{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Visibility Simulator Examples\n",
    "\n",
    "Although ``hera_sim`` is primarily aimed at simulating instrumental effects on top of existing visibility data, the package also has the `visibilities` module that offers a uniform visibility simulation interface to wrappers for several visibility simulators, as well as a few analytic beam models of the HERA antenna."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visibility Simulation Interface\n",
    "\n",
    "Starting from versions of `hera_sim >= 2.0.0`, wrappers for [VisCPU](https://github.com/HERA-Team/vis_cpu), [healvis](https://github.com/rasg-affiliates/healvis), and [pyuvsim](https://github.com/RadioAstronomySoftwareGroup/pyuvsim) visibility simulators are provided through the `VisCPU`, `Healvis`, and `UVSim` classes.\n",
    "\n",
    "The new `ModelData` object serves as a container for the visiblity data and all information required to perform visibility simulation. A preferred method to initialize this object is to use the classmethod `from_config`, providing [pyuvsim configuration files](https://pyuvsim.readthedocs.io/en/latest/parameter_files.html) that specify the observation and telescope parameters, beam model, and sky model. Direct initialization is also supported and currently required to utilize analytic HERA beam models in the `hera_sim.beams` module (see [Manual Initialization](###-Manual-Initialization)). Under the hood, ModelData wraps three objects used for facilitating simulation in pyuvsim, each encapsulating different information required for simulating visibility.\n",
    "* `pyuvdata.UVData`\n",
    "    * A data container for observation parameters, array layout, telescope location, and visibility.\n",
    "    * Accesible from the `uvdata` property.\n",
    "    * Package reference: https://pyuvdata.readthedocs.io/en/latest/index.html\n",
    "* `pyradiosky.SkyModel`\n",
    "    * A data container for sky models, including catalog of sources and diffuse sky maps in HEALPix.\n",
    "    * Provide methods for conversion from a catalog of point sources to a HEALPix map and vice versa, which is used by the visibility simulator wrappers.\n",
    "    * Accesible from the `sky_model` property.\n",
    "    * Package reference: https://pyradiosky.readthedocs.io/en/latest/index.html\n",
    "* `pyuvsim.BeamList` and the associated `beam_ids`\n",
    "    * Specify the antenna beam models and which beam model each antenna uses. (See [Manual Initialization](###-Manual-Initialization) for supported beam types.)\n",
    "    * Accesible from the `beams` and `beam_ids` properties.\n",
    "    * Reference: https://pyuvsim.readthedocs.io/en/latest/classes.html#pyuvsim.BeamList\n",
    "\n",
    "The `VisibilitySimulation` class is the new uniform visibility simulation interface. It takes an instance of a `VisibilitySimulator` class (of which `VisCPU`, `HealVis` and `UVSim` are examples) and a `ModelData` object as inputs. Visibility simulation can then be executed simply by calling the `simulate` method on an instance of a `VisibilitySimulation` class. This returns a Numpy array of visibilities in the same shape as in the `UVData.data_array` specification, which is also added to the object property `VisibilitySimulation.data_model.uvdata.data_array`.\n",
    "\n",
    "The motivation behind the `VisibilitySimulation`-`VisibilitySimulator`-`ModelData` setup is to provide a single uniform interface to many simulators, and the ability to create wrappers for simulators that are not already provided. This interface is heavily based on the pyuvsim configuration specifications and infrastructures, and thus it provides the ability to perform simulation with any simulator given the same static configuration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 1: Demonstrate the Uniform Visibility Simulator Interface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example simulates visiblities with VisCPU at 10 frequencies and 60 \n",
    "times with the 50 brightest GLEAM sources as a sky model and the HERA Phase I \n",
    "array as the instrument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "basis_vector_type was not defined, defaulting to azimuth and zenith_angle.\n",
      "Unknown polarization basis -- assuming linearly polarized (x/y) feeds for Telescope.feed_array.\n",
      "mount_type, antenna_diameters are not set or are being overwritten. mount_type, antenna_diameters are set using values from known telescopes for HERA.\n",
      "The default baseline conjugation convention has changed. In the past it was 'ant2<ant1', it now defaults to 'ant1<ant2'. You can specify the baseline conjugation convention in `obs_param` by setting the obs_param['ordering']['conjugation_convention'] field. This warning will go away in version 1.5.\n",
      "Parameter skycoord not found in skyh5 file. This skyh5 file was written by an older version of pyradiosky. Consider re-writing this file to ensure future compatibility\n",
      "No frame available in this file, assuming 'icrs'. Consider re-writing this file to ensure future compatibility.\n"
     ]
    }
   ],
   "source": [
    "from hera_sim import DATA_PATH\n",
    "from hera_sim.visibilities import MatVis, ModelData, VisibilitySimulation\n",
    "\n",
    "# Path to the example pyuvsim configuration files.\n",
    "# These can be found in `hera_sim.DATA_PATH` to follow along.\n",
    "config_file = (\n",
    "    DATA_PATH\n",
    "    / \"tutorials_data/visibility_simulator/obsparam_hera_phase1_gleam_top50.yaml\"\n",
    ").as_posix()\n",
    "\n",
    "\n",
    "# Initialize a ModelData object.\n",
    "# The pyuvsim configuration files consist of a few yaml and csv files, and a\n",
    "# sky model file readable by `pyradiosky`. `ModelData.from_config` takes the\n",
    "# outermost file obsparam_*.yaml as an input.\n",
    "\n",
    "data_model = ModelData.from_config(config_file)\n",
    "\n",
    "# Initialize a MatVis simulator.\n",
    "simulator = MatVis()\n",
    "# simulator = UVSim(quiet=True)\n",
    "\n",
    "# Construct a VisibilitySimulation object.\n",
    "simulation = VisibilitySimulation(data_model=data_model, simulator=simulator)\n",
    "\n",
    "# Executing the simulation by calling `simulate` method.\n",
    "visibility_array = simulation.simulate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Accessing Simulation Parameters and Visibility\n",
    "\n",
    "All simulation parameters and visibility data are contained in the `ModelData` object and can be retrieved from the corresponding object attributes and properties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 2: Plot the array layout of the simulation in Example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pyuvdata import utils as uvutils\n",
    "\n",
    "uvd = simulation.data_model.uvdata\n",
    "\n",
    "# Get antennas positions and corresponding antenna numbers\n",
    "antpos, antnum = uvd.get_enu_data_ants()\n",
    "\n",
    "# Plot the EN antenna position.\n",
    "plt.scatter(antpos[:, 0], antpos[:, 1])\n",
    "for i, antnum in enumerate(uvd.telescope.antenna_numbers):\n",
    "    plt.text(antpos[i, 0], antpos[i, 1], antnum)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 3: Plot the source positions in the SkyModel used in Example 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of sky model: point\n",
      "Number of sources: 50\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sky_model = simulation.data_model.sky_model\n",
    "print(\"Type of sky model:\", sky_model.component_type)\n",
    "print(\"Number of sources:\", sky_model.Ncomponents)\n",
    "\n",
    "# Extract the source positions and fluxes\n",
    "ra = sky_model.ra\n",
    "dec = sky_model.dec\n",
    "flux = sky_model.stokes[0, 0, :]\n",
    "\n",
    "# Plot the source positions with color and size showing source fluxes.\n",
    "plt.scatter(ra.deg, dec.deg, s=flux.to_value(\"Jy\"), c=flux.to_value(\"Jy\"))\n",
    "plt.colorbar(label=\"Flux Density [Jy]\")\n",
    "plt.xlabel(\"RA [degree]\")\n",
    "plt.ylabel(\"Dec [degree]\")\n",
    "plt.title(\"50 Brightest GLEAM Sources\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 4: Plot the waterfall of the visibility simulation in Example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "bls = (0, 1, \"xx\")\n",
    "waterfall = uvd.get_data(bls)\n",
    "extent = [\n",
    "    uvd.freq_array[0] / 1e6,\n",
    "    uvd.freq_array[-1] / 1e6,\n",
    "    uvd.time_array[-1] - 2458101,\n",
    "    uvd.time_array[0] - 2458101,\n",
    "]\n",
    "plt.imshow(np.abs(waterfall), aspect=\"auto\", interpolation=\"none\", extent=extent)\n",
    "plt.xlabel(\"Frequency [MHz]\")\n",
    "plt.ylabel(\"JD - 2458101\")\n",
    "plt.colorbar(label=f\"Visibility Amplitude [{uvd.vis_units}]\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more comprehensive visualization of visibility data (e.g. waterfalls of differences between visibilities, Fourier Transform of a waterfall, and etc), users may be interested in plotting routines in the HERA [uvtools](https://github.com/HERA-Team/uvtools) package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Initializing Model Data\n",
    "\n",
    "As mentioned, the `ModelData` object contains all the data and information required to perform visibility simulation with `hera_sim`.\n",
    "\n",
    "The prefered method to initlize a ModelData object is to generate a set pyuvsim configuration files (see https://pyuvsim.readthedocs.io/en/latest/parameter_files.html) and then pass the top-level <em>obsparams_*.yaml</em> file to the classmethod `from_config`.\n",
    "\n",
    "```python\n",
    "data_model = ModelData.from_config(config_file)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Manual Initialization\n",
    "\n",
    "If not using configuration files, the underlying objects can be first created and passed to the `ModelData` constructor for manual initialization. \n",
    "\n",
    "Users may consider using `hera_sim.io.empty_uvdata`, `pyuvsim.simsetup.initialize_uvdata_from_params`, or `pyuvsim.simsetup.initialize_uvdata_from_keywords` to construct a UVData object. \n",
    "\n",
    "See the next section on how to construct a sky model with `pyradiosky.SkyModel`.\n",
    "\n",
    "The beam models given to the `beams` parameter can be: i) a `pyuvsim.BeamList` object, ii) a `pyuvdata.UVBeam`, or iii) a list of `pyuvsim.AnalyticBeam`, including its subclasses.\n",
    "\n",
    "The `hera_sim.beams` module provides analytic models of the HERA antenna through the `PolyBeam`, `PerturbedPolyBeam`, and `ZernikeBeam`  objects (see HERA Memo [#081](http://reionization.org/wp-content/uploads/2013/03/HERA081_HERA_Primary_Beam_Chebyshev_Apr2020.pdf) and [#101](http://reionization.org/manual_uploads/HERA101_Analytic_polarized_beam.pdf), and [Choudhuri et al 2021](https://arxiv.org/abs/2101.02684)). These beam models are subclasses of `pyuvdata.analytic_beam.AnalyticBeam`, and so are recognizable by the pyuvsim configuration file parser."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 5: Manaully initialize a `ModelData` object with `PolyBeam`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Unknown polarization basis -- assuming linearly polarized (x/y) feeds for Telescope.feed_array.\n",
      "mount_type, antenna_diameters are not set or are being overwritten. mount_type, antenna_diameters are set using values from known telescopes for HERA.\n",
      "The default baseline conjugation convention has changed. In the past it was 'ant2<ant1', it now defaults to 'ant1<ant2'. You can specify the baseline conjugation convention in `obs_param` by setting the obs_param['ordering']['conjugation_convention'] field. This warning will go away in version 1.5.\n",
      "Parameter skycoord not found in skyh5 file. This skyh5 file was written by an older version of pyradiosky. Consider re-writing this file to ensure future compatibility\n",
      "No frame available in this file, assuming 'icrs'. Consider re-writing this file to ensure future compatibility.\n"
     ]
    }
   ],
   "source": [
    "from pyradiosky import SkyModel\n",
    "from pyuvsim.simsetup import _complete_uvdata, initialize_uvdata_from_params\n",
    "\n",
    "# Here, the configuration file uses the `PolyBeam` defined in hera_sim directly in the\n",
    "# pyuvsim configuration file, as the format accepts custom beams.\n",
    "uvdata, beams, beam_ids = initialize_uvdata_from_params(config_file, return_beams=True)\n",
    "_complete_uvdata(uvdata, inplace=True)\n",
    "\n",
    "\n",
    "# Load the SkyModel. Same model used in Example 1.\n",
    "sky_model_file = (\n",
    "    DATA_PATH / \"tutorials_data/visibility_simulator/gleam_top50.skyh5\"\n",
    ").as_posix()\n",
    "sky_model = SkyModel()\n",
    "sky_model.read_skyh5(sky_model_file)\n",
    "\n",
    "# Construct ModelData\n",
    "data_model_polybeam = ModelData(\n",
