Voltage_Losses_AJ.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
    "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\n",
    "from utils import bb, getAM15, linear, set_up_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/jungbluth/Desktop/git/Voltage-Loss-Calculations'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "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": 5,
   "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": 6,
   "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": 7,
   "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": 9,
   "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>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 (eV)</th>\n",
       "      <th>Err_l (eV)</th>\n",
       "      <th>f (eV2)</th>\n",
       "      <th>Err_f (eV2)</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>0.199</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.004235</td>\n",
       "      <td>0.001388</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>0.136</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.000794</td>\n",
       "      <td>0.000506</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>0.151</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.000019</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>0.159</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000035</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 × 21 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 (eV)  \\\n",
       "0  0.40    0.01      1.38          0.01    ...              0.01   0.199   \n",
       "1  0.33    0.01      1.45          0.01    ...              0.01   0.136   \n",
       "2  0.30    0.01      1.59          0.01    ...              0.01   0.151   \n",
       "3  0.29    0.01      1.61          0.01    ...              0.01   0.159   \n",
       "4  0.30    0.01       NaN           NaN    ...               NaN     NaN   \n",
       "\n",
       "   Err_l (eV)   f (eV2)  Err_f (eV2)  T1_exp (eV)  T1_calc (eV)  \\\n",
       "0        0.01  0.004235     0.001388         1.13          1.21   \n",
       "1        0.03  0.000794     0.000506         1.13          1.20   \n",
       "2        0.01  0.000097     0.000019          NaN          1.25   \n",
       "3        0.01  0.000035          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 21 columns]"
      ]
     },
     "execution_count": 9,
     "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = parameters['EQE File'][:-1]\n",
    "samples = [ZnPc_5, F4ZnPc_5, F8ZnPc_5, F16ZnPc_5]\n",
    "\n",
    "Voc = parameters['Voc (V)'][:-1]\n",
    "Jsc = parameters['Jsc (mA/cm2)'][:-1]\n",
    "ECT = parameters['ECT (eV)'][:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "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>Sample</th>\n",
       "      <th>Jsc [mA/cm2]</th>\n",
       "      <th>J0,rad [mA/cm2]</th>\n",
       "      <th>Voc,rad [V]</th>\n",
       "      <th>Delta Voc, nonrad [V]</th>\n",
       "      <th>Delta Voc, rad [V]</th>\n",
       "      <th>ECT [V]</th>\n",
       "      <th>Voc [V]</th>\n",
       "      <th>LED QE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DR46_D_5_ZnPc_C60_2</td>\n",
       "      <td>3.56</td>\n",
       "      <td>7.658051e-20</td>\n",
       "      <td>1.143369</td>\n",
       "      <td>0.363369</td>\n",
       "      <td>0.236631</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.78</td>\n",
       "      <td>5.618478e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DR40_B_5_F4ZnPc_C60_2</td>\n",
       "      <td>3.10</td>\n",
       "      <td>1.562512e-21</td>\n",
       "      <td>1.238141</td>\n",
       "      <td>0.348141</td>\n",
       "      <td>0.211859</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.026942e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DR72_D_5_F8ZnPc_C60_1_(0.5TC)</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.523239e-24</td>\n",
       "      <td>1.385125</td>\n",
       "      <td>0.245125</td>\n",
       "      <td>0.204875</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.14</td>\n",
       "      <td>6.075149e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DR71_B_5_F16ZnPc_C60_2 (2)</td>\n",
       "      <td>0.82</td>\n",
       "      <td>9.675607e-24</td>\n",
       "      <td>1.332937</td>\n",
       "      <td>0.122937</td>\n",
       "      <td>0.277063</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.21</td>\n",
       "      <td>7.679602e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Sample  Jsc [mA/cm2]  J0,rad [mA/cm2]  Voc,rad [V]  \\\n",
       "0            DR46_D_5_ZnPc_C60_2          3.56     7.658051e-20     1.143369   \n",
       "1          DR40_B_5_F4ZnPc_C60_2          3.10     1.562512e-21     1.238141   \n",
       "2  DR72_D_5_F8ZnPc_C60_1_(0.5TC)          1.02     1.523239e-24     1.385125   \n",
       "3     DR71_B_5_F16ZnPc_C60_2 (2)          0.82     9.675607e-24     1.332937   \n",
       "\n",
       "   Delta Voc, nonrad [V]  Delta Voc, rad [V]  ECT [V]  Voc [V]        LED QE  \n",
       "0               0.363369            0.236631     1.38     0.78  5.618478e-07  \n",
       "1               0.348141            0.211859     1.45     0.89  1.026942e-06  \n",
       "2               0.245125            0.204875     1.59     1.14  6.075149e-05  \n",
       "3               0.122937            0.277063     1.61     1.21  7.679602e-03  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = calculate_summary(columns, samples, Voc, Jsc, ECT)\n",
    "summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "markersize = 7\n",
    "capsize = 5\n",
    "\n",
    "plot_colors = ['#f0d314', '#ff7f1e', '#ed0b4f', '#a80078', '#070d8b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### EQE Fitting Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd1c0434f60>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot settings\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 = '$F_{x}ZnPc$ Weight Fraction in $F_{x}ZnPc$:$C_{60}$'\n",
    "y_label = 'CT State Energy / eV'\n",
    "plot_labels = ['CT Fit - Marcus Theory', 'Opt Fit - Marcus Theory']\n",
    "\n",
    "# Set up & draw plot\n",
    "fig = set_up_plot(x_label = x_label, y_label = y_label, values = values, labels = labels)\n",
    "\n",
    "plt.errorbar(values, parameters['ECT (eV)'][:-1], parameters['Err_ECT (eV)'][:-1], marker = 'o', markersize=markersize, capsize=capsize, color=plot_colors[1], label=plot_labels[0])\n",
    "plt.errorbar(values, parameters['Eopt (eV)'][:-1], parameters['Err_Eopt (eV)'][:-1], marker = 'o', markersize=markersize, capsize=capsize, color=plot_colors[4], label=plot_labels[1])\n",
    "\n",
    "plt.xlim(0.15, 1.1)\n",
    "plt.legend(fontsize = 12) #, loc=2, ncol=2, mode=\"expand\", borderaxespad=0.) # bbox_to_anchor=(0.05, 1.1, 0.9, .102),\n",
    "\n",
    "# plt.savefig('/home/jungbluth/Desktop/FxZnPc_CT_trends.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CT State & Voltage Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd1bfe18978>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot settings\n",
    "values = [ 0.25, 0.50, 0.75, 1]\n",
    "labels = ['ZnPc', 'F4ZnPc', 'F8ZnPc', 'F16ZnPc']\n",
    "x_label = None\n",
    "y_label = 'Energy / eV'\n",
    "plot_labels = ['$E_{CT}$', '$V_{OC}$', '$V_{OC},rad.$']\n",
    "\n",
    "# Set up & draw plot\n",
    "fig = set_up_plot(x_label = x_label, y_label = y_label, values = values, labels = labels)\n",
    "\n",
    "plt.plot(values, summary['ECT [V]'], 'D', markersize=8, linestyle='--', linewidth=2, label=plot_labels[0], color=plot_colors[3])\n",
    "plt.plot(values, summary['Voc,rad [V]'], 'D', markersize=8, linestyle='--', linewidth=2, label=plot_labels[1], color='#b7b7b7ff')\n",
    "plt.plot(values, summary['Voc [V]'], '^', markersize=8, linestyle='--', linewidth=2, label=plot_labels[2], color='black')\n",
    "\n",
    "plt.xlim(0.15, 1.1)\n",
    "plt.ylim(0.5, 2)\n",
    "plt.legend(fontsize = 15, loc=2, ncol=3, mode=\"expand\", borderaxespad=0.) # bbox_to_anchor=(0.05, 1.1, 0.9, .102),\n",
    "\n",
    "# plt.savefig('/home/jungbluth/Desktop/100_FxZnPc_Voc_Trends.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare to IAPP Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'Marcus'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2524\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2526\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Marcus'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-1f33e4fcce45>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Plot my results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Marcus'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarkersize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinestyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'--'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'Marcus Theory'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplot_colors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Marcus'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'^'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarkersize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinestyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'--'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'Marcus Theory'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplot_colors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Marcus'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m's'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarkersize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinestyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'--'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'Marcus Theory'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplot_colors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2137\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2138\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2141\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2144\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2145\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2148\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1840\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1841\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1843\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1844\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3842\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3843\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3844\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3845\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2525\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2526\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2529\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Marcus'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot settings\n",
    "values = [0.05, 0.25, 0.50, 0.75, 0.95, 1]\n",
    "labels = ['0.05', '0.25', '0.50', '0.75', '0.95', '1.0']\n",
    "x_label = '$F_{x}ZnPc$ Weight Fraction in $F_{x}ZnPc$:$C_{60}$'\n",
    "y_label = 'CT State Energy / eV'\n",
    "plot_labels = ['ZnPc - ', '$\\mathregular{F_{4}ZnPc}$ - ', '$\\mathregular{F_{8}ZnPc}$ - ', '$\\mathregular{F_{16}ZnPc}$ - ']\n",
    "\n",
    "# Set up & draw plot\n",
    "fig = set_up_plot(x_label = x_label, y_label = y_label, values = values, labels = labels)\n",
    "\n",
    "# Plot my results\n",
    "plt.plot(values, parameters['Marcus'][0:6], 'o', markersize=8, linestyle='--', linewidth=2, label=labels[0]+'Marcus Theory', color=plot_colors[1])\n",
    "plt.plot(values, parameters['Marcus'][6:12], '^', markersize=8, linestyle='--', linewidth=2, label=labels[1]+'Marcus Theory', color=plot_colors[2])\n",
    "plt.plot(values, parameters['Marcus'][12:18], 's', markersize=8, linestyle='--', linewidth=2, label=labels[2]+'Marcus Theory', color=plot_colors[3])\n",
    "plt.plot(values, parameters['Marcus'][18:24], 'D', markersize=8, linestyle='--', linewidth=2, label=labels[3]+'Marcus Theory', color=plot_colors[4])\n",
