tests/data/eda/01_lxcat_n2_fict.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "***Summary: This notebook provides an overview of the nitrogen cross sections from fict that are in NEPC.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nepc\n",
    "from nepc.util import config\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('max_colwidth', 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***NOTE: You may need to modify the `local` argument in the next command.  `local=True` points to a local copy of the NEPC database. `local=False` points to a computer serving a NEPC MySQL database. See `nepc.util.config.production()` and set the `$NEPC_PRODUCTION` environment variable.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnx, cursor = nepc.connect(local=True, test=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full fict dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fict = nepc.Model(cursor, \"fict\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cross sections in model: 30\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.io.formats.style.Styler"
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     "execution_count": 6,
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   ],
   "source": [
    "type(fict.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Number of cross sections in model: 30\n"
     ]
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       "            background-color:  #99159f;\n",
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       "            background-color:  #0d0887;\n",
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       "        }</style><table id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >cs_id</th>        <th class=\"col_heading level0 col1\" >specie</th>        <th class=\"col_heading level0 col2\" >lhsA</th>        <th class=\"col_heading level0 col3\" >rhsA</th>        <th class=\"col_heading level0 col4\" >process</th>        <th class=\"col_heading level0 col5\" >reaction</th>        <th class=\"col_heading level0 col6\" >threshold</th>        <th class=\"col_heading level0 col7\" >E_peak</th>        <th class=\"col_heading level0 col8\" >E_upper</th>        <th class=\"col_heading level0 col9\" >sigma_max</th>        <th class=\"col_heading level0 col10\" >lpu</th>        <th class=\"col_heading level0 col11\" >upu</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row0_col1\" class=\"data row0 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row0_col2\" class=\"data row0 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row0_col3\" class=\"data row0 col3\" >N2(X1Sigmag+)_jSCHULZ</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row0_col4\" class=\"data row0 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row0_col5\" class=\"data row0 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+ j=SCHULZ$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row0_col6\" class=\"data row0 col6\" >0.02</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row0_col7\" class=\"data row0 col7\" >2.3</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row0_col8\" class=\"data row0 col8\" >3.6</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row0_col9\" class=\"data row0 col9\" >6.9e-20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row0_col10\" class=\"data row0 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row0_col11\" class=\"data row0 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row1_col1\" class=\"data row1 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row1_col2\" class=\"data row1 col2\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row1_col3\" class=\"data row1 col3\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row1_col4\" class=\"data row1 col4\" >total</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row1_col5\" class=\"data row1 col5\" >e$^-$ + N${}_2$ $\\rightarrow$ N${}_2$ + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row1_col6\" class=\"data row1 col6\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row1_col7\" class=\"data row1 col7\" >2.5</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row1_col8\" class=\"data row1 col8\" >10000</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row1_col9\" class=\"data row1 col9\" >3e-19</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row1_col10\" class=\"data row1 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row1_col11\" class=\"data row1 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row2_col1\" class=\"data row2 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row2_col2\" class=\"data row2 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row2_col3\" class=\"data row2 col3\" >N2(X1Sigmag+)_jSLAR</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row2_col4\" class=\"data row2 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row2_col5\" class=\"data row2 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+ j=SLAR$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row2_col6\" class=\"data row2 col6\" >0.02</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row2_col7\" class=\"data row2 col7\" >2.3</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row2_col8\" class=\"data row2 col8\" >3.6</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row2_col9\" class=\"data row2 col9\" >2.77e-20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row2_col10\" class=\"data row2 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row2_col11\" class=\"data row2 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row3_col1\" class=\"data row3 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row3_col2\" class=\"data row3 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row3_col3\" class=\"data row3 col3\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row3_col4\" class=\"data row3 col4\" >excitation_v</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row3_col5\" class=\"data row3 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$ v=0) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+$ v=1) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row3_col6\" class=\"data row3 col6\" >0.29</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row3_col7\" class=\"data row3 col7\" >1.6</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row3_col8\" class=\"data row3 col8\" >50</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row3_col9\" class=\"data row3 col9\" >1.5e-21</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row3_col10\" class=\"data row3 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row3_col11\" class=\"data row3 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row4_col1\" class=\"data row4 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row4_col2\" class=\"data row4 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row4_col3\" class=\"data row4 col3\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row4_col4\" class=\"data row4 col4\" >excitation_v</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row4_col5\" class=\"data row4 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$ v=0) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+$ v=1) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row4_col6\" class=\"data row4 col6\" >0.292</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row4_col7\" class=\"data row4 col7\" >2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row4_col8\" class=\"data row4 col8\" >3.6</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row4_col9\" class=\"data row4 col9\" >4.8e-20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row4_col10\" class=\"data row4 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row4_col11\" class=\"data row4 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row5_col1\" class=\"data row5 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row5_col2\" class=\"data row5 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row5_col3\" class=\"data row5 col3\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row5_col4\" class=\"data row5 col4\" >excitation_v</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row5_col5\" class=\"data row5 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$ v=0) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+$ v=2) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row5_col6\" class=\"data row5 col6\" >0.59</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row5_col7\" class=\"data row5 col7\" >2.2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row5_col8\" class=\"data row5 col8\" >3.5</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row5_col9\" class=\"data row5 col9\" >3.3e-20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row5_col10\" class=\"data row5 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row5_col11\" class=\"data row5 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row6_col0\" class=\"data row6 col0\" >7</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row6_col1\" class=\"data row6 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row6_col2\" class=\"data row6 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row6_col3\" class=\"data row6 col3\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row6_col4\" class=\"data row6 col4\" >excitation_v</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row6_col5\" class=\"data row6 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$ v=0) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+$ v=3) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row6_col6\" class=\"data row6 col6\" >0.88</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row6_col7\" class=\"data row6 col7\" >2.3</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row6_col8\" class=\"data row6 col8\" >3.3</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row6_col9\" class=\"data row6 col9\" >2.7e-20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row6_col10\" class=\"data row6 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row6_col11\" class=\"data row6 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row7_col0\" class=\"data row7 col0\" >8</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row7_col1\" class=\"data row7 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row7_col2\" class=\"data row7 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row7_col3\" class=\"data row7 col3\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row7_col4\" class=\"data row7 col4\" >excitation_v</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row7_col5\" class=\"data row7 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$ v=0) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+$ v=4) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row7_col6\" class=\"data row7 col6\" >1.17</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row7_col7\" class=\"data row7 col7\" >2.3</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row7_col8\" class=\"data row7 col8\" >3.3</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row7_col9\" class=\"data row7 col9\" >2.385e-20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row7_col10\" class=\"data row7 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row7_col11\" class=\"data row7 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row8_col0\" class=\"data row8 col0\" >9</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row8_col1\" class=\"data row8 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row8_col2\" class=\"data row8 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row8_col3\" class=\"data row8 col3\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row8_col4\" class=\"data row8 col4\" >excitation_v</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row8_col5\" class=\"data row8 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$ v=0) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+$ v=5) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row8_col6\" class=\"data row8 col6\" >1.47</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row8_col7\" class=\"data row8 col7\" >2.2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row8_col8\" class=\"data row8 col8\" >3.3</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row8_col9\" class=\"data row8 col9\" >1.53e-20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row8_col10\" class=\"data row8 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row8_col11\" class=\"data row8 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row9_col0\" class=\"data row9 col0\" >10</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row9_col1\" class=\"data row9 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row9_col2\" class=\"data row9 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row9_col3\" class=\"data row9 col3\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row9_col4\" class=\"data row9 col4\" >excitation_v</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row9_col5\" class=\"data row9 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$ v=0) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+$ v=6) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row9_col6\" class=\"data row9 col6\" >1.76</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row9_col7\" class=\"data row9 col7\" >2.5</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row9_col8\" class=\"data row9 col8\" >3.2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row9_col9\" class=\"data row9 col9\" >1.72e-20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row9_col10\" class=\"data row9 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row9_col11\" class=\"data row9 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row10_col0\" class=\"data row10 col0\" >11</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row10_col1\" class=\"data row10 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row10_col2\" class=\"data row10 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row10_col3\" class=\"data row10 col3\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row10_col4\" class=\"data row10 col4\" >excitation_v</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row10_col5\" class=\"data row10 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$ v=0) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+$ v=7) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row10_col6\" class=\"data row10 col6\" >2.06</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row10_col7\" class=\"data row10 col7\" >2.7</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row10_col8\" class=\"data row10 col8\" >3.3</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row10_col9\" class=\"data row10 col9\" >9.6e-21</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row10_col10\" class=\"data row10 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row10_col11\" class=\"data row10 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row11_col0\" class=\"data row11 col0\" >12</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row11_col1\" class=\"data row11 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row11_col2\" class=\"data row11 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row11_col3\" class=\"data row11 col3\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row11_col4\" class=\"data row11 col4\" >excitation_v</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row11_col5\" class=\"data row11 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$ v=0) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+$ v=8) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row11_col6\" class=\"data row11 col6\" >2.35</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row11_col7\" class=\"data row11 col7\" >2.7</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row11_col8\" class=\"data row11 col8\" >3.3</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row11_col9\" class=\"data row11 col9\" >3.6e-21</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row11_col10\" class=\"data row11 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row11_col11\" class=\"data row11 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row12_col0\" class=\"data row12 col0\" >13</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row12_col1\" class=\"data row12 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row12_col2\" class=\"data row12 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row12_col3\" class=\"data