02-ajuste-seeing-visibilidad.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Revisamos las graficas de la Vexp en funcion de line base, agrupando por estrella y longitud de onda. Aseguramos ademas que sea el mismo dia!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from scipy.optimize import curve_fit\n",
    "import warnings\n",
    "import pickle\n",
    "from matplotlib import container\n",
    "\n",
    "# from functools import partial\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (6, 4)  # 8*4\n",
    "plt.rcParams['figure.dpi'] = 100\n",
    "\n",
    "output_format=\".pdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_dir(file_or_dir):\n",
    "    \"\"\"Given a dir or file path, creates the dir if needed and returns de original string\"\"\"\n",
    "    results_dir=os.path.dirname(file_or_dir)\n",
    "    if not os.path.isdir(results_dir):\n",
    "        os.makedirs(results_dir)\n",
    "    return file_or_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "styledict_exp1=dict(marker='x',color='black',linestyle='None',\n",
    "ecolor=(0, 0, 0, 0.4),elinewidth=1,capsize=2.5,)\n",
    "\n",
    "\n",
    "styledict_exp2=styledict_exp1.copy()\n",
    "styledict_exp2.update(dict(marker='^',ecolor=(0, 0, 0, 0.4),ms=4))\n",
    "\n",
    "ls1='--'\n",
    "ls2='-.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "r0dict={}\n",
    "r0errdict={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_V(B,r0):\n",
    "    return np.exp(-3.44*(B/r0)**(5/3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_errorr0(r0, B_exp,V_exp,delta=0.001):\n",
    "    \"\"\"Metodo para calcular el error segun las formulas del excel, el resultado es equivalente al de get_err() \"\"\"\n",
    "    B_exp=np.array(B_exp)\n",
    "    V_exp=np.array(V_exp)    \n",
    "    V_teor=np.exp(-3.44*(B_exp/r0)**(5/3))\n",
    "    errors2sum=np.sum((V_teor-V_exp)**2)\n",
    "    desvest=(errors2sum/(len(B_exp)-1))**0.5\n",
    "    dv=(np.exp(-3.44*(B_exp/r0)**(5/3))-np.exp(-3.44*(B_exp/(r0+delta))**(5/3)))/delta\n",
    "    print(dv)\n",
    "    dv2=dv**2\n",
    "    sumadv2=np.sum(dv2)\n",
    "    errorr0=desvest/sumadv2**0.5\n",
    "    return errorr0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_err(pcov):\n",
    "    \"\"\"Metodo correcto de calcular el error segun la documentación.\"\"\"\n",
    "    return np.sqrt(np.diag(pcov))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "B=np.linspace(0,0.3,1000)\n",
    "# V_sirio=get_V(B,0.284126682)\n",
    "# V_betel=get_V(B,0.478384865)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B_sirio_exp=[0.22,0.238,0.205,0.187]\n",
    "# V_sirio_exp=[0.094463463,0.015108305,0.138378891,0.226590674]\n",
    "\n",
    "# B_bet_exp=[0.147,0.238]\n",
    "# V_bet_exp=[0.641221919,0.32258731]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Objeto', 'Filtro', 'Disco', 'D (mm)', 'L (mm)', 'L1 (mm)', 'L2 (mm)',\n",
      "       'Notas', 'PATH', 'Imagen', '$\\lambda$', '$d\\lambda$', 'alphap',\n",
      "       'alphap_mas', 'filtername', 'starname', 'DATE', 'DAY', 'HOUR',\n",
      "       'V_media_antigua', 'S_antigua', 'S_tip_antigua', 'V_media', 'S',\n",
      "       'S_tip', 'E_acc'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "df_prev=pd.read_hdf('intermedio/tabla.h5', 'df')\n",
    "print(df_prev.keys())\n",
    "dispcols=['Imagen','Objeto','DATE','Filtro','L (mm)','V_media','S','E_acc','D (mm)',\"V_media_antigua\",\"S_antigua\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_S=pd.read_csv(\"v_individualesyajuster0inicial.csv\",sep=';',index_col='indice')[['V_media','S']]\n",
    "\n",
    "df=df_prev#.join(df_S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Objeto', 'Filtro', 'Disco', 'D (mm)', 'L (mm)', 'L1 (mm)', 'L2 (mm)',\n",
      "       'Notas', 'PATH', 'Imagen', '$\\lambda$', '$d\\lambda$', 'alphap',\n",
      "       'alphap_mas', 'filtername', 'starname', 'DATE', 'DAY', 'HOUR',\n",
      "       'V_media_antigua', 'S_antigua', 'S_tip_antigua', 'V_media', 'S',\n",
      "       'S_tip', 'E_acc'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_prev.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sirio (Dia 23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>Imagen</th>\n",
       "      <th>Objeto</th>\n",
       "      <th>DATE</th>\n",
       "      <th>Filtro</th>\n",
       "      <th>L (mm)</th>\n",
       "      <th>V_media</th>\n",
       "      <th>S</th>\n",
       "      <th>E_acc</th>\n",
       "      <th>D (mm)</th>\n",
       "      <th>V_media_antigua</th>\n",
       "      <th>S_antigua</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imagen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Sirio</td>\n",
       "      <td>17-03-23</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>238.0</td>\n",
       "      <td>0.015108</td>\n",
       "      <td>0.001886</td>\n",
       "      <td>0.010804</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.015108</td>\n",
       "      <td>0.001886</td>\n",
       "      <td>17-03-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Sirio</td>\n",
       "      <td>17-03-23</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.268869</td>\n",
       "      <td>0.013899</td>\n",
       "      <td>0.079645</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.268869</td>\n",
       "      <td>0.013899</td>\n",
       "      <td>17-03-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Sirio</td>\n",
       "      <td>17-03-23</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>222.0</td>\n",
       "      <td>0.139991</td>\n",
       "      <td>0.021037</td>\n",
       "      <td>0.120546</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.139991</td>\n",
       "      <td>0.021037</td>\n",
       "      <td>17-03-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Sirio</td>\n",
       "      <td>17-03-23</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.205609</td>\n",
       "      <td>0.051177</td>\n",
       "      <td>0.293257</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.205609</td>\n",
       "      <td>0.051177</td>\n",
       "      <td>17-03-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Imagen Objeto      DATE       Filtro  L (mm)   V_media         S  \\\n",
       "Imagen                                                                     \n",
       "14          14  Sirio  17-03-23  Halfa-35 nm   238.0  0.015108  0.001886   \n",
       "15          15  Sirio  17-03-23  Halfa-35 nm   133.0  0.268869  0.013899   \n",
       "16          16  Sirio  17-03-23  Halfa-35 nm   222.0  0.139991  0.021037   \n",
       "17          17  Sirio  17-03-23  Halfa-35 nm   162.0  0.205609  0.051177   \n",
       "\n",
       "           E_acc  D (mm)  V_media_antigua  S_antigua      DATE  \n",
       "Imagen                                                          \n",
       "14      0.010804    30.0         0.015108   0.001886  17-03-23  \n",
       "15      0.079645    30.0         0.268869   0.013899  17-03-23  \n",
       "16      0.120546    50.0         0.139991   0.021037  17-03-23  \n",
       "17      0.293257    65.0         0.205609   0.051177  17-03-23  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond=(df['DAY']==23) & (df['Filtro']=='Halfa-35 nm') \n",
    "df_sel=df.loc[cond]\n",
    "df_sel[dispcols+['DATE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'17-03-23'"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date=df_sel['DATE'].iloc[0]\n",
    "date[:2]+'-'+date[3:5]+'-'+date[6:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prueba de errores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25220956245896947 +- 0.015168958355392427\n",
      "[-0.9085143  -2.394583   -1.13893187 -2.09821755]\n",
      "0.015168958363374636\n",
      "7.98220864406396e-12\n"
     ]
    }
   ],
   "source": [
    "B_exp=df_sel['L (mm)']/1000\n",
    "V_exp=df_sel['V_media']\n",
    "\n",
    "popt, pcov =curve_fit(get_V, B_exp,V_exp,p0=0.2,bounds=(0.01,1))\n",
    "\n",
    "perr = np.sqrt(np.diag(pcov))\n",
    "print(popt[0],'+-',perr[0])\n",
    "err=get_errorr0(popt[0], B_exp,V_exp,delta=1e-8)\n",
    "print(err)\n",
    "print(err-perr[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r0= 0.25220956245896947\n"
     ]
    },
    {
     "data": {
      "image/png": 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//3369u3rdTkiIiLFonFSKsA4KSfinOPo0aNERER4XYqIiFQQGidFyoSZ5QsoU6dOPeYNtCIiIoFIIaUSmTVrFsOHD6d37958//33XpcjIiJyQgoplcjVV19NbGwsycnJXHHFFfzyyy9elyQiInJcCimVSPXq1fnggw9o2bIlW7dupVevXvz+++9elyUiIlIohZRKpl69enz00Uc0aNCAdevWMWDAAL3nR0REApJCSiUUExPDwoULqVatGkuWLOG2227TqLQiIhJwFFIqqY4dOzJv3jzCw8Np37691+WIiIgcQ4O5VWK9evXi+++/p1mzZl6XIiIicgy1pFRyeQNKamoqX331lXfFiIiI5KGQIgAkJSXRrVs3evTowbp167wuR0RERCFFfGrWrEnNmjVJTU2ld+/e7Ny50+uSRESkklNIEQAiIyP597//TevWrdmxYwd9+vRh//79XpclIiKVmEKK5KpduzYffPAB9erV49tvv+Xaa68lIyPD67JERKSSCoiQYmbDzOxnMztsZolm1u0E64aZ2f9kr3/IzH4ys3gzC4jvEuxiYmJ47733qFKlCh9++CEPPPCA1yWJiEgl5fn/2M1sEDAFmAB0Ar4AFppZk+Ns8jBwJzAcaAM8BDwI3O33YiuJc845h5dffhmAefPmkZyc7HFFIiJSGZnXI42a2ZfA1865oXnmbQDmO+ceKWT994Ak59yQPPPeAtKcczcV47jRwL59+/YRHR1dqu9QUf3rX//iiiuuoFGjRl6XIiIiASQ1NZUaNWoA1HDOpfrrOJ62pJhZBBALLCqwaBHQ5TibLQMuM7MzsvfRAegKfHCSY0WaWXTOBFQvVfGVwC233JIvoGRmZnpYjYiIVDZe3+6pC4QCSQXmJwENjrPNX4HXgI1mdhT4BpjinHvtJMd6BNiXZ9pR0qIro7lz59KpUydSUlK8LkVERCoJr0NKjoL3nKyQeTkGATcC1wNnA4OBB8xs8EmOMRGokWdqXOJqK5nDhw8zatQovTVZRETKldchJRnI5NhWk3oc27qS40lgknPudefcOufcK8DT+FpKjss5d8Q5l5ozARoEpIhOOeUUFixYQPXq1fn8888ZNmyY3posIiJ+52lIcc6lA4lAjwKLegDLj7NZFJBVYF4m3geuCq19+/a8/vrrhISEMGPGDCZPnux1SSIiUsEFwv/YJwO3mtlfzKyNmT0NNAGeBzCz4Wb2SZ713wUeNbM+ZtbMzPoDI4F/l3vllUzv3r1zw8mDDz7IggULPK5IREQqMs9DinPuDeBeIB5YA1wE9HbObc1epS7QIs8mdwNvAtOADcDfgReAMeVTceU2YsQI7rzzTpxzXH/99fzwww9elyQiIhWU5+OkeEXjpJTc0aNHufLKKzn77LOZOHEioaGhXpckIiLlqLzGSQnz146l4goPD2fhwoWEh4d7XYqIiFRgnt/ukeCUN6AcOXKE11472TA1IiIixaOWFCmVjIwMLr/8cpYtW8b+/fu5/fbbvS5JREQqCLWkSKmEhYXRq1cvAO666y6++OILjysSEZGKQiFFSm306NEMGjSIjIwMBgwYwNatW0++kYiIyEkopEipmRkvvfQSnTp1Ys+ePfTr14+DBw96XZaIiAQ5hRQpE1FRUcyfP5969eqxZs0abrnlFg2dLyIipaKQImWmSZMmvPXWW4SHh7No0SI2b97sdUkiIhLE9HSPlKmuXbvy6quv0qFDB04//XSvyxERkSCmEWc14qzfOecwM6/LEBGRMlJeI87qdo/41eLFiznnnHNISUnxuhQREQkyCiniN0ePHuWuu+4iMTGR6667joyMDK9LEhGRIKKQIn4THh7OW2+9RVRUFB9//DGPPPKI1yWJiEgQUUgRvzrzzDP517/+BcDf//53veNHRESKTCFF/O7aa6/l4YcfBmDIkCGsWbPG24JERCQoKKRIuZgwYQI9e/bk0KFD9O/fXx1pRUTkpBRSpFyEhoYyZ84cmjdvzqWXXkrVqlW9LklERAKcBnOTclO7dm2++uorateurXFTRETkpNSSIuWqTp06uQElMzNT/VNEROS4FFLEEwcOHCAuLo4uXbooqIiISKEUUsQTVapUISsrSx1pRUTkuBRSxBM5HWlbtGjBli1bGDRokEakFRGRfBRSxDO1atVi/vz5VK1alU8++YRRo0Z5XZKIiAQQhRTxVPv27Zk5cyYATz31FHPmzPG2IBERCRgKKeK5a665Jve9PnfffTf79+/3uCIREQkEGidFAkJCQgJ79uxh6NChVK9e3etyREQkAJhzzusaPGFm0cC+ffv2ER0d7XU5IiIiQSM1NZUaNWoA1HDOpfrrOLrdIwFp1apVTJw40esyRETEQ7rdIwFnx44dXHTRRRw+fJimTZty/fXXe12SiIh4QC0pEnAaN27MyJEjARgyZAjffPONxxWJiIgXFFIkII0fP54rr7ySw4cP079/f5KTk70uSUREyplCigSknBFpTz/9dLZu3cq1116rEWlFRCoZhRQJWDVr1mT+/PlUq1aNTz/9lAcffNDrkkREpBwppEhAa9euHbNmzQLgp59+UmuKiEglonFSNE5KUFiyZAmXXHIJISHK1SIiXtM4KSJ5dO/ePTegOOdIS0vzuCIREfE3hRQJKocOHeLmm2+mT58+HD161OtyRETEjxRSJKhs27aN+fPn89lnn6kjrYhIBaeQIkGlVatWvPLKKwD84x//yP2ziIhUPAopEnT69evHmDFjALj99ttJTEz0uCIREfEHhRQJSmPHjuWPf/xj7oi0u3fv9rokEREpYwopEpRCQkKYPXs2rVq1Yvv27Vx//fVU1sfpRUQqKoUUCVo1atRg/vz5NG/enEcffRQz87okEREpQ2FeFyBSGq1bt2bTpk2EhelSFhGpaNSSIkEvb0DZuHEjX3/9tYfViIhIWVFIkQpjxYoVnHvuucTFxZGUlOR1OSIiUkoKKVJhtGvXjkaNGrFz504GDhxIenq61yWJiEgpKKRIhREdHc38+fOJjo7miy++YOTIkV6XJCIipaCQIhVKq1atmD17NgBTp07lX//6l8cViYhISSmkSIVz1VVXMW7cOADuvPNOvvzyS48rEhGRkgiIkGJmw8zsZzM7bGaJZtatCNs0MrPZZpZiZmlmtsbMYsujXgl8jz32GP369SM9PZ2nn37a63JERKQEPA8pZjYImAJMADoBXwALzazJCbapBfwHOApcCbQF7gd+93O5EiRCQkKYNWsW48eP5+WXX/a6HBERKQHzeihxM/sS+No5NzTPvA3AfOfcI8fZZhJwoXPupC0uJzhuNLBv3759REdHl3Q3IiIilU5qaio1atQAqOGcS/XXcTxtSTGzCCAWWFRg0SKgywk2jQNWm9k8M9ttZt+Y2W0nOVakmUXnTED1UhUvQSUjI4ORI0cyY8YMr0sREZEi8nos8bpAKFBw5K0koMEJtmsODAUmA08A5wL/NLMjzrnjte0/AjxeunIlWM2ZM4enn36aiIgI2rVrx/nnn+91SSIichKe90nJVvCekxUyL68QfLeIRjvnvnHOvQBMxxdcjmciUCPP1LgU9UqQufHGG+nfvz/p6ekMGDCAX3/91euSRETkJLwOKclAJse2mtTj2NaVvHYB6wvM2wAct7Otc+6Icy41ZwL2l6BeCVI5HWnbtm3LL7/8woABAzQirYhIgPM0pDjn0oFEoEeBRT2A5SfY9D9AqwLzzgC2ll11UtFUr16d+fPnU6NGDZYvX86IESO8LklERE7A65YU8PUrudXM/mJmbczsaXwtIs8DmNlwM/ukwDZPA+eb2WgzO93MrgduB6aWa+USdFq2bMmcOXMwM1544QWee+45r0sSEZHj8DykOOfeAO4F4oE1wEVAb+dcTqtIXaBFgW1WAf2BPwHfAWOAe51zr5ZP1RLMevfuzRNPPEHVqlVp0OBE/bNFRMRLno+T4hWNk1K5OefYunUrzZo187oUEZGgUynGSRHxipnlCyjbt29n/371pRYRCSQKKVLprVixgs6dO3PjjTeSlZXldTkiIpJNIUUqvdDQUPbt28eCBQsYM2aM1+WIiEg2hRSp9M4991xefPFFAJ544glee+01jysSERFQSBEBfCPSPvTQQwD85S9/YfXq1R5XJCIiCiki2Z544gn69OnD4cOH6devH7t27fK6JBGRSk0hRSRbaGgoc+bMoU2bNuzcuVP9U0REPKaQIpJHdHQ0CxYsYMiQIfzjH//wuhwRkUpNg7lpMDcREZFi0WBuIgHAOcf//M//8MEHH3hdiohIpRPmdQEigWzGjBmMGTOG6OhoVq5cSZs2bbwuSUSk0lBLisgJ3HzzzXTr1o3U1FSuuuoqUlJSvC5JRKTSUEgROYGIiAjeeustYmJi2Lx5M1dffTVHjhzxuiwRkUpBIUXkJE499VTee+89oqOjWbp0KXfccQeVtcO5iEh5UkgRKYK2bdsyd+5cQkNDmTVrFpMmTfK6JBGRCk8hRaSIevbsyT//+U/Cw8Np0KCB1+WIiFR4GidF46RIMf3444+cfvrpXpchIuIZjZMiEqDyBpQ9e/awc+dOD6sREam4FFJESmjDhg2ce+65/PGPf+TAgQNelyMiUuEUOaSYWUc/1iESdKpUqUJaWhpr1qzhhhtuIDMz0+uSREQqlOK0pHxtZolmNtTMavitIpEg0axZM+bPn09kZCQLFizg4Ycf9rokEZEKpTgh5ULga2ASsMvMZpvZpf4pSyQ4XHDBBcycOROAp556iunTp3tbkIhIBVLsp3vMrApwLXAL0A3YArwEzHLO7SjrAv1FT/dIWRo3bhxjx44lLCyMjz76iO7du3tdUqWXlpZGWlraMfOjoqKIioryoCKRiqO8nu4p1SPIZtYCX1i5GWgILHbO9S6j2vxKIUXKknOOG2+8kTlz5nD++eezfPlyzMzrsiq1xMREEhMTSU9PZ9euXTRs2JCIiAhiY2OJjY31ujyRoFZeIaVUb0F2zm02s0nAduAJoGeZVCUSZMyMGTNmcOqppxIfH6+AEgDatGlD06ZNSUlJ4f3336dHjx7UqVNHrSgiQaTEIcXMLgb+AgwAMoG5wIwyqksk6JxyyilMmTIl3zznnAKLR/Le1qlWrRp16tShbt26HlclIsVRrHFSzOw0MxtjZpuBT4EWwN3AH5xztznnVvqjSJFgNG3aNPr160dGRobXpYiIBKUit6SY2WLgUmAP8DLwknNuk78KEwlm27dv54EHHuDQoUPcfffdTJs2TS0qIiLFVJyWlEP4bu00ds49rIAicnynnXYar776KmbG888/z5NPPul1SSIiQafIIcU5F+ece8c5p2E1RYqgf//+uX1UHn74YV5//XVvCxIRCTJ6d4+IH40YMYJ7770XgMGDB7N06VJvCxIRCSIKKSJ+9ve//52rr76a9PR0+vbty65du7wuSUQkKJRqnBQRObnQ0FBmz57N5ZdfTr9+/WjQoIHXJYmIBAWFFJFyUKVKFT7//HPCwvRXTkSkqHS7R6Sc5A0oqampxMfHc/ToUQ8rEhEJbPq1TqScOefo06cPy5YtY8uWLcycOZOQEP2+ICJSkP5lFClnZsaoUaMIDQ3llVdeYdSoUV6XJCISkBRSRDzQp08fXnzxRQCefPJJJk+e7HFFIiKBRyFFxCN//vOfmTRpEgD3338/r776qscViYgEFoUUEQ899NBD3HPPPYAvtCxatMjjikREAoc6zop4yMyYPHkySUlJLF26lIYNG3pdkohIwFBIEfFYSEgIM2fOZM+ePTRu3NjrckREAoZu94gEgMjIyHwB5T//+Y+GzxeRSk8hRSTALFy4kMsuu4yePXvy22+/eV2OiIhnFFJEAkyrVq2oXbs269ato3fv3hw4cMDrkkREPKGQIhJgmjdvzqJFi6hduzYrV66kX79+HD582OuyRETKnUKKSABq3749CxcupFq1anzyySf86U9/IiMjw+uyRETKlUKKSIA699xzWbBgAZGRkcyfP58hQ4aQlZXldVkiIuVGIUUkgF166aXMnTuX0NBQjhw5QmZmptcliYiUm4AIKWY2zMx+NrPDZpZoZt2Kse0jZubMbIofSxTxTFxcHMuWLePVV18lPDzc63JERMqN5yHFzAYBU4AJQCfgC2ChmTUpwrbnALcDa/1Zo4jXzj//fEJDQwHIysri448/9rgiERH/8zykACOBGc65F51zG5xz9wLbgaEn2sjMqgGvArcBGkxCKoWsrCxuvvlmevTowfTp070uR0TErzwNKWYWAcQCBd+qtgjocpLNpwLvO+eK9CulmUWaWXTOBFQvdsEiHgsJCckdmfaOO+7glVde8bgiERH/8bolpS4QCiQVmJ8ENDjeRmZ2Hb5w80gxjvUIsC/PtKNYlYoEiIkTJzJs2DCcc/z5z3/mjTfe8LokERG/8Dqk5HAFPlsh83wLzE4D/gHc4JwrzghXE4EaeSa9yU2CkpnxzDPPcOutt5KVlcUNN9zA22+/7XVZIiJlzuuQkgxkcmyrST2ObV3JEZu9PNHMMswsA7gYGJH9ObSwjZxzR5xzqTkTsL9svoJI+QsJCeGFF15g8ODBZGZmMmjQIN59912vyxIRKVOehhTnXDqQCPQosKgHsPw4m30CnAl0zDOtxteJtqNzTgNJSKUQEhLCjBkz+NOf/kRISAjOFdr4KCIStMK8LgCYDLxiZquBFfgeKW4CPA9gZsOB/s65ywCcc/uB7/LuwMwOAinOuXzzRSq60NBQXn75ZUaOHEnnzp29LkdEpEx5HlKcc2+YWR0gHmiIL4D0ds5tzV6lLtDCq/pEAl1YWFi+gPLTTz+xc+dOunUr8piIIiIByfOQAuCcmwZMO86yscDYk2x/SZkXJRKEtm7dysUXX8xvv/3GRx99xIUXXuh1SSIiJeZ1x1kRKUP169enTZs2HDx4kF69erFs2TKvSxIRKTGFFJEK5JRTTmH+/Pl0796dAwcO0KtXL5YuXep1WSIiJaKQIlLBREVF8e6779KjRw8OHjzIlVdeyaeffup1WSIixaaQIlIBRUVF8c4779CrVy/S0tLo06ePbv2ISNAJiI6zIlL2qlSpwr///W+uueYadu7cSdu2bb0uSUSkWBRSRCqwU045hbfeeouDBw9Su3Ztr8sRESkW3e4RqeAiIyPzBZTnnntOQ+iLSFBQS4pIJbJo0SKGDRtGeHg4c+bM4ZprrinSdmlpaaSlpR0zPyoqiqioqLIuU0QEUEgRqVS6d+/Oddddx+uvv86gQYOYMWMGf/7zn0+63YYNG0hMTCQ9PZ1du3bRsGFDIiIiiI2NJTY21v+Fi0ilpJAiUomEhYUxe/ZsqlatyowZM7jlllvYv38/d9999wm3a9OmDU2bNiUlJYX333+fHj16UKdOHbWiiIhfKaSIVDKhoaFMnz6d6Ohonn76aUaMGEFqaiqjR4/GzArdJu9tnWrVqlGnTh3q1q1bnmWLSCWkjrMilZCZ8dRTTzF27FgAHnvsMb744gtvixIRKUAtKSKVlJnx+OOPEx0dze+//85FF13kdUkiIvkopIhUcvfdd1++z/v37+eUU04hPDzco4pERHx0u0dEcqWlpdG7d28GDBhQ6CPHIiLlSSFFRHKtWbOG1atX8+6773LFFVewd+9er0sSkUpMIUVEcnXp0oXFixdTs2ZN/vOf/9CtWze2b9/udVkiUkkppIhIPl27duWLL76gUaNGrF+/ni5durB+/XqvyxKRSkghRUSO0b59e5YvX07r1q3ZsWMHXbt2ZcWKFV6XJSKVjEKKiBSqSZMmLFu2jAsuuICQkBBq1arldUkiUsnoEWQROa46derw8ccf89NPP9G6dWuSk5O9LklEKhG1pIjICUVFRdG+ffvcz//973+ZNGkSzjkPqxKRykAtKSJSZHv27OGFF17gyJEj7Nq1i5deeonIyEivyxKRCkotKSJSZKeeeiqDBg0iNDSUOXPmcPnll5OSkuJ1WSJSQSmkiEixXHjhhbzxxhtER0fndqz98ccfvS5LRCoghRQRKbaLL76Y5cuX07RpU3744QfOP/98li1b5nVZIlLBKKSISIm0a9eOlStXcs4555CSksKrr77qdUkiUsGo46yIlFiDBg347LPPmDx5Mg8//LDX5YhIBaOWFBEplaioKB577DHCw8MByMjIYOLEiRw8eNDjykQk2CmkiMgJjR07loSEhEKXJSQkMHbs2HzzRo0axejRo+natSvbtm0rhwpFpKJSSBGREwoNDSU+Pv6YoJKQkEB8fDyhoaH55vfr14969eqxZs0aOnfurA61IlJi6pMiIic0ZswYAOLj40lLSyMmJoannnqKSZMmMX78+NzlObp27cqqVavo27cva9asoXv37kybNo1bb73Vi/JFJIgppIjISeUNKmFhYWRkZBQaUHLkvJzwlltuYd68edx2222sXbuWyZMnExamf3ZEpGh0u0dEimTMmDFERESQkZFBRETEcQNKjqpVq/LGG2/k3iZ66aWX2LJlSzlUKiIVhX6lEZEiSUhIID09nbCwMNLT00lISDhpUDEzHnvsMdq1a0dWVhann356OVUrXkhLSyMtLe2Y+VFRUURFRXlQkQQ7hRQROamcTrKjRo0iJiaGn3/+mfj4eICTBhWA/v375/u8dOlSvvvuO4YOHYqZ+aVmKX8bNmwgMTGR9PR0du3aRcOGDYmIiCA2NpbY2Fivy5MgpJAiIieUE1DGjx/P0KFDefvtt7n//vuJiooqVlDJkZyczLXXXktSUhJffvklzz33nH7LriDatGlD06ZNSUlJ4f3336dHjx7UqVNH/32lxNQnRUROKDMzs9BOsmPGjGH8+PFkZmYWa3916tThgQceICQkhJdffpkLLriAzZs3l2XJ4pGoqCjq1q1LnTp1qFatGnXq1KFu3boKKVJiakkRkRMqOFhbXsVpQclhZjzwwAPExsZy3XXXsXbtWmJjY3n55ZeJi4srRaUiUtGoJUVEPHHppZfy9ddfc8EFF7Bv3z769u3LyJEji90yIyIVl0KKiHimUaNGfPbZZ9x3330AJCUlERKif5ZExEe3e0TEUxEREUyePJkrrriCCy+8MPdpn6NHj+a+tFBEKif9yiIiAaFXr15Ur14dAOccAwcOZOjQoRw6dMjjykTEK2pJEZGAs2LFCt555x0Ali9fzhtvvEHr1q09rkpEyptaUkQk4HTp0oUPP/yQU089lbVr13L22Wfz3HPP4ZzzujQRKUcKKSISkHr27Mm3335Ljx49OHToEMOGDeOqq64iKSnJ69JEpJwopIhIwGrYsCEffvghU6ZMITIykvfff59+/fqpRUWkklBIEZGAFhISwj333MPq1as5++yzmTx5st73I1JJqOOsiASF9u3bs2rVqnzjqMyZM4cWLVpw3nnn5Vt37NixhIaGFjoibkJCApmZmSccSVdEAkPAtKSY2TAz+9nMDptZopl1O8G6j5jZKjPbb2a7zWy+mbUqz3pFpPzlDSgbNmxgyJAhdOnShYcffpjDhw/nLgsNDSU+Pp6EhIR82+e8LDE0NLTcahaRkguIkGJmg4ApwASgE/AFsNDMmhxnk4uBqcD5QA98LUKLzKyq/6sVkUBQv359rrnmGrKysvjb3/5Gp06d+PLLL4H/f/lhfHw8Tz31FABPPfVU7tucS/LOIREpf4Fyu2ckMMM592L253vNrCcwFHik4MrOuV55P5vZLcBuIBZY6udaRSQA1K5dm1deeYWBAwdyxx13sHHjRrp06cIDDzzAuHHjcoNIfHw8YWFhZGRkKKCIBBnPW1LMLAJfuFhUYNEioEsRd1Mj++feExwn0syicyagerGLFZGAExcXx3//+19uvPHG3FaV8847j6NHjzJmzBgiIiLIyMggIiJCAUUkyHgeUoC6QChQcPCDJKDByTY2Xzf/ycAy59x3J1j1EWBfnmlHiaoVkYCT06ryzjvv0KBBA6655hrCw8NJSEggPT2dsLAw0tPTj+mjIiKBLVBu9wAUHPjACplXmGeBs4CuJ1lvIr4wk6M6CioiFUpcXBxdu3alevXquZ1kBw8eTPXq1alWrRrx8fEAalERCRKBEFKSgUyObTWpx7GtK/mY2TNAHHCRc+6EgcM5dwQ4kmfbEhUrIoGtdu3auQHl8ccfZ968eaxfv564uDgefPBBBRWRIOJ5SHHOpZtZIr6ndP6dZ1EP4J3Ctsm+xfMM0B+4xDn3s98LFanE0tLSSEtLIyUlhQMHDpCSkgJAVFQUUVFRHld3rMzMTMaPH8+DDz5IcnIyGzZsYMGCBdSoUYOrrrqKjIwMr0sUkSLwPKRkmwy8YmargRXA7UAT4HkAMxsO9HfOXZa9/lTgeqAvsN/Mclph9jnn9F53kTK2YcMGEhMTSU9PJyUlhcWLFxMREUFsbCyxsbFel3eMvAO1jR07llq1avHee++xZs0a3n33XZKTkxkwYABnnXWWd0WKyEkFREhxzr1hZnWAeKAh8B3Q2zm3NXuVukCLPJsMzf75WYFd3QLM9F+lIpVTmzZtaNq06THzA7EVpTCnnXYaH374IXPnzmX06NGsWLGCTp06sXLlSs455xyvyxOR4wiIkALgnJsGTDvOsrHA2Dyf1aFEpBwF6m2d4ggNDeXuu++mf//+3HfffezatYvOnTt7XZaInEAgPIIsIlJuGjduzLx581i0aFFuB/rU1FT69u3LqlWrPK5ORPJSSBGRSilvy9DEiRNZsGAB5513Hrfffjt79uzxsDIRyaGQIiKV3ogRI7jppptwzjF9+nRatGjBpEmT8r20UETKn0KKiFR6DRs25OWXX+aLL77g7LPPZv/+/TzyyCO0atWK1157zevyRCothRQRkWxdu3Zl1apVvPzyyzRu3Jht27bx4Ycfel2WSKWlkCIikkdISAg33XQTmzZtYsKECUyYMCF32datW9m0aZOH1YlULgopIiKFiIqKYvTo0TRu3Dh33v3330/btm0ZMmQIW7duPcHWIlIWFFJERIrgyJEjHD16lKysLF566SXOOOMM7rnnHpKSTviKsUpj7Nixx33LdEJCQr5RgEWKSiFFRKQIIiMjeeedd1ixYgWXXnop6enp/POf/6R58+aMHj2a3377zesSPRUaGkp8fPwxQSXnZY+hoaEeVSbBLGBGnBURCQbnn38+S5Ys4ZNPPmH06NF89dVXTJw4kdq1a/PAAw94XZ5nct4qHR8fT1paGjExMTz11FNMmjSJ8ePH663TUiIKKSIiJXDZZZexcuVKFixYwLPPPsuwYcNyl61bt45GjRpRu3ZtDyssf3mDSlhYGBkZGQooUirmnPO6Bk+YWTSwb9++fURHR3tdjoj4SXJyMm+//TZXX301devW9fvxsrKy6NChA1u2bGH48OHcd9991KtXz+/HDSSRkZGkp6cTERHBkSNHvC5H/CA1NZUaNWoA1HDOpfrrOOqTIiJShpKSkggJCeHAgQNMmjSJZs2aMXLkSHbt2uV1aeUiISGB9PR0wsLCSE9PP25nWpGiUEgRESlDDRs25JtvvuGdd96hc+fOHDp0iKeffpqYmBhuv/12fvrpJ69L9JucTrKjRo1i6tSpjBo1qtDOtCJFpZAiIlLGQkJCiIuL46uvvmLhwoV06dKFI0eOMH36dH744Qevy/OLnIAyfvx47r//fsA3rsz48eMVVKTE1HFWRMRPzIxevXrRs2dPli1bxrx587jiiityl8+YMYOaNWvSr1+/oH9ENzMzM7eTbHJycu78nE6zmZmZXpUmQUwhRUTEz8yMbt260a1bt9x5Bw8e5MEHH+S3336jRYsW3HPPPQwePDhoO/KfaLA2Pd0jJaXbPSIiHsjMzOSuu+6iVq1abN68mREjRtCoUSOGDx/Oxo0bvS5PJCAopIiIeCA6OpqEhAS2b9/Os88+S+vWrTlw4ABTp06lTZs2TJ061esSRTynkCIi4qGqVaty1113sX79ehYvXkzfvn0JDQ2lR48euets2bJF7wiSSkkhRUQkAJgZl19+OfPnz+eXX37hjDPOyF324IMP0rhxYwYMGMDChQvVCVUqDYUUEZEAk3eE2qNHj7Jr1y4yMjJ4++236d27NzExMYwdO5atW7d6WKWI/ymkiIgEsPDwcJYtW8batWu55557qF27Ntu3b2fcuHHExMQwfPhwr0sU8RuFFBGRIHDmmWcyZcoUdu7cyWuvvUb37t1xztGyZcvcdVJTU/n888/JysrysFKRsqOQIiISRE455RSuu+46PvnkE3788UcGDx6cu+ytt97ikksuISYmhtGjR7N+/XoPKxUpPYUUEZEg1aJFC2rWrJn7+ffffyc6Oppt27YxceJE2rVrR2xsLJMnT2bbtm3eFSpSQgopIiIVxH333cevv/7K3LlziYuLIywsjK+//pr777+f5s2bs3fvXq9LFCkWDYsvIlKBVKlShYEDBzJw4ECSk5OZO3cub7zxBuHh4dSuXTt3vbvvvpsmTZowcOBAmjVr5l3BIiegkCIiUkHVrVuXYcOGMWzYMI4ePZo7PykpiWnTppGVlcVDDz1EbGwscXFxxMXF0aFDB8zMw6pF/p9u94iIVALh4eG5f65SpQrPPPMMl1xyCSEhISQmJvL444/TqVMnmjZtyosvvuhhpSL/TyFFRKSSiY6OZtiwYXz66af88ssvvPjii/Tt25cqVaqwfft2QkNDc9fdsWMHL7/8soblF0/odo+ISCVWv359hgwZwpAhQzh06BBLlizh/PPPz13+5ptvct999wHQsWNHevbsyRVXXMGFF15IZGSkV2VLJaGWFBERAXy3gfr06UOdOnVy59WqVYuzzz4bgDVr1vDXv/6Vyy67jDp16vDHP/5RjzaLX6klRUREjmvw4MEMHjyY3bt3s3jxYj766CMWLVpEUlISixcvpm7durnrzpkzh8OHD3PWWWfhnPOwaqkoFFJEROSk6tWrxw033MANN9yAc461a9eyfv16oqKictd58sknWbNmDQC1a9dmyZIl9OrVi0suuYRmzZrpqSEpNoUUEREpFjOjQ4cOdOjQIXeec464uDiqVq3Kl19+yd69e5k7dy5z584FoGvXrnzxxRf51ldokZNRSBERkVIzM8aNG8e4cePYunUrU6ZMwcz48ssv+eqrr2jSpEnuullZWTRp0oSWLVvSpUsXunTpwvnnn5+vL4wIKKSIiEgZq1q1Km3atOHqq6+mbt26HDx4kN9//z13+caNG9m5cyc7d+7ks88+y53fqlUrzjvvPK655hquuuqq8i9cAo5CioiI+FXVqlWpWrVq7ufWrVvz3//+l+XLl+dOmzZtyp2aNm2aG1J2797NQw89ROfOnYmNjaVDhw75+sFIxaaQIiIi5SokJIS2bdvStm1bbr31VgBSUlJYuXIlq1atolevXrnrrlq1ilmzZjFr1iwAQkNDadu2LR07duSss87iqquuolWrVp58D/E/hRQREfFcnTp16NOnD3369Mk3v0WLFsTHx7N69WoSExNJSkpi3bp1rFu3DvANRpcTUr755htmzZrFmWeeyVlnnUW7du3U6hLkFFJERCRgtW7dmnHjxgG+J4J27txJYmIia9euZd26dXTu3Dl33aVLl/KPf/wj97OZ0bx5c1q1akXr1q254447OOOMM8r9O0jJKaSIiEhQMDMaN25M48aN6du37zHLO3fuzH333ce6detYu3Ytu3fvZvPmzWzevJkPPviAgQMH5q77yiuv8Pzzz9O6dWtat25Nq1ataNmyJc2aNaNKlSrl+bXkBBRSRESkQrjwwgu58MILcz8nJSWxYcMGNm3axMaNG2nTpk3ussTExNxOuwU1atSIjz76iHbt2gHwww8/sHfvXpo3b07dunU1vks5UkgREZEKqX79+tSvX59LLrnkmGXDhw/nggsuYOPGjbnT5s2b2b9/Pzt37uTUU0/NXfe5557j6aefBqB69eq0aNGCZs2a0aRJE5o0acJf/vIXatWqVV5fq1JRSBERkYA3duxYsrKyGDFixDHL/vnPfxISEsLYsWOLvL/TTz+d008/Pd885xwpKSls3rw5X0ipUqUKjRo1YufOnezfv581a9bkDv8PcNNNN+X++eGHH2bu3Lk0adKE0047LTfINGrUiIYNG3LWWWcRERFR9C9eySmkiIhIwAsNDWXcuHF8++239OjRg127dtGwYUMWL17MggULGD9+fKmPYWbUrVs330sTASZMmMCECRM4fPgwP//8M5s3b2bbtm1s27btmFaXzZs3s2XLFrZs2VLoMXbv3p27/pNPPsmnn35Kw4YNC51OO+00QkNDS/29gplCioiIlIm0tDTS0tJISUnhwIEDpKSkABAVFVXqR4HHjBnD0aNHSUhIoFmzZjRt2pQffviBBQsWMGbMGMaMGVMWX+GETjnlFNq0aZOvb0tBzz77LCNHjswNMdu3b88NM3v27Mk39P+XX37JwoULj7uvlJQUateuDcDf/vY3Pv30U0499dRCp9jY2ArZQqOQIiIiZWLDhg0kJiaSnp5OSkoKixcvJiIigtjYWGJjY0u9//HjxxMeHk58fDxhYWFkZGQwfvz4cgkoRdWgQQMaNGhAly5dTrruAw88QO/evdm1axe7du3il19+yf1zcnIyNWvWzF131apVfPjhh8fd1969e3NDyoMPPsiCBQuoVatWodMdd9xBtWrVAPj1119JT0+nVq1aVKtWLeA6BZtzzusaPGFm0cC+ffv2ER0d7XU5IuInycnJvP3227nvkRH/yWlJKagsWlLyioyMJD09nYiICI4cOVJm+w0kBd8SvXz5cjZu3EhycjJ79uzJN/322298//33uesPHDiQN99887j7/u2333ID0O2338706dMB3y216OhooqOjqVGjBtHR0bz99tu5t6fee+89vvnmG6Kjo2nTpg09e/YEqOGcS/XLSSBAWlLMbBjwINAQ+C9wr3Pui7LeRkRE/Kesw0hhEhISSE9PJywsjPT0dBISEgKqJaWsFGzRyHlbdFH89a9/Zfjw4fz222+FTnl/Mc/KyiIiIoL09HQyMzNz18mR9xbSggULcgPNXXfdVZqvV2SehxQzGwRMAYYB/wHuABaaWVvn3Lay2kZERIJbfHw8CQkJjBgxgqZNm7J161bi4+M5evRomXScrSiaN29O8+bNi7Tuiy++yPTp0zl06BC//fYb+/fvJzU1NXfKuS0EcPHFF2NmpKam0rFjRz9Vn5/nt3vM7Evga+fc0DzzNgDznXOPlNU2hexDt3tEKgHd7qkYEhISiI+PJy4u7rhP91TEFpVAlZqaSo0aNaAi3+4xswggFphUYNEioNB2rZJsk71dJBCZZ1Z18J1oEam49u/fz6FDh9i/f3+FfPqhskhLS+Ohhx7izjvvzDe/T58+tG7dmrS0NP17Xo7K61x72pJiZn8AdgIXOueW55k/GhjsnDvm/dsl2SZ7+Vjg8bL9BiIiIpVajHNui7927nmflGwFk5IVMq+020wEJuf5XB3YATQG9hehRtE5Kymdt+LTOSsZnbfi0zkrmZzzttefB/E6pCQDmUCDAvPrAUlluA3OuSNA7rNqeXpO7/fn/bSKROesZHTeik/nrGR03opP56xkyms8lZByOcpxOOfSgUSgR4FFPYBjX01Zwm1EREQk+HjdkgK+WzCvmNlqYAVwO9AEeB7AzIYD/Z1zlxV1GxEREQl+nocU59wbZlYHiMc3MNt3QG/n3NbsVeoCLYq5TVEcAcaR5xaQnJTOWcnovBWfzlnJ6LwVn85ZyZTLefN8nBQRERGRwnjaJ0VERETkeBRSREREJCAppIiIiEhAUkgRERGRgFRhQoqZDTOzn83ssJklmlm3stimJPsNFv44Z2Y21sxcgelX/32L8lfc82ZmF5nZu2b2S/b56FcW+w0m/jhnutaOWfcRM1tlZvvNbLeZzTezwl4tUmGvsxxlfd50rRW6/lAzW2tmqdnTCjO7srT7PYZzLugnYBCQDtwKtAGmAAeAJqXZpiT7DZbJj+dsLL5HwhvkmU71+vt6fN6uBP4HuBrfqxv6lcV+g2Xy4znTtZZ//Q+BPwPtgA7Ae8BWoGpluM78fN50rR27zVVAb+CM7GlC9j7aleX15vnJKaMT/CXwXIF5G4CJpdmmJPsNlsmP52wssMbr7xdI563Ausf7H66uteKfM11rJ97+1Oxzd1FZ7TMYJj+dN11rRdvPXmBIWe436G/3mFkEEAssKrBoEdClpNuUZL/Bwl/nLI+W2c30P5vZ62bWvAzK9py/rglda6Wia+34amT/3FuG+wxo/jhveehaO/4+Qs3sOqAqvlHgy+x6C/qQgm9E2lCOfblgEse+hLA425Rkv8HCX+cMfMn5ZqAncFv2suXmGyE42PnrmtC1VjK61o7DzAzf60OWOee+K4t9Bgl/nDfQtVYoMzvTzA7gG3X2eXyvsFlf2v3m5fmw+GWo4NC5Vsi8kmxTkv0GizI/Z865hXmWrTOzFcBmYDC+v/wVgb+uCV1rxdmhrrUTeRY4C+hahvsMJmV63nStHdcmoCNQExgAzDKzi/MElZLuN1dFCCnJQCbHJrN6HJvgirNNSfYbLPx1zo7hnDtoZuuAliUrNaD465rQtVYGdK35mNkzQBy+PhU7ymKfQcQf5+0YutZ8nHPpwI/ZH1eb2TnAPcAdpdlvXkF/uyf7JCUCPQos6gEsL+k2JdlvsPDXOSuMmUXi69W9q6T1Bgp/XRO61spGZb/WzOdZfE9EdXfO/VzafQYbf5y342xTqa+1EzAgskz363Wv4jLqmZzzmNNf8F04T+N7zKlp9vLhwCfF2aao6wTr5Mdz9nfgYiAGOA94F0itCOesFOetGr4m0Y74mjnvy/5zYY+761or+jnTtZbnvAHTgN+zz0neR2WrVIbrzM/nTdfasX9HnwC6Ac2AM/E9gpwJ9CjL683zk1OGJ3kYsAVfB55Ejn18bEtxtinOOsE6+eOcAa8Dv2RfmDuBt4C2Xn9XL88bcAm+/9EWnGbqWiv5OdO1lv+8Hed8OeDPleU689d507VW6N/RGXnW3w18TJ6AUlbXm2XvRERERCSgBH2fFBEREamYFFJEREQkICmkiIiISEBSSBEREZGApJAiIiIiAUkhRURERAKSQoqIiIgEJIUUERERCUgKKSISMMysjpntNrNmpdzPm2Y2sozKEhGPKKSISJkys5lm5vJMKWb2oZmdVYTNHwHedc5tKWUZ44FHzSy6lPsREQ8ppIiIP3wINMyeLgMygPdOtIGZVQGGAC+W9uDOubX43hdyQ2n3JSLeUUgREX844pz7NXtaA/wVOM3MTj3BNlcCGc65FTkzzOyS7NaYnmb2jZkdMrMlZlbPzK40sw1mlmpmr5lZVIH9LQD+VObfTETKjUKKiPiVmVXD16LxI5ByglUvAlYfZ9lYfK+L7wKcBswF7gWuB/oAPYC7C2zzFXCumUWWsHQR8ViY1wWISIX0RzM7kP3nqsAu4I/OuawTbNMM+OU4yx5zzv0HwMxmABOBFs65n7LnvQlciq/FJsdOIBJoAGwt4fcQEQ+pJUVE/OFToGP2dB6wCFhoZk1PsE0V4PBxlq3N8+ckIC0noOSZV6/ANoeyfxa8DSQiQUItKSLiDwedcz/mfDCzRGAfcBvw2HG2SQZqHWfZ0Tx/dgU+58wr+EtX7eyfe4pSsIgEHrWkiEh5cEAWvtaS4/kGaFuGx2wP7HDOJZfhPkWkHCmkiIg/RJpZg+ypDfAMUA149wTbfAS0M7PjtaYUVzd8t5lEJEjpdo+I+EMvfJ1lAfYDG4GBzrnPjreBc26dma0GrgVeKM3BzewUoD/QszT7ERFvmXPO6xpERAAws97A34H2J3kS6GT7uQvo65y7osyKE5Fyp5YUEQkYzrkPzKwl0AjYXopdHeXYcVNEJMioJUVEREQCkjrOioiISEBSSBEREZGApJAiIiIiAUkhRURERAKSQoqIiIgEJIUUERERCUgKKSIiIhKQFFJEREQkICmkiIiISED6PxvKsjgh4WNnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "B_exp=df_sel['L (mm)']/1000\n",
    "V_exp=df_sel['V_media']\n",
    "\n",
    "popt, pcov =curve_fit(get_V, B_exp,V_exp,p0=0.2,bounds=(0.01,1))\n",
    "print(\"r0=\",*popt)\n",
    "V_fit=get_V(B,popt[0])\n",
    "for n in df_sel['Imagen']:\n",
    "    if n in r0dict:\n",
    "        warnings.warn(\"Calculando r0 para una imagen dos veces\")\n",
    "    r0dict[n]=popt[0]\n",
    "    r0errdict[n]=get_err(pcov)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.ticklabel_format(useMathText=True)\n",
    "plt.xlim([0, 0.3])\n",
    "plt.ylim([0, 1])\n",
    "plt.plot(B, V_fit,ls1,color='black',label=f\"Best fit curve with $r_0$ = {popt[0]:0.3F} m\")\n",
    "plt.errorbar(B_exp, V_exp, yerr=df_sel['E_acc'],label=\"Experimental points\",**styledict_exp1)\n",
    "#ax.legend()\n",
    "\n",
    "date=df_sel['DATE'].iloc[0]\n",
    "ax.set_title(f'{date}')\n",
    "ax.text(0.47, 0.95, \"(c)\", transform=ax.transAxes, fontsize=14, verticalalignment='top')\n",
    "ax.set_xlabel(\"B (m)\")\n",
    "ax.set_ylabel(\"V\")\n",
    "\n",
    "\n",
    "plt.savefig(make_dir(\"productos/ajustes/ajuste-sirio-23\"+output_format))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# a parte-  Betelgeuse (Dia 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Imagen</th>\n",
       "      <th>Objeto</th>\n",
       "      <th>DATE</th>\n",
       "      <th>Filtro</th>\n",
       "      <th>L (mm)</th>\n",
       "      <th>V_media</th>\n",
       "      <th>S</th>\n",
       "      <th>E_acc</th>\n",
       "      <th>D (mm)</th>\n",
       "      <th>V_media_antigua</th>\n",
       "      <th>S_antigua</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imagen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Betelgeuse</td>\n",
       "      <td>17-03-15</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.641222</td>\n",
       "      <td>0.008581</td>\n",
       "      <td>0.049168</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.641222</td>\n",
       "      <td>0.008581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Betelgeuse</td>\n",
       "      <td>17-03-15</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>238.0</td>\n",
       "      <td>0.322587</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>0.021777</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.322587</td>\n",
       "      <td>0.003800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Betelgeuse</td>\n",
       "      <td>17-03-15</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.211979</td>\n",
       "      <td>0.018689</td>\n",
       "      <td>0.107091</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.211979</td>\n",
       "      <td>0.018689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Imagen      Objeto      DATE       Filtro  L (mm)   V_media         S  \\\n",
       "Imagen                                                                          \n",
       "8            8  Betelgeuse  17-03-15  Halfa-35 nm   147.0  0.641222  0.008581   \n",
       "10          10  Betelgeuse  17-03-15  Halfa-35 nm   238.0  0.322587  0.003800   \n",
       "11          11  Betelgeuse  17-03-15  Halfa-35 nm   133.0  0.211979  0.018689   \n",
       "\n",
       "           E_acc  D (mm)  V_media_antigua  S_antigua  \n",
       "Imagen                                                \n",
       "8       0.049168    51.0         0.641222   0.008581  \n",
       "10      0.021777    30.0         0.322587   0.003800  \n",
       "11      0.107091    30.0         0.211979   0.018689  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond=(df['DAY']==15) & (df['Filtro']=='Halfa-35 nm') & (df['Objeto']=='Betelgeuse')&(df['Imagen']!=9)\n",
    "df_sel=df.loc[cond]#.loc[df['Imagen'].isin([8,10])]\n",
    "df_sel[dispcols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nuevo r0= 0.3629309047496448\n",
      "inicial r0= 0.3629309047496448\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'17-03-15'"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "B_exp=df_sel['L (mm)']/1000\n",
    "V_exp=df_sel['V_media']\n",
    "\n",
    "popt, pcov =curve_fit(get_V, B_exp,V_exp,p0=0.2,bounds=(0.01,1))\n",
    "poptold, pcovold =curve_fit(get_V, B_exp,df_sel['V_media'],p0=0.2,bounds=(0.01,1))\n",
    "print(\"nuevo r0=\",*popt)\n",
    "print(\"inicial r0=\",*poptold)\n",
    "V_fit=get_V(B,popt[0])\n",
    "for n in df_sel['Imagen']:\n",
    "    if n in r0dict:\n",
    "        warnings.warn(\"Calculando r0 para una imagen dos veces\")\n",
    "    r0dict[n]=popt[0]\n",
    "    r0errdict[n]=get_err(pcov)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.ticklabel_format(useMathText=True)\n",
    "plt.xlim([0, 0.3])\n",
    "plt.ylim([0, 1])\n",
    "plt.plot(B, V_fit,ls1,c='black',label=f\"Best fit curve with $r_0$ = {popt[0]:0.3F} m\")\n",
    "plt.errorbar(B_exp, V_exp, yerr=df_sel['E_acc'],label=\"Experimental points\",**styledict_exp1)\n",
    "#ax.legend()\n",
    "date=df_sel['DATE'].iloc[0]\n",
    "ax.set_title(f'{date}')\n",
    "ax.text(0.47, 0.95, \"(b)\", transform=ax.transAxes, fontsize=14, verticalalignment='top')\n",
    "ax.set_xlabel(\"B (m)\")\n",
    "ax.set_ylabel(\"V\")\n",
    "plt.savefig(make_dir(\"productos/ajustes/ajuste-betel-15\"+output_format))\n",
    "date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# x Betelgeuse comparacion (Dia 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>Imagen</th>\n",
       "      <th>Objeto</th>\n",
       "      <th>DATE</th>\n",
       "      <th>Filtro</th>\n",
       "      <th>L (mm)</th>\n",
       "      <th>V_media</th>\n",
       "      <th>S</th>\n",
       "      <th>E_acc</th>\n",
       "      <th>D (mm)</th>\n",
       "      <th>V_media_antigua</th>\n",
       "      <th>S_antigua</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imagen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Betelgeuse</td>\n",
       "      <td>17-03-15</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.641222</td>\n",
       "      <td>0.008581</td>\n",
       "      <td>0.049168</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.641222</td>\n",
       "      <td>0.008581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Betelgeuse</td>\n",
       "      <td>17-03-15</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>238.0</td>\n",
       "      <td>0.322587</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>0.021777</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.322587</td>\n",
       "      <td>0.003800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Betelgeuse</td>\n",
       "      <td>17-03-15</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.211979</td>\n",
       "      <td>0.018689</td>\n",
       "      <td>0.107091</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.211979</td>\n",
       "      <td>0.018689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Imagen      Objeto      DATE       Filtro  L (mm)   V_media         S  \\\n",
       "Imagen                                                                          \n",
       "8            8  Betelgeuse  17-03-15  Halfa-35 nm   147.0  0.641222  0.008581   \n",
       "10          10  Betelgeuse  17-03-15  Halfa-35 nm   238.0  0.322587  0.003800   \n",
       "11          11  Betelgeuse  17-03-15  Halfa-35 nm   133.0  0.211979  0.018689   \n",
       "\n",
       "           E_acc  D (mm)  V_media_antigua  S_antigua  \n",
       "Imagen                                                \n",
       "8       0.049168    51.0         0.641222   0.008581  \n",
       "10      0.021777    30.0         0.322587   0.003800  \n",
       "11      0.107091    30.0         0.211979   0.018689  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond=(df['DAY']==15) & (df['Filtro']=='Halfa-35 nm') & (df['Objeto']=='Betelgeuse')&(df['Imagen']!=9)\n",
