Machinelearning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Machine learning algo to identiy if a firm should be prioritized or not based on certain features for one year period\n",
    "# The firms in the final priority list would be used for testing the model\n",
    "# Three labels \"error\", \"outlier\" and \"typical\" are used to classify the firms\n",
    "import numpy as np\n",
    "import torch \n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as Data\n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd\n",
    "import statistics as st\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LassoCV, Lasso\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Firm           NWP           SCR  SCR coverage ratio           GWP  \\\n",
      "0      4  24996.021042  19600.262144            1.414507  34922.702554   \n",
      "1      5     81.273653     66.230812            1.899498     83.668353   \n",
      "2      6   3041.876078   3281.203179            1.398558   4452.508624   \n",
      "3     10   7893.064120   2880.316429            1.421303   7923.371752   \n",
      "4     14     11.852087      8.858272            2.778387     13.775816   \n",
      "..   ...           ...           ...                 ...           ...   \n",
      "92   316     -0.037675    225.332967            1.797814     -0.879752   \n",
      "93   317   2150.070000    164.189033            1.353203   2150.070000   \n",
      "94   321     24.632234    167.547783            1.697523     46.127035   \n",
      "95   322   2029.697013   1395.278396            1.381756   2179.242094   \n",
      "96   324     26.546638     30.191048            1.707757    147.947620   \n",
      "\n",
      "           Assets        Equity  Gross Claims Incurred  Net Combined ratio  \n",
      "0    94065.081033  22827.370034               6.567988           -3.408470  \n",
      "1      235.481902    130.559790             373.786832            1.062102  \n",
      "2    64743.030244   4927.076783             178.323198            0.835525  \n",
      "3   142144.987135   5168.099949             106.373170            0.945358  \n",
      "4       61.976207     27.689665             328.634930            1.087818  \n",
      "..            ...           ...                    ...                 ...  \n",
      "92    1277.728155    450.178988              70.880339            1.752051  \n",
      "93   15949.164507    234.432154             191.050779            1.051514  \n",
      "94    6850.295493    295.164914               3.465836            0.816870  \n",
      "95    5885.118080   2135.697712             200.694626            1.168521  \n",
      "96     299.339725     53.507509               5.098240            0.780065  \n",
      "\n",
      "[97 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "# I would be calculating the zscore for all firms for one year period (2020)\n",
    "# With the zscore I would be able to identify if a firm should be prioritized or not\n",
    "# Selected metrics are made in excel file \n",
    "# firms with missing values for those features are filtered out in excel sheet\n",
    "# Firms left without missing values are used for machine learning technique analysis\n",
    "\n",
    "# Creating the dataset and cleaning it up\n",
    "Raw = pd.read_excel(r'C:\\Users\\tobio\\OneDrive\\Bank of England\\MachineL.xlsx', sheet_name='Machine Learning')\n",
    "# adjusting the column names\n",
    "Raw = Raw.drop(['Unnamed: 0'], axis=1) # drop the first column as the firm number has been seprated into another column inn excel sheet\n",
    "Raw.columns = [\"Firm\",\"NWP\", \"SCR\", \"SCR coverage ratio\", \"GWP\", \"Assets\", \"Equity\", \"Gross Claims Incurred\", \"Net Combined ratio\"]\n",
    "# 96 frims in total\n",
    "\n",
    "\n",
    "print(Raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19396.340851777604\n",
      "57216.33532155488\n",
      "    Firm           NWP           SCR  SCR coverage ratio           GWP  \\\n",
      "0      4  24996.021042  19600.262144            1.414507  34922.702554   \n",
      "1      5     81.273653     66.230812            1.899498     83.668353   \n",
      "2      6   3041.876078   3281.203179            1.398558   4452.508624   \n",
      "3     10   7893.064120   2880.316429            1.421303   7923.371752   \n",
      "4     14     11.852087      8.858272            2.778387     13.775816   \n",
      "..   ...           ...           ...                 ...           ...   \n",
      "92   316     -0.037675    225.332967            1.797814     -0.879752   \n",
      "93   317   2150.070000    164.189033            1.353203   2150.070000   \n",
      "94   321     24.632234    167.547783            1.697523     46.127035   \n",
      "95   322   2029.697013   1395.278396            1.381756   2179.242094   \n",
      "96   324     26.546638     30.191048            1.707757    147.947620   \n",
      "\n",
      "           Assets        Equity  Gross Claims Incurred  Net Combined ratio  \\\n",
      "0    94065.081033  22827.370034               6.567988           -3.408470   \n",
      "1      235.481902    130.559790             373.786832            1.062102   \n",
      "2    64743.030244   4927.076783             178.323198            0.835525   \n",
      "3   142144.987135   5168.099949             106.373170            0.945358   \n",
      "4       61.976207     27.689665             328.634930            1.087818   \n",
      "..            ...           ...                    ...                 ...   \n",
      "92    1277.728155    450.178988              70.880339            1.752051   \n",
      "93   15949.164507    234.432154             191.050779            1.051514   \n",
      "94    6850.295493    295.164914               3.465836            0.816870   \n",
      "95    5885.118080   2135.697712             200.694626            1.168521   \n",
      "96     299.339725     53.507509               5.098240            0.780065   \n",
      "\n",
      "       NWP_Z  Assets_Z   Total_Z  \n",
      "0   4.427720  1.305025  5.732745  \n",
      "1  -0.313818 -0.334884 -0.648702  \n",
      "2   0.249616  0.792548  1.042164  \n",
      "3   1.172848  2.145343  3.318191  \n",
      "4  -0.327029 -0.337917 -0.664946  \n",
      "..       ...       ...       ...  \n",
      "92 -0.329292 -0.316669 -0.645960  \n",
      "93  0.079896 -0.060248  0.019648  \n",
      "94 -0.324597 -0.219274 -0.543871  \n",
      "95  0.056988 -0.236143 -0.179155  \n",
      "96 -0.324233 -0.333768 -0.658001  \n",
      "\n",
      "[97 rows x 12 columns]\n",
      "    Firm           NWP           SCR  SCR coverage ratio           GWP  \\\n",
      "0      4  24996.021042  19600.262144            1.414507  34922.702554   \n",
      "1      5     81.273653     66.230812            1.899498     83.668353   \n",
      "2      6   3041.876078   3281.203179            1.398558   4452.508624   \n",
      "3     10   7893.064120   2880.316429            1.421303   7923.371752   \n",
      "4     14     11.852087      8.858272            2.778387     13.775816   \n",
      "..   ...           ...           ...                 ...           ...   \n",
      "92   316     -0.037675    225.332967            1.797814     -0.879752   \n",
      "93   317   2150.070000    164.189033            1.353203   2150.070000   \n",
      "94   321     24.632234    167.547783            1.697523     46.127035   \n",
      "95   322   2029.697013   1395.278396            1.381756   2179.242094   \n",
      "96   324     26.546638     30.191048            1.707757    147.947620   \n",
      "\n",
      "           Assets        Equity  Gross Claims Incurred  Net Combined ratio  \\\n",
      "0    94065.081033  22827.370034               6.567988           -3.408470   \n",
      "1      235.481902    130.559790             373.786832            1.062102   \n",
      "2    64743.030244   4927.076783             178.323198            0.835525   \n",
      "3   142144.987135   5168.099949             106.373170            0.945358   \n",
      "4       61.976207     27.689665             328.634930            1.087818   \n",
      "..            ...           ...                    ...                 ...   \n",
      "92    1277.728155    450.178988              70.880339            1.752051   \n",
      "93   15949.164507    234.432154             191.050779            1.051514   \n",
      "94    6850.295493    295.164914               3.465836            0.816870   \n",
      "95    5885.118080   2135.697712             200.694626            1.168521   \n",
      "96     299.339725     53.507509               5.098240            0.780065   \n",
      "\n",
      "       NWP_Z  Assets_Z   Total_Z    State  \n",
      "0   4.427720  1.305025  5.732745    Error  \n",
      "1  -0.313818 -0.334884 -0.648702  Typical  \n",
      "2   0.249616  0.792548  1.042164  Outlier  \n",
      "3   1.172848  2.145343  3.318191    Error  \n",
      "4  -0.327029 -0.337917 -0.664946  Typical  \n",
      "..       ...       ...       ...      ...  \n",
      "92 -0.329292 -0.316669 -0.645960  Typical  \n",
      "93  0.079896 -0.060248  0.019648  Outlier  \n",
      "94 -0.324597 -0.219274 -0.543871  Typical  \n",
      "95  0.056988 -0.236143 -0.179155  Typical  \n",
      "96 -0.324233 -0.333768 -0.658001  Typical  \n",
      "\n",
      "[97 rows x 13 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tobio\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 69.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "NWP_mean = st.mean(Raw['NWP'])\n",
    "NWP_sd = st.stdev(Raw['NWP'])\n",
    "\n",
    "Assets_mean = st.mean(Raw['Assets'])\n",
    "Assets_sd = st.stdev(Raw['Assets'])\n",
    "print(Assets_mean) # for validation in excel sheet\n",
    "print(Assets_sd)\n",
    "\n",
