Voltage_Losses_AJ_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import json\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy import integrate as ig\n",
    "from functions import J0, J0_rad, SQ, Voc_rad, LED_QE, calculate_summary, Vloss_CT, Vloss_SQ, calculate_Jsc, Voc_CT, Voc\n",
    "from utils import bb, getAM15, linear, set_up_plot\n",
    "from utils_EQE import extend_EQE, Marcus_Gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/jungbluth/Desktop/git/Voltage-Loss-Calculations'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 293 # [K] ambient & cell temperature\n",
    "h = 6.626 * 10**(-34) # [kgm^2/s]\n",
    "h_eV = 4.1357*10**(-15) # eV s\n",
    "c = 2.998 * 10**(8) # [m/s]\n",
    "k = 1.3806 * 10**(-23) # [kgm^2/s^2K]\n",
    "k_eV = 8.6173*10**(-5) # eV / K\n",
    "q = 1.60217662 * 10**(-19) # [C]\n",
    "q_eV = 1\n",
    "Vth = k_eV*T/q_eV # thermal voltage [eV]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import sEQE Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (Replace and read in from file !!!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ZnPc_5 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR46_D_5_ZnPc_C60_2')\n",
    "ZnPc_25 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR45_D_25_ZnPc_C60_5')\n",
    "ZnPc_50 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR39_C_50_ZnPc_C60_4')\n",
    "ZnPc_75 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR45_A_75_ZnPc_C60_4')\n",
    "ZnPc_95 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR61_D_95_ZnPc_C60_5')\n",
    "ZnPc_100 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR61_A_100_ZnPc_5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "F4ZnPc_5 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR40_B_5_F4ZnPc_C60_2')\n",
    "F4ZnPc_25 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR41_B_25_F4ZnPc_C60_2')\n",
    "F4ZnPc_50 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR40_A_50_F4ZnPc_C60_5')\n",
    "#F4ZnPc_75 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR41_C_75_F4ZnPc_C60_4')\n",
    "F4ZnPc_75 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR41_A_75_F4ZnPc_C60_5')\n",
    "F4ZnPc_95 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR66_B_95_F4ZnPc_C60_2')\n",
    "F4ZnPc_100 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR66_C_100_F4ZnPc_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# F8ZnPc_5 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR55_D_5_F8ZnPc_C60_5')\n",
    "F8ZnPc_5 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR72_D_5_F8ZnPc_C60_1_(0.5TC)')\n",
    "F8ZnPc_25 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR43_D_25_F8ZnPc_C60_1')\n",
    "F8ZnPc_50 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR42_C_50_F8ZnPc_C60_7')\n",
    "F8ZnPc_75 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR43_A_75_F8ZnPc_C60_4')\n",
    "F8ZnPc_95 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR56_B_95_F8ZnPc_C60_5')\n",
    "F8ZnPc_100 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR56_C_100_F8ZnPc_8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# F16ZnPc_5 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR50_A_5_F16ZnPc_C60_5')\n",
    "F16ZnPc_5 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR71_B_5_F16ZnPc_C60_2 (2)')\n",
    "F16ZnPc_25 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR44_D_25_F16ZnPc_C60_1')\n",
    "F16ZnPc_50 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR50_D_50_F16ZnPc_C60_7')\n",
    "F16ZnPc_75 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR57_C_75_F16ZnPc_C60_7')\n",
    "F16ZnPc_95 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR58_D_95_F16ZnPc_C60_1')\n",
    "#F16ZnPc_100 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR58_A_100_F16ZnPc_5')\n",
    "F16ZnPc_100 = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_sEQE/sEQE_analysis/Measurements_RT/DR72_A_100_F16ZnPc_1_(0.5TC)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Material</th>\n",
       "      <th>Number</th>\n",
       "      <th>Voc (V)</th>\n",
       "      <th>Err_Voc (V)</th>\n",
       "      <th>Jsc (mA/cm2)</th>\n",
       "      <th>Err_Jsc (mA/cm2)</th>\n",
       "      <th>FF</th>\n",
       "      <th>Err_FF</th>\n",
       "      <th>ECT (eV)</th>\n",
       "      <th>Err_ECT (eV)</th>\n",
       "      <th>...</th>\n",
       "      <th>Err_Eopt (eV)</th>\n",
       "      <th>l_opt (eV)</th>\n",
       "      <th>Err_l_opt (eV)</th>\n",
       "      <th>f_opt (eV2)</th>\n",
       "      <th>Err_f_opt (eV)</th>\n",
       "      <th>T1_exp (eV)</th>\n",
       "      <th>T1_calc (eV)</th>\n",
       "      <th>EQE File</th>\n",
       "      <th>Best_Voc</th>\n",
       "      <th>Best_Jsc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ZnPc</td>\n",
       "      <td>19</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3.56</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.135589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.032461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.21</td>\n",
       "      <td>DR46_D_5_ZnPc_C60_2</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F4ZnPc</td>\n",
       "      <td>13</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.179000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.030680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.20</td>\n",
       "      <td>DR40_B_5_F4ZnPc_C60_2</td>\n",
       "      <td>0.88</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F8ZnPc</td>\n",
       "      <td>12</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.098000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001544</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.25</td>\n",
       "      <td>DR72_D_5_F8ZnPc_C60_1_(0.5TC)</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F16ZnPc</td>\n",
       "      <td>15</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.035000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.18</td>\n",
       "      <td>DR71_B_5_F16ZnPc_C60_2 (2)</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C60</td>\n",
       "      <td>11</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR39_D_C60_2</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Material  Number  Voc (V)  Err_Voc (V)  Jsc (mA/cm2)  Err_Jsc (mA/cm2)  \\\n",
       "0     ZnPc      19     0.78         0.02          3.56              0.17   \n",
       "1   F4ZnPc      13     0.89         0.02          3.10              0.17   \n",
       "2   F8ZnPc      12     1.14         0.02          1.02              0.05   \n",
       "3  F16ZnPc      15     1.21         0.02          0.82              0.05   \n",
       "4      C60      11     1.13         0.07          1.08              0.08   \n",
       "\n",
       "     FF  Err_FF  ECT (eV)  Err_ECT (eV)    ...     Err_Eopt (eV)  l_opt (eV)  \\\n",
       "0  0.40    0.01      1.38          0.01    ...              0.01    0.135589   \n",
       "1  0.33    0.01      1.45          0.01    ...              0.01    0.179000   \n",
       "2  0.30    0.01      1.59          0.01    ...              0.01    0.098000   \n",
       "3  0.29    0.01      1.61          0.01    ...              0.01    0.035000   \n",
       "4  0.30    0.01       NaN           NaN    ...               NaN         NaN   \n",
       "\n",
       "   Err_l_opt (eV)  f_opt (eV2)  Err_f_opt (eV)  T1_exp (eV)  T1_calc (eV)  \\\n",
       "0             NaN     0.032461             NaN         1.13          1.21   \n",
       "1             NaN     0.030680             NaN         1.13          1.20   \n",
       "2             NaN     0.001544             NaN          NaN          1.25   \n",
       "3             NaN     0.000161             NaN          NaN          1.18   \n",
       "4             NaN          NaN             NaN         1.50           NaN   \n",
       "\n",
       "                        EQE File  Best_Voc  Best_Jsc  \n",
       "0            DR46_D_5_ZnPc_C60_2      0.76      3.74  \n",
       "1          DR40_B_5_F4ZnPc_C60_2      0.88      3.36  \n",
       "2  DR72_D_5_F8ZnPc_C60_1_(0.5TC)      1.14      1.09  \n",
       "3     DR71_B_5_F16ZnPc_C60_2 (2)      1.24      0.87  \n",
       "4                   DR39_D_C60_2      1.26      0.99  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = pd.read_excel('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_Voltage/Voltage_analysis/5_FxZnPc_Summary.xlsx')\n",
    "parameters.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Relevant Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pull out relevant parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = parameters['EQE File'][:-1]\n",
    "samples = [ZnPc_5, F4ZnPc_5, F8ZnPc_5, F16ZnPc_5]\n",
    "\n",
    "EIP = [1.75, 1.68, 1.78, 1.71] # from inflection point\n",
    "Voc = parameters['Voc (V)'][:-1]\n",
    "Jsc = parameters['Jsc (mA/cm2)'][:-1]\n",
    "ECT = parameters['ECT (eV)'][:-1]\n",
    "f = parameters['f_CT (eV2)'][:-1]\n",
    "l = parameters['l_CT (eV)'][:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Voc [V]</th>\n",
       "      <th>ECT [V]</th>\n",
       "      <th>Jsc [mA/cm2]</th>\n",
       "      <th>Voc,SQ [V]</th>\n",
       "      <th>Voc,rad [V]</th>\n",
       "      <th>Delta Voc,SC [V] (Rau)</th>\n",
       "      <th>Delta Voc,rad [V] (Rau)</th>\n",
       "      <th>Delta Voc,nonrad [V] (Rau)</th>\n",
       "      <th>Voc,rad [V] (CT properties)</th>\n",
       "      <th>Delta Voc,rad [V] (CT properties)</th>\n",
       "      <th>Delta Voc,nonrad [V] (CT properties)</th>\n",
       "      <th>LED QE</th>\n",
       "      <th>J0 [mA/cm2]</th>\n",
       "      <th>J0,rad [mA/cm2]</th>\n",
       "      <th>J0 (CT properties) [mA/cm2]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DR46_D_5_ZnPc_C60_2</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.38</td>\n",
       "      <td>3.56</td>\n",
       "      <td>1.473443</td>\n",
       "      <td>1.143944</td>\n",
       "      <td>0.044832</td>\n",
       "      <td>0.284667</td>\n",
       "      <td>0.363944</td>\n",
       "      <td>1.127264</td>\n",
       "      <td>0.252736</td>\n",
       "      <td>0.347264</td>\n",
       "      <td>5.491917e-07</td>\n",
       "      <td>1.363012e-13</td>\n",
       "      <td>7.485547e-20</td>\n",
       "      <td>2.642582e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DR40_B_5_F4ZnPc_C60_2</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.45</td>\n",
       "      <td>3.10</td>\n",
       "      <td>1.406645</td>\n",
       "      <td>1.239143</td>\n",
       "      <td>0.050652</td>\n",
       "      <td>0.116851</td>\n",
       "      <td>0.349143</td>\n",
       "      <td>1.233344</td>\n",
       "      <td>0.216656</td>\n",
       "      <td>0.343344</td>\n",
       "      <td>9.870037e-07</td>\n",
       "      <td>1.521519e-15</td>\n",
       "      <td>1.501744e-21</td>\n",
       "      <td>1.917339e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DR72_D_5_F8ZnPc_C60_1_(0.5TC)</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.501703</td>\n",
       "      <td>1.386659</td>\n",
       "      <td>0.075308</td>\n",
       "      <td>0.039735</td>\n",
       "      <td>0.246659</td>\n",
       "      <td>1.396065</td>\n",
       "      <td>0.193935</td>\n",
       "      <td>0.256065</td>\n",
       "      <td>5.716851e-05</td>\n",
       "      <td>2.507328e-20</td>\n",
       "      <td>1.433402e-24</td>\n",
       "      <td>1.730546e-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DR71_B_5_F16ZnPc_C60_2 (2)</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.434923</td>\n",
       "      <td>1.335010</td>\n",
       "      <td>0.083265</td>\n",
       "      <td>0.016648</td>\n",
       "      <td>0.125010</td>\n",
       "      <td>1.436082</td>\n",
       "      <td>0.173918</td>\n",
       "      <td>0.226082</td>\n",
       "      <td>7.074156e-03</td>\n",
       "      <td>1.259910e-21</td>\n",
       "      <td>8.912799e-24</td>\n",
       "      <td>2.304384e-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Sample  Voc [V]  ECT [V]  Jsc [mA/cm2]  Voc,SQ [V]  \\\n",
       "0            DR46_D_5_ZnPc_C60_2     0.78     1.38          3.56    1.473443   \n",
       "1          DR40_B_5_F4ZnPc_C60_2     0.89     1.45          3.10    1.406645   \n",
       "2  DR72_D_5_F8ZnPc_C60_1_(0.5TC)     1.14     1.59          1.02    1.501703   \n",
       "3     DR71_B_5_F16ZnPc_C60_2 (2)     1.21     1.61          0.82    1.434923   \n",
       "\n",
       "   Voc,rad [V]  Delta Voc,SC [V] (Rau)  Delta Voc,rad [V] (Rau)  \\\n",
       "0     1.143944                0.044832                 0.284667   \n",
       "1     1.239143                0.050652                 0.116851   \n",
       "2     1.386659                0.075308                 0.039735   \n",
       "3     1.335010                0.083265                 0.016648   \n",
       "\n",
       "   Delta Voc,nonrad [V] (Rau)  Voc,rad [V] (CT properties)  \\\n",
       "0                    0.363944                     1.127264   \n",
       "1                    0.349143                     1.233344   \n",
       "2                    0.246659                     1.396065   \n",
       "3                    0.125010                     1.436082   \n",
       "\n",
       "   Delta Voc,rad [V] (CT properties)  Delta Voc,nonrad [V] (CT properties)  \\\n",
       "0                           0.252736                              0.347264   \n",
       "1                           0.216656                              0.343344   \n",
       "2                           0.193935                              0.256065   \n",
       "3                           0.173918                              0.226082   \n",
       "\n",
       "         LED QE   J0 [mA/cm2]  J0,rad [mA/cm2]  J0 (CT properties) [mA/cm2]  \n",
       "0  5.491917e-07  1.363012e-13     7.485547e-20                 2.642582e-13  \n",
       "1  9.870037e-07  1.521519e-15     1.501744e-21                 1.917339e-15  \n",
       "2  5.716851e-05  2.507328e-20     1.433402e-24                 1.730546e-20  \n",
       "3  7.074156e-03  1.259910e-21     8.912799e-24                 2.304384e-23  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = calculate_summary(columns, samples, Voc, Jsc, ECT, EIP, f, l)\n",
    "summary\n",
    "# summary.to_csv('/home/jungbluth/Desktop/Voc_Loss_comparison')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### J0 / Voc Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test the full derivation of J0 with only CT fit properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Limit of saturation current density based on CT properties\n",
    "def J0_CT(EQE_EL, E_CT, l_CT, f_CT):\n",
    "    \"\"\"\n",
    "    Function to calculate the limit of the saturation current density (J0) based on CT properties as defined by Koen Vandewal\n",
    "    :param EQE_EL: LED quantum efficiency (unitless) [float]\n",
    "    :param ECT: CT state energy(eV) [float]\n",
    "    :param l: Reorganization energy (eV) [float]\n",
    "    :param f: Oscillator strength (eV**2) [float]\n",
    "    :return: J0: limit of the saturation current density [float]\n",
    "    \"\"\"\n",
    "    a_CT = (q/(10*EQE_EL))*f_CT/(h_eV**3 * c**2)*np.sqrt(4*np.pi*l_CT*k_eV*T)*np.exp(-(E_CT + l_CT)**2 / (4*l_CT*k_eV*T))\n",
    "    b_CT = (q/(10*EQE_EL))*f_CT/(h_eV**3 * c**2)*2*np.pi*(E_CT-l_CT)*np.exp(-(E_CT/(k_eV*T)))\n",
    "    J0_CT = a_CT + b_CT\n",
    "    \n",
    "    return a_CT, b_CT, J0_CT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_CT_list, b_CT_list, J0_CT_list = J0_CT(summary['LED QE'], parameters['ECT (eV)'][:-1], parameters['l_CT (eV)'][:-1], parameters['f_CT (eV2)'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SHBXhobW1lUceeYTp06dTVVXFo48+esBjL730Uj799FPuvfdeJk+ezN///neuvPJKEokEV111Vb+37W9/+xtPPvkkb7/9NuvWrcvIjXYwGETTNJbWfo3zp7yBXpegqxn7NicSS/64vV4vRaHbsMZDRHxmIjETvpiJz7Ydz56OPApdjeQ7WgjHzURiZjSdg6gygphSgNls7AwYNkwm0wFDx/5f6/X6Qf++CCGEEGLo+9u/t/JabRMT8ux9Or7Zf+CAIQbeUREexowZg8fjQVEU9uzZc8Dw8Oqrr/LGG2+kAgPA2WefzdatW7n11lu54oorUjex5557LsuXL+/xPLfeeiv33HNPn9tnMBg4//zz+c1vfsOkSZO44IILDvEZHhmbzYaiKNQ3ldHWkZPW09Dl3Q2n41edqa93tJfgNPsxGSKYDWFcVh96XQKAsTlbOWncCnTK3m7B2sYpvL7ufLJtHi47+a9EYkYicRPhmJlwyMyzn18OKJw45lOsxhCRWOe+uIntnlGEYjm47TFcdg1F70RndGM0OzGbDxw49v/aZDJJL4gQQghxlEgkNCLxBBZj8t6rxR/m5qc/o9Gr8uFtZ6f+pm9oCvD++hZclr7dluY7LQPWZtG7oyI89PWG8cUXX8ThcHD55Zenbf/Od77DVVddxb///W9mz54NwFtvvdXv7YzH42zcuPGgx8ycObNP9RYWLlzIwoUL+3Td0tJS1q1bl7YtoYFO2ft5p6eE5kA+kAxOo0d/h3A4nPrwqCqTZ4QZEw4TCk/ntcZvEw37SMR8aDEfwVACvV5PMGLl9XXnYjZEksFDH0aviwPJn1GeYw95jpZUKDHqY/zry4vYtMfFOPdKTp+4N7DFEzrWN09i2eq5WAwhLp72CpG4iUDMRFtn+Ph488kkND0lWdsxGWIkFDuK3gl6Jwn9CExmZ1rY6G0YlsVikV4QIYQQoh+p0ThbWjsoLXCm7tl++9YGfvfORr53+jhu/VoZAFk2I6u3txNLaOz2qRS5rQBcVFlEaaGT6SVZfLbVw26v2uO8BwUodCeXbRWZc1SEh75au3YtU6ZM6XZzXllZmdrfFR76KhaLEYvFiMfjxGIxVFXFaDTS0tLC8uXLmTt3LmazmX/+85+888473HvvvQc9X1NTU5+u6/P5+tzGiooKli1bhqqqBKNWOsI2/GEna3dVMLW4FqfZTzCa/A9qsVg4+eSTD7tgXCwWSwsdqqoSDoepKk/+OxQ+mw3hMGpIJRKJEFY7iFrD5OfH2BGcxYtrRqMkAqnw4VcdACiKhj/sxKQP4zQHMNmT+5d/lfx5nTrhY4rd6SssvFY3h7pt5ZQX1nH8hI+SvR0+E5G4iUZvER9vPgWdEmfW2E87e0JMRGJmYpqFPepEjCY7DmsCo8mK0ew6aC/I/j0iZrNZekGEEEIMK341yvqmAGaDjqkjkwvJhGNxpt31GtG4xqc/OY88pxkAu9lAOJZg856O1OONeh1//NYJFGVZGOEwp7bPHJvDzLHJQHDnxeXc/PTnKJAWILr+4t55cbnUe8iwYyo8tLa2Mn78+G7bc3JyUvsP1c9+9jPuvvvu1Nf/+7//y+OPP87cuXN58MEHuf7661EUhUmTJvHcc88xffr0g56voKCgTzfuLperz200GAxUVVVRXV1NIOzksY++Q1zTAwprdk1Fr8SJa8lrVlVVHVGlaYPBgMFgwG7v27jEnmialgwW+wWQcPhaVFWlfZ9wMmNGcv9Kzy1ozcmeEOI+lESAZl/yF1drRy5f7pyG2RDu7A2JoHX+mjHqo0wrXotJH8FkiKba8If3b8QfCHNGZQ3jRmwlkVA6h2GZ+HjDKazbPYWR7p1UjvwST8yc2ucNZbG+eTIAY0c0oegdYHCi6N3oTVmYzLY+D8Pq6gWRECKEEGKo+XDDHjY0+7nshBJcFiMAz6zYxs9freeiyiJ+d9XxAJgNeorcVjzBCE0+NRUevj6jmHPK8hmVY0s773nlBQe97typRTz8reO5++W6tMnThW4Ld15cztypRf35NMVhOKbCAxx8iNPh3KTdddddPa7qBPDBBx8c8vlWrlxJSUnJIT+uN6WlpSxYsKCHCtM64pqCxWIZMhWmFUVJ3UAfiXg83kMASf47OxzmnHHJrz9XTyUSiaCqQRJRH4mYF4vdghKOsGLrSaxrmoJZnwweJkOE9mAWAHp9DLs5SLbdg1mf3NcSGMH65snolDiXVFZ3a9NjH12HT3Vz+sQPKMraQaQ9OfyqPW5ifdNktrSNxWXxMTp7G5G4iWjCAjoHcV0WEYo7w4UprRektwBiMpnQ6YbnqsuxWIza2loaGhoIBoPYbDZKS0upqKg4opAshBDDxaaWAMvW7ibLZuTqk8aktt/6/Bc0elUqS9ycMCb5JuzEfAeFLgtuqzHtHEv++zScZkPafVauw0yu4/D+zs+dWsSc8kKpMD1EHVN/XXNzc3vsXWhrawP29kAcq0pLS1m0aBF1dXXU19enbqbKysooLy8/5m6m9Ho9NpsNm83W+8EHoGka0Wg0LYCM3yeE+PYNJ4Hkv8ePDxMOh3hx3fch4YN4B3o6MBsidESSPTJtHTmY9JHO4Vlh7KYgVlMIgHxnM3OmpM+5afaP4G+fXg3Af53xBxKaQiRuJhIxEQ6aeK3ua3hVd2fdjpbkEKzOCevN/jz80WLsVoUsewTF6EZvcGE0O/o8DMtisWAwGI6qXpCGhoYewrLCunXrWLZs2ZAJy0IIMVT88b1NfL7Vw8LzJ1NWmBzhsKEpwK9ea2DaSHdaeDhzch5tHRGM+r1vTp1dms8nPz6323m7eib6k16nyHKsQ9QxdTc5bdo0nnnmGWKxWNqN8po1awCYOnVqppo2aAwGA5WVlal5HuLgFEXBZDJhMplwOp29P+AAEokE4XCY8/bpAdk3kPjDYXKsKjNLwoTD03hlx1ziUR+JiBct7icSiXTeACd4f+PpyV4QfXLSudkQIZZIvp5z7a1MyNuYGp5l0Mf5ZPMsPt48gkLHVqrK9lYaj8X1tHbk8vdPkiuPXTj1FTQlgS9mpqUzfHy5cxqBsJMR9hZc1iCazoGmc6IYnCiGbAwm1yEFELPZPCi9IA0NDVRX7+35yXfs5rQJH/LhptNo8hegqirV1dUsWLBAAoQQ4pinaRoJjdQ782t3erl3aT12s54/XTMzddy7Dc188lUbc6cWpsJDRbGLS44bSUVx+nDpey/rfh9xNL3BJAbOMRUeLrnkEv785z/zwgsvcMUVV6S2P/nkkxQXF3PSSSdlsHXiWKbT6bBarVit1sM+h6ZpqQnp+w/DOndS17/PpD4cJtyR/DoaDhCOhRkxIk5HzEDNmitQtGQviNkQJp7Yu7JUJGbCZgritrZ3roYVYX3zJAJhJ9NGrmVGyZdp7Vm1fQbvrj+TfEcz8ytfJhw1E1FNRGImAmE7L9bPAaBy5JfolERqUnocO75oCYohC7tVh8lswWiyp4Zi9TYMq7fihLFYjJqamrRtUwrXMTpnB1M66mny7x1PW1NTw6JFi465XjchxPAUT2i0+MMUuvcuVfrjF9ew5Itd/OySacyfXgwkQ8SHG/fgshhSvbIAV84azdcqCpkxKiv1+FE5Nh64YsbgPhFxVDtq/qIuXbqUjo4O/H4/AHV1dTz//PMAXHDBBdhsNubNm8ecOXO4+eab8fl8TJw4kWeeeYZly5bx9NNPyxKdYkhTFAWj0YjRaMThcBz2eRKJROccj2QAmZkKI5cSDofp2CecFI8PkxsOsy1SzJb1F6DFfRAPQNyPX00OBwvFLNQ2lqeW3zXpIxj0sdT1phWvJdvmwbjPtn98fik7WkdROv4jThrzKfGEjnDnxPP6baW8t/kU7KYAp09cTmvnsrxdtUHW7JqGoigUZ3sxmkwoBhc6gwud0Y3ZbCUQCKCqKk6LD6sxBJpCacF6AEoLGqhrnAKKRihqxa8mf1dIT5wQ4miiRuNsagmQbTNRnJV8U2p9k5+LfvMhLquBlYvnpI6NxzV8aoxNzYHUtnEj7Nx3WSUT8tP/lnx9xsjBeQLimKZomtbTUrpDztixY9m6dWuP+zZv3szYsWMBCAQC/OQnP+G5556jra2NsrIyfvSjH7FgwYJBbG13O3bsYNSoUWzfvn1AJkwL0Z80TSMej3frAelpSFbXRyTcQTziRYv5aA+aCIYSOAw7yXPs6VwJKzkEq9mfz/rmyWRZPcyZ8mZqMrrZEAbg4Q9uAuDaWU+R62hLa9dLX17EV3smUF5Uy9emvLlPe5PV1Ls+d3ng7e9TWFjI+eefT3Z2Ni6Xa9hOLhdCDD2RWIIvdrSzvS3IpcfvvTdY+Oxq/rlqJ//3/Mn8n3MmAdARjlFx52uYDTpWLj4PZ+c8g69aAkTiCcbm2lPF2MThO9j9WiAc47dvbaCu0UftLh9tHRG+f+4kbpkzuc/nf712N49+uJm6XT7iCY2SbCvfOXUcV500Ou24f32xi4ff3cSmlgBZViMXVhbxf88vxW7O/Pv+mW9BH23ZsqVPxzkcDh566CEeeuihgW2QEMcwRVEwGAw4HI4j6gXRNC0tYITDYUarKtNSxQkv37svGCYcVhk7Nvn1e9u+hZLwQiKAUQlhMoRp8ecBEAzb2e4ZSUnWThRlb2DYNzjEEjqumfU07SE3Wz56jr9uPB2dTk9xng6bcySurDyys7PJyckhOzub7OxsTCbTkXzbhBDigGp3efl4UyuTC5ycMTn5uywUiXP5Hz8GYE55QSoQTMh3kGUzEkvsfX/Xbjbw4W1nU+S2pq06ND7v8H9Hi0Pj6Yjw9xXbmFLk4vzyAqo/3X5Ij//Duxv5f681cPVJY/jPsyZg1OvY1BIgGk+kHVezaic/eHY1C04cxR0XTWHzng7uXVrPxuYAT30380Pwj5rwIIQ4+ihKcplgi8XS+8EHsW9xwpdffpktW2BL21jyHc1cPeuZbse/t+F0ANxWL26rlyLXbkAhkUhw3ri/kGX14g878O5x07bdzevbTsATzCHXDW53Nnb3SLKz94aK7OxsnE6nTBYUQvQqFk/wh3c3sbE5wC8vq8RqSvYGvFHXxINvbuDyE0pS4cFtM1JZ4ibLZsKvxlLh4cYzxvOfZ03o9junJPvwVxcUR64k28qXd56Poii0dUQOKTys2eHl/73WwA/nlnHTmRNS20+dOCLtuHhC4+evruP0SSNSk9ZnTxiBw2zg+9WreaehmbNL8/vnCR0mCQ9CiCFv3+KEM2bM6NYTmdBAp+z9vMNTQnMg+cu1srISm8lGaakHj8fDOxvm4jDtIcvqxW31McLRil6JAzA1/z2OH7W6syCgG2+bm4+aJ7G+eTIWk0bhCD0m+1iyskekQkVOTg5ZWVkYjf2/VKEQYmh7f30Lf/14KxXFrtTQFb1O4S/LN9MejHLjmeOpKE4WND1+dDbzphZy/JjstHP86/+c1u28Br0MrxyKjuQNpCc/3oLJoOO62WMPetyqbR6a/WEWX1Setv2CaUX8+J9reL12t4QHIYQ4FBUVFSxbtgxVVQlGrXSEbfjDTtbuqmBqcS1Os59gNDnB0GKxcPHFF6ettqRpGh0dHXg8yTDR6PEwcqIHm8fDRs9sdrUXp3os3FZvclI2kG/fxiVlNSQSCv6wk/YmNzs25lO9KfmHf1ReCKN9NE53cVqPRXZ2Nna7XXothDiKaJpGRySOY5/x5f/x15V8sb2dv33vJCYVJJf2buuI8Oa6JnxqlFs6j1MUhe+eOg6DXkeOfe9QyDMm56V6HMTgu//++7n//vt7PS4ejw/I9VdsbmNivoOlaxv57Vsb2dLaQb7TQtVxI1k4ZzImQzIwNjQlFwaaUpi+fLxRr2NCvoOG3f4Bad+hkPAghDiqGAwGqqqqqK6uJhB28thH3yGu6QGFNbumolfixLXkr7aqqqpuy7QqipKayzFq1Khu549Go7S3t+PxeGhra8Ns8jDZ3o6/3c6LX+hxmj2pYGE3BTsfpVFV8RgGXZxQ1II34MLbksWzG06nI+Ig19mBy5WFyTGW7OzctGCRlZUlS8kKkSGxeIKtbUEKXJZUUHijrolbnl3N9FFu/va9k1PHNvvDNPvDbGwOpMLDzLHZ/M/XK9o7i24AACAASURBVCgvSq+R8P+dO2nwnoToE5/Px86dOzN2/d0+lbYOHXf9q45F509mYr6Djza28vB7m2j0hnhowXEAtAejQHJI2/7cViM7PaFBbXdP5C+WEOKoU1payoIFC3qoMK0jriXnWRxuhWmj0UheXh55ed3fIdQ0Db/fn+q18Hs8TDN48HhaebnuGkw04ba247b6cFu9qTobs8e+zcS8r4gndPhUF95dbj5eMZ3NreOwGoMU5IDBNh6Hu7DbJG6r1Sq9FkIcITUaZ0NTAL8aZfY+Y8wvffgjvtzh5c/XzmROebJGTK7DRCAcY3NLR9o5Fl84BaNex+SCvROUS7JtXHvK2EF5DuLIuFwuRo7sfanaeDzO7t27+/36mqYRCMf4zZXHpepxzJ4wgmAkzl+Wb+aW8yYzdoQ9dbzCAX7vD4E/BxIehBBHpdLSUhYtWkRdXR319fUEg0FsNhtlZWWUl5cPyLv5iqLgcrlwuVyMGTOm2/5IJJIKFh6Ph6kz2mhvb2d1y3zWNm7Fleq18AHJVVQmjPiKOVPeAiAYseL1udm6ZTQvbD4F0Bib14JiHoPFObpbr4Xb7Zb6NULs58sd7azZ6WXW2JxUD8EX29u54pFPKMm28uFt56SOHZNrZ0NTAE8wktpWXuTi9VvOYExu+uTkE8fmDM4TEANi4cKFLFy4sNfjupZq7W9ZNhMt/jBnTkp/Y+qs0jz+snwza3d5GTvCTlZnj0N7MEKe05x2rDcUJcua+fl1Eh6EEEctg8FAZWXlkCkCZzKZKCgooKCgoNu+RCKB3++nra0Nj8fDSHdynoXfm82La4qx6JpTw6G0zreW7KYgl0xLriYVi+vxqi68291UL72AWMJIgasZqy0Lg20crqyCtEnc2dnZR7zKlRBDmacjwj8+244nGOW2uWWp7X98bxOvrtnN4gunpMLDxHwHuXYTJdlW4gkttdTpLy6dxkNXzEC3z9KnFqOeyQXp482FOFJlhU5a/OFu27sW49V19jCXdc51qN/tT71+ITnEblNzgPkzige8rb2R8CCEEINAp9Phdrtxu92MGzeu235VVVM9FgmPh+Oz2/C2t/Liuv9EF92By+Ily+rFYfYTSyR/dZ9X+ib5zhYAAmE7Xo+L5Z+dys72kbit7WQ74iiWsdhco7otPSsF88TR5OUvdvFa7W7mTi3kosrkzVMknuDnr9aj1yn84LxJmA3JXriZY3IIReKpyswAuQ4zn90xp9t5HUOg4JYYHuZNLeKDDXt4d31zWqXvd+qb0SlQWZJclWvGqGzynWae/2wHF0/fGxReXbubjkicr1UUDnrb9yf/a4QQYgiwWCwUFRVRVFTUbV88Hsfn86Umcc92JUPG8sbvoW3eit3Qkuq1iMSSXdrlhes4edwKAKJxA96Qm3VflPHytpkY9RFKshvRjKMw2sbizs7vtvSs2Wzu1g4hBkIsnkgtTepTo/ygejVbWzt47QdnpLbX7vKx5MtGcuymVHjId5q55LiRjMqxEY1rdOWA608bx/WndQ/oQvSHdxqaCUXiBMIxADY2B3h1TSMAZ5fmYzXp+eHzX/DC5zt579azUrU5Lp9Zwt9XbGVxzVraOiJMynfy4cY9PPXJVq45eUzqOL1O4UcXlHHLs1/wo3+uYf70Yra0dvCLztoPZ2V4mVaQ8CCEEEOeXq9P3dyPHz8+bZ+maYRCoVSvxbTC5Ofm9iJerD0OXXxnZ00LL6HOJWxz7a1UVdZ0Ph78YQetTbk890YVAGNytoLejd46Drt7JFlZ6ZO4pWCeOFSaprHLq1LksqSGCD350RZ++/YG5k8fyU8vTq5p7zAZ+HhTK6FonG1twVT15POm5JNjNzJzn3kHiqLwwBUzBv/JiGFt8Ytr2dm+d8WjV9Y08kpnePjgh2czKsdGPJEs9qbtLRCOUa/j6e+exH2vNfD7dzbhDUUYlW3jtrmlfO+09N/rlxxXgk5RePjdTbzw2Q7cNiOXHl/CrV879EVABoKiafs+NTFQuibgbN++nZKSkkw3RwgxTMRiMbxebypctLW10d7ehurfAuGt2I1tuK1eDLoY7288A4CbT/8jFmNybG5XwbxldefT2jGCAmcTNnMEzTSmW8G8rg8pmDd8RWIJtrZ2kNCgtHPsdiKhcfzP3qA9GE3dXAH8/d/b+PGLazhjch5/vX5W6hxLvtxFrt3MjFFZqerMQgwWuV/rnfQ8CCHEMcxgMJCbm0tubm63fZqmEQwGU5O4zypJBozXtt9BIrQFQ2JXqtdCjSYnX88oWU15UT1AqmDeyrUn8NrOSuymAMXuRqJKITrrOJxZI8nKykrrtZCCeceOL3e0s74pwHlT8smyJYuhPfvpNu54qZbzpuTz6LdPBECnU8h3mgmoMXa2h1LhYU55AVNHupiQ50g7b9ewJCHE0CThQQghhilFUbDb7djt9l4L5p0+LtlrsaV9Auvrt0BkGw5TstfCG0oWyCp2N3LRtFdTj1ejZra1juLF9y4EYErhOkKxLBTzaCmYdxTZ2R7irXVNmPQ6Fswandr+g+rVfLWng6e/exKnTUrWTpiQ58BhNmDUp0/Gf/q7J5FtN6Vtz3Oauy1FKYQY+uS3tBBCiB71VjAvEAjQ1tZGrsfDGI8Hj2cqL22cRULdkiqYF4snhzAZdFHOn/IGOiU5UjZZMM/JP966nI6InTE5WzDoYsT1xeit43BmFXVbelYK5iXHUa/Y3EazXyXfaWHWuJzUsqP9oXrFNlZu9XDd7LFMHZlc/WVDk5+fvlTLpHxHWniYOTabApcl7fonj89lzV3nd/s55btk2WAhjhUSHoQQQhwyRVFwOp04nc5eC+bldX5+dfskoh1bUKLbcJo9ZFm9hDqHQ50wehVjcralHh+MWHmn9kzeaS4l195KgbOJYHzEsC6Yt2xtI3e/XEejV01tK3JbuPPicuZO7b5K18Fsagnw69cbAPjD1SfsvUbtbt5taOG40Vmp8FBa6OTcsnymFLnSznHfN6Z3O6+uH4OMEGJokvAghBCi3/WlYJ7H48E9JTnfYn17Bas2bCehbsGiaybL6sUTTK6sU5K1g3NK3009PhbXU99UyisfzUGvi1FZvAavmkXCMLJbwbyunoujvWDesrWN3Pz05+y/wslur8rNT3/Ow986PhUgfGoUl2XvpPX7ltXz0upd/OC8SVw+Mzk8TacovLpmN2aDLq1oWtWMkRw/OpvpJVmpxxe5rTx23YkD+wSFEEcNCQ9CCCEG1b4F88aOHdttf1fBvNGdPRYezwm8vPVCYh1bUGI7cFvaCUaSk26dZj+nTViOQR9PPd6vOnjs7e+gaTrKC+vQUAjG89CZx2Jzj+629OxQL5gXT2jc/XJdt+AAyeq0CnD3y3VMKXJR9fvlRONa2tChjnByovLG5kDqcaNzbCy+cAoT8h0kF13sDA/Hjex+ESGE2IeEByGEEEPKwQrmJRKJ1NKzkzuXnn2r5RRC/u0kQluwGfZgMwbRtGQYmF7yBYWu5tTjo3EDL315Mcs9oynJ2kGuvRV/OIuEsQSjbVxawbyuj0wXzFuxuS1tqNL+NKDRq7KtNYhPjRFPaLT4w6l5Bt86eQwXVhYzuWDvqkZ6ncL3Th9/gDMKIcSBSXgQQghx1NDpdKmb+v1pmoaqqrS1tTG2s9ei3nMcKzbvJh7cnCqY5wkmHzsqezsnjlmJXpfofDx8tu143lh5OnZTgIriOrwhN2EtH8UyDoe7hKys9Encg1Ewr9l/4OCwr7ZghGXfP52SbFtafYRJBc6BapoQYhiS8CCEEOKYoCgKVquVkSNHMnJk9+E38Xic9vZ2pqSGQ53C67tbCQe2oqlbsBvbaA8lJwm7rT6OL1mF1bT3xr2tI4snP/g2ACeOWUE4asEfyQbTaIz2cQNWMC/f2bf5GvlOiwQFIcSAk/AghBBiWNDr9b0WzOuqwu3xePjYMw+/d1eqYJ5B1zWvQqO8sB631ZvqtUhoCk+vuIrWjhFMzm/AZfHjDblTBfMc7uJuk7j7WjDPENiNQgINha65Cfu1HjsR3JEWoPtzE0KI/iThQQghxLC3b8G8kpKSbvu7CuYdlxoOdTLtu/YQDmzpLJjnwdvZa1Hs3s2Uwjosxkjq8R9sPJX3vpxJnqOFyfnr8YbcBKK5qYJ5WVk5aZO4uwrmxWIx3li6hALdaHYnnOydIt0lOY16lmk7L/9rA5MnTZRCe0KIAdXvv2ESiQSqqmKz2fr71EIIIURG9KVg3omdwaKt7QyWt7fjb99BQt2Cmd20diR7BJwWH6UFDTgtgVTBvJ3tRTz372+ikOCsye/REHLhVd3EdMWEEvlEwyqXu1ayPpbLR8HJ+BPW1LWdOpVzHXWM1nvxqy7q6uqorKwcnG+KEGJYOuLwoKoq1dXVvPLKKyxfvpzm5mY0TcNsNlNeXs4555zD1VdfzfTp3YvJCCGEEEe7fQvmjR49utv+fQvmeTwe1ngW0L69pbNg3nbi8WSIMBvCjHTvorxwHUZ9lH+3VnDyiFr+8P6N/MepjwMQ1xQ+ay1lTziLEeZ2TshtQN8ZQh54+/u8//77+P1+bDZb2ofVapUK3UKIfqFoyQWeD1koFOK+++7joYcewuv1UlZWxvHHH09+fj4Wi4W2tja++uorVqxYgc/nY/bs2dx3332ccsop/f0cjgo7duxg1KhRbN++vccucSGEEMPPvgXzuuZbtLd7eHVzB2/tGcHprjomRoOUFdQzt/w1err3jyd0rNtdyvgRWwhFrahRC6GolZ3txXy+/Xj0SozSgvWoMSsJxQ2GbHSmERgtuVit3UPGvl9bLJYhXQNDiP4m92u9O+yeh0mTJmG321m8eDFXX311j1VEIdmd+8477/D4449z9tln87vf/Y7vfe97h91gIYQQ4lhxoIJ5re9v4q1X68lyjANPLfVNZbR15HD1rGe6naN65RUoioY3lIXVGMJqUrEYQliMyZWirKYQXyt/o9vjfvvufxJLGDmv7E1yVA+hJgutUSs7oxbWN02mOZCP3RQgPysMhhwUQw4GSy42m6NbyNg3eFitVgkcQhzDDjs8/M///A/f/va30ev1Bz1OURTOOecczjnnHO6++262bdt2uJcUQgghhoUbzpjAqRNHEGvZQk1Nbdq+hAY6Ze9ngCZ/AU3+Ak477TQchYUEg0FMwSAn5YUIBoMs2TaNeLiVRLQV4m0Y8BNLJG8B2jpy0CsJLMYQI+x7sJpUdvsKaA7kMyl/I2dPfm+faytsahnPko8vwqCLckHFMtqiFnZFrYSiFkJRC/VNZRhNDnJdMcwWJwZLLlar/YC9GxI4hDi6HHZ4uP766w/5MePHj2f8eKloKYQQQuxvU0uA0Tk2jPrkTXRFsZtYfgXLli1DVVWCUSsdYRv+sJO1uyqYWlyL0+wnGE1OoLZYLJx55pl9Wm0pFosRCiWDRTAYTP27qfNrW1GIie4gbWoONesq0GIeDPiwGFVCkeT19Lo4ipIg196GxRjCalSxGMM0NJWiqionl73A6JwdaBqoMQuhiIWPvpzNhpZJFLoamZS3sXN7MniEtTyC2jisVgt2mwWrzdktaOz7tdVq7fUNTCFE/5P13IQQQogM29js5xt//JjjRmXx+6uPx2ZK/nk2GAxUVVVRXV1NIOzksY++Q1zTAwprdk1Fr8SJa8ljq6qq+rxMq8FgSE3y7qt4PJ4WOJIfl9HcuS3kCxEK+ikoChMMBvlk2zms3tGK1ahiNYWwGFT84eT1XBY/E/K+wmoMYTGGAdjaNpp/rr4Eoz7C/znzYdSoOdmb4bWi7rHwUt3XCMcsTBixEatJRY1aiOECQzaavhCTNSdt6NSBhlVJ4BDiyBxyeFBVlQ0bNjBhwoRuy7EuX76cU089td8al2nhcJibbrqJN998E6/XS3l5Offffz+zZ88+6D4hhBDiUOxsV1GjcdqC0W77SktLWbBgATU1NaiqiqIoaJqGouiIawoWi4WqqipKS0sHtI16vR6Hw4HD4ejzY7oCR1foKNoneDSEvk3QFyQU9BOLtBEOBbBYDEQjMV5fd24ydBjVVK9G1zCr8qJ1TBjxVdrk8bcazuLLDdOZlLeBEyYsJ+SzEmq14Ita2ejPY9WO4wCNiXkbieNC02ejM+aiN4/o1sPRU2+H1M4QYq9D+t/w8ccfM3/+/FQthzvuuIPbb789tX/evHn4fL5+b2SmxGIxxo0bx/LlyykpKeGpp55i/vz5bNu2DU3TDrhPalwIIYQ4FGdOzqP6hlMYnWNL9Trsq7S0lEWLFlFXV0d9fX1yaJHNRllZGeXl5UP25vZwAkcikehxSNVZ+cmvt4dm0LAzQExNzuFQ4h6a280AeFUXG1omdvZoqLit7cS15DAwoz7KxdNe7Xa9Rz78Lh0RB6eOX47b0YI/aqWlc8WqLW1jaPYX4LTFGeGKoRhz0ZlysdpcB+3dsNlsQ/ZnIsSROqSlWmfPns1NN93EtddeS319Pddeey1Tp07l0UcfRafT4XQ68fv9A9nejMvJyeGdd97psW7FwfbJ0l9CCCH2pUbjhKMJ3DZjppty1Ot6UzN9SFVwvxASJKq2kojsgZgHXaIdi1GlvqmUhKbnhFGfUZzVmAoeVmOIj746hTW7pjGlcB1zy19PXS8cM7HdU8LLay4GNL425XXUmCW1TK4atbDDOwmjJQuXQ4fF4sRic/c6rMpolNdCpsn9Wu8OKRbX1dVx7bXXAlBWVsZ7773H5Zdfzje+8Q2qq6sHpIEAfr+fe+65h9WrV7Nq1Sr27NnDnXfeyV133dXt2EAgwOLFi3nuuedoa2ujrKyM22+/nQULFhxxO+rr6wmFQkyYMOGQ9gkhhBD7iic0bnl2NZtaAjzxnVkUZ1l7f5A4IJ1Ol7oJ76uuwHF2KmQsIBgM0tYVNjpCRJxBRo0K4o3YqFlbgJLwppbBVWPJ3g69Lo7L6qPA0IzFFMJqUNHpNB75cCRer8Lpo5YwKX8TkZiRUNSCGrDy2brjaGgqI9feysS8jcntUStRzUFcN4KIMvqgw6j2/VoChxhshxQeXC4XO3fuZOTIkQBYrVZqamq49tprmTt3LolEYkAa2drayiOPPML06dOpqqri0UcfPeCxl156KZ9++in33nsvkydP5u9//ztXXnkliUSCq6666rDbEAwGueaaa1i8eHG37teD7RNCCCH21+RT+XybB09HlK2tQQkPGXA4gUPTtLQejsrO4NEenMeuYJBgR1cPRxtWVxwtqPLZ9pls2jMhrUcj1LlCltvazvSRX2Ixquh1yXuoRm8B1Z8tADRuPv1PROImQh1WQu0WglELSzeeQTBipyRrOzZTkGjC0TmHIwedKR+z1dnrpHGj0Tikq43HYjFqa2tpaGhIDdErLS2loqJChoMNAYc0bOn6669n/PjxLF68OG27pmnccMMNPPbYYwMSILqaqCgKe/bsIS8vr8eeh1dffZULL7wwFRi6nH/++dTW1rJt27bUKgvnnnsuy5cv7/F6t956K/fcc0/q62g0yiWXXEJubi5PPPFE2n+4g+3bl3SDCSGE2NfO9hD1jT7OndJzkVVxbOgKHN1Xqtp3WFUH0bCHRLiVcLiDxjYrEGdGyerOieNdS+GqvLJ2HqGojYumvsKk/I1p1/pw02w+3Xoio7K3c8bEDzprb1gJRax4gtl8sTM5rHpM7i4UgwvFmIveNAKzNeugk8YHM3A0NDT0sDhA8vNgLA4g92u9O6TwEIlEiMViB0zp27ZtY/To0f3WuJ4cLDz8x3/8B9XV1Xg8nrRk+swzz3DVVVexfPnyQ14NKZFIcPXVVxMMBnnhhRfSznuwffvrejEWFBT0KTUvXLiQhQsXHlJbhRBCDG2hSByrSZYKFQenaRrhcLjHuRv7zuGIhDzEI3sg7kGJt+PpcNIeyibP0cy04rXJwNE5lMqnOvnXmvmAxvfP/i06Ze/tXzRu4OkVV9Eeyub4UZ9T6GrqHEqVDB+72otpDRXhchjIcmrojLmdgcN2wN4Nm82GyWQ6pMDR0NCQNgy+wNnEaRM+5MNNp9Hk3xuyFyxYMGABQsJD7w6p78dkMmEymQ64f6CDQ2/Wrl3LlClTut2cV1ZWpvYfani48cYbaWxsZNmyZd3Oe7B9B9LU1NSn446lVauEEELA2/VN3PbCGh655gSOG52d6eaIIUxRkkvwWiwWcnJy+vQYTdOIRCLdQkYgFKI5GCSYCFJeHiIY7OBf6xehxVoh1o7FEMRiVAlGkm8MK2hYjSGybW2pXo9PtpxE89Z8so0b+PrklwGIxfWoMQtN7fn864P5AJw+4QNiCUMqdITjNtojEzBasrHbzFhtjlTg2L93w2Qy8eKLL6Y9pymF6xids4MpHfVp4aGmpoZFixbJEKYM6bfvek1NDX/729/YunUrqqqm7VMUhS+++KK/LnVAra2tPVaw7vqP19raekjn27p1K48++igWi4URI0akti9dupTRo0cfcN/pp59+wHP2tefB5XIdUluFEEIMXZqm8af3vqLFH+Yfn+2Q8CD6naIomM1mzGYz2dl9e33tGzhOTPVuXEIgGKS5q8ejvYOQLkhBgYovoqfmy0sx6YOdQ6lCRGJdbyprjMregc2U3GfQxwF48pNraG6OMqfsDcryG5LBImwhFLBS11jOut1TcFl8TBixifHZFtA0NJJVyUsL1gNQWtBAXeMUUDRCUSt+NbmIT9ebw2Jw9Ut4+NWvfsVtt91GXl4eEydOxG6398dpD8vBuscOdazemDFjONiorkMY8ZWycuVK6QYTQohhRlEU/nLdifz5g6/4r7MnZro5QgCHHzii0Wha78aYzuCxI3hmckiVN0Q41E48soe40YBer1LXWE6zPz9t4ng8kRzCl23zcOqEjzDqY/tdK/nZagxx9axnUtsffOcH1NfXS3jIkH4JD3/4wx+4/vrr+dOf/pTRsu+5ubk99i60tbUB9LnrTwghhOgP8YSGXpd848puNvCD8yZnuEVCHBlFUVLD2LOysvr0mK7Asf/cjfHBIEWd2/7y6RTCqo+pxWs5a9L76BQtVUW863M8oeP1dXPQNI1gMDhAz1D0pl/CQ2trK1dddVVGgwPAtGnTeOaZZ4jFYmlDg9asWQPA1KlTM9U0IYQQw4xPjfKtR//Nd08bx9dnjMx0c4TImH0Dh9vt7vGYQCBAfX09X+yYQWN7cVpPQ5fqlVfQHMhHUZRDWmJX9C9df5zk1FNPZd26df1xqiNyySWXEAgEeOGFF9K2P/nkkxQXF3PSSSdlqGVCCCGGm6c+3sqXO7z87yvr6AjHen+AEMNYaWlpt+HgCS39cxdN0ygrKxuklon99UvPw4MPPsgll1zCqFGjmDt37kFXZDpcS5cupaOjA7/fDyQnyjz//PMAXHDBBdhsNubNm8ecOXO4+eab8fl8TJw4kWeeeYZly5bx9NNPZ7xnRAghxPBx85kT8KsxLqoswm6WVWGEOJiKigqWLVuWLMIXtdIRtuEPO1m7q4KpxbU4zX6CncX1LBYL5eXlGW7x8HVIdR4OJB6Pc8stt/D73/++x64kRVHwer1HdI2xY8eydevWHvdt3ryZsWPHAslur5/85Cc899xztLW1UVZWxo9+9CMWLFhwRNc/UrJusBBCCCHEge1b50GvxIhrekABNPRKnLiWDOFS5yGz+iU8LFq0iAceeIAZM2YwZcqUHnseHn/88SO9zFFNXoxCCHHs+8O7GwmG4yw6f/KgVOMV4lgjFaaHvn7pR33iiSe47bbb+MUvftEfpxNCCCGOOusafdy3rAGAk8bncPqkvAy3SIijT2lpKYsWLaKuro76+nqCwSA2m42ysjLKy8ulMNwQ0C8/gXg8zpw5c/rjVEIIIcRRaUqRi19cOo3tbUEJDkIcAYPBQGVlpdRxGKL6JTycf/75fPLJJ5xzzjn9cTohhBDiqHTlrNGZboIQQgyofgkPd9xxB1dccQV2u50LL7ywx2JsUqBNCCHEsearlgB/eu8r7v56BRajrOgnhDj29Ut4mD59OgALFy5k4cKFPR4Tj8f741JCCCHEkBCLJ/jeX1fyVUsHZqOO//m6FCIVQhz7+iU8/PSnP5VVJYQQQgwrBr2O/62axi+WruO/z52U6eYIIcSg6JelWkXvZOkvIYQ4NnUtJSmEOPrJ/VrvdIf7wIceeogdO3b0Z1uEEEKIIS2R0Pj16w00+9XUNgkOQojh5LDDw89//nPGjBnDSSedxK9+9Ss2bdrUn+0SQgghhpwH3lzPb9/eyJWPfEI0nsh0c4QQYtAddnhobGzkzTff5MQTT+TBBx9k8uTJzJgxg5/97GfU1dX1ZxuFEEKIIeEbJ5QwNtfG98+bjFF/2H9ChRDiqNVvcx6WL1/O888/z4svvsj27duZPHkyl112GZdddhnHHXdcf1ziqCZj6IQQ4tgQjsUxG2RZViGORXK/1rt+e9vk1FNP5YEHHmDLli18/PHHzJ8/n2effZYTTjiB8ePH88Mf/rC/LiWEEEIMmg82tFC7y5v6WoKDEGI4G5A+11mzZvHLX/6SDRs2sGrVKubOncurr746EJcSQgghBszanV5u+OtnXPGnT1jX6Mt0c4QQIuP6pc5DTzRNY+nSpTz22GMsWbKEcDg8UJcSQgghBsToXBvTR7kx6nVMyHNkujlCCJFx/R4eNm3axF/+8heefPJJGhsbMZlMXHbZZf19GSGEEGLAuSxGnvjOLOIJDZNBJkgLIUS/hAdVVfnHP/7BY489xgcffJAqmLNw4UJuv/12cnNz++MyQgghxIDzq1FWbWvnjMl5AFiMMsdBCCG6HNHbKJ9++ik33XQThYWFXHfddXz++edcd911LFmyBE3TuPjiiyU4CCGEOGqEY3FufOozrnt8Bf9YuT3TzRFCiCHnsHseKisrqa2tBeCUU07h+uuv54orrsBuGVgv2gAAIABJREFUt+P1ent5tBBCCDH06BWFMbk2vtjeTlmhK9PNEUKIIeeww8PatWtRFIULL7yQe++9l/Ly8v5slxBCCDHoDHodP79kGjeeMYGxI+yZbo4QQgw5hz1s6cEHH6SyspIlS5Ywbdo0TjnlFB599FH8fn9/tk8IIYQYcJ9v89BVM1VRFAkOQghxAIcdHv77v/+bVatWsWLFCm644Qbq6+u54YYbKCoq4oYbbkBRFBRF6c+2CiGEEP3uhc92cOkfPuLOf9WmAoQQQoieHfG6czNnzuThhx+msbGRJ598kpkzZ/L888+jaRrf/e53+fWvf01ra2t/tFUIIYTod341iqKA2aCTN72EEKIXijYAb7Ns2vT/s3ffcVXX+wPHX4cNHvZeigriADQVTbQUEU3cK7NhmubIaxn3alcrNVve6tdOM81VqTmznLkyc+beC5GlKDJkr8P5/UFSCAgcvngY7+fj4eNyvt/v+XzecOnwfX8/4x3Bt99+y/Lly7lx4wZmZmZkZmYq3U2tEhsbi6enJzExMXh4eOg7HCGEEP9wLCqJRzxtMTCQ5EGI+kzu18pXLRVvmjZtynvvvUd0dDQ///wzTzzxRHV0I4QQQujk5t0scvMLil63a2QniYMQQlRAtZbLNDAwoG/fvqxfv746uxFCCCEq7HZaNk8uOMiYZX+SnpOv73CEEKJWUaTCtBBCCFFbRCZkkJiei4FKRVauBrWp/CkUQoiK0nnkwc/Pjw0bNlT4+ps3b/Lyyy8zd+5cXbsUQgghqqxjE3tWvvgoy0Z3wNHSVN/hCCFEraLz45Ynn3ySkSNHYmtryzPPPEO3bt1o27YtDg4OqFQqsrKyiIiI4NChQ2zcuJHt27cTGBjIhAkTlIxfCCGEKFdBgZa0nHyszY0BaO1po+eIhBC1TXpOPl/susL5m6mcu5FKUkYur4T48Gpos3Lfu+ZoDFPXni713JHXQ3CyNCt63XnubuJSskpc93THhrw3yF/3b0AhOicPM2fO5MUXX+TTTz9l0aJF/O9//yuq7WBsbExubi4AWq2Wxx57jFWrVjF48GDFAhdCCCEq6r0tF9h96TbLRnfA085C3+EIIWqh5IxcVhyJpoWrFT1bOrPqz5hKt/Hh0ACaOqmLHbO1MClxXftGtszo06LYMUd1zRgprdJET1dXV/73v//xzjvvcPjwYQ4ePMiNGzfIysrCwcGB5s2b061bt1q91dVTTz3Fnj17yMrKwsvLi/fee4++ffsCMH/+fBYuXMiZM2d4/fXXmT17tn6DFUIIUUJyRi5bztzkxt1sTsakSPIghNCJh605p2f1RKVSkZSRq1Py4OtiSYBH+SOfVubGtG1oq0uY1U6RVWLGxsZ06dKFLl26KNFcjfLmm2+yfPlyTExMOHLkCKGhoVy7dg17e3tcXV156623WL58ub7DFEIIUQbbBiasf6kz+64k0K+1m77DEULUUrWtiOTlW2kcvZ5MfGo2OXkabBuY4OOkpkNjOyzNjHVuV7aYKEerVq2KvjYyMiI3N5e4uDjs7e0ZOHAgABs3btRXeEIIIcqQm1+AiVHhviAu1mYMa++p54iEEPXdC0uPkpSRg6WZMY82sSM81BdfF8sS1x2JTKLVzG3k5Bfg5dCA4e09eaFLYwzLqUdzNzOPH45EsfJINHHJWZRWCdrIQEU3XydGB3kR5O1Q6e+hViQPaWlpvP3225w8eZITJ05w584dZs2aVeo0ofT0dN544w1Wr15NUlISzZs357///S9PPfWUzv0/88wzrFu3jpycHMLCwvD31/9iFSGEEGU7HZvChO+O8fmIR2jvZafvcIQQevbxxx/z8ccfl3udRqOplv4dLU35V7A3jzS0QW1qxKVbacz/LYJB8/azdkIQLd2siq7t3twJfw9rGtlZcDcrjy1nbvLulgucv5nKJ8PblNnHkv2RfLbrCiqgb4Abjzaxx8/dCnu1KaZGBqRk5hGdlMmJ6GR+PX+LZ789TBcfR+b0b4WXQ4MKfy+1InlITEzkm2++oXXr1gwcOJBFixaVee3gwYP5888/mTt3Ls2aNWPFihWMGDGCgoICnn76aZ36/+GHH1i2bBm7d+/mwoULtW7YSggh6psvdl/lxt1svt4bwSJJHoSo91JTU4mLi9Nb/918nejm61T0umMTe4J9nXji09/5eMdlFj3fvujc2wP9ir23ZysXrM2NWXYwijFdGuPnbl1qH0sPXOfNPi3p38YNY8OS1RgcLU1xtDSlXSNbxj7WhKjEDL7cfZXNZ24yKdi7wt9LrUgeGjVqRHJyMiqVijt37pSZPGzZsoUdO3YUJQwAwcHBREVFMXXqVIYPH46hoSEAISEh7N+/v9R2pk6dyttvv13smJGRET179uTzzz/Hx8eHsLAwBb9DIYQQSvr8qUf4ZOdlJnev+B9EIUTdZWVlhbu7e7nXaTQa4uPjH0JE4GlnQXsvO07GJJd77cBH3Fl2MIoTMSllJg+7wrtiVErSUJZG9g34cFhrNAWlTW4qW61IHir6pH/Dhg2o1WqGDRtW7Pjo0aN5+umnOXz4MEFBQQDs2rVLp1g0Gg1Xr17V6b0A7du3x8io/B97eHg44eHhOvcjhBD1jVarLfp7YW5iyIywFuW8QwhRX1T0vio2NhZPz4e3PkpLxe5z793eP2jJw6VbabRyKz2xeJDy1lHcT+cK0/907NgxJZqpsrNnz9KiRYsSN+cBAQFF5ysjPj6edevWkZGRQX5+PqtXr2bPnj107doVgPz8fLKzs9FoNMW+fpBbt24RFxdX7r/U1NRKxSqEEPVZbn4BLyz9k3XHYvUdihBCVEhMUibHrifxSAWKVq4/XvjZ9ohn2du39v3iD/p98QffHYoiNTtPsTjvp8jIQ2BgIB07duRf//oXTz75JMbGum//VBWJiYk0adKkxHE7O7ui85X16aef8sILL6BSqfDx8WH16tW0bt0agHfeeYe33nqr6Np3332XJUuWMGrUqDLbc3Z2rtDIg5WVVbnXCCGEKLTmWAx7LiVwJDKJrr6OONSQYkpCiLplz6XbZOVqSM/JB+Dq7XS2nLkJQLCvE+Ymhkxbe4p1x+PYO7UbHraFdWWeWXSIDl72NHe1xNLUiIvxaSz4PQKVSsW/e/oWtb/xZBzbzsYT3NwJDxtzUrPz2Hwmnl9O3WBoO49iC6vv91K3pmw4HsfMjWd5d/N5nmjlwpOBngQ1rfyOSg+iSPKwdOlSvvrqK5577jn+/e9/8+KLLzJ+/Hi9FId70NBPZRc6u7i4sG/fvjLPz549u9KF4Y4ePVqri+YJIURNNCKwITFJWXRqai+JgxCi2ryx4SxxKVlFrzefucnmv5KHfdOC8bSzQFMAmgIt2n8sJfB1tmLT6Rss3JdNdp4Ge7UJQU0dmNzdmyaOf1ec9rSzIDU7jw+3XyIlMxcjAwOaOat5e6Afz3Ro+MDYpvZqzn96+rL3cgJrjsWy9Ww8P5+6gbutOU+282RIOw/cbMyr/DNQabXayq2SeIA///yTL774gjVr1qDRaOjXrx+TJ0+mW7duSnXBnTt3cHR0LHWr1k6dOqHRaDhy5Eix4+fOncPPz48FCxYwbtw4xWKpjHtz6GJiYiR5EEIIIYSogerS/drdrDx+OhHH2mOxnL1xF0OViiBvB54K9CS0pXOpOzJVhCJrHu4JDAxk+fLlxMTEMHv2bI4ePUpISEjRjXt2draS3ZXg7+/PhQsXyM/PL3b8zJkzAPj5+ZX2NiGEELXQxpNxvL/1AgWV3ClECCHqA2tzY54P8uKXyV3Y8vJjPNepEefi7vKvFcd59D3dNg4ChZOHe0xMTLCwsMDExAStVktmZiYTJ07Ex8eHQ4cOVUeXAAwaNIj09HTWrVtX7PiyZctwc3OjY8eO1da3EEKIhycuJYupa06zYO81fj51Q9/hCCFEjdbC1YpBj7jTo4UzAMmZuTq3pehWradPn+arr75ixYoV5ObmMmzYMFasWEFgYCCnT59m3LhxjB8/nlOnTlW67a1bt5KRkUFaWhoA58+fZ+3atQCEhYVhYWFB7969CQ0NZeLEiaSmpuLt7c3KlSvZtm0b33//fVGNByGEELWbu4057w7y40BEIv1bu+k7HCGEqJGSMnJZfzyWtcdiuXwrDUMDFSEtnBneXvftaBVZ8/Djjz/y1VdfsX//fhwdHRk/fjwTJ07ExcWl2HW7d++mV69e5OVVfvsoLy8voqKiSj0XGRmJl5cXAOnp6bz++uusXr2apKQkmjdvzvTp03nqqacq3aeS6tIcOiGEqCn+WdtBCCGqqi7crxUUaPnt8m1W/xnL7ku3ydMU0NihAU+292RIWw8cLau2qYQiIw8jRozgkUceYfHixYwYMQITE5NSr/Py8uLZZ5/VqY/r169X6Dq1Ws1nn33GZ599plM/QgghaqY76Tl8tvMKM8JaYG5SOJIsiYMQQhS6lpDO6qOxbDgRS0JaDmbGhvRv7cbwQE8CvewU60eR5OH333+nS5cu5V7XpEkTlixZokSXQggh6hGtVstL3x/nyPUk7mbl8fmIR/QdkhBC1CghH+8FoLWHDVN6NKN/azcamCq6QgFQKHkoKCggPT0dtVpd4lx6ejrHjx/n8ccfV6IrIYQQ9ZBKpWLqE768tvY0U3r46DscIYSocV7o3JjhgZ40c7as1n4U2W0pODiY8+fPl3ru0qVLBAcHK9GNEEKIeizQy45fX328WEElIYQQhd7s27LUxCEiIZ2j15PIzM0v5V2Vp0jy8KA113l5eRgYVMuOsEIIIeq4b/+IJP7u3zWCjHQsaiSEEPXNumOxPPreLkI/3suTCw5yLSEDgEk/HGflkWid29X5Uzg1NZXo6Giiows7j4+PL3p979+lS5dYtmxZiV2XhBBCiPJ8dyiKtzedZ8j8A6TnKPPETAgh6oPNp2/yn7Wn8HO34q0BfvzzMX8rdys2n76pc9s6r3n45JNPmDNnDlA4F3XQoEGlXqfVapkxY4au3QghhKinujVzpIlDA4a190RdDYv+hBCirpr321WGtfPgg6Gt0RRombnxbNE5b0c1yw5c17ltnT+Ne/bsiVqtRqvVMm3aNCZPnkzDhg2LXWNqaoq/vz9du3bVOUAhhBD1k6edBb9M7oKFiRT4FEKIyrh6O53/9m5e6jkbCxOSMytfc+0enZOHTp060alTJwAyMjJ48cUXcXOTKp9CCCF0dzbuLgVaLQEeNgDVss2gEELUdeYmhqRllz7dMz41G2tzY53b1vlTOTo6GldXV4yNjRk9ejT5+flF6x9Kc/+ohBBCCPFPMUmZjFryJ5m5+Xw3piPtGtnqOyQhhKiV2jeyZdmB6/T2K7nueO2xWB5tYq9z2zonD40bN+bgwYN06NABLy+vcqt8ajQaXbsSQghRD9g2MKG5iyWJGbn4OMt2rEIIoauXQ3wY+vVBBny1n/6t3VAB287G88mOyxyJTGTjpPKLO5dF5+Rh8eLFNG3atOjr8pIHIYQQ4kHUpkYsHhVIWnYeVma6D6kLIUR9F+Bhw9LRgbz501ne3XIBgK9+u0pj+wYsGdUBXxfdC8mptA8q0iAUExsbi6enJzExMXh4eOg7HCGEqBHyNAX8eT2JoKYO+g5FCCHq5P1aVGIGd9JzsLUwUaTIplTbEUIIoRcFBVpeW3uaZxYd5rtDUfoORwgh6qRG9g1o18hOkcQBFEoedu/ezZo1a4pe37p1i7CwMFxcXBg5ciTZ2dkPeLcQQoj6SAuozYwwUKnwsDHXdzhCCFGrbTp9o9LvuZWazdHrSZV6jyLJw8yZMzl//nzR62nTprFv3z6CgoJYu3YtH374oRLdCCGEqEMMDVS81b8VmyZ3Ibi5k77DEUKIWm3mxnP0/mwfq45Ek5b94DoOZ2Lv8uZPZ+n24W9cuJlaqX4UWfPg5OTEt99+S79+/cjPz8fOzo65c+fy0ksv8dFHH7F48eJiyUV9VBfn0AkhhC4uxafRzFktG20IIWqc2ny/lpqdx6c7rrDySDQarZZWblb4uVljrzbB1MiQlKxcohMzORGdwu20bJo5WzI9rAVdmzlWqh9Fqu+kpqZiY1NY0OfYsWNkZGTQv39/ADp06MDs2bOV6EYIIUQtt//qHUYtOcLQdh68PcAPI0NZeieEEEqwMjNmZr+WvBLiw5pjMey5dJt1x2PJyvu7XEJDOwu6NnNkwCNuOm9UoUjy4OTkxJUrV3jsscfYuXMnjRo1KsrW0tLSMDaWLfeEEEIUFoLLL9CSmpWPgYw8CCGE4qwtjBn7WBPGPtYEKByRyM7TYGthgrECD2wUSR6eeOIJZsyYwblz51i6dCnPP/980bmLFy/i5eWlRDdCCCFquac6NKShvQVtG9piYCDJgxBCVDcrM2NFa+cokjy89957REdHs3DhQjp06MAbb7xRdG7FihUEBQUp0Y0QQohaKCUzF3MTQ0yNDAGkpoMQQtRiiiQPDg4ObNu2rdRze/bswczMTIluhBBC1DIZOfk8v/gI5iaGfDOyvVSOFkKIWk6R5OGfEhISyMrKKnG8YcOGSnclhBCihotISCciIQNjQxV30nIkeRBCiFpOkeQhLS2NV199lZUrV5ZZEE6j0ZR6XAghRN0V4GHDj+MfJTe/QLHqpkIIIfRHkeRhypQprFixgjFjxhAQEICpqakSzQohhKilMnPzsTAp/BPTys1az9EIIYRQiiLJw+bNm5k7dy6vvPKKEs0JIYSoxZbsj2Tpgessf6EDjewb6DscIYSod2ZtPMvIIC+aVsOIryLVebKzs/H391eiKSGEELVYdp6GpQeuE5WYyY7zt/QdjhBC1Evrj8cR+vFenl10mF/PxaPVahVrW5HkISwsjH379inRlBBCiFrMzNiQNRM6MSOsOWO6NNZ3OEIIUS8dfj2Et/q34nZaNuO/P0aX/+1h3m9XScrIrXLbKq0Cqcj58+cZOnQoY8eOpV+/ftjb25e4xs7Orqrd1GqxsbF4enoSExNTVH1bCCHqCk2BFkMp+iaEqOXq4v3agat3WHbwOrsu3MbQQEXfADeeD2pEgIeNTu0pkjwYGBQOYKhUZf/hqO+7LdXFX0YhhACIScpk1JIjvDvIn0eblHx4JIQQtUVdvl+7eTeLf68+xaFriQD4e9gwOdibHi2dK9WOIgumZ86c+cDEoS44ePAgnTt3Zs6cOUUVtBMSEhg1ahS//fYb7u7ufPXVV4SGhuo5UiGEeLg+33WFiIQM3t9ygQ0vdcZARiCEEKLGyM7T8NOJOJYfjOJCfCo+Tmr6+Lux88Itxn13lCk9mvFyiE+F21MkeZg9e7YSzdRYBQUFTJkyhQ4dOhQ7PmnSJFxcXEhISGDnzp08+eSTXL16tdRpW0IIUVe9PdAPcxNDJgV7S+IghBA1RFRiBssPRrH2WCzpOfl0a+bIjLAWdPFxAOCVHj58sO0iyw5cf/jJQ123YMECOnfuTFJSUtGx9PR0fvrpJyIiIrCwsKB///60bt2ajRs38sILL+gxWiGEeLjMjA2ZM8BP32EIIYT4y/OLj/DH1TtYGBsytL0Ho4K8St06O6SFM/P3RlSqbUV2W7rn7t27bN++nR9++IHk5GTF2k1LS2PatGn07NkTR0dHVCpVmaMd6enpTJkyBTc3N8zMzGjTpg2rVq3Sue/ExEQ+++wzZs2aVez4lStXUKvVeHp6Fh3z9/fn3LlzOvclhBC1gVar5bW1p1l1JFrfoQghhChFdFImb/ZpwaEZIczq16rMmju+LpasfPHRSrWtWPLw9ttv4+bmRu/evRk5ciSRkZEAhISEMHfu3Cq1nZiYyDfffENOTg4DBw584LWDBw9m2bJlzJo1i61btxIYGMiIESNYsWKFTn1Pnz6d8PBwrK2LV0hNT0/Hysqq2DErKyvS09N16kcIIWqL7efi+fFoDK//dJbrdzL0HY4QQoj77PlPN0Z1bkwD0wdPMlKbGlV6owtFkod58+bx1ltvMWbMGDZv3lysEEXfvn3ZvHlzldpv1KgRycnJ7N27l/fff7/M67Zs2cKOHTuYN28e48ePJzg4mIULFxIaGsrUqVOL7fgUEhKCmZlZqf/efPNNAI4dO8bx48cZO3Zsib7UajWpqanFjqWmpqJWK1/JTwghapJerVz4V7A37w3yw8tBKkgLIUR9osiahy+//JLw8HA++OCDEluy+vj4cOXKlSq1X9GdnDZs2IBarWbYsGHFjo8ePZqnn36aw4cPExQUBMCuXbvKbW/fvn2cP38eJycnoHC0wdDQkMuXLzNv3jzS09OJjY0t2srr7NmzPPfccw9ss3379hgZlf9jDw8PJzw8vNzrhBDiYVOpVPynl6++wxBCCFGGLv/bTVm3zwYqFVZmxgR4WDO6sxfeTpaValuR5OHatWv06tWr1HOWlpakpKQo0U25zp49S4sWLUrcnAcEBBSdv5c8VMTYsWMZOnRo0evw8HC8vLx47bXXUKvVDBgwgNmzZ/PFF1+wa9cuTp48yZo1ax7Y5q1btyrU9/2jGkIIoU8HIxLZc+k2/32iueyoJIQQNVzHxvYcjkzkdmoO7RrZ4mBpSkJaNsejUnCyMsXV2ozt5+JZfzyOH8c/WqmCcYokD9bW1mXeFF+/fr3oyX11S0xMpEmTJiWO36tunZiYWKn21Gp1sWlIFhYWWFlZFW3FOm/ePJ5//nns7e1xd3fnxx9/xMHB4YFtOjs7V2jk4f71FEIIoS8pmbmM/+4oqdn5uFiZ8UKXxvoOSQghxAM83syBEzHJ/Da1G2425kXH41KyeO7bw/Rs5cJHT7bmqQWH+GTHZZaM7vCA1opTJHkICQnhgw8+YMCAAZiZmQGFw9r5+fnMnz+/zFGJ6vCgKU5VLWS3dOnSYq8dHR3ZsmVLpdo4evRonatYKISo22wsTHh7oB9rjsbydMeG+g5HCCFEOb7ac5UpPZoVSxwA3G3MeSXEh893XWFoOw/GdGnMnE3nK9W2IsnDnDlzCAwMpGXLlgwaNAiVSsWXX37JiRMniI6OZvXq1Up0Uy57e/tSRxfu1We4NwIhhBCicga0cad/a7cqP4QRQghR/a4nZmJpVvptvpW5MbHJWQB42JqTlacp9bqyKLLbkre3N/v376dFixbMmzcPrVbL8uXLcXBwYN++fTRs+HCeVPn7+3PhwgXy8/OLHT9z5gwAfn5SxEgIISoiMzef97dcICPn789TSRyEEKJ28LAxZ+2x2FLPrTkag/tfIxLJmXnYmBtXqm3FKky3bNmSbdu2kZOTQ2JiIra2tpibm5f/RgUNGjSIhQsXsm7dOoYPH150fNmyZbi5udGxY8eHGo8QQtRWU9ecZvOZm1yIT2P5CxWfCyuEEEL/Xny8CTM2nGHI/AOE+bvioDbhTnoum0/f4GRMCu8P9gfg0LVEAjysy2mtOMWSh3tMTU1xc3NTulm2bt1KRkYGaWlpAJw/f561a9cCEBYWhoWFBb179yY0NJSJEyeSmpqKt7c3K1euZNu2bXz//fcYGhoqHpcQQtRFYx9rzInoZF4J8dZ3KEIIISppRIeGaLXw6c7LvLP57zUNjmpT3h3kz/DAwllBk4K9MTGq3EQklfafFd0qwcDAoFJD2PfXf6gsLy8voqKiSj0XGRmJl5cXUFiL4fXXX2f16tUkJSXRvHlzpk+fzlNPPVWl/qsqNjYWT09PYmJiZMG0EKJWyMnXYGokD12EEPVHXbtf02q1RCRkkJKZi42FCU0dG1R5CqrOIw8zZ84s1vmSJUtIT0+nX79+uLi4cPPmTTZt2kSDBg144YUXqhQkFG75WhFqtZrPPvuMzz77rMp9CiFEfbLxZBztveyK5sJK4iCEELVPdp6Grh/u4d2B/vRo6Yy3k7r8N1WCzsnD7Nmzi77+v//7P1xcXNi5c2exughpaWn06NEDCwuLKgUphBCiev16Lp4pP57E2dKMXyZ3wdHSVN8hCSGE0IGZsSHZeQVYmFTPAyBFdluaN28e06ZNK5Y4QGF16WnTpjFv3jwluhFCCFFN/Nyt8XZUE9LCCQe1ib7DEUIIUQWdve354+qdamlbkQXTcXFxZVZNNjIyIj4+XoluhBBCVBM3G3PWvRREAxMj2ZJVCCFquZe6eTPxh2OYGhnyhJ8LTpam3P/RbmOh24MinRdM/9MjjzyCtbU1O3bswNj4771ic3Nz6dGjB2lpaZw4caKq3dRqdW0BjhCi9otNzuR2Wg5tG9rqOxQhhKgR6sr9WuPpm4u+Lutx0LX3++jUtiIjD++88w4DBw6kSZMmDB48GBcXF+Lj41m/fj3x8fH89NNPSnQjhBBCIckZuYxcfIQbKVl8+3wgnb0d9B2SEEIIhbzc3afESINSFEke+vTpw7Zt23j99df56quvKCgoQKVS0aFDB5YsWUKPHj2U6EYIIYRCTI0NaGhnQVauhiaODfQdjhBCCAW9Gtqs2tpWrEhcSEgIISEhZGZmkpycjK2treyypEfnzp1j69at9O7dm1atWuk7HCFEDWNhYsTCke25lZqNq7W5vsMRQghRTbLzNKRk5uGgNsHIsOp7JSmy29I/WVhY4O7uLomDHmVkZLBp06Zi/yuEEFqtlmNRSUWvjQ0N8LCVz2ohhKiLDkTcYdC8/bSatZ3O/9vNxfg0AN786Szbzt7UuV3FkwehX1qtlk2bNpGTkwNATk4OmzdvLuddQoj64OMdlxky/yALf7+m71CEEEJUowNX7zDy2yPk5BXw4mNNKPjH/ki2DUxYeyxW57Yleahjzp07x8WLF7m3iZZWq+XChQucO3dOz5EJIfRJq9WSlasBQG2m2IxVIYQQNdDHOy7TzdeJLa88xn96Fl//0NLVkvM3UnVuW/6C1CEZGRlljjJs2rQJLy8vGjSQhZFC1EcqlYo3+rakt78r7RrJ1qxCCFGXnbuRyrxn2gKUqN1j18CJsYabAAAgAElEQVSUOxm5OrctIw91xP3Tle4n05eEqJ+iEzP5ZzkfSRyEEKLuMzJQkacpKPVcYnoOalPdxw8UGXn4/fffadu2LWq1usS59PR0jh8/zuOPP65EV6IMCQkJXLx4sczz96Yv3b59Gycnp4cYmRBCXy7GpzLs64P0bOnC3CH+GCuwy4YQQtRX6Tn5fLHrCudvpnLuRipJGbm8EuJToW1R1xyNYera06WeO/J6CE6WZsWO/XzqBvN/iyAiIR0bc2P6BLjyn56+NKjgTX+ApzUbTsTRs5VLiXNbzsbTtqFNhdopjSLJQ3BwMAcPHqRDhw4lzl26dIng4GA0Go0SXYkyODo60rx5cy5dukRZRcN9fHwkcRCiHrkUn0ZmrobopAw0BVqMDfUdkSiNVqtl3fE4Oja2w9NOdr8SoqZKzshlxZFoWrha0bOlM6v+jKl0Gx8ODaCpU/GH7bYWJsVe/3Qijik/nuSpQE/e7NuCyDsZzN16kau30/luTMcK9TOxqzcjFx9m3PKjDG7rgQo4EZPC6qMxbD1zk5XjHq107PcokjyUdbMKkJeXh4GBPO2qbiqVir59+3L9+nWys7NLPR8aGqqHyIQQ+jKgjTt2DUwIcLfBTDKHGisnv4BPdlzmVmo2Q9p68K/u3pJECFEDediac3pWT1QqFUkZuTolD74ulgR4lP3UX1Og5b0tF3jMx4G5QwIACGrqgNrUiFdWnWTPpdsE+5b/ILiLjwP/92Rr5vxynh0XbgEwc+NZrMyM+WhYawK97Cod+z06Jw+pqamkpKQUvY6Pjyc6OrrYNVlZWSxbtgwXl5JDJkJ5DRo0oE+fPqxbt67EucGDB+Po6EhaWhqHDh2iW7duGBsb6yFKIUR1ysrVoFJRlCw85uOo54hEecyMDdkZ3pUfDkfx9d4I1h2PlSRCiBro/oXH1eFEdDK303J4o2/LYsfD/F2Zsf4Mv56Lr1DyADDoEQ96+7lyLCqZO+k52FqY0N7LFguTqo0d6PzuTz75hDlz5gCFP8xBgwaVep1Wq2XGjBm6diMqqVWrVpw7d65o+pJKpaJ58+b4+fkBcOPGDQ4fPsyVK1cYMmQIzs7Oeo5YCKGUfE0Bk1ceJyUzj0XPt0dtasS1O1IksrZ4vJkjHRrbsen0TVYfjWHt8VhCWzrzcogPLV2t9B2eEEIBLyw9SlJGDpZmxjzaxI7wUF98XSyLzl+6VVjIrcU/jkFhUc+mTmou/VXoraLMjA3p7O1Q9cD/QefkoWfPnqjVarRaLdOmTWPy5Mk0bNiw2DWmpqb4+/vTtWvXKgcqKub+6Uumpqb06dOn6Lyvry/jxo1j/fr1LFy4kB49etCxY8eHkk0LIarX9cQMDkcmkZtfwLU7GRyMSOTD7Zf0HZaogm1n49l3OYFzc57QdyhC1Goff/wxH3/8cbnXVdcaXUdLU/4V7M0jDW1Qmxpx6VYa83+LYNC8/aydEERLt8IHBCmZeQBYW5ScHWJtbkxcclaF+9RqtZyKvUtcchbZeSW/ryHtPHT6XnROHjp16kSnTp2AwvoCL774Im5ubro2JxTUoEED+vbty9atW+ndu3eJ2g5OTk6MHTuWXbt2sXPnTry9vXFwUDYrFUI8fN5OlqyZ0IkbKVm0bWhLKzcrHm1ir++wRCXkawr47VIC647Hcjsth05N7Xm1h4++wxKi1ktNTSUuLk5v/XfzdaLbP6YbdWxiT7CvE098+jsf77jMoufbF7teRRkPdSv4rPdaQjpjlx/l+p0MSluZrEIPycM/zZo1S4lmhIJatWpFq1atyjxvZGREr1696NSpE1ZWVuTn5xMVFUXTpk0fYpRCCCXk5GswNSpc49DcxYrmLoVPsEyNDKWuQy2Rpylg3bFYvtxzldjkLPr4u/JyiE+x6QxCCN1ZWVnh7u5e7nUajYb4+PiHEBF42lnQ3suOkzHJRcds/hpxSMnMxdHStNj1d7PysDGv2HrVmRvPkZNXwJdPt6W5iyUmRsptXqR4hemEhASyskoOqdw/pUnUDFZWhTcZZ86c4eeff6Zdu3b06tVLFlMLUUtsOxvP+1svsHhUIE0dS9baETVfdp6GPp/vIyIhgz7+rnz7fKAkDUIoLDw8nPDw8HKvi42NxdPT8yFEVEhL8YXYzf/6b/9ifBo+zn9/DuRrCoi4nU7/NhWb5XMqJoX3h/gT5u+qaLygUPKQmppKeHg4K1euLHWbUKi+OWRCGW3atEGj0bB9+3auX7/OkCFDcHVV/hdOCKEcTYGWT3deJioxk1VHonm9T8vy3yRqHFMjA559tBFBTR0kaRCiHolJyuTY9aRiC5rbeNriZGnK2mOx9Gv9d6Kw5Ww8GbkaepVS9K00FqaGVaoi/SCKtPrqq6+yYsUKxowZQ0BAAKampuW/SdQoKpWK9u3b4+Xlxbp161i0aBETJ06UtRBC1GCGBip+GNuRb/+IJLwCFU5FzaRSqRjdubG+wxBCVMCeS7fJytWQnpMPwNXb6Ww5cxOAYF8nzE0Mmbb2FOuOx7F3ajc8bAu3W35m0SE6eNnT3NUSS1MjLsanseD3CFQqFf/u6VvUvqGBiulhzXn1x1NMX3+G/q3duJ6Ywft/1X7oVsFtWoe18+TnkzcqfH1lKJI8bN68mblz5/LKK68o0ZzQIwcHB8aOHcv58+eLEoesrCzMzc31HJkQ4p572zAD2KtNmfZEcz1HJIQQ9cMbG84Sl/L39PzNZ26y+a/kYd+0YDztLNAUFI4M/7OGsq+zFZtO32Dhvmyy8zTYq00IaurA5O7eNLlvyumgRzwwUKmY/1sE647FYm1hzOC2Hkzt5UtFNXOx5OdTNxi77E9CWjhjW8ruTU/46TbDRKV9UHnoCrKxsWH9+vV07969qk3VWffm0MXExODhodvqdn04d+4cmzZtol+/frRsKVMihNC3lMxcRi35k//09KWLj4wMCiGEkmrr/dr9Gk/f/MDzKuDa+30eeE1ZFBl5CAsLY9++fZI81EGNGzemcePGrFmzhjZt2tC7d29MTEz0HZYQ9da83yI4GZPC9A2n2RXeTdEdNIQQQtQNK198tNraViR5eOONNxg6dCiWlpb069cPe/uS+4rb2dkp0ZV4yCwsLBg2bBgnT55k69atREdH8+yzz2JrK9s/CqEP/+7ZjLTsPEYFNZbEQQghRKmqs8aPItOWDAwK/4A9qEpxfd9tqS4MgyUmJrJ//3769OmDoaGhvsMRQgghhFBUXbhfK0+epoCbKdk0tLfQ6f2KjDzMnDnzgYmDqBvs7e3p378/APHx8ezYsYN+/fphY2Oj58iEqNs+23kF2wbGjOzkpe9QhBBC1FBNpm9m/UudaeNZeF+m1WoZufgIbw/ww8uhQdF1Z+PuMmT+Af2ueZg9e7YSzdRoBw8epHPnzsyZM4c33ngDgNOnTzNp0iROnTqFh4cH8+bNo1u3bvoN9CHJz88nMTGRr7/+mr59++Ln56fvkISokw5fS+STnZcBCPCwKfqjIIQQQvzT/VOJCrTwx9U7RdvKKkXRCbN3795l+/bt/PDDDyQnJ5f/hlqioKCAKVOm0KFDh6JjeXl5DBo0iBEjRpCcnMysWbMYOHAgiYmJeoz04fHw8GDChAl4e3uzbt06NmzYQE5Ojr7DEqLO6dDYjvDQZrzc3VsSByGEEHqnWPLw9ttv4+bmRu/evRk5ciSRkZEAhISEMHfuXKW60YsFCxbQuXNnmjf/ey/1S5cukZKSwksvvYShoSHDhw/H2dmZDRs26DHSh8vMzIwhQ4YwcOBAYmJiyM3N1XdIQtQ5KpWKl0N8CO9Z8f29hRBCiOqiSPIwb9483nrrLcaMGcPmzZv55xrsvn37snnzg/eaLU9aWhrTpk2jZ8+eODo6olKpypwqlZ6ezpQpU3Bzc8PMzIw2bdqwatUqnftOTEzks88+Y9asWcWOl7bOvKCggHPnzuncV22kUqlo3bo1kyZNwtLSkszMTPbv309BQYG+QxOi1rp8K433tlxAU1Dl/SyEEEIIRSmSPHz55ZeEh4fz+eef07Nnz2LnfHx8uHLlSpXaT0xM5JtvviEnJ4eBAwc+8NrBgwezbNkyZs2axdatWwkMDGTEiBGsWLFCp76nT59OeHg41tbWxY77+vqiVqv5/PPPycvL44cffuDq1atkZGTo1E9td2/3pcjISHbt2sWSJUvq1NQ1IR6W7DwNo5f8yTe/X+OzXVX77BRCCFG/PIztixRZMH3t2jV69epV6jlLS0tSUlKq1H6jRo1ITk5GpVJx584dFi1aVOp1W7ZsYceOHaxYsYIRI0YAEBwcTFRUFFOnTmX48OFFN7khISHs37+/1HamTp3K22+/zbFjxzh+/Dhff/11iWtMTEzYsGEDkydP5q233uLxxx+nW7dudXZbr4pq1aoVVlZWrF+/nq+//pqwsDACAgJkNy4hKsjM2JDX+7Tgy91XeaGzl77DEUIIUYuMWHgIg/vuuZ5ccLDYsYIqVmlQJHmwtrbm1q1bpZ67fv06Tk5OVWq/ojeeGzZsQK1WM2zYsGLHR48ezdNPP83hw4cJCgoCYNeuXeW2t2/fPs6fP18Uf3p6OoaGhly+fJnly5fTtm3bogREo9HQtGlTXnvttQe22b59e4yMyv+xh4eHEx4eXu51NZGnpycTJkxg69atbNy4EXd3dxwcHPQdlhC1Rpi/K71auWBoIEm3EEKIihnS9uE8wFYkeQgJCeGDDz5gwIABmJmZAYU3/Pn5+cyfP7/MUQmlnT17lhYtWpS4OQ8ICCg6fy95qIixY8cydOjQotfh4eF4eXkVJQhnz57Fx8eH3Nxc5syZg7OzM0888cQD2ywrybpfampqheOsiUxNTRk4cCCdO3fGwcGBgoICbt68ibu7u75DE6LG0RRo+WrPVUZ39sLSzBhAEgchhBCV8tGw1g+lH0WShzlz5hAYGEjLli0ZNGgQKpWKL7/8khMnThAdHc3q1auV6KZciYmJNGnSpMRxOzu7ovOVoVarUavVRa8tLCywsrLC3r6w5PfSpUtZtGgRBQUFhIaGsnHjxnLbdHZ2rtDIg5WVVaVirakcHR0BOHnyJJs2baJLly507dpVKlQL8Q/vbr7A4v2R/HbpNmsnBGEgiYMQQogaSpHkwdvbm/379xMeHs68efPQarUsX76c4OBgfvjhBxo2bKhENxXyoClOVZ13v3Tp0mKvP/roIz766KNKtXH06NF6uS6iTZs2ZGRk8NtvvxEREcHgwYOLkjAh6rtBj7iz6fQNxj3eRBIHIYQQNZoiyQNAy5Yt2bZtGzk5OSQmJmJra4u5ublSzVeIvb19qaMLSUlJwN8jEOLhMzAw4LHHHqNJkyasX7+eBQsWMH78eEkghAD8Paz5bWo3LEwU+0gWQgghqkWVt2rNysrC3d2dX375BSic6+7m5vbQEwcAf39/Lly4QH5+8TLcZ86cAcDPz++hxySKc3d3Z/z48YSGhhYlc3l5eXqOSoiHb+/lBGKSMoteS+IghBCiNqhy8mBubk5WVhYNGjRQIp4qGTRoEOnp6axbt67Y8WXLluHm5kbHjh31FJn4JxMTEwIDA1GpVFy8eJEvvviCa9eu6TssIR6aY1HJjFt+lMHzDxCdmFn+G4QQQogaQrHdlnbu3En37t2VaK5UW7duJSMjg7S0NADOnz/P2rVrAQgLC8PCwoLevXsTGhrKxIkTSU1Nxdvbm5UrV7Jt2za+//57WaRbA7m5ueHg4MB3331HUFAQ3bt3l/+fRJ3nYWtOY4cGuNuY42Zjpu9whBBCiApTabVVrBQBnDhxgiFDhjBq1CgGDx6Mq6tricXJVV1v4OXlRVRUVKnnIiMj8fLyAgprMbz++uusXr2apKQkmjdvzvTp03nqqaeq1H9VxcbG4unpSUxMTL1cMP0gWq2WgwcPsmvXLpycnHjqqadKVPQWoq5Jzc7D2MAAcxNJloUQoqaoa/drey8ncOhaIskZuUwO8cHdxpxTMSl42JpjrzbVqU1FkgcDg79nP5W1o5FGo6lqN7VaXftlrA43b97k999/Z/DgwRgbG+s7HCEUdTczj4g76bRtaKvvUIQQQpShrtyvZeVqeHH5UfZH3OHenfnP/+qCn7s1k1Ycx83ajNf7tNSpbUWmLc2cObPK26AK4erqyvDhwwFISEhg79699O7du0aspxGiKrLzCj/ET8amMO/ptvRo6azvkIQQQtRhH26/xOnYFOY/047HfBzwm7296NzjPg4sPVD6bJ6KUCR5mD17thLNCFEkPT2da9eu8fXXXzNw4ECaNm2q75CEqBJrC2NMjQzwsHv4O9EJIYSoX7acucm/e/ryhJ8LmoLik4zcbMy5kZKlc9uKb9UqhBIaN27MxIkTcXZ25vvvv2f79u0ltuAVorYwMzZk/jNt2fBSEM1d6kb1eCGEEDVXUkYuPs7qUs8ZqFRk5+m+nKBObdUq6hZLS0ueeeYZevXqxZkzZ8jIyNB3SEJUyoWbqUVfGxka4O1kqcdohBBC1BfO1qZcik8r9dyFm6l42lno3HaVkwf4e6tWIZSmUql49NFHeeWVV7C2tiY7O5tjx46hwDp/IarVqiPR9P5sH1/tuarvUIQQQtQzT7Ry4as9Vzkbd7fY8djkTBb/EUmYv6vObSuy5mHGjBkMGTIEMzOzatuqVdRv93Zfunz5Mps2beLixYsMGDAAtbr0ITkh9C02uXA+aVZu/d5pTgghxMP3So9m7L+ayMCv9tPM2RIVMHXtaaITM2jiqOalbrqvJZWtWh+SurL1V01w5coVNm7ciFarZcCAATRr1kzfIQlRqn1XEuji7SC70QkhRC1Rl+7XsvM0LN4fyZ6Lt7mTnouthTEhLZx5oXPjKtUYUiR5mD17drl/HGfNmlXVbmq1uvTLWBNkZGSwceNGrl69yqRJk7C3t9d3SEJwKzUbR7UpBgaSLAghRG0k92vlk61aRa3UoEEDRowYQWxsLPb29mi1WpKSkiSJEHoTfzebwfP2E9jYjg+HtsbESJElZUIIIUSlPfbBbhY8256WbiV3+LsUn8bY5X+yb1p3ndqWv26i1lKpVHh6egJw4sQJ5s+fz8GDB2UxtdCL07Ep3E7L4UzcXTJyZFthIYQQ+hObnEWupqDUczn5GuKSda/zoMjIg4GBQbnTlur7mgdRvQICAkhISODXX3/l6tWrDBw4EEtL2RZTPDw9W7mwZHQgXvYNsG1gou9whBBC1HNl3ZlHJ2XSwFT3FECR5GHmzJklkod7N3IajYaRI0cq0Y0QZTIyMqJXr154e3vz008/MX/+fMaOHSu7fIlqpSnQkptfULTw7DEfRz1HJIQQor5aeyyWdcdii16/8dNZ1PclCdn5Gi7cTKVjY92neVfrmofc3Fx69eqFk5OTEt0IUa6mTZsyceJEjh49iq2tLQAFBQXFdgQTQglarZbZP5/jTNxdFo8KxE5GG4QQQuhRVp6GpIxcoHDUITU7j9z84lOXTIwM6Bvgxquhuu9UqUjyUBYTExMmT57Ma6+9xsSJE6uzKyGKWFhY8PjjjwMQERHBtm3bGDRoEG5ubnqOTNQl8anZ/HL6Bnez8jgelUyPls76DkkIIUQ99tyjjXju0UYAdPnfbuY/067UBdNVVa3JA4C5uTk3b96s7m6EKJW1tTXGxsZ8++23BAcHExQUJKMQQhGu1uasndCJ49EpkjgIIYSoUf54TbedlCqiWpOHhIQEPvzwQ3x9fauzGyHK5ODgwJgxY9izZw+7du0iIiKCIUOGSGVqobN8TQFGhoUJqLeTJd5OsjBfCCFEzZWYnkN2fsmdl9xtzHVqT5HkoXHjxiUWTOfk5HD79m0MDAz4+eeflehGCJ0YGhrSo0cPvL292bt3L8bGxvoOSdRSx6KSmbrmFAuea4ePsyQNQgghaq4vdl1h6YHrJGfmlnr+2vt9dGpXkeSha9euJZIHMzMzvLy8GD58OF5eXkp0I0SVeHl5Ff0uJicnc+DAAXr06IGpqal+AxO1glar5f0tF7h2J4Mvdl/l8xGP6DskIYQQolSr/4xh/t4IJnZtysc7L/OvYG+0WthwIg5TYwMmdG2qc9uKJA9Lly5VohkhHpqEhAROnTpFREQEgwcPlhL0olwqlYqFI9vzfzsuMSOshb7DEUIIIcq0/NB1JgV7M+Gv5KFXKxf83K35V3dvnlxwkOSM0kcjKkKRlaN5eXlkZGSUei4jI4O8vDwluhFCMc2aNWPChAlYWFiwePFifv/9dwoKSq/EKOq3f1Yst21gwjsD/bEwqfa9JoQQQgidRd3J5BFPGwz+mhh0r9q0mbEhYx9rwsoj0Tq3rUjyMHbsWMaOHVvquXHjxsk2raJGsrOzY/To0XTp0oUDBw5w9+5dfYckapjsPA1jlh1lz6Xb+g5FCCGEqDBDw8KsQaVSoTY1Iv5udtE5OwsT4lOzy3pruRRJHn777Tf69+9f6rl+/fqxa9cuJboRQnGGhoZ0796dl19+GVtbW3Jzc7lw4YK+wxI1xOL9key+eJtXfzxJWraMoAohhKgdvOwbcOOvhKG1hw0rj0STpylAU6Bl5ZFoPGwtdG5bkbH3W7du4erqWuo5FxcX4uPjlehGiGpjYVH4H9HZs2f55ZdfCAgIoHfv3piZmek5MqFPY7s0IepOJgPauGFpJrt0CSGEqB2CfZ04EpnI0HYevNStKSMXHyFg9q8YGajIyM3ng6GtdW5bkeTBxsaGq1ev0q1btxLnrl69iqWlbGkoaoe2bdtiZGTE5s2biY6OZtCgQTRs2FDfYQk9MTEy4H9DA/QdhhBCCFEpr/TwKfo6yNuBdROD+OXUDVQqCG7uRFBTB53bVmTaUnBwMO+//z5JSUnFjiclJTF37ly6d6++KndCKC0gIIAJEyZgaWnJ0qVLS/xei7pt9dEYFu27pu8whBBCCMW09rThjb4teb1PyyolDqDQyMPs2bMJDAzEx8eH4cOH4+7uTmxsLGvWrCEvL4+33npLiW6EeGhsbW0ZNWoUERER2NnZodVqSUtLw8rKSt+hiWp0+VYa09efQVOgpamTmmBfJ32HJIQQQlRak+mbWf9SZ9p42pQ4dyb2LgO++kO/ReJ8fX3Zt28f4eHhLFy4EI1Gg6GhIV27duXjjz/G19dXiW6EeKgMDAzw8Skc9jt58iTbtm0jLCyMgICAEkURRd3g46TmPz19iUhIp1szR32HI4QQQuhE+4BzBVptle5jFNusvHXr1uzatYusrCySk5Oxs7OTxaaizmjZsiVRUVH89NNPXLlyhT59+mBubq7vsITCVCoVE7s1RVvFD1YhhBBC38r6K3Ym7i6WZrqnAIpXOjI3N5ebKlHnmJqaMnDgQLy9vdm0aRNff/01o0ePxsam5HCgqF1upWaz7MB1wkObYWRYuAxMEgchhBC1zeI/IllyIBIoTBzGfXcUE6Piy5uz8wpITM+ht3/pu6RWhJRJLUe3bt04dOgQRkaFP6oOHTqwe/duoHAqy6RJkzhz5gwODg7MmDGjzGJ5om7w8/PD09OTw4cPF61/kKfUtZemQMuYZX9yNi6VjJx83hrgp++QhBBCCJ3Yq01o5lS4w2lschYN7Sywum+bcRMjA3xdLBndubHO/UjyUAGLFi3i2WefLXH8ueeeY9iwYezbt4+TJ0/StWtXOnfuTIsWLfQQpXhYrK2t6dmzJwBRUVHs2LGDQYMGYW9vr+fIRGUZGqh4ubsPb28+z9jHmug7HCGEEEJnA9q4M6CNOwBPfXOQdwb64+2kVrwfRbZqra+uX7/OiBEjMDAwoG3btrRo0YKLFy/qOyzxEJmYmJCVlcWCBQs4fvw4Wu2DliiJmqhnKxd2hXfD0073aptCCCFETbJqXKdqSRygliQPaWlpTJs2jZ49e+Lo6IhKpWL27NmlXpuens6UKVNwc3PDzMyMNm3asGrVqir1/+qrr+Lo6EhISAgnT54sOj558mS+//578vPzOXLkCDExMXTq1KlKfYnaxdXVlfHjx+Pn58cvv/zC6tWrycrK0ndY4gG0Wi3LD17nblZe0bH754QKIYQQtU1GTj5xKSXvQSLvZPCvFcfp+clenvv2MAeu3qlSP7XiL2ZiYiLffPMNOTk5DBw48IHXDh48mGXLljFr1iy2bt1KYGAgI0aMYMWKFTr1/cEHHxAZGUl0dDR9+vThiSee4O7duwD06tWL5cuXY2ZmRlBQEHPmzMHFxUWnfkTtZWJiQv/+/XnyySdJSUnRdziiHIv2RTJz4zmGLzhIbn6BvsMRQgghFPHBtos8t+hwsWNJGbkMmX+AzWducis1hwMRiYxa8icnY3S/X1E8eUhISCA6OrrEv6po1KgRycnJ7N27l/fff7/M67Zs2cKOHTuYN28e48ePJzg4mIULFxIaGsrUqVPRaDRF14aEhGBmZlbqvzfffLPoug4dOqBWqzE3Nyc8PBwHBwcOHDhAYmIiffv2Ze7cueTk5HD58mU+/fRTfvnllyp9r6L2atGiBePGjcPc3Jy7d++ye/du8vPz9R2WuE9nbwecLE15sr2njDgIIYSoM45FJ9O3tVuxY0v2R5KcmcubfVpyalZPDvy3Ox625iz8/ZrO/SiyYDotLY1XX32VlStXkp2dXeo1/7xxr6yK7mSzYcMG1Go1w4YNK3Z89OjRPP300xw+fJigoCAAdu3apVMsBgYGaLVarl27hlqtZvjw4QA0adKEfv36sX37dvr161fm+9u3b1+0c9ODhIeHEx4erlOMQn/u/a7GxcWxf/9+rly5wuDBg3F0lIJjNUVLNyt2vNoVawvj8i8WQgghaokbKdm0dLUsdmzPpdu4WZvzQpfC3ZWcrcwY3aUxX+2+qnM/iiQPU6ZMYcWKFYwZM4aAgABMTU2VaLbSzp49S4sWLUrcnAcEBBSdv5c8VERKSgp//vknjz/+OCqVirVuQjAAACAASURBVK+//pr4+Hg6deqEoaEhWVlZrFu3jsGDBxMdHc3GjRuZMmXKA9u8detWhfpOTU2tcJyi5mnZsiW2trasX7+eb775hl69etGuXTvZ0lVPTsWkYGVuTGOHBgCSOAghhKhzMnLysbUwKXqdnafh4s00+t83GuHtqCYpI1fnfhRJHjZv3szcuXN55ZVXlGhOZ4mJiTRpUnK7RTs7u6LzlZGXl8f06dO5ePEiJiYmtG7dmi1btmBrawvAmjVreO211xg9ejRqtZoRI0aUW+fB2dm5QiMP92oIiNrL1dWVcePG8euvv7J9+3aaNm1a9LsjHp5rCemMWnIEA5WKleMepZmzZflvEkIIIWoZZyszYpOz6PjX61MxKWi0Wvw9rItdV6DVYm5iqHM/iiQP2dnZ+Pv7K9FUlT3oyW5ln/o6Ojpy9OjRMs+HhoYSGhpaqTaPHj2Kh4dHpd4jai9jY2P69OlDly5dsLa2Jj8/n9jYWLy8vPQdWr1haWaMu605BioV7jbm+g5HCCGEqBbtGtmy7OB1wvxdMTcxZMWRaFTA482KT52+FJ+Gi5WZzv0okjyEhYWxb98+unfvrkRzOrO3ty91dCEpKQn4ewRCiIfN2row6z958iSbN2+mY8eO9OjRo0KjUKJqHC1NWTWuEzl5GhqYys9bCCFE3TQp2Jt+X/xB4Ls7UZsacSstm14tXWjqWLzew7az8TzS0EbnfhT5S/rGG28wdOhQLC0t6devX6mVdh/Gjbu/vz8rV64kPz+/2E3ZmTNnAPDz86v2GIR4kHbt2qHRaNixYweRkZEMHjwYZ2dnfYdV5+TkF87zbO1Z+OGoNjVCLYmDEEKIOszbSc2aCZ1YvD+Su5l5PNeoES8+Vnw6/+20bKzMjegb4FZGK+VTaRUoiWtgULjd4YOmBVVlt6V/unPnDo6OjsyaNatEobitW7cSFhbGqlWrinZBAujduzenT58mOjoaQ0Pd53hVRWxsLJ6ensTExMi0JcGtW7dYv349iYmJTJo0SdZCKKigQMvkVSfYce4WnwxvQ58AV32HJIQQopaQ+7XyKfIobubMmdW+i8zWrVvJyMggLS0NgPPnz7N27VqgcNqUhYUFvXv3JjQ0lIkTJ5Kamoq3tzcrV65k27ZtfP/993pLHIS4n7OzMy+++CIXLlzA1tYWrVZLdnY25uYyJ7+yNAVajkQmcTstGydLM9p42qDVatGixdpcdlUSQgihjPScfL7YdYXzN1M5dyOVpIxcXgnx4dXQZpVu66Ptl/hyz1WaOav59dWuxc4NX3CQw5FJJd7zeDNHlr/QQef4laJI8nD/CEB1mDhxIlFRUUWv16xZw5o1a+D/2bvv+KiqvPHjnzuTZNIbaaSQhISQ0EsqooAsHRRBqq7YsC4/lRWx7SLq4/qwPuy6NhRRBAEbskoVQZReBKSEDklIQgqkJ6RMZu7vj5jRMQkJYZJJ+b5fr3nB3HPnnjPJOZn7ndOApKQk0wTUr7/+mhdeeIG///3v5ObmEhkZyapVq5g6dWqTl1GI62FjY2NaaODYsWN899133H777UREXP8fofZq0/EM5q89QUbBb/vLdHSz529juvHwLWGmYUtCCCHEjcorqWDl/otEdXRleDdfPjuQ2qjrJF4q4IMdF/Byrntrg06ejvx7ah+zY672LeMLMYsPAj5z5gw5OTl4eXnRpUsXi103OTm5Qec5Ozvz5ptv8uabb1osbyGaWlhYGIGBgaxatYro6GiGDx+OrW3L+CPRUm06nsGjnx7ij+MuMwvKeHzlId67u58ED0IIISwm0MOBo/OGoygKuSUVjQoeKg1G5nx5lOmxnTiZUUje1dr3W7C31dCvU8sc0qyx1IW+/PJLgoODiYqKYuDAgURGRhIcHGwaWiSEqJuTkxNTp05l9OjR/PLLL3zwwQcUFBRYu1gtlsGoMn/tiRqBA2A6Nn/tCQzGG57SJYQQQgBVc3tvdJj+ez+ep6BUz5wRXS1UquZnkZ6HDRs2MHXqVLp3785f/vIX/P39SU9P59NPP2Xq1KmsXbuWUaNGWSIrIdosRVGIiYkhJCSE3bt34+zsXP+L2qn9SblmQ5X+SAUyCsrYn5RLQljN1d+EEEK0LwsXLmThwoX1nmepBX5qczariLe2neP9u/vXu3R4Ss5Ves/fTHF5JQHuDozr3ZFZt3bB3tb683ctEjz8z//8D8OHD2f9+vWmlZcA5syZw6hRo3j11VcleBCigby9vbn99tsBSE9P58cff2TcuHGy6/jvZBfWHTiYnVfUsPOEEEK0bYWFhaSnp1stf6NRZc5XRxnZ3Y8hkT7XPDcmxJOxvf0J83aiXG/kx9PZvP/TBQ4k5/HZzHg0mtp7P25e8APv3x1NN/+mvV+wSPDwyy+/8Nlnn5kFDlD1Tepjjz3G9OnTLZGNEO1OZWUlWVlZLFq0iHHjxhEVFWXtIlldRkEpH+y40KBzfVwav4OmEEKItsPV1ZWAgIB6zzMYDGRmZlo8/w93XiA5p4QPZ0TXe+7TfxjSNCTSh0APR/5nw0k2n8hiZA+/Wl+XlldKhcFokfJei0XmPGi1Wioqap/wodfrawQVQoiGCQ4O5pFHHiE4OJgvvviCb7/9ts621l64O9hxpaj8mucoVK26FBsqu8oLIYSA2bNnk5aWVu/jwIEDFs87Pb+Uhd+f4YmhXbDVaigo1VNQqsdgVDGqUFCqp0x/7eFS4/tWBT6HU/MsXr7rZZGeh5iYGBYsWMDo0aPN1qkvLy/njTfeIC4uzhLZCNEuOTo6MnnyZA4fPszOnTvR6/XY2dlZu1jNpqhMz3eJWdzZv2qzHgc7Lf+Z1pez2cX87b/HAcwmTld35s4b1w1tHV27QgghRHO5mHOVMr2R+WtPMH/tiRrpvedv5r6bQpg3rnu919I08b5qDWGR4GH+/PkMHTqUzp07M2nSJPz8/MjIyDDtoPvDDz9YIhsh2i1FUejXrx+9e/dGq9VSXFzMsWPHiIuLa9M9e6UVBoYt3E5mYRn+bvYMCPcCIK5zB+I6d8DL2a7GPg9+bvbMG9eNkT1kZ2khhBDW183flVUz42scf3ndCYrK9Pzzzt50dLv2MNvVh9IA6FvPEuSLd1zA+xr7R/zeS7fVH6zUxiLBw8CBA9m8eTPPPvss77zzDqqqotFoiIuLY9WqVQwYMMAS2QjR7lXvkn7+/Hk2b97MmTNnGD9+PG5ublYuWdNwsNMyvLsvO85eqTV9ZI+ODOvmZ7bDdGyop/Q4CCGEaBLbTmdTWmGguLwSgHPZxWw4lgHAkK4+ONhpeearI6w+lM5PcwYT6OGIm4NtrSv/udrbYDAazdL2J+Xy9rZzjOjuSydPx6oJ02eyWbU/lQFhHfhTlO81y1ddlvooWDl4ABg0aBB79uzh6tWr5OXl4eHhgaOjo6UuL4T4nd69e+Pm5saaNWtYtGgRY8eOpXv3xv0RaEmOpxew8PszvD6hJz6uVd/CPDMykr+N1WCrrb2HRatRZDlWIYQQzeLFNcdJzy81PV9/LIP1v96w73hmCEGejhiMVfsRqY3YasjHRYdWgbe2niP3agUKEOrlxOxhETx4c2idKy1VW/PYTfRp4g1SFVVtzFsT1ystLY2goCBSU1MJDAy0dnFEG1FaWsq6des4deoUs2bNwt29de+oPPG93RxMyWNabCf+MaGntYsjhBCinWnN92uhz61vluCh0T0PFy9epGPHjtja2nLx4sV6z+/UqVNjsxJC1MHBwYE777yTy5cv4+7ujsFgIDs7m44dW8d4/+LySuxtNNj82qvw/OhIPtmdwmODw6xcMiGEEELUptHBQ2hoKHv27CE2NpaQkJB6t+tuyh37hGjPFEXBx6dqw5lDhw6xceNGBg0axM0339yiJ1OvOZzG/6w/yVPDIrgrLhiA/sGe9A+W5VWFEEKIlqrRwcNHH31EWFiY6f/1BQ9CiKbXr18/iouL+emnnzh//jwTJkxosUOZCq7quVJcwX8PpzM9tpP8DRFCCCFuwD/v7E2wZ9PPN5Y5D82kNY+hE63PxYsX+frrrykrK+ORRx5pEQFE4qUCbDQauvq5AFBRaWTN4TTu6BuInU3L7SERQgjRfrTm+7WCq3rmrj7KpOhAhtaxKtPWk1l8+XMa/5jQEw+nxu0ZZZFP7O3bt1NcXFxrWnFxMdu3b7dENkKIBurUqROPPPIIt956q2kZV71eb7XyfLb/ImPf2snfvjlO9fcVdjYapsR0ksBBCCGEsIDPDlzkZGYhgyK86zxnUIQ3p7OKWLYnpdH5WORTe8iQIZw4UXPHPIDTp08zZMgQS2QjhLgO9vb2xMbGoigKiYmJvPPOOw1a3KAp3BLhjc5Gg6+rPWV6o1XKIIQQQrRla49eYmpMJ9MiJLWx0WqYGhPElpNZjc7HIsHDtUY+6fX6Fj1pU4j2ICAgAFdXV5YuXcq2bduadAGDSoORFftSeGfbOdMxf3cHts8ZwlvT+uJgp22yvIUQQoj2KulyCb0C6980tkeAG0lXShqdT6MnTBcWFpKfn296npmZWeNbzdLSUj755BP8/PwaXUAhxI1zd3fn3nvvZefOnfz444+cP3+eKVOm4OLiYvG89ifl8sKa49hpNdzW25+gXydvVW/6JoQQQgjLqzSq2NSziRyAjUZBb2j8KIBGBw//+te/ePnll4GqpSLvuOOOWs9TVZXnn3++sdkIISxEo9Fwyy230LlzZ3bt2oWDg4PFrn21ohJHu6o/JwlhHRjX259+ndzxlYBBCCGEaBY+rjrOZhcT17nDNc87k12Mt4uu0fk0OngYPnw4zs7OqKrKM888w6xZs2psBKfT6ejZsyeDBg1qdAGFEJYVGBjIlClTAMjKymLXrl2MGjWqUcFE/tUKXl1/kv1JuWx+6hbsbbUoisJb0/pauthCCCGEuIa40A58ujeFKTFB2NYx70FvMLJibwoJ9QQY19Lo4CEhIYGEhAQASkpKmDlzJv7+/o0uiBCi+RUVFXH27FlSUlK44447CAkJua7X62y07Dh7mazCcnacvcKwbrUvDSeEEEKIpvXAwFBue3snDy8/yD8m9KzR+59VWMazq49y4XIJ/57ap9H5yD4PzaQ1rxss2raCggL++9//kpyczE033cSQIUPQamuf1FxpMLLj3BWGdPUxHdt2Oht3B1v6dvJoriILIYQQTaK136+t2n+Rv/33OBpFoUeAq2neYWruVY6nF2JUVV4d34OpsZ3quVLdGt3z8Ednz57l/fff5+TJk5SWlpqlKYrC1q1bLZWVEMKC3Nzc+POf/8yePXvYs2cPcXFxtU6k1huM3P72Lk5kFLJyZhwDwrwAzAIJIYQQQljPtNhORPi68O62c+y5kMPh1KrFjRxstdwS4c1jQ8Lod4Nf9lkkeDh+/Djx8fEEBARw7tw5evXqxZUrV0hPTycoKIiwsDBLZCOEaCIajYabbrqJmJgY7OzsKC0t5fTp0/Tu3RtFqVq5wVarITrEg0sFpeSWVFi5xEIIIYSoTf9gD5bcG4PRqJJ7terz2tPRDk0DVmJqCItswPD8888zYsQIEhMTUVWVJUuWkJqaytq1aykrK+PVV1+1RDZCiCZmZ1e1Vf3JkydZ+d8NTHnjG1Ky80zpfx3WlZ+eHsLYXjK/SQghhGjJNBoFL2cdXs46iwUOYKHg4dChQ8yYMcO0GZzRWLV27JgxY3j66ad57rnnLJGNEKKZ9OvXj9PucezPseWxdzdw4cIFANwcbXFztLVy6YQQQghhLRYJHvLy8vD09ESj0WBra0te3m/fVEZHR3Po0CFLZCOEaEIGo4rB+Nv6CS+O70ufQFcGdNSwYsUKs00hhRBCCNE+WWTOQ0BAAFeuXAEgPDyc7du3M2zYMACOHj2Ks7OzJbIRQjSRXeeu8PLaE9x7UwjTfl2BISbEkzWPDwSqVp9wd3fHaDSSn5+Pp6enNYsrhBBCCCuxSPAwcOBAdu/ezfjx47nrrruYN28eGRkZ2NnZsXTpUu6++25LZCOEaCKnM4s4nVXERzuTmBoTZJokXf1vUFAQAAcPHmTz5s0MHz6c6OhoU7oQQggh2geLBA8vvPACly5dAmDu3LlkZmayYsUKFEVh8uTJvPHGG5bIxmo++ugjXnvtNTIzMwkKCmLdunWEhYXV6FG5evUq//znP/nrX/9qpZIK0TBZhWVcrTAQ6uUEwN3xwZSUV3JPQsg1A4I+ffpw+fJlNmzYwNmzZ7n99ttxcnJqrmILIYQQwspkk7h6rF27lhdffJFVq1YRFRXF+fPn6dChAx4e5mvkZmRkEBQUxNmzZwkNDa1xnda+6YhoOzYdz+Spz3+hd5Abq2bGN6r34MyZM3zzzTcoisKDDz6Iu7t7E5RUCCGEaF5yv1a/G54wXVpaSkBAAGvXrrVEeVqcV155hYULF9KtWzcURSE8PLxG4ACwYsUKEhISag0chGhJega6YVRV9AaVwrLKRl0jIiKCRx99lNjYWNzc3IDfVlkTQgghRNt1w8GDg4MDpaWlTTp0oaioiGeeeYbhw4fj7e2Noii89NJLtZ5bXFzMk08+ib+/P/b29vTp04fPPvusUfkaDAYOHz7M0aNHCQwMJDQ0lPnz51NbZ83y5cu55557GpWPEE3ppzOXWboryfQ8wN2BtbMG8tUjCbg5NH7ZVWdnZ2655RYUReHMmTO8//77ZGVlWaLIQgghhGihLLJU69ChQ9myZYslLlWrnJwcPvjgA8rLyxk/fvw1z50wYQKffPIJ8+bNY+PGjcTExDBt2jRWrlx53flmZWVRWVnJ999/z/Hjx9m2bRsrVqxg2bJlZucdO3aM06dPM2nSpOvOQ4imdOhiHjM+2s9rG06RmnvVdDzC18Wik52rhy0tXryYvXv31hpgCyGEEKL1s8iE6eeff56JEydib2/PhAkT6NixY40bkxtZ2jE4OJi8vDwUReHKlSt8+OGHtZ63YcMGvv/+e1auXMm0adMAGDJkCCkpKcyZM4cpU6ag1WqBqoBn165dtV5nzpw5vPLKKzg4OABVk8Dd3d1xd3fn4YcfZtOmTcyYMcN0/rJly7jttttk3LdoEQxGFe2vO0n2DXLn1kgfOns54WrfdJu7+fj4MHPmTLZs2cJ3333HuXPnmDBhAo6Ojk2WpxBCCCGan0WCh/79+wPw0ksvMX/+/FrPMRgMjb5+Q78hXbNmDc7OzjV6AO677z6mT5/Ovn37GDBgAABbt26t93oeHh74+/tfM3+j0cjKlStZtGhRg8oYHR2NjU39P/bZs2cze/bsBl1TCIDSCgOLfjrPpuOZfDvrJnQ2WhRF4cN7oi26LX1dbGxsGDlyJOHh4ezatatB9VwIIYQQrYtFPt3//ve/t4j13o8fP05UVFSNm5ZevXqZ0quDh4a69957WbBgAX379qWgoIDFixfz4osvmtK3bt2KXq9n1KhRDbpeQ8eEFxYWXlc5hVBRWbX/ItlF5aw7ksHE/lWrRDRH4PB74eHhhIWFoSgKOTk57N+/nz/96U/Y2jZdz4cQQgghmodFgoe6Ji83t5ycHDp37lzjePWQqZycnOu+5rx583j88ccJDAzExcWFhx56yGzTu+XLlzN16tQGf8vq6+vboHNdXV2vu6yi/TmeXkCPgKrVjhztbHj59u4YjDC6p59Vy1X9ZUJ2djaHDh3iwoULpiGNQgghhGi92ty4gmv1gDSmd8TOzo7FixezePHiWtP/OHm6Pj///LOsGyxumNGo8uCyn/nhVDafPRRPfOcOAIzs0bJuzqOionjooYf4+uuv+fDDDxk6dCgJCQktoqdSCCGEENfPIqstaTQatFrtNR/NoUOHDrX2LuTm5gI3NmlbiJZEo1EIcHfAVqtwKqNlD3Hz9vbmgQceID4+nu3bt1NUVGTtIgkhhBCikZpszsPly5fZvHkzBoOh2fY/6NmzJ6tWraKystJsaNCxY8cA6NGjR7OUQwhLu1pRyZIdSUyJDcLHxR6Ap4ZF8ODNoQR3aLo9VizFxsaGYcOGcdNNN+Ho6Eh5eTnJycl07drV2kUTQgghxHVo0jkPFRUVjBgxAh8fH0tkU6877riDxYsXs3r1aqZMmWI6/sknn+Dv709cXFyzlEMIS3vis1/4/kQWlwrK+MeEngB4Otnh6WRn5ZJdn+qlW48cOcLGjRvp27cvI0eOxM6udb0PIYQQor1q0jkPdnZ2zJo1i7lz5/Loo4/e0LU2btxISUmJacjDiRMn+OqrrwAYPXo0jo6OjBo1imHDhvHoo49SWFhIeHg4q1atYtOmTXz66afNNnxKCEtQVdXUo/fQLZ05lVnIzV28rFwqy4iJicHGxoZNmzaRkpLChAkTCAgIsHaxhBBCCFEPRW3irWA3btzIpEmTKC4uvqHrhISEkJKSUmtaUlISISEhABQXF/PCCy/wxRdfkJubS2RkJM899xxTp069ofxvVFpaGkFBQaSmpsqEaXFNpzOLeG3DSUb18GNqbCfT8UqDERutRaYptRg5OTl8/fXXZGVlMWvWLNzc3KxdJCGEEO2Y3K/Vr0mDh8uXLzNlyhQKCgo4ePBgU2XTKkhlFA314Y4LvLr+JAHuDvw0Z3CbCxj+yGAwcOHCBbp06YKqqhQXF+Pi4mLtYgkhhGiH5H6tfhYZthQaGlpjwnR5eTnZ2dloNBq+/fZbS2QjRJt0taKS/Kt6/N0dALgnIYSLuVe5/6bQNh84AGi1Wrp06QLAwYMH2bJlC2PHjpUFDoQQQogWyCLBw6BBg2oED/b29oSEhDBlyhTTkCIhhLk953N44rPDdPZ2YtXMeBRFwc5Gw8u3t88b5x49epCcnMzq1as5d+4co0aNQqfTWbtYQgghhPiVRYKHpUuXWuIyQrQ7QZ4O5JfqSc8v5UpxBd4u7ftG2d7enokTJ9KlSxc2bNhASkoK9913n+y4LoQQQrQQFhkTsX379jonRBcXF7N9+3ZLZCNEq3c6s4gvf041PQ/0cGT5/bFsmT2o3QcO1RRFoXfv3jzyyCNERUWZ5j808doOQgghhGgAiwQPQ4YM4cSJE7WmnT59miFDhlgiGyFatdOZRYx6czsvrDlOau5V0/G4zh3Q2cgywn/k4eHB8OHDURSFpKQkli5dSl5eXq3nJiYm8sYbb5CYmNjMpRRCCCHaF4sED9f6RlCv16PRtP1Jn0LUJ8LXmYSwDgyN8kGjUep/gTCxtbWlsLCQRYsWcfToUbO0kpIS1q1bZ/avEEIIIZpGo+c8FBYWkp+fb3qemZnJxYsXzc4pLS3lk08+wc/Pr/ElFKIVMhhVvj6Uxpc/p7H8wVh0NloURWHJjBjsbaWX4XoFBgby8MMPs3HjRtasWcPZs2cZM2YMOp2OdevWUV5eDlSt8rZ+/XomT55s5RILIYQQbVOjg4d//etfvPzyy0DVGOU77rij1vNUVeX5559vbDZCtErllQb++d1psovK+eJAKn9OCAGQwOEG2Nvbc8cddxAeHs7u3buBquFKp06dMp2jqionT54kMTGR7t27W6uoQgghRJvV6OBh+PDhODs7o6oqzzzzDLNmzaJTp05m5+h0Onr27MmgQYNuuKBCtHTp+aUE/LpXg6OdDS+MiSKrsIxJ0UFWLlnb0rNnT3r06MHVq1dZt25dreesW7eOkJAQnJycmrl0QgghRNvW6OAhISGBhIQEoGrM8cyZM/H397dYwYRoLVRV5e/fJLJiXwqrZsYT17kDALf3CbByydq2devWUVFRUWuaDF8SQgghmoZFZjLPmzdPAgfRbimKglFVMaqw4+wVaxenXbh8+TKnTp2qc7GG6uFLKSkpzVwyIYQQom2zyCZxAGfPnuX999/n5MmTlJaWmqUpisLWrVstlZUQVmUwqqw5nM6gCG/T3gxP/imC2/sEEBvqaeXStQ/e3t5ERkZy+vTpWgMIRVHw8PBg6dKleHt7ExISYno4OjpaocRCCCFE22CR4OH48ePEx8cTEBDAuXPn6NWrF1euXCE9PZ2goCDCwsIskY0QLcLc1Uf56mAad8V14n/u6AmAt4tONnlrRoqiMHbsWJKTkykrK6uRrtPpmDx5MtnZ2SQlJXH+/HkOHDjAwIEDGTp0KPn5+WRkZBAcHCzBhBBCCHEdLBI8PP/884wYMYLPP/8cOzs7lixZQr9+/Vi/fj33338/r776qiWyEaJFmBwdxObETEK9ZDKuNTk5OTFmzBhWr15dI23s2LH4+vri6+tLz55VAV5hYSGKUrW/xvnz502TrX19fQkODqZr16507ty5+d6AEEII0QpZJHg4dOgQ7777rmkzOKPRCMCYMWN4+umnee655/jpp58skZUQzepyUTn/2nKGPkHuTP511aTYUE92PzcUZ53FRv2JRurevTuJiYmm4UuKohAZGVnrMq2urq6m//fv35/w8HCSk5NJTk7mzJkz6PV6OnfuTFFREbt27SIkJITg4GAcHBya8y0JIYQQLZpF7n7y8vLw9PREo9Fga2tLXl6eKS06Otq0H4QQrc23Ry6xct9FtpzI4vY+/uhsqvZpkMChZfjj8CWdTseYMWMa9Fo3Nzd69+5N7969AaisrASgoKCA06dPs2/fPgD8/PyIiIhgyJAhTfMmhBBCiFbEIndAAQEBXLlStcpMeHg427dvZ9iwYQAcPXoUZ2dnS2QjRJMzGlUKSvV4ONkBcHd8Jw6l5DFjQIgpcBAti5OTE2PHjmXjxo2MGjWq0Xs72NhU/TkMDAzkiSeeID8/39QzUVxcDIBer+eTTz4hKCjI1DNhb29vsfcihBBCtHQWCR4GDhzI7t27GT9+PHfddRfz5s0jIyMDOzs7li5dyt13322JbIRoUicuFfL0l0dwd7RlxYNxKIqCzkbLO3f1s3bRRD26d+9u8R2l3d3d6dOnD3369DEdKy8vx8vLixMnTrB3714URcHf35/7pY14+AAAIABJREFU778fjUZDZWWlKQgRQggh2iKLfMq98MILXLp0CYC5c+eSmZnJihUrUBSFyZMn88Ybb1giGyGalIu9Deeyi9HZaEjLKyXIU1bhEeacnZ0ZP348qqqaeiby8/PRaDSoqsq///1v3NzcCA4ONvVM6HSyCpcQQoi2Q1Hr2mVJWFRaWhpBQUGkpqYSGBho7eIIqiZDH7qYx4jufqZjmxMziQ7xxPPXYUtCNJTBYODIkSOmoU5FRUUoisJTTz2Fi4sLly9fxtXVVYIJIYRoweR+rX4W71/Pzc1lwYIFHD9+nICAAJ544gm6detm6WyEuCEXc64y6s3t6I0qW2cPMvUyDP9dICHE9dBqtfTr149+/fqhqip5eXmkpqbi4uICwBdffEFOTg7+/v6mDeuCg4OxtbW1csmFEEKIhmt08PD000/zxRdfcPHiRdOxkpISYmJiSE5ONu36+tlnn7F//366du1646UVwkKCPB3oFehOSUUlJRWV1i6OaGMURcHT0xNPz992HJ86daqpV+LIkSPs2rWLBx98kICAAC5cuIDRaKRTp07Y2UmvlxBCiJar0cHD7t27mTp1qtmxt99+m6SkJJ566inmzZvHiRMnmDhxIq+//joff/zxDRdWiMbaff4Ky3an8Oa0PuhstCiKwrt39cPNwRaNRrF28