permutation visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import math\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "from statannot import add_stat_annotation\n",
    "from scipy.stats import iqr\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\R-3.6.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>0.435611</td>\n",
       "      <td>0.705858</td>\n",
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "base_dir = 'C:\\\\R-3.6.3'\n",
    "excel_file = 'base_crc.xls'\n",
    "excel_dir = os.path.join(base_dir, excel_file)\n",
    "\n",
    "cb=pd.read_excel('base_crc.xls', header = None)\n",
    "cb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1=pd.read_excel('1year_crc.xls', header = None)\n",
    "c1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.644645</td>\n",
       "      <td>0.333457</td>\n",
       "      <td>0.319980</td>\n",
       "      <td>0.479563</td>\n",
       "      <td>0.543491</td>\n",
       "      <td>0.356243</td>\n",
       "      <td>0.582218</td>\n",
       "      <td>0.156055</td>\n",
       "      <td>0.202898</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084208</td>\n",
       "      <td>0.049779</td>\n",
       "      <td>0.495902</td>\n",
       "      <td>0.274562</td>\n",
       "      <td>0.424156</td>\n",
       "      <td>0.389223</td>\n",
       "      <td>0.321327</td>\n",
       "      <td>0.317955</td>\n",
       "      <td>0.565477</td>\n",
       "      <td>0.851773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.644645</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.410688</td>\n",
       "      <td>0.663260</td>\n",
       "      <td>0.900596</td>\n",
       "      <td>0.655756</td>\n",
       "      <td>0.608535</td>\n",
       "      <td>0.877840</td>\n",
       "      <td>0.392785</td>\n",
       "      <td>0.280784</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241429</td>\n",
       "      <td>0.334406</td>\n",
       "      <td>0.829823</td>\n",
       "      <td>0.538558</td>\n",
       "      <td>0.627937</td>\n",
       "      <td>0.812082</td>\n",
       "      <td>0.691500</td>\n",
       "      <td>0.462624</td>\n",
       "      <td>0.944493</td>\n",
       "      <td>0.576546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.333457</td>\n",
       "      <td>0.410688</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.644440</td>\n",
       "      <td>0.486352</td>\n",
       "      <td>0.724412</td>\n",
       "      <td>0.743006</td>\n",
       "      <td>0.439607</td>\n",
       "      <td>0.619110</td>\n",
       "      <td>0.456020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137220</td>\n",
       "      <td>0.261373</td>\n",
       "      <td>0.440731</td>\n",
       "      <td>0.461147</td>\n",
       "      <td>0.495023</td>\n",
       "      <td>0.408496</td>\n",
       "      <td>0.498504</td>\n",
       "      <td>0.683026</td>\n",
       "      <td>0.374213</td>\n",
       "      <td>0.234318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.319980</td>\n",
       "      <td>0.663260</td>\n",
       "      <td>0.644440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.789084</td>\n",
       "      <td>0.602860</td>\n",
       "      <td>0.725639</td>\n",
       "      <td>0.618226</td>\n",
       "      <td>0.569407</td>\n",
       "      <td>0.384951</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128963</td>\n",
       "      <td>0.331509</td>\n",
       "      <td>0.604099</td>\n",
       "      <td>0.487129</td>\n",
       "      <td>0.477599</td>\n",
       "      <td>0.692569</td>\n",
       "      <td>0.801355</td>\n",
       "      <td>0.530654</td>\n",
       "      <td>0.629866</td>\n",
       "      <td>0.203451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.479563</td>\n",
       "      <td>0.900596</td>\n",
       "      <td>0.486352</td>\n",
       "      <td>0.789084</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.667172</td>\n",
       "      <td>0.645943</td>\n",
       "      <td>0.753534</td>\n",
       "      <td>0.432698</td>\n",
       "      <td>0.274465</td>\n",
       "      <td>...</td>\n",
       "      <td>0.208063</td>\n",
       "      <td>0.334767</td>\n",
       "      <td>0.717025</td>\n",
       "      <td>0.542038</td>\n",
       "      <td>0.641975</td>\n",
       "      <td>0.863997</td>\n",
       "      <td>0.757536</td>\n",
       "      <td>0.485719</td>\n",
       "      <td>0.840387</td>\n",
       "      <td>0.407120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 166 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5         6    \\\n",
       "0  1.000000  0.644645  0.333457  0.319980  0.479563  0.543491  0.356243   \n",
       "1  0.644645  1.000000  0.410688  0.663260  0.900596  0.655756  0.608535   \n",
       "2  0.333457  0.410688  1.000000  0.644440  0.486352  0.724412  0.743006   \n",
       "3  0.319980  0.663260  0.644440  1.000000  0.789084  0.602860  0.725639   \n",
       "4  0.479563  0.900596  0.486352  0.789084  1.000000  0.667172  0.645943   \n",
       "\n",
       "        7         8         9    ...       156       157       158       159  \\\n",
       "0  0.582218  0.156055  0.202898  ...  0.084208  0.049779  0.495902  0.274562   \n",
       "1  0.877840  0.392785  0.280784  ...  0.241429  0.334406  0.829823  0.538558   \n",
       "2  0.439607  0.619110  0.456020  ...  0.137220  0.261373  0.440731  0.461147   \n",
       "3  0.618226  0.569407  0.384951  ...  0.128963  0.331509  0.604099  0.487129   \n",
       "4  0.753534  0.432698  0.274465  ...  0.208063  0.334767  0.717025  0.542038   \n",
       "\n",
       "        160       161       162       163       164       165  \n",
       "0  0.424156  0.389223  0.321327  0.317955  0.565477  0.851773  \n",
       "1  0.627937  0.812082  0.691500  0.462624  0.944493  0.576546  \n",
       "2  0.495023  0.408496  0.498504  0.683026  0.374213  0.234318  \n",
       "3  0.477599  0.692569  0.801355  0.530654  0.629866  0.203451  \n",
       "4  0.641975  0.863997  0.757536  0.485719  0.840387  0.407120  \n",
       "\n",
       "[5 rows x 166 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2=pd.read_excel('2year_crc.xls', header = None)\n",
    "c2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROI data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <td>0.951355</td>\n",
       "      <td>0.973599</td>\n",
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       "      <td>0.951941</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.945582</td>\n",
       "      <td>1.076068</td>\n",
       "      <td>1.063864</td>\n",
       "      <td>1.089820</td>\n",
       "      <td>1.031460</td>\n",
       "      <td>1.093310</td>\n",
       "      <td>1.090832</td>\n",
       "      <td>1.073810</td>\n",
       "      <td>1.059597</td>\n",
       "      <td>1.123413</td>\n",
       "      <td>...</td>\n",
       "      <td>1.027151</td>\n",
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       "      <td>1.077409</td>\n",
       "      <td>1.008228</td>\n",
       "      <td>1.075569</td>\n",
       "      <td>0.971197</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.975771</td>\n",
       "      <td>1.060798</td>\n",
       "      <td>0.949275</td>\n",
       "      <td>1.015583</td>\n",
       "      <td>1.049425</td>\n",
       "      <td>0.996281</td>\n",
       "      <td>1.054733</td>\n",
       "      <td>1.095039</td>\n",
       "      <td>1.065628</td>\n",
       "      <td>1.065500</td>\n",
       "      <td>...</td>\n",
       "      <td>1.058089</td>\n",
       "      <td>1.070296</td>\n",
       "      <td>1.119465</td>\n",
       "      <td>1.050540</td>\n",
       "      <td>1.038854</td>\n",
       "      <td>1.061846</td>\n",
       "      <td>1.000564</td>\n",
       "      <td>0.946300</td>\n",
       "      <td>1.063773</td>\n",
       "      <td>0.996566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.861998</td>\n",
       "      <td>1.040186</td>\n",
       "      <td>1.142825</td>\n",
       "      <td>1.082765</td>\n",
       "      <td>1.016943</td>\n",
       "      <td>1.069252</td>\n",
       "      <td>1.119733</td>\n",
       "      <td>1.096673</td>\n",
       "      <td>1.100890</td>\n",
       "      <td>1.115353</td>\n",
       "      <td>...</td>\n",
       "      <td>1.117484</td>\n",
       "      <td>1.135948</td>\n",
       "      <td>1.071848</td>\n",
       "      <td>1.151971</td>\n",
       "      <td>1.039047</td>\n",
       "      <td>0.972968</td>\n",
       "      <td>1.066161</td>\n",
       "      <td>1.159888</td>\n",
       "      <td>1.024227</td>\n",
       "      <td>0.868109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.847126</td>\n",
       "      <td>1.036831</td>\n",
       "      <td>0.956991</td>\n",
       "      <td>1.142631</td>\n",
       "      <td>1.072392</td>\n",
       "      <td>0.993812</td>\n",
       "      <td>1.067336</td>\n",
       "      <td>1.046514</td>\n",
       "      <td>1.063084</td>\n",
       "      <td>1.023787</td>\n",
       "      <td>...</td>\n",
       "      <td>1.066062</td>\n",
       "      <td>1.089793</td>\n",
       "      <td>1.018724</td>\n",
       "      <td>1.097091</td>\n",
       "      <td>1.076730</td>\n",
       "      <td>1.054937</td>\n",
       "      <td>1.127381</td>\n",
       "      <td>1.073188</td>\n",
       "      <td>1.009547</td>\n",
       "      <td>0.860305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 166 columns</p>\n",
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       "        0         1         2         3         4         5         6    \\\n",
       "0  0.940687  1.043152  0.948859  0.961372  0.972362  1.033010  1.033285   \n",
       "1  0.945582  1.076068  1.063864  1.089820  1.031460  1.093310  1.090832   \n",
       "2  0.975771  1.060798  0.949275  1.015583  1.049425  0.996281  1.054733   \n",
       "3  0.861998  1.040186  1.142825  1.082765  1.016943  1.069252  1.119733   \n",
       "4  0.847126  1.036831  0.956991  1.142631  1.072392  0.993812  1.067336   \n",
       "\n",
       "        7         8         9    ...       156       157       158       159  \\\n",
       "0  1.055696  1.019223  1.083222  ...  1.066735  1.073867  1.052342  1.083282   \n",
       "1  1.073810  1.059597  1.123413  ...  1.027151  1.030608  1.052224  1.079168   \n",
       "2  1.095039  1.065628  1.065500  ...  1.058089  1.070296  1.119465  1.050540   \n",
       "3  1.096673  1.100890  1.115353  ...  1.117484  1.135948  1.071848  1.151971   \n",
       "4  1.046514  1.063084  1.023787  ...  1.066062  1.089793  1.018724  1.097091   \n",
       "\n",
       "        160       161       162       163       164       165  \n",
       "0  1.031008  0.969623  0.951355  0.973599  1.030525  0.951941  \n",
       "1  1.089496  1.064728  1.077409  1.008228  1.075569  0.971197  \n",
       "2  1.038854  1.061846  1.000564  0.946300  1.063773  0.996566  \n",
       "3  1.039047  0.972968  1.066161  1.159888  1.024227  0.868109  \n",
       "4  1.076730  1.054937  1.127381  1.073188  1.009547  0.860305  \n",
       "\n",
       "[5 rows x 166 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yb=pd.read_excel('y_base.xls', header = None)\n",
    "yb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 140 entries, 0 to 139\n",
      "Columns: 166 entries, 0 to 165\n",
      "dtypes: float64(166)\n",
      "memory usage: 181.7 KB\n"
     ]
    }
   ],
   "source": [
    "yb.info() # shape : tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>165</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
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       "      <td>140.000000</td>\n",
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       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>140.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.919852</td>\n",
       "      <td>1.092447</td>\n",
       "      <td>1.002825</td>\n",
       "      <td>1.045312</td>\n",
       "      <td>1.050381</td>\n",
       "      <td>1.065651</td>\n",
       "      <td>1.082289</td>\n",
       "      <td>1.090795</td>\n",
       "      <td>1.068812</td>\n",
       "      <td>1.106371</td>\n",
       "      <td>...</td>\n",
       "      <td>1.070076</td>\n",
       "      <td>1.064730</td>\n",
       "      <td>1.076781</td>\n",
       "      <td>1.084623</td>\n",
       "      <td>1.065511</td>\n",
       "      <td>1.040747</td>\n",
       "      <td>1.039017</td>\n",
       "      <td>0.991231</td>\n",
       "      <td>1.073760</td>\n",
       "      <td>0.929625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.066850</td>\n",
       "      <td>0.060999</td>\n",
       "      <td>0.082484</td>\n",
       "      <td>0.060779</td>\n",
       "      <td>0.057971</td>\n",
       "      <td>0.058877</td>\n",
       "      <td>0.052178</td>\n",
       "      <td>0.059875</td>\n",
       "      <td>0.045872</td>\n",
       "      <td>0.048314</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048556</td>\n",
       "      <td>0.047160</td>\n",
       "      <td>0.064121</td>\n",
       "      <td>0.055391</td>\n",
       "      <td>0.058192</td>\n",
       "      <td>0.060897</td>\n",
       "      <td>0.062564</td>\n",
       "      <td>0.075446</td>\n",
       "      <td>0.062716</td>\n",
       "      <td>0.066647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.739333</td>\n",
       "      <td>0.956862</td>\n",
       "      <td>0.781412</td>\n",
       "      <td>0.889291</td>\n",
       "      <td>0.913846</td>\n",
       "      <td>0.917903</td>\n",
       "      <td>0.935325</td>\n",
       "      <td>0.925453</td>\n",
       "      <td>0.970019</td>\n",
       "      <td>0.941276</td>\n",
       "      <td>...</td>\n",
       "      <td>0.926210</td>\n",
       "      <td>0.923289</td>\n",
       "      <td>0.855256</td>\n",
       "      <td>0.930731</td>\n",
       "      <td>0.950371</td>\n",
       "      <td>0.906326</td>\n",
       "      <td>0.900142</td>\n",
       "      <td>0.803858</td>\n",
       "      <td>0.942075</td>\n",
       "      <td>0.777169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.867864</td>\n",
       "      <td>1.048206</td>\n",
       "      <td>0.954409</td>\n",
       "      <td>1.005193</td>\n",
       "      <td>1.010593</td>\n",
       "      <td>1.027936</td>\n",
       "      <td>1.047956</td>\n",
       "      <td>1.049484</td>\n",
       "      <td>1.039769</td>\n",
       "      <td>1.079183</td>\n",
       "      <td>...</td>\n",
       "      <td>1.042119</td>\n",
       "      <td>1.039741</td>\n",
       "      <td>1.040210</td>\n",
       "      <td>1.050034</td>\n",
       "      <td>1.023395</td>\n",
       "      <td>1.001232</td>\n",
       "      <td>1.002790</td>\n",
       "      <td>0.945432</td>\n",
       "      <td>1.028087</td>\n",
       "      <td>0.878743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.925345</td>\n",
       "      <td>1.093436</td>\n",
       "      <td>0.997650</td>\n",
       "      <td>1.041477</td>\n",
       "      <td>1.039603</td>\n",
       "      <td>1.064149</td>\n",
       "      <td>1.074889</td>\n",
       "      <td>1.094668</td>\n",
       "      <td>1.063895</td>\n",
       "      <td>1.102878</td>\n",
       "      <td>...</td>\n",
       "      <td>1.075401</td>\n",
       "      <td>1.067157</td>\n",
       "      <td>1.068570</td>\n",
       "      <td>1.083432</td>\n",
       "      <td>1.059900</td>\n",
       "      <td>1.037626</td>\n",
       "      <td>1.036847</td>\n",
       "      <td>0.985762</td>\n",
       "      <td>1.069941</td>\n",
