Autres_Dimensions_2-Copy1.ipynb

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    "# **Les aventuriers des Axes Perdus**\n",
    "#Epoch 2\n",
    "\n",
    "***Olivier Thöni*** *(novice en IA, et sinon... professeur de mathématiques)*\n",
    "\n",
    "*School of AI - Le Mans*\n",
    "\n",
    "Lien vers le présent NoteBook : https://frama.link/OT_dimension2   \n",
    "\n",
    "\n",
    "\n"
   ]
  },
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    "id": "EigAUqpVFdPl"
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   "source": [
    "# **1. Diminuer la dimensionnalité**\n",
    "\n",
    "## **1.1.la régression linéaire**\n",
    "\n",
    "https://frama.link/OT_dimensions \n"
   ]
  },
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   "source": [
    "## **1.2. L' \"Analyse en Composantes Principales\" (ACP)**\n",
    "\n",
    "\n",
    "![ACP](https://drive.google.com/uc?id=1Ha2-PZQkoQghBFH13R0gCyr57h2c5AsE)\n",
    "### **1.2.a/ la classification des iris**\n",
    "\n",
    "https://frama.link/OT_dimension2 \n",
    "\n",
    "Pour que les choses restent visibles : commençons par passer de 3 à 2 dimensions, avec ce grand classique des *data sets*.\n",
    "\n",
    "![Iris](https://drive.google.com/uc?id=1eIZvGr1rIH-Af9AtnLzOY7rmO05oyGpZ)\n",
    "\n",
    "Il s'agit d'un jeu de données, directement accessible dans le module **Scikit Learn**, au format *txt* ou ici, au format csv :\n",
    "\n",
    "\n",
    "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data \n",
    "\n",
    "**Récupération des données**\n",
    "\n",
    "\n",
    "Récupérons-le et transformons-le en DataFrame **Pandas**.\n",
    "\n",
    "Pour les 150 \"individus\" du jeu de données, on mesure la longueur du sépale, la largeur du sépale, la longueur du pétale et la largeur du pétale, et enfin, on indique son espèce (qui sera l'objet des prédictions)."
   ]
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_len</th>\n",
       "      <th>sepal_wid</th>\n",
       "      <th>petal_len</th>\n",
       "      <th>petal_wid</th>\n",
       "      <th>classe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_len  sepal_wid  petal_len  petal_wid       classe\n",
       "0        5.1        3.5        1.4        0.2  Iris-setosa\n",
       "1        4.9        3.0        1.4        0.2  Iris-setosa\n",
       "2        4.7        3.2        1.3        0.2  Iris-setosa\n",
       "3        4.6        3.1        1.5        0.2  Iris-setosa\n",
       "4        5.0        3.6        1.4        0.2  Iris-setosa"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\n",
    "    filepath_or_buffer='https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', \n",
    "    header=None, \n",
    "    sep=',')\n",
    "\n",
    "df.columns=['sepal_len', 'sepal_wid', 'petal_len', 'petal_wid', 'classe']\n",
    "df.dropna(how=\"all\", inplace=True) # drops the empty line at file-end\n",
    "\n",
    "df.head()"
   ]
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.iat[3,2]"
   ]
  },
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   "execution_count": 3,
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    "colab_type": "code",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_len            4.7\n",
       "sepal_wid            3.2\n",
       "petal_len            1.3\n",
       "petal_wid            0.2\n",
       "classe       Iris-setosa\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[2]"
   ]
  },
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   "execution_count": 4,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = len(df)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "A18H9WEDQ3Nf"
   },
   "source": [
    "Séparons données et classes à prédire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "source": [
    "# split data table into data X and class labels y\n",
    "\n",
    "X = df.iloc[:,0:4].values # données mesurées, \"iloc\" : localisation indexée\n",
    "y = df.iloc[:,4].values # espèce de l'iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N2soQtVeJ4Kg"
   },
   "source": [
    "Nous affichons la dimension de la matrice, nous récupérons le nombre d’observations (n =\n",
    "150) et de variables (p = 3), enfin nous affichons les valeurs-mêmes.\n"
   ]
  },
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     "timestamp": 1591644680799,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]\n",
      " [5.4 3.9 1.7 0.4]\n",
      " [4.6 3.4 1.4 0.3]\n",
      " [5.  3.4 1.5 0.2]\n",
      " [4.4 2.9 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [5.4 3.7 1.5 0.2]\n",
      " [4.8 3.4 1.6 0.2]\n",
      " [4.8 3.  1.4 0.1]\n",
      " [4.3 3.  1.1 0.1]\n",
      " [5.8 4.  1.2 0.2]\n",
      " [5.7 4.4 1.5 0.4]\n",
      " [5.4 3.9 1.3 0.4]\n",
      " [5.1 3.5 1.4 0.3]\n",
      " [5.7 3.8 1.7 0.3]\n",
      " [5.1 3.8 1.5 0.3]\n",
      " [5.4 3.4 1.7 0.2]\n",
      " [5.1 3.7 1.5 0.4]\n",
      " [4.6 3.6 1.  0.2]\n",
      " [5.1 3.3 1.7 0.5]\n",
      " [4.8 3.4 1.9 0.2]\n",
      " [5.  3.  1.6 0.2]\n",
      " [5.  3.4 1.6 0.4]\n",
      " [5.2 3.5 1.5 0.2]\n",
      " [5.2 3.4 1.4 0.2]\n",
      " [4.7 3.2 1.6 0.2]\n",
      " [4.8 3.1 1.6 0.2]\n",
      " [5.4 3.4 1.5 0.4]\n",
      " [5.2 4.1 1.5 0.1]\n",
      " [5.5 4.2 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [5.  3.2 1.2 0.2]\n",
      " [5.5 3.5 1.3 0.2]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [4.4 3.  1.3 0.2]\n",
      " [5.1 3.4 1.5 0.2]\n",
      " [5.  3.5 1.3 0.3]\n",
      " [4.5 2.3 1.3 0.3]\n",
      " [4.4 3.2 1.3 0.2]\n",
      " [5.  3.5 1.6 0.6]\n",
      " [5.1 3.8 1.9 0.4]\n",
      " [4.8 3.  1.4 0.3]\n",
      " [5.1 3.8 1.6 0.2]\n",
      " [4.6 3.2 1.4 0.2]\n",
      " [5.3 3.7 1.5 0.2]\n",
      " [5.  3.3 1.4 0.2]\n",
      " [7.  3.2 4.7 1.4]\n",
      " [6.4 3.2 4.5 1.5]\n",
      " [6.9 3.1 4.9 1.5]\n",
      " [5.5 2.3 4.  1.3]\n",
      " [6.5 2.8 4.6 1.5]\n",
      " [5.7 2.8 4.5 1.3]\n",
      " [6.3 3.3 4.7 1.6]\n",
      " [4.9 2.4 3.3 1. ]\n",
      " [6.6 2.9 4.6 1.3]\n",
      " [5.2 2.7 3.9 1.4]\n",
      " [5.  2.  3.5 1. ]\n",
      " [5.9 3.  4.2 1.5]\n",
      " [6.  2.2 4.  1. ]\n",
      " [6.1 2.9 4.7 1.4]\n",
      " [5.6 2.9 3.6 1.3]\n",
      " [6.7 3.1 4.4 1.4]\n",
      " [5.6 3.  4.5 1.5]\n",
      " [5.8 2.7 4.1 1. ]\n",
      " [6.2 2.2 4.5 1.5]\n",
      " [5.6 2.5 3.9 1.1]\n",
      " [5.9 3.2 4.8 1.8]\n",
      " [6.1 2.8 4.  1.3]\n",
      " [6.3 2.5 4.9 1.5]\n",
      " [6.1 2.8 4.7 1.2]\n",
      " [6.4 2.9 4.3 1.3]\n",
      " [6.6 3.  4.4 1.4]\n",
      " [6.8 2.8 4.8 1.4]\n",
      " [6.7 3.  5.  1.7]\n",
      " [6.  2.9 4.5 1.5]\n",
      " [5.7 2.6 3.5 1. ]\n",
      " [5.5 2.4 3.8 1.1]\n",
      " [5.5 2.4 3.7 1. ]\n",
      " [5.8 2.7 3.9 1.2]\n",
      " [6.  2.7 5.1 1.6]\n",
      " [5.4 3.  4.5 1.5]\n",
      " [6.  3.4 4.5 1.6]\n",
      " [6.7 3.1 4.7 1.5]\n",
      " [6.3 2.3 4.4 1.3]\n",
      " [5.6 3.  4.1 1.3]\n",
      " [5.5 2.5 4.  1.3]\n",
      " [5.5 2.6 4.4 1.2]\n",
      " [6.1 3.  4.6 1.4]\n",
      " [5.8 2.6 4.  1.2]\n",
      " [5.  2.3 3.3 1. ]\n",
      " [5.6 2.7 4.2 1.3]\n",
      " [5.7 3.  4.2 1.2]\n",
      " [5.7 2.9 4.2 1.3]\n",
      " [6.2 2.9 4.3 1.3]\n",
      " [5.1 2.5 3.  1.1]\n",
      " [5.7 2.8 4.1 1.3]\n",
      " [6.3 3.3 6.  2.5]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [7.1 3.  5.9 2.1]\n",
      " [6.3 2.9 5.6 1.8]\n",
      " [6.5 3.  5.8 2.2]\n",
      " [7.6 3.  6.6 2.1]\n",
      " [4.9 2.5 4.5 1.7]\n",
      " [7.3 2.9 6.3 1.8]\n",
      " [6.7 2.5 5.8 1.8]\n",
      " [7.2 3.6 6.1 2.5]\n",
      " [6.5 3.2 5.1 2. ]\n",
      " [6.4 2.7 5.3 1.9]\n",
      " [6.8 3.  5.5 2.1]\n",
      " [5.7 2.5 5.  2. ]\n",
      " [5.8 2.8 5.1 2.4]\n",
      " [6.4 3.2 5.3 2.3]\n",
      " [6.5 3.  5.5 1.8]\n",
      " [7.7 3.8 6.7 2.2]\n",
      " [7.7 2.6 6.9 2.3]\n",
      " [6.  2.2 5.  1.5]\n",
      " [6.9 3.2 5.7 2.3]\n",
      " [5.6 2.8 4.9 2. ]\n",
      " [7.7 2.8 6.7 2. ]\n",
      " [6.3 2.7 4.9 1.8]\n",
      " [6.7 3.3 5.7 2.1]\n",
      " [7.2 3.2 6.  1.8]\n",
      " [6.2 2.8 4.8 1.8]\n",
      " [6.1 3.  4.9 1.8]\n",
      " [6.4 2.8 5.6 2.1]\n",
      " [7.2 3.  5.8 1.6]\n",
      " [7.4 2.8 6.1 1.9]\n",
      " [7.9 3.8 6.4 2. ]\n",
      " [6.4 2.8 5.6 2.2]\n",
      " [6.3 2.8 5.1 1.5]\n",
      " [6.1 2.6 5.6 1.4]\n",
      " [7.7 3.  6.1 2.3]\n",
      " [6.3 3.4 5.6 2.4]\n",
      " [6.4 3.1 5.5 1.8]\n",
      " [6.  3.  4.8 1.8]\n",
      " [6.9 3.1 5.4 2.1]\n",
      " [6.7 3.1 5.6 2.4]\n",
      " [6.9 3.1 5.1 2.3]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [6.8 3.2 5.9 2.3]\n",
      " [6.7 3.3 5.7 2.5]\n",
      " [6.7 3.  5.2 2.3]\n",
      " [6.3 2.5 5.  1.9]\n",
      " [6.5 3.  5.2 2. ]\n",
      " [6.2 3.4 5.4 2.3]\n",
      " [5.9 3.  5.1 1.8]]\n"
     ]
    }
   ],
   "source": [
    "#dimension\n",
    "print(X.shape) \n",
    "#nombre d'observations\n",
    "n = X.shape[0]\n",
    "#nombre de variables\n",
    "p = X.shape[1]\n",
    "#affichage des données\n",
    "print(X)\n"
   ]
  },
  {
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    "id": "Y9xqKth3vwZ8"
   },
   "source": [
    "**Visualisation**\n",
    "\n",
    "Visualisons le nuage dans le plan de deux des variables...\n",
    "Pour le fun !"
