JV-Analysis-Code-Outdoor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# JV Analysis Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import rc_context\n",
    "from matplotlib import rcParams\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.signal import savgol_filter\n",
    "import colormath as cm\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from lmfit import minimize, Parameters, fit_report\n",
    "\n",
    "#%matplotlib qt\n",
    "%matplotlib inline\n",
    "\n",
    "data_file = r'C:\\Users\\tl457\\OneDrive - University Of Cambridge 1\\1_Data\\211125-aSi-JV\\211125_aSi_JV_Outdoor_Curves.txt'\n",
    "export_folder = r'C:\\Users\\tl457\\OneDrive - University Of Cambridge 1\\1_Data\\211125-aSi-JV\\Summary'\n",
    "\n",
    "# specify matplotlib configuration file name\n",
    "plot_config_path = r'C:\\Users\\tl457\\Documents\\GitHub\\optofluidics\\optofluidics\\optofluidics'\n",
    "plot_config_file = 'plotting_params.txt'\n",
    "dir = os.path.abspath(os.path.dirname(plot_config_path))\n",
    "rc_fname=os.path.join(dir, plot_config_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_adff3_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Dark_1</th>        <th class=\"col_heading level0 col1\" >Forward_2</th>        <th class=\"col_heading level0 col2\" >Reverse_1</th>        <th class=\"col_heading level0 col3\" >Dark_1.1</th>        <th class=\"col_heading level0 col4\" >Forward_1</th>        <th class=\"col_heading level0 col5\" >Reverse_1.1</th>    </tr>    <tr>        <th class=\"index_name level0\" >Run Id</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_adff3_level0_row0\" class=\"row_heading level0 row0\" >Device</th>\n",
       "                        <td id=\"T_adff3_row0_col0\" class=\"data row0 col0\" >aSi_001</td>\n",
       "                        <td id=\"T_adff3_row0_col1\" class=\"data row0 col1\" >aSi_001</td>\n",
       "                        <td id=\"T_adff3_row0_col2\" class=\"data row0 col2\" >aSi_001</td>\n",
       "                        <td id=\"T_adff3_row0_col3\" class=\"data row0 col3\" >aSi_002</td>\n",
       "                        <td id=\"T_adff3_row0_col4\" class=\"data row0 col4\" >aSi_002</td>\n",
       "                        <td id=\"T_adff3_row0_col5\" class=\"data row0 col5\" >aSi_002</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adff3_level0_row1\" class=\"row_heading level0 row1\" >Curve</th>\n",
       "                        <td id=\"T_adff3_row1_col0\" class=\"data row1 col0\" >Dark Curve1</td>\n",
       "                        <td id=\"T_adff3_row1_col1\" class=\"data row1 col1\" >Illuminated IV3</td>\n",
       "                        <td id=\"T_adff3_row1_col2\" class=\"data row1 col2\" >Illuminated IV3</td>\n",
       "                        <td id=\"T_adff3_row1_col3\" class=\"data row1 col3\" >Dark Curve1</td>\n",
       "                        <td id=\"T_adff3_row1_col4\" class=\"data row1 col4\" >Illuminated IV2</td>\n",
       "                        <td id=\"T_adff3_row1_col5\" class=\"data row1 col5\" >Illuminated IV3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adff3_level0_row2\" class=\"row_heading level0 row2\" >Size [cm2]</th>\n",
       "                        <td id=\"T_adff3_row2_col0\" class=\"data row2 col0\" >1.96</td>\n",
       "                        <td id=\"T_adff3_row2_col1\" class=\"data row2 col1\" >1.96</td>\n",
       "                        <td id=\"T_adff3_row2_col2\" class=\"data row2 col2\" >1.96</td>\n",
       "                        <td id=\"T_adff3_row2_col3\" class=\"data row2 col3\" >1.96</td>\n",
       "                        <td id=\"T_adff3_row2_col4\" class=\"data row2 col4\" >1.96</td>\n",
       "                        <td id=\"T_adff3_row2_col5\" class=\"data row2 col5\" >1.96</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adff3_level0_row3\" class=\"row_heading level0 row3\" >Temperature [C]</th>\n",
       "                        <td id=\"T_adff3_row3_col0\" class=\"data row3 col0\" >25</td>\n",
       "                        <td id=\"T_adff3_row3_col1\" class=\"data row3 col1\" >25</td>\n",
       "                        <td id=\"T_adff3_row3_col2\" class=\"data row3 col2\" >25</td>\n",