       "      <td>0.926519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.967586</td>\n",
       "      <td>1.135441</td>\n",
       "      <td>1.060696</td>\n",
       "      <td>1.090152</td>\n",
       "      <td>1.087064</td>\n",
       "      <td>1.093572</td>\n",
       "      <td>1.113473</td>\n",
       "      <td>1.124472</td>\n",
       "      <td>1.101283</td>\n",
       "      <td>1.139875</td>\n",
       "      <td>...</td>\n",
       "      <td>1.105441</td>\n",
       "      <td>1.089874</td>\n",
       "      <td>1.114315</td>\n",
       "      <td>1.116692</td>\n",
       "      <td>1.099556</td>\n",
       "      <td>1.078190</td>\n",
       "      <td>1.078328</td>\n",
       "      <td>1.037842</td>\n",
       "      <td>1.117553</td>\n",
       "      <td>0.976058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.114468</td>\n",
       "      <td>1.289483</td>\n",
       "      <td>1.215251</td>\n",
       "      <td>1.199043</td>\n",
       "      <td>1.256901</td>\n",
       "      <td>1.292869</td>\n",
       "      <td>1.250749</td>\n",
       "      <td>1.246100</td>\n",
       "      <td>1.201223</td>\n",
       "      <td>1.250836</td>\n",
       "      <td>...</td>\n",
       "      <td>1.167164</td>\n",
       "      <td>1.197848</td>\n",
       "      <td>1.269918</td>\n",
       "      <td>1.270800</td>\n",
       "      <td>1.367021</td>\n",
       "      <td>1.278236</td>\n",
       "      <td>1.238708</td>\n",
       "      <td>1.226102</td>\n",
       "      <td>1.287774</td>\n",
       "      <td>1.135708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 166 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0           1           2           3           4           5    \\\n",
       "count  140.000000  140.000000  140.000000  140.000000  140.000000  140.000000   \n",
       "mean     0.919852    1.092447    1.002825    1.045312    1.050381    1.065651   \n",
       "std      0.066850    0.060999    0.082484    0.060779    0.057971    0.058877   \n",
       "min      0.739333    0.956862    0.781412    0.889291    0.913846    0.917903   \n",
       "25%      0.867864    1.048206    0.954409    1.005193    1.010593    1.027936   \n",
       "50%      0.925345    1.093436    0.997650    1.041477    1.039603    1.064149   \n",
       "75%      0.967586    1.135441    1.060696    1.090152    1.087064    1.093572   \n",
       "max      1.114468    1.289483    1.215251    1.199043    1.256901    1.292869   \n",
       "\n",
       "              6           7           8           9    ...         156  \\\n",
       "count  140.000000  140.000000  140.000000  140.000000  ...  140.000000   \n",
       "mean     1.082289    1.090795    1.068812    1.106371  ...    1.070076   \n",
       "std      0.052178    0.059875    0.045872    0.048314  ...    0.048556   \n",
       "min      0.935325    0.925453    0.970019    0.941276  ...    0.926210   \n",
       "25%      1.047956    1.049484    1.039769    1.079183  ...    1.042119   \n",
       "50%      1.074889    1.094668    1.063895    1.102878  ...    1.075401   \n",
       "75%      1.113473    1.124472    1.101283    1.139875  ...    1.105441   \n",
       "max      1.250749    1.246100    1.201223    1.250836  ...    1.167164   \n",
       "\n",
       "              157         158         159         160         161         162  \\\n",
       "count  140.000000  140.000000  140.000000  140.000000  140.000000  140.000000   \n",
       "mean     1.064730    1.076781    1.084623    1.065511    1.040747    1.039017   \n",
       "std      0.047160    0.064121    0.055391    0.058192    0.060897    0.062564   \n",
       "min      0.923289    0.855256    0.930731    0.950371    0.906326    0.900142   \n",
       "25%      1.039741    1.040210    1.050034    1.023395    1.001232    1.002790   \n",
       "50%      1.067157    1.068570    1.083432    1.059900    1.037626    1.036847   \n",
       "75%      1.089874    1.114315    1.116692    1.099556    1.078190    1.078328   \n",
       "max      1.197848    1.269918    1.270800    1.367021    1.278236    1.238708   \n",
       "\n",
       "              163         164         165  \n",
       "count  140.000000  140.000000  140.000000  \n",
       "mean     0.991231    1.073760    0.929625  \n",
       "std      0.075446    0.062716    0.066647  \n",
       "min      0.803858    0.942075    0.777169  \n",
       "25%      0.945432    1.028087    0.878743  \n",
       "50%      0.985762    1.069941    0.926519  \n",
       "75%      1.037842    1.117553    0.976058  \n",
       "max      1.226102    1.287774    1.135708  \n",
       "\n",
       "[8 rows x 166 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yb.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.989589</td>\n",
       "      <td>0.997885</td>\n",
       "      <td>0.987448</td>\n",
       "      <td>1.084156</td>\n",
       "      <td>1.064257</td>\n",
       "      <td>1.029333</td>\n",
       "      <td>1.047766</td>\n",
       "      <td>1.116636</td>\n",
       "      <td>...</td>\n",
       "      <td>1.096027</td>\n",
       "      <td>1.104974</td>\n",
       "      <td>1.035624</td>\n",
       "      <td>1.098077</td>\n",
       "      <td>1.047405</td>\n",
       "      <td>1.016846</td>\n",
       "      <td>0.956288</td>\n",
       "      <td>1.007331</td>\n",
       "      <td>1.055225</td>\n",
       "      <td>0.896300</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.892159</td>\n",
       "      <td>1.080733</td>\n",
       "      <td>1.055629</td>\n",
       "      <td>1.073830</td>\n",
       "      <td>1.025173</td>\n",
       "      <td>1.065538</td>\n",
       "      <td>1.095337</td>\n",
       "      <td>1.059834</td>\n",
       "      <td>1.071237</td>\n",
       "      <td>1.137690</td>\n",
       "      <td>...</td>\n",
       "      <td>1.042850</td>\n",
       "      <td>1.046162</td>\n",
       "      <td>1.038515</td>\n",
       "      <td>1.084443</td>\n",
       "      <td>1.060072</td>\n",
       "      <td>1.068793</td>\n",
       "      <td>1.057969</td>\n",
       "      <td>0.997008</td>\n",
       "      <td>1.077413</td>\n",
       "      <td>0.922787</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.937973</td>\n",
       "      <td>1.078918</td>\n",
       "      <td>0.969811</td>\n",
       "      <td>1.057107</td>\n",
       "      <td>1.083738</td>\n",
       "      <td>0.999604</td>\n",
       "      <td>1.078901</td>\n",
       "      <td>1.082811</td>\n",
       "      <td>1.102869</td>\n",
       "      <td>1.102146</td>\n",
       "      <td>...</td>\n",
       "      <td>1.071600</td>\n",
       "      <td>1.096139</td>\n",
       "      <td>1.108038</td>\n",
       "      <td>1.086849</td>\n",
       "      <td>1.047880</td>\n",
       "      <td>1.094300</td>\n",
       "      <td>1.021267</td>\n",
       "      <td>1.014394</td>\n",
       "      <td>1.084803</td>\n",
       "      <td>0.960560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.838961</td>\n",
       "      <td>1.012241</td>\n",
       "      <td>1.106243</td>\n",
       "      <td>1.043771</td>\n",
       "      <td>0.978500</td>\n",
       "      <td>1.040478</td>\n",
       "      <td>1.081377</td>\n",
       "      <td>1.026765</td>\n",
       "      <td>1.073370</td>\n",
       "      <td>1.107048</td>\n",
       "      <td>...</td>\n",
       "      <td>1.101429</td>\n",
       "      <td>1.095226</td>\n",
       "      <td>1.013200</td>\n",
       "      <td>1.114353</td>\n",
       "      <td>1.010747</td>\n",
       "      <td>0.974159</td>\n",
       "      <td>1.007733</td>\n",
       "      <td>1.095245</td>\n",
       "      <td>1.003814</td>\n",
       "      <td>0.842369</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.819423</td>\n",
       "      <td>0.996565</td>\n",
       "      <td>0.940067</td>\n",
       "      <td>1.108173</td>\n",
       "      <td>1.034461</td>\n",
       "      <td>0.978046</td>\n",
       "      <td>1.064465</td>\n",
       "      <td>1.003088</td>\n",
       "      <td>1.074253</td>\n",
       "      <td>1.060513</td>\n",
       "      <td>...</td>\n",
       "      <td>1.063611</td>\n",
       "      <td>1.055265</td>\n",
       "      <td>0.998028</td>\n",
       "      <td>1.033712</td>\n",
       "      <td>1.041595</td>\n",
       "      <td>1.033391</td>\n",
       "      <td>1.104201</td>\n",
       "      <td>1.012379</td>\n",
       "      <td>0.960953</td>\n",
       "      <td>0.786165</td>\n",
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       "<p>5 rows × 166 columns</p>\n",
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       "\n",
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       "2  1.082811  1.102869  1.102146  ...  1.071600  1.096139  1.108038  1.086849   \n",
       "3  1.026765  1.073370  1.107048  ...  1.101429  1.095226  1.013200  1.114353   \n",
       "4  1.003088  1.074253  1.060513  ...  1.063611  1.055265  0.998028  1.033712   \n",
       "\n",
       "        160       161       162       163       164       165  \n",
       "0  1.047405  1.016846  0.956288  1.007331  1.055225  0.896300  \n",
       "1  1.060072  1.068793  1.057969  0.997008  1.077413  0.922787  \n",
       "2  1.047880  1.094300  1.021267  1.014394  1.084803  0.960560  \n",
       "3  1.010747  0.974159  1.007733  1.095245  1.003814  0.842369  \n",
       "4  1.041595  1.033391  1.104201  1.012379  0.960953  0.786165  \n",
       "\n",
       "[5 rows x 166 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1=pd.read_excel('y_1year.xls', header = None)\n",
    "y1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.044085</td>\n",
       "      <td>1.013435</td>\n",
       "      <td>0.963763</td>\n",
       "      <td>1.014240</td>\n",
       "      <td>1.075801</td>\n",
       "      <td>0.903827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.906495</td>\n",
       "      <td>1.116981</td>\n",
       "      <td>1.096400</td>\n",
       "      <td>1.108960</td>\n",
       "      <td>1.065649</td>\n",
       "      <td>1.098683</td>\n",
       "      <td>1.109077</td>\n",
       "      <td>1.079941</td>\n",
       "      <td>1.082601</td>\n",
       "      <td>1.146823</td>\n",
       "      <td>...</td>\n",
       "      <td>1.075134</td>\n",
       "      <td>1.081224</td>\n",
       "      <td>1.071562</td>\n",
       "      <td>1.132229</td>\n",
       "      <td>1.115198</td>\n",
       "      <td>1.128571</td>\n",
       "      <td>1.111167</td>\n",
       "      <td>1.064516</td>\n",
       "      <td>1.126946</td>\n",
       "      <td>0.958630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.958556</td>\n",
       "      <td>1.068974</td>\n",
       "      <td>0.952114</td>\n",
       "      <td>1.032088</td>\n",
       "      <td>1.073294</td>\n",
       "      <td>0.971025</td>\n",
       "      <td>1.031583</td>\n",
       "      <td>1.071922</td>\n",
       "      <td>1.048734</td>\n",
       "      <td>1.031844</td>\n",
       "      <td>...</td>\n",
       "      <td>1.058036</td>\n",
       "      <td>1.092201</td>\n",
       "      <td>1.104072</td>\n",
       "      <td>1.063333</td>\n",
       "      <td>1.048441</td>\n",
       "      <td>1.081654</td>\n",
       "      <td>1.005959</td>\n",
       "      <td>1.019509</td>\n",
       "      <td>1.087444</td>\n",
       "      <td>0.994957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.836775</td>\n",
       "      <td>0.995787</td>\n",
       "      <td>1.128041</td>\n",
       "      <td>1.068563</td>\n",
       "      <td>0.961357</td>\n",
       "      <td>1.045921</td>\n",
       "      <td>1.106812</td>\n",
       "      <td>1.045187</td>\n",
       "      <td>1.094093</td>\n",
       "      <td>1.110553</td>\n",
       "      <td>...</td>\n",
       "      <td>1.094133</td>\n",
       "      <td>1.101152</td>\n",
       "      <td>1.038217</td>\n",
       "      <td>1.141276</td>\n",
       "      <td>1.063933</td>\n",
       "      <td>0.991501</td>\n",
       "      <td>1.074024</td>\n",
       "      <td>1.150976</td>\n",
       "      <td>1.008033</td>\n",
       "      <td>0.849588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.841793</td>\n",
       "      <td>1.032868</td>\n",
       "      <td>0.943739</td>\n",
       "      <td>1.124615</td>\n",
       "      <td>1.071300</td>\n",
       "      <td>1.014653</td>\n",
       "      <td>1.064497</td>\n",
       "      <td>1.008631</td>\n",
       "      <td>1.050162</td>\n",
       "      <td>1.015721</td>\n",
       "      <td>...</td>\n",
       "      <td>1.008034</td>\n",
       "      <td>1.018286</td>\n",
       "      <td>1.004166</td>\n",
       "      <td>1.053296</td>\n",
       "      <td>1.067734</td>\n",
       "      <td>1.033354</td>\n",
       "      <td>1.095741</td>\n",
       "      <td>1.024025</td>\n",
       "      <td>0.989215</td>\n",
       "      <td>0.818509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 166 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5         6    \\\n",
       "0  0.903053  1.085981  0.978892  0.998352  1.001821  1.061554  1.080558   \n",
       "1  0.906495  1.116981  1.096400  1.108960  1.065649  1.098683  1.109077   \n",
       "2  0.958556  1.068974  0.952114  1.032088  1.073294  0.971025  1.031583   \n",
       "3  0.836775  0.995787  1.128041  1.068563  0.961357  1.045921  1.106812   \n",
       "4  0.841793  1.032868  0.943739  1.124615  1.071300  1.014653  1.064497   \n",
       "\n",
       "        7         8         9    ...       156       157       158       159  \\\n",
       "0  1.063138  1.052151  1.119438  ...  1.099845  1.126464  1.058903  1.122482   \n",
       "1  1.079941  1.082601  1.146823  ...  1.075134  1.081224  1.071562  1.132229   \n",
       "2  1.071922  1.048734  1.031844  ...  1.058036  1.092201  1.104072  1.063333   \n",
       "3  1.045187  1.094093  1.110553  ...  1.094133  1.101152  1.038217  1.141276   \n",
       "4  1.008631  1.050162  1.015721  ...  1.008034  1.018286  1.004166  1.053296   \n",
       "\n",
       "        160       161       162       163       164       165  \n",
       "0  1.044085  1.013435  0.963763  1.014240  1.075801  0.903827  \n",
       "1  1.115198  1.128571  1.111167  1.064516  1.126946  0.958630  \n",
       "2  1.048441  1.081654  1.005959  1.019509  1.087444  0.994957  \n",
       "3  1.063933  0.991501  1.074024  1.150976  1.008033  0.849588  \n",
       "4  1.067734  1.033354  1.095741  1.024025  0.989215  0.818509  \n",
       "\n",
       "[5 rows x 166 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2=pd.read_excel('y_2year.xls', header = None)\n",
    "y2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### random permutation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 4, 9, 8, 5, 0, 7, 3, 6])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.permutation(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.subplots_adjust(hspace=0.5, wspace=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": [
    "for i in range(100):\n",
    "    df1 = yb.sample(n=20)\n",
    "    df2 = y1.sample(n=10)\n",
    "    sns.distplot(df1)\n",
    "    sns.distplot(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 69,
     "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": [
    "for i in range(100):\n",
    "    df1 = yb.sample(n=20)\n",
    "    df2 = y1.sample(n=20)\n",
    "    sns.distplot(df1)\n",
    "    sns.distplot(df2)\n",
    "sns.distplot(df1-df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 55,
     "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": [
    "sns.distplot(df1-df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 56,