    "    uvdata=uvdata, sky_model=sky_model, beam_ids=beam_ids, beams=beams\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Constructing a SkyModel\n",
    "\n",
    "`pyradiosky.SkyModel` is both a container and an interface for representing astrophysical radio sources. \n",
    "\n",
    "A `SkyModel` object will usually consist of many components that represent either compact point sources or pixels of a HEALPix map. This choice can be set by specifying `component_type=\"point\"` or `component_type=\"healpix\"` when constructing the object. Doing so will determine which parameters are required as followed.\n",
    "* If `component_type=\"point\"`, the `name` of each source and the source position (`ra` and `dec`) are required.\n",
    "* If `component_type=\"healpix\"`, the `nside` parameter of the HEALPix map, the indices of the HEALPix pixel `hpx_inds`, and the HEALPix pixel ordering `hpx_order` must be set. \n",
    "* If `component_type` is not set, the type is infered from weather `nside` is given.\n",
    "\n",
    "The component flux is given per frequency as an array of Stoke IQUV via the `stokes` argument (Shape: (4, Nfreqs, Ncomponents)). In addition, the `spectral_type` must be specified, which can be:\n",
    "* \"flat\" : Flat spectrum.\n",
    "* \"full\" : Flux is defined at each frequency given in the `freq_array` attribute.\n",
    "* \"subband\" : Flux is defined at a set of band centers given in the `freq_array` attribute.\n",
    "* \"spectral_index\" : Flux is defined at a reference frequency set in the `reference_frequency` attribute and follows a power law given a spectral index set in the `spectral_index` attribute.\n",
    "\n",
    "The method `at_frequencies` can be used to evaluate the stokes array of a SkyModel object at specified frequencies, converting the object to a \"full\" spectral type when `inplace=True`, or yielding a new instance of the object with a \"full\" spectral type if `inplace=False`.\n",
    "\n",
    "The classmethods `pyradiosky.from_gleam_catalog`, `pyradiosky.from_votable_catalog`, and `pyradiosky.from_text_catalog` can be used to `SkyModel` object from the GLEAM EGC catalog file, a votable catalog file, or from a tab separated value file. The `pyuvsim.simsetup.create_mock_catalog` method can also be used to create a mock catalog of point source and/or diffuse sky\n",
    "\n",
    "Loading a HEALPix map into a SkyModel object is currently only possible via the native skyh5 file format. Thus, a diffuse sky map in a HEALPix FITS format (e.g., Haslam 408 MHz) must be manually initialized from the HEALPix array values and indices.\n",
    "\n",
    "A method for converting a `SkyModel` object from one component type to another is provided, which can be used to convert a  \"healpix\" model to \"point\" and combine with another \"point\"-type model, for example. This conversion is used by `hera_sim` in its `matvis` and `fftvis` wrappers.\n",
    "\n",
    "Below, we show how to create a random point source model and a uniformly distrubuted diffuse model, and combine them into a single SkyModel object. Please refer to the [pyradiosky developer API](https://pyradiosky.readthedocs.io/en/latest/index.html) for details of the objects parameters and methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Example 6: Construct a random point source SkyModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SkyModel History:\n",
      "  Generate a random polarized source catalog with 50 sources.\n",
      "  Read/written with pyradiosky version: 1.0.1.\n",
      "Type of sky model: point\n",
      "Number of components = number of sources: 100\n"
     ]
    }
   ],
   "source": [
    "from astropy import units as u\n",
    "from astropy.coordinates import Latitude, Longitude\n",
    "from pyradiosky import SkyModel\n",
    "\n",
    "# Point Soruce Model ##\n",
    "component_type = \"point\"\n",
    "\n",
    "# Each source is a component in a SkyModel.\n",
    "# Create a sky model with 50 random sources (i.e. 50 point source components).\n",
    "nsources = 100\n",
    "\n",
    "# `ra` and `dec` must be Longitude and Latitude astropy quantity\n",
    "ra = Longitude(np.random.uniform(0, 360, nsources) * u.degree)\n",
    "dec = Latitude(np.random.uniform(-90, 90, nsources) * u.degree)\n",
    "\n",
    "# `names` are required for point soruces\n",
    "names = [f\"rand{i:02d}\" for i in range(nsources)]\n",
    "\n",
    "# SkyModel supports several spectral type. We will use \"spectral_index\" here,\n",
    "# where the flux of each source is describe by a power law given a reference\n",
    "# frequency and a spectral index.\n",
    "spectral_type = \"spectral_index\"\n",
    "spectral_index = np.random.uniform(-4, 3, nsources)\n",
    "reference_frequency = 200e6 * np.ones(nsources) * u.Hz\n",
    "\n",
    "# Source fluxes must be given in Stokes IQUV as an array of astropy quantity\n",
    "# with an appropriate unit. Shape (4, Nfreqs, Ncomponents).\n",
    "# Nfreqs is 1 here because we are using \"spctral_index\".\n",
    "stokes = np.zeros((4, 1, nsources)) * u.Jy\n",
    "stokes[0, :, :] = np.random.uniform(10, 100, nsources) * u.Jy\n",
    "# Let's also give our sources ~10% linar polarization\n",
    "stokes[1, :, :] = np.random.uniform(1, 5, nsources) * u.Jy\n",
    "stokes[2, :, :] = np.random.uniform(1, 5, nsources) * u.Jy\n",
    "\n",
    "# Collect all parameters in a dictionary to be passed to a SkyModel constructor\n",
    "source_params = {\n",
    "    \"component_type\": component_type,\n",
    "    \"name\": names,\n",
    "    \"ra\": ra,\n",
    "    \"dec\": dec,\n",
    "    \"stokes\": stokes,\n",
    "    \"spectral_type\": spectral_type,\n",
    "    \"reference_frequency\": reference_frequency,\n",
    "    \"spectral_index\": spectral_index,\n",
    "    \"history\": \" Generate a random polarized source catalog with 50 sources.\\n\",\n",
    "    \"frame\": 'icrs'\n",
    "}\n",
    "\n",
    "source_model = SkyModel(**source_params)\n",
    "\n",
    "print(\"SkyModel History:\\n\", source_model.history)\n",
    "print(\"Type of sky model:\", source_model.component_type)\n",
    "print(\"Number of components = number of sources:\", source_model.Ncomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 7: Construct a uniformly distributed diffuse HEALPix SkyModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SkyModel History:\n",
      "  Create a diffuse \"rainbow\" sky with a flat spectra.\n",
      "  Read/written with pyradiosky version: 1.0.1.\n",
      "Type of sky model: healpix\n",
      "Nside: 8\n",
      "Number of components = number of healpix pixels: 768\n"
     ]
    }
   ],
   "source": [
    "# Create a diffuse HEALPix SkyModel with a \"rainbow\" sky and flat spectra.\n",
    "component_type = \"healpix\"\n",
    "nside = 2**3\n",
    "npix = 12 * nside**2\n",
    "hpx_order = \"ring\"\n",
    "hpx_inds = np.arange(npix)\n",
    "\n",
    "spectral_type = \"flat\"\n",
    "\n",
    "stokes = np.zeros((4, 1, npix)) * u.K\n",
    "stokes[0, :, :] = np.random.uniform(400, 500, npix) * u.K\n",
    "\n",
    "diffuse_params = {\n",
    "    \"component_type\": component_type,\n",
    "    \"nside\": nside,\n",
    "    \"hpx_inds\": hpx_inds,\n",
    "    \"hpx_order\": hpx_order,\n",
    "    \"spectral_type\": spectral_type,\n",
    "    \"stokes\": stokes,\n",
    "    \"history\": ' Create a diffuse \"rainbow\" sky with a flat spectra.\\n',\n",
    "    \"frame\": 'icrs'\n",
    "}\n",
    "diffuse_model = SkyModel(**diffuse_params)\n",
    "print(\"SkyModel History:\\n\", diffuse_model.history)\n",
    "print(\"Type of sky model:\", diffuse_model.component_type)\n",
    "print(\"Nside:\", diffuse_model.nside)\n",
    "print(\"Number of components = number of healpix pixels:\", diffuse_model.Ncomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 8: Convert one SkyModel type to another and concatnate two SkyModel objects\n",
    "\n",
    "One model type can be converted to another type and concatenated to form a composite model. The concatnation can only be done if `component_type`, `spectral_type`, and `freq_array` of both objects matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate both sky models at frequencies in Example 1 simulation before\n",
    "# concatenating.\n",
    "frequencies = simulation.data_model.uvdata.freq_array * u.Hz\n",
    "source_model_full = source_model.at_frequencies(frequencies, inplace=False)\n",
    "diffuse_model_full = diffuse_model.at_frequencies(frequencies, inplace=False)\n",
    "\n",
    "# Drop spectral_index from the source model before combining.\n",
    "# See GitHub issue https://github.com/RadioAstronomySoftwareGroup/pyradiosky/issues/160\n",
    "source_model_full.spectral_index = None\n",
    "\n",
    "# Convert the diffuse model to \"point\" type, making sure the flux unit\n",
    "# is also converted to jansky\n",
    "diffuse_model_full.healpix_to_point(to_jy=True)\n",
    "composite_model = source_model_full.concat(diffuse_model_full, inplace=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run some simulations with these sky models and other speciication the same as in [Example 1](####-Example-1:-Demonstrate-the-Unifrom-Visibility-Simulator-Interface) and compare the output visibilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Unknown polarization basis -- assuming linearly polarized (x/y) feeds for Telescope.feed_array.\n",
      "mount_type, antenna_diameters are not set or are being overwritten. mount_type, antenna_diameters are set using values from known telescopes for HERA.\n",
      "The default baseline conjugation convention has changed. In the past it was 'ant2<ant1', it now defaults to 'ant1<ant2'. You can specify the baseline conjugation convention in `obs_param` by setting the obs_param['ordering']['conjugation_convention'] field. This warning will go away in version 1.5.\n",
      "Unknown polarization basis -- assuming linearly polarized (x/y) feeds for Telescope.feed_array.\n",
      "mount_type, antenna_diameters are not set or are being overwritten. mount_type, antenna_diameters are set using values from known telescopes for HERA.\n",
      "The default baseline conjugation convention has changed. In the past it was 'ant2<ant1', it now defaults to 'ant1<ant2'. You can specify the baseline conjugation convention in `obs_param` by setting the obs_param['ordering']['conjugation_convention'] field. This warning will go away in version 1.5.\n",
      "Unknown polarization basis -- assuming linearly polarized (x/y) feeds for Telescope.feed_array.\n",
      "mount_type, antenna_diameters are not set or are being overwritten. mount_type, antenna_diameters are set using values from known telescopes for HERA.\n",
      "The default baseline conjugation convention has changed. In the past it was 'ant2<ant1', it now defaults to 'ant1<ant2'. You can specify the baseline conjugation convention in `obs_param` by setting the obs_param['ordering']['conjugation_convention'] field. This warning will go away in version 1.5.\n"
     ]
    }
   ],
   "source": [
    "# Define a function to run the simulation.\n",
    "def run_simulation(sky_model):\n",
    "    \"\"\"Create and run a simulation given a sky model.\"\"\"\n",
    "    # Use the same config file as in Example 1 to initialize UVdata and beams.\n",
    "    uvdata, beams, beam_ids = initialize_uvdata_from_params(\n",
    "        config_file, return_beams=True\n",
    "    )\n",
    "    _complete_uvdata(uvdata, inplace=True)\n",
    "\n",
    "    # Initialize ModelData given the SkyModel\n",
    "    data_model = ModelData(\n",
    "        uvdata=uvdata, sky_model=sky_model, beams=beams, beam_ids=beam_ids\n",
    "    )\n",
    "\n",
    "    # Initialize a VisibilitySimulation instance, using MatVis\n",
    "    simulation = VisibilitySimulation(data_model=data_model, simulator=MatVis())\n",
    "    _ = simulation.simulate()\n",
    "\n",
    "    return simulation\n",
    "\n",
    "\n",
    "source_sim = run_simulation(source_model_full)\n",
    "diffuse_sim = run_simulation(diffuse_model_full)\n",