       "                        <td id=\"T_adff3_row3_col3\" class=\"data row3 col3\" >25</td>\n",
       "                        <td id=\"T_adff3_row3_col4\" class=\"data row3 col4\" >25</td>\n",
       "                        <td id=\"T_adff3_row3_col5\" class=\"data row3 col5\" >25</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adff3_level0_row4\" class=\"row_heading level0 row4\" >Material</th>\n",
       "                        <td id=\"T_adff3_row4_col0\" class=\"data row4 col0\" >Silicon</td>\n",
       "                        <td id=\"T_adff3_row4_col1\" class=\"data row4 col1\" >Silicon</td>\n",
       "                        <td id=\"T_adff3_row4_col2\" class=\"data row4 col2\" >Silicon</td>\n",
       "                        <td id=\"T_adff3_row4_col3\" class=\"data row4 col3\" >Silicon</td>\n",
       "                        <td id=\"T_adff3_row4_col4\" class=\"data row4 col4\" >Silicon</td>\n",
       "                        <td id=\"T_adff3_row4_col5\" class=\"data row4 col5\" >Silicon</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adff3_level0_row5\" class=\"row_heading level0 row5\" >Date</th>\n",
       "                        <td id=\"T_adff3_row5_col0\" class=\"data row5 col0\" >25/11/2021</td>\n",
       "                        <td id=\"T_adff3_row5_col1\" class=\"data row5 col1\" >25/11/2021</td>\n",
       "                        <td id=\"T_adff3_row5_col2\" class=\"data row5 col2\" >25/11/2021</td>\n",
       "                        <td id=\"T_adff3_row5_col3\" class=\"data row5 col3\" >25/11/2021</td>\n",
       "                        <td id=\"T_adff3_row5_col4\" class=\"data row5 col4\" >25/11/2021</td>\n",
       "                        <td id=\"T_adff3_row5_col5\" class=\"data row5 col5\" >25/11/2021</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adff3_level0_row6\" class=\"row_heading level0 row6\" >Time</th>\n",
       "                        <td id=\"T_adff3_row6_col0\" class=\"data row6 col0\" >11:10</td>\n",
       "                        <td id=\"T_adff3_row6_col1\" class=\"data row6 col1\" >11:13</td>\n",
       "                        <td id=\"T_adff3_row6_col2\" class=\"data row6 col2\" >11:13</td>\n",
       "                        <td id=\"T_adff3_row6_col3\" class=\"data row6 col3\" >11:19</td>\n",
       "                        <td id=\"T_adff3_row6_col4\" class=\"data row6 col4\" >11:19</td>\n",
       "                        <td id=\"T_adff3_row6_col5\" class=\"data row6 col5\" >11:20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adff3_level0_row7\" class=\"row_heading level0 row7\" >Temperature Corrected</th>\n",
       "                        <td id=\"T_adff3_row7_col0\" class=\"data row7 col0\" >yes</td>\n",
       "                        <td id=\"T_adff3_row7_col1\" class=\"data row7 col1\" >yes</td>\n",
       "                        <td id=\"T_adff3_row7_col2\" class=\"data row7 col2\" >yes</td>\n",
       "                        <td id=\"T_adff3_row7_col3\" class=\"data row7 col3\" >yes</td>\n",
       "                        <td id=\"T_adff3_row7_col4\" class=\"data row7 col4\" >yes</td>\n",
       "                        <td id=\"T_adff3_row7_col5\" class=\"data row7 col5\" >yes</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adff3_level0_row8\" class=\"row_heading level0 row8\" >Irradiance</th>\n",
       "                        <td id=\"T_adff3_row8_col0\" class=\"data row8 col0\" >darkcurve</td>\n",
       "                        <td id=\"T_adff3_row8_col1\" class=\"data row8 col1\" >999.94388152767</td>\n",
       "                        <td id=\"T_adff3_row8_col2\" class=\"data row8 col2\" >999.94388152767</td>\n",
       "                        <td id=\"T_adff3_row8_col3\" class=\"data row8 col3\" >darkcurve</td>\n",
       "                        <td id=\"T_adff3_row8_col4\" class=\"data row8 col4\" >999.94388152767</td>\n",
       "                        <td id=\"T_adff3_row8_col5\" class=\"data row8 col5\" >999.94388152767</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adff3_level0_row9\" class=\"row_heading level0 row9\" >Irradiance Corrected</th>\n",
