TLUSTY/hotsd/synspec_test/hhe.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wave</th>\n",
       "      <th>flux</th>\n",
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       "      <td>1400.00972</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>1400.01938</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>1400.02905</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>1400.03841</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>1409.96605</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>1409.97505</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>1409.98405</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>1409.99305</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>1410.00005</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            wave  flux\n",
       "0     1400.00005   NaN\n",
       "1     1400.00972   NaN\n",
       "2     1400.01938   NaN\n",
       "3     1400.02905   NaN\n",
       "4     1400.03841   NaN\n",
       "...          ...   ...\n",
       "1195  1409.96605   NaN\n",
       "1196  1409.97505   NaN\n",
       "1197  1409.98405   NaN\n",
       "1198  1409.99305   NaN\n",
       "1199  1410.00005   NaN\n",
       "\n",
       "[1200 rows x 2 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('./results/hhe35nl.spec',comment='#',names=['wave','flux'],sep='\\s+')\n",
    "cont = pd.read_csv('./results/hhe35nl.cont',comment='#',names=['wave','flux'],sep='\\s+')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wave</th>\n",
       "      <th>flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2999.89195</td>\n",
       "      <td>339322000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2999.89853</td>\n",
       "      <td>339318000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2999.90511</td>\n",
       "      <td>339314000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2999.91169</td>\n",
       "      <td>339310000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2999.91827</td>\n",
       "      <td>339305000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>403271</th>\n",
       "      <td>7409.94150</td>\n",
       "      <td>14113600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403272</th>\n",
       "      <td>7409.95803</td>\n",
       "      <td>14113300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403273</th>\n",
       "      <td>7409.97456</td>\n",
       "      <td>14112900.0</td>\n",
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       "    <tr>\n",
       "      <th>403274</th>\n",
       "      <td>7409.99108</td>\n",
       "      <td>14112600.0</td>\n",
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       "    <tr>\n",
       "      <th>403275</th>\n",
       "      <td>7410.00026</td>\n",
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       "</table>\n",
       "<p>403276 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              wave         flux\n",
       "0       2999.89195  339322000.0\n",
       "1       2999.89853  339318000.0\n",
       "2       2999.90511  339314000.0\n",
       "3       2999.91169  339310000.0\n",
       "4       2999.91827  339305000.0\n",
       "...            ...          ...\n",
       "403271  7409.94150   14113600.0\n",
       "403272  7409.95803   14113300.0\n",
       "403273  7409.97456   14112900.0\n",
       "403274  7409.99108   14112600.0\n",
       "403275  7410.00026   14112400.0\n",
       "\n",
       "[403276 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('fort.7',comment='#',names=['wave','flux'],sep='\\s+')\n",