    "df_sel=df.loc[cond]#.loc[df['Imagen'].isin([8,10])]\n",
    "df_sel[dispcols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nuevo r0= 0.3629309047496448\n",
      "inicial r0= 0.3629309047496448\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'V')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "B_exp=df_sel['L (mm)']/1000\n",
    "V_exp=df_sel['V_media']\n",
    "\n",
    "popt, pcov =curve_fit(get_V, B_exp,V_exp,p0=0.2,bounds=(0.01,1))\n",
    "poptold, pcovold =curve_fit(get_V, B_exp,df_sel['V_media_antigua'],p0=0.2,bounds=(0.01,1))\n",
    "print(\"nuevo r0=\",*popt)\n",
    "print(\"inicial r0=\",*poptold)\n",
    "V_fit=get_V(B,popt[0])\n",
    "# for n in df_sel['Imagen']:\n",
    "#     if n in r0dict:\n",
    "#         warnings.warn(\"Calculando r0 para una imagen dos veces\")\n",
    "#     r0dict[n]=popt[0]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.ticklabel_format(useMathText=True)\n",
    "plt.xlim([0, 0.3])\n",
    "plt.ylim([0, 1])\n",
    "plt.plot(B, V_fit,ls1,c='black',label=f\"Best fit curve with $r_0$ = {popt[0]:0.3F} m\")\n",
    "# plt.plot(B_bet_exp, V_bet_exp, color='r', linestyle='None',marker='x')\n",
    "plt.errorbar(B_exp, V_exp, yerr=df_sel['S'],label=\"Experimental points\",**styledict_exp1)\n",
    "plt.errorbar(B_exp, V_exp, yerr=df_sel['E_acc'],label=\"Experimental error nuevo\",marker='x',color='red',linestyle='None',\n",
    "ecolor=(1, 0, 0, 0.4),elinewidth=1,capsize=2.5)\n",
    "ax.legend()\n",
    "ax.set_title('Betelgeuse')\n",
    "ax.set_xlabel(\"B (m)\")\n",
    "ax.set_ylabel(\"V\")\n",
    "#plt.savefig(\"productos/ajustes/ajuste-betel-15\"+output_format)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparacion lambda Sirio (Dia 29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>Imagen</th>\n",
       "      <th>Objeto</th>\n",
       "      <th>DATE</th>\n",
       "      <th>Filtro</th>\n",
       "      <th>L (mm)</th>\n",
       "      <th>V_media</th>\n",
       "      <th>S</th>\n",
       "      <th>E_acc</th>\n",
       "      <th>D (mm)</th>\n",
       "      <th>V_media_antigua</th>\n",
       "      <th>S_antigua</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imagen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Sirio</td>\n",
       "      <td>17-03-29</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.372131</td>\n",
       "      <td>0.017874</td>\n",
       "      <td>0.102422</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.372131</td>\n",
       "      <td>0.017874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Sirio</td>\n",
       "      <td>17-03-29</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0.138379</td>\n",
       "      <td>0.003587</td>\n",
       "      <td>0.020552</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.138379</td>\n",
       "      <td>0.003587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Sirio</td>\n",
       "      <td>17-03-29</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.226591</td>\n",
       "      <td>0.002237</td>\n",
       "      <td>0.012818</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.226591</td>\n",
       "      <td>0.002237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Imagen Objeto      DATE       Filtro  L (mm)   V_media         S  \\\n",
       "Imagen                                                                     \n",
       "19          19  Sirio  17-03-29  Halfa-35 nm   162.0  0.372131  0.017874   \n",
       "21          21  Sirio  17-03-29  Halfa-35 nm   205.0  0.138379  0.003587   \n",
       "22          22  Sirio  17-03-29  Halfa-35 nm   187.0  0.226591  0.002237   \n",
       "\n",
       "           E_acc  D (mm)  V_media_antigua  S_antigua  \n",
       "Imagen                                                \n",
       "19      0.102422    66.0         0.372131   0.017874  \n",
       "21      0.020552    66.0         0.138379   0.003587  \n",
       "22      0.012818    90.0         0.226591   0.002237  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond1=(df['DAY']==29) & (df['Filtro']=='Halfa-35 nm') & (df['Objeto']=='Sirio')&(df['Imagen']!=9)\n",
    "df_sel1=df.loc[cond1]\n",
    "df_sel1[dispcols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>Imagen</th>\n",
       "      <th>Objeto</th>\n",
       "      <th>DATE</th>\n",
       "      <th>Filtro</th>\n",
       "      <th>L (mm)</th>\n",
       "      <th>V_media</th>\n",
       "      <th>S</th>\n",
       "      <th>E_acc</th>\n",
       "      <th>D (mm)</th>\n",
       "      <th>V_media_antigua</th>\n",
       "      <th>S_antigua</th>\n",
       "      <th>PATH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imagen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Sirio</td>\n",
       "      <td>17-03-29</td>\n",
       "      <td>Hbeta-8.5nm</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.072809</td>\n",
       "      <td>0.004383</td>\n",
       "      <td>0.025113</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.072809</td>\n",
       "      <td>0.004383</td>\n",
       "      <td>datos/imagenes\\Sirio\\20Sirio0002 Bx3 Hbeta8.5n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>Sirio</td>\n",
       "      <td>17-03-29</td>\n",
       "      <td>Hbeta-8.5nm</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.139635</td>\n",
       "      <td>0.002157</td>\n",
       "      <td>0.012362</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.139635</td>\n",
       "      <td>0.002157</td>\n",
       "      <td>datos/imagenes\\Sirio\\23Sirio0007 Bx3 Hbeta8.5n...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Imagen Objeto      DATE       Filtro  L (mm)   V_media         S  \\\n",
       "Imagen                                                                     \n",
       "20          20  Sirio  17-03-29  Hbeta-8.5nm   162.0  0.072809  0.004383   \n",
       "23          23  Sirio  17-03-29  Hbeta-8.5nm   187.0  0.139635  0.002157   \n",
       "\n",
       "           E_acc  D (mm)  V_media_antigua  S_antigua  \\\n",
       "Imagen                                                 \n",
       "20      0.025113    66.0         0.072809   0.004383   \n",
       "23      0.012362    90.0         0.139635   0.002157   \n",
       "\n",
       "                                                     PATH  \n",
       "Imagen                                                     \n",
       "20      datos/imagenes\\Sirio\\20Sirio0002 Bx3 Hbeta8.5n...  \n",
       "23      datos/imagenes\\Sirio\\23Sirio0007 Bx3 Hbeta8.5n...  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond1=(df['DAY']==29) & (df['Filtro']=='Hbeta-8.5nm') & (df['Objeto']=='Sirio')&(df['Imagen']!=9)\n",
    "df_sel2=df.loc[cond1]\n",
    "df_sel2[dispcols+['PATH']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r0= 0.3151585549724359\n",
      "r0= 0.21815983603464414\n"
     ]
    },
    {
     "data": {
      "image/png": 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Qjqpy5cqEh4czfvx4zpw5wzvvvEORIkU4ffo0ly9fNjpegkiRYid27txJgwYN4hzwLT45c+Zk4sSJZMiQAT8/PypXrsyWLVtsmFKIpItqhs6bN2+M+VeuXGH79u1UrFiRLl26xFg2bNiw51pdoovaV/QmbiGEWfHixRk9ejTfffcdVapUwd3dHT8/PwoVKkSTJk2MjpcgUqTYgdDQULp3787OnTvx9PRMcEcqpRQ9e/bkwIEDlCtXjps3b9K0aVOGDx9OWFiYjVMLkTgBAQEAZM+ePcb8o0ePAlC3bt3ntsmUKROVK1eOd585cuQAzK2KQqQlCxYsiHP8rIYNG6K1thT3I0aM4O7duzx9+pQFCxaQK1cuDh06xL///puygZNIihQ7kC5dOtasWUOOHDnYt28fHTp0IDQ0NMHblytXjv3799O7d2+01vzvf/+jcePGzw04JISRMmTIAPDc4FaBgYGAeayJuMRueYkual/SkVyI1EmKFDtRoUIF1q9fj5ubGxs3bqRXr16J6qPi5ubG7Nmz+fnnn8mcOTNVqlQhffr0NkwsROLkzp0b4LkBCbNmzQqYb+OMy4v6WkXtK2rfQojURYoUO1KzZk1+/fVXTCYTixYt4quvvkr0Pjp37syxY8eYMGGCZd7jx48T3SlXCGsrV64cTk5OzzUzV6pUCYBdu3Y9t83jx4/x9/ePd59R4ww5yu2UQojEkSLFzrRs2ZLZs2cDMHbsWKZPn57ofRQtWhQXFxfA3N+lZcuWdOrUiUePHlk1qxCJkS1bNipWrMjBgwdjFM2FCxemfv36HDt2jKVLl8bY5ptvvnnhAzb37dsHmJ+RJYRIfaRIsUM9evRg9OjRAAwcOPC50Q0TY9++fezevZsVK1ZQs2ZNzpw5Y62YQiRamzZtCAwM5MCBAzHmz5gxgyxZsvDee+/RsWNHhg0bRtOmTZkxYwb16tWLd3+bNm0ie/bs1K9f39bRhRAGkCLFTg0fPpwPP/wQrTVdu3a1/MWYWHXr1mX79u3kz5+f06dPU716dVatWmXltEIkTK9evTCZTCxZsiTG/PLly/P333/TvHlzNmzYwPTp00mXLh1///03xYoVi3Nfly9f5u+//6Zbt27S/0qIVEql1b4KSqksQGBgYCBZsmQxOk6cwsPDadOmDevWrSNv3rzs37+fwoULJ2lft27dolOnTmzfvh2AUaNGMWLECJRS1owsxEt5eXnh5+fH5cuXkzVE+MiRI/n22285ffo0r7zyihUTirQiODiYixcv4uHhIYVuHF50fh4+fBjV6T2r1vqhrTJIS4odM5lMLFu2jIoVK3Lr1i3efPPNJPcryZs3L5s3b2bQoEEAfP3113z66adWTCtEwvzvf//j8ePHzJgxI8n7ePDgAd9//z19+/aVAkWIVEyKFDuXOXNm1q1bR758+ahcubKlQ2xSODs7M2XKFObNm0euXLno2bOnFZMKkTAeHh4sXLgwWa0oly5dYtCgQYwcOdKKyYQQ9kYu99jx5Z7orl27Rv78+a12eebRo0dkzpzZ8j4gIICcOXNaZd9CCOEI5HLPi8nlHpFgBQoUsBQo4eHh7NmzJ1n7i16g/PXXXxQpUoSFCxcma59CCCGENUmR4mCePXtG+/btqVevHn/99ZdV9rl06VKePHlC9+7d+eyzz4iIiLDKfoUQQtiHhQsXUrZsWdzc3ChdujTr1q0zOlKCSJHiYFxcXMiYMSPOzs7cvHnTKvucNWsWI0aMAGDChAl07NiRoKAgq+xbCCGE9XXv3p02bdo8N/+vv/5CKRVjEMTVq1fTv39/hg8fzokTJ/D09KRPnz4pFzYZpEhxMEop5syZw969e+ncubNV9unk5MTo0aNZtmwZLi4urFq1ikaNGr3wmSlCCCEcw6RJkxg8eDBeXl4UK1aMFi1a8PjxY6NjJYiz0QFE4rm5ucV4fP2DBw/ImjVrsjvVvvPOOxQsWJA2bdqwf/9+atWqxZ49e8iXL18yEwshhGN58uRJordxdXXF2dn8azUsLIxnz57h5ORkeQJ4fPtNzp1uL/Po0SP27NnDpEmTLPM2bNgQ43eIPZOWFAd36NAhKlasyNSpU62yv3r16rFnzx5eeeUVateuTd68ea2yXyGEcCSZMmVK9LR69WrL9qtXryZTpkx4enrG2G/RokWf286Wjh49ilKKihUrEhQUxJw5c5g2bRpDhgyx6XGtRVpSHNzu3bv577//+PTTT6lYsSKvv/56svdZsmRJ9u7dS+bMmS2tMxERETg5SU0rhBD2Yt26dc8VOeHh4THe+/v7U7p0afz9/alduzYAbdu2pWXLlimWMzmkSHFwAwYM4NChQyxcuJBOnTpx4MABPDw8kr3fXLlyWV5HRETw9ttvU65cOby9vWUofSFEqpeUPhuurq6W123btuXx48fP/XF36dKl5EazaNSoET/++GOMefv27aNr166W9/7+/lSpUoXy5cuzb98+9uzZw1dffcXXX3+Nj4+P1bLYihQpDk4pxcyZMzl58iQHDx6kQ4cO/P3331YdmGjjxo34+vri6+vLlStXmD17NunSpbPa/oUQwt4kt5+Is7OzpX+KNfcbe1/FixePMe/q1asx3vv7++Pl5UXmzJmpUaMGNWrU4MyZM+zdu9dqOWxJ2u9TgfTp07Nq1Spy5crF4cOH+fjjj626f09PT2bPno2TkxMLFiygTZs2SepUJoQQIuWEhYVx8uRJSpcuHWP+0aNHqVevnkGpEkeKlFSiUKFCLFu2DKUUs2fPZtGiRVbdf+/evVmzZg3p06dn/fr1vP7669y9e9eqxxBCCGE9Z86cITg4mDFjxnDkyBHOnj3LoEGDuHjxIr179zY6XoJIkZKKNG3aFG9vbwD69OnD8ePHrbr/Vq1asWXLFnLkyMG+ffuoU6eOVa+vCiGEsB5/f3/c3d3JmDEj9erVo379+vz3339s27YNd3d3o+MliDxg0EEeMJhQERERtGjRgo0bN1KiRAkOHjxo9e/v9OnTNGvWjDt37rB582bq1Klj1f0LIURKSO0PGPz00085f/58jFujE0MeMCiszsnJiSVLllCoUCH+/fdfevTogbUL0TJlyrBnzx7Wrl0rBYoQQtgpf39/KlasaHSMZJEiJRXKlSsXK1asIF26dPj6+vLdd99Z/RgFChTgjTfesLw/duwYvr6+Vj+OEEKIpDl69KgUKcI+1axZk8mTJwOwbds2q7emRHfjxg2aN29Ox44d+eGHH2x2HCGEEAl3584d2rdvb3SMZJEiJRXr378/K1euZPXq1TYdgC1Pnjy0bt0arTX9+/dnzJgxNi2KhBBCpA1SpKRiSinat28fY8RDWxQPJpOJH374gZEjRwIwYsQIPv30UylUhBBCJIsUKWnEkydP6NWrF1OmTLHJ/pVSjBo1yrL/yZMn06tXL8LCwmxyPCGEEKmfFClpxJo1a5g3bx7Dhg3jxo0bNjvOoEGD+Omnn3BycuKnn35i7NixNjuWEEKI1E2e3ZNGeHl5sWfPHtq1a2fzQXzef/99smbNyuTJk60+RL8QQoi0wy5aUpRS/ZRSF5VSwUqpQ0qpeB8qoJRyVkqNiVz/qVLqglJqpFLKLr4Xe6WUYvr06TRu3DhFjteuXTt27NgRYyC5p0+fpsixhRBCpA6G/2JXSnUCpgL/A6oAO4E/lVKF49nkc6APMAAoA3wGDAU+snnYVOTff/9N8iiECRW9w+748eOpVasWt27dsukxhRBCpB6GFynAYGCe1nqu1vq01noQ8B/QN571XwPWaq3/0Fpf0lqvBPyAV1MmruM7deoUVatWxcvLixMnTtj8eA8ePGDKlCkcO3aMunXrcvnyZZsfUwghhOMztEhRSrkA1TAXGdH5AbXj2WwX8LpSqmTkPioBdYH1LzmWq1IqS9QEZE5WeAdWunRp6tatS3BwMJ06dSIoKMimx8uWLRs7d+6kaNGinDt3jjp16nD27FmbHlMIIYTjM7olJRdgAmJfA7gF5Itnm3HAz8AZpVQocASYqrX++SXH+hIIjDZdTWpoR+fk5MTChQtxd3fn1KlTKdK5tXjx4uzatYsyZcpw7do1GjRokCKtOEIIYSve3t74+PjEuczHx8fyVHpb6N69O23atHlu/l9//YVSigcPHtjs2CnJ6CIlSuxRv1Qc86J0AroCXkBVoBvwqVKq20uOMRbIGm0qmOS0qUCePHlYsmQJSinmzp3LL7/8YvNjFihQgO3bt1OpUiVu3bpFw4YNOXz4sM2PK4QQtmAymRg5cuRzhYqPjw8jR47EZDIZlCz1MLpIuQuE83yrSR6eb12JMgH4Vmv9i9b6uNZ6MTAFc0tJvLTWz7TWD6Mm4FEyszu8xo0bM3z4cAA+/PBDLl26ZPNj5s6dm23btlG9enUCAgLYt2+fzY8phBC2MGLECEaPHh2jUIkqUEaPHs2IESMMTmi2f/9+GjZsSIYMGShdujQHDhxg9uzZvPXWW0ZHeylDixStdQhwCGgaa1FTYHc8m7kBEbHmhWN8weWQRo4cSa1atXj48CHvvvtuiowQmz17djZv3szixYvp2ze+/tFCCGH/ohcqrq6udleg7N27lwYNGtC8eXOOHTtG2bJl8fb2ZsKECYwaNcroeC+ntTZ0wnz5JgTogfmW4inAY6BI5PIBwJZo6y/A3J+kJVAUaAvcAcYl8rhZAB0YGKjTuvPnz+vMmTNrQPv4+BiS4d69e3r79u2GHFsIkTY9ffpUnzp1Sj99+jTZ+3JxcdGAdnFxsUKyl+vWrZs2mUw6Y8aMMab06dNrQN+/f19rrfVrr72mu3TpYtlu+fLl2snJSbdt2/alx3jR+QkMDNSYu2Vk0TasEQxvfdBaLwcGASMBf6A+0EJrHXWfai7glWibfASsBH4ATgMTgVmAfZStDqhYsWLMmDEDMHcES+lLMI8ePcLT05OmTZvy+++/p+ixhRAiuXx8fAgJCcHFxYWQkJB4O9NaW6NGjfD3948xzZ0717L86tWr7NmzJ0aLtYuLC1prx2hFwU4ukWitf9BaF9Vau2qtq2mtd0Rb5q21Lhrt/SOt9SCtdRGtdQat9Sta6+HafOlIJFHXrl3p3Lkz4eHheHl58ehRynXZcXV1pUCBAoSEhNCuXTtWrFiRYscWQojkiN4H5dmzZ8/1UbGljBkzUrx48RhTgQIFLMtPnz4NwKuv/v8wYmfPnqVGjRpUqFDB5vmswS6KFGE8pRQ//vgjhQsX5sKFCwwcODDFju3i4sLy5cvx8vIiLCyMzp07s2TJkhQ7vhBCJEVcnWTj6kxrlMDAwBh3GN27d4/x48fj6upqYKrEkSJFWGTLlo0lS5bg5OREhgwZCA8PT7FjOzs7s2jRInr06EFERATvvfcec+bMSbHjCyFEYoWHh8fZSTaqUEnJn6FxqVy5MuHh4YwfP54zZ87wzjvvUKRIEU6fPu0wI3/LU5BFDPXq1eP06dOULFkyxY9tMpmYM2cO6dOn54cffuCDDz7AycmJnj17pngWIYR4mRcN1mYPd/cUL16c0aNH89133/HNN9/QqVMnli5dSrNmzWjSpAn//vuv0RFfSmkd35hpqVvk0PiBgYGBMZ7UK2KKiIhAa52igxJprRk6dChLlixh+/btlCpVKsWOLYRIO4KDg7l48SIeHh6kT5/e6Dh250Xn5+HDh2TNmhUgqzaPPWYTcrlHxOvq1as0adKEcePGpehxlVJMmDCBY8eOSYEihBBpmBQpIl5bt25l27ZtTJgwIcWfA6GUIk+ePJb3fn5+TJw4MUUzCCGEMJb0SRHxevfdd7lw4QJdunQhW7ZshuW4ePEirVu3Jjg4mJCQEIYNG2ZYFiGEEClHWlJEvJRSeHt7U6JECUNzeHh48NVXXwHw1VdfGX5bnxBCiJQhRYpIsB07drBx40ZDjj18+HC++eYbwPy8IW9vb9Jqp28hhEgr5HKPSJD169fz5ptvkitXLk6cOBGjv0hK+fLLLzGZTHz++eeMGjXKMkaBUirFswghhLA9aUkRCfL6669Tvnx57ty5Q+/evQ1rxfjss8+YNGkSAGPGjOHXX381JIcQQgjbkyJFJIirqytLlizBxcWF3377jZ9++smwLIMHD2bKlCl06tSJ9u3bG5ZDCCGEbUmRIhKsYsWKjBkzBoCPP/6Y8+fPG5Zl0KBB/Pzzzzg7m69YRg06J4QQIvWQIkUkyuDBg6lfvz5PnjzhvffeM/TZFFF9USIiIujVqxeffPKJFCpCCJGKSJEiEsVkMrFw4UIyZ87M7t27GT9+vNGR2LlzJ/Pnz+e7777j448/lkJFCCFSCSlSRKIVLVqUadOmAebbgY8cOWJongYNGjBnzhyUUkybNo0BAwZIoSKEENEsXLiQsmXL4ubmRunSpVm3bp3RkRJEihSRJO+99x7t2rUjLCyMrl278vTpU0Pz9OrVi3nz5qGU4ocffqB///5SqAghUq3u3bvTpk2b5+b/9ddfKKViPMpk9erV9O/fn+HDh3PixAk8PT3p06dPyoVNBilSRJIopZg1axZ58+bl1KlTdjFU/fvvv8/8+fNRSvHjjz9KoSKESDGbN2+mbNmybN682egoz5k0aRKDBw/Gy8uLYsWK0aJFCx4/fmx0rASRIkUkWa5cuSy3Ik+dOpUtW7YYnAi6detmKVTmzJnD0aNHjY4khEjltNYMGzaM06dPM2zYMLv64+jRo0fs2bOHli1bWuZt2LCBypUrGxcqEWTEWZEsLVq0oE+fPsyfP5/Lly8bHQcwFypaa7Jnz+4w/xGFEI7Lz8+PAwcOAHDgwAH8/Pxo1qyZwanMjh49ilKKihUrEhQUxNKlS5k2bRq+vr5GR0sQKVJEsk2cOJEBAwZQrlw5o6NYdO/ePcb7O3fukCtXLhlCXwhhVVprRowYgclkIjw8HJPJxIgRI3jjjTds/vNm3bp1ZMqUKca82MNC+Pv7U7p0afz9/alduzYAbdu2jdGyYs/kco9ItowZM8YoUOypqRPg0qVLVK9enYEDB9pdNiGEY4tqRYkqDsLDwy2tKbbWqFEj/P39Y0xz586NsY6/vz9VqlShfPny7Nu3j6lTp+Ln58fXX39t83zWIEWKsKr9+/fz2muvcfXqVaOjWOzdu5crV64wffp0KVSEEFYT1Yri5BTzV6mTkxMjRoyw+c+ajBkzUrx48RhTgQIFYqwTVaRkzpyZGjVq8PHHH/Puu++yd+9em2azFilShNVorRk0aBD79u3jyy+/NDqORefOnZk7dy5KKaZPny4DvgkhrCIkJIQrV64QERERY35ERAT//fcfISEhBiUzCwsL4+TJk5QuXTrG/KNHj1KvXj2DUiWO9EkRVqOUYv78+XzzzTdMmTLF6Dgx9OjRA4CePXtaBqL77rvvpI+KECLJXF1dOXDgAHfu3HluWZ48eXB1dTUg1f87c+YMwcHBjBkzBnd3d9zc3Pjxxx+5ePEivXv3NjRbQkmRIqyqVKlSLFy40OgYcerRowdaa3r16sW0adNQSjF16lQpVIQQSVaoUCEKFSpkdIw4+fv74+7uTsaMGalXrx4ZM2akbt26bNu2DXd3d6PjJYgUKcJmtNasXr2aVq1akS5dOqPjAOaWFDCPULtt2zYePXpElixZDE4lhBCJs2DBgjjnN2zY0HI529/fn5o1a7J69eoUTGZd0idF2Ezv3r1p3749Pj4+RkeJoWfPnvz6669s2bJFChQhRKrl7+9PxYoVjY6RLFKkCJtp2rQpAN98843d9STv2LEjuXPntrzfs2ePdKYVQqQqR48elSJFiPh06tQJLy8vwsPDeffdd3ny5InRkeI0Z84cateuzeDBg6VQEUKkGnfu3KF9+/ZGx0gWKVKETU2fPp0CBQpw7tw5hg4danScF5o6dSpDhgyRQkUIIeyEFCnCprJnz27p4PXjjz/y559/GhsoDr1792bWrFkATJkyhU8//VQKFSGEsANSpAiba9KkCR9//DFgvg347t27Bid63gcffMDMmTMBmDx5MkOHDpVCRQghDCZFikgRY8eOpUyZMty8eZM+ffrYZQHw4YcfWgqVSZMm8dlnn9llTiGEdcn/87jZw3mRIkWkiAwZMrB48WKcnZ3x9fVlyZIlRkeK04cffsiPP/4IYPhokUII24oavykoKMjgJPYp6rwYOc6VsodKyQhKqSxAYGBgoIyVkYL+97//MXz4cLJkycLx48cpXLiw0ZHitH//fqpXry6j0QqRyt24cYMHDx6QJ08e3Nzc5P885haUoKAgbt++TbZs2eIcnfbhw4dkzZoVIKvW+qGtskiRIkVKigoLC6NevXrs3buXRo0asXnz5ueeIGpvgoKCWLZsGT179pQfYEKkMlprbt68yYMHD4yOYneyZctGvnz54vy5l1JFigyLL1KUs7MzixcvplKlSpw4cYILFy5QvHhxo2PFKzw8nNatW7N582b+/fdfvv32WylUhEhFlFK4u7uTJ08eQkNDjY5jN9KlS4fJZDI6hhQpIuUVL16cVatWUaVKFfLkyWN0nBcymUy0adOGzZs3M378eJRSjB07VgoVIVIZk8lkF7+URUxSpAhDNGvWzOgICda/f38ABgwYwLhx4wCkUBFCiBRg350BRJqwYsUKvv32W6NjvFD//v2ZPn06AOPGjWPYsGF2cXueEEKkZtKSIgy1f/9+3n77bZycnHj99depXr260ZHi1b9/f7TWfPTRR3z77be4ubkxYsQIo2MJIUSqJUWKMFSNGjXo0aMHBQoUoHLlykbHeakBAwagtebrr7/G09PT6DhCCJGqyS3Icguy4bTWDte/4+7du+TKlcvoGEIIYYiUugVZ+qQIw0UvUEJCQjh16pSBaRImeoGyd+9eRo8eLX1UhBDCyuRyj7AbV69e5a233uLatWscP37c7m9PBrhz5w7NmzcnMDCQoKAguetHCCGsSFpShN3IlSsXoaGh3L59mw8++MAhWiZy587NmDFjAPNdP59//rlD5BZCCEcgRYqwG+nTp2fx4sWkS5eOtWvXsmDBAqMjJciAAQMstydPmDCBTz/9VAoVIYSwAilShF2pXLkyo0ePBmDgwIFcvHjR4EQJ079/f3744QcAJk+ezODBg6VQEUKIZJIiRdidoUOHUqdOHR4/fky3bt0IDw83OlKC9O3bl5kzZwIwdepU5syZY3AiIYRwbHZRpCil+imlLiqlgpVSh5RS9RKwTQGl1BKlVIBSKkgp5a+UqpYSeYVtmUwmFi1aRKZMmdi5cyeTJk0yOlKCffjhh8yePRtPT0/ee+89o+MIIYRDM3ycFKVUJ2Ax0A/4G/gQ6AWU1VpfiWeb7MARYBvwI3AbeAW4pLU+n8Djyjgpdm7evHn06tWLdOnSceDAASpVqmR0pASLiIjAycn8N0DU/zG560cIkVqk1Dgp9lCk7AMOa637Rpt3Glijtf4ynm2+BeporV/a4vKC40qRYue01rRu3Zrff/+dChUqcODAAVxdXY2OlShaaz777DMeP37MjBkzLIWLEEI4sjQxmJtSygWoBvjFWuQH1H7Bpm8BB5VSK5RSt5VSR5RSvV9yLFelVJaoCcicrPDC5pRSzJkzh9y5c3P8+HGHfE7O4cOHmTRpEjNnzqRv375EREQYHUkIIRyG0X/W5QJMwK1Y828B+V6wXTGgL/Av0AyYCXyvlHpRJ4AvgcBo09UkZhYpKG/evJYOqBMnTmT79u0GJ0qcatWqsWjRIpycnJg9ezYffvihFCpCCJFARhcpUWJfc1JxzIvOCfMlomFa6yNa61nAHMyFS3zGAlmjTQWTkVekoNatW9OjRw+01owfP97oOInWtWtXFi9ejJOTE3PnzqV3795SqAghRAIYPSz+XSCc51tN8vB860p0N4DYD3g5DbSPbwOt9TPgWdR76cToWKZOnUrRokUZOnSo0VGSxMvLCycnJ7p06cJPP/1EREQEc+fOxWQyGR1NCCHslqEtKVrrEOAQ0DTWoqbA7hds+jdQKta8ksBl66UT9iRz5syMGDGC9OnTGx0lyTp37syyZcswmUwsXLiQ3btf9BEXQghhdEsKwGRgsVLqILAH+AAojLmfCUqpAUBbrfXr0baZAuxWSg0DfgVqRG73QUoGF8YICwtj6tSpdO3alXz5XtR1yf506tQJJycnnjx5Qr16Sb45TQgh0gTDixSt9XKlVE5gJOAOnABaaK2jWkVyYR4DJfo2B5RSbTH3MxkJXAQGaa2XplxyYZR+/foxZ84ctm/fzm+//eZwl+46duwY4/3du3fJli0bzs6G/3cUQgi7Yvg4KUaRcVIc14kTJ2jcuDETJ07k3XffdbgiJbqbN2/SqFEjKleuzOLFi6VQEUI4hJQaJ0V+IgqHU758eS5fvkyGDBmMjpJsR48e5fz585w5c4bw8HCWLl1KunTpjI4lhBB2wV5uQRYiUaIXKAEBAYSEhBiYJumaNWuGr68v6dKlY8WKFXTs2JFnz569fEMhhEgDpEgRDm3r1q1UqFCB4cOHGx0lyVq1asWaNWtwdXVl7dq1tG3blqdPnxodSwghDCdFinBoDx8+5MaNG0yYMIEtW7YYHSfJWrRowR9//EGGDBn4888/efPNN3ny5InRsYQQwlBSpAiH1qZNGz74wHzn+XvvvUdAQIDBiZLu9ddfZ+PGjWTKlIlLly4RGBhodCQhhDCU3N0jd/c4vCdPnlCtWjXOnj1L27Zt8fX1deg7fvbv30/evHkpUqSI0VGEECJOaeIpyEJYQ8aMGfn5559Jly4dq1evZu7cuUZHSpYaNWrEKFD++OMPh24hEkKIpJIiRaQKVapU4ZtvvgFg0KBBnD171uBE1rF27Vpat25No0aNuHXrRY+zEkKI1EeKFJFqDB48mCZNmhAUFISXl5fD3pYcXcmSJcmTJw/Hjx+nYcOGXLt2zehIQgiRYqRIEamGk5MTCxcuJGfOnBw+fNihb0uOUqZMGXbs2EGhQoU4c+YMDRo04MqVK0bHEkKIFCFFikhV8ufPz7x58wAc/rbkKMWLF2fHjh14eHhw/vx56tevz/nz542OJYQQNidFikh1WrduzYcffgg4/m3JUYoWLcqOHTsoWbIkly9fpn79+ty8edPoWEIIYVNSpIhUafLkyZQuXZrr168zZ84co+NYRcGCBdm+fTtly5aldevW5M2b1+hIQghhUzJOioyTkmodOXKE3bt3069fP4ceNyW2Bw8ekCVLFpyc5G8MIYQxZJwUIZKpSpUq9O/fP1UVKADZsmWzFCihoaF4eXmxY8cOg1MJIYT1SZEi0oSHDx/yxRdfEBQUZHQUq5owYQI///wzzZo1488//zQ6jhBCWJUUKSLV01rz5ptvMm7cOAYPHmx0HKv65JNPaNmyJcHBwbRu3ZoVK1YYHUkIIaxGihSR6iml+Prrr/Hw8KBbt25Gx7GqDBkysHr1ajp16kRoaCidO3dm/vz5RscSQgirkI6z0nE2zQgJCcHFxcXoGDYRHh5Onz59LM8tmjp1Kh9//LHBqYQQqZXddZxVSlW2VQghUkL0AuXMmTOpYtj8KCaTidmzZ1suZw0fPpzr168bnEoIIZInMZd7DiulDiml+iqlstoskRA2tnjxYqpUqcIXX3xhdBSrUkoxceJExowZw2+//Ub+/PmNjiSEEMmSmCKlDnAY+Ba4oZRaopRqZJtYQthO1qxZCQ4OZsqUKaxbt87oOFallOKrr76iUaP//6954cIFwsPDDUwlhBBJk+AiRWu9R2vdG8gH9AUKApuVUueVUl8ppQraKqQQ1vTWW29Z+mt0796dq1evGpzIdk6ePEmNGjXo2rUroaGhRscRQohESfTdPVrrp1rrhVrrhkBJ4GfgQ+CiUmq9lfMJYRPjxo2jatWqBAQE0KVLF8LCwoyOZBP//vsvDx8+5JdffqFt27Y8ffrU6EhCCJFgyboFWWt9HvPln/8BD4Fm1gglhK25urryyy+/kClTJnbs2MGYMWOMjmQTbdq0Ye3ataRPn54//viD5s2b8+DBA6NjCSFEgiS5SFFKNVBKLQRuAuOBVZj7rQjhEEqUKMGsWbMAGD16NNu2bTM4kW14enri5+dHlixZ2LFjBw0aNODGjRtGxxJCiJdKVJGilCqklBqhlDoPbANeAT4C8mute2ut99oipBC24uXlxfvvv4/WGi8vL27evGl0JJuoV68eO3bsIG/evBw7dow6depw5coVo2MJIcQLJWaclE3ARaAfsBIoo7Wuq7Wer7V+YquAQtjatGnTKFeuHDdv3sTLyyvV3glTqVIl/v77b4oVK0aBAgXInTu30ZGEEOKFEjzirFLqN2AesE5r7fA/xWXEWRHd6dOnqV69Ok+ePOGrr75KtX1UAG7evEn69OnJli2b0VGEEA4qpUaclWHxpUgRkX7++We8vLwAWL9+PZ6engYnShne3t5UrlyZNm3aGB3FqoKCguJ86rWbmxtubm4GJBIi9UipIsXZVjsWwtG888477Ny5kx9//BF/f/80UaSsXbuWUaNG4eTkxOzZs+nZs6fRkazm9OnTHDp0iJCQEG7cuIG7uzsuLi5Uq1aNatWqGR1PCJEA0pIiLSkimmfPnrF79+4YI7amZmFhYXz44Yf89NNPAIwdO5bPP/8cpZTByZIvqiUlICCAP/74g5YtW5IzZ05pSRHCCuzuAYNCpAWurq4xCpTU2ok2irOzM3PnzrU8x+jLL79kyJAhREREGJws+dzc3MiVKxc5c+YkU6ZM5MyZk1y5ckmBIoQDkSJFiHhcvnyZ2rVrs3LlSqOj2JRSirFjxzJp0iQApkyZQrdu3WQYfSGE4aRIESIeP/30E/v372fo0KFp4hf24MGDWbRoESaTiSVLlrB9+3ajIwkh0jjpOCtEPEaMGMG9e/cYMmQI6dKlMzpOinj33XfJlSsX//zzD02aNDE6jhAijZMiRYh4ODs7M23aNKNjpDhPT88YdzZdu3aNhw8fUqZMGQNTCSHSIrncI0QCrVu3znIXTFoRGBhIixYtqFOnDrt27TI6jhAijZEiRYgE+Pvvv2nVqhV9+vRhz549RsdJMWFhYWTMmJH79+/TpEkTfH19jY4khEhDpEgRIgFq165Nhw4dCA0NpUOHDqn2QYSx5cyZk82bN9OmTRuePXtGx44d+e6774yOJYRII6RIESIBlFL89NNPlC1bluvXr9OxY8c0cccPmMcbWblyJf369UNrzaBBg1LNWCpCCPsmRYoQCZQ5c2ZWrVpFlixZ2LVrF59++qnRkVKMyWRi+vTpfPvttwBMnjyZ4cOHG5xKCJHaSZEiRCKUKlWKRYsWAfD999+zZMkSgxOlHKUUn3/+OUuWLMHDw4O+ffsaHUkIkcpJkSJEIrVu3drSitC7d2+OHDlicKKU1aVLF06fPk2hQoUs8x49emRgIiFEaiVFihBJ4O3tjaenJ8HBwbRr14579+4ZHSlFubq6Wl6vXLmS4sWLs3v3bgMTCSFSIylShEiCqKHjixUrxqVLl3jnnXdS/cMI46K15rvvvuP27ds0btyYn3/+2ehIQohURIoUIZIoR44crFq1igwZMuDn52d5knBaopRiw4YNtG7dmmfPnuHl5YWPjw9aa6OjCSFSASlShEiGSpUqsWDBAgAmTpzI/v37jQ1kgIwZM+Lr68uQIUMAGDlyJN26dePZs2cGJxNCODp5do8QyfT2229z5swZ8ufPT40aNYyOYwiTycTEiRMpXrw4AwYMYPHixVy+fBk/P78Y/VeEECIx7KIlRSnVTyl1USkVrJQ6pJSql4htv1RKaaXUVBtGFOKFRo4cSa9evYyOYbg+ffrwxx9/kDlzZqpXry4FihAiWQwvUpRSnYCpwP+AKsBO4E+lVOEEbFsd+AA4ZsuMQiTG3bt3+eijj3j69KnRUQzRrFkz/P39GTdunGWe9FERQiSF4UUKMBiYp7Weq7U+rbUeBPwHvHCkKKVUJmAp0Bu4b/OUQiSA1poWLVowffp0Bg4caHQcwxQrVgyTyQTAs2fPeOONN5g/f77BqYQQjsbQIkUp5QJUA/xiLfIDar9k8xnAH1rrzQk8lqtSKkvUBGROdGAhXkIpxYQJEyhdujSDBg0yOo5dmDdvHps3b6ZHjx4MHjyYsLAwoyMJIRyE0S0puQATcCvW/FtAvvg2Ukp1xlzcfJmIY30JBEabriYqqRAJ1KBBA44fP065cuWMjmIX+vTpw9dffw3AlClTePPNN3nw4IGxoYQQDsHoIiVK7AvWKo555gVKFQK+A7porYMTcYyxQNZoU8Ek5BQiQZyd///GuR07dnDixAkD0xjLyckJb29vfv31VzJkyMDGjRupVasW//zzj9HRhBB2zugi5S4QzvOtJnl4vnUlSrXI5YeUUmFKqTCgATAw8r0pro201s+01g+jJkAeNiJs7vfff+f111/nzTff5Nat+D7SaUPHjh35+++/KVSoEGfPnqVGjRr89ddfRscSQtgxQ4sUrXUIcAhoGmtRUyC+B4FsASoAlaNNBzF3oq2stU57Y5MLu1W7dm2KFi3K5cuXeeuttwgKCjI6kqGqVKnCgQMHqF3b3OXM3d3d4ERCCHtmdEsKwGSgl1Kqh1KqjFJqClAYmAmglBqglNoStbLW+pHW+kT0CXgCBES+FsJu5MyZkz/++IMcOXKwf/9+3nvvPSIiIoyOZai8efOydetWtm/fTqlSpSzzrXmbsre3Nz4+PnEu8/Hxwdvb22rHEkLYjuFFitZ6OTAIGAn4A/WBFlrry5Gr5AJeMSScEFZQsmRJVq9eTbp06fD19WXYsGFGRzKcq6srlSpVsrzfvHkzjRs35vbt21bZv8lkYuTIkc8VKj4+PowcOdJye7QQwr6ptDrIUuRtyIGBgYFkyZLF6DgiDVi0aBHdunUDYM6cOTJCbaTQ0FBKlSrFxYsXKViwIL6+vlZ5vEBUQfLFF1/g4eHBxYsX+fbbbxk9ejQjRoywQnIh0q6HDx+SNWtWgKyR/TxtQooUKVJECvr6668ZPXo0zs7ObNiwgddff93oSHbhzJkztGnThrNnz+Li4sIPP/xAz549k73fqELF2dmZsLAwKVCEsBIpUmxMihRhBK01Xbt2ZdmyZWTNmpXdu3dTtmxZo2PZhYcPH9KtWzfWrFkDwIcffsh3332X7Of/uLq6EhISgouLizyZWQgrSakixfA+KUKkJUop5s2bR506dQgMDKRly5ZW64fh6LJkyYKvry9jxoxBKcWsWbNo2LAhjx8/TvI+fXx8CAkJwdnZmZCQkHg70woh7JPzy1cRQlhT+vTpWbNmDbVq1eL8+fO8+eabbN26lUyZMhkdzXBOTk589dVXVK1aFS8vL8qUKUPGjBmTtK+4+qSMHDkSQC75COEgpCVFCAPkypWL9evXkzNnTg4cOMCiRYuMjmRXPD09OXz4MDNmzEApBUBwcHCCb1OOKlBGjx7NkCFDABgyZAijR4+O864fIYR9kpYUIQxSsmRJ/vjjD7Zs2ULfvi986Hea5OHhYXkdERFB+/btyZkzJzNnzsTNze2F24aHh1s6yd69e9cyP6oFJTxcxnwUwhFIx1npOCvsSEREBE5O0sAZ2+7du6lfvz7h4eFUqlSJlStXUrx48QRte/fuXVatWkW7du3IlSuXjZMKkTZIx1kh0pgnT57QqlUrfvzxR6Oj2J3atWuzadMmcufOzdGjR6lWrRq+vr5GxxJC2JgUKULYiWXLlrF+/Xo+++wz7ty5Y3Qcu9OoUSOOHDlCnTp1ePjwIR06dGDQoEGEhIQYHU0IYSNSpAhhJ3r16sVnn31maTEQzytQoADbtm1j6NChAHz33XeWUXyFEKmPFClC2AmlFOPGjaNWrVqWeWm1z9iLpEuXjvHjx7N27Vry5s1rKViEEKmPFClC2KkjR45QtWpVLly4YHQUu/TWW29x8eJFqlatapm3b98+wsLCDEwlhLAmKVKEsENaaz766CP8/f1p2rQpN27cMDqSXcqQIYPl9cGDB6lfvz5Nmzbl5s2bBqYSQliLFClC2CGlFL/++ivFihXjwoULNG3alICAAKNj2bXr16/j4uLCX3/9RaVKlfDz8zM6khAimaRIEcJO5c+fn82bN+Pu7s7Jkydp0aIFjx49MjqW3Xrrrbc4ePAgFSpU4Pbt2zRr1ozPPvtM7v4RwoFJkSKEHfPw8GDTpk3kyJGD/fv306ZNG4KDg42OZbdKlSrFvn376NevHwATJkygZcuWcku3EA5KihQh7Fy5cuXYsGEDmTJlYuvWrXTu3Fk6h75AhgwZmDFjBqtWrSJ79uz4+/vj7+9vdCwhRBJIkSKEA6hevTq///47rq6urF27lvfff5+IiAijY9m1tm3b4u/vz8CBA3n99deNjiOESAIpUoRwEA0bNmTFihWYTCaWLFnCBx98IIXKSxQuXJgRI0ZYnof05MkT2rRpw5EjRwxOJoRICClShHAgrVq1YunSpTg5OTFv3jz69+8vA74lwqhRo1i7di21atVi8uTJUuQJYeekSBHCwXTq1ImFCxeilGLmzJnMmjXL6EgO4/PPP6d169aEhIQwZMgQmjRpwn///Wd0LCFEPKRIEcIBde3alfnz59OqVSu6d+9udByHkTNnTlavXs2sWbNwc3Nj27ZtVKhQgWXLlhkdTQgRBylShHBQ3bp1Y+3ataRPnx4wj1Irl35eTinFBx98gL+/PzVr1iQwMJAuXbowceJEo6MJIWKRIkUIB6aUAswFytChQxk+fLgUKglUokQJdu3ahbe3N3nz5qVLly5GRxJCxCJFihCpwK5du5g0aRLffPMNhw8fNjqOw3B2dubrr7/m33//xd3d3TL/l19+4enTpwYmE0IAOBsdQAiRfPXq1WPy5MmkT5+eatWqGR3H4WTOnNnyes2aNbzzzjuUK1eOhQsXyvkUwkDSkiJEKvHJJ5/Qt29fy/vAwEC59JMEbm5u5M2bl5MnT1KzZk1GjBjBs2fPjI4lRJokRYoQqVBAQAD16tWjX79+MhZIIr3xxhscP36ct99+m/DwcMaMGcOrr77KoUOHjI4mRJojRYoQqdCuXbs4ceIEM2fOpFevXoSHhxsdyaHkzp2b5cuXs2LFCnLnzs2JEyeoWbMmkydPNjqaEGmKFClCpEKtW7dmyZIlmEwm5s+fz3vvvScPJUyCDh06cPLkSUurSsmSJY2OJESaIkWKEKmUl5cXy5cvx9nZmWXLltG5c2fpW5EEUa0q+/bt480337TMP3z4sJxPIWxMihQhUrH27duzatUqXFxc8PX1pVWrVjx+/NjoWCkiKCiIu3fvEhAQwOPHjwkICODu3bsEBQUlaX81atSwvL5+/Tqvv/46r776Kvv27bNWZCFELFKkCJHKtWrVij/++IOMGTOyadMmmjRpQkBAgNGxbO706dOsWrWKTZs2ERAQwKZNm1i1ahWnT59O9r4vXLhAunTpOHHiBK+99hoDBw7k0aNHVkgthIhOpdVbFJVSWYDAwMBAsmTJYnQcIWxu//79eHp6cu/ePcqUKYOfnx8FCxY0OpbNBAUFxdlq4ubmhpubW7L3f/fuXYYMGcKiRYsAKFiwID/88AOtWrVK9r6FsHcPHz4ka9asAFm11g9tdRwpUqRIEWnI6dOnadq0KdeuXaNw4cJs2rRJOoMm06ZNm+jTpw8XLlwAzE+pXrZsGU5O0lAtUq+UKlLkf5EQaUiZMmX4+++/KVmyJFeuXKFu3bocPXrU6FgOrWnTphw/fpzPPvsMk8mEu7u7FChCWIm0pEhLikiD7ty5g6enJ4GBgezatYu8efMaHSlV8Pf3p3jx4mTKlAmAc+fOERoaSpkyZQxOJoR1SUuKEMJmcufOzdatW9myZYsUKFZUuXJlS4ESERFBjx49qFixIp9//nmauatKCGuSIkWINCpLliwULlzY8n7evHn4+PjI836s5PHjx2TNmpWwsDDGjx9P2bJl8fX1lfMrRCLI5R653CME//zzD2XLliU8PJxVq1bRtm1boyOlGr///jsDBw7k0qVLADRr1oxp06ZRokQJY4MJkQxyd4+NSZEiREw//vgj+/fv56effkIpZXScVCUoKIixY8cyfvx4QkJCSJcuHWvXrqV69eqWdax1a7QQKUGKFBuTIkWI52mtLQVKUFAQDx48IH/+/AanSj0++ugj1qxZg9aaoUOHcvv2bdzd3XFxceHIkSPkzZsXb29vo2MK8VLScVYIkeKiCpTw8HC6du1KjRo1OHTokMGpUo88efJw9epVOnToQPPmzcmZMyf169dn+vTpzJw5E5PJZHREIeyKs9EBhBD25969e5w5c4Zr165Rr149FixYwNtvv210LIc3YsQIAEaOHEmGDBnw8PBg4MCBHD9+HKUUd+7c4d69e+TIkcPgpELYB7ncI5d7hIhTYGAgnTt3ZsOGDYD5F6y3t7cMVGYFPj4+jBw5EmdnZ8LCwihdujRnzpwBIEeOHIwaNYo+ffrg7Cx/Rwr7JJd7hBCGypo1K+vWrWPIkCGA+Rdrhw4dZLwPKxgxYgQuLi6EhYXh4uLC6dOn2bx5M+XLl+fevXt89NFHVKpUiU2bNhkdVQhDSZEihIiXyWRi4sSJzJ8/HxcXF1avXk2dOnW4fPmy0dEcmo+PDyEhITg7OxMSEoKPjw+vv/46R44cYcaMGeTMmZNTp07x/fffGx1VCEPJ5R653CNEguzevZu2bdty+/ZtcufOja+vL/Xq1TM6lsOJutTzxRdf4OHhwcWLF/n2228ZPXq0pc/K/fv3GT16NH379rU8APL27dtERESQL18+I+MLAcjlHiGEnalduzYHDhygcuXK3Llzh0aNGjFlyhQZQTURohco3bt35/Hjx3Tv3p0vvviCkSNH4uPjA0D27NmZMmVKjCdUDxs2jOLFizNq1Ci55CbSDLspUpRS/ZRSF5VSwUqpQ0qpeP9EU0p9qZQ6oJR6pJS6rZRao5QqlZJ5hUiLChcuzK5du/Dy8iI8PJzBgwfTuXNnHj16ZHQ0hxAeHk6fPn3w8PBg06ZNBAQEsGnTJjw8POjTpw/h4eFxbhcaGsrp06d58uQJ3t7eFC9enJkzZxIaGprC34EQKcsuLvcopToBi4F+wN/Ah0AvoKzW+koc628AfgEOYL6N+n9Ahcj1nyTwmHK5R4gk0lozY8YMPvnkE/LkycOhQ4fkMkQCBQUFERQU9Nz8l404q7XG19eXL7/8knPnzgFQqlQpvv32W1q3bi2jBIsUlaZGnFVK7QMOa637Rpt3Glijtf4yAdvnBm4DDbTWOxJ4TClShEimPXv2APDaa68ZnCTtCAkJYfbs2YwePZo7d+4AMGPGDPr162dwMpGWpJk+KUopF6Aa4BdrkR9QO4G7yRr59d4LjuOqlMoSNQGZEx1WCBHDa6+9FqNAWbZsGYMHDyYkJMTAVKmbi4sLAwYM4Ny5cwwfPpz8+fPj5eVlWR5XK40QjsrwIgXIBZiAW7Hm3wJe2n6szG2ck4FdWusTL1j1SyAw2nQ1SWmFEHG6ffs2H3zwAVOmTGHhwoVGx0n1smTJgo+PDxcuXCBbtmyA+ZLQG2+8QcuWLTly5IixAYWwAnsoUqLEvu6k4pgXl+lAReCdl6w3FnOLS9RUMLEBhRDxy5MnD0uWLKF9+/b06NHD6Dhphqurq+X1yZMn2bt3L+vXr6dq1ap06NCBU6dOGZhOiOSxhyLlLhDO860meXi+dSUGpdQ04C2gkdb6hS0jWutnWuuHURMgtyMIYWVt2rRh5cqVlgflPX36FG9vb548SVB/dpFM5cuX58yZM3Tp0gWlFL6+vpQvX56uXbtaht0XwpEYXqRorUOAQ0DTWIuaArvj2kaZTQfaAY211hdtm1IIkRRDhw5l1KhRVKtWDX9/f6PjpAnFixdnyZIlHD9+nHbt2qG1ZunSpZQtW5bdu+P8kSqE3TK8SIk0GeillOqhlCqjlJoCFAZmAiilBiiltkRbfwbQFfACHiml8kVOGVI8uRAiXh06dCB//vycPXuWmjVrMnXqVCIiIoyOlSaUK1cOX19fDh48SOvWrSldujQ1a9a0LL9//76B6YRIGLu4BRnMg7kBnwHuwAngk6jbiZVS3kB3rXXRyPfxhX5fa70ggceTW5CFSAF3797l/fffZ926dQA0atSI+fPnU6RIEYOTpS1PnjwhY8aMgPky3CuvvELVqlUZMWJEjOJFiIRIU+OkGEGKFCFSjtaamTNn8umnnxIUFETmzJn5/vvv6datmwxCZoCNGzfSokULS6tW06ZNGT58OPXq1ZN/D5EgaWacFCFE6qeUom/fvhw9epTXXnuNR48e8f7771seWChSVrNmzThz5gzdu3fHZDKxadMmGjRowGuvvcaqVaviHZ5fiJQmLSnSkiJEigoPD2fChAmMHDmS0NBQcufOzaxZs2jbtq3R0dKkixcvMm7cOBYsWMCzZ88AOHHiBOXKlTM4mbBncrnHxqRIEcJYR48e5b333uPYsWMAdOzYkblz58r/R4PcunWLadOmcenSJZYsWWKZ//vvv1OnTh1y5MhhYDphb6RIsTEpUoQw3rNnzxg1ahTjx4+nWrVq7N692zLGijDe1atXKVasGC4uLvTq1YtBgwZRtGhRo2MJOyB9UoQQqZ6rqyvffPMNBw4cYP78+TEGgbtw4YLB6cStW7coW7YsT5484bvvvuOVV16hffv2bN++nbT6B65IWVKkCCEMV6VKFcqWLWt5P2rUKMqXL8+8efMMTJU6BAUFcffu3eemhDyIsFq1ahw5coSNGzfSpEkTIiIiWLVqFQ0bNqRy5cocP348Bb4DkZY5Gx1ACCGiCw8P5/Dhwzx9+pTcuXMbHcfhnT59mkOHDhESEsKNGzdwd3fHxcWFatWqUa1atZdur5TijTfe4I033uDUqVN8//33LFq0iHPnzlGw4P8/Ai0sLAxnZ/mVIqxL+qRInxQh7I7Wmi1bttCkSRPLvK1bt1K+fHny5MljYLLkCwoKirMVw83NDTc3N5sdLyAggD/++IOWLVuSM2fOZB3v3r17HDp0iKZN//9pJvXq1aNAgQIMGDCAOnXqyHgrqZx0nLUxKVKEcBx37tyhZMmSAIwZM4Y+ffo4bAfbQ4cOJatlIzG8vb0xmUyMGDGCu3fvsmrVKtq1a0euXLnw8fEhPDwcb2/vZB/n5MmTlC9f3vK+bNmy9OnTh3fffZds2bIle//C/kjHWSGEiBQQEEDRokV58OABAwYMoHr16uzZs8foWElSpkwZ2rVrR8aMGZk7dy4ZM2akXbt2lClTxurHMplMjBw5Eh8fnxjzfXx8GDlypNUKvXLlynHkyBF69uyJm5sbp06dYuDAgeTPn58ePXpw8uRJqxxHpEFa6zQ5AVkAHRgYqIUQ9i8sLEzPmDFDZ8uWTQMa0O+8846+dOmS0dESLSIiQleuXFkDunLlyjoiIsJmxxo9erQG9BdffKFnzZqlv/jiCw3o0aNH2+R4Dx480NOnT9fly5e3/DutXr3aJscSxgkMDIz6982ibfm72pY7t+dJihQhHNOtW7d0jx49dOSDRrWrq6v+4osvHOr/8oYNGyy/wAG9YcMGmx4vqlBxdna2aYESXUREhP777791//79dWhoqGX+hAkTdM+ePfWuXbtsWpwJ20qpIkX6pEifFCEc0pEjRxgyZAjbtm0DIHfu3IwePZpevXrZ9V0mWmtq1qzJ4cOHCQ8Px2QyUbVqVfbt22fTzqaurq6EhITg4uJiGf4+pYWFhVG0aFGuXbsGQMmSJenevTvvvfceBQoUMCSTSBrpkyKEEC9QpUoVtmzZwm+//UbJkiW5c+cOffv2pWLFivz+++/Y6x9gfn5+HDhwwPIQv/DwcA4cOICfn5/Njunj40NISAjOzs6EhIQ810clpZhMJpYtW0b37t3JmDEj//zzD8OGDaNw4cI0b96c3377zZBcwo7ZspnGnifkco8QqUZISIieNm2azpkzpwZ00aJF9bNnz4yO9ZyIiAhdtWpVy6WqqEkppatWrWqTyx8p3ScloR4+fKh/+uknXa9ePct5+PDDDy3LIyIi5HKQHZM+KVKkCCES6f79+/rzzz/XS5cutcwLCQnR+/fvNzDV/wsODtY5cuSIUaBETTlz5tTBwcFWPV5UgTJ69Gh9584dPWvWLH3nzp0Y8+3Bv//+q7/66it96NAhy7ydO3dqDw8P/dVXX+lTp04ZmE7ERfqk2Jj0SREibZg7dy69e/emR48edjHM/j///MN///333PzChQtTokQJqx4rpcZJsYWPPvqI6dOnW95XrlwZLy8v3nnnnRgj3QpjyGBuNiZFihBpw/Dhwxk7diyTJk1i0KBBgLkFOa2MiGqLEWdTwpMnT/j9999ZtmwZf/75J2FhYYB5mP769euzYsUKeWyCgaRIsTEpUoRIO/755x8KFy5M+vTpAfjll19YuHAhw4cPp06dOgans62UHOHWVgICAli5ciVLly5l586dFC5cmIsXL+LkZL73Y/v27ZQpU8bhH5ngSKRIsTEpUoRIm7TWVK1aFX9/fwAaNmzI8OHDady4capsXUnpZwXZ2pUrV7h48SINGjQAIDQ0lHz58vHgwQMaNGhAhw4daNu2Le7u7gYnTd2kSLExKVKESLsuXLjAuHHjmD9/PqGhoQC8+uqrDB48mA4dOpAuXTqDE4qEunLlCu3bt+fgwYOWeUop6tatS4cOHWjfvr2MwWIDUqTYmBQpQoirV68yYcIEZs+eTXBwMACFChXi448/plevXlE/hIUDuHTpEr6+vqxcuZK9e/da5o8YMYLRo0cD5jFpnJycUmWLWUqTIsXGpEgRQkS5c+cOM2fOZPr06dy+fRuAzJkz06tXLwYOHEjRokWNDSgS5b///mPVqlWsXLmSKVOm8OqrrwKwZs0aBgwYQKtWrWjVqhWNGze29FMSiSNFio1JkSJE6paUvhjBwcEsW7aMyZMnW57c6+TkRPfu3e3i9mWRPB988AFz5syxvM+YMSNvvPEGb731Fp6enuTNm9fAdI5FihQbkyJFiNQtOXe1aK3x8/Nj0qRJbNq0iY8//pipU6dalj148IDs2bOnwHchrCk4OJht27bx22+/8fvvv1ueIRTl2rVr5M+f36B0jkWKFBuTIkWI1M1a44OcPHmSLFmyUKhQIcB8u2vz5s3p1asX06ZNs1V8YWNaa44cOWIpWJ4+fcqpU6csy7t06cKTJ09o3rw5zZo1w8PDw8C09ielihT7fVSoEEIkQ/RiJFOmTOTMmZNcuXIlej/lypWL8f6PP/4gODiYkJAQyzytNffu3SNnzpzJCy1SjFKKqlWrUrVqVby9vXn69Kll2bNnz1i7di1Pnjxh7dq1gPmJzc2aNaNJkybUr1+fbNmyGZQ8bZGnIAshRCKMGzeO3bt3M3ToUMu8ffv24e7uTrt27fjtt98stzULx5EhQwbL63Tp0rF9+3bGjBlDvXr1MJlM/PPPP0ybNo3WrVvTsWPHGNtGL3CEdUlLihBCJIJSitdeey3GvM2bNxMaGsrq1atZvXo1efLkoWvXrrz77rtUqlRJbnl1ME5OTpa+S1999RWBgYFs2bIFPz8/tm3bRsOGDS3r3rp1i8KFC1O9enUaN25M48aNqVWrltw1ZCXSJ0X6pAiRqvn6+jJgwACmT59O+/btbXac48ePs3DhQhYvXmy5jRmgdOnSdO7cmc6dO1OqVCmbHV+knPDwcEwmEwCrV6+mXbt2MZa7urpSq1Yt6tatS+fOnSlfvrwRMW1KOs7amBQpQqR+0YfAr1y5MocPH7Z5q0ZoaCgbNmxgwYIF/PHHHzx79syyrHLlyrzzzjt06tSJIkWK2DSHSDkXL15k69atbN26lW3btnHjxg3LssWLF9O1a1cAzp8/z8GDB6lbt67Dj4IrRYqNSZEiROq3ceNGmjdvbnm/YcMGmjVrlmLHf/jwIWvXruWXX37Bz8/P8iRfgHnz5tGjR48UyyJShtaas2fPsmvXLnbu3ImPjw+FCxcGYOLEiZa+TEWLFqVu3brUrl2bmjVrUqFCBYd6HIMUKTYmRYoQqZvWmpo1a3L48GFL83zVqlXZt2+fIX1EAgICWLVqFb/88gvbtm3j5MmTlClTBoA///yTnTt30qlTJypVqpTi2UTKmDdvHj/88AP+/v5ERETEWJYhQwYOHDhguZssODgYV1dXu+3PJEWKjUmRIkTqFrsVJUpKt6bE5datWzFGN+3UqRO//vorw4YN43//+x9gvg02PDzcIZ9ULF7s0aNH7N27l507d7J37172799PUFAQjx49wtXVFYC+ffuyatUqatSoQc2aNalZsybVqlUjR44cBqc3kyLFxqRIESL1impFOXToUIy/WKPu2jCqNSU+q1evZvny5Xz22WdUrVoVgLVr19KpUyfq169P8+bN8fT0pHTp0naVW1hHREQE//33X4x+SjVq1ODAgQPPrVukSBGqVq3Kr7/+irOzcTfoSpFiY1KkCJF6PXv2jCJFinDr1q3nluXLl49Lly5Z/mK1V1988QXjxo2LMa9w4cI0b96c5s2b07BhQxmaPxULDg7G39+fffv2sX//fvbt28f58+cB8PDw4MKFC5Z1O3XqRGBgIFWrVqVKlSpUqVKFYsWK4eRku6HQpEixMSlShEjd/vvvP+7cucP9+/fZtm0bjRo1Inv27OTJk4eCBQsaHe+lojpgbtiwgT///JPt27fHuFNIKUWVKlVo1KgRjRo1ol69evKzLJULDAzE39+fhw8f0qpVK8D8OcmRIwcPHjyIsW6mTJkoV64cDRo0eK7YtQYpUmxMihQh0oa7d++yatUq2rVrl6Rh8e1FUFAQ27dvZ8OGDWzcuJGzZ8/GWG4ymfj000/59ttvDUoojBAREcHBgwc5cuQIhw8f5siRIxw7dsxS0DZt2hQ/Pz/L+lWqVCF79uxUqFCB8uXLU6FCBcqVK0fmzJkTdVx5do8QQiRD9AcMPn78mICAAIBEP2DQXri5ueHp6YmnpycA169f56+//mLbtm1s27aN8+fPx2ghOnPmDG+//TZNmjRh8uTJRsUWNubk5ESNGjWoUaOGZV5YWBj//vsvx48fj1F83Lt3D39/fwC2bdsWYz8FChSgc+fOTJw40TLv2rVruLu72/Sy0ctIkSKESJVOnz7NoUOHCAkJISAggE2bNuHi4mIZ7tzR5c+fHy8vL7y8vAC4cuUKmTJlsizfvXs3x48ff+5ukAEDBlC4cGFq1qxJlSpVpCU5FXJ2dqZMmTKWW9yjZM6cmYMHD3L8+HFOnDhh+Xr9+nWuXbtGUFCQZd3Hjx9TsGBB3NzcKFWqFKVKlaJ06dKW1/ny5UuR70Uu98jlHiFSpaiWlNgctSUlse7evcvOnTtJnz69pfXl/v37zxUtJUuWpFq1arz66qtUq1ZNCpc06P79+5w9e5YsWbJQtmxZAE6ePEnlypVjDEAYXbdu3Vi4cCFInxTbkCJFCJHW3L9/n59++ondu3dz8OBBrly58tw6SilL4VKpUiXKly9PgwYNyJgxowGJhZFCQ0O5ePEiZ8+e5cyZMzG+Dhw4kJEjR4IUKbYhRYoQIq27c+cOhw8f5uDBgxw6dIhDhw7FWbicO3eOV155BTAPhnfx4kUaNWpE6dKlUzqysBMPHjyIugVeOs4KIYSwvty5c9OsWbMYI/DeuXOHQ4cOcfjwYY4dO8a5c+fw8PCwLF+wYAHLly9n3LhxliLl/PnzTJw40dJfoVSpUhQpUsTypGCR+qRUZ1opUoQQQljkzp3bMmBcXF577TUePXpEzZo1LfOOHDnCzJkzY6zn6upK8eLFLUXLK6+8QrFixfDw8KBgwYKGjpYqHIdc7pHLPUIIkSzHjh1j+fLllj4L586dizHwXGzOzs4UK1aMM2fOWIb537NnDyaTibJly8a4S0nYJxnMzcakSBFCCNsIDw/nypUrnD171jJduHCBixcvcunSJUJCQihWrJhlmHeAWrVqsW/fPlavXk2bNm0A+Ouvv5g9ezYFCxakYMGCFCpUyPI6T548cjnJQDKYmxBCCIdkMpnw8PDAw8PjuctGERERXL9+nfv378eY7+7uToECBShUqJBl3uHDh/n555/jPIazszPu7u7kzZuXfPnykTdvXvLmzUvRokXp3bu3Zb2goCAyZMggD2Z0UNKSIi0pQghhl44cOcLWrVu5evWqZfrvv/+4ceNGjKdbR/fKK69w7tw5y/tXX32VEydOsG7dOpo0aQLAjh07WLlyJTlz5ox3ypQpkxQ2LyAtKUIIIdK0qCf6xhYWFsbNmze5fv06t27d4ubNm5av2bJli7HuzZs3efbsWdQvVAAOHDjAtGnTXnjsdOnSkSNHDnLmzEnWrFnx8PBg6dKlluWLFy8mMDCQVq1aUaRIEcD8AMB79+6RNWtWsmTJIp2DrcAuWlKUUv2AoYA7cBIYpLXeae1tYm0vLSlCCJHKPX36lNu3b5MvXz5cXV0B2LVrF+vXrycgICDOKa5OvyVKlOCff/6xvK9cuTJHjx5lw4YNllu4582bR69evSzrZMiQgYwZM5IpU6Y4v2bMmJGcOXPGeCjkpk2buHfvHrVr17Zc+goMDOTGjRukT5+eDBkykD59etKnT4+Li4thrT1ppiVFKdUJmAr0A/4GPgT+VEqV1Vo/P6pQErcRQgiR9mTIkMHS0hGlbt261K1bN871tdaWB1MGBARw7949AgMDn2sV8fT0pESJEhQuXNgyLzQ0lAwZMvD06VPAXCA9ffqUu3fvxpsvd+7cMYqUMWPGsGPHDpYvX24pUjZu3EinTp2e21YpZSlYohcvUa937txpKWK+//57jhw5Qrdu3WjYsCEAFy5cYMGCBaRLlw4XF5cYU+x50d/XqlUr3u/H2gwvUoDBwDyt9dzI94OUUs2AvsCXVtxGCCGEeCGllKWVI3oBEtvYsWOfm9enTx/69OlDSEgIDx8+5PHjxzx58sTyNfrrqK/p0qWLsY8qVaqglKJAgQIxMmXPnp3g4GBLAQTmgiqqEIrdETl2K8uWLVv47bffqF27tqVIOXfuHD4+Pok6P2B+vEKaGMxNKeUCVAO+jbXID6htrW0it3MFXKPNygzmJishhBDCmlxcXMiRI8dzD3SMS/TfQ6NHj35ufrNmzbh06RJgLkxCQkIIDg62TM+ePePp06c8e/bMMi80NDTGft9++22qVq1K2bJlLfOzZctG7969CQkJISQkhLCwMMvr0NBQQkNDn3sddez4HjxobUa3pOQCTMCtWPNvAfE9Bzop24C5heXr2DOj3+4mhBBCpGbe3t7J3oe7u3v0tzmA1NsnJVLs3rsqjnnJ3WYsMDna+8zAVaAg8CgBGYWcs6SS85Z4cs6SRs5b4sk5S5qo83bPlgcxuki5C4TzfAtIHp5vKUnONmitnwGWLtvRrtU9smXP5NREzlnSyHlLPDlnSSPnLfHknCVNSt1VlDI9X+KhtQ4BDgFNYy1qCuy21jZCCCGEcDxGt6SA+RLMYqXUQWAP8AFQGJgJoJQaALTVWr+e0G2EEEII4fgML1K01suVUjmBkZgHZjsBtNBaX45cJRfwSiK3SYhnwCiiXQISLyXnLGnkvCWenLOkkfOWeHLOkiZFzptdjDgrhBBCCBGboX1ShBBCCCHiI0WKEEIIIeySFClCCCGEsEtSpAghhBDCLqWaIkUp1U8pdVEpFayUOqSUqmeNbZKyX0dhi3OmlPJWSulY003bfRcpL7HnTSlVXyn1u1LqeuT5aGON/ToSW5wz+aw9t+6XSqkDSqlHSqnbSqk1SqlSydmno7L2eZPPWpzr91VKHVNKPYyc9iilPJO73+dorR1+AjoBIUAvoAwwFXgMFE7ONknZr6NMNjxn3phvCc8Xbcpt9Pdr8HnzBMYA7TA/uqGNNfbrKJMNz5l81mKuvwHoDpQDKgHrgMtAxrTwObPxeZPP2vPbtAJaACUjp/9F7qOcNT9vhp8cK53gfcCPseadBsYmZ5uk7NdRJhueM2/A3+jvz57OW6x14/uFK5+1xJ8z+ay9ePvckeeuvrX26QiTjc6bfNYStp97QE9r7tfhL/copVyAaoBfrEV+QO2kbpOU/ToKW52zaEpENtNfVEr9opQqZoXYhrPVZ0I+a8kin7X4ZY38es+K+7Rrtjhv0chnLf59mJRSnYGMmEeBt9rnzeGLFMwj0pp4/uGCt3j+IYSJ2SYp+3UUtjpnYK6c3wOaAb0jl+1W5hGCHZ2tPhPyWUsa+azFQymlMD8+ZJfW+oQ19ukgbHHeQD5rcVJKVVBKPcY86uxMzI+wOZXc/UZn+LD4VhR76FwVx7ykbJOU/ToKq58zrfWf0ZYdV0rtAc4D3TD/508NbPWZkM9aYnYon7UXmQ5UBOpacZ+OxKrnTT5r8ToLVAayAe2BhUqpBtEKlaTu1yI1FCl3gXCer8zy8HwFl5htkrJfR2Grc/YcrfUTpdRxoETSotoVW30m5LNmBfJZM1NKTQPewtyn4qo19ulAbHHeniOfNTOtdQhwLvLtQaVUdeBj4MPk7Dc6h7/cE3mSDgFNYy1qCuxO6jZJ2a+jsNU5i4tSyhVzr+4bSc1rL2z1mZDPmnWk9c+aMpuO+Y6oxlrri8ndp6OxxXmLZ5s0/Vl7AQW4WnW/RvcqtlLP5KjbnHpg/uBMwXybU5HI5QOALYnZJqHrOOpkw3M2EWgAeAA1gd+Bh6nhnCXjvGXC3CRaGXMz5yeRr+O63V0+awk/Z/JZi3begB+AB5HnJPqtshnSwufMxudNPmvP/x/9BqgHFAUqYL4FORxoas3Pm+Enx4onuR9wCXMHnkM8f/vYpcRsk5h1HHWyxTkDfgGuR34wrwG+QFmjv1cjzxvQEPMv2tjTAvmsJf2cyWct5nmL53xpoHta+ZzZ6rzJZy3O/6Pzoq1/G9hMtALFWp83FbkTIYQQQgi74vB9UoQQQgiROkmRIoQQQgi7JEWKEEIIIeySFClCCCGEsEtSpAghhBDCLkmRIoQQQgi7JEWKEEIIIeySFClCCCGEsEtSpAgh7IZSKqdS6rZSqmgy97NSKTXYSrGEEAaRIkUIYVVKqQVKKR1tClBKbVBKVUzA5l8Cv2utLyUzxmjgK6VUlmTuRwhhIClShBC2sAFwj5xeB8KAdS/aQCmVAegJzE3uwbXWxzA/L6RLcvclhDCOFClCCFt4prW+GTn5A+OAQkqp3C/YxhMI01rviZqhlGoY2RrTTCl1RCn1VCm1VSmVRynlqZQ6rZR6qJT6WSnlFmt/vwHvWP07E0KkGClShBA2pZTKhLlF4xwQ8IJV6wMH41nmjflx8bWBQsCvwCDAC2gJNAU+irXNfqCGUso1idGFEAZzNjqAECJVelMp9TjydUbgBvCm1jriBdsUBa7Hs2y41vpvAKXUPGAs8IrW+kLkvJVAI8wtNlGuAa5APuByEr8PIYSBpCVFCGEL24DKkVNNwA/4UylV5AXbZACC41l2LNrrW0BQVIESbV6eWNs8jfwa+zKQEMJBSEuKEMIWnmitz0W9UUodAgKB3sDweLa5C2SPZ1lotNc61vuoebH/6MoR+fVOQgILIeyPtKQIIVKCBiIwt5bE5whQ1orHLA9c1VrfteI+hRApSIoUIYQtuCql8kVOZYBpQCbg9xdssxEop5SKrzUlsephvswkhHBQcrlHCGELzTF3lgV4BJwBOmqt/4pvA631caXUQeBtYFZyDq6USg+0BZolZz9CCGMprbXRGYQQAgClVAtgIlD+JXcCvWw//YHWWus3rBZOCJHipCVFCGE3tNbrlVIlgALAf8nYVSjPj5sihHAw0pIihBBCCLskHWeFEEIIYZekSBFCCCGEXZIiRQghhBB2SYoUIYQQQtglKVKEEEIIYZekSBFCCCGEXZIiRQghhBB2SYoUIYQQQtglKVKEEEIIYZf+D83LGHXBzeqOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "B_exp1=df_sel1['L (mm)']/1000\n",
    "V_exp1=df_sel1['V_media']\n",
    "lambda_1=df_sel1['filtername'].iloc[0]\n",
    "\n",
    "\n",
    "popt, pcov =curve_fit(get_V, B_exp1,V_exp1,p0=0.2,bounds=(0.01,1))\n",
    "print(\"r0=\",*popt)\n",
    "V_fit1=get_V(B,popt[0])\n",
    "r01=popt[0]\n",
    "for n in df_sel1['Imagen']:\n",
    "    if n in r0dict:\n",
    "        warnings.warn(\"Calculando r0 para una imagen dos veces\")\n",
    "    r0dict[n]=r01\n",
    "    r0errdict[n]=get_err(pcov)\n",
    "\n",
    "B_exp2=df_sel2['L (mm)']/1000\n",
    "V_exp2=df_sel2['V_media']\n",
    "lambda_2=df_sel2['filtername'].iloc[0]\n",
    "\n",
    "popt, pcov =curve_fit(get_V, B_exp2,V_exp2,p0=0.2,bounds=(0.01,1))\n",
    "print(\"r0=\",*popt)\n",
    "V_fit2=get_V(B,popt[0])\n",
    "r02=popt[0]\n",
    "for n in df_sel2['Imagen']:\n",
    "    if n in r0dict:\n",
    "        warnings.warn(\"Calculando r0 para una imagen dos veces\")\n",
    "    r0dict[n]=r02\n",
    "    r0errdict[n]=get_err(pcov)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.ticklabel_format(useMathText=True)\n",
    "plt.xlim([0, 0.3])\n",
    "plt.ylim([0, 1])\n",
    "plt.plot(B, V_fit1,ls1,c='black',label=\"H${\\\\alpha}$ \") #35 nm\n",
    "plt.plot(B, V_fit2,ls2,c='black',label=\"H${\\\\beta}$\") #8.5 nm\n",
    "\n",
    "plt.errorbar(B_exp1, V_exp1, yerr=df_sel1['E_acc'],label=\"H${\\\\alpha}$\",**styledict_exp1)\n",
    "plt.errorbar(B_exp2, V_exp2, yerr=df_sel2['E_acc'],label=\"H${\\\\beta}$\",**styledict_exp2)\n",
    "# linestyle='None',color='green',marker='s',ms=1,ecolor=(0, .7, 0, 0.4),elinewidth=1.5,capsize=2.5)\n",
    "\n",
    "\n",
    "# get handles\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "# remove the errorbars\n",
    "handles = [h[0] if isinstance(h, container.ErrorbarContainer) else h for h in handles]\n",
    "# use them in the legend\n",
    "ax.legend(handles, labels)\n",
    "#ax.legend()\n",
    "\n",
    "date=df_sel1['DATE'].iloc[0]\n",
    "ax.set_title(f'{date}')\n",
    "ax.text(0.47, 0.95, \"(d)\", transform=ax.transAxes, fontsize=14, verticalalignment='top')\n",
    "\n",
    "ax.set_xlabel(\"B (m)\")\n",
    "ax.set_ylabel(\"V\")\n",
    "plt.savefig(make_dir(\"productos/ajustes/ajuste-sirio-comp-lambda\"+output_format))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# x Sirio (Dia 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>Imagen</th>\n",
       "      <th>Objeto</th>\n",
       "      <th>DATE</th>\n",
       "      <th>Filtro</th>\n",
       "      <th>L (mm)</th>\n",
       "      <th>V_media</th>\n",
       "      <th>S</th>\n",
       "      <th>E_acc</th>\n",
       "      <th>D (mm)</th>\n",
       "      <th>V_media_antigua</th>\n",
       "      <th>S_antigua</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imagen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Sirio</td>\n",
       "      <td>17-03-10</td>\n",
       "      <td>Halfa-7 nm</td>\n",
       "      <td>220.0</td>\n",
       "      <td>0.094463</td>\n",
       "      <td>0.017632</td>\n",
       "      <td>0.101035</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.094463</td>\n",
       "      <td>0.017632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Sirio</td>\n",
       "      <td>17-03-10</td>\n",
       "      <td>Halfa-7 nm</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0.033639</td>\n",
       "      <td>0.014732</td>\n",
       "      <td>0.084420</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.033639</td>\n",
       "      <td>0.014732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Sirio</td>\n",
       "      <td>17-03-10</td>\n",
       "      <td>Halfa-7 nm</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.277885</td>\n",
       "      <td>0.002123</td>\n",
       "      <td>0.012167</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.277885</td>\n",
       "      <td>0.002123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Imagen Objeto      DATE      Filtro  L (mm)   V_media         S  \\\n",
       "Imagen                                                                    \n",
       "1            1  Sirio  17-03-10  Halfa-7 nm   220.0  0.094463  0.017632   \n",
       "2            2  Sirio  17-03-10  Halfa-7 nm   158.0  0.033639  0.014732   \n",
       "3            3  Sirio  17-03-10  Halfa-7 nm   178.0  0.277885  0.002123   \n",
       "\n",
       "           E_acc  D (mm)  V_media_antigua  S_antigua  \n",
       "Imagen                                                \n",
       "1       0.101035    22.0         0.094463   0.017632  \n",
       "2       0.084420    22.0         0.033639   0.014732  \n",
       "3       0.012167    61.0         0.277885   0.002123  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond=(df['DAY']==10) & (df['Filtro']=='Halfa-7 nm') \n",
    "df_sel=df.loc[cond]\n",
    "df_sel[dispcols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r0= 0.24107341172972285\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "B_exp=df_sel['L (mm)']/1000\n",
    "V_exp=df_sel['V_media']\n",
    "\n",
    "popt, pcov =curve_fit(get_V, B_exp,V_exp,p0=0.2,bounds=(0.01,1))\n",
    "print(\"r0=\",*popt)\n",
    "V_fit=get_V(B,popt[0])\n",
    "for n in df_sel['Imagen']:\n",
    "    if n in r0dict:\n",
    "        warnings.warn(\"Calculando r0 para una imagen dos veces\")\n",
    "    r0dict[n]=popt[0]\n",
    "    r0errdict[n]=get_err(pcov)\n",
    "    \n",
    "fig, ax = plt.subplots()\n",
    "plt.ticklabel_format(useMathText=True)\n",
    "plt.xlim([0, 0.3])\n",
    "plt.ylim([0, 1])\n",
    "\n",
    "plt.plot(B, V_fit,ls1,c='black',label=f\"Best fit curve with $r_0$ = {popt[0]:0.3F} m\")\n",
    "plt.errorbar(B_exp, V_exp, yerr=df_sel['E_acc'],label=\"Experimental points\",**styledict_exp1)\n",
    "\n",
    "# ax.legend()\n",
    "date=df_sel['DATE'].iloc[0]\n",
    "# ax.set_title(f'Sirius ({date})')\n",
    "ax.set_xlabel(\"B (m)\")\n",
    "ax.set_ylabel(\"V\")\n",
    "plt.savefig(make_dir(\"productos/ajustes/ajuste-sirio-10\"+output_format))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# a parte - Rigel (Dia 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>Imagen</th>\n",
       "      <th>Objeto</th>\n",
       "      <th>DATE</th>\n",
       "      <th>Filtro</th>\n",
       "      <th>L (mm)</th>\n",
       "      <th>V_media</th>\n",
       "      <th>S</th>\n",
       "      <th>E_acc</th>\n",
       "      <th>D (mm)</th>\n",
       "      <th>V_media_antigua</th>\n",
       "      <th>S_antigua</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imagen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Rigel</td>\n",
       "      <td>17-03-15</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.439454</td>\n",
       "      <td>0.007861</td>\n",
       "      <td>0.045045</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.439454</td>\n",
       "      <td>0.007861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Rigel</td>\n",
       "      <td>17-03-15</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>222.0</td>\n",
       "      <td>0.144100</td>\n",
       "      <td>0.008017</td>\n",
       "      <td>0.045938</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.144100</td>\n",
       "      <td>0.008017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Imagen Objeto      DATE       Filtro  L (mm)   V_media         S  \\\n",
       "Imagen                                                                     \n",
       "6            6  Rigel  17-03-15  Halfa-35 nm   147.0  0.439454  0.007861   \n",
       "7            7  Rigel  17-03-15  Halfa-35 nm   222.0  0.144100  0.008017   \n",
       "\n",
       "           E_acc  D (mm)  V_media_antigua  S_antigua  \n",
       "Imagen                                                \n",
       "6       0.045045    51.0         0.439454   0.007861  \n",
       "7       0.045938    51.0         0.144100   0.008017  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond=(df['Objeto']=='Rigel') & (df['Filtro']=='Halfa-35 nm') \n",
    "df_sel=df.loc[cond]\n",
    "df_sel[dispcols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r0= 0.3330430770908755\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "B_exp=df_sel['L (mm)']/1000\n",
    "V_exp=df_sel['V_media']\n",
    "\n",
    "popt, pcov =curve_fit(get_V, B_exp,V_exp,p0=0.2,bounds=(0.01,1))\n",
    "print(\"r0=\",*popt)\n",
    "V_fit=get_V(B,popt[0])\n",
    "for n in df_sel['Imagen']:\n",
    "    if n in r0dict:\n",
    "        warnings.warn(\"Calculando r0 para una imagen dos veces\")\n",
    "    r0dict[n]=popt[0]\n",
    "    r0errdict[n]=get_err(pcov)\n",
    "    \n",
    "fig, ax = plt.subplots()\n",
    "plt.ticklabel_format(useMathText=True)\n",