       "                        <td id=\"T_adff3_row9_col0\" class=\"data row9 col0\" >darkcurve</td>\n",
       "                        <td id=\"T_adff3_row9_col1\" class=\"data row9 col1\" >yes</td>\n",
       "                        <td id=\"T_adff3_row9_col2\" class=\"data row9 col2\" >yes</td>\n",
       "                        <td id=\"T_adff3_row9_col3\" class=\"data row9 col3\" >darkcurve</td>\n",
       "                        <td id=\"T_adff3_row9_col4\" class=\"data row9 col4\" >yes</td>\n",
       "                        <td id=\"T_adff3_row9_col5\" class=\"data row9 col5\" >yes</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x24ee859cf08>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display meta data for measurements contained in the data_file\n",
    "meta_data=pd.read_csv(data_file,sep='\\t',nrows=10,index_col=0)\n",
    "for col in meta_data.columns:\n",
    "    if 'Id'in col:\n",
    "        del meta_data[col]\n",
    "    if 'Unnamed' in col:\n",
    "        del meta_data[col]\n",
    "\n",
    "IV_arr=[]\n",
    "S_arr=[]\n",
    "for i in meta_data.columns:\n",
    "    S_arr.append(i)\n",
    "    S_arr.append(i)\n",
    "    IV_arr.append('V')\n",
    "    IV_arr.append('I')\n",
    "    \n",
    "arrays=[S_arr,IV_arr]\n",
    "tuples = list(zip(*arrays))\n",
    "index = pd.MultiIndex.from_tuples(tuples, names=[\"run\", \"param\"])\n",
    "\n",
    "meta_data.style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Short circuit current\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 275\n",
      "    # data points      = 100\n",
      "    # variables        = 2\n",
      "    chi-square         = 244466.902\n",
      "    reduced chi-square = 2494.56022\n",
      "    Akaike info crit   = 784.166502\n",
      "    Bayesian info crit = 789.376843\n",
      "[[Variables]]\n",
      "    I_sc:  2408.15383 +/- 9.42077960 (0.39%) (init = 2400)\n",
      "    G_sh:  0.99999979 +/- 5.15131016 (515.13%) (init = 0.0015)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(I_sc, G_sh) = -0.848\n",
      "\n",
      " Open circuit voltage\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 57\n",
      "    # data points      = 39\n",
      "    # variables        = 2\n",
      "    chi-square         = 14626.8012\n",
      "    reduced chi-square = 395.318950\n",
      "    Akaike info crit   = 235.154918\n",
      "    Bayesian info crit = 238.482041\n",
      "[[Variables]]\n",
      "    V_oc:  5.46856445 +/- 0.00407201 (0.07%) (init = 5.5)\n",
      "    R_s:   7.4594e-04 +/- 5.2217e-06 (0.70%) (init = 1e-08)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(V_oc, R_s) =  0.812\n",
      "\n",
      " Summary\n",
      "{'max_P': 0.0072489421755290995, 'max_I': 0.0019919795255981714, 'max_V': 3.6390646, 'PCE': 3.6986474479184945, 'I_sc': 0.002408153831216402, 'G_sh': 9.99999787497659e-07, 'V_oc': 5.468564454478096, 'R_s': 745.9357548137393, 'FF': 0.5504489837807696, 'n': 14.347387801835694, 'I_0': 8.556514019649975e-10, 'I_L': 0.0024100616301374386}\n"
     ]
    }
   ],
   "source": [
    "# Import datafile data and specify key to analyse\n",
    "data=pd.read_csv(data_file,sep='\\t',skiprows=12,index_col=False,names=index)\n",
    "dark=pd.Series(-savgol_filter(data['Dark_1.1','I'],21,1),index=data['Dark_1.1','V'])\n",
    "\n",
    "key='Reverse_1.1'\n",
    "area=float(meta_data[key]['Size [cm2]']) #cm^2\n",
    "irr=float(meta_data[key]['Irradiance'])*0.1 #mW/cm^2\n",
    "T=int(meta_data[key]['Temperature [C]'])\n",
    "\n",
    "# Apply Savitsky-Golay filter\n",
    "rolling_data=pd.Series(savgol_filter(data[key,'I'],21,1),index=data[key,'V'])\n",
    "\n",
    "# Calculate power\n",
    "power_data=np.multiply(data[key,'I'],data[key,'V'])\n",
    "power_rolling_data=np.multiply(rolling_data,rolling_data.index)\n",
    "max_V=power_rolling_data.idxmax(axis=0)\n",
    "max_I=rolling_data[max_V]\n",
    "max_P=power_rolling_data[max_V]\n",
    "\n",
    "# Initialise sample summary parameter dictionary\n",
    "summary={}\n",
    "summary['max_P']=max_P\n",
    "summary['max_I']=max_I\n",
    "summary['max_V']=max_V\n",
    "summary['PCE']=(100*summary['max_P']*1000)/(area*irr) #mW/cm^2\n",
    "\n",