    "cont = pd.read_csv('fort.17',comment='#',names=['wave','flux'],sep='\\s+')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x70ddb009b310>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.ion()\n",
    "plt.plot(data['wave'],data['flux'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x70ddabf58b50>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DzLdqvjl2WeJKiIiIbB/DSA+4Z1gIBAE4dKGaz6ohIiK6BYaRHhCkVWFkhA8ADmQlIiK6FYaRHjJnuHkg6zfHeauGiIjoZhhGesis2CC4yAQcv6TDhYp6qcshIiKyWQwjPcTPU4mx/XwBAP9l7wgREVGHGEZ6kOVWzTGOGyEiIuoIw0gPmhkTBIWLgLwrtcgrrZW6HCIiIpvEMNKDtG4KTBoYAADYcqxY4mqIiIhsE8NID5s7wnyrZnPWZZhMosTVEBER2R6GkR42fUggPJVyFNc04NCFKqnLISIisjkMIz1MpXDBrNggAMCmLN6qISIiuh7DSC+4744+AICtOSVobGqRuBoiIiLbYlUYSU5OxsiRI6FWqxEQEIB58+YhLy/vpuekp6dDEIQbttOnT99W4fZkTKQvQrQq1DY244fTZVKXQ0REZFOsCiMZGRlYsmQJ9u/fj7S0NDQ3N2PGjBmor7/1CqN5eXkoKSmxbAMGDOhy0fZGJhMwN87cO7LxKG/VEBER/Zzcmsbbt29v8/rDDz9EQEAAjhw5gokTJ9703ICAAHh5eVldoKO4P64PUtLPIT2vDFX1Rvh4uEpdEhERkU24rTEjOp0OAODj43PLtnFxcQgODsbUqVOxa9eum7Y1GAzQ6/VtNns3IFCN2D4aNJtELg9PRET0M10OI6IoYunSpRg/fjxiY2M7bBccHIw1a9YgNTUVGzduRHR0NKZOnYrdu3d3eE5ycjK0Wq1lCw0N7WqZNmXeCN6qISIiup4gimKXVuJasmQJtm7dir1796Jv375WnTtnzhwIgoAtW7a0e9xgMMBgMFhe6/V6hIaGQqfTQaPRdKVcm1BW24gxr3wPkwj88IdJiPL3lLokIiKiHqPX66HVam/5/d2lnpGnn34aW7Zswa5du6wOIgAwZswY5Ofnd3hcqVRCo9G02RxBgFqFCQP8AQCbueYIERERACvDiCiKeOqpp7Bx40b88MMPiIyM7NKbZmVlITg4uEvn2rv7W9ccST1azOXhiYiIYOVsmiVLluCzzz7D119/DbVajdLSUgCAVquFm5sbAGDFihUoLi7GunXrAABvvPEGIiIiEBMTA6PRiPXr1yM1NRWpqand/FHsw8yYIGhU5uXh952rxPgBflKXREREJCmrekZSUlKg0+kwefJkBAcHW7YvvvjC0qakpASFhYWW10ajEcuWLcOwYcMwYcIE7N27F1u3bsX999/ffZ/CjqgULpjbOpD1i8NFEldDREQkvS4PYO1NnR0AYy9yLukw5629cJXLcHDlVHi5c80RIiJyPD06gJVuT2wfDQYHa2BsNmHLMa45QkREzo1hRAKCIOAXCeZZSF/yVg0RETk5hhGJzBvRB64uMpwo1uPkZZ3U5RAREUmGYUQi3h6umD4kEADwf4cvSVwNERGRdBhGJPRQ662azdnFMDS3SFwNERGRNBhGJDRhgD+CtSrUXG1C2qkrUpdDREQkCYYRCbnIBDwYb+4d+eIQB7ISEZFzYhiR2EPx5icS7z1bgaKqqxJXQ0RE1PsYRiQW5uuOCQP8IIrA5wcLb30CERGRg2EYsQELRocBMK85Ymw2SVwNERFR72IYsQFTBwciQK1ERZ0RO0+VSl0OERFRr2IYsQEKFxkeHmkeO/LZAd6qISIi58IwYiPmjwqDTAD2navEufI6qcshIiLqNQwjNqKPlxumRAcAAD5n7wgRETkRhhEbsmCMeSDrV0cvobGJK7ISEZFzYBixIZMGBqCPlxtqrjZhW06J1OUQERH1CoYRG+IiEywDWT/lrRoiInISDCM2Zv7IULjIBBy5WI3cEr3U5RAREfU4hhEbE6BRYcaQQADAJ/svSlwNERFRz2MYsUGLEiMAAJuOFkN3tUnaYoiIiHoYw4gNGhPlg0FBajQ0teDLw3yaLxEROTaGERskCAIeGxsBAPg48wJaTKK0BREREfUghhEbNXdEH3i5K3CpugHf516RuhwiIqIewzBio9xcXfDwSPMiaB9nXpC2GCIioh7EMGLDFiaGQyYAP56txJkrtVKXQ0RE1CMYRmxYHy83zIwJAgB8tO+CtMUQERH1EIYRG3dtIOvGo5c4zZeIiBwSw4iNGxVpnubb2GTCfSk/os7QLHVJRERE3YphxMYJgoDHx0UAAM6X1+Pet/ZKWxAREVE3YxixA3NH9LH8fr68Hpeqr0pYDRERUfdiGLEDKoUL/jEv1vL6g70XpCuGiIiomzGM2IlHx4Tjw8dHAgA2HCrkYFYiInIYDCN2ZPJAfwwKUuOqsQXrD/CJvkRE5BgYRuyIIAj49cQoAOZ1RwzNLRJXREREdPsYRuzMPcNCEKRRobzWgK+zLktdDhER0W1jGLEzrnIZnhgfAQBYs+c8THyiLxER2TmGETv0yKgwqJVynC2rw668MqnLISIiui0MI3ZIrVLgf0abn+j77u7zEldDRER0exhG7NRj4yIglwk4WFCFrMJqqcshIiLqMoYROxWsdbOszPr2rnMSV0NERNR1VoWR5ORkjBw5Emq1GgEBAZg3bx7y8vJueV5GRgbi4+OhUqkQFRWF1atXd7lg+snvpvSDIADf5V5Bbole6nKIiIi6xKowkpGRgSVLlmD//v1IS0tDc3MzZsyYgfr6+g7PKSgowOzZszFhwgRkZWVh5cqVeOaZZ5CamnrbxTu7fv6emD00GADw9q6zEldDRETUNYIoil2eG1peXo6AgABkZGRg4sSJ7bZZvnw5tmzZgtzcXMu+pKQkHDt2DJmZmZ16H71eD61WC51OB41G09VyHVJuiR6z3txj7iFZOgn9/D2lLomIiAhA57+/b2vMiE6nAwD4+Ph02CYzMxMzZsxos2/mzJk4fPgwmpraf76KwWCAXq9vs1H7BgdrMG1wIEQRSEnn2BEiIrI/XQ4joihi6dKlGD9+PGJjYztsV1paisDAwDb7AgMD0dzcjIqKinbPSU5OhlartWyhoaFdLdMpPHVnfwDApqxiFFVdlbgaIiIi63Q5jDz11FM4fvw4Pv/881u2FQShzetrd4au33/NihUroNPpLFtRUVFXy3QKI0K9MGGAH1pMIt7dzd4RIiKyL10KI08//TS2bNmCXbt2oW/fvjdtGxQUhNLS0jb7ysrKIJfL4evr2+45SqUSGo2mzUY399QUc+/Il4cu4Yq+UeJqiIiIOs+qMCKKIp566ils3LgRP/zwAyIjI295TmJiItLS0trs27lzJxISEqBQKKyrljo0OsoXoyJ8YGwxYQ1XZSUiIjtiVRhZsmQJ1q9fj88++wxqtRqlpaUoLS1FQ0ODpc2KFSuwaNEiy+ukpCRcvHgRS5cuRW5uLj744AOsXbsWy5Yt675PQQB+Gjuyfv9FlLF3hIiI7IRVYSQlJQU6nQ6TJ09GcHCwZfviiy8sbUpKSlBYWGh5HRkZiW3btiE9PR0jRozASy+9hFWrVuGBBx7ovk9BAIAJA/xwR5gXDM0mpGRw7AgREdmH21pnpLdwnZHO+/FsBRa8fwCuchky/jgZwVo3qUsiIiIn1SvrjJDtGdvPF6MifWBsNuEdPrOGiIjsAMOIgxEEAUunDwQAbDhUiEvVXHeEiIhsG8OIAxoT5Ytx/X3R1CLymTVERGTzGEYc1LXekf87fAmFlewdISIi28Uw4qDiw30waaA/mk0iVv2QL3U5REREHWIYcWC/b+0d2Xj0Es6W1UpcDRERUfsYRhzYiFAvTB8SCJMI/HNHntTlEBERtYthxMH978xoyARgx8krOHKxWupyiIiIbsAw4uAGBKrxUHwoAOC1b0/DDta4IyIiJ8Mw4gSemz4ASrkMBy9UYVdemdTlEBERtcEw4gSCtW54bFwEAOC1b/PQYmLvCBER2Q6GESfxu0n9oVHJkXelFpuyiqUuh4iIyIJhxElo3RVYMqU/AOBfO/PQ2NQicUVERERmDCNOZPHYCARrVbisa8S6zAtSl0NERASAYcSpqBQu+P0080Job/1wFtX1RokrIiIiYhhxOg/E98WgIDX0jc1483suE09ERNJjGHEyLjIBf71nCADgk/0XcbasTuKKiIjI2TGMOKFx/f0wbXAAWkwiXtmWK3U5RETk5BhGnNSK2YMhlwn44XQZ9uSXS10OERE5MYYRJ9XP3xOPjgkHALy8NZcLoRERkWQYRpzYc9MGQOumwOnSWnxxqEjqcoiIyEkxjDgxL3dXPDt1AADgX2l5qG1skrgiIiJyRgwjTm5hYjii/DxQUWfEO+nnpC6HiIicEMOIk1O4yLBi9mAAwNq9BSiqutpuO1EU8cn+i5j15h5MeP0HLP/qeIdtiYiIrMEwQpg2OABj+/nC2GzCq9tP33BcFEWs3HQCf918ArklehRVNeCLw0WY/eYefHfqigQVExGRI2EYIQiCgL/cPQSCAGw9XoLDF6raHP/0QCE+P1gIF5mAP80ahHVPjEJ8uDdqDc34zfoj2Hq8RKLKiYjIETCMEABgSIgGv4gPBQC88M0py1TfE8U6vPjNKQDA8ruikTSpHyYO9MeGX4/B/Xf0QYtJxDMbsrhWCRERdRnDCFn8YeZAqFVy5BTrMO/tH6G72oSnP8+CscWE6UMC8asJUZa2ChcZ/vngcNw7PAQtJhG/+/Qol5YnIqIuYRghiwC1Cn9uHcyaU6zDqFe+Q0FFPUK0KvzzwWEQBKFNexeZgH8+NAwJ4d6obWzGkx8f4vRgIiKyGsMItTF/ZCgmDfQHABiaTfBXK7H2sZHwcndtt71S7oJ3F8ajj5cbLlRexZ9ScyCKXM2ViIg6TxDt4JtDr9dDq9VCp9NBo9FIXY5TuFzTgPyyOsSHe8NTKb9l+6OF1fjF6kw0m0S8ODcGixIjer5IIiKyaZ39/mbPCLUrxMsNkwb6dyqIAMAdYd6W9Ur+8d9cHL9U04PVERGRI2EYoW7zxLgIzIwJhLHFhCWfHYWe40eIiKgTGEao2wiCgNcfHI5QHzcUVTVg5UaOHyEioltjGKFupXVTYNXDcZDLBPz3eAm+PMynARMR0c0xjFC3iwvzxh9nRgMAnt9yEvlXaiWuiIiIbBnDCPWIX02IwsSB/mhsMuGpz7LQ2NQidUlERGSjGEaoR8hkAv7fQ8Ph56lE3pVavPTfU1KXRERENophhHqMv1qJN+aPgCCYH7b3bQ4fqEdERDdiGKEeNX6AH347qR8A4H9Tj6Oo6qrEFRERka1hGKEe9/vpAxEX5oXaxmY8uyELTS0mqUsiIiIbYnUY2b17N+bMmYOQkBAIgoDNmzfftH16ejoEQbhhO336dFdrJjujcJFh1cNxUKvkOFpYg3+nnZG6JCIisiFWh5H6+noMHz4cb731llXn5eXloaSkxLINGDDA2rcmOxbq447XHhgGAEjJOIc9+eUSV0RERLaicw8e+ZlZs2Zh1qxZVr9RQEAAvLy8rD6PHMfsocFYMDoMnx4oxO+/OIZvn50Af7VS6rKIiEhivTZmJC4uDsHBwZg6dSp27dp107YGgwF6vb7NRo7hr/cMQXSgGhV1Biz9MhsmE5eLJyJydj0eRoKDg7FmzRqkpqZi48aNiI6OxtSpU7F79+4Oz0lOToZWq7VsoaGhPV0m9RKVwgX/+Z84qBQy7MmvwHt7zktdEhERSUwQb+NJZoIgYNOmTZg3b55V582ZMweCIGDLli3tHjcYDDAYDJbXer0eoaGh0Ol00Gg0XS2XbMiGg4X408YcyGUC/i8pEXFh3lKXRERE3Uyv10Or1d7y+1uSqb1jxoxBfn5+h8eVSiU0Gk2bjRzL/JGhuGdYMJpNIp7+PAu6hiapSyIiIolIEkaysrIQHBwsxVuTjRAEAa/cPxShPm64VN2AlRtzcBuddEREZMesnk1TV1eHs2fPWl4XFBQgOzsbPj4+CAsLw4oVK1BcXIx169YBAN544w1EREQgJiYGRqMR69evR2pqKlJTU7vvU5Bd0qgUWPVwHB5anYmtOSUYf8gPj4wKk7osIiLqZVaHkcOHD2PKlCmW10uXLgUALF68GB999BFKSkpQWFhoOW40GrFs2TIUFxfDzc0NMTEx2Lp1K2bPnt0N5ZO9iwvzxh9nRiP529N44ZuTiA/3xsBAtdRlERFRL7qtAay9pbMDYMg+mUwiHvvoEHafKcfAQE9seWo8VAoXqcsiIqLbZNMDWIl+TiYT8P8eGg4/TyXOXKnDC9+ckrokIiLqRQwjZBP81Uq8MX8EBAH4/GAhNh69JHVJRETUSxhGyGaMH+CHp6f0BwCs2JiDE8U6iSsiIqLewDBCNuW5aQNx56AAGJpN+M0nR1BZZ7j1SUREZNcYRsimyGQC/j1/BCL9PFBc04CnPstCc4tJ6rKIiKgHMYyQzdG6KbBmYTw8XF2Qeb4Sr357WuqSiIioBzGMkE0aEKjG//vFcADA+3sL8HV2scQVERFRT2EYIZt1V2wwnmod0Lo89TgHtBIROSiGEbJpv58+EJOj/dHYZMKv1x1Gmb5R6pKIiKibMYyQTXORCXjz4Tj08/fAZV0jfrXuMBqbWqQui4iIuhHDCNk8rZsCHzw2Et7uChy7pMMfvjwGk8nmn2JARESdxDBCdiHc1wOrH42HwkXA1pwS/Pu7M1KXRERE3YRhhOzG6ChfvHLfUADAf344i01ZXDKeiMgRMIyQXXkoIRS/ndwPALD8qxwculAlcUVERHS7GEbI7vxxRjRmxgTC2GKeYVNQUS91SUREdBsYRsjuXFsyfmgfLaqvNmHRBwdQXstn2BAR2SuGEbJL7q5yfPDYSIT5uKOoqgGPf3QQdYZmqcsiIqIuYBghu+WvVuLjJ0bBx8MVJ4r1+N2nR9HEh+oREdkdhhGya5F+HvjgsZFwU7hg95lyLE89DlHkGiRERPaEYYTs3ohQL7y9IA4uMgEbjxbjnzvypC6JiIiswDBCDuHOQYF45b5YAMA76eewdm+BxBUREVFnMYyQw5g/Mgx/mD4QAPDSf0/hi0OFEldERESdwTBCDuWpO/vj1xOjAAB/2piD/x6/LHFFRER0Kwwj5FAEQcCKWYPwyKgwiCLw3IZs7DpdJnVZRER0Ewwj5HAEQcA/5sVizvAQNJtEJK0/gv3nK6Uui4iIOsAwQg7JRSbgX78YjqmDAmBoNuHJjw8jq7Ba6rKIiKgdDCPksBQuMry94A4kRvmiztCMRWsPIruoRuqyiIjoOgwj5NBUChe8vzgBoyJ8UGtoxsK1B3CMgYSIyKYwjJDD81DK8eHjI82BpLEZj649gOOXaqQui4iIWjGMkFO4FkhGRnibA8n7B5BzSSd1WUREBIYRciLmQDIKCeHe0LOHhIjIZjCMkFPxVMrx0ROjEB/uDV1DE/7nvQM4WFAldVlERE6NYYScjqdSjo+fGIUxUT7mWTYfHMDuM+VSl0VE5LQYRsgpeSrl+OjxUZgS7Y/GJvM6JDtOlkpdFhGRU2IYIaelUrjg3YUJmD00CMYWE3736VFsziqWuiwiIqfDMEJOzVUuw6qH4/BgfF+0mET8/stsfHrgotRlERE5FYYRcnpyFxlef2AYFieGQxSBP286gZT0cxBFUerSiIicAsMIEQCZTMDf743Bbyf3AwC8tv00XvjmFFpMDCRERD2NYYSolSAIWH7XIPz1niEQBOCjfRfw1GdH0djUInVpREQOjWGE6Dq/HB+J/zwSB1cXGb49UYpFaw9Cd7VJ6rKIiBwWwwhRO+4ZFoKPnxgFtUqOgxeq8ODqfSiuaZC6LCIih2R1GNm9ezfmzJmDkJAQCIKAzZs33/KcjIwMxMfHQ6VSISoqCqtXr+5KrUS9KrGfL75KGosgjQr5ZXV44J19OHVZL3VZREQOx+owUl9fj+HDh+Ott97qVPuCggLMnj0bEyZMQFZWFlauXIlnnnkGqampVhdL1Nuig9TY+LuxGBjoiVJ9Ix5cvQ9pp65IXRYRkUMRxNuYvygIAjZt2oR58+Z12Gb58uXYsmULcnNzLfuSkpJw7NgxZGZmdup99Ho9tFotdDodNBpNV8sl6jJdQxOWfHoUe89WQBCAP901CL+eGAVBEKQujYjIZnX2+7vHx4xkZmZixowZbfbNnDkThw8fRlNT+4MCDQYD9Hp9m41ISlo3BT58fCQeHRMGUQSSvz2N//3qOIzNJqlLIyKyez0eRkpLSxEYGNhmX2BgIJqbm1FRUdHuOcnJydBqtZYtNDS0p8skuiWFiwz/mDcUL9wbA5kA/N+RS3j0/QOoqjdKXRoRkV3rldk013dlX7sz1FEX94oVK6DT6SxbUVFRj9dI1FmLx0bgw8dHQa00z7SZ+/Ze5F+plbosIiK71eNhJCgoCKWlbZ+GWlZWBrlcDl9f33bPUSqV0Gg0bTYiWzJpoD82LRmLMB93FFU14L539mH7CT71l4ioK3o8jCQmJiItLa3Nvp07dyIhIQEKhaKn356ox/QPUGPzknEYHemDOkMzktYfwWvbT3MJeSIiK1kdRurq6pCdnY3s7GwA5qm72dnZKCwsBGC+xbJo0SJL+6SkJFy8eBFLly5Fbm4uPvjgA6xduxbLli3rnk9AJCEfD1esf3I0nhwfCQBIST+HxR8c5DgSIiIrWB1GDh8+jLi4OMTFxQEAli5diri4OPztb38DAJSUlFiCCQBERkZi27ZtSE9Px4gRI/DSSy9h1apVeOCBB7rpIxBJS+Eiw1/uGYL/PBIHN4UL9p6twJz/7EXOJZ3UpRER2YXbWmekt3CdEbIXeaW1SFp/BAUV9XCVy/DS3Bj8IiGU65EQkVOymXVGiJxJdJAaXz81DtMGB8LYbMLy1Bws/fIY6gzNUpdGRGSzGEaIuplGpcCahfH448xouMgEbMoqxr3/2YuTl3nbhoioPQwjRD1AJhOwZEp/fPHrMQjWqnC+oh73vbMPn2RegB3cGSUi6lUMI0Q9KCHCB9uemYBpgwNgbDbhr1+fxO8+PQpdQ/uPQiAickYMI0Q9zNvDFe8tSsBf7xkChYuAb0+UYvabe3CwoErq0oiIbALDCFEvEAQBvxwfidTfmldtLa5pwPw1mXj129MwNLdIXR4RkaQYRoh60bC+Xtj6zHj8IqEvRBFYnXEO897eh7xSPtuGiJwXwwhRL1OrFHj9weFY/Wg8fDxckVuix5y39uL9Pedh4lLyROSEGEaIJHJXbBC2PzcBdw4yD279x9ZcLHj/AC5VX5W6NCKiXsUwQiShALUKaxcn4OX7YuGmcEHm+UrM/PdufJJ5gb0kROQ0GEaIJCYIAhaMDse2ZydgZIQ36o0t+OvXJ/Hwe/tRUFEvdXlERD2OYYTIRkT6eeCLXyfihXtj4O7qgoMFVbjrjd14b/d5tLCXhIgcGMMIkQ2RyQQsHhuBHc9NxLj+vjA0m/Dytlzcn7IPZ65wxg0ROSaGESIbFOrjjvW/HI3XHhgKtVKOY0U1mP3mHry2/TQajFyXhIgcC8MIkY0SBAHzR4YhbekkTBsciGaTiJT0c5j+7wx8n3tF6vKIiLoNwwiRjQvSqvD+4gSsWRiPEK0Kl6ob8MuPD+M3nxzG5ZoGqcsjIrptDCNEdmJGTBC++8Mk/GZSFOQyATtOXsG0f2Vgze5zaGoxSV0eEVGXCaIdPM9cr9dDq9VCp9NBo9FIXQ6R5PJKa/GXzTk4dKEaANA/wBN/vWcIJg30l7gyIqKfdPb7m2GEyE6ZTCK+OnIJr24/jap6IwBg6qAA/PnuwYjy95S4OiIihhEip6FraMJ/vs/HR/suoNkkQuEi4LGxEXh66gBoVAqpyyMiJ8YwQuRkzpXX4R//PYVdeeUAAF8PVyybGY1fJITCRSZIXB0ROSOGESIntet0GV7aegrny81LyQ8M9MT/zhyEqYMDIAgMJUTUexhGiJxYU4sJ6zIvYtX3+dA1NAEARkX44E+zB+GOMG+JqyMiZ8EwQkTQXW1CSsY5fPhjAQzN5um/d8UE4Y93RaMfB7kSUQ9jGCEii8s1DXjjuzP46sglmETARSZg/shQPH1nfwRr3aQuj4gcFMMIEd0gr7QW/9xxGt/llgEAXF1k+J/RYfjt5H4I1Kgkro6IHA3DCBF16GBBFf6/nXk4WFAFAFDKZVgwOhxJk6MQoGYoIaLuwTBCRDcliiL2navEv9LO4MhF80quKoUMC8eE4zeT+sHPUylxhURk7xhGiKhTRFHEnvwK/CvtDLKLagAAbgoXLBgdhicnRCFIy54SIuoahhEisoooikg/U45/p53B8Us6AOYxJQ/E98FvJvZDhJ+HxBUSkb1hGCGiLrkWSlJ2ncPBC+YxJTIBmD00GL+d3A8xIVqJKyQie8EwQkS37dCFKryz66xliXkAmBztj99N7o+REd5c0ZWIbophhIi6zanLeqRknMPW45dhav0vxvC+WjwxPhKzhwZD4SKTtkAiskkMI0TU7S5U1OPd3eeRevQSjK0rugZrVViUGIH/GRUGrTufEkxEP2EYIaIeU1FnwKf7C/HJ/ouoqDMAMM/AeTC+Lx4fF4EoLjVPRGAYIaJeYGhuwZbsy1i7twCnS2st++8cFICFY8IxcaA/XGQcV0LkrBhGiKjXiKKIzHOVWLu3AN+fLrPs7+vthgWjw/GLhL7w5SJqRE6HYYSIJHG+vA7r9xfiqyNF0Dc2AzCvVzJraBAeHROOhHDOwiFyFgwjRCSpBmMLvjl+GZ/uv4hjrYuoAcCgIDUWjA7DvSP6QOvGAa9EjoxhhIhsxvFLNVi//yK2HLuMxibzLBylXIa7YoPwi4RQJEb5QsaxJUQOp7Pf311aHOCdd95BZGQkVCoV4uPjsWfPng7bpqenQxCEG7bTp0935a2JyA4N6+uF1x8cjgMrpuFv9wxBdKAahmYTvs6+jAXvH8CE13fhje/O4FL1ValLJSIJWN0z8sUXX2DhwoV45513MG7cOLz77rt4//33cerUKYSFhd3QPj09HVOmTEFeXl6bVOTv7w8XF5dOvSd7RogciyiKyCnW4cvDRfg6+zJqW8eWCAIwrp8fHkroi5kxQVApOvffCCKyTT12m2b06NG44447kJKSYtk3ePBgzJs3D8nJyTe0vxZGqqur4eXlZc1bWTCMEDmuxqYW7DhZii8PF+HHs5WW/Z5KOWbGBGFeXAjG9vPjFGEiO9TZ72+5NX/UaDTiyJEj+NOf/tRm/4wZM7Bv376bnhsXF4fGxkYMGTIEf/nLXzBlypQO2xoMBhgMBstrvV5vTZlEZEdUChfMHdEHc0f0QVHVVXx15BJSj17CpeoGpB41/+6vVmLOsBDMiwvB0D5azsYhcjBWhZGKigq0tLQgMDCwzf7AwECUlpa2e05wcDDWrFmD+Ph4GAwGfPLJJ5g6dSrS09MxceLEds9JTk7GCy+8YE1pROQAQn3c8fvpA/HctAE4crEam7OLsfV4CcprDfjgxwJ88GMBovw8cO+IEMwb0QcRfh5Sl0xE3cCq2zSXL19Gnz59sG/fPiQmJlr2v/zyy/jkk086PSh1zpw5EAQBW7Zsafd4ez0joaGhvE1D5ISMzSbsyS/H5uzLSDtVapmNAwAxIRrMHhqMWbFBXIKeyAb1yG0aPz8/uLi43NALUlZWdkNvyc2MGTMG69ev7/C4UqmEUsnVGokIcJXLMHVwIKYODkSdoRk7T5Zic/Zl/Hi2Aicv63Hysh7/3JGHQUFqzB4ajNlDg9A/QC112URkBavCiKurK+Lj45GWlob77rvPsj8tLQ1z587t9N/JyspCcHCwNW9NRARPpRz339EX99/RF1X1RqSdKsXWnFLsO1uB06W1OF1ai3+lncGAAE/Mag0m0YFqjjEhsnFWhREAWLp0KRYuXIiEhAQkJiZizZo1KCwsRFJSEgBgxYoVKC4uxrp16wAAb7zxBiIiIhATEwOj0Yj169cjNTUVqamp3ftJiMip+Hi4Yv7IMMwfGYaaq0aknbqCbTkl2Hu2Avlldcj/Ph+rvs9HuK87pg0OxLTBgUiI8IbCpUvLKxFRD7I6jMyfPx+VlZV48cUXUVJSgtjYWGzbtg3h4eEAgJKSEhQWFlraG41GLFu2DMXFxXBzc0NMTAy2bt2K2bNnd9+nICKn5uXuiocSQvFQQih0DU34PvcKtuWUYveZclysvIq1ewuwdm8BNCo5pgwKwLTBgZgU7Q+NisvRE9kCLgdPRA6r3tCMPfnlSDtVhl15ZaiqN1qOyWUCxkT5YtrgAEwdHIhQH3cJKyVyTHw2DRHRz7SYRGQVViMt9wq+O3UF58rr2xyP8vfApIH+mDTQH2OifLn6K1E3YBghIrqJ8+V1+D63DGm5V3DkYjVaTD/9p1Apl2F0lK8lnPTz9+AgWKIuYBghIuokXUMTMs9VIONMOTLyynFZ19jmeB8vN0wc6I+JA/yQ2M8XXu6uElVKZF8YRoiIukAURZwtqzMHkzPlOHC+CsaWnxZaEwRgSLAG4/qbg8moCB94KK2eC0DkFBhGiIi6wVVjMw6cr0LGmXL82Dpt+OfkMgHDQ70wtp8vxvbzQ1yYF8ebELViGCEi6gFl+kZknq/EvrOV2He+AkVVDW2OK+UyxId7Y1SkD0ZF+CAuzBturgwn5JwYRoiIekFR1VVknqvEj+cqsO9cJcprDW2Oy2UCYvtoMSrSBwnh3hgZ4QNvD445IefAMEJE1MtEUcS58jpknqvEoQvVOHShCiXXDYYFgAEBnhjZ2nMyMtIHIVoVZ+uQQ2IYISKSmCiKuFTdgEMXqnDoQhUOFlTdsL4JAASolYgL88KIUG/EhXlhWF8t3F05KJbsH8MIEZENqqwz4PDFahwqMAeUE5f1bdY4AQCZAEQHaTAi1AtxYV6IC/VCP39PyGTsPSH7wjBCRGQHGowtOHFZh6zCamQX1SCrsKbdWztqlRzD+3pheKgWQ/toEdtHiz5ebry9QzaNYYSIyE6V6hqRXVSNrNZwknNJh4amlhvaebsrENtHi5gQc0AZ2keLUB8GFLIdDCNERA6iucWEvCu1yCqswYliHXKKdThzpRZNLTf+51ujkiO2NZjE9NEiJkSDCF8PuPAWD0mAYYSIyIEZmltwprQOOa3h5ORlHU6X1LZZLfYapVyG6CA1BgWpMShIg0HBagwO0nCKMfU4hhEiIidjbDYhv6zW0ntyoliPvNLadm/xAECQRoVBweaAMrj1Z5S/BxQusl6unBwVwwgREaHFJKKw6ipOl+iRW1qL0yV6nC6tRWHV1XbbK1wE9PP3xIBANQYEeGJAgCf6B3gi3NcDrnKGFLIOwwgREXWoztCMvNJanC7V43TJTz9rDc3ttpfLBET4eVgCSr8ATwwIUCPK34PP4qEOMYwQEZFVri3Sll9Wi/wrdcgvM2/nyupQ10FIkQlAmI87+gd4IsrfE5F+Hoj080CUnwf81UrO7HFyDCNERNQtRFFEqb7RElDO/iys6BqaOjzPw9UFET8LJ5H+Hoj0MwcWrZuiFz8BSYVhhIiIepQoiiivM+BsWR3OltXhfHk9LlTWo6CiHkVVV2G6ybeLr4erJahE+nkg1McdYa2bt7uCPSoOgmGEiIgkY2huQVFVAwoq6lFQUYeCinqcLzcHlbLrnmx8PU+lvDWcuFkCyrWw0sfbDUo5x6jYC4YRIiKySXWGZlyoqLcElMKqqyisMv+8or95UBEEIFijatOT0sfbDX283BDi5YYgrYpTk20IwwgREdmdxqYWXKq+ag4olVdRWNWAwqqrKKoy7+tozZRrZAIQqFEhxOungNLHS4U+3ubfQ7zcoFFxvEpvYRghIiKHIooiKuqMbcJJYdVVFFc34LKuASU1je2uQHs9tUqOPl5te1OCNCoEaVUIbP3pqZT3widyfAwjRETkVEwmERV1BhTXNOByTSOKa662/mywBJaaqx3P/vk5tVKOwNaQYg4oSgRp3cyhRaNCoFYJPw8lZHzmz0119vub0Y+IiByCTCYgQKNCgEaFuLD229QbmlGia8Cl6p8CS6nOgCv6RpTqG1Gqa0SdoRm1hmbUts4S6ohcJiBArTT3rGhV8PdUwl/9s81TBX+1Er6erhzHcgsMI0RE5DQ8lHL0D1Cjf4C6wzZ1hmaU6hrNAUX3U0gp1f+0r7zOgGaTiMu6RlzWNd7yfX08XNsJKze+9nLSac0MI0RERD/jqZSjf+szeTrS1GJCea3BHFBag0pFnQHlta1b6+8VdUa0mERU1RtRVW9E3pXam763wkWAn6e5N8XHQwlfD1f4tG7Xfrcc83SFWil3iPDCMEJERGQlhYvMMjvnZkwmEdVXjZZw0ma7LrzUXG1CU4uIEl0jSjrR22KuQ2gNK+0EF0/zT19PJbzdXeHtroCXuytcbHCcC8MIERFRD5HJBPh6KuHrqcSgoJu3NTS3oLLOiPJaA6rqjaioM1h6VCrb/DSgqs6IemMLmlpEXNEbbrk+y89pVHL4eLjCqzWgeLubf58XF4Jhfb1u7wN3EcMIERGRDVDKXTrV23JNY1OLOZzUGVFZf11wad13LcRU1RtR22h+2KG+sRn6xmag8mqbvzcizIthhIiIiDpPpXCxrJfSGc0tJtQ0NKHmqhFV9U2ovmpEzVUjqq+afx8U1PGg3p7GMEJEROQE5C4y+Hkq4eeplLqUG3DiMxEREUmKYYSIiIgkxTBCREREkmIYISIiIkkxjBAREZGkGEaIiIhIUgwjREREJCmGESIiIpJUl8LIO++8g8jISKhUKsTHx2PPnj03bZ+RkYH4+HioVCpERUVh9erVXSqWiIiIHI/VYeSLL77Ac889hz//+c/IysrChAkTMGvWLBQWFrbbvqCgALNnz8aECROQlZWFlStX4plnnkFqauptF09ERET2TxBFUbTmhNGjR+OOO+5ASkqKZd/gwYMxb948JCcn39B++fLl2LJlC3Jzcy37kpKScOzYMWRmZnbqPfV6PbRaLXQ6HTQajTXlEhERkUQ6+/1tVc+I0WjEkSNHMGPGjDb7Z8yYgX379rV7TmZm5g3tZ86cicOHD6OpqandcwwGA/R6fZuNiIiIHJNVYaSiogItLS0IDAxssz8wMBClpaXtnlNaWtpu++bmZlRUVLR7TnJyMrRarWULDQ21pkwiIiKyI116aq8gCG1ei6J4w75btW9v/zUrVqzA0qVLLa91Oh3CwsLYQ0JERGRHrn1v32pEiFVhxM/PDy4uLjf0gpSVld3Q+3FNUFBQu+3lcjl8fX3bPUepVEKp/OkRx9c+DHtIiIiI7E9tbS20Wm2Hx60KI66uroiPj0daWhruu+8+y/60tDTMnTu33XMSExPxzTfftNm3c+dOJCQkQKFQdOp9Q0JCUFRUBLVafdMeGGvp9XqEhoaiqKiIA2NtDK+NbeJ1sU28LraJ18XcI1JbW4uQkJCbtrP6Ns3SpUuxcOFCJCQkIDExEWvWrEFhYSGSkpIAmG+xFBcXY926dQDMM2feeustLF26FL/61a+QmZmJtWvX4vPPP+/0e8pkMvTt29faUjtNo9E47f9RbB2vjW3idbFNvC62ydmvy816RK6xOozMnz8flZWVePHFF1FSUoLY2Fhs27YN4eHhAICSkpI2a45ERkZi27Zt+P3vf4+3334bISEhWLVqFR544AFr35qIiIgckNXrjDgSrl9iu3htbBOvi23idbFNvC6d59TPplEqlXj++efbDJYl28BrY5t4XWwTr4tt4nXpPKfuGSEiIiLpOXXPCBEREUmPYYSIiIgkxTBCREREkmIYISIiIknZfRhJSUnBsGHDLIvKJCYm4ttvv7UcF0URf//73xESEgI3NzdMnjwZJ0+ebPM3DAYDnn76afj5+cHDwwP33nsvLl261KZNdXU1Fi5caHl438KFC1FTU9MbH9EhJCcnQxAEPPfcc5Z9vDa97+9//zsEQWizBQUFWY7zmkinuLgYjz76KHx9feHu7o4RI0bgyJEjluO8NtKIiIi44d+MIAhYsmQJAF6XbiPauS1btohbt24V8/LyxLy8PHHlypWiQqEQT5w4IYqiKL766quiWq0WU1NTxZycHHH+/PlicHCwqNfrLX8jKSlJ7NOnj5iWliYePXpUnDJlijh8+HCxubnZ0uauu+4SY2NjxX379on79u0TY2NjxXvuuafXP689OnjwoBgRESEOGzZMfPbZZy37eW163/PPPy/GxMSIJSUllq2srMxynNdEGlVVVWJ4eLj42GOPiQcOHBALCgrE7777Tjx79qylDa+NNMrKytr8e0lLSxMBiLt27RJFkdelu9h9GGmPt7e3+P7774smk0kMCgoSX331VcuxxsZGUavViqtXrxZFURRrampEhUIhbtiwwdKmuLhYlMlk4vbt20VRFMVTp06JAMT9+/db2mRmZooAxNOnT/fSp7JPtbW14oABA8S0tDRx0qRJljDCayON559/Xhw+fHi7x3hNpLN8+XJx/PjxHR7ntbEdzz77rNivXz/RZDLxunQju79N83MtLS3YsGED6uvrkZiYiIKCApSWlmLGjBmWNkqlEpMmTcK+ffsAAEeOHEFTU1ObNiEhIYiNjbW0yczMhFarxejRoy1txowZA61Wa2lD7VuyZAnuvvtuTJs2rc1+Xhvp5OfnIyQkBJGRkXj44Ydx/vx5ALwmUtqyZQsSEhLw0EMPISAgAHFxcXjvvfcsx3ltbIPRaMT69evxxBNPQBAEXpdu5BBhJCcnB56enlAqlUhKSsKmTZswZMgQlJaWAgACAwPbtA8MDLQcKy0thaurK7y9vW/aJiAg4Ib3DQgIsLShG23YsAFHjx5FcnLyDcd4baQxevRorFu3Djt27MB7772H0tJSjB07FpWVlbwmEjp//jxSUlIwYMAA7NixA0lJSXjmmWcsDxzltbENmzdvRk1NDR577DEAvC7dyeoH5dmi6OhoZGdno6amBqmpqVi8eDEyMjIsxwVBaNNeFMUb9l3v+jbtte/M33FWRUVFePbZZ7Fz506oVKoO2/Ha9K5Zs2ZZfh86dCgSExPRr18/fPzxxxgzZgwAXhMpmEwmJCQk4JVXXgEAxMXF4eTJk0hJScGiRYss7XhtpLV27VrMmjULISEhbfbzutw+h+gZcXV1Rf/+/ZGQkIDk5GQMHz4cb775pmWWwPXJsqyszJJkg4KCYDQaUV1dfdM2V65cueF9y8vLb0jEZHbkyBGUlZUhPj4ecrkccrkcGRkZWLVqFeRyueV/N14baXl4eGDo0KHIz8/nvxcJBQcHY8iQIW32DR482PIEdF4b6V28eBHfffcdnnzyScs+Xpfu4xBh5HqiKMJgMCAyMhJBQUFIS0uzHDMajcjIyMDYsWMBAPHx8VAoFG3alJSU4MSJE5Y2iYmJ0Ol0OHjwoKXNgQMHoNPpLG2oralTpyInJwfZ2dmWLSEhAQsWLEB2djaioqJ4bWyAwWBAbm4ugoOD+e9FQuPGjUNeXl6