{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model spectra and synthetic photometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, we will have a look at some spectra of the DRIFT-PHOENIX atmospheric model. The spectra are first downloaded and added to the database. Then we will use the functionalities of [ReadModel](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_model.ReadModel) to extract a spectrum and calculate a photometric flux."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing the required Python modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we initialize [species](https://species.readthedocs.io/en/latest/species.html) with [SpeciesInit](https://species.readthedocs.io/en/latest/species.core.html#species.core.init.SpeciesInit), which creates a default configuration file and the HDF5 database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initiating species v0.5.2... [DONE]\n",
      "Creating species_config.ini... [DONE]\n",
      "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n",
      "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n",
      "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n",
      "Grid interpolation method: linear\n",
      "Creating species_database.hdf5... [DONE]\n",
      "Creating data folder... [DONE]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<species.core.init.SpeciesInit at 0x14ba61c90>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.SpeciesInit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding model spectra to the database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To store the spectra, we first create an instance of [Database](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "database = species.Database()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check which atmospheric models are available by running the [available_models](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database.available_models) method of the [Database](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database) object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available model grids:\n",
      "   - AMES-Cond:\n",
      "      - Label = ames-cond\n",
      "      - Model parameters: ['teff', 'logg']\n",
      "      - Teff range (K): [100, 6600]\n",
      "      - Wavelength range (um): [0.5, 40]\n",
      "      - Resolution lambda/Dlambda: 4000\n",
      "      - File size: 150 MB\n",
      "\n",
      "   - AMES-Dusty:\n",
      "      - Label = ames-dusty\n",
      "      - Model parameters: ['teff', 'logg']\n",
      "      - Teff range (K): [500, 4000]\n",
      "      - Wavelength range (um): [0.5, 40]\n",
      "      - Resolution lambda/Dlambda: 4000\n",
      "      - File size: 59 MB\n",
      "\n",
      "   - ATMO:\n",
      "      - Label = atmo\n",
      "      - Model parameters: ['teff', 'logg']\n",
      "      - Teff range (K): [200, 3000]\n",
      "      - Wavelength range (um): [0.4, 6000]\n",
      "      - Resolution lambda/Dlambda: 10000\n",
      "      - File size: 425 MB\n",
      "      - Reference: Phillips et al. (2020)\n",
      "      - URL: https://ui.adsabs.harvard.edu/abs/2020A%26A...637A..38P/abstract\n",
      "\n",
      "   - blackbody:\n",
      "      - Label = blackbody\n",
      "      - Model parameters: ['teff']\n",
      "      - Teff range (K): [10, 20000]\n",
      "      - Wavelength range (um): [0.1, 5000]\n",
      "      - Resolution lambda/Dlambda: 1000\n",
      "      - File size: 56 MB\n",
      "\n",
      "   - BT-Cond:\n",
      "      - Label = bt-cond\n",
      "      - Model parameters: ['teff', 'logg']\n",
      "      - Teff range (K): [800, 4000]\n",
      "      - Wavelength range (um): [0.1, 100]\n",
      "      - Resolution lambda/Dlambda: 10000\n",
      "      - File size: 162 MB\n",
      "\n",
      "   - BT-Cond [Fe/H]:\n",
      "      - Label = bt-cond-feh\n",
      "      - Model parameters: ['teff', 'logg', 'feh']\n",
      "      - Teff range (K): [2600, 4000]\n",
      "      - Wavelength range (um): [0.1, 100]\n",
      "      - Resolution lambda/Dlambda: 10000\n",
      "      - File size: 390 MB\n",
      "\n",
      "   - BT-NextGen:\n",
      "      - Label = bt-nextgen\n",
      "      - Model parameters: ['teff', 'logg', 'feh']\n",
      "      - Teff range (K): [2600, 30000]\n",
      "      - Wavelength range (um): [0.1, 50]\n",
      "      - Resolution lambda/Dlambda: 4000\n",
      "      - File size: 368 MB\n",
      "\n",
      "   - BT-Settl:\n",
      "      - Label = bt-settl\n",
      "      - Model parameters: ['teff', 'logg']\n",
      "      - Teff range (K): [400, 4000]\n",
      "      - Wavelength range (um): [0.1, 100]\n",
      "      - Resolution lambda/Dlambda: 10000\n",
      "      - File size: 227 MB\n",
      "\n",
      "   - BT-Settl CIFIST:\n",
      "      - Label = bt-settl-cifist\n",
      "      - Model parameters: ['teff', 'logg']\n",
      "      - Teff range (K): [1200, 7000]\n",
      "      - Wavelength range (um): [0.1, 5000]\n",
      "      - Resolution lambda/Dlambda: 20000\n",
      "      - File size: 1.8 GB\n",
      "\n",
      "   - DRIFT-PHOENIX:\n",
      "      - Label = drift-phoenix\n",
      "      - Model parameters: ['teff', 'logg', 'feh']\n",
      "      - Teff range (K): [1000, 3000]\n",
      "      - Wavelength range (um): [0.1, 50]\n",
      "      - Resolution lambda/Dlambda: 4000\n",
      "      - File size: 229 MB\n",
      "      - Reference: Helling et al. (2008)\n",
      "      - URL: https://ui.adsabs.harvard.edu/abs/2008ApJ...675L.105H/abstract\n",
      "\n",
      "   - Exo-REM:\n",
      "      - Label = exo-rem\n",
      "      - Model parameters: ['teff', 'logg', 'feh', 'c_o_ratio']\n",
      "      - Teff range (K): [1000, 2000]\n",
      "      - Wavelength range (um): [0.9, 5]\n",
      "      - Resolution lambda/Dlambda: 10000\n",
      "      - File size: 706 MB\n",
      "      - Reference: Charney et al. (2018)\n",
      "      - URL: https://ui.adsabs.harvard.edu/abs/2018ApJ...854..172C/abstract\n",
      "\n",
      "   - Koester WD:\n",
      "      - Label = koester-wd\n",
      "      - Model parameters: ['teff', 'logg']\n",
      "      - Teff range (K): [5000, 80000]\n",
      "      - Wavelength range (um): [0.09, 3]\n",
      "      - Resolution lambda/Dlambda: 2000\n",
      "      - File size: 291 MB\n",
      "      - Reference: Koester (2010)\n",
      "      - URL: https://ui.adsabs.harvard.edu/abs/2010MmSAI..81..921K/abstract\n",
      "\n",
      "   - Morley et al. (2012):\n",
      "      - Label = morley-2012\n",
      "      - Model parameters: ['teff', 'logg', 'fsed']\n",
      "      - Teff range (K): [400, 1300]\n",
      "      - Wavelength range (um): [0.6, 30]\n",
      "      - Resolution lambda/Dlambda: 10000\n",
      "      - File size: 141 MB\n",
      "      - Reference: Morley et al. (2012)\n",
      "      - URL: https://ui.adsabs.harvard.edu/abs/2012ApJ...756..172M/abstract\n",
      "\n",
      "   - petitCODE cool clear:\n",
      "      - Label = petitcode-cool-clear\n",
      "      - Model parameters: ['teff', 'logg', 'feh']\n",