     "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": [
    "sns.distplot(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 57,
     "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": [
    "sns.distplot(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>156</th>\n",
       "      <th>157</th>\n",
       "      <th>158</th>\n",
       "      <th>159</th>\n",
       "      <th>160</th>\n",
       "      <th>161</th>\n",
       "      <th>162</th>\n",
       "      <th>163</th>\n",
       "      <th>164</th>\n",
       "      <th>165</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.956928</td>\n",
       "      <td>1.079615</td>\n",
       "      <td>1.023897</td>\n",
       "      <td>1.013119</td>\n",
       "      <td>1.025478</td>\n",
       "      <td>1.046051</td>\n",
       "      <td>1.063160</td>\n",
       "      <td>1.044002</td>\n",
       "      <td>1.022710</td>\n",
       "      <td>1.083664</td>\n",
       "      <td>...</td>\n",
       "      <td>1.082380</td>\n",
       "      <td>1.043659</td>\n",
       "      <td>1.058169</td>\n",
       "      <td>1.064797</td>\n",
       "      <td>1.092426</td>\n",
       "      <td>0.988967</td>\n",
       "      <td>1.042249</td>\n",
       "      <td>1.032641</td>\n",
       "      <td>1.062877</td>\n",
       "      <td>0.991529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.913395</td>\n",
       "      <td>1.119264</td>\n",
       "      <td>1.123939</td>\n",
       "      <td>1.066251</td>\n",
       "      <td>1.041448</td>\n",
       "      <td>1.100613</td>\n",
       "      <td>1.178734</td>\n",
       "      <td>1.123978</td>\n",
       "      <td>1.114587</td>\n",
       "      <td>1.132316</td>\n",
       "      <td>...</td>\n",
       "      <td>1.003328</td>\n",
       "      <td>1.051805</td>\n",
       "      <td>1.104316</td>\n",
       "      <td>1.028037</td>\n",
       "      <td>1.093335</td>\n",
       "      <td>1.064840</td>\n",
       "      <td>1.064468</td>\n",
       "      <td>0.969125</td>\n",
       "      <td>1.095968</td>\n",
       "      <td>0.982248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.951249</td>\n",
       "      <td>0.978979</td>\n",
       "      <td>0.904065</td>\n",
       "      <td>0.939870</td>\n",
       "      <td>0.919407</td>\n",
       "      <td>1.007054</td>\n",
       "      <td>1.003281</td>\n",
       "      <td>1.014608</td>\n",
       "      <td>0.989124</td>\n",
       "      <td>1.064404</td>\n",
       "      <td>...</td>\n",
       "      <td>1.049336</td>\n",
       "      <td>0.983926</td>\n",
       "      <td>0.996721</td>\n",
       "      <td>0.959498</td>\n",
       "      <td>1.005522</td>\n",
       "      <td>0.949680</td>\n",
       "      <td>0.944909</td>\n",
       "      <td>0.867931</td>\n",
       "      <td>0.956460</td>\n",
       "      <td>0.921341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.824432</td>\n",
       "      <td>1.013625</td>\n",
       "      <td>0.864525</td>\n",
       "      <td>0.891568</td>\n",
       "      <td>1.018534</td>\n",
       "      <td>0.988746</td>\n",
       "      <td>0.935325</td>\n",
       "      <td>1.025481</td>\n",
       "      <td>0.995412</td>\n",
       "      <td>1.040247</td>\n",
       "      <td>...</td>\n",
       "      <td>0.983742</td>\n",
       "      <td>0.970143</td>\n",
       "      <td>0.962538</td>\n",
       "      <td>0.951079</td>\n",
       "      <td>0.979529</td>\n",
       "      <td>1.010153</td>\n",
       "      <td>0.924300</td>\n",
       "      <td>0.892729</td>\n",
       "      <td>0.979081</td>\n",
       "      <td>0.832729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.901518</td>\n",
       "      <td>1.103613</td>\n",
       "      <td>1.027391</td>\n",
       "      <td>1.003586</td>\n",
       "      <td>1.025185</td>\n",
       "      <td>1.091792</td>\n",
       "      <td>1.129188</td>\n",
       "      <td>1.089848</td>\n",
       "      <td>1.055400</td>\n",
       "      <td>1.116682</td>\n",
       "      <td>...</td>\n",
       "      <td>1.067677</td>\n",
       "      <td>1.057763</td>\n",
       "      <td>1.048781</td>\n",
       "      <td>1.105681</td>\n",
       "      <td>1.079535</td>\n",
       "      <td>0.991612</td>\n",
       "      <td>0.959368</td>\n",
       "      <td>0.960405</td>\n",
       "      <td>1.067110</td>\n",
       "      <td>0.903624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 166 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6    \\\n",
       "82  0.956928  1.079615  1.023897  1.013119  1.025478  1.046051  1.063160   \n",
       "35  0.913395  1.119264  1.123939  1.066251  1.041448  1.100613  1.178734   \n",
       "31  0.951249  0.978979  0.904065  0.939870  0.919407  1.007054  1.003281   \n",
       "19  0.824432  1.013625  0.864525  0.891568  1.018534  0.988746  0.935325   \n",
       "67  0.901518  1.103613  1.027391  1.003586  1.025185  1.091792  1.129188   \n",
       "\n",
       "         7         8         9    ...       156       157       158       159  \\\n",
       "82  1.044002  1.022710  1.083664  ...  1.082380  1.043659  1.058169  1.064797   \n",
       "35  1.123978  1.114587  1.132316  ...  1.003328  1.051805  1.104316  1.028037   \n",
       "31  1.014608  0.989124  1.064404  ...  1.049336  0.983926  0.996721  0.959498   \n",
       "19  1.025481  0.995412  1.040247  ...  0.983742  0.970143  0.962538  0.951079   \n",
       "67  1.089848  1.055400  1.116682  ...  1.067677  1.057763  1.048781  1.105681   \n",
       "\n",
       "         160       161       162       163       164       165  \n",
       "82  1.092426  0.988967  1.042249  1.032641  1.062877  0.991529  \n",
       "35  1.093335  1.064840  1.064468  0.969125  1.095968  0.982248  \n",
       "31  1.005522  0.949680  0.944909  0.867931  0.956460  0.921341  \n",
       "19  0.979529  1.010153  0.924300  0.892729  0.979081  0.832729  \n",
       "67  1.079535  0.991612  0.959368  0.960405  1.067110  0.903624  \n",
       "\n",
       "[5 rows x 166 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 20 entries, 54 to 80\n",
      "Columns: 166 entries, 0 to 165\n",
      "dtypes: float64(166)\n",
      "memory usage: 26.7 KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 20 entries, 97 to 75\n",
      "Columns: 166 entries, 0 to 165\n",
      "dtypes: float64(166)\n",
      "memory usage: 26.7 KB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 38 entries, 6 to 132\n",
      "Columns: 166 entries, 0 to 165\n",
      "dtypes: float64(166)\n",
      "memory usage: 49.6 KB\n"
     ]
    }
   ],
   "source": [
    "df3=df1-df2\n",
    "df3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.78249488 0.79087363 0.87809854 0.81662542 0.87057141\n",
      "  0.85521316 0.90325198 0.8762295  0.79708834 0.9152324  0.83132586\n",
      "  0.79506105 0.84676775 0.83386787 0.86061694 0.77229235 0.85444501\n",
      "  0.62084984 0.82573541]\n",
      " [0.78249488 1.         0.63049876 0.76700846 0.67937413 0.8943899\n",
      "  0.73319448 0.83041914 0.69115721 0.74590963 0.83996445 0.71440844\n",
      "  0.81069145 0.80854397 0.85378887 0.6614308  0.69807982 0.92177402\n",
      "  0.29912025 0.7780326 ]\n",
      " [0.79087363 0.63049876 1.         0.79729628 0.62869989 0.81611601\n",
      "  0.86151726 0.76503932 0.64007888 0.65251511 0.8354542  0.85841533\n",
      "  0.711936   0.73449492 0.80170239 0.88336036 0.7158786  0.7437067\n",
      "  0.43999625 0.6106986 ]\n",
      " [0.87809854 0.76700846 0.79729628 1.         0.74373332 0.87160313\n",
      "  0.82174955 0.82046467 0.79065868 0.71755486 0.89230508 0.81975173\n",
      "  0.79154984 0.82036553 0.85461786 0.89642472 0.73220291 0.79980148\n",
      "  0.50284972 0.82995955]\n",
      " [0.81662542 0.67937413 0.62869989 0.74373332 1.         0.70669778\n",
      "  0.7426748  0.79614843 0.83791792 0.70387764 0.81371664 0.62050242\n",
      "  0.68176437 0.83615586 0.77571002 0.72132605 0.68724688 0.72782052\n",
      "  0.74730601 0.75949073]\n",
      " [0.87057141 0.8943899  0.81611601 0.87160313 0.70669778 1.\n",
      "  0.85284977 0.86245299 0.76828534 0.78740297 0.91603399 0.87965575\n",
      "  0.83715484 0.858095   0.92514616 0.81869266 0.76790323 0.91272512\n",
      "  0.41       0.79305722]\n",
      " [0.85521316 0.73319448 0.86151726 0.82174955 0.7426748  0.85284977\n",
      "  1.         0.86879554 0.75703928 0.82327407 0.87959851 0.89058941\n",
      "  0.85978444 0.79366184 0.83699547 0.85642    0.85634871 0.84024349\n",
      "  0.54424632 0.81198443]\n",
      " [0.90325198 0.83041914 0.76503932 0.82046467 0.79614843 0.86245299\n",
      "  0.86879554 1.         0.83854968 0.84677901 0.91856644 0.7997278\n",
      "  0.83919019 0.87409887 0.83027346 0.82261831 0.83256142 0.84857084\n",
      "  0.58829145 0.83471086]\n",
      " [0.8762295  0.69115721 0.64007888 0.79065868 0.83791792 0.76828534\n",
      "  0.75703928 0.83854968 1.         0.7675921  0.82564332 0.73329713\n",
      "  0.64828462 0.83752643 0.7284093  0.75759882 0.72330273 0.74869532\n",
      "  0.79743082 0.80516389]\n",
      " [0.79708834 0.74590963 0.65251511 0.71755486 0.70387764 0.78740297\n",
      "  0.82327407 0.84677901 0.7675921  1.         0.84956627 0.79153903\n",
      "  0.81169288 0.69988343 0.73098982 0.77690305 0.90329486 0.7861759\n",
      "  0.55661553 0.8005382 ]\n",
      " [0.9152324  0.83996445 0.8354542  0.89230508 0.81371664 0.91603399\n",
      "  0.87959851 0.91856644 0.82564332 0.84956627 1.         0.84782847\n",
      "  0.88255193 0.85606861 0.8907457  0.8857949  0.84349179 0.87423652\n",
      "  0.55075345 0.82082756]\n",
      " [0.83132586 0.71440844 0.85841533 0.81975173 0.62050242 0.87965575\n",
      "  0.89058941 0.7997278  0.73329713 0.79153903 0.84782847 1.\n",
      "  0.77811245 0.73001128 0.82089004 0.8584483  0.79738687 0.80941366\n",
      "  0.48299043 0.75067568]\n",
      " [0.79506105 0.81069145 0.711936   0.79154984 0.68176437 0.83715484\n",
      "  0.85978444 0.83919019 0.64828462 0.81169288 0.88255193 0.77811245\n",
      "  1.         0.74644768 0.8477369  0.77122538 0.83287668 0.84851834\n",
      "  0.33611899 0.8113308 ]\n",
      " [0.84676775 0.80854397 0.73449492 0.82036553 0.83615586 0.858095\n",
      "  0.79366184 0.87409887 0.83752643 0.69988343 0.85606861 0.73001128\n",
      "  0.74644768 1.         0.89075992 0.74337787 0.68106136 0.84075468\n",
      "  0.61074174 0.76068054]\n",
      " [0.83386787 0.85378887 0.80170239 0.85461786 0.77571002 0.92514616\n",
      "  0.83699547 0.83027346 0.7284093  0.73098982 0.8907457  0.82089004\n",
      "  0.8477369  0.89075992 1.         0.78697642 0.71171893 0.89891498\n",
      "  0.4194548  0.74226359]\n",
      " [0.86061694 0.6614308  0.88336036 0.89642472 0.72132605 0.81869266\n",
      "  0.85642    0.82261831 0.75759882 0.77690305 0.8857949  0.8584483\n",
      "  0.77122538 0.74337787 0.78697642 1.         0.81304256 0.74313824\n",
      "  0.56147203 0.75576499]\n",
      " [0.77229235 0.69807982 0.7158786  0.73220291 0.68724688 0.76790323\n",
      "  0.85634871 0.83256142 0.72330273 0.90329486 0.84349179 0.79738687\n",
      "  0.83287668 0.68106136 0.71171893 0.81304256 1.         0.75010639\n",
      "  0.53584281 0.82445797]\n",
      " [0.85444501 0.92177402 0.7437067  0.79980148 0.72782052 0.91272512\n",
      "  0.84024349 0.84857084 0.74869532 0.7861759  0.87423652 0.80941366\n",
      "  0.84851834 0.84075468 0.89891498 0.74313824 0.75010639 1.\n",
      "  0.4056196  0.77724993]\n",
      " [0.62084984 0.29912025 0.43999625 0.50284972 0.74730601 0.41\n",
      "  0.54424632 0.58829145 0.79743082 0.55661553 0.55075345 0.48299043\n",
      "  0.33611899 0.61074174 0.4194548  0.56147203 0.53584281 0.4056196\n",
      "  1.         0.59291473]\n",
      " [0.82573541 0.7780326  0.6106986  0.82995955 0.75949073 0.79305722\n",
      "  0.81198443 0.83471086 0.80516389 0.8005382  0.82082756 0.75067568\n",
      "  0.8113308  0.76068054 0.74226359 0.75576499 0.82445797 0.77724993\n",
      "  0.59291473 1.        ]]\n",
      "[[1.         0.84343352 0.67282569 0.86150158 0.79791149 0.86170657\n",
      "  0.82323281 0.88009891 0.82845831 0.87238777 0.83841244 0.8881967\n",
      "  0.65606794 0.91144681 0.66622643 0.72092681 0.83762768 0.76402337\n",
      "  0.88173112 0.84536792]\n",
      " [0.84343352 1.         0.81153727 0.92575039 0.760886   0.77734046\n",
      "  0.89282613 0.8611457  0.75575681 0.76019755 0.8270608  0.83879208\n",
      "  0.76740084 0.88760355 0.83338824 0.67635184 0.88019501 0.83834446\n",
      "  0.86607439 0.85844344]\n",
      " [0.67282569 0.81153727 1.         0.8268163  0.67802993 0.68764534\n",
      "  0.85157055 0.72906078 0.70527296 0.49730027 0.78257199 0.65646428\n",
      "  0.8740977  0.78841258 0.86711047 0.60784886 0.80710727 0.88272695\n",
      "  0.71521348 0.83436261]\n",
      " [0.86150158 0.92575039 0.8268163  1.         0.75262581 0.83439205\n",
      "  0.9067924  0.83934546 0.82193161 0.80033424 0.82048873 0.8615113\n",
      "  0.73527556 0.8829275  0.83977635 0.69143128 0.87830298 0.85240673\n",
      "  0.86612545 0.88308039]\n",
      " [0.79791149 0.760886   0.67802993 0.75262581 1.         0.6720053\n",
      "  0.77835871 0.71629208 0.60351806 0.66316856 0.69810133 0.64318701\n",
      "  0.68745034 0.78781549 0.70460479 0.49057749 0.80360214 0.68153235\n",
      "  0.78681013 0.75410846]\n",
      " [0.86170657 0.77734046 0.68764534 0.83439205 0.6720053  1.\n",
      "  0.77064759 0.87786025 0.868786   0.76551569 0.8665157  0.85744102\n",
      "  0.66061335 0.86447974 0.60796066 0.84942264 0.84517905 0.77271163\n",
      "  0.83149676 0.88102578]\n",
      " [0.82323281 0.89282613 0.85157055 0.9067924  0.77835871 0.77064759\n",
      "  1.         0.78143337 0.7938416  0.76010954 0.76731019 0.80544697\n",
      "  0.73248138 0.83772077 0.83767993 0.62736895 0.85817319 0.81209717\n",
      "  0.87132826 0.84861385]\n",
      " [0.88009891 0.8611457  0.72906078 0.83934546 0.71629208 0.87786025\n",
      "  0.78143337 1.         0.77096278 0.7604503  0.88661205 0.83537221\n",
      "  0.74448575 0.9361307  0.65859595 0.83057008 0.84869478 0.7871152\n",
      "  0.8097436  0.86958675]\n",
      " [0.82845831 0.75575681 0.70527296 0.82193161 0.60351806 0.868786\n",
      "  0.7938416  0.77096278 1.         0.74443922 0.79848227 0.81514577\n",
      "  0.64773783 0.80354129 0.63288265 0.77961538 0.8084049  0.81198925\n",
      "  0.8089231  0.85735088]\n",
      " [0.87238777 0.76019755 0.49730027 0.80033424 0.66316856 0.76551569\n",
      "  0.76010954 0.7604503  0.74443922 1.         0.6349376  0.85712326\n",
      "  0.39186813 0.77373589 0.53514757 0.58609088 0.74615999 0.56790297\n",
      "  0.82947724 0.69265316]\n",
      " [0.83841244 0.8270608  0.78257199 0.82048873 0.69810133 0.8665157\n",
      "  0.76731019 0.88661205 0.79848227 0.6349376  1.         0.7728179\n",
      "  0.79756189 0.89281358 0.67241587 0.84700374 0.86904886 0.82187271\n",
      "  0.81236502 0.89230508]\n",
      " [0.8881967  0.83879208 0.65646428 0.8615113  0.64318701 0.85744102\n",
      "  0.80544697 0.83537221 0.81514577 0.85712326 0.7728179  1.\n",
      "  0.57246156 0.84279288 0.65670425 0.69149527 0.79854636 0.75461291\n",
      "  0.86498748 0.81192635]\n",
      " [0.65606794 0.76740084 0.8740977  0.73527556 0.68745034 0.66061335\n",
      "  0.73248138 0.74448575 0.64773783 0.39186813 0.79756189 0.57246156\n",
      "  1.         0.79654277 0.83759776 0.6725215  0.793212   0.88213044\n",
      "  0.66317579 0.84821037]\n",
      " [0.91144681 0.88760355 0.78841258 0.8829275  0.78781549 0.86447974\n",