bfWfOnEF4eDgA8NrYgA8//BABAQG4++67Lft4XbpR74+Z7V4rVqwQd+/eLRYUFIjHjx8XV65cKcpkMnHnzp2iKJqnXWm1WnHjxo1iTk6O+Mgjj7Q77apv377id999Jx49elS888472512NWzYMDEzM1PMzMwUhw4d6lTTrrrDz2fTiCKvjRT+8Ic/iOnp6eL58+fF/fv3i/fcc4+oVqvFCxcuiKLIayKVgwcPinK5XHz55ZfF/Px88dNPPxXd3d3F9evXW9rw2kinpaVFDAsLE5cvX37DMV6X7mH3YeSJJ54Qw8PDRVdXV9Hf31+cOnWqJYiIonlK3PPPPy8GBQWJSqVSnDhxopiTk9PmbzQ0NIhPPfWU6OPjI7q5uYn33HOPWFhY2KZNZWWluGDBAlGtVotqtVpcsGCBWF1d3Rsf0WFcH0Z4bXrftTUQFAqFGBISIt5///3iyZMnLcd5TaTzzTffiLGxsaJSqRQHDRokrlmzps1xXhvp7NixQwQg5uXl3XCM16V7CKIoilL3zhAREZHzcsgxI0RERGQ/GEaIiIhIUgwjREREJCmGESIiIpIUwwgRERFJimGEiIiIJMUwQkRERJJiGCEiIiJJMYwQERGRpBhGiIiISFIMI0RERCQphhEiIiKS1P8PhtiO8e5jBxcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.ion()\n",
    "plt.plot(cont['wave'],cont['flux'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1358811/340378805.py:39: UserWarning: Glyph 24402 (\\N{CJK UNIFIED IDEOGRAPH-5F52}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_1358811/340378805.py:39: UserWarning: Glyph 19968 (\\N{CJK UNIFIED IDEOGRAPH-4E00}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_1358811/340378805.py:39: UserWarning: Glyph 21270 (\\N{CJK UNIFIED IDEOGRAPH-5316}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_1358811/340378805.py:39: UserWarning: Glyph 35889 (\\N{CJK UNIFIED IDEOGRAPH-8C31}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_1358811/340378805.py:39: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.interpolate import interp1d\n",
    "data = pd.read_csv('./results/hhe35nl.spec',comment='#',names=['wave','flux'],sep='\\s+')\n",
    "cont = pd.read_csv('./results/hhe35nl.cont',comment='#',names=['wave','flux'],sep='\\s+')\n",
    "# 创建连续谱的插值函数\n",
    "cont_interp = interp1d(cont['wave'], cont['flux'], kind='linear', bounds_error=False, fill_value='extrapolate')\n",
    "\n",
    "# 在data的波长点上插值得到连续谱\n",
    "cont_flux_interp = cont_interp(data['wave'])\n",
    "\n",
    "# 创建新的连续谱数据框，使用data的波长点\n",
    "cont_new = pd.DataFrame({\n",
    "    'wave': data['wave'],\n",
    "    'flux': cont_flux_interp\n",
    "})\n",
    "\n",
    "# 计算归一化通量\n",
    "data['flux_nor'] = data['flux'] / cont_new['flux']\n",
    "data['flux_nor'] = data['flux_nor'].fillna(1.0)\n",
    "\n",
    "# 可视化结果\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# 原始数据\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(data['wave'], data['flux'], label='spec')\n",
    "plt.plot(cont['wave'], cont['flux'], label='cont')\n",
    "plt.legend()\n",
    "# plt.title('原始数据')\n",
    "\n",
    "# 归一化谱线\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(data['wave'], data['flux_nor'])\n",
    "plt.title('归一化谱线')\n",
    "# plt.axhline(y=1, color='r', linestyle='--')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x70dd73175420>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['wave'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wave</th>\n",
       "      <th>flux</th>\n",
       "      <th>flux_nor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2999.89195</td>\n",
       "      <td>339322000.0</td>\n",
       "      <td>0.999988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2999.89853</td>\n",
       "      <td>339318000.0</td>\n",
       "      <td>0.999984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2999.90511</td>\n",
       "      <td>339314000.0</td>\n",
       "      <td>0.999980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2999.91169</td>\n",
       "      <td>339310000.0</td>\n",
       "      <td>0.999976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2999.91827</td>\n",
