{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 2\n",
    "\n",
    "## Extraction of Radial Velocity Time Series using Different Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial demonstrates how `ARVE` can be used to extract radial velocity (RV) time series with the cross-correlation function (CCF) and line-by-line (LBL) techniques."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by importing the `ARVE` package and other useful packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import arve\n",
    "import matplotlib.pyplot as     plt\n",
    "from   matplotlib.ticker import AutoMinorLocator\n",
    "import numpy             as     np\n",
    "import pandas            as     pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our analysis, we make use of a synthetic stellar spectral time series computed in [Tutorial 0](https://arve.readthedocs.io/en/latest/tutorials/tutorial_0.html) and downloadable [here](https://github.com/almoulla/arve/tree/main/docs/tutorials/example_data).\n",
    "\n",
    "The spectral time series consists of 100 observations each containing the same 100 spectral lines. 95% of the lines are displaced with a sinusoidal signal of 10 m/s amplitude, whereas the remaining 5% of the lines are randomly shifted by RVs drawn from a $\\mathcal{G}(\\mu=100\\,\\mathrm{m/s}, \\sigma=100\\,\\mathrm{m/s})$ distribution acting as spurious outliers (from, e.g., telluric contamination). The spectra are thereafter noised to a S/N of 200.\n",
    "\n",
    "We begin by reading the true RVs of the sinusoidal signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read true RVs\n",
    "df = pd.read_csv(\"example_data/tutorial_2/vrad.csv\")\n",
    "time_val      = df[\"time_val\"     ].values\n",
    "vrad_val_true = df[\"vrad_val_good\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the RV extraction, we begin with the CCF technique.\n",
    "\n",
    "An `ARVE` object is initiated and some relevant information about the input spectra is provided:\n",
    "- `path`: relative path to the spectra (saved in individual files)\n",
    "- `format`: whether the spectra are echelle-order merged (`\"s1d\"`) or echelle-order separated (`\"s2d\"`)\n",
    "- `extension`: the extension of the files that the spectra are saved in (acceptable extensions are `\"fits\"`, `\"npz\"` and `\"csv\"`); if `\"fits\"`, additionally the `instrument` keyword must be specified; if `\"npz\"` or `\"csv\"`, the wavelength, flux and flux uncertainty variables must be named `wave_val`, `flux_val` and `flux_err`\n",
    "- `medium`: medium of the recorded wavelengths\n",
    "- `resolution`: spectral resolution, $\\lambda/\\Delta\\lambda$, of the spectrum\n",
    "- `same_wave_grid`: whether the spectra are recorded/interpolated onto the same wavelength grid\n",
    "\n",
    "For the CCF computation, in this case, we notably do not exclude lines affected by tellurics (because there are none in the synthetic data), and we weigh the CCF by the RV information per line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting CCF RVs.\n",
      "~~~~ Processed spectra:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 807.86it/s]\n"
     ]
    }
   ],
   "source": [
    "# Compute CCF RVs\n",
    "example = arve.ARVE()\n",
    "example.star.target = \"Sun\"\n",
    "example.star.get_stellar_parameters()\n",
    "example.data.add_data(path=\"example_data/tutorial_2/\", format=\"s1d\", extension=\"npz\", medium=\"vac\", resolution=100000, same_wave_grid=True)\n",
    "example.data.get_aux_data()\n",
    "example.data.compute_vrad_ccf(exclude_tellurics=False, vrad_grid=[-20,20,1], ccf_err_scale=True, weight_name=\"vrad_info\")\n",
    "\n",
    "# Extract CCF RVs\n",
    "time_val     = example.data.time[\"time_val\"]\n",
    "vrad_val_ccf = example.data.vrad[\"vrad_val\"]\n",
    "vrad_err_ccf = example.data.vrad[\"vrad_err\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we move onto the LBL technique.\n",
    "\n",
    "The steps are similar to the CCF technique, with the notable addition that a reference spectrum (sometime called a template) must first be built from all input spectra.\n",
    "\n",
    "In addition to the average RVs, the LBL technique also provides the per-line RVs and an optional mask that can be applied to remove outlier lines.\n",
    "\n",
    "Note:\n",
    "The LBL function can also be used to compute part-by-part (PBP) RVs of line segments formed within a specified range of average formation temperatures, as described in [Al Moulla et al. (2022)](https://doi.org/10.1051/0004-6361/202243276). This is done by provding the `compute_vrad_lbl()` function with the `bins` keyword consisting of a list of ranges. For example `bins=[[5000,5500],[5500,6000]]` computes the PBP RVs between 5000-5500 K and 5500-6000 K."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building reference spectrum.\n",
      "~~~~ Processed spectra:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 4636.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting LBL RVs.\n",
      "~~~~ Processed spectra:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 127.70it/s]\n"
     ]
    }
   ],
   "source": [
    "# Compute LBL RVs\n",
    "example = arve.ARVE()\n",
    "example.star.target = \"Sun\"\n",
    "example.star.get_stellar_parameters()\n",
    "example.data.add_data(path=\"example_data/tutorial_2/\", format=\"s1d\", extension=\"npz\", medium=\"vac\", resolution=100000, same_wave_grid=True)\n",
    "example.data.get_aux_data()\n",
    "example.data.compute_spec_reference()\n",
    "example.data.compute_vrad_lbl(exclude_tellurics=False, vrad_err_lim=0)\n",
    "\n",
    "# Extract LBL RVs (weighted average)\n",
    "time_val     = example.data.time[\"time_val\"]\n",
    "vrad_val_lbl = example.data.vrad[\"vrad_val\"]\n",
    "vrad_err_lbl = example.data.vrad[\"vrad_err\"]\n",
    "\n",
    "# Extract LBL RVs (per-line RVs before and after outlier rejection)\n",
    "vrad_val_lbl_before = example.data.vrad[\"vrad_val_lbl\"][0].flatten()\n",
    "vrad_val_lbl_after  = example.data.vrad[\"vrad_val_lbl\"][0][example.data.vrad[\"vrad_mask_lbl\"][0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We convert the RVs from km/s to m/s for easier interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert from km/s to m/s\n",
    "vrad_val_true       *= 1e3\n",
    "vrad_val_ccf        *= 1e3\n",
    "vrad_err_ccf        *= 1e3\n",
    "vrad_val_lbl        *= 1e3\n",
    "vrad_err_lbl        *= 1e3\n",
    "vrad_val_lbl_before *= 1e3\n",