      "  0.83772077 0.9361307  0.80354129 0.77373589 0.89281358 0.84279288\n",
      "  0.79654277 1.         0.77035288 0.79712757 0.89464309 0.83680114\n",
      "  0.83971479 0.90529724]\n",
      " [0.66622643 0.83338824 0.86711047 0.83977635 0.70460479 0.60796066\n",
      "  0.83767993 0.65859595 0.63288265 0.53514757 0.67241587 0.65670425\n",
      "  0.83759776 0.77035288 1.         0.52540514 0.74865647 0.82698008\n",
      "  0.71615398 0.78682107]\n",
      " [0.72092681 0.67635184 0.60784886 0.69143128 0.49057749 0.84942264\n",
      "  0.62736895 0.83057008 0.77961538 0.58609088 0.84700374 0.69149527\n",
      "  0.6725215  0.79712757 0.52540514 1.         0.73766022 0.70990404\n",
      "  0.66655906 0.82700068]\n",
      " [0.83762768 0.88019501 0.80710727 0.87830298 0.80360214 0.84517905\n",
      "  0.85817319 0.84869478 0.8084049  0.74615999 0.86904886 0.79854636\n",
      "  0.793212   0.89464309 0.74865647 0.73766022 1.         0.83226985\n",
      "  0.87803152 0.92229527]\n",
      " [0.76402337 0.83834446 0.88272695 0.85240673 0.68153235 0.77271163\n",
      "  0.81209717 0.7871152  0.81198925 0.56790297 0.82187271 0.75461291\n",
      "  0.88213044 0.83680114 0.82698008 0.70990404 0.83226985 1.\n",
      "  0.79501106 0.89302411]\n",
      " [0.88173112 0.86607439 0.71521348 0.86612545 0.78681013 0.83149676\n",
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      "  0.71074835 0.88663581 0.85945997 0.74631093 0.80552938 1.\n",
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      "  1.         0.79733637]\n",
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      "  0.79733637 1.        ]]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
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     "name": "stdout",
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    {
     "name": "stdout",
     "output_type": "stream",
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      "  0.77208044 0.81240897 0.81935719 0.73115299 0.61338653 1.\n",
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     "name": "stdout",
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     "name": "stdout",
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     "name": "stdout",
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    {
     "name": "stdout",
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      "  0.74882992 0.87027153 0.83107141 0.85506095 0.79743218 1.\n",
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     "name": "stdout",
     "output_type": "stream",
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      "  0.73899091 0.78036659 0.61160575 0.75607063 0.68376713 1.\n",
      "  0.71375541 0.70554962 0.71527731 0.75838924 0.76010954 0.58465647\n",
      "  0.66992492 0.85562086]\n",
      " [0.83397209 0.7324969  0.77171797 0.80798422 0.73959926 0.75397999\n",
      "  0.81550688 0.79088588 0.64567482 0.7624654  0.78471773 0.71375541\n",
      "  1.         0.85526322 0.71035143 0.71037077 0.75416098 0.82387251\n",
      "  0.67938836 0.7028204 ]\n",
      " [0.87454591 0.70913376 0.76942334 0.82034294 0.78474938 0.77637015\n",
      "  0.82767671 0.76183565 0.72177455 0.79261748 0.80565439 0.70554962\n",
      "  0.85526322 1.         0.78208591 0.74623336 0.7281473  0.856817\n",
      "  0.78086611 0.78844774]\n",
      " [0.83927724 0.82953095 0.80896659 0.79420647 0.79295518 0.89499037\n",
      "  0.77198752 0.788151   0.89698241 0.88336951 0.83763213 0.71527731\n",
      "  0.71035143 0.78208591 1.         0.77395145 0.85144793 0.74124002\n",
      "  0.77618694 0.86942672]\n",
      " [0.76338345 0.77451812 0.7951259  0.8541604  0.87371167 0.87601833\n",
      "  0.67654373 0.71802975 0.69715839 0.8576878  0.73026175 0.75838924\n",
      "  0.71037077 0.74623336 0.77395145 1.         0.85230699 0.78111764\n",
      "  0.85317766 0.80701195]\n",
      " [0.80161769 0.8781413  0.81720062 0.78143337 0.7938416  0.9067924\n",
      "  0.77202146 0.81715373 0.8157685  0.82463585 0.80805729 0.76010954\n",
      "  0.75416098 0.7281473  0.85144793 0.85230699 1.         0.78931603\n",
      "  0.72789382 0.81455608]\n",
      " [0.87115614 0.82943046 0.84367656 0.77371971 0.79323957 0.79102551\n",
      "  0.79889259 0.71310439 0.69078918 0.7539651  0.81212869 0.58465647\n",
      "  0.82387251 0.856817   0.74124002 0.78111764 0.78931603 1.\n",
      "  0.74743119 0.66192345]\n",
      " [0.74215422 0.72322006 0.80367638 0.86932464 0.84163945 0.81136745\n",
      "  0.69331416 0.65421505 0.70798553 0.87206727 0.62939742 0.66992492\n",
      "  0.67938836 0.78086611 0.77618694 0.85317766 0.72789382 0.74743119\n",
      "  1.         0.81158673]\n",
      " [0.76432612 0.7424202  0.75120154 0.85497839 0.77001223 0.87568477\n",
      "  0.79920637 0.77966214 0.8498717  0.86739411 0.76248091 0.85562086\n",
      "  0.7028204  0.78844774 0.86942672 0.80701195 0.81455608 0.66192345\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.81158673 1.        ]]\n"
     ]
    },
    {
     "data": {
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IOTyEzBSNzHFUbCDuCOD2xPmlfV/Dyhf51hVv4cE11/OWY09RZrhYJkC1KAV1Vjgw0HBYpf7s4UIz/0UzVay84EgT5fKkWtSKcglIAKEhsF7wcUcihnQ4qWUJiYYhdOplaVutiugSDkycRhGnJrn90bso6HaONXdjCjsLsowKElimYCFWT5QgQZECu443u0xSejkpVhPP5i7eCSs/FRXUinJJ/M9X7wWC2rKwFUpdFVUso2GhSclB7xriTje+TJJiuI6sp4GCM4wvm2LtyEkGa5pIOMpYpIyU8LIQqSdfdJHV7JRrKzg9Flo+y1vldwkRZ3w5gm3pLM70zI+WQbmsqK4PRbkIWvuGwTTBKI15zng8zNV6qZ+eRV/KYThKk1Ysm43KZISko9QVUs0yGenAQYHPNL2NprFFMi09z25kK3UP+fpWtp3ay6nOLZxoWEvb6CC9bMe0dGpCMxRTOpVGFrtLkIoLcobOzfpj3Km9mcmsRvn/7IyuXLZUi1pRLoGi3QGAvVh43v2hWIwtx09w46FTbD90CF8+iV9myGgu+v0dlGfTCCTO+DTu9AwgweXFly3QHJlluKKJaa2LnHRyk+NJXPYcaeHCLQu43SYgWCz4qBUrtLDEvAiRE46LXwHKT0S1qBXlImuScwzY/diNAi3WLP5CBtPScSeytJyYpGjTWayspHl8gonFCtpuW+aYfzU39B1FaBquyUECrWGqygocc7q4t34niStfTygVYePkEEXh4kbrCTrNCU4UVxGRQQDK3HmWcBHJe/F4czSzwLisIqIHKbu0VaK8BBXUinKBrKxEyKZLw+As0wFWqa8ZJBm7G3cxgzy/1L9mWNSfm8Nw2jjQUc9SzxbWRvupPjpMB0ssTWpUBaLYCmn0bIqM1si4O8gXOm/GXchTlYgwVV5D0h2gaNPYvhgiaJ5mOVqDho09HMfCDxSIF5zoQuKWeWqIMq+FMC0LXVN/YF+u1CejKJdA1u7BU3xu6njXwhjOXBFzjY2U042l2ylqGc51VAIwbW8DTcM5M46w2UhIL//W9gtYQqN7epBbzxykZWmGtMvDM52b+O0rPsaD5Vezyn0Wn6s0ocWJhbQ7SBbOd7tQoJ4VLKERz+YvfiUoL5sKakW5yEyhkbe7cBdL07yTNjf5GRsD7W186sq3ky4LY0tEmV7QafHHMNBZcdQhLJNisIKit4x9LWvJ2px0JSapj61gCsENfUexGQYIgW4Z/OqaP2MlEMRmK2Cg0aBNYTo95I3SH9IOCgRJY5MGMTVU77Kmuj4U5SLL2kuzA31GEmeVwZJeiSdzhnuvvZ4HV13DTkLs3Hc/Fa40m6vmGKSVvL00CqQQrsHQdIarG7EbRbb3n+Vowyrqh04hgR0jvZxta2fP5F46Tp9mLq1R8AoSFR7KZAK3rUAxZWHJUotaAH4rRTxnx7IkmqamwVyOVFArygXQazhI634K7tIfrQmHF6REsyyy56eAb6kcpEWbofaJRTI+J7fPP0Xnd4+TsjxgWbyhro9hewejRiuaZXLt449xqKGCEzuuI+dwctOZgxxu7GHHeB/SKiI0G++fv4frFw/gtvJQDgeCXRwYqiLuduD35CizJ1jGTqzgZtHr4YStiVxGYEnJcDJNV5nvUlab8iJU14eiXGSGrdQ+8mhp3tH2N6QXnVQ0J+m0TeBaWCG0NEu0roW808+yEWaIFtyFDJ5kCoHkeMcGbEaRlpV5NkyP4C3kaUicZY88wBt4krNaM7+49u/414a3s1MfpHuLSTzvwGPlOVfXA8BiwUvK5uRIWRfnqltL5SoaL1pm5dJSQa0oF5nbUcBDhg+t+RjrD/UhgHydky7nBBKBpdvwhWzcw+tICj8WOnmXi6NbNhDzh4j6w9RElxisbqQ8k8RupFhvG2OPOMQJq4fPJ6/hqfLt/HXrr3NfxdXcmj5AKhTEoxVwFksXDefzQcqLCQCkJrDZbBSLxUtYK8qPo4JaUS6wmAFaPo+Wz2ErZJF2DZfI8VT5Nq45foic28GY1sBsxoeFRqGilvrMCotUsEIQn0yh5UwiPh8D7WsBaF6YRbMsJJKQlWSLZ5KxYhX3ajdxcMt1AAgkv9f9v5lw1tBuL+36Urc0Td7uZKngI2QmQZZW7rPZbZiGiWm98NojyqWlglpRLqJW9yxRUcZYoI4dgycpiyVIBX1kLDdPLbbj1guIYIApRwNuPUXECtFiTbHnxH5qYrMMtK3BVcjxoTu/QNfSDFohx62Opylodh6a6sRCo/z8jjGmZiNh83Fv+W5qi6Vp4lXZFfJOF9GCCz9pnOe7O2z2UndMpqBa1ZcjFdSKchF1eqdI4mco2MwbDzyKJiFV5iVvwGLOx6bQHGGRoIgdEmAKG7XWAou/sIqTHatZqGqgPrrEvl27cORyXJfaTzOzPL7cxaIeIOb20rkwRWN8/tn3/GzDHSzZgwAE7DmEtEjnbXhkGs/5ZVf189t6pfIqqC9HKqgV5QJZyRnEDEhbIIpFvDKFz1karyylpHN8DEsTZAIuVrIaIKkMFIgSpC4+R9brwyELJMJe/rb5zdy1++0ALPmDLIfKWT3bx86yM5zS2hlcKuPMmh1EnR7CmSQxu5fzb8S8q5Lvlu9BAmV+A1c+h2UJLMPEmy+VR9M0NF0jrVrUlyUV1IpykbS450mI0pZbzStTeBNZsj4XphBEswIQHLDtIC+cyLQEKWkXE0wGaphyVVCWiODIZ8k6XHx709WsbThHXrfzyGw7ptOD5vETdXnQpUUg9wP7MArBN6puQgDV3jR2oxTG0byHsJlCO98vbbPZyBSKSPmjO84ol5YKakW5SFrcC0xRDcCa8XM4swUyfjdpU5ZGewAThWqahwYJJBIgBD0MEdXLcOSypLwBapZmecejd/L+/q/Q5+1h31wjWjRL0e1joK6F4frSULtbzhwovakQICWng6tJay4qRPLZjb8iBQ9+0rjOt6J1m45hWeTzajr55UZNeFGUi8AmDBpcS9zruhErBy2zpcX6M34X8aIF0qIpbdD58GO4czlOr1uLlJIOMcE1z+jo5PiH9/w671w6yoat/cjQMFI30DdU0DrdyBfiLcwHyvntk1/BJh0g7eiWganZ8JoZ0jYvY+561qRHKPrd2JNZInkPHjK4iwbY7c/2UycSCVwu16WsLuWHqBa1olwEtc4VbJrFqKsJSwh8iSyWgGWXE0yLbRPzrB+ZIu31cHLrOs6tXU3a48JDDioEJ7pX88lzn2Pd+rshME1uaifJ4RuR2XLKuh7h9zof5X1Hvo22AM3M4BE5epbGQEoac6ULiyd8q0rdH1WlqeOLeQ8uPffsyA9dfy6olcuLalEryit09OjR5922PfEE+YP7seViRPzlaEIgpERz2GksX6KITs5yUlGI4o+lyXldJEyT7aMLBDN5TmzcgCUEw+1dSCkoejTIwt9vez9vXDhH5aoH0FZWERt08p32bSSn8xwIvYV/6Lufmu4HuKVd4/HcBhJJQcbr5srhQ5yp7mTBUQFSciS4jvcs3E+Xb5lDhFjOeWnTsjiLBlJKNE3DoWsqqC9DqkWtKBeAsAyEtEiUhVhs7qAqmKTX00X1ygrbDx7Glc6jFQpsGZonmMlzfHU7g6tWkfF4SDndaEBtcRGAneOj1Hc/hB5tx9W3whWHjpCbtrjy2JN4fB4+2vMeZvtvxQyNsqt5noC7tILeTfkjAEQdZYQLMSZcdQCsMSewhKAobTjMDAIwDRMAt92ugvoypIJaUS6QpcpqFuoa8BaT1MpFDuhrecODD1Azv4gANBMCuQLRgIeZlk5CkQjL1ZVEvX4MTWNjpo9zmRBb244iLBvTg7VEii4GY438whPfRhMWBd2OJXRGIg6KU1dib9pPIlwKZLsuaUlNArAqM8a0qwaAkJnEGyhdUnTmShsbGOf3cnQ7bKTTaUzTvMi1pfw4KqgV5QKIlIWIVFQTiK6wKn4GgJbHpnAUCpxd00Pc6cBuWiTcTjRhJxsoIxCLEfMG8OWyJF0e8MDp1tswQ6PMDV9H1fQ5HDLPxPX1LPg8aHiwSROk5ETDWp4eKkfPVNDTdhAzE2NOVvFPx/8cIS3KizHmneUUz3/lKypKIz1yaYmhiee1qKWUJJPJS1NxygtSQa0or7KMw8lUbSPuTJrquWlqnVGiEx7axiY5tXED5ZkVHJZJ1mHj8at30dfVgbAsXNkcw5X1hLIp/PkUeiJNTdMB9Fgr92aDnF6pQlb6qV8cxtIEu8/28eahR/nWid/lM3Mf5yPm14mMbcT0z9LZKJilmiusQTz5NDOuGiw05pyV5IWd8kBpCF4s5aAgNMxsady1+/xUctX9cXl52UEthNCFECeEEPddyAIpymvduaZ2pBDUzkwigEpnlIW+IEvlYUba26mYX8JdNOltqKYQqmCxto7qhQVWnztHxuGiimU+YH2ZZM1qTHeUgcnN2NIaHeVRdq8c4WiiiaHWHkJr8vzj8j/QlZ3gifIdpBxebl26Gz1VQ1PjcSazHgyh87qFvfT6OqnIR5hy1ZLW3XTKOUASzztJOF0Yms6k1BnGhq7rKqgvMz9Ji/rDQN+FKoii/CxY8XqZC1dRvbyA3SjS5F8gGE1DHDZ0jvBrha/iyBgs+t0kqsKElxfIeTzYinme2byDbfGz/DJ3MZv3EGo6hLbSzemaCrYsneKWqn7+QXsLtqLBDf5+2roXiE+4+ZDno/yvrj/kY3Uf5uxoFeZEK2Zgms56yaxVxS3JAxQ1O12ZCaZdNejSoCk/j9dRJGM4CGkxBLAucZQ1kScJOUzyS+OXuiqVH/CyhucJIRqAW4G/BH7vgpZIUS4zPzwM739MTEw873ZwJUJ/TR32YgFvKkVn9Qxbw/1M7Q1R9Ng4VL+BVf1DaBa4egrUG2miKSeRcouFymqO93TxpfE/pmjoDNbvocr1ALNDV/Pv0b/EKocj9m70s6XlSidPSz7v2kLjaIY75h5krLOZ0epWnsm20DU5hbPTR2vlKKNL9WxPlvrI6wpLTLuq8Zul1fUCFZL0rIbHSmBSTt5ygp6jzAlz6QtYocpP7OW2qD8J/AHwoovVCiF+XQhxVAhxdGlp6dUom6K8psQ1QcLrpWFxnlZvKaQXEmWk5lw8sXUng8V2YgMeZJXFjrYp1oQmiIcrCEYjpHw+Pr74n9gpIqwctXW96Mla/r78Sj7a9GEmjXKOnw3iMIusKZtne9U0k3oNp2vLCcQWWH/yHOOBOsJWAdfeFObcWmTVGY54NlBdiFCfnSei+Zly1aABFgJHuR0ATzwKUpKVdvKWxNQMMsXnRoIol95LBrUQ4g3AopTy2I97npTyM1LKrVLKrZWVla9aARXltWLKZsNuGLQkJtlReZqFXIjopA8swcGu9dRMLWA3TPaXNTAcr6a8PEWyrAxHLk2rb4INmQESBQd3Bd+DWTbF4vQG3nX/1/CdHONLM9swpMZ7W4/zutohbNIigIdArsDJ5hqWvH4sTedLt/wqA82rGF2uAM1kTTiOJWFr8iy9vi7m7WEAZp2VVLpKLWt7PI2DHHlZmjbucZSG7qXTqll9uXg5LepdwO1CiHHgTuA6IcRXLmipFOU1JpfLEdV1GqenWVsxgJBwcHIdjsUiRpmN3fljNA1NEvW6SLmcnJ3p4GSmtH+hr9bgF5MPMJYJ87WxjTTVLiEMFyOzRU6s3oZnMYYnm8ar5yh3ZpASQodz5Gwe/uV9HyHj8rC6/xCaUWSgtY0//c2P8uWq3ejxZrzVY+TQ2Zw4x6KnkpRW2gF9zlHJqsIEEolIF3GSoyCdAHjspaBOpVKXpjKVH/GSQS2l/CMpZYOUsgV4O/CElPLdF7xkivIasrKygmZZrMsMUF6ZoG+5FbMoKCzrTHTVc+34UfypFFG3g675KJ5Ehr58B3XWHNc6DrGY8XN3dBf5hkb0mlMU5zZybtNGcl4v2vndy6+rGaNoaEgDzJs93H39LXTmpjEClYTjy9y0/2HKjCQfO/wfjNU1EV/qwCyb4DHX9WxIDQLgzGaxECRtXqoLEew+HTMvccgsJnZMqeF2SECqoL6MqLU+FOWnZJom0WiUukSCtvpJ8jk7gytN1BQWkJZgsrmW9gcnkEDbcmnYWzbpInJFOU2LwzxUuYcZVx3UQ0/1IlIv0L9ci28+zfqTe/HkDV4XG6BlTYzvz7eRHPbSOb3CV85f10+53Rxsq2Vt31Ee2XULt1tPEfxChINXbuUGHsdV5mHNcimow7kYc84KLAQA3gqN+LiJ4bFBDubclRSEjs0Ow8k0Y4XS8/pml5893/fUVVzE2lXgJ5zwIqV8Ukr5hgtVGEV5LYrH40gpWZcdpqwsxcxUJZbUCSYTCJvFVQ8fRqYh4XFzsK2Ocx2NjDU3AjBZ2c6SFcK5MIU9G6W6egA90UA0lqF+9BxCSvRQNRUbMhQyOtOTZQyFg6Q36JRvSoCQ2KTFusl5hDS45en7+ZZnD28KPcXKioWeqsFRuUhB2GnLTBLzhJh21uAzsxho+EISEIhiaaeXomUjZVpoNklezU68bKiZiYryU4pEIrhcLrp8/RQKNhZnypBSokcNpNRwZA0EcLqxElfRIONxMdrZBUCbMYJz6BxhuYKrwoUZHCcyvxqSNtyxJVzCgbtBEAjlOJbp4d/f8buk/AEO2JrwtudJXu8n73Ay3tyOJ19k5/GnyY468ZUXeOOT95Ne6sYMDTMmmlmfGmKsrJkFe5i6wiL93laa9NKmtyRzgIVplv7I1u0SWzatdnu5TKigVpRXSB8YoNDXRzabpaWwRMi3xMJMGKsAWqGIzAqkBEfAIOlxk3TaKMsVSJdVkPb5CGSX8cyN4tCKLFSspju8DFIwMesiaxSwNJ0rewe4Pf4wSenhkYrraEsuIyobyJs2vjmzkXbnLEev2EouHGSpuom418WNTz3F4NlKeryzjCcqQbNI+BvZFT3OtLsGw4C63CKn/F305MdLFxSTBTTdxDJLa1Lr9lK/OD+4pZdyyaigVpSfwopV6u3dxGkAvhm+iUe2XUU4HgPAvd4gF3UwWxECIZhraGW+vgmAK3J7mUiHcZR7yRRNfDWn0ZZ72DXwOJVL03TOLRMOZSgLZ/jn5nez4C+na3Eav55nobmbtjMRFu8PcP0jT3DN03u5pvcsAMfbmzHP2jlduYG5BQ1huCAIzclZAFK6BxsWE656QkYSPWBHZopouolpPdeiBpBpdUHxcqAuJirKK2RJSdSyCAlJi3WGaDRMQvNRvTxP7cISCEmZniIng8wFfWCzMdPWjjObJRhdYdH04NSKiEwlre0S0x1hbmwXOSOPTVg0L8VwvEGyYivji3W3c9vwA9TPzWOTkpsOHaZg0xmoCdHVsMzsbD3VC0vU+uwsBP0slwVpOjbGyFW1EOlAC0/QOr0CUrLkqYAIxGx+AOyVTnJjGTTdwCi4kJaJ7fxYatIpqKj6ySvn6Odf+P6t73+Ftf3zTbWoFeUnMbH/2SOdXcYE1meO4iTD0owf3Sywpe8MUgN3VYHlKT9ph520sChWNSAR7Nx/gOsWnmQkVU6tL8d0QzNt4alSy/epERK6nYZIgpFrWqhzR/iPxrdzzd4HqR/ppXJqgCsOHmKpspLHr97JRE0Zz8gmfKElsrqgInl+i6+gH1uhyNbDR4jE6jG9i6REiG0rvZz0rwbAFKWvf9BXQFgSm1m6oGglc+i5DJZuQ2bUpJfLgQpqRXmFFjU3TmnSpQ2RM51EIl66x8fwZzIIC5zlRYpLOrPhAB7dSa4sxLozvWiWyXh1EF1YZCJO1pw7hV5zGnNhPc5IDCFhqHMd3bWLRGwBDqeaeZf5OO8tP07XTJSFqkoWKytIVdVhlDeTNBxMlwU43tlMXSwFEopGjrn6eiqTaZaXSjMOZwJd3LryNMd8awCoLESZcNVSYysNGXSk4gDITBFbNkXRZoNXEtRHP/+8/6E971BeEdX1oSg/4IUWYPrBxZd8kQgAWQOSmoM2Y4UaxyLD2RaEKVk1OsxKWRnl8ThI0CRoUpCtacKXSGBLJZlZ42Ql6adRi7H6bJRjH3wd0naahclyFsMJ6qNJNs0+QVftPP9Y926u7duL0+Ni/FAdwi3Zd9VVVC4tsf7kKU5v3IDPIxif1Aj78iS9Xvx5i5lwGavPjBAJBtn8jUdIbylDBrNcP3OCT3R/kAU9RGN+nnPeNrpS45yiDZnJg0OSxYGbLEW7A6dqUV8WVItaUV6B/hggJTVMoAlJr+yCvIVumhQCDoQuWVnxYWgCyx8kGQhQOTnJUNhFvW2FvGWjdjDDUHs79VVD6NlytCdPg4BT264id72PuO6jcCzH03XbGR6oxZPJcGjHNq59/Al80+MEF2bpPtdHyluNva6KeN7Gwc5GOucWkEKyUObHlc/jLZpYsVa04BSd2Sk60+Mc8m2gKTtHn7edlvwcRacdK22gCQPr/AVFw+GAbAZpvehabMpFooJaUX5CloR8ARzSoF2bIG25iaW9BOJJhptaCKfiuEIFWAKERu+6NYhclhWRpcafZTYTIGDlmGppZmxbF1b5AMnZdSyW+cgHwgT9BdZrwzzivILbn9rPG/fvY+uZUwx1dtLVP0BvQwVzASd716xi7ZkzrJrvJ1rWRLG8FqwcBV1HAmdamvBks8SCQeLxakzPMoP2Dm5a2c9x32o6U5Oc87WjY2GvcFIwNHRhPDuWumh3lE44q1rVl5oKakX5CcVyICX4rCxNYpoxo4GGsRkAeju6ccbzmFJDl3B6TQ9FpwvX4hR2S7KufIWZbBn1qSSPb7matvpS//B0n4mpCXKVdaz19pPS3JiTBWaDXrYMDlC02zm0eQtPXHsdC21dOFqreE/DYewtsO7pU7iMLJnKJvL+cs41VhJO58jpkpGGRsoSCYqTpVEcY/41XBc5xKC/mUozypC7NFTQH7DQ8ga6KGCaOlJC0VEKaqlW0bvkVFAryk9oMQNCQAcT2IXBRKaOuslZ0j4vDsMAS5CP2DE0jaGubrR0Els6QVUZ5DKlTWWzmyt48wPfxV93Cm2lm8LkAqa/jERdkJujzzDsaSKi+Rlv78RmmpxZuwZhGkxW1JINh1hxNXEnb2JiYxPPrN/GycpWHlq/jbtveTcP73kTK2Vh7MUcZxtbkEJQ9+RJhOFE85tsT/Qy4a1Bx6J+eZGs5qTansBmGuhmDtCwpI5hL62m94ouKCqvKhXUivITKJgQzYHTAa3aBHnpQIwWsJkm8WCAhmhpUgkS+js7wGbDuTKHP2+wuXyCvlgllTJNYd7F8q2bMd0RFsdrMXSddGUjV2rHyGt2mlPTRNy1rBqbJOXx0Nnfz7vv/QY2X5rPX3Ebz7St4WDjan7zmj/lT3/j9zjSsY6wiGHpgjNdG/nML/42AOUrsxxev5HyaAwt1oI9OINdmnRlJ0hrLq5YPFOaSk5psw8tX5qJaFg2LF0Hu10N0bsMqFEfivIyRSIRzFgecOKSOVrEJOOFeurHZ1mpDFN0OFg9O/Ls84e7uhD5LFo2he4vENIjLBdauSowhnf/IgvXCbSCl4Vj80iPl/GOJj46/UUO2DdywlxHWTJLKBolW+fEN5NDInjn5+5k9g4Pvau3AbA6MkLr9ALXHtjLtRzAsUrwb+I99FY2ESkrZ7rWz1jbRrb1nsJaqEKufoZpUcn1kUOc9XXQkZhiuKKJG9L7gRqyBQnOUlAD4PH9xC3q6f6zsLL8go81vJKKV1SLWlFeLikl0YIdj27SyCxukWd5tAxHochMcx2aYeCM5JEarJSXk/d6cUQW0SyLq8tn6Y9XAhLXsGB0fTdU91KcWU0RiNR3sHZplH/i13i6uIukCLBSVcX3fuF2RnZ3sxwOMdzSyL+87bfpXb2NhvQsd5/6XT7X+3/oXBpieFU3J7JrSBkO3iQfZf3iNHagenGagcZWzvSsxj1kgJCcdW/nushh7i+/ijAxokYZISsFPge5og5Yzwa18HiRGTWN/FJTQa0oL1OxWCRvaYQcRdrMMYqmhmcoQTwUIBEMEIzFQELOpnNm9SowDWzxFfxeSYd/jrORaqpFGse8ibGnGjSDmbOQq2/FbrNj5W3UaAsgLbbHjrHt0GE8+QR9chX3vf5W/u+v/j4DzW188O4v8Mwz78ERM/nTit/ha+tvxJWIcXz9ZoYG69nAOTqKQ+i+IHbTIJRY5p/e+j5sZ0sXPIt+b2mjW3ctldoKRrp00dAdEmi5IppmPi+oyeWw1P6Jl5QKakV5CYWJSQoTk6QiEQSSQD5OuznKxEwtrkyO6eZ6HPk8/mQaEBTsLhbq6rHHlhHSYk9wnPmsj6jNR4ZKHrhmF+HGo4iVVqZsDRQD5QS8cT7AF0lZPq7iKI1949RPTfH1zTfwrU3XkLM7ufXMAf7u2/+Ptz75EMMHanjI2s1O8wQr4Sq+dPUb0A2DI63bOCfauN3+BJbTiQRuOnQ/U1V1nKxpQs+GcPtjALRnpqiWy6SKPgAqHGk8qSS6VvyBrg8vAMVE/OJXvPIs1UetKC+DJSU50yQgcpSLCAFSjA5XIV2S5eoKOs8NIWRpEf7RjnYAHNFFAn4Nq8rGb1Z8guM3bcCwO/jjwbsx3d9nYHAdptvLsY5Ovj3yIb5ZdROfbn4TVekl/uObH+d7176OqdomhCUpH5/GHdD45pbbmLn+feSlneWyEAWXA1O3EQlWcM/WPdzae4Dvha/nj1b+i4meBrrnJvDGU7QujfKp29/OP0f/EwIzZOIOro0cIeIIkM27iGoB6mwxpkw7OWmBtIGUCO/5oI7HcIbLL+En8PNNtagV5WVIGUUkEBI56qxpsgkbzsUcs+db0zXTC9h9BkVdY7S9DVsqhigWmdrQw3XbP8ephnVs6z3MX3zjr1hb9Qz5TBmL0XomPX7uSD+EAL7lfx21qWV+89Gvo1smRiHCp//iozQtzPCpt76HT970Tp7o2cpMqAZPPsemwXPopglAoJhkQ/Is3mic4oqbqB7ggzNfIuEPI4wC133/AWYrayishDF9CwynmtiW6OW7ldfidGdYIkytFgUgKXRAYCsWn2tRn1+2Vbk0VItaUV6GRLGIhsArCtRbU8wMVWJpgtnmetr6hhBITDQmGuoxHE4cSzM8+vq3cappHTcOP0333r1sGZ1k6W07scqOMdG/E9fCJCM3/wL/efqvOSFX0zI7xxdT/5u5wwEW6kN4UxH+6pc/zFRNPVLTcZhZNk+cYNXEHC6jwE2PPkKdf5GBWzr4ePeHuK/5OuoGvoazaHLUsZY9+UPsW6mggA2vrrH7zPc5Y9WxFpipXM96hlmylbNKG2XFCrGJGUCSL4JfgL2YR9js4HBSjKuuj0tJtagV5SUYlkXaMHDrOj6S+IoJ8mMai7XVaKZFzfQColFgpnQGVnUjjAJP7LqVU03r+MjI53j9976HO5+lKhinuuUAmUyAeK+dwaZV/MridxBScnfgJj6a/QyxWQ9mXudUdxtfefNvMNbYgmGzc8fcQzyw/zfYNj5CY3oWW9HgwJU7ic94mXvE5DNPfpSdY8f5zE3vZMhfw0ChEzsmHdWlfQ9Fscj6vjPcV70OAE8IzCKsXx5gXaGPRSpwUcTu1xDZPAC2QqFUAR4vxUTsZdXV0aNHWVlZYSVdfMFDeWVUUCvKS4gXSwHj0nXq5TTxcTeaIZlpbaBhbAqBpLomwnBbG6lgmMFQDWeau/nNqTt5933fJupw4s/m6b91DzbfMouD69ATMfrWreGdc/dzjHVkwzYcdpPMWTuWT/D3t32Q5XANSMkfDP83/zLw1/RZqyhg5/H121h/4iSxQIgj27eydnaJA5ON/Nv4n/OmmUd5aMdujlT0MEMVG4MzSCT2ZAwrXE3jeB96pgKtOk50zMvu6FGSdh/nvG0ABD0FvMkkUlg44jGi585RLBTJLi4y9NTTZI4cu5Qfxc8tFdSK8mNIKUkUC7h0HZumUW+MsjLsJxXyYrgEdRPTCA+MaQ2cXLuKmMfHU+t3sm3lFH9w5rOMzFaS1YGWWiraniIZrSV6OMqpdVfy27N3IYBPtrybt6w8xqedb8WKaXxt9+2Ymo7UND42/Bl+b+ZLjMt6TrOadSdOY8TgG9fcQvvQMBPNLST9QRqnk5yer+CvR/6JK4eP8diq7Xyl7HZqZZSgPYcwi0gJLTOzmIkGzPAMs7MVVNlj/GPD+zhVVgrqWkeSUGyZmMeD/fyqeZbNjjANsMyXrK+JiQnS6TTpvPmCh/LKqKBWlB8jmUxSsCwCdjtumcK9ksGI68x31FI9tYDNtPA053m49nqkbmNv+zocVpHPDn6C6Ckv0+FyLGFRdeUCmm4wd7AdqdsZXNvDO+Yf5LBYz9HK1YQ/t8B1X38GUwgeuXo3RYeLO/q/wwdm7yQn7XxFvIm66Wk8+SR/9h//xEBXG2WLy7iyOQ7tvIKGSJK52QCJtM6/Hf8L6pbm+c+et7JgC9MTKk0PD87PQriSaKq0kp5hBTALgpuG9hPzeYkSoNqZxmaZxO0ObOeD2bLZS5XxP10hykWnglpRfozZ2dLaHX6bnRZziNioF2kTrDSEaRyeBiQH1+7ALOTpb+pipryGPxn5NI7FLLEJNxkdGvbU468aZmlkG5nJFQ5v3s3/mvoKAH+26jdZszLM+FuasHTBt264hdmyajrGzvE7B7+CjsV3rZsIRJPE6z088tbrSQe9/O0n/4Y7b7mNDSeOk/b5OLd6Feuml7hvsocx4eW37v4sObuDD3f8EZtDs4DEH53D0u0spUo7vuTXNhMZ9nLj4gFO1axh1NFIla3Up10smNgsk65jB2gaOleqjEL+Yle/cp4KakV5EVJKZmdnKdMKlBvjtGdPk5h0k27y0dI7gW5aLHZX0Se6kJbJodbVrFke5L0L32PpiJ+l8jDm7h4qOp8ivtJE5PEVLJuDye5O7lh4mAG9lVNVqyiL53jIcQ2aJfn662+nJrvEzQceoKKpwAIVLEZCtE4MMd1Wj+Gw89htuymYBXYceZSzISd6Kk5/Tw92S1A/l+LgSgtRW5Erjj3Jk9Xb+X7lFQRseVK6jj2fZylVGuyV63SwOFZGvXuJ1tlxDlauo1JEQRNYhoEUAkPX0Y3zXRbFHx/U49/6BOmpXox0DLOQfcFDeWVUUCvKi4jFYmQyGcJ6AZss4BmPIU1BrsZD1fACpiY4vmELtmSUo2u2k3G6+cvpf6WQsmGu2Bi9aQvNmx4jm/WTPFlNXtjZt+06fm/ii4Dgzqab8BlpFsJBNp06zd++9zfJONz83rkv011vEtYTjE7Wsuf7T9I5MM713/4+geFJnH0jnG6qxp7LYpWVseXYcSzdxrHNm+hYjFGWz/I61wDvfPgeKqKLfKzjw9SH0xR1nfDCPJblQU9XQ3UMLSkpZnTev/8eegOd6Fj4Kpz4kzGSLg+GTUcDdNNEvETXx+jIEqlkjmLRxDStFzyUV0YFtaK8iJmZGTRNI6QVqDSniY94MIM6ZadXEMC5DWuIa2VoqRQnm1exc+EEW6O9HD65mmd+9fU0X/F9Coad4d6rsCYWMW0OJro6ecvSYyTwcGfdrTRG55l1V5D0+ji4bjN/MP45qiKzXFvcSy5hI7bi5p5338x4ZRlTGgQHxin6PCxv6mHTfJTdZ4ZIh8ponJxisqWFuN/P6qkVxrQg3YtR3vTkI8y7Kjm08WoAnPEFAIxEAyI4RdblJDbsYffyUZaMMgC8IY2KyALzgTCGXmp92y3zJbs+5gwXWUvHRGBI7QUP5ZVRNacoQPSubxC96xvYnngC2xNPoD/+OHPj41QLgTYfp3x6lHzMTkHasKUMTE1jvKUZV3yFQxuvpGCz86dT/8Hd8T2M3raN9g0PkC866D1xAzcsPUqk6OGpK27id8a/hobFqWA3SZuPFVeIXSeO8O9vex9luTi/NXUXVdk5KuxJ5kZDPL1rE/bhSc7VVeAqGuwYnmH1UpxMbQV7b76SsmgSnIL1p0+DlDy25zpC6TyZKTeyRvLmJx+lJrXE51veSrqsjKguEIUc8VQY0xXl6at3k5h2U+WKsOPYaYpCx6h0EoyvsOTxY9p0AOym9ZJdH8qFo2YmKj9zTj/2EADjE+Mv+3f0/gHC5eVEVlYASGg6ebcPTzpBIpNGn8mC5sIdz2NqGhMtzWScPipXhjnZ8R7WLw9izkiKna00r7oXI13JiTNXETM1ji7XYjqdDHf18I4DH8dC8MXaN+IoFlj0BhlsacPQdd60+Bg6Fo2uKIspD9+o3U7FsXMUPS6Wtq6BWIrw8BOU9w4TDwc4sWsDA+va6Tk1xFRbPZ1DQwx2ddHX0cGq8VFGm/x0F+LUT8wSW+XnyM497HnwHjxjfSxWtRMErI4gPCowMjrNYzOM7ainlmUOt20gR6mPGsBumgjTVKvoXSKqRa383BrL5hnL5jk7v8hsIsnZ+QXGU2nGU2kmpABpEY8n8NriJCed6C6LWCiAblnM11fjyCY42HklebuDXxm4m8GuK6nruRcRa+bIiV2YsSLrpo8RLXi4f/cb+dDI13GTJ6c5eLx8B1IIWuZnONfWRfv0BO+b/R5SwmwuwNemN6FHk8Q6m5jfvYVsdTmT3c08ffNOALY/dZyayXkOXL+dgtOBO51j9dnS2h+P774WmylxxyxMm+DWA0+yMTLAkZZtLJbXIIQgUlrWgybPMnONlSQmPKxf6WOWKlYnR3hm63XYcmnSjtJ2XLbza4oYeXVB8FJQQa383HJNz+CansG7uIgjmcC7uEhgZQX/ygpFuwNvMknZyjJdi9NIQyNXsKMXLZJ+L7N19YSXhzixehu1iWX0ikqqVt2PtrKKs0+1U5QuYkWDVEKQCwWYaO7gPYvfA+ArNW+gqDso2uxMV9Wy7ewp3pO/F08sxl3j63h4rpuc083SplUkm+uQ2nNf077Nqzh+xToE8PpvPAoSju7eRPXcMpHqIKvPnaMyk+DbN9xCYyTJssfFrtNHGXLV4S+m2LftBpwuJ/aJGfRMBSHvInfvvoVC0kbAlUGOQmNhgbTfhyYt5kOVWEKU+qgBM5+7FB/Vzz3V9aEoPyTvcmPa7XgWEwgkzpkipkdwqn0DG3rPEtlQgcwXyLWGWfaE+KfB/ybU9RBiYT0rdxZY2VSDY3mOQC5LVtr5ztV38IGhuwiQJqW5+NeGd4O0sBsmnlyWPceeQjenuCff/WwZ7Lk8Vcf7ALA0jXy4jGx1OcWaMlauqKQoXNRElvmNg1/l0NbNmGM2gksJKuYiDHZ3M9XUSMzvx5fP4igarBoZpzm0wNda38B07z78NidmsoDun+Wp1b/O7zi+RCFvwzmahTrYHDnH6bo2nJFFDF3HaZpIwHiRoP73x59k1FNPTmp4zWYELzzC44ZX/dP6+aBa1IryQ9L+AEiJN5WkqbhAPmYnU+GhenEJoUuOd22kM3aCR3pu4vcH7qGq40HE4lqMT88z0dqJPZfFsTxHMLHMUl0DC5X1vHvxXkwET4S2seIO4UsnKdrt/P5X/wtvdJqlvA+fLc/m0AxCWCxsXcPCtjWsrOuk2FBOIB0jfHaY+icOs/7UPtrrp6jelKCtdZ53rDzA2s2TbLlxhNbrl1i3eJaadIzPv/FtePMGRSG4bd+jLDqC+HMJnty8B8PjI5WqxPQusGnyDNNraskt2QnORzHyGjdOHyQeCDNeUYupl4boYbO/aFCzMopD5rBRRGCiIV/wUF4ZFdSK8kPSgQDudIrQ0hKNS/MITXK6ZjU1C4voHYJMQaO7MUJ7LMKW9rvQ482c3F9FNlBJMhjEMz+NqemYQuO7u9/Ce4bvo5ZlJpx1fMZ+Kzc9eQ9pj5/rD+2lZ+QMRY/GL9Sc4Tc6j9ASTKB77GRrKygPFnmT6zB/6L2HD7U8zeu6xvGWaxyLNPCpsV38ufEOznyrnsF7q/ii5zbGFmpxagVuaDiAQ+bZnBzmZOcqNGDzuTPsC2/mHUP3MNHQwazHz0rWD8B25rh/13VgCRwug8ikl3XRfoKpOFGPn6TNQcwU5HUb5ov0UUdnpjBNC7tZQJMWvNihvCIqqJWfSQdjqWcvFr7YkTJNUqZJBkERQQZB0uWm6HThTCRoWFwmH7Oh10nW9pamUfe3tHG79TBfr3grb677HFo+wP6+zfT0T9C7fh2VC/OY2Tg2s8jZni0Ybg9vX3yAgtR5eKCJXXsf4eDm3fjTCX73zs9zYP1G3h08SZu/dHWvP15BlT/Lr8zdzR9O/zdr04PsDWzhLxt+g//qfC+9O65hdvdW8n4vnqFpHtm6hlzWzto7z/Ll9l9g5MEqJveXszFzlgVXNRu6RhGA3ZLsPHWcrbOn8afiHGlfw8L5GYq17jSHylfhqihgGDqRcR9VIsLWyQGkpnGsYzUARb3UopbyR1vGllUE5PlDebWpoFaUH5DzlVqZXcMjBPQc0tAwnDoeI4e7Og8OC7sPbq18HKkXONa3h01PnWK+rg5T06gZGUQApm7n0Z2v552n72atHOJ0pIYZZzVffOsHSPhD/NY3v4ClafRv3IjHXUA7f7Wo07PILwYP0Z0Z5f7wbj7e8iG+U3ETi46KZ8to+D0s7txAtKcNK5vl6dUtWIbBjiePc3ZzD+Yk1D4ygW6Z9LlWEe5KIYE3ff9Bvtl8Czeeeoi58lqmCi60fICgN0Lz9CSRtWFkTiBXBOFsnPalWbzZNCdburEQGDY70jTJZDI/Um+bsme4IneKK3MnWSUmXvRQXhkV1MrPpRptiIbWPA2teVq7Jf6rKvFdVUGxLozXyNA6O4epaTgCRWyLBayiRqzGz42uwww07sQMjjE2+DrKzi2R9HqZaGmhfXiY6YAHgCevvJlgOsHrJh9DAINaLfs3XkMsWEHD/CQ3HjvCg1fuYfuREzi85rNt0WZvjLOilU+0fJCHwrvJ6a4XPgEhSLY1sLh9HVmnnYOdDYRmF8k77RgOO0JC09gEZ/Ruok1BNE2yemKUA3VbuHHqaQLJKPs2Xo1MNGD3z9GUivHkph1odgs0iTkBvmKMzaNnyTpd9De2YpxfRW/l/Fjz//Gur36DJXeAiNtLxO1luqzmRQ/llVFBrbzm/c+swv850keP4us9RXB4+EWPF2JYgqxho2VglKTLiUxrOMuK2F0WusOgrDXDNyvehavlaQpTu5haDtA1MMBEezvuVIrW/j4MTZJzeTi+eju/dvJzbCxfYCRbwbl0Hee6NyEsiw/fexe6ZXFsyzp+0fMw4vy3sCBtfG5kGyf9a8nonpd17vmKEPO7NlF02DnUXkf7iQGGe1pwFgqs6z2DQHI0voFCuwNNwrsevR9pwBXHn2KhrILD5lZM3xxuYTEo6yhrySClIDbmpro4R8/iNJpl8kzPBnLnuzwikcjzyjBtc5N2eMicP/K640UP5ZV5yaAWQjQKIb4vhOgTQpwVQnz4YhRMUS62TLq0qWvV8hKWS4CQWKYgu+xAbtI4W7aRhu4n0eONfDO2hdDiAn2draR9Plr7ztLbVAVC8MjVt7Np4TS/rT2ER+TZm+nkC2/7HRCCmyf3srb/HMlGH9+e+F8Ea0rdCDnsTBdCZIQL3fGTtZ8Mn4fZqzajAUfaaqkanSbtcWMvFmianmS8uYUhRxtSwO17HyPqC7Fu4DihdJRvl12J1EzC/hzJjAdHp4WQUEzZaUgvoWkawUyKlbIwk6FKJHDmzBmOHj367Pu7VhbRC0VshQK2QgHLsF70UF6Zl/MvwgB+X0rZA1wBfEAIsfrCFktRLjIpMafzuLJZUhurKItnQUicQRPhsvA0FPB1xZCayWfj76Zxfo58com5ljbcK4v44lHSLgcFu5PBlh7+ePyzGNLGjAjzsTf+OYbdgcMs8L/3/heubJ6OthlO610IUXp7F0WWcx5sXv0VFb/o92A216NLycnGKiZra7CZFmtOn8XSNHION1aNjiefQ0u7Cetpbjj1MJOeSnrZSJU7R202y7GmNYiKUkdM1dQyWCZrp4bx5DKMV9Zh6TbS6fTz3ls31IYCF9pLBrWUck5Kefz8z0mgD6i/0AVTlFeblUo/ewCIHzgqj46z4g8RNFI0PjkIUuBZnePcciX6boPe9j2YoVHOjL2JtOHCkY4jyuuxF4qs6T3DYE0YCZzu3syGoWPsyp3GJQr8d/NbkXqpb/ej459D78+TL3Ow5xe+QLmMY6A/W5bFlBu795XPQRvd0ElnPI2lacy6JIuhEN50mob5acba2oi4S6vjrT8yhC5Nms+cJZiN8235dkLeGP5inkGrnpq2GCDQxwto+RztK3OEk3GSLg+jFbVEo9Hnve9tqb2syQ0/e6wqTLzoobwyP9HfWEKIFmATcOgFHvt1IcRRIcTRpaWlV6l4ivLqi9if31caGFrANpSgaLfTduo0o2U+9nXVc7e+lqV2D4fb34Cz5WkKU1fy/9rfyKr5CbyWTjoYoruvj8mQBylKYTvS0s27x+5DCDjt7eTTTW9HSAvdKhKaiFGM2PjKnjfSHZ+kxZolagtgUmpWL2R92H2vrEUNgBCc2bWRrWNz5HWN4221IGHNybMYdjvznlrSXheefB73skRYkrcMP8CQ1slAKIAlBFNWHYGmHIZdh4KgNrKE0zTwFnO4CnlONnSQzeWwzu+n+C9f/nPyDhempj17WEK86KG8Mi87qIUQPuBbwO9KKRM//LiU8jNSyq1Syq2VlZWvZhkV5acWQSNidxCxO8icb+ECuOfjVD/ez7mWGsrPHqG3vIzRqiBOt0F7LoLZ3UVt9xPosRZ+t+KX6Zkfx1ksEA+HqZqfZyLgIGvXEWjE/EFuz+2jy7bIpKOaX9zwD1hCRyIwNTudj4+Qcbn45nWv5yMTX6SAjbxwYAgbOWknXnRh9/90qzpMtjeQC5fRuRTHMvKcaKunLB6nammB4c5OZuprsIDO2Qgakt0DTxA2Etzt20M+m0Gknay4ykj1+ADJqvERsCxqE1FqI4us+MqYDlWRGnwGjn4e9xNDBNJp7Ibx7KEZ5gse4WIMnvwbyERe4iyUH/aygloIYacU0l+VUn77whZJUX46sblZzKV5rHgUKx4lszKPtjKHLb4EuRR6NoGtkMO2ECF/YIInuxtJF7P4snnWTC9xw9kxbvIPYnUGqF01gpAa35p/A3FPiNXTYzjzeXTDIC4LkM/QvhDF0ATDbWu4evggVZ4Y71r/d8RtfpAShGBH3wkCkRTfv2IXq7Mj7Midps/dgcfKoUnJQjGAZtfQnT/lQCwhOLxnC51zK3g0O/N+FzMhH+tO9lJwOkm4g2iAu2jgswrMJPxct9TLkOgi21qLU1qc0dtoa59HIiifXsaey9C2PEvb9Dj+TIojLatInpsk+sRJ6goj1ExMUhzPPnvkxvM/cjhnY6y2JuHJv4Zv/WqpXpSX7eWM+hDAfwN9Usp/vPBFUpRXR87IkzPyGKaBZRpYlglGHmc6iXFkhqmzUSZDfmpjaa4emOKKkVmaVxLYTcnkITf+DSFM/wzDAzfxWN0mVk2OEMqmyLtciGQUR2yR2liG5VA5AkmtM89JvYdf2fDnTLhrsRAgBJpl8ivfuYui3cbXbnoDnxj+D9K4GXC34TXT2DCYy/iw+2yIH+wekBJnNk94MULt5DxNQ1M0DU3RODJN1cwS/lgSYf3oSIr5xmomOhrYcW4U0+Wlt6EKPZOiLBZjprmR6cpqMl4XdfMJogUPmyZ7KZdLHG7oIeVwMmPVUu2MMt1WXRqPPTWLwzTI+X3sOnOMxUCYg1Ut58v48j6LjuAShiUg3AYjj8PEvp/+A/458nL+ztoFvAfoFUKcPH/fx6SUD1ywUinKBaCbFs1Li0Q8dqY9LirSGdqTScqXU8TKyhhvqGG92ccZvZr463dQXv8gmdFrOR2tILo+yI39h0EIbIkorsUphO5l48QI39mxnqTXzzX5k/zFzt9gyR3m9sUn+E7lDZi6je7xUTqnJji0dQttzLAj1cu9zms5ElzD2yKlr9FswkfKH8A+olExH+Eti98jtBzHUSj+2HMydY1oeZCF+kqmW+uYaaml4HJy+Jot3PHf36U5VWTKZed4aw2rT5/mwO7dnFi3gVufeITqokF/HXTN7Od2y87nPb9CfV0tNePLAIztaKFxdIFVg4MMdnYQKGRZPT7EwTWbeaBtA7/5ma8g/PYfWz4At61AmfP8yJBMBBBw7IvQctVP9Xn+PHnJoJZSPgOoqwDKqyZ61zde1dfLHD2KLTP07G1bLI1f07CKpeZeTgpCmQLl0TT9teVoUrJxYgFfmZ3AcorJxgaWrqjljuL9PLrcwfiq3azu/B5iuYen+j08tnsHHdMjhLNptFwG18wo3bMrLFV6WAr4cOXSjHWs46Ob30fRZueLZ/6IX1v9Z5iajiOf5wN3fxnDpfO162/jb0f/kRh+jtrWsvb0OVJJB56aAjPZALtO91KRypJ1O1mpDtO/oZNkmY+030Pe5aTgtIMQCMvClc3jSWUJRuKEF6N0nh1lzYkBTF1jvKORgQ2djKxqpmdwiIHbbkIb7mfaLfCkUqTLy0otfTd4CwUW0n6uSwxwbyDKQFUTa8fHmdEr6QjMkAm68MXSlC8vgq5jVtnYM3iCezbt5u82vYk9U4/jEXmK/GBgP7+Z3eiOAbCS81D+y9+Az90E5+6BN34KdLXS8suhakn5mVceS0NRcq6+EnfBYOfQNFbYjWciymRzPcHteV6nf5fHZto5Wbudq1Y9DYUAxwZaSTo8NM6McuXMKEJKXDOj7Bye4ciaLdxwZB8PbN/EQEMTD++8GadZ4LsnP0hlfoWEzQtC8ObvP8iasSHmNtfQ4Jln00Q/3+VG9tz3DDULC2g3SCIZLwXLxqFbrmS5toK03wM/0AWiWS/dv6CZFlWzS7T3jdFxboz2gQniAR+2osGavhFObOpAnh2hYnKEzOoNHNy8lW2Dx6iMZ5h0lBFIBnmr/5t82v3rnGzoYGq2jp3xU/z5Te/nvd/4HuvPDfCMz0uuxsv28XM807GeR7Zfxc7ZJ7FZoP2Ytly1pzT24GSkluqRBKtD7WjREZg9Do3bf/oP+OeACmrltefkl55/eyoBIv7cbcOLXdOwDCehWIaUzclCpQ9vweSqvgkybif+xRTzbdX0bJ2mSZvjiYkODmWCXLluEtMVZerAtXBmjGaHi3rLwnI40ZMxvJaPcDpLML5MxuniwStvpK9rI45igX87/lesyw3xn/VvxZ3LYTeKvP/+b2EBn9t2B38y8RkiRoBTttVUbZwmX+ZilZjlZLqRXCjAfHfzK64SS9eYb6xmvrGaA9dvp61/nA0HexEJ6OkbYKkxjGG3EykWcWbTjHW0s+PEMexeiYVGMu5lV+OjfDPzNk40dTEw08EV8hSTa5swvqtRPT+PnsuRTgdZ4zjLztEz3LvhKp5Yey039z+OeJHOag0Lv71A3tQ5Fa9FfvqfKXSVs1kfgbPfUUH9Mqm1PpTLx8S+l3eklp5/5JNg5J47zgskc0RdHhaCPvy5PLsGJinYbfizeVbaytm4bYQ6scD3ZldxIl3Duj3NyKoz5I9vYuXMDIbDSbJtDabLA0JjprqZTYP9jNTUsxKu5F1//kn6O9YTji7yxtNPckNuP0LAw5VXk3c4+YtP/yN2w2BgbRu15UusyozxsHc3RZtO35ZVNHvmEQKmI17y4TI0S77g8ZOydI3hNW1865dv5+kbrgDgmkcO0LkUw1cwsC1MYxOSkfY2QoU0ummRXcihYfGGpeMU7A7uq72aLA5uiezjsWt2ogFrRycom5sgFvBSH1uiLrLAo2uvZ7yikRVf6AUPe2mODf2JSoRD43W/+WGOjp4v6OBDP+U/mJ8fKqiVn0la1iTmcBP1uvDmcmwfmUOK0rC0RGuADdtGccks3x7rYSReTlVzEG/nXrTpHhJnJ8nUt7O4eju6pqMVCkghKOYshhpb+Jtf+i3+7r2/jaOQ480Pfok9o6d4feoZ7MIkh51jvlVs6e9l3eggd93wBr745l/kI9NfYNjZSJ/RiXl+CF5dcRFDasykA+TLy179ShCCc9tWMdbagqUJqudWqM4LnIkY5DKcXr+esqSBy4LE2BJYNnZbvdRFlzjYupZj9jXcsriXT9/0LixN0D46hrAk+1NrKCfC1SMnsYTGg6tvetEitNkWEQLG0yFcYTtrr72R8jU7iRY9yNikGqb3MqmuD+Wy0mu8jBXWnI3P/mizZchXgsSgJzKINEE3s0zKcuIeJ7WpBN1TEeyGhQak2r2s2zqOZQkeGOlkxgxis9to3D0M+SCu8RjzddspusLYTYOxUAUNiShna1tZdvq475qbKEsm+K1vfAFPdIRsZT3pfJ4r5VGKUmNv+RbK4in+8EufJhb287nb7+BPxv+T6kKEr1TfhrYAhlNDSInfzDBVCGFoNnLh4IWpUCE4tXU9LeMTjK1qpqg7qI+lyCxMkWvuZqS1HU2YpE0NPdGANzDHrpFevrnlWv61+T3cNfx7dBozPLF5IzccPUHPzCJDqSi2xnKqiXPl6BGe7ryS3rkR2pd/dIp4lZ4EYMasIO8OA7Du+tcx+bW7CNgW0COjUN5+Yc79Z4hqUSuvHaaJHkngnInh6V/At38C3/dnqDg0RfUz08TOuIn2uZkuBom5nWycWGDj6DLuYimkC20ONmwdJy8dPNrfyoQZxkLQc4ML07PC+OAO+ordxJwhom4v39hyLSdaVvPlnTdztG01CMGmc6e482MfwJ1ZACGQgTBXFY8TEGnsmsUz/s381af/Hk8+x7+/7720Zad4/9w9PO3ewqyoLZ2GU6M7M4KGpC9eWWpN6xfuqxirCjDW1krnmVFO71jFZPcqKiIp9FSCs2vX0LqwAhKKkQqswCR1o0Osmhvn6bpNnPB187bpB/j87W9HCugYGQVvgPhChoK089vT36A8tcJDa64lZ3P+yHu7ZI6VvJust4yC7uWxxx7D39jCklWFLiT033fBzvtniQpq5fJlWtjnV/CcGCD4wDNUfO0hwvc9TeDEDJ6RZWzZAlLXKPicZGq8OCsL9LWXsxTw0r20gvcHVnUrtmqs3TpJlADfP9fMqFaORFCxswPRdIzlyS1MxYIcCq7n/rVX8M2t15Nxecg6XayZHeOv/u1v+MMvfIrdp45iagIKGXKhSky7nV0cJV5wYRmCdfecpXVmkv1Xb2dv2xb+ZuST5IWDJ7w70QulySmmQ2Nn8iRSwmA0TLYyfEGrsejWObtmDVIItj1zisXGCioMyaLNjuFwMNvYjHR5SC/oSFseT1uA7eN9uIoFPtz1MW5e2ct8RT37Nq1Ftyw2jE5jy6ZwRBZwaPDRs58l43DzyOprnve+Dgq4tSLTmTKyZXVk7SGeeeYZPvvfn8No3AmANfzEBT33nxWq60O5vFgWWu8onrklnCtRNNNCAsUyP6nmeop+D0W9gOG2Y+ZAdxYxs4BmEc1JJhxBWpejVBopAmkTgEKLoHvLPBPUcXCkmalAEMvuwKwMU939CGPpLr7L7Yxur2fFEwQp0S2LXQPHWbU0iyuTZOfZU3zyjvfxa9+7i32ru9GsLGZZJRvoI0yc6YyfhWNBVi0P85m3vJN8vY+3Lz7MzsRp7rTdStQfwLtYwLQJhCZZlR0jWnBTtGzkqi5sUFt2QdrvpX9NN2tOn+PM+h4Ge3oYbFnLlcMnGOvspLb/LNEZCz9Q48kwJkPsGj7NY6u38+36m7l+bh//cdt72HXyD2mYmmG2aR3LSzM841nLR/kyh0Y38J2Om2ldnmTdbD8AbdocNk2yYIYw7N7S5rbFIqbdwemUxVU2B9p0Hy9vi4SfbyqolctCcX6e7MF+ykfn0XIFTLudTG0V+fIg+VAZ0v4D/1Qzy+d/eG76dEwKphy1hIqQra2hX7STWeMhFfRScLr4jjg/IaPj/PtpOsnVBf7K+ccMunoQXov2hUmSdjcFu5PXnT2Et5BFQ7LjwBEGmlronhrDZhYpijxV3jweZ4KrOcLMTBmZYy6KeZ3/+6sfpkJmGKuu5ttn/4Yz9k5OOXtACPS8henUWJUdxSULHIq3UvB7MbzuC1u5QlB0a/R3ddM5OMzWgyfYv+Mq6iNLPLDpam4ZOEG0phbbwgQtRTchX4TGcwW0BkHzyix/3fKr/MWpv+PRzbv59ze/gw/e/XVWnxrj0Oo6ctMzfLdlN/8083f0hrt5ZPUeKpMr1CSXqLMWABi3t55f80QDhxNhGmQcLhYyPmq16LProSgvTnV9KJeMlJL88DCRL36RlU99ikz/FMXqclY29jC/eyvxnnZyVeVEbQ5iUnv2KJiSgikpWhLLkiw53AxXt5HpXM/U+i0Mdaxjrq6Wos9Gs2OWjcYZOmbO4ZobI52IcrC6ka/sfB3fKH8LSaOKj47+N0/c+z7Wjg5j6Trti9P4CwmC2RSWYVG3tMh0KMymwXOcbqtFs0w2VS/QmpykeMAisdeDrks+9+u/yPHVa5iorObPJv4DISWPF68iH7AhTIluSAynxhWJkxhoHF+pI1Nb8dIV9SooenRMm4MT29fRNDpD1fICNofGRF0rZQvz5MKVWN4yRLQOR3CKq88dZW/nRq4ZOIXLLPDfXe+kYXmCh3dcQ8Tnx59K0RXPoJkFBma8PGnu4M5zHyFgpvne1lvwN+gERZKipTHvbgJpcpN8ki2cRuo20HTmjDBurQCLfRelDl7LVItaecW+PLv80k867z11zwWStCxyZ86Q3rcPY34eze/Hd+ONeKoMzupeCnPPX5g+V3j+DiKWVVqz2bI5mK1qJeELI0yThulpmiYnqVxawt+dpKkrhswIzvTV8mTHVezfuIeRlh5sZpHthT5ukN/FfdTP+wvf4fS5Zo7++lqEBbuGe6nR50lYIbYeO8pURRmztU3s6O8lVuelQeZYOhimZW6YpOYmvCqFa4PJN9bcQjgRZ491mJ3x03zFcTvLMoTp1LBnSt0wbnuGtZEhFrMeLDQyNRdnSWDDVaqzgZ4uenoH2Xz0BHMVNWiWyUygmvJMmmxNE7nFAvY1o0Q3d2DaJZbH4uah/dy15nVcGTvEdEUzv/e7f8IX/uIPaRyYRu5q4Gwqxcn5cqK1V/D53j/ibRs/yadb38x1c4+zUCzDcrhKW5SxB11Y1MgF5kU1M6IGIYYxznwXW7XaNOrHUS1q5aKRUpJ4+BGW/+M/iH/rW2CaBN74Rip/93fxXXUVmvOlF/iB0koSy2UVDLT3kPCU4Vqc4XX338eVBw7QMDOD1pOjqTOGLAi+lbiG33rn/+XLb/wtZmqa2XPsEX733Pf4gOvPqJ8o547Cg6TmnXz66vcwHa5h2/wZTF1wwrf6fPhP8fC2q3nTkw9TtGlcd3aSdadnqY4sEV6V5viOWsIbMzxetRMdi1XWOL838QXuC+5muNBOrqy0PoctayEF7MyfQMPi4FIjut+G4b84PbTSJjAcAr0A+6/fTjAap2dggMrYMsdXr2P7ocNIh5PJlA+EZKmpi9/53hd5aNUVeCJ53jd1D/sbdlC5NMtUTR1fv/5WAGoPL7C2LI49EWV80cETyav543P/yay7go9u/iNGXB3PlqFg07GEjXmq0aTBtL0BgOS5py5KHbyWqRa1cuFJSWrvMyx98pPkzp5Fr6wk+Iu/iHPVKoT2k7UVTKExV9NC2hPAnojiWpjgqoFRApkCEsHyOhc728cZcLbyf1o/wIGOLfhSca47+ADrzhxBVLvYujOGzAdYWMjjI8fDsa3cc8f1VOZX2D55jjvX3MAdB59gy7HjZO12fuXB+xBA2mFjuKUGo7mct1Q9wrd916D3RbBj8nDFLv7tzF9wbewIU85a7hfXU6HHKZzfscWWM8FhclXiBHOinNF0BanVtRegsl+c4dZxJgwm2huYaaljbe8ZGq5b5kRLN8FojLahIca7W2i3NELeBJuW9/PPqfdT6VyieirCjZ79PF6+A90o8qU3vJnt507RNjdN+dE03dsEA1EoSJilidv6nuZ7Pdcwu66a686coLe+jcMtPTiMIm8/+gSeIiTdVSSKDorLYxe1Hl6LVItauaDcI8N0ffR/MfVrv4YZi1H7N39NxW//Nq7Vq3/ikC7oNqYra8m4fXjnJ3DNDHPFxBiBVBGJYGqNj+7VEf6g+yPcdNVnOdG4mt0HH+LX7v5XdpzaS6DcTXlzPWb5IMvjV/JL+ftYiAb5vbf/CXZp8NkTH+ewv5uP/fd/8Mbvfo/mqSmyLi9pp4Nnuuo5sqGBp66/kRtr9hPFz/Sgl6ZwkqzmYMZRxRtie7Fh8n/qP0R1NEYhYANNgCXRC5KtoheflaE/EkYXFrH6hgtU6y+s6NYREmx5yYGbdqFZFtcfegZLt3HfrdfQMjSIL5ImlQrjLpumrCvNR776GZZ8ZRxzrOVv+v6J9swUpm4j53Dx4d//OCuBMjyZLNVH07SHszhiS5TPn+PqyFH+b/+/MR6s5/61O6nV5/nI+Of51blvcXhNB1mbHanpDBUbcBjxly78zznVolYuCFskQv3nP0vFg/dj+nxUf+xjhN7+iwiH4xUtc5q3OZgprwYgNDlIMZNi/fQi4ZU8EsF4j4/c1fVcv+pvidoC7Di7ly1H9+Iu5qn3Jhh3tZDyNXBD037IVKCdHcReYfD7nR9lJRDkru9/mKmT5fzl0N9TsNuZamjg6a4uXIZG9Xw/Rbfg2KYb+UTis3hklrsi15ApFGkPxngquI3fnfwyFiCw6JqbIuXwkisrfb1sOQsnBfYUDjHkaOT0QhXhSosxx8vr6nm1GC4NKcCeNUmGApy4ahPX79vHP73tl3m050quePIA2w8dZmxTJZ66QeYcYXqsc/RPddDqmOH7vh185/SHuHHtf7HgrSDt9fPeP/0H/v3/fZzWhVnan87j7KphcsHAI1NkjNXcxlO8N38PV8VPPFuOP+a/+EzdWxle7uCguZMt+lexVsbQylsvan28lqgWtfKqEoUCNXd+lXXvfQflDz3A4pveQu+X7yT83vcgHC9jevgLyNkdzFTUoElJ48RpipkUHfMRGpNJJILBTVXc9fb38tsbPkEoEuXX7vkXrtn/EO58ju5gjDFbG7naVtpqcphlk8xN7uCm4Gn+tuqX2du5jf/84p9QcfcyzdMzHFu7nntvv43hhnoObbiSpqkzpJ12mtxF/k/iczQyw3eL1zBib6Ten6DKjDHibuCa6FG+LV5HQTj4lew3iVZ6S61pSt0e17EPr5XhZKIOU2rEWhtf4qwvAE1guDTsaROk5Oz2daTKfGwcOMdSsIb+nnpWAh6MSCOabvJg8FqC3Tk2Hd7HmpEz9HuasYo63zjzEcqTEZCSrMfLr/3xX/PFm9+Ibpq09M2wu3+CnqdX2PmtfXzkS5+i/t5p7hm8ig/wB/yp7YPs19bxW7Pf4IrAEabttcxRycozX7749fEaIuQFWBRl69at8ujRo6/66yqX1g+3hA/GU8/dkBLP0CChvU9hj8fJtLUT2b2H+ff+EvD8UR8v2qKe2Eev4XjeqI+0buNUoBKbYdBx6hhTPgdNiThr7BEYtPjezXv43OvfSV5z8I4nvk3teC86JnnTTkXAIOMOQzhEi3MCd1cBXDG2Hx7DIQxGsvUUngRb3OSZ7Vs4snoT66bHSHs83NOzifd978tkHIKbqkeoD6cJkuQ7vI7efAvesT6u2BhltTFBUnPzcGEb4+4u1mUHeSf3cti3lq9V3oYUGjsnT/I24372eTdx7LiHZl+cp6+6DUtc/HaSI1HEu1wkW+tBOjTKFqNY4xn+/W3v45Z932Xj6cO4qmvoeONTTEysw5qoxDM9SGHehqMjTO+6Hv56+pM8pW3hf3d8mLHaZjTTwNJtXH3iAL//1c/hy6RZqCkn5FvB7SqihSwywkHB0nAZJuO2elLd5bwr9TD/Uv0uGhZWaLfm2fB/f74vKgohjkkpt77QY6rrQ/mpORYWCD/5BK6ZaQoVFcy/5Q5yzS3Pe84PhnPmxf4nvjJNwRvCTGQAyOk2ztZUYS8UWX9oP/2Vfuplgs72JDO9lXzyw7/MsVXr2b50mnff+VVmHToeW4Gk5cWuG4SDkp3ec3Qxxl77HgqhEbIDexh3FRmarafz8RGcTov6PRF+reZefin1ABMVDUyJCnafeBRfU4Z27wpeR5EVyvgcdzAt6/BMnKSsSWNH/hyHA2v5ZMO72fGte/EHxzjavI5ouYcPzN5FXWGJZVuI9cYA43oDh+frEFYcva3skoQ0lMZTQxE9a2A4HMSrQlSPrwDgNmwYQmCLZtHjTTSWrbCfdXgbAljGJP7JOI3V49wduIm3Jh7hzwY/zacSd3CocwP2YoHDPRt4zyf+gT/60qfY2XuSRI2DkVCYyXjweWVwaAbr+gY51NDFby7exZ8FP0jr7MDFr4zXEBXUyiumJ5OE9u3Fe+4sltvD8g03kVq7Dn7Ci4Q/wrKwxZMMdq8GKVl9/AgDVX7qXQkaQhbf1m7kS3/0FnRh8onD/0L7A6fprammwpVmINxFeGaWhsost3hPYkiN/9f0S2zzjaHl/ZyMeDk3fiW37X2CWEOA93/wb1i/1MuffP/zOGokQX+SndYMjpCBYehM2+o4Z3RwyLYOHZ1Qcpwbqs+yv2M3jlmDT9fdwYJZTTFchXNlnkywikfad+E1CtwU20dLbprDbORhfSO+6WE2hOf4fvUbXp0P4BWQNg3DqaFnDIyyUldUrD5AzcoiY9XNXHn4SZZqIRVtxd38NIHZQWRdHanWHszIMra+eb6+5zoqzCg3pffTOLfAd+LX863uG5gpq6Y2OY1/d54Zp5tThVrsaYtsqJJv3fAWCk4XG4ZPcfW5JziWaqBmLEljtZc7PA8y7mlhnWFgs6lIeiGqVpSfmJXPk963j/p9+xBSkti2ndj2K5DOH1097SchDRPf2DSO2UWe2r2bnNPJqpFzjIacVNrTaIUwH9/wLs61dLFz8Bh/fOJTDI+6OV1TQ9iRJlRmEJ6ZpSMY4xcqepmRVRRtAk/MidV6luTwDTQfnWdrXy8zTVX86kf/lqqlWd5+9/1Eki6e7LwJe2SB8qkxuhbi7Lv+BmrzixjTSRqrx9jgm2SL7zR3V93I1bHjDNgaeKzqKm4+c5BcTQMTtbV0nTlBT2KIztZlJpy1HJfriGZdBAf7CDhyLK7qImnzvUqfxCtT8Op4IkVE0ULaNQqVQRoXJznavZk/WUqQ0WEmuYkOzaKpVpCeOElba4iToTXkw5WsH57iK5W3cdC3iatSx7kqe4KNJwd4qmoL9zXv5nfWfpxga4JNEwOsmpvAl87wK089wFh1HQ9uvoqnNu/hjv3fwnb2BA/Pd/BmenmqcTsP/Pcnuf03PnJJ6+ZypYL659Ar3Vw2feQIxakp8v39yHyeYnUN6Y4OLLcH70D/j/3dzNALP16YmEAWixhLS5grK3h0ne9ffx2JQID26UFmrBwe06C3/kq+fsub8BazfOxz/8a2/EH6XWEWQgG6A4tcWz3CV8Y2E3DkeX31WU4ZnWSx0N1OempGEYYL66EVNvefI3BFju6WU+w99G5m97sZppx92zaDlDRMTLJmcpFnrr2a6237WD3fh9GsUWFPY0qNp4qr+G7ltby97xH+tvr92I0i9ZFFEg0u9tbv4MbUQSbG4VzCSWdohYyRwhOdwKkbODZUcDC46RXV/aup6NUhUny2VS0dDjp6T3KkZysPX7mbtz18H3f1dNO52oG/RTI+4KF+fowrOjMUzulMOBpB08nZvTzG1edfVeJaNLhj8iHGKms51bmG76/extG21WwcH2DD+CB+DN52/EnSup2nenYzG65h194H+d78Gt7oe5BvWq+7pPVyOVNBrbws+dFR0k8/jZVIoIdCOLduZdnt/ale00qnKU5NYcZiICXS7+GJa64h5vXTPtbPcjZJJFjNI9e+ifHaJm6beYL3//OdhIJR+psqWEgF2BiaYXl1C18erCZjWLy95RQPFnfQP+5g355fYHdimI0130Tv30nHwAm0HTr/uubdlM9M45ucI+O3IW1OCsEKKufm2TQ6RWZPkF8vuwsPOYwajQmzkiNsY2hc8rXr3s1fzv8XCbz8V9sdtC7PIj2CBW+Yvxj9V97oPsLX1l7H1KSXxKITQYR8RZCZ9V0Ybter82H8lCy7hunQsKWf6/7w5TIEE1EevPp69hzbz5ZjZ2DTKhw1w1QvezlJHduHp3A0hPmN1J2MDNeRseCeuqs4U99F6+wI9XOTVLuSvNGIYt1pcqppPd+44Vb2rtrM0dYedvce4arTx4lVV/D6MweZDYTpveJqeg7s58R4JRXr4piGga66P36EqhHlxzIWF0k++ij5wUGE2417yxZstbUIISCbf0WvaaXT5AcHKc7MAKCHw4jKSvY21RHzBWjr72ValxzeeQuHNuyiohjhc70fo+c7/SSKLo5U17Oc8nJ99TBf3f4ONvYfI70kuaJ8kuO2HgZGnEx1rqcxHmV9zTjCsuG8ZxZjh+AJsYrKo6Uxvbaigd/uZqxrA26jyLbjxzl+1Rra7dN4yDF3pIxH6GBs8y5EvkDvqnL8Ibh+7DDfcF1LyuFl1fxxcmU2GouzvHnxcb4X3sOR4BXQDN7ZNPasJNb63FC9y4XpteGIFhAFE+nQyfkDrBo6yaHN1/DAzbdy9eMPMR9porr6NInXX0fTA/s5TCMN2Tj93iraOxboNdvYleql5uQ4OdPGtvA0mrB4ZrSe8nCWzqVzfOa7+5k418iXN7+R+zfv4bHNu7jqzBFuPHoAvdkgb3eQ6m5jZAC295/l43/7Cf7yj//iUlfPZUcF9c+Do59/3k1H9NhL/ooRzxI/OEa6fw5h1wle1cE9HVswPM9Ne14qGj/2NRZsTharSztrB91BfMkE1z/4XbqGBpCaIFrXwFJFJUWbncVQgOVwmJrhQZ5p7eD7u15PyuNnz5lDvOuBewgvR4jnfUgEnX0ZQkEnx81NVD8zy3LcoNaVordxEw8HrqbJHyWcy+DOL6PVHcM+vI3e4DKJdDOalaVtOUqwUGQ0FGCqpRuntGgdGWX/6nV86vW/xMnH30wuaWd+MsDM7nbQdWakjWd23MC3jnyIrGnjkz3vx59LU5GLEi338NGZLzPqqueJstKC+FgSe15Q8Nsuu5AGMLx27NECtpRBMayT8/pYO3SSg5v30FfXgn/X1WiLOaq7dVqCS/TUT+OfKjBcGWY608mjS53PvlaDJ8YO9xSpfi8nXA2UezIsBHzMBv0cN+qo7kvzzoNf4+3273DvLTfx7Stex741W7hl/5N0ROfJloWx6nOcmDPYmThwCWvl8qWCWnkeM50nfnicVO8MCIF/UxOBbc3obgdWSn/B38npNiJuDwXdRlk+S1ku+7yZVLZikR37nmLnvifRTYNofQMrjc3knU6WiwZLFSEsj5OoYfLAzW9lsr6Njqlx/ubf/o7KeJTFynLOtHaStdvQMam1YrjjWRxuSSg3RgaNDclFtC8usclzjsnqWsarq7niigwF4MyAk1pfgq6+HP68SabHzZFiBZXSTiiTJqUJZsrL+eSbf5nPf+mjeJqLzB4KMlYVohiqYtrl5aHN13DzxJPsyvbyHf9uRgPNXDFyhkLQxur8CG25Gf6p7r3I88PuHCkTISEfuEy/YrrA9Niwpf9/e+cdHtdV5/3PuXd6lTQqo2pZsiXbcu+xHceOCemkEggBQoAAC8s2dmnLy7Ivu/RlCQt5WSC0EAiwCSEhPXGc4hL3Ilm2epdHZTRNU+7ce8/7xxhvAnYsk8RSwnyeZ56Ze+fce7/33DlfHZ3yO1myhTYQAqc0qB3opLWilhU9rcisHSXcgKu0lX1mMdeta2P42BY8Fp0qOYTXkiJgpGinnk/O/jTuyig3HH2Ei+uO8dWC29B0E+eJcTKhMQYCPiyGwernt3LR/gM8uGojD6+/GGcmzSX7d1LmqyCLnfHQUb7ymQ/z6S//93Tn0Ixihv6K8rxWHH7qMejtetm+zFD8TxNqOrZjw1jbQ2Ca6LNL0BZUknDZGJ7IwESGEWcpxS+ZXBizOdhdOYsefxHmS4bkubUMC0aHKR0fZ37LYS59+LeUjo+yr2kJv918GUXxKGWhYYKjI1jsCuFAEfvKZ9NePQdPMsGtL97HjeEnKG8aIdMs6M7aiTpNHE6FD1S8wETaxyONWygY6yY05qKmTOdReSmlZidSyzBe6OWigV4S9X2Ygyu51f0gD7KJp7fMpqz3EJaMwvL+ceKBYjoCAQwTHrr4Yjbu3clF7oNkUioTfS4O37CWtuBsnm1YSkn4BF/quIO41cG3Gm/DYug0jPaSrlB5x8Bj7PU20WM/GbtDSuyxLIZVnFpxfCaieyxYkjrqpA4SNL+PpS0v8kD1LXT7CpgTHiM0NpuSea2MrriBiaEQvpIQ31z1MbpqG5Enn7ktq7Hp6G7mNbeybk4HWdVCpCQAQLK8BAwTx9gEvr4TCGuErKmzYs/jrO08wgOrN/PA+rcwZ7CXlYNtGEJl7sRh0qkUDufrvKDCG4i8Uf+lkzWwtoewHR+GrIFeE0BrqkR67VjIYCGGgonAZJZpw6OnyQgXLxbW8LuqeYDCgtET1MTC2AyDiMNJe1EJI4qFm596mOrQMJ3Vs2idVUfN8CD/+t2voUqJKQS/vOoatq7aQHdxOVY9y0faf84nQveQGFNJHrGzTw/SGQwAkgojzTuq9nBEzuNh92ZWJHbRPOam0Gdy15L3Ut7dghgBt8XLW/cfZOzjK5BKN7FhwdbgBfxT46fQFZW3P+XlAw/9hvHCQnavWgXAmNePK5Hk3174Jq5lGoO7/WxfsoQnFm6iq6SSyuEe/u+LX6eiMML3gm+nzVvLwsFupE9QYYxQmg3zo+B1p1YpsaRMLJpkssQ2o1cuMR0qplXBGsuChIzPR93RY/gSEfY2LqfpN3dyzFJL6RwHc0p6+UHoZjaoz3D11l8T9hczUFkH3kKqI6MUd3fQsqKRovADNL8ktCmAYzjCisJJCtekURQLyWQJ7cM+Us1tbHnqVzR0tfLgpqu4r6yC5X3Hka0Kd332fXzsm/fO6Pw7n+SN+i8V3cDaMYL1+DBKJotlrgPHPBcu1wQu2YFNyaC+ZKkrgAVZIJv7/J4ofLtHkBBuIoqXiPARzXrQTlix9SRQozreWAKLaVLcEiHi8TJWWEh3QxVHF9fzq2XXMlRYhl1Lc0P7Q3wy9DPiIUHHkUJGzAKOBQvQVQWbASv6hqjbEuUecR2dYhYXas/ROujA6lT4j0s+yqbdT1DX10ZF3GRpdytH1qyieO4e5PByLjB38WHH/2HnL26kt7uMohNxti9azr41qyhMxtlTO58Rj5+bn7mf5s0L+Z2liucvX87upqUoUnLB3me4/PAjXFbXwoiliLsqr0c1TRadaCdW5uC9oWfpclQybMkFjEJKnBNZTFWcCnE6E8mouWcrCyy4RzUMu4uYTcFRUMi6vVt5bNP1NC9ay9K928jWL8NasQe9r46nbZfjUsMUSxNXJoVITcLEBD+45lbu2PYvUAD73blFAI4dPMZtc92ULX8O05ZASfsRhgNX6WGW1EK2+gJ274VZXYd4b6ibnRdeyot1y+grCnLZgW189Qu386nP/ifYvdOXUTOEvFH/hdB1coSG0HT8x0MEjg/g90/iWG3iK0/iUHKrqCRxkjCddIvZxIWbJHYMVKwIjjpnEQqUkXJZqY8OMCfST0E6SmFHGNmTIRCKI8gFUWqpayC61EepP0y9a4D+gip+XXwpu8qWo6tW/MkE1xx+jJs7f0Uk5uBwdwmhoiomKnQkBqaioDi8XLj7EP1vrecp2yJAstHYSW+/QFOsPLj2em587G4KYhHmjSSoHRnlvk2XsnrdCFIYxAcUPl39d/z7T79JJOTGKTS+fsvt2CxZgokILcFZtAVryFhtfOv6D5/Kq0AszLK+NlYd2E4g1M7b6ltRhORr1e+n31PBit5j4JPodpVFkx38pPSaUzU/66SBJWPO+Nr0H9A8Ks6wwJ/NErXaSARKaDp+gJ0rt7Bz4VrmHd3L8bFKmqp3sqh4grZmSHkKAXCGR+nzFHHfjbfz9od/Sp0vhgkc9MxHdI3yT8sTiIpnEeG5tPYtob04wB3v/SRf/trnWF7Wg7VmOxt9s3nh2EpWD03gf+LXVNW388RF13DPhW9jy5Fd3Pcft3DD+78EwYXTm1HTTN6o/0JQMlkKD3cT7DxGoCaG78oUFquJjsp4opDOyQATKT8Zw44zG6Pf/vKVsc1Mlq0Xr+bA7IU0DnRR2TyE0a5R0D+CxTDoCVby7BVrGPcXoHlUSifHGCsqpLliPv2FubZbdzLOgsEu6seGaGjejZmM05qtoH3WQhLlCnV9x7DqOqFZNaylB6M7zaPXXkXGYkNFZ725h/HBScJaAUfrF3HltvvxprIsGhzDn0zx7ZtuZY4/jlq5m2zfOg4EKrn9zl9gncjSXNfAd264mQtaXsTh82MATSd6uengg6wvOUjU5afrcICCjjgvblhP2qKijvaxOdhJ0Bpnr2c+vy3fgj+ZoGm0k+b6Oj469AtiqptmVyMAwpC4xjV0uzKja9MvQwhShVbcYxo2wyTpL8S0WNm06zF+t+Umnrj4Bq567B6UqgX4Z23HO7aWD6d/zPtmfYmMLtjfsIyVR3YStCnUiX4mLH58Q3E2LTyCUdTOZOfF/LzVoHxp4NQlP/PJ3PC7n/7XP1A97xE2NGnsVDfwsWUZDu37PcH/6eexS97Bo8s20ttfif3Ov+Wqa98Hy98zTZk0/eSN+k3IywIgtRzCdWQ7yzK9BGYncL1Fw5CCASNIf7qckFGMry9yKr0kgy6zGLqBBExFQUpJv7eUha1HePujv6VuaABPKgkI4g47fcEKjs6eS0dFJcdnz2W0KLcOoE3LUBnqZVPPkxQlUwSyBkKauPs7iAAH1lxCYGKUxs5mKg2deHmApqIQ87OD7GM5RpOFYnOMjCxkPXuID8bomywm7C+iqeMQFbEMC/uGmPD4+dfbP8iGx+9jyQcFMutiqFfhLY8+S2Esys8uu5au0gLe/cT/MDivCZCowqTUDHNjcCvpmIXEVgt14xmam5qIeb0og52s9fWyuCCEjuCj8z5PUnVwdcsLdNRUMWb3c/nYCzxdsJasYgUpcY9kEAYkg2+M2vQf0LwqtnGBJ50l7LYTKwkyt+MI8xuX0lrdyM61l2Lv62FhcSuzgjpqL5gZnf3zV1PX28bScIgG6xBWYXCX7WY2Nb6A4Q4xfPRq9vgqKF96+uve+vFv8v1vqcxp+j0XNOzgG21r+UJJL+lxB7Oe+w6PNLyNXQsv4B+L/oW+332fj/Zsh6u/BdaZMXHofJI36jcp2VCI1IvPMTv5IkVz41hdJgnDxX5zHu3RACndgq6b6HqWcSzoiJMvkKIIfyxFSSxJ0WQafzKD1WwHcusVHqup5anVF9Jc30hrXQNRrx8AfyLGoo5j3PT0oyzqOEb9QC+9FVUcXLYU3e0kI2Fc05BlNRRPjHDRi09iqAr+UgvlHoU+i4995mwUYVDVP0C21MYJZ5CL5Aukh2N0JMrIKip1wyeYMxqnNBLh+SUrufuK6/jnn3yX6I0LMIoeYaz1Clb+5hkiXh93Xncjc7uOUqiV0D9/IW6SLKCNfXIpF6R3cuR4OZbjgBSMlJbQ0rQAMT7IGvU4Fwe7mDSsvH/Jl+hzV7Ch7SA9lUGOFM/hG8e/gQRe8K0AwDGRxZoymSy2zuiRHqdFCCbsNkrTGdyaTrS0nMLhftbt3Urc7WPnwrUMDQf5t6Ee7DUv8IvYewgGEszua2NdXzsWYIU8xLf9H2HVvCcxVY3O5mtoKTr7wr0f+ruvc9cdktqmB9lcd5j7969lk/cwT7kv42/af0TVcB+PbryaL278B/buf5zvfnM1ztsfgKK61z1bZhL5eNRvIqSUJHfuZPTrX8Bra8ZTn2JU99A7WUZ7NkgsbSGbefkkFUUBm2GCouBNGZTEEpSHI9gzOhKYKPIy4C+lL1jJvvmL2bdgMXF3rnOnIhHigsQh1sQOszZ6mOrUMEdcc3jEciFD6WIC0ThOC0hFML+1labmFsYKCjk8Zz7HZ81mvKwQbFbKIhH8qUlq5CD1g11o3YJd69ZiqAobXLtItcVojxfjS6aZPxwhkJhkwuPhG+/+EAOlZXzooZ9gWVFCzeIXUGLV+D8/wEBlJQOrC0h0pkhX1GL4iihhjOt5lB/Id1EWHqSwtY1FA+PEvC5smsGD112NGh5lVXovby1vp0cP8I/LP8v2opXMHh1kwuMj4vSyKNrG7w99lIPu+fyi9OpTMZ4zHpXkG6Rt+nS4RjVscZ1EuQNvRyeegRMMbl7D0cIGjvtmccmx57il9ndIe4yWo5fhjobooRY90szChkrK5j6NknVy4Ngl9JQUnvYad7z3k6fdf893/5bg/N/DiaWUH9zH04m1mI0Bag8f4IBtAU9ddA0dZdXMHuzmq0e/zLq1V2C58nOgvnnqmq8Ujzpv1G8CjESC2EMPEX3gLiatvUx4nQyk/JxI+zBkzjRUVWC1KthsSm69Pn+A9mAdaeli1dHDXHDoII7JDMIiGZtdxL3rrqS1uJ6M3UFnZQ1SUfAl4qztO8BFyX1ckt1FjXaCmHAxavjpS5fxgwXvZFv1aionRtnQcRh/OpkbJKJpFMWiFEViFIXDBEMj+CZziw5E3R6a6xsJFxdgVbI4oxpZtwMrWVZl9tGS8DMq3MwbGqduLErM6+HhdRfxo6veiTsZY2Wshb/v+RmJhR6ka5zx+5Yxd/8+uNLCI8OLyRZXYtrszBdtXCsf57/kraQMJ1UH97Kqs4+Y04EvleH+665FJCNsyT7PupI+9lvm8K6V/0HC6kYXFhACvxanKdbFB4bu48qJZ/lK1e2Mpwtxj2XRXCqTZW9ckwbAlHiH0ii6JFkoCG7fS7KihPGl87CPZ0kYbmYzwqoFT2DYohBagqa5cRb2YPgGUCfqeKFzDaNB3xkvcSajBrj/v/8K/9wn0PvWkd61h+eq3sMtlvs42uplyFrFoWWbeH7uEhTD4KZd9/Kp1MN4rvk69vXXvQ6Zcf7JG/WbECkl6eYWBu75KQMtzzLmtzKk+9ClikBSaLEQkDYcY+MUpuKgS45X1vLColUcq65j/dF9XLVrKwVjcYQi0WbZuHf9lfx89bWohknKkZtsML+7nYv69rAlvYul1nbG1CJ6zQq69SpiaQ/jLj87GpfRUlFL7fgJ1nU2482kciJNE1VLYwqVqNuHgaQoMsSidCtLRA+B6CQnwkWMR3z4JhJYDOPU/UWdNg7MKiNltTI3FGY4WMvj67aws2kppsWCaup8pvMH3Nj9EEcXr0GWHWb44NWUP7qf1IpC9isLMG12XIk4KwuPsDG7h++ZNzOmlOLv72DzrgMYFgs2LcvzF24g6la5lieZ7x/lV6WX8qk5f8+G2AGOiTpKExGKLBGKjBguI83n+u+kx1bB3Y7rcUb0N4dJn0TJmniHMghTIrNRfO3tjK5aSLq4EGdfBrtpoqhp1tYMYC8+hmGPoUZqOTHSyC+bJ6ldO+8Vz/9KRg3wyI9uw177HInOLRxptpCeVczHwj/mwe55pJx++upWsnX+Sk4UFLPo2H5ub72bC80Y7ss/h/fSqxFv4IBOeaN+ldw9NPaqjn/pMlSvlmRXF+2/uJvuPTsZserE1VzHis+SpjwTxzekURjJYDElOBw019TxzMrV7KtfwKLO49yw4zFqe3LBkCylBs+svIDvbLyFEX/pqWvM725n88GdXKbtwFom6BHV9MkKJkTBqTSGaXKkopbmmkbqxoZY1teGU88idA1reJSy3h5GKuvZuXQ1KbedQjOCx5IBu8qk6mLc4mfQWUYCB7dsexS3yGJPJyn0jbOg9yC7J6sQwFCgkq0XXkPG5iDi9ZG1WHhrxzY+0X8PvXo57kUOCB4g3HYprYMBTFUFKbEmYrhjCdY0tLJmspmfyuvopwpnYoK3PP4MQiiYiuDhSy7H9Ezy8cz/UGBL8+2ymziSrOSLqR/zoHkZQ0o58Qo7pjXX7nzt+FOsjh7km47bMdJWMl4LyWLrm8Kk/4DQTTwnMlg0idBSqMkEqWAJahoEuX4Ki9S4Kv0oqjHIQOFy7rduBiHwlL3yuc9m1Hf++AfMtT6FUrGXsWNXcHCslKqiQS7teYKHBxoRHjuD1Stpqa5jf00jNl1j7f5t3Np7P02TGVzrb8d/7Y1Yy8tf8TozkbxRv0qm06illIR2bOf4b+6lv6eTMavAUHLNF9WuCFVqjGJZjKt6EwzuR1gU2k0Lj9Wv5JmaJhrb2rlq11bmd3UiJCg2k32LF/LDS97B0eq5pwym7kQfF+/aTtNwJ7ZywWBBBWGRa2dUsxrWRByZTUEmRVtFHd0NS6iMR6gfHUSREksyjoiGidpcdNfM49CCxcRc/lP3UZiN4tTT6EIlbnVj6IJ17YdZPNSNZrczJ3ycxdaDtIwXM5Tyo0qTupE4msvDz666kT3zluHOpHhL616CsQl87kmWz9mL4esn2vEWQkck1Z5hOsMu9LhGT/1Sri9+josie7hXXk0XNahZnS2PPoktm+X5pau594qr+GTb97hK7iYhHfxQ30J5eoy3e3bzuLKJ3erSl5l0STbMu/t/x73ibaSkk1TAmovl8SYy6VNIiT2mY49lUTMGmCaagJjNijMSpnSwm7TLQ2/DYqT6v0MRX61RA3zxC59gw/w2zOJWQq1XciBSysXeHdR0t/PkiXqsToXW+ZuxSMn+yrm0VdZSNDHK+n1Pc3VoG/XhCbwFK/C97SZ8l1yC4n514XjPF3mjPgtSSsazBn2pDONZnXDWIJzVmcjqZKTkaCKFRQhcqoJLUXCqCm5Vociq4rOoKGcpqOdq1JGOdtp+cTd9LYcJZdOkrbl/53xqmjpfmFp3hLKSWVD9VtIRG1rLfiZG+xj2WNDtFgKjE3hDkxCWSFOg2A2OrJ7Pzy+6hj3lizGU3PnKJkfZdORF5nb2YbjsTBQWgRAoho41EYd0AnUyhppOEvcWcXThamxWG1WRMRx6FmmaWKNhBj0+umvqOV67gJTNiUtPsSW8iy3hXZRMhBg2S/jZvOs5HJhPU8cxrm3disWiEnKVUR3rZ21sBwOKm+ZoGaoA3V1IuriS1llz2VM7j5jTw6Khdm46/hDSU8TsYAilYh+K7qT3+BZWbn2SYMMYvxpcTH9hPds2XsrXBu9gY2wfvzavoE3UIXSTi5/cStpi5Z8/8gnWZ/fzme67KLJMsk1ZxHcabuXvBu9hQ/wA21nBo/ZNJMpsSEvOpBXDYMPAAVqMRgxVIV7mwHC8QcZKv0os8STFB1uxxSZP7Yv7ixiqbcSwWl+W9rUwaqTkW1/4IEsWDmMGjpPs2szWriJuDmzHOzDC0yfqkQKaV15MWSzJsC/A3ppGhgOlFMTCLG3exabO51mSHiAQMnE2bMSzaTOeTRdhLTuLwGkkb9Qn0U1JX1qjI5mmPZmhI5mmYzL3PqEbf5LeIsCmKBimJCvlH02ozmEVgoBVJWCzUGKzUGqzUmazUGKz4lRzhfyVjNrIZhl87BH6tj5FqK+HcV0j7sj9+O3oVDuj1PrD1Lgj+EQWPSmQUmJxSFS7iTQEqXEryRE7kyEbqXEbSMFYWQE7LljJE0svpLWkHlPJNQmUxMOs7jhCbegE0m7FVFWEaWJJJhCpOOpkHFsyjktoDJfX0la7BCeC8ug4FmmiKSq6niVjMThW10hL+Xx0xUqxFubS8R34u0JYVRdWi4pFMbErEoFECHLvgBASp0zj1mKo6QyaJkCA5nKRdnvRVDuaasUudAqUSTyWBA5nBOHrw3REEbqN7NBKnh+axd8c/TEnCt3cZXkre5ZtRity86uDn6A2O8jP5PX0UYVNy7DpmWfZvWARI+tL+Ku+e6lSxgllPHy5+jaiBWX8a9d3qdGHeYGVPOy5iGSxPReeVEpsCZ3isRgp6cTtiDNYVopU34S16FfCNDE7Y9gyKTJONym397T/SbwmRg3QsZXHf/h5lKYqlMo9qOE5HOhZzmplB7NPdPLE8FzGNTei3EtrzVoqYhMM+gIcK62ho3IW9kyauT2t1PccZe3IAeqSYxSPp3D6GnCvXIdz2TKcS5diDQb/jMx4fXjVRi2EuAy4A1CBH0opv/JK6afbqFOGSVcqQ/tkmrZkmrbJnDF3JzNoL7nfUpuFOS4Hc1x25roc1DptBGwWAlYLhVYLXlVBCMHdQ2NIKdGkJGmYJA2ThGESzuqMaTpjJ9/HNZ2X2r1PVSi1W9lQ6KUeg/K2FryHd5LtaSURnSBqSqLCTlbkamZ2RSfoiFHjjjLLPUGpY/JUWZAmZOIqqTEb6bCN5KQdbdKCnlQIFZXQF6ygtaGeQw0LaA/WkrLlOgNVQ6dqPMSckX6qIuM4jSzCNPFGo9gSUZJGhjQS0+7AgkK8sJiUP4Anq2Ezc3cz4fIQdrtJ6ZPM06IEVQ2fVcNjS+GyJ7E5oiiOCNI5jmlNvebPUxhWlFQxeqyC0UiQXVqQzR07WFTQxr8XvZsd9evxGxrv77mfdyYepkvW8IS5kZTqomJ0kAVjLdAoWEY7biXDaNrF/faN/HzR2/lE/z1cFX0WHQu/VS5hb+lCdJeK0E1sCQNnTEPoglJGCbjH2VG2/DW/vzcKidDZ07xmRg3wm9swm+/nl6Xvo2LO05hqBnN4Be2j1dx04occmKijOVYGAszKAEPFjXjSOqPeAroKg7SX1zDpdGHNatT3tVM6Okj1eB+Lw8eomgzjT2TwKH5c1Ytx1Ddgq6/DXl+PrbYW1Xv+44u8KqMWQqhAG3AJMADsAW6WUh490zF/rlEPpDUkuQ6LP7xMQDMlGdMkffI9phtEdYNI1iCi64xnDYbSGkOZLEMZjXD2f+1SAWY5bcx1OZjrdjDXZafB5aDeZcd/skmBTAK03HCx7OgIGCamYRIZ7EcPjXAgEkUYJoqpoxgaiqmhpBLYI8OgJZFZDXQN09RJC0EaBU0IsqZAMxTMSUFaV0/eUQ6HquNwmSheGwWWFKbDhilVdE1FS1nJZK2kTAdJ1UlWtaILlYzNzoTPT9hXwLi/gMGSIKMFRbkOtJP4k3FKYhOUJKJURsYITEaxahq2yRiZQg9SSnQhcu2uUsUiDKxCx6LqKIqOqmZx2LK4rDoei4HHmsXuSCAcE5jOcaSafdkzU7JORKoImSogm/agZeyYWTCyILMSNA2yBmZWohsCaeY6o3SHnZCvjJA/SMpqx0SgmCaBZJTyyAT+yQTWeAZfzwn2lNfw9LpLyNisXNy2i8ZMLxY3uGUKm5nFKTI4hEYKB3HpxikylOqjlJojlFqjKEKiGSodk8U84lnHc/XrecfYNtbED6Ng0irmsM/VRMLupjgVwdRU0kauk7aSYdZwgPaiGp7zr3pztkdPkfNu1IYOP78Bs2sbd9rexZzKSWwVe5GWDGqqCCNSSyrhxRhLEx/TSMYl8YyBWVJM2hUga3ExXBCgpyjIUEEx496CU6f2JWIUxSMURCcITIziTUfxZhO49QQePYU7k6Y8naDa0LC6C7D4A1hdXqweP/6Kuaj+AIrXg+J0Iqw2hM2KsNpQ7DaslZVTv8eX8GqN+gLgC1LKS09ufwZASvnlMx3z5xr17GcPkzJP18DwyhRaVMrtViocNirsVirtNmpdNhpcDmY77TjUs8wU234HPPn5l+0yJfznsQvPcMAro2DiVHWclix+PU3j/shp0z22diNfvfWvzuncFl3HpaVx6BredBJfahJ/ahJ/KkFgMobNOP2qK6qaZd36e8/tRqRAyfgQ6UKMdAGZtJek5iaqWdEzOgUdxxiIvPJ0XlWYWJ2CjMdFf7CW3TUr6QnWYc9mqQ4NUzs8wJyBXpYdb6FqZIihkiC9wcrclPTqWdhVjYZ4DzHdT9lkDCEEDXQyj06W03LG6yayNsY1F/1GMUfs9bSVzqHONcbbJp6jJB3mLt7JOEV/cpwNjSqGqaOPoBoi5Cpmm38NI7bAaa7yl8V5N2oAKWH399Ge/hpKZoKvcwuzy6C0cADFN4DhDJ9KqmgeEr9xEs46mdAcZBU7hsvDROUsMnY/dj3LuM/PiK+IqNNNzOEmYXMw6XC9LJ76H1jUupfLnn3gZfusis7Vve2kw7Y/SQ+gBgI0bH/h3O7xJK/WqG8ELpNSfvDk9nuANVLKv/6jdB8CPnRysxE4/mepPTvFwKsbhjG95PVPL3n900te/5mZJaU87bz7qYwOP93/en/i7lLK7wPfP0dh54wQYu+Z/uq8Ecjrn17y+qeXvP4/j6lEjxkAql+yXQUMvT5y8uTJkyfPHzMVo94DzBVCzBZC2IB3Ag++vrLy5MmTJ88fOGvTh5RSF0L8NfA4ueF5P5JSnrkX5/XndW9eeZ3J659e8vqnl7z+P4PXZcJLnjx58uR57XiDRTjPkydPnr888kadJ0+ePDOcGWvUQojLhBDHhRAdQohPn+Z7IYT49snvDwshZtTc3inov+Wk7sNCiB1CiCXTofNMnE3/S9KtEkIYJ8fbzximol8IsUkIcVAI0SKEePZ8a3wlpvD78QshHhJCHDqp/7bp0Hk6hBA/EkKMCCGaz/D9TC+7Z9N//suulHLGvch1WnYCdYANOAQs+KM0VwCPkhvnvRZ4cbp1n6P+dUDhyc+Xv9H0vyTdVuAR4Mbp1n2O+V8AHAVqTm6XTrfuc9T/WeCrJz+XAGHANt3aT+rZCCwHms/w/Ywtu1PUf97L7kytUa8GOqSUXVJKDbgXuOaP0lwD/Ezm2AUUCCFmSrTws+qXUu6QUk6c3NxFbnz6TGEq+Q/wceA+YOR8ipsCU9H/LuB+KWUfgJRyJt3DVPRLwCuEEICHnFGfPn7AeUZK+Rw5PWdiJpfds+qfjrI7U426Euh/yfbAyX3nmma6OFdtHyBXw5gpnFW/EKISuA743nnUNVWmkv8NQKEQYpsQYp8Q4r3nTd3ZmYr+7wDzyU0+OwL8rZTy3APlTA8zueyeK+el7M7UBcamMm19SlPbp4kpaxNCbCb3sDe8rorOjano/xbwKSmlIWZeRLmp6LcAK4AtgBPYKYTYJaVse73FTYGp6L8UOAhcDNQDTwohnpdSxl5nba8FM7nsTpnzWXZnqlFPZdr6TJ7aPiVtQojFwA+By6WU4+dJ21SYiv6VwL0nTboYuEIIoUspHzgvCl+Zqf5+xqSUk8CkEOI5YAm5kL7TzVT03wZ8ReYaSjuEEN3APGD3+ZH4qpjJZXdKnPeyO90N92dorLcAXcBs/rczpemP0lzJyzskdk+37nPUXwN0AOumW++fo/+P0v+EmdWZOJX8nw88fTKtC2gGFk639nPQ///IhR8GKAMGgeLp1v4SfbWcuTNuxpbdKeo/72V3Rtao5RmmrQshPnLy+++RG2lwBbkMS5KrYcwIpqj/80AAuPNkrVSXMySq2BT1z1imol9K2SqEeAw4TG59ih9KKU87HOt8M8X8/yLwEyHEEXKG9ykp5YwIHyqE+CWwCSgWQgwA/wJYYeaXXZiS/vNedvNTyPPkyZNnhjNTR33kyZMnT56T5I06T548eWY4eaPOkydPnhlO3qjz5MmTZ4aTN+o8efLkmeHkjTpPnjx5Zjh5o86TJ0+eGc7/B3vCVmu0CaMXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(100):\n",