UQ70KFDBzp06ED//v1RVZWcnBxTcHHgwAFOnTqFRqMx9Uz07t0bLy8vK5daCCGEMKdp7AsvXLhAdHS02bG1a9fi7e3NggULcHV1JT4+ntmzZ/Pjjz/eaDmFaLQyvYEnP/uFTYmZrNj7W0+Zh5OdBA7CKhRFwcvLy7Sx5uTJk3n88ccZOXIkbm5uHD58mNzcXABOnTrFDz/8wIULF9Dr9dYsthBCCNH4nof8/Hw6duxoel5ZWcmBAwcYP348Wu1v6+H37duXjIyMGyulENepqEyPi33VWHJ7Wy1zRnTleHoBt/fxt3LJhKipOpjw8vIiJiYGVVVNQz/z8vI4ePAgO3bsQKPREBgYSFxcnMwlE0IIYRWNDh58fX3NgoJDhw6h1+tr9EZoNBpZXUQ0q/d/Os9bP5zjo3tjiA2tGhYyKTqISdFBVi6ZEA2jKAqKUtUrlpCQQHx8PJcvXzbNmTAajQCcPn2a3bt3myZgBwYGygRsIYQQTarRwUP//v1ZvHgxkyZNQlEU074OQ4cONTvv1KlTZj0UQjS1lNyrFJdXsvpgmil4EKI1UxQFHx8ffHx8iI2NNR3X6XQ4OTlx4MABtm/fjlarJTY2luHDh2M0GjEajbJpnRBCWEhxeSVvbT3LiYxCEi8VkltSwRNDu/DUsIjrvtYb353m7W3niPB1ZvNTg2qk7zx7hf/7/jQnMwpxsNVya6Qvz42OxMvZ+l/IN/pTZe7cudx000107doVLy8v9u7dy80330y/fua78a5du5aYmJgbLqgQddlzPocuvs6mBvXUnyKIDfHktt4yREm0bdU9Dqqqkp2dTXJyMq6urgCkpqayfPlygoKCCA4OJjQ0lICAAAkmhBCikfJKKli5/yJRHV0Z3s2Xzw6kNuo6iZcK+GDHhToDgb0Xcrj34/0MifRh8T3R5BRX8PrGU9y1eB/fzroJnY221tc1l0ZPmI6Li+Obb77B39+foqIiHnzwQdasWWN2TmZmJmlpadx+++03XFAhavP6xlNMW7yXf285Yzrm7aJjfN8AmQwt2g1FUfD19SUuLo6oqCgAPD09+dOf/oROp2Pfvn0sXbqUpUuXAqCqKhcvXqSyUpYpFkKIhgr0cODovOF88XACz4yMbNQ1Kg1G5nx5lOmxnQjzdqr1nH9sOEmolxPv3dWPm7t4M75vAO/c1ZfTWUV88XPajbwFi7ihr6DGjBnDmDFj6kz38/PjyJEjN5KFVTk7O5s9v3r1Kv/85z/561//CsBHH33Ea6+9RmZmJkFBQaxbt46wsDBrFLXdGtzVmw93XMBWq0FVVdM4cSHaOxcXF+Lj44mPj8doNJKdnU1ZWRlQtZnnxx9/jI2NDUFBQaYejKCgIGlDQghRB0v8fXzvx/MUlOqZM6Ir9y89UCM9s6CMI2kFPDOyKzba377j7x/sSWcvJzYnZvLn+OAbLseNkP7rayguLjb9PyMjg6CgICZMmABUDcd68803+fbbb4mKiuL8+fNmG0IJyyvTG1iyM4lADwdu7xMAQHznDuyYO4SObg5WLp0QLZdGo8HP77fd0z08PHjooYdME7B3797NgQMHmD17NgAHDx7Ex8cHf39/s9XzhBBCNN7ZrCLe2naO9+/uj5Ou9lvw01lFAET5udZIi+zows/JeU1axoaQ4KGBVqxYQUJCAqGhoQC88sorLFy40LRcYnh4uDWL1y588XMq//zuND4uOoZ188XRrqr6SuAgxPXRaDR07NiRjh07kpCQgNFopLCwEEVR0Ov1bNmyhbKyMmxtbU09E7GxsbJynhCi1Vq4cCELFy6s9zyDwdAk+RuNKnO+OsrI7n4MifSp87z8qxUAuDnWXDnPzcGO/KvW3++n0XMemktRURHPPPMMw4cPx9vbG0VReOmll2o9t7i4mCeffBJ/f3/s7e3p06cPn332mUXKsXz5cu655x6gqmIdPnyYo0ePEhgYSGhoKPPnzzetyy4sp0z/WyOeEhNEXKgnz42OxN7Kk4WEaEs0Gg3u7u4A2NraMmfOHGbOnMngwYPRarXs27fP1AOxdetWdu7cSVpaWpN9yAohhKUVFhaSnp5e7yMzM7NJ8v9w5wWSc0r4+7iG7dFT5wCpFjCytMX3POTk5PDBBx/Qu3dvxo8fz4cffljnuRMmTODAgQO8/vrrREREsHLlSqZNm4bRaGT69OmNLsOxY8c4ffo0kyZNAiArK4vKykq+//57jh8/Tn5+PsOHDyckJIQZM2Y0Oh/xm9Tcq8xfe4JSfSWfPhCHoijobLR8/nCCtYsmRJun0Wjw9/fH39+fAQMGmM0nysnJ4fz582zduhVbW1s6derEqFGj6NChg5VLLYQQdXN1dSUgIKDe8wwGg8UDiPT8UhZ+f4a5IyOx1WooKK3qPTAYVYwqFJTq0dlosLfV4u5oB1BrD0NBaQXuDtbfy6fFBw/BwcHk5eWhKApXrlypM3jYsGED33//vSlgABgyZAgpKSnMmTOHKVOmmL45Gzp0KLt27ar1OnPmzOGVV14xO7Zs2TJuu+020zdzDg5Vw2Tmzp2Lu7s77u7uPPzww2zatEmCBwvafuYyBlXlbHYxEb4u1i6OEO3W7ycJTp48GYPBQEZGhmnOhL29PQDr168nPz/fNAG7Y8eOaDQtvoNbCNEOzJ492zSv61rS0tIICrLsprIXc65Spjcyf+0J5q89USO99/zN3HdTCPPGdafrr/c7pzKLagxvOpVZRFc/698PtfjgoaEz29esWYOzs7Opd6Dafffdx/Tp09m3bx8DBgwAqrrdG8poNLJy5UoWLVpkOubh4YG/v3+jZt1HR0c3aJ31hlbytqJMb+CX1HziO1d9exnk6cjrE3vSK9CdcB/nel4thGhOWq2WwMBAAgMDGThwoOm4r68veXl5/PTTT2zZsgWdTseECROIiIigvLwcW1tbCSaEEO1ON39XVs2Mr3H85XUnKCrT8887e9PRrepLGD83e3oHufPfw+k8dEtntL8uO3/oYh4XLpdw/02hzVr22rT44KGhjh8/TlRUVI0b8169epnSq4OH67F161b0ej2jRo0yO37vvfeyYMEC+vbtS0FBAYsXL+bFF1+s93pZWVkNyrewsPC6y9paZReWcce7u7lcXM4Pfx1EoIcjABP6BVq5ZEKI6xEdHU10dDQGg4FLly6RnJyMt7c3AD/99BOHDh0iODiY4OBgQkJC8PPzk2BCCNGqbDudTWmFgeLyqn1yzmUXs+FYBgBDuvrgYKflma+OsPpQOj/NGUyghyNuDrYkhNUc2ulqb4PBaKyR9uzISP68ZB+PrTjIn+NDyCkp5383nqKrrwuToq1/b9RmgoecnBw6d+5c43j18qk5OTmNuu7y5cuZOnVqjaBk3rx5PP744wQGBuLi4sJDDz3E3XffXe/1fH19G9TzUL1LbHvg7aIjyNMBo6pyKb/MFDwIIVonrVZLUFCQWdd/r169sLe3JyUlhW3btlFZWcngwYMZNGgQhYWFlJSU4OvrK8GEEKJFe3HNcdLzS03P1x/LYP2vwcOOZ4YQ5OmIwVg1n6Gx6+gkhHXg4/tiWPj9GR745AAOdlpujfTh+dFRVt9dGtpQ8ADXHuLU2I09li1bVutxOzs7Fi9ezOLFi6/rej///DOBgdaPGq3p/OViPtqZxLxx3bGz0aAoCv+a0gcPRzvsba3fKIQQlufn52faa6KyspJLly7h4lI1dvfYsWNs2bIFe3t7U89EeHi4qddCCCFail3P3lrvOf83uTf/N7l3veddaxGYm7t4c3OXlvk3sM0EDx06dKi1dyE3NxdANnBrISoNRu7+cB8ZBWWEeTtz/8CqsXuyV4MQ7YeNjQ2dOnUyPY+LiyMwMNA0AXvr1q3k5uYyZswYioqKSExMJCQkBF9fX9kBWwghrKzNBA89e/Zk1apVVFZWmg0LOnbsGAA9evSwVtHaPb3BiO2vW6zbaDX8v6Fd2Hoyi1sivKxcMiFES2BjY2PqcRg0aBCVlZVUVFRtlJSZmcmWLVswGAw4ODgQHBxMREQEffv2tXKphRCifWozg0vvuOMOiouLWb16tdnxTz75BH9/f+Li4qxUsvbt2yOXGPzPHzmQnGs6NjUmiA9nxBDuY/3lxoQQLY+NjQ2OjlVzn7p06cLcuXOZMWMGsbGxlJaWkpSUBEB5eTlffPEF+/btIzs7WzbqFEKIZtAqeh42btxISUkJRUVFAJw4cYKvvvoKgNGjR+Po6MioUaMYNmwYjz76KIWFhYSHh7Nq1So2bdrEp59+atrjQTSvPedzSM8vZfH2C8SEVA0dk2EHQojrYWtra9o7AjAFCSUlJZSUlLB582aMRiOOjo6EhYVxxx13yN8ZIYRoIoraCr6qCQkJISUlpda0pKQk0wdKcXExL7zwAl988QW5ublERkby3HPPMXXq1GYsbe2qNx1JTU1t0xOmL1wuxs3Blg7OOgCyi8r46mAa998UKpOhhRBNQq/Xk5aWRlJSEmVlZYwePRqj0cg777yDn5+fKfDw8vKSoEIIcU3t5X7tRrSK4KEtaA+VccnOJP6x4STTYjvxyniZYyKEsJ6Kigp27txJcnIy6enpGI1GnJyc+Mtf/oK9vT0lJSU4OjpKMCGEMNMe7tduVKsYtiRah24dXak0qmQVlmE0qmg08qEshLAOOzs7br21aknFiooKUlNTyczMxN6+ahfXJUuWUFFRYeqVCAkJoUOHDhJMCCFEPaTnoZm0tUjWaFRZe/QSOhsNI3t0NB0/camQbv7tZ4M7IUTro6oqFy5cICkpiZSUFNLT01FVlYcffhg/Pz/S09Oxt7fH09NTggkh2pm2dr/WFKTnQTTK6kNpzPnqKL6uOm6J8MbRrqoqSeAghGjpFEUhLCyMsLAwoGrVptTUVHx8fAD47rvvSE1NxcXFhZCQEIKDg+natSvOzs7WLLYQQrQIEjyIBvv9UKRxvf1ZsjOJsb06opFv5oQQrZhOpyM8PNz0/K677uLixYumTeuOHz+Ou7s7zs7OnDt3jsLCQkJCQvDw8JCeCSFEuyPBg6hXXkkF/95yhou5V/no3hgURcHeVsuG/3ezzGsQQrQ5Op2OLl260KVLF6CqZ6J689ELFy6wd+9eVFXF1dWVkJAQ+vXrR3BwsDWLLIQQzUaCB1Gv4vJKVu1PpcJg5EhaAX2C3AEkcBBCtAs6nc70/+HDh3PLLbeY9UwUFBQAcObMGU6cOEFwcDChoaG4u7tbq8hCCNFkJHgQNaiqyqnMIqI6Vs1fCPJ05IUxUYT7OJsCByGEaK/s7e2JiIggIiLC7HhlZSVZWVkcOXIEADc3N+Lj44mPj0dVVYsNcUpMTGTjxo2MGjWK7t27W+SaQgjRUBI8CDMFV/Xc8/F+TmUU8sPTgwlwdwBgxoAQ6xZMCCFauG7dutGtWzdKS0tJSUkhOTkZR0dHoGpD07Vr15otDevm5nbdeZSUlLBu3TrKyspYt24dISEhODk5WfqtCCFEnSR4EGZcHWywt9Fgo1E4canQFDwIIYRoGAcHByIjI4mMjDQdc3FxoWvXriQnJ/PLL78AEBkZyZQpU1BVlaKiIlxdr71anaqqrFu3jvLycqBqLsb69euZPHly070ZIYT4Awke2rnckgo+2Z3M40PCsbPRoCgK/zuxF446LT4u9tYunhBCtAne3t6MHDkSgKtXr5KSkmIaxnT58mXee+89PDw8zHom/hhMJCYmcurUKdNzVVU5efIkiYmJMnxJCNFsJHhox4xGlUmLdnP+cgnujrbcd1MoACFe0gUuhBBNxdHRkaioKNNzNzc3Jk+ebJqAffjwYdzc3HjyyScBOHXqFB4eHqxfv77W68nwJSFEc5LgoZ35/aQ9jUbhgYGdWb43hUg/2dxNCCGsQafTERUVZQooSkpKyM/PB6p6KT7//PNrvr68vJyvvvqKkSNHotFocHV1RafTodfrqaysRKvVotVq0Wg0si+FaFVkcYCWSVFVVbV2IdqD5tzu3GBU2Z+US3ZRGT4u9sSGeqLVKPycnMur60/yt7FR9A/2NJ0LoJVlV4UQokVKTk7mk08+afD5EydOpEePHuzdu5fvvvvOLK1r165MnTqV0tJS3n77bVNgUf24//770el0fP/992RkZJil9e3bl/DwcNLT0zly5IgpINFqtbi7u9OvXz8A9u/fb5am1WoJDw9Hp9Nx+fJlrl69apbm7OyMk5MTlZWVlJeXmwU7EvC0XyUlJbz99tuUlZVhb2/PX/7yl2bpXWvO+7XWSnoe2phNxzOYv/YEGQVlpmMd3eyZN64bP56+zC+p+Sz8/gwrHowHJGgQQoiWLjg4mMjISE6fPk1t3/cpikJISAhDhw7FYDDQoUMHACIiInBzc8NgMJgeLi4uAGi1WuLj483SDAYDWq0WqJr07ejoiMFgwGg0Ul5ejsFgAKpu6i5evGhKMxgM+Pr60q9fP1RVZfPmzaZzq/2///f/0Ol07Nixg2PHjpmlDRo0iMGDB5OUlMTKlSvN0jw9PZk1axYAb731lllwodVqmThxIn5+fuzbt4+TJ0+apXXp0oV+/fqRn5/Pjh07zNJ0Oh0333wzAEeOHKlx3ZCQEFxcXMjLyyM/P98s2HF0dMTNzQ2j0UhJSYlZmlarRVEUCXhukCwO0LJJ8NCGbDqewaOfHuKPHy2ZBWU8+ukhXp/QE1uthllDw61SPiGEENdPURTGjh1LcnIyZWVlNdJ1Oh0TJ06s8a2sp6cnnp6etV7Tzs7OdPNcm4EDB9aZVtseF78v64svvghgCiwMBgN2dnYAjBgxgkGDBtUa0Pj7+zN16lSzNFtbW9O1Y2JiqKioMEt3cKhaEdDBwQFXV1ezPKsDmIqKCjIyMszSbG1tTe9/z549XLlyxSzgmT59Oi4uLhw9epQff/zR7D326NGDiRMnUlBQwH/+858aP4O//e1vKIrCp59+yqVLl8x6UoYNG0ZUVBQnT55kz549ZmkdO3ZkyJAhVFZWsn79+hq9QgMHDkSn03Hq1Cny8/PN0vz9/fH29qa4uJjMzMwagZKXlxcABQUFZsGORqPBxsamxQU7sjhAyybBQxthMKrMX3uiRuAAoAIK8O+tZ9k591bpbRBCiFbGycmJMWPGsHr16hppY8eObZGTpauHHf0+AHBycqqzrE5OTnTt2rXO68XHx9eZ1qtXL3r16lVrmo+PDw899FCdr33kkUeAqhtUo9GI0Wg09cDExcXRq1cvU9BhNBpNO447Ozszffp0szSDwWC6Ee/duzchISFmwUz13h729vZ06NDBLK26V8loNJqCmd8/EhISADh+/Dhnzpwx5QlVO597e3uTkpLCV199Zfb+/Pz8ePjhh4Gq3ps/9go9+uij+Pj4sH79eo4dO2YWWMTGxjJgwAAuXbrEhg0bzIajubi4cPvttwOwadMmKioqzIKW6OhoPDw8SE5ONguitFotXl5eBAUFUV5eTkpKillaRUWFLA7Qwknw0EbsT8o1G6r0RyqQUVDG/qRcEsI6NF/BhBBCWET37t1JTEw0DV9SFIXIyEj5JtZCFEUx3cBWs7e3x96+9mXLbW1t6dKlS53X69mzZ51poaGhhIaG1ppmZ2fHAw88UOdr77zzTtP/qwOeahERETz55JNmvSwajcaUPm3atBrBTvWSwJGRkXh4eJil+fj4mMrk4+Njdt3f91bk5+dTXFxsFux069bNFDzs3bvXLEjq168fQUFB5Ofns2rVqhrvsa6eEBm+1DJI8NBGZBfVHTg05jwhhBAtyx+HL+l0OsaMGWPtYgkrqg54qtna2l5z5/KwsLBrptWV7uXlxW233Vbna6dOnVpn2uDBgxk8eLDpuaqqpl4WLy8vZs+ebQpKsrOz+fzzz2ud21P92pMnT5KdnW0KbETz09R/imgNGrqhm2z8JoQQrZeTk5NpmFJLHa4kxLUoimLqDake/uTm5oanpyddu3YlMjKyzp4HRVGIioqSwMHKpOehjYgN9aSjmz2ZBWW1zntQAD+3qmVbhRBCtF7du3eXoUqiTWrI4gDS22Z90vPQRmg1CvPGdQOqAoXfq34+b1w3mSwthBBCiBarenGA2khvW8sgwUMbMrJHR967ux9+buZDk/zc7Hnv7n6M7NHRSiUTQgghhGiY7t27mw1fqh6uJD1uLYMMW2pjRvboyLBufrXuMC2EEEII0dLJ4gAtmwQPbZBWo7Dnm08oLCzE1dWVhNmzrV0kIZrUwoULTfV9ttR30cZJfRftQfXwpOqVl2S4UsuhqHWthyUsKi0tjaCgIFJTUwkMDGzy/AIDA0lPTycgIIC0tLQmz08Ia5L6LtoTqe+ivbBGXW/u+7XWSOY8CCGEEEIIIRpEggchhBBCCCFEg0jwIIQQQgghhGgQCR6EEEIIIYQQDSLBgxBCCCGEEKJBJHgQQgghhBBCNIgED0IIIYQQQogGkeBBCCGEEEII0SCyw3QzMRqNAGRkZDRLfpWVlaZ/ZRMh0dZJfRftidR30V5Yo65X36dV37eJmmSH6WZy4MABYmNjrV0MIYQQQghRj/379xMTE2PtYrRIEjw0k8rKSg4fPoyvry8ajYwWE0IIIYRoaYxGI1lZWfTt2xcbGxmgUxsJHoQQQgghhBANIl+BCyGEEEIIIRpEggchhBBCCCFEg0jwIIQQQgghhGiQNhc8KIpS7+Oll166rmsuXbrU7PU2NjYEBgZy3333kZ6e3jRvpBZHjx7lvvvuIzQ0FHt7e5ydnenXrx8LFiwgNze3yfK99957Te+9R48eZmk//vhjnT/nvXv3Nuj6+/fvZ8SIEbi4uODs7MyQIUPYtWtXjfMamtd///tfs7Sff/75xn4AVvLHevf7x9NPP92gazRFe6itbNZoE9ZqD1B3m7BEe4CGtYnryas1tAmp79cm9b3113dL1PH6SBuwPLkHqqnNTSPfs2dPrccrKyu55557SE9PZ/To0Y269scff0xkZCSlpaVs376df/zjH/z0008cO3YMJyenGyl2vRYvXsxjjz1G165dmTNnDt26dUOv1/Pzzz+zaNEi9uzZw5o1a5osfz8/P9asWYOjo2Ot6a+99hpDhgwxO/bHRlabAwcOcMsttxAbG8vy5ctRVZUFCxYwdOhQtm3bRkJCwnXnNWjQIPbs2cP69et59dVXG/L2WrTqevd7/v7+DXptU7aH35etuduEtdsDXLtNNLY9wPW3iYbk1ZrahNT3mqS+X19eLb2+30gdr4+0gaYh90B/oLYTs2bNUgH1/fffv+7XfvzxxyqgHjhwwOz43/72NxVQP/30U0sVs1a7d+9WtVqtOnLkSLWsrKxGenl5ufrNN980Wf4zZsxQg4ODa03btm2bCqhffvllo649YsQI1dfXVy0pKTEdKywsVL28vNQBAwbcUF51/d5ai6Ys/420B1W1bpuwdntQ1brbxI22B1VteJtoTF4tuU1Ifa+d1PfG59XS6rs1yyNtoPHkHqimNjdsqTbLly/nrbfe4oEHHuChhx4yHX/ppZdQFIXExESmTZuGm5sbvr6+3H///RQUFNR73fj4eABSUlLMjp86dYpp06bh6+uLTqejU6dO3HPPPZSXlzeq/K+99hqKovDBBx+g0+lqpNvZ2XHbbbc16trWtmvXLgYPHmwWzbu4uHDLLbewe/fuZtuRuz2pqz1A07QJaQ/XR9qEZUl9b9mkvje9proHAmkDN6q11v82HzwcPnyYhx9+mJiYGN55551az5k4cSIRERGsXr2aZ599lpUrV/LUU0/Ve+1z584B4O3tbTp25MgRYmJi2Lt3Ly+//DIbN27kH//4B+Xl5VRUVNR6HUVRGDx4cK1pBoOBH374gf79+xMUFFRvmazl8ccfx8bGBldXV0aMGMHOnTtrnFPb+6yoqKj1j0H1sWPHjjUqr7bEYDBQWVlp9mishrQHsFybaEx7gLrbRFtqD2CZNtHW2oPU999IfW+b9d2Sdbw+TXkPBJZpA3IP1ArvgSzel9GCXL58WQ0ODla9vb3Vixcv1kifN2+eCqgLFiwwO/7YY4+p9vb2qtFoVFX1t66fvXv3qnq9Xi0qKlLXrVunent7qy4uLmpmZqbptbfeeqvq7u6uZmdnN7icWq1WvfXWW2tNy8zMVAF16tSpDb6eqqrqkiVL1LCwMNXJyUmNjIxUz507p6qqqmZnZ6ujR49WHR0d1S5duqibN2+u91rX6rI7dOiQ+sQTT6hr1qxRt2/frn700UdqVFSUqtVq1U2bNtX7Pvv06aNGREThoRE4AAAIqklEQVSoBoPBdEyv16udO3dWAXXlypWNyktVW16X9fWqLn9tD71eb3bu7t27VUVR1FdeeaXO69XXHlTV8m2iMe1BVetuE5ZuD6pq2TZxvXX0RtrE9ealqi27TTSkvh8+fFgdMGCA6uLiooaGhqqLFy+u83pS36W+t7T63pA6/u6776p9+/ZVbWxs1Hnz5tV6nWv9fn/PUvdAvy97U7QBuQdqffdAbTZ4qKysVIcOHapqtVr1hx9+qPWc6oZz6tQps+OLFi1SAVNjqKvB9+zZU925c6fpdSUlJapWq1Ufeughi72PxjScb7/9Vu3Vq5eamJioGo1G9ezZs2pubq6qqqo6adIk9f7771dLSkrUb775RnV3d1evXLlyzetdq+HUJi8vTw0MDFR79epV77lLlixRAfXRRx9V09LS1IsXL6oPPPCAqtVqVUD97LPPGp1XS/vguF7V5V+2bJl64MABs8fvGQwGNTY2Vo2Li6szeGhIe1BVy7aJ1tAeVLXp28T1tAdVvbE2UV9eLblNNKS+9+jRQ50/f75qMBjUgwcPqs7OzuqJEydqXEvqu9R3VW159b0hdXzNmjXqt99+q9555521Bg/1/X6rWfIe6Pdlb+1tQO6BLKPNBg+zZ89WAfWNN96o85zqhnP58mWz49U/8KSkJLPn1Q3+8OHD6qVLl2pcLy0tTQXUl19+2WLvo7KyUnV0dFTj4uIa/JqYmBh1y5YtNY4XFRWptra2Zt9ADBo0SF2yZMk1r3e9DUdVVfWRRx5RAfXq1av1nvv666+rzs7Opj9GCQkJ6ty5c1VA3bFjR6PzamkfHNeroeV/99131aeeekqdMWNGncFDQ9qDqlq2TbT09qCqzdcmrqc9qOqNtYlr5dWS20RDyubs7KyeOXPG9DwmJkb9+uuva5wn9V3qu6q2vPp+PeV54IEHag0ervX7/T1L3gP9/lhrbgNyD2Q5bXLOw6pVq1i4cCFTpkzhr3/9q8WuGxUVRXR0NH369KFjx4410j09PdFqtaSlpVksT61Wy9ChQzl48GCDrmswGDh8+DBHjx4lMDCQ0NBQ5s+fj6qqnD17FmdnZ7Nxgz179iQxMdFi5a2mqipQNcavPnPnzuXKlSscO3aM5ORkdu/eTV5eHk5OTvTv39+iebU1OTk5vPnmm8ybN6/Oc5qqPcC120RLbw9As7WJ662jN9Im2nJ7mDVrFp9++imVlZXs37+f1NTUGksZSn2X+t5W1ff7rSZtQO6BmlqbCx6OHj3Kgw8+SI8ePViyZEmz5u3g4MCgQYP48ssvuXLlisWu+9xzz6GqKjNnzqx1wpFer2ft2rUAZGVlUVlZyffff8/x48fZtm0bK1asYNmyZRQXF+Pq6mr2WldXV4qLiy1WVoC8vDzWrVtHnz59sLe3b9BrdDodPXr0IDg4mIsXL/L5558zc+ZMHBwcLJ5XW/Lcc88xe/Zs3Nzcak2X9lB3ewCapU00to42pk209fYwYsQIli1bhr29PQMGDODll1/Gz8/PlC71Xep7W1bf7xekDcg9UPNoU5vE5eXlMX78eMrLy5k7d26ts9ShalWAsLCwJinDwoULGThwIHFxcTz77LOEh4eTlZXFt99+y/vvv4+Li0uN19jY2DBo0CC2bt1a6zUTEhJ47733eOyxx+jfvz+PPvoo3bt3R6/Xc/jwYT744AN69OjBuHHjTBVt7ty5uLu74+7uzsMPP8ymTZt45plnKCwsNLt2YWEhzs7OjX6/06dPp1OnTkRHR+Pl5cXZs2f5v//7P7Kysli6dGm97/P48eOsXr2a6OhodDodR44c4fXXX6dLly688sorjc6rPTh48CCHDh1i0aJFtaa31vYA124TlmoPM2bMwNnZ2aJt4nrr6I20ifbWHnJychg7diwffvghd955JykpKYwbNw4/Pz/GjRsn9V3qe5tX3++3tbYBuQdqffdAbSp4OHLkCElJSQD8+c9/rvO8GTNmNNkPunfv3uzfv5958+bx3HPPUVRUhJ+fH7feeit2dna1vsZgMGAwGK553ZkzZxIbG8u//vUv/vd//5fMzExsbW2JiIhg+vTp/OUvfwHAw8MDf3//WruvunTpQnFxMWlpaQQGBgJVFfdaP6v69OrVi88//5xFixZRXFyMp6cnAwcOZPny5cTExNT7Pu3s7Pjhhx/4z3/+Q3FxMZ06deKRRx7h2WefrbFb5fXk1R7s2LGDEydO4OPjA1R9q6jVajlz5gzLli1rte0B6m8TlmgPYPk2cb119EbaRHtrDxcuXMDZ2ZkpU6YA0LlzZ8aNG8d3333HuHHjpL4j9b2tq+/321rbgNwDtcJ7IIvPohBW9/zzz6tjxoxRCwsL1dTUVLVr167q8uXLVVVV1TvvvFN94IEH1KtXr6pr165V3d3da0yW+qPqyUJ6vV6trKxsjrdwQ4xGo6rX602rGLSUyXKWVlRUpKamppoekyZNUufMmVPvyhHtzbXag6pKm2gtCgoKVDc3N/Wrr75SjUajmpycrEZGRqqLFi2ydtFaFKnvrbu+6/V6tbS0VL333nvVF154QS0tLTX7mdf3+xVyD9Qc9V+ChzaovLxcffDBB1VXV1c1ICBAnT9/viktOztbHTVqlOrg4KCGh4er3333Xb3XmzFjhmkVgO7duzdl0S1izZo1ZkvJtaYPjhtxrdWW2rNrtQdVlTbRmmzevFnt27ev6uLionbs2FGdPXt2q/gwb05S31t3fa9eAen3j48//tiUXt/vV8g9UHPUf0VV/zBNX4g/SE5ONk1+cnD4/+3dsQmAQAyG0bSuctl/rxvBThBBfhARvPcmuCIpvuayVXd//KJ7c87j6mVV1RjjdPodnrITrMS8szLzfyUeAACAyO++agUAAN4hHgAAgIh4AAAAIuIBAACIiAcAACAiHgAAgIh4AAAAIuIBAACIiAcAACAiHgAAgIh4AAAAIjvnxI4vH9jk6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "markersize = 6\n",
    "\n",
    "values = [0.25, 0.50, 0.75, 1]\n",
    "labels = ['$\\mathregular{ZnPc:C_{60}}$ [5:95]', '$\\mathregular{F_{4}ZnPc:C_{60}}$ [5:95]', '$\\mathregular{F_{8}ZnPc:C_{60}}$ [5:95]', '$\\mathregular{F_{16}ZnPc:C_{60}}$ [5:95]']\n",
    "x_label = ''\n",
    "y_label = 'Saturation current density $\\mathrm{(A/m^2)}$'\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(8,5), dpi=100)\n",
    "\n",
    "plt.grid(False)\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.minorticks_on()\n",
    "plt.rcParams['figure.facecolor'] = 'xkcd:white'\n",
    "plt.rcParams['figure.edgecolor'] = 'xkcd:white'\n",
    "ax1.tick_params(labelsize=fontsize, direction='in', axis='both', which='major', length=8, width=2)\n",
    "ax1.tick_params(labelsize=fontsize, direction='in', axis='both', which='minor', length=0, width=2)\n",
    "\n",
    "plt.xlabel(x_label, fontsize=fontsize, fontweight='medium')\n",
    "plt.ylabel(y_label, fontsize=fontsize, fontweight='medium')\n",
    "\n",
    "plt.xticks(values, labels)\n",
    "\n",
    "plt.semilogy(values, J0_CT_list, label='$\\mathrm{J_0 = J_1 + J_2}$', marker='o', markersize=markersize+2, ls='-', linewidth=2, color='grey')\n",
    "plt.semilogy(values, a_CT_list, label='$\\mathrm{J_1}$', marker='d', markersize=markersize, ls='--', linewidth=1, color='grey')\n",
    "plt.semilogy(values, b_CT_list, label='$\\mathrm{J_2}$', marker='*', markersize=markersize, ls='--', linewidth=1, color='#f1c211')\n",
    "\n",
    "plt.legend(fontsize=fontsize, ncol=3, bbox_to_anchor=[0.06, 1])\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "ax2.plot(values, parameters['ECT (eV)'][:-1], marker='o', markersize=markersize, color='tab:blue', linestyle='dotted')\n",
    "ax2.set_ylabel('CT State Energy (eV)', fontsize=fontsize, fontweight='medium', color='tab:blue')\n",
    "\n",
    "ax2.tick_params(labelsize=fontsize, direction='in', axis='both', which='major', length=8, width=2, labelcolor='tab:blue')\n",
    "ax2.tick_params(labelsize=fontsize, direction='in', axis='both', which='minor', length=0, width=2, labelcolor='tab:blue')\n",
    "\n",
    "ax2.annotate('', xy=(0.75, 1.5), xytext=(0.59, 1.5), arrowprops=dict(arrowstyle=\"->\", color='tab:blue'), fontsize=12, color='grey')\n",
    "\n",
    "plt.savefig('/home/jungbluth/Desktop/J0_contributions.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_opt_list, b_opt_list, J0_opt_list = J0_CT(summary['LED QE'], parameters['Eopt (eV)'][:-1], parameters['l_opt (eV)'][:-1], parameters['f_opt (eV2)'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.6, 1.65, '')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "markersize = 6\n",
    "\n",
    "values = [0.25, 0.50, 0.75, 1]\n",
    "labels = ['$\\mathregular{ZnPc:C_{60}}$ [5:95]', '$\\mathregular{F_{4}ZnPc:C_{60}}$ [5:95]', '$\\mathregular{F_{8}ZnPc:C_{60}}$ [5:95]', '$\\mathregular{F_{16}ZnPc:C_{60}}$ [5:95]']\n",
    "x_label = ''\n",
    "y_label = 'Saturation current density $\\mathrm{(A/m^2)}$'\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(8,5), dpi=100)\n",
    "\n",
    "plt.grid(False)\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.minorticks_on()\n",
    "plt.rcParams['figure.facecolor'] = 'xkcd:white'\n",
    "plt.rcParams['figure.edgecolor'] = 'xkcd:white'\n",
    "ax1.tick_params(labelsize=fontsize, direction='in', axis='both', which='major', length=8, width=2)\n",
    "ax1.tick_params(labelsize=fontsize, direction='in', axis='both', which='minor', length=0, width=2)\n",
    "\n",
    "plt.xlabel(x_label, fontsize=fontsize, fontweight='medium')\n",
    "plt.ylabel(y_label, fontsize=fontsize, fontweight='medium')\n",
    "\n",
    "plt.xticks(values, labels)\n",
    "\n",
    "plt.semilogy(values, J0_opt_list, label='$\\mathrm{J_0 = J_1 + J_2}$', marker='o', markersize=markersize+2, ls='-', linewidth=2, color='grey')\n",
    "plt.semilogy(values, a_opt_list, label='$\\mathrm{J_1}$', marker='d', markersize=markersize, ls='--', linewidth=1, color='grey')\n",
    "plt.semilogy(values, b_opt_list, label='$\\mathrm{J_2}$', marker='*', markersize=markersize, ls='--', linewidth=1, color='#f1c211')\n",
    "\n",
    "plt.legend(fontsize=fontsize, ncol=3, bbox_to_anchor=[0.06, 1])\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "ax2.plot(values, parameters['Eopt (eV)'][:-1], marker='o', markersize=markersize, color='tab:blue', linestyle='dotted')\n",
    "ax2.set_ylabel('S1 Energy (eV)', fontsize=fontsize, fontweight='medium', color='tab:blue')\n",
    "\n",
    "ax2.set_ylim(1.22,1.98)\n",
    "\n",
    "ax2.tick_params(labelsize=fontsize, direction='in', axis='both', which='major', length=8, width=2, labelcolor='tab:blue')\n",
    "ax2.tick_params(labelsize=fontsize, direction='in', axis='both', which='minor', length=0, width=2, labelcolor='tab:blue')\n",
    "\n",
    "ax2.annotate('', xy=(0.75, 1.65), xytext=(0.6, 1.65), arrowprops=dict(arrowstyle=\"->\", color='tab:blue'), fontsize=12, color='grey')\n",
    "\n",
    "# plt.savefig('/home/jungbluth/Desktop/J0_contributions.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test the full / approximated derivation of J0 with CT and Opt Fit properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Limit of saturation current density based on CT properties\n",
    "def J0_CT_opt(EQE_EL, E_CT, l_CT, f_CT, E_opt, l_opt, f_opt):\n",
    "    \"\"\"\n",
    "    Function to calculate the limit of the saturation current density (J0) based on CT properties as defined by Koen Vandewal\n",
    "    :param EQE_EL: LED quantum efficiency (unitless) [float]\n",
    "    :param ECT: CT state energy(eV) [float]\n",
    "    :param l: Reorganization energy (eV) [float]\n",
    "    :param f: Oscillator strength (eV**2) [float]\n",
    "    :return: J0: limit of the saturation current density [float]\n",
    "    \"\"\"\n",
    "#     a_CT = np.sqrt(4*np.pi*l_CT*k_eV*T)*np.exp(-(E_CT + l_CT)**2 / (4*l_CT*k_eV*T))\n",
    "    a_CT = 0\n",
    "    b_CT = 2*np.pi*(E_CT-l_CT)*np.exp(-(E_CT/(k_eV*T)))\n",
    "    J0_CT = (q/(10*EQE_EL))*f_CT/(h_eV**3 * c**2)*(a_CT + b_CT)\n",
    "    \n",
    "#     a_opt = np.sqrt(4*np.pi*l_opt*k_eV*T)*np.exp(-(E_opt + l_opt)**2 / (4*l_opt*k_eV*T))\n",
    "    a_opt = 0\n",
    "    b_opt = 2*np.pi*(E_opt-l_opt)*np.exp(-(E_opt/(k_eV*T)))\n",
    "    J0_opt = (q/(10*EQE_EL))*f_opt/(h_eV**3 * c**2)*(a_opt+b_opt)\n",
    "    \n",
    "    J0_sum = J0_CT + J0_opt\n",
    "    \n",
