{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial: fitting a BL Lac broad-band SED using angpy and sherpa\n",
    "\n",
    "In order to perform a fit of the broad-band SED of a jetted AGN, `agnpy` includes a `sherpa` wrapper.\n",
    "The wrapper defines a custom [model.RegriddableModel1D](https://sherpa.readthedocs.io/en/latest/model_classes/api/sherpa.models.model.RegriddableModel1D.html), representing the emission due to a combination of radiative processes. The model can be used to fit flux points.\n",
    "\n",
    "Several combination of radiative processes can be considered to model the broad-band emission of jetted AGN. For simplicity, we provide wrappers for the two scenarios most-commonly considered:\n",
    "\n",
    " * `SycnhrotronSelfComptonModel`, representing the sum of synchrotron and synchrotron self Compton (SSC) radiation. This scenario is commonly considered to model BL Lac sources;\n",
    " \n",
    " * `ExternalComptonModel`, representing the sum of synchrotron and synchrotron self Compton radiation along with an external Compton (EC) component. EC scattering can be computed considering a list of target photon fields. This scenario is commonly considered to model flat spectrum radio quasars (FSRQs).\n",
    "\n",
    "In this tutorial, we will show how to use the `SynchrotronSelfComptonModel` to fit the broad-band SED of Mrk 421, measured by a MWL campaign in 2009 [(Abdo et al. 2011)](https://ui.adsabs.harvard.edu/abs/2011ApJ...736..131A/abstract).\n",
    "\n",
    "[sherpa](https://sherpa.readthedocs.io/en/latest/index.html) is required to run this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy, astropy and matplotlib for basic functionalities\n",
    "import numpy as np\n",
    "import astropy.units as u\n",
    "import matplotlib.pyplot as plt\n",
    "import pkg_resources\n",
    "\n",
    "# import agnpy classes\n",
    "from agnpy.spectra import BrokenPowerLaw\n",
    "from agnpy.fit import SynchrotronSelfComptonModel, load_sherpa_flux_points\n",
    "from agnpy.utils.plot import load_mpl_rc, sed_y_label\n",
    "\n",
    "load_mpl_rc()\n",
    "\n",
    "# import sherpa classes\n",
    "from sherpa.fit import Fit\n",
    "from sherpa.stats import Chi2\n",
    "from sherpa.optmethods import LevMar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `sherpa` wrapper of `agnpy`\n",
    "\n",
    "\n",
    "The `SynchrotronSelfComptonModel` wraps the `agnpy` functions to compute synchrotron and SSC radiation and returns a `model.RegriddableModel1D`. To initialise the model, only the electron distribution has to be specified, the remaining parameters (the ones of the emission region) will be initialised automatically and can be modified at a later stage.\n",
    "\n",
    "The `SynchrotronSelfComptonModel` class provides both the `sherpa` and `gammapy` wrappers. You should specify, through the `backend` argument, which package you want to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# electron energy distribution\n",
    "n_e = BrokenPowerLaw(\n",
    "    k=1e-8 * u.Unit(\"cm-3\"),\n",
    "    p1=2.02,\n",
    "    p2=3.43,\n",
    "    gamma_b=1e5,\n",
    "    gamma_min=500,\n",
    "    gamma_max=1e6,\n",
    ")\n",
    "\n",
    "# initialise the sherpa model\n",
    "ssc_model = SynchrotronSelfComptonModel(n_e, backend=\"sherpa\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us set appropriate parameters for the emission region. The size of the blob, $R_{\\rm b}$, is set by the variability timescale, $t_{\\rm var}$, via\n",
    "\n",
    "\\begin{equation}\n",
    "R_{\\rm b} = \\frac{c \\delta_{\\rm D} t_{\\rm var}}{1 + z},\n",
    "\\end{equation}\n",
    "\n",
    "where $c$ is the speed of light, $\\delta_{\\rm D}$ the Doppler factor, and $z$ the redshift."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssc_model.z = 0.0308\n",
    "ssc_model.delta_D = 18\n",
    "ssc_model.t_var = (1 * u.d).to_value(\"s\")\n",
    "ssc_model.t_var.freeze()\n",
    "ssc_model.log10_B = -1.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>/*\n",
       "Copyright (C) 2020  Smithsonian Astrophysical Observatory\n",
       "\n",
       "\n",
       " This program is free software; you can redistribute it and/or modify\n",
       " it under the terms of the GNU General Public License as published by\n",
       " the Free Software Foundation; either version 3 of the License, or\n",
       " (at your option) any later version.\n",
       "\n",
       " This program is distributed in the hope that it will be useful,\n",
       " but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
       " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
       " GNU General Public License for more details.\n",
       "\n",
       " You should have received a copy of the GNU General Public License along\n",
       " with this program; if not, write to the Free Software Foundation, Inc.,\n",
       " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n",
       "\n",
       "*/\n",
       "\n",
       ":root {\n",
       "  --sherpa-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --sherpa-background-color: var(--jp-layout-color0, white);\n",
       "  --sherpa-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --sherpa-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "\n",
       "  /* https://medium.com/ge-design/iot-cool-gray-is-a-great-background-color-for-data-visualization-ebf18c318418 */\n",
       "  --sherpa-background-color-dark1: #EBEFF2;\n",
       "  --sherpa-background-color-dark2: #D8E0E5;\n",
       "}\n",
       "\n",
       "div.sherpa-text-fallback {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "div.sherpa {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "div.sherpa details summary {\n",
       "    display: list-item;  /* needed for notebook, not lab */\n",
       "    font-size: larger;\n",
       "}\n",
       "\n",
       "div.sherpa details div.datavals {\n",
       "    display: grid;\n",
       "    grid-template-columns: 1fr 3fr;\n",
       "    column-gap: 0.5em;\n",
       "}\n",
       "\n",
       "div.sherpa div.dataname {\n",
       "    font-weight: bold;\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa div.dataval { }\n",
       "\n",
       "div.sherpa div.datavals div:nth-child(4n + 1) ,\n",
       "div.sherpa div.datavals div:nth-child(4n + 2) {\n",
       "    background: var(--sherpa-background-color-row-odd);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tbody {\n",
       "    border-bottom: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model tr.block {\n",
       "    border-top: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd ,\n",
       "div.sherpa table.model th.model-even {\n",
