UVVIS_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a61101e",
   "metadata": {},
   "source": [
    "# UV-VIS Spectroscopy Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e9d851db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean refractive index in visible region (n) =  3.19  ± 2.34e-03\n",
      "Mean Absorption Coefficient in visible region (α) =  7.67e+04  ± 1.61e+02  cm-1\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wavelength (nm)</th>\n",
       "      <th>T (%)</th>\n",
       "      <th>R (%)</th>\n",
       "      <th>Absorptance (%)</th>\n",
       "      <th>Absorbance (%)</th>\n",
       "      <th>h$\\nu$</th>\n",
       "      <th>alpha (cm$^{-1}$)</th>\n",
       "      <th>$(\\alpha h\\nu)^2 (eV^2/cm^2)$</th>\n",
       "      <th>k</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>300.0</td>\n",
       "      <td>0.018</td>\n",
       "      <td>23.088</td>\n",
       "      <td>76.894</td>\n",
       "      <td>3.744727</td>\n",
       "      <td>4.125000</td>\n",
       "      <td>75380.621822</td>\n",
       "      <td>9.668683e+10</td>\n",
       "      <td>0.179958</td>\n",
       "      <td>3.478888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>301.0</td>\n",
       "      <td>0.017</td>\n",
       "      <td>23.062</td>\n",
       "      <td>76.921</td>\n",
       "      <td>3.769551</td>\n",
       "      <td>4.111296</td>\n",
       "      <td>75919.006855</td>\n",
       "      <td>9.742232e+10</td>\n",
       "      <td>0.181847</td>\n",
       "      <td>3.477079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>302.0</td>\n",
       "      <td>0.016</td>\n",
       "      <td>23.031</td>\n",
       "      <td>76.953</td>\n",
       "      <td>3.795880</td>\n",
       "      <td>4.097682</td>\n",
       "      <td>76490.866466</td>\n",
       "      <td>9.824166e+10</td>\n",
       "      <td>0.183826</td>\n",
       "      <td>3.474947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.016</td>\n",
       "      <td>22.995</td>\n",
       "      <td>76.989</td>\n",
       "      <td>3.795880</td>\n",
       "      <td>4.084158</td>\n",
       "      <td>76499.572531</td>\n",
       "      <td>9.761649e+10</td>\n",
       "      <td>0.184456</td>\n",
       "      <td>3.472636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>304.0</td>\n",
       "      <td>0.017</td>\n",
       "      <td>22.967</td>\n",
       "      <td>77.016</td>\n",
       "      <td>3.769551</td>\n",
       "      <td>4.070724</td>\n",
       "      <td>75941.981641</td>\n",
       "      <td>9.556681e+10</td>\n",
       "      <td>0.183715</td>\n",
       "      <td>3.470961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>1396.0</td>\n",
       "      <td>23.739</td>\n",
       "      <td>12.726</td>\n",
       "      <td>63.535</td>\n",
       "      <td>0.624538</td>\n",
       "      <td>0.886461</td>\n",
       "      <td>10852.672064</td>\n",
       "      <td>9.255352e+07</td>\n",
       "      <td>0.120562</td>\n",
       "      <td>2.920676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>1397.0</td>\n",
       "      <td>23.775</td>\n",
       "      <td>12.709</td>\n",
       "      <td>63.516</td>\n",
       "      <td>0.623879</td>\n",
       "      <td>0.885827</td>\n",
       "      <td>10842.191870</td>\n",
       "      <td>9.224265e+07</td>\n",
       "      <td>0.120532</td>\n",
       "      <td>2.919878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>1398.0</td>\n",
       "      <td>23.832</td>\n",
       "      <td>12.718</td>\n",
       "      <td>63.450</td>\n",
       "      <td>0.622840</td>\n",
       "      <td>0.885193</td>\n",
       "      <td>10817.980587</td>\n",
       "      <td>9.169982e+07</td>\n",
       "      <td>0.120349</td>\n",
       "      <td>2.920315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>1399.0</td>\n",
       "      <td>23.885</td>\n",
       "      <td>12.726</td>\n",
       "      <td>63.389</td>\n",
       "      <td>0.621875</td>\n",
       "      <td>0.884560</td>\n",
       "      <td>10795.594543</td>\n",
       "      <td>9.119019e+07</td>\n",
       "      <td>0.120186</td>\n",
       "      <td>2.920704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>1400.0</td>\n",
       "      <td>23.940</td>\n",
       "      <td>12.725</td>\n",
       "      <td>63.335</td>\n",
       "      <td>0.620876</td>\n",
       "      <td>0.883929</td>\n",
       "      <td>10774.396491</td>\n",
       "      <td>9.070271e+07</td>\n",
       "      <td>0.120036</td>\n",
       "      <td>2.920668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1101 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Wavelength (nm)   T (%)   R (%)  Absorptance (%)  Absorbance (%)  \\\n",
       "0               300.0   0.018  23.088           76.894        3.744727   \n",
       "1               301.0   0.017  23.062           76.921        3.769551   \n",
       "2               302.0   0.016  23.031           76.953        3.795880   \n",
       "3               303.0   0.016  22.995           76.989        3.795880   \n",
       "4               304.0   0.017  22.967           77.016        3.769551   \n",
       "...               ...     ...     ...              ...             ...   \n",
       "1096           1396.0  23.739  12.726           63.535        0.624538   \n",
       "1097           1397.0  23.775  12.709           63.516        0.623879   \n",
       "1098           1398.0  23.832  12.718           63.450        0.622840   \n",
       "1099           1399.0  23.885  12.726           63.389        0.621875   \n",
       "1100           1400.0  23.940  12.725           63.335        0.620876   \n",
       "\n",
       "        h$\\nu$  alpha (cm$^{-1}$)  $(\\alpha h\\nu)^2 (eV^2/cm^2)$         k  \\\n",
       "0     4.125000       75380.621822                   9.668683e+10  0.179958   \n",
       "1     4.111296       75919.006855                   9.742232e+10  0.181847   \n",
       "2     4.097682       76490.866466                   9.824166e+10  0.183826   \n",
       "3     4.084158       76499.572531                   9.761649e+10  0.184456   \n",
       "4     4.070724       75941.981641                   9.556681e+10  0.183715   \n",
       "...        ...                ...                            ...       ...   \n",
       "1096  0.886461       10852.672064                   9.255352e+07  0.120562   \n",
       "1097  0.885827       10842.191870                   9.224265e+07  0.120532   \n",
       "1098  0.885193       10817.980587                   9.169982e+07  0.120349   \n",
       "1099  0.884560       10795.594543                   9.119019e+07  0.120186   \n",
