{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "178c0a2a",
   "metadata": {},
   "source": [
    "# Thermal Emission Retrievals of Bare Rocky Exoplanets \n",
    "\n",
    "Tidally locked, hot, bare-rocky exoplanets provide a unique avenue to explore surface geology by probing mid-infrared absorption features characteristc of specific rocks.\n",
    "\n",
    "In this tutorial, we explore how to perform retrieval analysis when assuming a bare rocky exoplanet with no atmosphere (though, we note that all the surface + atmosphere + clouds models featured in 'Rocky Planets with Reflecting and Emitting Surfaces' can be used in a retrieval, here we only explore planetary bodies without atmospheres in since, as we will see below, the retrieval run very fast compared to models with atmospheres and clouds). \n",
    "\n",
    "This tutorial assumes a basic understanding of the 'Atmospheric Retrievals with POSEIDON' tutorial. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89224c6f",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "  **Note:**\n",
    "\n",
    "   As of POSEIDON v1.4, there is a surface albedo database that is now included in the `inputs` folder. \n",
    "   If you already have downloaded the v1.3 version of the inputs, and don't want to redownload them, you can \n",
    "   access the surface_reflectivities folder [here](https://github.com/MartianColonist/POSEIDON/tree/dev_bugfix/POSEIDON/reference_data/surface_reflectivities_txt_files/albedo_database_for_v1.4) to add it manually.\n",
    "\n",
    "   Download the `surface_reflectivities.zip` folder, unzip it, and add it to your `inputs` folder. \n",
    "   \n",
    "   As of 1.4 you should have four top-level folders in `inputs`: `stellar_grids`, `opacity`, `chemistry_grids`, and `surface_reflectivities`. \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6954571f",
   "metadata": {},
   "source": [
    "## Case Study: TOI 1685b Retrieval\n",
    "\n",
    "Lets define the stellar and planetary properties for our system: TOI 1685b. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3ec87aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from POSEIDON.constants import R_Sun, R_J, M_J, R_E, M_E\n",
    "from POSEIDON.core import create_star, create_planet, load_data, define_model, \\\n",
    "                          wl_grid_constant_R, set_priors, read_opacities\n",
    "from POSEIDON.visuals import plot_data, plot_spectra_retrieved, plot_PT_retrieved\n",
    "from POSEIDON.retrieval import run_retrieval\n",
    "from POSEIDON.utility import read_retrieved_spectrum, read_retrieved_PT, \\\n",
    "                             read_retrieved_log_X, plot_collection\n",
    "from POSEIDON.corner import generate_cornerplot\n",
    "\n",
    "import numpy as np\n",
    "from scipy.constants import au\n",
    "from scipy.constants import parsec as pc\n",
    "\n",
    "#***** Model wavelength grid *****#\n",
    "\n",
    "wl_min = 0.3     # Minimum wavelength (um)\n",
    "wl_max = 15     # Maximum wavelength (um)\n",
    "R = 1000         # Spectral resolution of grid\n",
    "\n",
    "wl = wl_grid_constant_R(wl_min, wl_max,R)\n",
    "\n",
    "#***** Define planet properties *****#\n",
    "\n",
    "planet_name = 'TOI-1685b'  # Planet name used for plots, output files etc.\n",
    "\n",
    "R_p = 1.468*R_E    # Planetary radius (m)\n",
    "M_p = 3.03*M_E\n",
    "d = 37.6153*pc\n",
    "a_p = 0.01138*au   # For reflection, we have to set the semi-major axis \n",
    "\n",
    "# Create the planet object\n",
    "planet = create_planet(planet_name, R_p, mass = M_p, a_p = a_p)\n",
    "\n",
    "\n",
    "#***** Define stellar properties *****#\n",
    "\n",
    "R_s = 0.4555*R_Sun      # Stellar radius (m)\n",
    "T_s = 3575        # Stellar effective temperature (K)\n",
    "Met_s = 0.3          # Stellar metallicity [log10(Fe/H_star / Fe/H_solar)] <--- note: for PHOENIX, only the solar metallicity models are used  \n",
    "log_g_s = 4.778       # Stellar log surface gravity (log10(cm/s^2) by convention)\n",
    "\n",
    "# Create the stellar object\n",
    "star = create_star(R_s, T_s, log_g_s, Met_s, wl = wl, stellar_grid = 'phoenix')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f7fda0f",
   "metadata": {},
   "source": [
    "## Loading JWST Simulated Data from Online Version of PANDEXO\n",
    "\n",
    "Here, we have created a simulated JWST MIRI dataset with the online version of PANDEXO (https://exoctk.stsci.edu/pandexo/calculation/new) \n",
    "\n",
    "The online version of PANDEXO is a great alternative to the downloadable version featured in `Supporting a JWST Proposal with POSEIDON' tutorial.\n",
    "\n",
    "In order to make the simulated data spectrum, we \n",
    "\n",
    "1. Resolved the target from its name \n",
    "2. Used the default stellar spectrum from phoenix \n",
    "3. Uploaded a forward model spectrum made in POSEIDON (micron, Fp/Fs) [we will keep the forward model here a secret, as to not ruin the suprise of the retrieval analysis]\n",
    "4. Baseline of 1, with 10 transits \n",
    "5. MIRI LRS Spectroscopy, Slitless LRS, Optimize, 80% Full Well, and a Constant Minimum Noise of 0 \n",
    "\n",
    "This process is a bit different than the one done in the 'Supporting a JWST Proposal with POSEIDON' tutorial. In particular, we are scattering simualted data around a spectrum that we then bin down, instead of using simulated error bars to generate synthetic data around. \n",
    "\n",
    "Lets load in our pandexo output! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9d8aed3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#***** Specify data location and instruments  *****#\n",
    "\n",
    "data_dir = '../../../POSEIDON/reference_data/observations/TOI-1685b'         # Change this to where your data is stored\n",
    "datasets = ['TOI-1685b-MIRI-PandexoOutput.txt']  # Found in reference_data/observations\n",
    "instruments = ['JWST_MIRI_LRS']             # Instruments corresponding to the data\n",
    "\n",
    "# Load dataset, pre-load instrument PSF and transmission function\n",
    "data = load_data(data_dir, datasets, instruments, wl, skiprows = 1)\n",
    "\n",
    "# Plot our data\n",
    "fig_data = plot_data(data, planet_name, y_unit = 'eclipse_depth')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d5b9704",
   "metadata": {},
   "source": [
    "That looks very noisy. Lets go ahead and bin it down!\n",
    "\n",
    "We have introduced a new function that takes pandexo's output and bins it down. \n",
    "\n",
    "Since we often want to try many different binning resolutions, we also specify a `saved_directory` to store the binned down data at. \n",
    "\n",
    "Note that, for MIRI data, you have to specify that it is MIRI since pandexo returns a txt file that is flipped in wavelength compared to other datasets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cab42c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from POSEIDON.instrument import create_binned_down_data_from_pandexo\n",
    "\n",
    "data_dir = '../../../POSEIDON/reference_data/observations/TOI-1685b'\n",
    "saved_directory = data_dir + '/binned_spectra/' #<- specify where to save the data at\n",
    "file_name = 'TOI-1685b-MIRI-PandexoOutput.txt'  #<- name of the file we are loading to bin down\n",
    "R_to_bin = 15                                   #<- R to bin down to, here an R of 15 \n",
    "\n",
    "create_binned_down_data_from_pandexo(file_name,\n",
    "                            wl, R_to_bin,\n",
    "                            stored_directory=data_dir, \n",
    "                            saved_directory = saved_directory, \n",
    "                            is_miri = True,)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "badcc159",
   "metadata": {},
   "source": [
    "Lets see what this dataset looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e66d61f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#***** Specify data location and instruments  *****#\n",
    "\n",
    "data_dir = '../../../POSEIDON/reference_data/observations/TOI-1685b/binned_spectra'         # Change this to where your data is stored\n",
    "datasets = ['TOI-1685b-MIRI-PandexoOutput_R15.txt']  # Found in reference_data/observations\n",
    "instruments = ['JWST_MIRI_LRS']             # Instruments corresponding to the data\n",
    "\n",
    "# Load dataset, pre-load instrument PSF and transmission function\n",
    "data = load_data(data_dir, datasets, instruments, wl, skiprows = 1)\n",
    "\n",
    "# Plot our data\n",
    "fig_data = plot_data(data, planet_name, y_unit = 'eclipse_depth')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6481be",
   "metadata": {},
   "source": [
    "Since MIRI's sensitivity falls off extremely at the longest wavlenegths, lets zoom-in a bit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73c2ce3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#***** Specify data location and instruments  *****#\n",
    "\n",
    "data_dir = '../../../POSEIDON/reference_data/observations/TOI-1685b/binned_spectra'         # Change this to where your data is stored\n",
    "datasets = ['TOI-1685b-MIRI-PandexoOutput_R15.txt']  # Found in reference_data/observations\n",
    "instruments = ['JWST_MIRI_LRS']             # Instruments corresponding to the data\n",
    "\n",
    "# Load dataset, pre-load instrument PSF and transmission function\n",
    "data = load_data(data_dir, datasets, instruments, wl, skiprows = 1)\n",
    "\n",
    "# Plot our data\n",
    "fig_data = plot_data(data, planet_name, y_unit = 'eclipse_depth', y_min = 0, y_max = 3e-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6c0552",
   "metadata": {},
   "source": [
    "Great! We now have binned down synthetic data to run a retrieval on."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "331d5457",
   "metadata": {},
   "source": [
    "## Surface Composition and Geological Implications \n",
    "\n",
    "Now, lets set up our forward model for the retrieval. For bare rocks we are interested in two main properties: surface temperature, and surface composition.\n",
    "\n",
    "For surface retrievals, lets try three different surface albedos simultaenously and see which the dataset favors the most. \n",
    "\n",
    "To do this, we define three surface components: \n",
    "\n",
    "1. Black = Albedo of 0 at all wavelengths. Would be synonymous with a non-detection of surface geology, but helps in constraining a surface temperature. \n",
    "\n",
    "2. Granite = A felsic (silica-rich) rock. In the Solar System, only Earth has a large amount of granite when compared to other rocky planets due to plate tectonics (where mafic crust is reprocessed as granitic continental crust). However, granite by itelf doesn't always mean plate tectonics, as it could form due to a planet just being silica-rich from formation. \n",
    "\n",
    "3. Basalt = A mafic rock. In the Solar System, basalts represent the most ubiqituous surface, as it is the natural consequence of what forms from the partial melting of ultramafic mantle material. If this pattern extends beyond the Solar System, we would expect basalts to be common as an exoplanet surface. \n",
    "\n",
    "If you are more interested in how to start interpreting specific rocks in the context of exogeology, please check out Mullens et al (2026) Appendix material, which is also present in the surface albedo database!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65d4c385",
   "metadata": {},
   "source": [
    "## CLR vs Uniform Priors for Surface Components\n",
    "\n",
    "Surface component percentages can be defined in both linear (0-1) and log space. For the retrievals in this tutorial, we'll demonstrate both for the uniform prior. \n",
    "\n",
    "Uniform priors are the normal, default prior, where percentages for each surface component are drawn seperately and then normalized to 1. \n",
    "\n",
    "The CLR (Center-Log Ratio) Prior can also be utilized for surfaces. This prior is useful when you can't make any assumptions about which surface component will be the dominate surface component. In particular, this prior draws for N-1 surface component percentages, and fills the rest of the surface with the remaining surface component until they add up to 100%. In particular, it has less one less free parameter when compared to the uniform prior. Additionally, the CLR prior requires the percentages to be in log-space. \n",
    "\n",
    "In this tutorial, we will demonstrate both the uniform and the CLR prior. \n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "As described in Mullens et al. (2026), both uniform and CLR priors have advantages and disadvantages. The CLR prior (with log percentages) is useful in determining which surface is most likely the bulk surface component, while the uniform prior (with linear percentages) is useful in discovering degeneracies that come from linearly combining different albedo datasets.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827d4528",
   "metadata": {},
   "source": [
    "**Model Definitions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48f7afb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['R_p_ref' 'T_surf' 'Black_percentage' 'Granitoid_H12_percentage'\n",
      " 'Tholeiitic_basalt_H25_percentage']\n"
     ]
    }
   ],
   "source": [
    "model_name_linear_uniform = 'TOI-1685b-Linear-Percentages'\n",
    "\n",
    "#***** Define model *****#\n",
    "\n",
    "bulk_species = []\n",
    "param_species = []\n",
    "\n",
    "# List the surface components here \n",
    "surface_components = ['Black','Granitoid_H12','Tholeiitic_basalt_H25']\n",
    "\n",
    "# Create the model object\n",
    "model_linear_uniform = define_model(model_name_linear_uniform, bulk_species, param_species, \n",
    "                            radius_unit = 'R_E', surface = True,  # <----- Set surface = True\n",
    "                            reflection = True, thermal = True, thermal_scattering = True, # <----- Set reflection and thermal to True \n",
    "                            surface_model = 'lab_data',           # <----- Set surface_model to 'lab_data'\n",
    "                            surface_components = surface_components,  # <----- Input surface_components                    \n",
    "                            disable_atmosphere = True,                # <----- Set disable_atmosphere = True for bare rocks\n",
    "                            surface_percentage_option = 'linear',     # <----- Use linear percentages (0 to 1)\n",
    "                            surface_percentage_apply_to = 'models')    # <----- Apply percentages to models (3 models for each spectra)\n",
    "\n",
    "print(model_linear_uniform['param_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d995bcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['R_p_ref' 'T_surf' 'log_Black_percentage' 'log_Granitoid_H12_percentage'\n",
      " 'log_Tholeiitic_basalt_H25_percentage']\n"
     ]
    }
   ],
   "source": [
    "model_name_log_uniform = 'TOI-1685b-Log-Percentages-Uniform'\n",
    "\n",
    "#***** Define model *****#\n",
    "\n",
    "bulk_species = []\n",
    "param_species = []\n",
    "\n",
    "# List the surface components here \n",
    "surface_components = ['Black','Granitoid_H12','Tholeiitic_basalt_H25']\n",
    "\n",
    "# Create the model object\n",
    "model_log_uniform   = define_model(model_name_log_uniform, bulk_species, param_species, \n",
    "                            radius_unit = 'R_E', surface = True,  # <----- Set surface = True\n",
    "                            reflection = True, thermal = True, thermal_scattering = True, # <----- Set reflection and thermal to True \n",
    "                            surface_model = 'lab_data',           # <----- Set surface_model to 'lab_data'\n",
    "                            surface_components = surface_components,  # <----- Input surface_components                    \n",
    "                            disable_atmosphere = True,                # <----- Set disable_atmosphere = True for bare rocks\n",
    "                            surface_percentage_option = 'log',        # <----- Use log percentages\n",
    "                            surface_percentage_apply_to = 'models')    # <----- Apply percentages to models (3 models for each spectra)\n",
    "\n",
    "print(model_log_uniform['param_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdb16f0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['R_p_ref' 'T_surf' 'log_Black_percentage' 'log_Granitoid_H12_percentage'\n",
      " 'log_Tholeiitic_basalt_H25_percentage']\n"
     ]
    }
   ],
   "source": [
    "# Here we duplicate the model for the CLR prior\n",
    "\n",
    "model_name_log_clr = 'TOI-1685b-Log-Percentages-CLR'\n",
    "\n",
    "#***** Define model *****#\n",
    "\n",
    "bulk_species = []\n",
    "param_species = []\n",
    "\n",
    "# List the surface components here \n",
    "surface_components = ['Black','Granitoid_H12','Tholeiitic_basalt_H25']\n",
    "\n",
    "# Create the model object\n",
    "model_log_clr   = define_model(model_name_log_clr, bulk_species, param_species, \n",
    "                            radius_unit = 'R_E', surface = True,  # <----- Set surface = True\n",
    "                            reflection = True, thermal = True, thermal_scattering = True, # <----- Set reflection and thermal to True \n",
    "                            surface_model = 'lab_data',           # <----- Set surface_model to 'lab_data'\n",
    "                            surface_components = surface_components,  # <----- Input surface_components                    \n",
    "                            disable_atmosphere = True,                # <----- Set disable_atmosphere = True for bare rocks \n",
    "                            surface_percentage_option = 'log',        # <----- Use log percentages\n",
    "                            surface_percentage_apply_to = 'models')    # <----- Apply percentages to models (3 models for each spectra)\n",
    "\n",
    "print(model_log_clr['param_names'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7c3585e",
   "metadata": {},
   "source": [
    "**1. Uniform Prior, Linear Percentages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8055dcb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialise prior type dictionary\n",
    "prior_types = {}\n",
    "\n",
    "# Specify uniform priors for all free parameters\n",
    "prior_types['R_p_ref'] = 'uniform'\n",
    "prior_types['T_surf'] = 'uniform' #<- Surface temperature \n",
    "\n",
    "prior_types['Black_percentage'] = 'uniform'\n",
    "prior_types['Granitoid_H12_percentage'] = 'uniform'\n",
    "prior_types['Tholeiitic_basalt_H25_percentage'] = 'uniform'\n",
    "\n",
    "# Initialise prior range dictionary\n",
    "prior_ranges = {}\n",
    "\n",
    "# Specify prior ranges for each free parameter\n",
    "prior_ranges['R_p_ref'] = [0.9*R_p, 1.1*R_p]\n",
    "\n",
    "prior_ranges['T_surf'] = [100,2000]\n",
    "\n",
    "# Percentages range from 0 to 1, or 0 to 100%\n",
    "prior_ranges['Black_percentage'] = [0,1]\n",
    "prior_ranges['Granitoid_H12_percentage'] = [0,1]\n",
    "prior_ranges['Tholeiitic_basalt_H25_percentage'] = [0,1]\n",
    "\n",
    "# Create prior object for retrieval\n",
    "priors = set_priors(planet, star, model_linear_uniform, data, prior_types, prior_ranges)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0bdda4",
   "metadata": {},
   "source": [
    "Lets run the retrieval. \n",
    "\n",
    "Surface retrievals are very fast, since they do not need to compute atmospheric radiative transfer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52f3e9de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POSEIDON now running 'TOI-1685b-Linear-Percentages'\n",
      " *****************************************************\n",
      " MultiNest v3.10\n",
      " Copyright Farhan Feroz & Mike Hobson\n",
      " Release Jul 2015\n",
      "\n",
      " no. of live points =  500\n",
      " dimensionality =    5\n",
      " *****************************************************\n",
      " Starting MultiNest\n",
      " generating live points\n",
      " live points generated, starting sampling\n",
      "Acceptance Rate:                        0.990991\n",
      "Replacements:                                550\n",
      "Total Samples:                               555\n",
      "Nested Sampling ln(Z):               -500.042794\n",
      "Acceptance Rate:                        0.980392\n",
      "Replacements:                                600\n",
      "Total Samples:                               612\n",
      "Nested Sampling ln(Z):               -457.751338\n",
      "Acceptance Rate:                        0.943396\n",
      "Replacements:                                650\n",
      "Total Samples:                               689\n",
      "Nested Sampling ln(Z):               -380.846297\n",
      "Acceptance Rate:                        0.919842\n",
      "Replacements:                                700\n",
      "Total Samples:                               761\n",
      "Nested Sampling ln(Z):               -324.388131\n",
      "Acceptance Rate:                        0.892857\n",
      "Replacements:                                750\n",
      "Total Samples:                               840\n",
      "Nested Sampling ln(Z):               -245.684053\n",
      "Acceptance Rate:                        0.854701\n",
      "Replacements:                                800\n",
      "Total Samples:                               936\n",
      "Nested Sampling ln(Z):               -153.022941\n",
      "Acceptance Rate:                        0.828460\n",
      "Replacements:                                850\n",
      "Total Samples:                              1026\n",
      "Nested Sampling ln(Z):               -101.870007\n",
      "Acceptance Rate:                        0.816697\n",
      "Replacements:                                900\n",
      "Total Samples:                              1102\n",
      "Nested Sampling ln(Z):                -57.049488\n",
      "Acceptance Rate:                        0.808511\n",
      "Replacements:                                950\n",
      "Total Samples:                              1175\n",
      "Nested Sampling ln(Z):                -27.717481\n",
      "Acceptance Rate:                        0.796178\n",
      "Replacements:                               1000\n",
      "Total Samples:                              1256\n",
      "Nested Sampling ln(Z):                  2.384433\n",
      "Acceptance Rate:                        0.794852\n",
      "Replacements:                               1050\n",
      "Total Samples:                              1321\n",
      "Nested Sampling ln(Z):                 23.114734\n",
      "Acceptance Rate:                        0.785153\n",
      "Replacements:                               1100\n",
      "Total Samples:                              1401\n",
      "Nested Sampling ln(Z):                 41.189163\n",
      "Acceptance Rate:                        0.775455\n",
      "Replacements:                               1150\n",
      "Total Samples:                              1483\n",
      "Nested Sampling ln(Z):                 58.467278\n",
      "Acceptance Rate:                        0.764331\n",
      "Replacements:                               1200\n",
      "Total Samples:                              1570\n",
      "Nested Sampling ln(Z):                 68.420132\n",
      "Acceptance Rate:                        0.758495\n",
      "Replacements:                               1250\n",
      "Total Samples:                              1648\n",
      "Nested Sampling ln(Z):                 77.635345\n",
      "Acceptance Rate:                        0.752750\n",
      "Replacements:                               1300\n",
      "Total Samples:                              1727\n",
      "Nested Sampling ln(Z):                 85.510435\n",
      "Acceptance Rate:                        0.745445\n",
      "Replacements:                               1350\n",
      "Total Samples:                              1811\n",
      "Nested Sampling ln(Z):                 91.289144\n",
      "Acceptance Rate:                        0.738786\n",
      "Replacements:                               1400\n",
      "Total Samples:                              1895\n",
      "Nested Sampling ln(Z):                 95.807781\n",
      "Acceptance Rate:                        0.733064\n",
      "Replacements:                               1450\n",
      "Total Samples:                              1978\n",
      "Nested Sampling ln(Z):                 99.408687\n",
      "Acceptance Rate:                        0.729217\n",
      "Replacements:                               1500\n",
      "Total Samples:                              2057\n",
      "Nested Sampling ln(Z):                102.490473\n",
      "Acceptance Rate:                        0.724638\n",
      "Replacements:                               1550\n",
      "Total Samples:                              2139\n",
      "Nested Sampling ln(Z):                104.900934\n",
      "Acceptance Rate:                        0.725295\n",
      "Replacements:                               1600\n",
      "Total Samples:                              2206\n",
      "Nested Sampling ln(Z):                107.070595\n",
      "Acceptance Rate:                        0.727513\n",
      "Replacements:                               1650\n",
      "Total Samples:                              2268\n",
      "Nested Sampling ln(Z):                108.843677\n",
      "Acceptance Rate:                        0.722789\n",
      "Replacements:                               1700\n",
      "Total Samples:                              2352\n",
      "Nested Sampling ln(Z):                110.602513\n",
      "Acceptance Rate:                        0.721649\n",
      "Replacements:                               1750\n",
      "Total Samples:                              2425\n",
      "Nested Sampling ln(Z):                111.844032\n",
      "Acceptance Rate:                        0.720576\n",
      "Replacements:                               1800\n",
      "Total Samples:                              2498\n",
      "Nested Sampling ln(Z):                112.850102\n",
      "Acceptance Rate:                        0.716221\n",
      "Replacements:                               1850\n",
      "Total Samples:                              2583\n",
      "Nested Sampling ln(Z):                113.590005\n",
      "Acceptance Rate:                        0.715901\n",
      "Replacements:                               1900\n",
      "Total Samples:                              2654\n",
      "Nested Sampling ln(Z):                114.207673\n",
      "Acceptance Rate:                        0.712719\n",
      "Replacements:                               1950\n",
      "Total Samples:                              2736\n",
      "Nested Sampling ln(Z):                114.783020\n",
      "Acceptance Rate:                        0.709975\n",
      "Replacements:                               2000\n",
      "Total Samples:                              2817\n",
      "Nested Sampling ln(Z):                115.290955\n",
      "Acceptance Rate:                        0.707140\n",
      "Replacements:                               2050\n",
      "Total Samples:                              2899\n",
      "Nested Sampling ln(Z):                115.727626\n",
      "Acceptance Rate:                        0.706120\n",
      "Replacements:                               2100\n",
      "Total Samples:                              2974\n",
      "Nested Sampling ln(Z):                116.090344\n",
      "Acceptance Rate:                        0.703534\n",
      "Replacements:                               2150\n",
      "Total Samples:                              3056\n",
      "Nested Sampling ln(Z):                116.412854\n",
      "Acceptance Rate:                        0.699746\n",
      "Replacements:                               2200\n",
      "Total Samples:                              3144\n",
      "Nested Sampling ln(Z):                116.687415\n",
      "Acceptance Rate:                        0.697026\n",
      "Replacements:                               2250\n",
      "Total Samples:                              3228\n",