   ]
  },
  {
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    "executionInfo": {
     "elapsed": 2407,
     "status": "ok",
     "timestamp": 1591644681840,
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   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "colors = {'Iris-setosa':'#0D76BF', 'Iris-versicolor':'#00cc96', 'Iris-virginica':'#EF553B'}\n",
    "\n",
    "        \n",
    "for Swidth, Slength, Specie in zip(df['sepal_wid'], df['sepal_len'], df['classe']):\n",
    "    plt.scatter(x=Slength, y=Swidth, color=colors[Specie])\n",
    "\n",
    "ax.set(xlabel=\"Sepal Length\",\n",
    "       ylabel=\"Sepal Width\",\n",
    "      title=\"Relationship between Sepal Length and Width\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "edCBfw1zyjce"
   },
   "source": [
    "Pour visualiser l'ensemble du nuage à quatre dimensions, on n'a d'autre recours que de visualiser dans les plans de deux dimensions.\n",
    "Voici toutes ces vues..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6898,
     "status": "ok",
     "timestamp": 1591644686371,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "a3d2vJhjSQUF",
    "outputId": "8da3a7bf-abd4-48c4-8bff-be5b5ddad69b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = {'Iris-setosa':'#0D76BF', 'Iris-versicolor':'#00cc96', 'Iris-virginica':'#EF553B'}\n",
    "for i in range(4) :\n",
    "    for j in range(0,i) :\n",
    "        plt.subplot(3,3,(i)+3*(j))\n",
    "        \n",
    "        for vi,vj,specie in zip(df.iloc[:,i],df.iloc[:,j],df.iloc[:,4]) :\n",
    "            plt.scatter(x=vi,y=vj,color = colors[specie], s=4)\n",
    "        \n",
    "        plt.xlabel('x'+str(i))\n",
    "        plt.ylabel('x'+str(j))\n",
    "        #pd.scatter_matrix marcherait aussi, voir pour les couleurs...)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5ok36H_OyO-C"
   },
   "source": [
    "Bon... j'ai un peu galéré, mais je suis content du résultat...\n",
    "On voit clairement qu'une espèce est clairement séparée des deux autres.\n",
    "\n",
    "\n",
    "Pour que la visualisation soit possible, et afin de donner du sens à la **réduction de dimensions** par **Analyse en Composantes Principales**, oublions la 1ère dimension et travaillons sur les trois autres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ouNFllfO0GAg"
   },
   "outputs": [],
   "source": [
    "X = df.iloc[:,1:4].values # données mesurées\n",
    "#X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cwGmA73a0194"
   },
   "source": [
    "**Visualisation du nuage à 3 dimensions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 7993,
     "status": "ok",
     "timestamp": 1591644687492,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "36Xl4NBH01Rb",
    "outputId": "ad85b646-9351-4ec1-e096-9ccbdd78ce6f"
   },
   "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",
       "        fig.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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "x =X[:,0]\n",
    "y =X[:,1]\n",
    "z =X[:,2]\n",
    "colors = {'Iris-setosa':'#0D76BF', 'Iris-versicolor':'#00cc96', 'Iris-virginica':'#EF553B'}\n",
    "for xx,yy,zz,specie in zip(x,y,z,df.iloc[:,4]) :\n",
    "    ax.scatter(xx,yy,zz, color = colors[specie], marker = 'o')\n",
    "\n",
    "ax.set_xlabel('Sepal Width')\n",
    "ax.set_ylabel('Petal Length')\n",
    "ax.set_zlabel('Petal Width')\n",
    "\n",
    "ax.set_title(\"Relationship between Sepal Width, Petal Length and Petal Length\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xsS8Z0rcDK4n"
   },
   "source": [
    "**Normalisation**\n",
    "\n",
    "Avant tout travail de réduction de la dimensionnalité, il faut **normaliser** les données, c'est-à-dire les **centrer** (leur soustraire leur moyenne) et les **réduire** (diviser par leur écart-type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 8897,
     "status": "ok",
     "timestamp": 1591644688416,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "bQpbeB8FE3De",
    "outputId": "ae6a0ead-d1d2-44b4-a6e7-bbe69a05bd42"
   },
   "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",
       "        fig.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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def centrage(s):\n",
    "    return s - np.mean(s)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "x_centree = centrage(x)\n",
    "y_centree = centrage(y)\n",
    "z_centree = centrage(z)\n",
    "\n",
    "colors = {'Iris-setosa':'#0D76BF', 'Iris-versicolor':'#00cc96', 'Iris-virginica':'#EF553B'}\n",
    "for xx,yy,zz,specie in zip(x_centree,y_centree,z_centree,df.iloc[:,4]) :\n",
    "    ax.scatter(xx,yy,zz, color = colors[specie], marker = 'o')\n",
    "\n",
    "ax.set_xlabel('Sepal Width')\n",
    "ax.set_ylabel('Petal Length')\n",
    "ax.set_zlabel('Petal Width')\n",
    "\n",
    "ax.set_title(\"Variables centrées\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 9789,
     "status": "ok",
     "timestamp": 1591644689321,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "ANkC2uZNG0BP",
    "outputId": "ec87cc5c-c82d-4320-9c19-e6d27ac0a9b2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "def reduire(s):\n",
    "    return centrage(s) / np.std(s)\n",
    "\n",
    "x_reduite = reduire(x)\n",
    "y_reduite = reduire(y)\n",
    "z_reduite = reduire(z)\n",
    "\n",
    "colors = {'Iris-setosa':'#0D76BF', 'Iris-versicolor':'#00cc96', 'Iris-virginica':'#EF553B'}\n",
    "for xx,yy,zz,specie in zip(x_reduite,y_reduite,z_reduite,df.iloc[:,4]) :\n",
    "    ax.scatter(xx,yy,zz, color = colors[specie], marker = 'o')\n",
    "\n",
    "ax.set_xlabel('Sepal Width')\n",
    "ax.set_ylabel('Petal Length')\n",
    "ax.set_zlabel('Petal Width')\n",
    "\n",
    "ax.set_title(\"Variables réduites\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "M_Dcl18iEEXG"
   },
   "source": [
    "Les trois figures sont les mêmes... à l'échelle des axes près !\n",
    "\n",
    "Vu en 3D animé, voici notre nuage de points...\n",
    "\n",
    "![Iris 3D animé…](https://github.com/njanakiev/blender-scripting/blob/master/img/fisher_iris_visualization.gif?raw=true)\n",
    "\n",
    "L'ACP va consister à trouver l'axe de perspective dans lequel le nuage sera le plus dispersé, et ceci étant fait, le 2ème axe après ce premier (il est perpendiculaire au premier)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "b6gsenyWDIvs"
   },
   "source": [
    "***Remarque :***\n",
    "Le module *ScikitLearn* comprend une commande, *StandardScaler*, permettant la normalisation (standardisation) en une ligne... une fois qu'on a compris les paragraphes précédents..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 9781,
     "status": "ok",
     "timestamp": 1591644689327,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "f8mnV6fjDAiW",
    "outputId": "fcfc169b-6b31-4ba4-a332-2652eb18bc97"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.03205722e+00 -1.34127240e+00 -1.31297673e+00]\n",
      " [-1.24957601e-01 -1.34127240e+00 -1.31297673e+00]\n",
      " [ 3.37848329e-01 -1.39813811e+00 -1.31297673e+00]\n",
      " [ 1.06445364e-01 -1.28440670e+00 -1.31297673e+00]\n",
      " [ 1.26346019e+00 -1.34127240e+00 -1.31297673e+00]\n",
      " [ 1.95766909e+00 -1.17067529e+00 -1.05003079e+00]\n",
      " [ 8.00654259e-01 -1.34127240e+00 -1.18150376e+00]\n",
      " [ 8.00654259e-01 -1.28440670e+00 -1.31297673e+00]\n",
      " [-3.56360566e-01 -1.34127240e+00 -1.31297673e+00]\n",
      " [ 1.06445364e-01 -1.28440670e+00 -1.44444970e+00]\n",
      " [ 1.49486315e+00 -1.28440670e+00 -1.31297673e+00]\n",
      " [ 8.00654259e-01 -1.22754100e+00 -1.31297673e+00]\n",
      " [-1.24957601e-01 -1.34127240e+00 -1.44444970e+00]\n",
      " [-1.24957601e-01 -1.51186952e+00 -1.44444970e+00]\n",
      " [ 2.18907205e+00 -1.45500381e+00 -1.31297673e+00]\n",
      " [ 3.11468391e+00 -1.28440670e+00 -1.05003079e+00]\n",
      " [ 1.95766909e+00 -1.39813811e+00 -1.05003079e+00]\n",
      " [ 1.03205722e+00 -1.34127240e+00 -1.18150376e+00]\n",
      " [ 1.72626612e+00 -1.17067529e+00 -1.18150376e+00]\n",
      " [ 1.72626612e+00 -1.28440670e+00 -1.18150376e+00]\n",
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      " [-1.24957601e-01 -1.34127240e+00 -1.18150376e+00]\n",
      " [ 1.72626612e+00 -1.22754100e+00 -1.31297673e+00]\n",
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      " [ 5.69251294e-01 -1.34127240e+00 -1.31297673e+00]\n",
      " [ 3.37848329e-01  5.35295827e-01  2.64698913e-01]\n",
      " [ 3.37848329e-01  4.21564419e-01  3.96171883e-01]\n",
      " [ 1.06445364e-01  6.49027235e-01  3.96171883e-01]\n",
      " [-1.74477836e+00  1.37235899e-01  1.33225943e-01]\n",
      " [-5.87763531e-01  4.78430123e-01  3.96171883e-01]\n",
      " [-5.87763531e-01  4.21564419e-01  1.33225943e-01]\n",
      " [ 5.69251294e-01  5.35295827e-01  5.27644853e-01]\n",
      " [-1.51337539e+00 -2.60824029e-01 -2.61192967e-01]\n",
      " [-3.56360566e-01  4.78430123e-01  1.33225943e-01]\n",
      " [-8.19166497e-01  8.03701950e-02  2.64698913e-01]\n",
      " [-2.43898725e+00 -1.47092621e-01 -2.61192967e-01]\n",
      " [-1.24957601e-01  2.50967307e-01  3.96171883e-01]\n",
      " [-1.97618132e+00  1.37235899e-01 -2.61192967e-01]\n",
      " [-3.56360566e-01  5.35295827e-01  2.64698913e-01]\n",
      " [-3.56360566e-01 -9.02269170e-02  1.33225943e-01]\n",
      " [ 1.06445364e-01  3.64698715e-01  2.64698913e-01]\n",
      " [-1.24957601e-01  4.21564419e-01  3.96171883e-01]\n",
      " [-8.19166497e-01  1.94101603e-01 -2.61192967e-01]\n",
      " [-1.97618132e+00  4.21564419e-01  3.96171883e-01]\n",
      " [-1.28197243e+00  8.03701950e-02 -1.29719997e-01]\n",
      " [ 3.37848329e-01  5.92161531e-01  7.90590793e-01]\n",
      " [-5.87763531e-01  1.37235899e-01  1.33225943e-01]\n",
      " [-1.28197243e+00  6.49027235e-01  3.96171883e-01]\n",
      " [-5.87763531e-01  5.35295827e-01  1.75297293e-03]\n",
      " [-3.56360566e-01  3.07833011e-01  1.33225943e-01]\n",
      " [-1.24957601e-01  3.64698715e-01  2.64698913e-01]\n",
      " [-5.87763531e-01  5.92161531e-01  2.64698913e-01]\n",
      " [-1.24957601e-01  7.05892939e-01  6.59117823e-01]\n",
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      " [-8.19166497e-01  8.03701950e-02  1.75297293e-03]\n",
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      " [-1.24957601e-01  4.78430123e-01  2.64698913e-01]\n",
      " [-1.05056946e+00  1.37235899e-01  1.75297293e-03]\n",