    "    df1 = yb.sample(n=20)\n",
    "    df2 = y1.sample(n=10)\n",
    "    print(np.corrcoef(df1))\n",
    "    dfc2 = np.corrcoef(df2)\n",
    "    sns.distplot(dfc1)\n",
    "    sns.distplot(dfc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.80651668, 0.76523209, 0.69594447, 0.81709583, 0.77034814,\n",
       "       0.80109226, 0.81428419, 0.81139747, 0.88672912, 0.83513379,\n",
       "       0.84982365, 0.84247089, 0.8289649 , 0.84178274, 0.81845574,\n",
       "       0.70010858, 0.87060999, 0.86495407, 0.81721811, 1.        ])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(df1)[19]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs=[]\n",
    "for i in range(100):\n",
    "    dfc = pd.DataFrame(index=range(0,20))\n",
    "    df1 = yb.sample(n=20)\n",
    "    df2 = y1.sample(n=20)\n",
    "    dfs.append(dfc)\n",
    "    for j in range(20):\n",
    "        dfc[j]=np.corrcoef(df1)[j] - np.corrcoef(df2)[j]\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.220446e-16</td>\n",
       "      <td>0.124748</td>\n",
       "      <td>2.802136e-02</td>\n",
       "      <td>-1.286097e-02</td>\n",
       "      <td>0.202506</td>\n",
       "      <td>-0.025433</td>\n",
       "      <td>0.003296</td>\n",
       "      <td>0.100179</td>\n",
       "      <td>0.013202</td>\n",
       "      <td>0.121962</td>\n",
       "      <td>-0.074193</td>\n",
       "      <td>-0.047020</td>\n",
       "      <td>0.079634</td>\n",
       "      <td>0.112847</td>\n",
       "      <td>0.190872</td>\n",
       "      <td>0.030316</td>\n",
       "      <td>0.062691</td>\n",
       "      <td>0.014325</td>\n",
       "      <td>0.089343</td>\n",
       "      <td>-0.029938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.247477e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.874577e-02</td>\n",
       "      <td>4.384231e-02</td>\n",
       "      <td>0.100344</td>\n",
       "      <td>0.006235</td>\n",
       "      <td>0.082877</td>\n",
       "      <td>0.051468</td>\n",
       "      <td>-0.075090</td>\n",
       "      <td>-0.048097</td>\n",
       "      <td>-0.119422</td>\n",
       "      <td>-0.004451</td>\n",
       "      <td>-0.085151</td>\n",
       "      <td>0.108021</td>\n",
       "      <td>0.137469</td>\n",
       "      <td>0.010462</td>\n",
       "      <td>0.043596</td>\n",
       "      <td>-0.003565</td>\n",
       "      <td>0.081985</td>\n",
       "      <td>0.236945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.802136e-02</td>\n",
       "      <td>-0.038746</td>\n",
       "      <td>-1.110223e-16</td>\n",
       "      <td>4.857645e-02</td>\n",
       "      <td>0.348929</td>\n",
       "      <td>-0.027157</td>\n",
       "      <td>0.091829</td>\n",
       "      <td>-0.052313</td>\n",
       "      <td>-0.107639</td>\n",
       "      <td>-0.011846</td>\n",
       "      <td>-0.033623</td>\n",
       "      <td>0.093184</td>\n",
       "      <td>0.123861</td>\n",
       "      <td>-0.017794</td>\n",
       "      <td>0.110341</td>\n",
       "      <td>-0.094809</td>\n",
       "      <td>0.104139</td>\n",
       "      <td>0.038251</td>\n",
       "      <td>0.024321</td>\n",
       "      <td>0.140226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.286097e-02</td>\n",
       "      <td>0.043842</td>\n",
       "      <td>4.857645e-02</td>\n",
       "      <td>-2.220446e-16</td>\n",
       "      <td>0.052392</td>\n",
       "      <td>0.083872</td>\n",
       "      <td>0.001917</td>\n",
       "      <td>-0.010878</td>\n",
       "      <td>-0.001912</td>\n",
       "      <td>0.024176</td>\n",
       "      <td>-0.000885</td>\n",
       "      <td>-0.064205</td>\n",
       "      <td>0.082568</td>\n",
       "      <td>0.013441</td>\n",
       "      <td>0.069973</td>\n",
       "      <td>0.055250</td>\n",
       "      <td>0.019517</td>\n",
       "      <td>-0.023954</td>\n",
       "      <td>-0.013347</td>\n",
       "      <td>0.076581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.025061e-01</td>\n",
       "      <td>0.100344</td>\n",
       "      <td>3.489291e-01</td>\n",
       "      <td>5.239209e-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.149797</td>\n",
       "      <td>0.341999</td>\n",
       "      <td>-0.023707</td>\n",
       "      <td>-0.037960</td>\n",
       "      <td>0.153908</td>\n",
       "      <td>0.179024</td>\n",
       "      <td>0.026391</td>\n",
       "      <td>0.088627</td>\n",
       "      <td>0.092552</td>\n",
       "      <td>-0.063809</td>\n",
       "      <td>0.105389</td>\n",
       "      <td>0.155227</td>\n",
       "      <td>0.050654</td>\n",
       "      <td>0.154182</td>\n",
       "      <td>0.537274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1             2             3         4         5   \\\n",
       "0 -2.220446e-16  0.124748  2.802136e-02 -1.286097e-02  0.202506 -0.025433   \n",
       "1  1.247477e-01  0.000000 -3.874577e-02  4.384231e-02  0.100344  0.006235   \n",
       "2  2.802136e-02 -0.038746 -1.110223e-16  4.857645e-02  0.348929 -0.027157   \n",
       "3 -1.286097e-02  0.043842  4.857645e-02 -2.220446e-16  0.052392  0.083872   \n",
       "4  2.025061e-01  0.100344  3.489291e-01  5.239209e-02  0.000000  0.149797   \n",
       "\n",
       "         6         7         8         9         10        11        12  \\\n",
       "0  0.003296  0.100179  0.013202  0.121962 -0.074193 -0.047020  0.079634   \n",
       "1  0.082877  0.051468 -0.075090 -0.048097 -0.119422 -0.004451 -0.085151   \n",
       "2  0.091829 -0.052313 -0.107639 -0.011846 -0.033623  0.093184  0.123861   \n",
       "3  0.001917 -0.010878 -0.001912  0.024176 -0.000885 -0.064205  0.082568   \n",
       "4  0.341999 -0.023707 -0.037960  0.153908  0.179024  0.026391  0.088627   \n",
       "\n",
       "         13        14        15        16        17        18        19  \n",
       "0  0.112847  0.190872  0.030316  0.062691  0.014325  0.089343 -0.029938  \n",
       "1  0.108021  0.137469  0.010462  0.043596 -0.003565  0.081985  0.236945  \n",
       "2 -0.017794  0.110341 -0.094809  0.104139  0.038251  0.024321  0.140226  \n",
       "3  0.013441  0.069973  0.055250  0.019517 -0.023954 -0.013347  0.076581  \n",
       "4  0.092552 -0.063809  0.105389  0.155227  0.050654  0.154182  0.537274  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5</th>\n",
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       "      <th>12</th>\n",
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       "      <td>0.031992</td>\n",
       "      <td>-0.110735</td>\n",
       "      <td>0.195099</td>\n",
       "      <td>0.083937</td>\n",
       "      <td>0.114450</td>\n",
       "      <td>-0.089381</td>\n",
       "      <td>0.092252</td>\n",
       "      <td>-0.017369</td>\n",
       "      <td>0.124119</td>\n",
       "      <td>-0.070807</td>\n",
       "      <td>-0.034410</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.796830e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.167823</td>\n",
       "      <td>0.118653</td>\n",
       "      <td>0.364835</td>\n",
       "      <td>0.262143</td>\n",
       "      <td>0.145135</td>\n",
       "      <td>0.337368</td>\n",
       "      <td>0.099687</td>\n",
       "      <td>0.261464</td>\n",
       "      <td>0.025014</td>\n",
       "      <td>0.256887</td>\n",
       "      <td>0.188500</td>\n",
       "      <td>-0.133229</td>\n",
       "      <td>0.063229</td>\n",
       "      <td>0.164732</td>\n",
       "      <td>0.256250</td>\n",
       "      <td>0.184944</td>\n",
       "      <td>0.047521</td>\n",
       "      <td>0.163943</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.254240e-02</td>\n",
       "      <td>0.167823</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.107754</td>\n",
       "      <td>0.099808</td>\n",
       "      <td>0.036799</td>\n",
       "      <td>-0.011195</td>\n",
       "      <td>-0.060073</td>\n",
       "      <td>-0.093803</td>\n",
       "      <td>-0.030847</td>\n",
       "      <td>-0.072711</td>\n",
       "      <td>0.204279</td>\n",
       "      <td>0.181494</td>\n",
       "      <td>-0.079781</td>\n",
       "      <td>-0.024681</td>\n",
       "      <td>0.088638</td>\n",
       "      <td>-0.063913</td>\n",
       "      <td>0.029787</td>\n",
       "      <td>-0.016489</td>\n",
       "      <td>-0.158700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.021191e-02</td>\n",
       "      <td>0.118653</td>\n",
       "      <td>-0.107754</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.303646</td>\n",
       "      <td>0.177629</td>\n",
       "      <td>-0.057371</td>\n",
       "      <td>0.117835</td>\n",
       "      <td>0.025063</td>\n",
       "      <td>0.072305</td>\n",
       "      <td>0.014275</td>\n",
       "      <td>0.036758</td>\n",
       "      <td>-0.022482</td>\n",
       "      <td>-0.076267</td>\n",
       "      <td>0.056770</td>\n",
       "      <td>-0.031450</td>\n",
       "      <td>-0.158261</td>\n",
       "      <td>0.213324</td>\n",
       "      <td>0.104391</td>\n",
       "      <td>0.051540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.017241e-01</td>\n",
       "      <td>0.364835</td>\n",
       "      <td>0.099808</td>\n",
       "      <td>0.303646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.257237</td>\n",
       "      <td>0.112149</td>\n",
       "      <td>0.048336</td>\n",
       "      <td>0.114838</td>\n",
       "      <td>0.018995</td>\n",
       "      <td>0.257974</td>\n",
       "      <td>0.412132</td>\n",
       "      <td>0.362429</td>\n",
       "      <td>0.235017</td>\n",
       "      <td>0.212074</td>\n",
       "      <td>0.055925</td>\n",
       "      <td>0.001649</td>\n",
       "      <td>0.114292</td>\n",
       "      <td>0.201280</td>\n",
       "      <td>0.045589</td>\n",
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       "             0         1         2         3         4         5         6   \\\n",
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       "1  1.796830e-01  0.000000  0.167823  0.118653  0.364835  0.262143  0.145135   \n",
       "2 -1.254240e-02  0.167823  0.000000 -0.107754  0.099808  0.036799 -0.011195   \n",
       "3  4.021191e-02  0.118653 -0.107754  0.000000  0.303646  0.177629 -0.057371   \n",
       "4  1.017241e-01  0.364835  0.099808  0.303646  0.000000  0.257237  0.112149   \n",
       "\n",
       "         7         8         9         10        11        12        13  \\\n",
       "0  0.045625  0.110995  0.031992 -0.110735  0.195099  0.083937  0.114450   \n",
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       "2 -0.060073 -0.093803 -0.030847 -0.072711  0.204279  0.181494 -0.079781   \n",
       "3  0.117835  0.025063  0.072305  0.014275  0.036758 -0.022482 -0.076267   \n",
       "4  0.048336  0.114838  0.018995  0.257974  0.412132  0.362429  0.235017   \n",
       "\n",
       "         14        15        16        17        18        19  \n",
       "0 -0.089381  0.092252 -0.017369  0.124119 -0.070807 -0.034410  \n",
       "1  0.063229  0.164732  0.256250  0.184944  0.047521  0.163943  \n",
       "2 -0.024681  0.088638 -0.063913  0.029787 -0.016489 -0.158700  \n",
       "3  0.056770 -0.031450 -0.158261  0.213324  0.104391  0.051540  \n",
       "4  0.212074  0.055925  0.001649  0.114292  0.201280  0.045589  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[3].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfp=[]\n",
    "for i in range(100):\n",
    "    dfp.append(dfs[i][0][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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": [
    "sns.distplot(dfp)\n",
    "pvalue=np.percentile(dfp, 95)\n",
    "plt.axvline(x=pvalue)\n",
    "plt.axvline(x=yb[0][2]-y1[0][2], color = 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = [4, 10, 38, 85, 193]\n",
    "interquartile_range = iqr(dataset)\n",
    "interquartile_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "기각 x\n",
      "기각 x\n",
      "기각 x\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "for i in range(100):\n",
    "    for j in range(19):\n",
    "        dfp.append(dfs[i][0][j])\n",
    "        pvalue=np.percentile(dfp, 95)\n",
    "        x=yb[0][j]-y1[0][j]\n",
    "        if x>pvalue:\n",
    "            print('유의')\n",
    "        else :\n",
    "            print('기각 x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "add_stat_annotation(그래프명, data = 데이터명, x = \"x축 변수\", y =\"y축 변수\",\n",
    "\n",
    "                                       box_pairs =[(\"그룹1\",\"그룹2\")],\n",
    "\n",
    "                                       test ='원하는 통계분석',: 원하는 통계분석은 실험자료에 따라 \n",
    "\n",
    "                                                            t-test_ind, t-test_welch, t-test_paired, Mann-Whitney, Mann-Whitney-gt, \n",
    "\n",
    "                                                            Mann-Whitney-ls, Levene, Wilcoxon, Kruskal.를 테스트 할 수 있다. \n",
    "\n",
    "                                                            지금 자료에서는 독립적인 두 그룹의 비교이므로 t-test_ind를 사용했다. \n",
    "\n",
    "                                       text_format ='star', loc ='inside' or 'outside')\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from numpy import mean\n",
    "\n",
    "data1 = [4,6,17,16,8,9]        # 관측치\n",
    "data2 = [10,10,10,10,10,10]    # 기대치\n",
    "chis = stats.chisquare(data1, data2)\n",
    "print(\"statistic = %.3f, pvalue = %.3f %(chis))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 20 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   0       20 non-null     float64\n",
      " 1   1       20 non-null     float64\n",
      " 2   2       20 non-null     float64\n",
      " 3   3       20 non-null     float64\n",
      " 4   4       20 non-null     float64\n",
      " 5   5       20 non-null     float64\n",
      " 6   6       20 non-null     float64\n",
      " 7   7       20 non-null     float64\n",
      " 8   8       20 non-null     float64\n",
      " 9   9       20 non-null     float64\n",
      " 10  10      20 non-null     float64\n",
