hierarchical_clustering_case_study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Context:\n",
    "The data set has information about features of silhouette extracted from the images of different cars\n",
    "Four \"Corgie\" model vehicles were used for the experiment: a double decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400 \n",
    "cars. This particular combination of vehicles was chosen with the expectation that the bus, van and either one of the cars \n",
    "would be readily distinguishable, but it would be more difficult to distinguish between the cars.\n",
    "\n",
    "Here let's apply Hierarchial Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries and Read the dataset using function .dropna() - to avoid dealing with NAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('vehicle.csv').dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(813, 19)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 813 entries, 0 to 845\n",
      "Data columns (total 19 columns):\n",
      "compactness                    813 non-null int64\n",
      "circularity                    813 non-null float64\n",
      "distance_circularity           813 non-null float64\n",
      "radius_ratio                   813 non-null float64\n",
      "pr.axis_aspect_ratio           813 non-null float64\n",
      "max.length_aspect_ratio        813 non-null int64\n",
      "scatter_ratio                  813 non-null float64\n",
      "elongatedness                  813 non-null float64\n",
      "pr.axis_rectangularity         813 non-null float64\n",
      "max.length_rectangularity      813 non-null int64\n",
      "scaled_variance                813 non-null float64\n",
      "scaled_variance.1              813 non-null float64\n",
      "scaled_radius_of_gyration      813 non-null float64\n",
      "scaled_radius_of_gyration.1    813 non-null float64\n",
      "skewness_about                 813 non-null float64\n",
      "skewness_about.1               813 non-null float64\n",
      "skewness_about.2               813 non-null float64\n",
      "hollows_ratio                  813 non-null int64\n",
      "class                          813 non-null object\n",
      "dtypes: float64(14), int64(4), object(1)\n",
      "memory usage: 127.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>48.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10</td>\n",
       "      <td>162.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>159</td>\n",
       "      <td>176.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>197</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>41.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9</td>\n",
       "      <td>149.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143</td>\n",
       "      <td>170.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>199</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "      <td>50.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10</td>\n",
       "      <td>207.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>158</td>\n",
       "      <td>223.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>196</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>93</td>\n",
       "      <td>41.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>9</td>\n",
       "      <td>144.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143</td>\n",
       "      <td>160.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>207</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>85</td>\n",
       "      <td>44.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>52</td>\n",
       "      <td>149.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>144</td>\n",
       "      <td>241.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>183</td>\n",
       "      <td>bus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "0           95         48.0                  83.0         178.0   \n",
       "1           91         41.0                  84.0         141.0   \n",
       "2          104         50.0                 106.0         209.0   \n",
       "3           93         41.0                  82.0         159.0   \n",
       "4           85         44.0                  70.0         205.0   \n",
       "\n",
       "   pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "0                  72.0                       10          162.0   \n",
       "1                  57.0                        9          149.0   \n",
       "2                  66.0                       10          207.0   \n",
       "3                  63.0                        9          144.0   \n",
       "4                 103.0                       52          149.0   \n",
       "\n",
       "   elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "0           42.0                    20.0                        159   \n",
       "1           45.0                    19.0                        143   \n",
       "2           32.0                    23.0                        158   \n",
       "3           46.0                    19.0                        143   \n",
       "4           45.0                    19.0                        144   \n",
       "\n",
       "   scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "0            176.0              379.0                      184.0   \n",
       "1            170.0              330.0                      158.0   \n",
       "2            223.0              635.0                      220.0   \n",
       "3            160.0              309.0                      127.0   \n",
       "4            241.0              325.0                      188.0   \n",
       "\n",
       "   scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "0                         70.0             6.0              16.0   \n",
       "1                         72.0             9.0              14.0   \n",
       "2                         73.0            14.0               9.0   \n",
       "3                         63.0             6.0              10.0   \n",
       "4                        127.0             9.0              11.0   \n",
       "\n",
       "   skewness_about.2  hollows_ratio class  \n",
       "0             187.0            197   van  \n",
       "1             189.0            199   van  \n",
       "2             188.0            196   car  \n",
       "3             199.0            207   van  \n",
       "4             180.0            183   bus  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print/ Plot the dependent (categorical variable) and Check for any missing values in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "car    413\n",
       "bus    205\n",
       "van    195\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Since the variable is categorical, you can use value_counts function\n",
    "pd.value_counts(df['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2897a4bee48>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "pd.value_counts(df[\"class\"]).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2897a57d308>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.value_counts(df['class']).hist(bins=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "compactness                    0\n",
       "circularity                    0\n",
       "distance_circularity           0\n",
       "radius_ratio                   0\n",
       "pr.axis_aspect_ratio           0\n",
       "max.length_aspect_ratio        0\n",
       "scatter_ratio                  0\n",
       "elongatedness                  0\n",
       "pr.axis_rectangularity         0\n",
       "max.length_rectangularity      0\n",
       "scaled_variance                0\n",