    "# creating a new dataframe and calculating the z-score to identify outliers both for NWP and Assets\n",
    "Raw['NWP_Z'] = (Raw['NWP'] - NWP_mean)/NWP_sd\n",
    "Raw['Assets_Z'] = (Raw['Assets'] - Assets_mean)/Assets_sd\n",
    "\n",
    "Raw[\"Total_Z\"] = Raw[\"NWP_Z\"] + Raw[\"Assets_Z\"]\n",
    "print(Raw)\n",
    "\n",
    "# creeating a method for condition \n",
    "def condition(row):\n",
    "    if row[\"Total_Z\"] >= 2:\n",
    "        return \"Error\"\n",
    "    elif row[\"Total_Z\"] < 2:\n",
    "        if row[\"Total_Z\"] > 0:\n",
    "            return \"Outlier\"\n",
    "        else:\n",
    "            return \"Typical\"\n",
    "Raw[\"State\"] = Raw.apply(condition, axis=1)\n",
    "print(Raw)\n",
    "# catplot plotting according to state\n",
    "sns.catplot(x=\"State\", y=\"Total_Z\", data=Raw, kind=\"swarm\")\n",
    "plt.show()\n",
    "#sns.catplot(x=\"Firm\", y=\"Total_Z\", data=Raw, hue=\"State\", kind=\"swarm\", height=10)\n",
    "# The firms in yellow which represent firms with typical values are: the firms which should be prioritized when taking the year 2020 into account "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting the raw dataframe containing firm states to csv file\n",
    "Raw.to_csv(r'C:\\Users\\tobio\\OneDrive\\Bank of England\\MLData.csv', index=False)\n",
    "# CSV is then converted to excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xeMmgS5Ak9VmwaHjQJawTZ4IkSVKTDEGSJKlJhiBJktQkQ5AkSWqSIUiSJDXJECRJkppkCJIkSU0yBEmSpCYZgqZIkv2TVJLdJnnM3SdrPEmSWmYImjqvAb7e/Zws+wOGIEmSJoEhaAok2Rx4NnAo8OqubfskFyZZmeS6JM9JsnGSpd32tUmO6vrukuSLSVYkuSjJbkkWAi8DTujG2CXJEUluSHJNkrMGdsGSJM1A/u6wqfFy4ItVdWOS25MsAPYFvlRV70myMbAZMB94XFXtAZBk6+74JcBhVfXtJM8APlRVz09yHvD5qvps1/8dwBOq6r6+Yx8myTAwDLDjjjtO/tVKkjQDORM0NV4DrJqZOavbvgI4JMlxwJ5V9XPge8DOSU5J8iLgrm4WaSFwTpKVwD8C249znmuAM5O8Drh/vGKqaklVDVXV0Ny5c9f/6iRJmgWcCZpkSbYBng/smaSAjYECjgaeC/wRsDTJ+6rq9CR7AX8IHAa8CjgSuLOq5k/gdH/UjflS4Jgke1bVuGFIkiQ9xJmgyXcAcEZV7VRV86pqB+AmemHl1qr6KPAxYO8k2wEbVdXngGOBvavqLuCmJK8ESM9e3dg/B7bo2jcCdqiq84G/ArYCNt9wlylJ0szmTNDkew3w3lFtnwOWAr9I8mvgbuANwOOAU7tAA/DX3c+DgA8nORbYhN4jtau7nx9NcgS9BdcfT7IVEODkqrpzqi5KkqTZxhA0yapqvzHaTgZOHueQvcfofxPwojHal/HbH5F/9jqWKUlS83wcJkmSmmQIkiRJTTIESZKkJhmCJElSkwxBkiSpSX46TDPOgkXDgy5BkjQLOBMkSZKaZAiSJElNMgRJkqQmGYIkSVKTDEGSJKlJhiBJktQkQ5AkSWqSIUiSJDXJECRJkppkCJIkSU0yBEmSpCYZgiRJUpMMQZIkqUmGIEmS1CRDkCRJapIhSJIkNckQJEmSmmQIkiRJTTIESZKkJhmCJElSkwxBkiSpSYYgSZLUpDmDLkBaWysWLxl0CZKkPgsWDQ+6hHXiTJAkSWqSIUiSJDXJECRJkppkCJIkSU0yBEmSpCYZgiRJUpMMQZIkqUmGoAFJUklO7Nt+e5Ljkmyd5PYk6dr36fo+vtveKskdSTZKsjTJTUlWJrkyyT6Duh5JkmYaQ9Dg3Af8aZLt+hur6k7gFuDJXdNC4KruJ8Azgcur6sFu++iqmg+8A/jHKa5ZkqRZwxA0OPcDS4Cjxth3MQ+FnoXASaO2l41xzIXArpNcoyRJs5YhaLA+CByUZKtR7ct4KPTsDJwDDHXbC+mFpNFeClw71kmSDCdZnmT5yMjI+lctSdIsYAgaoKq6CzgdOGLUrouBhUmeANxcVfcCSbI5sAC4rK/vCUlWAsPAoeOcZ0lVDVXV0Ny5cyf7MiRJmpH8BaqD9w/AlcCpqxqq6ttJtqY3u3NJ17wCOIReKLq77/ijq+qzG6ZUSZJmD2eCBqyq7gA+w8NncS4F3sZDIegS4EjGXg8kSZLWkiFoejgR2G5U2zJgB2B5t30JvfVBY60HkiRJa8nHYQNSVZv3vb8V2GzU/hOAE/q2bwYyqs/BU1qkJEmzmDNBkiSpSYYgSZLUJEOQJElqkiFIkiQ1yRAkSZKa5KfDNOMsWDQ86BIkSbOAM0GSJKlJhiBJktQkQ5AkSWqSIUiSJDXJECRJkppkCJIkSU0yBEmSpCYZgiRJUpMMQZIkqUmGIEmS1CRDkCRJapIhSJIkNckQJEmSmmQIkiRJTTIESZKkJhmCJElSkwxBkiSpSYYgSZLUJEOQJElqkiFIkiQ1yRAkSZKaZAiSJElNmjPoAqS1tWLxkkGXIEnqs2DR8KBLWCfOBEmSpCYZgiRJUpMMQZIkqUmGIEmS1CRDkCRJapIhSJIkNckQJEmSmmQIkiRJTTIETVCS/ZNUkt0meczd1+G4U5Os7HvdnOTWyapLkqQWGIIm7jXA17ufk2V/YK1DUFUdUlXzq2o+sDfwA+CYSaxLkqRZzxA0AUk2B54NHAq8umvbPsmF3UzMdUmek2TjJEu77WuTHNX13SXJF5OsSHJRkt2SLAReBpzQjbFLkiOS3JDkmiRnTbC8dwIjVfWx1dQ/nGR5kuUjIyPrdS8kSZot/N1hE/Ny4ItVdWOS25MsAPYFvlRV70myMbAZMB94XFXtAZBk6+74JcBhVfXtJM8APlRVz09yHvD5qvps1/8dwBOq6r6+Y8eV5OnAn9ObDRpXVS3pamBoaKjW7tIlSZqdDEET8xrg/d37s7rt84BPJNkEOLeqVib5HrBzklOALwBf7maRFgLnJFk13iPHOc81wJlJzgXOXV1B3bifBA6tqjvW9cIkSWqVIWgNkmwDPB/YM0kBGwMFHA08F/gjYGmS91XV6Un2Av4QOAx4FXAkcGe3fmdN/qgb86XAMUn2rKr7x+l7CvAvVfWVdb44SZIa5pqgNTsAOKOqdqqqeVW1A3ATvbBya1V9FPgYsHeS7YCNqupzwLHA3lV1F3BTklcCpGevbuyfA1t07RsBO1TV+cBfAVsBm49VUJIDgL1wMbQkSevMmaA1ew3w3lFtnwOWAr9I8mvgbuANwOOAU7tAA/DX3c+DgA8nORbYhN4jtau7nx9NcgS9BdcfT7IVEODkqrpznJreQ28N0uV9j9gA9qmqX67jdUqS1BRD0BpU1X5jtJ0MnDzOIQ9bpFxVNwEvGqN9Gb/9EflnT7CmJ02knyRJGp+PwyRJUpOcCZrGkhwCvG1U87Kqessg6pEkaTYxBE1jVXUqcOqg65AkaTbycZgkSWqSIUiSJDXJx2GacRYsGh50CZKkWcCZIEmS1CRDkCRJapIhSJIkNckQJEmSmmQIkiRJTTIESZKkJhmCJElSkwxBkiSpSYYgSZLUJEOQJElqkiFIkiQ1yRAkSZKaZAiSJElNMgRJkqQmGYIkSVKTDEGSJKlJhiBJktQkQ5AkSWqSIUiSJDXJECRJkppkCJIkSU2aM+gCpLW1YvGSQZcgqc+CRcODLkFaJ84ESZKkJhmCJElSkwxBkiSpSYYgSZLUJEOQJElqkiFIkiQ1yRAkSZKaZAiSJElNMgRNkSQPJFnZ93rHeox1cfdzXpLXTl6VkiS1y2+Mnjq/rKr5kzFQVS3s3s4DXgt8ajLGlSSpZc4EbWBJXpTkm0muTHJyks937ccleXtfv+uSzOve3901Hw88p5tZOirJhUnm9x3z9SR7bbirkSRp5jIETZ1HjXocdmCSTYGPAi8FFgD/bS3HfAdwUVXNr6qTgI8DBwMkeSKwaVVdPfqgJMNJlidZPjIysj7XJEnSrGEImjq/7MLKqtfZwG7ATVX17aoq4JPreY5zgD9OsgnwZ8DSsTpV1ZKqGqqqoblz567nKSVJmh1cEzR93M9vh9JN13RAVd2T5N+BlwOvoje7JEmSJsCZoA3rm8C8JLt026/p23czsDdAkr2BJ4xx/M+BLUa1fQw4Gbiiqv5rUquVJGkWMwRNndFrgo6vqnuBYeALSa4EftrX/3PANkmuB94K3DjGmNcADyS5OslRAFW1ArgLOHVKr0aSpFnGx2FTpKo2Hqf9i/TWBpFkX+DtXfsvgT8Y55jNu5+/Bp7fvy/JY+mF2S9PTuWSJLXBmaAZLMkbgMuAY6rqwUHXI0nSTOJM0ABV1QXABetx/OnA6ZNVjyRJLXEmSJIkNckQJEmSmmQIkiRJTXJNkGacBYuGB12CJGkWcCZIkiQ1yRAkSZKaZAiSJElNMgRJkqQmGYIkSVKTDEGSJKlJhiBJktQkQ5AkSWqSIUiSJDXJECRJkppkCJIkSU0yBEmSpCYZgiRJUpMMQZIkqUmGIEmS1CRDkCRJapIhSJIkNckQJEmSmmQIkiRJTTIESZKkJhmCJElSkwxBkiSpSXMGXYC0tlYsXjLoEqTmLVg0POgSpPXmTJAkSWqSIUiSJDXJECRJkppkCJIkSU0yBEmSpCYZgiRJUpMMQZIkqUmGoGkiyTFJrk9yTZKVSZ6RZJMkxyf5dpIrk1yS5MVd/5uTXNv1/1qSnQZ9DZIkzSR+WeI0kGQf4I+BvavqviTbAY8A3g1sD+zRtT8GeF7foftV1W1JFgPHAm/a0LVLkjRTORM0PWwP3FZV9wFU1W3AnfRCzeF97bdW1WfGOP4S4HEbqFZJkmYFQ9D08GVghyQ3JvlQkucBuwI/qKq7JnD8i4Bzx9uZZDjJ8iTLR0ZGJqdiSZJmOEPQNFBVdwMLgGFgBDgb2HcCh56f5MfAi4FPr2b8JVU1VFVDc+fOnYSKJUma+QxB00RVPVBVF1TVIuCtwEuBHZNsuZrD9gN2AlYCi6e+SkmSZg9D0DSQ5ElJfq+vaT7wLeDjwPuTPKLrNzfJK/uPrar7gSOBNyTZZsNULEnSzGcImh42B05LckOSa4DdgePofeJrBLghyXXA54GHrRGqqlvoPQ57ywarWJKkGc6PyE8DVbUCWDjO7r/sXqOPmTdq+/DJr0ySpNnLmSBJktQkQ5AkSWqSIUiSJDXJECRJkppkCJIkSU3y02GacRYsGh50CZKkWcCZIEmS1CRDkCRJapIhSJIkNckQJEmSmmQIkiRJTTIESZKkJhmCJElSkwxBkiSpSYYgSZLUJEOQJElqkiFIkiQ1yRAkSZKaZAiSJElNMgRJkqQmGYIkSVKTDEGSJKlJhiBJktQkQ5AkSWqSIUiSJDXJECRJkppkCJIkSU0yBEmSpCbNGXQB0tpasXjJpI21YNHwpI0lSZpZnAmSJElNMgRJkqQmGYIkSVKTDEGSJKlJhiBJktQkQ5AkSWqSIUiSJDVpjSEoyWOSfCrJ95KsSHJJkj/ZEMUl2STJ8Um+neTK7twv7vbdnGS7NRz/r0m2nuSa7p7M8SZLkoOTfGDQdUiSNFOs9ssSkwQ4Fzitql7bte0EvGyMvnOq6v5Jru/dwPbAHlV1X5LHAM+b6MFV9ZJJrmfKJdm4qh4Yb1uSJE2ONc0EPR/4VVV9ZFVDVX2/qk6B38w+nJfkq8BXkmyT5Nwk1yS5NMlTu37PS7Kye12VZIsk2ye5sGu7Lslz+k+cZDPgTcDhVXVfd+5bq+ozo4vszrkiyfVJhvvab06yXZJ5Sb6ZZGmSG5OcmeSFSZZ1s0xPH6/O8W5Mkn2TXJDks93YZ3ahkSRPS3JxkquTXN5d72/N1CT5fJJ9u/d3JzkxydXAPmNsv64bZ2WSf0yycXfcId31XA48aw3/LCVJUp81haCnAFeuoc/ewAFV9TxgMXBVVT0VeCdwetfn7cBbqmo+8Bzgl8BrgS91bXsBK0eNuyvwg6q6awLX8WdVtQAYAo5Isu0YfXYFTgR2616vBZ7d1fbO1dS5Or8PHAnsDuwMPCvJI4CzgbdV1V7ACycwzqOBy6pqr6r6ev82cDtwIPCsrq4HgIOSbE/vfj+ru47dxxs8yXCS5UmWj4yMrKEUSZLasFYLo5N8sJvduKKv+d+r6o7u/bOBMwCq6qvAtkm2BJYB70tyBLB199jsCuCQJMcBe1bVz9fjOo7oZk0uBXYAfm+MPjdV1bVV9SBwPfCVqirgWmBe12esOlfn8qr6UTfmym6cJwG3VNUVAFV11wTGeQD43DjbLwAWAFckWdlt7ww8A7igqkaq6lf0gteYqmpJVQ1V1dDcuXPXUIokSW1YUwi6nt5MDwBV9RZ6fwn3/036izWdpKqOB/4ceBSwLMluVXUh8Fzgx8DSJG8Yddh3gB27EDWu7pHSC4F9upmTq4BNx+h6X9/7B/u2H6RbGzVWnWu4tP4xH2D1a6zu57fvd3+N945a99O/HXprsuZ3rydV1XFrqEuSJK3BmkLQV4FNk7y5r22z1fS/CDgIfhNObququ5Ls0s3CvJfeDNBu3QLrW6vqo8DH6AtbAFV1D/Bx4P3dIyaSzE3yylHn3Ar4r6q6pwstz1zDNY1rrDrXYZhvAdsneVo35hZJ5gA3A/OTbJRkB+DpExzvK8ABSX63G2+b7t5dBjwvybZJNgFG3xdJkrQaq/10WFVVkv2Bk5L8JTBCb+bnr8Y55DjgE0muAe4B3ti1H5lkP3qzLtcD/wa8Gjg6ya+Bu4HRM0EAxwL/B7ghyb3duf9mVJ8vAocl+Qa9AHLp6q5pDcaqc61U1a+SHAickuRR9NYDvZDeo7abgBuAb7DmtVarxrshybHAl5NsBPya3rqlS7tHiZcAd/LwNVWSJGk10lsWo1YMDQ3V8uXLB13GelmxeMmkjbVg0fCaO0mSZqwkK6pqaKx9fmO0JElqkiFIkiQ1yRAkSZKaZAiSJElNMgRJkqQmGYIkSVKTVvs9QdJ05MfaJUmTwZkgSZLUJEOQJElqkiFIkiQ1yRAkSZKaZAiSJElNMgRJkqQmGYIkSVKTDEGSJKlJhiBJktQkQ5AkSWqSIUiSJDXJECRJkppkCJIkSU0yBEmSpCYZgiRJUpMMQZIkqUmGIEmS1CRDkCRJapIhSJIkNckQJEmSmmQIkiRJTZoz6AI0+61YvGRSx1uwaHhSx5MktcmZIEmS1CRDkCRJapIhSJIkNckQJEmSmmQIkiRJTTIESZKkJhmCJElSkwxBkiSpSVMWgpIck+T6JNckWZnkGV37JkmOT/LtJFcmuSTJi7t9Nye5tjvma0l2mqr6Zook85O8pG/7ZUneMciaJEmaDaYkBCXZB/hjYO+qeirwQuCH3e53A9sDe1TV3sD+wBZ9h+/XHXMBcOxU1DdRSTbIN2qv4Tzzgd+EoKo6r6qOn/KiJEma5aZqJmh74Laqug+gqm6rqp8k2Qx4E3B4375bq+ozY4xxCfC4sQZP8qJuFunqJF/p2rZJcm43i3Rpkqcm2aibXdq679hvJ3lMkrlJPpfkiu71rG7/cUnOSLIMOCPJvCQXdee7MsnCrt9GST6U5JtJ/j3JvyY5oNu3oJvJWpHkS0m2H+Malib5SJLLgL9L8vRuVuyqJBcneVKSRwDvAg7sZtMOTHJwkg90Y8xL8tXumr+SZMd1+YclSVKLpioEfRnYIcmNXVB4Xte+K/CDqrprAmO8CDh3dGOSucBHgVdU1V7AK7tdi4GrulmkdwKnV9WDwL8Af9Id+wzg+1V1K/B+4KSqehrwCuBjfafZHXhhVb0G+Cnw37tZqwOBk7s+fwrM6/q+HtinO8cmwCnAAVW1APgE8J5xrvHxwMKq+gvgm8Bzqur3gb8B/raqftW9P7uq5lfV2aOOPwU4rbvmM/tqG33PhpMsT7J8ZGRknFIkSWrLlDzuqaq7kywAngPsB5zdrWO5cgKHn59kG+Bu4H+Psf+ZwIVVdVN3rju69mfTCzNU1VeTbJtkS+BsekHiVODV3Tb0HtHtnmTVuFsm2bx7f15V/bJ7vwnwgSTzgQeAJ/ad75wuaP1nkvO79icBewD/3o29MXDLONd6TlU90L3fCjgtye8B1Z13TfahF8YAzgD+bqxOVbUEWAIwNDRUExhXkqRZb8rWvHR/uV8AXJDkWuCNwGeAHZNsuZrZoP2AO+nNbCwG/mI9S7kE2LWbQdof+D9d+0bAM6vq3v7OXXD5RV/TUcCtwF7dMb/VfwwBrq+qfSZQW/953g2cX1V/kmQevXsnSZKmyFQtjH5SN6Oxynx6j6HuAT4OvL9b70K3NueV/cdX1f3AkcAbulmhfpcCz03yhO74VfsvAg7q2valtybprqoq4J+B9wHfqKrbu/5fBg7vq3n+OJezFXBLN+PzenozOwDLgFd0a4MeA+zbtX8LmNstDl/1abinjDP26PP8uHt/cF/7z/ntheP9LqY3uwW9a79oAueRJElM3Zqgzek92rkhyTX01s0c1+07FhgBbkhyHfB54GGzQlV1C/Bp4C2j2keAYeCfklzNQ4+3jgMWdOc7nt7M0ypnA6/r6wtwBDDULSq+AThsnGv5EPDG7ly78dDszeeAHwE3AJ+k96jvZ906ngOA93bHrAQWjjN2v78D/m+Sq/jtGbrz6T22W5nkwFHHHA4c0l3z64G3TeA8kiQJSG+iROsiyebd+qdtgcuBZ1XVfw66rtUZGhqq5cuXb9Bzrli8ZFLHW7BoeFLHkyTNXklWVNXQWPs2yPfgzGKf7z5+/wjg3dM9AEmSpIcYgtZDVe076BokSdK68XeHSZKkJhmCJElSkwxBkiSpSa4J0pTz01ySpOnImSBJktQkQ5AkSWqSIUiSJDXJECRJkppkCJIkSU0yBEmSpCYZgiRJUpMMQZIkqUmGIEmS1CRDkCRJapIhSJIkNckQJEmSmmQIkiRJTTIESZKkJhmCJElSkwxBkiSpSYYgSZLUJEOQJElqkiFIkiQ1yRAkSZKaZAiSJElNMgRJkqQmzRl0AZrdVixeMuljLlg0POljSpLa40yQJElqkiFIkiQ1yRAkSZKaZAiSJElNMgRJkqQmGYIkSVKTDEGSJKlJkx6CklSSE/u2357kuDUcs2+ShavZ/+Iky5PckOSq/vHXs9a7x2l/V5IXTtI5LkgytB7H759k96moTZKklk3FTNB9wJ8m2W4tjtkXGDMEJdkD+ADwuqraHRgCvrO+Ra5OVf1NVf3HVJ6jX5KNV7N7f+A3IWhD1yZJ0mw1FSHofmAJcNToHUnmJvlckiu617OSzAMOA45KsjLJc0Yd9pfAe6rqmwBV9UBVfbgbb16Srya5JslXkuzYtS9N8uEklyb5XjfT9Ikk30iydFRNJyW5vjt+bt/xB3Tvb06yOMmVSa5NslvX/uhuzMu72amXd+2PSnJWd65/Bh411k3qxn1vkiuBVyZ5U3dPru7u0Wbd7NjLgBO6e7PLqNpe0J372q6WR67tPyxJklo1VWuCPggclGSrUe3vB06qqqcBrwA+VlU3Ax/p2udX1UWjjtkDWDHOeU4BTquqpwJnAif37fsdYB96Yew84CTgKcCeSeZ3fR4NLK+qpwBfAxaNc57bqmpv4MPA27u2Y4CvVtXTgf3oBZVHA28G7qmqJ3fjLRhnTIDbq2rvqjoL+KeqelpV7QV8Azi0qi7uaj+6uzffXXVgkk2BpcCBVbUnvV+B8uaxTpJkuHucuHxkZGQ15UiS1I4pCUFVdRdwOnDEqF0vBD6QZCW9v9y3TLL5epxqH+BT3fszgGf37ft/VVXAtcCtVXVtVT0IXA/M6/o8CJzdvf/kqOP7/VP3c0XfsX8AvKO7lguATYEdged2Y1FV1wDXrKb+s/ve75HkoiTXAgfRC2yr8yTgpqq6sds+rTv3w1TVkqoaqqqhuXPnrmFYSZLaMJW/QPUfgCuBU/vaNgKeWVX39ndMsrpxrqc3m3L1Wp7/vu7ng33vV22Pd921hrEe6Ds2wCuq6lv9HddwLaP9ou/9UmD/qro6ycH01klJkqQpMmUfka+qO4DPAIf2NX8ZOHzVRt9jqZ8DW4wz1AnAO5M8sTtmoySHdfsuBl7dvT8IGP0obU02Ag7o3r8W+PpaHPsl4PB0qSfJ73ftF3ZjrVrU/dQJjrcFcEuSTehdyyrj3ZtvAfOS7Nptv57eIz1JkjQBU/09QScC/Z8SOwIY6hYy30BvQTTA/wP+ZKyF0d0jpSOBTyf5BnAdsHO3+3DgkCTX0AsBb1vL+n4BPD3JdcDzgXetxbHvBjYBrklyfbcNvXVDm3e1vovx1zON9r+By4BlwDf72s8Cju4WQO+yqrGbTTsEOKd7hPYgvbVVkiRpAtJbNqNWDA0N1fLlyzfY+VYsXjLpYy5YNDzpY0qSZqckK6pqzO/r8xujJUlSkwxBkiSpSYYgSZLUJEOQJElqkiFIkiQ1aSq/LFHyk1ySpGnLmSBJktQkQ5AkSWqSIUiSJDXJECRJkppkCJIkSU0yBEmSpCYZgiRJUpMMQZIkqUmGIEmS1CRDkCRJapIhSJIkNckQJEmSmmQIkiRJTTIESZKkJhmCJElSkwxBkiSpSYYgSZLUJEOQJElqkiFIkiQ1yRAkSZKaZAiSJElNMgRJkqQmzRl0AZrZVixessHPuWDR8AY/pyRp9nEmSJIkNckQJEmSmmQIkiRJTTIESZKkJhmCJElSkwxBkiSpSYYgSZLUJEPQgCV5IMnKvte8JBcPui5JkmY7vyxx8H5ZVfNHtS0c3SnJnKq6f8OUJEnS7OdM0DSU5O7u575JLkpyHnBDt/21JP+S5HtJjk9yUJLLk1ybZJcBly5J0oxhCBq8R/U9CvvnMfbvDbytqp7Ybe8FHAY8GXg98MSqejrwMeDwsU6QZDjJ8iTLR0ZGpuASJEmaeXwcNnhjPQ7rd3lV3dS3fUVV3QKQ5LvAl7v2a4H9xhqgqpYASwCGhoZqvSuWJGkWcCZo+vvFqO37+t4/2Lf9IIZaSZImzBAkSZKaZAiSJElN8vHJgFXV5uO1VdUFwAV97aO39x1vnyRJWj1ngiRJUpMMQZIkqUmGIEmS1CRDkCRJapIhSJIkNclPh2m9LFg0POgSJElaJ84ESZKkJhmCJElSkwxBkiSpSYYgSZLUJEOQJElqkiFIkiQ1yRAkSZKaZAiSJElNMgRJkqQmGYIkSVKTDEGSJKlJqapB16ANKMkI8P1B17GetgNuG3QRM4j3a+K8VxPnvVo73q+Jm+x7tVNVzR1rhyFIM06S5VU1NOg6Zgrv18R5rybOe7V2vF8TtyHvlY/DJElSkwxBkiSpSYYgzURLBl3ADOP9mjjv1cR5r9aO92viNti9ck2QJElqkjNBkiSpSYYgSZLUJEOQpq0kL0ryrSTfSfKOMfY/MsnZ3f7LkswbQJnTwgTu1V8kuSHJNUm+kmSnQdQ5XazpfvX1e0WSStLsR5sncq+SvKr783V9kk9t6Bqniwn8e7hjkvOTXNX9u/iSQdQ5HST5RJKfJrlunP1JcnJ3L69JsveUFFJVvnxNuxewMfBdYGfgEcDVwO6j+vxP4CPd+1cDZw+67ml8r/YDNuvev7nVezXR+9X12wK4ELgUGBp03dP1XgG/B1wF/E63/buDrnsa36slwJu797sDNw+67gHer+cCewPXjbP/JcC/AQGeCVw2FXU4E6Tp6unAd6rqe1X1K+As4OWj+rwcOK17/1ngBUmyAWucLtZ4r6rq/Kq6p9u8FHj8Bq5xOpnIny2AdwPvBe7dkMVNMxO5V28CPlhV/wVQVT/dwDVOFxO5VwVs2b3fCvjJBqxvWqmqC4E7VtPl5cDp1XMpsHWS7Se7DkOQpqvHAT/s2/5R1zZmn6q6H/gZsO0GqW56mci96ncovf/DatUa71c39b5DVX1hQxY2DU3kz9YTgScmWZbk0iQv2mDVTS8TuVfHAa9L8iPgX4HDN0xpM9La/ndtncyZ7AElTV9JXgcMAc8bdC3TVZKNgPcBBw+4lJliDr1HYvvSm2G8MMmeVXXnIIuapl4DLK2qE5PsA5yRZI+qenDQhbXKmSBNVz8GdujbfnzXNmafJHPoTS/fvkGqm14mcq9I8kLgGOBlVXXfBqptOlrT/doC2AO4IMnN9NYjnNfo4uiJ/Nn6EXBeVf26qm4CbqQXilozkXt1KPAZgKq6BNiU3i8L1cNN6L9r68sQpOnqCuD3kjwhySPoLXw+b1Sf84A3du8PAL5a3Yq6xqzxXiX5feAf6QWgVtdsrLLa+1VVP6uq7apqXlXNo7eG6mVVtXww5Q7URP49PJfeLBBJtqP3eOx7G7DG6WIi9+oHwAsAkjyZXgga2aBVzhznAW/oPiX2TOBnVXXLZJ/Ex2Galqrq/iRvBb5E71MXn6iq65O8C1heVecBH6c3nfwdegvsXj24igdngvfqBGBz4Jxu7fgPquplAyt6gCZ4v8SE79WXgD9IcgPwAHB0VTU3IzvBe/W/gI8mOYreIumDG/0fN5J8ml543q5bI7UI2ASgqj5Cb83US4DvAPcAh0xJHY3ef0mS1Dgfh0mSpCYZgiRJUpMMQZIkqUmGIEmS1CRDkCRJapIhSJIkNckQJEmSmvT/AReOWnp181DXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# machine learning Feature selection technique to show correlation between the metrics\n",
    "# Pearson correlation is used (meausures linear relationship between two variables)\n",
    "# The correlation is performed against the total z score as that is a direct indidcator of the state of the firm (outlier, typical or error)\n",
    "Metrics = Raw.corr()[\"Total_Z\"]\n",
    "plt.rcParams['figure.figsize'] = (8, 10)\n",
    "imp_Metrics = Metrics.sort_values()\n",
    "imp_Metrics.plot(kind=\"barh\", title=\"Metric importance\", color=[(0.8, 0.5, 0.6)])\n",
    "plt.show()\n",
    "\n",
    "# From the chart below, NWP, GWP, Assets, Equity, and SCR are strong metrics in determining the priority of a firm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hAnzyCZjBBRfAL36RvvdT8iAiIlmnstK3ZUrn3n/fE4clS6C4GK65BvbfP73vqeRBRESySlWV+lV01Qsv+BqH2loYOBCmToWvfz3976s1DyIikjWWLoUVK+KOIjc88QQce6wnDhttBA88kJnEAZQ8iIhIlli+3Bf9Sefuvht++UtoaoItt4SHHoKttsrc++uyhYiIxG7lSr9mLx1rbfU1DXfd5eNddvFLFYMHZzYOJQ8iIhKr1at9nYN0rLERJk+Gxx/38Xe/C7/9LZSWZj4WJQ8iIhKbujr1q+iK2lo45RT4z398/LOf+XbMoqJ44lHyICIisWhoUOLQFUuW+MLI997z8WmnwfHHez2HuCh5EBGRjGtq8uqR6lfRsU8+8RoOCxb4LMOll8Ihh8QdlZIHERHJMPWr6Jq33oKJE30XSt++cMMNsNdecUfllDyIiEjGtLZ64tDUFHck2e2552DSJF8TMmQITJsGO+wQd1Rrqc6DiIhkRFu/ioaGuCPJbo88Aiec4InDyJHw4IPZlTiAZh5ERCRDFi1Sv4qOhOA1G2680cfbbuszDsOHxxtXe5Q8iIhI2lVV+XZDaV9LC/z61z7LALDHHnDTTTBgQLxxrY+SBxERSSv1q+hYfT2ceSY8/bSPf/ADuOIK75CZrZQ8iIhI2qhfRcdqauDEE+HVV308frwnEr2yfEWikgcREUkL9avo2GefeQ2HOXN8PHkyHHVUrCF1mZIHERFJudpa9avoyAcfeNXIqiro0weuvhoOPDDuqLpOyYOIiKRUXZ3vrFDZ6fa9/LJfqli1yhdETpkCu+8ed1TJyfKrKiIikksaGrzstBKH9v2//wfHHOOJw/DhcP/9uZc4gJIHERFJkaYmrx7Z2hp3JNnpvvu8qVVTE2y+OTz0EGyzTdxRdY8uW4iISI81N3vioEZXXxYCXH893H67j3fcEW67zctO5yolDyIi0iOtrX6pQv0qvqypCS64AP7yFx/vvbcnEn37xhpWj+myhYiIdFsInjioX8WXrV7tPSraEoef/ARuuSX3EwdIY/JgZqVm9rKZvWlms8zskuj45mb2kpnNNrOHzKw4Ol4SjWdH95cnvNbk6PgHZrZfwvH9o2OzzezcdJ2LiIh8WVujq7q6uCPJPsuWwRFHwL/+5eNTToFLL4XeeTLfn86Zhwbg2yGErwE7Avub2e7AVcD1IYQtgeXA+Ojx44Hl0fHro8dhZtsC44DtgP2BW82syMyKgCnAAcC2wGHRY0VEJAOqqvyva/miefNg3Dh45x2vFHnppXDyyWAWd2Spk7bkIbi2Nih9oo8AfBv4Y3T8HuDg6PZB0Zjo/n3MzKLjM0IIDSGET4DZwK7Rx+wQwschhEZgRvRYERFJsyVLvIKkfNHbb8NPf+oJREmJX6b46U/jjir10rrmIZoheANYDDwNzAFqQgjN0UMWACOi2yOA+QDR/SuAYYnH13nO+o63F8dEM5tpZjOXqFaqiEiPVFd7zwr5on/9yy9VVFdDWRncfTfss0/cUaVHWpOHEEJLCGFHYCQ+UxDLjtYQwrQQQkUIoWJ4NjZGFxHJEStWeJdM+aK//AWOPx7WrIFNN4UHHoCdd447qvTJyNKNEEKNmT0LfAMoM7Pe0ezCSGBh9LCFwGbAAjPrDQwGliUcb5P4nPUdFxGRFFO/ii8LAe64A6691sdbb+3jjTaKN650S+dui+FmVhbd7gt8F3gPeBY4JHrYkcCj0e3HojHR/X8PIYTo+LhoN8bmwFjgZeAVYGy0e6MYX1T5WLrOR0SkkK1Z4/0qZK2WFrj88rWJw267ebnpfE8cIL0zD5sA90S7InoBD4cQHjezd4EZZnYZ8DowPXr8dOD3ZjYbqMaTAUIIs8zsYeBdoBk4KYTQAmBmJwNPAkXAXSGEWWk8HxGRglRf71sy1a9irYYGOOssePJJHx94IFx1FRQXxxtXplgosJ+GioqKMHPmzLjDEBHJCY2NMH++yk4nWrkSTjrJu2MCHHUUnHOOb8vMRltvba+GECpS+Zp5Uq5CRERSrbnZq0cqcVirqgomTIAPP/Tx2WfD+PEdPycfKXkQEZEvaWnxRlfqV7HW7NmeOCxaBH36wBVXwA9/GHdU8VDyICIiX9DWr6KxMe5IssfMmXDiib5VtV8/L/60xx5xRxWfLL1CIyIicWjrV1FfH3ck2ePpp+GYYzxx2GADuO++wk4cQMmDiIgkqKxUv4pEDzwAp57quyvKy2HGDNhuu7ijip8uW4iICACLF8OqVXFHkR1CgBtugNtu8/EOO8Dtt8PQobGGlTWUPIiICMuWQU1N3FFkh+ZmuOgieOQRH3/rW55I9OsXa1hZRZctREQK3IoVnjyIV9I86aS1icOPfgRTpihxWJdmHkRECtiqVepX0aa62ptbvfmmj48/Hk47DcxiDSsrKXkQESlQa9b4AknxKpoTJsDcuZ4sXHQR/OxncUeVvZQ8iIgUIPWrWOvdd+HYY73VeHExXHcdfPe7cUeV3ZQ8iIgUmMZGLwLV2hp3JPH7z398jcOaNTBoEEydChUp7QKRn7RgUkSkgDQ3e9lp9auA//1fmDjRE4dNNoEHH1Ti0FWaeRARKRBt/Sqam+OOJH533eUttAG22gruuAM23jjemHKJkgcRkQLQ2qp+FeBfh6uugrvv9vHXvw633uqXLKTrlDyIiOQ59atwjY0weTI8/riP99sPrrkGSkrijSsXKXkQEclzlZV+Xb+Q1db6wsgXX/TxL34B550HRUXxxpWrlDyIiOQx9avwr8HEifDeez4+4wzfmqniT92n5EFEJE+pXwV8/LEXf1q4EHr3hssvh4MPjjuq3KfkQUQkD9XUqF/F6697iemaGu9NceON8N//HXdU+UF1HkRE8syqVT5VX8j+/nc46ihPHIYOhXvvVeKQSkoeRETyyOrV6lfxhz/44sj6ehg1CmbMgO23jzuq/KLLFiIieaLQ+1WE4O2zb77Zx1/9KkybBsOGxRtXPlLyICKSB9r6VRRq4tDcDJdcAg8/7OM994SbboL+/eONK18peRARyXFNTYXdr6KuDk4/3dc5gO+muOwy6NMn1rDympIHEZEc1tLiMw6F2q9i+XI44QTfWQFez+H001XDId2UPIiI5KjWVp9xKNR+FQsXeg2Hjz/2ZOH88+Hww+OOqjAoeRARyUFt/SoaGuKOJB7vv++Jw5IlfnnimmvggAPijqpwKHkQEclBixYVbr+Kl16CE0/0fhUDB/oOi912izuqwqI6DyIiOaaqyn9xFqInnoDx4/38N9wQ7r9fiUMclDyIiOSQpUthxYq4o4jHPff4YsimJhgzBh56CLbeOu6oCpMuW4iI5Ijly6G6Ou4oMq+1FX77W5g+3cc77wxTp0JZWaxhFTQlDyIiOWDlSl8cWGgaG30XxWOP+fg734Frr4XS0njjKnRKHkREstzq1b7OodDU1sKpp8K//+3jww6DCy+EoqJ44xIlDyIiWa2urjD7VSxZAscdB7Nm+fi007y9too/ZQclDyIiWaqhoTD7Vcyd6zsqFizwWYZLL4VDDok7Kkmk5EFEJAs1NXni0NoadySZ9dZbXmJ6+XJf13DjjbDXXnFHJevSVk0RkSzT0uJ/dRdav4rnnoMjjvDEoawM7r1XiUO2UvIgIpJF2vpVNDXFHUlm/elP3uCqrg5GjIAHH4SvfS3uqGR9dNlCRCRLhOCXKgqpX0UIcNttcMMNPt52W5g2DYYPjzUs6YSSBxGRLLFokf/lXShaWuCyy+CBB3z8zW/CzTfDgAHxxiWdU/IgIpIFKisLq19FQwOceSY89ZSPv/99uPJKKC6ONy7pGiUPIiIxW7LEK0gWihUrvCvmzJk+PuYYOOss6KVVeDlDyYOISIyqq313QaFYtAiOPRY++sjHkyfDUUfFGpJ0g5IHEZGYrFjhXTILxUcfwYQJfommTx+46ir43vfijkq6Q8mDiEgMamsLq1/FK6/4pYqVK6F/f5gyBb7xjbijku7SFSYRkQyrq/Pp+0Lx5JO+rmHlSt+Cef/9ShxynZIHEZEMKrR+FffdB5MmeWvtzTeHGTPgK1+JOyrpKV22EBHJkKYmrx5ZCP0qQoDrr4fbb/fxjjvC1KkwdGisYUmKKHkQEcmA5mZPHFpa4o4k/Zqa4MIL4c9/9vHee3si0bdvvHFJ6uiyhYhImrW2+qWKQuhXsXq196hoSxwOPRRuuUWJQ77RzIOISBoVUr+KZcu8nfY77/j45JP9wyzeuCT10jbzYGabmdmzZvaumc0ys0nR8V+Z2UIzeyP6ODDhOZPNbLaZfWBm+yUc3z86NtvMzk04vrmZvRQdf8jMVNhURLJGCPDZZ4XRr2LePBg3zhOHXr3g0kvhlFOUOOSrdF62aAbOCCFsC+wOnGRm20b3XR9C2DH6eAIgum8csB2wP3CrmRWZWREwBTgA2BY4LOF1ropea0tgOTA+jecjIpKUqiqfxs9377zjicO8eVBS4pcpfvrTuKPqREMDzJlTGFNCaZC25CGEsCiE8Fp0exXwHjCig6ccBMwIITSEED4BZgO7Rh+zQwgfhxAagRnAQWZmwLeBP0bPvwc4OC0nIyKSpELpV/H883D44X7JoqwM7r4b9tkn7qg60dDgCzOOO84/K4FIWkYWTJpZObAT8FJ06GQze8vM7jKzIdGxEcD8hKctiI6t7/gwoCaE0LzO8fbef6KZzTSzmUuWLEnFKYmIrFeh9Kv4y1/89++aNbDppt5ae+ed446qCxYs8GmSQYP884IFcUeUc9KePJjZAOAR4LQQwkpgKjAG2BFYBFyb7hhCCNNCCBUhhIrhw4en++1EpIAVQr+KEOCOO+Ccc3wL6tZbe/GnMWPijqyLRo6EUaN8amjUKB9LUtK628LM+uCJw/0hhD8BhBCqEu6/A3g8Gi4ENkt4+sjoGOs5vgwoM7Pe0exD4uNFRDJu1ar871fR2gpXXAG//72Pd90Vbr0VBg6MN66klJR4xaoFCzxxKCmJO6Kck87dFgZMB94LIVyXcHyThIf9DxBt6uExYJyZlZjZ5sBY4GXgFWBstLOiGF9U+VgIIQDPAodEzz8SeDRd5yMi0pE1a7xbZD5raIBf/nJt4nDAATB9eo4lDm1KSnyqRIlDt6Rz5mEP4HDgbTN7Izp2Hr5bYkcgAHOB4wBCCLPM7GHgXXynxkkhhBYAMzsZeBIoAu4KIcyKXu8cYIaZXQa8jicrIiIZVV/vWzLzuV/FypVw0knw8ss+PvJIOPdc35YphcdCPv+0t6OioiLMnDkz7jBEJE80NsL8+flddrqqCiZMgA8/9PHZZ3uXTNVwyA1bb22vhhAqUvmaqjApItJNhdCvYs4cGD/eW4j37g1XXgk//GHcUUnclDyIiHRDS4snDs3NnT82V736qpdBWLEC+vWDm2+GPfeMOyrJBrpaJSKSpLZGV42NcUeSPn/7Gxx9tCcOw4bBffcpcZC1lDyIiCShrV9FfX3ckaTPjBnel6KhAUaP9vF228UdlWQTXbYQEUlCZaVvy8xHIcBNN3ndBoDtt4fbb/eZB5FESh5ERLpo8WIvBJWPmpvh4ovhj1G3oP/+b7jhBujfP9awJEvpsoWISBcsWwY1NXFHkR51dXDyyWsThx/9yGcflDjI+mjmQUSkEzU1njzko+pq31Hxxhs+Pv54OO001XCQjil5EBHpwKpVfrkiHy1Y4DUc5s71ZOHCC+HnP487KskFSh5ERNYjn/tVvPceHHssLFkCxcVw7bWw775xRyWp1Ls3bLRRml47PS8rIpLb8rlfxQsveJ+K1ath0CBvMFmR0uLFEreyMthgg/T1HlHyICKyjsZGLwLV2hp3JKn3+OPe0KqpCTbeGO68E8aOjTsqSZXiYp9t6Ns3ve+j5EFEJEE+96u46y646iq/PXasJw4bbxxvTJIaZjBkiNfkyMRiVyUPIiKRfO1X0doKV18Nv/udjysqfCvm4MHxxiWpUVLiSWBJSebeU8mDiAj526+isREmT/bLFQD77QfXXJPZXzSSHmY+0zBkSOa31ip5EJGCl6/9KmprfWHkiy/6+Oc/h/PPh6KieOOSnist9dmG4uJ43l/Jg4gUvEWL8q9fxeLFMHGib8kEOP10H6v4U27r1WvtbEOclDyISEGrqvK/0PPJxx/DhAl+GaaoCC6/HP7nf+KOSnqqXz/fSdGnT9yRKHkQkQK2dCmsWBF3FKn1xhtw3HFeUrtfP7jxRm9yJbmrVy8YPjy7FrgqeRCRgrR8ufd1yCfPPut9KerrYehQb6e9ww5xRyU9MWAAbLihV4vMJlkWjohI+q1c6WWZ88kf/uAttVtaYLPNYPp0GD067qiku4qKPGkYODDuSNqn5EFECsrq1b7OIV+E4DUbbrrJx9ttB9OmeWliyU2DBvllimzeFaPkQUQKRl1dfvWraGmBSy6Bhx7y8Z57+hqHAQPijUu6p62RVf/+cUfSOSUPIlIQGhryK3Gor/ftl8884+Mf/tB3VcS17196Jt2NrFJNyYOI5L2mJt+2mC/9Kmpq4Pjj4fXXfTxhApxxRu784pG1MtXIKtWUPIhIXsu3fhULF3qy8PHHXvDpvPPgiCPijkq6Y8gQn23IxcJdSh5EJG+1tnri0NQUdySp8f77njgsWeKFgq6+Gg48MO6oJFklJT7bUFoadyTdp+RBRPJSW7+Khoa4I0mNl16CE0/0apgDBvgOi912izsqSYaZ198YOjQ3ZxsSKXkQkbyUT/0qnngCzj7bZ1A23BDuuAO22SbuqCQZcTeySjUlDyKSd/KpX8W998IVV/hMypgxcOedsOmmcUclXWXm6xribmSVakoeRCSv5Eu/itZWuPZaTxYAdtoJbrvNt/RJbsimRlappuRBRPJGvvSraGyE88+Hxx7z8T77wHXX5fYCu0KSjY2sUk3Jg4jkhXzpV1FbC5MmwfPP+3jcOLjoouwuVSxr9e/vsw3Z1sgq1fL89ESkENTWQmVl3FH03NKlMHEizJrl40mT4IQTcn9lfiHI9kZWqabkQURyWl2d76zIdZ9+CuPHw/z5/ovokkvg0EPjjkq6YuBATxwKaXZIyYOI5KyGBq+4mOv9Kt56C447ztdrlJbCDTfA3nvHHZV0JpcaWaWakgcRyUlNTV49srU17kh65rnn/PJEXZ3vpLj9dthxx7ijks4MHuyLIgu1n4iSBxHJOc3NnjjkeqOrP/0JLrjAz2PECN+WucUWcUclHenTx2cb+vWLO5J4KXkQkZzS2uqXKnK5X0UIPsNw/fU+3mYbrxq54YbxxiUdy+VGVqmm5EFEckYInjjkcr+Klha47DJ44AEf7747TJni/SokO+VDI6tUU/IgIjmhrdFVXV3ckXRfQwOceSY89ZSPv/99uPLK/Ol3kG/yqZFVqil5EJGcUFUFq1fHHUX3rVgBJ50Er7zi42OOgbPOKtwFd9mutNRnG0pK4o4kOyl5EJGst2SJV5DMVZWVMGECfPSRj889F44+Ot6YpH352sgq1ZQ8iEhWq672nhW56qOPPHGorPSV+r/5jV+ukOyTz42sUk3Jg4hkrRUrvGRzrpo508tLr1zphYSmTIFvfCPuqGRdhdDIKtWUPIhIVqqt9XUOueqpp+CMM7xD5vDhvhXzK1+JOypZV6E0sko1fblEJOusWZPb/Sruvx9+/WvfIbL55l78aeTIuKOSRIXWyCrVlDyISFapr/ctmbnYryIE70tx220+/trX/PbQobGGJesoxEZWqdbhJiEz283M3jSzWjN7wcy2zVRgIlJ4Ghu9CFQu9qtoaoLzzlubOOy9N9xzjxKHbNK7t5cB32QTJQ491dkO4ynAmcAw4Drg+rRHJCIFqbnZE4dc7FexejWceKL3qgA45BC45Rbo2zfeuGStwYOhvLwwO2CmQ2eXLXqFEJ6Obv/BzCanOyARKTwtLd7oKhf7VVRXw8SJ8PbbPj7pJDjlFFUkzBZqZJUenSUPZWb2o/WNQwh/Sk9YIlIo2vpVNDbGHUny5s+H8ePh0099u9/FF8O4cXFHJW3UyCp9Okse/gn8IGH8XMI4AEoeRKTb2vpV1NfHHUny3nnHZxyWLfMSxtddB9/5TtxRCXivkI03ViOrdOoweQghHNXdFzazzYB7gY3wRGNaCOFGMxsKPASUA3OBn4QQlpuZATcCBwJrgKNCCK9Fr3UkcEH00peFEO6Jju8C3A30BZ4AJoWQi2u0RQpTZWVu9qt4/nm/NLFmjV9LnzoVdtkl7qhEjawyp7PdFsvM7AkzO9/M9jazZK4aNQNnhBC2BXYHTop2a5wLPBNCGAs8E40BDgDGRh8TgalRDEOBi4HdgF2Bi82srer4VODYhOftn0R8IhKjxYth1aq4o0jeo4/Cccd54rDppvDgg0ocskFpKYwaBcOGKXHIhM52W2wO3AD0ASYD881sppndaGY/6eiJIYRFbTMHIYRVwHvACOAg4J7oYfcAB0e3DwLuDe5FfH3FJsB+wNMhhOoQwnLgaWD/6L5BIYQXo9mGexNeS0Sy2LJlUFMTdxTJCcGLPZ19tu8M2WormDEDxoyJO7LCZuYVPEeNUgfMTOrsssVK4KnoAzPrDxwNnAacDDzclTcxs3JgJ+AlYKMQQlvtuEr8sgZ4YjE/4WkLomMdHV/QzvH23n8iPpvBqFGjuhKyiKTJihWePOSS1la48kq4914f77qr96kYNCjeuApd376+k6K4OO5ICk+HyYOZbQp8M/r4enT4VXz9wQtdeQMzGwA8ApwWQlhpCfNJIYRgZmlfoxBCmAZMA6ioqNCaCJGYrFqVe/0qGht9tuGvf/XxAQfA1VfrF1acevXyXRRlZXFHUrg6222xAHgNLw51bgghqc1UZtYHTxzuT9jWWWVmm4QQFkWXHhZHxxcCmyU8fWR0bCGw1zrH/xEdH9nO40UkC61Z4wskc8mqVV634aWXfHz44V5FsldnF3wlbdTIKjt09k9gD+AB4H+AF8zsETM708z2MLMOry5FuyemA++FEK5LuOsx4Mjo9pHAownHjzC3O7AiurzxJLCvmQ2JFkruCzwZ3bfSzHaP3uuIhNcSkSySi/0qqqrg5z9fmziceSacf74Sh7gUFfn2yxEjlDhkg87WPLyAX564Dj5fu/ADfKHjSKCjXbR7AIcDb5vZG9Gx84DfAA+b2XjgU6Bt4eUT+DbN2fhWzaOjGKrN7NfAK9HjLg0hVEe3T2TtVs2/Rh8ikkVysV/FnDkwYYInPL17w+WXw8EHxx1V4VIjq+xjnZVFMLNtWLvuYQ+gDHgR+HcI4bfpDjDVKioqwsyZM+MOQ6QgNDfDvHn+OVe89hqccILvBunXD26+GfbcM+6oClPv3p40DBgQdyS5zcxeDSFUpPI1O1swuRT4DJ99+CfwmxDC7FQGICL5qa1fRS4lDs88A7/8JTQ0eL2A22+H7bePO6rCNHiwb8HUZaLs1NmVozEhhBUZiURE8kZra+71q3joIfjVrzz2UaNg+nT/LJmlRla5obPkYZKtv1RXCCH8OsXxiEiOy7V+FSH4pYkpU3z81a/CtGk+8yCZNWSIf90125D9Okse2qs63w+YAAwDlDyIyBdUVvq2zFzQ3OyzDX/4g4//67/gxht9O6BkjhpZ5Z7Odltc23bbzAYCk4BjgBnAtet7nogUpqqq3OlXUVfn6xuefdbHP/oRXHqpT5tLZpitnW1QP4rc0ulu2agx1enAz/EtmjtHPSZERD63bJmXns4F1dW+o+KNN3x8/PFw2mn6BZZJJSU+26B+FLmps90W1wA/wks7bx9CqM1IVCKSU2pqcqdfxYIFMH48zJ3rycKFF3oxKMkMM59pGDo07kikJzqbeTgDaMB7WZyfsHjS8AWTagsjUuBWrfL22rngvffg2GNhyRK/zn7ttbDvvnFHVTjUyCp/dLbmQWteRWS9Vq/OnX4VL7zgfSpWr/ZumFOnQkVKy+bI+qiRVf5RhXAR6ZZc6lfxf/8H55wDTU1+nf3OO2Hs2LijKgxqZJWf9O0UkaS19avIhcThd7+D3/zGb2+5pScOm2wSb0yFoKjIK0QO0sXtvKTkQUSS0tTkiw5bWuKOpGOtrXD11Z48gF+iuPVWL3ss6TVggM82qJFV/lLyICJd1tLiMw7Z3q+isREmT4bHH/fxvvvCb3+rbYHppkZWhUPJg4h0SWurzzhke7+K2lo4+WRfIAnws5/BBRfor+B0GzTIL1Po61wYlDyISKfa+lU0NMQdSceWLPGtmO+95+PTTvMCUCr+lD5qZFWYlDyISKcWLcr+fhWffOLFnxYu9L9+f/1r+PGP444qv5WV+RZMNbIqPEoeRKRDVVV+KSCbvfkmTJzolS779vXmVt/6VtxR5a/iYp9t6Ns37kgkLkoeRGS9li7N/n4Vzz7rlyfq673J0rRpsMMOcUeVn9TIStooeRCRdi1f7g2kstkf/gAXX+y7QEaOhOnTobw87qjykxpZSSIlDyLyJStX+uLDbBWC12y46SYfb7edzzhssEG8ceWjtkZWQ4ZotkHWUvIgIl+werWvc8hWLS1w6aUwY4aP99jDkwjVFkg9NbKS9VHyICKfq6vL7n4V9fVwxhnwt7/5+Ac/gCuu0C+3VFMjK+mMkgcRAbyGQzb3q6ipgRNOgNde8/H48XDmmdommGr9+vlsQ58+cUci2UzJg4jQ1OSJQ2tr3JG077PPYMIEmDPHr7tPngxHHhl3VPlFjawkGUoeRApcS4uXnc7WfhUffOCJw+LF/tfw1VfDgQfGHVV+USMrSZaSB5EC1tavoqkp7kja99JLcOKJXqRqwACYMgV23z3uqPKHGllJdyl5EClQIfilimztV/HXv8JZZ3liM3w43HknbLNN3FHlDzWykp5Q8iBSgNoaXdXVxR1J+37/e7j8co9ziy08cRgxIu6o8oMaWUkqKHkQKUBVVV7PIduEANdeC3fc4eOddoKpU71AkfRcW2lp7VCRnlLyIFJglizxCpLZpqkJzj8fHn3Ux/vsA9ddB6Wl8caVD4qLvbS0vpaSKkoeRApIdbX3rMg2q1fDqafC88/7+Kc/hYsu8gV90n1qZCXpon+aIgVixQrvkpltli71dtqzZvn41FN9h4V+2fVMaamvbVAjK0kHJQ8iBaC2Njv7VXz6qddwmDfPr8NfeikcemjcUeU2My8trXUikk5KHkTy3Jo1sGhR3FF82dtv+4xDdbX/lXz99fDtb8cdVW7r29fXNqi0tKSbkgeRPNbQkJ2Nrv75T5g0yRObsjK47TbfWSHd06uX12wYPDjuSKRQKHkQyVONjV49Mtv6Vfz5z3DBBV4Oe8QIr+GwxRZxR5W7+vf3tQ1aXCqZpB83kTzU3OzVI1ta4o5krRC8fsO11/p4m21g2jT/xSfJKyry0tIDB8YdiRQiJQ8ieaalxROHbOpX0dLiFSPvv9/Hu+8Ot9yiX3zdNXCgJw4qLS1xUfIgkkfayk5nU7+KhgbvUfHkkz4+8EC46iovXCTJ6d3bZ2r69487Eil0Sh5E8kQ29qtYsQJOOgleecXHRx8NZ5+t8sjdMXiwL4rU106ygZIHkTyRbf0qKiu9hsNHH/n4nHPgmGPijSkXqZGVZCMlDyJ5INv6VXz0kScOlZX+y++KK+CHP4w7qtwzZIgXfFK1Tck2Sh5Ecly29auYORNOOMGTmX79YMoU+OY3444qt5SU+GyDGllJtlLyIJLDsq1fxVNPwRlneI2JDTbwrZnbbht3VLnDDIYO9Q/NNkg2U/IgkqNWrcqufhUPPOC9KUKA8nIv/rTZZnFHlTtKS720tHahSC5Q8iCSg9as8fUE2SAEuOEGLzEN8LWv+e2hQ2MNK2f06uUts9XISnKJkgeRHFNfnz39Kpqa4KKL4E9/8vFee3mDK+0M6Jp+/XxtgxpZSa5R8iCSQxobvXpkNvSrWLPGm1v9858+/vGP/bKFeix0rqjIazYMGhR3JCLdo3/mIjmiudkbXWVDv4rqajjuOHjrLR+feCKceqoW+XXFgAFeWlpJluQy/fiK5ICWFk8cmpvjjgTmz/caDnPn+vX6iy6Cww6LO6rsp0ZWkk+UPIhkudZWv1TR2Bh3JDBrFkyc6NtDS0q8Q+Z3vxt3VNlv0CC/TKFGVpIvlDyIZLEQYNEiXyQZt3//G04+2dc6DB4MU6fCLrvEHVV2U2lpyVdpa7FiZneZ2WIzeyfh2K/MbKGZvRF9HJhw32Qzm21mH5jZfgnH94+OzTazcxOOb25mL0XHHzIz7Y6WvFNZmR39Kh57zGcc1qyBTTbxmg5KHDpWVgajRytxkPyUzv5sdwP7t3P8+hDCjtHHEwBmti0wDtgues6tZlZkZkXAFOAAYFvgsOixAFdFr7UlsBwYn8ZzEcm4xYu9EFScQoDp072ldnMzbLUVPPQQbLllvHFls+JiL4614YbqgCn5K20/2iGEfwLVXXz4QcCMEEJDCOETYDawa/QxO4TwcQihEZgBHGRmBnwb+GP0/HuAg1MZv0icli2Dmpp4Y2hthSuvhKuv9vGuu8L99/s0vHxZW2np0aOhb9+4oxFJrzjy4pPN7K3oskZbTbURwPyExyyIjq3v+DCgJoTQvM7xdpnZRDObaWYzlyxZkqrzEEmLmhpPHuLU2Og9Ku65x8f77+/lplWXoH0lJTBqlDpgSuHIdPIwFRgD7AgsAq7NxJuGEKaFECpCCBXDhw/PxFuKdMuqVX65Iu4YJkyAJ57w8eGHe9XIkpJ448pGZp4wjB6tr48UlozutgghfN7Gx8zuAB6PhguBxBY6I6NjrOf4MqDMzHpHsw+JjxfJSatXx9+voqoKjj0WPvjAx2ec4WP9Nf1lffv6JRw1spJClNGZBzPbJGH4P0DbTozHgHFmVmJmmwNjgZeBV4Cx0c6KYnxR5WMhhAA8CxwSPf9I4NFMnINIOtTX+5bMOPtVzJnjxZ4++MCrH151le+wUOLwRb16+WLIzTZT4iCFK20zD2b2ILAXsIGZLQAuBvYysx2BAMwFjgMIIcwys4eBd4Fm4KQQQkv0OicDTwJFwF0hhFnRW5wDzDCzy4DXgenpOheRdMqGfhWvvQYnnODrLfr1g5tugv/6r/jiyVb9+/tsg0pLS6GzkA2t+TKooqIizJw5M+4wRADvSjl/frxlp595Bn75S2ho8N0C06bB9tvHF082UiMryWVm9moIoSKVr6n8WSQmLS0+4xBn4vDQQ/CrX/msx6hRvqNi9Oj44slGAwf6ZQqVlhZZS8mDSAzi7lcRAtxyi38AbLcd3HEHDBsWTzzZqHdvTxoGDIg7EpHso+RBJMNCgM8+i69fRXMzXHIJPPywj/fc09c49O8fTzzZaPBgv0yhCpEi7VPyIJJhixZ5j4g41NX5+oZnn/XxwQfDZZd5AydRIyuRrlLyIJJBVVVQWxvPe1dX+46KN97w8XHHeSKhrZhuyBBViBTpKiUPIhmydCmsWBHPey9Y4FUjP/nEfzlecAH84hfxxJJtSkp8tqG0NO5IRHKHkgeRDFi+3P/yj8P773visGSJFzW65hrvVVHo2hpZDR2q2QaRZCl5EEmzlSv9F3ccXnwRTjrJL5UMHAhTp8LXvx5PLNmktBQ23lgVIkW6S8mDSBqtXu3rHOLwf/8H55zjhag22shrOGy1VTyxZIu2RlZDhnT+WBFZPyUPImlSV+dbMuMo4nr33XDllX57yy09cdhkkw6fkvf69fMkSjtLRHpOyYNIGjQ0xJM4tLb6moa77vLxLrvArbdCWVlm48gmvXp5zYbBg+OORCR/KHkQSbGmJq8e2dKS2fdtbITJk+HxqNH9d78Lv/1tYe8iGDDAq0SqkZVIaumflEgKtbT4tshM96uorYVTToH//MfHhx0GF15YuP0Yioo8aRg4MO5IRPKTkgeRFGlt9cShqSmz77tkCUycCO++6+NJk7wYVKFuPxw0yC9TFGriJJIJSh5EUiAEv1TR0JDZ9/3kE6/hsGCB/7K89FI45JDMxpAtevf2BZHq0SGSfkoeRFJg0SLfXZFJb77pJaaXL4e+feGGG2CvvTIbQ7YoK/MtmGpkJZIZSh5EeiiOfhXPPeeXJ+rqvGbBtGmwww6ZjSEbFBf7bEPfvnFHIlJYlDyI9EAc/SoeecQXQ7a0wMiRXsNh880zG0PczDxpGjascNd2iMRJyYNIN2W6X0UIXl76xht9vO22PuMwfHjmYsgGJSVeWrqkJO5IRAqXkgeRbsh0v4qWFvj1r+HBB328xx5w001ex6BQmPlMw5Ahmm0QiZuSB5Ek1dZCZWXm3q++Hs48E55+2sc/+AFccUVhNXXq29fXNhTSOYtkMyUPIkmoq/OdFZlSU+M1G157zcfjx3siUSi7Cnr18l0UhVxeWyQbKXkQ6aKGBq/lkKl+FZ995jUc5szxafrJk+HIIzPz3tmgf3+fbVBpaZHso3+WIl3Q1OSFmFpbM/N+H3wAxx7r20D79IGrr4YDD8zMe8etqMgXgQ4aFHckIrI+Sh5EOtHc7IlDphpdvfwynHgirFrlCyKnTIHdd8/Me8dt4EDvSaHS0iLZTcmDSAdaW/1SRab6Vfz1r3DWWf5+w4d7DYdttsnMe8epd29PGgpp94hILlPyILIeme5X8fvfw+WX+/tuvjlMnw4jRmTmveM0eLAnSoWyCFQkHyh5EGlHCL5gMRP9KkKA667zgk8AO+3kxaCGDEn/e8epTx9fENmvX9yRiEiylDyItKOqClavTv/7NDXBBRfAX/7i429/2xOJfO/VMGSIb8FUsSeR3KTkQWQdS5Z4Bcl0W70aTj0Vnn/exz/9KVx0UX5vTSwu9tLSpaVxRyIiPZHH/02JJK+62ntWpNuyZTBxIrzzjo9POQVOOil//xI3g6FD/SNfz1GkkCh5EImsWOFdMtNt3jyvFDlvni8SvOQS+MlP0v++cSkt9bUNamQlkj+UPIjg/SqqqtL/Pm+/Dccd5zMPpaVw/fW+ziEfmfm6hnxf+ClSiJQ8SMFbsyYz/Sr+9S9f47BmjfdquO0231mRj/r189mGPn3ijkRE0kHJgxS0+nrfkpnufhV/+Qucf75Xq9x0Uy/+NGZMet8zDr16ec2GwYPjjkRE0knJgxSsxkYvApXOfhUhwB13wLXX+njrrX280Ubpe8+4qJGVSOHQP3MpSM3Nnjiks19FSwtceaVXjgTYbTfvUzFwYPreMw5FRV5aOt/OS0TWT8mDFJyWFm90lc5+FQ0N3qPiySd9fOCBcNVVXucgnwwa5Jcp1MhKpLAoeZCC0tboqrExfe+xcqXXbHj5ZR8feSSce25+9W7o3dsvUfTvH3ckIhIHJQ9SMNr6VdTXp+89Kivh2GPhww99fM45cMwx6Xu/OJSV+RbMfEqGRCQ5Sh6kYFRW+jbJdJk9GyZM8G2fffrAFVfAD3+YvvfLtOJin23I974bItI5JQ9SEBYvhlWr0vf6M2fCiSd6lcp+/Xxh5De/mb73y7ShQ2HYMJWWFhGn5EHy3rJlUFOTvtd/+mk44wxfJLnBBr4Vc9tt0/d+mVRS4rMNamQlIol01VLy2vLlnjyky4MPetXIhgYoL4cZM/IjcWgrLT1qVCeJQ309vP9+eheSiEjW0cyD5K2VK729djqEADfeCFOn+niHHeD22316P9f17euzDZ1uK62vh4MOgjlzvFzmo49qikKkQGjmQfLS6tXpa3TV3OylptsSh299C+65J/cTh169vNjTZpt1sR7F3LmeOJSV+ee5c9MboIhkDSUPknfq6tLXr2LNGq/h8MgjPv7xj+HWW32RZC7r1w9Gj/Y8oMvKy33GoabGP5eXpyU2Eck+umwhuau+3v/aLS//fLq8ocGLQKUjcaiu9nbab73l4xNOgEmTcnsHQlGRV4gcNKgbTy4t9UsV63wPRCT/KXmQ3NTO9famotK0NbqaP99rOMyd68nCRRfBz36W+vfJpAEDfG1Dj0pLl5bCNtukLCYRyQ1KHiQ3rXO9vXn2XBb034bm5tS/1bvvwsSJvviyuBiuuw6++93Uv0+m9O7taxsGDIg7EhHJVUoeJDe1XW+fM4fWLbZkYZ/ytDS6+s9/4OSTfQHmoEG+SLKiIvXvkylqZCUiqaDkQXJTdL09fDKXhb3LaQipv97+v/8Lkyd7981NNoE774Qtt0z522REnz5+iSLXF3aKSHZI224LM7vLzBab2TsJx4aa2dNm9lH0eUh03MzsJjObbWZvmdnOCc85Mnr8R2Z2ZMLxXczs7eg5N5nl8rI16Y5QUspng7ahLg2Jw113wZlneuKw1VZe/ClXE4eyMt9JocRBRFIlnVs17wb2X+fYucAzIYSxwDPRGOAAYGz0MRGYCp5sABcDuwG7Ahe3JRzRY45NeN667yV5rrLSLyekUmsrXHklXHWVj7/+dbj/fth449S+TyYUF3uFyA03VAdMEUmttP2XEkL4J1C9zuGDgHui2/cAByccvze4F4EyM9sE2A94OoRQHUJYDjwN7B/dNyiE8GIIIQD3JryW5KN1yiCno9FVY6PPNtx9t4/32w+mT+/mNsYYmXnBqtGjtXtSRNIj02seNgohLIpuVwIbRbdHAPMTHrcgOtbR8QXtHG+XmU3EZzQYNWpUD8KXWKyzLXPZXY9Sszq1vxVra73404sv+vgXv4Dzzsu9hYWlpb62oaQk7khEJJ/FNpkZzRikoZRPu+81LYRQEUKoGD58eCbeUlIpYVtmzYeLWfbWwpS+/OLF8POfr00czjgDLrggtxIHM99FMWqUEgcRSb9MJw9V0SUHos+Lo+MLgc0SHjcyOtbR8ZHtHJd8FG3LXFXdxOIRO8HIkZ0+pas+/hjGjfMrIr17+1qHiRNzq2pk377+JRoypNOHioikRKaTh8eAth0TRwKPJhw/Itp1sTuwIrq88SSwr5kNiRZK7gs8Gd230sx2j3ZZHJHwWpJvSktZ/cCjVN76Jy+0kKI/rV9/HQ47zMtZ9+vnL33wwSl56Yzo1csvUWy2mW/FFBHJlLSteTCzB4G9gA3MbAG+a+I3wMNmNh74FPhJ9PAngAOB2cAa4GiAEEK1mf0aeCV63KUhhLZFmCfiOzr6An+NPiQP1dXBZ9WlhC3GpOw1//53+OUvfTnF0KEwbRpsv33KXj7t+vf3xKG3KrWISAwspKODUBarqKgIM2fOjDsM6aKGBu8rkcp+FX/4g/emaG31NQJ33uk7E3JBUZFvvRw4MO5IRCRXmNmrIYSU1sbV3y2StZqaSGmjqxBgyhS4+WYff/WrPuMwbFhqXj/dBg70xCGXFnKKSH5S8iBZqbkZFiwgZY2umpvhkkvg4Yd9vOeecNNNPv2f7Xr39ksUuRCriBQGJQ+SdVpbfcYhVY2u6urg9NN9nQP4osjLLsuNRYaDB/sWTFWIFJFsouRB4lVf73UcysuhtJQQPHFoaEjNyy9fDiec4DsrwLdhnn569m/FVCMrEclmSh4kPutUjgx/eZTPqkupq0vNyy9cCOPHwyefeLJw/vlw+OGpee10GjIENtgg+xMcESlcSh4kPgmVI5kzh6qZ81m90diUvPT778OECbBkif8Vf801cMABKXnptCkp8dkG9aMQkWyn5EHiE1WOZM4clmy2MyvLUtN35MUXvU9Fba3vUJgyBXbbLSUvnRZtjayGDtVsg4jkBiUPEp/SUnj0UarfmMfy/qNTUjnyiSfg7LN9seWGG3oNh623TkGsaVJa6u2+i4vjjkREpOuUPEisVjSUsnToVil5rXvugSuv9HoOY8Z44rDppil56ZTr1cvrS6gfhYjkIiUPEptVq6Cqquev09oKv/0tTJ/u45139j4VZWU9f+106NfP1zbkwlZREZH2KHmQWKxZA5WVPX+dxkbfRfHYYz7+znfg2muzc9Fhr15es2Hw4LgjERHpGSUPknH19fDZZ355oSdqa2HSJHj+eR+PG+c9K7KxfPOAAb4GQ42sRCQf6L8yyajGxtT0q1i61As+zZrl40mTvBhUtu1WUCMrEclHSh4kY9r6VbS09Ox15s71Gg7z5/sv50sugUMPTUmIKTVokF+myMaZEBGRnlDyIBnR0pKaRldvveUzDsuX+7qGG2+EvfZKSYgpo9LSIpLv1G5H0qe+Ht5/n1BXz8KFfsmiJ557Do44whOHsjK4997sSxzKymD0aCUOXRL9fFBfH3ckIpIkzTxIekR9K8LsOXw2clfqb/ldj4pA/elPcMEFPoMxYoTXcNhiixTG20PFxT7b0Ldv3JHkiHX6mvDoo9m5RUZE2qWZB0mPqG9F1YAxrP5ksV+z6IYQvGbD5MmeOHzlKzBjRvYkDm2lpUePVuKQlHX6mjB3bswBiUgylDxI6iROQ5eXe7+K5S0wahSMHJn0y7W0wKWXwg03+Pgb34D77vPdC9mgpMRPTR0wu6Gtr0lNjX8uL485IBFJhi5bSGqsMw1d/btHWX7T733GYeTIpC9Z1NfDWWfBU0/5+Pvf99LT2dADwmxtaWklDd0U9TVh7lxPHHTJQiSnKHmQ1EiYhl7xYRVL31zof1GOGZP0S61Y4TUbXn3Vx8cc44lEryyYJ+vb19c2ZEMSk/NKS2GbbeKOQkS6QcmDpEY0DV370SKqRlR06zIFwKJFXsNh9mwfT54MRx2Vsii7rVcvvzyRrf0yREQyScmDpEZpKXUzHmXRSwthRPKXKQA+/NATh6oqr5Vw1VXwve+lIdYk9e/vsw0qLS0i4vTfoaREQwMsXFZK2CL5yxQAr7wCJ54IK1f6L+spU3yBZJyKirxC5KBB8cYhIpJtlDxIjzU1+brI7varePJJOPNMLyI1fLjXcIj7UvjAgb6rQ6WlRUS+TMmD9EhP+1Xcdx9cdpnXc9h8c08curlcIiV69/akYcCA+GIQEcl2Sh6k21pbvUNmU1Pyzw0Brr8ebr/dxzvu6MWghg5NaYhJGTzYZz6yYVeHiEg2U/Ig3RKCJw4NDck/t6kJLrwQ/vxnH++9tycScVVoVCMrEZHkKHmQblm0COrqkn/e6tUwaRL8618+PvRQ+NWv4tvJMGSIKkSKiCRLyYMkraoKamuTf96yZd5O+513fHzSSXDKKfH84i4p8dkGFTYUEUmekgdJytKlXgEyWfPmeQ2HTz/1NQUXXwzjxqU+vs60NbIaOlSzDSIi3aXkQbps+XKork7+ee+84zMOy5b5X/zXXQff+U7q4+tMaSlsvLFKS4uI9JSSB+mSlSthyZLkn/evf8Gpp8KaNV7aeepU2HnnlIfXITNf1zBkSGbfV0QkXyl5kE6tXu3rHJL1l7/A+ed7LYhNN/UaDt3ok9Uj/fr52oY+fTL7viIi+UzJg3Sovh4++8y3ZnZVCHDHHXDttT7eemsfb7RRemJsT69eXrNh8ODMvaeISKFQ8iDr1djotRySSRxaW+GKK+D3v/fxrrvCrbd6uedMGTDAq0SqkZWISHrov1dpV1u/imTKTjc0wNlnw//7fz4+4AC4+urMLVAsKvKkIZOJiohIIVLyIF/S0uIzDs3NXX/OypVet+Hll318+OFw3nmZK/U8aJBfplAjKxGR9FPyIF/Q1q+isbHrz6mq8hoOH37o47POgvHjM1NHoXdvX0vRv3/630tERJySB/lcCL44sr6+68+ZM8cThUWL/Bf5FVfAQQelL8ZEZWW+BVONrEREMkvJg3xu0SKvx9BVr74KJ5zgFSf79YObb4Y990xffG2Ki322Ia5GWiIihU7JgwDJ96v429/g9NN9keSwYTBtGnz1q+mLD/wyyJAh/n4qLS0iEh8lD5J0v4oHH4RLL/X1EaNHw/TpsNlm6YsPvKz1xhv7ZxERiZeShwKXTL+KEOCmm7xuA8D22/uMw9Ch6YvPzGcahgzRbIOISLZQ8lDAkulX0dzsnTD/+Ecff+tbcMMNvtYhXfr29bUNamQlIpJdlDwUqGT6VaxZA7/8JfzjHz7+0Y/8skW6+kX06uW7KMrK0vP6IiLSM0oeClBdXdf7VVRXw/ETW3nzbd8PefzxcNpp6buE0L+/zzaotLSISPbSDvkC09DQ9X4VCxbAYeM8cTBauWiL3/PLExvSkjgUFfmCyBEjlDiIiGQ7JQ8FpKnJE4fW1s4f+957MG4czP20F8U0ctNm1/Dzpns8o0ixgQOhvNxLTIuISPbT33gFoqXFf+93pV/FCy94n4rVq2HQwMDU0ddSseIZGDUKRo5MWUy9e3sjqwEDUvaSIiKSAUoeCkBrqycOTU2dP/bxx+Hcc/2xG28Md95pjB11Oiz4iScOKSq0MGiQJw4qLS0iknuUPOS5EPxSRUND54/93e/gN7/x22PHwh13wCabAJTAmDEpiadPH18Qmc4tniIikl5KHvLcokW+u6Ijra1w9dWePABUVHghqMGDUxuLGlmJiOSHWP4bN7O5Zva2mb1hZjOjY0PN7Gkz+yj6PCQ6bmZ2k5nNNrO3zGznhNc5Mnr8R2Z2ZBznks0qKzvvV9HY6C202xKHffeFu+5KbeJQXOzlq3WZQkQkP8T5X/neIYQdQwgV0fhc4JkQwljgmWgMcAAwNvqYCEwFTzaAi4HdgF2Bi9sSDvHKkStXdvyY2lo49lhf5wDw85971chU9o8YOtT7X6gDpohI/simvwMPAu6Jbt8DHJxw/N7gXgTKzGwTYD/g6RBCdQhhOfA0sH+GY85K1dXes6Ijixd7svDiiz4+/XS48EKvt5AKJSW+OWODDdSTQkQk38S15iEAT5lZAG4PIUwDNgohLIrurwQ2im6PAOYnPHdBdGx9x7/EzCbisxaMGjUqVeeQlVas8C6ZHfn4Y5gwwRdSFhXBZZd5yelUMPPZhqFDlTSIiOSruJKHPUMIC81sQ+BpM3s/8c4QQogSi5SIkpNpABUVFSl73WxTW9t5v4o33oDjjoOaGr+UcOON3uQqFdQ2W0SkMMRy2SKEsDD6vBj4M75moSq6HEH0eXH08IXAZglPHxkdW9/xgrRmje+s6MizTzVx5BGt1NT4zMC996YmcWhrmz1qlBIHEZFCkPHkwcz6m9nAttvAvsA7wGNA246JI4FHo9uPAUdEuy52B1ZElzeeBPY1syHRQsl9o2MFp6Gh80ZXf3iwiZNO6UV9Qy82K13MjHsb2GGHnr93aaknDcOG6TKFiEihiOOyxUbAn81/0/QGHggh/D8zewV42MzGA58CP4ke/wRwIDAbWAMcDRBCqDazXwOvRI+7NIRQnbnTyA6NjV49cn39KkLwmg033eT9s7crncO0oeewQa+rgO4XfmqbbRgyREmDiEihyXjyEEL4GPhaO8eXAfu0czwAJ63nte4C7kp1jLmiudkXPba0tH9/Swtccgk89JCP9yx7hxv7T2ZA+fAe9agoLfW1DcXF3X4JERHJYaowmaPaGl2tr19Ffb1vv3zmGR8fdBBcdtFYiqtu6HaPCjPfejlE1TRERAqakocc1NavorGx/ftrauD44+H11308YQKceSaYlcCA7l2q6NvXe1JotkFERJQ85JgQfHFkfX379y9c6MnCxx/7TMH558Phh3f//Xr18tmGsrLuv4aIiOQXJQ85prISVq9u/7733/dy04sXe/fKa66BAw7o/nv16+ezDX36dP81REQk/yh5yCGLF8OqVe3f99JLcOKJXihqwADfYbHbbt17n169YPjw1HfVFBGR/KDkIUcsW+ZrGdrzxBNw9tm+eHLDDeGOO2Cbbbr3Pv37+2xDb/1kiIjIeuhXRA5YscKTh/bcey9ccYWvhRgzBu68EzbdNPn36NXLE49Bg3oWq4iI5D8lD1lu1ar2+1W0tsK113qyALDTTnDbbd1b2DhggCcOmm0QEZGu0K+LLLZmjS+QXFdjo++ieOwxH++zD1x3nRdvSkZRkScNAwf2PFYRESkcSh6yVH19+/0qamth0iR4/nkf//SncNFFyc8aDBzoiUNRUWriFRGRwqHkIQs1Nnq9hnX7VSxdChMnwqxZPj71VN9hkUxviT59PGno3z918YqISGFR8pBlmpu97PS6/So+/RTGj4f583224JJL4NBDk3vtIUO8mVWvWBqxi4hIvlDykEXa+lU0N3/x+FtvwXHHQXW1r2u44QbYe++uv25JiW+/THZNhIiISHuUPGSJ1tb2+1U895yvcair850Ut98OO+7Ytddsa2RVVqa22SIikjpKHrLA+vpV/OlPcMEFPiMxYoRvy9xii669pkpLi4hIuih5yAKVlb4ts00IPsNw/fU+/spXYNo0X+jYmaIiLy2tYk8iIpIuSh5iVlX1xX4VLS1w2WXwwAM+/sY34JZbvJATAA0NvjBi5EhfzJBA2y9FRCQTlDzEaOlSLz3dpqEBzjwTnnrKx9//Plx5JRQXJzzghBNg3jwYNQqmToWSEnr39ksU2n4pIiKZoE17caivp+blD6le1PD5oRUr4Jhj1iYOxxzjLbU/TxzAZxzmzfNrEvPmwYIFDB4M5eVKHEREJHM085Bp9fWs/N5hLP5o1eezB4uqSzj2WPjoI3/IuefC0Ue389yRI/058+bRe/QINt5tBP2GZjR6ERERJQ+ZtvrdT6n6aCUMGgzz5vHRv6uYcMkoKit9Z8RVV8H3vreeJ5eUwNSpDF4xj+E7b0avfircICIimafkIYPq6uCz4nLCqNEwbx4zB+/DCWdvxspV0L9vK1NubOYb3ype7/N794aNRpTQv//YDEYtIiLyRUoeMqShIWp0VeyzB089tJwzrtmIxkZjeJ8a7hh0Dl+5pwl2n/qlXRTgyxyGD9dOChERiZ+ShwxoavLqkW39Ku7/Ywm/vmJjQoDyEY3c2Xoymw1dA/NW+qLIMWM+f25Rke+k+HyrpoiISMy02yLNEvtVhOCFny691G9/7Wvw4AOBzbYohpUrfTHkyJGfP3fQIN9JocRBRESyiWYe0qi11ROHpib/uOgiLzkN3tjquuugXz+/jJFY+KlPH59t6Ncv3vhFRETao+QhTULwSxUNDV56etIk+Oc//b5DDvGW2r3bvvolJTBmDGZr22arkZWIiGQrJQ+pVF8Pc+dCeTmLqkupq/M22hMnwttv+0NOPBFOPfXLyUFJCWy8cbtrJUVERLKKkodUqa/3etIffkjl6N2ovfU+5i8uYfx4+PRT6NULLr4Yxo374tPMYOhQ/9Bsg4iI5AIlD6ny1lvw/PMsaR3GysUfMOtvn3Hs5ZuzbJnPJlx3HXznO198Smmpr23QbIOIiOQSJQ+pUF8PkyZR3dCP5Qzg33125+TzRrGmHgYP9vWQu+yy9uFmsMEGvr5BREQk1yh5SIW5c1mxaA1L+5fz6JrvcF7TFTQ3FbHppnDnnV8o20C/fj7b0KdPfOGKiIj0hJKHFKjdoJzKEbswvWY3rgnHAbDVVp44bLSRP6aoyCtEDhoUY6AiIiIpoOShh9asgYXLSrli82nc+x//cu66K0yZsjZR6N/fk4je+mqLiEge0K+zHqivh08+gbPOgr/+1b+UBxwAV18NxcW+w2LDDTXbICIi+UXJQzIS6jg09irl/ffhuOPg5Zf97iOOgMmTPWnQbIOIiOQr/Wrrqvp6OOggmDOHps23Yub5f+bo40r48EO/+8wzYcKEtWsbBg+ON1wREZF0UfLQFfX18Pe/w0cf0TxkOP+YtQFH/aQXny3xmYUrrvC8ol8/rxKp2QYREcln+jXXmbYZh9mzaalZxWO1+3LM8t+yorkP/frBzTfDf/+3ZhtERKRwqCV3Z+bOhTlzaC0byu+KxvOzmimsaB7AsGHw+9/Dd78Lo0dHiUN9Pbz/vn8WERHJU5p56Ex5OWGLMVzz6j6cV30mrfRi9GiYPh123raeISvmQks5tPD5mgjGjIFHH/X60yIiInlGyUMnWotLOXOb/+P6p/1Ltf32cPfd8NUt6yk+NCFZuPpqv11W5p/nzoVttokzdBERkbTQZYt1JVx6aGqCX/wCrr/ZE4f/+i+fUNhpJyj+bO4XkwXwJKKmxj+Xl8d0AiIiIumlmYdECdsxV4/eloPDn/nbs0UA/OhHfqmirCx6bHm5JwltMw9bb+2ZRVQHQpcsREQkXyl5SBQtjlzcdzQH/OtyXmvyxOHkk72l9heaWZWWtp8s6FKFiIjkOV22SFRezscb7s43Z93Ba03bY7Ry5a+buPnm9XTBLC31ZEGzDCIiUkCUPCR45e1Sdn//d8wJW1Bsjdyz4Vmce8icuMMSERHJKkoeogWS//tII3vvDUuW92FQUS3/t9F4Dt/xHS8ZqdoNIiIinyvsNQ/19YTvfZ9pr1dwSs1lNAXYuLiaJzY4nJ02XwH3/BF++lPVbhAREUlQuDMP9fU0zXiEi/7xbY5f/huaQm/GblLLCxsfzE6bVEFlJbz22pdrN4iIiBS4wkweamqo+eaBnHB0HZe1ngfAbrzICxc+QfnYYqiu9ssVO++s2g0iIiLrsBBC3DFkVMWOO4U/rRnKSR9N4nF+CMABPMEf+TH9RgyDsWN9fcPixbDllvDQQz4LodoNIiKSg8zs1RBCRSpfM+fXPJjZ/sCNQBFwZwjhNx09vu6tDzksPMV/2AOAo/gddzKBIlp9xmH2bDCDDTf0SxWVlardICIikiCnL1uYWREwBTgA2BY4zMy27eg5H4fyzxOH87icuzjGEweAujrYdFPYaitdqhAREVmPXJ952BWYHUL4GMDMZgAHAe+u7wn19KWIZm5gEidzq1+KqK/32YbiYrj5ZthhB5WZFhERWY9cTx5GAPMTxguA3dZ9kJlNBCYCGDvxIOM4lEf8zmHDYPhwWLbMZxx22GFt5UgRERH5klxPHrokhDANmAawtQ0Mh/K631Fc7EnCH/+oRZEiIiJdlOvJw0Jgs4TxyOjYeg2k1pOEKVNgxAjvhllamtAuU0RERDqS68nDK8BYM9scTxrGAT/r8Bm77AIzZ2YgNBERkfyU08lDCKHZzE4GnsS3at4VQpgVc1giIiJ5LaeTB4AQwhPAE3HHISIiUihyus6DiIiIZJ6SBxEREUmKkgcRERFJipIHERERSYqSBxEREUmKkgcRERFJipIHERERSYqSBxEREUmKkgcRERFJipIHERERSYqSBxEREUmKkgcRERFJipIHERERSYqSBxEREUmKkgcRERFJipIHERERSYqSBxEREUmKkgcRERFJipIHERERSYqSBxEREUmKkgcRERFJioUQ4o4ho8xsFfBB3HGkyQbA0riDSCOdX+7K53OD/D6/fD43KIzz6x9CGJ7KF+2dyhfLER+EECriDiIdzGxmvp4b6PxyWT6fG+T3+eXzuUHBnF95ql9Xly1EREQkKUoeREREJCmFmDxMizuANMrncwOdXy7L53OD/D6/fD430Pl1S8EtmBQREZGeKcSZBxEREekBJQ8iIiKSlIJJHsxsfzP7wMxmm9m5cceTDDOba2Zvm9kbZjYzOjbUzJ42s4+iz0Oi42ZmN0Xn+ZaZ7ZzwOkdGj//IzI6M6VzuMrPFZvZOwrGUnYuZ7RJ9rWZHz7UsOL9fmdnC6Pv3hpkdmHDf5CjWD8xsv4Tj7f68mtnmZvZSdPwhMyvO4LltZmbPmtm7ZjbLzCZFx/Pi+9fB+eX898/MSs3sZTN7Mzq3SzqKx8xKovHs6P7y7p5zzOd3t5l9kvC92zE6nlM/m9H7F5nZ62b2eDSO93sXQsj7D6AImANsARQDbwLbxh1XEvHPBTZY59jVwLnR7XOBq6LbBwJ/BQzYHXgpOj4U+Dj6PCS6PSSGc/lvYGfgnXScC/By9FiLnntAFpzfr4Az23nsttHPYgmwefQzWtTRzyvwMDAuun0bcEIGz20TYOfo9kDgw+gc8uL718H55fz3L/p6Dohu9wFeir7O7cYDnAjcFt0eBzzU3XOO+fzuBg5p5/E59bMZvf/pwAPA4x39LGXqe1coMw+7ArNDCB+HEBqBGcBBMcfUUwcB90S37wEOTjh+b3AvAmVmtgmwH/B0CKE6hLAceBrYP8MxE0L4J1C9zuGUnEt036AQwovB/7Xcm/BaGbGe81ufg4AZIYSGEMInwGz8Z7Xdn9foL51vA3+Mnp/4tUq7EMKiEMJr0e1VwHvACPLk+9fB+a1Pznz/ou9BbTTsE32EDuJJ/J7+Edgnij+pc07vWa3VwfmtT079bJrZSOB7wJ3RuKOfpYx87woleRgBzE8YL6Dj/xSyTQCeMrNXzWxidGyjEMKi6HYlsFF0e33nms1fg1Sdy4jo9rrHs8HJ0fToXRZN65P8+Q0DakIIzescz7hoKnQn/C+8vPv+rXN+kAffv2ja+w1gMf5LcU4H8Xx+DtH9K/D4s/b/l3XPL4TQ9r27PPreXW9mJdGxXPvZvAE4G2iNxh39LGXke1coyUOu2zOEsDNwAHCSmf134p1RJpwXe27z6VwSTAXGADsCi4BrY42mh8xsAPAIcFoIYWXiffnw/Wvn/PLi+xdCaAkh7AiMxP/a3CbeiFJr3fMzs68Ck/Hz/Dp+KeKc+CLsHjP7PrA4hPBq3LEkKpTkYSGwWcJ4ZHQsJ4QQFkafFwN/xv/hV0VTaUSfF0cPX9+5ZvPXIFXnsjC6ve7xWIUQqqL/2FqBO/DvHyR/fsvw6dXe6xzPGDPrg/9ivT+E8KfocN58/9o7v3z6/gGEEGqAZ4FvdBDP5+cQ3T8Yjz/r/39JOL/9o0tRIYTQAPyO7n/v4vzZ3AP4oZnNxS8pfBu4kbi/d50tisiHD7wB2Mf4IpG2BSHbxR1XF2PvDwxMuP0ffK3CNXxxkdrV0e3v8cWFQC9Hx4cCn+CLgIZEt4fGdE7lfHFBYcrOhS8vajowC85vk4Tbv8SvOwJsxxcXMH2ML15a788r8Ae+uEjqxAyel+HXem9Y53hefP86OL+c//4Bw4Gy6HZf4F/A99cXD3ASX1x093B3zznm89sk4Xt7A/CbXPzZTDjPvVi7YDLW711GTzzOD3x17Yf4db7z444nibi3iL6ZbwKz2mLHr2E9A3wE/C3hB9yAKdF5vg1UJLzWMfgimdnA0TGdz4P41G8Tfm1tfCrPBagA3omecwtRFdWYz+/3UfxvAY/xxV9G50exfkDC6u31/bxGPw8vR+f9B6Akg+e2J35J4i3gjejjwHz5/nVwfjn//QN2AF6PzuEd4KKO4gFKo/Hs6P4tunvOMZ/f36Pv3TvAfazdkZFTP5sJMezF2uQh1u+dylOLiIhIUgplzYOIiIikiJIHERERSYqSBxEREUmKkgcRERFJipIHERERSYqSBxHpETPbyMweMLOPoxLqL5jZ/0QdAHeMHtPbzGrN7BcJz3vVzHY2s6PMbEnU9fBdMzs2tpMRkS5R8iAi3RY13PkL8M8QwhYhhF3wwjQjgX8D34we+jV8H/k3o+f1x0s+vxnd/1Dw0sJ7AVeYWVt/DBHJQkoeRKQnvg00hhBuazsQQvg0hHAzXg21LXn4Jl4Fb8dovCvwagihJfHFgpdgnwOMTnPcItIDSh5EpCe2A15bz32JMw/fBP4JNJjZwGj8n3WfYGZb4JXzZqc+VBFJld6dP0REpGvMbApe5rkxhPB1Mys2s43xzoYfAK8Au+HJw80JT/2pme0JNADHhRCqMxy6iCRByYOI9MQs4MdtgxDCSWa2ATAzOvQf4FBgUQghmNmLeJfAXYEXEl7noRDCyRmKWUR6SJctRKQn/g6UmtkJCcf6Jdz+D3AaaxOFF4AjgMoQwoqMRCgiKafkQUS6LXhnvYOBb5nZJ2b2MnAPcE70kH/jaxheiB6/CG8D/KX1DiKSO9RVU0RERJKimQcRERFJipIHERERSYqSBxEREUmKkgcRERFJipIHERERSYqSBxEREUmKkgcRERFJyv8HHwCL5ER8kp8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Pp3qr2JPAW8BhzrktzrnvA7cD/8859zHw9fhYREQioKzMCrFI6rz0Epx1VufP6Z/KALz33+ngoRNT+boiIpJ8FRVW+lRSw3t46CFYtKjr/fKqsCYiIl2qrLRmI5Iazc1w882wcKEl7ilTOn++kreIiHSqutrae0pq1NbChRfC6tU2PvVUeOSRzn8mpafNRUQk2urq1GgklUpL4YIL4MMPbTx7Nlx2GfTrYmqt5C0iIu2qr1e98lR6/32YNQu2b4f+/e20+Wmnde9nlbxFROQzGhqsCIsSd2q89hpccYVtuRs6FO65B/76r7v/87rmLSIie2lqsrKnajSSGo8/bte4d++GsWPtWndPEjdo5i0iIm2o0UjqtLTAHXfYdjCAI4+EFSugN9W/lbxFRATYU6+8qSl0JNmnvh6uvhpeftnGJ54Id94Jgwf37nhK3iIigveWuBsaQkeSfcrL7TT5e+/Z+Nxz4dprIS+v98dU8hYRyXGJRiP19aEjyT4ff2wryrdute1fN9zQdenT7lDyFhHJcWVltp9bkuutt+CSS6wW/ODBsGQJTJuWnGNrtbmISA7bvt0qqElyPfUUzJxpibuoCB57LHmJGzTzFhHJWTt3QlVV6CiyS2srLF1qq8gBJk2CVatgzJjkvo6St4hIDtq1y7qESfI0NMC8efD88zY+/nhL5EOHJv+1dNpcRCTH1NSoXnmy7dwJ5523J3GfcQasXJmaxA2aeYuI5JTdu60ZhiRPSYk1F9m82cbXXAPf+x44l7rXVPIWEckRsZgajSTb2rUwZ46tHcjPtwpqJ52U+tfVaXMRkRzQ2Gh7jVWvPHnWrLFT5VVVMGoUPPpoehI3aOYtIpL1VK88uby31eR33WXjQw+1FeXjx6cvBiVvEZEs1tJiibu5OXQk2aGxEebPh2eesfExx8CyZTB8eHrjUPIWEclSiUYjjY2hI8kO1dVWMe3tt208YwbceisMHJj+WJS8RUSyUKJeeSwWOpLs8OmnVqN80yYbX3KJLVRL5Yryzih5i4hkoW3bbFuY9N2771pXsB07YMAAuO02mD49bExK3iIiWaasDGprQ0eRHV56CebOteppw4fb9e1jjgkdlZK3iEhWqaiw0qfSN97DQw/BokV2e/x4W1F+6KGhIzNK3iIiWaKy0sp0St80N9tCtNWrbTxlCixfbnu5M4WSt4hIFqiuhvLy0FFEX20tXHEFvPGGjU85BRYutOppmUTJW0Qk4mprVa88GUpLrUb5hx/aeNYsuPxy6JeBtUiVvEVEIqy+3laWS9+8/74l6+3bIS8PfvhDOO200FF1TMlbRCSiGhqsCIsajfTN66/bDHv3bmvheffdcNxxoaPqXAaeDBARka40NVnZUzUa6ZvHH4fZsy1xjx0LTz6Z+YkbNPMWEYkcNRrpu5YWa9/50EM2njzZmo3sv3/YuLpLyVtEJEJaWuxUeVNT6Eiiq74err4aXn7ZxiecAIsXw+DBYePqCSVvEZGI8N4Sd0ND6Eiiq7zcSp2+956Nzz0Xrr3WFqlFiZK3iEgEqNFI3338sa0o37rVtn/dcAOcdVboqHpHyVtEJAJKS6GuLnQU0fXWW9YJrKbGTo8vWQLTpoWOqve02lxEJMNt325JR3rnqadg5kz7DIuK4LHHop24QTNvEZGMtmMHVFWFjiKaWlth6VJbRQ4waZI1FxkzJmxcyaDkLSKSoaqqLHlLzzU0wLx58PzzNj7+eEvkQ4eGjStZdNpcRCQD1dTY6XLpuZ074bzz9iTuM86w2Xe2JG7QzFtEJOPU1anRSG+VlFhzkc2bbTx3rl3vdi5oWEmn5C0ikkFiMdsSpnrlPbd2LcyZY5cb8vNh0SI4+eTQUaWGTpuLiGSIxkY1GumtX/7STpVXVcGoUfDoo9mbuEEzbxGRjJBoNKJ65T3jvV3PvusuGx96qK0oHz8+aFgpp+QtIhJYol55c3PoSKKlsRHmz4dnnrHxMcfAsmUwfHjYuNJByVtEJKDWVptxNzaGjiRaqqutYtrbb9t4xgy49VYYODBoWGmj5C0iEkiiXrkajfTMp59ajfJNm2x8ySW2UC3bVpR3RslbRCSQbdtg9+7QUUTLu+9aV7AdO2DAALjtNpg+PXRU6afkLSISQFkZ1NaGjiJaXnrJ9m03NNh17XvvhS99KXRUYSh5i4ikWXk57NoVOoro8B4eesj2bXtvK8lXrbKV5blKyVtEJI127oTKytBRREdzs50af+IJG0+ZAsuX217uXKbkLSKSJrt2QUVF6Ciio7YWrrgC3njDxqecAgsXWvW0XKfkLSKSBrW1dp1buqe01FaUb9hg41mz4PLLoZ/qggJK3iIiKbd7t60sl+754ANrLrJ9O+TlwQ9/CKedFjqqzKLkLSKSQg0NajTSE6+/bjPs3buthefdd8Nxx4WOKvPoBISISIo0Nlr1tNbW0JFEw+OPw+zZlrjHjoUnn1Ti7ohm3iIiKdDcbPXK1Wikay0tcMcdth0MYPJkazay//5h48pkSt4iIknW0mIz7qam0JFkvvp6uPpqePllG59wAixeDIMHh40r0yl5i4gkUWurzbjVaKRr5eVW6vS992x87rlw7bW2SE06p+QtIpIk3tuq8lgsdCSZ7+OPbfvX1q22/euGG+Css0JHFR1K3iIiSVJaCnV1oaPIfG+9ZZ3Aamrs9PiSJTBtWuiookWrzUVEkmD7dktG0rmnnoKZM+2zKiqCxx5T4u4NzbxFRPpoxw6oqgodRWZrbYWlS20VOcCkSdZcZMyYsHFFlZK3iEgfVFVZ8paONTTAvHnw/PM2Pv54S+RDh4aNK8p02lxEpJdqaux0uXRs504477w9ifuMM2z2rcTdN8Fm3s65K4CZgAfeA77rvdcaTRGJhLo6W6AmHSspsRrlmzfbeO5cu97tXNCwskKQmbdz7iDgUmCq9/5IIA84M0QsIiI9FYupXnlX1q61WfbmzdbCc+lSOP98Je5kCXnavD8wyDnXHxgM/ClgLCIi3dLQYHuTlbg7tmaNnSqvqoJRo+DRR+Hkk0NHlV2CJG/v/VbgTuATYBuwy3v/0r7Pc85d4Jxb65xbW15enu4wRUT20tSkeuWd8R7uuw+uuso+q0MPhZ/9DI46KnRk2SfUafORwHTgEGAsMMQ595naOt77Vd77qd77qUVFRekOU0TkzxL1ypubQ0eSmRob4frr4a67bHzMMbB6NYwfHzSsrBXqtPnXgf/13pd775uAZ4C/DhSLiEinWlvVaKQz1dV2PfuZZ2w8YwY88AAMHx40rKwWarX5J8CXnXODgXrgRGBtoFhERDrkvZ0qb2gIHUlm2rLFVpRv2mTjSy6BOXO0MC3VgiRv7/1vnHNPAe8AzcD/AKtCxCIi0plt26xtpXzW+vUwe7YVqRkwAG67DaZPDx1Vbgi2z9t7Px+YH+r1RUS6UloKtbWho8hML79s+7ZjMTs9vmyZXeeW9FB5VBGRdpSX27Vc2Zv38NBDsGiR3R4/3mqUH3po6Mhyi5K3iMg+du6EysrQUWSe5mZYsACefNLGU6bA8uW2l1vSS8lbRKSNXbugoiJ0FJmnthauuALeeMPGJ58MCxdCQUHYuHKVkreISFxtLZSVhY4i85SWwqxZsGGDjWfNgssvh35qbRWMkreICLB7t60sl7198IFtBdu+HfLy4Ic/hNNOCx2VKHmLSM5To5H2vf66zbB377YWnnffDccdFzqq7DdwIBx4YOfPUfIWkZzW2GhFWFpbQ0eSWR5/3BantbbC2LGwciVMmhQ6quw3YgQUFXVd5EbJW0RyVnOzVQhTo5E9WlrgjjtsOxjA5MmwYgXsv3/YuLJd//422x48uJvPT204IiKZSY1GPqu+Hq6+2gqwAJxwAixe3P2EIr0zfLjNtnuyAFDJW0RyTmurnSpvbAwdSeYoL4cLL4T33rPxOefAddfZIjVJjf794YADYMiQXvxs8sMREclc3tvitFgsdCSZ4+OPbfvX1q02+7v+ejj77NBRZbdhw+xSRG//OFLyFpGcUlpqq6fFvPWWdQKrqYFBg2DJEjtdLqmRl2dJe9iwvh1HyVtEckZZmSUpMU89BfPn23X/oiJbUT55cuiosteQIXaavH8SMq+St4jkhIoKK30qdungrrtsFTnYFrBVq2DMmKBhZa1+/eyPo+HDk3dMJW8RyXpVVdZsRKChwa5pr1lj4+OPh6VLrQiLJN/gwTbbHjAgucdV8haRrFZdbaU9xf6AufhiWLfOxmecATfemPzEIlZkpajIiq6kgpK3iGStujo1GkkoKbEa5Zs323juXJg5s+tKXtJzBQVWcGXgwNS9hpK3iGSl+nrVK09YuxbmzLHLBwMHwqJFcMopoaPKPs7B6NHp6W+u5C0iWaehwfYsK3HD88/DtddCUxOMHAn33QdTpoSOKvvk59tsOz8/Pa+n5C0iWaWpSY1GwP5wWbkSfvITGx9yCNx/P4wfHzaubDRqlM2403kJQslbRLKG6pWbxkbbv/3MMzY+5hi4557ULZ7KVYnWnQUF6X9tJW8RyQqtrZa4m5pCRxJWdbVVTHv7bRtPn26tPVO5eCoXjRwJ++0XbsGfkreIRJ73dqq8oSF0JGFt2WI1yjdutPEll9hCNa0oT54BA2zfduhOa0reIhJpiUYj9fWhIwlr/XqYPRt27LAEc9ttNuuW5OlN685UUfIWkUgrK7P93Lns5Zdt33YsZglm2TK7zi3J0ZfWnami5C0ikVVebtd4c5X38PDDsHCh3R4/3mqUH3po6MiyR19bd6aKkreIRNLOnVBZGTqKcJqb7dT4E0/YeMoUWL48PQVCckGyWnemipK3iETOrl3WJSxX1dbClVfC66/b+OSTbfYdYstSNho61E6TZ9psuy0lbxGJlJqa3K5XXlZmK8o/+MDGF1wAV1yRGYuooq5fP5ttFxYm6YCxmBWVLy5O+l9WSt4iEhm7d0NpaegowtmwwZJ1WZnNCm++GU4/PXRU2WHwYCu40j9ZWTEWs+X+mzbBxInw3HNJTeD6W01EIiEWy+1GI6+/Dt/5jiXuIUNsYZoSd985Z7PtceOSmLjBZtybNllZu02bbJxESt4ikvEaG3O7XvkTT9ge7t27YcwYWL0ajj8+dFTRV1AABx+corKxxcU2466qsu/FxUk9vE6bi0hGa262ymEtLaEjSb+WFrjzTnjwQRtPngwrVthMUXovLa07CwrsVLmueYtIrsnlRiP19XD11VaABeDEEy2Rhy7LGXVpbd1ZUACHH56SQyt5i0hGam21U+WNjaEjSb+KCrjwQit5CnDOOXDddZm9dSnTOWcz7VGjsqPWu5K3iGScRL3yWCx0JOm3caOtKN+61bYu3XADnHVW6KiiLWTrzlRR8haRjFNaaouzcs1bb1knsJoaOz2+ZAlMmxY6qmgL3bozVZS8RSSjlJVZ8so1Tz8NN91k1/eLimDlSlugJr0zYIDNtgcNCh1Jaih5i0jGqKiw0qe5xHu46y5bRQ4waZLt4R4zJmhYkZZJrTtTRclbRDJCZaU1G8klDQ1w/fWwZo2Njz8eli612trSc5nYujNVlLxFJLjqamvvmUt27oSLL4Z162x8xhlw4412uld6LlNbd6aKkreIBFVXl3uNRkpKbEX55s02njsXZs7MvkVV6ZDprTtTRclbRIKpr8+9euVr18KcOVY1c+BAWLQITjkldFTRFIXWnami5C0iQTQ02F7mXErca9ZYsZWmJisWsnw5TJkSOqroSXrrzghS8haRtGtqyq1GI97bavK77rLxIYfA/ffD+PFBw4qkpLfujKgcf/sikm6JRiO5Uq+8sRHmz4dnnrHxMcfAPfekqJNVFuvXz4qt6HMzSt4ikjaJeuVNTaEjSY/qaquY9vbbNp4+HRYssGvd0n2DBtlsWyvx91DyFpG08N4Sd0ND6EjSY8sWmDXLapWDJfE5c7SivCecs9n2yJGhI8k8St4iknKJRiP19aEjSY/1660rWEWFzRZvu81m3dJ9+flWZU5nKdrXafE459ws51y7fVicc5elJiQRyTZlZbafOxe8/DKcfbYl7uHD4cEHlbh7wjkYPRomTFDi7kxXlV/vAtY5545q57Fzkx6NiGSd8nK79pvtvIeHHrLT47GYrSRfvdoWqEn3DBxon9vo0bq80JWukvcG4ArgX51zc/d5TB+tiHRq506rWZ7tmpvhllvg9tstiU+ZAj/7GRx6aOjIomPkSDj44OzquZ1KXSVv771/CZgKHO+c+5VzLtHrJodKK4hIT+3aZaeOs11tLVx0ETzxhI1PPhkeftiKsEjXBgyw2XZRkWbbPdGtBWve+wpghnNuNvAb59wVqQ1LRKKspiY36pWXldmK8g8+sPH558OVV2Z3K8pkyoXWnanSVfLe6+8g7/0K59xrwOOA2sSLyGfs3g2lpaGjSL0NG6y5SFmZ1da++WY4/fTQUUVD//62b3vw4NCRRFdXyXvWvnd47zc4574M/EPiPufcZO/9H5IdnIhESyyWG41GXn8dLr/c/lAZOhTuvhuOOy50VNFQWGh1yTXb7ptOPz7v/X93cH+T9351m7t+mtSoRCRyGhtzo175E0/A7NmWuMeMgSefVOLujrw8GDvWZtxK3H2XrCItWmYgksMS9cpbWkJHkjqtrXDHHbZvG2DyZGs2sv/+YeOKglxu3ZkqyUreWX6STEQ60tKS/Y1G6uvh6qutAAvACSfA4sW6ZtsVte5MHZVHFZFeSzQaaWwMHUnqVFRYqdP16218zjnWk1uzyM4NGWKz7Vxv3ZkqyfpYs/h/XRFpT6JeeSwWOpLU2bjRVpRv3WqzyOuvt9Kn0rF+/Wz71/DhoSPJbp0mb+fc0Z097r1/J/79y8kMSkQy37ZttmgrW731lpU6ramxlpRLltjpcumYWnemT1cz78WdPOYB/SqL5KCyMqsslq2efhpuusmu4xcVwcqVtkBN2qfWnenXafL23k9LVyAiEg0VFVb6NBt5D0uXwn332XjSJFi1yraESfsKCmy2rQ5g6dXta97OuSOBI4A/l4333j/a2xd2zo0A/hk4EpvFf897/1ZvjyciqVdZac1GslFDg13TXrPGxscfb4l86NCwcWUq56x++6hRqkkeQreSt3NuPvA1LHm/AJwC/CfQ6+QNLAX+3Xv/befcQECbLkQyWHW1tffMRjt3wsUXw7p1Nj7jDLjxRl277cjAgXY2Ij8/dCS5q7sz728DXwD+x3v/XefcAcBjvX1R59xw4KvAeQDe+0a0Yl0kY9XWZm+jkZISW1G+ebONr74avv99zSY7MnKkXd/W5xNWd4vU1XvvW4Fm51whsB0Y34fXPQQoBx5yzv2Pc+6fnXND9n2Sc+4C59xa59za8mz9k18kw9XX28rybKxXvnatzbI3b7bZ5F13wcyZSkztUevOzNLd5L02fo