{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial: fitting a FSRQ 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 `ExternalComptonModel` to fit the broad-band SED of PKS1510-089, measured during its gamma-ray flaring activity in 2015 [(Ahnen et al. 2017)](https://ui.adsabs.harvard.edu/abs/2017A%26A...603A..29A/abstract). We select the MWL SED corresponding to the period identified in the paper as \"Period B\" (MJD 57164-57166).\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",
    "from astropy.constants import c, G, M_sun\n",
    "import matplotlib.pyplot as plt\n",
    "import pkg_resources\n",
    "\n",
    "# import agnpy classes\n",
    "from agnpy.spectra import LogParabola\n",
    "from agnpy.fit import ExternalComptonModel, 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 `ExternalComptonModel` wraps the `agnpy` functions to compute synchrotron, SSC, and EC radiation. It returns a `model.RegriddableModel1D`. To initialise the model, the electron distribution and a list of targets for EC has to be specified, the remaining parameters (the ones of the emission region and of the line / thermal emitters) will be initialised automatically and can be modified at a later stage.\n",
    "\n",
    "The `ExternalComptonModel` class provides both the `sherpa` and `gammapy` wrappers. You should specify, through the `backend` argument, which package you want to use.\n",
    "\n",
    "In this case we choose to consider only EC on the DT radiation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# electron energy distribution\n",
    "n_e = LogParabola(\n",
    "    k=1 * u.Unit(\"cm-3\"), p=2.0, q=0.2, gamma_0=1e2, gamma_min=1, gamma_max=3e4\n",
    ")\n",
    "\n",
    "# initialise the sherpa model, consider only EC on the DT radiation\n",
    "ec_model = ExternalComptonModel(n_e, [\"dt\"], ssa=True, 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.\n",
    "\n",
    "The parameters of the accretion disk and the DT are taken from [(Ahnen et al. 2017)](https://ui.adsabs.harvard.edu/abs/2017A%26A...603A..29A/abstract)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = 0.361\n",
    "\n",
    "# - blob parameters\n",
    "Gamma = 20\n",
    "delta_D = 25\n",
    "Beta = np.sqrt(1 - 1 / np.power(Gamma, 2))  # jet relativistic speed\n",
    "mu_s = (1 - 1 / (Gamma * delta_D)) / Beta  # viewing angle\n",
    "B = 0.35 * u.G\n",
    "# size and location of the emission region\n",
    "t_var = 0.5 * u.d\n",
    "r = 6e17 * u.cm\n",
    "\n",
    "# - disk parameters\n",
    "L_disk = 6.7e45 * u.Unit(\"erg s-1\")  # disk luminosity\n",
    "M_BH = 5.71 * 1e7 * M_sun\n",
    "eta = 1 / 12\n",
    "m_dot = (L_disk / (eta * c ** 2)).to(\"g s-1\")\n",
    "R_g = ((G * M_BH) / c ** 2).to(\"cm\")\n",
    "R_in = 6 * R_g\n",
    "R_out = 3e4 * R_g\n",
    "\n",
    "# - DT parameters\n",
    "xi_dt = 0.6\n",
    "T_dt = 2e3 * u.K\n",
    "# Ghisellini and Tavecchio's Formula\n",
    "# relating the DT radius to the disk luminosity (check the reference)\n",
    "R_dt = 2.5 * 1e18 * np.sqrt(L_disk.to_value(\"erg s-1\") / 1e45) * u.cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# emission regions parameters\n",
    "ec_model.z = z\n",
    "ec_model.delta_D = delta_D\n",
    "ec_model.log10_B = np.log10(0.35)\n",
    "ec_model.mu_s = mu_s\n",
    "ec_model.t_var = t_var.to_value(\"s\")\n",
    "ec_model.t_var.freeze()\n",
    "ec_model.log10_r = np.log10(r.to_value(\"cm\"))\n",
    "\n",
    "# we freeze the reference energy of the log parabola\n",
    "ec_model.log10_gamma_0.freeze()\n",
    "\n",
    "# target parameters\n",
    "# - disk\n",
    "ec_model.log10_L_disk = np.log10(L_disk.to_value(\"erg s-1\"))\n",
    "ec_model.M_BH = M_BH.to_value(\"g\")\n",
    "ec_model.m_dot = m_dot.to_value(\"g s-1\")\n",
    "ec_model.R_in = R_in.to_value(\"cm\")\n",
    "ec_model.R_out = R_out.to_value(\"cm\")\n",
    "# - DT\n",
    "ec_model.xi_dt = xi_dt\n",
    "ec_model.T_dt = T_dt.to_value(\"K\")\n",
    "ec_model.R_dt = R_dt.to_value(\"cm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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;ExternalComptonRegriddableModel1D model instance &#x27;ec&#x27;&gt;</div><div hidden class=\"sherpa\"><details open><summary>Model</summary><table class=\"model\"><caption>Expression: ec</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=20>ec</th><td>log10_k</td><td><input disabled type=\"checkbox\" checked></input></td><td>0.0</td><td>-10.0</td><td>10.0</td><td></td></tr><tr><td>p</td><td><input disabled type=\"checkbox\" checked></input></td><td>2.0</td><td>1.0</td><td>5.0</td><td></td></tr><tr><td>q</td><td><input disabled type=\"checkbox\" checked></input></td><td>0.2</td><td>0.001</td><td>1.0</td><td></td></tr><tr><td>log10_gamma_0</td><td><input disabled type=\"checkbox\"></input></td><td>2.0</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>0.0</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>4.477121254719663</td><td>4.0</td><td>8.0</td><td></td></tr><tr><td>z</td><td><input disabled type=\"checkbox\"></input></td><td>0.361</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>25.0</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>-0.4559319556497244</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>43200.0</td><td>10.0</td><td>31415926.535897933</td><td>s</td></tr><tr><td>mu_s</td><td><input disabled type=\"checkbox\"></input></td><td>0.9992498439462307</td><td>0.0</td><td>1.0</td><td></td></tr><tr><td>log10_r</td><td><input disabled type=\"checkbox\"></input></td><td>17.778151250383644</td><td>16.0</td><td>22.0</td><td></td></tr><tr><td>log10_L_disk</td><td><input disabled type=\"checkbox\"></input></td><td>45.82607480270082</td><td>42.0</td><td>48.0</td><td></td></tr><tr><td>M_BH</td><td><input disabled type=\"checkbox\"></input></td><td>1.135382036168587e+41</td><td>1e+32</td><td>1e+45</td><td>g</td></tr><tr><td>m_dot</td><td><input disabled type=\"checkbox\"></input></td><td>8.945706450671092e+25</td><td>1e+24</td><td>1e+30</td><td>g s-1</td></tr><tr><td>R_in</td><td><input disabled type=\"checkbox\"></input></td><td>50589173803597.266</td><td>1000000000000.0</td><td>1e+16</td><td>cm</td></tr><tr><td>R_out</td><td><input disabled type=\"checkbox\"></input></td><td>2.5294586901798634e+17</td><td>1000000000000.0</td><td>1e+19</td><td>cm</td></tr><tr><td>xi_dt</td><td><input disabled type=\"checkbox\"></input></td><td>0.6</td><td>0.0</td><td>1.0</td><td></td></tr><tr><td>T_dt</td><td><input disabled type=\"checkbox\"></input></td><td>2000.0</td><td>100.0</td><td>10000.0</td><td>K</td></tr><tr><td>R_dt</td><td><input disabled type=\"checkbox\"></input></td><td>6.471089552772392e+18</td><td>1e+17</td><td>1e+20</td><td>cm</td></tr></tbody></table></details></div>"