    "# Fitting to determine I_sc, G_sh\n",
    "def Isc_model(G_sh,I_sc,V):\n",
    "    return (-G_sh*V)+I_sc\n",
    "\n",
    "def Isc_residual(params, V, data):\n",
    "    G_sh = params['G_sh']\n",
    "    I_sc = params['I_sc']\n",
    "\n",
    "    model = (-G_sh*V)+I_sc\n",
    "\n",
    "    return data-model\n",
    "\n",
    "print('\\n Short circuit current')\n",
    "params1 = Parameters()\n",
    "params1.add('I_sc', value=2400, min=2000, max=3000)\n",
    "params1.add('G_sh', value=1.5e-3, min=1e-3, max=1)\n",
    "out1 = minimize(Isc_residual, params1, args=(rolling_data.index[0:100], rolling_data.values[0:100]*1e6), method='leastsq')\n",
    "print(fit_report(out1))\n",
    "\n",
    "Isc_model_plot=pd.Series(Isc_model(out1.params['G_sh'].value,out1.params['I_sc'].value,np.linspace(0,0.5,20)),index=np.linspace(0,0.5,20))\n",
    "summary['I_sc']=out1.params['I_sc'].value*1e-6\n",
    "summary['G_sh']=out1.params['G_sh'].value*1e-6\n",
    "\n",
    "# Fitting to determine V_oc and R_s\n",
    "def Voc_model(R_s,V_oc,V):\n",
    "    return (-1/R_s)*(V-V_oc)\n",
    "\n",
    "def Voc_residual(params, V, data):\n",
    "    R_s = params['R_s']\n",
    "    V_oc = params['V_oc']\n",
    "\n",
    "    model = (-1/R_s)*(V-V_oc)\n",
    "\n",
    "    return data-model\n",
    "\n",
    "print('\\n Open circuit voltage')\n",
    "params2 = Parameters()\n",
    "params2.add('V_oc', value=5.5, min=5, max=6)\n",
    "params2.add('R_s', value=1e-8, min=1e-9, max=1)\n",
    "out2 = minimize(Voc_residual, params2, args=(rolling_data.index[-40:-1], rolling_data.values[-40:-1]*1e6), method='leastsq')\n",
    "print(fit_report(out2))\n",
    "\n",
    "Voc_model_plot=pd.Series(Voc_model(out2.params['R_s'].value,out2.params['V_oc'].value,np.linspace(5,5.5,20)),index=np.linspace(5,5.5,20))\n",
    "summary['V_oc']=out2.params['V_oc'].value\n",
    "summary['R_s']=out2.params['R_s'].value*1e6\n",
    "\n",
    "# Calculate fill factor and PCE\n",
    "summary['FF']=summary['max_P']/(summary['V_oc']*summary['I_sc'])\n",
    "\n",
    "# Calculate dark current, ideality factor and illuminated current\n",
    "V_t=(1.38e-23*(T+273))/1.602e-19\n",
    "\n",
    "n=(summary['max_V']+(summary['max_I']*summary['R_s'])-summary['V_oc'])/(V_t*(np.log(summary['I_sc']-(summary['max_V']*summary['G_sh'])-summary['max_I'])-np.log(summary['I_sc']-(summary['V_oc']*summary['G_sh']))+summary['max_I']/(summary['I_sc']-(summary['V_oc']*summary['G_sh']))))\n",
    "summary['n']=n                                                        \n",
    "summary['I_0']=(summary['I_sc']-(summary['V_oc']*summary['G_sh']))*np.exp(-summary['V_oc']/(n*V_t))\n",
    "summary['I_L']=(summary['I_sc']*(1+(summary['R_s']*summary['G_sh'])))+summary['I_0']*(np.exp((summary['I_sc']*summary['R_s'])/(n*V_t))-1)\n",
    "\n",
    "print('\\n Summary')\n",
    "print(summary)\n",
    "\n",
    "export_folder_dir=os.path.abspath(os.path.dirname(export_folder))\n",
    "export_path=os.path.join(export_folder_dir, str(meta_data[key]['Device'])+'_outdoor_'+key+'.txt')\n",
    "\n",
    "f=open(export_path,\"w+\")\n",
    "f.write('\\r\\n Short circuit current \\r\\n')\n",
    "f.write(fit_report(out1))\n",
    "f.write('\\r\\n Open circuit voltage \\r\\n')\n",
    "f.write(fit_report(out2))\n",
    "f.write('\\r\\n Summary \\r\\n')\n",
    "for i in summary:\n",
    "    f.write(i+'\\t'+str(summary[i])+'\\r\\n')\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 5000x7500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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6dcrhUF9RzZo1y+7/l319fXXq1Cm3vsISAAAAQOng6eoCAAAAAAAAKL1Gjhxpd33FihW6cOGCU/c8d+6cVq1aZTfjjFNWfv31V02dOtVuxsvLS/PmzdM777xT5CsSb7nlFn3zzTd65plnHGYnTJigrKysIj3/ZlOjRg1NnTpVx48f17Rp05x2wo+vr69efvllJSQkKDAw0GYuJSXF4ZWgRkRFRTkc6Bo3bpw+++yzYl8jOGLECC1dutSUq1FtefHFF+0OdHl6euqNN95QXFxcsQa6pP+czBYbG+vw7/+4ceMMXYHqrmJiYq470CXdGKd0/eHRRx+Vh4eHzfX09HStXLnS6fs6unqxb9++DHQBAAAAsARDXQAAAAAAADDNQw89ZHewJDc3VwsXLnTqngsWLLB7wlC5cuU0cODAEu8zZcoUh9fETZ8+XcOHDy/RPu+++67DIZQTJ04oKiqqRPuUdtHR0XruuecUEBBgyvNbtmypuLg4u8N7s2bNKtEeeXl5dq80laROnTrpww8/LNE+ktSnTx+9+eabJX6OEXv27NHMmTPtZmbMmKHJkyfbHfIxwsfHR3PnzlW/fv1sZg4fPqzPP/+8RPu4kr2rF+29b3dTu3ZthYeH2804GsAqql9++UVbt261