      "      - Teff range (K): [500, 1000]\n",
      "      - Wavelength range (um): [0.1, 250]\n",
      "      - Resolution lambda/Dlambda: 1000\n",
      "      - File size: 86 MB\n",
      "      - Reference: Mollière et al. (2015)\n",
      "      - URL: https://ui.adsabs.harvard.edu/abs/2015ApJ...813...47M/abstract\n",
      "\n",
      "   - petitCODE cool cloudy:\n",
      "      - Label = petitcode-cool-cloudy\n",
      "      - Model parameters: ['teff', 'logg', 'feh', 'fsed']\n",
      "      - Teff range (K): [500, 850]\n",
      "      - Wavelength range (um): [0.1, 250]\n",
      "      - Resolution lambda/Dlambda: 1000\n",
      "      - Extra details: log(Kzz) = 7.5\n",
      "      - File size: 224 MB\n",
      "      - Reference: Mollière et al. (2015)\n",
      "      - URL: https://ui.adsabs.harvard.edu/abs/2015ApJ...813...47M/abstract\n",
      "\n",
      "   - petitCODE hot clear:\n",
      "      - Label = petitcode-hot-clear\n",
      "      - Model parameters: ['teff', 'logg', 'feh', 'c_o_ratio']\n",
      "      - Teff range (K): [1000, 2000]\n",
      "      - Wavelength range (um): [0.1, 250]\n",
      "      - Resolution lambda/Dlambda: 1000\n",
      "      - File size: 42 MB\n",
      "      - Reference: Mollière et al. (2015)\n",
      "      - URL: https://ui.adsabs.harvard.edu/abs/2015ApJ...813...47M/abstract\n",
      "\n",
      "   - petitCODE hot cloudy:\n",
      "      - Label = petitcode-hot-cloudy\n",
      "      - Model parameters: ['teff', 'logg', 'feh', 'c_o_ratio', 'fsed']\n",
      "      - Teff range (K): [1000, 2000]\n",
      "      - Wavelength range (um): [0.1, 250]\n",
      "      - Resolution lambda/Dlambda: 1000\n",
      "      - File size: 126 MB\n",
      "      - Reference: Mollière et al. (2015)\n",
      "      - URL: https://ui.adsabs.harvard.edu/abs/2015ApJ...813...47M/abstract\n",
      "\n",
      "   - Sonora Cholla:\n",
      "      - Label = sonora-cholla\n",
      "      - Model parameters: ['teff', 'logg', 'log_kzz']\n",
      "      - Teff range (K): [500, 1300]\n",
      "      - Wavelength range (um): [1, 250]\n",
      "      - Resolution lambda/Dlambda: 10000\n",
      "      - Extra details: [Fe/H] = 0.0\n",
      "      - File size: 497 MB\n",
      "      - Reference: Karalidi et al. (2021)\n",
      "      - URL: https://zenodo.org/record/4450269\n",
      "\n",
      "   - Sonora Bobcat:\n",
      "      - Label = sonora-bobcat\n",
      "      - Model parameters: ['teff', 'logg', 'feh']\n",
      "      - Teff range (K): [200, 2400]\n",
      "      - Wavelength range (um): [0.4, 50]\n",
      "      - Resolution lambda/Dlambda: 10000\n",
      "      - File size: 1.1 GB\n",
      "      - Reference: Marley et al. (2021)\n",
      "      - URL: https://zenodo.org/record/5063476\n",
      "\n",
      "   - Sonora Bobcat C/O:\n",
      "      - Label = sonora-bobcat-co\n",
      "      - Model parameters: ['teff', 'c_o_ratio']\n",
      "      - Teff range (K): [200, 2400]\n",
      "      - Wavelength range (um): [0.4, 50]\n",
      "      - Resolution lambda/Dlambda: 10000\n",
      "      - Extra details: [Fe/H] = 0.0, log(g) = 5.0\n",
      "      - File size: 112 MB\n",
      "      - Reference: Marley et al. (2021)\n",
      "      - URL: https://zenodo.org/record/5063476\n"
     ]
    }
   ],
   "source": [
    "_ = database.available_models()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will import the model spectra with the [add_model](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database.add_model) method of [Database](https://species.readthedocs.io/en/latest/species.data.html#species.data.database.Database). This step will automatically download the DRIFT-PHOENIX spectra (R = 2000) to the [data_folder](https://species.readthedocs.io/en/latest/configuration.html). The dowloaded data will then be unpacked and added to the database. We restrict the temperature range to 1300-1700 K, so not all spectra are added to the databse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading DRIFT-PHOENIX model spectra (229 MB)... [DONE]\n",
      "Unpacking DRIFT-PHOENIX model spectra (229 MB)... [DONE]\n",
      "Please cite Helling et al. (2008) when using DRIFT-PHOENIX in a publication\n",
      "Reference URL: https://ui.adsabs.harvard.edu/abs/2008ApJ...675L.105H/abstract\n",
      "Wavelength range (um) = 0.1 - 50\n",
      "Spectral resolution = 4000\n",
      "Teff range (K) = 1300.0 - 1700.0\n",
      "Adding DRIFT-PHOENIX model spectra... [DONE]                                            \n",
      "Grid points stored in the database:\n",
      "   - Teff = [1300. 1400. 1500. 1600. 1700.]\n",
      "   - log(g) = [3.  3.5 4.  4.5 5.  5.5]\n",
      "   - [Fe/H] = [-0.6 -0.3 -0.   0.3]\n"
     ]
    }
   ],
   "source": [
    "database.add_model(model='drift-phoenix', teff_range=(1300., 1700.))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two of the grid points were missing in the original data and have been added with a linear, multidimensional interpolation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpolating the model grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will read the spectra from the database by creating an instance of [ReadModel](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_model.ReadModel)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "read_model = species.ReadModel(model='drift-phoenix', wavel_range=(0.5, 10.))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what the grid boundaries are from the spectra that are stored in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'teff': (1300.0, 1700.0), 'logg': (3.0, 5.5), 'feh': (-0.6, 0.3)}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_model.get_bounds()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now interpolate the grid in the ($T_\\mathrm{eff}$, $\\log(g)$, $[\\mathrm{Fe}/\\mathrm{H}]$) space at some specific parameter values, which need to be provided in a dictionary. The radius and distance are optional, otherwise the emitted flux is given at the planet surface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_param = {'teff':1510., 'logg':4.1, 'feh':0.1, 'radius': 1., 'distance': 100.}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To interpolate a spectrum, we use the [get_model](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_model.ReadModel.get_model) method and provide the parameter dictionary, and also an optional spectral resolution. Together with `smooth=True`, the spectrum will be smoothed (but not resampeld) to the given spectral resolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_box = read_model.get_model(model_param=model_param, spec_res=200., smooth=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is stored in a [ModelBox](https://species.readthedocs.io/en/latest/species.core.html#species.core.box.ModelBox). Let's have a look at its content."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening ModelBox...\n",
      "model = drift-phoenix\n",
      "type = None\n",
      "wavelength = [ 0.49989727  0.50002105  0.50014486 ...  9.99710369  9.99957902\n",