       "scaled_variance.1              0\n",
       "scaled_radius_of_gyration      0\n",
       "scaled_radius_of_gyration.1    0\n",
       "skewness_about                 0\n",
       "skewness_about.1               0\n",
       "skewness_about.2               0\n",
       "hollows_ratio                  0\n",
       "class                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KDE plot for all the features to see how many of it can show separate clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P79/JpqK20e5ylPIs6EVkoYhki0iOiNzdzvpwEXnJWr9JRFKt5TNFZLv12CEiX/Bu+ao3GWPIKqjQs/lOiAj3XTOeyromHnpXu1sq+3Ua9CLiBB4FFgHjgBtFZFybzW4Dyo0xI4GHgBXW8t1AhjFmCrAQ+JuIBPeYeT92tKKO4qoGHSjlgbGDYrl5VgrPfnJE56xXtvPkjH4mkGOMyTXGNAIrgcVttlkMPGM9fwWYLyJijKk1xpyeACQC0KtTfmx7gc5YeT5+cMUoYvuEct+qPRijP/rKPp4E/RCgoNXrQmtZu9tYwV4JJACIyAUisgfYBXyrVfCfISK3i0imiGQWF2u3NF+VlV9BeIiDMYNi7C7FL8RFhvGjBaP5JLeMt3Ydt7scFcQ8Cfr2RsW0PT3pcBtjzCZjzHhgBnCPiESctaExjxtjMowxGUlJSR6UpOyQlV/OpKF9CXXqNXxP3TgzmbGDYvnfN/dS19hidzkqSHnyP7YQGNbq9VDgWEfbWG3wfYGy1hsYY/YBNcCErhar7NPQ3MLuY6f0Qux5cjqEX1wznmOV9fz1gxy7y1FBypOg3wKki0iaiIQBS4FVbbZZBdxiPV8CvGeMMdZ7QgBEJAUYDeR5pXLVq/YVVdHY7NKBUl0wMy2eayYP5rGPcskrqbG7HBWEOg16q019ObAG2Ae8bIzZIyL3i8g11mZPAQkikgP8ADjdBfNiYIeIbAf+BXzHGFPi7YNQPW+7NWOl9rjpmnuvGku408G9/96lF2ZVr/Ooq6Mx5i3grTbL/rvV83rg+nbe9yzwbDdrVD4gq6CCgbERDOrbx+5S/NKA2Ah+umgMP/v3bl7bdpTrpg+1uyQVRPSqmvJIVn6Fns13000zk5me0o9fvbmXshodMat6jwa96lRJdQP5ZbV6IbabHA7hN1+cSHVDM/+zao/d5aggokGvOrXdmshML8R236gBMdw1P53XdxzjjZ1tO68p1TM06FWnthdU4HQIE4f0tbuUgPCtS0cwZVgcP/v3br3HrOoVGvSqU1kF5YwdFEOfMKfdpQSEEKeD339pMvVNLfzk1Z24dN561cM06NU5tbgMOwoqtX3ey0YkRXPvlWP5ILuYv32Ua3c5KsBp0KtzOnCiiuqGZqZp+7zX3TwrhasmDeLBd7LZlFtqdzkqgGnQq3PaZg2U0qD3PhFhxXWTSImP5I4Xs7S9XvUYDXp1TtuOVBAfFUZKQqTdpQSk6PAQHl02jeqGZm59Zgs1DWdN7qpUt2nQq3PKyi9nWnI/RNqboFR5w9hBsTx60zT2HjvFHS9m0dzisrskFWA06FWHymsayS2pYVqKXojtaXPH9Of+xRN4b/9J7v3Xbu2Jo7xKb+unOpRVoO3zvenmWSmcPFXPH9/LwWUMD1w3CadD/5JS3adBrzq07Yh7oNSkoTpQqrd8/4pRiAiPrD1Ii8uwYskkvdGL6jYNetWhbfnugVKRYfpj0ltEhO9fMYoQh/D7dw9wrLKOvy6bTr+oMLtLU35MTxVUu9wDpSq02cYmd8xP56EbJrMtv4LFj25g77FTdpek/JgGvWpX9vEqahpbNOht9IWpQ1l5+yzqm1q49tENPP7RIb1Iq7pEg161SwdK+YZpyf14+3uXMHdMEr9+az9Ln/iEnJNVdpel/IwGvWrXtiPlJEaHMSxe7yhlt/ioMB67eTq/XTKJ7ONVLHpkHQ+uyaa+qcXu0pSf0KBX7dqWX85UHSjlM0SEL2UMY+0PL+XqyYP58/s5LHjoIz7IPml3acoPaNCrs5RWN5BXWsv0FG228TWJ0eH84UtTeOEbFxDiFL76jy189/ltnNB5ctQ5aNCrs2RZd5TS9nnfdeGIRFbfNYcfXjGK/+w7wfzff8jTGw7rxVrVLg16dZZt+eWE6EApnxce4uSO+em88/1LmJbSj/te38vXnt5CSXWD3aUpH6NBr86yLb+ccYNjiQjVO0r5g5SEKJ752gx+ee0ENuaWsvDhdWzJK7O7LOVDNOjVZzS3uNhRUKnNNn5GRPjyrBRWLb+I2IgQlj25iTd3FtldlvIRGvTqM/Yfr6KuqYWpyTpjpT8aMzCWV799IZOG9GX5i9v4x4bDdpekfIAGvfqMLB0o5ff6RYXx3NcvYMG4Afzi9b28sCnf7pKUzTTo1Wdsy68gKSacof10oJQ/iwh18uebpjF3dBL3/nsXq3Ycs7skZSMNevUZW/LKyEjRgVKBINTp4C/LpjMjJZ4fvLRdb0AexDTo1RnHKuooLK9jRmq83aUoL+kT5uTJr2aQHB/Jcr0BedDSoFdnnO6SNzNNgz6QxEaE8tiXp1Nd38x3X9hGk96TNuho0KszNh8uIzo8hLGDYu0uRXnZqAExPHDdRLbklfPgO9l2l6N6mQa9OmNLXhnTU/rpfUoD1OIpQ7hx5jCe+Cj3TO8qFRw06BUA5TWNHDhRrc02Ae6/rhzLwNgIfvzKTp3mOIho0Cvg0/Z5vRAb2GIiQvnNdZPIOVnNH9cetLsc1Us8CnoRWSgi2SKSIyJ3t7M+XERestZvEpFUa/kVIrJVRHZZX+d5t3zlLVvyyghzOnQisyBw6agkrp8+lL99lMuBE3q3qmDQadCLiBN4FFgEjANuFJFxbTa7DSg3xowEHgJWWMtLgKuNMROBW4BnvVW48q7NeeVMGRanE5kFiXuuHEtUmJNfvrEXY3Rq40DnyRn9TCDHGJNrjGkEVgKL22yzGHjGev4KMF9ExBiTZYw5PSRvDxAhIuHeKFx5T21jM3uOVjIjTac9CBbxUWHcdfko1h0s4b39epeqQOdJ0A8BClq9LrSWtbuNMaYZqAQS2mxzHZBljDlrsmwRuV1EMkUks7i42NPalZdk5pXT7DLMTGv7LVOB7CuzUxieFMWv3txHY7P2rQ9kngR9e33t2v6td85tRGQ87uacb7a3A2PM48aYDGNMRlJSkgclKW/6+FApIQ4hQ28dGFRCnQ5+ftU4DpfU8PymI3aXo3qQJ0FfCAxr9Xoo0HaGpDPbiEgI0Bcos14PBf4FfMUYc6i7BSvv25hbyuRhcUSFh9hdiuplc8f0Z9bweB59/xB1jdrdMlB5EvRbgHQRSRORMGApsKrNNqtwX2wFWAK8Z4wxIhIHvAncY4zZ4K2ilfdU1Tex+2glF47QZptg9cMFoympbuDZT/LsLkX1kE6D3mpzXw6sAfYBLxtj9ojI/SJyjbXZU0CCiOQAPwBOd8FcDowEfi4i261Hf68fheqyLXlltLgMs4dr0AerGanxzElP5LEPc6luaLa7HNUDPOpHb4x5yxgzyhgzwhjzv9ay/zbGrLKe1xtjrjfGjDTGzDTG5FrLf2WMiTLGTGn10Ev8PmTjoVLCnA6maft8UPvhgtGU1TTyzMd5dpeieoCOjA1yG3NLmZqs/eeD3ZRhccwb058n1+VS26hn9YFGgz6IVdY2sefYKWZr+7wCvjt3BOW1Tby0paDzjZVf0aAPYpsOl2IM2j6vAJieEs+M1H48ue6wzlkfYDTog9jHh0qJCHUwJTnO7lKUj/jWpSM4WlHHGzv1HrOBRIM+iK3PKWFmWgLhIdo+r9zmju7P6AExPPZBrs6BE0A06INUUWUdOSeruSQ90e5SlA9xOIRvXjqc7BNVfHhApyMJFBr0QWrdwRIALtagV218ftJg+seE848NeXaXorxEgz5IrTtYQlJMOKMHxNhdivIxYSEObp6VwocHisk5WW13OcoLNOiDkMtl2JBTwpyRiYjo/WHV2W66IJkwp0MHUAUIDfogtLfoFGU1jdpsozqUGB3ONVMG8+q2QirrmuwuR3WTBn0QOtM+P1KDXnXsaxelUtvYwss6gMrvadAHoXUHixkzMIb+sRF2l6J82PjBfZmR2o/nNh3B5dKulv5Mgz7I1DQ0k5lXzhxttlEeuHlWCkdKa9lwqMTuUlQ3aNAHmQ05JTS2uJg7WmeLVp1bOGEg8VFhPP9Jvt2lqG7QoA8y72cXEx0eQkZqvN2lKD8QHuLk+oyhvLvvBMcr6+0uR3WRBn0QMcbwQfZJLh6ZSFiIfuuVZ26amUyLy+isln5M/7cHkf3HqyiqrGfeGG22UZ5LSYhiTnoiK7fk06yzWvolDfog8t5+9829LhudZHMlyt8suyCFosp63s/W+W/8kQZ9EPkg+yQThsRqt0p13i4f258BseE898kRu0tRXaBBHyQqahvZeqScedrbRnVBiNPB0hnJfHSwmPzSWrvLUedJgz5IfHigGJeBy7R9XnXR0pnDcIjw4hbtaulvNOiDxDt7T5AYHc6UoXo3KdU1g/r2Yf6Y/ry8pYCG5ha7y1HnQYM+CDQ0t/DB/pNcMW4ADofOVqm6btmsFEprGlmz54TdpajzoEEfBD4+VEpNYwsLxg+wuxTl5+aMTCQ5PpIXNulFWX+iQR8E3tlzgqgwJxeOSLC7FOXnHA5h6cxhfJJbpjcl8SMa9AHO5TK8u/cEl43przcBV15x/fRhhDiEFzfrRVl/oUEf4LIKKiipbmDBOG22Ud6RFBPO5yYM5JWthdQ36UVZf6BBH+De2XucUKcwV7tVKi9aNjOZyrom3tpVZHcpygMa9AHMGMPqXceZNTyB2IhQu8tRAWT2iATSEqN4YZM23/gDDfoAtutoJflltVw9abDdpagAIyLcNDOZzCPlZB+vsrsc1QkN+gD25s4iQhyi3SpVj7hu+lDCnA7taukHNOgDlDGGN3YWMSc9kbjIMLvLUQEoPiqMRRMH8lrWUWobm+0uR52DBn2A2l5QwdGKOq7SZhvVg5ZdkEJVfTNv7NCLsr5Mgz5AvbGziDCngyu0W6XqQTNS+zGyfzTPa596n+ZR0IvIQhHJFpEcEbm7nfXhIvKStX6TiKRayxNE5H0RqRaRP3u3dNURl8vw1q4iLhmVSN8+2ttG9ZzTF2V3FFSw+2il3eWoDnQa9CLiBB4FFgHjgBtFZFybzW4Dyo0xI4GHgBXW8nrg58CPvFax6tSWvDKKKuu5atIgu0tRQeC66UPpE+rkmY/z7C5FdcCTM/qZQI4xJtcY0wisBBa32WYx8Iz1/BVgvoiIMabGGLMed+CrXvLatqNEhjn53PiBdpeigkDfPqF8cdoQ/m/HMcpqGu0uR7XDk6AfArS+/XuhtazdbYwxzUAloDNo2aC+qYW3dhWxaMIgIsNC7C5HBYmvzE6lsdnFSr0piU/yJOjbm8DcdGGbjncgcruIZIpIZnGx3ny4O97de4Kqhma+OK3t72Kles7ogTHMHp7AcxuP0Nzisrsc1YYnQV8IDGv1eihwrKNtRCQE6AuUeVqEMeZxY0yGMSYjKSnJ07epdvwr6yiD+kYwa7j+QaV61y0XpnKssp7/7NObkvgaT4J+C5AuImkiEgYsBVa12WYVcIv1fAnwnjHG4zN65R3FVQ18eKCYa6cOwal3klK97PKx/RkS14e/r8+zuxTVRqdBb7W5LwfWAPuAl40xe0TkfhG5xtrsKSBBRHKAHwBnumCKSB7wB+CrIlLYTo8d5SX/t/0oLS7DF6dqs43qfSFOB7denMbmvDKy8svtLke1Ir524p2RkWEyMzPtLsPvGGO44qGPiA4P4d/fvcjuclSQqm5oZvZv1jInPZG/LJtudzlBRUS2GmMy2lunI2MDROaRcnJOVnPTBcl2l6KCWHR4CDfPSuHt3cc5UlpjdznKokEfIF7YlE9MeAif10FSymZfvTAVp0N4av1hu0tRFg36AFBR28ibu4q4duoQ7TuvbDcgNoJrpwzh5cwCiqsa7C5HoUEfEF7ddpTGZhc3ztRmG+Ubvn3ZCBqbXTyxLtfuUhQa9H7PGMMLm44wZVgc4wbH2l2OUgAMT4pm8ZQh/HNjHiXVelZvNw16P/fhgWIOFdfwldkpdpei1GcsnzeSxmYXj3+kZ/V206D3c0+tP0z/mHA+rzcYUT5mhJ7V+wwNej+WfbyKdQdLuOXCVMJC9FupfM/yeSNpajH8ae1Bu0sJapoOfuyp9blEhDq4SS/CKh81IimapTOG8dymfHJOVttdTtDSoPdTxVUN/DvrGEumD6VflN78W/mu718xij6hTh5Yvc/uUoKWBr2fenJ9Lk0uF7delGZ3KUqdU2J0ON+dO+Ji5NUAAA6JSURBVJL/7DvJhpwSu8sJShr0fqi0uoFnNx7h6kmDGZ4UbXc5SnXqaxelMiSuD794fQ+NzTpffW/ToPdDT6w7TF1TC3fOH2l3KUp5JCLUyS+uGc+BE9X89YNDdpcTdDTo/UxZTSP/3JjH5ycNZmT/GLvLUcpjl48bwDWTB/Pn9w9y8ESV3eUEFQ16P/PEulz32fw8PZtX/ud/rh5HdHgIP3l1Jy0u35oiPZBp0PuRoxV1/H39Ya6ZPJj0AXo2r/xPQnQ4910znqz8Ch7RvvW9RoPej6xYvR+AnywcY3MlSnXd4ilDWDJ9KH967yAfHSi2u5ygoEHvJ7YeKWfVjmPcfslwhsT1sbscpbrll4snMKp/DN97aTtFlXV2lxPwNOj9gMtl+OUbe+kfE863Lh1hdzlKdVufMCd/uXkaDU0t3Pp0Jqfqm+wuKaBp0PuB5zcdYXtBBT9ZOIaocL2xiAoMI5Ki+cvN0zl4oorb/5lJfVOL3SUFLA16H1dQVstvVu9nTnoi100bYnc5SnnVpaOSePD6yXySW8ZdK7N0MFUP0aD3YcYY7nltFwL85osTERG7S1LK666dOoT7rh7Hmj0nuO2ZLdQ0NNtdUsDRoPdhz31yhPU5Jdx95ViG9ou0uxylesxXL0rjt0smsSGnhJue3KTz13uZBr2P2l5QwS/f2Melo5JYptMQqyDwpYxh/O3LGewvOsWVj6xjU26p3SUFDA16H1Ra3cB3nttK/9hwHlk6BYdDm2xUcLhi3AD+9Z2LiAoP4aYnN/HHtQdpatF2++7SoPcxDc0tLH8hi5KaRh67eTpxkTrXvAou4wbHsmr5RVw5cRB/ePcAV/9pPdsLKuwuy69p0PuQ5hYXd7yQxcbcUlZcN5EJQ/raXZJStoiJCOVPN07l8S9Pp7y2kS/8ZQM/fHkHxyp0cFVXaKdsH+FyGX7yyk7e2XuC+64exxemDrW7JKVst2D8QGaNSODR93L4x8d5vL7zGDfNTOa2i9MYFq8dFDwlxvjWDHIZGRkmMzPT7jJ6VV1jC99/aTtv7znOjxaMYvm8dLtLUsrnHK2o4+F3D/Dv