      ],
      "text/plain": [
       "<ExternalComptonRegriddableModel1D model instance 'ec'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec_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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "systematics_dict = {\n",
    "    \"Fermi-LAT\": 0.10,\n",
    "    \"KVA1\": 0.05,\n",
    "    \"KVA2\": 0.05,\n",
    "    \"MAGIC\": 0.30,\n",
    "    \"Metsahovi\": 0.05,\n",
    "    \"NICS1\": 0.05,\n",
    "    \"NICS2\": 0.05,\n",
    "    \"SMART1\": 0.05,\n",
    "    \"Swift-XRT\": 0.10,\n",
    "    \"TCS1\": 0.05,\n",
    "    \"TCS2\": 0.05,\n",
    "    \"TCS3\": 0.05,\n",
    "    \"TCS4\": 0.05,\n",
    "    \"TCS5\": 0.05,\n",
    "    \"TCS6\": 0.05,\n",
    "    \"UVOT\": 0.05,\n",
    "}\n",
    "\n",
    "# min and max energy to be considered 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(\n",
    "    \"../../agnpy/data/mwl_seds/PKS1510-089_2015b.ecsv\", E_min, E_max, systematics_dict\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us take a look at the initial model and at the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x3rx5iAhKKVJSUpg3b96Arrdt2zbefPNNPvvsM0SEtrY2RIQ777wT2HXSid/v32UGUeixrsfb2tp48cUXdxuy+e9//9vjtaA9iZg2bVrUvVJnnXUWr7zyCjNnzmTRokUUFRX1+bnp6em71Z1++umn/OhHP+L111/vTD5Gjx7d2eNUX1/P5s2b+1w0/txzz+3SexOiqalpwDuT2yGqKDFhN3Hd30jBOuqCCY5ghmcsHOfMmcOECRMoKCjgjTfeGHDvzeLFiznzzDPZtGkTGzdu5KuvvmLcuHGdPRfRctRRR3Hvvfd29nh88sknfX7upEmT2Lp1a2eC09raysqVK4H2v0u1tbURn1dbW8vIkSNpbW3l6aefjireKVOm7LLLe0lJCSeccAJPPfUUEydO7Dy+//77s3btWjZs2EBycjLPPfccxx57bK/XDwaDvPDCC5x66qm7HFdKUV5ezp577hlVvOHYBCdKTKjB6TrGrCvWUQ9McAQzPGPlmJWVRX5+/oCTG2gfnjr++ON3OXbiiSfyzDPP9Ot6V1xxBa2treyzzz5Mnz6dG2+8sc/PTUlJYfHixVx99dXMnDmTWbNm8cEHHwDtvTQXXHBBxCLjn//85xx44IEcccQR3RZyP/jggzz44IO7HZ88eTI7duzoTJ5uvfVWtm3bxkUXXcSsWbPYb7/9AEhKSuK+++7jyCOPZPLkyZxyyilMmzYNaJ9iP3r0aJYsWcLRRx+9yxDZO++8w+jRo9lrr712uW9xcTEHHXQQSUkDG2QSL+0b4XVEpGju3Llzo+niS0SCwaCnVqN0AuuoByY4ghmekRw///xzpkyZEtV1Qj1BxcXFMYstViil+lyb4hXuuususrOzO9fC6Y1YOF522WUce+yxzJ8/f7fHwl8T8+bN4+23335bKTUv/Fy9/8c4gAlFxl0Xs9IV66gHJjiCGZ6xciwuLvZkcgOJuY7ahRdeGNUCt7FwnD59esTkJlpsghMlaWlpbofgOOPGjXM7BMexjnpggiOY4WmCYyKuhJ+WlsYZZ5zR5/Nj4XjuuecO+BpgE5yocWolSi/h1IqYXsI66oEJjmCGZ3eOOpVRtLS0uB2C4zjpGO1rwSY4UZKcnOx2CI6Tm5vrdgiOYx31wARHMMMzkmNaWhrbtm3TJskZaNFsIuCUo1KKbdu2RTWKov+/9gAJX+ivoqLCzXDiQkNDQ+dy3LpiHfXABEcwwzOS4+jRo9m8eTNbt251KarYEggEtE9ynHRMS0vrXFixL+j9Lx0Dwhf6Gz58uPYL/ek+WwOsoy6Y4AhmeEZyTE5O1qo2p6ysrE+rDycyXnLU/39NjEm0KX79wYRhOOuoByY4ghme1lEPvORoE5woaWtrczsExzFhMUPrqAcmOIIZntZRD7zkaBOcKPFSduoUw4YNczsEx7GOemCCI5jhaR31wEuONsGJEhOmiW/evNntEBzHOuqBCY5ghqd11AMvOdoEJ0oGurtpIjB+/Hi3Q3Ac66gHJjiCGZ7WUQ+85GgTnCipr693OwTHCe1QqzPWUQ9McAQzPK2jHnjJ0W62GQWmbLZpsVgsFksiYDfbjCGhbeN1xqsb1cUS66gHJjiCGZ7WUQ+85Gh7cKLA9uBYLBaLxeIdbA9ODLE9OHpgHfXABEcww9M66oGXHG0PTi+E7UU1Z8qUKbmrVq1yMySLxWKxWCzYHpwBoZR6Ril1jFLqGGBFZmam2yE5zooVK9wOwXGsox6Y4AhmeFpHPfCSo01wosSEdXAmTpzodgiOYx31wARHMMPTOuqBlxxtghMlJqxkXFJS4nYIjmMd9cAERzDD0zrqgZccbYITJSkpKW6H4Dh5eXluh+A41lEPTHAEMzytox54ydEmOFESCATcDsFxampq3A7BcayjHpjgCGZ4Wkc98JKjTXCixOfT/58sLS3N7RAcxzrqgQmOYIanddQDLznq/9faYrFYLBaLcdgEJ0qCwaDbIThOU1OT2yE4jnXUAxMcwQxP66gHXnK0CU6UJCUluR2C4wwePNjtEBzHOuqBCY5ghqd11AMvOdoEJ0paWlrcDsFxKioq3A7BcayjHpjgCGZ4Wkc98JKjTXCiJDU11e0QHGfMmDFuh+A41lEPTHAEMzytox54ydEmOFHS2NjodgiOs2bNGrdDcBzrqAcmOIIZntZRD7zkaDfbjAIRKZo7d+7coqIit0OxWCwWi8V47GabMaS2ttbtEBzHS9vdO4V11AMTHMEMT+uoB15ytD04UWB7cCwWi8Vi8Q62B6cDEfGLyNMi8paIPC4iUc/5tj04emAd9cAERzDD0zrqgZccjerBEZGTgJlKqRtF5Cpgo1LqT1E83/bgWCwWi8XiEWwPzv/YG1jW8fNS4LBoL1BXVxfLeDzJ8uXL3Q7BcayjHpjgCGZ4Wkc98JKjZxMcEblYRD4WkWYRWRT2WK6IvCwi9SKySURO6+NlVwFf7/j5G8CQaOPKzMyM9ikJx7Rp09wOwXGsox6Y4AhmeFpHPfCSo2cTHKAUuA14PMJj9wMtQB5wOvCAiEwDEJF8EXkvwlc+8BrQJCJvAplA1EsumrAOzrp169wOwXGsox6Y4AhmeFpHPfCSo2c3VlJKvQQgIvsBo0PHRSQTOBGYrpSqA94TkVeBM4BrlFLlwKE9XPqnHddZALwZ6QQRKermubNMWMl49OjRvZ+U4FhHPTDBEczwtI564CVHL/fgdMdEoE0p1XW5xOVAr/1iHb07RSLyBtCilHon2ps3NDRQVVVFWVkZW7ZsYfv27axfv57GxkZWrVpFMBhk6dKlwP+qyZcuXUowGGTVqlU0Njayfv16tm/fzpYtWygrK6OqqoqNGzdSV1fH6tWrCQQCneOYoWuEvq9YsYLm5mbWrl3Lzp07KSkpobKyksrKSkpKSti5cydr166lubmZFStWRLzG8uXLCQQCrF69mrq6OjZu3LiLU0lJiXZO4e20detW7ZzC26mqqko7p/B2Ki0t1c4pUjtt3LhRO6fwdqqoqNDOKbydqqqqtHMKb6fy8vK4OvWE52dRichtwGil1Fkdvx8GvKCUyu9yzrnA6ZGqqGMcS9HBBx889/3333fyNq5TVVXFsGHD3A7DUayjHpjgCGZ4Wkc9iLejbrOo6oCcsGM5QFwWqPF6QhgLWltb3Q7BcayjHpjgCGZ4Wkc98JKjZ2twemANkCQiE5RSazuOzQRWOnGzjhlaoVlaM7Zu3erEbTxFMBh0OwTHsY56YIIjmOFpHfXAS46e7cERkSQRSQP8gF9E0kQkSSlVD7wE3CoimSJyCHAc8JQTcSilnlFKHaOUOgZYkZeX58RtPEVGRobbITiOddQDExzBDE/rqAdecvRsggPcADQC1wDf7/j5ho7HLgLSgUrgWeBCpZQjPTjheKn7zSmqq6vdDsFxrKMemOAIZnhaRz3wkqNnh6iUUguABd08Vg18N47hdGLCNPGCggK3Q3Ac66gHJjiCGZ6J4FhYWAj0vt9Sd+clguNA8ZKjl3twPIGInCYir4nIa8CMLVu2uB2S42zYsMHtEBzHOuqBCY5ghqd11AMvOdoEpxfCa3D22GMPt0NynMmTJ7sdguNYRz0wwRHM8LSOeuAlR5vgRIkJm20uW7bM7RAcxzrqgQmOYIZnIjjW1dVRXl7OkiVL+vX8RHAcKF5ytAlOlGRlZbkdguPMnj3b7RAcxzrqgQmOYIan1x2XLFnC2rVrKS0tZf78+T0mOd0lQl53jAVecrQJTi+E1+Bs2rTJ7ZAcp7cCOh2wjnpggiOY4el1x6Kios6FXltaWigqKop4Xk+JkNcdY4GXHG2C0wvhNThjx451OyTHCc0A0BnrqAcmOIIZnl53nDdvHiICQEpKCvPmzYt4Xk+JkNcdY4GXHG2CEyUm1OCENmPTGeuoByY4ghmeXnecM2cOEyZMoKCggDfeeIM5c+ZEPK+nRCiWjkopWkvKqHv5DXY89hJt1Ttidu2B4KV29Pxmm15CRIrmzp07t7uuSV0IBoP4fHrnvtZRD0xwBDM83XQc6Po24UyaNIm6ujoWL168SyIUraNqa6OteidtW6tpq9pOW1UNgU2lNC39nObiVbRt/d+iepKRRs4Z32Hwhf9H0ij3VtyPdzv2tNmmZxf68yoNDQ1uh+A4q1evZurUqW6H4SjWUQ9McAQzPHVyzMrKIisra7denr44qmCQxrc+Yscji2ko+gja2nY7x5edSeq+k0n9/jGkFU4Fn48dDzzHjodeYMdjL5F90jfJ+eF3kfQ0VHMLqqkF1dwCwSD+EbkkjRyOb0hOZ09TLPFSO9oEpxfCN9vcuXOnm+HEhXHjxrkdguNYRz0wwRHM8DTdMVjXQO1zr7Pj0RdpXf8VkpZC1rHzSBpbgH/YEPzD27+SRg4nea/RSFgvSeYRc2hauoqae56h9vm/U/vc6z3GIqkp+POHkjJhLMPu/CnJe+Q77hhvbILTC0qpZ4BnoH2IatCgQXNdDslxSktL2Xvvvd0Ow1Gsox6Y4AhmeLrpWFdXR11dHUuWLOm2tgYGPkOoq6NSisDGUhrfK6bxvU9o+NcSgrX1JI0aQe6NF5Dz/WPw5w6K6vqHnHsGAEvee5GGN/+L+P1IWgqSkoKkpgAQqNhGW/lWAuVVBMqqaHj7Y0qPvZiCl35H8rhRA/ILd3Qbm+BESXJystshOE5ubq7bITiOddQDExzBDE+3HEPTupVSzJ8/v8cC4oGSm5tL438/pfaPr9H43lICmyvaH0hJJn3OTHJ+cByZRx2KJA3sT3PKxD1Jmbhnn86t/+cHVJx9I1uOu4SCl+4mZfyYAd3bS69VvavWHKAtwniobphQZ2Qd9cAERzDD0y3Hvq5vEw3FxcW79fa0rN1Ezfk/p/SYH1P7wj/xjxzO4J+cycgX72LcutcpWHwXWd+ZN+DkJloyv3kw+U/9guD2HZQeewktXwxsLykvvVZtD06UOFGU5TV0n60B1lEXTHAEMzzdcgxN61ZK9bi+TX8JVGxj+6+fYOdTrwGKnB8cx5CfnUVS/rCY3mcgZHztAEY+92vKTruaLd+9lILFd5E6bXy/ruWl16pNcHohvMi4qqrKzXDiggnDcNZRD0xwBDM83XIMrW8TaVp3f1HBIE0ffkbdS/+m9vm/oxoayTjqUHw/PoXhB+4bg6hjT/oh+zLyT7+m7NQrKT3+svYkZ5+JUV/HS69V76RaHiV8JWMvjS86hQmLGVpHPTDBEczwdNMxKyuL/Pz8ASU3Simal39B1YL7KZl9MqXf+TE7n3iZ1JkTKXj1PkY++Qsa84bEMOrYk37gPhQs/i0E2ig96Sc0r1gb9TW89Fq1PThR4qXs1CmGDfNO16lTWEc9MMERzPBMVEcVCFD/l7fZft8ztHy6BoCUGRPIPecEsr47f5fp14ngmFY4jZEv/Iayk39K6YmXU/DS70id3vfhKi852h6cKGlubnY7BMfZvHmz2yE4jnXUAxMcwQzPRHMMNjaz4/GXKZlzOhXnLSCwuYIhV/yAPZY8zR5vPs6QS07fbW2Z/joWFhbGdY+ntMJpjPzTb1CtAUpPvJzmlev6/FwvtaPtwYmS9PR0t0NwnPHj+1dclkhYRz0wwRHM8HTTMdr1bWpf+AdVN91HsKqGpD3yGbbwMrJPOxpfZs9/HxKpHdP2m0bBn35D6clX/K8nZ2rv69t4ydH24ERJfX292yE4zsqVK90OwXGsox6Y4AhmeCaKY83vn6PyotvwD8pmxIM3MebDZxl07km9JjeQOI4h0vafzsjnf41qaqH0hMtoWVfS63O85GgTnCjJyspyOwTHmTlzptshOI511AMTHMEMT687KqXY9vMH2Xbz/aTtP51Rf3+I7BOPiGrdGq87RiL9wH0Y+dyvUfWNlJ36MwKV1T2e7yVHm+D0goicJiKvichrwIxNmza5HZLjDHQ58kTAOuqBCY5ghqeXHVVbG1t/+itq7nmajPkHMfKF3+IfnB31dbzs2BPpB+3DiAdvJlBSTvn3ryFY39jtuV5ytAlOL4RPEx87dqzbITlOPIvZ3MI66oEJjmCGp1cdVXMLFecuoPapv5B14hHkP/WLPg1HRcKrjn0h6+jDGXb7pTR/8jkV59+CCgQinuclR5vgREltba3bITiOlzJwp7COemCCI5jh6TXH1g1bqP7Fo2w64HvU/6WInLNPYMTvb0CS+z83x2uO0TLo3JMYdNGpNPzjfaqu+13nFhdd8ZKjnUUVJdnZ0XdLJhpeysCdwjrqgQmOYIanFxyDjc3U/flNap/9G00fLAMgZepe5P7sB2R//zsD3qrHace+7oo+EIbefCGBLZXsfOIVkvbIZ8glp+/yuBfaMYTtwYkSE2ZRrVixwu0QHMc66oEJjmCGp9uOTctWs/lrP2TrJQtpWbmOnLNPYPS/H2V00SJyzjg2JvsQOukY2hW9tLSU+fPns2TJEkfuIz4fI+67jrSDZlJ964M0vPXhLo+73Y5dsQlOlJiwDs7EidHvP5JoWEc9MMERzPB0y1G1tbH9nqfZctQFBCq2Mfw3VzL2s1cY/sufkDpzUkw3WA53jOUCfk7sit4dvrRU8hfdhm/YYLb+9FcE6/63g7iXXqs2wYkSE1YyLinpfa2DRMc66oEJjmCGpxuOgS0VlJ74E6p//iCp+05hj7eeIOfMY/GlpTpyPycdQ7uiA47sih6Of+hghi+8nMBX5VQvfKTzuJdeqzbBiZKUlBS3Q3CcvLw8t0NwHOuoByY4ghme8Xas/+f7fDXvhzQtWc6QK3/IqFfvJXnPAkfv2V/Huro6ysvLexx2Cu2KXlBQwBtvvOFYDU5XMr/7dTK+dSg7Hn2Rpg/bh6a89Fq1CU6UBLqZGqcTNTU1bofgONZRD0xwBDM84+lY+8I/KD/zenxZGRS8ei+5V50d1YJ9/aU/jtHU1sRiV/RoEBGG33kFvqwMKi//JcGmZk+9Vm2C0wvhC/1t27bN7ZAcJy0tze0QHMc66oEJjmCGZ7wcdzzxMpUX3Uby3qMZ9bcHSD9wn7jcF/rnGM/amv6QNHI4QxdcROvaTdTc9ZSnXqs2wemF8IX+hg8f7nZIFovFYukH2+95mqqrfkvqzEmMevU+kkZ6//083rU1/SH7+8eQdsi+bL/nj7St3uh2OJ3YBCdKgsGg2yE4TlNTk9shOI511AMTHMEMTycdlVJsu/1hqn/+IGkH7sPIl+7GP3SwY/frjv44ulFbEy3i8zHit1chSX4ab7iv21WO441NcKIkKQ7jtG4zePBgt0NwHOuoByY4ghmeTjkGyrZSef4t1Nz9FOlfO4CRf/oN/hx3Nk3ur2O8a2v6Q/Jeoxly+ZmozzfQ9OFnbocD2AQnalpaWtwOwXEqKircDsFxrKMemOAIZnjG2jHY2Mz23z5JyUGnUffyG2R/79uMfOoX+DLcqxEJd+zL7KhEInXfyQC0VW13OZJ29O+OiDGpqc6sj+AlxowZ43YIjmMd9cAERzDDM1aOSinqXy1i2y2/J/BVOamzpzDs9stI229aTK4/ELo6hmZHKaWYP3++Z4efosE3JAeAYI039my0PThR0tjY/TbxurBmzRq3Q3Ac66gHJjiCGZ6xcAzWN1L+g+uo+NFNqJZWRtx3PaNef9ATyQ3s6uj12VH9wT+4PcFp277T5UjasT04UZKZmel2CI4zY8YMt0NwHOuoByY4ghmeA3UMVFZT/v1raP7kc3LOOYGhN5yPLysjRtHFhq6OodlRSinPzo6Klv/14HgjwbE9OFFSW+uNrjcn8dJ2905hHfXABEcww3Mgji3rS9jy7QtoXraaYQsvY/gdP/FccgO7OibC7Kho8eVkokRo88gQle3BiZLs7Gy3Q3AcL2137xTWUQ9McAQzPPvr2PThCsrOuBbV0EjeE7eRdfThMY4sdoQ7ZmVlkZWVpUVyA+3Txf2Dswlu90aCY3twosT24OiBddQDExzBDM/+ONa99jalJ14OQMFLv/N0cgNmtGNrWgptdogqMbE9OHpgHfXABEcwwzNaxx1/+DMVZ9+IP384o/72AGn7T3costhhQjtm5A+3NTiJSl1dndshOM7y5cvdDsFxrKMemOAIZnj21VEpRfWvn6DqZ78mZfp4Rv3196TsvYfD0cUGE9qx3g9tHhmisjU4vSAipwGndfw6o76+3s1w4sK0ad6YUukk1lEPTHAEMzz74qja2qi69nfsfOJl0g+bTf4fFuLLTpyZrSa0Y84eI2lctcHtMADbg9Mr4ZttmjBEtW7dOrdDcBzrqAcmOIIZnr05quYWKs67hZ1PvEzmsV9j5LO/SqjkBsxox1pRqIYmgk3NbodiE5xoMWEl49GjR7sdguNYRz0wwRHM8OzJMVhbT9n3rqT+1bfI+eHx5D18M5KaEsfoYoMJ7ZgzKh/wxmrGNsGJktbWVrdDcJyqqiq3Q3Ac66gHJjiCGZ7dOQbKq9jynYtpfHcpQ645h2G//Ani98c5uthgQjs2JLWnFV5IcGwNTpT4E/Q/VjRkZbmz0248sY56YIIjmOEZybFlXQllp/yUwJZKhv/2KnLO+I4LkcUOE9oxbcRQGsETi/1FleCISB7wTWAmMBioAZYD/1JKlcc6OC8S2jtEZ0zopbKOemCCI5jhGe7Y9PFKyk6/GtXYRP6TC8k88hCXIosdJrRjMCu9/bsHpor3aYhKRKaIyGJgFXAGkAyUd3w/A1gpIotFZKpjkXoEExKcYDDodgiOYx31wARHMMOzq2P9Pz+g9ITLQCkKXrxbi+QGzGhH1bFFhhc23OxrD84i4FfA6Uqp3UqjRSQFOA54DNBjzeluMGGIKiPDe3u4xBrrqAcmOIIZniHHulffouL8W0jKH8bIP/2GlAljXY4sdpjQjul5w2ghgXpwlFIHKqUWR0puOh5vUUq9oJTSOrkBM7oYq6ur3Q7BcayjHpjgCGZ4VldXU7v4n1Scu4DkMSMZ9dr9WiU3YEY77lABwBuL/dki4ygxYZp4QUGB2yE4jnXUAxMcwQzPQe8sp/Lqu0meOJaCxXeRlD/M7ZBiTn/bMZH2sBo5aTxlJFAPTjgicmasA0kUmpqa3A7BcTZs8MYqlE5iHfXABEfQ33PHYy+x46q7SJm6N6NeuUfL5AZ2b8fi4uKESl76wsYtm5HMdIJer8HppmhYgPOBJx2JyOOYMIY6efJkt0NwHOuoByY4gr6eSilq7nma6tseImXWZAr+9Bv8Q3LcDssxdG3HrkyePJmvhuR4Ypp4bz04/wF+BlzZ5etngF4Do1Fgwmaby5YtczsEx7GOemCCI+jpGWxqpvLihVTf9hBpB+7D1pvO0jq5AT3bMZxly5bhG5zj/R4c4HPgSqXUtq4HReSvzoXkbUxYqGn27Nluh+A41lEPTHAE/TwD5VWUn3U9zcWryD71KIb/+meMSsCtF6JFt3aMxOzZsykdkk3rV+4vjddbD84RtC/mtwtKqaMdiSYBqK11v9vNaXQbE46EddQDExxBL8+mZavZ/M3zaP5kNUN/fjHD77kWSU3RyrE7THH0Sg9OjwmOUmqnUqot9LuIjHA+JG9jwm7ihYWFbofgONZRD0xwBH086157m9Lv/BhV38jIZ+5k8AX/h4gA+jj2hCmO/iE5BHfUodraen+Cg0Q7i2qxI1E4gIgMEpEPRaRORKZ3OX67iLzbsfJy1BXDJtTgLF261O0QHMc66oEJjqCHZ8v6Eiov+jn+/OGM+sdDZMw/cJfHdXDsDVMcfYPaOwKCO9z9exltgiOOROEMDcDRdEnKOhKdvZVShwH/Bs6O9qIm1ODMmjXL7RAcxzrqgQmOkPieKhCg8se3o9qC5C+6jZTxY3Y7J9Ed+4Ipjr4h7QmO29s1RJvgJMxGTEqpVqXU1rDDhwGvd/z8OnBotNdtaGgYaGieZ/Xq1W6H4DjWUQ9McITE96y55xmai1eRe805pE4bH/GcRHfsC6Y4+ge3z4Zze7G/fi30F2tE5GIR+VhEmkVkUdhjuSLysojUi8gmETltALcaAuzo+HkHkBvtBdLS0gZw+8Rg3LhxbofgONZRD0xwhMT2bF7+BdW/epy0A2Yw+KJTuz0vkR37iimOvo7p/m5v1+CVIapS4Dbg8QiP3Q+0AHnA6cADIjINQETyReS9CF/53dxnOzCo4+dBQNQbgzQ3R9yOSytKS0vdDsFxrKMemOAIiesZbGqm4se3ISkpjLjveqSHzYoT1TEaTHEMrWfkdg9OtHtRneREEEqplwBEZD9gdOi4iGQCJwLTlVJ1wHsi8ipwBnCNUqqc6IaZ3gOuBf4AHAm8H+kkESnq5vmzkpOTo7hdYpKbG3XHVsJhHfXABEdIXM/qXzxK6xcbGf6bK0keN6rHcxPVMRpMcfQ1tVezBF1ezTiqHhylVIWIfE1ExgGIyEgR+YOIPN5Dr8lAmAi0KaXWdDm2HJjWlyeLyN+AbwKPiMhZSqkVwCYReZf2BCdSj1GPNDU1UVVVRVlZGVu2bGH79u2sX7+exsZGVq1aRTAY7KyUD615sHTpUoLBIKtWraKxsZH169ezfft2tmzZQllZGVVVVWzcuJG6ujpWr15NIBBg+fLlu1wj9H3FihU0Nzezdu1adu7cSUlJCZWVlVRWVlJSUsLOnTtZu3Ytzc3NrFixIuI1li9fTiAQYPXq1dTV1bFx48ZdnCorK7VzCm+n+vp67ZzC26mhoUE7p/B2qqmp0c4pUjtVVFQknNPyRX9ixwPP03bQdJJO+kav7VRbW+t5p4H+f2poaHDU6cUXX+Qf//iHq3+fduzYQUlN+9rA1Ru/crydekKUiq5uWEQ+B45USpWIyDMdhxuB4UqpY6O62O7Xvg0YrZQ6q+P3w4AXlFL5Xc45FzhdKTVvIPfqZ3xFc+bMmfvBBx/E+9ZxpaysjJEjR7odhqNYRz0wwRESz7P+X0uovPBW8PnY450/9GnzzERz7A+mOOYNGsKGsUcw6LyTGXb7pY7eb968ebz99ttvR8oJoh2iAhjVkdwk0d4LMpb2GhknBhfrgPDNSXIA1/q9QotS6YwJw3DWUQ9McITE8VTBINt/8we2/+oJ/COHk7/otj7vDJ4ojgPBFEdJT0VSU2hLwFlUO0UkD5gLrOqojQFwouXWAEkiMqHLsZnASgfuFREROU1EXhOR14AZFRUV8bq1a5iwmKF11AMTHCExPNt21FJ+xrVsv/Nx0g7Zl9H/fpS0faf0+fmJ4DhQTHEUEXyDslzfrqE/PTj3Ah8BKcDlHccOAfo9wb+jNygJ8AN+EUkDAkqpehF5CbhVRH4EzAKOAw7u772iRSn1DPBMR5xFI0eOnBuve7vFsGF9+8SVyFhHPTDBEbzv2bxqPeU/uJ7Axi0M+vGpDL3hfCQpuj8vXneMBSY5+obk0JZIRcYASqlfAt8ADlFKPddxeAvwowHEcQPtdTzXAN/v+PmGjscuAtKBSuBZ4EKlVNx6cMIxYZr45s2b3Q7BcayjHpjgCN72bPliA6XHXUJbZTV5j97KsAU/jjq5AW87xgqTHP0e2HCzPz04hM1q2u33flxvAbCgm8eqge8O5PqxJD093e0QHGf8+MgrjeqEddQDExzBu56BLRWUnvIzVEuAgpfvJm321H5fy6uOscQkR9+QHFrWl7gaiydWMvYy4TU4X331ldshOc7Kla51kMUN66gHJjiCNz3bqndQespPaavcRv4TPx9QcgPedIw1OjsWFhZSWFjY6egfnE1wey3RztSOJTbB6QWl1DNKqWOUUscAK8aM2X2TON2YOXOm2yE4jnXUAxMcwXuewfpGyk6/mtY1mxhx73VkfP3A3p/UC15zdAKTHH1DcqCtDVXf6FosPSY4IuIXkRNE5LsdhcCh4yc7H5o3qa11t2gqHoQWXNIZ66gHJjiCtzxVa4CKH91E88crGfrzS8g+6Zsxua6XHJ3CJMfQhptu7ijeWw/Ok8Bs2mcvvScioQHEC50MystkZ2e7HYLjFBYWuh2C41hHPTDBEbzjqZRi6xV30vDv/zD4ktMZfMEpMbu2VxydxCRH35D2v5VuFhr3luAUKKVu6CgC/h7wqIjMczooLxFeg7Np0ya3Q3Ickz5l6Ix11AeveNbc9RS1z71O1infIvfG82N6ba84OolJjp09OC4u9tdbgpMqIqkASqkNwHeAnwHTnQ7MK4TX4IwdO9btkBzHpE8ZOmMd9cELnnWvvEn1Lx4h7eBZjLjrqpiv6u4FR6cxydEX2lF8u3tlHb0lOFcAQ0K/KKVqaV9o7wong/Iy9fX1bofgOKFN0HTGOuqBCY7gvmfTxyupvPh2kvcaTf6i25GU2C9c77ZjPDDJ0Te4fYjKzR6cHtfBUUr9p+vvIjJCKVUJ/NHRqDyMCevgTJw40e0QHMc66oEJjuCuZ2tJGeVnXotkpJH/zJ34h4RvDxgbTGhLkxxDQ1RersEJZ7EjUSQQJqxkXFLi7uJM8cA66oEJjuCeZ9vOOspOv5q2mlryn7iNlL33cOxeJrSlSY6hImPP9uBEQP+ttMMQkdOA0zp+nVFTU+NiNPEhLy/P7RAcxzrqgQmO4I6nam6h4pybaF29geH3Xkf6Ifs6ej8T2tIkR192Jvh8nq7BCce9JQldIrzIODc31+2QHMeEJM466oEJjhB/TxUIUHHBrTQWfcSQn/6AnFOPcvyeJrSlSY7i8+EbnE1wR+IkOMbj8+n/T5aWluZ2CI5jHfXABEeIr6cKBtl6xa+of+1tcs45gSFXnxOX+5rQljo71tXVUV5evkshtX9wtqcX+gvHuCEqi8ViMQWlFNtuuo/aZ/9G1ilHMmzhZTGfDm7RjyVLlrB27VpKS0s56aSTWLJkCdA+VTzo4XVwwjnJkSgSiGAw6HYIjtPU1OR2CI7jhOOkSZMYNWpU539ut7HtqA/x8tz+m0XseOgFMr99GCN+dw0Sxx5rE9pSV8eioqLOTTVbW1spKioC2mdStSVKDY5SqsKpQLxK+ErG1dXVbofkOIMHD3Y7BMeJtWPXTzDz58/3RJJj21Ef4uFZ8/Bitv/ycdIPL2TEQzcjSdHOQRkYJrSlro7z5s3r7OlLSUlh3rx5QPtMKjd7cKJ+BYvIIOBSYF8gq+tjSqnY7LrmIZRSzwDPAIhI0eDBg+e6HJLjVFRUkJPjzFoXXiHWjl0/wbS0tFBUVMScOXNidv0QbTW1tK4rAb8PSUlGUlM6v/uH5OyyAJttR31w2rP+X0vYdsM9pBZOJf8PC/GlpTp2r+4woS11dZwzZw4TJkygrq6Ou+66q/O9zzcoG9XYTLCxGV96/F9T/UnRXwD8wMuAe/ugu0RqavwbKd6MGTPG7RAcJ9aO8+bNIz09nZaWll0+wQwE1dZGc/EqmpZ+TvOy1TR/8jmtX27u8TmSlYF/6CD8uYNJz8mkfHAOvqx0fNmZ+LIy8A3KJmXiWFKmjScpb+iAY3QbE16r4Kxny7oSKs+/Bf/I4eQ/+Qt8WRmO3asnTGhLnR2zsrLIysriuOOO6zwWWhQyuKM2YRKcg4ChSqnWWAeTCDQ26p/TrVmzhhkzZrgdhqPE2nHOnDm88cYbFBUVMW/evAH13gTKq6h9+q/s/ONfCGzuGBVOSSZ1+nhyzj6BlKl7ISKoltb2r+YWVFMLbdt30la9g2B1DW3VO2haWYa/qQVV3whq9xUe/MOHkDJtPKkzJpJ10hGkTt273zG7hQmvVXDOs21HLeXfvwbV2krBkwtJGuHeMhgmtKVpjr7Qhpvbd5KUPyzusfQnwXkPmAJ8GuNYEoLMzEy3Q3Ac3f8DgjOOc+bM6Xdio5Si8Z1idi56hfq/vweBNpInjmXoLT8m/ZB9SZmyV7/3/1HBIKqhiWBdA23bamj5/EtaVq6jeeV6Wlauo7HoI2rufZq0Q/Zl0DknkHnUoXGvv+gvJrxWwRlP1dZG5fm30rr+K0Y8eBOpMyfF/B7RYEJbmubo71jN2K3tGvrzLnYW8DcR+S+wS9GxUurWWATlZWpr3asIjxfFxcXa73rrFUfV3ELti/+i5vfP0frFRkhJJuvYr5Hzg+NImzNzQFN0Q47i8yFZGfiyMkjKH0bqtPFw0v/K5VrWbmLnE6+w89m/UXH2jfgLRjDorOMYdP4p+DK8vW6HV9rRaZzwrL79ERre+A+DLzmN7BOPiOm1+4MJbWmaY6gHx63F/vqT4NwO7AFsBLpWSxmxynF2drbbITiO7v8BwX3Htppadv7hz+x4ZDFtFdvw5w0l9/rzyPn+MfiHDYnJPfrqmDJhLMMWXkbudedS+6d/sOOxF6le+Ah1f36L/CduI3ncqJjE4wRut2O8iLVn7Uv/pubep8mYfxC5158X02v3FxPa0jTHUA2OW4v99WeRg1OBWUqpk5RSZ3T5OjPWwXmB8GnimzZtcjskxykuLnY7BMeJt6MKBGj65HNqHniOsjOvZdPME6m+7SF8g7MZ/rtrGFv8J4ZcfkbMkhuI3tGXlcGgs49nj/eeYtivfkrL2k1s/saPqP/n+zGLKdaY8FqF2Ho2vP0xlZcsJHnvPRjx0E2I3x+zaw8EE9rSNEdfqMi4JnF6cL4EjCkwDp8mPnbsWO2niZv2KSOWqOYWWr8qp3VjKYGNW2jdVErL6g00ffRZe7Ev7TOd0g+ZRc4Pjydj/oGOLabWX0cRYdBZ3yV1n4lUnH0j5adfw+CfnEnu1Wd75o9hCBNeqxA7z6aPPqP8zGvx5w5i5HO/xj/IOz3SJrSlaY7+wR07iidQDc5TwKsici+71+C8GZOoPExdXZ3bITjO8uXLmTlzptthOEqsHVvWlbD9l49R92oRhK127Rs2mIy5+5E2Zybpc2aRMm3vuBTxDtQxbfZURr/xGBUX3ErNXU/SvHQVeY//HH9OVu9PjhMmvFYhNp7Nn62j7HtXImmpFCz+Lcl7FsQouthgQlua5ujrSHDcWuyvP++yP+74vjDsuAL2Glg43seEWVTTpk1zOwTHiZVja0kZ23/1BLV/+gcAmcfMJXXmJH7029v5omkHDz71Bw494hssWbKkfQp5wxjmJMVntkosHP1DBzPyuV+x/c4n2P7bP1B58e3k/2GhZ/YnMuG1CgP3bFlXQtkpV0BQUfDib0iZNC5GkcUOE9rSNEdJSsKXnenadg1R940rpcZ186V9cgNmrIOzbt06t0NwnIE6tlXvYOtVv6HkoNOofe51Mo+Zyx7v/oH8x25l9f578XLJalZWlPLN447l4Ycf5pBDDuG6666L6zYOsWpH8fvJvfZHDDrvZBpef4+a+56JyXVjgQmvVRiYZ+tX5ZSe+BOCdQ3kP/1L16eDd4cJbWmio2+we9s1RJ3giMg9InJw2LGDReTumEXlYUxYyXj06NFuh+A4A3FsWVfC5iPPZ+cTr5Axb39Gv/EY+Y/dSsrEPYHdt2148cUXd9vGIR7Euh2H3nwhqftPp/q2h2l8/5OYXru/mPBahf57BpuaKTv1Z7RtrSb/idtJn+Pd4RET2tJER9/g7ISaRfU94OOwY8XAaQMPx/u0tupfX11VVeV2CI7TX8fG9z9hy1EX0Fa+lbzHf87IZ+4kdZ+Ju5wTvvHciSeeGHEjOqeJdTtKSjL5j96Cb0g2FecuIFDu/uvEhNcq9N9z+68X0bpmE8Pv+AkZ8w+McVSxxYS2NNHRPyTHtVlU/UlwVITn+ft5rYTD77FZJE6QleWdIlKn6I9j7Qv/oPTkK5DkJApeuYes78zb5fExY8aQk5PDihUr8Pv9+Hw+7r77bs477zzef/99Fi5cyBtvvNGv1Y6XLFnCL37xi6iGt5xox6SCEeQ9tIC2qu1UnLcAFQjE/B7RYMJrFfrn2bxiLTX3PUv6YbPJPuM7DkQVW0xoSxMdfYPdS3D6U2T8LnCbiFyllAqKiA9Y0HFce1SEPX10w4ReqmgclVJs//Uitt/5OMkTxzLymTtJHrvrDJSHH36Yr776CoDzzz+/8/gll1zCjBkz+L//+z9qamoYOnRonxOcUGHy0KFDueCCC1BKkZ6e3uckyal2zJi7H7lXn0P1HY9SvfARht50oSP36QsmvFYhek8VCFB5+R1IShLDf3u1Z4rCe8KEtjTR0T8kh+DOOlQgEPctYPpzt8uA14AyEdkEjAHKAO9/ROgHInIa/xt+m7F161Y3w4kLwbBpzjrSV0cVCLD1il9R++zfSD9sNnmP39a5tkNXXnzxxYjPb2lp4Zprrtkt+TnvvJ5Xj12yZAmHHHIISimSkpJ2q+HpS4LjZDsO/skZNH38GTX3PkP61w4g4zB31vcw4bUK0XvWPPA8LZ+uYeitP/bcdPDuMKEtTXTsnCq+ow7/0MFxjaU/s6g2A7OB44BfAd8FCjuOa4dS6hml1DFKqWOAFXl5eW6H5DgZGRluh+A4fXEMNjZT/sMbqX32b2Sd/M32hdEiJDcAs2bN6vY6q1ev3uX3xx