    "\n",
    "# Plot IAPP results\n",
    "plt.plot(0.04, 1.35, 'X', markersize=8, linestyle='--', linewidth=0, label='ZnPc - IAPP Result', color=plot_colors[1])\n",
    "plt.plot(0.05, 1.47, 'X', markersize=8, linestyle='--', linewidth=0, label='F4ZnPc - IAPP Result', color=plot_colors[2])\n",
    "plt.plot(0.05, 1.55, 'X', markersize=8, linestyle='--', linewidth=0, label='F8ZnPc - IAPP Result', color=plot_colors[3])\n",
    "plt.plot([0.06, 0.55, 0.96], [1.50, 1.45, 1.33], 'X', markersize=8, linestyle='--', linewidth=0, label='F16ZnPc - IAPP Result', color=plot_colors[4])\n",
    "\n",
    "plt.xlim(0, 1.05)\n",
    "plt.ylim(1.11, 2.1)\n",
    "plt.legend(fontsize = 10, loc=2, ncol=2, mode=\"expand\", borderaxespad=0.) # bbox_to_anchor=(0.05, 1.1, 0.9, .102),\n",
    "\n",
    "#plt.savefig('/home/jungbluth/Desktop/FxZnPc_CT_trends_Marcus_MLJ.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Open Circuit Voltage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAF4CAYAAABdHJF/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdeXxM5/4H8M/JvohEhJgsQiKkKRIkiERQVOxqLwm1VDeK6KqoUrS/9urmXq229qh9F1VbySKEIEISZBNJkH0E2ef3R5qpkW0ymclMks/79cqrd55z5pzvuPc2H898z/MIEolEAiIiIiKiJkBL3QUQEREREdUXhl8iIiIiajIYfomIiIioyWD4JSIiIqImg+GXiIiIiJoMhl8iIiIiajIYfomIiIioyWD4JSIiIqImQ0fdBTQEpaWlSE1NhYmJCQRBUHc5RERERPQCiUSCx48fw8rKClpaVc/vMvzKITU1Fba2tuoug4iIiIhqkJycDBsbmyqPM/zKwcTEBEDZH2bz5s3VXA0RERERvUgsFsPW1laa26rC8CuH8laH5s2bM/wSERERabCaWlT5wBsRERERNRkMv0RERETUZDD8EhEREVGTwfBLRERERE0Gwy8RERERNRkMv0RERETUZDD81oKTkxNsbGxkftauXavusoiIiIhITlzntxbS0tIqjInFYjVUQkRERESKYPitBZFIVGGvaG56QURERNRwMPzWQkxMDMMuERERUQPGnl8iIiIiajIYfomIiIioyWD4JSIiIqImg+GXiIiIiJoMhl8iIiIiajIYfomIiIioyWD4JSIiIqImg+GXiIiIiJoMhl8iIiIiajIYfmvByckJNjY2Mj9r165Vd1lEREREJCdub1wLaWlpFcbEYrEaKiEiIiIiRTD81oJIJIKWluxkefPmzdVUDRERERHVFsNvLcTExDDsEhERETVg7PklIiIioiZDrvA7YMAAbNq0if2tRERERNSgyRV+z507h9mzZ6NNmzaYPHkyjh49ipKSElXXRkRERESkVHKF399++w39+/dHYWEhdu/ejdGjR0MkEmHevHm4ePGiqmskIiIiIlIKQSKRSOQ9OTU1FTt27MD27dsRGRlZdgFBgIODA/z8/DB16lTY29urrFh1EYvFMDU1RW5uLh94IyIiItJA8ua1WoXf5926dQvbtm3Dzp07kZSUBEEQAAAeHh7w9fXFxIkTYW5urlj1Gobhl4iIiEizqTz8Pi8oKAjbt2/H3r17kZ2dDUEQoKurCx8fH/j5+WHcuHF1vYVaMfwSERERabZ6Db/lioqKEBgYiO3bt+PYsWPIz8+HlpYWiouLlXULtWD4JSIiItJs8uY1pa7zq6OjAyMjIxgaGkJHp2z/DCVma7VzcnKCjY2NzM/atWvVXRYRERERyUkpO7xduXIFAQEB2LlzJx4+fAiJRAJtbW0MGTIEfn5+yriFRkhLS6swxrWPiYiIiBoOhcNvQkICAgICEBAQgNu3b0tneF1dXeHn54cpU6bA0tJSaYVqApFIBC0t2clytkEQERERNRy1Cr+ZmZnYvXs3tm/fjrCwMABlbQ02NjaYMmUK/Pz88PLLL6ukUE0QExPDsEtERETUgMkVfnft2oWAgACcOHECxcXFkEgkMDExwdixY+Hn54cBAwZIlzojIiIiItJUcoXf119/HQBk+njHjBkDQ0NDlRZHRERERKRMcoVfV1dXTJs2DVOmTEHr1q1VXRMRERERkUrIFX4jIiJUXUcFV65cwcmTJ3Hp0iVcvHgRqamp0NfXR35+fq2uk5OTg8DAQBw9ehTXrl1DUlIStLS04OzsjClTpuDdd9+Frq6uij4FEREREWkSpW5yoUxjxozBoUOHZMYUCb9LlizBqlWroKWlhW7duqFDhw5IT09HSEgICgoK4OXlhRMnTsDIyKjKa3CTCyIiIiLNJm9eU8o6v6rg4eEBFxcXuLu7w93dHW3atFHoOs2aNcPixYvx7rvvwtraWjp+584dDBo0CMHBwfjyyy+xevVqZZVORERERBpKY2d+XyQIgkIzv9X5448/MGXKFLRr1w4JCQlVnseZXyIiIiLNppbtjRsaFxcXAEBqaqqaK6lo9dcXYGj6LVZ/fUHdpRARERE1Ghrb9lAf4uPjAUDhlgpVWf31BaxYFQIA0n8u/thDnSURERERNQpyhd8HDx5oXEBUhh9++AEAMHr0aLnOd3JyqrC9cWX8/f3h7++vUE3PB99yDMBEREREyiFX+LW2toa7uztGjhyJESNGSNsFGrKff/4Zp06dgpmZGT755BO53pOWlibXeWKxWKGaKgu+5RiAiYiIiOpOrvA7bdo0BAYGYunSpVi2bBlsbGwwatQojBgxAq+88kqDWyf33LlzmD9/PgRBwMaNG2FlZSXX+0QikVwzv4o8FFdd8C3HAExERERUN3Kv9iCRSBAWFobDhw/jyJEjuHXrFgRBgLGxMYYMGYKRI0di2LBhsLCwUE2hSlrtITIyEv369UNOTg5+/PFHzJs3r8b3qHq1B3mC7/OWfebJAExERET0HHnzmsJLnSUkJODQoUM4cuQIgoODUVRUBG1tbfTs2ROjR4/GiBEj4OzsrPAHqFCoEsJvXFwcvLy88ODBAyxfvhyff/65XO9TZfitbfAtxwBMRERE9C+Vh98XbxYYGIjDhw/jxIkTyM7OhiAIaNeuHUaNGoWRI0eiX79+0NbWVvgedQ2/qamp8PLyQkJCAubPn4/vv/9e7veqKvwqGnzLMQATERERlanX8Pu8kpISBAUF4fDhwzh69Cju3r0LQRCwfPlyLF26VOHr1iX8Zmdnw9vbG1FRUZgxYwZ+//13CIIg9/tVFX4NTb9FXf70BQF4lvuB0uohIiIiaqjUtsmFtrY2+vfvj7Vr1+L27du4desW1qxZg44dOyr7VjLWrVsHJycnfPrppzLjT58+xbBhwxAVFYWJEyfi119/rVXwVaWliz3V+n4iIiKipkblm1w4OTnBycmp1u87duwYVq5cKTNWWFiI3r17S18vXboUw4cPBwBkZGQgNja2wnJkn332GcLCwqCtrQ0dHR3MmjWr0vtt3ry51jXWVXnLAnt+iYiIiOqHxu7wlp6ejosXL8qMSSQSmbH09PQar5OdnQ2grB1jx44dVZ6njvALKBaAjY114dq1tapKIiIiImq0lN7z2xipeqkzQLGH32a+0RX/t7o/mjXTU0lNRERERA2F2np+STGLP/bAss+q7+H1n++OoUPspa//2HULaWl5qi6NiIiIqNHQ2LaHpqi6FojyHl+JRILfN0fi48V/Y9UX3nB0NK/vMomIiIgaLLY9yKF8Gr2y7Y39/f3h7++v1Pu92AJR2cNtyffFsLE2kVm54tmzIiTdE8OpU0ul1kNERESk6dj2oAJpaWlISUmR+RGLxUq/T3kLhCBUvaqDrU3zCku2LV0ehF5eW/HTf6+gtJR/pyEiIiJ6UZ1nfqOiohAeHo6MjAw4OztLlx4rKipCUVERjIyMlFKoOtX3zK8igkKSMXjoLunr/v3aYsP/fNDWVjUP6BERERFpEpXP/MbGxsLT0xMuLi6YPXs2PvnkE+zbt096/Ndff4WJiQlOnDih6C00TkxMDO7fvy/zownBFwDcurfBvHd7SF//fe4e3PtswY6dt8DOFiIiIqIyCoXf5ORkeHt748KFCxg+fDjWrFlTIWBNnjwZurq6MoGYVMfQUBfffDUAgYcnwNraBACQm1uAmXMC4fvGEWRmPlNzhURERETqp1D4Xb58OTIyMvD777/j8OHD+OijjyqcY25uDmdnZ4SFhdW5SJLfK/3tcOXCdLw+yVk6tu/Abbh5bMZfJxPUWBkRERGR+ikUfk+cOAEXFxfMmDGj2vPatm2LlJQUhQojxZmZGWDTr8OwffMItDAzAACkPXiCUeP2MQATERFRk6ZQ+E1PT4ejo2PNF9fSwrNn/LpdXcaPdcKVi29g8MB2AIDePa3wygA79RZFREREpEYKbXJhYWGBxMTEGs+LiYmBlZWVIrcgJbESNcPh/ePw6+/XMXCAHXR0ZP++I5FIKiyZRkRERNRYKTTz269fP1y+fBkXLlyo8pzAwEDExMRg0KBBChdHyiEIAubMdoWDQwuZ8UvhaRgw+A/cvpOlpsqIiIiI6pdC4feTTz6Bjo4ORo4ciS1btiA7O1t67NmzZ9i5cydmzJgBQ0NDLFq0SGnFkvI8eVKImXMCEXYpFb28tmL9LxHcGIOIiIgaPYU3udi9ezdmzJiB/Px8CIIAiUQCbW1tlJSUAAD09fWxdetWjB8/XqkFq4O8iyY3JHFx2Xht4gGZWd+BA+ywYb0PrK1M1FgZERERUe2pfJOLiRMn4saNG3jnnXfQoUMH6OnpAShb4WHmzJm4evVqowi+jZWDQwuEBfnhnTndpGOnzybBrfcW7N4bo8bKiIiIiFSnztsbNwUNYXvjujh1JhFz3vkTqWl50rEJ45zw49pBaNHCQI2VEREREclH5TO/TVFaWhpSUlJkfsRisbrLqrNBr7TD5QvTMWGck3Rsz74YuHlsRsiF+2qsjIiIiEi5FFrqrKmqbOa3sfQAm5sbYtumERgxzAHzF51CTk4B0jOeobmJvrpLIyIiIlIahcJveX+vPOe1bNkSrq6umDp1KiZOnKjI7TRGTExMowm7VZk04SV49rHBW+/+iYGv2KFL51bqLomIiIhIaRTq+bWxsYEgCDJbF5uYlK0Q8PjxY+mYlZUV0tPTUVRUBEEQMHr0aOzdu7fC7Kmma4yrPdSkfNkzLa1/N8AoLCzB5q03MPONrhU2yyAiIiJSJ5X2/CYlJaF3796wtbXFhg0bkJ2djdzcXOTm5iI7OxsbNmyAnZ0devfujdzcXAQFBaFz5844dOgQ1q9fr/CHovqjpSXIBF8AWLkmFO/7n8Irr/6BO9wYg4iIiBoghWZ+V65ciW+++QY3b96Era1tpeckJyfD2dkZH374IZYtW4aEhAQ4OzvDxcUFYWFhdS68PjXFmd8XpaQ+xktdf0NhYdk6zkZGOvjqy/54c5YLt0cmIiIitVPpzO+WLVswcODAKoMvANja2mLQoEHYunUrAKB9+/Zwc3NDdHS0IrckNbO2MsHJwElwsDcDADx9Woz3/U9hzPj9SHuQV8O7iYiIiDSDQuE3JSUF2traNV9cS0umL9jGxgaFhYWK3JI0QK+eVrgUMg1vznKRjp04mYAevTZj/8FYNVZGREREJB+Fwq+1tTVOnz6N9PT0Ks9JT0/HmTNnYG1tLR3LyMiAubm5IrckDWFsrIefvhuMQ3vHoo2lMQAgKzsfU6Ydwcw5gcjNLVBzhURERERVUyj8Tps2Dbm5uejXrx8OHDiA0tJS6bHS0lIcOHAA/fv3h1gsxvTp0wEAJSUluHbtGjp37qycykmthrxqj8th0/HaaEfp2I6dt/Dzr1fVWBURERFR9RR64K2oqAhjx47FsWPHIAgCdHR0IBKJIAgCUlNTUVxcDIlEgqFDh+LgwYPQ1dVFVFQUFi5ciFmzZmHy5Mmq+CwqwwfeqiaRSLBj5y0s/PA02rczQ9CZqdDTq7klhoiIiEiZ5M1rCoXfcps3b8bPP/+MiIgIFBcXAwB0dHTQvXt3vPXWW3jjjTcaxUoADL81u5csRkF+MRwdZdtasrKewdzcUE1VERERUVNRL+G3XGFhobT/18LCAvr6jWtL3PI/zMq2N/b394e/v7+aKtNsN6LSMeDVHfhoUW8sWuAObW1ujEFERESqIW/4VWh74xfp6enJPNjWWKWlpVUYE4vFaqhE8xUUFGPGm8eQl1eEZV8EIfDPOPz+yzDpUmlERERE6sCpuFoQiUSwtraW+WEbROUEQcBQHwfpLnFhF1PR03MLft8cCSV82UBERESkkDq1PezcuROHDh3CnTt38Pjx40pDjSAIiI1t2GvAsudXcaFhKZg1JxAJibnSsWE+9li/bggsWxursTIiIiJqTFTa81tUVISRI0fi5MmTVc7iCYIgPfb8UmgNEcNv3Tx+XIiPFp/Fpi03pGMWLQ3xv59exagRjtW8k4iIiEg+Kt3eeO3atfjrr7/g4+OD6Oho+Pr6QhAEPHnyBDdu3MCSJUtgaGiIDz/8EEVFRQp/CGocTEz0sP6nIdi36zW0bmUEAMjIfIaJUw5hxaoQNVdHRERETYlC4Xfnzp1o0aIFdu3ahU6dOkm3OjY0NMTLL7+MFStW4OjRo/jPf/6Dbdu2KVTYlStX8NVXX2Hs2LGwtraGIAgwMDBQ6FoAkJOTgwULFsDOzg76+vqws7PD/PnzkZOTo/A1qXaGD3XAlYtvYNSIDgAAbW0BPq/aq7kqIiIiakoUanto1qwZvL29ERgYCACYOXMmtmzZgsLCQmkQBgBvb288e/YM4eHhtS5szJgxOHTokMyYvr4+8vPza32tzMxMeHh44M6dO7C3t4ebmxtu3ryJmzdvokOHDggLC0PLli2rfD/bHpRLIpFga0AUMjKeYdGCnuouh4iIiBoBlbY9aGlpyVy0WbNmACBd67ecjY0NYmJiFLkFPDw8sGzZMhw5cgQPHjxQ6BrlFi5ciDt37mDs2LGIjY3Frl27EBUVhXnz5uHu3btcp7eeCYKA6b5dKgTf4uJSvD33BG5EpVfxTiIiIqK6UWjm19nZGWZmZggNDQUA/PDDD/D398f+/fsxevRo6XldunRBenp6ncMrUBaYFJn5ffDgAaytraGtrY3k5GRYWlpKjxUUFMDW1hZZWVlISUmROfY8zvzWj6+/DcPnK4Khp6eN5Us9MX+uGzfGICIiIrmodOa3V69euHnzpjSIDhs2DACwYMECnDx5EtHR0ViwYAFu3bqFXr16KXILpTl+/DhKS0vh7e1dIdzq6+tj5MiRKCkpwfHjx9VUIQFls76HjtwBABQWlmDx0vN4dfhuJCSyJ5uIiIiUR6HwO3bsWOjp6eHEiRMAAEdHR8ybNw9JSUnw8fFB586d8eOPP8LQ0BBfffWVUguurevXrwMAunfvXunx8vHy80g9dHS0cPav1+E/3x1C2b4YCAm9D/c+W7Bl+w1ujEFERERKodD2xiNHjqzQ3/v999/Dzc0NBw8eRHZ2Njp27Ij58+fDyclJKYUq6t69ewDK+o8rUz5efl51nJycoKVV898X/P392UesAH19Haxe2Q9Dfewx663juHdPjLy8Irz17gkcPRaH//44GK1bcWMMIiIiUpxC4bcqvr6+8PX1VeYl6ywvLw8AYGRkVOlxY2NjmfOqk5aWJtc9xWKxnNVRZfp62uJy6HQs+vgMtgXcBAAcOXYXFy+lYv26IRg+1EHNFRIREVFDpVD4Xb16NVxcXDB8+PBqzwsMDMS1a9ewePFihYpThvKvy4Xy79KrOC4PkUgk18wvH4qru+bN9fHr+qEYMawD3nv/L2RkPsOj9Kc4+3cSwy8REREpTKHwu2TJErzxxhs1ht8DBw5g48aNag2/JiYmAIAnT55Uevzp06cA/l2urToxMTEMtvVs9EhH9OpphXfnnUB8Qg5WLu+r7pKIiIioAVNq28OLSkpKqpxxrS9t27YFANy/f7/S4+Xj5eeR5mljaYx9u15DRuYzGBrqyhy7eCkV3VwtoaenXcW7iYiIiP6l0kVUo6OjYW5urspb1MjFxQUAEBERUenx8vGuXbvWW01Ue4IgoJWFbN/27TtZ8Bm5G31fCcCt6Aw1VUZEREQNidwzv3PmzJF5HRoaWmGsXHFxMWJjY3Hp0iWMGjWqbhXWkY+PD7S0tBAUFIRHjx6hdevW0mMFBQU4cuQItLS0MHToUDVWSbUlkUjw1nsn8OxZMa5HPoKH9zas/Lwv5r7bA1pa6v22gYiIiDSX3Du8Pf+glyAIcj0o5uzsjIMHD6JDhw6KV/jcPavb4W3dunVYt24dXnvtNaxZs0bmmK+vLwICAjBu3Djs3LkTOjplmX/+/Pn48ccf4evri23btlV5b+7wppmuXX+IGW8GIjomUzrWz9sWv64fira2/O+JiIioKZE3r8k983vy5EkAZTNur776KoYMGYIPPvig0nP19PRgZWUFBwfFn8o/duwYVq5cKTNWWFiI3r17S18vXbpU+tBdRkYGYmNjK12O7Pvvv0dYWBj27dsHJycnuLm54ebNm4iKioKDgwO+++47hesk9XF1scSF835Y9kUQfvzvFQDAufPJcPPYjO++GYgpk53V3nNOREREmkXu8Dtw4EDpf546dSr69esnM6Zs6enpuHjxosyYRCKRGXtxo42qWFhYIDw8HJ9//jkOHjyIAwcOwNLSEnPnzsUXX3yh9r5kUpyBgQ7+b80ADBvqgNlvH8f9+48hFhdi1lvHcex4HH76bjBatjRUd5lERESkIeRue2jK2PbQMOTk5MP/ozPYsfOWdKyXuwh/n5rCGWAiIqJGTt68ptLVHojqk5mZATZuGIYdW0fCvIUBBAFY+YU3gy8RERFJydX20LFjR4VvIAgCYmNjFX4/UW2NHdMJHr2tcfJ0Iry9bGWOlZSUQlubf+cjIiJqquRqe5BnS9/qlJaW1un96lY+jV7Z9sb+/v7w9/dXU2VUG6WlEowauw/ubm2w+GMP6OpyYwwiIqLGQqmrPRQVFSmtsIasspUkxGKxGiohRfzv5wicOpOIU2cSceJkAjZuGAanTi3VXRYRERHVI7nCr7Y2Z8gAVDrzywfgGo78gmJoawsoKZEg4upD9O67Dau+8MY7b3XjxhhERERNBFd7kANXe2g8rkQ8wIw3A3H7TpZ07JUBdtjwPx/YWJuosTIiIiKqC3nzWp3Cb2ZmJrZs2YKgoCCkpqZCEASIRCJ4e3tj2rRpaNmycXylzPDbuDx9WoQln5/H/365Kh0zM9PHD/8ZhEkTXlJjZURERKQolYffgwcPYsaMGRCLxRW2OhYEAaampti0aRNGjx6tyOU1CsNv43TqTCLeevdPpKTmSccmjO2ETb8Nh44OV4QgIiJqSFS6zm9ERAQmTZoEsViMkSNHYs+ePQgPD0d4eDj27t2LUaNGITc3F5MmTUJERITCH4JIlQa90g6XL7yBieOdpGPNTPQYfImIiBoxhWZ+J0yYgP3792Pnzp2YMGFCpefs3bsXEydOxPjx47F79+46F6pOnPlt/HbvjcEPP4Xjz6OTYGKip+5yiIiIqJZU2vbQpk0bdOjQAcHBwdWe5+XlhTt37uDhw4e1vYVGYfhtGiQSSYXd4A4duQNrq2Zw6yFSU1VEREQkD5W2PeTk5MDOzq7G8+zs7JCbm6vILYjq3YvBNzEpF7PfPo5+g3Zg1VehKC5u2Ju1EBERkYLh19LSEtevX6/xvOvXr8PS0lKRWxCp3Xc/hOPx40KUlEiwcnUoBgzegTvPLZFGREREDY9C4XfIkCGIjo7G8uXLK6z0AJR9fbx8+XJER0fDx8enzkUSqcO3Xw/A4o89oK1dNiMcfuUBenptxYbfrlX6v3siIiLSfAr1/N67dw/dunVDTk4OHB0dMWnSJLRr1w6CICAhIQE7d+7E3bt3YW5ujoiICNja2qqi9nrDnt+m7eKlVMycE4i4+Bzp2KuD2uHn//rAStRMjZURERFROZWv83vt2jVMnToV0dHRMr2S5ZdzdnZGQEAAXFxcFLm8Rin/w6xse2N/f3/4+/urqTKqL0+eFOLTJeew4fd/233MWxjgp+8HY9xrndRYGREREQHyh18dRW/g6uqKqKgonD59WrrDGwBYWVmhb9++GDhwYIUHiBq6tLS0CmNisVgNlVB9MzbWw4/fDcbwoQ54670TePDwCbKy87HoozN4dVB7Lo9GRETUQCgcfoGyp+MHDRqEQYMGKasejVbZzC/bIJqWIa/a40rYG5i38CT2H7yNX/47hMGXiIioAZGr7cHOzg6+vr6YOnUqnJ2d66MujcKeX3qRRCJB+OUH6Okuu/5vVtYzGBrqwNBQt9L3rf76AlauDsHSxZ5Y/LFHfZRKRETUJCi151dLS0vawtCtWzf4+vpi8uTJaNOmjfIq1mAMvyQPiUSCcZMOICExF5t+HQZXF9ll/lZ/fQErVoVIXy/7jAGYiIhIWZS6ycW5c+cwe/ZsmJmZISIiAosWLYKtrS2GDh2KHTt24OnTp0ornKih2rjlBgL/jEd0TCa8BgTg62/DpBtjvBh8AWDFqhCs/vqCOkolIiJqsmq12kNRURECAwOxbds2BAYGIj8/H4IgwMjICGPHjsXUqVMxePDgRvegG2d+SR63ojMwc04grl1/JB3r3dMKbj3aYN36iCrfxxlgIiKiulP5UmdisRh79uxBQEAAzp8/j9LSUgiCAEtLS0yZMgW+vr5wdXVV+ANoEoZfkldhYQm+/CoU3669hNJS+f+vxQBMRERUNyoPv89LTU1FQEAAtm/fjhs3bpRdWBDw0ksvwc/PDx9//HFdb6FWDL9UWxcupuC1CfuRk1Mg93sYgImIiBRXr+H3ebdu3cK2bdvw22+/ITMzE4IgoKSkRJm3qHcMv1RblfX4yoMBmIiISDFKfeBNXpmZmTh37hzOnz+PrKwsZV6aqMFQNPgCZQ/BfbEyWMkVERERUbk6z/zm5+fj0KFDCAgIwIkTJ1BcXAyJRAILCwtMmjQJfn5+6Nmzp7LqVQvO/FJtGJp+i7p+nyJqY4z27c3gYG8G+/ZmcLBvgVEjOsDAoE770hARETVaKt3eWCKR4PTp09i+fTsOHDiAvLw8SCQS6OvrY9y4cfDz88PQoUOho8Nf1NT0LF3sqfDMb7m0B0+Q9uAJQi+kSMeyH86XOefQkTu4eu0h7O3/DcltLI0b3WorREREylSrdBoREYGAgADs3LkTDx48gEQigSAI8PLygp+fHyZMmABTU1NV1ap2Tk5OFbY39vf3h7+/v5oqIk1U3rOrSADu2qUVjIx0kZCQg4eP/l0/29rapMKucUeO3cX2HTdlxoyNddG+nSkc7FvAwd4M7duboUc3S3Tv1jQ2pCEiIqqJXOF31apVCAgIQGxsLICymd9OnTrB19cXvr6+sLOzU2mRmiItLa3CmFgsVkMlpOkUCcAvPuz2+HEhEhJzEJ+Qg/z8ig+NxsXnVBh78qQIUTczEFRPGnMAACAASURBVHUzQzo23a8zfvmvj