       "1100  0.883929       10774.396491                   9.070271e+07  0.120036   \n",
       "\n",
       "             n  \n",
       "0     3.478888  \n",
       "1     3.477079  \n",
       "2     3.474947  \n",
       "3     3.472636  \n",
       "4     3.470961  \n",
       "...        ...  \n",
       "1096  2.920676  \n",
       "1097  2.919878  \n",
       "1098  2.920315  \n",
       "1099  2.920704  \n",
       "1100  2.920668  \n",
       "\n",
       "[1101 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import pandas as pd\n",
    "\n",
    "# Set the path to the foldercontaining text files\n",
    "folder_path = \"C:\\\\Users\\\\w16025698\\\\OneDrive - Northumbria University - Production Azure AD\\\\PhD\\\\Characterisation\\\\UV-VIS\\\\221027B\"\n",
    "file_name_T = \"271022Bsel T.txt\"\n",
    "file_name_R = \"271022Bsel DR.txt\"\n",
    "file_title = file_name_T.replace(' T.txt', '')\n",
    "t = 1074.22*1E-7 # film thickness in cm\n",
    "\n",
    "# Read the data from the text file, skipping the first 12 lines\n",
    "data_T = np.loadtxt(os.path.join(folder_path, file_name_T), delimiter=',', skiprows=2)\n",
    "data_R = np.loadtxt(os.path.join(folder_path, file_name_R), delimiter=',', skiprows=2)\n",
    "\n",
    "df_T = pd.read_csv(os.path.join(folder_path, file_name_T), sep=\",\", skiprows=2, header=None, names=[\"Wavelength (nm)\", \"T (%)\"])\n",
    "df_R = pd.read_csv(os.path.join(folder_path, file_name_R), sep=\",\", skiprows=2, header=None, names=[\"Wavelength (nm)\", \"R (%)\"])\n",
    "df_data = df_T.merge(df_R, on='Wavelength (nm)') # assumes same wavelength scale \n",
    "\n",
    "# Extract data\n",
    "df_data['Absorptance (%)'] = 100 - (df_T['T (%)'] + df_R['R (%)']) # Absorptance\n",
    "df_data['Absorbance (%)'] = np.log10(100/df_T['T (%)']) # Absorbance\n",
    "WL_T = data_T[:,0] # wavelength\n",
    "WL_R = data_R[:,0] # wavelength\n",
    "df_data['h$\\nu$'] = 1237.5 / df_data[\"Wavelength (nm)\"] # hv in eV\n",
    "df_data['alpha (cm$^{-1}$)'] = -(1/t) * np.log( (df_data['T (%)']/100)/ (1-df_data['R (%)']/100)**2)\n",
    "df_data['$(\\\\alpha h\\\\nu)^2 (eV^2/cm^2)$'] = (df_data['alpha (cm$^{-1}$)']*df_data['h$\\nu$'])**2\n",
    "df_data['k'] = df_data['alpha (cm$^{-1}$)'] * 1E-7 *df_data[\"Wavelength (nm)\"]/(4* np.pi)\n",
    "df_data['n'] = np.sqrt(((1 + df_data['R (%)']/100)**2) / ((1 - df_data['R (%)']/100)**2) - (df_data['k']**2-1)) + (1 + df_data['R (%)']/100)/(1 - df_data['R (%)']/100)\n",
    "\n",
    "def absorption_coefficient_mean(wl_min, wl_max, df):\n",
    "    wavelength_range_start = wl_min  # nm\n",
    "    wavelength_range_end = wl_max  # nm\n",
    "\n",
    "    # Filter the DataFrame to select data within the specified wavelength range\n",
    "    filtered_data = df[(df['Wavelength (nm)'] >= wavelength_range_start) &\n",
    "                            (df['Wavelength (nm)'] <= wavelength_range_end) &\n",
    "                            np.isfinite(df['alpha (cm$^{-1}$)'])]\n",
    "\n",
    "    # Calculate the mean or median of the α values within the specified range\n",
    "    alpha_visible = filtered_data['alpha (cm$^{-1}$)'].mean()  # You can use .median() instead of .mean() if you prefer the median\n",
    "    alpha_sem = filtered_data['alpha (cm$^{-1}$)'].sem() # standard error of the mean (SEM)\n",
    "    \n",
    "    # Display the representative α value\n",
    "    print(\"Mean Absorption Coefficient in visible region (α) = \", \"{:.2e}\".format(alpha_visible), \" ± {:.2e}\".format(alpha_sem), \" cm-1\")\n",
    "    return alpha_visible, alpha_sem\n",
    "\n",
    "def refractive_index_mean(wl_min, wl_max, df):\n",
    "    wavelength_range_start = wl_min  # nm\n",
    "    wavelength_range_end = wl_max  # nm\n",
    "\n",
    "    # Filter the DataFrame to select data within the specified wavelength range\n",
    "    filtered_data = df[(df['Wavelength (nm)'] >= wavelength_range_start) &\n",
    "                            (df['Wavelength (nm)'] <= wavelength_range_end) &\n",
    "                            np.isfinite(df['n'])]\n",
    "\n",
    "    # Calculate the mean or median of the α values within the specified range\n",
    "    n_visible = filtered_data['n'].mean()  # You can use .median() instead of .mean() if you prefer the median\n",
    "    n_sem = filtered_data['n'].sem()\n",
    "    # Display the representative α value\n",
    "    print(\"Mean refractive index in visible region (n) = \", \"{:.2f}\".format(n_visible), \" ± {:.2e}\".format(n_sem))\n",
    "    return n_visible, n_sem\n",
    "\n",
    "n_visible, n_sem = refractive_index_mean(380, 750, df_data)\n",
    "alpha_visible, alpha_sem = absorption_coefficient_mean(380, 750, df_data)\n",
    "\n",
    "excel_file_path = os.path.join(folder_path, f\"{file_title}_data.xlsx\")\n",
    "df_data.to_excel(excel_file_path, index=False)\n",
    "\n",
    "display(df_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5450bcf",
   "metadata": {},
   "source": [
    "## Transmission Reflection Spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5bd20095",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.plot(df_data['Wavelength (nm)'], df_data['T (%)'], label='Transmittance', color='red')\n",
    "ax1.set_xlabel('Wavelength (nm)')\n",
    "ax1.set_ylabel('Transmiattance (%)')\n",
    "ax1.xaxis.set_minor_locator(ticker.MultipleLocator(50))\n",
    "ax1.yaxis.set_minor_locator(ticker.MultipleLocator(1))\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(df_data['Wavelength (nm)'], df_data['R (%)'], label='Reflectance', color='k')\n",
    "ax2.set_ylabel('Reflectance (%)')\n",
    "ax2.tick_params(axis='y', labelcolor='red')\n",
    "ax2.yaxis.set_minor_locator(ticker.MultipleLocator(0.5))\n",
    "plt.title(file_title)\n",
    "\n",
    "# Figure Save settings \n",
    "save_dir = os.path.join(folder_path, 'UVVIS Plots')\n",
    "if not os.path.exists(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "plt.savefig(os.path.join(save_dir, file_title + 'T and R plot.png'), dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca740e82",
   "metadata": {},
   "source": [