      "Nested Sampling ln(Z):                116.930292\n",
      "Acceptance Rate:                        0.694235\n",
      "Replacements:                               2300\n",
      "Total Samples:                              3313\n",
      "Nested Sampling ln(Z):                117.147595\n",
      "Acceptance Rate:                        0.690770\n",
      "Replacements:                               2350\n",
      "Total Samples:                              3402\n",
      "Nested Sampling ln(Z):                117.334735\n",
      "Acceptance Rate:                        0.687482\n",
      "Replacements:                               2400\n",
      "Total Samples:                              3491\n",
      "Nested Sampling ln(Z):                117.500715\n",
      "Acceptance Rate:                        0.681502\n",
      "Replacements:                               2450\n",
      "Total Samples:                              3595\n",
      "Nested Sampling ln(Z):                117.647924\n",
      "Acceptance Rate:                        0.679532\n",
      "Replacements:                               2500\n",
      "Total Samples:                              3679\n",
      "Nested Sampling ln(Z):                117.782219\n",
      "Acceptance Rate:                        0.674782\n",
      "Replacements:                               2550\n",
      "Total Samples:                              3779\n",
      "Nested Sampling ln(Z):                117.904310\n",
      "Acceptance Rate:                        0.667180\n",
      "Replacements:                               2600\n",
      "Total Samples:                              3897\n",
      "Nested Sampling ln(Z):                118.015624\n",
      "Acceptance Rate:                        0.663661\n",
      "Replacements:                               2650\n",
      "Total Samples:                              3993\n",
      "Nested Sampling ln(Z):                118.120426\n",
      "Acceptance Rate:                        0.659663\n",
      "Replacements:                               2700\n",
      "Total Samples:                              4093\n",
      "Nested Sampling ln(Z):                118.215131\n",
      "Acceptance Rate:                        0.657737\n",
      "Replacements:                               2750\n",
      "Total Samples:                              4181\n",
      "Nested Sampling ln(Z):                118.300431\n",
      "Acceptance Rate:                        0.654206\n",
      "Replacements:                               2800\n",
      "Total Samples:                              4280\n",
      "Nested Sampling ln(Z):                118.378442\n",
      "Acceptance Rate:                        0.652025\n",
      "Replacements:                               2850\n",
      "Total Samples:                              4371\n",
      "Nested Sampling ln(Z):                118.450240\n",
      "Acceptance Rate:                        0.650078\n",
      "Replacements:                               2900\n",
      "Total Samples:                              4461\n",
      "Nested Sampling ln(Z):                118.516185\n",
      "Acceptance Rate:                        0.648922\n",
      "Replacements:                               2950\n",
      "Total Samples:                              4546\n",
      "Nested Sampling ln(Z):                118.576964\n",
      "Acceptance Rate:                        0.645717\n",
      "Replacements:                               3000\n",
      "Total Samples:                              4646\n",
      "Nested Sampling ln(Z):                118.633236\n",
      "Acceptance Rate:                        0.645776\n",
      "Replacements:                               3050\n",
      "Total Samples:                              4723\n",
      "Nested Sampling ln(Z):                118.684671\n",
      "Acceptance Rate:                        0.644759\n",
      "Replacements:                               3100\n",
      "Total Samples:                              4808\n",
      "Nested Sampling ln(Z):                118.732349\n",
      "Acceptance Rate:                        0.641679\n",
      "Replacements:                               3150\n",
      "Total Samples:                              4909\n",
      "Nested Sampling ln(Z):                118.776846\n",
      "Acceptance Rate:                        0.638850\n",
      "Replacements:                               3200\n",
      "Total Samples:                              5009\n",
      "Nested Sampling ln(Z):                118.817690\n",
      "Acceptance Rate:                        0.636381\n",
      "Replacements:                               3250\n",
      "Total Samples:                              5107\n",
      "Nested Sampling ln(Z):                118.855304\n",
      "Acceptance Rate:                        0.633397\n",
      "Replacements:                               3300\n",
      "Total Samples:                              5210\n",
      "Nested Sampling ln(Z):                118.890163\n",
      "Acceptance Rate:                        0.631718\n",
      "Replacements:                               3350\n",
      "Total Samples:                              5303\n",
      "Nested Sampling ln(Z):                118.922670\n",
      "Acceptance Rate:                        0.631266\n",
      "Replacements:                               3400\n",
      "Total Samples:                              5386\n",
      "Nested Sampling ln(Z):                118.952571\n",
      "Acceptance Rate:                        0.629218\n",
      "Replacements:                               3450\n",
      "Total Samples:                              5483\n",
      "Nested Sampling ln(Z):                118.980642\n",
      "Acceptance Rate:                        0.627015\n",
      "Replacements:                               3500\n",
      "Total Samples:                              5582\n",
      "Nested Sampling ln(Z):                119.006736\n",
      "Acceptance Rate:                        0.626323\n",
      "Replacements:                               3550\n",
      "Total Samples:                              5668\n",
      "Nested Sampling ln(Z):                119.030782\n",
      "Acceptance Rate:                        0.624025\n",
      "Replacements:                               3600\n",
      "Total Samples:                              5769\n",
      "Nested Sampling ln(Z):                119.053134\n",
      "Acceptance Rate:                        0.624038\n",
      "Replacements:                               3650\n",
      "Total Samples:                              5849\n",
      "Nested Sampling ln(Z):                119.073805\n",
      "Acceptance Rate:                        0.622019\n",
      "Replacements:                               3678\n",
      "Total Samples:                              5913\n",
      "Nested Sampling ln(Z):                119.084730\n",
      " ln(ev)=   119.31761235164392      +/-   8.3682731900666718E-002\n",
      "POSEIDON retrieval finished in 0.008 hours\n",
      " Total Likelihood Evaluations:         5913\n",
      " Sampling finished. Exiting MultiNest\n",
      "Now generating 1000 sampled spectra and P-T profiles from the posterior distribution...\n",
      "This process will take approximately 0.068 minutes\n",
      "All done! Output files can be found in ./POSEIDON_output/TOI-1685b/retrievals/results/\n"
     ]
    }
   ],
   "source": [
    "#***** Specify fixed atmospheric settings for retrieval *****#\n",
    "\n",
    "# For bare rocks, P and opac arrays are empty \n",
    "P = []\n",
    "P_ref = []\n",
    "opac = []\n",
    "\n",
    "run_retrieval(planet, star, model_linear_uniform, opac, data, priors, wl, P, P_ref, R = R, \n",
    "                spectrum_type = 'emission', sampling_algorithm = 'MultiNest', \n",
    "                N_live = 500, verbose = True, resume = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b57774f",
   "metadata": {},
   "source": [
    "## Plotting Emission Retrieval Results\n",
    "\n",
    "Thats pretty fast! This is because for bare rocks, we don't have to propogate radiation throughout a pesky atmosphere. \n",
    "\n",
    "Lets go ahead and plot the results \n",
    "\n",
    "Note that for emission retrievals, POSEIDON saves Fp (not Fp/Fs), and therefore we must convert the retrieved spectra ourselves to Fp/Fs space. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4cf51ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1280x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 853.36x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#***** Plotting magic *****#\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from POSEIDON.visuals import plot_histograms\n",
    "\n",
    "fig_combined = plt.figure(constrained_layout=True, figsize=(16,9))    # Change (9,5.5) to alter the aspect ratio\n",
    "\n",
    "# This function is the magic. Each letter corresponds to one matplotlib axis, which you can then pass to POSEIDON's plotting functions\n",
    "axd = fig_combined.subplot_mosaic(\n",
    "    \"\"\"\n",
    "    AAAA\n",
    "    AAAA\n",
    "    abcd\n",
    "    \"\"\"\n",
    ")\n",
    "\n",
    "# Read retrieved spectrum confidence regions\n",
    "wl, spec_low2, spec_low1, spec_median, \\\n",
    "spec_high1, spec_high2 = read_retrieved_spectrum(planet_name, model_name_linear_uniform)\n",
    "\n",
    "# Convert to Fp/Fs \n",
    "d = 1        # This value only used for flux ratios, so it cancels\n",
    "\n",
    "# Load stellar spectrum\n",
    "F_s = star['F_star']\n",
    "R_s = star['R_s']\n",
    "\n",
    "# Convert stellar surface flux to observed flux at Earth\n",
    "F_s_obs = (R_s / d)**2 * F_s\n",
    "\n",
    "spec_low2 = spec_low2 / F_s_obs\n",
    "spec_low1 = spec_low1 / F_s_obs\n",
    "spec_median = spec_median / F_s_obs\n",
    "spec_high1 = spec_high1 / F_s_obs\n",
    "spec_high2 = spec_high2 / F_s_obs\n",
    "\n",
    "# Plot\n",
    "# Create composite spectra objects for plotting\n",
    "spectra_median = plot_collection(spec_median, wl, collection = [])\n",
    "spectra_low1 = plot_collection(spec_low1, wl, collection = []) \n",
    "spectra_low2 = plot_collection(spec_low2, wl, collection = []) \n",
    "spectra_high1 = plot_collection(spec_high1, wl, collection = []) \n",
    "spectra_high2 = plot_collection(spec_high2, wl, collection = [])\n",
    "\n",
    "# Produce figure\n",
    "ax_spectrum = axd['A']\n",
    "_ = plot_spectra_retrieved(spectra_median, spectra_low2, spectra_low1, \n",
    "                        spectra_high1, spectra_high2, planet_name,\n",
    "                        data, R_to_bin = 150, show_ymodel = False,\n",
    "                        colour_list = ['brown'], \n",
    "                        spectra_labels = ['Retrieved Spectrum'],\n",
    "                        plt_label = 'Linear Percentages, Uniform Prior',\n",
    "                        figure_shape = 'wide', save_fig = False,\n",
    "                        ax = ax_spectrum,\n",
    "                        sigma_to_plot = 2,\n",
    "                        y_unit = 'eclipse_depth',\n",
    "                        legend_location = 'lower right',\n",
    "                        wl_axis = 'linear',\n",
    "                        data_marker_size_list = [5],\n",
    "                        data_colour_list = ['black'],\n",
    "                        y_min = 0, y_max = 3.6e-4,\n",
    "                        data_labels = ['Simulated MIRI Data (R=15)'],\n",
    "                        wl_max = 13,\n",
    "                        line_alpha_list = [1]\n",
    "                        )\n",
    "\n",
    "# Plot histograms \n",
    "axes_histograms = [axd[\"a\"],axd[\"b\"],axd[\"c\"],axd[\"d\"]]\n",
    "\n",
    "models = [model_linear_uniform]\n",
    "\n",
    "_ = plot_histograms(planet, models, plot_parameters = ['T_surf','Black_percentage',\n",
    "                                                       'Granitoid_H12_percentage',\n",
    "                                                       'Tholeiitic_basalt_H25_percentage'], \n",
    "                    span = ((1000,2000),(0,1),(0,1),(0,1)),\n",
    "                    N_bins = [25,25,25,25],\n",
    "                    parameter_colour_list = [\"#892a2a\",'black','#a29b84',\"#363431\"],\n",
    "                    axes = axes_histograms, save_fig = False,\n",
    "                    tick_labelsize = 14,                          # x axis tick labels\n",
    "                    title_fontsize = 16,                          # Title size\n",
    "                    alpha_hist = [1,1,1,1],\n",
    "                    title_alpha_list = [1,1,1,1],\n",
    "                    custom_labels = ['T$_\\mathrm{surface}$ (K)', 'Black %','Granite %','Basalt %'],\n",
    "                    title_vert_spacing = 0.2,\n",
    "                    title_colour_list = [\"#892a2a\",'black','#a29b84',\"#363431\"],\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5708cd",
   "metadata": {},
   "source": [
    "It looks like we have some mid-infrared feature around 8-9 microns! We can see how granite is the highest percentage, but that the forward model can produce degeneracies by combining the three albedo spectra together. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd61aada",
   "metadata": {},
   "source": [
    "**Uniform Prior, Log Percentages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0db64781",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialise prior type dictionary\n",
    "prior_types = {}\n",
    "\n",
    "# Specify uniform priors for all free parameters\n",
    "prior_types['R_p_ref'] = 'uniform'\n",
    "prior_types['T_surf'] = 'uniform'\n",
    "\n",
    "prior_types['log_Black_percentage'] = 'uniform'\n",
    "prior_types['log_Granitoid_H12_percentage'] = 'uniform'\n",
    "prior_types['log_Tholeiitic_basalt_H25_percentage'] = 'uniform'\n",
    "\n",
    "# Initialise prior range dictionary\n",
    "prior_ranges = {}\n",
    "\n",
    "# Specify prior ranges for each free parameter\n",
    "prior_ranges['R_p_ref'] = [0.9*R_p, 1.1*R_p]\n",
    "\n",
    "prior_ranges['T_surf'] = [100,2000]\n",
    "\n",
    "# Now, since its log 10 percentage, we do 1e-12 to 0\n",
    "prior_ranges['log_Black_percentage'] = [-12,0]\n",
    "prior_ranges['log_Granitoid_H12_percentage'] = [-12,0]\n",
    "prior_ranges['log_Tholeiitic_basalt_H25_percentage'] = [-12,0]\n",
    "\n",
    "# Create prior object for retrieval\n",
    "priors = set_priors(planet, star, model_log_uniform, data, prior_types, prior_ranges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02b179db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POSEIDON now running 'TOI-1685b-Log-Percentages-Uniform'\n",
      " *****************************************************\n",
      " MultiNest v3.10\n",
      " Copyright Farhan Feroz & Mike Hobson\n",
      " Release Jul 2015\n",
      "\n",
      " no. of live points =  500\n",
      " dimensionality =    5\n",
      " *****************************************************\n",
      " Starting MultiNest\n",
      " generating live points\n",
      " live points generated, starting sampling\n",
      "Acceptance Rate:                        0.994575\n",
      "Replacements:                                550\n",
      "Total Samples:                               553\n",
      "Nested Sampling ln(Z):               -497.290081\n",
      "Acceptance Rate:                        0.978793\n",
      "Replacements:                                600\n",
      "Total Samples:                               613\n",
      "Nested Sampling ln(Z):               -464.367428\n",
      "Acceptance Rate:                        0.948905\n",
      "Replacements:                                650\n",
      "Total Samples:                               685\n",
      "Nested Sampling ln(Z):               -408.026753\n",
      "Acceptance Rate:                        0.930851\n",
      "Replacements:                                700\n",
      "Total Samples:                               752\n",
      "Nested Sampling ln(Z):               -325.612091\n",
      "Acceptance Rate:                        0.915751\n",
      "Replacements:                                750\n",
      "Total Samples:                               819\n",
      "Nested Sampling ln(Z):               -217.984041\n",
      "Acceptance Rate:                        0.893855\n",
      "Replacements:                                800\n",
      "Total Samples:                               895\n",
      "Nested Sampling ln(Z):               -163.650959\n",
      "Acceptance Rate:                        0.886340\n",
      "Replacements:                                850\n",
      "Total Samples:                               959\n",
      "Nested Sampling ln(Z):               -107.132191\n",
      "Acceptance Rate:                        0.881489\n",
      "Replacements:                                900\n",
      "Total Samples:                              1021\n",
      "Nested Sampling ln(Z):                -73.647208\n",
      "Acceptance Rate:                        0.870761\n",
      "Replacements:                                950\n",
      "Total Samples:                              1091\n",
      "Nested Sampling ln(Z):                -33.658462\n",
      "Acceptance Rate:                        0.858369\n",
      "Replacements:                               1000\n",
      "Total Samples:                              1165\n",
      "Nested Sampling ln(Z):                 -4.718458\n",
      "Acceptance Rate:                        0.846774\n",
      "Replacements:                               1050\n",
      "Total Samples:                              1240\n",
      "Nested Sampling ln(Z):                 14.942105\n",
      "Acceptance Rate:                        0.837776\n",
      "Replacements:                               1100\n",
      "Total Samples:                              1313\n",
      "Nested Sampling ln(Z):                 32.554890\n",
      "Acceptance Rate:                        0.833938\n",
      "Replacements:                               1150\n",
      "Total Samples:                              1379\n",
      "Nested Sampling ln(Z):                 47.370113\n",
      "Acceptance Rate:                        0.826446\n",
      "Replacements:                               1200\n",
      "Total Samples:                              1452\n",
      "Nested Sampling ln(Z):                 60.310144\n",
      "Acceptance Rate:                        0.815927\n",
      "Replacements:                               1250\n",
      "Total Samples:                              1532\n",
      "Nested Sampling ln(Z):                 69.199596\n",
      "Acceptance Rate:                        0.809465\n",
      "Replacements:                               1300\n",
      "Total Samples:                              1606\n",
      "Nested Sampling ln(Z):                 78.649121\n",
      "Acceptance Rate:                        0.800712\n",
      "Replacements:                               1350\n",
      "Total Samples:                              1686\n",
      "Nested Sampling ln(Z):                 85.258672\n",
      "Acceptance Rate:                        0.796813\n",
      "Replacements:                               1400\n",
      "Total Samples:                              1757\n",
      "Nested Sampling ln(Z):                 90.586589\n",
      "Acceptance Rate:                        0.792350\n",
      "Replacements:                               1450\n",
      "Total Samples:                              1830\n",
      "Nested Sampling ln(Z):                 94.542451\n",
      "Acceptance Rate:                        0.791139\n",
      "Replacements:                               1500\n",
      "Total Samples:                              1896\n",
      "Nested Sampling ln(Z):                 98.285131\n",
      "Acceptance Rate:                        0.784413\n",
      "Replacements:                               1550\n",
      "Total Samples:                              1976\n",
      "Nested Sampling ln(Z):                102.426136\n",
      "Acceptance Rate:                        0.775194\n",
      "Replacements:                               1600\n",
      "Total Samples:                              2064\n",
      "Nested Sampling ln(Z):                104.744664\n",
      "Acceptance Rate:                        0.768872\n",
      "Replacements:                               1650\n",
      "Total Samples:                              2146\n",
      "Nested Sampling ln(Z):                106.570850\n",
      "Acceptance Rate:                        0.763016\n",
      "Replacements:                               1700\n",
      "Total Samples:                              2228\n",
      "Nested Sampling ln(Z):                107.955014\n",
      "Acceptance Rate:                        0.757576\n",
      "Replacements:                               1750\n",
      "Total Samples:                              2310\n",
      "Nested Sampling ln(Z):                109.187412\n",
      "Acceptance Rate:                        0.753769\n",
      "Replacements:                               1800\n",
      "Total Samples:                              2388\n",
      "Nested Sampling ln(Z):                110.255889\n",
      "Acceptance Rate:                        0.748988\n",
      "Replacements:                               1850\n",
      "Total Samples:                              2470\n",
      "Nested Sampling ln(Z):                111.317092\n",
      "Acceptance Rate:                        0.739012\n",
      "Replacements:                               1900\n",
      "Total Samples:                              2571\n",
      "Nested Sampling ln(Z):                112.267070\n",
      "Acceptance Rate:                        0.731707\n",
      "Replacements:                               1950\n",
      "Total Samples:                              2665\n",
      "Nested Sampling ln(Z):                113.136777\n",
      "Acceptance Rate:                        0.728597\n",
      "Replacements:                               2000\n",
      "Total Samples:                              2745\n",
      "Nested Sampling ln(Z):                113.954385\n",
      "Acceptance Rate:                        0.725921\n",
      "Replacements:                               2050\n",
      "Total Samples:                              2824\n",
      "Nested Sampling ln(Z):                114.668223\n",
      "Acceptance Rate:                        0.723140\n",
      "Replacements:                               2100\n",
      "Total Samples:                              2904\n",
      "Nested Sampling ln(Z):                115.287082\n",
      "Acceptance Rate:                        0.713575\n",
      "Replacements:                               2150\n",
      "Total Samples:                              3013\n",
      "Nested Sampling ln(Z):                115.768089\n",
      "Acceptance Rate:                        0.708763\n",
      "Replacements:                               2200\n",
      "Total Samples:                              3104\n",
      "Nested Sampling ln(Z):                116.173126\n",
      "Acceptance Rate:                        0.703785\n",
      "Replacements:                               2250\n",
      "Total Samples:                              3197\n",
      "Nested Sampling ln(Z):                116.510841\n",
      "Acceptance Rate:                        0.700579\n",
      "Replacements:                               2300\n",
      "Total Samples:                              3283\n",
      "Nested Sampling ln(Z):                116.781945\n",
      "Acceptance Rate:                        0.695884\n",
      "Replacements:                               2350\n",
      "Total Samples:                              3377\n",
      "Nested Sampling ln(Z):                117.008291\n",
      "Acceptance Rate:                        0.691842\n",
      "Replacements:                               2400\n",
      "Total Samples:                              3469\n",
      "Nested Sampling ln(Z):                117.204145\n",
      "Acceptance Rate:                        0.685506\n",
      "Replacements:                               2450\n",
      "Total Samples:                              3574\n",
      "Nested Sampling ln(Z):                117.410263\n",
      "Acceptance Rate:                        0.681570\n",
      "Replacements:                               2500\n",
      "Total Samples:                              3668\n",
      "Nested Sampling ln(Z):                117.622027\n",
      "Acceptance Rate:                        0.678733\n",
      "Replacements:                               2550\n",
      "Total Samples:                              3757\n",
      "Nested Sampling ln(Z):                117.816309\n",
      "Acceptance Rate:                        0.673052\n",
      "Replacements:                               2600\n",
      "Total Samples:                              3863\n",
      "Nested Sampling ln(Z):                117.996285\n",
      "Acceptance Rate:                        0.668854\n",
      "Replacements:                               2650\n",
      "Total Samples:                              3962\n",
      "Nested Sampling ln(Z):                118.166624\n",
      "Acceptance Rate:                        0.665680\n",
      "Replacements:                               2700\n",
      "Total Samples:                              4056\n",
      "Nested Sampling ln(Z):                118.319180\n",
      "Acceptance Rate:                        0.658998\n",
      "Replacements:                               2750\n",
      "Total Samples:                              4173\n",
      "Nested Sampling ln(Z):                118.453970\n",
      "Acceptance Rate:                        0.656045\n",
      "Replacements:                               2800\n",
      "Total Samples:                              4268\n",
      "Nested Sampling ln(Z):                118.578945\n",
      "Acceptance Rate:                        0.653670\n",
      "Replacements:                               2850\n",
      "Total Samples:                              4360\n",
      "Nested Sampling ln(Z):                118.695072\n",
      "Acceptance Rate:                        0.649787\n",
      "Replacements:                               2900\n",
      "Total Samples:                              4463\n",
      "Nested Sampling ln(Z):                118.800485\n",
      "Acceptance Rate:                        0.644950\n",
      "Replacements:                               2950\n",
      "Total Samples:                              4574\n",
      "Nested Sampling ln(Z):                118.895242\n",
      "Acceptance Rate:                        0.642949\n",
      "Replacements:                               3000\n",
      "Total Samples:                              4666\n",
      "Nested Sampling ln(Z):                118.979136\n",
      "Acceptance Rate:                        0.639011\n",
      "Replacements:                               3050\n",
      "Total Samples:                              4773\n",
      "Nested Sampling ln(Z):                119.055518\n",
      "Acceptance Rate:                        0.635897\n",
      "Replacements:                               3100\n",
      "Total Samples:                              4875\n",
      "Nested Sampling ln(Z):                119.125844\n",
      "Acceptance Rate:                        0.635209\n",
      "Replacements:                               3150\n",
      "Total Samples:                              4959\n",
      "Nested Sampling ln(Z):                119.189696\n",
      "Acceptance Rate:                        0.632536\n",
      "Replacements:                               3200\n",
      "Total Samples:                              5059\n",
      "Nested Sampling ln(Z):                119.247825\n",
      "Acceptance Rate:                        0.628627\n",
      "Replacements:                               3250\n",
      "Total Samples:                              5170\n",
      "Nested Sampling ln(Z):                119.300561\n",
      "Acceptance Rate:                        0.624054\n",
      "Replacements:                               3300\n",
      "Total Samples:                              5288\n",
      "Nested Sampling ln(Z):                119.347996\n",