    "composite_sim = run_simulation(composite_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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MCUlGs3RPT0/i4+OfakVFKimFSPZy2oHOr2KnsS9maR5AQKuOJocdfIVAK15foRFwlraIdhjOF0L2k4D3E7nvrl2s6MS6fgZTBjsTlqrPdVuSkZFBQpKRk7954eKS9btv3zbh0/gyGRkZUkmRFA7Z//j2pVyx0zoUszQPIKBCIORDEoRU6ES8fkL2k4gKgYjKHAh5/RTBrp9iTAcK91nlUEbBoUxWwK2DAIG33bt3z/c5CxYswMPDw2YySCVFIpFIJBIByFRMZCr/fC9u1q9fT48ePXB2draqfGxsLKmpqVJJKWloSjuj0ToWtxj/IOCoSUiZAEUwkzMgZl+JKJO8dtYjoFyKTiyZFOOjyzwuaYqC/X0LSpoAlhSAzz77zGqlY82aNTZvXyopEolEIpEIgAENmWjU78XNjh07KF++vNXlN23aROXKlW0qg1RSigDF0RFFJ5AlRbC5XkDIkRyAIqIPgYAiiehroYh4T4nXTVkIef2KWwJzTIbCN6WkKVrsFO3974Xe3CNp1apVvsq3bNnS5jLYREk5e/YsAwYMYPv27bao7olDcXZA0YnjOCvkw1sw066KgH0l2sMbRJ0WK24BckDA+wnEc1IFhFPoTIbCH9OnKXYPKCnF75PyIK1ataJfv37861//stpHxRbY5DZITU1l165dtqhKIpFIJJKnEoOiJfP+x6CIpaX5+fkxatQoPD09GTBgAPv27SuSdq1SDT/77LM88y9dumQTYZ5UjE52aOxEWieluAWwRMiRHIg58hXr2QWIef1EtBgK9t75ByGvX3FLYI7BUPih/2mKHTpFd/97EXjq5oPo6GhmzZrFhg0b+PrrrwkMDKRmzZr07duX3r17U7FixUJp1yol5Z133qFSpUo4OOQ8ZfHwUr4SiUQikUjyR6aiI/O+kpIpgE/Kw9jZ2dG9e3e6d+9OUlISX375JePHj2fcuHF06tSJESNG0KZNG9u2aU0hvV7Pxx9/TI8ePXLMj4uLo1GjRjYV7EnC5KTDZCfOAlwijjBFtO6AeGGQIN4IE8S0pIh4TwnZTyBoXxW3BOYYMwtfoHTFQbWkpAtmSXmQAwcOsHTpUr755hs8PDwIDw/n0qVLdO7cmSFDhjBr1iybtWWVktKoUSMOHjyYq5Ki0WhQBInpFhGjgw6NvUBKimD//ICQD0kQ86Ui4vUTsZ9EvKdEVDBBzHtKtKlWYxFEQGUoOuzuKykZgr1Sk5KSWLFiBUuXLuX06dN06dKFVatWERwcrK7CGx4eTocOHYpeSZkyZQp3797NNd/X15f4+HibCSWRSCQSydNGumKH1mR3/7tYSpq3tzc1atSgb9++hIeH4+7ublGmXr16NGnSxKbtWqWk+Pr65plvb2+PXq+3iUBPIkYnDRp7cW44Ead7hBzJgZijcQGtFoI9TwFB7ykB+wnE7CvRnlPGIpAnXbFHq4ippGzbto0XX3wxzzKurq7s2LHDpu3mO/A7OTmZhIQEIGsbaTc3N5sKJJFIJBLJ00iGYofuvpIi2nTPoxSUwsJqJWXRokXMmTOHkydPmqX7+Pjw3nvv0a9fP5sL96RgcNSCvUBDFbEUdEDMkRyIaSEQ8/qJJ5SI107U+1zMe6q4JTDHWAR9lGGyV6d7MkxiXBQ/P79H7vxsZ2eHp6cnL730Em+//XaukcAFwSolJSoqikmTJjFixAiCg4PVeOjExES2bNnCyJEjuXnzJqNGjbKZYE8SJnsNRgcxbjgQ758fEPIhCfJFZzWyn6xDwH4CMftKuOmeIrh4mYpOje7JFCQYpVu3bo8sYzKZSEpKYurUqfz555988cUXNmvfKiXl888/Z+nSpRbRPbVr1yYoKIj69eszevRoqaRIJBKJRFJA0kx2YLK//72YhbnPxIkTrS77xhtv0KNHj6JXUpKSkqhbt26u+XXr1uXatWs2E+pJw+gAiLN1j5grloo1aFIRcYQp4mhcxOkeMfupuCXIGSH//wTrq6LQGQwPLOZmEGzvHmto2LAhvXr1smmdVt0GTZo04aOPPsJgMFjkGY1GPv74Y5uHHUkkEolE8jSRbrIz+xQ3DRs25ObNm1aX79ChA++9955NZbB6uic4OBhPT08CAwPNfFJ2796Ng4MDW7ZssalgTxJGRw0I5JMiR5j5QPaVdch+sgohLRYgnNUCxOsrYxG4iKSb7CB7nRRT8fukxMXFcfjwYcqXL291+fT0dJvKYJWSUq9ePU6dOsXKlSvZt28ff/31F5AVgjx16lR69eqFq6urTQV7kjDZg0ak6R7B/vlBzBcKiPegBOQLxUpEvadERMi+EuyeMhXB7ItB0aEVbLqnbdu2Vq8o/6gooIJgtT3JxcWFwYMHM3jwYJsLIZFIJBLJ006GSadaUjKKQit6BAVZSd7b29umMthk0iszM5MrV67w7LPP5vvcefPmERUVRUJCAvXr12fu3Lk0bdo0x7LLli2jT58+ZmmOjo6kpaWpx6mpqYwZM4b169dz/fp1qlWrxogRIxg0aBAAN27cYOLEiWzZsoULFy7g7u5Ot27diIyMNFuYLieNcNWqVfTs2TPfv9HkIJYlRY56SzZC9pW8p6xDwH4CUfuq+Kc7HsRUBNMvmSYdGpNO/V7ciLCSvE2UlOPHj9OwYUOMxvzt2rh69WoiIiJYsGABzZo1Izo6muDgYE6ePImHh0eO57i6upotKPewMhEREcH27dtZuXIlVatWZcuWLQwZMgQvLy+6du3K5cuXuXz5MrNmzcLX15fz588zaNAgLl++zJo1a8zqWrp0KR06dFCPy5Ytm6/fJ5FIJBKJtWQqOshWUpTiV1JEoFjdh+fMmcOAAQNU68iCBQv48ccfWbJkCWPGjMnxHI1Gg6enZ6517tmzh7CwMIKCggAYOHAgCxcu5MCBA3Tt2pU6derw3XffqeVr1KjBtGnTeOuttzAYDNjZ/dMlZcuWzbMtazHaA/aPXY3tEHA0J+RIDmRfWYtgo14QtZ+KW4CckX31aIrCRcTwgCXFIIAlRQSsUlIaNmyYZ/69e/fy3XBGRgYHDx5k7NixappWq6Vdu3bs3bs31/NSU1PR6/WYTCYaNmzI9OnTeeGFF9T85s2bs2HDBvr27YuXlxc7d+7k1KlTfPLJJ7nWmZycjKurq5mCAjB06FD69+9P9erVGTRoEH369MnTMSg9Pd3MszklJQUAxQ4UgZQURSvgC0WwB5KKgA9vIftK9pN1iCgTYj4TROsrUxGE92SYdCgCTfeIgFVKyvHjx+nZsyfVqlXLMf/KlSucOnUqXw1fu3YNo9GohjNnU7FiRU6cOJHjOT4+PixZsoR69eqRnJzMrFmzaN68OceOHVOddebOncvAgQPx9vbGzs4OrVbLV199RWBgYK5yREZGMnDgQLP0KVOm0KZNG0qVKqVOGaWmpjJixIhcf9OMGTOYPHlyfrpBIpFIJBIAMk1aVUkxmATU/IsBq5SUOnXq0KxZs1wje+Li4vjqq69sKlhOBAQEEBAQoB43b96c2rVrs3DhQiIjI4EsJWXfvn1s2LABvV7P7t27GTp0KF5eXrRr186svpSUFEJCQvD19WXSpElmeePHj1e/+/n5cefOHaKiovJUUsaOHUtERIRZ/VWqVMHkoICDQCMVwUYoIOioF6SFwFoEHImL2U/FLUDOiNlXYt1TRWFJMZi0cF85EVFJuXXrFmvWrOHs2bOMHj2a8uXLc+jQISpWrEjlypULpU2rlJQWLVpY7H78IC4uLrlaKnKjQoUK6HQ6EhMTzdITExOt9gOxt7fHz8+PM2fOAFnTTuPGjWPdunWEhIQAWWu8xMXFMWvWLDMl5fbt23To0AEXFxfWrVuHvX3e8zHNmjUjMjKS9PR0HB0dcyzj6OiYa55EIpFIJHmRadShGO9bUoxiTfccOXKEdu3a4ebmxrlz5xgwYADly5dn7dq1XLhwgeXLlxdKu1YpKZ9++mme+TVq1GDHjh35atjBwYFGjRqxbds2dZdFk8nEtm3bGDZsmFV1GI1G/vjjDzp16gRkhUJnZmai1ZproDqdDtMDMecpKSkEBwfj6OjIhg0bcHJyemRbcXFxlCtXrkBKiGKX9REFEeefhXTcAzGtTiL2lYCOs0JaLQS8n0DMZ4JofaUYisCSomjVf3CDYP/oERERhIeHM3PmTFxcXNT0Tp062Xy/ngcp1ldnREQEYWFhNG7cmKZNmxIdHc2dO3fUaJ/Q0FAqV67MjBkzgCw/EX9/f2rWrMmtW7eIiori/Pnz9O/fH8gKT27VqhWjR4/G2dkZvV7Prl27WL58OXPmzAGyFJT27dtz9+5dVq5cSUpKiurg6u7ujk6n44cffiAxMRF/f3+cnJzYunUr06dPL/Auz6JN90jTbj4Q6zkBgCIVgpKLsPe5gHKJ9pwyFH54j8GoA0EtKb/++isLFy60SK9cuTIJCQmF1q7VSkpSUhJHjx6lUaNGuLm5kZiYyNdff43JZCIkJCTPXZJz4/XXX+fq1atMmDCBhIQEGjRowObNm1Vn2gsXLphZRW7evMmAAQNISEigXLlyNGrUiD179uDr66uW+eabbxg7dixvvvkmN27cQK/XM23aNHUxt0OHDrF//34AatasaSZPfHw8VatWxd7ennnz5vHuu++iKAo1a9ZUw6UlEolEIikMDIoW5b4vilEwS4qjo6M6oH+QU6dO4e7uXmjtahQrFuXfuXMnnTt35u7du1SsWJHNmzfTuXNnnJ2d0Wq1nDt3jg0bNtC+fftCE7QkkpKSgpubG1VmR6J1fvSUUlEhTbv5QFotrEP2k3WI2E+ARsBngmgyme6mcWFgpLpkhS3JflfU+XY0ulJZLgXGu+kc7RFVKO0VhP79+3P9+nW+/fZbypcvz5EjR9DpdHTr1o3AwECio6MLpV2r/o3Hjx9PeHg4KSkpvPfee4SEhPDyyy9z6tQpTpw4wfDhw2XorUQikUgkj4HRqDX7iMTs2bNJTU3Fw8ODe/fu0apVK2rWrImLiwvTpk0rtHatmu45cuQIy5Yto0yZMrzzzjuMHTtW9QOBrFVdiyIEuaSi2JlQ7Ip/sygVse79LAQbNakIaOERbYQJCGkhELGfNCL+74GQ108rmEyaIniGG00aMGn++S4Qbm5ubN26lV9++YUjR46QmppKw4YNLZb2sDVWKSkODg7qJn4ZGRmYTCazTf3u3bv3yBDepxp7JesjCCI+vEVVUjSCPShB0BedgP0k2ksOBP3fQ8z7XCtYXxnt87c3XYHaMGnUdVJEU1KyadmyJS1btiyy9qxeJ2XMmDGMGTOG5cuX07BhQ6ZOncrq1avRaDRERkbSuHHjwpZVIpFIJJInFsWkQbmvnCgCKCmfffaZ1WXzWuj0cbBKSYmKiiIkJIQXX3yRWrVqsXXrVoYMGaLuClyuXDk2b95cKAI+CWjtjGiLQAu3FhFH4iKO5EBMuUQcjYvYT6KNxEFMmUDQ6yeYTNoimO4xGbVw3xfFJIBPysN73l29epW7d++q7/5bt25RqlQpPDw8Ck1JsaoXnnvuOU6dOsXVq1c5fvw4lStX5vvvv2fz5s2sW7eOP//885GbEEokEolEIskdxagx++SH3bt306VLF7y8vNBoNKxfv94sPzw8HI1GY/bp0KFDnnXGx8ern2nTptGgQQP+/PNPbty4wY0bN9R3f/a2NIVBvhZze+aZZ8yO27Zta1NhnlR0dqYi0cKtRY7ErUfEka+IMol4/UQbiQPotOI8Bx5ExOunE+w+N9obCr0NRXlguiefq27euXOH+vXr07dvX7p3755jmQ4dOrB06VL1OD8rqI8fP541a9bg4+Ojpvn4+PDJJ5/w2muv8eabb+ZLXmsp0IqziqKwc+dOzpw5g5eXF+3bt5eOs3mgszeiE2m6R8AHkogvXhDzRSekTAK+fEV7yYGY1w7EVJ50gvWVQVcESorpn8Xcsv8+vIBabnvEdezYkY4dO+ZZv6Ojo9V74z3MlStXMBgs+8BoNFrswWdLrJru6dSpE8nJyQDcuHGDgIAA2rZty//93//RtWtX6tWrx9WrVwtNSIlEIpFInnhMD32AKlWq4Obmpn6yt4kpCDt37sTDwwMfHx8GDx7M9evXrT63bdu2vP322xw6dEhNO3jwIIMHDy7UMGSrLCmbN28mPT0dgA8//JDbt29z9uxZqlWrxsWLF+nWrRsTJkxg/vz5hSZoScbOzoTOThxLimgjFBBzJA6gLX4HewuEHPUKKJOAlw47AfsJxLx+olmddHZFYEl5wBcl++/ff/9ttuJsQTa5haypnu7du1OtWjXOnj3LuHHj6NixI3v37kWne/Q+QUuWLFH32sueOTEYDAQHB7No0aICyWQN+Z7u2b59OzNnzqRatWoAeHt78/HHH8t9bSQSiUQieQw0Jg2a+z4p2X9dXV1tsix+z5491e9169alXr161KhRg507d1rlX+ru7s5PP/2krjQPUKtWLZ5//vnHli0vrFZSNJqsDrt58yY1atQwy6tZsyaXL1+2rWRPEI52RrEsKQKOmkT0kwExrU4ijsZFvH4i9pNOI55MIJ7VAsS7fplFYEnBqMn6ZH8vRKpXr06FChU4c+ZMvoJgnn/++UJXTB7EaiUlPDwcR0dHMjMziY+P54UXXlDzEhIS1LhpiSX2OgN2duJsuy3ii1dExQkEfXgL+KIT8frJa2c9dlpxBlHZiHb9dLrMwm+kCJWUixcvcv36dSpVqmRV+b59++aZv2TJEluIZYFVSkpYWJj6/eWXX+bu3btm+d999x0NGjSwqWASiUQikTxNaExZn+zv+SE1NZUzZ86ox/Hx8cTFxVG+fHnKly/P5MmTefXVV/H09OTs2bO8//771KxZk+DgYKvqv3nzptlxZmYmR48e5datW7Rp0yZ/wuYDq5SUB+Oqc2LixIlWOd48rTjojNjpxBmpiGZGBWkGzw8iXj87jTj3dzYi9pOI9xOIaeGxF8y6k6HLKPQ2NMoDPin5XCflt99+o3Xr1upxREQEkGVkmD9/PkeOHOHrr7/m1q1b6tIhkZGRVjvirlu3ziLNZDIxePBgCxcQW1KgdVKyiY+Pp0qVKpQuXdpW8kgkEolE8nTyQOgx+dQbg4KCUJTcleD//Oc/BRYrN7RaLREREQQFBfH+++/bvH54TCXFx8eHw4cPU7t2bVvJ80TiqDNgJ5ClScQRpogjOZBz9dYi4vUT8dqJ6A8GYl4/0SwpReGTojFmfbK/lwTOnj2b4yJvtsIqJSW3JXaNRiMjRozAxcUFgLVr19pOsicIR50R+yJYrdBaRHx4i6g4gZgPb62AMtkLKJOI97m4Sop4fWUvmExabREoKTmEIItC9vRRNoqicOXKFX788Uczv1VbY5WSsn79egIDA9W1UR6kTJkyuLm52VwwiUQikUieJkS2pPz+++9mx1qtFnd3d2bPnv3IyJ/HwSolJTY2ltGjRxMWFkafPn3U9JUrVzJt2jR8fX0LTcAnASe7TOztxNGKRbQOiGbazUaLeCNfES0E0pJiHbr8OhoUESJaMoWzpBRJCPIDyolYP58dO3YUS7tW7d3Ts2dP/vvf/7J48WJeffVVi1AkiUQikUgkj4dGMf+IRJs2bbh165ZFekpKSvGHIANUrVqV3bt3M3nyZOrXr89XX32lrkIryRsnbSYOOnH6SkRLioh+FiAtBNYi2qgXxHQwFrGfQEy5hFuWoEh8Uh6Y7hHs5+/cuZOMDMsw7LS0NP773/8WWrv5iu7RarVMnjyZl156idDQUIxG8W5sEXHWiaWkyCkM6xHx4S1fvtYhZbIe4RQCxOsrrbbwgx8eZzG3wuLIkSPq9+PHj5OQkKAeG41GNm/eTOXKlQutfaumex6mZcuWHDlyhEOHDlGzZs3HEmDevHlUrVoVJycnmjVrxoEDB3Itu2zZMjQajdnHycnJrExqairDhg3D29sbZ2dnfH19WbBggVmZoKAgi3oGDRpkVubChQuEhIRQqlQpPDw8GD16dKGGWUkkEonk6SbbcfZBB9ripkGDBvj5+aHRaGjTpg0NGjRQP40aNWLq1KlMmDCh0Nov8DopZcqUoX79+o/V+OrVq4mIiGDBggU0a9aM6OhogoODOXnyJB4eHjme4+rqysmTJ9Xjh6ecIiIi2L59OytXrqRq1aps2bKFIUOG4OXlRdeuXdVyAwYMYMqUKepxqVKl1O9Go5GQkBA8PT3Zs2cPV65cITQ0FHt7e6ZPn57v3+mgNeJQBFq4tYg2QgExrQMgZl9JmaxDWgesR0S57DXiPDMBNEVhSREwuic+Ph5FUahevToHDhzA3d1dzXNwcMDDw6NQV5y32pLy008/0b9/f95//33+/PNPs7ybN28WyHFmzpw5DBgwgD59+qgWj1KlSuW5UZFGo8HT01P9VKxY0Sx/z549hIWFERQURNWqVRk4cCD169e3sNCUKlXKrJ4Ht8LesmULx48fZ+XKlTRo0ICOHTsSGRnJvHnzcpyTk0gkEonkccme7nlw2qe40ev1VK1aFZPJROPGjdHr9eqnUqVKhb4ljtUhyKGhoXTo0IGTJ08yd+5cFi1axJtvvglARkYGu3btylfDGRkZHDx4kLFjx6ppWq2Wdu3asXfv3lzPS01NRa/XYzKZaNiwIdOnTzfbkbl58+Zs2LCBvn374uXlxc6dOzl16hSffPKJWT0xMTGsXLkST09PunTpwvjx41Vryt69e6lbt66ZAhQcHMzgwYM5duwYfn5+OcqWnp5Oenq6epySkgKAg9aAo1YcnxQxR03iyQRiyiUtBNYhpkxiWQeyEdGS6SBYXylPoSVlw4YNdOzYEXt7ezZs2JBn2QdnKmyJVUpKVFQUc+bMYcSIEQB8++239O3bl7S0NPr161eghq9du4bRaLSwhFSsWJETJ07keI6Pjw9LliyhXr16JCcnM2vWLJo3b86xY8fw9vYGYO7cuQwcOBBvb2/s7OzQarV89dVXBAYGqvX06tULvV6Pl5cXR44c4YMPPuDkyZPqirkJCQk5ypWdlxszZsxg8uTJFulO2kwcC+T9UziI+fAWTyYQUy4RZRIxOkvEfhLtxZuNiH0l2poypiLoowdDj0XQG7t160ZCQgIeHh5069Yt13IajabQAmmsUlJOnz5Nly5d1OMePXrg7u5O165dyczM5JVXXikU4R4mICCAgIAA9bh58+bUrl2bhQsXEhkZCWQpKfv27WPDhg3o9Xp2797N0KFD8fLyol27dgAMHDhQraNu3bpUqlSJtm3bcvbs2cfazXHs2LFmSwenpKRQpUqVAtcnkUgkkqcH0SwpJpMpx+9FiVVKiqurK4mJiWbL4rdu3ZqNGzfSuXNnLl68mO+GK1SogE6nIzEx0Sw9MTERT09Pq+qwt7fHz8+PM2fOAHDv3j3GjRvHunXrCAkJAaBevXrExcUxa9YsVUl5mGbNmgFw5swZatSogaenp4UPS7acecnm6OiY47bXztoMnLQCqMX3kaNe6xFRLhFlEm3UC2JOrTgIeO1A0HtKsOdUkVlSTP98l1ippDRt2pRNmzbh7+9vlt6qVSt++OEHOnfunO+GHRwcaNSoEdu2bVPNSCaTiW3btjFs2DCr6jAajfzxxx906tQJgMzMTDIzM9FqzedWdDpdnlpgXFwcAJUqVQKyLDbTpk0jKSlJjTLaunUrrq6ucgsAiUQikRQKollSPvvsM6vLZruD2BqrlJR3332XPXv25JgXFBTEDz/8wPLly/PdeEREBGFhYTRu3JimTZsSHR3NnTt31P2BQkNDqVy5MjNmzABgypQp+Pv7U7NmTW7dukVUVBTnz5+nf//+QJbFp1WrVowePRpnZ2f0ej27du1i+fLlzJkzB8jaVjo2NpZOnTrxzDPPcOTIEd59910CAwOpV68eAO3bt8fX15fevXszc+ZMEhIS+PDDDxk6dGiOlpJHYa8xCTVSEUmWbEQc9YLsK2sRcXdfEftJXEuKeH2lE2zRySKxpAimpDwccJIbGo2meJWUVq1a0apVq1zzW7duTevWrfPd+Ouvv87Vq1eZMGECCQkJNGjQgM2bN6tOqhcuXDCzity8eZMBAwaQkJBAuXLlaNSoEXv27DGzbnzzzTeMHTuWN998kxs3bqDX65k2bZq6WJuDgwM///yzqhBVqVKFV199lQ8//FCtQ6fTsXHjRgYPHkxAQAClS5cmLCzMbF2V/OCgzcRRoOkeEV+80qHQesScWhGvn0R88YqqpGgFvKdE6ytjUSgpgq04Gx8fX9wioFEURZy35xNGSkoKbm5ufPzriziXKfC6eTZHxBeKVFKsRyop1iGVFOuRSsqjuXPbSNf6Z0lOTjZbV8sWZL8r/N6chs4haxV1Y0Yav8f8X6G097hkqw1FsX+fOG/OJxgnTSZOApnDRXx4izhdAGL2lWgPbxCzn+xF2+se8ZxBsxFRyXQQ7PoZi+DaiWZJeZjFixfzySefcPr0aQCee+453nnnHdXlojCQSopEIpFIJAIgmk/Kg0yYMIE5c+YwfPhwdSmQvXv38u6773LhwoUCu0M8CqmkFAH2GgP24iw4K+hIXDyZQEwLgYjXT04XWIeI9xOI+f/nINg9ZSgK04aioDEp6neRmD9/Pl999RVvvPGGmta1a1fq1avH8OHDxVNSVq1aRdeuXSldurQt5XkicdQYhFonRcSHt4gvORCzr0R80Yn5khNPJhHXKALxFALIiooUiaKQR2uA7B1UBNqTFsha4qNx48YW6Y0aNcJgKDxhC7xY+9tvv22xEJtEIpFIJJKCoTEqZh+R6N27N/Pnz7dI//LLL9V9/AqDAltSZFCQ9ThojDgINN0j5EhcwFEviOnoKK0W1iFiP4lmHchGtDVJQLy+Kgp5SoLj7JYtW9SFXffv38+FCxcIDQ012xIme10yWyB9UiQSiUQiEQCtUUF73zVAK5gl5ejRozRs2BDIWhQVsra3qVChAkePHlXL2TosucBKyqZNm6hcubItZXliyXKcFeeGE9FqIaLvB4hpdRJz1Cve9RNtJA5gL6DvB4CDgH0l0MbxANgVwf+dyJaUHTt2FEu7BVZSWrZsaUs5nmjsMGJf3EI8gIgKgYjKAIj58hVTSRHsiYqYCoGIygAgVPRhNqIpKUXRRxqjgub+gFY0n5TiQk73SCQSiUQiABrTPyHIaiiyIKSlpTF37lx27NhBUlKSxaa9hw4dKpR2pZJSBEjH2UcjosUCpNXCWqTVwjpEtFiAeFYLAIciWHI9PxSFPBqDgub+M0djEOvZ069fP7Zs2cJrr71G06ZNi2RJfJBKikQikUgkQqAxmtDcV641RrGU7I0bN/LTTz/RokWLIm1XKilFgL3GKJTjrIjWARFlAmm1sBYRrRYiWgdElAnEs1oA2COWTEUhj8jTPZUrV8bFxaXI27VaSTGZTCxbtoy1a9dy7tw5NBoN1apV47XXXqN3795FZvopiehQhHoJizi1IqIyAGIqBCJuxijiy1fEqRURlQEQTyEA0ArWV0XxjhN5umf27Nl88MEHLFiwAL1eX2TtWqWkKIpC165d+emnn6hfvz5169ZFURT+/PNPwsPDWbt2LevXry9kUSUSiUQieXLRmExo7jukakxiDZAaN25MWloa1atXp1SpUtjbm8es3rhxo1DatUpJWbZsGbt372bbtm20bt3aLG/79u1069aN5cuXExoaWihClnS0GpO6H4MIiGTVyUZEmUBMq4W9gH0lotVCV9wC5ICIFicQz2oBYC/YFSyS6R6jggaT+l0k3njjDS5dusT06dOpWLGiWI6zq1atYty4cRYKCkCbNm0YM2YMMTExUkmRSCQSiaSAaAwmNIqYjrN79uxh79691K9fv0jbtUpJOXLkCDNnzsw1v2PHjnz22Wc2E+pJwx4TDsUtxANoBbQOaAW0DoCYFh6deINewca8WdgLaB3QCej7AWLKpRPs+hWJPKYHlpzN53TP7t27iYqK4uDBg1y5coV169bRrVu3HMsOGjSIhQsX8sknn/DOO+9YVX+tWrW4d+9evmSyBVYpKTdu3KBixYq55lesWJGbN2/aTKgnDa1GEUoxEPPFK55MIKZ5XkSZRHuhgJgvXhGnVQC0At5VoslUFNI8jiXlzp071K9fn759+9K9e/dcy61bt459+/bh5eWVr/o/+ugj3nvvPaZNm0bdunUtfFJcXV3zVZ+1WKWkGI1G7OxyL6rT6TAYxFsgTCKRSCSSEoPRBNkRhflUUjp27EjHjh3zLHPp0iWGDx/Of/7zH0JCQvJVf4cOHQBo27atWbqiKGg0GozGwokatTq6Jzw8HEdHxxzz09PTbSqUpHARcWpFROsOyKkVaxFrzCsuIlp3QExLmFawvioSeUxGyN4A1pT1NyUlxayIo6Njru/iPKs2mejduzejR4/mhRdeyPf5Qm8wGBoa+khPXuk0K5FIJBLJY2A0gmKupFSpUsWsyMSJE5k0aVK+q/7444+xs7NjxIgRBRKtVatWueYdPXq0QHVag9UhyJKCI9pibiL6f4g6EhdVLsmjEdH/QzQ/i2xElEunEUumIrGqGoygNVdS/v77bzN/j4JYUQ4ePMinn37KoUOHbBY6fPv2bVatWsWiRYs4ePBg8U735OWEo1ZkZ4enpycvvfQSXbp0sVqAefPmERUVRUJCAvXr12fu3Lk0bdo0x7LLli2jT58+ZmmOjo6kpaWpx6mpqYwZM4b169dz/fp1qlWrxogRIxg0aBAA586do1q1ajnW/+233/Kvf/0LyHl1wVWrVtGzZ0+rf1s2WhQhp1gkJRMRTfMiTmOIKJNEkicGI2jv+3feV1JcXV0f2yn1v//9L0lJSTz77LNqmtFo5L333iM6Oppz585ZXdfu3btZvHgx3333HV5eXnTv3p158+Y9lnx5YZWS4ubm9sgyJpOJ06dPs2jRIkaNGsWUKVMeec7q1auJiIhgwYIFNGvWjOjoaIKDgzl58iQeHh45nuPq6srJkyfV44eViYiICLZv387KlSupWrUqW7ZsYciQIXh5edG1a1eqVKnClStXzM758ssviYqKsnA6Wrp0qeosBFC2bNlH/iaJRCKRSAqE6QHHWRuuONu7d2/atWtnlhYcHEzv3r0tBv45kZCQwLJly1i8eDEpKSn06NGD9PR01q9fj6+vr83kzAmrlJSlS5daXeHGjRsZMmSIVUrKnDlzGDBggNpJCxYs4Mcff2TJkiWMGTMmx3M0Gg2enp651rlnzx7CwsIICgoCYODAgSxcuJADBw7QtWtXdDqdxfnr1q2jR48elClTxiy9bNmyebb1MOnp6WZOxA87PImCSFNP2YjooCqR2BoRrWAgnpPq04piMKDcn+ZSlPxFzKampnLmzBn1OD4+nri4OMqXL8+zzz7LM888Y1be3t4eT09PfHx88qy3S5cu7N69m5CQEKKjo+nQoQM6nY4FCxbkS76CYvNJv5YtW9K4ceNHlsvIyODgwYNm2p1Wq6Vdu3bs3bs31/NSU1PR6/VUqVKFl19+mWPHjpnlN2/enA0bNnDp0iUURWHHjh2cOnWK9u3b51jfwYMHiYuLo1+/fhZ5Q4cOpUKFCjRt2pQlS5agKHm/3GfMmIGbm5v6edjhSSKRSCSSXDEazT/54LfffsPPzw8/Pz