    "## Absorptance Spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "46571ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IxCmaANna2iIsLCzfYe8eHh4oSB/t2NhYvefz58/H/PnzZYiwBJMk4JNPxP6wYYC/vzJx+PgAAwcCa9eKBVT37StW9/npOTIyxJw8O3YAv/4K/Puv/uudOwNduoj1t8xkNCUZSceOwJtvilbjiAigb1/g3j3RMkRUSCVv6A0VTFSUWOi0TBmxRo+SZs0STdsHDohJ0d5+W9l4KH8ajeiPcfCg6M/z35xZAAAvL7EQqZeXuMWVx5QTRIWmVouW4rJlgRUrgJEjgeRkYMoUpSMjE8UEKIfw8HCEh4cjMzNT6VCMS5JEx2MAeO89sU6PkqpUEaPApk0T2y5dxDpBVHycPSv6aK1ZI37pZHNwEC2ITZuKFh/e2iJjsrQUI0hdXMTPi//9T7REhoay5ZgMxgQoh+DgYAQHB0Oj0cC+GM5AKpucrT/FZfj5J5+IfkiJiWJ49AcfKB2ReZMk0a9n2zaxxtbBg09fe/VVkTi3aQPUr8/FRqloqVTA1Kli9GD2H05aLTBjBpMgMggTIHOTkSGGGwPAqFHKt/5kK1tWrAj97rviltzQoeIYFa2UFOCrr0TfnsTEp8ctLUULz1tviSkL2EJHSvv4YzGB6tix4jZ6err47jIJogLi2FNzs2mTGKVToULxu3f+zjtAjRril/CCBUpHY16uXhV/VdesCXz55dPkp2ZNMWnhtWvAzz+LfyMmP1RchIQ8Hck6bx6wZImi4ZBpYQJkTiRJ/DIDgDFjgPLllY3nWWq1+CUMiL/obtxQNp6S7uRJ4P33xa2sGjVEPwqNRszNs3EjcOcOcP48MGGC6HNBVBy9/75o+QFEa1B8vLLxkMlgAmRO9u0DEhLEkOT331c6mrz16wf4+opZX0NClI6mZIqLE0nP668D4eFipubMTKBtWzEdwZEjYiRe+fK8nUCmYdw4oGdP0RcoMFAk70QvwATInHz/vdj26wcUYkmRImFhASxdKvqcbNwIbN+udEQlQ3q6mJW5VSugeXOR9FhainW31qwBTp8Gdu8GBgzgrMxkelQqMVN09eridu7gwWIZFqLn4E86c/H4MfDTT2J/0CBlY3mR+vWftv68/z6QmqpoOCbtxg1xa6taNdGx/MABkeAMHQpcuSK+E0FBgKen0pESvRx7e/FHk7W1GL04b57SEVExxwTIXGzbJvp3VK0qWgGKu9BQMT9QYqIY3kqGOXFCzJLr4SH6Vd26JRagnTVLLFexcqWoX6KSxNv76QCKzz4T69ER5YMJUA7h4eHw9PSEj4+P0qHIb+1asTWVWxyvvPJ0dMeXX4qRa/R8WVlAZKRYT61BAzGvUkaGSHjXrxfJ5KefcjVtKtlGjgT69BH9gbp1E33eiPJgAr8Ji05wcDDOnDmD+JI2iuD27ad9aQYOVDYWQ3TvDnTtKn6QjR4tRrFRbpmZov9DtWqiI+ixY2JEXWCgWPB23z7xC6FUKaUjJTI+lUosmdGlC5CWJm73pqcrHRUVQ0yAzMGPP4qWAG9v0+rroVKJViAbG9FpN7sTNwl37gAzZwKvvSaWo0hKAuzsxLD1GzfEv3vLlkpHSVT0SpUSSZCTE3Du3NNh8kQ5MAEyB9m3v0yp9Sebu7uYIRoAPvqIw1sB8dfs3LmiP9ekSWKuHnt74P/+D/j7bzHXk4OD0lESKat8+acdoWfMEJ3+iXJgAlTS/fWXuAduYSGGv5uisWNFK8ft22LNMHOUlQX88APQo4eYwmDCBODRIzGXz9Kl4t953DigdGmlIyUqPvr1E/NbPX4MDBnC2+ikhwlQSZfd+vPmm6Y7m69aDSxbJvZXrgQiIpSNpyhJErBnD9CihWjB27JFJD6ursDq1WJiy1Gjiu+8TkRKUqmAFSue3kZfs0bpiKgYYQJUkkmSad/+yqllS2DiRLE/fDjwxx/KxmNs6emiz1PTpsAbbwCHDomRcVOmAEePirW5Bg/mTM1EL1KjhhgSD4gRYr/8omw8VGwwASrJjhwBLl0Sf/306KF0NC9v+nSgY0fRnN2jh7glVtKkp4sWrjp1xASF8fHitlZwMHD2LDBtmliryxSmMiAqLiZOfLpUxvDh7EtIAJgA6Slx8wBFRopt166i9cDUWVqKfjA1aojp7nv2LDmzRKekiM7eVaqIuZquXBG3LL/4Qlzr4sWcuJCosCwsgHXrxCjYlJSni0KTWWMClEOJmwcou6m3e3dl45BT+fKiH4ytrZjjpnt3MdeHqXrwQPRvqlFDtO7cvi3690yfDly8KEZ5OToqHSWR6StVSvwfA8TkqkuWKBsPKY4JUEn1119i9mRLS6BDB6WjkVe9esDOnUDZskBMjGgJMrWJztLTxQ9jV1fgvfeAhw/Fra0ffxQtPpMni+sjIvn07Al88IHYf/99cYuZzBYToJIqu/WndWvRalLSNG8O/PorUKYMsGOHmPXYFG6H/f23SHzc3MQtr4cPgVq1gDlzREfnwEDAykrpKIlKJpVKrBXWv78YJPLee2ImdTJLTIBKquwEqGtXZeMwJj8/YOtWsfrzL78Avr7A9etKR5W3y5fFcPWqVUXi888/YpbadevETLWffMLEh6goqFRigkQ7O7FszDffKB0RKYQJUEl0755Y/wko2QkQIOY3io4W/WT+/FO0eB0/rnRUOnZXrsAyKAioWRNYvhx48kTc6oqIEEPZ+/blUHaioubk9LQ/0GefAf/+q2w8pAgmQCXRzp1i7a+6dYFXX1U6GuNr1UoM+a9RQ6x43qwZEBamXNN2Vhbw66+w7NABbUNCYLF+vTjWoYNITI8eFTPUqtXKxEdEYmqJevXEkPjsZIjMChOgksgcbn89y91dJEFvvSXm+hg7VkwgmJhYdDE8eCDW6KpTB+jSBRa7d0OysEBWr16iVWrHDpGsEZHyrKyerhW2bJlYTJjMChOgHErEPEBaregcDJhXAgQAFSoAmzYBCxeKEVT79okOxp98Yryh8qmpwIYNYmJGJyexRtfFi4C9PTLHjUP0smXIjIgAvL2N8/lEVHhvvin6Ej55IubcIrPCBCiHEjEPUGwscP++6BPj66t0NEXPwkIMcz1xAmjXTtwK/PJLoHJloFcvYNeul18QMSVFdL7+4AMxjL1vXzE30ePHIuFauRK4fh1Zs2fjMefwISq+VCqxUjwAfPutmDmfzAaHnZQ02bM/d+8u5gAyVzVqiDmCfvlFrP+TnCxahzZtEp2Q27cXiUu9es/vhPzPP8Dhw8Dp04BGIxKdM2f0z/HwEH16evYULT3Z9a7VGuvqiEguLVqIJXZ27ABCQ5+un0glHhOgkiQrC/j5Z7FfEtb+kkPXruLe/tGjYpLBr78WQ1+PHQNmzhTzCLm5if4AkiRaiu7dE8Ppy5TJvw+Rp6doYQsMBPz9uTYXkSmbPl0kQBER4g+m1q2VjoiKABOgkuT0aeDmTbH46RtvKB1N8aFWi2TF11cMeV2zBtizR4yWe/wYuHDh6blnz+Z+v6enaCmysxMdnIcMASpWLLLwicjIGjUC+vQR/fkGDADOnxc/R6lEU/TP1oyMDEyePBnVqlVDmTJlUL16dUybNg1ZWVm6cyRJQmhoKFxdXVGmTBm0adMGp0+ffmHZmzZtgqenJ6ytreHp6YnI7FtDJdmePWLbsqWYHJByc3AAPv5YdBS/f1+MzoqOBnbvBrZtE5Oi/fij6EC9c6fo73P6tPjBuGIF8NFHTH6ISqJvvhGjSa9fF/0GqcRTtAVozpw5WLZsGdasWYPXXnsNR48exTvvvAN7e3t8+OGHAIC5c+di3rx5WL16NWrVqoUvvvgC/v7+OH/+PGxtbfMsNy4uDn369MH06dPRo0cPREZGonfv3jhw4ACaNm1alJdYtGJjxbZNGyWjMB2vvMLRWUQkvPKKmMaiTx+xNM2IEWKQA5VYirYAxcXFoXv37ujcuTM8PDzQq1cvBAQE4OjRowBE609YWBgmTZqEnj17ol69elizZg1SU1MRERGRb7lhYWHw9/fHxIkTUadOHUycOBHt2rVDWFhYEV2ZAjIynrYAtW2rbCxERKYoMFCsM/j4MTB1qtLRkJEp2gLUsmVLLFu2DBcuXECtWrVw4sQJHDhwQJeoJCYmIjk5GQEBAbr3WFtbw8/PDwcPHsS7776bZ7lxcXEYO3as3rH27dvnmwClp6cjPcdq4hqNBgCg1WqhNZGRPKpDh2B17x6kcuWQUb/+S41Ayr5mU7n24or1KA/WozxYjwWj+uILWL3xBqQ1a5AxeTLg7Kz3OutRHsaqR0PKUzQBmjBhAu7fv486derA0tISmZmZmDFjBvr16wcASE5OBgA4OTnpvc/JyQlXr17Nt9zk5OQ835Nd3rNmzZqFqXlk+1FRUbAxkY5wtdevRx0ANz09cTQqSpYyo6OjZSnH3LEe5cF6lAfr8QUkCa1q1UKFCxdw8513kPDBB3mexnqUh9z1mJqaWuBzFU2ANmzYgLVr1yIiIgKvvfYaEhISEBISAldXVwwePFh3nuqZeVokScp17FmGvGfixIkYN26c7rlGo4GbmxsCAgJgZ2dn6GUpwnL2bACAU1AQOnXq9FJlabVaREdHw9/fH2quV1VorEd5sB7lwXosOFWlSkCrVqi6dy9cly8HqlbVvcZ6lIex6jH7Dk5BKJoAjR8/Hp9++in69u0LAPDy8sLVq1cxa9YsDB48GM7/NT0mJyfDxcVF976UlJRcLTw5OTs752rted57rK2tYZ3HqCm1Wm0aX/C7d8U6WACsOnaUbZFNk7n+Yo71KA/WozxYjwXQsiXQrh1Uv/0G9YIFYnmdZ7Ae5SF3PRpSlqKdoFNTU2HxzARylpaWumHw1apVg7Ozs14T2ZMnT7B37140b94833J9fX1zNatFRUU99z0m7bffxCSIdero/aVCRESFNHGi2K5YIabDoBJH0QSoa9eumDFjBn799VdcuXIFkZGRmDdvHnr8N4uxSqVCSEgIZs6cicjISJw6dQpDhgyBjY0N+vfvrysnKCgIE7O/rAA+/PBDREVFYc6cOTh37hzmzJmDmJgYhISEFPUlFo1du8S2fXtl4yAiKineeANo0kQspFySRxCbMUVvgS1atAhTpkzB6NGjkZKSAldXV7z77rv43//+pzvnk08+wePHjzF69GjcvXsXTZs2RVRUlN4cQElJSXotSc2bN8f69esxefJkTJkyBTVq1MCGDRtK5hxAkgRkd3pmAkREJA+VSswc/9ZbQHg4MGECYG+vdFQkI0UTIFtbW4SFhT13fh6VSoXQ0FCEhobme05s9gSAOfTq1Qu9evV6+SCLu/PnxVpX1taAn5/S0RARlRxdu4qlcM6cAZYseXpbjEoEruBo6rJvf7VqxbVriIjkZGHxNOmZPx8wYIg1FX9MgHIIDw+Hp6cnfHx8lA6l4Nj/h4jIePr2BapVA/75B/j2W6WjIRkxAcohODgYZ86cQXx8vNKhFExa2tP1v5gAERHJz8pKLIIMiFagzExl4yHZMAEyZb//LtascXEB6tVTOhoiopJpyBCgQgXg8mWotmxROhqSCRMgU7Zzp9gGBIgRC0REJL+yZYHgYACAxbx5YvQtmTwmQKZs+3ax7dhR2TiIiEq64GDA2hoW8fGocO6c0tGQDJgAmaorV8TQTAsL0QJERETG4+QEDBgAAKgaE6NwMCQHJkCmascOsW3eHChfXtlYiIjMwdChAIAqe/cCV68qHAy9LCZApmrbNrHt3FnZOIiIzEWLFsh64w1YZmTA8osvlI6GXhIToBxMZh6gf/99uvxFt27KxkJEZEaypk0DAKjWrgVu3VI4GnoZTIByMJl5gH76CcjIABo0ENO0ExFRkZCaNMG/detClZkJrF6tdDj0EpgAmaKVK8V24EBl4yAiMkNX33xT7KxcCWRlKRsMFRoTIFNz9Kh4lCoFBAUpHQ0Rkdm52aIFJFtb4K+/gH37lA6HCokJkKlZtUps334bcHBQNhYiIjOUWbo0svr0EU+++UbZYKjQmACZkidPgPXrxf6QIYqGQkRkzqT/hsRj40bg7l1lg6FCYQJkSrZtA+7cEWt/tWundDRERGZLatQI8PIC0tOBiAilw6FCYAJkSrI7PwcFAZaWysZCRGTOVCpg+HCxv2IF1wczQUyATMWNG08XP81ueiUiIuUMHAhYWwMnTgDHjysdDRmICVAOxXoixNWrxXDL1q2BWrWUjoaIiCpUALp3F/tr1yobCxmMCVAOxXYixCdPRBMrAAwbpmwsRET01KBBYhsRISaoJZPBBMgUrFolFt5zdgYCA5WOhoiIsrVvD1SqBKSkANHRSkdDBmACZAq+/lpsP/0UKFNG2ViIiOgptRro10/sf/+9srGQQZgAFXd//ik611lYPP1PRkRExUf2bbDISECjUTYWKjAmQMWZJIlWHwDo1QtwdFQ2HiIiyq1xY6BOHSAtDdi0SeloqICYABVnkZHAjh2AlRUwbZrS0RARUV5UqqetQN99p2wsVGBMgIqrzExg9myxP3YsULu2svEQEVH+Bg4U29hYIClJ0VCoYJgA5VCs5gHatQuIjwdsbYExY5SOhoiInqdqVaBNG7H/ww+KhkIFwwQoh2I1D9DcuWI7dChQpYqysRAR0YsFBYntd99xaQwTwASoOEpMBPbuFet9ffyx0tEQEVFBvP22WBrj3Dng5Emlo6EXYAJUHK1aJbatWrH1h4jIVNjZAR07iv2fflI2FnohJkDFzZ07QFiY2H//fUVDISIiA/XqJbY//cTbYMWcogmQh4cHVCpVrkdwcDAA5PmaSqXCl19+mW+Zq1evzvM9aWlpRXVZL2fqVODBA+D114EePZSOhoiIDNG1q7gNdv48cOqU0tHQc1gp+eHx8fHIzMzUPT916hT8/f0R+N96V7du3dI7f8eOHRg2bBjefvvt55ZrZ2eH8+fP6x0rXbq0TFEb0dGjwMKFYn/2bDH7MxERmQ47O7E+2NatohXIy0vpiCgfiiZADg4Oes9nz56NGjVqwM/PDwDg7Oys9/rPP/+Mtm3bonr16s8tV6VS5XpvsSdJTzs8Dxz49D4yERGZlsBAkQBt3MhJbIsxRROgnJ48eYK1a9di3LhxUKlUuV7/+++/8euvv2LNmjUvLOvhw4dwd3dHZmYmGjRogOnTp8Pb2zvf89PT05Genq57rvlvLRetVgutVluIqzGcatcuWO3dC6l0aWSEhgJF9Ll5yb7morr2kor1KA/WozxYj/IoUD126ACrUqWgOnsW2oQE4LXXiiY4E2Ks76Mh5akkqXj00vrxxx/Rv39/JCUlwdXVNdfrc+fOxezZs3Hz5s3n3s46dOgQLl26BC8vL2g0GixYsADbt2/HiRMnULNmzTzfExoaiqlTp+Y6HhERARsbm8JflAFaTJqESqdP41K3bjg9dGiRfCYRERlHkxkz4BIfj3N9+uA8F7IuMqmpqejfvz/u378POzu7555bbBKg9u3bo1SpUvjll1/yfL1OnTrw9/fHokWLDCo3KysLDRs2ROvWrbEwu3/NM/JqAXJzc8Pt27dfWIFyUMXFwcrPD5JajYzz5xUf+q7VahEdHQ1/f3+o1WpFYzFlrEd5sB7lwXqUR0HrUbV2LayGDoVUty4yTpwowghNg7G+jxqNBpUqVSpQAlQsboFdvXoVMTEx2Lx5c56v79+/H+fPn8eGDRsMLtvCwgI+Pj64ePFivudYW1vD2to613G1Wm38HxSSBEyYAABQBQVBXa2acT/PAEVy/WaA9SgP1qM8WI/yeGE99ugBvPsuVGfPQn3xIuDpWXTBmRC5v4+GlFUshhmtWrUKjo6O6Ny5c56vr1y5Eo0aNUL9+vUNLluSJCQkJMDFxeVlwzSO338HDh0Swya/+ELpaIiISA7lygEBAWKfkyIWS4onQFlZWVi1ahUGDx4MK6vcDVIajQY//fQThg8fnuf7g4KCMHHiRN3zqVOnYteuXbh8+TISEhIwbNgwJCQkYNSoUUa7hkJLTgb69BH7QUGAqY1cIyKi/P03pQsToOJJ8VtgMTExSEpKwtB8Ov6uX78ekiShXz6dyJKSkmCRY76ce/fuYeTIkUhOToa9vT28vb2xb98+NGnSxCjxF9qjR+Kvg5s3ARsb4PPPlY6IiIjk1L07oFYDp08DZ88CdesqHRHloHgLUEBAACRJQq1atfJ8feTIkUhNTYW9vX2er8fGxmL16tW65/Pnz8fVq1eRnp6OlJQU7Nq1C76+vsYIvfDS0sRcPydPAhUqAHv2AJUrKx0VERHJqVw5wN9f7LMVqNhRPAEyO1lZQO/ewJYtYrX3yEiguLVOERGRPLJvg23cqGwclAsToBzCw8Ph6ekJHx8f433ItGnAL78ApUoBERFA69bG+ywiIlJWt26AlZVo8X9miSZSFhOgHIKDg3HmzBnEx8cb5wM2bxaLnQLA8uWiJYiIiEquChWAN98U+7wNVqwwASpKTZsCjRoBH30EDBmidDRERFQUevUS223blI2D9Cg+CsysVK4M7N8vbn8REZF5yG4BOnoUePgQeOUVZeMhAGwBKnplyojOz0REZB7c3cUjMxOIi1M6GvoPEyAiIiJjyx7wsnevsnGQjkG3wO7fv4/IyEjs378fV65cQWpqKhwcHODt7Y327dujefPmxoqTiIjIdPn5Ad9/D+zbp3Qk9J8CtQDdunULI0aMgIuLC6ZNm4ZHjx6hQYMGaNeuHapUqYI9e/bA398fnp6ehVqwlIiIqETz8xPbQ4cAjUbZWAhAAVuA6tevj6CgIBw5cgT16tXL85zHjx9jy5YtmDdvHq5du4aPP/5Y1kCLQnh4OMLDw5GZmal0KEREVJK8+ipQu7aYC2j7dqBvX6UjMnsFagE6ffo0vvrqq3yTHwAoU6YM+vXrh8OHD2Pw4MGyBViUjD4PEBERma8ePcQ2MlLZOAhAARMgBwcHgwo19HwiIqISLzsB2r4dSE9XNhYq/CiwBw8eYPz48fDx8UHDhg3xwQcf4Pbt23LGRkREVHI0bgy4uoq5gH77TelozF6hE6ARI0bg9u3bmDp1Kj7//HNcvnwZAwYMkDM2IiKiksPCAnjrLbG/dauioZABw+Dnz5+PkJAQqFQqAEB8fDwuXLgAy/8m9atduzaaNWtmnCiJiIhKgk6dgCVLgOhopSMxewVOgC5duoSmTZti+fLl8Pb2hr+/Pzp37oy33noLWq0W33//Pdq3b2/MWImIiExb69ZidfjLl8WjenWlIzJbBU6AwsPDERcXh6FDh6Jt27aYNWsW1q5di+joaGRmZiIwMBDvv/++MWMlIiIybba2QLNmwIEDQFQUMGqU0hGZLYP6APn6+iI+Ph4VKlSAr68vPDw8sGnTJmzZsgXjx49HmTJljBUnERFRydCxo9j++quycZg5gztBW1lZYfLkyfjll18QFhaGXr16ITk52RixFbnw8HB4enrCx8dH6VCIiKik6tJFbGNigNRUZWMxYwVOgE6ePIkmTZrA1tYWLVq0QFZWFn777Td06tQJzZs3x9KlS40ZZ5HgRIhERGR0Xl5A1apAWhqwZ4/S0ZitAidA77zzDlq2bIn4+HgEBgZi1H/3LYcOHYrDhw/jwIED8PX1NVqgREREJYJK9bQVaNs2ZWMxYwXuBH3+/HmsX78er776KmrWrImwsDDdaw4ODvjhhx8QFRVljBiJiIhKlo4dxXB4/t5UTIEToDZt2mDkyJHo27cvdu/ejRYtWuQ6JyAgQNbgiIiISiQ/Pw6HV1iBb4F99913aNiwIX7++WdUr169RPT5ISIiUoStLZDdbYSTIiqiwC1A5cuXx1dffWXMWIiIiMxHQACwf7+4Dfbuu0pHY3YK1AKUlJRkUKE3btwoVDBERERmw99fbHfvBjIzlY3FDBUoAfLx8cGIESNw5MiRfM+5f/8+VqxYgXr16mHz5s2yBViUOA8QEREVmcaNgXLlgHv3gKNHlY7G7BToFtjZs2cxc+ZMdOjQAWq1Go0bN4arqytKly6Nu3fv4syZMzh9+jQaN26ML7/8Eh2zZ7k0McHBwQgODoZGo4G9vb3S4RARUUlmaQm88QawebPoB9S0qdIRmZUCtQBVqFABX331FW7evImlS5eiVq1auH37Ni5evAgAGDBgAI4dO4bff//dZJMfIiKiIpd9G4wdoYtcgTtBA0Dp0qXRs2dP9OzZ01jxEBERmY/sBCguDnj4EHjlFWXjMSMGrwVGREREMqlRA6hWDdBqgb17lY7GrCiaAHl4eEClUuV6BAcHAwCGDBmS67VmzZq9sNxNmzbB09MT1tbW8PT0RGRkpLEvhYiIqHB4G0wRiiZA8fHxuHXrlu4R/d8/fmBgoO6cDh066J2zffv255YZFxeHPn36YNCgQThx4gQGDRqE3r174/Dhw0a9FiIiokJ5802x/e03ZeMwMwb1AZKbg4OD3vPZs2ejRo0a8PPz0x2ztraGs7NzgcsMCwuDv78/Jk6cCACYOHEi9u7di7CwMKxbty7P96SnpyM9PV33XKPRAAC0Wi20Wm2BP7ukyL5mc7x2ObEe5cF6lAfrUR5GqceWLaEGgFOnoL1+HXBykq/sYspY30dDylM0AcrpyZMnWLt2LcaNGweVSqU7HhsbC0dHR5QrVw5+fn6YMWMGHB0d8y0nLi4OY8eO1TvWvn17vcVbnzVr1ixMnTo11/GoqCjY2NgYfjElRDSbY2XBepQH61EerEd5yF2PftWqoVxiIk7Mn48brVvLWnZxJnc9pqamFvjcQiVA33//PZYtW4bExETExcXB3d0dYWFhqFatGrp3716YIrFlyxbcu3cPQ4YM0R3r2LEjAgMD4e7ujsTEREyZMgVvvPEGjh07Bmtr6zzLSU5OhtMz2bOTkxOSk5Pz/eyJEydi3LhxuucajQZubm4ICAiAnZ1doa7HlGm1WkRHR8Pf3x9qtVrpcEwW61EerEd5sB7lYax6tNi7F5g/Hw3v3EH9Tp1kK7e4MlY9Zt/BKQiDE6ClS5fif//7H0JCQjBjxgxk/jd9d7ly5RAWFlboBGjlypXo2LEjXF1ddcf69Omj269Xrx4aN24Md3d3/Prrr88dip+zBQkAJEnKdSwna2vrPBMqtVpt1j8ozP365cJ6lAfrUR6sR3nIXo/t2wPz58Ni1y5YWFkBz/mdVZLIXY+GlGVwJ+hFixZhxYoVmDRpEiwtLXXHGzdujJMnTxpaHADg6tWriImJwfDhw597nouLC9zd3XUTMObF2dk5V2tPSkpKrlYhIiKiYqNNG6BsWeDWLeDECaWjMQsGJ0CJiYnw9vbOddza2hqPHj0qVBCrVq2Co6MjOnfu/Nzz/v33X1y7dg0uLi75nuPr65vrnmJUVBSaN29eqNiIiIiMztoaaNlS7HM+oCJhcAJUrVo1JCQk5Dq+Y8cOeHp6GhxAVlYWVq1ahcGDB8PK6ukduYcPH+Ljjz9GXFwcrly5gtjYWHTt2hWVKlVCjx49dOcFBQXpRnwBwIcffoioqCjMmTMH586dw5w5cxATE4OQkBCDYyMiIioy2SOgmQAVCYP7AI0fPx7BwcFIS0uDJEk4cuQI1q1bh1mzZuGbb74xOICYmBgkJSVh6NChesctLS1x8uRJfPfdd7h37x5cXFzQtm1bbNiwAba2trrzkpKSYGHxNI9r3rw51q9fj8mTJ2PKlCmoUaMGNmzYgKZcZI6IiIqzNm3Edt8+ICsLsOBiDcZkcAL0zjvvICMjA5988glSU1PRv39/VK5cGQsWLEDfvn0NDiAgIACSJOU6XqZMGezateuF74+Njc11rFevXujVq5fBsRARESmmcWPAxgb491/gzBmgXj2lIyrRCpVejhgxAlevXkVKSgqSk5Nx7do1DBs2TO7YiIiIzIdaDWT3V+Ws0EZXqE7Q2aOwKlWqpJuU8OLFi7hy5YqswRW18PBweHp6wsfHR+lQiIjIHAUEiG0B7oDQyzE4ARoyZAgOHjyY6/jhw4f1JjE0RcHBwThz5gzi4+OVDoWIiMxRx45iGxsLPH6saCglncEJ0B9//IEWLVrkOt6sWbM8R4cRERFRAb32GlC5skh+9u9XOpoSzeAESKVS4cGDB7mO379/XzcrNBERERWCSgV06CD2d+5UNpYSzuAEqFWrVpg1a5ZespOZmYlZs2ahZfYkTkRERFQ4b74ptvv2KRtHCWfwMPi5c+eidevWqF27Nlq1agUA2L9/PzQaDXbv3i17gERERGYluzHhjz+ABw+AHHPfkXwMbgHy9PTEn3/+id69eyMlJQUPHjxAUFAQzp07h3qcs4CIiOjlVKkCeHiIyRDj4pSOpsQyuAUIAFxdXTFz5ky5YyEiIiIAaNUKuHIFOHDg6dB4klWhEqB79+7hyJEjSElJQVZWlt5rQUFBsgSmhPDwcISHh7MzNxERKatlS+D77zkSzIgMToB++eUXDBgwAI8ePYKtrS1UKpXuNZVKZdIJUHBwMIKDg6HRaGBvb690OEREZK7+62OLQ4eAJ0+AUqWUjacEMrgP0EcffYShQ4fiwYMHuHfvHu7evat73LlzxxgxEhERmZc6dYBKlYC0NODoUaWjKZEMToBu3LiBMWPGwMbGxhjxEBERkUoFtG4t9jkc3igMToDat2+Po8xGiYiIjCs7Adq7V9k4SiiD+wB17twZ48ePx5kzZ+Dl5QW1Wq33erdu3WQLjoiIyGy1bSu2+/axH5ARGJwAjRgxAgAwbdq0XK+pVCqOoCIiIpJDvXqAgwPwzz+iM3R2ixDJwuBbYFlZWfk+mPwQERHJxMICeOMNsf/bb8rGUgIZnAARERFREcleF4wJkOwKNRHio0ePsHfvXiQlJeHJkyd6r40ZM0aWwJTAiRCJiKhYyW4BOnwYePQIKFtW2XhKEIMToD/++AOdOnVCamoqHj16hAoVKuD27duwsbGBo6OjSSdAnAiRiIiKlWrVxNpg16+LdcGyW4TopRl8C2zs2LHo2rUr7ty5gzJlyuDQoUO4evUqGjVqhK+++soYMRIREZknlQpo00bsczi8rAxOgBISEvDRRx/B0tISlpaWSE9Ph5ubG+bOnYvPPvvMGDESERGZLz8/sd29W9k4ShiDEyC1Wq1b/8vJyQlJSUkAAHt7e90+ERERyST7ttfhw8D9+8rGUoIYnAB5e3vrZoJu27Yt/ve//+GHH35ASEgIvLy8ZA+QiIjIrHl4ALVqAZmZQGys0tGUGAYnQDNnzoSLiwsAYPr06ahYsSLee+89pKSkYPny5bIHSEREZPYCAsQ2KkrZOEoQg0eBNW7cWLfv4OCA7du3yxoQERERPcPfH1i8mAmQjAxuAXrjjTdw7969XMc1Gg3eyJ6vwESFh4fD09MTPj4+SodCRET0VJs2YmboS5eAa9eUjqZEMDgBio2NzTX5IQCkpaVh//79sgSllODgYJw5cwbx8fFKh0JERPSUnR3QqJHY53B4WRT4Ftiff/6p2z9z5gySk5N1zzMzM7Fz505UrlxZ3uiIiIhI8PMD4uNFAjRwoNLRmLwCJ0ANGjSASqWCSqXK81ZXmTJlsGjRIlmDIyIiov/4+QFffcUWIJkUOAFKTEyEJEmoXr06jhw5AgcHB91rpUqVgqOjIywtLY0SJBERkdlr2VL0A7p4Ebh5E3B1VToik1bgPkDu7u7w8PBAVlYWGjduDHd3d93DxcWlUMmPh4eHrlUp5yM4OBharRYTJkyAl5cXypYtC1dXVwQFBeHmzZvPLXP16tV5lpmWlmZwfERERMVGuXJAgwZin61AL83gTtAAcP78ebz//vto164d3nzzTbz//vs4d+6cweXEx8fj1q1bukd0dDQAIDAwEKmpqTh+/DimTJmC48ePY/Pmzbhw4QK6dev2wnLt7Oz0yr116xZKly5tcHxERETFSva6YHv2KBpGSWDwPEAbN25Ev3790LhxY/j6+gIADh06BC8vL0RERCAwMLDAZeW8jQYAs2fPRo0aNeDn5weVSqVLiLItWrQITZo0QVJSEqpWrZpvuSqVCs7OzgWOIz09Henp6brnGo0GAKDVaqHVagtcTkmRfc3meO1yYj3Kg/UoD9ajPJSuR1WrVrCaNw/Sb78hw4T/LY1Vj4aUp5IkSTKk8OrVq2PgwIGYNm2a3vHPP/8c33//PS5fvmxIcTpPnjyBq6srxo0bl++iqjExMQgICMC9e/dgZ2eX5zmrV6/G8OHDUblyZWRmZqJBgwaYPn06vL298/3s0NBQTJ06NdfxiIgI2NjYFOp6iIiI5Gb1+DE6DhgAi6wsRC9fjlQnJ6VDKlZSU1PRv39/3L9/P988IZvBCZCNjQ3+/PNPvPrqq3rHL168iPr16yM1NdXwiAH8+OOP6N+/P5KSkuCaR8eutLQ0tGzZEnXq1MHatWvzLefQoUO4dOkSvLy8oNFosGDBAmzfvh0nTpxAzZo183xPXi1Abm5uuH379gsrsCTSarWIjo6Gv78/1Gq10uGYLNajPFiP8mA9yqM41KOlnx8s4uKQ8fXXkIYMUSSGl2WsetRoNKhUqVKBEiCDb4G1adMG+/fvz5UAHThwAK1atTK0OJ2VK1eiY8eOeSY/Wq0Wffv2RVZWFpYsWfLccpo1a4ZmzZrpnrdo0QINGzbEokWLsHDhwjzfY21tDWtr61zH1Wq1Wf+gMPfrlwvrUR6sR3mwHuWhaD22bg3ExcHqyBFgxAhlYpCJ3PVoSFkGJ0DdunXDhAkTcOzYMV2icejQIfz000+YOnUqtm7dqnduQVy9ehUxMTHYvHlzrte0Wi169+6NxMRE7N692+AWGQsLC/j4+ODixYsGvY+IiKhYyv4j/9AhZeMwcQYnQKNHjwYALFmyJFdrTPZrgOiInJmZWaAyV61aBUdHR3Tu3FnveHbyc/HiRezZswcVK1Y0NFxIkoSEhAR4eXkZ/F4iIqJiJzsBOn0a0GjEMhlkMIOHwWdlZRXoUdDkJysrC6tWrcLgwYNhZfU0H8vIyECvXr1w9OhR/PDDD8jMzERycjKSk5P11iILCgrCxIkTdc+nTp2KXbt24fLly0hISMCwYcOQkJCAUaNGGXqpRERExY+zM+DhAUiSWBqDCsWgBEir1aJt27a4cOGCbAHExMQgKSkJQ4cO1Tt+/fp1bN26FdevX0eDBg3g4uKiexw8eFB3XlJSEm7duqV7fu/ePYwcORJ169ZFQEAAbty4gX379qFJkyayxUxERKQo3gZ7aQbdAlOr1Th16hRUKpVsAQQEBCCvgWgeHh55Hn9WbGys3vP58+dj/vz5coVHRERU/LRoAaxfD0RHA5MmKR2NSTL4FlhQUBBWrlxpjFiIiIioILp2Fdv9+4G7d5WNxUQZ3An6yZMn+OabbxAdHY3GjRujbNmyeq/PmzdPtuCKWnh4OMLDwwvcf4mIiEgR7u5ArVrAhQvAgQNPEyIqMIMToFOnTqFhw4YAkKsvkJy3xpQQHByM4OBgaDQa2NvbKx0OERFR/vz8RAK0dy8ToEIwOAHawwXYiIiIlOfnB6xYwZXhC6lQq8Fnu379Om7cuCFXLERERFRQfn5ie/y4mA+IDFKoeYCmTZsGe3t7uLu7o2rVqihXrhymT5+OrKwsY8RIREREz6pSBaheHcjKEv2AyCAG3wKbNGkSVq5cidmzZ6NFixaQJAm///47QkNDkZaWhhkzZhgjTiIiInpWmzbA5cviNlinTkpHY1IMToDWrFmDb775Rm+dr/r166Ny5coYPXo0EyAiIqKi0qYN8O23wDNz4tGLGXwL7M6dO6hTp06u43Xq1MGdO3dkCYqIiIgKILsf0LFj7AdkIIMToPr162Px4sW5ji9evBj169eXJSilhIeHw9PTEz4+PkqHQkRE9GJVqwLVqgGZmcDvvysdjUkx+BbY3Llz0blzZ8TExMDX1xcqlQoHDx7EtWvXsH37dmPEWGQ4DxAREZmcNm2AxETRD6hjR6WjMRkGtwD5+fnhwoUL6NGjB+7du4c7d+6gZ8+eOH/+PFq1amWMGImIiCg/bdqILefpM4jBLUAA4Orqys7ORERExUF2AnTsGPDgAWBrq2g4psLgFqCdO3fiQI75BsLDw9GgQQP0798fd7kgGxERUdFiP6BCMTgBGj9+PDT/9TQ/efIkxo0bh06dOuHy5csYN26c7AESERHRC2SPBuNw+AIzOAFKTEyEp6cnAGDTpk3o2rUrZs6ciSVLlmDHjh2yB0hEREQvkH0bjAlQgRmcAJUqVQqpqakAgJiYGAQEBAAAKlSooGsZIiIioiKU3QJ09KjoB0QvZHAC1LJlS4wbNw7Tp0/HkSNH0LlzZwDAhQsXUKVKFdkDLEqcB4iIiEySh4d4sB9QgRmcAC1evBhWVlbYuHEjli5disqVKwMAduzYgQ4dOsgeYFEKDg7GmTNnEB8fr3QoREREhuFtMIMYPAy+atWq2LZtW67j8+fPlyUgIiIiKoS2bYHVq5kAFVCh5gHKzMxEZGQkzp49C5VKhTp16uCtt96ClVWhiiMiIqKX9Ww/IM4H9FwGZyynTp1Ct27d8Pfff6N27doARP8fBwcHbN26FV5eXrIHSURERC/g7i7mA0pMFP2ATLxbirEZ3Ado+PDhqFevHq5fv47jx4/j+PHjuHbtGl5//XWMHDnSGDESERFRQbAfUIEZnACdOHECs2bNQvny5XXHypcvjxkzZiAhIUHO2IiIiMgQTIAKzOAEqHbt2vj7779zHU9JScGrr74qS1BERERUCNkJEOcDeqECJUAajUb3mDlzJsaMGYONGzfi+vXruH79OjZu3IiQkBDMmTPH2PESERFRfnKuCxYXp3Q0xVqBOkGXK1cOKpVK91ySJPTu3Vt3TJIkAEDXrl2RmZlphDCLRnh4OMLDw036GoiIyMz5+oqO0HFxwH+rNVBuBUqA9uzZY+w4ioXg4GAEBwdDo9HA3t5e6XCIiIgM17w5EBHBFqAXKFAC5Jc9t8ALsBM0ERGRwnx9xfbQIXErzNJS2XiKKYM7QT/r/v37WLJkCRo2bIhGjRrJERMREREV1uuvA3Z2wP37wB9/KB1NsVXoBGj37t0YOHAgXFxcsGjRInTq1AlHjx6VMzYiIiIylJWVWBYDAKKjlY2lGDMoAbp+/Tq++OILVK9eHf369UP58uWh1WqxadMmfPHFF/D29jbowz08PKBSqXI9goODAYjO1aGhoXB1dUWZMmXQpk0bnD59+oXlbtq0CZ6enrC2toanpyciIyMNiouIiMikZXd+jopSNo5irMAJUKdOneDp6YkzZ85g0aJFuHnzJhYtWvRSHx4fH49bt27pHtH/ZaqBgYEAgLlz52LevHlYvHgx4uPj4ezsDH9/fzx4ztwGcXFx6NOnDwYNGoQTJ05g0KBB6N27Nw4fPvxSsRIREZkMf3+x/f134NEjZWMppgq8FlhUVBTGjBmD9957DzVr1pTlwx0cHPSez549GzVq1ICfnx8kSUJYWBgmTZqEnj17AgDWrFkDJycnRERE4N13382zzLCwMPj7+2PixIkAgIkTJ2Lv3r0ICwvDunXr8nxPeno60tPTdc81Gg0AQKvVQqvVvvR1mprsazbHa5cT61EerEd5sB7lYTL16O4OKw8PqK5cQcbu3ZCK2bpgxqpHQ8orcAK0f/9+fPvtt2jcuDHq1KmDQYMGoU+fPoUKMC9PnjzB2rVrMW7cOKhUKly+fBnJyckIyDGHgbW1Nfz8/HDw4MF8E6C4uDiMHTtW71j79u0RFhaW72fPmjULU6dOzXU8KioKNjY2hbugEiCa945lwXqUB+tRHqxHeZhCPdavWRMeV67g6ooVOJWVpXQ4eZK7HlNTUwt8boETIF9fX/j6+mLBggVYv349vv32W4wbNw5ZWVmIjo6Gm5sbbG1tCxUwAGzZsgX37t3DkCFDAADJyckAACcnJ73znJyccPXq1XzLSU5OzvM92eXlZeLEiRg3bpzuuUajgZubGwICAmBnZ2fopZg8rVaL6Oho+Pv7Q61WKx2OyWI9yoP1KA/WozxMqR5VqalAdDSq//UXqnbqpHQ4eoxVj9l3cAqiwAlQNhsbGwwdOhRDhw7F+fPnsXLlSsyePRuffvop/P39sXXrVkOLBACsXLkSHTt2hKurq97xnDNQA6Jj9LPHnmXoe6ytrWFtbZ3ruFqtLvZfcGMy9+uXC+tRHqxHebAe5WES9di+PaBSQXXmDNT//AM88/u1OJC7Hg0p66XmAapduzbmzp2L69ev59u/piCuXr2KmJgYDB8+XHfM2dkZAHK13KSkpORq4cnJ2dnZ4PcQERGVOBUqAI0bi30TuGVX1F56IkQAsLS0xFtvvVXo1p9Vq1bB0dERnTt31h2rVq0anJ2d9e4PPnnyBHv37kXz5s3zLcvX1zfXPcWoqKjnvoeIiKhEevNNsd27V9k4iiFZEqCXkZWVhVWrVmHw4MGwsnp6R06lUiEkJAQzZ85EZGQkTp06hSFDhsDGxgb9+/fXnRcUFKQb8QUAH374IaKiojBnzhycO3cOc+bMQUxMDEJCQorysoiIiJTXurXY7tunbBzFkMF9gOQWExODpKQkDB06NNdrn3zyCR4/fozRo0fj7t27aNq0KaKiovQ6WyclJcHC4mke17x5c6xfvx6TJ0/GlClTUKNGDWzYsAFNmzYtkushIiIqNpo3BywsgL/+Am7cACpXVjqiYkPxBCggIACSJOX5mkqlQmhoKEJDQ/N9f2xsbK5jvXr1Qq9evWSKkIiIyETZ2QHe3sCxY0BMDDB4sNIRFRuK3wIjIiIiI8oeAv/LL8rGUcwwAcohPDwcnp6e8PHxUToUIiIieXTrJra7dgE5Vj0wd0yAcggODsaZM2cQHx+vdChERETyaNgQcHICHj4EuC6mDhMgIiKikszCAmjTRuzn0W/WXDEBIiIiKunathXbPXuUjaMYYQJERERU0mW3AMXFAWlpioZSXDABIiIiKulq1QKcnUUnaPYDAsAEiIiIqORTqXgb7BlMgIiIiMwBO0LrYQKUA+cBIiKiEis7ATp0iP2AwARID+cBIiKiEqtmTcDFRfQDiotTOhrFMQEiIiIyBzn7AfE2GBMgIiIis8F+QDpMgIiIiMxFzn5Ajx8rGorSmAARERGZi1dfBSpXBp48Mft+QEyAiIiIzIVKBbRrJ/Z//VXZWBTGBIiIiMicdO8utpGRgCQpG4uCmADlwHmAiIioxGvfHihdGkhMBE6dUjoaxTAByoHzABERUYlXtizg5yf2zXg0GBMgIiIic9O6tdju3atsHApiAkRERGRusluA9u0z235ATICIiIjMjY+P6Af0zz/AuXNKR6MIJkBERETmplQpwNdX7EdFKRuLQpgAERERmaMePcT2u++UjUMhTICIiIjMUd++Ynv8OJCSomwsCmACREREZI4cHABvb7G/ZYuioSiBCVAOnAiRiIjMSnYr0KZNysahACZAOXAiRCIiMivt24vtwYOAVqtsLEWMCRAREZG58vICnJyAhw+BbduUjqZIMQEiIiIyVxYWwKBBYj8yUtlYihgTICIiInPWubPY7twJZGUpG0sRUjwBunHjBgYOHIiKFSvCxsYGDRo0wLFjx3Svq1SqPB9ffvllvmWuXr06z/ekpaUVxSURERGZjhYtAFtbMSv0H38oHU2RsVLyw+/evYsWLVqgbdu22LFjBxwdHfHXX3+hXLlyunNu3bql954dO3Zg2LBhePvtt59btp2dHc6fP693rHTp0rLFTkREVCKo1cCbb4pbYDt2AI0aKR1RkVA0AZozZw7c3NywatUq3TEPDw+9c5ydnfWe//zzz2jbti2qV6/+3LJVKlWu9+YnPT0d6enpuucajQYAoNVqoTWzXvEAdNdsjtcuJ9ajPFiP8mA9yqOk1qPK3x9WkZHI2rYNmRMmGP3zjFWPhpSnkiTlloH19PRE+/btcf36dezduxeVK1fG6NGjMWLEiDzP//vvv1GlShWsWbMG/fv3z7fc1atXY/jw4ahcuTIyMzPRoEEDTJ8+Hd7ZEz49IzQ0FFOnTs11PCIiAjY2NoW7OCIiIhNR+t9/0X7YMEgqFXatWoX0HHdiTElqair69++P+/fvw87O7rnnKpoAZd+SGjduHAIDA3HkyBGEhIRg+fLlCAoKynX+3LlzMXv2bNy8efO5t7MOHTqES5cuwcvLCxqNBgsWLMD27dtx4sQJ1KxZM9f5ebUAubm54fbt2y+swJJIq9UiOjoa/v7+UKvVSodjsliP8mA9yoP1KI+SXI+Wvr6wOHYMGcuXQ3rnHaN+lrHqUaPRoFKlSgVKgBS9BZaVlYXGjRtj5syZAABvb2+cPn0aS5cuzTMB+vbbbzFgwIAX9uVp1qwZmjVrpnveokULNGzYEIsWLcLChQtznW9tbQ1ra+tcx9VqdYn7ghvC3K9fLqxHebAe5cF6lEeJrMe33gKOHYPVr78CI0cWyUfKXY+GlKXoKDAXFxd4enrqHatbty6SkpJynbt//36cP38ew4cPN/hzLCws4OPjg4sXLxY6ViIiohKtWzexjYoCHj1SNpYioGgC1KJFi1wjtS5cuAB3d/dc565cuRKNGjVC/fr1Df4cSZKQkJAAFxeXQsdKRERUonl5AR4eQFoaEB2tdDRGp2gCNHbsWBw6dAgzZ87EpUuXEBERga+//hrBwcF652k0Gvz000/5tv4EBQVh4sSJuudTp07Frl27cPnyZSQkJGDYsGFISEjAqFGjjHo9REREJkulErfBALNYHV7RBMjHxweRkZFYt24d6tWrh+nTpyMsLAwDBgzQO2/9+vWQJAn9+vXLs5ykpCS9+YLu3buHkSNHom7duggICMCNGzewb98+NGnSxKjXQ0REZNKyE6Bffinxi6Mq2gkaALp06YIuXbo895yRI0di5HM6ZMXGxuo9nz9/