      "Acceptance Rate:                        0.619224\n",
      "Replacements:                               3350\n",
      "Total Samples:                              5410\n",
      "Nested Sampling ln(Z):                119.391144\n",
      "Acceptance Rate:                        0.616836\n",
      "Replacements:                               3400\n",
      "Total Samples:                              5512\n",
      "Nested Sampling ln(Z):                119.430202\n",
      "Acceptance Rate:                        0.614098\n",
      "Replacements:                               3450\n",
      "Total Samples:                              5618\n",
      "Nested Sampling ln(Z):                119.465497\n",
      "Acceptance Rate:                        0.613045\n",
      "Replacements:                               3487\n",
      "Total Samples:                              5688\n",
      "Nested Sampling ln(Z):                119.489617\n",
      "POSEIDON retrieval finished in 0.0063 hours\n",
      " ln(ev)=   119.81119829943626      +/-   8.7005552518691201E-002\n",
      " Total Likelihood Evaluations:         5688\n",
      " Sampling finished. Exiting MultiNest\n",
      "Now generating 1000 sampled spectra and P-T profiles from the posterior distribution...\n",
      "This process will take approximately 0.052 minutes\n",
      "All done! Output files can be found in ./POSEIDON_output/TOI-1685b/retrievals/results/\n"
     ]
    }
   ],
   "source": [
    "#***** Specify fixed atmospheric settings for retrieval *****#\n",
    "\n",
    "P = []\n",
    "P_ref = []\n",
    "opac = []\n",
    "\n",
    "# We redefine the model wavelength grid and star just for convenience in the Jupyter notebook (in case cells are run out of order)\n",
    "\n",
    "#***** Model wavelength grid *****#\n",
    "\n",
    "wl_min = 0.3     # Minimum wavelength (um)\n",
    "wl_max = 15     # Maximum wavelength (um)\n",
    "R = 1000         # Spectral resolution of grid\n",
    "\n",
    "wl = wl_grid_constant_R(wl_min, wl_max,R)\n",
    "\n",
    "#***** Define stellar properties *****#\n",
    "\n",
    "R_s = 0.4555*R_Sun      # Stellar radius (m)\n",
    "T_s = 3575        # Stellar effective temperature (K)\n",
    "Met_s = 0.3          # Stellar metallicity [log10(Fe/H_star / Fe/H_solar)] <--- note: for PHOENIX, only the solar metallicity models are used  \n",
    "log_g_s = 4.778       # Stellar log surface gravity (log10(cm/s^2) by convention)\n",
    "\n",
    "# Create the stellar object\n",
    "star = create_star(R_s, T_s, log_g_s, Met_s, wl = wl, stellar_grid = 'phoenix')\n",
    "\n",
    "run_retrieval(planet, star, model_log_uniform, opac, data, priors, wl, P, P_ref, R = R, \n",
    "                spectrum_type = 'emission', sampling_algorithm = 'MultiNest', \n",
    "                N_live = 500, verbose = True, resume = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "756baa9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1280x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 853.36x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#***** Plotting magic *****#\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from POSEIDON.visuals import plot_histograms\n",
    "\n",
    "fig_combined = plt.figure(constrained_layout=True, figsize=(16,9))    # Change (9,5.5) to alter the aspect ratio\n",
    "\n",
    "# This function is the magic. Each letter corresponds to one matplotlib axis, which you can then pass to POSEIDON's plotting functions\n",
    "axd = fig_combined.subplot_mosaic(\n",
    "    \"\"\"\n",
    "    AAAA\n",
    "    AAAA\n",
    "    abcd\n",
    "    \"\"\"\n",
    ")\n",
    "\n",
    "# Read retrieved spectrum confidence regions\n",
    "wl, spec_low2, spec_low1, spec_median, \\\n",
    "spec_high1, spec_high2 = read_retrieved_spectrum(planet_name, model_name_log_uniform)\n",
    "\n",
    "# Convert to Fp/Fs \n",
    "d = 1        # This value only used for flux ratios, so it cancels\n",
    "\n",
    "# Load stellar spectrum\n",
    "F_s = star['F_star']\n",
    "R_s = star['R_s']\n",
    "\n",
    "# Convert stellar surface flux to observed flux at Earth\n",
    "F_s_obs = (R_s / d)**2 * F_s\n",
    "\n",
    "spec_low2 = spec_low2 / F_s_obs\n",
    "spec_low1 = spec_low1 / F_s_obs\n",
    "spec_median = spec_median / F_s_obs\n",
    "spec_high1 = spec_high1 / F_s_obs\n",
    "spec_high2 = spec_high2 / F_s_obs\n",
    "\n",
    "# Plot\n",
    "# Create composite spectra objects for plotting\n",
    "spectra_median = plot_collection(spec_median, wl, collection = [])\n",
    "spectra_low1 = plot_collection(spec_low1, wl, collection = []) \n",
    "spectra_low2 = plot_collection(spec_low2, wl, collection = []) \n",
    "spectra_high1 = plot_collection(spec_high1, wl, collection = []) \n",
    "spectra_high2 = plot_collection(spec_high2, wl, collection = [])\n",
    "\n",
    "# Produce figure\n",
    "ax_spectrum = axd['A']\n",
    "_ = plot_spectra_retrieved(spectra_median, spectra_low2, spectra_low1, \n",
    "                        spectra_high1, spectra_high2, planet_name,\n",
    "                        data, R_to_bin = 150, show_ymodel = False,\n",
    "                        colour_list = ['brown'], \n",
    "                        spectra_labels = ['Retrieved Spectrum'],\n",
    "                        plt_label = 'Log Percentages, Uniform Prior',\n",
    "                        figure_shape = 'wide', save_fig = False,\n",
    "                        ax = ax_spectrum,\n",
    "                        sigma_to_plot = 2,\n",
    "                        y_unit = 'eclipse_depth',\n",
    "                        legend_location = 'lower right',\n",
    "                        wl_axis = 'linear',\n",
    "                        data_marker_size_list = [5],\n",
    "                        data_colour_list = ['black'],\n",
    "                        y_min = 0, y_max = 3.6e-4,\n",
    "                        data_labels = ['Simulated MIRI Data (R=15)'],\n",
    "                        wl_max = 13,\n",
    "                        line_alpha_list = [1]\n",
    "                        )\n",
    "\n",
    "# Plot histograms \n",
    "axes_histograms = [axd[\"a\"],axd[\"b\"],axd[\"c\"],axd[\"d\"]]\n",
    "\n",
    "models = [model_log_uniform]\n",
    "\n",
    "_ = plot_histograms(planet, models, plot_parameters = ['T_surf','log_Black_percentage',\n",
    "                                                       'log_Granitoid_H12_percentage',\n",
    "                                                       'log_Tholeiitic_basalt_H25_percentage'], \n",
    "                    span = ((1000,2000),(-12,0),(-12,0),(-12,0)),\n",
    "                    N_bins = [25,25,25,25],\n",
    "                    parameter_colour_list = [\"#892a2a\",'black','#a29b84',\"#363431\"],\n",
    "                    axes = axes_histograms, save_fig = False,\n",
    "                    tick_labelsize = 14,                          # x axis tick labels\n",
    "                    title_fontsize = 16,                          # Title size\n",
    "                    alpha_hist = [1,1,1,1],\n",
    "                    title_alpha_list = [1,1,1,1],\n",
    "                    custom_labels = ['T$_\\mathrm{surface}$ (K)', 'Log Black %','Log Granite %','Log Basalt %'],\n",
    "                    title_vert_spacing = 0.2,\n",
    "                    title_colour_list = [\"#892a2a\",'black','#a29b84',\"#363431\"],\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d62cb511",
   "metadata": {},
   "source": [
    "In log space, it becomes a lot more obvious that granite is most likely to be the 'bulk' surface. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5960882a",
   "metadata": {},
   "source": [
    "**CLR Prior, Log Percentages**\n",
    "\n",
    "Here, we set the prior type to 'CLR_surface'\n",
    "\n",
    "The first surface component, 'black', will be the 'fill' surface (i.e., the non-free parameter that is added to granite + basalt to make the percentage 100%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f30a9167",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialise prior type dictionary\n",
    "prior_types = {}\n",
    "\n",
    "# Specify uniform priors for all free parameters\n",
    "prior_types['R_p_ref'] = 'uniform'\n",
    "prior_types['T_surf'] = 'uniform'\n",
    "\n",
    "prior_types['log_Black_percentage'] = 'CLR_surface'\n",
    "prior_types['log_Granitoid_H12_percentage'] = 'CLR_surface'\n",
    "prior_types['log_Tholeiitic_basalt_H25_percentage'] = 'CLR_surface'\n",
    "\n",
    "# Initialise prior range dictionary\n",
    "prior_ranges = {}\n",
    "\n",
    "# Specify prior ranges for each free parameter\n",
    "prior_ranges['R_p_ref'] = [0.9*R_p, 1.1*R_p]\n",
    "\n",
    "prior_ranges['T_surf'] = [100,2000]\n",
    "\n",
    "prior_ranges['log_Black_percentage'] = [-12,0]\n",
    "prior_ranges['log_Granitoid_H12_percentage'] = [-12,0]\n",
    "prior_ranges['log_Tholeiitic_basalt_H25_percentage'] = [-12,0]\n",
    "\n",
    "# Create prior object for retrieval\n",
    "priors = set_priors(planet, star, model_log_clr, data, prior_types, prior_ranges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "85eb597e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POSEIDON now running 'TOI-1685b-Log-Percentages-CLR'\n",
      " *****************************************************\n",
      " MultiNest v3.10\n",
      " Copyright Farhan Feroz & Mike Hobson\n",
      " Release Jul 2015\n",
      "\n",
      " no. of live points =  500\n",
      " dimensionality =    5\n",
      " *****************************************************\n",
      " Starting MultiNest\n",
      " generating live points\n",
      " live points generated, starting sampling\n",
      "Acceptance Rate:                        0.998185\n",
      "Replacements:                                550\n",
      "Total Samples:                               551\n",
      "Nested Sampling ln(Z):               -496.907870\n",
      "Acceptance Rate:                        0.993377\n",
      "Replacements:                                600\n",
      "Total Samples:                               604\n",
      "Nested Sampling ln(Z):               -473.018264\n",
      "Acceptance Rate:                        0.977444\n",
      "Replacements:                                650\n",
      "Total Samples:                               665\n",
      "Nested Sampling ln(Z):               -408.653354\n",
      "Acceptance Rate:                        0.953678\n",
      "Replacements:                                700\n",
      "Total Samples:                               734\n",
      "Nested Sampling ln(Z):               -338.723462\n",
      "Acceptance Rate:                        0.924784\n",
      "Replacements:                                750\n",
      "Total Samples:                               811\n",
      "Nested Sampling ln(Z):               -256.291887\n",
      "Acceptance Rate:                        0.897868\n",
      "Replacements:                                800\n",
      "Total Samples:                               891\n",
      "Nested Sampling ln(Z):               -195.659961\n",
      "Acceptance Rate:                        0.861196\n",
      "Replacements:                                850\n",
      "Total Samples:                               987\n",
      "Nested Sampling ln(Z):               -148.015015\n",
      "Acceptance Rate:                        0.845070\n",
      "Replacements:                                900\n",
      "Total Samples:                              1065\n",
      "Nested Sampling ln(Z):               -104.330046\n",
      "Acceptance Rate:                        0.823224\n",
      "Replacements:                                950\n",
      "Total Samples:                              1154\n",
      "Nested Sampling ln(Z):                -64.168163\n",
      "Acceptance Rate:                        0.802568\n",
      "Replacements:                               1000\n",
      "Total Samples:                              1246\n",
      "Nested Sampling ln(Z):                -33.734339\n",
      "Acceptance Rate:                        0.784753\n",
      "Replacements:                               1050\n",
      "Total Samples:                              1338\n",
      "Nested Sampling ln(Z):                 -6.166869\n",
      "Acceptance Rate:                        0.771930\n",
      "Replacements:                               1100\n",
      "Total Samples:                              1425\n",
      "Nested Sampling ln(Z):                 13.530437\n",
      "Acceptance Rate:                        0.757077\n",
      "Replacements:                               1150\n",
      "Total Samples:                              1519\n",
      "Nested Sampling ln(Z):                 30.912013\n",
      "Acceptance Rate:                        0.730816\n",
      "Replacements:                               1200\n",
      "Total Samples:                              1642\n",
      "Nested Sampling ln(Z):                 43.217085\n",
      "Acceptance Rate:                        0.722961\n",
      "Replacements:                               1250\n",
      "Total Samples:                              1729\n",
      "Nested Sampling ln(Z):                 57.610068\n",
      "Acceptance Rate:                        0.711939\n",
      "Replacements:                               1300\n",
      "Total Samples:                              1826\n",
      "Nested Sampling ln(Z):                 67.002485\n",
      "Acceptance Rate:                        0.697314\n",
      "Replacements:                               1350\n",
      "Total Samples:                              1936\n",
      "Nested Sampling ln(Z):                 76.009742\n",
      "Acceptance Rate:                        0.688299\n",
      "Replacements:                               1400\n",
      "Total Samples:                              2034\n",
      "Nested Sampling ln(Z):                 81.163701\n",
      "Acceptance Rate:                        0.682032\n",
      "Replacements:                               1450\n",
      "Total Samples:                              2126\n",
      "Nested Sampling ln(Z):                 86.237929\n",
      "Acceptance Rate:                        0.676895\n",
      "Replacements:                               1500\n",
      "Total Samples:                              2216\n",
      "Nested Sampling ln(Z):                 91.912643\n",
      "Acceptance Rate:                        0.673328\n",
      "Replacements:                               1550\n",
      "Total Samples:                              2302\n",
      "Nested Sampling ln(Z):                 96.009663\n",
      "Acceptance Rate:                        0.669176\n",
      "Replacements:                               1600\n",
      "Total Samples:                              2391\n",
      "Nested Sampling ln(Z):                 99.282987\n",
      "Acceptance Rate:                        0.661058\n",
      "Replacements:                               1650\n",
      "Total Samples:                              2496\n",
      "Nested Sampling ln(Z):                102.660150\n",
      "Acceptance Rate:                        0.654349\n",
      "Replacements:                               1700\n",
      "Total Samples:                              2598\n",
      "Nested Sampling ln(Z):                105.055374\n",
      "Acceptance Rate:                        0.647908\n",
      "Replacements:                               1750\n",
      "Total Samples:                              2701\n",
      "Nested Sampling ln(Z):                106.940995\n",
      "Acceptance Rate:                        0.644699\n",
      "Replacements:                               1800\n",
      "Total Samples:                              2792\n",
      "Nested Sampling ln(Z):                108.343922\n",
      "Acceptance Rate:                        0.642584\n",
      "Replacements:                               1850\n",
      "Total Samples:                              2879\n",
      "Nested Sampling ln(Z):                109.389434\n",
      "Acceptance Rate:                        0.638012\n",
      "Replacements:                               1900\n",
      "Total Samples:                              2978\n",
      "Nested Sampling ln(Z):                110.222470\n",
      "Acceptance Rate:                        0.632706\n",
      "Replacements:                               1950\n",
      "Total Samples:                              3082\n",
      "Nested Sampling ln(Z):                111.042736\n",
      "Acceptance Rate:                        0.631512\n",
      "Replacements:                               2000\n",
      "Total Samples:                              3167\n",
      "Nested Sampling ln(Z):                111.868714\n",
      "Acceptance Rate:                        0.630381\n",
      "Replacements:                               2050\n",
      "Total Samples:                              3252\n",
      "Nested Sampling ln(Z):                112.697638\n",
      "Acceptance Rate:                        0.628931\n",
      "Replacements:                               2100\n",
      "Total Samples:                              3339\n",
      "Nested Sampling ln(Z):                113.570229\n",
      "Acceptance Rate:                        0.625728\n",
      "Replacements:                               2150\n",
      "Total Samples:                              3436\n",
      "Nested Sampling ln(Z):                114.334800\n",
      "Acceptance Rate:                        0.625711\n",
      "Replacements:                               2200\n",
      "Total Samples:                              3516\n",
      "Nested Sampling ln(Z):                114.954227\n",
      "Acceptance Rate:                        0.622751\n",
      "Replacements:                               2250\n",
      "Total Samples:                              3613\n",
      "Nested Sampling ln(Z):                115.468008\n",
      "Acceptance Rate:                        0.617118\n",
      "Replacements:                               2300\n",
      "Total Samples:                              3727\n",
      "Nested Sampling ln(Z):                115.907567\n",
      "Acceptance Rate:                        0.613577\n",
      "Replacements:                               2350\n",
      "Total Samples:                              3830\n",
      "Nested Sampling ln(Z):                116.268601\n",
      "Acceptance Rate:                        0.609601\n",
      "Replacements:                               2400\n",
      "Total Samples:                              3937\n",
      "Nested Sampling ln(Z):                116.555117\n",
      "Acceptance Rate:                        0.605387\n",
      "Replacements:                               2450\n",
      "Total Samples:                              4047\n",
      "Nested Sampling ln(Z):                116.793411\n",
      "Acceptance Rate:                        0.600384\n",
      "Replacements:                               2500\n",
      "Total Samples:                              4164\n",
      "Nested Sampling ln(Z):                117.002750\n",
      "Acceptance Rate:                        0.595516\n",
      "Replacements:                               2550\n",
      "Total Samples:                              4282\n",
      "Nested Sampling ln(Z):                117.223113\n",
      "Acceptance Rate:                        0.593066\n",
      "Replacements:                               2600\n",
      "Total Samples:                              4384\n",
      "Nested Sampling ln(Z):                117.442757\n",
      "Acceptance Rate:                        0.589151\n",
      "Replacements:                               2650\n",
      "Total Samples:                              4498\n",
      "Nested Sampling ln(Z):                117.657374\n",
      "Acceptance Rate:                        0.585429\n",
      "Replacements:                               2700\n",
      "Total Samples:                              4612\n",
      "Nested Sampling ln(Z):                117.849484\n",
      "Acceptance Rate:                        0.581764\n",
      "Replacements:                               2750\n",
      "Total Samples:                              4727\n",
      "Nested Sampling ln(Z):                118.023829\n",
      "Acceptance Rate:                        0.578871\n",
      "Replacements:                               2800\n",
      "Total Samples:                              4837\n",
      "Nested Sampling ln(Z):                118.180755\n",
      "Acceptance Rate:                        0.572749\n",
      "Replacements:                               2850\n",
      "Total Samples:                              4976\n",
      "Nested Sampling ln(Z):                118.316998\n",
      "Acceptance Rate:                        0.569521\n",
      "Replacements:                               2900\n",
      "Total Samples:                              5092\n",
      "Nested Sampling ln(Z):                118.439084\n",
      "Acceptance Rate:                        0.567526\n",
      "Replacements:                               2950\n",
      "Total Samples:                              5198\n",
      "Nested Sampling ln(Z):                118.549499\n",
      "Acceptance Rate:                        0.563698\n",
      "Replacements:                               3000\n",
      "Total Samples:                              5322\n",
      "Nested Sampling ln(Z):                118.649964\n",
      "Acceptance Rate:                        0.558199\n",
      "Replacements:                               3050\n",
      "Total Samples:                              5464\n",
      "Nested Sampling ln(Z):                118.742595\n",
      "Acceptance Rate:                        0.553769\n",
      "Replacements:                               3100\n",
      "Total Samples:                              5598\n",
      "Nested Sampling ln(Z):                118.826865\n",
      "Acceptance Rate:                        0.551954\n",
      "Replacements:                               3150\n",
      "Total Samples:                              5707\n",
      "Nested Sampling ln(Z):                118.904039\n",
      "Acceptance Rate:                        0.549356\n",
      "Replacements:                               3200\n",
      "Total Samples:                              5825\n",
      "Nested Sampling ln(Z):                118.973797\n",
      "Acceptance Rate:                        0.544024\n",
      "Replacements:                               3250\n",
      "Total Samples:                              5974\n",
      "Nested Sampling ln(Z):                119.036283\n",
      "Acceptance Rate:                        0.542585\n",
      "Replacements:                               3300\n",
      "Total Samples:                              6082\n",
      "Nested Sampling ln(Z):                119.092879\n",
      "Acceptance Rate:                        0.538326\n",
      "Replacements:                               3350\n",
      "Total Samples:                              6223\n",
      "Nested Sampling ln(Z):                119.143941\n",
      "Acceptance Rate:                        0.538315\n",
      "Replacements:                               3400\n",
      "Total Samples:                              6316\n",
      "Nested Sampling ln(Z):                119.190254\n",
      "Acceptance Rate:                        0.536714\n",
      "Replacements:                               3450\n",
      "Total Samples:                              6428\n",
      "Nested Sampling ln(Z):                119.232367\n",
      "Acceptance Rate:                        0.533781\n",
      "Replacements:                               3500\n",
      "Total Samples:                              6557\n",
      "Nested Sampling ln(Z):                119.270761\n",
      "Acceptance Rate:                        0.531995\n",
      "Replacements:                               3550\n",
      "Total Samples:                              6673\n",
      "Nested Sampling ln(Z):                119.305966\n",
      "Acceptance Rate:                        0.528405\n",
      "Replacements:                               3581\n",
      "Total Samples:                              6777\n",
      "Nested Sampling ln(Z):                119.326292\n",
      "POSEIDON retrieval finished in 0.0076 hours\n",
      " ln(ev)=   119.65087682520353      +/-   8.9294505223800461E-002\n",
      " Total Likelihood Evaluations:         6777\n",
      " Sampling finished. Exiting MultiNest\n",
      "Now generating 1000 sampled spectra and P-T profiles from the posterior distribution...\n",
      "This process will take approximately 0.052 minutes\n",
      "All done! Output files can be found in ./POSEIDON_output/TOI-1685b/retrievals/results/\n"
     ]
    }
   ],
   "source": [
    "#***** Specify fixed atmospheric settings for retrieval *****#\n",
    "\n",
    "P = []\n",
    "P_ref = []\n",
    "opac = []\n",
    "\n",
    "#***** Model wavelength grid *****#\n",
    "\n",
    "wl_min = 0.3     # Minimum wavelength (um)\n",
    "wl_max = 15     # Maximum wavelength (um)\n",
    "R = 1000         # Spectral resolution of grid\n",
    "\n",
    "wl = wl_grid_constant_R(wl_min, wl_max,R)\n",
    "\n",
    "#***** Define stellar properties *****#\n",
    "\n",
    "R_s = 0.4555*R_Sun      # Stellar radius (m)\n",
    "T_s = 3575        # Stellar effective temperature (K)\n",
    "Met_s = 0.3          # Stellar metallicity [log10(Fe/H_star / Fe/H_solar)] <--- note: for PHOENIX, only the solar metallicity models are used  \n",
    "log_g_s = 4.778       # Stellar log surface gravity (log10(cm/s^2) by convention)\n",
    "\n",
    "# Create the stellar object\n",
    "star = create_star(R_s, T_s, log_g_s, Met_s, wl = wl, stellar_grid = 'phoenix')\n",
    "\n",
    "run_retrieval(planet, star, model_log_clr, opac, data, priors, wl, P, P_ref, R = R, \n",
    "                spectrum_type = 'emission', sampling_algorithm = 'MultiNest', \n",