36fmAd8A7Ql+vT/YGjgfu891OAOuC6fZ/kvV/lvZ/qvZ9aVFTUh5cTkd5oaLA9ztmYuNesgfPOg6oqm00++iicckroqDLTiBFw8MG2FUwyQ3f7eV8Uv7nCOffvQKH3fn0fXncLsMV7/5v4+CnaSd4iEk5Tk5U9zbZGI95bTfK77rLxIYfYivIJE4KGlZHUujNzdWvm7Zz7VeK2977Ee7++7X095b0vBT51zh0Wv+tE4P3eHk9EkitbG400NtqK8kTiPuYYWL1aibs9hYVQXKzEnam6qrBWgK0C3885N5I93cMKgYP6+NqXAI/HV5r/EfhuH48nIknQ0mKnypuaQkeSXNXVcOmlVjkNYPp0WLBA+5P3lZdnNcm1RS6zdXXafBZwOTAWu86dUA0s68sLe+9/B0ztyzFEJLkS9cobGkJHklxbtsCsWVarHKzs6Zw5Wni1L7XujI6uKqwtBZY65y7x3t+TpphEJIBE4q6vDx1Jcq1fb13BKipsxfSCBTBjRuioMktenq0iV+vO6OjuPu+VzrlLsb3ZAK8BK733WXZiTSR3lZZCXV3oKJLr5Zdh7lzrfDZ8OCxbZte5ZQ+17oym7v7nWg4MiH8HOBu4D5iZiqBEAPsXt6TEVs0UFHT1bOmD7dute1a28B4eeQRuv91ujx9vK8oPPTR0ZJlDrTujrasFa/29983Al7z3X2jz0K+dc++mNjTJabGYrSjatAkmToTnnlMCT5EdO2yvc7ZoboYf/Qgef9zGU6bA8uVWg1uMWndGX1dbxf47/r3FOTcxcadz7lAgyzaRSEYpKbHEPWKEfS8pCRxQdqqqsuSdLWpr4aKL9iTuk0+Ghx9W4k5wzmbb48crcUddV6fNE2sx5wKvOuf+GB8Xo61dkkrFxTbjTsy8i4tDR5R1amrsdHm2KCuzFeUffGDj88+HK6+008Oi1p3ZpqvkXeScuzJ+eyWQ2EDQAkwBXk1VYJLjCgrsVLmueadEXZ0tUMsWGzZYc5GyMls5ffPNcPrpoaPKDGrdmZ26St55wFD2zMDb/tywlEQkklBQAIcfHjqKrBOLZVejkddfh8svh927beX03XdbL26xlp0HHqjWndmoq+S9zXt/S1oiEZGUa2y06mnZUq/8iSfg1lvt/YwZAytXwmGHdf1zuWDUKBg9WrPtbNXda94iEnGJRiPZUK+8tRXuuAMefNDGkydbs5H99w8bVyYYONBm27rSlN26St4npiUKEUmpRL3y5ubQkfRdfT1ccw289JKNTzgBFi9WAw2wzRnqt50buiqPujNdgYhIarS22oy7sTF0JH1XUWGlTtfHGxKfcw5cd51qcat1Z+5RQTyRLJZNjUY2brQV5Vu32vav66+Hs88OHVV4hYV2uUBb4nKLkrdIFtu2zVZhR91bb1knsJoaqw62ZImdLs9l/ftbTfIhQ0JHIiEoeYtkqbIyqzgWdc88AzfeaNfri4psRfnkyaGjCmvYMJtt5/rlglym5C2ShSoqYNeu0FH0jfewdCncd5+NJ02y5iJjxoSNK6S8PEvaw1RlI+cpeYtkmcpK2BnxpaYNDXZNe80aGx9/vCXyoUPDxhWSWndKW/o1EMki1dVQXh46ir6prIQ5c2DdOhuffjrcdFPuNtJQ605pj5K3SJaorY1+vfKSEltRvnmzjefOhZkzc3ff8uDBtgVMs23Zl34lRLJAfb2tLI+ytWttxl1VZVXCFi2CU04JHVUYidadI0aEjkQylZK3SMQ1NNje5yg3GlmzxoqtNDVZTe7ly2HKlNBRhTFokM22c/UygXSPkrdIhDU2WvW0qDYa8d5qkt91l40POQTuvx/Gjw8aVhDOwX77wciRoSORKFDyFomo5mabcUe10UhTky1Ee+YZGx9zDNxzT26eKi4osNn2wIGhI5GoUPIWiaCWFptxNzWFjqR3qqvh0kutchrA9OmwYEHuJS/n7DLBqFG5uyhPekfJWyRiWlttxh3VRiNbt9qK8o0bbXzJJbZQLdeSV36+zbbz80NHIlGk5C0SId7bqvJYLHQkvbN+vXUFq6iwBVkLFsCMGaGjSr9Ro2D06Nz7g0WSR8lbJEJKS6GuLnQUvfPKK3DVVfaHR2EhLFsGxx4bOqr0GjjQZtsFBaEjkahT8haJiO3bratW1HgPjzwCt99ut8eNsxrlEyeGjiy9Ro601eSabUsyKHmLRMCOHVa8JGqam+FHP4LHH7fxUUdZo5FRo4KGlVYDBthse9Cg0JFINlHyFslwVVWWvKOmthauvBJef93GJ58MCxfm1inj4cOtUlq/fqEjkWyj5C2SwWpq7HR51JSVwaxZ8MEHNj7/fEvkuZLE+ve32fbgwaEjkWyl5C2SoerqotloZMMG2wpWVmb9p+fPhzPOCB1V+hQWWs/tXPlDRcJQ8hbJQLEY/OlP0atX/vrrcPnlsHu39Z+++27rxZ0L+ve3fttDhoSORHKBkrdIholqo5Enn4Rbb7Xqb2PGwMqVcNhhoaNKj2HDbLadlxc6EskVSt4iGaSpKXr1yltb4Y474MEHbTx5sq0oP+CAsHGlQ16eJe1hw0JHIrlGyVskQyTqlTc3h46k++rr4Zpr4KWXbDxtGixenBunjocOtT9QNNuWEJS8RTJAa2v0Go1UVFip0/XrbXz22TBvXvYns379bLZdWBg6EsllSt4igXlvp8obGkJH0n0bN9qK8q1bLZnNmwfnnBM6qtQbPNi2gPXXv5wSmH4FRQLbts1OP0fFW29ZJ7CaGqsatmQJnHBC6KhSq18/K7YyfHjoSESMkrdIQKWlVoksKp55Bm680a7LFxXBihVw5JGho0qtQYNstj1gQOhIRPZQ8hYJpLwcqqtDR9E93sPSpbaKHGDSJNsKNnZs2LhSyTlrJDJyZOhIRD5LyVskgJ07obIydBTd09ho17TXrLHx8cdbIh86NGxcqVRQYLPtgQNDRyLSPiVvkTTbtctWakdBZSXMmQPr1tn49NPhppuy9xSyczB6dG51PZNoUvIWSaPaWqv5HQWbN9uK8pISG8+dCzNnZm8/6vx8m23n54eORKRrSt4iabJ7t60sj4K1a23GXVVlp44XLYJTTgkdVWo4ZzPtUaOy9w8TyT5K3iJpEKVGI88/D9ddZ9e6R460RWpTpoSOKjUGDrTZdi71GJfsoOQtkmKNjVbMpLU1dCSd895WkP/kJzYuLob774cJE4KGlTKjRtn1bc22JYqUvEVSqLnZyp5meqORpibru/300zY+5hi45x4YMSJoWCkxcKDVJB80KHQkIr2n5C2SIlFpNFJdDZdeapXTAKZPhwULsnOb1IgRVlxGs22JOiVvkRRobbVT5Y2NoSPp3JYtMGuW1SoHK3s6Z072JbcBA2y2PXhw6EhEkkPJWyTJvLfFabFY6Eg6t369dQWrqLDktmABzJgROqrkGz7cZtv9+oWORCR5lLxFkqy01LaFZbJXXoGrrrI/MAoLYdkyOPbY0FElV//+NtvOhd7iknuUvEWSqKzMum1lKu/hkUfg9tvt9vjxtsJ84sTQkSVXYaHNtrO9t7jkLiVvkSTZscNKn2aq5mb48Y/hscdsfNRRtoc7m0qB5uXZbDub666LgJK3SFJUVVnyzlR1dXDllfDaazY++WRYuDC7ipMMHWqJW7NtyQVK3iJ9VF0N27eHjqJjZWW2ovyDD2w8c6Zd786WBVx5eXaKvLAwdCQi6aPkLdIHdXWZ3WhkwwZrLlJWZknu5putM1i2GDLEZtv99S+Z5Bj9yov0Un19Ztcrf/11uPxyW/k+ZAjcfbf14s4G/frZbHv48NCRiISh5C3SCw0NVoQlUxP3k0/CrbdalbcxY2xF+WGHhY4qOQYPtmYimm1LLtOvv0gPNTVlbqOR1la44w548EEbT55sK8oPOCBsXMngnM22s7HeukhPKXmL9EAm1yuvr4drr4UXX7TxtGmwZEl2lAQdNMhm2wMGhI5EJDMoeYt0U2urJe6mptCRfFZFhZU6Xb/exmefDfPmRWTbVEODfbDjxkF+/l4POQf77Wd9xUVkDyVvkW7w3k6VNzSEjuSzNm60FeVbt9pCrnnz4JxzQkfVTQ0N9lfHJ59Y4/D77vtzAi8osNl2NnY3E+mrLNnpKZI6iUYj9fWhI/mst96CM8+0xD1oENx7b4QSN9iM+5NPbJP2J5/Ali04B6NHW+lWJW6R9gVN3s65POfc/zjn1oSMQ6QzZWW2nzvTPPOMFVypqbGFXI89BiecEDqqHho3zmbc1dUwYQL5hx7EhAmWvLOtLalIMoU+bX4Z8AGg2kiSkcrLLa9kEu9h6VI7wwwwaZJtBRs7NmxcvZKfb29kyxZG/eVBjD6oQElbpBuCzbydc+OAvwX+OVQMIp3ZuRMqK0NHsbfGRpg7d0/iPu44eOKJiCbuuIHD8pkwbSL7jVPiFumukDPvu4BrgGEdPcE5dwFwAcCECRPSE5UI1h2soiJ0FHurrIQ5c2DdOhuffjrcdFO0t0+NGGGn/JW0RXomyMzbOfcNYLv3fl1nz/Per/LeT/XeTy0qKkpTdJLramszr1755s22MC2RuK+6Cm65JbqJu39/u9y9//5K3CK9EWrmfRzwLefcqUABUOice8x7f1ageEQAqwO+bVvoKPa2bh1cdJG1HR040Fp5nnpq6Kh6r7DQkna2dDUTCSHI/z7e+3ne+3He+2LgTODXStwSWiyWeY1Gnn8ezj3XEvfIkfDoo9FN3Hl5dm3+wAOVuEX6KvRqc5GM0NiYWfXKvbcV5D/5iY0POQRWrbJdVVE0dKjVV49ExTeRCAievL33rwGvBQ5Dclhzs9UKaWkJHYlpaoL58+Hpp218zDFwzz3RbMjRr5+dIi/UZlCRpAqevEVCyrRGI9XVcOmlVjkNYPp0WLAgmpXGhgyx2bZad4okn/63kpzV2mqnyhsbQ0ditm61GuUbN9r44ovtK2qrsfv1s+1fw4eHjkQkeyl5S05K1CuPxUJHYtavt/4cFRW2/WvBApgxI3RUPafWnSLpoeQtOam01LaFZYJXXrF927GYXRtetgyOPTZ0VD2j1p0i6aXkLTmnrMyaeYTmPTzyCNx+u90eN85WlE+cGDqynsnPhzFjonldXiSqlLwlp1RUWOnT0Jqb4Uc/gscft/FRR8Hy5dZNKyqcg1Gj7Ctq1+VFok7JW3JGZaU1Gwmtrg6uvBJee83GJ50EixZBQUHQsHpk4EC7th2lmEWyiZK35ITqamvvGVpZGcyaBR98YOOZM+16d5Qqjo0cade3NdsWCUfJW7JeXV1mNBrZsMG2gpWVWaWx+fPhjDNCR9V9AwbYbHvQoNCRiIiSt2S1+vrMqFf++utw+eW2wn3IEFi6FL7ylbAx9cTw4bZ3O+1nCGIxKCmB4mKdoxdpQ8lbslZDgxU+CZ24n3wSbr3VqrmNGWM1yw87LGxM3dW/v1VJGzIkwIvHYlZibtMmW4L/3HNK4CJxSt6SlZqawjcaaW2FO+6ABx+08eTJcN99lgyjYNgwq0serJlISYkl7hEj7HtJCRx+eKBgRDKLkrdknUSjkZD1ymMxuOYaePFFG0+bBosXB5rB9lBeniXtYcMCB1JcbDPuxMy7uDhwQCKZQ8lbskqiXnlTU7gYKirgoovg3XdtfPbZMG9eNNphDhlii9IyItaCAjtVrmveIp+h5C1Zw3tL3A0N4WLYtAnOP9/icA6uvx7OOSdcPN2Vsa07Cwp0qlykHUrekhUSjUbq68PF8PbbcMkltqd80CA7TX7iieHi6a7Bg+06vJqJiESHkrdkhbIy288dyrPPwg9+YNfZi4pgxQo48shw8XSHcxbriBGhIxGRnlLylsgrL7fZbgjew913W11ygEmTbCvY2LFJOHhDg628GzfOun8kUUGBXdtWMxGRaFLylkjbudNqlofQ2GgL0dassfHxx8NddyVplXZDgzX4/uQTmDDB9pglIYE7Z81PRo1KQowiEkyEKiqL7G3XLlvZHUJlJXz3u3sS9+mn26nypG2v2rLFEndhoX3fsqXPh8zPt78DlLhFok8zb4mkmppw9co3b7Ya5SUlNr7qKlthntRGHePGWaZNzLzHjevT4UaNshm3momIZAclb4mc3buhtDTMa69bZ3u4q6rsevGiRXDKKSl4ofx8O1Xex2veat0pkp2UvCVSYrFwjUZeeAGuvdaudY8cabl1ypQUvmB+vlUW66URI2w1uWbbItlHyVsio7ExTL1y720F+U9+YuNDDoFVq+xsdibq399m24MHh45ERFJFyVsiIVGvvKUlva/b1AQ33wxPPWXjY46Be+7J3L3RhYVWKS3trTtFJK2UvCXjtbSEaTRSUwOXXgpvvmnj6dNhwYLM3Budl2dV0oYODR2JiKSDkrdktESjkcbG9L7u1q0waxZ8/LGNL77YvjLx+vHQoZa4M6KZiIikhZK3ZKxEvfJYLL2v+957Vh+lvNzqfS9YADNmpDeG7sjYZiIiknJK3pKxtm2zbWHp9MorMHeuNTgpLIRly+DYY9MbQ3cMGWKz7f76P1gkJ+l/fclIZWVQW5u+1/MeHnkEbr/dbo8fbyvM+7BTKyX69bPtX8OHh45EREJS8paMU1FhpU/TpbkZfvxjeOwxGx91lO3hzrQyooMG2RYwte4UESVvySiVldZsJF3q6uDKK+G112x88smwcGFmVSRzDvbbzwrDiIiAkrdkkOpqWySWLmVlMHs2vP++jWfOtDrlmbRHOj8fxozJzO1pIhKOkrdkhNra9DYa2bDBtoKVltoWq5tvts5gmcI5O20/alRmbk8TkbCUvCW4+npbWZ6ueuVvvAGXXWYr2YcMgaVL4StfSc9rd4eaiYhIV5S8JaiGBiuIkq7EvXo13HKLVW078EBbUX744el57e4YOdKub2u2LSKdUfKWYJqarOxpOhqNtLbCHXfAgw/a+IgjYMUK2yudCQYMsD8mBg0KHYmIRIGStwSRzkYjsRhccw28+KKNp02DxYvtlHkmGD7c9m5n0kI5EclsSt6Sdi0tdqq8qSn1r7Vjh5U6ffddG599Nsyblxl1wPv3t5l/pvwRISLRoeQtaZWoV97QkPrX2rQJzj/f/lBwDq6/Hs45J/Wv2x3Dhlld8kz4I0JEokfJW9Imkbjr61P/Wm+/DZdcYnvHBw2y0+Qnnpj61+1KXp4l7WHDQkciIlGm5C1pU1pqFc1S7dln4Qc/sOvqRUW2MO3II1P/ul1RMxERSRb9MyJpsX071NSk9jW8h7vvhuXLbTxpkm0FGzs2ta/bFTUTEZFkU/KWlNuxA6qqUvsajY22EG3NGhsff7wVXxk6NLWv25XBg222rWYiIpJMSt6SUlVVlrxTqbISLr4Y1q618emnw003hU2Yztlse8SIcDGISPZS8paUqamx0+WptHkzXHABlJTY+KqrbIV5yAplBQVWcEXNREQkVZS8JSV277YFaoDtC9uyBcaNszZZSfLOO7aHu6rKEuXChXDqqUk7fI85B6NHZ14fcBHJPkre0jOxmE1zi4s77JwRi9mWMO+xxH3hhfDJJzBhAtx3X1IS+AsvwLXX2rXukSPtsFOm9PmwvZafb7PtJP5tIiLSIRVkFBOLWZ/MWKzz50yfDt/4hn1v57mNjVYU5c/1yrdsscRdWGjft2zpU5je2wryK66w1zrkEPjZz8Im7lGj7O8SJW4RSRclb+lWUgZsxr1pk63C2rRpz4XmuESjkb3qlY8bZ5mtutq+jxvX6zCbmmz/9pIlNv7Sl6xL2IQJvT5knwwcaK+tLmAikm46bS7tJ+X2+mQWF8PEifaciRNtHJeoV97cvM/P5OfbOe0+XvOuqYFLL4U337Txt74Ft90WblHYiBG2mlxJW0RCUPKWTpPyXgoK4LnnPnPNu7XVcnNjYwfHz8+34/bS1q0waxZ8/LGNL7kE5swJkzj797dr24MHp/+1RUQSlLylw6Tc4XPbzMpT3WjkvfdsvVt5ue3bXrAAZsxIzWt1pbDQ6pKrdaeIhKbkLWafpNxd27bZtrBUeOUV27cdi1niXLYMjj02Na/Vmbw8q5IWulqbiEiC5hDSa2VlUFvbyRMaGuxUfA+n5d7Dww9b1bRYDMaPt4VpIRL30KF2MkKJW0QyiWbe0isVFbBrVydP6OX+7uZm+NGP4PHHbfyFL9iPjh6dnLi7q18/O0VeWJje1xUR6Q7NvKXHKith584untSL/d11dbYQLZG4TzoJHn00/Yl7yBCbbStxi0im0sxbeqS62haPdSmxvzsx8+5if3dZGcyeDe+/b+OZM+16dzoXh6l1p4hEhZK3dFttbZt65V3pwf7uDRtsK1hpqS0Omz8fzjgjOTF316BBtgVMrTtFJAqUvKVb6uttZXmPdGN/9xtvwGWX2Yr1IUOsB/dXvtL7OHvKOauQNnJk+l5TRKSvlLylSw0NVijF++Qed/VquOUWq8524IFWs7wXu9V6Ta07RSSqlLylU42Ndub7z41GkqC1Fe68Ex54wMZHHAErVthe6nRwzpqJjBql8qYiEk1K3tKh5mabce/VaKSPYjG45hp48UUbT5sGixfbKfN0GDjQZtudFZETEcl0St7SrpYWm3E3NSXvmBUVcNFF8O67Nj7rLLj+eluklg4jR6oDmIhkByVv+QzvbcbdYaORXti0Cc4/347rnCXtc85J3vE7M2CAzbYHDUrP64mIpFqQIi3OufHOuVedc+875/7gnLssRBzyWYlGIx219O6Nt9+GM8+0xD1okNUoT1fiHj4cDj5YiVtEskuomXczcJX3/h3n3DBgnXPuZe/9+4HikbjSUqt0lizPPAM33mjXz4uKbGHakUcm7/gd6d/fFsCl61q6iEg6BUne3vttwLb47Rrn3AfAQYCSd0Dbt0NNTXKO5T3cfTcsX27jSZNsK9jYse08uaGhW8VcumvYMKtLnq5r6SIi6Rb8mrdzrhiYAvymnccuAC4AmDBhQnoDyzE7dkBVVXKO1dgI8+bBmjU2Pu44K74ybFg7T+5lA5P25OVZ0m73dUREskjQxiTOuaHA08Dl3vvqfR/33q/y3k/13k8tKipKf4A5oqrKkncyVFbCd7+7J3GffrrNuDtMqL1oYNKeRDMRJW4RyQXBZt7OuQFY4n7ce/9MqDhyXU2NnS5Phs2b4YILoKTExlddZSvMO92a1cMGJvtS604RyUVBkrdzzgEPAB9475eEiEFsYVq3G410Yd0628NdVWWFUBYuhFNP7cYP9qCByb4GD7YtYP2DX/wREUmvUKfNjwPOBk5wzv0u/tWdf+olSWIxazSSjHrlL7wA551niXvkSOvB3a3EnZBoYNLNxO2czbbHjVPiFpHcFGq1+X8CqnMVSGOj7bnua71y72HVKlgSP3dSXAz3329nv1NFzURERDJgtbmkV1OTnaHua73ypib44Q/h5z+38Ze+ZMVXRozoc4jtcg5Gj7ZmIiIiuU7JO4e0tNiMu7m5b8epqYFLL4U337Txt74Ft92Wutlwfr7NtpOwBVxEJCsoeeeI1tbk1CvfuhVmzYKPP7bxxRfbV6qafYwaZTNuNRMREdlDyTsHJKte+XvvWT2V8nJr9rFgAcyYkZQQP0OtO0VEOqbknQO2bYPdu/t2jFdegblzob7e9lQvWwbHHpuc+PY1YoTVQddsW0SkfUreWa6sDGpre//z3sMjj8Dtt9vtceNshfnEicmLMWHAAGsmMnhw8o8tIpJNlLyzWEUF7NrV+59vboYf/xgee8zGRx1ljUZGj05KeHsZPtxm2/2CFuwVEYkGJe8sVVkJO3f2/ufr6uDKK+G112x80kmwaFHyr0GrdaeISM8peWeh6mpbVNZbZWUweza8H2/QOnOm1SlP9qxYrTtFRHpHyTvL1Nb2rV75hg22Fay01JLq/PlwxhnJiw/UulNEpK