PubPny9HeEREROajZUvAwUHMCr1vH9CundIRGY3iS2EQERFRMWFpCXTvLvY3b1Y2FiNjApRDeHg4PD094ePjo3QoREREyujZU2wjI0v04qhMgHIIDg7GmTNnEB8fr3QoREREymjXDrC3B27dAn7/XelojIYJEBERET1VqhTQo4fY37BB2ViMiAkQERER6evdW2w3bgQyM5WNxUiYABEREZG+N98EypcH/v5bjAYrgZgAERERkT61+mln6J9+UjYWI2ECRERERLllD4ePiVE2DiNhAkRERES5+fmJeYEuXgSuXVM6GtkxAcqB8wARERH9x84OyP59GBWlbCxGwAQoB84DRERElEP2UlXLlpW4SRGZABEREVHehg0DXnkFOHq0xK0QzwSIiIiI8ubsDHzwgdhfvFjZWGTGBIiIiIjy9957gIUFsGcPkJSkdDSyYQJERERE+XNzA5o0Efu//aZsLDJiAkRERETPFxAgtj/+qGwcMmICRERERM83aJDYRkUBN24oG4tMmADlwHmAiIiI8vDqq0CrVmIo/HffKR2NLJgA5cB5gIiIiPLxzjtiu3o1IEmKhiIHJkBERET0Yr16ATY2wIULwOHDSkfz0pgAERER0YvZ2gJvvy3216xRNhYZMAEiIiKighk8WGwjIoB//1U2lpfEBIiIiIgKpm1b4PXXAY0G+PprpaN5KUyAiIiIqGAsLICQELH/7bcm3RmaCRAREREVXGCgWCD10iXgwAGloyk0JkBERERUcK+8AvTpI/aXLVM2lpfABCgHToRIRERUAKNHi+2PPwLXrikbSyExAcqBEyESEREVQMOGQJs2QEYGsHCh0tEUChMgIiIiMtzHH4vtsmXAnTvKxlIITICIiIjIcJ06AfXqAQ8fAj//rHQ0BlM8Abpx4wYGDhyIihUrwsbGBg0aNMCxY8d0rw8ZMgQqlUrv0axZsxeWu2nTJnh6esLa2hqenp6IjIw05mUQERGZF5VKLI8BmOQCqYomQHfv3kWLFi2gVquxY8cOnDlzBv/3f/+HcuXK6Z3XoUMH3Lp1S/fYvn37c8uNi4tDnz59MGjQIJw4cQKDBg1C7969cbgErF1CRERUbLzzDqBWA7GxwN69SkdjECslP3zOnDlwc3PDqlWrdMc8PDxynWdtbQ1nZ+cClxsWFgZ/f39MnDgRADBx4kTs3bsXYWFhWLdu3UvHTURERACqVgWGDweWLgWmTwf8/JSOqMAUTYC2bt2K9u3bIzAwEHv37kXlypUxevRojBgxQu+82NhYODo6oly5cvDz88OMGTPg6OiYb7lxcXEYO3as3rH27dsjLCwsz/PT09ORnp6ue67RaAAAWq0WWq22kFdnurKv2RyvXU6sR3mwHuXBepQH6zEP778P9dKlkPbtQ8aDB0Dp0i98i7Hq0ZDyVJKk3DzWpf+rpHHjxiEwMBBHjhxBSEgIli9fjqCgIADAhg0b8Morr8Dd3R2JiYmYMmUKMjIycOzYMVhbW+dZbqlSpbB69Wr0799fdywiIgLvvPOOXqKTLTQ0FFOnTs11PCIiAjY2NnJcKhERUckkSegweDCsNRrsmzsXd2vVUiyU1NRU9O/fH/fv34ednd1zz1U0ASpVqhQaN26MgwcP6o6NGTMG8fHxiIuLy/M9t27dgru7O9avX4+ePXvmW+6aNWvQr18/3bEffvgBw4YNQ1paWq7z82oBcnNzw+3bt19YgSWRVqtFdHQ0/P39oVarlQ7HZLEe5cF6lAfrUR6sx7xZ9uwJi23bkBkaiqzPPnvh+caqR41Gg0qVKhUoAVL0FpiLiws8PT31jtWtWxebNm167nvc3d1x8eLFfM9xdnZGcnKy3rGUlBQ4OTnleb61tXWerUlqtdqsv+Dmfv1yYT3Kg/UoD9ajPFiPz+jeHdi2DZa//grLzz8v8NvkrkdDylJ0FFiLFi1w/vx5vWMXLlyAu7t7vu/5999/ce3aNbi4uOR7jq+vL6Kjo/WORUVFoXnz5i8XMBEREeXWpYvYxscDZ88qG0sBKZoAjR07FocOHcLMmTNx6dIlRERE4Ouvv0ZwcDAA4OHDh/j4448RFxeHK1euIDY2Fl27dkWlSpXQo0cPXTlBQUG6EV8A8OGHHyIqKgpz5szBuXPnMGfOHMTExCAkJKSoL5GIiKjkc3YWrUAA8MUXysZSQIomQD4+PoiMjMS6detQr149TJ8+HWFhYRgwYAAAwNLSEidPnkT37t1Rq1YtDB48GLVq1UJcXBxsbW115SQlJeHWrVu6582bN8f69euxatUqvP7661i9ejU2bNiApk2bFvk1EhERmYXsW18bNgCJicrGUgCK9gECgC5duqBLdtPZM8qUKYNdu3a9sIzY2Nhcx3r16oVe2TNUEhERkXF5ewMBAUBUFBAaCqxZo3REz6X4UhhERERUQoSGiu1PPwEPHigayoswAcohPDwcnp6e8PHxUToUIiIi09OsGVCjBvD4MbB7t9LRPBcToByCg4Nx5swZxMfHKx0KERGR6VGpgA4dxH4xX4ScCRARERHJ57+BTFi/Hvj3X2VjeQ4mQERERCSfZs2Ahg2B9HRg5Uqlo8kXEyAiIiKSj0oFvP++2F+8WCRCxRATICIiIpJXv36AkxNw7RowZYrS0eSJCRARERHJq3RpYMkSsb9gAXDsmLLx5IEJEBEREcmvRw+xPMaTJ8AHHygdTS5MgIiIiEh+KhWwbBlgZQXExQGnTikdkR4mQDlwIkQiIiIZOTs/XSl+1SplY3kGE6AcOBEiERGRzN55R2zXrgW0WmVjyYEJEBERERlPx46AoyOQkgLs2KF0NDpMgIiIiMh41Gpg0CCxX4xugzEBIiIiIuMKChLbHTvEQqnFABMgIiIiMi4vL8DFRcwKvXOn0tEAYAJERERExqZSPb0NNm0aIEnKxgMmQERERFQUPv5Y9AdKSIBF9izRCmIClAPnASIiIjISBwfgf/8DAFgsXAhkZSkaDhOgHDgPEBERkRGNGweUKwdVYiIcTpxQNBQmQERERFQ0bGyAwYMBANUU7gzNBIiIiIiKzrvvQlKpYPHkCZCZqVgYVop9MhEREZmfunWRcekSDp08iU6WloqFwRYgIiIiKlpubkpHwASIiIiIzA8TICIiIjI7TICIiIjI7DAByoETIRIREZkHJkA5cCJEIiIi88AEiIiIiMwOEyAiIiIyO4onQDdu3MDAgQNRsWJF2NjYoEGDBjh27BgAQKvVYsKECfDy8kLZsmXh6uqKoKAg3Lx587llrl69GiqVKtcjLS2tKC6JiIiIijlFZ4K+e/cuWrRogbZt22LHjh1wdHTEX3/9hXLlygEAUlNTcfz4cUyZMgX169fH3bt3ERISgm7duuHo0aPPLdvOzg7nz5/XO1a6dGljXQoRERGZEEUToDlz5sDNzQ2rVq3SHfPw8NDt29vbIzo6Wu89ixYtQpMmTZCUlISqVavmW7ZKpYKzs7PsMRMREZHpUzQB2rp1K9q3b4/AwEDs3bsXlStXxujRozFixIh833P//n2oVCpdK1F+Hj58CHd3d2RmZqJBgwaYPn06vL298zw3PT0d6enpuucajQaAuAWn1WoNvzATl33N5njtcmI9yoP1KA/WozxYj/IwVj0aUp5KkiRJ1k83QPYtqXHjxiEwMBBHjhxBSEgIli9fjqCgoFznp6WloWXLlqhTpw7Wrl2bb7mHDh3CpUuX4OXlBY1GgwULFmD79u04ceIEatasmev80NBQTJ06NdfxiIgI2NjYvMQVEhERUVFJTU1F//79cf/+fdjZ2T33XEUToFKlSqFx48Y4ePCg7tiYMWMQHx+PuLg4vXO1Wi0CAwORlJSE2NjYF15YTllZWWjYsCFat26NhQsX5no9rxYgNzc33L5926DPKSm0Wi2io6Ph7+8PtVqtdDgmi/UoD9ajPFiP8mA9ysNY9ajRaFCpUqUCJUCK3gJzcXGBp6en3rG6deti06ZNese0Wi169+6NxMRE7N692+CkxMLCAj4+Prh48WKer1tbW8Pa2lr3PDsnfPz4sVl+wbVaLVJTU/H48WNkZGQoHY7JYj3Kg/UoD9ajPFiP8jBWPT5+/BjA09/jz6NoAtSiRYtcI7UuXLgAd3d33fPs5OfixYvYs2cPKlasaPDnSJKEhIQEeHl5Fej8Bw8eAADc3NwM/iwiIiJS1oMHD2Bvb//ccxRNgMaOHYvmzZtj5syZ6N27N44cOYKvv/4aX3/9NQAgIyMDvXr1wvHjx7Ft2zZkZmYiOTkZAFChQgWUKlUKABAUFITKlStj1qxZAICpU6eiWbNmqFmzJjQaDRYuXIiEhASEh4cXKC5XV1dcu3YNtra2aNKkiexLY/j4+MhaptzlZd8CvHbtmmy3AOWO0Rhlsh6LZ3msR3mwHuXBepSHserxyJEjePDgAVxdXV94vqIJkI+PDyIjIzFx4kRMmzYN1apVQ1hYGAYMGAAAuH79OrZu3QoAaNCggd579+zZgzZt2gAAkpKSYGHxdE7He/fuYeTIkUhOToa9vT28vb2xb98+NGnSpEBxWVhYoEqVKgAAS0tL2fsByV2mMWIExFxKcpXLemQ9vizWozxYj/JgPcpD7nq0t7d/YctPNkUTIADo0qULunTpkudrHh4eBbqPFxsbq/d8/vz5mD9/vhzhITg4WJZyjFmmMWKUG+tRHqxHebAe5cF6lAfrUR6GxqjoKDAqnjQaDezt7QvUi57yx3qUB+tRHqxHebAe5VEc6lHxtcCo+LG2tsbnn3+uNzKODMd6lAfrUR6sR3mwHuVRHOqRLUBERERkdtgCRERERGaHCRARERGZHSZAREREZHaYABEREZHZYQJkhmbNmgWVSoWQkBDdMUmSEBoaCldXV5QpUwZt2rTB6dOn9d6Xnp6ODz74AJUqVULZsmXRrVs3XL9+vYijV9aNGzcwcOBAVKxYETY2NmjQoAGOHTume531+GIZGRmYPHkyqlWrhjJlyqB69eqYNm0asrKydOewHvO2b98+dO3aFa6urlCpVNiyZYve63LV2927dzFo0CDdpHKDBg3CvXv3jHx1Red59ajVajFhwgR4eXmhbNmycHV1RVBQEG7evKlXBuvxxd/HnN59912oVCqEhYXpHVeyHpkAmZn4+Hh8/fXXeP311/WOz507F/PmzcPixYsRHx8PZ2dn+Pv769ZFA4CQkBBERkZi/fr1OHDgAB4+fIguXbogMzOzqC9DEXfv3kWLFi2gVquxY8cOnDlzBv/3f/+HcuXK6c5hPb7YnDlzsGzZMixevBhnz57F3Llz8eWXX2LRokW6c1iPeXv06BHq16+PxYsX5/m6XPXWv39/JCQkYOfOndi5cycSEhIwaNAgo19fUXlePaampuL48eOYMmUKjh8/js2bN+PChQvo1q2b3nmsxxd/H7Nt2bIFhw8fznN5CkXrUSKz8eDBA6lmzZpSdHS05OfnJ3344YeSJElSVlaW5OzsLM2ePVt3blpammRvby8tW7ZMkiRJunfvnqRWq6X169frzrlx44ZkYWEh7dy5s0ivQykTJkyQWrZsme/rrMeC6dy5szR06FC9Yz179pQGDhwoSRLrsaAASJGRkbrnctXbmTNnJADSoUOHdOfExcVJAKRz584Z+aqK3rP1mJcjR45IAKSrV69KksR6zEt+9Xj9+nWpcuXK0qlTpyR3d3dp/vz5uteUrke2AJmR4OBgdO7cGW+++abe8cTERCQnJyMgIEB3zNraGn5+fjh48CAA4NixY9BqtXrnuLq6ol69erpzSrqtW7eicePGCAwMhKOjI7y9vbFixQrd66zHgmnZsiV+++03XLhwAQBw4sQJHDhwAJ06dQLAeiwsueotLi4O9vb2aNq0qe6cZs2awd7e3mzr9v79+1CpVLrWXtZjwWRlZWHQoEEYP348XnvttVyvK12Piq8FRkVj/fr1OH78eJ6r+SYnJwMAnJyc9I47OTnh6tWrunNKlSqF8uXL5zon+/0l3eXLl7F06VKMGzcOn332GY4cOYIxY8bA2toaQUFBrMcCmjBhAu7fv486derA0tISmZmZmDFjBvr16weA38fCkqvekpOT4ejomKt8R0dHs6zbtLQ0fPrpp+jfv79uyQbWY8HMmTMHVlZWGDNmTJ6vK12PTIDMwLVr1/Dhhx8iKioKpUuXzvc8lUql91ySpFzHnlWQc0qKrKwsNG7cGDNnzgQAeHt74/Tp01i6dCmCgoJ057Een2/Dhg1Yu3YtIiIi8NprryEhIQEhISFwdXXF4MGDdeexHgtHjnrL63xzrFutVou+ffsiKysLS5YseeH5rMenjh07hgULFuD48eMGX29R1SNvgZmBY8eOISUlBY0aNYKVlRWsrKywd+9eLFy4EFZWVrq/GJ/NplNSUnSvOTs748mTJ7h7926+55R0Li4u8PT01DtWt25dJCUlARB1BLAeX2T8+PH49NNP0bdvX3h5eWHQoEEYO3YsZs2aBYD1WFhy1ZuzszP+/vvvXOX/888/ZlW3Wq0WvXv3RmJiIqKjo/UW7GQ9vtj+/fuRkpKCqlWr6n7vXL16FR999BE8PDwAKF+PTIDMQLt27XDy5EkkJCToHo0bN8aAAQOQkJCA6tWrw9nZGdHR0br3PHnyBHv37kXz5s0BAI0aNYJardY759atWzh16pTunJKuRYsWOH/+vN6xCxcuwN3dHQBQrVo11mMBpKamwsJC/0ePpaWlbhg867Fw5Ko3X19f3L9/H0eOHNGdc/jwYdy/f99s6jY7+bl48SJiYmJQsWJFvddZjy82aNAg/Pnnn3q/d1xdXTF+/Hjs2rULQDGox5fqQk0mK+coMEmSpNmzZ0v29vbS5s2bpZMnT0r9+vWTXFxcJI1Goztn1KhRUpUqVaSYmBjp+PHj0htvvCHVr19fysjIUOAKit6RI0ckKysracaMGdLFixelH374QbKxsZHWrl2rO4f1+GKDBw+WKleuLG3btk1KTEyUNm/eLFWqVEn65JNPdOewHvP24MED6Y8//pD++OMPCYA0b9486Y8//tCNTpKr3jp06CC9/vrrUlxcnBQXFyd5eXlJXbp0KfLrNZbn1aNWq5W6desmValSRUpISJBu3bqle6Snp+vKYD2++Pv4rGdHgUmSsvXIBMhMPZsAZWVlSZ9//rnk7OwsWVtbS61bt5ZOnjyp957Hjx9L77//vlShQgWpTJkyUpcuXaSkpKQijlxZv/zyi1SvXj3J2tpaqlOnjvT111/rvc56fDGNRiN9+OGHUtWqVaXSpUtL1atXlyZNmqT3y4X1mLc9e/ZIAHI9Bg8eLEmSfPX277//SgMGDJBsbW0lW1tbacCAAdLdu3eL6CqN73n1mJiYmOdrAKQ9e/boymA9vvj7+Ky8EiAl61ElSZL0cm1IRERERKaFfYCIiIjI7DABIiIiIrPDBIiIiIjMDhMgIiIiMjtMgIiIiMjsMAEiIiIis8MEiIiIiMwOEyAiIiIyO0yAiKhYCw0NRYMGDZQOQ0elUmHLli0Gv+/8+fNwdnbGgwcP5A8qh5SUFDg4OODGjRtG/RwiU8cEiIiwbNky2NraIiMjQ3fs4cOHUKvVaNWqld65+/fvh0qlwoULF4o6zCIld+I1adIkBAcHw9bWVrYy8+Lo6IhBgwbh888/N+rnEJk6JkBEhLZt2+Lhw4c4evSo7tj+/fvh7OyM+Ph4pKam6o7HxsbC1dUVtWrVUiJUk3T9+nVs3boV77zzTpF83jvvvIMffvgBd+/eLZLPIzJFTICICLVr14arqytiY2N1x2JjY9G9e3fUqFEDBw8e1Dvetm1bAMDatWvRuHFj2NrawtnZGf3790dKSgoAICsrC1WqVMGyZcv0Puv48eNQqVS4fPkyAOD+/fsYOXIkHB0dYWdnhzfeeAMnTpx4bryrVq1C3bp1Ubp0adSpUwdLlizRvXblyhWoVCps3rwZbdu2hY2NDerXr4+4uDi9MlasWAE3NzfY2NigR48emDdvHsqVKwcAWL16NaZOnYoTJ05ApVJBpVJh9erVuvfevn0bPXr0gI2NDWrWrImtW7c+N94ff/wR9evXR5UqVXTHVq9ejXLlymHXrl2oW7cuXnnlFXTo0AG3bt3SnTNkyBC89dZbmDlzJpycnFCuXDlMnToVGRkZGD9+PCpUqIAqVarg22+/1fs8Ly8vODs7IzIy8rlxEZkzJkBEBABo06YN9uzZo3u+Z88etGnTBn5+frrjT548QVxcnC4BevLkCaZPn44TJ05gy5YtSExMxJAhQwAAFhYW6Nu3L3744Qe9z4mIiICvry+qV68OSZLQuXNnJCcnY/v27Th27BgaNmyIdu3a4c6dO3nGuWLFCkyaNAkzZszA2bNnMXPmTEyZMgVr1qzRO2/SpEn4+OOPkZCQgFq1aqFfv366W3y///47Ro0ahQ8//BAJCQnw9/fHjBkzdO/t06cPPvroI7z22mu4desWbt26hT59+uhenzp1Knr37o0///wTnTp1woABA/KNFwD27duHxo0b5zqempqKr776Ct9//z327duHpKQkfPzxx3rn7N69Gzdv3sS+ffswb948hIaGokuXLihfvjwOHz6MUaNGYdSoUbh27Zre+5o0aYL9+/fnGxOR2Xvp9eSJqET4+uuvpbJly0parVbSaDSSlZWV9Pfff0vr16+XmjdvLkmSJO3du1cCIP311195lnHkyBEJgPTgwQNJkiTp+PHjkkqlkq5cuSJJkiRlZmZKlStXlsLDwyVJkqTffvtNsrOzk9LS0vTKqVGjhrR8+XJJkiTp888/l+rXr697zc3NTYqIiNA7f/r06ZKvr68kSZKUmJgoAZC++eYb3eunT5+WAEhnz56VJEmS+vTpI3Xu3FmvjAEDBkj29va6589+bjYA0uTJk3XPHz58KKlUKmnHjh151okkSVL9+vWladOm6R1btWqVBEC6dOmS7lh4eLjk5OSkez548GDJ3d1dyszM1B2rXbu21KpVK93zjIwMqWzZstK6dev0yh87dqzUpk2bfGMiMndsASIiAKIf0KNHjxAfH4/9+/ejVq1acHR0hJ+fH+Lj4/Ho0SPExsaiatWqqF69OgDgjz/+QPfu3eHu7g5bW1u0adMGAJCUlAQA8Pb2Rp06dbBu3ToAwN69e5GSkoLevXsDAI4dO4aHDx+iYsWKeOWVV3SPxMRE/PXXX7li/Oeff3Dt2jUMGzZM7/wvvvgi1/mvv/66bt/FxQUAdLfnzp8/jyZNmuid/+zz58lZdtmyZWFra6srOy+PHz9G6dKlcx23sbFBjRo19OJ8tpzXXnsNFhZPf1Q7OTnBy8tL99zS0hIVK1bM9b4yZcro9d0iIn1WSgdARMXDq6++iipVqmDPnj24e/cu/Pz8AADOzs6oVq0afv/9d+zZswdvvPEGAODRo0cICAhAQEAA1q5dCwcHByQlJaF9+/Z48uSJrtwBAwYgIiICn376KSIiItC+fXtUqlQJgOgn5OLiotf3KFt2f5ycsrKyAIjbYE2bNtV7zdLSUu+5Wq3W7atUKr33S5KkO5ZNkqQX1lFeZWeXn112XipVqpRnh+S8ynk2jrzOKcjn37lzBw4ODvlfBJGZYwJERDpt27ZFbGws7t69i/Hjx+uO+/n5YdeuXTh06JBuJNO5c+dw+/ZtzJ49G25ubgCgN4osW//+/TF58mQcO3YMGzduxNKlS3WvNWzYEMnJybCysoKHh8cL43NyckLlypVx+fJlDBgwoNDXWadOHRw5ckTv2LOxlypVCpmZmYX+jJy8vb1x5swZWcoqqFOnTula5IgoN94CIyKdtm3b4sCBA0hISNC1AAEiAVqxYgXS0tJ0HaCrVq2KUqVKYdGiRbh8+TK2bt2K6dOn5yqzWrVqaN68OYYNG4aMjAx0795d99qbb74JX19fvPXWW9i1axeuXLmCgwcPYvLkyXkmU4CYn2fWrFlYsGABLly4gJMnT2LVqlWYN29ega/zgw8+wPbt2zFv3jxcvHgRy5cvx44dO/RahTw8PJCYmIiEhATcvn0b6enpBS7/We3bt0dcXJxsCdWLpKam4tixYwgICCiSzyMyRUyAiEinbdu2ePz4MV599VU4OTnpjvv5+eHBgweoUaOGrrXHwcEBq1evxk8//QRPT0/Mnj0bX331VZ7lDhgwACdOnEDPnj1RpkwZ3XGVSoXt27ejdevWGDp0KGrVqoW+ffviypUrep+f0/Dhw/HNN99g9erV8PLygp+fH1avXo1q1aoV+DpbtGiBZcuWYd68eahfvz527tyJsWPH6vXTefvtt9GhQwe0bdsWDg4Oun5MhdGpUyeo1WrExMQUugxD/Pzzz6hatWquSSyJ6CmVZMiNbyKiEmrEiBE4d+6c0YaOL1myBD///DN27dpllPJzatKkCUJCQtC/f3+jfxaRqWIfICIyS1999RX8/f1RtmxZ7NixA2vWrNGbUFFuI0eOxN27d/HgwQOjLoeRkpKCXr16oV+/fkb7DKKSgC1ARGSWevfujdjYWDx48ADVq1fHBx98gFGjRikdFhEVESZAREREZHbYCZqIiIjMDhMgIiIiMjtMgIiIiMjsMAEiIiIis8MEiIiIiMwOEyAiIiIyO0yAiIiIyOwwASIiIiKz8//rbcpl1/0LsAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.plot(df_data['Wavelength (nm)'], df_data['Absorptance (%)'], label='Absorptance', color='red')\n",
    "ax1.set_xlabel('Wavelength (nm)')\n",
    "ax1.set_ylabel('Absorptance (%)')\n",
    "ax1.grid()\n",
    "ax1.xaxis.set_minor_locator(ticker.MultipleLocator(50))\n",
    "ax1.yaxis.set_minor_locator(ticker.MultipleLocator(0.5))\n",
    "\n",
    "# Figure Save settings \n",
    "save_dir = os.path.join(folder_path, 'UVVIS Plots')\n",
    "if not os.path.exists(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "plt.savefig(os.path.join(save_dir, file_title + 'Absorptance plot.png'), dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c5a2d38-f276-492c-9f78-ad58d4500a26",
   "metadata": {},
   "source": [
    "## Absorbance Spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "373b4b2b-cc11-408c-bb21-d832a88bd931",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.plot(df_data['Wavelength (nm)'], df_data['Absorbance (%)'], label='Absorbance', color='red')\n",
    "ax1.set_xlabel('Wavelength (nm)')\n",
    "ax1.set_ylabel('Absorbance (arb. units)')\n",
    "ax1.grid()\n",
    "ax1.xaxis.set_minor_locator(ticker.MultipleLocator(50))\n",
    "ax1.yaxis.set_minor_locator(ticker.MultipleLocator(0.5))\n",
    "\n",
    "# Figure Save settings \n",
    "save_dir = os.path.join(folder_path, 'UVVIS Plots')\n",
    "if not os.path.exists(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "plt.savefig(os.path.join(save_dir, file_title + 'Absorbance plot.png'), dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d6b5994",
   "metadata": {},
   "source": [
    "## Refractive Index ($n$) & Extinction coefficient ($k$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fad82c74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.plot(df_data['Wavelength (nm)'], df_data['n'], label='Refrcative Index', color='k')\n",
    "ax1.set_xlabel('Wavelength (nm)')\n",
    "ax1.set_ylabel('Refrcative Index, n')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(df_data['Wavelength (nm)'], df_data['k'], label='Extinction coefficient', color='red')\n",
    "ax2.set_ylabel('Extinction coefficient, k')\n",
    "ax2.tick_params(axis='y', labelcolor='red')\n",
    "ax2.xaxis.set_minor_locator(ticker.MultipleLocator(50))\n",
    "ax2.yaxis.set_minor_locator(ticker.MultipleLocator(0.05))\n",
    "ax2.set_ylim(0,1)\n",
    "plt.title(file_title)\n",
    "\n",
    "# Figure save settings\n",
    "save_dir = os.path.join(folder_path, 'UVVIS Plots')\n",
    "if not os.path.exists(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "plt.savefig(os.path.join(save_dir, file_title + 'n k plot.png'), dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e593eee7",
   "metadata": {},
   "source": [
    "## Tauc $\\alpha h \\nu^2$ Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "afbcec48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def linear_fit(x_min, x_max, hv_series, ahv2_series):\n",