    "                N_live = 500, verbose = True, resume = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d5a6a9f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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62zx/7jktDWLODUASQgghhBBCCCGEkI5Rd+MBJjc3F5mZmTh79iz8fj9kMlmHY48fPw6FQoHCwsIu592zZ0+P15aYmAi1Wg2Xy4WkpKSI51JTUwEADQ0N4WPffPMNAGDWrFltxg4dOhRbtmyB2+3usqlLWloaysrKYDabuwyIMsaifj2EEEIIIYQQQgghJIQyCZsNlJqEAHD77bfD7/dHZOWd69ChQ6ioqMAvfvELKBSKPlkXx3GYMmUKALRpUtLSUbglWAgALpcrfN65RCIRGGNwOp0AgIMHD+Ljjz9u97p1dXUQi8Xt1mmsq6uL+LilPuOgQYOiek2EEEIIIYQQQggh5zOqSRhnA6km4bJlyzBs2DA8+eST8Pl8bZ5njGH58uVIS0vDU0891adru+WWWwD8mCXYYsuWLQB+rAkIIJzhuHPnzoixTU1NOH36NLKzs8NBxYMHD+KZZ55pc72jR4+ivLw83PjkXC3XbfGvf/0LAHDNNdfE9LoIIYQQQgghhBBCzkdUk/ACptFosHHjRtjtdlx55ZU4ePBg+LkzZ87gxhtvxL59+7Bx48ao6hHG05IlS1BUVIQ//OEPOHDgAADg8OHDePTRRzFmzJiIWofLly+HXq/H/fffjyNHjgAIbUe+7bbb4PF48Nxzz0XMffjwYSxfvjycgXj8+HEsWbIEarUaL7zwQrvrefnll3H48GEAoYDiypUrMW7cOPzyl7+M+2snhBBCCCGEEEIIuVBRkLCP3XfffTAajQCAHTt2wGg0Yu3atW3GDRkyBAcOHMCCBQtw++23hxuOLFq0CEOHDsWxY8fa1CK866672sz93nvvdbmmsrKy8PyVlZWorKwMf1xWVhYxViwWY+PGjbj55pvx05/+FAaDAVdffTUWLVqEbdu2QaPRhMcOHz4cu3fvRmFhIS6//HIYDAYUFBTA7Xbj3//+NxYvXhwee+211+J///d/sXv3bgwfPhyJiYm49NJLMWrUKOzduxfjx48HABQXF0fcszfeeAMPPvggMjIyMGvWLFxyySX46quvOq3lSAghhBBCCCGEEEIicewi7/Rgt9uh1+ths9mg0+n6ezmEEEIIIYQQQgghhMQkHvEtyiRsNpAalxBCCCGEEEIIIYQQEo14NS6hTELKJCSEEEIIIYQQQggh5zHKJCSEEEIIIYQQQgghhPQYBQkJIYQQQgghhBBCCLnIUZCwGdUkJIQQQgghhBBCCCHnG6pJGCdUk5AQQgghhBBCCCGEnM+oJiEhhBBCCCGEEEIIIaTHzosg4WeffYYrr7wSl19+OSZPnow5c+Zg165d7Y5ljOHtt9/G3LlzMW/ePEyYMAF33XUXrFZr3y6aEEIIIYQQQgghhJDzxHmx3fiKK67Ar3/9a1x33XUAgEceeQRvvvkm6uvrIRJFxjmdTieMRiP279+PgoICuN1uzJs3D1lZWVi3bl2buWm7MSGEEEIIIYQQQgg5n100242fffZZ/OxnPwt/PHv2bDQ1NcFms7UZK5VK8eSTT6KgoAAAoFKpsHjxYvz73/9GZ/HQgdS4pLi4GEVFRTAYDDAYDCgqKsLx48f7ZS233347hg8fDo7jMG3aNBQVFWHq1KkYMWIE7rnnHjQ0NPTLunrKarXiySefRFlZWX8vJa6qq6tx3333YcaMGbjkkkswa9YsTJs2DQ8++CA2b94c/hq4+uqrkZeXB47jUFRUhA8++KDd+f7whz9g/Pjx4DgO48ePR1FREWbNmoWCggLMmTMH3377bafrcTqdKCoqgtFohEKhQFFREYqKijBp0iSMGjUKzz//PILBYFSvbePGjUhJSUFFRUVM94QQQgghhBBCCLmQXbSNSxwOB2655RYYDAa8/fbbUZ3zl7/8Ba+//nq7AaGBnElYVFQEAF0GYnrbO++8g9tuuw2lpaXIy8sDAJSVlWHatGnIzc3Frl27wHFcv64xVmVlZRg0aBC2bNkSvs/nu127duHqq6/GY489hvvvvx9isRgAcPbsWdx0003Yt28fLBYLDAYDAODJJ5/EU0891WnwHAi9/y655JKIe+X3+3HTTTdh06ZNOHz4MIYMGdLpHLfeeiu+/fbbiK/Bf/3rX7jmmmvw4IMPYuXKlV2+vu3bt+ORRx7Bp59+ipSUlC7HE0IIIYQQQgghF4uLJpOwxU033YSMjAxIpVK8/PLLUZ3DGMO6devw8MMP9/LqLi55eXm48cYb8cMPP+DUqVP9vZyLnsPhwM9+9jMsXLgQDz30UDhACAD5+fn4+OOP4xrIlclkWLp0KTweD7788stuzXHVVVdh1KhRWL16dVTjZ86cie+//54ChIQQQgghhBBCSC84r4KEa9euhdlsRlZWFiZPngyn09nlOS+99BJycnJw991398EK+8c333yD2bNno7CwEGPGjMG1116Ls2fPRozZvXs3pk2bhuHDh+PSSy/FU089Fd4Gevnll3frun6/HwDA8zwA4OWXX8aECRMwa9YszJgxAw899FD4c/Tdd9+hqKgIHMfhpZdewl133YVLL70USqUS999/PwCgoaEBv/zlLzFq1CjMnTsXhYWFeOCBB1BTUxO+5ldffYWZM2di2rRpmD17Nm644YZwdprNZgtv0166dCmefvppLFiwAEOGDMFvfvOb8Dq/+uor/PznPwcA3H///SgqKgp/XF9fj1/96leYPHkyioqKUFhYiGeffTZ8bqz3c+/evbjsssswefJkzJ07Fz/5yU9w6NCh8PPBYBCPPPIICgsLcckll2Dy5Mm4++67YTKZYvpcvPvuuzCZTPjlL3/Z7vODBg3Cp59+CrVaHdO8nWnZJtyT4KPf7w/f25/85CcwGo0oKirCqlWrcPXVVyM3NxcGgwFr167FtGnTwHFcRGZtMBjEn/70J4wePRrTpk3D6NGj8eSTT4bXFs37jhBCCCGEEEIIIQDYecjn8zGJRMLefPPNTsd9+OGH7Oqrr2Yej6fDMTabjQFg99xzD3vggQfYAw88wL744ot4L7lb5s6dy+bOndvpmM2bNzOxWMzWrFnDGGNMEAR2xx13sNTUVFZbW8sYY6y+vp7pdDp27733hs97/fXXmUQiYUuXLu1yHW+//TYDwEpLS8PHduzYwbRaLZs3bx5jjLEnnniCpaamsvLycsYYYy6Xi82dO5ddc801EXMBYCNHjmRVVVWMMcbee+899uijjzKPx8NGjhzJrrzySubz+RhjjFVXV7P09HT26aefMsYY+/rrr5lEImH//ve/w/M9/PDDLDMzk9nt9oj7ZjAY2J49exhjjFVWVjKpVBq+R4wxVlpaygCwLVu2RKxv586dbNy4ccztdjPGGLNarWzEiBFs5cqV4THR3s9jx44xlUrFVq1aFT726quvMrVazSoqKhhjjL344ots0KBBzOv1MsYYs1gsbPDgwW3W1ZUbb7yRAWAWiyXqc5544gkWzbeALVu2tLlXTU1NbNasWSw7O5s1NjZ2OcfSpUtZbm5u+GNBENirr77KALCnnnoqYpxWq2XvvvsuY4wxh8PBhg8fzhhr/3N22223sdzcXFZTU8MYC71ncnNz2S233BJx/Y7ed4QQQgghhBBCeu79999nCxYsYKNHj2YLFixg77//fn8v6aLwxRdfhGNZ99xzDwPAbDZbt+c7L4KEK1asiPhYEASmUqnY3//+9w7P+eijj9iVV14ZDr6cPn06/PfWWoKEPbmJvSWaIOGcOXPYsGHDIo6ZTCYGgC1fvpwxxtjjjz/OALC6urrwGEEQWHp6ekxBwqlTp7K5c+eyKVOmsKKiIvb8888zt9vNnE4nUyqV7LHHHos4b+3atQwAO3HiRPgYAPaHP/yhzTXeeustBoDt3LmzzfG9e/cyxhibPXs2mz59esTzdXV1DAB7/fXXw8fau2/jxo1j999/f/jjjoKELpcrHEhq8eijj7IZM2aEP472fi5ZsoSlp6dHzNXy3n3kkUcYY4zdf//9EcFVxhjbv39/VIG31i699FIGgAWDwajPiTVIOG7cODZ37lw2cuRIJpFI2LJly6IOSi5dupTJ5fLw52bSpEnsJz/5CVu3bl2bcdnZ2e3Oce7n7OzZs4zjOPbcc89FjPvLX/7CALBTp06Fj3X0viOEEEIIIYQQEh9Op5NNmjSJOZ3O/l7KRSke8S1J3+Qr9szTTz+NhQsXYvTo0QCAN954AzzP47LLLgMAvPLKK9ixYwc+/PBDAKFtya+99hrWrVuHQCCAQCCAp556Cs8880y48caFomU7a2tpaWlITEzE3r17AQBHjx6FXq9HampqeAzHcRg0aFBM1/roo4/avX8//PADPB4P1q9fjx07doSPe71e5ObmorKyEsOHDw8fb2+OlrW2HgcAt912W8QYrVbbptFIbm5umy7L2dnZER/r9XpYrdbOXh6AUHfstWvX4vPPP0cwGIREIkFZWVnEltpo7+fevXvhcrnarDc9PR0WiwUAcN9992Hz5s3Iz8/HJZdcgkWLFuHmm29GQkJCl2ttLSkpCUCom7Ber4/p3Gi99NJLKCoqAs/zuOeee/DKK6/g5ptvxsSJE6M632g0RtWEJzc3N6r59u3bB8ZYm/fMiBEjws+3dDkH2n/fEUIIIYQQQgghJOS8CBI+99xzuOOOOyCTyeD3+yGRSPCvf/0rHAyoqKjAyZMnAQAmkwmLFy8Gz/MRQRwAeOaZZ/p87f2FtepYyxjrk+7Dd955Jx588MEux7VuqtGCRdFkm+M4TJ06FZ9//nnM1+A4LqprPPLII3j99dexbds2FBYWAgh1AX7nnXci1hrt/czNze00MJaXl4dDhw5h+/bt+Pjjj/HUU0/h8ccfx9dff43x48dHdQ0AmDVrFtauXYvDhw9j9uzZUZ/XHWKxGC+88AI2bNiAZcuWYfPmzXGfPxodfT47Oh7tvIQQQgghhBBCyMXovGhcct9992HHjh349ttvsWPHDmzduhXz588PP79y5UocOHAAQChbKRgMgoW2Ukc8OsskWr58OTZt2tTbLyVuWhptFBYWhgOkLerq6mCxWDB58mQAwNixY2G1WlFfXx8ewxhDaWlpXNYyevRoKJVKHD16NOJ4MBjEL37xizZZfu1pWeu5nZI//fRTbNu2DUDotR47dqxNEOjpp5+OKkOtNZEo8q3vdDrB8zy+/vprjBgxIhwgBH5s0NIi2vs5efJklJaWwu12RxxftWoV/vnPfwIINZ0pLy/HzJkz8dJLL6G4uBgqlQpvvfVWTK/n1ltvRUZGBt5+++12n//yyy8hlUpRUlIS1Xz3339/p81TVCoVHn30UWzZsiXmex8vhYWF4Diuzfu/5ePWn0NCCCGEEEIIIeRCtWnTJixfvrzH85wXQcK+sGLFCixYsKC/lxG1Xbt2AQhluRUXF2PNmjUAQsGqxx9/HKmpqfjtb38LALjnnnug0+kiMinfeOMN2O32uKxFpVLhsccew9q1a8PbhoHQPTWbzUhJSelyjptvvhkjR47Es88+i0AgAAAoKSnBvffei+Tk5PBrLS8vx6pVq8Lnff3113jzzTdjyroDgJSUFIjFYjQ2NgIAJk2ahNOnT2PcuHE4depUuDu00+nExo0bI86N9n4++uijYIzhqaeeCh87fPgwnn32WUyaNAkAsHr1arz44ovh5xljEAQhvLUeAP7rv/6ry9en0WiwYcMG/Otf/8ILL7wAQRDCz+3btw+/+tWv8PTTT2Pw4MHR3B4cPHgQXq+30zF33HEHsrKy8MQTT0Q1Z7wNHjwYt912G1atWhXugF1bW4vXXnsNt9xyC4YOHdov6yKEEEIIIYQQ0ruEQAABp7O/lzFgLFiwACtWrOjxPByLZg/mBcxut0Ov18Nms0Gn0/X3cgAAZ86cwZIlS3D8+HEAwMiRI9uMMZlMKCsrAxDKRnviiSfg8Xjg9/sxdOhQ/PWvf8WQIUPC43fv3o3//u//htVqxaBBg/CTn/wE69evx7Bhw/CPf/yjw7Xcfvvt+P7773Hq1ClMnToVGRkZ+OSTT9od+9prr2HVqlVQKBSQy+UYO3Ys/vznP0Or1WLfvn146KGH8N1332HYsGEwGo345ptvIraANjQ04OGHH8YPP/wQDgw+/vjjEVmjmzdvxhNPPAGr1YqUlBTodDr85S9/wbBhw8DzPObPn4+DBw9CoVDg8ssvx3vvvYef/OQn2LlzJxQKBYqKivDRRx8BCAUx3333XSQmJmLSpEn4n//5HzQ2NuLee+/Ftm3bMGrUKCQkJIDneWzYsAHTpk3DRx99BKPRGPX9PHDgAB577DGUl5cjPT0dMpkMf/rTnzBlyhQAoQy/F154ARaLBSqVCk6nE4sWLcLy5cvDW5pHjx6NOXPm4LXXXuv4TdOstrYWf/nLX7Bz504olUoIggCFQoF7770XCxcuDI+77LLLcOLECVRXV2Pq1Klt5jl+/DgOHz6MN954Axs3bsShQ4cwbtw4jBo1KhyQBoDXX38dv/nNbzBz5kzcdttt+NWvfhUxj9PpxNVXX42TJ0/CarVi2rRpmDdvHh5//PE217zhhhuwbds2eL1ejB8/Hr/73e9w1VVXAQjVGX3xxRexe/dujBs3Drfddhvuu+8+BINBPPvss/jnP/8JrVYLh8OB66+/Hn/84x8hkUiiet8RQgghhBBCCOk5l8uFuXPn4rvvvoNare7VazmrqxF0uaAfMgSciPLfgPjEtyhIOACDhL3BZDLBaDRGHMvPz8cdd9yBRx55pJ9Wdf7qq/v5xhtv4I9//CP27NmDrKysuM1LCCGEEEIIIYTEU18FCYNeL+zNu/8USUlQnfOz+cUqHvEtCrdeJMaMGYPy8vLwxx9//DHq6+tx88039+Oqzl99dT/NZjN27dpFAUJCCCGEEEIIIQSAu64u/Hev2Yygx9OPq7mwnBfdjfvC8uXLsXDhwvOqLmEslixZgmuuuQaJiYkIBoNQKpXYvHkzcnJy+ntp56W+up8PP/xwXOcjhBBCCCGEEELOV36HA8HWtQgZg6umBrrBg8Mluy5GmzZtwoYNG3o8D203vki2GxNCCCGEEEIIIYT0lt7ebswYg/3sWfA+X5vnVEYjFElJcb/m+YS2GxNCCCGEEEIIIYSQC57Pam03QAgA7vp68H5/H69oYPGazT2eg4KEhBBCCCGEEEIIIRch3u9HwOWCwPP9vZROMUGAp76+4wGCALfJ1HcLGmCEQACehoYez0M1CZtd6DUJCSGEEEIIIYQQcvFigoCgx4Og2x3+k7UKDopkMkgUCogVCkiUSogVCogkAyNs5GlsBAsGOx0TcDjgt9shuwhLyX2+di02/utfPZ6HahJSTUJCCCGEEEIIIYRcoHxWK7xNTaGtujGGgEQyGdQZGZBGUWOwt2oSCoEArMXFgCB0OZaTSKAfMgQisThu1x/o/A4HnBUVsDudGDRtGtUkJIQQQgghhBBCCCGRvBYLXNXV4L3emAOEACD4/XCUl8el3l13uevrowoQAgALBjvflnyBYYIAd21t3OajICEhhBBCCCGEEELIBcZrNsNdU9PziRiDu7YWrpoa9PVm1KDXC7/V2ua43+GAo6Ki3XN8ZjOCbncvr2xgcNfVQQgE4jYfBQkJIYQQQgghhBBCLiCexsa4ZpgBgM9igaO8HEIXtQHjyV1X1+7xxsOH0XDgAIIddDt21dT06Tr7Q9Djgc9iieucFCRstnz5cmzatKm/l0EIIYQQQgghhBDSbZ6GBng6CK71VNDlgr2kBEGvt1fmby3gdCLodLY57q6vD22h9vnQePBgu+fyPh9sZ86EGp70QyuO3r4m7/fDVV0d3kK+eft2PPPSSz2elxqXUOMSQgghhBBCCCGEXADc9fXwNjT0/oVEImgyMiDT68OHOmtcwgQBQiAAIRAA3/ynEAgAghAKqDHW5k8hEIjovgyEgm+VX30FX6styBmzZ0Odnt7xUmUyqNLS+qzrccDpDGUy8jzEUilEMhnEMhlEcjnELX+XSrs1txAMwtPQEMogPCecF4/GJQOjlzUhhBBCCCGEEEII6TZ3XR28jY19czFBgLOqClKbDZxIBDAGp8sVanRSUQFBpQIDwHgegt/fJtjXXfbS0ogAIQA07N8PxeWXQ9xB4E3w++GsrIRErYbKaIREoYjLWtpch+fhNpkiaijyPh94nw9tqgaKRJBpNJDp9ZBqteA4rtO5GWPwNjbC29gIFmUTl+6gICEhhBBCCCGEEELIeUoIBH7MLutjAYfjx7+73RB4HgGnE4FeCGTxgQCajh5tuwaXC+ajR5EyYUKn5wddLtjPnoU8IQHKlJRuZ/O1x2+zwWUygUVbB1EQ4Lfb4bfbwYnFkOn1kOv1kKhUbYb6rFZ46uu7blASh43CFCRstnz5cixcuBALFizo76UQQgghhBBCCCGEdCrgdMJrNiPgdMYlQDTQWU6cAN9BLURbcTG0ublQJCZ2OY/PYoHPZoPcYIAiMRFiubzbaxICAbhqayOCpbFiPA+f2Qyf2QyRXA65Xg+ZXg8hEIC7rg68x9PlHJu3b8dn/+//dXsNLShI2GzFihVUk5AQQgghhBBCCCEDlhAMwme1wmexQPD7ezRX0OOBt6kJQZ8PioQEyPR6iMTiOK00vvwOB6ynT3f4PGMM9Xv3IuvSSyESRdGjVxDCgTmJRhN6/THGhLwWCzwmU1y3/wo+Hzz19fDU18d03uwJE5BUXo73//OfHl2fgoSEEEIIIYQQQgi5qK1ZswarV69GdXU1MjMzsWTJEixevDjmefx2O3wWCxTJyZCe07yjI0wQ4LNYQp14BQEisRicWAxOIoFILIZIIgEnFoP3++G324FuBKX4QAA+sxne5ofPbEbwnAw1TiSCXK+HPCkJioQEyBMTIdPpuqyX1xeajhzpMhjns1phPXkSiSNHxjR30OmE0+mESCqFPCEB8oQEiCQ/hssYYxD8fvBeL4I+HwSfD0Gvt8dB2niq37cvLnUfKUhICCGEEEIIIYSQi9rixYuxaNEizJ07F+vXr2/TnTcarRuHBJxOSNRqKFNSOgwWMsbgM5tDwcFWtewEQQC6qj8XJSYIqN+3D46yslDX4C7Gei0WeC0W2JqPiaRSpBUWQpOdHZf1dIe7vh7OqqqoxppPnIA6KwvybuwUFQKBUBZfYyNkWi0AgPd6wfv9A3o7t728HO7a2rjMFUUO5sVh+fLl2LRpU38vgxBCCCGEEEIIIecRgefhKC9v01k46HLBUVYGW0kJ/K1q1jHG4DWbYTtzBu5Yml3Eui5BgGnXLthLS7sMEHY4RyCA2p070XjkSLfn6AnGGBoPHox+PM+jYd++nq1VEOC32eC32cD7fAM6QBj0+dB48CB2FRfjtW++6fF8lEnYjGoSEkIIIYQQQgghJBa8zwdHRUWnW095jwfOigqIFQrIdLpQPcE4ZQp2RAgGUbtjB9wmU1zms5w4AZ/FgrRp0yCRyeIyZzTspaXwWa1tjgecTgS9XiiTk9s852logK2kBIb8/D5YYf9qPHgQvM+HaUOGYEx2Ntbv3duj+SiTkBBCCCGEEEIIISRGfrsd9pKSqGvT8V4vPPX1vR4g5P1+1GzdGrcAYQu3yYSqr7+Gt52gXW/gAwGYjx5tc5wxhrrdu1H7/fcIuN3tntt0+DDsUWyxPp85a2vhKC+P65yUSUgIIYQQQgghhBDSCu/zIejxhJp2iETgRKKIv/usVngbGvp7mW0EvV7UbNsGn8XSK/MHnE5Ub96M1MJCaHNy4jYvYwxCIAAhGAz9GQjAXlaGoNfbZqyjrAy8zwd1RgYa9u5F+uzZbZqrCIEA6n74AdbTp5E4ejQ0GRlxW2s0nNXV4H0+SDUayLRaSJTK+M5fVYWGAwfiOidAQUJCCCGEEEIIIYSQMK/ZDJ/T2a0uwv0p6Haj6rvvEGhV/7A3CMEgTLt2wWexIGnMGHCijjepCoIA3u1GwO1GsPkR/rvHA8HvhxAIgPF8VFl/vN+PpiNHkDppEhSpqaj88ks4SkuhGzy43fE+qxW1338PZUoKkseOhSIpqduvOxoBpxP1+/e3yeIUSSSQajQ/PrRaqNPSIFGpYprfXV+PpiNH4G1qiueywyhI2Gz58uVYuHAhFixY0N9LIYQQQgghhBBCSB8TAgEIfj/cJhPUMQZv+pvf4UDN1q0IuFx9dk3LqVOwl5eHs/g8Ph/8VivKNm6EQioFALBgMK5bfs3HjkGekABVRgY4jkPqpEkw7doFZVpah12kgVCdwspvvoEmKwuJo0d3q/txZwRBgPXUKZhPnGi3EY0QDMJntUbUV+Q4Dsq0NGhzc6HJzIRI0nGIzmuxoOnIkQ63kO8qLsa206d7/DooSNiMGpcQQgghhBBCCCH9x2+zwWs2Q5GYCKlO12YLaa9e226HraQEAs/32TXjwWuxwF5aCkd5ea/XOmwP32o7cNDvB2MMvN+P3sjB9FmtcJSWIuuyy8LvDZXRCE12Nhr27Wt32/G5nFVVcFVXQ5uXB8OwYXEJFnoaGlC/fz/8NltM5zHG4DaZ4DaZ0CCVQpOVBW1uLpQpKeHX4Xc4YD56FM6qqk6DrdOGDMHorKweNy7pUZAwEAigtLQUFosFcrkcqampSEtLg1gs7tGiCCGEEEIIIYQQcvEQgkG4TCawYBBOtxucRAJFYiLkBgNEzVlp3eE1mxH0eCBRKCBufohaxSyYIMBtMsFnsYCdJwFCPhCAo6ICjpISeHup9uBAwxhD44ED0A8dCplWG/Fc8tixqPzyS9hLSqCPoqMxYwz20lLYS0uhTE2FIT8f6szMTrdNtyfo96Pp0CE44tAgRQgEwmuSqtXQ5uYi6PHENLe1uLhHawC6ESSsrq7Gm2++ic8++wxHjhyBSCRCYmIigsEgrFYrRCIRJk6ciJ/97Ge48847KTuPEEIIIYQQQgghnXLX1UVs02TBIDz19fA0NECm1UKemNjpdtL2+G02uGtrQ39vdVwkk4WDhj6rNeruxNEIut1w1dVBm5MTEYzs8jyPB9YzZyAEgxDL5RDL5ZDI5aGgZvPHAYcDtpISuKqqILSzpTVaAs/Db7XCZ7HAa7GA93oh1+shT0iAzGCAVKPp0yzOaDgrKxFwuZA+e3ab50RSKVIKC2HauRMqozGm94mnvh6e+npIlEroBg+GfvDgDpuMMMYQdLvhs9vht1phPXMmIpMyXgIuF8zHj8d0jt9uh/XkyR5fO+ogodfrxaOPPort27fjpz/9KV588UVMnDgRGo0mYpzZbMbu3buxbds2zJ07F7/4xS9w//33D/jsQqpJSAghhBBCCCGE9L2A0wl/q1ptERiD326H326HIiUFqtTU6OZ0ueCsrm73OcHvh9/vB+z2bq64fS6TCXU//ADe64X56FEYhg6FLj8f4k4yIQMuFywnT8JeVtZrmYwBlwueurpwUNBvs0EklUKekABFYiIUiYnw22ywnj4Nn80GTiSC3GAIPRISoO6iXl5vEwIBNB0+jKRx4zpchyotDdrs7FC34zlzYg5yBj0emI8dg+X4cagzM6EbNAgMQMBuh89mQ6D5PdiT4GxvYYKAL7/8Egc8nh7PxbEo8hZrampw11134Y477sDVV18d9eTBYBDvv/8+/vWvf+Gtt96C9pyU0IHAbrdDr9fDZrNR1iMhhBBCCCGEENKHmCDAVlwcdT09eUICVOnpnQaBeJ8P9pISsBi7E7vcbiy89VZseOedmBqXMMZCAaYTJ9psDRXLZNAPGQL90KGQyOXh436HA+YTJ+AsL49rY49zuU0mmHbtCgUEExIgT0yEPCEBEpWq3XvIBAF+ux0+iwU+qxWehgYAgHH69DbbfM/l8ftx97vv4vWlS6GUyeL2GpoOH4bXbEbG3Lmdft6FQACVX30Fw7BhUW07vlBYTpyAo7ISCbNm4bLnn+9RfCuqUPBLL72E9957DwaDIbbJJRLceuutmDdvHl5++WX8/ve/784aCSGEEEIIIYQQcgFy19XF1HDDZ7FACASgyc5ut4acEAjAUV4ec4Cwu4IeD0y7d8NTX9/u87zfD/Px47CePg3d4MHQZGbCdvYsnJWVvRocBABHRQUa9u1DyqRJ0ObkRHVO6yxCIBQ0NB89iupvvkHK5MnQZGb24orb8tvtsJ09i8x587rMDhRJpUiZNKlb247PVz6rFZaTJ5Exdy74OOzgjaoq41//+teYA4St5eTkdCtAyBjD22+/jblz52LevHmYMGEC7rrrLlg7SkMG0NTUhFtuuQWTJk3CnDlzMHv2bOzYsaPbayeEEEIIIYQQQkj8Bd1u+MzmmM8LOJ2wl5W12fop8DwcFRV91uXXXV+Pyq+/7jBA2JoQDMJ6+jSqtmyBo6Ki1wOE1jNn0LB/P4zTp0cdIGwPJxIhaexYpBQWomHPHjQdOdJnAVjGGBoPHoRu0CDI9frIdXFcu7UDVWlp0ObkoH7v3l6/x/2NCQLq9+6FfuhQKBIT4zJn/20qj4LL5cK9996L/fv3o6CgAG63G/PmzcPtt9+OdevWtXvOf//3f6O8vBy7du2CVCrF//zP/+Cqq65CRUXFgNzuTAghhBBCCCGE9AWB52NqptGbGGNw1tR0+3ze44G9rAzanByIZbLQfFVVcWsk4bVY4G1qgkgigUgiAdfqT7FEAkdlJcxHjw64QFTL1md7SQky5syJW/BIk5UFmU4H086d8JnNSJ06FRKFIi5zd8RVUwOfzYa0adPaPGcoKIA6IwPV337b5nOQNGYMKr/6Cg379yNx5MgOG5Gc7ywnTgCMIXHkyLjNOaCDhFKpFE8++SQKCgoAACqVCosXL8YjjzwCxli7qaaHDh3CVVddBWlzYdB58+bh3nvvxalTp1BYWNjhtahxCSGEEEIIIYSQC1XQ64WjrAxihQLKlJRe34rZVUDS09AAwefr2TV8PthLS6HNzYW3qQlBp7NH87UwnzgBX2lpn2XMxQtjDA3798NjMiGzqAiyOPddkOl0yJo/H/V796Lqm29gnDYNiqSkbq+V9/nAgkEIPA8WDILxfOjvzQ/zsWNIGjMG4nPqG0qUSiSMHAmxVIqEkSNhPnYs4nmRVIr0WbNgPnoUFf/5D7SDBsEwbBikMdSZ7ImAyxVakyBAotFAqtFAqlZDqtFArFD0uHM0YwzOykpYT59G5iWXgBOJsKu4GNtOn+7x2gd0kFAul2PZsmURx9xuN1JTUzu8qddeey0+++wzLF++HHq9Hh999BFSUlIwbNiwTq+1YsUKalxCCCGEEEIIIeSCw/v9oTp9PI+gywWHywWJRgNVSgokvRA48TY1wW0yQSSThYIjajUkanW4M23Q64W3sbHNeX6HA56GBmiyszvtCNwaCwZhLykB4pDR53c4ELDbYTlxIq6NN/qCwPOo370bfqcTmZdc0iufVwAQSSRImzoVtjNnULN1KwzDhoUy9TgOnkAg9F6rqEBQKgU4DkIgAN7rRdDrBe/xhP5sfgAAOC6UoSkWhx+i5j9VRiO0ublt1pAyfnz4/ZE4YgQ8dXXwnPN+kul0MM6YAZ/NBuuJE6j84gtocnORMGwYpBpNr9wbgedhPXUK1lOnoMnOhlSnQ8DlCgewgx4POLEYkuaAoTIlBZrs7KgzMlnzdnrrqVNgPI+USZPCtSOnDRmCMdnZWL93b49eQ1yDhDzP4/jx4xgzZgz27duHSZMmxXN6MMawbt06PPzwwx2O+dOf/gS73Y7c3FykpKRApVLh22+/pa3GhBBCCCGEEEIuOrzfD0dZGdg59fuCTifsTmcoWJGaGrEls3U2F+P5UCfcdpqEtMdtMsHb1BSax++Hz++Hz2IBAIgVCkjVagRcrjZBPcYY6vfuhaehAY2HDkGbmwt9fn6bWnTt6mGAkDEWqhe4fz8Enu/RXP3BZ7Oh8eBBMJ5HZlFRm8y7eOM4DoaCAsgTEmA9dQre5oxLbzAIweeDo7wcAbEYYAycRAKJUgmxQgG5Xg+xUgmJQgGxQgGJQgEuxu3vKqMRmuzsH9ciEiFt2jRUbNrUbi1KuV6PtGnT4LfbYTl5EpVffglNdjYMw4d32a05Fq7aWjQePAiRVBra5t1OhqXQHKQPOJ0IOJ1w1dSg6fBhKFNToc3JgTozMxxIjzivORBuPXMGIokEhuHDoc3JifprMhZxzyR89tln8dJLL+HVV1/FW2+9Fde5X3rpJeTk5ODuu+/ucMy9996L3bt3o6SkBImJiXj//fdx8803Y8uWLUiM0158QgghhBBCCCFkoGvp9NtZI4+WgIVIJgsFBQWhTdBNJJNBZTR2GlRhjMFVXQ2/zdbhmIgMsnPYiovhaWgIr9tWXAxbcTGUycnQ5edDk5UVcz1Fxhjqdu+G12KBwmCAPCEB8oQEyAwGSORyAKHswfo9e+BpbAQ7jwKEvN8PZ0UFHOXl8Nvt0GRnI3n8+HaDTL1FmZICZUpK+GOP3w/p2bPImD27VzIxObEYKRMmtDkuVamQVliI2p07OzxXptMhbcoUBEaOhOXkSVR99RWURiN0gwZBZTR2ewtwwOVC06FD8DQ0IHH0aOgGD+5wLpFYDJlOF94GbigoQMDthrM5O7Bh/36oMzKgycmBKi3tx6+Ds2chVauRPH481BkZHc4vjUPQk2NxqrJ59OhR/PDDD3A6nfjwww9x++23QywW49Zbb43H9Pjoo4+wZs0afPzxx1B0kIrpdruh1WrxzjvvYMmSJeHj+fn5uPXWW/HHP/6xzTl2ux16vR733HMPZM1v4gULFlBtQkIIIYQQQgg5z6xZswarV69GdXU1MjMzsWTJEixevLi/l9UvhGAQjrIy8D2s+9eaVKuFymhsk6km8DyclZUIulzdmjfgdKLiyy/bdCtuTaxQwDhtGlSpqVHPW7d3b2grcjukKhVken2oNmLzdT1+P+5+9128vnRpt4NcfrsdPosF6hiDmoyxUJdkxiBuzrQTy2QR2WJMEOCuq4OjrAyu2looEhKgzc2FOobt2b0pHvevM4kjRyJp9OgOn6/bswf20tKo5gq43XCUlMBeVgZOJII2Lw+6QYOibnLCeB7W06dhOXkSmuzsUO3E5sBzd/ms1lDgt7ISTBDAgkEokpJgGDYMyg7K7u0qLsaus2fBcRxk6el499NPYbPZul1OL24hZqPRiPHjx+Ps2bMYNGgQ8vLykJCQEJe5165di9WrV+OTTz6BXC7HmTNnkJOTA/k5n4BgMAhBEMLBvhYymQzmLtqqU01CQgghhBBCCDm/LV68GIsWLcLcuXOxfv16qHu5OcdAJfA8HOXlcQ0QAkDA4YDN5YIyJQWKpCRwHBfOaOvutRhjqNuzp9MAIRDKQqzZtg3GKVMitpt2pPHw4Q4DhEAoSBRwu2Neb0cYY3CUlaHx4EGIFQo0HTkCQ0EBdIMHd5rdJ/A8nOXlsJ4+DSEYhEgqBe/1hrM/RTJZaGuuXA6/wwFwHLQ5Oci+7LIeb5flJBIoEhIgUSjgdzjgdzgGbDalVKNBwogRnY5JmTABnsZGBByOrudTqZA4ejQSRo6E22SCvaQE5SdOQJWeHpFdyHgegZYtwg4HAk4n/E4n/HY7JEolMmbPhiI5OS6vUW4wQG4wIHHMGHgbGyGSSCDvIq42bcgQTBsyBMnjxkGcmYl3P/20R2uIW5AwOTkZWq0WzzzzDD744APcfPPN+LSHiwNCAcLXXnsN69atQyAQQCAQwFNPPYVnnnkGeXl5eOWVV7Bjxw58+OGH0Ol0mDNnDt59911ce+21kEql2Lp1K06fPo2XXnqp5y+SEEIIIYQQQgjpI7zfH6oJGGV2U8s5rurqDrf19pggwFNXB7/VCkVyMjz19Z1uZ+6K7ezZ8DbjrjCeh2nXLiR7vTAMHdrhOPOJE7CcPNntNcWKDwTQuH8/PPX1MM6YAWVqKty1tbA0r8MwdCh0Q4ZEZPvxfj/sZ8/CVlwMsVyOhBEjoMnODmcOMp4H7/OFGn34fOC9XkiUSijT0rq1NZbjOEi1WiiSkiBPTIQiMRFyvb5NpqLf6YTfZgs97Hb4rFYE4tQ1uieSx4/vMjNTJJHAOG0aqjZvjjrYyYlEUGdkQJ2RgYDLBUdZGRr27QM4DhzHIeh2gxOLQx2Kmx/anJzQvUxM7JW6gBzHRWzj7ooyNRWGggI4upnJ21pcN6vL5XK8/fbbUCgUePvtt3s8n8lkwuLFi8HzPFLPSSl+5plnAAAVFRU42eqL/8MPP8QjjzyCadOmQaVSwe1246233qLtw4QQQgghhBBCzgu83w9vYyN8VivAGEQyWaiWmV7fbifUoNeLgMMBv80W9+zBDtfo88FVXd2jOQIuF5oOH47pHMYYGg4cQNDrRfKYMW2etxYXo+nIkR6tKxZesxl1u3dDqtEg67LLwp8fdUYGVOnp8NTVwXLiBKynT0Ofnw91VhYcZWWwl5ZCnpiI1MmT2w38cWIxJCpVj7sUcxyHxNGjoT8nSNnuWJEIcp0Ocp0OaJWtaT19Go2HD4fqVfYDdWYmNBkZUY1VJCQgecwYNBw8GPN1pGo1EkeNQsKIEfDU14MTiSDVaCBWKrtds7C3iWUyGKdMidv6og4Sbt26FZmZmcjNzYWkk1RZfXPnIUNzG+aeMBqNCHaRcrxy5cqIjzMyMrB69eqYr7V8+XIsXLiQgomEEEIIIYQQQvrFucHBFkLzcW9jI0RyOWQ6HaQqFQIuF/wOB4QoA4NBr7fdIGN/iHabcUcsJ06A93qROmlSOJvLXl6OxgMH4rnMDjHGYDt9Gubjx5EwciQMBQVtA30cB5XRCJXRCE9DQzizUJ2VhYy5c6Ho5eaqHMchdfJk6PLyejSPoaAAiuRkmHbt6vOsQk4iabdZSWf0Q4fCVVcHd21t964pEkFlNHbr3L6WMmkSJCoVNm/fji+2bOnxfFEHCYuKijBz5kxMmDAB06dPx80339zjiw8kVJOQEEIIIYQQQkh/EAIBeBob4bNY2nQWbjPW54O3oQGxbib2NjWheutWyPV6JI4eHVMDkN5gKykJNeroAXtpKXifD8Zp0+Cur0f9Dz8gHr1ZGWOhrsEOB0QSCTixOPSnRAKRWAxOLIbtzBn47XZkzJkDRVJSl3O2dAIWeD7mLs3dwUkkME6fDk16elzmUyQmIvvSS1G/dy+cVVVxmTMaiSNGQBpjNiXHcUibPBlNR46EmoB0MxDdE5xIBDAWl/djR7R5edA2Z3zOmzkThePG4c2PPurRnFEHCYcPH45t27b16GKEEEIIIYQQQsjFjjGGoNsdbojAezy9ej2v2YzqrVvDwcjqb7+FMi0NyaNHRxXgipWzthaN+/ZBZjBAmZoKZUoK5AZDONOuO9uMO+KqqUHVli3w2+3xCRAKAmq3b4fE44HSaETQ5YLA82DBIIRgEIznITR3nc269NI2nZ670hcBQrFMFmqoEefPrVgmQ/qMGbCeORPaftzLTU6kWi0Mw4Z161yJQoG0yZORPG4cHOXlsJeWhjJ0exnHcdDk5CB57FiI5HIEXa7Q17nLFfq70xn+uu9uLU+JUglDQQH0+flxXn0MQcJx48bF/eKEEEIIIYQQQsjFgDEGv80W7pDaV/XdvGYzqr/7rk1AwlNXh8q6OqjT05E4ejQUrbqoCsEghEAAfCAAFghAlpAAUZQNGqxnzqDx4EEwxhBwu+GqqQEAiKTSUDZdaipcNTXtBkjMR4/CZ7P92F02ymv6LJaoxnWGCQKsp0/Db7dDptUic/ZsiLqo4TcQSVUqpM+ZE6or2EsMQ4eGth/v3Nmt7cdimQwSlQpihSJUJ7E5UzOcrdmcvalMSYn6fdfZtQxDh8IwdCi8TU2wlZTAWVnZ7W3unVEkJCB5wgQoW3U7lmm1HXah9jY1wV5eDmdVVVSNhuQGAwwFBdDk5PT4vnQk6iChslU3pXXr1mHjxo0YP348ioqKMH78+N5YGyGEEEIIIYQQct4LejyhjsN91FSkRUcBwtZctbVwm0yQaDRgLYHBczLEpFotUiZMgLqTOm1MENBw8CBsxcXtPi8EAnDV1ISDhudy19XBWlwMfX4+GprrCury8qAbNKhHzTv8djuEYBAyvb7DLD6fxYL6ffvg8fsh1WiQPG7ceRkglOl0yJwzp8fNTqKhSEhA9mWXwXbmTCjgxnGQe70QKxQwDBsGVXPtS7FMBolSCYlSCbFCAYlSCVEnfS56dc1JSVAkJSF5/PgfswvjEGCWKBRIHDMGury8mBqItKwnZfx4uOrq4Cwvh7Omps32aGVqKhKGDYM6TlvHOxP1Z6Z12u7111+PiRMnYsSIEbj99tuh1+vx7LPPDthuL9GgxiWEEEIIIYQQQuKJMQZvYyM8DQ1d1hqMN6/FgprmLcZdYYwh4HB0+HzA4UDN1q3QZGUhefz4NjXi+EAApl27ut0ogvf7Ub93L5LHjoVu8GAkjh4Nd20t7KWlKP/Pf6BKS4Nu8GCo0tNjiju4TSaYdu4EJxKFAoU6HWQGA+R6PeQGA6Q6HWxnzsBWXAxDQQGSBg+G6P33u/Ua+psyORnGWbMgiXH7c0+IpVIkjhwZ/tjldkOiVCJp1Cio+yBQ2V1iqRSGIUNgGDIEXosF9tJSOCsqwPv9Mc3DiUQwDB2KhJEju+wc3dU8mvR0aNLTwTcH0x3l5aF1DhsWVYObPm9cIpyTCj148GDMnz8fr776ao8XMRBQ4xJCCCGEEHI+G0hdSwkhAO/zRb2NMN68Fgtqvvsu5qBHV5xVVXCZTEgcMQKGYcMgEokQcLtR+/33Par31njgAOR6PbSDBgEI1XVTZ2RAnZGBoNsNe1kZGg4cgKy4GGnTp0cVkHHX1cG0cydSCwuhzspC0O2G32qFz2qFp6EB1jNnwHs8kCcmInPePMj1enjifL96m0gigTorC7q8PChTUs7rxK3+okhICG0THjcOrupq2EpK4G1oaLe+JcdxkKhUkKjVkGo0MBQUxH1bt1gqhS43F7rc3JjO6/PGJZ988gk0Gg2uuOIKzJ8/HyqVCsYOUo1PnTqFYd0sLkkIIYQQQgiJTcDlgrOqCoahQ6Ou4UUI6T2exsZQ594eZA8GPR5Yz5yBEAhAk50NZXJyl1/ffocDrupqWE6dinuAsAULBtF05AjsZWUwFBTAcuwYgj0IhDorK+Guq0P25Ze3G+SSqFRIHDkShqFDUbd7N6q3bEH6zJmQqtUdzulpaIBp506kTJwITXP3V6laDalaDXVmZngc7/dDJJWeV8E1juOgSE6GLi8P6qysHmWwkR+JxGJoc3KgzclBwOmEvbwcYCwUEGx+SJTKgf1vbBzex1EHCYPBID744AO8/vrrkMvlmDVrFqxWK7Zv347CwkLI5fLw2KeeegoffPBBjxdHCCGEEEII6ZrbZAILBuE1myMKphNC+k5LYxJvY2OPag8G3G5YTp6EvbQ0XBvQdvYsxAoFNJmZ0GRlhbLGRKLwdmZXbS2c1dXtbhn+4sgRfHH4MOrtdqTqdLhi7FhcMWZMt9cXXqfDgYZ9+3o0R9DjQcP+/UiZNKnLTGiRVArjjBloPHgQ1Vu2wDhjRrvbML1NTTBt347kceOg7SIbK9bOxNESSaUxd67lJBLocnMhVijAeD70YCz0pyCA8Txkej10ubmQajS9sm4SItVokDRqVH8vI2bx+Pc/6iDhokWL8MEHH2D//v3YsmULvv32W5w+fRqzZ8+GTCbDxIkTMXv2bMyYMQNnzpzp8cL6GtUkJIQQQggh5yOf1RrezuhtbIQiMXFgZzoQcoFhjMFntcLb2AihB9l7focDlpMn4Sgvb7fzMe/1wnb2bDhgqEhKgrepqcvtzFeMGYO5w4bh7nffxYrrr4eqVYJPf2KMoX7vXqjS06HJyorqHE4kQvKECZBqNKjZuhVpU6ZAnZERft5nsaD2+++ROHo0dM1bl/uSSCoNbW9OT4ejogLWM2fgt9m6PEc/ZAgMQ4dSyQjSbd/t349vdu/u8Twca2+jdTs2b96MefPmRRwTBAF79uwJBw23b98Ol8sFjuPAn9MNaaCy2+3Q6/Ww2WxUk5AQQgghhJxXmCDAeuZMRCdEZWoqlCkp/bgqQvqXy+XC3Llz8d1330HdyZbUnmKCAK/ZDG9TU5tupNHyOxxw19fDXVsLd21tu3XQ4sHj9+PON9/EY0YjkrKykDBiRL9no9nOnoXl5ElkX3ZZtzL6nNXVqP/hBySOHg3D0KHwWa2o2boVCcOHw1BQ0K01efx+3P3uu3h96VIoY1yTMjkZaVOnttkG7a6rg/XMmTafX7FcDsPQodAPGdJrGY19zeV2Y+Gtt2LDO+8M6MYlFxqRXA7doEFwulw9jm9FnUl4boAQAEQiEaZOnYqpU6fi0UcfRTAYxK5du7B06dJuLYYQQgghhBASPU9jY5vghLepCfLERIjE4n5aFSEXPr/DAVdNTczBwaDHA3ddHdz19fDW1yPgdvfSCiO5GxoQcDphGDoU8HhQ+eWX0GRn91qw0Gs2w15SAnlCApQpKZBqtRF1//wOB5qOHIFx+vRuB8g0mZmQzJ0L0/bt8FutcJlMMBQUdDtA2F0cxyFhxAgkjhzZbha3Ki0NqrQ0+B0O2IqLQ+vMz4du8GCIJFGHZMi5OA5iubxfGgMNKCIRtNnZcfs3P67vSIlEglmzZqGwsDCe0xJCCCGEEELOwfv98DY1tTnOeB4+sznqbMI1a9Zg9erVqK6uRmZmJpYsWYLFixfHe7mEXBAEnofbZII/xk6+fCAQavZx9myvZQt2xG0yoW7XLkhUKhgKCqCUyZAwYgQsJ06EgoU5OUgYPrzdYCFjDEIwCJFEEnVzD0d5ORr274c2Nxeu6mo0HToEkUwGZUoKlKmpUCQno37PHujy8qBKS+vRa1M0dyY27dwJfX4+EoYP79F8sZKqVEibOjWq77cyrRYpEyaA8rzjQ5GUBGVqKryNjfA0NgLtbNHvT5xEAlEXTWV4r7dHzY0AQJORAXEcSwj0Stj6scce641pCSGEEEIIIc089fUd/lDkbWyMOptw8eLFWLRoEebOnYv169f36vZMQs5n3c0edNfXo37PHgRcrl5aWcdcNTWo270byePHQ7x5c/i4VKNB6uTJMAwfHg4WypOSwHgeQjAIIRCAEAiEX6s8IQHJ48dDkZTU4bUYY2g6cgSO0lIYp0+HymgMHed5eM1meOrrwwFEqUaDxDg0TwFCXYuzL700LnO1xonFkCiVkQ1EGAOa/9RkZSGlsBCSC2Sr8PlEJJWGmvdwHJQpKZDpdHBWV4P3ePp7aQDHQZ6QAFVaWpf1gXm/H97GRvis1m4FCxVJSZDp9d1caPt6JUg4fvz43pi2V1HjEkIIIYQQcr4IuFxtiuEHvd5w0XsmCPA2NvY4S4cQ0sPswcOHYS8p6fPsQeDHmn2pkydDnJra7hiZVou0KVPgHzEC3oYGcFIpRBIJxFIpRM0PTiyG7exZ1GzdCnVmJpLGjIFEqYyYh/f7Ubd7N4JuNzLnzYNMqw0/x4nFoSzC5mw7IRgEx3HgBnBJBGVaGnKnT+9wKzYTBGoQ1Y9URmPE/RfL5dAPHgxPYyM8DQ39llUoViigzsho8/XR4XiZDOqMDChTUuBpCRZ2sXaRVAqZTgeZXh9xnU2bNmHDhg09WT6AKIOE//Vf/4WRI0fiv/7rvzB48OAeX3QgWrFiBTUuIYQQQgghAxJjDEG3GwGnEwGHA7zPF/G8z25H3a5dyL700vAPTl6zGYrExC63OxFC2icEg/BZrfCZzRACgZjOddfVoX7v3n7JHgQAZ2Ul6vfuRdrUqVBnZMDTRddlmVYbEdg7V+LIkdDm5aHp8GFUbNoUag4ydCg4sRh+hwOmHTsgVauROW8exF18z+msDh/XHKAM9lNGmFguh1StRsbMmZB20niDAoT9R6LRQNZB7EaZnAyZVtthVqFIKoVYoYBEqQzVM/T7EXA6EXS7e7btVySCMiUFiqSkqLfln7sudXo6lMnJ8DY1wWuxRAQLOYkkFBjU6do0xmmxYMECTJ8+Ha+++mq3XwYQZZBwzZo1+O677/Dcc8+huroaV111FW666SYkJyf36OKEEEIIIYRc6IJuN/wOBwIuF1SpqZ02CeD9fvhtNjBBCG1t43kIggDe4wHj+Q7Pazx0CD6rFfbycugHDQodFATYS0uhzsjo9JpBrxdsgNVyIqQ/BVwu+Mxm+B2OmAMHXqsV1hMn4Kis7NEaGGNw1dTAeuoUmCBAk5UFTVZWp1/LjDH4zGa4ampgKy6O2PIbD1KVCsZp0+BpaEDjwYNwlJVBO2gQrCdPQjd4MBJHj+5WgKS15HHjoB80CC6TCY6ystD27j76/qTNy4OyoACijz/uk+uRbuA4qNPTOx3SOquQ9/kglstDQUGFot0SHMrkZAg8j6DTCb/TiYDTGVNJAalGA1V6elw6VIukUqiMRiiag4WM5yHT6zsMDPaGqIKEHMehqKgIRUVF8Pv92LhxI+655x4Eg0Fcf/31WLRoEZRRplMSQgghhBByoROCQXjq6xFwOiMykBzl5VCmprZb5N5rNsNTVxfzD8TO2lq4a2sBAJZjx6DNyQn/ICQEAnCUl4fqI52zPYv3++Gprw8FJTvJICLkYsAEAV6zGT6LBUIXWXft8TQ0wHzyZPhrsSfrcFRUwHrqFIRgEIahQyGWy+GsqoL5+HHIdLqIgGFLt2SPyQR3XR3AcVClpSF99mwoeympR5mSgqz582EvLYXt7FkkT5gAbU5Oj+dVZ2bCkJ8PINSMQZORgaDXC0dFBeylpW1KLESD4zikTJoEucEAn9UKv80Gn80Gv80WzsiWajRInTQJqrQ0uPqo2zTpHkVyctTBuFje/yKxGDK9PlzfL+jxhP/9ZowBjIV+edfq7wCg7IWagEAo27a/yoXEXJNQJpPh2muvxbXXXgubzYaPP/4YN9xwA5KSknDzzTfj8ssvh+g8TL2lmoSEEEIIISQehEAA9vJyCOdsCW7hqa9HwO2GJjMTIokEvN8PV00Ngt3YligIAhoPHgx/HHC7YT97FoaCgohxPosFAZcL6uYuiJ6GBvgslnCWlNdiifnahFwohEAAjoqKUKfRGDDG4KqthfXkyVB31Z6sIRiEvbQU1tOnIRKLYRg2DNqcnHDdPm1uLni/H+7aWjgrK2E+fhwSuRxBrzf8S4D0oUMhT0zscTZfNDiRCPr8fOibg3o9JVEqkVpY2Pa4QoGEggIkFBTA29SEur17ow4WchyH1MJC6JqzqxWJiRHPBz0e+O12KJKSOt0CTQYGkVTaa4Hvc0mUyqjrCg4UfVqTsCN6vR633347br/9dlRVVeGDDz7A888/j1GjRmHx4sWYMmVKjxfYV6gmISGEEEII6amuAoQtgk4n7CUlkBkM8DY1dbvIuq24GAGHI+KY+eRJaAcNalMXTPD74SgrA0SiNtcT/P5OtzMTcqEKer1wVlTEXHPQa7Ggfu/eULC9Gxhj8Nvt8NTXw9vQAE9DA6QaDZLHj4c6I6PdQJ9YJoM2NzccMPSZzZAnJEAsl3drDQMFx3FImzIFki5ehyIpCVnz5qHuhx/gqq7ucs7WAcL2nI+BoIvZudnwJFK8ahLG7Q5nZWXh4Ycfxtdff41f//rX+OSTTzB//nw8+eSTOHPmTLwuQwghhBBCyIDE+/2wl5V1GSBsIQQC8PagC2PQ64X52LG26/B6Ye3s/98dXE+IoQYTIQNVdXU1HnroIZw4cQIPPfQQqjsJJgWcTjhKS2MKEAqCgMYjR1D19dcxBwh5nw+2s2dh2rULZRs3onrzZrhNJsgTE5E+ezYy582DJjMzqkxAsUwGldF43gcIAcBQUBD11kqxVIr0GTOQMGJEh2OiCRCS84u0k2YlJL56Jad29OjR+POf/wwA+O6777By5Uo4HA58+OGHvXE5QgghhBBC+hXfnKUXazZSTzQdPRq+HhME+CwWKJKSAADWU6egz8/vMjOnNSYIoU6sfVggnVy41qxZg8WLF/fpNaurqzFhwgSYzWbwPI833ngDn3zyCQ4ePIiMjIyIsV6zGW6TKabGJF6LBfV79sBntXY59osjR3DFmDEAQlmDzooKNB46BJlWC2VaGvRDhkCRmNjnmVFcO40b+pM8IQGJzfcpWhzHIXnMGMj1etTt3RvRZIIChBcgkQiqLpqVkPjp9Y33c+fOxdy5c3v7Mj1GNQkJIYQQQkh3Oauq+jRA6LVY4CgtDX9sPXMG5iNHkDlvHhSJiRACAVhPnkTyuHGxzdvUBKSmxnu55CK0evVqLFq0qE+v+fTTT4cDhADA8zzMZjOefvppPP/88+Fx7rq60Hs9SoIgwHLiBGynT4caF0Thi8OHccWYMQg4nWjYvx9+ux0pkyZ1uJW4L3BiMXIWLIDY5wvVUWxo6Jd1hNcjkSBt6tRu9zTQ5uRAqtGgdvt2BD2eUIBw8mTo8vLiu1DSr5QxNCu5mA2ImoTtaWpqgtlsxtChQ+M9da+imoSEEEIIISTo9UIklYa7A0cj4HKB93i6HCcEg90uji8IAryNjaEupnV18Fks4WBF0OOB5cQJKFNTYT5yBOlz5oDjOFiLi6HPz4dUo4n+tTid4H2+C2ILI+lf1dXVfZ4scuLEiXCAsAXP83jv3Xexa/v2UNYgY1EH+oBQvc6g1xtzzU5/MAjLqVOwHD8ObW4u0qZPb1MntC8pkpIg02ohUSigTkyEJj0dXrMZ1lOn4KyqiumeRIPjOChSU0NdhDtoCJMyfjzkPfwZXJGYiOxLL4Vp507oBg+mAGFvaKeObZ9dWiYLZ8iTzsWrJmFcg4RPP/00vvjiC9TU1CAQCODJJ5/E7bffHs9LEEIIIYQQ0mt8ZjOCHg+0ublRB/S8HXQ1ZYIAb1MTXCYT3CYT/FYrDMOHI2nUqA63GHoaG+EoL4cQDIYfLBiE32brsGZg0+HDUKenI3nCBFT85z/w1NVBZTSC8TwqvvoKyePGQT94cIdrtJWUgGvV9dPb1AT1OVszCYlVZmYm1q9f36fXfPDBB/Hmm29GBArFYjFuuuYaPPXQQ1HP47NYYC8vh7OyslsZwl6zGY+uXQtHWRnSZ8/us46sHVEZjdBNmAC8/XbEcUViIozTp8PvcMB65gzcNTWAWAyRWBz6/tfyd7EYLpMp6nshlsuRNmUK1M1bRH12O7yNjfA0NMDb0BDq7p6V1eH3pVhJlEpkzZsXl7kuSiIR5Ho9RFIpOIkk/PnnJJLQnyIRfFYr3CZTnzS4EslkEMtkEMvlkOn11Kykj8U1SGg0GrF9+3YAQHl5OVatWoUXX3wRDzzwQDwvQwghhBBCSK8IOJ0QAgE4ysujChQGvV4EnM42xx0VFajft6/ND9WWEyfgbWyEcdq0iK6avN+PpiNHYC8piSmjx1NfD1dtbWgLoUyGhBEj0HTkCJRpaeA4DkIggPq9e+GorETapEnhrELGGJyVlWg6ehQBpxPyVtk3PqsVypQUiPox64mc/5YsWQJ1H9W3FIJBeM1m/PfNN+OTdetgsdnA8zzEYjES9Ho8dOedUKtUXc5hLy2FvbQ0XHNQznFAlNscGWNw1dTAduYMfBYLLh82DNmXXtrvNQC12dlInToVng6y+QBAptUideJEYOLEDscEPR40HjwIR2Vlp9dTJCXBOH06pK3ut1yng1ynCwcFA243fX8ZICQqFdSZmV1u55UbDJBqNHDX1cEfRU3OaHBiMSQqFcQKBSRyOURyOcQyGQUF+1lcg4RKpRJ2ux06nQ65ubl47rnn8Prrr8fzEoQQQgghhPQK3ucLB/V4rxeOsjJo8/I6DRS2V9fMVlKChn37Ogz2eRoaUPHVV6FMG6MRjooKNB48iGAnP8S3hwkCGg4eROKIEeGAoy4/H7biYjgrKqDNzf3xmnV1qPjySySNHg2pToemI0ciOrPaS0t/bODAGLwWC1RUm5D0QF81LfGazXDX1QGCgPSUFHy7bh2eX7UKazdswE0LF2LZXXchvYv3ctDjQe327fCazTFfnw8E4Cgtha24GACgz8+HccYMDB4ANdR0gwYhtbAwLjUQJUoljNOnQ5uXh4YDB9r95Yhh6FAkjRvXZY1BaRcBW9IHRCIoU1JiynIVSSTQZGYiYDDAVVsLweeL+ZoSlQpStRpStTriF2Vk4IhrkPDgwYN49NFHcckll2DWrFkYMWJExDeIkpISDI5TSnG8UeMSQgghhJCLm9/hiPiY9/lgLyuDLje33awX3u+H32aLOGY5dQqNhw51eS3e60Xttm2QGQwRwbpY2IqLAcagb1ULXCQWI3HUKJiPHYM6KyuitqIQDKLh4MF25xICAfCtfuDzNTVBCATAgkEIPA/W/BDL5ZBqtZDpdFRInvQ7IRAIBwhbpKem4k/LluHA0aP407JlXWYQ+mw21H7/faizdwx4nw+WEydgLyuDXK9H0tixoaYkcc6CUiQmghOL4W1sjCnLOGHYsJgbF0VDnZ4OZUoKzCdOwHrqFJggQCSVIrWwENrs7Lhfj8SfWKmEJjOz27VnpWo19Pn54S3k7XYIF4lCmYFSKcRKZSgoqFL1W9Oei0GfNy7ZunUrMjMzkZubC0kHv03NyMhAcXExDh06hF27duH111/HsWPH8NFHH2HixIk4evQoNm3a1ONF9wZqXEIIIYQQMjD4bTaIZLI+zzJoLzNGaA4UanNz2wTFfGZzxA9HTUePwnz8eNTXY4x1O0AY9HhgPn4cxunT2wQlNDk5sJ4+DfvZszAUFEQ9J+/zQWiuN8UEod0tZUG3G0G3G566OogVCsh0ulDAkBqdkH7gaWzsUUMFl8kE086dMdUdZIzBUVaGpsOHoUhORsacOVC0qukZD5xIBE1WFgxDh4abNgQ9HjirquCsqmo3YCiWyaBMTYUyNRWqtDTItNq4rqk1kUSC5DFjoM3NheXkSSSOGNGr1yNxwnFQpqRAkZzc42Ad1zyXTKeDt6kp1PBLKoXI74dEoUDi8OF9Vm6AhPR545KioiLMnDkTEyZMwPTp03HzzTe3GbNkyRKsW7cOixYtwtSpU3HfffcBAOrq6rBjxw7s2LGjR4slhBBCCCEXPp/VCq55W1NfEXgeQbe7/ef8fthLS6HNzYVEoQiP97YK8DUdOxZTgLAngl4vGg8ehMpohCotrc3zHMchacwY1P3wA7R5eV1m/NXb7Xh72zaU1tfjsaeewvLf/Q4Z7cx7Lt7rhcfrhae+HiKZDDKtFlKtlrJFSJ8QAoFw7cDusJ49i8b9+2PKzvPZbGjcvx9BjwepkyfHvcGPRKGAbvBg6PPz2/ySRKJUwjB0KAxDh4YChtXV8DY0QGYwQJWWBnlCQp9/3cl1OhinTOnTa5LuEcnl0GRlhf8NixexXB7xdRB0uQD6/n9eizpIOHz4cGzbtq3TMSkpKfjFL37R5nhaWhp+9rOfYdSoUbGvkBBCCCGEXDSEYDC87U9ITe2z4vZBpxNgDAGXC2Klsk1NLRYMwlFaCk1ODqRqdSiLsDmDKejxwHLyZIdzM8bgt1rhaWyEJjMTkijrcTFBgBAI4IvDhzE3PR2ehgZ4GhoQsNshMxiQPmNGeCwnFiNh+HCYjx0DACjT0iDX62E5fhxJ48Z1GDyot9txy//+L+xeLwTGsHbTJnz1ww/4dt26Luu4tSb4/fA2NcHb1AROJIJYpQp3pxQ3F6Nv+VyuWbOmz+rVkQuXp6GhW1mEjDE0HT4My6lTUZ8jBIOwnDgBW3Ex9Pn5SBg5Muru553hOA4yvR6K5GQoU1Kgzszssp4f0BwwHDIEGDKkx2sgFz5OJII2O5syvklUov7ONi4O9QwKYtju0NeoJiEhhBBCSP/z2+3hLbxes7ndTLleuW7zVmPrmTMIOBwwzpgRUc8PCAXtHBUV0GRmRjQ4sJ45A9a8TbeFEAzCbTKFH0IwCJlOB8uJE0gtLGw3Ayno8aDpyBG4TSawYBCsOQDyWXk5CkaNgjI5GcqCAiQmJUEslyMAIOD3AwASRoyAPC8PrLw8vDbVqFGo27EDtqYmJI8bB9k5pXV4vx+vffop7B4PWkItAmOwWK14ftUq/GnZsm7fT7SzdVubkwOpRoPVq1dTkJD0CO/3d5pF6G1sRNDlQvXWrVBKpWCCEKqrKQgQgkHwUTYJ4v1+OKuqYD15EmKFApmXXAK5wdDtdXMcB0VycvihTEqi2p6k16l7UH+QnD/6vCahslW687p167Bx40aMHz8eRUVFGD9+fI8X0t+oJiEhhBBCSP9r3QjEZ7FAmZIS90YA7Qk0Ny1x1dQg4HSidvt2pM+c2SZQCEGAs7Iy/CHv98N29mzEkKDHg9pt28AYgzo9HWlTpoRqQIlEcFRUoO6HH6DLy0PSmDHgxGIwQYCtuBjm48ehTk9H+qxZP9Z3kkjg/Mc/8GxlJdDquq1xYnE4AMh4PhRobcEYgl4v+P37IZbLQ1vNOA683w/e7Ua1z4dzc7F4QcDaDRtw4OjR7t3MDnAiEcRyOXyxdsQk5BzexsY2zRKYIMBZXQ3r6dOw1NaC9/vhbWwEF2MQjvE8XCYTnOXlcJlMkBsMSBg5Etrc3B5t55UbDEiZODGmbrKE9JSiuW4gufD1eU3C1rUarr/+ekycOBEjRozA7bffDr1ej2effZZqjxBCCCGEkG7j/f6IuoCM5+GzWuPeFOBcQbc7dC27Pdy8xG0yoeb775Exc2an2wqtxcURTQ/8Dgdqt22DMjUVKRMntglwanNyoEhMhGnXLlR/+y0MBQWwnDgBxhjSZ8yAsp0tvqk6HZ674YYO15A+c2ZExmXd3r1wVlREjPHZbGg8eBC81wuxQgHe40HimDF489gxfH7gAIRW/9cXcRyuv/RSPPvHP3Z4ze5SZ2Tgv371q7jPSy4e52YR8oEAHGVloSzgdjJYo+W32WA7exbOykqIZDJoc3KQOGZMjxtyiKRSJI4aBcOQIX3yCw9CWkg0GqhiKBtBCBBDkFA4p97D4MGDMX/+/B5HKTvDGMM777yDd955B2KxGBaLBVOnTsWf//xnGKJI877pppuwe/dulJWV9doaCSGEEEJIfLRkEQrBYDgw5zWbez1I2LLV2F1TE3HcU1eHmm3bkD5rFsTt1EbkAwHYzpwJf+w1m1H7/ffQDRqExNGjO/wFulSjQdYll6DpyBE07N8Pw/DhMAwd2mEA4YqxY6HsIBtKm52NlEGDIo7lFBairLERLBgMH1OmpEB/6aVwlJWB9/mgHzIEIokEv0xKwncnT8Lm8UBgDCKOg16pxNIZMyDnOAS9XgQ9HgQ9HvAeD1RpaVCmpLS7lmiIXK52a5gTEi1PQ0NEFmHttm2hLsc94KioQMO+fdDk5MA4cyYUSUlxSYDRZmcjedy4qOuQkv5VU1eHv61ahdMlJXh85Uo8dNddUTVxGohEMhk0WVn9vQxyHoo6SPjJJ59Ao9HgiiuuwPz586FSqWA0Gtsde+rUKQwbNqzHi3O5XLj33nuxf/9+FBQUwO12Y968ebj99tuxbt26Ts/dvHkzvvzyS+j1+h6vgxBCCCGE9D5fc5DQXl4OqVoNtdEIweeD3+HocTZPZ1pvNT6Xp6EBNVu3In32bEjOCdTZS0vBN2+ddZtMMO3aFcoYGjq0y2tyYjGSx49HchRle64YM6bd4yKptN3zJSpVqInJOduFOY6D7pyAYqpOh/fuvBNvbd2Kfx88iEvS03HPDTdA4/GgtJ3aRubjx6FKT0fS6NFQJCR0ufZzCYEArrviipjPIwQAeJ8voiSBq7a2RwFCxhjMR47AXlqKtGnToE5Pj8cyIdPrkTxuHNQd/LxMfvTxxo244eqr+3sZqKmrwyU33ACL1QpeELB6/Xps/OabmJs49ZaY7pNIBE12dttyGYREIeogYTAYxAcffIDXX38dcrkcs2bNgtVqxfbt21FYWAh5q0KYTz31FD744IMeL04qleLJJ58MNzxRqVRYvHgxHnnkETDGOvztTiAQwKOPPorf/e53+L//+7+orkWNSwghhBBC+k/Q44HQHHBzlpcDYnH4B2xvU1OvBQmFQAC814ugzwdvU1O7Y7xNTajevBmZc+aEM4L+uWED3nvrLdRZrUhWqVDI8/jZpZdCm5PTK+tsT9KoUZC0qhveWkJBARwlJQi02r7dkRStFv992WU4XlWFG3U6GLrIoHLX1sJjMkGdmYnE0aMhj7HelbexEfKEBPoBlsTs3CzCcwPhseD9ftTt3o2g243MefN6/D2GE4mgycqCPj+/R9m2F5t/btiAK+fN6+9l4G+rVoUDhECoNqvFZut5E6c4+eeGDVEHCdXp6aH6s+Si0ueNSxYtWoQPPvgA+/fvx5YtW/Dtt9/i9OnTmD17NmQyGSZOnIjZs2djxowZONNq20VPyOVyLDvnC9LtdiM1NbXT9O8XX3wRN9xwA1Ji+OZMjUsIIYQQQvpPS40xv8MRzgzyWixQJCQg6HLB09AAriWoxHHgOA5StRqidrYBxyK81bi2Fowx1GzdCmVKCgzDh0f8f9Nvt6NqyxZkzJkDmVaLBaNGYfh11+Hud9/Fg/n5UKvVfRogVCQkQD9kSIfPiyQSJI0dC9OuXVHPyYlEUGdmwnLqFFInTep0LGMMzqoquKqrkT5rVkwZWIzn4W1s7LPO1eTCEPR6I7IInVVV8Fos3ZrLb7fDtGMHpBoNMufNa7ecQLSkajV0gwdDN2gQBWa6oaauDgtvvbW/l4HTJSXhAGELnud7pYlTd/iaO9l3RZ6Y2KMO3OT81eeNS37961+D4zhMmjQJkyZNwrJlyyAIAvbs2RMOGr722mtYuXJlrzUwYYxh3bp1ePjhhzscU11djY8//hg7duzAmjVremUdhBBCCCEkfhhj4Y689la1pK0nT8I4fToAwFNf3+65YrkcUo0GUo0GnFgMIRD48REMQggGwVr+5HlwEgnECgUkcjnECkX4uq6aGgSag5F+ux2834+ksWMj/l8bcLlCgcJZs2A5eTK0dp6H22RC1pVXdvoaOY6DVKuN7DwcA5FUCmVyMlRpaVCkpkKu13f5f25