7UdxGbhy4iBunzOciUP1L18AEdlqjMlod50Gvb1OVtXzjWcy2Xm0kp9dNY5bL0rV/vJKncPxynr+seEwL2zKp6qhmdnDE7h5VgqXj+tPeIjT7vJso0Hvo97de4J7XttJTUMLjyydwoLxA+0uSSm/caq+iZWb83l6Qx7HKuuJiwzl2ilDuD5jKOMHB99Zvga9jzlxqp4Vq/fzWtZRxg2K5Q83TGbMwFi7y1LKL7W4DBtySng5s4B39pygscXF2EGxfH7SIBZOGMiIILmvsga9jyitbuCJdYd5+uPDtLgM37p0BHfMSycsRDs/KeUNFbWNrNpxjNe2HT3TJXPUgGgWThjEgnEDGDcoNmDHpWjQ26i5xcXmw2Ws3FLA27uP0+RysXjyYH5wxWiSE7TXgFI95VhFHWv2HGf17uNsySvDGEiMDuPCEYlcnJ7InPREBvUNnHs7aND3ImMMBWV1bM4r4+NDJby3/yQVtU3ERIRw3bSh3DwrWW/qrVQvO1lVz0cHSlh/sJj1OaVn5tJJjo9k8rA4Jg/ty9TkOMYP7ktEqH9e0O120IvIQuARwAk8aYx5oM36cOCfwHSgFLjBGJNnrbsHuA1oAe40xqw51778JejrGlsoLK+loLyW/NJa8svqyD5xin1FVZTVNALQt08o88b0Z8G4AVw6OonIMB22oJTdjDFkn6hi/cEStuWXsz2/gmOV9QA4HUJqQiQj+0czIin6zNch/foQHxnm080+5wr6TpNHRJzAo8AVQCGwRURWGWP2ttrsNqDcGDNSRJYCK4AbRGQcsBQYDwwG/iMio4wxPX6HAWMMLuO+UOMy7keLy+By4X5uva5vaqGuqYW6RvfX+qYWahvdj1N1TZTVNFJe20hptfur+3XTmTA/LSLUQXr/GK4YO4AJQ2KZkRbPqP4xPv2DoVQwEhHGDIz9TAeIk6fq2VFYyY6CCg6erCLnZDVr952k2fXpiXCY08HAvhHuR2wE/SJDiYsMIy4ylLjIUGLCQ4kIdRIR6jjzNTzESUSok/BQB04RHCI4HLi/iuAQeqU7tSenmDOBHGNMLoCIrAQWA62DfjFwn/X8FeDP4q5+MbDSGNMAHBaRHOvzNnqn/E/tLKxgyV83nglxb7VIhTiEflFhxEeG0S8qlNEDY+gXGcbA2AiSEyIZ2i+SYfF9SIoO1/7vSvmp/rERXDEugivGDTizrKnFxZHSWnKLqymqrOdYZR3HK+spqqhnR2EFFbVNnKpv6nbWiLiD3ynClRMH8vDSqd08mrN5EvRDgIJWrwuBCzraxhjTLCKVQIK1/JM27z3rNkkicjtwu/WyWkSyPaq+5yUCJXYX4SV6LL5Jj8U32XIsjwCP3Njlt6d0tMKToG/vNLXt77COtvHkvRhjHgce96CWXiUimR21efkbPRbfpMfimwLpWMCz2SsLgWGtXg8FjnW0jYiEAH2BMg/fq5RSqgd5EvRbgHQRSRORMNwXV1e12WYVcIv1fAnwnnF351kFLBWRcBFJA9KBzd4pXSmllCc6bbqx2tyXA2twd6/8uzFmj4jcD2QaY1YBTwHPWhdby3D/MsDa7mXcF26bge/2Ro8bL/K55qRu0GPxTXosvimQjsX3BkwppZTyLp1kRSmlApwGvVJKBTgNeouIjBaR7a0ep0TkeyISLyLvishB62s/u2v1hIh8X0T2iMhuEXlRRCKsC+qbrGN5ybq47tNE5C7rGPaIyPesZX7zPRGRv4vISRHZ3WpZu/WL2x9FJEdEdorINPsqP1sHx3K99b1xiUhGm+3vsY4lW0Q+1/sVd6yDY/mdiOy3/u3/JSJxrdb57LF4QoPeYozJNsZMMcZMwT1nTy3wL+BuYK0xJh1Ya732aSIyBLgTyDDGTMB9Ef301BQPWcdSjnvqCp8lIhOAb+AeTT0Z+LyIpONf35OngYVtlnVU/yLcPdPScQ8g/Gsv1eippzn7WHYDXwQ+ar2wzfQnC4G/WNOp+IqnOftY3gUmGGMmAQeAe8AvjqVTGvTtmw8cMsYcwT2NwzPW8meAa22r6vyEAH2scQ2RQBEwD/cUFeAfxzIW+MQYU2uMaQY+BL6AH31PjDEf4e6J1lpH9S8G/mncPgHiRGRQ71TaufaOxRizzxjT3kj2M9OfGGMOA6enP/EJHRzLO9bPGbhH9A+1nvv0sXhCg759S4EXrecDjDFFANbX/rZV5SFjzFHgQSAfd8BXAluBilY/yO1OR+FjdgOXiEiCiEQCV+IegOd335M2Oqq/velGfP171BF/P5ZbgdXWc38/Fg36tqx262uA/2d3LV1ltfkuBtJwzxoahbtZoC2f7ltrjNmHu7npXeBtYAfu8RiByqMpQ/yE3x6LiNyL++fs+dOL2tnML47lNA36sy0CthljTlivT5z+89n6etK2yjx3OXDYGFNsjGkCXgMuxN0UcHqQnF9MR2GMecoYM80YcwnuP7UP4p/fk9Y6qj+Qpgzxy2MRkVuAzwPLzKeDjPzyWFrToD/bjXzabAOfnd7hFuD/er2i85cPzBKRSGu66Pm4Rye/j3uKCvCTYxGR/tbXZNwX/V7EP78nrXVU/yrgK1bvm1lA5ekmHj/kd9OfiPsGSz8FrjHG1LZa5XfHchZjjD6sB+6LlqVA31bLEnD3jDhofY23u04Pj+UXwH7c7dzPAuHAcNw/oDm4m6bC7a7Tg+NYh/uX1A5gvr99T3D/YioCmnCfGd7WUf24mwgeBQ4Bu3D3mrL9GDo5li9YzxuAE8CaVtvfax1LNrDI7vo9OJYc3G3x263HY/5wLJ48dAoEpZQKcNp0o5RSAU6DXimlApwGvVJKBTgNeqWUCnAa9EopFeA06JXqJhGJE5Hv2F2HUh3RoFeq++IADXrlszTolV8Rka9Y84XvEJFnRSRFRNZay9ZaI2gRkadF5K8i8r6I5IrIpdYc5PtE5OlWn1ctIr8XkW3W+5Os5d8QkS3Wfl61JlVDRAZYc5XvsB4XAg8AI8R9H4PfichlIvKBiLxizW/+vDVCGRGZLiIfishWEVnTaiqEO0Vkr3UcK61ll8qn90fIEpGYXv3HVoHD7hFb+tCHpw/c84FnA4nW63jgdeAW6/WtwL+t508DK3GPNl0MnAIm4j652QpMsbYzuOc1Afhv4M/W84RW+/0VcIf1/CXge9ZzJ9AXSAV2t9r+Mtwzhg619rcRuBgIBT4GkqztbgD+bj0/hjVSGYizvr4OXGQ9jwZC7P4e6MM/H3pGr/zJPOAVY0wJgDGmDJgNvGCtfxZ3oJ72ujHG4J5O4IQxZpcxxgXswR3OAC7c4Q3wXKv3TxCRdSKyC1iG+5fM6Rr+au2/xRhT2UGtm40xhdb+tlv7Gw1MAN4Vke3Az/h0zvOdwPMicjOfztC5AfiDiNyJO/wDeeZO1YM06JU/ETqfHrb1+gbrq6vV89OvQ2jf6fc/DSw3xkzEPW9QxHlV+tn9tVj7E2CPse5kZoyZaIxZYG1zFe55bqYDW0UkxBjzAPB1oA/wiYiMOc8alAI06JV/WQt8SUQSwH3vVdxNIUut9cuA9ef5mQ4+ndHzplbvjwGKRCTU+tzWNXzb2r9TRGKBKmv7zmQDSSIy23p/qIiMFxEHMMwY8z7wE9wXd6NFZIT1V8gKIBPQoFdd0tFZjVI+xxizR0T+F/hQRFqALNz3xv27iPwYKAa+dp4fWwOMF5GtuNvVb7CW/xzYBBzB3fRzOsjvAh4Xkdtwn6l/2xizUUQ2iPtG06uBNzuov1FElgB/FJG+uP//PYz7/qTPWcsE9319K0TklyIy19rPXj6945FS50Vnr1RBTUSqjTHRdtehVE/SphullApwekavlFIBTs/olVIqwGnQK6VUgNOgV0qpAKdBr5RSAU6DXimlAtz/B8MyF+ADlQDIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pPregMJ2uXhelVbpguFKBpEHvMDuqminODa1mG4/5Be52+g0V2nyjVCBp0DtIfWsXNc0dIdc+75ESH8PUMUnaIatUgGnQO4hn6GFxiAY9wMUT09l6tEnnvVEqgDToHaS0qpnICGH6uCS7S/GbSydn0t3r0tE3SgWQBr2D7KhuYXL2aOJjouwuxW/mF6QRGxXBOwfq7S5FqbDhVdCLyFUisl9EDonIPYM8v1hEtolIr4hcN+C5lSJy0Lqt9FXhocYYQ2lVM7PzQm9YZX9x0ZHMK0jToFcqgIYNehGJBB4CrgamATeLyLQBux0FbgWeGnBsGvADYD4wD/iBiKSef9mh58jJdlo6ekJ2xE1/SyZnUl5/mprmDrtLUSoseHNGPw84ZIypMMZ0A08DK/rvYIypNMaUAQNnrLoSWGOMaTTGNAFrgKt8UHfIKQ2DjliPxZMzAfSsXqkA8Sboc4Cqfo+rrW3e8OpYEblTRLaIyJb6+vD84d9R1cyo6EiKshLtLsXvirISGZMUp0GvVIB4E/SDTaFovHx9r441xjxqjCkxxpRkZmZ6+dKhpbSqmZk5yURFhn7/uIiweHIG7x1qoLdPpy1Wyt+8SZVqIK/f41zgmJevfz7Hho2ePhe7jp2iOMQ7YvtbPDmT1s5edlTptMVK+Zs3Qb8ZKBKRAhGJAW4CVnv5+q8Cy0Qk1eqEXWZtU/3sP95Kd68rLNrnPRZPziQ6Uliz94TdpSgV8oYNemNML/Bl3AG9F1hljNktIj8SkeUAInKRiFQD1wOPiMhu69hG4Me4f1lsBn5kbVP9eM5qw2HEjUdSXDQLCtN5bfcJjPG2JVApdS68ujLHGPMy8PKAbff2u78Zd7PMYMc+ATxxHjWGvNKqZtITYshNHWV3KQG1bPoY/uXFXZTXtzEpa7Td5SgVskK/5y8IlFY3Mys3tJYO9MYVU7MBeHW3Nt8o5U8a9DZr6+rlYF1bWLXPe4xJjqM4L4XXdh+3uxSlQpoGvc12VrdgTHhcKDWYZdOyKa1uobZFr5JVyl806G125orYMOqI7e/K6e7mm9f3aPONUv6iQW+zbUeamJAeT1pCjN2l2GJiZiKFmQn8dWet3aUoFbI06G1kjGHb0SYunBC+87yJCCuKc9h4uJFjYTDJmTGGQ3Wt7D7WQk1zB509ugCL8r/Qnfg8CBxtbKehrTusgx7gmjnj+MXrB/jzjmN8celEu8vxi+1Hm3hiXSXryxtoaOs+sz0+JpIVs8fxmfkTmJETPldGq8DSoLfR1iNNAJRMSLO5EntNSE9g7vgUXthezV1LCkNqmGlVYzv3/20ffy2rJTU+mqVTslhYmE7SqGhaOrrZdqSZF7bX8MdNVdxYkscPl09nVEyk3WWrEKNBb6OtR5oYHRsVFjNWDufaubn8y4u72FvbyrQQWUrxtd3H+eaqUnpdhq99tIjPLy4kMfaDP3I3XjSe7318Kg+/Xc7Db5ezo6qZhz4zRy8gUz6lbfQ22nqkiTkTUomICJ0z2HP1DzPHEh0pvLijxu5SzpvLZfiP1/Zz5++2kp+RwJpvLObrV0z+UMh7JI+K5v9ddQFP3jaPhrYurn94PQdPtAa4ahXKNOhtcqqzh/0nWrlwfHi3z3ukJsSwdEoWL26vCeqpi/tchnueL+NXbx7i+gtzeeauheSmxnt17OLJmTz/pYuJiozglsc3UtXY7udqVbjQoLfJjqPNGAMl+Rr0HtddmEtdaxev762zu5Rz0tPn4ut/2sGqLdV89bJJ/Oy6WcRFj6y9fUJ6Ar+7fR6dPS4+89hGGk93D3+QUsPQoLfJ1iNNREj4XhE7mI9ekEVOyiiefL/S7lJGzOUyfHNVKatLj/Gdq6bwjWVTzrlT+YIxSfzfbRdxvKWTbz9TqrN7qvOmQW+TrUeauGBM0pDttuEoKjKCWxZMYH3FSfYfD542amMM9/1lN6tLj/HtK6fwpaWTzvs1545P5bsfu4A39tXxxLrK8y9ShTUNehv09rnYHuYXSg3lpovyiI2K4Mn1lXaX4rVfvXmIJ9cf4Y5LCviSD68DuPXifC6fms39r+ylrFpX4lLnToPeBntqT3G6u495BeE9fn4wqQkxrJg9jhe21dDS3mN3OcP63YYj/OeaA3xyTg7f+9hUn14DICI8cN0s0hNi+c6zZfQEcSe1spcGvQ02HXYvsqVBP7iVF+fT0dPHU5uO2l3KWf21rJZ7/7yLyy7I4qfXzfLLMNnUhBh+uHw6+4638n/rDvv89VV40KC3wcbDjeSnx5OdFGd3KY40fVwySyZn8sg75bR2OvOs/t2D9fzTn7ZTMiGVhz49l+hI//0oXTk9m8unZvGLNQepbtIhl2rkNOgDzOUybK5sZH5But2lONo3l02mub2HJ96rtLuUD9l6pIk7f7uViZmJPPaPF/l9ygIR4YfLpwPww9V7/PpeKjRp0AfYgbpWmtt7tNlmGLNyU7hyejaPvVtBc7tzxpLvrT3Fbf+3ieykWH57+zyS46MD8r65qfF87fIiXt97gnWHGgLynip0aNAHmLbPe+/rV0ymrbuXR96psLsUAA43nOazj28iPiaK398xn6zRgW16u/XifHJSRvGTV/bicunYeuU9DfoA23i4kXHJceSmjrK7FMe7YEwSK4rH8fh7hymvb7O1ltqWDm55bCMuY/j9HfO8ntbAl+KiI/nWlZPZVXOKv5QdC/j7q+ClQR9Axhg2HW5kXkFaSE3F60/f+/hU4qIiuOe5MtvOYutOdXLLYxtp6ejhydvm2Tqz5IriHKaNTeJnf9tPV68uWqK8o0EfQIcbTlPf2sX8Qu2I9VbW6Dj++R+msbmyiT/YMNyyuqmdGx5ZT21LJ4+vLGFmrr2Lg0RECN/92AXUNHfw1EZnDz9VzqFBH0AbKrR9/lxcf2Eul0zK4P6X93L0ZOCGF5bXt3HDw+tpPN3N7++Y75hf0JcWZTK/II1fry3XpQiVVzToA2hdeQPZSbEUZiTYXUpQERF+8smZREVGcPuTmzkVgLH16w41cO1D6+jqdfHHOxcw12HTSX/t8iLqWrt42uEXlSln0KAPEJfLsL78JIsmZmj7/DnIS4vn17fM5XDDab7y1Ha/zVlvjOG36yv5xyc2MTZ5FC/evYjp45y3luvCwnTmFaTx67f1rF4NT4M+QPYdb6XxdDcXT8qwu5SgdfHEDH58zQzePlDPd5/f6fOwbzzdzRd+t5V7/7ybJZMzefaLC8lLC/zoGm+ICP90eREnTnXxp81VdpejHE7nyA2Q98vdF7ksmuSMdt5gdfO88Rxv6eTBNw5S39bFf3967nlP9WyM4eWdx7nvL7tpbu/hnz8+lc8tKnD8Eo8LC9OZl5/GI2+X8+n54/06DYMKbvrJCJB1hxoozEhgbLKOnz9fX79iMj/55EzePdjAdb9+/7ym8C2rbuamRzdw91PbSEuI4YW7L+aOSwsdH/LgPqu/a2khx1o6+WtZrd3lKAfTM/oA6OlzsfFwI5+cm2N3KSHj5nnjGZscx7eeKWPFQ+u4ed547v7IJHJShv9F2tXbx9v763li3WE2VDSSGh/Nv14zg5vnjScyCAK+v6WTsyjKSuSRdypYMXuc9v+oQWnQB8COqmbau/tYNFHb531p6ZQs3vzWEv5rzUGeXF/JUxuPMr8gjSumZTM5ezT56QlEREBvn6GmuYMDJ1rZeqSJtfvraevqZVxyHN//2FRunJdHUlxg5qzxtYgI4fOLC/nOs2W8e7CBxZMz7S5JOZAGfQCsO9SACCycqO3zvpYUF829n5jGbYvyeWF7