m+HqRQAAAABWYagLAAAAAAAApqlYsaLuv/9+uxln/1De0fMGDBggf3//Eu3x66+/6ptvvrGb6devn8aPH1+iff7wySefqG7dunYzH3zwgS5fvuyU/VA8rVu3tnuy2pYtW5SSklLs5y9evFi///67zXUfHx99/PHHTjth6+mnn1ajRo2c8ix7pkyZooKCApvrzzzzjMaNG+fUPefPn6/Q0FCb69OmTXPqfla5fPmy1q1bd921KlWqqFWrVhY3KhlHA1SxsbE6c+aM0/Zz9PmjWrVq6tGjh9P2AwAAAAB7GOoCAAAAAACAqRxdwbhjxw4dOHDAKXvt3btXe/bssZtxxikrs2bNsjuE4ufnpw8++KDE+/yhTJkymj59ut3M2bNntXTpUqftieKZPHmy3avZVq9eXexnf/nll3bXJ06cqDvvvLPYz/9v3t7e+uijj5z2vOvZt2+f3Sv06tSpozfeeMPp+1asWNHuSWTJycnasmWL0/c125o1a5STk3PdtT59+pT4pDOrPfjgg3aHcPPz8xUdHe2UvQoKChz+HXvkkUcsvZYUAAAAwM2NoS4AAAAAAACYqkuXLqpZs6bdjLNO65o3b57d9dq1a6tDhw4l2uPatWsOr4x85plnFBYWVqJ9/lvPnj3Vq1cvu5kb+cq40qJcuXJ2/z398MMPxXpuenq63dd6eXlp0qRJxXq2PeHh4WrdurXTn/uHOXPm2F1/99135evra8reAwcOVO3atW2uL1myxJR9zbR8+XKba3379rWwiXMYuS7XWZ8/1q5dq5MnT9rNjBgxwil7AQAAAIARDHUBAAAAAADAVJ6enho+fLjdTHR0tK5du1aifa5evaqvvvrKbmbEiBElPqlm27Ztdq/A8/T01JgxY0q0hy2OrnPcsGGDMjIyTNkbxt13330213bv3l2sZ8bHx9v9O9K1a1dVqVKlWM92ZOjQoaY899q1a3b/ztatW1eRkZGm7C39ZxDO3sl9a9asMW1vMxQUFGjVqlXXXStXrpy6du1qcSPncHS64r59+5SYmFjifRwNBd97771OPQkPAAAAABxhqAsAAAAAAACmczRM9fvvv5foWjpJWrVqlc6cOWNz3cPDw+FwmRFr1661u961a1cFBweXeJ/r6datm6pWrWpz/dq1a4qPjzdlbxgXEhJic+3IkSPKzs4u8jM3bNhgd92swStJGjx4sHx8fJz+3J9++knp6ek21wcNGuT0Pf9bx44dba4dPHjQ7scUd7NlyxadPXv2umvdunWTn5+fxY2co23btrrjjjvsZkp6WtfFixftnnImOefqXgAAAAAoCoa6AAAAAAD/X3t3HmV1ed4B/BkGGJBhF1QQEUTADdAECLJoBAEDQoMRBQ3ibmxJbFJzEvW01WMb0/YkIZ4G27hGZKz1nAoiKKCoxMiWKAiukUVWGXYVhhlg+kcOTaNzf3eWe+9c5PM5h3/meX/v88x4mTnM/fq+AFl32mmnxZAhQxLX1PVN+XTPDx06NE455ZQ69YiIWLRoUWJ94sSJde6RSmFhYVx55ZWJa9LNR/YlBe8qKyvjo48+qvGe6U74quu1oknatm0bZ511Vsb3ff755xPrl112WcZ7fl6/fv2iqKgoZf3NN9/M+gyZMmvWrJS1sWPH5nCSzEsXqJoxY0aUl5fXev8nn3wyysrKUtarcw0kAABApjWs7wEAAAAAODZce+218corr6Ssz5o1K3bt2hWtW7eu8d6lpaUxZ86ctP0zYeXKlYn1QYMGZaRPKoMHD45f/OIXKesrVqzIav9jweHDh2Pjxo3x8ccfR2lpaXzyySdRXl4e5eXlUVlZmfb5vXv3Jta3bNkSZ5xxRo1mWr16dcpamzZtolOnTjXar6Z69+6d8YDT0qVLU9aKiorinHPOyWi/qjRu3DhOPPHEWL9+fZX1VatWxfDhw7M+RyakOmmqQYMGMXr06BxPk1mTJk2Ku+66K+UVpDt27IjZs2fHuHHjarV/ulDwZZddFi1atKjV3gAAALUl1AUAAABATnzrW9+KKVOmxCeffFJl/cCBA1FSUhK33nprjfd+4oknoqKiImW9ZcuW8c1vfrPG+37ekZBPKi1atIiuXbvWuU+Sc889N7H+1ltvZbX/l9GmTZtizpw58frrr8eyZcvij3/8Y+KpPXWVdOVgqvV79uxJWe/du3ddR0qrT58+8dhjj2V0z6TTx7p37x6FhYUZ7ZdK27ZtU4a6Nm7cmJMZ6uq9996L999/v8ragAEDol27djmeKLM6dOgQw4cPj7lz56Zc8+ijj9Yq1PXuu+/GkiVLEte4ehEAAKgPQl0AAAAA5ESzZs1i/Pjx8dBDD6Vc8+ijj9Yq1JXulJUJEyZE06ZNa7zv56W7Nq93795RUFBQ5z5JunTpEq1atYrdu3dXWd+2bVuUlZVFkyZNsjrH0a6srCxKSkriP//zP2PJkiXVOoErU/bv31+j9Zs3b06sn3766XUZp1q6d++e0f127NgR27dvT1kvLCyMBx98MKM9U9m3b1/K2qZNm3IyQ12lOqUrImLMmDE5nCR7rrvuusRQ19y5c2Pbtm3Rvn37Gu2b7udHly5d4sILL6zRngAAAJkg1AUAAABAzlx77bWJoa5ly5bF22+/HWeeeWa191yxYkXaKwczdcpKunBNt27dMtInnW7dusXy5ctT1rds2RJdunTJySxHo4cffjj+/u//vt4COwcOHKjR+m3btiXWW7ZsWZdxqiXTV89t2LAhsf7mm2/GjTfemNGetZHuKs18MWvWrJS1sWPH5nCS7BkzZky0bds25Ul3Bw8ejOnTp8f3v//9au95+PDhmD59euKayZMnZz2sCwAAUJUG9T0AAAAAAMeOgQMHpj3xJ92pKZ/3yCOPJNbPPPPM6NevX432TCXpZKGI3IRrItIHbJKuiDyWlZaWxvDhw+P666+v1xOYDh06VKP1SSdJRWQ+cJWLHukCkvmipqeq1YfS0tJ4/fXXq6x17949evTokeOJsqNx48YxceLExDU1vSJ03rx5id8LCgoK4pprrqnRngAAAJki1AUAAABATqU7NWv69OnVDr1UVFTEjBkz6tSvJtIFPHIRrolIHx47GoIoubZ27doYMGBAzJ8/v75HqbF0J3s1b9486zNk+rX9ySefZHS/bKnpqWr1Yfbs2XH48OEqa1+WU7qOuO666xLrK1eujD/84Q/V3i9diPiiiy6Kzp07V3s/AACATBLqAgAAACCnJk2aFIWFhSnrW7ZsiRdeeKFaez333HOJp1I1bNgwvv3tb9d4xlTKysoS6/kS6ko357Fmx44dMWLEiPjwww/re5SsyMXVcA0aZPZXyUdL8LCysrK+R0hr5syZKWtjxozJ4STZ16dPn+jTp0/imuqe9rh79+7Er11EZkPBAAAANSXUBQAAAEBOdejQIYYPH564prpvyqe7evGSSy6JE044obqj1VkuwjURR0fQJJ/ceOON8cEHH1RrbePGjWPgwIExZcqUuP/++2P27NmxfPnyWLduXWzfvj32798fFRUVUVlZmfLPli1bMjp/UVFRYn3v3r0Z7VeVPXv2ZHS/ioqKjO53rCorK0t5+ly7du3i/PPPz/FE2ZcuaFVSUlKt11dJSUliALZly5Yxbty4Gs8HAACQKQ3rewAAAAAAjj3XXXddzJ07N2V91qxZsWvXrmjdunXKNaWlpYl7HOmTSU2aNEmsZzr4kkq6EE+6OY8lzz33XPzP//xP2nUXXHBBfOc734nRo0dHs2bN6tQz06G74447LrF+NIa60gXVqJ758+fHvn37qqyNGjUq4yes5YOrrroqbr/99igvL6+yvn379nj22WfTBrLShYevvPLKaNq0aW3HBAAAqLMv37/oAAAAAMh7Y8aMibZt26asHzhwIEpKShL3mD59euJpLO3atYtRo0bVesaqpHuDPxfhmur0EUT4s7vvvjux3rRp03jsscfi5ZdfjiuuuKLOga6IzAeg2rdvn1jPxesu0z3SBdV+8pOfJJ6Glqs/ixcvzujnnWmzZs1KWRs7dmwOJ8mdtm3bpr1W8rHHHkusv/vuu7F06dLENa5eBAAA6ptQFwAAAAA517hx45g4cWLimnRvyqerX3311dGoUaMaz5bk+OOPT6znKtSVLjTUrl27nMyR7956661YtmxZynphYWHMmzcvJk2alNG+u3btyuh+J510UmK9uldL1kWmeySFOiMi1q5dm9F+X0aVlZUxe/bsKmtNmjRJe83t0Sxd4GrOnDmxbdu2lPV0V/eeccYZ0b9//1rNBgAAkClCXQAAAADUi3RXIy5dujTeeeedKmtvvPFGrFixIvH5bJyy0qFDh8T6hx9+mPGetemTLgR0rJg5c2Zi/Y477ohBgwZlvO/OnTszut/xxx8fLVq0SFlP93chE958882M7nfKKack1tesWZPRfl9GS5Ysia1bt1ZZGzZsWNrT0I5mI0aMSPx+fPDgwXjiiSeqrB06dCimT5+euL9TugAAgHwg1AUAAABAvejTp0/06dMncc2jjz5ao48f8ZWvfCXOOeecWk6WWufOnRPrmQ6+VGXdunWJJ0G1b98+mjRpkvU5jgaLFi1KWWvUqFHcdtttWembjUDSmWeembK2Y8eO2LBhQ8Z7/n+Zfm136tQpCgsLU9ZzcfrY0S4ptJjuesKjXWFhYdoT9lL9nJg3b15s3rw55XMNGzaMb3/723UZDwAAICOEugAAAACoN+lOQ5k+fXocOnToLz5WUVERM2bMqNO+tdW+ffto3759yvqePXuyfm3cG2+8kVjPRpjtaJXqpLeIiKFDh0abNm2y0ve1117L+J7nnntuYv3VV1/NeM8jdu7cGatXr87ono0bN46zzjorZX39+vWCXWnMmjWryo8XFBTEpZdemuNpci/d9/mVK1dW+f0yXSj4kksuiRNPPLEuowEAAGSEUBcAAAAA9eaqq66Kxo0bp6xv3rw55s2b9xcfmz17dmzfvj3lM0VFRTFx4sSMzfh5vXr1SqxnI9BTk/179+6d1f5Hi4MHD8amTZtS1pNOvqqrbLwGLrzwwsR6uuvk6uK//uu/oqKiIuP79u/fP7E+e/bsjPf8svjwww/j7bffrrLWr1+/YyKU1L179xg4cGDims8HuHbv3p32WlZXLwIAAPlCqAsAAACAetO2bdu01weP0DUAABUPSURBVIR9/k35dKes/NVf/VW0bt26jpOlNmTIkMR6ulPE6uLw4cNRUlKSuOaCCy7IWv/aSLpiLyKyEhaKiPjss8/i8OHDKevZCr2sWLEiNm7cmPF9L7roosSv5YIFC2Lbtm0Z7xsR8fjjj2dl32HDhiXW04VvjmVJX5uxY8fmcJL6lS6ANWPGjL/4HlNSUhIHDhxIuf7444+P0aNHZ2w+AACAuhDqAgAAAKBepXtTfubMmbFr166IiNi2bVvMmTOnTvvV1cUXX5xYnz9/ftbCNS+99FJs3rw5Zb1hw4ZpT3TKtaST2CIi9u/fn5W+5eXlifV0YbPamjp1alb2Pf744+PrX/96yvrBgwfj5z//ecb7/va3v43XX3894/tGRHzjG9+IoqKilPVXXnklli5dmpXeR7ukUFe6oOyXyfjx46NZs2Yp69u3b/+LE98eeeSRxP2uvvrqaNSoUcbmAwAAqAuhLgAAAADq1YgRI6JDhw4p6wcOHIgnn3wyIv50xdzBgwdTrj355JPThq7qql+/ftGxY8eU9YMHD8avf/3rrPT+93//98T6RRddFC1atMhK79oqLi6OgoKClPW9e/dmpW/Tpk0T69kI3pWWlqY9Sa0urrrqqsT6z372s3j//fcz1u/QoUPxN3/zNxnb7/OKi4vTBpDuuuuurPU/Wu3cuTPlFZ+nnXZanHXWWTmeqP40b948LrvsssQ1R053fOedd2LZsmWJa129CAAA5BOhLgAAAADqVWFhYUyaNClxzZE35R977LHEdZMmTYoGDbL7K68GDRrExIkTE9fcd999sWnTpoz2XbBgQTzzzDOJa9J9HetDYWFh4nWYa9asyUrf4uLixGBXunBHbdx2221RVlaW8X2PmDBhQpxwwgkp6+Xl5XHrrbcmXjtZE7/4xS9ixYoVGdkrlSlTpiTW58+fH88++2xWZzjazJ49Ow4dOlRl7Vg6peuI6667LrE+Z86c2LZtW9pTus4777zo1atXJkcDAACoE6EuAAAAAOpdutNRli5dGk888USsXLmyTvtkyk033ZR4fd+nn34at99+e8b6VVRUpA2/nHDCCTFu3LiM9cykTp06pay9/fbbWevbtWvXlLVFixbF9u3bM9brqaeeihkzZmRsv6oUFRXF3/7t3yauefHFF9OuqY45c+bEj370ozrvk87gwYPj/PPPT1xz9dVXZ/V1crSZNWtWytrYsWNzOEl+GDJkSOLf9YMHD8ZvfvObmD59euI+6cJhAAAAuSbUBQAAAEC96969e9pgxy233JJYHzx4cHTr1i2TY6XUrVu3GD9+fOKakpKS/zthrK6mTJkS7777buKa73//+2mvHKwvPXv2TFlbuXJlbNy4MSt9+/btm7JWUVER99xzT0b6rFq1Ku3rM1O+973vxamnnpq45pe//GXceOONceDAgVr1ePzxx+Ob3/xm4lWnmfSzn/0s7RWdl156adZeJ1WZM2dOlJeX56xfdR04cCBeeOGFKmtt2rSJQYMG5Xii+ldQUBCTJ09OXHP33XfHli1bUtaLiopiwoQJGZ4MAACgboS6AAAAAMgL6U5J+fTTTxPruTql64h77rknioqKEtfcdNNN8fTTT9epz5133hn/8R//kbjmlFNOib/+67+uU59s6t+/f8paZWVl/OQnP8lK3+HDhyfWf/WrX9X5ar/ly5fHsGHDYteuXXXap7qaNGkS999/f9p1Dz74YPTt2zcWLFhQ7b3Xrl0bV1xxRUyaNOkLgaZsXkvXv3//uPHGGxPXrFmzJs4777yYP39+1uaoqKiIJ598Mr761a/GqFGj8jLU9dJLL6X8Xjhq1KjEEwS/zCZPnpx49W66nx9jx46NNm3aZHosAACAOhHqAgAAACAvjB8/Ppo1a1arZ5s1axaXX355hidK1q1bt/jxj3+cuKaioiKuuOKK+Md//Mcan3q0Z8+euPLKK+Of//mf0669//77a/21y4Vhw4Yl1qdNmxY/+MEPYseOHRnte+mll0aLFi1S1g8dOhRXXHFFPPHEEzXe+/DhwzF16tQYMmRIfPzxx39Ry3awZvTo0XHrrbemXffWW2/FxRdfHL1794577703XnnlldiyZUscOHAgDh06FLt27Yrf//738cADD8SoUaOiW7du8dRTT31hn6ZNm8avfvWrbHwq/+fnP/954oluERGlpaUxcuTI+N73vpfRU7tWrVoVP/rRj6Jz584xYcKE+P3vf5+xvTNt5syZKWtjxozJ4ST5pVOnTjF06NBaP5/rUDAAAEB1FFRWVlbW9xAAAAAAEBFxzTXXxG9+85saPzd58uR45JFHsjBRsoqKivj6178er732Wtq1PXv2jDvuuCPGjx+feMLX7t274+GHH4777rsvSktL0+578803xwMPPFCjuT9v8eLFMWDAgJT1uXPnxsiRI+vU48wzz4x33nkncU2jRo1i4MCBcc4550THjh2juLg48Wt13HHHxcSJExP3vP322+Pf/u3f0s43duzYuP3222PgwIGJ6z799NP47//+7/jXf/3XlJ/PXXfdFffee2/KPaZNm1bn6xoPHDgQI0eOjJdffrlO+1THQw89FN/4xjfipJNOSrnmgQceiJtvvrlOfd5///0YOHBgbN++Pe3aRo0axYQJE+Kqq66K888/P4qLi6vdZ9u2bbF48eJYuHBhPPfcc/HBBx9Uue6TTz6p0b7ZVllZGZ06dYpNmzZ9oVZUVBSlpaXRvHnzepgsP5SUlKT9flCVjh07xvr164/ZU84AAID8JdQFAAAAQN545ZVX4sILL6zVc0OGDMn8QNWwefPm6NevX5VBi6o0a9YshgwZEr169YqTTjopmjZtGvv27YuPPvoo/vCHP8Tvfve7qKioqNZeAwYMiIULF6a9BjKdXIS6fv3rX8dNN91Upz0+74QTToitW7cmrtm9e3d07969WgG5iD9dZTlw4MDo0aNHtG7dOoqKiuLTTz+N9evXx8qVK2Px4sVx4MCBlM+PGDEiHnnkkejQoUPKNZkIdUX86TS3oUOHZvVkqR/+8Ifx05/+NLZu3ZoY6nrwwQfj+uuvr3O/JUuWxPDhw2Pv3r3VfqZhw4Zx7rnnRq9evaJNmzbRpk2baN26dRw6dCjKyspi586dsXXr1li7dm28++67sXnz5mrtm2+hrmXLlkW/fv2qrI0cOTLmzp2b44nyS1lZWZx00kmxe/fuGj334x//uFonIgIAAORaw/oeAAAAAACOGDJkSHTt