row12 col3\" >N2(A3Sigmau+)_v0-4</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row12_col4\" class=\"data row12 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row12_col5\" class=\"data row12 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (A ${}^3\\Sigma_u^+ v=(0-4)$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row12_col6\" class=\"data row12 col6\" >6.17</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row12_col7\" class=\"data row12 col7\" >16</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row12_col8\" class=\"data row12 col8\" >70</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row12_col9\" class=\"data row12 col9\" >1.5e-22</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row12_col10\" class=\"data row12 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row12_col11\" class=\"data row12 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row13_col0\" class=\"data row13 col0\" >14</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row13_col1\" class=\"data row13 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row13_col2\" class=\"data row13 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row13_col3\" class=\"data row13 col3\" >N2(A3Sigmau+)_v5-9</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row13_col4\" class=\"data row13 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row13_col5\" class=\"data row13 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (A ${}^3\\Sigma_u^+ v=(5-9)$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row13_col6\" class=\"data row13 col6\" >7</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row13_col7\" class=\"data row13 col7\" >16</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row13_col8\" class=\"data row13 col8\" >70</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row13_col9\" class=\"data row13 col9\" >6.5e-22</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row13_col10\" class=\"data row13 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row13_col11\" class=\"data row13 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row14_col0\" class=\"data row14 col0\" >15</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row14_col1\" class=\"data row14 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row14_col2\" class=\"data row14 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row14_col3\" class=\"data row14 col3\" >N2(B3Pig)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row14_col4\" class=\"data row14 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row14_col5\" class=\"data row14 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (B ${}^3\\Pi_g$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row14_col6\" class=\"data row14 col6\" >7.35</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row14_col7\" class=\"data row14 col7\" >13</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row14_col8\" class=\"data row14 col8\" >70</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row14_col9\" class=\"data row14 col9\" >2.003e-21</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row14_col10\" class=\"data row14 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row14_col11\" class=\"data row14 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row15_col0\" class=\"data row15 col0\" >16</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row15_col1\" class=\"data row15 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row15_col2\" class=\"data row15 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row15_col3\" class=\"data row15 col3\" >N2(W3Deltau)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row15_col4\" class=\"data row15 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row15_col5\" class=\"data row15 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (W ${}^3\\Delta_u$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row15_col6\" class=\"data row15 col6\" >7.36</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row15_col7\" class=\"data row15 col7\" >16</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row15_col8\" class=\"data row15 col8\" >100</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row15_col9\" class=\"data row15 col9\" >2.546e-21</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row15_col10\" class=\"data row15 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row15_col11\" class=\"data row15 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row16_col0\" class=\"data row16 col0\" >17</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row16_col1\" class=\"data row16 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row16_col2\" class=\"data row16 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row16_col3\" class=\"data row16 col3\" >N2(A3Sigmau+)_v10-</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row16_col4\" class=\"data row16 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row16_col5\" class=\"data row16 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (A ${}^3\\Sigma_u^+ v=(10-)$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row16_col6\" class=\"data row16 col6\" >7.8</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row16_col7\" class=\"data row16 col7\" >17</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row16_col8\" class=\"data row16 col8\" >70</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row16_col9\" class=\"data row16 col9\" >6.7e-22</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row16_col10\" class=\"data row16 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row16_col11\" class=\"data row16 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row17_col0\" class=\"data row17 col0\" >18</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row17_col1\" class=\"data row17 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row17_col2\" class=\"data row17 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row17_col3\" class=\"data row17 col3\" >N2(Bp3Sigmau-)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row17_col4\" class=\"data row17 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row17_col5\" class=\"data row17 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (B${}^{'} $ ${}^3\\Sigma_u^-$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row17_col6\" class=\"data row17 col6\" >8.16</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row17_col7\" class=\"data row17 col7\" >15</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row17_col8\" class=\"data row17 col8\" >70</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row17_col9\" class=\"data row17 col9\" >8.38e-22</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row17_col10\" class=\"data row17 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row17_col11\" class=\"data row17 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row18_col0\" class=\"data row18 col0\" >19</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row18_col1\" class=\"data row18 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row18_col2\" class=\"data row18 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row18_col3\" class=\"data row18 col3\" >N2(ap1Sigmau-)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row18_col4\" class=\"data row18 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row18_col5\" class=\"data row18 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (a${}^{'}$ ${}^1\\Sigma_u^-$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row18_col6\" class=\"data row18 col6\" >8.2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row18_col7\" class=\"data row18 col7\" >15</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row18_col8\" class=\"data row18 col8\" >150</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row18_col9\" class=\"data row18 col9\" >6.97e-22</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row18_col10\" class=\"data row18 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row18_col11\" class=\"data row18 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row19_col0\" class=\"data row19 col0\" >20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row19_col1\" class=\"data row19 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row19_col2\" class=\"data row19 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row19_col3\" class=\"data row19 col3\" >N2(a1Pig)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row19_col4\" class=\"data row19 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row19_col5\" class=\"data row19 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (a ${}^1\\Pi_g$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row19_col6\" class=\"data row19 col6\" >8.55</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row19_col7\" class=\"data row19 col7\" >17</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row19_col8\" class=\"data row19 col8\" >1000</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row19_col9\" class=\"data row19 col9\" >2.023e-21</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row19_col10\" class=\"data row19 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row19_col11\" class=\"data row19 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row20_col0\" class=\"data row20 col0\" >21</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row20_col1\" class=\"data row20 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row20_col2\" class=\"data row20 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row20_col3\" class=\"data row20 col3\" >N2(w1Deltau)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row20_col4\" class=\"data row20 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row20_col5\" class=\"data row20 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (w ${}^1\\Delta_u$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row20_col6\" class=\"data row20 col6\" >8.89</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row20_col7\" class=\"data row20 col7\" >13</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row20_col8\" class=\"data row20 col8\" >50</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row20_col9\" class=\"data row20 col9\" >7.82e-22</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row20_col10\" class=\"data row20 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row20_col11\" class=\"data row20 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row21_col0\" class=\"data row21 col0\" >22</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row21_col1\" class=\"data row21 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row21_col2\" class=\"data row21 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row21_col3\" class=\"data row21 col3\" >N2(C3Piu)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row21_col4\" class=\"data row21 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row21_col5\" class=\"data row21 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (C ${}^3\\Pi_u$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row21_col6\" class=\"data row21 col6\" >11.03</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row21_col7\" class=\"data row21 col7\" >12</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row21_col8\" class=\"data row21 col8\" >100</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row21_col9\" class=\"data row21 col9\" >6.5e-21</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row21_col10\" class=\"data row21 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row21_col11\" class=\"data row21 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row22_col0\" class=\"data row22 col0\" >23</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row22_col1\" class=\"data row22 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row22_col2\" class=\"data row22 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row22_col3\" class=\"data row22 col3\" >N2(E3Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row22_col4\" class=\"data row22 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row22_col5\" class=\"data row22 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (E ${}^3\\Sigma_g^+$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row22_col6\" class=\"data row22 col6\" >11.87</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row22_col7\" class=\"data row22 col7\" >11.93</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row22_col8\" class=\"data row22 col8\" >50</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row22_col9\" class=\"data row22 col9\" >4.96e-22</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row22_col10\" class=\"data row22 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row22_col11\" class=\"data row22 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row23_col0\" class=\"data row23 col0\" >24</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row23_col1\" class=\"data row23 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row23_col2\" class=\"data row23 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row23_col3\" class=\"data row23 col3\" >N2(ap1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row23_col4\" class=\"data row23 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row23_col5\" class=\"data row23 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (a${}^{''}$ ${}^1\\Sigma_g^+$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row23_col6\" class=\"data row23 col6\" >12.25</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row23_col7\" class=\"data row23 col7\" >20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row23_col8\" class=\"data row23 col8\" >1000</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row23_col9\" class=\"data row23 col9\" >3.89e-22</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row23_col10\" class=\"data row23 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row23_col11\" class=\"data row23 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row24_col0\" class=\"data row24 col0\" >25</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row24_col1\" class=\"data row24 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row24_col2\" class=\"data row24 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row24_col3\" class=\"data row24 col3\" >N2(1SUM)_Z-M</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row24_col4\" class=\"data row24 col4\" >excitation</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row24_col5\" class=\"data row24 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ $(1SUM)_{Z-M}$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row24_col6\" class=\"data row24 col6\" >13</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row24_col7\" class=\"data row24 col7\" >60</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row24_col8\" class=\"data row24 col8\" >1000</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row24_col9\" class=\"data row24 col9\" >1.76e-20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row24_col10\" class=\"data row24 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row24_col11\" class=\"data row24 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row25_col0\" class=\"data row25 col0\" >26</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row25_col1\" class=\"data row25 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row25_col2\" class=\"data row25 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row25_col3\" class=\"data row25 col3\" >N2+</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row25_col4\" class=\"data row25 col4\" >ionization_total</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row25_col5\" class=\"data row25 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2^+$ + 2e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row25_col6\" class=\"data row25 col6\" >15.6</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row25_col7\" class=\"data row25 col7\" >100</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row25_col8\" class=\"data row25 col8\" >1500</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row25_col9\" class=\"data row25 col9\" >2.3436e-20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row25_col10\" class=\"data row25 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row25_col11\" class=\"data row25 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row26_col0\" class=\"data row26 col0\" >27</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row26_col1\" class=\"data row26 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row26_col2\" class=\"data