8x5S78IgmUro7IZY3sztLMzhb4+v6khIqLGT67fdkuXLgUAWFhYYPLkyfDz84O7u7tKC9NEIpGowswve4CpKrUJwJWt8mBiooeuXVqja5fWlb7n1//54PadLMQn5CAuPkf6z6R7uSgqKpWe52DfQuZ9eXmF+OY/F2XGBAGwsTGBg32Lf3qMy9oo+nu3RYsWBnJ9XiIiooZArvA7YcIE+Pn5wcfHp0n38cbExDDsUq3IE4AVXd6sQ4cW6NChRYXxkpJSJN9/LA3EvdxFMscTEnMrvEciAZKTHyM5+TH+PndPOn7hvJ9M+L0S8QAnTibIBGRzcwP2GRMRUYOh9HV+GyOu9kB1VdXyZ+pY1/fp0yJcvf6wLBzHl80WJ/wza5ydky9z7sPkeTA11Ze+XvvDJSxeel7mHDMzfdi3LwvC9v8E4pc6tUSvnlb18nmIiIgAFa/2QES1U9kMsLo2tDAy0oWnhw08PWwqHMvKeob4hBzEJ+Qi+b5YJvgCQHxCxVnjnJwCRFx9iIirD6VjPd1EOH9mqsx5v268DkmpRBqQ29o2h46OUpcaJyIiqpFGh9/8/HysWbMGf/zxB+7duwdzc3P4+PhgxYoVsLGp+Iu7On/++Sd++OEHhIeHIzc3Fy1atEDPnj2xcOFCDBw4UEWfgOhf5UF35eoQLF2smTu5mZsbwtzcEG49RJUeX/i+OwYPbCftL46Lz0Z8Qg6Skx+jtPTfL5Hatzer8N5vv7uIpKR/HxDV0dGCXdvmMmsZ29ubwa1HG1i2Nlb+hyMiIoIGtz3k5+dj4MCBCA0NhUgkQt++fZGYmIhLly6hVatWuHDhAhwcHOS61tq1a7Fo0SIIggBPT09YW1sjPj4e4eHhAID169fj7bffrvL9bHsgql5hYQmSknKlfcb27c3gM8Re5rhZ6+9lAnJVfvt5KHynvCx9nfYgD9t33JSGZPv2ZmjeXL+aKxARUVMkb17T2PC7bNkyrFy5Eh4eHvjrr7/QrFkzAP8GWW9vb5w7d67G66Snp0tniU+fPg0vLy/psX379mHChAkwNDTEw4cPpfd4EcMvUd0UFZUgODQFcfHZ0v7isvaKHOTlFcmce+av19Gnt7X09Ym/4jF6/H6Zc1pZGMLBvsULu+CZwd1NBC0tPnxHRNQUNejwW1RUhNatWyMnJwcRERHo1q2bzHEXFxdERkbi8uXL6NGjR7XXOnr0KEaOHAkfHx8cP368wnFXV1dcv34dFy9erHIbZoZfItWQSCR4lP60rI0iriwMv/d2d7RsaSg9Z/0vEVj44Zkar2VsrIuM1PdlVp4IPB6Hh+lPYd++bOMPK1EzhmMiokaqQT/wFhwcjJycHDg4OFQIvgAwfvx4REZG4siRIzWGX319+b4eNTc3V6hWIlKcIAiwbG0My9bG8OhlXek5w4Y6wMLCSGY94/j4HKSm5cmcZ9/erMKSa79tuo7AP+Olrw0MdP7ZAc9MZrm2l19uBStR5d/8EBFR46JQ+LW3t8eECRPw9ddfV3vep59+it27dyMuLq5W179+/ToAoHv37pUeLx8vP6867u7uMDU1xZkzZxAcHCzT9rB//35ERkaiT58+6NChQ61qJKL6YdfWFHZtK26b/vRp0T874OUiLj4bxsZ6Fc55cRe8/PxiRMdkIjomU2Z84ftuWPNlf+nr0lIJ1v3virSton070wrbS2uC1V9f0OgHKImINJFC4TcxMRHp6ek1npeRkYHExMRaX//evbJF9qta0aF8vPy86piZmeG3337D1KlT4e3tLX3gLSEhAeHh4fDx8cHmzZtrXSMRqZeRkS5edm6Fl51bVXnOd98OxO3bsrvgxSfkoKBAdrto+xd2wUtJfYyPFv8tM2Zt1axCj7F9ezN0frkV9PS0lfa55PX82tHl/2QAJiKqmUrbHp48eQJd3drPluTllX2daWRkVOlxY2NjmfNqMn78eJibm2PSpEkIDg6WjltaWuKVV15By5Yt5bqOk5NThe2NK+Pv7w9/f3+5rklEqvNKfzu80t9OZqy0VIKU1Mcygbj3CxtyxCfIzhgDQEpqHlJS8xAccl9mPObGm2hn9+/M9NVrD3ErOgP29mUBuZWFkdJ3wKts0xQGYCIi+agk/JaWliI2NhZnz55F27Zta/3+8mfwqvqFUdtn9P7zn//go48+wpgxY7B8+XLY29sjPj4ey5Ytw4cffoiwsDDs3bu3xuukpaXJdT+xWFzzSUSkFlpaAmxtmsPWpjn69a38309OnVpi44Zh/6xjnIv4f9YzTs94JnOerq4WbG1MZMb2HYzFt2svSV83a6ZbtoZxe9N/dsFrAfv2Zujo2ALWVrLvlUdVuwUCDMBERPKQO/xqa8t+rbdlyxZs2bKl2vdIJBLMmTOn1kWZmJT9Qnjy5Emlx58+fQoAVS5N9rxz587hgw8+QPfu3bFnzx7pzG2XLl2wd+9euLu7Y9++ffjrr7/w6quvVnstkUgk18wvV4QgatgsWxtjymTnCuNiccFzG3zkQPy4ANrasv9OSHhh1jgvrwjXIx/heuQjmfEhg9vj0L5xMmO79kTDzLRsu2g7O9MK7RTVBd9yDMBERNWTO/za2tpKZ2Lv3bsHIyMjWFhYVHqunp4erKysMGrUKLz//vu1Lqp8tvj+/fuVHi8fl2dWeevWrQCAsWPHVgiu2traGDt2LK5evYq///67xvAbExPDYEvUhDVvrg9XF0u4ulhWec7bc7qhl7uVTEhOupeLkhLZb6zsX9gFTyKRYN7CkxCLCwH8M0NtawIH+xZwsDdDQmIuTp1OlKtOBmAioqrJHX6ff3BNS0sLEyZMwMaNG1VRE1xcXAAAERERlR4vH+/atWuN1yoPylWF1vLxrKysWtdJRPSivp626OtpKzNWVFSCe8mP/1mmrayFwvuFlouMzGfS4AuU9SYnJYmRlCTGmbNJta6DAZiIqHIK9fyePXsWbdq0UXYtUp6enjA1NUVcXByuXr1aYa3f8v7cESNG1Hit8jovX75c6fHyLY7btWtXh4qJiKqmq6sNh38egMPAdpWeY6Cvg5//OwRx8Tkyu+Dl5hYofF8GYCKiimpuYK1Ev3790KlTJ2XXIqWnp4e5c+cCAObOnSvT+7t27VpERkbCy8sL7u7u0vF169bByckJn376qcy1xowZAwAICAjAkSNHZI4dOnQIO3bsgJaWFl577TVVfRwiohqZmOjhDb8uWPl5X2zfPBIXzvvhwb25qOtCEStWheDXjdcRE5tZ64eFiYgaI7lmfs+fPw8A6NmzJwwMDKSv5eXt7V3rwpYsWYJTp04hNDQUjo6O6Nu3L5KSknDx4kW0bNkSmzZtkjk/IyMDsbGxFVZkGDNmDCZMmIA9e/Zg1KhRcHNzQ/v27ZGQkCCdDV61apVKwzwRkSIEQcDSxZ41PuRWk3kLTgIABg6ww7FDE5RRGhFRgyVX+O3fvz8EQUB0dDQ6duwofS2vkpKSmk96gYGBAc6ePYs1a9Zgx44dOHjwIFq0aIHp06dj5cqVsLW1rfkiKPvlsWvXLvj4+GDLli2IjIzEtWvXYGZmhmHDhmHevHnw8fGpdX1ERPWhvGVBkQCsq6uFoqJS6esunStuCDL77eNwfqklvPrYoptra+jq1v+GHURE9UmQyPE92BtvvAFBEPDVV1/B0tJS+lpeL87SNjRisRimpqbIzc3lag9EpBbyLHP2vGWfeeJD/564eu0RgkOTERxyH+/M6YbBg9pLz0m6l4tOnX+VvjYy0kHvXtbw6mMDL08b9HQTwcBApXshEREpjbx5Ta7w29Qx/BKRJpA3AC/7zFOuh9x27o7GG7OPVXlcT08b7m4iePWxwQcLe8LERK9W9RIR1SeGXyVi+CUiTVFTAJY3+AJlawvH3s5CUMh9BIfcR3BIMlJSK24bb2ysiwf35sq0RMQn5KCFmQFatDCo/YcgIlIBhl8lKv/DrGyHN39/f/j7+6upMiJqiqoKwLUJvpWRSCRITMotC8KhZYE4Lj6n0gflxk7cj+Mn4tH55VbSNgnPPjZoY2ms8P2JiOpC3vCrUDPXihUr5D5XEAQsXbpUkdtonBdXkgDK/qCJiOpTZQ/B1TX4AmX/vm7fzgzt25nBb2pnAEBqWh5yc/NlzispKUXIhRRIJMCNqHTciErH+g1XAQCOHVrAy9NGGojt2prWqSYiImVTaOZXS0sLgiBUuWZk+cNwEokEgiAotNqDJuHMLxFpotVfX8DK1SFYurjuwbc2Hj8uxIpVIQgOvY/rkY9QWlr1r5HffxmKqa+/XG+1EVHTpdK2hy1btlQ6XlpaiuTkZJw4cQIXLlzAe++9Bzc3N0yfPr22t9Ao7PklIqpcbm4Bwi6mIOifNokrEQ9klle7cWUmHB3Npa+DQpLxy6/XpDPDzi9ZQEurjjt5EBFBA3p+16xZg1WrVuHChQvo0qWLKm5Rbxh+iYjk8/RpES5dTkNwyH1E3UzHH9tGySyNuWxFEP7v24vS1y3MDODZx1raKuHqYgkdHYU2HyWiJk7t4RcAnJyc4OjoWGFb4YaG4ZeISDmGj96D02eTqjzerJkueve0xoTxnTDdt2FPnBBR/VLpA2/y6tKlC06dOqXKWxARUQNyaN84RN54JF1RIiQ0BZlZz6TH8/KKcOpMIjp1NK/w3uDQ++jm0hrGxlxvmIgUp9LwGxcXh+LiYlXegoiIGhAdHS1079YG3bu1wftz3VBaKkFMbCaCQ+7/s95wMtIePIFnH2uZ96WkPsYgn53Q0dFCj26W0jYJj97WMDPjWsNEJD+VhN+cnBysXLkS165dw4ABA1RxCyIiagS0tAQ4v2QB55csMGe2KyQSCeITctG6lZHMeSGhKQCA4uJSXAxPw8XwNPzn+3AIAtC1S2vpA3RenjZoZWFU2a2IiAAo2PNrb29f5bG8vDxkZmZCIpHA0NAQZ8+eRc+ePetUpLqx55eISL3CL6dh87YbCA65j9jbWVWeZ2iog4fJ86Cnp13lOUTUOKm05zcxMbHKY7q6urC1tUW/fv3w8ccfw9nZWZFbEBERSbm7ieDuJgIAPEp/gpDQFGnfcOSNRyifxnF3E1UIvv4fnsbjvMJ/WiVsYd/eVGYFCiJqWri9sRw480tEpLlycvJxISwFwaH30aGDOWZM+3eVCIlEAlv7/yEj89+H6qxEzeDZxwZ9/2mTcOrUkmsNEzUCGrHUWWPB8EtE1DAlJuXCxW0jCgqq3mm0pbkhPPtYY+liT3Tp3KoeqyMiZWL4VSJub0xE1HDl5xfjcsSDsjaJkPu4cDEFT54UVTgv4uIbcH7JQvo6ITEHDx8+RfduluwhJmoAVNrz++OPP2LhwoU4cuQIhg0bVuk5x48fx4gRI/DTTz/h3XffVeQ2GictLa3CmFgsVkMlREQkLwMDnbLVIPrYAB+WrRhx7fpDBP+zJXNIaAq0tAQ4dWop877tO25i1VcXYGiog149raQrSvR0E8HISFdNn4aI6kqhmd9+/fohPj4eycnJVZ4jkUhga2uLjh074syZM3UqUt0480tE1HiVlkpwP+Ux2trKzhS9OnwXzgdV/D2nq6uFHt3bwMvTBn3/WWu4eXP9+iqXiKqg0raHNm3aoFu3bjh+/Hi15w0dOhTXr19HampqbW+hUdjzS0TU9GzfcROnzyYhKCQZ9+8/rvK82TNdsO77wfVYGRFVRqVtDzk5OTA3r7j15ItatGiBrKyq12MkIiLSVL5TXobvlJcBAEn3cqVLqwWH3Medu9nS8/p62si8Lz3jKXxG7Ja2SXj2sYGVqFm91k5EVVMo/LZp0wY3btyo8byoqChYWFjUeB4REZEms2trCru2ppj6elkYfvDwCUL+CcJ9vWxlzg0JvY+btzJw81YGfvntGgDAwd5MuiWzl6cN2tlxrWEidVEo/A4YMABbt27Fvn37MG7cuErP2b9/P6KiojB16tQ6FUhERKRp2lgaY9xrnTDutU4VjiUk5kJbW0BJyb9dhXHxOYiLz8GWbVEAAGtrE7zSvy02/M+HIZioninU8xsdHY3u3btDIpHg7bffxpw5c2Bvbw9BEBAXF4cNGzbg559/BgCEh4ejS5cuNVxRs7Hnl4iIauPx40KEXUxB0D+zw5evPEBhoexaw717WeHvk1Nkxk78FQ+RqBk6v9yKG28Q1ZLK1/nds2cPpk+fjoKCgn8vJgiQSCSQSCQwMDDAxo0bMXnyZEUur1EYfomIqC6ePStC+JUH0r7hsIspePft7vhyubf0HIlEAvtOPyPtwROYmemjT29r6ZbM3VxbQ1eXaw0TVadeNrm4e/cu1q5di9OnT0uXPbO1tcWgQYOwYMECODo6KnppjcLwS0REylRUVIJnz4pllkiLi8vGy91+r/R8IyMd9O5lLe0Z7uUugr6+Qp2LVVr99QWsXB2CpYs9sfhjD6Vem6g+cIc3JWL4JSIiVUvPeIrde2IQHJqM4JD7SM94VuW5YUF+cHWxVNq9V399AStWhUhfL/uMAZgaHpWG3+7du8PBwQF79uypU5ENBcMvERHVJ4lEgtjbWQj6Z0vm4JBkpKTmAQBMTfWRmvgetLX/3XTpP99fwsFDt8vaJDxt0ae3NVq0MJDrXi8G33IMwNTQqHSd39jYWDg5OSlcHBEREVVNEMq2W3bq1BJvznSBRCJBYlLZWsM5OQUywRcA/j53D+FXHiD8ygN89+NlCALQ+eVWMmsNt7E0rnCfqoIvAOk4AzA1NgqFX0dHR2RmZiq7Fo3n5OTE7Y2JiKjeCYKA9u3M0L6dWYVjEokEYnHBC2PAjah03IhKx/oNVwEAjh1a4KNFveA3tTOA6oNvOQZgaowUCr+zZs3Chx9+iJiYmCY1A5yWllZhTCwWq6ESIiKiMoIg4NzpqcjIfIqQ0BTpihLXIx+htPTfzsbnd6WTJ/iWYwCmxkah8Dtv3jzcvHkT/fr1wyeffIKRI0eibdu20NPTU3Z9GkUkElWY+WUPMBERaQKLlkYYPdIRo0eWrbSUm1sgs9bwlYgH8PK0qVXwLccATI2JQg+8aWuXrTUokUhq3JlGEAQUFxcrVp2G4ANvRETU0D19WoTvfwrHilWhCl+DD8GRJlPpag/t2rWr1XaMCQkJtb2FRmH4JSKixsDQ9FvUZYFTQQCe5X6gvIKIlEilqz0kJiYqWhcRERGpydLFnrVueXjx/UQNnVbNp6hPfn4+Pv/8c3Ts2BEGBgawsrLCzJkzcf/+fYWud/fuXbz55pto164dDAwM0KpVK/Tp0wfffPONkisnIiLSPIs/9sCyzxQLsGx5oMZCY3d4y8/Px8CBAxEaGgqRSIS+ffsiMTERly5dQqtWrXDhwgU4ODjIfb0DBw5gypQpKCgoQLdu3dCxY0dkZmbixo0bMDY2xt27d6t8L9seiIioMantQ28MvtQQKLXt4fz58wCAnj17wsDAQPpaXt7e3rU6HwBWr16N0NBQeHh44K+//kKzZs0AAGvXrsWiRYswc+ZMnDt3Tq5rXb9+HZMnT4aJiQlOnjwJLy8v6bHS0lJERETUuj4iIqKGqjzIyhOAF853lwm+paUS7NkXg3GvdYKOjkZ/gUxUKblmfrW0tCAIAqKjo9GxY0fpa3mVlJTUqqiioiK0bt0aOTk5iIiIQLdu3WSOu7i4IDIyEpcvX0aPHj1qvJ63tzeCgoJw5MgRjBgxola1AJz5JSKixqmmGeDKZny3BkRhzjt/omuXVvhh7SB49LJWdZlEclHqzO+0adMgCAJMTU1lXqtKcHAwcnJy4ODgUCH4AsD48eMRGRmJI0eO1Bh+o6OjERQUhI4dOyoUfImIiBqr6maAKwu++fnFWLKs7NvfyBvpGDD4D0z364xVK7xh0dJI9QUTKYFc4Xfz5s3Vvla269evAwC6d+9e6fHy8fLzqnP69GkAwODBg5Gfn49du3bh8uXLEAQBXbt2xcSJEzmbS0RETVZlAbiqHl8DAx3s3jEG8xedwvXIRwCALduicOToXaxY3hczp3eFlpbqJseIlEGhpc5U7d69ewAAGxubSo+Xj5efV52bN28CAAwNDeHq6orY2FiZ459++in27dunUF8yERFRY1AedFeuDsHSxdU/3Na7lxVC/vbFht+uYfmXwRCLC5GVnY+5809iy9Yb+GHtIHTv1qa+SieqNbk71aOjo3H+/Hm5AmdSUhLOnz+PmJgYhYrKy8sDABgZVf4VirGxscx51cnOLtvL/Pvvv0dWVhb279+PnJwcxMbGYsqUKcjIyMCYMWOQlpZW47WcnJxgY2NT48/atWvl/ahEREQaYfHHHniW+4Fcqzro6Gjh3be7I/LKLEye+JJ0PPzKA3j2344Fi06htFQjF5Mikm/mNyMjAx4eHjAyMsKVK1dqPF9PTw+TJ09GYWEh7t69CzMzs1oVVf4MXlV9xbVZna38Ybvi4mJs374dr776KgDA1NQUAQEBuHPnDsLDw/Hf//4XX375ZbXXkicgA2UN10RERI1dG0tjbP5tOGZM74IFi04jOiYTEgnw5GkR2x9IY8k187tp0yaIxWKsXr0aIpGoxvNFIhG++uorZGVlYdOmTbUuysTEBADw5MmTSo8/ffoUAKTLn8lzLWtra2nwfd6MGTMAAH///XeN1xKJRLC2tq7xhz3ERETUlPTr2xYXg6dh1Qpv2NiYYPXKfuouiahKcs38Hjt2DKampvD19ZX7wlOnTsWCBQtw+PBhLFy4sFZFtW3bFgCq3MmtfLz8vOq0a9cOAGBnZ1ft8UePHtV4rZiYGAZbIiKiSujpaWPRgp6Y924P6OlpyxzbtPUGYmMz8dknfWBioqemConKyDXze/PmTfTu3Rs6OvI/H6etrY1evXpJHzirDRcXFwCocvOJ8vGuXbvWeK3ypdKysrIqPZ6ZmQlAvllkIiIiqt6LwTc94ykWLzmH73+6DFf3jdh3ILZW7YtEyiZX+M3NzUXLli1rffGWLVsiNze31u/z9PSEqakp4uLicPXq1QrH9+7dCwByrds7cOBAGBsbIy4uDsnJyRWOl7c7VLWsGhERESnuUnganj4rAgCkpOZh6vQjGDFmL+7cqXxSikjV5Aq/ZmZmVc6cVicrK0uhNgE9PT3MnTsXADB37lyZ3t+1a9ciMjISXl5ecHd3l46vW7cOTk5O+PTTT2WuZWRkhHnz5qGoqAjvvPOOzLX+/PNPbNmyBYIgYM6cObWuk4iIiKo3fKgDIi7OgM+r7aVjp88moYfHFixfGYynT4vUWB01RXKF3w4dOuDixYu12qa4uLgYYWFhcHR0VKiwJUuWoFevXggNDYWjoyMmTZqE3r17Y9GiRWjZsmWFB+kyMjIQGxtb6YoMn3/+OTw9PXHs2DE4Ojritddeg6enJ4YPH47i4mJ8+eWX6Nmzp0J1EhERUfUc7M1wYM9Y7N4xGra2ZQ+iFxaW4KtvwtCt1yYcOx6n5gqpKZEr/A4dOhQ5OTlYt26d3Bdet24dcnNzMXToUIUKMzAwwNmzZ7F06VIYGRnh4MGDSExMxPTp03H16lV06NChVtc6c+YMVq1aBTMzMxw/fhw3b97EgAEDcPToUSxevFihGomIiEg+giBg1AhHXLs0Ax/494SublkESUoSY9ykA/jl14ptjkSqIEjk6DrPysqCvb09nj17ht9//73GVR+2bduGWbNmwcjICPHx8TA3N1daweogFothamqK3NxcrvZARESkBLG3MzF/0Wn8fe4eLFoaIvLKTJibG6q7LGrA5M1rcoVfADh8+DDGjh0LiUSC3r174/XXX0f37t3RqlUrAEB6ejoiIiLwxx9/ICwsDIIgYN++fRg9erRyPpEaMfwSEREpn0Qiwe69MdDWFjB+rJPMsbQHeRC14UpMJD+lh18AOHHiBKZNm4b09PRqd19r1aoVNm/erHDLg6Zh+CUiIqo/aQ/y4OK2EUMGtcdXq/vD2spE3SVRA6CS8AsAz549w5YtWxAYGIirV69K18lt2bIlXF1dMXz4cEybNg1GRkZ1+wQahOGXiIio/rwx+xh27o4GADRrpouliz3x7lvdoKurXcM7qSlTWfhtisr/MEUiEbS0ZJ8R9Pf3h7+/v5oqIyIiany2BkRh8ZJzyMh8Jh3r/LIFflg7CJ4eNmqsjDSZvOFXrtUeqExaWhpSUlJkfsRisbrLIiIialSmTe2MyCszMXumC8q7LKNuZmDgkJ2Y/fZxPEp/Uv0FiKqhtPD74YcfwsHBQVmX00gikQjW1tYyP2yDICIiUj5zc0Os+34wzp+eim6ultLx7TtuomuPjdjw2zWUlJSqsUJqqJTW9jBjxgxs3bq1VhthNBTs+SUiIlKfkpJS/LbxOpatCEZubgEAoI2lMSKvzETz5vpqro40BdseiIiIqFHQ1tbCW292Q+SVmZj6ujMA4KtV/Rl8SSEMv0RERNQgWLY2xu+/DENYkB8mTZBdF/h+ymNsDYhCaSmf46fqMfwSERFRg+LqYllhv4GPF5/FnHf+xKChOxF1M11NlVFDoKOsC3l5eSnrUkRERERyux75CPsO3AYAhF5IQS+vrXjv7e5YutgTJiZ6aq6ONA3X+ZUDH3gjIiLSbCdPJ2LhB6dxNy5bOmYlaoavV/fH+LGdqtyZlhoPPvBGRERETcbgge1wJWw6Pl/iCQODsi+2U9Py4DfjKIaP2Yvbd7LUXCFpijrN/P799984f/480tLSUFBQUPkNBAG///67wgVqAs78EhERNRwJiTlY9NEZBP4ZLx3T1dXCqi+88f5cNzVWRqokb15TqOc3NzcXo0ePRlBQEGrKzo0h/BIREVHD0b6dGfbvHoujgXfh/9EZ3LsnRlFRKWxtOYFFCobfjz/+GOfPn0eHDh3wzjvvoGPHjmjWrJmya9M4Tk5O0NKS7RTx9/eHv7+/mioiIiKiqowY1gGv9LfD4f8nHQAAIABJREFUV9+G4datDIwZ5ShzXCKRsBe4CVIo/B46dAiWlpYICwuDubm5smvSWGlpaRXGxGKxGiohIiIieRgZ6WLFsr6VBt1pM47ipZcssGiBO/T1lbYAFmk4hdsehg4d2qSCLwCIRKIKM7/sASYiItJ8LwbfY8fjsGd/LIBY7Nh5C999OxCDB7ZTS21UvxQKv46OjkhPb3oLSMfExDDsEhERNQKxtzOhrS2gpESCu3HZGPnaXox7rSO+Xj0ANtYm6i6PVEihpc7mzZuHS5cu4caNG8quh4iIiEjl/Of3xMXgaejjYS0d23fgNlzcNuK7H8NRVFSixupIlRQKv7Nnz8b8+fMxdOhQbN68GSkpKcqui4iIiEilOr/cCqf/nIxf1/uglYUhAODJkyJ8uuQcenltRVBIsporJFVQaJ1fbW1tAPI9JSkIAoqLixWrTkNwnV8iIqLGLTs7H5+vCMKvG6/j+WQUFuQHVxdL9RVGclPpOr+2trZcGoSIiIgajRYtDPDjd4Mxzbcz5vufwpWrDzFimAODbyNUpx3emgrO/BIRETUdJSWl2Lg5EoMHtUc7O1PpuEQiQUxsJl5yslBjdVQVefOaQj2/RERERI2VtrYW3pzlKhN8AeDg4Tvo3msz3pv/FzIzn6mpOqorpYTfgoICpKWlISsrSxmXIyIiItIoeXmF+OCTs5BIgN83RaJrj43YtPUGSkv5BXpDU6fwu379eri6usLY2Bg2Njb44IMPpMd2796NsWPH4s6dO3UukoiIiEidDAx0MP+9HmjWTBcAkJn1DO/MPYFXXv0D1yMfqbk6qg2Fwm9xcTFGjhyJuXPnIjY2Fs7Oznixdfill17CwYMHsWvXLqUUSkRERKQuOjpaeH+uGyKvzMKEcU7S8bBLqfDw3oYPPj4DsbhAjRWSvBQKvz/88AOOHTuGESNGICkpCZGRkRXO6dKlC9q3b4/jx4/XuUhN4eTkBBsbG5mftWvXqrssIiIiqidWombYtmkEAg9PQEdHcwBAaakE69ZHoGuPjdi1J1rNFVJNFAq/W7duhUgkws6dO9G6desqz7O3t0dSUpLCxWmatLQ0pKSkyPyIxWJ1l0VERET17JX+dggPnYYVn/eFoWHZyrEPHj7B4aNs99R0Cq3ze+fOHfj4+MDQ0LDa