n",
      "\n",
      "avg / total       0.94      0.95      0.94       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Combined report with all above metrics\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, tree_predicted, target_names=['not 1', '1']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random class-proportional (dummy)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      not 1       0.90      0.91      0.90       407\n",
      "          1       0.03      0.02      0.03        43\n",
      "\n",
      "avg / total       0.82      0.83      0.82       450\n",
      "\n",
      "SVM\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      not 1       0.99      0.99      0.99       407\n",
      "          1       0.88      0.88      0.88        43\n",
      "\n",
      "avg / total       0.98      0.98      0.98       450\n",
      "\n",
      "Logistic regression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      not 1       0.99      0.99      0.99       407\n",
      "          1       0.86      0.86      0.86        43\n",
      "\n",
      "avg / total       0.97      0.97      0.97       450\n",
      "\n",
      "Decision tree\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      not 1       0.96      0.98      0.97       407\n",
      "          1       0.79      0.60      0.68        43\n",
      "\n",
      "avg / total       0.94      0.95      0.94       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Random class-proportional (dummy)\\n', \n",
    "      classification_report(y_test, y_classprop_predicted, target_names=['not 1', '1']))\n",
    "print('SVM\\n', \n",
    "      classification_report(y_test, svm_predicted, target_names = ['not 1', '1']))\n",
    "print('Logistic regression\\n', \n",
    "      classification_report(y_test, lr_predicted, target_names = ['not 1', '1']))\n",
    "print('Decision tree\\n', \n",
    "      classification_report(y_test, tree_predicted, target_names = ['not 1', '1']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, -23.170881270151199),\n",
       " (0, -13.541051928132356),\n",
       " (0, -21.718405203954628),\n",
       " (0, -18.903016323207897),\n",
       " (0, -19.726964302382928),\n",
       " (0, -9.7476306403865181),\n",
       " (1, 5.235052141213675),\n",
       " (0, -19.310534365033241),\n",
       " (0, -25.095755964721327),\n",
       " (0, -21.825189330505872),\n",
       " (0, -24.151842401582744),\n",
       " (0, -19.576855649771879),\n",
       " (0, -22.568654099789118),\n",
       " (0, -10.827495453773645),\n",
       " (0, -11.914204413203882),\n",
       " (0, -10.978195904503959),\n",
       " (1, 11.208246659191989),\n",
       " (0, -27.654781403042065),\n",
       " (0, -12.865795473669293),\n",
       " (0, -25.854659987077181)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y_binary_imbalanced, random_state=0)\n",
    "y_scores_lr = lr.fit(X_train, y_train).decision_function(X_test)\n",
    "y_score_list = list(zip(y_test[0:20], y_scores_lr[0:20]))\n",
    "\n",
    "# show the decision_function scores for first 20 instances\n",
    "y_score_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 8.6499604847538486e-11),\n",
       " (0, 1.315816405950478e-06),\n",
       " (0, 3.6967192074750047e-10),\n",
       " (0, 6.1733985673851152e-09),\n",
       " (0, 2.70824691804493e-09),\n",
       " (0, 5.8429534301319143e-05),\n",
       " (1, 0.99470167503164109),\n",
       " (0, 4.1071586681042176e-09),\n",
       " (0, 1.261977655328713e-11),\n",
       " (0, 3.3223142359023995e-10),\n",
       " (0, 3.2433074314941097e-11),\n",
       " (0, 3.1468759034769616e-09),\n",
       " (0, 1.5796397741384883e-10),\n",
       " (0, 1.984585676488723e-05),\n",
       " (0, 6.6945880760630995e-06),\n",
       " (0, 1.7069574056481497e-05),\n",
       " (1, 0.99998643829059386),\n",
       " (0, 9.765197660745108e-13),\n",
       " (0, 2.5849663728969238e-06),\n",
       " (0, 5.9083176472926785e-12)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y_binary_imbalanced, random_state=0)\n",
    "y_proba_lr = lr.fit(X_train, y_train).predict_proba(X_test)\n",
    "y_proba_list = list(zip(y_test[0:20], y_proba_lr[0:20,1]))\n",
    "\n",
    "# show the probability of positive class for first 20 instances\n",
    "y_proba_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision-recall curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_scores_lr)\n",
    "closest_zero = np.argmin(np.abs(thresholds))\n",
    "closest_zero_p = precision[closest_zero]\n",
    "closest_zero_r = recall[closest_zero]\n",
    "\n",
    "plt.figure()\n",
    "plt.xlim([0.0, 1.01])\n",
    "plt.ylim([0.0, 1.01])\n",
    "plt.plot(precision, recall, label='Precision-Recall Curve')\n",
    "plt.plot(closest_zero_p, closest_zero_r, 'o', markersize = 12, fillstyle = 'none', c='r', mew=3)\n",
    "plt.xlabel('Precision', fontsize=16)\n",
    "plt.ylabel('Recall', fontsize=16)\n",
    "plt.axes().set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC curves, Area-Under-Curve (AUC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y_binary_imbalanced, random_state=0)\n",
    "\n",
    "y_score_lr = lr.fit(X_train, y_train).decision_function(X_test)\n",
    "fpr_lr, tpr_lr, _ = roc_curve(y_test, y_score_lr)\n",
    "roc_auc_lr = auc(fpr_lr, tpr_lr)\n",
    "\n",
    "plt.figure()\n",
    "plt.xlim([-0.01, 1.00])\n",
    "plt.ylim([-0.01, 1.01])\n",
    "plt.plot(fpr_lr, tpr_lr, lw=3, label='LogRegr ROC curve (area = {:0.2f})'.format(roc_auc_lr))\n",
    "plt.xlabel('False Positive Rate', fontsize=16)\n",
    "plt.ylabel('True Positive Rate', fontsize=16)\n",
    "plt.title('ROC curve (1-of-10 digits classifier)', fontsize=16)\n",