row26 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row26_col3\" class=\"data row26 col3\" >N2+(B2Sigmau+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row26_col4\" class=\"data row26 col4\" >ionization</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row26_col5\" class=\"data row26 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}^+_2$ (B ${}^2\\Sigma_u^+$) + 2e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row26_col6\" class=\"data row26 col6\" >18.8</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row26_col7\" class=\"data row26 col7\" >90</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row26_col8\" class=\"data row26 col8\" >10000</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row26_col9\" class=\"data row26 col9\" >1.73e-21</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row26_col10\" class=\"data row26 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row26_col11\" class=\"data row26 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row27_col0\" class=\"data row27 col0\" >28</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row27_col1\" class=\"data row27 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row27_col2\" class=\"data row27 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row27_col3\" class=\"data row27 col3\" >N2*</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row27_col4\" class=\"data row27 col4\" >excitation_total</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row27_col5\" class=\"data row27 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2^*$ + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row27_col6\" class=\"data row27 col6\" >0.02</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row27_col7\" class=\"data row27 col7\" >2.29087</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row27_col8\" class=\"data row27 col8\" >1000</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row27_col9\" class=\"data row27 col9\" >1.23556e-19</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row27_col10\" class=\"data row27 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row27_col11\" class=\"data row27 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row28_col0\" class=\"data row28 col0\" >29</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row28_col1\" class=\"data row28 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row28_col2\" class=\"data row28 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row28_col3\" class=\"data row28 col3\" >N2*</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row28_col4\" class=\"data row28 col4\" >excitation_total</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row28_col5\" class=\"data row28 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2^*$ + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row28_col6\" class=\"data row28 col6\" >6.17</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row28_col7\" class=\"data row28 col7\" >30.1995</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row28_col8\" class=\"data row28 col8\" >1000</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row28_col9\" class=\"data row28 col9\" >1.98645e-20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row28_col10\" class=\"data row28 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row28_col11\" class=\"data row28 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row29_col0\" class=\"data row29 col0\" >30</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row29_col1\" class=\"data row29 col1\" >N2</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row29_col2\" class=\"data row29 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row29_col3\" class=\"data row29 col3\" >N2(X1Sigmag+)_v1-8)</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row29_col4\" class=\"data row29 col4\" >excitation_total</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row29_col5\" class=\"data row29 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+ v=(1-8)$) + e$^-$</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row29_col6\" class=\"data row29 col6\" >0.29</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row29_col7\" class=\"data row29 col7\" >2.29087</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row29_col8\" class=\"data row29 col8\" >48.9779</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row29_col9\" class=\"data row29 col9\" >9.83258e-20</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row29_col10\" class=\"data row29 col10\" >-1</td>\n",
       "                        <td id=\"T_0ba214ac_04cd_11eb_97ed_acde48001122row29_col11\" class=\"data row29 col11\" >-1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc571d43128>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fict.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To inspect the `metadata` or `data` attribute of a particular cross section, use the `Model.subset` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cs_id': 10,\n",
       " 'species': 'N2',\n",
       " 'process': 'excitation_v',\n",
       " 'units_e': 1.0,\n",
       " 'units_sigma': 1.0,\n",
       " 'ref': '\\\\N',\n",
       " 'lhsA': 'N2(X1Sigmag+)',\n",
       " 'lhsB': None,\n",
       " 'rhsA': 'N2(X1Sigmag+)',\n",
       " 'rhsB': None,\n",
       " 'threshold': 1.76,\n",
       " 'wavelength': -1.0,\n",
       " 'lhs_v': 0,\n",
       " 'rhs_v': 6,\n",
       " 'lhs_j': -1,\n",
       " 'rhs_j': -1,\n",
       " 'background': 'N2 VIBRATIONAL (V=6) Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.',\n",
       " 'lpu': -1.0,\n",
       " 'upu': -1.0,\n",
       " 'lhsA_long': 'N${}_2$ (X ${}^1\\\\Sigma_g^+$)',\n",
       " 'lhsB_long': None,\n",
       " 'rhsA_long': 'N${}_2$ (X ${}^1\\\\Sigma_g^+$)',\n",
       " 'rhsB_long': None,\n",
       " 'e_on_lhs': 1,\n",
       " 'e_on_rhs': 1,\n",
       " 'hv_on_lhs': 0,\n",
       " 'hv_on_rhs': 0,\n",
       " 'v_on_lhs': 1,\n",
       " 'v_on_rhs': 1,\n",
       " 'j_on_lhs': 0,\n",
       " 'j_on_rhs': 0}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fict.subset(metadata={'cs_id': 10})[0].metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Electron-impact excitations provided by Model `fict`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the `fict`, `fict_min` or `fict_min2` models should **NOT** be used for simulations. These are populated with fictitious cross section data for testing and demonstration purposes only.\n",
    "\n",
    "See the `01_lxcat_n2_fict.py` script in  `curate` for details on how the data are curated starting from the LXCat text file.\n",
    "\n",
    "All excitations (pure electronic, rotational, and vibrational) of $\\text{N}_2$ are from the ground electronic state, $\\text{N}_2 (\\text{X} {}^1\\Sigma_g^+)$.\n",
    "\n",
    "There is no data in `fict` to support follow-on excitation of excited states.\n",
    "\n",
    "Vibrational excitations, $v_{0\\rightarrow n}, n \\in \\{1, ..., 8\\}$, of the ground electronic state, $\\text{N}_2 (\\text{X} {}^1\\Sigma_g^+)$, are supported. $v_{0\\rightarrow 1}$ is provided in two separate data sets. \n",
    "\n",
    "Excitation from $\\text{N}_2 (\\text{X} {}^1\\Sigma_g^+)$ to $\\text{N}_2 (\\text{A} {}^3\\Sigma_u^+)$ are vibrationally resolved, but not completely. The cross sections are lumped into three fictitious vibronic levels: $v=(0-4)$, $v=(5-9)$, and $v=(10-)$.\n",
    "\n",
    "Cross sections for excitation to the three singlets above the N${}_2$ (a${}^{''}$ ${}^1\\Sigma_g^+$) state are lumped together in the fictitious N${}_2$ $(1SUM)_{Z-M}$) state.\n",
    "\n",
    "Rotational excitations within the $\\text{N}_2 (\\text{X} {}^1\\Sigma_g^+)$ state are supported via the single level approximation to rotation (SLAR). Another option would be to use the cross sections for the resonance region (provided) along with the CAR approximation (not provided) in place of the SLAR approximation.\n",
    "\n",
    "De-excitation rates should be computed using detailed balance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc571d89b70>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fict.plot(units_sigma=1E-20, process='excitation',\n",
    "              plot_param_dict = {'linewidth':.8}, \n",
    "              #xlim_param_dict = {'left': 0.01, 'right': 120.0}, \n",
    "              ylog=True, xlog=True, max_plots=40, width=8, height=4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5728b1da0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tVYGIXCrOAgNLReSZDPOFNOPci0uAYpy51O4GvqGq8T1wFgLLEnq/pEVVnwcWZPqwGSfbeI7WWF4DtOM8FJ/v/nxN3P6sY5nKCLgn5Ayrn7qx+gmrn9Jg9VNmcjWWfuC/cb6Y2Y2zQNtnVTV+cam8qJ8kvrdZPpk/f74uX758uIthTGqxKCw5Ab7evWd9R6SDCx67gJtPupkJJRPg1Vt5df1T/LNpDV9r/iK7//dWKl59iiueu4Kz557NWXPPApzeol945Av834k3w/1fgaO+7gydP+H7w/HujDHDQEReU9X5w10OY7Ll9o77G3CDOguC9JX+p8BOVf3NoBfOOd884Ic4jRtHAz9W1VOG4tzpEpGXga+q6rB/K5pJPC2WPeVSLI0Z7ax+6r9cuqdZ/dQ/uRTL/rAeosYMl8AmqNinx+Y/vvNHFu670GkMBZj7GfYPhtjsGUfL3+4iNKGIbz/9ba488squxlAAEXdKj20rYMqhMOlg2JE4ZYsxxhiT087F+fB/rYgsE5FUvaoAUNXvD9UDiussoFadHgUR4O0hPHdaVPXoHHpASTueFsueciyWxox2Vj/1U47d06x+6occi2XW8nZRJWNyXpIV5jc1beKFLS9w54I7926smE5583aO/rey8vgJbN3wFW4++QQmlkxMmq1uW4FMPhQqZ0LjhsF8B8YYY9IkIjOAd1PsPkhVNw5leXKVqi4lbr6xHDQ27ueL6T6U1STI8XhaLI3B6qd05fj9DOyelpEcj6fFcohYg6gxwyVhQSVV5fpXr+fy+Zfj88T91xShJTyRkm2reOzUj3HQ1nFM3LEKZvVsEC0rKKNl2xuMOeAM8HjAXwLBFigsG4p3ZIwxJgX3gdJuxiPfUmCpiKwCxuEsrGBGJoulMVj9lEfsnpY/LJZDxIbMGzNcEnqIPr3paSoLKzl80uHdkgWCAZ5+aj0Pn1jCJ/V0Z+Pj34fAlh5ZVhZW0tiwFsbt62yYeCDsHKiFII0xxphR721VPRj4Os6iAyuGuTwmexZLY0w+sXta/rBYDhHrIWrMcIlrEG2PtPOHN//ALaf0XJTx/Wf+SklJOf8xdx9Wvd1OcbgdTvsfeOBrcOFD4CvsSlvhLaHRX8Q+Hq+zYdIhzkrz+xw5JG/JGGOMyXM/FZGj3Z//U/N1ddLRwWJpjMkndk/LHxbLIZK3DaKBQIBFixZRU1NDTU3NcBfHmJ5ad0HpeACWvLWEz839HOOLx/dIVv7X53hmwX6cvXMTb0TaKY+2wcxj4KCF8Pj/gzN+1ZV2bCRI49gZew+edDC88+CgvxVjzPCqq6ujrq4OoGK4y2JMPlPVK4a7DGZgWCyNMfnE7mn5w2I5dPJ2yHxFRQW1tbXWGGpyUyQI3kJwV4Z/bvNzfGH/ngvbhbdswd/SwaqJESJN+7Ft7Oq9O4++GDoC8OZdXZsq2ptorJi8N83Eg2BnqjnSjTH5oqamhtraWoDAcJfFGGOMMcYYY3Jd3jaIGpPT6tdB1SwAWsOtlPpL8XYOc4/TcM+9jD/3PFrCLWwMHUX7+PV7d4rAwpvglSWwzZlWpLJ5F40lcYvSFVc6jabWy94YY4wxxhhjjDEGsAZRY4ZH3Pyh65vWM7N8Zo8ksWCQlmefpfzTp4AKu9snMn1MI7H4RAWl8Plb4W+XEtm8nSnbS2j0F3bPqHwaNG3hkQ8fIRQNDd57MsYYY4wxxhhjjBkBrEHUmOEQ3yAaWM+silk9kjQ9+hhjTjkFKShgSstsxs4s44C2AEpCb89x+xL+6A/Yc+uLTNxzJu0t7d33TzoYdqzkj+/8kVX1qwbrHRljjDHGGGOMMcaMCNYgasxw2LMGxu0LwLrAuh4NoqpK4733UvmFcwCYWj+X0gNL2L8loYcoEN7RSv2/J1H10Q14Zn7AIW9P655g0sHo9rfZ2rKVlXtWDtY7MsYYY4wxxhhjjBkRrEHUmOGwZy1UOQ2i65vWU11e3W13x4oV+KdOxT9xInu2bKLgtVdZ/8ZfCTZMJxSrJ9Th9AINb2+l/p73qTr3APwLL6P8i1+ntLWQ0KbmvZlN2J+m3avwe/y8u8cWWDLGGGOMMcYYY8zo5hvuAhgzKgWboagcgM3Nm5k2pnuvzvq/3MXY888HoKB4HKJhwmUCvpmEout44CfXUhgs5BD/x6mfG6B+dTPjO2ZSOWUq91U/zf73z8Jb9DoTvnExFFexpX0Xx844ltUNq3sUxRhjjDHGGGOMMWY0sQZRY4Zayy4oHQ9ATGPENIbf4+/aHdm1i9DGDRR/9DAneUOYsQefRv1bD3DI17/C669u4fMXn03D/R9QeNpEfM3b2L1pA6tefJaqqdPZWdGAdmyhfVW9k2FxJVuDjVSXV7OxaSPtkXaKfcVD/raNMcYYY4wxxhhjcoENmTdmqO1cCRMPAmBb6zamlE7ptrvhvvsYe845iAgAk2eX4+mYQPuccTy37G3KfT4a7v+AcecfSOXc6cw+/EiOOvNsTrjwIpp27gAg+M7DUFDtZOgvYUusjallU9nPX8H7L/1myN6qMcYYY4wxxhhjTK6xBlFjhtrO92CS0yC6LrCO6orqrl0aDtP8xJOUn3561zYR4cBPTSEU2Y/glg4OLq9i3AUH4RvXvZdnSWUlrYEGxrd6iHkj4C0gFo4S2N1O/frPM61sGgd7inl328tD8jaNMcaYXCYiFSLyioi0iMghw10ekz2LpTEmn9g9LX9YLHObNYgaM9R2rISJBwKwPrC+2wrzzU89Rdlxx+IpKup2yGHHzGLm1unMKz6O53c+iK+q+36AgqJiwh1BPvJ+CM9xRyMaoOOD7bQFQvj3HM54zySmx7xsbd81uO/PGGOMGRnagNOB+/uTiYgsG5DSmP6wWBpj8ond0/KHxTKHWYOoMUNt1/swwWkQXRdY122F+Ya77qbyi+f2OCS2qZVDCwt5zwft0eYe++PNfbuB9mPm4SkOEly1g3AwStQTpvHtEOPCQepDTemV863/g1g07bdljDG5RERWisjxuZqfGRgi8jMR+W42x6pqWFXz7ltCtyfKwcNdjkxZLHsaqbE0vbP6aXSwe1pPI/WeZrHsaaTGMtGIbBAVkVIRuUNElojIecNdHmPSFotBpB0KSgBY37S3h2jHqlV4xoyhYHr3FefDO1pp/PuH/OnQR1izPoxKLGX2Pr+PwrYoDVV+fOO9hDY1EwpG2TH+DdYu30FVqI090VDfDZ0r7oWHvw0b/92/92uMMcNEVQ9W1WUAIrJeRE5O99hk6ePzM9lzr+0OESmN2/a1bHo+iMgE4ELgf93fy9z8vxSXZoyIbBSRszPMe52IhNwhbp2vWzIt42AQkSoR+auItIrIhvj36/olcN0QlWVA4jmKY/lNEVkuIkERuT1JkiGLpRk6Vj/lJquf+s/qp/yJJYCIfFFE3nPjuVZEjovbnRf1U840iIrIH0Vkp4i8k7D9VBF5X0TWiMjV7ubPAfer6kXAwiEvrDHZCmyEypldvzaFmqgorACg4S93UXVeYp0BkT0dFB1QRcGEIvwFMaAwZfaFoQgN82YRCAbwT6sg0hilubUFLWmAWITCpjCNhcXQvC11GXevgZd+B5+rhXcfyvqtGmOMGRlEZLKIvJDkNTmDPBaLyOI0k/uA72RV2O6+DDyqqu0AqtoCLAJudB9gAH4BLFfVTIeq/RzYDMxS1TL3dXHnThGZISLL3Aerwzp/FpEZ/XxP6fg9EAImAecBf5DuvTQeBk4QkSnJDh4EAxHPLzM6Y7kV+G/gjyn2px3LDP8P9pVXv+8JxgwEq5+SyuV7mtVPmcnZWIrIKW75vgKMAT4JfBiXJC/qp5xpEAVuB06N3yAiXpz/VAuAg4BzReQgYDqwyU1mY3rNyLHj3a4V5ltCLZT4nJ6i0cZGOt59l5JjjulxiPg9EIkxqWQS48ftAQpQ1aTZ+3bvoeOgOTR0NOCfPAm0nfqdu6go8FA5VmltVNRfDI0bk5cvEoSHLoaFv4X9T4P1Lzq9Wo0xZhCJyFQReUBEdrnfln/b3b6viNSLyOFx6XaLOzRQRPYRkQfd4/aIyO/i8lwvIieLyFJgBlDnfvN+pbv/avfb7mYReVdEznK3p0rf1StHRA50P5A2ijNUcWHCeS8XkbdEJCAi94pIz4mfu7//q0Xk/oRtN4rITf28tJ15Jb2+nVR1u6oem+S1fSDOn8T/AJeLSGU/81kAPBu/QVWfBP4O3OT+nZwDXJppxqp6C/Ag8HCy+KnqRlU9XlWPB97s/FlVU1SwAxNncXq6fB74oaq2qOoLOA8lF8SVrQN4Dfh0uvkmnEMyPGQg4jnqYume+0FVfQjYk2J/v2KZSg7eE3KW1U9WP2Vp1N3TrH7Kn1i6fgRcp6r/VtWYqm5R1S1xZcuL+ilnGkRV9TmgPmHzUcAaVf1QVUPAPcCZOK3o0900OfMejOnTzrgFleKGyzc++FcqPncWye7x4vWgboNoONQGRNn0XuJ/FdBQCH9jE95xE5weopMmQWgHoR2NTCouprw0SFN7CfiKiNWvT16+f1wLh5wNUw8Drw+mHwGbbNi8MWbwiIgHqANWANOAk4DvishnVHUtcBXwFxEpAf4E3K6qy8T50vQRYANQ7R57T2L+qnoBsBGocb95/4W7ay1wHFCB86HvzyIypZf0neX1u+V9EpgIfMst3/5xyc7B+ZJ3FvARnN4FvbkbOE1Eyt1zeN087kpxzW4WkZv7yLMzbcrrm87xg2Q5sAy4vJ/5zAPeT7L9MuB4nAUMLlfVlMMiRORRnA/zS0Tkywm7r8DpwXdnFg9hyfQaZxF5xG3ESPZ6xM1jPyCqqh/E5bsCSJzH6z3g0EwLKM5wuEf7aiRJMBDxHI2xTFdWsUwlR+8JOcnqJ8Dqp2yNxnua1U8pjLRYusfMByaIM1J7s4j8TkSKE8414uunrBoTxZnD0zvQhUliGnt7goLTEDoNpxX98yLyB5wL1sOuXbuYP39+16u2tnbwS2tMX3a+B5OcOmFdYF1Xg2jTE49TceaZSQ8Rv9sgWjqJYEeUmLeZN//R80uh9hUrGDNzJr52pTHYiG/yZKIN6wjVBwi3FxJ98TkCbRWUF1bQVL+m54lWPQqNm+Dor+/ddtCZsNKGzRuTy2pra7vqOmD8cJcnC0cCE1T1OlUNqeqHwBLgiwCqugRYDbwMTAF+4B53FDAVuEJVW1W1w+2NkBZVvU9Vt7rfet/rnuOoNA79GFAGXO+W92mcB9/4FfFucvOux/mcclgfZdkAvA581t10ItCmqkm/kVLVS1T1kjTKCn1c32F0LfAt2TvcDAAROUZEXhKRZ0XkbvcBP5VKoMdKg6raAKwESnA+M6akqqep6lRVPUZVb0/YpzgPECfRy/8tt+dGn/qKs6qeoaqVKV5nuMeUAYGErAM4w9niNeNcn0y9COwkRW+VXvQ3nqMxlunKNpap5Oo9IRdZ/WT1Uxern6x+ipfnsZwE+IGzcb6cOQz4KHBNwulGfP3kSyeR21L7RZx5II4EgkChiOwCHgVqVXX1IJQvWQu5qmorzlwGKU2YMIHly5cPQpGM6Yc9a6FqNuA0iB420f0MEoniLStLfoxX0IgysWQiW4PbiHnD+Aq87NrYzIQZe+uXjvc/oHzWbBrbIjQEG/COHUtk92oCE6ezW6OweQ2BknmMK5lEfeO6bneuNz58gsOe+QnyH3UQ/8XUrE/Bk9c6w+Y91hnbmFy0aNEiFi1aBICI7E7roMUVjzO4jae7WRw4te9kAMwEpopIY9w2L/B83O9LcIZdLVLVoLttH2CDqkayKaCIXAh8D6f3Djgf5NO5JlOBTaoaP5/IBpwvbDvFD9tpc4/py104D613Al8iRe+bLKRzfbPi9iQ41v21yN3WuQrrC701/KjqO+7xV+P0MOi0AThRVdtF5Cc4I4NSzcnVQM8HLUTkfJy4/hNn/quLE9OkQ0SmArcAF+nArRDb3zi3AOUJ28rp+bA2BmgkBRE5FXisj3N9A/h1OoUagHiOxlimK2Uss/w/OGj3hIGy+ernB72Omn79cenUUVY/Oax+clj91Durn7KQo7Fsd//9rbo9X0XkVzgNoj+ISzfi66e0GkSBZ3AC/f+AdzpvsiJSBeZ1ntQAACAASURBVJwAXC8if1XVPw9w+TbjVCidpuN0J+5TIBBg0aJF1NTUUFNTM8DFMiYLkRCIB7zOl0/rm9bz2Tmf7eOguB6iJZOIRHzERPnoKTN448kNfPprh3SlC65ZTcXRR6Lr3iUQDCAiaDTELgIcMGk8DZ522sPjqBoTZs+255jZ0oKnpATxeLj0+Su5dv7/x6klVd1P7vXDtI/C5ldgxscG9HIYYwZOXV0ddXV14Ayv61v6jZVDYROwTlXnJtspImXAb4DbgMUi8oDbs2UTMENEfGk8dHabeFlEZuI8xJ4EvKSqURF5k71fxCafqNmxFdhHRDxxD50zgA96OSYd9wE3iMh04Cyg56TS2en1+vZH/IdZcSfLV9XFGWTxXzi9GG6IyzP+c14E6G0i67dwhui9GleOiTgPSecAq4CVInKXOlMzpc3tDLAUeFBVe+35kaGUcRaRx3B6YiTzvKouwPk784nI3LjOCIfi9FKJdyCQ8nO5qj5Oko4H7vv+E05vt0xXuu1PPEdjLNOVMpZZ/h8ctHvCQEmzsXIoWP3ksPoJq5+w+mnUxFJVG0RkM73fbyAP6qd0u3ydrKo/VtW34r9xUtV6VX1AVT8P3DsI5XsVmCsis0SkAKeX6sPpHFhRUUFtba01hprcsWc1jN/7f3tLyxamlvX9pax4nUWVJpZMRGMe1ANT5lTS0hikaXd7V7rgmjVUHjKPcHMrgaAzWmF75RhKKWa/sdPYOraC9mgZ48qmsifcxI4f/5iWZctoadrCVPWyZNtz1Hf0nJuUGR+HLa/3//0bYwZNTU1N59QwiUOVRoJXgCYRuUpEikXEKyKHiMiR7v4bgddU9Ws4E9jfEnfcNpwvZUtFpEhEPpHiHDuA2XG/l+J8yNsFICJfAQ7pJX28l4FW4EoR8YszmX4NSeaHy4TbK2AZzof9dar6Xu9HpK2v6ztsVHUNzufHbyfuE5FZOAsZ9Dbf4qPApxK2/Q54SFWfcXs1XIkzZ1dhhsW7Gueh67IMj+tVb3F2H0LKUrwWuGlacYbmXef+3X8Cp1fL0s583Pd6BPCPLIr4CZyhcmequ6JuBu+tP/EcdbEEEBGfO/TTC3jd+5gvbn9/YplKzt4TcpDVT1j9lLjP6iern8jzWLr+hDPVwEQRGQt8l7hrlC/1U1oNoqoaHog0vRGRu4GXgP3FmbT1q+43at8EnsDp3vx/qpr4DYMxI8OOd7sWVIrGoqgqPk/fnbTF5/QQLfAWQExR9/uyw06ewZv/dKbYVVVirW2UTZlKeyBAzP3eYs2EKsZoFRNDY2msqCQaE6qKxlEvQturywl9+CHb1z/NnOJJXHrYpVz/8vU9C+AZiumCjTGjlapGcR7YDgPWAbuBW4EKETkTZ/GHzmFI3wMOF5Hz4o6bg7PIxGbgCylO8zPgGnEmjL9cVd/F6SXwEs7D5TycuamSpk8obwhYiPPheTdwM3Chqq7qx2XodBdwMn0MbRKRW0Qkrd4RvV3f/hV1wFyH0wDQRZyFAO4ALnCvdyp34iwcUOwe91mc4VlXdCZQ1Vtx/jauzbBcF+H8be0UZzXnFrdXBSJyqYi8KCJLReSZDPOFNOPci0uAYpy51O4GvpHw+XghsCyh90taVPV5YEGmD5txso3naI3lNThDE68Gznd/jp+jLetYpjIC7gk5w+qnbqx+wuqnNFj9lJlcjuWPcToofoDTFvcG8JO4/XlRP4lq771gReQUnK7Av1fVN0Vkkarm/ApFc+fO1RNOOMGGzJvc8c8fwYxjYL9Ps6l5E79a/it+fYIz9cm6z5/NrAeST0ETawuz565VjP/qISz52p/Z6b2Xa2ofQWPKfdcvp+bbh+JrbWTbD69hRm0tS6/6DnXHbuOe