tTg7cdXVwlJVFNCGMWJdYDMPQoQiWlAAADAUFaPruOySOHNlhlmJrjDGYdu1C1vz5MWUUttSa7GmQl1wcGGNwm0wRHzd1EbhhjCHgckHk8YDxPBjPQwgGEXS7YT5yBLohQ5A4alS3f3aVajShLcUZGdTAswcy0tLwzosv9vcy8PjKlVi9fn1EoFAsFuPn11yDpx56qB9XFnLrAw90OUaiUkFFW9xJD0UdJJzXTgqwSCTC1KlTMXXqVDz66KMIBoPYtWsXli5dGtdFtnjppZeQk5ODu+++u8MxDz74IJ577jlI6T8chBBCCCED1po1a7B69WpUV1cjw2jEtZdeiuuvuiqiK6+zqgoBpxNSjabDeXifD3wnW4XPxYJBBJ1OBFt1QRUEAe66OrhqaqBMSUHKxImo2bYNgt+PlEmTIhqK8F4vqr75Jhx0C3q90ObmttuYQKrRQGU0QpWaCmVqKsQyGZxVVajfuxd8B1kh2uxsaLKzwYnFEEkk4CQSiCQSSNXqLjujStRqBF2uiGNpkydDP2QImo4ciQiyAKEtacnjx0Om1SLY/H9nmVYLldEI6+nTSB43rtPrtRACAdR+/z2y5s+HpFUJos5PEuBpaIA6IyO68eSixRiDs7Iy4r3tqKhoN+DOGIO3qQl1J0/Cb7Wi8quvoJTJIBKLwUkkoa8rsRgphYXdburAicVIGDECCcOG0Zb5OLhx4UKoB0Bjl4fuugsbv/kGFpsNPM9DLBYjQa/HQ3feOSDWd+PChZ0+L9FooMnKooA16bGog4RRTSaRYNasWSgsLIzntACAjz76CJs3b8bHH3/c4Rt/8+bNYIzh0ksvjXn+5cuXQ9ZcjHrBggVUm5AQQgghpBctXrwYixYtwpzZs/HmX/4ClVwOd309Aq0CAYwxWE6fRurEib26Fk99PYRAAK6aGmgyM0NbEIuKULNtG+p270balCk/bnVuXhcQ2hot+P3Qn9OohOM4JI8bB0NzXe3WNFlZUCQmwrR7d6i+WjO5wYDk8eOh6maBfJlOB3VmJmzFxRACgYjnFAkJyJwzB+76ejQdPgw+EEDy+PHQtNoi3BKYBADD8OGo+e47GIYNAycWg7VkZQYCkOl07Wb/BZxOmHbsQMbcuRB1Ecxs4bNaIVWrIaNGg6QTrpqacHMhAGCCAPOxYxFjmCDAWVkJW3Fx6BcLWVmQ6fUY9NOfQhVt4DoK6vR0pEyY0OkvLkhsBkLTEiCU0fjtunV4ftUqrN2wATctXIhld901IJqWAJ3fJ3lCAlTp6RQgvEht2rQJmzZtAgD4o9yW3pm4BglbPPbYY3Gdb+3atVi9ejU++eQTyOVynDlzBjk5ORHNUgDgP//5D8rKylBUVAQAMJlMMJlMKCoqwqRJk/C3v/2tw2tQTUJCCCGEkL7ldzjA+/1gPA/gx63G5mPHINVqoc3Jgb2sDImjRkWfodYNrpoa8H4/vI2NSJs8GQAgUSqROXcuar//HrXbt8M4fXqb4Jj15MnQdudWWSacRALjlCmdZilJVCpkFhXBcvIkbGfOIGHkSOgHD+4yU7AjnEgEldEY/tNZWdnuOFVqKlSXXgomCG2uxYlE4XpqisREKFNSUL5xY8Tr4sTiUAB0wgRoMjPbzO9paEDDvn3he9il5rqGEosFKqOR6rmRNly1tfA31yttYS8rQ6A5E5gxBuupU7AVF0MkkUA/ZAi0eXnwCQK4/fvjFjSRqtWhwHo773ty4UhPTcWfli3DgaNH8adlywZEBmGnOA4qoxGKxMT+XgnpR62T3Ox2e9/VJIzF+PHj4zbX2rVr8dprr2HdunUIBAIIBAJ46qmn8MwzzyAvLw+vvPIKduzYgQ8//BArV66MOPedd97Bk08+iW+//TZu6yGEEEIIIT3ns1rhrKoKdyoVgkG4qqogBAKwnj4NsUwWCrQFg7CdOYOk0aN7bS3u2lq4a2sh0+sjtg2L5XJkzJkD065dqP7uO6TPnBmu0+e32+GqrYW4VWBLrFAgY+ZMKJKSurwmx3FIHDECCec0SOkOZVpaOIAp0+kg0WgitlO3uXYHwUiRTAapVgv4fDDOmAHe64VIKg0FCDkutO2zogIN+/bBWVGB5PHj29QttJeWQqbXI6GdLMqOBF0u2EtKIE9IgDI1lbZwEgCAu74ePrM54pjA8xFZhC3ZgymTJoUC5S1fS3HIpuFEIqjS0qDNzYU6IwMiSa/86Nwnaurq8LdVq3C6pASPr1yJh+66CxnUOOi8xolE0GRnU1Yribsuf13597//HU1R1nhpj81m63ZmoclkwuLFi7F161akpqZCq9VCq9VGNCSpqKjAyebC0a1dffXV+POf/xzOJFy3bl23XwMhhBBCCIkfb1MTXNXV4QAhADirqyEEg3BWVYWbkDia6xPazp6FEAz2zlqsVgRcLrhqatqtjyeSSpE+cybkej2qt2wJ10GznDgR2qLbHHCTarXInjcvqgBhaz0NEEpUqjZZJOr0dKCb8yYMHx5al0gEiUoVChI2z8VxHLS5uchZsACcSITKL7+EvaSkTVOUpkOH4O6g0UyHGIPPbIbtzBl4zeYOG62Qi4OnsRHeVtvxW9jPnkXQ4wEQahpkPnoUiaNHQx2nrZYcx0GZkoLUwkLkXXMNMmbPhjYnp0cBwo9bZeT2h5q6Olxyww1YvX49PF4vVq9fj0tuuAG1sX6NxlF/35PznUgmg27wYAoQkl7R5Xe73/zmN3jggQcwY8YM/PznP4+6xggAfP7553jnnXe6ne5oNBoR7OI/hOdmD7bYGOM3nuXLl2PhwoVUi5AQQgghpBcFXK42DTSAH7caO8rKoM3Lg0gigfXUKWhzc8H7fCj57LNQ4Ks5eMSJRKFuvxkZ0KSnt2kcIgSD4L1e8D4fgj4fBJ8PfCAAiVIJucEQCkRyHNw1NRCaOxQnjBgBsUzWpqkIJxIhpbAQlmPHUP3tt0gaOxau6mokz5sHHD4MjuOQPnNm3//AxnFQtaor2EIsk0GRnNxukKUrmsxMeEtKImrAtZlfLkfa1Klw1dai8cAB+Gw2pEyYEH6eMYa63buRvWABJM01v6PFeB7u2lp4GxuhSEmB3GCgOlsXGZ/VCk9dXZvjfCAAc6vkEFtJCUQSCbS5uXG5rjY3F8ljxrTbhKgn/rlhA65spwloX/nbqlWwWK3hrr28IMBis+H5Vavwp2XL+mVN/9ywYcDUIjzfSNRqaLKyzuvMVtI7Nm3ahA0bNvR4ni7fWVKpFC+//DJee+01TJ06FVdffTVmzpyJKVOmtKnh53a7sW/fPmzduhUbNmzAZZddhvfffx+qgb6XH1STkBBCCCGkt7VkCp4r6PHAW18Pv8MBr8UC44wZEEmlMB8/Dld1NTRZWeG6hS0Yz8NVWwtXbS0aEGr8IZLJwHu9CHo8bZp3nEsklUJuMCDocsFTXw+xXA6ZXg/DsGEQy+Vo2LcvIpuN4zgkjh4NsUqFhr17oc3LC9ciVGdnQ94P/49UJCV1WMdPmZwMv80GIcZtl5xIhKRRo2DatavLser0dMh0OlR9/TWUycnQZGeHnwt6PKjfswcZM2fGdP0WQiAAd00NvI2NUKakQKbXU7DwIuHpILhtPXMGvNcLIPT+sJw4gdTJk3v8vuDEYiSPHw9Dfn6P5ulITV0dFt56a6/MHY3TJSXhAGELnuexdsMGHDh6tF/W5IvDdvCLDScWQ5GcDEVSEn0vJO1asGABpk+f3jc1CUUiEX77299i6dKlWL16NV544QV8++23YIzBYDBAJBLBbDaD53lMmTIFV1xxBT755BNktLNlgxBCCCGEXJyc1dVg7ewSaTp6FIwxOMrLoU5Ph7i5SYmhoADWU6egzszs8oci3znNDboiBALhYETLVmOO46DOzIRcp4NYKoVp926wc3641g8eHM5EbPkxN7F5i25fEslkUKakdPh8uIlJ85btTueSy8E1B1+AUKdj6bFjnWYTtpCq1UidPBl1P/wAmcEAmVYbfs5VXQ1rcTEMQ4Z0OU9HBL8frupqBJzOTpvBkAtD0OttN7BtLy+HpVUtQuvp05DpdFAZjT26nlSthnH69F5t/JCRloZ3Xnyx1+bvyuMrV2L1+vURgUKxWIyfX3MNnnrooX5Z060PPNAv1z0fhYODiYndbm5FSCxiylHVarX4zW9+g9/85jfgeR51dXUwmUzgeR4pKSkwGo1QUFcyQgghhBByDk9DQ5tmGgLPI+B0wllZCYVUCkd5OVImTgw/rxs0CJYTJ+Cpq+txMKAjjDG4a2uRNmUKZDpdOCNQk52NdIkEtTt3tglshgMKfn+ou3EU24w5kQgiqRS8z9etdbbUCAw/lMouf2CUabVQpqbC29jYJtjZQpGUBGVqKgLl5T9ei+OQNGoU6nbvhkgqhUgmg1guh0gqhbexsU19SHVGBvSDB6Nu1y5kXnJJxDa4xkOHoExJgVyv79brbuG32RBISIBUre7RPGRga6n52ZqjshL1P/wQzuwNer2wnj6NjDlzepRRpTIakTZ1aq92TgeAGxcu7NcuuQ/ddRc2fvMNLDYbeJ6HWCxGgl6Ph+68s9/WdePChf1y3fMJJxZDkZQUyhyk4CDpQ93eyC4Wi5GRkUHZgoQQQgghpFNBt7vNFsKgz4farVvD24I9dXUAY1ClpUEsk0EIBCCSSKAfMgSWU6d6LUjoM5vBeB6K5GSoMzMjnlOnpyNzzhzUbNvW7vZlTiSK6G4c8ZxYDHlCAiQKBcRKJcTNtfn8djtcNTVttk+HTuKgSEyETKcDRKJQAITjwHFcuHtxrJQpKZAnJsLb1ARfU1M4WCiSy6HJyAjXXzt3C5smOxuarKw2P5wG3G407NsHV21txPHE0aPhbWpC48GDSC0sDB9nPA/Trl3Inj+/xzW03LW10OXn01a7C1jgnCChs7oadbt2RWz9txw/DpXRGHOToBYcxyFh5EgkjhzZJ++l/q69l5GWhm/XrcPzq1Zh7YYNuGnhQiy76y6kp6b225r6+54MZJxI9OO2YgoOkn5A1S6bUeMSQgghhJxrzZo1WL16Naqrq5GZmYklS5Zg8eLF/b2s8woTBDjP6WQccDpRvW0bvBZL+JijvBzanBxwIhF0gwcj6HLBUVkJ/ZAhsJ4+DW9TU7eDAp1x1dRAlZ4OTiRqdzurMjkZWfPmoWbr1nBX1RbavLx2f4gTy+XQ5OSEA4OtyXQ6SFQquGpqIrbzSjQaqI3G8FbreBKJxVClpkKRmAiv2QwIApSpqRFrF4nFEUG8lgDluaQqFTJmz4ajogINBw+Ga8RxIhHSpk5F5ddfw15WBl1eXvgcv82GxkOHkDppUo9eB+/zwWex9OrWUNJ/eL8/ItPWWVsL086dEQFCv8MBR1kZsi67rMN5xHI5DLm5kOn1yL3yyjbZcpxI1O7X5oUsPTUVf1q2DAeOHsWfli3r18xG0jFOIoEuL69X/h0gF74+a1xysaDGJYQQQgg51+LFi7Fo0SLMnTsX69evh5q2OsbMazZH1BgL+nyo/vZbBNzu8DG+ue5c1qWXAggF38AYnFVVEMtk0A8eDNOuXaFGIc2Bq5ZsC5XRCHlCQocZQYyxiE7HEoUCErU6PN5VU4PEUaMgVauhSEhodw65Xo/s+fNRvW0b/DZb6PpiMRKGDWszVqrVtpuB15pIIoE2JwdeiwW+piYoU1ND2YO9TCSRQNVJ9hAnkUSdsajNyYHSaETjwYNwNHemlqhUSJsyBaadOyFPSIjYYmw7exb6oUN73ODFU1cHmU43YDt7MsbABAEisbi/l3LeafnaAgB3XR1MO3a02SZvPnoU2ry8iNqXLRRJSdDn5+OLw4fx8VtvwR8M4vbHHsONCxdS5hoZ8ERSKbR5eRddAJvET582LiGEEEIIISRWTBDgbWz88WPGUPfDDxEBQiDU4EJmMECm00GZnBwOJKkzM+GsqkLCqFGQJySEMoqas4qEYBCe+nrYTp8GJ5FAZTRCrFCEg4Gt/2SCAHAcxHI5eJ8PnFgMmU4HmVaLgMsFVVpam63G55KoVMi65BLUbt8OT0MD9IMHQ6JURoxRpKR0GoQ7lyIhocPAZH9RpqQAUTaBkchkME6ZAk4kgr2kBECozpth6FDU7d6NrPnzI4JlzooKyEeP7tH6mCDAU18P9QAteeRtbIQQCAzY9Q1k/ubMWk9DA2q2b2+zJd/b1AS3yYScK66IOK5OT0fi6NHhr6Ub8/Jw4zXX9M2iCYkDkUwGbW4uBQjJgEBBQkIIIYQQ0iu8TU0RP+hbTp6E+5xadgBgLytDanMHXN2gQeHjCSNGwFlVBZFYDE12dpvz9Pn54UCky2QKZwrKDQaI5XKIFYrwnyKpFBzHgQkC/A4H/DYb/FYrkkaNgkgqhaYlSMhxEVujWxPLZMiYOxcN+/bBm5iIP69cidMlJXh85Ur84fHHkd+PNb7iRW4wIOjxxNRgJWXiRATsdniaA8IJI0fCXV+PpsOHkTJhQnico6ICST0MEgKAz2IJ1Xs8J0jb33i/P3QPBGFArm8gEwIB8M3b+RsPHmzTLIgxhqYjR6AfOjTivkoUCqRNmwZxN2t2EtLfRHI5dLm53a47S0i8USXMZsuXL8emTZv6exmEEEIIIRcEIRiMyCJ019fDfPRo5JhAIFSXjzFoc3IgkkqhblUXUJGQAFV6eqfX4UQiKFNTkTx2LFInTULi6NHQDxkCTXY2lCkp2FxeDrFMFt5ezIlEkOv10ObkIGnsWBiGDYNEoYAiOTl0zaQkyDupeScSicDn5ODSJUuwev16eLxerF6/HtPnzUNNTU3M96kra9asifuc7V3juuuug8/nw3XXXYf/9+23MZ0vEolgnD49HLxpqU/oLC+Hq9U9CTidoZqIceA2meIyTzy5amuB5u2x5zZ2IZ1rySIMut3wtZPJajl+HAGnE4ZztvgnjR1LAUJy3hLL5dDl5VGAkMTFpk2bsHz58h7P0ytBwjfffLM3pu1VK1asoKYlhBBCCCFx4m3VSTfo8bTpUFpbV4c/r16NKrcb6/1+NHo80GRnt/mBP2nkyB6t44vDh+Hx+zt9cMnJcHs8cLndCIjFgE4HXqmEy+1u9/G3VatgsVrBN78+XhBgNpvx9NNPw+VyxfWxevXqHr3+aCxevBhffPEFjhw5gi+++AJLb78d4hiz4CRKJYzTp4drMUrVaiRPnIj6vXsjGr44ysvjsuaOgkn9xW+zIeh0hj/mPZ64BUQvBv7mrsbOmpqI7xMAYD52DPaSEmTMmRPx/UGZnAxtbm6frpOQeBErFNDm5Q3Y+qrk/LNgwQKsWLGix/P0yjvyj3/8I371q1/1xtSEEEIIIaSPrVmzJqauzkIgEBEgMf3wA4LNXXABoKqqCr987z04g0EIAD4/dAjfnT6Nr99/v81ciqQkKNPS4Kmri2nNXxw5givGjEG93Y67332307FSrRYiiSTU9bRVV0khGIQQCLQZf7qkJBwgbMHzPN577z3s2bMnpnV2xRfDtt94UqWlwV1XB5FYDK75IZJIEHA6ETynpmQLZXIyUpoDg0CouYnbZEL9nj1Inz0bHMfBWVWF5PHjO2w0Ewu3yQSJStXvdbwEnoerncxGT339gG6yMlAIwWD4PeU6Jxs3HCCcOzeiuQ/HcUieMCEu7yNC+ppEpYImJ4caHJEBKeZ/sTweD5599lmsW7cOVVVV8LT6zSAhhBBCCLnwrF69GosWLYp6vMtkgq85q8pZXQ1zZWX4Oa/Fgtf+3/+Dk+fREmYTGIPd68UrH32EPy1bBolKBSEQCAfoFHl5EXNE44vDh3HFmDFI1enw3A03dDhOLJMh58orIRKJoEhKgiotLeJ5r9ncZmvr4ytXYvX69RGBQrFYjFtuuQXPP/98TOvsynXXXRfX+aIlVauhHzy4zXFlSgoCLhc89fXtBgv1gwfDZ7HAdvYsACBlwgRUfv01bGfOwFBQgKDHA099fZv73B2M5+GsrIRu8OB+DRZ56ura1NADQutz19X9WO+StCvgdAKMgQ8E4KmvBxCqQWg5frzdACEA6PLzB1zTH0JaE8lkEMtkEX+KpNJQ+QsRVX0jA1fMQcJ77rkHq1evxowZMzBp0iTIzvnNHWMMH3/8cdwWSAghhBBC+ld1dTXmzp3b+SDGwBgDEwQIwWC4+Yffbg83L2GMIWC3o9rng3DOlkJeELB2wwYcOHo0lBnWHDQIP+/ztWkoIgSDEddqzd8ctLli7Fgom/+/ynFcm62M2rw8aDUaAIAuIwMShSLiebVaDa1OB2d1dfg6D911FzZ+8w0sNht4nodYLEZiYiL++Mc/Qq1Wd36fYrRkyZK4zhcPUrUa0kGDOgwWJk+YAK/ZDJ/FApFUirQpU1CzdSuUKSmQJyTAUVERlyAhAPBeL9y1tf3WTTjo8cBnsUQcEwQBouYggN9qRcBggDTO74sLid9mAxDKDGWC0GWAUCyXIzEODXAI6S3yxESou6inS8hAFXOQcMOGDdi2bRumTZvW4ZjzsQHI8uXLsXDhQqpLSAghhBByjszMTKxfv77d51wmE/xWa7j+YGu20lI0HjgAIBQgNO3cCZFEgo+sVnx+4EBEoFAsFuPn11yDPz38MAwFBQBjsBYXt5uh1ZogCPA1NcFdVwc+EIBEoYBYocBv//pXZM2bhzuuuAJiqRScRAKxVIqg1wu/3R5+aJobpYjk8jYBwhYyvR5aiQTOykownkdGWhq+XbcOL3/wAd7/4APccsst+OMf/4iMXghUxbLNu6+1BAudlZXhmnJAqJFJ2tSpqPz6a7BgEIqkJOjz82E+fhzpM2fCWVWFlIkT47bVzmexQKJSQW4wxGW+aDHG2myP9TQ0wNvUhIThw8PH3LW10OXn09bYdjBBQKDVVuOuAoQAkDRmDCT9vMWckI4okpPj9ksQQmKxadMmbNiwocfzxBwkNBgMnQYIgdBvm883K1asgK6df4QIIYQQQi52S5YsaTdDzlVbC4nX225wjQ8EUH/2bDiLz3zsGCReL7Lmz8cvPR58d/IkbB4PBMYgFomQoNfjoTvvRGJGBlTNmX3i7OyoahFqNRrgnAYGN99wA5TNHYtbkygUkCgUUKWmRhxvLxjRmlSthm7wYDjKyyH4/cgdMgQvvPQS9h04gOeffz7uGYTnE1VGBoI+H4RW9RPlOh2Sx45Fw/79AADtoEGo/Oor8H4/gFDgTNOqk3VPuWprIW7+3PYFIRCAp6EBfKtam0AoMO6qroZ20CBImutb8j4fvE1N7b4fL3YBhwMQBDBBgLu2Fr6mJtiKi5F5ySXtfk0qEhKgGzSoH1ZKSNcUKSlt/m0hpK8sWLAA06dPx6uvvtqjeWLeDP+b3/ymy+hkf9VOIYQQQggh8ddeNpurtha+Trq32s6cCTcrcdXWwnrmDNKmT4dIKkWqTof37rwTP504EUqZDEuuuw7frluH9NRUyFvVGVMkJv5/9v48Pq76uh//X/fOvo+kGY1mRqv33Xi3MWBjMCYJbhpMaKhLaJrwTSmENj+y/KpP3Zol/iSUNkkJ+TSfOAnEcZs0QJqYb4oCAePdyNjGNt5traNtRrPvc5fvHzO6aDSjfaSR7PN8PHjEc7c5o0gj3TPnfQ6YMVabffa++0Z1/HBJQiDdv9BYVwe5TgcNVYpIWJkMhqoqYECfLfOsWdBmltwpDQaoTCZEMsUEodbWwgYhCAi3t0PILG2fKKlIBOG2NvivXMlZZswnk+kYUin4zp/P2hfr6UEyFJrQ2Kajvq9JzOORvn46pzPvzyPDMLAuX04VmWRK0pSXU4KQ3BBGXUn41a9+Fbt378ZDDz2ElStXwmKx5LxR79+/v1DxEUIIIYSQKSba1TVkgpBLJOC7dAlAOqnS8/77sC5fDpXJJB1TbjTima99DdeffRbPfP3r0Gm1kOt0WdOFmcwwkb5hBhNlqKXGOcfK5TDW1qYf5Jl8fLOSqVTQOxwIt7dnbbetXInWt94CH49DX1WFcGsrjHV1iHR2gk+lIFMoChaDkEgg4nJBX1VV8ERSwu9HvLc3p3Kwv1BLi7Q8PnDtGkyzZkFpMKR3iiLC7e0wVFdTf8IMURTTlYQAIi6XtHzbsmxZ3uMNtbVQl5VNZoiEjIimvBwaq7XYYRBSEKNOEjY2NuJ//a//BbfbjV/96ld5j5mOn+5QT0JCCCGEkKEJPI9Yd3dOBdVAvvPnpcnEntOnoaushKG6WtrPKhSoWLsW6Jc0BJC3p5y6rAzx3l5p+MlEGEkVIRme0mSCOhZDvLdX2ibXaFC+ciU6Dx2CvqoKvWfPgovFINdoEGlvL/jS0VQohFBLC/ROJ9gCJSDjPh+iA3oP5hNsapL+LQoCPGfOwLF+/ccHCALCra3ppciTtCx6KkuFw1Iv00hHB5J+P/hkMm81lnn2bJQtXTrZIRIyLI3NRq0EyJRQtJ6ETzzxBGbNmoXnn38eTqcTigG/fEVRnJbLjaknISGEEELI4JLBICKdnXkHiQiCgFQ4jFQohGQwiMC1awDSQxxibjdqPvEJ6VhGLodz40aoS0oQ6TcVl5HJoByQNAQmp5qQkoSFo7HZwMXj4CIRaZve4YBxxgwEr1+HxmJBuL0d5tmzEWprm5D+clwkgsD169BXVg5atScKAvhkcthkHRePI9rVNexzxnt7kfD708udRRGsXI6Iy4WY251VYSQKAkItLTDW1aWneN9AREEAw468m1Xfhw1xvz+9jLu9HTq7PavFACOXo3z58o+rdwmZKhgGWpuNqlvJlFGonoSjThJevHgRHR0dQzZn/vM///NxBUUIIYQQMhW4XC48++yzuHDhAp566ins2LEDTqez2GFNKiGVQqSzU1oW2J8oCOg+cQLhlhaI/SYVA+kPjnvPnEHJ3LlZS4htK1dC3a/vYB+V2TzoahRVaSkSPl+66ijzPCIA5JmoPFqjWWpMhscwDPSVlQhcu5aVULbeckt6KXB1NYLXr8M8ezZi3d3golHItdqCxyFyHEItLellgP2qfPhkEgmvF4nMRG6dwzHoVGSB5xFuaxvR91ng+nUAQO+HHyLh98N5551gGAae06dReffdWd/bIsch1NoKY20tWPmob8emJIHnEbx+HdqKio+XWA8h0tHx8VLjzFTjiMuF0kWLpGMUej0q1q3L+35BSDEwLAuFXg+FwQCFwVCwCe2ETCWj/q00f/58yIf5Zfb1r399zAERQgghhBTC3r178w7cGCmXy4Vly5bB6/WC53ns3r0br7/+Ok6fPg2Hw1HU2CaLKAgINjdDyEykHbiv+/hxhNra8p4baW8HF4/DNHu2tM08Z07WsuP+VEMkAliZDOY5c3K2c9EoUpEIkqFQuldcJoHIsCwYhQKsTAZGJgMrl4PJ/FvkOHDxOPh4HCLPZ/VJJIXByuXQO50ItbRkb6usRDIUgvvUKSRDISgNBnjOnUPF6tUTE4goItbdDT4Wg9JoRNzny6pwBCANUsmXKIx2dOT93h+IT6UQbmuDkEn+AUj3H6yqQtznSycEB0zfFhIJhFpbYaipuSESDdHOTgjJJMJtbdBXVg5ZnRvp7MxqWRB1uZAKhcBFo9BWVAAAtBUVsK1dC/kNVm1Jph9WqYRCr4fSaIRcq52WrdUIGY1RJwmff/55PPXUU3j22WdRMsgfc2vXrkXHCPp2TCXUk5AQQgi5sezZswd/+qd/Oubzn332WSlBCAA8z8Pr9eLZZ5/FCy+8MO7YpkOSMNrVNWiCsOvYsZwhFdJ+nkfvuXMoXbhQqpTSWK2wLFmS93i5VptVbThScq0Wcq0WGqsVAs9D5Pl0QnCESx6FVCpnIi8pDIVeL/WT7GOorkbg2jVoKyoQbmtD6YIFCLe0ID579oRWiyWDQSSDwUH3R1wuiIIAdWmptC3e2zvkOf2FWlshcBwiLhfkWi3Mc+bAe/YsdA4HWJkM3rNnoa+szEkG8rEYAlevQldRkXep/XSRDASQDATSDzIDWvROZ97XNHDoEReNIu7zIdzeDm1FBVi5HKaZM2mKMSk6hmWhczoHrTQmZKopWk/CnTt3orW1FT/5yU8wa9asvNONvUNMu5uqqCchIYQQcmNxuVzYsGHDmM+/cOGClCDsw/M8fv7zn6OxsXFcsSUSiXGdPxmSoVDeASWCIKB7iAQhkJ7sysrlMGSqpxRaLSrWrcubvGNYFvoCLOFmZTJglBVZhRpsQfLT2GxIRSLSRGC1xQKFVgtDVRW858+jZP58AOnhNpV33jlhcfz6jTfwX/v2oaO7Gw6bDQ9u3YrP3ndf1jHRzk5AFKEuKwMXjSLa3T3i6wczS41Dzc0w1tTAUFODwNWrCFy5gpJ585CKRuG7dAllCxbknCtyHMLt7VD4/dDa7dOuT2FfO4IsooiwywWdKGYlWKLd3VlJYwAIZwpLIh0dMM+eDYZhULpgASUISVExLAvjjBmUICTTStF6Eh44cABVVVWoqKhAOBxGOBzOOWbgH9SEEEIIIZPN6XTitddeG/P5Tz31FHbv3p31d41MJsPnP//5cVcSTvUhbwLHIZJnVUgyFELPBx8MOUSETybhu3ABtjVrwDAMGIZBxa235u37pzQaIVOpKFl3g5L6E16/DghC+nF1NRLBIHo++ABJvx+qkhLE3G6E2tpgqKqakDg+e999+OSmTdj6l3+Jl7/7XegG6YEY7eqCwHHpqrgBPTYHE/f5kPD5kIpEEPN4UJ75vrcsWYKuI0dgqK2FXK2G76OPoLFY8k7uBdKTfgPXrkFjsUCdpwhjqop0dOSfPC6K0nuIymxGtKcHcY8n7/mpcBjJQABaux3q8nLINZqJDpuQQalKSyFTqaZdwn4qoD7ON4ZRJwmtViuampqGPMZut485IEIIIYSQQnj44YeHHLQ2nB07duD111+XlhzLZDKUlpZix44d47puX2xTWaSjI2vohCgI8F26BO+FCznTjQWeT081DoWQCgYR6+mBqqQEGpsNAGCcMSNrGWcftcUClV4/sS+EFJ1MpYLWZktX6gHQV1fDd/EidA4Hwm1tUi/K3jNnpOW5xZQvkTUUqYqwpQXaigopGa4pL4e6vBy+8+dhXb4coiii6+hRVN1996ATlyEIiPX0IBkMwlBbW/SvxXDiPh9SeQpGJJlhJMlgMO/gIz6VQqynBxGXC5rycsiUyglLFBMyHEYmg87hQGqK/9wBU7Ov8UT2cS60qfj1m0rG1JNwOC+//PJYYiGEEEIIKZjx/gHodDpx+vRpPPvss/j5z3+Oz3/+89ixY0dB/tgt5h+nyUBgyP5nCb8/64Y+7vOhp7ERCb8/99hAAJ0HD0LkOCiMRigNBmjtdhhqasAwDFiFAqULF+acp3U4oC4pQWTAEAlyY1KXliIVDiMVCkFtNkNpMkFfVQX3yZMoXbwYDMMgFYnAf/kySjNLkKcDPpVCqLUVoigi1NyMsqVLwchkkCmV4GIxlC1ejPa33oJp1iwojUbwiQQ6Dx9G5aZNQ0415uNxhFpaYKytHXF/zcnGJ5OIdXWN6Nh8CUIgvcRbFASEXS7pteoqKwsZJiEjItdqoXM6IVMqkZoGv5fG23N5IkxkH+dCmy59oYtl1EnCkXzyPR2Hf9DgEkIIIYQM5HA48MILL6CxsREvvPDCuCsIiy0ZDCLscsEgk0GRp4ovFYlk9RfjYjF0HDgAPk8PxYTPh46DB2GaNQsl8+fnXR5ZOn9+zjJjdVnZhA6pIFOTzuFA4MoViIIAQ3U1Ej4fRJ5H3OOBxmoFAPguXoSxtnbaLDcNtbRASKUQd7shcBx0djv0Dgd0Tie6jh2D0mCAccYM9J45A/tttwFIJ+F7GhtRsW7dkNfmY7H09OPq6imZKIx0dEAUhPFdw+UCF4sh4fNBe+ut0NpsNM2YTAyGkabcs3I5WIUCjFye/rdcDoXBMG2W+APj77k8ESayj3OhTYe+0GNRtMElI/GZz3wGv/nNbybi0hOGBpcQQggh5EYX83jSQwXa2mCcOTOr5xIXiyHc2gpkbvxFUUR3Y2PeBGG8txedhw6hZP58mOfMyftcCr0eptmzs7bJ9XppGTK5ubByOZQmExI+HwzV1ejNTPwNtbRISUIhlULvuXOwrVpV5GiHx8Vi8J47BwAINjdLyTzjjBnQ2mwIXLuGmNuNkvnz0frmm4h2d0Ob+d4PtbVBaTYPWzXJRSIIt7VBX11d9ASGwHHgEwlwsRi4aBTcOKutuGQS4Y4ORFwuqMvKIFeroa+uLlC0hPTDMDBUV+f9YGy6Gm/P5YkwkX2cC22q94Ueq6IOLhnOwYMHxxQMIYQQQggZGheLjanSKhWJgI/FAKR7DIZaW2GsqwMrk4FPJBBqacmqDApcvYponuWEsZ4edB45grLFi2GaOXPQ57MsXpzVU41VKqF3Ooue7CDF05ckVOh00FgsMM2ahfa334Z57lwoDQYA6QnBppkz8/axnEo8p0+DTybT031dLjg3bUq/rsxgEsstt6D97bchU6lQMn8+es+cgeauu6SqQO+5c1CZzdAN08s9FQ4j0t4OXWXliH92+GQSCb8fDMumBzCMYQiDwPPSQBY+Hs/pRTpe4ZYWiDyPiMsFndMJRi6Hbor1LSM3AIaBvqrqhkoQAuPvuTwRJrKPc6FN9b7QxTbqJOHGjRvpjztCCCGEkCKIe72IdnZCrtOll+aNIlkYGzCQQUgkEGlvh9ZuR7C5OWtCaSIYhOfs2ezjeR7+ixfhv3wZlmXLYKytHfS5NBYL9P0HELAs9FVVQ/Zhm2727t2LPXv2IJFIYNu2bXj44Yepx9EwFDodWIUCQioFfVUVYh4PDHV18J49i4pbbwWQrmDtOnIElZs3Q65SFTni/MIdHQi1taX/3d4OpdEIlckEQ12ddJ+kLimBoa4OwevXYZo5E8HmZng/+ghlixcDyFTqHj8O+223QWOxDPl8yWAQ6OiAfpgpoXwyibjHk+4fOnA6M8tCplRCplZDodNBodfn/XnkEwnEe3uRCASkquKJEGxqAp9IpCdCr1oFXUUFZDTlfOIxDHQOB3Q6Hfh4HFwiMSFJ4HFjmBFPGB/qGvqqKukDiBvJVPxdM5F9nAttKn79ppJR/6VmNBrx/e9/P2ubIAhwu904evQoWlpa8OSTTxYswMlCPQkJIYQQMpUlg0FpQiwXiSB4/TqUZjM0VuuwVUJcLAYuzxTSVDiMwLVrWckAQRDQfexY1k1jtKcHnpMnwSoUcG7cKE2kzYdhGFhuuSVrm97hyOlNON1t376dbjTGQGk2I+52Q19VBc/p09Jy3JjbLS07TkWj6Dp6FM477phy/fj4VAruDz6QHgebmqRBPQMT52WLFiHc1gYBQMXatWh/5x2oy8qkijk+mYTr3XdhnjsXpQsXDjnNOOn3IxCLQabRQKZSQa5Wg1Uq00NS4nHEPZ50MnGwxIoggI/HwcfjSGaGEMk0Gij0+vS0ZVFEvLd36GnFBRL3+ZDw+xHp6IDKbIZcq6WpxpOAkcshUyqhMpuhGlDZJfA8+HgcQioFkechCkLW/wqCkPd3yETQlJdDY7VC4Lj0c3McRI6DkPkvGQxCSCaHvsgNnCCcym60Ps7TTdF6Et5111145JFHBt3/+uuv41ymP8d0Qj0JCSGEEDJVpSIRhF2unO1Jvx9Jvx9yvR4qkwlKozFvUiWeqSIUBAGBK1dQMnfuxzsHVAv1njkjTTIWeR49J08i4nKhdOFCmGbNyllRojAYoLPbocxMN1YYjVkVYAqDIe80ZarEuzmpTCbE3W7I1WpobDaIXV0omTcPvWfOwLlpk/T9FevpgefDD2FdtqzIEWfrPXsWXGbZfjIYRNLvh/6226Cx2aDQarOOlavVKFu4EO7Tp6E0GlG+YgV6GhtReddd0vJHURThu3gRkc5O2FatGnKZNZ9I5PQIZVh2zANE+FgMfCyGuNs9pvPHKtjUBADSUmNWoYB2mGXXQ2Hk8nQl9Hgrz25grEoFo8MxaNKdlcnADpPQSfj9iOT5PVRIqpIS6cMCVi4H5HLIBlQUa8rLkQwEEHO78ycLGQb6ykpKEJKbTtF6Eg7XIPP+++/HN77xDfzjP/7jmIMihBBCCCFpfCKBcFvbkEv/uHAYXDiMaGcnlCYTlCZTujooc34yGASQnsbq+fBDsAoFTDNm5FzHd+kS/JcvS497z55FMhBA9T33QD4gAQKkl1TaN2wYfCIpw0BbUZF3F1Xi3ZxkKhVkGg34WAyGmhpEu7pgmj0bgWvXEG5rg6Hf8Ar/lStQlZbCWFNTxIg/FvN4ELx2TXocammB1uGATKmEqa4OQLr3plytln7mTLNmIXD9OpLBIPRVVYj39qarJDdtyqocTAYCaP/jH1Eyfz5KFiwAO8IKyvFOGJ5sAs8j3NoKIZVCtKcHZUuXQudwjLkVgbqsLD0MSRTTFdOZxCcXi0FIpQoc/SRgGKlCVKZSSf/m43HEPJ4xLQuW63Tp5f3x+LhCU5nNANKTrSciIavQ60eULGYYBiqzGUqTKZ0s9Hgg9CXP+xKEVPxDyJgVvDHM5cuX4fP5CnItURTx8ssv4+WXX4ZMJoPP58OaNWvw7W9/G+bMm1Q+P/zhD/HjH/8YGo0GZrMZP/rRj1BFJeyEEEIImeL6Enp8IgEhlUr/x3EjviETBQEJnw8Jnw9sZlkZn7kx7KtYAgD3yZPpG7LMkAUACFy/Ds+HH0qPI52dCDY3o/Kuu/ImCDUWC+y33z5kHzF1WdmoByaQG5/KbEY0FstKDpUtWgTvuXPpyrJ+ybOeDz6A0miEeogl7pNBEAT0nDgBMfOzKHAcQi0tKF+5EjK1GtpMv0CV2QyV2YxUOAxREMCwLCxLl6IjM9ixbMkSuPbvh+fUKZSvXJn1HKIownv+PGI9PXBs2DDk8uPpKuJygU8mEXa5oDQYoDQYsvuXjhAjk0HndH5cLcYw6V6L/arh+GQSCa8XcZ9vQvsrjhSrUEBhMECu0aQrZlkWDMtm/ZtVKPL2/1fodFCVlCDe24u4xzPi5LDSZEoPhinQTAGV2QyGYdKV7QVMFMrUauirqkYVZ75kodZqpQQhIeM06iThM888k3c7x3FwuVx44403sHHjxvHGBQCIRCL4yle+gpMnT2LOnDmIRqPYtGkTvvSlL+HVV1/Ne87rr7+OZ599FmfPnoXFYsEzzzyDT3/60zhx4sSIP5EjhBBCCJksQiqFhN+PZCCQs5QwHy6ZHLxyr/91k0nEenqkx+G2NqRCIQDpZGLXkSOovOsuKA0GhFpbs/qscfE4ek6cgHXZsrxLtjQ2Gxzr1w9Z/cPI5dKyMUL6UxqNiHZ1QaZQwDR7NnwXLkBfXQ3/lSsIXL2atRxe5Lj09+rddxd1kInvwgWpOrDvsVyrhcZmg6G6On2fwTBQlZSAlcuhtloR6+4GAOjsdugcDkQ6OsCwLCrWrkXbH/+IYFMTjJkKxP5iHg96PvgAFatXT9rrmyzB5mYAQLi1FYbqasiUypxqY43N9nEFdJ5kmEyjgb6yctgPIPqurbZYEPd6kejtnfTKS5lanW65kEkOjgfDstBYrVCVliLu8SDu9Q6Z/FRbLNDabON6znyUJhP0QMEShaxSme7rOcZ79b5koWqIIiJCyMiNOkm4c+fOwS8ml2Pz5s34P//n/4wnJolCocDOnTsxZ84cAIBWq8X27dvxzW9+E6Io5v2k4YUXXsBf/MVfwJKZEvbkk0/i6aefxh//+Eds3rx50OeiwSWEEELIjUHguEmdoiuKIpKBwJhuUFKRCMJtbVmThYcSbm9H17Fj0FdXo2zhwqyqmeH0VRH24ZNJdBw6hNL589HT2ChVSImiiJ7334e2vBz6fks/+xiqqlC+evWwVU5am23KDZ0gUwMrl0Oh1yMVCqFk3jyEmpvBxWIoW7IE3UeOwFhbm9WHLBWJoOvw4aJV1yWCQfguXJAeJ4NBBK5ehfPOO9MDSzJL95UGg/Teoy4rQ9LvlxL/5StWoM3rBRePQ67VwrZ6NbqOHEknN/JUSYaam6EuKYF59uxJeIWTIxWNItbdDS4WQ8ztTk81djqzCjkUer007Vm029N9HwMBpCIRQBSl5cWjqThj5XJoy8uhLitLVxb29gKiCJlanV7+rlanB8FkvueERAJcIpH1vwLPg5XLwcrlYGSy9L8VCjCZ/78HxsMwTHrIzARUUrMyGbQ2G9SlpVL/WFahyIqNkcsLVj2Yj9Jkgp5hEG5vz58oZFnItVrIFAqkwuFBl34zMlk6yT6Jv7MJuVEVbXCJxWLBiX6l9tKF5HKUl5dDUcDR9SqVCl/72teytkWjUZSXl+d900smk2hsbMRjjz0mbTObzaipqcHhw4eHTBLS4BJCCCFk+ktFIkh4vWNavjYWfCKBcHs7+HgcXDQqTS0dib7pniOtxIh2d6Pr+HGIgoBQczPCra0wzZyJknnzhq1QCXd0IOH3Q+R5pMJhaZBIKhRC9/vvZx0buHwZqUgEFevWZf29pdDrYbnlFuhH8BrlWi1VdZAhqcxmpEIhyBQKlC5ciJ4TJ9KJHKsV3vPncwaWxDwedB07Bvu6dZOefHafPClVoImiCPfJkzDOmAGV2Qx1WRlUmXuI/sk+hmGgtdsRylTOyTUaVKxbB9f+/RBFEVqbDaULF8L13nsoW7IExrq6nPsbz+nTUJpMWW0BprNgczNEUUS4rQ0aiwVyjSarByWQriLsw7CsVCEmcByEZDJv64ORYmUyaKxWqC2WIRNorFY7rueZLKxCUdRqbaXRCH1VVbpnbibpqtDrodDr00uq+/2cctEokqFQ9mRiloW+qipnMAkhZGwKNbhk1L9hH3vsMbz88stwu92oqamR/nM6nQVNEOYjiiJeffVVfOMb38i73+PxgOM4lA6YClZaWorOzs4JjY0QQgghxSUKAiIdHeleYJMw5TLu8yFw/brU8y/h8yHa1TWic6Pd3ekpkSOMM+71ovPw4ayKQ1EQ4L9yBc1vvIH2d96BL5Pcy8efqSL0XbqEtrffRrTfMuSsuHp64D1/HrbVq8Fm/q5jWBYl8+ej+p57RpQgBDDosBJC+igMBjCZqkBjXZ2UVC5bsgShlhZE8vztHnG50HPy5GSGiWBLS/ay/dZWpMJhlC5YAADScmFWqZQmFvdR6HRQ9kuWa6xWlC1dKj02z5mDinXr4L9wAZ2HDoGLRrPOF0URXceOITVg+3QkiqKUMA21tkJfU5OecN0vyaU0myFXq/Oez8rlBUvcTWSF3VS1d+9ebNu2TZomv3fv3oJcV2kwwDhjBsxz58I0cya0NhsUOl1OIl+u1UJrs8E8ezaMM2dCbbVC73SOqhqeEDI5Rp0kfPbZZ7F//34IRWj++r3vfQ/V1dVZlYL55Hvjvxl/GRBCCCE3k2h3N4RkEqIg5NxsF1rM40G0oyOnH1S8txcxt3vQ84RUCsGWFsQ9nhE/VyIYRMfBg+kBJnmIooiYxwPP6dNo/n//X7S9/Tb8ly+DyyQvoz096emPqRQCV6/CWFuL7qNHkQgEsl+T242uI0dgWbYM6rIyAOm/n5wbN8KyePGgy8FYlQpqiwU6pxPGujqUzJ8/7t5b5MbHMIw0YIBhGFgyyTOlwQDrsmXoaWzM+3McvH4dvefOTUqMfDKZNcyHTybhOXMGlltukZZ39lUtD1Y5O3DZfcmcOTD0q3TW2myovOceyDUatL31FkItLVkfcvDxODoPHx7053+6iLndSIXD6aXDwSB0Tid0lZUff20YhnqYTqDt27fjzTffxNmzZ/Hmm28WdLK8XK0e1XJhuVoNbXk5DRghZIoa9XLj0tJSvPXWW5BNcj+QX/7yl3jnnXfw61//etCEn9VqhVwuh9frzdru9XpRMcwn2vX19VBmekZs2bKFehMSQggh00jfMmPpcSg06gqFkfYy5GKxrMqigWI9PRBSKShNpqwYksEgIh0dg/YfFEUR8d5eRDo6kAwGwcVi4GIxCInEsJWRb549izfPnEFPMIhyoxH3LlmCTyxZAo3NBiHTEy1w/Xp6yfDy5ZBpNOg6dAjOTZsg12gQ83jQefgwLEuXwlhbK123dNEiqT9YPnKtFvrq6htyCiuZeCqzGQmfD0A6WdY33MNQU5NOWh87BufGjTlVSd7z5yFTq2GeNWtC4+s9e1aqFAYA77lzUJnN0GUmGRtqatLTvTMDS/Jh5XJobDZE+1VGWleuRCIQkAahyBQKlK9ciYjDAffJkwi7XLCtWiVV8yZ8vvQgkzVrJuqlTrhgUxOAdBWh1uGATKHIWmqsKimhSeiEEDIGDQ0NaGhoAJBuwTdeo04Szps3D5FIZMj+ff/wD/+A5557blyB9ferX/0Ke/bsweuvvw6VSoUrV66guroaqgH9CxQKBVatWoUzZ85I2wKBAFpaWrB+/fohn4N6EhJCCCHTk8Dz6d5+/STDYYxmYVpfnz9tRUXOksGBx41komPC50PC5wOrVEJpNELkeSkZMlAyFELw+vX09OFhKiD5ZBLhtrZ0H6d+N9T3Ll6MDXPn4rFXXsH//uxnoVEqIYqitPxZ4DgELl+GdeVKMAyDkvnzwUUi6Dx8GGWLF6P76FGULV6cNWlVY7OhZN68QWNRGAzQ968EImSU5FotZGq1lIizLF2KaFcXREGAZdkyuN55B73nzsGyZEnOuZ5Tp8BmEk2jXTEk8Dz8ly8j2NwM67Jl0OUpJoh7vQhev571ONTSgqrNm9NDKZRKlC5aBCB7YEk+6tJSJHw+6XXKFApU3Hor2v/4x6yBDjqHA+qyMnQfO4bu999Hxa23Sq8t1NIChmVRtmRJUac8jwWfSiHsckn9CC233AKFwSB9ANE3tZcQQsjo9S9yCwaDk9+T8Dvf+Q7++q//Gt3d3YMe89Of/nRcQfX3q1/9Cj/84Q/x8ssvI5VKIRwO4+mnn5Z6DL744ot46KGHpOO/9rWv4Re/+AV6e3ul/UuWLMFdd91VsJgIIYQQMjUIPI9QS8vHjdD7ticS4PN8mprw+yHkqeSLdHaCTyQQ6erKqtob2MfpZz/8oVSZ1198kASgkEwi7vEMmiAMd3ai7e234bt0adgEocBx6Dp8GL4LF9D6+9+j99w5aXLqcIJNTZBpNFKvQIZhYF2xAjKlEp0HD6J00SKYZs6Ujpep1ahYvXrQ5IvSbIa+qooShGTc+vevVBoM0vchK5PBtnYtgtev53wIAKQrb7uPH0fTvn3oOnYMwaYmcLFY3ufo6O7GP/7zP+Py9ev4///TP+HEr36F3rNnkQqF0HnwINynTmW9L4iCgJ4PPsia+O0+eRLmuXOlDxFKFy+WknWDVRH2p3M6gX4/LyqjEbZVq3J+xmQqFWzr1iEVCsH30UdZ+4JNTWj9n/+B/+rVSem7Wiih1laIHIe4xwOB46CtqIChpkbar7ZYaLotIYRMEaN+N96xYwdaW1tRXV2NWbNmwWq1Zo2tB5Cz3Hesurq6sH37dvA8j/IBU736KhVbW1txMdOMGwDuv/9+dHV1YfPmzVCr1TCbzfjd736XEyMhhBBCpjeB4xBqaclaDgikb+gZhkEqHIas3zAzPplEpKMDMqUS+upqqRIv7vMh6fenr5lIIOH1Sj35tm/fLvVuSoZCCLe25sTgPnUKwaYmWJYsGbLybiDfxYvoPXt2RDf7oiCg+9gxgGFQ/YlPIOHzwXfhAlp+/3sY6+qgczohGgz5z81UTVluuSUrIcGwLCrWrUPc64W2/0RRhoFt1ar8fQUzfcOo6ocUwt69e7F9+3YoDAakQiEAQMnChemf62Qy3Z9wxQr0NDai8u6787YQ4ONxhFpbEcr8bCpNJqhMJsh1Oij0evTGYvjEl78MbyAAQRDwyzffxP+89x5+/uUvw2owQBRF+K9cQbS7G7Y1a6AuKUHg6tWsxH7w2jWIHAfz3LkAAHVZGUwzZgDIP7Bk4OsD0n3Y9E5nehJshr6yEvbbbkPPiRNZCU6ZUomKdevQ/u676YR8ZeXHrzeZhPvkSQSbmmBdtmzIdgBTQbClRerrGG5thb6yEqxMBmNmqTEjl0vvt4QQQoqPEUf5MZRcLkdVv2a7+bS3tyPVr3R+KgsGgzCZTHj88cexdetW6kVICCGETAMCxyHU3JxTScclk/BfugTL4sVQ6PVZ1Sphl0tKBjJyeXp4AMMg2NycNYCEYVmYZs/OqmxJRSIIt7Vl9RNMBIPoOnoUyX4DQMoWL0bp/PlDx87z6PngA2nS53BEUUTP8eNIhsNwbNiQ7oGWEe/tRfD6dUS7uhBLpfBcayu+e999KOuXBA1cv47A1avSMsn+CZl8SubOlYZI9CfX66GrqIBsmi11JFPXvffei9deew18MonAtWvSMv6Iy4Wu48el4zynTyPh98N+222jrjj7t7fewm9PnoTQ75aHZRh8evlyPLl5c9axfT/7wevXpWXAXCyG9j/+EeWrV0NbXp4e5nPnndKgkqGS5tu2bcObb76ZtS3mduf0NO0bkNLXt69PpKMD3e+/D+fGjXkHozAMA311NdSlpRBFESLPQxQEiIIAVi6HadasrPeLySQIAjynTyNw9SqA9IcVzW+8Afv69SiZPx+Vd94JANDa7VD3+zCHkGKLRCLYsGED3nvvPeho+vKo0deveBoaGrBv3z689NJLCAQCY26nN+pKQqvViqYBv8AGstvtYwqmmKgnISGEEFJ8oiCMaAlruK0t71Lb3g8/RLSzE2WLFiEVjUpVhVw8LiUIAUDkOAQzPb4GTigWBQGxnh7oHI705GC3G/EBE4ujPT3oOHQI4oCJo71nzwLAoInCRDCI7mPHkOgXy2D4ZBJcNIrAtWtIBAJwbtyYc8OvLiuDuqwMoigi4HaD+dWvELh2DaEzZ6C12aCvqoL/4kWULloEhmGgsVhgv/12dOzfn3eJtKG2FmUD+r8xcjl0FRVQmkzDxkzIaLhcLmzYsAFAevJ3/wm+XCz2cZWwKCIVDgMffZSuJhxFD8JmjycrQQgAgijif86cwYU8y5gHSkUiAABFppWRTK2G/Le/Te9kmPSS40HiSeR5j9JYreDjcWloCZCuHLStWgV9ZWVWVaHO4YB5zhx0HT2Kyk2bchL0oigi1NKCUEtL3ucPXr+O8pUrsyqFJwMXi6Hr6FHE+k1xj3R1gVUqoSorkz68YZXKES3VJoQQMrwtW7Zg3bp14+5JOOok4fPPPz/sMS+//PJYYiGEEELITWrv3r34+csvo7W5GU6HA9sfegif/6u/yjvtMhUOg8vTvy/mdiPU3AxRFJHw+aAuLQUXiUCh1yOar5dypuImn4TfD4XBgLjHk/NcXCyG7mPHchKEfXrPnkUyGIShpiZdeZRJevqvXYPnww8HPS8ZCiHc1oZoZydSoVB62rJCAaXRCMfttw9ZwccwDFRmM+RqNSrvvBOyRAKR9nb4L14EI5dDn1kFUrJgAWQKBey33472d9/Nqig0VFXBlhlsIl2XZWGsq6Opo2RCOJ1OvPbaawDSyXn/1avSz4coCOg4eBDxTHJOSKXQefgw5FotyleuHHE/zMEqCT+5ZAm+MqCScKBoTw963n8flXfdBblGk/75uuceyDLVjEqTCfrMpON8tm3blne7zukEn0zmtErQ2e2o3rIF7lOnpMRfyfz5SPr96D52DPbbbx9VH9BUJALXe+/BOGMGLEuXTkpVYcztRtfRo+AGvLZwaysM1dVg5XJp+bTGah310BlCCCETa9RJwugQTbW/853v4NixY/j2t789rqAIIYQQcnPZvn077lm3Dpu3bsXPXngBOq0WodZWmGbOzLmJjA2o6gPSS9u6T5yQ+vuFXS6oS0uRDIUAhgEXDg8bg/fCBRhra9O9+EQxp/8gkBmW0NiYcwM8UF91j0ythr6yElwshojLlfd6wevXEWxqQjIYhLaiAqZZs6DM9FQb60290mCAcv58lMyfL1VTaiwWaYqrXK2G84470P7OO+BiMegrK2FbsyYnAaFzOChBSCbMww8/nLUcTVFXl/VzMmPjRrS99Vb6502pRN2GDXC99x5CZ8+mE4UjSDB94fbbsf/CBQRiMQiiCJZhYNJo8IU77oBmiO9tkefhPncO9sWLYchU0VasXg1D38ojloWptnbIn4+HH34473aGZaGvqkLw+vWsFgZApqpw9WoIHIeIywWGYVC+ahXa330X7lOnYLnlFrAy2bCvu7/g9euIdXXBunJl3knOhRJsakJPv/fhPnwyiWhnJ0oXLYI+857CsCyUtIqLEEKmnFFP83j66acH3feZz3wGy5cvxxe+8IVxBUUIIYSQm09qQCJPSCQQ77dcre+YfFWEvosXs6riop2d6eMjEUS7uoZ97nBHB7znzkkN9gfjv3Qp53qiKCLe25t3AAkfjyNw9WreBKHAceg+fhz+S5dgmjULtffdB/utt8JQUwOV2Zw3QcjIZCiZP3/QQQn59CVSShYsyNqu0OnguOMOGGprYVu7NidBqCopoSXGZEL1DfXoozKbIVOrpcdyjSb9vZn5HpapVHDcfjviHg88p0+PaOhPudGIn3/5y/j08uVQKxT49PLl+PmXvwyTKMJ7/vygE5F9ly6Bkculacsamw2GzLANIF0FN1wCfeDr60+mVKYrfPNUBjIMA9vq1VIfQlahgH39eiR8PrS/9VZOT8ORSEWj6DhwIF3NPAGTkRPBIHpOncp77YjLBaXJBKXBAH1mqbHSZKLp6IQQMgWN+p15qF8qc+bMwY4dO3D9+vVxBVUM9fX1aGhoKHYYhBBCyE2JTybz9hiMud1Z2/NVEcYzk377S/j9SEUiEBKJrCV9PR98kHONVCSCnvffT/f3am1FdJAb8HhvL3rPncuOO5VC97FjcL37LnoaGwddvjxQKhKB6913wScSqLzrLhiHqUgCPp5GbFm8GDX33ouKtWtH3M+rfxVhfyqTCRWrV4MdcLMuU6mgncCKI0IGM/D7TltejrLFi6XHco0GjjvuQCST2B/Jz5zVYMCTmzej1mLB43fcAVy5gva330aspwdtf/hDuqKv3z1OKhyG/9IlWJctA8OyYGQyWJctk/bLVKqCTORV6HQwzZgBWZ5J4jKFAvbbboM8kzRV6HSo3LQJxpkz0XnkCHoaG/O+Zw7Hd+kSut9/f8TvVSMhCkL6/W+QVgqhlpb0RHm1WnofyjeIhRBCyNg1NDSgvr5+3NcZ0XLjZ555Rvp3JBLBs88+mzdZyHEczp8/D4PBMO7AJhsNLiGEEEImB59M5iTEBp22K4qIdHTAWFeHVCSSU0UY93rRceCAtGSPi8cRuHIFpYsWIdLRAfPs2dKxMbcbgWvXEGxqgmXJEpjnzIEgCOg6ehR8Mikd5z55ElX33JOVOEsEg+g6dizrxjru9aL7+HEo9HpUbt6MnuPH0X3sWHrZ7hDLAaM9Peg+dgz6qipYli4dUTUNwzCwrVkDvcORfsyyMFRXw1BdjXhvLyKdnegdZHgBkFtFOKTMUkiq8iHFoNDpoCopQaLfYJ2SefMQ93oRbm+XjnHccQc63nsPgStXoDSZoCopSf9nNkOh14ORy7OWI4s8Dz4eR9tbb8Fss6Fy82YoDQZEOjvhOXUKodZWWFesgEKvh/vUKRhqaqREYMn8+VD1u0/QORwF66UnU6lgmjED8d7e9AcU/d5jFFot7OvXo33/fog8D4ZlYZ49G3qnE+5Tp9Da0ICyJUvSFY4MM+KYQi0t4JNJ2NetG/W06Hx8ly5JvSMHinZ3I+71wrZ2LQyZ9xWZSgW5Vjvu5yWEEPKxSR1csnPnTunfDMPgn/7pnwY9tqysDD/+8Y/HFRQhhBBCbkypSAQJr1capNEnOViSEAAXjSLu9SIZCGRtj/f2wnXgAIRUStrW++GHCLe1QV9ZifCAJGHfUmJREOA+fRrx3l6wSiXiXm92LMEgApcvo2TePABAoKkJ7lOnsqpkgk1N8Jw+jZL582GeOxcMw8CxcSM6Dx5E55EjqMhz8y0KArznzyNw5Qost9wCY13dSL5k6Z5kK1fCMOBr1qdvwrF6xgyoXn4ZttWrIWYShwLH5VQRylSqISuQdHb7kENSCJloWrsdXDwOvt9SYNuaNRA4TlrurzQYUPOpTyEVCiHh9yPh8yHU2oreM2cgpFJgZLL0JGK1GjK1GoHeXvDJJMpXr0ZZZnAGkP5+11it8J47h/a334bO4UDC74dtzZr085hM0nsBkF6GPxEJLnVZGRQGAyIdHeAyE5X7tttWrULXsWPSNnkmeRhxueA+fRruEyc+vlAmWcjI5ShdtAjGurq8ycNoZyc6DhxAxW23QT6OvqOJQADe8+fz7ot2d6PryBGUr1wJuVoNQ20tANBEY0IImcJGlCRsamoCkF5qvHr1ajQ2NuY9TqfTwWKxFC46QgghhNxQkoEAksEguFgsPSAEgMDzefsM9hft6gL6rWKIud3oOHQoK0EY6exEtKsLmvLy9OCSsjLwqRRkCgVCbW05ycBQW9ugz+c9fx5aux2+8+dzjou53fCcPg37bbdBY7VK22VKJRx33IHOw4fhevfd9HTjigooDAZwkQi6jx+HKAio3LRpyF5/MqUSCr0ecq0Wco0GGqtVmgY6LIaBvrISujlzIPA8ot3d0tcZAOQ6HQzV1Qi1tmYlIvpoHQ5aBkiKjsl8H/cf7MHKZLDfeis6Dh2SevIxDAOl0Qil0Sj1CxRFEUIqBT4eBx+Pp5ON8ThYqxXKzk5oy8tzno+Vy2G55Rboq6vhOX0a1ltuSQ/XYBjYVq6UqooZuRwam23CXrdMqYSxthYxjwexfhPZDdXVSAaDOck4ndMJrd2eroQWhPRKL1GEKIpIhUJwnzyJWHc3rCtW5G1nEPN44Hr3XThvv31MiU9RENKtGgYMXwE+ThBaV6yAoboaSpMJ6pISgGGo1ykhhExhI0oS1mQazALA3/3d32U9vlHU19dj69at2LJlS7FDIYQQQm5IoiAgkakGjPX0wJD5eyIVCmUlAPOf/PF+QRDQffx4VoJQ4Dh4Tp1C2eLFYBUKeC9cQNmiRYh2dUHndKL37NlhLi/Ce+4cdA4H1GVlEDgObX/4Q+6UzkQC3e+/j7LFi7MShH1YhQL2229HqKkJkc5OeM+dg0yjAZ9IwFhbi9LFi/NOJpVrNOnkntMJjdVakKWMrEwmLU/uo7FapaXKAxOFWrs9fRNPyBTQN9gj1NIi/fyzcjns69ej8+BBxAYMNerDMAxkSmU6KdZviXCsX0uBwahLS1G5aZP02DhzZlbvQa3NNurJwmOhsVggCgLi/fqnli5cCFEQ4L90Ket9iWFZqW9hnzfPnsWbZ86gJxCAqbkZ669exQOf/GTePorJQADt77yDkoULobVaRzUUyXfxIuL9loX3GZggBCD9r9JgKMgSZ0IIIdkaGhqwb9++cV9n1O/QhWiEOBVRT0JCCCFkYiUCAanfVt+UYrlWmzPVeDih5makBlQeej/6CHKtFoa6Oogch+7GRiRDIUQ6OsDHYsM+R+DKFQSuXEHE5ULl5s1gZbKcBKEoiuhpbIS6tBTGzMTTfFiZDKZZs2CaNQsCxyHW0wNWocifVJTLUb5yZboHYIF6nA1GoddDodMB+LinYailBVw0Cm1FBdSlpRP6/ISMlkKng6a8PKuqTpZJxHe8915OdfBosHI5dHY7wu3teXutyzWa7IEpev2kVtlqy8shJBJIBoMA0slPy5Il0FdVwX3iRN7kXJ97Fy/Ghrlz8dgrr+BfPv95JFta0HHgAErmzYN53ryc95pUNIqezEoxhVYLdXk5NBYLtOXlgyYN4z5f3mXGUoJw+XIpMcgwDIyZD4VoqTEhhEyMQvUkHHVH6mPHjmH58uVYvnw5jvXrjeFyuVBTU4Of/vSn4wqIEEIIITempN+f9TjqdkvL4gCASybBRaPwnj8P/7VriA84HkhXEQ6cZBz3ehG8fh3W5cvBMAxYhQLa8nJEXC5EOzvhzRwvcBy6jh7NmW4c6+mB96OP4NiwAaxCAf+A6/fxX76MZCgE68qVWTfZDMPAUFubN8nHyuXQORx5E4QKvR7OTZtgqK4eeYKQZaEpL8/pGdjR3Y1//Od/xuXr1/GP//zP6OiXVOmjGbDMkmFZ6KuroXM6CzKplZCJoLFYcpanyhQK2O+4Y0xJO4ZhYJwxAzWf+AQq1q1D5d13502QW1esgEyhSD9gWejs9rGEPy46pzNn8rG6pASVd90F6y23jKgij2EYmOfMgXPjRoRaWtB56BCEQaYQA+mEYai5GT0nTqD5979H0+9+h84jR+C7eBExtxsCz0Pom2Y8YEJyVoKw38ozdXk55FotWIViVJWKhEy2vXv3Ytu2bUgkEti2bRv27t1b7JAImXSjriTcvXs3wuEwdu3ahaVLl0rbbTYbdu7ciR07dkChUODhhx8uaKCEEEIImb74RCKn7yAXDiPu8Ug3mrHOTvCJRHoJW2a5YNXdd2fdVIaam5Hqt0SWi0bRffw4SubNg7L/9FGnE8GmpqyBA/5Ll5Dw+9F56JBUUcPHYug6fhyWpUuhLiuDdflyuPbvh76qKisxEenshO/8eTg2bPg4cYB0tVHF2rXQWK0IV1amJyAPcQPeR1tRgYNeLz43miQHw8BQVQWFXg+N1YpUJIJ4by9arl7FnZ/9LHx+P3hBwJ7XXsMbf/wj9r/6KuyZxKDCYMjqTdiHlcmGTbTs3bsX27dvH3mchBSYzuFI9xjsN3BHrlSi8u67031O/f708JLMf/1bEfTHKhSovOsulPQb5NOXdAtcvYrec+cgpFIwVFVlLdXXWK15e/pNNIZlYaiqQrCpKes1MSwL85w50FVWwn3yZHoqMs/nrYjso8q8zs4jR9B58CDst90Gtt972WC4eBzh9nZpsjTDsnkrwJPBYN4EISOXoywzXV1J/U7JFLd9+3b6fUdueqNOEh46dAi/+93vMK/fH90AIJfL8YUvfAHLli3DF77wBUoSEkIIIUSSyFMVCEAaQAAAkczU0j58MonOI0fgvPNOyBQKiIIA38WL0n4uFkPHgQPQlJfD3O/vEkYmg9Zuh/vkSWlACheNwn/5Mhx33AFGJkP3sWPpqhiOg85uh3HGDADpG2njzJlwnzwJx8aNgCCg96OPELx2DdYVK7IqjrR2O2yrV0OeqerTOxxw3n57zkCV/hiGQcmCBSidPx+vPfYYtt5zz8i+gAwDvdOJJMMg2S9JypaV4cXnnoMvEACfSbbyggBfIIAX/v3f8czXvgYAMFZUIJJnUMlI7Nmzh26aSFExLAtDTQ2CLS0Q+iUKWZaFuqQkp5cml0yCj8chJBLS4BJOoYDid7/L+jBBuj7DwDx7NvSVlfCcPQtLv2XGMo2mqJW2rEIBfXU1gk1NUruGPgqtFo7bbpMeCzwPgecBnkfQ6wX7i1/kXMu+fj26jhxBx8GDsN9+e9aHHiMhCkJOglDMVBaaZs7MSRA61q+XKqlpKBIhhEx9o04SBoPBnARhf7fccgu68yxxmepocAkhhBAyMURRHDRJ2EcQhKyEYZ+E3w/3iROoWLcOoZYW6eaUi8XQ8d57UFss0jJjIN3DrGTBgnTvwLIyRDo6YJo5E70ffSQNJQGAyrvugvvkSYg8D8uyZVnPWbpgAdr+8Ad4z51DNPM3TeVdd2UlFwy1tbCtWpWzTFhjtaJy0yZ0Hj6ccyOt0OtRsWaNFENHdze2/uVfDvPVA8AwkCkUYAYZmHDhwgXwA6aL8jyP/9q3D6c++ggsy4IdRxVUol9ShpBiYRUKGPMkCvORK5WQD/iejwwzQR3IVAavXv3xBoaBzuGY8H6hw5Gr1dBXViLc2jrkcaxMJg1WUZWUQGE0wjx7NpKtrVKVISuXo2L9enQfPYrOAwfSicJxVkn6LlyAyPMoWbhQ2taXINRmpkHLdbqiVGMSQsjNomiDS1iWhcfjgcViybvf4/EU/RfpWNDgEkIIIWRoAs8j4fNBplJBodOBYdOtjQWOAxeNQqZW570JTAYCwy7Bjbvdg1bfhdraoDSbEWxuBpCuMOw4cCC9PHjFiqy/O8pXroSqtBTukyehczgQcbmgKilBpL0dVf2q9liFArY1a/I+HyuXw7p8OToPHYJ5zhyULlokvVYAUBqNKO+XmBxIZTKh9pOfRCIQQLSrC9Hubsi1WliWLs2q2nHYbHj5u9/NOZ+RySBTqcAqFOkeXlrtkH28nnrqKezevTsrUSiTyfD5Rx7B89/5DiCK45omum3btjGfS0ghsQoFjLW1CDU3Zy09nihqiyVncnCxKA0GGGpqEOnshDCCSc19yhYvBqqr0dPYCC4eB5BOJlasW4euY8fQceAAHLffntPndKTiXi/8ly/DeeedUoJyYIIQoCpCQgiZaIUaXDLqvxjvuecePPDAA3jxxRexuF8pPgB89NFHePLJJ3HvvfeOKyhCCCGEFJeQSkn9qkRRRLy3F3G3++NG9QwDuUaTXt6WuVmX63Qw1tZmX4fjEB2wjDif6+fO4d/eegvNHg/+7a238IXbb0d5vw/ves+elf7tv3wZMpUqZ4CIccYM6aZUW1GBZDCI3rNn4Tl1CqbZs6XJvoPROZ2I9fRASKWgrahA7Z/8SU7Sk5HLUbFu3YiSbiqTCSqTCSVz5+bd/+DWrdBptQDSyQ+l0QiFwTBsnAPt2LEDr7/+OrxeL3ieh0wmQ2lpKXbs2AFDAT4ApRYyZCph5XIYCpAoZGQyKAyG9PTgAct4AUCmUuUdOFRMCr0eppkzEfN4EPd4gCF6EPans9tRtXkzuhsbpfdjJpMo7D5+HB2ZisLRJkQFjkNPYyNK5s+XkoD5EoQMy+Zd5k0IIWTqGfV0429961u4du0abrnlFlRXV2PNmjVYs2YNqqursWTJEly9ehXPPffcRMRKCCGEkEkQ6eiA//Ll9JThK1cQuHIFse7u7EmWogguGpUShL9+4w1wmUEa/UU7OyEOWAqbGtAbr6O7G39aX4/fnjyJeCqF3548iUf+7/+FOzP1uD8+kUDg6tV0dV+/BKFco4Gl30C1t69cgUKng9JkAheNDpqo66MqKUHF2rXpqpuMfFWR1mXLoBowaXWsPnvffekJw1VVMM+ZA21FxagThADgdDpx+vRpPProo9BqtXj00Udx+vRpOPoNXhgP6kdIppq+ROGYqt9YFmqLBabZs6F3OmGaORMKgyH7mCmyzDgfhmWhLS+HaeZMyEfxfiHXaOC4/XaUZoaI9F3LtmYNlCYTWn//e3QePoxQSwv4EVYqes+dA6tQwDxnTvp6eRKEAKA0mbKqsQkhhExdo363ttvtOHHiBB555BEEg0E0NjaisbERoVAIX/jCF/D+++/DbrdPRKwTqr6+Hg0NDcUOgxBCCCmqaFcXEj5f+oEoQkgmB10G3N9/7duHSDQKd3Mzgl4vIpEIfJ2d8HV1IRKNSv95XS407d+PcCQibXv+xRcRiEYhZKpiBFFEIBrFD375S4RDIcSSSem/rvPnAbMZMBqztusWLkQ8lZKu+bsjR8CwLEoXLED5qlVSVaTO6cxJ/slUKthvvRWsTAbTzJmDVg8ZampgqqvL3cEwUJWUAKNMKMhUKhhnzixIhY3D4cALL7yA+fPn44UXXihYgpCQqYqVy2GcORPGmTOhczqhtlig0OvTP+v5fhYZBqxcDlOm4rhvaaxMqYShuhr66mqpd6e6tBTyTJXvVCVTqWCsrYXO6YTSbE5PMNfrIddqIVOpwOSpdmYYBmWLFmV9GMKwLGyrV6Ny82aoS0vhv3IFzfv2oePgQQSbmqQlygNFu7sRbG6GbfVqKQFYsXp1ToJQrtVCM2AbIYSQwmtoaEB9ff24r8OI4gjr1PMQRRFutxuiKKK8vHxKfto2nGAwCJPJhEAgQD0JCSGE3DSEVAqhtjbIlMp0g3udDjG3O+/wkJFY/6d/ClXmBpthWciUSvCJBAb+mcHFYuDjcelGFgAuX7uGWJ5lgyqWRaVWK1X5iKKIZCCQTgT0uwGWazSQDVgml0gm8eunnkKks1PaptDpUH3vvRASCXQ1NiLW3Q2GYeC4446sG9tkKITWt97K6qNoqKmBdfnynEmgrEoFfWUl5Go1UpEIwq2t2RWXg1CazdDZ7QWtrolEItiwYQPee+896MZQkUjIjUTgeYg8D5HjIPA84qkUNt1zz5A/H6IoIuH1QlVSMu0r38LhMO647Tb8dvdu6DSanP3+y5fhPn0677mpcBhhlwuR9nYk/H6oSkqgs9uhdTigNBohpFJoe+stlMybB9PMmQAAY10dbKtWZV1HrtPBUF097b+WhJCRo79FiqsQ+a2xd7FG+tOo8vLy8VyCEEIIIZOMTyYRam2FkEiAj8WQDATAKhQjqhgczMAhHPmuJwoCWt98E1w8DlYuR+XmzVBoNPj/ffWreP3IEamSEABYhsEnly7F/UolVGYzLLfcAt9HHyEZCqFi3TrpuJIFC1A6b15OPH/51a9CX1mZlSQsW7IkPf1Tq4XzjjsQuHoVoijmLo0zGGBZtAju06fBKhSwLl8OY01NznOoSkqgraiQboAVOh0MdXUIt7YO+rVkWBZau52a+BMywViZDJDJgMyHFwPbHOTDMIw0fXy6YzKVk8aaGoiBQM5EaPOcOYBMBs/Jkzkf5ij0epTMnYuSuXPBxWKIdnUh0tkJ38WLkKlUkKlUUBqNMM6YkT7eYIDllluyriHX62GoqqIEISGETDNjThKePHkSDQ0NcLvd+Nd//Ve0t7fD4/HglgG/IAghhBAydfDJJEItLTnTMceTIASyh3BIBlTdRTo7oRAEKDI37dELF2BbvRqPrF6Nt06cQCiZhCCKYBkGJo0Gf7VxI0pkMrS/8w5iV64g0dICx4YNUGfOL1u0KKu/FpDuiaUuK8ODW7dC63SC+eADiIIAjdUKQ1XVx8cxDMyzZw/6ekyzZyMVicA8e3beycJqqxXaPB+UytVqGGprEenoABeNZg0WkOv10Dsc0tJnQgiZaHKtFtqyMkS7u5HwerP2mWfOBMuy6DlxIidRKJ2v0cBYVwdjXR0Enkespwdxtxum2bPBMAwYhkHFmjVZVdYKvR766uppucqMEEJudqNOEvI8j7/8y7/Ef/zHf0AURej1evzrv/4rXC4X7rjjDnz2s5/Fz372Myim2R/A9fX12Lp1K7Zs2VLsUAghhJCC6Vuiy0Wj4BOJdH+pESyHHa3P3nffsMeEWluzHkc6OuD+4ANYDQY8v3gx9gWDeLupCZ9YsgR/dccdsGaWGVfceis69u+HxmaDurQUAFAyf35OghBIT/GU63T47J/8CSAI0JSXI9bdnVPlMhyGYWBdtizvPrlenzdB2EemVMJYWwtREMBFo0hFImAVCil2QgiZTAzLQme3Q2kwIOxyZbVSMNbVgZHJ0H3iRNb2fFiZDDq7Hbp+/edLFy7Mem9TGAzQV1VRgpAQQiZZQ0MD9u3bN+7rjLr++/nnn8e+fftQX1+P1157DZpMj4s1a9agra0NXV1deOGFF8Yd2GTbtWsXJQgJIYTcULhYDMFr1xBxuZDw+dKVbROQIBwJPpVCxOXK2R5qa0MqGoU+FsPf3Xcfai0WPLl5s5QgBNJDBBwbNkhJO4VWi9KFC3OupTSZoDQawcpk0pRgfWUlDLW1UJeUDBujprxcGlwwGFahgN7pHPZaQPrGXKHXQ9svuUkImXx79+7Ftm3bkEgksG3bNuzdu7fYIRWFQq+Hsa4u533OUF2NGX/yJ7CvWwdDVdWIq501FgtK+rV7oAQhIYQUz5YtW7Br165xX2fUlYR79uzB66+/jk2bNgEAHn/8cWlfeXk5fv7zn+Puu+/G3//93487OEIIIYSMjMDzSAaDYFkWjEyGVDSKuMeTtdy1mCIdHRA4DpHOTkRcLlhXrJBuJKOdnVBbLDlTh/vr3yesZMECsAP6XDFyObQVFdJjlcmEVCgEndOZVfUyGFVJCTRWK+RaLULNzfkPYhjoq6qyhqYQQqa+7du3Y/v27cUOY0roq3QOtbSA79enkJXLoa+qgr6qCgLPI9rdjXB7O6IdHeAHtKcA0h+Y2NaskXoOskoldE4nJQgJIWSaG/VfuT09PVKCMB+Hw4FAIDCuoAghhBAycqIoItzeDi4cLnYogwq1tAAAApcvI+Z2Q22xwFhbCyCdQNTZ7ZCp1VDodLDccgs0CgUSXi9CbW1Z11Ho9dJ5/ens9qzkncJgAFgW8swE5T6a8nKkIhFw/YYYyLVaaDOJRIVOB3VZGeK9vTnPobXbIc8zJZQQQqYTVqGQhixx0WjufpkMeocDeocDoiAg3tuLSGcnwi4XUqEQAMC6bJlUsQ2Ggd7pTA+LIYQQMq2NOkkol8vh8XhgsVjy7m8d0G+IEEIIIRMr1t09pROEXCyGWHc3UtEoYh4PrMuXo/fDD6G12cDK5Yi53bAuWwZNRQVYpRKmGTOg02ohiqKUAO1TumBBzrRMtdUKpdGYtY1hWSgNBiT7fXAp1+mgsVqhsVoR9/kQ6+oCI5PlLI/T2GxIhcNSlQ0jl0PncEDZbwk0IYRMZ6xMBkNNDUJtbUP+/mBYVnrftCxZgmQohITPB0N1tXRMXxU2IYSQ6W/UPQnvvfdePPjgg2jJVAT0d+LECTz44IP41Kc+VZDgJlN9fT0aGhqKHQYhhBAyKgm/P2/V21Tiu3QpnexrbYXWZoNxxgxo7Xa4P/gA0e5uKPR6KPT6nGXBDMPAtno1NJkPJhUGQ9aNKZCuDBxsiEhW4pBhspYjq0tKYJo1C4aampzlwwzDQOd0AgwDpdEI08yZlCAkhNxwGJaFobo650OWoSgHvA/L9XporNaJCI8QQsgoNDQ0oL6+ftzXGXWScNeuXbhy5QpmzpyJuXPnwuv1Ys2aNaisrMSaNWvQ3d2NZ599dtyBTTYaXEIIIWS6Sfj9iHR2FjuMIaXCYQSuXoUoigi1tMBQUwMAsNxyCxI+H3rPnJGWCmvyJPtYuRwV69dDYTCgbOHCrCpCTXn5kDenCoNBOl5VUgK5Wp19bYUCsgHLkfvINRqYZs6kHoSEkBsawzDQVVZCX1mZThayI789ZDLLkgkhhBRfoQaXjDpJ6HA4cOLECTzyyCPo6elBMplEY2MjIpEI/uqv/grvv/8+bDbbuAMjhBBCSH7JUAiBq1fT04KLNK14pHrPnYMoCEj4fOBjMWgzN5QypRLW5cvBRaPQ2e3pROEg/azkKhUqN2yAvqpK2jZcghBI3/wqTSYwMlneBORwBksgEkLIVOZyufDUU0/hwoULeOqpp+DKM1m+v773Sn1VFUrmzoW+qir93jlMwlDndI54EjIhhJDpYUwfjdtsNvzkJz/B7t274Xa7IYoiysvLaZoVIYQQUmBCKoV4by+4WAwCz0PkOIg8X+ywcnDxOLhoFOrSUmlb3OtFODN4JNTSAl1VVVYiUOd0ovKuu6A0m9PLe4fQv98Vq1RCPUhv5IGURiNkGg011CeEFM3evXsnbbqyy+XCsmXL4PV6wfM8du/ejddffx2nT5+GYwRVfwzLQmk0Qmk0QhRFcJEIUuEwkuEwhH7TkNVlZXnbMEzmayWEEFJ4Y14/EwgE8NZbb6GpqQkAMGPGDNx9990wmUwFC24y1dfXY+vWrbTkmBBCyJQgcBziHg/iPt+UrxZMhcNwHTwILhJB2ZIlKJkzBwDgOXMmPXxEEBBua0PFunVgGAaq0lKpj6KqpAQMy0JrtyOeSo3o+TRW64g/mJTrdFDQh5iEkCLas2cP/vRP/3RSnuvZZ5+VEoQAwPM8vF4vnn32WbzwwgujvyDDAAYDFAYD+GRSGuok6PWI9JsS32fPnj2UJCSEkCJoaGjAvn37xn2dMSUJv/vd72LHjh2IxWIQRRFAukxdo9Hg6aefxlNPPTXuwCbbrl27YBxF015CCCFkvPhkEuG2Nsg1GmjKy6Xedwm/H9GurilZMThQ3OdD58GD4OJxAIDn9GnE3G7oKysR6+kBAEQ7O8EqFFBbLNDYbLCtWoXWhgbwySSA9NJhmUIB8DxYuRyq0lKotFrwyWTO1E2ZSgWV2Tzi+G6WVQ579+7Fnj17kEgksG3bNjz88MN0o07IFOFyubBhw4ZJea4LFy5ICcI+PM/j5z//ORobGyf8+RP9qg0JIYRMni1btmDdunV46aWXxnWdUScJX3rpJTz11FNYtmwZPvnJT8Jut0MURXR2duKNN97AN77xDajVajz++OPjCowQQgiZ7kRBABeNgk8mIaRSkKlUUBqNYFgWXCyGUGsrRI4DH48jGQxCU16OVCiE1IDE2FQk8DzCra1wnz4NYUAFYMTlSvdLzAg2N8NQXQ2GYWCsq4Nco4F1xQp0HT0KANJSY6VeD1ahgK6iAjqdDqIoItLejmQwKF1LQ32P89q+fTslBQmZopxOJ1577bVJea6nnnoKu3fvzkoUymQyfP7znx9bJeEobdu2bcKfgxBCyMQZdZLwu9/9Lr7//e/jK1/5Ss6+5557Dt///vfx3e9+t6BJQo/HgyeeeALHjx+XljcPpbGxEf/wD/+AcDiMaDSK8vJy7N27F5YR9i8ihBBCCiHicmUluAAg0tkJpV6PZDictYxY5HlEp/ikYgDgolH4r11DsKkJfKZ6cCjx3l7EenpgXb4cMqUSukxPLENVFSIdHQi3tkrTMRV6fda5fVM3xdZWpMJhyLXavD2wCCFkKnv44Yeh0+km5bl27NiB119/XVpyLJPJUFpaih07dkxKDA8//PCEPwchhJCJM+rpxn6/P2+CsM+TTz6J4IAbovE4d+4cHnjgAZSVlUlLm4dy4cIFPPjgg/je976Hw4cP44MPPoDBYEA0Gi1YTIQQQshwoj09OQlCAIAgpLdP8T6D+XCxGNrfeQe+CxdGlCAURRG9Z8/CNHs25BoN9NXVWQNErMuWQV9ZCblGAzAMFHkSgAzDQF9VBblON6YJxYQQUmyTWeXrdDpx+vRpPProo9BqtXj00UdHPLSkEKiimRBCprdRJwltNtuQCbdIJAK73Z61rbW1dfSRZVgsFjQ0NGDVqlUjOn7nzp146KGHMH/+fAAAy7J49dVXUV1dPeR59fX1aGhoGHOchBBCSJ9kIIC4213sMApKEAR0HT2K1Cg+dIt2diIZCqFk7lwAgLG2Nmu/TKlE+Zo1AACFTjfoBGKGZWGoqYFikipxCCFkOnM4HHjhhRcwf/58vPDCC5OWICSEEFI8DQ0NqK+vH/d1Rp0kfPLJJ/H4448jFovl7IvFYvjbv/1b/MM//EPW9rVr1445wIqKCqhUqhEdK4oi3nzzTRgMBjz44IO49dZb8elPfxqnT58e9txdu3bRZGNCCCHjInAcYm43wh0dxQ6l4NwnTyLm8Qy6P9TWht4zZ6T+hKIgoPfsWZTOnw9WoYDKbIa6tDTnPJZN/ynyWkMDtm3bJg3e2Lt3b9ZxN8sAEkIIIYQQQkZry5Yt2LVr17ivM+qehMeOHUNDQwMqKyuxatUq2DINxLu7u9HY2Ijy8nKIooj/+Z//kc4JBALjDnQkPB4PgsEgXnjhBRw4cAALFy7ET37yE6xfvx4XL15EVVXVpMRBCCHk5sInk4i53dN2GfFw/FevInj9+qD7g9evw3PmDJRGI8J/+AMsy5eDi8UAUYRxxgwA2VWErEKRPeyEYfD5L34Rf/n//D8T9RIIIYQQQgghwxh1kvCVV16R/v2HP/whZ7/P58OlS5eytk3Wp/+JRAIAsHXrVixcuBAA8MUvfhE7d+7Ez372M/zjP/7jpMRBCCHk5iAKAmIeD+K9vTdkcpBPJuE5cwahIYaG+a9cge+jj2Bfvx5qiwWh5mb0vP8+RJ5H+Zo1YFgWDMtCn2n7IdNooHM4EGxqkr5m8iGWGhNCCCGEEEImx6iThDabDZ2jnL44sEfhRCkpKQHDMDl9NyorK9HS0jLkufX19VAqlQDSZZq09JgQQkg+AseBi0SQikaRCoWyK+JuIGGXC+6TJ9MVgXkIPA//hQsIXLsG+x13SEuJjXV10NrtiHZ0SJOM9dXVkKvVAABVSQnkajW0FRWIZpZlK43GSXhFhBBCCCGE3FgaGhqk+RrJZHLc1xt1knCoycaFPGcsdDodFixYgK6urqztbrd72KXGu3btgpFuUgghhAxB4HkErl2DyHHFDmVChV0udB4+nHefKAgINTfDe+ECZCoVHBs2QGU2Zx0jV6ulZcasXA7L4sXpHSwLlckEAFCXlICLRJAMBKDMM9WYEEIIIYQQMrT+RW7BYBAvvfTSuK436sElY5mWUogJK4N58cUX8dBDD0mPv/rVr+K3v/0tXC4XgPSS6M7OTjzyyCMTFgMhhJCbQ6yn54ZPEHLJJNwffJB/XyyGtrfegv/KFViWLkXlXXflJAgHMs+bB7lGAwBQmUxg2I//9NA5HFCVlICVj/ozS0IIIYQQQkiBDftXeSgUwoULFwAAVqsVdXV1Wfu+9a1v4d1330U0GsXKlStRX1+P2bNnFyzAWCyGT3ziE+jq6kJXVxc2btyIBx54AE888QQAoLW1FRcvXpSO/+IXvwifz4d77rkHpaWlkMvlePvtt7PiJoQQQkaLi8eR8PmKHcaE85w+DS4ez9kuCgK6jx+HymxG+apVWcm+wSi0WpTMmSM9HphQZFhWWpJMCCGEEEIIybZ3717s2bMHLpcLTqcTDz/8MLZv3z5hz8eIoigOdcAPf/hDfOUrX4FMJsM3vvENPPfccwAAQRBw++2349ixYwAAi8UCj8cDk8mEDz74YNok5YLBIEwmEx5//HFs3bqVehESQgjJK9jUBC4aLXYYEyrc2YnOgwfz7us9exaRjg5U3nVX3so/fWUldHY7es+elZKMtjVrYKypAQDIVCqYZs2auOAJIYRIIpEINmzYgPfeew86na7Y4RBCbhL03jMxRvJ1bWhowL59+/DSSy8hEAiMuZ3esGUABw8exG233Yb29nYpQQgA//Vf/4WjR4+isrISly5dQnd3N7q6urBkyRJ861vfGlMwxbRr1y5KEBJCCJHEfT4Em5sR7epCtKvrhk8QcskkPIMsM450diJw7Roq1q3LmyA01NaiYu1aGOvqUH3vvTDPng2NxQJDZqIxkB5YQgghhBBCCCm8LVu2YNeuXeO+zrDLjc+ePYtXX30V5eXlWdt/9rOfgWEY7NixA7MylQFWqxU/+MEP8OlPf3rcgRFCCCHFlPD5wMdi4CKRYocyYeJeL8JtbYh5PEj4fBAFIeeYhN+PnsZGWJcvzzuF2DRrFqzLloFhGACATKmEddkyCIIgbQPDQJkZWEIIIYQQQgiZmoZNEvp8PsybNy9rWyAQwP79+6FQKPDggw9m7Vu0aBG8Xm9hoySEEEImEZ9Mgo/Fih3GhIr7fHDt3w9hkEEsIs/Dd/Ei/Jcvwzx3blZVIADINRqULlwIU2aK8UBsv56FKrOZhpMQQgghhBAyxQ37F3u+loW//e1vkUqlcM899+Rd56xWqwsTHSGEEFIEyUCg2CFMqFQkgs6DBwdNECb8fnS//z4YhoFz48aspcIytRql8+bBOHMmWJls2OdiWBaaAasRCCGEEEIIIVPPsD0JKyoqcPz48axtP/jBD8AwTE4VIQA0NzePuUFiMdXX16OhoaHYYRBCCJkCEn5/sUOYMFwiAdeBA3knGAOAwHHoOnoU2ooKVN51V1aCkFUoUHnnnTDPmTOiBCEAaMrLqYqQEEIIIYSQCdTQ0ID6+vpxX2fYv9ofeugh/Nmf/Rl27twJq9WKPXv24MSJE6ioqMBDDz2UdWwymcQ3v/lNLF68eNyBTbZdu3ZNy+QmIYSQwuKiUQjJZLHDKLhEIIBIRweCzc1IhUKDHuc9dw5ytRplixd/3FMQAMMwsK1eDaXBMOLnlKlUUJWWjituQgghhBBCyNC2bNmCdevW4aWXXhrXdYZNEn7lK1/Ba6+9hr/6q78CwzAQRRE6nQ4/+9nPoNFopOM+97nP4ciRI3C5XPi///f/jisoQgghpFgSN9hS43BnJzynTw+ZGOwT83gQbGpC5d13ZyUIAcA8dy70Tueg5yoMBiiNRsTcbinJqrXbc65DCCGEEEIImZqGTRKq1WocPnwYv/3tb3H58mXYbDZ86lOfypl2/Cd/8if4xCc+AQA03ZgQQsi0wsXjYFgWDMsiGQwWO5yC4JJJeE6fRqi5eUTHCxwH94kTKF24MKdaUGO1omzRokHPVZrN0DkcYDJTjBNeL/hEAgqdbjwvgRBCCCGEEDKJRtQkSCaT4f777x/ymD//8z8vSEDFUl9fj61bt2LLli3FDoUQQsgkElIpBK9dK3YY4yYKAiIuF5KhEJLhMGJdXYP2HRx4XsLnQ+DKFbBKJUyzZ2ftN1RXw7J8ORg2fxtjVWkpdHa79JhhGKjLysb3YgghhBBCCCEj1tDQgH379o37OtRJPIN6EhJCyM3pRqkc9F28iN5z50Z8vMBxcJ88iWhnJ8Aw0JaXw7Z6tbQ8WK7RwLpiBfQOR97zWYUCmvJyqMzmQoRPCCGEEEIIGaNJ60lICCGE3MiSI+jVN9UlAgF4z58f8fGiIKD72DEIHAf77bdDVVKS1TtQZTbDeeedkCkUOecycjk0VmvOOYQQQgghhJDpLf/aIUIIIeQmIHAcuGi02GGMiygI6GlshCgIIzteFNFz4gS4eBwV69dDXVqaPcWYZVG+alX+BKFMBmNdXc45hBBCpo69e/di27ZtSCQS2LZtG/bu3VvskAghhEwTVElICCHkppUMhQBRLHYY4+K/fBlxr3dEx4qCgN4zZxDv7R20UrB04UKoS0pyT2YY6KuqIFMqxxsyIYSQCbR9+3Zs37692GEQQgiZhihJmEGDSwgh5OaTmub9CONeL3o/+mjIY0SeR7ijA9GODkS7usAqFHDccQfkanXOsRqLBSVz5+a9jraigqYVE0IIIYQQMgXR4JICo8ElhBBycxF4HqlIpNhhjFnc64Xrvfcg8vzgx3g86PngAwCAzumE/bbboBpkqTArl6N81aq8U4xVJSVQl5YWLnhCCCGEEEJIwUzpwSWLFy/G2bNnJ+LShBBCyJiJoiglyFLTeKlx3OtFx4EDEFKpnH2iKIKLRhG4cgXBpiaULFgA8+zZeZN/MrUaOrsdOrsdGpstZ/kxw7LQVFTkX35MCCGEEEIIuaGMKUnY1dWFhoYGdHZ2IplM5uxvaWkZd2CEEEJIocV6epDw+SBTqSAMUYE3lUV7etB15Aj4Ab9/w+3t8F+6hGQoBJHnobFaUXn33VAaDHmvo9Bq4dy0CQqtNu9+pdEIbUUF2Dx9CwkhhBBCCCE3nlEnCRsaGvCZz3wG8Xh80GOm48RD6klICCE3vlQ4DJHnp+VEYz6VQu/ZswheuwZxQAVkKhxGz4kTKFu0CGqrFUq9HoxMNui1ZGo1HBs2DJ4gNJmgr6wsaPyEEEIIIYSQiVG0noTf+MY3sHnzZvzt3/4tqqqqoBww5VAURaxevXrcgU026klICCE3NoHjwA/xAddUFvN40H38eN4eiqIooufECRhqamCaNWvYa7EKBRy33z5ohSGrUEBrt487ZkIIIYQQQsjkKFpPwtbWVjQ2NuYkB/v75je/Oa6gCCGEkEJLhcPFDmFMwh0d6Dp2DCLH5d0fuHIFXCwG+223DXkdhVYLndMJQ10d1GbzoMfpnE6wQ1QhEkIIIYQQQm5Mo04Szps3D4IgDHnM5z73uTEHRAghhEyE6TjJONjcjJ7GxpzlxX2SoRC8H30E+223gZXn/5WusVhgWboU6rKyYZ9PbbFAodONK2ZCCCGEEELI9JQ76nAY//t//2/83d/9Hfx+/6DHrFq1ajwxFUV9fT0aGhqKHQYhhJAJMlUrCeNeL7gBQ0hEUYT3/PkhE4RcPI6uI0dgnDEDGqs1Zz+rUMC6fDmcd945bIKQkcmgNJuhKS8f+wshhBBCCCGEFEVDQwPq6+vHfZ1RVxI+88wzaG1thd1ux8yZM2GxWMCy2blGr9c77sAmG/UkJISQGxcXjw+6XLeY4n4/XO+9B5lCAduaNdBYreBTKXQfP45IR8eg53GxGDoOHICqpARlS5ZI29WlpVCazVCZzdA7HJAPMpikj9JohKq0FHKtdloOHSOEEEIIIYQUsSfhgQMHUFVVhYqKCkQiEUTyLN/ieX5cQRFCCCGFNBWrCFPRKDoPHoSQSkFIpeDavx/mOXMQ7uhAKhQa9DwuGkXHgQNQl5XBunKllNwzzZqF8uXLR/z8SrMZeqdz3K+DEEIIIYQQcmMYdZLQarWiqalpyGPsNBWREELIFDLV+hFyySQ6DhwAF4tJ20RRhO/SpSHPC7tc8Jw6Ba3dDuvy5VKCUGEwwNKvonA4SqMROodjbMETQgghhBBCbkijThI+//zzwx7z8ssvjyUWQgghpOAEngcXjRY7DAkXi8F14ACSweCozvGcPo2Y2w3L0qXQV1dLCUKGYVCxZs2gg0sGUhqN0FVW0vJiQgghhBBCSJZRJwkffvjhYY/ZsmXLmIIppvr6emzdunVaxk4IISSXKAiIe72IezyAIBQ7HADpacQdBw+OavlzwudD56FD0FitqLrnHsjV6qz9JQsWQF1aOuQ15DodlEYjFHo9ZErlmGInhBBCCCGETE0NDQ3Yt2/fuK8z6iRhnyNHjuC//uu/cPXqVQDArFmz8OCDD+LWW28dd1DFQINLCCHkxiBwHBI+HxI+H4RUqtjhSBLBIDr27wcXj4/4nGhXF7qOHUPp/PkwzZmTU/2ntdtROn/+oOezSiV0djsUev2Y4yaEEEIIIYRMbUUbXAIAf/M3f4Mf/ehHEEUxa/uLL76IRx99FP/+7/8+rqAIIYSQkRIFAVw0Ci4eBx+LIRkKAQN+PxUbF4uh8+DBvAlCgefBsGxOAjDY3AzPqVOwrlgBQ3V1znn6ykrY1q4Fw7I5+1iFAkqTCRqrNe9+QgghhBBCCBlo1EnC73//+9izZw/+7u/+Dlu3bpWGlHR2dmLfvn348Y9/jPnz5+Nv//ZvCx4sIYQQ0kcUBMR7exHv7YXI88UOZ1ACx6Hz8OG8w1NS4TBc+/dDaTKhfMUKyLVaiDwPz4cfItzWhopbb4XWZss5z1hXh/IVK7ISgH1VgzK1esT9CQkhhBBCCCGkz6jvInbv3o3f/va32LRpU9b2uXPnYuPGjdi6dSsef/zxaZckpJ6EhBAyfaTCYYRdLogcV+xQhiSKIrrffx9xrzdnHxeLoePgQeicTogch7a33kLpwoUItbQAACrvvhsKnS7rHEYuR9mCBTDPnZtdecgw0DudkGu1E/p6CCGEEEIIIVNP0XoSejyenARhfxs3boQ3z83QVEc9CQkhZHqIud2I9fQUO4wR8V++jHB7e852PplE56FDUJeVwXLLLWAYBpGODrhPnYLObkfZ0qVgZbKsczTl5ShfsQJKgyHnehqrlRKEhBBCCCGE3KSK1pOQZVl4vV6UDjJJsbe3N6evEiGEEDJeXCyGaHc3uDzLdqeiRDCI3nPncrYLqRS6Dh+GXKNB+cqV0u9MncMBrd2e8zuUYRiULV6Mknnz8j6PXKuFxmot/AsghBBCCCGE3FRG3c38nnvuwQMPPIBzeW58zp07hz/7sz/DvffeW5Dg+vN4PPjc5z6Hurq6IY8TRRE/+9nPsGHDBmzatAnLli3DX//1X8Pv9xc8JkIIIROHywwhSfj9CLW2Inj9+rRJEIqCgJ7jx3N6JfKpFDoOHgQjk+UdOpKTIJTLUbF27aAJQkYmg87pLGzwhBBCCCGEkJvSqCsJv/Wtb2HNmjVYunQpnE5n1uASl8uFyspK7Nmzp6BBnjt3Dk888QQWLlyYM1F5oEgkgq985Ss4efIk5syZg2g0ik2bNuFLX/oSXn311YLGRQghZGJEe3oQd7uLHcaYeS9eRNzny9rGJ5PoPHgQMpUKtnXrcpYTDyRTq+FYvx7qsrK8+1mFAoaaGsiUyoLFTQghhBBCCLl5jbqS0OFw4MSJE3jkkUcQDAbR2NiIxsZGhEIhfOELX8D7778vJQ4LxWKxoKGhAatWrRr2WIVCgZ07d2LOnDkAAK1Wi+3bt+P3v//9kAnG+vp6NDQ0FCxmQgghoyeKIsLt7dMiQSiKIvhUCoIgAAC4eByh1lZ0NzbCd/581rECz6Pz0CHI1GpUDEgQGmfMgGbABGON1YrqzZsHTxAqlTDU1kKmUhX4VRFCCCGEEEKmm4aGBtTX14/7OqOuJAQAm82Gn/70p/jJT34Ct9sNURRRXl4+Yb0IKyoqRnysSqXC1772taxt0Wh02PhocAkhhBSXwPMIt7VN6SXFoiCg4/BhJH0+8ImE9OETwzCDfhAliiLcjY1gGAYVa9eC6Z8grKuDbeVKAECorQ2e06dhqK1F2cKFOUuR+8i1WugrK8EqFAV+dYQQQgghhJDpqGiDS/pjGAbl5eU52w8fPoz169eP59IFI4oiXn31VXzjG98odiiEEEIGwSeTCLe2gk8kih3KkALXriHa2ZmzfahKdd+FC4h7vXBu2pSVINRXVqJ8xQrpsaGqCnqnc9DkIMOy0NhsUA8yOIwQQgghhBBCxmNcScLBfPazn0VHR8dEXHrUvve976G6uhqPPfZYsUMhhBAygCgISPh8iHk8EDmu2OEMiYvF8k4rHkq4vR3+y5fhvPNOyNVqabvObs8/uGSQBKFCr4fO4aDqQUIIIYQQQsiEGTZJ+PTTT8Pv9+O73/0uAGDTpk3DXtTr9Y4/sgL45S9/iXfeeQe//vWvh10KXV9fD2Wm+fuWLVuwZcuWyQiREEJuOnwyCS4aTU8vDgRyJgBPVZ4zZyCkUiM+notG4f7gA5SvWgWVySRtN1RVoXzNGrCDJAQHUpeVQTuKthuEEEIIIYSQm0NDQ4M0XyOZTI77esMmCX/0ox8hHA7j+eefh0KhwIEDB1BVVTXkOfwUuOH71a9+hT179uD111+HSqXClStXUF1dDdUgTd6pJyEhhEwsURAQ6exE0u8vdiijFu3uRqilZcTHi6KInhMnoHM6oXc6pe2mmTNhXb58ZD18GQZaux3qkpKxhEwIIYQQQgi5wfUvcgsGgxPfk/DUqVNIJpNQZJY4Wa1WNDU1DXlOoacbD+fFF1/EkSNH8J//+Z8A0gnCH/7wh3j11VeRSqWQSqXw9NNP47nnnkNtbe2kxkYIISQ9+Tfc3g5hivccHCgZCsF74QLCo0gQAkDw+nWkwmFUrFsnbTPNnJnVg7CPTK2GkEplVVSySiX0TifkWu3YgyeEEEIIIYSQURg2SWiz2bIe7969e9iLjuSY0YjFYvjEJz6Brq4udHV1YePGjXjggQfwxBNPAABaW1tx8eJFAEBXVxe2b98Onudzhqo899xzBY2LEELI4ERRRCoYRNznm9ITiwcTbmtD17FjQw4lyScZDKL37FlU3Hqr1ENQZ7fDumxZzrEyjQaGmhoAQNzjQdzrhVKvh9bhANtvyAkhhBBCCCGETLRRDy751Kc+lXd7U1MT/H4/li5dOugxY6XRaLB///5B9//zP/+z9O+KigpwY2h+X19fj61bt1IvQkIIGSeB55Hw+ZDwekfVw2+qCTQ15SQIBZ5HqLkZfCwGPlP9xyqVkCmV4BMJxHp6kAwEYJ47F9rMB1WqkpK8Q0pkajUMNTVSMlBrs0FtsVBykBBCCCGEEDIqDQ0N2Ldv37ivM7Ku6f28++672LRpEx5++GFp29e//nXMmjULK1euxJIlS9DT0zPuwCbbrl27KEFICCHjIIoi4r29CFy5glh397ROEHKxGGLd3TnbfRcuwH/5MrhEAqxMBrlGA5Hn0wNYOA4l8+ahdutWlC1eDACQazRw3HYbZAOmEg9MEPahBCEhhBBCCCFktLZs2YJdu3aN+zqjriTcvXs3YrGYlCQ8evQo/uVf/gVr167F9u3b8Z//+Z945pln8IMf/GDcwRFCCJm6BI5D3OuFkEwCogguHk//+wYQbmvLqSJMBoMIXLkC5513QmU2D3sNhmFgW70aco0ma7tcr4e+spISgoQQQgghhJApZdRJwuPHj+PQoUOoqKgAAPz4xz+GSqXCb37zG9hsNjzwwAO49dZbCx4oIYSQqUEUBES7u5Hw+wFBKHY4E2LgJGNRFOE+eRLGGTNGlCAEAPOcOdAO6OurNJmgczpHNt2YEEIIIYQQQibRqJcbx2IxKUHI8zzeeOMNfPrTn5YGnNhsNsTj8cJGSQghpGj6V9QJHIdQSwsSXu8NmyBMZIat9BdqaUEqEkHpwoUjuobKbEZpZsmxtK2kBPrKSkoQEkIIIYQQQqakUVcSajQauN1uWK1W/Pd//zd6e3vxuc99TtofDochl4/6skVHg0sIISRXvLcXsZ4eyNRqKPR6JPz+G2ZJ8WDCra3Sv0WeR7C5Gd5z51C+ciXYEfx+UxqNsK1dC7bfoBKFwQCt3T4h8RJCCCGEEEJuboUaXDLqbN7999+Pe++9F5s2bcIrr7wCp9OJrVu3AgB6e3vxzW9+E4sHVE9MB7t27YLRaCx2GIQQMiWkIhHE3G5wkQgAgItGwUWjRY5qcoQyScJQayt6z54Fq1DAumIFdE4nAEBjs0FlNEKu0YBRKCDyPESOg0ythraiAgqtNut6cp0O+qoqqiAkhBBCCCGETIgtW7Zg3bp1eOmll8Z1nVEnCXfu3Inu7m785Cc/gdVqxU9/+lPIZDLwPA+r1QqGYfDKK6+MKyhCCCGTRxRFcNEohFQKfDKJVDAIPpEodlhF4b1wAalwGHwyCfeJE7CuWAF9dbWU4FOZzXDefjsYdmTdOliVihKEhBBCCCGEkGlh1ElCrVabNwkok8kg3KD9qQgh5EaS7EsCMgyEZBLJYBAizxc7rKLzXbqE3rNnAQCR9nYoTSYYamqk/QzDoHzVqhEnCMGyNMWYEEIIIYQQMm0UrHlgU1MT/H4/li5dmtWHabqgnoSEkBudKIqIdnWlh46QLP7Ll+H58EPpcailJStBCADmuXOhLikZ8TW1FRWQq9UFi5EQQgghhBBC8ilUT8JRZ/PeffddbNq0CQ8//LC07etf/zpmzZqFlStXYsmSJejp6Rl3YJNt165dlCAkhNwwBI5DzONB4No1eC9cQODqVQSuXaMEYR5cIpGVIEyFw4j7fNBXVUnbFAbDiCcbA4DSZBpVQpEQQgghhBBCxmrLli3YtWvXuK8z6iTh7t27EYvFpCTh0aNH8S//8i9Ys2YN/u3f/g1msxnPPPPMuAMjhBAycqlIBHwyCVEQEHO7EbhyBbHubvDxOCAI4BMJCDdpn8HhhNvaIIqi9DjU0gJdRQVkKhUYuRzmOXNQuXHjiJYNyzQaqK1W6ByOiQyZEEIIIYQQQgpu1MuNjx8/jkOHDqGiogIA8OMf/xgqlQq/+c1vYLPZ8MADD+DWW28teKCEEHKzi3R2Qmk0QqHTSdv4ZBIRl+vjycMMA/RLeJHhhTPTjIH0kuxQSwssS5dCY7WiYt26nCXDSpMJcq0WMqUSjPzjX6OsXA5WXrAuHoQQQgghhBAyqUZdSRiLxaQEIc/zeOONN/DpT38aNpsNAGCz2RCPxwsbJSGE3GRSkQgiHR1ShVvC70fC60WopQUxjwfJQABhlwuBa9c+ThAClCAcpVQ0inhvr/Q47vFA4Dho7XaY587NSRDK9XroKyuhLi2FQq+HXK2W/qMEISGEEEIIuVnt3bsX27ZtQyKRwLZt27B3795ih0TGYNR3NBqNBm63G1arFf/93/+N3t5efO5zn5P2h8NhyKfhjRINLiGETBUJvx+Rjg5AFMEnk9BWVCDa2ZneKYqIdXcXN8AbSLi1VUrEioIA/6VL0FdVQaHTQZf5QKwPw7LQ2e3FCJMQQgghhJApbfv27di+fXuxw7hpFWpwyaizeffffz/uvfdebNq0Ca+88gqcTie2bt0KAOjt7cU3v/lNLF68eNyBTbZdu3bBaDQWOwxCyDQlcFxWJdnAx3wyCYgiGLlc6m0n8Dzivb3g43EwLAuB58FHoxAFQTqPi0QQvH6dKgQnSCiz1FgURfR88AFS0SjKV62CobYWDJtdbK+12yFTKosRJiGEEEIIIYQMasuWLVi3bh1eeumlcV1n1EnCnTt3oru7Gz/5yU9gtVrx05/+FDKZDDzPw2q1gmEYvPLKK+MKihBCphM+mUTw+nWpAi3h9yPS2Qmd3Q6V2YxkMIiwywVkkn+sQgGZWg0uEslKCA6KEoQTIhEMIuH3QxRFeE6fRqK3F44NGyBXq2Gqq8s6Vmk2Q2U2FydQQgghhBBCCJkEo04SarXavElAmUwGYSQ3u4QQMo3F3G6oSkuzJt3Genog8jzCra1QlZUh7vEAooiIy4VUOIxkIJB1DSGVgpBKTXbopB9RENIVmgBCzc2IdHTAuXEj5JnpxAq9HgCgNBqhtlgg12iKGS4hhBBCCCGETLgJaR54+PBhrF+/fiIuPWGoJyEhZDhxrxexnh4kfD5obTYojEbw8biUBBQFAXG3O+ucgQlCUlzRnh74Ll5EvLdXStQGm5pQMm+eNDXaNGMGAEBttUJbXl60WAkhhBBCCCFkJArVk5ARxcKvY3M4HOjo6Cj0ZSdEMBiEyWRCIBCgnoSEkEEJqRQCV69mLQ9mWBaMTEZVgdNI+/79iPX0SI9T4TBaGxpQe999kKlUkGs0qPnkJ8HK5TDNmkU9CAkhhBBCCCFFFYlEsGHDBrz33nvQZQob8ilEfmvYSsKnn34afr8f3/3udwEAmzZtGvaiXq93TMEQQshUIvA8Ii4XIIoQOC6nf6AoCCPrKUimBC4azan0DLW2pgeSZBKEzo0bwcpkUOj1lCAkhBBCCCGE3FSGTRL+6Ec/QjgcxvPPPw+FQoEDBw6gqqpqyHN4ni9YgIQQMpmSoRDkajUYmQzh1lZw0WixQyIFEmptRf/ieVEUEW5tRemiRZCr1XBs2AClwQAAUJeWFitMQgghhBBCCCmKYZOEp06dQjKZhEKhAABYrVY0NTUNeY7dbi9MdJOIehIScnMTeB7Rjg4kg0EA6QnEtIz4xhJsacl6nPD5wMfj0DkcsN9xB1SZknxWpZIGlxBCCCGEEELIVFeonoTDJgltNlvW4927dw970ZEcM9Xs2rWLehISchOKdHQgGQikK8z6VZlRgvDGEvf5cobIhFtboaushNZuh9pslrarS0omOTpCCCGEEEIIGbstW7Zg3bp1eOmll8Z1HXa0J3zqU58a9piurq4xBUMIIRNJFEWEXS5Eu7ogiiJibjcSPl+6r2DhZziRKSTc2pr1WEilEGprg6G6Gqa6Omk7w7JQ9ksYEkIIIYQQQsjNYthKwrHYsWMHvvjFL07EpQkhZEwEns/qMZgMhyEkEkWOikwGURQRyiQJg83NCLW0IO7xQF1WBq3DAZ3DAQBgZDIYamrAymTFDJcQQgghhBBCimLUScJYLIZvfetbePXVV9He3o5YLDYRcRFCSMEIPI9QczP4ePzjbZQgvCnwySS6GxvBxWJIRSJwf/ABLEuXonzlSih0OhhrasDK5WDkchhqaiBXq4sdMiGEEEIIIYQUxaiThI8//jj27NmDW2+9FStWrIBSqczaL4oifv3rXxcswMlCg0sIuTGJgoBQS0tWgpDcHOJeL7qOHkUqEgEAhJqbobXbYZo1SzrGWFsLMAyMtbWQqVRFipQQQgghhBBCxq5Qg0sYURxdIy6r1Yp9+/Zh7dq1gx5jt9vR2dk57uAmQzAYhMlkQiAQoMElhNyAwi4Xkn5/scMgkyju88F/+TLCra3o+xUniiJaf/97WJYtk5YXK41G1Nx7LxR6PQw1NcUMmRBCCCGEEELyikQi2LBhA9577z3odLpBjytEfmvUlYRms3nIBCEAuFyuMQVDCCGFFPd6KUF4E4l2d8N78SJi3d05+2Ld3RBFEdqKCgAAq1DAPGcOAEBVWjqpcRJCCCGEEELIVDTqJOHf/M3fYN++fdi6deugx2zbtg2/+c1vxhUYIYSMB59MIponWURuPDG3G54PP0Tc6x30mGBzMwzV1dDabChduBBqiwUsy4JVKKDQ6ycxWkIIIYQQQgiZmkadJPzqV7+K3bt346GHHsLKlSthsVjAMEzWMfv37y9UfJOGehIScmOJdHQAglDsMMgEEgQB3nPn4L90CUN1zuATCUQ6OlC1eTPKFi+GxmKR9inN5pzfYYQQQgghhBAynRStJ2FjYyPuu+8+uN3uwS/KMOB5ftzBTQbqSUjI9CXwPJKBAJKhEGQKBZRGI8Aw4KJRxHp6ih0emUDJUAhdR48iMYLl5P7LlxFxuTDjM59B9T33fLyDYWCaNQuyAQO4CCGEEEIIIWSqmNI9CZ944gnMmjULzz//PJxOJxQKRdZ+URSxbdu2MQVDCCEjFXO7Efd4IGaqBTkACZ+vuEGRSRFqa0PPiRMQUqkhjxNFEf7Ll+E7fx62NWtgrKvL2q/Q6ShBSAghhBBCCCEZo04SXrx4ER0dHUNmL//8z/98XEEN9MMf/hA//vGPodFoYDab8aMf/QhVVVV5j+3t7cVXv/pVfPTRR9DpdBBFEd/5zndw6623FjQmQkjxJAMBqhS8CQk8D8+HHyJw9eqwx3LRKNwnTyIZCsGxYQO05eUw1NYCAFiVCgqdDmoaWEIIIYQQQgghklEnCefPnw+5fOjTvv71r485oIFef/11PPvsszh79iwsFgueeeYZfPrTn8aJEyfAsmzO8U8++SRaWlpw7NgxKBQK/OAHP8CnPvUptLa2wmAwFCwuQkhxCByHSGdnscMgkywRDKL72LFhlxcng0H4L11CuK0NOqcTlXfdBZlSCX1VFWQKBTQ2W1ZPQkIIIYQQQgghablZtmE8//zzeOqpp+AbYlnf2rVrxxVUfy+88AL+4i/+ApbMTd2TTz6JDz/8EH/84x/zHv/hhx9i/fr10jLoTZs2we/349KlS0M+T319PRoaGgoWNyGk8ERRRMTlgjhNep6S8Yv7fPB8+CHa3n57yARhKhJB9/vvo/3ttwGGQeXmzbCtWSMtJzbNnAmwLFQlJZMUOSGEEEIIIYRMjoaGBtTX14/7OqOuJNy5cydaW1vxk5/8BLNmzco73djr9Y47MABIJpNobGzEY489Jm0zm82oqanB4cOHsXnz5pxz7r//fvz2t79FfX09TCYTfvnLX8JqtWLu3LlDPteuXbtocAkhU5goigi3tSEVDhc7FDIJYh4P3CdPDls5KPI8vOfPI3DlCnSVlajasgWKfu0wGIaBed48qEtLoTKZwMpkExw5IYQQQgghhEyuLVu2YN26dXjppZfGdZ1RJwkPHDiAqqoqVFRUIBwOI5znhr1Qk409Hg84jkPpgL5RpaWl6BxkueEzzzyDYDCImpoaWK1WaLVa7N+/n5YaEzLNxbq7kQqFih0GmWACz8N77hz8ly9DFMUhj00EAuh5/30wLAvnpk1Qmc1Z++UaDWxr1kBbXg4AUJeVTVTYhBBCCCGEEDLtjTpJaLVa0dTUNOQxdrt9zAHlM7BScbBtAPCVr3wFx48fx/Xr11FaWopf/OIXeOihh/Duu+/mJBsJIdNDKhJBvLe32GGQCSQKAoLNzfCdP49UNDr0saKIYFMTej/8EKbZs1G6YAGYfj1q5Wo1jLNmwTxrlrTcWK7TQaZSTehrIIQQQgghhJDpbNRJwuefX4pWfQAAzJFJREFUf37YY15++eWxxJLDarVCLpfnLF/2er2oqKjIOT4ajeKHP/whXn75ZSkh+Bd/8Rf4p3/6J7z00kvYsWPHoM9VX18PZeZmcsuWLdiyZUtBXgMhZHz4RAKRjo5ih0EmAJdIIO7xIOZ2I9LRMaKl5ALPw3PqFKKdnbDfdhs0Vqu0T6HToWT+fBhqanKWFdMkY0IIIYQQQsiNpqGhQZqvkUwmx329YZOEP//5z6V/f/7zn8fDDz886LFf+tKXIAgCABQkyaZQKLBq1SqcOXNG2hYIBNDS0oL169fnHM9xHARBkJJ9fZRK5bB9EqknISFTiygICLW0gBumqoxML3G/H/5LlxDv7R11f8lUOIyuY8fAsCwq774bco0GACBTq1E6fz6MM2fmnXqvMBigpPd3QgghhBBCyA2mf5FbMBgcd0/CYacbP/roo/jZz36Gl19+edj+UDU1NaipqcEvfvGLcQXV39e+9jX84he/QG9mqeGLL76IJUuW4K677pIeP/TQQwAAo9GIO+64A6+88gpSqRSAdA/Fy5cv49577y1YTISQiRfv7aUE4Q1EEAT0fvQR2t9+G6GWllEnCEMtLWh7+21oLBY4N2yQEoSGqipUb9kC8+zZOQlChmWhczphqK4u2OsghBBCCCGEkBvVsJWEpaWlePfdd6XHd955Z04/wHfeeQcApOW83/72twsW4P3334+uri5s3rwZarUaZrMZv/vd76SbwdbWVly8eFE6/j//8z/xzW9+E2vXroVWq0U0GsVPf/pTWj5MyDTCJ5OIeTzFDoMUABeLIdjcjGBT06gTg6IoItrVheC1a4j7fLCtWQNdpuctq1CgfOVKGKqqcs5jWBaqkhKoy8rAKhQFeR2EEEIIIYQQcqNjxGHKAx0OBzr69QN75ZVXIIoivvrVr+J73/seAOCRRx7JOqcvOTcdBINBmEwmBAIBWm5MyBQRbm9HMhAodhhklERRRDIUQsLnQ8LrRdzjQcLvH7YKPZ9wezt6z56FyPMw1tXBOHMm5Go1gHR/wYp166DQ6bLOYRUKqMvKoCopyRpkQgghhBBCCCHTVSQSwYYNG/Dee+9BN+AeqL9C5LdGPbikLyH493//9znJwemsvr4eW7dupYpDQoqMi8cpQTgNCDyPcFsbgk1N4GIxiDwPPpWCyHHjui4Xj8Nz6hRibjfKliyBobpaSvgxDAPTrFkoW7o0a2kxq1BAbbVCZTbnVLoTQgghhBBCyI2uoaEB+/btG/d1Rp0k7HOj3YjR4BJCpoa4213sEEg/XCyGRCAAdVkZZAoFBJ5H4MoV+C5eBF+A6Vn9n8d/+TKCTU3QVlSg6p57pMpBAFBotShftQpamy3rPFVpKbQ2G1UOEkIIIYQQQm5aW7Zswbp168Y9uGTMSUJCCCkkURCQDAaRDAaLHcqQ+GQSsgET1G80cZ8P4dZWhDs6kAqFAACMXA6tzYakz4dUAdpJ8Mlkeklyb2/6P48H2ooKOG6/HeqyMuk4uUYDQ20tSubOzfq6syoV9A4H5FrtuGMhhBBCCCGEEDKCJGE0GsWePXtyekrFYrG82wGA5/nCRUgIuSGJgoCEz4e4zweIIgSOAwRhwp835nZDrtVK/exS0Sj4eBxgmHQvPb8fxro6qEtKkAgEEOnogL6yEnKNBu7TpxFua4P91ltzKtqmu2QohFBrK0KtrVJisD+R4xBxucb1HKIoInjtGgLXryMVDEKu00FdVgad0wnr8uVQ6PXSsQq9HpYlS6BzOHKqBFUlJdBWVFD1ICGEEEIIIYQU0LCDS1iWzbu0WBTFIbdPl0RhX2PHxx9/nHoSEjIJ+EQC4fZ28IkEMIaBFuPhu3gRvWfPglWpYF+3DgmfD55z5/L20VOaTFm9EWUqVTpmpKfnWpYuhWnWLDAMAy4WQ6SrC6a6uoLFKnAc4l4vtOXlBbvmQKIoItbTA9+lS4h2dU3Y8wBAKhxGz4kT4GIxlC1aBLXVmrWcuA/DsjDPnYvSBQvAymTZ++Ry6BwOKA2GCY2VEEIIIYQQQqaKkQwu6etJ+NJLL41rcMmwSUKz2Yzvf//7I76gKIr48pe/jETmZnqqo+nGhIxf3OcDH4tBoddDmfk54mIxyDWanGODLS3gwuFJjU/gefQ0NiLU2lrQ62qsVhhra+E5exZ8PI7SBQtQtmgRBEFIfz2GmDw1FC4eR+ehQ0j4fLCtXQtDVVXBYk4EAvBduoR4by+4aBTiBH6gI4oi4h4Pgk1NiLS3w1Bbi7IlS8DKc4vYWbkcxro6mOfMyft1U5rN0FZU5CQOCSGEEEIIIeRGNqWmG2s0mlFPMX7sscfGFAwhZPpJ+P2IdnRI/9Y5HOCiUSR8PrAqFTQWC5QmExiGQTIQmPQEIZ9KofPgQcQ8noJfO+Z2I9Zv0Ir3/HnE3G4k/H6IPI/yFStgzFQX8qkUZArFkNdLRaOItLfDf+UKUpEIAKD72DGkQiEkw2HE3W6pPx8rl0PgOPCJBBiWBatUSgm0vuRctLsbca8XfDwOmUYDiCJi3d1520SMlcDzEJJJKZZUKIRUKISE34+E3w+IIgw1Nai86y4oTSbpPIZloSopgcZigbqsDJry8pxej4xcDpXJBFVJCWQqVcFiJoQQQgghhNw8ent74fF4MHfu3GKHMmJHjx7FmjVrwE5yi6Vhk4QtLS2jvmig3xI9QsiNSxQERDIJwvQGMatvnZBIIOJypZeyMsyEVq3lM5EJwsH0Txp2NzYi0tmJVDiMZCAAVqWCuqQE+qoq6CsrIXAcYj09iPb0IO7xIBUK5STwRFFE77lz0mPvRx8heP06WIUi63iGYaAqKYHSbEaspwepgclYv79gr1EURSS8XgSuX0ekrQ2iIEiJSoXBAKXBAJ3TidJFi6AymcD0q/7TVlSgZN48qEtL81YUAgBYNp08tFjytrUghBBCCCGk0A4dOoSdO3fizJkzePDBB8HzPK5cuYIvfelL+NznPjfs+QzDDPth/JkzZ/DYY4/h8OHDhQo7rx//+MfYuXMn9u7di40bN+bsf+ONN/DKK6/AYrEgGAzC5/Ph29/+NpYsWVLwWPbv34+dO3di//79Bb/2SLS2tuLxxx/H3r17cfLkSTz77LP47//+bxw6dAjr16/POvbxxx/Hnj178Oijj+Jf/uVfRnR9l8uFJ554Aj6fT3qNt99+O37wgx9g6dKl44p9+/bt+I//+I9xXWO0hl1ufKOjnoSE/H/s3Xd4VMXXwPHv9k2vpFICJPTeEZCANCkiglhAigIiUqQIioUoiojSfhRR4QVUbCCgoogU6dKkS6+BQEIK6cnuZnffP5KsWVIIELKU83mePLL3zsw99xJi9uyZmduXlZ5O8vnzjg6jQFaLhSvbt5foWntWsxljcjI6L687HkuhVoPZXKJVfSXJkpVFZnw8hoQETGlpOPn54RwYiCE+nuvHj2NMSsK1QgXcK1VC6+Z2001EVDodvnXr4h4SUngjhQKtu3uBVYVCCCGEEELcbUuWLGHu3Lns27cPgDNnzlCrVi2SkpLQ3WRmS3GShGazmcjISCqW4FrmhQkPDyciIiJfktBgMFC+fHnOnz+Ps7MzAOPHj6dJkyb06tWrxONwdJKwU6dOvPXWW7Rq1coWz6BBg6hYsSLr16+3tYuKiqJjx46kpqZy4cKFW7rGjfd4/vx5ypcvj+oOl0p69dVXadCgAc8++2yprUl400rCh8WUKVNkTUIhblHWPbz2aPzRowUmCI3JySg1Gtt6iabUVMxGI+RM0TUkJuISHIxLUBDpMTFkxMRkJ630emL/+QdjUhKeVargXbv2HVW5FbRZiqNZLRbSrl4lNTKS9OhoVDodem9v1M7OJJ48ybU9e1BqtXiGhRHYsiXKm0yfVigU6H19ca9UCdfg4EIrB1U6HRo3t+zqwpuMKYQQQgghHnxWq5WsnOV37oTaxeWOfmePiYnBaDSSkZGBTqfj0KFDfPLJJwQGBnLx4kVGjx5N8+bNeeuttwAYOnQoHh4edO7cmZdffplatWrh6urKtm3b6NatGwCzZ8+2JRMLGs/Dw4Pnn38ehULB/PnzqVChAs8//zxubm78+uuv/Pjjj6xduxYfHx+uXLnC9OnTCQwMJCoqioEDB+Lr60tQUFCh+0RkZmaSkpLClStXCA0NBSAiIgLIXvtuyJAhbNmyhUGDBnH58mVOnTrFnDlzqFu3LhaLhXfffZekpCSsVisqlYoZM2agUqk4fPgwkydPply5cpw7d46WLVsycOBAZsyYwalTpxg6dCiNGzcmNTWVjz76iH79+nHq1Cm2bNnC66+/zvr166lQoQJLlixh6dKlvPnmm3z77beEh4fz+OOP88cffzB//ny+//57DAYDc+fOZerUqRw5coTx48fz0ksv5bvXq1evsnPnTh555BG748OHDyciIoIdO3bYqglnzJjBiBEj+Oijj2ztEhISeP311/Hy8iImJobw8HDbdebMmcOKFSuoXr06HnmWVfrhhx949913efPNNxkwYABbtmzh448/pkqVKkRHRzN8+HBatmzJli1bbN8jbm5u7N+/n8aNG7Nw4ULbWB07duTTTz8tViVrx44dad68OfPmzbtp26JIklAIcdvMmZmODiEfq8VCSmQk10+cyHf8+vHjJJ48iUKpxKduXYxJSbapuwA6b2+07u7EHzqUnRDT6XD29yfu4EGyMjLwqlYN/yZNiN61C0NSEn6NGqF2ciI9OpqUyEh86tQpcMfe25ERG0vq5cv41K5d+LTcEpKVkUHSmTOkXLiAQq3GrXx5vGrUQJfnf3Y+gCktDZVOZxePQqlEpdWi0GhQ6/Vo3NzQubvbpj4XtA6j2sUFtbMzGmdnVE5OshmJEEIIIYSwk5WWxvKmTe94nKd370bj6npLfSIjIxk6dCjHjx/n2LFjvP/++3h6emIymXjqqafYtGkTFSpU4PTp07Rp04bIyEg+/PBDpkyZwoIFC2zjvPHGG7z33nscOXKEzMxMNm7cyDPPPGPbGLao8T755BMmTJhA8+bNAQgNDWX27NmcPHmSSZMmcfToUVQqFV9++SVjxozhu+++Y9SoUbRv357XX3+dxMREKlSoUOD9eXh4MGHCBOrVq0eHDh3o0qULPXv2xNPTE4APP/yQSpUq0bdvX0JDQ1mzZg19+vTh6NGjLF68mKioKBYvXgxkT4dduHAhL774Ij169OD777+3JQL79+/PuHHjGDNmDBEREXbP5sCBA5w/f57Vq1ezY8cOXFxcCAoKslXi9e/f33YNgLVr16JQKKhatSpbtmyhb9++vP322/z++++cPXvWLnmX19GjRwkICMhX0efp6cmoUaOIiIhg/fr1REVFoVQqCQ4Otms3atQo2rVrR//+/TGbzYSGhvLII4+QlZXFBx98wIULF3BycuLdd9+19XnmmWdYu3at7bVOp2PmzJlUrVqVa9eu0bp1a44fP07r1q154403+OCDDzhy5Agajcb2fRAWFgZA+fLlOXz4cIF/j3eLJAmFELfNfI9UEhpTUki9dAlDYiIZ165lVwbmkZWZSczff2M2mQhu25as9HSu/fMPWnd3yrZvj9bNza69d82aGBIT0Xl6olAqsVqtWEwm2xTY4DZtiDt4kEvr1qEvU4bM2Fh03t5E/fUX/k2bYs7MxJSWhnvFirec4LNarSSfPUv8kSOoXVy4umMHgS1alHii0Go2Y0xJIeXCBZLPn8fJ3x+/Jk1w8vMr9NPWvLsO6zw9ca9UCbfy5Ys9NVil1+MSGIg6Z1qDEEIIIYQQBVG7uPD07t0lMs6tKl++PAsWLCAjI4Pnn3+ewYMHA3Dy5EmioqKYOXOmrW3ZsmWJi4vDz8+vwLGaNWuGq6srrq6uPPPMM3bnihqvXbt2JCYm8s8//1ChQgWcnZ1xcXFh/fr1mEwmxo4dC2Qvn2YymQDYtGkTr7/+OpCdBKtRo0ah9zhp0iQGDx7M6tWr+f777xkzZgyrV6+mTZs2APj7+9uqDMPDw/n333+5evUqa9euJS4ujtdeew3InrocHx/PyZMnuXLlCo0bNwbA1dWVn376qcjn3K5dOwBbJd/BgweLbJ+3beXKlSlXrhxKpZLQ0FCuXr1aYPu4uLhCp+eOHj2a2bNns2PHDlauXMmYMWM4cOCAXZu1a9ditVptx8PCwrh48SKnTp2iQYMGOOXMTmvRogVbt24t8Dply5ZlypQpqFQqVCoVp06dsjvfuHFj2ziVKlXi6tWrtiShm5sbSUlJtr/j0iBJQiHEbXN0ktBqtZJ05gxxR44UOn03MyGB6J07cSpThsCGDVGq1eg8PakYFITVai0wIaZQKtF7e//3WqGwS4SptFr8mzQhPSaGtCtXKNuhA2onJxL+/ZeoTZvQuLqiUKtJPnsWv8aNsVosGJOS0Hl5ofP2LvCaptRU0qKiSL18mayMDIJatULr6cnVHTuI2rwZrZsbVrMZ13LlcClbttjTJqxWK1itKJRKLFlZpEZGknTuHMakJBQqFc7+/gS3aYMu55PDvPes9fBA7+ODxs3NtluyzssL7+rVcfb3L9b1AdTOztn3fsM1hBBCCCGEKIhCobjlCsCS5uTkRNeuXZkwYQJLlizJLhywWJg5c6btd/G0tDTbun4FKWodw6LGUygUvPjiiyxcuJAqVarwwgsv2PqEhIQwa9Ys2zipN25YWAwHDhygfv36DBs2jGHDhjFx4kRmzpxpSxIW9l7DarXSrl073n77bSB7jUWj0ciZM2duOYYbn41CocBisdheF5QYy+2jUCjs/lzYWpAeHh5kFjL7LbeacPTo0bRu3Zrg4OB8SULInkLesmVLIDspqlQqOXnyZDHuMNtLL71Eu3btbAnc3ErSG+8JQKVS2T2D9PR0nJ2d0ZTikkySJMwxceJE2bhEiFtgycpy6Lp6lqwsru3dS8qlS4W2yYiNJXrHDjyrV8ezSpV8/7O7051znf397ZJlPrVq4VW1KkqNBqvVyvXjx4n66y9Uej1aDw8S/v0XlEpcAgJwDgjAkpWF4fr17N2IczYHca9UCZfgYFtSMrBFC5LOns25aQtxhw9z/eRJNC4uGJOSsJrNaNzd0Xl64hIYiM7HB2NiIqlRUWTGx2NMTMRiMtli0ri44BEaipO/P2onJ7tnkLuGoGu5crgGB9vWbQSgSpVCk6p2lEpUGg0qJyfUTk5oXF1lExIhhBBCCHFf6tOnD2+99RZnz56lWrVqlCtXjs2bN9OmTRsyMjJ4/PHH2bJlC5Cd7DGbzfz666+2pFJRbjbewIEDqVOnDu3atWP06NEAdOjQgYiICJKSkvDw8ODQoUP873//Y9GiRbRt25atW7fStGlTkpKSOHbsWKHX7t+/PwcPHkSZZ/PBcuXK2f4cExPDuXPnqFSpEps3b6ZGjRoEBgbSpUsXvvrqK1uScO7cuXh5efHcc88RFBTEP//8Q8OGDUlKSmL48OF8/fXX6PV6zGYzAAsXLmTQoEEFxhQQEGCrCMzIyOD48eM3fYY3ExYWRkxMTKHnR48ezbp162yVkTfq3Lkz69evt/199unTh08++YQ2bdowZcoUMjIycHJyYufOnYVeIz4+Hq+cjS8jIyNvKf7o6GhbRefN5G5ccqckSZhDNi4R4tY4sorQYrEQtWULmfHxhbbJiIvj6o4d2TvqltDuYWonJ1yCgkg+d67QT6ty1zdUKBR416iRnZxUqbI/4bJYyIyPJ/3qVa6fOIFSo0Hn5YVPnTo4lSlT4KYdSrUar6pVba89qlQh5cIFLFlZuFesiEKlwpicTGZCAld37sxO3CqVuAQG4la+PLo6dVDp9dl/X1YrWk/PfIk+lVaLe+XKuIeE5Jt6nVe+fjpd9rqDWi0KlQqlRiMJQSGEEEIIcV/avn0733zzDZGRkbzxxhtMnToVvV7PwIED6du3L+PGjWPVqlW8/fbb/PLLLyQlJTFjxgzb78h9+/alX79+pKenU7lyZb755huioqKYPn26bXpw7n/Hjx/PtGnTihwvODiYFi1a2CUcq1atyoIFC+jXrx+hoaEkJiYybdo0ILtCbeDAgRw+fBg/Pz/bOoaVKlWifPnydvfarFkzevbsSXBwMKmpqRiNRubMmWM7X7ZsWduzOHXqFN9++y0AAwYM4Ny5c/Tp0wd/f3+0Wi0jR45EoVCwatUqJk+eTPny5YmJibHda506dYDsijofHx9++eUXdu/ezeXLl/H29uaJJ54AoG3btsyZM4cBAwYQFhZG9erVmT17NmFhYbbNOCZNmkTnzp35/fffAXj88cdtf544cSJTpkyxu8+wsDAqVKjAsWPHqFGjBsePH2f27NmkpKQQGhpKy5Yt2bVrF5A93XnBggUkJCTYxpo1axbDhw9n2LBhWK1WunXrZtuZ+u2336ZTp07Url0btVrN6dOnWbBgAT4+Prb7a9CgAR999BHjx4/n4MGDtqnP77zzDs8//7zte+T3338nISGB06dPM3v2bGrVqoWvry/btm2jd+/exfr+LamNSxTWm+3R/YBLTk7Gw8PjjraIFuJhlBkfX+DuwaUh7sgRrhfxyZIpNZXLmzbhXbMmHpUr251T6/VYzGYsOeXrem9vNO7uKNVqnHx90Xp6kh4dTXp0NC5BQbgEBJARG4spPR3PKlVQaTSkX7tGzO7dZGVkAKBQqdB7e5MRG1ti9+gRGop7hQpc3bnTdp2bsVosGJOT0bi53XQzEKVGg87DA5eyZXGvWLHADUbyts19XuROQ/b2tq80FEIIIYQQQtz3Lly4QHh4OBcuXHB0KCVi165dzJo1i2+//daucvJeFxUVRZ8+ffj999+xWq20bt2aLVu2FLrGIpRMfkuShJIkFOKW5E6RNSQlYXFANWFmQgKXN27MV8lntVrJSksj/do1kk6dwjkwEN+6de3auIWEUKZePaxmMwnHjqH38cE9JOS24rBkZZF07hzpMTH41q2Lzt2dxNOniTt8GK2bG2pnZ9KvXi204rAgCoUCnbc3XtWq4Zqzs1ZWRgYx+/ZhtVhQqtVkxMRguYVp3gqFAoVKhSUrC6VajVtICB6VK9vtXHwjpU6H1s0te6qwXo8yp39WRgYqnU6qBYUQQgghhHgApaWlMWTIEH755RfGjx/PO++84+iQSsTevXtRKBQ0atTI0aEU2w8//ED79u3x9vYmLS1NkoSlJfchvvrqq7ImoRDFkH7tGpklWDF3K7LS07n811+Y0tL+O5aRQfyRI6RHR2MxmdD7+OASGIhHnjUIlRoN/o0a4ZpnnY27xWI226r4MuLiiP/3XzTOzug8PTGlpWFISCDz+nWsOetyqPR6nHx9cfL3xyUwEM1Ndv7NMhpJPnsWi8mUvfuyRkNWWhqmlBTSr13DlJyM1tMTl+BgnHx90Xl5ocpZj9BqtRb66ZlSo0Hr4YHWwwO1Xl+yD0UIIYQQQgghxG0pTpIwd03CefPmSZLwTkgloRDFZ7VaSTx50pbgKk1ZRiNRf/2FMSnJdizl4kXiDh7EOSAAj9BQtJ6e+abZaj08CGrZEk0Rn7iUNovFgjExEZVOV+Jx5VYMFpfKyQknHx807u53vJGLEEIIIYQQQoiSVZqVhLJxiRCi2HJ303WEuP37bQlCi9lM3IEDpF25gl/jxrgEBRXYx8nfn8DmzUtseqxSp0Pr7o4hPh5rnq3pb3kcpRK9t3fhDRQKnMqUQevuTurly5gzM4s/9k0ShLmbpaidnVHpdLeUUBRCCCGEEEII8eCSd4dCiGIxG41kXLvmkGunX7tGSp7t4uMPHcKQmEi5du1QFzI918nPj6CWLW+6gYeNUona2RmNszPmzEyyDAa07u5o3dwwGwxYLRZ0Xl4oFAr0Xl5kxMZiSEoCiwWNmxs6Ly8yYmJsuz4r1OrsnYZvgUKpROPmlr0pSM59uVesaNvFWaFSYUpNxZSaClYrCrUapUqF1WLJXqcwT2G4SqfLTgQ6OWVPN7ZYUKhU91RFpRBCCCGEEEKIe4ckCYUQN2UxmUi5ePG/HW5LUVZGBtf++cf2OjUqitRLlyhbVILQ15fAFi3yJQgVSiX6MmXAaiUzLg6VkxPOfn6o9HoURex0deMuvkqNBpegIJz8/TFnZtoSbxpXV0ypqaidnFCq1RiTksi8fh2VVovKyQlrVhZmg4GsjAwsRmN2YlKvR+3igsbFBbWzc74pvwqlEqcyZWyv9d7etoSgMs+OxFarFbPBgMVgyE4MyuYiQgghhBBCCCFugSQJc0ycOFE2LhGiABazOTtBaDSW6nWzMjK4sm0bhsRE2zFjcjKx+/ZRpmFDu4o4tV6PU0AAei8vnPz90RWw/oLG1RWXoCBbYk3v41NkYrA4lCoVyjxxKBQKtG5utte5G4EUxGI2o1Aqb2sdwIKmCCsUiuwNR2TTESGEEEIIIYR4qORuXHKnZOMS2bhEiAKZjUZMKSkYk5PJSk8v1WtbLBaubN5MRlyc7ZgxJYUrW7bgHhKCd61aQHaVnWeVKnhVr44qT1WdHaUSl8BAdJ6epRC5EEIIIYQQQghRcmTjEiGEw6VHR2NKSXHItRP+/dcuQWgxmbi6fTuu5cvjVbMmADpPT/waN0bv5VXoOEqtFtdy5bIr7IQQQgghhBBCPHSysrLYv38/TZo0ueW+MTExpKamUrly5bsQ2b1HkoRCiHxyqwgdcm2TiaQzZ+yOxR85gtrZGZ/atVEoFOg8PSnbtm2RO/Oq9HrcKlSQ3XuFEEIIIYS4TxyeN++ujFvn1VeL1W779u1ERERw+PBhevfujdls5vTp0wwaNIhnn322yL7h4eFEREQQHh5eZLtWrVoxd+5c6tatW9zwb1lUVBTDhw/n+vXrbN68ucA2a9asYenSpfj6+pKcnMz169eZOnUqderUKfF4ivts7gaTyUTv3r354IMP7I5//vnnVK9enUcffbTI/r6+vowbN46hQ4fSokWLuxnqPeHOFuR6gEycOJF169Y5OgwhHMqQmIghMZHMPFV8pS35/HnbBimWrCxSIiNJiYzEr2FDFAoFKp0ue1OSIpJ/amdn3EJCJEEohBBCCCGEKLaWLVvSt29fypcvz9y5c/nss89YsGABAwYMwGAwlMg1vvrqK2rlLJ90twQHBzNq1KhCzxsMBl566SWWLl3KZ599xrJly6hVqxanTp26q3E5wvTp02nQoAE1c2akGQwG5s6dyxdffIHFYrlpf5VKxbRp0+jfv3+x2jvKunXrmDhx4h2PI0nCHFOmTJFNS8RDzZKVRfrVq6RFRWG4ft0hMVgtFpJOnwYg6cwZzv/8MwlHj1Kmfn00rq6otFqCWra027QEQKnToffxwSU4GI+wMNwrVsy3s7EQQgghhBBC3KqYmBiMRiMZGRkA/PjjjwwcOJBx48bx/PPPc/XqVZYuXcqpU6eYMWMGQ4cOJSYmhtmzZxMQEMD48eN58skn8fLy4ocffqBTp058/fXXtvFvHO/SpUt07twZf39/li5dCsCQIUNo2LChLYlXUAwAc+bMoXXr1gwdOpS1a9cWek+ZmZmkpKRw5coV27GIiAg6d+7M/PnzCQoKYsyYMYwcOZI2bdowffp0u/6FXX/FihU888wzjB07lo4dO7Ju3bpiP5vp06cTFBRkq3zs06cPISEhALb248aNo0ePHoSFhbFq1SrefPNNmjVrRpcuXTCbzQXe69KlS2nfvr3ttU6nY/jw4dSuXduuXVxcHC+++CItW7akefPm9OjRgzM5M9wCAwNxd3cvtCrzXtCxY0emTJlyx+NImY0QDzlTWhoWkwlTWhpWB38ykhoVhSktjdSoKOKPHiXo0UdxKlMGyJ4+HPToo+jzbECiUKtx9vdH6+FxW7sECyGEEEIIIcSNIiMjGTp0KMePH+fYsWO8//77eHp6cvLkSSZNmsTRo0dRqVR8+eWXjBkzhu+++47FixczZswY25TaUaNGceDAAc6fP8/q1avZsWMHLi4uNG/e3HadgsYbP3483333HSEhIXTu3BmAevXq8cILL1ClSpVCY5g4cSIffPABFy5cwMnJiXfffbfQ+/Pw8GDChAnUq1ePDh060KVLF3r27ImnpyfDhg3jxx9/xNXVlffffx+DwUBoaCitWrWiSZMmhV4/IiKCkSNHcvbsWZycnNi2bRtbtmzh7bffLvazybs774cffpiv/bVr11i1ahUbNmygR48e7Nu3j48++ogWLVqwadMmu2QggNFo5OTJk5QvX77Iv++srCwGDx7MggULcHJyonfv3qxatcquTfny5Tl8+DBt27Ytcqz7nSQJhXjIZVy7Vuq7Fxcm8eRJTGlpXNuzB/+mTW0JQoVCQWDz5nYJQp23N87+/iiUUhAthBBCCCGEKDnly5dnwYIFZGRk8PzzzzN48GAA1q9fj8lkYuzYsUD2brKmnKWSCtOuXTuAAtezK2w8Dw8PunfvzldffcXYsWPZvn07w4YNK7LPX3/9RYMGDXBycrJdb+vWrYXGNWnSJAYPHszq1av5/vvvGTNmDKtXr6ZNmzZ28ep0Opo2bcrGjRtp0qRJoddfv349derUsV2/VatWtGrV6rafTUFy21WuXBlXV1eqVq0KQGhoqK2aMa/4+HisVmuROwIDtgpPf39/rFYrqamp+dq4ubkRGxtbrDjvZ5IkzDFx4kS6desmU47FQyUrI+OeSRBmxMaSmZBA4smTuAQF4RIUZDvnVbOmLWEIoC9TBmc/P0eEKYQQQgghhHhIODk50bVrVyZMmMCSJUuwWq2EhIQwa9YsW5uCEkp56XS6Qs8VNd6gQYMYPHgwbdu2pX79+jfts2jRolu6twMHDlC/fn2GDRvGsGHDmDhxIjNnzrQlCfPO1LJarTe9/uLFi295dteNz0ahUNjW/Sso+ZrbXqFQ2PXN2y8vDw8PIHt6de6fC7J792769esHwJEjR+yed6709HQ88xSt3GvWrVtnV4l5u6QEJ4esSSgeJhmxsaRevkzGPfRJyPVTp8jKyCDlwgU8q1VDoVDgERpKQPPmeFevbmun9/WVBKEQQgghhBCiVPTp04c//viDs2fP0qFDBw4cOEBSUhIAhw4dsm0QotfrMZvNHD58mD179hRr7KLGa9myJQqFghEjRtC/f/+b9mnTpg379++3rZ24c+fOIq9d0EYc5cqVs/05t7/BYGDPnj089thjRV6/Q4cOHD58mMzMTAA2b95sW8uwuM8mICDAVhH4zz//FBl/cTg7OxMUFER0dHSR7apUqWJLzs6fP58333wzX5vo6GhCQ0PvOKa7RdYkFELcNkNiIhaj0dFhANnVjPFHj5J+5QqJJ0/iHBiI3tMTv0aNcK9Y8b+GSiUugYHo7uFPb4QQQgghhBD3r+3bt/PNN98QGRnJG2+8wdSpU9Hr9QwcOJC+ffsybtw4FixYQL9+/QgNDSUxMZFp06YB0Lt3bz755BPUajUzZszgl19+Yffu3Vy+fBlvb2+eeOIJli9fbjvWoEED6tSpU+h4kF1NuGPHDvzyFElUrVq1wD4+Pj68/fbbdOrUidq1a6NWqzl9+jQLFixg6NCh+e61WbNm9OzZk+DgYFJTUzEajcyZM8d2Pj09ndGjR3Pw4EFee+01mjRpctPrz549mwEDBhAcHEx8fLyt2rA4zwbgtddeY8yYMezdu5fKlSuTkJDA1KlTqVGjhq19ixYt+OCDD0hISGD27NmEhYXZzjVt2pTqeQpMcq+9Y8cO6tatC2RXQn722Wfs2bMHi8WCxWLhlVde4dtvv+Xbb7/lzTffJCjPrDaAtLQ0zp49S6dOnW73W+u+obDmrRu9R82fP58vv/wSJycnPD09+fzzz+0y3IV55pln2L17NxcuXCi0TXJyMh4eHiQlJeHu7l6CUQtxbzKmpJAaGenoMABIPHuW+EOHsGRlYTaZuLhmDUGtW1M2PByvatX+a6hQ4FquHFo3N8cFK4QQQgghhBAPgfDwcCIiImwbh9zPEhIS6NWrFytWrMDb2/u2xnjzzTepXr26bUpyaUtLS6N169Zs2bKlyPUVSyK/dc9PN165ciWTJ09m/fr17Ny5k2bNmtG9e/cC55vntWnTJv78889SilKIe58lK4vM69fJuHbNoXFkJiYSs3cvF9etI/aff7BkZQGQcuECWg8P3ENC8KxSxa6Pc0CAJAiFEEIIIYQQ4i6bP38+p06dYsaMGUTeI8Uld8Lb25tly5YVuYlLUaKiomjevLnDEoSl7Z6vJHzkkUdo0aIFn3zyCQCJiYn4+Pjwxx9/5NveOpfJZKJFixY8+eSTfPHFF8WqJHz11Vdl4xLxwMrKyCD10iUsN9l563YZU1IwJiWh1GpxKlOGrPR0ks+fR+3sjNbNDbWLC5asLDJiYog7cgRrTmIwl9Vq5dK6dXjVqEGVPn1wDQxEoVbj7O+P2tkZlVZ7V+IWQgghhBBCCCHuZcWpJMzduGTevHl3VEl4T69JaDQa2bt3L6+88ortmKenJxUqVGDHjh2FJglnzpzJ008/TZk8u6HezJQpU2S6sXggWK1WTCkpoFCQlZGBKSUFc87isSV+LYuFuEOHSDpzxrbjlcbVlaz0dKw3qfbNK+3KFSwmE74NGuAaGIhSp8OtfHlJDgohhBBCCCGEEDfRsWNHmjdvzrx58+5onHs6SRgXF0dWVla+eePe3t62HW9uFBUVxfLly9m5cyfLli0rjTCFuKekXbmCMTGxVK6Vfu0aiadP2x0z5ewKVRxmg4H4I0dIvXQJ33r18KtbF4VSKQlCIYQQQgghhBCilN3TScJcCoWiWMcAxowZw0cffYRGo7nbYQlxz7BaLBiuX8dw/Tpmg6HUrpty8eJt9bNkZZEeE0PcgQPovLwo16EDHqGh6H18cAoIkAShEEIIIYQQQghRyu7pJGGZMmVQq9UkJCTYHU9ISCAgICBf+02bNmG1WmnXrt0tX2vixIlocxITHTt2lLUJxX0hKzMTY3IyhuvX863zd7eZTSbSoqIKP28woNRoUCj/2x/JmJxM/OHDpEdHo9Lr8a5ZE7eQEJRqNT41a6JxdUXv5VUa4QshhBBCCCGEEPe1devWsW7dOiB7yb47dU8nCTUaDY0bN+bw4cO2Y0lJSVy8eJEWLVrka7927VouXLhg26Y7Ojqa6OhowsPDadiwIdOnTy/0WrImobgf5K43aExOJis9/a5tRFIcaVFRWLKyMKWmknT2rG0dRCc/P9Kjo8mIiQFAqdOh1umwmExkGQx4VKpEhc6dUTs7o1AocK9UCe/q1VG7uuIcGOiw+xFCCCGEEEIIIe4neYvckpOTH+w1CQHGjRvH8OHDmTBhAj4+PsyZM4c6derw2GOPATBnzhx27tzJd999Z9sBOdeSJUuIiIhg8+bNDohciJuzmEwoVCq7arsbWS0WTGlp2cnBlJRSrxi8UeqVK1w/dgxDcjLJ584Rd/AgLsHBOAcGZk8jvnoVnacnfo0bg9VKVkZGdlWhWo3G1RW1kxNKjQa9jw8+tWqhz1lz1MnXV6YZCyGEEEIIIYQQDnLPJwmfeuopoqOjad++PXq9Hk9PT3755ReUOUmVyMhITpw4ka9f165dOXPmjK2ScPjw4fTq1au0wxeiSIbERKwWC87+/nbHrVYrhsREjElJZKWnQ87OwY6WERtL9N9/YzWbsZrNJPz7L35Nm+IaHGxr41W1KgBqJyesZjMaZ2esVisKpRKdlxde1avjEhhoW1dUpdej8/ZG5+npiFsSQgghhBBCCCEEoLBa75Hsg4MkJyfj4eFBUlKSTDcWpS7pzBnMJhNu5cujcXHBmJSEKS3tnqgYzCsrM5Prx4+TdO4cVrMZgOQLF0g8eZJKPXviFRaG1sMDrFYy4+PR+/ri7OcHZCc8LVlZKNVqW2JQoVSicnJC7+2NVv7dCSGEEEIIIYQQBUpLS6N169Zs2bIFFxeXQtuVRH7rnq8kLC0TJ06kW7dusmGJuKvMBgOGxESc/f3Jysy07USccuECCrX63koMGo0YExPJiIsj8eRJu/UPzQYDSadP41uvHiGPP26323huVaRSo8muILRYUCiVKFQq1Ho9eh8fNK6upX4/QgghhBBCCCHEg2jdunX8+uuvdzyOVBJKJaEoRSmRkZhSUtD7+mJMSnLoxiNFycrI4PLmzdmbkeRhNhqJ/vtvMmNj0Xp60vjdd/GqWhWdp6dtPUGz0YhKr0et1zsidCGEEEIIIYQQwiYrK4v9+/fTpEmTW+4bExNDamoqlStXvguRFY9UEgrxAMrdfAQgMy7OwdHkZzaZiP3nH8xGo12suTITEoj95x/Uzs6EPPEErmXL4lOjBq7lytm1Uzs7l2bYQgghhBBCiAdEwr//3pVxvWvWLFa77du3ExERweHDh+nduzdms5nTp08zaNAgnn322SL7hoeHExERQXh4eJHtWrVqxdy5c6lbt25xw79lUVFRDB8+nOvXrxe6keuaNWtYunQpvr6+JCcnc/36daZOnUqdOnVKPJ7iPpu7wWQy0bt3bz744APbsfnz56PRaNiyZQuffvopAQEBhfb39fVl3LhxDB06lBYtWpRGyA4lSUIhSkn61auODqFA10+dwhAfjyEpCWNysu241WLBbDCQGRdH0pkzGJKS8KhUiQqPP45HWBh6Hx+cbthwRQghhBBCCCHuVy1btqRv377MnTuXuXPnAnDmzBlq1apFjx490Ol0d3yNr776ivLly9/xOEUJDg5m1KhRREREFHjeYDDw0ksvcf78eZxzijzGjx/PqVOn7kqS0JGmT59OgwYNqJmTKP79999p2bIlderUwdfXl8GDBxc5TVelUjFt2jRatWrFqVOnbJvoPqge7Lu7BRMnTmTdunWODkM8QCwmE+nR0SRfvEjy+fO29QfvBZkJCaReucL1kyeJO3iQlEuX7BKEGbGxXPztNy7+9hsJ//6LS3AwlZ96imr9+hHYqhXulSrhVr68bYqxEEIIIYQQQjyIYmJiMBqNZGRkAPDjjz8ycOBAxo0bx/PPP8/Vq1dZunQpp06dYsaMGQwdOpSYmBhmz55NQEAA48eP58knn8TLy4sffviBTp068fXXX9vGv3G8S5cu0blzZ/z9/Vm6dCkAQ4YMoWHDhpw6darQGADmzJlD69atGTp0KGvXri30njIzM0lJSeHKlSu2YxEREXTu3Jn58+cTFBTEmDFjGDlyJG3atGH69Ol2/Qu7/ooVK3jmmWcYO3YsHTt2ZN26dcV+NtOnTycoKMhW+dinTx9CQkIAbO3HjRtHjx49CAsLY9WqVbz55ps0a9aMLl26YM7ZYPNGS5cupX379rbXp0+f5vPPPwegSpUqXLx4EYC4uDhefPFFWrZsSfPmzenRowdnzpwBIDAwEHd390KrMu8F69atY+LEiXc8jlQS5pgyZYqsSShKhCktDWNSEsakJKwWi6PDySfz+nWitmyxrYeYuyxpenQ08YcO2SoIfevWxat6dXReXnhUqoRLUBAaV1dcgoMlOSiEEEIIIYR4YEVGRjJ06FCOHz/OsWPHeP/99/H09OTkyZNMmjSJo0ePolKp+PLLLxkzZgzfffcdixcvZsyYMbYptaNGjeLAgQOcP3+e1atXs2PHDlxcXGjevLntOgWNN378eL777jtCQkLo3LkzAPXq1eOFF16gSpUqhcYwceJEPvjgAy5cuICTkxPvvvtuoffn4eHBhAkTqFevHh06dKBLly707NkTT09Phg0bxo8//oirqyvvv/8+BoOB0NBQWrVqRZMmTQq9fkREBCNHjuTs2bM4OTmxbds2tmzZwttvv13sZ5O3ou/DDz/M1/7atWusWrWKDRs20KNHD/bt28dHH31EixYt2LRpk10yEMBoNHLy5Em7ys1hw4aRmpoKwM6dO+nQoQNZWVkMHjyYBQsW4OTkRO/evVm1apXdWOXLl+fw4cO0bdv21r6ZSknHjh1p3rw58+bNu6NxJEkoRAkx5OwEbLkHKgatVisKhQKLxYLFYMCUmkpKZCSZ8fEYU1Kw5nzKkpmQQPTOnZgzM1FqNPjUro3W3R29ry/lO3VC5+6OQqVC6+GBztMTtZOTg+9MCCGEEEIIIe6u8uXLs2DBAjIyMnj++ecZPHgwAOvXr8dkMjF27Fgge6MI0002o2zXrh1AgevZFTaeh4cH3bt356uvvmLs2LFs376dYcOGFdnnr7/+okGDBjjlvGdr0aIFW7duLTSuSZMmMXjwYFavXs3333/PmDFjWL16NW3atLGLV6fT0bRpUzZu3EiTJk0Kvf769eupU6eO7fqtWrWiVatWt/1sCpLbrnLlyri6ulK1alUAQkNDbdWMecXHx2O1Wu02+9BoNHh5eZGcnMxvv/3G119/bavw9Pf3x2q12pKIebm5uREbG1usOO9nkiQUogSY0tJIu3IFHLRZeFZmJpkJCWSlpZEaFUVmbCwoFAVWMhqTk4n++28sJhMWkwnfunXxrFoVva8v/o0aodLpUKjVuAYHo3Z2RqmWHxNCCCGEEEKIh4+TkxNdu3ZlwoQJLFmyBKvVSkhICLNmzbK1KSihlFdR6xgWNd6gQYMYPHgwbdu2pX79+jfts2jRolu6twMHDlC/fn2GDRvGsGHDmDhxIjNnzrQlCRUKhd01b3b9xYsX2/UpjhufTW6hC1Bg8jW3vUKhsOubt19eHh4eQPb06tw/A1gsFqZOncrChQtxc3Nj9+7d9OvXD4AjR47YPe9c6enpeHp63tL93Y9kTcIcsiahuFVWqxVDYiKpUVGkREbeVoIw/do1MorY6dhsMmFMSSH92jWSzp0j4dgx4g4dImbfPqL//pvLmzZx/pdfOP/LL1zdvp3YAwdIj4nBbDJhMZuxmM1kZWSQGR9Pwr//Er1zJ1F//YV7hQqUb9+e0Geeod64cYT17k2Fxx/Hu2ZNPKtWxbNKFbTu7pIgFEIIIYQQQjzU+vTpwx9//MHZs2fp0KEDBw4cICkpCYBDhw4xatQoAPR6PWazmcOHD7Nnz55ijV3UeC1btkShUDBixAj69+9/0z5t2rRh//79trUTd+7cWeS1+/fvny+xVq5cOdufc/sbDAb27NnDY489VuT1O3TowOHDh8nMzARg8+bNtrUMi/tsAgICbBWB//zzT5HxF4ezszNBQUFER0fbHV+4cCEjR47E19eXn376iSpVqtiSs/Pnz+fNN9/MN1Z0dDShoaF3HNPdImsSljBZk1Dciqz0dNKuXLnlzUgMyclkxsXhUakSWQYDV7Ztw2Kx4FurFnpvb4zJyWSlp2NMTcWQkEBWzg/4vKwWi60K0GIyYTYas/skJ2POzCQzLi5fP6VWi7O/Py5BQQS2akXlp55CpdOh1GpxDQpCqdHc0fMQQgghhBBCiDvlnbMDraNs376db775hsjISN544w2mTp2KXq9n4MCB9O3bl3HjxrFgwQL69etHaGgoiYmJTJs2DYDevXvzySefoFarmTFjBr/88gu7d+/m8uXLeHt788QTT7B8+XLbsQYNGlCnTp1Cx4PsasIdO3bg5+dnO1a1atUC+/j4+PD222/TqVMnateujVqt5vTp0yxYsIChQ4fmu9dmzZrRs2dPgoODSU1NxWg0MmfOHNv59PR0Ro8ezcGDB3nttddo0qTJTa8/e/ZsBgwYQHBwMPHx8bZqw+I8G4DXXnuNMWPGsHfvXipXrkxCQgJTp06lRo0atvYtWrTggw8+ICEhgdmzZxMWFmY717RpU6pXr253n71792bHjh3UrVsXgFWrVtnWcATo0qUL//d//8e3337Lt99+y5tvvklQUJDdGGlpaZw9e5ZOnTrd1vdVaSipNQkVVquD5kfeI5KTk/Hw8CApKUmShOKmcncszrsTcHGlRUdzeeNGjCkpeFSqREZsLJc2bEChVFKmfn20Hh6YUlMxZ2aSlZGBKTUVU1oaFqMxuzLQaMRiMtnWE0ShQKnRoNJqUTk5ofP0ROPqinvFirhXqoQ5MxOVkxNaNze0rq7o/fzQODujcXHBOSBAqgSFEEIIIYQQQuQTHh5ORESEbeOQ+1lCQgK9evVixYoVeHt739YYb775JtWrV7dNSS5taWlptG7dmi1bttitr3ijkshvSZZAiGKwWq1kxsVlTw0uxo7FWenpZMbHkxEXh9lgwGqxcHXnTuIOHMBqsRB/6BCmtDRcy5VD4+JiWyNQpdOhcnJCrdejdXfHPSQEtZsbWhcXNG5u2V9OTtlrBep0qHU6VHo9WldXNK6uKDQa1Ho9Kp3OtnmJQqVCqdFk91GpSuFpCSGEEEIIIYS4H82fP59Tp04xY8YMKlWqZLcz8P3I29ubZcuWsXXrVp588slb7h8VFUXz5s1t1Y4POqkklEpCUQSrxYIhMRFDQkKRU4tN6enEHz1KZmws6TExGJOTMSYlYUxOxmo2k5WZSVZaGuU6dMC7enWubN+OU5kyVHnuOZRaLUnnzmFMTETv64tar0ft5ITW3R2FWo1Spcr+r1qNQqlEoVSCUvlfAlCtRqnRSGWgEEIIIYQQQgjxgJFKQgeYOHEi3bp1o2PHjo4ORdwDTKmppF+7hsVo/G96b47cxGHSuXMknTmDISGBtKtXST5/HovRaKva07q74+zvj0qvR6lWU65DB/wbN0bl5ERQeDgqjQbngAAUSiWuZctiMZmyk305CT+FUvYVEkIIIYQQQgghRNHWrVvHr7/+esfjSCWhVBIKwGw0YkxKyt4IxGDI3vgjzz8NU2oq18+cIeHQIRJPnyb92jWMiYnZU35z1gMMbNECj7AwnHx8spOEHh5onJ1RqNXZW7R7e6PW6x14l0IIIYQQQgghhLifSCWhEHeJ1WLBkJREVlqabd0+i8mUvRFJzlqDFouFxFOniDt0CGNSEsnnzpFx7Rqm1FS0Hh44BwYS2KIFPnXqZK8Z6OSEzsMDnZdX9o7BOdWAUgkohBBCCCGEEEKI+4UkCcV9z5KVRVZGBpasLBQKBVarFXNmJgql0nbOajaDxYLFYiEjJobkyEh0np5YTCYM8fHEHjhAenQ0WRkZZGVkYEhMROPigkqvx61cOfwaNcK3bl1cy5bN3jzExQWNq2v2ZiAyNVgIIYQQQgghhBD3OUkS5pA1Ce99VosFc2YmFrOZrPR0stLTs5N8iYlkJiTYdh42pqSQHhODKTUVY2IihuvXMaWl2fqYDQZQKGzTiZUaDU7+/rgEBeHq5obOwwOv6tXxrlEjOwGoUKBQq9F5eaHNSQwKIYQQQgghhBBC3AtkTcISImsS3pusVisWoxFDUhIWo5GszExSLl0i5exZMhMTST53jrSoqOwdhFNTMWdk2PoqtVrUTk6onZzQuLqi9fBA6+6O1sMDnYcHruXL4xIUhDk9HaVen10VWEDiT6XToXZ2lrUEhRBCCCGEEKKULFu2jK+//pqoqCiCg4N54YUX6NOnT6n1Fw+P+Ph44uLiqFq1qkPjOHv2LK6urvj7+xd4vjTXJJQkoSQJHcpiNmM1m7M3C0lPx5SWRlpUFMkXL5IaGUncoUPZlYApKRhTUlCoVKj1ejRubrgEBeHk44NzYCAuwcE4+fujVKlQ6fVoXV1vbwqwQoFKp8PJ1xeth0fJ37AQQgghhBBCiCIVNylS0v3XrFnD0qVL8fX1JTk5mevXrzN16lTq1KnD4cOHeeWVV9ixY8ctx1OY5cuX8+677zJhwgQGDBhQZNuIiAi7/xbX+PHj+eSTTygo9TN//nzmzJmD1Wrl2LFjKPO8h05PT6dixYpUqFCB119/nVq1ajFx4kT++OMPhg0bxvTp05k4cSJz587l0UcfJSQkhKSkJNLS0li8eDEeHh4sXryYadOmodfrmThxIk8//bTd9adPn86MGTMIDQ2levXqxMfH4+TkREREBJUqVbrpvQ0YMIDw8PCbPruCREZG8uqrr7Js2TKioqJs9/bSSy9hsVg4f/48nTp1YtSoUbc07sWLFxk5ciRRUVHs27fP7lyVKlVITk62vZ42bRr9+vUjLS2N5557junTpxMWFpZvTNm4RDxwrBYLFpPJtnuwMSWFtJgYrm7ZQvq1a2Rcu0balSsYExPJysjITga6uOASFIR7aChOvr54hoXhEhxsqxBUltA6gAqVCr2vL1p3d1RabYmMKYQQQgghhBDi1kVFRTF58mSOHz/O2LFjeeeddwgODr7r/Q0GAy+99BLnz5/HOWem2fjx4zl16hR16tShZs2afPPNN7d9XwV5+umn+e2330p0zBtNmzaNTz75pMBzw4YNY8+ePWzfvp3vvvvOruJy/vz5uLm50blzZ1tyb9SoUWzbto3p06cDMGXKFL799luGDh1K165dAejbty+zZs1i0qRJDBw4kC1btuDr65svQQgwduxYfv31V3r16sXw4cOB7ERt06ZN2bVrF5UrVy7RZ5HXkCFDeOutt3B3d8fd3d12b3PnzgWyk6Te3t50796dkJCQYo1psViYOXMm4eHhLFu2LN/5Rx55hCVLluQ77uLiwjvvvMOLL77Itm3b7uS27pgkCcUdsVqtWC0WrFlZ2QnAnEQgFguWrCzSY2JIOnOGxDNnSLtyhYyYGDLj4zEkJmIxGlE7O2dPBXZ3x6t6dVyDg3ErXx7ngIDsDUJKcv0/pRKVRoPOywuNqytWsxmFWi2JQSGEEEIIIYRwoGXLltGnTx+ioqKoX78+CQkJmM1mFi5cyMqVKzl48CBBQUF2bW/sC9y0/41988rMzCQlJYUrV64QGhoK2FftjR07ltmzZ2O1WtmyZQsvv/wyNWvWxN3dnd27d/PUU09RrVo1lixZQlxcHKtXryYkJIRp06YxefJkjhw5gpubGwMHDiQ5OZnNmzfni2HLli18/PHHVKlShejoaIYPH07Lli35+++/WbNmDQDR0dH06dOHFi1a8O6775KUlITVakWlUjFjxgxUKhVbt25l4sSJhIaG0rBhw5s+/7feeovJkyfz3HPPoVQqSU9P58SJE9SrV684f302VquV2NhYypQpc0v98uratSvt2rXjww8/5P/+7//48ccf+frrrwkLCyMqKopJkyZRo0YN1qxZw99//83FixfZtWsXI0eOJDY2tsDnd6OrV6+yc+dOHnnkkULjiI+Px2QykZSUVOzYlUols2bNKjARCNnfn2PGjMFisRAUFMTo0aPRaDQANGrUiGPHjnH27Nm7mhy9GUkS5nhYNi6xWiz//dlsxmI2Z7+wWLKTfblfueesVqxms+1Y3jYWk4mM+HhbFWD61aukRUXZNg3J3SREoVajcXVF5+GB3seHMg0b4pazLqBLcPDtTw0ugtrZGYVSidrZGY2LC0qtFqVavt2FEEIIIYQQ4l7z9ddf8+STTzJ58mRbgg/AbDaTkJDA5MmT+fTTT21t8yb6cvsCN+1/Y9+8PDw8mDBhAvXq1aNDhw506dKFnj174unpCcCsWbOYPXs2AK1bt+aNN95gypQpHD58GJPJREBAAAsWLGDDhg289dZbfPnll3z44YeMHz+e+fPnA+Dj48OYMWMKnTKs0+mYOXMmVatW5dq1a7Ru3Zrjx4/TvHlzW6Vebt9FixYRFRXF4sWLAejTpw8LFy5k4MCBPPfcc6xevZrGjRuzadOmmz7/F154gQ8//NBWTTh//nyGDh3K1KlTb9oXsqsOf/zxRzZv3oyfnx8jRowoVr/CNG7cmEWLFgHg6urKokWL8PPzY8+ePYwdO5a1a9fStWtXVqxYYTfdeNeuXQU+vxsdPXqUgIAAVCqV3fG0tDSGDh3KhQsX+OeffxgyZAh169YFsP19FmTEiBGMHTv2pvfVu3dvBg4ciFqtZuTIkbz22mvMmzcPAIVCQbly5Thy5MhtJQlLauMSyZrkmDJlSomvSZhbZUeexJvVas1OvOWuB1DUkpA553Lb5k3gYbVmJ/Fyj+VW9OU5j0KBQqGwnSdPgrAoZpMJc2YmWenpGK5fJyM+nszYWDLi4jAkJGBITMzeMCQlBXNmJigUqJ2dUTs7o/fywi0kBL23Nzpvb1zLlsU5IACdh8ddq9hT5axRqHZyQqFUotLpJCEohBBCCCGEEPeJqKgoW0InN8GXy2w289VXX7F3714ge1pwQX2Bm/a/se+NJk2axODBg1m9ejXff/89Y8aMYfXq1bRp06bA9g0bNkSv16PX6/H19aVJkyYAhIaG3ta00bJlyzJlyhRUKhUqlYpTp04V2nbt2rXExcXx2muvAdnPJT4+nhMnTpCQkEDjxo0BaNGixU2vq1arbdWETzzxBCdOnGDcuHHFjnvYsGF07dqVr776ivj4+GKtJ1gUq9WKQqEAoGLFikycOBFnZ2dSU1OLfCbFfX5xcXEFru3n4uLCggULsFgs9O/f3y7ZOXjwYAYPHnxH95W3/4ABA2jTpo0tSQjg5uZGbGzsbY3dsWNHmjdvbjfe7ZBMSjFZrVYsJlN2os9sxpKVlZ2Yy8qyVd1Zcv5sNZuzjxczKVcSzCYT1qys7Om+BgNWiwWFUonVYsFsNGbHljsdOCsLU0oKmQkJGK5fz14jMDk5e4OQtDTMGRlkZWRkJwDJs1uwszNqJye07u64VaiAk58frsHBaL280Hl4oPXwQK3T3fV7VajV2VOU3dxQ6fWSEBRCCCGEEEKI+1hwcDA//fQTY8eOZeHChXaJPpVKRb9+/WyVhD179iywL3DT/jf2vdGBAweoX78+w4YNY9iwYUycOJGZM2cWmiTU5Xn/q1AobK8VCgWWPPmAvK9NJlOh13/ppZdo164dr7/+OoCtcrEgVquVdu3a8fbbbwPZyVCj0cipU6dsCbZbkVtN2KVLF2bMmHHL/SG7mrFq1ap069bNNmX7duzbt4+mTZsC8OSTT/LBBx/w9NNPc+HCBcLDwwvtV9zn5+HhQWZOvqMgSqWSAQMGMGzYMNu08DutJExMTCQjI4PAwEAAtFotBoMBi8Vi228hPT3dVrnqKJJdKUDuxhpmgwEsFrIyMrBkZRVd9ZfDlpTLWZvPkpWVnaDLSeBZ81b/5Vb42Tpb/6s2zHltMZuzqwFzEpK5VX62HYFzknlmg+G/6xqN2W0gu0/OObPBYGuL1YpSq7Ul2dR6PRp3d9zKlUPr7o7O2xsnHx+03t5oc9YGVDk5oXF2LvWknEKpRJW7dqGbmyQFhRBCCCGEEOIB8sILL9g2b1i5cqVtyrBKpcLb25t33nnHVvn1wgsvFNgXuGn/G/veqH///hw8eNBuk8xy5crd8f0FBARw9epVKlWqxD///FNou/j4eLy8vIDs3Xfz0uv1pKWlkZqaypo1a+jSpQtfffWVLUk4d+5cvLy8eOaZZ/Dy8mLv3r00btyYnTt3FitGtVrNBx98wMaNG2nUqNFt3adKpeLll1/mww8/tE2DvlVr1qxhw4YN7N69G7j5MzGbzVy6dIljx44V2TavsLAwYmJiiozjscceY9SoUWzZsoXWrVvfcSXhwYMHWbNmjS3ZvX79etq0aWP3vRYdHX1HydWSINmWHG+++Sad2rTh0QYNMGdkANk702C1YkxOJiunus5iMGA2GOwSfxaDITt5ZzBkVxvmJPQseaoMrXm/8qz5Z73hy5J37T+zGYvRiCInKWbbHCQn4WcxGrODVypRabXZSb+c/+ZW9Kl0OrQeHmhcXFC7uKDLqfrTeXnZkm4KtTq7UlCvd8izz0ehQKXTZU9f9vGRjUWEEEIIIYQQ4gGWu05gcHAwBw8eZPLkyXz11Vf069ePd955x7ZpSd62Bb2+Wf/C1iPM1axZM3r27ElwcDCpqakYjUbmzJkDYKsUGz9+PAMHDuSbb74hKiqK33//ndOnT5OQkMCkSZN4++23bed++eUXnnjiCSZOnMibb75JkyZN8PDw4PTp03z55Zd4enqye/duLl++TIMGDfjoo48YP348Bw8etEt8Tp48mY4dOzJ8+HBOnTpFr169GDBgAOfOnaNPnz74+/uj1WoZOXIkCoWC7777jtGjR1OjRg1b0mns2LG2XYlzLV68mN27dzN8+HBmzZrFs88+y7PPPgvAggULOHjwIJGRkTRo0ICwsDBmz55NWlqabayJEyeSkJDAggULcHd359FHH2XQoEGEhITg7OxMrVq12L17N3q9nuXLl+fb4Xj69OmcPn2a5cuXc/ToUeLj43FycmL37t22KcuzZs1i/PjxtG7dmszMTBISEpg5cyajR4/mySefZOrUqaxZs4axY8cW+fzyCgsLo0KFChw7dowaNWpw/Phx270NHz6cGTNmoNVqGTFiBK+++iqjRo0qdoJw+vTprF27lsjISIYPH8748eMpX748FStW5NSpU7z88stoNBqio6NZuHChrd+5c+dwcXGhfv36xbrOjUpqTUKF1VqM8rgHWHJyMh4eHlzcvx8XtZrM+HhSczffSEkhKz3dliA0Z2ZmJwVzpu/aqgUL+LJRKlEqlShUKvsvpRLljcdUKpQqFcqcpJhCpULj6oolMxOrQoFKq0Xj7Jxd3efqiiYnyZeb4FNqtWhcXGzVgbdTYuwoSq0Wrbu7baORkt7IRAghhBBCCCHE/SEtLY3WrVuzZcuWAteOu9v9xYNv165dzJo1i2+//daums8RrFYr/fr148UXXyxwantxv59z81tJSUm3veeGVBLmuH78OFeOHyfl4kXSY2LIjI/PXpMvZ1qu2skJlV6PSqOx7ZSrdnLKrnjLs16f2sUFjasrmpyNPJQaTXbCTqVCqdGg0mqztyfPGSd3cxGFQoFCpUKl199Xyb3bpdRqUel02RuaODlJtaAQQgghhBBCCCFKRbNmzRg7diz79++/7enVJeXo0aMMHTq0WJvM3G2SJMzx7+efQ3w8eh8fXMuWJbB5c5z8/NB6eKDz9ESl09m+FGp1duJPqXwoEnp3TKFApdej1uttuyBLUlAIIYQQQgghxI2WLVvG119/jcFgoGfPnrzwwgs3nSZckv3FwyN3B2hHq127tqNDsJHpxjnlmF1DQ+nesyfdnnoKjZQj37bcTUZUGk12UlWvz57+rFI5OjQhhBBCCCGEEEKI+0pxphvnrkk4b968O5puLEnCnCTh+V27cHd1dXQ49x2VXo9So8leSzBnB2SprhRCCCGEEEIIIYS4c7ImobgnKTWa7GnDTk7Z6zE6OUmFoBBCCCGEEEIIIcQD4L5IEs6fP58vv/wSJycnPD09+fzzzylXrlyJtRc5lEqUarVtgxXbf3U6VFqt7DgshBBCCCGEEEII8YC655OEK1euZPLkyRw5cgRfX1/ef/99unfvzr59+wrcpvpW2z8QFIrsTVRUKtvXf6eyd01W5GkD2P6s1GhQKJW2XZiFEEIIIYQQQgghxMPnns+affrpp/Tt2xdfX18ARo4cyaFDh9i4cWOJtM/1waxZbNqxo2SDz5WTqFNqtahdXFC7uKBxc0Pn5ZX95e2N3scHva8vzgEBuJYti0twMK7lyuFeqRJuISG4VaiAW0gIHqGheFarhleNGrYv7xo18KpWDc+wMDwqVcK9QgXbl1v58rgGB+MSFIRzQABOZcrgVKYMeh8fdJ6eaFxcUDs5oVSrWbdu3d25f3FT8uwdS56/48izdxx59o4jz95x5Nk7jjx7x5Fn7zjy7B1Lnr/jyLMvfevWrWPixIl3PM49nSQ0Go3s3buXOnXq2I55enpSoUIFdhSQ0LvV9nlNjoigS48eOAcG2hJ0ruXL25Jz7hUr4l65cr4vj7Aw25dnlSrZX1WrZn/lJPPyJvHcQ0JwDwnBrXx5XIKCsr8CA3EOCMDZ3x+9jw9aDw90np5o3d1ROzmhcXFB4+qKxsUFlU6HMrcyMOerpMg/ZMeRZ+9Y8vwdR56948izdxx59o4jz95x5Nk7jjx7x5Fn71jy/B1Hnn3p69ixI1OmTLnjce7pJGFcXBxZWVl4e3vbHff29ubq1at33D4vl8DA7Ao7b29bgk7r5mZLzm3ctg21Xp/vS6XV2r6UGg3rN23KXtdPrbYl825U3H8wxWlXkmMVV2nH9TCMVVz38z2W9PMqyWuW1PWK207GujX3+z3eq2OV1PWK207GujX3+z3ey9csqbHu1Xu8V8cqrvv5Hu/VsYrrfr7He3Ws4rrf7/Fejb+krlfcdjLWrbnf77G0x0pOTi7WWHfqvliErqBEW1EVdLfS3mq1Ajd/4L/++ivNmzcvsk1x292rYxmNxmJ9493P93ivjiXP3rHXLM7zv1efxf0+ljx7x40lz95xY8mzd9w15f+3jhtLnr3jxpJn77ixHoZn74hryv9v7/2x5NmX7FhpaWkkJiaSnJyM2WwutF3uM8/Nc90OhfVOet9lJpMJZ2dnFi9eTN++fW3HK1WqRL9+/YiIiLij9gCXL1+WnY+FEEIIIYQQQgghxH3v0qVLlC1b9rb63tOVhBqNhsaNG3P48GHbsaSkJC5evEiLFi3uuD1AUFAQly5dws3NrUTX9xNCCCGEEEIIIYQQojRYrVZSUlIICgq67THu6UpCgJUrVzJ8+HCOHDmCj48PH3zwAT/99BP//PMPSqWSOXPmsHPnTr777rtitRdCCCGEEEIIIYQQQti7pysJAZ566imio6Np3749er0eT09PfvnlF1vCLzIykhMnThS7vRBCCCGEEEIIIYQQwt49X0kohBBCCCGEEEIIIYS4ux6K8rr58+dTv359HnnkETp37sylS5cKbTtt2jTatGlDu3btqFu3Lr179yY6OroUo3WMuLg4nn32WSpWrOjoUB4aVquVxYsX07p1a9q2bUv9+vUZOnQoiYmJjg7tgffzzz/TuXNnOnToQOPGjXn00UfZtWuXo8N6KD3zzDOEhIQ4OoyHRkREBPXq1SM8PNz2NXbsWEeH9VDZu3cvHTt2pEWLFtSvX5+OHTsSFxfn6LAeaNWqVbP7ng8PD8fX15eZM2c6OrSHQnx8PP369aNhw4Y8+uijtGrVip07dzo6rIeCwWDgzTffpHnz5jRu3Jh27dpx8uRJR4f1wCrq/dSlS5fo3LkzjzzyCPXr12f+/PkOiPDBdbP3sj/99BP+/v4sWbKkdAN7CBT27M+ePctLL71k+7nftGlTfvvtNwdF+WAq7NnHx8czZswYWrduTZs2bahRowYTJkwoclfkvO756cZ3auXKlUyePJkjR47g6+vL+++/T/fu3dm3b1+BU5A/+eQT9u7dS0hICCaTibZt2/LKK6+watUqB0RfOo4ePcrw4cOpWbPmHW2VLW5NWloaI0aMYP/+/VSpUoX09HTatm3LoEGDWLFihaPDe6B99tlnDB48mJ49ewIwYcIEunbtyrVr12RpglK0adMm/vzzTzw8PBwdykNl1qxZhIeHOzqMh9Lx48fp3bs3v//+O9WrV8disdC7d2/S09MdHdoDrWrVqvz888+21yaTiZCQEJ555hkHRvXwGDlyJBcvXmTXrl1oNBrmzp1Lly5diIyMxM3NzdHhPdBef/119uzZw+bNm9Hr9bz33nt07NiRY8eO4ezs7OjwHihFvZ+yWCw88cQT9OrVi7feeovY2Fhq1apFYGAgPXr0cFDED46bvZcdMWIETk5OqNUPfOqj1BX17D/77DPUajVbtmxBoVCwcuVKnnzySfbs2UP9+vUdFPGDo6hnf/r0abZt28b27dvR6XRcvXqVGjVqEBQUxKhRo2469gP/bvjTTz+lb9+++Pr6Atm/qBw6dIiNGzcW2P63336zVbVoNBqaNm3K+fPnSytch/D19WXdunU0btzY0aE8VDQaDREREVSpUgUAZ2dn+vTpw++//y7J2rvsww8/tPulrFWrVsTHx5OUlOTAqB4uJpOJN954g9dff93RoQhRaiIiInjuueeoXr06AEqlkhUrVlC+fHkHR/Zgy5sgzH3duHHjO9r5TxTfoUOHaNGiBRqNBoC2bduSmJgoFW13mcViYeHChQwaNAi9Xg/AqFGjuHjx4gNd/OAoRb2f2rBhA0eOHGHEiBEAlClThj59+vDpp5+WdpgPpJu9lx03bhzTpk2z/QwSJaeoZ9+0aVPGjh2LQqEAsveOcHd3588//yztMB9IRT376tWrs2zZMnQ6HQCBgYFUrly52HmtBzpJaDQa2bt3L3Xq1LEd8/T0pEKFCuzYsaPAPk2aNLH9+cKFC/zyyy+MHDnyrsfqSAEBAbZvIFF6dDod48aNszuWnp6On5+f7YepuDsaNmxoqxhMSUlh0aJFDBgwAC8vLwdH9vCYOXMmTz/9tLxJd4BFixYRHh5Oy5YtGT58uEx1LSVWq5U//vgDNzc3evfuzSOPPEL37t05ePCgo0N76CxcuJAhQ4Y4OoyHxlNPPcUff/xh+yDu+++/p0yZMlStWtXBkT3Y4uLiyMjIwM/Pz3bM09MTvV7Pnj17HBjZg6mo91M7duygQoUKuLu7247Vq1ePvXv3YjKZSivEB9bN3stWqFChFKN5uBT17J9++mlbMQxkf3BhMBjw9/cvrfAeaEU9ew8PD7tnv3HjRi5dusTAgQOLNfYDnSSMi4sjKysLb29vu+Pe3t5cvXq10H6xsbE0atSIWrVq8cILLxT7YQpxJ6xWKytWrGD8+PGODuWh8cwzzxAUFIRGo+F///ufo8N5aERFRbF8+XJee+01R4fy0AkJCaFJkyZs3LiRzZs3YzKZePTRRzEajY4O7YEXFxdHcnIyn376KZMmTWLnzp088cQTtGjRosi1kkXJioyM5OTJk3Tq1MnRoTw03n//fdq0aUOFChUICwvj559/ZvPmzTLV+C4rU6YMzs7OREZG2o4lJCSQmZlJQkKCAyN7+Fy9erXA96Mmk0k+qBMPjd9++w1fX1969+7t6FAeGuvXr6d69er06dOHL774grp16xar3wOdJMxVUFVWUZVaZcqUYd++fVy8eJGNGzfywgsv3M3whACy1wkrX748r7zyiqNDeWj88MMPJCQkULZsWRo3bkxqaqqjQ3oojBkzho8++kimfTjAgAEDGDFiBCqVCrVazccff8zx48f59ddfHR3aA89gMADQrVs3atasCcBLL72Et7c3ixcvdmRoD5VFixYxcOBAWX+2FI0YMYKdO3dy7tw5Tp8+zeuvv85zzz0niaq7TKFQ8PLLLzN//nzi4uKwWq28//77aLVamUHkAIW995TZQ+JhEB8fz5tvvsn3338v66GWovbt23P8+HE2bNjAK6+8wpdfflmsfg/0b0hlypRBrVbn+yUkISGBgICAm/b38fHh3XffZdmyZZw7d+5uhSkE33//PZs2bWLZsmXyy0Ip02g0TJ06lbNnz/Ljjz86OpwH3qZNm7BarbRr187RoQiyp555eHjI/+NKgZeXFwqFIt8U+7Jly3Lx4kUHRfVwsVgsfP3117z00kuODuWhkZ6ezvz58xkxYoStkqpv376kpqYyb948B0f34Js2bRrPP/883bt3p02bNtSrV48qVapQuXJlR4f2UAkMDCzw/ahGo8HHx8dBUQlROlJSUujVqxfTp0+nWbNmjg7noVSrVi0GDRrE5MmTi9X+gU4SajQaGjduzOHDh23HkpKSuHjxIi1atMjX/tSpU/z00092x1xcXACkwkjcNT/88ANff/01K1asQK/Xc/r0aVvFibg7PvroI7vXGo0GrVYr/85Lwdq1a7lw4QLh4eGEh4czdepUoqOjCQ8PZ+zYsY4O74F34zNOS0sjOTmZcuXKOSiih4eLiws1atQgOjra7nhsbKw8/1Lyxx9/UKtWLYKDgx0dykMjKysLi8WCVqu1O67VaqWSsBSo1WrefvttduzYwebNm3n66ac5f/48nTt3dnRoD5UWLVpw4cIFUlJSbMcOHTpE48aNZVaFeKClpKTQvXt3JkyYQMeOHTEYDJw+fdrRYT3w1qxZw5EjR+yOubi4FPu97gOdJITs3Yy++eYb4uPjAZgzZw516tThscceIzExkRYtWrBlyxYArly5wqRJk0hOTgayf7GZP38+oaGhtp0IhShJP/zwA/Pnz2fJkiWYTCZSU1N57733ilwzU9y5yZMnc/ToUdvrhQsXYjabad++vQOjejh88skn7Nmzh82bN7N582beeOMNAgIC2Lx5M9OnT3d0eA+8lStX8scff9hef/TRRwQHB9OlSxcHRvXwGD16ND///DNRUVEA/Pnnn1y9epX+/fs7OLKHg2xYUvrc3d159NFHWbp0qW2Dhq1bt3Lq1ClZF7IUvPPOO6xdu9b2etKkSTz55JPFXpdKlIx27dpRu3Zt5s6dC2SvUbts2bJ8GxgK8SBJSUmhS5cuDBkyhJYtW5KamsrZs2f58MMPHR3aA2/fvn3MmDEDs9kMwPXr1/n666/p1q1bsforrFar9W4GeC+YP38+CxcuRK/X4+npyeeff065cuW4evUqtWrV4osvvqBnz54kJCTw8ccf89dff+Hs7ExKSgqVK1dmypQphIaGOvo27pqMjAwef/xxoqOjuXDhAs2aNaNXr14MHz7c0aE90KKjoylbtqztH29e58+fJyQkpPSDekjMnj2bH374Aa1Wi9FoRK1WM2nSJB577DFHh/ZQ6dq1K2fOnLH93Bk+fDi9evVydFgPtGXLltnWI8nMzMTPz49PPvlEdhktRZ9++imLFy/G29sbtVrNlClTaN68uaPDeuBdu3aNZs2acfr0aVQqlaPDeahcuXKFCRMmcOzYMZydnUlPT2fkyJGSHC8FixYtYvr06fj4+GA0GmnTpg3vvfeerEl4F9zs/dSlS5d4+eWXSUxMJCMjg8GDBzNs2DAHR/1guNmz/+yzz/jhhx/YtWsXISEhBAQEsGbNGlxdXR0c+f2vqGc/fPjwApeV6N+/P0uWLCn9YB8wRT37f//9l6lTp3L69Gl0Oh1JSUk89thjvPfee8X6vn8okoRCCCGEEEIIIYQQQojCPfDTjYUQQgghhBBCCCGEEEWTJKEQQgghhBBCCCGEEA85SRIKIYQQQgghhBBCCPGQkyShEEIIIYQQQgghhBAPOUkSCiGEEEIIIYQQQgjxkJMkoRBCCCGEEEIIIYQQDzlJEgohhBBCCCGEEEII8ZCTJKEQQgghhHhoWSwW0tLSHHb9lJQUh11bCCGEECIvSRIKIYQQQtyiOnXq4OzsjEKhwMfHh3bt2tmd//PPP1EoFHz55Zd2xz/++GMCAgJQKpUEBASwf//+0gzbpkuXLnh7e6NQKFiyZIlDYrgdJR33hQsXaNq0KSdPnrzz4G5Tly5d+Pbbbx12fSGEEEKIXJIkFEIIIYS4RYcPH+add94B4H//+x8bNmywO79p0yYANm7caHd8woQJbNiwgSZNmhAdHU2DBg1KJ+Ab/Pbbb6xcudIh1y6OiIgIFAoFFy5csDteknFfvXqVVq1a0a9fP4f9PQAsXLiQMWPGsGzZMofFIIQQQggBkiQUQgghhLgtbdu2Bf5LCOa1ceNG3N3d2bRpE1ar1e7cpk2bbH2F4wwbNowKFSowYsQIh8ZRpUoVIiIieOWVV7h69apDYxFCCCHEw02ShEIIIYQQt6FRo0Z4eHjkqxZMTEzk6tWrPPfcc8TGxnLkyBG785s2beKxxx4rzVDFDY4ePcrq1asZO3aso0MB4MUXX0StVjN79mxHhyKEEEKIh5gkCYUQQgghboNKpeLRRx/l4sWLnDt3znZ88+bNtG7d2lYtmDeJaLFY2L17Ny1atABgy5YtPPvss1SsWBFfX1/KlSvHqFGjSE1NtfXp1KkTOp0OhUJBQECAbZ3D/fv3ExAQgFqtxtfXlz179gCQmZnJO++8Q6VKlfD29iY4OJiXX36Z2NjYYt2XxWJhxowZVK9eHW9vb/z9/Xn++eft7vHDDz8kICAAhULBgAEDWL58OfXr18fd3Z2mTZuyb9++fOPGxsbSt29fvLy8CAoKon379hw9ehSFQoGTkxMBAQGcOXOGOnXq8OmnnwLQuHFjAgICCAgIICEhIV+cERERhIaG4uXlRY8ePbh27Vqx7vGHH34AyFfRWbVqVVxdXe2mOl+6dImAgAC0Wi0hISG2tl999RUBAQGoVCrCw8PZsmULTZs2xd3dnSZNmtj+PmbMmEFoaCi+vr4MHDiwwE1StFotLVu25Pvvvy9W/EIIIYQQd4MkCYUQQgghblNBU443btxI27ZtadOmDQqFwi5JuH//fqpVq4Zerwdgzpw5xMTEsHfvXuLi4vj1119ZtWoVL774oq3PH3/8wejRo21/Hjx4MAANGjTgyJEjeHp6cunSJZo0aYLFYqF79+4sXryY5cuXk5CQwNatW/n7779p2bKlXfKxMEOGDCEiIoI5c+aQkJDA4cOHiY6OplmzZkRFRQHw1ltvER0dDcCePXs4cOAAu3fvJjIyEoDHH3+czMxM25hGo5H27duzdetWduzYwZUrV5g+fTqDBg0C4JlnniE6OprQ0FAOHz7MuHHjANi7dy/R0dFER0fj7e1tF+f//vc/atWqxenTp/n777/Ztm0bL7300k3vD2D79u0EBATg4eFhd/zkyZO2a+cqV64c0dHRPPLII3bH+/XrR3R0NOXKlSMyMpIffviBzZs3ExUVhVqtpmvXrnz55ZdUrlyZ06dP8+uvv/L111/z4YcfFhhT9erVuXjxIpcuXSrWPQghhBBClDRJEgohhBBC3KaCqgVzk4RlypShdu3abN26laysLCD/eoSVK1fmk08+wdfXF4B69erx1ltvsXz5ck6fPm1rN2DAAACWLl1qd/1ly5bRo0cPnJycbK///PNPJk+eTMOGDW3XmDFjBqdOnWLBggVF3s+2bdtYtGgRo0ePtu3Y7O/vzxdffEFsbCwfffRRvj4JCQm8//77aLVaPD09GTJkCHFxcfz999+2NosWLeLQoUNMmDCBGjVqANk7RN/JeoCVKlWiV69eKBQKqlWrRpcuXfjjjz8wGo037Xv69Gn8/f1v+9o3ioqKYvLkyTg5OeHm5saQIUOIjY1lw4YNdO/eHYVCQfPmzXnkkUdYsWJFgWMEBAQAcOrUqRKLSwghhBDiVkiSUAghhBDiNtWuXRs/Pz/++usvIHvHXIPBQMWKFYHsJGJKSopt6umN6xF+/PHHNGrUyG7MsLAwAI4fP247Vq1aNZo0acKyZcswmUy240uWLKF///621z/++CMAHTp0sBszN2G4bt26Iu+nsP6hoaG4u7sX2L9Bgwao1Wrb63LlygHYbcLxyy+/ANlTp/MKDw8vMp6iNGvWzO51cHAwWVlZxMXF3bRvfHw8Li4ut33tG4WGhuLj42MXC5Dv77Zs2bJcvny5wDFy44mPjy+xuIQQQgghboUkCYUQQgghbpNCoSA8PJyYmBiOHj3Kpk2baNOmje183kpDk8nEoUOHaNKkie18VFQUI0aMoFatWvj7+xMQEECvXr0A8q3BN2DAAGJjY1m7di0Ahw4dIjU1lZYtW9ranDlzBshOCuau5RcQEED16tVxcXHJN+aNcvs/+eSTdv0DAgKwWq1cv349X5/cKshcWq0WwK6i7/z58wAEBgbatc2tnrsdxbluYYxGo11i807dGItGoynwuFarJSMjo8AxcuMpTvxCCCGEEHdDyf12JIQQQgjxEGrbti0//vgjmzZt4uDBg7ZpugCtW7dGpVKxceNG2rRpQ/369W3JoJSUFJo3b45CoWDlypU0aNAAhULB5s2b7RKNuZ599llGjx7NkiVLeOKJJ/JVEQJYrVYAzp49e1uVcrn9N2/eTM2aNYvVR6m8+WfOuePm/rckFOe6hXF2draryCwOi8VS6DmFQnFLxwuSmxx0dna+pbiEEEIIIUqKVBIKIYQQQtyB3OnDmzZtyrfmoLu7O40aNeLvv/9mzZo1dlONN27cyKVLl3j11Vdp2LDhTRNKXl5ePPHEE6xZs4bo6Gi+++47+vXrZ9emSpUqALYNRvI6efIkhw8fLvIaRfWPjIy0TZu+VZUqVQKwbXaS68bXpSUwMJDExMQi2+SuI5mruLtD367ceIKCgu7qdYQQQgghCiNJQiGEEEKIOxAaGkr58uX5448/cHFxyTeFtm3bthiNRubPn2+XQNTpdED+arOidrft378/JpOJ/v37U6NGDSpUqGB3vnfv3gCsXr3a7rjVauWZZ55hw4YNRd5LYf0BRowYwTfffFNk/8J069YNwDZVOtfmzZsLbJ9bBZmbqPv666/tdpC+UzVr1iwwEZpX3vOxsbGcPXu2xK5fkMuXL6NUKqlevfpdvY4QQgghRGEkSSiEEEIIcYfatGmDwWCwSwLmyj2m0WioW7eu7fgjjzxCYGAg8+bN48SJE0D2rrsffPBBodfp1KkTAQEB/Pnnn7Ydj/N6/vnnefzxx5k2bRrbtm0DID09nbFjx5KZmclLL71U5H20bNmSl19+mcWLF7Nq1SqsVismk4np06eza9cuxo0bd9NnUZCXXnqJ2rVr8/HHH3Ps2DEADh8+XGjSsVq1agAcO3YMg8HA1KlTSUpKuq1rF+Sxxx4jOTmZixcvFtpm3rx5GAwG0tPTmThxImXLli2x6xfk0KFDNG7cGA8Pj7t6HSGEEEKIQlmFEEIIIcQd+eqrr6yAdeXKlfnOpaenW3U6nfWpp57Kd+7QoUPWDh06WD09Pa2VKlWydujQwTpz5kwrYHV3d7d27NgxX5+xY8daXVxcrKmpqQXGYjAYrO+99561cuXK1jJlylgrVqxoHTRokDUqKsrWpnPnzlYvLy/bderXr287Z7FYrP/73/+sNWvWtPr4+FgrVKhg7d27t/XEiRO2Nl988YXV39/fClj1er3V39/farVarSNHjrQbN2/8MTEx1ueff97q4eFhDQ4Otnbr1s167tw5K2AdOHCg3T2YzWbrSy+9ZC1Tpow1ODjYOnjwYKvZbLYOGDDAbvwePXpYrVartVGjRlYXFxcrYPX19bW+9957BT6bXHFxcVYnJyfrzJkz852bNGmSFbDWrVvX6u7ubq1Zs6Z1/fr11tatW1uVSqXV39/fumzZMuvatWut/v7+VqVSadVoNFZ/f3/r+fPnra+++qpdjE888YQ1KyvL6u/vb9Xr9VbA6u/vb/3iiy9s17xy5YpVo9FYP//88yLjFkIIIYS4mxRWawmuIC2EEEIIIUQxxcTEEBAQwOuvv860adNK9drvv/8+n3/+OWfOnMHJycl2PCIigvfee4/z588TEhJSKrGMGjWKDRs2cPDgQdvOyEIIIYQQpU2mGwshhBBCiLuuZcuW+TYD+fvvvwF49NFHSz2et956i6ZNm9KzZ08MBkOpXz/XkiVLWL58OatWrZIEoRBCCCEcSpKEQgghhBDirtu1axdvvfWWLSF36NAhJkyYwKOPPkrnzp1LPR6VSsWKFSto0aIFhw4dKvXr59qzZw+7d++27SwthBBCCOEoakcHIIQQQgghHnxTp05l1apVVKpUiYyMDNzc3Hj66ad57733UCod87m1Uqnkrbfecsi1c82fP9+h1xdCCCGEyCVrEgohhBBCCCGEEEII8ZCT6cZCCCGEEEIIIYQQQjzkJEkohBBCCCGEEEIIIcRDTpKEQgghhBBCCCGEEEI85CRJKIQQQgghhBBCCCHEQ06ShEIIIYQQQgghhBBCPOQkSSiEEEIIIYQQQgghxENOkoRCCCGEEEIIIYQQQtyBgwcPkJSUhMlk4sjhQ6Snpzs6pFumdnQAQgghhBBCCCEeLFcunyU2Jgqd3gmTyUjtei1uqU1x+gshRGmKjr7Ksm++Yey41ws8//ln89m//x90Oh2vDh9J7Tp1MZlMPNamNRrNf+k3b28fvvthOWr1vZeSu/ciEkLcc8xGI7ObN0el0diOOfv48NLPP3N2yxayDAbizpxBqVbTYtgwB0YqhBCOt3r1ajIzM/n3339Rq9VMmjTJ7rzRaMTT0xOtVms75u/vb2u/Y8cODhw4gF6vJzExkbFjx6JQKEr7NoQQ4qair1zAy9sPnd7Z7rjJaGD3jj/o/vQrKJVKtm5cSWzMZcr4ly1Wm+L0F0KIkrRv317CQsPw8PQstM2XX3yO1WIt9HynxzvzzrsReHp54eyc/XPx/PnzvPf+ZKpWrQYKBadOnsBssdyTCUKQJKG4zxjS0tC5uDis/4Pu4u7d+FWtitMNPxjjz52j69Sp+FWvjkKhIOb4caxmM6aMDFaPHs2gNWuo1qkT/2vRgnKNGlG+SRPH3IAQQpSSv/76izp16uDj42N3PCkpiV69enHy5El69+6Nj48PrVu3Jjw83Nbm+PHjfPPNN9SvXx+FQsGBAwcwm82o1WoyMzP5+eefmTZtGgALFy7kzz//pGPHjqV5e0IIUSxpqUm4unnmSxJGX72Ih6cvSmX26lZl/MtyOfK0XZKvqDbF6S+EECUp+upVgoKCC00Sbt+2lVq1anPk8OFCx1CpVAQFB9sdc3VxodWjj6JWazCZTKz59WeGvPxKSYZeoiRJeJ9a0L49xtRUu8qutPh4lGo1Th4etmPG9HSqduhA5ylTHBFmibGYzWyZOROr2UzbCRPIMhr5vF07TAYDxtRUtK6uaHQ6Xt6wAbVWy4Hvv2fHvHmkJySgc3PDp1Il+n77LX+8+y5BtWvTeMAAR9/SPSn5yhU8y5bNlyTUurpSOTwclUaD2WjkyKpVtBw+HIB+P/yAZ7ly2VUuVitWi8UBkQshROm6ePEiFStWzJck9PDwYO/evVSqVAmFQoHVasVyw89Fd3d3unXrhkajwWg0snjxYt577z0A0tLS+P333xk/fjy+vr6o1Wrc3NxK7b4eZmZzFiu/n4s5KwuTyYBGo0OlVtPzuZG2RMW9wGq1cvH8cc6dPkxCfAxWixWlSomLizt+AeUpWz4MX7/geyLmjPRUflu9iLCq9ajbsHWpXNNiMXNg719cOHcMhUJBSKWa1GsUnu95nDq+n2NHdtGt58uoVKpSie1hkp6WYpc41Gr1XIuOLHab4vQXJcdoNNK9WxcMBgNpaam4uLii0+lY8/sfDv/3UVhsVquF9PR0yvj50bz5I7w0aAheXl4OjXXx/y3kxx9+ICEhni5duvFuxHt3NF5Wlol5c+eyYf2fKJUK2nfoyNBXXs1XgbZy5QqWffM13/+wAk2e/IAoOampqVy/fp3yFSoUmSS8ePEiP69ehdVqJTU1lb4v9LNLGq5Y/gNdu3UvjZBvm+N/exC37cnZs3l161bbF0Bw/fp2xxoPHOjgKEvGuvfe4+qhQ7QaORIAtVbLq1u38tgbbwDw2Btv8OrWrahzpm7V7dWLCs2bU6F5c4b99Rd9v/0WgHYTJ3JoxQr+/uILx9xIMZlNJixms90xq9WK2Wh0SDyeZcvaEtL7v/uO2j162M7551QXXjl8GK8KFSjboIFDYhSOZzAYCAgIwMPDA4VCgYeHBwEBAZhv+F6+l2Lz8/PDycmJsLAwRowYQWxsrK3Phx9+SEBAAAqFggF38YOFkrqOyWRi3LhxlCtXjgoVKjBhwgSysrLytfv8888JCwvD6KCfJw+D3ArB3bt3U6VKFVq2bGl3vmLFirZf4ufNm8fAPP+v9vHxoXnz5tSqVYvZs2eTmJjII488UqrxP6xUKjVP93mNxs3bA9C4eXue7vPaPZFsy2UyGdm07gf279lIaNX6PPXscJ7u+xpP9BpKWLX6nDl5kHVrvuLK5bOODhXITtiZjAYMhkzbsdSURL5e+CGH/tl6V6559OBOIi+cpPOTL/F49xe5cO4Y/x7+266N0ZDJwX+20LBpO4cnQB5URmOm3bNVqdSYTMZitylOf1FytFota9etZ8zYcQCMGTuOtevW3xP/PgqL7Y8/N7Jh0xZ69OjJ8h9/YNzY1xz+O+fAFwexdt36As9duXKFpo0b8OUXC4o93pLFi9n81yaWfr2MxUu/YcOG9Xzz9VK7NikpKXyx4DNGjRpzXyYIs7JM+f7erFYrJpPppn1TU1OIj4sjJiaGq1evcOVK9ldCQkKJx7nuj7U83rnzTdu1adOW7k/24MkeT3H27Bn27N5tO5eRkcG///5LUFBQicdXkqSSUNzzTm/axNFVq3jxl1/QODndtL3ZaOTX118ny2Si57x5qHU62zkXHx/aTpjAT6++SuVHH8WvWrW7GXqR4s+dY9PUqTR7+WXKNWxody7q4EF+GTuWCk2b4uLrS/r168SdOkX3WbPwKl++wPFOb9rEua1b6RgRYXf86OrVXN6/HxdfX1JiYqj5xBNUaNr0tmI2pqdz9cgRGvfvb3f8zF9/ceKPP2j/zjuo8qyxJR4uOp2O6OholixZwsCBA5k9e/ZdTa7diqJiMxgMzJkzh9dff519+/axfft2VCoVb731Fm+99dZdXwuupK7z0UcfsXLlSv755x8AmjZtioeHBxMnTrS1SUxM5J133mHhwoV26+GJkvfrr7/yww8/MH/+/EKfdVpaGnv27GH06NF2x0eOHElCQgJvvPEGr7xy705HEaVv55ZfuRYdSdenBuPm/l/FjEajpXKVuji7erDh92UOjNCei6sHvV8Yg0pVem85zp05QuWwOjg5ZS8vU7lKHc6eOmS36cWh/Vvx9vGnXIUqpRZXSbt08RQXzh3Dw8OHxMQ4KoRUo0Kl6nfc52Zt0tNSOPHvXtvrxOvXiLt2BY02+/dttVpDnQat0Gjsf+6Zsoxodfa/xxfVpjj9hdBqtfR9oR8bN6zn6JEjnDxxgho1azo6rBKz9vc1dOnaDW9vbwC6du3Gr7/+woCBL9nafPnFAsKqVOXR1qVTrV3SDh86zMSJE2jcuAk+Pj5cv36ds2fOMHXaJ5QtW67Qfv+3aCGfL5hf4Lm+L/RjxMjXcqb2/kJycjJpaamcPHGCLt2eoEOH7CVcYmNj+fH772z9zpw9w7//HsXVxRUAnV7PoMFDOPbvv1StVg21+uZJ2Kp58gtBQUFs27aVJjnvvTes/5OyZe/9JRMkSXifembRIlzLlLlpuwbPP4+lgEqS+8nOBQuo3Lo1PhUr3rRtlsHA6tGjUWu19Jg92246dq5KrVrhU7EiuxYu5IlPP70bIRfp3LZtnFi7Fq2LC+d37KDpoEH5G1mtKJRKzmzejFqno1KrVvSYOxePQj51SL9+nXUREVS6oVrln2++4ciqVbzw3XeotFqMaWl82bkzz331Fd4VKpBy7Rr7v/nG1j729GmuHjmCzjX7B6Nar7fbiOTkunV4lcv/wzq0TRsqtmzJ0l69aPP661S8IQ4h7mU6nY5x48bx448/smvXLvbv30/jxo0dHdYt++qrrxgwYAB+fn4ADBgwgMWLF9slCSMiIqhXrx5PPPGEo8K8Y2azmfnz57N//37Kli2LSqWif//+VCzG/yMAfv75Z37//Xc+//xzu+NLly5l+/btBAQEcPnyZfr160ebNm1s569cucL//vc/2+sjR46we/duPHKW+HB2dubdd9+1ne/WrRudOnWifv36TJ8+vcA1BX/88UcqV65sd+zixYssW7aMn376iX///Zdnn32Wb775hr59+xbr/sSD62rUeSIvnKB6rSZ2CcK8AoNC8PL2u6c2uinNBCFkJ7H0zv+tP+3k7Ep6WortddL1OE6fOEDnJ18s1bhK0rXoS/y97Te6Pz0Unc4JoyGT1cs/Q6vTExhc8M/C4vQpThtnFzcaNGlrG/fsqUP4B1bA1c3T7npu7t7EXL1oe23ITMfFxa3YbYrTX4hcQUFBHDv2L1evXnmgkoSxsbF2y5r4+PgSe+2/WS/nz59j9aqVLPnq3vlw6FZZsaJSKtm+bStarZbmj7Tgk+kzCAwsutouLjaW9z+YgkajQalQoFAoyMrKYtmyrxk85GUA5vxvFkePHOHzLxeh0WjY/NcmJowfhwJo36EjZcqU4dURI21jrvn1Fxo0bJSv0u/w4UMYDAb+2beP8+fPce7cWVavWsmTPZ6ya7dzxw4WLJjHV19nz2JMT89Anacad8+e3dSqVftOHlepkCThfaqwarIbOTt4XYY7dT0ykph//6VWMd7QmjIyWDliBM7e3nT56COURZTHl2/alMMrVpBlMNhVGpaGSq1aUalVK5KiotifMw26II+OGmU3rbco+5YuzZdANGVmsn3uXJq8+KKtuk/r4kJQvXoc/P572k6YgJufH63HjLH1ObJqFeWbNMHjhsVWc13YuZOgunVtr89t28bmTz/lxZ9/RqXR4BsWxj/LlkmSUNyXKlasyN69e7lw4cJ9mSSMiooiICDA9jowMJCoqCjb6+PHj/P555/bKg3vV0OGZK859H//938oFAoaN27Mv//+y/Lly2/aNy4ujiFDhvD444/bHf/f//7H4sWL2b17N1qtltTUVMLCwti6dSthYWFA9huQqVOn2vosWbKE8PBwQkJC7MZau3Ytr7/+OkePHkWj0VC7dm3+97//FZgkXL9+Pc2aNbM7tmLFCp599lkAatasyS+//MK0adMkSXiPSk9L5uC+LVyJOofVYkWndyKsWn2q1WycL1GXEBfNvl3rSYiPRqPVExgcQtnyYWzZ8BMajQ4XVw+69Rxc6LXOnDoEQNnyYUXG1PWp/8Y4e/ow+/dswpCZjp9/Oeo1Cmf/nk0kJcVjNGRQp34r6jZ8lJTk6xw/uoerUecxGjOxWCwEBJanfuO2uHt4FzhWnQatOLD3L5KS4nFx8aBBk7YEl/sv6X058jR/b/vN1r5D1xc4cmA7x3Oq0I4d2cWpE/sBqFO/FVVr/DerIj0tmQP7tnDl0lksVjN6vQuVwmpTs07zm07/dnZxs0sKpqcm4+zibnu9b/d6QqvWw9Pr5h+236sO7d9G+ZCq6HIq67Q6PeVDqnL86O5Ck4TF6XM74xbGP7A8e/9eh8ViRqlUcS36EjXrZP+8u3juOAFBIUW2KeqcuDdcu3aNBZ/NY/euvzFbLHh6eNL9yR48+9zz+X7+nTx5glkzpnPi5AncXN1o3KQJrVo9yoTx43BxcSUwMIBl3/1427FcunQJgLJ5ihmiLl/m+++/ZfeuXaSkpJBlzqJBg4YMHzGScuXs30tnZGSw8MvP+WvTJjIzM9HpdNSqXZvu3Z+kUeMmtzzejRb/30K+/y77fd+yb75m5U8/ATBo8BB69nq60H5l/Py4du2a7XVMTAx+/n6217NmTOeJ7k9SqVKl4jyme9Yrw4bTtdutfYDt4elJx46d7I4t+Gweo8eMQ6/P/hmmVCqJj48jKysLjUZDhZzf2fbv/4f2HYq/Idyzzz1v+/OaX39BqVDaEoQbN6ynUeMmeHh44OfvxzPPPGdre+L4MYaPHGV7ffHCBRo1uvffY0iSUNzTovZn//LofcObsBsZ09NZ/vLLeIWE0CkiAsVNfoH0qVSJLIOBq0eP5pvqm1fy1at8/cwzxY43d23I0nTst98IbdOGqIMH7Y7HnTmDISUFlxsW1XcPDOT8jh23da348+cpf8NU5eA8axAmX70qOxuLWxIVFcXbb7/NunXrMJvN+Pr6MnjwYEaNGpXvF8wDBw4wevRo9u/fj6enJ+3ataNbt2489dRTuLu7U6FCBQ4XsZDwzZw+fRqA0NDQm7Y9d+4cs2bN4s8//yQxMRGTyUR4eDgff/xxvv5Xrlyxu0d3d3datWrFoEGDaN68eaHXaNeuHbt27cJkMuHl5cW8efPo2bNnoe3Lli3L5cuXba8vXbpkN6Vh9OjRDBo0iBo1atz0/u5VP/30E3/++Sfnz5+3fX907ty52Pc0Y8aMfEm9jIwM3n33XcaPH2+bFuzq6krz5s357LPPmDFjxi3FqFAo7NYgvHjxoq0icfny5Tz22GO2aUMnTpygbdu2dv0rVqzIv//+S7169YDsKclFfZ8Ix8lIT2XtL0twc/Oia49B6J1cuBp1ns3rl5OUGEezlv+tXZScFM+fv32DT5kgejwzHK1OT+SFk+zblb12VePm7alcpW5hlwIg7lp20t/jFpJblcPqUDmsDiu/n0taWjInju2jTcfeaDRaNvz+3weVUZfOEHn+OI89/jxe3n5kpKeyffMvbFj7LU/0ehm1WpNvrPNn/6V9l74oULB100o2b1hOj96v4pxT7VW2fBhP93mNld/PtV2ndv2WVAytxaof5lGjdjPqNny06Of6VPZzjb5ygS0bfiLxeiyt2jxZ5D1XCqvN6RMHqBxWBxQKzp4+TJXq2b/rXbp4irjYq7QML3qMgqSlJvP7z/9X7PZP93ntlq9RHGZzFjFXL1Kugn2y2NOrDOfOHMVszspXvVmcPsAtj1sUtVpDvYbhHNq/Db3eGd8yQfgHVgDgyMHtODm74hdQrtA2RfUXjhcfF8dLA/tTtmxZvvn2B7y8vNi7ZzevjxvDxYsXeOPNt2xtIy9e5JWXh1CzZk1W/7wGNzc3Nm/+i5kzsmd0jRk77pYTRLlSUlL44ftvOXnyBE/2eIqqVf+b6rlz53Y2btjAnLnzqRwaSnx8PBHvvs2IV1/h+x9X2BJJANM+/ogzp0/z5cL/w8fXl9jYWCaMH8uihV/akoS3Mt6NBr44iI6dOtOje1f69H2BwUOGFuv+OnfuyupVK+nStStKhZLf1vxKz169ANi2dQvHjh/j/Q9ufYPSmOhoBvR/odjtC1tn0ZFe6Ge//NWBA/tRKpXUrl3Hduy10WN5bfRY2+tLkdnJ5LxtbsX27dtY8+svxMTEsPzHH3i69zMs/r9F+Pj6Uq9efUJDw7JnhHzzNWlpqXTr3p2aNWvZ+ru7uxMQGHhb1y5NkiQU97TrF7OnGTjfkOi60daZM7GYzTz+wQc3TRACuPj6Zo9/4UKRSUL3wECHJP4Arp04wdZZs9C6uJBw/jxVO3ak8g1rTaTExJBy9So1unTJ1z93Exer1Wp33JKVRfy5c1jM5iKrLQvi5OFhV7FYqVUrUmNj+eebb0iLiyOwdm1avvrqLY0pHl7R0dE0a9aMypUrc+jQIcqUKcPGjRvp3r07J06cYMGC/xZ2PnXqFOHh4TRp0oQLFy7g6enJqlWreO211wDuaP3DxMREZs+ezYEDBxgyZAj169e/aZ/ff/+d5cuXs379emrVqkVMTAx9+/alXbt2HDt2DGdnZ9s9Nm3alIoVK7J//378/f05duwY3bt3559//uHgDcn9vObNm0fnzp1ZvHgxjz6a/430jfr168cXX3xB//79USqVLFmyxLae3a+//srevXv5tojq5cJcunTplioro6Ojb/kaxTVt2jQef/xxu139cncFvpnvvvuOJ554gp07d9od//fff0lKSsLf39/ueIUKFVi3bt0tx9ipUyfb1OTcv//cGD/88EMCAwNtSURvb28qVLB/0/vUU08xZ84cPvjgA9zc3HB3d7fb2ETcOw7s+4v0tBTadHgGfc76d4HBFQmrVp/jR/dQKbQ2fgHZVS0H923BZDLQuHl7dDlvJCtUrMaVy2c5c/Jgsa6XkZ4KZO/yejvSUpPp2LWfrUqsfpO25H4Uo3dyoU6DR/Hyzq5QcXJ2pckjHfllxQLOnzlKWDX7n4vpaSk0aNzGtj5TtZqNuRx5mitR5wi9SbLzZg7s20x6WgoduryAk3P28ieBwRWpWrMRRw/uoEatJviUKXwaWq26j5BlMrE+Z23GSqHZFYhms5l/dm+gXsNH0er0HNi3mbMnD6JQKqlRuxnVaxX9IaeLq/tdS/zditSURKxWi20NwFxarR5zlom01GRb9eet9LFaLbc8LoCTsxtqTcHrroZUrkEI+T/EyVvtWlibm50TjjV//lyuXYth+szZth2FGzdpypM9nuK7b5fR6fHO1KuX/XPj8wXzSUtLZfSYcbYlOtq2fYxdO3fy88+rbvnaM6Z/yry5c7BaLSQmJqLVanmhX/98iTcvL28GDRlC5ZwPb318fBj3+gR6P/0Uf6xdazdVdNvWLXTr1h2fnPeIZcqUYfDgl1n7+++3NV5J6T9gAJkZGQwfNhQUCjp36ULfF/pjMpmYNWsGQ4YMxd3dnQWfzeOXn39GpVLRp+8LdpVvBfEPCLinEn+nTp1k/rw5ODu7EHnxIm3btaNly1ZF9sn9XRuyNz9Z9OUXTJ85u9D2FouFb5d9TYeOnej0eMEbkJTx88PZufBkb8uWrfLF9c2339u9fuyxdoX2nzu/+JvWOJIkCcU9LSMpCQBNnh8CBfGqUIFrJ06wfMgQ+nzzjS0JWJjcDVAyEhNLJM6SplAqsZjNPJqTAMlITOTLzp15au5cu92D9331Fa1GjSpwDJ/KlXH19yctLs7ueNyZM2C1kpGYmK/K0M3fv8hn/cyiRfmO1Xmq5P+HKB4OEydO5PLly6xZs4YyOWusPvbYYwwZMoSZM2fSt29fWzLlnXfeITk5mVmzZtmqsHr27Mkff/zBwoULb/nao0aN4o033sBisRAXF4dOp2P8+PHFTjj5+fkRERFBrVrZnw76+/szd+5cqlWrxrJlyxg8eLDdPf7666+2JFSNGjWYMmUKH374YaHjnzlzhi5duvDll18WK0EI8MYbb5CWlsZjjz2GQqGgX79+jB8/HqPRyJgxY3j//ffx8vLi7bffZtGiRajVasaNG8eoQn6G5CpXrtxdTfwVV3x8PHv37qV9+/bMnz+fpKQkzp49S6dOneiV86l6YaKiooiMjOS5557Ld06Xs+TEjR+omEwmjh8/jtlsLnB3x+DgYFxz1m+90YsvFrzW2Y1J4Q0bNhTYbsSIEQUeF/cOq9VK5PmTODu74e1jn2AuWz6M40f3cOHcMfwCymGxWIi6dAZnF7d8U1z9AysUO0mYq6DlBs+cOsSBvX8BkGUy4hdQjsc62X+/u7l72ar8AMr4/be0SEil/ImY3IRQUmJcvnNu7l7o9P/9vuDimj2dNzeRebusViuRF07g5u6Vb91FH9/s5RSuXD5XZJJQqVTRoElbu3XzAI4e2olSpSasWgPOnDrEqeP76dStP0ZDBn/+9g2e3n4EBoXcUfylIXenaLXaPjGXm6gzGjNvq4/FYrnlcQGCyt7f0xzFrbNYLPy1aRNl/PyoUsV+85+WrR7lu2+XsXHDeurVq4/ZbGbHju34+flT6YY1eBs0bHhbScK8lYcGg4EjRw7z0YeT2b5tG7P+N4eAgOxKrXbtO+TrWy5nya4LF87bHff08uK339ZQq04dWrcOR61W0/yRFjR/5L8Nj25lvJKiVmt4dcRIu3XzAL5augStRstTPXvx6y8/89NPK1i4cDHJyUm8MnQIoaGhtgrIe51SocBsNjPs1ezffZISE+nVqwefTp9J3br1ijXGiuXLadS4ie13uht99+03nD17Fv+AAN54861Cl61o2lSWNABJEopblBIdze5Fi0i/fp1a3btTqVXRGf47Zc7Z+vxmFW8N+/Yl5vhx9i9bxvKXX+a5pUttm28UJHc8czG2VneEco0aUa5RI9trJ09PKjRtyt8LFvD0F18AcGjFCmp07WqrGLyRUqWi/dtvs33OHBr164fW2ZlL//xDlsFgO3+jkEceuQt3I0R+FouFn376ieDgYOrWta866datGzNnzuTHH3+kZcuWmM1mfvvtN8qWLUvNGxajDg8Pv60kYd7Kw8zMTP7++28GDx7MmjVrWLt2LeVvsu5r79698x3LXbvu+PHj+e4xd+pori5duhBcyNqf58+fp23btsydOzffVNSiaDQapk6darduHsDHH3+MTqdj6NCh/N///R+fffYZO3fuJCEhgfDwcGrXrn1L13GU8+fPY7Va+fXXX1m/fj1+fn6YzWZq1aqFSqWiRxFruM6cObPQpGyNGjUIDg7Olwg9evQoVquV+Ph422YwebVv3/7Obkjc1zIz0jCZDIVUVmX//pGSfB3I3nAhK8uEh1f+DzBzd+AtDr2TC2mpSRgyM2zXyBVapa6tgu/rhR9iLmDTOr1T4R8CZmWZOHlsHxfOHSMjLYW8KfPcBFNeuhum1SmV2b9TWMzm4t5OgTIz0jAZDZizTCxfNsvunNVqRa3WkJmZfsvjZqSncvTgDlq364VSqeT08f1UrFwTD8/sD0sDgyty+vj++yJJqMzJEt+4JIc1J8mXm+y71T63M+7ddi36Eh6evqg1WhLiruLp7Zdv12NR+q5fv05aWmqBvyv55hRq5K4RmHj9OhkZGVSsmD+Z7HOTmWLFodPpaNSoMWPHTWD0ayOYPWsmH02dBmT/frd8+Q9sWP8nsddi7T4MTE5Kthvn7Xcm8fZbbzLxjfF4enrSomUrnnzyKerk+R31Vsb7//buPLypKv/j+Cdd0qYtLUjZ6bBXZN+KskhR1EEQcENFUBkVZ5CRWdRxFB30N4rLKDPuCCq4izgoCIMgDCAgaEGRzWFfy14LXWna5vz+KI2EljalTdPkvl/Pk6dPb05uvnfJN8k3557jS2nHj2v69Lf07LPPKzQ0VLP//akGDrzaPd7exRdfotmz/x0wRcKu3bqra7dfruyLq11bPXok6e03p+nFl18t9/Eul0szP/5IT0165pxtRtxaNK7zokULNfyGazX5Xy+XKHDjFxQJUSHfTZ+uZr16KdzhUOTp7uK+FB5ZdEmNNzM0D3j4YZ3KyNCWL77Q7N//XsOnTj1nAa24OFi8/nOpSWMShjsc7rEE0/ftU15WlhpcdFGZj2lz+eWKqVdP306bpvCoKNVt1UpNu3XTz3v2VMvxA87l2LFjysjIKPUNutHpsTp27NjhbpudnV3quHNnTtRxviIjI3XZZZfp5Zdf1qBBg3T//feXOwlGbm6uXnnlFc2cOVOpqakeHxR//vlnd9zn2saoqCj1LqUof+DAAV1xxRXav3+/Vq5cWelZiA8fPqynnnpKs2fPVmhoqKZMmaKRI0fqwgsvlCRdddVVmjJlSkAUCYu/oLZt29ZdtAsNDdWAAQP0t7/97ZxFwjfffFMjR44856/LoaGheu211/TYY4/pT3/6k6Kjo7VixQqdOnXKfb8vrFy5Uu3atVNMTIzWrVunjh07nrNnIgKbKS65mbLbladeg6bKzjqpE+nHShQJvWFTKV0QT1ux9HOl7tuhSy+/VgnNEt1Fv/feLL24Xta6qkKdCxpU6ezD36csVYPGzd2Tb2Rk/KxWib+MSRVXu64OHdxT5jp8MSbh/r3b9L/TE7mUJ6nXVapdp54iigvLJXo/F/0IbLeXzHXePCb09KXjFVlveQ4e2KljR1IVEelQfr5THbv0qVCb9WuX6cjhfQoNDVPXpMtVr0HTEo+HHxjvk1lx/ju7t35V69CxaMbY79etdS977NFHtGrlSv39qUlKTk52D49wcVK3Eo/v0qWrPp/zhVZ8/bW++mqRFi38UvPnfaFbbx2lP/zpzxVeny+9+srL6tEjST1PjxO/f/8+Db5miPv+XzVrrrUp35W5Dl+MSfj18uX6ZOZHXq3vz/c/WKJn6ZkckQ6tWbPaq3WtW5uigwdT1cKLyVuuuurXeulfk/XYow/r45mflvhRxJfWr/9BLVq0VFRUlP730xa1at3G45LpmoQiISokLzNTETEx1TY5RfTpSxDzMsr/dcZms2nQk0/qVEaGdi1fri8efFDDJk8utcdcXmbRrHcxpfQOOZM/xiTMy87WjOuuU5srrtDlf/mLx/Libdnx3//q6E8/6T+PPOK+/9jWrco8fFj/eeQRdbrhBjU9PdZio44d1ajjL1Otb5k3T8179TqvpHhi/3598/rrGjRpUqWWAxX5sFjc1tcfMItnmV22bFm5bUeMGKH58+fro48+0rBhwxQeXvRB8czX1fnEu3z5cs2aNUtTp07V5MmTddNNN6nHGb2KK+qvf/2rLr/8cl1xRdH4KNu3b/cYu/HCCy/UkiVLylyHL8YknDt3rl566SWv1vfiiy+qffv27h4Hrc76UFmnTh1t3rxZWVlZJYpsO3fu1MmTJ8sdZ3Lo0KFq1KiRnn76acXExOiiiy5S3759tXXrVvfl7RXx+eef69SpU9q8ebPCwsI0ceLEEm0effRRLV++XJGRkXr22Wc9Jifx5vHwn53bfizq0WSPKPXy2uJlxZfLRkZGKywsXLm5pbTNzfb6eVu16aQ9Ozdr/95tFZ5ptix5ebk6sHebGjdtpWYtyv7x0dciHdEKt0coJyez1PuPHU1VVFQt9+XN3jh+7KD27tqiITfc88tCI8mj0Gkrt/DhizEJE5olKqFZxXqzREXFKDQ0rMS5k5eXK5vNplq16pzfY2yq8HrLku/M07ervtSw4WMVEhKir5fM1rEjBzwKfeW1adG6g3olD1FkZBQ9CGuIeV/MVes2bRQTE6PjaSWHIjh+epijhNOzDNepc4EcDofS0tJKtC1t2fkq/vxV/ANfRkaGvl6+TL169ylzjLhiBQUFCgsL12WXD9Bllw/Q8ePH9NBfHtCHH76vG4YPV2xsXIXW5ytbNm/W4sWL9OHHv8wEbYw8Lp8NCbGV+xnUF2MS9ktOVr+zxs8vT3Z2tkbeerMu63+5uxhbtDxLYV7+SPvtt2sUGRkph8Ozh3va8eO64/aR6t2nrx6Z8Jh7ecOGjbRx4wadOHHCPZ5mRRw+fEgfvP++7n/gwVLvX7P6G23cuEG1a9dWdnaORv+m6AevN15/Td9/v04REREa9/vx6tipcuP3+hJFQos4vmOHDm3cqIiYGO1YulR9f/97HdywQYsnTdLQ559X/Qsv1LyHH1Z+To5GzJihDbNna/kLL+jKxx7TtsWL1fbXv5Y9OlrHtm/Xlvnz5czJUev+/fXtm28qqm5dpe3apXaDB6t+26IZpTZ9/rkyjxxRVN26yj5+XL1/9zv98PHHMoWFOpWZqXqJiWrjRc+V+NMDw2YePSpv5gEKCQvTsMmTNeuee7R98WIt+r//08BSxhjLPHLEY/01ic1mU0Fenuqc1YU/fc8eNTv9JTKplAkaplx5pZp26+ZRkJvzpz/JFhqqoc8XzR5W4HRq37ff6rqXX65wXN9/+KEOb9qkkwcPVmo5MGPGDHXq1ElxcXE6dOhQifuLlxXPEly/fn1FR0eXWnyqyrHyij9g5uSUfSlbenq65syZo6uvvrrMsfDq169/zm3Mz8/XkSNH1LhxY48PdjfffLOuvfZade/eXR06dNBvfvMbrVu3zj3rbkWkpKRo5syZ2rRpk3uZMeasD5Ih5X6Q9MWYhEOHDq1wL8lmzZqpdu3acjqdHstdLpeMMSoopcf5nDlz9MMPP3gURjds2KADBw5o9OjRuvvuu93jXiYlJXkUQz/44ANdeeWVFf5B5eTJk7rxxhu1detW3XTTTapbt66Sk5PVv39/j3ajRo3S9OnTVa9ePY/iprePh//s3LZBrRI76VfN22rnth+VnnZEdc4Yl/DAvqJe0M1aFhXcQkJC1CShtfbu/knpPx91Tw4iFc0m663GTVuqabNE7di6Xhe266642mWPv+yt4l6DZ5/q2Vm+uXyuuPeNyxT1Dj55Ik37925Vh869ZbPZ1Kx5W+3Y9qOOH01V/BnjJubmZmvRvHc1cOhor4uExhilrF6ktu2TPMY4rBVb2305uCRlZaarVmzFfxDwh9DQMDVs3FwZJz0LLGnHD6t+w18pNKzk1ztvH1PR9Zbl8KG9iqsd737PqdegqQ7s2+5RJCyvTUhIiGrVql2h54VvzZ/3hQZfM0SXXT5AX8ydo+3bt6lNm18K3atWrJAkXX66kBYaGqreffpqyeKvtHPHDvekH5L0/bp1VRbX5s1Fn3UuPP1dNDw8XDabrcR7+JFzfJ7p06unvly0xF00io+vp4EDr9amjRuVmZmpunXjK7S+0hQXsQpPD8uwd88eLVu2VHeM9m5yMmOMXnjhOd108y1q2jTBvbxp06Y6cPrybqnoqpSmCQmlraLGCQkJkTPPWSLeffv2KannxV6tY+v//idHKT3y0n7+WceOHdPJ03McFEtP/9k9Mdz5mDb1DRlX6Z+ds7Oz9ewzT+vT2Z8rNDRUEx5+SBs3/KiOnTpr4NWD9NjfHlftOnVqbA/CYuVPA4ugsH7mTDW7+GIlXnml2l1zjWwhIWo7cKBa9Cnq0h8ZF6crHn7Y3b7T9derbsuWMsbo4rvuUv2LLlLz3r1Vr00btRs8WK3791fuiRP6bvp0dbj2WvW88059ebqnw8ENG/Tjp5+q129/qzaXX649q1Zp/7p1Sv3hB3UbOVKXjBmjlS+/7B7jpCwJ3bsrJCxMx7Zt83pbwyMjdcOrr6p+27ba8Omn+vrFkrMcHd26VdHx8Yo/PYaYPxRv/9n7wR4VpQ7DhqnZJb8MnHpowwZlHj2qfmVMMGBcrhLjxeSkp6vB6TdLSVr+wgtqd801HpOfeKvbrbeqw7XXVno5MGPGDG3YsEE33HCDUlNTtWHDBo/7582bJ0kaPny4pKIPmIMGDdKBAwc8Cl6Sd73+vPXdd0WXZnQr5/Vht9tL/aC4/4wPaFLRB5/ibdy4caPHfe+//75Hz7FixbP2JiQk6LnnntOmTZs06Tx64hpjNH78eI0fP96j512rVq3cl3FLRT3tWtfAH0tKExYWpquuukr79u3zWH7kyBF17NhRtWvXLvGYP//5z3r//fc1Y8YM9y02NlZ9+/bVjBkz3AXC4cOHe0xqkpeXp//+97/lTupSmri4OKWkpKhly5buImxpY3mFhYWpRYsWJXo/evt4+F/XHv0VFV1LKWu+co+Td/jgXm3f+oNaX9hFDRr+8mNf5+7JCgu3a+2ar5R3KlfGGO3d/T+lHS/5I0JZ+iYPVf2GCfrqPx9oz64tKiwsKo4XFhQodf8OLZz3riQptrb3Y32Fh9vVuGkrHTywWwf2bZdUNElFyuqKz+7tjYjIKEVEOHQyvajH0d7dW3TsyAH3/V2SLlN0TKy+W71QWZlFX+5ysjO1cunn+lXztqob783PxkV279ik7MwTJS5zbX1hF+3ZuUk52Zk6kX5Mqft3qvWFXSq/cdWkTduu2rNri/KdRZcC5+RkKnXfdo/tTN2/Q5+8/08dPLDL68d408ZbOdmZHpPb2O2RJQqQ5bU5efJnbf/fD9r+vx+0ecOaCscA3xk7dpzq12+gf05+XidOFBXc161bq88/n61hw65T166/fJb67W/HKioqSv+c/LxOnjwpl8ul//53iX76aUul4zDGaPPmTZr8/D8UGhqme07PcOxwOHRJr976ds0arVxRdFVYVlamXnjhH+dc1ysvv6js7KKetGlpaVq0cKGaN2+uNm0Sz2t9Z6tdu7bi4mpr966i1+TixV9p48YN5TzqFwsWzNehg4f0mzvv9lg+ZOgwLVz4pY4dO6Zdu3Zp9TffaNiwa71erz85HA4NHnyNks4YP3Hz5k06duyoxt47zr3sm1Wr9OsrL9e335bMA2lpaQovHi7hDG3atFHPnhdrzD2/dS/bsnmzDhw4oN+NHXdew8msXPG1OnToeM77v1+3Vi1atnCvu0PHTlpx+nwJDQ1V4yZNanyBUKInYVBY8vTT+t+CBZKk1B9+0Kv9+unyhx/WRVdf7W7TZsAAvTN8uJp07aouN9+sWqfH8TrzS67trFl+bKGhqtemzTl72zlq19bwqVP146xZiqhVSzmnx+H634IF7ktdoy64QLe+956WPP20JGn76cvamnbvLmduriKiyx6wOzIuTm0GDND2JUvU59573csLnE69ccUVyj89CceSZ57R1//8p367eLHC7HZF1Kql4W+8oQ9uu01rpk7Vxn//W91GjlSv3/5W+adOac+qVep0ww3VOg5BsYMbNijlnXd09PTkBv+ZMEGNOnZUx+uuU6vTXbT7jBunb15/XTnp6QqPjFReZqZu++gj1WnWrMT6NsyerU1z5ijz8GFt++orfZqRoV5jxqhJ1666YsIEpcyYocWTJikvM1P127ZVj9u8H38C8KWnnnpKixYt0h//+Ed98sknio+P17JlyzR16lTdfffduvSMiZH+/ve/a8GCBe62tWvX1meffaa1a9eW8QzeMcYoJSVF48ePV1hYWLkzHEdHR2vgwIFatGiR5s2bp2uuuUYnT57U+PHjS7Qt3sbx48frk08+Ub169bR+/Xo9+uijevzxx885u5ok3XPPPZo5c6YmTZqk66+/Xp06dTpn27O9//772rNnjxYtWuSx/K677tKTTz6p+++/XydOnNCCBQs0e/Zsr9frbw888ICuvvpqHT9+XPHx8UpPT9eXX36pN954w91mwYIFuv322/Xhhx+WOrlIYWGh+1f8YsePH9fAgQPd/z/00EMaOXKk+vSp+BdjSe7Lm7/99lslJia6i5Fn2rp1q958800ZY3Ty5Ek98MADFXo8ql5hYYE+fvd5dw+B1Sv+ozWrFpRo5yosVKvETnJExejqoaP1w9rlmvfvqTJGskdEqnO3frqog+ewLHG16+qqwaO0ds1izZ75iiIiHGr6qzbq0j1ZSxd9UvqUxaUIt0dowMAR2rNzs3ZsXa+1qxfJqOiHwuiYWNVrkKBuPQe4Zy5O3b9T33z9hfJO5Sg3J1OzPviXWid2VtekyzzW26f/UH3/3X+1ZsV8SVJUTJzad7pE+/du095dW3TwwE717neNvvl6nse6Bg27U3t2bdHmDUVjR23ZuEZ7d/+koTf+Vgf2bdfqFfM92vcbcL0aNPyVLuk7SOu+W6JP3v+natWqrd79fhlPy+GI1sCho/Xj2uX6cu7009sdqRatO6h9J+9nnszPd+r7lP+qS9JlCj9rPL02bbsp4+TPmjd7mmy2EHXqdqmaJJx7fKyaJqFZok7lZmv1ivmqc0F9/Zx2RL37DSlxGbqrsFDmdI9Nbx7j7Xq94XSe8vgSHhoapvx8Z4Xa/Kr5hYo/PZP1quVzdSh1d5Veao9fOJ1ODbisnwoLi86XSU89qWefKfkjZX5+vgZfM0R14+P11vR3NOW1V3XrLTfLZVyKi43VmHt+p1tG3OrxmGbNm+u1KVP14j8n69qhgxVXu7YuvbSffjv2Xt3/pz+U+VmoOLZhQwYr7/T3vskvPK9XXym6KiovL08xMTHq2KmTHn/i72p3xgR3jz/xf3rl5Zc06aknJRWNYz3yttu1fNlSLV68SKtXf6PP5sxVZKRDEx//Py1atFAjbh6ugoIC2SPs6t2rj+66e4z7B1xv1nfTzTfrk5kzJcm97K3p76hx48ay2Wx6+JEJevmlFzXwqgFq3KSJHvvb414dn5ycHL32yssae+84RZ/1/fn6G27Uvn37NHLEzQoJDdFdd4/xmJW5prv7nt/qrTen6sSJE4qMjFRWZpbenv6uEhJ++aHNyCg/v6DUq0ZatmpV6nKbzaZJzzynGdPfUm5urmSkgwdT9eRTT+vKq35d4TizsrKUnp6uXzVrpo0bSi/uHj16VLXjfum1Hhsbq/Xrf5Ak7d27V3M+/0zGGGVlZWnUbbdXOIZqY2AJJ1NTTU56uvlpwQIz/brrzOEtW4wxxsx/+GGz99tvjTHGpO/fbz684w73Yz684w5z4sABj/Wc2T5t927zzvDh5uc9e4wxxrx+xRWmsKDAfPXkk2bZCy94PG7JM8+UWOattN27zQtdu5ptS5ac1+PP9t2MGeblSy81uSdOVMn6rGTvt996nCPnuxzB6dSpUyYiIsKEhYUZSSYsLMxERESUuNlsNjN9+nRjjDEHDhwwd9xxh2nYsKGpX7++adu2rXnhhRdMYWFhifWvXbvW9OvXz8TExJjmzZub++67z8ybN89IMu+88065sTVo0MDExsYaSSY2NtY0aNDAvSwhIcHcfPPN5rvvvnM/5sknnzQNGjQwkkxkZKRp0KCB2b17tzHGmGPHjpk777zTNGzY0DRs2ND07NnTzJo1y6Ntdna2extHjx5tGjVqZBo0aGA6derk3n5jjJk6dWqJ5zl58qS5++673fFGR0ebHj16eHUcMjMzTePGjc3bb79d4r6CggIzfvx4U7duXVO/fn3z9NNPe7XOmmTu3LnmmmuuMePHjzcjRowwn3/+ucf98+fPN7GxsWb+/Pkey9966y3Tr18/I8k4HA4zePBgs2rVKmOMMRs3bjSjR4829913n7n99tvN5MmTSz0HKxrnyJEjTUpKSqn3n3mu3X777earr76q0OMRHHbv2GzenfakObBvu79DAby2af035vuUpaXeis/lnzZ9Z1av+CUPb/vfD2b5ktke6ymvzZl5eP3a5ea7bxb6apPgB4sWfml69uhqVq5c4e9QgHJ9OusTk5/vNGvXppgnJv6t1DbT337TPD3pSff/ixZ+aX4/bqwxxpjNmza5lz8+8THz7Zo1vg24EuhJaBFr339fl44fr7YDB8oeHa2TqalqcNFFCo+Kcs8cnLZzp8xZvSvKsmPpUv2qZ0/VadZMmafHYzi4fr0Sr7pKyydPljFGNptNe9esUevLL9fX//qX+7FbFy1Sq/79zzn78JkuaN5cgyZN0qLHH1dso0blzuhblt2rVmnNtGm67qWXmN33tG/fekvOrJKDqUtSk27d1PKM3lxAWSIiItwDRnurSZMmmjFjhldtu3fvruXLl3ssm3n619r4+LLH5oqIiKjwuHoTJkzQhAkTSr0vPj5eb731VonlppTx/Zo0aaLp06ef83nGjBmjMWPGlFg+bdo0TZs2rQIRF4mJiVFqamqp94WGhurFF1/Ui6UMwxAohgwZoiFDhpzz/kGDBpUYf0aS7rzzTt15Z+mzpXbo0KHMY1TsueeeU8Y5JtLq06ePrj6jB/+QIUM0cOBAde3aVS+88IJ+/WvPX63PvKy9RYsW+uKLL9wTzHjzeASWn9MOa9tP3+uSvoM8lh87ekAhIaHu3lJAIGjfueRwGWerFXuBx5ibeadyFB1dy+s2qft3aP3a5Rp83V2SpIICp2w2RsoKRNu2btXs2Z/qrw97fqbauHGDwsPD1aF9Bz9FBkjvvTvDfZn52Tp16qLeffpoy+bNurBtW/eYuucSHR3t8V0gNzfXPfbhhWcMAda4cWOtWPG1e4bqmoYioUWERUTox1mz5KhdW5mHD6vHHXdIkjrfeKO2zJun7OPHFRIerp/37NHGzz5TSFiY0nbu1KpXX9XFd92luq1aaefy5Trwww86lZGhsIgIJV55pVa9+qq2//e/Mi6XmvfqpT1r1qjP2LHqNmKElr3wguJbtZKjTh217t9fXYYP1/LJk1U7IUGxjRp5VSAs1nbgQEXHx2vPqlWVKhLu++473TJ9uur5cSzCmubiu+7ydwhAudavX68pU6ZoypQpHstXr14tu92ui2vomyyCy1/OmHH+XBYsWKAHH3xQmzZtUnh4uDp27KiXXnrJo8i3YMECPfroo1p3etD2rKws9+VM3jwegacgP187tv6oRk1aqFmLi+RyuXRg33bt2Lpe7Ttd4jEuGxAMGjT6lVJWL5TLVaiQkFAdPbzffbn43l0/qWHj5mW2iYqOVdv2v0wmlXbskLr1LH/SQ9Q8Obk5mjtnjpJ6XqwBA65QYWGhVq74WnPnfK5Rt92huFLGFAaqy223jy63zYYNPyovL0/r1q7V7t27tGvXTn3+2Wxde931Hu2aNk3wGAopPT1d9es30DerVmnKlFf17nsfSpJycnK9nr3ZHygSWsS5Jryo37ate0ZiSR7jGLY/q6dGq+Rk95h5xQafHmt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      "text/plain": [
       "<Figure size 1280x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 853.36x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#***** Plotting magic *****#\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from POSEIDON.visuals import plot_histograms\n",
    "\n",
    "fig_combined = plt.figure(constrained_layout=True, figsize=(16,9))    # Change (9,5.5) to alter the aspect ratio\n",
    "\n",
    "# This function is the magic. Each letter corresponds to one matplotlib axis, which you can then pass to POSEIDON's plotting functions\n",
    "axd = fig_combined.subplot_mosaic(\n",
    "    \"\"\"\n",
    "    AAAA\n",
    "    AAAA\n",
    "    abcd\n",
    "    \"\"\"\n",
    ")\n",
    "\n",
    "# Read retrieved spectrum confidence regions\n",
    "wl, spec_low2, spec_low1, spec_median, \\\n",
    "spec_high1, spec_high2 = read_retrieved_spectrum(planet_name, model_name_log_clr)\n",
    "\n",
    "# Convert to Fp/Fs \n",
    "d = 1        # This value only used for flux ratios, so it cancels\n",
    "\n",
    "# Load stellar spectrum\n",
    "F_s = star['F_star']\n",
    "R_s = star['R_s']\n",
    "\n",
    "# Convert stellar surface flux to observed flux at Earth\n",
    "F_s_obs = (R_s / d)**2 * F_s\n",
    "\n",
    "spec_low2 = spec_low2 / F_s_obs\n",
    "spec_low1 = spec_low1 / F_s_obs\n",
    "spec_median = spec_median / F_s_obs\n",
    "spec_high1 = spec_high1 / F_s_obs\n",
    "spec_high2 = spec_high2 / F_s_obs\n",
    "\n",
    "# Plot\n",
    "# Create composite spectra objects for plotting\n",
    "spectra_median = plot_collection(spec_median, wl, collection = [])\n",
    "spectra_low1 = plot_collection(spec_low1, wl, collection = []) \n",
    "spectra_low2 = plot_collection(spec_low2, wl, collection = []) \n",
    "spectra_high1 = plot_collection(spec_high1, wl, collection = []) \n",
    "spectra_high2 = plot_collection(spec_high2, wl, collection = [])\n",
    "\n",
    "# Produce figure\n",
    "ax_spectrum = axd['A']\n",
    "_ = plot_spectra_retrieved(spectra_median, spectra_low2, spectra_low1, \n",
    "                        spectra_high1, spectra_high2, planet_name,\n",
    "                        data, R_to_bin = 150, show_ymodel = False,\n",
    "                        colour_list = ['brown'], \n",
    "                        spectra_labels = ['Retrieved Spectrum'],\n",
    "                        plt_label = 'Log Percentages, CLR Prior',\n",
    "                        figure_shape = 'wide', save_fig = False,\n",
    "                        ax = ax_spectrum,\n",
    "                        sigma_to_plot = 2,\n",
    "                        y_unit = 'eclipse_depth',\n",
    "                        legend_location = 'lower right',\n",
    "                        wl_axis = 'linear',\n",
    "                        data_marker_size_list = [5],\n",
    "                        data_colour_list = ['black'],\n",
    "                        y_min = 0, y_max = 3.6e-4,\n",
    "                        data_labels = ['Simulated MIRI Data (R=15)'],\n",
    "                        wl_max = 13,\n",
    "                        line_alpha_list = [1]\n",
    "                        )\n",
    "\n",
    "# Plot histograms \n",
    "axes_histograms = [axd[\"a\"],axd[\"b\"],axd[\"c\"],axd[\"d\"]]\n",
    "\n",
    "models = [model_log_clr]\n",
    "\n",
    "_ = plot_histograms(planet, models, plot_parameters = ['T_surf','log_Black_percentage',\n",
    "                                                       'log_Granitoid_H12_percentage',\n",
    "                                                       'log_Tholeiitic_basalt_H25_percentage'], \n",
    "                    span = ((1000,2000),(-12,0),(-12,0),(-12,0)),\n",
    "                    N_bins = [25,25,25,25],\n",
    "                    parameter_colour_list = [\"#892a2a\",'black','#a29b84',\"#363431\"],\n",
    "                    axes = axes_histograms, save_fig = False,\n",
    "                    tick_labelsize = 14,                          # x axis tick labels\n",
    "                    title_fontsize = 16,                          # Title size\n",
    "                    alpha_hist = [1,1,1,1],\n",
    "                    title_alpha_list = [1,1,1,1],\n",
    "                    custom_labels = ['T$_\\mathrm{surface}$ (K)', 'Log Black %','Log Granite %','Log Basalt %'],\n",
    "                    title_vert_spacing = 0.2,\n",
    "                    title_colour_list = [\"#892a2a\",'black','#a29b84',\"#363431\"],\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06f27eb7",
   "metadata": {},
   "source": [
    "There we go! Looks like granite is the most likely surface component of this planet due to its strong feature at 8-9 microns (which is indicative of the mineral quartz), whereas basalt has a broader feature that is disfavored by the simualted data. Indeed, the data above was made from a forward model where granite was assumed to be 100% of the surface of this planet. \n",
    "\n",
    "From here-on, one can perform retrievals with just a single surface component and compare bayesian evidences to see if we are truly detecting a singular surface component, or one can explore what the absorption feature would look like if the planet's surface was a combination of 50% granite and 50% basalt.\n",
    "\n",
    "If this was for a JWST proposal, once could explore adding on existing datasets (i.e., the recently published G395H eclipse dataset for this planet) to see if helps constrain the surface in conjunction with the MIRI data, or test whether or not you would need more MIRI transits to confidently detect granite, basalt, or a mix of the two. \n",
    "\n",
    "Either way, JWST provides the unique mid-infrared capability to begin explore exo-geology on exoplanet surfaces. "
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "POSEIDON-V12",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
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   "file_extension": ".py",
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