    "vrad_val_lbl_after  *= 1e3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have RVs from two different methods, we can compare them. We can see that the CCF RVs are more scattered around the true curve compared to the LBL RVs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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uuovWrFlDRYsWpf/85z+ig+f9999PmZmZ9Oqrr5IdRUh6s+Md5OTJk1SuXDmxAMuWLWv25AAAAAB4nDt3jnbv3i1qbPNolGHHaSOlS+feP32aSKUUodud87OM9MSZaCEHAAAAADARyh4CAAAAQH7cIu7eRIqwQgs5AAAAAICJEJADAAAAAJgIATkAgBVw56mIiNybSv1dAABwLgTkAAAAAAAmQkAOAGA1+/ebPQUAABBGCMgBAKxg1qzL9xs3Jpo+3cypAQCAMEJATkTx8fEUFxcnhmsFAAi7jAyiESMuP87JIRo0KPd5AACTZGVlUUREhLjxfQgMx5McV3J8GSjUISeiDRs2YKROADBPWlpuEK6UnU20YwdRdLRZUwUAAEEYNmyYuMkjdQYCLeQAAGaLjSWK9NkdR0UR1a9v1hQBAJjioYceou7du6u+VqdOHU+LfVRUFNWoUYMGDBhAx48f97znxx9/FK+fOHGC7AQBOQCA2bgVfNIk72B8yhS0jgMA+Hj55Zfp4MGDtG/fPpo9ezatWrWKHnvsMbI7BOQAAFbQr9/l+1u3Eg0YYObUAAB42W+R6k9lypShatWqUc2aNalt27bUr18/2rx5M9kdAnIAAKupWdPsKQAAoFmK6k+NGzem6Rar/rR//3769ttv6YYbbiC7Q0AOAAAAAF4yMjJohKL6U05ODg0aNEg8b6ZnnnmGSpcuTSVLlqTo6GiRL/7OO++Q3SEgBwCwglKliCQp98b3AQBMlJaWJoJwpezsbNrB1Z9M9NRTT9Fvv/1Gf/zxB61YsUI816VLFzFtdmabgHz8+PGiniPnDlWpUkX0wE1NTfV6z7lz50SZmUqVKomzp549e9Lhw4dNm2YAAAAAO4qNjaVIn+pPXNmkvsnVnypXriymgaevXbt29N5779GaNWsoJSWF7Mw2AflPP/0kgu1169bR8uXL6eLFi9SxY0evQvVPPPGEyCWaP3++eP+BAweoR48epk43AAAAgN1wOsgkRfUnDsanTJkinreSKK5KRURnz54lO7PNwEBLlizxejxz5kzRUr5p0ya65ZZbKDMzU3Q2mDNnjjhjYjNmzBCdEDiIb9WqlUlTDgAAAGA/XMGEG0PZ1q1bqUGDBmH53szMTJGWosTZD+zUqVN06NAhkiSJ0tPT6emnn6Yrr7yS2rRp4/X+LVu2iKwKGeeaN2/enKzKNgG52sJiFStWFH85MOdW8/bt23ve06hRI6pduzatXbvWb0DOIykpFS9eXNwAAAAAgIs/ha/6048//kjXXnut13M8ABAbM2aMuDEOxDmdedmyZZ6AXcaNtb4t6ZcuXQrpdJ8/f17ctOJLxwXk3Mlg5MiRdOONN1KTJk3Ec3y2VKxYMSpfvrzXe6tWrSpe86dWrVpej8eOHUvjxo0LwZQDAAAAgJaZM2eKW7Buu+020XpuVn/Hl156Kaj/a8uAnC+f/Pnnn7R69WpDPo8veZQtW9bzGK3jAAAAAKDH6NGjadSoUV4t5L6Nvo4JyIcPH07fffedGCpV2bGAR226cOECnThxwquVnKus8Gv+cDCuDMgBAAAA3K5UqVKmtTbbUWFSnm1TZYVXCA7GFy5cSCtXrqS6det6vd6iRQsqWrSopyYl47KI+/bto9atW5swxQAAAAAADmoh5zQVrqDy9ddfi16zcl54uXLlxGhN/JcT/vlSAXf05BZvHmGKg3FUWAEAAAAAq7JNQD558mRPsr4SlzZ86KGHxP13331XFLHnAYG4l2tCQgIlJSWZMr0AAAAAAI4KyAPJYSpRogQlJiaKGwAAAIAT+A5hD85bNrYJyAEAAADchMs585V/Hnmca27zYx7gBqzRUMzFRP755x+xjHjZFAYCcgAAAAAL4kCPi1gcPHhQBOVgPVdccYUYhJKXVWEgIAcAAACwKG555YCPR5nMzs42e3LAZ/TPIkWKGHLVAgE5AAAAgIVxwMelnfkGzmSbOuQAAFBIWVl8ZM+98X0AALAEBOQAAAAAACZCQE5E8fHxFBcXh3KJAAAAAFAoHE9yXMnxZaAipEAKfDvUyZMnxQifmZmZYmRPAABH4zSV0qVz76emEjVoYPYUAQA4lp44Ey3kAABuMWvW5fuNGxNNn27m1AAAQB4E5AAAbpCRQTRixOXHPLrcoEG5zwMAgKkQkAMAuEFaWm4QrsQ1jXfsMGuKAAAgDwJyAAA3iI3lYf+8n4uKIqpf36wpAgCAPAjIAQDcIDqaaNIk72B8ypTc5wEAwFQIyAEA3KJfv8v3t24lGjDAzKkBAIA8CMgBANyoZk2zpwAAAPIUke8AAIDDlSpF5N6hJwAALAst5AAAAAAAJkJADtYcTTAiIvfG9wEAAAAcDAE5EcXHx1NcXBwlJiaaPSkAAAAAYGMcT3JcyfFloCIkyb0JhSdPnqRy5cpRZmYmlS1b1uzJARm3ipcunXs/NZWoQQOzpwgAAAAgZHEmWsjBembNuny/cWOi6dPNnBoAAACAkEJADtaSkUE0YsTlxzzU96BBuc8DAAAAOBACcrCWtLTcIFwpO5toxw6zpggAAAAgpBCQg7XExhJF+qyWPMR3/fpmTREAAABASCEgB2uVGYyOJpo0yTsYnzIl93kAAAAAB0JADtbTr9/l+1u3Eg0YYObUAAAAAIQUAnLwtn8/WUrNmmZPAQAAAEBIISAH65UZLFWKiMvj843vAwAAADgYAnK3Q5lBAAAAAFMhIHc7lBkEO7FSB2QAAACDICAnovj4eIqLi6PExERyHZQZBAAAADAMx5McV3J8GagISeJEXXc6efIklStXjjIzM6ls2bLkWklJRMOGeZcZRGUTb9waW7p07v3Tp5HbbhYsBwAAcGCciRZyQJlBsCerVQQCAAAIEgJy8IYygwVDIGgeq1UEAgAAMAACckCZwUAgEDQfKgIBAIBDISAH+zCrwgYCQWtARSAAAHAoBOQABUEgaA2oCAQAAA6FgBzsKZx53HYLBJ1aqzs6mmjSpMuP5YpA/DwAAICNISAH+zArjxuBoHWgIhCA80++AVwIATnYg9l53AgErQcVgQAAwCEQkIMz8rjD2VJkp0CwEKk9WVlZFBERIW583xJQEQgAABwIATnYg93yuM2EEo0A7oPxEQBsDQE52IOePO5QHJjs0jIbotSe/U472CP3FpwAJ98AjoGAnIji4+MpLi6OEhMTzZ4UCDaPGwcmw0s0zlLM08aNG9N0C8xTS6bRALixXw0AaOJ4kuNKji8DFSFJ3OTnTidPnqRy5cpRZmYmlS1b1uzJgYJwAFa6dO7906cvt1TzASgmxjsQ5Rb0PXvcVwklI4OkmBjan5NDaZzpwxcXgpgXGRkZFBMTQzmKeRoVFUV79uyhaBPnKQfhpfPWgdOnT1OpYK9WaK1LAHaRkkLUrp3687fdZsYUAUAh4ky0kIP9YeCey6KjaUqvXhRDRHyo5r/T+/bVfWKSlpbmFYyz7Oxs2mGheWpYGo3T0nHAHdCvBsBREJCDfWjlcePA5NWyPWzBApJDaf476LPPxPN6xMbGUqTPPOUW8vomz1PD0miQ4gR2h/ERABwFATnYHw5MAbVs68m/5rSUSYp5ysH4lClTTE1X4ZOKEYqcWf6dgwYN0n2ygdxbcAyMjwDgGAjIwRkccGAyosNioC3bgaR79FPM061bt9IAk+epYWk0SHEqPFSpsR47jY8AAPkgIAfnKcSBKevIkctB8ZEjZDf+WrYLk+5RM8B5GsoqKIal0SDFCZzCLuVYAaBACMgBCiFUAWggLdha363Wsh1MugdXMOEiTHwLupqJgQxLo0GKEwAAWIytAvJVq1ZRt27dqEaNGiII+eqrr7xe58BhzJgxVL16dSpZsiS1b99eXOYGFwhBS9H+AwconEJR91tu2TYqt5z5fb/i8f4QbHuGpdE4IMXJMlClBgDAXQE5H/ybN2+uOYDPW2+9RR988AF99NFH9Msvv4hWvYSEBDp37lzYpxXsadbs2Z77jVu00BUUF6YMX2E7LCq/W61lO1xVUwoz//QKNI0mgA8y5nPcBFVqAACMJdkUT/rChQs9j3NycqRq1apJb7/9tue5EydOSMWLF5f++9//qn5GZmam+Bz+C5Ceni5FRkSIdUK+RUVGiudPnz7teY7vyxITEz3PR0ZGStOmTfP7HVqfs3LlSq/vlW8pKSman6X3u5Xvj4qK8rxfOU2pqakFziet94v5FxnpPf+iosTz4CC8PCMj5etRubeoqNznAQAgqDizCDnE7t276dChQyJNRcajI91www20du1auu+++/yOpKRUvHhxcQN3SVuzhnJ8Bq7NzsmhHWvXUnznzgG3avNVGb15zXILtu/ImNyCzVd4Vq9eLf5euHCBzp8/L9b1J598Mt938/o/f/588Zg/r0iRIp6bclBeTvdo0KABrVmzxiv1i1NlkpKSxGcFmlqTnJwsUkf8pcVozQ/DRt6E8PFXpQZ5+ADgYufPnxc3rfjSH8cE5BygsKpVq3o9z4/l17TUqlXL6/HYsWNp3LhxIZhKsDIeZp6TOpShRhQR1VdJD+FgNpgAVCklJYUqVKhAx48fF7cePXrQggULxGscTMsdFnnj5oDc9wRUGWDL371z504R2KrhYF6Z7sH//4svvqB3333X8zz/niFDhtCePXuoZcuWdPfdd3te4+dOnTqleRISW7q0+vwrVQqBt5PIVWqU6z6q1AAA0Pjx4+mll14K6v86JiAvjPT0dCpbtqznMVrHnU8tQIxu04amRETQYEmi7Lxgkh9Ht25NSSqtwhyEarVqywHy0aNH6ciRI+JWsWJFcbVGduedd1LXrl3puuuu87SSyzhIljssXrx40bOBr1ixQlz54QD+s88+y/fd/BlXX3216HDJAfelS5e8bkOHDvWcLPD08TT5Bvb8+ODBg+L9Mv4e/j6tk5Dff/+dulxxBSXzyKD8nDz/uKhJVhYFUn9GPtEJhlbAjxOBEJCr1AwblvsYVWoAAITRo0fTqFGjvFrIfRt9HR+QV6tWTfw9fPiwqLIi48fXXHON3//LwbgyIAf7MSTwio6mByZOpPajRtEeIroqMpJqJScTd6tUaxXmFmMuwzcsLzDhgPjFF1+kP//8k5YuXZovqOXWcOWGyoHvokWLqG/fviIQLVasmDi7ZsrqIRxcyzgFS/5tvt/NLeqBbvjy/+GqJVyZyDewf+KJJ7w6TZ49e5YqVapEmZmZnmBfOX08L+iuu+jhyEhK4DSfvCsL0Sotp8rAWyv9xTA+VV8aFLAvAB1VauSAnKvUBHkiBQDgJMULkfJsqyor/tStW1cE5dyCqDwz4WorrVu3NnXaHMdho/QpK5Rc6tOHbiIiTliqtGmTKIen1SrMrd033nij57m//vpLbIjr1q0TqSMcnHPLNqdNXX/99SKQ9v0cflylShVx0hgXF1dg3W/ltBpRAlCrtjdXM6pcubLneZ4eTmXhSkZ8k/EVgv/85z/UrFkzcUJzYcIE4lPj24ioRkQE/dC7N325fj29/PLL+Uo6FrayjBblPApn1RfXQpUaAIDCk2zk1KlT0q+//ipuPOnvvPOOuL93717x+htvvCGVL19e+vrrr6U//vhDuuuuu6S6detKZ8+eVf08VFkJElcHkasrKCqFmElvpRDNCiUqv02tekhERIT0xBNPiHVOWTVl8eLF0vfffy+tXbs2XzWVYKuQ6K2mopdRVVZ8598Hw4dLY8eOFfOJ55fv7547d67uyjJ65hGqvgAAgJn0xJm2Csj5QK12AO/Xr5+n9OGLL74oVa1aVZQ7vP322/0GGAjIg6QMWgMI4MJBT9CqN1Bbvny51L17d69gvFu3btL48eOlOXPmSBcuXAg4aNUqPWjUtAZDqxRjUO9XrBtnjx6VduzYIU2cOFF1u503b54hv01rHhkZ8AMAAJhe9lDudBYozin95ptvjBu4I89tt92WrwOa7/fypXHl5XEIw6AgycmmjnSot/ygVgoKP88pJ9u3b6c2bdqI9A25OgmnWcjlAT///HO65ZZbRKqJ72A7Mq3caE4zkfO+5dKD/hS2kksg5IGEjH5/iRIl6KqrrqJ77rmHnnrqqXx56pxK9thjj9F7773n2X4ffvhh0e+DO66WKVMmoOnRmkf8eZEREV6lLKMiIw0fDAkAAKCwIjgqL+hNHHRwzWO505w//HFvvPGGCDbq1atHVsY55nzg545q6NQZIM7vjYnJX/KMO/WFuMqCVsdNLh/Yrl27fO/n5/kkTi2Aj4mJ8QrieB3nUpccyLGHHnqI6tSpI+7/888/4sbVS3y/W43a53MAyh0fuWOnns6n/j7LqIA8HLi2uW8HVD5BSU1NpUaNGonn+XVl3jqf0Pfq1UvMs6Dm0dq1tOSGG/JVzRmwbx8qggAAgKXizICrrHALF7cIBmLixImBfizYjQUHBfE3qI4aDmTffPNNsU4zbknt0qWLCMb5/3GrLg+kI7vyyivFLdBWZH+t2gVd5VGbVrVqKnYKxpnWlQHl7+AKNNwhk4P0Xbt20cCBA8VNPnHZu3ev2LGVL18+sHl0+jQ9IknUiehy1Ree9zt2UJbOEyMAAADTW8j5QFi7dm2v8msF1fWuUaOG55K/VaGF3L4t5By0KdM9tFpgleTRK9nff/9NDRs2FPdHjhwp0ie4ygkH94WtQ290q7a/3+3UspSctiKXMuX3X3HFFfTBBx+I+us8D5s0aUJNmzZVrTfumUcZGSTFxFCEyrqKgBwAAKwUZwZU9pCDi0CDcca1kK0ejLuSEeUK5UFBZGEcFMQ3L1tZwk6rBCAPzMPrLt94VEoZnzAqR2blPGcO8owYFEqrlGCwrdpyzjbf7BiM+/6GQIJfZXoct5qfO3dOtIzzcuQTniVLlogrcf/9739p27ZtIl893zyKjqaIANZVZZlEAAAAM+iuQ84HQuUw3omJiaKGMtci5tYrcAFF8CsGBQlDh049Nas52OaUBw7AuVVVxi2ncqt1aR7cJq/sRvmiRQ2fXiNqhDu17nswJ19z5swR85T7snTu3Fnkl/Oy5GU6d+5csV/Ss676O7kDAACwfEDOebfcBM+2bNniOUDu3r3baxRCcIkwDQriLy9b2QK7YcMGEVx98sknYv1UjpTJeeJaVVFCyehqQ07n7+SLW85btmxJjz76qEhR4oGZuBoLX92Q8VWRzZs3i+o4XvKWQ6gGJAIAAAiW7uiEA2/OtWXcAtm1a1d6/fXXRUv54sWLyY7i4+PFb+Lf4BoWvkzP+cBymgnfV3bcVJI7YCq7QfDJIl+p4dQTHh1TWTqPH6u2hIZgXuhN0YDAT75k3NG2Q4cO9MQTT4g+LjIenZfLrnJKy/c//USHDx3KrYyetxwC/XwAt1+pAoDgcDzJcSXHlyELyIsVK0ZnzpwR93/44Qfq2LGjuF+xYkVPy7ndcKsqpxXInQJdUz882Mv0HNjIw7+EONiU83vV8rKfffZZEXhxgKWsmX/33XfT//3f/4lUqhdeeEG9JdSoeRHug7SFT6SMonXypVU1h9+r7OPC1aB4f3T+/Hlav349TZ48mT7++GP666+/xDqg9/PBJhAwA4BFcDzJcSXHlyELyG+66SaRmvLKK6+Igx2nAchVK+xWis1VOAhVXKYXVVIGDcp93mK08nuVedk8oAyXJuT64JyeIOOOf82bN6eiRYtqt4SuXWubeSEYdfJgE4XtFMstEpyS8uCDD4oWCg6+9+3bR/Pnz6epU6eKFCIjO92GHAJN63HBiTEAhJfugPzDDz8UgdCCBQtEy5OcH8vpKp06ccVfsF39cAvRyu9dt26dCKhkXFWDB5Hh/gvcIq5GsyWUW/ZtMC/sdiJlpU6x3GLOA5Nx9RxOaeH675w6xOsEv2Zop9twpHDxb8q7DyZx2YkxAIRXwAMDrVy5km699VaRq/ndd9/le/3dd981etrASLGxfG0/f/1wi12m12rV5koaXMuTSxRyWbtWrVpR3bp1/Zbj1Bwwpk2b0MwLbjUzuiyhBQdislunWO5HwAE5X92TR2JV4qpRfIKnzEO3Gp4DsRyUHzjAOTlmT471Gb0tap0YJyS4ZjsEAIu0kD/yyCOiExWXN5w3bx6dOnUqtFMGxjKxfrgeWq3a3BLeokULEVzzOsitn4HUxldtCTVyXoS61Uw+kVKy4ImUHTrF8pU9uc48f+ZPP/1E48aNE+ksnGPOqVHbt2/XNZKqVidkIxWZPZt+JqIxRPTvddehZdaMbdEmVxgBwOEjdcr++OMP0YmOb1xSjluc7rzzTrrrrrss3bqkxXUjdXKwIA+4kppqfGtuIXEnPO4AwdUxvvrqK7+jbhZ6hEgj5kW4Ri1NSuIeIt4nDxZOsbCTf//9l9asWUO//fabp/WcGx7atGkj+iIEUiZT7yikut6fkUHTatemQZJEvJbx1CRHRNCAffssdzJtqlBviyaOUAwA9mX4SJ2yZs2aiaoV3Jlz586d1LNnT5E7zkOQc0WLMWPG0MaNGws7/RAOhUgDMKpFUPk533//vUh74so93JHTqPzegFpag50X4Wo1M2EgJreoVKkSdevWjUaOHCkaGHhdHD58uKjWI1eT0sPoUT8z1qzxBOOM//LjDO6YDOHbFm1yhRHAcOhUHjZBj5LCoyEOHjxYBFI8EAcH6nv27BEdO7kuOVhQGMsVBkJ5cYbzeHl4dO6oyQGSLQbVMSOdxMrzw8Y4z7x9+/ZeHYoPcL42EV28eFE0NCgHmVIqzKifBQXwXNDTJ8wkbsdHooQJ2yJOjAEghAwZtpBbHnv16iVGRzx8+LAYRQ/ccWZcmBZBZQ445/ZyRYyhQ4dS69atQz+ojhEnJ/5azYxsVbDYiZSTff755/mC602bNomO7O+//74YdIgDdFkwo37qCeBj27ShSJ++ElGRkVS/deugfp9jhbsFGyfG4EYo92m9gJzzfN966y0x+ArXJJdvTz75pMj55RxMcC69LYLK1JRFixaJXCrxOcnJnve8/PLLtHbtWlOGti8UtJo5hlZwzR3YOfeP/3KK3gcffCD2gZxzrnfUT70BvKgUNHGiVzA+JTnZujXTzYRtEcB4KPdpzU6djNNROD2F88arVq3q1crJ97k8ol04slOnsrPi6dOFa1FV6fjIgUNMTIxXEMInYZyupBUkHDt2TOTqstGjR4sOc9znQO/n2Gp+W7wDLeSXkpJC7dq1U32e88t///13WrVqleeEkgeh4hNSrgAU6Hrs7zu4NKOarCNHqHTVquJ+6q+/UoNrrinU73QsI/d9AIDOzGGOMwOuQy7jy7ZcIuyhhx4ip+CR/fggyiX15JrVoHJmnJxMafXqabYI+gYgnHPLLYkrVqzwSk3h0ob+WhZtFZDL6SQBzDu02FmbXHLTN7iuX7++KJnIZTe58gqPDMuB+YkTJ+j48ePqte6jo1Wrqfj7jkDUrFHD8N/tGFrbIgAEB+NgBC0xMVHc1Ma+MKyFvHr16uJgxAcWu0MLuR8ZGSTFxFCEz5kxV3eIadWqwBZBbk3kQJznMQfl3PmXcTCSnJxMCQkJFFO7NuUoVj++HL9n7157BeRq0KpgW0lJSfmCa7nKjzLA5kCcKwBdddVV4jn5+WXLlonOoXIFIrXyhv6+AwDAMnAss27ZQ8bDUHPUDw7vgJGW5h2Ms+xsis7KEi2CMmWLoNKRI0fEisjne0uWLMmXM0sHD9IU/v/y5xDRFEkiR2ziGETEtlQHklJRtGhRkXrFaXtyaU0en+Hnn3+mTz/91FOhRa3zc6Df4QooqQZgXSj3GVa6U1a4I2eXLl1Ey1BcXJw4MCl9+eWXRk4f6GVUqkRsLEmRkbQ/J0eUXuPrIdF5ZcT6xcd7Wvg4oOCh7Hm0Q05H4QCFcc4tl5LjlkHV1JTVq+kRSaJOeSXc+IJ9NLeWO+FSmFyCzbdVwaqjayL3VpW/kpscYPN6L+OAvESJEuIEddeuXeIqkLKjJuea83O+wbely3oCAHADgpzKy52l0R8qZHS3kD/22GOiAxIfjLijHjfFK29gIg4AFBUcREDIrdF+SrBpio6mKb16UQwRcRc0/ju9b998wTIHIXPnzhX9CjgtRc6AKlmyJLVq1YoaNWqUr3KKyJm96SYR8POncVe2aKsHrXqgVcG2/A0k5a+6EKeodOjQQVRQ4TxzvjrE24RaNZWABqsCALAaNCCElO4ccm715Fq93Epud47LIU9JIVKp4CCe16jgwNRyXQuqpsL/56effhIDpvB7OCC5vnFj6nLvvflaWjVzZp08JLydqqyghbxAeqsLffHFF2JsBj3VVFzJTtsJgBvh+GDdKisVK1YU6Srg7FQJrSooqampIj2Fg/Hz58/nfW2saB3kzVQugnk6K8vT+tevV6/LKS4bN14u2+aWS2F2alXgXGenLodC0FsV6IYbbtCspsIj0nJ6l7JkrGuhGhGAtaF6kXVTVsaNG0djx46lM2fOhGaKwNRUCbnzmVyeTYkDCi5lyJUkOBivVq0aPfjgg9SnTx+qUqVK4cq22SlodRoM/FAgre1Bq1yhGNBHpfMz54zzFcYZM2YUapRbRzAyxc6K0GEVAEIZkPModTxaHXfea9q0KV133XVeN7DfaHVqubFaAQW3hPMVkrvuuosGDhxI9erV8/qsakTUnIgO/Pqr5zmRM8sd3/j+iRPkCnYZ7t7pQZFBtLYHfyU61aqp8CBZHIjzVaapU6fSV199JUYAdSVUIwIACD6H/KWXXvL7Oree24XjcsgLyPfSmyterFgxT9WUv/76S1TV8eeje++lYfPmUU7emV5yv340YOZM71xxbmXEZWnb9ztwI+X2w6lbyiorBb1fWYec9ztco59r9TPezm699VaR5sIDELmG02scI/cWwPVO6ogzdQfkTuLIgFxngKA1lDfXmuf5w/mu7N577xWt51oyNmygmJYtRTBOitrie779lqLvusu5B127c3pQZGF8MsxXG+XUFe6f88ADD1CFChXINdCxGwAc7GQoBwYCZ/CXK86PubMaB+OcJ96/f3+/wThL+9//vIJxxgPG7li8GJelrQwlGk3D6S6PPPIIde/eXZwo85UpNzQMFDbFzjbQNwPMgv4LthRQQM4tN0ePHg34Q2vXrk179+4lu4iPjxfpGE4fgTSQXHGu/MAlLTlVpWvXriJPnFNaChJ78835ViZuIa9/xx25aSpeLzik3rhTdrpODoosfpDj7e2aa64R9ct5e+OUFX6OW1N41M8LFy74mZws8V6+8X3bc1LHbvTNAHA1jic5ruT4MlABpaxwiykHc4EO/HP//ffTli1b8nX4sxo7p6xo5adq8ZcrzpfI5c8aPnw4derUidq2bUtXXHGFrmniHPLh8+aJlnEOxqeo5ZA77bK0E3JUketqifmk3KZnz55Nf//9t9gvJSQkiB27b5lEvfsACCP0zQAzYZ/u3Bxy35SGQHDKAwLy0NF7MNbKFefRNW+55RbPZ+3cuTPo5ZZ15AjVr1qVuBvo/CVLKDYhQZ5Y5FKGAuaro+a3cgAt3uf27t1bjHTLeJu844476MorryywkykCdQtA3wwwE44Nzs0h51ZVvTerB+NOEkg9Y7VccW5xO3DggNdQ3oVZbvw5B4noN/6+m25y/mVpsyFH1THzm69gcdqKjPehCxYsoKuvvlqksezatYsmT55MP/zwgyeNRS0FDSwCfTPATDg22BI6ddqU3oMx54q///77XsF4t27dqEaNGiIQD2ntbbvU5LZTHjJyVMMrxPNbayRQbhHnVvOGDRuK11evXk2LFi1SDeAHDRoknldy/eBDZkLfDDADjg22hYDchgI9GCvxQCQXL170PH7llVfEwCZ8GRxDeNsQBlVx1Pz2NxIo9/Hgfjl8q1Spkkgx0wrgOVXQ38m64zqC2gWuDEK44NhgWwjIbcjfwVjNH3/8QR9//DH9888/nucef/xxUdIQTFSY1svYWFSvCacQz+9ARgLlVnLudM1BuVYAz2ljek/W/UEAXwi4MghmwLHBthCQ25C/1jS1Ayh39uJOXq1atRLPcYqK3OkLbJrbhxzV8ArD/O6nSHHYunUrDVBJcZCvZnGg/vrrr3s9/9prr4lOnIGerCOdBcCF+yrUKLd/QH7s2LHQTgkY2prG5FaxEiVKiJa1O++8U3cpQ7Bwbp/eHFWjdsRu3aGHMSe4ZgApDnJFFjZ06FA6e/asqJKkdbJe2I6gCOABbAL9F5wdkHPnv/vuu4+WL18e2imCoFvTLl26RM8995znea5dLB9wOSgHB+f2IUc1vAKZ3zpPXJTVjgIpVajs+3FbXm1rPgnnE2+1k/Vg+p6gkguAg/dVypNstza02DEgnzp1qshB5kFj6tSpQ+PGjRODyoD5uDWNy6LxJWxly3lh80chzLl9dtwhotXUNMoAvlevXuKknEfY5cBZLfVFb9+TYAJ4ALB4/wWURLR/QP7AAw/QihUrxM6bW2e55YQvg3bo0IHmzp3rd4hnCN3BmHNGly1bRp988om4XO1bwtDfARdsnodcmE5jhQmk3bpDt3gnvVq1atHAgQO9BgCTU194P+Cv74kavQE8AFgcSiI6q1Nn3bp16aWXXqLdu3fTkiVLqEqVKvTwww9T9erV6bHHHgvNVIImPhn6/fffxeXr22+/XdcBFyyc2xeKlmcjAmns0C19JYG39/bt23ulvmzfvl0MKsTBtFbfE7XO4HoDeABwSNokrnzar8oK7/hnz54tWmdZYmIi2VF8fLzIt7bj9HNrGJcv5MvSDz74YECdPcGiuX2hbHk2KpBGjVtbXUngoHzVqlV09OhRmjFjBlWuXLnASi56O49DGNgxnQ3slTZpgf2Vk3A8yXElx5eBipCCHKZx7969YgfPqSvp6enUtm1bsXPnjp92cfLkSSpXrhxlZmZS2bJlyeq4hWvt2rVUtGhRuuGGGzzP8yKUO3hx65Zc0jA1NVWUPAQL4YOpXHLy9OnLqQ8cGMfEeAe7vKPkfhpGBEApKXz2pv58XofAgIR6Op3CQvOJq6/88MMPtGnTJpFaOH78ePH8qVOnPPsKrf0G9icW328A6JWUxCWavNMmExIss79yGj1xpq4W8vPnz9OcOXNEy/hVV10lAnJuleWcQq6+Yqdg3G64I1VycrI4sPKNF7JMa6TNQEqngUXykEPd8mzUYBGof267KwklS5akbt26Uf/+/cU+YezYseL2zTffiIOEv2oqyo6jymAcAwYBOCht0kL7KzcLOCDnOrecJ8754jxS3Pfffy+qrHA+OVddgdDgk6DFixeLA+Thw4dFHfGuXbtSmTJlDCmdBi4ZXc3IQBo1bm05Wl5MTIyoksIlEjn95O+//6YjR46Ep5oKUi6Mg/xeMIrcaGfB/ZUbBRyQr169WrSq8OAQ3JGwY8eO+Vpm+dIoGIcPmO+++y517txZlJmUh85u3ry5Zqs42FQ4Wp5DEUjjKoytriQUKVJEBOSDBw8WncC542Zhq6lgwKAwQH4vuHB/5TZB55D7tuJ++OGH9Pbbb9OhQ4fILqycQ87T9v7774uTHDnnk0scotXbJXmiqalERufrGpWHinxWayxPg3BLOLeeK4NybkHnK6BaHTiTkpI8I4VyJRZOp/PXQdTR60yof5uF+iOAg9lkf2U3Ickh585Ao0ePpuuvv57atGlDX331lXie88i5FOJ7771HTzzxROGnHgRecLfccgu1atXK8xxaolwkFC3Peutoa6UZGFmP2y2pDBa+ksBB98iRIz2P+eobDzKmFYwXOsUF+zF9kN8L4Wbh/ZWTBRyQv/jii6KWLeeLc8tJ7969xSAUnFLxzjvviOeeeeaZ0E6tA2h1hjp+/LgoIXnw4EHPc7feeqvXQQ5DV4Nr2ClQ13viYsHfxn2BlP2Fzpw5Q9999x2dO3cu33v9prho/TakXAQP+b0QDhYf+MwNAg7I58+fL+qNL1iwQIwMyTvgS5cuiUFpuLoKX+K0Uv1HPnEoUaKEKA+4fv16sio+sHEpQ77C0LdvX6pRo4ZITWEYutplrLxDRKumoyn7pPCVObZx40aRmsLlDpV0DxjkpsGkQrGdIL8XICB2r/4UcEDOQWCLFi3E/SZNmlDx4sVFiorVOhdyh9NRo0aJDqibN28WHSATEhJENQGr+e2332jatGm0dOlSunjxoud5eZ5i6GowVbhbNbWCGTudDOidVov8NmV1pnvuuYf69etHFStWFPmPfPUz0AGD+CDIYWJbIjrDqRZuSLkIx3aCykYAuihTfO0SqAcckHMgWKxYMa/e+vKAEVbC6TOPPvqoqLnLoyR99NFHolTgxx9/bPak5av3e/PNN4vLwtyS36VLl3wrEoauBtOEq1VTK5ixU4qD3mm1wW/jfkFDhgwRlVh40DcZp7Bw0M4Bu9qIn0Vmz6a9PN4U1z/nBhz+bU5OuTCj9R/5veBiWX6Ca63xFBxXZYUDwzvuuEO0jLNvv/1WDNvuW/Xjyy+/JLNwx1MOvjmtpnv37p7n+eBx4sQJ+vrrr1V7v/JIo8rer/wb5d8Z6moGPF//+usvWrlypWrVAmU1A7klym81AwAjGDWyZzDVI9auJeLOzHaoKqG3AoaNK2bwoYI78XNjDAfptWvX9q7+lJFBUkwM7c/JIW4bj+XW9Lzfdn7+fIoaNYqK8OdERVEEp1w4YT8Wju0EADyUowcrK89pVYvavXs37du3j2666aawjDjMVQf5powza9WqZWyVFQ5qq1SpIgJYvsn5zvJj+Wamo0ePipb8qlWrej3Pj/2VY+SZpfwNcplBo6mloPDjLVu2aOaKa7VEAYRUOFo1tVIZVq+2T4qD3nQMG6dv8MBkBw4coF27don+RD///LPY33oaZdLS6OOcHIohIg5R+e/0vN92qU8fqkVEzYlox6JFzgjGmZNb/wFslJaSppHiu23bNpo4cWLYWs45flTGkxxfBoobLALCLSNOpdZCHgpyCorvGRy3PGnlisfHx3ue42GvAcJC7kiWd3UmJB3J5GDGt7WYWzLUnrdikKP1G7SmVe/7LaRatWqiAgtfHeVWJ+7c/+eff9Kdd94pXssoXZoGcoNC3vv57yAiSihVir6ZPZu4SYRvjTp3LrhuuV2EYzsBAM20FHlfohVfVahQwSs7Qm7w5L6FWqVdC4PLg3M/Rt8W8kAE3EJuB5UrVxYLgFtylPgxHzC0cDCuvIUqINfqDMV13bVyxZWdrTAoEIRVqDuSaVWP4JNQu1SV0FsBw+YVM7ij54MPPiiCcO77wi3mfED84YcfaHtmpicYl2UT0do9e2jE//2fc6tFocMlQFhk+Kk85xtfcUzF8RWntYSzOAbHj74xZaAcFZBzp1OuBLNixQrPc7wg+HHr1q3JCtRSUMSKNGGCatUCAEsI1dUZrWDGTkGO3mm1029TwZ2prrvuOtG3hTvO8z6WD25andD9XQF0HFzFBAiZND+V57hSnTJmevbZZ0VxDzsVx3BUQM74UsHUqVPFZQ3OHeJKAdwJgBeMJSh6BdcsX95zv1+fPlQtL8dy26JFzricC2BEMGOnIEfvtNrpt/koU6aMKJHI41DcddddokOVWsOCvyuAjmDl8QMALE5PScJYjeC6aNGiogDGunXrPM8/9thj4r3+yrRajeMC8nvvvZcmTJhAY8aMoWuuuUbU+l6yZEm+jp5mESkoXGkg776My4XxBdzfiKh+586WLIUGLhOOQEPrOxDk2EajRo2oevXqnoYF2Tdz59LDDz9sqwMiAFhXtMq+ZPDgwbR8+XIx2jmnLW/fvl1clVPGfHYpjhFw2UMnksseBlKOxjB8BijXb+cR8Lj8jo1LoQFYgnK74pFuEcSbuk+VOzc1a9aMOnfuLA6ccqkyZdkxrRJmAOAsWtu68vnUAPYNyud//PFHcWNcAIPHTVDrA2jmfkZPnBlwlRUwiO+gIMnJRPXqaZdCQ0AOADbBBxzO5Vy1ahWtXr1atFZxRZYbb7zR8x5UiwJwNy5XKAfeWlVTtChHNb/++uvFuAg8JoK/SiZycQyrc1zKii1HdeMzN9SyBQAH4AMkDxrHl5L5EjMPksEDn02bNk2MFaHVOqWsKWz41ZOIiNybhYfNdgzMb1ChNoqmv6opvvsGfu2XX34RfQR5PyJXnuOTfT21vq0MAXk4aQ0KwjstG5dCszQcHNwnVIEd6MIDyXEOOaescAUsHpzNt0OW3Ye6BoCCaQXea9as0ayaMstn38CFORYvXizSTzZu3EhOhIDcKqO62bwUGoClUsEQ2FkCB+AtW7YUJRJ79uwpBumQ+RuhOJiKDFqv8X1u2mhLRGe4UQTCByfH4KdcIW+ralVTSpUqlW/f8Omnn4qrbd26daOOHTuSEyEgD6dABwVBjmVo4ODgrlQwpww84wDcqYmrscj27t0rhrMOR31yrmC1l4hSiKhkixY4WQs1nBy7m8pVaa1yhTw+jFoFptMqg/lwisrNN98sxprhQN6JEJDn9c7lAS4SExND/2VaLeEo8xYaODi4NxXMiQPPOMS+ffvEqJ++B1Y99cn95Zx7XsvIoGL/9390MC8g34+TtdDCybFr6Kkf7q/0qVpJwtjYWNV9A1dtsguOJzmu5PgyUAjIiWjDhg1iReDLqmGFlvDQwsHBHfylgoElcUvXyJEjqXfv3p7nuAXtnXfeEQdprYO9v5xz1dfS0ujjnByKIaJ2ROLvdJyshQ5OjkFJcdIcSC3wcnklU3kf8MEHH9h67AKOJ/l3cnwZKATk4Fw4OLhDoKlgYClcqowrJsh4VGWu2cuVFNRau/1VZNB6bUNWFg3kx/LzRDSIPwtXIUMDJ8eu5HW1KoCr0srSp5wvfuTIEfr444/pyy+/FGlrTDm6upUH8zESAvJwQ2pK+ODg4B7oFG1L3PIl45GVL126RCVKlFBt7dbqGMY551qvrebnfb6TD/c7UHEpNHBy7BqqV6T8XJWWa4HL5QrlmuIp331HV1apQv0ffphOHTokqjH5csvYBRipM9wjdUJ4JSXxtSPvgwOCNefBSJ22x4einTt3ipH26tSp4xVgc+C+du1aatWqVb7n9/CIxpyOEhOj6//Y6fK3raiNRg2Owlek1La3PXPmUPS99+b/DykpRLfd5vUUn0R///33dPrwYXp+/Hjx3ImMDCpfmODbgscBPXEmWsjB2dBy6g648mR7nC/OHTq5xVuttZsrL0yaMEE1r1Sr0xh3qNL6PxAGLmnZdBvNq1URESQVcFX6woULNG/ePJo9ezYdP37cK0gt7/IrVwjIwT1wcACwPLUSaRys82AgnTp0oGpE1JyIti1a5JVXqtVprN/AgarPm8bpg5Xh5NhR1DpYa5UxrN+6NV2YMIH25FU1Suf3+KQsFS1alM6cOSP+f5s2bWjIFVeEphLafvuVOUZADs6GgwOArfi2dvOBu3v37iIY2PJ//0dcI+k3IqrfubPmwVsr59QtuagAoeSvjCFvkXXzqhrV4apGeeMOnDt3TryXA3se3Ic7XXeMi6MiTzxhXCW0WfYuc4wccuSQA4CTWDCPUi8Ovkvn/YbU1FSqVq0arZozhzoPHUqRykMWXw7nHPIgUlCU38HpMHJHs5BzwPKxJDPnq4OXqe+22CCvT4Da82q55XxC/fjjj1OHDh3ojjvuyJ9b3o5Ddx8qOecF4iA+Jsa7sloh9g9GQQ45AAA4Ardq84Gsa8OG3sG4E8qY2vCyOriLv9r/vlee1HLL+fGxY8fE33ztv0ZWQkuzf5ljBOQAAGApaiXStA7emVdeScuWLfNcEg+Gv1E/DWfzy+rgHv5q/6tto2q55fyYB8np0qVL/iHvjSyTGWv/MscIyAEAnMpJLbAaB+8lf/5Ja9asoQ8//JB+//33/K1whWj5MxxGD3b+em/TbU6t86a/2v9qeICfTp06eQXjH330kf/h442qhBbtJ7i3SUdqBOQAAE7i5BZYlYN3ixYtqFKlSiIPfOHChWLEvwMHDgTd8qcnYHHjZXXLMnO9d+g2p1lNRaPVmfPIb7jhBs/jbdu20aOPPhr4F9as6eoyxwjIicTZW1xcHCUmJpo9KQAAwXNTC2zewZuDgyFDhlD79u1FSbX09HSaOnUqffPNNyJIV6O35c8wDrisbklmrvcW3OYKe/Iop3D5q6Yib0eLFy/2vM6dPIcOHaqvqlGoKqHVrGnqFQyOJzmu9Ht1wAcCciLasGGDqE/LeU4AALbl9BZYjYN3kSJF6KabbhKt3s2aNRNpK5s3b6ZNmzYZ0vJnWM45hpZ33nrvkG1OK4VLrb7/P//8Iwb24dsvv/zidSJbuXLl/P0/zDYr/FcwOJ7k+cXxZaAQkAMAOIXLW2C5GkuPHj1E0NCoUSMx8Ijs7Nmznvxyfy1/Wq2LhuWc2/yyetBCmcdr5npv8W0ukJPHQFO4KlasKFrEJ0+eLFrHebvhbaxWrVpk2RP2DOtdwdCCgBwAwCnQAitwgHDfffeJFBbGgfgnn3xCn376KR06dMjvyJ5G5ZwHBAMV2X+9t+A2p/fk0V8KF7dyX7p0idatWyc+h1vE+b0NGzYU6SkdO3ak4sWLk2Wl2ecKBgJyAAAncWsLrB8chHMFiF27domWcM4vP3XqVIG5rnLroqE55xg9ODR5vGau9xba5oI5eSwohYtPaDkg56tMVapUoQcffJDuv/9+0Zna8mKtfQVDCQE5AIBToQVWqF69Og0fPpyaNGniyS/naiw//vgjXbhwwSvXVa11sbA55+AnjzcUqSxmrvcmb3MFnTyqpWSppXC9+eabYruR+2jwKJtdu3alwYMHU7169cg2oq13BUNLhBRo0VYH0jOkKQAA2B9XYVm6dKmnxbBMmTIiXaV8+fKqQ39zcLJnzx7Rqi53/Jdzzv2luUCAw5pXqGCtYec5SDVregz4bn/rsNxHQh7ynqsQySejyufff/99MbomD+ajp0qIZWUp5mtqKtdntGSciRZyAABwVX45B9K9evUSQTgfJPmAWVDrop6ccwgyj9emg+pYSUGlCrU6fHI6ikzuZ8FBuePUtO5VwyJmTwAAAEA48eV6Tl/hSizyJXxWu3ZtcV954VgtNUWZc67V4ggqeby+LeQ8X31TWZKT0e+hkPjkUb6awyePPGCPVkpWUlKSqJf9888/09ixY8XznJLSoUMHT8oKhAdayAEAwJU4N1ZuHZfTWYY2bkxReY/575S+fUXrIgfaBdVXDqTEnCEjftqNVh4vs3JJOjNb7A36buXJo1qHT66UwiPcnjt3jqpWrUp9+vShBx54wFnBeCl7dKRGQA4AAEBELapWpUnbttEeIkohEn/7f/opHd+yRfP/GFafPAi2Cu7VKpFYsSSdCYPIGP3dWiePailZ/PjixYvUs2dP0WGTOzDLV4xcV8/eZAjIAQAAuIPnoUNc6YA42/Y2btjlg2RODn09caLo1OlbA6Gw9cnztag7ONjwIrfaWq0knZmDyATx3XpOyHjd5f4QvsE2p2Q99dRT1LRpU2sH4i6AgJxI9CLmHKrExESyDbfsuAHCCduVu6kEiDmRkXSsYkXVYCaY+uShalEPJF3Gcuu31UrSmdliH6Lv5kD8r7/+EqNrrl69WpQv9O3wyX0nwFgcT3JcqadKDcoe2rXsoZmlmQCcCtsVJCUR5XWIkwPEjIQE0XGTq7Kwf/75RwyUwp3fmjVrplpirkKFCvk6exZUkk5veTbukCd33uM66cnJyf6rv1hx/TaxJJ2u8oy+y8fo+VfQd6tQdihOTU316rzJod327dtFrf3Dhw+L50qUKEHXXHONJyj3/T+2kGWh9SUAKHvoNigVBQAQslxnDpblYJz973//o02bNtEXX3xBjzzySEAl5gJqUdeRQ1zYdBlLHjfMLklnZot9EN/t72rL2rVrae7cuSIY56Htb731Vho5ciTdfPPNBY5Qa2mzTMzxDzEE5Hbl4JUSwBKsGLC4hVVSKzQCluuvv56uuuoqEQQrhw/nQF2thVpOJ/E74qfOHGJ/wb1mbrEVjxtWq4Ch1vk0TLJ69fL0XzizcaPf7y7ohIyv3PBVmVtuuUUE4m3bthUt5IFUC7LsNpphYo5/GCAgtzq1ld7hKyWAaawYsIDlcM4tl4bj1nEOzGXffvut6Pyp1Xrpd9AWnTnEfoN7NThu6OevBbkQOfv+OmPyp/7EKSc1avj96IKutnAqy6hRo6hdu3ZUsmRJcoQ0C1blMRACcjty+EoJYAo3ByxWaZG2Ah0tthxIc