DS9sr+Ff/7p3yP0zR8fyieKxXDEtm0uLMkOiXXvF7HH8/NX9PPpOhQa9GpQGfQCsO9TA9HFJpMTH2F1KyMpLi+erHy3iK5dNoqGtm0N1bVRZc7dHijAmOY6i7EQyE2NDrnkjNiqS2xYV8NO/7WNXTQszcpw3HFTZS4Pez1o6eth2tJm7lhTaXUpYEBEyR8eSOTqWhYTPX1Cfnj+eh946xP++W8GDN82xuxzlMMH/d6vDvXewgT6XYemULLtLUSEseVQ0N8/L46WyWl2FSn2IBr2frd1fR1JcFHPyUuwuRYW42xYVIMDj7+nasuqDNOj9yBjD2wfqubQok6gQ6PRTzjYuZRTLi8fxp81VtLQ7c61dZQ+v0kdErhKR/SJySETuGeT5WBH5k/X8RhHJt7bni0iHiOywbg/7tnxn21N7irrWLpZM0ZEQKjA+v7iQ9u4+fr/xiN2lKAcZNuhFJBJ4CLgamAbcLCLTBux2O9BkjJkE/AL4ab/nyo0xs63bXT6qOyis3V8PwFId8qYCZOrYJC4tyuA371fqwiTqDG/O6OcBh4wxFcaYbuBpYMWAfVYAT1r3nwU+KqE2hu0cvL2/nmljk8hKCuzaoiq8ff7SQupbu3ipVKdFUG7eBH0O0H96vGpr26D7GGN6gRY4M7atQES2i8jbInLpYG8gIneKyBYR2VJfXz+iL8CpTnX2sPVoE0u12UYF2KVFGUzOTuSx9w5jjC4irrwL+sHOzAd+eobapxYYb4yZA3wDeEpEkj60ozGPGmNKjDElmZmhEYxv7aujz2W47AIdVqkCS0S445JC9taeYn35SbvLUQ7gTdBXA3n9HucCA5egP7OPiEQByUCjMabLGHMSwBizFSgHJp9v0cHgtT0nyEiMZY7DViZS4WH57HFkJMbwmA61VHgX9JuBIhEpEJEY4CZg9YB9VgMrrfvXAW8aY4yIZFqduYhIIVAEVPimdOfq6u1j7b46rpiWFXSzIarQEBcdyWcX5PPmvjoO1bXZXY6y2bBBb7W5fxl4FdgLrDLG7BaRH4nIcmu3x4F0ETmEu4nGMwRzMVAmIqW4O2nvMsY0+vqLcJr3y09yuruPZdPH2F2KCmO3LBhPTFQET6zTs/pw59VcN8aYl4GXB2y7t9/9TuD6QY57DnjuPGsMOq/tPk5CTCQX62yVykbpibF8am4Oz22t5lvLppCWoJPqhSu9XNPH+lyGNXtOsPSCLGKjIu0uR4W5zy0qoKvXxR826AVU4UyD3sd2VDXR0NbNsmnZdpeiFEXZo1k6JZMn1x/RC6jCmAa9j/1t13GiI4WP6LBK5RB3XFJIQ1sXf94+cLCcChca9D7kchleKqtlcVFm0C5Np0LPoknpTB+XxMPvlONy6QVU4UiD3oc2VzZS29LJ8tnj7C5FqTNEhC8smUhF/WnW7D1hdznKBhr0PrS69BijoiO5fKq2zytn+diMMeSljeLXa8t1WoQwpEHvIz19Ll7eWcvl07JJiNUVGpWzREVGcOelheyoambj4ZC/lEUNoEHvI+8daqCpvYflxdpso5zp+pI80hNi+J+15XaXogJMg95H/rLjGElxUSyenGF3KUoNKi46ktsvLeCdA/XsqGq2uxwVQBr0PnC6q5dXdx/n6hlj9SIp5Wj/uDCf1PhoHnz9gN2lqADSoPeBl8qOcbq7jxsuyrW7FKXOKjE2ijsuLeSt/XpWH0406H3g6c1VFGUlMlenJFZBYOXF+aTER/PLNw7aXYoKEA3687T/eCvbjzZz40V56OqJKhgkxkbx+UsLeXNfHVuPNNldjgoADfrz9PTmo8RERvDJudpso4LHrRfnk5EYy/2v7NVx9WFAg/48dPb08cL2GpZNz9YpYFVQSYiN4utXFLG5sok1e/Rq2VCnQX8eXtlVS3N7DzdelDf8zko5zI0leRRmJnD/3/bR2+eyuxzlRxr058gYw2PvHmZSViKLJurYeRV8oiIjuOeqC6ioP80fNx21uxzlRxr052h9xUl2HzvF7ZcUEKHrwqogdcW0bBYWpvPAq/upb+2yuxzlJxr05+jxdw+TnhDDtXNy7C5FqXMmIvz4mhl09PTxb3/dY3c5yk806M/Bobo23thXxy0LJhAXrVfCquA2KSuRLy6ZyIs7jrHuUIPd5Sg/0KA/B4+/V0FMVASfXTjB7lKU8okvfWQS+enxfP+FnZzu6rW7HOVjGvQjdPRkO89sqeaGklwyEmPtLkcpn4iLjuT+T83iSGM79/1lt93lKB/ToB+h/3rjAJERwlcuK7K7FKV8akFhOncvncSqLdW8VKbry4YSDfoROHiilRe210PCKZIAAA9ZSURBVLDy4nyyk+LsLkcpn/va5UXMGZ/Cd5/fydGT7XaXo3xEg34E/nPNARJiorhryUS7S1HKL6IjI/jlTXOIjBBu/c0mmtu77S5J+YAGvZe2VDbyyq7jfO6SAp3uQIW0vLR4Hv1sCdWNHdz5u6109fbZXZI6Txr0Xujpc/G9F3aSkzKKu5YU2l2OUn43ryCNB66fxabDjXztjzvo7tUpEoKZBr0XHnv3MAdOtHHf8unEx+jC3yo8rJidw73/MI2/7T7OHb/dQnu3DrsMVhr0w6hqbOfBNw6wbFo2l0/LtrscpQLqc5cU8LNPzeK9g/Xc8thGTpzqtLskdQ406M+it8/FN1eVEiHCD5ZPt7scpWxxw0V5/M9n5rK3tpWrH3yXt/bV2V2SGiEN+rP4jzUH2FTZyL9fO5OclFF2l6OUba6aMZa/fGURWaNjue03m/nWM6Ucb9Gz+2ChQT+Et/bV8eu15dw8bzzX6MRlSjEpazQv3r2ILywpZPWOYyz9+Vv85JW9VDfpeHunE6ctI1ZSUmK2bNliaw17a09x4yPryUmN54UvXawTlyk1QFVjOz97dT8vlR1DgI9MyeLqmWNZMjmTzNE6NYgdRGSrMaZk0Oc06D+oor6NGx5ZT3RkBKu+sJC8tHjbalHK6aqb2nl6UxXPbK3ixCn3fPZFWYnMzElm2rgkxqWMYkxyHGOT48hMjCUqUhsR/EWD3kuHG07zmf/dQFevi1V3LWRiZqItdSgVbIwx7D52irX769h2tJmdNS0fWsgkQiAtIZbkUVEkj4omeVQ0KfExJI+KJsl6PPCWOTqW1PhoRHRxn+GcLeh1ULjlvYMN3P3UNiIEfn/HfA15pUZARJiRk8yMnOQz25pOd1Pb0snxUx3uf1s6aWjroqWjh5aOHurbujhU30Zzew+tnUOP0R8dF0V+egIT0uPJT0+gKDuR6eOSKchIIFJXd/NK2Ad9b5+Lx947zAOv7mdSZiKPrSzR5hqlfCA1IYbUhBimjUsadt8+l6G1s+fMLwHP7cSpLo6cPE3lyXZ21rTwyq7j9LncrRDxMZFMG5vEjJxkZuYkU5yXQmFGgi7tOYiwDvrtR5v4/gu72FN7iqumj+HnNxSTGBvW3xKlbBEZIaTEx5ASf/Z5pLp7XZTXt7GrpoXdx06x+1gLq7ZU8Zv3KwEYHRvFrLxkinNTKM5LYXZeis40SxgGvTGG98tP8ug7Fbx9oJ7spFh+/Zm5XDVjjLYDKuVwMVERTB2bxNSxSVxvbetzGcrr29hR1UxpVTNl1S08+k4FvdaZ/5ikOIrz3Gf8s3NTmJmbzOi4aPu+CBt41RkrIlcBDwKRwGPGmPsHPB8L/Ba4EDgJ3GiMqbSe+y5wO9AHfNUY8+rZ3ssfnbEd3X2UVjfzxt4T/G33caoaO8hIjGHlwnxuXZQfdv/pSoW6zp4+9tSeotQK/9LqFg43nAZABHJSRjExM5HCzIQz/+amxJOdHEtsVHAOpz6vzlgRiQQeAq4AqoHNIrLaGNN/yfjbgSZjzCQRuQn4KXCjiEwDbgKmA+OA10VksjHG5/Oe9vS5OHiijfq2Lupbu6hp6uBwQxsH69rYd7yVPpchOlK4ZFIGX72siE8Uj9Px8UqFqLjoSOaOT2Xu+NQz25rbuymrbqGsupmDdW2U17ex6XAjHT0fjKOMxFjGpcSRnRRHany0u68hPobU+GiS4qIZFRNJfEwUo6IjGRXjvsVHRxIXHUlEBESKEBkhXrcQ9LkMPX2uM3+B+KP52JtXnAccMsZUAIjI08AKoH/QrwB+aN1/FvhvcX+VK4CnjTFdwGEROWS93nrflP93Te3dfOyX7555LALjkkdRmJnAXUsKmTs+lYsK0kjSs3elwlJKfAyLJ2eyeHLmmW0ul+H4qU4q6k9zrKWD2uZOals6ONbSSVVjO2XV3TSd7qG7b+TTNIv8PfQjI4RIESIiBJcx9PYZel3ucO/fqDI7L4UX717kiy/3A7wJ+hygqt/jamD+UPsYY3pFpAVIt7ZvGHDsh+YTEJE7gTuth20ist+r6odRCbzvixeCDKDBNy/lM06sCZxZlxNrAmfWpTV5z+d1HQHky+d8+IShnvAm6Af7+2Ngw/5Q+3hzLMaYR4FHvajFFiKyZai2L7s4sSZwZl1OrAmcWZfW5D2n1jUYb65Hrgby+j3OBQYuEX9mHxGJApKBRi+PVUop5UfeBP1moEhECkQkBnfn6uoB+6wGVlr3rwPeNO7hPKuBm0QkVkQKgCJgk29KV0op5Y1hm26sNvcvA6/iHl75hDFmt4j8CNhijFkNPA78zupsbcT9ywBrv1W4O257gbv9MeImAJzYrOTEmsCZdTmxJnBmXVqT95xa14c4blIzpZRSvqVzhiqlVIjToFdKqRCnQT+AiDwhInUismvA9q+IyH4R2S0iP7O7JhGZLSIbRGSHiGwRkXkBrilPRN4Skb3W9+Rr1vY0EVkjIgetf1OHe60A1fWAiOwTkTIReUFEUuyuqd/z3xIRIyIZTqjJ5s/6UP9/tn3eRSRORDaJSKlV033W9gIR2Wh91v9kDVZxJmOM3vrdgMXAXGBXv20fAV4HYq3HWQ6o6TXgauv+x4C1Aa5pLDDXuj8aOABMA34G3GNtvwf4qUPqWgZEWdt/Gsi6hqrJepyHe6DDESDD7poc8Fkfqi7bPu+4rwdKtO5HAxuBBcAq4CZr+8PAFwP5vRrJTc/oBzDGvIN75FB/XwTuN+6pHDDG1DmgJgN4JvpOJsDXJxhjao0x26z7rcBe3Fc9rwCetHZ7ErjGCXUZY14zxnhWt9iA+5oOW2uynv4F8B0GuZDQpprs/qwPVZdtn3fj1mY9jLZuBrgM95QvYMNnfSQ06L0zGbjU+jPtbRG5yO6CgH8CHhCRKuDnwHftKkRE8oE5uM90so0xteD+oQWyHFJXf58DXgl0PfDBmkRkOVBjjCm1o5bBasJBn/UBddn6eReRSBHZAdQBa4ByoLnfycOg07s4hQa9d6KAVNx/rn0bWCX2T17/ReDrxpg84Ou4r2UIOBFJBJ4D/skYc8qOGgYzVF0i8n3c13T8wc6arBq+D9wb6DqGqsn6Pjnisz5IXbZ+3o0xfcaY2bj/EpwHTB1st0DWNBIa9N6pBp63/oTbBLhwT2hkp5XA89b9Z3B/+AJKRKJx/zD+wRjjqeWEiIy1nh+L+wzICXUhIiuBfwA+Y6yGVRtrmggUAKUiUok7QLaJyBgbawIHfNaHqMv2zzuAMaYZWIv7F2GKNeULOHx6Fw1677yIuz0OEZkMxGD/bHrHgCXW/cuAg4F8c+ss73FgrzHmP/s91X86jJXAn51Ql7gXz/l/wHJjTLvdNRljdhpjsowx+caYfNwBO9cYc9yumiy2ftbPUpdtn3cRyfSM0hKRUcDluPsO3sI95QvY8FkfEbt7g512A/4I1AI9uH/4bsf9Yf89sAvYBlzmgJouAbYCpbjbMC8McE2X4P5TtQzYYd0+hnt66jdw/yC+AaQ5pK5DuKfS9mx72O6aBuxTSWBH3Qz1fbL7sz5UXbZ93oFZwHarpl3Avdb2Qtxzdx3C/VdGbCC/VyO56RQISikV4rTpRimlQpwGvVJKhTgNeqWUCnEa9EopFeI06JVSKsRp0CulVIjToFdBS0SWi8g9AX7Pa0RkWoDf81YR+e8RHlMiIr+07i8VkYv9U50KBsOuGauUv4hIpDmPNYSNe73igQvVj+T9o8zfJ6Xy1jXAS7jXQXYk6+vaAmyxNi0F2oD3bStK2UrP6JVfiEi+tdDHk9ZiH8+KSLyIVIrIvSLyHnD9gGPmicj7IrLd+neKtf0bIvKEdX+miOyyXuvMma6IXG9tLxWRd85S160i8oyI/AX3HOeIyLdFZLNV53399v1Ha1upiPzOOitejnsWxR0iMlFEPm8dWyoiz4lIvHXsb0Tkl9bXUSEi11nbI0Tkf6wFLF4SkZf7PVcp1uIj1hn52kHq/4Q1s+R2EXldRLKt7T8UkUdF5DXgt9ZZ/EvWDJB3AV+3ar5URA5b88kgIknW+0aP9P9YBQ89o1f+NAW43RizzgrqL1nbO40xlwyy/z5gsTGmV0QuB/4d+BTwX8BaEbkW94yPXzDGtA+YVPFe4EpjTI0Mv3rUQmCWMaZRRJYBRbgnyRJgtYgsBk5a77XIGNMgImnW/quBl4wxzwKISLMx5n+t+/+Ke3qKX1nvMxb3pfsX4P7L41ngk0A+MBP3FM57gSeGqbe/94AFxhgjInfgnsv+m9ZzFwKXGGM6RGQpgDGmUkQeBtqMMT+36lwLfBz3vDY3Ac8ZY3pGUIMKMhr0yp+qjDHrrPu/B75q3f/TEPsnA0+KSBHu+U6iAYwxLhG5FfdcI4/0e83+1gG/EZFV/H2Ww6GsMcZ4FnJZZt22W48TcQd/MfCsMabBqmHgwi8eM6yAT7GOfbXfcy8aY1zAHs+ZN+7gf8baflxE3hqm1oFygT+Je2bQGOBwv+dWG2M6vHiNx3D/gngRuA34/AhrUEFGm26UPw2cSMnz+PQQ+/8YeMsYMwP4BBDX77ki3O3M4wZ9I2PuAv4Z99J8O0Qk/Sx19X9/AX5ijJlt3SYZYx63tnszEdRvgC8bY2YC9w2ouWvA+/T/dzC9/P1nMm6IfX4F/Lf1fl8YsN9Q39cPsH5R5ovIEiDSGLNruGNUcNOgV/40XkQWWvdvxt3scDbJQI11/1bPRhFJBh7EvXZuuqdNuz8RmWiM2WiMuRf3tLp5Xtb4KvA5cS90gYjkiEgW7pk3b/D8whCRNGv/VtxrmXqMBmqtNu7PePF+7wGfstrqs3F3lHpU4m5+AXeT1WD6f49WDrHPQANrBvgt7llR/8/L11BBTINe+dNeYKWIlAFpwK8H7iAid4nIXdbDnwE/EZF1QGS/3X4B/I8x5gDuNvD7rTDu7wER2Skiu4B3cE9nOyxjzGvAU8B6EdmJux19tDFmN/BvwNsiUgp45kZ/Gvi21Rk6EfgX3NPmrsHdxzCc53BPNb0LeMQ6tsV67j7gQRF5FxhqNNIPgWesfbydJ/4vwLWezlhr2x9wryT1Ry9fQwUxnaZY+YU12uMlqxlG9SMiicaYNuuvhU24O3wDsuBIvxquA1YYYz4byPdV9tDOWKUC7yVrZFAM8GMbQv5XwNW4F/RQYUDP6FVIEpErgZ8O2HzYGHOtHfUoZScNeqWUCnHaGauUUiFOg14ppUKcBr1SSoU4DXqllApx/x+AhnY5rzUczgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jU1OjGBwbypvrD+j8Nx5Og155jI+2FBHsb+Pikdpt4wgR4bazU9h+8BhbCo9aXY7qQRr0yiPUNjTxybYSZo2KJyTAobn6FHDNuATCAnx56+sDVpeiepAGvfIIK3Yd5nhtI9eOH2B1KW4lJMCXaycMYEl2ic5/48E06JVH+HDzQfpFBDIlVaffPV23Tk2mocmw4BsdaumpNOiV2ys7XseaPWVcPS4Bm493LzByJgbFhDJ9cDTzMwpobGq2uhzVAzToldv7cHMRTc2Ga7Tb5ozNm5rCoWO1fL7zsNWlqB6gQa/cmjGGhRsLmZjSh7TYUKvLcVvnDYtlQJ8g/qEfynokDXrl1jLzK8k7Us0N6YlWl+LWbD7CbVOTydhfwY7iKqvLUU6mQa/c2oJvCgkN8OUynZK4225MTyLY38YbXx2wuhTlZBr0ym0dq21gSXYxV47tT7C/jp3vrohgP66bMIBFW4t1qKWH0aBXbmvR1mJqG5q5UbttnOb2s1Oob2rWBcQ9jAa9ckvGGOZnFDC8XzhjBkRYXY7HSI0J5QfDYnl7Qz51jTqrpafQoFduaVN+JbtKjnHrlGREdOy8M905bSBHTtSzOKvE6lKUk2jQK7f01vp8wgJ8uWqcLjDibNPSohgSF8rrX+7XWS09hAa9cjtlx+tYtr2EaycM0A9he4CIcOe0gewsOUbG/gqry1FOoEGv3M7CjQU0NBlunZpsdSke66pxCfQJ9uP1L/dbXYpyAg165VYampqZn1HAOWnRDIrRO2F7SqCfjZsnJ/P5rsMUlOsC4u7OoaAXkVkikiMiuSLyaAePB4jIQvvjGSKSYt8/SUS22r+yRORq55avvM3S7BJKqmqZd3aK1aV4vFunJmMT4Y2v9are3XUZ9CJiA14CLgFGAHNFZES7ZncBlcaYNOA54Gn7/u1AujFmLDALeEVEtFNVnRFjDH9bt5/U6BDOHxZrdTkeLy48kCvO6s/CjYVU1TRYXY7qBkeu6CcBucaYPGNMPbAAmN2uzWzgTfv2v4DzRUSMMTXGmEb7/kBAP8JXZyxjfwXZB6u4a/pAfHQ64l5x9/RUauqbmP+N3kDlzhwJ+gSgsM33RfZ9HbaxB3sVEAUgIpNFZAeQDdzbJvi/JSL3iEimiGSWlZWd/k+hvqO+sZlN+RXkl1d71PC419bm0TfEX1eR6kUj+oczfXA0//jqgN5A5cYc6Ubp6NKpfXp02sYYkwGMFJHhwJsisswYU/udhsa8CrwKkJ6e7jnJ1MtyS0/w0qrcb5fVAwgL8OXcYbH84tLhxEcEWlzhmcstPcEXu0v58fmDCfSzWV2OV7l7eiq3vf4Ni7YWc71ON+GWHAn6IqDt3+4AoLiTNkX2PvgI4DsDcI0xu0SkGhgFZJ5xxapDG/LKueetTIyBi0bGc8HwWKpONrDtYBUfbCpi1e5S/vviodw21T3vJP3f1fsI8PXhNh1S2eumD45mWHwYr63L47oJA9zy/ePtHAn6jcBgERkIHATmADe1a7MImAesB64DVhpjjP05hcaYRhFJBoYCB5xVvGqxNLuEhxZsJbFvEG/eOYkBfYK/fWwOcO+MQfzi39n8atEO9pYe54krR7lVH3dBeQ3/3nqQeVNTiA4NsLocryMi3DMjlYffy2L1njLOG6ofhLubLvvo7X3qDwDLgV3Ae8aYHSLyhIhcaW/2dyBKRHKBh4HWIZjnAFkishX4CLjPGHPE2T+EN9t96BgPLdjKmAERfPCjs78T8q2SooJ5685J3DtzEG9vKODRD7fR1Ow+PWR/XZ2LzUf4r5mpVpfitS4f05/48EBeW5tndSnqDDg01NEYsxRY2m7f4222a4HrO3jeP4F/drNG1Yn6xmZ++l4W4UG+vHLrBCKD/TttKyL8bNZQ/H19+MsXe7H5+PC7q0e5/K/hRZU1fLC5iLmTkogLd9/PGNydv68Pd0xL4ffLdrP9YBWjEnTGUHeid8a6sRdX5bKj+Bi/vXo0UQ50aYgID184hB+dO4h3vyngTTdYH/TlNfsAuHfmIIsrUXMnJxEa4MurelXvdjTo3dSukmO8tCqXa8YlcPHI+NN67n9fNJQLR8TxxCc7WbvHdYezHjhSzYJvCrkhPZH+kUFWl+P1wgP9mDspkSXZJRRV6rQI7kSD3k09v2Ivwf42Hr+i/U3KXfPxEZ67cSxD4sJ44J3NFFa45j/aP32Wg5/NhwfPH2x1KcrujmkDEeBv63RaBHeiQe+Gcg4d59Mdh7jj7JRT9sufSmhAS79+s4EfL9hCQ1Ozk6vsnqzCo3yyrYS7pw8kVvvmXUb/yCBmj01gwcYCyk/ourLuQoPeDb2wci8h/jbuPGdgt14nOSqE318zmi0FR3nmsz1Oqq77jDE8tWw3fUP8uXuGjrRxNT86N5Xahma3+IxHtdCgdzO5pSdYkl3CvG5czbd1xVn9mTspiZfX7GONi/TXf77zMOvzyvk/P0gjLNDP6nJUO2mxYVw0Io5/fH2AE3Xfm9FEuSANejfz8pp9BPrauKubV/Nt/eqKEQyNC+PhhVs5fKy26yf0oJP1Tfx68U6GxIVyyxS9C9ZV3XdeGsdqG3knQyc7cwca9G6k6mQDi7OKuWZ8gkPDKR0V6GfjxZvGUV3fyEMLtlp6M9VLq3I5ePQkT8wehZ9N356uamxiJGcPiuJv6/brZGduQP8luZFFWw9S19jM3ElJTn/twXFhPHHlKNbnlfPSqlynv74j8spO8OraPK4el8CU1ChLalCOu+/cNEqP1/Hh5oNWl6K6oEHvJowxvPtNISP7h/fYXYnXpw9g9tj+/HnFHjLyynvkGJ1pajY8+kE2Ab4+PHbpsF49tjoz09KiGJ0QwStr9rnVlBreSIPeTWw/eIydJceYM7HnpokVEX579WiS+gbz4IKtVFTX99ix2vvbujy+OVDBr64cSWyYDqd0ByLCfecO4kB5Dcu2l1hdjjoFDXo3sWBjAQG+Plw5tv2aL84VGuDLizeNp6K6nkfez+qVhUt2lRzjmc/2cPHIOK4d37M/n3Kui0fGkxoTwl9X7fOoRW48jQa9GzhZ38SircVcNrofEUE9P9xwVEIEv7hsOCt3l/KXL3q2v/5kfRM/WbiV8CBffnf1aJefZE19l4+PcO/MQewsOcbqHNcYnqu+T4PeDazOKeV4XSPX9OISerdNTeaacQk8t2IPy7J75tdyYwz/94Nt5Bw+zh+vP8upI4lU77lqbAIJkUE8/8Vevap3URr0buCT7BKiQvyZktq3144pIvzumtGMS4rk4fey2H6wyunHeHlNHouzivnvi4fqYhZuzN/Xh/vOG8TWwqN8mavLTbgiDXoXd7K+iZW7Srl4VDy+vTyuPNDPxiu3TCAy2I95r39DbukJp732suwS/rB8N5eP6cePdApit3fdhAH0iwjkL3pV75I06F3cqpxSTjY0cfnofpYcPzY8kLd/OBkR4ea/bSC/vLrbr/np9hL+z7tbGJ/Uhz9ed5b2y3uAAF8b984cxMYDlWzIq+j6CapXadC7uCX2bptJA3uv26a9QTGhzP/hZOobm5nz6oZudeMs2VbCA+9sYcyACP5xx0SC/G1OrFRZ6caJicSGBfCXL/ZaXYpqR4PehbV228yyoNumvaHxYcz/4RQEuO7lr1mUVXxaz29oaub3S3dx/zubOSsxkjfvnKQTlnmYQD8b/zVzEOvzytl4QK/qXYkGvQtr7ba5zKJum/ZG9A/n4wfOYXRCBD9+dwsPLdhC8dGTXT4vu6iKua9u4JW1edwyJYn5P5ysIe+hbpqURHSov17VuxiHFgdX1li+4xB9gv0s7bZpLyYsgPk/nMLzX+zhtXX7+XTHIeZMTOKikXFMTOn77URkR2vq2XigkoUbC1ixq5SIID+enzOW2T18w5eyVpC/jbunp/L7ZbvZXFDJ+KQ+VpekAHG1T8jT09NNZmam1WVYrrGpmQlPruD84bE8e8NYq8vpUGFFDX/6LIdl2Yeob2omwNeH0ABf/Gw+HD5eizEQHujL3dNTuX1ail7Fe4nqukbOeXolYxMjeeOOSVaX4zVEZJMxJr2jx/SK3kVl5ldSdbKBC4bHWV1KpxL7BvP8nHH87upG1u09wqb8Ck42NFHX0ExS32AmDezLWYmRBPrpB67eJCTAlx9OT+WPy3PYUlDJOL2qt5wGvYtaubsUP5swfXC01aV0KSTAl1mj4pk1Kt7qUpSLuP3sFP7+5X6e/XwP/7xrstXleD39MNZFrdh1mCmpUdrdodxSSIAv985MZd3eIzoCxwVo0Lug/UeqySurduluG6W6cuuUFKJDA3jmsxyrS/F6GvQu6ItdhwE4f7jO/6LcV5C/jfvOHcSGvAq+1jlwLOVQ0IvILBHJEZFcEXm0g8cDRGSh/fEMEUmx779QRDaJSLb9zx84t3zPtGLXYYbFhzGgT7DVpSjVLTdNTiI+PJBnPt+jc+BYqMugFxEb8BJwCTACmCsiI9o1uwuoNMakAc8BT9v3HwGuMMaMBuYB/3RW4Z7qeG0DmQcqOW+YXs0r9xfoZ+P+H6SxKb+SNXt0vnqrOHJFPwnINcbkGWPqgQXA7HZtZgNv2rf/BZwvImKM2WKMab1XfgcQKCI66fgpfL2vnMZmw7lDYqwuRSmnuDE9kYTIIJ7Vq3rLOBL0CUBhm++L7Ps6bGOMaQSqgKh2ba4Fthhj6s6sVO+wZk8ZoQG+jE/WscfKM/j7+vDj89PYVlTFil2lVpfjlRwJ+o7mkG3/3/Ip24jISFq6c/6rwwOI3CMimSKSWVbmvb/eGWNYk1PGtLSob6cSUMoTXDN+AMlRwTzzWQ7NzXpV39scSZMiILHN9wOA9lMXfttGRHyBCKDC/v0A4CPgNmPMvo4OYIx51RiTboxJj4nx3i6LfWXVHDx6khnabaM8jJ/Nh4cvHMLuQ8f5OOug1eV4HUeCfiMwWEQGiog/MAdY1K7NIlo+bAW4DlhpjDEiEgksAR4zxnzlrKI9VeuHVTMGa9Arz3PFmP6M6BfOM5/toa6xyepyvEqXQW/vc38AWA7sAt4zxuwQkSdE5Ep7s78DUSKSCzwMtA7BfABIA34pIlvtXzqcpBNr9pQxKCaExL46rFJ5Hh8f4WeXDKOo8iTvZBRYXY5XcWiuG2PMUmBpu32Pt9muBa7v4HlPAk92s0avUNvQREZeOTdPTra6FKV6zIzB0UxNjeLFlblcn55IaIBOt9Ub9BM/F7Ehr5y6xmZmDtVuG+W5RFqu6sur63ltbZ7V5XgNDXoXsXbPEQJ8fZjsQouMKNUTxiZGcsmoeP62Lo8jJ3S0dW/QoHcRa/aUMjk1SuduV17hkYuHUtvYzIsrc60uxSto0LuAwooa9pVVM1OHVSovMSgmlBvSE5mfkU9BeY3V5Xg8DXoXsHZvy7BKDXrlTR66YDA2H+GZz3Ua456mQe8C1uSUkRAZxKCYEKtLUarXxIUHcse0gXy8tZgdxVVWl+PRNOgtVt/YzNf7ypkxJAaRjmaSUMpz3TtzEBFBfvzhU72q70ka9BbbXFDJibpG7bZRXikiyI/7zxvEmj1lrN9XbnU5HkuD3mJr9pTh6yOcndZ+sk+lvMNtU1PoFxHIU5/u1mmMe4gGvcVW55QxPrkP4boIuPJSgX42fnLhELIKj7Iku8TqcjySBr2FDlXVsqvkGOcN1el/lHe7dvwAhsWH8fSnu3XCsx6gQW+hNXtaFmE4b5j2zyvvZvMRfnHZcAorTvLW1/lWl+NxNOgttGp3Gf0iAhkaF2Z1KUpZbvrgGGYOieGFlXuprK63uhyPokFvkfrGZr7MPcK5Q2N1WKVSdj+/dDgn6hp5QadGcCoNeotk5ldwoq6Rc3W2SqW+NTQ+jBvSE/nnhgMcOFJtdTkeQ4PeIqtzyvCzCdPSoq0uRSmX8vCFQ/Cz+fCH5butLsVjaNBbZHVOKZMG9tWFF5RqJzY8kP+aMYil2YfYlF9hdTkeQYPeAkWVNew5fEKHVSrVibtnDCQ2LIAnl+zSm6icQIPeAqtzWmarPFeDXqkOBfv78shFQ9lSoDdROYMGvQVW55SS2Fdnq1TqVK6doDdROYsGfS+rbWjiq9xyzh2iwyqVOhWbj/DzS1tuovrnelZnZk4AABCDSURBVL2Jqjs06HvZxgMVnGxo0rthlXLAjCExzBgSw1++0JuoukODvpet2l2Gv68PU1N1WKVSjviF/SaqP6/YY3UpbkuDvpetzillamoUQf66CLhSjhgaH8ZNk5N4O6OAPYePW12OW9Kg70UHjlSTd6Sa8/RuWKVOy8MXDiXE38ZvPtmpwy3PgAZ9L1qV0zJbpQ6rVOr09A3x58ELhrBu7xFW7i61uhy3o0Hfiz7bcZi02FBSonVYpVKn67apyaTGhPDkkl3UNzZbXY5b0aDvJRXV9WTsL2fWyHirS1HKLfnZfPjlZSPYf6Sat9YfsLoct+JQ0IvILBHJEZFcEXm0g8cDRGSh/fEMEUmx748SkVUickJEXnRu6e5lxc7DNBuYNUqDXqkzdd6wWGYOieH5L/ZSfqLO6nLcRpdBLyI24CXgEmAEMFdERrRrdhdQaYxJA54DnrbvrwV+CTzitIrd1PIdh0iIDGJk/3CrS1HKrf3y8uHU1DfxzOc63NJRjlzRTwJyjTF5xph6YAEwu12b2cCb9u1/AeeLiBhjqo0xX9IS+F7rRF0j6/Ye4eKR8Xo3rFLdlBYbxq1TklnwTQG7So5ZXY5bcCToE4DCNt8X2fd12MYY0whUAVGOFiEi94hIpohklpWVOfo0t7Fqdyn1Tc3abaOUkzx0wWDCg/x4YrEOt3SEI0Hf0SVo+zPrSJtOGWNeNcakG2PSY2I8b4z58h2HiA71Z0JyH6tLUcojRAb78/CFQ1ifV85nOw9bXY7LcyToi4DENt8PAIo7ayMivkAEoCsGADX1jazcXcqFI+Kx+Wi3jVLOctOkJIbEhfK7pbt0dssuOBL0G4HBIjJQRPyBOcCidm0WAfPs29cBK43+PgXA5zsPU1PfxOyx/a0uRSmP4mvz4ZeXjyC/vIbX1uZZXY5L6zLo7X3uDwDLgV3Ae8aYHSLyhIhcaW/2dyBKRHKBh4Fvh2CKyAHgWeB2ESnqYMSOR/t4azH9IwKZlNLX6lKU8jjTB8dwyah4XliZS0F5jdXluCyHFiw1xiwFlrbb93ib7Vrg+k6em9KN+txaRXU9a/eUcdf0gfhot41SPeLxK0awdk8Zv1q0nddvn6gj2zqgd8b2oCXbimlsNlw1tv0gJaWUs/SLCOInFw5hVU4Zy3ccsrocl6RB34P+vbWYoXFhDO+nN0kp1ZNuPzuF4f3C+fXinVTXNVpdjsvRoO8hhRU1bMqv5Er9EFapHudr8+HJq0ZRUlWrC5R0QIO+hyzcWIiPwFXjtNtGqd4wIbkPcycl8vpXB/SO2XY06HtAQ1MzCzYWct7QWBIig6wuRymv8bNZw4gI8uMXH2XT3KwjvFtp0PeAz3ce5siJOm6ekmR1KUp5lchgf35+6XA2Fxzl7Yx8q8txGRr0PWB+Rj4JkUHMHKIrSSnV264dn8CMITE8tWw3hRU6th406J1u/5FqvsotZ+6kRJ3yQCkLiAi/v2Y0PiL87INtOukZGvRON39DPr4+wg3piV03Vkr1iITIIH5+6XC+3lfO2xkFVpdjOQ16JzpaU8+73xRw6eh+xIYHWl2OUl5t7qREpg+O5rdLdpJbesLqciylQe9Eb36dT3V9E/edN8jqUpTyeiLCn64/iyA/Gw8t3OLVC4pr0DtJdV0jb3y9nwuGxzEsXu+EVcoVxIUH8tS1Y9h+8BjPfJ5jdTmW0aB3kncyCjha06BX80q5mItHxjN3UhKvrMlj5W7vXKREg94JTtY38dq6PM4eFMX4JF1FSilX86srRjCiXzgPLdjqlUMuNeid4JW1+yg9XsdDFwyxuhSlVAcC/Wy8fMsEAO59exO1Dd61IpUGfTcdPHqSl9fs47Ix/Zg0UBcXUcpVJUUF8+wNY9lRfIxH3s/yqikSNOi76alluzEGHrtkmNWlKKW6cMGIOH42axifbCvh2c+9Z5ZLh1aYUh3LyCtncVYxPz5/MAP6BFtdjlLKAffOTKWgopoXV+WS2DeIGyd6/pxUGvRn6FhtAz99P4vEvkHcOzPV6nKUUg4SEZ6YPYqDR2t57MNsAv1szPbwVeC06+YMPf7v7S2LHNw4jmB//f9SKXfiZ/PhlVsmMDGlLw+/l8XS7BKrS+pRGvRn4KMtRfx7azEPnj+YCck6nFIpdxTkb+P12ycyLjGSH7+7hX9tKrK6pB6jQX+aNuVX8NiH2UxM6cP956VZXY5SqhtCAnx5446JTE7tyyPvZ/HSqlyPnO1Sg/405Bw6zh1vbKRfRBD/e8sEnYZYKQ8QFujHG7dP4upxCfxxeQ4/fS+LmnrPWmBcO5cdlFt6gttezyDQz8Zbd04iOjTA6pKUUk7i7+vDszecRUpUCH/+Yg/bDlbx15vHMyQuzOrSnEKv6B2wbm8ZV//1K5qaDW/dNYnEvjqUUilPIyI8eMFg3r5rMkdr6rn8hS95fsVe6hrd/y5aDfpTaGhq5q+rc7n9jY0kRAbx7/un6cyUSnm4aWnRLH1wOheNiOO5FXu45Pl1LN9xyK377sXVik9PTzeZmZlWl8Gm/Ap+8dF2dh86ziWj4vnj9WcRGqA9XUp5k9U5pfx68U72H6lmZP9w7js3jYtGxuFnc71rZBHZZIxJ7+gxTa42mpsNa/eW8dq6PL7KLSc+PJBXbp3AxSPjrS5NKWWBc4fGck5aNB9vLeYvK/dy/zubiQ4N4LoJA7hkVDxjBkQg4vqDMhy6oheRWcDzgA34mzHmqXaPBwBvAROAcuBGY8wB+2OPAXcBTcCPjTHLT3Ws3r6iP1pTz5aCo6zcXcryHYcoPV5HXHgAd0wbyC1TkvUqXikFQFOzYe2eMuZn5LNydynNBuLDA5mWFs3ElD6MT+7DwOgQy672u3VFLyI24CXgQqAI2Cgii4wxO9s0uwuoNMakicgc4GngRhEZAcwBRgL9gRUiMsQY02OfbhhjqG9qpqHJUN/YzMmGJiqr66msqaeiuuXrUFUteUeq2Vd6grwj1QAE+vlw7pBYLh3Tj1kj4/H3db1fzZRS1rH5COcNi+W8YbFUVtezcncpn+88zKqcUj7Y3HKzlb/Nh9SYEBIig4iPCCQ+PJD4iEBiwwMJDbAREuBLiL8vIQG+BPnZsPkIvj6CTw8P1XbkcnUSkGuMyQMQkQXAbKBt0M8G/se+/S/gRWn5fWY2sMAYUwfsF5Fc++utd075/5FVeJTrX15PfVPX60L623xIjgpmcFwo104YwLikSMYl9iHI3+bsspRSHqhPiD/XThjAtRMGYIxhX1k12QePsvvQcXIPn6CkqpYthUepqK536PVEwM/Hh8vG9OO5G8c6vV5Hgj4BKGzzfREwubM2xphGEakCouz7N7R77vdmDxKRe4B77N+eEJEeX9xxb08f4LuigSO9e0i3oufn1PT8dM6jzs2fgT/POeOnJ3f2gCNB39HvFO079jtr48hzMca8CrzqQC1uSUQyO+s7U3p+uqLnp3N6bhzjSEd0EZDY5vsBQHFnbUTEF4gAKhx8rlJKqR7kSNBvBAaLyEAR8aflw9VF7dosAubZt68DVpqW4TyLgDkiEiAiA4HBwDfOKV0ppZQjuuy6sfe5PwAsp2V45evGmB0i8gSQaYxZBPwd+Kf9w9YKWv4zwN7uPVo+uG0E7u/JETcuzGO7pZxEz8+p6fnpnJ4bB7jcnbFKKaWcSweLK6WUh9OgV0opD6dB300i8rqIlIrI9jb7xorIBhHZKiKZIjLJvl9E5C8ikisi20RkvHWV945Ozs9ZIrJeRLJFZLGIhLd57DH7+ckRkYutqbr3iEiiiKwSkV0iskNEHrTv7ysin4vIXvuffez7veo9dIrzc739+2YRSW/3HK96DznEGKNf3fgCZgDjge1t9n0GXGLfvhRY3WZ7GS33F0wBMqyu36LzsxGYad++E/iNfXsEkAUEAAOBfYDN6p+hh89PP2C8fTsM2GM/D38AHrXvfxR42hvfQ6c4P8OBocBqIL1Ne697DznypVf03WSMWUvLSKPv7AZar1Ij+M+9A7OBt0yLDUCkiPTrnUqt0cn5GQqstW9/Dlxr3/52ygxjzH6gdcoMj2WMKTHGbLZvHwd20XL3+GzgTXuzN4Gr7Nte9R7q7PwYY3YZYzq6g97r3kOO0KDvGQ8BfxSRQuBPwGP2/R1NJ/G9KSG8wHbgSvv29fznpjqvPj8ikgKMAzKAOGNMCbSEHRBrb+a156jd+emM156fU9Gg7xk/An5ijEkEfkLLfQbg4JQQXuBO4H4R2UTLr+OtMz957fkRkVDgA+AhY8yxUzXtYJ/HnyM9P92jQd8z5gEf2rff5z+/OuqUEIAxZrcx5iJjzATgXVr6UcFLz4+I+NESYvONMa3vm8OtXTL2P0vt+73uHHVyfjrjdefHERr0PaMYmGnf/gH/mSxzEXCbfeTEFKCq9ddzbyIisfY/fYD/B7xsf8jrpsywT+f9d2CXMebZNg+1nVZkHvBxm/1e8x46xfnpjNe9hxyhyyd1k4i8C5wLRItIEfAr4G7gefsEb7X8ZwrmpbSMmsgFaoA7er3gXtbJ+QkVkfvtTT4E3gCvnTJjGnArkC0iW+37fg48BbwnIncBBbR8lgHe9x7q7PwEAC8AMcASEdlqjLnYS99DXdIpEJRSysNp141SSnk4DXqllPJwGvRKKeXhNOiVUsrDadArpZSH06BXSikPp0Gv3IqIpLSd8tiB9v8Qkevs26vbT2lrNfuU1pe2+f5KEXnUypqU59GgV6qH2W+c68xYWm6AAsAYs8gY81TPV6W8iQa9ckc2EXnNvvDEZyIS1Gaxl20i8lHrQh2dEZG59oVPtovI0/Z9N4jIs/btB0Ukz749SES+tG8/JSI77cf50yle/x8i8qyIrAKeFpFJIvK1iGyx/zlURPyBJ4AbpWWRmhtF5HYRedH+Gski8oX9WF+ISJJTzp7yOhr0yh0NBl4yxowEjtIyn/1bwM+MMWOAbFqmWuiQiPQHnqZlHqKxwEQRuYqWOfKn25tNB8pFJAE4B1gnIn2Bq4GR9uM82UWdQ4ALjDE/BXYDM4wx44DHgd8ZY+rt2wuNMWONMQvbPf9FWuaeHwPMB/7S1YlRqiMa9Mod7TfGtM57sgkYBEQaY9bY971Jy8pWnZlIy6pfZcaYRlpCdIYx5hAt8/CE0TID4jv215kOrAOO0TJ30d9E5Bpa5po5lffbzLMSAbxv/3zhOWCkAz/nVHsNAP+k5T8cpU6bBr1yR3VttpuAyNN8fkdzlrdaT8tEYTm0hPt0WgL3K/t/CpNomTL3KuDTLo5T3Wb7N8AqY8wo4Aog8DRrBp1XXZ0hDXrlCaqAShFp7Xa5FVhzivYZwEwRiRYRGzC3Tfu1wCP2P7cA5wF1xpgq++IXEcaYpbSsIjb2NGqMAA7at29vs/84LYuvdORrYI59+2bgy9M4nlLf0mmKlaeYB7wsIsFAHqeYvtcYUyIijwGraLm6X2qMaZ3vfR0t3TZrjTFN9uUgd9sfCwM+FpFA+/N+chr1/QF4U0QeBla22b8KeNQ+Be/v2z3nx8DrIvLfQNmpfialTkWnKVZKKQ+nXTdKKeXhtOtGqW4QkV/wn9WfWr1vjPmtFfUo1RHtulFKKQ+nXTdKKeXhNOiVUsrDadArpZSH06BXSikP9/8BdYCer6Z0RPwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "for i in df.columns[df.columns!='class']:\n",
    "    sns.distplot(df[i],hist=False,)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the dimensions of the data are not really known to us, it would be wise to standardize the data using z scores before we \n",
    "#go for any clustering methods. You can use zscore function to do this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "interest_df = df.drop('class', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import zscore\n",
    "interest_df_z = interest_df.apply(zscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.163231</td>\n",
       "      <td>0.520408</td>\n",
       "      <td>0.060669</td>\n",
       "      <td>0.264970</td>\n",
       "      <td>1.283254</td>\n",
       "      <td>0.299721</td>\n",
       "      <td>-0.198517</td>\n",
       "      <td>0.129648</td>\n",
       "      <td>-0.217151</td>\n",
       "      <td>0.766312</td>\n",
       "      <td>-0.397397</td>\n",
       "      <td>-0.339014</td>\n",
       "      <td>0.301676</td>\n",
       "      <td>-0.321192</td>\n",
       "      <td>-0.071523</td>\n",
       "      <td>0.371287</td>\n",
       "      <td>-0.321809</td>\n",
       "      <td>0.171837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.322874</td>\n",
       "      <td>-0.619123</td>\n",
       "      <td>0.124067</td>\n",
       "      <td>-0.836393</td>\n",
       "      <td>-0.599253</td>\n",
       "      <td>0.085785</td>\n",
       "      <td>-0.591720</td>\n",
       "      <td>0.514333</td>\n",
       "      <td>-0.606014</td>\n",
       "      <td>-0.337462</td>\n",
       "      <td>-0.590034</td>\n",
       "      <td>-0.618754</td>\n",
       "      <td>-0.502972</td>\n",
       "      <td>-0.053505</td>\n",
       "      <td>0.538425</td>\n",
       "      <td>0.147109</td>\n",
       "      <td>0.003400</td>\n",
       "      <td>0.442318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.256966</td>\n",
       "      <td>0.845988</td>\n",
       "      <td>1.518823</td>\n",
       "      <td>1.187734</td>\n",
       "      <td>0.530251</td>\n",
       "      <td>0.299721</td>\n",
       "      <td>1.162569</td>\n",
       "      <td>-1.152637</td>\n",
       "      <td>0.949438</td>\n",
       "      <td>0.697326</td>\n",
       "      <td>1.111591</td>\n",
       "      <td>1.122486</td>\n",
       "      <td>1.415804</td>\n",
       "      <td>0.080339</td>\n",
       "      <td>1.555006</td>\n",
       "      <td>-0.413338</td>\n",
       "      <td>-0.159204</td>\n",
       "      <td>0.036596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.079822</td>\n",
       "      <td>-0.619123</td>\n",
       "      <td>-0.002729</td>\n",
       "      <td>-0.300595</td>\n",
       "      <td>0.153750</td>\n",
       "      <td>0.085785</td>\n",
       "      <td>-0.742952</td>\n",
       "      <td>0.642562</td>\n",
       "      <td>-0.606014</td>\n",
       "      <td>-0.337462</td>\n",
       "      <td>-0.911095</td>\n",
       "      <td>-0.738643</td>\n",
       "      <td>-1.462359</td>\n",
       "      <td>-1.258099</td>\n",
       "      <td>-0.071523</td>\n",
       "      <td>-0.301249</td>\n",
       "      <td>1.629444</td>\n",
       "      <td>1.524243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.052030</td>\n",
       "      <td>-0.130753</td>\n",
       "      <td>-0.763506</td>\n",
       "      <td>1.068668</td>\n",
       "      <td>5.173770</td>\n",
       "      <td>9.285029</td>\n",
       "      <td>-0.591720</td>\n",
       "      <td>0.514333</td>\n",
       "      <td>-0.606014</td>\n",
       "      <td>-0.268476</td>\n",
       "      <td>1.689501</td>\n",
       "      <td>-0.647299</td>\n",
       "      <td>0.425468</td>\n",