0+/hl4t/QHX443xyeiX/WF6Lln2caZcexNp/XssJ90Yp/eSn+PATAZb74Luf/h1XPnslp846lRNnnLi3ACvugbZ6OCbd+dGNMUOtc8j8kiVL1gzlMBNjBprbO+5vwA3qLAjSV/qfAjtV9TeDXjjnfPOAH+I0bhwN/FhVTxmKc6dLRF4Gvqqq7+RAWdKOp8Wyp1yKpTGjndVP/ZdL9zSrn/onl2LZH+n0EL0Ep/X7fBE5kT5WossVNmTe5Jyde3uIrguso7qiOr3jfB40HCPUHsEXCxLWKADiEQ755DTeeXYLwTWrKZzjtoEI+MXHmnVr8NBCJOJFW5XJHTuIeAIUt5fj36ZUnHQ0oXXr2b5rJVMmHQrA1Udfzc1v3kxTqGmg370xZhCN8CHzxsQ7F+fD/7UiskxEUvWqAkBVvz9UDyius3AWE1WcOcfeHsJzp0VVj86hB5S042mx7CnHYmnMaGf1Uz/l2D3N6qd+yLFYZi2dBtFdqtqoqpcDn8ZZZd4Yk6nAFqiYDsD6wHpmVcxK6zDxedBojI7WMAWRdsJEu/btd/Qk1r6+k7b311I412kQLSobQ5WU8+Zbb9Li345GQEPKdLbT7m3D31rCmI1Byg6YQKytjW3hZiZX7QdAVVEVX533VX756i8H+M0bY8zoJiIz4oZDJb5mDHf5coWqLlXV8ap6vPsajDnq+2Ns3M8Xk4MPKbkkx+NpsTQGq5/SleP3M7B7WkZyPJ4WyyGSToPo3zt/UNWrceZQyHmdq8y7q+4aM7yCzVBQCuJMALquaR2zytNsEPUIKHS0RCiIdXTb5/N7mXvkJD5Y2UrhnDkAlFaOZWy4lM2bN9M2NoSGIxCNMr6wnXZ/DG32UbWlA295CE+xl3otY3Lp5K48T60+lcZgI//a8q8BevPGmMFWV1fHokWLIHfm3TIJVHVjL5PYbxzu8pm0LQVuFJEHcOb9emOYy2OyZ7E0Bquf8ojd0/KHxXKI9Nkgqqp/6/xZROYDJ4rI6yLyloi8LSJvDWoJs2RD5k1O2bmqa7g8ZDhk3tXRGu7RIApwyCensS4wDv8sp4G1tHIsxYFCYhUxqmbOhUgY6MBfPpHCaIy2PQGCBR4efnwFe8og2iTdGkRFhB9+7If8+vVfE23ZAcv/BFM+ktXbNsYMDRsyb8yQeVtVDwa+DviBFcNcHpM9i6UxJp/YPS1/WCyHSN9LXHf3F5z5RN8GYgNfHGPy1M6VMOngrl/bwm2MKRiTURYdrWEKNdhje0Gxj6rgJta/38qcI0oprajEt97P5vGbObL6cGKRCERbkDHTGNsSY+f6VeyeXk54Vwf7VEDxnhjlBeXd8pxQMoHq4olsuOvzzD7pOqg+Nrv3bYwxxuSXn4rI0e7P/6l9rU5qcpnF0hiTT+yelj8slkMk0wbRXar68KCUZIB1Dpm3VeZNTtjxLhy0sF9ZtLeEKCTUY3tk2zbmlm5lxVOb2PfwCZRWjiXSIrxW/hrnVn2J1bqGWEcrMmM+4ze0sbFhG80yho/PbKejIcr4+hjiDuWPd0BgF+9Gmpj9zx/BizdB6XgonQAl46BsIkw/Cibs3zUNgDFm+HSuMo8NmTdmUKnqFcNdBjMwLJbGmHxi97T8YbEcOpk2iP6XiNwKPAV0dVVT1QcHtFQDoHPIvDE5Yee7cPzVAGT7BU9HoINiX89jg2vWULnfdCqqitn6QSMFY8rRqNJII7NL9mUd7xML7EDnnkN5wyO07GwgMraEinFNbGgrZfK25qTnO/Bj3+VfW//FGUdcBu2N0LoL2nZD625o3g7Lfga7V8PC38L0I7J6T8aYgdH55d+SJUtsyLwxxhhjjDHG9CHTBtGvAAfgzGPQOWRegZxrEDUmZ6hCewOUVAEQjoUp8BZknE17oJ2xRV480n3q3+Dq1RTOncOB+01h7Ru7qJgdoUhiTCqZRDGl+Ao8hNa/j4a9VFSNpSOwndhxswnuDNGwvZB9A41Jz3fAuAP44zt/BI8XSsc5r3gfuxhiUVCbPcMYY4wxxhhjjDEjR6YNooeq6rxBKYkx+ap5uzPE3NUabqXEV5JxNh1NHRQVe/BH/N22B1evofTjHydWUUBHa5hgQwNF4RD7V+1POBiloMiHdjQSbQoRK/IS8/mp2mcqLb4OWt/ahd9XgIbDiL97vlVFVTQGG1HVpEPqAaexFG/G78UYY4wxxhhjjDFmuPS5ynyCf4vIQYNSEmPy1coHYf/Tun5ti7RR6i/NKAvxCsHmMIXFPqrLq7vtC677kILZsyks8RNsC7Nl6zZKgyGuP+56wsEo/pJCPCUeooEQ7ePH4CkooaqoioY5nySEB50ykdDmzUnPO6V0Cttat2X8lo0xxhhjjDHGGGNyVaYNoscCb4rI+yLyloi8LSJvDUbB+qtzUSV3kQljhocqvH0fzDu7a1NruJUSf4Y9RL0ewu1RisoKug2Z11gMDYfxFBZSWOqjrSVIMBTCozHGFIxxeoiOKcI7dgwajtK0ezuIl6rCKva07yFCBG/1TJr+sTHp3KYHjDuA9+rfy/rtG2OGRl1dHYsWLQJbVMkYY4wxxhhj+pTpkPlTB6UUg8AWVTI5YcvrULUvFI/t2tQWbst4yLz4PUg0hre0BNr2bg9v2ULBtGkAeL0eWiP17LPPPjRsfJ9oJEI4GKWwohTf2CrwCI3btuGTcZRpGfUd9USLlIKp1QRXC7GmEN6Kwm7nPaDqAFbVr+KkGSdlfw2MMYPOFlUyxhhjjDHGmPRl1ENUVTckew1W4YwZ8d64Ez56frdNbeG2jHuIilcQYnhKux/nLKg0d2/euofq6mpKyitobwoQDkYpmjaJynPOwVtRiDaH8UsJRdEi9rTvobkgTHHpDEAIbW7pcd4Dqw5k1Z5VGZXVGGOMMcYYY4wxJpdlOmTeGJOuUBtsfg1mfarb5mzmEMUr+IjhKel+XHD1GgrnzNmbt9Yzc8ZMSivH0trY4PQQLS+h7Lhj8Y4vYoy3Cp8U4Q17qe+op70ohic2hljHFkJbmnuc1uYQNcYYY4wxxhhjTL6xBlFjBsu7f4MDa8DT/b9ZNqvMRxUKJdqzh+iaNRS4DaKhUAj1xPB5CpwG0UAD4Y4o/kJnFfhQYQfjyqbhl0JiHTEag42ES4RoQ4RY/VuENvQcaSsiVBZW0tDRkFF5jTHGGGOMMcYYY3JV3jaI2qJKZti9+Rc47Es9NmfTQzSmUEAYT0n3BtHQ+vUUVlcDsG3bNsoLq+hojVAS10O0s0G0OdZAuW8cPimiubmZ5lAzJWMroTHGmFM+SnD9tuQLK1XZwkrG5DpbVMmYzIlIhYi8IiItInLIcJfHZM9iaYzJJ3ZPyx8Wy9yWdoOoiBwgIleJyE0icqP784GDWbj+6FxUqaamZriLYkajPWvBWwCV+/TY1RpupdhfnFF20ZhSoGE8pXsbUjUSgWgUKSgAoL6+nrKSSoJtYUorx9LW2Eg45DSIRqNt7GneQFG0GL/HaRCtKKygctwkpAPGnnsWsZZdtL/5fo9z71u5L+sC6zK8AMaYoVRTU9O5kKAtqmRM+tqA04H7+5OJiCwbkNKY/rBYGmPyid3T8ofFMoel1SAqIlcB9wACvAK86v58t4hcPXjFM2aEeuPPPRZT6tQWbqPUl1kP0UhM8Wv3HqKhjZvwz5zR9XtjYyPlZWMItkYordjbQzTm2ci/XjqJ5uIbCFa9T6GvmKamZqqKxzKlYgbhaAcen4/Sow9k1y139+gl6vP4iGkso/IaY8xwE5GVInJ8ruZnBoaI/ExEvpvNsaoaVtVdA12m4eb2RDl4uMuRKYtlTyM1lqZ3Vj+NDnZP62mk3tMslj2N1FgmSreH6FeBI1X1elX9s/u6HjjK3WeM6RSNwAdPwAGnJ93dFsl8lflIVPFFg90aRINrVndbUKmxsZHy8ko62sIUl1fQFmgkHIwivmYmTvwMjSuOZ9fMexhbsYaiogdZ6HmRmbKbpmg9qkrJ4bPxTdyPpocf7nbu13e+zsSSiRmV1xhjhpuqHqyqywBEZL2InJzuscnSx+dnsude2x0iUhq37WvZ9HwQkQnAhcD/ur+Xufl/KS7NGBHZKCJnZ5j3OhEJuUPcOl+3ZFrGwSAiVSLyVxFpFZEN8e/X9UvguiEqy4DEczTGUkQKReQ2N4bNIvKGiCxISDZksTRDx+qn3GT1U/9Z/ZRXsWxJeEVF5LdxSfKifkq3QTQGTE2yfYq7zxjTae1TMOs48BUm3d0WznwO0XAkhj8a6jZkPrhmDYVz5nb93tjYSFXVWIKtYQqKiwkHOwh3RBFvCK+nhEibj2neiygc9xyBwCzCU79HUfgVWmni3v9+hSceWEOLfzZv3P0KO97bRiwa48n1TxIIBvj0zE9ndy2MMcbkPBGZLCIvJHlNziCPxSKyOM3kPuA7WRW2uy8Dj6pqO4CqtgCLgBvdBxiAXwDLVTXToWo/BzYDs1S1zH1d3LlTRGaIyDL3weqwzp9FZEaqDAfQ74EQMAk4D/hDQi+Nh4ETRGTKEJQFBiaeX2b0xdIHbAI+hTP/8w+B/xOR6rg0accyw/+DfeXV73uCMQPB6qekcvWeBlY/5U0s48pThhPPduC+uCR5UT+l2yD6XeApEXlMRGrd1+PAU+4+YwyAKrz0ezj8wpRJslllPhxRvOGO7j1EV6+mcO7eBtFgMEhZeQkdrREKiooIdbQ7PUQ9QbzeEmKRCBOnnUzVmktobalm4f5foShSSXDsZpQINVfOp7TYR8kxx/JK7bPcce2L3LriNr5ceAarXliW8aUwxph0ichUEXlARHa535Z/292+r4jUi8jhcel2izs0UET2EZEH3eP2iMjv4vJcLyIni8hSYAZQ537DfaW7/2oRWStOz6x3ReQsd3uq9F29ckTkQPcDaaM4QxUXJpz3chF5S0QCInKviBT18f6vFpH7E7bdKCI39fPSduaV9Pp2UtXtqnpsktf2gTh/Ev8DXC4ilf3MZwHwbPwGVX0S+Dtwk/t3cg5waaYZq+otwIPAw8nip6obVfV4VT0eeLPzZ1XdmCrPgYizOD1dPg/8UFVbVPUFnIeSC+LK1gG8BmT1baaISIaHDEQ8R10sVbVVVRer6npVjanqI8A64Ii4NP2KZSo5eE/IWVY/Wf2UpVF3T7P6KX9imcTZwE7g+biy5UX9lFaDqKo+DuwH/Ah4AngSWAzsr6qPZXNiY/LS63fCxANhUurpNLIZMh8OR/GGuw+ZD2/YSMEMZ9GmaDSKx+OhsNRPsC2Mr7CQcEeQcDCCeDucBtFYlKL9qyjpiFDoK6S9vZ2xO07BN/s9YuFmvH4PRZNK+ej5x/ORhidoimziK3O/StumbbTW78nuehhjTB9ExAPUASuAacBJwHdF5DOquha4CviLiJQAfwJuV9VlIuIFHgE2ANXusfck5q+qFwAbgRr3m+5fuLvWAsfh9Mz6EfBnEZnSS/rO8vrd8j4JTAS+5ZZv/7hk5wCnArOAj+D0LujN3cBpIlLunsPr5nFXimt2s4jc3EeenWlTXt90jh8ky4FlwOX9zGce0HM1QLgMOB5nAYPLVXVbqgxE5FGcD/NLROTLCbuvALYCd2bxEJZMr3EWkUfcRoxkr0fcPPYDoqr6QVy+K4DEDx7vAYdmWkAROQ54tK9GkgQDEc/RGMvE8k/Cie/KhF1ZxTKVHL0n5CSrnwCrn7I1Gu9pVj+lMAJjmeg/gDtVExYbyYP6Ke1V5nH+wEuBJ1T1flX9t6pGReTUQSpbvwQCARYtWkRdXd1wF8WMFk1b4dUlcOIPe03WFm7LuIdoKBzDFwkixc7q9BoKgceD+HzOqZuaqKiooKjUR0dbBI/Hi2qMSCgGEsTrLUZV8Xg9tEwsobjdQ3NzMyX1B+EdU494nXmeC6aVEd7SwuRrfsDUbY20f/9XbL3zdtrfXJHFBTHGDJW6ujoWLVoEzsPTSHMkMEFVr1PVkKp+CCwBvgigqkuA1cDLOFP1/MA97iic6XyucHtadbi9EdKiqvep6la3Z9a97jmOSuPQjwFlwPVueZ/GefA9Ny7NTW7e9Tgf7A7roywbgNeBz7qbTgTaVPXfKdJfoqqXpFFW6OP6DqNrgW/J3uFmAIjIMSLykog8KyJ3uw/4qVQCzYkbVbUBp1GpBKfnRUqqepqqTlXVY1T19oR9ivMAcRIwvpc8ju/tHHHpeo2zqp6hqpUpXme4x5QBgYSsA8CYhG3NONcnUy/i9AJJ2lulF/2N52iMZRf3uvwFuENVVyXszjaWqeTqPSEXWf1k9VMXq5+sfoqX57HsIs7w/E8BdyQ53Yivn3zpJHK7qV6K0wJ8m4h8R1X/5u7+KfD4IJUvaxUVFdTW1g53Mcxo8vf/hFN+DIVlvSZrj7RT7CvOKOtgMIpXY4jH+Q4jtGEDBdXVXfsbGxuprKyksMRPsDXc7dhYrJ2Cgr331o7JpRS/6yWwZQ9jywrxR+ZTPOkl4AIKppfR8WGAygVzqJy/Azmgmjfu+z1V0e55GmNyS01NDTU1NSxZsiTxg2hS8+6Y9zi9fOgaALvf/o+30/3CdCYwVUQa47Z5iRuWg/Nh6GFgkaoG3W37ABtUNZJNAUXkQuB7OL13wPkgn841mQpsUtX4OdQ34HyT3Sl+2E4byedhT3QXzkPrncCXSNH7JgvpXN+suD0JjnV/LXK3dU6l9EKyD9adVPUd9/ircT5fdtoAnKiq7SLyE+BMnN4XyTTQ80ELETkfJ67/xJmf6+LENOkQkanALcBFOnArxPY3zi1AecK2cno+rI0BGknB7dDQ1yivbwC/TqdQAxDP0RjLzrJ5gKU48+59M0mSlLHM8v/goN0TBsrixYsHvY5avHhxOnWU1U8Oq58cVj/1zuqnLORoLONdiPN/Zl2SfSO+fkqrQRS4CDhCVVvEmej7fhGpVtUbgYHo1mvMyNe4CfY9Ia2kmfaGj8YUj+zt0O0sqLR3hfmGhgYqKyspKvXTkdAgGo224fUWd53TV+SlaFwlO174kImzD6LEdyKtk5wRN/5pZTQ/txmA0mmT2GefA1nruZN39qzk2FgEnyfdW4YxJpdl0Fg5FDYB61R1brKdIlIG/Aa4DVgsIg+4PVs2ATNExJfGQ2e3IT4iMhPnIfYk4CV1Rry8yd7PNIlDguJtBfYREU/cQ+cM4INejknHfcANIjIdOAs4pp/5der1+vZH/IdZcSfLV9XFGWTxXzi9GG6Iy3Nr3P4IvS/e+RbOCKZX48oxEech6RxgFbBSRO5S1ecyKFd8I9WDqtprz48MpYyziDyGM0w2medVdQHO35lPROaq6mp336H0HGZ9IPDnVIVQZzqsHh9G3Pf9J5zebpmudNufeI7GWHbOh3cbzoIVp6lqsm+gU8Yyy/+Dg3ZPGChpNlYOBaufHFY/YfUTVj+NpljGuxC4PkX6EV8/pTtk3qvOalqo6nqcuRIWiMivsAZRYxyeTGagyEwMQLxdvzsLKu1tEO3sIeor8DjD5HEaXVWVaLQNj3fvEH1fgRf/xEqaG5vwTyqhqHgKaIxYLIy3rIBYWwSNKQUlPoJtEbwxL+UF5fz05Z+iPaYNMcaYfnsFaBKRq0SkWES8InKIiBzp7r8ReE1Vv4Yzgf0tccdtA64XkVIRKRKRT6Q4xw5gdtzvpTgPlbsAROQrwCG9pI/3MtAKXCkifnEm068hyfxwmXB7BSzD+bC/TlXf6/2ItPV1fYeNqq4B7gW+nbhPRGbhLGSQaj4rgEdxhnHF+x3wkKo+o858XlfizNlVmGHxrsZ56Losw+N61VucVXWBxq3qmvBa4KZpxRmad537d/8JnF4tSzvzcd/rEcA/sijiJ3Aa585Ud0XdDN5bf+I56mLp+gPOA2VNsuvdz1imkrP3hBxk9RNWPyXus/rJ6ifyPJadROTjOD3M41eX79yXF/VTui0420Wka34Rt3H0DJyu+/MGo2DGGEcspqgAcb0zg6vXdFthvrNBNL7nqdfnB40Sjbbj9e4dou8r8FLgLyE6u5jCuWMpKKog0lFAMLjT2V9VRKS+g8JiH6H2CG3tbVSXVwNw2zu3De6bNcaMOqoaxXlgOwxnheXdwK1AhYicibP4Q+cwpO8Bh4vIeXHHzcFZZGIz8IUUp/kZcI04E8Zfrqrv4vQSeAnn4XIeztxUSdMnlDcELMT58LwbuBm4UHvO+5eNu4CT6WNok4jcIiJp9Y7o7fr2r6gD5jqcBoAu4iwEcAdwgXu9U7kTZ+GAYve4z+IMz7qiM4Gq3orzt3FthuW6COdva6c4qzm3uL0qEJFLReRFEVkqIs9kmC+kGedeXAIU48yldjfwDVWN74GzEFiW0PslLar6PLAg04fNONnGc9TF0u0J+HWc/5vb48p2XlyyrGOZygi4J+QMq5+6sfoJq5/SYPVTZnI5luAspvSgqvaYQ5U8qZ/SHf96IU434i7qdP+/UET+d8BLZUyeisai3Ya+pyPYFsZb4EH8e79wCm3ejH/69K7fOxtE4/kKC1FPxBky7ynp6t3pK/Dg9xTRXhDGV1mIr7CccIuPYHAbxcXT8E8fQ3hLM4UlPpp2txAOhxGB7x/9fS575jLq1tZRs29NP66CMcZ0536YOjfF7r/FpWvB+eDY+ftG9k4an5hnddzPf4vPx932A/YugJF4bLL08fmtpGdvgR7p3N8XJ0uX4tilxPWk6CVdRvNU9XF9B0S67zPJ9dmEO7cUgIj4cB6iFqtqslVd44/dLSJ34jQq/UZVHwIeSpLupHTKlnDMrGTbRWQeTuyPBY4GfpxF3mnFuZfj60nxd++6HPhqP/JPezjIQMVzNMZSnYUv+hppl3YsM7zXDPo9IV9Y/dSV1uonq5/SOd7qpwzkcizdPL7ey+68qJ/SaplR1c2qGj8BMyIy2d33YvKjjDGJsllQqaMljK9A9jaIqiJ+f9cCSwChUIjCQme/z+8hEori8xfh9UWIxhJ6iPq9+KSQlpYWd4ufcFsBHR3OlzsF08sIbW6hoNhHS0P93uM8Pn7+yZ9z3wf38a+t/8ri3RtjjDF9Ohfnw/+1IrJMRFL1qgJA43exngAAIABJREFUVb+vqr8ZmqIBzlxcte5DWQR4ewjPnRZVPVpV3xnucrjSjqfFsqcci6Uxo53VT/2UY/c0q5/6IcdimbX+rJDyKHD4QBXEmNGgLdJGqb+074RxOloj+AvoahDVYJDCWXu/TIpEIni9e+cXLSz109EawesvdIfMt+H1luxdVKnAA1EvoZAzKiDcEUFDZbQ0bYDJUDCtjKanN1F4wDjaGhvw+/xdeZf4S/j18b/mm099k3nj5zGmoMeCe8YYY5IQkRnAuyl2H+T2Jhr1BqJHwyAbG/fzxXQfymoS5Hg8LZbGYPVTunL8fgZ2T8tIjsfTYjlE+tMgaospGZOh1nArJb6SvhPGCbaG8fmi4CsAIBYMUnjI3gWVmpqaqKjYO61GYYmPYFsYj7cA1QjRaDsihXENol4ioSjA/8/encfHXVf7H3+dWbM2XWhLCy2trGXfdxUBf4IagYsXRUTxorks1+0qyL1uXLyuV67CVcRWRUVFL4tgEFGvWgUE2XeKlKUF2tIWmjTNMpnl/P74zrSTdJLMZCaZSfJ+Ph7zaDPzXU7mk8w3c+Z8Pgd3J5lI0zR9bzaseZTd9oBQQxTvSxGrC7Ol8zUys+ayOS+eWfWz+NHJPyIWjo3yWRARmXqybyibqh2HlO1a4FozWwHMImisIBOTxlIEXZ8mEb2mTR4ay3FSTlvsZRWLYgx0dnbS1tZGe3t7tUMR2aon1UNDtLSEaDqdwTyNZROQ3pco2FApp64hGiREIzFCoSTpdA/mMULZSs9cJ/p4PE4ikaA/kWbOgiPZvOm5rceIzK7nyUcfZGXiWRJN0+kY9FKhZKhIbWlvb6etrQ1qpxGByGT1mLvvQ7CWWBR4pMrxyOhpLEVkMtFr2uShsRwnJVWIWlBidhbwOne/LFtev6O73zsm0ZWhpaWFpUuXVjsMkQF6kqVPmQfIpFIQDhKankgQ321bhejghGi8MUJfd4pQKE4mkwIyZNJOOBL8ukeiYVLJNM3NzXR1dZFMpJm94EBefW4T6VSScCRKbKcmHn3g/5jTGcPqUwSvwyJSq1pbW2ltbWXZsmWd1Y5FZJL7kpkdkf3/J0pp8CA1R2MpIpOJXtMmD43lOCl1yvxVQAY4HrgM6AJuBA6rcFwik1JPsvQKUYBMqh8LBb+unuwnMn/+1sc2bdrEggULtn5d1xilrzsZVJSmkgCkU0lC2XVGcxWiTXOb6O7uJtmXJl5fT6w+zssrnmThvgcQ3akZ7nFSiS00TlM1qIiICIC7X1TtGKQyNJYiMpnoNW3y0FiOn1KnzB/h7hcCfQDuvglQtkSkSN3Jbhojo6gQTSYhFCHT0wOh0Nb1QKFwhWiiO4VZFEgCRiaV2lohGs2uIRqNRunv7yeZSBOtCxNvaGHlA0H3+NhOTXgyTTq5hVhUv+IiIiIiIiIiMnmUmhBNmlkYcAAzm01QMSoiRRjNGqIQVIhiYRLPPofF4wMe2y4hml1DFIuBBxWimXSacN4aosn+DJFIhFQqFSRE42Gapy9i7fMP4O5kohDyEOlUtxKiIiIiIiIiIjKplJoQvRL4JTDHzL4I3Al8qeJRiUxS3cnuUSVEvT8JhEmsXEloUEI0mUwSi21LWtY1ROnrCdb99EwfRiiYMp+3hmg6GVSIbkuIRqirn8+0Het5bc1L9PX10bTzdCAN4QShaF8Z37WIiIiIiIiISO0oaQ1Rd/+pmT0AnAAYcKq7PzUmkYlMAPe98BqNsQh7z59W1PY9yR4WNC/Y/oER1kn2VBKrC5FY+QwWr9t6fyqVIpxdGzQnmDKfxIjgdBMK15POmzIfihjplBOJREgmkyQTRrQuTF3dPHbcI8RzD9zL4qPfSENTA5tDISIzXqDOe4r6/kREREREREREal2pFaK4+wp3/7a7f8vdnzKzD4xFYCK1LpXO8KkbHuXRlzqK3qcnNbou897fD6EQiWeeGTBlvrOzk5aWlgHb5poquUdx7yGcTYiGwkFCNLf+6NYK0b400ViIuvg8WuY38txD99HX10ckEiYSb4BQf8nxioiIiIiIiIjUqpITogX8RwWOITLh/O/9L9HTny5pn+5kNw2RUUyZTyYhFCK1fgMW3VbY3dHRwYwZMwZsG6uP0N+bIpOJ4PQQDjcMaKqUk1tDNJNxQuEQ8bp5ZKyDTCpN56ZNhNyJN7Tg4WTJ8YqIiIiIiIiI1Kqipsyb2aNDPQTMrVw4IhNDT3+KH9/9Au8/elFp+42yQjSTDJKSocaB+w5uqAQQChnukMmEyXgP4VA96XThhGgymcQsWH+0Lj6PRGIdiw98Ey+teBLDqGtqwUL9wMB1S0VEREREREREJqpi1xCdC7wF2DTofgP+WtGIRCaAa+56gTMOXUA0UlqR9eibKvXj/Uniu+4K/dum6Hd0dLBw4cKC+2RSkSAhGp5BJpna2lQpJzdlPicWm0Mi8QqvO+Rwbv3FdczeZRH1zS0QTqKEqIiIiIiIiIhMFsVmc24Fmtx91aDbC8DyMYtOpAZt6u7n1kfXctaRhRORw+lN9o4uIZpM4skk8d13HxjLpk3bVYgCmEEqGSKT6d26hmg4Eh2wTSQcVIh6tqFTKBTBPc3sXRbT1dlBureHxukzsbDWEBURERERERGRyaOohKi7n+vudw7x2HsqG9LIzOx1ZvZ9M7thvM8t8u0/raTtDYuJR8IjbzzIaNcQJZWCZILYbrsOuLujo2O7pkoA8cYo3Z1pLJQkFG7INlXaFm8kFgJCJPuTRKLbXgZCoTiZTIKGmbN55ZkVNM6YoYSoiIhMSmbWYmb3mtkWM9u32vHI6GksRWQy0Wva5KGxrG1FJUQt15a6zG2y2/3AzNab2eOD7j/JzJ42s5Vmdslwx3D359z93GLOJ1JJL23q4b5VmzjlgJ1GtX/a00RCxa5UsY3jZHp7iC8amBBNpVLEYrHttq9riNCzuR8LpwiH68mkkgPWEI3EwoQI0Z9IEq3bdn9dfEcSiXU0ztqBzpdfpHnmLExNlUREZHLqAd4GlPUBu5ktr0g0Ug6NpYhMJnpNmzw0ljWs2CnzfzKzD5vZgDnCZhYzs+PN7EfA+4s81g+BkwYdJwx8GzgZ2Bs408z2NrP9zOzWQbc5RZ5HpOK+8ftn+NiJuxMKFZX/ryhP9hGatm16fDKZJBIpnFyNN0QJhQwLpwmHG0in0wMTotEQeJj+/n6i8W2Vo/G6efQl1hJpaCQSDjNtziwsogpREZl4zOwJMzuuVo8nlWFmXzazj41mX3dPuvuGSsdUbdlKlH2qHUepNJbbm6hjKcPT9Wlq0Gva9ibqa5rGcnsTdSwHKzYhehKQBq4zszVm9qSZPQc8A5wJfMPdf1jMgdz9L8Brg+4+HFiZrfzsB34OnOLuj7n72wfd1hcZs0hFPbV2M2s7ezluj9njfm5P9GNhC34Lszo7OwtOlweIN0aIxsNbE6KZVIpQ3hqikVgYyxj9/ckBCdG6+DwSfWtJ9Pfztgs/TtOMaYQi/YSttOZRIiLV5u77uPtyADN7wcxOLHbfQtvnH09GL/vcvmJmjXn3fXA0lQ9mNht4H/Dd7NdN2eO/J2+bZjNbbWbvLPHYz5tZf3aKW+52dakxjgUzm2lmvzSzbjNblf/9Zn0duGycYqnIeE7hsfyJma01s81m9ncz++CgTcZtLGX86PpUm3R9Kp+uT5NqLBeZ2W1mtsnM1pnZt8wsvxprUlyfil1DtM/dr3L3Y4BdgBOAg919F3f/kLs/XGYcOwEv5n39Uva+gsxsVvYH5SAz+7dC22zYsIFDDz10623p0qVlhihT1drOXi5rf5KLbniEz7xtb6y41SG2k2teVPJ+6QyZzk5CjXV4KrP1/o6OjoINlQDqGqPBtPhohpDFSW83ZT4EmRDJZGpQheiO9CXW0tfXx24HHkJdY5RwNElj/SjWPRWRcbF06dKt1zpgh2rHIxOPme1oZncWuO1YwjEuNbNLi9w8Anx0VMEOdA5wm7v3Arj7FqANuCL7Bgbga8D97l7qVLWvEvw9utjdm7K383IPmtlCM1uefWN1YO7/Nmg21Rj5NtAPzAXOAr4zqErjV8CbzGzeOMQClRnPc5iaY/llYJG7TwPeAfynmR2S93jRY1ni7+BIxyr7NUGkEnR9KqiWX9N0fSpNLY/lVcB6YB5wIPBG4IK8xyfF9anksq9sye9ad+8YzQmHUCjDNGT2yN1fdffz3H1Xd/9yoW1mz57N/fffv/XW1tZWsWBlanhuwxYuvuERLvzpgxy2aAa3XHgse8+fNurj9Wf6iYfjJe/n/QkAQs1N2yVEZ8yYUXCfeEOUaDxMJGZ4OpLtMj9wDVHPGKlkgQrRxLpgin04TDSewUKpkmMWkfHT1ta29VoHbKx2PKNhZvPN7EYz25D9tPwj2ft3NbPXzOzgvO02WnZqoJktMLObsvu9ambfyjvmC2Z2opldCywE2rOfvF+cffwSM3vWzLosmPlyWvb+obbfWpVjZkuyf5B2WDBV8R2DzvtJM3vUzDrN7BdmVjfC93+JDWoUaWZXmNmVZT61uWMVfH5z3H2dux9b4LauEucv4L+AT5pZ4U/1incy8Of8O9z9d8CvgSuzPydnABeWemB3vxq4CfhVofFz99Xufpy7Hwc8nPu/u68e6piVGGcLKl1OBz7r7luyTU9/BZydF1sf8ADw/4o97qBzlPrJbyXGc8qNZfbcT7h7Ivdl9rZr3uNljeVQavA1oWbp+qTr0yhNudc0XZ8mz1hmLQb+N1scuQ64Hdia3J4s16damQf7ErAg7+udgTVVikWmuMdf7uSCnz7AZ295nNYD5nPj+Udz8n7zCJe5bmh3spuGaOmVlp4I1vAMtzSXUCEaTJkPx5xMKpSdMr8tIRqNhSAdIpVMEasbtIZo39pt57bukuMVESmFmYWAduARgtkhJwAfM7O3uPuzwKeAn5pZA3AN8EN3X27B+uO3AquARdl9fz74+O5+NrAaaM1+8v617EPPAq8HWoD/AH5iZvOG2T4XbzQb7++AOcCHs/HtmbfZGQTLDS0G9ieoLhjOdcBbzWxa9hzh7DF+NsRzdpWZXTXCMXPbDvn8FrP/GLkfWA58sszj7Ac8XeD+jwPHETQw+KS7ry2wDQBmdhvBH/PLzOycQQ9fRPD36I9H8SaskGHH2YK18juGuN2aPcYeQNrd/5533EfIe5OS9RRwQKkBmtnrgdtGSpIMUonxnIpjmYv7KjPrAVYAa4HbBp1rVGM5lBp9TahJuj4Buj6N1lR8TdP1aQgTcCwBrgDebWYNZrYTQWL49kHnmvDXp1pJiN4H7G5mi80sBryb4NOEUevs7KStrY329vaKBCiTm7tz97Ov8r4f3MsVf3iGD77+dfz0g0fy+t1nj3qK/GA9yR4ao40jbzhIJtEHQHhGS9EJ0XhDlGhdmFA2IVqoQjSVzJBxJxrfdn88Npv+xLZletPpLSXHK1KqZzds4eWO3mqHMaG1t7fnZkIUXli4th0GzHb3y9y9392fA5YR/C2Auy8jWLP8bwTTdj6d3e9wYD5wkbt3Zz/BvrPYk7r79e6+xt0z7v6L7DkOL2LXI4Em4CvZeP9I8Mb3zLxtrswe+zWCP+wOHCGWVcCDwKnZu44Hetz9niG2v8DdLyj0WAHDPr9V9Dngw7ZtuhkAZnaUmd1tZn82s+uyb/CHMh3oGnynu28CngAaCCovhuTub3X3+e5+lA9aD9/dneANxAkMsxxFtnJjRCONswdr5U8f4vb27D5NQOegQ3cCzYPu6yJ4fkp1F8EUuYLVKsModzyn4ljmjnUBwfi9Pvs9JhhotGM5lFp9TahFuj7p+rSVrk+6PuWb5GMJQVXsPsBmggLG+4GbB51uwl+fCreoHkNmdh1BJn0HM3sJ+Ly7f9/M/gX4LRAGfuDuT5RznpaWFq0bKiPKZJw/rFjPsr88x04z6vn0W5ew546DX7MrozvZTX2kvuT9vC+BNTYSbqyD1LaVJDo6OoZsqtQwLUa8IQKRDJnk9hWi4WiIVDKNZ3zAlHmzMBnPEIvFAEilushkwjXzyYlMLq919/ON3/+dFes209Of5ifnHsGMxli1w5qQWltbaW1tZdmyZYP/EC3oqb2W3M7Yrje6ccmKp04qcttdgPlmlr8UTxi4I+/rZQQflLb5tumlC4BV7j6qdT3M7H3AvxJU70Dwh3wxz8l84EV3z+Tdt4qBa5/nT9vpye4zkp8RvGn9MfAehqi+GYVint9RyVYSHJv9si57X64L652DEz/53P3x7P6XEFQY5KwCjnf3XjP7InAKQfVFIZvY/o0WZvZegnH9P4L1uc4bvE0xzGw+cDXwIa9ch9hyx3kLMHj9nmls/2atGRhyeSszOwn4zQjnOh/4RjFBVWA8p+JYbuXuaeDO7Pd7PpA/tXHIsRzl7+CYvSZUyh/+uOuYX6NOOP7ZYq5Ruj4FdH0K6Po0PF2fRqEWxzJbqflbgkZSRxO8Bv2A4Pu8OG/TCX99KikhamZxgnUhFuXv6+5Fd5dy9zOHuP82tp8iMmq5CtHcm0SRfKl0hlsfXcs1dz3P/jtP5/IzDmDBzLFtHNSb6h26QnSYKlRP9BOZNROLhAZUiKZSKaLRwh9kTZ/bwPHvXcLy32XI9EM6PbBCNBoLk+rP4M6AhCiAEaOuLkiBptJbyKTqsNH1gxIpqD+V4cd3v8CND77MBcftymWn7MPypzfwkZ8/xDXnHEYkrBR8qdrb23MzIoqqEC0hWTkeXgSed/fdCz1oZk3AN4HvA5ea2Y3ZypYXgYVmFiniTeeAVzEz24XgTewJwN3unjazh9m2pvlwr3prgAVmFsp707kQ+Psw+xTjeuByM9sZOA04qszj5Qz7/JYj/49Zyy6W7+6XlnCIzxNUMVyed8z8JZNSQGbwTnkeJZiid19eHHMI3iSdQTAF+Qkz+5m7/6WEuHJvBq4FbnL3YSs/SjTkOJvZbwiqBAu5w91PJvg5i5jZ7u7+TPaxAwiqVPItAX4yVBDufjsF1vDPft/XEFS7ldrptpzxnIpjWUiEvDVEs4Ycy1H+Do7Za0KlFJmsHA+6PgV0fULXJ3R9mkpjOZPgg51vZT/oSZjZNcB/MjAhOuGvT6W+67yFIHOeArrzbjUnVyGqZKjk60umufaeVbzjW3exYl0Xy95/KF84dd8xT4ZCdg3RSOnnyfT3EZk1C/ISoslkcshkaE44GiIUTpNKEkyZD2/bPhILBQnRjBOtG5gQDYd3oKEhmKafTnXhqTiF+56JlMbd+d0T6zjtqrtIpDL88oKjaT1gPmbGm/aaw1G7zuJLt62odpgTUmtra25WRFEVojXmXmCzmX3KzOrNLGxm+5rZYdnHrwAecPcPEixgf3XefmuBr5hZo5nVmdkxQ5zjFeB1eV83Eryp3ABgZh8A9h1m+3x/I/jb52Izi1qwmH4rBdaHK0W2KmA5wR/7z7v7U8PvUbSRnt+qcfeVwC+Ajwx+zMwWE6xXdevgx/LcRtD1NN+3gJvd/U8erOd1McGaXaV2NbyE4E3Xx0vcb1jDjbO7n+zbuswOvp2c3aabYGreZdmf+2MI/ja/Nnec7Pd6CPD7UYR4DEF34FM821G3hO+tnPGccmNpZnPM7N1m1pT9vXwLQUXPH3PHKXMsh1Kzrwk1SNcndH0a/JiuT7o+MfnHciPwPHC+mUUsaEr1foK1PYHJc30qNSG6s7u/y92/5u6X525jEplIBXX1JfnO8mc59dt3sbk3yXVtR3LJyXsxp7mU5UfK05Ma3Rqi8V13pf6QQ4MK0XSQEB1uuvwA4RSpPieTShKKbEt8RmJhUv1p3H37ClGbSbyuB8hOmU/H0KR5KdeTazZz9vfv5f+eeoVrzjmMC9+0G3XRgT97579xV9Z39XHDAy9VKUqpBg+mirYSrGP2PLAR+B7QYmanEDR/yE1D+lfgYDM7K2+/3QiaTLwEvGuI03wZ+IwFC8Z/0t2fJKgSuJvgzeV+BGtTFdx+ULz9wDsI/njeCFwFvM/dK5HN/xlwIiNMbTKzq82sqOqI4Z7f8kKtmMsIEgBbWdAI4EfA2dnneyg/JmgcUJ/d71SC6VkX5TZw9+8R/Gx8rsS4PkTws7Xegm7OW7JVFZjZhWZ2l5lda2Z/KvG4UOQ4D+MCoJ5gLbXrgPN94FJT7wCWD6p+KYq73wGcXOqbzTyjHc+pOJZOMO3zJYIpmV8HPubut+RtM+qxHPKktf+aUDN0fRpA1yd0fSqCrk+lqeWx/AeC17gNwEqCosj8xO2kuD6Ze/FzYc1sKfA/7v7YWAVUKYceeqjff//91Q5DakBvf5q3XnkH5xy9iDMOXUB9LDzyTkW69p5VREPGuw9fCN99A/zz0FXyN6+8me5kN2ctOWu7x54//Z0svrHwEjTPPrSeTWt72LM5Qqgpxk2/+CJHnvsvrFq1ihNPPHHY+Jb/4Y3MCn2a9c8/y5Jjj2P+HkuC8z2ygQ2ru/jL4+2cfeY5zF207TXm/ge+SmdnPycc/1lefPFHPHzfD2nu3p3jztaavFK6DV0J/vv3T7Pq1R7+/a1L2Hen4a9nPf0p3rPsb1z6jn04cEEl1+ieGszsAXc/tNpxiIyWmUUIZiRd7kFDkJG2/xKw3t2/OebBBefbD/gsQXLjCOAL7v7m8Th3sczsb8C57v54DcRS9HhqLLdXS2MpMtXp+lS+WnpN0/WpPLU0luUotezrWOABM3vazB41s8fM7NGxCKxc6jIvOWYwszHG+49eVNFkaKl6kj2jmjKfY5EQpLZViA7VYX7APhYi2ddHJpUmHMmfMh90mTcPYeGBH4pk0tOJRIK1r4MK0Sjm1XveZGJKpNJ8Z/mzvP8H9/LGPWbz0w8eMWIyFKAhFuF/zjyIf7vpMdZv7huHSCeHCd5lXiTfmQR//H/OzJab2VBVVQC4+7+P1xuUrNOApR5UFKSAmisScPcjaugNStHjqbHcXo2NpchUp+tTmWrsNU3XpzLU2FiOWqld5odaALzmqMu85MQjIRKpdLXDoCfVw6z6WaPeP7+pUkdHB4sXLx55n1CQEE0P6jIfyTZVMg8RigxMiKZSTYRDwTKEqXQXqXQUc02Zl+K5Oxdd/yi7zm7ilxceTTxSWkJ9wcwGPvf2vfmX6x7i2nMPL3n/qajULvMy/sxsIfDkEA/v7e6rxzOeWuXu15K33lgNmpH3//MYOJVVBqnx8dRYiqDrU7Fq/PUM9JpWkhofT43lOCkpIeruq8zsALZ1pbrD3R8Zbh+RarNhOriPp57k6NYQBcCChGimL2hU2dHRwYwZM0bYKfje+/t6SaeSA7rMB02V0lCgQrS/v4lIdBMQVIim01FK/+xEprLrH3iJumiIj544+gaBR+06i6fX7cjnb3mCr5y+fwWjE6mO7BvKpmrHIWW7FrjWzFYAswgaK8jEpLEUQdenSUSvaZOHxnKclFT2ZWYfBX4KzMnefmJmHx6LwMqlKfNSa0bbZX6riA2oEB2pqZJ7mlAonJ0ynxqYEI1mE6KZEB4amBDt64vivhmAVGoLHm5k5n57jj5umVJWru/i2rtXcek79in7WO8/ehH9qQw3PagmSyPRlHmRcfOYu+8D/DMQJa/jqkw4GksRmUz0mjZ5aCzHSallX+cCR7h7N4CZfZWgA97/VDqwcmnKvNSa0XaZz7FwCE8Fyct0Ok0kMvyvbzrdSzhcH1SIplOEwoOmzGfXEE2nUwP26+vrp7k5e4xUF25xps2uH1XM6YyzcUuCudPqRrW/TCx9yTSfuP5RvvbO/WmIlV9VbGZcduq+vHvp3Ry8cAaLdhj9789kpynzIuPmS2Z2RPb/n/BSupNKrdFYishkote0yUNjOU5KfcdqQP5ijOnsfSI1LRIK0Z/KEItUby3McitELRo0VcoA0Wh0xO3T6V7CkcaCFaLRWJhUfzpoupRMDtivr6+PcKSRVGoLyVQXnpk96pifXLOZf/zuX/mnYxbzL8fvVpEkmdSuL/76Kd558E4smTetYsdsikf40mn78cnrH+FnHzqyqr/DIiLuflG1Y5DK0FiKyGSi17TJQ2M5fkp9Z3kN8Dczu9TMLgXuAb5f8ahEKqwpHqE7kRp5wzHUk+qhIVpGQjQcNFVKWaioDvPpdA+RSCPJvt4CTZVCpPozhIiQSg2uEO2jvm4+fYm1pNN9hCOjr+7MuHPaQTsxr6WO079zN795bC36gGtyuv3xtazv6uO9R+5S8WPvv/N03rz3XC7//dMVP7aIiIiIiIhMPSUlRN39v4F/Al4DNgEfcPdvjkVg5dIaopKvMR5mS7UToskyE6LRICGaLDYhmuklGm2iP9tlPr9CNBwNBVPmCW2XEE0kEjQ07ESibx2ZjBOLxUcdM0DIjLOPWsRPzj2cPz29nnOuuY/nNmwp65hSW17a1MMVf1jJV0/ff8yamH3o9a/jqbVd3PnMxjE5/kSnNURFRERERESKV/LcQ3d/wN2vdPcr3P2hsQiqEnJriLa2tlY7FKkBjfEI3f3VTYj2pfqoC4+i2jJXUBk2POWkLFxUQjST7iESbSLVn8hOmd82zd7MSPWnCYcj202Zd3fq6uaRSKwlk0kTi8VKj7mAWU1xvvbOA/jICbvziesf4Wu3r6CnymMi5UulM/zr/z7CF07Zh+kNlflZKSQUMr7+zv354m1P8eqWxJidZ6JqbW3NrZutNURFRERERERGUFRC1MzuzP7bZWab825dZrZ5bEMUKV8tTJkHyqqes0iJFaLpXsKRBtydTCaNhQb+uicTaaKR7afMA9TVzaO372U8A7FoZZNch+wygxvOO5p50+v5h6v+qmn0E9w3/+8Z3rD7Dhy6aOaYn2vOtDoufsueXHzDo/qZERERERERkVErKiHq7sdm/21292l5t2Z3r1z3DJEx0hiPsCWRHnnDGmaRoKlSKWuIhsPbpugXSsZGowMTopkQkB14AAAgAElEQVRMBjMjHp9Hd/dKnDixeOWr/sIh4+wjd+GnHzyC5U9v4P3X3MezmkY/4fx15UYefrGD84/bbdzO+aa95rBwVgM//OsL43ZOERERERERmVxKmjJvZl8t5j6RWlMrFaLlyFWIpixES8vIywSm072Ew/VDPh6JhYlGowOmzCcSCeLxOHV18+jufoZMJl7xCtF8s5rifPWd+/OxE3fnousf4auaRj9hbNyS4LJbn+TyMw4gHBqbdUOHcsnJe3HLw2t4co0mKIiIiIiIiEjpSl1D9M0F7ju5EoGIjKXGWHWbKqUzaUJW8pK9A1jE8LTjQCSvQdKQ50z3EA4FFaKFphdHYiGiseiACtG+vj7q6uqIRmfS27uKdDo2JhWigx28cAbXn3c0O2Wn0d+mafQ1x9158bUefvXIGi5rf5L3LLuHT520F3OnjWJd3DLFI2G+/o/786kbH6W3f2JXfouIiIiIiMj4GzmrApjZ+cAFwK5m9mjeQ83AX8cisHLlusy3traqsZJkp8xXLyHakyqvwzwAkRD9iX5CFJcoTGd6iUZaiMbj9HZ1bX+4aJhwbGCFaF9fH/X19ZiFcE+RSkXHtEI0XzhkvPfIXXjrfvP42u0ruO7e1Xy+dR92m9M0LueXgbYkUjz6YgcPvdjBQ6s7eGlTDzvPqOeghTM4YckcPv7m3Wmui458oDGy25xmzjpiIZfd+iRf/of9qhZHrWhvb6e9vR3UZV5ERERERGRERSVEgZ8BvwG+DFySd3+Xu79W8agqINdlXgSCKfOvbO6r2vm7k900RhrLOoZFQnQmtxDxTFHbp9M91MXnEa2rp+vVjds9HomFiMajpFL9W+/LVYjmpJKe7TI/flV4MxtjfOX0/Xlo9SYuvuERDl88iw8fvxuN8WJfrqRU6YzzzPouHl4dJD+fXLuZWCTE/ju3cNDCGbzjgPnsPKO+rKZgY+Fdhy3gjmc2cttja3nrfvOqHU5V5T78W7ZsmbrMi4iIiIiIjKCoDIO7dwKdZtYPdLp7B4CZzTCzH7j7P41lkCLlqnZTpZ5UD/XRodfzLIaFjK50D9GiE6LBGqKxeB2hUHi7xyOxMLF4jFSqZ+t92yVEU6lsQrS3rNhH46DsNPqf37ea07/zV3aeUd7zJ4VtSaTY3Jti97lNHLhgOmcesZAl85qJR7b/mak1ZsaXTtuPdy+7hwMWTGen6foZEZHimVkL8Htgb+BId3+8yiHJKGksRUREpFSlllztn0uGArj7JjM7qMIxiVRcY5WbKvUke2iMllchCtCV7imhQrSbcLiRaF09oQJrjkZiIeJ1MTrzpsz39vZulxCNxqKk0+OfEIVgGv1ZR+zCaQftxOZeNVsaC/XRMC0N1Zv6Xq6WhiiXnbIPn/jfh/npB48c9wZPIjKh9QBvA/6rnIOY2XJ3P64iEcloaSxFRESkJKUmRENmNsPdNwGY2cxRHENk3FW7y3xPsoeGSJlriAJdmVISokGFaLSujnDBhGiYeL2R2jywqVJj47bEreOELDSOE+YLa4hFaIjppUYKO2zRTI5YPItv/XElHz1x92qHIzXCzJ4ALnT35bV4PKkMM/sy8Iq7f7PUfd09CWyoteVAymVm9wIfcPcnqh1LKTSW25uoYykiIjIRlNr2+nLgbjP7gpldRtBQ6WuVD0ukshrj1e0y353srkiF6OZMKVPmewiHG4ZOiEaDCtFCXea3Uad3mRg+fPxu3P3cRu57oSaXtZYqcPd9cslLM3vBzE4sdt9C2+cfT0Yv+9y+YmaNefd90MyWj+JYs4H3Ad/Nft2UPf578rZpNrPVZvbOEo/9vJn1m9mWvNvVpcY4Fsxsppn90sy6zWxV/veb9XXgsnGKpSLjOVXHMsfMdjezPjP7yaCHxm0sRUREppqSEqLu/mPgH4BXgA3AP7j7tWMRWLlyXeazXXdlimuKR+jur3KX+QpUiHane4uuEM2kewnl1hCNbD8leuc9ZzBzx2nbdZnPT4ialfqZiUh1RMIhLj/jQD5/yxP09le7pnn8tbe309bWBuoyL6NgZjua2Z0FbjuWcIxLzezSIjePAB8dVbADnQPc5u69AO6+BWgDrsgm2CD44P5+d7+hxGN/FXgJWOzuTdnbebkHzWyhmS3PJv4OzP3fzBaW+T0V49tAPzAXOAv4jpntk/f4r4A3mdl4dZurxHiew9Qcy5xvA/cVuL/osSzxd3CkY5X9miAiIlLrSsp2WDAP5WBgprv/D7DFzA4fk8jKlOsy39raWu1QpAZUu6lSd7Kbhmj5CdEMTrGTwdLpHsKhBqJ19YTD2zfI2fXgOcyc27RdhWh9/bbGNJEClaUitWqn6fW8+/AFXPGHZ6odyrhrbW1l6dKlABOyy7yZzTezG81sQ7aa6yPZ+3c1s9fM7OC87Taa2XHZrxeY2U3Z/V41s2/lHfMFMzvRzK4FFgLt2cqwi7OPX2Jmz5pZl5k9aWanZe8favutVaNmtiSbMOkwsyfM7B2DzvtJM3vUzDrN7Bdmll96X+j7v8TMbhh03xVmdmWZT23uWAWf3xx3X+fuxxa4ravE+Qv4L+CTZja9zOOcDPw5/w53/x3wa+DK7M/JGcCFpR7Y3a8GbgJ+VWj83H21ux+XXW/y4dz/3X31UMesxDhnKzFPBz7r7lvc/U6CpNnZebH1AQ8A/6/Y4w46R6nzzisxnlNuLPP2ezfQAfyhQGxljeUw56y11wQREZFxV2r511XAUcCZ2a+7CD7RFKlp0XCIdKa4ysqx0JvqLTsh2tfXR9SKT1CmM9vWEC3UVAmChOdwFaJKiMpEc9YRu/DAqtd4au3maociRbKgFL0deATYCTgB+JiZvcXdnwU+BfzUzBqAa4AfuvtyMwsDtwKrgEXZfX8++PjufjawGmjNVobllvp5Fng9QVXtfwA/MbN5w2yfizeajfd3wBzgw9n49szb7AzgJGAxsD9B9dtwrgPeambTsucIZ4/xsyGes6vM7KoRjpnbdsjnt5j9x8j9wHLgk2UeZz/g6QL3fxw4DrgB+KS7rx3qAGZ2G0GyaZmZnTPo4YuANcCPR5EkLGTYcTazW7NJ9kK3W7PH2ANIu/vf8477CJBfIQrwFHBAqQGa2euB20ZK4g9SifGcimNJdv/LgE8Mc65RjeVQavQ1QUREZNyVmhA9wt0vBPog6DIPxCoelcgk053spjFS3hqiHR0dTIs0YEX+2rqnCIWixIZYQxSChGd+hejgLvNRJURlggmHjMtO2ZfP3fI46YzWwJ0gDgNmu/tl7t7v7s8By4B3A7j7MuAZ4G/APODT2f0OB+YDF7l7t7v3ZavliuLu17v7GnfPuPsvsucoZtbLkUAT8JVsvH8kSMyembfNldljv0aQeDhwhFhWAQ8Cp2bvOh7ocfd7htj+Ane/oIhYYYTnt4o+B3zYtk2HBsDMjjKzu83sz2Z2XTYBPZTpBB/OD5D9+/QJoIGgMnBI7v5Wd5/v7ke5+w8HPeYECa4TgB2GOcZxw50jb7thx9nd3+7u04e4vT27TxPbV4J3As2D7usieH5KdRewniGqKYdR7nhOxbEE+ALwfXd/cZjTjXYsh1KrrwkiIiLjqtRsRzL7CajD1gXQq1d2JzJB9CR7hq8Q9ZETNx0dHTRHGtlc4ucY0bp6wgXWEAUIh8N43rmTySTRaLBtNLpn0clXkVqyZN40DtllJj+5ZxXvP3pRtcOpSd8+74+3M0xSoAI2Xnj18ScVue0uwHwz68i7Lwzckff1MoJpwW3unsjetwBY5e6jWiDazN4H/CtBdSkEiaZinpP5wIvuAxZ0XkVQaZWTP620J7vPSH5GkFT9MfAehqgOHYVint9RyVa6HZv9si5738eyX985KPEzgLs/nt3/EoIKuJxVwPHu3mtmXwROIagOLGQT2ycCMbP3Eozr/xGsH3ne4G2KYWbzgauBD7n7htEco4Byx3kLMG3QfdPYPpnYTDANuyAzOwn4zQjnOh/4RjFBVWA8p9xYmtmBwInAQSNsOuRYjvJ3cMxeE0RERCaSUhOiVwK/BOZk/6h5J/CZikclMkbcncrMlCpNd6r8LvMdHR1MizWxpcQkZWyYKfOF5J6fuvi59PqtI2wtUps+esLunP6dv/KWfXZkx5ZSipymhhKSlePhReB5d9+90INm1gR8E/g+cKmZ3ZitvHwRWGhmkSKSogM+dTKzXQiSrCcAd7t72swehq3LNA/3KdUaYIGZhfKSoguBvw+zTzGuBy43s52B0wiWKKqEYZ/fcuQnWyzbzMXdLy3hEJ8nqLK7PO+Ya/IeTzH8B++PEkwh39qMxszmECTxzgBWAE+Y2c/c/S8lxJWbVnwtcJO7D1uZWKIhx9nMfkOwjEMhd7j7yQQ/ZxEz293dcwsmH0BQRZlvCTC4Y/lW7n47bL8sefb7voagGrvUTuzljOdUHMvjCJK9q7N/ezUBYTPb290Pztt+yLEc5e/gmL0miIiITCSldpn/KXAx8GVgLXCqu18/FoGJVFpdJEwiVZ2C5p5k+V3mg4RoIyG2b5A0nKGaKo0k0R8mHOoveb98mrAs1VIfC/Nvb92Lz//q8WqHIiO7F9hsZp8ys3ozC5vZvmZ2WPbxK4AH3P2DBA1Wrs7bby3wFTNrNLM6MztmiHO8Arwu7+tGgpeoDQBm9gFg32G2z/c3oBu42MyiFjR7aaXA+qWlyFatLSdIRj3v7k8Nv0fRRnp+q8bdVwK/AD4y+DEzW0zQaGe4T+ZuA9446L5vATe7+5+y601eTLCmZLzE8C4hSAp+vMT9hjXcOLv7yb6tC/rg28nZbboJpo5flv25P4ag6vLa3HGy3+shwO9HEeIxBN3rT/Fsx/cSvrdyxnPKjSWwFNiVYEmNAwle234NbF3Ls8yxHErNviaIiIiMp5Lnw7r7Cnf/trt/q4J/rIuMuaDT/KhmVpZtxCnzRejo6KClvplQEb+2QdFSUPixw4JdOOTtp424TyqVGpA47esFs75RxytSba/ffTb10TC/fUJNcWuZu6cJEooHAs8DG4HvAS1mdgpBc6LcNNl/BQ42s7Py9tuNoAnSS8C7hjjNl4HPZBuafNLdnySoYrubIPm5H8HaiQW3HxRvP/AOguTORoKGk+9z9xVlPA05PyOYQjvs1Fszu9rMiqreG+75LS/UirmMIEG9VbbRzI+As7PP91B+TNDYpj6736kE04cvym3g7t8j+Nn4XIlxfYjgZ2u9mW3J3n6TPc+FZnaXmV1rZn8q8bhQ5DgP4wKgnmCtz+uA8909v0L0HcDyQdWZRXH3O4CTS02G5hnteE65sXT3Hg+6ua/zoHv7FqBv0JT+UY/lMOet9dcEERGRcVHUPNrsJ4YvZi/WuXW3TidYF+jS7NQ1kZrWFI/QnUixQ1OphQXl60n1lD1lfvPmzTRPK65CNJ3uJRQKpglHYjHmLBqq0GmbRCIxoKFSb6/T0JgYZg+R2veZt+/Ne7/3N47edRbNdcP1ZpFqyr7ZP3OIh2/J224LQWIj9/VqtjU1GXzMRXn/vyX/ONn7Ps22Bk2D9y20ff7xnmD7arbttst+fWmh7YbY91ryKv2G2a6kdRRHeH4rotjvs8Dz8yLZtQ8BzCxCkOS71N0LdR3P33ejmf0Y+Gfgm+5+M3Bzge1OKCa2QfssLnS/me1HMPbHAkcQNMUp9dhFjfMw+7/GED/3WZ8Ezi3j+EVP8KjUeE7VsRx0rEsL3F30WJb4WjPmrwkiIiK1rtgK0e8C/QBm9gbgKwSf5HYSTPcQqXmN8XBVK0TrI/VlHSOTyRCORopqdJTO9BIJF1+R6u709fUNSoj2Y6ZJ7zKx7dAU55+OXczlvyt3eUcRGSdnEiSnPmdmy81sqKpfANz93939m+MTGhCsFbk0mzRMAY+N47mL4u5HuHutrBdS9HhqLLdXY2MpIiIyqRSbEA3nVYG+i+CPhxvd/bPkVWrUks7OTtra2mhvb692KFIjGmIRuhPpqpw77WkioVJ7mG2TSieIx+MQsaIqRDPpHkJFJkTD4TDpdJre3t4BCdGenh5CofFvQCVSaf94yM48s76Lh18csuHyhNfe3k5bWxtoymPNMrOFedN1B98WVju+WuHu17r7Du5+XPb2i2rHNMiMvP+fRw0m0WpJjY+nxlJERGQKKzohmp3yAkFH1j/mPTb6LM8YamlpYenSpbS2tlY7FKkRuSnzE1FPopsZM2ZgkVBRa4hmMklCoeKmB0ejUZLJ5HYVogceeODWjvMiE5mZcdkp+/L5Xz1BMl2dxmpjrbW1laVLl0Iwc0NqkLuvHqbJyupqxydFuxa4wsxuJCgKeKjK8cjoaSxFRESmsGITotcBfzazW4Be4A4AM9sNvfmSCaKaTZXK1ZvYwvTp07MJ0WI7xheXzIxEIqRSqe0SogcddBChUIzMsP0sRCaGXWc3ccJec/j+nc9XOxQRmdgec/d9CNa6jAKPVDkeGT2NpYiIyBRWVELU3b8IfAL4IXBs3mLrIeDDYxOaSGU1xcNVqRAtoTfBkHoSXXkJ0WI/xyhOJBLZWiFaX18/6LFmUqmuso6vIlOpFee9cVdue2wtL77WU+1QRGTi+pKZ/QW4CfhEKQ2IpOZoLEVERKawoqe7u/s9Be5TlwqZMBrjEV7rHv9qx0Q6QV2kbuQNh9GTqxDtrHxCNBqNbq0QnT59+oDHcgnRaHT6EHuLTByxSIjPvn1vPnvL41xzzmFaEkJESubuF1U7BqkMjaWIiMjUVtnMikgNa4xXp6lSd7KbhkjxHd8LySVEi22qVIqhpswDRMJNZVeIitSSwxbNZF5LPb96ZE21QxEREREREZEqqcmGSCJjoSkeobt//KfM96Z6qY/Uj7zhMPr6e2hubqY30jtmU+YHd5kPHmsmmdxU0fOJVNslJ+3FOT+8l6V/eY7pDVHmNtcxZ1odc6fFmZv9d05zHXOmxYlHKvsBhIiIiIiIiFRfSQlRM/tH4HZ37zKzzwAHA//p7g+OSXQiFVStpkqOlzU1191xd8LhMBYJYRWuEM2fMl8oIdrbq+bHMrm0NET55QXH4O509iZ5ZXOCVzb38crmPp7bsIW7n32VVzb3sb4rQTKdwQxmNca3JkznTKtjbnMueVrHDk0xImFNuBAREREREZkoSq0Q/ay7X29mxwJvAb4OfAc4ouKRiVRYUzzC8xu66epL0lwXrXY4RetPJohGYgBYxAiPQYXocAnRVHpLRc8nUivMjOkNMaY3xNhzx+Yht8tknFe7+7NJ0j5e2ZzgybWb+dPTG1i/uY8NWxKk0k4kbMxuijNnWh075lWczsn+O7MhRiikdUtFRERERESqrdSEaG4BxrcB33H3W8zs0sqGJDI2XrdDIycsmcO7vnsPByyYznuPXMg+81uqHdaIunu7aIg3AYxZhWiuy3zBhKjWEJUpLhQyZjfHmd0cB4Z+zUimM2zckthacbp+cx8PrNoUVJ92JXitO4E7RMMh5k6Lc8KSuZxx6ILx+0ZEREREREQEKD0h+rKZfRc4EfiqmcVRYyaZAHr6U6zr7GPv+dOY2Rjjxgdf4m1X3skb9pjNjz5Q292mu3u7qY81Bl9EKt9lPlchmk6niUQigx5TQlSkWNFwiHkt9cxrGX7N4L5kmg1diXGKSkRERERERAYrNSF6BnAS8HV37zCzHYGLKh+WSPmu+L9n+P1T6wCoi4SZ2xJMY91xWh3vOmwhHz1hDxbMrK/pZCiAewazIAlq4dCYdJlPJpMFHwsrISpScXXRMAtmNlQ7DBERERERkSmr1ITo2yjQVKnyYQ3PzE7NxjIH+La7/268Y5DaVx8L8a5DF3D2UYuqHUpZ3Lf936KVrxCNRqP09PQUfCyoENUaoiIiIiIiIiIyeZSaWflsNhmaa6r0I4KmSkUzsx+Y2Xoze3zQ/SeZ2dNmttLMLhnuGO5+s7t/CDgHeFdp34JMFacfvDM3PPgynp9RnOAsbGMyZb63t5dodPtGU5FwM+79FT2fiIiIiIiIiEg1lZpZ2a6pEhAr8Rg/JJh2v5WZhYFvAycDewNnmtneZrafmd066DYnb9fPZPcT2c6spjgLZtTz6Eud1Q6lYoIK0cpPme/q6tquoVLw2NCdt0VEREREREREJqJxb6rk7n8xs0WD7j4cWOnuzwGY2c+BU9z9y8DbBx/DgkUfvwL8xt0fLPF7kCnkPYcv5Lp7V3PAgunVDqUigjVEKz9lfsuWLUMkRJsqei4RERERERERkWorNbNyBvBb4CR37wBmUpmmSjsBL+Z9/VL2vqF8mCAp+04zO6/QBhs2bODQQw/delu6dGkFwpSJ5sjXzeLRlzrZkkhVO5TKiIawMaoQra/fvjN2KBQjFIpX9HwiUllLly7deq0Ddqh2PCIiIiIiIrWupApRd+8xs2eBt5jZW4A7KtTQqFCb7yEXfnT3K4Erhzvg7Nmzuf/++8uNSya4UMh4x4Hz+dXDa3jPEQurHU7ZLGyEx7FCFMqbNj+Z1m8VqVVtbW20tbUBYGYbqxyOiIiIiIhIzSsps2JmHwV+StDdfQ7wEzP7cAXieAlYkPf1zsCacg7Y2dlJW1sb7e3tZQUmE9uqV7t5aPUmnt84Np3SC2Xyx5JFxqZCtLu7e0wSoiIyPtrb23NJ0ZZqxyIiIiIiIlLrSl1D9FzgCHfvBjCzrwJ3A/9TZhz3Abub2WLgZeDdwHvKOWBLS4umyU9hr2zu48o/PMPT67r4+Jv34JjdJscsUotUfg3RSCSCuw+dEA0rISpS61pbW2ltbWXZsmWTp4uciIiIiIjIGCk1IWps6zRP9v8lFcmZ2XXAccAOZvYS8Hl3/76Z/QvB+qRh4Afu/kSJsYnQ0dPPd/78LH9d+SoXvmk3/vPUfQl6cE0SYRuTpkqAKkRFREREREREZEooNSF6DfA3M/tl9utTge+XcgB3P3OI+28DbisxniHlpsznqmZkcuvpT3HNXS9w66Nr+adjFnHxW/YiHJpEidCssUjuRiLBy8BQCdGwEqIiNa+9vT23RIymzIuIiIiIiIyg1KZK/21my4FjCSpDP+DuD41FYOXSlPmpoT+V4bp7V3Pdvat512ELuPnCo4lHKrvG5mSXS4gW6jIfPK6EqEit05R5ERERERGR4hWdELWgNG1nd38QeHDsQhIZWTrj3PzQy3zvzuc5ed8dueH8o2mKl1rwLADhcBgz05R5EREREREREZkSil6M0N0duHkMY6kodZmfnNyd3z6xjtOuuounX+niZx88go+csPvET4ZWcZ1TMyMSiSghKjKBqcu8iIiIiIhI8UrNIt1jZoe5+31jEk0Facr85OLu3LlyI1f+4Rl2m9PEd88+hHkthad4S+mi0SjxeLzgY0qIitQ+TZkXEREREREpXqkJ0TcB/2xmq4Du3J3uvn9FoxLJSmec2x9fx/fufI7dZjfx1dP353Wzm6od1qQzc+ZMQqHCBeOx6ExSqa5xjkhEREREREREZGwUlRA1s92AucDJgx7aBVhT6aBEEqk0v3zwZa69ZxVHLJ7Ft99zMPOnqyJ0rHzgAx8Y8rG5c1sBH79gRERERERERETGULEVot8E/t3dV+XfaWazgW8ArZUOrFy5NURz0whlYtiSSPGzv63ipgdf5qR9d+Qn5x7BjMZYtcOa9MLh8JCPmRW91LCIVEl7e3tuzWytISoiIiIiIjKCYhOii9z90cF3uvv9ZraoohFViNYQnVj6kmmu/vOz3P74Os44dAE3XXA0DbEJ3ihJADCq1zBKZKrQGqIiIiIiIiLFKzbjVLj9dEDzmKUsf3vuVb7w6yf5h4N25lf/ciyxiCoSRURERERERERkbBSbEL3PzD7k7svy7zSzc4EHKh+WTAWb+5J85TcrWNPRy3fOOoQFMxuqHZKIiIiIiIiIiExyxSZEPwb80szOYlsC9FAgBpw2FoGVS2uI1rbfP/kK//37v9P2hsWceuC+mGlatYjIaGkNURERERERkeIVlRB191eAo83sTcC+2bt/7e5/HLPIyqQ1RGvThq4El7Y/QdiMa889nB2a4tUOSURkwtMaoiIiIiIiIsUrqWuNu/8J+NMYxSKTmLtzwwMv8YO7XuCit+zB8XvNrXZIIiIiIiIiIiIyBamNt4y51a/28OmbH2PxDo1cf95RNMX1YyciIiIiIiIiItWhzJSMmXTGueau57nl4TV8vnVvDl00s9ohiYiIiIiIiIjIFBeqdgBjJddUKdtkQsbZU2s3867v3s3m3iTXn3eUkqEjca92BCIygbW3t9PW1gZqqiQiIiIiIjKiSVshqqZK4y+dcZ7fuIWbH1rDPc+9yn+eti977Tit2mFJFSnNKzI+1FRJRERERESkeJM2ISpja3NfkhVru3hq7WaeWruZFeu6SKYzLN6hkaN33YFf/PNRhENW7TBFREREREREREQGUEJUhuXuvLSplyfWbN6a/HxxUy9N8TBL5k1jybxpvPvwhew5t5n6WLja4YqIiIiIiIiIiAxLCVHZqi+Z5ul1QdXnk2s3s2JtF12JFDvPqGfJvGnsPW8apx+8MzvPqCek6k8REREREREREZmAlBCdgtydDV0Jnlibq/rs4rkNW4iGQ+w5t5kl85p5237z+MT/25OW+mi1wxUREREREREREakYJUQnuWQ6w7MbtgRVn2uC5Oer3f3Mbo6z97xpLJnXzJuXzGXxDo1EwqFqhysiIiIiIiIiIjKmJm1CtLOzk7a2tq2dd6eCjp5+nsxWfD61djPPvNJFxmHX2Y0smTeNN+wxm39+467s0BSvdqhTjqElBkRk7LS3t9Pe3g7QUu1YREREREREat2kTYi2tLSwdOnSaocxJjIZZ9VrPdmKz+D2ckcv0xuiWxsdnXP0Inab00RdVI2OREQmu9yHf8uWLeusdiwiIiIiIiK1btImRCeL7kSKFXmNjp5e10Vvf5pdZjWw91cJejoAAAzkSURBVLxpHLhgOmcesZD5LXWYqQpxMtKoioiIiIiIiIhUjhKiNeTVLQkeWt0RVH2u28wLG3uoj4XZc8dmlmQ7vO+1YzONcQ2biIiIiIiIiIjIaCizVgP6kmm+d8dz3P7EOo7bYw5L5k3jbfvPY5dZjYRDqg8UERERERERERGpFCVEq+yPK17h8t/9nVMP3Imbzj+GWESd3kVERERERERERMaKEqJV8sLGbr5w65NMq4/yg3MOY+60umqHJCIiIiIiIiIiMukpITrOevpTXPWnZ7lj5UY+/dYlHL54ZrVDEhERERERERERmTKUEB0n7s5vHl/H//xxJWcevoAbzzuKSFjT40VERERERERERMbTpE2IdnZ20tbWRmtrK62trVWNZeX6Lv6j/Ul2ml7PT849nFlN8arGIyIik0t7ezvt7e0ALdWORUREREREpNZN2oRoS0sLS5curWoMXX1JrvzDMzy0uoPPvn1vDlgwvarxyMRlVu0IRKSW5T78W7ZsWWe1YxEREREREal1kzYhWk3uzs0Pv8x3//wc/3TMYv7t5CWEQspoydSkZK6IiIiIiIiI1BIlRCvsyTWb+Y/2J1gybxq/aDuKloZotUMSERERERERERGRLCVEK2j95j7+9X8f5hvvOpAl86ZVOxwREREREREREREZRG3OK+iLtz3FxSftqWSoiIiIiIiIiIhIjVJCtEL+unIjvf1pjt9rbrVDEakZ7tWOQERERERERERkICVEK6A/leHLv1nB51r3rnYoIiIiIiIiIiIiMgwlRCvg+3c+z0n77sjOMxqqHYqIiIiIiIiIiIgMQwnRMr3c0cuvH1vDh17/umqHIiIiIiIiIiIiIiNQQrRMl7U/wb+dvIRYRE+liIiIiIiIiIhIrVMWrwx/WrGeWCTMMbvtUO1QREREREREREREpAhKiI5SXzLNf/32aT7ztiXVDkVERERERERERESKNCEToma2xMyuNrMbzOz8asTwneXPcvohOzN3Wl01Ti8iIiIiIiIiIiKj8P/bu9cYu6oyDuPPX9oqINYgxSAgeAWaKKhFRPzgPagMGDBy0yghjJd4SYyJ4C1ePmg00XiXqWLBaKEagh1sAlgghJsUL2CB1hDA2GhCEVJNFSv19cPZI4fJzDCdOXPOmXOeXzKZs9de+13vnqxMMu+stXfXC6JJLkryYJLNk9pPTLI1yb1Jzp8pRlXdU1XvB94JrFrIfKfyp7/t5Po/buc9xx/W7aElSZIkSZIkzUMvVoiuAU5sb0iyF/Ad4C3ASuDMJCuTvCTJlZO+DmyuORm4EdjYzeSris+tv4vPvO0oluy1KBfYSpIkSZIkSUNrSbcHrKobkhw+qfmVwL1VdR9AkkuBU6rqS8BJ08RZD6xP8kvgpwuX8RPddv/DLN97KasO379bQ0qSJEmSJEnqkK4XRKdxMPDntuNtwHHTdU7yWuBU4KnAhqn6bN++nVWrHt9NPzo6yujo6LwTfeSfu3jhgU+fdxxJkjphbGyMsbGxicMDepmLJEmSJC0G/VIQzRRtNV3nqroeuH6mgCtWrOD222+fX1aSJPW59n/4JXmox+lIkiRJUt/rl4dgbgMObTs+BPjLfALu2LGD0dFRxsfH55WYJEn9bnx8fKIourzXuUiSJElSv+uXgugm4EVJnpdkGXAGsH4+AZcvX87Y2BgjIyMdSVCSpH41MjIysW1+R69zkSRJkqR+1/WCaJK1wC3AEUm2JTm3qh4DPgRcBdwDrKuqu7qdmyRJkiRJkqTB1ou3zJ85TfsGpnlB0lxMbJkfGRlxlagkaaCNj49PPCLGLfOSJEmS9CT65aVKHTexZV6SpEE38c+/1atXu2VekiRJkp5EvzxDVJoVi9waNM5pSZIkSZK6a2ALogv5lvktW7Z0POaETuQ7nxh7eu1s+3eq3yAXjxZirvbD+Pff/0BH4sw1v4Wa07Pt65wezPH77Xe1b5mXJEmSpNkb2ILoQr5lfsuWrR2POaHf/sjuVP9O9+sn1aE4M917p8aY6/jz8cAD93ckzqAWRAdZr+99mAqivmVekiRJkmYvVd0otXRfku3AnxYo/HIW7o/OTsSeT4w9vXa2/TvV7wDgoVnEWYwWcl71cvxOxZ1rnIWa07Pt65wezPH79Xf1YVW1Yu4pSZIkSdLgG9iCqCRJkiRJkiRNNrBb5iVJkiRJkiRpMguikiRJkiRJkoaGBVFJkiRJkiRJQ8OCqBa1JPsmuTjJ6iRn9zofab6SPD/JD5P8vNe5SJIkSZI0iCyIqu8kuSjJg0k2T2o/McnWJPcmOb9pPhX4eVWdB5zc9WSlWdiTOV1V91XVub3JVJIkSZKkwWdBVP1oDXBie0OSvYDvAG8BVgJnJlkJHAL8uem2u4s5SntiDbOf05IkSZIkaQFZEFXfqaobgIcnNb8SuLdZPbcLuBQ4BdhGqygKzmf1qT2c05IkSZIkaQFZQNJicTCPrwSFViH0YOBy4LQk3wPGe5GYNEdTzukkz0ryfeBlSS7oTWqSJEmSJA2uJb1OQJqlTNFWVbUTOKfbyUgdMN2c/hvw/m4nI0mSJEnSsHCFqBaLbcChbceHAH/pUS5SJzinJUmSJEnqAQuiWiw2AS9K8rwky4AzgPU9zkmaD+e0JEmSJEk9YEFUfSfJWuAW4Igk25KcW1WPAR8CrgLuAdZV1V29zFOaLee0JEmSJEn9I1XV6xwkSZIkSZIkqStcISpJkiRJkiRpaFgQlSRJkiRJkjQ0LIhKkiRJkiRJGhoWRCVJkiRJkiQNDQuikiRJkiRJkoaGBVFJkiRJkiRJQ8OCqNQnkuxO8vu2r/Ob9uuTrJpDvGOSvLXzmf4//pT59lpark3yjBn6rEnyvkltb0+yIcmyJDckWbLw2UqSJEmSpG7zD36pf/yrqo7pYLxjgFXAhsknkiypqsfmGb/T+XYqr7cCd1TV32fosxY4H7iwre0MYG1V7UqyETgd+Mk8c5EkSZIkSX3GFaLSIpLkzUluSfLbJD9L8vSm/dgkNye5I8ltSZYDXwBOb1Zvnp7kc0nGklwNXJLksCQbk9zZfH9uE2tNkm828e5L8o49zPGBJJ9vcvxDkiOb9n2TXJRkU5LfJTmlaX9vcy/jwNVJ9kmyrsnrsiS/TrIqyblJvt42znlJvjZFCmcDv2jr967mZ/L7JBcm2Qv4FXBkkoOaPvsAbwSuaC67ookjSZIkSZIGjAVRqX/sPWkL+untJ5McAHwaeGNVvRy4HfhYkmXAZcBHq+poWoW9ncBngcuq6piquqwJ8wrglKo6C/g2cElVvZTWSshvtg13EPAa4CTgy3PI96Emx+8BH2/aPgVcW1XHAq8Dvppk3+bc8cB7qur1wAeBR5q8vtjkDHApcHKSpc3xOcCPpsjrBOA3zc/sKForPU9oVrPuBs6uqt3A5cA7m2tOBq6rqn80x5uBY6e5b0mSJEmStIi5ZV7qH0+2Bf1VwErgpiQAy4BbgCOAv1bVJoCJreJNn8nWV9W/ms/HA6c2n38MfKWt3xVV9V/g7iTPnkO+lzfff9M2xptpFTQnCqRPA57bfL6mqh5uPr8G+EZzL5uT3Nl83pnkWuCkJPcAS6vqD1OMvX9bYfMNtAqqm5qfx97Ag825tcBXm7HOAC6ZCFBVu5PsSrJfWyxJkiRJkjQALIhKi0doFQ7PfEJj8lKgZhlj5wzn2mP8e9K4e2ri+t08/nsmwGlVtbW9Y5LjJuU103g/AD4JbGHq1aEAjyV5SlPQDXBxVV0wRb+bgIOSHA28mlZRtN1TgUdnyEWSJEmSJC1CbpmXFo9bgROSvBBaz71M8mJaxcHnJDm2ad+veUP6P4D9Zoh3M48XAc8GblywzFuuAj6cZqlmkpdN0+9Gmq3sSVYCL5k4UVW/Bg4FzqK1wnMqW4HnN583Au9IcmATb/8khzWxClgHXAxsqKr/Fz+TPAvYXlX/mcN9SpIkSZKkPmZBVOofk5/J+YRnd1bVduC9wNpmG/mtwJFVtYvWczK/leQO4Bpa29GvA1ZO9TzSxkeAc5pY7wY+2sl8p/BFYClwZ5LNzfFUvgusaPL6BHAnsKPt/Drgpqp6ZJrrfwm8FqCq7qb13NWrm3jX0Ho+6oS1wNG0nk/a7nXAhie5H0mSJEmStAiltUhKkvpD8xb4pVX1aJIX0Frl+eKm8EuSK4GvV9XGaa4/iNbLot40jxwuBy6YvL1fkiRJkiQtfj5DVFK/2Qe4rnmbfIAPVNWuJM8EbgPumK4YClBVf02yOskzJl4wtSeSLKP1UimLoZIkSZIkDSBXiEqSJEmSJEkaGj5DVJIkSZIkSdLQsCAqSZIkSZIkaWhYEJUkSZIkSZI0NCyISpIkSZIkSRoaFkQlSZIkSZIkDQ0LopIkSZIkSZKGxv8AFqI/o8iA3PUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fict.plot(units_sigma=1E-20, process='excitation_v',\n",