+UvLPI7t22sry33ngDLrvMjjNkiPXg/spXkhcf2HEPOEA1yUVE+kL/hGaJWMz2cve20cjq1XDLLVaF7cADrQf34YcnL75+/WxB2vDhyTumiEiuUvLOAn1pNNLaCnfeCQ88YOMjjoAVK2x2nCyDB9vxBgxI3jFFRHKZknfENTf3vtFILAbXXAMvvmjjadNg8eLkrfx2Dvbbz9qEiohI8ih5R1hLiyXu3jQa2bHDSp2++66Nzz4b5s1L3spvte4UEUkdJe+I6kujkU2b4Pzz7eedg+uvh3POSU5czlkzkVGjVN5URCRVlLwjqC+NRt5+Gy65xPaCDxpkp8lPPDE5camZiIhIeih5R1Bpae8ajTz7LPzgB3aavajIFqYdeWRyYho50q5va7YtIpJ6St4Rs3071NT07Ge8h3vugXvvtfGkSbYVbOzYvsczYIDNtgcN6vuxRESke5S8I2THDqiq6tnPNDbaNe1f/tLGxx1nxVeSUd1MzURERMJQ8o6IXbssefdEVRVcfDH89rc2Pu00K3fa1/3WaiYiIhKWkncE1NZas5Ce2LwZLrgASkpsfNVVtsK8r9ekCwutLrlm2yIi4Sh5Z7je1Ct/5x246CJrCzpwICxcCKee2rc48vJstj10aN+OIyIifafkncF6U6/8hRfg2mvtWvfIkbB8ORx9dN/iUOtOEZHMouSdoXpar9x7WLUKliyxcXEx3H8/TJjQ+xjUulNEJDMpeWegntYrb2qCm2+Gp56y8Ze+BMuWwYgRvY9h6FA7Ta7ZtohI5lHyzjA9rVdeUwOXXgpvvmnj6dNhwYLe1xTv189m24WFvft5ERFJPSXvDNLTeuVbt9qK8o0bbXzJJTBnTg9XlDc02F8L48YxZFQ+BxxgW8FERCRz6Z/pDNHTeuXr11tXsIoK27e9YAHMmNHDF21ogAsvpN+nmymaWMjwF56E/ipMLiKS6bRbN0P0pF75K69YC8+KCju9/cADvUjcAFu2MHjLRxy8Xy3DP3lvz6ZwERHJaEreGaC79cq9h4cftqppsRiMGwerV8Oxx/b8NZ2D/accxLjDhjBg1w6YONGWqIuISMbTafPAuluvvLkZfvxjeOwxG3/hC3DffTB6dM9fs6DAmokMHFgAzz1nM+7iYvXyFBGJCCXvgKqqulevvK7Oypu++qqNTzoJFi3qea51zpL9qFFt7iwogMMP79mBREQkKCXvQGpq7HR5V8rKYPZseP99G3//+zB3bs9riw8cCGPGQH5+z2MVEZHMouQdQF2dLVDryoYNMGuWPTcvD266Cc48s+evN3Ik7Ldf35uSiIhIZlDyTrP6+u7VK/+P/4DLLrNEP3iw9eD+6ld79loDBti17UGDeh+viIhkHiXvNGpo6F7iXr0abrnFqq0deCCsXNnzy9LDh0NRkVp3iohkIyXvNGlqsopondUrb22FO++0fdsARxwBK1ZYjfHu6t/fnj9kSN/iFRGRzKXknQaJRiOd1SuPxeCaa+DFF208bRosXtyzJFxYaHXJNdsWEcluSt4plqhX3tTU8XN27LBSp+++a+OzzoLrr+9+R6+8PJttDx3a93hFRCTzKXmnkPeWuBsaOn7Opk3WXGTLFlsNPm8enHtu919DrTtFRHKPkneKJBqN1Nd3/Jy337ZOYNXVtiL8zjvh61/v3vHVulNEJHcpeadIaalt8+rIs8/CjTfa6fSiIit1+pd/2b1jDx5sq9DVulNEJDfpn/8U6KzRiPdwzz1w7702/tznbCvYQQd1fVznLNGPGJG0UEVEJIKUvJOss0YjjY22EO2Xv7TxccdZ8ZVhw7o+7p5mIkkLVUREIkrJO4k6azRSVWWtPH/7WxufdhrMn29V0DrjnDUSGTVK5U1FRMQoeSdJZ41GNm+2FeUlJTa+6io4//yuk7GaiYiISHuUvJOgs0Yj77wDF10ElZWWjBcuhFNP7fqYaiYiIiIdUfLuo84ajbzwAlx7rV3rHjkSli+Ho4/u/HhqJiIiIl1R8u6DhgYrwrJv4vYeVq2CJUtsXFwM998PEyZ0fjw1ExERke5Q8u6lRKOR1tbP3v/DH8LPf27jqVNh2TKbeXdEzURERKQnlLx7oaNGIzU1cOml8OabNv7mN+FHP+p8e9ewYVYpTeVNRUSku5S8e6ilpf1GI1u3wqxZ8PHHNp4zx0qfdrTgLC/PknZ39niLiIi0peTdA4kOYfs2GnnvPesKVl5uC85uvRX+7u86Ps6QIbYoTbNtERHpDSXvbko0GonF9r7/lVdg7lxbdV5YaKVPv/zl9o+hZiIiIpIMSt7d4D1s2wa7d+9936OPwo9/bLfHjbMV5hMntn8MNRMREZFkUSrphrIyqK3dM25utqT92GM2/sIXrCvY6NGf/dl+/azYipqJiIhIsih5d2H7duu3nVBXZ+VNX33VxiedBIsWWeOQfQ0aZLPtruqXi4iI9ISSdyfKy/fuEFZWBrNnw/vv2/j737fr3fsWVXHOZtud7e0WERHpLSXvDuzYYfXIEzZssK1gpaW2SvzGG+E73/nsz6l1p4iIpJqSdzsqK/du7fkf/wGXXWanzIcMgbvugq9+de+fUetOERFJFyXvfezaZafLE/7lX6zcaUuLzahXroTDD9/7ZwYOtMfau+4tIiKSbLmXvGMxa6xdXPyZbLtrl13XBivIcued8MADNj7iCFixwmqQt6XWnSIikm65lbxjMZg+HTZtsg3Zzz335wReXb0nccdicM018OKLNv7a16xDWNvGIf3722x78OD0vgUREZFgzSedcyc75z50zm10zl2XlhctKbHEPWKEFSH/9a8hFqOmxhaigV3rPuecPYn7rLOsD3fbxF1YaBN3JW4REQkhSPJ2zuUB9wKnAEcA33HOHdGng8ZitiR83/qlbRUX24x75047R37JJdT87ZlsK7Fi5Zs2wemnw7vv2mnw66+3VeWJGuR5eTB2rM241XNbRERCCZWCjgE2eu//6L1vBFYD03t9tMTp8G98w753lMALCuxU+bJlMGIEtYVjKf24BrZs4e234cwzrdXnoEH2lHPP3fOjw4ZZ7h86tNdRioiIJEWo5H0Q8Gmb8Zb4fXtxzl3gnFvrnFtb3nYJ+L7ang7ftMnGHSkogBNOoK54Mtt2DMRPOJhn101g5ky77l1UBD/9KXz96/b0vDwYM8a+1AVMREQyQUYvWPPerwJWAUydOtV3+MTE6fDEQrTi4k6Pu7u1gD8t/Tmtn2zhnl8czL032sfwuc/ZVrCD4n9GDBliq8vVTERERDJJqLS0FRjfZjwufl/vJE6Hd7AFrK3du+M9ucnnhpUT+cUv7P7jjoOlS+30eL9+NgMfPrzXEYmIiKRMqOT9W+BzzrlDsKR9JvCPfTpiQcFnq6fso77eenJXVsLFF8Nvf2v3n3YazJ9vDUTUTERERDJdkOTtvW92zl0MvAjkAQ967/+Qytesr7cZ9//+L1xwwZ7L4lddBeefb7Pt0aOtvKmIiEgmC3Y113v/AvBCUg7WSdU02JO4166Fiy6ymffAgbBwIZx6KuTn22w7Pz8p0YiIiKRU9JdidVI1DaC+MsbW/97KmvcmcO0NA2hstEXpy5fDF7+o8qYiIhI90U/e7W0Ti1/7rq+MseWbF7Ly98exeNdMwCbnq1ZZnld5UxERiaJo1Qlrr4paYptYVdVe28R274aSN7dy/e9O+3Pinjq5ntWrYfJkOPhgJW4REYkm533H26czydQvftGv3W+/9k+P73PNu7YWPvoILr24lf96y/4++WbRW/z4xaMZd2i+toCJiEjGc86t895Pbe+x6My8Gxo6rqJWUGCJu6SE6u0xfvtbK3WaSNwX/+NO7n5jCp+brMQtIiLRF51r3vn5VvqsvSpq8UVrVR9t51eFf8eFf7qR8grHgAGwYAF897ujtChNRESyRnSSN8CiRfb9sMP23hJWUkLFRzv5WcvpzH3vcuq9o7DQVpTPmLF3O08REZGoi07y3rgR/uEf9lzvBojF8Bs+ZNvWVu7lMn786T/i6ce4gzyP/tTxla+oLrmIiGSf6KS2hoa9r3cXF9N86rf49L8+YX7Ljfy05SwAvvCXrTz2RD8mT97nNHkXhVxERESiIjrJOz9/r+1gDe99xEfrG7msaQWv+q8BcNJxtTz086GMGbPPz3ZRyEVERCRKorPa/C/+Atasgeeeo7oafnvhA5y5874/J+7zx6zh6Tv+yJiRsc/+bE/6fYuIiGS46CRv5/CHHc726gJeW/0nTvufG3jff548mllwxnvce/g9DDn77+Eb34B33+1WIRcREZEoisxpc+/h049jvPzEdi67dQx1rYMYQi3Lj1jGP11zCnnf/tj6ef7Xf8E3vwmf//ye0+M96PctIiKS6SIz826sjvHwSU8y64djqWsdxIH9y3l25Pc464ETyBt7AOzcCR9+CI2N1tezvUIuhx+uxC0iIpEXmZl36aY65vvvAnAEf+CJ5n/kr3Z/hLu+wnp+1tVZYm5psUT++c/r9LiIiGSlyCTvCj8agGn8mgf4PodQAv2H2P5v7+2UeX09TJ0KK1Z8tpCLiIhIlohM8gY4i59yJ1dxAOV2R0ODlUwdMgT++EcYO9ZWpI8YETROERGRVIpMV7Ex7iD/e2KMZueeO486Cl591WbYWowmIiJZpLOuYpGZeR9AGaNp2XPHX/2VJe7ELPvww4PEJSIikm6RWW3ev23iPuooeP11nR4XEZGcFJnk/WezZsFbbylxi4hIzopO8u7XD37yE1tJruvaIiKSw6KTvKdMgcsvDx2FiIhIcNFJ3iIiIgIoeYuIiESOkreIiEjEKHmLiIhEjJK3iIhIxCh5i4iIRIySt4iISMQoeYuIiESMkreIiEjEKHmLiIhEjJK3iIhIxCh5i4iIRIySt4iISMQoeYuIiESMkreIiEjEKHmLiIhEjJK3iIhIxCh5i4iIRIySt4iISMQoeYuIiESMkreIiEjEOO996Bi6xTlXDmwOHUeG2A+oCB1EhtBnsYc+iz30Weyhz2KPqH0WB3vvi9p7IDLJW/Zwzq313k8NHUcm0Gexhz6LPfRZ7KHPYo9s+ix02lxERCRilLxFREQiRsk7mlaFDiCD6LPYQ5/FHvos9tBnsUfWfBa65i0iIhIxmnmLiIhEjJK3iIhIxCh5B+ScK3HOveec+51zbm38vlHOuZedcx/Hv4+M3++cc3c75zY659Y7545uc5xz48//2Dl3bpv7vxg//sb4z7r0v8v2OecedM5td879vs19KX/vHb1GSB18Fjc757bGfzd+55w7tc1j8+Lv60Pn3Elt7j85ft9G59x1be4/xDn3m/j9/+KcGxi/Pz8+3hh/vDhNb7lDzrnxzrlXnXPvO+f+4Jy7LH5/zv1udPJZ5NzvhnOuwDn33865d+OfxQ97G3+yPqPgvPf6CvQFlAD77XPfIuC6+O3rgIXx26cC/wY44MvAb+L3jwL+GP8+Mn57ZPyx/44/18V/9pTQ77nN+/wqcDTw+3S+945eIwM/i5uBue089wjgXSAfOATYBOTFvzYBhwID4885Iv4zPwPOjN9eAVwYv30RsCJ++0zgXzLgsxgDHB2/PQz4KP6ec+53o5PPIud+N+L/rYbGbw8AfhP/b9ij+JP5GYX+Ch5ALn/RfvL+EBgTvz0G+DB+eyXwnX2fB3wHWNnm/pXx+8YAG9rcv9fzMuELKGbvhJXy997Ra4T+auez6Ogf6HnAvDbjF4H/E/96cd/nxf/RqwD6x+//8/MSPxu/3T/+PBf6s9jn/T4H/L9c/t1o57PI6d8NYDDwDnBsT+NP5mcU+kunzcPywEvOuXXOuQvi9x3gvd8Wv10KHBC/fRDwaZuf3RK/r7P7t7RzfyZLx3vv6DUy0cXxU8EPtjmF29PPYjRQ5b1v3uf+vY4Vf3xX/PkZIX6qcwo2y8rp3419PgvIwd8N51yec+53wHbgZWym3NP4k/kZBaXkHdbx3vujgVOAOc65r7Z90Nufejm5ly8d7z3DP9/7gInAUcA2YHHQaNLMOTcUeBq43Htf3faxXPvdaOezyMnfDe99i/f+KGAccAxweNiIwlLyDsh7vzX+fTvwLPYLWeacGwMQ/749/vStwPg2Pz4ufl9n949r5/5Mlo733tFrZBTvfVn8H6tW4H7sdwN6/lnsAEY45/rvc/9ex4o/Pjz+/KCccwOwZPW49/6Z+N05+bvR3meRy78bAN77KuBV7BR2T+NP5mcUlJJ3IM65Ic65YYnbwN8Avwd+ASRWxp6LXecifv858dW1XwZ2xU/xvQj8jXNuZPz02d9g12S2AdXOuS/HV9Oe0+ZYmSod772j18goiSQS93fY7wZY/GfGV9MeAnwOW4D1W+Bz8ZWxA7FFOr+IzyBfBb4d//l9P9fEZ/Ft4Nfx5wcT/+/1APCB935Jm4dy7nejo88iF383nHNFzrkR8duDsGv/H9Dz+JP5GYUV+qJ7rn5hqxrfjX/9Abghfv9o4FfAx8ArwKj4/Q64F7vO8x4wtc2xvgdsjH99t839U7H/sTcBy8iQBSfx2J7ETvk1YdeRvp+O997Ra2TgZ/HT+Htdj/2DM6bN82+Iv68PabODAFt5/VH8sRv2+V377/hn9HMgP35/QXy8Mf74oRnwWRyPna5eD/wu/nVqLv5udPJZ5NzvBvBXwP/E3/PvgZt6G3+yPqPQXyqPKiIiEjE6bS4iIhIxSt4iIiIRo+QtIiISMUreIiIiEaPkLSIiEjFK3iI5wjk3wznnnXNJq0wVP+YRyTqeiHSPkrdI7vgO8J/x78kyA+vUJCJppH3eIjkgXh/7Q2Aa8Evv/WHxSl3/AhRinZcuBN7EqnpNxQqEPOi9/4lzbiJWDKUI2A2cj7XbXIM1fdgF/APwt8BsoBl433t/ZtrepEgO6d/1U0QkC0wH/t17/5Fzbodz7ovA17CSobc55/KwVotHAQd5748ESJSkBFYBs733HzvnjgWWe+9PcM79AljjvX8q/vzrgEO89w1tflZEkkzJWyQ3fAdYGr+9Oj7+BfBgvPnFv3rvf+ec+yNwqHPuHuB5rGXtUOCvgZ9buW0A8jt4nfXA4865fwX+NRVvRER02lwk6znnRmE108uxU+F58e8HA2OwU91zgCXe+0fjyfok4GxgJ3A58KH3fkw7x36YvWfeecBXgW9irW7/0u/phSwiSaIFayLZ79vAT733B3vvi73344H/xZJsmff+fuCfgaOdc/sB/bz3TwM/AI721kP6f51zp4F1u3LOfSF+7Bog0R2vHzDee/8qcC3WhnFo+t6mSO7QaXOR7PcdYOE+9z0NPAzUOeeagFqsPeZBwEPxRAwwL/79n4D7nHM/AAZgp97fjX+/3zl3KdZG8QHn3HCs29fd3novi0iS6bS5iIhIxOi0uYiISMQoeYuIiESMkreIiEjEKHmLiIhEjJK3iIhIxCh5i4iIRIySt4iISMT8f/ooZRomUGk1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Assets', ylabel='Net Combined ratio'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# performing a regression plot between equity and SCR  \n",
    "sns.regplot(x=\"Equity\", y=\"SCR\", data=Raw,  color=\"red\", scatter_kws={\"s\": 6}, line_kws={\"color\": \"blue\"})\n",
    "plt.title(\"Equity vs SCR\", fontsize=13, fontweight=\"bold\")\n",
    "plt.show()\n",
    "\n",
    "# performing a regression plot between GWP and NWP\n",
    "sns.regplot(x=\"GWP\", y=\"NWP\", data=Raw,  color=\"red\", scatter_kws={\"s\": 6}, line_kws={\"color\": \"blue\"}, label=\"GWP vs NWP\") \n",
    "plt.title(\"GWP vs NWP\", fontsize=13, fontweight=\"bold\")\n",
    "plt.show()\n",
    "\n",
    "# performing a regression plot between SCR coverage ratio and net combined ratio\n",
    "sns.regplot(x=\"SCR coverage ratio\", y=\"Net Combined ratio\", data=Raw,  color=\"red\", scatter_kws={\"s\": 6}, line_kws={\"color\": \"blue\"}, label=\"SCR coverage ratio vs Net Combined ratio\")\n",
    "plt.title(\"SCR coverage ratio vs Net Combined ratio\", fontsize=13, fontweight=\"bold\")\n",
    "plt.show()\n",
    "\n",
    "# performing a regression plot between SCR Coverage ratio and Gross Claims Incurred\n",
    "sns.relplot(x=\"SCR coverage ratio\", y=\"Gross Claims Incurred\", data=Raw,  color=\"red\", label=\"SCR coverage ratio vs Gross Claims Incurred\")\n",
    "plt.title(\"SCR coverage ratio vs Gross Claims Incurred\", fontsize=13, fontweight=\"bold\")\n",
    "plt.show()\n",
    "\n",
    "# performing a regression plot between Assets and Equity\n",
    "sns.regplot(x=\"Assets\", y=\"Total_Z\", data=Raw, color=\"red\", scatter_kws={\"s\": 6}, line_kws={\"color\": \"blue\"}, label=\"Assets vs Total_Z\")\n",
    "plt.title(\"Assets vs Total_Z\", fontsize=13, fontweight=\"bold\")\n",
    "plt.show()\n",
    "\n",
    "# performing a regression plot between Assets and Net Combined ratio\n",
    "sns.regplot(x=\"Assets\", y=\"Net Combined ratio\", data=Raw, color=\"red\", scatter_kws={\"s\": 6}, line_kws={\"color\": \"blue\"}, label=\"Assets vs Net combined ratio\")\n",
    "#The Net combined ratio which indicates profit is seen from the plot To have a fall within a certain range irrespective of asset size. And therefore, points indicated far above this range are likely errors.\n",
    "\n"
   ]
  }
 ],
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