    "plt.legend(loc='lower right', fontsize=13)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--')\n",
    "plt.axes().set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gamma = 0.01  accuracy = 0.91   AUC = 1.00\n",
      "gamma = 0.10  accuracy = 0.90   AUC = 0.98\n",
      "gamma = 0.20  accuracy = 0.90   AUC = 0.66\n",
      "gamma = 1.00  accuracy = 0.90   AUC = 0.50\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y_binary_imbalanced, random_state=0)\n",
    "\n",
    "plt.figure()\n",
    "plt.xlim([-0.01, 1.00])\n",
    "plt.ylim([-0.01, 1.01])\n",
    "for g in [0.01, 0.1, 0.20, 1]:\n",
    "    svm = SVC(gamma=g).fit(X_train, y_train)\n",
    "    y_score_svm = svm.decision_function(X_test)\n",
    "    fpr_svm, tpr_svm, _ = roc_curve(y_test, y_score_svm)\n",
    "    roc_auc_svm = auc(fpr_svm, tpr_svm)\n",
    "    accuracy_svm = svm.score(X_test, y_test)\n",
    "    print(\"gamma = {:.2f}  accuracy = {:.2f}   AUC = {:.2f}\".format(g, accuracy_svm, \n",
    "                                                                    roc_auc_svm))\n",
    "    plt.plot(fpr_svm, tpr_svm, lw=3, alpha=0.7, \n",
    "             label='SVM (gamma = {:0.2f}, area = {:0.2f})'.format(g, roc_auc_svm))\n",
    "\n",
    "plt.xlabel('False Positive Rate', fontsize=16)\n",
    "plt.ylabel('True Positive Rate (Recall)', fontsize=16)\n",
    "plt.plot([0, 1], [0, 1], color='k', lw=0.5, linestyle='--')\n",
    "plt.legend(loc=\"lower right\", fontsize=11)\n",
    "plt.title('ROC curve: (1-of-10 digits classifier)', fontsize=16)\n",
    "plt.axes().set_aspect('equal')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation measures for multi-class classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi-class confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"550\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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\" width=\"550\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = load_digits()\n",
    "X, y = dataset.data, dataset.target\n",
    "X_train_mc, X_test_mc, y_train_mc, y_test_mc = train_test_split(X, y, random_state=0)\n",
    "\n",
    "\n",
    "svm = SVC(kernel = 'linear').fit(X_train_mc, y_train_mc)\n",
    "svm_predicted_mc = svm.predict(X_test_mc)\n",
    "confusion_mc = confusion_matrix(y_test_mc, svm_predicted_mc)\n",
    "df_cm = pd.DataFrame(confusion_mc, \n",
    "                     index = [i for i in range(0,10)], columns = [i for i in range(0,10)])\n",
    "\n",
    "plt.figure(figsize=(5.5,4))\n",
    "sns.heatmap(df_cm, annot=True)\n",
    "plt.title('SVM Linear Kernel \\nAccuracy:{0:.3f}'.format(accuracy_score(y_test_mc, \n",
    "                                                                       svm_predicted_mc)))\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "\n",
    "\n",
    "svm = SVC(kernel = 'rbf').fit(X_train_mc, y_train_mc)\n",
    "svm_predicted_mc = svm.predict(X_test_mc)\n",
    "confusion_mc = confusion_matrix(y_test_mc, svm_predicted_mc)\n",
    "df_cm = pd.DataFrame(confusion_mc, index = [i for i in range(0,10)],\n",
    "                  columns = [i for i in range(0,10)])\n",
    "\n",
    "plt.figure(figsize = (5.5,4))\n",
    "sns.heatmap(df_cm, annot=True)\n",
    "plt.title('SVM RBF Kernel \\nAccuracy:{0:.3f}'.format(accuracy_score(y_test_mc, \n",
    "                                                                    svm_predicted_mc)))\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi-class classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.65      0.79        37\n",
      "          1       1.00      0.23      0.38        43\n",
      "          2       1.00      0.39      0.56        44\n",
      "          3       1.00      0.93      0.97        45\n",
      "          4       0.14      1.00      0.25        38\n",
      "          5       1.00      0.33      0.50        48\n",
      "          6       1.00      0.54      0.70        52\n",
      "          7       1.00      0.35      0.52        48\n",
      "          8       1.00      0.02      0.04        48\n",
      "          9       1.00      0.55      0.71        47\n",
      "\n",
      "avg / total       0.93      0.49      0.54       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test_mc, svm_predicted_mc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Micro- vs. macro-averaged metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Micro-averaged precision = 0.49 (treat instances equally)\n",
      "Macro-averaged precision = 0.91 (treat classes equally)\n"
     ]
    }
   ],
   "source": [
    "print('Micro-averaged precision = {:.2f} (treat instances equally)'\n",
    "      .format(precision_score(y_test_mc, svm_predicted_mc, average = 'micro')))\n",
    "print('Macro-averaged precision = {:.2f} (treat classes equally)'\n",
    "      .format(precision_score(y_test_mc, svm_predicted_mc, average = 'macro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Micro-averaged f1 = 0.49 (treat instances equally)\n",
      "Macro-averaged f1 = 0.54 (treat classes equally)\n"
     ]
    }
   ],
   "source": [
    "print('Micro-averaged f1 = {:.2f} (treat instances equally)'\n",
    "      .format(f1_score(y_test_mc, svm_predicted_mc, average = 'micro')))\n",
    "print('Macro-averaged f1 = {:.2f} (treat classes equally)'\n",
    "      .format(f1_score(y_test_mc, svm_predicted_mc, average = 'macro')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear model, coefficients:  [-698.80206267]\n",
      "Mean squared error (dummy): 4965.13\n",
      "Mean squared error (linear model): 4646.74\n",
      "r2_score (dummy): -0.00\n",
      "r2_score (linear model): 0.06\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.dummy import DummyRegressor\n",