8ga1bBz8+PCRMmPJZImzZtol+/fkyePJmQkBB0uqKNL7R6F2RrKVu2LGvXrn1kuWvXrmE0Gi0WiatYsSInTpzI8RwfHx+WLFlCvXr1SE5OZtasWTRv3pxjx47h7e0NwNy5cxk4cCDe3t7Y2dmh1Wr56quvCAwMzLHOxYsXU7t2bZo3b26WPmXKFNq0aUOpUqXUKaPU1NQ8PaPHjh1LRESEepySkiIVlRKOiOG+EonkyeRxLClBQUGPHEg/iLV+KL/88guLFy+mUaNG1K5dm969exfIN7Og2FxJKUwCAgIICAhQj5s3b07t2rVZuHAhkZGRQJaSsm/fPjZs2IBer2f37t0MHToULy8vizm5e/fuERsby/jx4y3aejDNz8+PO3fuEBUVlaeSUtD4dYlEIpFIMJpAc9+Cooixd4+/vz/+/v5ER0ezevVqlixZQkREBCaTia1bt1KlShVcXFwKrf1iU1IqVKiATqcjMTHRLD0xMdFqPxB7e3v8/PzUebh79+4xbtw41q1bp66mV69ePeLi4pg1a5aFkrJmzRru3r1r1RovzZo1IzIykvT0dKsVkWyt9k6qGDdbNg4aseQBsBN0Stz+0UWKHBH9Gopih9j8IuI9JeJ+QgB2Al4/0fzUUu4/x/NjrcgvGZn3MJFlQTGQWWjtFITSpUvTt29f+vbty8mTJ1m8eDEfffQRY8aM4aWXXmLDhg2F0m6xKSkODg40atSIbdu2qZsgmUwmtm3bxrBhw6yqw2g08scff9CpUycAMjMzyczMRKs1d7XR6XSYcvCUXrx4MV27dsXd3f2RbcXFxVGuXLl8WUquX78OQFv/JKvPkUgkEom43L5926qI1/zg4OCAp6cnvyRsNEv39PTEwUGk7Wmz8PHxYebMmcyYMYMffviBJUuWFFpbxTrdExERQVhYGI0bN6Zp06ZER0dz584dNdonNDSUypUrM2PGDCDLT8Tf35+aNWty69YtoqKiOH/+PP379weywpNbtWrF6NGjcXZ2Rq/Xs2vXLpYvX86cOXPM2j5z5gy7d+/mp59+spDrhx9+IDExEX9/f5ycnNi6dSvTp09n1KhR+fp95cuXB+DChQs2v6mfRrJ9fB5e3EhScGSf2hbZn7ZHlD5VFIXbt2/ne2M+a3ByciI+Pp6MjAyzdAcHB5ycnGzenq3Q6XR069Yt192WbUGxKimvv/46V69eZcKECSQkJNCgQQM2b96sOtNeuHDBzCpy8+ZNBgwYQEJCAuXKlaNRo0bs2bPHLE77m2++YezYsbz55pvcuHEDvV7PtGnT1MXcslmyZAne3t45Rv3Y29szb9483n33XRRFoWbNmmq4dH7Ilt3NzU0+sGyILRY3kpgj+9S2yP60PSL0aWEONp2cnIRWSIoLjVKYE2xPOSkpKbi5uZGcnFzs/1xPArI/bY/sU9si+9P2yD59uhFrcwSJRCKRSCSS+0glpRBxdHRk4sSJMizZRsj+tD2yT22L7E/bI/v06UZO90gkEolEIhESaUmRSCQSiUQiJFJJkUgE4dy5c2g0GpYtW2aWvnnzZho0aICTkxMajYZbt24BsGLFCmrVqoW9vX2J3qE7PDzcYnPPkkZu184adu7ciUajYefOnTaXSyIp6UglRfJUs2zZMjQajdnHw8OD1q1bs2nTpseq+8E67ezsKF++PI0aNWLkyJEcP37cqjquX79Ojx49cHZ2Zt68eaxYsYLSpUtz4sQJwsPDqVGjBl999RVffvnlY8laGKSmpjJx4kTq1KlD6dKleeaZZ2jQoAEjR47k8uXLhdLmpEmT0Gg0aLVa/v77b4v8lJQUnJ2d0Wg0Vi8aKZFIio8StXePRFJYTJkyhWrVqqEoComJiSxbtoxOnTrxww8/0Llz5wLX+9JLLxEaGoqiKCQnJ3P48GG+/vprvvjiCz7++GOzDSn1ej337t3D3v6fxfh//fVXbt++TWRkpNm2Djt37sRkMvHpp59Ss2bNAstXWGRmZhIYGMiJEycICwtj+PDhpKamcuzYMWJjY3nllVcKZVGsbBwdHVm1ahXvv/++Wbo1m59KJBJxkEqKRAJ07NiRxo0bq8f9+vWjYsWKrFq16rGUlOeff5633nrLLO2jjz6iS5cuvPfee9SqVUvd1kGj0Vgs5pSUlLWlwsPTObmli8L69ev5/fffiYmJoVevXmZ5aWlpFitr2ppOnTrlqKTExsYSEhLCd999V6jtSyQS2yCneySSHChbtizOzs7Y2Znr8VeuXOHEiRNkZhZ8869nnnmGb775Bjs7O6ZNm6amP+zXEBQURFhYGABNmjRBo9EQHh5O1apVmThxIgDu7u5oNBomTZoEYPb9QapWrUp4eLh6nJmZyeTJk3nuuedwcnLimWeeoWXLlmzdutXsvBMnTvDaa69Rvnx5nJycaNy4sVUbiZ09exaAFi1aWOQ5OTk9clGuuLg43N3dCQoK4ocffkCj0bBu3TqLcrGxsWg0Gvbu3WuW3qtXL+Li4jhx4oSalpCQwPbt2y2UpmySkpJU5dTJyYn69evz9ddfW5S7desW4eHhuLm5UbZsWcLCwlQ/oYcpaP9JJJIspJIikQDJyclcu3aNq1evcuzYMQYPHkxqaqqFFWTs2LHUrl2bS5cuPVZ7zz77LK1atWLfvn2kpKTkWOb//u//GDhwIJA1HbVixQrefvttoqOjeeWVVwCYP38+K1asoHv37vlqf9KkSUyePJnWrVvz+eef83//9388++yzHDp0SC1z7Ngx/P39+fPPPxkzZgyzZ8+mdOnSdOvWLUeF4UH0ej0Ay5cvz/eusb/++itt2rTBz8+PTZs20blzZ6pUqUJMTIxF2ZiYGGrUqEFAQIBZemBgIN7e3sTGxqppq1evpkyZMuoO6Q9y7949goKCWLFiBW+++SZRUVG4ubkRHh7Op59+qpZTFIWXX36ZFStW8NZbbzF16lQuXryoKpMP8jj9J5FI7qNIJE8xS5cuVQCLj6Ojo7Js2TKL8mFhYQqgxMfHP7JuQBk6dGiu+SNHjlQA5fDhw4qiKEp8fLwCKEuXLrWQ79dffzU7d+LEiQqgXL161aLNiRMnWrSl1+uVsLAw9bh+/fpKSEhInvK3bdtWqVu3rpKWlqammUwmpXnz5spzzz2X57l3795VfHx8FEDR6/VKeHi4snjxYiUxMdGibFhYmFK6dGlFURTll19+UVxdXZWQkBCzdseOHas4Ojoqt27dUtOSkpIUOzs7s9/7YL+MGjVKqVmzpprXpEkTpU+fPoqiWF6b6OhoBVBWrlyppmVkZCgBAQFKmTJllJSUFEVRFGX9+vUKoMycOVMtZzAYlBdffNHi2lnbfzt27FAAZceOHXn2qUTyNCItKRIJMG/ePLZu3crWrVtZuXIlrVu3pn///haOlsuWLUNRFKpWrfrYbWaH3d6+ffux68ovZcuW5dixY5w+fTrH/Bs3brB9+3Z69OjB7du3uXbtGteuXeP69esEBwdz+vTpPK1Jzs7O7N+/n9GjRwNZ/davXz8qVarE8OHDSU9Ptzhnx44dBAcH07ZtW9auXWu2wmhoaCjp6emsWbNGTVu9ejUGg8HC2pVNr169OHPmDL/++qv6N7epnp9++glPT0/eeOMNNc3e3p4RI0aQmprKrl271HJ2dnYMHjxYLafT6Rg+fLhN+08ikWQhlRSJBGjatCnt2rWjXbt2vPnmm/z444/4+voybNiwQnPyTE1NBcDFxaVQ6s+LKVOmcOvWLZ5//nnq1q3L6NGjOXLkiJp/5swZFEVh/PjxuLu7m32y/WGynXdzw83NjZkzZ3Lu3DnOnTvH4sWL8fHx4fPPPycyMtKsbFpaGiEhIfj5+fHtt9/i4OBgll+rVi2aNGliNuUTExODv79/rtFNfn5+1KpVi9jYWGJiYvD09KRNmzY5lj1//jzPPfec2a7rALVr11bzs/9WqlTJYl0XHx8fs2Nb9J9EIpHRPRJJjmi1Wlq3bs2nn37K6dOneeGFF2zextGjR9HpdFSrVs3mdT+M0Wg0Ow4MDOTs2bN8//33bNmyhUWLFvHJJ5+wYMEC+vfvj8lkAmDUqFEEBwfnWGd+Qp/1ej19+/bllVdeoXr16sTExDB16lQ139HRkU6dOvH999+zefPmHCOqQkNDGTlyJBcvXiQ9PZ19+/bx+eef59lur169mD9/Pi4uLrz++usWSkhhYev+k0ieVqSSIpHkgsFgAP6xeNiSCxcusGvXLgICAmxqSSlXrpxFpElGRgZXrlyxKFu+fHn69OlDnz59SE1NJTAwkEmTJtG/f3+qV68OZE15PLg+iy3kq1GjBkePHjVL12g0xMTE8PLLL/Ovf/2LTZs2ERQUZFamZ8+eREREsGrVKnU9mddffz3P9nr16sWECRO4cuUKK1asyLWcXq/nyJEjmEwmM0UmOzoo2xFYr9ezbds2UlNTzawpJ0+eNKuvsPpPInnakNM9EkkOZGZmsmXLFhwcHFSTP9gmBPnGjRu88cYbGI1G/u///s8W4qrUqFGD3bt3m6V9+eWXFpaU69evmx2XKVOGmjVrqr4iHh4eBAUFsXDhwhwVnKtXr+Ypx+HDh7l27ZpF+vnz5zl+/LjF9AiAg4MDa9eupUmTJnTp0oUDBw6Y5VeoUIGOHTuycuVKYmJi6NChAxUqVMhTjho1ahAdHc2MGTNo2rRpruU6depEQkICq1evVtMMBgNz586lTJkytGrVSi1nMBiYP3++Ws5oNDJ37lyz+h63/yQSSRbSkiKRAJs2bVJHzUlJScTGxnL69GnGjBljtqbH2LFj+frrr4mPj7fKefbUqVOsXLkSRVFISUnh8OHD/Pvf/yY1NZU5c+bQoUMHm/6O/v37M2jQIF599VVeeuklDh8+zH/+8x+Ll7mvry9BQUE0atSI8uXL89tvv7FmzRqzpeLnzZtHy5YtqVu3LgMGDKB69eokJiayd+9eLl68yOHDh3OVY+vWrUycOJGuXbvi7+9PmTJl+Ouvv1iyZAnp6ek5ruUCWQ63GzdupE2bNnTs2JFdu3ZRp04dNT80NJTXXnsNwMKvJTdGjhz5yDIDBw5k4cKFhIeHc/DgQapWrcqaNWv43//+R3R0tGrt6tKlCy1atGDMmDGcO3cOX19f1q5dS3JyskWdj9N/EonkPsUbXCSRFC85hSA7OTkpDRo0UObPn6+YTCaz8vkNQc7+aLVapWzZsoqfn58ycuRI5dixYxblbRGCbDQalQ8++ECpUKGCUqpUKSU4OFg5c+aMRQjy1KlTlaZNmyply5ZVnJ2dlVq1ainTpk1TMjIyzOo7e/asEhoaqnh6eir29vZK5cqVlc6dOytr1qzJ87f/9ddfyoQJExR/f3/Fw8NDsbOzU9zd3ZWQkBBl+/btZmUfDEHO5tq1a4qvr6/i6empnD59Wk1PT09XypUrp7i5uSn37t2zaDe3fnkYcggPT0xMVPr06aNUqFBBcXBwUOrWrWt2LbK5fv260rt3b8XV1VVxc3NTevfurfz+++8W105RrOs/GYIskeSORlHyudKSRCKRFBMGgwEvLy+6dOnC4sWLi1sciURSyEifFIlEUmJYv349V69eJTQ0tLhFkUgkRYC0pEgkEuHZv38/R44cITIykgoVKpgt3y+RSJ5cpCVFIpEIz/z58xk8eDAeHh4sX768uMWRSCRFhLSkSCQSiUQiERJpSZFIJBKJRCIkUkmRSCQSiUQiJHIxt0LEZDJx+fJlXFxc0Gg0xS2ORCKRSAqIoijcvn0bLy+vQtkDKi0tzWIzUwcHB5ycnGzeVklCKimFyOXLl6lSpUpxiyGRSCQSG/H333/j7e1t0zrT0tKopi9DQpL59hWenp7Ex8c/1YqKVFIKkeyltPccqECZMuLMrIkjyT/oiluAEoROQKucmPeUeP0kKiJaekW7frdTTfg2TrDphqDZZGRkkJBk5ORvXri4ZP033b5twqfxZTIyMqSSIikc1H/8UvZQWqTHuHgBXcZHFykWdBrx+krE6yciIt5TOrHeuyoaAe8p0WTSmExZfwtRoXMqA873N9fOFOvnFxtSSZFIJBKJRADSFSNp91cFSVdMxSzNo+nevXu+z1mwYAEeHh5Wl5dKShGQbHLEYBLHkqITbIQCoNWI+Q+pk8sIWYWY95R4Msn7yXq0gt1TqUXwiMpEIfP+784U7PfnxPr16+nRowfOzs5WlY+NjSU1NVUqKRKJRCKRlDTSFQ32ikb9XhL47LPPrFY61qxZk+/6pZJSBKSanDCZxHEN1SKm1UJEdAJaeMS0WojXTwJ2EzpB//fkPfVo7hTB9EuaosVO0d7/XujNPTY7duygfPnyVpfftGkTlStXzlcbNlFSzp49y4ABA9i+fbstqnviSDE5YRBISRERER+SIKaSIiIiKr4iXjtR73MREe2eumMsfFfsTEVL5n0lpSQ4zrZq1Spf5Vu2bJnvNmziKJGamsquXbtsUZVEIpFIJE8l6Yodafc/6UrJmuho1aoVy5cv5969ezat16pe+Oyzz/LMv3Tpkk2EeVJJNpUiwyjODSeiyVnEUa+oiDbCBFFDtcVDxP89EG9qBcSzOt01FYUlRUemorv/XcQg+tzx8/Nj1KhRDB8+nB49etCvXz/8/f0fu16r3pzvvPMOlSpVwsHBIcf8h5fylUgkEolEkj/SFDt095WUtBLiOJtNdHQ0s2bNYsOGDXz99dcEBgZSs2ZN+vbtS+/evalYsWKB6rVKSdHr9Xz88cf06NEjx/y4uDgaNWpUIAGeBu4YnTAKZEkRcdQkKqKN5kBeP2sR0WohYli0qIh2/e6ZDIXeRqZiR4ZqSSlZSgqAnZ0d3bt3p3v37iQlJfHll18yfvx4xo0bR6dOnRgxYgRt2rTJX53WFGrUqBEHDx7MVUnRaDQoMv4/V1KMTqQb7YtbDBXR1h8AOd2TH+T1sw6pzFmPVMYfTVoRKClpij1a1ZIiztpa+eXAgQMsXbqUb775Bg8PD8LDw7l06RKdO3dmyJAhzJo1y+q6rFJSpkyZwt27d3PN9/X1JT4+3upGJRKJRCKRmJOu2KE12d3/XrIsKUlJSaxYsYKlS5dy+vRpunTpwqpVqwgODla3EggPD6dDhw62V1J8fX3zzLe3t0ev11vd6NNGqsGRTINAlhRpcrYaEftKNDO4qAh57QSzDmQjonVONNINmYXeRqZih939qJ6SEIL8IN7e3tSoUYO+ffsSHh6Ou7u7RZl69erRpEmTfNWbb0eJ5ORkEhISgKxtpN3c3PJbhUQikUgkkodIU+zR3FdSSprj7LZt23jxxRfzLOPq6sqOHTvyVa/VSsqiRYuYM2cOJ0+eNEv38fHhvffeo1+/fvlq+GnijtGBTGPOkVGSLEQNYZUjTOsQzX8AxLSkiIqI1jnRrl+6sfB9RAwPhCAbSpif56MUlIJilZISFRXFpEmTGDFiBMHBwWooUWJiIlu2bGHkyJHcvHmTUaNGFYqQJZ1UgxP2meIoKaL984uMiC9fURU60RBRwRT1f0/E+1w0MgyFr6Skm+zRZPukmEqGJcXPz0/1OckNOzs7PD09eemll3j77bdzXc4kx3OtKfT555+zdOlSi+ie2rVrExQURP369Rk9erRUUiQSiUQiKSCZilZdJyWzCPYKsgXdunV7ZBmTyURSUhJTp07lzz//5IsvvrC6fquUlKSkJOrWrZtrft26dbl27ZrVjT5t3DU4YG8Qx5IiIqKO5EQd+YqGiP0kokyiIq1OjybTWPiWjXSTPZjs738v9OZswsSJE60u+8Ybb9CjR498KSlW2a+aNGnCRx99hMFgGSduNBr5+OOP8+2xK5FIJBKJ5B/STXZmnyeNhg0b0qtXr3ydY/V0T3BwMJ6engQGBpr5pOzevRsHBwe2bNmSf4mfEu4a7LETyJIi4qhJI9ioSWREG2GCmDKJiKj9JOIzQTQyMwvfkpKp6NTF3ErCdE/Dhg3Ztm0b5cqVs6p8hw4dWL16db7asEpJqVevHqdOnWLlypXs27ePv/76C8gKQZ46dSq9evXC1dU1Xw0/TdzLtMcuU5x1UkRE2Ie3oHKJhohKprx21iNiX4mmOBkMha80ZJjs4L4FJcMk1u/Pibi4OA4fPkz58uWtLp+enp6vNqy2J7m4uDB48GAGDx6crwYkEolEIpE8GoNJi9Z0PwQ5H7suz5gxg7Vr13LixAmcnZ1p3rw5H3/8MT4+PmqZtLQ03nvvPb755hvS09MJDg7miy++MNv478KFCwwePJgdO3ZQpkwZwsLCmDFjBnZ2uasKbdu2tXpbnEdFAeWETSa9MjMzuXLlCs8++2y+z503bx5RUVEkJCRQv3595s6dS9OmTXMsu2zZMvr06WOW5ujoSFpamnqcmprKmDFjWL9+PdevX6datWqMGDGCQYMGAXDjxg0mTpzIli1buHDhAu7u7nTr1o3IyEizhely6sxVq1bRs2fPfP/GNIMdukxx5he1Aka2iTgSFxURR73y+lmHiNcOQMBHgnAYimAJ2HSTHYpqSbHecrNr1y6GDh1KkyZNMBgMjBs3jvbt23P8+HFKly4NwLvvvsuPP/7Iv//9b9zc3Bg2bBjdu3fnf//7H5DlXxoSEoKnpyd79uzhypUrhIaGYm9vz/Tp03NstyDb4Xh7e+ervE3enMePH6dhw4YYjdZrfgCrV68mIiKCBQsW0KxZM6KjowkODubkyZN4eHjkeI6rq6vZgnIPKxMRERFs376dlStXUrVqVbZs2cKQIUPw8vKia9euXL58mcuXLzNr1ix8fX05f/48gwYN4vLly6xZs8asrqVLl9KhQwf1uGzZsvn6fRKJRCKRWEumogOT7p/vVrJ582az42XLluHh4cHBgwcJDAwkOTmZxYsXExsbq+5CvHTpUmrXrs2+ffvw9/dny5YtHD9+nJ9//pmKFSvSoEEDIiMj+eCDD5g0aVKOa5sUxXY4xTq8nzNnDgMGDFCtIwsWLODHH39kyZIljBkzJsdzNBoNnp6euda5Z88ewsLCCAoKAmDgwIEsXLiQAwcO0LVrV+rUqcN3332nlq9RowbTpk3jrbfewmAwmJm1ypYtm2db1pJhsENnEMeSIke91iPiCFPU0bhoiHifiyiTqIhm8c3vILwgGEw6NOp0T9bflJQUszKOjo44OjrmWU9ycjKA6ity8OBBMjMzadeunVqmVq1aPPvss+zduxd/f3/27t1L3bp1zaZ/goODGTx4MMeOHcPPz+/xf2ABsOrN2bBhwzzz7927l++GMzIyOHjwIGPHjlXTtFot7dq1Y+/evbmel5qail6vx2Qy0bBhQ6ZPn84LL7yg5jdv3pwNGzbQt29fvLy82LlzJ6dOneKTTz7Jtc7k5GRcXV0t5t2GDh1K//79qV69OoMGDaJPnz55zqmlp6ebOQVl31yZmVpMmdZrxU8jBZiqfGoR8UUnokwiIup9Lq/fozFmFr6SkmHSoWRbUu7/rVKlilmZiRMnMmnSpFzrMJlMvPPOO7Ro0YI6deoAkJCQgIODg8VsQMWKFdW9+BISEswUlOz87Lziwiol5fjx4/Ts2ZNq1arlmH/lyhVOnTqVr4avXbuG0WjMsVNOnDiR4zk+Pj4sWbKEevXqkZyczKxZs2jevDnHjh1T57nmzp3LwIED8fb2xs7ODq1Wy1dffUVgYGCuckRGRjJw4ECz9ClTptCmTRtKlSqlThmlpqYyYsSIXH/TjBkzmDx5cn66QSKRSCQSIMtxVmPSqt8B/v77b7Po2UdZUYYOHcrRo0f55ZdfCk/QIsQqJaVOnTo0a9Ys18ieuLg4vvrqK5sKlhMBAQEEBASox82bN6d27dosXLiQyMhIIEtJ2bdvHxs2bECv17N7926GDh2Kl5eXmakLsiwdISEh+Pr6Wmim48ePV7/7+flx584doqKi8lRSxo4dS0REhFn9VapUwZhphyKQ4yxy1GQ1Ig585ajXSgTsJ1EtKSIi2n1uzCz8vXsyTVrVkpKtpLi6ulq9xMewYcPYuHEju3fvNnNQ9fT0JCMjg1u3bplZUxITE1WXBk9PTw4cOGBWX2JioppXXFjV6y1atLDY/fhBXFxccrVU5EaFChXQ6XRqJ2TzYKc9Cnt7e/z8/Dhz5gyQNe00btw45syZQ5cuXahXrx7Dhg3j9ddfZ9asWWbn3r59mw4dOuDi4sK6deuwt897HZNmzZpx8eLFPGO8HR0d1RsqPzeWRCKRSCQGk9bsYy2KojBs2DDWrVvH9u3bLWY9GjVqhL29Pdu2bVPTTp48yYULF9SBf0BAAH/88QdJSUlqma1bt+Lq6oqvr69Vcty6dYtFixYxduxYbty4AcChQ4e4dOmS1b/lYawa3n/66ad55teoUYMdO3bkq2EHBwcaNWrEtm3b1A2KTCYT27ZtY9iwYVbVYTQa+eOPP+jUqROQFQqdmZmJVmt+cXU6HaYHwrlSUlIIDg7G0dGRDRs24OTk9Mi24uLiKFeu3CNNbTlhMmihCLTwEo1goyYVOfK1CtFGvYC8dvlASAuPYPeUyVD4foWZD/ikZDvOWsPQoUOJjY3l+++/x8XFRfUhcXNzw9nZGTc3N/r160dERATly5fH1dWV4cOHExAQgL+/PwDt27fH19eX3r17M3PmTBISEvjwww8ZOnSoVe+9I0eO0K5dO9zc3Dh37hwDBgygfPnyrF27lgsXLrB8+fIC9EgxR/dEREQQFhZG48aNadq0KdHR0dy5c0eN9gkNDaVy5crMmDEDyPIT8ff3p2bNmty6dYuoqCjOnz9P//79gSyzWKtWrRg9ejTOzs7o9Xp27drF8uXLmTNnDpCloLRv3567d++ycuVKUlJSVAdXd3d3dDodP/zwA4mJifj7++Pk5MTWrVuZPn16gXd5VjK0KDqBlBT5QCrZCHj9FAFlEhJB73MhlRTBKIrgB4NRB0bdP9+tZP78+QBqVGs2S5cuJTw8HIBPPvkErVbLq6++araYWzY6nY6NGzcyePBgAgICKF26NGFhYUyZMsUqGSIiIggPD2fmzJm4uLio6Z06dcr3fj0PYrWSkpSUxNGjR2nUqBFubm4kJiby9ddfYzKZCAkJyXOX5Nx4/fXXuXr1KhMmTCAhIYEGDRqwefNm1Zn2woULZlaRmzdvMmDAABISEihXrhyNGjViz549Zqaob775hrFjx/Lmm29y48YN9Ho906ZNUxdzO3ToEPv37wegZs2aZvLEx8dTtWpV7O3tmTdvHu+++y6KolCzZk01XFoikUgkksLApGgwmjTqd2uxZsVXJycn5s2bx7x583Ito9fr+emnn6xu90F+/fVXFi5caJFeuXLlx4oO0ihW/LqdO3fSuXNn7t69S8WKFdm8eTOdO3fG2dkZrVbLuXPn2LBhA+3bty+wIE8iKSkpuLm54T1nClrnR08pFRly1GQ9sq9KLoJaLYRExPtcMJlM99K4OHyiumSFLcl+V9T5djS6UllTK8a76RztEVUo7RUGHh4e/Oc//8HPzw8XFxcOHz5M9erV2bp1K3379uXvv/8uUL1WzUGMHz+e8PBwUlJSeO+99wgJCeHll1/m1KlTnDhxguHDh8vQW4lEIpFIHgOTSWP2KUl07dqVKVOmkJmZCWQtvHrhwgU++OADXn311QLXa5Ulxc3NjUOHDlGjRg0MBgPOzs78+uuvNGjQAIDTp0/TpEkTbt26VWBBnkSyteMqH08Vy5IiInLUaz0iPrtElElEBO0nRcT/P8H6ynQvjYvvTihUS8rzMWPMLCmn3vyoxFhSkpOTee211/jtt9+4ffs2Xl5eJCQkEBAQwE8//aTuIZRfrPJJcXBwUDfxy8jIwGQymW3qd+/evUeG8D7NaDM1aHUC/ceJ+EAS7YmUjYBiSSdVKxGxn0SUCaTnrBVoMgo/+MFk0qiLuZU0S4qbmxtbt27ll19+4ciRI6SmptKwYUOL9cnyi1VKSosWLRgzZgxjxoxh+fLlNGzYkKlTp7J69Wo0Gg2RkZE0btz4sQSRSCQSieRpJktJ0ajfSyItW7akZcuWNqvPKiUlKiqKkJAQXnzxRWrVqsXWrVsZMmSIunJduXLlLHZhlPyD1pD1EQY5arIaaXOyEhGFElEmQRHSOieaxbcInuEmoxaM2n++C85nn31mddm8VmvPC6uUlOeee45Tp05x/fp1nnnmGQC+//57tm3bxr179wgICFDTJRKJRCKR5B/FpEG5b0FRSoAl5eGNe69evcrdu3dVA8atW7coVaoUHh4ehaukZPOwItK2bdsCNfq0ockErdwEOU+EHMkhjU5WI2A/iXpPiYiQ97loQhkKXx7FpEExlhwlJT4+Xv0eGxvLF198weLFi/Hx8QGylt4fMGAAb7/9doHbKNCKs4qisHPnTs6cOYOXlxft27eXjrN5oM3UoNUKdMMJJEo28oWSD0TsKxFlEhBh73PRplYERMkogjZKmCXlQcaPH8+aNWtUBQXAx8eHTz75hNdee40333yzQPVapaR06tSJVatW4ebmxo0bN+jUqRMHDhygQoUKXL9+neeff57du3fj7u5eICEkEolEInnqMWmyPtnfSxBXrlzBYLB03DEajRYbCecHq5SUzZs3q7v/fvjhh9y+fZuzZ89SrVo1Ll68SLdu3ZgwYYK6f4DEHK1BrOkeEUdzAookLrKzrELI+1xAmbIQUDDBRFKKarqnhFpS2rZty9tvv82iRYto2LAhAAcPHmTw4MGPFYacb/fh7du3M2PGDHUraG9vbz7++GP+85//FFgIiUQikUiedjRGjdmnJLFkyRI8PT1p3Lgxjo6OODo60rRpUypWrMiiRYsKXK/VPima+0OAmzdvUqNGDbO8mjVrcvny5QIL8aSjzQSt+NFkxYqIo15AuNGcsAjYT0LeUyLKJCiiXT9NUSwjYdRkfbK/lyDc3d356aef1O1yAGrVqsXzzz//WPVaraSEh4fj6OhIZmYm8fHxvPDCC2peQkKCGnIksUQqKY9GtAeSiqhyiYaA/STsPSUiAvaVaNdPySyCRkz3Pzzwt4Tx/PPPP7Zi8iBWKSlhYWHq95dffpm7d++a5X/33XfqPj4SiUQikUjyz4PTPCVtuqdv37555i9ZsqRA9VqlpCxdujTP/IkTJ6LTCeQZKhjaTAWtSCF+At77irgeheIhYlcJKJNoI3GhEbCvRLt+RWFJ0fBPNLhgP/+R3Lx50+w4MzOTo0ePcuvWLdq0aVPgegu0Tko28fHxVKlSpcC7G0okEolEIrlPCfZJWbdunUWayWRi8ODBFn6s+eGxlBQfHx8OHz5M7dq1H6eaJx5tJoi0lpuQKrpIlibBEW2ECQh5TwnZT6IiYF+JZl1VisBxVmPK+mR/L+lotVoiIiIICgri/fffL1AdVikp3bt3zzHdaDQyYsQIXFxcAFi7dm2BhHjS0WWCTqD/NyEf3iLKJCjy+lmHkP0kKiL2lWgDl8zCl6ck+6TkxtmzZ3Nc5M1arFJS1q9fT2BgoLo2yoOUKVMGNze3AgsgkUgkEokENMasT/b3kkRERITZsaIoXLlyhR9//NEs+Ca/WKWkxMbGMnr0aMLCwujTp4+avnLlSqZNm4avr2+BBXga0Bqk4+yjkKPefCBgX4lmmgeE7CdREfL/TzCZiioEWVNCQ5B///13s2OtVou7uzuzZ89+ZORPXlilpPTs2RN/f3/eeustNm7cyKJFiyhXrlyBG5VIJBKJRGJOSfZJ2bFjR6HUa7XjbNWqVdm9ezeTJ0+mfv36fPXVV+oqtJK80WYq6BDHkiLmqElEocREzOsnzv2djZj9VNwClBxEu35KUfiklGAlpU2bNqxdu9ZiYdeUlBS6devG9u3bC1RvvqJ7tFotkydP5qWXXiI0NBSjsYRNmhUT2kwFrUBKiogPStEeSCqiyiUYQl4/AWUSsp9ADhKsoMiie4z/fC9J7Ny5k4yMDIv0tLQ0/vvf/xa43gIt1t6yZUuOHDnCoUOHqFmzZoEbB5g3bx5Vq1bFycmJZs2aceDAgVzLLlu2DI1GY/ZxcnIyK5OamsqwYcPw9vbG2dkZX19fFixYYFYmKCjIop5BgwaZlblw4QIhISGUKlUKDw8PRo8e/VgeyhKJRCKR5EW24+yDDrSic+TIEY4cOQLA8ePH1eMjR47w+++/s3jxYipXrlzg+gu8TkqZMmWoX79+gRsGWL16NRERESxYsIBmzZoRHR1NcHAwJ0+exMPDI8dzXF1dOXnypHr88JRTREQE27dvZ+XKlVStWpUtW7YwZMgQvLy86Nq1q1puwIABTJkyRT0uVaqU+t1oNBISEoKnpyd79uzhypUrhIaGYm9vz/Tp0/P9O7UZClpFWlLyRMDpApAOoSUZRcT9sgS9dkJaeASTSWuQ0z050aBBA3Wwn9PKss7OzsydO7fA9Vv9b/zTTz/Rv39/3n//ff7880+zvJs3bxZo2ds5c+YwYMAA+vTpo1o8SpUqleca/xqNBk9PT/VTsWJFs/w9e/YQFhZGUFAQVatWZeDAgdSvX9/CQlOqVCmzelxdXdW8LVu2cPz4cVauXEmDBg3o2LEjkZGRzJs3L0dzlkQikUgkj0tJtKTEx8dz9uxZFEXhwIEDxMfHq59Lly6RkpJS+NE9sbGxhIaG0qFDB06ePMncuXNZtGgRb775JgAZGRns2rUrXw1nZGRw8OBBxo4dq6ZptVratWvH3r17cz0vNTUVvV6PyWSiYcOGTJ8+3WxH5ubNm7Nhwwb69u2Ll5cXO3fu5NSpU3zyySdm9cTExLBy5Uo8PT3p0qUL48ePV60pe/fupW7dumYKUHBwMIMHD+bYsWP4+fnlKFt6ejrp6enqcUpKStbvMpjQlrR4sqJGqCV5/0ER0cIjoHVHxJG4gN0kLgLe56JZMbXScTZH9Ho9kLUEfmFglZISFRXFnDlzGDFiBADffvstffv2JS0tjX79+hWo4WvXrmE0Gi0sIRUrVuTEiRM5nuPj48OSJUuoV68eycnJzJo1i+bNm3Ps2DG8vb0BmDt3LgMHDsTb2xs7Ozu0Wi1fffUVgYGBaj29evVCr9fj5eXFkSNH+OCDDzh58qS6Ym5CQkKOcmXn5caMGTOYPHmyRbou04ROEeeOE+2fHxDyISkqYl6/4hbAEhEVJ3GV8eKWwBLRbnM53WPJhg0b6NixI/b29mzYsCHPsg+6W+QHq5SU06dP06VLF/W4R48euLu707VrVzIzM3nllVcK1Hh+CQgIICAgQD1u3rw5tWvXZuHChURGRgJZSsq+ffvYsGEDer2e3bt3M3ToULy8vGjXrh0AAwcOVOuoW7culSpVom3btpw9e/axNkIaO3as2ap7KSkpVKlSpcD1SSQSieTpoaQpKd26dSMhIQEPDw+6deuWazmNRlPgaGCrlBRXV1cSExPNlsVv3bo1GzdupHPnzly8eDHfDVeoUAGdTkdiYqJZemJiIp6enlbVYW9vj5+fH2fOnAHg3r17jBs3jnXr1hESEgJAvXr1iIuLY9asWaqS8jDNmjUD4MyZM9SoUQNPT08LH5ZsOfOSzdHREUdHR4t0TYYJTSGZwgqCaCMUQFChRB1hCiiUgE6qQlqcRFqK4EEE7CrRrp/WUPjP8JIWgvzgFE9hTfdY9Whp2rQpmzZtskhv1aoVP/zwA9HR0flu2MHBgUaNGrFt2zY1zWQysW3bNjNrSV4YjUb++OMPKlWqBEBmZiaZmZloteY/S6fT5dmBcXFxAGo9AQEB/PHHHyQlJalltm7diqurq9wCQCKRSCSFQkl0nC1srLKkvPvuu+zZsyfHvKCgIH744QeWL1+e78YjIiIICwujcePGNG3alOjoaO7cuaPuDxQaGkrlypWZMWMGAFOmTMHf35+aNWty69YtoqKiOH/+PP379weyLD6tWrVi9OjRODs7o9fr2bVrF8uXL2fOnDlA1o6MsbGxdOrUiWeeeYYjR47w7rvvEhgYSL169QBo3749vr6+9O7dm5kzZ5KQkMCHH37I0KFDc7SUPAqtwYhWEeiOE2yEAmJaLEDIAaaQfg2ijXpByNtcUKHE/P8TzWJYZJaUEjTd89lnn1ldNtunNb9YpaS0atWKVq1a5ZrfunVrWrdune/GX3/9da5evcqECRNISEigQYMGbN68WXVSvXDhgplV5ObNmwwYMICEhATKlStHo0aN2LNnj5l145tvvmHs2LG8+eab3LhxA71ez7Rp09TF2hwcHPj5559VhahKlSq8+uqrfPjhh2odOp2OjRs3MnjwYAICAihdujRhYWFm66rkB02GEY1OoIXgBPvnB/EeSNmI+PIVcWpFyOsnoEwiKgMAGqn4PhJNUSgpJWwX5IejZnNDo9EUWEnRKIpIq4w9WaSkpODm5kabOqOx0+XfAlNoCPbPD4gpE+I9KAEhlRQhr5+AMomqpEjr3KMxGNLYdWAaycnJZutq2YLsd0WDt6ahc8haRd2YkUbcyv8rlPZKEgVecVZiPZpMIxqTQGqxYP/8gJgygZhySSXFOqRM1iPgPSWadU5bBHvVaY1ZHwCRPATyS7btwxbXUMBbUyKRSCSSp49sn5QHfVOsZffu3XTp0gUvLy80Gg3r1683y1cUhQkTJlCpUiWcnZ1p164dp0+fNitz48YN3nzzTVxdXSlbtiz9+vUjNTXVahkWL15MnTp1cHJywsnJiTp16rBo0aL8/ZCHkJaUoiDTACZdcUvxD4KNUAAxZUK80RwgpGleyOsnZbIeEYergvWVxlgEnqwmBY1RUb/nhzt37lC/fn369u1L9+7dLfJnzpzJZ599xtdff021atUYP348wcHBHD9+XN2o98033+TKlSts3bqVzMxM+vTpw8CBA4mNjX1k+xMmTGDOnDkMHz5cjdDdu3cv7777LhcuXCi4T2dBfVJWrVpF165dKV26dIEafhrInmds9+wQ7LTSJyVPRJQJhJRLkUqKdWgFfPOKeO1AzOsnmEwGYzrb/pxVqD4pTV6Zip19lsJgyEzj13UfFqg9jUbDunXr1AXWFEXBy8uL9957j1GjRgGQnJxMxYoVWbZsGT179uTPP//E19eXX3/9lcaNGwOwefNmOnXqxMWLF/Hy8sqzTXd3dz777DPeeOMNs/RVq1YxfPhwrl27lq/fkE2B/4vffvtti4XYJBKJRCKRFIycpntSUlLMPg/uD2ct8fHxJCQkmC1o6ubmRrNmzdS98vbu3UvZsmVVBQWgXbt2aLVa9u/f/8g2MjMzzc7NplGjRhgMBY9uLfB0jwwKygcZBrFGdSLJko2IMoGQI1+NSTyZRBv1AkIuNCGkFQzEvH6iPROKwHFWY1TQaBX1O2CxtcrEiROZNGlSvurN3nMupz3psvOyl7d/EDs7O8qXL5/nnnXZ9O7dm/nz56trkmXz5ZdfqpsRFwTpkyKRSCQSiQBoTAqa+74o2X///vtvs+megiwoWlQsXryYLVu24O/vD8D+/fu5cOECoaGhZvvaPazI5EWBlZRNmzZRuXLlgp7+VKEYjChageLJtOKNMIV0UAU5wrQWIWUS79oJaQUDIe9zRSeYtb6oQpC1/3yHrJXUH9cHJnvPucTERHX7l+zjBg0aqGUe3AoGwGAwcOPGDav20zt69CgNGzYEslZ2h6w9+ipUqMDRo0fVcvl91hdYSWnZsmVBT336MBrAJNBDXMCHt2gLN6loBLpu2YioZAqppAgok7D3uXhyaQRzKSiSta6MCtyf7sFou99frVo1PD092bZtm6qUpKSksH//fgYPHgxk7Vl369YtDh48SKNGjQDYvn07JpNJ3YQ3L3bs2GEzeR9ETvdIJBKJRCIAWpOC9r5yos1nCHJqaipnzpxRj+Pj44mLi6N8+fI8++yzvPPOO0ydOpXnnntODUH28vJSI4Bq165Nhw4dGDBgAAsWLCAzM5Nhw4bRs2fPR0b2FCZSSSkCFIMBRaARuZBTKwJadwAxLSk68WRS8vlALRKkxcl6RJRLMEsKRWBJ0RgUNCjq9/zw22+/me2hl+0DEhYWxrJly3j//fe5c+cOAwcO5NatW7Rs2ZLNmzera6QAxMTEMGzYMNq2bYtWq+XVV1+1ehPBtLQ05s6dy44dO0hKSsJkMv//O3ToUL5+TzZSSZFIJBKJRABycpy1lqCgoDyjbjUaDVOmTMlzUbXy5ctbtXBbTvTr148tW7bw2muv0bRpU5sNhqWSUhRkZgo156uIOGrSCbQi7wNoNIKN5gAU8SwEQl4/AXfzE9LiBGhEc1IFoZ6ZAJiKYhdkE5r7ofNFssKtDdm4cSM//fQTLVq0sGm9VispJpOJZcuWsXbtWs6dO4dGo6FatWq89tpr9O7dW8wpBEFQjAqKSGs2iGZGBUS9e4R06FXEUzIF7CVBp+qKW4CcEXIzO8GmgJUiUVIUdWCksaHjbFFQuXJlXFxcbF6vVf/FiqLQtWtX+vfvz6VLl6hbty4vvPAC58+fJzw8nFdeecXmgkkkEolE8jShMZrMPiWJ2bNn88EHH3D+/Hmb1muVJWXZsmXs3r2bbdu2mTnmQFaIUrdu3Vi+fDmhoaE2Fe5JQTEaxXKcFdEMXtwC5IK0EFqHiNdPI6LVQkSLBfwT9ioUgl3AIpiq0xgVNJjU7yWJxo0bk5aWRvXq1SlVqhT29vZm+Tdu3ChQvVYpKatWrWLcuHEWCgpAmzZtGDNmDDExMVJJkUgkEomkoBgVVJW/hCkpb7zxBpcuXWL69OlUrFixaB1njxw5wsyZM3PN79ixo9VhSk8ligkQx3SniLSw3H2EdFBFSPcP4Ra5AorEqTC/CNhLYlp3QKzFJrMRyY8PisRhXWM0orlvbtMUwQq3tmTPnj3s3buX+vXr27Req5SUGzduWGxM9CAVK1bk5s2bNhPqiUN5QDsWAsH++UHISAxAPrytRcSIMRERNLpHtJkVQDwH/6KQpwRbUmrVqsW9e/dsXq9VTxaj0YidXe76jE6ne6ytmCUSiUQiedrRGIxmn5LERx99xHvvvcfOnTu5fv06KSkpZp+CYpUlRVEUwsPDc919MT09vcACSCQSiUQiIWtKKXvqVMT1kPKgQ4cOALRt29YsXVEUNBoNxgJOX1mlpISGhj7SCUY6zUokEolE8hgYDKC9P/dmKlmzE8W6weCyZcsKpXGJRCKRSCT3MRhBe9/iUBS7LtuQVq1a5Zp39OjRAtdrlZLSvXv3R1dkZ4enpycvvfQSXbp0sVqAefPmERUVRUJCAvXr12fu3Lk0bdo0x7LLli2jT58+ZmmOjo6kpaWpx6mpqYwZM4b169dz/fp1qlWrxogRIxg0aBAA586do1q1ajnW/+233/Kvf/0LyHl9jFWrVtGzZ0+rf5uwCLRmi/AItuqlsMj1ZKxD3k/WI9o9VRTymBTUwAZRnayt5Pbt26xatYpFixZx8ODBwp3ucXNze2QZk8nE6dOnWbRoEaNGjcpzE6NsVq9eTUREBAsWLKBZs2ZER0cTHBzMyZMn8fDwyPEcV1dXTp48qR4/rExERESwfft2Vq5cSdWqVdmyZQtDhgzBy8uLrl27UqVKFa5cuWJ2zpdffklUVBQdO3Y0S1+6dKk6zwZQtmzZR/4miUQikUgKhMHwT6RcCZvuyWb37t0sXryY7777Di8vL7p37868efMKXJ9VSsrSpUutrnDjxo0MGTLEKiVlzpw5DBgwQLWOLFiwgB9//JElS5YwZsyYHM/RaDR4enrmWueePXsICwsjKCgIgIEDB7Jw4UIOHDhA165d0el0FuevW7eOHj16UKZMGbP0smXL5tmW1Wg0Qo0KNCKO5gS17gi54qyI4b4CyiTktRP0PkcnoFyi9VURyKMYjSj3N1JSStB0T0JCAsuWLWPx4sWkpKTQo0cP0tPTWb9+Pb6+vo9Vt817vWXLljRu3PiR5TIyMjh48CDt2rX7Rxitlnbt2rF3795cz0tNTUWv11OlShVefvlljh07ZpbfvHlzNmzYwKVLl1AUhR07dnDq1Cnat2+fY30HDx4kLi6Ofv36WeQNHTqUChUq0LRpU5YsWZLnNtiQFeVkq7AriUQikTxlGAyQef9TQpb16NKlCz4+Phw5coTo6GguX77M3LlzbVa/1bsgW0vZsmVZu3btI8tdu3YNo9FosUhcxYoVOXHiRI7n+Pj4sGTJEurVq0dycjKzZs2iefPmHDt2DG9vbwDmzp3LwIED8fb2xs7ODq1Wy1dffUVgYGCOdS5evJjatWvTvHlzs/QpU6bQpk0bSpUqpU4ZpaamMmLEiFx/04wZM5g8ebJFukanQyPSUpOijVAAjYgjORDSQoBOoHvpPkJaLQTsJ2F9UgR8JojXV4UvT9Y+b/ctKUJuTW3Jpk2bGDFiBIMHD+a5556zef02V1IKk4CAAAICAtTj5s2bU7t2bRYuXEhkZCSQpaTs27ePDRs2oNfr2b17N0OHDsXLy8vMagNw7949YmNjGT9+vEVbD6b5+flx584doqKi8lRSxo4dS0REhHqckpJClSpVsh6WAikp8oWSDwRUUoRU6ER8ycl+shox7ymxnlMa2088WGIwgua+BaWEKCm//PILixcvplGjRtSuXZvevXvbNMCk2JSUChUqoNPpSExMNEtPTEy02g/E3t4ePz8/zpw5A2QpHePGjWPdunWEhIQAUK9ePeLi4pg1a5aFkrJmzRru3r1r1RovzZo1IzIykvT09FwXtXN0dDTLy54eMiiZVv2eokKsf/37KIIqKQJu3qMRUKZCmDl+fITc0kBAmQCNiHIJpqQYTBkAj5z2fxwyMu9hIktJMSDWeyM3/P398ff3Jzo6mtWrV7NkyRIiIiIwmUxs3bqVKlWq4OLiUuD6i01JcXBwoFGjRmzbto1u3boBWRFC27ZtY9iwYVbVYTQa+eOPP+jUqRMAmZmZZGZmon1o9KvT6TDlsAHa4sWL6dq1K+7u7o9sKy4ujnLlyuWqoOTE9evXAfhvxjqrz5FIJBKJuNy+fduqiNf84ODggKenJ78kbDRL9/T0xMHBwaZtFRalS5emb9++9O3bl5MnT7J48WI++ugjxowZw0svvcSGDRsKVG+xTvdEREQQFhZG48aNadq0KdHR0dy5c0eN9gkNDaVy5crMmDEDyPIT8ff3p2bNmty6dYuoqCjOnz9P//79gazw5FatWjF69GicnZ3R6/Xs2rWL5cuXM2fOHLO2z5w5w+7du/npp58s5Prhhx9ITEzE398fJycntm7dyvTp0xk1alS+fl/58uUBuHDhgs1v6qeR7Omzv//+G1dX1+IW54lA9qltkf1pe0TpU0VRuH37Nl5eXjav28nJifj4eDIyMszSHRwccHJysnl7hY2Pjw8zZ85kxowZ/PDDDyxZsqTglSnFzNy5c5Vnn31WcXBwUJo2bars27dPzWvVqpUSFhamHr/zzjtq2YoVKyqdOnVSDh06ZFbflStXlPDwcMXLy0txcnJSfHx8lNmzZysmk8ms3NixY5UqVaooRqPRQqZNmzYpDRo0UMqUKaOULl1aqV+/vrJgwYIcy+ZFcnKyAijJycn5Ok+SM7I/bY/sU9si+9P2yD59utEoimj7YT85pKSk4ObmRnJyshxV2QDZn7ZH9qltkf1pe2SfPt0I6C0lkUgkEolEIpWUQsXR0ZGJEyfmy9lWkjuyP22P7FPbIvvT9sg+fbqR0z0SiUQikUiERFpSJBKJRCKRCIlUUiQSiVWEh4dTtWrV4hbDjPDwcIuNQUsa586dQ6PRsGzZsnyfu3PnTjQaDTt37rS5XBKJCEglRfLEcPbsWd5++22qV6+Ok5MTrq6utGjRgk8//ZR79+4VuF6j0cjSpUsJCgqifPnyODo6UrVqVfr06cNvv/1mw19Qsrh79y6TJk0qlBdkamoqEydOpE6dOpQuXZpnnnmGBg0aMHLkSC5fvmzz9gAmTZqERqNBq9Xy999/W+SnpKTg7OyMRqOxesFJiUTyeJSovXskktz48ccf+de//oWjoyOhoaHUqVOHjIwMfvnlF0aPHs2xY8f48ssv813vvXv36N69O5s3byYwMJBx48ZRvnx5zp07x7fffsvXX3/NhQsX1A0un2S++uors5Wb7969q26oGRQUZLN2MjMzCQwM5MSJE4SFhTF8+HBSU1M5duwYsbGxvPLKK4WyoFY2jo6OrFq1ivfff98s3ZqNUyUSiW2RSoqkxBMfH0/Pnj3R6/Vs376dSpUqqXlDhw7lzJkz/PjjjwWqe/To0WzevJlPPvmEd955xyxv4sSJfPLJJ48jeonC3t6+SNpZv349v//+OzExMfTq1cssLy0tzWJVTlvTqVOnHJWU2NhYQkJC+O677wq1fYlE8g9yukdS4pk5cyapqaksXrzYTEHJpmbNmowcOVI9vnbtGidOnODu3bt51nvx4kUWLlzISy+9ZKGgQNaeUKNGjTKzovz+++907NgRV1dXypQpQ9u2bdm3b5/ZecuWLUOj0fDLL78wYsQI3N3dKVu2LG+//TYZGRncunWL0NBQypUrR7ly5Xj//ffNNjXL9mGYNWsWn3zyCXq9HmdnZ1q1asXRo0ct5Ny+fTsvvvgipUuXpmzZsrz88sv8+eefZmVu377NO++8Q9WqVXF0dMTDw4OXXnqJQ4cOqWUe9Ek5d+6cuufV5MmT0Wg0aDQaJk2apJY/ceIEr732GuXLl8fJyYnGjRtbtX/H2bNnAWjRooVFXvY0Xl7ExcXh7u5OUFAQP/zwAxqNhnXrLPfPio2NRaPRsHfvXrP0Xr16ERcXx4kTJ9S0hIQEtm/fbqE0ZZOUlES/fv2oWLEiTk5O1K9fn6+//tqi3K1btwgPD8fNzY2yZcsSFhbGrVu3cqyzoP0nkTxJSCVFUuL54YcfqF69Os2bN7eq/Oeff07t2rU5cOBAnuU2bdqEwWCgd+/eVtV77NgxXnzxRQ4fPsz777/P+PHjiY+PJygoiP3791uUHz58OKdPn2by5Ml07dqVL7/8kvHjx9OlSxeMRiPTp0+nZcuWREVFsWLFCovzly9fzmeffcbQoUMZO3YsR48epU2bNmY7i//8888EBweTlJTEpEmTiIiIYM+ePbRo0YJz586p5QYNGsT8+fN59dVX+eKLLxg1ahTOzs4Wykw27u7uzJ8/H4BXXnmFFStWsGLFCrp37672hb+/P3/++Sdjxoxh9uzZlC5dmm7duuWoMDyIXq9Xf19+V0j49ddfadOmDX5+fmzatInOnTtTpUoVYmJiLMrGxMRQo0YNAgICzNIDAwPx9vYmNjZWTVu9ejVlypRRd1d/kHv37hEUFMSKFSt48803iYqKws3NjfDwcD799FO1nKIovPzyy6xYsYK33nqLqVOncvHiRcLCwizqfJz+k0ieKIpxSX6J5LHJ3tfj5ZdftvqciRMnKoCyY8eOPMu9++67CqD8/vvvVtXbrVs3xcHBQTl79qyadvnyZcXFxUUJDAxU05YuXaoASnBwsNmeUgEBAYpGo1EGDRqkphkMBsXb21tp1aqVmhYfH68AirOzs3Lx4kU1ff/+/QqgvPvuu2pagwYNFA8PD+X69etq2uHDhxWtVquEhoaqaW5ubsrQoUPz/H1hYWGKXq9Xj69evaoAysSJEy3Ktm3bVqlbt66SlpampplMJqV58+bKc889l2c7d+/eVXx8fBRA0ev1Snh4uLJ48WIlMTExR5lKly6tKIqi/PLLL4qrq6sSEhJi1u7YsWMVR0dH5datW2paUlKSYmdnZyZ79n1x9epVZdSoUUrNmjXVvCZNmih9+vRRFEVRALO+io6OVgBl5cqValpGRoYSEBCglClTRklJSVEURVHWr1+vAMrMmTPVcgaDQXnxxRcVQFm6dGm++2/Hjh1W3csSSUlFWlIkJZqUlBQAXFxcrD5n0qRJKIrySGfP/NRtNBrZsmUL3bp1o3r16mp6pUqV6NWrF7/88otaXzb9+vVDo9Gox82aNUNRFPr166em6XQ6GjduzF9//WXRZrdu3ahcubJ63LRpU5o1a6bu7H3lyhXi4uIIDw9Xd+QGqFevHi+99JLZDuBly5Zl//79NomcuXHjBtu3b6dHjx7cvn2ba9euce3aNa5fv05wcDCnT5/m0qVLuZ7v7OzM/v37GT16NJA1PdavXz8qVarE8OHDSU9Ptzhnx44dBAcH07ZtW9auXWu2OmloaCjp6emsWbNGTVu9ejUGg4G33norRxl69erFmTNn+PXXX9W/uU31/PTTT3h6evLGG2+oafb29owYMYLU1FR27dqllrOzs2Pw4MFqOZ1Ox/Dhw23afxLJk4RUUiQlmmz/hNu3bxdr3VevXuXu3bv4+PhY5NWuXRuTyWQR1vrss8+aHbu5uQFQpUoVi/SbN29a1Pvcc89ZpD3//PPqNM758+cBcpXp2rVr3LlzB8jy6zl69ChVqlShadOmTJo0KUfFyBrOnDmDoiiMHz8ed3d3s8/EiROBLB+OvHBzc2PmzJmcO3eOc+fOsXjxYnx8fPj888+JjIw0K5uWlkZISAh+fn58++23ODg4mOXXqlWLJk2amE35xMTE4O/vT82aNXNs38/Pj1q1ahEbG0tMTAyenp60adMmx7Lnz5/nueeeQ6s1f5zWrl1bzc/+W6lSJYt1XR6+PrboP4nkSUFG90hKNK6urnh5eeXoMPq41KpVC4A//viDBg0a2Lx+nU5ndbpSyLtX9OjRgxdffJF169axZcsWoqKi+Pjjj1m7di0dO3bMV13ZYcqjRo0iODg4xzK5KQc5odfr6du3L6+88grVq1cnJiaGqVOnqvmOjo506tSJ77//ns2bN9O5c2eLOkJDQxk5ciQXL14kPT2dffv28fnnn+fZbq9evZg/fz4uLi68/vrrFkpIYWHr/pNISjJSSZGUeDp37syXX37J3r17LZwgH4eOHTui0+lYuXLlI51n3d3dKVWqFCdPnrTIO3HiBFqt1sJC8ricPn3aIu3UqVNqBE62A2puMlWoUIHSpUuraZUqVWLIkCEMGTKEpKQkGjZsyLRp03JVUh6cqnqQ7Okue3t72rVrl6/flBflypWjRo0aFgqpRqMhJiaGl19+mX/9619s2rTJYiqvZ8+eREREsGrVKu7du4e9vT2vv/56nu316tWLCRMmcOXKlRwdl7PR6/UcOXIEk8lkpshkRwdlXwe9Xs+2bdtITU01s6Y8fH0Kq/8kkpKInO6RlHjef/99SpcuTf/+/c0iW7I5e/asWZSFtSHIVapUYcCAAWzZsoW5c+da5JtMJmbPns3FixfR6XS0b9+e77//3ixqJjExkdjYWFq2bPnI0Nn8sn79ejPfhAMHDrB//35VqahUqRINGjTg66+/NgtzPXr0KFu2bKFTp05Alj9NcnKyWd0eHh54eXnl6P+RTalSpQAsQmg9PDwICgpi4cKFXLlyxeK8q1ev5vm7Dh8+zLVr1yzSz58/z/Hjx3OcvnJwcGDt2rU0adKELl26WERuVahQgY4dO7Jy5UpiYmLo0KEDFSpUyFOOGjVqEB0dzYwZM2jatGmu5Tp16kRCQgKrV69W0wwGA3PnzqVMmTK0atVKLWcwGNSoKMjq+4fvrcftP4nkSUJaUiQlnho1ahAbG8vrr79O7dq1zVac3bNnD//+978JDw9Xy3/++edMnjyZHTt2PNJ5dvbs2Zw9e5YRI0awdu1aOnfuTLly5bhw4QL//ve/OXHiBD179gRg6tSpbN26lZYtWzJkyBDs7OxYuHAh6enpzJw50+a/u2bNmrRs2ZLBgweTnp5OdHQ0zzzzjNkiZFFRUXTs2JGAgAD69evHvXv3mDt3Lm5ubuqaJrdv38bb25vXXnuN+vXrU6ZMGX7++Wd+/fVXZs+enWv7zs7O+Pr6snr1ap5//nnKly9PnTp1qFOnDvPmzaNly5bUrVuXAQMGUL16dRITE9m7dy8XL17k8OHDuda7detWJk6cSNeuXfH396dMmTL89ddfLFmyhPT0dLO1WB6WZ+PGjbRp04aOHTuya9cu6tSpo+aHhoby2muvAVj4teTGg+vr5MbAgQNZuHAh4eHhHDx4kKpVq7JmzRr+97//ER0drTped+nShRYtWjBmzBjOnTuHr68va9eutVAQgcfqP4nkiaJYY4skEhty6tQpZcCAAUrVqlUVBwcHxcXFRWnRooUyd+5cs1BOa0OQszEYDMqiRYuUF198UXFzc1Ps7e0VvV6v9OnTxyI8+dChQ0pwcLBSpkwZpVSpUkrr1q2VPXv2mJXJDkH+9ddfzdIfDIF9kAfDbBXlnxDkqKgoZfbs2UqVKlUUR0dH5cUXX1QOHz5sIf/PP/+stGjRQnF2dlZcXV2VLl26KMePH1fz09PTldGjRyv169dXXFxclNKlSyv169dXvvjiCws5HgxBVhRF2bNnj9KoUSPFwcHBIhz57NmzSmhoqOLp6anY29srlStXVjp37qysWbMm175WFEX566+/lAkTJij+/v6Kh4eHYmdnp7i7uyshISHK9u3b8+wbRVGUa9euKb6+voqnp6dy+vRps99Zrlw5xc3NTbl3755Fu7n1/8PwUAiyoihKYmKi0qdPH6VChQqKg4ODUrduXbOQ4myuX7+u9O7dW3F1dVXc3NyU3r17K7///rtFCLKiWNd/MgRZ8qSjUZRC9siTSCQ25dy5c1SrVo2oqChGjRpV3OKUGAwGA15eXnTp0oXFixcXtzgSicQKpE+KRCJ5Kli/fj1Xr14lNDS0uEWRSCRWIn1SJBLJE83+/fs5cuQIkZGR+Pn5qY6sEolEfKQlRSKRPNHMnz+fwYMH4+HhwfLly4tbHIlEkg+kT4pEIpFIJBIhkZYUiUQikUgkQiKVFIlEIpFIJEIiHWcLEZPJxOXLl3Fxccl1CXGJRCKRiI+iKNy+fRsvL69C2ccpLS2NjIwMszQHBwecnJxs3lZJQiophcjly5dtvl+LRCKRSIqPv//+G29vb5vWmZaWRjV9GRKSjGbpnp6exMfHP9WKilRSCpHs5bD3HahAmTLizKzlvPeuJCd0AlrAxLmTxEaHeNdOWlStR7TrdzvVhG/jBPW5bksyMjJISDJy6jdvXF2y/sNTbpt4vvFFMjIypJIiKRzUB1IpOygtzqvF+OgiRY5OI2qQmXhymYpbgBwQ5+5+ALHecQAoAt5PIOb10wjWVxpT1n9eYSqajmVMON7fINtREfE/veiRSopEIpFIJAJgwETmA99Fp3v37vk+Z8GCBXh4eFhdXiopRUCKyRGjSZyxilYj3s2vk8v1WI1OsBEmgFZES5iAIol47QC0gsolEqlF8NhMU0zYK/98F53169fTo0cPnJ2drSofGxtLamqqVFIkEolEIilppCsa7BWN+r0k8Nlnn1mtdKxZsybf9UslpQjIsqSI466qE9CSIioijnxFtIQJ2E3oBDSXi3g/iYpo9/mdIrBsZCoaMu8rJ5klQEnZsWMH5cuXt7r8pk2bqFy5cr7asImScvbsWQYMGMD27dttUd0TR6rJGZNASoqIiKo4aQV80YmIiNdPKgTWI+J9Lto9dcdU+CEHaYoOO0V7/7v4Skp+N+ts2bJlvtuwiaNEamoqu3btskVVEolEIpE8lRgUHZn3PwalZA1sW7VqxfLly7l3755N67XKkvLZZ5/lmX/p0iWbCPOkkmxyJsMozsyaaCMUEHMkJyoihmuLOLUiIqJNYYiMaJawu8aisKTYobuvnKQpIi4WkTt+fn6MGjWK4cOH06NHD/r164e/v/9j12vVm/Odd96hUqVKODg45Jj/8FK+EolEIpFI8keGosP+vpKSIZaO9kiio6OZNWsWGzZs4OuvvyYwMJCaNWvSt29fevfuTcWKFQtUr1VKil6v5+OPP6ZHjx455sfFxdGoUaMCCfA0kGpywmASyJIi2AhFZEQc+UqrhXUIGRYtKCLeU6JZfO8phkJvI12xVy0p6Yo4y1ZYi52dHd27d6d79+4kJSXx5ZdfMn78eMaNG0enTp0YMWIEbdq0yV+d1hRq1KgRBw8ezFVJ0Wg0KHKdi1xJNTpiMNoXtxhCI+o6DaI9KEVFRGVOREQdIMjr92jSjIWvpKQp9mjV6Z6Sp6Rkc+DAAZYuXco333yDh4cH4eHhXLp0ic6dOzNkyBBmzZpldV1WKSlTpkzh7t27ueb7+voSHx9vdaMSiUQikUjMyVTsyLyvpJSEEOQHSUpKYsWKFSxdupTTp0/TpUsXVq1aRXBwsLqVQHh4OB06dLC9kuLr65tnvr29PXq93upGnzZSDM6kG8SxpIhoBhfR3CwqIl4/ERHRCiaqxVBERLt+acbMRxd6TNIVO7T3XQNKymJu2Xh7e1OjRg369u1LeHg47u7uFmXq1atHkyZN8lVvvh0lkpOTSUhIALK2kXZzc8tvFRKJRCKRSB4iU9Fhp1pSSpZCu23bNl588cU8y7i6urJjx4581Wu1krJo0SLmzJnDyZMnzdJ9fHx477336NevX74afpq4a3TAYMw5Mqo4kKM565Fz9dYhonVHWuesR8TrJxrphsIPfkhX7NEoJdOS8igFpaBY1etRUVFMmjSJESNGEBwcrIYSJSYmsmXLFkaOHMnNmzcZNWpUoQhZ0rljdCDDII6SIiIirv0hKiIqmfIlZx1S6bUe0Z4JGcbCVxoyFZ0a3VNSLCl+fn6qz0lu2NnZ4enpyUsvvcTbb7+d63ImOZ5rTaHPP/+cpUuXWkT31K5dm6CgIOrXr8/o0aOlkiKRSCQSSQFJN9mhMdnf/279eTNmzGDt2rWcOHECZ2dnmjdvzscff4yPj49aJi0tjffee49vvvmG9PR0goOD+eKLL8zWL7lw4QKDBw9mx44dlClThrCwMGbMmIGdXe6qQrdu3R4pn8lkIikpialTp/Lnn3/yxRdfWP3brFJSkpKSqFu3bq75devW5dq1a1Y3+rSRmumIfaY4lhQ56rUeEftKRJlERESLk6iIeE+JZnXKLIIFYNNN9lAAJWXXrl0MHTqUJk2aYDAYGDduHO3bt+f48eOULl0agHfffZcff/yRf//737i5uTFs2DC6d+/O//73PwCMRiMhISF4enqyZ88erly5QmhoKPb29kyfPj3XtidOnGi1nG+88QY9evSwvZLSpEkTPvroIxYvXmyhURmNRj7++ON8e+xKJBKJRCL5h0xFp66Tknl/1+WUlBSzMo6Ojjg6Opqlbd682ex42bJleHh4cPDgQQIDA0lOTmbx4sXExsaqi6ktXbqU2rVrs2/fPvz9/dmyZQvHjx/n559/pmLFijRo0IDIyEg++OADJk2alK8pmtxo2LAhvXr1ytc5Vk/3BAcH4+npSWBgoJlPyu7du3FwcGDLli35l/gp4Z7BnkyBfFJEHDWJiuwr65D9ZB2iWndEs1qISGZm4fukpJvsIDsE2ZR1r1SpUsWszMSJE5k0aVKe9SQnJwNQvnx5AA4ePEhmZibt2rVTy9SqVYtnn32WvXv34u/vz969e6lbt67Z9E9wcDCDBw/m2LFj+Pn5WbTTsGFDtm3bRrly5az6fR06dGD16tVWlc3GKiWlXr16nDp1ipUrV7Jv3z7++usvICsEeerUqfTq1QtXV9d8Nfw0kWawx06gdVJERCNfclYjFQLrELGfRFVSRES0Z4LBUPjyGE06DCbd/e9ZiuPff/9t9n592IryMCaTiXfeeYcWLVpQp04dABISEnBwcKBs2bJmZStWrKguKZKQkGCxv072cXaZh4mLi+Pw4cOqMvQo4uLiSE9Pt6psNlbHVLm4uDB48GAGDx6crwYkEolEIpE8mnSTDuW+JSXjvpLi6uqaLyPA0KFDOXr0KL/88kuhyPgwbdu2tXpbnEdFAeWETQK/MzMzuXLlCs8++2y+z503bx5RUVEkJCRQv3595s6dS9OmTXMsu2zZMvr06WOW5ujoSFpamnqcmprKmDFjWL9+PdevX6datWqMGDGCQYMGAXDjxg0mTpzIli1buHDhAu7u7nTr1o3IyEizhely6sxVq1bRs2fPfP/GewZ7dJniWFJEHGGKimijOVER8Z4qWatMFC8iXj/RMGQWfh+lm+wslJT8MGzYMDZu3Mju3bvx9vZW0z09PcnIyODWrVtm1pTExEQ8PT3VMgcOHDCrLzExUc3LiYJsh/OgXNZgEyXl+PHjNGzYEKMxf+7Pq1evJiIiggULFtCsWTOio6MJDg7m5MmTeHh45HiOq6ur2YJyDysTERERbN++nZUrV1K1alW2bNnCkCFD8PLyomvXrly+fJnLly8za9YsfH19OX/+PIMGDeLy5cusWbPGrK6lS5fSoUMH9fhhU5lEIpFIJLbCiBbD/Y0FjVi/waCiKAwfPpx169axc+dOqlWrZpbfqFEj7O3t2bZtG6+++ioAJ0+e5MKFCwQEBAAQEBDAtGnTSEpKUt+/W7duxdXVNdetcYpiO5zCX0IvD+bMmcOAAQNU68iCBQv48ccfWbJkCWPGjMnxHI1Gk6tWB7Bnzx7CwsIICgoCYODAgSxcuJADBw7QtWtX6tSpw3fffaeWr1GjBtOmTeOtt97CYDCYRS+VLVs2z7asJT3TDl1msXa18Ig6kpOWFOuQ/WQdWmnesRrR7qn8DsILQobRDsWY9a7INFpvSRk6dCixsbF8//33uLi4qD4kbm5uODs74+bmRr9+/YiIiKB8+fK4uroyfPhwAgIC8Pf3B6B9+/b4+vrSu3dvZs6cSUJCAh9++CFDhw59pB9MYWLVm7Nhw4Z55t+7dy/fDWdkZHDw4EHGjh2rpmm1Wtq1a8fevXtzPS81NRW9Xo/JZKJhw4ZMnz6dF154Qc1v3rw5GzZsoG/fvnh5ebFz505OnTrFJ598kmudycnJuLq6WoRXDx06lP79+1O9enUGDRpEnz598pxTS09PN3MKyg4dyzRoMRl0uXeGRLgHksgUYFr3qUTeU9YjYl+Jdpsbi2ChFIOiRXPfkpJtUbGG+fPnA6iD82yWLl1KeHg4AJ988glarZZXX33VbDG3bHQ6HRs3bmTw4MEEBARQunRpwsLCmDJlyuP9qMfEKiXl+PHj9OzZ08KElM2VK1c4depUvhq+du0aRqMxR2/iEydO5HiOj48PS5YsoV69eiQnJzNr1iyaN2/OsWPH1HmuuXPnMnDgQLy9vbGzs0Or1fLVV18RGBiYqxyRkZEMHDjQLH3KlCm0adOGUqVKqVNGqampjBgxItffNGPGDCZPnpyfbpBIJBKJBIAMow7FeH+dFKP1A1trHFednJyYN28e8+bNy7WMXq/np59+srrdosAqJaVOnTo0a9Ys18ieuLg4vvrqK5sKlhMBAQHq/BlkWU1q167NwoULiYyMBLKUlH379rFhwwb0ej27d+9m6NCheHl5mcWIQ5alIyQkBF9fX4u48/Hjx6vf/fz8uHPnDlFRUXkqKWPHjiUiIsKs/ipVqmDIsMOUx7LCRY2IoyZhkX1lFdK6Yx2i/u/J6/dojJmGwm9D0aiWFGMJ22CwsLDqzdmiRQuL3Y8fxMXFJVdLRW5UqFABnU6neg9n86C38aOwt7fHz8+PM2fOAFnTTuPGjWPdunWEhIQAWWu8xMXFMWvWLDMl5fbt23To0AEXFxfWrVuHvX3e0TfNmjUjMjKS9PT0XOfncloJUCKRSCQSa8gw6jDdt6AY8mFJEYVbt26xZs0azp49y+jRoylfvjyHDh2iYsWK/D97Zx4XxbHt8V/PsCugqIAIIoqiREHAyBIj7qgI8ZnEmBgBF9zjjSRG9LkbNRGjRK8RoyDxumZRgxvRIMrNg2iCIbhEVIJilMGFTZRlZrrfH0DLOAP0DLMUWt/Ppz/09FJ1urrpPnXqnFOdOnXSqExBSsqXX37Z6P5u3bohNTVVrYpNTEzg4+ODlJQUfoIilmWRkpKCuXPnCipDLpfj0qVLGD16NICaUGipVAqRSHEsTywWg60XzlVWVoagoCCYmpoiKSkJZmZmTdaVlZWFtm3baqSEyOUicDLh44svI6T2MImEwA4W7YkLhD7ngiHtkWKlulcapKwYXG0yt7qkbi2F7OxsDBs2DNbW1rh16xYiIyNhY2ODQ4cOIT8/H7t379aoXIOOQURFRSE8PBz9+vVD//79ERsbiydPnvDRPmFhYejUqRPWrVsHoMZPxM/PD66urigpKUFMTAxu376NadOmAagJTw4MDMSCBQtgbm4OZ2dnnDt3Drt378bGjRsB1CgoI0aMwNOnT7Fnzx6UlZXxDq4dOnSAWCzG0aNHUVhYCD8/P5iZmeH06dNYu3atxrM8c1IROCOqpDQGtWyqAf3QCYIqTmpAn6kmYfXQ0ZSzIjCsiF9vSURFRSEiIgLr16+HpaUlv3306NFqz9dTH8FKyv3793H58mX4+PjA2toahYWF+Oabb8CyLIKDgxudJbkh3nnnHTx48ADLli2DRCJB3759kZyczDvT5ufnK1hFiouLERkZCYlEgrZt28LHxwfp6ekKMdwHDhzAokWLMHHiRBQVFcHZ2Rlr1qzhk7ldvHgR58+fBwC4uroqyJOXl4cuXbrA2NgYW7duxfz588FxHFxdXflwaQqFQqFQdIGcFQHylqmk/Pbbb9i+fbvS9k6dOjWYVl8IDCfALfjs2bMYM2YMnj59Cjs7OyQnJ2PMmDEwNzeHSCTCrVu3kJSUhBEjRmgsyItIWVkZrK2t4Ri7CiLzpoeUKARCe5jCoFYLYZDaTvQ5bxK2ohL/zF3Bp6zQJnXfCo/vPobYosalQP60Ctlvb9BJfbrA1tYWP/30E7y8vGBpaYk///wTXbt2xenTpzFlyhTcuXNHo3IFqWpLly5FREQEysrK8NFHHyE4OBhvvPEGrl+/jmvXruGDDz6gobcUCoVCoTQDmVyksLQkQkNDsWrVKkilUgA1iVfz8/OxcOFCPsutJgiypFhbW+PixYvo1q0bZDIZzM3N8dtvv6Fv374AgBs3buDVV19FSUmJxoK8iNRpx04xq6klpSmI7WEaWoCWAUd74sKgz5NwCGsrtqIS/3y4TKeWFLd90QqWlJz3PmsxlpTS0lK89dZb+P333/H48WM4ODhAIpHA398fJ06cQKtWrTQqV5BPiomJCT+JX3V1NViWVZjUr6KioskQ3pcZRsaAkRL2H0catHmEQ2BbMQQKRZ2x1YBEJZOw+6ePd7hczjzzSZET1gBNYG1tjdOnT+OXX35BdnY2ysvL4e3trZSfTF0E50mJjo5GdHQ0du/eDW9vb3z66ac4ePAgGIbB6tWr0a9fv2YJQqFQKBTKywzLMmBYhl9viQwYMAADBgzQWnmClJSYmBgEBwfj9ddfR8+ePXH69GnMnj2bnxW4bdu2SE5O1ppQLxqiagYikmYWI0gU4qFtJQgSrRY0944a0HjtptGDJYVjGV454VqAkrJ582bBxzaWrb0xBCkp3bt3x/Xr1/Ho0SO0a9cOAPDjjz8iJSUFFRUV8Pf357dTKBQKhUJRH1b+LASZbQGOs89P3PvgwQM8ffqUN2CUlJTAwsICtra2ulVS6nheERk6dKhGlb5siGQ1CzGQr6ATA4kWAhIhsyNOoFAEikQqpNnB9OGTwrEMb0FpCZaUvLw8fn3fvn346quvEB8fDzc3NwBATk4OIiMjMWPGDI3r0CjjLMdxOHv2LG7evAkHBweMGDGCOs42AiNlIBKT/8AZFNo8gqGKk0AIHO6h9044xCm+euhociwDTt5ylJT6LF26FN9//z2voACAm5sbNm3ahLfeegsTJ07UqFxBSsro0aOxf/9+WFtbo6ioCKNHj8aFCxfQvn17PHr0CD169EBaWho6dOigkRAUCoVCobz0cMwzTbaFabQFBQWQyZQ1OblcrjSRsDoIUlKSk5NRVVUFAFiyZAkeP36M3NxcuLi44J9//sHYsWOxbNkybNu2TWNBXmREMkAkNbQUZNPC/h8NCnE9TFKhDSUYIv//CJOJ08dwj7yeJaWFhSAPHToUM2bMwM6dO+Ht7Q0AyMzMxKxZs5oVhqy2Z86ZM2ewbt06uLi4AAAcHR3x+eef46efftJYCAqFQqFQXnYYOaOwtCQSEhJgb2+Pfv36wdTUFKampujfvz/s7Oywc+dOjcsV7JPC1PZKiouL0a1bN4V9rq6uuHfvnsZCvOiIpICIJEftlvXsGxTaw2y5EHnvCIU2VdNw+rCGs0zNUrfegujQoQNOnDjBT5cDAD179kSPHj2aVa5gJSUiIgKmpqaQSqXIy8vDK6+8wu+TSCR8yBFFGaqktFzoh04gtJ0EQezzRKpcBMHpI0JTXrug3t8WRo8ePZqtmNRHkJISHh7Or7/xxht4+vSpwv4ffviBn8eHQqFQKBSK+jD1Ms4yLcySMmXKlEb3JyQkaFSuICVl165dje5fvnw5xGKxRgK8DIhk1JLSUiG250satJ0EQezzRKpcBKGP4R6GAxj22XpLori4WOG3VCrF5cuXUVJSgiFDhmhcrkZ5UurIy8uDk5OTxrMbUigUCoVCqUXO1Cx16y2Iw4cPK21jWRazZs1S8mNVh2YpKW5ubvjzzz/Rq1ev5hTzwiOSchC1NLVY3xD6/0hsz5c0aDsJgqNh0cIhrKn04ZPCsPUsKazu69M1IpEIUVFRGDRoED755BONyhCkpIwbN07ldrlcjnnz5sHS0hIAcOjQIY2EeNERSQGS5hckEto+giFScSJRJhKhnRXBkPacc1Ld37v6occtLQS5IXJzc1UmeROKICXlyJEjGDhwIJ8bpT6tW7eGtbW1xgJQKBQKhUJp2ZaUqKgohd8cx6GgoADHjx9XCL5RF0FKyr59+7BgwQKEh4dj8uTJ/PY9e/ZgzZo1cHd311iAlwGxlIOY9qAahZrB1YDApiKt10sstJ0EQ9ozpZc8KXKAaaEhyH/88YfCb5FIhA4dOuCLL75oMvKnMQQpKRMmTICfnx/ef/99HDt2DDt37kTbtm01rpRCoVAoFIoiLdmSkpqaqpNyBTvOdunSBWlpaVi5ciU8PT2xY8cOPgstpXEYGfVJaRJqaRIMaT1MANRCIBAi7x1A758Q9OGT0oKVlCFDhuDQoUNKiV3LysowduxYnDlzRqNy1YruEYlEWLlyJYYPH46wsDDI5S3MHmUgxFIOYtCPcGMQ+/ImEQLbit4/gRDasaP3r2lYfUX3yJ+ttyTOnj2L6upqpe2VlZX473//q3G5GqUYGzBgALKzs3Hx4kW4urpqXDkAbN26FV26dIGZmRl8fX1x4cKFBo9NTEwEwzAKi5mZmcIx5eXlmDt3LhwdHWFubg53d3fExcUpHDNo0CClcmbOnKlwTH5+PoKDg2FhYQFbW1ssWLCgWR7KFAqFQqE0Rp0lpb5FhXSys7ORnZ0NALh69Sr/Ozs7G3/88Qfi4+PRqVMnjcvXOE9K69at4enpqXHFAHDw4EFERUUhLi4Ovr6+iI2NRVBQEHJycmBra6vyHCsrK+Tk5PC/nx9yioqKwpkzZ7Bnzx506dIFp06dwuzZs+Hg4IDQ0FD+uMjISKxatYr/bWFhwa/L5XIEBwfD3t4e6enpKCgoQFhYGIyNjbF27Vq1r1NUzUHEUUtKoxDak+NIyhRMMgTePzKdsQl9DxDYVKRZd0QyfYQg17OktJCBir59+/KdfVWZZc3NzbFlyxaNyxf8Cj5x4gSmTZuGTz75BH/99ZfCvuLiYo3S3m7cuBGRkZGYPHkyb/GwsLBoNMc/wzCwt7fnFzs7O4X96enpCA8Px6BBg9ClSxdMnz4dnp6eShYaCwsLhXKsrKz4fadOncLVq1exZ88e9O3bF6NGjcLq1auxdetWleYsCoVCoVCaDfvc0gLIy8tDbm4uOI7DhQsXkJeXxy93795FWVmZ7qN79u3bh7CwMIwcORI5OTnYsmULdu7ciYkTJwIAqqurce7cObUqrq6uRmZmJhYtWsRvE4lEGDZsGDIyMho8r7y8HM7OzmBZFt7e3li7dq3CjMwBAQFISkrClClT4ODggLNnz+L69evYtGmTQjl79+7Fnj17YG9vj5CQECxdupS3pmRkZKBPnz4KClBQUBBmzZqFK1euwMvLS6VsVVVVqKqq4n+XlZXVXJeMhailPHGGgsheL3m9OWIhsZ2oM7ZgiLQ6ESaSPpK5ieQ1CwBwLcSS4uzsDKAmBb4uEKSkxMTEYOPGjZg3bx4A4Ntvv8WUKVNQWVmJqVOnalTxw4cPIZfLlSwhdnZ2uHbtmspz3NzckJCQAA8PD5SWlmLDhg0ICAjAlStX4OjoCADYsmULpk+fDkdHRxgZGUEkEmHHjh0YOHAgX857770HZ2dnODg4IDs7GwsXLkROTg6fMVcikaiUq25fQ6xbtw4rV65U2i6uZiHW0Q3UBCJfSNQMLhiqOAmEwJA6Uu8dka8EwhDpQUlRsKCQ88lokKSkJIwaNQrGxsZISkpq9Nj67hbqIEhJuXHjBkJCQvjf48ePR4cOHRAaGgqpVIr/+Z//0ahydfH394e/vz//OyAgAL169cL27duxevVqADVKyq+//oqkpCQ4OzsjLS0Nc+bMgYODA4YNGwYAmD59Ol9Gnz590LFjRwwdOhS5ubnNmghp0aJFCln3ysrK4OTkpHF5FAqFQnl5ELE1CwBwaiopaWlpiImJQWZmJgoKCnD48GGMHTuW389xHJYvX44dO3agpKQEr732GrZt24bu3bvzxxQVFeGDDz7A0aNHIRKJ8Oabb+LLL79E69atVdY5duxYSCQS2NraKtT1PAzDaBwNLEhJsbKyQmFhoUJa/MGDB+PYsWMYM2YM/vnnH7Urbt++PcRiMQoLCxW2FxYWwt7eXlAZxsbG8PLyws2bNwEAFRUVWLx4MQ4fPozg4GAAgIeHB7KysrBhwwZeSXkeX19fAMDNmzfRrVs32NvbK/mw1MnZmGympqYwNTVV2i6qZiEiypJiaAlUQLtywiHQmZda5wRCYjOB0PtHmEgime7f4c0JQX7y5Ak8PT0xZcoUlfPtrV+/Hps3b8Y333wDFxcXLF26FEFBQbh69SofJTtx4kQUFBTg9OnTkEqlmDx5MqZPn459+/aprLP+EI+uhnsEve769++PkydPKm0PDAzE0aNHERsbq3bFJiYm8PHxQUpKCr+NZVmkpKQoWEsaQy6X49KlS+jYsSMAQCqVQiqVQiRSvCyxWNxoA2ZlZQEAX46/vz8uXbqE+/fv88ecPn0aVlZWdAoACoVCoeiE5oQgjxo1Cp9++qnKkQ2O4xAbG4slS5bgjTfegIeHB3bv3o179+7hyJEjAIC//voLycnJ2LlzJ3x9fTFgwABs2bIFBw4cwL1797RwdZohyJIyf/58pKenq9w3aNAgHD16FLt371a78qioKISHh6Nfv37o378/YmNj8eTJE35+oLCwMHTq1Anr1q0DAKxatQp+fn5wdXVFSUkJYmJicPv2bUybNg1AjcUnMDAQCxYsgLm5OZydnXHu3Dns3r0bGzduBFAzI+O+ffswevRotGvXDtnZ2Zg/fz4GDhwIDw8PAMCIESPg7u6OSZMmYf369ZBIJFiyZAnmzJmj0lLSFIxUDoYgLyjCOig1EOg/ABDawyQQIpuJQKGItGJCOZUDRRm9WFJUhCDXBWDU0ZDFvjHy8vIgkUgURhOsra3h6+uLjIwMTJgwARkZGWjTpg369evHHzNs2DCIRCKcP39epfKzefNmwTLU+bSqiyAlJTAwEIGBgQ3uHzx4MAYPHqx25e+88w4ePHiAZcuWQSKRoG/fvkhOTuadVPPz8xWsIsXFxYiMjIREIkHbtm3h4+OD9PR0BevGgQMHsGjRIkycOBFFRUVwdnbGmjVr+GRtJiYm+Pnnn3mFyMnJCW+++SaWLFnClyEWi3Hs2DHMmjUL/v7+aNWqFcLDwxXyqqiDSMpCxJKjpNAPr3AYAodWSPz4kigTiQoBgSLVQGAngbT3FKOv4Z7n0uI/79e4fPlyrFixQq1y6wI+VAWE1O2r8y2pj5GREWxsbBoMGHk+arYhGIbRrZKiS+bOnYu5c+eq3Hf27FmF35s2bWqyUezt7bFr164G9zs5OQkKl3Z2dsaJEyeaPI5CoVAoFG3AyDkwco5fB4A7d+4o5PHSxJqvK/Ly8nReh8GVlJcBploGRiw2tBg8JJp2Ses18VBLSouFPudqQOBzTtr9E+lhrjpVlhQrKysFJUUT6gI+CgsLed/Lut99+/blj6nvhwkAMpkMRUVFgoNZ6uBqM6xr4x4S+GhSKBQKhfLyUZfMrX5SN23g4uICe3t7hUCVsrIynD9/ng9U8ff3R0lJCTIzM/ljzpw5A5Zl+QjYpoiPj0fv3r1hZmYGMzMz9O7dGzt37myW7NSSog+kMoAlx5JCYk+ctF4TD4Fj9URC4v2jMgmHxO4qYW3FyPWQRoJ9NtwDVr0Q+vLycj4dB1AzFJOVlQUbGxt07twZH374IT799FN0796dD0F2cHDg85v06tULI0eORGRkJOLi4iCVSjF37lxMmDABDg4OTda/bNkybNy4ER988AGv+GRkZGD+/PnIz8/X2KdTYyVl//79CA0NRatWrTQt4qWBkcnAUCWlcUiUiVA4EhUnEu8fgTJRZVwNCGsrxkDDPUL5/fffFQJY6hKLhoeHIzExEZ988gmePHmC6dOno6SkBAMGDEBycjKfIwWomS5m7ty5GDp0KJ/MTWgEz7Zt27Bjxw68++67/LbQ0FB4eHjggw8+0L+SMmPGDPj6+qJr166aFkGhUCgUCqUWkeyZviiSqXfuoEGDeF8QVTAMg1WrVjWqLNjY2DSYuK0ppFKpQvhyHT4+PpDJ1LyYemispDTWGJTnkMoAEbWkNAqJMgFE9jAZljyZiLx/BMpEpBUMAOgz1TR6saRwYGqHeRg1h3sMzaRJk7Bt2zY+J1kdX3/9NT8ZsSZQnxQKhUKhUAiAkXNgRIohyC2J+Ph4nDp1Cn5+fgCA8+fPIz8/H2FhYQrz2j2vyDSGxkrKyZMn0alTJ01Pf6ngqmXgRAR5phHYmyN2rJ5EuUh6luogsZ0IlIkh8H8PAJFtRdpzzughIWdzfFIMzeXLl+Ht7Q2gJrM7UDNHX/v27XH58mX+OHXf9RorKQMGDND01JcPWTVZqSYJTKPKicmTCQCRbcVPk0oQRCqZJMpE2IeXh8S2EhNmSdCHkiLjwDAcv96SSE1N1Um5dLiHQqFQKBQCELEcRLXDPKIW5pOiK6iSogc4qQwcST1yAntzjJzAnhwAEJQpmIfAIQMiM6mS9D9XC0OadaAOEu8facEZehnuabmOs5WVldiyZQtSU1Nx//59sKyixffixYsalUuVFAqFQqFQCICRcWDQMod7pk6dilOnTuGtt95C//79tTYETJUUPcDJ5OAYzePEtQ1J8wjVQWRPHCCvNwdC/T8IfKYgIu/ecRx5/kQAqNVJCKw+ZkFuuZaUY8eO4cSJE3jttde0Wq5gJYVlWSQmJuLQoUO4desWGIaBi4sL3nrrLUyaNInMFycpyOVEvQQ4Ah9+hlTHWQKVFBIVOvIkAsARKBVB7wEFCNQxOd2PrqgFpw8lRcaCAcuvtyQ6deoES0tLrZcr6D+G4ziEhoZi2rRpuHv3Lvr06YNXXnkFt2/fRkREBP7nf/5H64JRKBQKhfIywchZhaUl8cUXX2DhwoW4ffu2VssVZElJTExEWloaUlJSFOYGAGpmSRw7dix2796NsLAwrQr3osDJ5UQ5zjK01yQcAi0pRA7X6SEbp7owJJoHCAwfBwCQd/vIs+7oYaiOqTfBYEsb7unXrx8qKyvRtWtXWFhYwNjYWGF/UVGRRuUKUlL279+PxYsXKykoADBkyBBER0dj7969VEmhUCgUCkVTZNwzZaiFZZx99913cffuXaxduxZ2dnb6dZzNzs7G+vXrG9w/atQowTMlvpRwHAByHjgie72k9ZrqYMmxgPGQmIqSwLB2EucXI/HWASDPagGQZ8XUgzwMy4KpfUgYPfjAaJP09HRkZGTA09NTq+UKUlKKiopgZ2fX4H47OzsUFxdrTSjKyweJzrwAmcoTkR9fQwugChJf8gQO1QHQy1CG+hDaVrpExtazpJB4TxqmZ8+eqKio0Hq5gro/crkcRkYN6zNisbhZUzFTKBQKhfKyw7CswtKS+Oyzz/DRRx/h7NmzePToEcrKyhQWTRFkSeE4DhERETA1NVW5v6qqSmMBKBSA4InXCISG+wuEwCEoYiHIsf+lRi5/FkWghwy32mTkyJEAgKFDhyps5zgODMNArqGbgSAlJSwsrMkXI3WapVAoFAqlGchkgKh2mIttWaMTBp1gMDExUSeVvzQwDFlzY5DYayJRJoDIeXKItBCQ9HzXQqTFicTniVRIu3/6kEdezyelhQ33BAYGNrjv8uXLGpcrSEkZN25c0wUZGcHe3h7Dhw9HSEiIYAG2bt2KmJgYSCQSeHp6YsuWLejfv7/KYxMTEzF58mSFbaampqisrOR/l5eXIzo6GkeOHMGjR4/g4uKCefPmYebMmQCAW7duwcXFRWX53377Ld5++20Aql9w+/fvx4QJEwRfGw8jIuojTGKeDVJf3kS2FWkvb4BMxYnEZ4qg94ACJGZ8Jq2t9CGPnK033NOylJTnefz4Mfbv34+dO3ciMzNTt8M91tbWTR7Dsixu3LiBnTt34uOPP8aqVauaPOfgwYOIiopCXFwcfH19ERsbi6CgIOTk5MDW1lblOVZWVsjJyeF/P69MREVF4cyZM9izZw+6dOmCU6dOYfbs2XBwcEBoaCicnJxQUFCgcM7XX3+NmJgYjBo1SmH7rl27+HE2AGjTpk2T10ShUCgUika0YJ+UOtLS0hAfH48ffvgBDg4OGDduHLZu3apxeYKUlF27dgku8NixY5g9e7YgJWXjxo2IjIzkrSNxcXE4fvw4EhISEB0drfIchmFgb2/fYJnp6ekIDw/HoEGDAADTp0/H9u3bceHCBYSGhkIsFiudf/jwYYwfPx6tW7dW2N6mTZtG6xIKIxaDISiWlch5ckjsiQNEhozSYQyBEHjviLSCAUS2FWnPOaMHSwonk4GrfRdyLcgnRSKRIDExEfHx8SgrK8P48eNRVVWFI0eOwN3dvVlla73VBwwYgH79+jV5XHV1NTIzMzFs2LBnwohEGDZsGDIyMho8r7y8HM7OznBycsIbb7yBK1euKOwPCAhAUlIS7t69C47jkJqaiuvXr2PEiBEqy8vMzERWVhamTp2qtG/OnDlo3749+vfvj4SEhCbzU1RVVWkt7IpCoVAoLxksWzPkI2dbzHBPSEgI3NzckJ2djdjYWNy7dw9btmzRWvmCZ0EWSps2bXDo0KEmj3v48CHkcrlSkjg7Oztcu3ZN5Tlubm5ISEiAh4cHSktLsWHDBgQEBODKlStwdHQEAGzZsgXTp0+Ho6MjjIyMIBKJsGPHDgwcOFBlmfHx8ejVqxcCAgIUtq9atQpDhgyBhYUFP2RUXl6OefPmNXhN69atw8qVK5W2M8ZiMIzWm1pzCOuhAIT6fgBkWghIG6sHqE+DUEh8ngAwJFoySZNJH5YUqZSf5ZzjpDqvTxucPHkS8+bNw6xZs9C9e3etl0/Ql7Np/P394e/vz/8OCAhAr169sH37dqxevRpAjZLy66+/IikpCc7OzkhLS8OcOXPg4OCgYLUBgIqKCuzbtw9Lly5Vqqv+Ni8vLzx58gQxMTGNKimLFi1CVFQU/7usrAxOTk5gjIzIUlJIfFGS+EEB6MdXKAQ+U/TDqwYEdlyI+99j9dCRkskBpnaYh9hZVxX55ZdfEB8fDx8fH/Tq1QuTJk3SLMCkAQz25Wzfvj3EYjEKCwsVthcWFgr2AzE2NoaXlxdu3rwJoEbpWLx4MQ4fPozg4GAAgIeHB7KysrBhwwYlJeX777/H06dPBeV48fX1xerVq1FVVdVgUjtTU1OFfXXDQzKuWtD16A2OwBeS9kcetQORc/cQKBOBifEZIp8p8toJAJlKCmHPuYyteY/rcloKqbwSXO2U1DK0DEuKn58f/Pz8EBsbi4MHDyIhIQFRUVFgWRanT5+Gk5MTLC0tNS7fYEqKiYkJfHx8kJKSgrFjxwKoiRBKSUnB3LlzBZUhl8tx6dIljB49GgAglUohlUoheq63IhaLwaoY34uPj0doaCg6dOjQZF1ZWVlo27ZtgwqKKh49egQAOPf4W8HnUCgUCoVcHj9+LCjiVR1MTExgb2+P/0qOKmy3t7eHiYmJVuvSFa1atcKUKVMwZcoU5OTkID4+Hp999hmio6MxfPhwJCUlaVSuQccgoqKiEB4ejn79+qF///6IjY3FkydP+GifsLAwdOrUCevWrQNQ4yfi5+cHV1dXlJSUICYmBrdv38a0adMA1IQnBwYGYsGCBTA3N4ezszPOnTuH3bt3Y+PGjQp137x5E2lpaThx4oSSXEePHkVhYSH8/PxgZmaG06dPY+3atfj444/Vuj4bGxsAQH5+vtYf6peRuuGzO3fuwMrKytDivBDQNtUutD21DyltynEcHj9+DAcHB62XbWZmhry8PFRXK1rdTUxMYGZmpvX6dI2bmxvWr1+PdevW4ejRo0hISNC8MM7AbNmyhevcuTNnYmLC9e/fn/v111/5fYGBgVx4eDj/+8MPP+SPtbOz40aPHs1dvHhRobyCggIuIiKCc3Bw4MzMzDg3Nzfuiy++4FiWVThu0aJFnJOTEyeXy5VkOnnyJNe3b1+udevWXKtWrThPT08uLi5O5bGNUVpaygHgSktL1TqPohrantqHtql2oe2pfWibvtwwHEfgvO8vCGVlZbC2tkZpaSntVWkB2p7ah7apdqHtqX1om77ckOWZRKFQKBQKhVILVVJ0iKmpKZYvX66Wsy2lYWh7ah/aptqFtqf2oW36ckOHeygUCoVCoRAJtaRQKBQKhUIhEqqkUCgUCoVCIRKqpFAoLyFfffUVGIaBr6+vwWSIiIgAwzD8YmRkBCcnJ0yYMAFXr141mFwUCoUcCJpQhkKh6Iu9e/eiS5cuuHDhAm7evAlXV1eDyGFqaoqdO3cCAGQyGXJzcxEXF4fk5GRcvXpVJ4mzKBRKy4FaUiiUl4y8vDykp6dj48aN6NChA/bu3atxWbdu3QLDMDh79qxG5xsZGeH999/H+++/j4iICKxevRqJiYkoLS3F8ePHNZaLQqG8GFAlhUJ5ydi7dy/atm2L4OBgvPXWW81SUnRB3QSjRkbU0EuhvOxQJYVCecnYu3cvxo0bBxMTE7z77ru4ceMGfvvtN4PJ8/DhQzx8+BCFhYXIyMjA/Pnz0a5dO4wZM8ZgMlEoFDKgXRUK5SUiMzMT165dw5YtWwAAAwYMgKOjI/bu3YtXX31V7/I8efJEaRbyTp064dSpU4JmJ6dQKC82VEmhUF4i9u7dCzs7OwwePBgAwDAM3nnnHezZswdffPEFxGJxo+eXl5ejsrKS/11cXAwAKC0txcOHD/ntxsbGgmb+NjMzw9GjNdPTsyyLW7duYePGjRg9ejTS0tLQo0cPta+RQqG8ONCMsxTKS4JcLoeTkxMCAwOxevVqfvsff/yB8ePH46effsKIESMaLSMiIgLffPNNk3UFBgY26UwbERGB77//HuXl5Qrb//nnH3Tv3h2jR4/GDz/80GRdFArlxYUqKRTKS8Lp06cbVULCwsKaVECuXr2Ke/fu8b8LCwvx/vvvY8OGDfD09OS3t23bFj4+Po2W1ZCSAgB9+/bF3bt38eDBg0bLoFAoLzZ0uIdCeUnYu3cvbG1tsXXrVqV9hw4dwuHDhxEXFwdzc/MGy3B3d4e7uzv/+9atWwAAHx8fDBo0SGuyymQylcoLhUJ5uaBKCoXyElBRUYFDhw7h7bffxltvvaW038HBAfv370dSUhLeeecdA0j4jOvXryMnJ6dJSwyFQnnxoUoKhfISkJSUhMePHyM0NFTlfj8/Pz6xmz6VFJlMhj179gB45jgbFxcHlmWxfPlyvclBoVDIhCopFMpLwN69e2FmZobhw4er3C8SiRAcHIy9e/fi0aNHaNeunV7kqqqqwqRJk/jfVlZWePXVV/Gf//wHQ4cO1YsMFAqFXKjjLIVCoVAoFCKhGWcpFAqFQqEQCVVSKBQKhUKhEAlVUigUCoVCoRAJVVIoFAqFQqEQCVVSKBQKhUKhEAlVUigUCoVCoRAJzZOiQ1iWxb1792BpaQmGYQwtDoVCoVA0hOM4PH78GA4ODhCJtN+/r6ysRHV1tcI2ExMTmJmZab2ulgRVUnTIvXv34OTkZGgxKBQKhaIl7ty5A0dHR62WWVlZCRfn1pDclytst7e3R15e3kutqFAlRYdYWloCAC79bgfL1uSMrIlBnlVHREceBSMm0CpH758wRAT+7wGAmKH3rynKylk4e9/i3+vapLq6GpL7clz/3RFWljX3ouwxix79/kF1dTVVUii6oW6Ih7HgwLQiJ7EvA3JkqYMDa2gRVEKiQkCeRCDwiSJUGSfweQLo/ROCiK15R+ly6N68NQfz1jV3Q0qTwQOgSopeKGdFAEt7Ko0hZsj8hxTTF4UgSHy6iXymCBQJIPT+GVqA5yhndd+RqoYcVbUPSTWhHbf6jBs3Tu1z4uLiYGtrK/h4qqRQKBQKhUIAlRwLY+7ZOukcOXIE48ePh7m5uaDj9+3bh/LycqqkkEYZawo5QZYUEUPew08tFsIRE9gdF1GrhSBIvHcAICJULpIo18NrU8rVLHXrLYHNmzcLVjq+//57tcunSgqFQqFQKARQxYlgzIlq1w0sjABSU1NhY2Mj+PiTJ0+iU6dOatWhFSUlNzcXkZGROHPmjDaKe+GosaSQM8IqJtCSQiok9nxJtIQR2EwQEzimT+LzRCqkPedP9DD8IuVEkNYqKS3BkhIYGKjW8QMGDFC7Dq0oKeXl5Th37pw2inohKWfNwRKkpJAIqYqTiMAPHYmQeP+oQiAcEp9z0p6pJ6y86YOaSRVnBCPekkLW9TdFYGAgpk6dirfffluwj4oQBCkpmzdvbnT/3bt3tSIMhUKhUCgvK9WcmB/uqebICsFuCi8vL3z88cf44IMPMH78eEydOhV+fn7NLleQkvLhhx+iY8eOMDExUbn/+VS+FEVKWXNUy8lx/yGthwKQ2ZMjFRJDa0kcWiER0oYwSIY0S9hTue4tKZWcEcScuHZd9/Vpk9jYWGzYsAFJSUn45ptvMHDgQLi6umLKlCmYNGkS7OzsNCpXUMiJs7MzNm3ahLy8PJXL8ePHNaqcQqFQKBRKDVWcMSprlyrO2NDiqI2RkRHGjRuHH3/8Ef/88w/ee+89LF26FE5OThg7dqxGfquCuvc+Pj7IzMzE+PHjVe5nGAYcDSFtkHLWDDKWIEsKYT0UkiGx50utFsIgMiyaUEh8pkiz+FZwMp3XIeXEkNZaUlqC42xDXLhwAbt27cKBAwdga2uLiIgI3L17F2PGjMHs2bOxYcMGwWUJ+nKuWrUKT58+bXC/u7s78vLyBFf6slEuN4VM3vK0Yn1Cap4G0l6UpEKiMkcipHYQ6P1rmkq57pWUSs4EohY63HP//n385z//wa5du3Djxg2EhIRg//79CAoK4qcSiIiIwMiRI7WvpLi7uze639jYGM7OzoIrpVAoFAqFoogMIt6SIiNUoW0IR0dHdOvWDVOmTEFERAQ6dOigdIyHhwdeffVVtcpVewyitLQUEokEQM000tbW1uoW8dJRJjNHlYwcSwqJZnASzc2kQuL9IxESrWCkWgxJhLT7VymX6r4O1hhMrWtAJduyontSUlLw+uuvN3qMlZUVUlNT1SpXcK72nTt3wt3dHTY2NnB3d1dYj4+PV6tSCoVCoVAoitT5pNT3TWkpNKWgaIogS0pMTAxWrFiBefPmISgoiA8lKiwsxKlTp/Cvf/0LxcXF+Pjjj3UiZEvnqdwEMrnq8G1DQHtzwqFj9cIg0bpDrXPCIfH+kUaVTPfBD1WcMRjOqHa9ZVhSvLy8eJ+ThjAyMoK9vT2GDx+OGTNmNJjOROW5Qg7697//jV27dilF9/Tq1QuDBg2Cp6cnFixYQJWUBngiN0G1jBwlhURIzP1BKiQqmfQjJwyq9AqHtHdCtVz3SkMVawSGNa5dF37eunXrcOjQIVy7dg3m5uYICAjA559/Djc3N/6YyspKfPTRRzhw4ACqqqoQFBSEr776SiF/SX5+PmbNmoXU1FS0bt0a4eHhWLduHYyMGlYVxo4d26R8LMvi/v37+PTTT/HXX3/hq6++EnxtgpSU+/fvo0+fPg3u79OnDx4+fCi4UgqFQqFQKIrI6g3zyNRIi3/u3DnMmTMHr776KmQyGRYvXowRI0bg6tWraNWqFQBg/vz5OH78OL777jtYW1tj7ty5GDduHP7v//4PACCXyxEcHAx7e3ukp6ejoKAAYWFhMDY2xtq1axuse/ny5YLlfPfddzF+/HjtKymvvvoqPvvsM8THxytpVHK5HJ9//rnaHrsvE+VSUxhLybGk0F6vcEhsKxJlIhESLU6kQuIzRZrVSaqHiOBK1hicBpaU5ORkhd+JiYmwtbVFZmYmBg4ciNLSUsTHx2Pfvn0YMmQIAGDXrl3o1asXfv31V/j5+eHUqVO4evUqfv75Z9jZ2aFv375YvXo1Fi5ciBUrVqg1RNMQ3t7eeO+999Q6R/BwT1BQEOzt7TFw4EAFn5S0tDSYmJjg1KlT6ktMoVAoFAoFQI0lRfycJaWsrEzhGFNTU5iamjZaTmlpKQDAxsYGAJCZmQmpVIphw4bxx/Ts2ROdO3dGRkYG/Pz8kJGRgT59+igM/wQFBWHWrFm4cuUKvLy8lOrx9vZGSkoK2rZtK+j6Ro4ciYMHDwo6tg5BSoqHhweuX7+OPXv24Ndff8Xff/8NoCYE+dNPP8V7770HKysrtSp+maiQGUNKkE8Kib0mUqFtJQzaTsIg1bpDmtWCRKRS3fukVMuNgNp53qrlNc+Kk5OTwjHLly/HihUrGiyDZVl8+OGHeO2119C7d28AgEQigYmJCdq0aaNwrJ2dHZ9SRCKRKM2vU/e77pjnycrKwp9//skrQ02RlZWFqqoqQcfWIdhd2dLSErNmzcKsWbPUqoACVMqMYURQnhQSYehHTjBUIRAGie1EqpJCIqS9E2Qy3ctTxYrB1eZJqWZrFMc7d+4oGAGasqLMmTMHly9fxi+//KI7QesxdOhQwdPiNBUFpArBeVIaQyqVIj8/X6Nzt27dii5dusDMzAy+vr64cOFCg8cmJiaCYRiFxczMTOGY8vJyzJ07F46OjjA3N4e7uzvi4uL4/UVFRfjggw/g5uYGc3NzdO7cGfPmzePNY3U8Xw/DMDhw4IBG10ihUCgUSlNIOZHCAtQkQKu/NKakzJ07F8eOHUNqaiocHR357fb29qiurkZJSYnC8YWFhbC3t+ePKSwsVNpft08VeXl5+PvvvxucfPj55e+//1Y7O71WAr+vXr0Kb29vyNWcyvrgwYOIiopCXFwcfH19ERsbi6CgIOTk5MDW1lblOVZWVsjJyeF/P6+ZRUVF4cyZM9izZw+6dOmCU6dOYfbs2XBwcEBoaCju3buHe/fuYcOGDXB3d8ft27cxc+ZM3Lt3D99//71CWbt27cLIkSP538+byoRSITOGWEqOJYXEHiapkNabIxUSn6mWkWWCDEi8f6Qh08OMf1LOCKi1pEjViO7hOA4ffPABDh8+jLNnz8LFxUVhv4+PD4yNjZGSkoI333wTAJCTk4P8/Hz4+/sDAPz9/bFmzRrcv3+f//6ePn0aVlZWDU6No4/pcAw6Ne/GjRsRGRmJyZMnAwDi4uJw/PhxJCQkIDo6WuU5DMM0qNUBQHp6OsLDwzFo0CAAwPTp07F9+3ZcuHABoaGh6N27N3744Qf++G7dumHNmjV4//33IZPJFKKX2rRp02hdFAqFQqFoCxkrBsOK+XWhzJkzB/v27cOPP/4IS0tL3ofE2toa5ubmsLa2xtSpUxEVFQUbGxtYWVnhgw8+gL+/P/z8/AAAI0aMgLu7OyZNmoT169dDIpFgyZIlmDNnTpNDTLpEkJLi7e3d6P6Kigq1K66urkZmZiYWLVrEbxOJRBg2bBgyMjIaPK+8vBzOzs5gWRbe3t5Yu3YtXnnlFX5/QEAAkpKSMGXKFDg4OODs2bO4fv06Nm3a1GCZpaWlsLKyUgqvnjNnDqZNm4auXbti5syZmDx5cqNjalVVVQpOQXVe2VVSI4ilBtUHiYfUnhy1pAiDtpMwRNS8IxjSnil1Rwo0oZoVg6tVTqRqKCnbtm0DAL5zXseuXbsQEREBANi0aRNEIhHefPNNhWRudYjFYhw7dgyzZs2Cv78/WrVqhfDwcKxatap5F9VMBH05r169igkTJiiZkOooKCjA9evX1ar44cOHkMvlKr2Jr127pvIcNzc3JCQkwMPDA6WlpdiwYQMCAgJw5coVfvxty5YtmD59OhwdHWFkZASRSIQdO3Zg4MCBDcqxevVqTJ8+XWH7qlWrMGTIEFhYWPBDRuXl5Zg3b16D17Ru3TqsXLlSabtUJgIra1nzMOgb0l5IJKOB79lLCX2mhENiW5H2mMv1kChFxorAsCJ+XShCHFfNzMywdetWbN26tcFjnJ2dceLECcH16gNBSkrv3r3h6+vbYGRPVlYWduzYoVXBVOHv78+PnwE1VpNevXph+/btWL16NYAaJeXXX39FUlISnJ2dkZaWhjlz5sDBwUEhRhyosXQEBwfD3d1dKaRr6dKl/LqXlxeePHmCmJiYRpWURYsWISoqSqH858PHKBQKhUJRhZQV8ZYUdZSUFxlBSsprr72m4Kz6PJaWlg1aKhqiffv2EIvFKr2JhfqBGBsbw8vLCzdv3gRQM+y0ePFiHD58GMHBwQBqcrxkZWVhw4YNCkrK48ePMXLkSFhaWuLw4cMwNm7csdXX1xerV69GVVVVg+NzDSXZkVUbgW1k7gN9Q2KviVhoWwmCWneEQer/Hr1/TSOXynReR7VcDFZeq6TIW571vaSkBN9//z1yc3OxYMEC2NjY4OLFi7Czs0OnTp00KlOQqvbll18iNja2wf3dunVDamqqWhWbmJjAx8cHKSkp/DaWZZGSkqJgLWkMuVyOS5cuoWPHjgBqQqGlUilEIsXLEovFYNlnntJlZWUYMWIETExMkJSUpBTGrIqsrCy0bdvWoA5EFAqFQnlxkXMihaUlkZ2djR49euDzzz/Hhg0b+HDnQ4cOKfieqotBu/dRUVEIDw9Hv3790L9/f8TGxuLJkyd8tE9YWBg6deqEdevWAajxE/Hz84OrqytKSkoQExOD27dvY9q0aQBqwpMDAwOxYMECmJubw9nZGefOncPu3buxceNGAM8UlKdPn2LPnj0oKyvjHVw7dOgAsViMo0ePorCwEH5+fjAzM8Pp06exdu1ajWd5lstF4GQt64HTN6T2MImEwF4v7YkLhD7ngiHtkWKlurdsSOVicC3UkhIVFYWIiAisX78elpaW/PbRo0erPV9PfQQrKffv38fly5fh4+MDa2trFBYW4ptvvgHLsggODm50luSGeOedd/DgwQMsW7YMEokEffv2RXJyMu9Mm5+fr2AVKS4uRmRkJCQSCdq2bQsfHx+kp6crxHAfOHAAixYtwsSJE1FUVARnZ2esWbMGM2fOBABcvHgR58+fBwC4uroqyJOXl4cuXbrA2NgYW7duxfz588FxHFxdXflwaU3gpCJwRlRJaQyOtDcSydAPnSCo4qQG9JlqElYPHU2WFUFe64vCtjCflN9++w3bt29X2t6pU6cG0+oLgeEEuAWfPXsWY8aMwdOnT2FnZ4fk5GSMGTMG5ubmEIlEuHXrFpKSkjBixAiNBXkRKSsrg7W1NRy3roDIvOkhpZca+kERDv2gCIIqKWpAn6kmYSsqcWfGKj5lhTap+1Z4fPcxxBY1LgXyp1XIfnuDTurTBba2tvjpp5/g5eUFS0tL/Pnnn+jatStOnz6NKVOm4M6dOxqVK8iSsnTpUkREROCzzz5DXFwcgoODMXbsWPz73/8GACxYsAArV66kSkpDSEUAtaS0TIh8eRP49SVQJBLvHIntBIDQ55wsOKnu3+FylgFY5tl6CyI0NBSrVq3Ct99+C6Am8Wp+fj4WLlzIZ7nVBEGtnp2djfnz56N169b48MMPUVhYyPuBADVZXa9cuaKxEBQKhUKhvOzI5GKFpSXxxRdfoLy8HLa2tqioqEBgYCBcXV1haWmJNWvWaFyuIEuKiYkJKisrAdRkimVZlv8N1IT+NhXC+zLDSBkwRi1LK9Y7pDYPHTMQBEd74sIg9nEiUDDSRNKDT4pczgBy0bP1FoS1tTVOnz6NX375BdnZ2SgvL4e3t7dSfjJ1EZwnJTo6GtHR0di9eze8vb3x6aef4uDBg2AYBqtXr0a/fv2aJciLDCNjwEhb1gOnd2jzCIfAtmIIFIo6Y6sBiUomYfdPH+9wlmPA1D64bAt9gAcMGIABAwZorTxBSkpMTAyCg4Px+uuvo2fPnjh9+jRmz57Nzwrctm1bJCcna00oCoVCoVBeNjg5A7bWgsK1AEvK5s2bBR/bWLb2xhCkpHTv3h3Xr1/Ho0eP0K5dOwDAjz/+iJSUFFRUVMDf35/fTlFGVM1ARNLMYgSJQjy0rQRBYqeP5t5RAzqs2TR6sKRwrAhcbegx1wJCkJ+fuPfBgwd4+vQpb8AoKSmBhYUFbG1tdauk1PG8IjJ06FCNKqVQKBQKhaIIK2eAWgsK2wIsKXl5efz6vn378NVXXyE+Ph5ubm4AgJycHERGRmLGjBka16FRxlmO43D27FncvHkTDg4OGDFiBHWcbQSRrGYhBvKffWIg0UJAImR2xAkUikCRSIU0O5g+fFI4jgFXG3rMtbCXz9KlS/H999/zCgoAuLm5YdOmTXjrrbcwceJEjcoVpKSMHj0a+/fvh7W1NYqKijB69GhcuHAB7du3x6NHj9CjRw+kpaWhQ4cOGgnxosNIGYjELeuB0zu0eQTTwt5dhoPA4R5674RDnOKrj45mvTwpaGF5UgoKCiCTKTeSXC5XmkhYHQQNeiUnJ6OqqgoAsGTJEjx+/Bi5ubm4f/8+bt++jVatWmHZsmUaC0GhUCgUyssOJ2cUlpbE0KFDMWPGDFy8eJHflpmZiVmzZjUrDFnt4Z4zZ85g/fr1cHFxAQA4Ojri888/13hem5cBkQwQSQ0tBdnQHqZwiOthkgptKMEQ+f9HmEycPtJItGBLSkJCAj9hcJ37h0wmQ1BQEHbu3KlxuYKVFKb2H764uBjdunVT2Ofq6op79+5pLASFQqFQKC87jJwBU2tBYVqYJaVDhw44ceIErl+/jmvXrgEAevbsiR49ejSrXMFKSkREBExNTSGVSpGXl4dXXnmF3yeRSPiQI4oyIikgIimarGU9+waF9jBbLkTeO0KhTdU0nD6s4Wztgnp/Wxg9evRotmJSH0FKSnh4OL/+xhtv4OnTpwr7f/jhB/Tt21drQr1oUCWl5UI/dAKh7SQIYp8nUuUiCE4PjrMt2ZIyZcqURvcnJCRoVK4gJWXXrl2N7l++fDnE4pY1GRKFQqFQKCTBcADDPltvSRQXFyv8lkqluHz5MkpKSjBkyBCNy9UoT0odeXl5cHJyQqtWrZpTzAuPSEYtKS0VYnu+pEHbSRDEPk+kykUQ+hnuabmOs4cPH1baxrIsZs2apeTHqg7N+nS6ubnhxo0bzSmCQqFQKBQKAEauuLR0RCIRoqKilNLnq4MgS8q4ceNUbpfL5Zg3bx4sLS0BAIcOHdJYkBcZkZSDqKXZ7vQNoZ0GYnu+pEHbSRAcDYsWDmFNpRefFLbecE8LdZx9ntzcXJVJ3oQiSEk5cuQIBg4cyOdGqU/r1q1hbW2tsQAvAyIpQNL8gkRC20cwRCpOJMpEIrSzIhjSnnNOqvt715IdZ6OiohR+cxyHgoICHD9+XCH4Rl0EKSn79u3DggULEB4ejsmTJ/Pb9+zZgzVr1sDd3V1jASgUCoVCoQCoZ0lpaSHIf/zxh8JvkUiEDh064Isvvmgy8qcxBCkpEyZMgJ+fH95//30cO3YMO