Working with Matrix Plots-Heatmaps and Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matrix Plots with Seaborn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips = sns.load_dataset('tips')\n",
    "flights = sns.load_dataset('flights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949</td>\n",
       "      <td>January</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949</td>\n",
       "      <td>February</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949</td>\n",
       "      <td>March</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949</td>\n",
       "      <td>April</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949</td>\n",
       "      <td>May</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     month  passengers\n",
       "0  1949   January         112\n",
       "1  1949  February         118\n",
       "2  1949     March         132\n",
       "3  1949     April         129\n",
       "4  1949       May         121"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#heat maps will be our primary way to show matrix data.\n",
    "#e.g \n",
    "tc=tips.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20854edfc50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(tc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20854d67128>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#annotation will give you numerical values into heat map. Cmap let's change colors too.\n",
    "sns.heatmap(tc,annot=True,cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>year</th>\n",
       "      <th>1949</th>\n",
       "      <th>1950</th>\n",
       "      <th>1951</th>\n",
       "      <th>1952</th>\n",
       "      <th>1953</th>\n",
       "      <th>1954</th>\n",
       "      <th>1955</th>\n",
       "      <th>1956</th>\n",
       "      <th>1957</th>\n",
       "      <th>1958</th>\n",
       "      <th>1959</th>\n",
       "      <th>1960</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>112</td>\n",
       "      <td>115</td>\n",
       "      <td>145</td>\n",
       "      <td>171</td>\n",
       "      <td>196</td>\n",
       "      <td>204</td>\n",
       "      <td>242</td>\n",
       "      <td>284</td>\n",
       "      <td>315</td>\n",
       "      <td>340</td>\n",
       "      <td>360</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>118</td>\n",
       "      <td>126</td>\n",
       "      <td>150</td>\n",
       "      <td>180</td>\n",
       "      <td>196</td>\n",
       "      <td>188</td>\n",
       "      <td>233</td>\n",
       "      <td>277</td>\n",
       "      <td>301</td>\n",
       "      <td>318</td>\n",
       "      <td>342</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>132</td>\n",
       "      <td>141</td>\n",
       "      <td>178</td>\n",
       "      <td>193</td>\n",
       "      <td>236</td>\n",
       "      <td>235</td>\n",
       "      <td>267</td>\n",
       "      <td>317</td>\n",
       "      <td>356</td>\n",
       "      <td>362</td>\n",
       "      <td>406</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>129</td>\n",
       "      <td>135</td>\n",
       "      <td>163</td>\n",
       "      <td>181</td>\n",
       "      <td>235</td>\n",
       "      <td>227</td>\n",