      " 11  11      20 non-null     float64\n",
      " 12  12      20 non-null     float64\n",
      " 13  13      20 non-null     float64\n",
      " 14  14      20 non-null     float64\n",
      " 15  15      20 non-null     float64\n",
      " 16  16      20 non-null     float64\n",
      " 17  17      20 non-null     float64\n",
      " 18  18      20 non-null     float64\n",
      " 19  19      20 non-null     float64\n",
      "dtypes: float64(20)\n",
      "memory usage: 3.2 KB\n"
     ]
    }
   ],
   "source": [
    "dfc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pearsonr_ci(x,y,alpha=0.05):\n",
    "    r, p = stats.pearsonr(x,y)\n",
    "    r_z = np.arctanh(r)\n",
    "    se = 1/np.sqrt(x.size-3)\n",
    "    z = stats.norm.ppf(1-alpha/2)\n",
    "    lo_z, hi_z = r_z-z*se, r_z+z*se\n",
    "    lo, hi = np.tanh((lo_z, hi_z))\n",
    "    return r, p, lo, hi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' calculate Pearson correlation along with the confidence interval using scipy and numpy\n",
    "    Parameters\n",
    "    ----------\n",
    "    x, y : iterable object such as a list or np.array\n",
    "      Input for correlation calculation\n",
    "    alpha : float\n",
    "      Significance level. 0.05 by default\n",
    "    Returns\n",
    "    -------\n",
    "    r : float\n",
    "      Pearson's correlation coefficient\n",
    "    pval : float\n",
    "      The corresponding p value\n",
    "    lo, hi : float\n",
    "      The lower and upper bound of confidence intervals\n",
    "    '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "for i in range(100):\n",
    "    sns.distplot(dfs[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans=[]\n",
    "for i in range(100):\n",
    "    trans.append(pearsonr_ci(dfs[i][0], dfs[i][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r, p = stats.pearsonr(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "math.atanh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "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></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.073393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.029970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.047474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.011853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.034635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.073393\n",
       "1  0.029970\n",
       "2  0.047474\n",
       "3  0.011853\n",
       "4  0.034635"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccb=pd.read_excel('base_cc.xls', header = None)\n",
    "ccb.head()"
   ]
  },
  {
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.085110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.057288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.035948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.005069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.045832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.085110\n",
       "1  0.057288\n",
       "2  0.035948\n",
       "3  0.005069\n",
       "4  0.045832"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc1=pd.read_excel('1year_cc.xls', header = None)\n",
    "cc1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.108641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.081165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.055204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.033307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.071435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.108641\n",
       "1  0.081165\n",
       "2  0.055204\n",
       "3  0.033307\n",
       "4  0.071435"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc2=pd.read_excel('2year_cc.xls', header = None)\n",
    "cc2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(100):\n",
    "    dfcb = ccb.sample(n=20)\n",
    "    dfc1 = cc1.sample(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 37 entries, 2 to 159\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   0       3 non-null      float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 592.0 bytes\n"
     ]
    }
   ],
   "source": [
    "dfc=dfcb-dfc1\n",
    "dfc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# T - test는 여기서 불가능함\n",
    "# Example of the Paired Student's t-test\n",
    "from scipy.stats import ttest_rel\n",
    "stat, p = ttest_rel(dfcb-dfc1, df1+df2)\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "    print('Probably the same distribution')\n",
    "else:\n",
    "    print('Probably different distributions')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=0.096, p=0.908\n",
      "Probably the same distribution\n"
     ]
    }
   ],
   "source": [
    "# Example of the Analysis of Variance Test\n",
    "from scipy.stats import f_oneway\n",
    "data1 = [0.873, 2.817, 0.121, -0.945, -0.055, -1.436, 0.360, -1.478, -1.637, -1.869]\n",
    "data2 = [1.142, -0.432, -0.938, -0.729, -0.846, -0.157, 0.500, 1.183, -1.075, -0.169]\n",
    "data3 = [-0.208, 0.696, 0.928, -1.148, -0.213, 0.229, 0.137, 0.269, -0.870, -1.204]\n",
    "stat, p = f_oneway(data1, data2, data3)\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "\tprint('Probably the same distribution')\n",
    "else:\n",
    "\tprint('Probably different distributions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=4.048, p=0.018\n",
      "Probably different distributions\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import f_oneway\n",
    "stat, p = f_oneway(cc1, cc2, ccb)\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "\tprint('Probably the same distribution')\n",
    "else:\n",
    "\tprint('Probably different distributions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=0.982, p=0.000\n",
      "Probably not Gaussian\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda3\\envs\\env\\lib\\site-packages\\scipy\\stats\\morestats.py:1681: UserWarning: p-value may not be accurate for N > 5000.\n",
      "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
     ]
    }
   ],
   "source": [
    "# Example of the Shapiro-Wilk Normality Test\n",
    "from scipy.stats import shapiro\n",
    "stat, p = shapiro(cb)\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "    print('Probably Gaussian')\n",
    "else:\n",
    "    print('Probably not Gaussian')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "only size-1 arrays can be converted to Python scalars",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-77-65dbba4da495>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstats\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnormaltest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mstat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnormaltest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'stat=%.3f, p=%.3f'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mstat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mp\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0.05\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Probably Gaussian'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars"
     ]
    }
   ],
   "source": [
    "# Example of the D'Agostino's K^2 Normality Test\n",
    "from scipy.stats import normaltest\n",
    "stat, p = normaltest(cb)\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "\tprint('Probably Gaussian')\n",
    "else:\n",
    "\tprint('Probably not Gaussian')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.8660254037844386, 0.011724811003954649)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.7426106572325057, 0.1505558088534455)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "a = np.array([0, 0, 0, 1, 1, 1, 1])\n",
    "b = np.arange(7)\n",
    "print(stats.pearsonr(a, b))\n",
    "stats.pearsonr([1, 2, 3, 4, 5], [10, 9, 2.5, 6, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWRUlEQVR4nO3dfWzd1X3H8ffXduzEjvPoPJHEcZxCSklpYW5HoeuqBhilCLqJSlSloitapKmj9EmlCHXVpK3q+lytaquoUDpB2Vag6sM2RgSlaBJlTVIgkKSFexMSkwRfJyHOvXb8+N0f9zoYJ04c3/O7Px/fz0tCsa+tcz+K7A8nv98552fujoiIxKcm7QAiIjI1KnARkUipwEVEIqUCFxGJlApcRCRSdZV8s5aWFm9ra6vkW4qIRG/btm3d7r5k/OsVLfC2tja2bt1aybcUEYmemb18utd1CUVEJFIqcBGRSKnARUQipQIXEYmUClxEJFIqcBGRSKnARUQipQIXEUlQ7ng/X31kN9lcPvjYKnARkQTtPtTD957I8GpPf/CxVeAiIgnK5goArFvSFHxsFbiISIIyuTzNDXUsaW4IPrYKXEQkQdlcgfYlTZhZ8LFV4CIiCcrm8qxbMjeRsVXgIiIJ6R0Y4sCxE7QncP0bVOAiIol5/QamZuAiIlHJlNZ+t6vARUTiks0VqDFYs7gxkfFV4CIiCcnk8qxa2MjsWbWJjK8CFxFJyOgSwqSctcDN7B4z6zKz50/ztc+ZmZtZSzLxRETiNDLi7OkuJHYDEyY3A78XuGb8i2a2GrgK2Bc4k4hI9A72nKBvcDjRGfhZn0rv7k+aWdtpvvQt4PPAz0OHEhFJw//tOcKe7jCnBma7k11CCJMo8NMxs+uBV9z92bNtDzWzTcAmgNbW1qm8nYhI4oaGR/jo3U/TPzQSbMyGuhrWL2sONt5451zgZtYI3AVcPZnvd/fNwGaAjo4OP9f3ExGphP1H++gfGuGL172F929YHmTMubPrmDd7VpCxTmcqM/B1wFpgdPa9CthuZu9090Mhw4mIVEqmq3jp5JLWBZy3YE7KaSbnnAvc3XcAS0c/N7O9QIe7dwfMJSJSUdnSte91Lcldsw5tMssIHwCeAtabWaeZ3Zp8LBGRysrmCrTMrWd+Y3KXPEKbzCqUD5/l623B0oiIpCSTy9Me0ewbtBNTRARIftdkElTgIlL1Xusd4HBhINE120lQgYtI1cuUzu3WDFxEJDJJn9udFBW4iFS9bK7ArFpj9cI41n+PUoGLSNXL5PKsWdxEXW1clRhXWhGRBBSfHB/X9W+Y4mFWIiJpOlIYYOeBniBjOc7Lh3u5+qIw559UkgpcRKJzx0PPsWXnq0HHvHDFvKDjVYIKXESis/tQD392fguf3Hh+kPHqa2vYsHJ+kLEqSQUuIlE5MThM59E+/uqSVbyjbVHacVKlm5giEpW9hwu4w7qlca3ZToIKXESikukq7ZpsiW/VSGgqcBGJSvbkrkkVuApcRKKS7S5w3vzZNNbrFp4KXESiksnldf27RAUuItFw9+K53br+DajARSQiueP95PuHojs1MCkqcBGJxkulG5ixPXghKZN5qPE9ZtZlZs+Pee1rZrbbzJ4zs5+Z2YJEU4qIUDz2FbQCZdRkZuD3AteMe20LsMHdLwb+CNwZOJeIyCkyuTyN9bUsnzc77SjTwmSeSv+kmbWNe+3RMZ/+FrgxcC4RmQHcnf1H+hgaGQky3q6DPaxtaaKmxoKMF7sQCyk/Dvx7gHFEZIb5j637ueOhHUHH/MtLVgYdL2ZlFbiZ3QUMAfef4Xs2AZsAWltby3k7EYnMc53HaJ5dxz9+cEOwMd/VvjjYWLGbcoGb2S3AdcBGd/eJvs/dNwObATo6Oib8PhGZebK5Am9aOpcb3q5ZcxKmtIzQzK4B7gCud/fesJFEZKbI5PJa8pegySwjfAB4ClhvZp1mdivwXaAZ2GJmz5jZDxLOKSKROX5ikK7j/Vryl6DJrEL58GlevjuBLCIyg4yu2dYMPDnaiSkiich2j+6a1Aw8KSpwEUlENlegtsZoXaQCT4oKXEQSkcnlaV3USH2daiYp+psVkUTo2NfkqcBFJLjhESfbXdAKlISpwEUkuAOv9TEwNKIVKAlTgYtIcC+dfPCwCjxJeiqoiAAwNDzC8MSnYpyTl17VEsJKUIGLCAeP9bHxG7+hd2A42JgLGmexqKk+2HhyKhW4iPBc5zF6B4b52OVtLGluCDLmhpXzMdO53UlSgYvIyW3vn7n6AubNnpVyGpks3cQUETK5PEuaG1TekVGBiwjZXF43HCOkAhepcu5OJlfQkr8IqcBFqtyRwgDH+ga16SZCKnCRKpftLt7A1Lb3+KjARapcpqu06aZFM/DYqMBFqly2u0B9XQ0rF85JO4qcIxW4SJXL5vKsXdxEbY023cRGBS5S5TK5AuuW6vp3jCbzVPp7zKzLzJ4f89oiM9tiZi+W/lyYbEwRScLA0Aj7jvTSruvfUZrMDPxe4Jpxr30BeMzdzwceK30uIpHZd6TA8IhrBh6ps56F4u5PmlnbuJdvAN5b+vjHwBPAHSGDicjp/c2/buX3+44GGWtgaARAM/BITfUwq2XufhDA3Q+a2dKJvtHMNgGbAFpbW6f4diIC0DcwzJadr3Jp6wIuXDEvyJgLG+vZsHJ+kLGkshI/jdDdNwObATo6OsKcFi9SpbLdxTXbt767nQ9cvCLlNJK2qa5CedXMVgCU/uwKF0lEJjJ67Kt2TQpMvcB/AdxS+vgW4Odh4ojImWRyecxgbYsKXCa3jPAB4ClgvZl1mtmtwFeAq8zsReCq0ucikrBsrsDKBXOYPas27SgyDUxmFcqHJ/jSxsBZROQsMrm8Tg2Uk7QTUyQS7s6e7oKuf8tJKnCRSBzqOUHvwLAevCAnqcBFIpHpKq5A0aPPZJQKXCQSo2vAdQ1cRqnARSKR6crTVF/L0uaGtKPINKECF4lEtrvAuqVzMdO53VKkAheJRKYrT7s28MgYiZ+FIlKtfrp1P/f99uVg4x04dkLXv+UNVOAiCfnp1k72H+3j4lVhTvq78sJlvP+ty4OMJTODClwkIdnuPFdeuJSv3vi2tKPIDKVr4CIJONY7SHd+QJc8JFEqcJEEZEprtrVrUpKkAhdJwOi53do1KUlSgYskIJvLU1djrF7UmHYUmcFU4CIJyOTyrFncyKxa/YpJcvTTJZKAbK6g69+SOBW4SGBDwyPsPVzQChRJnApcJLDOo30MDrsevCCJU4GLBPb6sa8qcElWWQVuZp82sxfM7Hkze8DMZocKJhKr0QcvtLfoEooka8oFbmYrgU8CHe6+AagFbgoVTCRW2e48i5rqWdhUn3YUmeHKPQulDphjZoNAI3Cg/EgilbW3u8A3tvyRoeGRIOP9bu9RXT6Riphygbv7K2b2dWAf0Ac86u6Pjv8+M9sEbAJobW2d6tuJJOY/dxzkl88e4IJlYS55LGqaxQcvWRlkLJEzmXKBm9lC4AZgLfAa8FMzu9nd7xv7fe6+GdgM0NHR4VOPKpKMTC7PivmzefTTf552FJFzUs5NzCuBPe6ec/dB4GHg8jCxRConkytoyZ9EqZwC3wdcZmaNVnxI30ZgV5hYIpXh7mRzea0YkShNucDd/WngQWA7sKM01uZAuUQqIpfv5/iJId10lCiVtQrF3b8EfClQFpGKGz32VeeWSIy0E1OqWiZX2jW5VAUu8VGBS1XL5grMnlXDinnaRCzxUYFLVcvk8qxtmUtNjaUdReScqcClqmVzBd3AlGipwKVqnRgcpvNor25gSrRU4FK1Xj7cy4jr2FeJlwpcqlZ2dAWKZuASqXJPIxSpmJER5/u/yXC0MBBkvB2vHANgbYtm4BInFbhEY+fBHr72P3+goa6GukCrRq5402KaGvRrIHHST65EY3TTzS9vezcXLGtOOY1I+nQNXKKR6cpTY7BmcWPaUUSmBRW4RCPTXWD1okYa6mrTjiIyLajAJRqZrjztuuEocpIKXKIwMuLsPVzQkj+RMVTgEoUDx/o4MTiiXZMiY6jAJQqZ0rnd2jUp8joVuERhdNekZuAir1OBSxQyuTzzZtfRMrc+7Sgi04YKXKKQzRVoXzKX4vOzRQTKLHAzW2BmD5rZbjPbZWbvChVMZKxigev6t8hY5W6l/w7wiLvfaGb1gLbISXD5/iEO9ZzQEkKRcaZc4GY2D3gP8DEAdx8AwhwTJ9F7OnuYnQd7goyVO94PaAWKyHjlzMDbgRzwIzN7G7ANuN3dC2O/ycw2AZsAWltby3g7icknfrKd7ny4/5/X19awYeX8YOOJzATlFHgdcClwm7s/bWbfAb4AfHHsN7n7ZmAzQEdHh5fxfhKJo4UBuvMDfO7qC7j5sjVBxmyoq2VOvc5AERmrnALvBDrd/enS5w9SLHCpctnu4prti86bz4JGLfsTScqUV6G4+yFgv5mtL720EdgZJJVELdNVvIqmVSMiySp3FcptwP2lFShZ4K/LjySxy3Tnqa+tYdVCLUoSSVJZBe7uzwAdYaLITJHpKtDW0khtoMeeicjpaSemBJftztPeojXbIklTgUtQg8Mj7Dvcq+vfIhWgApeg9h3pZWjEtWtSpAJU4BJUpmv02FfNwEWSpgKXoLLdo0sINQMXSZoKXILK5vK0zG1g/pxZaUcRmfFU4BJURse+ilRMuRt5JHJHCgP8bu+RYOO91JXn2reuCDaeiExMBV7lvvxfu3hwW2fQMS86b17Q8UTk9FTgVe4Ph47TsWYh/3DDRUHGq6up4fyluoEpUgkq8Crm7mRzeT7UsZqLztNZ2yKx0U3MKvZqTz+FgWHddBSJlAq8imVzxU032jUpEicVeBXL5LRrUiRmKvAqlskVaKyvZfm82WlHEZEpUIFXsWx3cdONmc7tFomRCryKZbryuv4tEjEVeJXqGxjmwLE+PXhBJGIq8Cq1p7uAO6xbqhuYIrEqu8DNrNbMfm9mvwoRSCoj211agaIZuEi0QszAbwd2BRhHKiibK2AGa1s0AxeJVVlb6c1sFfAB4J+AzwRJJKfl7rzUlad/aCTIeM/uf43z5s9hTn1tkPFEpPLKPQvl28Dngebyo8iZPPL8If72/u1Bx3zfm5cGHU9EKmvKBW5m1wFd7r7NzN57hu/bBGwCaG1tnerbVb0XDvRQW2N87yOXEmrV9ttXLwg0koikoZwZ+BXA9WZ2LTAbmGdm97n7zWO/yd03A5sBOjo6vIz3q2qZXJ41ixr5i4uWpx1FRKaJKd/EdPc73X2Vu7cBNwGPjy9vCSerR5WJyDhaBx6B4RFnz+GCdk2KyBsEeaCDuz8BPBFiLDnVK0f7GBga0QxcRN5AM/AIZHRut4ichgo8Aq+f260CF5HXqcAjkMkVWNA4i0VN9WlHEZFpRAUegWxOx76KyKlU4BHI5Aq068wSERlHBT7N9ZwYpDvfz7qlmoGLyBupwKe5bK4AoBm4iJwiyDpweaMTg8P0DQwHGWvHK8cArUARkVOpwAM71jvIFf/8OPn+oWBjzqo1Whc1BhtPRGYGFXhguw/1kO8f4mOXt9G2OEzpti+ZS32drnaJyBupwAPLdhevWd/67rWs1qxZRBKkaV1g2VyehroaVi6Yk3YUEZnhVOCBZXIF1rY0UVMT6rELIiKnpwIPTLsmRaRSVOAB9Q8Ns+9Ir459FZGKUIEHtO9wLyOuY19FpDJU4AFlRndNagYuIhWgAg9I53aLSCWpwAPK5gosm9fA3AYtrxeR5KnAA8poBYqIVNCUC9zMVpvZr81sl5m9YGa3hwwWG3cnm8vr+reIVEw5/9YfAj7r7tvNrBnYZmZb3H1noGxROVwYoOfEkGbgIlIxUy5wdz8IHCx9fNzMdgErgWgK/O9+sp3Hd3cFGWvEHdANTBGpnCB328ysDbgEePo0X9sEbAJobW0N8XZBjIw4j+3qYv3yZt7RtjDImE0NdVzWvijIWCIiZ1N2gZvZXOAh4FPu3jP+6+6+GdgM0NHR4eW+XyiHek7QNzjMhzpW8ZE/XZN2HBGRc1bWKhQzm0WxvO9394fDRKqMk2u2W3TJQ0TiVM4qFAPuBna5+zfDRaqM0WdNrtOqERGJVDkz8CuAjwLvM7NnSv9dGyhX4jK5PM0NdSxpbkg7iojIlJSzCuV/gWgPvc7mCrQvaaL4DwkRkfhU7U5M7ZoUkdhVZYEX+oc4eOyEdk2KSNSqssD3dI8e+6oZuIjEqyoLfHQJoS6hiEjMqrTAC5jBmsWNaUcREZmyqizwbC7P6oWNzJ5Vm3YUEZEpq9ICL+gGpohEL4pHx/zLYy/yi2cPBBsv213gsvbFwcYTEUlDFAW+pLmB85eFu+G4fnkzN/7JqmDjiYikIYoCv+mdrdz0zulzFK2IyHRQldfARURmAhW4iEikVOAiIpFSgYuIREoFLiISKRW4iEikVOAiIpFSgYuIRMrcvXJvZpYDXq7YG05OC9CddohJiikrxJU3pqwQV96YssL0zLvG3ZeMf7GiBT4dmdlWd+9IO8dkxJQV4sobU1aIK29MWSGuvLqEIiISKRW4iEikVOCwOe0A5yCmrBBX3piyQlx5Y8oKEeWt+mvgIiKx0gxcRCRSKnARkUhVZYGb2Woz+7WZ7TKzF8zs9rQzTYaZ1ZrZ783sV2lnORMzW2BmD5rZ7tLf8bvSznQmZvbp0s/B82b2gJnNTjvTWGZ2j5l1mdnzY15bZGZbzOzF0p8L08w4aoKsXyv9LDxnZj8zswUpRnyD0+Ud87XPmZmbWUsa2SajKgscGAI+6+4XApcBnzCzt6ScaTJuB3alHWISvgM84u5vBt7GNM5sZiuBTwId7r4BqAVuSjfVKe4Frhn32heAx9z9fOCx0ufTwb2cmnULsMHdLwb+CNxZ6VBncC+n5sXMVgNXAfsqHehcVGWBu/tBd99e+vg4xYJZmW6qMzOzVcAHgB+mneVMzGwe8B7gbgB3H3D311INdXZ1wBwzqwMagXBP0A7A3Z8Ejox7+Qbgx6WPfwx8sJKZJnK6rO7+qLsPlT79LTBtHkg7wd8twLeAzwPTepVHVRb4WGbWBlwCPJ1ylLP5NsUfqJGUc5xNO5ADflS63PNDM2tKO9RE3P0V4OsUZ1oHgWPu/mi6qSZlmbsfhOKEBFiacp7J+jjw32mHOBMzux54xd2fTTvL2VR1gZvZXOAh4FPu3pN2nomY2XVAl7tvSzvLJNQBlwLfd/dLgALT55/3pyhdO74BWAucBzSZ2c3pppqZzOwuipcv7087y0TMrBG4C/j7tLNMRtUWuJnNolje97v7w2nnOYsrgOvNbC/wb8D7zOy+dCNNqBPodPfRf9E8SLHQp6srgT3unnP3QeBh4PKUM03Gq2a2AqD0Z1fKec7IzG4BrgM+4tN788k6iv8zf7b0+7YK2G5my1NNNYGqLHAzM4rXaHe5+zfTznM27n6nu69y9zaKN9ged/dpOUt090PAfjNbX3ppI7AzxUhnsw+4zMwaSz8XG5nGN13H+AVwS+njW4Cfp5jljMzsGuAO4Hp37007z5m4+w53X+rubaXft07g0tLP9bRTlQVOcUb7UYoz2WdK/12bdqgZ5DbgfjN7Dng78OV040ys9C+FB4HtwA6KvxPTaiu1mT0APAWsN7NOM7sV+ApwlZm9SHG1xFfSzDhqgqzfBZqBLaXftR+kGnKMCfJGQ1vpRUQiVa0zcBGR6KnARUQipQIXEYmUClxEJFIqcBGRSKnARUQipQIXEYnU/wPVKS1/CCy8HgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def f(x):\n",
    "    return np.int(x)\n",
    "f2 = np.vectorize(f)\n",
    "x = np.arange(1, 15.1, 0.1)\n",
    "plt.plot(x, f2(x))\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}