       "      <td>7.307905</td>\n",
       "      <td>0.538425</td>\n",
       "      <td>-0.189159</td>\n",
       "      <td>-1.460039</td>\n",
       "      <td>-1.721531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "0     0.163231     0.520408              0.060669      0.264970   \n",
       "1    -0.322874    -0.619123              0.124067     -0.836393   \n",
       "2     1.256966     0.845988              1.518823      1.187734   \n",
       "3    -0.079822    -0.619123             -0.002729     -0.300595   \n",
       "4    -1.052030    -0.130753             -0.763506      1.068668   \n",
       "\n",
       "   pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "0              1.283254                 0.299721      -0.198517   \n",
       "1             -0.599253                 0.085785      -0.591720   \n",
       "2              0.530251                 0.299721       1.162569   \n",
       "3              0.153750                 0.085785      -0.742952   \n",
       "4              5.173770                 9.285029      -0.591720   \n",
       "\n",
       "   elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "0       0.129648               -0.217151                   0.766312   \n",
       "1       0.514333               -0.606014                  -0.337462   \n",
       "2      -1.152637                0.949438                   0.697326   \n",
       "3       0.642562               -0.606014                  -0.337462   \n",
       "4       0.514333               -0.606014                  -0.268476   \n",
       "\n",
       "   scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "0        -0.397397          -0.339014                   0.301676   \n",
       "1        -0.590034          -0.618754                  -0.502972   \n",
       "2         1.111591           1.122486                   1.415804   \n",
       "3        -0.911095          -0.738643                  -1.462359   \n",
       "4         1.689501          -0.647299                   0.425468   \n",
       "\n",
       "   scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "0                    -0.321192       -0.071523          0.371287   \n",
       "1                    -0.053505        0.538425          0.147109   \n",
       "2                     0.080339        1.555006         -0.413338   \n",
       "3                    -1.258099       -0.071523         -0.301249   \n",
       "4                     7.307905        0.538425         -0.189159   \n",
       "\n",
       "   skewness_about.2  hollows_ratio  \n",
       "0         -0.321809       0.171837  \n",
       "1          0.003400       0.442318  \n",
       "2         -0.159204       0.036596  \n",
       "3          1.629444       1.524243  \n",
       "4         -1.460039      -1.721531  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interest_df_z.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "sc = StandardScaler()\n",
    "X_std = pd.DataFrame(sc.fit_transform(interest_df),columns=interest_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.163231</td>\n",
       "      <td>0.520408</td>\n",
       "      <td>0.060669</td>\n",
       "      <td>0.264970</td>\n",
       "      <td>1.283254</td>\n",
       "      <td>0.299721</td>\n",
       "      <td>-0.198517</td>\n",
       "      <td>0.129648</td>\n",
       "      <td>-0.217151</td>\n",
       "      <td>0.766312</td>\n",
       "      <td>-0.397397</td>\n",
       "      <td>-0.339014</td>\n",
       "      <td>0.301676</td>\n",
       "      <td>-0.321192</td>\n",
       "      <td>-0.071523</td>\n",
       "      <td>0.371287</td>\n",
       "      <td>-0.321809</td>\n",
       "      <td>0.171837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.322874</td>\n",
       "      <td>-0.619123</td>\n",
       "      <td>0.124067</td>\n",
       "      <td>-0.836393</td>\n",
       "      <td>-0.599253</td>\n",
       "      <td>0.085785</td>\n",
       "      <td>-0.591720</td>\n",
       "      <td>0.514333</td>\n",
       "      <td>-0.606014</td>\n",
       "      <td>-0.337462</td>\n",
       "      <td>-0.590034</td>\n",
       "      <td>-0.618754</td>\n",
       "      <td>-0.502972</td>\n",
       "      <td>-0.053505</td>\n",
       "      <td>0.538425</td>\n",
       "      <td>0.147109</td>\n",
       "      <td>0.003400</td>\n",
       "      <td>0.442318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.256966</td>\n",
       "      <td>0.845988</td>\n",
       "      <td>1.518823</td>\n",
       "      <td>1.187734</td>\n",
       "      <td>0.530251</td>\n",
       "      <td>0.299721</td>\n",
       "      <td>1.162569</td>\n",
       "      <td>-1.152637</td>\n",
       "      <td>0.949438</td>\n",
       "      <td>0.697326</td>\n",
       "      <td>1.111591</td>\n",
       "      <td>1.122486</td>\n",
       "      <td>1.415804</td>\n",
       "      <td>0.080339</td>\n",
       "      <td>1.555006</td>\n",
       "      <td>-0.413338</td>\n",
       "      <td>-0.159204</td>\n",
       "      <td>0.036596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.079822</td>\n",
       "      <td>-0.619123</td>\n",
       "      <td>-0.002729</td>\n",
       "      <td>-0.300595</td>\n",
       "      <td>0.153750</td>\n",
       "      <td>0.085785</td>\n",
       "      <td>-0.742952</td>\n",
       "      <td>0.642562</td>\n",
       "      <td>-0.606014</td>\n",
       "      <td>-0.337462</td>\n",
       "      <td>-0.911095</td>\n",
       "      <td>-0.738643</td>\n",
       "      <td>-1.462359</td>\n",
       "      <td>-1.258099</td>\n",
       "      <td>-0.071523</td>\n",
       "      <td>-0.301249</td>\n",
       "      <td>1.629444</td>\n",
       "      <td>1.524243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.052030</td>\n",
       "      <td>-0.130753</td>\n",
       "      <td>-0.763506</td>\n",
       "      <td>1.068668</td>\n",
       "      <td>5.173770</td>\n",
       "      <td>9.285029</td>\n",
       "      <td>-0.591720</td>\n",
       "      <td>0.514333</td>\n",
       "      <td>-0.606014</td>\n",
       "      <td>-0.268476</td>\n",
       "      <td>1.689501</td>\n",
       "      <td>-0.647299</td>\n",
       "      <td>0.425468</td>\n",
       "      <td>7.307905</td>\n",
       "      <td>0.538425</td>\n",
       "      <td>-0.189159</td>\n",
       "      <td>-1.460039</td>\n",
       "      <td>-1.721531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "0     0.163231     0.520408              0.060669      0.264970   \n",
       "1    -0.322874    -0.619123              0.124067     -0.836393   \n",
       "2     1.256966     0.845988              1.518823      1.187734   \n",
       "3    -0.079822    -0.619123             -0.002729     -0.300595   \n",
       "4    -1.052030    -0.130753             -0.763506      1.068668   \n",
       "\n",
       "   pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "0              1.283254                 0.299721      -0.198517   \n",
       "1             -0.599253                 0.085785      -0.591720   \n",
       "2              0.530251                 0.299721       1.162569   \n",
       "3              0.153750                 0.085785      -0.742952   \n",
       "4              5.173770                 9.285029      -0.591720   \n",
       "\n",
       "   elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "0       0.129648               -0.217151                   0.766312   \n",
       "1       0.514333               -0.606014                  -0.337462   \n",
       "2      -1.152637                0.949438                   0.697326   \n",
       "3       0.642562               -0.606014                  -0.337462   \n",