      " [-1.74477836e+00 -2.60824029e-01 -2.61192967e-01]\n",
      " [-8.19166497e-01  2.50967307e-01  1.33225943e-01]\n",
      " [-1.24957601e-01  2.50967307e-01  1.75297293e-03]\n",
      " [-3.56360566e-01  2.50967307e-01  1.33225943e-01]\n",
      " [-3.56360566e-01  3.07833011e-01  1.33225943e-01]\n",
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      " [ 3.37848329e-01  1.10395287e+00  1.44795564e+00]\n",
      " [-5.87763531e-01  6.49027235e-01  1.05353673e+00]\n",
      " [-5.87763531e-01  1.67260991e+00  1.05353673e+00]\n",
      " [-8.19166497e-01  6.49027235e-01  7.90590793e-01]\n",
      " [ 5.69251294e-01  1.10395287e+00  1.18500970e+00]\n",
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      " [-5.87763531e-01  5.92161531e-01  7.90590793e-01]\n",
      " [-1.24957601e-01  6.49027235e-01  7.90590793e-01]\n",
      " [-5.87763531e-01  1.04708716e+00  1.18500970e+00]\n",
      " [-1.24957601e-01  1.16081857e+00  5.27644853e-01]\n",
      " [-5.87763531e-01  1.33141568e+00  9.22063763e-01]\n",
      " [ 1.72626612e+00  1.50201279e+00  1.05353673e+00]\n",
      " [-5.87763531e-01  1.04708716e+00  1.31648267e+00]\n",
      " [-5.87763531e-01  7.62758643e-01  3.96171883e-01]\n",
      " [-1.05056946e+00  1.04708716e+00  2.64698913e-01]\n",
      " [-1.24957601e-01  1.33141568e+00  1.44795564e+00]\n",
      " [ 8.00654259e-01  1.04708716e+00  1.57942861e+00]\n",
      " [ 1.06445364e-01  9.90221459e-01  7.90590793e-01]\n",
      " [-1.24957601e-01  5.92161531e-01  7.90590793e-01]\n",
      " [ 1.06445364e-01  9.33355755e-01  1.18500970e+00]\n",
      " [ 1.06445364e-01  1.04708716e+00  1.57942861e+00]\n",
      " [ 1.06445364e-01  7.62758643e-01  1.44795564e+00]\n",
      " [-8.19166497e-01  7.62758643e-01  9.22063763e-01]\n",
      " [ 3.37848329e-01  1.21768427e+00  1.44795564e+00]\n",
      " [ 5.69251294e-01  1.10395287e+00  1.71090158e+00]\n",
      " [-1.24957601e-01  8.19624347e-01  1.44795564e+00]\n",
      " [-1.28197243e+00  7.05892939e-01  9.22063763e-01]\n",
      " [-1.24957601e-01  8.19624347e-01  1.05353673e+00]\n",
      " [ 8.00654259e-01  9.33355755e-01  1.44795564e+00]\n",
      " [-1.24957601e-01  7.62758643e-01  7.90590793e-01]]\n"
     ]
    }
   ],
   "source": [
    "#classe pour standardisation\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "#instanciation\n",
    "sc = StandardScaler()\n",
    "#transformation – centrage-réduction\n",
    "Z = sc.fit_transform(X)\n",
    "print(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hC8ZJpoWHfGT"
   },
   "source": [
    "\n",
    "\n",
    "### **ACP et aide à l'interprétation**\n",
    "\n",
    "*inspiration : http://eric.univ-lyon2.fr/~ricco/tanagra/fichiers/fr_Tanagra_ACP_Python.pdf*\n",
    "\n",
    "L' \"Analyse en composante Principale\" consiste à tenter d'observer le nuage de points sous un angle (ici projection sur un plan, donc déterminé par deux vecteurs) où la dispersion est la plus importante, afin de faire apparaître au mieux les clusters.\n",
    "On cherche pour cela des tendances dans le nuage, ce sont les vecteurs propres de la matrice de corrélations qui déterminent ces tendances : les valeurs propres associées indiquent la part que prend chaque tendance dans la variance totale du nuage.\n",
    "Pour passer de 3 à deux dimensions, il s'agit donc de trouver les deux vecteurs propres associés aux deux plus fortes valeurs propres.\n",
    "\n",
    "Le fait que nos données soient étiquetées, ici, permettra de donner du sens aux tendances dégagées.\n",
    "\n",
    "\n",
    "**Matrice des corrélations**\n",
    "\n",
    "( covariances divisées par le produit des écarts-types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 137
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 9770,
     "status": "ok",
     "timestamp": 1591644689330,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "ooFIZActJ9C8",
    "outputId": "c927c384-9165-4a3a-827d-cce74867063e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_wid</th>\n",
       "      <th>petal_len</th>\n",
       "      <th>petal_wid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_wid</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_len</th>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_wid</th>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sepal_wid  petal_len  petal_wid\n",
       "sepal_wid   1.000000  -0.420516  -0.356544\n",
       "petal_len  -0.420516   1.000000   0.962757\n",
       "petal_wid  -0.356544   0.962757   1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3var = df.iloc[:,1:4]\n",
    "df_mat_corr = df3var.corr()\n",
    "df_mat_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "peqr_bspSZdu"
   },
   "source": [
    "Longueur et largeur du pétale sont très positivement corrélées et suffiraient à classifier nos iris\n",
    "\n",
    "**visualisation en couleur de la matrice des corrélations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10203,
     "status": "ok",
     "timestamp": 1591644689777,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "9GOCne3dR4Sx",
    "outputId": "6001722d-486c-4729-f4e1-af416f90a3be"
   },
   "outputs": [
    {
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       "    } 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",
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       "}\n",
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       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
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       "        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",
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       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
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       "\n",
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       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
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       "\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",
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       "\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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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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bk2XYL377rv03HPPuR/cnCe/WXIOfK3cSdm7d6+Lz120Pn360P79+6lQoUJJClZMTAzVr1/fvalzJy5wjBw5krp16+a6MH4WrNTqFcwcSOnWEQv5tddem2D+ckeUOwXc/erbt6+ngsXziX9p4LV0y5YtS7Cm7sUXX6S33nqLfvnlFydSfLBg7dmzh+bMmePEP3DcdtttlDVrVlq5cqWn33tawQKCFS51rlmzpvuFLVAP/r7k72mWL36PefXVVx1a7q7ffPPNxN+/fCS1yJ1/qWLZ5l/4Ah12js2/RLGc+7mDteDzfHSrx7cIN264QA0fQgdL63uXx4FgadL2eCy+XXL99de77hG/GSU+HnvsMfdGw92JxAd3ibgdffjw4QSCxbd84v+w5H/ktj634LmDFP+2DP8b3x48deqU+w2UxYi7H3we357hI5QOFv/g5ttOo0ePdu3+t99+290K5VuE/Aa9bds2KlWqlBMnvkUZX+gS3yLkTse4ceOc+HHnJXBw3v/4xz+ocuXKvhas1OrFP7TSOgfSujaH682SzRJ03333uVrwHAwcXnSweM5yd46lmOdq/IOln2Weu6vPP/98nGBxTnz7Mv7BP8C548mdWz8eAcEKlzrz+8PQoUNdZypXrlyuxnXq1HGCtXHjRtcF//333+mGG25wt58D70WJBYtve3M3rHr16q7zFf8I3F6EYCWc8RAs/XcACJY+c89G5B9qTz31lLvdV758efdDh3+g1qpVy43B3Z/NmzenOB6/UbE0Bd7I586dSyxm8Q++PcQ/zA4ePEgFCxZ0P1iHDx/uxIVlKPHeRdxJuOmmm0IWLH7hjTfe6K6Df0Pl9VfcMeMOGr+p8g9dXi8W6ODxrarAkViwHnzwQdcB4bVXiQ+Wqzx58vhasFKrF+8dlNY5kJJgsbhwt3DBggXuh2P8g8dYvHixp4LFdedbfSkd//nPf9z6LD647tzF5F8E4h9+3/4hrd+Xfqkzr9usV68effbZZ06O+Lbep59+6n5J41/GeG7xHOPu1PLly90SAD4SCxa/F3G9+dYgd6PjHyzg3Ln0s2B9KtTBaowOlmc/f9MSCIKVFkqGzzly5IhbX8RvQPzmxT8IuevD3R++fcKdmgkTJiR7BfzbI3ccAm/kLG28violweL1E71793adBX6z5E4QCxAvkOdu065du+L2UQqlg8Vj8+1A/qHN18NvwvyDlEUvMB7f3uPFr7wGjc9NTrD4fP6hy+vJEh/8JszC5udbhKnVi2+BpnUOJCdY3J1kVtwF5AXFLPN8C5nF/IUXXnD1ic/Qiw5WoAvBG5YGfmFIXD/u4JYsWTJOsHitFndAwlGwwqXO/Msg/2LE84gFi38h5O4ib8HAv1RxN4qv9f3333eyxYvfkxIsfl/g8/k9gdcOxj/Wrl3rOtMQrITfMdzBgmDp/jCHYOnyFh2Nn6pj6eHf/vnWGUsI/+DjJ3UCb1TJJZDWWxH8QzW5zg+381977TVPBIu7UoGFrnxNfMuI1/vwbQNec8bX9tJLLxGvJ+M32uQEK9JvEXL3L61zgBe481N3iXfLTqkjwOz5QQevBWvNmjXuac8hQ4a4zllqR+ApwnAVrNRuEfqlzlxH7kqxtLNgcb241nzwPOI1oyxYLMv8C2PgSOoWIXefOVY43iL8+PP8VN7jNVibNlygRx/CRqOpvZd4+e8QLC9pKsbiW3t824s7MPEPXiT673//2z0hyGuVWrZsmezHlvBTeoGnv+IvpmWZCSxoDixy54XDLG588A8+/vfvvvsubmhey8WdDf6vFx0sXlfFHTj+w106zpU7I3y7jzsa/HVmwLco4x+JbxHyk2S8mD+cF7mnVC9+iCGtcyDQNUrcLeG1MSy3kyZNcrdmAwdv5cEdysQPCnjRweIfwDyfuIvBc5BvTcc/eO7zLw3Zs2d3Xw53weJF7uFQZ64V/yL2xhtvuJqxGPLf+eDtN1i4tmzZ4tZc8nqt5ASLv863kPkhjnBc5A7BUvxhKjgUBEsQrmTo48ePu87NI4884m7f8JYD/MbEtwYDTxxxt4lvn/HaJV6o/sADD7iFpbxGip/O4TVagS0PEj8Ozj84+bYar2/gxcP8JhbY7yggcRyb137wU3zcceI1Ebxw3gvBYnYcj9dasNzx02F88FNCLJW8KJa7LbywPiXB4n9jwWLR4jdklgH+gR1u2zQkVy9eH5fWOcBCzl0E3vuMO3+8Zxrfhi1atKgTLF58zJ1DrstPP/3ktv/g2z387153sLhuPAbPWRYp3vuMtxRh4WLhYwnkW0G8wD4SBCuwHYff68y1CjzZy3/n9VYNGjRwNeRtGQL76LFo8QMtKQkWz1f+ZY/f5/hWIb8Hhss2DXM/k+lgNXkYHSzJn8uJY0OwNGl7OBavq+FFvl9//bW7HcjCwW31Ro0aud8QeV0UH/wDltdgcfeBtz3g/w8sIOffGHmfIz6S2tCQby3yD1teV8VvZIGDb0Xy5o3cwWAB4rUw/DQXr8vhDodXgsULXfkNkyWPF7MGDl7M/8033zg5TPyEWXIbjTIT7ubwYtpw3Wg0uXqldQ4wXxZ03gKBJZw7hIF1LLznEK+b4R98LLl8e4fX4vEaLD4kBIvj8g9R7mbwbSDujvJTsyxaPM95TrAERoJgxd8A2M915lrx9yCLOc9LXn/Ft/r44DV+LOu82Sj/Ahl/WUNyn0XIkt2zZ88EG43ye5HfNxqFYHn4wzIDQ0GwMhC+paEDghV/sz9L+SGXhARQr8iYEahzZNQ5cJWBndxnf5afylXI6unF/7LhPD2ODpanTFMLBsFKjVCE/DveyP1VaNTLX/UKNVvUOVRy/nxdQLBmfPYPEcFq/vBhfFSO4tSAYCnCtjyUxhs5b0DKj2OndPDtgsBtH8u8Mjo3jXp5cY18Czm1gzeVxJE0AdQ5smYGBCu86g3BCq96hnw1Gm/kvECX122ldPhx75qQoafjhRr1Skd6cS9Ny4csJ96o1otxwyUG6hwulUzbdQQEa/rCAhTl8S3CzRvOU4sGh9DBSlspPDkLguUJRgRJCwH+7Dh+4jClgx/ND3yuYFpi4hzbBPghjNSOwAf8pnYe/t0uAdTZm9pAsLzhaCUKBMtKJZAHCIAACIBARBMICNa0hQVFOlitG/yODpbiDINgKcLGUCAAAiAAAiCQHAEIVnjNDQhWeNUTVwMCIAACIOBTAgHBen/hDVS2QjZPr2LLhnP0VIOD6GB5SjXlYBAsRdgYCgRAAARAAARS62BBsMJjjkCwwqOOuAoQAAEQAAGfEwh0sKYsLERlb/W4g7XxHD3d4AA6WIpzBIKlCBtDgQAIgAAIgEBqHaxJC2+kMh4L1taN5+iZBvshWIrTD4KlCDvShjpw4ID7EOgOHTpg64UIKD7qHQFFjneJqLf39Q50sCBY3rPNiIgQrIygHiFjBt4s1qxZQ9HR0RFy1ZF7mah3ZNUe9fa+3gGm7y4oItLBat8wFh0s78uWbEQIliLsSBsKb8CRVXHUG/WOLALeXy0Ey3umGRkRgpWR9MN8bPzADfMCJ7o81Bv1jiwC3l9t4HtowoKidMut2T0d4NeNf9FzDfeig+Up1ZSDQbAUYUfaUPiBG1kVR71R78gi4P3VQrC8Z5qRESFYCvSPHDlCX375JRUrVoxy5MihMKKNITZv3kytW7emadOmUVRUlI2kkIUYAdRbDK3JwJFa77Nnz9Lu3bupXr16lD9/fk9rExCssQtuEulgdWr4GzpYnlYMHSxFnEkP9eGHHzrRwAECIAACIBAeBPgXx1atWnl6MRAsT3FmeDB0sBRKsGzZMqpVqxZ9MKoAlS2dVWFEDJHRBJ6vVyajU8D4igQy58qpOBqGykgCpy4dpw1nvqOlS5dSzZo1PU0lIFij5xej0h6vwdq28S96vtFudLA8rRg6WIo4kx4q8E2z6ssiFF3R24WLGX5xSCBJAvVuvA1kIohA5uvyRNDVRvalnrx4hFb8+amIqECwwmtuoYOlUE8IlgJkY0NAsIwVRDgdCJYwYEPhNQRrpOtgebted9vGs9QVHSzVmQTBUsANwVKAbGwICJaxgginA8ESBmwovIZgDZ9fgkp5LFjbN56l7o12inTeDJXHVCoQLIVyQLAUIBsbAoJlrCDC6UCwhAEbCg/BMlQM46lAsBQKBMFSgGxsCAiWsYIIpwPBEgZsKLyGYL39aSkq6XEHa8fGs9Sj8XZ0sBTnEgRLATYESwGysSEgWMYKIpwOBEsYsKHwECxDxTCeCgRLoUAQLAXIxoaAYBkriHA6ECxhwIbCawjWm5+WppK3XuPpVe/YeIb+2XgbOlieUk05GARLATYESwGysSEgWMYKIpwOBEsYsKHwECxDxTCeCgRLoUAQLAXIxoaAYBkriHA6ECxhwIbCawjWsE9uoRIed7B2bjxDvR75FR0sxbkEwVKADcFSgGxsCAiWsYIIpwPBEgZsKDwEy1AxjKcCwVIoEARLAbKxISBYxgoinA4ESxiwofAagvXaJ2VFOlh9HtkScgfr0qVLNGzYMJo4cSLt3buXihYtSu3ataNevXpR5syZU60QfybvyJEjaevWrXTlyhUqVaoUPffccy7GVVddlerr/XgCBEuhahAsBcjGhoBgGSuIcDoQLGHAhsJrCNaQT6KoePlrPb3qXZtOU99HNocsWJ07d6axY8dS27ZtqUaNGrR8+XKaPHky8ddHjx6dYq6DBg2if/3rX1SvXj1q1KiRE6y5c+fSt99+S//85z/pjTfe8PRarQSDYClUAoKlANnYEBAsYwURTgeCJQzYUPhIFKwNGzZQpUqVqGvXrjR8+PC4anTv3t11pdatW0cVKlRItkoFChSgm2++mVauXEmZMmVy512+fJmio6Np9+7ddPz4cUMV9i4VCJZ3LJONBMFSgGxsCAiWsYIIpwPBEgZsKLyGYL36SXmRDta/HtkUUgerf//+NHjwYNq5cycVL148rhq7du2iEiVKUL9+/Yi7VMkdOXLkoDp16tDChQsTnMIdLZa3/fv3G6qwd6lAsLxjCcFSYOmXISBYfqmUN3lCsLzh6IcokShYLELcpTp48ODfSlSwYEGqXLkyxcTEJFu+Bg0a0BdffOFuBTZu3NjdIpw9ezaxuI0aNYo6derkh9IHnSMEK2hkwb8AHazgmfn9FRAsv1cwuPwhWMHx8vPZGoL1349vpWIer8Havek0/efRjTRt2jSKiopKUIJChQoR/0nu4Nt/WbNmdd2vxAff5rtw4YLrRCV3sJg9+eST9PXXX8edkj17dnr33XepdevWfp4OKeYOwVIoLQRLAbKxISBYxgoinA4ESxiwofB+F6ykUA4YMIAGDhyYLOWSJUsSd6p4YXvigxe8Hzp0iLZv357s648dO0YvvfQSnT9/nh566CEnZO+//z4tXryYpk+fTk2bNjVUYe9SgWB5xzLZSBAsBcjGhoBgGSuIcDoQLGHAhsJrCNbAjytQsfI5Pb3q3ZtO0cBHN6h3sHh7h2rVqhFL2qxZs+KuiW8T1qpVy23bwNs+8DqtcDsgWAoVhWApQDY2BATLWEGE04FgCQM2FN7vgsW3+fi2XjBHetZg8VYM9913H82cOZOaNWuWYNg333yTXnzxRVq1ahVVqVIlmJR8cS4ES6FMECwFyMaGgGAZK4hwOhAsYcCGwmsI1n/mVaKbPe5g7dl0iv772LqQniLkpwSHDBkS0lOEfAuwZcuW9NFHH1GLFi0SVPL111+nPn360IoVK6h69eqGquxNKhAsbzimGAWCpQDZ2BAQLGMFEU4HgiUM2FD4SBQsfoKQnxRMbh+stWvXUsWKFd3aqh07dlCePHniFs3//PPPrmP24IMPuicJA8fFixdd14pvER4+fJhy5vT2lqiFKQPBUqgCBEsBsrEhIFjGCiKcDgRLGLCh8BqC9a95t4l0sF59bG1IHSzG37FjRxo/frzbyb1mzZq0bNkyt5N7hw4daNy4ca5CvGko75PVpk0bmjJlSrhL2KkAACAASURBVFzVeJuGzz77jGrXrk2PPfYYsVzx04wsX6ktsDdU+qBTgWAFjSz4F0Cwgmfm91dAsPxeweDyh2AFx8vPZ2sIVr+5lemm8rk8xfTbpj9pcJOfQxYslqKhQ4e6zyKMjY2lIkWKuM8R7N27N2XJkiVFwTp37hyNGTPGPTnIm5Xy04TlypVz+19xjHA9IFgKlYVgKUA2NgQEy1hBhNOBYAkDNhQ+UgXLUAl8kwoES6FUECwFyMaGgGAZK4hwOhAsYcCGwmsI1ktzbxfpYL3eZE3IHSxDJfBNKhAshVJBsBQgGxsCgmWsIMLpQLCEARsKD8EyVAzjqUCwFAoEwVKAbGwICJaxgginA8ESBmwovIZg9Z5ThYp6vAZr76Y/aWjT1ehgKc4lCJYCbAiWAmRjQ0CwjBVEOB0IljBgQ+EhWIaKYTwVCJZCgSBYCpCNDQHBMlYQ4XQgWMKADYXXEKx/zqlKRcvn9vSq9246SW82XYUOlqdUUw4GwVKADcFSgGxsCAiWsYIIpwPBEgZsKDwEy1AxjKcCwVIoEARLAbKxISBYxgoinA4ESxiwofAagtVzzh1UpJy3HazYX07SW01/QgdLcS5BsBRgQ7AUIBsbAoJlrCDC6UCwhAEbCq8hWN3nVBcRrOFNf4RgKc4lCJYCbAiWAmRjQ0CwjBVEOB0IljBgQ+EhWIaKYTwVCJZCgSBYCpCNDQHBMlYQ4XQgWMKADYXXEKxus++kwuXyeHrV+345QSMeX4EOlqdUUw4GwVKADcFSgGxsCAiWsYIIpwPBEgZsKDwEy1AxjKcCwVIoEARLAbKxISBYxgoinA4ESxiwofAagvX87BoiHazRjy9HB0txLkGwFGBDsBQgGxsCgmWsIMLpQLCEARsKD8EyVAzjqUCwFAoEwVKAbGwICJaxgginA8ESBmwovIZgdZ5Vi270eA3W/l9O0JhmS9HBUpxLECwF2BAsBcjGhoBgGSuIcDoQLGHAhsJDsAwVw3gqECyFAkGwFCAbGwKCZawgwulAsIQBGwqvIVgdZt5FN5a7ztOr3v/LcRr/xA/oYHlKNeVgECwF2BAsBcjGhoBgGSuIcDoQLGHAhsJDsAwVw3gqECyFAkGwFCAbGwKCZawgwulAsIQBGwqvIVjPzbybCnncwTrwy3Ga8MT36GApziUIlgJsCJYCZGNDQLCMFUQ4HQiWMGBD4TUE69kZ94gI1nvNl0CwFOcSBEsBNgRLAbKxISBYxgoinA4ESxiwofAQLEPFMJ4KBEuhQBAsBcjGhoBgGSuIcDoQLGHAhsJrCNYzM+6lG6K8XeR+cPNxmtT8W3SwFOcSBEsBNgRLAbKxISBYxgoinA4ESxiwofAQLEPFMJ4KBEuhQBAsBcjGhoBgGSuIcDoQLGHAhsJrCFabGffRDVF5Pb3qg5uP0dTmi9HB8pRqysEgWAqwIVgKkI0NAcEyVhDhdCBYwoANhYdgGSqG8VQgWAoFgmApQDY2BATLWEGE04FgCQM2FF5DsJ6aXocKetzB+n3zMXq/xTfoYCnOJQiWAmwIlgJkY0NAsIwVRDgdCJYwYEPhIViGimE8FQiWQoEgWAqQjQ0BwTJWEOF0IFjCgA2F1xCs1tMfEOlgTWvxFTpYinMJgqUAG4KlANnYEBAsYwURTgeCJQzYUHgNwWr50QNUIOp6T6/60OY/6KOWECxPoaYSDIKlQBuCpQDZ2BAQLGMFEU4HgiUM2FB4CJahYhhPxZRgPf3007RkyRLavXu3CrZixYrRPffcQ1OmTEl1PD6PD84v2AOCFSwx/58PwfJ/DYO5AghWMLT8fa6GYDX/sK5IB2tGq0W4Rag4/SBYECzF6RY5Q0GwIqfWfKUQrMipNwQrcmqd3iuNaME6c+YMZc6cmbJly5YqR3SwUkWEE+IRgGBF1nSAYEVOvTUEq9m0B0U6WLNax6CDpThVI1qwguEMwQqGFs6FYEXWHIBgRU69IViRU+v0Xmmm0aNHX+ncubOLc+nSJRoxYgRNnjyZfv31V8qRI4dbozR48GCKioqKG4vXLLVt25Y+//xz+v7772nq1Kl07Ngxqlq1Kr3zzjsUHR0dd+4ff/xBr7/+Oi1atIh27txJ58+fp3LlylGPHj2odevWCfIPZQ1WkSJF6M4776TZs2fHxWrcuDHNnz+fJk6cSM8++6z7+sWLFylv3rxuzLFjx7qvJbUGi7ta/fv3p48++oj+/PNPqlKlCr355pvUq1cv9xqswUrvlIuM10OwIqPOgauEYEVOvTUEq+m0+vQPj58iPLz5D5rT+gt0sBSnaqYnnnjiyowZM9yQjz/+OH3yySf05JNPUrVq1ejo0aM0ZswYOn36NK1atYpKly7tzgsIVqVKlejKlSvUpk0bOnnyJI0aNYouXLhAq1evjjuX//7oo49SkyZN6JZbbqFz587RvHnzaOnSpfTee+/RM888E3e5oQhWq1at6KuvvqJDhw65OJcvX6b8+fPTiRMnqEWLFjRt2jT39R9//NGJ2PTp06l58+bJClbDhg1p4cKF9MQTT9Ddd99N69evp5kzZ1K+fPmIZQ6CpTg7fTwUBMvHxQshdQhWCNB8+hIIlk8LlwFpx90i5A5Qs2bNXCeoadOmcans27fPdZweeughJyfxBatEiRK0bt06ypkzp/s6/527V/x6lhI+WKiyZMni1joFDpay+++/n2JjY2nr1q3pEqx3332XnnvuOdq0aZPL8+eff3Y5sFz98MMPtHfvXhefu2h9+vSh/fv3U6FChZIUrJiYGKpfvz51797ddeICx8iRI6lbt25Uu3btVAXrwIEDxH/iH5s3b3ads1VfFqHoitkzoMwYUpsABEubeMaOB8HKWP6ao2sI1mPcwSqbz9PLOrzlKM1DB8tTpqkFixOsxx57zHV5uGOT+OAuEW81cPjw4QSCxbcO+/btm+D0unXr0ooVK1wH6aqrrkrwb3x78NSpU67LxGLUr18/d17u3LndeaF0sPhWZpkyZWj06NHEtzrffvttGjRokLtFWLNmTdq2bRuVKlXKiRPfoowvdIlvEXbq1InGjRvnxK9w4cJxuXPe//jHP6hy5cqpCtbAgQPp5ZdfTpI7BCu16Rg+/w7BCp9apuVKIFhpoRQe52gI1qMfPEz5PRasI1uO0sdPfoZbhIrTME6wuPvDnZaUDl6jxdIUuEU4d+5cYjGLf3Tt2tXdKjx48CAVLFjQ3UIcPny4ExeWIf7/+MeePXvopptuClmw+IU33ngj1apVi2bNmkW8/oo7ZtxBu+6661wniteL8for7mqNHz8+bvjEgvXggw/SsmXL3NqrxAfLVZ48eVIVLHSwFGev4aEgWIaLI5AaBEsAqtGQECyjhTGYVqbOnTtf4e5P2bJl3QL0CRMmJJtmnTp1KFOmTHGCxWupeH1VSoI1bNgw6t27N3EXrF69eq4TxALEC+S527Rr1y632JyPUDpY/DoWp8WLF7tbc7z+ijtILHqB8fj23h133OEWrvO5gSOxYPH53H3j9WSJj9tuu80JG9ZgGZzFBlOCYBksimBKECxBuMZCawhW4w8aiHSwPn1yITpYivMpExFd4VtnLCEsD/zU39VXX51iCoEOVlpuESbX+eFbi6+99pongsVdqY4dO9KHH37oRI5vc1aoUME9/ciL9PnaXnrpJeL1ZNztSk6wvLhFmBQ47OSuOKONDAXBMlIIpTQgWEqgDQwDwTJQBJ+k4ASLnxDktUotW7akAQMGEK8jSnzwU3oFChRwXw4IFi9yZ5m59tpr3dcDi9z5iUG+XccHb3PA//7dd9/FheS1XOXLl3drurzoYPG6Ku7A8Z8jR464Jwq508a3+/jWIX+db2/yLcr4R+IO1hdffOEW86dnkTsEyyczXzhNCJYwYGPhIVjGCiKYjoZgNXy/IeXzeA3W0S1HacFTC9DBEpwbiUNnKl++/BUWI15bxbfPeO0SL1R/4IEHKFeuXMRrpPjpOl6jFdjyIPE2DXxrj2+r8dN2/NQgb83AC8/5ePXVV+nf//63i33vvfe6p/i448RP8nFnxwvB4nE4Hq/7YrmbM2eOG5u3jODbery3Vbt27dzC+pQEi/+NBYtFi7dp4KcGmQ22aVCckWEyFAQrTAqZxsuAYKURVBicBsEKgyIqXUKmQ4cOXeF1UXzwAnRegzVp0iS37QH/f2ABefv27alGjRruvKQ2GuVbi7zRKK+r4q5V4OANPl955RW3GSkLUPHixen55593Wzvw4nOvBIv3tmIRYsnr0qVL3Pi8HcQ333zj5JBvH6YmWCxjfPuSt6TgJx6x0ajSTAyzYSBYYVbQVC4HghU59dYQrIenNqJ8ZfN7CvXoliP0WZv56GB5SjXlYCF9VE5AsHiDTxYYHCkTwBqsyJshEKzIqjkEK3LqDcGKnFqn90ohWOklmIbXQ7DSACnMToFghVlB0cGKrIKmcLUaglV/SmORDtYXT3+KDpbiTDYrWLwB6dmzZ1NEwftS8eclWj8gWNYr5H1+ECzvmVqOiA6W5ep4mxsEy1ue4RzNrGDxwnlet5XSwR9KzedZPyBY1ivkfX4QLO+ZWo4IwbJcHW9z0xCsB6c8Qtd7vAbrjy1HKObpT9DB8nY6pBgtJMHSyO+XX35xTxymdPBWD4HPFdTIKdQxIFihkvPv6yBY/q1dKJlDsEKh5s/XaAhW3SmP0vVlvF3k/sfWI7To6Y8hWIrTzqxgKTIQHwqCJY7Y3AAQLHMlEU0IgiWK11RwCJapcphOBoKlUB4IlgJkY0NAsIwVRDgdCJYwYEPhNQTrgcmPUd4y/7d9klfHsa2H6au289DB8gpoGuJAsNIAKb2nQLDSS9B/r4dg+a9m6ckYgpUeev56LQTLX/XKyGwhWAr0IVgKkI0NAcEyVhDhdCBYwoANhdcQrDqTmoh0sL55Zi46WIpzCYKlABuCpQDZ2BAQLGMFEU4HgiUM2FD4SBUs/jzfYcOG0cSJE2nv3r1UtGhR9xF0vXr1osyZM6epQjNmzKBRo0a5zzDmT4opWbIkderUiTp06JCm1/vtJAiWQsUgWAqQjQ0BwTJWEOF0IFjCgA2F1xCseyc1FelgffvMnJA7WJ07d6axY8e6j7jjj81bvnw58VZJ/PXRo0enWqGePXvS8OHDqVmzZu5zflmwtm3bRtmzZ6fBgwen+no/ngDBUqgaBEsBsrEhIFjGCiKcDgRLGLCh8JEoWBs2bKBKlSpR165dnSQFju7du7vP/123bh1VqFAh2Sp99tln1KBBA/roo4+oRYsWhqopmwoES5aviw7BUoBsbAgIlrGCCKcDwRIGbCi8hmDd897jdJ3HTxEe33qYljw7O6QOVv/+/V2XaefOnVS8ePG4auzatYtKlChB/fr1o0GDBiVbpbvvvpvOnDlDq1evdp2rU6dOUa5cuQxVVSYVCJYM1wRRIVgKkI0NAcEyVhDhdCBYwoANhdcQrLsnNhMRrO/bzQpJsOrVq+e6VAcPHvxbJQoWLEiVK1emmJiYJKvEMsUfa8drrfLnz08jRoygY8eOUd68eenZZ5914nb11VcbqrB3qUCwvGOZbCQIlgJkY0NAsIwVRDgdCJYwYEPh/S5Y06ZNo6ioqARE+RNRUvpUFL79lzVrVidniY/o6Gi6cOEC8W3EpI61a9c6AWO5unz5Mv373/+mIkWK0PTp02nevHnUqlUr4pzC8YBgKVQVgqUA2dgQECxjBRFOB4IlDNhQeA3BuuvdZpSnTAFPr/rE1kP0Q/tZScYcMGAADRw4MNnx+Gk/7lTxwvbEBy94P3ToEG3fvj3J1y9dupTuuusu92/ff/993N/5/+vUqUOLFy+mTZs2Ubly5Ty9XgvBIFgKVYBgKUA2NgQEy1hBhNOBYAkDNhTe74Kl3cHirleVKlWoWLFixGu24h9TpkxxTyWOGTPG3UIMtwOCpVBRCJYCZGNDQLCMFUQ4HQiWMGBD4TUEq+a7T1CeWzzuYP16iJa1n6m+BovXbfHtxzvuuIN+/PHHBJXkdVv169d3C+R5oXy4HRAshYpCsBQgGxsCgmWsIMLpQLCEARsKH4mCxfIzZMiQkJ8i5E1J+eANSuMfvGlp+/bt6d1333WblobbAcFSqCgESwGysSEgWMYKIpwOBEsYsKHwGoJ157vNRTpYK9rPCKmDxU8Q8kL15PbB4oXsFStWdIvdd+zY4Z4ajL9o/qWXXqKhQ4fSggUL3H5YfPDO8Lx+i28h8votvoUYbgcES6GiECwFyMaGgGAZK4hwOhAsYcCGwkeiYDH+jh070vjx492aqZo1a9KyZcvcTu78MTfjxo1zFdq9e7fbJ6tNmzbE66sCB2/LwOuwDhw4QLw5aeHChWnWrFn0ww8/UJ8+fVx3LBwPCJZCVSFYCpCNDQHBMlYQ4XQgWMKADYXXEKzqE1pQbo/XYJ389RD9+Nz0kDpYjP/ixYuuC8W39WJjY91WC3xbr3fv3pQlS5YUBYv/keWqb9++9Pnnn9OJEyeoVKlS1KVLl7Bc3B6YrhAshW9cCJYCZGNDQLCMFUQ4HQiWMGBD4SNVsAyVwDepQLAUSgXBUoBsbAgIlrGCCKcDwRIGbCi8hmBVG99SpIO1ssNHIXewDJXAN6lAsBRKBcFSgGxsCAiWsYIIpwPBEgZsKLyGYFUd15Jy3VLQ06v+89ffaVVHCJanUFMJBsFSoA3BUoBsbAgIlrGCCKcDwRIGbCg8BMtQMYynAsFSKBAESwGysSEgWMYKIpwOBEsYsKHwGoJ1+9hWIh2sNZ0+xC1CxbkEwVKADcFSgGxsCAiWsYIIpwPBEgZsKDwEy1AxjKcCwVIoEARLAbKxISBYxgoinA4ESxiwofAaghU9tjXlKu3xGqxtv9P/Ok1DB0txLkGwFGBDsBQgGxsCgmWsIMLpQLCEARsKD8EyVAzjqUCwFAoEwVKAbGwICJaxgginA8ESBmwovIZgVR77JOX0uIN1atvv9HOnD9DBUpxLECwF2BAsBcjGhoBgGSuIcDoQLGHAhsJDsAwVw3gqECyFAkGwFCAbGwKCZawgwulAsIQBGwqvIViVxjwl0sFa1/l9dLAU5xIESwE2BEsBsrEhIFjGCiKcDgRLGLCh8BqCVXE0C9YNnl71qW0Haf3zECxPoaYSDIKlQBuCpQDZ2BAQLGMFEU4HgiUM2FB4CJahYhhPBYKlUCAIlgJkY0NAsIwVRDgdCJYwYEPhNQSrwqg2dK3HHazT2w7Shi5TcYtQcS5BsBRgQ7AUIBsbAoJlrCDC6UCwhAEbCg/BMlQM46lAsBQKBMFSgGxsCAiWsYIIpwPBEgZsKLyGYN068mmRDtbGrlPQwVKcSxAsBdgQLAXIxoaAYBkriHA6ECxhwIbCQ7AMFcN4KhAshQJBsBQgGxsCgmWsIMLpQLCEARsKryFY5Ua2pWtLefsU4entB+mXrpPRwVKcSxAsBdgQLAXIxoaAYBkriHA6ECxhwIbCQ7AMFcN4KhAshQJBsBQgGxsCgmWsIMLpQLCEARsKryFYUSNkOlibu6GDpTmVIFgKtCFYCpCNDQHBMlYQ4XQgWMKADYWHYBkqhvFUIFgKBYJgKUA2NgQEy1hBhNOBYAkDNhReQ7DKjniGrvF4DdaZ7QdpS7dJWIOlOJcgWAqwIVgKkI0NAcEyVhDhdCBYwoANhdcQrDLDWbAKeXrVZ7YfoK3dIVieQk0lGARLgTYESwGysSEgWMYKIpwOBEsYsKHwECxDxTCeCgRLoUAQLAXIxoaAYBkriHA6ECxhwIbCawhW6XeeFelgbXvhPdwiVJxLECwF2BAsBcjGhoBgGSuIcDoQLGHAhsJDsAwVw3gqECyFAkGwFCAbGwKCZawgwulAsIQBGwqvIVil3pbpYG3vgQ6W5lSCYCnQhmApQDY2BATLWEGE04FgCQM2FB6CZagYxlOBYCkUCIKlANnYEBAsYwURTgeCJQzYUHgdwWpHOUp6+xTh2R0HaHuPiViDpTiXIFgKsCFYCpCNDQHBMlYQ4XQgWMKADYWHYBkqhvFUIFgKBYJgKUA2NgQEy1hBhNOBYAkDNhReQ7BKvtVepIO1o+e76GApziUIlgJsCJYCZGNDQLCMFUQ4HQiWMGBD4TUEq/ibz4kI1q5/ToBgKc4lCJYC7IBgVaM6lDtTXoURMURGE/hy/9qMTgHjKxI4cfms4mgYKiMJrNtwnu598LCIqAR+VkCwMrLC3o0NwfKOZbKRIFgKkI0NAcEyVhDhdCBYwoANhdcQrGJvyHSwdr+IDpbmVIJgKdCGYClANjYEBMtYQYTTgWAJAzYUHoJlqBjGU4FgKRQIgqUA2dgQECxjBRFOB4IlDNhQeBXBGvYcZS95o6dX/deO/bS7FzpYnkJNJRgES4E2BEsBsrEhIFjGCiKcDgRLGLCh8BAsQ8UwngoES6FAECwFyMaGgGAZK4hwOhAsYcCGwmsI1s3DOlD2Eh53sHbupz29xosszjdUHlOpQLAUygHBUoBsbAgIlrGCCKcDwRIGbCg8BMtQMYynAsFSKBAESwGysSEgWMYKIpwOBEsYsKHwGoJ1k1AH6zd0sFRnEgRLATcESwGysSEgWMYKIpwOBEsYsKHwECxDxTCeCgRLoUAQLAXIxoaAYBkriHA6ECxhwIbCawhW0aEdRdZg7e09DmuwFOcSBEsBNgRLAbKxISBYxgoinA4ESxiwofAqgvW6kGC9BMHSnEoQLAXaECwFyMaGgGAZK4hwOhAsYcCGwkOwDBXDeCoQLIUCQbAUIBsbAoJlrCDC6UCwhAEbCq8hWEVe6yRyizC2z1jcIlScSxAsBdgQLAXIxoaAYBkriHA6ECxhwIbCQ7AMFcN4KhAshQJBsBQgGxsCgmWsIMLpQLCEARsKryJYQzpRNo83Gj23cz/F9kUHS3MqQbAUaEOwFCAbGwKCZawgwulAsIQBGwoPwTJUDOOpQLAUCgTBUoBsbAgIlrGCCKcDwRIGbCi8hmAVHtKZshX39qNyzu3aT/v6jsEaLMW5BMFSgA3BUoBsbAgIlrGCCKcDwRIGbCg8BMtQMYynAsFSKBAESwGysSEgWMYKIpwOBEsYsKHwKoI1mDtYhT296nO79tG+fuhgeQo1lWAQLAXaECwFyMaGgGAZK4hwOhAsYcCGwmsI1o2DZARrf38IluZUgmAp0IZgKUA2NgQEy1hBhNOBYAkDNhQ+UgXr0qVLNGzYMJo4cSLt3buXihYtSu3ataNevXpR5syZg6pQ7dq16fvvv6dWrVrRtGnTgnqtn06GYClUC4KlANnYEBAsYwURTgeCJQzYUHgVwXpVqIP1r9A7WJ07d6axY8dS27ZtqUaNGrR8+XKaPHky8ddHjx6d5gq9//777jWnT5+GYKWZGk5MlgAEK/ImBwQrsmoOwYqcekeiYG3YsIEqVapEXbt2peHDh8cVu3v37jRy5Ehat24dVahQIdVJcPz4cSpTpgz16NGD+vbtC8FKlRhOSJUABCtVRGF3AgQr7Eqa4gVBsCKn3hqCVeiV50UWuR/49+iQtmno378/DR48mHbu3EnFixePK/auXbuoRIkS1K9fPxo0aFCqk+D555+nr776ijZu3EjZsmWDYKVKDCekSgCClSqisDsBghV2JYVgRVZJk73aSBSsevXquS7VwYMH/8alYMGCVLlyZYqJiUlxhqxZs4aqVatGCxcupPr161OmTJkgWPieSj8BCFb6GfotAgTLbxVLX77oYKWPn59erSZYxTzepmH3PuIOFi8qj4qKSoC8UKFCxH+SO/j2X9asWV33K/ERHR1NFy5cIL6NmNxx+fJlql69uhvj008/dadBsPw06w3nCsEyXByh1CBYQmCNhoVgGS2MQFp+F6ykkAwYMIAGDhyYLK2SJUsSd6p4YXvigxe8Hzp0iLZv357s68eNG+fWXf3yyy9xtxghWAKTMxJDQrAir+oQrMiqOQQrcuqtIVg3/LcLZRPoYB38zyj1Dtbhw4fdwvYuXbrQf//737iJAsGKnO8Z0SuFYIniNRkcgmWyLGJJQbDE0JoLrCJYLwsJ1oBRIS1yT88aLH7y8KOPPqIlS5ZQjhw54upZunRpaty4Mb3xxhuUP39+uu6668zVOr0JYR+s9BJMw+shWGmAFGanQLDCrKCpXA4EK3LqHYmCxU8JDhkyJKSnCB955JG4dVfJzRLewPTFF18Mu0kEwVIoKQRLAbKxISBYxgoinA4ESxiwofA6gtVV5hbhgJEhdbD4CUJ+UjC5fbDWrl1LFStWdIvdd+zYQXny5IlbNL9ixQrat2/f3yr4+OOP01133UXdunVzr73lllsMVdmbVCBY3nBMMQoESwGysSEgWMYKIpwOBEsYsKHwkShYjL9jx440fvx4t5N7zZo1admyZW4n9w4dOhAvYudj9+7dbhF7mzZtaMqUKSlWDWuwDE1qP6cCwfJz9ULLHYIVGje/vgqC5dfKBZ+3imANFOpgDQytg8WULl68SEOHDnWfRRgbG0tFihRxn0XYu3dvypIlCwQriamEDlbw319BvwKCFTQy378AguX7EgZ1ARCsoHD5+uRIFSxfFy2DkodgKYCHYClANjYEBMtYQYTTgWAJAzYUXkWwBgh1sF4OvYNlqAS+SQWCpVAqCJYCZGNDQLCMFUQ4HQiWMGBD4SFYhophPBUIlkKBIFgKkI0NAcEyVhDhdCBYwoANhdcRrG6U7WaPPypnzz46+PKIkJ4iNITfV6lAsBTKBcFSgGxsCAiWsYIIpwPBEgZsKDwEy1AxjKcCwVIoEARLAbKxISBYxgoinA4ESxiwofAqgvWfbpTV4w7Wee5g/RcdLM2pBMFSoA3BUoBsbAgIlrGCCKcDwRIGbCi8imD9W0iwXoFgaU4lCJYCbQiWAmRjQ0CwjBVEOB0IljBgQ+EhWIaKYTwVCJZCgSBYCpCNDQHBMlYQ4XQgWMKADYVX+7LzdQAAIABJREFUEax/cQeriKdXfX5PLB18FR0sT6GmEgyCpUAbgqUA2dgQECxjBRFOB4IlDNhQeAiWoWIYTwWCpVAgCJYCZGNDQLCMFUQ4HQiWMGBD4VUEqz9v0+BtB+scd7AGoYOlOZUgWAq0IVgKkI0NAcEyVhDhdCBYwoANhYdgGSqG8VQgWAoFgmApQDY2BATLWEGE04FgCQM2FF5NsG7yuIP1GzpY2tMIgqVAHIKlANnYEBAsYwURTgeCJQzYUHgIlqFiGE8FgqVQIAiWAmRjQ0CwjBVEOB0IljBgQ+FVBKtfd8om0cEaPBwflaM4lyBYCrAhWAqQjQ0BwTJWEOF0IFjCgA2FVxGsvkKCNQSCpTmVIFgKtCFYCpCNDQHBMlYQ4XQgWMKADYWHYBkqhvFUIFgKBYJgKUA2NgQEy1hBhNOBYAkDNhReRbD6CHWwXkMHS3MqQbAUaEOwFCAbGwKCZawgwulAsIQBGwoPwTJUDOOpQLAUCgTBUoBsbAgIlrGCCKcDwRIGbCi8imC9JNTBeh0dLM2pBMFSoA3BUoBsbAgIlrGCCKcDwRIGbCg8BMtQMYynAsFSKBAESwGysSEgWMYKIpwOBEsYsKHwOoL1AmUr6vFGo3tj6eDr72CbBsW5BMFSgA3BUoBsbAgIlrGCCKcDwRIGbCg8BMtQMYynYlqwMmXKRAMGDKCBAwemGSOf+/LLL9OVK1fS/BrpEyFY0oTtxYdg2auJZEYQLEm6tmJrCFah3jIdrAND0cHSnE0igsVCMX/+fHr66aepWLFiIV8PBCtkdHhhBhOAYGVwAZSHh2ApA8/A4SBYGQjfZ0OLCNbEiROpffv29O2339I999wTMhIIVsjo8MIMJgDByuACKA8PwVIGnoHDqQhWL6EO1jB0sDSnDgRLgTZuESpANjYEBMtYQYTTgWAJAzYUHoJlqBjGU8k0efLkK23btqXPP/+cvv/+e5o6dSodO3aMqlatSu+88w5FR0fHXcKlS5doxIgRNHnyZPr1118pR44crkM1ePBgioqKcucF1kAlvm5+Dd8y/OGHH2js2LG0YsUKOnDgAOXMmZNq167tYpQpUybBy7zsYG3atMmtzeKu2smTJ6l48eKuy9ajRw+66qqr4sblW5pFihRxOXbv3p1+/PFHyp07N7Vr145eeeUV4pyCPSBYwRLz//kQLP/XMJgrgGAFQ8vf50Kw/F0/zezjBKtSpUpuYXibNm2cgIwaNYouXLhAq1evptKlS7ucHn/8cfrkk0/oySefpGrVqtHRo0dpzJgxdPr0aVq1apU7b/369U7C3nvvPerXr1+ceNWoUYNKlChBXbt2pQ0bNjgxK1y4MP322280fvx4NzZLUIECBeKu3yvBYkm6//77nTjx9eXNm9eJ1qxZs6hTp07uGgIHC1bmzJnpzJkz9MQTT1DZsmXpiy++cGvK+Nbns88+G3R9IFhBI/P9CyBYvi9hUBcAwQoKl69P1hCsG1+UuUW4/w3cItScfHGCxfKzbt0611Hig//O3aumTZvSzJkzafbs2dSsWTP3X/5a4Ni3bx+VK1eOHnroIZo+fbr7ckprsFjGrr322gTXyN2wChUquO5X3759PRUsFreKFStStmzZaNmyZe6/gePFF1+kt956i3755RcnUnywYO3Zs4fmzJlDTZo0iTv3tttuo6xZs9LKlStTrA935fhP/GPz5s3UunVrqkZ1KHemvJr1xVgZRACClUHgM2hYCFYGgc+AYSFYGQDdp0PGCRbfoosvN3w9devWdbfyTpw44aSKO0HcoUp8tGrVirhLc/jw4VQFK/5rT506RefOnXPdq/vuu49KlSpF8+bN81SwOF/uzvHtTs4z/sESyZ0t7tY9//zzcYLFOSWWJL5dOG3aNNe1S+lI7hYpvwaC5dPvkhDShmCFAM3HL4Fg+bh4QaauIlj/7CGy0ej+N9/GRqNB1js9p8cJ1ty5c+mxxx5LEItv57F8HDx4kO69917iTkxKB6/R4vVMKXWwWFz69OlDCxYscGu94h88xuLFiz0VLL4NyLf6Ujr+85//uPVZfHAHq1ChQk4s4x9p3V8LHaz0TMfweS0EK3xqmZYrgWClhVJ4nAPBCo86alxFnGBx5+jRRx9NVrB4Ifr58+dpwoQJyeZVp04dtwg8OcG6fPky8a222NhYt7i8fPny7pYkS9kLL7xA+fPnpyVLlngqWDNmzKAWLVq4DUtr1aqVZO684L1kyZJxgsVrtZYuXRqSYCU1ANZgaUxlW2NAsGzVQzobCJY0YTvxVQSrp1AH6y10sDRnUppvETZu3NjJzx9//EFXX311ijnyAnd+6i7xPlh8S44FK/BEYfwgN954I91yyy2eC9aaNWuoSpUqNGTIENc5S+0IPEUIwUqNFP49JQIQrMiaHxCsyKk3BCtyap3eK02wyJ3XKwUWoAcWufNCb77NxgvYW7ZsmexH1xw6dCjuCcBA1yhxV2zjxo1uMfukSZOIt4YIHLw1BG/hwF0yrztY3DXjThnfjuRrKliwYAJmf/75pxPG7Nmzo4OV3tmE18cRgGBF1mSAYEVOvdUEq4jHH/YcG0v70cFSnah/26aBRYe3aRg5cqRbgM7bNPD+VLwQnW+18ROFvPj9gQceoFy5crkn7mJiYtyThLwInI9t27a5bhTvpcXbIPB+WXfccQcVLVrUCdbvv/9O3bp1c2udfvrpJ7f1A2+dwP/utWBxPjwG58sixdss8HYSLFwsfCyBa9eudQvs+UAHS3X+he1gEKywLW2SFwbBipx6awhW4R49KJuAYO17G7cINWdqkhuN8m1AlqO3337b3V4LHCxZvAaLO1C8ZxX/P9/a47VNvGkn73UVOEaPHu22QGAB48XvgduCO3bscOuv+BYc77NVvXp1GjZsmFuDxYeEYAWkb9CgQfTVV1+5px2vv/56J1qNGjWiLl26OAmEYGlOvfAeC4IV3vVNfHUQrMipNwQrcmqd3iuNEywWD96yAIf3BLDI3Xum1iNCsKxXyNv8IFje8rQcTUWwXhDqYL2DDpbm3IJgKdCGYClANjYEBMtYQYTTgWAJAzYUHoJlqBjGU/GNYPFeXKkdN9xwQ2qnZMi/Q7AyBHuGDgrBylD86oNDsNSRZ9iAKoLVXaiDNRwdLM2J4xvBSsuHLPOaMIsHBMtiVWRzgmDJ8rUWHYJlrSJy+UCw5NiGW+RMV6xaSSLSX3/9darsra4hg2ClWrqwOwGCFXYlTfGCIFiRU28NwSrSTaaDFTsCHSzNmeobwdKE4vVYECyvidqPB8GyXyMvM4RgeUnTdiwIlu36WMoOgqVQDQiWAmRjQ0CwjBVEOB0IljBgQ+F1BKsnZSvs8Uaj+2IpdsRb+LBnxbkEwVKADcFSgGxsCAiWsYIIpwPBEgZsKDwEy1AxjKcCwVIoEARLAbKxISBYxgoinA4ESxiwofAqgtVVqIM1Eh0szakEwVKgDcFSgGxsCAiWsYIIpwPBEgZsKLyKYHXpSdk9vkX4F98iHAXB0pxKECwF2hAsBcjGhoBgGSuIcDoQLGHAhsJDsAwVw3gqECyFAkGwFCAbGwKCZawgwulAsIQBGwqvIljPC3WwRqODpTmVIFgKtCFYCpCNDQHBMlYQ4XQgWMKADYWHYBkqhvFUIFgKBYJgKUA2NgQEy1hBhNOBYAkDNhReQ7CKdpbpYO0dgw6W5lSCYCnQhmApQDY2BATLWEGE04FgCQM2FB6CZagYxlOBYCkUCIKlANnYEBAsYwURTgeCJQzYUHg1wbrR241G/9ofS+hg6U4kCJYCbwiWAmRjQ0CwjBVEOB0IljBgQ+EjVbAuXbpEw4YNo4kTJ9LevXupaNGi1K5dO+rVqxdlzpw52QqdOXOG3n//fZo/fz5t2LCBjh49SsWKFaMGDRpQv3796LrrrjNUXW9TgWB5yzPJaBAsBcjGhoBgGSuIcDoQLGHAhsKrCFannpRdooM1NvQ1WJ07d6axY8dS27ZtqUaNGrR8+XKaPHky8ddHjx6dbIU2btxIFStWpLvuuovq1atHBQoUcB/Xw6LGosV/z507t6EKe5cKBMs7lslGgmApQDY2BATLWEGE04FgCQM2FF5DsG7qKCNYv40LTbC481SpUiXq2rUrDR8+PK4a3bt3p5EjR9K6deuoQoUKSVbpyJEjtH//fidZ8Y9JkybRs88+S2+++Sb17NnTUIW9SwWC5R1LCJYCS78MAcHyS6W8yROC5Q1HP0SJRMHq378/DR48mHbu3EnFixePK9OuXbuoRIkS7lbfoEGDgirfyZMnKU+ePPTMM8/Qe++9F9Rr/XIyBEuhUuhgKUA2NgQEy1hBhNOBYAkDNhQ+EgWLb+1xl+rgwYN/q0TBggWpcuXKFBMTE1SVtm7dSmXLlqU+ffrQkCFDgnqtX06GYClUCoKlANnYEBAsYwURTgeCJQzYUHi/C9a0adMoKioqAdFChQoR/0nu4Nt/WbNmdeulEh/R0dF04cIFt4A9mOOpp54izuXnn392tx/D8YBgKVQVgqUA2dgQECxjBRFOB4IlDNhQeBXB6iC0Bmv8W0mSHDBgAA0cODBZyiVLliTuVPHC9sQHL3g/dOgQbd++Pc1VmjBhAnXo0MGtveI1WOF6QLAUKgvBUoBsbAgIlrGCCKcDwRIGbCi83wUroztYn3zyCTVt2pQeeughmjdvHmXJksVQdb1NBYLlLc8ko0GwFCAbGwKCZawgwulAsIQBGwqvIVg3PyfTwdozIbSnCL1ag7Vo0SJq1KiR2+bh888/p+zZsxuqrPepQLC8Z/q3iBAsBcjGhoBgGSuIcDoQLGHAhsJHomDxU4K8ED09TxF+9913VL9+fbedwzfffEM5c+Y0VFWZVCBYMlwTRIVgKUA2NgQEy1hBhNOBYAkDNhReTbAKefxROQdiKdQOFj9ByE8KJrcP1tq1a90+V7zYfceOHW77hfiL5n/66Se6//773RYPLFp58+Y1VFG5VCBYcmzjIkOwFCAbGwKCZawgwulAsIQBGwofiYLF+Dt27Ejjx493O7nXrFmTli1b5nZy58Xq48aNcxXavXu3k6g2bdrQlClT3Nf27Nnj5OzPP/+k1157zS2Wj3/w/z/wwAOGKuxdKhAs71gmGwmCpQDZ2BAQLGMFEU4HgiUM2FB4FcFq35OyS3Sw3g1tDRbjv3jxIg0dOtR9xE1sbCwVKVLEfRZh79694xaqJyVYS5YsoXvvvTfZCtauXZv4nHA8IFgKVYVgKUA2NgQEy1hBhNOBYAkDNhReQ7CKtZMRrN0TQxcsQyXwTSoQLIVSQbAUIBsbAoJlrCDC6UCwhAEbCg/BMlQM46lAsBQKBMFSgGxsCAiWsYIIpwPBEgZsKLyKYD0r1MF6Dx0szakEwVKgDcFSgGxsCAiWsYIIpwPBEgZsKDwEy1AxjKcCwVIoEARLAbKxISBYxgoinA4ESxiwofAqgvVMT8rh8SL3swdiafckdLA0pxIES4E2BEsBsrEhIFjGCiKcDgRLGLCh8BAsQ8UwngoES6FAECwFyMaGgGAZK4hwOhAsYcCGwqsJ1g3ebjR69iA6WNrTCIKlQByCpQDZ2BAQLGMFEU4HgiUM2FB4CJahYhhPBYKlUCAIlgJkY0NAsIwVRDgdCJYwYEPhVQSrbU/KIdHBmow1WJpTCYKlQBuCpQDZ2BAQLGMFEU4HgiUM2FB4DcEq/rSMYO2aAsHSnEoQLAXaECwFyMaGgGAZK4hwOhAsYcCGwkOwDBXDeCoQLIUCQbAUIBsbAoJlrCDC6UCwhAEbCq8iWG2EOlhT0cHSnEoQLAXaECwFyMaGgGAZK4hwOhAsYcCGwkOwDBXDeCoQLIUCQbAUIBsbAoJlrCDC6UCwhAEbCq8iWE8JdbDeRwdLcypBsBRoQ7AUIBsbAoJlrCDC6UCwhAEbCg/BMlQM46lAsBQKBMFSgGxsCAiWsYIIpwPBEgZsKLyGYJXgDlZBjzca/T2WdqKDpTqTIFgKuCFYCpCNDQHBMlYQ4XQgWMKADYWHYBkqhvFUIFgKBYJgKUA2NgQEy1hBhNOBYAkDNhReRbCeFOpgfYA1WJpTCYKlQBuCpQDZ2BAQLGMFEU4HgiUM2FB4DcEq2VpGsHZMg2BpTiUIlgJtCJYCZGNDQLCMFUQ4HQiWMGBD4SFYhophPBUIlkKBIFgKkI0NAcEyVhDhdCBYwoANhVcRrFZCHawP0cHSnEoQLAXaECwFyMaGgGAZK4hwOhAsYcCGwkOwDBXDeCoQLIUCQbAUIBsbAoJlrCDC6UCwhAEbCq8iWC2FOlgfoYOlOZUgWAq0IVgKkI0NAcEyVhDhdCBYwoANhYdgGSqG8VQgWAoFgmApQDY2BATLWEGE04FgCQM2FF5DsEoJdbC2o4OlOpMgWAq4A4J1Z67GlDtLfoURMURGE5i+MSajU8D4igTyXJVDcTQMlZEE/rf+L6paL5bWrFlD0dHRnqYS+FkBwfIUa4YFg2ApoIdgKUA2NgQEy1hBhNOBYAkDNhReTbAKePxROYdiCR0s3YkEwVLgDcFSgGxsCAiWsYIIpwPBEgZsKDwEy1AxjKcCwVIoEARLAbKxISBYxgoinA4ESxiwofAqgtWiJ13jcQfrDHewpuMpQs2pBMFSoA3BUoBsbAgIlrGCCKcDwRIGbCi8imA1FxKsGRAszakEwVKgDcFSgGxsCAiWsYIIpwPBEgZsKDwEy1AxjKcCwVIoEARLAbKxISBYxgoinA4ESxiwofAqgvWEUAdrJjpYmlMJgqVAG4KlANnYEBAsYwURTgeCJQzYUHgIlqFiGE8FgqVQIAiWAmRjQ0CwjBVEOB0IljBgQ+E1BKt0M5kO1rZZ6GBpTiUIlgJtCJYCZGNDQLCMFUQ4HQiWMGBD4SFYhophPBUIlkKBIFgKkI0NAcEyVhDhdCBYwoANhVcTrH94u9HomcOxhA6W7kSCYCnwhmApQDY2BATLWEGE04FgCQM2FB6CZagYxlOBYCkUCIKlANnYEBAsYwURTgeCJQzYUHgNwbrl8Z50jUAH69fZWIOlOZUgWAq0IVgKkI0NAcEyVhDhdCBYwoANhVcRrKZCgjUHgqU5lSBYCrQhWAqQjQ0BwTJWEOF0IFjCgA2Fh2AZKobxVCBYCgWCYClANjYEBMtYQYTTgWAJAzYUXkOwyjSR6WBtnYsOluZUgmAp0IZgKUA2NgQEy1hBhNOBYAkDNhQegmWoGMZTgWApFAiCpQDZ2BAQLGMFEU4HgiUM2FB4FcF6TKiDNQ8dLM2pBMFSoA3BUoBsbAgIlrGCCKcDwRIGbCg8BMtQMYynAsFSKBAESwGysSEgWMYKIpwOBEsYsKHwaoKV3+ONRo/E0lZ0sFRnEgRLATcESwGysSEgWMYKIpwOBEsYsKHwECxDxTCeCgRLoUAQLAXIxoaAYBkriHA6ECxhwIbCawhW2Ud70jUCHawtH2MNluZUgmAp0IZgKUA2NgQEy1hBhNOBYAkDNhQegmWoGMZTgWApFAiCpQDZ2BAQLGMFEU4HgiUM2FB4FcF6RKiD9Qk6WJpTCYKlQBuCpQDZ2BAQLGMFEU4HgiUM2FB4DcGKatxD5Bbh5k/fpjVr1lB0dLQhouGbCgRLobYQLAXIxoaAYBkriHA6ECxhwIbCQ7AMFcN4KhAshQJBsBQgGxsCgmWsIMLpQLCEARsKryJYjYQ6WPPRwdKcShAsBdoQLAXIxoaAYBkriHA6ECxhwIbCR6pgXbp0iYYNG0YTJ06kvXv3UtGiRaldu3bUq1cvypw5c6oV2rhxI/Xu3ZuWLl3qzq1VqxYNHTqUbr311lRf69cTIFgKlYNgKUA2NgQEy1hBhNOBYAkDNhReRbAa9qBrPd6m4fSRWNq8IPQOVufOnWns2LHUtm1bqlGjBi1fvpwmT55M/PXRo0enWKFt27ZR1apV6frrr6euXbu6c0eMGEHHjx+nlStXUunSpQ1V2LtUIFjesUw2EgRLAbKxISBYxgoinA4ESxiwofCRKFgbNmygSpUqOTkaPnx4XDW6d+9OI0eOpHXr1lGFChWSrVLTpk0pJiaGNm/e7DpffHAXLCoqiurXr0+zZ882VGHvUoFgeccSgqXA0i9DQLD8Uilv8oRgecPRD1HUBCuftx+Vc/po6B2s/v370+DBg2nnzp1UvHjxuDLt2rWLSpQoQf369aNBgwYlWb5Tp05Rvnz5