4s4t4BzegCnBnDuuL/WS80RP/1UHFEL4AIdkdHT2RPHjcIvf50n7GK5cWe9vPsBfXee/SdO+H1rjRo18nUy5sfKNBReJxwl1mJVeQyGgNyOHL5SApgCAYs12SB1iIOje+65R3Sw5KoKgVZf8crhDSKHWCu4V80tjo0lCceNkJywB1J+cP+BA+rPK9ZvrZxw5efv3btXXIWZP3++V8UUvlry0UcfiSsnjt1Goy1WlcdgCMjtRF7pHb5SApgCJ7rWYdPUocqVK4s8XbV0Eg6mDh06JAJzzRzeAPKXlQGcMh9Yrpah2TpPRBcmTPAMepTO04fjRkhP2GfNnu2537hFC0+ArRZ4B9pJ9+DBg7R582a6dOkSde7c2fP8tm3baODAgeT4bbSfeTn+oYaA3Oq0VnoHr5QApsCJrjVapMOVOhTCNB3fdBIOxrt27SrK0L333nuiOkuB1VAUred6apf7a53n/1WXiNoRUR0i8djpDBvNNO8KQ0beCU2Gxgm7fMIkAuwnn/QOsAcOpA0bNqgG3mvWrFFdbtwvYYtipFguoXfttdeKqyEPPfSQORVTrJLeV9OGVWL8QEBuZYGu9A5bKQFM49YTXSu1SDskdcg3neTNN9/01CDnAGvZsmUBfY7e8oZanT25NX3E//1fwJ8DPqKjaUqvXhSTd0LDf6f37SueVzthSuMA22eYl+ycHFr93XeqgTefMKhdVeETrAkTJniea9KkCR09epRq1aplXMUUO22jpSxWlcdACMitzCEHJgBbcsuJrlVau8xMHQrBVQFlsNSoUSMRqPXv318EwTw4S8uWLT3vPXPmDH3//fd0mAeb8Qk29I4GKlrnFQGc3NmTByfSO6qo0wQ0mqkGPnEZtmAByXOQ/w767DPNFu/S//6bL8DiLpY3Va+uesLEpTSfeeaZfFdVuJPwwoULrXUihfS+kEBAbmX+VnoHnyUCmMaN25XVTvzDlTpk0lWB6tWrU/fu3UVLp+y3336j9evX0/vvv+9Jrzh27Fhw5Q25db5PH8/9rRs3ivSGYD7HCWYlJwec7uOP1okRD0ev9nxW5co0JSJCBOGM//Lj+K5dVavj/Pnnn16fMXHiRJHedOONN1rvRArpfSGBgNzKsNIDgBtbu0KdOmSxqwKcfhAXF+cVMHNw/uWXX9KFCxfogw8+CHg0UFnNvAFm5HcFWibRSUS6j0aajt7ccq0Tmptuukn9RKd1a3pg4kTakZdzvjsykgZMnSqO3717985XHYcH8uH8cBl30KxXr57orGvJEym3pveFEAJyIlGmineGiYmJZDlY6QHAzSf+oUgdsthVAQ7IuWyiMvXh33//pT/++INmz55N9957r3rtcg2lFizwdD68okULT+u/Zg10h9Kb7sO0AnWtExqOH7ROdC716UM3EdE4Iiq7bh1tadmS5syZI062fDtj8mBTTz/9dL6ccFucSLklvU8Hjic5ruT1I1AREi95lzp58qQ4I83MzKSyZcuSJfEOoXTp3PucX+iWy+gAYM5+JjWVKK+EnmNxS3hMjHdQzicie/aYeiKSlJQkapkzbhUdPnw4tW/fXoy2yCMvss8++0wEY5zyUqlSJV2/LatCBc/ncE55KDoCchAb6u8IFLeEx9Su7dW5Mioykvbs3Stys+XpTE1N9ZSNVE6/8nl/r2k9L50+TRFlyoj7b48dS1k+qUscrF133XUF/g5/0wTOiTOLhG2qoHA5rQAA4eCG1i75qkBe8GuFqwJq1VS4le2pp57ydBDlvHJOX+GW35SUFKpWrRpdffXVoiXOE5z7af0vddtt4nPswIjAnpfmFCIazLNAzuOWJPF8kk9llOTkZHHFYJbG876UZQbl5cO85m+pUvTOxIkiKGN8EtC0aVNxu/LKKwP+HcrPB+dCC7nVW8gBAMDxVwU4wOaWcLXnb7uNs8GJzp8/L1JN/vrrL9q1a5dXOgYPStS2bVu6mvOQTWz9N6o1V29Arvr+lBSidu1E+g4nqXDWNc+BjHnzKOa++7zmH6eCrF27llq1apXv+T179vhNETl79qxIg+HBeQ4cOECPPfaYJ+/7xx9/FNPGOeL8Gb7D3YOznUQLOQAAgH2uCsidBn2DQWXnveLFi4tBYfjGpRI5AOTgnANGrk0tgkCf1n8pKooiwtj6H2gLs95yhUEF9nmD+UTn5Hg6t/LJSZok6aqYwsG2MiDndkweMZOvVPBrfHVD2baZnp4uas4z+WQKoCBoIUcLOQCA+1iwf44yh1zuvBdIMHvu3DnauXOnCOqLFSvm9dsmDR9OxZo0ERU7rrrqKtGBtGjRoqHL2c4b/EhPC3Mg+fQFBfaaLfNJSflSkzISElSnU6uFfPfu3V6t2//73/9oxYoVXt9fpUoVatiwoTgJ4fxwtISD3jgTATkCcgAAsADDOu8pAvLXRo+mixyk5ylSpIgIynv16uVJAzGqI2YgaTehCuw1A3iN1CStkx/fzxk8eLC4StGhQwdP7Xievk8++USc5PBJEL+uLFkIEEycibKHAAAAFqPsNFgYjz/+OPXo0UOMDlqmTBm6dOmSyHMuWbKk5z0//fST5/7hw4dFmoa/EoBazwcz+JDaZ+ktV6jWIVZ1NEvFPO3Xq5fX4En33XefmA8lSpTwPD9kyBDR+ZKDqb179yo+pqYYVZP/T4sWLRCMgyFsk0P+2muv0aJFi8SIZnxJ7sSJE/nes2/fPrEB8dk4n+1zzdXx48eLFgEAAAArM6yahqI6F7cNN6taVXQq5M/mXPPjx497Bc7KUSLfeecdUb2FUzDKly+v62vlmtm+Lc/8vJ5W+EDy6ZX8BfDRijrQ/Nyxf/6hI0eOUDq3luepWaOG+P8cO/BATDKuYMNXKerUqSOuKsj45IGnB8BItolUeSPh0a1at26tOvQtb2hdunQRO5I1a9aIDhcPPvigyJV7/fXXTZlmAAA35UCDtXEgyS2+ypJ7HKSfOnXK85hTNrp27SrqY3OKiG/HSnn0UNnmzZtFJ1MOsPnzuSFMDsi5IoyetBv5O0RgP2ECDRs1KqDBcLQC+Lp169Kyn3+mE3PnioGWjr7/vqf1v+iFC+Q59eEGvipVqGXLlqJ85NChQ0UsgcY8CCfbrG0vvfSS+Dtz5kzV15ctWyY2/h9++IGqVq0qLs+98sor4rLSuHHjcju6AAAAgFcQrGy04gD9+++/p5EjR9LXX3/teZ47K3700Ud06NAhWrdunef5W2+9VQTwPMgNB8bdunXzumrNrfGcHqNs+ecWeW595hZz36osfELAQf09PXrQK6NGUVUi+njuXLquZ0/P+3gEU25x55Z2vvF9Hun0888/9wrgefRLHhmTy0XKOBbg1v+WGzdengmNGxMlJ1Nnh49cCtZmm4C8INw7movtczAuS0hIECksXBaKz+C1yEX7laWl+AYAYAv794emjjZa4R1PK92DW5Q//PBDz3P8Hj6e8kihY8eO9TzPgfZ3330n0km4lVpOu+Hbq6++6mmR3rBhg+f/8JXu/v37i4Y239xv/g7u0HrrX3+J+uGcGJLTuzfR1KlEeQHz4sWLRe1vJW4Nlylb5m+++WZxpZwH5eFAnPO9I3h7GTTo8n/m38+PExJMHRwK7I9P/pQngL7xpSsCcj5rVwbjTH7Mr/mjzA1jvLPhVnUAAMtStCzKLXxywAIQKK10Dw6o1QJ1Tgf1zXPnx1xd5aabbvI8x/+XG8I4cOZWeA6ile/nq93caOb7HfzaufR06rZ8uafqRCR/nyJg5mCbp4Vb2OUbp8twnzHfDrHKafLwM5opAnIoDF4H5YwOvUwNyJ999ll68803/b6HBz5o1KhRSKeDi/gry9GgdRwALI2rRyhaFsPSwheqVngwlVZHzDZt2qgG6hzgqj3PaaLKTqD8HKeyMO4s6RvE8//nIFrts57p0YMiFyzQDJjvvvtu1d8ScIfY2FiuaZh/NFM/1WCgkFxytW306NE0Kq/vg9xC7tvoa8myh08++aQIuP3duM5nILgDBpdrUpIf82v+cDCuvCEgBwBL89fCZyTfVniVDvVgf5yzrUz34HrccqAukwN1zhVXe97fwD9a5RA5dUXts2Juvjk3YPb+D8YFzPJopsrPDuNopuBcxYsXzxdTBkyymRkzZkjlypXL9/z3338vRUZGSocPH/Y8N2XKFKls2bLSuXPnVD8rMzOTT6fFXwAA20hPl6RIcSH/8i0qKvd5O30HWMLp06fFsZBvfF/t+dTU1AKf9ycxMdHzf6KioqRp06b5/6zERO/1Lu/9huHfKX9+gL8BDJrfinXM6TJ1xJm2GRiIe2tzDXL+y7ljfJ9v3MOadezYkeLi4uiBBx6g33//nZYuXUovvPCCuAyHFm8AcJRwtPCFqxUeTKfsiKmsD658Xqt8YaADGKm1wvv9DsX7aevW0PaPMGgQJtCR/gb52CYgHzNmjOggwh0uOQjn+3zbmFe6iC91cU9v+TJY3759RR3yl19+2exJBwAwXqgDFjnPVgl5tuAngA/ZKKShCJjlwZP45tB8ZktB+luBIriZnFyKk+25BBIPi6srzwcAwA2dpJKSiPI6+3la4VHJBQD0dkKPicnfiZYHnnJ43v5JHXGmbVrIAQAgzMKZNgAAzoT0t4AgIAcAsKNwX3JHni1YAI/KyeUS+cb3wQaQ/hYQBOQAAAAAEBooMxkQBOQAAKAOHd/AwngEULAJpL8VCAE5AAAA2MIsRbWOxo0b03Q3VOvg1JyIiNybE9J0kP6mCgE5kRh5jGuYJyYmmj0pAAAAoCIjI4NGjBjheZyTk0ODBg0Sz4PFuexqW2JioogrOb4MFMoeouwhAACA5aWkpFC7du1Un7/tttvIFSVOU1OJNAZpAutB2UMAAABwlNjYWIr0qdbBgwHWd3q1Dgyq4woIyAEAAMDyoqOjaZKiWgcH41OmTBHPOxan4yjSdEQ970GDcp8HR0FADgAA4GY26jTYT1GtY+vWrTTA6dU6MKiOayAgBwAAAHtQnDDULF+eHA+D6rgGAnIAAACwhVKlShFXopDy7jseBtVxDQTkAAAAkMtOg+3YaVoLA4PquAICcgAAADfTW8XDzJxzt1ccwaA6joU65KhDDgAAbsXVOmJivDsOclrEnj3aaRHKutinT4dvoJdgptUJzJrfUGioQw4AAAChr+IRzrQRt1Yccdkol26FgBwAAMCtgqniYVbaCCqOgIMhIAcAAHArvVU8zByoBhVHwMEQkBNRfHw8xcXFUWJiotmTAgAAYN0qHmanjaDiCNgAx5McV3J8GSh06kSnTgAAcDM9nQbN7liJDo6XYV5YHjp1AgAAgPGdBpE2AhASCMgBAADAHmkjqDji7kGSHAwBOQAAAAQHA9WYx+2DJDkMAnIAAAAAOzGz2g2EBAJyAAAACBzSRsxndrUbMBwCcgAAAAA7wSBJjoOAHAAAAMBOUO3GcRCQAwAAANgNBklyFATkAAAAAHaGaje2h4AcAAAAAMBECMiJKD4+nuLi4igxMdHsSQEAAAAoGKrdWBbHkxxXcnwZqAhJ4iXpTidPnqRy5cpRZmYmlS1b1uzJAQAAAICsLKLSpXPvnz5t2xMOPXEmWsgBAAAAAEyEgBwAAAAArGn/fnIDBOQAAAAA4UzHiIjIvfF9yG/WrMv3Gzcmmj6dnA4BOQAAAABYQ0YG0YgRlx/n5BANGpT7vIMhIAcAAAAAa0hLyw3ClbKziXbsICdDQA4AAABgBpfkR+sSG0sU6ROeRkUR1a9PToaAHAAAAPJDrnNouDA/WpfoaKJJk7yD8SlTcp93MNQhRx1yAAAAx9aCthTOg46J8U7J4IBzzx7HB5xBr3upqUQNGpAdoQ45AAAAGAepFcZwaX50odSsSW6AgBwAAADyQ2qF8VyaHw0FQ0AOAAAA3lxaei7kXJofrVupUkScUc03l6RKISAnovj4eIqLi6PExESzJwUAAMB8SK0InX79Lt/fupVowAAzpwZCgONJjis5vgwUOnWiUycAAIA3dD4MHXSWdY2T6NQJAAAAQUNqRei4MB0DCoaAHAAAAPJDagVA2CAgBwAAAP9cUnoOwCwIyAEAAAAATFTEzC8HAAAAi+c6A0DIoYUcAAAAAMBEtgjI9+zZQwMGDKC6detSyZIl6aqrrqKxY8fShQsXvN73xx9/0M0330wlSpSgWrVq0VtvvWXaNAMAAAAAOCZlZfv27ZSTk0NTpkyh+vXr059//kmPPvooZWVl0YQJEzy1Hjt27Ejt27enjz76iLZs2UIPP/wwlS9fngYOHGj2TwAAAAAAcNbAQG+//TZNnjyZdu3aJR7z/eeff54OHTpExYoVE889++yz9NVXX4mAXg0GBgIAAACAUHDFwED84ypWrOh5vHbtWrrllls8wThLSEig1NRUOn78eIEzTHk7f/58SKcdAAAAAJzl/Pnz+WLKQNkyIN+xYwdNmjSJBg0a5HmOW8arVq3q9T75Mb/mD+eb8xmMfBs/fnyIphwAAAAAnGj8+PFe8STHl7YIyDmlJCIiwu/NN91k//791KlTJ+rdu7fIIzdCenq6aHGXb6NHjzbkcwEAAADAHUaPHu0VT3J8aYtOnU8++SQ99NBDft9Tr149z/0DBw5Q27ZtqU2bNpScnOz1vmrVqtHhw4e9npMf82v+cF4PcsgBAAAAIFjFixcXt2CY2kJ+5ZVXUqNGjfze5Jxwbhm/7bbbqEWLFjRjxgyKjPSe9NatW9OqVavo4sWLnueWL19ODRs2pAoVKpDVcozGjRuHXHWHw3J2Dyxrd