       "      <td>269</td>\n",
       "      <td>313</td>\n",
       "      <td>348</td>\n",
       "      <td>348</td>\n",
       "      <td>396</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>121</td>\n",
       "      <td>125</td>\n",
       "      <td>172</td>\n",
       "      <td>183</td>\n",
       "      <td>229</td>\n",
       "      <td>234</td>\n",
       "      <td>270</td>\n",
       "      <td>318</td>\n",
       "      <td>355</td>\n",
       "      <td>363</td>\n",
       "      <td>420</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>135</td>\n",
       "      <td>149</td>\n",
       "      <td>178</td>\n",
       "      <td>218</td>\n",
       "      <td>243</td>\n",
       "      <td>264</td>\n",
       "      <td>315</td>\n",
       "      <td>374</td>\n",
       "      <td>422</td>\n",
       "      <td>435</td>\n",
       "      <td>472</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>148</td>\n",
       "      <td>170</td>\n",
       "      <td>199</td>\n",
       "      <td>230</td>\n",
       "      <td>264</td>\n",
       "      <td>302</td>\n",
       "      <td>364</td>\n",
       "      <td>413</td>\n",
       "      <td>465</td>\n",
       "      <td>491</td>\n",
       "      <td>548</td>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>148</td>\n",
       "      <td>170</td>\n",
       "      <td>199</td>\n",
       "      <td>242</td>\n",
       "      <td>272</td>\n",
       "      <td>293</td>\n",
       "      <td>347</td>\n",
       "      <td>405</td>\n",
       "      <td>467</td>\n",
       "      <td>505</td>\n",
       "      <td>559</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>136</td>\n",
       "      <td>158</td>\n",
       "      <td>184</td>\n",
       "      <td>209</td>\n",
       "      <td>237</td>\n",
       "      <td>259</td>\n",
       "      <td>312</td>\n",
       "      <td>355</td>\n",
       "      <td>404</td>\n",
       "      <td>404</td>\n",
       "      <td>463</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>119</td>\n",
       "      <td>133</td>\n",
       "      <td>162</td>\n",
       "      <td>191</td>\n",
       "      <td>211</td>\n",
       "      <td>229</td>\n",
       "      <td>274</td>\n",
       "      <td>306</td>\n",
       "      <td>347</td>\n",
       "      <td>359</td>\n",
       "      <td>407</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>104</td>\n",
       "      <td>114</td>\n",
       "      <td>146</td>\n",
       "      <td>172</td>\n",
       "      <td>180</td>\n",
       "      <td>203</td>\n",
       "      <td>237</td>\n",
       "      <td>271</td>\n",
       "      <td>305</td>\n",
       "      <td>310</td>\n",
       "      <td>362</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>118</td>\n",
       "      <td>140</td>\n",
       "      <td>166</td>\n",
       "      <td>194</td>\n",
       "      <td>201</td>\n",
       "      <td>229</td>\n",
       "      <td>278</td>\n",
       "      <td>306</td>\n",
       "      <td>336</td>\n",
       "      <td>337</td>\n",
       "      <td>405</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year       1949  1950  1951  1952  1953  1954  1955  1956  1957  1958  1959  \\\n",