      " 10.00205496]\n",
      "flux = [9.44339097e-20 9.38011967e-20 9.31147817e-20 ... 1.53571943e-18\n",
      " 1.53924349e-18 1.54301688e-18]\n",
      "parameters = {'teff': 1510.0, 'logg': 4.1, 'feh': 0.1, 'radius': 1.0, 'distance': 100.0, 'luminosity': 4.729862212008143e-05, 'mass': 4.857062223118246}\n",
      "quantity = flux\n",
      "contribution = None\n",
      "bol_flux = None\n"
     ]
    }
   ],
   "source": [
    "model_box.open_box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now use the same atmospheric parameters but we will add some [ISM extinction](https://species.readthedocs.io/en/latest/species.util.html?#species.util.dust_util.ism_extinction) with the relation from [Cardelli et al. (1989)](https://ui.adsabs.harvard.edu/abs/1989ApJ...345..245C/abstract). Therefore, we add the V band extinction and reddening parameters to the dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_param = {'teff':1510., 'logg':4.1, 'feh':0.1, 'radius': 1., 'distance': 100., 'ism_ext': 5., 'ism_red': 3.}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use again the [get_model](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_model.ReadModel.get_model) method and store the result in a different [ModelBox](https://species.readthedocs.io/en/latest/species.core.html#species.core.box.ModelBox)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ext = read_model.get_model(model_param=model_param, spec_res=200., smooth=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two boxes with the model spectra are provided to the [plot_spectrum](https://species.readthedocs.io/en/latest/species.plot.html#species.plot.plot_spectrum.plot_spectrum). We also include some filter profiles to indicate where the telluric windows are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding filter: MKO/NSFCam.J... [DONE]\n",
      "Adding filter: MKO/NSFCam.H... [DONE]\n",
      "Adding filter: MKO/NSFCam.K... [DONE]\n",
      "Adding filter: MKO/NSFCam.Lp... [DONE]\n",
      "Adding filter: MKO/NSFCam.Mp... [DONE]\n",
      "Plotting spectrum... [DONE]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "species.plot_spectrum(boxes=[model_box, model_ext],\n",
    "                      filters=['MKO/NSFCam.J', 'MKO/NSFCam.H', 'MKO/NSFCam.K', 'MKO/NSFCam.Lp', 'MKO/NSFCam.Mp'],\n",
    "                      offset=(-0.15, -0.04),\n",
    "                      xlim=(0.8, 5.),\n",
    "                      ylim=(0., 5.5e-17),\n",
    "                      legend={'loc': 'lower right', 'frameon': False, 'fontsize': 10.},\n",
    "                      figsize=(7., 3.),\n",
    "                      output=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting a spectrum at a grid point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to extract a spectrum at one of the grid points, which doesn't require any interpolation. Let's check with the [get_points](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_model.ReadModel.get_points) method what parameter values are stored in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'teff': array([1300., 1400., 1500., 1600., 1700.]),\n",
       " 'logg': array([3. , 3.5, 4. , 4.5, 5. , 5.5]),\n",
       " 'feh': array([-0.6, -0.3, -0. ,  0.3])}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_model.get_points()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a dictionary with values at one of the grid points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_param = {'teff':1500., 'logg':4., 'feh':0.}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now use the [get_data](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_model.ReadModel.get_data) method to extract a spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_full = read_model.get_data(model_param)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make another plot with [plot_spectrum](https://species.readthedocs.io/en/latest/species.plot.html#species.plot.plot_spectrum.plot_spectrum). The spectrum is now shown at the spectral resolution as stored in the database ($R = 2000$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting spectrum... [DONE]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vv4x9x4rx7oKt+GTpTjz01WpUeBgeD+OrFQX4dtVu/HPaWsN+M9bsxcNfrUZpuQe3fbTC9ngfXt8fY7o0BgC8e00//O+GAYbtKW4XamWkBuz33jX9AAC3jGgXsM0uH/y6HS/O2ggA+Gzpzoj7EQRBSGZE2RHiAjOj40NKIr2yCo+jff/5tYXYfugkXpi5AQ99tdq7vs3fp+Ef367Bj2v3Yc+xYgDAjkMnsU/9rI+ACsWWJ8/CzDtPC1jvtrDemDG4XT3D8rRbh9jeV+PjJTsAAPd8uhJ3frIyRGtBEITqiSg7QlzYX6j4v+w+WoQ+//wxrH0LjhShwhM8omb59iM4bKK8bDlwwpCr5r2F23DzB8vCGh8AiAht6tcMez+NZnUzAQCTJ/TyruvcpFbE/U1dtRuAcnwfL96OR776PeK+BEEQko2kc1AWEpu7PlmJT3XTLQOenAVAUWDycjKD7ltcVoGMVDcunrwAH13fH3P+2I+jJ0tNp4EmfrzCtI+5fxgjcWat24cN+47bkr17sxys2HHEsO7MLo1s7evPT3crzspjTPbv27IuFm0NL7w8xe0CUIF//fAHDh4vwS+bDuLRc7tEJJsgCEKyIZYdodJYueOIQdHRM2jSLBSXVeCuT1aCmfHoN6sxddVuFJdVePOidHt0BgAlUuqiyQvw0Je/Y8ovW6OSya6iAwC1MwP9bl7RWWbssuLh0wMS29VMV947PriuH6Zc3SfsPo8WlQEAvlm5C+UmVi/NElZSHhs/KUEQhEQm6ZSdvLw8EJHk2EkwSss9OPe/84O26fjQdHy6dCda3T8Nb8/fips/WIZr/m8x/vXDHzh6sgwl5R7c/P4y7Dxc5PW5OXC8FA99+TveXbAV037bHdNjSEtx5uuSUyMwseDSh5QkhAPb1AvYFi4l5T4fqN1Hi/Dewm0YOGkmjp4sw4hn50bdvyAIQmWQn5/vSNg5kITTWJJnJzG559PInGfnbzyIhtkZ6PYPxaoz1USheXfhNow5pRGmr96D3x89Iyo5g/HwuM74Yc3emPSdnuL2fiZEV85gpTrV1vK+qahXMx0tc2tg77ESeJi9BUYFQRASHcmzI1QZDp8oxUWTf8GXKyIvYGknuX+KW1EQ7v1sVcTjhKJxbSWF+b//3D1mYzjNgeMl3vw++pkzq+lEQRCEZESUHSGm9HjsByzeejiqPr5YXhCyjRbyvWDTwajGCoaLCOO6No5Z/xpO16nU19JiZpSUK75RgiAI1YW4KTtElEFEU4hoIREtIaLRFu2yiehdIlpMRIvUfSKP+RUqjTv+t6LSxvpKtRyFkysnXIhQpStmvz1/KzwM9H7cGOpfVCpOy4IgJDfxtOzkAyBm7g/gUgAfEVFDk3YPAWgBoL/61wLAg5UlpBAZhcVl+HyZ0SJTr6ZSHPPBsZ3iIVLUVGVFBwD+PXMDmBkn/Px2hjw9G0dPlsVJKkEQhNgTF2WHiFwArgXwJgAw8x8AlgOYYNL8FACLmLmCmT0AFgHoUVmyCuFTVuHBqfkzAtYveXAUtk4ai5GdfDpt/RCVxAVnMfN/KpVwdEEQkpx4WXZaA8gFsE63bg2A3iZtpwEYQURZRFQDwAgAv8ZeRCFSej32g2G5RW4Nw3KrellY+48xAIAPru1XaXJVFWJpQDpZWoEQyacFQRCSjngpO9qr/VHduiMAGvg3ZOb/ApgDYDOArQCWAfhHTKUTIuaPvYU4VuybJmmRWwNz1WzBejLT3Hj6wq5oVrdGwLZEo6pOu4UD62w+knhQEIRkI97RWP7vmAHvtET0AIBuUHx1mkOxCl1p1aGWVFD7k+SClQcz48EvjTWZejavY9n+4t7NkO5Qor5YovkaAcC53ZqgS17tmI4XbZ4du+wvLMG6PccMvkjbD57Eef/9xbv8wo9/hOxnX2Exdh4+GRMZBUGofuTn53uf4U4lFYzXk2af+n+Obl2Obr2eWwG8yszFzFwMYDKAx6w6LigoADN7/0TZqTxmrdtnCHMGgDR38Fss0Z1+OzWuhea6abhRnRtGVQDUDpV1Sub+sR//nb3JEI1VwYyTpT7L3As/bgjZz1fLd2HK/K2xEFEQhGpIfn6+9xleUBA69Ygd4pVBeROAQwA6wKfgdIbin+NPGgB9qEgZgOyYSieEzeKth3D9u0stt79wSffKE8ZBxvdtFtQ6VZX5akUBMlLdKK3wlZcgANsOnsSy7YexxGYxUpeLTOtxCYIgJApxseyoUVWvA7gaAIioHYDuAN4nok5ENJOItPz5PwK4hFSghKnPjoPYQhAe/3aNt9gkAEye0BM3DmuDywe0AACc18MZU2R1oLJsXYXF5d5rdv/nv4GZvValH9fsxRPT1gXZ20eKi+BhUXYEQUhc4p5nh4gWAvgQwHhm3gOgNoCOALQS0zdD+f1fCCUKKwXA9ZUurWDJ1gMnsHLnUcO6MV0a494xHWPu3yJEzoodR7wV5b/7fQ/O/PfP+ODX7QCApdvsZ73WLDtz/9gfEzkFQRCiJW7KjuqDcyUz92fm3sw8Q12/kJnzVP8cMPM+Zr6Emfsxc19mvpCZY1veWgiLYc/OMSyvePj0sPZfcP8IB6Wp+lSmH5N+9mndnkK8+tNmAMDOw0W29p/wxq9eS9Rf3lrksHSCIAjOkPihMEJCw37TFx0aZiOnRlpYfTSunemkSEIY2Jl8+nDRdqzZdQyAUntMf83nbTwARuVNvQmCIERCRA7KRDQEwKkA6kPJj7MBwA/MLDnnqxFHT5ah/5MzvctEwPSJQ+IokTOM7doYU1fFz3hYmYpDcVnonDrzNhxAdkYKOjephfGvL0S3prVxcZ9mOFakRm3pfH3ssOtIEepnpyM1RKSeIAiCU4T1a0NEA4hoLYB/ARgFoA2AwQDuA7CJiC5zXkQhEVm4+SC6/WMGinQPyxkTh0Y8BfPyZT2dEi1qqpOVwj9VgIbeeuNykcH5fNuhk9hztBh7jxUrbWHMDfTmvC04eLzEcsxr/28J1u8pjFJyQRAE+9hWdtSIqesBnMbMfZj5Ama+gpkvYuahUJyKOxDR2bES1g5aUkHJrxNb1u4+Zli+cmBLtGsYeUaAUZ3MasAmDo+de0qljZVoqYdcBHiYMe6ln9VlRfnR5GSGIRrrw0XbLavPV3jY1tSZIAhCfn6+Y0kFw5nGOgLgGjVsPABmPgngYYvK5ZVGQUEBmjRpEk8Rkp59hcV49Js13uWtk8ZG3acrwR7w/lw+oGW8RYgbbhehwgP8XqAouIdOlMLDPmsOM+N9NYorGEeLynD+f+cjPdWdcAqdIAiJR35+Pq6//npHFB7blh1m3m+l6Pi12xudSEKis++Y9RRFpLhVbef2Ue0d7ztc4m15SISs0vpz4CaCxy9p4ImSckz5ZUtA2/IKT4DTuobHwzhoYfHROOvfP0ciriAIQlAi9hAkos5OCiJUDYrLKjDupXne5b+NaOtIv9oDPi0RamWpz+pL+zWPrxwJgosImw4cN6x7d+E2b9i6XrcZ//pCAIFTca+rIe2hWOM3PSoIguAE0TxZXnBKCKFqUOFhbNxnfOhlpLotWkdGInh0JIBhJe7oFZi9hcV4da61srJxv++eWLz1sOkV/Oe0tVithq9rlp9VO49Y+vYIgiA4SUhlh4iOEdEMIvpB/X8GEf0AoEclyCckEJPnbjJYdQClVICTJELVgZwaqaEbJTl6pfNoUfCMEh+Y+usQvl21yzD9tfXgCcMU14szN1hmal623X4GZ0EQhFDYsez8AeBiZj6dmUerf6cDWBFb0YREYuuBE3jm+/UB6zs2rhUHaSqH6mzg0bvouCIwdR06UYpbPliOCmYcK1aUpU37j/u1sq6pdcHLv4Q9piAIghV2lJ3TAQQkxVAVHqGasHDzQdP1p7Wv7+g4Vs6tlUkCiBB3yvwqoYcFA2t2+WqlHTmhKDtvz9/qXVdYXI6dh0/KuRYEoVIIqeww82Fm9maOI6J7YitSdEieHef54NftOFkaOtOuE2gPvx7NcyplPH8eObszWtevGZexEwmO0rKjWYYOnyz1+kBlpfn8u+as3491ewoTQrkVBCExcTLPTiQOymMcGTlGFBQUgJlF2XGIsgoP/v7Fb/jHt2tCN3YA7SH53EXdKmU8f8Z2bYwmtTMAVG9HZb0SEvZ5IF+SwSk6a07tTJ8vlDchoY3uyio8tspaCIKQXOTn56OgoMCRviJRdqrxI6D68VqQkOGBbXIdH09zjI3n+37bBoplh6rxra43uISr7LiIvPuz3/7+19WOYWfK/K14/oc/whNCEARBRyTKjtidqxFmTsl3n9EBAPDBdf0dH48ZmHTBqTH35dBPqRgFQFRlL5IFvePwws3m9bOscBFQoe7P7MuhpPWov7Z2Ug0wGK/azNMjCIJghlh2BFPenr8FBUeKAtanugk3D2/rSIkIMxjAn/s2R6x1aqssxcmgyWvTcNEQzXnQamdprNxxxLLtyh1H8NUKZ8zU4aKFxT8xbW1cxhcEofKIRNm5yXEphITj0W/WYOJHywPWp7hinOFYZxGIJcmksd99RoeE8i8iv/IShsrq6uqlW5U8Omt3F+K2j1Zg9vp9mL/xgO0xLn/z14jl+5c6JXbpGwuxef/xoFO1giAkB2E/uZjZ9DVIrYouJAFbD5wAoGTDrWzibVmpasFBbhehed0aOKNzo3iL4mXt7mO+aSz46mHpz+2ircapsTnr9hmVIvicpM18p37eYF8x0lNe4cGLMzcAAA4eL0W5x/yCl5RX4PaPV0Q0hiAIiUdEr+lE5CKikUQ0gYiuIKIrAPzXYdmEODHs2TmW2/55fpeYjq13bI0pNiwhd4/pgIt7N4u1JFHhH7rtVD0vq2R/dnnhxw3ez9+s3GXYpvfT0T6fLK2I+TU/WVqO695ZohvbGo8H+O733TGWSBCEyiLSOYmvAeQDGAVguPrnTDC8EFcumhw8c+0FPZvGbOzbR7X3RWPFycKifxDXykh1vPZXLNBPYZ3ZpZEjVdNjcf6DOSN/snRnwKDMwH9mbcD+4yWG9Zov2bHisrDy9HR7dAZO+OWL0p+pCg8b+isu80AQhOQgUmUnh5mHMPOVzHwVM18F4C4nBYsUSSoYOUeLyuIydaWR4iZfJe04TWhVtWmsWOGYD5A+8ooRlrMUA/hm5W4cPG4sFjpo0iwAQNf8GXh5zia8u3Cb13q0+2gRVlg4RJdVMNzqgd3w7hLDtp837Mfdn6xEq/ungZlN7z+PxZSXIAixId5JBQFgLhG18VvXNlphnECSCkZOt0dnmK4f1alhpYx/xYAW+OtQ5bZyQumoVzPdchsBqJWREv0gccbsNNWrmRZ9vw491826CadvouCK7/7CEmzefxyTvlsHQClrMmX+lqD9AcD3q/ca1l/+5iJvwdMKD2P673sC9h0waaZtuY+XlNtuKwiCOfFOKggAiwEsJ6ICItpMRFsAPOaIRELCUVmRPtkZqaitVhx34mE7qlMDy21EZDrdUxXe3fu1qmtY9nfg/fymQVGP4dR50E8LMYCSMg827PMVBDXI7nc97E5R6ctZuIhQoe62fPthfLZ0p6GtPiR+4z5jYVJtS7mH8XvBMcO2ez9dhb3HjNNpGku3HcZ7C7cZ1vX8xw/esUrKK7wKWHmFB6/9tAkAsGFvQMlBQRBiRKTKzpMAzgMwCD6fnS+dEUmobLYfPImW90213K49StLcMQ47d5hQSloihWuHw8A29dC3ZV3L7W5XYh4YM1BaYfSD4YB5LmDHoZPqNgVNITpWXIYZq40Wl3cXbgNB8eOp8LAS9q72s2HfcfyyyVjA1o7+VO7hAAftj5fssGy/9cAJLNumTP96PIwKD8PlAkrLlWM9WVKB51WH7XIP49nvldD305//KbQwgiA4QqRPr9+YeRYzb2Xmbcy8FWLZqbJMVt80Q/H+df1iLImPWPvsVFVFB/CLZvI7TY6VuHBqGiuMfrSm2w4qys4vmw4arG9bD5zA9e8uNeyjKDjK573HiuEiX0dmZ8L/vtLC4hVZlW0VFWywAM1ev8/7+enp6yzlBoD3f92Gf8/cgPQUt7ee17Lth71TZECgwrd+TyF+2WQdSv/izA34ecN+y+2CIIQmUmVnOxG9TUTX6ULPX3FSMKHy+ODX7bbaVaZ+UBmOwv7HM3FUO+TlZMZ+YIeJheLm/0B2hsCLqlfOfGkHlA/FZRUg3XJJublM2jTWwEmzQCCDVUbbd3+hMgXlP3X51rxA/54yj8ebJ+jhr37H7iPF3m0vz9mEnYcVZay8woMKD+OuT1YCBBSVVuChr1Zj7e5jOFpUhh6P/YCS8gpc839L1ONjdHxoesB4i7YcxLTfAsPcV+44gn2FxSg4XISdh43ZzPcVFqOkXIqjCoJdIlV2xqv/D4SEnic92vOhKltDQkGEhA4z9/c/ile0WrjopQw3mIlZiRD8fJnioHjFm4tM2+kVmJs/WGZQdhao01h9/vkjAHijsTRmrDE6KgPAsm2HvZFX8zYcQIrbuM/gp2YDANo+8B3+/aMyJUUg/Hf2RgDAD7o+t6tWKsD8+ItKKwBSohA1y9KFr/yCxVsP4ZU5m7B062FkpLoCqr7f+b+V+DXMmmWhYGaDBUoQkolIlZ3HtJBzXej5PU4KJiQilaftSLkII1qZjll3nmYydaX7nGAH9qbOcmLX4Vjf7MhJ3zRTUZm5JcP/mFfsOIItB07Aw4zdR4sN24JVO9GGvf7dpThyUnnoHzxRahrKXqZavr5Ztdsrw39UZUdPoS4q6+PFPr8fzZ/Hw4yHvvwdm/Ydx9VTFmPHoZNYsu0wnpm+3itTRpo74Ng9zAbH7EjZcegkbn5/GTbuK8SxonIMfVpR5D5fpjh2M3NQf75QfLtqF+bopgEFIV5EpOww82ST1YmTr16wjeYMWh3RWwQSTEcIIK+OMr3Wun7NOEsSOaZh6CbZlH/6w+ef4p8E0Ax/f+y9x0ow/Nk5mLfxYEDbtbutI6DKK3yybD6gRGodLSoznebVlJ0tamkV//tHk+mCl31JOt/WhcRrmZyvVae49h8vwbaDJ73fR305DY+H8d1vewzRWxUeNhz3K3OC+93tPVYcsI6Z8dWKAkz9bTf2FZbgZFk5MlJd2LT/OO7430os2nIId3+6yitDeYUH2w6esJ1vaH9hCW75YDnW75GoMyH+RFouYpb/H5SMynFHkgraw+Nh7DtWjCHqm1wwCIRTmtSKm9XglCa1ItzTWuDDJ8sCtia6wnP7qPYAFKWhX6tcLLx/ZHwFcgCPzg1H8995Q7UG2bUEWTll+5epAIDCYutpmnm6QqR/7FWUnZa5NUzbllUYZfvEL8R9YJt6AfvoQ+61sPcFmw/ihqGtMfbUxgCAS9/wFTidvnoPbnp/GV7/eQt+KziK05//CcyMJVsPYeHmQ4Yb9ikTx2kAXkWj3xNKjiDtnJZXeHDT+8vw7AxlGg4M3PbRClR4GCOfmwsAOFFa7vV1+mDRdjw1fR1GPjcXbR6Yhme+X4fpIcpplMXE70uoTiRCUsEjAB5V/54AMA3Am45IFCWSVDA4Wr6PHYdPou8T9pKkje3aGJf1a1Gpzrt6xSpyZcf+GEBi5dj5+Z7hAIyJEW8b5au1m+ImNKqdEbCfdkj3jOkQU/kiwUx38S8Foceuj08oJVyfayecaLXze+ShS15t022hpmaa1gn+XdErAq9qVddtiHbmv3/2Ror9S1VUNCfrXo/9YPDtufG9pTjjBWN4+9n/mYdjxWV49afN+E6XONHDSnX6A7ps1fd9tspbib64rALHSypQ7mEwA8u3H8Ff31sGQJmqWrpNsUQdLSoL8C8ShEhJhKSCE5h5rvr3IzM/CyAHAIioviOSCTEh/5s1KCmvwK0fLre9z9ndmuDSfs3RsFbgwzVW6B+MVg+om4b5J/GOHCfqSTlJs7o10ESnzNgxcgxonev9fNOwhEhobsCsuKh/Yj89T39vbq3wJ9SVm6ObFgvHfJfqtm5820crgu67ab/1cQGBVo+pq3Zj8/4TIWVat6cQV09ZDABYoub22a5OfR08UYpjOsvVzLWBCtmOQ0WoqGDDlB0ATHjzV5x1qtETYe+xEm9VeA8zPLqpM33+ovkbD2KdakHq9ugMvDlvC77TRZfNWb/fMDUpCPEgUmWnNxENVf+GEdF4AKcR0VAArzson2CDVTuPWG4rUn0eynU/rgOenIWVO4/a6vvy/i2iks0JrCKPzNZq0wH28D3M2tavmXDTWPPuHWG+wULzSfQIrcLi8EoobDtoz58slKIa6XV1WWTZtkMoC5K/srH5QGhFR0Pvx9TyvqkGn6ISXfFSLX3AITWXELOSk+iBL3/zKkh6pv0WWCJDY/7Ggyj3mDtFM7PheJ/5fj12HS32KrdLtx/G8u1HbB6dIMSGSJWdD6D46DwK4BEA18M3tdXbTgdElEFEU4hoIREtIaLRQdoOJKKZRPQTEa0hor9FKHdSceRkKZgZ5/xnvmWbTg9Px+pdR9H2ge+8Pgx26/akpbjw2HldHJE1Uro1NZ9GsMS6+kAA47o29tbHOq1D4hkkXfoMeX44ljwwCQgVlfS1ie+OrX6jyUQdYlernEGRoM+JpE0h6f2dFqvOzq3un4YjJ8uwbk8h9hUGOiyf2cU6xmTuH/vx2bKdFspOoJM4Mxv8sZ7/8Q+cLJV6YUL8iLQS4v3M/K7ZBiK60WYf+QCImfsTUXsAC4moEzMbEl8QUSsALwA4i5kPENEpAK6OUO6kovs/fvB+9njY8sf5oskLAAB/U6euSm3+0Cb64zRa+c7p3gQrdhzBih1H8KeeTZGeUrXKYSQbiWaZ8s/JEw6LtviiqXq1qIOl6pSTRmySNir9Hi8pxzkvzfOum+g35ZbmduHnDdYZm0P1r+fWD5cjPcUVoAQxw5uYUaOotAI10lLw5rwtOKdbE9TPti7UKwhOE2nouamio24LmUmZiFwAroXq1MzMfwBYDmCCSfM7ALzNzAfUtquZ+c5I5E4mXp1rDDVt/fdplm3NfCXs4EQej0RHOzMdGmWjZb2suMpiF/3V/HOfZr71jMTXUGPArHWBiQGdwO0iR4p1VoSbTTEKxr44D10e+d4wLeafo0fvT5SjFt4FIsvR9PXKXfCwUiZDb006WVqBWesUnyHt5aqsgnHz+8vwyZIdppYlQYgl8XqVbQ0gF4DeA3ENzKfARgJII6JpRDSfiJ4losrzlE1AVu86iie/s+e8CQDFZZG9RY7oaF01PBEwfYSE8VzRfttvG9kuaLt488PtQ3H90Nbe5Ya1MtCwlvJWPOlPXeMlVsJg1/8MsG/VBJSH/zoHcsRE+rIRK/Rh806I9tmynVi586g30zWgRHs+9u0aQ7v8r1dj6m+74XYRnpy2DptDOHELgpPES9lpqP6v/5U6AsDs6doSwF8BXAFgGIDOUKa1TNHy7Gh/yRiCPvbFeaEbOcAzF1XBBymZfjQlp0YaejWvg9tPbx9TkaKBWZFTH2Y+oX8L/Llv8zhKFRum/27tIBsPorVsNlavWXkFIzs9Uo+B2OJkeQj9FNenfnmHACVvEKDk/lmw+SAOn5TSFII5+fn53md4vPPsOIX/e4XZr0s6gA+Y+QAzl0FRdK5Up8IC0PLsaH/JpuwEK/6nmZEPnSjF8ZLyqJN6xdMJNlr/jY6NsnFBz6ZB27Sql4WHz+4c1TiVgd1nrjKLVXXnsTbZCL2uTNwROCg/faHvBeGVCb0AJJ5lJxTPXdTNVrs0t/En+NtV9hzByz0MD3PAfX3bR8ttZ2cWkpv8/HzvM9ypPDu2XjfUaSMPM5fq1jVm5uApNK3REkDkWHzWcxiAflJ+JxQFqJ5F+6Smw4OBVZM1npq+HjsPn8S3qyK9LEYS3WXHVDz1t7JlbhZ6tahTmeIISUYkt3+9mmkB+3dvloNdR4rMd0ggCtTK6n/q1RR3frLSsG1gm1xDbh0AyExzo7TI90I136Q8hxWa/jd7/T4M76AY9L9asQs7DxfhuYu6VRn/OaHqENKyQ0S3Q8mQ/C0RvUJEWmrQ96MYdxOAQwD0aV47A1hs0nYFjNNb9QGUArD/zUoCmDlkevajRaWOKTpVgVC1lgQhGiLJsePWVRrV34lOJa1sXte8fEUo2jWoicxUd9A2et+nZy40TmGbWbnMohdbBVFScrPSAtZd9bbxJ3/ptsN46KvfA9pVppO3kJzYmcb6EzOPYObRAD4H8A0RReXRycweKMkHrwYAtb/uAN4nok5qTh3tm/k6gPFEpH3LrwbwHjNXq5zkHoY3PbsVdjKwJju5WfbCWYP9KCcS4f7EJ7o1rirhjmCSX68TaA9oJ2exGoWZxfy87k0AAG0b1ESHRtmmbfT1v16+rCcA4KLeSpTftYNbATBX1tJMlB1/heb+Mzt6Pw9sa6wX9uAXilIz9sWfDdNXR06W4c7/GS1L/WyWthEEK+x8nd1ElAIAzPwDgCsBvAwgWq/OfABERAsBfAhgPDPvAVAbQEcAqeqYHwN4D0oenvkAKgBMjHLsKke5J7T/za+63B5OkBAPzjCFuHZIK3vdRiJLnLAr6+uX90aflnVjKos/retXDaUxEvQOyvoQ7WDofab0odjR5CfUk5oSXkcpqsa2ef+JAEuMVg6jrk5BOcsvA/mVg1qiSe0MU/nNlB1/P8F+uhIm/l2s2X0MALB61zEMfmqWd31puQefLduJ7QdPwuNR/DYOBKmhJgh2sKPs3AXFPwYAwMw7AYwF8GA0AzNzMTNfycz9mbk3M89Q1y9k5jxmLta1fZKZuzLzIGa+mpmjjwetYlRbMy4zOjSyXwhUe0DVzAjhjlZFtJ1rh7RCjTR7kTy1a6RG5FQbDYNMqnsnC3prht3vH5lYdvz70jjr1EZhJ7LUT5OFYliH+l6r0vq9hUj3m8ZKT/Etd2hobvUhIqSaJA28ZnArZKa60bN5jmG9fzX4FN396J9kUE+xLiXAzsNKKYuhz8zGqz9txlvztwIA3vh5s+X+ghCKkN8cZp6vWlwAAER0DzOXMvOUmEomGFjksNXGDokS2XPN4FYY1DY3dEOVJrUzMOmCU4O2SYwjC81Nw9oiMy24r0Uw+sbY0lPJulWl4ibyFmO1GyWkf557p7F0k5H+EUyt69e07KuWicKeZlKctLXFlGzdGmnesV0U6GPjIuCD6/oBAL68eRC+vmVQQB9Namfgu9uGGK7zae3r454xHXDtkFbw020M4ecdG2UblCTtN+zff+4eMI7esVtf+2tfYTFOquVtHp+61rDPxWpmeEGwQySh52Mcl0IIyX9mbayUcW4e7qsknhDTWBFARF7zfbA2wahsC0k09G5ZF3+1qAD/v78OqGRpkgf9LWKl6vRrFahM3jNGibuoYEazupm4uHczr+XE0GcI/clMv0o1ua8Z5kpnipsABi7okYdUtytA2XG7CGluF4gImWludG2aE9AHEaFGWorh+8JQrEJul8t7EJqvzutX9EZGqjKOiwgeZnRqXAuX9muO/YXKVJRZsV4r5+lDJ0oDzv1Pf+zHF8t3YtHWyn8BFKoukSg7Cf0U0JIKJlt+nSV+tXUA4LXLe2HRAyMTNmFZPHDKGXTVI5Z1aROO+tnp6N4sJy5jOxVllKhot1OtDHOfnRp+VjcixRoHKJadVvVqonfLul4LiqFvDv5jajZ1ZlV1nBFYyLNDo1ro3zoXz1zUDWUVHu+01ejOSk5Xt8tl2wHeqk6YpnylqBanVvWysOJh5bvjcik5hr67bQieOP9Ub9ZvsxeRDfvMsyl/tcKYu+fZ79dj1c4juP1jnwNzeYzqjAnxJz8/P65JBRPaeURLKphMyk7L+6ZabmuQnWHqKJg0qD+ybfzM/avyfcqI3sHSqWdvliiQ1R6CT3m2SgwYTNnzMEObddLa6XuxSpNww2lKaRB/Zeel8T1MtSOtlb8Tdc/mObi4TzO4XQQPA+mqxeW1K3ojNysNqW4KqXBpWLkKNaqdgf9e2hMpugYZqW48PK4zmtWpYfALumJAS8O+2bppupPq1NXSB0cFjPGvH/7wfv7P7I14doZv+YvlO9H2ge9sHIFQFcnPz3csqWDSWXaSjcJi65TqsdY6Eynx6xi/t1arN+1gXNDT94YgN7EzJLlhx4uVy06w2c7yCg6cDtX1w2x+/jSn3mAOvYYuWfuuGjtL8dNQ9NNYNw9vi5waykuCnWtIIGRnpODawa0MUWaAorT5H+fVg1vh5ct6GsLd7UwNazLZRbPw/GfWhrD2E6ofkSg7NzkuhWDJih1HKm2sbk1rez8vuH9EQlmMnHCWnnSBL1FadXhIv3N135iPkShO7LGACNhzTAkK9X/A+9pYH/+pTWvj6kHGVAgMRs0QVkMt4srfsmNV1F6zEPmL4q9c6K0sVw9uhVoZKZbHFQABE0e1x11ndPCWxND29bAx6sq7i59A53Rr4v087dYhpk7fkfrKadaeD37dHtH+QvIT9tOMmdearY820aDg40RJOdbtOYYTJeX4dqUvI3JH3VvSn3o29Tr1OWWA0ZuZa6TGdxrH/zfY6czIyfyQ1hjavn68RajSGELPLZSCYM/mxrUzAxLpMeujtMyVbjP/mJwaqWBmU+VKE81/S4pf5Fanxsbw8rQUV9B6alcObOn9TFB8wzJS3WhcO9O3nggej4kFy4QmOZm4baTymOjcpBY8DNw+yrkivLd/vAJ//+I3x/oTkouIXt2JyEVEI4loAhFdQURXAPivw7JVW1btPIoxL/yMUx75Hh8v2eFd//Utg72m6CcvONX7MJs+cUhc5Iw1retl4aGxnUK2s/N2+u3fBgPwJVITnKFpnczQjSKgrklpATNGdGwQupED6K0Ql/XzVZz3VxRC3V0Mn/+P/22bleZGHYvkhcFucW2biwjDO/gUXH8F5NzuRkdPbxi8hdD555ziN465EB62p+wAwO2n+5Qbp62rXyw3+nY8+KUoPoKPSOcpvoaSAXkUgOHqnzMu04JlAjO3i7DykdH48Y6hhod2g+wM2w+HYGg/PreOaIvaNjPGxpKs9BT0DpInRktzbyciqEueMkWXSH5IyYDdzMKxwiw6KRZ96++be3UlEPwdd+3cXr6+2KAsXdynGRY/4HPQfdBE0Q92tERKWQgNs6klPWkpLvRonoMX/9wjpMxm37FTmtTG2FMbo1GtDHRubD/xp8bnNw1EZppyAr/92+CwS2FYsXHfcbS8byreWyhTWoKPSJWdHGYeomZAvoqZr4KSaVlwgCve+tV0vYuUSIe2DbIDfny+ujkwIVgkXNSrmbcuTiJhZmq/pE9zdZuuXZDfd/226uCzo5EVRVLCUMT7PIZ6oJsl5gvGBT1872xW2X+zdBmtw50OZWaDZcf//KW4Xd511w5pbdhPa6/PhaVtU2QxEsraouTecaNR7ciUjLYNamJMl0YY2LYe/jqsjUHRskPHRrW8CmWDWum2SuJoBUqD1bb7++di0RECiVTZmUtE/lnM2kYrjKD9GJpvC2bBcDmUBK9lvSw0i7CystMYk7pZvzMHe5u+dYTclssePt37eVSnho727ZRl5Y0reke0X2oIJ/pQySX9+dcl3X1966yn+tDzYMq1nWmsd67ui3o10w33bSj/KobvO3D3GR0DtimyGEcPpew4GYAQ7V1QLysdU28NPR2vlU4Jdtv5Jxv8veBoQM0uofoR6d2+GMByIiogos1EtAXAYw7KFTFVPangtoMnI9ov2h+bO05vj3o17VUMjxf6aDE9wXx27hjdwfvZTWTwGagu6KNwQtViqpHmxichsi7rLQtOKTvZYVpgNDJTYxcxqFeU9EYHo4UwXMuO8kLhr4MNbWevxphpNJbmoBwiGssfs2zM8cLlIjT0m8bSkqVOuaqPd50m8ub9J2z3Pe6lefjn1LX4Y2+1K6lY5Yl3UkEAeBLAeQAGweez86UjEkVJVU8q+NBXv5uun3v3sKD7RfvMuXVku4SP3rGqEWX3geNyEW4eLpaeYKSluNA2SL0mwDh1E2oayS49mtcxLB86UWprPwIF1JvSoynCdhT5h8Z1NiwP1zk/Gyw76v320LjOgdFYdnLWkFE2fZ9aFwElFdja38wbeh4iz44/ZnW2osF2GLuOkZ0amtbKAoDcmmnIzkjxWnOuHNgSQ9pF9hs1e/0+7DlaHLohgBdnSs6eRCHeSQUB4DdmnsXMW5l5GzNvRYJYdqoypeUe/LzhgOm2FrnWc9RA8oVSm/1sinNx9NgJ4Q/nNDtVQyzSKLmPl+xAmWp2ubBXU8t2dvThawYbc+Lk5fgizcxCz3s2zwn8Xto4eQQlc7FV3hzA3CdF8dkJEnrut6l2ZnDncUensSK8DVrVywqIEgOUPF9vX9UXHg97FcqWuTUsM5sPMbGMtcj1TceXlnts3avFZRWGjM1C8hDp3b6diN4mout0oeevOClYdWRfob03DzOqUN1Kx4nkjVIwJ9zbyCn/rmhqbGmX36pIphNY3WJ3RDAt6rPswFJL8FdKvX45unWNVcdis22As9NYlf3z0rh2JlrVy0K5h1E/Ox0TR7XD5WoesMkTeuEWnYU2I9Vlev8cLy73pgwoq2BsOWCc+iot9wT48gyaNMvhIxEShUiVnfHq/wMhoeeO8ea8LZHvnGTKjtnhaL9npyX4dFsy06a+z+IwpF09dIog5DhWmOkNdr8WjUNEJK3QOXn7xot8QEZwy44dRnRsgGcu7Gpp2QnFsA7O5iiKxSuHhxmNamdg4qj2XuVtTJdGuOsMxRevb8u6WPHwaNMXnoMnSrFy5xEAQFmFBw9+aXQReHnORrz202YAQFFpBeb+sR/FZRUxOAohEYg0Te5jzDxZv4KIxjogT7WmuMw8YmBQ29yQ+ybbNFYwQj2YhOgIZilL5CrnwQwZoaQOtd2/ZlPXprVNz5Od8OtIzqBW2dx/54t6N8NT09cBCD/yrG8r6xxW4ROb++Ki3s2C+h49/+fuyEh1WxZqfezbNQCAo0VKjUGPh+FyEcoqPCgqrYArXZF72fbDePDL3xL6/haiIyLLjr+io66zLs0t2OLDRYFJsDJSXXj/2v4h9430O9qjeU5kO8aYgJ8uvwKKQmwI9WOfyI8CM4XfF5YdYt8wv0BW7Rtk21PEleKdbG6N8ltXp0aqYgliYxJC/3Z/GdDS0fILicAT559qOR23ddJYr1/Vm3/pY9pm2fYjhuV5Gw/gWHEZ3py3Ba+qVh0AuOyNX7HnaHFC399CdCRO7GE1x998etfo9nhwbCfkZtkLB480BPiGof7pkhIHu0f0xl98OVrqZqXhnjEdgrQWQlFVdUmzZ6KvblTsHmOfhgjVD0AnilW2dD0/3zsCgPV10Y4xxU0xLd4b8iUjjjdOhlonMFQW5iveWoQV24+gtDzQik4gx/KVCYlHWN8MIuoYulV8qYp5dj5duhNDnp5tWHfLiHa4dkjrkCHnGpF+RdNjmKfEUdQDfPKCUwM2dW5cG4+crYQN10hLMY3uEOwRcrongZ8F5pFK5hXBnSRYSRNrlIzIRaUVpmUh9IoFacsmGZeVnnztYsVj53bBmac2styeKPfFuK6+kH0rfzIXEcpVJbO4rMIwHWk1HSbEh3jm2XmNiPpTAk9sVsU8O3d9shL7C0tMt9mdh4/4iiTwd9tKNP9jzUxzY3gYzpaf3Rjmm3gSof2WZ6W5kaFTdNs3DC/Vf1UhkX6qrhuihLZrViYG47ohrfGnnsaQeX+ZDUkMTfqtjGjE2jVSDckp/WlSOxNPmLyIVCZ5OZn4i65Su1UOqI+X7PDm0nl5zia0un+asoHsWdqEyiOeeXZ2AWgH4EUieomI/kJEzuafr2aY/VCd3yN8TTZiM33iPAuC4+BvUGZqpH75VR/twdm3VV2D9aBjI99bsNWz07/CeKg8LpWNqdXDYUVAmyYa0aFB2BnHHxjrS1rIapLAJjmZqGOjiC+r/8y3KQRT7F65rGdYsoZLZpob/VuHDqSwQ78IHaddLt+9+/3EoZYJRL9ZucuyD39lZ+JHyyOSRUg8wlV2LmPmd5n5bwBuBbAWwPVE9B0RPeG8eMnPJ0t3Bqx7XlefxzYWv3OJ9kAKh8DktFVFM0tctIfB21f1Nd1u9rz8x7mnAADq1UxDVnqKt82/bVTLrkyC+a05NT2XqloLbhvVLuIcQ3YK0uofucGckgG9X5I1Z/pnZE5gPr4hMsvrB9f2R5OcDHRtWhsdGmVjWAdjiorxfYMXOC4t9wRMY325Ypc3V49QtQlL2WHmCgAgoloALgBwg/rXC4B1+lLBkke/Xu1IP1Z+dXb87cyyjyYiDMaQdvVwWb8W8RYl6WDDZ+OPex/VJ+WRs0/B8A4NvA9Xp7InO4VeGs1fw+nHlGNJCmFtQRveoQHGnBLoH2NZLkL8TAAoCS5T3C58fctgAIH3p75avRVlFYxc1dL27x83wO0i/N+CrWBmHD1Z5rzQQqURroPy34noJwB7AEwEsAVKjayGzHyF49IlMcyM+z9fhROlziSxsjJhV9VQ4oAfcFXQpnVq4FSLgqBC5PjeaAPvCO0WSiD3F1P0kTQfXW9M1+CU744TeoUmCYMNZ1vru3OTWujWLCdwbEQXXl/d0Hx27lYTENpVzmtlpmLdnmN4/sc/QAAe/WYNVu86hv5PzgQArN9TiNd1YetC1SDcaaxTATQG8BCAi5n5cWZewvJqERaz1+1Dq/un4cNFOwK2PX5el4j6tPoaV+nfP/2vt407TF8LRwgTmzWdgNg+VBfePzLiffVi2ZVRswDZbW+ntpitfpjVchH2xjQ7995ke6w80K0K5VZXiAgX9WqKK1Wn5WEdGuAb1eoTjIxUN06UlAOAN2pr3EvzUFRWgfkbD6DgyEn8c9ramMktxIZwp7HGA+gAYD4UX51XieifRDSUiOSbZpOrpiy23Dahf2RTNJE+gBIpWiUAPx06lM+OqNwOEeI8xvI8N7KZHdusJpV/5XAAGN+3OQDg9St6B7QHlGKewcipYfR5C3bsWyfZSyKvyelvqTH7Kk7o31wtABq4TcstwwAuH9DCEC11drcmpmN/Em5OoCrOMxd1A5GSnHVAm1xbqTbW7j5m+bu4cucRqHVnJXKrihF2khVm9jDzQmZ+FIqF5yCAL9X/hRB8tcI6jO77iUMj7tfKObOqvu0ltBKWhGgWi78MaIEafpWlw3EMr5WRErT6eCzRbpk0t68wZIYaPdW5SS2kBUnjoM98rJ/t8P9eOemzc2Gvpmgewhr5+Hmnesf0V3o6Nc5W13PAFXppvLnzeJ+IcgJVbVxEXiVVX8k+GFaTFeUV7J3yNUtMKCQu4frsvEJEg4jocSJaAmA7gHMAPA3gtFgIGC6JnlTwto9WWG6LxTTMQ+M6h26UgEQyMyr6UfhoviHa6f7byHaomR7ckTPYeWb4ordiCQE4vbMx64WmpEy7bYh3XXqqT9m3Ssg3smMDZGf4jvmmYeYhy94dokQT4+LezWw9fDNSXHhpfA9VIfUdhJY8UymeLjd/MLT7O1Sl9xrqy+GfXllgur28wgPNoFNS7vO31FdPF68O54hnUsEbAHwEoBGAJwE0YOZhzDyJmVc6IlGUVMWkghoZqZFbYax+61LdoaZ+EviLKT/gjsMMfKw676aluKDdHnacN4mAVvWy0FpX+Vyjskt0EAGPnnMKGmSn48ZhSskTvV+LdjSZoZQdkykivV+OftOoTs6lFDP72ll9FVPcLozs1DBINFYV982LMUS+a6r/PTRLOlgeYmqqtIJxTC0q+tOGA971vR//EQBw4HgJJrz5a9QyCwrxTCr4KjM3Y+ZrmfkzZj7miBQCBraJLiGX5KARnx279FOTvy36+yjvNOezF3ULaKcl0NM//M/u1gTn9wicpsrQ+YtU1r3odhEYwL1jlCo23qgxmCs2kdaP0/jvZc7kFYpUDLbYl2FeUFRQIPimsfQWMLPSEOUVwaemTpaW457PVgEAbv3Ql3DwWHGZ14dn7e7CaEUWYkC4Dso3xkqQZEdv8jTjg+tCVzYPhtWPXagHT3U0fzsVUVOV0PK26C+33p/LzKrY1KZ/g6HfME5tNPmdiALvbO1e1t/TenE0ZSfbME2n1s7Sr6mE2yMSi2r9mmmm016KZaf6fY/t4iLz21K/blQnJTu4h4HRna0teO8s2GZY/lRNCpvqcmH8awvh8XDYSvU7C7aG1V6IjLhWgSSiDCKaQkQLiWgJEY0O0T6ViDYQUX4liegIzIwOD07Hha/8ErMxon1rTTRa16uJO3URN3YeDeGcguqm5F3ar3n4OwU5RWZ5YMLlkj7BM9rawb9gpva/2cNfW3Pvmb56xsr+1k7IQ9vXx+y7hqn7kyMO/5EqJmO6NDYtgWBl8REUiMhgxRncth4eHNvJcO/cNtL3W9Oglq8MyNtX9Qna90uzlBpbLhewaOshVDBb1uSy4uGvnEksKwQn3iWv8wEQM/cHcCmAj0LU2roegP2KjwnAlgMn8Oa8LQCAJdsOm7ap4cgPaHJRu0YqhravH7qhDpnGskYf0RP2vib7fHXzoKDjWFE/O7x6UkEh44h6hT88xde4rD/e5y/pjlb1FB+ltBQXfv27vTxA/iHr/kR7q2pTd450luQQjNf0vWv74dohrY1tyFcM97FzfbnOOjUyr5yuod1z2v8b9x3HseLATMvLth/G9oMnIxFfcIi4KTtE5AJwLYA3AYCZ/wCwHMAEi/Y1AVwM4OvKkjEaisuUaavhz87B41ODJ6CaPKFX1ONZ/rgniRbk9GEktGN2DIhlZYdv/zbYdkK/cyzyv1jRsVG25Ta9L4Z+XEMuSj/Tj/+LhamjcBDtwW4QweIHRllvdOBaaE7ZgPjshCLYuXGRkliSCPjutqFqe50Tc4gAjy0HTqBCN3V1+ZuLcFKXFX/RlkNoed9UXPDyL1iw+UDA/tN+2x3OoQhREG7oeR0i6u1QpfPWAHIBrNOtWwPAPPsXcCeAlwA4U18hRpRVeDB7/T50fGi67X3CtWCYUd2mZcywewqqo3/DoDbm/jGNakXgl+NHl7zIyncQKGIljEiLsvGh9aW/vumpbjSqpeTQSXW7MPfu4aZ1wAzfHwf04FAhzmZjROy4LD47QQn220hEap4iMkQkDm5bD5f0boZUV+hHZJu/T8NxNeOyPxe/6gth/2J5YFTRTe8vC9m/4AwhryQRTVH/vxRK8sDzAPwfEd0e5diawnRUt+4ITKapiKg+gKHM/GmoTrU8O9pfZYegr9p5BFe9rWRIrgpae25WGjo3CW6qTQTsPH/sGmuqo4Oyy0KrePairo6OE67FbFX+GRGNQ+rjXT+ez0FZ+XO7CJf1bY5f7hvhbWOlXBkclCOSKHoiNTaKz05k9Gieg/N75IFZ8bkBfNODDMa4bo2RmhLZiT1aVBaQdHDh5kNK39XMqhwJ+fn53md4ZebZ0TwbbwBwBjM/COBMABc6IkHgb4vZ3fUQgH/a6UzLs6P9Vbayo09G9fT0daZterWoU1nihHzf65JXG/ef2alSZIkWsx/0m7QcK2H+JoklTMGp82C3H/9WoRIYhjOmvwguUpQ8K0UPiKOfl8O3n767xyohqWMy8PmNA/HsRd3gYV/tMW16ULOWpdiw7JjR7dEZGPWvuabbWt0/DQBMtxfppsCOFpXh94KjAW2qA/n5+d5neGXm2elIRK8BaKWtUAt/pkU59j71/xzduhzdegAAEbUG0IqZZ0U5XqVj9QDQwoCdJlS9oKqO2YPpHu1NTF6WbBGWVUttaufcardZsKYNa6WjTytnyhVox6Efr0ezOl5Z/P15jDv7NjCAfq3q+kVoxfhmcrh7f3kvH9DS2QGSFH2NMn99WEs2GSopazC2H7J2SF6/pxAb9x3Xjadcw+7/mOFdt2LHETylvjAfLfI5PS/ddihimaozdpSdAVCsKkOg+ssQURaAv0c59iYAh6AUFtXoDMC/SuZpAJoQ0RwimgNgDIAr1eVWSCD2F5YYlq0KxQV704yG5FFrhFiRlWbfkhJoco3uDsvLyUTP5j6rZrR6uH+UzYA2uWiiFhINVdLC+5mBWpmpaN8w27Au1jj5Xb3htDZJl3qiMvGwuYO3kpzS+rzqnd1HdgwvSNg/t46vBIUHs9cp7/su8t2LeiXIqpSFxnsLtwXdXl0Jqeww8zbdn6ZeZgMoJKL+RBSRmYKZPQBeB3A1ABBROwDdAbxPRJ2IaCYRuZn5bWbupZalGAZgOoAp6vKWSMaOFVsOnDAsW2n2UbwsBCXZf++CHV+wbTcPb2NYrq5WoFuGt0XLeoGlHiqLz24cGHXo+VN/OtX7mSjQqqGvE2XLzwuBRTRjjdM+Y/eO6RizF6jqQMdG2cj0ewnQX6Pxfc3zQQ3v4FNw6tW0d19vO6g8IxZsNtbN1ucB+njxDgCqdVKzYNq4ZTQn6Qe//N2WLNWNcKOxWhLRDwC2QXFW/hLANiL6UZ1uCpd8pVtaCOBDAOOZeQ+A2gA6AvAmqyCiND/LzucRjBdTzNKPm1GZP0zyEwjcfUbH0I2qCWb3Q9sGNR3rN9hXIJIp1UFtlSgybTrhvB556nikPgyMaHWi/J2XLeFAuSpDF06m6eWqzsuX9QrITP3Mhd3QU/WtTE+xSDegu4R2XXv+M2sjAOBYkTF6Sz8L8NOG/Xjm+3VYuu1wWC9mXR75HvuOFdvfoZoRrvfVCwCeB1CTmRsxcyMANdV1z4c7ODMXM/OVzNyfmXsz8wx1/UJmzmPmYl3bUtWa04iZWzLzBeGOF2vibTGI9/jxJJxjr87PGbPT9MPtQ83bsuYXo4Vn2xuDCEgLFXoNe4r4Q+M6++1D+oWAAzpwvESNxgrs/ZI+zZCR6g64V/xbRvI9WvJgkLw6DvQvRMf1Q8N7F29Wt4Y3p5K9+97el+MTtbyE/42rvydOllbg/V+3439LdhjWP/7tmpD9bwviJ6Tnrk9W2mqXTISr7Oxm5mm66SwwcxkzTwWwy1nRqh6JGFKYLA/2vJxMnBEjx+7qgmX9NIsNYd3N3qmjwL3MqqQDQO3M4FmGgwwDAEodIj8rabnHF1njL8m9YzoiK91/uiKQSKaZ7E5jaPif8iT5miYsfz8r8ojTTJuJJKOhwu/Zcby43FutXXuuvDHP2mvjyMlSAECZGu7+9nxj2woPexPdAr6aXtWJcJWdRkQ0joi8kVjq9NLZAKr9k8juT2SsftiSRbExo2W9LPxlYEvL7cl87E4Rri7ulO5uptTcOKwNBrYNvxCo3hE3I9WN+840n6L0d162gi2cU2NJqKKUQtXAo5t6Cv9F13jTHT5RalguV7MyFxwpwsjnAkPU9YoLABQWK9NiJWrV9ke/8VmBpv++Gze+txSPTw1tGUpmwlV2bgXwVwAniOgQER0CcBxKDp6JDssWEVpSwcrOrwOE9tnR3nAr84ctWATN039yNqFcVSEBDXAJR62MFKSnuHBRr6be8xWOTqBZR87t3sRR5V7fV2aaG+P7BhY41ZIKWsrmdwMEq43lNH3V0Hu3aOdVhgpmXNy7acB6fdZkq8hbuwx5enbANBWp0VibdYEvmpLjH82lWTjL/BIZAsC2gycxf+MBVHiMeXyqAvn5+ZWaVNALM+9g5nFQsh+PVP8aMfM4Zk6IeDctqWB8lJ3w2tcJUSwwFrx+ha8ax8UOVJ2uqlTHR004z9elD52OFLfLYE0LdnvrHZT1CvaE/i0cccbV+rTTVXhKWeXyvxsGoE6NVMy5e1gljyxESremORjbtQneu6Yf3tD9ft40zFeB3n8aKhRmldH9p6kIgWVH/vLWIgDAgeOlqPAwTpaW46/vLsWuI0UAgOvfXRrQr4c1ZYgNIexVgfz8/EpNKhgAMx9i5uXqn2Q4Uom3z87IToEly9JTjZe4gZNVp4WkRfuRDccp2QwtOiqgvck6O8k2bSlOFg7KZpjVlYr195iIkJ1R+S86QmScdWpjnNa+Pga3q2e4zwe0yfV+9oT5puu2GZHrP1vw6xblcXvgeAlmrN6Dzg9/j+mr9wRMa+lh+AqVlphYfn76Yz9emrnBruhVFseqnhPROU71VVX5xzfxnRPt1NhY4yoz1Y0+LZ3JWJtsVMeZrNb1s9Ait4bt9uE89Ed3VhQV/z06Ns7GA2PtOYdOvrwXAOCnu4fbHteMUMkPA5IlJoCZLwFEECJg8oSeABQfm2D859IehuUC1RITjHIPW7pGlFcwynRjBhv+QGGpIUP0mBd+MmzfV1iCLWr+n0MnSrFgkzEHULIQbp6d9lZ/cK5WVpVk+8GThrlVjQ66zKyV/YNm9+0hGUh1u9BIzZ4bikR4uMWD83s0xbndw5v/tps12T9ZoPYbXSsjFT10WZPt0NxEIYvVNRNnYSE6lBvTSinp1rR2xD0fKyqDJ9AQo4zql1AzmGXprflbcPikr9zEuj2FAIBnv18PQKnhWFLmwZYDJ7B29zG8NCs5rTzhWnaWAVgLYJ3J32XOilZ1mLVuL4Y+M9tW28v7t/B+jnVisXhPq1UmTXIy8fH1/W21rUanJSpeHN/DMmw8GOHe1v93dd+QbXL8/NuCl4OI7gLbTQ4qVD8a1jK+UGn3oZlS0rROZsgXzqcvDB4kYuX4vGz7YRzRKTBXTfGvshT4+z9rnaHsJP4zW0lwuK+wBOUeD/63ZIe6X1CRqizhKjuPABjMzC7/PwAvx0C+KsHVU5YYltNTXPjq5kFY/MAo7w9vz+Y5AcrN1FsHV5qM1YFwlMdqatwJi/YNs72J1TJT3aZOlQHY/KHUKy+nta8ftG0rkxIXo0z80+zKoP8xr04vBEL0dMkzWmo0Xxh/B+VTmtTC3LuHm9Ys0ytAwWqaMYA9FhmRdxwqwt4Q2ZI/WLTdsLz3WIlFSyWj84kSY1bnk6Xl+Hqlefq8G99binEv/QwAuON/K4LKkShEkkH5FItt70cnSvKw7rEx6NYsx2Da1x7EDbLTkVNDSVPUuHam6f6CkGi0rp/lqCXyuiH2M9p+cF0/AMBXNw8CAGSnp6BBrcgd7TN0SeL0tbS860T/EWyi3Tn+00h/7C2E20WmpYGm3zbE+znY+4OWO8eKl+dsCro9nBpZCzYfxNw/9gMAilRn57W7j+HWD5d728xZvw9fLi/AOwu2Yvn2I/i94BgA4PNlzkRLxZpwQ88rmPkNi20LnRGp6mP2UNDeIG8Z0RbndGtS2SIJQqXgLVwYpI1VOQc91w5uFeBw361Zjk0ZFC7qFZgbBVAKO2ovIpmpbqT5VeaNh64j+lXVRLuNW+QarY9lFcoVNcunpN37g9rmxtR/0I7SXlbhm39LT3Hh1g+XY8WOIwB81dWvelsJd7/y7cWY+PEKPD19vaXFCVCm3m77aLlh3YNf/ham9M5jW9khoj5qZfJgbWoR0djoxYqceCYVBIC3r+pjWNZuOIZaoFC9uy/o4UyiJCF8MtPcaFZXrGqxItTv96pHRofs48FxnfGd7g1Yz22j2iE9xfqnS/vOPXNRN3P5iLy1u5684NSAMiRi2RHs4iLCsA710STHPDjC32cnNyvNa83JTHXbDgCIlFDJDns+9oOh7UG/TM4AMHv9fsNyVnpg+Yyr3l6EEyXlWLfnGG77aDm+WqFMf13+5q8AgPcWbg/Yxw7xSiq4AsCzRHQvEfUkonpElElEdYioExFdB+BzKE7McaOykwqOffFnw/LwDg1M2/nf0v+6pHtsBApBXh3lIZ+R6ljWgSpHm/o1MXlCr3iLkXA4Eb3HHNpKEUmOGf1D4dohra0rUcOeg3JOjVR8dfMgpLpdCVGBPP4SCBFBStj3Rb3ME7T631qn5NX2+ulkpqXEPDK0zd+nBd2unyrTR2z5M2e9z7nZzPdn9vr9eHXuJizbdsTgCP3zhgOo8ERekiUuSQXV4p8XAnBD8c/ZB6VUxAEAP0Dx5bmEmXc7IlkVYPLcTVi961jQNvF6SRx7amPTsbWChSseDv12ncwkwgMu0VhpYXGJpKZWVch6YO0cKqYdwR4uIjAzausc7pWixYEO9G/+Rcm+TAQ0q5uZcAruIROrjsZ3v+0Juf+6PYUoKa/wFk59eY4S7eVh9k7nHTlZiuveWeL9rOHxMBZsOojCYmuFK1rC9dkpY+YnmLkTgHQATQFkM3NTZp7IzMmZjciCeRsOGJYfP6+LZdtK//kM8U3KqIRKvkLVoqZfRXA94eiGbhfh3jHmBToTiWBZnwXBDvqCs1qE4KQ/nWrw4UlTp1xdLsU+qShIifFCYFeGzLTQz4sZa/biWFG599ny9HQlj8+uI0XeF4s3ft6CH9bsBQB0/4dvCm3wU7PwxLS12GKSq84pIp7LUBWf3cx80kmBqhLbDvkuTOPaGZigy6HjD3PiJLM7pUmt0I0EIQIYirJzrV+01ec3Daw0GdLcVW+KtmW9GujYKDt0QyGhcBF5p01HdFRcGIa0q4+/n+XLGq7dj/Wy0tG5SS0QKZaM+8/qZFripzJJC+L7pse/7JAevcJUXF6BFD+H/4e+Wg0i4Pp3lqBUdYi+9v+M6Vp2HS2Gi4B5G40GBCeper8KCcSOQ76U3y9f1tO0TbzyeATTq6beau74KQhmRJukDwB6hplF2TB+mMPn1oyu/ls8vrIjOjbEn00quAuJjaK4KJ+DJaMkEE5tWhv3jukIFxE8rCQoDGZNrQysXgyWbrNX8vLXzQeRouvDRYHPnvIKD0rKPZixZi9K1LD2H9fuDczbRYSf/xBlJ+GxSokfL4s4qaZSQahMqkqSvmBiOqHcCcnNz/co9duUWuKh0y3oITK/x4Z1CJ5cMxYUmxQGBYDL3vjV+1ktmG7KJa8tNCgtiqXLSHmFb02p7nNmqhsz1+7FzsPK5BABKCmvQLlq/XlnwVZv7h8niK9aWUV5Z8FW5GaF9/ZYNyvNa+asbGqkuXGy1LoqriAIPqqIvibEiYmj2qFZXaV+m/6lsonN2nyaZccfTWVo16AmNuw77oCkoSm1UHaKy3zryUSB0aN/trw0a2PA9jJdLY0K3ecyjwfX6KazXARsOXACf5q8AF/dPAjLth1GhYexeas9K1Moks6yE4s8O/5vq58t3YmbP/BF2N99RoeQfTTITsetI4OmKXKUS3r7QiGXP3x6pY0rJCd284HEKsotVLfjujZ2dLyn/xS8ZpFQfZk4qr33c5e8WnhoXGcAMPW/6dm8TkCRXKWIZ2C/mhNv75Z1HZQ2elwUvNBoKPSJC8uDVGonIpR7GEdPlmLRlkPISHXj7ZeewdVn9I54bD1RKztE1IiITiOi64joOSL60gG5IiYWeXZGPjfX+5mZsXLnUe/y/24YgJuHt7XeWb2gNw0L0iYG9GyR4/2cnuLG85eYJ1gTBCfJP7tzTPoNpUL951Jzn7lIEMOOYJfsjFRDvaw3rjA+mG8/vT06NTY6nvsHq9wwVHHm79tKU3IS6w4kBLfsAIH17Ya0q+f9XFbu21uf5NDfZUdTqrYePImLX12AjFQ3+lxwA/Ju+r+IZTf0H+mORPQOEc0FMBfAvwEcA/AOgD87IlkCsVkXDrdcTaWt4btBg9M8t4aTIoUkxeXCJX181p3ze5inzheEUIQzrXP5gJaOj9+4dga+v32oo30GOyTW0p0LQpiM6hxo3Ulzu6D3Ay4p8xgygN+vRm7dcFobW2NkB3Fqzs5ICZpdPBSmDssWlig9/s7Z+gSl6/cWej+XG5Qd45eMQCjRTavVTE/BZ8t22hHbFtH47Kxj5iuIyAXgegCXApjEzMFLsVZhPB7GBS//4l2+cmDL+AkTgrQUF/LPMdZsPd3kiygIdohn2gQiiijrcuh+zdenuMhehXdBsMGkP3U1KBG5NdPwyNlW9bRDKxZBN0eZ4iQj1eUND9dwWThUG4bVbc7NSjOtCTa6c0PDdFiAHykZlSEnMrrrCUsFJKLmRNQIAJj5CfV/DzNPhqLsnElEbzsqYQLxyyZjzsR+Nqw6DOCzGysvx0gwXr/CmblPQUgGrB4qj557Cs7tLrXrBGfISHUbqp9npacEffEM14rivy2aeltmyQOLyzwhZdJPT1mVYHGpPjlWxPr9IlzLzmcA3iQireBGETPvBwBmPgHgYSJKmpLe8zYcwIszN3iXH/rqd8P2tg1q2uqnTg3n30oFIdGIR+hsLJDs4kIiE0zZYURr2Yns3q8ImMYKbEMEVFRYy+6vpB0+aV2+IhLCndxbolpxrgLwLYAz/Bsw8y4nBEsEJrz5KxapYW8HjpcYUlm3qZ+Fdg1DZzytKnlHBMEKu3fwlKv6xlQOp0mUjOaCoEc/ZdTB5BkTNEdUlI8bqySDoZ5j+ukpBiPFFdiPiyhoFfZVO48YlssqzMPiIyVcyw4DADM/SkQdmfk9bQMRpTNzYDnUKkq534nu/fiPhuVw5hOl6KRQ1ZE7WBAqB71eYWbFCZUQ0xXFt7WGhfNzKB3Kf3rKZfJ8XLz1UFAF5oSfD095ECtQJEQTeu4vyTPRCJJozFCLlVnRINteAqnPbhyIFnWtI7H8QxUFQYgcq8rtglBV0D9Y/aeHgBDTWFHqB3ee3h7j1bIlNXT+O+8s2GZo16Z+Fno2z0FuVhoAoNxjVGLcJvrWvsISHD5pv6q5v6N0tISr7FxARL8Q0S8ATtc+q8sXOipZhDiVVPCm95dZbju7WxNMuaqPrX5ya6abarkaZqGK0VJXvQEFwQmq0lRs7Ux7/nFV6ZiE6kmT2hmmJpXQPjuRW3ZS3GTqb+NPvZrp6NS4Fnq3VMok+Vthgj3z7FJa7sGRee+j4OW/RN0XEP401hoAVhl+JkQpiyMUFBSgSZPofKR/LzgadPvj53YxFD8TBKHqEU3UiiDEikzVSbhOVhpOlJQHbA+WzDgcJX5c18Y4t3se3lmwFT9v8BXg1LoI9u1IcRNcREhLUWTVT2MxwzT0PFxKyz3IGXwZanYb44jCE+4T+0Fm/j+zPwAPRS1NgvDx4h2G5Tb1s7yfF94/ErUTPLrqpmH2klMJQnXl8fO6oGW9yk30KQihuKhXU3RqXAuAojT4KzahDCbMwC0jArP11zAJKa+blRYQAq9/ASAiyxB5t8sFFwGp6nyV5niszSqkmM1jhYnZFF40hKXsMPMvQbYtjF6cxODL5QXez63rZ2HPUV+exMwqEJZ67ZDW8RZBSDaSzMm+d8u6qJEmdZCFxGJo+/ro0MgXgeU/ZWXlk6ZFbTGA64e0xs3DzV94B7TO9X7WMhjrp72IfF/19BSXpYUm1UVwucgbvaU5Hmvy+mdHjoREclCOCiLKIKIpRLSQiJYQkelVJKKziWgGEc0iomVEdGss5dp7rBiFqunw2Yu64cPr+nsjr766eVDCW3UEwWnaN8zG4+d2ibcYgpD0nN2tCXq1qIPmalCLv3HDKpN492Y5OL9HHpgZLhfB7Rf6rakeZhYX8vvMDDxydmeM7drYMurY7dKmsZRxNMsOs6Jw+e9nlmsuVPFep9+v4ul4kg+AmLk/lOzLHxGRmc3seQD3MvMIAGcBeISIzo2VUPd//pv38+mdG6JhrQzvvGR6qvjpCNWPrPQUnNq0duiGgiBEhL9OMffuYXj47M64Z0wH2330blnH68+s72/qrYNNnZaDKRMpLmVCy8rRONWtTGNpSk2ZaoXxMMPjUfLs9Gye421vNiOilWQ5s0ujgG3DOtR3vGRLXJ7eaj2tawG8CQDM/AeA5TB3cv4vMy9X2+0BMBuAY/Glv2w8gF6P/eBdnrVun/ezFtlx4LiSPsgswZMgCIIgRMOqfGN+XiJC/9a5OLd7HvLP7hzQ/skLTjUsMxguIq8l6G8j2qGmmjOnaU4Nr/VG294ytwYu7KUUhw4W3aUpHL1a1DGs1yw72jSXN38OK742qSlkULDMRtCsT8M7NgjYlp7icjwIKF6mitYAcgGs061bAyAg6QwzP++3KgPAfqcEufSNX3HwhJKWes2uY6ZtHh7XGVcNainJAQVBEATHqRmkkvmVg1oFrOuo+vVoFdCZjdYct4vQqp4aWKNbr2Vn7t2yLk5polhr9cqO9xmn/q/53tx5enuDJShF9dnRLDsl5RXevio8jHQ/RUUbQl+RXWui9+85Na+22s6dHJYdANp0lT7G+wiAQBVPBxHVAtAHQEyKjT7y9e+m668e3CpolVpBEARBiBU3DDUGnbRXZxmm3TYEgJpfxy9Y/OtbBgEInK6qm5WG+87s6F3OTjf604zv2xzD2tc37OtykcF/SAk9901jFZd5vHKAFauNflgGo1vT2mhQKx0A0CWvlndfvU6jKV5pKa74Vj2PAf7WrVBH9xSAfzDzNqsGWlJB7c9ucsEDx0uweOthW20FQRAEobK4/6xOhuWs9BTk5WQCALIzUsCsKCZjT/U5/RIRWuaqjs7qOrMZq2Ed6uMidUqLCOiSVxvNVAdpAtC0Tib6tKxr2EcJPSev9eWG0xRlzMOsOigbLTYeBq4b2hr1ayrKTkaKW6fs6Ka7VPl+/XQy/ntZL2x7apxjSQXjpexojjE5unU5uvUBENH1AMqY+b/BOi4oKAAze//sKjtz1htnxq4dHGg6FARBEIREgJlBBCx98HT1M+G/l/U0tJl91zA1wsoXLQUYrQp/7tscXZvloFvT2t4cP4BvSqtmekqAlSXFpRgTNAfm+89UlDEPK2P5R4Mp02w+P55TmtTyFgvVO0Frlp1RE27Bnf9bgRb3fotznvoq3FNjSryUnU0ADgHQu5p3BrDYrDERnQ9gOIDb1OV2Tggxe71Pt7rrk5WGbQ+OC3QKEwRBEIR48u8/dwfgKw2RluJSp7EC0WY4vJYdq5KezOiSVzvAd8jKTTXFrTgnB+ThURMhprrJ6CvEmgM1e+Uym8bSlKG0FJfXavTKBGfqR8ZF2WFmD4DXAVwNeJWX7gDeJ6JORDSTiNzqtqEAbgVwC4AsIqoJ4EEn5LjzfysD1vX28zoXBEEQhETh3O553s++KCu2VEzMVpupPGb7E8h06ivFpfrs+OXtKfd4MLR9PaSroeZ9W9X1juffv6bs6H2NNMUnXTfN5RRxz7NDRAsBfAhgvBpaXhtARwCa19SHAIYBOACgUP1r4YQAx03qjnx640DHvcAFQRAEIRo+vK6/YVmvhJgpE2ZtmZWwc7vPOJfLN7U0eYJviizF7VKisfwG9TAweUIv1Ex3g6AkO1TGZYNvDpF5lmVtXbrOsuMUccuXzszFAK40Wb8QQJ5uOc+/jVOUlpuXkLdKyS0IgiAI8WBAm1zDMsNnzWG2LmxLZJy++uzGgQFpVMwsPSXlFUhPcXu3jenic35WfHYCMyUr4xFOzauNE90qsOPQSaW92+XLxQNF1lb1NOdp3+g+y07y5NmJO/uO+epdrXj4dMO2rCA5DxKdUZ3MC7cJgiAIyYNewQlm2dErQZqfT6h2AFBS5kGNNLchN47GFQNaIs1tbX1p2yAbZ5zSyDvWA2d1QlZ6infZRUoyQX30GGDus+MU1VbZWbD5IADF2SunhlKpVatHUpV54y/OOHMJgiAIiYtewfGo0ViWbX0eyrYpKfegY+Na+OzGgQHb6men46pBrVAjiGHART5LzXk98pClq7xOBG80lv8+AJDmTr48O46j5dkJFXJ+20crAPicvd6+qo/jJ1cQBEEQYg5bJ6lTprHC56bhbTCoTS4yTOpaAYFTWDcOM1ZadxEZc+gYZDKJ5ILOZydVsewcmfc+8vKc8WRJOmVHy7MTTNn5cnlBwLqmOZkBxdgEQRAEIRFxkc95WO+/Y4rmoGyh9phFXJ3SpDZy1SSAdrh3TEfDsqLsGMfQFpUio4F9aMpO/9a5aJyTiZzBl6GgIPB5HQlV1zklQio8jIkfrwAAbH7iLO/61vVr4uMbBsRJKkEQBEGwjz4sPJSDskaQmp/BlSVLGZQpJ43xfZv5trkC/YOI1ILaJtNYNdLcqJmhqCSNa2difN/mSgHSisLwBTMh6Sw7odC8wwFj5ka3i1AvDC1WEARBEOKFfnpKKxcRjP/dMCCi6azgMhDGdfM5GT95QVfvZzeRqUzTJw4BgQIsO8seOh2TJ/QyrNNqgDlBtbPsDHt2DgDgifNPja8ggiAIghAh+ozEDLZ0w3ARoVeLOkHdNDiYyScUFrtqPjsjOxrre5OqBGk+O9rQVr5BTlHtlB2NS/s1j7cIgiAIghAxmqLwzEXdkGmhLKS6Xfjw+v5YsvVQUKXGzixWmtuFUkO+HGvnZ1Kjsd68so8qqzGfjttF9gZ1iGo3jQUA6x4bE28RBEEQBCFiiHzKTq2MVKSGSMIXiU+OP/4JdxUZzNWdNLcLY7o0MrZXtZs0tztoqHwsSGplh5lRXFbhXT54vAQpLoq5uUwQBEEQYol/ZmQbe1i2tttLZprx2RlMX3G5CG0bmPvc3DS8jen6WJKU01hLtx1G+4Y10fOxH1BWoVzGD67rh6enr0e5x2kXLUEQBEGoXDJS3EGjq/zRW4LMt4dvacnNSkczm8l49UOHskLFgqSz7OTl5aF3y7q48pZ7vIoOAEyeuxkrdhzBPWM6xFE6QRAEQYier28ZjBa58c36P7R9fdw5Ooxnqp8+dUnvZujWNAdf3BSYpRkA8vPzJamgFQUFBWhx77dYWm+UYf1Pf+wHANRRS0MIghBb9Dk3BEFwlsy08PxeamWkolPjWlGPe3n/FlH3oTG0fX00z62BHs3rmG7Pz893LKlg0ik7z81Y7/38/CXd8MlfjYkCz/IrPCYIQmzQ59wQBCG+tG1QE09eYJ5yJZzpsMfO6+KQRJVL0vnsfLx4B1KycwEA5/doGrC9dmZqZYskCIIgCNUauwrVe9f0i8n4SWfZ0dg6aaz387iuijUnzaRUvSAIgiBUZxrWykBeTmbMx7Ez6Ta4Xb2YjJ10lh3AqOgAwH8u7YnL+x/EtN92x0kiQRAEQUhMxnaNvXtHeGHyzpOUyo4Z/Vrnom+ruvEWQxAEQRCqHzbqd8WSpJvXuXyAtad4ZWdsFARBEARBwaoye2WQdMrO2d2axFsEQRAEQRASiKRTdgZ1bQ8iQn5+frxFEQRBEAQhQpxMKph0PjsFBQVo0kSsO4IgCIKQKETinpyfn4/rr7/eEYUn6Sw7giAIgiAkHuKgLAiCIAhC0tK0TibOjGMFg6SbxhIEQRAEIbFokZuFy3Oz4ja+WHYEQRAEQUhqRNkRBEEQBCGpEWVHEARBEISkJumUnby8PMmzIwiCIAhVHCfz7BDbrbuewBBREwAFgOTZEQRBEIRkYdeuXXqFJ4+Zd0XST9JZdgRBEARBEPSIsiMIgiAIQlITV2WHiDKIaAoRLSSiJUQ0OkjbO4loqfp3d2XKqVHd/IDkeJMbOd7kRo43ualuxxstcfXZIaJJABoz81+IqD2AhQA6MfNev3ZjALwAoLu6agWAO5l5qrq9Unx2iAjJ4ONkFzne5EaON7mR401uqsvxVnmfHSJyAbgWwJsAwMx/AFgOYIJJ8xsAfMDMxcxcDOB9AH+NVoZYa8bh9p9o7cMl0eSX43WWRJNfjtdZEk1+OV5nSTT5K9syFTfLDhG1BbABQENm3qeuewlAPWYe79d2J4A7mPl/6vKfAPyHmRury00B7ACApUuXolGjRrZkyMvLQ0FBgW2Zpb20l/bSXtpLe2lfee337NmDXr16aYvNmHmn7UF0xFPZGQRgHoAMZi5R1z0GYCAzj/RrWwZgHDN/ry6PBDCDmd3qcg8AyypTfkEQBEEQKpWezLw8kh0TIRrLX9uyKgKf/JOTgiAIgiA4Tjyrnu9T/8+x+OzfNke3nANgv275dwA91c/7AXicEVEQBEEQhDjiAlBf/fx7pJ3EU9nZBOAQgA7wKTidAUwzabtYbQddu8XaAjOXQXFuFgRBEAQhuYjIT0dP3KaxmNkD4HUAVwMAEbWDElr+PhF1IqKZRORWm08GMF7Ny5MB4FJ1nSAIgiAIQlDi7bOTD4CIaCGADwGMZ+Y9AGoD6AggFQCYeTqUEPX56t9bWo4dpyGiPkS0kYiuDNHuMjXB4RIieo6IrHyNEho7x0tEw4hoHRHN0f1dWIliRg0RpRLRRFX2uUS0QHV0t2pfpa9vOMebDNcXAIjob+pL0iwi+o2I/hakbZW+voD9402W66tBRO2IqIyIhgVpE/cktE4R6niJ6EoiWuF3fQdXrpTRoyYYnuP3VzNI+/C+w8wsf+ofgPMBfABgKYArg7TrAmAPgHpQFMbZAG6Ot/wxPN5hwbZXhT8ALQFsAVBbXT4dwHEoSaqS7vqGebxV/vqqx7ESQAP1czsA5QD6JuP1DfN4k+L66o7nQwAnAAyz2D4GwDoAGerfOgBj4y13DI/3SqttVekPwJQw2ob9HY63ZSfRWMzMlwIoDNHuGgDTmPkAK9Nxb8GBJIdxwO7xJgOFAB5m5qMAwMw/ACgGMNCkbTJc33CON1m4nNWcXcy8AcBhKEqfP8lwfQH7x5s0EFEfKEr7/iDNYpKENh7YPN7qSNjfYVF2dLD9ZEV9oLwtaKwBcAoRZTovVewI43gB4Dwimk1EPxPRg0QUT+f2sGHmg8z8rrasmjzTYP4jUuWvb5jHC1Tx6wsAzLxK+6wmHj0OYIZJ0yp/fYGwjhdIguur8qj6Fwyz69s7ZhLFFjvHCwA3qNPVc4joplgLFSuIaLJ6j35HRMODNA37O1xVb/h40xDAUd3yESj5gepBzeScZBwFsADAc1DMwt8CqAPgzngKFSWnAdgG4CeTbcl4fYMdb9JcXyI6FcDHALIAXMzMR0yaJc31tXm8SXF9iehMAKuZeWcI9wyz69sghqLFhDCOdy8UJXcKlBDtOUTkZuaXYi+lo6wFMIuZF6sWrdlENJiZV5i0Dfs7LJadyDFLcljlnBztwMzLmfkpZi5n5uMAngLw16ro1AkApET0PQHFj8EqJ1PSXN9Qx5tM15eZf2PmzgDOA/AVEfW1amqyLimPNxmuLym1FO8F8KTNXap0EtpwjpeZv2Pmt1lhH4CXAFQ56456jy5WPy8GMBXKlKTlLibrLO9pUXYiwyzJIaP6zKtuB1ADvkRPVQb1B/41AM8z81KLZklzfW0erz9V9vpqsJJSfiqAW002J8311QhxvP5Uxet7KYDvmfmQjbahktBWBcI5Xn+2A2jhsDzxINhxhP0dFmUnMsySHK5m5qI4yRNTiOhW1Tqg0RBAKYCDcRIpGp4DsIiZPyGidCJqbtImma5vyONNhutLRLmq34qeE1Cmd/yp8tc3nONNhusLYAiAcVpIMoBGAF4goq9M2gZNQltFsH28RHSP36qGAHbFXkRnCfM4wv8OxyvMLJH/AMyBLlQTyjzgzwDqsi/sbTeAXCgK40xUwdDVMI53CoAr1M9uAF8AeDveckdwnPcCeBFATfWvM5RcT0l5fcM43ip/faFEIa0AUENdrg/lzfCWZLy+YR5vlb++Jse/FWq4NYBO6jV0q8tjoDisaqHna1GFQ89tHO9sAKepn2sA+BXAI/GWOYJj3ANfKoVWUHxytOOK+jsc9wNMpD8AvaA8+I9A8fT+XF3fDIp5rKmu7WVQ8tMshvL2TPGWP1bHC2AQlDIes9XjfQ1ArXjLH+axtodi5vT/y0/G6xvO8SbJ9c0A8BgUR9y5UHLQ5Ks/hMl4fW0fbzJcX91x91V/s4qhKHsPA+gPoABAhq7dner1XQrg7njLHcvjBTAOysNeu76TAKTFW/YIjvUuKEmD5wJYAiW1grYt6u8wqTsJgiAIgiAkJeKzIwiCIAhCUiPKjiAIgiAISY0oO4IgCIIgJDWi7AiCIAiCkNSIsiMIgiAIQlIjyo4gCIIgCEmNKDuCIAiCICQ1ouwIgiAIgpDUiLIjCIIgCEJSI8qOIAiCIAhJjSg7giAIgiAkNaLsCEKSQkTfElEFEa0jout16/9HRO/ols8hojXq32kxkuUFItpDRPmx6D8MOWYSUTERDQtzvxpE9BQR1YyRXK2J6O9ElBKL/gWhuiPKjiAkKcw8Dkql5G+Y+TUAICIXgBEARurafQ3gcwCXMvPcGMkyEcD0WPRtBRFdSURz/OQYCWBPBN1NATCHmY87IFoAzLwZwFEAT8aif0Go7oiyIwjJzQ8ARumWewCYA6AJEZ2iW98VwMpKlKvKQERnAajLzN/FeKjJAM73uy6CIDiAKDuCkNz8AKAbEdVXl0cBeBXAFgCnAwAR1QZwhJmZiMaoUz0/ENHPRDRRbdOKiFYTUSkRfauuG0FE64loLRG1JaJ0InqWiBYS0Vwieo+I6lgJRkRNiOgzdZz5RPSIankCEb1PREfUdR8T0VJVpjq6/Tuq+/5KRF+r00xHiOgjIjoHwH0AuhPRHCKa6Td8dyL6nIh+U6f1UoOcw8sAePcnohuJaCsRTVGXBxDRCiLaqmujTZdNJKIv1KnEF4moGRF9QEQr1XG901bMXAFFEb00iCyCIESAKDuCkNzMA1AM37TVIAA/Q1GCTlfXDQcwW/2cDeBaZj4dwFAA5xHREGbeAmA0AAJwFwAw8ywAvwI4k5k3AngKQF8AQ5n5NAB7AbxhJpSq1HwDYAMzD4EytXYGgJvVvi+DMgV3GoAJzNxL3fUadf8Udf/pzNwPwCVqHyuY+c/q1NwkdXmYOn2lpxeAP0GxdHUFcG6Qc9gXwEZtgZlfgTKtpS0vADBRv4Nuuqw7M5+vjjMewGMArgDQE0Bnk3H/ANAniCyCIESAKDuCkMQwcwkUhed0IkpX1xUD+BHAUNWiMVJdBhQF4wkimg9FAeoIoL+6XwGA7wBcDwCqlaU2M29VlZfrAbzKzKVqX/8HZVqmrolofaE88F/SyfkRgGv92n3DzGXq56UA2qmf+wNoC1WZYuYiAB+HcWo+Y4Vy9ZjbBWnbCMCxMPrW86VOvvVQlK9y1YqzEkB7v/bH1PEEQXAQ8fwXhOTnBwC3ABgMYL66biaAGgAGAGjGzDvU9VOhOBJfqk5rTQGQpevrdQBvE9H9AC4H8K66vh6ATAB3EdF16jo3gO0AGgM45CdTc/X/94lIW5eFwBeww7rPxQDS1c956v/7ddsP+B94EI5Y9GuGCwCH0bfVOOV+y2Um43ogL6GC4Dii7AhC8vMDgKcB3ATgCQBg5kNEtBzA1QB2AIDq19MOwM3MrD3c0/z6mgagCMoU0DkAzlTXH1DXP6JOIUHtMxfGB7zGdvX/c5j5mNqWoChNdihQ/28AX3SV3X3DZS+U6T1/MnSfzaxXkVBbHU8QBAeRNwhBSH5WQrGADAWwXLf+ByjWmR/U5YNQlJahgNdxeYi+I3X65S0o/jlLtCkmZvYAeA3A1ZrTLRF1gDKFZsYiAMsA3KBbdwWAf9k8poVQ/GiuU8fKhKKA6TkGVUlRHaf72ezbn2UAWpms705EKaqSdn6EffvTGsZrJAiCA4iyIwhJjmql+RHAbFUp0fgByrTJHLWdB4oT7UVENA/AfwFsAHAlEd2o2+8NKH4lr/sNdS8Uv5R5RDQLwIsALmbmCiJ6AcAYta+71bHOBtCXiOap+XBGQ5luAxG9CqA7gPuI6HxSkiJeCWAMET2q+tqcA+AMIloE4AMAM2CcbvoRQLl6LF0BrCCir1TZX1CjqB7WyaU/Rj0fQnHi9qcOFEXoGwCLATTSor5MxvE/nkd142rHTACGqeMJguAg5LNWC4IgVB2IqB4zH9At3wOgBzOPd3gcgjJ9909mnqeuywfQkpmvdHCcCVAi2a4P2VgQhLAQy44gCFWV/1OnykBEWVCsUt86PYhqGRsPYALFrlxEKyiWn1tj0b8gVHfEsiMIQpVEndq6AUAhlEiuz5k55uUWiOg+AH+F4qD8CTP/LdZjCoIQHaLsCIIgCIKQ1Mg0liAIgiAISY0oO4IgCIIgJDWi7AiCIAiCkNSIsiMIgiAIQlIjyo4gCIIgCEmNKDuCIAiCICQ1/w/2BipgaOGWpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "species.plot_spectrum(boxes=[model_full],\n",
    "                      filters=None,\n",
    "                      offset=(-0.12, -0.05),\n",
    "                      xlim=(0.8, 5.),\n",
    "                      ylim=(0., 1e5),\n",
    "                      legend={'loc': 'upper right', 'fontsize': 10.},\n",
    "                      figsize=(7., 3.),\n",
    "                      output=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating synthetic photometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [ReadModel](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_model.ReadModel) class can also be used for calculating photometric fluxes and magnitudes. To do so, we create a new instance and set the `filter_name` argument to the [VLT/NACO M' filter](http://svo2.cab.inta-csic.es/svo/theory/fps/index.php?id=Paranal/NACO.Mp&&mode=browse&gname=Paranal&gname2=NACO#filter). This will automatically downloadd and addd the filter profile. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding filter: Paranal/NACO.Mp... [DONE]\n"
     ]
    }
   ],
   "source": [
    "read_model = species.ReadModel(model='drift-phoenix', filter_name='Paranal/NACO.Mp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create again a dictionary with the parameters but now run the [get_flux](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_model.ReadModel.get_flux) method, which returns the flux in W m-2 um-1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flux (W m-2 um-1) = 1.33e-17\n"
     ]
    }
   ],
   "source": [
    "model_param = {'teff':1510., 'logg':4.1, 'feh':0.1, 'radius': 1., 'distance': 100.}\n",
    "flux = read_model.get_flux(model_param)\n",
    "print(f'Flux (W m-2 um-1) = {flux[0]:.2e}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we provided a radius and distance, the emitted flux at the planet surface has been scaled by $(\\mathrm{radius}/\\mathrm{distance})^2$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can use the [get_magnitude](https://species.readthedocs.io/en/latest/species.read.html#species.read.read_model.ReadModel.get_magnitude) method to calculate the magnitude for the NACO M' filter. Note that the returned absolute magnitude is set to `None` if the parameter dictionary does not contain a radius and distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading Vega spectrum (270 kB)... [DONE]\n",
      "Adding Vega spectrum... [DONE]\n",
      "Apparent magnitude = 15.53\n",
      "Absolute magnitude = 10.53\n"
     ]
    }
   ],
   "source": [
    "app_mag, abs_mag = read_model.get_magnitude(model_param)\n",
    "print(f'Apparent magnitude = {app_mag:.2f}')\n",
    "print(f'Absolute magnitude = {abs_mag:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, at a distance of 100 pc, the difference between the apparent and absolute magnitude is 5."
   ]
  }
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