8BydgcsZ/c4b6FlbYsqK3IwHhMTQ7NmzaKoqCjPa3LrN18a4OCbSx8+88wzojQilz189913NcsemlVlBdVd3AHL2T2wrN0By9kdsJzd42SIl7XjqqxwSzd35FyxYgVFR0dT9erVPTcZ/+Bly5bR7t27RSs6p8OMGTMGNchVJCYmuup7zf5us7hxfmM5u+O73bic3Tq/3bis3Ti/E124nG3ZQh4qbm0hj4uLo61bt7rme836brcuZzO/26zvNXNZYzmHj1u3abetY25dzmZ+t1O3aT2fb4uROkMlOztb/M3IyAjrRnfq1ClPKo6eGpVGuXDhgvjNbvles77brcvZzO8263vNXNZYzuHj1m3abeuYW5ezmd99waHbtPyZcrzpj6tbyDds2EAtW7Y0ezIAAAAAwKHWr19P8fHxft/j6oCcR/Dk0T65TmS4U1a4WHy4v1fGKwWfjLjle836brcuZzO/26zvNXNZYzmHj1u3abetY25dzmZ+d7xDt2n5848dO1ZgxT9Xp6zI1VrMqkNu1vfy73bT95r93W5bzmZ+t5m/2axljeUcfm7bpt24jrlxOZv53VEO36aV1QFtXWUFjDVs2DBXfa/Z320WN85vLGd3fLcbl7Nb57cbl7Ub5/cwFy5nX65OWXFrlRUIDyxn98CydgdLLmfuCJeWRhQbSxQdbfbUOIIllzM4vsqKbVrIx48fL3KMypQpQ1WqVKHu3btTamqq13vOnTsnzrIqVapEpUuXpp49e9Lhw4fJanhY1bFjxwY9vCrYA5aze2BZu4PllnNSElGtWkTt2hHFxBBNn272FDmC5ZYzuGJZ26aFvFOnTnTfffeJoPzSpUv03HPPidE4uW5lqVKlxHuGDBlCixYtopkzZ4ozkuHDh1NkZCT9/PPPqp+Js2AAALAlbhnnIDwn5/JznKe6Zw9aygEsQk+caZuA3Nc///wjWsp/+uknuuWWW8SPvfLKK2nOnDnUq1cv8Z7t27dT48aNae3atdSqVat8n4GAHAAAbCklJbdlXO35224zY4oAwI0DA/GPY1y2kG3atIkuXrxI7du397ynUaNGVLt2bc2AXOZbDJ4vXVjh8gUAAIAqzhmPjMzfQl6/vplTBeBq58+fFzeZnsGGbJNDrpSTk0MjR46kG2+8kZo0aSKeO3ToEBUrVozKly/v9d6qVauK1/zhGpF8BiPfOF8dAADAsjgtZdIk72B8yhSkqwCYiONHZTzJ8WWgbNlCzh03OX989erVhnyeb0F4tI4DAIDlDR1KdOedRDt25LaMIxgHMNXo0aNp1KhR+QYGcmRAzh01v/vuO1q1ahVFK3Y+1apVowsXLtCJEye8Wsm5ygq/ZsXi/wCukZVFVLp07n2ujtSggdlTBOAMfBxEIA5gCYVJebZNygr3PeVgfOHChbRy5UqqW7eu1+stWrSgokWL0ooVKzzPcVnEffv2UevWrU2YYgDwmDXr8v3GjVGeTeukJSIi9/b332ZPDQAAhFGkndJUPvvsM1FFhWuRc144386ePSte51ydAQMGiEsFKSkpopNn//79RTDur0MnAIShPNuIEZcfcye0QYNyn4fLcNICAOBatil7GMGtRipmzJhBDz30kGdgoCeffJL++9//il6uCQkJlJSUpJmygrKHAGGA8mwFQ01pAADHcWTZw0DOG0qUKEGJiYniBgAWgfJsBeOhz5Xzh2Vn53bWQ0AOAOB4tklZAQCb4oAyOTk3CGcoz6Z90qKEkxYAANdAQA4AoTdgQG76Baep8F9+DOactHB6DC8H5PADAFiGbXLIQwE55ABgKRwkh7KmdFIS95DPvc8t8nwSgJMjAADT40wE5AjIAcAN0HEUAMCycSZSVgAA3N5xFAAATIWAHADADdBxFADAsgIqe/jNN9/o/uAOHTpQyZIlyQ7i4+MpKipKDD7ENwAAx3Yc5UGZuGUc1W4AAEJCLsGdzfvaAAWUQx7p26pS0IdGRFBaWhrVq1ePrAw55ADgOqHuOOr0ecepP3y1AfMOAMzIIedh6nNycgK6XXHFFYF+LAAAhBMHkjxCKgJK/RVqatXKHXWWO8dOn272FAGAgwQUkPfr109X+knfvn3R4gwAAM5pGR8x4vJj7hzLqT+o5Q4A4cwhnzFjhq4PnTx5crDTAwAAYJ8KNbjSAABWqLLC+TFfffUVbdu2zYjpAQAAsBZUqAEAqwXk99xzD3344Yfi/tmzZ+n6668XzzVr1oy++OKLUEwjAACAebgVfNKky49RoQYAzA7IV61aRTfffLO4v3DhQuIiLSdOnKAPPviAXn31VaOnDwAAwHxDhxKlpxOlpOSObjpgQMH/h3PM+f3INQcAowNyLt1SsWJFcX/JkiXUs2dPUVWlS5cuotQhAAC4gNWCzawsrrmbe/v7b/Mr1KAqCzhBOLYrCC4gr1WrFq1du5aysrJEQN6xY0fx/PHjx6lEiRJ6Pw6sABscAOhhxWBz1qzL9xs3NneaUJUFnMJK25XD6Q7IR44cSX369KHo6GiqUaMG3catBXmpLE2bNg3FNEKoYYMDADsHm1abJn9VWQDswmrblcPpDsiHDh0qWsg//vhjWr16tWcUTx6VEznkNoQNDgDsHmxabZpQlQWccNXbatuVwwUckHNHzgkTJog8ca6scvfdd1Pp0qU9r3MO+Y033hiq6YRQwQYHAHYPNq02TZxjnpycOw1GVGVBWiGYcdXbatuVwwUckD/66KOiZfy6666jxo0b0zPPPEM///yzqLJid/Hx8RQXF0eJiYnkOtjgAMDuJQCNDoCNwFVYuBqLnqosWpBWCGZc9bbidmUTHE9yXMnxZaAiJJ0R9fnz52nFihX09ddf07fffkvZ2dmidfzOO++khIQEKlmyJNkFD2pUrlw5UTmmbNmy5Fq8c+cNllvG5Q2uMAcPI/DOg1vv+YQBGz+A9fA2ylfS+OTdKtuoFafJiN/EHWeVVzJ5P81BvlN+IxiHTwC5s7Xa83l9/nRz4nZlwThTd0Du65dffqFvvvlG3Hbu3Ent2rWj0aNH2yJ9BQG5RTc4ruAwbFjufW695zN0s08QAACcEmCBcwVyAocGL0vGmbo7dfq64YYb6LXXXqMtW7aI2+23304HDx4s7MdCuOmprxtK6GQKAGDftELku5uroDQTK5YshcK3kJ8+fZpyfDoE2qmlGS3kFoTWIAAA66cVasEVTute9Ub6k7NayHfv3i1yxkuVKiW+pEKFCuJWvnx58RfAVa1BAOCsET+t+BuM7CAaSrjCae2r3v6qquHKhumK6P0Pffv2FZVVuA551apVKYIXHoDRl9t8W4Nw9g7gPE5oTQ3Xb+B9oNX3g/4CPqtPu5savHxbyLnBy7eSjx23RbelrHDt8U2bNlHDhg3J7pCyYmFW6mQKAMZzwuVzJ/wGI2F+2DP9KSEBy82OKStcUzE9Pb0w0wdgn06mABAaThiUzAm/wUhm1612QvpTqKmlP2E9tmfKyrRp02jw4MG0f/9+atKkCRUtWtTr9WbNmhk5fQAA4LbL53bhhN9gNA7wuMU13Fc4nZD+FC6+6U9Yjy1Bdwv5P//8I+qN9+/fX7SWX3PNNXTttdd6/gKEDDqdAEMrmDOY3ZpqBCf8Bidc4URn0sLBemzPHHIeCrRx48b09NNPq3bqjOE8JJtADrnNoAUEsA44jxP6izjhN4SjQaV06dz7qalEDRo4q1yuEwbbwXpsr5E6udzh77//TvUdcCnDdQF5KHeIoYbOQmD0OuCEAyiA3U+mjTgumX18QEMBmNGps127diIgdxJOveGW/8TERHI037JGhRmhK9xpA4F0OgnlNCFdxnxGdjzCaHVgJrelXflLKTHiuGRmygXSZUAFx5McV3J8GSjdLeTJycn06quv0sMPP0xNmzbN16nzzjvvJLtwVQu5kS0IZrQGFDT9oZ4mtIA4Zx02uzUtGGjNdw437ku0UkrmzSO67z5jr3oZkXKhZ3uzQroMOCPOlHSKiIjQvEVGRkp2kpmZyScj4q/jrVzJZ175bykp+j4nPV2SeDkrPyMqKvf5UJs2Lfe75O/kx+GYJjN/MwS2DpixLYRLYqKUTiStJJLSIyKC+81gDW7dl2j97rlzrbctJiZeng6e5oK2N7cuUzA8ztSdspKTk6N5y+bLx2DNy6FGDUlvZr1SreGjQz1NqNFqHQMGUMbatZTyzjvib1Ati0ZtC+GQkUHThg8n7irPbXAxkkTTH30Ul8PtmiLi1n2JVkpJmzbW2haDST9BhRIwiO6AHCxOKzfWqJ2G2cGMWjmtUE+T2b8ZPJKSkqhWy5bUbtQoimnViqYHm286adLlxxY+gGasWUODJInkEI7/8mNxMmI3ZgXFSUmUUasWpbRrRxm1a5vbXyAE+5KMjAxKSUkRfy3dF0atQcVqwWywJ0xajUUAegTS5P7+++9LZ8+eDax9XpKkyZMnSydPnpSsznEpK4FcOuP7fDmwMJfTjEgbMFqop8mKv1nL6dOXl39qaqE+Kj09XVq5cqX4azaeBk6L421WvkVFRQU/bUZsCyG2cu5cr98r31LmzQvbNBiyDuhNAzBKero0ldMp8+Yb/53GaT9mLnPlvCjkviQxMdGzTvC2MU3xWarLzazlYJdtsaBjqIH7Vr/TwGl1Zs8LCHucGVBAzhv6kSNHAp6AMmXKSDt37pSsznEBeThzY62yA1UK9TRZ8TerMeig6+9gbwYOLlSD0wLWbyudVOidJnESEhHhfRISGRm232LIOhDGHFvf+Zo+d64nGPfMP87FD/KExrB1yYB9ib8TVNXllrccPP0RXJTrrGu5+Wt8CfUJjYGfH479nt7vsOK+2HYBOXfYbNq0qXTttdcGdCtSpAgC8jDIt3IbfeDz0xpgxQ3LkGk6ffryAeunnyTbMeiga3hrtAEKmia15W+1k4pgpolf598p/95w/YZg5re/hgKvdbIQDQVa36s2Xwu6wqBnn2HkuqT5vTpaYLVOUOfNm6e+3ObOlablXSXwXC0wuwNlGAS13NROmArYt2ot04DXMQOP30bv93TvW1Va+XVfzXEIwwPycePG6b4dP35csjo7B+SaK3cBqRW6dhoa1R3MDHL0HIyD+azJ99zjfcDq1y/oaTLFypV+D7qBTmuwrdF6viMYWsGp2vIvqHXZyOnU1eIdxIkOv87z3oiWKCPWAc3tTe2E1sC0Ea3v1Zqv69ev11wH9AQIhp2cFLSv8tNCGug0zdU4CZk3ebL61YL16yWjGbVtFfZzDN0H+Nm3ai3TxHfeCTwINegKd7D7mNOnT3ven6o4GdTct2p9h8o6rPtqThj2b7YNyO3mww8/lGJiYqTixYtLLVu2lH755RdHBeQFbnAal0M1dxoalzfVDqK88w5Hy6meM/JgdkCqOxn+bX4OWOFogS30AcjPb9AzrcHu1I3euQYSnGoGJklJ6gHlvHmGLjc9n1WYEx2teaFnnTQiCBVBrsa6oXZCa1RQ5G+d9Ddf1U7i9AYIQZ2c6A3s/bSQan2Hnt+mFagHu+5pMWrbMmJf4u8KidbnawWmWvvW9d9+a8zJYJBXN/P95gD2MYHuN3SvS3zlSeU3aC0Hzas56fqO3YVZZ0LN1QH5559/LhUrVkz6+OOPpb/++kt69NFHpfLly0uHDx+2TEBe2KArmIO63oPr+qQk1Z3P3DFjQr5T13tGrnd+aO5kXnxR/XPefVd/K4FJrfy6L2P7CYq0WqM1D1hBBF5G7EA1f/OYMeoHUG4pNOikUm/LabDrjBq962QwrVRq64Dm/NZogdU8MdIZzPrbzgNZDsqTOL3bieZ+0s/Jid7AXquFlPPd9fw2xidGUYpl4DkxCnFjSrBXEozYTlS3B40+BP72AVrrpNaye0fRCu71vMaxUmwnWie0ffroukIbzHHJiMBbb1qU1jpc0Eliuo71IhzrdzBcHZBzi/iwYcM8j7Ozs6UaNWpI48ePt0RA7rsxfPTRR9L58+e9bhcvXvT6P76vc36+1oonv7506VLxV/4//FhtxX/rrbdUn3/7+edVn5/9wQcFrvQXLlxQnWZ5mpR27drlNa1av01rw122bJnm/1F+pvI7tOaF+G0qO+9fvvlG9fM/++wzvzuTS5cu5ZsPXLFIbWe/Z88ev7+B12OZ/Lm+n8Xrkta80JrWFStW+F0n+fPkeSxPg9b7l2p9x+ef65pW5Xrre+PfLuPp8bfO7Fm7VlzVUQYmfNVnaQHLLScnR/P7fbdPnn9a66Xvb546dar4//zblUGuPP8K2u6Vt927d+teJ7UCitmzZ2tu07wtK9cBf/N7znPPqX7+nOefV33/6tWrNZe/2jomvtfPCZ/vyYPavlWmdfDWmn8//PCD6nLT3JdozFN/v/n8zp1Sjk8LeY6f1kVeJsrf5tlHpKeLz+HWyZS8Vkr+HP5839/A80zen6gdN/i2d+9eT1Ck3KepvZ/nU6DbQ3Jysub+RGu+8nosb/eBHBP3rlsntvlA9wFay40/X2sfrbVMtRq1ZmtsJ1pBrnK7W758eYHB6U6N5exvPvHv1rtvUNvedvK8UPnNfHVB9f1+YpkPPvgg4PWC54vWa7zumZnKoifOLEIOcuHCBdq0aRONHj3a81xkZCS1b9+e1vqp28tDmyoVL15c3IzGdWJHKAYd4MGUhgwZQqmpqWJIVZ6Of//9l1q0aEGPPfaY530vvPACHT58mCpVquQZerVLly703Xff8QkVRUVF0ZQpUyg6Opruvvtu+uqrr8R7IiIiqGvXrnTdddeJz+bH/H4ZP/77779