    "    hv_series = np.nan_to_num(hv_series, nan=0.0, copy=False).astype(float)\n",
    "    ahv2_series = np.nan_to_num(ahv2_series, nan=0.0, copy=False).astype(float)\n",
    "\n",
    "    mask = np.logical_and(hv_series >= x_min, hv_series <= x_max) # a mask based on the condition for x_min and x_max\n",
    "    hv_subset = hv_series[mask]\n",
    "    ahv2_subset = ahv2_series[mask]\n",
    "\n",
    "    # Fit line of best fit\n",
    "    coefficients = np.polyfit(hv_subset, ahv2_subset, 1)\n",
    "    poly_func = np.poly1d(coefficients)\n",
    "\n",
    "    # Generate x values for the line\n",
    "    line_x = np.linspace(x_min - 1, x_max + 0.5, 100)\n",
    "\n",
    "    # Calculate y values for the line\n",
    "    line_y = poly_func(line_x)\n",
    "\n",
    "    # Calculate x-axis intercept\n",
    "    x_intercept = -coefficients[1] / coefficients[0]\n",
    "\n",
    "    return x_intercept, line_y, line_x\n",
    "\n",
    "x_intercept, line_y, line_x = linear_fit(1.8, 1.9, df_data['h$\\nu$'], df_data['$(\\\\alpha h\\\\nu)^2 (eV^2/cm^2)$'])\n",
    "\n",
    "### Tauc Plot figure ###\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot line of best fit\n",
    "ax.plot(line_x, line_y, label='Tangent', color='r')\n",
    "# Plot Tauc plot\n",
    "ax.plot(df_data['h$\\nu$'], df_data['$(\\\\alpha h\\\\nu)^2 (eV^2/cm^2)$'], color='k')\n",
    "ax.scatter(df_data['h$\\nu$'], df_data['$(\\\\alpha h\\\\nu)^2 (eV^2/cm^2)$'], label='Tauc plot', c='k', marker='o', alpha=0.8, s=10)\n",
    "\n",
    "# Plot X intercept\n",
    "#ax.plot(x_intercept, 0, 'ro')\n",
    "ax.set_xlabel('Photon energy (eV)')\n",
    "ax.set_ylabel('$(\\\\alpha h\\\\nu)^2 (eV^2/cm^2)$')\n",
    "ax.set_xlim(0.7, 2.5)  # Set x-axis limits Selenise\n",
    "ax.set_ylim(0, 1.2E9) # Set y-axis limits Selenise\n",
    "ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.05))\n",
    "ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.5E13))\n",
    "ax.grid()\n",
    "#ax.legend()\n",
    "plt.title('CZTS' + ' Tauc plot')\n",
    "\n",
    "# Add Egap as text onto the figure\n",
    "# ax.text(0.4, 0.1, \"Band gap : {:.2f} eV\".format(x_intercept), \n",
    "#         transform=ax.transAxes, fontsize=10, position=(0.55, 0.05))\n",
    "\n",
    "# Add a table with parameters\n",
    "table_data = [\n",
    "    ['Parameter', 'Value'],\n",
    "    [\"α ( cm⁻¹)\", \"{:.2e} ± {:.2e}\".format(alpha_visible, alpha_sem)],\n",
    "    [\"n\", \"{:.2f} ± {:.2e}\".format(n_visible, n_sem)],\n",
    "    [\"Band gap (eV)\", \"{:.2f}\".format(x_intercept)],\n",
    "    [\"Thickness (nm)\", \"{:.2f}\".format(t*10000000)]\n",
    "]\n",
    "\n",
    "table = plt.table(cellText=table_data, colWidths=[0.35, 0.52], loc='upper left')\n",
    "table.auto_set_font_size(False)\n",
    "table.set_fontsize(7)\n",
    "table.scale(0.5, 1.5)\n",
    "table.set_zorder(10)\n",
    "\n",
    "# Figure save settings\n",
    "save_dir = os.path.join(folder_path, 'UVVIS Plots')\n",
    "if not os.path.exists(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "plt.savefig(os.path.join(save_dir, file_title + 'Tauc plot.png'), dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dbad682",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Tauc Plot figure ###\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "x_intercept, line_y, line_x = linear_fit(1.9, 2, df_data['h$\\nu$'], df_data['$(\\\\alpha h\\\\nu)^2 (eV^2/cm^2)$'])\n",
    "\n",
    "# Plot line of best fit\n",
    "ax.plot(line_x, line_y, label='Tangent', color='r')\n",
    "# Plot Tauc plot\n",
    "ax.plot(df_data['h$\\nu$'], df_data['$(\\\\alpha h\\\\nu)^2 (eV^2/cm^2)$'], color='k')\n",
    "ax.scatter(df_data['h$\\nu$'], df_data['$(\\\\alpha h\\\\nu)^2 (eV^2/cm^2)$'], label='Tauc plot', c='k', marker='o', alpha=0.8, s=10)\n",
    "\n",
    "# Plot X intercept\n",
    "#ax.plot(x_intercept, 0, 'ro')\n",
    "ax.set_xlabel('Photon energy (eV)')\n",
    "ax.set_ylabel('$(\\\\alpha h\\\\nu)^2 (eV^2/cm^2)$')\n",
    "ax.set_xlim(0.7, 2.5)  # Set x-axis limits Selenise\n",
    "ax.set_ylim(0, 0.2E10) # Set y-axis limits Selenise\n",
    "ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.05))\n",
    "ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.05E13))\n",
    "ax.grid()\n",
    "#ax.legend()\n",
    "#plt.title(file_title + ' Tauc plot')\n",
    "\n",
    "# Add a table with parameters\n",
    "table_data = [\n",
    "    ['Parameter', 'Value'],\n",
    "    [\"α ( cm⁻¹)\", \"{:.2e} ± {:.2e}\".format(alpha_visible, alpha_sem)],\n",
    "    [\"n\", \"{:.2f} ± {:.2e}\".format(n_visible, n_sem)],\n",
    "    [\"Band gap (eV)\", \"{:.2f}\".format(x_intercept)],\n",
    "    [\"Thickness (nm)\", \"{:.2f}\".format(t*10000000)]\n",
    "]\n",
    "\n",
    "table = plt.table(cellText=table_data, colWidths=[0.35, 0.52], loc='upper left')\n",
    "table.auto_set_font_size(False)\n",
    "table.set_fontsize(7)\n",
    "table.scale(0.5, 1.5)\n",
    "table.set_zorder(10)\n",
    "\n",
    "# Create a second axes for the additional plot in the top right corner\n",
    "ax2 = fig.add_axes([0.68, 0.65, 0.3, 0.3])  # Adjust the position and size as needed\n",
    "\n",
    "ax2.set_xlabel('Photon energy (eV)')\n",
    "ax2.set_ylabel('$(\\\\alpha h\\\\nu)^2 (eV^2/cm^2)$')\n",
    "#ax2.set_xlim(0.6, 3)  # Set x-axis limits nanoparticles\n",
    "#ax2.set_ylim(-0.1E13, 2E14) # Set y-axis limits nanoparticles\n",
    "ax2.set_xlim(0.6, 4.5)  # Set x-axis limits Selenise\n",
    "ax2.set_ylim(-0.0, 2E11) # Set y-axis limits Selenise\n",
    "#ax2.xaxis.set_minor_locator(ticker.MultipleLocator(0.05))\n",
    "#ax2.yaxis.set_minor_locator(ticker.MultipleLocator(0.05E13))\n",
    "# Plot line of best fit\n",
    "ax2.plot(line_x, line_y, label='Tangent', color='r')\n",
    "# Plot Tauc plot\n",
    "ax2.plot(df_data['h$\\nu$'], df_data['$(\\\\alpha h\\\\nu)^2 (eV^2/cm^2)$'], color='k')\n",
    "ax2.scatter(df_data['h$\\nu$'], df_data['$(\\\\alpha h\\\\nu)^2 (eV^2/cm^2)$'], label='Tauc plot', c='k', marker='o', alpha=0.8, s=2)\n",
    "ax2.grid()\n",
    "\n",
    "# Add Egap as text onto the figure\n",
    "#ax.text(0.4, 0.1, \"Band gap : {:.2f} eV\".format(x_intercept), \n",
    "#         transform=ax.transAxes, fontsize=10, position=(0.55, 0.05))\n",
    "\n",
    "# Figure save settings\n",
    "save_dir = os.path.join(folder_path, 'UVVIS Plots')\n",
    "if not os.path.exists(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "plt.savefig(os.path.join(save_dir, file_title + 'Tauc plot2.png'), dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2594f289",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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