       "month                                                                         \n",
       "January     112   115   145   171   196   204   242   284   315   340   360   \n",
       "February    118   126   150   180   196   188   233   277   301   318   342   \n",
       "March       132   141   178   193   236   235   267   317   356   362   406   \n",
       "April       129   135   163   181   235   227   269   313   348   348   396   \n",
       "May         121   125   172   183   229   234   270   318   355   363   420   \n",
       "June        135   149   178   218   243   264   315   374   422   435   472   \n",
       "July        148   170   199   230   264   302   364   413   465   491   548   \n",
       "August      148   170   199   242   272   293   347   405   467   505   559   \n",
       "September   136   158   184   209   237   259   312   355   404   404   463   \n",
       "October     119   133   162   191   211   229   274   306   347   359   407   \n",
       "November    104   114   146   172   180   203   237   271   305   310   362   \n",
       "December    118   140   166   194   201   229   278   306   336   337   405   \n",
       "\n",
       "year       1960  \n",
       "month            \n",
       "January     417  \n",
       "February    391  \n",
       "March       419  \n",
       "April       461  \n",
       "May         472  \n",
       "June        535  \n",
       "July        622  \n",
       "August      606  \n",
       "September   508  \n",
       "October     461  \n",
       "November    390  \n",
       "December    432  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#flights data revisited. Need to get the data into matrix form\n",
    "fp=flights.pivot_table(index='month',columns='year',values='passengers')\n",
    "fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20854c90320>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#use heatmap to quickly visualize data\n",
    "sns.heatmap(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2085819d9e8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(fp,annot=False,cmap='coolwarm',linecolor='white',linewidths=1) #adding line widths to make data easier to read"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cluster Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x208582e7630>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Use heirchical clustering based on smilarity. Here most similar months and years are clustered together.\n",
    "sns.clustermap(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x20858acbb00>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8ttm/omffHWu87vHAAcCfAp9cR42d8ppFSZI0zLYAftUExd2Bh03gmOuBXZvtvdaY66aqugd4FbC2m1m+BDwXeCLw9UGLnk6GRUmSNHSSzGN05fBzwBOS/JjRVcafTuDwI4A3JPkesFVP/0eB/ZP8gNFT0GuuJv6vqvoDcB5wSlWtWr93MT08DS1JkobRo4Frqupm4CktY3ZavVFVR/Rs/xTYuWfcO5r+q9bo/4emfzmwvHfi5saWJwN7r+8bmC6uLEqSpKGS5PWM3qn8jo5ef0fgauDcJmDOaK4sSpKkoVJVxwHHdfj6VwDbdvX6g3JlUZIkSa0Mi5IkSWplWJQkSVIrw6IkSZJaGRYlSZLUyrAoSZKkVoZFSZIktTIsSpIkqZVhUZIkSa1SVV3XoAlIsrSqlk7SdP7SJUnDIF0XMBe4sihJkqRWhkVJkiS1MixKkiSp1byuC1B39tj3R12XwLkn79bX/j/7X9xRJWO+fuIufe1DP3ZnR5WMOeINm/a133zM7zuqZMwxb968r/3yt17fTSE9Tvngor72G/7t1m4K6fGxv9+yr33p1b/sqJIxj3nEA/rav76i+78FW+/Y/7fgGz+5u6NKxjznsZv0tb97xe0dVTLmaTsu7Gt/8D+6vwT9rS/