    "plt.xlim([0, 0.3])\n",
    "plt.ylim([0, 1])\n",
    "\n",
    "plt.plot(B, V_fit,ls1,c='black',label=f\"Best fit curve with $r_0$ = {popt[0]:0.3F} m\")\n",
    "plt.errorbar(B_exp, V_exp, yerr=df_sel['E_acc'],label=\"Experimental points\",**styledict_exp1)\n",
    "\n",
    "# ax.legend()\n",
    "date=df_sel['DATE'].iloc[0]\n",
    "# ax.set_title(f'Rigel ({date})')\n",
    "ax.set_xlabel(\"B (m)\")\n",
    "ax.set_ylabel(\"V\")\n",
    "plt.savefig(make_dir(\"productos/ajustes/ajuste-rigel-15\"+output_format))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rigel vs Betelgeuse "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>Imagen</th>\n",
       "      <th>Objeto</th>\n",
       "      <th>DATE</th>\n",
       "      <th>Filtro</th>\n",
       "      <th>L (mm)</th>\n",
       "      <th>V_media</th>\n",
       "      <th>S</th>\n",
       "      <th>E_acc</th>\n",
       "      <th>D (mm)</th>\n",
       "      <th>V_media_antigua</th>\n",
       "      <th>S_antigua</th>\n",
       "      <th>HOUR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imagen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Betelgeuse</td>\n",
       "      <td>17-03-15</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.641222</td>\n",
       "      <td>0.008581</td>\n",
       "      <td>0.049168</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.641222</td>\n",
       "      <td>0.008581</td>\n",
       "      <td>21:41:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Betelgeuse</td>\n",
       "      <td>17-03-15</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>238.0</td>\n",
       "      <td>0.322587</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>0.021777</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.322587</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>22:01:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Betelgeuse</td>\n",
       "      <td>17-03-15</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.211979</td>\n",
       "      <td>0.018689</td>\n",
       "      <td>0.107091</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.211979</td>\n",
       "      <td>0.018689</td>\n",
       "      <td>22:05:53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Imagen      Objeto      DATE       Filtro  L (mm)   V_media         S  \\\n",
       "Imagen                                                                          \n",
       "8            8  Betelgeuse  17-03-15  Halfa-35 nm   147.0  0.641222  0.008581   \n",
       "10          10  Betelgeuse  17-03-15  Halfa-35 nm   238.0  0.322587  0.003800   \n",
       "11          11  Betelgeuse  17-03-15  Halfa-35 nm   133.0  0.211979  0.018689   \n",
       "\n",
       "           E_acc  D (mm)  V_media_antigua  S_antigua      HOUR  \n",
       "Imagen                                                          \n",
       "8       0.049168    51.0         0.641222   0.008581  21:41:53  \n",
       "10      0.021777    30.0         0.322587   0.003800  22:01:11  \n",
       "11      0.107091    30.0         0.211979   0.018689  22:05:53  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond1=(df['DAY']==15) & (df['Filtro']=='Halfa-35 nm') & (df['Objeto']=='Betelgeuse')&(df['Imagen']!=9)\n",
    "df_sel1=df.loc[cond1]#.loc[df['Imagen'].isin([11,10])]#.loc[df['Imagen'].isin([8,10])]\n",
    "df_sel1[dispcols+['HOUR']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>Imagen</th>\n",
       "      <th>Objeto</th>\n",
       "      <th>DATE</th>\n",
       "      <th>Filtro</th>\n",
       "      <th>L (mm)</th>\n",
       "      <th>V_media</th>\n",
       "      <th>S</th>\n",
       "      <th>E_acc</th>\n",
       "      <th>D (mm)</th>\n",
       "      <th>V_media_antigua</th>\n",
       "      <th>S_antigua</th>\n",
       "      <th>HOUR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imagen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Rigel</td>\n",
       "      <td>17-03-15</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.439454</td>\n",
       "      <td>0.007861</td>\n",
       "      <td>0.045045</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.439454</td>\n",
       "      <td>0.007861</td>\n",
       "      <td>21:14:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Rigel</td>\n",
       "      <td>17-03-15</td>\n",
       "      <td>Halfa-35 nm</td>\n",
       "      <td>222.0</td>\n",
       "      <td>0.144100</td>\n",
       "      <td>0.008017</td>\n",
       "      <td>0.045938</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.144100</td>\n",
       "      <td>0.008017</td>\n",
       "      <td>21:19:47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Imagen Objeto      DATE       Filtro  L (mm)   V_media         S  \\\n",
       "Imagen                                                                     \n",
       "6            6  Rigel  17-03-15  Halfa-35 nm   147.0  0.439454  0.007861   \n",
       "7            7  Rigel  17-03-15  Halfa-35 nm   222.0  0.144100  0.008017   \n",
       "\n",
       "           E_acc  D (mm)  V_media_antigua  S_antigua      HOUR  \n",
       "Imagen                                                          \n",
       "6       0.045045    51.0         0.439454   0.007861  21:14:55  \n",
       "7       0.045938    51.0         0.144100   0.008017  21:19:47  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond1=(df['Objeto']=='Rigel') & (df['Filtro']=='Halfa-35 nm') \n",
    "df_sel2=df.loc[cond1]\n",
    "df_sel2[dispcols+['HOUR']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Betelgeuse r0= 0.3629309047496448\n",
      "Rigel r0= 0.3330430770908755\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "B_exp1=df_sel1['L (mm)']/1000\n",
    "V_exp1=df_sel1['V_media']\n",
    "lambda_1=df_sel1['filtername'].iloc[0]\n",
    "\n",
    "\n",
    "popt, pcov =curve_fit(get_V, B_exp1,V_exp1,p0=0.2,bounds=(0.01,1))\n",
    "print(\"Betelgeuse\",\"r0=\",*popt)\n",
    "V_fit1=get_V(B,popt[0])\n",
    "r01=popt[0]\n",
    "# Aqui sobra porque ya los guardamos en los ajustes individuales\n",
    "# for n in df_sel1['Imagen']:\n",
    "#     if n in r0dict:\n",
    "#         warnings.warn(\"Calculando r0 para una imagen dos veces\")\n",
    "#     r0dict[n]=r01\n",
    "\n",
    "B_exp2=df_sel2['L (mm)']/1000\n",
    "V_exp2=df_sel2['V_media']\n",
    "lambda_2=df_sel2['filtername'].iloc[0]\n",
    "\n",
    "popt, pcov =curve_fit(get_V, B_exp2,V_exp2,p0=0.2,bounds=(0.01,1))\n",
    "print(\"Rigel\",\"r0=\",*popt)\n",
    "V_fit2=get_V(B,popt[0])\n",
    "r02=popt[0]\n",
    "# Aqui sobra porque ya los guardamos en los ajustes individuales\n",
    "# for n in df_sel2['Imagen']:\n",
    "#     if n in r0dict:\n",
    "#         warnings.warn(\"Calculando r0 para una imagen dos veces\")\n",
    "#     r0dict[n]=r02\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.ticklabel_format(useMathText=True)\n",
    "plt.xlim([0, 0.3])\n",
    "plt.ylim([0, 1])\n",
    "plt.plot(B, V_fit1,ls1,c='black',label=f\"Betelgeuse\")\n",
    "plt.plot(B, V_fit2,ls2,c='black',label=f\"Rigel\")\n",
    "\n",
    "plt.errorbar(B_exp1, V_exp1, yerr=df_sel1['E_acc'],label=f\"Betelgeuse\",**styledict_exp1)\n",
    "plt.errorbar(B_exp2, V_exp2, yerr=df_sel2['E_acc'],label=f\"Rigel\",**styledict_exp2)\n",
    "\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "# remove the errorbars\n",
    "handles = [h[0] if isinstance(h, container.ErrorbarContainer) else h for h in handles]\n",
    "# use them in the legend\n",
    "ax.legend(handles, labels)\n",
    "#ax.legend(prop={'size': 8})\n",
    "\n",
    "\n",
    "ax.set_title(f'{df_sel2[\"DATE\"].iloc[0]}')\n",
    "ax.text(0.47, 0.95, \"(a)\", transform=ax.transAxes, fontsize=14, verticalalignment='top')\n",
    "\n",
    "ax.set_xlabel(\"B (m)\")\n",
    "ax.set_ylabel(\"V\")\n",
    "plt.savefig(make_dir(\"productos/ajustes/ajuste-betel-vs-rigel\"+output_format))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exportamos los resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('intermedio/valoresr0.pkl', 'wb') as f:\n",
    "    pickle.dump(r0dict, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "valoresr0=pd.DataFrame(r0dict.values(),index=r0dict.keys(),columns=[\"r0\"])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "erroresr0=pd.DataFrame(r0errdict.values(),index=r0errdict.keys(),columns=[\"error\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r0</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.252210</td>\n",
       "      <td>0.015169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.252210</td>\n",
       "      <td>0.015169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.252210</td>\n",
       "      <td>0.015169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.252210</td>\n",
       "      <td>0.015169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.362931</td>\n",
       "      <td>0.101940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.362931</td>\n",
       "      <td>0.101940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.362931</td>\n",
       "      <td>0.101940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.315159</td>\n",
       "      <td>0.015988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.315159</td>\n",
       "      <td>0.015988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.315159</td>\n",
       "      <td>0.015988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.218160</td>\n",
       "      <td>0.035400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.218160</td>\n",
       "      <td>0.035400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.241073</td>\n",
       "      <td>0.051223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.241073</td>\n",
       "      <td>0.051223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.241073</td>\n",
       "      <td>0.051223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.333043</td>\n",
       "      <td>0.016267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.333043</td>\n",
       "      <td>0.016267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          r0     error\n",
       "14  0.252210  0.015169\n",
       "15  0.252210  0.015169\n",
       "16  0.252210  0.015169\n",
       "17  0.252210  0.015169\n",
       "8   0.362931  0.101940\n",
       "10  0.362931  0.101940\n",
       "11  0.362931  0.101940\n",
       "19  0.315159  0.015988\n",
       "21  0.315159  0.015988\n",
       "22  0.315159  0.015988\n",
       "20  0.218160  0.035400\n",
       "23  0.218160  0.035400\n",
       "1   0.241073  0.051223\n",
       "2   0.241073  0.051223\n",
       "3   0.241073  0.051223\n",
       "6   0.333043  0.016267\n",
       "7   0.333043  0.016267"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r0df=valoresr0.join(erroresr0)\n",
    "with open('productos/valoresr0.json','wb') as f:\n",
    "    r0df.to_json(f,indent=4)\n",
    "r0df.to_excel(\"productos/r0errors.xlsx\",engine=\"openpyxl\")\n",
    "r0df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En teoria la formula del error seria esta? No sabemos\n",
    "\n",
    "$E_V=2E_{Cut}V\\left(\\frac{1}{I_{Max}+I_{Min}}\\right)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel(\"productos/tabla_completa.xlsx\",engine=\"openpyxl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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