2jXWrFlT7WdOO+20egt0RUR06NAhXnzxxbjgggu+cA1fVT777LOYO3dunQMY5557bsyZM6fOga5cufbaa2PatGnxxhtv5LRvq1at4uGHH44xY8ZEdf7/1o8++ig++uijWvU6++yz48knn4yysrJaPV9TLVu2jIULF8a3vvWtmDdvXsb3v/POO//vxLH9+/cnrm3SpElGevbv3z9eeeWVuOSSS9IG9o44ePBgLFu2LJYtW5aRGfLVrFmzUtbGjh2bw0nyU5MmTWLChAkxbdq0Gj3n6kUAACBfNajvAQAAAADgiIKCgpg8eXKNnsmHN+R79OgRr776avTo0SMn/YYNGxYvvvhitGrVKif9MqFhw4bx9NNPx4knnpjz3qNHj45/+Zd/yWqPnj17xvPPP5/z/ybNmzePuXPnxr333huNGjXKyJ5t2rSJJ5544i+ukEx3+lGmQl0REX369ImlS5fG4MGDM7bnl8HMmTOr/HhBQUFceumlOZ4mP9X058GgQYPi9NNPz9I0AAAAdSPUBQAAAEBemTx5cjRoUL1fWzVo0CAmTZqU5Ymqp3v37rFkyZK48sors9ajYcOGcccdd8Tzzz8frVu3zlqfbOnatWssWbIkBg4cmPPef/d3fxcPP/xwRsNHRwwbNixef/316NixY8b3ro4GDRrEnXfeGatXr47LL7+82n9/Pq9JkyZx8803x+rVq2PixIl/UUsX6mrbtm2teqbSqVOnWLhwYfz0pz+N5s2bZ3TvdBo1ahRjx47Nq1Pw1q1bF2+99VaVta985Sv19trLN3379o2zzz672uvzIRQMAACQilAXAAAAAHmlU6dOMXTo0GqtHTZsWHTq1CnLE1Vfy5Yto6SkJGbPnl2jYEF1DB06NJYvXx7/9E//FIWFhRndO5dOOeWUWLRoUTz99NNxwQUX1DqAVBvXXntt/O53v8vYiWqtWrWKadOmxbx58/Li1LTTTz89nnrqqVi7dm3cfffd8bWvfS3ta+W4446LoUOHxtSpU2P9+vXxwAMPVHma2q5duxL3ycYJbIWFhfHDH/4wPvjgg5gyZUoUFxdnvMf/d95558XUqVNj8+bN8cwzz2Ts5LNMSHVKV0TEmDFjcjhJ/qtuUKtZs2Yxfvz4LE8DAABQewWVlZWV9T0EAAAAAPx/JSUlXzgpKNW6bJ6MVReVlZXx7LPPxkMPPRTPP/98lJeX13iP5s2bx7hx4+KWW26Jr33ta1mY8k8WL14cAwYMSFmfO3dujBw5Miu9P/7443j55Zdj6dKl8d5778X69eujtLQ09u7dG+Xl5XHo0KGUz55wwgmxdevWGvc8dOhQPPXUU3HffffFypUra/x8165d4zvf+U7ccMMNVYa5duzYESNGjEj5/B133BHjxo2rcd/aKCsri/fffz/Wr18fe/fujYqKiiguLo4WLVrEaaedFl26dKlWsO4f/uEf4p577klZ37NnT7Ro0SKTo3/B3r17Y/r06fH000/HokWL4uDBg3Xar127dnHRRRfFsGHD4uKLL47OnTtnaNLMGzp0aLz00ktV1lasWBG9evXK8UT5a9u2bXHyySdHRUVF4rprrrkmHn300dwMBQAAUAtCXQAAAACQZbt3745FixbFa6+9FqtXr441a9bExx9/HJ999lmUl5dHkyZNori4ODp06BBdu3aN3r17x6BBg+L888/PynWB/Nk777wTCxcujJdffjn++Mc/xo4dO2LHjh1RVlYWzZo1i+bNm8cpp5wSPXr0iD59+sTw4cPjjDPOqO+xc27kyJHxwgsvVFnr2LFjbNy4Mafz7Nq1KxYvXhzLli2L1atXx/r162PTpk3xySefxL59+6KysjKKi4ujefPm0bx582jdunV069YtevbsGT179owzzzwzTj/99CgoKMjp3LWxe/fuaNeuXZUhts6dO8e6detyPxQAAABZJ9QFAAAAAEBKlZWV0bZt25RXMA4fPjxl4Iu6mzFjRlx11VVV1r773e/G1KlTczwRAAAAuZD+XG0AAAAAAI5Zc+fOTRnoioisXg1KxMyZM1PWxowZk8NJAAAAyCWhLgAAAAAAUrr//vsT6xdeeGFuBjkGVVRUxPPPP19lrVWrVjFkyJAcTwQAAECuCHUBAAAAAFClt956K/FqxVatWsX555+fw4mOLQsXLoy9e/dWWbvkkkuiUaNGOZ4IAACAXBHqAgAAAADgC/bv3x8TJ06MysrKlGvGjRsXRUVFOZzq2DJr1qyUtbFjx+ZwEgAAAHJNqAsAAAAAgC/47ne/G6tWrUpcc8MNN+RommNTqlBXo0aNYuTIkTmeBgAAgFwS6gIAAAAAOArdcsstsWDBgozvu3///rjyyivjwQcfTFw3ePDgGDBgQMb78ydvvPFGbNiwocraBRdcEC1btszxRAAAAORSQWXS2dkAAAAAAOSlnj17xnvvvRd9+/aNH/zgBzFmzJho2rRpnfZ89dVX47bbbos33ngjcV1BQUG8+uqrMWjQoDr1AwAAAKom1AUAAAAAcBQ6Euo6ori4OMaMGROXX355DBw4MNq1a1etfUpLS+Pll1+OX/7yl/Hb3/62Ws9cf/31aU/yAgAAAGpPqAsAAAAA4Cj0+VDX53Xq1CnOO++8OPXUU6NVq1bRunXraNy4cezcuTN27twZ27dvj+XLl8fbb79do75nn312LF68OJo1a1bXTwEAAABIoWF9DwAAAAAAQOZt2LAhNmzYkNE9O3XqFLNmzRLoAgAAgCxrUN8DAAAAAACQ/3r06BGvvvpqdOnSpb5HAQAAgC89oS4AAAAAABJdffXVsXz58jj11FPrexQAAAA4Jgh1AQAAAAAchUaNGhWtWrXKao+vfvWrsXDhwnj88cejuLg4q70AAACAPyuorKysrO8hAAAAAACoufLy8liwYEE888wzsWjRonjvvfeirr/ybdu2bYwePTpuuOGGGDRoUIYmBQAAAGpCqAsAAAAA4Eti586dsXjx4li1alWsW7cu1q5dGxs2bIg9e/bEvn37Yt++fVFeXh6NGzeOpk2bRtu2bePkk0+OLl26xLnnnht9+/aNvn37RmFhYX1/KgAAAHBME+oCAAAAAAAAAADIIw3qewAAAAAAAAAAAAD+TKgLAAAAAAAAAAAgjwh1AQAAAAAAAAAA5BGhLgAAAAAAAAAAgDwi1AUAAAAAAAAAAJBHhLoAAAAAAAAAAADyiFAXAAAAAAAAAABAHhHqAgAAAAAAAAAAyCNCXQAAAAAAAAAAAHlEqAsAAAAAAAAAACCPCHUBAAAAAAAAAADkEaEuAAAAAAAAAACAPCLUBQAAAAAAAAAAkEeEugAAAAAAAAAAAPKIUBcAAAAAAAAAAEAeEeoCAAAAAAAAAADII0JdAAAAAAAAAAAAeUSoCwAAAAAAAAAAII8IdQEAAAAAAAAAAOQRoS4AAAAAAAAAAIA8ItQFAAAAAAAAAACQR4S6AAAAAAAAAAAA8ohQFwAAAAAAAAAAQB4R6gIAAAAAAAAAAMgjQl0AAAAAAAAAAAB5RKgLAAAAAAAAAAAgjwh1AQAAAAAAAAAA5BGhLgAAAAAAAAAAgDwi1AUAAAAAAAAAAJBHhLoAAAAAAAAAAADyiFAXAAAAAAAAAABAHhHqAgAAAAAAAAAAyCNCXQAAAAAAAAAAAHlEqAsAAAAAAAAAACCPCHUBAAAAAAAAAADkEaEuAAAAAAAAAACAPCLUBQAAAAAAAAAAkEeEugAAAAAAAAAAAPKIUBcAAAAAAAAAAEAeEeoCAAAAAAAAAADII0JdAAAAAAAAAAAAeUSoCwAAAAAAAAAAII8IdQEAAAAAAAAAAOQRoS4AAAAAAAAAAIA8ItQFAAAAAAAAAACQR4S6AAAAAAAAAAAA8ohQFwAAAAAAAAAAQB4R6gIAAAAAAAAAAMgjQl0AAAAAAAAAAAB55H8BtP8Lah8jR+oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2500x1550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot IV and Power curves\n",
    "with rc_context(fname=rc_fname):\n",
    "    fig=plt.figure(figsize=(10,15))\n",
    "    fig,axs=plt.subplots(2,sharex = True)\n",