qHnz5jR16tQE57Rp04ZmzpxJR44coZw5c/qh/EHlCMEKCldoJ6ODFRo3P78KguXn6gWfOwQreGZ+fUUkCla9evVcl+rgwYN/K1vBggWpcuXKrkOV1LFixQp3S5FvL3bs2DHBKfw1vsXI51SvXt2vUyLZvCFYCiWFYClANjYEBMtYQYTTgWAJAzYUXkOwyjXoQdcKdLB+Wfg2TZs2zd2ai38UKlSI+E9yB9/+y5o1q9tDK/HBe2pduHCB+DZiUsfcuXOJbxHOnz+fGjZsmOAU/lrjxo1pzpw51KRJE0NV9iYVCJY3HFOMAsFSgGxsCAiWsYIIpwPBEgZsKLyKYD0sJFifvZ0kyQEDBtDAgQOTpVyyZEniThUvbE98cHfq0KFDtH379iRf/8EHH9BTTz1FX375JdWtWzfBOYsWLSLujvE5rVu3NlRlb1KBYHnDEYKlwNFPQ0Cw/FSt9OcKwUo/Q79E8LtgoYOlN9MgWAqs0cFSgGxsCAiWsYIIpwPBEgZsKLyGYJV/SKaDtenz0LZpwBqs0CYgBCs0bkG9CoIVFK6wOBmCFRZlTPNFQLDSjMr3J0aiYPFTgkOGDBF5inDGjBl09OhRPEXo+++MDLoACFYGgc/AYSFYGQg/A4aGYGUA9AwaUkWw6gt1sL4IrYPFTxDyk4LJ7YO1du1aqlixolvsvmPHDsqTJ0+CRfO8gJ3XYG3ZsoWKFPm/7ScC+2Bxd4wXwofjgQ6WQlUhWAqQjQ0BwTJWEOF0IFjCgA2Fj0TBYvy8xcL48ePdTu41a9akZcuWuZ3cO3ToQOPGjXMV2r17t9sni/e3mjJlSlzVtm7dStWqVXP7YXXr1s19nXdy584V7+RepkwZQxX2LhUIlncsk40EwVKAbGwICJaxgginA8ESBmwovIZg3codrOs93mj0j1jaGGIHi/FfvHjRfXYgfxZhbGys60TxZxHy5wtmyZIlRcHif1y/fr07l8WMD/4swtdff911vsL1gGApVBaCpQDZ2BAQLGMFEU4HgiUM2FD4SBUsQyXwTSoQLIVSQbAUIBsbAoJlrCDC6UCwhAEbCq8iWA++INPBinnHbRbKm4PikCcAwZJnTBAsBcjGhoBgGSuIcDoQLGHAhsJDsAwVw3gqECyFAkGwFCAbGwKCZawgwulAsIQBGwqvIVgV6sl0sDZ8iQ6W5lSCYCnQhmApQDY2BATLWEGE04FgCQM2FF5FsOoKCdYiCJbmVIJgKdCGYClANjYEBMtYQYTTgWAJAzYUHoJlqBjGU4FgKRQIgqUA2dgQECxjBRFOB4IlDNhQeBXBeuAFypnX220aTh2LpQ1foYOlOZUgWAq0IVgKkI0NAcEyVhDhdCBYwoANhYdgGSqG8VQgWAoFgmApQDY2BATLWEGE04FgCQM2FF5FsO4X6mB9jQ6W5lSCYCnQhmApQDY2BATLWEGE04FgCQM2FB6CZagYxlOBYCkUCIKlANnYEBAsYwURTgeCJQzYUHgNwapYR6aDtf4bdLA0pxIES4E2BEsBsrEhIFjGCiKcDgRLGLCh8BAsQ8UwngoES6FAECwFyMaGgGAZK4hwOhAsYcCGwqsI1n3dRZ4iXL94OD4qR3EuQbAUYEOwFCAbGwKCZawgwulAsIQBGwqvIViV7pURrHXfQrA0pxIES4E2BEsBsrEhIFjGCiKcDgRLGLCh8BAsQ8UwngoES6FAECwFyMaGgGAZK4hwOhAsYcCGwqsJ1nUebzR6PJbQwdKdSKYEK1OmTDRgwAAaOHCgOIUlS5bQvffeS99++y3dc889KY63e/duKl68OE2ePJmefvrpoHODYAWNzPcvgGD5voRBXQAEKyhcvj4ZguXr8qkm74lgsUDMnz/fyUexYsVCvgAIVsjo8EJjBCBYxgoinA4ESxiwofAagnXbPd0pp0AHa+0SrMHSnEqeCNbEiROpffv2aeoGpXRxmoJ16dIlOnv2LOXIkYMyZ86MDpbmrIuAsSBYEVDkeJcIwYqcekOwIqfW6b3SiBWsYMDhFmEwtHAuE4BgRdY8gGBFTr1VBKt2N5kO1ncjsE2D4lTNVKhQoSvHjh2jqlWr0jvvvEPR0dFxw3OXZ8SIEW7t0a+//uq6PbxeafDgwRQVFeXO4/VSL7/88t9SDqxX+uGHH2js2LG0YsUKOnDgAOXMmZNq167tYpQpUybB64LtYH311VdUt25dWrhwIT388MMu1h9//EH58+enq6++mvi6rrnmGvf1GTNmUIsWLejHH3+kO+64g5Jbg7V+/Xrq0aOHy5dzbdq0KXXu3JkqVKiANViKE9PvQ0Gw/F7B4PKHYAXHy89nQ7D8XD3d3DO9+eabV06ePEmjRo2iCxcu0OrVq6l06dIui8cff5w++eQTevLJJ6latWp09OhRGjNmDJ0+fZpWrVrlzmMhYQl77733qF+/fnHiVaNGDSpRogR17dqVNmzY4MSscOHC9Ntvv9H48ePpypUrtGnTJipQoEDcFQcrWGfOnKHrrruOunfvTsOGDXNxPv74YydFly9fJhaw+++/3329Y8eO9OGHHzrpypIlS5KCtWPHDrr99tuJ8+jSpYsTtZkzZ9Jff/1FP//8MwRLd276ejQIlq/LF3TyEKygkfn2BRqCVflumQ7Wz9+jg6U58eJuEa5bt851r1hOWCpmz55NzZo1c//lrwWOffv2Ubly5eihhx6i6dOnuy+ntAaLZezaa69NcE3cDeOOEHe/+vbtG7J05KVvAAAa8klEQVRg8Qtr1qzpxHDlypUuDssWd81YvjjvV1991X2dO24333wzxcTEuP9PqoPVvHlzmjVrlpPMQCePY999992u85WWpwi5S8d/4h+bN2+m1q1b0525GlPuLPk164uxMogABCuDwGfQsBCsDAKfAcNCsDIAuk+HTLAGi2+38a2xEydOODlhqeAOVeKjVatWxE8OHj58OFXBiv/aU6dO0blz51z36r777qNSpUrRvHnz0iVY3DUbOnSo60zlypWLKlWqRHXq1HGCtXHjRlq6dCn9/vvvdMMNN7jbkgGhSyxYfDuUu2HVq1d3na/4R+D2YloEK7lbphwPguXT75IQ0oZghQDNxy+BYPm4eEGmriJYdwl1sH5AByvIcqfr9ASCxbfz+FbhwYMH3R5R3HlJ6WApueqqq1LsYHE3p0+fPrRgwQInQfEPHmPx4sXpEqxFixZRvXr16LPPPnNyxLf1Pv30U2KZ420jeEwem7tTy5cvpzvvvNONl1iw+JoLFSrkbg2OHDkyQZ7c3bvtttvQwUrXVIusF0OwIqveEKzIqbeWYOXK4+1Go3+eiKWfIViqEzVZweKF6OfPn6cJEyYkmxB3ini9UnK3CHkdFItJbGysWzhevnx5t3CcpeyFF15wMsSiEziCXYPFr+NbkHnz5nXxWbC488ZrxXgLhhtvvNF1o7hL9v777zvZ4sXvSQkWiyCfz5LJa8riH2vXrqXKlSunSbCSgoWNRlXntInBIFgmyqCWBARLDXWGDwTByvAS+CaBZG8RNm7c2MkPP5UXkJLkrooXuLdr1+5v+2Cl1PlhmbnlllvSLVicE3elWOZYsPiW4Jo1a1yqHJ/XkbFgFSlShLjbFTiSukWYJ08eFys9twghWL6Z+6KJQrBE8ZoLDsEyVxKxhFQEq2Y3ypWnsKfX8OeJffTzMtwi9BRqKsH+tsi9SZMmbqE3L2Bv2bJlsh9dc+jQobgnAANrlFhkHn300bgheQ0UL2afNGkStW3bNu7rU6dOdbfvuEuW3g4WB+V1VW+88YbbRZ7FkP/OB29+ysK1ZcsWGjRokHvKMTnB4q8/8cQTblF/eha5Q7A0p6/dsSBYdmsjkRkES4KqzZgQLJt1sZhVprfeestt08DrjngBOssF70/FC9F53yh+opAXvz/wwANuEfmePXvck3j8JOG0adPcNW3bts11i3gvrU6dOrn9snivqaJFizrB4kXm3bp1c2ucfvrpJ7f1A9/W43/3QrA4n/r167tceL1VgwYN3N95WwZ+eo8PFi1+4jAlweLrqFKliruFybcK8+XLh20aLM5aH+QEwfJBkTxMEYLlIUzjoVQEq4ZQB2s5Olia08ttNMq3AVmO3n77bScYgYMli9dgcQeK96zi/+dbe7Vq1XLdId7rKnCMHj2a3nrrLSdgvPg98MQd7y3F66NYcHjLA76Nx3tW8RosPrwQLF7QzsLG+fH6K77Vxwev/WKJ481Gjx8/nuBWZ3IbjfJ6q549eybYaPT555/HRqOaszIMxoJghUERg7gECFYQsHx+KgTL5wVUTN+Tj8pRzNeXQ2GRuy/Llq6kIVjpwue7F0OwfFeykBPWEKzoO2U6WP9bgQ5WyIUP4YUQrBCgBfsSCFawxPx/PgTL/zUM5gogWMHQ8ve5ECx/108ze7OCxftSpXbw5qF+OCBYfqiStzlCsLzlaT0aBMt6hbzLT0ewulKu3B4/RXhyH/1vxUh82LN3UyHVSGYFi/fESu3gNVd+OCBYfqiStzlCsLzlaT0aBMt6hbzLT0Owbq8uI1hrfoRgeTcTUo9kVrC+/vrrVLMPfJBzqidm8AkQrAwuQAYMD8HKAOgZOCQEKwPhKw8NwVIG7uPhzAqWj5n+LXUIVjhVM23XAsFKG6dwOQuCFS6VTP06VATrDqEO1k/oYKVeYe/OgGB5xzLZSBAsBcjGhoBgGSuIcDoQLGHAhsJDsAwVw3gqECyFAkGwFCAbGwKCZawgwulAsIQBGwqvIVhVqnURWeS+euUoLHJXnEsQLAXYECwFyMaGgGAZK4hwOhAsYcCGwkOwDBXDeCoQLIUCQbAUIBsbAoJlrCDC6UCwhAEbCq8iWFW7UK5cHm/T8Oc+Wr0KHSzNqQTBUqANwVKAbGwICJaxgginA8ESBmwoPATLUDGMpwLBUigQBEsBsrEhIFjGCiKcDgRLGLCh8BqCVbXK8yIdrFWrR2MNluJcgmApwIZgKUA2NgQEy1hBhNOBYAkDNhReRbBuFxKsNRAszakEwVKgDcFSgGxsCAiWsYIIpwPBEgZsKDwEy1AxjKcCwVIoEARLAbKxISBYxgoinA4ESxiwofAqghXdWeYW4f/G4Bah4lyCYCnAhmApQDY2BATLWEGE04FgCQM2FB6CZagYxlOBYCkUCIKlANnYEBAsYwURTgeCJQzYUHgVwarcmXJ7vE3DyT/30aqf0cHSnEoQLAXaECwFyMaGgGAZK4hwOhAsYcCGwkOwDBXDeCoQLIUCQbAUIBsbAoJlrCDC6UCwhAEbCq8mWDlv9PSqT57ajw6Wp0RTDwbBSp1Rus+AYKUboe8CQLB8V7J0JQzBShc+X70YguWrcmVoshAsBfwQLAXIxoaAYBkriHA6ECxhwIbCawhWtUqdKbdAB2vlOqzB0pxKECwF2hAsBcjGhoBgGSuIcDoQLGHAhsJDsAwVw3gqECyFAkGwFCAbGwKCZawgwulAsIQBGwqvIlgVO8l0sNaPxT5YinMJgqUAG4KlANnYEBAsYwURTgeCJQzYUHgVwaogJFgbMkawNm7cSL1796alS5e6StaqVYuGDh1Kt956a6qVXb16NU2bNo0WL15Mu3btomuvvZbKly9Pffv2pfvvvz/V12fkCRAsBfoQLAXIxoaAYBkriHA6ECxhwIbCQ7CCK8a2bduoatWqdP3111PXrl3di0eMGEHHjx+nlStXUunSpVMM2LRpU/ruu++oSZMmFB0dTadOnaLJkycTS9uYMWOoU6dOwSWkeDYESwE2BEsBsrEhIFjGCiKcDgRLGLCh8BqCdUeFjpT7Wo+3aTi9n37aME79FiELUkxMDG3evJmKFi3qKrl3716Kioqi+vXr0+zZs1Os7rJly6hKlSqULVu2uPPOnj1Lt912Gx0+fJgOHTpEWbJkMTRD/l8qECyFskCwFCAbGwKCZawgwulAsIQBGwoPwUp7MbjblC9fPmrevDlNnTo1wQvbtGlDM2fOpCNHjlDOnDnTHvT/P/Of//wnvfXWW/Tbb7/FiVvQQYRfAMESBszhIVgKkI0NAcEyVhDhdCBYwoANhVcRrFs7yHSwNo5X7WCtWLGCatSoQWPHjqWOHTsmqCJ/rXPnzsTnVK9ePegKt2jRgubMmeNuNfK6LIsHBEuhKhAsBcjGhoBgGSuIcDoQLGHAhsL7XbB4wTjfnot/FCpUiPiP18fcuXOJbxHOnz+fGjZsmCA8f61x48ZOknh9VTAH327kW4QNGjQgHsPqAcFSqAwESwGysSEgWMYKIpwOBEsYsKHwKoJVTqiD9cv4JEkOGDCABg4cmCLlCxcu0I4dO9JUiWuuuYZuuukm+uCDD+ipp56iL7/8kurWrZvgtYsWLaJ69eq5c1q3bp2muHzSiRMn6M4776QDBw7QunXr3DhWDwiWQmUgWAqQjQ0BwTJWEOF0IFjCgA2F97tghdrB2r17NxUvXjxNlahduzYtWbLEdZe87GDx4naWslWrVrmF8zyO5QOCpVAdCJYCZGNDQLCMFUQ4HQiWMGBD4TUEq3rUcyJrsH7cPCHkNVgsN7xdQloO3pKhWrVqbn2VV2uwzp8/T40aNXL7Yc2bN8/dHrR+QLAUKgTBUoBsbAgIlrGCCKcDwRIGbCi8mmBd4+2aqJNnDlB6BCuUEqT2FOGMGTPo6NGjqT5FePHiRdcJW7BgAX344YfuqUQ/HBAshSpBsBQgGxsCgmWsIMLpQLCEARsKD8EKrhi8gJ3XYG3ZsoWKFCniXhzYB4tv98VfpM7rqniNVcmSJenqq692516+fJlatmzptnSYMGECtW/fPrgEMvBsCJYCfAiWAmRjQ0CwjBVEOB0IljBgQ+FVBKtse8ot0cHa8m7ItwhDLcHWrVvd7ULeD6tbt24uDO/kzp0r3sm9TJkycaGffvppt18WfyROsWLF3Nd79uxJb7/9tltv1a5du7+l8cADD1DBggVDTU/0dRAsUbz/FxyCpQDZ2BAQLGMFEU4HgiUM2FB4CFbwxVi/fr37LELelZ0P/izC119/nSpWrJggWFKCdc8996S49uvbb78lPsfiAcFSqAoESwGysSEgWMYKIpwOBEsYsKHwKoJ1i1AH61f9Dpah0qmnAsFSQA7BUoBsbAgIlrGCCKcDwRIGbCg8BMtQMYynAsFSKBAESwGysSEgWMYKIpwOBEsYsKHwGoJ1Z+l2ImuwVmybqL4Gy1Dp1FOBYCkgh2ApQDY2BATLWEGE04FgCQM2FB6CZagYxlOBYCkUCIKlANnYEBAsYwURTgeCJQzYUHgVwSr1rEwHa/t76GApziUIlgJsCJYCZGNDQLCMFUQ4HQiWMGBD4SFYhophPBUIlkKBIFgKkI0NAcEyVhDhdCBYwoANhdcRrGcodw6Pd3I/e4BWbJ/0/7V3bzFWVWccwJcXrAJCBbxMAwREYxCjUSMlSuKDQUobAi20pUiBJiYaE41PPpmoT0afjBoNTy0FW669WNtyiUYTcCyJiTE2eAkBoWEUB2WQm+Cl2aedo7QOZ8S9v9lz1u8kRmLOrG+v37eEP9/Zs8cEK/AsCVgB2AJWAHLNSghYNWtIxZcjYFUMXKPlQwLWpCJgXVLqrg8efS917hCwSkVtsZiAFaAtYAUg16yEgFWzhlR8OQJWxcA1Wl7AqlEzan4pAlZAgwSsAOSalRCwataQii9HwKoYuEbLhwSsiRVNsHaaYEUeJQErQFvACkCuWQkBq2YNqfhyBKyKgWu0vIBVo2bU/FIErIAGCVgByDUrIWDVrCEVX46AVTFwjZYPCVgTfpVGlnwPVk9xD9auX7vJPfAsCVgB2AJWAHLNSghYNWtIxZcjYFUMXKPlBawaNaPmlyJgBTRIwApArlkJAatmDan4cgSsioFrtHxMwFqaRp5b7ncR9hwrJli/McEKPEsCVgC2gBWAXLMSAlbNGlLx5QhYFQPXaHkBq0bNqPmlCFgBDRKwApBrVkLAqllDKr4cAati4BotHxKwxi+pZoK1e7kJVuBZErACsAWsAOSalRCwataQii9HwKoYuEbLhwSscYurCVh7fitgBZ4lASsAW8AKQK5ZCQGrZg2p+HIErIqBa7S8gFWjZtT8UgSsgAZt3bo1TZ8+Pa1cuTJNnjw5oKISBAgQIFCFwPbt29OiRYvSli1b0k033VRqieZfxscWE6yLS12759j7qfNfJlilorZYTMAK0H7mmWca/0N6ESBAgEB7CBR/Yb7ttttK3YyAVSrngC8mYAW0oLu7O23cuDFNmDAhnXfeeQEV61Gi9296Jnf16EfVV6HfVQvXa/1c+3306NG0a9euNHPmzDRmzJhSm/JlwPplGvmdkidYnxQTrBXuwSq1Y6deTMAKxM6tVO9vFq+++mq67rrrctt+dvvV77xart/l91vAKt90IFcUsAZSv81r+w24zRv8P9vTb/3OS6D83TYD1vcWVTPB2rvSBKv8tvW5ooAViJ1bKX/g5tVx/dbvvATK362AVb7pQK4oYA2kfpvX9gdumzfYBCuvBut35f1uBqyOhdVMsLp+Z4JVeRe/LCBgBWLnVqqrqystW7Ys3XHHHamjoyO37We3X/3Oq+X6XX6/BazyTQdyRQFrIPXVJkCAAAEC/xVoBqxLignWRaW69HyyL3W+Z4JVKmqLxQSsSG21CBAgQIBAHwLNgHXxL9LIc0oOWMf3pc73f+8jwsDTJ2AFYitFgAABAgT6EhCw2utsCFjt1U+7IUCAAIFBKtAMWBctqGaCtW+VCVbg2RCwArGVIkCAAAECLSdYAlZbHBIBqy3aaBMECBAgMNgFmhOsC39ezQTrg9UmWIGHRMAKxFaKAAECBAi0nGAJWG1xSASstmijTRAgQIDAYBdoTrDG/KyaCVb3GhOswEMiYAViK0WAAAECBFpOsASstjgkAlZbtNEmCBAgQGCwCzQnWKN/mkYOubDU7fSc+CB17l9rglWq6qkXE7ACsZUiQIAAAQItJ1ij5lcTsD5cJ2AFHj8BKxBbKQIECBAgIGDlcQYErDz6bJcECBAgUHOB5keEF8yrZoL10XoTrMAzIGAFYitFgAABAgRaTrAErLY4JAJWW7TRJggQIEBgsAs0J1jf/UkaeXbJN7l/+kHqPPAHE6zAQyJgBWIrRYAAAQIEWk6wBKy2OCQCVlu00SYIECBAYLALNCdYI3+cRpw9ptTtHPy0O3X2/NEEq1TVUy8mYAViK0WAAAECBFpOsASstjgkAlZbtNEmCBAgQGCwCzQnWOfPrWaC9fGfTLACD4mAFYitFAECBAgQaDnBErDa4pAIWG3RRpsgQIAAgcEu0JxgDZ9TzQTr0J9NsAIPiYAViK0UAQIECBBoOcEaNjuNOKvkm9w/606dh/8iYAUePwErEFspAgQIECAgYOVxBgSsPPpslwQIECBQc4HmR4RDiwnW6FKv9uBn+1PnEROsUlFbLCZgRWqrRYAAAQIE+hAQsNrraAhY7dVPuyFAgACBQSrQG7CmnfujSiZYrxz7q3uwAs+GgBWIrRQBAgQIEOhLoF0D1htvvJHuu+++tGXLlsbWp0+fnh599NF01VVXfePD8MILL6Rbbrml8XXvvPNOuuyyy77xGlFfIGBFSatDgAABAgROIfBlwPphGnFmyfdgfb4/vXLsb+ETrCIE3XDDDWnUqFHp7rvvbuz+8ccfTwcOHEjbtm1Ll19+eb/PxPHjx9M111yT9uzZkw4fPixg9VvOGwkQIECAQMYC7Riw5s+fnzZs2JC2b9+exo0b1+huEZAmT56cZs2aldauXdvvjj/88MPpscceSwsXLmz82wSr33TeSIAAAQIE8hVoBqxzZlUzwTr+99AJ1qFDh9Lo0aPTggUL0vLly09q7JIlS9Lq1atTd3d3Gj58eMumv/vuu+nKK69MTz75ZCp+/dBDDwlYLdW8gQABAgQIEEjNgDXkB2nEmaNKFTn4+YfplRMbQgNWZ2dnuvHGG9PTTz+d7rzzzpP2U/y3u+66KxXvmTZtWsu9zpkzJ+3bty+9/PLLjXAlYLUk8wYCBAgQIECgEIgIWCtXrmx8PPfVV0dHRyr+Kfu1fv36VHxE+Oyzz6bZs2eftHzx34rQtG7dujRv3rxTln7uueca7y3u2br++uvTgw8+KGCV3SzrESBAgACBdhXoDVjfP3tmGnFGyROsLz5M//h049fSPfDAA43QcqrXiRMn0o4dO/pFP3To0DR+/Pi0YsWKtHjx4rRx48Z06623nvS1mzZtSjNnzmy8Z9GiRX2ue/To0TRlypQ0Y8aMtGzZssb7BKx+tcGbCBAgQIAAga9OsKoMWKc7wdq1a1eaOHFivxp18803pxdffDGVMcG6//7701NPPZXefvvtNGbMf34+o4DVrzZ4EwECBAgQIHBSwDprRjUTrM82n/Y9WMUk6aWXXupXo4pHMkydOrVxf9W3uQdr79696dJLL0333ntvuv3225u1i8c8PPHEE+n5559PEyZMaLynji/PwapjV1wTAQIECGQn0PyIsIYB63Sa0eq7CFetWpX279/f53cRvvbaa+naa689Zelhw4alok4dXwJWHbvimggQIEAgO4HegDX1jGKCdUGp+z/4xUdp2xenP8E63YspbmAv7sF6880309ixYxvL9D4Hq7gHq/gYsffV1dWVenp60qRJk9KQIUMav968efP/lV6zZk3j+VnFFKtYc+7cuad7eZV+nYBVKa/FCRAgQIBA/wTaMWC99dZbjY8Li+dh3XPPPQ2I4iO+YnJVfFfgFVdc0cRZunRp43lZO3fubHz019fLPVj9O0/eRYAAAQIECHzlMQ1T0tQ0LJ1fqsnh9HH6Z9p22vdgfZuLef311xs/i3Dr1q2NZYqfRfjII4+kq6+++qRlBaxvo+xrCRAgQIAAga8V2L17d+MZVUeOHKlEqHh8QvEja4pHKHhVL+AjwuqNVSBAgAABAv0SKEJW8eNjqngVjzkQrqqQ/fo1Baw4a5UIECBAgACBTAQErEwabZsECBAgQIBAnICAFWetEgECBAgQIJCJgICVSaNtkwABAgQIEIgTELDirFUiQIAAAQIEMhEQsDJptG0SIECAAAECcQICVpy1SgQIECBAgEAmAgJWJo22TQIECBAgQCBOQMCKs1aJAAECBAgQyERAwMqk0bZJgAABAgQIxAkIWHHWKhEgQIAAAQKZCAhYmTTaNgkQIECAAIE4AQErzlolAgQIECBAIBMBASuTRtsmAQIECBAgECcgYMVZq0SAAAECBAhkIiBgZdJo2yRAgAABAgTiBASsOGuVCBAgQIAAgUwEBKxMGm2bBAgQIECAQJyAgBVnrRIBAgQIECCQiYCAlUmjbZMAAQIECBCIExCw4qxVIkCAAAECBDIRELAyabRtEiBAgAABAnECAlactUoECBAgQIBAJgICViaNtk0CBAgQIEAgTkDAirNWiQABAgQIEMhEQMDKpNG2SYAAAQIECMQJCFhx1ioRIECAAAECmQgIWJk02jYJECBAgACBOAEBK85aJQIECBAgQCATAQErk0bbJgECBAgQIBAnIGDFWatEgAABAgQIZCIgYGXSaNskQIAAAQIE4gQErDhrlQgQIECAAIFMBASsTBptmwQIECBAgECcgIAVZ60SAQIECBAgkImAgJVJo22TAAECBAgQiBMQsOKsVSJAgAABAgQyERCwMmm0bRIgQIAAAQJxAgJWnLVKBAgQIECAQCYCAlYmjbZNAgQIECBAIE5AwIqzVokAAQIECBDIREDAyqTRtkmAAAECBAjECQhYcdYqESBAgAABApkICFiZNNo2CRAgQIAAgTgBASvOWiUCBAgQIEAgEwEBK5NG2yYBAgQIECAQJyBgxVmrRIAAAQIECGQiIGBl0mjbJECAAAECBOIEBKw4a5UIECBAgACBTAQErEwabZsECBAgQIBAnICAFWetEgECBAgQIJCJgICVSaNtkwABAgQIEIgT+DfMgXvH6Ut7bwAAAABJRU5ErkJggg==\" width=\"480\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(df3var.corr())\n",
    "plt.xticks(range(len(df3var.columns)), df3var.columns)\n",
    "plt.yticks(range(len(df3var.columns)), df3var.columns)\n",
    "plt.colorbar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "upoOkqvF4ieU"
   },
   "source": [
    "**Valeurs propres et vecteurs propres de la matrice de corrélation :**\n",
    "\n",
    "ça marche avec simplement Numpy :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10191,
     "status": "ok",
     "timestamp": 1591644689780,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "YL1s3no6Ffvi",
    "outputId": "66b57ff8-acd6-4a45-cabf-a35480a5013f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.4205161 , -0.35654409],\n",
       "       [-0.4205161 ,  1.        ,  0.9627571 ],\n",
       "       [-0.35654409,  0.9627571 ,  1.        ]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np_mat_corr = np.array(df_mat_corr) # On retype le Dataframe Pandas en Array de Numpy\n",
    "np_mat_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10179,
     "status": "ok",
     "timestamp": 1591644689782,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "lPs8DmVjF6KG",
    "outputId": "5f9e7645-1623-4afe-bcf4-513ab2d56422"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.2120434 , 0.75324395, 0.03471265]),\n",
       " array([[ 0.41314159, -0.90895312, -0.05584127],\n",
       "        [-0.64998631, -0.25137587, -0.71716663],\n",
       "        [-0.6378337 , -0.33258742,  0.69466091]]))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.eig(np_mat_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z5y_oje1GIgr"
   },
   "source": [
    "On obtient les trois valeurs propres (*Eingen Value in english*) et les vecteurs-colonnes propres associés.\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "On peut aussi fort bien faire faire le même travail à *ScikitLearn* , en travaillant directement sur les valeurs de notre tableau Z, des données normalisées :\n",
    "\n",
    "On importe l'objet PCA du module *ScikitLearn*.*decomposition* et on l'instancie :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4MTrifA5GSmE"
   },
   "outputs": [],
   "source": [
    "#classe pour l'ACP\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "#instanciation\n",
    "acp = PCA(svd_solver='full')\n",
    "# Le paramètre svd_solver placé à \"Full\" indique que\n",
    "# l'on va travailler sur la totalité des données, vu qu'elles sont peu nombreuses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xlHKvfiXQfuH"
   },
   "source": [
    "Nous pouvons lancer les traitements : la fonction ***fit_transform()***\n",
    "renvoie en sortie les coordonnées factorielles F<sub>ik</sub> que nous collectons dans la variable *coord*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k0B03_7nRGLP"
   },
   "outputs": [],
   "source": [
    "#calculs\n",
    "coord = acp.fit_transform(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aVV3Jz4ZRoT6"
   },
   "source": [
    "La propriété ***.explained_variance_*** semble faire l’affaire pour obtenir les variances (valeurs propres, λ<sub>k</sub>) associées aux axes factoriels.\n",
    "\n",
    "Il est nécessaire de débiaiser les variances expérimentales par le coefficient (n-1)/n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10150,
     "status": "ok",
     "timestamp": 1591644689787,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "L3kx3ZXgSQ3d",
    "outputId": "e29cf469-0d7e-4133-f84c-02f0173d0581"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.2120434  0.75324395 0.03471265]\n"
     ]
    }
   ],
   "source": [
    "#valeurs propres corrigées\n",
    "eigval = (n-1)/n*acp.explained_variance_\n",
    "print(eigval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PIGwaHpHSXmR"
   },
   "source": [
    "Youpi, les mêmes que ci-dessus !!!\n",
    "\n",
    "PCA fournit également les proportions de variance associées aux axes :\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10139,
     "status": "ok",
     "timestamp": 1591644689790,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "sLnb4SZXTjWt",
    "outputId": "7f5dc120-8bf2-44da-e164-a8d863206e7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7373478  0.25108132 0.01157088]\n"
     ]
    }
   ],
   "source": [
    "#proportion de variance expliquée\n",
    "print(acp.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T6C2AMpsTnxV"
   },
   "source": [
    "C'est là que commence l'analyse des résultats !!!\n",
    "\n",
    "La 1<sup>ère</sup> composante accapare 73,73% de l'information disponible.\n",
    "Avec la 2<sup>ème</sup>, on arrive à 73,73 + 25,10 = 98,83% de l'information globale ! La dernière semble anecdotique.\n",
    "\n",
    "Confirmons cela par l' **\"éboulis des valeurs propres\"** (*Scree Plot*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10124,
     "status": "ok",
     "timestamp": 1591644689793,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "MTpcCIeaVBJr",
    "outputId": "d4bfcde1-b3c2-43f8-efd0-cca65cc4c3cd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scree plot\n",
    "plt.plot(np.arange(1,4),eigval)\n",
    "plt.title(\"Scree plot\")\n",
    "plt.ylabel(\"Eigen values\")\n",
    "plt.xlabel(\"Factor number\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dY7tZphZVSju"
   },
   "source": [
    "... ou par la courbe de cumul de variance restituée selon le nombre de facteurs : courbe ***ROC*** (*Receiving Operator Characteristic*) qui est communément utilisée pour mesurer la performance d'un classifieur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10535,
     "status": "ok",
     "timestamp": 1591644690220,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "t-FoCC4BV2rI",
    "outputId": "c5333072-1a80-4549-c539-a7453fa511c8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cumul de variance expliquée\n",
    "plt.plot(np.arange(1,4),np.cumsum(acp.explained_variance_ratio_))\n",
    "plt.title(\"Explained variance vs. # of factors\")\n",
    "plt.ylabel(\"Cumsum explained variance ratio\")\n",
    "plt.xlabel(\"Factor number\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dO-6crEZWHbG"
   },
   "source": [
    "Dans les deux cas les \"coudes\" (cassures) indiquent le nombre de facteurs à retenir.\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "Il existe deux outils pour une interprétation facile de l'ACP :\n",
    "* la représentation des individus dans le plan factoriel\n",
    "* la projection des variables dans le plan factoriel.\n",
    "\n",
    "**Représentation des individus : coordonnées factorielles**\n",
    "\n",
    "Les coordonnées factorielles (F<sub>ik</sub>) des individus ont été\n",
    "collectées dans la variable *coord*. Nous les positionnons dans le premier plan factoriel avec leurs labels pour situer et comprendre les proximités entre les iris.\n",
    "Deux commentaires au préalable :\n",
    "1. L’ajout d’une étiquette dans un graphique nuage de points n’est pas très pratique sous Python (librairie Matplotlib), ma solution a le mérite de fonctionner, il y a plus simple avec la bibliothèque *Seaborn*.\n",
    "2. Les outils graphiques calculent souvent automatiquement les échelles en fonction des plages de valeurs. Ce n’est pas une bonne idée en ce qui concerne l’ACP. En effet, les axes n’ont pas la même importance (% de variance restituée). Pour ne pas fausser la perception des proximités, il est très important de veiller à ce que les échelles soient identiques en abscisses et en ordonnées. Respecter cette règle nous dispense de faire afficher les pourcentages de variance portés par les axes. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 541
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 11214,
     "status": "ok",
     "timestamp": 1591644690915,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "GqCovFLqDG3M",
    "outputId": "fc80509b-1c43-47eb-9de2-beb8b58a4a14"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#positionnement des individus dans le premier plan\n",
    "fig, axes = plt.subplots(figsize=(9,9))\n",
    "axes.set_xlim(-4,4) #même limites en abscisse\n",
    "axes.set_ylim(-4,4) #et en ordonnée\n",
    "\n",
    "#placement des étiquettes des observations\n",
    "\n",
    "for i in range(n):\n",
    "    plt.annotate(df.index[i],(coord[i,0],coord[i,1]))\n",
    "    \n",
    "#ajouter les axes\n",
    "plt.plot([-4,4],[0,0],color='silver',linestyle='-',linewidth=1)\n",
    "plt.plot([0,0],[-4,4],color='silver',linestyle='-',linewidth=1)\n",
    "\n",
    "#affichage\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KM-U4a-pDPMu"
   },
   "source": [
    "Et si nous ajoutions quelques couleurs en fonctions de nos espèces, qui sont connues..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 541
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12495,
     "status": "ok",
     "timestamp": 1591644692211,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "Z2UF-fJ_dmsa",