    "    return J0_CT, J0_opt, J0_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "J0_CT_list, J0_opt_list, J0_sum_list = J0_CT_opt(summary['LED QE'], parameters['ECT (eV)'][:-1], parameters['l_CT (eV)'][:-1], parameters['f_CT (eV2)'][:-1], parameters['Eopt (eV)'][:-1], parameters['l_opt (eV)'][:-1], parameters['f_opt (eV2)'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YEBERERFRlzGxICIiIiKiLmNiQUREREREXcYF8pyMxWJBWloasrKyUF9fD5VKBa1WC71eD6mU/24iIiIi6h6saTqRrKwsbN++HSaTCYIgQBRFCIKAjIwM7N69G7NmzYJWq3V0mERERETkhNgVyklkZWVh06ZNMJlMAAB/TTHuTtwKf00xAMBkMmHTpk3IyspyZJhERERE5KSYWDgBi8WC7du3t9gWF5iBMO98xAVmtti+fft2WCyWngyPiIiIiPoBJhZOIC0tDSaTCW7Kavi7lcBfUwptwDkAgDYgC/6aUvi7lcBNWQ2TyYT09HQHR0xEREREzoZjLJxAVlYWBEHAw6Pft20TxebvrjIj7hvxiW37W/ufRGZmJgYNGtTTYRIRERGRE2OLhROor6+HKIrYlXYnrE3N/1JBQIvv1iYBu9LuhCiKqK+vd1CkREREROSsmFg4AZVKBUEQkFmiw6YT89o8pqnJBRpFLQCgrKwMycnJTDCIiIiIyG7YFcoJaLVaZGRktNjWJAIS4afvaUVxqDR6AgA8pBdQm7UTH32vg8p3DAyGBGi1WiiVSkeET0REREROgImFE9Dr9di9ezdMJhPqza6oa1ChpsENqYV6GILT4KaowfHc4ahtcIMgCNAo6hDtl40hoSmoMu5C5hktvturh9+AkdDr9YiNjYVcLnf0ZRERERFRH8LEwglIpVLMmjULmzZtQm2DG9b+8BtYRRcAAs4WGuAiWGEVm//Vc+fOhbu7O1JTz6AiaxfC3E5icMgZWJtccDTTA5ezT+JbuQQDIodBr9cjOjqaK3YTERER0U2xxugktFot5s+f38bK2xJYRQFKpbLFytvBwcEQxTuRl5eHk2mnkVVxFoAFiQNSMDLiGPIrdyDtOy12JcUjMnoIDAYDIiMj4eLi4tgLJSIiIqJeSRDFaxOTUnfKz89HaGgo8vLyMGDAgG47j8ViQXp6OjIzM1FfXw+VSgWdTof4+Pgbtjw0NTXh8uXLyEg9jsbyrzDQ6yzCvPMgigIOnP8VzhQMgqurK+Li4mAwGBAeHg6JhGP/iYiIyHn0VH3NWbHF4ibeffddrF69GmfPnsVf/vIXvPDCC7Z98+fPx/79+2E0GhEREYFXXnkF06ZNc1ywaO4WNWjQoA6vUyGRSBAZGYnIyEhYrXfj4sWL+CH9B4iVSSiu8gcARHichJ/xCxxI0uJqYxzi4g0wGAwYMGAAhGvz2hIRERFRv8TE4iaCgoLw4osvYuPGja32Pf/889i4cSPkcjmOHTuGiRMn4uLFi/Dx8XFApPbj4uKCmJgYxMTEwGK5DxcuXEBqaiqEynMIdC9CfFAG6hq+wbnSWGzfZIBVNhB6vR56vR5BQUFMMoiIiIj6ISYWNzFr1iwAwBdffNFqn16vt/0slUrR2NiIgoKCPp9Y/JxUKoVOp4NOp0Nj4wycy8pCxvldcDXtQax/JgqrgnCu1AeZp5OQcXoHBGUU9Ho9DAYD/P39HR0+EREREfWQPtFJvqamBsuWLcOkSZPg5+cHQRBadEn6udraWjz55JMIDg6GUqlEYmIiNm3a1G2x3XfffVAqlRg6dCjGjx+PhISEbjuXo8nlchgSEjBlzjLcOucrlPpsh+h+JwRBwPDwE1h060ZMHvgO6i69jY1r38S///1vHDp0CBUVFY4OnYiIiIi6WZ9osaioqMCqVaswePBgzJo1C2vWrLnusXPmzMHx48fx2muvITY2Fh9//DEWLFiApqYmLFy40O6xffTRR9iwYQP27duHjIyMftMNSKlUInHIUCQOGYr6+npkpk/E0Zwt8JZ8hzEDf8Ad0d9hZ9oU7N9fhv379yMoKMjWXcrT09PR4RMRERGRnfWJxCI8PBxXr16FIAgoLy+/bmKxc+dO7Nmzx5ZMAMC4ceNw+fJlPP3005g3b55tutQJEybg8OHDbZbz9NNP46WXXmp3fFKpFJMmTcKKFSsQExODu+66q4NX2LepVCrcMmwUMGwUampqkJl2DNX5nyG/0h0AMGbgYXipriIzRYv9+yIRHBJpSzI0Go2DoyciIiIie+gTiUV7WwE+//xzaDQa3HvvvS22/+Y3v8HChQtx9OhRjB49GgDw7bff2j1Oq9WKCxcu3PCYYcOGtWvBuaVLl2Lp0qX2Cq3HuLm5YfitEwBMwLCqKqSlpaEurwDuylxMT9iJBoscF8qicPTQMOze7Y2IiAgYDAbExcVBpVI5OnwiIiIi6qQ+kVi0V2pqKuLi4lpV3K9NvZqammpLLNrLYrHAYrHAarXCYrHAZDJBJpOhrKwMhw8fxuTJk6FQKLBt2zbs378fr7322g3LKykpadd5q6urOxRnb+Th4fGfv/doVFRUICPtG5ivbEGYx0mcklgBAJbK73D84FHs3BmIgQObB37rdDoolUrHBk9EREREHeJUiUVFRQUGDhzYaru3t7dtf0e9/PLLePHFF22//+1vf8P777+PyZMn46233sLixYshCAJiYmKwefNmDB48+IblBQQEtKvFwt3dvcOx9mY+Pj7wuWMBgAUoLSmBTpYKa1o6RoRtQ7h3Hq7UeSKrRIuD32iRlOSLmJgY6PV6xMbGQi6XOzp8IiIiIroJp0osgBt3m+rMwOoXXnjhujNQfffddx0u78SJE/1+JUf/gACMDwjAuHHjUVw0CxkZmyE1JuGWsFMYNfAoNp+8B5mZVmRlpkMqUyA2NhYGgwHR0dHtSsqIiIiIqOc5VS3Nx8enzVaJK1euAPip5YJ6B0EQEBQciqDgpyCKS5GfewE5WR+h2qoCYMSMQUlwkViQWaLF51tOQpC6Q6fTQa/XY+DAgbaB+ERERETkeE6VWCQkJOCTTz6BxWJpcWf77NmzAACDweCo0OgmBEFAaHgMQsNfwLBfN+Hy5csoP1cPhflrTNLtxYTY/cipiMShrNuRkpICV1dXxMXFwWAwIDw8HBJJn1iShYiIiMhpOVVtbPbs2aitrcXWrVtbbN+wYQOCg4MxcuRIB0VGHSGRSBAZGYnhd76O2CnJqAj4Cjmmu+HuWocGS/N4i0iPZJTlfIEPNq7H8uXLsXPnTuTm5kIURQdHT0RERNQ/9ZkWi127dqGurg41NTUAgPT0dGzZsgUAcNddd0GlUmHKlCmYOHEiHnvsMVRXVyM6OhqffPIJdu/ejQ8//JBdZ/ogFxcXDNSOAbRjYLFYMGvgBaSlpWKw6yb4u5WgtkGNrJJYZGbk4PjxY3B394Ber4fBYEBQUFC/WbCQiIiIyNEEsY/c4o2IiMDly5fb3JeTk4OIiAgAQG1tLf7yl79g8+bNuHLlCnQ6HZ599lnMnz+/B6NtLT8/H6GhocjLy+v3g7ftoaGhAbmZX8BYuAn+8qNQyY1Y+8MiVJs8IHNphNkqh5eXFwwGAwwGA/z9/R0dMhEREfVyrK91TZ9JLPo6PlG7j9FYi8vp23EyS4Ls7At4aMT7MJpdkVmixbmSGNSb1fDz87O1ZPj4+Dg6ZCIiIuqFWF/rGiYWPYRP1J5RV1eD/NRVwNXt8HfNgAARuVfDsCvtTpgsrgCAwMBAGAwG6PV6eHp6OjhiIiIi6i1YX+uaPjPGgqg91Go3aEc+BeAp1FTmoShjDVzkB2GyNK/kPSryCEpq/LHv2wLs3bsXAwYMgF6vh16vh5ubm2ODJyIiIurD2GLRQ5gBO1ZVVRXSU08iuP5xeCkLYTIrcK40BpklWhRUhgAQEBERAb1ej/j4eKhUKkeHTERERD2M9bWuYWLRQ/hE7T2uFP6I8gvroDF/C7mkFu99/ztYmmRwU1SjpsENgiDBwIEDYTAYoNPpoFQqHR0yERER9QDW17qGXaGo3/EOvhXewbdCFEWUF5zAaNQiK/0E5upXosrogcwSLTILKvFFdjaSkpIQHR0NvV4PrVYLuVzu6PCJiIiIeiW2WPQQZsC9W1OTGeUXt6E2/yN44ChkLmbkVwZj66k5aBKb1z+RSqWIjY2FwWBAdHQ0ZDKZg6MmIiIie7pRfa2uwYK/f5OFHWeKUGk0I8pPg8fGRmHG4OAblvn9+XL838FsnCupQWW9GW5KKWID3PDIHQMxTtd6Ovzvz5fjH3uykFFUDVeZC8brAvDsXTr4ahR2vdbuwBYLIgASiQz+0fPgHz0PTZZalJ7/CGLjUajUHqirrcbk+K+RUxGJ81n1SE9Ph1wuh06ng16vR1RUFBdfJCIicnKPfpiMlLxK/HmKDpG+anx5uhB/+uQURFHEzMSQ6z7uan0jYgI0mDc8FH5uClTWm/HR0cv4zfrj+Oe8wZg95KcE5seLFVj0/jGM0/lj9YPDUFHbiNd2ZeK+1Ufx5eNjoJD27voGWyx6CFss+qampibk5SRDkvs4PKTnYLZKkV0+EJnFWuRURAIQoFQqERcXB4PBgIiICEgkEkeHTURERJ1wvfra/sxS/Gb9cbw9P7FFEvHA2qM4V1KDH56ZABeJ0O7zmK1NuP31/QjzVmHzo6Ns22eu/B71jVbseuJ2SF2a6xPJl6/g7neP4KVZBjxwa7gdrrL7sAZEdAMSiQThUcMROu5HuN6SjAbPJRjgU4fbo4/YjvGQXcKpUyfxwQcfYPny5di5cydyc3PBnJ2IiMg5fJ1WDLXcBVMTglpsv2foAJRUN+B03tUOlSdzkcDdVdoiGSmuMiElvwqzbwmxJRUAMDTcGwN91fgmrbhrF9ED2BWKqJ1k6kgEJ74E4CWYjWWYH3kFORnfYLTP26g2uSGrJBYZxTocP16H48ePw93dHfHx8TAYDAgODoYgtP9OBhEREfUeWSU1iPbXtKjwA0BckHvz/uJaDA33vmEZTU0imkQRFXWN+OhoLnLK6/DMFF2LcwBAXKB7q8fqgtxw4lLHkhdHYGJB1AkyVz9otX6IjY1Bw5VESLLXYbB8L4aHJyP3ygBsPX03qqur8eOPP+LHH3+El5cX9Ho9DAYD/P39mWQQERH1oOXLl2P58uU3Pc5qtba5vbLejFDv1mtcebo2T+Rytb7xpmUvWn8ch86VAQDcFFL8a8EtGK8L+Nk5msvwULWeHMbDVY7KevNNz+Fodk8smpqaYDKZuMCYAxRWGnGl7vpPbB+NHEEerj0YkfMTBAmUPrcj2Od2iE2NMJZ+A9e8NMTUhaIw9yzuik9CVmkszpXG4Pvvr+L777+Hr68vDAYD9Ho9fH19HX0JRERETq+6uhoFBQVdKuNGtwTbc7/wxRl6VBvNKK1pwPZTBXj8k5P4+72DWw38vm5RfeCeZJcTC5PJhE2bNmHHjh04fPgwSktLIYoiFAoF4uPjMX78eNx3330YPHiwPeKl62iwWDFj5fcor71+YuGnUeD7Z8b1+hkF+ipBIocqcBqiAqchajhQf/UsqjJOYpznQYyNOYjLV8KQXhSP82XAgQMHcODAAQQGBtpaMjw9PR19CURERE7J3d0dISHXn7npGqvViuLi1mMZPFUyW4vCz1Uam1sRPF1vvs5VpK/a9vPE+AA8tO4Y/ueLNEwfFAyJRICnqrmMtlomqoyNttaR3qzTiYXRaMQbb7yBt99+G1VVVdDpdJgwYQL8/f2hVCpx5coVXLx4EatXr8Y//vEPjB49Gm+88QZGjRp188Kpw+QuEgR7uqKirhFtjRkWBCDIUwm5C8fr9xSVVwJUo79GU2MZ6go+RaDLJ1Ao83C+LAYuEgtCvfKQW9L8Bvbtt98iJCQEBoMB8fHxcHdv3b+SiIiIOmfp0qVYunTpTY+7NivUL+kC3fDl6UJYrE0txllkFjePi9AGajoc0+BQTxw8V4aKukb4uSmgDXCzlfnL9S0yi2ugDXTr8Dl6WqcTi5iYGKjVajz33HO47777EBAQ0OZxoihi//79eP/99zFu3DisXLkSDz/8cKcDprYJgoCnJmnx0Lpjbe4XReCpSVr27XcAidwPbpF/hFvkHxHQ1IgnRxuRm7oR4U3voL7RFedKY5BZrEVBgYiCggJ8/fXXCA8Ph16vR3x8PNRq9c1PQkRERN1mkj4QnxzLw67UYkz/2YJ4W5PzEeCuQGKoV4fKE0URRy9WwF0phdd/xlQEeigxONQT208V4JE7BtpmjDqZexUXy+qweEyk/S6om3R6HYt169bhoYce6tDCYBcvXtHnU7QAACAASURBVERubi7Gjh3bmVP2aT2xjoUoipj5zmGkFlSh6Wf/VYkAGEI88MUfxjCx6CVEUURTXSpqcj+EpWIb5KjA+dIoJKVOa3GcIAgYOHAg9Ho9dDodXF05RoaIiKi73Ki+9sDaoziTX4VnpugQ7qPCVymF+ORYHt6al4hZQ5q7WS3bkoKtJwtw8OmxGODVPN744Q0nEB/khvhgd3iq5CipNmFLcj6+O1+Ol2bq8cCoCNs5jmRX4IG1RzEhzh8P3BqBiroGvL4rE25KGRfIo5/01AJ5B8+VtdlqsWHxCPwq1q/bzkudJ4pNsFYdQWVlBVIvuaP44i4MC/wcmSU6ZJXEorahuelTIpEgOjoaBoMBsbGxUCgUDo6ciIjIudyovlbXYMGbX2dhx9kiVNWbMdBPjd+Pi8aMn7VgPLU5BVtP5uO7ZeNss0j938Fs7DpbhEsV9ahtsMBdKUXCAE8sGh3eYlaoa747X4ble84hvbAarnIXjNf54//dFQdfTe//3Gdi0UN6KrEQRREzVx7G2YIqiGieQCBhAFsr+hJLzVlUX3gFkpp9EGBGfmUIzhQMwrnSWNsxUqkUsbGx0Ov1iImJgUzW+wd0ERER9XY9VV9zVh0eY2EymXD+/HlERUW1mlL28OHDGDNmjN2Co44TBAFP3fnTWAsRHFvR10jdEuA95BOIlmqYy3cgKP8jQK1AYb0GYmMxQjwLcbE8Eunp6UhPT4dcLodWq4XBYEBUVFSHuicSERER2UuHEosjR45gxowZtrUqnn/+eTzzzDO2/VOmTEF1dbXdg6SOuSPGF4MGeOBMfhUGDfDAHTG+aLBYcb6kFoYQD0eHR+0kSN0hD1wA78AF8BJFxI0TUZy+Apqra9FoleNC6UBkluiQezUUZ8+exdmzZ6FUKqHT6WAwGBAZGQmJhLOAERERUc/oUK3jqaeewj/+8Q9UVFQgOTkZ27Ztw+LFi9HU1ASguRsOOZ4gCFh2pw7R/hosu1OHBksTlnyQjHv/7wiOX7ri6PCoEwRBgEQiQbDhSWiGnYAmciliQ42Yk7gdv9Z9959jmmAyGXH69Gl8+OGH+Mc//oEdO3bg8uXLfG0SERFRt+vQGAtPT09UVlbafjcajbj33nshl8uxadMm+Pj4oKamplsC7esc2WevwWLFwxtO4MSlq1j94DDcFsPVnp1B88xSZ2BtkiGnyAVXLqzCANk2ZBRrkVmixdV6b9uxbm5utoX4goOD2TWOiIioDRxj0TUdSizCwsJw5MiRFisXWiwWPPjggyguLsbRo0dRV1fXLYH2dY5+ohobrcguY1coZ2atTYEpfzXM5V9CItaitMYfRy8Nw4WymBbHeXp62pKMgIAAJhlERET/4ej6Wl/XocRi8eLFGDhwIJ577rkW20VRxCOPPIK1a9faukVRS73tiVpe24AqoxlRfh1fKZJ6N7HJBMuVvWgo3owSUyKOnQ9CTcn38FGV4nxZNBosStuxPj4+MBgMMBgM8PVlSxYREfVvva2+1td0KLFobGyExWJpNRvUNbm5uQgLC7NbcM6kNz1RS6pNWLj6R9Q2WLB5ySiE+3BlZ2dXm7Mc1vxX0CQKyCmPQGaxFhcrImFt+mn+hoCAAFtLhpdXx1YQJSIicga9qb7WF3VoVii5XA65XH7d/Uwq+gaZiwQuEgEugtBihW5yXprIpWgKWQhz2eeIVm9GtN9OnC2/C3vPxEDm0giLVYqSkhKUlJRg3759CAkJgV6vh16vh7u7u6PDJyIioj7Abgvkbd++HR999BEuX74Mk8nU8iSCgJSUFHucps/qbRlwaY0JDeYm26qQ1L9Y6y9AkHqg1qRA2Zll0Jh3IaMoCpnFWpTUBKB5acVmYWFhMBgMiI+Ph1rN1i0iInJeva2+1td0eIG8trz55pv485//DD8/P0RHR7Py0Qf4uylb/J5VXANfjRw+fWC5eOo6F1U0AMBdDqgT/wBziTeGKLfiltDTqGnwwd7MMbhUEQmguYtjbm4udu3ahcjISOj1esTFxcHV1dWRl0BERES9jF1aLCIjIzFhwgS89957XPX3OnpzBnwmvxIPrD2GEE9XfPK7W+Ghkjk6JHIAUbTCWvkdzGVbUO86G2ezXVCd9ylgLsS5Ei3qGn+6YSCRSBAVFQWDwQCtVguFggkpERH1fb25vtYX2KXFoqKiAgsXLmRS0Uep5FLIXAQoZZKf94ChfkYQXCD1Ggup11i4AhgbCpgufY+G/G34VfT3KKqJQGpBFM6XRqPRqsD58+dx/vx5SKVSxMTEQK/XIzY2FjIZE1MiIqL+yC6JxZgxY5CRkYHx48fbozjqYdH+Gny6ZBQC3ZVQK+zylCAnoYx4DoqQP8Jc8RVCS7cgyP1bhA9MwHdn1GhqKILJooTFAmRkZCAjIwMymQw6nQ56vR5RUVGQSvl8IiIi6i/s0hUqKysLs2fPxmuvvYbJkyffcOao/qqvNa39eLECiaGeUMrYCkU/aWoohCDzAQQ5KpLvhaT+R1woi0ZqQTTyr4ZAhMR2rEKhQFxcHAwGAyIjIyGRSG5QMhERkeP1tfpab2OX24nR0dH49a9/jdmzZ0MQhFbrXAiCgKqqKnucinrAlymF+K9PT2Oc1g/v3j8UMhdWCKmZRBFs+9kr/hWYS7cgznULdP7b0NDkiZ1pU3GprHmhvYaGBpw+fRqnT5+GSqWyJRlhYWFMMoiIiJyQXRKLZcuWYeXKlUhMTERcXBxbLPo4X7UcLhIBHq5ySAQOuqC2uahi4RLx/6AIfxbWmmTIy7bgvhFP4mJeNWqy38SVimKkFUajyuiJ+vp6JCcnIzk5GW5uboiPj4fBYEBISAgEPseIiIicgl26Qvn4+OCRRx7Bq6++ao+YnFJfa1rLKKqGNsANEgkrfdRxpot/QWPRRqCpDpWNETiTG4nUwlg0WFpOc+zp6WlbiC8wMJBJBhEROVRfq6/1NnZpsbBarZg4caI9iqJeIi6o5WrL+7NKMTbWjxU/ahflwL9BEf4XWK58DWnpFtyh2IvoIQ/hVEYdrhb9iKp6NRqtClRWVuLw4cM4fPgwfHx8oNfrYTAY4Ofn5+hLICIiog6yS2IxadIk/Pjjj5wVykn9/essrNx/AQ/fFonnpsU7OhzqIwQXFWR+syHzmw3RUgWd1AO6RKD6xL/QZLyIorp4nLw4ADnl4bCKUlRUVODQoUM4dOgQ/P39YTAYoNfr4e3tfd1zWCwWpKWlISsrC/X19VCpVNBqtdDr9ZyRioiIqIfZ5ZP3+eefx7x586BWqzF16tQ2KwI3qhxQ7xbmrYIgAIEeypsfTNQGQeph+1mTsBnmsm0ILd2CEHUSrNBgX+4fkHrBZDumtLQU+/btw759+xAcHGxLMtzdf2pJy8rKwvbt22EymSAIAkRRhCAIyMjIwO7duzFr1ixotdoevU4iIqL+zC5jLK7N8HKjbjJWq7Wrp+nT+nqfvaziGmgD3RwdBjkZa10GLBVJkIcuRU1NHSrP/BbFFWacuBCCslo//HLFxrCwMOj1esjlcnzxxRc3LX/+/PlMLoiIqN36en3N0ezSYvE///M/7Hvv5H6eVJitTfjufBnG6wIcGBE5Axd1HFzUcQAANzc3yIOi4OWyFTrvQzAhFFmlcTicORANFgUAIDc3F7m5ue0uf/v27XjqqafYLYqIiKgH2KXFgm7OWTJgi7UJj354EnszSvDK7AQsHBnm6JDIyYhNZlgqD8BSugWWq/vQMHAP0jIvo+LSdlwokMNoVrV6TIBbCW6L+h7fZ9+GkpqWCe/s2bMxaNCgngqfiIj6MGeprzlKp2/jvf3227j77rv5R+9nXCQCovzU+O68BEGeHHNB9idIZJB5T4TMeyLEJjPcJDLc4ReCGvliTBhYjcLqSJzJi0J2+UCYrc1r5sQFZiDMOx9xdZktEgtBEJCZmcnEgoiIqAd0evnbV155BeHh4Rg5ciTefPNNZGdn2zMu6qUEQcAzU3TY+cTtGKf1d3Q45OQEicz2XTPsGFyj34BCZsUU/dd4ZMxqDPDMg7+mFNqAcwAAbUAW/DWl8HcrgZuyGqIoor6+3pGXQERE1G90OrEoKirC3r17MXz4cLz11luIjY1FYmIiXn75ZaSnp9szRuplBEFAlJ/G9vvVukb8kF3uwIioP5DIfCAP+g1OVP4Za39YDLnUgntv2Yb7RnwCV5kRAOAqM+K+EZ/gvuGb8PDo9yEIAlSq1l2niIiIyP46nVhIJBKMGzcOK1euREFBAQ4dOoRx48ZhzZo1SEhIQFxcHJ577jmcOnXKnvFSL3O1rhHzV/2IReuO4/vzTC6o+2m1WlSb3LAr7U5Ym67NSIcW361NEuxKuxOiKEKn0zkoUiIiov6l04nFL40ZMwb//Oc/cenSJRw5cgQzZszAp59+iqFDh2LgwIFYtmyZvU5FvYhGKUW4jwpeahkCPRSODof6Ab1eD6VSicwSHTadmNfmMZtP3oPMkuaEQqPRtHkMERER2ZfdEoufGzFiBF5//XWcP38ep06dwuTJk7Fz587uOFW3e/fdd3HLLbdAJpPhhRdeaLFv7NixUCqV0Gg00Gg0/XLlcZmLBP9aOATbfj8G0f5c54K6n1QqxaxZs1psa/rP3HbX5rgLcCux7fv444+RkpLSU+ERERH1W92SWACAKIrYuXMn/vd//xdr165Fampqd52qWwUFBeHFF19sVZG5Zs2aNaitrUVtbS327dvXw9H1DgqpC0I8XW2/nyupQVphlQMjImen1Woxf/58WCWeqGtQobQmAHszx6OkJgD1ja7Iq45HQIA/3JTVsFqt2L59O/bu3QvOrk1ERNR97L5qVHZ2NtatW4cNGzagqKgIcrkcd999t71P02OuJRTtWeWXmpOKee8dgSAI2LxkFKL92Q2FuodWq0VU1ItIT5uNzKxs1MuMOF13L3QDovDYnxJhKdsM4/mPsOPsRGSXR+Pw4cOoqKjA7NmzIZfLHR0+ERGR07FLi4XJZMIHH3yAsWPHIjY2Fq+++iqKioqwdOlS5Ofn4+OPP+5S+TU1NVi2bBkmTZoEPz8/CILQqlvSNbW1tXjyyScRHBwMpVKJxMREbNq0qUvnv5H/+q//gp+fHyZMmIDTp09323n6ikAPJQZ4qRDq5QpfDStv1L2kUikGDR6GuXPnYdGiRZg7dx4GDR4GqVQKhf8sKPymYMagHRgRcRyAiMzMTLz//vuorq52dOhEREROp0uJxfHjx/Hoo48iMDAQixYtwsmTJ7Fo0SIkJSVBFEVMnz4dPj4+XQ6yoqICq1atQkNDw3W7JF0zZ84cbNiwAX/961+xa9cuDB8+HAsWLOhyctOWN954Azk5OcjNzcXUqVMxefJkVFX17y5A7koZNi4egQ8fHglPFRMLchzBRQVX3VrIw5ZhzMAfcJdhD1wkFhQXF2P16tUoKChwdIhEREROpdOJxaBBg3Drrbdi9erVMBgMWL16NYqKirB27VqMGTPGnjEiPDwcV69excGDB/Hqq69e97idO3diz549+Pe//40lS5Zg3LhxWL16NSZOnIinn34aVqvVduyECROgVCrb/Hr++efbFdeIESOg0Wjg6uqKpUuXwtfXFz/88EOXr7ev81LL4aaU2X4/dK4MJdUmB0ZE/ZUgSKAMfwauujWIjgyEu7sXgOaWzfXr1yMtLc3BERIRETmPTo+xSE1NhSAImDp1Kl577TXEx8fbM64WhGuT09/E559/Do1Gg3vvvbfF9t/85jdYuHAhjh49itGjRwMAvv32W7vHKZFIbjo4dNiw5m4aN7N06VIsXbrUXqE5zN70Ejz6YTLCfVTY8uhoeKnZikE9T+Y3B56+s/FbbT32fPEmCovKUFbrjy1btqC8vBx33HFHu99niIiIqG2dbrF46623MGjQICQlJSEhIQGjRo3CmjVrUFNTY8/4OiQ1NRVxcXGtKu6DBg2y7e8oi8UCk8kEq9Xa4ufKykrs2bMHDQ0NaGxsxIoVK1BcXIxRo0bdsLySkhIUFBTc9MtZ+oBrA93g56aAPtgDGqXd5wogajdBEKBWq/HrhHQsGL4VUb4XAAAHDhzAtm3bYDabHRwhERFR39bpxOJPf/oTTp06hWPHjuGRRx5BZmYmHnnkEQQFBeGRRx6BIAg9fgewoqIC3t7erbZf21ZRUdHhMl9++WW4urpi/fr1+Nvf/gZXV1d88MEHMJvNePbZZ+Hj44PAwEB8/vnn2LlzJ7y8vG5YXkBAAEJCQm765e7u3uFYe6NQbxU+//0Y/HNeImQu3Ta7MVG7qePX2wZ1Dw8/BkBEamoqNmzYgNraWkeHR0RE1Gd1+RbysGHDMGzYMPzzn//EZ599hrVr12LLli0QRRG//e1vsWTJEixatMgug7jb40bJTGcSnRdeeOG6M1CdOHGiw+WdOHECAwYM6PDj+rJAD2WL3784XYCJ8QFQydmCQT3v2qDuhlwtbsMb0Cgasf/cbSgoKMDq1auxYMECBAYGOjpMIiKiPsdut5CVSiUeeOABHDhwAOfOncMzzzyD+vp6PP300wgNDbXXaW7Ix8enzVaJK1euAECbrRnUs949kI0nNp3GIxuTYbE2OToc6qdsg7q1q6G9dZmthbC6uhrr1q1DVlaWgyMkIiLqe7qlb0pUVBReeeUV5Obm4ssvv8TkyZO74zStJCQkICMjAxaLpcX2s2fPAgAMBkOPxEHXNyLSG2q5C0ZF+UDKrlHkYDL/uxEQORG/Xfwg5gw9CD9NGcxmMzZt2oTDhw9zpW4iIqIO6NaanUQiwbRp07Bt27buPI3N7NmzUVtbi61bt7bYvmHDBgQHB2PkyJE9Egdd39BwL3z71Fj8YVy0o0MhstG4iogIbsKC4Vtsg7r37t2LL7/8ssU01URERHR9faaT+65du1BXV2ebdSo9PR1btmwBANx1111QqVSYMmUKJk6ciMceewzV1dWIjo7GJ598gt27d+PDDz+Ei4uLIy+B/uPnYy4s1iZsPpGP+cNDIZFwuk9yDIncF5rBO2HM+j1mDPoS32ePwvHLw3H69GlcvXoVc+fOhUqlcnSYREREvZogdrKt32Aw4KWXXsLs2bPbdXxRURFeffVVBAcH45lnnunw+SIiInD58uU29+Xk5CAiIgJA88JXf/nLX7B582ZcuXIFOp0Ozz77LObPn9/hc9pTfn4+QkNDkZeX1+8Gb9/I45+cwlcphXjg1nC8NItd1cixRLEJDbmvozH3TWw/Mwc55c3jw7y8vLBw4UL4+vo6OEIiIupOrK91Tae7Qs2dOxcPPvggwsLC8Oyzz+Lrr79GWVmZrU+y0WhEamoq1qxZg+nTpyM8PBzJycmYMWNGp8536dIliKLY5te1pAIANBoN3n77bRQVFaGhoQEpKSkOTyro+ibFB0Apk+COWD9Hh0L0n0Hdz0KduA/jZ74AtVoNqcSMq1evYs2aNcjOznZ0iERERL1Wp1ssgOZWiLfeegvr1q1DRUWFbe0KmUyGxsZGAIAoirj99tvxxBNPYM6cOXYLvK9hBnx9pTUm+Lspb34g0XWkpaVh165dmDJlCvR6vd3Krbr0Aeqz/wefn56Kslo/CIKAyZMnY8SIEXY7BxER9R6sr3VNlxKLa8xmM44ePYojR46gsLAQRqMRvr6+0Ol0GDt2LP8x4BO1varqzfg6vRhzh/XMFMXU99XV1WHlypUwmUxQKpX44x//CLVabZeymxoKUJd6H8y1mdiZOgnZ5VEAgOHDh2Py5MmQSDizGRGRM2F9rWvsMnhbJpPhtttuw2233WaP4qifMpmtWLD6R6QXVcNktuLBURGODol6OVEUkZSUhIaGBgBAQ0MDduzYgblz59qlfIkiBJrEnai3DeoejeOXh+H48eO4cuUK7rnnHiiVbG0jIiICunm6WaKOUMpcMNkQCF+NAiMiuZgh3VxaWhoyMzNtY7tEUURGRgbS0tLsdg7BRQVV3DrIw57GsJgKyKTN58rOzsbatWttC3ASERH1d0wsqFd5fHw0vn7ydugC3R0dCvVydXV12LFjR5v7kpKSUFdXZ7dzXRvU7TfqAO67fzGCvOqhktehvLwca9asue6MdURERP0JEwvqVQRBgI9GYfs9u6wWu1OLHRgR9Ua/7AL1S9e6RNmbIFEgLCwM88acxP0jPoOfpgxGoxEbN27EqVOn7H4+IiKivqTPLJBH/U9BpRHz3juCq/VmrFs0HL/ilLT9mtlsRmlpKYqKipCTk4PMzMzrHnutS1RpaSn8/f3tGocgCHAzbIAkdQHmD9tiG9T95ZdfoqysDL/+9a85qJuIyAnVNVjw92+ysONMESqNZkT5afDY2CjMGBx8w8ftTi3CjrPFOJNfieIqE3w1CgyL8MKTv45FpG/LyUbmvXcER3Nad7G9I9YPGxf3/hkJmVhQrxXorsRt0b44V1ILQzC7RvUnRqMRRUVFKC4uhl6vh4eHB/bs2YPjx49DEAT4+vrC3d0dNTU1aGtiO0EQoNPp7J5UXCNRBEOTuMs2qPvAuTtwKn8Ijhw5gitXrmDOnDmQy+Xdcm4iInKMRz9MRkpeJf48RYdIXzW+PF2IP31yCqIoYmZiyHUf9+7Bi/DTKPCHcdEI81ahqMqId/ZnY9qK7/D5H8YgNsCtxfFh3iq8NT+xxTZ3paxbrsne7JJYJCcnY+jQofYoisjGRSLg7/cORr3Z2mdeUNQxoiiitrYWbm7Nb6pJSUm4cOECqqqqADTPOOfv7w8PDw+MGDECiYmJ8PPzg0wmazHN7C/J5XJMnTq1W2O/Nqi7IfcNRMm8cbqgGKIoIisrC+vWrcOCBQvg4eHRrTEQEVHP2J9Ziu/Ol+Pt+Ym2JGJ0lC8KKo14ZWcGpg0KhotEaPOxax8aBt+fdfO+9tjbXt+Htd/l4PV7BrXYp5RJcEuYV/dcSDezS2IxfPhwjBw5En/84x8xd+5cyGSsBJJ9SF0kcHf5qVvJoXNl0CilffYF199ZLBakp6ejuLjY9mU0GvHMM89AoVBArVZDr9cjMDAQQUFB8Pb2tnUr8vX1bVGWWq3G1KlTsXXr1lbn8fb2tttaFjfSPKj7GejDAaXveVw+9iSSL+lRUlKC1atXY/78+ZwHnYjICXydVgy13AVTE4JabL9n6AA8sek0TuddxdDwtme0/GVSAQAB7koEeihRWGXslngdxS6Jxfr16/HOO+/ggQcewFNPPYXf/e53WLJkCT9Qya6O5VzBwxtOQCGT4PPfj0G0v8bRIdF1/Hw8RHFxMQRBwNSpUyEIAr788ku4ubkhMDAQI0eORGBgIFxcXAAA48aN69B59Ho90tLSkJWVBVEUIQgCYmJiMHPmTFscPXWjI3KAEr5l2dAFpOHz09NQXtv83jhz5kwkJCT0SAxERNS25cuXY/ny5Tc9zmq1trk9q6QG0f4aSF1ajqGLC2ruqp1VXHvdxKItuRX1KLhqxKT4wFb7LlfUY/CL36C2wYIQT1dMHxyEx8fHQClzaXf5jmKXxOLBBx/Egw8+iOPHj+Nf//oX/v73v+P111/H9OnT8fjjj2Ps2LH2OA31c4YQdwwO9YCfmwJh3ipHh0P/YTQaUVxcDIVCgeDgYOTk5OCDDz6wVfT9/PwQHh4OAHBxccHTTz8NhaL13ZvOEAQB06ZNw6VLl2AymaBQKDBjxgyoVCrU1tZizZo1uP3223ukq6ZEGQq3W76FkLoQC342qHvbtm0oLy/H2LFjIQhtN5MTEVH3qq6uRkFBQacfX1lvRmgbdQ9P1+abV1frG9tdlsXahGVbU6CWS/Hb2yJb7Bse4Y1pg4MR5adGg7kJB7JK8d7Bizh+6So2/e5WSK7T3aq3sOvg7eHDh2Pjxo1Yvnw5Vq1ahffeew8TJkxAXFwcHn/8cTz00ENcpZY6TSWXYv1vRkAhlbS6Y0DdTxRFiKIIiUSCc+fO4eTJkyguLraNh0hMTMTMmTMREBCAqVOnIjAwEP7+/q1aDOyVVFyjVqsxbdo07Nq1C1OmTLF1gVKpVIiNjUVSUhJqamrwq1/9qtsr9hJFCNz+s1L39IQkbD55NwqrQnDo0CFUVFRg5syZ7CpKROQA7u7uCAm5/gDra6xWK4qL257m/kafIO39eBFFEcu2nsHxS1fx7n23INjTtcX+/75T2+L3cTp/DPBS4W87M/BNegkmG1q3cPQm3TIrlFwuh0qlglwuhyiKqK+vx2OPPYaXX34Zn332GW699dbuOC31A2pFy6fspmO5GBPt2+ZdBOqaiooKFBYW2rozFRcXY9q0aYiPj4fRaITZbG41HgJortD39GQOer0eer2+xTaJRIIpU6bAzc0N+/btQ3V1NaZNm9btU8EKLmqo4t6HuewL6F38Ubh3LwARaWlpuHr1KubPn28brE5ERD1j6dKlWLp06U2Py8/PR2hoaKvtnioZKttolag0mpv3u958JkBRFPHnrWew/VQB/jF3MCbp25ckzBoSgr/tzMCpvKu9PrGw6yfsmTNnsGTJEoSEhODPf/4zRo4ciaNHj+LixYs4ffo0QkJCsGTJEnuekvqxj4/m4pltZ3HfmqOoNpkdHU6fZTabUVBQgOTkZCQlJcFsbv5b7t69G9u2bUNGRgYUCgVGjhwJP7/mtUQGDx6MBx54ABMnTkRCQgJ8fX175doNgiDg9ttvx8yZM5GWlobS0tIeOq8Ecv/ZGD1mDB6aPQDzhm6FSlaHwsJCrF69GkVFRT0SBxER2Ycu0A0XSmthsTa12J5ZXAMA0AbeeNzntaTis+R8vHb3IMwe0vFxyJI+0J3WLi0Wn376Kd555x0cPnwYfn5+WLp0KR577DEEBv6UVQ0aNAivvPIK7rzzTnuckgjjdf4I81Zhzi0hcFNwSZb2MBqNqKqqQmBgIJqamrBq1SqUlpbaxkP4+vqipqYG3t7euOuuu6BUKuHq6nrzgnu5xMRExMbGtU01dwAAIABJREFUQqVSoampCQ0NDT12XaGRQ1Bb24j7Rm5uHtRdA7z//vuYPXs24uLieiQGIiLqmkn6QHxyLA+7Uosx/WcL4m1NzkeAuwKJodefrVIURTyz9Sw+S87HK7MTMHdY6xaRG9l6Mh8AMCTUs3PB9yC71MYWLFiAIUOG2OZuv97CUBEREbj//vvtcUoiBHooseNPt8GNa1xcV01NDZKTk21dmaqqqqBSqfDf//3fkEgk0Ol0GD58eJvjIby8nGtKX5Wqubvct99+i8zMTNx///09co0ubrfAbeg+CGcXtBjUvXnzZkyYMAFjxozhoG4iol5unNYft8f44rntqahtsCDcR4WvUgr/P3v3HRfVmT1+/DPD0Kv0LogIiKIR0WgssaGoWBAVNIktW5LdbHbNpu8mpuzGTbLuN9lsko29G6NrYq+xx2gssWDAgoooIL23Kb8/+EkkYoPLDOB5v168DHNn7nMwI3Of+zznHPaey+b/Jnat7WHx0pqTrD1+jb0vPo5vm5rPnVnrk/jy6FUmdPclxNOe42n5tee1MFPTyaem59GRS3l8svsCQ8M98He2qUnePneDlUeu0jvIhcFhHsb/wR+QIhOLffv20adPn3s+r127dixcuFCJIYUAqDOp0OkNfL73Ik/2avtQNdTT6/Xk5uaSmZlZmw/h6elJdHQ0Wq2WI0eO4OXlVZsPcetK4sNYsS0yMpLk5GTmz5/P5MmT8fLyuveLGklt6YP9I1soS36GQR2Pcml/AHqDGbt27SInJ4eRI0ei0ciqmxBCNGefPxHJB9tSmLPjHIVl1bRzs+XjxEcYdcsKhk5fcz1iMPz8up0/1WzDXX00ndVH0+uc08fJmoOvDATA3d4SMxX8e9cF8sqqUAGBrrbMHNKBp/sGNvuKUAAqg+HWH71h9u3bR7du3bCzu31/WUlJCcePH6dfv36NHaZFu5kMdPXqVenv0UT++vUZln5/hZ6BzqxsASXZGuJmf4jMzEx8fHzw9PTk8OHDbN26FQAnJyc8PT0JDg6mW7du3PznLXfE6yotLWXFihXk5OQwYcIEgoKCjDKuwaBHX5XDwcPJHDm4kQqtJTq9Bj8/PyZOnGiUpn5CCCHuTK7XGkeRiYWZmRmHDh2iR48etx07duwYPXr0uGPDkYeFvFGb3plrhUxdeIS3R3dieOemvwvd1MrLy7G0tEStVnPgwAFOnTpFTk5ObT7EsGHD6NGjB4WFheTl5eHp6dkq8iGMpaqqijVr1mBvb09sbKxRxzYYDOQc6kNeXgHrT42grNoWJycnEhMTcXd3N2osQgghfibXa42jyNr73eYm1dXVzbJajGh9Ovk4svfFAbeVpG0pzp07x/Xr1+vkQ/z617/Gy8sLCwsL/P39aztV35oP4ejoiKOjo4mjb3ksLCyYOHFi7fe5ubk4OzsbZXVHpVLh3PljzE8nMqnHar4+OZKcApg/fz7x8fEEBwc3eQxCCCGE0hp8BVZUVERBQUHt95mZmaSlpdV5Tnl5OYsXL66zp1uIpnTrpKKoopr5+y/x3MD2zaah3i/zIYqKioiPjwdg+/btlJWV4enpSceOHfHy8sLJqaYCRH2rgaLxzMzMACgsLOTzzz+nW7duDB061Cg3Q8zsI3GI3I36dCIJ3dew+cxQUnPasXLlSqKjo+nZs6dsYRNCCNGiNHhi8a9//Yu3334bqLn7Nnbs2HqfZzAYeO211xo6jBANYjAYmLHoB364nE9OSSV/G9vZ6DFotVqysrLQ6XT4+/tTWFjIJ598glarBWpWGry8vNBqtWg0GmbMmIGVlZVcTJqAo6MjQ4cOZfPmzZSUlDB27FijJFPfTOouTX4Wfz9PUnNq3rvbtm0jJyeHmJiY2smPEEII0dw1+JMzOjoaOzu7mtbkL73Ec889h7+/f53nWFpa0rlzZ/r379/oQIV4ECqVit/0C+JK7mkSe/jf+wWNdDPv4fr16xw+fJiMjIzafAhfX19mzJiBg4MDAwcOrK3M9Mt8CMmPMK3u3btjZ2fH2rVrWbZsGQkJCVhZWTX5uCozW2w7LqJfR9Da7iL/4n9Jzgrl2LFj5OXlMX78eHlvCCGEaBEUSd5+6623+NWvfoW3t/e9n/yQkmQg0yiv0mFtoewd35KSEq5du1abC5GRkUFkZCR9+/blypUr7NixA09PT7y8vOrtDyGat7S0NDZt2kRiYmLtVjRj0ZWdo/hYP7IKXWqTul1cXEhMTMTFxcWosQghxMNIrtcap8ETi7S0NLy8vDA3N78tt6I+v1zNeNjIG9X0LueUsjUpk9/0a3df241uzYfIzMwkMjISZ2dntmzZwpEjR7C2tq5dfQgJCaFt27ZG+CmEMdxcgSovL6ekpAQ3Nzejja0rPkbx6URKy6pqkrpL3LCysmLChAkEBgYaLQ4hhHgYyfVa4zR4K1RgYGBtidmAgIB7Xqg97OVmhWkVV1Qz8YtDZBVVYm1uxpTeAXWOa7VacnJyagsNfPXVV5w7d65OPkT79u1xdnamd+/e9O7dGwcHB8mHaKVu/n/duXMnZ8+eJSEhwWgTx5tJ3apTCSREruHLY/Fkl7ixbNkyhg8fTmRkpFHiEEIIIR5UgycWCxYsqG0qtWDBArnAEs2avZU5v+rbjq+OpjO8sxfV1dUcPXq0djUiOzu7Nl/I2toaPz8/fH19682HkNKuD48hQ4aQl5fH0qVLiYuLo2PHjkYZV23pg0O3rZSl/Qena55kX7iEXq9n48aN5OTkMGTIECnjLYQQotlRJMdC3JssrRmfwWCguLiYjIyM2gmEQa0hYfw49Ho9H374Ic7OznXyITw9PaUKj6hDq9XyzTffcObMGWJiYoxe+lev13No58eo8tfw7bkB6PQagoODGTduHJaWlkaNRQghWju5XmucltlJTIhfuDUfwt7enoCAAC5evMjy5csBavMh/P09AFCr1TwWN42MwkpGRsovDnFnGo2GuLi42ip4xqZWq+nxSCClyedxts1nw6kRnD9/ngULFpgkwVwIIYS4E0UmFt9++y25ubmMHz8egKysLKZNm8bx48eJjo7miy++MErZRvFwuJn3oNFoOHPmDIcPHyYrK4vq6moAoqKiCAgIwNfXl4SEBDw9PW/LhzifVcy0RUep1unxdLCiT7CrSX4W0TKoVCqGDh1a+31ycjLBwcFGW90ydxuNnaUPnrd06r5xA+bOnUtCQgJ+fn5GiUMIIUTLpdcbqNTqFa+WeStFJhZvvPEGQ4YMqf3+pZdeYv/+/QwZMoQ1a9YQHBzMX//6VyWGEg+h9PR0rl69WicfIi4ujk6dOqFWq2nTpg1hYWG125lu5kNYWVkREhJS7znbu9sRH+nLjaJKogLbGPPHES1cQUEBa9asISAggPHjxxttO5LGoTsO3fegOjWRURHbWfhdImVlZSxevJhRo0YRERFhlDiEEEK0DBXVOjacvM7ulBscvZxPbmkVBoMBC42aYHd7ege5MLqrDx29HRQbU5EcC3d3d+bPn09sbCxarRZnZ2dmz57Ns88+y4cffsiCBQs4e/asEvG2WLJn7+7qy4cYM2YMlpaWrFy5ktTUVDw8PGrzINq3b9/oLSB6vQGdwYC5mSTBigdz6dIlVq1ahYuLC5MmTcLOzs5oYxt0pZQXpbJmwwnS01Ko1pkDKvr06cPAgQOlkIYQQjRCa7heq6jW8fneiyw8eJniimqC3Ozo5OOIi60FluZqCsqqScsr4+TVAkoqtUS2bcMrMWFEtm38jVZFJhZWVlbs2LGDvn37cvjwYXr37s2VK1fw9fVl3759DB8+nJKSkkYH25K1hjeqUm7mQxQXF9OuXTv0ej3/93//R3FxMfBzPsSoUaNwcnKirKwMKyurJq+Cs+z7K7Rzs6V3kGyLEveWmZnJ8uXL0Wg0TJkyxei5DlptNRn7+3It24ydyQPRGTSEhYUxZswYLCwsjBqLEEK0Fq3heu3Rv+/CxsKMST39Gd3VBzf7+lfWDQYDhy7m8tWxdDadzuDtUeEk9Ghc3zlFtkK5u7tz/vx5+vbty86dO2nbtm3t/4zi4mLpOvwQu9lorLCwkAMHDpCZmVmbD2Ftbc2LL76IWq2mb9++2Nvb4+XldVs+hI2NTZPHuS0pk798fQYbCzO2Pt8Pf5emH1O0bJ6ensyYMYPdu3cb5T36SxqNOR4dX8D+3O9xsilg/amR/PTTTxQUFJCQkICDg3JL20IIIVqOmUM6MC7SFzP13VewVSoVvdu70ru9K38a3IFrBeWNHluRicWwYcN47bXXSEpKYtGiRUyZMqX2WHJyMgEBAUoMI5q5yspKrl+/XruVKTMzEx8fH0aNGgXAlStX8PT0rJMPcXMCERUVZcrQ6d/Bjb7BrkT4OuLnbH3vFwgBODk5MXbsWACys7MpKCggODjYaONbeIxHbR2Ix+lEJkd9ydcnR5KRAfPmzSMhIQFvb2+jxSKEEKJ5GBTmfs9JxS/5u9goclNVka1QOTk5PPHEE3z33Xf06NGD1atX4+zsDEBkZCSPPvoo//nPfxodbEtm7KW1pKQktmzZQkxMDOHh4Yqe+5f5EO3atcPPz48jR46wZcsWNBpNbT5Eu3btjNZUrLGqdXrJtxANtmnTJo4dO8bIkSPp1q2bUcfWV6ZTdHIih84FceRiTeNSjUbD2LFjW8y/PyGEaA5aw1aoDq9vYVCYOxOi/Hi8g5tRc+8UWbFwdXVl69at9R7bvXu3lJo1stLSUjZu3EhFRQUbN24kICAAW1vbBp1Lr9eTl5dHmzZtMDMzY9euXRw/fpyysjKgJr/GwcEBPz8/wsPDCQwMxMXFpUV2Bb51UqHXG/hgewrxkb4EuRkvMVe0XDExMRgMBjZs2EBxcTH9+vUz2i9ztaUvjt130y+8mowvv8RQ/B3pBb589dVXDBgwgL59+0pStxBCPCRiOnuyPSmLbUmZuNtbER/py/juvrR1adi14INQvPN2dnY25eW379Hy929cMkhLZ6wZsMFgYPXq1aSkpNTmN4SGhjJhwoT7PseJEydqtzTdzId4+umn8fHx4eTJk+Tn59d2q/5lPkRr8dHO8/xr5zl8nKzZ9UJ/rMylG7e4N4PBwP79+9m9eze9evUiOjra6DFUFZ6g4tQgzmaE1SZ1d+7cmVGjRqHRSE9UIYS4m9awYgFQXFHNNz9e56tj6ZxKL0AF9Ah0ZmKUHzGdvJrsukaRiUVxcTF/+tOfWLlyJRUVFfU+R6fTNXaYFs1Yb9QzZ86wdu3a2x6Pj4+vsyWqvLycrKys2u1MRUVFtbkxn332GQaDoba0q5eXFz4+Pg9VpZnckkomzzvMM48HMbqrj6nDES3MiRMncHJyIjAw0CTjV2V9Rdm535NZ4Mr60yMpr7bB19eXiRMnGrU0rhBCtDStZWJxqws3ivnyh6t8/eN1ckoqsbPUMKqLNxO6+9HFT9mKhopMLGbMmMGKFSuYMWMGERER9TaMujWh+2FkjDdqaWkpn3zySb2TO3Nzc0aOHElERAQFBQV89NFHAHXyIWJiYjAzM0Ov17fIrUxK0+r0aCTnQjSCXq9n79699OzZ0+iVo7RFRyk+lUhpeRVrTsRRWO6Eo6MjiYmJeHh4GDUWIYRoKVrjxOImnd7At8k3WH30KntTstHq9XTwsGfrH/spNoYi6+KbNm1i9uzZPP/880qcTjSAwWBg48aNVFZW1nu8urqabdu2ERERgaOjI3FxcXh6etabDyGTihq3TipKKrX8ffNPvBgdQhvbh2flRjROYWEhR48eJSkpiSeeeMKovS40Dt1xjNqNPvktVOaeUF5BYWEhCxYsYNy4cXTo0MFosQghhDA9M7WKIR096OrnxH/3XmTBwUucyypWdAxFriArKiro3LmzEqcSDZSdnU1ycjJ3W4AqKyvjxo0bqFQqOnfujJubm0wi7tOLX51kxeE0nl1+/K5/x0Lcqk2bNsyYMQO9Xs/8+fPJzMw06vhqS19cusxl+tPPEhaoopvfcaqqKlm5ciWHDh2S97IQQjwkdHoDW89k8vTiH+g9exfzD14ixNOBN0YqWzlQkavK4cOHs3//fiVOJRrIzc2N0NDQOyZSq1QqwsLCcHd3N3JkrcML0R1o52bLKzF3/jsWoj7Ozs5Mnz4de3t7Fi5cSG5urtFjsLe3J/ZxZ/oH72do2A7MVFq2b9/Ohg0bHvr8NyGEaM3OZRXz7saz9Pz7Tp5dfowjl/KYGOXH+t/1YcvzfZn6mLK5gIpshfrLX/5CfHx8zYdXbCwuLi63PedmXwvRNFQqFSNHjuTy5cv15lhYWloyYsQIE0TWOrR3t2fHn/o/cMMZIQDs7OyYOnUqx44dM9nvQmv/32Nm6UGo4fc4Whew4fRITpw4QX5+PuPHjzdJ93AhhBDKq60IdfQqp68VAsapCAUKJW/f3E5ztzu5D/tdseZWFUo0ztW8Mj7dc5FZozpiqZFStOLBnDt3jtzcXHr16mX0sWuSuhPILdKw/IcEQIWzszOJiYm4uroaPR4hhGhOWkPyduhft1Cl1eNub8W4SB8mdPczSg8LUGjF4o033mi120M+++wz5s6dy+nTp3n99deZNWtW7bFflm0sKyvjgw8+4IUXXjBylD8LDw8nKSnptj4WMqlQjlanZ+rCI1zMLsXCTMVbozuZOiTRwmRkZLBnzx6KioqIjo42bldUh+44Ru2hPO0EtmdTKC0tIS8vj3nz5jFhwgTatWtntFiEEEIor38HNyZG+fF4B3fURt5poXiDvNbm66+/xszMjCVLlhAeHl5nYnGrjIwM/Pz8OH/+fL216405A7617KyVlRW///3vG9x5W9TvwPkc/r75JxZMjcLTUTrLiwd35MgRtmzZQnh4OGPGjDFJ87qC/FyufRfL5RsuHE2LRKVSExMTQ1RUlNFjEUKI5qA1rFiYkpQEuocxY8YQGxuLo6PjXZ+3fPlyevXqZbKGWLeytbVl5MiRdf4UyuoT7MrG5/rIpEI0WI8ePZgwYQLJycls2rTJJDE4OrUhIHQofdsfJDpsB2qq2bx5M1u2bEGv15skJiGEEMorqqhm77lsvj5xjcKy6iYbR9GJRWFhIdu2bWP58uXk5+crdt7i4mJeeukloqOjcXNzQ6VS3XHloKSkhD/+8Y94e3tjZWVF165dWbVqlWKx3MnSpUt56qmnmnyc+xUeHs6f//xn2QLVhG5dXjxyKY/3Nv8k5TvFAwkLC+Opp56iXz/lmhM9CJVKjW3Qm1h2+JwwzwuMe+R/WJuXceTIEVasWFFvIQghhBAty8e7ztPzb7uYuvAIM1f/yNX8MgAmzf2eT/dcUHQsxSYW77zzDt7e3sTExPDUU09x6dIlAAYNGsTs2bMbde7c3Fy++OILKisrGTNmzF2fGxcXx+LFi3nzzTfZsmULUVFRJCYmsmLFikbFcDenT58mJSWF8ePHN9kYovnKLalk2sIj/HdfKiuOpJk6HNHC+Pv706ZNGyoqKli5ciXZ2dlGj8HSYwJ2XTfh0aaCMM9zAFy8eJH58+crepNICCGEcS09dJmPdp1nYpQfC6ZGcevtz4Gh7uxOvqHoeIpMLD799FPeeustZsyYwaZNm+rctR05cmSjl/nbtm1Lfn4+e/fu5b333rvj8zZv3syOHTv49NNP+c1vfsOAAQOYO3cuQ4YM4cUXX6xTmWrQoEFYWVnV+/XXv/71geJbsmQJo0aNMmpXXdF8uNhZ8peRHRkQ4sa4brIfUzRMdXU1BQUFLFiwgLQ0409QNQ7dcepxmM4D5mBtbY2DVSE5OTnMnTuXK1euGD0eIYQQjbf40BWe7hPIrFHh9At2q3Ms0NWWSzllio6nSLbgJ598wsyZM3n//fdvKysbHBzM+fPnG3X++62Ysm7dOuzs7G5bOZg2bRqTJk3i8OHD9O7dG4Bdu3Y1Kqab9Ho9K1as4PPPP7+v53fv3v2+kjRnzpzJzJkzGxueMJLEHv5M7O5n9OoLovWwt7dn2rRprFq1iqVLlzJu3DhCQ0ONGoPawpWAQFeenhSJOnUiBy/24mhaJEuWLCE2NpauXbsaNR4hhBCNk5ZXRr8ObvUes7XUUFShbL6FIisWqampDB06tN5j9vb2FBQUKDHMPZ05c4awsLDbLtwjIiJqjz8orVZLRUUFOp2uzn/ftGvXLqqrq4mJibmv82VlZXHt2rV7fhUVFT1wrMK0bp1UfPlDGosOXjJhNKIlsrKy4oknnqBDhw6sXr3aZNuQ2vgMQOP9XG1St8pQxTfffMOOHTskj0gIIVoQBysNOSWV9R5Lzy/H1dZC0fEUWbFwdHQkKyur3mOXL1/G3d1diWHuKTc3t94a7Dc73ebm5j7wOd99913eeuut2u//9re/sXDhQqZOnQrUJG0nJCTcd6lIDw+P+3qug4PDA8cqmocTafm8vPY0AOE+jkQFSNd5cf80Gg3x8fFcvnyZNm3a1F7IG7PXhUqlxq79m1TahRJm+ANtrAtYf3ok3333Hbm5ucTFxWFhoeyHkRBCCOX1DnLl872pDOnoUaehr1anZ9n3V+64mtFQikwsBg0axPvvv8/o0aOxsqopv6lSqdBqtXz22Wd3XM1oCnf78G3IB/OsWbPuWIEKavIrHsTRo0elLnIr19XPiV/1rSk73L1tGxNHI1oilUpVW7r64MGD5ObmMnLkSMzMjNvl3dJzImY27XA89RJ6Q83HRUpKCgsWLCAxMfGeZbiFEEKY1swhHRj1yQGGzNlHdLgHKmDJocskXS/iekE5/5ncTdHxFNkK9fbbb3PlyhU6duzICy+8gEql4pNPPqFHjx5cuHDhgZOhG8rFxaXeVYm8vDzg55ULIZqSSqXiteFhvDY8rNV2pBfG4+DgwKlTp1i1ahVVVVVGH1/jEIVXn92MT5iOq0M5gS6pZGVlMW/ePK5du2b0eIQQQty/AFdb1j7TmyB3O5Z9fwUD8L/j13C2teCr3/bCx8la0fEUmVi0b9+egwcPEhYWxqefforBYGDJkiW4urqyf/9+/P39lRjmnjp37sxPP/2EVqut8/jp0zXbUjp16mSUOIRQqVS1kwq93sC7G88qXtJNPBwiIiKYPHkyaWlpLF68mNLSUpPEERQURGK0jtERG+juf5SSkmIWLVrUoNw1IYQQxhPsYc+S6T0489ZQvn91EGfeGsrSGT1p726v+FiK9bHo2LEjW7dupbi4mPT0dIqKiti+fTthYWFKDXFPY8eOpaSkhLVr19Z5fPHixXh7e9OzZ0+jxSLETV8du8q8A5d4ZvkxbhRJwzHx4Nq1a8fUqVMpKiri22+/NVkcbTr+A7XXH2qTug26CtauXcuePXskqVsIIZo5S40ZHg5WWJk33bZaRXIsbmVpaYm3t7fSp2XLli2UlpZSXFwMwNmzZ1mzZg0Aw4cPx8bGhpiYGIYMGcIzzzxDUVER7du3Z+XKlWzdupVly5YZfX+yEABx3XzZnZzN4I4euDtYmToc0UJ5eXkxY8YMrK1rlq11Op3Rf6epVGrsg2dRaR9GqOE5bC1KWXdyDHv37iU3N5dRo0Zhbm5u1JiEEELUFf2vvcwcEsKwTp739fwbRRV8uuci7g6WPPt4+0aNrTI08DaTWq1+oP3jv+xv8aACAgLu2KTp0qVLBAQEAFBSUsLrr7/O6tWrycvLIzQ0lFdffZWEhIRGjd9Y6enp+Pn5cfXqVUnefggZDAbJtxCKycvLY8mSJYwYMYLg4GCTxKAtPMLZ0wdYt6u49jEfHx8SEhKws7MzSUxCCNFYreF67aOd5/li30Ucrc0Z/YgPj7ZzoZO3A862FqhUKiqqdVzJLeNEWj47zmax73w2Eb5OzI7rTLBH47ZHNXhiMWvWrDoXSgsXLqSkpITY2Fg8PT3JyMhg48aN2NraMn36dN58881GBdrStYY3qlBGWZWWF9ec4vcD2hPmJWWFxYOrrq5mzZo1nD9/nlGjRpm0cV1KcjLZJ5/lUrYfqbntcHBwIDExEU/P+7tTJoQQzUlruV67UVTB/IOX+OpoOvllVaioyf/UqFVU6/QAGICoAGemPxbAsE5eiozb4K1Qt5Zg/ec//4mnpyc7d+6sc6equLiYwYMHY2Nj06gghWhN3t+awqZTGZxOL2TXC/0xN1Ms1Uk8JMzNzZk4cSKbNm3im2++obi4mD59+phkVaxDh0Dcylzp7LWB/Rf7cCytGwsWLGDcuHGEhIQYPR4hhBDg7mDFqzFh/Dk6hB+vFnD8Sj5ZRZVUaHU421gQ5G7Lo+1c8HJUtipUg1csbhUUFMQHH3xAXFzcbcfWrl3Ln//8Zy5deri7ELeWGbBovMLyan679Bh/HtqByLZSAlk0nMFgYN++fXz33Xc888wzODk5mSgOPcXn34Ksf5OUEcau5IHoDBoGDx5M7969ZRugEKLFkOu1xlEkefvatWt37Cat0WjIzMxUYhghWgVHa3NW/KqnXGyJRlOpVPTv359u3bphb2+PVqvFYDAYPYFapVLj0OEtyu1DCTU8T3axGyfSH2Hnzp3k5OSYpLmfEEII41NkD0ZYWBhz5syhurq6zuNVVVX885//JDQ0VIlhhGg1bp1UXC8oZ9rCI1KKVjSYvX1Nst2mTZtYunQp5eXlJonD2isR+267cWz/BwDMzar48ccfWbJkCWVlZSaJSQghhPEoMrF49913OXjwIO3ateP555/nvffe4/nnnycoKIhDhw7x7rvvKjGMEK3SC6tPsjslm5fXnjJ1KKKFi4yMJCcnhwULFlBYWGiSGDR24fTvP5BJY8KY0Xsh7VxSSUtLY968eWRnZ5skJiGEEMahSI4FwK5du3j99dc5evQoer0elUpFjx49eOeddxg8eLASQ7RosmdP3Elabhkvrz3FhxO64OOkbBKVePjk5uaybNkydDodkydPxsPDwyRxGHQl5P04FU3pt7VJ3ZaWVsRgmpN/AAAgAElEQVTHx9O+fePqpAshRFOR67XGUWxicVNZWRn5+fm0adNGqkHdQt6oQghjKSkpYfny5bRv355BgwaZLA6DQU/xuTfhxn9qk7r1mDNs2DB69OhhsriEEOJO5HqtcRTvvG1jYyMTCiEa4diVfD7fe5GPEx7B2kISXsWDs7OzY9q0abVJ3IWFhTg6Oho9DpVKjUPIO5TbhxGgfRvLC5WUVWvYsmUL2dnZDBs2TJK6hRAtRmmllg+315SMLyivJsjNjmceD2JUF++7vm7rmQw2nc7kVHoBmYUVuNpZ0j2gDX8c3IFAV9vbnn/gfA7/3JHCTxlFWJubMTDUg1eHh+JqZ9ng2DMKyxUvLVsfKaAvRDNSqdXxu+XH2XE2i492nTd1OKIFs7Co6bCamZnJv//9b77//nuTxWLtPQmPfid5pEc0NualuNjmcPToUVasWEFFhRQtEEK0DL9ddoy1x9J5fnAwi6ZF0cXXkT+sPME3P1676+s+25tKeZWO3w1oz+LpPfjz0A4kXS9i5Mf7OZdVXOe536fmMnXhEVztLJn7VHfejA3n4IUcJs89TKVW1+DYh8zZ1+DXPgjFVyyEEA1nqTHjk0mP8N99qfxhkOxDF43n4eFBz5492bZtG0VFRQwZMsQkpY7VZpYMHjyYULtFWJXvYHPSMFJTYd68eUyaNAlnZ+npIoRovnYn32D/+Rw+SujK6K4+APQOcuVaQTl/3/wTIyO8MVPX/7t1/pTut6029A5ypc8/vmX+/kv8Iz6i9vH3Nv9EoKstn03uhub/N9D1c7Zm3GeHWH00nScfbdug+BXOfLgjWbEQopnpHuDM3Ke6Y2Mh837ReCqViiFDhjBs2DAOHTrEunXr0OkaftersXyiPgf7PoyO2ECk/zFyc3OYN28ely9fNllMQghxL9uSMrG1MGNEZ686j8dH+pJVVMmPV/Pv+Nr6tjB5OFjh6WjF9cKfy4NnFlZwMr2Qsd18aicVAJFtnWnnasv2pMb1hbteUM61u3wpQa5chGjm1h5L5/ClXGbHRaC+w90QIe6lZ8+e2NnZsXfvXioqKrC1vX1frzGozOxwifyKwpS/0k/1GW2sC9iZMoilS5cyYsQIunXrZpK4hBCt25w5c5gzZ849n3enGy8pWcW0d7erc8EPEOblUHM8s4TItve/8pqWW8a1/HKiO3rWGQMgzNPhtueHetlz9PKdJy/3Ulato/8Hu7nTwoVKBef/NrzB579JkYnFvn376NatG3Z2drcdKykp4fjx4/Tr10+JoYR4qFwrKOeV/52iWmegd5ArYx7xMXVIogULDw8nLCwMtVpNSUkJBoOhtrmeMalUapxC/0apXUd0uYcA0Ov1bNiwgZycHAYPHoxaLQvqQgjlFBUVce3a3XMh7qagrBo/59uLEzlZ1xTJyC+ruu9zaXV6Xlp7ElsLDTP6BN4yRs05HG3Mb3uNo7UFBWXVtz1+v2zMzUh6e1iDX3+/FJlYDBgwgEOHDtVbPjAlJYUBAwaYdOldiJbKx8maD8d34URawT2rTghxP25esG/YsIGsrCyeeOIJXF1dTRKLre9k+ngnUmq5DX3mp1zKDeDQoUPk5uYSFxeHpWXDK6AIIcStHBwc8PG59805nU5HZmb9W47utmfgflPXDAYDL609xQ+X8/lscje86+lfdcdTNWLTgrFy6xS5JXS3hJDq6mq58yREI4zu6sOsUeG126CMlYAlWrfhw4djYWHBggULuHr1qsniUKvVDIvuT1RwBgmRqwl0SeXcuXMsWLCAgoICk8UlhGhdZs6cSXp6+j2/fvjhh3pf72RjXruicKuC8ppVBCdri3vGYDAYeHntKb4+cY0Px0cQHe5Z57iTTc056luZKCyvql0daYhmn7xdVFREWloaaWlpAGRmZtZ+f/MrJSWFxYsX4+npeY+zCSHuh8Fg4O+bf+I/uy+YOhTRwjk6OjJt2jTc3d1ZsmQJKSkpJotFZWaDW689GOweq0nq9jvGjRtZzJs3z6STHiGEuCnU054LN0rQ6vR1Hk/OrMmLCPG8PR3gVjcnFV8dS2f2uAjGPnJ7870QD/s65/zlOCGeDd+6uuG5Piw5dLneY0sOXeZKbmmDz32rBk8s/vWvfxEYGEhgYCAqlYqxY8fWfn/zq2PHjvz3v/9lypQpigQrxMPuwIUc5u6/xAfbUki6XmjqcEQLZ21tzRNPPEGHDh1M3k9CZWaHa/c16F1/Q7/gA3TySqK0tJTFixdz6tQpk8YmhBDR4Z6UVunYcqbuNqm1x9LxcLCkq1+bO77WYDDwytrTfHUsnb+P7cyE7n71Ps/T0Youfk58feIaOv3PKwzH0/JJzS5laHjDb9R/tOs8dpb1Z0A4Wpvzrx3nGnzuWzU4xyI6Oho7O7uavWIvvcRzzz2Hv79/nedYWlrSuXNn+vfv3+hAhRDQN9iNPw3ugIeDJeHexu+kLFofjUZDfHw8KpUKg8HAhQsXaN++vUl6XahUatp0fI+S670ou3QDuIZOp2XdunXk5OQwYMAAk8QlhBADQtzpG+zKX74+Q0mllrYuNmw4eZ2957L5v4lda3tYvLTmJGuPX2Pvi4/j26Ym2XvW+iS+PHqVCd19CfG053jaz9WdLMzUdPL5+fP8lWGhPDn/MM8uP8aTjwaQW1rJP7YkE+Jhz/jut69y3K8fLuXx/i39Mm41rJMn/9iS3OBz36rBE4tevXrRq1cvAEpLS/nVr36Ft7cklwrR1J4fHFzne4PBIBdbolFuvn+uX7/OihUr6Nq1K7GxsSbLj7PzHsWkyTr2bP2MQPVHbE4axv79+8nNzWXMmDGYmzd8n7EQQjTU509E8sG2FObsOEdhWTXt3Gz5OPGROsVVdHrQ6Q11yrru/OkGAKuPprP6aHqdc/o4WXPwlYG13/cKcmHhtCjm7DjHjMU/YG1hxsBQd14bHoalxqzBsReUV6O6S/Z3YXnDK07dSmWQTFCjSE9Px8/Pj6tXr+Lr2/AZpxC3qqjW8fsVx4nr5svwXzTtEaIhTp48yfr16wkKCiI+Ph4Li3snJDYVXflVco6OwUybzuakYVzKbYe3tzcJCQkmKZMrhGj9Wuv1WsxH+3l5WAiPh7jfdmxPyg1mb0lm6x8b3xpC8dtR2dnZtyVx30zwFkIoa9n3V9j50w1eXnuKwkbUtxbipi5dujBp0iQuX77MkiVLKC9XphtrQ5hZ++Heey962961Sd3Xr19j7ty5ZGRkmCwuIYRoaab1DuDltafYeTYL/f/P39DrDew8m8Wr/zvN9Fv6aTSGIn0sioqKmDlzJitXrrxjAqD0sRBCedMeC+RidgljuvrU21BHiIYICgpi6tSpHDp0yOTbjlRmdrhFrSXv7Gv00C3lp8xQiotVLFiwgLi4OMLCwkwanxBCtAQTovzILqnkuZUn0Or1tLGxIL+sCnMzNb8f2P6OCeUPSpGtUDNmzGDFihXMmDGDiIiIepsaPeyVoVrr0ppofiTnQijt+vXrACbPoyspSOPLtTu4kXERM7WO8mobBg4cSJ8+feQ9L4RQRGu/XiuqqOb4lXwKyqpxsjGnW9s2OFgpdwNJkRWLTZs2MXv2bJ5//nklTieEaKCsogp+s/QYs0aF09XPydThiFZi7969XL58mQkTJhAUFGSyOOyc/JkyZQoXdo/G1pDEN6di+fbbb8nJySE2NhaNRpGPNCGEaLUcrMzrzbNQiiI5FhUVFXTu3FmJUwkhGuHDbSn8eLWAV9aeqt1DKURjjRs3jrZt27JixQpOnjxp0lg0Gg3BfT7Dwtq5tlP3qVOnWLJkCaWlyjR4EkKIluxwai6lldp7Pi+vtIrVPyjbhFSRicXw4cPZv3+/EqcSQjTCrFHhjO7qzdynuqNWy9YQoQwLCwsSEhKIiIjg66+/5vDhwyaNx8zaH/fe+9BZP8roiA108zvO1atXmTdvHjdu3DBpbEIIYWqJc7/n/I2S2u/1egPBr2/mzLW6jXWv5Jbyyv+UbUCqyLrxX/7yF+Lj47G3tyc2NhYXF5fbnuPs7KzEUEKIu7C11PBRwiN1HpOcC6EEtVrNqFGjcHJyahb7jlVmdrj3XEfumVewsb4IQEFBAfPnzyc+Pp7g4OB7nEEIIVqnX+5XMABaI+1iUGTFolOnTiQnJ/Piiy8SGhqKm5vbbV9CCOM7ebWAER8fID2/zNShiFZApVLRv39/fHx80Ol07N+/H6323svtTRePGtfO7xM1YiFeXl6EeiRjZihg5cqVfP/990ibJiGEMC5FVizeeOMNuSMqRDNjMBh445sznM0o4v2tKXyc+Mi9XyTEfcrMzGTfvn1cuHCBhIQErK2tTRaLg4MDU5+Mp+Dw36msOsQ3p2LZtm0b2dnZDB8+HDOzhnerFUIIcf8UmVjMmjVLidMIIRSkUqn4/MlIPtiawttjOpk6HNHK+Pj48NRTT7Fy5UoWLlzI5MmTcXR0NFk8FtbOuPTcR/YPo0mIXM3mpBiOH4f8/HzGjx9v0omPEEI8LBTtvF1YWMi2bdtYvnw5+fn5Sp5aCNEAXo7WzJnYFTvLn+8hSLUooRQ/Pz+mT59OVVUV8+fPN3lVJjNrfzwe24/WqgejI9bT3u0Cly5dYt68eeTm5po0NiGEMKbU7BLOXCus/QK4+IvHLmYr/ztbsaLf77zzDrNnz6a8vByVSsUPP/xAmzZtGDRoEEOGDOGVV15RaighRAOtP3mdxd9dZtG0KOwVbIgjHl6urq7MmDGD06dPY2NjY+pwUJnZ4fHoN9xIeo+cSgOgIy8vj3nz5jFhwgQCAwNNHaIQQjS5P391e2nwP335Y53vDYDSiQyKTCw+/fRT3nrrLZ599lliYmIYMWJE7bGRI0fyv//9TyYWQphYSaWWtzckkVNSxdLvr/Ds4+1NHZJoJezt7enduzcAp0+fRq1WEx4ebrJ4VCo1Hp1eZ4pvARvWfkJ4m2/YmTyQZcuWMXz4cCIjI00WmxBCNLUP4ruYbGxFJhaffPIJM2fO5P3330en09U5FhwczPnz55UYRgjRCHaWGhZN68HXJ67x236m654sWreLFy9y8uRJSkpK6Nmzp0ljcXJyIn7MEEpOzyWx+5d8cyqWjRs3kp2dTXR0NGq1oruBhRCiWYiPNF1JcEUmFqmpqQwdOrTeY/b29hQUFCgxjBCikTr5ONLJp26CrU5vwEya6QmFjB49GhsbG7Zu3UpRURGDBw82adVAa5eemPeom9R9+DDk5uYSHx+PpaWlyWITQojWRpHbNY6OjmRlZdV77PLly7i7uysxjBBCQQaDgQ+2JfOHlSfQ6vSmDke0EiqViujoaKKjo/nuu+/Yu3evqUNCY9MWzz4HqLKMIiZ8K5aaCi5cuMD8+fOl0IgQQihIkYnFoEGDeP/99+tUBFGpVGi1Wj777LM7rmYIIUznYnYJX+xLZdPpDPZfyDF1OKKV6dWrFxMmTGg2+QwqMzu8eq2n3GcpKo0TZmoteTkZzJs3j7S0NFOHJ4QQrYLKoEBr0gsXLhAVFYWDgwNjx47l3//+N1OnTuXEiROkpaVx/Phx/P39lYi3xUpPT8fPz4+rV6/i62u6vW9C3Gp7UiZZRRU82SvA1KGIVqy0tJTNmzcTExODnZ2dqcMhNzeXy/snYmOWyYbTI6jS2xMbG0uXLqZLeBRCNA9yvdY4iqxYtG/fnoMHDxIWFsann36KwWBgyZIluLq6sn///od+UiFEcxUd7llnUqHV6VHgXoMQdZSVlXH16lXmz5/fLPpJuLi4EPrYG7jYF5HY/UucrLL4+uuv2bVrl7z/hRCt1k8ZRRSWVzfpGIqVxOjYsSNbt26luLiY9PR0ioqK2L59O2FhYUoNIYRoQpVaHc8sP84/t58zdSiilXFzc2P69OloNBoWLFhAenq6qUPC1q0fbaL2YWbhyMTIrwh0ucSBAwdYvXo1VVVVpg5PCCEUN+Lj/Ww7k9mkYzR6YlFeXo6Pjw8bNmwAwNLSEm9vb6ytrRsdnBDCePady2HH2Sy+2J/K5RzTdlAWrY+TkxPTp0/HxcWFZcuWUV5ebuqQMLcNqEnqNu+GX5uayU5ycjILFy6kqKjIxNEJIYSyjLEe2+hys9bW1pSXl2Nra6tEPEIIExnS0YM3RnYkxNOeAFf59yyUZ21tzZNPPsnVq1extrbGYDCYtBQtgFpjj/djGyn3OI/l1XW4Wl8kI0vH3LlzSUhIwMfHx6TxCSFES6JYVaidO3cqcSohhAlN7xPIY+1da7+vljK0QmHm5ua0a9cOgJ07d7Jnzx6T5zWoVGraB4cwY8pYxnbZwLhH/oeu8gaLFi0iKSnJpLEJIURLosjE4rXXXmPVqlW8/fbbnDlzhtzcXPLy8up8CSFaluziSmL/fYC1x0y/H160PgaDASsrK/bu3cvGjRvR600/iXXzCsEy7Etc7WqSuh0tM1mzZg179+41+eRHCCFaAkUmFpGRkVy+fJlZs2bRpUsX3N3dcXNzq/MlhGhZVh+9SnJmMR9uT6G8SmfqcEQro1Kp6Nu3L6NHj+bEiRN8+eWXVFc3bbWS+2Hn0R+nHntRmzuQELkaH6dr7Nmzh3Xr1qHVak0dnhBCNGuNzrEAeOONN0y+T1YIoaxnHw+ivEpHfKQv1hZmpg5HtFJdu3bFzs6O1atXc+DAAQYMGGDqkLCwDcSr70GuHPot+aVOAJw+fZq8vDwSEhKaRS8OIYRojhRpkNdaffbZZ8ydO5fTp0/z+uuvM2vWrNpjp06d4ne/+x0nT57E19eXTz/9lMcff/yO55KGK6I1qNLqsdAoVqVaiFpZWVm4uLig0WjQ6/Wo1c3jfZacnMzWDUvp7HWE71J7YWffhkmTJuHh4WHq0IQQTaA1X68FvrqJf8RFMCHKr8nGULzcbGvi5eXFW2+9xZgxY+o8Xl1dzdixY0lMTCQ/P58333yTMWPGNIvGT0I0laTrhQz4cA+HU+V9LpTn4eGBRqMhMzOTTz/9lMzMpq21fr9CQ0OZFBdBN78fGdd1HVXlWcyfP5+UlBRThyaEEM1OoycWrbnc7JgxY4iNjcXR0bHO4ykpKRQUFPDss89iZmbGxIkT8fDwYN26dSaKVIim98W+VK4VlPN/O89LIqtoMvb29lhaWrJw4UJSU1NNHQ4A7kETMQtZjat9AZO6r8LBIpNVq1bx3Xffyb8FIYS4RbMvN1tcXMxLL71EdHQ0bm5uqFSqOluSblVSUsIf//hHvL29sbKyomvXrqxatUrxmOr7INHr9VKWULRq/xgXwa/7teO/T0VKTpVoMra2tkyZMgV/f3+WL1/OqVOnTB0SAA6eA3CM2ota48D4R9ZgblbFjh07WL9+PTqdFDcQQjR/K3/1KI+HNm1BpWZfbjY3N5cvvviCysrK27Yk/VJcXByLFy/mzTffZMuWLURFRZGYmMiKFSsaPH59QkJCsLOz4+OPP6a6uprly5dz4cIFSkulW7FovazMzXhteBgOVua1j1VUywWVUJ6FhQUJCQlERESwbds2KioqTB0SAJZ27fDqd5B0s5eo1lkABn788QRLly6lrKzM1OEJIcRdPdrOBXd7qyYdQ5Hk7VuT7O50J7Ohd3RuhqdSqcjJycHNzY0333zztlWLzZs3M2LECFasWEFiYmLt49HR0SQlJZGWloaZWU1lm0GDBnHw4MF6x3vxxRd555136jz29NNP4+vrW2fM48eP89xzz5GcnEy/fv0oKChgwIABvPHGG/WetzUnA4mH05bTGby76SeWPd2TQOnULZqAwWCgsLAQJycnqqur0Wg0zWa17MyZM+Sefh5zdSW7Ugbi6OTKpEmTcHV1vfeLhRDNllyvNU6zLzd7v+ddt24ddnZ2jB8/vs7j06ZNY9KkSRw+fJjevXsDsGvXrkbH1a1bt9rJiU6nIygoiJdffvmer+vevTsazb3/2mfOnMnMmTMbHacQTUGnN/CfPRe4VlDO0kNXeCO2o6lDEq2QSqXCyckJg8HAV199hYWFBWPGjLmv36FNrVOnTmSZxWFxYxZO1oVsODOCefPmMX78eIKCgkwdnhBCmIQiv53vlPNgTGfOnCEsLOy2D5yIiIja4zcnFvdLq9Wi1WrR6XRotVoqKiowNzfHzMyMM2fOEBwcTFVVFW+//TYeHh4MGzbsnufMysq6r7GLiooeKFYhjMlMrWLRtB4s+e4yzw/uYOpwRCunUqno1q0ba9euZdmyZSQkJGBl1bTL+ffDI+x3FDqG4ZoyhUndV/HNqVEsX76cmJgYoqKiTB2eEEIYXfMoFK6A3NxcnJ2db3v85mMNKQX77rvvYm1tzaJFi/jb3/6GtbU1S5cuBWDRokV4eHjg4+NDamoq33zzzX2d8+Zr7vXl4ODwwPEKYUyudpbMjA7BTP3zqqJ06BZNJTQ0lCeffJKsrCwWLlzYbG6+OHoPxLH7HlQaO7r4nMRgMLB582Y2b96MXq83dXhCCFHH/46n1/v41yeuKXJ+RVYs1Gr1PbcsGaNqxt1iaMhWrVmzZt1xNebDDz/kww8/fOBzHj16VPbsiVbHYDDw0a7zbD2TyapfP4qTjYWpQxKtkL+/P9OnT2fZsmWcPXuWRx991NQhAWBpH4RXn4Mk7z0A547iaF3ADz8cIS8vj/j4+GaxuiKEEABvfJNEXLfbr0PfXJ/EmEd8Gn3+JsuxyM7OZvv27eh0Op566iklhrkrFxeXelclblakqm81QwihjPyyapYfTiO7uJLtZ7OY0L3punqKh5ubmxu//e1vay/WS0pKsLOzM3FUYGbhyOAhI3B3tsSneBLJWSF8m6Jn/vz5JCYmymeQEMKkjl2puR7WGwwcu5IP/Fy7KS2vDBsLM0XGadIci6qqKoYOHYq7u7sSw9xV586dWblyJVqttk6exenTp4GaRDshRNNwtrVgxdM9+f5SnkwqRJOztrYGIDU1lZUrVxIXF0dYWJiJo6oRETmYjKS/0lH1Nm2sC2qTuidOnEjbtm1NHZ4Q4iH1/KofgZoy8c+vOlH7uEpVs7X5zdhwRcZp0hwLCwsLnnvuOebMmdOUwwAwduxYSkpKWLt2bZ3HFy9ejLe3Nz179mzyGIR4mAV72PPkoz9fOGl1eulzIZqUv78/ISEhrF69miNHjpg6nFpe4c9hCFpZ26nbWnWNJUuWcOLEiXu/WAghmsCBlwdy4OWBTOjuV/vfB14eyP6XBrLu2ccY1slTkXGavGaftbU1GRkZjTrHli1bKC0tpbi4GICzZ8+yZs0aAIYPH46NjQ0xMTEMGTKEZ555hqKiItq3b8/KlSvZunUry5Ytq+1hIYRoelVaPc+vOkFFtY7/PtkdC02rqRMhmhGNRsO4ceOws7Njy5YtFBcXM3DgwGbR68LZdzDl9rspPvwEWr0Zer2e9evXk5OTw6BBg+r0fxJCCGOZPS6iSc+vSIO8O8nOzmbixIkUFhZy7NixBp8nICCAK1eu1Hvs0qVLBAQEADV7bV9//XVWr15NXl4eoaGhvPrqqyQkJDR4bKVIwxXxMEm6Xsi4z75Dr4eVv36UyLZtTB2SaOW+++47Tp48yYwZM7CwaD7FA/R6PVu3buXk8QMEu58nKSOckJBQ4uLimlWcQogaD8P12qGLuWw4dZ0bRZW4O1gysrMXvdsr09xTkYlFYGDgbXeIKisruXHjBmq1mvXr1zN06NDGDtOiPQxvVCFute9cNgagfwc3U4ciHhI3c+wKCwuxtrZuVhfuKd//Ha/qDzl9PZxvUwbg5u5NYmIijo6Opg5NCHGL1n699sW+i8zbf4kJ3f3wdrImo7Cc1UevMv2xQH7Tv/HNPRXZCtW/f//bJhZWVlYEBAQwceLE2hUFIcTDo98vJhSllVqszc1Qq02/TUW0ThqNBoPBwOrVqzEYDEyaNKlZVIwCCHn0NdJP29FR/05tUvfcuXNJSEholRcvQojmad7+S6z89aMEuf38u3HMIz4kfPF985lYLFq0SInTCCFaqbzSKp6cf5ioAGfejO3YLPbAi9ZJpVIRGxvL8uXLWbBgAU888USzKfXq2/kP5KaF4HJhBgmRX7LsyGQWLVrEmDFjpHKhEMIozM3UeDnW7a3j4WCFhZkyeV+KnKW6uprS0tJ6j5WWllJdXa3EMEKIFurIpVySrhex8dR1bhRXmjoc0cp5enoyY8YM1Go18+fP59o1ZTrKKsHFfyh2j+ziYkkMWr05Op2OtWvXsnv3bpow5VEIIQD4db92PLPsOEcu5XE1r4zDqbk8t+I4v+nfjqyiitqvhlIkx2LKlClUVVWxcuXK245NnjwZa2tr5s2b19hhWrTWvmdPiHtZcyydrn5OtHdvHltTROtXVlbGl19+Se/evQkJCTF1OHXodDo2bdqEJu8LtHozTlx9hPDwTowePRpzc3NThyfEQ6u1X68Fvrrpns9RAanvjWjQ+RXZCrVnzx5mz55d77HY2FheffVVJYYRQrRg8ZF1f0EXllfjaC0XUKLp2NjYMHXqVFQqFQaDgbS0tGbTpM7MzIzY2FhSD27EzbAGF9s8vj2rJz8/n4SEBOzt7U0dohCiFbrUwAnD/VJkK1RWVhZeXl71HvP09CQzM1OJYYQQrURKZjGD/rmXpYcumzoU0crdzOdJSUlh0aJF7Nu3r9lsOVKpVAT1+YIC+7/Q0TOZcV3XkZd9kXnz5jW6/5MQQtxJUUU1G05eZ97+VDaeuk5xhXIpC4pMLJycnLhw4UK9xy5cuCB3XoQQdew4m0lOSSWrfrhKlVZv6nDEQyAkJITHH3+c3bt3s2nTJvT65vO+8+86E23bJbjY5dM78BBFRUUsXLiQ5ORkU4cmhGhljl3Jo9/7u1l48BIn0wtZePAyfd/fzbEreYqcX5GtUAMGDOC9994jLi6uTvWNvLw8Zs+ezcCBA5UYRgjRSvxuQHtsLTXEPeIrXbmFUahUKvr374+9vXUnXmUAACAASURBVD0bN26ktLSUcePGodEo8jHYaG4BMRQ77CT1m11ADip9CV9++SWDBg3iscceQ6fTkZSUREpKCmVlZdjY2BASEkJ4eHiz+RmEEM3fWxvO8u6YToyM8K59bNOpDGatP8uG5/o0+vyKJG+npKQQFRWFubk5EydOxMfHh/T0dL766iuqq6s5cuRIs0ucM7bWngwkRGMVlFXhZNN8GpqJ1uvcuXOcOXOGMWPGoFY3r4mtVqtl28ZFRNq/yZEr3Tlx9RECAgLJzMykoqKiNl/k5p9WVlaMGTPmof+MFUIpd7teK63U8uH2FDadyqCgvJogNzueeTyIUV2873C2GhmF5XyxL5Wk60X8lFFEcYWWD+IjGN/d77bnTvzvIQ5fun31oF8HN5ZM79G4Hw6ImLWNE29EY3ZLTymd3sAjb2/n1KzGN7NW5DZHSEgI+/fvZ+bMmcydOxedToeZmRn9+/dnzpw58gtPCHFXO85m8acvf+STSY/weIi7qcMRrVyHDh3o0KEDAGlpaTg4OODk5GTiqGpoNBpiRk3l4oFDPB68riapO2UAeoMZQG1+yM0/KyoqWLVqFQkJCfJZK0QT++2yY5y8WsDLMaEEutqy/sfr/GHlCQwGA6O7+tzxdZdzyvjmx+t09HJgQIg7609ev+s4/s42/F9C1zqPOVgpU+wk1NOBZd9fYUrvgNrHVhy+QgcPZdIWFFs/7dKlC7t27aK8vJz8/HycnZ2xsrK69wuFEA+9jaeuU1Kp5esT12RiIYzGYDCwZcsWSktLmTx5Mh4eHqYOCQC1WkNwv/mkHu1AR88PcLIuYOOZEVRUW+Nhn0WfoAMcuNiHrOKf4/3666954YUXZFuUEE1kd/IN9p/P4aOErrWTiN5BrlwrKOfvm39iZIR3nVWAW/UMdOb4X4cAcCq94J4TCytzNd382yj7A/x/fxvbiRmLjzLvQCpejtZcLyhHrVIx96nuipxf8TVga2trvL29ZVIhhLhvH8R34a8jO/Lh+C6mDkU8RFQqFZMnT8bOzo6FCxdy6dIlU4dUR7H5MNaciMPeqgRbi5omtGGeP+HvnE6YZ93E7oqKCs6ePWuKMIV4KGxLysTWwowRnetWQY2P9CWrqJIfr+bf8bXqO0w4TCHYw55dL/RnzoSuPNWrLf+a2JVdL/QnxFOZFYvmtblUCPFQstComdEnEI3Zz7+S8kqrTBiReFjY2dkxZcoUfH19WbZsGT/99JOpQ6qVkpJCRpEv/zsxGjO1Dk+HjNoJRYhHCu52N3C3z8LeqgiVSiVVpIS4izlz5uDr63vPr6ioqHpfn5JVTHt3uzqfUwBhXg41xzNLFIv1Sm4ZXd7aTtBrm+n3/m4+2JZMRbXuvl9/ODWX0krtHY+bm6mJCnCmd5Arl7JLMTdTbjoga6ZCiGbnP7svMG9/Kqt+3UuxuyhC3ImlpSWJiYls3boVFxcXU4dTq6zs/7F33/FRlWnj/z9nJr33HpKQQEIgCYT0BCMiIIKIHXHtu88+urqr7Oqq61p2Xdev7Np+rq4FF2yURUBaQB4klBBIAtJCEiCShDTSSS9Tfn/EDEQglAxMyvV+vXgBZ+4555rJYbivucvVil6v55Gkzw3HerZbsTZv4764JYbjb3//O1pbW691iEIMGo2NjZSVlV3x8xtau/B3sTnnuNNPhV7rW43zZVhsoAuzonwIdrelo0tHekEVH237keyiepb+KuGSRj/u/WQ3Kx9PZrx/99oxnU5P6J/TWPV4MuN8HQ3timtbeG7lQe6OPXcR+ZWSxEIIMaB0aLR8d+QU9a1dbDtaJYmFuCbUajUzZ3ZXpO3q6mLfvn3ExcUZCuyZgo2NDYqikJY7nWljNqNW6egJp+d3rU7hu7xpAANuhyshBhIHBwd8fS+8wLqHVqu9YGHnvj4NjPVR8YfpvTdhmBzmgZ+zDX/bkMd3R05x0zivi57j59u96gGN7toUBpVPISHEgGJppubzh+N4845I/ue6YFOHI4ahEydOsHHjRlauXIlGc+HpBFdbaGgoer2e/FNhLM2557xtNh2ZTv6pMKA77iVLlnDy5MlrGaYQg8L8+fMpLS296K/s7OzzPt/JxpyG84xKNLR1V612sr5626XPmdCdEP3QxzqOgUISCyHEgONoY95raFar01Pb3GHCiMRwMnr0aO666y7y8vL4+uuvaW9vN0kcY8eOPWcjlJ4vHXt+r291BvTcMf4bYgOyKSo8xGeffcaiRYs4fvw4RihVJYQAwrzsOV7VjEar63U8v7IJgFAvu6seg8qEI6iXyuiJRXV1NSUlJef8EkKIK9Gl1fH0sv3c9e9MqpskuRDXRnh4OPfffz8VFRUsWrSIrq6uax6DmZkZc+bMAaC1y5qWDhuqmjz5v/wbqGrypKXDhtYua8xUGtS2o0kI2sMvkz8jeWQGVeV5fPXVV3z88cfk5uai0+kucjUhRF+mjfWipVNL2uHe06S+2VuKp4Ml4/2vzvawAN/sKwVggv/AqLfTF6OssWhqauLpp59myZIlF/xmR6u99NXsQgjRo76lk73F9ZxqbOdw2Wkmh0mdC3FtBAQE8PDDD1NYWIi5uXGKU12u0NBQ5s6dy+rVq1m462F0mKHXw+GKCFRoMLe04867uytvd7WVUXHgNSb4r2KUxzEW7X6QyspKVqxYgYuLC8nJyURGRkqtCyGuwORQDyaNcuPF1Ydp7tAQ4GrD2gPlbDtazTv3jDfUsHh2xQG+2VfGtmeux8/5zGLvDYcqACip695k4VDZaWwtu/8t3vzTFrZZJ+p4f+txpo/1ZISLTffi7aNVLMk6SVKwKzeOufRaOz9WN2P2U0zan4Y4C6t771xVWN1yJW9FnxS9EcZJH330Ub7++mseffRRIiMjsbS0PKfNgw8+2N/LDGp9lYgXQvStuLaFH2tamCzF84QJ7du3Dzc3N0aMGHHNr63RaDhy5Aj5+fm0trZiY2NDWFgY4eHh5yQKus46fszfwtY9DTTX5RMfuIeckonUt7pgb29PYmIiEydOxMLi6s0JF2Kw6qu/1tKhYcGmAtYfquB0axcj3W15fHIIs6N8DG1+v/wA3+wrZcezk3vtIhX43PoLXrPoje6NI4pqWnh1bS55FU3UtXaiAEFuttwS5cMvJwVhaaa+pNcQ9Pz6cxaa6zl38XnPsR//PvOSznspjJJYeHl58fzzz/O73/3OGDENSZJYCGE8Te1dqBTF8G2PEFebXq/n888/p7S0lDvuuIOwsDBTh3RRer2ek3nLsTz1HFbq0xyvDiGrOIaqJk+sra2Ji4sjPj4ea2trU4cqxIAxFPprK/aWXlb7Oyca73UaJbFwcnJi5cqV3HDDDcaIaUgaCjeqEANBQ2snD36WhVql8KeZYy74DY6rnQXejtJhEsaj0WhYtWoVeXl53HzzzcTExFyT65Y3tPVZMPJi97pe10FVwb/RVX6ArVk1O44nk1PSHbu5uTkTJ04kMTERBwcHo8cuxGAj/bX+McrXfTfffDM7duyQxEIIcdWV1rdxvKqZ1k4td3yYecF27naW7Hxu8iUPHQtxMWZmZtxxxx1s2rSJ9evXo1KpiI6OvqrX7NBomf3+TmqaL5xYXOxeV1SWeI75Hfqw31Bb+AVm9W0oJ2sIcD6BoujYvbuT7OxsIiMjSU5OHlBFAoUQg4tREosXX3yRO++8E3t7e2655Zbzfii5uLgY41JCiGFunK8jix6O5U+rD3OsqpnzjbkqCng7WWGhlh21hXGpVCpuuukmPDw8GDNmzFW/noVahY+TNbUtnf2+1xXFDLeQh5keAvHXNVC99wE8LXZS3exKdnEs+/d3sX//fsLDw0lJScHL6+KFuIQQ4mxGmQrVU+2zrwqlw31XKBlaE8K4th2t5sHPsi74+HtzJxDl74iTjQWO1t07+mi0Osoa2gAIcLU1tK1t7qC5Q4OjtTlONt0LWrU6PaX13bt3jHCxMXy+1bV00tTehYOVOc623W31er1hpw8/ZxvD7iANrZ2cbuvCztIMV7szm1oU13bvxOHrZI3ZTx3C061dNLR1YmtphttZbU/WtaLT6/Fxssb8p7aN7V3Ut3RibaHGw97qnLZejlaGb6+b2ruoa+nE2lyNh8OZtqX1rWh1ejwdrLAy727b0qGhprkDSzM1Xo5n2pY3tNGl1eFhb4W1RXfb1k4N1U0dWJipek3DqTjdRqdGh7u9JTYW3d9dtXdpOdXYjvlPneQepxrbae/S4mpniZ1l77ZqldJrR5WqxnbaurS42Fpgb9X98+zQaKk83Y5KUXotkqxu6qC1U9PrZ9+l1VHe0IaCwgjXM21rmjto6dDgZG2Bo03f98n5fvb1DQ2s3LSN1NRURno4ovrpZ1/f0kljexf2Vua4XOQ+6fnZ//w+KaltRY+ewupmHlmUw4UsfiSO1NHuF3z8QvR6Pc2nvqe+4DWcVAdoaHNk5f45nG7r3tIyJCSElJQUAgICLvvcQgxW0l/rH6N8nffSSy/x8ssv89JLL13wlxBCGNN1o9yI9HNE9bPvM1QKRPo5srXgFKkL0lmWfaaOTnVzB6kL0pn61vZez3lr81FSF6SzaFeR4Vhzu4bUBemkLkg3bNUH8GH6cVIXpPPv7YWGY51anaFtS+eZSs2fZRSRuiCdd7cc63W9G/65jdQF6b3mzX+dVULqgnTeSMvv1fbmd3eQuiCdsvo2w7FV+8pIXZDOq2uP9Gp7+4e7SF2QTmHVmS0E0w5XkrognedWHurV9r5P95C6IJ3c8kbDse/zq0hdkM5Ty37o1faRRdmkLkhnb/GZqq+ZhbWkLkjnf7/c16vt41/tI3VBOjuP1RiO7SupJ3VBOg/9p3ciOH/5flIXpLMl75ThWF5FI6kL0pn78e5ebV9YdZjUBemsP1hhOFZU00rqgnRu+yCjV9u/rDtC6oJ0vjlrAWPl6XZSF6Rz07u9f/YLNhaQuiCdL/cUG47Vt3YZfp5ne2/LMVIXpLNw5wnDsYrqWv76gxk3vpNBfWOT4fjHO34kdUE6739/vNc5es57uu1MXYzPM7vvk398d7RX26lvd98nozzs+rzXrxvlxpVQFAV7rymMSN2KWfhGNNaJaJTuUYog1x8pPnGERYsW8dlnn3H06FEptieEuCijTIV65ZVXjHEaIYS4ZIqi8PtpoeeMWuj08PtpoWw8XImdpZnhW34ABeWnY717aJZm6nPaAoZv0c9mYabCztIMy0toa9nT1qx3W1sLdXfl5LPCMFcr529rada9JeBZbc1+amv1szn1thZq2izNUJ11ip7zWpn3Pq+NhRl2lmaGb80BzFTdba3N1T9r2/3+nH1etaFt7/Nam3e3NTvrPVYrP7W1MDt/27NO3HNe25+1tTJX/XTeM21VSvf7bvPztj+97+Zm5/6MekZcelj+dN6zpxIpSt8/T4uzzhswIgAbi6N0dXWx+PPPeegX83BxccFCfW7bnhig97aP5he4T7rvSR0qlarPe12vh7kfZzLO15HfThllGKW5HDaucYyc9DW/S+ji4L50gtrfp0trzv6TUfxQ2s6SJSfx9PQkJSWF8PBww0wFIYQ4m1GmQp3t6NGj1NbW4ubmxqhRo4x56kFNhtaEMD69Xs+t/8rgcNlpdPrujuY4X0e+/U1yn1MzhTC2+vp6vvzyS7RaLU888YTRi9D1da/nljcy6//biY2Fmv0vTTMkM3uL61ApCpF+Tr0SyEvR1VpM5aG/YNu+Dp0O9pdGkfFjMgDOzs4kJycTFRUlxfbEkCP9tf4x2lcO//3vfwkICGDMmDGkpKQQFhZGQEAAK1asMNYlhBCil55Ri56ZSj3f4EpSIa41Z2dnHn30UWbPnn1VOtt93esBrja8P28Cz0wP7TVC8o9NR7ntg118nXVmOqBOp7+kKU3mNgH4xy/EMeEQHQ734ujQvTZGrdKgaz/BunXrePfdd9m1axcdHR3GfbFCiEHLKJ9+GzZsYO7cuYwdO5YnnngCHx8fysrK+PLLL5k7dy5r165lxowZxriUEEL00rPW4mDp6X7NNxeiv2xsbBg5ciR6vZ4NGzbg6+vL+PHjjXb+C93r9lbmzIr06dVWr9fjbm+Jg5UZycFndmpMP1rFn1fncke0L/OnhV70mmpLD3wnvoNPtB63yCJO7n+TSNflFJwaTXZxDJs3N7Njxw5DsT0bG5uLnlMIMXQZZSpUcnIyDg4Ohn29e+j1embMmEFTUxMZGRl9nGHok6E1Ia6encdqeGVtLq/cMpYUSSyEiel0OtatW8cPP/zADTfcQEpKitFG0S73Xtfq9KiUM7s2/mXtET7LOMG9cSP4++0RhnYfphcS4etI/EiXc9YanU2vbaW64F/oT32EtVkdhdVBZJ5IoLrZA3Nzc6Kjo0lKSpJie2LQkv5a/xglsbC1tWXp0qXccsst5zy2Zs0a5s2bR3Nzc38vM6jJjSqEEMOHXq9n27ZtbNu2jdjYWG666aYBseC5pUNDVlEdHvaWjPVxBKCsoY3kN75HpcD+l6fh8NN2vqdbu7C3MjNsoXs2va6Luh//Q2fpu6TnR3P0VAjm6k66tOaoVGpDsT03N0n0xeAi/bX+McpUKLVaTWfn+auCdnV1DYgPUyGEEOJaURSF66+/Hnt7e9avX4+HhwcxMTGmDgtbSzMmh3r0OqbR6rgj2o/mji5DUgHw/KqDZBbW8rfbIrg5wrvXcxSVOa4h/4M++JfcPP40Tpm78Wp/GVuLRrKKYtm/X9ur2J63d+/nCyGGJqOMWEyZMoXm5mbS09Oxtj5T/Kijo4Prr78eOzs7Nm/e3N/LDGqSAQshxPB08uRJfH19UalU6PX6QbG5gF6vZ9KbWymtb+Obx5KYGOAMQG75aZZklTBljOc5CUpzRRoNR1/HQZVLbYsL2cUxFJwajU6vJjg42FBsbzC8fjF8SX+tf4wyYvHqq68yZcoURo4cyV133YWXlxcVFRWsXLmS2tpavv/+e2NcRgghhBh0/P39ASgpKWHjxo3cc889ODo6mjiqvimKwtY/XM/+kw1E+Z2JdUteFV/uLqG2ubNXYnG47DSjPafj5z2DtpoMzPP+yo1h31NcN4LWTlsKC49TWFiIn58fKSkpjB49WhIMIYYgoyQWKSkpfPfddzz33HP861//Qq/Xo1KpiI+PZ8mSJSQlJRnjMkIIIcSgZWNjQ2trKwsXLuS+++7D09PT1CH1yVytIjbQpdexpGBX6loCex1vbO/i1n9lYKFWsfOPk3F1S8Zv0ka6Wsu43qacrN3pzAj5mLzKMA6WdbB0aSkeHh4kJyczbtw4mS4txBBi9AJ5ra2t1NfX4+zsLNvOnUWG1oQQQjQ1NfH1119TX1/P3LlzCQwMNHVI/Xao9DQPL8rC3sqcrX+43nD8H5sKOFnfygOxTnjVvIJN+wa6tGoOlEXxw8nxtHXZ4OTkRFJSEhMmTJBie2JAkP5a/xg9sRDnJzeqEEII6F5/uHz5curq6njiiSdQq9WmDqnf9Ho91U0deDhYGY6lLthKcW0rnz4Qw43hnmjbyzmc/Q9+rMjFz6yODYdmGdra2tqSkJBAbGwslpaWpngJQgDSX+uvK/56oKSkBG9vb8zNzSkpKblo+xEjRlzppYQQQoghw9LSknnz5tHY2IharUaj0Qz6b+sVRemVVOj1et64PZKdx6uJH9k9bUpt5cNBs9/y5325RHuruD24lraaHYz1ziW7OIYtW7awc+dOQ7E9W1tbU70cIcQVuuJPsqCgIDIzM4mLiyMwMPCii7C0Wu2VXkoIIYQYUtRqNc7Ozmi1Wj7//HP8/f258cYbh8yCZkVRSAx2JfGsqt8AapWKQFcbpkT684vJMzh1TI/25P/xVN5ERlhVkaQ+yY4dO8jMzDQU2xvoC92FEGdccWLx2WefERwcbPjzUPkwFEIIIa4VtVpNeHg4mzZtoqmpiVtvvXVITI26kHnxI5gXPwKNVgeA56j7yTabRtV3OXRqLfh06utszruRI5XhLM88yrLdhdw4zo/p1yfj7u5u4uiFEBcjayyuEZmzJ4QQ4kJyc3NZtWoVAQEB3H333cNqnYFOpye3vJHyhmYS7L4nO9+SrH3HSevw4WSHOynmPzLKrI6wsDBiE5Lw8vbGxmJwTx0TA5f01/rHKP8yt2/fTnR0NHZ2duc81tzczL59+7juuuuMcSkhhBBiyBk7diy2trYsXbqUo0ePEhERYeqQrhmVSiHCz5EIP0fgfm4MhMRJLbR89SZ7a1r4zZjPKaocTUG+jrTDp9ihGclNoc68/2CSzJYQYoAxyoiFWq02rLf4ub179xIXFzfs11hIBiyEEOJimpubDV/StbW1YW1tbeKITEev19Nek059/mvY8QP1rY78dudzHOjyY6y6ktuC9IZie8+vPEyEnyO3R/vKaIboF+mv9Y9R/vX1lZt0dXVJ8RshhBDiEvQkFYcPH2bDhg3MmzevV+cmNzeXtLQ0ZsyYwdixY00V5jWhKArW7pOxdp9MR0MOurzF3ODWQXDdQUa7H+dUhTfLlpWhcvJmWYUvq/aXcefEM+/ViZoWnKzNcba1MOGrEGJ4ueLEorGxkYaGBsPfKysrz9l2tq2tjcWLF+Pl5XXlEQohhBDDTHBwMO7u7ixevJg777yT0NBQWlpaWLduHe3t7axbt47AwMBhsyWrpVMMQYkxPBmv4+ihDXid/icdXRb8UDqejJJYos0ACxsO/rCXCRMmYG5uzqtrc9l2tJoFd0b1SjiEEFfPFQ8lvP322wQFBREUFISiKNx2222Gv/f8Cg8P56OPPuLBBx80ZszXTEdHBw8//DD+/v44ODiQkJDArl27DI9/+OGHREdHY25uziuvvGK6QIUQQgwp1tbW3H///YwaNYply5aRk5PDunXr6OjoALr/f1q/fr2Jo7z2VCoVYVGzsI/dh97pFuIDc/hdyr/5ZcgaovSFpKWl8c4777B9+3bqWzrQ62Gsj4Ph+bt/rOX+hXtYmnXx+ltCiMt3xSMW06ZNw87ODr1ez7PPPsuTTz55ThE8S0tLIiIiSE1N7XegpqDRaAgKCiIjIwM/Pz+++OILZs+eTUlJCTY2Nnh7e/Pqq6/y+eefmzpUIYQQQ4yZmRl33nknaWlpZGZmUldXZ3hMr9eTl5dHbm7ukJ8SdT5q6xF4TfwIXedfqc57E6emBigGK/M2zHSn2bp1K4mWltw/aSK+dmcWeKcXVLPjWA3u9pbMjTvTZ1l/sIIof0f8nG1M8XIGnb3F9fzxm4PcGzeC++JHYGU+dLdIFpfHKIu3X331VX71q1/h4+NjjJgGNBcXF7Zu3UpUVJTh2C9/+Uv8/Pz6HLWQxUBCCCGuRHNzM++//75htOJsVlZWPPHEE8NmSlRfKisrqfjhGYJsNlBQFUp2cQy1La6YmZkxfvx4kpOTadCY831+FWHe9iQFuwFQ3dRB7N/+D4Af/jzVsCZDr9fLrlMX0Nyh4S9rc/lmXxkuthb8b2rwkEkwpL/WP0ZZVf3yyy9f1aSiqamJZ599lmnTpuHu7o6iKBfsxDc3N/PUU0/h4+ODlZUV48ePZ+nSpUaJIz8/n7a2NkNhQCGEEOJq0uv1rF+/ns7OzvM+PlynRJ2Pl5cX46d9hN7nzwR5VPFA/JfcErEWB4tT5OTk8N577/HDju+YNdrWkFQA1LZ0ED3CiUg/x14LvZ9ZcZA5/8pgx7FqU7ycAc3O0ow374xi6++vZ3KoO69vyGPSm1tZuPME7V3DexfQ4c5oe7IdO3aMjz76iLy8PNra2no9pigKW7ZsueJz19bW8vHHHxMVFcWcOXP49NNPL9j29ttvJzs7mzfeeIPRo0fz9ddfc++996LT6Zg3b94Vx9Da2sr999/Piy++eN56HUIIIYSxVVdXk5+ff8HHe6ZEVVVV4eHhcQ0jG5gUtQ3Oo55CH/w4TSVf4Kt7G+tSPbSCpbqVgwcPcPDgQUJDQ0lJScHPz48wLwdWPp5sqAYO3e/rtqPVVDd10Nap5aVvD9PaqaW5XUP56Ta8HKxwsDY34SsdWKaGe5Jf0chr646wYGM+86eF8j/XjTR1WMIEjJJYHD58mISEBHx9fTl+/DiRkZHU1NRQVlaGv79/v7/hDwgIoL6+HkVRqKmpuWBisWHDBjZv3mxIJgAmT55McXExzzzzDPfccw9qdfcw3ZQpU8jIyDjveZ555hn++te/Gv7e1dXF3XffTXh4OC+88EK/XosQQghxqdzd3QkLC6OgoOCCW7uHhYXh4eEhU3fOoqgscAh8FPuAh5kX205W1h5GtD9Gl0YhuziWggI9BQUFBAYGkpKSwsiRIzFTn5nEoSgK3/4mmZ3HawjztufLPSW0dmiobGyntL6NYutWRnmc+ZJRo9Njphq+771Wp6dLq0OlKHRodJTVt138SWJIMkpi8cILLzB9+nSWLVuGhYUFCxcuJDo6mvXr1/PII4/w2muv9ev8l/pBuWrVKuzs7Ljrrrt6HX/44YeZN28ee/bsISkpCeCSR1B0Oh0PPPAAarWahQsX9vtDOyYmBjOzi7/t8+fPZ/78+f26lhBCiMFNURRmzZpFUVER7e3t5zxuaWnJrFmzKC8vZ8WKFSQkJDB+/HgsLKR2A4CiqLCxsSE19Xraq9+loeB1Zkeuo7bZheySGPKLdRQVFeHt7U1KSgpjxowx/D/v42TN3TH+AHz+SHcB4I2HK/g66yTTwj35RUIAAG2dWqL+8h0BLjYs/3XisKqb0dyhYfGuIj7Z8SOtHVruSxjBY9cH4+04fAs7DndGSSz27dvHBx98YCiEp9N1DyfOnDmTP/zhDzz//PNs27bNGJfq0+HDhxkzZsw5HffIyEjD4z2JxaX69a9/TUVFBRs3bjznvBqNBo1Gg1arRaPR0N7ejrm5uWFU5HxOnTp1SddtbGy8rDiFEEIMTba2tsycOZNvGPMP+QAAIABJREFUvvnmnMduueUWbG1taWtrw9fXl40bN7J161ZiYmKIi4vD3t7eBBEPPIqiYO0xHWuP6XTU78Iq7zWuG5XJ0apRaPUqKivK+O9//4urqyvJyclERkae9//ym8Z5c9M4717HjlScpkuro6VDg5PNmelRn2cWUdfSya3jfQlyG1qL67u0Oj7e/qMhoZgb5y8JhQCMlFjU19fj4uKCSqXC3Nyc+vp6w2MxMTH85S9/McZlLqq2tpaRI8+d0+fi4mJ4/HIUFxfz6aefYmVlhZvbmYVeaWlpTJo0iddee41XX33VcPxvf/sb//nPf3jooYcueE5PT89LGrFwcHC4aBshhBDDw9ixY8nNzTVMiVIUhbCwMMNWs25ubtxxxx1MmTKFPXv2kJWVhVarZdq0aTJF6mcsnZPwTtqAtrOe2zzL2bt7PTeMeJ8DpVEcLItgzZo1pKenk5SUZKhV1ZeJAS788OeplNS19nqfv9xdzNFTzYz2tDckFqdbu6hu7iDY3XZQ/0xyiup59/+OSUIhzmGUxMLX15eamhoAQkJC2L59O1OnTgXg4MGD13Sxc1//UC/3H3FAQMAF57QCvPLKK5ddGC8nJ0e2LxNCCHFZfj4lytLSkpkzZ57TzsnJienTp5Oammr4/2vnzp0UFRWRmJhIcHDwoO7QGpPawpmxY50JC3am5nA+yZZriQ3IYX9pFD+Ujmfjxo1s376d+Ph4YmNjsba+cOfZycYCJ5szU6D0ej2PJAex41gNiSNdDcc35Vby7DcHmRruyScPxFzV13c1JQa7kv/Xm1AN43Ul4vyMklikpKSwa9cu5syZw3333cfLL79MRUUFFhYWLFq0iF/84hfGuMxFubq6nndUoqeoUM/IhRBCCDHY2NraMmvWLNLS0pgxY0aftSusrKwMf/bw8CAvL4+vvvoKd3d3EhMTiYiIuKTR8+FAbeWDZ8xn6DrKqM3/f8Sol+No3cjGI9NpbW1l69atZGRkEBMTQ0JCwiVNL1MUhblxI3oV4QOoamrHwkzFGK8z59Dq9Nz7yW6i/Bx5csooHKwGx25TklSI8zHKp8qf/vQnysvLAfjjH/9IZWUlX331FYqicPfdd/OPf/zDGJe5qIiICJYsWYJGo+n1gXno0CEAxo0bd03iEEIIIa6GsWPHXnal7dDQUEaPHk1xcTGZmZmsWbMGR0dHRo4cKdOkzqKy9MU96j10XS8TdOok41SFdFWvxN+5hJziiezatYs9e/YYiu05Oztf9jWeuGEUj6aMpFNzZmvb3PLTZJ2o40h5I8/eFGY4nlNUh7laxThfR9TSiReDhFEqb19LNTU1uLu78/LLL58zDSktLY2bb76ZpUuXcs899xiOz5gxg4MHD1JSUtLnwuqrSSo5CiGEGAjq6upwdnZGURS++uornJycSEhIwNXV9eJPHmbqf/wMzcm/Yq40cqwqhOziWKqbuwv1jhs3juTkZDw9Pft1jcb2LtILqqlr7uCh5CDD8Xs+ymTPiTpevy2CefHdIx86nR5Fufyp3eLSSX+tf/o9YtHW1kZISAj//ve/ueWWW4wR03mlpaXR0tJCU1MTAEeOHGHFihUA3HzzzdjY2DBjxgymTp3KY489RmNjIyEhISxZsoSNGzfy5ZdfmiypEEIIIQaKnmnBOp0OPz8/srKyyMnJITQ0lMTEREaMGCEd1584j3wEfeA8mooXEaB/h1DPr1m5/1aK6wI5dOgQhw4dYvTo0aSkpODv739F13CwMmd2lE+vY3q9Hnd7S+wtzUgKPpPwbcmv4tW1udw50Y+nbhzdr9cmxNVglBELFxcXVqxYwQ033GCMmM4rMDCQ4uLi8z524sQJAgMDAWhubuZPf/oTy5cvp66ujrCwMJ5//nnmzp171WK7FJIBCyGEGIi6uro4ePAgu3fvpqmpifnz52NhYSHTpH5Gr9fQWr6CfYXu7N6zj7Hu26hqcqe4LgBQCAgIICUlxagL5DVaHWqVYjjfK2tyWbSriF8kjOC1ORGGdh+kHyfKz4m4IBfMzyr0Jy6f9Nf6xyiJxV133cWoUaN4/fXXjRHTkCQ3qhBCiIFMr9dTW1uLm5sbra2tLFq0iOjoaCZMmIClpaWpwxtQOjpaqc+eho3+CKea3MkuiuV4dTB6VHh5eRmK7fXU9zKWlg4NWSfq8HSwItyne1v6k3WtTHpzK2qVwoGXp2Fn2T0Z5XRrF/ZWZrLI+jJJf61/jFZ5+4477sDKyorbb78db2/vc7J12ZFJCCGEGLgURTHUbNJoNHh7e7N582bS09OZOHEi8fHxUmPpJ5aWNngm76Crfjv2+a8xK2IDtc0ufJk9j8rKSlasWIGLiwvJyclERUUZbSq2raUZk8M8eh3T6PTcHu1LW6fWkFQAPPvNAXKK6vnbbRHcNM7LKNcX4mKMMmJxdkZ+oeE/rVbb38sMapIBCyGEGGwaGxvZs2cPe/fuJSAggHvvvdfUIQ1IXadzKD+exqa9nlRVnmSs9xGOVI5BozPH3t6exMREJk6ciIWFxcVPZgR6vZ6kN76n4nQ7q3+TzHh/JwAOl51mWfZJbgz3JHW0+zWJZbCR/lr/GGXE4qWXXpJ5mEIIIcQQ4+DgwNSpU7nuuutoa2sDoKCggN27d5OYmMioUaPk/3/A3DGGgIkx/Cpaz8m8/+JQ8yGJI3fzQ+l4DpRG8t1337Fjxw7i4uKIj4/vs9ieMSiKwrZnJrP/ZAMRvo6G45uPnOKL3cXUtXT2SiwOl51mtKc9FmayPkP0z6DbbnawkgxYCCHEUHDixAm2bNlCWVkZbm5uJCQkEBUVJQX3zqJrL6Eu/++oG79Bo1WRUZjEgbIoAMzNzZk4cSKJiYnXfGpZ1ok61h0sJ3GkKzMivIHutRgT/vodVuZqdj13Q68K4sOR9Nf6Rz4FhBBCCHHJgoKCePTRRzl58iSZmZmsW7cOrVZLXFycqUMbMFRWI3Ab/yG6zldoOPYWbq1mKOUKDpYN6IHdu3eTnZ1NVFQUycnJ12wdalyQC3FBva9VVNuCi60FjtbmvZKKBZvyKW9o56GkQKJ+mkolxMUYbY3FxYZCZY2FZMBCCCGGnrq6Ouzs7LCwsGDTpk10dnaSmJhoWAguutXX11O972E8zHeQXxlKdnEMda2uKIpCeHg4KSkpeHmZZpG1TqenprkDDwcroHuNxqQ3t1Ja38Z/Hoo1LBivPN3OkYrTxAW59looPpRIf61/rtoai+rqar777ju0Wi0PPPCAMS4jhBBCiAHm7G/bHRwc2LVrF/v27WPUqFEkJSUREBAg6zAAZ2dnnK7/iubiTwnmfcK9v+R4dTDbj6eQm5tLbm4uISEhpKSkEBAQcE1jU6kUQ1LR4807I9l5rKbXCMem3EpeXpPLpFFufPFovOG41DwRPYySWLzyyivnPd7Z2cn06dPx8PA47+NCCCGEGDoSExOJjY3l0KFD7N69m8WLF/Pkk0/KlvM/UdS22I/8HXaBj9Fa9hW+vId5iT20ga1FM8ePH+P48eOMGDGClJQUQkJCTNJhVxSFpGA3koJ7jzqpVQojXGxIPKsaeKdGx6Q3vyfC14l/3hWFo435tQ5XDCBXffH2ypUr+eMf/8ixY8eu5mUGPBlaE0IIMZzo9XoqKirw8fFBp9PxxRdfEBISwsSJE7Gysrr4CYYBvV6PRqNh/749+Lfcy+lWe7KKY/mxJghQ8PT0JCUlhfDwcKMX2+sPjVaH2U8Vvvf8WMs9H+/Gzc6CrBduNBTkSztUQadWx3Wj3HG2HTwLwqW/1j9XfYKctbU1FRUVV/syQgghhBhAFEXBx8cH6J7B4OjoyPfff8/27duJjo4mPj4eJ6fhvShYURTMzc2JiUums3YhqqOvc6vjWmqaXckujiH/lJ5vvvmG77//3lBsbyDsvtWTVADEBLqw5olkKk+396ry/UF6IYfKTvPW3VHcHt3dQW/v0qLXg7XFhQsGlje0UdfSecHHXe0s8Ha8utv1XkhLh4Z/fFfA+oMVNLR1Eexux2PXBzM7yqfP51WcbuPj7T+SW95IXkUjTe0aFtwZyV0x/udtv/NYDf/cXEBeRSPW5mpuCPPk+ZvDcLOzvBovy6iu6t1ZXV3NggULCA0NvZqXEUIIIcQAZmVlxZw5c5gyZQpZWVnk5ORw7NgxfvOb38jcfLoTDEu3aXi4TaOrIRO7gteIVx8m/1R3/+l0Qw3r1q0jPT3dUGzP0nJgdDLVKoVIPyciz/pyX6/XkxTiikanJyXkzHSqTbmVPPPfg9wZ48frt0Wcc64OjZbZ7++kpvnCiYW7nSU7n5uMpZlxqplfjv/9ci8HTjbwxxlhBLnZsmZ/Ob9d8gN6vZ5bx/te8HlFNa18u7+ccG8HJod6sOZA+QXb7v6xlof+k8XkMA8+eSCG2uZO3kjL575P9rDmyWSTvO7LYZTEIigo6JwPho6ODqqqqlCpVKxZs8YYlxFCCCHEIGZvb8+UKVOYNGkS9fX1KIpCbW0ta9euJSEhgdDQ0GGfaJg7JeIRvx6dppn7Q6o4kvMVE10+YW9JNAfLI9i8eXOvYns2NjamDvkciqLw/IwxPD+j9/GDpafp1Op67Sil1+t57ptDRPo7ctt4X3ycrKlt6eR8E/UVBbydrLBQX/tpYVvzq9hxrIZ35443JBFJwW6UNbTx+oY8ZkX6oFad/96ND3Jh35+nAnCwtKHPxOLvG/IIcrPlw/uiDSND/i7W3PFhJstzSrk/4dou7L9cRkksUlNTz/kgsLKyIjAwkHvuuYfAwEBjXEYIIYQQQ4CFhQWenp5A9zQpnU7HsmXLcHFxISEhgfHjx2NuPrwXAavM7Bg50o5Anwepyy8mxeJb4gKz2V8axQ8nx7N9+3YyMzOJjo4mKSnpmhfbuxIvzhzDffEjelX4PlbVzLKck6zeX8Yd0X78flooD36Wdd7n6/Xw+2mmST435VZia6Fm5k+FBXvcOdGP3y3dz/6T9UwMOP8mBaoLJBw/V3m6nQOlp3n2ptBe080mBrgw0s2W73Irh0disWjRImOcRgghhBDDjLe3N4888gilpaVkZmaSlpZGTU0NM2bMuPiTh4HuYnufout4hYZj/yDWbCkqFWQUJtLV1cWePXvIzs4mMjKSlJQUXF1dL35SE1EUhZHudr2OOViZM3/qaJo7NFiZq7lulBuRfo4cLD3dq51KgXG+jlw36srqo7z11lu89dZbF213obprBaeaCPGw69XhBxjj3Z3QFVQ2XzCxuFQFp5q6z+l1bpIY5m1PTlF9v85/LRglsehZiGVnZ3fOY83Nzezbt4/rrrvOGJcSQgghxBDk5+fHXXfdRX19PWp19zzyvXv3UlpaSmJi4rDful5l6YfLuHfQdb5IzNgWurKOQNW/cbCqI7s4hv3797N//35DsT1vb++Ln3QA8HK04rdTRhn+rigK86eO5qH/ZPdqp+vnaEVjYyNlZWVXHGdDaxf+LudOO3Oy7h5Zq2+98LqQS79G9znOt2Wvo7UFDa1d/b7G1WaUxGLy5MlkZmYSFxd3zmMFBQVMnjx52FfeFkIIIcTFOTs7G/6sUqkoLCxk//79hISEkJiYeN51ncOJysINJ1c3ZswIoKmoiM6SBYz1PsLRqlFkF8dw5MgRjhw5QnBwsKHY3mB7v1JHuxPp58jhstPo9P0frYDu4o2+vhdeYN1Dq9VSWVl53sf6eheN+RZf8FSD4MdolMSir1IYXV1dA2rvZSGEEEIMDhMmTCAyMpLDhw+TmZnJF198wX333UdISIipQxsQ7AMfQ+//EG1lnzNSeZswz6/5ImseNc3uFBYWUlhYiJ+fHykpKYwePXrQJBiKovRaa9Hf0QqA+fPnM3/+/Iu266lj8XNONuaGEYWzNbR1jyI4Wfe/VoeTTfc5zjcycbqt0zA6MpBdcWLR2NhIQ0OD4e+VlZWUlJT0atPW1sbixYvx8vK68giFEEIIMWyp1WqioqKIjIykuLiYESNGALB27VqcnZ2ZOHEi1tamqWswEChqa2xG/Bprv0foqNlEnKUHuzJ3EeW+hqLaQIpL9SxduhQPDw+Sk5MZN27coPjC9+y1FpF+/RutMIYwL3vW7C/vVRwQIL+ye11EqNe5ywEuV6inveGck8N6T/3Lr2wi1Mu+39e42q44sXj77bf5y1/+AnRnlrfddtt52+n1el544YUrvYwQQgghBIqiGHaZ7JkpkZ6ezvbt25kwYQIJCQm9plENN4rKHCuPWcR6QPT4UdTlLCfafzWnGj3ILo7heJWOVatWsXXrVpKSkpgwYcKAKLZ3IYqi8Oz0MF5Zm8uz08NMPtoybawXS7JOkna4klvOKoj3zd5SPB0sGe/f/3vPy9GKKH8nVv9Qxv9cN9Kwfe2+knp+rG7hkeSgfl/jalP0fc1j6kNmZia7du1Cr9fz7LPP8uSTTxq+RehhaWlJREQEqampRgl2MJMS8UIIIYRxNTc3k52dTXZ2NhqNhj/84Q9YWPR/SspQoNfr0TRso6Hgb1h27aW8wZtl++6iZ6K+nZ0dCQkJxMTEDJhiewNBX/21+xfu4WDpaZ6bEUaAqw1rD5SzJOsk79wznjkTutdvPLviAN/sK2PbM9fj53xmsfeGQxUAlNS18kZaPg8kBpAwsnsHr5vP2sI2s7CW+xfuYcoYD+5PCKS2pYP/l5aPvZX5oCiQd8WJxdleffVVfvWrX+Hj03dJ8+FMEgshhBDi6ujq6qK8vJyAgAA6Ozv573//S3R0NKGhoYNi2s/VpmnMpqr0B77fZ0N5ySFCPY9yuHwsGp05VlZWxMbGEh8fj62tralDNbm++mstHRoWbCpg/aEKTrd2MdLdlscnhzD7rBGM3y8/wDf7Stnx7OReu0gFPrf+gtcsemNmr7/vOFbNW5uPcqS8EWsLNTeEefDCzWNwsxv4CaBREgtxcZJYCCGEEFdffX093377LcXFxTg7OxsK7slIRreqo59hUfks7V2W7Ds5noNlUXRoLDEzMzMU23N0dDR1mCYj/bX+MVpicezYMT766CPy8vJoa2vrfRFFYcuWLca4zKAlN6oQQghx7ZSXl5OZmUlubi4hISHMmzfP1CENGLr2Yk4fWwD1y9FoVXxfMJn8U2FA9xa/kZGRJCcn4+Zm2gXTpiD9tf4xyqqdw4cPk5CQgK+vL8ePHycyMpKamhrKysrw9/cnODjYGJcRQgghhLgkPj4+3HHHHUyZMoX29nYAioqK2L9/P4mJiXh6epo4QtNRWQXgHPE+us4XafrxXXy7fCisa8LJsox2jZWh2N6YMWNISUmRqe7ikhllxGL27NmYm5uzbNkyLCwsyMnJITo6mvXr1/PII4+watUqkpKSjBHvoCUZsBBCCGFa+fn5pKWl0djYyMiRI0lMTCQ4ONjkOw4NBK2trdRmz8SOQ+SfCiWnOIa6VhcARo4cSUpKCoGBgUP+vZL+Wv8YJbHw8/Pjgw8+YNasWZiZmZGVlUVMTAwACxYsYN26dWzbtq3fwQ5mcqMKIYQQpqfVajly5AiZmZlUVFQwY8YM4uLiTB3WgKDXttBa+hntJe9hpq/leHUIW49eT0tn96JuX19fUlJSCA3tX7G6gUz6a/1jlKlQ9fX1uLi4oFKpMDc3p76+3vBYTEyMod6FEEIIIYQpqdVqIiIiGDduHMXFxXh4dBciy8jIQKvVEhMTg42NzUXOMjQpaltsA57Exv9/6KhYgp/6U6zLPWipa8HRuoGyMj3Lli3D3d3dUGxPrR7Y25+Ka8soiYWvry81NTUAhISEsH37dqZOnQrAwYMHsbPrfzVCIYQQQghjObvgHnRPBcrKymLHjh2MHz+ehIQEXF1dTRegCSkqS6x8H8LK9yEeS9SRd2g7Pg13carJg+yiGH6s1rN69epexfbMzc1NHbYYAIySWKSkpLBr1y7mzJnDfffdx8svv0xFRQUWFhYsWrSIX/ziF8a4jBBCCCHEVTF16lSSkpLIyckhKyuLnJwcnnzySVxcXEwdmkmpVCrCI1PR1H2J9tjr3Oq4lupmV7KKYjlaFUpaWhrbt28nPj6e2NhYrKysTB2yMCGjrLEoLCykvLycSZMmodVqefrpp/nqq69QFIVZs2bx3nvv4eDgYIx4By2ZsyeEEEIMDl1dXRQWFhIWFoZer2fNmjWEhIQwZsyYYV1wT6/Xo23M5PTR16mua2XZ7lQUdKgUHVq9GZaWlsTExJCQkDBoZ6tIf61/pEDeNSI3qhBCCDH4tLW1sXz5coqKinB0dCQhIYEJEyZgaTnwqyBfTXpdJ6eq6jie/RYjrZewtySaQ+Xj6NJaYGZmxvjx40lOTsbJycnUoV4W6a/1j9ETi7q6Ot58800OHz6Mr68vv/vd7wgPDzfmJQYluVGFEEKIwauiosJQcM/FxYXHH398yO6MdDm0bYU0HX8D6lfToTFn/8kofigdT4fGCkVRiIiIIDk52bBIfqCT/lr/XHFi8Yc//IHly5dTUlJiONbS0kJkZCRFRUX0nNbe3p6srCxCQ0ONE/EgJTeqEEIIMfg1NjZSW1tLUFAQjY2NbNmyhYSEBLy9vU0dmknp2ktpPvEW2uqv2X48lf0nwwE90J18hYaGkpKSMuD7QNJf658rnii4a9cu5s6d2+vY+++/z4kTJ3jqqadoaGhg165d2NnZ8cYbb/Q7UCGEEEIIU3NwcCAoKAiAhoYGiouL+fjjj1m8eDFHjx5luM4wV1n54TDmLRwTDnPj3f8mNTWVaeHbmRK6BUfrBgoKCli4cCGLFy+msLBw2L5PQ90Vj1h4eXnx3nvvcffddxuOpaSkcOzYMcrLyw37Gv/zn/80JBzDmWTAQgghxNCj0+nIy8sjMzOTsrIyEhISmD59uqnDGhBai9+jveQdVPrTFJwaTXZxDLUtbgD4+PiQkpJCWFjYOVPKNBoNubm5FBQU0Nraio2NDaGhoYwdOxYzM6NsaHpB0l/rnyv+6TQ0NPQa9tNoNGRnZzNnzpxexVImTJhARUVF/6IUQgghhBiAVCoVY8eOJTw8nJMnT2JtbQ1AXl4ep06dIjY2FltbWxNHaRo2Ab/F2u9XtFd8TojqHUI9l/BJxiO0dtpSXl7O8uXLcXNzIzk5mYiICNRqNQUFBaxevZr29nYURUGv16MoCnl5eWzcuJE5c+YM++n1A9kVJxaenp69EoZ9+/bR1dVFTExMr3YqlWrY75wghBBCiKFNURRGjBhh+Ht9fT27du0iIyODyMhIEhMTcXNzM2GEpqGorbH2+zVWPo+gOb2Hm12dydi5jfGun5NbHk5JjZ5vv/2WrVu3EhISwr59+wzP7ZlU0/N7e3s7S5cuZe7cuZJcDFBXvMZi4sSJfPLJJ4Yfdk/diilTpvRql5+fP+wXNAkhhBBieElKSuKpp55i0qRJHD16lH/9618UFhaaOiyTUVTmmDunMHbsWB594BaC/dTcMWE198YsJdjtOI2Np3slFX1ZvXo1Go3mKkcsrsQVj1j88Y9/JDk5mdDQUNzc3Ni9ezeTJk0iOjq6V7u1a9cSGxvb70CFEEIIIQYTGxsbrrvuOpKSksjNzSUwMBCA9PR0XF1dCQ8P7zV9fLhQW/vjEr8TbUM6rsfeYLbDeo5XB7P20Kxe7TztT5ESvJOdhSmcavI0HG9vb+fIkSNERkZe69DFRVzxiEV8fDzffvstPj4+NDU18ctf/pJVq1b1alNZWUlpaSm33nprvwMVQgghhBiMzMzMiIqKQq1Wo9frKS8vZ+XKlbz33nvs2rWL9vZ2U4d4zSmKgpnzZFzjNmETtZFKTfeCd1fbWqJ8D6BWaRjjlccIl1LGeOWf89z8/PzznVaYWL+W1s+cOZOZM2de8HEvLy8OHDjQn0sIIYQQQgwZiqIwb948Tp06RWZmJlu2bGHHjh08/fTTWFhYmDo8kzBziKOq9QhQTLBbIYkjM0kcmYla0QEQ6lnAkYoxoOhp67Kmqd2B1tZW0wYtzuvq7tklhBBCCCHO4enpyZw5c5gyZQrFxcVYWFig1WrZsGED0dHR+Pr6mjrEa8rGxgZFUUgOzgTA2ryDnoII1uZt3Be3xND2na1PYWNjY4owxUVc8VQoIYQQQgjRP/b29owbNw7o3knqxIkTfPrpp/znP/8hPz9/2BSSCw0NRa/Xk5Y7Ha2uu3vaU96i53etTkVa7nT0ej1hYWEmilT0RRILIYQQQogBwM3NjSeeeIK7774bvV7PsmXLWL58uanDuibGjh2LlZUV+afCWJpzz3nbLM25h/xTYVhZWREeHn6NIxSXQqZCCSGEEEIMECqVijFjxjBmzBhKS0sN26pWVFSQl5dHXFwcdnZ2Jo7S+MzMzJgzZw5Lly41HNPpQaWc+b3HnDlzrnoFbnFlZMRCCCGEEGIA8vPzM2xRW1VVxe7du3nnnXf49ttvqaqqMm1wV0FoaChz585Fq3KipcOGqiZP/i//BqqaPGnpsEGrcpLieAOcJBZ96Ojo4OGHH8bf3x8HBwcSEhLYtWuX4fG5c+fi6emJg4MDkZGRrFu3zoTRCiGEEGKoioqK4umnn+b666+nsLCQDz/88JILyg0moaGh/O+Tr1Ltvpr9La/QaD6L/S2vUO2+mv998lVJKgY4GUfqg0ajISgoiIyMDPz8/Pjiiy+YPXs2JSUl2NjY8Oc//5nPP/8cCwsLsrKymDp1Kj/++COurq6mDl0IIYQQQ4y1tTUpKSkkJiZy+PBhgoKCANi/fz+KojBu3LghUXDPzMyMyKgYIqNiTB2KuEwyYtEHW1tbXnrpJUaMGIFKpeLBBx9Ep9Nx7NgxoHuhUc+e02ZmZnR2dlJWVmbKkIUQQggxxKnVaqKionBwcACguLiY1atX8+6777Jz507a2tpMHKEYrgZFYtHU1MSzzz7LtGnTcHd3R1EUXnnllfO2bW5u5qmnnsLHxwcrKyvGjx/fayGsZUKAAAAZkklEQVRQf+Tn59PW1kZwcLDh2H333YeVlRUTJ07khhtuICIiwijXEkIIIYS4FLfeeiuPP/44ISEhpKen8/bbb1NbW2vqsMQwNCgSi9raWj7++GM6OjqYM2dOn21vv/12Fi9ezMsvv0xaWhqxsbHce++9fP311/2KobW1lfvvv58XX3yx124MX331Fc3NzWzatIlp06ahKEofZxFCCCGEMD53d3dmz57NU089xZQpU3BxcQFg69atnDx50sTRieFiUCQWAQEB1NfXs23bNv7+979fsN2GDRvYvHkzH3zwAb/+9a+ZPHkyn3zyCVOnTuWZZ55Bq9Ua2k6ZMgUrK6vz/vrzn//c67xdXV3cfffdhIeH88ILL5xzXTMzM6ZNm8bmzZvZsGGD8V64EEIIIcRlsLOzIz4+HkVRaG9v5/Dhw3z22WcsXLiQvLw8dDqdqUMUQ9igWLx9qaMAq1atws7OjrvuuqvX8Ycffph58+axZ88ekpKSANiyZcslnVOn0/HAAw+gVqtZuHBhn7FotVqOHz/e5/liYmIuae/l+fPnM3/+/EuKUQghhBDi56ysrHjiiScoKCggMzOT5cuX4+3tza9+9SuZYSGuikGRWFyqw4cPM2bMmHM67pGRkYbHexKLS/XrX/+aiooKNm7c2Ou8lZWVZGRkcNNNN2FpacnKlSvZunUrb7zxRp/nO3Xq1CVdt7Gx8bLiFEIIIYT4OUVRCAsLIywsjLKyMurr61EUhdbWVnbv3k1sbCz29vamDlMMEUMqsaitrWXkyJHnHO+ZZ3i5C5mKi4v59NNPsbKyws3NzXA8LS2NUaNG8c477/DII4+gKAqjRo1i+fLlREVF9XlOT0/PSxqx6NnpQQghhBDCGHx9ffH19QW6vyDds2cPGRkZREREkJiYiKenp4kjFIPdkEosoO9pU5c77BcQEIBer7/g4zt27Lis8wHk5OTg5+d32c8TQgghhDCWkSNH8vTTT7Nv3z727NnDgQMHuO6665g8ebKpQxOD2JBKLFxdXc87KlFXVwecGbkQQgghhBjurKysSEpKIj4+niNHjuDs7AzAiRMnaGhoICIi4pJmWQjRY1DsCnWpIiIiyMvLQ6PR9Dp+6NAhAMaNG2eKsIQQQgghBiy1Wk1ERIRhRsWJEydYs2YN7777Ltu3b6e1tdXEEYrBYkglFrfddhvNzc188803vY4vXrwYHx8f4uPjTRSZEEIIIcTgcMMNN/Cb3/yG0NBQduzYwdtvv01RUZGpwxKDwKAZ30pLS6OlpYWmpiYAjhw5wooVKwC4+eabsbGxYcaMGUydOpXHHnuMxsZGQkJCWLJkCRs3buTLL79ErVab8iUIIYQQQgwKbm5uzJo1i8mTJ7N37158fHwA2Lt3L+7u7vj7+8uWteIcir6v1ckDSGBgIMXFxed97MSJEwQGBgLQ3NzMn/70J5YvX05dXR1hYWE8//zzzJ079xpGe67S0lL8/f05efKkLN4WQgghxKCj1+v55JNPqKiowNfXl8TERMaMGYNKNXQmwEh/rX8GTWIx2MmNKoQQQojBTq/Xc+zYMTIzMykqKsLJyYnHHnsMCwsLU4dmFNJf659BMxVKCCGEEEKYlqIojB49mtGjR1NRUcGJEyewsLBAp9ORkZFBVFSU1OIaxiSxEEIIIYQQl83b2xtvb2+guwhxRkYG6enpjB07lsTERMNjYviQxEIIIYQQQvSLu7s7Tz/9ND/88AN79uzh0KFDjB8/nltvvdXUoYlrSBILIYQQQgjRb5aWliQkJBAXF0deXp5h16ja2lqKioqIioqSgntDnPx0hRBCCCGE0ahUKsaOHWv4+4kTJ1i/fj3ff/89sbGxxMbGYmtra8IIxdUiiYUQQgghhLhqYmJiCAoKYvfu3WRkZJCRkcHs2bOJiIjo83m5ubmkpaUxY8aMXomKGLgksRBCCCGEEFeVq6srM2fOZPLkyeTk5BgK7h07dgxzc3MCAgJ6FdxraWnh/2/v/qOiLvM9gL/HQX4OI5r8UtRUVBBUSMFwW3/eu1SKt3PWNLt3I3+VWt1OlKHt7aC2q61b7JZ3W8w1PWC/bhGpWGmlliUuiCwqij9KFCjAHyigOMwMn/uHMTHNwAzMDDMM79c5czg8z/f7PM+X7+cDz8N8v9/Jzc3FrVu3kJubizvvvJPvcnQDXFgQERERUZfw9fXF5MmTDd8XFhbi9OnTCA0NRUJCAkaPHo1evXohNzcXGo0GAKDRaLB7927MnTvXWcMmK3FhQUREREROMW/ePHz33XfIy8vDRx99hC+++AIJCQkoLS01bCMiOHXqFEpKSnhJlIvjwoKIiIiInEKhUCA8PBzh4eGorq7GkSNHcODAAbPbOvuSqBsaHV7Zexq7j/2Ia41aDA9UYdnU4Zg9boDFfS83aLD+k1LsK61Go1aPyFA1nvvNKPwqvL/RdvM25eGf56+a7D95ZCAyF8bb7VgchQsLIiIiInK6oKAgNDQ0oKmpyWy9sy+JWrq9EMXl15B6XwSG9vfDzn/9gP9+twgigv+IGdjmfhqdHv+5+Z+ou6VFWlIU7lB5IjPvApLfysf2xRNx97A7jLYf3M8Xf30oxqhM7d3bIcdkb1xYEBEREZHTXbp0yegSqF9quSSqpqYGQUFBXTgyYH9pDQ6evYzXHooxLCImDe+PymuNWPfJKcwaOwDKXgqz+/5fQTlOV9cje9kkjB/SFwCQMOwO3PfaQaz/tBQ7nviV0fbevXvhrsF9HXtADtLL2QMgIiIiIgoMDERERITR06FaUygUiIyM7PJFBQDsKamCn6cSM8eEGpXPGR+G6joN/lVe286+1RgW6GdYVACAh7IXHogdiOLya6i6fsth4+5qXFgQERERkdMpFArMmjULXl5eZuu9vLwwc+bMLh7Vbaer6xEepIKH0njqHBmqvl1f1dDuvpEhapPyyFB/AMCZ6nqj8gtXbmLcmr0Y/sInmLxhP/68pxS3tHpbD6FL8FIoIiIiInIJfn5+mDlzJrKzs03qZs2a1ekbt9PT05Genm5xO73e/AT+2k0tBvXzNSkP8Ll970PtTfP3hdzetwl9fE3vkejj42myb9yd/TBr3AAMD/SDRtuMA6drsOmr71FQVov3ltyNXm1cbuUquLAgIiIiIpcRFRWFkpISnD59GiIChUKBiIgImx41W1dXh8rKSpvG1d6Uvo2rt6zc9+fa5xJHGdVNiwhCWF9f/PGTU9h7shr3RodYHqgTcWFBRERERC6j5ZKosrIy3Lp1yy6XQKnVagwc2PaTm1ro9XpUVVWZlAf49sY1M+9KXGvU3q7/6d0HcwJ8PXHtptak/Hpj00/7tv/EpwdiB+KPn5xCUXktFxbkHOnp6airq4NarUZKSoqzh0PkUIx36ikY69RTtFzyJCJG33dWSkqKVTlTUVGBQYMGmZRHhPhj579+gE7fbHSfRWnV7fsjRoWo2mwzIsQfpVV1JuU/7+tvcVwA0MvS2yIugAsLN5Weno7KykoMHDiQf3zI7THeqadgrFNPsnHjRkO8p6amOnUsv4kKwbv55fj0RBWSWn0gXnZhBYLVXogZ1PbjYX8TFYIXPz6Boou1iP3pMbI6fTM+LqpEzKAABKu92+07+2gFACB2UIAdjsSxuLAgIiIiImrHtFFB+PWI/vifj0+gQaPDkDt8sav4B3x15hL+Oi/G8BkWz39YjOyjlfhqxVSE9b19s/fcCWHIyivDE28fRep9EbjDzwtZh8vw/aUb2L54oqGP/PNX8b/7zyExKhiD+/nevnn7TA3ezS/HpOF34N8ig51x6B3ChQURERERkQUZ/zUef95zGumfn8H1m1oMC/TD6/NjMbvVOxj6ZkDfLPjpCi4AgJeHEm8vvhvrPz2FtJ0laGzSY/QANbYtiDf61O0gfy8oFcDGL8/h6s0mKAAM7e+HlH8ficW/HuryT4QCuLAgIiIiIrLIz8sDq2dHYfXstp9O9erccXh17jiT8kB/L6TPjWm3/Tv7+2Hrgnibx+lM/IA8IiIiIiKyGRcWRERERERkMy4siIiIiIjIZlxYEBERERGRzbiwICIiIiIim/GpUF2kubkZAPDjjz92SX86nc7wtaKiokv6JHIWxjv1FIx16kmcEe8t87SWeRt1jEKk9ZN2yVEKCgoQH9+9HyFGRERE1BPk5+cjLi7O2cPodriw6CI6nQ5FRUUIDg5Gr168Ao2IiIjI1TQ3N6O6uhqxsbHw8OCFPR3FhQUREREREdmM/zonIiIiIiKbcWFBREREREQ248KCiIiIiIhs5nYLC4VCYfG1evXqDrW5bds2o/09PDwQFhaGBQsWoLKy0jEHYsaxY8ewYMECDB06FN7e3lCpVLjrrruwYcMGXL161WH9Pvroo4Zjj46ONqo7cOBAmz/nw4cPW9V+fn4+EhMT4e/vD5VKhWnTpuHbb7812c7avj7++GOjuiNHjtj2A3CSX8Zd69dzzz1nVRuOyAdzY3NGTjgrH4C2c8Ie+QBYlxMd6as75ATjvX2M9+4d7/aIb0sY//bH+U/Hud3t7nl5eWbLdTodHnnkEVRWVuL+++/vVNtbt25FREQEGhsb8fXXX2P9+vX46quvcPz4cfj5+dkybIs2b96M5cuXY9SoUVixYgVGjx4NrVaLI0eOICMjA3l5ecjJyXFY/yEhIcjJyYGvr6/Z+nXr1mHatGlGZb9MQnMKCgowefJkxMfHIysrCyKCDRs2YMaMGdi/fz8SEhI63NeUKVOQl5eH3bt34w9/+IM1h+fSWuKutQEDBli1ryPzofXYujonnJ0PQPs50dl8ADqeE9b01Z1ygvFuivHesb5cOd5tiW9LGP+OwflPB0kP8dRTTwkA2bRpU4f33bp1qwCQgoICo/IXX3xRAMj27dvtNUyzDh06JEqlUu699165deuWSb1Go5EdO3Y4rP/k5GQZMmSI2br9+/cLAPnggw861XZiYqIEBwfLjRs3DGV1dXXSv39/mTRpkk19tXXeugtHjt+WfBBxbk44Ox9E2s4JW/NBxPqc6ExfrpwTjHfzGO+d78uV4t2ZY2H8dx7nPx3ndpdCmZOVlYWNGzdi0aJFeOyxxwzlq1evhkKhQElJCebPn48+ffogODgYCxcuxPXr1y22e/fddwMALly4YFReWlqK+fPnIzg4GF5eXhg8eDAeeeQRaDSaTo1/3bp1UCgUePPNN+Hl5WVS7+npidmzZ3eqbWf79ttvMXXqVKP/BPj7+2Py5Mk4dOhQl31SeU/SVj4AjskJ5kPHMCfsi/Hu2hjvjuWo+Q/A+LeVu8a+2y8sioqK8PjjjyMuLg5/+9vfzG7z29/+FiNHjkR2djZWrlyJd955B88884zFts+dOwcACAwMNJQVFxcjLi4Ohw8fxtq1a/Hpp59i/fr10Gg0aGpqMtuOQqHA1KlTzdbp9Xrs27cP48ePx6BBgyyOyVmeeOIJeHh4QK1WIzExEd98843JNuaOs6mpyewvi5ay48ePd6ovd6LX66HT6YxenWVNPgD2y4nO5APQdk64Uz4A9skJd8sHxvvPGO/uF+/2jG9LHDn/AewT/5z/uOH8p8vfI+lCly5dkiFDhkhgYKBcvHjRpD4tLU0AyIYNG4zKly9fLt7e3tLc3CwiP7+ldPjwYdFqtVJfXy+5ubkSGBgo/v7+UlVVZdh3+vTpEhAQIDU1NVaPU6lUyvTp083WVVVVCQB56KGHrG5PRGTLli0yfPhw8fPzk4iICDl37pyIiNTU1Mj9998vvr6+MmLECNm7d6/Fttp7K/Do0aPy9NNPS05Ojnz99dfy1ltvSWRkpCiVSvnss88sHmdMTIyMHDlS9Hq9oUyr1cqwYcMEgLzzzjud6kvEtd4G74yW8Zt7abVao20PHTokCoVCXnrppTbbs5QPIvbPic7kg0jbOWHvfBCxb050NEZtyYmO9iXi2jlhTbwXFRXJpEmTxN/fX4YOHSqbN29usz3GO+PdleLdmvh+4403JDY2Vjw8PCQtLc1sO+2d29bsNf9pPXZHxD/nP+43/3HbhYVOp5MZM2aIUqmUffv2md2mJbFKS0uNyjMyMgSAIVna+oUwZswY+eabbwz73bhxQ5RKpTz22GN2O47OJNbOnTtl7NixUlJSIs3NzXL27Fm5evWqiIg8+OCDsnDhQrlx44bs2LFDAgIC5PLly+22115imVNbWythYWEyduxYi9tu2bJFAMiyZcukoqJCLl68KIsWLRKlUikA5L333ut0X670R6UzWsafmZkpBQUFRq/W9Hq9xMfHy8SJE9tcWFiTDyL2zYnukA8ijs+JjuSDiG05YakvV84Ja+I9Ojpa1qxZI3q9XgoLC0WlUsnJkydN2mK8M95FXCverYnvnJwc2blzp8yZM8fswsLSuW1hz/lP67F39/jn/KdruO3CIiUlRQDIK6+80uY2LYl16dIlo/KWE3L+/Hmj71t+IRQVFckPP/xg0l5FRYUAkLVr19rtOHQ6nfj6+srEiROt3icuLk6++OILk/L6+nrp3bu30X8vpkyZIlu2bGm3vY4mlojI0qVLBYDcvHnT4rYvv/yyqFQqwy+rhIQESU1NFQBy8ODBTvflSn9UOsPa8b/xxhvyzDPPSHJycpsLC2vyQcS+OeHq+SDSdTnRkXwQsS0n2uvLlXPCmrGpVCo5c+aM4fu4uDj56KOPTLZjvDPeRVwr3jsylkWLFpldWLR3bluz5/yndVl3jn/Of7qOW95j8e677yI9PR3z5s3Ds88+a7d2IyMjMWHCBMTExCA0NNSkvl+/flAqlaioqLBbn0qlEjNmzEBhYaFV7er1ehQVFeHYsWMICwvD0KFDsWbNGogIzp49C5VKZXSt4pgxY1BSUmK38bYQEQC3ryu0JDU1FZcvX8bx48dRVlaGQ4cOoba2Fn5+fhg/frxd+3I3V65cwWuvvYa0tLQ2t3FUPgDt54Sr5wOALsuJjsaoLTnhzvnw1FNPYfv27dDpdMjPz0d5ebnJIxkZ74x3d2Tp3LZg/HP+42xut7A4duwYFi9ejOjoaGzZsqVL+/bx8cGUKVPwwQcf4PLly3Zrd9WqVRARLFmyxOwNUFqtFrt27QIAVFdXQ6fT4fPPP8eJEyewf/9+vP3228jMzERDQwPUarXRvmq1Gg0NDXYbKwDU1tYiNzcXMTEx8Pb2tmofLy8vREdHY8iQIbh48SLef/99LFmyBD4+Pnbvy52sWrUKKSkp6NOnj9l65kPb+QCgS3KiszHamZxw93xITExEZmYmvL29MWnSJKxduxYhISGGesY7491dWTq3AOOf8x/X4FYfkFdbW4sHHngAGo0GqampZu+oB24/wWD48OEOGUN6ejruueceTJw4EStXrkR4eDiqq6uxc+dObNq0Cf7+/ib7eHh4YMqUKfjyyy/NtpmQkIC///3vWL58OcaPH49ly5YhKioKWq0WRUVFePPNNxEdHY2kpCRDIKampiIgIAABAQF4/PHH8dlnn+H5559HXV2dUdt1dXVQqVSdPt6HH34YgwcPxoQJE9C/f3+cPXsWr776Kqqrq7Ft2zaLx3nixAlkZ2djwoQJ8PLyQnFxMV5++WWMGDECL730Uqf76gkKCwtx9OhRZGRkmK3vrvkAtJ8T9sqH5ORkqFQqu+ZER2PUlpzoaflw5coVzJo1C//4xz8wZ84cXLhwAUlJSQgJCUFSUhLjnfHu1iyd2+4a/5z/uN/8x60WFsXFxTh//jwA4He/+12b2yUnJzvsRIwbNw75+flIS0vDqlWrUF9fj5CQEEyfPh2enp5m99Hr9dDr9e22u2TJEsTHx+Mvf/kL/vSnP6Gqqgq9e/fGyJEj8fDDD+PJJ58EAPTt2xcDBgww+7bYiBEj0NDQgIqKCoSFhQG4Hdjt/awsGTt2LN5//31kZGSgoaEB/fr1wz333IOsrCzExcVZPE5PT0/s27cPr7/+OhoaGjB48GAsXboUK1euNPkkz4701RMcPHgQJ0+eRFBQEIDb/41UKpU4c+YMMjMzu20+AJZzwh75ANg/Jzoao7bkRE/Lh++//x4qlQrz5s0DAAwbNgxJSUnYs2cPkpKSGO9gvLszS+e2u8Y/5z9uOP/p8rs6yOFeeOEFmTlzptTV1Ul5ebmMGjVKsrKyRERkzpw5smjRIrl586bs2rVLAgICTG7e+qWWm5e0Wq3odLquOASbNDc3i1arNTxxwRVu3HOE+vp6KS8vN7wefPBBWbFihcWnXPQ07eWDCHOiu7h+/br06dNHPvzwQ2lubpaysjKJiIiQjIwMZw/NpTDeu2+8a7VaaWxslEcffVR+//vfS2Njo9HP29K5Jc5/XCH2ubBwQxqNRhYvXixqtVoGDhwoa9asMdTV1NTIfffdJz4+PhIeHi579uyx2F5ycrLhiQVRUVGOHLpd5OTkGD0Sr7v8UbFVe0+F6snaywcR5kR3snfvXomNjRV/f38JDQ2VlJSUbvHHvisx3rtvvLc8qan1a+vWrYZ6S+eWOP9xhdhXiPzikQJEv1BWVma4GcvHxwdRUVFOHlH7rl27ZvhEUAAYPXo0fH19nTgicjfMCepJGO/UUzH2O44LCyIiIiIispnbPW6WiIiIiIi6HhcWRERERERkMy4siIiIiIjIZlxYEBERERGRzbiwICIiIiIim3FhQURERERENuPCgoiIiIiIbMaFBRERERER2YwLCyIiIiIishkXFkREREREZDMuLIiIiIiIyGb/D0OFuYNMN9R1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "markersize = 6\n",