    "    axs[0].scatter(data[key,'V'],np.multiply(data[key,'I'],1e6),marker='x',s=2,alpha=0.7,color='grey',label='raw')\n",
    "    axs[0].plot(rolling_data.index,np.multiply(rolling_data.values,1e6),color='red',label='filtered')\n",
    "    axs[0].plot(dark.index,np.multiply(dark.values,1e6),color='navy',label='dark')\n",
    "    #axs[0].plot(Isc_model_plot.index,Isc_model_plot.values,color='purple')\n",
    "    #axs[0].plot(Voc_model_plot.index,Voc_model_plot.values,color='purple')\n",
    "    axs[0].axhline(0,lw=1,color='black')\n",
    "    axs[0].axhline(summary['max_I']*1e6,ls=':',color='orange')\n",
    "    axs[0].axvline(summary['max_V'],ls=':',color='orange')\n",
    "    axs[0].set_ylim(-500,3500)\n",
    "    axs[0].set_ylabel('Current / $\\mu$A',fontsize=12)\n",
    "    axs[0].legend(ncol=3,fontsize=8,loc='upper right',frameon=True)\n",
    "    axs[0].set_title(str(meta_data[key]['Device'])+' | '+key,fontsize=10)\n",
    "    axs[1].scatter(data[key,'V'],np.multiply(power_data,1e6),marker='x',s=2,alpha=0.7,color='grey')\n",
    "    axs[1].plot(rolling_data.index,np.multiply(power_rolling_data,1e6),color='blue')\n",
    "    axs[1].axhline(summary['max_P']*1e6,ls=':',color='orange')\n",
    "    axs[1].axvline(summary['max_V'],ls=':',color='orange')\n",
    "    axs[1].set_xlim(0,5.6)\n",
    "    axs[1].set_ylim(0,9000)\n",
    "    axs[1].set_xlabel('Voltage / V',fontsize=12)\n",
    "    axs[1].set_ylabel('Power / $\\mu$W',fontsize=12)\n",
    "    plt.savefig(os.path.join(export_folder_dir, str(meta_data[key]['Device'])+'_outdoor_'+key+'_JV.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x1550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create model\n",
    "V_t=(1.38e-23*(T+273))/1.602e-19\n",
    "\n",
    "def func(V,I_L,I_0,n):\n",
    "    return I_L-(I_0*(np.exp(V/(n*V_t))-1))\n",
    "\n",
    "model_x=np.linspace(0,5.5,100)\n",
    "model_y=func(model_x,summary['I_L'],summary['I_0'],summary['n'])\n",
    "\n",
    "# plot IV and Power curves\n",
    "with rc_context(fname=rc_fname):\n",
    "    plt.scatter(data[key,'V'],np.multiply(data[key,'I'],1e6),marker='x',s=2,alpha=0.7,color='grey',label='raw')\n",
    "    plt.plot(rolling_data.index,np.multiply(rolling_data.values,1e6),color='blue',label='filtered')\n",
    "    plt.plot(model_x,model_y*1e6,color='orange',label='model')\n",
    "    plt.ylim(0,2700)\n",
    "    plt.xlim(0,5.6)\n",
    "    plt.xlabel('Voltage / V')\n",
    "    plt.ylabel('Current / $\\mu$A')\n",
    "    plt.legend(ncol=2,fontsize=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 12\n",
      "    # data points      = 99\n",
      "    # variables        = 2\n",
      "    chi-square         = 49.4889504\n",
      "    reduced chi-square = 0.51019536\n",
      "    Akaike info crit   = -64.6436726\n",
      "    Bayesian info crit = -59.4534329\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    n:  at initial value\n",
      "[[Variables]]\n",
      "    n:  14.0000000 (init = 14)\n",
      "    c:  23.1290434 (init = 24)\n",
      "I_0 9.019533213291291e-11\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dark current\n",
    "dark_ln=np.log(-dark.values[-100:-1])\n",
    "\n",
    "# Fitting to determine n\n",
    "def n_model(n,c,V):\n",
    "    return (V/(n*V_t))-c\n",
    "\n",
    "def n_residual(params, V, data):\n",
    "    m = params['n']\n",
    "    c = params['c']\n",
    "\n",
    "    model = (V/(n*V_t))-c\n",
    "\n",
    "    return data-model\n",
    "\n",
    "params3 = Parameters()\n",
    "params3.add('n', value=14, min=12, max=16)\n",
    "params3.add('c', value=24, min=22, max=27)\n",
    "out3 = minimize(n_residual, params3, args=(dark.index[-100:-1], dark_ln), method='leastsq')\n",
    "print(fit_report(out3))\n",
    "\n",
    "n_model_plot=pd.Series(n_model(out3.params['n'].value,out3.params['c'].value,np.linspace(2.5,5.5,20)),index=np.linspace(2.5,5.5,20))\n",
    "plt.plot(dark.index[-100:-1],dark_ln,label='exp')\n",
    "plt.plot(n_model_plot.index,n_model_plot.values,label='model')\n",
    "plt.legend()\n",
    "\n",
    "print('I_0 '+str(np.exp(-out3.params['c'].value)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}