    "\n",
    "diabetes = datasets.load_diabetes()\n",
    "\n",
    "X = diabetes.data[:, None, 6]\n",
    "y = diabetes.target\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "lm = LinearRegression().fit(X_train, y_train)\n",
    "lm_dummy_mean = DummyRegressor(strategy = 'mean').fit(X_train, y_train)\n",
    "\n",
    "y_predict = lm.predict(X_test)\n",
    "y_predict_dummy_mean = lm_dummy_mean.predict(X_test)\n",
    "\n",
    "print('Linear model, coefficients: ', lm.coef_)\n",
    "print(\"Mean squared error (dummy): {:.2f}\".format(mean_squared_error(y_test, \n",
    "                                                                     y_predict_dummy_mean)))\n",
    "print(\"Mean squared error (linear model): {:.2f}\".format(mean_squared_error(y_test, y_predict)))\n",
    "print(\"r2_score (dummy): {:.2f}\".format(r2_score(y_test, y_predict_dummy_mean)))\n",
    "print(\"r2_score (linear model): {:.2f}\".format(r2_score(y_test, y_predict)))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(X_test, y_test,  color='black')\n",
    "plt.plot(X_test, y_predict, color='green', linewidth=2)\n",
    "plt.plot(X_test, y_predict_dummy_mean, color='red', linestyle = 'dashed', \n",
    "         linewidth=2, label = 'dummy')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model selection using evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross-validation example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation (accuracy) [ 0.91944444  0.98611111  0.97214485  0.97493036  0.96935933]\n",
      "Cross-validation (AUC) [ 0.9641871   0.9976571   0.99372205  0.99699002  0.98675611]\n",
      "Cross-validation (recall) [ 0.81081081  0.89189189  0.83333333  0.83333333  0.83333333]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "dataset = load_digits()\n",
    "# again, making this a binary problem with 'digit 1' as positive class \n",
    "# and 'not 1' as negative class\n",
    "X, y = dataset.data, dataset.target == 1\n",
    "clf = SVC(kernel='linear', C=1)\n",
    "\n",
    "# accuracy is the default scoring metric\n",
    "print('Cross-validation (accuracy)', cross_val_score(clf, X, y, cv=5))\n",
    "# use AUC as scoring metric\n",
    "print('Cross-validation (AUC)', cross_val_score(clf, X, y, cv=5, scoring = 'roc_auc'))\n",
    "# use recall as scoring metric\n",
    "print('Cross-validation (recall)', cross_val_score(clf, X, y, cv=5, scoring = 'recall'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid search example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid best parameter (max. accuracy):  {'gamma': 0.001}\n",
      "Grid best score (accuracy):  0.996288047513\n",
      "Test set AUC:  0.999828581224\n",
      "Grid best parameter (max. AUC):  {'gamma': 0.001}\n",
      "Grid best score (AUC):  0.99987412783\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "dataset = load_digits()\n",
    "X, y = dataset.data, dataset.target == 1\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "clf = SVC(kernel='rbf')\n",
    "grid_values = {'gamma': [0.001, 0.01, 0.05, 0.1, 1, 10, 100]}\n",
    "\n",
    "# default metric to optimize over grid parameters: accuracy\n",
    "grid_clf_acc = GridSearchCV(clf, param_grid = grid_values)\n",
    "grid_clf_acc.fit(X_train, y_train)\n",
    "y_decision_fn_scores_acc = grid_clf_acc.decision_function(X_test) \n",
    "\n",
    "print('Grid best parameter (max. accuracy): ', grid_clf_acc.best_params_)\n",
    "print('Grid best score (accuracy): ', grid_clf_acc.best_score_)\n",
    "\n",
    "# alternative metric to optimize over grid parameters: AUC\n",
    "grid_clf_auc = GridSearchCV(clf, param_grid = grid_values, scoring = 'roc_auc')\n",
    "grid_clf_auc.fit(X_train, y_train)\n",
    "y_decision_fn_scores_auc = grid_clf_auc.decision_function(X_test) \n",
    "\n",
    "print('Test set AUC: ', roc_auc_score(y_test, y_decision_fn_scores_auc))\n",
    "print('Grid best parameter (max. AUC): ', grid_clf_auc.best_params_)\n",
    "print('Grid best score (AUC): ', grid_clf_auc.best_score_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation metrics supported for model selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['accuracy', 'adjusted_rand_score', 'average_precision', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'log_loss', 'mean_absolute_error', 'mean_squared_error', 'median_absolute_error', 'neg_log_loss', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_median_absolute_error', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.scorer import SCORERS\n",
    "\n",
    "print(sorted(list(SCORERS.keys())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two-feature classification example using the digits dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optimizing a classifier using different evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid best parameter (max. precision): {'class_weight': {1: 2}}\n",
      "Grid best score (precision): 0.5247707213186056\n",
      "Grid best parameter (max. recall): {'class_weight': {1: 50}}\n",
      "Grid best score (recall): 0.9139383300815119\n",
      "Grid best parameter (max. f1): {'class_weight': {1: 4}}\n",
      "Grid best score (f1): 0.5080729218813851\n",
      "Grid best parameter (max. roc_auc): {'class_weight': {1: 20}}\n",
      "Grid best score (roc_auc): 0.8911525501411692\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "from adspy_shared_utilities import plot_class_regions_for_classifier_subplot\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "dataset = load_digits()\n",
    "X, y = dataset.data, dataset.target == 1\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "# Create a two-feature input vector matching the example plot above\n",