    "\n",
    "values = [0.25, 0.50, 0.75, 1]\n",
    "labels = ['$\\mathregular{ZnPc:C_{60}}$ [5:95]', '$\\mathregular{F_{4}ZnPc:C_{60}}$ [5:95]', '$\\mathregular{F_{8}ZnPc:C_{60}}$ [5:95]', '$\\mathregular{F_{16}ZnPc:C_{60}}$ [5:95]']\n",
    "x_label = ''\n",
    "y_label = 'Saturation current density $\\mathrm{(A/m^2)}$'\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(8,5), dpi=100)\n",
    "\n",
    "plt.grid(False)\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.minorticks_on()\n",
    "plt.rcParams['figure.facecolor'] = 'xkcd:white'\n",
    "plt.rcParams['figure.edgecolor'] = 'xkcd:white'\n",
    "ax1.tick_params(labelsize=fontsize, direction='in', axis='both', which='major', length=8, width=2)\n",
    "ax1.tick_params(labelsize=fontsize, direction='in', axis='both', which='minor', length=0, width=2)\n",
    "\n",
    "plt.xlabel(x_label, fontsize=fontsize, fontweight='medium')\n",
    "plt.ylabel(y_label, fontsize=fontsize, fontweight='medium')\n",
    "\n",
    "plt.xticks(values, labels)\n",
    "\n",
    "plt.semilogy(values, J0_sum_list, label='$\\mathrm{J_0 = J_{0,CT} + J_{0,opt}}$', marker='o', markersize=markersize+2, ls='-', linewidth=2, color='grey')\n",
    "plt.semilogy(values, J0_CT_list, label='$\\mathrm{J_{0,CT}}$', marker='*', markersize=markersize, ls='--', linewidth=1, color='#f1c211')\n",
    "plt.semilogy(values, J0_opt_list, label='$\\mathrm{J_{0,opt}}$', marker='d', markersize=markersize, ls='--', linewidth=1, color='grey')\n",
    "\n",
    "plt.legend(fontsize=fontsize, ncol=3, bbox_to_anchor=[0.85, 1.14])\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "# ax2.plot(values, parameters['ECT (eV)'][:-1], marker='^', markersize=markersize, color='tab:blue', linestyle='dotted', label='$\\mathrm{E_{CT}}$')\n",
    "# ax2.plot(values, parameters['Eopt (eV)'][:-1], marker='v', markersize=markersize, color='tab:blue', linestyle='dotted', label='$\\mathrm{E_{opt}}$')\n",
    "ax2.plot(values, parameters['Eopt (eV)'][:-1]-parameters['ECT (eV)'][:-1], marker='v', markersize=markersize, color='tab:blue', linestyle='dotted', label='$\\mathrm{E_{opt}} - \\mathrm{E_{CT}}$')\n",
    "ax2.set_ylabel('$\\mathrm{E_{opt}} - \\mathrm{E_{CT}}$ (eV)', fontsize=fontsize, fontweight='medium', color='tab:blue')\n",
    "\n",
    "ax2.set_ylim(0.04,0.34)\n",
    "\n",
    "ax2.tick_params(labelsize=fontsize, direction='in', axis='both', which='major', length=8, width=2, labelcolor='tab:blue')\n",
    "ax2.tick_params(labelsize=fontsize, direction='in', axis='both', which='minor', length=0, width=2, labelcolor='tab:blue')\n",
    "\n",
    "ax2.annotate('', xy=(0.98, 0.12), xytext=(0.87, 0.12), arrowprops=dict(arrowstyle=\"->\", color='tab:blue'), fontsize=12, color='grey')\n",
    "# plt.legend(fontsize=fontsize, ncol=2, bbox_to_anchor=[1.02, 1.14])\n",
    "\n",
    "plt.savefig('/home/jungbluth/Desktop/J0_CT_opt_contributions.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding optical terms to the equation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelled EQE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "kwargs = {'CT_df':parameters, 'n':0}\n",
    "ZnPc_5_extended = extend_EQE(orig_EQE=ZnPc_5, mode='gaussian', **kwargs)\n",
    "\n",
    "kwargs = {'CT_df':parameters, 'n':1}\n",
    "F4ZnPc_5_extended = extend_EQE(orig_EQE=F4ZnPc_5, mode='gaussian', **kwargs)\n",
    "\n",
    "kwargs = {'CT_df':parameters, 'n':2, 'num':-10}\n",
    "F8ZnPc_5_extended = extend_EQE(orig_EQE=F8ZnPc_5, mode='gaussian', **kwargs)\n",
    "\n",
    "kwargs = {'CT_df':parameters, 'n':3, 'num':-28}\n",
    "F16ZnPc_5_extended = extend_EQE(orig_EQE=F16ZnPc_5, mode='gaussian', **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 0\n",
    "\n",
    "# Something is wrong with this code\n",
    "# EQE_Fit_df = Marcus_Gaussian(np.arange(0.8, 2.5, 0.05), \n",
    "#                               E_CT = parameters['ECT (eV)'][n], \n",
    "#                               l_CT = parameters['l_CT (eV)'][n],  \n",
    "#                               f_CT = parameters['f_CT (eV2)'][n], \n",
    "#                               E_opt = parameters['Eopt (eV)'][n], \n",
    "#                               l_opt = parameters['l_CT (eV)'][n], \n",
    "#                               f_opt = parameters['f_opt (eV2)'][n], \n",
    "#                               T=295)\n",
    "\n",
    "\n",
    "EQE_Opt_df = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Fits/ZnPc/5_ZnPc/5_ZnPc_Fit_Marcus_Opt')\n",
    "EQE_CT_df = pd.read_csv('/home/jungbluth/Desktop/ZnPc_Fits/ZnPc/5_ZnPc/5_ZnPc_Fit_Marcus_CT_subOpt')\n",
    "\n",
    "fontsize = 12\n",
    "markersize = 6\n",
    "\n",
    "x_label = 'Energy (eV)'\n",
    "y_label = 'EQE (a.u)'\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(7,5), dpi=100)\n",
    "\n",
    "plt.grid(False)\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.minorticks_on()\n",
    "plt.rcParams['figure.facecolor'] = 'xkcd:white'\n",
    "plt.rcParams['figure.edgecolor'] = 'xkcd:white'\n",
    "ax1.tick_params(labelsize=fontsize, direction='in', axis='both', which='major', length=8, width=2)\n",
    "ax1.tick_params(labelsize=fontsize, direction='in', axis='both', which='minor', length=0, width=2)\n",
    "\n",
    "plt.xlabel(x_label, fontsize=fontsize, fontweight='medium')\n",
    "plt.ylabel(y_label, fontsize=fontsize, fontweight='medium')\n",
    "\n",
    "plt.semilogy(ZnPc_5_extended['Energy'], ZnPc_5_extended['EQE'], label='$\\mathrm{ZnPc:C_{60}}$ [5:95]', markersize=0, ls='-', linewidth=2, color='tab:blue')\n",
    "plt.semilogy(EQE_CT_df['Energy'], EQE_CT_df['Signal'], label='CT State Fit', markersize=0, ls='--', linewidth=1, color='grey')\n",
    "plt.semilogy(EQE_Opt_df['Energy'], EQE_Opt_df['Signal'], label='$\\mathrm{S_1}$ Fit', markersize=0, ls='dotted', linewidth=1, color='grey')\n",
    "\n",
    "plt.legend(fontsize=fontsize)\n",
    "plt.ylim(10E-5, 1)\n",
    "plt.xlim(1, 3.5)\n",
    "plt.savefig('/home/jungbluth/Desktop/EQE_Marcus_Fit.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Testing the Jsc calculations...__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original EQE:  3.2338247230431394\n",
      "Extended EQE:  3.2350843421530944\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jungbluth/Desktop/git/Voltage-Loss-Calculations/functions.py:61: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
      "  If increasing the limit yields no improvement it is advised to analyze \n",
      "  the integrand in order to determine the difficulties.  If the position of a \n",
      "  local difficulty can be determined (singularity, discontinuity) one will \n",
      "  probably gain from splitting up the interval and calling the integrator \n",
      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n",
      "  result = ig.quad(lambda e: q*EQE_intp(e)*AM15_intp(e), min(E), max(E))\n"
     ]
    }
   ],
   "source": [
    "print('Original EQE: ', calculate_Jsc(F4ZnPc_5['Energy'], F4ZnPc_5['EQE']))\n",
    "print('Extended EQE: ', calculate_Jsc(F4ZnPc_5_extended['Energy'], F4ZnPc_5_extended['EQE']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Comments__:\n",
    "\n",
    "> ### The difference does not seem to be super big!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Testing the Voc,rad calculations...__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Voc_rad_from_CT(Jsc, n, df=parameters):\n",
    "    \"\"\"\n",
    "    Function to calculate the Voc,rad from CT properties\n",
    "    :param Jsc: short-circuit current [float]\n",
    "    :param n: number of sample [int]\n",
    "    :param df: dataFrame with CT / Opt fit information [dataFrame]\n",
    "    :return \n",
    "    \"\"\"\n",
    "    \n",
    "    ##### This was wrong: ########################### \n",
    "#     a_CT = k_eV*T/q_eV * np.log((2*np.pi*q)/(10*Jsc*h_eV**3 * c**2) * df['f_CT (eV2)'][n]*(df['ECT (eV)'][n]-df['l_CT (eV)'][n]))\n",
    "#     b_opt = k_eV*T/q_eV * np.log((2*np.pi*q)/(10*Jsc*h_eV**3 * c**2) * df['f_opt (eV2)'][n]*(df['Eopt (eV)'][n]-df['l_opt (eV)'][n])*np.exp(-(df['Eopt (eV)'][n]-df['ECT (eV)'][n])/(k_eV*T)))\n",
    "    ################################################################################################################\n",
    "\n",
    "    a_CT = k_eV*T/q_eV * np.log((2*np.pi*q)/(10*Jsc*h_eV**3 * c**2) * df['f_CT (eV2)'][n]*(df['ECT (eV)'][n]-df['l_CT (eV)'][n]))\n",
    "    b_opt = k_eV*T/q_eV * np.log((2*np.pi*q)/(10*Jsc*h_eV**3 * c**2) * df['f_CT (eV2)'][n]*(df['ECT (eV)'][n]-df['l_CT (eV)'][n]) +  df['f_opt (eV2)'][n]*(df['Eopt (eV)'][n]-df['l_opt (eV)'][n])*np.exp(-(df['Eopt (eV)'][n]-df['ECT (eV)'][n])/(k_eV*T)))\n",
    "        \n",
    "    Voc_rad = df['ECT (eV)'][n] - a_CT\n",
    "    return a_CT, b_opt, Voc_rad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Radiative limit of the open-circuit voltage\n",
    "def Voc_rad_from_EQE(EQE_df, n, df=parameters):\n",
    "    \"\"\"\n",
    "    Function to calculate the radiative limit of the open-circuit voltage\n",
    "    :param EQE_df: dataFrame of EQE values [dataFrame]\n",
    "    :param n: number of sample [int]\n",
    "    :param df: dataFrame with CT / Opt fit information [dataFrame]\n",
    "    :return: Voc_rad: Radiative upper limit of the open-circuit voltage [float]\n",
    "    \"\"\"\n",
    "    \n",
    "    bb_df = bb(EQE_df['Energy'])\n",
    "    j0_rad = J0_rad(EQE_df, bb_df)\n",
    "    \n",
    "    jsc = calculate_Jsc(EQE_df['Energy'], EQE_df['EQE'])\n",
    "    \n",
    "    Voc_rad = k*T/q * math.log((jsc/j0_rad)+1)\n",
    "    \n",
    "    print('Calculated Jsc: ', jsc)\n",
    "    print('J0,rad: ', j0_rad)\n",
    "    print('Ratio: ', (jsc/j0_rad))\n",
    "    print('Voc,rad: ', Voc_rad)\n",
    "    \n",
    "    # Experiment: Opt extension to Vandewal equation\n",
    "    a_CT, b_CT, Voc_rad_CT = Voc_rad_from_CT(jsc, n, df=df)\n",
    "    print('A_CT: ', a_CT)\n",
    "    print('B_Opt: ', b_CT)\n",
    "    print('Voc,rad (CT): ', Voc_rad_CT)\n",
    "    \n",
    "#     # Experiment: Test with original Jsc\n",
    "#     Voc_rad_2 = k*T/q * math.log((df['Jsc (mA/cm2)'][n]/j0_rad)+1)\n",
    "#     print('                    ')\n",
    "#     print('Original Jsc: ', df['Jsc (mA/cm2)'][n])\n",
    "#     print('Ratio: ', (df['Jsc (mA/cm2)'][n]/j0_rad))\n",
    "#     print('Voc,rad: ', Voc_rad_2)\n",
    "#     a_CT, b_CT, Voc_rad_CT = Voc_rad_from_CT(df['Jsc (mA/cm2)'][n], n, df=df)\n",
    "#     print('A_CT: ', a_CT)\n",
    "#     print('B_Opt: ', b_CT)\n",
    "#     print('Voc,rad (CT): ', Voc_rad_CT)\n",
    "    \n",
    "#     Delta_Voc_nonrad = Voc_rad - Voc\n",
    "    return Voc_rad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------\n",
      "Extended EQE Spectrum\n",
      "---------------------------\n",
      "Calculated Jsc:  3.816226098852374\n",
      "J0,rad:  1.642677133748058e-19\n",
      "Ratio:  2.323174786115748e+19\n",
      "Voc,rad:  1.1258552362184222\n",
      "A_CT:  0.25098138587451435\n",
      "B_Opt:  0.25098138587501767\n",
      "Voc,rad (CT):  1.1290186141254854\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jungbluth/Desktop/git/Voltage-Loss-Calculations/functions.py:61: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
      "  If increasing the limit yields no improvement it is advised to analyze \n",
      "  the integrand in order to determine the difficulties.  If the position of a \n",
      "  local difficulty can be determined (singularity, discontinuity) one will \n",
      "  probably gain from splitting up the interval and calling the integrator \n",
      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n",
      "  result = ig.quad(lambda e: q*EQE_intp(e)*AM15_intp(e), min(E), max(E))\n"
     ]
    }
   ],
   "source": [
    "# print('---------------------------')\n",
    "# print('Standard EQE Spectrum')\n",
    "# print('---------------------------')\n",
    "# Voc_rad_from_EQE(ZnPc_5, n=0);\n",
    "\n",
    "print('---------------------------')\n",
    "print('Extended EQE Spectrum')\n",
    "print('---------------------------')\n",
    "Voc_rad_from_EQE(ZnPc_5_extended, n=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Comments__:\n",
    "\n",
    "> #### The Jsc calculation is not the limiting factos\n",
    "> #### The calculation is limited by J0,rad, which strongly dependes on how far the EQE extends\n",
    "> #### For the calculations of Voc,rad from CT properties, the full Jsc needs to be passed in, rather than the Jsc contribution of the CT / Opt Fit. This is because we assume Jph~Jsc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare CT fit approaches & Voltage loss calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Material</th>\n",
       "      <th>Standard</th>\n",
       "      <th>Separate</th>\n",
       "      <th>Sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ZnPc_5</td>\n",
       "      <td>[1.37, 0.175, 0.004183]</td>\n",
       "      <td>[1.37, 0.174, 0.004170]</td>\n",
       "      <td>[1.38, 0.199, 0.004235]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F4ZnPc_5</td>\n",
       "      <td>[1.48, 0.119, 0.003259]</td>\n",
       "      <td>[1.47, 0.202, 0.003218]</td>\n",
       "      <td>[1.45, 0.136, 0.000794]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F8ZnPc_5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1.59, 0.083, 0.000036]</td>\n",
       "      <td>[1.59, 0.151, 0.000097]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Material                 Standard                 Separate  \\\n",
       "0    ZnPc_5  [1.37, 0.175, 0.004183]  [1.37, 0.174, 0.004170]   \n",
       "1  F4ZnPc_5  [1.48, 0.119, 0.003259]  [1.47, 0.202, 0.003218]   \n",
       "2  F8ZnPc_5                      NaN  [1.59, 0.083, 0.000036]   \n",
       "\n",
       "                       Sum  \n",
       "0  [1.38, 0.199, 0.004235]  \n",
       "1  [1.45, 0.136, 0.000794]  \n",
       "2  [1.59, 0.151, 0.000097]  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approaches_df = pd.read_excel('/home/jungbluth/Desktop/ZnPc_Data/FxZnPc_Voltage/Voltage_analysis/5_FxZnPc_Approaches.xlsx')\n",
    "approaches_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 2\n",
    "column = 'Separate'\n",
    "\n",
    "V_rad, Delta_V_rad_eV, Delta_V_nonrad = Vloss_CT(parameters['Jsc (mA/cm2)'][n], parameters['Voc (V)'][n], json.loads(approaches_df[column][n])[0], json.loads(approaches_df[column][n])[2], json.loads(approaches_df[column][n])[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 F8ZnPc - Separate Optimization\n",
      "-------------------------------\n",
      "1.4199255361698242\n",
      "0.17007446383017585\n",
      "0.2799255361698243\n"
     ]
    }
   ],
   "source": [
    "print('5 F8ZnPc - Separate Optimization')\n",
    "print('-------------------------------')\n",
    "print(V_rad)\n",
    "print(Delta_V_rad_eV)\n",
    "print(Delta_V_nonrad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 F8ZnPc - Sum Optimization\n",
      "-------------------------------\n",
      "1.4199255361698242\n",
      "0.17007446383017585\n",
      "0.2799255361698243\n"
     ]
    }
   ],
   "source": [
    "print('5 F8ZnPc - Sum Optimization')\n",
    "print('-------------------------------')\n",
    "print(V_rad)\n",
    "print(Delta_V_rad_eV)\n",
    "print(Delta_V_nonrad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 F4ZnPc - Standard Optimization\n",
      "-------------------------------\n",
      "1.4199255361698242\n",
      "0.17007446383017585\n",
      "0.2799255361698243\n"
     ]
    }
   ],
   "source": [
    "print('5 F4ZnPc - Standard Optimization')\n",
    "print('-------------------------------')\n",
    "print(V_rad)\n",
    "print(Delta_V_rad_eV)\n",
    "print(Delta_V_nonrad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 F4ZnPc - Separate Optimization\n",
      "-------------------------------\n",
      "1.4199255361698242\n",
      "0.17007446383017585\n",
      "0.2799255361698243\n"
     ]
    }
   ],
   "source": [
    "print('5 F4ZnPc - Separate Optimization')\n",
    "print('-------------------------------')\n",
    "print(V_rad)\n",
    "print(Delta_V_rad_eV)\n",
    "print(Delta_V_nonrad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 F4ZnPc - Sum Optimization\n",
      "-------------------------------\n",
      "1.4199255361698242\n",
      "0.17007446383017585\n",
      "0.2799255361698243\n"
     ]
    }
   ],
   "source": [
    "print('5 F4ZnPc - Sum Optimization')\n",
    "print('-------------------------------')\n",
    "print(V_rad)\n",
    "print(Delta_V_rad_eV)\n",
    "print(Delta_V_nonrad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 ZnPc - Standard Optimization\n",
      "-------------------------------\n",
      "1.4199255361698242\n",
      "0.17007446383017585\n",
      "0.2799255361698243\n"
     ]
    }
   ],
   "source": [
    "print('5 ZnPc - Standard Optimization')\n",
    "print('-------------------------------')\n",
    "print(V_rad)\n",
    "print(Delta_V_rad_eV)\n",
    "print(Delta_V_nonrad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 ZnPc - Separate Optimization\n",
      "-------------------------------\n",
      "1.4199255361698242\n",
      "0.17007446383017585\n",
      "0.2799255361698243\n"
     ]
    }
   ],
   "source": [
    "print('5 ZnPc - Separate Optimization')\n",
    "print('-------------------------------')\n",
    "print(V_rad)\n",
    "print(Delta_V_rad_eV)\n",
    "print(Delta_V_nonrad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 ZnPc - Sum Optimization\n",
      "-------------------------------\n",
      "1.4199255361698242\n",
      "0.17007446383017585\n",
      "0.2799255361698243\n"
     ]
    }
   ],
   "source": [
    "print('5 ZnPc - Sum Optimization')\n",
    "print('-------------------------------')\n",
    "print(V_rad)\n",
    "print(Delta_V_rad_eV)\n",
    "print(Delta_V_nonrad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def comparison(df, samples=[ZnPc_5, F4ZnPc_5, F8ZnPc_5, F16ZnPc_5]):\n",
    "    \n",
    "    summary = pd.DataFrame()\n",
    "    \n",
    "    J0_rad_list = []\n",
    "    Voc_rad_list = []\n",
    "    Sub_CT_list = []\n",
    "    Sub_opt_list = []\n",
    "    Voc_rad_CT_list = []\n",
    "    Voc_rad_opt_list = []\n",
    "    \n",
    "    for n in range(len(df)):\n",
    "        energy = np.arange(0.4, 2.5, 0.01)\n",
    "        EQE_df = Marcus_Gaussian(energy, df['ECT (eV)'][n], df['l_CT (eV)'][n], df['f_CT (eV2)'][n], df['Eopt (eV)'][n], df['l_opt (eV)'][n], df['f_opt (eV2)'][n], 300)\n",
    "        \n",
    "        orig_EQE_df = samples[n]\n",
    "        \n",
    "        kwargs = {'CT_df': df, 'n':n}\n",
    "        \n",
    "        EQE_extended = extend_EQE(orig_EQE = orig_EQE_df, mode='gaussian')\n",
    "\n",
    "        E = EQE_extended['Energy']\n",
    "        bb_df = bb(E)\n",
    "\n",
    "        # Calculate Parameters        \n",
    "        j0_rad = J0_rad_test(EQE_extended['Energy'], EQE_extended['EQE'], bb_df)\n",
    "        \n",
    "        Jsc = df['Jsc (mA/cm2)'][n]\n",
    "        print(Jsc)\n",
    "        Jsc = calculate_Jsc(EQE_extended['Energy'], EQE_extended['EQE'])\n",
    "        print(Jsc)\n",
    "        \n",
    "        voc_rad = Voc_rad(df['Voc (V)'][n], Jsc, j0_rad)[0]\n",
    "        \n",
    "        sub_CT, sub_opt = Voc_CT_opt_test(df['Jsc (mA/cm2)'][n], df['ECT (eV)'][n], df['l_CT (eV)'][n], df['f_CT (eV2)'][n], df['Eopt (eV)'][n], df['l_opt (eV)'][n], df['f_opt (eV2)'][n])\n",
    "        voc_rad_CT = df['ECT (eV)'][n] - sub_CT\n",
    "        voc_rad_opt = df['ECT (eV)'][n] - sub_CT - sub_opt\n",
    "        \n",
    "        J0_rad_list.append(j0_rad)\n",
    "        Voc_rad_list.append(voc_rad)\n",
    "        Sub_CT_list.append(sub_CT)\n",
    "        Sub_opt_list.append(sub_opt)\n",
    "        Voc_rad_CT_list.append(voc_rad_CT)\n",
    "        Voc_rad_opt_list.append(voc_rad_opt)\n",
    "        \n",
    "    summary['J0,rad'] = J0_rad_list\n",
    "    summary['Voc,rad'] = Voc_rad_list\n",
    "    summary['Voc,rad_CT'] = Voc_rad_CT_list\n",
    "    summary['Voc,rad_opt'] = Voc_rad_opt_list\n",
    "    summary['sub,CT'] = Sub_CT_list\n",
    "    summary['sub,opt'] = Sub_opt_list\n",
    "    \n",
    "    return summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def revert_Voc_rad(V_rad, ECT, Jsc,l):\n",
    "    f = (Jsc/10)*(h_eV**3*c**2) /(q*2*math.pi*(ECT-l)*math.exp((ECT-q_eV*V_rad)/(k_eV*T)))\n",
    "#     f = (Jsc/10)*(h**3*c**2) /(q*2*math.pi*(ECT-l)*math.exp((ECT-q*V_rad)/(k*T)))\n",
    "    return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6568895306088396e-13"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revert_Voc_rad(V_rad = summary['Voc,rad [V]'][0], ECT=summary['ECT [V]'][0], Jsc=summary['Jsc [mA/cm2]'][0], l = parameters['l_CT (eV)'][0])"
   ]
  }
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