       "    border-right: 1px solid var(--sherpa-border-color);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-odd {\n",
       "    background: var(--sherpa-background-color-dark1);\n",
       "}\n",
       "\n",
       "div.sherpa table.model th.model-even {\n",
       "    background: var(--sherpa-background-color-dark2);\n",
       "}\n",
       "\n",
       "div.sherpa .failed {\n",
       "    background: orange;\n",
       "    font-size: large;\n",
       "    padding: 1em;\n",
       "}\n",
       "</style><div class=\"sherpa-text-fallback\">&lt;SynchrotronSelfComptonRegriddableModel1D model instance &#x27;ssc&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: ssc</caption><thead><tr><th>Component</th><th>Parameter</th><th>Thawed</th><th>Value</th><th>Min</th><th>Max</th><th>Units</th></tr></thead><tbody><tr><th class=\"model-odd\" scope=\"rowgroup\" rowspan=10>ssc</th><td>log10_k</td><td><input disabled type=\"checkbox\" checked></input></td><td>-8.0</td><td>-10.0</td><td>10.0</td><td></td></tr><tr><td>p1</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.02</td><td>1.0</td><td>5.0</td><td></td></tr><tr><td>p2</td><td><input disabled type=\"checkbox\" checked></input></td><td>3.43</td><td>1.0</td><td>5.0</td><td></td></tr><tr><td>log10_gamma_b</td><td><input disabled type=\"checkbox\" checked></input></td><td>5.0</td><td>2.0</td><td>6.0</td><td></td></tr><tr><td>log10_gamma_min</td><td><input disabled type=\"checkbox\"></input></td><td>2.6989700043360187</td><td>0.0</td><td>4.0</td><td></td></tr><tr><td>log10_gamma_max</td><td><input disabled type=\"checkbox\"></input></td><td>6.0</td><td>4.0</td><td>8.0</td><td></td></tr><tr><td>z</td><td><input disabled type=\"checkbox\"></input></td><td>0.0308</td><td>0.001</td><td>10.0</td><td></td></tr><tr><td>delta_D</td><td><input disabled type=\"checkbox\" checked></input></td><td>18.0</td><td>1.0</td><td>100.0</td><td></td></tr><tr><td>log10_B</td><td><input disabled type=\"checkbox\" checked></input></td><td>-1.3</td><td>-4.0</td><td>2.0</td><td></td></tr><tr><td>t_var</td><td><input disabled type=\"checkbox\"></input></td><td>86400.0</td><td>10.0</td><td>31415926.535897933</td><td>s</td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<SynchrotronSelfComptonRegriddableModel1D model instance 'ssc'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssc_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting with `sherpa`\n",
    "Here we start the procedure to fit with `sherpa`.\n",
    "\n",
    "#### 1) load the MWL flux points, add systematics\n",
    "A function is provided in `agnpy.fit` to directly load flux points in a `sherpa.data.Data1D` object. It reads the data from a file, included in the package, containing a MWL SED following [these specifications](https://gamma-astro-data-formats.readthedocs.io/en/v0.2/spectra/flux_points/index.html).\n",
    "\n",
    "The same function allows to add a systematic error on the flux points. This can be done with a dictionary specifying the instrument name and the systematic error, expressed as a relative error on the flux. The systematic error is summed in quadrature to the statistical error.\n",
    "\n",
    "In this example, we use a very rough and conservative estimate of the systematic errors ($30\\%$ of the flux for VHE instruments, $10\\%$ for HE and X-ray instruments, $5\\%$ for all the other instruments).\n",
    "\n",
    "Specifying the systematic errors through the dictionary is optional.\n",
    "\n",
    "We can also set the minimum and maximum energy to be used in the fit. We exclude points below $10^{11}\\,{\\rm Hz}$, as they are measured in the radio band with large integration regions. They hence include the extended emission of the jet, while in our model we are considering the emission from a finite region of the jet, the blob."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sed_path = pkg_resources.resource_filename(\"agnpy\", \"data/mwl_seds/Mrk421_2011.ecsv\")\n",
    "\n",
    "systematics_dict = {\n",
    "    \"Fermi\": 0.10,\n",
    "    \"GASP\": 0.05,\n",
    "    \"GRT\": 0.05,\n",
    "    \"MAGIC\": 0.30,\n",
    "    \"MITSuME\": 0.05,\n",
    "    \"Medicina\": 0.05,\n",
    "    \"Metsahovi\": 0.05,\n",
    "    \"NewMexicoSkies\": 0.05,\n",
    "    \"Noto\": 0.05,\n",
    "    \"OAGH\": 0.05,\n",
    "    \"OVRO\": 0.05,\n",
    "    \"RATAN\": 0.05,\n",
    "    \"ROVOR\": 0.05,\n",
    "    \"RXTE/PCA\": 0.10,\n",
    "    \"SMA\": 0.05,\n",
    "    \"Swift/BAT\": 0.10,\n",
    "    \"Swift/UVOT\": 0.05,\n",
    "    \"Swift/XRT\": 0.10,\n",
    "    \"VLBA(BK150)\": 0.05,\n",
    "    \"VLBA(BP143)\": 0.05,\n",
    "    \"VLBA(MOJAVE)\": 0.05,\n",
    "    \"VLBA_core(BP143)\": 0.05,\n",
    "    \"VLBA_core(MOJAVE)\": 0.05,\n",
    "    \"WIRO\": 0.05,\n",
    "}\n",
    "\n",
    "# define minimum and maximum energy to be used in the fit\n",
    "E_min = (1e11 * u.Hz).to(\"eV\", equivalencies=u.spectral())\n",
    "E_max = 100 * u.TeV\n",
    "\n",
    "sed = load_sherpa_flux_points(sed_path, E_min, E_max, systematics_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us take a look at the initial model and at the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# array of energies to plot the model\n",
    "E = np.logspace(np.log10(E_min.to_value(\"eV\")), np.log10(E_max.to_value(\"eV\")), 100)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.errorbar(\n",
    "    sed.x,\n",
    "    sed.y,\n",
    "    yerr=sed.get_error(),\n",
    "    marker=\".\",\n",
    "    ls=\"\",\n",
    "    color=\"k\",\n",
    "    label=\"Abdo et al. (2011)\",\n",
    ")\n",
    "ax.loglog(E, ssc_model(E), ls=\"-\", color=\"crimson\", label=\"SSC model\")\n",
    "ax.set_ylabel(sed_y_label)\n",
    "ax.set_xlabel(r\"$E\\,/\\,{\\rm eV}$\")\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) run the fit\n",
    "Now we define the `Fit` procedure choosing the statistics ($\\chi^2$) and the optimiser (Levenberg-Marquardt)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitter = Fit(sed, ssc_model, stat=Chi2(), method=LevMar())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit succesful =  True\n",
      "Method                = levmar\n",
      "Statistic             = chi2\n",
      "Initial fit statistic = 3533.13\n",
      "Final fit statistic   = 270.786 at function evaluation 71\n",
      "Data points           = 86\n",
      "Degrees of freedom    = 80\n",
      "Probability [Q-value] = 1.47258e-22\n",
      "Reduced statistic     = 3.38482\n",
      "Change in statistic   = 3262.35\n",
      "   ssc.log10_k    -7.88461     +/- 0.0702959   \n",
      "   ssc.p1         2.05281      +/- 0.0231709   \n",
      "   ssc.p2         3.53711      +/- 0.0517896   \n",