uvyzw1B/c01ElY/Z+smthk8mfpiRJkloZFiVJktTKsChJkqRWhkVJkiS1MixKkiSplWFRkiRJrfzonA2Q5HBgwTS93KJpeh1JkqT/ZVjcMAsm8fua1yrJtLyOJElSL09DS5IkqZVhUZIkSa0Mi5IkSWplWJQkSUMpyaokF/c8Fq1l7OIkX56+6mYOb3CRJEnD6q6q2mUyJ0yycVWt2oDj51XVysmsaUO5sihJktRIsnGSf0vyn0kuSfI3Pbvvk+RLSa5IclySjZpjbk/y7iQ/BJ6S5PokWzX7npBkebO9W5LvJbmoeX5k039AklOTnAWck+QzSfbsqelzSV40XT+DNbmyKEmShtW9k1zcbF9XVS8BDgRuq6onJtkE+G6Sc5oxuwE7Av8NfA14KXAasBlwWVW9CyBJ2+v9FHhGVa1M8mzgX4GXNfueAuxcVbckeSbwFuCMJFsATwX2n7R3PSDD4iy0vh8GPl2fCSlJ0kyQZAmwpKdrpKpGetrjnYb+C2DnJHs17S2A7YE/AD+qqmubuU8Cns5oWFwFfHECJW0BnJhke6CA+T37vlFVtwBU1beSfCTJ/RkNpF/s8tS0YXF2mrYPA5ckabZqguHIOgf2C/DGqvp6X2eymNGA1/cSzfOKNa5TXMnYpX69izvvAc6rqpc0N9Ms79l3xxpzfwZ4JbAv8JqB3sEk85pFSZKkMV8H3pBkPkCSHZJs1uzbLcnDm2sV9wHOb5njemDXZvtlPf1bAP/TbB+wjjpOAA4GqKrLB6h/0hkWJUmSxhwPXAFcmOQy4OOMnYn9PrAMuAy4DvhSyxz/DByT5DuMnqJe7QPA+5J8F9h4bUVU1S+BK4FPref7mDSehpYkSUOpqhaO03cP8Pbm0Ws5/aeNW+epqu8AO4wz7vtr9L+z6T+B0ZXE/5VkU0avlTxpbe9hOriyKEmSNIM0d0r/FPhwVd3WdT2uLEqSJM0gVfX/AQ/tuo7VXFmUJElSK8OiJEmSWhkWJUmS1MqwKEmSpFaGRUmSJLUyLEqSJKmVYVGSJEmtDIuSJElqZViUJElSq1RV1zXMWkmWVtXS6X6tSXhdf+mSpGGQrguYC1xZlCRJUiu/G3r2WJFkabO9qMM6JEnSEDEszhJVtWz1dk9olCRJmlKGxSH29Bd+q+sSOP+sZ/a1F+/1/Y4qGbP8tKf0td/wb7d2VMmYj/39ln3tw/99RUeVjFn2ugV97b3efG1HlYw57Zht+9qvfe/NHVUy5vh/3Kqvfc213f+cttu2/+f06yt+1FElY7becbe+9rmXdv/f+B6P6f9vfPlld3VUyZjFO927r330md1fgn7wi/ovCzzpu93XtN/TvFRxMnnNoiRJkloZFiVJktTKsChJkqRWhkVJkiS1MixKkiSplWFRkiRJrQyLkiRJamVYlCRJUivDoiRJkloZFiVJktTKsChJkqRWhkVJkjSUklSSD/a0D02ytKNabu/idSfCsChJkobV3cBLk2zVdSEbIsm8qZzfsChJkobVSmAEeMuaO5I8LMm5SS5pnh+aZIsk1yfZqBmzaZIbksxPsl2SryW5IMl3kjyqGXNCko8lOS/JtUmemeSTSa5McsIar/nBJBc2r7d107e2eY9Mch7w/qn8IRkWJUnSMPsI8MokW6zRfyzw6araGfgc8KGqug34CfDMZswLga9X1R8ZDZ1vrKpdgUOBj/bM9SfAsxgNpWcBRwGPBh6TZJdmzGbAhVX1eOBbwD81/Wubdwfg2VX11g35AazLlC5basqsWJ9rKqpq4GMkSZqtkiwBlvR0jVTVSO+Yqvpdkk8DbwLu6tn1FOClzfZngA80218A9gHOA/YFPppkIfBU4NQkq4/fpGeus6qqklwK/LKqLm3quxxYBFwM3NPMDfBZ4PQJzHtqVa2awI9igxgWZ6GqWtZ1DZIkzXRNMBxZ50A4GrgQ+NTapmuezwTel+S+wK7ANxldFby1qnZpOfbu5vmenu3V7bYsVoyeAV7bvHespd5J42loSZI01KrqFuAU4MCe7u8xunII8Erg/Gbs7cCPgGOAL1fVqqr6HXBdkr0BMuqxA5axEbBXs/0K4PxJmneDGRYlSZLgg0DvXdFvAl6d5BLgVcCbe/Z9Afgrxk4bw2igPDDJT4DLgT0HfP07gEcnuYDR6xvfPUnzbjBPQ0uSpKFUVQt7tn8JbNrTvp7R0DbecacBWaPvOuC544w9YI05d2rZt7qWdw4671RzZVGSJEmtDIuSJElqZViUJElSK8OiJEmSWhkWJUmS1MqwKEmSpFaGRUmSJLXa4M9ZTHI4sGASapmNFnVdgCRJ0lSajA/lXlBVSydhnlknydKua5AkSZpKnoaWJElSK8OiJEmSWqWqNmyCZOkwn4aepe99w37pkiTNDln3EK2LK4uSJElqZViUJElSK8OiJEmSWk3GR+dolnr+ay/rugS+cvxOfe2XHHRVR5WM+dKx2/e1jz6z+0s8D35R/2U3R57RfU2H7Nlf0+vf/9uOKhlz3Nv+pK/9lmNv76iSMUcdtLCvfetF3+yokjFbPu5Zfe3f/+grHVUyZvPdnt/X/tk1P++okjE7bPfQvvY1117bUSVjttt22772Z7/T/d+Cv/rz/r8F372i+//dPW3HhesepAlzZVGSJEmtDIuSJElqZViUJElSK8OiJEmSWhkWJUmS1MqwKEmSpFaGRUmSJLUyLEqSJKmVYVGSJEmtDIuSJElqZViUJElSK8OiJEkaSkkqyWd62vOS/DrJlydp/uVJnjAZc3XJsChJkobVHcBOSe7dtJ8D/M8gEySZN+lVzTCGRUmSNMy+Cjy/2d4POGn1jiS7Jflekoua50c2/QckOTXJWcA5Td9hSS5N8pMky3rm3zvJj5L8LMmfT9N7mlRzPg1LkiStxcnAu5pTzzsDnwRWh7qfAs+oqpVJng38K/CyZt9TgJ2r6pYkfwm8GHhSVd2Z5L4988+rqt2SPA/4J+DZ0/CeJpVhcQZLcjiwYLLmq6qlkzWXJEkzXZIlwJKerpGqGukdU1WXJFnE6Kri2WtMsQVwYpLtgQLm9+z7RlXd0mw/G/hUVd3ZzHlLz7jTm+cLgEXr/WY6ZFic2RYY8CRJWj9NMBxZ50A4EzgCWAzcr6f/PcB5VfWSJlAu79l3R892GA2T47m7eV7FLM1dXrMoSZKG3SeBd1fVpWv0b8HYDS8HrOX4c4DXJNkUYI3T0LOeYVGSJA21qrqxqo4ZZ9cHgPcl+S6w8VqO/xqjq5M/TnIxcOjUVNqNWbkcKkmStKGqauE4fctpTjdX1feBHXp2v7PpPwE4YY3jlgHL1uhb3LN9M7P0mkVXFiVJktTKsChJkqRWhkVJkiS1MixKkiSplWFRkiRJrQyLkiRJamVYlCRJUivDoiRJkloZFiVJktTKsChJkqRWhkVJkiS1SlVt2ATJ0qpaOjnlzC5T/d6ncP4N+6VLkjQ7pOsC5gJXFiVJktTKsChJkqRWhkVJkiS1mtd1AerOHvv+qOsSOPfk3fraL/7bn3VUyZj/+OgOfe23jdzVUSVj3r/k3n3tD3zxno4qGXPYy/r/rXnA0l92VMmYE5Y+oK/91o/e0VElYz74t5v1tW+9eHk3hfTYcpfFfe07v31KN4X02PQZL+9rX3TVzR1VMuZx22/V1774ql93VMmYXbbfuq996g+6/1uw95P7/xace+mKjioZs8djFnRdwpziyqIkSZJaGRYlSZLUyrAoSZKkVoZFSZIktTIsSpIkqZVhUZIkSa0Mi5IkSWplWJQkSVIrw6IkSZJaGRYlSZLUyrAoSZKkVoZFSZI0lJKsSnJxksuT/CTJIUk6yUZJbu/idSdiXtcFSJIkdeSuqtoFIMn9gc8DWwD/1GlVA0oyr6pWTtX8hsUNsyLJ0imcf9EUzi1JkhpV9askS4D/bP6/fSNgGbAY2AT4SFV9HCDJYcCrgHuAr1bV4Um2Az4CbA3cCbyuqn6a5ATgLuBRwMOAVwP7A08BflhVB6yuIckHgd2B3wL7VtWv1zHvLcDjgAuBt07NT8awuEGqatlUzj/FQVSSJPWoqmub09D3B/YEbquqJybZBPhuknMYDX0vBp5UVXcmuW9z+Ajw+qq6KsmTgI8Cz2r2/Umz/SLgLOBpwGsZDaa7VNXFwGbAhVX11iTvYnR186B1zLsD8OyqWjV1PxXD4oyX5HBgwWTMVVVLJ2MeSZJmg2alcElP10hVjazrsOb5L4Cdk+zVtLcAtgeeDXyqqu4EqKpbkiwEngqcmqw+nE165jyrqirJpcAvq+rSpr7LGT2LeDGjq5RfaMZ/Fjh9AvOeOtVBEQyLs8ECQ54kSYNrguG6wuH/SrItsAr4FaOh8Y1V9fU1xjwXqDUO3Qi4dfX1j+O4u3m+p2d7dbsti9UE5r2jpX9SeTe0JEkaekm2Bo4Djq2qAr4OvCHJ/Gb/Dkk2A84BXpNk06b/vlX1O+C6JHs3fUny2AFL2AhYvYr5CuD8SZp3g7myKEmShtW9k1wMzAdWAp8Bjmz2Hc/oKeILM3oO+NfAi6vqa0l2AX6c5A/A2cDbgVcCH0vyjma+k4GfDFDLHcCjk1wA3Abs0/Rv6LwbzLAoSZKGUlVtvJZ99zAaAt8+zr5ljN4p3dt3HfDcccYe0LN9PbBTy76FzeY7B513qnkaWpIkSa0Mi5IkSWplWJQkSVIrw6IkSZJaGRYlSZLUyrAoSZKkVoZFSZIktTIsSpIkqZVhUZIkSa0Mi5IkSWplWJQkSVKrVNWGTZAsraqlk1OOeiVZCjAFP98N+6VLkjQ7pOsC5gJXFiVJktTKsChJkqRWhkVJkiS1mtd1AerOc155Qdcl8I3P7drX/j/7X9xRJWO+fuIufe0PfPGejioZc9jL+v9dd+QZ3V92esie/ZcCvfa9N3dUyZjj/3GrvvZBR97WUSVjjj1ki7727T88q6NKxix80gv72itOOaKjSsYsePmhfe2fXfPzjioZs8N2D+1rX3vNNR1VMmbb7bbra5/03e7/Fuz3tP6/Bd+94vaOKhnztB0Xdl3CnOLKoiRJkloZFiVJktTKsChJkqRWhkVJkiS1MixKkiSplWFRkiRJrQyLkiRJamVYlCRJUivDoiRJkloZFiVJktTKsDizrQAWdV2EJEkaXobFGayqlgHXd12HJElzUZJK8pme9rwkv07y5S7rmmkMi5IkaVjdAeyU5N5N+znA/3RYz4xkWJQkScPsq8Dzm+39gJNW70iyW5LvJbmoeX5k0/+dJLv0jPtukp2nteppZFiUJEnD7GRg3yQLgJ2BH/bs+ynwjKp6HPAu4F+b/uOBAwCS7ABsUlWXTFvF02xe1wVo/SQ5HFgwyDFVtXRqqpEkaeZJsgRY0tM1UlUjvWOq6pIkixhdVTx7jSm2AE5Msj1QwPym/1TgnUn+HngNcMKkFz+DGBZnrwWGP0mS2jXBcGSdA+FM4AhgMXC/nv73AOdVBZlmAAAeXUlEQVRV1UuaQLm8mffOJN8A9gReDjxh0oqegQyLkiRp2H0SuK2qLk2yuKd/C8ZueDlgjWOOB84CvlNVt0x5hR3ymkVJkjTUqurGqjpmnF0fAN6X5LvAxmsccwHwO+BT01Bip1xZlCRJQ6mqFo7Tt5yx083fB3bo2f3O1RtJHsToots5U1rkJEryVEa/7ON/819VfXpdxxkWJUmSBpDkr4H3AodU1T1d1zMRzYePbwdcDKxqugswLEqSJE2mZjVunSFrhnkCsGNV1aAHes2iJEnS3HcZ8Kfrc6Ari5IkSXNUkrMYPd28OXBFkh8Bd6/eX1UvWtcchkVJkqS564gNncCwKEmSNEdV1bcAkry/qt7Wuy/J+4FvrWsOr1mUJEma+54zTt9fTuRAVxYlSZLmqCRvAP4W2DbJJT27Nge+N5E5DIuSJElz1+eBrwLvAw7v6f/9RL+m0LAoSZI0R1XVbcBtwH5JNgYewGj+W5hkYVX9fF1zGBYlSZLmuCQHAUuBXwKrv3WmgJ3Xeex6fJD3mi++tKqWbtAkatX2893An/uG/dIlSZod0nUBM0WSq4EnVdVvBj3Wu6ElSZLmvhsYPR09ME9DS5IkzX3XAsuTfIX+b3A5cl0HGhYlSZLmvp83j3s1jwkzLA6x573m0q5L4OxPPqav/cK/ubKjSsac9fE/62sfeUb3l3gesmf/ZTfvP+2elpHT52179V/Fsv+7/m9HlYw58d1/2tf+uyNu7aiSMR85dMu+9m8um9DHmk2p++301L72ijOO7aiSMQv2PKivfcXVv+iokjE7PuJBfe2ZWNPnz+/+79Mrnt7/9+nbl9/RUSVjnvHozbouYcapqn8GSLL5aLNun+ixXrMoSZI0xyXZKclFwGXA5UkuSPLoiRxrWJQkSZr7RoBDquphVfUw4K3Av0/kQMOiJEnS3LdZVZ23ulFVy4EJna/3mkVJkqS579ok7wQ+07T/CrhuIge6sihJkjT3vQbYGvgicDqwFXDARA40LEqSJM192wHbMJr95gN7AN+eyIGehp75ViRZOk7/ommuQ5IkzV6fAw5l9G7ogT5/zbA4w1XVsvH6WwKkJEnSeH5dVWetz4GGRUmSpLnvn5IcD5xL/9f9nb6uAw2LkiRJc9+rgUcxer3i6tPQxejNLmtlWJQkSZr7HltVj1n3sP+Xd0NLkqShleQlSSrJo9bj2OOT7NhsX59kq8mvcNL8YHWtgzIsSpKkYbYfcD6w7yAHJdm4ql5bVVdMTVmT7unAxUn+K8klSS5NcslEDjQsSpKkoZRkIfA04ECasJhkcZJvJ/lSkiuSHJdko2bf7UneneSHwFOSLE/yhO7ewUCeC2wP/AXwQuAFzfM6ec2iJEkaVi8GvlZVP0tyS5LHN/27ATsC/w18DXgpcBqj36V8WVW9CyBJByWvn6r67/U91rA4iyU5HFgw0fFVtXTqqpEkaWZJsgRY0tM1UlUjPe39gKOb7ZOb9leAH1XVtc0cJzF6Cvc0YBWjX5c3VAyLs9sCA6AkSeNrguHIePuS3A94FrBTkgI2ZvSjZM5unvumap5XVNWqKSp3xvKaRUmSNIz2Aj5dVQ+rqkVVtQ1wHaOriLsleXhzreI+jN4AM7QMi5IkaRjtB3xpjb4vAq8Avg8sY/R7lK8bZ9xQ8TS0JEkaOlW1eJy+DzUfJ3NoVe0zzv6FbXNU1aLJr3JmcGVRkiRJrVxZlCRJalTVcmB5x2XMKK4sSpIkqZVhUZIkSa0Mi5IkSWplWJQkSVIrw6IkSZJaGRYlSZLUyrAoSZKkVoZFSZIktTIsSpIkqVWqasMmSJZW1dLJKUcTlWQpwHr+7Dfsly5J0uyQrguYC1xZlCRJUivD4uy1AljUdRGSJGluMyzOUlW1DLi+6zokSdLcNq/rAtSdZ+/3465L4P876Ql97Re87oqOKhnz5X/fsa/9rhP/0FElY969/7362h/44j0dVTLmsJf1/1vzr995U0eVjPn0ex7Y1z74w7d3VMmYo9+4sK9960Xf7KiSMVs+7ll97RVnHNtRJWMW7HlQX/uKq3/RUSVjdnzEg/raV19zXUeVjHnEdg/va5/y/e7/Frz8Kf1/C869dEVHlYzZ4zELui5hTnFlUZIkSa0Mi5IkSWplWJQkSVIrw6IkSZJaGRYlSZLUyrAoSZKkVoZFSZIktTIsSpIkqZVhUZIkSa0Mi5IkSWplWJQkSVIrw6IkSRpKSR6S5IwkVyW5JskxSe61lvEHJ9l0AvN2/8X0k8iwKEmShk6SAKcD/1FV2wM7AAuB967lsIOBdYbFDaxr3lTOvz4Mi5IkaRg9C1hRVZ8CqKpVwFuA1yTZLMkRSS5NckmSNyZ5E/Ag4Lwk5wEk2a8Zc1mS9/dOnuSDSS5Mcm6SrZu+7ZJ8LckFSb6T5FFN/wlJjmzm7ZtnJjAsSpKkYfRo4ILejqr6HfBz4LXAw4HHVdXOwOeq6kPAL4Ddq2r3JA9iNNg9C9gFeGKSFzdTbQZcWFWPB74F/FPTPwK8sap2BQ4FPtrz8jsAz66qt07+W90wM26pUwNZkWTpRAdX1YTHSpI02yVZAizp6RqpqpHVu4Ea7zDgGcBxVbUSoKpuGWfcE4HlVfXr5rU+1xz3H8A9wBeacZ8FTk+yEHgqcOroGXAANumZ79RmdXPGMSzOYlW1rOsaJEmaqZpgONKy+3LgZb0dSe4DbANcy/hBsm/4IKUwejb31qrapWXMHQPMN608DS1JkobRucCmSf4aIMnGwAeBE4BzgNevvtkkyX2bY34PbN5s/xB4ZpKtmmP3Y/SUM4zmq72a7VcA5zenuK9LsnczZ5I8dgrf36QxLEqSpKFTVQW8BNg7yVXAz4AVwNuB4xm9dvGSJD9hNPDB6CrlV5OcV1U3Af8AnAf8hNFrFM9oxt0BPDrJBYxe0/jupv+VwIHNnJcDe07x25wUnoaWJElDqapuAF7YsvuQ5tE7/sPAh3vanwc+P868C5vNd67Rfx3w3HHGHzBI3dPNlUVJkiS1MixKkiSplWFRkiRJrQyLkiRJamVYlCRJUivDoiRJkloZFiVJktTKsChJkqRWhkVJkiS1MixKkiSplWFRkiRJrTL6PdobMEGytKqWTk45miYb9kuXJGl2SNcFzAWuLEqSJKmVYVGSJEmtDIuSJElqNa/rAtSdvd9yXdclcOpRD+9rH/ieX3dUyZhPvHPrvvZnvt1RIT1e9Yz+9jd+cnc3hfR4zmM36WsfdtxdHVUy5gOvv3df+4jT7+mokjGHvrT/3+Q3/O3LOqpkzDYf/WJf+yvzH9lRJWOe/8f/6muvOP2YjioZs+Clb+5r3/Hv7+iokjGbve5f+trXXnNNR5WM2Xa77frav7vg6x1VMuY+u/6frkuYU1xZlCRJUivDoiRJkloZFiVJktTKsChJkqRWhkVJkiS1MixKkiSplWFRkiRJrQyLkiRJamVYlCRJUivDoiRJkloZFiVJktTKsChJkoZSktvXsX95kidMVz0zlWFRkiRJrQyLkiRpaCVZnOTLPe1jkxywxpgDkxzV035dkiOnscxOGRYlSZLW7mTgRUnmN+1XA5/qsJ5pNa/rArR+khwOLBjkmKpaOjXVSJI08yRZAizp6RqpqpFB56mqO5J8E3hBkiuB+VV16WTVOdMZFmevBYY/SZLaNcFwXeFwJf1nWtsWYo4H3g78lCFaVQTDoiRJGm7/DeyYZBNGg+IewPlrDqqqHybZBng8sPP0ltgtw6IkSRo6SeYBd1fVDUlOAS4BrgIuWsthpwC7VNVvp6PGmcKwKEmShtGjgWsAquow4LA1B1TV4jW6ng4ctea4uc67oSVJ0lBJ8nrgJOAdExy/ZZKfAXdV1blTWtwM5MqiJEkaKlV1HHDcAONvBXaYuopmNlcWJUmS1MqwKEmSpFaGRUmSJLUyLEqSJKmVYVGSJEmtDIuSJElqZViUJElSK8OiJEmSWhkWJUmS1CpVtWETJEuraunklKOJ2sCf+4b90iVJmh3SdQFzgSuLkiRJajXnvxs6yeHAgq7rmAKLui5AkiTNfXM+LAIL5uJp8iRLu65BkiTNfcMQFtVi77dc13UJnHrUw/vaB77n1x1VMuYT79y6r33y97q/xHPfp/ZfdnP2hX/sqJIxz3v8/L72Ycfd1VElYz7w+nv3tY89u/vf3UHP6//dXXvACzqqZMy2J3y5r33e9o/tqJIxu1/1k772ii8e1VElYxa87C197d8dfUhHlYy5z8FH9rWvuPoXHVUyZsdHPKivfevFy7sppMeWuyzuuoQ5xWsWJUmS1MqwKEmSpFaGRUmSJLUyLEqSJKmVYVGSJEmtDIuSJElqZViUJElSK8OiJEmSWhkWJUmS1MqwKEmSpFaGRUmSJLUyLEqSpKGV5CVJKsmjpvE1D06y6XS93oYyLEqSpGG2H3A+sO80vubBgGFRkiRpJkuyEHgacCBNWEyyOMmXe8Ycm+SAZvt5SX6a5PwkH1o9LsnSJIf2HHNZkkVJNkvylSQ/afr2SfIm4EHAeUnOm753u/7mdV2AJElSR14MfK2qfpbkliSPbxuYZAHwceAZVXVdkpMmMP9zgV9U1fObObaoqtuSHALsXlU3T8abmGqGxdlrRZKlgxxQVQONlyRpNkuyBFjS0zVSVSM97f2Ao5vtk5v2V1qmexRwbVVd17RPWmPu8VwKHJHk/cCXq+o7g9Q/UxgWZ6mqWtZ1DZIkzWRNMBwZb1+S+wHPAnZKUsDGQAFn0n+Z3oLVh6zlpVaOd0yzYrkr8DzgfUnOqap3r8976ZLXLEqSpGG0F/DpqnpYVS2qqm2A1auGOybZJMkWwB5N30+BbZMsatr79Mx1PfB4gOZU9sOb7QcBd1bVZ4EjVo8Bfg9sPhVvaiq4sihJkobRfsCaZ+m+CLwCOAW4BLgKuAigqu5K8rfA15LcDPxojeP+OsnFwH8CP2v6HwP8W5J7gD8Cb2j6R4CvJrmpqnaf9Hc2yQyLkiRp6FTV4nH6PtTTPGycw86rqkclCfAR4MfNcXcBfzHO+OuBr4/zOh8GPjx41d3wNLQkSdLEvK5ZPbwc2ILRu6PnPFcWJUmSJqCqjgKO6rqO6ebKoiRJkloZFiVJktTKsChJkqRWhkVJkiS1MixKkiSplWFRkiRJrQyLkiRJamVYlCRJUivDoiRJklqlqjZsgmRpVS2dnHIm30yvryMb9kuXJGl2SNcFzAWuLEqSJKnVZHw39IokSydhnqmyqOsCJEmSZqsNDotVtWwyCpkqMzzISpIkzWiTsbKoWeqFf3Nl1yVw1sf/rK/98rde300hPU754KK+9rFnd3+J50HP67/s5jPf7qiQHq96Rn/79e//bTeF9DjubX/S137PSSs7qmTMO/fr/zO74pQjOqpkzIKXH9rX/r9//1cdVTLmT//ts33t239wZkeVjFn45Bf1te/81skdVTJm02fu29c+/4o7OqpkzNN33KyvfcNVV3RUyZhttt+x6xLmFK9ZlCRJUivDoiRJkloZFiVJktTKsChJkqRWhkVJkiS1MixKkiSplWFRkiRJrQyLkiRJamVYlCRJUivDoiRJkloZFiVJktTKsChJkoZSktu7rmE2MCxKkiSplWFRkiQNrSSLk3y5p31skgOa7euT/HOSC5NcmuRRTf9mST6Z5D+TXJRkz47KnxaGRUmSpHY3V9XjgY8BhzZ9/wh8s6qeCOwO/FuSzboqcKrN67oArV2Sw4EFkzFXVS2djHkkSZoNkiwBlvR0jVTVyIDTnN48XwC8tNn+C+BFSVaHxwXAQ4Er17fWmcywOPMtMORJkjS4JhiuKxyupP9M65oLNHc3z6sYy00BXlZV/7XBRc4CnoaWJEnD7L+BHZNskmQLYI8JHPN14I1JApDkcVNZYNdcWZQkSUMnyTzg7qq6IckpwCXAVcBFEzj8PcDRwCVNYLweeMFU1do1w6IkSRpGjwauAaiqw4DD1hxQVYt6tn8MLG627wL+ZjqKnAk8DS1JkoZKktcDJwHv6LqW2cCVRUmSNFSq6jjguK7rmC1cWZQkSVIrw6IkSZJaGRYlSZLUyrAoSZKkVoZFSZIktTIsSpIkqZVhUZIkSa0Mi5IkSWplWJQkSVKrVFXXNUypJEuramnXdayvKap/bv/SJUkala4LmAtcWZQkSVKrYfhu6BVJlnZdxAZY1HUBkiRpeM35sFhVy7quYUPM8qArSZJmuTkfFtXuhX9zZdclcNbH/6yv/cp/+J+OKhnzufc9uK999JndX+J58Iv6L7v51HkdFdLj1bv3tw868rZuCulx7CFb9LXfccIfOqpkzL8ccK++9or/+FBHlYxZ8OI39bV/d+TBHVUy5j6HHN3XvvXi5d0U0mPLXRb3tWdiTd+6/M5uCunxzEdv2tf+2TU/76iSMTts99CuS5hTvGZRkiRJrQyLkiRJamVYlCRJUivDoiRJkloZFiVJktTKsChJkqRWhkVJkiS1MixKkiSplWFRkiRJrQyLkiRJamVYlCRJUivDoiRJGkpJ/jHJ5UkuSXJxkietxxyLkzx1Emu6PslWkzXfZJjXdQGSJEnTLclTgBcAj6+qu5uAdq/1mGoxcDvwvUksb70kmVdVKyd7XsOiJEkaRg8Ebq6quwGq6maAJLsCRwILgZuBA6rqpiTLgYuB3YD7AK8BfgW8HliV5K+ANwI/BY4DHtq8zsFV9d0kS4GHN6+7A3AI8GTgL4H/AV5YVX9sjvn7JLs326+oqquTbL2WeR8ELGrqfcVk/YBW8zS0JEkaRucA2yT5WZKPJnlmkvnAh4G9qmpX4JPAe3uO2ayqngr8LfDJqrqe0QB3VFXtUlXfAY5p2k8EXgYc33P8dsDzgT2BzwLnVdVjgLua/tV+V1W7AccCRzd9a5t3V2DPqpr0oAiuLM4GK5p/NWywqpqUeSRJmg2SLAGW9HSNVNUIQFXd3qwi/jmwO/AF4F+AnYBvJAHYGLip5/iTmmO/neQ+SbYc52WfDezYHA9wnySbN9tfrao/Jrm0mftrTf+ljK4M9r1O83zUBOY9s6ruWtvPYkMYFme4qlrWdQ2SJM1GTTAcWcv+VcByYHkT4P4OuLyqntJ2yDraMHrW9ilrhrcm5K0+5X1Pkj9W1erj76E/k9U422ub946WeieFp6ElSdLQSfLIJNv3dO0CXAls3dz8QpL5SR7dM2afpv/pwG1VdRvwe2DznjHnAAf1vM4u61HePj3P35/EedeLK4uSJGkYLQQ+3JxKXglczegp6xHgQ0m2YDQnHQ1c3hzz2yTfY+wGF4CzgNOS7MnoDS5vAj6S5JLm+G8zehPMIDZJ8kNGF/X2a/omY971YliUJElDp6ouAMb7fMSbgWe0HPbFqvqHNeb5GbDzGuP2WaP9/9w3UFULx9tXVYuazX9eY/zNE5l3KngaWpIkSa1cWZQkSVqHqlrcdQ1dcWVRkiRJrQyLkiRJamVYlCRJUivDoiRJkloZFiVJktTKsChJkqRWhkVJkiS1MixKkiSplWFRkiRJrVJVXdeg6ecvXZI0DNJ1AXOBK4uSJElqZVgcTpmsR5K/mcz5rMmauq5hptc00+qxJmua4TVpEhgWtaGWdF3AOKxpYqxpYmZaTTOtHrCmibKmiZmJNQ01w6IkSZJaGRYlSZLUyrCoDTXSdQHjsKaJsaaJmWk1zbR6wJomypomZibWNNT86BxJkiS1cmVRkiRJrQyL6pPkk0l+leSynr7HJvl+kkuTnJXkPmsc89Aktyc5tKfvzUkuS3J5koOnq6Yki5LcleTi5nFczzG7NuOvTvKhJOv9sQqTWNP/3969x9hR1mEc/z7liKQtoCktcmmtxGJpFCgUbRNaqlVUIoZqiG1AJdUQQAOJEY2JJK0tFxuNqCQCAbz8ASFI8BK1VRpK0bRCWujaGxWbRtFGpUKkNqZQfv7x/pYMmz1t95zZMwfzfJI3O+edM7PPzpw5+9t3ZvaskrQ5t9Ptko5qOlNl2Z9V19VkJkk3SvqLpH3d5Kkrk6Sxkn4haUfuu1uazpTzPiFpIDOt7FWmnHdmztua84/J/r467iQdW+l7StJzkm5tMlPOW5zPH8j3hRP6INNaSU9X5k3qg0xHS7pT0s48/j7eaSYbgYhwc3u1AfOAc4Atlb4ngAtyegmwfMgyDwIPAF/Mx+8EtgBjgRbwMDCtF5mAqdXnDVnP48Acyv/e+hXw4T7IdFx+VW7HRU1nyvkfA+491HN6vJ1mAycB+3r5Gm+XKV/b783po4HHmn49AROAPwMT8/EPgQU9ytQCBoCzKlmOyum+O+6GrHMjMK/hfdcC/gGckI9XAkub3k7AWmBWpzlGKdMyYEVOjxncZm6j2zyyaK8REeuAfw3pfgewLqd/A7z6l5ykS4BdwNbK888ANkTE/oh4GXgUWNirTMORdBKlMFsf5V3mR8AlTWbK9fw7J1uUoqPji4jryiRpPPAFYEWnWerOFBEbImJPt3nqypSv7Udy+gCwCTi1yUzAacDOiPhnPn74CJapK9OFwEBEbM5l90bEwX497gZJmgZMohT7TWYa/AfS43Lk9Tjgbw1nqlWNmZYAN+c6X4mI52oLaW25WLQjsQX4aE5fCkwGkDQO+DLlL72hz58naYKkscBFg8uMdqb0NklPSnpU0tzsOwV4tvKcZ7OvyUwASFpNGVV4EfhxH2RaDnwT2F9zlm4yjbaOM0l6E3AxsKbhTM8A0/MUXotSlPXquDsdCEmrJW2S9KXs79vjLi0G7s9CtrFMEfEScDXwB0qROAO4u8lMFd/PU8E3ZCHbWKY81gCW5+vsAUkn1pzJhuFi0Y7EEuBzkjYCxwIHsn8Z8K2IeM11ZBGxHfg65S/FVcBm4OUeZdoDTImImZTRsXvzOpjh3uTq/gUx0kwlRMQHKadY3wi8r8lMks4G3h4RD9Wco+NMo5ij60xZlN0HfCcidjWZKSKepxQc91NGynbTu+OuBZwPXJZfF0paQB8fd2kRZf/VbaTH3Rso+24mcDLllP5XmsyU8y6LiHcBc7N9suFMLcoI/u8i4hxgPfCNmjPZcJo+D+7Wf41DXy9yOvB4Tg/+QtoNvEA5xfD5YZa5CbimF5mGmbcWmEUpxnZU+hcDdzSZaZj+TwO3NbydrqaMbOymjAIdANb2y3aihmsW68wE3EMpFPsmU6X/SmBlLzJRiq4fVObdAFzfz8cdcBbltH3j+w44D1hT6Z8H/LIftlOl/4o+eH8S8B9gTPZPBrbWsQ/dDt08smiHpbwDTtIY4KvA7QARMTcipkbEVOBW4KaIuG3IMlMoN0vU+td7u0ySJirvKJZ0GjAN2BXlercXJc3OUymfAn7aZCZJ4/OarsERqouAHU1miojvRcTJuU/Pp/wynd9kpjq/d52ZJK0Ajge6utu/5kyDy7wZuAa4qxeZgNXAmSp3ibeAC4Bt/XjcVRZdzOiMKnaS6a/ADEkTcxUfALY3mUlSS3lHdo58foRy2rixTFEqxJ8D83MVC4BtdWayNpquVt36q1HePPcAL1FGlj4DXAfszHYL+c/chyy3lLwbOh8/RjmIN9PFHZkjzUS5QHprft9NwMWV9cyivNn9CbhtuJ+jl5mAEyl3Aw7k/O8Craa3U2V9U+n+bui69t3KXP6V/Lq04X13KuV06nbgqWyf7YPtdB/luNtGF3fWd/JeAFyeubZQGdGkz467yrp2AdO72UY177ur8vU0QCmIJjT8Gh9HuVN88P3p2+Qd7g1vp7dSbooZoFwnPKXbfeh2+OZPcDEzMzOztnwa2szMzMzacrFoZmZmZm25WDQzMzOztlwsmpmZmVlbLhbNzMzMrC0Xi2ZmZmbWlotFM7M+MvjPiM3M+oWLRTOzDklaLum6yuMbJV0r6XpJT0gakLSsMv8nkjZK2irpykr/Pklfk/R7YE6Pfwwzs0NysWhm1rm7KZ/pPfiRZYuAv1M+nuzdwNnAuZLm5fOXRMS5lE81uVbShOwfR/m0nPdExG97+QOYmR1Oq+kAZmavVxGxW9JeSTMpH9/4JHAecGFOA4ynFI/rKAXiwuyfnP17gYPAg73MbmZ2pFwsmpl15y7gCuAtwD3AAuDmiLij+iRJ84H3A3MiYr+ktcAxOfu/EXGwV4HNzEbCp6HNzLrzEPAhyoji6mxLJI0HkHSKpEnA8cDzWShOB2Y3FdjMbCQ8smhm1oWIOCDpEeCFHB38taQzgPWSAPYBlwOrgKskDQBPAxuaymxmNhKKiKYzmJm9buWNLZuASyPij03nMTOrm09Dm5l1SNIM4BlgjQtFM/t/5ZFFMzMzM2vLI4tmZmZm1paLRTMzMzNry8WimZmZmbXlYtHMzMzM2nKxaGZmZmZtuVg0MzMzs7b+B8fhiy2WDuCEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(fp,cmap='coolwarm',linecolor='white',linewidths=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x208591ccd68>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#normalize data from o to 1\n",
    "sns.clustermap(fp,cmap='coolwarm',linecolor='white',linewidths=3,standard_scale=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Will be useful for machine learning."
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}