       "4       0.514333               -0.606014                  -0.268476   \n",
       "\n",
       "   scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "0        -0.397397          -0.339014                   0.301676   \n",
       "1        -0.590034          -0.618754                  -0.502972   \n",
       "2         1.111591           1.122486                   1.415804   \n",
       "3        -0.911095          -0.738643                  -1.462359   \n",
       "4         1.689501          -0.647299                   0.425468   \n",
       "\n",
       "   scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "0                    -0.321192       -0.071523          0.371287   \n",
       "1                    -0.053505        0.538425          0.147109   \n",
       "2                     0.080339        1.555006         -0.413338   \n",
       "3                    -1.258099       -0.071523         -0.301249   \n",
       "4                     7.307905        0.538425         -0.189159   \n",
       "\n",
       "   skewness_about.2  hollows_ratio  \n",
       "0         -0.321809       0.171837  \n",
       "1          0.003400       0.442318  \n",
       "2         -0.159204       0.036596  \n",
       "3          1.629444       1.524243  \n",
       "4         -1.460039      -1.721531  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_std.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find out the linkage matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use ward as linkage metric and distance as Eucledian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(812, 4)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### generate the linkage matrix\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "Z = linkage(X_std, 'ward', metric='euclidean')\n",
    "Z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.27000000e+02, 3.70000000e+02, 1.31740844e-01, 2.00000000e+00],\n",
       "       [4.78000000e+02, 7.52000000e+02, 3.94147914e-01, 2.00000000e+00],\n",
       "       [4.48000000e+02, 5.25000000e+02, 5.40896298e-01, 2.00000000e+00],\n",
       "       ...,\n",
       "       [1.60100000e+03, 1.62100000e+03, 5.12738877e+01, 2.82000000e+02],\n",
       "       [1.61700000e+03, 1.62000000e+03, 5.35328071e+01, 5.31000000e+02],\n",
       "       [1.62200000e+03, 1.62300000e+03, 1.07910132e+02, 8.13000000e+02]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z[:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the dendrogram for the consolidated dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 10))\n",
    "dendrogram(Z)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recreate the dendrogram for last 12 merged clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the truncated dendrogram, find out the optimal distance between clusters which u want to use an input for clustering data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hint: Use truncate_mode='lastp' attribute in dendrogram function to arrive at dendrogram\n",
    "dendrogram(\n",
    "    Z,\n",
    "    truncate_mode='lastp',  # show only the last p merged clusters\n",
    "    p=3,  # show only the last p merged clusters\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_d = 52"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use this distance measure(max_d) and fcluster function to cluster the data into 3 different groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 1, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 1, 2, 1, 1, 3, 2, 3, 3, 1,\n",
       "       3, 2, 1, 1, 2, 3, 3, 3, 1, 3, 2, 1, 1, 2, 1, 2, 3, 3, 1, 3, 3, 2,\n",
       "       3, 3, 2, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 2, 2, 1, 2, 1, 3, 1, 1,\n",
       "       3, 2, 3, 1, 3, 2, 3, 1, 3, 2, 3, 1, 3, 2, 1, 2, 1, 3, 1, 3, 2, 1,\n",
       "       2, 2, 1, 2, 1, 3, 3, 3, 1, 1, 3, 2, 1, 3, 3, 2, 2, 3, 3, 1, 1, 3,\n",
       "       2, 3, 3, 3, 3, 3, 2, 3, 3, 1, 1, 3, 3, 2, 1, 1, 3, 3, 2, 3, 3, 1,\n",
       "       2, 3, 1, 3, 3, 3, 3, 1, 1, 3, 1, 3, 1, 3, 3, 3, 2, 1, 3, 3, 1, 1,\n",
       "       3, 1, 2, 2, 1, 1, 3, 1, 3, 3, 3, 3, 2, 1, 2, 3, 2, 1, 1, 3, 3, 1,\n",
       "       3, 3, 3, 1, 3, 2, 1, 2, 2, 2, 3, 3, 1, 1, 3, 3, 3, 2, 1, 3, 3, 3,\n",
       "       1, 3, 2, 1, 3, 3, 1, 3, 2, 2, 3, 1, 3, 1, 2, 2, 2, 2, 1, 3, 3, 3,\n",
       "       1, 2, 3, 3, 2, 1, 3, 2, 3, 3, 1, 2, 1, 3, 3, 3, 1, 3, 3, 1, 1, 2,\n",
       "       3, 3, 3, 1, 2, 3, 3, 2, 2, 1, 3, 1, 3, 2, 2, 1, 3, 1, 2, 2, 1, 3,\n",
       "       3, 1, 2, 3, 1, 3, 3, 1, 3, 1, 2, 3, 3, 1, 3, 3, 3, 2, 3, 1, 1, 1,\n",
       "       3, 3, 3, 1, 2, 2, 3, 1, 2, 1, 1, 2, 1, 3, 2, 3, 3, 3, 3, 1, 1, 2,\n",
       "       1, 1, 3, 1, 3, 3, 3, 2, 2, 1, 1, 1, 3, 3, 3, 1, 2, 3, 2, 3, 1, 3,\n",
       "       1, 3, 1, 1, 1, 3, 3, 2, 1, 3, 2, 2, 3, 1, 3, 3, 2, 1, 1, 2, 2, 1,\n",
       "       2, 1, 2, 1, 3, 3, 1, 3, 1, 1, 2, 3, 3, 3, 1, 3, 3, 3, 3, 1, 3, 1,\n",
       "       3, 1, 3, 2, 3, 3, 3, 3, 2, 2, 3, 3, 1, 3, 3, 2, 2, 1, 3, 2, 1, 3,\n",
       "       1, 3, 1, 3, 1, 1, 2, 2, 1, 3, 2, 2, 3, 1, 1, 3, 3, 1, 1, 3, 1, 1,\n",
       "       1, 3, 3, 3, 3, 3, 1, 2, 2, 3, 1, 1, 3, 1, 3, 2, 3, 2, 2, 1, 3, 3,\n",
       "       1, 1, 1, 2, 1, 1, 3, 1, 2, 1, 1, 3, 3, 3, 2, 1, 3, 2, 1, 1, 3, 2,\n",
       "       1, 1, 3, 3, 2, 1, 1, 1, 2, 2, 1, 1, 1, 3, 3, 1, 2, 3, 1, 3, 3, 2,\n",
       "       3, 1, 2, 3, 3, 3, 1, 1, 3, 1, 1, 3, 2, 3, 1, 1, 2, 2, 3, 1, 3, 1,\n",
       "       1, 3, 3, 3, 3, 3, 3, 2, 3, 3, 1, 2, 2, 3, 2, 1, 3, 1, 2, 2, 1, 1,\n",
       "       3, 1, 3, 3, 3, 1, 3, 2, 3, 1, 3, 3, 3, 1, 1, 1, 1, 3, 2, 2, 3, 1,\n",
       "       1, 1, 3, 1, 2, 3, 1, 2, 2, 2, 3, 2, 1, 3, 3, 1, 3, 3, 3, 1, 3, 3,\n",
       "       1, 3, 3, 3, 2, 1, 3, 2, 3, 2, 3, 3, 2, 2, 1, 1, 3, 3, 1, 3, 1, 3,\n",
       "       3, 3, 3, 2, 1, 2, 1, 2, 2, 3, 2, 3, 1, 1, 2, 1, 1, 3, 2, 3, 2, 1,\n",
       "       3, 1, 2, 3, 1, 3, 2, 3, 3, 3, 1, 3, 1, 2, 3, 3, 3, 1, 1, 3, 2, 1,\n",
       "       3, 1, 3, 3, 1, 2, 1, 2, 3, 3, 3, 3, 1, 3, 2, 3, 1, 2, 1, 1, 3, 1,\n",
       "       3, 1, 2, 3, 3, 2, 3, 1, 1, 3, 3, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 3,\n",
       "       1, 1, 3, 1, 1, 3, 1, 3, 1, 3, 3, 1, 3, 2, 1, 1, 1, 3, 1, 2, 2, 1,\n",
       "       1, 1, 3, 1, 3, 3, 1, 3, 2, 3, 2, 3, 1, 3, 2, 3, 3, 3, 3, 1, 3, 2,\n",
       "       3, 1, 1, 3, 1, 1, 2, 3, 3, 1, 3, 2, 1, 1, 2, 3, 3, 1, 1, 1, 2, 1,\n",
       "       3, 1, 1, 2, 2, 1, 3, 1, 3, 2, 3, 1, 1, 3, 2, 3, 1, 1, 3, 3, 2, 3,\n",
       "       3, 1, 2, 3, 1, 2, 2, 1, 2, 3, 3, 2, 3, 3, 1, 1, 1, 2, 1, 3, 1, 1,\n",
       "       3, 1, 1, 3, 3, 3, 3, 1, 3, 2, 1, 3, 3, 3, 3, 3, 3, 3, 1, 3, 2],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "clusters = fcluster(Z, max_d, criterion='distance')\n",
    "clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2634332134378479"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Avg Silhoutte Score\n",
    "\n",
    "from sklearn.metrics import silhouette_score\n",
    "silhouette_score(X_std,clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Silhouette Score is better when closer 1 and worse when closer to -1\n",
    "\n",
    "here, it is not so great."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final dendogram with 'ward linkage'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from scipy.spatial.distance import pdist\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(18, 16))\n",
    "plt.title('Agglomerative Hierarchical Clustering Dendogram')\n",
    "plt.xlabel('sample index')\n",
    "plt.ylabel('Distance')\n",
    "Z = linkage(X_std, 'ward')\n",
    "dendrogram(Z,leaf_rotation=90.0,p=5,color_threshold=52,leaf_font_size=10,truncate_mode='level')\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}