    "# We jitter the points (add a small amount of random noise) in case there are areas\n",
    "# in feature space where many instances have the same features.\n",
    "jitter_delta = 0.25\n",
    "X_twovar_train = X_train[:,[20,59]]+ np.random.rand(X_train.shape[0], 2) - jitter_delta\n",
    "X_twovar_test  = X_test[:,[20,59]] + np.random.rand(X_test.shape[0], 2) - jitter_delta\n",
    "\n",
    "clf = SVC(kernel = 'linear').fit(X_twovar_train, y_train)\n",
    "grid_values = {'class_weight':['balanced', {1:2},{1:3},{1:4},{1:5},{1:10},{1:20},{1:50}]}\n",
    "plt.figure(figsize=(9,6))\n",
    "for i, eval_metric in enumerate(('precision','recall', 'f1','roc_auc')):\n",
    "    grid_clf_custom = GridSearchCV(clf, param_grid=grid_values, scoring=eval_metric)\n",
    "    grid_clf_custom.fit(X_twovar_train, y_train)\n",
    "    print('Grid best parameter (max. {0}): {1}'\n",
    "          .format(eval_metric, grid_clf_custom.best_params_))\n",
    "    print('Grid best score ({0}): {1}'\n",
    "          .format(eval_metric, grid_clf_custom.best_score_))\n",
    "    plt.subplots_adjust(wspace=0.3, hspace=0.3)\n",
    "    plot_class_regions_for_classifier_subplot(grid_clf_custom, X_twovar_test, y_test, None,\n",
    "                                             None, None,  plt.subplot(2, 2, i+1))\n",
    "    \n",
    "    plt.title(eval_metric+'-oriented SVC')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Precision-recall curve for the default SVC classifier (with balanced class weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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d1+7fOy3c3/Hat7bIOfd7ksuNiDNzc1/29HxRee1faKrTrL/DxS+nNAY87XsxiR6eehOEXkiennnPtF1ft3RBiR6pnO3ZF7KlXvWQUSuL2EDskkLXiYZXX5kdJ5Rl/9WqvrL6Gf04fnfI/sLlK56AgCgW8qwAUlGg+s/Ci4/TUSGNjQ0HBy928t7InJdwXVuE5HWWOa1xvkbihNVdVP2tdN519+q0YnEBdfv6ByQUzXvN+7FbEA60t4GRFV/KyI7C4d2tLB8OhaLfVxV/zU6tjML7stgVZ2pql+M/j7/vIGYybyTks2eJNqdDUh0/dkiMq3guI7LLsxN5h3J9tq05b1U7rKuumlsbDxIM6/dvz36nrkTnot5THR03O3cj8c185v/eDt/9/+i2/5OwV/tKyJrok3WS1JwwnxeHyNUdVPv3r2b2/ne2begPrHYYzXGHN3O8ewjmROAB0W3mb8gHyEicwqO6dBoMfty9D3PFpFV+b+Zjs7xsCLSN9qct7bT9RwpOG+hK5o52XudiEzL/r+USCROlsxLwGao6s351+3qsV94f40xl0Sbwdz/19ECeWcJG5CBXW1AyiHvZYiP5F8umc+Kecdk3vXse9F1/rXgOkOjy+PRn3M/P0XknOj/kU/lfcl+kvlcGhuLfhFRzAZERJ4TkdWxvA1VdGK6jf5f7PLfKvuySlXtU3AfprIBAQCUXU83ILHMYm+Cqq43xpwZvZvLOZJ5u8h03tder5kT0a+I3tnmgujPv4/+PrfA7t27999o5vyPqYlE4thEIvENybyF5J7sW9MWLsijP78nIldo5p2z+kYL15diHw7zsm5AotfKL1HV11T1J4lE4usm8xkCu0Xk1ryvfTo6lj7xePybxpgbo/tybPQMQlv0W+/vmsw7bN0r0TkOxpij2jv2IjYgSck8C/JE9AF7P1XVDar6fCwWi8XjcY3u94tRX6dp5p2+thezAYmO4XnJnGMzueCvinpMmCLeBSs6hvWaeUOEi1X1W9Fr7M+RzHlCI2LtvExKMs8CLJfMM2BHtPe94/H44ZJ5R6+N0W96v6WZdxl6POru7vzrR8eaaO97RfaTzDM2G6PfKH/LGPOwZt6W9YtRZ/kbkMtEZLcx5jyTeeek06Nj3iMis2Kx3Fvkvhttlo6MTlx/TkR2NTc3/2301qlvRI/D7LspXVXMY7pQ7969m/XDl+V8Kbr9XtnL8t8xqRuP/fwN7V9F3cyTzDs1nRBtznZr3jMjxWxAVPUmEdmZSCS+07t377/pzv3sjrz/B95R1f7Rz6Lron+jC2Ox3DlR74vIC5I5lyMpIs9K5l3obDwe/+dYbO+fn9lNhKo+Gj2eT4g2y3tU1WZfAlvMBsQY8znJvJPYaBFJRudavSaZX0B8oth/q+hn7K7o8fi/knk2bAcbEABA2ZVhA5JdjNwsH37mwwpjzDUF7zC0j4j0jRZ8uzTzeSC/yvveey2wo894eFEzn22Q/Q3scR1dP5ZZ/F0oIss18xr4N4wxV7fzOSD5X9OjDUgsFoslEonPiMhAid5fXzOfl9EvtveJyJ+IBv4ayfy2d7aIHJ/9y2hBMUMy57m0isgL0Ynd2yXvBPrubECir/lutLHZJZlnBG7MP+k6eoboNY0+D0Uy7161pNgNiHx4kvKvC/+umMdEdlHZ1Wvxm5qaPiuZk8aXSObtindEi9h+sQ5eRqOZt6/dI5lzUzo8j6OhoeFgk/ksioWaORdjs2ZOlj6l8OuiLh7q7FijDeXdknlZ2tuqOin7RgLZ76EfPiPwZ6p6p6puiO7TElW9LHqmYFf2ZWPGmP9R1YmSecved0RkgkQnIEe3KdGCtzX6t1yg7XwWSjEk8xbVb8XyTtKWzEnu22IFb39czGO/8HEavfRtTPaxHt3XySIypKOviS7bawMSvbvcYlV9r/CZz3LKOwn959Fx79LMOTq/y7+eMeZEiT7HRzPPIj0rIv8tInuy/38U/vw0mc8tWh79/7lKRB5KJBLH6t7P2na5AYku+5JkTubfIdFJ6fnPNBb5c2o/zbzF8Nro+zwnvAQLAADAn+i33wN8H0e1EpH/187L5T4WPcN1c7tfBAAAANSpfSXzgYGdfl5MaKIT4Dv7ML4vdfZOYGU+lpOilxhdFr1U7LsiMjj6bXs5PxASAAAAqH7Zd7KqJpo5r6ezt0S2xb4Esxwk87au86OXkm1V1ZHRO1IBAAAAAAAAAAAAAAAAAAAAAAAAAADUH2PMn4nIgo4+FC26zudEZFr0mQAzEonEv1fwEAEAAADUgqampv1F5LnCD4/KF31w24boU7L/XkRuE5GW/A+O68qePXvsm2++ZTdu3E56mDfffMvSJ32GGvqkz5DTXp9lG6gAgK4ZY/5BROaq6rzONiDGmJ+p6orYh5++vI+IvN7VJ3Tns9baLVve9j58aiFbtrxt6ZM+Qw190mfIaa/Pnk9TAEDRjDFnisgtDQ0NB3SxAblXVR/Jv0xEHhKRe4q9LQZoJq2tbU4GKKHPUEKf9Bly2IAAQEA624CIyFBVvbbgsutEZHix399aa9vadtgtW96u24ycuNyecuFw+/zY13r0fdradlj6LF/okz5DDn2677OH4xMAUKrONiCqOlZVL8+/TESuUNUxxX5/C3vh3RNtMpW2l98/xfehAAAiPRyfAIBSdfEMyPAOngEZUuz3t5bf4J12xYs2mUrbmx+fWfbf4BH6DCX0SZ8hh2dAACAgXZ0DIiIPFVz2MOeAFJ/1G7bZo/sMtslU2j4ydEHZX8NM6DOU0Cd9hpz2+uzR8AQAlK6rd8ESkaWxvd8Fa5mInFbs96/3AbpgaatNptI2mUrbka8sK/sAJfQZSuiTPkMOGxAACEjhBqSpqemzjY2Nn4zFYjFjzF+KyEYRuS16697bVHVDdz4HpN4H6LjpK3MbkJmvriv7ACX0GUrokz5DDhsQAAhI4QZEVW3+53yo6hdFZLaI7BSRacaYz3Xn+9f7AH161OLcBmTl6i1lH6CEPkMJfdJnyGEDAgB1pN4H6F1Pz7bJVNqecuFwJwOU0GcooU/6DDlsQACgjtT7AL3snkk2mUrbs24Y62SAEvoMJfRJnyGHDQgA1JF6H6C/uW6sTabS9g83j7Pjpq20y1e+WdYBSkoPfdJnyKFP9336no8AAEfqfYCefMGw3DkgyVTannjeULtm7dayDVBSeuiTPkMOfbrv0/d8BAA4Uu8D9KI/TtxrA5JMpe2Cpa1lG6Ck9NAnfYYc+nTfp+/5CABwpN4HaGtrm52/pMXe9qdZNplK22P6DrYbWraVbYCS0kOf9Bly6NN9n77nIwDAEQZoJtc/PN0mU2n7sytfLOsAJaWHPukz5NCn+z59z0cAgCMM0Ez63TbBJlNpe96dL5d1gJLSQ5/0GXLo032fvucjAMARBmgmP7lspE2m0vbmx2aUdYCS0kOf9Bly6NN9n77nIwDAEQbodrtu/TZ7VHQC+mPDF5Z1gJLSQ5/0GXLo032fvucjAMARBuh2O2/xhtw7YI2avKKsA5SUHvqkz5BDn+779D0fAQCOMEC321GTV+Q2IHMXbSjrACWlhz7pM+TQp/s+fc9HAIAjDNDt9rHhC3MbkDFT3ijrACWlhz7pM+TQp/s+fc9HAIAjDNDt9o9Pz8ltQI7pO9iuWrOlbAOUlB76pM+QQ5/u+/Q9HwEAjjBAt9vp89fu9UnobEDCCH3SZ8ihT/d9+p6PAABHGKCZ3P5E5pPQf3jxiLIOUFJ66JM+Qw59uu/T93wEADjCAM3koj9OtMlU2v7h5nFlHaCk9NAnfYYc+nTfp+/5CABwhAGayS+uHmWTqbS99sFpZR2gpPTQJ32GHPp036fv+QgAcIQBut22tLTZY/sNtslU2j6Qnl/WAUpKD33SZ8ihT/d9+p6PAABHGKDb7aKlrbkT0Ie/vKysA5SUHvqkz5BDn+779D0fAQCOMEC323HTV+Y2IDPmryvrACWlhz7pM+TQp/s+fc9HAIAjDNDt9qkXFvX4LXg7GqCk9NAnfYYc+nTfp+/5CABwhAH64VvwJlNpu2hpa1kHKCk99EmfIYc+3ffpez4CABxhgG63Nz02I7cB4ST0cEKf9Bly6NN9n77nIwDAEQbodjtrwfrcBuTJFxaVdYCS0kOf9Bly6NN9n77nIwDAEQbodjtj/rrcBmTctJVlHaCk9NAnfYYc+nTfp+/5CABwhAG63Q6b8DrngAQY+qTPkEOf7vv0PR8BAI4wQLfbgen5NplK22P7DbYtLW1lHaCk9NAnfYYc+nTfp+/5CABwhAG63fZ/YKpNptL2l9eMLvsAJfQZSuiTPkMOGxAAqCMM0O327JvG2WQqbS++e2LZByihz1BCn/QZctiAAEAdYYBut9+/aLhNptL2zidnl32AEvoMJfRJnyGHDQgA1JF6H6ArV2/OnYD+1AuL7IaWbbaltbTzQFiQlDf0SZ8hhz7d9+l7PgIAHKn3ATpt3trcBiSbUy8daecu2lCWAUpKD33SZ8ihT/d9+p6PAABH6n2Avvpay0c2IMlU2j794uKyDFBSeuiTPkMOfbrv0/d8BAA4wgDdbqfMWWOffGGRvfOp2bkNyNipb5RlgJLSQ5/0GXLo032fvucjAMARBuiHGf7ystwGZGEJH0jIgqS8oU/6DDn06b5P3/MRAOAIA/TDDHx+XvSBhENK+kBCFiTlDX3SZ8ihT/d9+p6PAABHGKAf5uqBU2wylba/6l/aBxKyIClv6JM+Qw59uu/T93wEADjCAP0wZ90w1iZTaXvpPZPKNkBJ6aFP+gw59Om+T9/zEQDgCAM0k9bWNnvieUNtMpW2dz8zp2wDlJQe+qTPkEOf7vv0PR8BAI4wQDNZumJT7gT058e+VrYBSkoPfdJnyKFP9336no8AAEcYoJm8PGNVbgPy2LAFdtz0lXvl5Rmr7PoN27o9QEnpoU/6DDn06b5P3/MRAOAIAzSTQaOXtPuBhPm54K5Xuj1ASemhT/oMOfTpvk/f8xEA4AgDNJNp89ba484Z0ukG5OdXjer2ACWlhz7pM+TQp/s+fc9HAIAjDNAPs2LVZjtn0YaP5JfXjLbJVNpeMqDzd8diQVLe0Cd9hhz6dN+n7/kIAHCEAdp5Wlvb7AnRu2MNGNT5u2OxIClv6JM+Qw59uu/T93wEADjCAO08i1/fmHsJ1pDxS7s9QEnpoU/6DDn06b5P3/MRAOAIA7TzjJnyRm4DMmvB+m4PUFJ66JM+Qw59uu/T93wEADjCAO08jwxdYJOptD2qT9quW7+12wOUlB76pM+QQ5/u+/Q9HwEAjjBAO8+1D06zyVTant7FO2B1NEBJ6aFP+gw59Om+T9/zEQDgCAO08/zuxpdsMpW2F989saQBSkoPfdJnyKFP9336no8AAEcYoB2ntbXNnhi9A9ZZN4y1AwbNsWOmvNGtAUpKD33SZ8ihT/d9+p6PAABHGKAdZ+XqzR/5MMKjUmm7YtXmogcoKT30SZ8hhz7d9+l7PgIAHGGAdp7rHppmT7lwuD0++pT0o1Jpu3Zd+yejsyApb+iTPkMOfbrv0/d8BAA4wgAtLtc+FJ2MfuWL3RqgpPTQJ32GHPp036fv+QgAcIQBWlzOumGsTabS9pIBk7o1QEnpoU/6DDn06b5P3/MRAOAIA7TrtLS22ePPzbwE695n53ZrgJLSQ5/0GXLo032fvucjAMARBmjXWbC0NXcS+vCXl3VrgJLSQ5/0GXLo032fvucjAMARBmjXeXHi8twGZO6iDd0aoKT00Cd9hhz6dN+n7/kIAHCEAdp1Bqbn22QqbY/tN9iu37CtWwOUlB76pM+QQ5/u+/Q9HwEAjjBAu84V9022yVTannntmG4PUFJ66JM+Qw59uu/T93wEADjCAO06v7pmtE2m0vZ75w+zf7h5nP3DzePsuXe8bKfOXdvlACWlhz7pM+TQp/s+fc9HAIAjDNCu8/2Lhn/kE9GTqbTt/8DULgcoKT30SZ8hhz7d9+l7PgIAHOF1JpkAACAASURBVGGAdp2Rryyzl9872V52zyR7yYBJuQ3IPYP2fkteFiTlDX3SZ8ihT/d9+p6PAABHGKDdy/wlLbkNyIiCt+RlQVLe0Cd9hhz6dN+n7/kIAHCEAdq9jHh5WW4D8uqSli4HKCk99EmfIYc+3ffpez4CABxhgHYvAwbNtclU2h5/7hDb0trW5QAlpYc+6TPk0Kf7Pn3PRwCAIwzQ7uWiP060yVTa/u7Gl4oaoKT00Cd9hhz6dN+n7/kIAHCEAdq9/OSyF2wylbY3PDK9qAFKSg990mfIoU/3ffqejwAARxigxWfl6s258z+efGFRUQOUlB76pM+QQ5/u+/Q9HwEAjjBAi88rM1flNiCvzFxlW1raculogJLSQ5/0GXLo032fvucjAMARBmjxeWLkonY/kDCZStur7p/CgqTMoU/6DDn06b5P3/MRAOAIA7T4dLYBOen8YSxIyhz6pM+QQ5/u+/Q9HwGgrjQ1Ne0vIgNFZJuqblDVPh1dV1WPU9XFqvq2qk40xny+O7fFAC0+6zdss0PGLbVPjFyUy8+ufNEmU2l79k3jWJCUOfRJnyGHPt332fNpCgAomjHmDlWdZ4z5fLTB2G6MObGd6/2jiOxU1R8bYxKqeqeqbmhoaDig2NtigPYsP7x4hE2m0vaWx2ayIClz6JM+Qw59uu+zvJMVANChXr16HSgiO40xR2QvE5GLRGR84XWNMX9Q1ZnZPx988MF/oao2Ho9/odjbY4CWnqUrNuVegvXs6CUsSMoc+qTPkEOf7vvs6TwFABQpkUj8l4h8EIvFPpG9zBhzhIjsjMVi++ZfV1V/LCI7E4nEl2Ox2L4i8jsRaTv88MP/utjbY4CWnjFTVuQ2IDNfXceCpMyhT/oMOfTpvs+yDVYAQOdE5AQRaSm47O9V1RpjDsm/3BjzZyIySFWtiHygqu+JyLe7c3vWWtvWtsNu2fI26WYeHDzfJlNpe2y/wXbjpu22rW2Hpc/yhT7pM+TQp/s+yzFTAQBFMMacKiKr8i9T1Xi0AWnMv/ywww5riE48/00ikfh3Vb1fRNYmEonPFHt7FiW7+sFpNplK29/fPM73oQBAzSnXXAUAdMEYc1JHz4A0NjYelH+5qj4qInfnXbSvqr5mjDm32Nuzlt/glZqfXv6CTabS9oZHpnf4GzxSeuiTPkMOfbrvs4fjFABQrLxzQD6Wd9nXRWRHrOAcEBFZJCJn5F+mqk+LyB+LvT1reQ1zKXlj9ebc+R83PTrDjp++0r4ya7Vdt+kt+ixTtmzhNfb0GW7o032fPZ2nAIAiNTQ0HCAiO+Px+Feyl4nIxSIyofC6qjpGRG4vuGyeiPQr9vYYoKXllZmr2v1AwqP7DrbzX2vxfny1EBZ49Bly6NN9nz2bpgCAbhGRASKyIB6P/0cikThWRNpE5PhYLBZramr6bGNj4ydjsVgskUicHL1l76mJRMKo6rUisq2754AwQLufBa+12OPPGdLuJmTK3LXej68WwgKPPkMOfbrv09WMBQC0o6Gh4QBjzMPRp5uvM8acnf276B2vfpr9szHm9OiT0N9S1Vf4JPTK5Y3Vm+3shevt7IXr7e1PzLLJVNoe1Sdt17ds835stRAWePQZcujTfZ9lHKsAgJAwQMuTK++fYpOptD3zurH0WaawwKPPkEOf7vv0PR8BAI4wQMuT06980SZTaXvzn2bRZ5nCAo8+Qw59uu/T93wEADjCAO15Vua9I9aQl5fTZ5nCAo8+Qw59uu/T93wEADjCAO15JkxfmduALF65mT7LFBZ49Bly6NN9n77nIwDAEQZoz/PwkFczb8HbZ7Dd9d4H9FmmsMCjz5BDn+779D0fAQCOMEB7nsvunWyTqbT99XVjWJCUMSzw6DPk0Kf7Pn3PRwCAIwzQnuenl79gk6m0/eU1o+3YGavs5s1veT+mWggLPPoMOfTpvk/f8xEA4AgDtGdZt35rOx9EuMZuWrbGbu9/g333y1+17zfH7Qef6WXfb47bd7/8Vbu9/w1203I+rLCrsMCjz5BDn+779D0fAQCOMEB7ltbWNnvVwCn2hHOH2mQqbX/6i/vsppNPtXsOONDaWKzD7DngQLvjJ6fbN+cs8n4fQg0LPPoMOfTpvk/f8xEA4AgDtDwZMGiu7Xdyf7t9/7/odONRmN0HHWS3DBvt/fhDDAs8+gw59Om+T9/zEQDgCAO0PLn7D3fZXft9olubj9yzIfvvb7cMHeX9PoQWFnj0GXLo032fvucjAMARBmjPs2n2Qrv9kx995mPPfvvZXd892rbdda/d+nTatt11r9115FF2z777tvtMCC/H2jss8Ogz5NCn+z59z0cAgCMM0J7nzVN+/JENxbtf/6Z9c/bC9q8/e6F99+vf/MjX7PjJ6d7vS0hhgUefIYc+3ffpez4CABxhgPYsm5avte/vf8BeG4n3vvEtu3H9ls6/dv2Wj2xCdh/457w7Vl5Y4NFnyKFP9336no8AAEcYoD3L9v43fORlV1vnLynqa9+cvdDu2W+/vb5+e/8bvd+nUMICjz5DDn2679P3fAQAOMIA7Vne/fJX934p1fHHd6vPXUcetfdLt778Ve/3KZSwwKPPkEOf7vv0PR8BAI4wQHuW95vje20gnvjxRbbPLeNt6uZxReXxH5y/19dvPKTxI9e54ZHpdv2Gbd7va6XDAo8+Qw59uu/T93wEADjCAO1Z3j/kM3ttIC464bKPfDJ6Z7n4+Ev3+vrNB/51u9ebOGu19/ta6bDAo8+QQ5/u+/Q9HwEAjjBAe5bCZ0AG//Iye/m9k+2l90wqKs+edvFeX//mZw7L/d3pV42yyVTaHpVK2xWrNnu/r5UOCzz6DDn06b5P3/MRAOAIA7RncXkOyIV3vWKTqbQ989ox3u+nj7DAo8+QQ5/u+/Q9HwEAjjBAe5bCd8Gy3X0XrIIPJcy+C9aGlm32pPOH2mQqbW/70yzv99NHWODRZ8ihT/d9+p6PAABHGKA9y6bla+2eAw4s7XNAjvhGh58DMm3e2tz5Hy9OWu79fvoICzz6DDn06b5P3/MRAOAIA7Tn2fGT07v/SegFm4/CT0J/eMirmfM/+qTtytX1d/7Hxo0s8Ogz7NCn+z59z0cAgCMM0J7nzTmL7O6DDvrIhmLPfvvZXUceZdvuvMdufep523bnPXbXd5IfedmVjcXs7oMOsm/OWZT7nufd+bJNptL2N9eP9X7/fIUFHn2GHPp036fv+QgAcIQBWqbhOXSU3bP//h/ZWBSTPfvvb7cMG537Xus3bLMnnJs5/+POJ2d7v2/eOmWBR58Bhz7d9+l7PgIAHGGAli9tI0Zb++lPd2vzsfugg/bafGzcuN1OnrMmd/7HqMkrbEtr215pbW3zfl8rERZ49Bly6NN9n77nIwDAEQZomQfo6tV212mnf+TE9I9sPA78c7vjJ6fv9bKrbB5Iz+/0wwuPO2eIfX7sa97vb0X65PFJn4GGPt336Xs+AgAcYYC6GaCblq+12/vfaN/98lft+/GE/eAzvez78YR998tftdv735h7t6v2cu+zc7v8BPX+D071fn8r2afvY6mF0Cd9hhw2IABQRxigbgdoKVmzbqt9fuxr9k8jFu6Vu56enduAPDNqsff7Wy19Evqkz/DDBgQA6ggD1O0ALWeeHrU4twFZtLTV+/2t9j7rLfRJnyGHDQgA1BEGqNsBWs5cds8km0yl7c+ufNH7fa2FPust9EmfIYcNCADUEQao2wFarrS0tNmTLxhmk6m0venRGd7va7X3WY