       "      <td>339305000.0</td>\n",
       "      <td>0.999969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403271</th>\n",
       "      <td>7409.94150</td>\n",
       "      <td>14113600.0</td>\n",
       "      <td>0.999941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403272</th>\n",
       "      <td>7409.95803</td>\n",
       "      <td>14113300.0</td>\n",
       "      <td>0.999929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403273</th>\n",
       "      <td>7409.97456</td>\n",
       "      <td>14112900.0</td>\n",
       "      <td>0.999909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403274</th>\n",
       "      <td>7409.99108</td>\n",
       "      <td>14112600.0</td>\n",
       "      <td>0.999896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403275</th>\n",
       "      <td>7410.00026</td>\n",
       "      <td>14112400.0</td>\n",
       "      <td>0.999887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>403276 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              wave         flux  flux_nor\n",
       "0       2999.89195  339322000.0  0.999988\n",
       "1       2999.89853  339318000.0  0.999984\n",
       "2       2999.90511  339314000.0  0.999980\n",
       "3       2999.91169  339310000.0  0.999976\n",
       "4       2999.91827  339305000.0  0.999969\n",
       "...            ...          ...       ...\n",
       "403271  7409.94150   14113600.0  0.999941\n",
       "403272  7409.95803   14113300.0  0.999929\n",
       "403273  7409.97456   14112900.0  0.999909\n",
       "403274  7409.99108   14112600.0  0.999896\n",
       "403275  7410.00026   14112400.0  0.999887\n",
       "\n",
       "[403276 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('spec.csv',index=False,header=False,sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x70ddabdd9a20>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.ion()\n",
    "data['flux_nor'] = data['flux'] / cont['flux']\n",
    "plt.plot(data['wave'],data['flux_nor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       3.056000e+09\n",
       "1       3.061460e+09\n",
       "2       3.063140e+09\n",
       "3       3.063960e+09\n",
       "4       3.064560e+09\n",
       "            ...     \n",
       "1195    3.054650e+09\n",
       "1196    3.061830e+09\n",
       "1197    3.061170e+09\n",
       "1198    3.021170e+09\n",
       "1199    2.925480e+09\n",
       "Name: flux, Length: 1200, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['flux']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3.142010e+09\n",
       "1     3.138210e+09\n",
       "2     3.134510e+09\n",
       "3     3.131020e+09\n",
       "4     3.127420e+09\n",
       "5     3.123660e+09\n",
       "6     3.119950e+09\n",
       "7     3.116050e+09\n",
       "8     3.112360e+09\n",
       "9     3.108810e+09\n",
       "10    3.105200e+09\n",
       "11    3.104370e+09\n",
       "Name: flux, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont['flux']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.972626\n",
       "1       0.975543\n",
       "2       0.977231\n",
       "3       0.978582\n",
       "4       0.979900\n",
       "          ...   \n",
       "1195         NaN\n",
       "1196         NaN\n",
       "1197         NaN\n",
       "1198         NaN\n",
       "1199         NaN\n",
       "Name: flux_nor, Length: 1200, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['flux_nor']"
   ]
  }
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