8ywsLJCRkaFQYZpIJBJBS0v27wtc+oyIiKhp0tfXwUeLemHieCd8+MlZnD2XhP9bM0DdZVENFAq/+vr6yMvLq/G8e/fuwdTUtMbzGoqYmBiGXSIiIpLRzs4Ue/4Yg8SkXFhbmcgcO3LsLkpKSjF6pCM3CNMQCrU9dOnSBeHh4cjMzKzynHv37iEiIgJubm4KF0dERETUULy4LnBubgHmzv8Lk30PY8z4/YiLz1FTZfQ8hcLvrFmzkJubC19fX2RnZ1c4npeXh9mzZ6OwsBCzZ8+uc5FEREREDc3O3f/f3p3HNXWlfQD/JUAAUQIiyqaiqFAVcClSQajaWq1iVSrqa6uorbVj3ap0FLVox3EbBbXaOuPYam11xtZtiluVV0VRRrGKdECoO1WgCMgqe573D99kjAmQ5SYseb6fTz799J6bc59zDzl5vDn33DT8nvsUAPDT6Xvo578Lf157CRUVNY0cmWnTKfkNDw/H+PHj8dNPP6FLly4ICQkBAFy6dAnjx49H586dERcXh//5n//BuHHjdA6uoqICK1asQI8ePWBlZQUXFxfMmDEDDx8+1LlO4NmcZWtra4hEIowYMUKvuhhjjDHG1Png/T7Yt2c0XF1aAwAqK2vx57WX0M9/N346dbeRozNdOj/kYv/+/Vi3bh0kEgmOHz8OAPj1119x6NAhyGQyrFq1Ct9++63OgVVUVOC1117Dn/70J5SWlmLMmDHo2LEjdu3ahX79+uHOnTs61z1r1ixUVvJafIwxxhgzHJFIhNCxnkhOmoGP570Mc/Nnadfde4UYM/4QJr37L/z2kFeNMjYRvfh0Ci3V1tbi+vXruH//Pmpra+Hm5gY/Pz9IJBK9AouKisKqVaswcOBAnDp1Cq1bP/tXU0xMDBYtWoTg4GDEx8drXe9XX32F999/Hx988AF27NiB4cOH4+TJk/W+p7i4GFKpFEVFRXzDG2OMMcZ0knYzD/MWxiHh4n9/wW7Vyhx7d4/GmyM8GjGylkHTfE3v5NcQqqur0b59exQWFuLatWvo27evUrmvry9SUlJw9epV9O/fX+N6c3Nz4eXlhf79+2PZsmUYMmQIJ7+MMcYYMxoiwr5/piFyeTxyHz+FVGqJG1dnwKmDTWOH1uxpmq/ptNTZ86qrq5GSkoKsrCwAgIuLC3x8fGBhYaFznQkJCSgsLISHh4dK4gsA48ePR0pKCmJjY7VKfufNm4fy8nJs375d73nDjDHGGGPaEolEeOd/emHUmx747M8X4eXZViXxramRKaZIMOHpfGbLysoQERGB9u3bY8CAARg7dizGjh2LAQMGwNHREYsWLUJJSYlOdd+4cQMA0K9fP7Xl8u3y/TRx/Phx7N+/H0uXLkW3bt10iosxxhhjTAh2dlbYtPE1zJqpfJGvoKAcPv2/xhfbr6GmRtZI0elnzfpEWEs3Ys36xMYORS2drvwWFRVh8ODBSElJAfBsGoK7uzuICJmZmUhOTsbmzZsRFxeH8+fPa/2gi8zMTACAm5ub2nL5dvl+DSkrK8Ps2bPh6emJxYsXaxULY4wxxpixRP0pAXfvFWLR4jPYs/c/2BL9Ol7xd2nssDS2Zn0i/rT6IgAo/rt08cDGDEmFTslvVFQUbty4gaFDh2Lbtm3w8vJSKk9PT8fcuXNx5swZREVFYcuWLVrVL396XKtWrdSW29jYKO3XkOXLl+PBgwc4c+aMXjfieXl5qTzeWJ2FCxdi4cKFOh+HMcYYY6ZHJiPIZP+9FetGSi4GD9uHaVO98efPgtDOQX1e1FQ8n/jKNcUEWKdpDwcPHoSTkxN+/PFHlcQXeJYk/utf/0KHDh1w8OBBreuX34NX12MAtblH7+rVq9i6dSumTp2KIUP0e952dnY2Hj161OCruJiXLWGMMcaYdsRiEb78/A2cOz0ZPt6Oiu279/wCn35f46vdKUrJcVOiLvGV+9Pqi01qCoROyW9+fj5effXVOq/MAs+u2gYHB6OgoEDr+tu0efZc7LKyMrXlT58+e1qKfPmzutTU1GDmzJmQSqXYuHGj1nG8yNnZGa6urg2+eEUIxhhjjOnqFX8XXIqfguj1Q9GmzbNfrAueVOCjeacw+PV9uJ78eyNHqKy+xFeuKSXAOk176Nq1q9rHGr+oqKgIXbp00br+Tp06AUCdKzLIt8v3q8vDhw+RnJwMJycnhIWFKZUVFj57vvaVK1cwePBgtG7dGkePHq23vvT0dE5sGWOMMWZw5uZifPSHfggd1wNLlsVj/w83AQBXrmbjrdAD+DX1A1hbN7yy1pr1iVi15iI+XRpokKkHmiS+ck1lCoROye8HH3yAxYsXIzk5GX369FG7T3JyMs6cOYN169ZpXb+vry8A4Nq1a2rL5dt9fHw0qi8nJwc5OTlqy548eYL4+Hitb8pjjDHGGDM0Z6fW+OarUZge7o0Fi+KQnlGAlVFBGie+hrz5TJvEV64pJMA6TXuYP38+Zs6ciSFDhiAqKgqpqakoLS1FaWkpUlNTERUVhaFDh2LWrFn4+OOPta4/MDAQUqkUd+7cwfXr11XKDxw4AAAICQmptx75ChTqXmfPngUADB8+HESkuBLMGGOMMdbUDA7uhCsXw/HXL4Zj+lRvpbLHeU+RdjNPaVtdN58JNfVg1ZqLWie+hohDFzolv2ZmZvjyyy9RVFSE1atXw8fHB1KpFFKpFD4+Pli9ejUKCwvxxRdfwMzMTOllbt7wxWaJRII5c+YAAObMmaM09zcmJgYpKSkYNGgQ/Pz8FNvlq05ERkbq0iTGGGOMsSZNIjHDtCneEIuVFwRY+mk8BgTuwdJP41FaWmXQm88qKmrg+/LXWL1Ov+R11RrdEmch6DTtoWPHjnWuxCCU5cuXIy4uDpcuXUL37t0RFBSEBw8e4PLly3BwcMCuXbuU9s/Ly0NGRgays7MNGhdjjDHGWFPx78tZ+HZvKgAgZksS/v71DZSUVNX7HnVTD4gIWdmlSEvLw3/S8pCaloe0m3kIeMUVG9cPVexnZWWO0tL669fEp0sD9a5DVzolv/fv3xc4DFVWVlY4e/Ys1q5di3379uHIkSOwt7dHeHg4Vq1ahY4dOxo8BsYYY4yxpqyPb3ssjwzAhpjLqKysbTDxlfvT6ou4+nM23FzbIPVmPtLS8vCksEJlP4mFmcq2fn2d4OhYjNpawi//eax1zFHLDHPznaZEpM2iuSaquLgYUqkURUVFvNoDY4wxxpqciCVnse3LnwWtUywWoa9ve1yMn6K0nYgUMwC0venNkImvpvmaTld+5Wpra1FQUACRSAR7e3uYman+64AxxhhjjBnOmvWJeie+rq5t0OslB/Tq2Q69ejqiV8928PJsq3ZVieenvsoTWU0S4Ma+4iundfL722+/4YsvvsCJEyeQlpYGmUwG4NmJ6NWrF958803Mnj27wTV4GWOMMcaY/vS9eUwkAu7cnKXz+zVJgJtK4gtoudqDfEWFDRs24JdffkFtba1i6TCZTIZffvkFGzZsgKenJz7//HNDxcwYY4wxxv6fvjePCXHz2dLFAxG1TH09TSnxBbRIftevX4/58+ejoqICYWFhOHLkCH777TdUVFSgvLwcmZmZOHz4MN5++21UVVXh448/xtq1aw0Zu9F5eXnBzc1N6RUTE9PYYTHGGGPMhNWXeDZEyMRUXRxNLfEFNLzhLTU1FX369IFUKsXhw4cRFBRU7/7nz5/H2LFjUVJSguvXr6N3796CBdwY5BOo1VmxYgVWrlxp3IAYY4wxxl7QVG4+M/Qjleui6Q1vGl35/fzzzyGTyfDdd981mPgCQHBwMPbu3Yva2lps27ZN86ibOGdnZ7i6uiq9ePUHxhhjjDUF2lwBNuQV2aWLB6K8KKLJXfGV0+jKb/fu3SGRSJCamqpV5b169UJlZSVu376tc4BNAS91xhhjjLHmoqErwE1xKoIQBL3ym5WVBW9v74Z3fIG3tzc/cY0xxhhjzIia081njUGj5FcikaCyslLryisrK2Fhobo+HGOMMcYYM5zmcvNZY9Ao+e3SpQsSExMVa/pqQiaTITExEV27dtU5OMYYY4wxpht5AiwSceL7PI2S31GjRuHx48eIjo7WuOKNGzfi8ePHCAkJ0Tk4xhhjjDGmu6Z+81lj0Cj5XbBgAWxtbREZGYm1a9eitra2zn1ra2uxZs0aREZGQiqVYv78+YIFyxhjjDHGmD40Wu0BAOLi4hASEoLq6mq4uroiLCwM/fv3R/v27UFEyM3Nxc8//4wDBw7g0aNHMDc3R2xsLN544w1Dt8HgeLUHxhhjjLGmTdN8TePkFwCSkpIQHh6O9PR0iEQilXJ5VZ6enti9ezf8/f11CL3p4eSXMcYYY6xp0zRfM9emUj8/P6SlpeHEiRM4fvw4kpOTkZ+fDyJCu3bt4OvrizfffBMjR45UmxwzxhhjjDHWmLS68muq+MovY4wxxljTJuhDLtgzXl5ecHNzU3rFxMQ0dliMMcYYY0xDWk17MHXqnlZXXFzcCJEwxhhjjDFdcPKrBWdnZ4jFyhfLeRoEY4wxxljzwcmvFtLT0znZZYwxxhhrxnjOL2OMMcYYMxmc/DLGGGOMMZPByS9jjDHGGDMZnPwyxhhjjDGTwckvY4wxxhgzGZz8MsYYY4wxk8HJL2OMMcYYMxmc/DLGGGOMMZPBya8WvLy84ObmpvSKiYlp7LAYY4wxxpiG+AlvGti2bRsAIDs7W6WsuLjY2OEwgcTExKC4uBi2trZYuHBhY4fDBML92jJxv7ZM3K8tU1PvVxERUWMH0dS5uLggOzsbIpEILi4uSmULFy5skh3LGubm5oZHjx7B1dUVDx8+bOxwmEC4X1sm7teWifu1ZWqsfi0uLoZUKkVRURFsbW3r3I+v/GrBycmJP5yMMcYYY80Yz/lljDHGGGMmg5NfxhhjjDFmMnjaQxPVWJPFG+O4TX1ivJC4X1sm7teWyZT6tTGP2xhM6bNjSv2qMWINcnZ2JgDk7OxstGO6uroSAHJ1dTXaMRvruNzWlnlcbmvLPC63lY/b3I/ZWMflthpeUVERAaCioqJ69+NpD4wxxhhjzGRw8ssYY4wxxkwGJ7+MMcYYY8xk8A1vGqD/fw6ITCYz2hPdZDKZ0Y/ZWMfltrbM43JbW+Zxua183OZ+zMY6LrfV8OTHogae38ZPeNNAVFQUVq1a1dhhMMYYY4yxBvz2229wc3Ors5yTXw3IZDJkZWWhTZs2EIlEjR0OY4wxxhh7ARGhpKQELi4uEIvrntnLyS9jjDHGGDMZfMMbY4wxxhgzGZz8MsYYY4wxk2FyyW9FRQVWrFiBHj16wMrKCi4uLpgxYwYePnyodV2FhYVYsGABOnfuDEtLS3Tu3Bnz589HYWGhASJnDRGibwsLC7Fv3z5MnjwZPXv2hI2NDdq0aQN/f39s2bIF1dXVBmwBU0fIz+zzbt26BWtra4hEIowYMUKgaJmmhO7X27dvY+bMmXB3d4eVlRUcHR0REBCADRs2CBw5q4+Q/Xry5Em8+eabaNeuHSwsLNC+fXuEhITgf//3fw0QOavLzz//jHXr1iE0NBSurq4QiUSwsrLSub4mkTsZ9kFzTUt5eTkFBAQoHlU8YcIEGjBgAAEgR0dHun37tsZ15eXlUffu3QkAde3alSZMmEC9evUiANStWzfKy8szYEvYi4Tq22XLlhEAEovF1L9/f5o4cSINHTqULC0tCQANGjSIysrKDNwaJifkZ/ZFQ4YMIZFIRABo+PDhAkbNGiJ0vx46dIisrKxIJBJRv379aNKkSTRs2DBycnIiDw8PA7WCvUjIfo2OjiYAJBKJaNCgQTRx4kTy8/MjAASAtm/fbsCWsOeNGTNGcd7lL0tLS53qaiq5k0klv59++ikBoIEDB1JJSYliu/xDFhwcrHFdU6ZMIQAUGhpK1dXViu1z584lADR16lRBY2f1E6pv165dS0uXLqWHDx8qbf/111+pU6dOBIAiIyMFjZ3VTcjP7PN27txJAOiDDz7g5LcRCNmvycnJJJFIyMHBgS5cuKBUVltbS0lJSYLFzeonVL/m5uaSRCIhiUSi0qcHDhwgkUhErVq1UjoGM5x169ZRVFQUxcbGUk5Ojl7Jb1PJnUwm+a2qqiI7OzsCQNeuXVMp9/HxIQB09erVBuvKzs4msVhMFhYWlJOTo1RWUVFBjo6OZGZmplLGDEPIvq3Pvn37CAC5u7vrVQ/TjKH69ffffyd7e3t6/fXX6ezZs5z8GpnQ/RoUFEQAKDY2VuhQmRaE7NfY2FgCQCNGjFBb7uvrSwDo8uXLesfNtKdr8tuUcieTmfObkJCAwsJCeHh4oG/fvirl48ePBwDExsY2WNeJEycgk8kQHByMDh06KJVZWlpi9OjRqK2txYkTJ4QJntVLyL6tj6+vLwAgKytLr3qYZgzVr/PmzUN5eTm2b98uSJxMO0L2682bN3HhwgX06NEDISEhgsfKNCdkv1paWmp0zLZt22oXJGtUTSl3Mpnk98aNGwCAfv36qS2Xb5fvZ6y6mP6M1R93794FADg5OelVD9OMIfr1+PHj2L9/P5YuXYpu3brpHyTTmpD9Kr/xadiwYaioqMA333yDuXPnYt68edi5c6dRH6tq6oTsVz8/P0ilUpw5cwYJCQlKZYcOHUJKSgoCAgL4M9zMNKXcydzgR2giMjMzAaDOx93Jt8v3M1ZdTH/G6o8tW7YAAMaMGaNXPUwzQvdrWVkZZs+eDU9PTyxevFiYIJnWhOzX1NRUAIC1tTX69OmDjIwMpfLIyEgcPHgQwcHB+oTMNCBkv9rZ2WHnzp145513EBwcjMDAQLi6uuLevXtISkrCiBEjsHv3bsFiZ8bRlHInk7nyW1paCgBo1aqV2nIbGxul/YxVF9OfMfrjr3/9K+Li4mBnZ4clS5boXA/TnND9unz5cjx48ADbt2+HRCIRJkimNSH79cmTJwCAzZs3o6CgAIcOHUJhYSEyMjIwefJk5OXlYezYscjOzhYoelYXoT+v48ePx4kTJ+Dg4ICEhATs378fV65cQfv27TF06FA4ODgIEzgzmqaUO5lM8kv//xRnkUhUb7mx62L6M3R/xMfHY/78+RCJRPj666/h4uKiV31MM0L269WrV7F161ZMnToVQ4YMESQ+phsh+7W2thYAUFNTg++++w7jxo2DVCpFjx49sHfvXvj5+eHJkyf44osv9A+c1UvocTg6OhrDhg1DcHAwUlJSUFpaipSUFAwcOBCffPIJJk6cqHfMzLiaUu5kMslvmzZtADz76VOdp0+fAgBat25t1LqY/gzZHykpKRg7diyqqqqwZcsWjBs3TvdAmVaE6teamhrMnDkTUqkUGzduFDZIpjVDjMWurq544403VMqnT58OADh37pwuoTItCNmv8fHxiIiIQJ8+ffDDDz/A29sbNjY28Pb2xoEDB9C3b18cPHgQp06dEq4BzOCaUu5kMnN+O3XqBAB1PmVGvl2+n7HqYvozVH/cuXMHw4cPR2FhIVauXIm5c+fqFyjTilD9+vDhQyQnJ8PJyQlhYWFKZfInCl25cgWDBw9G69atcfToUX1DZ/UQ8vPq7u4OAOjcuXO95bm5uVpGybQlZL/u2bMHABAaGgqxWPkanZmZGUJDQ3H9+nWcO3dO7T96WNPUlHInk0l+5ctUXbt2TW25fLuPj49R62L6M0R/ZGVlYdiwYcjJycH8+fOxYsUK/QNlWhG6X3NycpCTk6O27MmTJ4iPj4dUKtUhUqYNIftVvqRWQUGB2vL8/HwA/CucMQjZr/IkyNbWVm25fHtd/c6apiaVOxl8JeEmorKykqRSaYMLcF+5cqXBurKyskgsFpNEIqHff/9dqUy+ULNYLKbs7GzB4md1E7JviYgKCgqod+/eBICmT59OMplM6JCZBoTuV3X4IRfGJ2S/lpWVkY2NDVlYWFBmZqZK+XvvvUcA6L333hMkdlY3Ift16tSp9T7t69133yUAtHbtWr3jZtqDjg+5aEq5k8kkv0REy5YtIwAUEBBApaWliu3yRy8OGjRIaf+tW7eSp6cnLVmyRKWud955hwDQ22+/rfSIvnnz5hEAevfddw3XEKZCqL4tKyujV155hQDQhAkTqKamxijxM/WE/Myqw8lv4xCyX5csWUIAaNSoUUp1nThxgszNzUkkEvGTwIxEqH49dOgQASAzMzP68ccflcqOHDlCYrGYxGIxpaenG64xrE4NJb/NIXcyqeS3vLyc/P39CQA5OzvThAkTFP/v4OBAt27dUtp/xYoVBIDCw8NV6nr8+DF5eHgQAPLw8KCJEycqrhZ6eHjQ48ePjdQqRiRc3y5YsEAx6E6ePJnCw8PVvphxCPmZVYeT38YhZL+Wl5dTYGCgoq6xY8dSQEAAicViAkCrV682UquYUP0qk8koLCyMABAAevnllyksLIxefvllxTbuV+M5evQo+fv7K14ASCQSKW07evSoYv/mkDuZVPJLRPT06VP69NNPycPDgyQSCXXo0IHCw8PV/mTW0BdpQUEBzZ07lzp27EgSiYQ6duxIc+bMofz8fAO3gqkjRN+Gh4crBtf6Xsx4hPzMvoiT38YjZL9WVlbS6tWr6aWXXiJLS0uSSqX02muvKX0hM+MQql9lMhl99dVXFBwcTHZ2dmRubk7t2rWjkSNH0okTJ4zQEia3a9euBr8Td+3apdi/OeROIiJelJYxxhhjjJkGk1nnlzHGGGOMMU5+GWOMMcaYyeDklzHGGGOMmQxOfhljjDHGmMng5JcxxhhjjJkMTn4ZY4wxxpjJ4OSXMcYYY4yZDE5+GWOMMcaYyeDklzHGGGOMmQxOfhljjDHGmMng5Je1SHPmzIFIJKr3df/+fcGPu2fPngaP+/xrwYIFeh1PXTstLCzQtWtXLFy4EMXFxQK17Jlr165BJBKhc+fOastrampgY2MDkUiE+fPnq93n2LFjEIlEePnll/WKRSQSwd3dXa86AOD+/fsQiUQYPHiw3nVpqqG/C2PGIhSh+kNoxozLVMYdwPhjjzrV1dXYs2cPxowZgy5duqBVq1Zo06YNfH19ERERgVu3bhk8BtY8mTd2AIwZwo0bNwAAISEhcHBwUCm3srIyyBeitbU1wsPD693nypUruHnzJgDA399fr+PJ2zlq1Ci0a9cOAJCTk4MzZ85g06ZNSEhIQGJiIszMzPQ6jpyvry9sbW2RmZmJzMxMdOrUSan8559/xtOnTwEAFy5cUFtHQkICACAoKEiQmBrD/fv30aVLF7z66qs4d+6czvXU9bfi5eWlc52GIFR7WzpTGXcA4489L0pKSsK0adOQlpYGS0tL9OvXD/7+/igqKkJiYiKio6Px+eef48CBA3jrrbcMEgNrxoixFkYmk5GtrS0BoJycnMYOR8n58+fJ2tqaANCHH36oV13PtzMrK0up7N///jeZm5sTAIqNjdXrOC8aPnw4AaDvvvtOpWzjxo0EgHx9fUksFlNRUZHKPoMGDSIAdPDgQb3iuHnzJt2+fVuvOoiI7t27RwDo1VdfNeh7ngeAmtPwq0l7heoPoRkrLlMZd4gab+yRS0xMpDZt2hAA+uijj1TOd2VlJX3++efUpk0bOnnypEFiYM0bT3tgLc69e/dQXFyMtm3bokOHDo0djkJycjJGjx6N8vJyTJo0CV988YVe9T3fTmdnZ6Uyf39/BAcHAwCuX7+u13FeJL9iK7+C+7yEhARYW1tjzpw5kMlkSExMVCqvqqrC1atXAQCDBg3SKw4vLy94eHjoVQcTTlPtD2PFZSqrSaUKAAAS0ElEQVTjDtB4Yw8A5ObmYvTo0SgpKcHGjRuxbds2lfMtkUgwd+5cnD9/Xu/pVaxl4uSXtTjyn+NeeumlRo7kv27fvo0RI0agqKgIb775Jvbs2QOx+L8fv/T0dIhEIowYMQKlpaVYsWIFvLy8YG1tjW7duiE6OlqlTnk7e/furfaYNjY2dcbz4MEDLFy4EL169YKNjQ3s7OzQv39/rFy5EmVlZfW2RZ78qpvWcOnSJfj5+WHIkCFq90lKSkJFRQU8PT3Rvn17pbL79+9j1qxZcHd3h6WlJRwdHTF+/HikpKSojaO+uZzff/89/Pz8YG1tjQ4dOmD69On4/fffMW3aNIhEojp/ui8vL8eSJUvQuXNnWFpaolu3bli/fj2ISLHPypUr0aVLFwBAfHy80pzHadOmqa1XH8/PSS4uLsaiRYvQpUsXWFhYKM3dPHbsGGbMmIGXXnoJtra2sLGxga+vL9asWYPKyso668/MzMScOXPQvXt3WFlZwcHBAQMGDMCaNWtQXl6ucXvr64/ExESMGTMGjo6OsLS0hLu7O2bPno2srKx626tJfzREXVxCHwNonuMOYNyxR59xR27evHnIy8tDSEgIFi1aVO++ffr0UTv9hLHm87sbYxpasWIFAaCZM2c2dihERPTw4UNyd3cnABQYGEhlZWUq+/zzn/8kADRhwgTq2rUrdezYkSZMmEDDhg0jsVhMAGjPnj1K75G3c/bs2WqP6+XlRQDo22+/Vdr+ww8/UKtWrQgAeXt708SJE2no0KFkb29P5ubmVF5eXm97ysvLydLSkkQiEeXn5yu2p6enEwBaunQpERE5OTlRcHCw0nvXrVtHAOj9999X2n7hwgXFz6i9evWi8ePH08CBA0kkEpG1tTWdOXNGJQ4A1LlzZ5XtmzZtIgBkZmZGr732Gk2cOJFcXV3J3d2d3nrrLQJAZ8+eVewv/0l/4MCBNGjQILK3t6fhw4fT8OHDycrKigDQsmXLFPsfPnyY3n77bQJAHTp0oPDwcMXr73//e73n7vnYNR1+5fENGDCA+vTpQ/b29jR27FgKDQ2llStXKvbr0KEDtW7dmvz9/SksLIyGDx9O9vb2BICGDh1KNTU1KnXHx8eTVColANS1a1eaMGECjRo1irp06UIA6N69exq3t67++Pbbb8nMzIxEIhEFBgbSpEmTqEePHor6bt68qba9mvZHQ9TFJfQxiJrnuENkvLFHm3FHXn94eLhSvRkZGSQSicjc3Jzu3bun24lhjIg4+WUtztixYwkAbdq0qbFDofz8fOrZsycBIB8fH3ry5Ina/SIjIxUJ0aeffkrV1dWKsq+++ooA0JgxY5TeI2/nl19+qVLfyZMnCQBZW1tTbm6uYvvp06fJwsKC2rdvr5JQlpWV0b59+zRqV2BgIAGgH3/8UbFt586dBICOHTtGREShoaFkZWVFlZWVin1CQkIIAH3zzTeKbUVFReTk5EQWFhb0ww8/KB3n9OnTJJFIyNXVVakeIvVJzZ07d0gikZCVlRWdP39esb28vJxGjRqlOMfqkl8AFBQURI8fP1aUJSUlkbm5ObVq1YpKSkpU3mOMOb/Pxzdw4MA6/4YOHz5MpaWlStuKi4vVnnMiooKCAnJ0dFR8VmQymVJ5fHw8FRYWKsVQX3vV9UdmZiZZW1uTubm50vzP2tpaWrBgAQEgPz+/OturaX/Up77kV6hjEDXPcYfIOGOPtuNOXclvREQEAaBx48ZpdB4Yqwsnv6zFkV/tqOt16tQpo8RRUlJC/v7+BIA8PDwoOzu7zn1HjhxJAGjixIkqZZmZmYrE53nydsqTPJlMRg8ePKDt27eTnZ0dAaC1a9cq9i8rKyMXFxeysLCgxMREvdq2ZMkSAkCffPKJYtu0adNIJBIpvmijo6MJAF28eFERX9u2bQkA3b17V/E++ZXayMhItceSJ0kv3iCnLqlZtmwZAaBZs2ap1HP79m3FlSx1ya9YLKaMjAyV940ePbrO9+ib/Nb1ej5ZeT5RS0pK0vpYt27dIgAUGhqqtH39+vUEgEJCQhqsQ9fkNyoqigDQlClTVPavqKggFxcXAqD096hLf9SnvuRXqGMQNc9xh8jwY48u487WrVvJ09OTlixZorTdx8dH7T/kGNMWL3XGWpTi4mLcv38fYrEYU6ZMUbuPMW6AqKqqwrhx43D58mW4uLjg9OnTcHJyqnN/+Ry65cuXq5Tl5OQAgNKNJfJ2AlDcXPI8a2trrF+/Hn/84x8V23bs2IGsrCz84Q9/wCuvvKJTu+SCgoKwbt06pZveEhIS0Lt3b9