+hT/oMOWxAAKCOMEDdDtByZdq8tbmXX418Zbn3+1rtfdZj6JM+Qw4bEACoIwxQtwO0XMm+Ne9RfdJ25erN3u9rtfdZj6FP+gw5bEAAoI4wQN0O0HKl763jbTKVtr+/8SXv97MW+qzH0Cd9hhw2IABQRxigbgdoObJm7VZ7TN/BNplK28vvnWwnzFjVbibOWm3XrtvqvYfQ+6zX0Cd9hhw2IABQRxigbgdoOTJu+souP5gwm4v+ONF7D6H3Wa+hT/oMOWxAAKCOMEDdDtByZM7C9blnQLpK6pbx3nsIvc96DX3SZ8hhAwIAdYQB6naAlivLV75pZy9Y325embkqtwF5ID3few/V0Gc9hj7pM+SwAQGAOsIAdTtAK5Gh41/PbUBmvrrOew/V3methj7pM+SwAQGAOsIAdTtAK5GrB061yVTa/vCSEba1tc17D9XeZ62GPukz5LABAYA6wgB1O0Bdp6W1zZ5y4XCbTKXtdQ9N895BtfdZy6FP+gw5bEAAoI4wQN0OUNeZOjf/E9KXee+g2vus5dAnfYYcNiAAUEcYoG4HqOvc99w8m0yl7TF9B9tVa7Z476Da+6zl0Cd9hhw2IABQRxigbgeo65x1w0s2mUrbvrfWztvv+uyzlkOf9Bly2IAAQB1hgLodoC6z7I037VHRy6/OvnmcvffZuVWTQaOX2JYuTphngVfdj89aD32679P3fAQAOMIAdTtAXWbUpBVFf0J6iBkz5Y2g+qz10Cd9hhw2IABQRxigbgeoyyxdscn++rox9uQLhlVNju03JLcBmbVgfVB91nrokz5DDhsQAKgjDFC3A5TsnT63jLfJVNr+6prRXX5mCX2WN/RJnyGHDQgA1BEGqNsBSj7MkmUbc89+PDh4Pn1WOPRJnyGHDQgA1BEGqNsBSj7Mw0NfzW1AFi5tpc8Khz7pM+SwAQGAOsIAdTtAyYf59bVjMu/YddM4+vQQ+qTPkMMGBADqCAPU7QAlmcxZtCH37McTIxfRp4fQJ32GHDYgAFBHGKBuByjJZMCgOTaZStuj+wy2y1a+SZ8eQp/0GXLYgABAHWGAuh2gZLttbW2zP738BZtMpe35d75Cn55Cn/QZctiAAEAdYYC6HaBku50yZ03u5Vc/uWykTd0yvqj0uXW8PffOV2yfW4u7fmc55/YJduzUzj/4sNbD45M+Qw4bEACoIwxQtwOUbLdPvrDI+6euJ1Npm7plvPcufIbHJ32GHDYgAFBHGKBuByjZbt9Yvdne+Mh0e8mASd3KpfdMslc/OM1eek/3vi4/qeiDD5OptL1n0FzvXfgMj0/6DDlsQACgjjBA3Q5Q4rfPgen5uQ3IgiI+e6SWw+OTPkMOGxAAqCMMULcDlPjrs6W1zZ52Rebk9363TfB+f3yHxyd9hhw2IABQRxigbgco8dfnyzNW5Z79GDxuqff74zs8Pukz5LABAYA6wgB1O0CJvz6vHjjFJlNpe9L5w+zadVu93x/f4fFJnyGHDQgA1BEGqNsBSvz0uXL1ZnvcOUNsMpW2Nz06w/t9CSE8Pukz5LABAYA6wgB1O0CJnz6fynvr3+nz13q/LyGExyd9hhw2IABQRxigbgco8dPnb68fa5OptD3z2jG2tbXN+30JITw+6TPksAEBgDrCAHU7QEnl+5y1YH3u2Y/HRyz0fj9CCY9P+gw5bEAAoI4wQN0OUFL5Pm95bKZNptL22H6D7YpVm73fj1DC45M+Qw4bEACoIwxQtwOUVLbPNeu22pPOH2aTqbS98v4p3u9DSOHxSZ8hhw0IANQRBqjbAUoq2+fzY1/Lvfzq5RmrvN+HkMLjkz5DDhsQAKgjDFC3A5RUts+zbx5nk6m0Pf3KFzn5vAx9Evr02afv+QgAcIQB6naAksr1OWfhhyefPzJ0gffjDy08Pukz5LABAYA6wgB1O0BJ5fq89U+Zk8+P6TvYLlv5pvfjDy08Pukz5LABAQDPmpqa9heRgSKyTVU3qGqfjq4bj8f/WVUnishOEXk1kUh8vTu3xQB1O0BJZfpct36rPfmCzMnnV9w32fuxhxgen/QZctiAAIBnxpg7VHWeMebzqnqcqm43xpxYeL14PP4pEWkxxtybSCSMql4uItsSicRnir0tBqjbAUoq0+fgcUtzL7+aMH2l92MPMTw+6TPksAEBAI969ep1oIjsNMYckb1MRC4SkfGF1xWR34nIslgstl/2MlWdoapHFnt7DFC3A5RUps/ULeNtMpW2J18wzE6YsYqXYPWwT0KfIfTZw3EKAChWIpH4LxH5IBaLfSJ7mTHmCBHZGYvF9s2/rog8p6o39eT2GKBuByhx3+fi1zfmnv3I5uQLhtlVa7Z4vw8hhccnfYYcNiAA4JGInCAiLQWX/b2qWmPMIQWXzzXGnGeMuVdEWkRkaiKR+HJ3bo8B6naAEvd9LnvjzdyHD2ZzbL8hdvVaNiCl9EnoM5Q+yzFTAQBFMMacKiKr8i9T1Xi0AWnMv1xElkUnql9ujPmciFwnIu8kEonDir09a61ta9tht2x5m/QwbW07LH366XPdhm12yty19pi+g20ylbb9H5zm/fhDC49P+gw57fVZrrkKAOiCMeakjp4BaWxsPCj/clVdIiLjCq47R1UvKPb2LFAjhk1ckXsGZPHKzb4PB0AP9WyaAgCKlncOyMfyLvu6iOyIffQckHEicnf+Zar6VOFlnbGW3+C5/A0eqUyfmze/ZX/Vf7RNptL2N9ePtZs3v+X9+EMLj0/6DDk8AwIAHjU0NBwgIjvj8fhXspeJyMUiMqHwuqraX0QmF1w2zxhzbrG3Zy2vYXb5GmZSmT5fmbkq9+zHoNFLvB97iOHxSZ8hp70+ezZNAQDdIiIDRGRBPB7/j0QicayItInI8bFYLNbU1PTZxsbGT8ZisVhzc3NvVX1bVS9LJBJGRK5Q1bdU9dBib4sB6naAksr0ecV9k20ylbYnnT/Mrlm71fuxhxgen/QZctiAAIBnDQ0NBxhjHo42F+uMMWdn/05VrYj8NPvnRCLxZRGZJSK7RGSOiPx3d26LAep2gBL3fb6+YlPu5PNbH5/p/bhDDY9P+gw5bEAAoI4wQN0OUOK+z4Hp+bmXX81bvMH7cYcaHp/0GXLYgABAHWGAuh2gxG2fLS1t9seXjbTJVNr2u22C92MOOTw+6TPksAEBgDrCAHU7QInbPl+YuDz37MeIl5d5P+aQw+OTPkMOGxAAqCMMULcDlLjts99tE2wylbY/umSEXb9hm/djDjk8Pukz5LABAYA6wgB1O0CJuz7nLFyfe/ZjYHq+9+MNPTw+6TPksAEBgDrCAHU7QIm7Pm98dIZNptL22H6D7bI33vR+vKGHxyd9hhw2IABQRxigbgcocdPnG6s32+PPHWKTqbS95oGp3o+1GsLjkz5DDhsQAKgjDFC3A5S46fPRYQtyL7+aPn+t92OthvD4pM+QwwYEANphjPlZT+L7+DvCAHU7QEn5+2xpabOnXfGCTabS9vc3veT9OKslPD7pM+SwAQGAdojIHhHZXWp8H39HGKBuBygpf5+jJq3IPfsxZPxS78dZLeHxSZ8hhw0IALRDRB4SkQdLje/j7wgD1O0AJeXv89zbM2+9+0PeercsfRL6DCFsQACgjjBA3Q5QUt4+5y3ekHv24/sXDbd9bx1vnx612PuxVkN4fNJnyGEDAgB1hAHqdoCS8vb57OgluQ1INiedP9T7sVZDeHzSZ8hhAwIA7eAkdFLKACXl7XPl6i32pkdn2HOil2ElU2l72T2TvB9rNYTHJ32GHDYgANAOTkInpQxQ4qbPx4cvzG1AJs9e4/1YqyE8Pukz5LABAYB2cBI6KWWAkvL32dLSZn96eeZteH9340u2tbXN+7FWQ3h80mfIYQMCAHWEAep2gJLy9zni5WW5Zz+GTXjd+3FWS3h80mfIYQMCAHWEAep2gJLy93n2TeNsMpW2P7lspN3Qwtvw9rRPQp8hhA0IAHSDMeY/ReQHInJawYnnP1fV34rIVSIy1/dxdoQB6naAkvL2OWXumtyzH48OW+D9GKspPD7pM+SwAQGAIsTj8U+JyLQiTkDfw0no9REWJO77vOK+yTaZStsTzhtqV67e4v0Yqyk8Pukz5LABAYAiqGr/aHOxTUSeE5E5IvKBqj6iqmkR2Rj9/avGmM/5Pt6OMEDdDlBSvj4Xvd5qj+4z2CZTaXvbn2Z5P75qC49P+gw5bEAAoAgiskhV343H4xr9+XgR2R2Px78Qi8VivXr1OlBEhovI7kQi8Q2/R9sxBqjbAUrK1+ddT83+yIcQktJy5rVj7Ko1PINUzscnKX+fvucjAARHVd8WkfHZPzc3N/eOnvE4K3vZoYce+mkReUdEhng5yCIwQN0OUFK+Pq95YKr3hXstZd7iDd7/jas5/P/uvk/f8xEAgiMiO1X1qYLLdonIPQWXjVLVDZU9uuIxQN0OUFK+Ples2myfHrXYPj58ISklIxbaY/pmXsJ21g1j+fyUMj8+Sfn79D0fASA4qrpCVV/Jv0xEFonI5ILLBqnqu5U9uuIxQN0OUEKfoWR43uenjHh5mffjqfbw+HTfp+/5CADBUdVHReQDY8x/5l32lIjsampq+mx00X6q+oaIrPZ0mF1igLodoIQ+Q0hra5v9zfVjbTKVtj+78kXb0sKzHz0Nj0/3ffqejwAQHGPM51X1fRHZKSJXxWKxWCKR+E50HshsETlLVUeKyO7Cl2qFhAHqdoAS+gwh46avzD378czoJd6PpxbC49N9n77nIwAEyRhzkohsVNVHspeJyJDsZ39k36Y3Ho+Lz+PsDAPU7QAl9BlCzr3jZZtMpe0PLh5hWzj3oyzh8em+T9/zEQBC9vFEInFY3p/3UdUfi8jdInJFc3Nzb29HVgQGqNsBSujTd2a+ui737MefXlhMn2UKj0/3ffqejwAQtMbGxoPy/9y7d++/aW5u/hdfx9MdDFC3A5TQp+9cPXCKTabS9vhzhthtb+2izzKFx6f7Pn3PRwAIkjHm8yIyV0Qm5F8uIt+PXoI1t7m5+W99HV8xGKBuByihT59Z/PrG3KfHn3fHy3b+sk128pw19pWZq6oqIX5oIo9P9336no8AEJympqa/iz6McI+qvpT/d4lE4usiMjU6B2RrIpEwvo6zKwxQtwOU0KfP3PvsXO8feFiOnH7li9675PFZ+T59z0cACI6I/CnaYJzR0XWMMWdGG5RHK3ls3cEAdTtACX36zOMjFnrfPJQjp1460nuXPD4r36fv+QgAwVHVlao6vavrRW/J21KJYyoFA9TtACX06TsLlrbama+us3MWbbDL126zcxZtsDNfXRd8RuR9cOK9z8713iOPz8r36Xs+AkBwRGRHMZ/voapPi8iuShxTKRigbgcooc9QUm193v7ELJtMpe3RfQbb15Zv8n481d5n6GEDAgBFUNXXRGRZEddbrKpvVOKYSsEAdTtACX2Gkmrqc826rfbkC4bZZCptL7tnkvfjqfY+qyFsQACgCCJyg4jsNsZc08l1Lo4+Cf2uSh5bdzBA3Q5QQp+hpJr6fHbMktzLrybMWOX9eKq9z2oIGxAAKEJzc3MvVV0fbTDmGWOuEZEzolwlIrOit+JtVdVDfR9vRxigbgcooc9QUi19tra22d9cP9YmU2n7i6tH2dZAP7m9WvqslrABAYAiJRKJf4pOMt8TZXeU7J8Xhv6BhAxQtwOU0GcoqZY+p85dm3v244mRi7wfT7