    "outputId": "874ecfc4-ebac-4c49-8754-52fe4da0dc64"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#positionnement des individus dans le premier plan\n",
    "fig, axes = plt.subplots(figsize=(9,9))\n",
    "axes.set_xlim(-4,4) #même limites en abscisse\n",
    "axes.set_ylim(-4,4) #et en ordonnée\n",
    "\n",
    "#placement des étiquettes des observations\n",
    "colors = {'Iris-setosa':'#0D76BF', 'Iris-versicolor':'#00cc96', 'Iris-virginica':'#EF553B'}\n",
    "for i,Specie in zip(range(n), df['classe']):\n",
    "    plt.annotate(df.index[i],(coord[i,0],coord[i,1]))\n",
    "    plt.scatter(x=coord[i,0], y=coord[i,1], color=colors[Specie])\n",
    "\n",
    "#ajouter les axes\n",
    "plt.plot([-4,4],[0,0],color='silver',linestyle='-',linewidth=1)\n",
    "plt.plot([0,0],[-4,4],color='silver',linestyle='-',linewidth=1)\n",
    "\n",
    "#affichage\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KLVcYioFCgCU"
   },
   "source": [
    "Yes !!!\n",
    "\n",
    "Comme nos données étaient étiquetées (ce qui n'est pas une obligation), nous avons pu colorer selon les espèces, et nous constatons que nous avons plutôt bien réussi à séparer les clusters.\n",
    "Ainsi, si d'autres individus se présentent, en les projetant également sur notre plan factoriel, nous serons en mesure de les classifier !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CDWJRPRwqeB9"
   },
   "source": [
    "**Qualité de la représentation des individus dans le plan factoriel : les Cos² (cosinus carrés)**\n",
    "\n",
    "Pour calculer la qualité de représentation des individus sur les axes, nous devons d’abord calculer les carrés des distances à l’origine des individus, qui correspondent également à leur contribution dans l’inertie totale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 251
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12484,
     "status": "ok",
     "timestamp": 1591644692214,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "HahX8K0TrFfR",
    "outputId": "90d0e323-10de-4396-9d25-331b5f8878fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      ID       d_i\n",
      "0      0  4.588062\n",
      "1      1  3.538534\n",
      "2      2  3.792840\n",
      "3      3  3.384939\n",
      "4      4  5.119251\n",
      "..   ...       ...\n",
      "145  145  2.783974\n",
      "146  146  2.991940\n",
      "147  147  1.797338\n",
      "148  148  3.608776\n",
      "149  149  1.222449\n",
      "\n",
      "[150 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "#contribution des individus dans l'inertie totale\n",
    "di = np.sum(Z**2,axis=1)\n",
    "print(pd.DataFrame({'ID':df.index,'d_i':di}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 251
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12468,
     "status": "ok",
     "timestamp": 1591644692215,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "xfkLNhNSsPP6",
    "outputId": "d1d2ebb3-9bcd-4969-b51b-2bd08f98a9f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      id    COS2_1    COS2_2\n",
      "0      0  0.994107  0.005880\n",
      "1      1  0.776532  0.222556\n",
      "2      2  0.937630  0.061012\n",
      "3      3  0.870217  0.129780\n",
      "4      4  0.972507  0.027409\n",
      "..   ...       ...       ...\n",
      "145  145  0.816759  0.118358\n",
      "146  146  0.830767  0.155068\n",
      "147  147  0.878198  0.109113\n",
      "148  148  0.398655  0.577758\n",
      "149  149  0.904755  0.095177\n",
      "\n",
      "[150 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "#qualité de représentation des individus - COS2\n",
    "cos2 = coord**2\n",
    "for j in range(3):\n",
    " cos2[:,j] = cos2[:,j]/di\n",
    "print(pd.DataFrame({'id':df.index,'COS2_1':cos2[:,0],'COS2_2':cos2[:,1]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 251
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12456,
     "status": "ok",
     "timestamp": 1591644692216,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "PimZtPDgtVOe",
    "outputId": "44196793-6924-4cc2-debe-e630ff786595"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      id     CTR_1     CTR_2\n",
      "0      0  0.013746  0.000239\n",
      "1      1  0.008281  0.006970\n",
      "2      2  0.010718  0.002048\n",
      "3      3  0.008878  0.003888\n",
      "4      4  0.015004  0.001242\n",
      "..   ...       ...       ...\n",
      "145  145  0.006853  0.002916\n",
      "146  146  0.007491  0.004106\n",
      "147  147  0.004757  0.001736\n",
      "148  148  0.004336  0.018453\n",
      "149  149  0.003333  0.001030\n",
      "\n",
      "[150 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "#contributions aux axes\n",
    "ctr = coord**2\n",
    "for j in range(3):\n",
    " ctr[:,j] = ctr[:,j]/(n*eigval[j])\n",
    "\n",
    "print(pd.DataFrame({'id':df.index,'CTR_1':ctr[:,0],'CTR_2':ctr[:,1]}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z3KhF6bbtnVC"
   },
   "source": [
    "**Représentation des variables : 2<sup>ème</sup> outil pour l'interprétation de l'ACP**\n",
    "\n",
    "Nous avons besoin des vecteurs propres pour l’analyse des variables. Ils sont fournis par le champ **.components_**\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12443,
     "status": "ok",
     "timestamp": 1591644692218,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "XuA_hD-st9AE",
    "outputId": "e5a367ab-1f64-4512-8ccb-e538fa2344ae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.41314159 -0.64998631 -0.6378337 ]\n",
      " [ 0.90895312  0.25137587  0.33258742]\n",
      " [-0.05584127 -0.71716663  0.69466091]]\n"
     ]
    }
   ],
   "source": [
    "#le champ components_ de l'objet ACP\n",
    "print(acp.components_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G8yvaXGcuBYe"
   },
   "source": [
    "*Note : les facteurs sont en ligne, les variables en colonne, nous devons en tenir compte pour obtenir les corrélations (variables x facteurs, 𝑟<sub>𝑗𝑘</sub>) en les multipliant par la racine carrée des valeurs propres :*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12431,
     "status": "ok",
     "timestamp": 1591644692219,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "kO3bYcCxuZGm",
    "outputId": "d99503e7-e284-4117-8864-c3aa51d77de5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.61446301  0.78887703 -0.01040397]\n",
      " [-0.96672072  0.21816818 -0.13361769]\n",
      " [-0.94864622  0.28865139  0.12942458]]\n"
     ]
    }
   ],
   "source": [
    "#racine carrée des valeurs propres\n",
    "sqrt_eigval = np.sqrt(eigval)\n",
    "\n",
    "#corrélation des variables avec les axes\n",
    "corvar = np.zeros((3,3))\n",
    "for k in range(3):\n",
    " corvar[:,k] = acp.components_[k,:] * sqrt_eigval[k]\n",
    "\n",
    "#afficher la matrice des corrélations variables x facteurs\n",
    "print(corvar)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U2MuaUFaup6O"
   },
   "source": [
    "Les variables sont maintenant en ligne, les facteurs en colonne.\n",
    "\n",
    "Si l’on s’en tient spécifiquement aux deux premiers facteurs :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12419,
     "status": "ok",
     "timestamp": 1591644692222,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "tZj3XStcuzwh",
    "outputId": "056abd06-354b-4141-dd99-ae8063e85b5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      COR_1     COR_2\n",
      "0  0.614463  0.788877\n",
      "1 -0.966721  0.218168\n",
      "2 -0.948646  0.288651\n"
     ]
    }
   ],
   "source": [
    "#on affiche pour les deux premiers axes\n",
    "print(pd.DataFrame({'COR_1':corvar[:,0],'COR_2':corvar[:,1]}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8eF3S2EQ8Rwc"
   },
   "source": [
    "Ainsi, la 1<sup>ère</sup> variable(largeur du sépale) intervient assez fortement et positivement sur les deux axes factoriels, tandis que les deux autres (longueur et largeur du pétale), très fortement négativement sur le 1<sup>er</sup> axe et de manière légèrement positive sur le 2<sup>nd</sup>. Ces deux dernières sont très proches l'une de l'autre, comme on le pressentait déjà dans la matrice des corrélations.\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "Nous pouvons dessiner maintenant le cercle des corrélations :\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 378
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12406,
     "status": "ok",
     "timestamp": 1591644692224,
     "user": {
      "displayName": "olivier thoni",
      "photoUrl": "",
      "userId": "04225242464974929302"
     },
     "user_tz": -120
    },
    "id": "bE36ziyv-Q40",
    "outputId": "272d825e-4d0d-4521-9e7b-7618fe6e82a3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cercle des corrélations\n",
    "fig, axes = plt.subplots(figsize=(6,6))\n",
    "axes.set_xlim(-1,1)\n",
    "axes.set_ylim(-1,1)\n",
    "\n",
    "#affichage des étiquettes (noms des variables)\n",
    "for j in range(3):\n",
    " plt.annotate(df.columns[j],(corvar[j,0],corvar[j,1]))\n",
    "\n",
    "#ajouter les axes\n",
    "plt.plot([-1,1],[0,0],color='silver',linestyle='-',linewidth=1)\n",
    "plt.plot([0,0],[-1,1],color='silver',linestyle='-',linewidth=1)\n",
    "\n",
    "#ajouter un cercle\n",
    "cercle = plt.Circle((0,0),1,color='blue',fill=False)\n",
    "axes.add_artist(cercle)\n",
    "\n",
    "#affichage\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gfBrGD5QGm4-"
   },
   "source": [
    "### **1.2.b/ Les décathloniens**\n",
    "\n",
    "\n",
    "https://www.kaggle.com/capedcrusader/pca-on-a-random-dataset/notebook (en R)\n",
    "\n",
    "\n",
    "### **1.2.c/ Un usage d'une ACP simplifiée : la SVD pour la compression d'image**\n",
    "\n",
    "https://frama.link/OT_dimension3\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_PSHUhZKIb6x"
   },
   "source": [
    "## **1.3. \"Réseau de Neurones Convolutionnel\" (CNN)**\n",
    "\n",
    "https://frama.link/OT_dimension4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Qr4MIOqNJFre"
   },
   "source": [
    "# **2. Récupérer l'information perdue : \"AutoEncoder\"**\n",
    "\n",
    "https://frama.link/OT_dimension5 "
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Autres_Dimensions_2.ipynb",
   "provenance": [
    {
     "file_id": "1gJUxZ7Clh1Hj-_PLcJyKLsdeZAkrED_O",
     "timestamp": 1578408594414
    }
   ]
  },
  "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}