57bLdzwoeguhYqd33DSElJ4YEHHmDUqFG9Dlc52Y7i8zHivuuRzHS23/mEY/fpDRNeqxCdZ8v6Erbf+Tips6cw6LyTHYwqtpjQliY6dm7X4MJMqn7VzSilgkqp/yilXuj4rn9a2oEJXYzV1dVuh+A4vTm27ail7JSf0vD39xh04f+1/zHvYTfvwYMHd/tYQUFBj7+Hemuuu+46Dj74YIYPH87QoUM7hxVSU1N56KGHWLhwIXfffTebN2+mtLS089zuEh2n29E/dDCDzj6epv8sp/GDZY7eqztMeK1C3z1VMMjWn9yJCrQx/K6rPbfydE+Y0JYmOnZuuOnCWjhGFAbHEhOmiYf/AdaRnhwD5VWUHncJTf9ZTu5NFzD0lh/3up1C15lTSV3GmX0+HwcddFDn70lJSVx11VW7PLdrbw20z0K49NJLufLKK/nmN7/ZWah87bXXsm3btt3O7W5tnXi046AL/g9JS2H7XU86fq9ImPBahb577nzqLzQtWc6Qy88gdereDkcVW0xoSxMdfdntPTrB2vq4x2ITnChpampyOwTH2bBhg9shOE53ji3rv2LLMRfR8vkGht99DUMuOb1PBZpz5szpnCl1//3379L7cuaZZ/LBBx+wcOFC3nnnnd3qZyJNG29ububOO+/kn//8J5dffnlnAtM1kQrR2NjIk0/unmDEox2TRuSSffoxNBZ9RNMnnzt+v3BMeK1C3zwbP1jGthvvJXnSngy5/Iw4RBVbTGhLIx07ehFVW/wHeqIuMhaRW7t5qBnYDPxdKVXRzTkJjwljqJMnT3Y7BMeJ5Nj4/ieU//AGVEMT+YtuI/Oow6K65pw5czqTlxkzZrRvzTBvXuex/kwPh10Li0OJ1JNPPsljjz3WOWT6xBNPcOaZZ+5yj3i14+CLT2Pnk6+y/a4nGfnkL+JyzxAmvFahd8+m4pWUnXYVvqwM8p+4DUlNiVNkscOEttTZMbSLeHiRsfg7+lGCbfEOqV89OBOBq4GvAeM7vl8N7AtcCHwpIt+KWYQew4TNNpctW+Z2CI4T7rjzmb+2r3GT5KfglXuiTm7CmTNnDtdee22fkppIKxsnJSV1uzjgnDlzeOCBBzjnnHM6jwUCgd2uE692TB6dR/YpR9Lw+ns0r1ofl3uGMOG1Cj17Nn+6hrL/+xmSkszIF35LyoSx8QsshpjQlkY6hurAAomR4PiAU5VShymlTlNKHQacArQppQ4CLgLuiGWQXsKEhZpmz57tdgiOE3JUwSDbbn2ArZfdQcr4MYz6x8Ok7RffDfHCVz6+4IILeOedd3pdHPDMM8/scYXkeLbjkEu/Dz4fNXc/Fbd7ghmvVejes2X1Bko7NtEc+affkDptfJwjix0mtKWJjqEeHDeGqPqT4BwJvBp27DXgqI6f/wgkVnVbFNTWurMiYzwJdTXqTHFxMcH6Rip+eAM19z5DxvyDGPXX35O8R37cY+lav1NUVMQDDzzQORzVUy9Q1+dFSoLi2Y7Je40m6/j51L3yJi3rSuJ2XxNeqxDZs2X9V5SeeDmqsYWRz/2KtFmJPfxhQlsa6RjqwUmEGhxgPe1DUfd1OXZBx3GAYUD8y6XjRHZ25HVQdKKw0J1F2+LJPrn5bDn6QlpWrmfQeScz9JaL4r7KZle61u/E6nnxbschl59B3Yv/ouZ3f2TEvdfF5Z4mvFZhd8+2bTWUnXwFwZ115D9zJ2kHzHApsthhQlua6NjZg+PCIof96cH5EfAzEflKRP4jIl8BVwKhgoBJwI2xCtBrmFCDs3TpUrdDcJT6f/+HTV//IS1rSxj+2ysZdvulriY3ThHvdkyZPI7Mow+ndvE/aS0pi8s9dX+thujqqQIBKs5bQOCrcvIeutm1VaRjjQltaaRjZw1O/Pet60+CswyYQPv2BXcBpwMTlFJLAZRS7yilHolZhB7DhBqcnlblTWRUMEj1bxZRftpVJGVnMerVe8k541i3w3IMN9px8CWnQaCN2hf+EZf76fpaDaerZ/XCR2l8p5ghV/yAzG8f7l5QMcaEtjTRMWFqcETET/vwk08p9a5S6vmOhEb/5X07aGhocDsExwnfWkAH2nbWUf6D69l+x2OkzZlJw++vIq0wvsXE8caNdkydPZWk0XnU/y0+e+/q+FqNRMiz7i9F1Nz7NOlfP5AhV/3Q3aBijAltaaRjaJq41xMcpVQbsAYY6kw43kNEThOR10TkNWDGzp3x308j3owbN87tEGJK86r1bPnGuZ3bLhS8eBfjCme6HZbjuNGOIkLmUYfR8ukaWjc7vxyWbq/V7hg3bhwtazZSeclCksaOJO/BmxJqG4a+YEJbGukYep0mSA3O08BrIvIDEZkvIl8PfcU6OC8QvtnmoEGD3A7JcUpLS90OIWbULv4nW466gEDFNkY8fDPDbr0YSUrynGP4ZpuxwC3HzG+3ryHU8LrzvThea0enKF27nvIfXA/BIPmLFnZuYKgTJrSlSY6FhYUUFhZ2bnOjXFgHpz+VlRd2fF8QdlwBew0omgQgObn7DRd1ITc31+0QBoxqaaXqpvvY+dhLJO+9B/mLbidl8v8+WXjJMbTZplKK9PT0bte9iRa3HNMO2gffkBzq/vYug849ydF7eakdnUIpRfIvn6R1XQkjfn8DqdMTd62bnjChLY10TApNE0+Ahf6UUuO6+dI+uQFoc6GR4k2i1xkFyray5buXsvOxl8g8+nBG/+uRXZIb8JZj1802Q9syxAK3HCUpicxvHkzTkuW0Ve9w9F5eaken2PHQn2j95xJyzjmB7JOPdDscxzChLU10TJgi4xAicoSIPCYif+n4vVDXIapw+rLxYqLj62XnbC/T+J9P2Tz/RzQXryL35gvJe+I2fNmZu53nJcfwlYwjbb7ZH9x0zPz2YdDWRsO/YjfkFgkvtaMTNH24gm23PIBv+niG3Xqx2+E4iu5tCYY6JlINjohcAjwArAVCcxSbgNtiGJdnMSHBScRhOKUUOx57idLjL0UF2xi5+LcMufi0btvLS469rUjcX9x0TJ93AJKeSr3DdTheasdY07athvJzF+DLyiDz7iuRFH1dQe+2DGGiY2cNTiIMUQGXA99QSt0BhFKy1bQv8Kc9JgxRJdpihsGmZrZe+guqrrmLlKl7M/pfj/a6+JnXHKPZnLOvuOnoy0gj42sH0PDmfwk2NDl2H6+1Y6xQwSAVF91GW2klI+6/noZBGW6H5Di6tmVXjHRMSqzNNrOBrzp+Vh3fk4GWmETkcUzIwIcNG+Z2CH0msKWC0u9cTO1zr5N1yrcY9Vrf9pNKJMf+4rZjxlGHoRqbaXz7I8fu4bajU9T87o80vvlfBl9yOpnfPERbz65YRz0IdwzV4Dxw//1x36qiPwnOO8A1YccuBd4aeDjep7m52e0QHGfz5s1uh9Anmj5cweYjzqX5s7UMW3gZI+67Dl96ap+emyiOA8Ftx8xvHgx+v6OL/rnt6ASN739C9R2PkXbQTHKv+xGgp2c41lEPdnPsqMHxqfiXd/RnmvglwF9E5FwgW0S+AHYC34lpZB4lPT3d7RAcZ/x4709D3fnc62z96a/wZaZTsPgu0g/ZN6rnJ4LjQHHb0Z87iLQ5M6n/5weoQMCR/b7cdow1gfIqKs5bgC83h7yHb+78N9PNMxLWUQ92c+yowXGjvLo/08TLgP2BU2jfj+oHwIFKqfIYx+ZJ6uu13Si9k5UrV7odQreotjaqbr6frZcsJHmv0Yz+5yNRJzfgbcdY4QXHzKMOI1i9g6b/rnDk+l5wjBWqpZWKc26iraqGvIduJmnk8M7HdPLsDuuoB+GO0lGDkxAJDoBq50Ol1AtKqf8opeI//8slTNhsc+ZMb25jEKytp/z717Dj98+R8c2DGf23B0jes6Bf1/KqYyzxgmNoVWOnhqm84Bgrtt18P00friD3+vPIOHy/XR7TybM7rKMe7ObYUYPjUxFOdpg+JTgicqmI9FjcICKpInJpbMLyLrW1tW6H4DjFxcVuh7AbrV+Vs/nbF9Lw7/8w+JLTyH9yYcT1bfqKFx1jjRcck0fnkbLPROpff7dzMcNY4gXHWFD7wj/Y8eiLZB49t31H9jB08ewJ66gHIce6ujrKy8v5z8cfA97uwckH1onIQx2bTxaKyMSO798TkYdoXxdnhHOheoPs7Gy3Q3CceFe690bz8i/Y8q3zaV1XwvB7rmXoTRcOeKNBrzk6gVccM799GIGvymlZsTbm1/aK40Bo/mwdW3/6K5InjGXEvddGXLtJB8/esI56UFhYyJIlS1i7di2lpaUc8Z2jWdq607sJjlLqOmBf2pOYc4DXgc+AvwFn074Ozr5KqRscitMz2B6c+FL/j/fZcuzFqKYWRj7/a3K+9+2YXNdLjk7hFcfMIw4GoOGdj2N+ba849pe2mlrKf3g9+HzkL4q86jYkvmdfsI56UFxcvNv2M/9t3YHfhSGqPk9rUEpVAb/u+DIW24MTP3Y8spiqG+4ladQIRj57JymTxvX+pD7iFUcn8YpjytS9kIw0mpd+HvNre8WxP6hgkMof30ZgYyl5j91KysQ9uz03kT37inXUg8LCQlpaWhARlFKkpKRwYPIgqlyIRduNMURkkIh8KCJ1IjK9u2PRYsIsqhUrnJnx0ldUMEjVTfdRdd3vSJ05iVGvPxjT5Abcd4wHXnGUpCRSZ06ieemqmF/bK479oeaep2n45wcMuuhUso79Wo/nJrJnX7GOerBixQrmzJnDhAkTKCgo4N///jezk3NcWQdH2wQHaACOBhb3ciwqTFgHZ+LEia7dW7W0Uvnj29jxwPNkfOtQCl65h6S8oTG/j5uO8cJLjmmFUwlsqSRQHtvPcV5yjIbG95ZS/YtHSTtoJkNvOL/X8xPVMxqsox6EHLOyssjPz+fggw8Gn48NzbWUl5ezZImzG/B2pc8Jjoh8JSIPi8h3RaT/01fihFKqVSm1tbdj0WLCSsYlJSWu3DdY10DZ96+hbvG/yD79aPKf+Dm+jDRH7uWWYzzxkmPqvlMAaP4ktsNUXnLsK6HF/PxDB5H3yAIkufdKgUT0jBbrqAeRHJcG6/htxeeUlpYyf/78uCU50fTgHAD8FzgD2Cgi/xKRn4jIgFNSEblYRD4WkWYRWRT2WK6IvCwi9SKySUR2n0MZR1JSUty8fVzIy8uL+z3bttVQeuLlNL71IYMvP4Phd13tyMq3IdxwjDdeckwrnApAU3Fsh6m85NgXVCBAxXkLaNu2gxEP3UxSft/2Jko0z/5gHfUgkuOHrTsIdCyX19LSQlFRUVxiiabIuAx4DHhMRJKAw4FvA6+ISArtM6r+BryllIq2m6MUuA04EggfA7qf9o0884BZwF9FZLlSaqWI5BN5uOmkgaysLCJF3Tw0KxAI9PeyCUNNTQ05OTlxu1/rV+WUnfJTWteVMPT2yxh83kmO3zPejm7gJUd/wQj8eUNjXofjJce+UL3wUZqWLCf32nN73fG+K4nm2R+sox5EcjwwPZfknRtoUUFSUlKYN29eXGLp70rGAaXUm0qpnymlpgLfAL6gfZ+qS/pxvZeUUq8A27oe7xgKOxG4USlVp5R6D3iV9l4klFLlSqlDI3w5tm1EIBCgqqqKsrIytmzZwvbt21m/fj2NjY2sWrWKYDDI0qVLgf9NCVy6dCnBYJBVq1bR2NjI+vXr2b59O1u2bKGsrIyqqio2btxIXV0dq1evJhAIsHz58l2uEfq+YsUKmpubWbt2LTt37qSkpITKykoqKyspKSlh586drF27lubm5s6CtvBrLF++nEAgwOrVq6mrq2Pjxo27OLW2tsbNqWV9CRu/dR6tm0oJ3nI+vlO/6YhTeDulpqYmfDv19tpLS0vzjNPSpUtJnT2FhuJVtDY399spvJ1EJGHaafuf36Tm3qdJn38gGw6fGtVrr6WlxZNOsXyPSE5O1s4pvJ3S0tK0cwpvJ7/fz9q1a2lra6OlpYXKykr2TR/C1cMmkZ+fz6JFi5g9e3bMnHpEKeWZL9p7cRZ1+X1foDHsnJ8Bf+nj9f5Ge+/QEuCs7o5FEV/RwQcfrHSnoqIiLvdpWrVebZhyrFo/+uuq/t//ics