X3WS1hAwIA3bNPIpH4L1W9wBhzh4jcbYy5JB6PfzMWi+3j++C6wgB1O0AJfYaSaunzmgem2mQqbU84d2iQH0BYbX1WS9iAAEAdYYC6HaCEPkNJNfS5fOWb9th+Q2wylbY/v2qUvf+5eV7y+IiFXW5+qqHPagobEADoJlX9ooicY4y5wxjz81gsFhORpDHmEN/H1hUGqNsBSugzlFRDn+mXXvP+oYfZ3P/8vKrvs5rCBgQAihSPxw8XkQl5537sVtVHYrFYTFWni8g7iUTiWN/H2RkGqNsBSugzlFRDn6++1mJ/cfUoe9L5w7zk+HOG5DYgg0Yvqfo+qylsQACgCIceeuinVfWN6GTzuSJynYjsyW5ARORP0Z/fU9V/9X28HWGAuh2ghD5DCX12nVsem2mTqbQ97pwhduVqXoJVybABAYAiqOpN0ebjiuxl+RuQ6M9nRNd53M9Rdo0B6naAEvoMJfTZedZv2GZPuXC4TabS9sr7p9BnhcMGBACKICLLRWRpwWV7bUCiy14VkeWVPbriMUDdDlBCn6GEPjvPixOX515+NXbqG/RZ4bABAYAiiMguVX264LL2NiCDRGRnZY+ueAxQtwOU0Gcooc/Oc+mASTaZStsfXjzCbmjZRp8VDhsQACiCiLSKyLSCy9rbgMwSkZbKHl3xGKBuByihz1BCnx1nxarN9pi+g20ylbZ3PDmLPj2EDQgAFEFEnhGRD+Lx+FfyLttrA5JIJL4RnQMyyM9Rdo0B6naAEvoMJfTZcZ56YVHu5VezFqynTw9hAwIARTDGfF5V3xORbSLye1X91+wJ56oaV9XfishWEfnAGPOfvo+3IwxQtwOU0Gcooc+O8/sbX7LJVNqeee0Y+vQUNiAAUCRjzI9EZGf+54AUfCbI+8aY3/g+zs4wQN0OUEKfoYQ+28/8JS25Zz9+dMkI2/fW8UWl320T7AV/nGj73Tah6K/pe+t4e+7tE+zIV5Z7v9+hhQ0IAHRDPB5XEblbVReLyDvRyelviMhDxpjP+z6+rrAgcTtACX2GEvpsP0PGLa34p6z/qv9o7/c7tLABAYDy+riIXOj7IDrCgsTtACX0GUros/2sXrvF3vTYDHvx3RO7lUsGTLJXDpxqLxkwqajrX3jXK7kNSP8Hpnq/36GFDQgAdEJVDzXGnC4i56jqkbFYbJ+OrptIJL4RPTOyu4KH2C0sSNwOUEKfoYQ+/fY5cdbq3AbkhYm8BKuYPn3PRwAIgjHmPFV9t+Bcj2kNDQ0H51+vubm5l4j8Kfr7PSKyw9cxd4UFidsBSugzlNCn3z7veHKWTabS9vhzhtg167Z6P/7QwgYEANqhqsdFm4k9qjpdVZ+OPgtkj4gMyV7PGPM9Vd2ct/kYHY/Hxeexd4YFidsBSugzlNCnvz5bW9vsz6540SZTaXvx3RO9H3uIDkuOYgAAIABJREFUYQMCAO0QkVEistsY84fsZYcccsifi8jU6PKEqp6f3Xio6noR+b7PYy4GCxK3A5TQZyihT399zl64Pvfyq2fHLPF+7CGGDQgAtENVN4jI8sLL4/H4N7Of/5G3+bgvHo9/ysdxdhcLErcDlNBnKKFPf30OfH6eTabS9qg+abts5Zvejz3EsAEBgHZE534MLby8qanpr6INyG4R2WaMOdrH8ZWKBYnbAUroM5TQp78+f3v9WJtMpW3qlvHejzvUsAEBgHZEz2w80s5f7ZPdgIT8iecdYUHidoAS+gwl9OmnzyXLNuZefvXYsAXejzvUsAEBgHZ0sgGJRRuQ8RU+pLJgQeJ2gBL6DCX06afPJ0Yuym1AFixt9X7coYYNCAC0o6sNiKo+WuljKgcWJG4HKKHPUEKffvrsc8t4m0yl7Rl8+nm3+/Q9HwHAuyI2IO3+XehYkLgdoIQ+Qwl9Vr7P15ZvskdFz34MfH6e92MOOWxAAKAdbEBIKQOU0Gcooc/K9/n4iIW5l1+9uqTF+zGHHDYgANCOaJOxXVVXFEZEdnf0d9Hff+Tte0PBgsTtACX0GUros/J9nn3TOJtMpe2vrxvj/XhDDxsQAGhH9lPQS8xu38ffERYkbgcooc9QQp+V7TP/3a8eGvKq9+MNPWxAAKAdxpiv9SS+j78jLEjcDlBCn6GEPivb5yNDF+Q2IAt596uS+vQ9HwEAjrAgcTtACX2GEvqsbJ9n3ZD58MGzbnjJ+7FWQ9iAAEAdYUHidoAS+gwl9Fm5Phcsbc09+/EoHz5Ycp++5yMAwBEWJG4HKKHPUEKflevzwcHzcxuQIeOW2omzVtdMZi1Yb1tb2yrSp+/5CABwhAVJZRYkhD59hz4r1+fvb3wptwGpxTw4eH5F+vQ9HwEAjrAgqcyChNCn79Bn5fq8+O6J3jcJLnPV/VMq0qfv+QgAcIQFSWUWJIQ+fYc+K9dnS0ubnb1gvZ0xf11N5YRzh9pkKm3vfmZORfr0PR8BoK40NTXtLyIDRWSbqm5Q1T5FfE2Tqr5tjDmiO7fFgqQyCxJCn75Dn/TZk6xcvSX3DMgzoxZXpM+ShygAoPuMMXeo6jxjzOdV9ThV3W6MObGzr1HVkapq2YD4S70tSOizukKf9NmTzHx1XW4DMm7ayor02aNBCgAoXq9evQ4UkZ35GwkRuUhExnf0NcaYH6rqRDYgflNvCxL6rK7QJ332JC9OXJ7bgLy6pKUifZY8SAEA3ZNIJP5LRD6IxWKfyF5mjDlCRHbGYrF9C69/6KGHflpV1xlj/pENiN/U24KEPqsr9EmfPcnjwxfaZCptj0ql7br12yrSZ48HKgCgOCJygoi0FFz299Hm4pDC6xtjHjbGXBOLxWJsQPym3hYk9FldoU/67Elu+9Msm0yl7amXjqxYnz0apgCA4hljThWRVfmXqWo82lw0Flz+LRFZ3tjY+MnozyVtQNradtgtW94mPUxb2w5Ln/QZauiTPnuSS++ZZJOptP3DzeMq1mePByoAoDjGmJM6egaksbHxoOxljY2NnxSR5caY/81eVuoGBACAzvzuxnE2mUrbGx+fWbHb7PFABQAUJ+8ckI/lXfZ1EdkRyzsHxBjzNVW1qvp2XqyI7BSRAcXenrX18xs816m334jSZ3WFPumz1Gze/Jb93vnDbDKVtvc+O7difZZ3ugIAOtTQ0HCAiOyMx+NfyV4mIheLyIT86zU2Nn4ykUiY/ETPgPwwkUh8ptjbs7Z+XsPsOlu21NdrwumzukKf9FlqVqzanHsHrGfHLKlYn+WcrQCALojIABFZEI/H/yORSBwrIm0icnwsFos1NTV9NnvORyFOQvebelqQ0Gf1hT7ps9RMmbsmtwGZOGt1xfoswzgFABSroaHhAGPMw9HLqtYZY87O/l30Mquftvd1bED8pp4WJPRZfaFP+iw1g8ctzW1Alq7YVLE+ezZJAQDBqpcBWonU04KEPqsv9Emfpea+5+bZZCptjz93iG1tbatYn77nIwDAkXoZoJVIPS1I6LP6Qp/0WWquHjjFJlNpe0b/MRXt0/d8BAA4Ui8DtBKppwUJfVZf6JM+S83vb3zJJlNpe+mASRXt0/d8BAA4Ui8DtBKppwUJfVZf6JM+S80pFw63yVTa3vXU7Ir26Xs+AgAcqZcBWonU04KEPqsv9EmfpWTl6g/fgveZUYsr2qfv+QgAcKQeBmilUi8LEvqsztAnfZaS6fPX5jYgE2asqmifvucjAMCRehiglUq9LEjoszpDn/RZSoaOfz23AVn8+saK9ul7PgIAHKmHAVqp1MuChD6rM/RJn6Xk3mfn5t6Ct8XRW/B21Kfv+QgAcKQeBmilUi8LEvqsztAnfZaSy++dbJOptP3NdWMr3qfv+QgAcKQeBmilUi8LEvqsztAnfZaSM/qPsclU2l51/5SK9+l7PgIAHKmHAVqp1MuChD6rM/RJn91NS0ubPbbfEJtMpe3A5+dVvE/f8xEA4EitD9BKph4WJPRZvaFP+uxuFixtzZ2APuLlZRXv0/d8BAA4UusDtJKphwUJfVZv6JM+u5vRk1fkNiBzFq6veJ++5yMAwJFaH6CVTD0sSOizekOf9NndPDJ0gU2m0vaoPmm7bv22ivfpez4CAByp9QFaydTDgoQ+qzf0SZ/dzXUPTbPJVNqefuWLXvr0PR8BAI7U+gCtZOphQUKf1Rv6pM/u5uybx9lkKm0v+uNEL336no8AAEdqfYBWMvWwIKHP6g190md30traZr93/jCbTKXtXU/N9tKn7/kIAHCklgdopVPrCxL6rO7QJ312J0uWbcydgD543FIvffqejwAAR2p5gFY6tb4goc/qDn3SZ3eS/w5Ysxe4fQesjvr0PR8BAI7U8gCtdGp9QUKf1R36pM/u5MHB820ylbbH9B1s129w+w5YHfXpez4CAByp5QFa6dT6goQ+qzv0SZ/dyRX3TbbJVNqe0X+Mtz59z0cAgCO1PEArnVpfkNBndYc+6bM7+eU1o20ylbZXD5zirU/f8xEA4EgtD9BKp9YXJPRZ3aFP+iw269Zvs0f3GWyTqbR9eMir3vr0PR8BAI7U6gD1kVpekNBn9Yc+6bPYzHx1Xe4E9LFT3/DWp+/5CABwpFYHqI/U8oKEPqs/9Emfxeb5sa/lNiCvLd/krU/f8xEA4EitDlAfqeUFCX1Wf+iTPovNHU/OsslU2p5y4XDb2trmrU/f8xEA4EitDlAfqeUFCX1Wf+iTPotN31vH22QqbfvdNsFrn77nIwDAkVodoD5SywsS+qz+0Cd9FpOW1jZ74nlDbTKVtnc9Pdtrn77nIwDAkVocoL5SqwsS+qyN0Cd9FpN5izfkzv8Y8fIyr336no8AAEdqcYD6Sq0uSOizNkKf9FlMBr+0NLcBWfR6q9c+fc9HAIAjtThAfaVWFyT0WRuhT/osJrc+PrPiJ6B31Kfv+QgAcKQWB6iv1OqChD5rI/RJn8Xk7JvG2WQqbc+/8xXvffqejwAAR2pxgPpKrS5I6LM2Qp/02VU2tGyzx58zxCZTaXvPoLne+/Q9HwEAjtTaAPWZWlyQ0GfthD7ps6vMWrA+d/7Hi5OWe+/T93wEADhSawPUZ2pxQUKftRP6pM+uMmj0kop/AnpnffqejwAAR2ptgPpMLS5I6LN2Qp/02VVufGS6TabS9keXjAiiT9/zEQDgSK0NUJ+pxQUJfdZO6JM+u8qvrhltk6m0vfSeSUH06Xs+AgAcqbUB6jO1uCChz9oJfdJnZ1mxanPu5VePDV8YRJ++5yMAwJFaGqC+U2sLEt+hT/oMObXW56hJK3IbkOnz1wbRp+/5CABwpJYGqO/U2oLEd+iTPkNOrfV511OzbTKVtiecN9RuaNkWRJ++5yMAwJFaGqC+U2sLEt+hT/oMObXW5+9veskmU2l77u0TgunT93wEADhSSwPUd2ptQeI79EmfIaeW+ly7bqs9pu9gm0yl7f3PzQumT9/zEQDgSK0M0BBSSwuSEEKf9BlyaqnPV2auyp3/MWHGqmD69D0fAQCO1MoADSG1tCAJIfRJnyGnlvocmJ5vk6m0PabvYLtm3dZg+vQ9HwEAjtTKAA0htbQgCSH0SZ8hp5b6PO/Ol20ylba/u/GloPr0PR8BAI7UygANIbW0IAkh9EmfIadW+ly/YZs98byhNplK2zuenBVUn77nIwDAkVoYoKGkVhYkoYQ+6TPk1Eqf+ed/jJ36RlB9+p6PAABHamGAhpJaWZCEEvqkz5BTK30OGDTHJlNpe2y/Id7O/+ioT9/zEQDgSC0