      "   ssc.log10_gamma_b   4.99003      +/- 0.0228676   \n",
      "   ssc.delta_D    19.809       +/- 0.612714    \n",
      "   ssc.log10_B    -1.33284     +/- 0.0389128   \n",
      "CPU times: user 19 s, sys: 6.93 s, total: 25.9 s\n",
      "Wall time: 26.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# perform the fit and time it!\n",
    "results = fitter.fit()\n",
    "print(\"Fit succesful = \", results.succeeded)\n",
    "print(results.format())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the final model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zTuXSUl5F2XevQdc1MOqvd5Iy6QBH6jCTkZHB/euWM2bhHbRsL2fHaVcQ3LKjx8fH4rSCG8l7yMjKTNoHxEPrIF0rTo0+//zzfPe73wXgu9/9Ls8//3zHfYcffjgHHnggfr+f733ve7z33nt8+OGHFBQUMHz4cFJSUro0RoqKijjnnHMAOO+883jvvfcM+/vf//7HnXfeyWGHHUZBQQGNjY2mPT2dBYNBLrroIqZPn863v/3tLo2qSFRVVTFo0KAutwUCAc4880zuv/9+Bg/ufe25gRoxYgQ7dvT8eyFSchWViFpVVRVDhzp3+sOtnMgl3NjEzvOuo6V0J6OeupX0IyJbdsEu7ZlkfuMYRj19O7v+7zdsP+0K8l66n5SvjAP2zWD70EMPUVJSgtaao48+mmXLlsXtKS15DxlZmUn7gPhAIMCiRdGfGq2srOStt97is88+QylFKBRCKcVdd90FYJhbqv37SOecMnuc1pp//OMf7L///l1O+Xz44Yc9bue+++5j5MiRrFq1inA4bHqqqDdJSUmEw+GOcT/BYJAzzzyTc889lzPOOKPjcSNHjqSsrIzRo0dTVlbGiBHRXcjQ2NhoyXw/0oMjopaXl+d0Ca5kdy5aa3ZffTdNKz5n2C1XkHnSsbbuPxKdM8k8IZ9Rz9xBuHoPO077Cc3rNwP7em0WLlzYcVpBax3XV1zJe8jI6kyysrIYNWqUJY3kRYsWcf7557N161a2bNnCl19+yfjx4zt6XpYvX87mzZsJh8O88MILzJkzhyOOOILCwkIqKysJBoO8+OKLHds76qijOsbxPPvss8yZM8ewzxNPPJEHH3yQ5ORkAD755BOgdTzM3r17Tevcs2cPo0ePxufz8cwzzxAKhfr1OidPnswXX3wBtL4Hf/jDHzJlyhSuuuqqLo877bTTePrppwF4+umnO8btDNSGDRsiusqrL9LAEVHbvHmz0yW4kt257Hn0BQJ//y+DvncSQy4+y9Z9R6p7JhnzDmf083cTDtSz48yf8+4/XukYDPrYY491eewjjzxiZ6m2kveQkZszef755/nWt77V5bYzzzyT5557DmjtMbruuuuYNm0a48eP51vf+hajR4/m5ptvJj8/n+OPP75jwC7AAw88wFNPPcUhhxzCM8880+US7HY33HADwWCQGTNmMG3aNG644QYA5s2bx9q1a00HGV9++eU8/fTTHHnkkWzYsMF0HasdO3Zw0kknmb7Ok08+ueODxbJly3jmmWd46623mDlzJjNnzuy4euu6667jjTfeYOLEibzxxhtcd911Hds44IADuOqqq1iwYAFjx47tOE32y1/+krFjx1JfX8/YsWO5+eabO56zbNkyjj/+eNOa+kVrLV8RfgGFc+fO1aKrUCjkdAmuZGcudW99qDeOOFZ/+fVLdLixybb99ldPmdS987HeNOY4/Yu8aZrWVR40oDMzM7t8/9hjj9lcsT3kPWTUWyZr1661sRJ3CYfDtu1rx44d+vjjj7dtf1prvWLFCv3973+/x/u7/+znzp2rgUJt8jdbenBE1FauXOl0Ca5kVy7BL7ax66Kb8I/IYdRTv0Olunem054yyThmNiP/fDOHNSSR3GkywLq6ui6Pu+SSS+JywLG8h4wkE3P19fW27Wv06NFcdNFF1NbW2rbPiooKbr31Vku2JQ0cEbXOXa1iHztyCdc1UPaDX6Ebmxn19G2uuRy8J71lkvmNY/j6Y3dyVurIjtu6T2oGref74428h4z6ykT3sQ5avDI7zRRL3/nOd2J2tZSZE044gQMOOMD0vv7+zKWB0wdZbLNv3WeuFa1inYvWmt2/vJfgus3k3vlz0mYdHNP9WaGvTAZ9+0T+76qfkoYPH62v0e/3d3lMZWVl3PXiyHvIqLdM0tLSqKysTMhGTvdezUShtaaysrJfV4KpRDxABkopVTh37ty58Xw1h/CO2mdeYfdVdzPou99gxIO/crocS102ZiqZNXVsT1O8k9ZMXV0de/bsW6jz0ksvjetBx6J3wWCQbdu2ycraCSYtLY2xY8d2XEkGrXMcLV26dKnWuqD742UeHBG1FStWSBe7iVjm0vTpBiqu/yMpBx9I7u+v6vsJLhFpJh+NTOPUFsXL5Z/TRBi6zQvy1FNPcf7558fNvDjyHjLqLZPk5GTGjx9vc0XuIMdK5OQUlYjazJkznS7BlWKVS6g2wM4f3ghJfkb+5VZ8Gf2bvMtJkWby8Ypiki/7Ns2E0bSOxek8s21TU1NczYsj7yGjeM4kmiVI4jkXq0kDR0Rt3bp1TpfgSrHIRWvN7p/eScuW7Yy4/zpSJoyzfB+x1J9M5p34NVAKP5AU0gzP6Dpl/P33329tcQ6S95CRZGJOcomcNHBE1BK1q7gvscil9qmXqXt1KYMvPIOsbx5n+fZjrT+Z5OfnM2HiRKaPGMOvMg9gWG3X8Rbl5eU8/vjjVpfoCHkPGUkm5iSXyEkDR0TNikXR4pHVuTSt2UjljQ+RMnUCw357uaXbtkt/M1m/fj1/evlFbq3fwsrmPYb7r7jiiri4okreQ0aSiTnJJXLSwBFRy8nJcboEV7Iyl3BdA7suvhn8Pkb++WZ8aamWbdtOA8mksLCQFh0mDHRfgjAYDMbFWBx5DxlJJuYkl8hJA0dEzc6ZNb3EylwqbniQ4Iat5N5+JSkT97dsu3YbSCYFBQUdqyunKh/HpXadzLCmpsaK0hwl7yEjycSc5BI5aeCIqJnNNiusy+XMAw7mrsf+xLqjJjHoHPNF8bxiIJnk5+czceJE8vLy+G7eZA71d53J9b777vP8aSp5DxlJJuYkl8hJUiJqnSddEvtEm8vkyZMZO2w4i7eu4776rXz7jef54IMPLKrOGQPNZP369Wzfvp2izBBraKTz3MbxcJpK3kNGkok5ySVy0sARUQsEAk6X4ErR5FJUVERJSQk7qipoRhMGmpubPf+HPNpMNpSU8J/6XYb7vH7JuLyHjCQTc5JL5KSBI6KWm+vuBR6dEk0uhYWFoDXtC6kopUhJSaGgoMCK0hwTbSbtS8uEu93n9UvG5T1k5NVMopnELxJezcUJ0sARUdu2bZvTJbhSNLnk5x1AKj78tDZsLrnkEpYsWeL5pQmiyaTzYOOU1FTDL6/LL7/cs2Nx5D1kJJmYk1wiJ2tRiahNmDDB6RJcaaC5hOsaqL7rKc7IGk3W6cdxwY8v83zDpl00x0r7YONAIEBWVhbDt+7m/abqjl6uUCjEwoULPZmVvIeMJBNzkkvkpAdHRG3NmjVOl+BKA83lPz/6BeesXcLzge08889FFlflrGiPlfbBxgBbc9KZkTSoj2d4g7yHjCQTc5JL5OK2gaOUGqKUWq6UCiilpnW6/Tal1LtKqUVKqYzetiEiM2PGDKdLcKWB5FL332W89crijkUm42FgcWdWHCvtA7C3le3g05auAy4HDx4c9fadIO8ho3jOJBAIsHPnzgGdUo3nXKwWtw0coB44Gej4CNzW0PmK1voY4E3gQodqiyvFxcVOl+BKveVSVFTEHXfc0eUXXKiimt0//z1Hjdof2seaxMHA4s6sOFY6DzbW3aY2vuuuuzw5DkfeQ0bxmknHFZI7djB//vx+H6/xmkssxO0YHK11ENjdPiixzTHA623/fx34PfCQzaXFnVheMeBlPeVSVFTE0Ucfjdaa9PR0lixZwpFHHsnuq+8mtLuar//9Xt5LC1FYWEhBQYEnx5T0xIpjpX2wsdaalJQUmpqautzvxXE48h4yitdMOjfQ23to+3O8xmsuseCKHhyl1BVKqY+VUk1KqQXd7stRSr2klKpTSm1VSp0Txa6GAu0r9u0BZFEPC8gnCnM95WL2C27v316n7rV3GfzDM8iYdzj5+flcf/31nvtD3RcrjpX2wcZJSUnsv//+jNtvvy6/yJ566inP9eLIe8goXjPpcjXgAHpo4zWXWHBFAwfYAfwOeNLkvoeBZmAkcC7wiFJqKoBSapRS6j2Tr1E97KcaGNL2/yFAlaWvIkHJJwpzPeXS/RfcnIOmUfGrP5I8YRzDbrzMzhJtZ9Wxsn79eg455BCysrLYWlrKtCHDOxbibGpq8ty4JXkPGcVLJt3nxem89MhApn6Il1zs4IoGjtb6n1rrl4HKzrcrpTKBM4EbtNYBrfV7wCvAeW3P26m1nmPytbOHXb0HnNj2/xOBZWYPUkoVmn0BM6N8qXFp9erVTpfgSma5TJ48mbPOOotHH32U22+/nTffeIMDF/wP3dDEiD/9Bl9GmgOV2sfKY6XzQM0//eMFUvF1NHKGDRtm2X7sIO8hI69mEskA4qysLEaNGjWgHlqv5uIEVzRwejEJCGmtN3S6bRUwNZInK6VeA74G/FkpdYHWejWwVSn1Lq0NHLMeo141NjZSUVFBWVkZ27dvp7q6mk2bNtHQ0MDatWsJh8OsWLEC2NeVuGLFCsLhMGvXrqWhoYFNmzZRXV3N9u3bKSsro6Kigi1bthAIBFi3bh0tLS2sWrWqyzba/129ejVNTU2UlJRQW1tLaWkp5eXllJeXU1paSm1tLSUlJTQ1NXW8EbpvY9WqVbS0tLBu3ToCgQBbtmyJ6jXtt99+cfeaovk5TZ48mREjRrBr164ur+nVV1/tGFx45ZVX8uCDD/Kb087m/aXvMPTqH7AmXO/a12TVzykYDFrympYtW9aR5XHHHUegpZlvD90Pf1sT52c/+xlLlizxzLHX2Njoqp+TG469SZMmee41vf7662zYsKHjuHz33XdpaGgwraehoWFAr6lzLm74OTl97PVKa+2aL1pPUy3o9P0xwM5uj7kIKHSovsK5c+dq0dWGDRucLsE13n//fa2U0oBOS0vT77//fsd9t99+uwY00PEYBTrN59fL3nnHwartY9Wx0jlLv9+vb7/9dv3bS67QvrbbAH377bdbsi87yHvIyIuZmB2Xs2bN0rNmzeryOLPbIuXFXGJp7ty5uqc2gdt7cAJA94ktBgN7HahF9GDkyJFOl+AanQcQd1/luvNpk/bHaKBJh3nmuefsLNMxVh0rnccxhcNhFixYwPHnfZdkj56mkveQkRcz6TxgOFZTPHgxF6e4vYGzAUhSSk3sdNsMwLapHJVS5yilFiulFgPTy8vL7dq1Z9TU1DhdgmsUFBSQnp6O3+8nOTm5yy+4yspK0+dorT155c9AWHWsdB6oOXbsWAKBAMrn4xu5+05TXXnllZ7JVN5DRl7MJD8/n5SUFJKSkmK2dpwXc3GKK+bBUUol0VqLH/ArpdKAFq11nVLqn8AtSqkf0TrI93TgKLtq01o/BzzXVmfhiBEj5tq1b69IS4vvgbH9kZ+fz5IlSygsLOSQQw7p8guuvfHT1NREONx1PeyWlpZ+z4fhRVYeK+vXr+8yp9D8+fP5/hlnEX72GWBgc4w4Rd5DRl7IpLlkK3WLl9K0agOhimpClTW8P2gWPmD4LU9TPnkph+xqYEWwlvfff5+jjor+T5cXcnELVzRwgN8AN3X6/vvAb4GbgctpHQxcTutVVpdprWUxDuFa+fn55Ofn0723Lz8/nzdee51XLriSwXub2Pr1r/Ln51r/GCclJcXVjMV26T6nkH9QJknKR0iH424WaOEOzSVbCSx6g8DiQoIbtgLgG5yFf/hQ/MOyKQ7tRaE4sbqWpQtf4G/V62kmzPxjjuX1Pz1BwSUXOPsCEogrGjha65tpbcyY3VcFfNPGckQ/NTY2Ol2CK5nlsvev/+bLnTtZm9bC5XPndDRw2v9Ixzurj5Xusxqff/75VL22lP0q6tmam8EVV1zhiYnR5D1k5KZMtNY0vr+Smoefp/6N1tOeyV/Zj+yffZ/MU+aSOmNyx5iwKzNalzis/2A1f731VhpvXAlAMBxi8VU3M+mNVUxo8rExNWy2qz65KRe3c0UDx83aZk5unz1ZxuCYyM7OdroEV+qcy+TJkwntrmJrdSUtaGiCyy+/vOP+UCjkmdMp0bD6WGkfixMIBFi0aBH5+flclZPObxpy2BgO8zua+t6IC8h7yMgNmWitqX/9Xar/sJCmVetRqSkM+v4pDPnRmaQc/JWORk1noVCIcDhMUVER844/HnXTTa0N8LR05n/nDBrfWMXtTWk8PrR5QDW5IRevcPsgY8dprZ/TWp+itT4FWD1ixAinS3KdXbt2OV2CK7XnUlRUxMaSEr6ormht3LQJh8MkJyfj9/sT5nRKLI6V7pOmFX1SzMhzTyW4N0DNjoGt2Gw3eQ8Z2ZlJ99mGAYKlZew891p2/uDXBEvLGHr1Dxi34kVG3HctqVMnmDZuioqKaG5upqWlhfnz5wPsm7X4rSV846k/MO7D5/giOczF1SnseeIf/a5VjpXISQ+OiNq4ceOcLsGV2nNpHyfS/SRUcnIyDz74IJWVlXG3qGZPYnGsmJ2C+my/wfxgzxqa9oSZP39+zK5osYq8h4ycykQHW6h55AWq73kK3RRkyMXfJue6H+IblNnncztPC9E+yD0rK4usrKyO4y9pzEhuHdnIr8tT4fr70S0tZF96dsT1ybESOenBEVHbsGFD3w9KQO255OeMJpl9n/Z8Ph/f/OY3KSws5OKLL47LRTV7YtexUrRnF82E0ez7Q+Nm8h4yciKTps+/YNsJP6Lq1kdJmTyesf97nNzbfhpR4wYinwfn4/Wfc171p6QeNo3KGx6i+oFnI65RjpXISQNHRG369OlOl+BK06dPJxyoZ/zjr3HWkP263Hf44YcnTKOmM7uOleO+fiJJ+PDReirQ7af/5D1kZGcmSsM3apPYfsJFBL/YxrDbfsaY/zxK6ozJXR5ndiqrs/7MgxMgTN7f7yXtyBlU3foogX+9HVGtcqxETho4fZCJ/vrmhatUnFBcXEzlb/9ES2kZR37vzI7bw+Gwp2bZtZJdx0p+fj4/yjuIn2fszxHjJ7m+MSnvISO7MmnZWcEZ25pp3LGLT0dlMHbJX8i++CyU3z+g7bVP8hnJMefLymD083eRNGYEFdffT6hm3yT9PTWm5FiJnDRw+iCDjPvW2yeaRDYloKld8C8yT5tHYNzwjtt9Pl+PsxrHOzuPlXv+/leOSB5Cyp461w80lveQkR2ZNLy/kn8dcTq37l7LH+q3cs7aJRRX7Ij5fjvzZWWQ+/urCO2uourWR/t8vBwrkZMGjohaIn+iKCoq4o477qCoqIjJkyczZswYxo0bxwF5Y1h8wZX4crPZeNbRlJaWdlx1kZqa6vpTJrFi57HySctezt/zGe9VbmP+/PmubuQk8nuoJ7HOZM/T/2LHmVfyQUMVjQ6P18o88WgyT5tH7cJXaCha1etj5ViJnFxFJaKWqJ8oOi8TkJqaSlPTvjlXFPADdnLXj6/kZyd/o2MiugsvvJDzzz/f9adMYsXOY2Xp+8toJkwY9y/bkKjvod7EKhMdbKHi13+k9qmXSZk2kdN+dg33nnFax3vUqQ8fubf9lIbCj9h9zd3s99aTPT5OjpXISQ+OiNqqVb1/4ohX3ZcJ6EwDQTSLSz7reEwoFGLcuHGu/SNrB7uOldmzZ7NgwQIUCj+QkpTs6l6zRH0P9SYWmYSqa9nx7auofeplMk+bx5jFD3PMN0/ZN1eNBdMJTJkyhSlTpvT7eUecfCKPJFUS3LCV6gd7vqpKjpXISQOnDzLIuG9Tp051ugRHtC8TAK2XhKakpODztb6lfEBKWipnnnlml8e4+Y+sHew8VrKysvhKWhZXZuzPS5dd6+qGZaK+h3pjdSbBrTvYfvLlNC77hKHX/ZCRT/wWX2Y6YJwsMtZCoRDBYNBw2vStzBBpRxxC9X3PMDponEgQ5FjpD2ng9EEGGfdt48aNTpfgiPz8fJYtW8btt9/O22+/TWFhIdcdcQK/y/wKN5x/EX958kkuvvjijse4fbI5O9h9rAyaMpFzs8ZSv+h/ru7aT9T3UG+symT27Nl8e9phbP/GZQS3bGfEozeSc/UFpjMR9yUQCLBzZ3SzY3ef7bjztrSC3LuvhuYgJ+01H0Eix0rkZAyOiNrYsWOdLsEx7SuHA9S9+QEjSurI+v75jHz0RgKBgOExic6uYyUQCBAIBPD7/dzRBFPrkikPNVJUVOTKn0Uiv4d6YlUmsxp8XFmRih7UTN6LfyD96EMHtJ2ioiJKSkrQWvdrduzug4LNZjvuvJ3UKQeSXnAYQ958k8qmSsMxK8dK5KQHR0StoqLC6RIcF9qzl91X3YV/eA65t/8MkFzM2JFJ+x+iHTt28OWXX/Lini+5ue4LtpXtcO3VVHKsGFmRyd4X/8svd6ey16cZ8+qfBty4AeOYu4FebRXJbMfrjprEj2rWUGpyzMqxEjlp4IioZWVlOV2C4ypveIhQ2W6G33sN/pwhgORixo5MOv8h6s6tyzbIsWIUbSZ7nv4X5T++jf/pWr6vtlBcvbPHxxYXF/d5+XX3MXcDHU8XyWzHH9RX9rjUiBwrkZMGTh9kkHHfgsGg0yU4qu6NIvY+/xpZZ55A5jeO6bg90XMxY0cmnf8QdaZonUV6wYIFMa+hv+RYMYomk5pHX6DimntYvd9gflr1GZt2Rt97l5+fb9nVVn3NdjzvuOPwKV/bFYBJXRpTcqxETsbg9EFr/RzwHIBSqnDEiBFzHS7JdcLhsNMlOCa0Zy+7r767y6mpdomcS0/syKT9D1H7GJyysjJOTRrGeF8aS8ak4nfhJ2A5VowGmkn1vU9TdecTpB42jTWHjSS84lXAmrmQuq8MbmagE/G1jxtrH3Mz7SsTKNjRxHFzjunY3+zZswkGg3z66acD2keikR4cEbWMjAynS3CM2ampdomcS0/syqT9st/hw4fj8/nYGG7gyOQhHJI82Jb995ccK0YDyaTqriepuvMJ0ubMIu/v93Lc10905TQN3efK6TxurL2nyTc4i6nDR3PwJ1sJbt23fET7VBSib5KUiFpVVZXTJTii7s0PWLrwbzx5YCqfZhs7QxM1l97YnUkgEKC5uZnVzbWcv+cz/HvqbN1/pORYMYokk84LUlbd/RTVdz9FesFhjH7uLnxZGZaeVoqlngYw/2dQELSm9ul/dTy2paXFiRI9SRo4Imp5eXlOl2C7UG2A/15yLeft+Yzbly8xPb+fiLn0xe5M2i/VBwgSZvOeyqjnMYkFOVaM+pNJ1b0LqL7rSdLnfpVRC+/Al57acZ/dk/gNRE8DmDenaFK/OpXavy4m3NC6FExycrJTZXqONHBE1DZv3ux0CbarvPEh3t+5lWZaxwmYXZ2TiLn0xa5M2q+KWbRoUccfDr/y8X5gd5fTAG4hx4pRJJkEAgGGbSlnya33kX7MLEPjxit662ka8sMzCFfXUvfK24BxWRjRM2ngiKgddNBBTpdgq/olH7L32VeZd8Lx0Mv5/UTLJRJ2Z9L5D8d3D5pFiOjnMYkFOVaM+sqkqKiIL0o2sqRqG+fvXcvmn3wLX0aaTdVZr6eepsyT56Iy0gn8uxCAtDTvvka7SQNHRG3lypVOl2CbUG2A8qvuwj98KF//yz29LsOQSLlEyolM2v9wnHPiyaS0/cpz04BTkGPFTF+Z/OcPfyKsW1eLDyrNOx+Y98hFMseNm/nSU8mYfwQNhR+RGob6+nqnS/IMaeD0QebB6dusWbOcLsE2lTc9TGhHObl3XY1/WDb5+flcf/31puf3EymXSDmZyZyTv87CIdM4OGWQ6wacyrFi1Fsme//xBtPeXE2ySxusVss8+Vh0UzOjqxuora111elVN5MGTh9ksc2+efnTUX/Uv72cvX9dTObpx5F1St/TISVKLv3hZCapU7/CrOTBnJo2wlWNG5BjxUxPmdS9/i7lP76NI6ZMY8qECa6/QspMf3uVMk7IZ4WuY3HpeleOIXMraeCIqLl5lWarhPfWsfuqu/DlZjP8zisjek4i5NJfTmTS/sfEPyqXpc3VlDQHXPfHQY4VI7NMGt5bwc4f3UTSmBHk/eM+fIPdf4WUFfyDs1iRl0mL7vmiBmEkDRwRtRUrVjhdQsxV/vYRWrbtYvjvr8KfOzSi5yRCLv3lZCYffPABl9Wu5eXGXa77BCzHilH3TJo+20jZedfjHzKIvEX3kTR6uO012Tmep/u+5n/zVFLwoYj/U3JWkQaOiNrMmTOdLiGm6t/5mNqn/0XmafPIOm1exM+L91wGwslMCgsLCaIJ475PwHKsGHXOJFhaRtl3rwFg9N/uJnn8GMD7A4j7o+CKH/H0kKmcnr2f507JOUUaOCJq69atc7qEmAkH6tl95e/xDRtC7p0/79dz4zmXgXIyk4KCAnwo0wUMnSbHilF7JqHKGsrOvoZQ1R5GPX0bqYdMcrgyZySNyCE9K4vfJO/HkYcf7nQ5niANHBG18ePHO11CzFTe8igtX+5k+J1XkTQ8slNT7eI5l4FyMpP8/HwmpA3iyoz9eflnN7jqE7AcK0bjx48nXN9I2bnXEtxYyoiHf03GsV91uixHLU8Pka19NH642ulSPEEaOCJqO3bs6PtBHtSw7BNqn3qJzJPnknl65Kem2sVrLtFwOpPsKRO4JGMsM5v8jtbRndO5uM3s2bM54quHsevS39JUvJZht/6EQd863umyYiqS020fZYQAqHvtHTtK8jxp4Iio5eTkOF2C5cJ1DZRfeSe+oYPJveuqjun++yMec4mW05kEFexI0jSv2eRoHd05nYudOi+Q2ZsL6zKpf/09hlzybbIv/Y4Nlbnf7iTNpuQQda++07E4Z6R5JiJp4PRBJvrrWzzOrFl12+O0bNlB7h1XkjRiYH984jGXaLkhk60pYZrXfYEOhfr93Fj9MXFDLnYJBAJ9Lng6tbyBbbt3sfar4xn22x/bWJ37fZAapGXbLpo/3eB0Ka4nDZw+yER/ffP54uswaihaxZ4/LyLj63PIOmPg3eLxlosV3JDJ1uQwurGZ4ObtTpfSwQ252KGoqIiSkpJeJ6t76/7HeHHbeu6r38rZSxfxwfLlDlTqXh+kBYHWCQ9F7xLjXSViKjk52ekSLBOub2T3z+7ENySL4XdfPaBTU+3iKRerOJ1JcXExV/3pPgCaP9voaC2dOZ2LXQoLCztOrZhdqt/06QZev/kemgm78nJ+N9ierEkaN5r6JR86XYrrSQNHRC0QCDhdgmWq7vgzwc3byL3tZySNyo1qW/GUi1XckEnqtAkANK11zzgcN+Rih4KCgo4PDd0nq2vZVUnZeddzZMYw6OExAsI6TMb8I2hauY6W3dWG+2VMzj7SwBFRy82NriHgFo3LV7PnsRfJOP5Isr5zYtTbi5dcrOSGTPyjh+PLHkTzGmMPjlN/HNyQix3y8/OZOHGiYf2ocGMTOy/4NaGdFXx94f1MmDiR0aNHy4R2JpKSksiYfyQADW9LL05vpIEjorZt2zanS4hauKGJ8p/diS8rg+H3/iKqU1Pt4iEXq7khE6UUKQd/hWYX9eC4IRcrRNJAzMrqun6U1prdV99D08drGHbzZWQefyRZWVkMHTpUGjcmmpubSZ8zC1KS5TRVH5KcLkB434QJE5wuIWrVdz9JcGMpw++/jqQ8awaSx0MuVnNLJqlTJ9D4/kpCe/biHzLI6XJck4sTah5+nsDf/8Og736DIZeeDbSOlWppaXG4Mvdpz8WXlER6/gzq316OygId/eexuCQ9OCJqa9ascbqEqDSuWEvNw38jfd7hDDrnJMu26/VcYsEtmaRMaZ05OLh+i7OFtHFLLnboPKFd3RtFVN3yKGmHTWP4Pdd06TlNpEz6oz2XjOOOIFxdS3ZNfZ+X3ScqaeCIqM2YMcPpEgZMNzVT/tM7UBlpDP/DLy05NdXOy7nEilsySdo/D4DglzsdrqSVW3KJViRz3LRr3lRK+SW/JSlvOCMX3IZKTelyf7xkYrX2XDLmH8GKYC3vbNnQ62X3iUwaOCJqXl7Nt+qeBQTXbyH3tz8meexIS7ft5VxixS2ZJI8dBcD91/7G4UpauSWXaEQyx027cKCenT/4Nbo5yMgFt5lOphkPmcRCey7Jkw7go4wQIR0G5JJ6M9LAEVHz6iWJTavWU/Pgc6QfO5tB551q+fa9mkssuSWTpDGt46yGh9wxeMEtuUSjrzlu2mmtKf/J7a0fLO6+mrSZB5k+Lh4yiYX2XJRSFBQUkNL2Z1wuqTeSBo6Imhc/aenmIOU/vR2VmsLw+6619NRUOy/mEmtuyUSlplDlDzO8peuvwP6cYrGSW3KJRm9z3HRW88Cz1C1eyuAfnsHg7/U85i0eMomFzrnMPe9sFg6ZxlG5Y+SSehPSwBFR8+Inrer7n6F57RcMu/FSkseNjsk+vJhLrLkpk91+TW7LvoZtf06xWM1NuQxUT3PcdFb/1odU3fY4aUccQu6tP+l1e/GQSSx0ziX9mNkckjyIizL268jbqUa6G0kDpw+y2GbfVq9e7XQJ/dL02Uaq71tI2lEzGfx/34zZfryWix3clMnuJM3wkOo4rRLpKZZYcFMu0eg+x01nwW272HXpLfhHDmPkX25BJfc+S0m8ZGK1zrn4sjL4PDXMjEY/OhRytJHuRtLA6YMsttm3SZMmOV1CxHSwpfXUVHISI+67FhXDRQ69lItd3JTJ7iRNqlaEKmqAyE+xxIKbcokF3Rxk149uJFxbx8g//5akkcP6fE68ZzJQ3XNZmR5icFjRtHKdo410N5IGjohaaWmp0yVErObB52heXULOry4m+cCxMd2Xl3Kxi5syWdlcyyP1X/Leq68DkZ1iiVX3v5tyiYXK3z5CU/Faht14KelHHhLRc+I9k4HqnssnaSEA6pd86Ggj3Y2kgSOiNnKktZdXx0rzus1U3fMUqYdNY8hFZ8Z8f17JxU5uyaSoqIi/bV/HffVbOemSCzsaLL2dYoll979bcolW50n82gX+9TZ7Hn+RzJOOYchlZ0e8rXjJxGrdc9mWrKnyh2lY9klEjfREIg0cEbWamhqnS+iTbmlpndDP52PEH69D+f0x36cXcrGbWzIpLCwkpDVhoLklGFFXfiy7/92Si9WaN5VSfuWdJB2Qx/AHru/X1Yrxmkm0DLko+Dw1TFPxWsKNTb020hONNHBE1NLS0pwuoU81j7xA0yefM/TaC0mZuL8t+/RCLnZzSyYFBQX4lMIPJPv8EXXlx7L73y25WCnc2MSuH94EwRZGPXFLv9f8isdMrGCWy9rUMLqpmaYVnztQkXvJYpsi7jWXbKX690+SOmsK2f3oIhfxKz8/nwkTJ3LC9iZOPGpORJ9227v/A4EAixYtkk/Ifaj67SM0r9lI7u+vInXGZKfLiWtr28bhNBStdLYQl5EGjohaY2Oj0yX0SIdClP/sTrTWjHjgelSSfYe8m3NxipsyycrK4tQhwzi4MfLTlVlZWWRlZVneuHFTLlao+8977HniH2SefOyAp2KIt0ysYpbL9iSNLzebxvdX2l+Qi8kpKhG17Oxsp0vo0Z4/L6Lpo8/IueYCUiaPt3Xfbs7FKW7LZLdfE/xyZ8fYGqe4LZdotOwop/ynd5A0ZkRUs4THUyZWMs1FQfqRM2j86DP8zh7KriINHBG1Xbt2OV2CqeAX26i6/c+kHDKJ7CvOsX3/bs3FSW7LZHeSRgfqCdfsdbQOt+UyUDoUYtdltxLeE2DEIzfiHzp4wNuKl0ys1lMu6UfNRDc08ZVm+bPeTpIQURs3bpzTJRjocJjyK+9EB1taT031MWtqLLgxF6e5LZPdSa0fd1u+3OloHW7LZaCq73+GxvdXMvQXF5CePyOqbcVLJlbrKZe0o2YCcHCj/FlvJ0mIqG3YsMHpEgxqn3qZxqJVDP35+aROneBIDW7MxWluy6SirT+/ZZuzDRy35TIQjcVrqL57AWn5Mxj68/Oj3l48ZBIL3XNpn3soZcqB+LIHMaUp9lNgeIU0cETUpk+f7nQJXQS37qDylkdJmfoVhl55nmN1uC0XN3BbJuVJYQCCpc42cNyWS3+FA/WUX/Y7fJnpjPjTDZbMM+X1TGKlp1yUz0fakTM4qMmHT8bhANLAERboPnOpk7TW7L7qLnRTMyP+eD0qJdmxWtyUi1u4LZOKpK49OGYz8drBbbn0V8WNDxHcvI3cu64ieaw1MxB7PZNY6S2X9KNmkK4V42UcDiANHGGB2bNnO11Ch73P/JuGd4rJ/sk5js+94aZc3MJtmTT4wJc9yPExOG7LpT/qXn+Xvc/8m6wzjmfQmSdYtl0vZxJLveWSftShABzcJH/aQRo4wgJu+aTVsn0XFTc9TPKk/cm55gKny3FNLm7ipkzae2uSxo4i6HADx0259EfLrkrKf/57/HkjyP39VZZu26uZxFpvuaRMm0C90v2a2ymeSQNHRM0Nn7S01uy++h50fWPrqanUFKdLckUubuPGTJLHjZIenAHQWrP7yjsJV9Uy8uFf48/u31IMffFiJnboLRfl95M7P5/DUoagQ6Euz0nEPOO2gaOUGqKUWq6UCiilpvV0m4jeqlWrnC6BwN//S/2SDxhy6XdI++pUp8sB3JGL27gxk6SxowjX7CUcqHesBjfm0pe9z71G/Zut77n0ObMs374XM7FDX7mkHTWTcG2A5jWbbKrIveK2gQPUAycDi/q4TURp6lRnGxQtOyuo+PUfSR4/lpxrf+hoLZ05nYsbuTGTpP1GATh6msqNufQmuG0XlTc8SPLE/cm5/qKY7MNrmdilr1zS2+bDaZBlG/rXwFFKjVRKnaeUukcp9UTbv+cppUbFqsCB0loHtda7+7pNRG/jxo2O7VtrTcW1fyC8J8Dw+6/Fl+GeFYidzMWt3JhJewOn5csyx2pwYy490Vqz++e/J1zXwIgHr8eXnhqT/XgpEzv1lUvqIZNRGWk0Ll9tU0XuFVEDRyk1RSm1CFgLnAckAzvb/j0PWKOUWqSUOnggRSilrlBKfayUalJKLeh2X45S6iWlVJ1SaqtSyv4590Wvxo4d69i+6/71NnWvvcvg//tWxycXt3AyF7dyYybtlzW3fOnc0gBuzKUne5/5Nw2FH5F9xfdImx27XhYvZWKnvnJRyUmkTp1A02qZKDHS+esXAHcD52qtm7rfqZRKAU4H/gIMZJndHcDvgBOB9G73PQw0AyOBmcCrSqlVWus1bT1HZqebztJaD7i/WSlV2MNdMwe6zXhWUVFBVlaW7fsNVe1h9/X3kTR2JMNuvNT2/ffFqVzczI2ZJI0bDUDQwR4cN+ZiJlhaRsWND5F80HhyfnlhTPfllUzsFkkuKYdMovEv/yRUs9fywd9eElEPjtb6CK31IrPGTdv9zVrrF7XWA2ncoLX+p9b6ZaCy8+1KqUzgTOAGrXVAa/0e8AqtvUZorXdqreeYfMXsZHpjYyMVFRWUlZWxfft2qqur2bRpEw0NDaxdu5ZwOMyKFSuAfZfzrVixgnA4zNq1a2loaGDTpk1UV1ezfft2ysrKqKioYMuWLQQCAdatW0dLS0vHQLL2bbT/u3r1apqamigpKaG2tpbS0lLKy8spLy+ntLSU2tpaSkpKaGpqYvXq1abbWLVqFS0tLaxbt45AIMCWLVuiek3JycmOvKbKmx4mXFHD8Ht+wepNJZa+Jit+Tqmpqa76Obnh2Nu1a5frXlMwPQWdkUrj5m0Rvab6+nrLf05lZWWu+jmZvabdu3ez7bJb0I1NpN92BV9s+zKmP6esrCxX/95z6ufUOZeeXlPj/q29kpVFK9i0aRPhcJjGxkbXvqZofk690lq75ovWXpwFnb4/FGjo9phrgH9HuL3XaO0dKgIu6Om2ftRXOHfuXC262rFjh+37rHvnY70xd47eeclvbd93pJzIxe3cmknpMefrL0+4qM/HzZo1S8+aNcvy/bs1l872LHxFb8ydoyt+95gt+/NCJk6IJJfG1SV6Y+4cXf3w81rr2B23bjB37lwNFGqTv9kDWmJZKXW+1nrhQJ7bT1nAnm637QEi6nPTWp8UyW0iOuFw2N79NTRRcfU9+LIHMezWn9i67/6wOxcvcGsmSWNH0rRynWP7d2su7VrKdlN508MkT9yfoVf/wJZ9uj0Tp0SSS8rkAyAlmaZPE3scTq8NnB4GDSvgEsCOBk4AGNzttsHAXhv2LSKUkZFh6/6q71tIcPM2hv/xOpKGD7V13/1hdy5e4NZMkvYbTf0bRYQbmmJ2VVBv3JoLtF019cs/EA7UM+q+X+JLsycfN2fipEhyUclJpB78FWng9HH/B7QO4lXdbt8/NuUYbACSlFITtdYlbbfNANbYtH/artpqv3Jrenl5uV279oyqqiqGDrWnodH0+RfUPPgsaUcfyqDvubszzs5cvMKtmSTt13Yl1badpEy069fbPm7NBVqvVKz/z3sM+dGZpB9xiG37dXMmToo0l9RDJlH7zL8J1zXYUJU79dXA+Rz4hda6++DfV60sQimV1FaLH/ArpdKAFq11nVLqn8AtSqkf0XoV0+nAUVbuvzda6+eA59rqLBwxYsRcu/btFXl5ebbsR4fDVFx9N8rvZ/i916BU93a3u9iVi5e4NZPkse1z4TjTwHFrLp2vVMz59cW27tutmTgt0lxSDpkEWtO8JnHnE+rrKqoTgJruN2qtT7a4jt8ADcB1wPfb/v+btvsup/XS8XLgeeAyrbVtPTiib5s3b7ZlP3v//l8aP/qM7Cu/T8pXxtmyz2jYlYuXuDUT/4gcAEIV1Y7s3625VNzwYOuVivf+Al+WvaeM3JqJ0yLNJfWQSQA0fbqBQCDAzp07KSoqimVprtNrD47Wurbz90qpEVpry8/RaK1vBm7u4b4q4JtW71NY56CDDor5PsKBeqpue5yk/UaRfYU35nq0IxevcWsm/raxXKGKGkf278Zc6t9eTuDv/yXrO18n47gjbN+/GzNxg0hzSZlyIPj9vPffNykpKUFrzfz581myZAn5+QOa0cVz+rsWVcKt4aSUOkcptVgptRgZg2Nq5cqVMd9HzUPPEdpZwbAbL7NtkGO07MjFa9yaiT+3vYHjTA+O23IJNzSx+5f34hs2hNxbr3CkBrdl4haR5uJLSyXloAN4p3h5+zQnNDc3U1hYGLviXKa/DRx3D3qIAa31c1rrU7TWpwCrR4wY4XRJrjNrlvUrCXfWsn0XNX/6G2mHTyfz9Hkx3ZeVYp2LF7k1E9+QLEjyE9rtTAPHbblU/+FpWrbsIPe3V+DPGeJIDW7LxC36k0vq9El8dU8YX9t4xZSUFAoKCmJUmfv0t4GjY1KF8LT2GShjpfJ3j6Ebmhj2u5+4fmBxZ7HOxYvcmony+fAPy3asB8dNuTSv20zNQ8+RNmcWWd850bE63JSJm/Qnl9RDJjHLl8XcAyaTl5eXUKenIPK1qITo0ezZs2O27cbiNQQWvUHWd04k7dApMdtPLMQyF69ycyb+3KGEKrvPK2oPt+Siw2F2X303+HwMv+dqRz9QuCUTt+lPLinTWwcafzV5EHuGZiRU4wbkFFWfZAxO39rXNrGa1pqKGx5CpacyzOZLVK0Qq1y8zM2Z+HOd68FxSy57n11M4/LVDP35eY5fqeiWTNymP7mkTpsASnFgc3//1MeH/r7qs2JShYvJGJy+zZw5Mybbrf/PezR99BnZV5xDUp73co9VLl7m5kz8uUMJVVR3DMi0kxtyaSmvovK3j5A8YRxDf3Ku0+W4IhM36k8uvqwMkr+yH+OlgdM3rfUupdQ8pdR4AKXUaKXU00qpJ5VSo2JTonC7deusX8NHa031HxbiG5JF9mVnW759O8QiF69zcyb+3Gx0QxPagZlf3ZBL5W8fIbwnwPC7r0alpjhdjisycaP+5pJ6yCTGNfvwJ+AI2oE06/4EhNr+fy+QTOvg48etKkp4y/jx4y3fZkPhRzStXMeQi87CNyjT8u3bIRa5eJ2bM9l3qXiN7ft2OpeGolUE/v4fss46gfQ57rh6yelM3Kq/uaQeMokUFGOCCTfCZEANnDFa69K25RVOBC4GLsPG5ROEu+zYscPybVbftxCVkc6Qi7x7VjQWuXidmzNxci4cJ3PRwRYqrv0DvkGZDLv5x47V0Z2bjxUn9TeXlLYZjRPxNNVArqKqVUqNBKYBa7XWAaVUCq09OXFHFtvsW05OjqXba/jgUxqLVjHkx991bA4OK1idSzxwcyb7ZjO2v4HjZC57/ryI5s+/IPf2n5E0cphjdXTn5mPFSf3NJbXtSqpEHGg8kFf8IPAR8CzwcNttRwNxecJUBhn3rb6+3tLtVd+3EJWaQvZl37V0u3azOpd44OZM/LnZgDOnqJzKpWVHOVV3PUnK9IkM/r9vOlJDT9x8rDipv7n4swex2x9mv2DiNXD63YOjtf69UuolIKS13tR283bgR5ZWJjzD57PujdO4ch0Nb33I4AvPcNWnyYGwMpd44eZM/MOc68FxKpeKGx5C1zUw/K6rUUnumhbNzceKkwaSy64kzYiWxBuDM6AjWmu9obfvRWJJTrbu7GTN/c9Akp/sn3hjQc3eWJlLvHBzJvt6cOxv4DiRS/3Sj6l75W0Gff8U0r461fb998XNx4qTBpJLeZLm4CYfOtiCSnZXQzaWpIksohYIBCzZTvOGLdS9+g6Dvn0iyWNHWrJNJ1mVSzxxcyYqMx2VnurIKSq7c9HNQSquvx9f9iCG/eYSW/cdKTcfK04aSC7lSWF8KFq2J9YYUmngiKjl5uZasp09T74EQPYV37Nke06zKpd44uZMlFIdk/3Zze5c9jzxD4IlW8m5/iL8w7Jt3Xek3HysOGkguZQntU6CEyxNrCvTem3gKKX8SqkzlFLfbLssvP32b8e+NHeQpRr6tm3btqi3Ea5rIPDi/0g7+lBSJh0QfVEuYEUu8cbtmTjVwLEzl5adFVTd/RQp0yYy+Aen2bbf/nL7seKUgeTS3sBp2VpmdTmu1lcPzkJgFjATeE8pNaHt9stiWZSbyFVUfZswYULfD+pD4OW3CNcGXP0Lt7+syCXeuD0Tf242od09N3CKi4tjssq1nblU3voYOlBP7h1Xovx+2/bbX24/VpwykFzKk8IABEulgdNZntb6N1rrm4HvAU8opQpiXZTwljVr1kS9jdqn/4UvN5usk461oCJ3sCKXeOP2TPy5QwlV7UGHw7bu165cGpev3jdj8ZGH2LLPgXL7seKUgeRS44NmpWmRBk4XqUqpVACt9WbgVOAaWif5EwKAGTNmRPX8plXrafrkcwZ/72RXrIFjlWhziUduz8Sfmw0tIcJ77B3gakcuOhRi93X3oTLTGXbT5THfX7Tcfqw4ZUC5KCj3a4JbZQxOZ1cBQ9u/0VrvBU5vu10IgKi77GsXvgLA4PNOtaIc14jFqQyvc3smTi3XYEcue599lebVJQy95gKSRrl/AK/bjxWnDDSX8iTpwelCa/2B1npn+/dKqRFa65DW+q+xL014xezZswf83PDeOvYueoP0gsNIHj/GwqqcF00u8crtmXTMhdPLOJxYiHUuodoAlXf8meQDx5J9sTeuEXH7seKUgeZSnhQmtLuacF2DxRW5V38vE18UkyqEp0XzSWvvP95A1zcw+IJvWleQS8gnUCO3ZxKvPTjV9z5NuKKGYbdcgUrxxgR6bj9WnBJNDw5Ay5c7+3hk/OhvAyfh5nqWy8T7NtBPFFprahf8C//IYWR+Lf4Wo5dPoEZuz2RfA6fG1v3GMpfmTV+y58+LSC84jAwPvc/cfqw4ZeA9OIk3F05/Gzg6JlW4mFwm3rfVq1cP6HlNK9bSvGYjg79/SlxOHz7QXOKZ2zNxakXxWOZSedPDENbk3voTlPLOZ1S3HytOGWgu7ZeKJ9JcOPH3V0XYbtKkSQN63t6//w+AQeecbGU5rjHQXOKZ2zNpn9XX7h6cWOVSX/gR9f9dxuALzyDloPEx2UesuP1YccpAcikuLia0Zy9bJpyUUHPhyCkqEbXS0tJ+P0e3tBB45S3SDp9O8rjRMajKeQPJJd65PROVkoxvcJbtPTixyEW3tFDxmwfwZQ8i59oLLd9+rLn9WHHKQHPxDxmEb0hWQl1J1d8GzlkxqUJ42siR/V8Ys+GdYsIVNWSdcXwMKnKHgeQS77yQiT832/YGTixyqX36FYLrtzD0Fxfizxli+fZjzQvHihOiySVp3GiCCXSKql8NHK31rlgVIryrpqam38/Z+483we8n87R51hfkEgPJJd55IZPW9ahqbN2n1bmEavZSddeTJE/cnyH/901Lt20XLxwrTogml+RxebSUlqF1Ygyn7fcYHKXUEOCnwKFAVuf7tNZfs6gu4SFpaWn9eny4oYm6V5eSfuxskoYP7fsJHtXfXBKBFzLxDx9Kc8lWW/dpdS7V9y0kXLWHEQ/+yrMD+L1wrDghmlySDhhN+NU6wtW1nuzV66+BHPkvAn7gJSBxZgwSlql/4310XQODzjzB6VKEMPDnDiVcXYsOtniycRD8Ytu+y8JPyHe6HOEiyePygNZFN6WBY+5IYJjWOmh1McKbGhsb+/X4wD/fRKWlkHnSMTGqyB36m0si8EImHbMZV9bYtqSBlblU3vIIhMKtk/p56LLw7rxwrDghmlyS2i7oaNlaBjMPsqok1+rvIGOA94ApVhfiVjLRX9+ys7Mjfmxoz17q3/yAjBOOwjcoM3ZFuUB/ckkUXsikY7K/yhrb9mlVLg3LPqHu1XcYfN6ppE450JJtOsULx4oTosklef/WBk6iTPY3kB6cC4DXlFIfAl0GHWutb7GiKDfRWj8HPAeglCocMWLEXIdLcp1du3YxePDgiB5b9+o76KbmuL56ql1/ckkUXsjEiblwrMhFh0JU3PAgvkGZDL32hxZV5hwvHCtOiCaXpP1GASTMpeIDaeDcBuwHbAE6p5wYw7KFwbhx4yJ+bOClJfgGZZJx/JExrMgd+pNLovBCJk7MZmxFLnv//l+aV5eQc+OlcTF43wvHihOiycWXlop/5LCEuVR8IKeovgvM1FqfpbU+r9PX+VYXJ7xhw4YNET2uZVclDe8Uk3nysfjSUmNclfMizSWReCGTjlNUNq4oHm0u4fpGqm7/M0njRjPkoviYrswLx4oTos0ledzohOnBGUgD5wtABhiLDtOnT4/ocXX/LoRwOCFOT0HkuSQSL2TSMcjYxh6caHPZ88gLhHZWkPObi+Pmw4MXjhUnRJtL0v6jCX65Ex0OW1SRew2kgfMM8IpS6ntKqeM6f1ldnPCG4uLiiB5X99o7+LIHkX7MrBhX5A6R5pJIvJCJb+hg8PlsbeBEk0vLrkqqH3iW1FlTyPrmfAurcpYXjhUnRJtL8v550BwktKvSoorcayBjcH7c9u/t3W7XgLeH7YsBmT17dp+PCVXX0vD+KrLOPAGV5L25RQYiklwSjRcyUX4//mFDbB1kHE0u1Xc/ha5vYNjNP/b0ZeHdeeFYcUK0ubRfKh7cWkbS6OFWlORa/e7B0VqP7+FLGjcJKpJPFPVvvA+hUNzPfdOZfAI18komrcs1uL8Hp3n9Zmqf+TeZJx9Lev4Mi6tylleOFbtZ0oMDtGyN/0vF+93AUUo9oJQ6qtttRyml7resKuEpkXyiqHvtPVR6KhnzDrehIneQT6BGXsnENyzb1nlwBppL5W8fAZ8i54ZLLK7IeV45VuxmWQ9OAgw0HsgYnO8BH3e7rRg4J/pyhBetWrWq1/vDDU3Uv/0h6QWH4ctInPVl+solEXklk6TcbFuvohpILvXvFlP/RhFDLvgmKV+Jv0uqvXKs2C3aXJLyhkOSX3pweqBNnucf4LZEHJg6dWqv9zcs/Qhd30jmNxLn9BT0nUsi8kom/tyh6LoGwvX2LBfQ31x0OEzlzX9qndTv6h/EqCpneeVYsVu0uSi/n6SxI6UHpwfvAr9TSvkA2v69ue12kYA2btzY6/11r70LPh+ZXzuq18fFm75ySUReycTu5Rr6m0vgpSU0f7qB7J+e21FrvPHKsWI3K3LxD8smvGevBdW420AuZ/kZsBgoU0ptBcYBZcCpVhYmvGPs2LE93qdbWqj77zLSjjykYwr8RNFbLonKK5l0ns04uW16+1jqTy66qZmq2/+Mf/Rwhlz87RhW5SyvHCt2syIXX1YGQRtPwTql3w0crfU2pdQs4HBal2z4EliutY7LWYOUUuewb3yRLLZpoqKigqysLNP7Gj9cTbhqD5knHWtzVc7rLZdE5ZVMOib7s+mPQH9y2fPUS7SUljH8j9fF9Zg2rxwrdrMiF19mOuFAvUUVudeAJiRpa8x80PYV12Sxzb719mare731zGXmN+bYVY5ryC9nI69k4utYcNOeBk6kuYRq9lJ979OkTDmQQWd/PcZVOcsrx4rdrMhFZWUkRANHBgaLqAWD5it3aK2pe/09UqZNJLnt0sRE0lMuicwrmXT04FTtsWV/keZS88BfCdfsJeeGS1F+f4yrcpZXjhW7WZGLLzMDmoPo5vjOWBo4ImrhHtY0aV6ziZbSsoSa3K+znnJJZF7JxD+sdQxO2KZBxpHkEty2iz2PLyJtziwyjj/Shqqc5ZVjxW5W5OLLymjdVl1D1Ntys36folJK3dLDXU3ANuA/WutdUVUlPCUjI8P09n2npxKzgdNTLonMK5n4BmeC32/bcg2R5FJ95xPopmaG3XRZXC3J0BOvHCt2syIXX2Y6AOFAPf6hg6PenlsNpAdnEnAtMA+Y0PbvtcChwGXAF0qp+D45LLqoqqoyvb3+zSKSxowgZepXbK7IHXrKJZF5JRPl8+HPGWLbKaq+cmlau4m9f/8vWd88jrSZB9lSk9O8cqzYzYpcVHsPTpyPwxlIA8cHfFdrfYzW+hyt9THAd4CQ1vpI4HLgTiuLFO6Wl5dnuK1ldzVNn6wj4/j8hPi0acYsl0TnpUz8ufYt19BXLlW3PQ5+HznXX2RLPW7gpWPFTlbk0n6KSsf5KaqBNHBOBF7pdtti4Btt//8rkJgf2RPU5s2bDbc1vP0haJ0QYwV6YpZLovNSJr6cIbY1cHrLpaFoFfX/e5/B551G8oGJMzeMl44VO1mRS+dTVPFsIA2cTbSeiurs0rbbAXKBumiKEt5y0EHGLvP6Nz+AlGTS58xyoCJ3MMsl0XkpE/+wbMKV9pyi6ikXrTWVtz6KykiL2yUZeuKlY8VOVuQip6h69iPgGqXUl0qpD5RSXwK/AH7Ydv9k4AarChTut3Llyi7f65YW6t9eTvpRMzu6QhNR91yEtzLxD8smvLcO3dQc8331lEv96+/S9NFnZF92Nkkjh8W8Djfx0rFiJytySZRTVAOZ6G8lMBE4EsijdZmGIq11EEBr/Q7wjlUFCvebNatrL01j8VrCNXvJmJ+4p6fAmIvwVib+YUOA1rlwkkYPj+m+zHLRLS1U3vY4vmFDyP7x92K6fzfy0rFiJyty8UkPjpFSyk/r6Sef1vpdrfULWut32hs3IjEVFxd3+b7+zdYJrjNOSOwGTvdchLcy8XfMZlwT832Z5bL3b/8huGErQ3/+A3yDMmNeg9t46VixkxW5yBgcE1rrELABSKy+UtGr2bNnd/m+/s0PSDpgDMkH7udQRe7QPRfhrUw6Gjg2XCrePZdwYxNVdz9F0n6jGHLB6THfvxt56VixkxW5yBicnj0LLFZK/UApNV8pdVz7l9XFCW9YsWJFx/9bynbT/FkJmccfmbCXh7frnIto5aVMfO3LNdhwJVX3XGqfeonQjnKG/vJCVGpKzPfvRl46VuxkRS4yBqdn7VdQ3dztdg0cGFU1wpNmzpzZ8f/6JR8CkHF8vkPVuEfnXEQrL2Xiz2kbg2PDKarOuYT31lF9/19JnnwAg779tZjv2628dKzYyYpcVFoK+P3Sg9Od1np8D1/SuElQ69at6/h//ZsfoNJTSTtqpnMFuUTnXEQrL2XSfooqXFUT8311zqXm0b8TrtpDzvUXxf2Cmr3x0rFiJytyUUrhy0yXBo4ZpdQJSqm/KKX+3fb9bDlFlbjGjx8PgA62UL/0I9LnzMKXnupwVc5rz0Xs46VMOsbg2HCKqj2XUGUNNX/6G6mzpiTsIrXtvHSs2MmqXFRWhjRwulNK/QR4BCgBjm27uRH4nYV1RU0pNUQptVwpFVBKTWu7LV8pVaSUWqqUel4plex0nfFgx44dADQuX40O1MvpqTbtuYh9vJSJSk7CNzjLllNU7blU//Gv6EA9Ob++OOHHsHnpWLGTVbn4stLjfgzOQHpwrgSO11rfCbSv276O1gn+3KQeOBlY1Om2rcBxWuu5wBdAYl6eYLGcnBygdXFNgIz5RzhZjmu05yL28Vom/mH2rEeVk5NDy/Zd1D75EunHzibj2K/GfJ9u57VjxS5W5eLLyiBcJz043Q0Cvmz7v277NxmI/XSf/aC1Dmqtd3e7bYfWur3J2sK+BpqIQn1965uk/u2PSP7KfiTvL4vkwb5cxD5ey8SXm23LZeL19fVU3fs0uqmZnF9fHPP9eYHXjhW7WJWLjMEx9w5wXbfbfgq8PdAilFJXKKU+Vko1KaUWdLsvRyn1klKqTim1VSl1zkD302mb42ldHHRxtNsS4PP5aNlVSfOajWTMO9zpclzD5xvQELe45rVM/HYtuPnlLvY+9xqZJx1D2qyDY78/D/DasWIXq3JRWRmE4/wU1UAuE/8J8G+l1EXAIKXUeqAWODWKOnbQOobnRCC9230P09o7NBKYCbyqlFqltV6jlBpF11NQ7c7SWu8025FSajDwNHCe1tpVvU5elZycTMPbHwOQPu8wh6txj+RkGeLVndcy8Q/LJlxViw6HUTH8gxt6dBGEw+Rcf1HM9uE1XjtW7GJVLr6sDHSc9+D0u4GjtS5TSh0GHAbsT+vpquVa6wGf7tFa/xNAKfVVYGz77UqpTOBMYJrWOgC8p5R6BTgPuK6tETMn0v0opZKA54Gbtdbre3lcYQ93zYx0X4kkEAgQKvwIkpNIP+pQp8txjUAgQG5urtNluIrXMvEPy4ZwmHDN3o55cazWtGYjwVffJevbXyPlILlyqJ3XjhW7WJWLLzMd3diMbmlBJQ2kr8P9BvSRRLdarrV+UWv9QTSNmz5MAkJa6w2dblsFTI3kyUqp14CvAX9WSl0AfA84ArhRKVWolDq7vwU1NjZSUVFBWVkZ27dvp7q6mk2bNtHQ0MDatWsJh8MdM022rxmyYsUKwuEwa9eupaGhgU2bNlFdXc327dspKyujoqKCLVu2EAgEWLduHS0tLaxatarLNtr/Xb16NU1NTZSUlFBbW0tpaSnl5eWUl5dTWlpKbW0tJSUlNDU1sXr1atNtrFq1ipaWFtatW0cgEGDLli1RvabMzEwCbxaRNHMyZXuq4+I1WfFzGjx4cNy9pmh/ThUVFZ56TcGsNAAayspj9nOquvMvaL+P7GsucM3PyQ3HXm5ubty9Jit+Tp1zieY1lQf2AvDJsg8cf03R/Jx6pbXu84vWMTapfTwmFfhpJNvrZRu/AxZ0+v4YYGe3x1wEFEaznyjqK5w7d64WXX3+7zf0xtw5uurep50uxVU+//xzp0twHa9lsuf51/TG3Dm6/v2VMdl+w0ef6Y25c/T6H/46Jtv3Mq8dK3axKpfKe57SG3Pn6OC2nZZszylz587VPbUJIu2XGgVsbOsRWQqsB/bSekXVJKCA1kG7CyPcXqQCwOButw1u27dwiRFbyqlGxt90N2HCBKdLcB2vZRLrBTerbn8clZbCuJt+HJPte5nXjhW7WJWLLzP+F9yM6BSV1vpXwKG0Tu73Q+B14DPgNeBCWufBOVRr/RuL69sAJCmlJna6bQawxuL99EgpdY5SarFSajEwvby83K5de8buxW/jGzqY1EMmOV2Kq6xZY9th6hley8TftuBmOAZXUtW/8zEN765g8IVnsL5Kfq9057VjxS5W5eLLbL2eJ54bOBGPLNJaVwD3tH1Zqm3wbxLgB/xKqTSgRWtdp5T6J3CLUupHtA7yPR04yuoaeqK1fg54rq3OwhEjRsy1a99eEG5oInnNF6SfOCeh180xM2PGDKdLcB2vZRKrBTe11lTd8QQqM52hPz2X3LaeIrGP144Vu1iVS/uK4uFA/F4q7paJBn4DNNA6v8732/7f3ht0Oa2XjpfTegXUZVpradq7ROOHn6Ibm8kokNNT3bUPmhP7eC2TWK1HVf9GEU0fryH70u/gH5btuVzsIJmYsyoX1dbA0XE8m7Errg3TWt8M3NzDfVXAN20sR/RDfeFyANKlgWMwe/Zsp0twHa9lojLTUWkphCxcUVxrTdWdT+AbksWQy1ov5PRaLnaQTMxZlcu+Hpz4beBE3IOjlPpSKfW4UuqbbfPTJAQZg9O7hsKPCY0dQfLYkU6X4jryCdTIa5kopVrXo7LwFFXdq+/QvLqE7Mu/h3/IIMB7udhBMjFnVS77xuDIKSqAw4EPaZ1kb4tS6g2l1M+VUnE9slRr/ZzW+hSt9SnA6hEjRjhdkmu0L8+Q8/VjnC7FleQTqJEXM/FZuFyDDoWo+v1f8OUMYcjFZ3Xc7sVcYk0yMWd5D04cn6KKuIGjtS7TWv9Fa30mMBq4AxgDvKyU2qiUekAp9XWlVGqsihXu0vBO6/IMu8ZLo89M+0RWYh8vZtK6XIM1l4kH/vU2wXWbyf7JOR1/YMCbucSaZGLOqlw6xuBID05XWusWrfVbWutrtNYHA8fTOjfOT9q+RAKoL/wIkvwceOY3nC7FlSZNiuvOzQHxYib+3GxClTXtk30OmG5pofquJ/EPz2HIhWd0uc+LucSaZGLOqlxkDE6EtNZbtNYPa61P1lpbfhm5k2QMjjmtNQ3vFJP21Wlsq6pwuhxXKi0tdboE1/FiJv6c7NY1e6JceXnvojcIbvqS7CvPw5eR1uU+L+YSa5KJOatyUemp4PNJAyeRyRgcc8GNpYR2VpB+7GxGjpQBxmYkFyMvZmLFpeI62EL1PU/hzxvB4PNPNdzvxVxiTTIxZ1UuSil8memEo2y4u1nUDRyl1DFKqYuUUlOsKEh4Q8M7rSP504+ZTU1NjbPFuJTkYuTFTNpnM45muYa9f3udlq1lDL3y+/jSjMMUvZhLrEkm5qzMRWVloKUHp1dHAzXAr5RSN1mwPeEBDe8WozLSSZs1hbS0tL6fkIAkFyMvZuKLcjZj3Ryk+r6FJI0dyeBzTzF9jBdziTXJxJyVufgy0+UUVW+01ndqrV/UWp8HbFdK3WtBXcLFdChEw3srKA4H+Gr+kU6XI0RMRXuKqvb512j5cidDrzoflZJsXWFCRMmXlRHXDZyoZzJWSv0ZyANGtG0vrk7oKaXOAc5p+1YGGQNNn24gvCfA66k17Ny5h2XLlvGtb33L6bJcp7Gx0ekSXMeLmXQsuDmAU1S6qZma+xaSNG40g757Uo+P82IusSaZmLMyF5WVQWjbLsu25zZWLNXwCLAD2KWjvY7ShWSxTaOGd4pZEaxlYeU6Qlpz7rnnsmTJEvLz850uzVWys7OdLsF1vJhJRw/OAE5R1T77Ki3byxl+/3Wo5J5/3Xoxl1iTTMxZmYsvKyOu16Lq11INZrdrrVdorXfGY+NGmGt4t5jlyU2E2n7kzc3NFBYWOluUC+3aFb+fjAbKi5n4sgeBz9fvU1Thxiaq73+GpAPyGPSdE3t9rBdziTXJxJyVucgYnH2yO3+jlFplbSnCC8KNTTR++ClzjzwKpRQAKSkpFBQUOFuYC40bN87pElzHi5konw9fzuB+X0W196+LCZXtZujVF/TaewPezCXWJBNzVubiy8pANzShQyHLtukm/WngdO+hkaMvATV9vAbd2MwxZ53OxIkTycvL4/HHH5fTUyY2bNjgdAmu49VM/MOy+9WD0957k3zgWAaddUKfj/dqLrEkmZizMheV1bbgZpzOhRPNGBw5JZWA6tvmv8k4Zjbr1693uBp3mz59utMluI5XM/HnDCG0szLix+995t+EdlUy7OFfo5L6/jXr1VxiSTIxZ2Uuvo71qOphcJZl23WL/vTgZCmldiulliqlHgFSlFKHKqWsGKjsWrJUQ1cN7xaTNHYkSePHdNxWXFzsYEXuJbkYeTWT/vTghBubqH7gWZIPHEvWGcdH9Byv5hJLkok5K3PxZbb14MTpOJz+NHBygG8DLwEZwCbgQ2CvUuqjtkZP3JGlGvYJ762j6ZN1pB8zu2P8DcDs2bMdrMq9JBcjr2biz80mXBtAB1v6fOzevy4mtLOCoVf9IKLeG/BuLrEkmZizMpeOBTfj9BRVxA0crXWN1roQeBx4GrgKyKV1JuPHgL7f+cLTGt5fCaEQ6cd2fYPJJy1zkouRVzPx52QDfU/2F25sovqPfyV5/Fiyzoys9wa8m0ssSSbmrMxFxfmK4gM5vbQVWAukA+OBy7XWT1halXCljvWn5szqcrt80jInuRh5NRNfp9mMk0bl9vi4vc++SmhnBTkP/iri3hvwbi6xJJmYs7QHp+0UVbyuRzWQpRomaK3naq0PB74B/FYpdZ3FdQkXanivmOTJBxh+wa9aJTMGmJFcjLyaSSQLbuqmZqr/+FeSDhgT0ZVTnXk1l1iSTMxZmYsvM757cPrdwNFa7+n0/4+Bw4H5SqlfWVmYcJdQRTXNa78g/ehZhvumTp3qQEXuJ7kYeTWT9tmMw73MZlz77Ktt895EPvamnVdziSXJxJyVucgYnG6UUicqpS5USt2glHoU+BswFrjV8uqEazS8vxKA9DmHGu7buHGjzdV4g+Ri5NVM2hs4wS3bTe9vH3szkN4b8G4usSSZmLMyl3gfgzOQU1SPABcC02ldWHMp8DtgvoV1uYZcJt6q4b1PAEg/aqbhvrFjx9pcjTdILkZezSTloANIPnAse558iXBDk+H+2oX/JrSjvHXF8H723oB3c4klycSclbnIGJxutNYHaq3naK2/o7X+udb6bq31s21XWMUduUy8VcOyFaRM/UrHJ9nOKioq7C/IAyQXI69mopKSGHrNBYR2VlC78JUu94XrGqi5/xmSJ4xj0Le/NqDtezWXWJJMzFmZi5yiEgmvZVclwQ1bTcffAGRlxd8MmFaQXIy8nEnWGceTPGEcNQ/8lXB9Y8fte578J6HdVeT88sIB9d6At3OJFcnEnJW5qMx0UEpOUYnE1fB+2+kpk/E3AMFg0M5yPENyMfJyJsrvZ+gv/o9QeRW1T78MtE5+WfPgc6QcfCCZp88b8La9nEusSCbmrMxFKYXKTJdTVCJxNb73CShFWv5M0/vD4bC9BXmE5GLk9UyyTp9H8qT9qXnwudZTU4/9nXB1LTnX/QjlG/ivU6/nEguSiTmrc/FlpksPjkhcDe+tIGX6RPzZg0zvz8jIsLkib5BcjLyeifL7ybnm/wjtrqb6D0+z508vkHroFDK+Pieq7Xo9l1iQTMxZnYsvK0PG4IjE1FK2m+AX2wyzF3dWVVVlY0XeIbkYxUMmmafPI/mg8dQ88CzhvXXkXP+jLmuzDUQ85GI1ycSc1bn4sjKkB0ckpob3VgCQfrT5+BuAvLw8u8rxFMnFKB4yUT4fOb/4PwDSjpxBesFhUW8zHnKxmmRizupcVFYGOiA9OCIBNby7Avx+0vNn9PiYzZs321iRd0guRvGSSeYpcxl2y48Zft8vo+69gfjJxUqSiTmrc4nnMTgDu6ZRJIyGZZ+QOmMSvkGZPT7moIMOsrEi75BcjOIlE+XzkX3Zdy3bXrzkYiXJxJzVucgYnASWyDMZB0vLaCkt63H+m3YrV660pyCPkVyMJBNzkouRZGLO6lx8WRno+gZ0HF61Jg2cPiTyTMYd4296mP+m3axZvTeAEpXkYiSZmJNcjCQTc1bnotqXa4jDXhxp4IgeNSz7BJL8pB0+vdfHFRcX21SRt0guRpKJOcnFSDIxZ3Uu8bxcgzRwRI8aP/iUtEOndLwBejJ79mybKvIWycVIMjEnuRhJJuaszsUXxyuKSwNH9Gjs208y/MHr+3zcihUrbKjGeyQXI8nEnORiJJmYszqXeG7gyFVUokf+wVn4B/e9sNvMmTNjX4wHSS5Gkok5ycVIMjFndS4dY3DisIEjPTgiauvWrXO6BFeSXIwkE3OSi5FkYs7qXGQMjhC9GD9+vNMluJLkYiSZmJNcjCQTc1bnEs+nqKSBI6K2Y8cOp0twJcnFSDIxJ7kYSSbmrM5FGjhC9CInJ8fpElxJcjGSTMxJLkaSiTmrc5ExOEL0or4+/t4YVpBcjCQTc5KLkWRizupcZAyOEL3w+eQwMiO5GEkm5iQXI8nEnNW5yCkqIXqRnJzsdAmuJLkYSSbmJBcjycSc1bmojDRAGjgJKZEX24xUIBBwugRXklyMJBNzkouRZGLO6lyUz4fKSJe1qBJRIi+2Ganc3FynS3AlycVIMjEnuRhJJuZikYsvK116cIQws23bNqdLcCXJxUgyMSe5GEkm5mKRiy8rQxo4QpiZMGGC0yW4kuRiJJmYk1yMJBNzschFZUoPjhCm1qxZ43QJriS5GEkm5iQXI8nEXCxy8WVlyDw4QpiZMWOG0yW4kuRiJJmYk1yMJBNzschFJSehw2HLt+s0aeCIqBUXFztdgitJLkaSiTnJxUgyMSe5RE4aOHFu9uzZzJ49O+b7EEaSi5FkYk5yMZJMzEkukZMGTgKaPHkyY8aMoaioyJLtyScKc5KLkWRiTnIxkkzMSS6RkwZOgikqKqKkpIQdO3Ywf/58Sxo58onCnORiJJmYk1yMJBNzkkvkpIGTYAoLC9FaA9Dc3ExhYWHU21y9enXU24hHkouRZGJOcjGSTMxJLpGTBk6CKSgoQCkFQEpKCgUFBVFvc9KkSVFvIx5JLkaSiTnJxUgyMSe5RE4aOAkmPz+fZcuWcfvtt7NkyRLy8/Oj3mZpaakFlcUfycVIMjEnuRhJJuYkl8glOV2AsF9+fr4lDZt2I0eOtGxb8URyMZJMzEkuRpKJOcklctKDk8CsuoS8pqYm+mLikORiJJmYk1yMJBNzkkvkpIEjopaWluZ0Ca4kuRhJJuYkFyPJxJzkEjlp4CSwQCDAzp07LZsPRwghhHCLuG3gKKWGKKWWK6UCSqlpbbeNVEq9r5RaqpR6Syk12uk6Y62nRoyV8+E0NjZGW2ZcklyMJBNzkouRZGJOcolc3DZwgHrgZGBRp9sqgDla67nAQuCHThRml94aMVbOh5OdnR1lpfFJcjGSTMxJLkaSiTnJJXJx28DRWge11ru73RbSWrcvmToIsH7deRfprRFj5Xw4u3btiqbMuCW5GEkm5iQXI8nEnOQSOVc0cJRSVyilPlZKNSmlFnS7L0cp9ZJSqk4ptVUpdU6U+5qplPoQuAJYEc223K63Rkx+fj4TJ04kLy8v6vlwxo0bF22pcUlyMZJMzEkuRpKJOcklcq5o4AA7gN8BT5rc9zDQDIwEzgUeUUpNBVBKjVJKvWfyNaqnHWmtV2qtjwBuAK43e4xSqtDsC5gZzYu0W1+NmPXr17N9+/ao58TZsGFDVM+PV5KLkWRiTnIxkkzMSS6Rc0UDR2v9T631y0Bl59uVUpnAmcANWuuA1vo94BXgvLbn7dRazzH52mm2H6VUaqdv99A6TqdfGhsbqaiooKysjO3bt1NdXc2mTZtoaGhg7dq1hMNhVqxo7RhqX/V1xYoVhMNh1q5dS0NDA5s2baK6uprt27dTVlZGRUUFW7ZsIRAIsG7dOlpaWli1alWXbbT/u3r1apqamigpKaG2tpbS0lLKy8spLy+ntLSU2tpaSkpKaGpqYvXq1WRlZTF48GDy8/M7trFq1SpaWlqYOnUqhx56KFu2bInqNU2YMMHW12S2jfbXtG7dOgKBQNSvyYqf06RJk+LuNUX7cwqHw3H3mqz4OQWDwbh7TdH+nKZPnx53r8mKn1PnXKx6TVpDsLnZk8deb1T7GA03UEr9Dhirtb6g7ftDgfe11umdHnMNMFdrfWoE23uN1l6XrcBjwHrg90AIaAQu1FqX9aO+wrlz5861YoFKu7RP5Nd+8HQ2efJkAoEAixYtiqoXp7i4WFa4NSG5GEkm5iQXI8nEXCxy2XHmlQRLy9j/oxcs3a4dCgoKWLp06VKtdUH3+9y+VEMWrT0tne2hdYBwn7TWJ5ncfGy0RcWD9iustNbMnz8/qnE48kvInORiJJmYk1yMJBNzkkvkXHGKqhcBYHC32wYDex2oJa5YeZm4We+QkFzMSCbmJBcjycSc5BI5t/fgbACSlFITtdYlbbfNwMbLu9uu2mq/cmt6eXm5XbuOqfYrrLTWUV8mLp8ozEkuRpKJOcnFSDIxJ7lEzhU9OEqpJKVUGuAH/EqpNKVUkta6DvgncItSKlMpdTRwOvCMXbVprZ/TWp+itT4FWD1ixAi7dh1TVl4m3j7gTHQluRhJJuYkFyPJxJzkEjlXNHCA3wANwHXA99v+/5u2+y4H0oFy4HngMq11XE/QZ5esrCxGjRoV9WXiU6dOtaii+CK5GEkm5iQXI8nEnOQSOVc0cLTWN2utVbevm9vuq9Jaf1Nrnam1Hqe1fs7O2pRS5yilFiulFhNHp6istHHjRqdLcCXJxUgyMSe5GEkm5iSXyLmigeNm8XCK6vPPP4/peduxY8fGbNteJrkYSSbmJBcjycSc5BI5aeAkgFAoZLqieE8rjfdXRUVFVM+PV5KLkWRiTnIxkkzMSS6RkwZOnPvss89obm42rCje20rj/ZWVlWVVuXFFcjGSTMxJLkaSiTnJJXLSwIlzoVCo4/+d57uxch6cYDAYTYlxS3IxkkzMSS5Gkok5ySVybp8Hx3FengenqKioSwMnKSmpY74bK+fBCYfDUVYanyQXI8nEnORiJJmYk1wiJz04ffDaIOPJkyczZswYioqKDL0y//d//9dxSbiV8+BkZGREU3LcklyMJBNzkouRZGJOcomcNHDiSPdxNcOGDeu4TynF+eef3+XxVs2DU1VVFdXz45XkYiSZmJNcjCQTc5JL5OQUVRzpPq6msrKSSZMm9bhiuFVrmuTl5VmynXgjuRhJJuYkFyPJxJzkEjnpwYkjBQUFpKen4/f7O8bVrF+/nu3bt0fdS9ObzZs3x2zbXia5GEkm5iQXI8nEnOQSOenB6YOXBhnn5+ezZMkSCgsLKSgoiGmjprODDjrIlv14jeRiJJmYk1yMJBNzkkvkpAenD14bZJyfn8/1119vW+MGYOXKlbbty0skFyPJxJzkYiSZmJNcIicNnDgze/bsmC7LYGbWrFm27s8rJBcjycSc5GIkmZiTXCInDRwRNasGK8cbycVIMjEnuRhJJuYkl8hJAyfOfPbZZ3z66adRry/VH3b3GHmF5GIkmZiTXIwkE3OSS+SkgRNHioqKaG5upqWlhYKCAtsaOStWrLBlP14juRhJJuYkFyPJxJzkEjm5iqoPXriKqn3W4uXLl3fc1tzczMKFC20ZbDxz5syY78OLJBcjycSc5GIkmZiTXCInPTh9cPtVVEVFRRx99NH86le/4t///rcjNaxbt86R/bqd5GIkmZiTXIwkE3OSS+SkgeNxnWcvbv8XIDU11bA0Q6yMHz/elv14jeRiJJmYk1yMJBNzkkvkpIHjUe2Xg7evCg6tjZqkpCSSkpJ4++23bZsLZ8eOHbbsx2skFyPJxJzkYiSZmJNcIidjcDwuPz+fZcuW2T57cWc5OTm279MLJBcjycSc5GIkmZiTXCInDRyPCgQCBAIBioqKyM/P72jYtF9CaOdcCfX19QwdOtS2/XmF5GIkmZiTXIwkE3OSS+TkFJUHFRUVUVJSwo4dO5g/f36Xy8EDgQA7d+60dR4cn08OIzOSi5FkYk5yMZJMzEkukZOk+qCUOkcptVgptRiXXCa+cOHCjgHFzc3NFBYWAr03fGIpOTnZlv14jeRiJJmYk1yMJBNzkkvkpIHTB7ddJl5UVMRjjz3W8b3f76egoADoekVV54ZPrAUCAVv24zWSi5FkYk5yMZJMzEkukZMGjsd0bsQAXHjhhR3jbzpfUZWSktLR8Im13NxcW/bjNZKLkWRiTnIxkkzMSS6RkwaOx3RuxCilusx1k5+fz8SJE8nLy2PJkiW2XVG1bds2W/bjNZKLkWRiTnIxkkzMxSyXTh+c44VcReUx7ZeFn3XWWWRlZRkaMevXr7e9pgkTJti+Ty+QXIwkE3OSi5FkYk5yiZz04HhQfn4+WVlZHZeJO23NmjVOl+BKkouRZGJOcjGSTMxJLpGTBo4HOXW1VE9mzJjh6P7dSnIxkkzMSS5Gkok5ySVy0sDxIKeuluqJnZMKeonkYiSZmJNcjCQTc5JL5KSB40Gdr46y82qpnrTPniy6klyMJBNzkouRZGJOcomcNHD64MaJ/vLz80lKSsLn83H//fc7sv5UZ/KJwpzkYiSZmJNcjCQTc5JL5KSB0we3TfQHrWNwQqEQ4XCYK6+80vExOPKJwpzkYiSZmJNcjCQTc5JL5KSB40FuG4OzevVqR/fvVpKLkWRiTnIxkkzMSS6RkwaOBzk1Y3FPJk2a5Oj+3UpyMZJMzEkuRpKJOcklctLA8SCnZizuSWlpqaP7dyvJxUgyMSe5GEkm5iSXyMlMxh6VlZVlOpOxE0aOHOl0Ca4kuRhJJuYkFyPJxJzkEjnpwfGo4uJi14ymr6mpcboEV5JcjCQTc5KLkWRiTnKJnDRwXG7y5MmMGTPG8SulepOWluZ0Ca4kuRhJJuYkFyPJxJzkEjlp4LiY25ZkEEIIIbxCGjgu5rbLwXvS2NjodAmuJLkYSSbmJBcjycSc5BI5aeC4WEFBAenp6fj9fldcDt6T7Oxsp0twJcnFSDIxJ7kYSSbmJJfISQPHxfLz81myZAm33nqrKy4H78muXbucLsGVJBcjycSc5GIkmZiTXCInl4m7XH5+vmsbNu3GjRvndAmuJLkYSSbmJBcjycSc5BI56cHpgxsX23SbDRs2OF2CK0kuRpKJOcnFSDIxJ7lETho4fXB6sc3Zs2e7fnG16dOnO12CK0kuRpKJOcnFSDIxJ7lETho4LhcIBNi5c6erLxF3y4SDbiO5GEkm5iQXI8nEnOQSOWnguJhX5sFxew+TUyQXI8nEnORiJJmYk1wiJw0cF/PKPDjyicKc5GIkmZiTXIwkE3OSS+SkgeNiBQUFKKUAXD0PjnyiMCe5GEkm5iQXI8nEnOQSOWnguFh+fj4TJ04kLy/P1fPgrFq1yukSXElyMZJMzEkuRpKJOcklcjIPjstlZWWRlZXl2sYNwNSpU50uwZUkFyPJxJzkYiSZmJNcIic9OC5XXFzs+nOuGzdudLoEV5JcjCQTc5KLkWRiTnKJnDRwRNTGjh3rdAmuJLkYSSbmJBcjycSc5BI5aeCIqFVUVDhdgitJLkaSiTnJxUgyMSe5RE4aOC4wefJkxowZ49p5bvqSlZXldAmuJLkYSSbmJBcjycSc5BI5aeA4zCuT+fUmGAw6XYIrSS5Gkok5ycVIMjEnuUROGjgO88pkfr0Jh8NOl+BKkouRZGJOcjGSTMxJLpGTBo7DvDKZX28yMjKcLsGVJBcjycSc5GIkmZiTXCInDRyH5efns2zZMm6//XZXT+bXm6qqKqdLcCXJxUgyMSe5GEkm5iSXyMlEfy6Qn5/vyYZNu7y8PKdLcCXJxUgyMSe5GEkm5iSXyMVtD45SaohSarlSKqCUmtbtvu8ppXY7VVu82bx5s9MluJLkYiSZmJNcjCQTc5JL5OK2gQPUAycDizrfqJTyAWcBXzpRVDw66KCDnC7BlSQXI8nEnORiJJmYk1wiF7cNHK11UGtt1ktzDq2NHhmKbpGVK1c6XYIrSS5Gkok5ycVIMjEnuUTOFQ0cpdQVSqmPlVJNSqkF3e7LUUq9pJSqU0ptVUqdE8V+/MB3gBeiLFl0MmvWLKdLcCXJxUgyMSe5GEkm5iSXyLmigQPsAH4HPGly38NAMzASOBd4RCk1FUApNUop9Z7J16ge9vN94O9aa+m9sZDbFwN1iuRiJJmYk1yMJBNzkkvkXHEVldb6nwBKqa8CHSuJKaUygTOBaVrrAPCeUuoV4DzgOq31TmBOP3Z1MHCoUur7wESl1ANa6592f5BSqrCH58/sx74SxuzZs50uwZUkFyPJxJzkYiSZmJNcIueWHpyeTAJCWusNnW5bBUyN5MlKqdeArwF/VkpdoLW+Vmv9Na3114ESs8ZNXxobG6moqKCsrIzt27dTXV3Npk2baGhoYO3atYTDYVasWAHsa2mvWLGCcDjM2rVraWhoYNOmTVRXV7N9+3bKysqoqKhgy5YtBAIB1q1bR0tLC6tWreqyjfZ/V69eTVNTEyUlJdTW1lJaWkp5eTnl5eWUlpZSW1tLSUkJTU1NrF692nQbq1atoqWlhXXr1hEIBNiyZUtUr2n58uVx95qs+Dl99NFHcfeaov05vfnmm3H3mqz4Ob3xxhtx95qi/TmtWLEi7l6TFT+nzrlY9Zq0hmBz0JPHXm9U+zIBbqCU+h0wVmt9Qdv3xwAvaq1HdXrMRcC5WusCB+ornDt37lwvLqcQS+FwGJ/P7W1l+0kuRpKJOcnFSDIxF4tcdpx5JcGtO9j/479bul07FBQUsHTp0qVmbQK3Hz0BYHC32wYDex2oRfRg3bp1TpfgSpKLkWRiTnIxkkzMSS6Rc8UYnF5sAJKUUhO11iVtt80A1thVQNtVW+1Xbk0vLy+3a9eeMX78eKdLcCXJxUgyMSe5GEkm5iSXyLmiB0cplaSUSgP8gF8plaaUStJa1wH/BG5RSmUqpY4GTgeesas2rfVzWutTtNanAKtHjBhh1649Y8eOHU6X4EqSi5FkYk5yMZJMzEkukXNFAwf4DdAAXEfrpdwNbbcBXA6kA+XA88BlWmvbenBE33JycpwuwZUkFyPJxJzkYiSZmJNcIueKBo7W+matter2dXPbfVVa629qrTO11uO01s85XK7opr6+3ukSXElyMZJMzEkuRpKJOcklcm4fg+M4GYPTN7nSwZzkYiSZmJNcjCQTc5JL5Fx1mbjbKaW2DRkyZMzMmTOdLsVVgsEgycnJTpfhOpKLkWRiTnIxkkzMxSKX5jUbCTc2kzb7YEu3a4eVK1eyZ8+e7Vrrsd3vkwZOPyilPgGGAxudrsVFZrb9u9LBGtxoZtu/Kx2swW1mtv270sEa3Ghm278rHazBbWa2/bvSwRrcaGbbvysdrMFtJgC7tdaHdr9DGjgiKu3LWjgx8aKbSS5Gkok5ycVIMjEnufSPnMwTQgghRNyRBo4QQggh4o40cIQQQggRd6SBI4QQQoi4Iw0cIYQQQsQduYpKCCGEEHFHenCEEEIIEXekgSOEEEKIuCMNHCGEEELEHWngiJhTSo1USr2vlFqqlHpLKTXa6ZqcopS6TSn1rlJqkVIqw+l63ECOj54ppQqUUkuUUm8rpb7ldD1OU0r5lVLPtuXxpFIqYReMVkoNUUotV0oFlFLT2m7LV0oVtb2XnldKJfRiXtLAEXaoAOZorecCC4EfOlyPI9p+CX1Fa30M8CZwocMluYUcHyaUUmnA1cA3tNbztNYvOV2TC3wL+EJrPQ9YB5zhcD1OqgdOBhZ1um0rcFzbe+kL4HQnCnMLaeCImNNah7TW4bZvBwFrnKzHQccAr7f9/3VgjoO1uIYcHz06CmgA/q2UekkpNcrpglzgK+xbaHIFre+phKS1Dmqtd3e7bYfWuqHt2xYgbHxm4pAGjuhCKXWFUupjpVSTUmpBt/ty2n7R1imltiqlzunHdmcqpT4ErqD1F5NnRZHRUGBP2//3ADk2lWyLaI6deDo+uosil5G0rpR8KvBn4Gbbio6xKDJZCxzX9v/jaX1PeVosfucqpcYD3wAWx6Bkz0jY85eiRzuA3wEnAund7nsYaKb1F+9M4FWl1Cqt9Zq2T5eLMDpLa71Ta70SOEIp9R3geuDSGNVvhwFlBFQDQ9oeNwSosqVa+ww0F+Ls+OhuoLnUAMu01s1KqSXAdbZVHHsDzWQxUKCUeovWnr5dtlUcOwN+35hRSg0GngbO01o3x6Rij5AeHNGF1vqfWuuXgcrOtyulMoEzgRu01gGt9XvAK8B5bc/bqbWeY/K1UymV2mlTe2g9d+xZA80IeI/WX2K0/bvMnortMdBc4u346C6K42U5MEUppWj947bJtqJjLIrfM1prfbXW+ri25/7L3sqtF8XxYdA26Pp54Gat9frYVe0N0oMjIjUJCGmtN3S6bRUwN4LnzlJK/R4IAY3E7+DaXjPSWq9u62Z+FygHznegRif0dewkyvHRXV/HS6VS6iVgKa1jKRIhl14zaesp/hutx8oSrfU79pdomz5/5yqlXqO18TtZKfUYrbkcAdyolLoReERr/YJ9JbuLNHBEpLLYN36k3R5aB4X2SmtdBBwbi6Jcps+MtNbX21qRO/SaSwIdH91Fcrw8TOtpikTR17GyEyiwuSanRHJ8nGTyvGdiWZSXyCkqEakAMLjbbYOBvQ7U4laSkTnJxZzkYiSZ7CNZREkaOCJSG4AkpdTETrfNQC7p7UwyMie5mJNcjCSTfSSLKEkDR3ShlEpqm2DMD/iVUmlKqSStdR3wT+AWpVSmUupoWieRSrjuUMnInORiTnIxkkz2kSxiSGstX/LV8UXrXBu629fNbfflAC8DdUApcI7T9UpG7vmSXCQXyUSycNOXagtRCCGEECJuyCkqIYQQQsQdaeAIIYQQIu5IA0cIIYQQcUcaOEIIIYSIO9LAEUIIIUTckQaOEEIIIeKONHCEEEIIEXekgSOEEEKIuCMNHCGEEELEHWngCCGEECLuSANHCOFZSqmhSimtlAp0+/pZt8flKaW2RbjN/yqlbjG5/XSl1E6lVJJV9QshYkcaOEIIL5sJVGmts7p9/bHb404C/hPhNhcA5ymlVLfbzwOe1Vq3RFWxEMIW0sARQnjZTGBtBI87CXit8w1tvTr/UErtVkptVkr9tO2ul2ldxfmYTo8dCpwCLLSiaCFE7EkDRwjhZYfSRwNHKZUMHAu80ek2H/BvYBUwBpgPXKmUOlFr3QD8HTi/02a+A6zTWq+ytnwhRKxIA0cI4WUzaT2dVNPp6w/dHnMssEprvbfTbYcBw7XWt2itm7XWXwB/Br7bdv/TwLeVUult35/fdpsQwiNksJwQwpOUUqnAFCBfa/1xLw81nJ4C9gfylFI1nW7zA+8CaK3fU0rtBk5XSi2ntUF0hlW1CyFiTxo4QgivmgZoYHUfjzsJ+Fa3274ENmutJ/byvIW09txMBv6ntd410EKFEPaTU1RCCK86FFijtW7q6QFKqfFAqtZ6Xbe7lgO1SqlrlVLpSim/UmqaUuqwTo9ZCBwPXIScnhLCc6SBI4TwqpnA9G7z3+xVSg3p9JiTMZ6eQmsdAk5t28ZmoAJ4AhjS6TFbgPeBTOCVGL0GIUSMKK210zUIIURMKKVeAx7SWhsaOUKI+CY9OEKIeFYIvO10EUII+0kPjhBCCCHijvTgCCGEECLuSANHCCGEEHFHGjhCCCGEiDvSwBFCCCFE3JEGjhBCCCHijjRwhBBCCBF3/h8AmJXv96H21AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.errorbar(\n",
    "    sed.x,\n",
    "    sed.y,\n",
    "    yerr=sed.get_error(),\n",
    "    marker=\".\",\n",
    "    ls=\"\",\n",
    "    color=\"k\",\n",
    "    label=\"Abdo et al. (2011)\",\n",
    ")\n",
    "ax.loglog(E, ssc_model(E), ls=\"-\", color=\"crimson\", label=\"SSC model\")\n",
    "ax.set_ylabel(sed_y_label)\n",
    "ax.set_xlabel(r\"$E\\,/\\,{\\rm eV}$\")\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit",
   "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": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}