    "              plot_param_dict = {'linewidth':.8}, \n",
    "              #xlim_param_dict = {'left': 0.01, 'right': 120.0}, \n",
    "              ylog=True, xlog=True, max_plots=40, width=8, height=4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5726e3320>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fict.plot(units_sigma=1E-20, process='ionization',\n",
    "              plot_param_dict = {'linewidth':.8}, \n",
    "              #xlim_param_dict = {'left': 0.01, 'right': 120.0}, \n",
    "              ylog=True, xlog=True, max_plots=40, width=8, height=4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5726577b8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fict.plot(units_sigma=1E-20, process='total',\n",
    "              plot_param_dict = {'linewidth':.8}, \n",
    "              #xlim_param_dict = {'left': 0.01, 'right': 120.0}, \n",
    "              ylog=True, xlog=True, max_plots=40, width=8, height=4) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `fict_min`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `fict_min` model consists of three cross section data sets.\n",
    "\n",
    "1. The `total` cross section (labelled \"effective\" in LXCat parlance) from the complete `fict` data set.\n",
    "\n",
    "2. The `ionization_total` cross section from the complete `fict` data set.\n",
    "\n",
    "3. An `excitation_total` cross section determined by interpolating and summing the electronic, rotational, and vibtrational excitations from the complete `fict` data set. See the `02_fict_excitation_total.ipynb` Jupyter Notebook in  `methods` for details on how it is constructed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_fict_min = nepc.Model(cursor, \"fict_min\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5735a3208>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_fict_min.plot(units_sigma=1E-20,\n",
    "              plot_param_dict = {'linewidth':.8}, \n",
    "              #xlim_param_dict = {'left': 0.01, 'right': 120.0}, \n",
    "              ylog=True, xlog=True, max_plots=40, width=8, height=4) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `fict_min2`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `fict_min2` model consists of five cross section data sets.\n",
    "\n",
    "1. The `total` cross section (labelled \"effective\" in LxCat parlance) from the complete `fict` data set.\n",
    "\n",
    "2. The `ionization_total` cross section from the complete `fict` data set.\n",
    "\n",
    "3. An `excitation` cross section for rotational excitations within the $\\text{N}_2 (\\text{X} {}^1\\Sigma_g^+)$ state using the single level approximation to rotation (SLAR). This, too, is directly from the complete `fict` data set.\n",
    "\n",
    "4. (and 5.) Two `excitation_total` cross sections determined by interpolating and summing the electronic (and vibrational) excitations from the the $\\text{N}_2 (\\text{X} {}^1\\Sigma_g^+)$ state. See the `02_fict_excitation_total.ipynb` Jupyter Notebook in  `methods` for details on how it is constructed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "fict_min2 = nepc.Model(cursor, \"fict_min2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cross sections in model: 5\n"
     ]
    },
    {
     "data": {
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       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col10 {\n",
       "            background-color:  #0d0887;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col11 {\n",
       "            background-color:  #0d0887;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col6 {\n",
       "            background-color:  #ba3388;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col7 {\n",
       "            background-color:  #8b0aa5;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col8 {\n",
       "            background-color:  #41049d;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col9 {\n",
       "            background-color:  #0d0887;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col10 {\n",
       "            background-color:  #0d0887;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col11 {\n",
       "            background-color:  #0d0887;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col6 {\n",
       "            background-color:  #370499;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col7 {\n",
       "            background-color:  #0d0887;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col8 {\n",
       "            background-color:  #100788;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col9 {\n",
       "            background-color:  #8808a6;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col10 {\n",
       "            background-color:  #0d0887;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col11 {\n",
       "            background-color:  #0d0887;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }</style><table id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >cs_id</th>        <th class=\"col_heading level0 col1\" >specie</th>        <th class=\"col_heading level0 col2\" >lhsA</th>        <th class=\"col_heading level0 col3\" >rhsA</th>        <th class=\"col_heading level0 col4\" >process</th>        <th class=\"col_heading level0 col5\" >reaction</th>        <th class=\"col_heading level0 col6\" >threshold</th>        <th class=\"col_heading level0 col7\" >E_peak</th>        <th class=\"col_heading level0 col8\" >E_upper</th>        <th class=\"col_heading level0 col9\" >sigma_max</th>        <th class=\"col_heading level0 col10\" >lpu</th>        <th class=\"col_heading level0 col11\" >upu</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row0_col0\" class=\"data row0 col0\" >2</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row0_col1\" class=\"data row0 col1\" >N2</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row0_col2\" class=\"data row0 col2\" >N2</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row0_col3\" class=\"data row0 col3\" >N2</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row0_col4\" class=\"data row0 col4\" >total</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row0_col5\" class=\"data row0 col5\" >e$^-$ + N${}_2$ $\\rightarrow$ N${}_2$ + e$^-$</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row0_col6\" class=\"data row0 col6\" >-1</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row0_col7\" class=\"data row0 col7\" >2.5</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row0_col8\" class=\"data row0 col8\" >10000</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row0_col9\" class=\"data row0 col9\" >3e-19</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row0_col10\" class=\"data row0 col10\" >-1</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row0_col11\" class=\"data row0 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row1_col0\" class=\"data row1 col0\" >3</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row1_col1\" class=\"data row1 col1\" >N2</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row1_col2\" class=\"data row1 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row1_col3\" class=\"data row1 col3\" >N2(X1Sigmag+)_jSLAR</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row1_col4\" class=\"data row1 col4\" >excitation</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row1_col5\" class=\"data row1 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+ j=SLAR$) + e$^-$</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row1_col6\" class=\"data row1 col6\" >0.02</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row1_col7\" class=\"data row1 col7\" >2.3</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row1_col8\" class=\"data row1 col8\" >3.6</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row1_col9\" class=\"data row1 col9\" >2.77e-20</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row1_col10\" class=\"data row1 col10\" >-1</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row1_col11\" class=\"data row1 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col0\" class=\"data row2 col0\" >26</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col1\" class=\"data row2 col1\" >N2</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col2\" class=\"data row2 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col3\" class=\"data row2 col3\" >N2+</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col4\" class=\"data row2 col4\" >ionization_total</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col5\" class=\"data row2 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2^+$ + 2e$^-$</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col6\" class=\"data row2 col6\" >15.6</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col7\" class=\"data row2 col7\" >100</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col8\" class=\"data row2 col8\" >1500</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col9\" class=\"data row2 col9\" >2.3436e-20</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col10\" class=\"data row2 col10\" >-1</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row2_col11\" class=\"data row2 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col0\" class=\"data row3 col0\" >29</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col1\" class=\"data row3 col1\" >N2</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col2\" class=\"data row3 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col3\" class=\"data row3 col3\" >N2*</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col4\" class=\"data row3 col4\" >excitation_total</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col5\" class=\"data row3 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2^*$ + e$^-$</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col6\" class=\"data row3 col6\" >6.17</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col7\" class=\"data row3 col7\" >30.1995</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col8\" class=\"data row3 col8\" >1000</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col9\" class=\"data row3 col9\" >1.98645e-20</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col10\" class=\"data row3 col10\" >-1</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row3_col11\" class=\"data row3 col11\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col0\" class=\"data row4 col0\" >30</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col1\" class=\"data row4 col1\" >N2</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col2\" class=\"data row4 col2\" >N2(X1Sigmag+)</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col3\" class=\"data row4 col3\" >N2(X1Sigmag+)_v1-8)</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col4\" class=\"data row4 col4\" >excitation_total</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col5\" class=\"data row4 col5\" >e$^-$ + N${}_2$ (X ${}^1\\Sigma_g^+$) $\\rightarrow$ N${}_2$ (X ${}^1\\Sigma_g^+ v=(1-8)$) + e$^-$</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col6\" class=\"data row4 col6\" >0.29</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col7\" class=\"data row4 col7\" >2.29087</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col8\" class=\"data row4 col8\" >48.9779</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col9\" class=\"data row4 col9\" >9.83258e-20</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col10\" class=\"data row4 col10\" >-1</td>\n",
       "                        <td id=\"T_d073e8b4_04cd_11eb_97ed_acde48001122row4_col11\" class=\"data row4 col11\" >-1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc573806358>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fict_min2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc57382a898>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UEVdXGmNCxHqOGmOMMS2XiHQHvvfx8LGquqUp4zHRQ0QeAd5T1dki8hzwuar+I9JxNcSuJjHRoln2HBWR3iLydxF5PdKxGNMcPffpBn716mIGZqfz6a2ncP8Fgzm+e7uwzCwvItx19rGcO7QL5z/1Bf9ZuJWm/lHmYEUVT81bxxmPfsKsZUENrWOMMcYYY0xEqeoWVU3xcbPEaOv2MvCYiLyBMy7pkgjH4y+7msREhajpOSoizwNn44zNMchj+Rk4swvGAs+p6gMej72uqhd6q896jhrj2/RZK+jevi1XjuvVpNtdtauIX/9zKe2S4zl1QBYn9unAMZ1SiYkJfVIWoLSimle/3szfPtnA6F7tOXtINn/63yoGd0nnD+cOIj0pPizbNSZSrOeoMcYYY0zrIyLxqlopIh1wemKO02hJ9hjTDMRFOgAPLwBP4szuBoCIxAJ/BU7Dmc1tgYjMcmfRM8Y0M8d0SuOd68cyd9UePl+Xz6tfb6G4rJLxfTM5qV8HxvfNpENKQlB1l5RXsXJnESu2F/LdjiJW7ChiQ14JE/pl8tKVoxjQOQ2ACf0yuf+/Kznj0U847dgsjumUxjGdU+mflUpyQjS9JRpjjDHGGGOMX+4TkdHu/7+1xKgxgYmanqMAItITeLe256iIjAGmq+qP3Pu/A1DV+9371nPUtBiqSnF5FYUHK9l/sJLSymoqq2uoqK6hsqqGqhp17lfVUFmtVNfUNFhnVY1SXFZFSXkVxWWVFJVVUVxWxaqdRVx/ytFcOqZnwDHW1CixsaEbkWPrvoPMX5PHJ2vy+HLDXrLSEkmKjz30eFys0Cktkc7pSXRKT6CyWiksrXTaqbSCwtJK9hSVs7OwjH5ZKQzsks7A7DQGZafTv1MqiR51eVq2dT8LNxewamcRq3YVs25PCX06JnNS30xO6pfJsO7taBPXLEceMa2Y9Rw1xhhjjDHGmMBEe3L0QuAMVb3avf8LnPEo7gbuxelR+lxtstRTjx49NDMz89D9qVOnMnXq1HDvgjF+OVBexadr8/jo+z0s2VLA/tJKCksrSYqPJT0pnvSkeJITYomPjSEuNoY2sUJ8bIzHTYiNERoaIjRWhJTEOFIT40mt/ZsQR2piHIO6pPtMHPqy6dt8Vny6g8nXDWnE3vtWWV3Dmt3FVNf88L5UXlXDrsIydhaWsquwnIT4mENtlJ4UT0ZSPB1SE+jVIZn4RiRtK6trWLy5gE/W5vHJmnw25R9gdO+jGN+3AyN6tqN/VipxIUwKGxMqM2bMYMaMGQAsWrRos6r2jGxExhhjjDHGGNN8RHty9CfAj+okR0ep6g0N1WU9R000KC6rZNHmAtbtKXESfEVl7NxfyprdJRzfPYNJx3RkTJ8OHJXShvSk+EYl95rCwvc3smzuNs65cSgJbeNI65AU6ZDCZm9JOZ+ty+fzdfks2lzA7qJyjuuWzvDu7eidmUJ628MTtGnN4PkzLZ/1HDXGGGOMMcaYwET7AHvbgG4e97sCfk01XVhYyNSpU8nJySEnJycswRlT196SchZs2sfXG/exYNM+NuQdYHCXdAZ0TiM7I5Eh3TLolJbIgM6ppCY2v8mACnYdpKykkn/ftwCAqx4eT2Jy89sPfxyVksC5Q7tw7tAuABQcqGDJ1gIWbS5gzqo97qX9zmX9haXOkAW1PX/T3IRpelI8GW3jSW8bz6RjshjVq32E98q0VLm5ueTm5gKkRzoWY4wxxhhjjGlOor3naBywBpgEbAcWAD9T1RUN1WU9R01T2Heggm827uWrDfv4cv1eduwvZViPdozu3Z5RPdszuGs6CXGBXboezf593wLythQz7id9WfPNLsZe2JfsvhmRDisq1NQoJRXOmLG1CdP97v/5JeX8Z9FWurVry68n9WV076MiHa5poaznqDHGGGOMMcYEJmp6jorIP4GJQAcR2Qbcrap/F5Hrgf8BscDz/iRGwXqOmvCpqKrh/W938sIXm1i/p4ThPdtxQu+jePDCIQzKTmuR41JuXbWPtKOSKNh9kKsfOYmEpDj27ihh384Dlhx1xcQIaYnxpCXGH9bdvdYvJ/bhrcXbueX15XTJSOKmUy1JakLHeo4aY4wxxhhjTHCiqudoKFnPURMKRWWVlFVWA1BeWcPbS7bzytebObpjClec2IuJ/TNbZDLUU0lBOS/+7nM6H51OUV4plz84DoBlc7ayf89BJvy0f4QjbF4qq2t4a8l2npy7ji4ZSfz61L6cYElSEyLWc9S0ViKSDnwEHAucoKrfRTikFsHa1RhjjDGtQdT0HDUmmhSVVfL47LX8a8HWQzO6xwicckxHXrxyFMd0SotwhE1nx9oC2nVOZue6Qjr3+aFTWmb3VNYs2B3ByJqn+NgYpozoxvnHd+HtJdu59fXlZGckctOp/SxJaowxwTsITAYeakwlIjJPVSeGJKKWwdrVGGOMMS1ei02O2mX1Jhg1Ncobi7fx0P9Wc3L/jsy7ZSIdUhIiHVZE7dlSTP/RWSybu434xB/eMjJ7pLJvRwnVlTXExrfs3rPhEB8bw09GdOM8N0l62xvL6ZyeyNSTejPu6EzaxFmbGv/ZZfVHEpEVwK9UdV401meOJCL3A7tV9dFA11XVSiBPREIfmEtEvgGu8HeIp2gR7e0KICIrgYtVdVlYNxQmzfXYMMYYY4zDLqs3rVpNjbJ6dzELN+1jwaYCFmzaR1ZaIvecM5DjutlYmgBvP7KEYad3p8sx7dBqJa7NDxNMvf7gQnod14HjTul22HITuKrqGt5euoN/frOFDXklnDGoEzlDshnd+yhiY8L7pdS0HHZZvXcisgm4WlVnh6N8a+W2UxLQW1UPuMuuBi4JtJegiGQCS4GjVbVURFKA74Dfq+prbplUYAVws6q+7qOeF4A/117+LSIbgS5AhUexV1T1Wh/r++zhKCJTgItU9ceB7FswQtW24WpXd5nfbdtUPUdF5OfAzUAfoAZYDvxcVbeLSAcgD+iqqtt9rB8HfAvEqmo/j+XtgH3AASDGrecPqvp39/EmOzaMMcYYE3ottueoMd7U1CgrdhTxydo8Fm7ax6LNBXRISWBEz3aM69uB35zWj55HtSXcPSSak9LiCpIzEoiNjXGmRfMw+pzezHpsKSkZCfQ/oXNkAmwh4mJjuHB4Vy4c3pVtBQd5b/lO7v/vKnYWljF5cCdyjstmWPd2xFii1BhTDxHpBHhLcF2oqrv8WH86gKpO93OTccCvgfv8LO/L5cD7qlrqbr9ERKYCr4rIR6qaB/wJWOgrgefDg8CtwGi3jiOISHfgJffuUBGZ5/5/qapu8Sg6C3hGRDqr6s4AYghWKNr2csLTrtBA2wbQriEhIpcDdwIXAYuBo4DzgAK3yPFAvq/EqOuXQEcgQ0SSaxPTwFB33Ux3Wz8DXhKRd1Q1nwCPjSBeZ/XV1ajXvDEmuthYz+FjbWvq02Kv26y9rN69zNC0YoWllby3fCfT/rOMUffN4aaZS8gvKeeikd2ZO20ic6dN5E8XHseUEd3o1SHZEqN1VJRW0SbJ++8o3Qa0Z9iPelC8r6yJo2rZurZry/+b0IfcG8bxn2vH0CElgd+9+S1jH5zL9Fkr+HL9XqqqayIdpokiubm5TJ06FZrpZfUiki0ib4hInohsFJEb3eV9RGSfiAzzKJcvIhPd+91E5E13vb0i8qRHnZtE5FQReRnoDuSKSImI3Oo+fruIrBeRYhH5XkTOd5f7Kr9JRE51/x8gIvNEZL+IrBCRc+psd5qILBeRQhGZKSKJ4WwnT6q6S1XHebmFK0nyEDBNRBp7ucWZwHzPBar6IfAe8Lj7nE8BfhVIpar6DPAmMMvX86CqW1R1otuzcWnt/3UTeKpaBiwCTg8kBk8S2IeMULRtWNrVrafetvW3XUXk5yLyv0C378VVwN9UdZE68lX1OVU96D4+FKcXrVduO98NXA9UA4M8Hh6Kk3CtNR/nJ+N2EJpjo5646n3dR+A1b4wJr9qxngP9weowHj9ImR9Y2xqfWmxyND09nRkzZth4o62QqrJyZxFPzVvHlGe+ZOwDc3l90VaGdE3nzV+eyJzfTuTunIGcMahTqx9P1B/lpVW0SfR9yXxKuwSKC8qbMKLWpVeHZG6Y1JePbp7AS1eOokNKG+57fyWj7pvDLf9ZxpyVuymrrI50mCbCcnJymDFjBkBhpGMJlIjEALnAMpxLdCcBN4nIj1R1PXAbTi+3tsA/gBdUdZ6IxALvApuBnu66/6pbv6r+AtgC5Khqiqr+yX1oPTAeJ6F8D/CK2+vLV/naeOPdeD/E6WF2gxtff49iU4AzgF7AEJzee772/ykReaox7dTQumG2EJgHTGtkPYOB1V6W/waYiPNFZlp9vfJE5H2c5NSzbi/CWrcAO3B6+jX2F9CVwHHBrCgi44H3A0iWh6Jtw9muEJq2HYRzqX/d7b7r/gDh7faul3pKgStFZIp7CX1d9SZHgf8D1qvqP4E1OK/dWsfjJD9rk6j3u/fXeZQJ+tjwJYpf962G+wPYxGitr6l5/lBofBOR+0XkpmDWVdVKX1c6hIqIfCMiA8O5jXCJ9rYNp8bsewhjOOLYac7Hk6cWmxw1rc/OwlIe/nA1Jz4wl//38iJ2F5bxy5P7sPDOU/nHFaO4dExPuh/VNtJhNitao1SVVx82EVNdqe0TKdlnydGm0DcrletP6UvuDeOYdf1YBnROY8YnGxh572x+9epiZi3bQXFZZaTDNCZQI4FMVf2Dqlao6gbgWeBiAFV9FlgLfA10Bu5w1xsFZAO3qOoBVS1T1c/83aiq/kdVd6hqjarOdLcxyo9VTwBSgAfceOfiJGl/6lHmcbfufTiJjaH1xHGdql7nx3brbacIuwu4QZzxLQ8RkTEi8qWIzBeRf7qJZV8ygOK6C1W1AGc8zLY4vRR9UtWzVDVbVceo6gseyxV4DSex5C1p5lnHxPoed2MMtifn58Ae6unF6kVj2zZs7eo+5lfbNtCug3DG+ay7ztmqmuHjdraXei4F/gf8GdgtIrki0tHjcZ/JURHpjdNj9DZ30QoOT44OBX4tIkU4l+l3BM5w979WY44NX6L5dd8qqOpAdSfiCzQx6K28Z33hEIoYWyu3LXaLSLLHsqsD7SXovl9fCvzNvZ/i1v0zjzKpIrJFRC4MsO6NIlIhzpUttbdnAqnD9WfgD0GsF7BQtau7XljaNoTtGjZe9v16EVkoIuXijAseym31FJH3RaRARHaJyJPijMkN3o+dJjuewqnFjjlqs9W3DqrK5+v28vJXm/hqwz7OG5rNC1eMol9Wil0eHwIV5dXEJcTWO85lSvsEDuy3y+qbWtd2bblyXC+uHNeLvSXlzF65m7eXbOf3b37LyJ7tOHtINqcPzCI1sb5chGkpAp6tfnp6+GdjnF7o75twDyBbRPZ7LIsFPvW4/yzOmH5TVbX215huwGZVrQomPBG5FGfilp7uohQaSJy5soGtquo5tsVmnF5dtTwvZz3ortNY/rRTUNxeeOPcu4nustqeCZ/5SEQdoqrfuXXcjtN7rtZm4BR1JgK6FzgX35eyFQCpXmK7BOc5mo0zxqXXiZTqIyLZwDPANSHoMZIK7K+vgIicAfy3gXp+CTzS0MZC0LZha1e3nlC07SCc3tuN4l5GfhNOz8rhOEnf24Dfusno/sASH6s/BMz2SFqtAGqH0UgABgDHqOp6Efkx8Heg7q+R9R4bQb7Owva6NybaSQjG05XIjKN9OdE/1nNzHEMbmsf45OFyOR77jnPVxv8H/AhnAsdQegrnx9zOOD/6fQRcBzyO92PH7+MpiNdkfXWFdsxtVW2Rt+HDh6tpufYfrNC/f7pBT/7zx3r6X+bry19u0pKyykiH1eIU7S3VF27/rN4ypcUV+uxv5jdRRKYhRaUV+vaSbXrVCwt00F0f6LUvL9T3l+/Q0oqqSIdmmgDOh8GIn4MDuQFjgLX1PJ6Ccwn8c8B2oL3HenuAOB/rbQJOdf/fWPu/e78HUI6TqIh1ly3FmaH+iPKe9eFcir8LiPF47DVget3tuven48zgHdZ2CuHzMb12X/wo69nGRwNFOGM2zvNS9h7ggnrqmo0zq7jnso44s4KfjPMBfR9wUoD7EwPMwQfW0WgAACAASURBVBmLMhTt8xFwWZDrxgAv4gzJkNQUbRuudg1V27qv7yqgrZfH/guU+Lj914+63wWecf8fifNDRayXcuMAxRmWZJd7KwQK3MeHu9sUj3WWA1cGe2z4+zprqtd9S7nh/BD1hnt8bwRudJf3cY/zYR7l8oGJ7v1uOMn0PGAv8KRHnZtw3vtfBmpwhm8oAW51H78d5xxVDHwPnO8u91Xe87U9AGfojP04Cflz6mx3mnusFQIzgcQG9t/XNr1uJ9B9qht/uJ6vEB8Tfr3WPPbtdvdYyXCXXY2X990G6pkLXOJl+QvAP3GGNNkLdG6gnheAQV6W/xn40o/jod64acT5zF1f/CwXknYNd9v62671tS1OJwUFOngsOxbYDaQF29YN7Pv/hzPkVNB1e6lzJXCWx/2H8DjXezt2/D2eAnxNhv09wvNml9WbZmXFjkJ+9+Zyxj84lyVb9/PABUP44KbxXHJCD5ITWmxH6IipbzKmWglt46gsq6baJgiKCqmJ8Zw7tAvPXTaCz247hYn9M3nl682Munc2N89cyser91BdE/5Og8YE4BugSERuE5EkEYkVkUEiMtJ9/DFgkapejTOJzDMe6+0EHhCRZBFJFJGxPraxG+jtcT8Z58NrHoCIXMHhk6/ULe/pa+AAcKuIxIszdlwOXsY7DbGG2imiVHUdzpf3IyaJEpFeOBMDeRsnstb7wIQ6y54E3lbVj9XpiXArzriXgQwYfjtOAvA3Aazjlbvd4ThfAIIxFsgCztUfen40qJFtG652hdC07UBgo/4wadIhqnqmOuP+erud6VlWnAnWxopIgnu7HOdL8j/cIkNxkjzx7ntFoltOgL/gvK/0d8sNxRljNcPtKXQ88J2639Rc7wOeE7E19tjwJapf99FEWvn41b5irG87ge5Tfdt298mvMbTdstE6nm5rGesZmt8Y2hDl45OraiGwDSchWute4EFVLfKIIdAxtcH3vofDY8DFItJWRLrgfM74wONxb8dOSMfdjsR7RFDJUfdLiO8ZWowJoYMVVfx7wVbO/evnXPPiQjqnJzH7txN44qfHM6pXe7t8PowamowJQGKEhJR4ykpsrMtok942notGdufVq09g9m8nMLhrOo98tIbTH5nPO0u3W5LURAVVrcZJLg7F+VU4H6eXaLqInIvzxbD2kt+bgWEi8nOP9Y7G+XK3DbjIx2buB+50P3ROU9XvgYdxegjsxvnA+bmv8nXircBJipzpxvoUziVVq4LZfxF5RvwY16q+dgpmu2HyB5zE8yEikobTW/IXbtv58hJwlogkueudh9Oj75baAqr6HM7zfFcAMV2Dc4zskR/GETt0ybuI/EpEPheRl0Xk4wbqOgent8iOALZ/iKp+CpwZSGLUQ7BtG652hdC0rdfJmIKQhpPw2ovzfnARMElVv3YfPw4neVnqcfse+DlOwvoWdWZ936XOpXjf4PSaG4LzmlteZ3sfAKd5JAYadWz40kxe99GiVY9f3cjthGSf1P8xtCG6x9NtDWM9Q/MbQxuax/jk3+H01kZERgHDgL/WWT/QMbXBx76HyXycHy+LcD4fLATe9njc27ET6nG3m/w9wq+udm7W9mKcDxAjcS5FSxCRPJxfTmeo6tpwBWlap1W7injt6y3MWraD4d3bceMpRzOxf0di6xn/0oSWPz1HAZJS4iktriQ5PdBOJ6apdExN5Iqxvbj8xJ58ti6fR2ev5bE5a/n1pL6cPSTbXlcmotyEgq8vau94lCvBScbU3t8CnOejzp4e/7/jWY+77A5++HJcd11v5T3rW8GRvfGOKOfen+6tnMfjfo/12EA7hURD8dYp27PO/a24YykCiDN4/z9xLp+qt7eDquaLyEvA/wMeVdW3OfyDeG25Sf7G55bv5esxERmM8zyOA0YDf2ygumnAVYFs30s8fv0qFaq2DVe7uuuEom2HUP8M8v7G8nvg9/U8fj3OhEvevOKlvOIkXMFLr1x1xib1TFYHdGwE+DoL++s+WH+9dm7Yf2X91TOn2PjVjmDHr/ZnO4dpxD4FoiWPo90cxnqG5jeGNjSP8cm/44eeo/fjnKdDMXux131viDhjo3r9zAp8rqrjPBe4ub//4Uz8dCLO6/95fhiXFbwfOz6PpyBfk00+5ra/1yF/jHOg/Q7nspIaABFpjzNe0QMi8paqHvHhIlJsQqbma+GmfTw2Zy1rdhdz0cjuvH/jeLIzQj3GsPFHRZmfydHUeEpL6usQZKKFiDC+bybjju7A5+v28sjsNTw2Zy03ntKXnOMsSdpcBTwhkzFN46c4ibG7ROQu4Gm3F5JXboKrKZ2P8wO/ikgVXmZM96Sqo5smLL/43bYRaFfwo21FJAWYDFzZ1MGFWpQdG00mgMRlU9iKM0RDX28PusfboziTaU0XkTfcHplbge4iEudHgvSwZLCI9MBJuE4CvlTVahFZCoi38nXsALqJSIxH4rI7sKaBGBpSd5sNbSfQfQqVep+vxvBMtEjwk7/cDSzGucqktl7PnuFVOOO1erMc6Acs8IijI04CcQqwClghIq+p6ieBBOUmrl4G3lTVentI+mEAXn4c8qSqH+DluXfj+AdOL+xAZnVvTLtCmNo2xO36HXCJiJyK0z4v1S3gXmUx3sf6n2qdoWNcR+y7P/zoQVxXe5wfjZ50k7rlIvIPnLFNa5Oj3o4dn8dTkK/JsL1H+OLvZfWnquofVXW5569OqrpPVd9Q1R/jjIUUNdLT05kxY4YlRpuRrzbs5WfPfsVNM5dy1uDOfHrrKdx8Wj9LjEZQRWm1nz1H21BabMnR5kREGNe3A69fO4Y/nDOIV77azGmPzOftJXa5fXOUk5PDjBkzwJm0wZiooKovq2oHVZ3o3qLqsyLQzuP/a2kgORpNmnvbijNW72rg39jM6yY0bPxq79tsaDuB7lOoRPV4utqyx3purmNoQzMYn5wfeo7eB/xeneFRDqMBjKnt4bB9F5E4d0iDWCDWfe9q9CQsqpqPM4zLL91tZACX4Yz96fXYCcHx5E2Tv0f4lRxV1QYHE/SnjDF1qSqfrc1nyjNfcvsbyzn/+C58PG0iPx3VnTZxNl9YpFWUVpGQ6E/P0TaUFttbQHNUmyT9z7Vj+OO5bpL0L/N5a8k2qmySLWNMy/Yy8JiIvIEzXMOSCMfTktTbtqo6T1W7qOrv/R1qwJj6aCsfv7qeGBvaTqD75JP4OYY2NJvxdFvqWM/QPMfQhuYxPvn3QCeg2h3KJlQO23fgTpzxs28HLnH/vzNE27oA5z0zD1iH06O3NnHs7dgJ+bjbkXiPkIY+j4jIaThdlP+qqktFZKqqzghXQKEyYsQIXbhwYaTDMF7U1Cjr8kpYsqWAmQu2sr+0khtOOZqcIdnExVpCNJp8+fZ64tvEMOIsn8OKAbD4w81sW1VA/9Gd6D+6UxNFZ8JBVfli/V4em72WXUVlXHNSb34yvCuJ8TYHX3MgIotUdUSk4zCmORCReFWtFJEOOGPMjrNEXWhY2xpjWhq3V947wMPqTGxVX9n7gD2q+miTBMehsZ7/Dye5Pxr4o6qe5qPs18BVqhqKCfEaJZB2dcs3adsG0q5NEEuTH1deYjji2Imm46kx/Ol2ex1wBc4vSu0JbpY804rtO1DB0q0FLNmynyVb9rNs637ap7Th+G4ZXDG2F2cN7mzjHEapitIqktPbNliua/92fPnmeorySi052syJCGOP7sDYozuwcNM+npm/nsdmr+XyE3vwixN6kt62vgkkjTGmWblPRGrHivytJe9CytrWGNPSNPuxnmtF2TjJLWp88nCK0HFVN4Yjjp0oO56C5k9yNE9V9wPTROQBnNnqo55NyBQZqsqqXcUs2LTPTYYWsLekguO6ZXB89wyuGNuTod0yOCrFZjVvDvydrT6zWypdj2lH3pZiivJLSetg48S2BCN6tue5nu1Zs7uYv83fwEkPfcxPhnflynG9bCzgKGMTMhkTOFW9peFSJhjWtsaEh4h0x7ls15tjVXVLU8bTmqjqyzhDhkSrumM9+zUcQqRZu5po4c9l9eeq6jse929Q1SfCHlkj2WX1Tae0opov1uczZ9UePl61h/jYGE7scxTHd8/g+O7t6JOZYj1Dm6n3nlrOgBM703toprNg30b44nE4+xGv5XOfWMbgCV3oOaRDE0ZpmsqO/aU8/9lG/rNoG6cOyOLaCb3pm5Ua6bCMB7us3hhjjDGm9RGRYThJxlXAUcBNqro0slE1f9aurUeDXcLqJEZHAKe4s9bFAeIU0SHhC9FEo+37S5m7ag9zV+5mwaYCBnVJ45RjOvLyVaPpk5mMiCVDW4KK0ioSPHuOLvw7LHzeZ3I0pX0CJQVlTRSdaWrZGUncefax3HBKX17+ahM/ffZrhnZL59oJfRjRs32kwzPGGGOMMaa1+lZVB3qM9bws0gG1ENaurYQ/l9V7ehVnJrBvAZvGuBWpqKph4eZ9zFudx7zVe8gvqWBiv0wuGNaVRy8+nvQkG4ewJSqve1n9tkX1lk/JSKCkoDzMUZlIS28bz/Wn9OXq8b35z6Jt3PzvZWSmJnDthD5MOqYjMdZT3BhjjDHGmKZkYz2Hh7VrKxFocjRPVWeFJRITdXYWljJvdR4fr9rDlxv20rtDMhP7d+TBHw9hSNcMu1S+FXDGHPWYpXzLF9DxWJ/lU9olsn11QRNE1jqoKgXlBWwq3MSmok3sLd1LcnwyqW1SD91S4lNIa5NGSpsUkuOTiZGYJosvMT6WX5zQg5+N6s5/v9vJY3PW8Of/rea6k/sweXBn4mKbLhZjjDHGGGNaKxvrOTysXVuPQJOjd4vIc8Ac4FD3MFV9M6RRmSZRVFbJ8q2FxMRAfGwM8bExHCiv4pO1ecxfncfuojLG983kzMGduP+CwTaJUit02IRMpfudv0N/DhvmQ2w8ZB8P8T9MzJPSLoGS/XZZfTCqaqpYvHsxy/OXs7FwI5uKNrGpcBOK0iutFz3Te9IhqQN7Du6huLKY4opiSipKKKoooqSyhOKKYkqrSkmOSyY9IZ3+7fsz8KiBDOwwkIFHDSQ9IXzz9MTGCGcPyWby4M58sjafv85dx18+WsO1E/pwwbAuJMTFNlyJMcYYY4wxxhgTAYEmR68AjgHi+eGyegUsOdpMVNcoX6zP5/VF25i7ag8DOqcRI1BVrVRW1xyaTOm+CwZznPUObfUqy6tpk+C+Tex0h1f5/h1Y8wFUHHBu139zqHxKuwRK9tll9f4qqyrjyx1fMmfLHOZvm092SjajOo1ieNZwLuh7AT3TetI+sb3fY/hW11RTUlnC3rK9rN63mhX5K3h2+bOs3LeSdgntDiVKT8w+kX7t+oV8bGARYUK/TCb0y+Sbjfv468freGz2Wq45qTc/HdWNtm0CPeUYY4wxxhhjjDHhFeg31eNUdXBYIjFhU9tD9Iv1+by1ZDsdUhK4cHhXpucMpF1ym0iHZ6JUdVUNKMTECVSWwcb50GsCXOaOrKEK93eDg/ugrTMZT0q7REr2l6OqLXZSrorNmylfv4GYxAQkMRFJSEDi4pG4WCQ2FuLinL+xscQkJhKTkoLEHH55+Yq9K3j+2+f5YscXDDhqAJO6T+JXQ39F55TOjYotNiaW9IR00hPS6Z3emzN7nQlAjdawqWgTK/JX8G3+t/z6418jCJO6T2JSj0kcl3lcyC/HH9WrPaN6jeLbbYU8NW8dT89bx+Un9uQXY3raGMXGGGOMMcYYY6JGoMnRr0TkWFX9PizRhFBhYSFTp04lJyeHnJycSIfTZCqra1i9q5glW/ezdMt+lm4tYGdhGQOz0xjeoz3PXz6SAZ3TIh2maQYqy6uJT4xF1n4I7/0WsgbB5Id/KCACHQdA3irocSIA8QmxxMXHUHagkqSUlpF4ryoo4OA3Czj4zTcc+OorqosKSRwwAK2oRMvKqCkvh6oqtLoara6Cqmq0qsq5lZVRU1pKTFISMamp1LRNYKcUkS8HuSC7L78ZcQntjxlDYq9jiUlMDNs+xEgMvdN70zu9Nzl9cvjdqN+xumA1c7bM4Y9f/ZF9pfs4ufvJTOo+idGdRhMfG7rk5eCu6Tx9yXDW7i7m6fnrmfjQx1w6pidXjutlSdIQys3NJTc3FyB84ycYY4wxxhhjTAskgUy2JSIrgT7ARpwxRwVQVR0SnvCCN2LECF24cGGkwwirggMVrN1Twto9xazdXcJ32wv5fmcRXTKSGNotg6HdMziuawb9O6USbxOjmAAV7yvjzYcWcVmfu2H0tTD4wiMLzboROg+BkVcfWvSvP37NpMuPJbNbahNGGzrVhYUc+PJLSpcs4eDiJVRs3EjS8GEkjxxJ29GjSRw48IieoPXR6mry927j1YUz+GrtXM7tdBqndRhLXOFBylasoHTpUsrXryd57FhSTzmZpCFDaNO7t9P7tIlsKdrCnC1zmLNlDhsKNzC+y3gmdZ/E+K7jSYpLariCAGzKP8ATc9cxd9VuLj+xF1eM60laoiVJQ0VEFqnqiEjHYYwxxhhjjDHNRaDJ0R7elqvq5pBFFCItKTm6t6ScNbtLWLen2EmG7nYSouWVNRydlULfjin07ZjKsdlpDO6abokGExL7dh7gg799y89SLoNr5kJ6lyMLffUM5K+Bs/9yaNG7Ty5j4Eld6DWkQxNG2zBVRcvLD/XQrC4pQeLjERHKVq+h6P33OfDVV1Ru3kzSyBG0HT6CpKHH0XboUKRNcL1gD1Ye5KXvX+LVla8yufdkpg6ZSvvE9keUqzl4kMJZszi4cBGl3y6nOn8viQMHkjRkMG1HjqTtyJHEtG3bqP33V97BPD7e+jGzN8/m+33fk9M7hyn9p9ArvVdIt7Mx/wBPzF3LvNV5XH5iT64Y25NUe+9qNEuOGhMZIpIOfAQcC5ygqt9FOCRjjDHGGOOngJKjzUlzTI7WJkHX7C5m7Z5iNyFaQmV1Df2yUp0k6KG/KXRKS2yx4zqayNu9qYhPXlvFT6onwx27IMZLT8YN82He/XDlB4cWffzKKjK7pzLoJC/J1Ag58PU3bL/pJqpLSkjs35+Evn0p/ugj55J4Vdr06EHqpEmknnIyicceG3Qy1NOczXO49+t7GdFpBDcMvYFuad38XreqoICy71ZQunwZB7/6mtIVK0gaPJjkE08k9dRJJPTp0+j4/LG9ZDuvr3mdN9e+Sd+Mvlx0zEVM7DaR+JjQJTE35JXwxNx1zF+Tx5Vje3LZiZYkbQxLjhoTGSISD2QADwF/DjY5KiLzVHViKGNrKs05dmOMMca0bpYcjYDCg5Ws2VPM6l3FrN1dfCghWlFdQ/+s1EMJ0H5ZqfTLSiEzNcGSoKbJbVtdwIK3v+f8hGvhpm+9FyorhL8MhFvWQrxz+fXXszYQEyuMnBzanoaNseXKq0g7J4f0nBwOfP45ldu3k3LyycRlZYX8tVVZU8kjix5h7pa5PDD+AYZ2HNroOmsOHODAggUc+Oxzij/8kNh27Ug7ezLpZ51FfJfwJ6ErqiuYvXk2M1fPZFvxNn7c78f8uO+PyUrOCtk21ueV8MSctXy6Np+rx/fmynE9SYhruqEFWormmhwVkRXAr1R1XjTUE+46m5qIbAKuVtXZkY4lmonI/cBuVX20EXW8QJiSoyLyDXCFqq4INr5wsuSoMcYYY5qrQCdkMg04WFHF7qJy9hSVsbvY/VtUxp7icnYVlrFp7wEOlFdzdMcU+mU5CdBJA7Lol5VKVpolQU30qCqvJj6mAtLr6fGYmO6MObrpM+h7GgBJqfHs33WwiaJsWHVREaXLltH1ySeQ2FhSTjopbNvadWAX0+ZPIyMhg5lnzyQ9ITRz48QkJ5M6cSKpEyeS9bvbObhwEUXvvcfGH19Im169SJs8mbQzfkRch/AMZdAmtg1n9T6Ls3qfxZqCNfx79b85f9b5nND5BKb0n8LoTqMb/d7VJzOFRy8+nnV7Snjgvyv598Kt3J1zLBP7dwzRXphopqoDo6WeuonEUMUWyDZDXb4lc9siCeitqgfcZVcDlwSSqBORTOBS4Gj3fgrwHfB7VX3NXZYKrABuVtXXA6h7I9AFqPBY/IqqXutvHa4/A38AfhzgegELVbuGOKYE4CngVKA9sA7n+flvJOIxxhhjTMvRYpOjqlBRVROy+qpqasgrLmd3UTm73YSnc7/MWVZcRl5RORXVNWSlJZKVlkDH1EQ6piWQlZbIsdlpdExNpMdRbemSkWRJUBP1KsuriecgpHetv+DRp8LaDz2So23YsbawCSL0z8HFi0kcPDjsY3Z+vv1z7vjsDi459hKuHHQlMRKeSdAkNpbk0aNIHj2KTnfeQckXX1D03vvkPfYYSYMHkzZ5Mqmnn0ZsangmxOrXrh93nnAnvxn+G95d/y4PfvMgVTVVTOk/hXP6nNPohPDRHVN47rKRzF21m7tnraBf1hbuOvtYurVvmjFXjTGNIyKdAG+JwwtVdZefdUwHUNXpfm42Dvg1cJ+f5b25HHhfVUvdbZeIyFTgVRH5SFXzgD8BCwNJjLoeBG4FRrv1HEZEugMvuXeHisg89/9LVXWLR9FZwDMi0llVdwYYQzAabNcAYvcpgOc7DtgKTAC2AGcB/xaRwaq6yY/tNPrYNMYYY0zL5HdyVESOAc7F+eVbgR3ALFVdGabYGmXFjkIG3v1BwwX9FCNCZmrCEYlPp8fnD8vSkuIs8WlahMryauK1pP6eowB9T4eZPwf9E4iQlNqG0uKK+tdpQqWLl9B22LCw1V9dU83Ty57mrbVv8dCEhxjZaWTYtlWXtGlzqEdpTWkpJfPmUfjee+y+/36Sx5xAWk4OKRMnEhOCMVTrSo5P5qJjLmJK/yks2bOEf63+F08ve5pz+pzDVYOuIrNtZqPqP+WYLE7s04HnPt1AzpOfcfmJPbl2Qh8S4+1S+5bIsyekiAwAngaGAtuB36nqLI9yT+L08OsBfABcpqplnvUARwF/99hEPPClqk4UkduBa4COOImWO1T1LXf9l4HuQK6IVOP00rsOj16ajYnPx75722ZuPds4oryq/qm+/QolEckGngBOAkqAR1T18drH3STTuFBvtwEPAbeKyFOquj/IOs4EnvdcoKofish7wOMi8jdgCjAo0IpV9RkRORqYJSIn1z0e3CTiRKj/0nRVLRORRcDpwIuBxuHWL+r/mFoNtqu/sYeC24N1useid91eucOBTVF6bBpjjDGmGfArOSoitwE/Bf4FfOMu7gr8U0T+paoPhCm+oA3qks7Ce8+KdBjGNFuV5dXEVxc23HM0ayBUV0L+WsjsR1JqfFQlRw8uXkSHX/4yLHXnl+Zz+6e3o6rMzJlJh6TwXNbuj5ikJNLOPJO0M8+kuqiI4g8/pOCVV9n1f3eR+qMfkX5ODknDhiExoe3RKiIMyxrGsKxh5B3M4/nvnue8d87jvKPP44pBVzSqTRLjY7n+lL6cd3wX7n1vJac9Mp+7zh7IqQM62o9QLZQ7qU0uTpLqdJxExjsiMkJVV7vFpgBnAGXA5zg9/p7xrEdVZwIz3TrTgK+Bf7oPrwfGA7uAnwCviMjRqrpTVX8hIuM5PBl6XajjqxPrYdt0t7HS1za8xdjQfvnatrtPT7lxXFdfObdsjLv/7+B8LuwKzBaR1ar6v4bWD6OFwDxgGnBnkHUMBlZ7Wf4b4HvgNGBafe0pIu/jJLT7i8jfVPUFj4dvwem1+JKIXBRAgrKulcBxwazoHje/F5Hz60vYewhFu4aNiGQB/YAVUXxsGmOMMaYZ8Pdb8lXASFV9QFVfcW8PAKPcx5qUiCSLyIsi8qyI/Lypt29Ma1BZXkV89T7IaKDnqIjTe3St892jbWobSosrmyDChtVUVFD2/UqSjmv8pEh1Ldq9iIvevYghHYYw47QZEU2M1hWblkbGhRfS46UX6fXWm8R368que+5h/Wmns+fRRynfsCEs281sm8lto27jrXPfoqqmivPeOY+/LPwL+8r2Nareru3a8vQlw7n3vMHc/9+VXPnCAjblHwhR1CbKnACkAA+oaoWqzgXexUl21HpcVXeo6j6cZIjPF7ibMHkNmKeqfwNQ1f+469e4SdS1OJ9nmjy+RmzjCMHul6pe509i1DUSyFTVP7ixbQCeBS72c/1wugu4wR079BARGSMiX4rIfBH5p5t89iYDKK67UFULcMYZbQu8WV8AqnqWqmar6pg6iVHcZOhrwCTA5wnDj56XxW6swfgc2IPTgzXRz3W8tqs3TTkWqfs8vgq8qKqriO5j0xhjjDFRzt/L6muAbGBzneWd3ccaTUSeB84G9qjqII/lZwCPAbHAc25S9gLgdVXNFZGZOB+OjDEhVFleTZuK/IYvqwfofyZ89iiceAOJyfFUVlRTWVFNfJvIXgJd9t0K2vTsQWxKckjrLSwv5Ma5N/LA+AcY33V8SOsOtfjsbDpccw1HXX015atWUTgrly2XXU5cVhbp5+SQdtZZIZ/IqWPbjvxu9O+4YtAVPPftc5zz9jn8pN9PuOzYy8hIDPY7PZzUL5MPfn0Sz3++kfOf+pxfnNCDGyf1JS42POO7tmSDXxwcbK81v3172bfBdO/NBraqqudni804Q/rU8hwb8KC7ji/3AqnAjbULRORS4Gagp7sohXqSVWGOL9htHKGR++WvHkC2iHheYh0LfNrYikXkXX645DnRXXaTe/8zVT27vvVV9Tu3jttxelfW2gycoqqlInIvzhBR3sadLMA5VurGdQlOm87GGTs00EmUauvJxulBfI23cUcDkArUO3SA+9m5oUmKfgk80tDG6mnXRmnM8+3+6PEyzgRX17uLw3ZsGmOMMabl8zc5ehMwR0TW4oxjBc54V0cDN4QolhdwxumqHdQdEYkF/opzKdM2YIGIzMK5VOZbt1h1iLZvjPFQfrCKlIpdkFbv93FH74nw5jVQvBtJzSKtQxKFe0rp0DUl3GHWq3TJUjBj7gAAIABJREFUYtoOGx7yet9Z9w5ju4yN+sSoJxEhccAAEgcMoOO033Lgq68ompVL3hNPknT8UNLPPZfUSZOISfS3M1HDOiV34s4T7uTKQVfy7LfPcvbbZ3NR/4u49NhLg564qU1cDNdO6MN5Q7twy+vLuOrFhTz5s+NJTfTVGcx4E2TisinsALqJSIxHcrA7sCbQikTkYpzeliNVtdJd1gOnN9kknDFIq0VkKeDZHvUljkMWXx2e2/RnG4fF6Od+hcJWYKOq9g1xvXgmw4KYkKnW3cBi4GGPend4PF6F7x/1l+Ncor3AI46OOAnEKcAqnMu3X1PVTwIJyiOZ96aq1tv71A8DgFfqK6CqH+DluXfj+AdO5wafQz14cUS7isgY4C84CcodOBMw+X3ZSLDPtzhjqvwdyALO8thm2I5NY4wxxrR8fnW3cT9k9QPuAf4HfIgzIHp/VW3ol2m/uB806157OQpYp6obVLUCZ8zTc3ESpbUDIVqXIWPCoOJgOW1iSiHBjwRnXIJzaf2qXAAyOiaxf/fBMEfYsIOLFtN2eGgnY6rRGmaunsnPjvlZSOttShIbS8rYsWQ/+AB9588jPSeHwjfeZN2EieycPp3S5csJfji8I2WnZHP3mLv51+R/kV+az+S3JvP44sfZXxbsvCnQKT2Rf1w+kq7tkrjw6S/ZVhD5482ExNfAAZxJYOJFZCKQg3P+95uIHI8zMct5dXrpJeMkFvPccldw5AQ7u4He4YzPC89t+rONujH6s1+h8A1QJCK3iUiSiMSKyCARabqZ6Oqhqutwxpq9se5jItILZ9Kld32s/j7OLOiengTeVtWP3bFGbwWeFZGEAEO7HSch+ZsA1zuMu93hwEdBVjEWJ6l4rqqW+ruSj3at7ZE7Afj/27vv8LjKM+/jv1vNkixZ7r0BNjYGU22wA6GGhCYgdCebTSEo7CYs75tks0k2y27Ibsrm3SRLQkjkhFBDCSWgQCihhNDBBgzuxrhhyZaLZHVpRvf7x4xAGLWZOaMp+n6uay5rzpzznN/jkQZz6ykbFfn3+WC4QZECcfl+fUjr700AAJDeYiksHqzIP74fdfd73P3F6MiEM5KUTYpMIdva7fm26LH7JF1oZjcosp7Xh9TW1mrBggXvPSorK5MYE8g+7c3tGpYfw6oZh10krbhbkjRyfLHqdqa2WOWdnWpZvlxFAe9U/8L2F1ScX6wjxsW1H0baySkuVll5uabf+NvI+qQTJujdr/+z3jn3XO2+8XcK7doV2L2mlk7Vdz/yXd11zl3a07pH5/zxHP1s2c+0t3VvXO3l5eboP88/TJcsnKYLb3her2+Nv9iaySorK9/7b52Cn0Y9qKK/CD1XkSLWLkm/VGRE2poYmzpP0ihJz5pZY/TxZ3dfpcjotxcUKTDOV2Qdxu5+IOk7ZlZnZl9PUr79vXdPRQpQ/d3jAxkH2K8emdmvzGxAowjdPaxIofZISe9E8/1GUnxDwZPjWkX+vfqe6KZcN0v6TPQ97Mktks4ys6LoNecrMu37n7tOcPffKPJv0WtizHSFIrOtdnb/foze58tm9pyZ3WpmT/XTzrmKrJ+7vZ/zeuTuf5N0ZiyF0W4+8PcaXd+2q52+RuQGJjpC+kuKfP/VdPu7/HSGfG8CAIA0ZQMZHWRm/yTpy4qsNXSkpKvd/YHoa8vdPZDqg5nNlPSnrjVHzexiSZ9w9y9Gn39G0rHu3u9U/gULFvirr74aRCxgSLrvB89pkf9Yk7/9x4FdEO6QfjJP+vyftXrdcG1dvUcfv/zQ5IbsQ9vbb2trxZc064m/9H9yDL742Bd19gFn65OzPxlou+nE3dXy6ququ+9+NTzxhIoXLNDICz6pkpNOkuUHN319e+N2/ebN3+ixzY/pwtkX6rOHflajC0fH1dbjq3boX+5dof88/zCdNX9SYBkzjZktc/cFqc4BpAszy1NkB/P/iW5u1de531dk7fufDVK2+ZL+TdKlko6T9D13P72P81+SdLm7vzUY+QYiOiL3Lkkn9FF4BgAASGsDHTl6haRj3P18SSdL+jczuzr6WjLXLdsmqftuMFMVWdeoX/X19aqoqFBVVY8DSwH0o70lpIJhMfx45+ZL8y+W3rhD46aXqnbLhzb9HVTNr7yqooCn1L+28zVta9imcw7qc1+QjGdmKl64UJN/8H3NevJJlZ52mnb/7iatP+VU7fjhj9S6LtHlFSMml0zWNYuv0R/O+YOaOppUfn+5fvLqT7SrJfbRqqfPm6BbvnCsvvenVfrl0xsCXRYgE1RVVamiokJilBSwvyWKFB6vMbOnzezS3k50928PVmE06pOSKqM72Yf0/nr6PXL349KsMDqQEbkAAABpb6DF0Vx3b5Qkd9+kSIH0TDP7iZJbHH1F0mwzO8DMCiRdJunBgVxYVlamyspKlZeXJzEekL3aWjtVUBjjkr5HLpFW3KXREwrVuLdV7a2h5IQbgIYnn1DpyScH2uYNr9+gK+ZfofycobP5T27JcI288ALNvP02zbztVtmwYdp6RYXeuehi7fn97xWur0/4HpNKJuk7i76je8+9V82hZp1z/zla8qclum75dXq5+mW1hwf2/9yHTSnT/f94vB5aUa1v3LNCze2p+/4bbOXl5V3LxyT+hiBwZja92xTg/R/TU50vm7n7re4+1t1Pjj7uSnWmbkZ1+/pK9VMcTSfREbl3SPoPd1+b6jwAAACJGGjlo8bMjux6Ei2UnqPI2mbzgwhiZncoslbWHDPbZmaXu3tI0lcU2QRqtaS73X1lEPcD0LeONldBYW5sF02cL+UVKqf2LY0YW6SG3a3JCdeP9q1b1fLGCg0/8cTA2nx95+vavG+zzj3o3MDazDQFM2dq/P/9P5r15BMad/XVan7lFW342Ol696tfU+Ozz8nD4YTa79rd/plLn9FXF3xVkvSz5T/TiXedqCv/cqVuXnmz1u1d1+eo0Illhbr7S4vVEe7Uif/9tCqfeXtIFUmRntx9i7uX9PLYkup8SJlbJf2vmd2ryJqkr6U4TywGPCIXAAAg3Q10zdGpkkLuXtPDa8e7+4AW/R9Ms2fP9lNOOUXl5eWMHgVi5O664R+f1JUf/Z1yPnVbbBc/+E/ShEP1x+cWasFZMzV1bnxrSCai5tprlTtylMb9U7/LEw/YlY9fqdNmnKaLD744sDazQbiuTvUPPaT6++5XaM8elZ13rkZ+8pMqmDEjsHvUt9Xr5ZqX9eL2F/VC9Qtq7mjWosmLtHjSYp1z4DnKzem5iL+6ep9+/uR6vfzOXn3xowfoM4tmaPiwvMBypZOqqipVVVVp6dKlG9x9dqrzAOifmeW7e4eZjVVkXdQTfKitCQIAAJAGBlQc7fFCs4k9FUvTBRsyAfFrbw3pd1//q7506l3SBZWxXfz6HdL6R/XI3q/roKPGafbCCckJ2Ye3zzhTU376ExUeckgg7a2oXaGv//XreuiTDyk/d+hMqY9V69q1qr/vPtVX/UnDDjxQZRdcoBGf+Lhyhg/v/+IYbGvYpheqX9Cq3at0zaJrZNb36i5raxp03ZPr9dLG3frCCQfo7xfPVEmWFknZkAnIHGb2Y0VGX0rSN9z9xVTmAQAAGKpiXFDwAx4OLEUSsCETEL/2lpAKCsJSQRxFrakLpHeXq7g0Xy2Ng78/Q8eOnQrt3athc+YE1uYNb9ygL87/IoXRfhTOmaMJ3/qWZj/9lEZ99u/V8PjjWn/yKXr3q19Tw5NPyduD+X6YWjpVFx98sf598b/3WxiVpDkTS3X9p47W769YpNXVDTrpv5/S9U9tUGNb9ky3Z0MmIPO4+z+7+4nRB4VRAACAFEmkOJrMjZgSxoZMQPxaGjpUNKwjvuJofrEU7lBhaYFaGjqCD9eP5pdfVvHCBbKcRD7e3vdm7ZvaULdB5886P5D2hgIrKNCI00/XtBt+qYMee1TFxy7U7ht/q/UnnqTqa/5dTS+/LO/sHPRcB08o1c+XHKU7KxZpbU2kSPqLJ9erKQuKpGzIBAAAAADxSaR6sDSwFADSSktju4oK2qWCkrjbKC7NV0vD4I8cbX75JQ0/9rj+TxygX634lS4/7HIV5BYE1uZQkjdqlEZddplm3nabDrj3HuVPm6od3/tPvX3mmdr92xsV2rt30DPNnlCq65Ycpbu+tFhrdzTqEz97Ri9u3D3oOQAAAAAAqRdTcdQi/s7MrnH3X5rZdDM7NlnhEsG0eiB+LQ0dKspviYwCjVNhyeCPHA3V1qrhscdV+rHTAmlv7Z61WrN7jS6YfUEg7Q11+VOmaOwVV+iABx/Q5B/+UG3r1untj39C737jG2pevrzPXeiTYdb4Ev18yVG69rxDdfWdr+naqlVq7QgPaoagMK0eAAAAAOIT68jRX0paLGlJ9HmDpOsDTRQQptUD8WtpaFdhXnN80+qj8oflKjTIhaa9d92t0rPOVP7kyYG0d+faO3XxnIsZNRowM1PxUUdp8o9+qIMee1SF8+ap+tv/qnfOPU97br9d4cbGQc1z6twJeuTqE7WrsU1nXfc3vbZl8EezJopp9QAAAAAQn1iLo8e5+5cltUqSu++VRNUAyDItjR0qzm1IaFr9YPNwWHX33atRl1wSSHsN7Q16dNOjuujgiwJpDz3LGzVKYz73OR3454c14V+/reZXXtW7X/vaoOcYNbxA1y05Sl87fY6uuGWZfvzoGrWFMnMUKQAAAABg4PJiPL/DzHIluSSZ2ThJg7+rBoCkam1oV2nOPqlgdnwN+OB/LDQ9/4LyRo1W4SGHBNLeg28/qOMnH6+xRWMDaQ99MzMNX7RIwxctkodTV5Q8+/BJOvaA0frWfW/qvF88p59ccqTmTR6RsjwAAAAAgOSKdeTodZLulzTezP5L0rOSvh94qgCw5igQv+aGDhVbXXzT6otHSy17pUFeP7Lu3ns18qILA2nL3XXnmjt12dzLAmkPsbHc3JTef1zpMC39+2N0xUcP1Gd++5J+8eR6hcLp/XtA1hwFAAAAgPjEVBx199slfUPSDyRVSzrf3f+QjGCJYs1RIH71tS0akVsT37T6/KLIo60h+GC9CO3Zo6bnntOIs88OpL01e9ao0zt19PijA2kPmcfMdOExU1V11Ql66Z09uvCG57Vh5+CuhRoL1hwFAAAAgPjEOnJU7r7G3a9391+4+2oz+3wyggFIDe907dvVorKcbfFvyDRistSyO9hgfah/4EGVnnqKckcEM/25LdymUYWjZGaBtIfMNXlkkW75wrG6aME0Xfyr5/Wbv21UZ+fgjooGAAAAACRPzMXRHnw3gDYApInGujYNK85TfqhOKiiOr5HSSVLz4Oz47e6qu/cejbyIjZOQHGamzyyaoT9++Xg9urJGly19UVv3NKc6FgAAAAAgAAMqjprZil4eb0qakOSMcWHNUSA++2pbVDauSGpvjH+3+hGTBm3kaNuaNfK2dhUtWBBYm+3hduVYEL87QjaZMWa47qxYrNPmjtf51z+nh1ZUpzrSe1hzFAAAAADiM9Dd6idI+oSk/YeCmaTnA00UkK41RwHEpr6rOLqlKf5p9aWTpZo9wQbrRcsbb6h44cJAp8Av27FMh489PLD2kD1yc0xfOukgLT5ojK664zU9u6FW15xzqIoKUruJVHl5ucrLy7V06VLWHAUAAACAGAx0aNSfJJW4++b9HpskPZ20dAAGXX1ts8rGDpM6O6S8wvgaGTFJah6k4uhbb6nwsEMDbfOv2/6qk6adFGibyC6HTx2pP111glrawzr3F89qTc2+VEcCAAAAAMRhQMVRd7/c3Z/t5bVPBRsJQCrV72xR2SiLTKmPdzRm6eRBK462rnhTRfPnB9ZebXOttjZs1ZHjjwysTWSn0sJ8/fTSI/Wlkw7Sp5a+pFtf3Cx3NmsCAAAAgEwy0DVH+62QDOQcAOmvbmezyso8/in1UnTN0eQXR8P79qlj2zYVzp0bWJsPbXxIH536UeXn5AfWJrKXmemiY6bqD1cu1h0vbdGVty1TXXN7qmMBAAAAAAZooNPqnzKzq8xseveDZlZgZqea2c2SPht8PACDqa25Q/t2tWrM6FBixdHSyVJz8jdkann9dRXOny/LD6aQ2dTRpJtW3qQvHvbFQNrD0HHQuBLd/+WPaPLIIp193bN6aePgbEgGAAAAAEjMQIujZ0gKS7rDzLab2Soz2yhpvaQlkn7q7jclKWNc2K0eiN329XWacMAI5XpzYsXR4jFSqFXq7AwuXA+aly1X8TFHB9bebatu06LJizRr1KzA2sTQMSwvV/9efqiuPe9Q/dOdr+lrd7+hnQ2tg3JvdqsHAAAAgPgMdM3RVnf/pbsfL2mGpNMkHe3uM9z9Cnd/Pakp49C1W315eXmqowAZo3Fvm0aOL5bam6T8BIqjOTlS4Sgp3BZcuB60LFumoqOPCaSt+rZ63b76dv3jEf8YSHsYuk47ZIKe+NrJGltSoDN+9jf95m8b1RFO7i8KysvLVVlZKUnsVg8AAAAAMRjoyNH3uHuHu1e7e10yAgFIMVOkOJrIyFFJKh4thZJXHO1sb1fLqlUqOvKIQNq7eeXNOnX6qZo+Ynr/JwP9KBmWp2+ddYju/tJi/XVdrc7637/p+Q27Uh0LAAAAALCfmIujAIaA9saAiqPJm1LcunKlCmbOUG5JSSDtPbb5MS2ZuySQtoAus8aX6JYvHKuvfXyO/vmeFfry7cv1bl1LqmMBAAAAAKIojgL4sKZdkeJmIpI8crRl+WsqPiqY9Ub3te9TbXOtZo1krVEEz8x0xmET9ZevnqRZ40t09nV/0y+eXK/WjnCqowEAAADAkEdxFMCH1a6Rxs5JrI2i5BZHO7ZvV8GMGYG0tWr3Ks0dPVe5ObmBtAf0pKggV//39INV9ZUTtGJbva6+87VURwIAAACAIS8vlpPNbJikCyXN7H6tu18bbCwAKVW7Vpp3XmJtFI+RwvuCydOD0J7dKjr6qEDaWlG7QoeNPSyQtoD+TBtdrMq/X6CG1o5URwEAAACAIS/WkaMPSDpPUkhSU7dH2qmvr1dFRYWqqqpSHQXILC5p52pp3CGJtVM0KqkjR8O79yhvdIJT/6OW71yuoycEM0UfGKjSwvzA2qqqqlJFRYUklQXWKAAAAAAMATGNHJU01d3PSEqSgJWVlamysjLVMYDME2qRvFMqGZ9YO0nekCm8d49yR49JuJ1O79SKnSv0X8f/VwCpgNQoLy9XeXm5li5dWp/qLAAAAACQSWIdOfq8mc1PShIA6aFlrzT+EMkssXaKRkuhdsk9mFz7Ce3eo7wxiY8c3dqwVaUFpRpTlHihFQAAAAAAZJZYR46eIOlzZvaOpDZJJsnd/fDAkwFIjZa90uQEN2OSpLxhUk5upL3iYKa/d/FwWOH6euWOHJlwW+v2rtPBow8OIBUAAAAAAMg0sRZHz0xKCgDpo6Uu8fVGuxxwYuCFUUkK19Upt6RElhfrR9iHrdu7TgePojgKAAAAAMBQFNO0enffLGmkpPLoY2T0GIBs0bJXGhfAyFEp8an5vQjt3Km8CRMCaWvdnnWaPWp2IG0BAAAAAIDMElNx1MyulnS7pPHRx21mdlUyggEYXLu2Napxb6vUVi+NTe+RlB01NcqbmHhx1N31eu3rOmLsEQGkAgAAAAAAmSbWOamXSzrO3Zskycx+JOkFST8POhiAwfVy1UbtrW7U1FC7VDop1XH6FNqxQ/kTJibcztaGrSrILdCkkvTuLwAAAAAASI5Yd6s3SeFuz8PRYwAy3M7NDVK4QyocIeXE+tEwuIIaOfpG7Rs6ctyRASQCAAAAAACZKNaRo7+T9JKZ3R99fr6k3wYbCcBga6prU1Ndm0aODEvFI1Idp1+hmh0qXrgg4Xa2NW7TtNJpASQCAAAAAACZKNYNmX4i6QuS9kjaK+nz7v6zZATri5kdaGa/NbN7BvveQDbasWlf5IvO6MjRNBcZOZr4tPra5lqNLx4fQCIAAAAAAJCJYp476+7L3P06d/9fd38t1uvN7EYz22lmb+13/AwzW2tmG8zsm/1k2Ojul8d6bwA927k5WhwNd0jDylIbZgA63n1X+ZMnJ9zOzuadFEcBAAAAABjCBjSt3syedfcTzKxBknd/SZK7eyxDzW6S9AtJt3RrP1fS9ZJOl7RN0itm9qCkXEk/2O/6L7j7zhjuB6AfO97Zp9LRhVLzbqlwbKrj9Mk7OxWqqaE4CgAAAAAAEjag4qi7nxD9szTRG7r7M2Y2c7/Dx0ra4O4bJcnM7pR0nrv/QNI5id4TQO/C4U7t2LRPM+ePVe3rm9J+Wn2otlY5ZWXKGTYs4bYojgIAAAAAMLTFNK3ezH40kGNxmCJpa7fn26LHessxxsx+JekoM/tWT+fU1tZqwYIF7z0qKysDiAlkn9rNDRoxpkiFhS55p5Q/PNWR+tTx7vZARo12dHaovr1eYwrHBJAKSJ3Kysr3/lsnKb2HfgMAAABAmol1t/rTJf3LfsfO7OFYrKyHY97DscgL7rslXdlXg+PGjdOrr76aYCwg+21fX6fJB4+UmnZJufmS9fTjmD46tgdTHN3dslujh41Wbk5uAKmA1KmoqFBFRYUkycx2pTgOAAAAAGSUAY0cNbN/MLM3Jc01sxXdHu9IejOAHNskTev2fKqk7Yk0WF9fr4qKClVVVSUUDMh2766r05TZIyPrjebmpzpOv4IqjlY3VWtiyYd3vN+3q0VP3rpaq5+vTvgewGCpqqrqKpCm/45qAAAAAJBGBjqt/veSyiU9EP2z63GMu386gByvSJptZgeYWYGkyyQ9mEiDZWVlqqysVHl5eQDxgOzUGe5Uzdt1mjyrTNqxUhqW3uuNSlLH9mB2qq9urNbk4R9u55k71ynUFtZz96xXqCOc8H2AwVBeXt61fEx9qrMAAAAAQCYZUHHU3evdfZOkdkn17r7Z3TdLcjO7MZYbmtkdkl6QNMfMtpnZ5e4ekvQVSY9KWi3pbndfGUu7+2PkKNC/XdsaNXxUoYr2LpNC7VJBeq83KgU7cnTS8EkfOLZ7e6N2bmnQqZ89RGOnlWjLW3sSvg8wGBg5CgAAAADxiXXN0cPdva7ribvvNbOjYmnA3Zf0cvxhSQ/HmKdXXSNHAfSuekO9Js8eKT3/XWnSxdLu9F5vVIoWR6cEUxw9sOzADxx7/fEtOvzkKcrLz9WBR47TO2/u0oFHjUv4XkCylZeXq7y8XEuXLmXkKAAAAADEIKbd6iXlmNmoridmNlqxF1gBpIm9NU0aU9YibX1JGntwquP0y93Vsb06kJGjNU01Hxg52ri3Te+8sUuHnThVkjRz/lhtfnOXOjt73RsOAAAAAABkuFiLo/8j6QUz+56ZXSvpeUn/HXysxDGtHuhffW2LynY+LC34vJST/psxhevqZLm5yi0tTbit6qZqTRz+/oZMK57aqoOPm6jCksjfw4ixRSoqLdDOTfsSvheQbEyrBwAAAID4xFQcdfdbJF0gaYekWkkXuPutyQiWKDZkAvpXv6NRZdvulRZekeooAxLUeqOStKtll8YVR6bMe6drzYs1OvzkqR8454DDx+qdN3YFcj8gmdiQCQAAAADiE1Nx1MxM0tGSRrv7zyU1mtmxSUkGIKnCHZ1qqm9T6WGLpdIJqY4zIEEVR0OdIe1r26eRw0ZKimxMNawoTyMnFH/gvJmHj9WmNymOAgAAAACQrWKdVv9LSYsldW2q1CDp+kATBYRp9UDfGnft0/CcPco5/supjjJgodpa5Y0fn3A7dW11GjFshPJyIksmb165W9Pnjf7QeRNmjlBLY4fqa1sSvieQTEyrBwAAAID4xFocPc7dvyypVYrsVi+pIPBUAWBaPdC3puWPqKS4XRp/yKDcb8OynWre155YIx0dsoLEP3J2t+zWmKIx7z3fsnK3ph825kPnWY5p5mFjtGkFo0eR3phWDwAAAADxibU42mFmuZJcksxsnKTOwFMBSK7OTjW+9oRKAlq/cyCWPbJJz9y5NqE2PBSW5eYmnGV3y26NKYwUQ9uaO7Rra6OmzB7Z47kz54/V5pW7E74nAAAAAABIP7EWR6+TdL+k8Wb2X5KelfT9wFMFgGn1QB82/EWN4TEaPnXaoN52y8o9env5zriv93BYlp+XcI7dre+PHN26eq8mzSpTXkHPRdcpc0ep5u16dbSHE74vkCxMqwcAAACA+MS6W/3tkr4h6QeSqiWd7+5/SEawRDGtHujFrvVS1dVqnHCqSkYVDuqtF51/kJ65a51amzriayAckgIeObpl5W5NP/TDU+q7DCvK09hpJdq+ri7h+wLJwrR6AAAAAIhPrCNH5e5r3P16d/+Fu69ORigASVK7Trr5XOmUb6tJk1Qyatig3n7SQWU66OjxevYP6+O63jtCstzgRo66u7as3K0ZfRRHJWn6oWO0ZRVT6wEAAAAAyDYDKo6a2UIzm9jt+d+b2QNmdp2ZfXiLZwDpp3addMu50qnfkY7+jBr3tmr4IBdHJWnReQdq+/o6bX4r9mKjh8OyvMRHjta31WvksJF6d12dCoryVDa+qM/zZxw6RltW7kn4vgAAAAAAIL0MdOToryW1S5KZnSjph5JuUWT6XmVyoiWGNUeBbmrXRgqjp10jHfVpSVJjXZtKRg7utHpJKijM0yl/N1dP/36N2ltCsV0cDkl5iY8cbWxvVOeGEj1x0yotOHumzKzP88dOLVFbc4f27WpJ+N5AMrDmKAAAAADEZ6DF0Vx37xo2damkSne/193/TdKs5ERLDGuOAlE710Sm0n/sP6QjPyVJCoc61drYoeKygpREmnbIaE07ZLSev//tmK6L7FafWHG0ZmO9Jj2+SPteytPHPjdPBy+c2O81lmOaNm+0tqxi9CjSE2uOAgAAAEB8BlplyDWzPHcPSTpNUkUcbQAYbDtXS7ecL51+rXTEpe8dbqpvU/GIAuXk9D1iMpkfLSC0AAAagklEQVSOv3CW7rj2ZT0R6lRu3sB+T9NcO125LcM17Pdr47pnw+4W7dnepO0zV+nUc8s1ZdKoAV87fd4Yvb18pw47cUpc9wYAAAAAAOlnoIXNOyT91cx2SWqR9DdJMrNZYpQKkJ52rJJu/aT08e9Jh1/ygZea9rZp+MjBX2+0u2HF+Sq/6ghVbxj4LvD1rzcor6RQw6cMj+ueUw4eqZmHj9UDj/yPSoYtiena6fNG65k71ykc7lRubsx72QEAAAAAgDQ0oOKou/+XmT0haZKkx9zdoy/lSLoqWeEAxGnHykhh9BPfl+Zf9KGXG+vaBn2n+p6MmVKiMVNKBnx+9V+2qXD6SI06aWpC923uaFZxfnFM1xSVFqhsXJF2bKzX5NkDH3EKAAAAAADS14CnxLv7iz0cWxdsHAAJq3lLuu2CXgujktS4NzWbMSUqqN3qmzqaNDw/9tGn0w4ZrW1r9lIcBQAAAAAgS2Tt3FB2q8eQVPNmZMToGT/otTAqRafVp8HI0ZiFQ1Ju6oqj46aXqnZrY8L3B4LGbvUAAAAAEJ+sLY6yWz2GnOoV0q0XSGf9t3TYhX2e2ljXmhbT6mPlHaGEd6tvD7fL5SrIKYj52nHTS7Rra0NC9weSgd3qAQAAACA+MVUZzOxiSY+4e4OZfUfS0ZL+092XJyUdsls4JDVUpzpFdqjbIv3hc9JZP5YOPb/f0yPT6jOwOBoOy/ITK452jRo1s5ivHTGmSO0tIbU2dqiwJD+hHAAAAAAAIPVirTL8m7v/wcxOkPQJSf9P0g2Sjgs8GbLfK7+RnvyeVDgy1UkyX06udPb/k+adN6DTm+oyc1q9BzCtvqmjScPz4tvt3nJMY6eVqnZrg6YdMjqhHAAAAAAAIPViLY6Go3+eLekGd3/AzP4j2EgYMkKt0oIvSB//XqqTDCmd4U4172vX8LLMK44qFE54Wn1TR1PMO9V3N3ZaCcVRAAAAAACyRKxrjr5rZr+WdImkh81sWBxtAEih5n0dKhyer9y8zPvR9VAo4d3qm0PNcW3G1GXctFLtYlMmAAAAAACyQqzVkUskPSrpDHevkzRK0j8HngpA0mTqZkxSgNPqEyiOjp1WyqZMAAAAAABkiViLo2dLetzd10c3ZPqlpF3BxwKQLE172zQ8AzdjkhSZVp+X2EZIiRZHR00qVsPuVnW0hfs/GQAAAAAApLVYi6P/Ft2pvmtDppsV2ZAp7dTX16uiokJVVVWpjgKklca9bSoZVZjqGHHxcDjhafXt4XYV5BbEfX1ubo5GTRqu3e8ytR7po6qqShUVFZJUluosAAAAAJBJYi2OfmhDJknxVxmSqKysTJWVlSovL091FCCtNNa1Ze60+lBIluC0ekkyWULXj51aol3bKI4ifZSXl6uyslKS6lOdBQAAAAAyCRsyAUNM097WDJ5WH5IS3K0+CGOnlbDuKAAAAAAAWSDRDZlGiw2ZgIyS0SNHw2FZfjoUR0u1czPFUQAAAAAAMl1MxVF3b5b0tqRPmNlXJI1398eSkgxAUkTWHM3U4mgw0+oTNeGAEWrY06p9u1pSHQUAAAAAACQgpuKomV0t6XZJ46OP28zsqmQEAxC8cKhTzfvaM3dafUd6TKvPzc3R4adM1d3ff0VP375GO97ZJ3dPdSwAAAAAABCjWKsMl0s6zt2bJMnMfiTpBUk/DzoYgODV7WhW6ehC5eWnfvRlPILYrT4oC88+QHMXT9LaF2v02I0rlZefo0M+MkkHHztRxSPScp86AAAAAACwn1iLo6b3d6xX9OvEtn0GMGh2bWvUmCklqY4Rt0hxNPUjR7uUji7UgrNm6pgzZ6h6Q51WP1et2x96UVPnjNLcj0zSjENHKyeXPesAAAAAAEhXsVYZfifpJTO7P/r8fEm/DTYSgGTZuXmfxk7L3OJouuxWvz8z0+TZozR59ih9tCWkDct2avkjm/T0bWs0Z9FEHfrRKSobV5TqmAAAAAAAYD+xbsj0E0mfl7RH0l5Jn3f3nyUjWF/M7HwzW2pmD5jZxwf7/kAm8k7XxtdqdcARY1MdJW4eCqXNtPreFBTlad4Jk3XhNxbo/K8epbaWkP7yu5WpjgUAAAAAAHow4OKoRUxz9+Xufp27/6+7vxbrDc3sRjPbaWZv7Xf8DDNba2YbzOybfbXh7n909yskfU7SpbFmAIai6o31KijK05jJmTty1MPhtNitfqBGTRyuwz46RaGOzlRHAQAAAAAAPRhwcdQjWzH/MYB73iTpjO4HzCxX0vWSzpQ0T9ISM5tnZvPN7E/7PcZ3u/Q70esA9GPDKzs0e8GEVMdITCgk5eWnOgUAAAAAAMgSse4U8qKZLUzkhu7+jCLT8rs7VtIGd9/o7u2S7pR0nru/6e7n7PfYGR3F+iNJf3b35YnkAYaCcKhT65ft1OyFmV0cTafd6gEAAAAAQOaLdWeTUyR9ycw2S2rqOujuhyeYY4qkrd2eb5N0XB/nXyXpY5LKzGyWu/9q/xNqa2u1YMGC955XVFSooqIiwZgIlqc6wJCx+a3dGjWxOOM3BfJQKKOm1QODobKyUpWVlV1PM3dRYQAAAABIgQEVR81slqQJikx7726GpO0B5LAejvVaOXP36yRd11eD48aN06uvvppoLiSb9fTWI2hrXqjW3MWTUh0jIZ1tbVJOjpSXfrvVA6nU/Zd/ZrYrxXEAAAAAIKMMdFr9zyQ1uPvm7g9JzZJ+GkCObZKmdXs+VQkWXevr61VRUaGqqqqEggGZrqWxXe+uq9Oso8f3f3IaC9XUKH/8eBkFdeBDqqqqugqkZanOAgAAAACZZKDF0ZnuvmL/g+7+qqSZAeR4RdJsMzvAzAokXSbpwUQaLCsrU2VlpcrLywOIB2Su9a/s0IzDxqigKLNHXHbU7FDepImpjgGkpfLy8q6p9fWpzgIAAAAAmWSgxdHCPl6LaRFDM7tD0guS5pjZNjO73N1Dkr4i6VFJqyXd7e4rY2l3f4wcBSLWvFCjuYszv6gYqqlW/sTMXhoASBZGjgIAAABAfAY6lOwVM7vC3Zd2P2hml0taFssN3X1JL8cflvRwLG31pWvkKDCU7d7eqOZ97Zo6d3SqoySso7pG+YwcBXpUXl6u8vJyLV26lJGjAAAAABCDgRZH/4+k+83s03q/GLpAUoGkTyYjGIDErX2xRnOOm6CcnMxfp7OjplrDZs9OdQwAAAAAAJBFBjSt3t13uPtHJH1X0qbo47vuvtjda5IXL35Mq8dQ19npWvdSjeYsyo6p6KHqGuVPZOQo0BOm1QMAAABAfGLaocXdn5L0VJKyBIpp9Rjqlj+yWSMnFGv0pOGpjhKIjh07lEdxFOgR0+oBAAAAID4D3ZAJQAbZ+FqtVv7tXZ1++aGpjhKYUHW18idlxyhYAAAAAACQHrK2OMq0egxVu99t1FO3r9GZV87X8LJhqY4TiM6WFnW2tip31KhURwHSEtPqAQAAACA+MU2rzyRMq8dQ1NnpeuLm1frIBQdp/IwRqY4TmI7qGuVNnCCzzN9YCkgGptUDAAAAQHyyduQoMBS99ddtKijK1dzF2TX9PFRTrfyJ2dUnAAAAAACQellbHGVaPYaapro2vfLQJp20ZE7WjbDsqNmh/IkTUh0DSFtMqwcAAACA+DCtHsgSz96zXoeeMFmjJmbH7vTdddRUK4+Ro0CvmFYPAAAAAPHJ2pGjwFCyddUe7dy0T8ecNTPVUZIiVF2j/EkTUx0DAAAAAABkGYqjQIYLdYT11zvW6qOXHqz8gtxUx0mKjh01ypvAtHoAAAAAABAsiqNAhlv+yGaNmVKimfPHpjpK0nQ2NytnePYtFwAAAAAAAFIra4ujbMiUAdxTnSDj1bxTrzefflcnXDI71VGSyts7lFNQkOoYQNpiQyYAAAAAiA8bMiHFsmtX9cGya1ujlj+ySVtX79Vpnz1EpaMLUx0pqby9XUZxFOgVGzIBAAAAQHyytjgKZKOajfVa9udN2rm5QUd8bJpO/vRcFRRl/48xxVEAAAAAAJAM2V9VATKcu2vb6r1a9sgm7dvVqqM/MV2fuOIw5WXp5ks9oTgKAAAAAACSgeIokKa80/XOG7u07JFN6mgL65gzZmjWwgnKzc3apYJ7RXEUAAAAAAAkQ9YWR7s2ZOpahw3IFJ3hTq1/ZYeWPbpFefk5OubMGTrwiHGynKG7PivFUaBvVVVVXRsQsiETAAAAAMQga4ujbMiETBPqCGvN89Va/tgWlY4u1AkXz9K0Q0bLbOgWRbtQHAX6xoZMAAAAABCfrC2OApmioz2sN5/epjee2Kpx00t1+ufnadKskamOlVY6OzoojgIAAAAAgMBRHAVSaOfmffrzr9/U+BkjdM5XjtC4aaWpjpR2vLNT6uiQ5eenOgoAAAAAAMgyFEeBFAl1hPWX363SceceqLmLJqU6TtryaGGU5QUAAAAAAEDQht6210CaePWhTRo1cbjmHDcx1VHSGuuNAgAAAACAZKE4CqRA7ZYGrXpuu05ccjAjIvtBcRQAAAAAACQLxVFgkIXDnXry1tVa/MlZGl42LNVx0h7FUQAAAAAAkCxZWxytr69XRUWFqqqqUh0F+IDXHtui4tICzV3MdPqB8PZ2NmMC+lFVVaWKigpJKkt1FgAAAADIJFlbHC0rK1NlZaXKy8tTHQV4z57tTXrjL1t10qfnMJ1+gBg5CvSvvLxclZWVklSf6iwAAAAAkEmytjgKpBt311O3rdax5QdoxJiiVMfJGJ0URwEAAAAAQJJQHAUGSX1tixp2t+qwE6ekOkpGYeQoAAAAAABIFoqjwCCp3lCnybNHynKYTh8L7+iQFbDmKAAAAAAACB7FUaSQpzrAoNq+PlIcRWy8vUM5jBwFAAAAAABJQHEUqTWENiXavr5OkyiOxiyyWz3FUQAAAAAAEDyKo8AgaNzbpraWkEZPHJ7qKBknvHePckdSVAYAAAAAAMGjOAoMguoNdZo8i/VG49G+Zavyp09LdQwAAAAAAJCFMrI4amaHmNmvzOweM/uHVOcB+sN6o/Fr37JZBdOnpzoGAAAAAADIQoNeHDWzG81sp5m9td/xM8xsrZltMLNv9tWGu6929yslXSJpQTLzAkHYvqFOk2ZRHI1Hx5atKpjGyFEAAAAAABC8VIwcvUnSGd0PmFmupOslnSlpnqQlZjbPzOab2Z/2e4yPXnOupGclPTG48YHYtDZ2qGFPq8ZNK0l1lIwU2rVLeePHpzoGAAAAAADIQnmDfUN3f8bMZu53+FhJG9x9oySZ2Z2SznP3H0g6p5d2HpT0oJk9JOn3yUsMJGbH5n0aP2OEcnIzchWL9JDD3x0AAAAAAAjeoBdHezFF0tZuz7dJOq63k83sZEkXSBom6eGezqmtrdWCBe/PuK+oqFBFRUUQWYGYhNs7VVCYm+oYALJUZWWlKisru56OTWUWAAAAAMg06VIc7WkLb+/tZHd/WtLTfTU4btw4vfrqq4mlAgAgzXX/5Z+Z7UpxHAAAAADIKOkyV3WbpO47rkyVtD2RBuvr61VRUaGqqqqEggEAkO6qqqq6CqRlqc4CAAAAAJkkXYqjr0iabWYHmFmBpMskPZhIg2VlZaqsrFR5eXkgAQEASFfl5eVdU+vrU50FAAAAADLJoBdHzewOSS9ImmNm28zscncPSfqKpEclrZZ0t7uvHOxsAAAAAAAAAIaOVOxWv6SX4w+rl82V4tE1rb68vJzRowCArFZVVdW1jAzT6gEAAAAgBumyIVPguqbVAwCQ7bp+Ebh06VKm1QMAAABADNJlzdHABb0h01AptNLP7EI/swv9zC5B9pMNmQAAAAAgPllbHO2+IVNPBdLux/Z/vafXgvqf2IEWa/s6r7fXMrGf6zdsiLmNvvrR3/NU9bOmpibmNjKxn32dV1/f84C2/vrV2tra6+vp2M+h8n7Sz/TqJxsyAQAAAEB8srY42l0QRcNkZon1vGQWR4My0PY2rE9NcTQo6V4cDUrc37f+/pf76vcN6Jr9n7e0tPT6etr0cwCvZcX7OYDX6Gf/z1PVTwAAAADAh5m7939WBjKzWkmbo0/L9OHRNN2P7f96T6+NlbQrgGg9ZYn1vN5eG6r9jOU5/YwP/Rz4efSTfvb3PJn9nOHu4wJoEwAAAACGhKwtjgIAAAAAAABAX4bEtHoAAAAAAAAA2B/FUQAAAAAAAABDEsVRAAAAAAAAAEMSxdEAmNkhZvYrM7vHzP4h1XmSxczON7OlZvaAmX081XmSxcwONLPfmtk9qc4SNDMbbmY3R9/HT6c6T7Jk83vY3RD6mRwSn7HSez+jy8zsnFRnAQAAAIChYMgXR83sRjPbaWZv7Xf8DDNba2YbzOybfbXh7qvd/UpJl0hakMy88Qqon3909yskfU7SpUmMG7eA+rnR3S9PbtLgxNjnCyTdE30fzx30sAmIpZ+Z9h52F2M/0/5nsjcx9jPtP2N7E8dn0r9IuntwUwIAAADA0DXki6OSbpJ0RvcDZpYr6XpJZ0qaJ2mJmc0zs/lm9qf9HuOj15wr6VlJTwxu/AG7SQH0M+o70evS0U0Krp+Z4iYNsM+SpkraGj0tPIgZg3CTBt7PTHaTYu9nOv9M9uYmxdDPDPiM7c1NGvhn0sckrZK0Y7BDAgAAAMBQlZfqAKnm7s+Y2cz9Dh8raYO7b5QkM7tT0nnu/gNJPU51dPcHJT1oZg9J+n3yEscniH6amUn6oaQ/u/vy5CaOT1DvZyaJpc+StilSIH1dGfbLkRj7uWpw0wUnln6a2Wql+c9kb2J9P9P9M7Y3MfazRNJwRQqmLWb2sLt3DmJcAAAAABhyMqo4Moim6P3RdVKkoDSlt5PN7GQzu87Mfi3p4WSHC1BM/ZR0laSPSbrIzK5MZrCAxfp+jjGzX0k6ysy+lexwSdJbn++TdKGZ3SCpKhXBAtZjP7PkPeyut/czU38me9Pb+5mpn7G96bGf7v6v7v5/FCn+LqUwCgAAAADJN+RHjvbCejjmvZ3s7k9Lejp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      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fict_min2.plot(ylog=True, xlog=True, width=8, height=4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnx.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}