M0FBSKwuSUEKf9BlyaqXP314/1iZTaXuOp8//6KxP3/MRAOBILQzQUFIrC5JQQp/0GXJqoc/lK9+0R0Uvv3p46KvB9el7PgIAHKn2ARpSamFBElLokz5DTi30OXT867nzP2YtWB9cn77nIwDAkWofoCGlFhYkIYU+6TPk1EKf/R+capOptP3hxSNsa2tbcH36no8AAEeqfYCGlFpYkIQU+qTPkFPtfba2ttkfXTLCJlNpe80DU70fDxsQAKgj1TxAQ0u1L0hCC33SZ8ip9j5nL1yfe/nVkHFLvR8PGxAAqCPVPEBDS7UvSEILfdJnyKn2Ph8dtiC3ATmqT9oe03ew15x0/lA7etpKNiAAUA+qeYCGlmpfkIQW+qTPkFPtfd7/3LzcBiSU9Lv9ZTYgAFAPqnmAhpZqX5CEFvqkz5BT7X2uWbvVDhq9xD4ydIH3nNF/jE2m0rbvbRPYgABAPajmARpaqn1BElrokz5DDn2WL5ffO5kNCADUEwZo+cKChD5DDn3SZ6hhAwIAdYYBWr6wIKHPkEOf9Blq2IAAQJ1hgJYvLEjoM+TQJ32GGjYgAFBnGKDlCwsS+gw59EmfoYYNCADUGQZo+cKChD5DDn3SZ6hhAwIAdYYBWr6wIKHPkEOf9Blq2IAAQJ1hgJYvLEjoM+TQJ32GGjYgAFBnGKDlCwsS+gw59EmfoYYNCADUGQZo+cKChD5DDn3SZ6hhAwIAdYYBWr6wIKHPkEOf9Blq2IAAQJ1hgJYvLEjoM+TQJ32GGjYgAFBnGKDlCwsS+gw59EmfoYYNCADUGQZo+cKChD5DDn3SZ6hhAwIAdYYBWr6wIKHPkEOf9Blq2IAAQJ1hgJYvLEjoM+TQJ32GGjYgABCApqam/UVkoIhsU9UNqtqno+uKyHdFZK6qvq2q840xR3fnthig5QsLEvoMOfRJn6GGDQgABMAYc4eqzjPGfF5Vj1PV7caYEwuv19zc/C+q+q6I/C6RSBhjzG9U9T1V/ddib4sBWr6wIKHPkEOf9Pn/27v3IDvr+o7jPyKChCqtJm442WXPPs/38xlTKx1AsANODY69eGkLXsZOHSxgp2PrlIKO1qkwI06xYarjIF4io51EO+OtUkalNJWr4gWpSgQLAbzQARPRRmIgyyX19I88B5497CbnJE92f8fn/Zr5zLDPPrvn2Q/Z/H7fnFuuYQABgCU2MTFxpKTZiFjbPybpfEnXD55re53tq+rHJG2KiIuGvT0W0ObChoQ+cw590meuYQABgCVWluXJknanlA7rH4uItZJmU0rL6udKWjN4b4ekTbY/MOztsYA2FzYk9Jlz6JM+cw0DCAAsMUmvkrRt4Nga272IWLm3r42I50rabfv0YW+PBbS5sCGhz5xDn/SZaxhAAGCJRcQZku6pH7NdVAPI5EJf1+l0Vti+Q9INaeCekr3p9Xq9HTt29bZvf5AcYHbs2NWjT/rMNfRJn7nmXR/9+uMDSL3PA1hKAQCjiIjXLHQPyOTk5DPn+5qZmZkJ29+VdOe+7iUZ1AMAYAmt2/jNxweQugNZSwEAI6g9B+TQ2rFTJe1K89yzYXu17S22t0xNTXVGvb1ej3/Bayr8iyh95hz6pM9cwz0gALDEOp3OckmzRVG8sH9M0gXVQ6vmqF4x61bbt3e73VX7c3u9Ho9hbirbt/OYcPrMN/RJn7mG54AAQAYkrZd0W1EUJ5ZleZqkHZJemVJK3W531eTk5BEppRQRF0naZfukbre7qp+iKI4a9rZYQJsLGxL6zDn0SZ+5hgEEADLQ6XSWR8TG6t3N74uIc/ufs92TdGb133fY7g1G0oZhb4sFtLmwIaHPnEOf9JlrGEAAoGVYQJsLGxL6zDn0SZ+5hgEEAFqGBbS5sCGhz5xDn/SZaxhAAKBlWECbCxsS+sw59EmfuYYBBABahgW0ubAhoc+cQ5/0mWsYQACgZVhAmwsbEvrMOfRJn7mGAQQAWoYFtLmwIaHPnEOf9JlrGEAAoGVYQJsLGxL6zDn0SZ+5hgEEAFqGBbS5sCGhz5xDn/SZaxhAAKBlWECbCxsS+sw59EmfuYYBBABahgW0ubAhoc+cQ5/0mWsYQACgZVhAmwsbEvrMOfRJn7mGAQQAWoYFtLmwIaHPnEOf9JlrGEAAoGVYQJsLGxL6zDn0SZ+5hgEEAFqGBbS5sCGhz5xDn/SZaxhAAKBlWECbCxsS+sw59EmfuYYBBABahgW0ubAhoc+cQ5/0mWsYQACgZVhAmwsbEvrMOfRJn7mGAQQAWoYFtLmwIaHPnEOf9JlrGEAAoGVYQJsLGxL6zDn0SZ+5hgEEAFqGBbS5sCGhz5xDn/SZaxhAAKBlWECbCxsS+sw59EmfuYYBBABahgW0ubAhoc+cQ5/0mWsYQACgZVhAmwsbEvrMOfRJn7mGAQQAWoYFtLmwIaHPnEOf9JlrGEAAoGVYQJsLGxL6zDn0SZ+5hgEEAFqGBbS5sCGhz5xDn/SZaxhAAKBlWECbCxsS+sw59EmfuYYBBABahgW0ubAhoc+cQ5/0mWsYQACgZVhAmwsbEvrMOfRJn7mGAQQAWoYFtLmwIaHPnEOf9JlrGEAAoGVYQJsLGxL6zDn0SZ+5hgEEAFqGBbS5sCGhz5xDn/SZaxhAAKBlWECbCxsS+sw59EmfuYYBBABahgW0ubAhoc+cQ5/0mWsYQACgZVhAmwsbEvrMOfRJn7mGAQQAWoYFtLmwIaHPnEOf9JlrGEAAoGVYQJsLGxL6zDn0SZ+5hgEEAFqGBbS5sCGhz5xDn/SZaxhAAKBlWECbCxsS+sw59EmfuYYBBABahgW0ubAhoc+cQ5/0mWsYQACgZVhAmwsbEvrMOfRJn7mGAQQAWoYFtLmwIaHPnEOf9JlrGEAAoGVYQJsLGxL6zDn0SZ+5hgEEAFqGBbS5sCGhz5xDn/SZaxhAAKBlWECbCxsS+sw59EmfuYYBBABahgW0ubAhoc+cQ5/0mWsYQACgZVhAmwsbEvrMOfRJn7mGAQQAWoYFtLmwIaHPnEOf9JlrGEAAoGVYQJsLGxL6zDn0SZ+5hgEEAFqGBbS5sCGhz5xDn/SZaxhAAKBlWECbCxsS+sw59EmfuYYBBABahgW0ubAhoc+cQ5/0mWsYQACgZVhAmwsbEvrMOfRJn7mGAQQAWoYFtLmwIaHPnEOf9JlrGEAAoGVYQJsLGxL6zDn0SZ+5hgEEAFqGBbS5sCGhz5xDn/SZaxhAAKBlWECbCxsS+sw59EmfuYYBBABahgW0ubAhoc+cQ5/0mWsYQACgZVhAmwsbEvrMOfRJn7mGAQQAWoYFtLmwIaHPnEOf9JlrGEAAoGVYQJsLGxL6zDn0SZ+5hgEEAFqGBbS5sCGhz5xDn/SZaxhAAKBlWECbCxsS+sw59EmfuYYBBABahgW0ubAhoc+cQ5/0mWsYQACgZVhAmwsbEvrMOfRJn7mGAQQAWoYFtLmwIaHPnEOf9JlrGEAAoGVYQJsLGxL6zDn0SZ+5hgEEAFqGBbS5sCGhz5xDn/SZaxhAAKBlWECbCxsS+sw59EmfuYYBBABahgW0ubAhoc+cQ5/0mWsYQACgZVhAmwsbEvrMOfRJn7mGAQQAWoYFtLmwIaHPnEOf9JlrGEAAoGVYQJsLGxL6zDn0SZ+5hgEEADLQ7XafJuljkh6wvdX2WxY6NyKOk3STpF22by7L8oRRbosFtLmwIaHPnEOf9JlrGEAAIAMRcantzRFxvO3Tbf8iIl49eN7ExMSRtrdG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width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At zero threshold, precision: 0.21, recall: 0.74\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from adspy_shared_utilities import plot_class_regions_for_classifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "dataset = load_digits()\n",
    "X, y = dataset.data, dataset.target == 1\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "# create a two-feature input vector matching the example plot above\n",
    "jitter_delta = 0.25\n",
    "X_twovar_train = X_train[:,[20,59]]+ np.random.rand(X_train.shape[0], 2) - jitter_delta\n",
    "X_twovar_test  = X_test[:,[20,59]] + np.random.rand(X_test.shape[0], 2) - jitter_delta\n",
    "\n",
    "clf = SVC(kernel='linear', class_weight='balanced').fit(X_twovar_train, y_train)\n",
    "\n",
    "y_scores = clf.decision_function(X_twovar_test)\n",
    "\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_scores)\n",
    "closest_zero = np.argmin(np.abs(thresholds))\n",
    "closest_zero_p = precision[closest_zero]\n",
    "closest_zero_r = recall[closest_zero]\n",
    "\n",
    "plot_class_regions_for_classifier(clf, X_twovar_test, y_test)\n",
    "plt.title(\"SVC, class_weight = 'balanced', optimized for accuracy\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.xlim([0.0, 1.01])\n",
    "plt.ylim([0.0, 1.01])\n",
    "plt.title (\"Precision-recall curve: SVC, class_weight = 'balanced'\")\n",
    "plt.plot(precision, recall, label = 'Precision-Recall Curve')\n",
    "plt.plot(closest_zero_p, closest_zero_r, 'o', markersize=12, fillstyle='none', c='r', mew=3)\n",
    "plt.xlabel('Precision', fontsize=16)\n",
    "plt.ylabel('Recall', fontsize=16)\n",
    "plt.axes().set_aspect('equal')\n",
    "plt.show()\n",
    "print('At zero threshold, precision: {:.2f}, recall: {:.2f}'\n",
    "      .format(closest_zero_p, closest_zero_r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}