jZ2QEAAgMDFdsDAgKQlpaGgoICuLq6KuaQAsDp06cBAGPHjlV7rEGDBmHz5s1ISkpCaGhovXFdunQJABAWFqZS5uHhgb59++Lnn39W+153d3f06NFDZbt8W3Z2dr3H1kVdy1NJJBKVbc7Ozg3+7d66dQvHjx/H7du3UVZWBplMppi7+uKap3FxcQCAWbNm6RK6RuRzvt955x2VMktLS4SFhWHLli24cOGCyt+kMfpDqGM013EHMPzYo8u4M2fOHMyZM0dpW21tLf7zn/8AAPr3769RPYzVhZNf1qLIB/Lu3btj9+7d9e57/vx5REdHIzk5GZmZmVixYgVWrlypdt/8/HwsX74c//rXv5Cfnw9nZ2csWbIEH374ocq+MpkMkydPRlxcHNq2bYtTp04pJXvq6n706BG6du2q9gYS+Q1f3t7eKu10dHTEyJEjAQBmZmawt7eHj48PQkJC0LZtW6V6Dh06BAB4//336zkrmgkMDIRYLMbVq1dRXl6OkpIS3L59W+l89OvXD9bW1rhw4QL++Mc/1rm+r/yLtKG1R/Py8hqMS34DVceOHdWWd+rUqc7k183NTe321q1bA0C9N43pqqG/0ee9uKby84gIERER2LRpU503apWUlCj9/2+//QYABl0JQd4fdd0IJ9+u7sY3Y/SHUMfQZtwBNB97DDnuyOs39Ngj1LiTn58PmUwGAA0m9HW9X5NzeerUKURGRiI1NRUODg6YPn06PvvsM4OtV8waBye/rEWRD8x9+vRpcN/S0lL07NkTkydPrveJR6WlpQgKCoKrqyv+8Y9/oHPnzsjOzkZ1dbXa/WfNmoWDBw/CxsYGx48fR69evTSK2c/PT215cnKySpvk73n99dc1TqCSk5NhYWGBvn37arR/faRSKby9vXHjxg1cvnwZ+fn5AJSXL7OwsICfnx8uXrwIIqoz+a2trQXw7Gptq1at6jymNgvzi0QitdvrSgzre09TYWVlVWfZ/v37ERMTAzc3N2zevBkDBw6Eo6MjLCwsUFVVBUtLyzrbbox2N3QMdeVNIS5NaTPuAJqNPYYed56P25Bjj1DjzvN9Vd/nWB1Nz+X169cREhKCjz76CN999x3S0tLw3nvvoaamBuvWrdMrfta0cPLLWhT5YK3JQDty5EjFlYvFixfXud+GDRvw9OlTHD16FJaWlgDqvpK1ePFi7Ny5ExKJBIcPH9YoYZN/mdQVs7ovIHXb6lNVVYWSkhLY2toK9oUfFBSEGzduICEhQZH8yqc6yAUGBuL8+fNITU2tM/l1c3NDRkYGli9fDh8fH71icnZ2RkZGBjIzM9G9e3eVcvnVzpbm8OHDAIDt27cjJCREqezu3btq39OxY0ekp6fj9u3bBnuinIuLCzIyMnDv3j210wsePHgAACprxTY32ow7gGZjj6HHHcDwY4+Q446DgwPatGmDkpIS/Prrr4qnymlC03O5ceNGeHt7Y9OmTQCeLVv36NEjLFmyBMuXL1f8IsCaP17nl7UoDQ3mujh48CAGDRqEjz/+GM7OzvD09MTChQtV1qX8y1/+gr/85S8Qi8XYt28fhg0bpnfMRIQbN27Azs5OabDW9kqTRCKBlZUViouLFfP49PX8er8JCQlwdXVV+UKRJ8P79+/H/fv3YWdnp/Lz6uuvvw4AOHLkiN4xBQQEAAAOHDigUnb37l3BFt2Xz8mtqakRpD59PXnyBID66R7ff/+92vfIz/uOHTsarF/X9sr/Rvbu3atSVlVVhR9++EFpv+aqOY47DcUtxNgj5LgjFovxxhtvAAC+/vrrevetrq5W+kefpufy4sWLin+UyI0aNQrl5eV1TpdizRMnv6zFqK2tRWpqKgBhv4Tu3LmDAwcOoKysDLGxsdiwYQMOHDiAGTNmKPbZuXOn4grO3/72N7z99tsa11/fl8mdO3dQUlICX19fxbbn2/n89oYMHToUABAREaEylzE5OVlxRUdT8oTl0qVLSE5OVvvEtoCAAIhEImzbtg3As2kRL14BmjVrFhwdHbFmzRrs2rVL5SfNsrIy7NmzBw8fPmwwpunTp8PCwgK7d+9W3PwGABUVFViwYIFizqC+2rVrBwsLC9y5c0cxbaMxya+q7tixQ+n8XbhwARs2bFD7nvfffx/t2rVDbGwstm3bpnLeL1y4gKKiIgC6t/e9996DtbU1/vGPf+DYsWOK7TKZDEuXLsWjR4/g5+en9w2Yjam5jjuAccYeXcadbdu2wcvLC5GRkUrbly1bBolEgq+//hoxMTEqf4vV1dX45z//iT59+iAjI0OpLQ2dS+DZDY4v/gohn1+sbl46a7542gNrMX799VeUl5fD1dUVjo6OgtUrk8nQrl07fPXVVzA3f/aRqaqqQlhYGLZu3YqCggLFHfMdOnRAQkKC2kf/Pm/lypVwd3dHTU0N0tLS4OrqqvLEM0D9T4zydjo5OWn1GNW1a9fi/Pnz2Lt3L86dO4f+/fuDiJCeno5bt24p7v7XlLOzMzw8PHDnzh0AqlMeAMDe3h4vvfQS0tLSAKi/wmdvb4/Dhw/jrbfewowZM/DZZ5+hd+/esLS0RGZmJm7evImysjJcv369zhuU5Lp164Y1a9bgk08+QXBwMIYMGQIHBwdcvHgRYrEYo0ePRmxsrNrVFLQhkUgwYsQIxMbGwtfXF/369YNEIkFgYCCmT5+uV926mDdvHnbv3o0vv/wS586dg4+PDx49eoSEhAQsWrQIGzduVHlP27Zt8f3332PMmDGYO3cuNm/ejP79++Pp06dITU3FvXv3cO/ePUilUp3b26lTJ+zYsQPTpk3D6NGjERgYiI4dO+LatWvIyMhAhw4dsGfPHkOeGoNrjuMOAKONPbqMO3l5ecjIyFBZbaNv37745ptvMH36dCxatAjR0dEYMGAArK2tkZOTgytXriiu5j6/IkRD51Jd++Xk/1hv6vcEMO1w8staDG2nAmjK2dkZ7u7uikETgOJmkgcPHiA9PV1xRfH333/HN998U299YrFYcSX05s2bqKqqqjPm+m440badPj4+uHr1Kv785z/jzJkzOHHiBKRSKbp06YLly5drdUOZXFBQkCL5VXflF3iWFMuT3/r2+eWXXxATE4Njx47hzJkzMDMzg4uLC0JCQhAaGoqePXtqFFNERATc3NywYcMGXLhwAba2thgxYgTWr1+PqVOnAoAgjzzduXMnIiIicPr0aezbtw+1tbWoqalplOS3R48eSEpKwuLFi3H58mX8+OOP8PT0xN/+9jfMnDlTbfILAEOGDEFycjLWr1+Pn376CUeOHIGtrS08PDzwwQcfKN1Vr2t73333XXTt2hXr1q3DpUuXcPnyZTg7O+MPf/gDli1bBldXV0HPhbE1x3EHMN7YI/S4M2nSJPTv3x9btmxBXFwcfvrpJ4hEIjg7O8Pf3x+vvvoqJk2apJTQNnQu5fs6OzurJNzy/2/u89KZMhFpe9skYy2Qu7s7pk2bpna5oSlTpuDcuXO4f/++YrmbI0eOYNy4ccjNzRX0ag8znLKyMri7u6O8vBxFRUW8dBFrEuoae3jcEY6m5/Kdd95BRkYGrl69qnjv1q1bsXjxYuTm5vINby0Iz/llJqu0tFQx56yqqgo5OTlITk5WXKWUi4iIQG5uLubMmYOMjAycPXsWERERmDx5Mn8BNUF3795VzFWVKy0txYcffoi8vDxMnDiRE1/WqDQZe3jcEY6m53LRokVISUnBokWLcPPmTRw6dAhRUVGYN28eJ74tjfEfKsdY03D27Fm1jyF98VGoRERxcXH08ssvk6WlJXXu3JkWLlxIpaWlxg+aNWjt2rVkaWlJAQEBNGHCBBo2bBi1a9eOAJC7uzvl5OQ0dojMxGk69vC4IxxNz+XJkyepb9++JJFIyNnZmZYuXUo1NTWNEDEzJJ72wBhrUZKSkhAdHY1///vfyM3NBRGhU6dOGD16NBYvXsxXzRhjzMRx8ssYY4wxxkwGz/lljDHGGGMmg5NfxhhjjDFmMjj5ZYwxxhhjJoOTX8YYY4wxZjI4+WWMMcYYYyaDk1/GGGOMMWYyOPlljDHGGGMmg5NfxhhjjDFmMjj5ZYwxxhhjJoOTX8YYY4wxZjI4+WWMMcYYYybj/wA1u3n367kEYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot settings\n",
    "values = [0.05, 0.25, 0.50, 0.75, 0.95, 1]\n",
    "x_label = '$F_{16}ZnPc$ Weight Fraction in $F_{16}ZnPc$:$C_{60}$'\n",
    "y_label = 'Open-Circuit Voltage / V'\n",
    "figsize=(8,4)\n",
    "\n",
    "# Set up & draw plot\n",
    "fig = set_up_plot(x_label = x_label, y_label = y_label, figsize=figsize)\n",
    "\n",
    "plt.plot(values, parameters['Voc'][18:24], 'D', markersize=8, linestyle='--', linewidth=2, label='$F_{16}ZnPc:C_{60}$', color=plot_colors[4])\n",
    "\n",
    "plt.ylim(0.21, 1.39)\n",
    "plt.xlim(0, 1.05)\n",
    "\n",
    "# plt.savefig('/home/jungbluth/Desktop/F16ZnPc_Voc_trends.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Short-Circuit Current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAGBCAYAAABhKKfBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd1iUR9cG8HtBiiBBFBCUKhZiARsaEFGxoIk9EjXWGI1JXtPWFhVExR5jzKt+yZto7BossaCxIooiYkFFURQbAiKCIkU67PcHYXXDUnZ9YHfh/l0XV8I8szOHROEwO3NGJJFIJCAiIiIiqqW0VB0AEREREZEqMSEmIiIiolqNCTERERER1WpMiImIiIioVmNCTERERES1GhNiIiIiIqrVmBATERERUa3GhJiIiIiIajUmxERERERUqzEhJiIiIqJajQkxEREREdVqCifEu3fvhre3N7744gtcv35d5llKSgqaNm0qWHBERERERFVNoYR4x44dGDlyJLKyshAZGYkuXbpgy5Yt0ueFhYWIjY0VPEgiIiIioqpSR5HOq1atwvLlyzF9+nQAwObNmzFlyhSIRCKMHTu2SgIkIiIiIqpKCiXEd+/exbBhw6Sfjx8/HvXr18fIkSOhr68PDw8PwQMkIiIiIqpKCm2Z0NfXR2pqqkzb4MGDsXHjRowfPx4HDx4UNDgiIiIioqqm0ApxmzZtcO7cOXTs2FGmfeTIkcjIyMCXX34paHBERERERFVNoYR43LhxCAoKkvts8uTJyMjIwJo1awQJTJ0VFRVh/vz5+P333yESiWSeTZ06FVOnTlVRZEREREQEABKJBBkZGWjcuDG0tMrfFCGSSCSSaoqrxoiPj4e1tbWqwyAiIiKiCsTFxcHKyqrcPgqtEFMxIyMjAECjRo1K/cbBFWIiIiIi1UtPT4e1tbU0bysPE2IllGyTuHv3Lt555x0VR0NEREREZfn39lZ5BE2IHz16hJCQECQmJiI3N7fMoHx9fYWcloiIiIhIaYLsIc7JycHkyZOxY8cOAMWbmMucUCRCYWHh206pUunp6TA2NkZaWhpXiImIiIjUkCL5miArxLNmzcL27dthbm6O0aNHw8HBAYaGhkIMTURERERUpQRJiAMCAmBqaopr167BwsJCiCGJiIiIiKqFQjfVlSUzMxPdunVjMkxEREREGkeQhLh169ZISkoSYigiIiIiomolSEI8ffp0XLx4EefPnxdiOCIiIiKiaiPIHmJvb2/Ex8dj8ODB+Oqrr9C7d280adKkzLpvNjY2QkxLRERERPTWBLu6OSgoCF988QXu379f/oQiEQoKCoSYUmVKynhYWlqWuqlOLBZDLBarKDIiIiIiAlRQdu3QoUMYNmwYCgoKYGZmBltb21pRdi0xMbFUW3p6ugoiISIiIiJlCZIQ+/n5QSKRYOPGjRg3blylrsirCeStEPOiDiIiIiLNIsiWCQMDA7i6uiIoKEiImNQeb6ojIiIiUm+K5GuCVJkwNTVFvXr1hBiKiIiIiKhaCZIQDx8+HBcuXOD+WSIiIiLSOIIkxIsWLULTpk0xbNgwPHz4UIghiYiIiIiqhSCH6gYMGABtbW0EBwejZcuWsLOzK7MOsUgkqjV7jYmIiIhI/QlyqO7flRbKnVAkQmFh4dtOqVI8VEdERESk3qq9DjG3SRARERGRphIkIba1tRViGCIiIiKiaifIobrc3Fw8fvwYGRkZZfbJyMjA48ePkZeXJ8SURERERESCECQhXrVqFezt7XH9+vUy+1y/fh329vb4+eefhZhSLTg6OsLKykrmY9WqVaoOi4iIiIgUIEhCvH//ftjb28Pd3b3MPu7u7rCzs8O+ffuEmFItJCYmIiEhQeaDtZiJiIiINIsge4jv378PNze3Cvu1bt0a4eHhQkypFiwtLUtV2GDVCSIiIiLNIkhC/OrVKxgaGlbYz8DAoEatoEZHRzMBJiIiItJwgmyZsLa2xuXLlyvsd+XKFVhaWgoxJRERERGRIARJiPv27YsHDx5gzZo1ZfZZt24d7t+/Dy8vL6XnuXLlCpYtW4Zhw4ZJb8LT19dXeryXL1/i22+/ha2tLfT09GBra4tvvvkGL1++VHpMIiIiItIsgtxUFxcXh7Zt2yIjIwMDBw7EZ599BgcHB4hEIty7dw+//fYbAgMDYWRkhGvXrsHOzk6peYYMGYIDBw7ItOnp6SEnJ0fhsZ4/fw5XV1fExMSgadOm6NSpE6KiohAVFYVmzZrhwoULaNiwodzX8qY6IiIiIvVW7TfVWVtb4+DBgxg+fDgOHjyIwMBAmecSiQSmpqbYtWuX0skwALi6usLZ2RkuLi5wcXGBhYWF0mN99913iImJwbBhwxAQEIA6dYr/U3z99ddYs2YNxGIxNm/erPT4RERERKQZBFkhLvHy5Uv89ttvCAoKQlxcHIDiZLl3796YNGkSTExMhJoKACASiZRaIX769CmaNGkCbW1txMXFoVGjRtJnubm5sLa2xosXL5CQkCDzrARXiImIiIjUW7WvEJeoX78+Zs6ciZkzZwo5rOCOHDmCoqIi9OzZs1TCq6enh4EDB+KPP/7AkSNHMGHCBNUESURERETVQqlDdc2bN8f06dNx+vRpFBYWCh1TlSu5Ua9Dhw5yn5e0l3fzHhERERHVDEpXmVi1ahV69eoFMzMzjBkzBgEBAUhLSxMytirz+PFjAICVlZXc5yXtJf2IiIiIqOZSastETEwM7ty5gwMHDiAwMBABAQHYuXMn6tSpA3d3dwwaNAgDBgyAg4OD0PEKIjMzE0DxRSHylFwyUtKvLI6OjqVuqpNHLBZDLBYrGCURERERVQel9xC3bNlSul/4+fPnOHz4MA4ePIgTJ04gODgYYrEYLVu2xKBBgzBw4EC4ublBJBIJGbvSSs4RlhVPZc8ZJiYmVqpfTbqdj4iIiKimEeRQXcOGDTFu3DiMGzcO+fn5OHXqFAIDA3Ho0CGsWLECP/zwAxo0aIAPPvgAAwcOhJeXF+rVqyfE1EoxMjICUHzltDxZWVkAUGGMlpaWlVohZiUKIiIiIvUlaJUJANDR0YGXlxe8vLywdu1aREZGSrdWbN26FVu2bIGuri7WrFmDyZMnCz19pdjY2AAA4uPj5T4vaS/pV5bo6Ggmu0REREQaTvCE+N+cnJzg5OQEX19fPH36FAcPHsShQ4dUegDP2dkZABARESH3eUm7k5NTtcVERERERKpR5QlxiVu3bmHbtm3Yvn07YmNjq2taufr16wctLS2cPXsWz549g7m5ufRZbm4uAgMDoaWlhf79+6swSiIiIiKqDkqXXauMpKQk/PTTT+jYsSPatm2LZcuWlblNoSqsXbsWjo6OmD17tky7paUlRo0ahby8PHz55ZcoKCiQPps5cyaSk5Px8ccfv9XV0ERERESkGQRfIc7KysK+ffuwdetWBAUFoaioCBKJBObm5hg+fDhGjRql9NiHDx+Gv7+/TFteXh7ee+896ee+vr744IMPAAApKSm4c+eO3GoQq1evxoULF7B37144OjqiU6dOiIqKws2bN+Hg4ICffvpJ6TiJiIiISHMIkhBLJBKcOHEC27Ztw759+5CVlSVT2uz48ePw9PSsVEWG8iQnJyM8PLzU3G+2JScnV2osU1NTXLp0CX5+fti/fz/27duHRo0aYerUqViwYAEaNGjwVrESERERkWYQSSpbdFeOa9euYevWrdi5cyeSkpIgkUhQp04deHl5YcyYMfjxxx9x5coVjbzeuTzp6ekwNjZGWloaq0wQERERqSFF8jWlVoiXL1+OrVu34vbt29KV4M6dO2PMmDEYOXIkTE1NARTv4SUiIiIiUmdKJcSzZ8+GSCSChYUFPvvsM4wePRrNmjUTOjYiIiIioiqn9B5iiUSCpKQknDlzBjY2NjA3N6912wccHR1L7YsWi8UQi8UqioiIiIiIFKXUKbcLFy7gyy+/RIMGDXD69GlMmjQJFhYWGDFiBA4ePChTxqwmS0xMREJCgsxHenq6qsMiIiIiIgUotULcuXNndO7cGatXr8bff/+NrVu34vDhw9i9ezf27NmDhg0bwtvbG8+ePRM6XrViaWlZaoW4tq2SExEREWm6t6oy8ab09HQEBARg69atCA0NhUQigUgkAlB82cXIkSOlVyZrOlaZICIiIlJviuRrgiXEb4qNjcXWrVuxfft23LlzR5oYt2zZEqNGjYKvr6/QU1YrJsRERERE6k3lCfGbLl26hC1btmDXrl1ITk6GSCTS+LrETIiJiIiI1Jsi+drbXR1XCS4uLlizZg2ePHmCAwcOYPjw4VU9JRERERFRpVX5CnFNxBViIiIiIvWmVivERERERETqTOmLOeR59OgRQkJCkJiYiNzc3DL7zZs3T8hpiYiIiIiUJsiWiZycHEyePBk7duwAUHyLXZkT8lAdEREREVUxRfI1QVaIZ82ahe3bt8Pc3ByjR4+Gg4MDDA0NhRiaiIiIiKhKCbJCbGFhgaKiIkRGRsLCwkKIuNRayW8c8m6qE4vFEIvFKoqMiIiIiAAVrBBnZmbCy8urViTDb0pMTCzVlp6eroJIiIiIiEhZgiTErVu3RlJSkhBDaRR5K8TcU0xERESkWQRJiKdPn47Ro0fj/PnzcHNzE2JIjRAdHc0EmIiIiEjDCZIQe3t7Iz4+HoMHD8ZXX32F3r17o0mTJhCJRHL729jYCDEtEREREdFbE6wOsZOTE0xMTLBgwQIsWLCgzH4ikQgFBQVCTUtERERE9FYESYgPHTqEYcOGoaCgAGZmZrC1tWXZNSIiIiLSCIIkxH5+fpBIJNi4cSPGjRtX5lYJIiIiIiJ1I0hCfPv2bXh4eGD8+PFCDEdEREREVG20Ku5SMVNTU9SrV0+IoYiIiIiIqpUgCfHw4cNx4cIFXkpBRERERBpHkIR40aJFaNq0KYYNG4aHDx8KMSQRERERUbUQZA/xgAEDoK2tjeDgYLRs2RJ2dnZl1iEWiUQICgoSYlqVc3R0LHVTnVgshlgsVlFERERERKQoQRLi06dPS/+9oKAA9+7dw7179+T2rUkVKBITE0u1cdsIERERkWYRJCGurdskLC0tS60Q8ypnIiIiIs0iSEJsa2srxDAaJzo6mgkwERERkYYT5FAdEREREZGmYkJMRERERLWaxiXEOTk58PPzQ4sWLaCvr4/GjRtj4sSJiI+PV3iso0ePon///jA1NYWOjg7Mzc0xYMCAGlMFg4iIiIgqJpJIJBJVB1FZOTk56NWrF86fPw9LS0t069YNjx49wsWLF2FmZoawsDA4ODhUaqxVq1Zh2rRpEIlE6Nq1K5o0aYIHDx7g0qVLAIBffvkFn3/+udzXpqenw9jYGGlpadxDTERERKSGFMnXNCohnjdvHvz9/eHq6orjx49Lr4suSW49PDxw5syZCsdJTk6GlZUVACAoKAju7u7SZ3v37oW3tzfq1q2LpKQkuVdSMyEmIiIiUm+K5Gsas2UiPz8fa9asAQCsW7dOJlEVi8VwcnJCSEgIrly5UuFY4eHhyMvLg6enp0wyDAAffvghnJyckJWVhVu3bgn7RRARERGR2lEqIW7evDmmT5+O06dPo7CwUOiY5Dp37hxevnwJBwcHtG/fvtTz4cOHAwACAwMrHEtPT69SczZo0ECxIImIiIhI4yi9Qrxq1Sr06tULZmZmGDNmDAICApCWliZkbDKuX78OAOjQoYPc5yXtJf3K4+LiAmNjY5w6dQrnzp2TefbXX38hMjISbm5uaNas2VtGTURERETqTqmLOWJiYnDnzh0cOHAAgYGBCAgIwM6dO1GnTh24u7tj0KBBGDBgQKUPuFXG48ePAUC69/ffStpL+pWnfv36WL9+PUaPHg0PDw/pobqHDx/i0qVL6NevHzZt2lThOI6OjqVuqpNHLBZDLBZX2I+IiIiIqp/SN9W1bNkSM2fOxMyZM/H8+XMcPnwYBw8exIkTJxAcHAyxWIyWLVti0KBBGDhwINzc3CASiZQONDMzEwBgYGAg97mhoaFMv4oMHz4cDRo0wIgRI2RWiRs1agRPT080bNiwwjESExMrNVd6enql+hERERFR9RPkUF3Dhg0xbtw47NmzBykpKThy5Ai++OILZGVlYcWKFfDw8IC5uTkmTJiAvXv3VjppfVNJMYyykmpFi2X8+OOP6NOnDzw8PBAZGYnMzExERkbC1dUVM2bMwIgRIyocw9LSEk2aNKnw420qUSxZHoa6xiuxdEWY3PYly8PKeCURERERVYbSK8Rl0dHRgZeXF7y8vLB27VpERkbiwIEDOHToELZu3YotW7ZAV1cXa9asweTJkys9rpGREQDg1atXcp9nZWUBgNwyaf925swZTJ8+HR06dMDu3bul2x7atm2LPXv2wMXFBXv37sXx48fRt2/fMseJjo6u0rJrS5aHYeHiUADAgkXF/5w901WmveSfc2a5VlkcRERERDVZlZddc3Jygq+vL8LDw5GQkID//e9/6Nu3r8IH8GxsbACgzBvpStpL+pVny5YtAIBhw4aV2gOsra2NYcOGAQBOnz6tUIxCejPpLbFgUSj6D9pVqn3h4lCuFBMREREpqVrrEFtYWGDy5Mk4ePAgpk+frtBrnZ2dAQARERFyn5e0Ozk5VThWSfJc1upuSfuLFy8UilFI/ktC5bYHn5Z/aLCs/kRERERUPo25mKNr164wNjbG/fv3cfXq1VLP9+zZAwAYMGBAhWNZWFgAAC5fviz3ecn1zXZ2dkpG+/Z853RVqP+8uYr1JyIiIqJiGpMQ6+rqYurUqQCAqVOnyuwlXrVqFSIjI+Hu7g4XFxdp+9q1a+Ho6IjZs2fLjDVkyBAAwPbt20td5HHgwAHs2LEDWlpaGDp0aFV9ORWaM8u10kmun09XzJ7JPcREREREyhD8UF1V8vHxwcmTJ3H+/Hk0b94c3bp1Q2xsLMLDw9GwYUNs3LhRpn9KSgru3LlTqjzakCFD4O3tjd27d2PQoEHo1KkT7O3t8fDhQ+mq8eLFi9GyZctq+9rkmTPLFWdD48rcJgEAnj1tmQwTERERvQWNWSEGAH19fQQHB8PX1xcGBgbYv38/Hj16hPHjx+Pq1auVvllOJBIhICAAGzZsgIeHB+7du4d9+/bh0aNHeP/993HkyBHMmTOnir+aii1ZHlZuMgwAp4JjMW1mUDVFRERERFTziCSKFvAlpKenw9jYGGlpaVVWdk1elYnytHM2x+6dQ2BtVXVl4IiIiIg0hSL5mkatENcmilaNuHb9Gdq034BZc4KR8jyriqIiIiIiqnkESYgnTpyIP/74o8J+mzZtwsSJE4WYUi04OjrCyspK5mPVqlWCjF1WlQnPnrZlviY3txA/r72C29HPBYmBiIiIqDYQJCHetGkTzp07V2G/0NBQbN68WYgp1UJiYiISEhJkPtLT0wUZW16VCT+frvj7gHep9hnizpj2rQv09evAq489unW1FiQGIiIiotqgWqtM5OXlQVtbuzqnrFKWlpalbroTck9xyXXM/ktCMW/u69Jqb7b7zukq/fzLzzsgJ6dQZgyJRIKxnxzC+/0cMMLbEdra3CVDRERE9CZBDtVpaWlhwoQJ5W6bkEgkcHJywosXL5CQkPC2U6pUdRyqE8qBwBiMGH0AANCmtSkWzuuG/v2aQiQSqTgyIiIioqqjSL6m9Aqxp6enzOdHjx4t1VaioKAA9+/fx9OnTzF27FhlpyQlnAqOlf77zagUDBuxD26uTeA/vxu6ulqpMDIiIiIi9aD0CvGbWwVEIhEqGkZHRwf9+vXDhg0bYGpqqsyUakOTVogBIPjMY/j6heByxFOZ9vf7NcVCv25o09pMRZERERERVQ1F8jWlE+LY2OKVR4lEgqZNm2L48OH44Ycf5PbV1dWFqakpdHR0lJlK7WhaQgwU/386EBiDeQvO4W7MC2m7SASM/KgV5vt2ha2NsQojJCIiIhJOtSTEb1qwYAHat2+PQYMGve1QGkETE+ISBQVF2LojCouWnkdCQoa0/VzwaHTqaKnCyIiIiIiEU+0JcW2jyQlxiezsfPxv/TWsWBmOHt1tsGOL7C8zEomEB++IiIhIY6ksIc7KysLly5eRmJiI3NzcMvuNGzdOqClVoiYkxCXS0nKRlZ0PS4t60rbCwiL0H7QbA/o74LNJ7aCvX63V+YiIiIjemkoS4nnz5uGnn35CVlbZ1waXrDoWFhaW2UcT1KSEWJ5tO6Iw6fMjAAArKyP4znHD6JGtUacOaxgTERGRZqiWsmtvWrFiBRYtWoQ6depgwIABaNGiBerVq1fxC0kt3Y5OgUgESCRAfHwGpnx5DD/9fAkL5nXDoAHNuJWCiIiIahRBVoibN2+OJ0+e4OzZs+jQoYMQcam1kt845N1UJxaLIRaLVRSZcCJvPIPfwnM4cuyBTHvnTpbwX9AN3bvZqCgyIiIiooopskIsyHvgcXFx6N69e61Iht+UmJiIhIQEmY/09HRVhyUIp7bm2Ld7GE4eHQnX95pI2y9eToTXB7swcOge3Ln7XIUREhEREQlDkC0TFhYWQgyjceStENe0PcXublY4dWwkjhx9AN8FZxF1KwUAEBQci6IiFighIiIizSdIQjxy5EisX78eqampMDExEWJIjRAdHV3jEmB5RCIR3u/vAK++9gjYHY0Fi8+hezcbvOsoe+NgYWERtLV58I6IiIg0iyB7iHNyctC3b19oa2tj/fr1cHBwECI2tVXTq0xUJDe3AFlZBTAx0Ze25eUVwq37VvTr2xTTvu0s84yIiIioulV7lYn3338fRUVFCA0NhaOjI+zs7GBlZSW3GoFIJEJQUJAQ05KK6OnVgZ6e7B+djZsjcTMqBTejUrB+43VM/64zvpzSAQYGNeO6biIiIqq5BFkh/vc+2nInZB3iGmnZDxewZHkY8vJe/7+1tDDEnO/dMGFsG+joaKswOiIiIqptqv1ijtjYWIX629ravu2UKsWEWL7HcelYtPQ8tu2Ikjlw18zBBPN9u2LYkJbQ0mINYyIiIqp6Kru6ubZgQly+29EpmO8figOBMTLt7ds1wq9rveDsZK6iyIiIiKi2qPY6xERvetfRFAHbB+PMyY/h0c1a2n4zKhnvvKOrwsiIiIiIShPkUF2JgoICHDp0CJcuXUJKSgq6dOmCiRMnAgCePHmClJQUtGrVCnXqCDotqakunRvj2KGPcPJULHznh8DtvSawt6sv0ycrK58H74iIiEilBMtMz5w5g7FjxyIhIQESiQQikQj5+fnShDgoKAgTJkzArl278OGHHwo1Lak5kUiEPr3s0KunLXJyCmSeZWXlw6nTH+jV0xZzv3eDjTW3nxAREVH1E2TLxI0bN/D+++/j2bNn+Oabb7B79278e2vyhx9+CAMDA+zdu1eIKdWCo6MjrKysZD5WrVql6rDUkpaWqNRK8C+/XUV8fAY2b72Jth02YNacYKQ8z1JRhERERFRbCbJCvHDhQuTm5uL48ePw9PSU28fAwADvvvsurl69KsSUaiExMbFUW3p6ugoi0UwGdevA2FgPaWm5yM0txM9rr+CPzTfw3dcu+Po/HVGvHvcbExERUdUTZIX4zJkzeO+998pMhkvY2NjgyZMnQkypFiwtLdGkSROZD1adqLwvpnTA7euTMO1bF+jrF/9ulpGRh4WLQ9HKeT3+79cImbrGRERERFVBkIQ4PT0dTZo0qbBfbm6uxl/K8abo6GjEx8fLfIjFYlWHpVEaNKiLxQu7I+rap/j0EydoaxfXKX6WnAXxzFNo23EDwi/WnF+iiKrCkuVhqGu8EktXhMltX7I8rIxXEhFVHU363iRIQmxpaYnbt29X2O/mzZsafykHVY0mjY2w7ue+uHbxEwwf1lLanpycBRsbrroTlWXJ8jAsXBwKiQRYsChU+oPnzfaFi0PV6gcPEdV8mva9SZCEuG/fvoiKisK+ffvK7LNp0ybExsbigw8+EGJKqqGaN2+AbZsG4vyZMejtaYepX3aEpUU9mT7Pkl+pKDoi9VLyg+VNCxaFov+gXaXa1ekHDxHVbJr4vUmwq5udnJyQm5uLGTNmYPDgwejcuTM++ugj+Pj4YP/+/ViyZAkMDQ1x48YNWFhYKD1XTk4Oli5dip07d+Lx48do0KAB+vXrh4ULF8LKykrh8e7du4fly5fjxIkTePr0KYyMjNC8eXMMHToUM2bMkPsa3lRXfQoLi6Ct/fr3trS0XLRyXo8unS2xYF43tG1jpsLoiFSrrvFKKPIdXCQCstOmV11ARERQn+9NKrm6+dy5c/D29kZSUhJEIpHMM4lEAjMzM+zbtw9ubm5Kz5GTk4NevXrh/PnzsLS0RLdu3fDo0SNcvHgRZmZmCAsLg4ODQ6XH27dvHz7++GPk5uaiffv2aNGiBZ4/f44bN27A0NAQ9+7dk/s6JsSqs2DROSxdcQFA8V+gkR+1wry5bqUu/CCqDeStwpTHz6crZs90rcKIiIjU53uTShJiAMjIyMCGDRtw8uRJPHr0CIWFhbCyskLv3r0xZcoU1K//dknLvHnz4O/vD1dXVxw/fhz16hW/lb5q1SpMmzYNHh4eOHPmTKXGun79Ojp37gwjIyPs378f7u7u0mdFRUWIiIhAp06d5L6WCbHq7Ay4BZ/5Z5GQkCFt09HRwqRPnPH9zPfQyNxQhdERVZ2CgiI8epSGZs1MZNq7uG/G9cjkCl/PZJiIqlNlk+Kq/N6ksoS4KuXn58Pc3BwvX75EREQE2rdvL/Pc2dkZkZGRuHz5Mjp27FjheB4eHjh79iwCAwMxYMAAhWJhQqxaOTkF+PX3q1ixMhwvUnOk7YaGOvjqy4747msXGBvrqTBCoreTnJKFGzeTcfNmMm5EJeNmVApu3U5Bbm4hnsV/hXfeef3ne826K5gxO7jc8Tp3skTIqdFVHTYRkYz+g3Yh+PTjMp979rTF3we8q2x+RfI1QQ7VicVi+Pv7CzFUmc6dO4eXL1/CwcGhVDIMAMOHDwcABAYGVjjW7du3cfbsWbRo0ULhZJhUT1+/Dr79ygW3Iyfj+xnvwcCguIbxq1f5WPbDBbzr9DvOhsapOEqiynv+PBuzfU5jwJA9sGv+C6yb/qim4PwAACAASURBVB/eH7QbM+ecxtbtUbh6LQm5ucUlK6Nup8i89m7M8wrHv3g5ER+N3l8lsRMRybNkeVi5yTAAnAqOLVWSTVUEualu7dq1GDx4sBBDlen69esAgA4dOsh9XtJe0q88QUFBAIA+ffogJycHAQEBuHz5MkQiEZycnPDRRx9x5VcDGBvrYb6vO76Y0h7LVlzA+o3XkZ9fhMIiCVq/a6rq8IikJBIJEp++kq74tmllCq++TaXP9fS0sXrN5TIPoWhpidC8mQnatjGDnq62tH3J8jD8/kdkpWI4GHgPS1eEcdsEEVU5RfYQL1hU3E/V35sESYitrKxQVFQkxFBlevz4sXSusmJ4s195oqKiAAB169ZFu3btcOfOHZnns2fPxt69e+Hh4VHuOI6OjtDSqniRXSwW88KOKtTI3BA/reyFr/7TEf5LzqN1q4Zo0KCuTJ+YmBdo1syk1IFPIqFlZ+fjdvRz3IhKxo0b/2x5uJmC5y+ypX3GfNxaJiGuV08XTe3r4/6Dl2hgoo+2bc3QtrUZ2rQp/merdxuibl2dUnP5L6n8oRWguLxRyQ+d29EpaN6sAerUEeSNQiIiqbf53qQqgiTEQ4cOxebNm5GRkQEjIyMhhiwlMzMTAGBgYCD3uaGhoUy/8qSmpgIAVq9eDRMTE/z111/w9PREUlISFixYgB07dmDIkCGIioqCpaVlmeMkJiZWKvb09PRK9aO309S+Pjb+/j7+vS0+OSULrt23opWjKfwXdEP3bjYqipBqEolEUuoXrDm+Z7B6zWUUFZV/NONmVOlDcNs3D4S5uSEsLQwr/Yub75yucldhPHva4lRwrNz+QHH5wj79A2BuboCl/t3Rt489f1kkIsHMmeWKxctKb4Wo6HuTKgmyNDB//nzY2Njg/fffx9WrV4UYspSSJKesb9qKnA0suT66oKAA27Ztw9ChQ2FsbIwWLVpg+/btcHFxQWpqKtatW1fuOJaWlmjSpEmFH9x+Ub3+/WdkxcpwZGbm4+LlRHh9sAsDh+7B1WtJKoqONFFmZh7CLz7B+o3X8e20k+jV7080sVuHrKx8mX5mZgZyk2GLRobo7WmH777uhA3/64/1v/Yv1aedcyM0tqynUGI6Z5Yr5s2V/UHi59MVfx/wLtU+b25XzJlVvALzw0/hSHmejVu3n2Pw8L8wYMgeRN54Vul5iYjKo/vG1q4SlfnepEqCrBAPHjwYenp6CA0NRadOnWBpaQkbGxvo6+uX6isSiaR7eBVRsvL86pX8W8qysrIAQFqKrTJjNWnSBH379i31/JNPPsGlS5dw+vTpcseJjo5msqsBPHvY4NTpWETdKj6MdCLoEU4EPYL3sJbw83EvVcaKarfs7HwcO/GwuMpDVApu3EzGg4cv5fa9dTsFnTq+fhepnXMjtG/XCG1am0q3PLRpbQpzs6orB1jyg8R/SSjmzX1dvujNdt85sj9wPujvgJCQOFy8XPwuV1BwLLq4b8G4MW3g5+OOxpYVfx8lIpLn4aOXMvcFAKj09yZVEqTsWmX20UonFImkK7SKWL16Nb777jt4e3tj165dpZ4fPnwYAwYMwJAhQ8q9QhoAFi5cCD8/P7i5uSE0tPTbjUeOHMH777+P5s2b4+7du6Wes+ya5iksLMKfu25j4ZJQxMa+3sKirS3CJ+OdMGeWK5OAWublyxzcvJUC43f0ZG48TEvLRSPrNRW+3srKCL/8ty/69LavyjCrjEQiwZ6/7sBnfojM3wkDg+IqLuJvXFCvnq4KIyQiTSORSPDhiH34++gDAMDULzpg5XJPlcWjSL4myArxw4cPhRimXM7OzgCAiIgIuc9L2p2cnCocq6Rs24sXL+Q+f/68uIxRZVabSTNoa2th9KjWGD6sJTZsjMTSFWFITslGYaEE6/+4ju07o7A3YCg8e9iqOlQSWEFBEe7dT8WNkpq+//wzLq74cpcJ49ri17Ve0v7GxnqwtX1HmiQaGNRB63dN0aaNGZza/LPq28oMJial3wHTJCKRCN4fOmLQgGb45berWLriAtLScpGVVYAly8Pwx6ZI7Nw2CK5dmqg6VCLSEIf+vi9Nhi0tDEttj1BngiTEP//8M0xMTODr6yvEcHJ17doVxsbGuH//Pq5evVqqFvGePXsAoFJ1hXv16gVDQ0Pcv38fcXFxsLa2lnleslWirBJvpLn09Orgy887YOzoNljzf1fw038vISMjD/UMddC5U9kHKEnz/P7HdWzcHIlbt58jJ6egzH435Oydne/jDn39Omjb2gz29sbQ1q65lRj09IpXhMd+3AZLV4Th19+voaCgCNk5BWjRrIGqwyMiDfHqVR7EM19viV2xtKfMJULqTpDv8mvXrkVkZOVqYSpLV1cXU6dOBQBMnTpVZi/xqlWrEBkZCXd3d7i4uMjE5ejoiNmzZ8uMZWBggK+++gr5+fn44osvZMY6evQoNm/eDJFIhM8++6xKvyZSHSMjXcyZ5YrbkZPwzdSOmOfjXurt4fCLT5Cfr/j2Hqp6eXmFuHEzGTv+vIU5vmcwaNge5ObKJr2pqTmIuJokNxl+5x1duLk2wZRJ7TDls9IX/Ywa0QpDB7dAs2YmNToZflPDhnWxcrknrl38BEMGNcf3M95Dw4ay5Qtfvswp49VEVNtt2nJT+s5br562GD6spYojUozG1CEGAB8fH5w8eRLnz59H8+bN0a1bN8TGxiI8PBwNGzbExo0bZfqnpKTgzp07csuj+fn54ezZszh8+DCaN2+OLl264NmzZ7hw4QKKioqwePFidO7cucq/JlIt04YGWL6kZ6n2+IQMeA3YBasmRpjv2xXDhrSElhbLUlW3f19oUXzQLRnRd16goED2e070nRdwdjKXft62tSm0tERo5mCCtm1M0aa1Gdq2Kf6wsX6HZcbK0KyZCf7cNlhu+UKnDn9g8KDm8PPpCksLbikjotc+/6wddHW1sHhZGFav7KVx32M1pg4xAOjr6yM4OBhLly7Fjh07sH//fpiYmGD8+PHw9/cvtfWhorFOnTqFlStXYtu2bThy5Aj09fXRs2dPfPfdd/jggw+q7Osg9bd42Xnk5BTg3v1UjJlwCO3bXcJCv27o7WmrcX/JNUV2dj6ysgpkViVTU3PQtOWvlXp91K0UmYTYs6ctUp58DQOD0hdaUMX+/ed88dLzSH2Zg01bbmD33mh897ULvvu6EwwNefCOiIrP6kz+tB3GjWkDPT1B0stqJUiViYyMDHTv3h2Ghob473//W2p/b03DKhM138VLifCZH4KQs3Ey7d09rOHv54HOLtxvrCyJRILHcenSkmY3/1n5jbmXik8/ccKan/rI9Hdw/BUJT15fuFOnjhZatmggXe0tKW+myIUWpLif117G4mXnkZ6eJ22ztDCEn687xn7cutZsLSEizaFIviZIQuzp6Yns7GyEh4dDJBJVSR1idcKEuHaQSCQ4eSoWvvNDcO267MGrwQObY8E8dzi2bKii6DRLUHAsAg/F4EZUCm5GJSMtLVduv/e6NMbpEx/LtPktPIe8vALploeWLRpo5OpDTZCckoUly8Lw24ZrKCx8/aOjbRszLF3UHb097VQXHBFVu9TUHNy5+wLvdWms6lDkqvaEuDrqEKsTJsS1S1GRBHv33cF8/3O4/+D1BQ1aWiIc2Psh+vSyU11waqKoSIKHj9Kkq72zpneBjs7rm4qWLA+Te8VwCV1dbbR6tyHe69wYq3/sXR0h01u4G/MCc+eFIPDwPZl2rz72WPffvrBqUnVb54hIfXz93Qn8tuE6Joxri8ULPEodxFW1ak+IY2NL30tdHltbza71WvIf2NLSstQvA2KxGGKxWEWRUVXKzy/Epq03sWTZeSQ+fQUrKyPcjPgU+vq1a7Wy5EKLm2/U9b15KwWvXr2+xvjKhfFo3er1ZReBh+/Be9R+AMUXWpRsc2jb2gxt25iiebMGqFOHb7lrmpBzcfh+7mlEXC2+Ct3czABR1ybByIj7iolqustXEtHNczskEsDQUAfXLn0Cayv1WiSs9os5ND3BVZa86hXp6elyelJNoKOjjckTnTF6ZCus+zUCtjbGpZLho8cewL2rVY284Ss9PRcdXTdJy+qU58bNFJmE2N3NCieOjKgRF1rQax7u1jgXPAYBu2/Dd8FZzJrWpVQyXFQkYYUWohqmsLAIX4tPomRJ1XeOm9olw4qqXUtbApO3QswtFDWfgYEOZoi7lGq/dy8VH47ch4YN6mL2zPfw6SfO0NXVljOC+kl5noUbN99Y9Y1KRvdu1li6qIe0zzvv6CE3R/52J3s7Y7RtYybd5+vmKnu7mYmJPrp1rXwVGNIcWloijBrRCkMGNZfZJgMUly/sN2AXZk3vgtGjWjMxJqohft9wXfrOUOtWpvjP55p/kZkgWyZCQkIU6u/h4fG2U6oU9xCTPOMmHsKuPdHSz+3sjDFvTleM8HZUqxP4Dx+9xIXwJ9L9vjduJiPx6atS/dy7WuHkkZEybSPHHEDSsyy0bW1anAD/c40x3yIneSZ9fgTbdkQBAJydzLFscQ/07G6j4qiI6G08TXoFp44bpBVngo6NRFdXKxVHJV+1b5no0aOHQuWONP1QHZE8vrPdUFQkwZ6/7gAAHj1Kw8TP/saqny9i4bxu6N+vqfTvyZLlYfBfEop5c7ti9kxX6Rgl7b5zumLOLFe581SGRCLB06TiCy08e9rKJORbtt3E0hUXyn29SATkyrnh7c9tg5WOiWqXgoIimWoi1yOfof/AXejv1RRL/D3wrqOpCqMjImV9P/e0NBkeN6aN2ibDihJkhXjChAlyE+KioiLExcUhIiIC6enpGDRoEExMTErdKKdpuEJM5Ym4+hTzFpzDyVOPZNrdXJvAf343nAmJk6m44OdTnBT/uxLDvLmVS4pzcgpw63bKPwfcUqQH3VKeZwMArl/+BC1bvC4P99f+O/h4XKD0c5P6+mjb1uyNg26maPWuKS+0IEGcDnmM2T5ncPVakrRNS0uEiROc4DvHDY3MDVUYHREp4nTIY/QbsAtA8c+OyIiJMDM1UHFUZav2KhMVefHiBSZNmoRbt24hLCwMJiYmVT1llWJCTJURfOYx5s0PwaUrTyvs27OHDYJPPy7VXlZSnJWVjylfHsWNqGTcjUlFUVHZf423bRqA4cMcpZ/HJ2Tgz123pAlwY8t6vNCCqlRRkQQ7A25h3sJzSEh4fSizXj0dTP+uC779qlOtq9ZCpGny8grh4rYZd+6+AACs+29ffDrBScVRlU/tEmIAyMrKgoODAwYPHoxff63cVazqigkxVZZEIsGBwBj4LTwn/SaiqM8nt4Njy4b4/LPXN0BKJBI0sVuHF6k5cl9j0cjwnwNuphjh/S7aOTdSam4iIWVl5eO/665g5U/hyMwsLtNnbW2EyMsTUbcu35EgUmdPEjPx8biDuBD+BF1cLBF84mO1PyirlgkxAAwePBiXLl3CkydPqmvKKsGEmBRVUFCEbTujcCbkMXYG3Fb49T262+Bo4EcybX0/CMCF8Cdo9W5DaXWH4koPpjA349vQpL6Snr2C/5Lz+GNTJDb8rz9GjWil6pCIqBKKiiTYsv0m2js3grOTuarDqZDaJsS9evXC+fPnkZ2dXV1TVgkmxPQ2Krq1TR7ThnUR9+BLma0Nz5JfoYFJXV5oQRorJuYFHBxMZFaZ7t9PxWzfM/Cf301m7zsRkaKqvcpEZQQGBiIkJAStWnElgGq3ObNccTY0Tu6e4RLvGOniw2Etpft827QyLbXPl6vApOmaN29Qqs1nfggOHrqHw0fuY9Inzpg725V/1omoygmSEE+cOLHMZ5mZmbh79y5u3LgBiUSCadOmCTElkcZasjys3GQYANIz8mBj/Q6+rAHFzokqKzU1BxcvFd8AWlgowf/WX8OOgFuYOa0Lpn7RgfuMiarZq1d5GDX2IKZ91xndu9XsGuKCbJn4921t8tjY2GD+/PmYMGHC206nciVL8PJuqhOLxRCLxSqKjNSdotslSkqyEdUWWVn5+HntZaz86SJevcqXtltbG2HhvG4Y4f2u2h/kIaopfOaHYOWqiwCA5Ut64JupnVQckWKqfQ/xmTNnynymq6sLS0tL2NnZve00aqPkP7A8fn5+mD9/fvUGRBqjrvFKKPI3TiQCstOmV11ARGrqadIr+C8JxcbNN2TKCnZo3wjLFveAhzuvAieqSrejU+DitgUFBUXQ1dXGlbDxcrc5qbNq30PcvXt3IYbROPJWiHnIjsrjO6er3BViz562OBUcK7c/UW1k0cgQ637ui/983gGzfc7g2ImHAICIq0n4z9fHce3SJ2p1JTpRTSKRSPDNtCAUFBQBAKZ966JxybCiWAn9LURHRzMBJoWUXLIh5E11RDVZq3dNcWDvhzh56hFm+5zBjZvJWLzQg8kwURXaGXAbIWfjAAD2dsaYOa2LiiOqekonxKdOnUJ8fDw6depUYeWIW7du4fLly7C2tkbPnj2VnZKoRihJcv2XhGLe3Nd7hN9s953DZJjoTb097dDzrA2OHHuAD/o7yDyLupWMI8ceYOoXHXnjHdFbSk3NwfdzT0s/X72yV6040KrUHuK4uDi0aNEC1tbWuHLlCoyMjMrtn5GRgU6dOiE+Ph4xMTFo3Lix0gGrA9YhJiJSHwOH7sGJoEewtjaCv58HPhruyIN3REr6RnwS/1t/DQAwZFBz/LltsIojUp4i+ZpS7zmtX78eeXl5WLFiRYXJMAAYGRnhhx9+QHZ2NjZs2KDMlERERKXcjk5B0D/77+PiMjBh0mF4eG7HufPxKo6MSPNciXiK3zYUJ8OGhjr4YVnteVdfqYT4xIkTMDMzw5AhQyr9mkGDBqFRo0Y4cuSIMlMSERGV8q6jKS6GjkOfXnbStssRT9G7358YMfoAYmJeqC44Ig0ikUgwbeYpaSUkn9lusLaqPe+CK5UQR0dHw8XFReHXderUCXfu3FFmSiIiIrnatDZD4L7hCPzrQ7RpbSptPxAYg/ZdNmHazFNIeZ6lwgiJ1J9IJMIva/vCo5s1WrcyxdQvatfFUEolxK9evSqzDm95jI2NkZmZqcyURERE5erT2x7h58bhl7VesGhUfN1zQUER1v0agVFjD6o4OiL1966jKY4d+ghHAr2ho6Ot6nCqlVIJsYmJCZKSkhR+XVJSEkxMTJSZkoiIqELa2lr4ZFxb3Lz6KeZ+7woDg+KqE7zxkahyRCIRzM0MVR1GtVMqIW7VqhUuXLiA7OzsSr8mKysLYWFhFZZoIyIielv16unCd05X3Ij4FD8s7QnPHrYyzyNvPMP5Cwkqio5Ifdx/8BK5uQWqDkPllEqIBw4ciFevXmHRokWVfs2iRYuQnZ2NgQMHKjOlWnJ0dISVlZXMx6pVq1QdFhER/aNJYyN89Z+OMm0lt3B59t2JUWMP4P6DlyqKjki18vIKMeyjv9Dxvc04eeqRqsNRKaUS4ilTpsDCwgLLli3DokWLUFRUVGbfoqIi+Pv7Y9myZWjUqBGmTJmidLDqJjExEQkJCTIf6enpqg6LiIjKcfTYA4T9szq870AM2rn8gRnfB+PFi8q/60lUE/y89jLu3H2Be/dTsXBRKJS4mqLGUOpiDgC4cOECevXqhZycHFhZWcHb2xsdOnSAmZkZACA5ORkRERHYvXs34uPjoaenh6CgILi6av4+rpJCz5aWltDSkv2dQiwWQywWqygyIiKqSEFBETZtvQH/xaFIeva6+kT9+nqYPdMVn09uBz093nhHNceS5WGlbkd9FJuGNu03oKCgCCIREBYyFu2cG6k4UmEpcjGH0gkxAERGRmLMmDG4efMmRKLStwKVDN26dWts27YNzs7Oyk6lVnhTHRGR5svIyMOPqy/i57WXkZ39eg+lvZ0xFi3wwLAhLeT+bCPSJEuWh2Hh4lDp534+xUmxU8cNuBuTKm2fN7cr5szS/EXLN1VbQlzi2LFjOHz4MK5evYrnz59DIpHA1NQU7dq1wwcffIB+/fq97RRqhQkxEVHNEZ+QgQWLzmHbjii8+RPx88ntsPrH3qoLjOgt/TsZLtG2jRlu3Ewu1V7TkuJqT4irU05ODpYuXYqdO3fi8ePHaNCgAfr164eFCxfCyspK6XFjYmLg5OSEnJwceHl54ejRo2X2ZUJMRFTzXLuehO99zuD0mccAgDMnP0aXzo1VHBWR8uoar4QiWZ5IBGSnTa+6gKqZIvmaUofqVCUnJwe9evXCwoULkZmZicGDB8Pa2hobN25Ehw4dcP/+faXHnjJlCnJzcwWMloiINEk750Y4ctAb+3cPw4xpXUolw3fuPkdqao6KoiNSnO+crgr1nzdXsf41iUYlxEuWLMH58+fh6uqKu3fvIiAgAOHh4fjxxx+RnJyMiRMnKjXuhg0bEBwcjMmTJwscMRERaRKRSIR+Xk3h79dNpr2oSIJxEw+jlfN6rFl3BXl5hSqKkKjy5sxyrXSSW7K3uLbSmIQ4Pz8fa9asAQCsW7cO9erVkz4Ti8VwcnJCSEgIrly5otC4z549w4wZM9C7d2+MGjVK0JiJiKhm2PHnLVyPfIbUlzmYMTsY7Vw2Yt+Bu7W6TBWpp6ysfBw99gDfTQ/CiZMPMWeWK3r2sCn3NZ49bWt1MgxoUEJ87tw5vHz5Eg4ODmjfvn2p58OHDwcABAYGKjTu119/jezsbPzyyy+CxElERDVPdw9rfDzy9U2rDx6+xKixB+Hp9ScuXkpUYWREwP37qfi/XyMwaNgeNLZbhyHef+GX365i9193sGR5GIJPPy739aeCY7F0RVg1RaueNKbQ4vXr1wEAHTp0kPu8pL2kX2X8/fffCAgIwMKFC9GsWTPEx8e/faBERFTjWFu9gz9+ex9ffdkRs+aeRsjZOABA2IUEePTaDu9hLbFwfjfY29VXcaRUG2Rn5+PsuXgcO/EQR48/KPO2xf0H7yI9Pa9SYy5YVFyNorauFGvMCvHjx8W/3ZRVSaKkvaRfRV69eoUvv/wSLVu2xKxZs4QJkoiIarT27Rrh2KGPsDdgKFo0byBt3/3XHTh32ohNW2+oMDqqLX5cfQmDPtyLdb9GlEqGmzSuh0/Gt8Wf2wYhI6NyyXAJeSXaaguNWSHOzMwEABgYGMh9bmhoKNOvIj4+PoiNjcWpU6egq6urVEyOjo6lbqqTh7fXERHVHCKRCB/0d0Df3nbYuPkG/JeEIjklG/n5hejQrmbd9EWqk5NTgLPn4nDsxENMmdQOzd/4Baxvb3ssWnoeAKCtLYKbaxN49bGHV5+maNPaVHqhjO+crnKTXM+etjgVHFuqXdGqFDWJIAnxxIkT4e7uXmGVh02bNiEkJAR//PGHwnOUHFwo69YgRQ42XL58GWvWrMG4cePQs2dPhWMpkZhYuX1j6enpSs9BRETqSUdHG59NaocR3u9i5eqLyEjPhVNbc5k+Sc9ewdzMgDfeUaU8fPQSx44/xLETD3E65LH0BkUrKyN8+0ZC3LFDI3w+uR26e9jAs4ctjI315I5XcsmGvJvq/n1pR027lENRgiTEmzZtAoAKE+LQ0FBs3rxZqYTYyMgIQPFWB3mysorvo3+z+oQ8BQUFmDx5MoyNjbFy5UqF43iTpaVlpVaIeXkHEVHNZWysV6pMGwDk5xeiT/8AmJkZYPni7ujU0VIF0ZE6y80twNnQeGkSfDfmhdx+x08+wrdfuUg/19bWqvQtiiVJrv+SUMyb+7q02pvtvnNqdzIMVPOWiby8PGhrayv1Whub4pIhZR18K2kv6VeW+Ph4XLt2DRYWFvD29pZ59vJl8T6cixcvokePHqhXrx4OHTpU5ljR0dFMdomISK4NGyNxN+YF7sa8gHvP7fhouCMW+nWDna2xqkMjNTHp86PYvTda7jNLC0P06W0Prz726NXT9q3mmTPLVW7CW1Z7bVRtCbFEIkFERATMzMyUer2zszMAICIiQu7zknYnJ6dKjff06VM8ffpU7rPU1FScOXMGxsb8pkVERMppal8fzZuZIOZeKgBg155oHAiMwdQvOmCGuAvq19dXcYRUHXJzC3DufAJOnHwIP5+uqFtXR/rMs6etNCHW1hahS+fG8Opjj359m8KprRm32lQjkUTJquKenp7Sfz99+jQsLCzg6Ogot29BQQHu37+Pp0+fYuzYsdItForIy8uDubk50tLSEBERUaoWsbOzMyIjI3Hx4kW4uLiUMUr5Tp8+jZ49e8LLywtHjx4ts58id2MTEVHtlZ9fiPV/XMeipWF4/iJb2t6wQV34zHbFpInO0NFR7p1TUl+xj9Nw/MRDHD1evBf41at8AMDBvR+ibx97ab8niZnwW3gW/fo2hWcPW5iY8JckISmSrymdEL+5d1YkElV4qE1HRwf9+vXDhg0bYGpqqsyU8PHxweLFi+Hm5objx49LK0usWrUK06ZNg7u7O86ePSvtv3btWqxduxZDhw7F0qVLKxyfCTEREVWFtLRcrPjxAtb+EoHc3NfXPjdvZoL1v/ZHl86NVRgdva3c3AKEhiXg2PEHOHbiIaLvyN8L/J/PO+DHFZ5yn5HwFMnXlN4y8fDhQwDFWyGaNm2K4cOH44cffpDbV1dXF6amptDR0ZH7vLJ8fHxw8uRJnD9/Hs2bN0e3bt0QGxuL8PBwNGzYEBs3bpTpn5KSgjt37lS6GgQREVFVMDbWw+KF3TH503aYt+Asdu0pfpv84aM0NGxQV8XR0dvq2XcnIq4myX3WyNwAfXrbo19fe/TqaVetcVHlKZ0Q29q+3uDt5+eH9u3by7RVBX19fQQHB2Pp0qXYsWMH9u/fDxMTE4wfPx7+/v6wtrau0vmJiIjehp2tMbb8MUB6411750Zo1sxEpk9+fiG3UaihvLxChIYl4Oq1pxB/01nmmbublTQh1tISobOL5T91ge3RzrkRtLS4F1jdKb1lojbjlgkiInpbEokEeXmF0NN7vTaVfVbLAQAAIABJREFUnZ2PLu5bMWhgM8wUd8E778ivL0vVIy4+HcdOPMTxEw9x6nQsMjOL9wLH3PoM1lavf/6fOx+PjZsj4dWnKXp72qIBV/3VQrXsIa7NmBATEVFVWPlTOHz8is/CmJnWhc9sN0yc4MQV42qSn1+I8xcSpHWBo26lyO239uc+mPSJczVHR4qq8j3ETZs2hUgkwsmTJ2Fvb4+mTZtW+rUikQj3799XZloiIqIaLTe3ELq62sjLK0RySja+mRaE//vfVSzx7473+zVlGa4qVFBQhKYtf0VySrbc52amdaV1gfv0sqve4KjKKbVCXFJhIjo6Gi1atKjUbW1vKioqUnRKtVLyG4e8m+rEYjHEYrGKIiMiIk338NFLzJt/Frv/uiPT3t3DGssW9UD7do1UFFnNULIK/ORJJkaNaCXzbMCQPTh56hEAQCQCXDpaom+f4gNxHdpbcC+whqnyFeJ/J7SanuAqS171ivT0dBVEQkRENYW9XX1s3TQQU/85eHch/AkA4ExIHFw9tuLjka2weKEHLC3qqThSzZHwJAPHTz7CseMPcOp0LNLT82BSXx/eHzqiTp3XC1vDP2wJU9O6xavAve1g2tBAhVFTdeIeYiVwhZiIiKqDRCLB/oMxmDsvBA8evgQA6OvXwY2IiTKHukhWfn4hLlx8gmMnHuLY8Ye4cTNZbr9Tx0fB7b0m1RwdVZdqqUNMxVtGeKiOiIiqikgkwtDBLfBBfwf87/drWLI8DJ9NasdkuBxx8eno5LoZaWm5cp83bFAXvXvZwauPPVo5Nqzm6EhdCZIQh4SEKNTfw8NDiGmJiIhqBV1dbXz1n44Y83Fr6OjIvjOZkZGH4aP24dupndDPq/YcvCsoKMKFi09QUFCEHh420narJkaoZ6gjkxB36mCBvv/UBe7U0QLa2oqdfaKaT5CEuEePHgr9BSwsLKy4ExEREckwMdEv1fbj6os4ExKHMyFx6NnDBssW9YCzk7kKoqt6iU8zcfxkcV3gk6dikZaWi/c6N8bpkx9L+4hEIozwfhfxCRnSvcDmZoYqjJo0gSAJ8bhx4+QmxEVFRYiLi0NERATS09MxaNAgmJiYyBmBiIiIFCWRSHDx8usD3sGnH+O9blsw5uPW8PNxh1UTIxVG9/YKCooQfumJ9HKMa9efleoTfukJnj/PRsOGry/DWOLfvTrDpBqgWg7VvXjxApMmTcKtW7cQFham8UkxL+YgIiJ1IZFIsO/AXcydF4KHj9Kk7XXr1sE3Uzth2redYWSkq8IIlRMWnoCh3n/h5Uv5e4FN6uujt6ct+vaxx9DBLVCvnuZ9jVS11PKmuqysLDg4OGDw4MH49ddfq2PKKsOEmIiI1E1ubgF+/f0alq4Ik0kiG5kbwHduV0wY21amxJi6KCwswsXLiTB+Rw+t3jWVtr94kQ2rpv+HoqLXaUr7do3Qt7c9vPrao3MnS7X8ekh9qGVCDACDBw/GpUuX8OTJk+qaskowISYiInX14kU2lq64gF9/v4r8/OJ7AoyMdBF17VO12Uub9OwVTvxTF/jkqVikvszBJ+Pb4pc1XjL9hgzfi3r1dP/ZC2wPi0bqET9pBrUtu5aZmYnU1NTqnJKIiKhWadCgLn5Y1hOfT24Hn/kh2HcgBjPEXVSaDBcWFuHS5ac4duIBjp14iIirSaX6HD/xEBKJROZM0v49H1ZnmFSLVVtCHBgYiJCQELRq1arizkRERPRWHBxMsHPrYISFJ6Ddv6pOvHiRjQWLQjFrxntobFm1N97t+SsaX393Ei9Sc+Q+NzbWk+4FLiyUoE6d2lE2jtSLIAnxxIkTy3yWmZmJu3fv4saNG5BIJJg2bZoQU6oFR0dH3lRH/9/efcdFdez9A/8ckCYiICAsIKBYiA0bcgF7id4IEY0lj0lsKeYmqIkaxa651qgYo7m51xijXq+Jsd4QYxK7IFiIhceGgCAqYEEpIorA/P7wt/u47gLL7mFpn/frta+EmdmZOTswfD3MmSEiqtYC/DVPYlv6xUn8a8N5/HvbRXw6yQ+fTvJDgwbmWLI8Fn9fcgLzZgdh5vQAVXll+txZQZg1I0CjPuD5XeC4PzPh6WmrtrTB1dVGIxj2bd9YtS/wX7q6ci0wVTlZAuJNmzaVW8bDwwMLFizA6NGj5WiyWsjIyNBIy83NrYKeEBER6SY/vxA//nQZAPD4cREWL4vFxk3x6NJZgch9SQCAhYtOAABmTg/AkuWx+Hzx86+V/1UGxffuP36+FvhACg4eSkXWgwKsWNobEz/urGqvaxcFPD0bonPH54djvNqvaaXflSaqKFkeqjt27Fipeebm5lAoFPDy8jK0mWpDuUhboVDwDjEREdU497MeY+ny53eJi4pKSi3Xu5cHjhxN00jv0b0JCh4/Q9zZTLwcRfTr44Vf9g5TS3t5bTCRMVTbXSZqC+4yQUREtUFS0kPMWXAce39ONLguGxtz9O3tiZBBzfHW/7SRoXdEhjH6LhNPnz7FnTt3YG9vDxsb7afi5OXl4eHDh3BxcYG5OTfPJiIiqmrNm9vjx62D8eHHv2HTvy9W+P1t2zhiQP+mGPBqMwT4u8LMzLQSeklU+WRZxR4REYGmTZviwoULpZa5cOECmjZtijVr1sjRJBEREcnkn18PxNxZgTqV9WnlgK+/ehVJVyYgLnYsFn/eEz26NWEwTDWaLEsm/P39kZWVhaSkpDLLeXt7w9nZGTExMYY2WaW4ZIKIiGqjv77+k9Y1w0q9e3pgf+QII/aISH8ViddkuUOcnJys0/7Cbdq0QXJyshxNEhERkYyWLI8tMxgGgCPH0rD0i1gj9YjIeGQJiPPz82FtXf4JOPXr1+e2ZERERNXMi1urlWfhohMMiqnWkSUgbtKkCeLi4sot9+eff0KhUMjRJBEREcnk70t0C4aVdA2eiWoKWQLiV199FdevX8fatWtLLfP1118jOTkZAwYMkKNJIiIiksncWUFa0/v09qxQeaKaSpaH6m7evIl27dohLy8PISEh+OCDD+Dt7Q1JkpCUlIT169cjMjISNjY2OH/+fI0/pIMP1RERUW3z8rKJ+XOCNE6qA4B5s0s/vpmoOqmSgzmOHz+OYcOG4f79+xqn0Qgh4OjoiJ9++gm9evWSo7kqxZPqiIioNlqyPBZ/X3IC82Y/D4ZfTp87i8Ew1RxVdlJddnY21q9fj0OHDuHmzZsAnq8v7tevH9577z3Y29vL1VSVUn7A2syfPx8LFiwwboeIiIiISI3RT6pTsrOzw/Tp0zF9+nQ5q622tN0h5hIKIiIioppFloB4ypQpsLe3x9y5c+Worsa4evUqA2AiIiKiGk6WXSbWrVuH+Ph4OaoiIiIiIjIqWQJid3d3lJSUyFEVEREREZFRyRIQDxkyBMeOHUNeXp4c1ZXpyZMnmD9/Plq2bAlLS0u4urpi/PjxuHXrls51ZGdnY9u2bRg1ahRat24Na2tr2NjYwN/fH2vWrMGzZ88q8QqIiIiIqDqRZZeJvLw89OzZE9bW1vjqq6/QsWNHOfqm4cmTJ+jbty9iYmKgUCjQvXt3pKam4vTp03ByckJsbCy8vb3LrWfOnDlYvHgxTExM0LFjRzRv3hz37t3DiRMn8PTpU3Tr1g2///476tevr/X93IeYiIiIqHoz+i4TgwcPhoWFBU6cOIEuXbpAoVDAw8MDlpaWGmUlScKhQ4f0amfJkiWIiYlBQEAA/vjjDzRo0AAAEBERgalTp2L8+PE4duxYufU0aNAAs2bNwkcffQQ3NzdVemJiIvr164fo6GgsWrQIS5Ys0aufRERERFRzyHKH+OWtx8psUJJQXFxc4TaePXuGxo0bIzs7G2fPntW4C+3r64v4+HjExcWhc+fOFa5f6YcffsCoUaPg5eWFlJQUrWV4h5iIiIioejP6HeLSAkc5RUdHIzs7G97e3lqXZAwbNgzx8fGIjIw0KCD29fUFAKSnp+tdBxERERHVHLIExJ6ennJUU6YLFy4AADp16qQ1X5muLKev69evAwBcXFwMqoeIiIiIagZZT6qrTGlpaQCeb/GmjTJdWU5fa9asAfB8XXR5fHx8dFouMmXKFEyZMsWgfhERERFR5agxAfGjR48AoNSdH6ytrdXK6eOf//wnDh48CDs7O4SHh5dbPiMjQ6d6c3Nz9e4TEREREVUuvQLiZs2aQZIkHDx4EE2bNkWzZs10fq8kSUhOTq5wm8pn/yRJKjNfX8eOHcPkyZMhSRI2btwIV1fXct+jUCh0ukPMB++IiIiIqi+9AuLU1FQAUB1gofy6MtnY2AAA8vPzteY/fvwYAFRbsVVEfHw8QkNDUVhYiK+++gpDhgzR6X1Xr15lsEtERERUw+kVEL98TLMxjm328PAAgFJPpFOmK8vpKjk5GQMGDEB2djYWLFiAiRMnGtZRIiIiIqpRZDm62RiU26GdPXtWa74yvX379jrXmZ6ejv79+yMzMxOTJ0/G/PnzDe8oEREREdUoegfEhw8fxpYtW3D58uVyy16+fBlbtmzBkSNH9G0OQUFBsLW1RXJyMs6dO6eRv3PnTgBAcHCwTvU9fPgQAwYMQEpKCsaNG4fVq1fr3TciIiIiqrn0Cohv3ryJQYMGYdGiRWjSpEm55Zs0aYLFixcjODhY7wMvzM3NERYWBgAICwtTW0scERGB+Ph4dOvWDX5+fqr0devWwcfHBzNnzlSr6/Hjx3jttddw8eJFjBgxAt9++22pD+sRERERUe2m1xriDRs2oLCwEF988YXqYbey2NjYYMWKFQgNDcV3332HuXPn6tMs5syZg4MHDyImJgYtWrRA9+7dcePGDZw6dQoODg74/vvv1crfv38fCQkJGtujzZ49GydPnoSpqSnq1auHd999V2t7mzZt0qufRERERFRz6BUQHzhwAE5OTggNDdX5Pa+//jqcnZ2xf/9+vQNiS0tLHDlyBEuXLsW2bduwd+9e2NvbY8yYMfj73/+u091q4PlyCQAoLi7Gtm3bSi3HgJiIiIio9pOEHhv4NmrUCEFBQYiMjKzQ+0JCQhATE4OsrKyKNlmt5ObmwtbWFjk5Odx2jYiIiKgaqki8ptca4vz8fNja2lb4fba2tgadJEdEREREJDe9AmJ7e3vcuXOnwu+7c+cO7O3t9WmyWvLx8YG7u7vaKyIioqq7RUREREQVoNca4tatW+PkyZMoKCiAlZWVTu95/PgxYmNj0bVrV32arJZeflgPeH57noiIiIhqDr3uEIeEhCA/Px+LFi3S+T2LFi1CQUEBQkJC9GmyWlIoFHBzc1N7cU0xERERUc2i10N1jx8/RvPmzXHnzh0sXLgQs2bNgomJ9ti6pKQEixcvxvz58+Hi4oKkpCTUr1/f4I5XJT5UR0RERFS9VSRe0ysgBoCTJ0+ib9++ePLkCdzd3TF8+HB06tQJTk5OAIB79+7h7Nmz2LFjB27dugULCwscOnQIAQEB+jRXrTAgJiIiIqrejBIQA0B8fDzefvttXLx4UetJb8qq27Rpg61bt8LX11ffpqoVBsRERERE1VtF4jW9HqpTat++PeLj4/H7779j3759OHfuHLKysiCEgKOjIzp06IBBgwZh4MCBhjRDRERERFRpDLpDXFfxDjERERFR9VbpB3MQEREREdUWlRYQ//e//8Xnn39eWdUTEREREcmi0gLivXv3YuHChZVVPRERERGRLLhkgoiIiIjqNAbEBvDx8YG7u7vaKyIioqq7RUREREQVYNC2a3VdRkaGRlpubm4V9ISIiIiI9FVpAbEQArV9RzeFQqFxZDW3YSMiIiKqWbgPsR64DzERERFR9Wb0fYjHjx+PjRs3lltu06ZNGD9+vBxNEhERERHJQpaAeNOmTYiOji633IkTJ7B582Y5miQiIiIikoVRd5koLCyEqampMZskIiIiIiqT0QJiIQTOnj0LJycnYzVJRERERFQuvXeZ6NOnj9rXv/32m0aaUlFREZKTk5GZmYl33nlH3yaJiIiIiGSnd0B89OhR1f9LkoTMzExkZmaWWt7MzAzBwcFYuXKlvk0SEREREclO74A4JSUFwPOlEM2aNcOwYcOwYsUKrWXNzc3h6OgIMzMzfZsjIiIiIqoUegfEnp6eqv+fPHkyGjVqpJZGRERERFQTyPJQ3ddff434+Hg5qqpRfHx84O7urvaKiIio6m4RERERUQXIcnSzu7s7SkpK5KiqRsnIyNBIy83NrYKeEBEREZG+ZAmIhwwZgs2bNyMvLw82NjZyVFkjKBQKmJio32TnUc5ERERENYskhBCGVpKXl4eePXvC2toaX331FTp27ChH36qtipyNTURERETGV5F4TZY7xIMHD4aFhQVOnDiBLl26QKFQwMPDA5aWlhplJUnCoUOH5GiWiIiIiMhgsgTEL+5JLIRAeno60tPTtZaVJEmOJomIiIiIZCFLQKzck9gYnjx5gqVLl+KHH35AWloaGjVqhIEDB+Lzzz+Hu7t7herKzs7GggULsGfPHmRmZsLFxQWhoaFYuHAh7OzsKukKiIiIiKg6kWUNsbE8efIEffv2RUxMDBQKBbp3747U1FScPn0aTk5OiI2Nhbe3t051ZWVlISAgAImJiWjWrBm6dOmCS5cu4dKlS2jevDlOnjwJBwcHre/lGmIiIiKi6q0i8Zos+xAby5IlSxATE4OAgABcu3YN27dvx6lTp7Bq1Srcu3cP48eP17muTz/9FImJiRg6dCgSEhKwfft2XLx4ERMnTkRSUhKmTJlSiVdCRERERNWF7HeIT58+jaioKKSnp0OSJNWd3K5duxpU77Nnz9C4cWNkZ2fj7NmzGjtZ+Pr6Ij4+HnFxcejcuXOZdWVmZsLNzQ2mpqa4efMmnJ2dVXlPnz5FkyZN8ODBA9y+fVstT4l3iImIiIiqN6PvMgEA165dw+jRo3HmzBkAzx+uA/7vIbquXbtiy5YtaNGihV71R0dHIzs7G97e3lq3dRs2bBji4+MRGRlZbkC8f/9+lJSUoHfv3hoBr4WFBUJCQrBx40bs378fY8eO1au/RERERFQzyBIQZ2RkoGfPnrhz5w5cXV0xfPhweHl5AQBu3LiBHTt24NSpU+jVqxfi4uKgUCgq3MaFCxcAAJ06ddKar0xXljO0ro0bN+pUFxERERHVbLIExIsWLcKdO3fw6aefYunSpTA3N1fLX758OWbOnImIiAgsWbIEa9eurXAbaWlpAFDqThLKdGU5Y9Tl4+OjcVKdNlOmTOGaZCIiIqJqSpaA+Ndff0WrVq2watUqrflmZmZYsWIF9u3bh19++UWvgPjRo0cAgPr162vNt7a2VitnjLoyMjLKbQt4voaFiIiIiKon2ZZMvPHGG2WWkSQJnTp1wq5du/Rq4+U1yaXlG7MuhUKh0x1iPnhHREREVH3JEhA3bNgQN2/eLLfczZs39Q4ObWxsAAD5+fla8x8/fgwAaNCggdHqunr1KoNdIiIiohpOln2IAwICEBMTg/3795da5tdff8WJEycQGBioVxseHh4AgFu3bmnNV6YryxmrLiIiIiKq2WQJiMPDwyFJEkJDQzFu3DgcOHAAiYmJSEpKwoEDBzB27FgMGTIEpqamCA8P16sNX19fAMDZs2e15ivT27dvb9S6iIiIiKhmk+1gjq1bt2LChAkoKCjQWJsrhICVlRXWr1+Pt956S6/6CwsL0bhxY+Tk5JR5MMfp06fh5+dXZl0ZGRlwd3dHvXr1cPPmTTRu3FiVpzyYIysrC7dv34aLi4vG+3kwBxEREVH1ViVHN7/99ttISEjA3Llz0atXL7Rs2RItW7ZEr169MG/ePFy9elXvYBgAzM3NERYWBgAICwtTW/8bERGB+Ph4dOvWTS0YXrduHXx8fDBz5ky1uhQKBf7nf/4HhYWF+Oijj1BUVKTKmz59Ou7du4dRo0ZpDYaJiIiIqHaR7aQ64Pn+vQsXLpSzSjVz5szBwYMHERMTgxYtWqB79+64ceMGTp06BQcHB3z//fdq5e/fv4+EhASt26N9+eWXOHnyJHbt2gUfHx906dIFly5dwsWLF+Ht7Y3Vq1dX2nUQERERUfUh2x1iY7C0tMSRI0cwd+5c1K9fH3v37kVqairGjBmDc+fOoXnz5jrX5ejoiDNnzmDixIkoLCzEnj17kJOTg7CwMJw+fRqOjo6VeCVEREREVF3ItoYYeL5dWVxcHDIyMvD06dNSy40ePVquJqsE1xATERERVW8ViddkWzIxb948rF69WrWHrzZCCEiSVOMDYiIiIiKqPWQJiL/44gssWrQI9erVQ3BwMFq2bKnTARlERERERFVNloD422+/hZWVFaKiotCpUyc5qqwRfHx8NI5unjJlCqZMmVJFPSIiIiKiipIlIL558yb69OlTp4JhAFp3r8jNza2CnhARERGRvmQJiOvafr3r1q0DADRs2BA2NjZqeXzIruaKiIhAbm4uGjZsyLv8tQjHtfbi2NZOHNfaqbqPqyy7TISHh2PDhg1ITEyEvb29HP2q1lxdXZGRkQGFQoH09PSq7g7JxN3dHbdv34abmxtu3bpV1d0hmXBcay+Obe3Eca2dqmJcjX5S3YIFC9C6dWsMHToUycnJclRJRERERGQUei2Z6NOnj0ZaSUkJTpw4AR8fH3h5ecHd3R2SJGmUkyQJhw4d0qdZIiIiIiLZ6RUQHz16tNS84uJiJCcnl3qnWFuQTERERERUVfQKiFNSUuTuB+mgKhakV9Ui+Oq++F5OHNfaqS59xhzX2tkux7V2tluXxrVCBFWYQqEQAIRCoTBqu25ubgKAcHNzq9VtVlW7vNba2W5dutaqapfXWjvb5bXWznbr0rXm5OQIACInJ6fcsrI8VEdEREREVFPpFRAXFRXh7t27yMnJ0ZqflZWFCRMmwN3dHZaWlmjWrBk+++wz5OXlGdRZIiIiIiK56RUQb9q0CQqFAmvWrNHIy8nJQWBgIDZs2ID09HQUFhYiNTUVERER6NevH4qKigzuNBERERGRXPTeZUKSJLz//vsaeUuWLEFiYiKsra2xbNky9OrVC0lJSZg6dSri4uLw3XffYcKECQZ3vCqJ/3+WSUlJiVGPai4pKTF6u1XRZlW1y2utne3WpWutqnZ5rbWzXV5r7Wy3Ll2rsh2hyxl0+ixSbt26tejQoYPWPGdnZ2FiYiJWrlypln7t2jVhamoq+vbtq0+T1crcuXMFAL744osvvvjiiy++qvnr5s2b5cZ2eh3d7OjoiH79+uHHH39US798+TLatm0LU1NTZGZmwsHBQS0/MDAQycnJuHPnTkWbrFZKSkqQnp4OGxsb7qtMREREVA0JIZCXlwdXV1eYmJS9SlivJRN5eXkoLi7WSI+NjQUAtG3bViMYBgAPDw