Vnz98/jxFRkRQjuJ5frxx926v7+b39urVS3y3bMaMGXTgwAHP4w0bNtD333/v+eypU6fSgAEDRE3pYXlDSMvTWqFCBTFflHjAKX5dbToXL14s5glPE38Hv5fnxyOPPEKffPJJvu/m76hfv75YL5TfE5H324Y0b04f/f478RBXXBl3cPPmdDQqSnWaeD77ThN/N38++/zzz2nZsmWe5cbL4L333vNa/oMGDaKEhAT68ssvVb/j5Zdfpjp16ojfd/r0aYqNjaW9e/fSF198ke+zeF0aOXIkvf766/T888+L/8+/s3PnzmK7UJt/x44d87tOyn7++Wfq3bu3GAJY6/3R+/aJgQ2Uv4LnYdb27TTypZfy/Z+ePXv6/c3y9qBc7zt06EA33nijuH/w4EGxLjHfdZJ/8+5z52jA1KmUMHAg7cjJoXpE9GvXrrREZV7wfOLllpWVRaVLlxbP8bpZuXJl8hUfHy++j/F6rzZf582b51UjnX/n4MGDaevWreK38PbN875ixYrid/Ayi4uLozZt2lBaWppYztOmTSMtFy9eDHidlH+bfN93vV+4cKHqZ+3YsYMWLFhAJ06c8KwDMv79ixYtEv9P3v8c3L5ddflvO3xYrDv8WfL2+Z///IeSk5NVv/fFF18U247v+vLCgAH0kSTREH5f3mdPzskhec/D+xRefps3b/aar0rjxo3zLLdHH31UTINynUn98UfV33Bg0yY6eOZMvuXG673a/Naap7y+8nb7xBNPeG2fvK9i13bpQtd++y3tJKKriKj8229TbJs2qsuN933KZcK/p2bNmqKWdkTefPHslbOzac7LL9PBOnXEb2jZsiXddNNNYj6sX7+eXnrppXz7ST5uKPefPA18LOLX1far/P6bb75ZdVq1todrrrlGdX/y2H33qS6H+qVK0b59+2jmzJle83b37t2q8/uXffvoyq5dade339IuIu99QF7E5vl8P9sC75PkdZb32fIxiZfd8uXL8+1/eN8R360bJQ8bJsYN8KyvRLT9yBHV4wY/1vruf/75x2s58HTUq1fP7/7znXfeoWbNmoltn5fza6+9Jo73av9n9erV6sfEjRvz/TaeD/x5vL3xseuNN96gYsWKie3hpTVrvJaZ+Hwi2pGVJd7P7+H9irz98HqvFsuwxx9/nHzXC/4MtXn3xx9/iPhKbV/My4f3qXKsIc8//qxQOH/+vLhpxZf+OCogP3r0qFi5qlat6vU8P96+fbvm/6vFA+kojB071rPjNhKvFL4bA688vIPnHbu8wX366adUqlQpT9D69ttv59vx8Y03NN4Qhg8fLjYQXiG//vprr8/mFZ3fx0FA3759ac6cOZ6DIgfSvAPnz1RucPyYN+jkqVM9Ox8Oxrt07So+R/5u+cBUmwfbyJOxYQP9s3w55ZQvT5E1a4qVkQ8cymniz+QdhXJHLE+r1gbXunVr8Rrv1JXTKQdqPE08r3hj52lbs2YNrVu3Lt9383dw0CqfDMgHRd4piM/q3p2ei4+nC+npVKxWLfEbtAJ4Xm985927774rloXWyYbydykDH63AjucvH/z4gMl4Op5++mkRqPp+lrwu8QG3T58+4nNPnTolgnGmtpzPnDmjuU4qA/JA1uHo2rXpIyKvgGlKRASdLFdOdVrl3+i7rOXfrHbA1+K7TnqmfcAAik5IoFIbNtCM//2PTpYtS2VV5gXv/OXlJuP7vt/L6zPv/Js3by7er7X9cOOA2rKW56t8U1q6dCnde++9nuXM66TWb+Z9WqDrZL9+/TwnzHwg4sBNOa38f7ROLH/77TfN+S0HdfIBPzExUfWElrehVuXL04QJEzzbJy9b3ierrfNMbR27sG8fPUpEd/ABnoMzOeDkQVvyfh+fvPG+KxDt2rWjK664wmudqbN7NyXz4EvKdZiI6l64IIJk3+XG93keKhsDeLvnv1rb8wMPPCAaTnhe7N+/X2xTsuTsbPpe8f6JRPREdLRYbgMHDhTzxXffl09sLDcpiqBclsMn3xUreqa5VatWnnWCG3vU9pO8jimf5+9+9dVX6eGHH1Z9Py/XatWq5QtatbYH/jwOBNWWdYl//1VdDtFZWbTnyivz/WRe7mrrMK8Li667jnbWr08Vjx2jzRUrin1A9MmT4vOU+yred+0rX15zuTE5COXtYuXKlV7HH+X+5/bbbxfrJDcIdBw4kHbKDQLdutF+n30Gb8cchPLJuNo2XbRo0XzLgecvNzL6m1aeJ7fxIHoFzCd+zNOuttzUjveNGjXy/F9eh/g1+aRdaznIDQJ8DPTdPgdmZ1OSJOVu03knkilpaarHDG6IUM47OYDnv7xvvuOOO/IdN5jvSZ/cEKZsSDTK+PHjPcdr3fQ2vSsvncv4MpYVOkbu379fXBpYs2aN1/NPPfWUSGXRupTAlzD4vnw7d+5cSKZP6xKT5iUvjfxE5SVCvqQs07okLV/a5Pcq8w2VqSW+l6Xlz5Xfz6klWpfP5ff6duia8sADmpeRtPLueBrULrcxvqTvO51ql6Pl9/q7hJXz4YeeS7r7IiKkiyqXt+UbpzD4uxyunCZ+r97lLF8KVrskrnVJz9+lb7X0Fq3lzJejA00b4c/1d5lUpF189JG0LzIy91J5Xg97f9/h+5v5Mra/VBa1lJVA0lvU0lCU84LfW9BvU0s3kj830LQOrXQcve/n9TvQdVK5j2Bq25DWNqeWfqa271G+ly9bL3/7bfG3oPfqWef589RSOpSd3ni++FsnfPcR+d6TlzaiTPfgTmkXd+/2+7n79u3z7Fflz9VKTeJ1Rm379Hf5nnHKhNa+zytlhbeN5OTceZM3j3z3b8r38uV+PemMWs/ztMmfy9PM6VwFbQ+ax7i8Ze27HHhZa233auuw1nsvLF3qKcXp+Xwi6YLK8UdebgXtT3y3T8979+0Tn8vrltr+h9PPZJzWFmhaFK9vWtPqOw1Maz7xY9/jvb+0Qa1tuaB9ier+RCVNS+J1voDYh/8q+034i2X8zb9Q4PhRGU/yNBqeQ/7ll19KsbGxUlZWVr7XuJNXgwYNpG+++UYyE898XmgLFy70ev7BBx+U7rzzTkvkkE/r0ydfZxu9nUX0dlYMR86U7h7o/LxGrlkwZd50zQ/+jiDqvQY6TYFWZPDdWal9h97qEcHQ+zkFvl+lyo+//6PnN4dSYToNFna+FmbQo8JuJ6H4rMJ+r+a8C8eIuQaOpKlnnhq+3mtU21KbRqM6svqjte/TXNZBLIeA53cBNb/N2hbUvltvh+VgviPUv0GTn1KP0ww6vpnd2TMknTo7dOggzt60TJ8+XerYsaNkNm4JHz58uOcxnyHXrFnTGp06FWWNlGf96RrBqVYwa1QQYKSVEyeqH0zefVf1JESu6er1fAgGqVCdH0aPaOozCIKRO9BQ7IyNqnMdqoOAWTtQf98b6oDZ7IOGFWnOuwADTbuNymvmOqB13ND7fLDLVO+yNqSDXjhO7gxi1nE95MJ0YjTNxPkXkoC8evXqUlpamubr/Bq/xwplD7n++MyZM6WtW7dKAwcOFGUPDx06ZH5ArhUIvvOOZnAazIpkxtm9Zv3rb79VPQmR8lqp1S5JGj5tvvOjgJ2ALhoDeRi5A3DsztiCv1nre8MRLLlxOYN11gG9wbJZrciG1po24cQrWGa22oeU0SdG6flHCTVz/oUkIC9RooS0bds2zdc5+OX3WMGkSZOk2rVri3rk3GK+bt061feZ1UKeLxAsIDi1y4bILd9qLeGardFmtlAY8d3huuyZN2Jqis+IqU5npRSKcAVLdtnWHUHHMPXhhHVAG64kWYxG8GvaiVGi9ki3ZglJQN6oUSPp008/1Xz9k08+kRo2bCjZiSkDA2kFggbmLZqJW8o5TcUzHHNBrdFmtlAU9ruNTn3RmkajWvMh/CdGRh2wwHgWPHiDf2b1LwEbbD/p1jxWhiQgf+6550Srs1rqx8GDB8Vr/B47MWukTs1A0EaXz5yaq6dLICcbhQ3GwhH0Q2h29lY7YIHlD94QQAu5n8GhwMXbz0prHiv1xJlcez8gzz77LJUpU0YMWDF06FB6//33xY2LtTdo0EAMpsHvgQBw7UuuD+pbA1PrebvjAvx79hClpOT+DVFB/rDj5TRp0uXHUVFEPKgBP8/1rLm+fbt2RDExRIpBMXSJjeWi1N7P8ffk1XWFMOFa0T71knmwFVEDW01GBhe/vfyY/++gQbnPg/2Wp9NlZXHh5tzb338H9n94XeZ9ehjXaT4yJufVDSe5PrkkXR4ACdy7/cQ64FipJ9I/ceKENGTIEKlixYpSRESEuFWoUEE8d+zYMcluTGshB2fxvbJhdOuBU68w2IneZVpQaw1SWQoWynlkxRY+M+m9mmPW1Z+87cq3frjZraCuY9XtJ9FP6q9J+9yQpKwocVH8I0eOSIcPH/Ya5MBuEJCDbS6dOTWdyU70nBj5O2AhlaVg4ZhHONENLrgyMxizaiDoRlbdftJVjpUm7nP1xJkR/E+wretvvPEGDR48mMqXL092xEOtlitXjjIzM7WHIQbQiy/hcpqK8pIeXzrjdB2rpiPxNPNlSL7sZ9VptMp84suyfBm0oPnEaUqcpsKXcuV0poQE+60bTt5+9CxPp+K0E06tU3veZ+j1oN5vNE4HHDYs9768XTklDdJfSlHp0rn3U1OJGjQgSzBr+8nQcbwy+XisJ84MOIdczeuvv07Hjh0rzEcAOI+/3HIrMirf3Q20+nmo5dOq9Z2wYu6l1YRzHjm1304oc2/NztUdOpQoPd15fZL8mTXr8v3Gja2zjzZj+0nSebyy0T63UAF5IRrXLSU+Pp7i4uIoMTHR7EkBp7DLQQOdD0N7gPA9YJkdzNgB5lF48bqZnJw7jwNpQLBCg4ObTqSwjy7cvDBpf8LxJMeVHF+GJSB3ig0bNtDWrVtpmHwZDMAtBw0btR444gChN/gJV8UMKwnHPILCVcKyS4ODE2AfXbh5YdL+hONJjis5vgxUoXLI09PTqUaNGhQl/1CbQQ45kNvzCu2Y724lwebThjL3Upljyy1DfDCyY8CE/G4A7KONmhcm7U/CkkN+9OhRKlmypG2DcQBTGNl6aUReIVojCyfYy6GhunripMvbdrjCBBBq2EcbMy9ssD/RFZCfOHFCNMNXrlyZqlatKm58f/jw4eI1AAhT5xwjA69gBm4yYVAQS7LawRKXtwGcx6mD6wVjgHPnRcApK1xNpXXr1rR//37q06cPNeZggkjkyMyZM4dq1apFa9asoQoVKpBdIGUFbHvpsTCpEoUtb+iUlAgnplfg8jaAu9ipZK2dptXKKSsvv/wyFStWjHbu3ElTpkyhkSNHiltycjLt2LGDihYtKt4DAEG2XupJZwkmVcKI8oZOSokwklUuh1qtxR7sD1fDrMtOJWvtNK0mCTgg/+qrr2jChAkiTcVXtWrV6K233qKFCxcaPX3gb4eIHaW9FBRE60ln0Rt4GRVIhyIlQm09tnulELtd0sX8BjUIoqzLTo0jdppWOwTkBw8epKuvvlrz9SZNmtChQ4eMmi4oaIeIHaX9+Auig9lh6Qm8jAqkja7pqrUeW3UgDKe22GN+gy8EUdZmp/4idppWOwTk3HlzDx/0NezevZsqVqxo1HSBvx0i17XEjtKetILoYHdYgQZewQbSvq3XRqZEYP22BgReoAZBVOgYcUXKTgNo2Wla7RCQJyQk0PPPP08XLlzI99r58+fpxRdfpE6dOhk9fe6mtUNcvRo7SjtTC6JDvcMKZnQ9rdZro3q5G7l+I+UieAi8QA2CqNAxqmSt2SOmBgp9W4ytspKRkUHXX389FS9eXJQ+bNSoEfF/3bZtGyUlJYmgfOPGjaLail1YvsqKVsWEtWuJWrVCJQWn9ezmnTK3THIwJO+wjK5eEmg1kHBU6zBy/Ubll+ChMguYuU9yG6O3N6tUeHLatJoRZ0o67Nq1S+rUqZMUGRkpRUREiBvfT0hIkNLS0iS7yczM5JMR8deypk2TpKgoPmvK/cuP/T0P3hITc+cR3yIjrT+f0tMlKSUl96+ZVq68PN+UN562UC2fYNdvnle8bJXTyf/H7HloJ9ifgNX3SU4Rrn0r2C7ODLiFXOn48eOUxi2OxCc69W2bOy6fuTRo0ECMOMot/3yzzVmlC882dUHLnz3mXWHX72BrskPgy8EOV5gA7ADHJVdITEwUt+zsbPr7778DaiEPKiB3CsunrEDhIFArHGUaiJUvV+MAFzpIBQIwHlKBXOOkjjgTATkCcudCoFZ4drkKgwOc8bD9AISOXfatELY4s0jhvgrAwuSe3b6BGnZ+geN5ZYf5xcF3QgIOcOGqvoL5C+COfSuEDQJycDYEau6BA1xoyt75tpCj7B0AgHl1yAFcM2ohgF34DtxkJNQOBgAIGwTkAADhCnKNpDVwk5GMGgQKAAD8QqdOdOoEALtVFUGHSwAAR8WZaCEHAJCD3BEjLj/mYJc7BFuxpRzD3QMAOAoCcgAAuwW5codLJXS4BACwLQTkAAB2C3I5LWXSpMuP0eESAJwmwyb9eQyCgBwAwI5B7tChROnp6HAJAM6TFIZO6xaDTp3o1AkAShhBDwDAPBnO6bSOTp06xcfHU1xcHCUmJpo9KQBgNtStBwAwT5qN+vNo4HiS40qOLwOFFnK0kAMAAABYQwZayAEAAAAAzBNts/48BkFAboLz58/TuHHjxF9wLixn98CydgcsZ3fAcnZPp/XzFlrWSFkxIWUFqTLugOXsHljW7oDl7A5Yzu5xMsTLGikr4JdZnVfN7DTrxg67bpzfWM7u+G43Lme3zm83Lms3zu9EFy5nXwjIXciNG5wbN3Y3zm8sZ3d8txuXs1vntxuXtRvnd6ILl7OvIuRicrYOX1IIJ/n7wv29suzsbFO+26zvNeu73bqczfxus77XzGWN5Rw+bt2m3baOuXU5m/nd2Q7dpuXPDSQ73NU55BkZGVSLR4ICAAAAAAiB9PR0ii6gSoyrA/KcnBw6cOAAlSlThiIiIsyeHAAAAABwCA6xT506RTVq1KDISP9Z4q4OyAEAAAAAzIZOnQAAAAAAJkJADgAAAABgIgTkAAAAAAAmQkBuQq3NOnXqUIkSJeiGG26g9evXmz1JUEjjx4+n+Ph40Tm4SpUq1L17d0pNTfV6z7lz52jYsGFUqVIlKl26NPXs2ZMOHz5s2jRD4b3xxhuiM/jIkSM9z2E5O8P+/fupb9++YjmWLFmSmjZtShs3bvS8zl2vxowZQ9WrVxevt2/fntLS0kydZgiu1N6LL75IdevWFcvxqquuoldeecWrRB2Wtf2sWrWKunXrJjpS8j76q6++8no9kGV67Ngx6tOnjxhds3z58jRgwAA6ffp0SKcbAXkYzZ07l0aNGkVjx46lzZs3U/PmzSkhIYGOHDli9qRBIfz0008iCFu3bh0tX76cLl68SB07dqSsrCzPe5544gn69ttvaf78+eL9XN2nR48epk43BG/Dhg00ZcoUatasmdfzWM72d/z4cbrxxhupaNGitHjxYtq6dStNnDiRKlSo4HnPW2+9RR988AF99NFH9Msvv1CpUqXEvpxPyMA+3nzzTZo8eTJ9+OGHtG3bNvGYl+2kSZM878Gytp+srCwRX2kNNhTIMuVg/K+//hLH9O+++04E+QMHDgzthHOVFQiPli1bSsOGDfM8zs7OlmrUqCGNHz/e1OkCYx05coSbV6SffvpJPD5x4oRUtGhRaf78+Z73bNu2Tbxn7dq1Jk4pBOPUqVNSbGystHz5cunWW2+VHn/8cfE8lrMzPPPMM9JNN92k+XpOTo5UrVo16e233/Y8x8u+ePHi0n//+98wTSUYoUuXLtLDDz/s9VyPHj2kPn36iPtY1vZHRNLChQs9jwNZplu3bhX/b8OGDZ73LF68WIqIiJD2798fsmlFC3mYXLhwgTZt2iQujci4JiU/Xrt2ranTBsbKzMwUfytWrCj+8nLnVnPlsm/UqBHVrl0by96G+GpIly5dvJYnw3J2hm+++Yauv/566t27t0hBu/baa2nq1Kme13fv3k2HDh3yWs7lypUTKYhYzvbSpk0bWrFiBf3999/i8e+//06rV6+mO+64QzzGsnae3QEsU/7LaSq8H5Dx+zlm4xb1UCkSsk8GL0ePHhX5alWrVvV6nh9v377dtOkC4web4pxivuTdpEkT8Rxv/MWKFRMbuO+y59fAPj7//HORbsYpK76wnJ1h165dIo2B0wufe+45sawfe+wxsWz79evnWZZq+3IsZ3t59tlnxdDmfOIcFRUljtGvvfaaSFdgWNbOcyiAZcp/+WRcqUiRIqKRLZTLHQE5gMGtp3/++adoZQHnDX38+OOPi5xC7pQNzj2p5pax119/XTzmFnLepjnflANycI558+bR7Nmzac6cOXT11VfTb7/9JhpUuDMgljWEG1JWwqRy5criDNy34gI/rlatmmnTBcYZPny46PyRkpJC0dHRnud5+XLK0okTJ7zej2VvL5ySwh2wr7vuOtFawjfuuMmdg/g+t7BgOdsfV16Ii4vzeq5x48a0b98+cV9eltiX299TTz0lWsnvu+8+UUnngQceEB2zuXIWw7J2nmoBLFP+61ts49KlS6LySiiXOwLyMOHLnS1atBD5asqWGH7cunVrU6cNCof7jXAwvnDhQlq5cqUooaXEy50rNiiXPZdF5AM8lr193H777bRlyxbRiibfuCWVL2/L97Gc7Y/TzXzLlnKOcUxMjLjP2zcflJXLmdMeOLcUy9lezpw5I/KClbjhjI/NDMvaeeoGsEz5LzescCOMjI/tvF5wrnnIhKy7KOTz+eefi568M2fOFL14Bw4cKJUvX146dOiQ2ZMGhTBkyBCpXLly0o8//igdPHjQcztz5oznPYMHD5Zq164trVy5Utq4caPUunVrcQN7U1ZZYVjO9rd+/XqpSJEi0muvvSalpaVJs2fPlq644grps88+87znjTfeEPvur7/+Wvrjjz+ku+66S6pbt6509uxZU6cd9OnXr59Us2ZN6bvvvpN2794tffnll1LlypWlp59+2vMeLGt7VsL69ddfxY3D3HfeeUfc37t3b8DLtFOnTtK1114r/fLLL9Lq1atFZa37778/pNONgDzMJk2aJA7YxYoVE2UQ161bZ/YkQSHxBq92mzFjhuc9vKEPHTpUqlChgji433333SJoB2cF5FjOzvDtt99KTZo0EQ0ojRo1kpKTk71e59JpL774olS1alXxnttvv11KTU01bXohOCdPnhTbLx+TS5QoIdWrV096/vnnpfPnz3veg2VtPykpKarHZD4BC3SZ/vvvvyIAL126tFS2bFmpf//+ItAPpQj+J3Tt7wAAAAAA4A9yyAEAAAAATISAHAAAAADARAjIAQAAAABMhIAcAAAAAMBECMgBAAAAAEyEgBwAAAAAwEQIyAEAAAAATISAHADAwR566CHq3r172L935syZFBERIW4jR470+946derQe++95/VY/r88hDUAgNMVMXsCAAAgOByw+jN27Fh6//33eURmMkPZsmUpNTWVSpUqpev/bdiwgf73v/9Rz549QzZtAABWgoAcAMCmDh486Lk/d+5cGjNmjAiAZaVLlxY3M08YqlWrpvv/XXnllVSxYsWQTBMAgBUhZQUAwKY42JVv5cqV8wTA8o2Dcd+Uldtuu41GjBgh0kgqVKhAVatWpalTp1JWVhb179+fypQpQ/Xr16fFixd7fdeff/5Jd9xxh/hM/j8PPPAAHT16VPc0HzlyhLp160YlS5akunXr0uzZsw2ZFwAAdoaAHADAZWbNmkWVK1em9evXi+B8yJAh1Lt3b2rTpg1t3ryZOnbsKALuM2fOiPdzHne7du3o2muvpY0bN9KSJUvo8OHDdM899+j+bj5BSE9Pp5SUFFqwYAElJSWJIB0AwM0QkAMAuEzz5s3phRdeoNjYWBo9ejSVKFFCBOiPPvqoeI5TX/7991/6448/xPs//PBDEYy//vrr1KhRI3H/448/FkH133//HfD38nu55Z1b5Fu1akUtWrSg6dOn09mzZ0P4awEArA855AAALtOsWTPP/aioKKpUqRI1bdrU8xynpDC55fr3338XwbdaPvrOnTupQYMGAX3vtm3bqEiRIiIQl3GAX758+UL9HgAAu0NADgDgMkWLFvV6zLnnyufk6i05OTni7+nTp0Xe95tvvpnvs6pXrx7y6QUAcDoE5AAA4Nd1111HX3zxhagPzi3cweLW8EuXLtGmTZsoPj5ePMdVYVBrHADcDjnkAADg17Bhw+jYsWN0//33ixrhnKaydOlSUZUlOzs74M9p2LAhderUiQYNGkS//PKLCMwfeeQRUXEFAMDNEJADAIBfNWrUoJ9//lkE31yBhfPNuWwi535HRuo7jMyYMUN83q233ko9evSggQMHUpUqVUI27QAAdhAhmTWEGwAAONbMmTNF0B5sOsqPP/5Ibdu2pePHj6PTJwA4HlrIAQAgJDIzM0VllmeeeUbX/7v66qvFIEQAAG6BFnIAADDcqVOnxOBBjFu4uc55oPbu3UsXL14U9+vVq6c7LQYAwG4QkAMAAAAAmAjNDgAAAAAAJkJADgAAAABgIgTkAAAAAAAmQkAOAAAAAGAiBOQAAAAAACZCQA4AAAAAYCIE5AAAAAAAJkJADgAAAABgIgTkAAAAAABknv8HZ4LMGtAnpZoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot comparison between CCF and LBL RVs\n",
    "fig, axs = plt.subplots(2, 1, height_ratios=[3,1], figsize=(7.5,5.0))\n",
    "\n",
    "ax = axs[0]\n",
    "ax.plot    (time_val, vrad_val_true, ls=\"--\", color=\"k\", alpha=0.5)\n",
    "ax.errorbar(time_val, vrad_val_ccf-np.nanmedian(vrad_val_ccf), vrad_err_ccf, fmt=\".\", color=\"r\", ecolor=\"r\", label=\"CCF\")\n",
    "ax.errorbar(time_val, vrad_val_lbl-np.nanmedian(vrad_val_lbl), vrad_err_lbl, fmt=\".\", color=\"k\", ecolor=\"k\", label=\"LBL\")\n",
    "ax.legend(title=\"RV Extraction\", loc=\"upper right\")\n",
    "ax.set_xticklabels([])\n",
    "ax.set_ylabel(\"RV [m/s]\")\n",
    "ax.xaxis.set_minor_locator(AutoMinorLocator())\n",
    "ax.yaxis.set_minor_locator(AutoMinorLocator())\n",
    "ax.tick_params(axis=\"both\", which=\"both\", direction=\"in\", top=True, right=True)\n",
    "\n",
    "ax = axs[1]\n",
    "ax.plot    (time_val, np.zeros_like(time_val), ls=\"--\", color=\"k\", alpha=0.5)\n",
    "ax.errorbar(time_val, vrad_val_ccf-np.nanmedian(vrad_val_ccf)-vrad_val_true, vrad_err_ccf, fmt=\".\", color=\"r\", ecolor=\"r\", label=\"CCF\")\n",
    "ax.errorbar(time_val, vrad_val_lbl-np.nanmedian(vrad_val_lbl)-vrad_val_true, vrad_err_lbl, fmt=\".\", color=\"k\", ecolor=\"k\", label=\"LBL\")\n",
    "ax.set_xlabel(\"Time [d]\")\n",
    "ax.set_ylabel(\"O-C [m/s]\")\n",
    "ax.xaxis.set_minor_locator(AutoMinorLocator())\n",
    "ax.yaxis.set_minor_locator(AutoMinorLocator())\n",
    "ax.tick_params(axis=\"both\", which=\"both\", direction=\"in\", top=True, right=True)\n",
    "\n",
    "fig.align_ylabels()\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(hspace=0)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, if we analyse the per-line RVs (from all observations), we can see that the LBL method allows the identification and removal of outliers originating from a different distribution than the primary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot comparison between per-line RVs before and after outlier rejection\n",
    "bins = np.arange(-200.0,200.1,2.5)\n",
    "plt.figure(figsize=(7.5,5.0))\n",
    "plt.hist(vrad_val_lbl_before, bins=bins, histtype=\"step\", color=\"k\", label=\"Before\")\n",
    "plt.hist(vrad_val_lbl_after , bins=bins, histtype=\"bar\" , color=\"k\", label=\"After\" )\n",
    "plt.legend(title=\"Outlier Rejection\", loc=\"upper left\")\n",
    "plt.xlabel(\"RV [m/s]\")\n",
    "plt.ylabel(\"Nr. of lines\")\n",
    "plt.xlim(bins[0],bins[-1])\n",
    "plt.yscale(\"log\")\n",
    "plt.gca().xaxis.set_minor_locator(AutoMinorLocator())\n",
    "plt.gca().tick_params(axis=\"both\", which=\"both\", direction=\"in\", top=True, right=True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "arve",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}