9Q8TL0U285lh915Nq3bBDVfPqL2N2Ta85dkfzuhL15V7fUhtnnagC22qifn6ieA4E66gHIcfZs2er2bNnK6WU+nLi0erpPQ7q/D2WzJ07VwFFKsLfbOeKHGJDFrAj7NgOoE+L0SildlsVLtKxaAgG9d92q6mpyfF7NC//gtKTr0C1tDLyuV/3a8PMgRAPR7fxmmNqlzqcWE3795pjJIJ1DZT/4DpUcwt5j/8Wf+6gqK+RCJ4DxTrqQSRH8fu8u5JxT4jIYSJyrohMiUVAYdQB4QOWOYBrywknOVj46hUGDx7s6PUb//sppcdfBsEgBYvvintyA847egGvOabNmgwiMV3wz2uO4ahgkMqLb6f1i40M+9VPSdu3f2+TXveMBdZRDyI6+nwJuw7OIUANcJ2I3ByD63VlDZAkIhO6HJsJxG3P+Y69t14TkdeAGWVlZfG6tWtUVFQ4du2Gd4spO+WnkJJEwcv3kLbfNMfu1RNOOnoFrzn6sjNJnjg2pjOpvOYYTs1dT1H/13cYdO5JA9pmxOuescA66kFER78/MXtwlFJ3KKVeUEqdAWwRkd9Eew0RSRKRNMAP+EUkTUSSlFL1wEvArSKSKSKHAMcBTw007r6ilHpGKXWMUuoYYEVBQUG8bu0aY8aMceS6je9/QvnpV+PLzmTUn+8ldcaE3p/kEE45egkvOqbNnkrL518SbIhNV70XHUPU/+N9qn/5GGmH7MvQW348oGt52TNWWEc9iOQofh8D2x65f8RiiOoREfmriHwE/BiY04/L3AA0AtcA3+/4ObRx50W0Tx2vBJ4FLlRKxa0HJ5zGxka3bh031qxZE/NrNv7nU8pOuxpfViYFr9wT860XosUJR6/hRcfUwqnQ1kbzp7GJzYuOAC1rN1F54c9JGjWC/Edv6dNifj3hVc9YYh31IKKj34/Py5tt9sADtM9KqlBK9UtBKbUAWNDNY9XAd/sZ24DpWFgwtLjgDBP2opoxY0ZMr9f00WeUnfozJCOVgpfvJmW8+59iYu3oRbzoGKpBaV66ivSD9hnw9bzoGGxoovwH16NaW8lfdDv+YUMGfE0vesYa66gHkRw9X2QsIl9FOq6UWqra16NxIT9znvAhKhP2ogqtRxALmj75nLL/+xmSmkLBi3e73nMTIpaOXsWLjilT90LSU2mKUaGxFx2rf/kYrWs3MeyXV5A6c1JMrulFz1hjHfUgoqPXExxgcNdfRGR5bENJDLKz+zRDPaEpLOz7Cqs90fzpGspOvgL8PgoW30Xq1L1jct1YECtHL+NFR0lKInWf2O0s7jXHpuKV7HjwT2TMP4jsARQVh+M1TyewjnoQyVFcGqKKJsEJD8/9cQYXqK11bYZ63IjFp4zWTaWUnfozUDDyhd+6WlAcCWM/SXmA1MKpBL4qJ1BZPeBreclRNbdQedkdSEYaw3/zM0RiNy3WS55OYR31IHIPTuLNotJySCqc8GniJhQZD/RTRltNLWXfu4q2mlryn1zYvv6JxzD1k5QX6FqHM1C85Lj9t0/S+sVGhi64iKRRsd1U0UueTmEd9SByD47318HJEpGtIvK2iDwApIjIvh0bb2pLeA1ORkaG2yE5Tmifkf6gmlso/8F1tK7dxIh7rnVlEb++MBDHRMGrjp0rGsegDscrjs0r1rL9nj+Sduhscs74Tsyv7xVPJ7GOehDRMQFqcHKBk4GXgQxgPfBfoFZEPupIerQnMzPT7RAcZ9q0/i2+p5Si8vI7aPpgGbnXnkv2Sd+McWSxo7+OiYRXHZNG5+EfnhuTHhwvOKrWAJWX/QJJTmLEb69CfLF/K/eCp9NYRz2I6Oj3e3sdHKVUjVKqCHgY+ANwBTCM9pWMHwICTgToNUwYolq3bl2/nlf9i0epW/wvsr9/DIN/ckaMo4ot/XVMJLzqKCKkFk6leennqAHu7eYFx5r7nqFlxVpyrzuP5HGjHLmHFzydxjrqQSRHt4qM+zO8tAlYRfvie+OAi5RSj8Y0Kg+TmprqdgiOM3r06Kifs/OZv1Jz15Okf+0Aht/505gWWDpBfxwTDS87pu07hYa/v0fruhJSJu7Z7+u47dhUvJLqXy8idf/pDDr3RMfu47ZnPLCOehDRMQGGqEKMV0rNVUodABwF3CIi18Q4Ls9g4l5UVVVVUZ3f+uVmqq69m+TJ48h/7NYBr9oaD6J1TES87BirOpyBOhYWFva78DNQsY3ys27Al55K3v03IH7nOuG93JaxwjrqQcixuLi4c0ZVe5Fx/GOJOsFRSu3o8vPHwAHAfBG5LpaBeYXwIuO8vNjOjvAiWVlZfT5XtbVReclCVGuAvAduwpedGDVK0TgmKl52TJ3WviZSy+dfDug6bjmqllYqfngDbZXV5D28wLGhqRBebstYYR31IKKjz8eo/Py4T5OP+qO2iBwJjOryNbrj6+fAwphG50E0XbB5F1pbW/t87o4H/0TThyvIvfZcUqePdzCq2BKNY6LiZUf/sCH4h+cOOMGJ1jHUWzPQN9qq635H00efkXvjBWR8/cABXasveLktY4V11INIjpLkR7UNrN6uP/RnLCG091QpsAV4m/ZNMLfEMC7PYkKCE+xj4WfLFxuo/sWjpO47hcGXntb7EzxEXx0TGa87pkwZR8vqDQO6hhuOO596lZ1/+DOZx32dwZfE53Xv9baMBdZRDyI6+v2QCAmOUmovJwJJFPwOjrN7hb6s9aMCASovbu+wG3HfdUiS9+tuumLCekZed0yZsheN7xTTVlOLf3D/tkDpzTFWPTYhmj5cwdar7yJl2t6M+N01cSum93pbxgLrqAeRHMXvQwXb4h6LG4XNCY0JXYzV1b0voV/zu6dpXraa3OvOHdAsGLfoi2Oi43XHlMntn5UGMkw1UMe6ujrKy8tZsmRJr+cGKqspP/tGfFkZ5C9aiC8zfhvver0tY4F11IOIjj4fBOKf4CTWx24XEJHTgFA/9IwdO3b0dLoWFBQU9Ph484q1VP9mEWkH7sOg80+OU1SxpTdHHfC6Y8qU9p3lW1Z/Sfqcmf26RrSOdXV11NXVdSY0a9euRSnF/PnzeeONN5gzZ07E56lgkMqLb6etYhsjn/81yXvG99/W620ZC6yjHkRydKsGx/bg9EL4LKqcnBy3Q3KcDRt6rououv537au23nudo1NjnaQ3Rx3wumPKpI4E5/P+x9mbY9cemiVLlrB27VpKS0uZP38+Tz75ZGdNXUtLC0VFRd1ep+b3z9H41ocMvuT0uBQVh+P1towF1lEPIjr6/eBC/ZHtwYkSE8ZQJ0/ufnPM5lXraVqynEHnn+z41Fgn6clRF7zu6MvKIGnsyAENUfXkGEpoQj00P/jBD3ZJaKB9VWWlFCkpKcybNy/idZo+Xkn17Q+Tut80cq/9Ub9jHQheb8tYYB31IKKj3+dKkbHtwYmSuro6t0NwnGXLlnX72M7HXwYg54ffjU8wDtGToy4kgmPK5L1oWf1lv2cn9uRYVFQUMaEBSElJ4cwzz2TChAkUFBR0OzzVtqOWivNvwZeRTt5DN7u2iGUitOVAsY56EMlRfD5Umy0y9jwmLNQ0e/bsiMfbdtZR+8I/SZ+3Pyl7j4lzVLGlO0edSATHlMnjCNbU0laxrV/P78lx3rx5vSY0WVlZ5OfnR0xulFJsveJXBErKGH7XVSSPGdmvGGNBIrTlQLGOehDRMcnvSpGxTXCipLa21u0QHKe7KbV1z/8d1dDIoLOPj3NEsSfeK2q6QSI4pkzt+4rGkbZV6Mlxzpw5USU04dQ+9RfqX32LnLOOI+vYr/V6vpMkQlsOFOuoB5EcQ7WaA91cN1psghMl2dn9W68jkYi0N49Sih1PvELS6DwyvnmwC1HFlv7uP5RIJIJjyuRQoXH/6nB6c4wmoelK8+dfUnX970iZuhdDb72kX7HFkkRoy4FiHfUgoqOvI9WIcx2OTXB6IXyzzU2bNrkdkuMsXbp0t2ON7xbTunYTOT84LmFnTnUlkqNuJIJjyvgxkOTvd4LT1XEgG2d2JdjQRMV5C0CEvIcX4EtPHfA1B0oitOVAsY56EMlR/O2pRrzrcGyC0wvh08THjh3rdkiOM2vWrN2O7Xz8FUhJJvv0Y+IejxNEctSNRHCUlGSS996jT1s2dJ3yHUpmIjkONNHZdtN9tK7ewLDbL+ucyu42idCWA8U66kFEx44EJ951ODbBiZKGhga3Q3Cc1atX7/J7oLSS+r+/R9ax80gaPsSlqGJLuKOOJIpj6pS9aPliQ+enu0gJSvgaNqHZjL05FhcX71ITEP57OHV/KercZyr7+95J5hOlLQeCddSDiI5JtgYnIUhLS3M7BMcZN27XT607//AqtLUx6OwTXIoo9oQ76kiiOKZM3gvV2ExgU1m354RP+a6urqa8vJzKysrOc0I9PNEs5dA14Wn9qpytP/klSWNGMvw3P4vbPlN9IVHaciBYRz2I5Ci+jrIGW4PjbZqbm90OwXFKS0s7f1Ytrex86i+kzJhA6n7TXIwqtnR11JVEcey6ZUN3dJ3y7ff72bZtG6WlpRx11FG7rVK8Zs0aSkpK+rS/VAgVCFB5/i0E6xrJe/Am/IO8NZkgUdpyIFhHPYjoGBqisjU43iY5OdntEBwnNze38+e6v75N29ZqBv3weE99oh0oXR11JVEc/7fpZvd1OF2nfJ999tmdvTmtra0UFRXt0sMDUFVVxfz58/uc5FTf+QRNH31G7jXnkLb/9AHYOEOitOVAsI56EMmxs8jY1uB4mzYXVmOMN13rjGqffR1fThZZJx7hYkSxx4RaqkRxTNqzAElPpbmXmVShKd9nnnlmZ7KdnJzMokWLWLRo0W4JeG/7S4VofP8Tau5+ivTDCxl86en99nCSRGnLgWAd9SCiY0cNTrz3o7IJTpTo1IvRHb6ONQtUMEjzxytJO3gWvgy9ao9CjjqTKI7i85EyaVyPQ1Rd6dqb86c//YmsrCyysrKYMGECw4YN22X14u72lwrRtn0nFRf+HF9uDiPuvwHx6L9ZorTlQLCOehDJMVSDE+8dxe1mm1FiQoITGoZrXbuJYG09aYVTXY4o9pgw1JhIjimTx1G7+J+o5pY+nR9KaubMmUNdXR11dXVkZWUxZswYcnNzqaurY/HixT0u8KeUYutP7qStbCv5f7yDpPxhsdKJOYnUlv3FOupBREe/XejPk4Qv9FdRUeF2SI4TmoXSVLwKgFQNExwTNk1NJMeUKXtBoI2W9V/tst5Nb7zzzjudxcVr167tTHT6snpx7R9fo/6vb5Nz9glkHnlIrFQcIZHasr9YRz2I6Ni50F8grrHYBKcXwhf6GznSvQ334sWwYe2fZJuXfg4ipM2a7HJEsSfkqDOJ5Jgypb3Q+N1XXttlvZuuSU6kNWw+/fTTzuJin8/HWWed1af7tazdRNUN95A8eRxDF1wUGwkHSaS27C/WUQ8iOUqSnSaeEJgwTXzz5s0ANH28kuRJe+LLznQ5otgTctSZRHIMJThvv7XrejfdFQmHkp1JkybtVnPT22J+qrmFivNvgbYgeQ/e5ImtGHojkdqyv1hHPYjo6FINjk1woiQ9Pd3tEBxn/PjxBOsbafn8Sy3rb6DdUXcSydGfNxTf4GwO8GVFVSR88skn77ZjeG9sW/gILSvWMvTmC0mdlhj/RonUlv3FOupBJEexNTiJQX19vdshOM7KlStpXv4FBIPaJjgrV650OwTHSSRHESFl8l7sU9UcVcKycuXKqHYMbyj6iB2/f46MbxxEzo9OjFX4jpNIbdlfrKMeRHS0C/0lBllZWW6H4DgzZ86kaWlHgfFsPROcmTNnuh2C4ySaY8qUvQiUlDEso+8JSzSObVXbqfzxbfiH5zL8nusSakZkorVlf7COehDJUfyhISqb4Hia2tpat0NwnOLiYpo/XoVkpJMyWc+9U3qq0dCFRHMMbdkwurXvb0t9dVRKUXnZHbRVVjPi3usSbtPYRGvL/mAd9SCio0tDVHYdnCjJzvbWHjVOUFhYyMalt5O67+TOzFs3wner1pFEcwxt2TC2VVjXx7rfwsLCPv3R2Pn4yzT88wMGXXAKGfMPHEiYrpBobdkfrKMeRHL8Xw+OrcHxNCb04Cz9xxu0lW0lTdPhKTD4k5SHSZ05CVKSmdbU96S6L47Nn3/JtgX3kzJtPENvOH8gIbpGorVlf7COetBzD44dovI0JvTgTGxt79jTcYG/EKZ+kvIyvow00vafzvQmP6jez4feHYONzVSefwuIkPfwzUhqSgwijT+J1pb9wTrqQc89ODbB8TQmzKLa/PciAG1nUAGsWLHC7RAcJxEdMw4rZHBQ2KO1bwXAvTlW3/oALZ9/ydCfX0LKxD1jEKE7JGJbRot11IOIjnaaeGJgwjo4mRsrSRo1wtN78wyUiRMnuh2C4ySiY/rh7Z/+pvdxmKonx/p/fsCOR18k89uHkXPmsTGJzy0SsS2jxTrqQURHW4PjTcL3oiotLXU7JEdRgQDNy1drOz08RElJidshOE4iOqbuO5kGUezTxwSnO8dAxTYqL/sF/vxhDL/r6oSaEh6JRGzLaLGOehDJUVyqwbGzqHpBKfUM8AyAiBSNHDlyrsshOUrL6o1IU4vWw1MAeXl5bofgOInoKElJfJ4aZEqzDxUIIEk9v0VFclTBIJWX/oLgth2MXPxb/LmDnAo3biRiW0aLddSDiI4+W2ScEAQC8d0NNd40FbevQplaOM3lSJylpqbG7RAcJ1Edv3n95WQoofmT1b2eG8lxx8OLaXzzvwy++HtkHL6fAxHGn0Rty2iwjnoQyTG02aYdovI4Pp/e/2TNxavA7yN1H73HitPS0twOwXES1TFUh9P4Tu9TasMdmz9bx7afP0jqzEnkXvMjR+Jzg0Rty2iwjnoQ0TG0nppNcCxu0rR0Fb6JY/Fl6P8f0eJNUqbshX/4EBrejW7NkGBDExXnL0CS/Ix46CYkJdmhCC0WSzSEanDsNHGPEwzGNwONJ20762hdswmm7e12KI7T1NTkdgiOk6iOIkL6obNp+ugzgg09O3R13LbgflrXbGLYwstJ2XuM02HGlURty2iwjnoQ0dHW4CQGSb0UPSYyzZ+sBqXIPGCG26E4zuDBg90OwXES2TH9sEJoaaXpw57XDQk51v/jfXY+8QqZ35lH9mnfjkOE8SWR27KvWEc9iORoa3AShJaWFrdDcIzm4vYdxHeM1nf9mxAVFRVuh+A4iezY1zqciooKApXVVF5+B/6Rwxn+mysTfkp4JBK5LfuKddSDiI52s83EIDW1j7sAJiBNn3yOLyeLUYfu73YojjNmjF5DGJFIZMfksQUkjR1JYy91OHvssQdbz76JYFUNI1+8C/+QnDhFGF8SuS37inXUg4iOdquGxKCxsdHtEByjdV0JKZPHsXbdOrdDcZw1a9a4HYLjJLpj+mGFNC//graa7je43fDrx2j41xIGXXSqNlPCI5HobdkXrKMeRHIUn92qISHIzMx0OwRHUMEgrSVlJO1ZwIwZ+tfgWEfvk3H4fqAUje9/EvHxlrWbSH34ZVKm7sXQ686Nc3TxJdHbsi9YRz2I6JgUmiZue3A8TW1t958mE5m2sq3Q0kry2ILI291rhnX0PumHzQag8e2Pd3tMtbRSccGtBANtjHjgpoTdJbyvJHpb9gXrqAeRHP83Tdz24Hia7Oxst0NwhNaN7XtsJY8tiLjdvW5YR+/jHzaElGl771aHo5Si+peP0fLpGobddCGpU/Vf1iDR27IvWEc9iOjotz04CYGuPTitm9oTnCTbg6MNOjimH1ZI67oSGoo+YsejL1L+o5vZNON4au55mvR5+7O+cJzbIcYFHdqyN6yjHkTswfG504NjZ1FFia49OIFNZQAk71lAYb7+08SN/SSVYKQfVsiOB/9E2clXACBpKaQWTiPnjO8w6LyTKdB01lQ4OrRlb1hHPYjoaGtwEoO6ujq3Q3CE1k2lSFoK/ryhLF++3O1wHMc6JgYZ8/Zn0EWnknvjBYz66+8Zt+51Rr1yD7lXn4N/SI4Wjn3BBE/rqAeRHDtrcOK8E4C2PTgiMgj4FzAVOEgp9ZmIzAF+C7QApcCZSqnWaK7rhVlUkyZNoq6ujsWLFzNnzpyYXLN1UylJYwsQEaZN03snccA6JgiSksywW37c7eM6OPYFEzytox5EdLSbbcacBuBoYHGXY5uAryul5gJfAsdFe1G318FZsmQJa9eupbS0lPnz57NkyZKYXDewqZTksQUArDNgHRzrqAcmOIIZntZRDyI6hmpwAnaIKiYopVqVUlvDjpUqpUIZSgCIOp10eyXjoqIilFJA+7YRRUVFA75msK6Btq3bOxOc0aNHD/iaXsc66oEJjmCGp3XUg0iOYnINjohcLCIfi0iziCwKeyxXRF4WkXoR2SQip8XgfuOAo4DXon1ua2tUI1oxZ968eZ177aSkpDBv3rwBX7PrDCqAqqqqAV/T61hHPTDBEczwtI56ENExtBeVoTU4pcBtwJFAethj99NeM5MHzAL+KiLLlVIrRSSfXYegQpyklCqPdCMRyQH+AJyhlIp650x/aCzRJebMmcOECRNiWoMT6EhwkvdsT3CysrIGfE2vYx31wARHMMPTOupBJEfxu7ObuCcSHKXUSwAish/Q2b8lIpnAicB0pVQd8J6IvAqcAVzTkcQc2tf7iEgS8CywQCn1RQ/nFXXz0KzQ8JCbfPFFt6H3i1APTmiIyu1eqnhgHfXABEcww9M66kFEx9BeVLYGZxcmAm1Kqa67dy0H+lSKLiJ/A74JPCIiZwHfAw4EbhKRIhH5v2gDamlpoaqqirKyMrZs2cL27dtZv349jY2NrFq1imAwyNKlS4H/LXi0dOlSgsEgq1atorGxkfXr17N9+3a2bNlCWVkZVVVVbNy4kbq6OlavXk0gEOicahe6Ruj7ihUraG5uZu3atezcuZOSkhIqKyuprKykpKSEnTt3snbtWpqbm1mxYkXEayxfvpxAIMDq1aupq6ujekV7wlSVImzZsoWdO3cmvNPGjRt7bKe2tjbtnMLbKfS7Tk7h7dTU1KSdU6R22rFjh3ZO4e3U2tqqnVN4OwWDQe2c+tJOW6u3AdDc1BRzp54QL/RIhBCR24DRSqmzOn4/DHhBKZXf5ZxzgdOVUvNciK/okEMOmfvee+/F+9aOUnbqlTR/tpY9P3sFgO3btzNkyBB3g3IY66gHJjiCGZ7WUQ8iOaq2Nr7Mn0fOWccx/Fc/i+n95s2bx9tvv/12pJzA6z04dUD4UqU5gGv7JejYxdi6cUvn8BRAdXW1i9HEB+uoByY4ghme1lEPIjq6tFWD1xOcNUCSiEzocmwmsDJeAYjIaSLymoi8BszYsWNHvG4dF1RbG61flZO05/8SnIKCgh6eoQfWUQ9McAQzPK2jHkRyFJH2JMfEGhwRSRKRNMAP+EUkTUSSlFL1wEvArSKSKSKH0L4431Pxik0p9YxS6hil1DHAipwcvfa+aSuvgpbWXXpwNmzY4GJE8cE66oEJjmCGp3XUg24d/T5je3BuABqBa4Dvd/x8Q8djF9E+dbyS9hlQFyql4taDE05GRoZbt3aE1o27zqACmDx5slvhxA3rqAcmOIIZntZRD7pzlCQ/BA3swVFKLVBKSdjXgo7HqpVS31VKZSqlxiilnnEzVt022wxf5A9g2bJlLkUTP6yjHpjgCGZ4Wkc96NbR54v7XlSeWAfHy3SsnBxaPXlGQ0ODm+HEnMCmMuB/i/wBzJ49261w4oZ11AMTHMEMT+uoB905it9v96LyGuE1OOnp4QstJzatG7cgaSn484Z2HgutR6Az1lEPTHAEMzytox506+j3xX2rBpvgREl2drbbIcSU1k2lJI0t6NzfCqCwsNDFiOKDddQDExzBDE/rqAfdOUqS39gi44RBxxqcrgXGQOdKlzpjHfXABEcww9M66kG3jj5f3HcTtzU4vRBeg1NfX+9mODElWNdAsKpmtwRn1qxZ7gQUR6yjHpjgCGZ4Wkc96NbR1uB4j/AaHJ2GqCLNoAJYvXq1G+HEFeuoByY4ghme1lEPunMUW4PjfdLS0twOIWYEQruI77lrgjNu3Dg3wokr1lEPTHAEMzytox506+i3NTiep7m52e0QYkZrNwlOaWmpG+HEFeuoByY4ghme1lEPunMUf/xrcGyCEyXJycluhxAzWjd0DFHtMXKX47m5uW6EE1esox6Y4AhmeFpHPejW0W/oXlReJnyzzYqKCrdDihmtm0rx5w3Fl7HrsJtuixlGwjrqgQmOYIanddSD7hzF70fZGhxvEV5kPHz4cLdDihmBCFPEAXw+/V8W1lEPTHAEMzytox506+j3x32rBv3/tWNM1wXxEhnV1kbrV+Uk7bl7gqPTMFx3WEc9MMERzPC0jnrQnaP4fShbg+Nt2uLcQE7RVl4FLa0Re3B0W8wwEtZRD0xwBDM8raMedOtoi4y9jy4ZeOvGyDOoAIYNGxbvcOKOddQDExzBDE/rqAfdOtpp4t5Hl2nioQQnfJE/gM2bN8c7nLhjHfXABEcww9M66kF3juJCDY7dqqEXwrdqqK2tdTOcmNG5Bk6EBGf8+PHxDifuWEc9MMERzPC0jnrQraPfZ4uMvUb4LKrMzEy3Q4oJgU2lSFoK/ryhuz22cuVKFyKKL9ZRD0xwBDM8raMedOdoi4wTgKysLLdDiAmtJWUkjRkZcVbYzJkzXYgovlhHPTDBEczwtI560K2jC7uJ2wQnSnQZogpsriBpdH7Ex4qLi+McTfyxjnpggiOY4Wkd9aA7R7FFxt5Hh93EVUsrbRXbSB6dF/HxwsLCOEcUf6yjHpjgCGZ4Wkc96NYxyS7053l06MEJlG0FpUgaFTnBMflThk5YR30wwdM66kH3PTi2Bsfz6NCDE9jcvp9W0ugRER83+lOGRlhHfTDB0zrqQbeOtgbHe4RvtllSUuJ2SAMmsKUjwemmB2fFihXxDMcVrKMemOAIZnhaRz3o1tHW4HiP8Gnio0ePdjukARP4KtSDEznBmThxYjzDcQXrqAcmOIIZntZRD7pzlCQ/2N3EvY0OKxm3bqkAEZJGRt4ZXYdeqt6wjnpggiOY4Wkd9aBbR78PFbBDVJ4mJSXF7RAGTGBzBf68oUhK5H218vIi9+zohHXUAxMcwQxP66gH3TmKz65k7HkCgYDbIQyYwJaKboenAGpqauIXjEtYRz0wwRHM8LSOetCto91N3Pv4fIn9T6aUIrC5stsCY4C0tLQ4RuQO1lEPTHAEMzytox505yhJ7VtfqjjW4ST2X2tL1ARralENjSTvoX9XqcVisVg8QqhzII7DVDbBiZJgnKvAY03nGjg99OA0NTXFKxzXsI56YIIjmOFpHfWgO0fxt6cb8Sw0TorbnRIUETkNOK3j1xnV1dVuhjNgOtfA6aEGZ/DgwXGKxj2sox6Y4AhmeFpHPejW0R/qwYlfgmN7cHohfB2cRH+B9qUHp6KiIl7huIZ11AMTHMEMT+uoB906JvkBW4PjaVJTU90OYUD0pQdnzJgx8QrHNayjHpjgCGZ4Wkc96M5RfO0Jjq3B8TCNjY1uhzAgApsrkIx0fIO731NrzZo1cYzIHayjHpjgCGZ4Wkc96NbRhRocm+BESWZmptshDIjWLZUkjR6BiHR7zowZM+IYkTtYRz0wwRHM8LSOetCdo9gaHO9TW1vrdggDIrC5osf6G+h+u3udsI56YIIjmOFpHfWgW8eOGpx47kdlE5woyc7ufmjH66jWAG3lVT3W30AP291rhHXUAxMcwQxP66gH3TmGanDiuaO4TXCiJJF7cAJlW0Epkm0PjnXUBBMcwQxP66gH3TqGhqhsDY53SeQenM4p4rYHxzpqggmOYIanddSDbh1DRcZBm+B4lrq6OrdD6Dd9mSIOsHz58niE4yrWUQ9McAQzPK2jHnTnKEl2mrjnSeRZVH1Z5A9g2rRp8QjHVayjHpjgCGZ4Wkc96NbR1uB4n0ReByewpRJESBo5rMfz1q1bF6eI3MM66oEJjmCGp3XUg+4cxdbgeJ9EXsk4sLkC/4hcJDWlx/NGjx4dp4jcwzrqgQmOYIanddSDbh1DCY6dJu4dROQ0EXlNRF4DZpSVlbkdUr9p3VLRa/0NQFVVVRyicRfrqAcmOIIZntZRD7pz7NxN3C705x3CN9vMy+s9QfAiSikCX5X3Wn8DkJWVFYeI3MU66oEJjmCGp3XUg24d/bbI2PMopdwOoV8Ed9ah6hv71IPT2toah4jcxTrqgQmOYIanddSD7hylI8Gxe1F5mERNcPo6gwogGMcxUrewjnpggiOY4Wkd9aBbR1uD4338oW62BCO0Bk7y6BG9npuRkeF0OK5jHfXABEcww9M66kG3jrYGx/skahdjYHMl0LcenOrqaqfDcR3rqAcmOIIZntZRD7pzFFuD430SdZp4X7dpACgoKHA6HNexjnpggiOY4Wkd9aBbR1uD432amprcDqFfBLZUIOmp+HIH9Xruhg0b4hCRu1hHPTDBEczwtI560J2j2Boc75OoY6iBzRUkjcpDRHo9d/LkyXGIyF2sox6Y4AhmeFpHPejW0deRbtgaHO+SqJttBvq4yB/AsmXLnA3GA1hHPTDBEczwtI560J1jaLNNuxeVh0nEhZpUa4BAWRVJo3qfQQUwe/ZshyNyH+uoByY4ghme1lEPunUMFRnbGhzvUltb63YIURMor4JgsM89OMXFxQ5H5D7WUQ9McAQzPK2jHnTn2LlVg63B8S7Z2dluhxA10SzyB1BYWOhkOJ7AOuqBCY5ghqd11INuHW0NjvdJxBqc0CJ/fe3BWbp0qZPheALrqAcmOIIZntZRD7pztDU4CUAi1uCEenCS+9iDM2vWLAej8QbWUQ9McAQzPK2jHnTrGJombmtwvEtDQ4PbIURNqAfHXzC8T+evXr3ayXA8gXXUAxMcwQxP66gH3TqGFvqzNTgDR0QGiciHIlInItM7juWJyAci8raIvCkiI6O9blpaWuyDdZjA5gr8w3PxpfVtFeZx48Y5HJH7WEc9MMERzPC0jnrQnaN01uDYBCcWNABHA4u7HKsCDlVKzQWeBM6J9qLNzc2xiS6OBCqr+1x/A1BaWupgNN7AOuqBCY5ghqd11INuHZNCe1HFb4gqKW53ijNKqVZga9eVe5VSXf9ls4GV0V43OTl54MHFmdH/fhRV1/ehtdzcXAej8QbWUQ9McAQzPK2jHnTnKKbuJi4iF4vIxyLSLCKLwh7LFZGXRaReRDaJyGkDvNcsEfkvcDEQdUl7WxwbJ1aICL7szD6fn4h1RtFiHfXABEcww9M66kG3ji7sJu6VHpxS4DbgSCA97LH7gRYgD5gF/FVEliulVopIPrsOQYU4SSlVHulGSqllwIEicgpwLXBBNIH2ZS+nRMfn80Te6yjWUQ9McAQzPK2jHnTnGKrBiec0cU8kOEqplwBEZD9gdOi4iGQCJwLTlVJ1wHsi8ipwBnBNRxJzaF/vIyKpSqlQEc0O2ut0Ip1X1M0l5qxcuZJ58+b19ZYJSWtra0IOxUWDddQDExzBDE/rqAfdOarmFppqVpJ8z20kvfRIzO7XsffV+EiPeSLB6YGJQJtSak2XY8uBuX15soj8jfZen0ki8hDwhYj8EmgDmoCzo4zHv3Pnzpa33357SZTPSyRmdXxf5mIMTjOr4/syF2Nwmlkd35e5GIPTzOr4vszFGOLBrI7vy1yMwWlmdXxf5mIMTjOr4/syF2Nwmlkd35d1e8b6nbA+ptPlxwNbIz3g9QQni/aelq7soL1AuFeUUt+OcPjwPjxvXqTjoZ6d7h7XAeuoB9ZRH0zwtI564DVHrw8I1gE5YcdygMTb8dJisVgsFkvc8HqCswZIEpEJXY7NpB/Tuy0Wi8VisZiDJxIcEUkSkTTAD/hFJE1EkpRS9cBLwK0ikikihwDHAU+5Ga/FYrFYLBZv44kEB7gBaASuAb7f8fMNHY9dRPvU8UrgWeBCpZTtwbFYLBaLxdItnigyVkotABZ081g18N04hmOxWCwWiyXBEaWU2zFYLBaLxWKxxBSvDFFZLBaLxWKxxAyb4FgsFovFYtEOm+BYLBaLxWLRDpvgxBgR2VNEtopIUcfXcLdjijUikiciH4jI2yLypoiMdDsmJxCRQSLyoYjUich0t+OJBTo6hWPQ69OE95pDu/itEZG73I4p1oiIX0SeFpG3RORxEfHE5J+BEum9Jt7vPzbBcYa3lVLzOr4i7pGR4FQBhyql5gJPAue4HI9TNABHE3nH+kRFR6dwTHl9gubvNUqp90J+wAfAK+5G5AjHA18qpb4GrAZOcDmeWBHpvSau7z82wXGGQ0TkXRFZKCLidjCxRinVppQK7XmfjaYrSyulWnX7o6GjUzimvD470Pq9JoSIJAMHAO+6HYsD7M3/NqdcChzmXiixI9J7Tbzff4xOcETkYhH5WESaRWRR2GO5IvKyiNSLyCYROa2Ply2jfXfTw4ERuJyNO+SIiMwSkf8CF9P+n9JVnPL0Kib4DsTRa6/PnhiAp6fea3oiBq/XI4A3uiSunmMAjquAr3f8/A1gSJxC7hOJ/F6jxVjfACgFbgOOpH215K7cD7QAebRvAf9XEVmulFopIvlE7mI7SSlVDjQDiMhLwEHAi86E3ycccVRKLQMOFJFTgGuBCxyKv6841ZZepV++cY1w4PTb0YOvz57ol6dSqhlvvdf0xEBfrycDT8QhzoHQX8fXgHki8ibtvY0VcYu4byTse43RCY5S6iUAEdkPGB06LiKZwInAdKVUHfCeiLwKnAFc0/GH79BI1xSRHKXUzo5fDwM+d1ChVxxyTO14cwXYQfu4qqs44ell+uvrRqz9pb+OXnx99sQAPD31XtMTA3m9Svvw1P54vJZqAO9BCvhpx7kLgDfjHHqPJPJ7jdFDVD0wEWhTSq3pcmw5MK0Pz50rIsUi8i4wCnjGiQBjwEAcZ4vIOyLyFnA58CsH4osVA/FERP4GfBN4RETOin14MadX3wR0Cqc3x0R6ffZEb56J8l7TE335//kN4E0vD0/1Qo+OIpIv7bPE3gBalFLvuBFkP+jXe00833+M7sHpgSzaP/l1ZQftBYs9opT6C/AXJ4KKMQNxXEL7uH8i0G9PAKXUt2MekbP06puATuH06Jhgr8+e6M0zUd5reqIvr9fXgdfjGVSM6a0dy4F5cY4pFvTrvSae7z+2BycydUBO2LEcoNaFWJzCBEcwxzOECb4mOIIZntYxcfG8l01wIrMGSBKRCV2OzUSv6aYmOII5niFM8DXBEczwtI6Ji+e9jE5wRCRJRNIAP+AXkTQRSVJK1QMvAbeKSKaIHAIcBzzlZrz9wQRHMMczhAm+JjiCGZ7WMXEdE9pLKWXsF7AAUGFfCzoey6V91cx6oAQ4ze14raP1NMnXBEdTPK1j4jomspd0BGmxWCwWi8WiDUYPUVksFovFYtETm+BYLBaLxWLRDpvgWCwWi8Vi0Q6b4FgsFovFYtEOm+BYLBaLxWLRDpvgWCwWi8Vi0Q6b4FgsFovFYtEOm+BYLBaLxWLRDpvgWCwWi8Vi0Q6b4FgsFovFYtEOm+BYLJaERUSGiIgSkbqwr8vCzisQkc19vOY/ROTWCMePE5FyEUmKVfwWi8U5bIJjsVgSmVlAtVIqK+zrd2HnfRv4ex+vuQg4Q0Qk7PgZwNNKqcCAIrZYLHHBJjgWiyWRmQWs6sN53wb+1vVAR6/OiyKyVUQ2iMilHQ+9QvsuyYd1OXcIcAzwZCyCtlgszmMTHIvFksjsSy8JjogkA4cD/+pyzAf8BVgOjALmA5eLyJFKqUbgT8CZXS5zCrBaKbU8tuFbLBansAmOxWJJZGbRPpxU0+Xrt2HnHA4sV0rVdjm2PzBcKXWrUqpFKfUl8AhwasfjfwBOFpH0jt/P7DhmsVgSBFssZ7FYEhIRSQWmAHOUUh/3cOpuw1PAWKBARGq6HPMD7wIopd4Tka3AcSLyIe0J0Qmxit1isTiPTXAsFkuiMh1QwIpezvs2cHzYsa+ADUqpCT0870nae24mAf9USlX0N1CLxRJ/7BCVxWJJVPYFViqlmrs7QUTGAalKqdVhD30I7BSRq0UkXUT8IjJdRPbvcs6TwDeAc7HDUxZLwmETHIvFkqjMAmaErX9TKyKDupxzNLsPT6GUagO+03GNDUAV8CgwqMs5G4EPgEzgVYccLBaLQ4hSyu0YLBaLxRFE5G/AfUqp3ZIci8WiN7YHx2Kx6EwR8JbbQVgslvhje3AsFovFYrFoh+3BsVgsFovFoh02wbFYLBaLxaIdNsGxWCwWi8WiHTbBsVgsFovFoh02wbFYLBaLxaIdNsGxWCwWi8WiHf8PPSEAA0Anf0UAAAAASUVORK5CYII=",
      "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=\"Ahnen et al. (2017)\",\n",
    ")\n",
    "ax.loglog(E, ec_model(E), ls=\"-\", color=\"crimson\", label=\"EC model\")\n",
    "ax.set_ylabel(sed_y_label)\n",
    "ax.set_xlabel(r\"$E\\,/\\,{\\rm eV}$\")\n",
    "ax.set_xlim([1e-5, 1e12])\n",
    "ax.set_ylim([1e-13, 1e-8])\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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitter = Fit(sed, ec_model, stat=Chi2(), method=LevMar())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit succesful =  True\n",
      "Method                = levmar\n",
      "Statistic             = chi2\n",
      "Initial fit statistic = 3396.08\n",
      "Final fit statistic   = 220.712 at function evaluation 65\n",
      "Data points           = 41\n",
      "Degrees of freedom    = 36\n",
      "Probability [Q-value] = 2.09563e-28\n",
      "Reduced statistic     = 6.13088\n",
      "Change in statistic   = 3175.37\n",
      "   ec.log10_k     0.244905     +/- 0.277713    \n",
      "   ec.p           2.1683       +/- 0.23791     \n",
      "   ec.q           0.336091     +/- 0.0631399   \n",
      "   ec.delta_D     25.8259      +/- 3.08421     \n",
      "   ec.log10_B     -0.406479    +/- 0.0632429   \n",
      "CPU times: user 15.2 s, sys: 4.75 s, total: 19.9 s\n",
      "Wall time: 20.8 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": 10,
   "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": [
    "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=\"Ahnen et al. (2017)\",\n",
    ")\n",
    "ax.loglog(E, ec_model(E), ls=\"-\", color=\"crimson\", label=\"EC model\")\n",
    "ax.set_ylabel(sed_y_label)\n",
    "ax.set_xlabel(r\"$E\\,/\\,{\\rm eV}$\")\n",
    "ax.set_xlim([1e-5, 1e12])\n",
    "ax.set_ylim([1e-13, 1e-8])\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
}