/8+eef+jRZrZiYmMDd3b2qu0FEREREZbC1tdWpnF4P1TVq1AjXrl3TSI+KioIkSfD399f6vmfPnqFhw4b6NElEREREVCn0Coj9/f1x8eJF/P7776q0+/fvY+/evQCA/v37a33flStX4Orqqk+TRERERESVQq+A+OOPP4YQAqGhoRgzZgymTZsGPz8/5ObmwtXVFa+//rrGe1JTU5GQkABfX1+DOy23J0+eYP78+WjZsiUsLS3h6uqK8ePH49atWxWuKzs7G5988gk8PT1hYWEBT09PTJ48GdnZ2ZXQcyqLHOOanZ2Nbdu2YdSoUWjdujWsra1hY2MDf39/rFmzBs+ePavEKyBt5Px5fVFiYiKsrKwgSRIGDhwoU29JV3KPa1JSEt5//314eXnB0tISTk5OCAwMxIoVK2TuOZVHzrH97bff8Ne//hWOjo4wMzND48aNERwcjEOHDlVCz0mbP//8E8uWLcPQoUPh5uYGSZJgaWmpd33VJm7Sd6eFefPmCUmShCRJwsTEREiSJKysrMSBAwe0lp8+fbqQJEls3bpV3yYrRUFBgQgMDBTA86OYR4wYIbp27SoACCcnJ5GUlKRzXffv3xctWrQQAESzZs3EiBEjRJs2bQQA0bx5c3H//v1KvBJ6kVzjOnv2bAFAmJiYiM6dO4uRI0eKPn36CAsLCwFAdOvWTeTn51fy1ZCSnD+vL+vdu7eQJEkAEAMGDJCx11Qeucd19+7dwtLSUkiSJDp16iTefPNN0b9/f+Hi4iK8vb0r6SpIGznHdtWqVQKAkCRJdOvWTYwcOVL4+fmpdhL45ptvKvFKSGnw4MEauzhYWFjoVVd1ipv0DoiFECIuLk7Mnj1bfPDBB+Lzzz8XycnJpZadM2eO+OSTT0RWVpYhTcpOuYVaQECAyMvLU6Urf/B69Oihc13vvPOOACCGDh0qnj17pkqfOHGiACBGjx4ta9+pdHKN69KlS8WsWbPErVu31NKvXbsmPDw8BAAxc+ZMWftOpZPz5/VFGzZsEADEBx98wIC4Csg5rufPnxfm5ubCwcFBREVFqeUVFxeLM2fOyNZvKp9cY3v37l1hbm4uzM3NNcZ1586dQpIkUb9+fbU2qHIsW7ZMzJs3T0RGRorMzEyDAuLqFDcZFBDXdIWFhcLOzk4AEGfPntXIb9++vQAg4uLiyq0rIyNDmJiYCDMzM5GZmamW9+TJE+Hk5CRMTU018kh+co5rWbZt2yYACC8vL4PqId1U1rjeuXNH2Nvbi379+okjR44wIDYyuce1e/fuAoCIjIyUu6tUQXKObWRkpAAgBg4cqDXf19dXABCnTp0yuN9UMfoGxNUtbtJrDXFtER0djezsbHh7e6Njx44a+cOGDQMAREZGllvX/v37UVJSgh49esDZ2Vktz8LCAiEhISguLsb+/fvl6TyVSs5xLYtyPXx6erpB9ZBuKmtcJ02ahIKCAnzzzTey9JMqRs5xvXLlCqKiotCyZUsEBwfL3leqGDnH1sLCQqc2GzVqVLFOUpWpbnFTnQ6IL1y4AADo1KmT1nxlurKcseoiwxhrLK5fvw4AcHHrtpLzAAAce0lEQVRxMage0k1ljOuvv/6K7du3Y9asWWjevLnhnaQKk3NclQ9W9e/fH0+ePMHmzZsxceJETJo0CRs2bDDqMbUk79j6+fnB1tYWhw8fRnR0tFre7t27ER8fj8DAQP4c1yDVLW7S62CO2iItLQ0ASj1kQ5muLGesusgwxhqLNWvWAAAGDx5sUD2kG7nHNT8/Hx999BFatWqFGTNmyNNJqjA5x/XSpUsAACsrK3To0AEJCQlq+TNnzsSuXbvQo0cPQ7pMOpJzbO3s7LBhwwa89dZb6NGjB4KCguDm5oaUlBScOXMGAwcOxKZNm2TrO1W+6hY31ek7xI8ePQIA1K9fX2u+tbW1Wjlj1UWGMcZY/POf/8TBgwdhZ2eH8PBwvesh3ck9rnPmzMGNGzfwzTffwNzcXJ5OUoXJOa4PHz4EAHz55Zd48OABdu/ejezsbCQkJGDUqFG4f/8+QkNDkZGRIVPvqSxy/8wOGzYM+/fvh4ODA6Kjo7F9+3acPn0ajRs3Rp8+fbSekEvVV3WLm+p0QCyEAABIklRmvrHrIsNU9lgcO3YMkydPhiRJ2LhxIw+bMRI5xzUuLg5r167F6NGj0bt3b1n6R/qRc1yLi4sBAEVFRdi6dSuGDBkCW1tbtGzZEv/5z3/g5+eHhw8f4uuvvza841QuuefiVatWoX///ujRowfi4+Px6NEjxMfHIyAgAJ999hlGjhxpcJ/JeKpb3FSnA2IbGxsAz/90qs3jx48BAA0aNDBqXWSYyhyL+Ph4hIaGorCwEGvWrMGQIUP07yhViFzjWlRUhPfffx+2trZYuXKlvJ2kCquMedjNzQ2vvvqqRv64ceMAAEePHtWnq1RBco7tsWPHMG3aNHTo0AE7duxAu3btYG1tjXbt2mHnzp3o2LEjdu3ahT/++EO+C6BKVd3ipjq9htjDwwMASj0tR5muLGesusgwlTUWycnJGDBgALKzs7FgwQJMnDjRsI5Shcg1rrdu3cL58+fh4uKC4cOHq+UpT0Y6ffo0evXqhQYNGuCXX34xtOtUBjl/Xr28vAAAnp6eZebfvXu3gr0kfcg5tlu2bAEADB06FCYm6vfyTE1NMXToUJw7dw5Hjx7V+o8hqn6qW9xUpwNi5bZZZ8+e1ZqvTG/fvr1R6yLDVMZYpKeno3///sjMzMTkyZMxf/58wztKFSL3uGZmZiIzM1Nr3sOHD3Hs2DHY2trq0VOqCDnHVbm114MHD7TmZ2VlAeBf6oxFzrFVBkcNGzbUmq9ML23sqfqpdnGTUXY7rqaePn0qbG1ty900/PTp0+XWlZ6eLkxMTIS5ubm4c+eOWp5yg2kTExORkZEhW/9JOznHVQghHjx4INq2bSsAiHHjxomSkhK5u0w6kHtcteHBHMYn57jm5+cLa2trYWZmJtLS0jTy3333XQFAvPvuu7L0ncom59iOHj26zJPL3n77bQFALF261OB+U8VAz4M5qlvcVKcDYiGEmD17tgAgAgMDxaNHj1TpymMlu3XrplZ+7dq1olWrViI8PFyjrrfeeksAEG+88YbaEYSTJk0SAMTbb79deRdCauQa1/z8fPGXv/xFABAjRowQRUVFRuk/aSfnz6s2DIirhpzjGh4eLgCIQYMGqdW1f/9+Ua9ePSFJEk8zMyK5xnb37t0CgDA1NRU///yzWt7evXuFiYmJMDExEVevXq28iyGtyguIa0rcVOcD4oKCAuHv7y8ACIVCIUaMGKH62sHBQSQmJqqVnz9/vgAgxowZo1HXvXv3hLe3twAgvL29xciRI1V3Fr29vcW9e/eMdFUk17h+8sknqkl41KhRYsyYMVpfZBxy/rxqw4C4asg5rgUFBSIoKEhVV2hoqAgMDBQmJiYCgFi8eLGRroqEkG9sS0pKxPDhwwUAAUB06dJFDB8+XHTp0kWVxrE1jl9++UX4+/urXgCEJElqab/88ouqfE2Jm+p8QCyEEI8fPxZz584V3t7ewtzcXDg7O4sxY8Zo/ZNbeb9gHzx4ICZOnCiaNGkizM3NRZMmTURYWJjIysqq5Kugl8kxrmPGjFFNtmW9yHjk/Hl9GQPiqiPnuD59+lQsXrxYvPLKK8LCwkLY2tqKvn37qv2SJuORa2xLSkrEd999J3r06CHs7OxEvXr1hKOjo3jttdfE/v37jXAlJIQQ33//fbm/E7///ntV+ZoSN0lCcINcIiIiIqq76vQ+xEREREREDIiJiIiIqE5jQExEREREdRoDYiIiIiKq0xgQExEREVGdxoCYiIiIiOo0BsREREREVKcxICYiIiKiOo0BMRERERHVaQyIiYiIiKhOY0BMRERERHUaA2KqM8LCwiBJUpmv1NRU2dvdsmVLue2++Prkk08Mak/bdZqZmaFZs2aYMmUKcnNzZbqy586ePQtJkuDp6ak1v6ioCNbW1pAkCZMnT9ZaZt++fZAkCV26dDGoL5IkwcvLy6A6ACA1NRWSJKFXr14G16Wr8r4vjNkXucg1HnIzZr/qyrwDGH/u0ebZs2fYsmULBg8ejKZNm6J+/fqwsbGBr68vpk2bhsTExErvA9VM9aq6A0TGcuHCBQBAcHAwHBwcNPItLS0r5ZeklZUVxowZU2aZ06dP48qVKwAAf39/g9pTXuegQYPg6OgIAMjMzMThw4exevVqREdHIzY2Fqampga1o+Tr64uGDRsiLS0NaWlp8PDwUMv/888/8fjxYwBAVFSU1jqio6MBAN27d5elT1UhNTUVTZs2Rc+ePXH06FG96ynte8XHx0fvOiuDXNdb29WVeQcw/tzzsjNnzmDs2LG4fPkyLCws0KlTJ/j7+yMnJwexsbFYtWoVvvrqK+zcuROvv/56pfSBajBBVAeUlJSIhg0bCgAiMzOzqruj5vjx48LKykoAEB9++KFBdb14nenp6Wp5J0+eFPXq1RMARGRkpEHtvGzAgAECgNi6datG3sqVKwUA4evrK0xMTEROTo5GmW7dugkAYteuXQb148qVKyIpKcmgOoQQIiUlRQAQPXv2rNT3vAiAqElTsi7XK9d4yM1Y/aor844QVTf3KMXGxgobGxsBQHz88ccan/fTp0/FV199JWxsbMRvv/1WKX2gmo1LJqhOSElJQW5uLho1agRnZ+eq7o7K+fPnERISgoKCArz55pv4+uuvDarvxetUKBRqef7+/ujRowcA4Ny5cwa18zLlnV3lnd4XRUdHw8rKCmFhYSgpKUFsbKxafmFhIeLi4gAA3bp1M6gfPj4+8Pb2NqgOkk91HQ9j9auuzDtA1c09AHD37l2EhIQgLy8PK1euxLp16zQ+b3Nzc0ycOBHHjx83eGkW1U4MiKlOUP4p75VXXqninvyfpKQkDBw4EDk5OfjrX/+KLVu2wMTk/34kr169CkmSMHDgQDx69Ajz58+Hj48PrKys0Lx5c6xatUqjTuV1tm3bVmub1tbWpfbnxo0bmDJlCtq0aQNra2vY2dmhc+fOWLBgAfLz88u8FmVArG1JRExMDPz8/NC7d2+tZc6cOYMnT56gVatWaNy4sVpeamoqJkyYAC8vL1hYWMDJyQnDhg1DfHy81n6UtTb0p59+gp+fH6ysrODs7Ixx48bhzp07GDt2LCRJKvXP/gUFBQgPD4enpycsLCzQvHlzLF++HEIIVZkFCxagadOmAIBjx46praEcO3as1noN8eIa59zcXEydOhVNmzaFmZmZ2lrQffv2Yfz48XjllVfQsGFDWFtbw9fXF0uWLMHTp09LrT8tLQ1hYWFo0aIFLC0t4eDggK5du2LJkiUoKCjQ+XrLGo/Y2FgMHjwYTk5OsLCwgJeXFz766COkp6eXeb26jEd5tPVL7jaAmjnvAMadewyZd5QmTZqE+/fvIzg4GFOnTi2zbIcOHbQuXSGqOX+fIzLA/PnzBQDx/vvvV3VXhBBC3Lp1S3h5eQkAIigoSOTn52uU+fHHHwUAMWLECNGsWTPRpEkTMWLECNG/f39hYmIiAIgtW7aovUd5nR999JHWdn18fAQA8e9//1stfceOHaJ+/foCgGjXrp0YOXKk6NOnj7C3txf16tUTBQUFZV5PQUGBsLCwEJIkiaysLFX61atXBQAxa9YsIYQQLi4uokePHmrvXbZsmQAg3nvvPbX0qKgo1Z9g27RpI4YNGyYCAgKEJEnCyspKHD58WKMfAISnp6dG+urVqwUAYWpqKvr27StGjhwp3NzchJeXl3j99dcFAHHkyBFVeeVygICAANGtWzdhb28vBgwYIAYMGCAsLS0FADF79mxV+T179og33nhDABDOzs5izJgxqte3335b5mf3Yt91nZKV/evatavo0KGDsLe3F6GhoWLo0KFiwYIFqnLOzs6iQYMGwt/fXwwfPlwMGDBA2NvbCwCiT58+oqioSKPuY8eOCVtbWwFANGvWTIwYMUIMGjRING3aVAAQKSkpOl9vaePx73//W5iamgpJkkRQUJB48803RcuWLVX1XblyRev16joe5dHWL7nbEKJmzjtCGG/uqci8o6x/zJgxavUmJCQISZJEvXr1REpKin4fDJEQggEx1QmhoaECgFi9enVVd0VkZWWJ1q1bCwCiffv24uHDh1rLzZw5UxUkzZ07Vzx79kyV99133wkAYvDgwWrvUV7nP/7xD436fvvtNwFAWFlZibt376rSDxw4IMzMzETjxo01gsz8/Hyxbds2na4rKChIABA///yzKm3Dhg0CgNi3b58QQoihQ4cKS0tL8fTpU1WZ4OBgAUBs3rxZlZaTkyNcXFyEmZmZ2LFjh1o7Bw4cEObm5sLNzU2tHiG0BzrJycnC3NxcWFpaiuPHj6vSCwoKxKBBg1SfsbaAGIDo3r27uHfvnirvzJkzol69eqJ+/foiLy9P4z3GWEP8Yv8CAgJK/R7as2ePePTokVpabm6u1s9cCCEePHggnJycVD8rJSUlavnHjh0T2dnZan0o63q1jUdaWpqwsrIS9erVU1tPWlxcLD755BMBQPj5+ZV6vbqOR1nKCojlakOImjnvCGGcuaei805pAfG0adMEADFkyBCdPgei0jAgpjpBeVektNcff/xhlH7k5eUJf39/AUB4e3uLjIyMUsu+9tprAoAYOXKkRl5aWpoqGHqR8jqVgV9JSYm4ceOG+Oabb4SdnZ0AIJYuXaoqn5+fL1xdXYWZmZmIjY016NrCw8MFAPHZZ5+p0saOHSskSVL98l21apUAIE6cOKHqX6NGjQQAcf36ddX7lHd0Z86cqbUtZeD08kN42gKd2bNnCwBiwoQJGvUkJSWp7nhpC4hNTExEQkKCxvtCQkJKfY+hAXFprxcDmBeDtzNnzlS4rcTERAFADB06VC19+fLlAoAIDg4utw59A+J58+YJAOKdd97RKP/kyRPh6uoqAKh9P+ozHmUpKyCWqw0haua8I0Tlzz36zDtr164VrVq1EuHh4Wrp7du31/qPO6KK4rZrVOvl5uYiNTUVJiYmeOedd7SWMcZDFoWFhRgyZAhOnToFV1dXHDhwAC4uLqWWV67JmzNnjkZeZmYmAKg9vKK8TgCqB1heZGVlheXLl2P69OmqtPXr1yM9PR1/+9vf8Je//EWv61Lq3r07li1bpvZgXXR0NNq2bQs7OzsAQFBQkCo9MDAQly9fxoMHD+Dm5qZakwoABw4cAACEhoZqbatbt2748ssvcebMGQwdOrTMfsXExAAAhg8frpHn7e2Njh074s8//9T6Xi8vL7Rs2VIjXZmWkZFRZtv6KG2rLHNzc400hUJR7vduYmIifv31VyQlJSE/Px8lJSWqtbAv78l68OBBAMCECRP06bpOlGvI33rrLY08CwsLDB8+HGvWrEFUVJTG96QxxkOuNmrqvANU/tyjz7wTFhaGsLAwtbTi4mJcvHgRANC5c2ed6iEqDQNiqvWUk3uLFi2wadOmMsseP34cq1atwvnz55GWlob58+djwYIFWstmZWVhzpw5+O9//4usrCwoFAqEh4fjww8/1ChbUlKCUaNG4eDBg2jUqBH++OMPtQBQW923b99Gs2bNtD6konyorF27dhrX6eTkhNdeew0AYGpqCnt7e7Rv3x7BwcFo1KiRWj27d+8GALz33ntlfCq6CQoKgomJCeLi4lBQUIC8vDwkJSWpfR6dOnWClZUVoqKiMH369FL3H1b+ci1vb9T79++X2y/lQ1pNmjTRmu/h4VFqQOzu7q41vUGDBgBQ5oNp+irve/RFL+/5/CIhBKZNm4bVq1eX+jBYXl6e2tc3b94EgErdgUE5HqU9bKdM1/ZwnTHGQ642KjLvALrPPZU57yjrr+y5R655JysrCyUlJQBQbpBf2vt1+Sz/+OMPzJw5E5cuXYKDgwPGjRuHhQsXVtp+ylQ1GBBTraecrDt06FBu2UePHqF169YYNWpUmSc3PXr0CN27d4ebmxt++OEHeHp6IiMjA8+ePdNafsKECdi1axesra3x66+/ok2bNjr12c/PT2v++fPnNa5J+Z5+/frpHFSdP38eZmZm6Nixo07ly2Jra4t27drhwoULOHXqFLKysgCob6VmZmYGPz8/nDhxAkKIUgPi4uJiAM/v6tavX7/UNitymIAkSVrTSwsWy3pPdWFpaVlq3vbt2xEREQF3d3d8+eWXCAgIgJOTE8zMzFBYWAgLC4tSr90Y111eG9ryq0O/dFWReQfQbe6p7HnnxX5X5twj17zz4liV9XOsja6f5blz5xAcHIyPP/4YW7duxeXLl/Huu++iqKgIy5YtM6j/VL0wIKZaTzmB6zL5vvbaa6o7HDNmzCi13IoVK/D48WP88ssvsLCwAFD6Ha8ZM2Zgw4YNMDc3x549e3QK4pS/YErrs7ZfStrSylJYWIi8vDw0bNhQtiCge/fuuHDhAqKjo1UBsXKZhFJQUBCOHz+OS5culRoQu7u7IyEhAXPmzEH79u0N6pNCoUBCQgLS0tLQokULjXzlXdHaZs+ePQCAb775BsHBwWp5169f1/qeJk2a4OrVq0hKSqq0k/FcXV2RkJCAlJQUrUsTbty4AQAae9nWNBWZdwDd5p7KnneAyp975Jx3HBwcYGNjg7y8PFy7dk11Op4udP0sV65ciXbt2mH16tUAnm+hd/v2bYSHh2POnDmqvxxQzcd9iKnWK2+C18euXbvQrVs3fPrpp1AoFGjVqhWmTJmisW/mF198gS+++AImJibYtm0b+vfvb3CfhRC4cOEC7Ozs1Cbwit6RMjc3h6WlJXJzc1XrAg314n7E0dHRcHNz0/glowyQt2/fjtTUVNjZ2Wn8abZfv34AgL179xrcp8DAQADAzp07NfKuX78u20EByjW+RUVFstRnqIcPHwLQvlTkp59+0voe5ee+fv36cuvX93qV3yP/+c9/NPIKCwuxY8cOtXI1VU2cd8rrtxxzj5zzjomJCV599VUAwMaNG8ss++zZM7V/COr6WZ44cUL1DxWlQYMGoaCgoNSlVlQzMSCmWq24uBiXLl0CIO8vpuTkZOzcuRP5+fmIjIzEihUrsHPnTowfP15VZsOGDao7Pf/617/wxhtv6Fx/Wb9gkpOTkZeXB19fX1Xai9f5Ynp5+vTpAwCYNm2axtrI8+fPq+786EoZxMTExOD8+fNaT54LDAyEJElYt24dgOdLKl6+UzRhwgQ4OTlhyZIl+P777zX+HJqfn48tW7bg1q1b5fZp3LhxMDMzw6ZNm1QP2AHAkydP8Mknn6jWIBrK0dERZmZmSE5OVi35qErKu6/r169X+/yioqKwYsUKre9577334OjoiMjISKxbt07jc4+KikJOTg4A/a/33XffhZWVFX744Qfs27dPlV5SUoJZs2bh9u3b8PPzM/ghz6pUU+cdwDhzjz7zzrp16+Dj44OZM2eqpc+ePRvm5ubYuHEjIiIiNL4Xnz17hh9//BEdOnRAQkKC2rWU91kCzx+ifPmvFcr1ytrWuVPNxSUTVKtdu3YNBQUFcHNzg5OTk2z1lpSUwNHREd999x3q1Xv+Y1RYWIjhw4dj7dq1ePDggepJfWdnZ0RHR2s91vhFCxYsgJeXF4qKinD58mW4ublpnNwGaP/zpPI6XVxcKnRE7NKlS3H8+HH85z//wdGjR9G5c2cIIXD16lUkJiaqdh3QlUKhgLe3N5KTkwFoLpcAAHt7e7zyyiu4fPkyAO13Au3t7bFnzx68/vrrGD9+PBYuXIi2bdvCwsICaWlpuHLlCvLz83Hu3LlSH4JSat68OZYsWYLPPvsMPXr0QO/eveHg4IATJ07AxMQEISEhiIyM1LqLQ0WYm5tj4MCBiIyMhK+vLzp16gRzc3MEBQVh3LhxBtWtj0mTJmHTpk34xz/+gaNHj6J9+/a4ffs2oqOjMXXqVKxcuVLjPY0aNcJPP/2EwYMHY+LEifjyyy/RuXNnPH78GJcuXUJKSgpSUlJga2ur9/V6eHhg/fr1GDt2LEJCQhAUFIQmTZrg7NmzSEhIgLOzM7Zs2VKZH02lq4nzDgCjzT36zDv3799HQkKCxi4fHTt2xObNmzFu3DhMnToVq1atQteuXWFlZYXMzEycPn1addf3xZ0oyvsstV2/kvIf8NX9GQOqGAbEVKtVdBmBrhQKBby8vFQTKQDVAys3btzA1atXVXce79y5g82bN5dZn4mJieqO6ZUrV1BYWFhqn8t6qKWi19m+fXvExcVh0aJFOHz4MPbv3w9bW1s0bdoUc+bMqdBDa0rdu3dXBcTa7hADzwNlZUBcVpn//d//RUREBPbt24fDhw/D1NQUrq6uCA4OxtChQ9G6dWud+jRt2jS4u7tjxYoViIqKQsOGDTFw4EAsX74co0ePBgBZjnPdsGEDpk2bhgMHDmDbtm0oLi5GUVFRlQTELVu2xJkzZzBjxgycOnUKP//8M1q1aoV//etfeP/997UGxADQu3dvnD9/HsuXL8fvv/+OvXv3omHDhvD29sYHH3yg9jS/vtf79ttvo1mzZli2bBliYmJw6tQpKBQK/O1vf8Ps2bPh5uYm62dhbDVx3gGMN/fIPe+8+eab6Ny5M9asWYODBw/i999/hyRJUCgU8Pf3R8+ePfHmm2+qBbnlfZbKsgqFQiMIV35d09e5kzpJVPTRTKI6wsvLC2PHjtW69dE777yDo0ePIjU1VbX1zt69ezFkyBDcvXtX1rtCVHny8/Ph5eWFgoIC5OTkcBslqhZKm3s478hH18/yrbfeQkJCAuLi4lTvXbt2LWbMmIG7d+/yobpahGuIiV7w6NEj1Rq2wsJCZGZm4vz586q7mUrTpk3D3bt3ERYWhoSEBBw5cgTTpk3DqFGj+EupGrp+/bpq7avSo0eP8OGHH+L+/fsYOXIkg2GqUrrMPZx35KPrZzl16lTEx8dj6tSpuHLlCnbv3o158+Zh0qRJDIZrG+MfjkdUfR05ckTrEasvH/MqhBAHDx4UXbp0ERYWFsLT01NMmTJFPHr0yPidpnItXbpUWFhYiMDAQDFixAjRv39/4ejoKAAILy8vkZmZWdVdpDpO17mH8458dP0sf/vtN9GxY0dhbm4uFAqFmDVrligqKqqCHlNl4pIJIqr1zpw5g1WrVuHkyZO4e/cuhBDw8PBASEgIZsyYwbtrRER1HANiIiIiIqrTuIaYiIiIiOo0BsREREREVKcxICYiIiKiOo0BMRERERHVaQyIiYiIiKhOY0BMRERERHUaA2IiIiIiqtMYEBMRERFRncaAmIiIiIjqNAbERERERFSn/T9IbEuhzkvaqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot settings\n",
    "values = [0.05, 0.25, 0.50, 0.75, 0.95, 1]\n",
    "x_label = '$F_{16}ZnPc$ Weight Fraction in $F_{16}ZnPc$:$C_{60}$'\n",
    "y_label = 'Short-Circuit Current / mA/$\\mathregular{cm^{2}}$'\n",
    "figsize=(8,4)\n",
    "\n",
    "# Set up & draw plot\n",
    "fig = set_up_plot(x_label = x_label, y_label = y_label, figsize=figsize)\n",
    "\n",
    "plt.plot(values, parameters['Jsc'][18:24], 'X', markersize=8, linestyle='--', linewidth=2, label='$F_{16}ZnPc:C_{60}$', color=colors[4])\n",
    "\n",
    "plt.ylim(-0.1, 1.15)\n",
    "plt.xlim(0, 1.05)\n",
    "\n",
    "# plt.savefig('/home/jungbluth/Desktop/F16ZnPc_Jsc_trends.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}