3fuRNu2bTWu9GVDLOUgpj2oRqFmcDUgsKlI6/USC20nwZD2TOnDcba+L0pL80lJTU3VSbmCHWe7dOmCtLQ09O7dG56envjpp5/4LLQUCoVCoVCaR51PSn3flJbCkCFDUFJSorS9rKxMfyHIIpEIK1euxPDhwxEWFga5vIWpegaCkVGflCahlibBkNbDBEAtBAIh8t4B9P4JQQ8+KeDwbJinhb0Sz549i+rqaqXtlZWV+O9//6txuRrlSRkwYACys7ORm5sLV1dXjSsHgK1btyImJgYSiQSenp7YsmUL+vfvr/LYxMREBZ8YADA1NUVlZSX/u7y8HNHR0Thy5AgePXoEFxcXzJs3DzNnzuSPGTRoEM6dO6dQzowZMxAXF8f/zs/Px6xZs5CamorWrVsjPDwc69atg5GR+k0mlnIQt7QnTs8Q+/ImEQLbit4/gRBqfab3r2lYPUT3iOQ1CwBwLcQGkJ2dza9fvXoVEomE/y2Xy5GcnIxOnTppXL7Gydxat24NT09PjSsGgIMHDyIqKgpxcXHw9fVFbGwsgoKCkJOTA1tbW5XnWFlZIScnh//9/JBTVFQUzpw5gz179qBLly44deoUZs+eDQcHB4SGhvLHRUZGYtWqVfxvCwsLfl0ulyM4OBj29vZIT09HQUEBwsLCYGxsjLVr1zbrmikUCoVCUUkLnLunb9++YBgGDMOoHNYxNzfHli1bNC5fsJJy4sQJHDp0CDY2Npg8eTJ69erF7ysuLsabb76JM2fOqFX5xo0bERkZyVtH4uLicPz4cSQkJCA6OlrlOQzDwN7evsEy09PTER4ejkGDBgEApk+fju3bt+PChQsKSoqFhUWD5Zw6dQpXr17Fzz//DDs7O/Tt2xerV6/GwoULsWLFCpiYmKh1naJqDiKOWlIahdCeHEdSpmCSIfD+kemMTeh7gMCmIs26I5LpIwS55TnO5uXlgeM4dO3aFRcuXECHDh34fSYmJrC1tW3WtDmCXsH79u1DaGgoJBIJMjIy4O3tjb179/L7q6urlYZPmqK6uhqZmZkYNmzYM2FEIgwbNgwZGRkNnldeXg5nZ2c4OTnhjTfewJUrVxT2BwQEICkpCXfv3gXHcUhNTcX169cxYsQIheP27t2L9u3bo3fv3li0aJHCpIkZGRno06cP7Ozs+G1BQUEoKytTqq8+VVVVKCsrU1goFAqFQhFCS3ScdXZ2RpcuXcCyLPr16wdnZ2d+6dixY7Pn9RNkSYmJicHGjRsxb948AMC3336LKVOmoLKyElOnTtWo4ocPH0IulysoAgBgZ2eHa9euqTzHzc0NCQkJ8PDwQGlpKTZs2ICAgABcuXIFjo6OAIAtW7Zg+vTpcHR0hJGREUQiEXbs2IGBAwfy5bz33ntwdnaGg4MDsrOzsXDhQuTk5PAZcyUSiUq56vY1xLp167By5Uql7SIZC1FLsd0ZCiJ7veT15oiFxHaiztiCIdLqRJhI+knm1rIsKUlJSRg1ahSMjY2RlJTU6LH1RzLUQZCScuPGDYSEhPC/x48fjw4dOiA0NBRSqRT/8z//o1Hl6uLv7w9/f3/+d0BAAHr16oXt27dj9erVAGqUlF9//RVJSUlwdnZGWloa5syZAwcHB95qM336dL6MPn36oGPHjhg6dChyc3ObNRHSokWLFLLulZWVwcnJCeJqFmKWHCWFyBcSNYMLhipOAiEwpI7Ue0fkK4EwRHpQUkRszQIAHDmfjAYZO3YsJBIJbG1tMXbs2AaPYxhG42hgQcM9VlZWKCwsVNg2ePBgHDt2DAsWLNDIKaZ9+/YQi8VK5RYWFjbqc1IfY2NjeHl54ebNmwCAiooKLF68GBs3bkRISAg8PDwwd+5cvPPOO9iwYUOD5fj6+gIAX469vb1Kuer2NYSpqSmsrKwUFgqFQqFQBFEXgsxC7X5bWloaQkJC4ODgAIZhcOTIEcWiOQ7Lli1Dx44dYW5ujmHDhilNEFxUVISJEyfCysoKbdq0wdSpU1FeXt5gnSzL8kEuLMs2uDQnXYkgS0r//v1x8uRJ+Pn5KWwPDAzE0aNHMWbMGLUrNjExgY+PD1JSUngNjGVZpKSkYO7cuYLKkMvluHTpEkaPHg0AkEqlkEqlEIkUdS+xWAy2EUtGVlYWAKBjx44Aaiw2a9aswf379/kbcPr0aVhZWWk0BYComoWIKEuKoSVQAe3KCYdAZ15qnRMIic0EQu8fYSKJZLp/hzcnBPnJkyfw9PTElClTVE4KvH79emzevBnffPMNXFxcsHTpUgQFBeHq1aswMzMDAEycOBEFBQU4ffo0pFIpJk+ejOnTp2Pfvn3NvTSNEaSkzJ8/H+np6Sr3DRo0CEePHsXu3bvVrjwqKgrh4eHo168f+vfvj9jYWDx58oSP9gkLC0OnTp2wbt06AMCqVavg5+cHV1dXlJSUICYmBrdv38a0adMA1Fh8AgMDsWDBApibm8PZ2Rnnzp3D7t27sXHjRgA1MzLu27cPo0ePRrt27ZCdnY358+dj4MCB8PDwAACMGDEC7u7umDRpEtavXw+JRIIlS5Zgzpw5MDU1Vfs6KRQKhUJpClWzID8fgGFqaqryOzRq1CiMGjVKZbkcxyE2NhZLlizBG2+8AQDYvXs37OzscOTIEUyYMAF//fUXkpOT8dtvv6Ffv34AatwnRo8ejQ0bNsDBwUGp3M2bNwu+tjqfVnURpKQEBgYiMDCwwf2DBw/G4MGD1a78nXfewYMHD7Bs2TJIJBL07dsXycnJvJNqfn6+glWkuLgYkZGRkEgkaNu2LXx8fJCenq5g3Thw4AAWLVqEiRMnoqioCM7OzlizZg2fzM3ExAQ///wzrxA5OTnhzTffxJIlS/gyxGIxjh07hlmzZsHf3x+tWrVCeHi4Ql4VdWCkcjAEZeYhrINSA4H+AwChPUwCIbKZCBSKSCsmlPNNUZTRhyVFleOsk5OTwjHLly/HihUr1Co3Ly8PEolEIZrW2toavr6+yMjIwIQJE5CRkYE2bdrwCgoADBs2DCKRCOfPn1fpe7pp0yZh18UwulVSdMncuXMbHN45e/aswu9NmzY12Sj29vbYtWtXg/udnJwEhUs7OzvjxIkTTR4nBJGUhYglR0mhH17hMAQOrZD48SVRJhIVAgJFqoHATgJp7ylGH0oKy4FhOX4dAO7cuaPg36iJNb8uKlVV1GrdvjoH2PoYGRnBxsamwajWvLw8tWVRF4MrKRQKhUKhUFRbUlpiEAZXm7xUGxY6qqToAaZaBqaZCW20CYmmXdJ6TTzUktJioc+5GhD4nJN2/0R6mFBXV3P31EWlFhYW8gEidb/79u3LH3P//n2F82QyGYqKigRH3MbHx2PTpk181FD37t3x4Ycf8n6jmkDgo0mhUCgUyksIV895VovBaS4uLrC3t0dKSgq/raysDOfPn+dzj/n7+6OkpASZmZn8MWfOnAHLsnyajsZYtmwZ/vWvfyEkJATfffcdvvvuO4SEhGD+/PlYtmyZxrJrbEnZv38/QkND0apVK40rf2mQygCWHEsKiT1x0npNPASO1RMJifePyiQcErurhLUVI9eH4ywHRsTx6+pQXl7O5/oCavxFsrKyYGNjg86dO+PDDz/Ep59+iu7du/MhyA4ODnwKkF69emHkyJGIjIxEXFwcpFIp5s6diwkTJqiM7Hmebdu2YceOHXj33Xf5baGhofDw8MAHH3ygceCJxkrKjBkz4Ovri65du2paxEsDI5OBoUpK45AoE6FwJCpOJN4/AmWiyrgaENZWjB6Gexj5M2d9ddPi//777wpRtnXZz8PDw5GYmIhPPvkET548wfTp01FSUoIBAwYgOTmZz5EC1MxpN3fuXAwdOhQikQhvvvmm4DBjqVSqEBlUh4+PD2QymXoXUw+NlRSOzupLoVAoFIrWEMk5iGotKSI1LSmDBg1q9LvMMAxWrVrVqEXDxsZG48RtkyZNwrZt2/icZHV8/fXXmDhxokZlAtRxVj9IZYCIWlIahUSZACJ7mAxLnkxE3j8CZSLSCgYA9JlqGr1YUjQf7iGB+Ph4nDp1is9Of/78eeTn5yMsLExhXrvnFZnG0FhJOXnyJDp16qTp6RQKhUKhUOqhKuNsS+Hy5cvw9vYGUJPZHaiZo699+/a4fPkyf5y6Q54aKykDBgzQ9NSXDq5aBk5EkGcagb05YsfqSZSLpGepDhLbiUCZGAL/9wAQ2VakPeeMHhJyMjIODMPx6y2J1NRUnZRLh3v0gayarFSTBKZR5cTkyQSAyLbi53InCCKVTBJlIuzDy0NiW4kJ+0jrQ0nh6mWcpX6fAKiSQqFQKBQKEYjkHESMZo6zhqayshJbtmxBamoq7t+/D5ZV7ExdvHhRo3KpkqIHOKkMHEk9cgJ7c4ycwJ4cABCUKZiHwCEDIjOpkvQ/VwtDmnWgDhLvH2mWBH1YUuT1hntamJIydepUnDp1Cm+99Rb69++vNesqVVIoFAqFQiGAlqykHDt2DCdOnMBrr72m1XIFKyksyyIxMRGHDh3CrVu3wDAMXFxc8NZbb2HSpElkjkkTAieTg2M0T2ajbUiaR6gOInviAHm9ORDq/0HgMwURefeO48jzJwJArU5CYPWQcVbGggHLr7ckOnXqBEtLS62XK0hJ4TgOoaGhOHHiBDw9PdGnTx9wHIe//voLEREROHToEI4cOaJ14V4Y5HKiXgIcS9g/PwCGVMdZApUUEhU68iQCwBEoFUHvAQUI1DG1OcGeNuD0oaSw9RxnCXxPN8YXX3yBhQsXIi4uDs7OzlorV5CSkpiYiLS0NKSkpCik3QVqJiAaO3Ysdu/ejbCwMK0JRqFQKBTKywQjr2dJ0cNcQdqkX79+qKysRNeuXWFhYQFjY2OF/UVFRRqVK0hJ2b9/PxYvXqykoADAkCFDEB0djb1791IlpQE4uZwox1mG9pqEQ6AlhcjhOj1k41QXhkTzAIHh4wAA8m4fedYdfQzVsdyzLG4tzJLy7rvv4u7du1i7di3s7Oz06zibnZ2N9evXN7h/1KhRgicholAoFAqFogwjY8FwLdOSkp6ejoyMDHh6emq1XEFKSlFREezs7Brcb2dnh+LiYq0J9cLBcQDI0YqJ7PWS1muqgyXHAsZDYr5sAsPaSZwElcRbB4A8qwVAnhVTH/Kw9fLi68EHRpv07NkTFRUVWi9XkJIil8thZNTwoWKxuFlTMVMoJDrzAmQqT0R+fA0tgCpIfMkTOFQHQD9DGWpDaFvpkJZsSfnss8/w0UcfYc2aNejTp4+ST4qVlZVG5QqO7omIiICpqanK/VVVVRpVTqFQKBQKpRa5/JmDnh6Sx2mTkSNHAgCGDh2qsJ3jODAMA7mGFnxBSkpYWFiTTjDUaZbSHIideI1AiMyTQiIEDkERC0GO/S81cvaZVYtES2AjGHSCwcTERJ1UTqFQKBQKpRaZDBDVDnOxLcuFIjAwsMF9ly9f1rhcQUrKuHHjmi7IyAj29vYYPnw4QkJCBAuwdetWxMTEQCKRwNPTE1u2bEH//v1VHpuYmIjJkycrbDM1NUVlZSX/u7y8HNHR0Thy5AgePXoEFxcXzJs3DzNnzgQA3Lp1Cy4uLirL//bbb/H2228DUN1b3b9/PyZMmCD42ngYhqy5MUjsNZEoE0DkPDlEWghIer5rIdLiROLzRCqk3T99yMNyAFpmCPLzPH78GPv378fOnTuRmZmp2+Eea2vrJo9hWRY3btzAzp078fHHH2PVqlVNnnPw4EFERUUhLi4Ovr6+iI2NRVBQEHJycmBra6vyHCsrK+Tk5PC/n38RRUVF4cyZM9izZw+6dOmCU6dOYfbs2XBwcEBoaCicnJxQUFCgcM7XX3+NmJgYjBo1SmH7rl27+HE2AGjTpk2T16QSRkTUR5jEPBukvryJbCvSXt4AmYoTic8UQe8BBUjM+ExaW+lDHpns2f9SC7Ok1JGWlob4+Hj88MMPcHBwwLhx47B161aNyxOkpOzatUtwgceOHcPs2bMFKSkbN25EZGQkbx2Ji4vD8ePHkZCQgOjoaJXnMAwDe3v7BstMT09HeHg4Bg0aBACYPn06tm/fjgsXLiA0NBRisVjp/MOHD2P8+PFo3bq1wvY2bdo0WheFQqFQKFqjhfqkSCQSJCYmIj4+HmVlZRg/fjyqqqpw5MgRuLu7N6tsrc+CPGDAAPTr16/J46qrq5GZmYlFixbx20QiEYYNG4aMjIwGzysvL4ezszNYloW3tzfWrl2LV155hd8fEBCApKQkTJkyBQ4ODjh79iyuX7+OTZs2qSwvMzMTWVlZKjW9OXPmYNq0aejatStmzpyJyZMnN2pCrqqqUoh0KisrA1DTG2cIimUlcp4cEnviAJEho3QYQyAE3jsirWAAkW1F2nPO6MGSwsll4GpzM3Fcy7CkhISEIC0tDcHBwYiNjcXIkSMhFosRFxenlfK1rqS0adMGhw4davK4hw8fQi6XKyWJs7Ozw7Vr11Se4+bmhoSEBHh4eKC0tBQbNmxAQEAArly5AkdHRwDAli1bMH36dDg6OsLIyAgikQg7duzAwIEDVZYZHx+PXr16ISAgQGH7qlWrMGTIEFhYWPBDRuXl5Zg3b16D17Ru3TqsXLmyyWunUCgUCuV5OKmUn0CU46QGlkYYJ0+exLx58zBr1ix0795d6+VrXUnRJf7+/vD39+d/BwQEoFevXti+fTtWr14NoEZJ+fXXX5GUlARnZ2ekpaVhzpw5cHBwwLBhwxTKq6iowL59+7B06VKluupv8/LywpMnTxATE9OokrJo0SJERUXxv8vKyuDk5ATGWAyGIaipCeuhAIT6fgBkWghIG6sHqE+DUEh8ngAwJFoySZNJH8+TvF7GWSIT7Cnzyy+/ID4+Hj4+PujVqxcmTZqkWYBJAxjsy9m+fXuIxWIUFhYqbC8sLBTsB2JsbAwvLy/cvHkTQI3SsXjxYhw+fBjBwcEAAA8PD2RlZWHDhg1KSsr333+Pp0+fCsrx4uvri9WrV6OqqqrBpHampqYq9zFGRmQpKSS+KEn8oAD04ysUAp8p+uFVAwI7LsT977G670hxMlk9S0rLGO7x8/ODn58fYmNjcfDgQSQkJCAqKgosy+L06dNwcnKCpaWlxuUb7MtpYmICHx8fpKSkYOzYsQBqIoRSUlIwd+5cQWXI5XJcunQJo0ePBgBIpVJIpVKInnsRiMVisCqckOLj4xEaGooOHTo0WVdWVhbatm3boIKiirr05TKuWvA5eoEj8IUEwl5IdRA5dw+BMhGYGJ8h8pkir50AkKmkEPacy9ia97gup6WQyivB1U5JLUPLGO6po1WrVpgyZQqmTJmCnJwcxMfH47PPPkN0dDSGDx+OpKQkzQrmDMiBAwc4U1NTLjExkbt69So3ffp0rk2bNpxEIuE4juMmTZrERUdH88evXLmS++mnn7jc3FwuMzOTmzBhAmdmZsZduXKFPyYwMJB75ZVXuNTUVO7vv//mdu3axZmZmXFfffWVQt03btzgGIbhTp48qSRXUlISt2PHDu7SpUvcjRs3uK+++oqzsLDgli1bptb15ebm1s0sSBe60IUudHkBljt37qj1HRBCRUUFZ29vr1SXvb09V1FRofX69IVMJuMOHz7MhYSEaFyGQccg3nnnHTx48ADLli2DRCJB3759kZyczDvT5ufnK1hFiouLERkZCYlEgrZt28LHxwfp6ekKIU4HDhzAokWLMHHiRBQVFcHZ2Rlr1qzhk7nVkZCQAEdHR4wYMUJJLmNjY2zduhXz588Hx3FwdXXlw6XVwcbGhr8OIblmKI1T5+Nz584djSeroihC21S70PbUPqS0KcdxePz4MRwcHLRetpmZGfLy8lBdrWh1NzExgZmZmdbr0xdisRhjx47lR0s0geE4AqdUfUEoKyuDtbU1SktL6QtLC9D21D60TbULbU/tQ9v05YasQT8KhUKhUCiUWqiSQqFQKBQKhUiokqJDTE1NsXz5crUigigNQ9tT+9A21S60PbUPbdOXG+qTQqFQKBQKhUioJYVCoVAoFAqRUCWFQqEoMHv2bAwfPtzQYmiFR48eoVWrVjhx4oShRaFQKBpAlRQKhXByc3MxY8YMdO3aFWZmZrCyssJrr72GL7/8EhUVFVqtKy8vDzt37sTixYtV7v/rr7/AMAzMzMxQUlKi1bqFkpiYCIZhFBZbW1sMHjwYJ0+eVDi2Xbt2mDZtmsr5uSgUCvlQJYVCIZjjx4+jT58++PbbbxESEoItW7Zg3bp16Ny5MxYsWIB//etfWq3vyy+/hIuLCwYPHqxy/549e/i5tb7//nut1q0uq1atwn/+8x/s3r0bn3zyCR48eIDRo0fj2LFjCsfNnDkTFy9exJkzZwwkKYVC0RTqOEuhEEpeXh48PDzg6OiIM2fOoGPHjgr7b968iePHj2tNUZFKpXBwcMDMmTP5WcXrw3EcunbtinHjxiEvLw/FxcVITU3VuL4uXbogIiICK1asUOu8xMRETJ48Gb/99hv69evHby8uLoadnR3efvtt7N27V+GcPn36wMvLC7t379ZYXgqFon+oJYVCIZT169ejvLwc8fHxSgoKALi6umrVkvLLL7/g4cOHSrOF1/F///d/uHXrFiZMmIAJEyYgLS0N//zzj9bqby5t2rSBubk5jIyUZ/sYPnw4jh49qtPJ4SgUivahSgqFQihHjx5F165dERAQoJf60tPTwTAMvLy8VO7fu3cvunXrhldffRUhISGwsLDA/v379SKbKkpLS/Hw4UM8ePAAV65cwaxZs1BeXo73339f6VgfHx+UlJTgypUrBpCUQqFoClVSKBQCKSsrw927d9GnTx+91Xnt2jXY2NionB9FKpXiu+++w4QJEwAA5ubmCA0NVRpW0SfDhg1Dhw4dYGtri969eyMxMREJCQkqI5O6du0KALh69aq+xaRQKM3AoLMgUygU1ZSVlQEALC0t9Vbno0eP0LZtW5X7Tp48iUePHuHdd9/lt7377rsICQnBlStX8MorrzRadlVVFR4/fqywjWVZPH36FA8fPlTY3r59e0Hybt26FT169AAAFBYWYs+ePZg2bRosLS0xbtw4hWPrruv5uigUCtlQSwqFQiB11oznP+zqUF5eDolEwi8PHjxo8pyGfDb27NkDFxcXmJqa4ubNm7h58ya6desGCwsLQdaU/fv3o0OHDgrLnTt3EBMTo7RdKP3798ewYcMwbNgwTJw4EcePH4e7uzvmzp2rNOV93XUxDCO4fMqLRVpaGkJCQuDg4ACGYXDkyBGD1ieVSrFw4UL06dMHrVq1goODA8LCwnDv3j2dytXSoEoKhUIgVlZWcHBwwOXLlzUuY8OGDejYsSO/vPrqq40e365dOxQXFyttLysrw9GjR5GXl4fu3bvzi7u7O54+fYp9+/Y16ZAaFBSE06dPKyx2dnaYNGmS0nZNEYlEGDx4MAoKCnDjxg2FfXXXJdRKQ3nxePLkCTw9PbF161Yi6nv69CkuXryIpUuX4uLFizh06BBycnIQGhqqF/laCnS4h0IhlDFjxuDrr79GRkYG/P391T4/LCwMAwYM4H+bm5s3enzPnj2xd+9elJaWwtramt9+6NAhVFZWYtu2bUof+ZycHCxZsgT/93//p1DX89QpSvUxMzND165dG4wm0gSZTAagxopUn7y8PABAr169tFYXpWUxatQojBo1qsH9VVVV+N///V/s378fJSUl6N27Nz7//HMMGjRIJ/VZW1srKeX//ve/0b9/f+Tn56Nz584a1fuiQZUUCoVQPvnkE+zduxfTpk3DmTNnYGdnp7A/NzcXx44dazAMuWvXrrzDqBD8/f3BcRwyMzMxZMgQfvuePXvQtWtXzJw5U+mcqqoqfPbZZ9i7d2+jSoo+kEqlOHXqFExMTJSUkczMTFhbWzfpO0N5eZk7dy6uXr2KAwcOwMHBAYcPH8bIkSNx6dIldO/eXS8ylJaWgmEYtGnTRi/1tQSokkKhEEq3bt2wb98+vPPOO+jVqxfCwsLQu3dvVFdXIz09Hd999x0iIiK0Vt+AAQPQrl07/Pzzz7yScu/ePaSmpmLevHkqzzE1NUVQUBC+++47bN68GcbGxlqTpylOnjyJa9euAQDu37+Pffv24caNG4iOjlaKUDp9+jRCQkKoTwpFJfn5+di1axfy8/Ph4OAAAPj444+RnJyMXbt2Ye3atTqXobKyEgsXLsS7776rMsLuZYUqKRQKwYSGhiI7OxsxMTH48ccfsW3bNpiamsLDwwNffPEFIiMjtVaXiYkJJk6ciO+++45/KR84cAAsyyIkJKTB80JCQvDDDz/g5MmTeh1PX7ZsGb9uZmaGnj17Ytu2bZgxY4bCcdeuXcPly5cRGxurN9koLYtLly5BLpfz0WJ1VFVVoV27dgBqnqOmhgsXLlyIzz77TO36pVIpxo8fD47jsG3bNrXPf5GhafEpFArP33//jZ49e+LkyZMYOnSoocXRCh9++CHS0tKQmZlJLSkUADVRXocPH8bYsWMBAAcPHsTEiRNx5coViMVihWNbt24Ne3t7VFdX4++//2603Hbt2qmMUHu+vvrUKSh///03zpw5wytFlBqoJYVCofB07doVU6dOxWefffZCKCmPHj3Czp078e2331IFhdIgXl5ekMvluH//Pl5//XWVx5iYmKBnz55arbdOQblx4wZSU1OpgqICqqRQKBQFXiRzc7t27ZQifSgvJ+Xl5bh58yb/Oy8vD1lZWbCxsUGPHj0wceJEhIWF4YsvvoCXlxcePHiAlJQUeHh4IDg4WKv1de7cGVKpFG+99RYuXryIY8eOQS6XQyKRAABsbGxgYmLS/It+AaDDPRQKhUJ54Tl79iwGDx6stD08PByJiYmQSqX49NNPsXv3bty9exft27eHn58fVq5cqdH0FE3Vd+vWLbi4uKg8NzU1VePQ5xcNqqRQKBQKhUIhEppxlkKhUCgUCpFQnxQdwrIs7t27B0tLS+q0R6FQKC0YjuPw+PFjODg4QCTSfv++srJSac4pExMTmJmZCTo/LS0NMTExyMzMREFBQYPRRHU0NBxVUFAAe3t7tWTXJVRJ0SH37t2Dk5OTocWgUCgUipa4c+cOHB0dtVpmZWUlXJxbQ3JfrrDd3t4eeXl5ghSVurmCpkyZojQLeGPk5OQoJI+ztbUVLrgeoEqKDrG0tAQA9ApbCrGJMG1YH9gl5RlaBCVuTxaevl2ftL0mb/ogPWOZW2ZoEZSQtm18XiBDIDMXN32Qnmn15z+GFkElLIERUJybs6FFUEAmr8J//9zEv9e1SXV1NST35bj5uxOsLGusNGWPWbj2u4Pq6mpBSkpTcwU1hK2tLdFp+KmSokPqhnhEpuYQEaSk/DPd3dAiKOGQKTW0CCrJn0ieX3mFJTnPUh0mP1o3fZCeqbYib4iVad2j6YMMQEEAee6JbpvvGFoEBcRsFQDodOjetDUL09a16xwLoGYWcoVjTE1hamqqtTr79u2Lqqoq9O7dGytWrMBrr72mtbK1AVVS9IB92kMYibX3UDWX+6+RN129zIK8lyQAdPyRvFwFFndZQ4ugBCMnrycueU37Pd7mYvUbmZYUy+utDS2CEmWvdjK0CArIpJWAjm9fNeSoAle7XvN//rzLwPLly7FixYpm19WxY0fExcWhX79+qKqqws6dOzFo0CCcP38e3t7eKs9RZxipjri4uGYNIVElhUKhUCgUApByHKS1WUHq/t65c0fBZ0RbVhQ3Nze4ubnxvwMCApCbm4tNmzbhP//5j8pzjhw5gvHjx8PcXNjw7r59+1BeXk6VFNK5Na49xAI9tPWBy75CQ4ugROFgspy16pCZkzdk0Po2eZaUO8PIs1qY+BUZWgQlrnfsbGgRVNI9/oGhRVBCXEHWc85JdS9PJcfBuFY5qaz9a2VlpbdZkfv3749ffvml0WM2b94sWOn4/vvvmy0TVVIoFAqFQiEAGcdAyjH8ur7JyspCx44dG9yfmpoKGxsbweWdPHkSnTo1b9iOKil6wPZ3KYyMyYk0KBhmZ2gRlGh3tdLQIqjE5D55vhZPnclzUjUtIc/B2H7SPUOLoMTdqcJf8Prkn2DyLJmVrz4xtAgKsE+rgdO6raOSE8OIE9Wuq6ekNDVX0KJFi3D37l3s3r0bABAbGwsXFxe88sorqKysxM6dO3HmzBmcOnWqwToCAwPVkmnAgAFqHa8KrSgpubm5iIyMxJkzZ7RR3AuHcbkURkbkKCnWtw0tgTKMjCzTbh1sLnmNZSHTbo4GbfDUjryP3F/rtTtjrTYwKiNPmQOAz8buNbQISizK/B9Di6B3qiFGdW0i+Gqop6T8/vvvCsnZoqKiADybK6igoAD5+fnP6qquxkcffYS7d+/CwsICHh4e+Pnnn1UmeFNFYGAgpk6dirfffluwj4omaEVJKS8vx7lz57RRFIVCoVAoLyVVrBGMWFHtunodt0GDBqGxqfgSExMVfn/yySf45JNP1JaxDi8vL3z88cf44IMPMH78eEydOhV+fn4al9cQgpSUzZs3N7r/7t27WhHmRUX8VAaxmKA8IASm6BdXygwtgkoqh3oYWgQlGAKNTuaPyEt657adrOECACjrQZ6DMQDEr/MxtAhKdOpvbGgRFJDJ5NB1GsxKzghiTly7Tt7/VH1iY2OxYcMGJCUl4ZtvvsHAgQPh6uqKKVOmYNKkSbCz045bgSAl5cMPP0THjh1hYqI6Z8Tz8w1QKBQKhUJRDxnEkEJcu04+RkZGGDduHMaNG4f79+/j66+/xtKlS7F48WKMHj0a8+bNw5AhQ5pXh5CDnJ2d8fnnn2P8+PEq92dlZcHHhzxNnBTyxllCRFAIsiV5bhYwLienfepT1pU8q5P9eYKscrU87E2eD75zLnnt9OANMh3ETUu6GVoEJYzLyfpMMzLdy1PJGkPE1lpSWDITXKriwoUL2LVrFw4cOABbW1tERETg7t27GDNmDGbPno0NGzZoXLagN4uPjw8yMzMbVFIYhml0LOxlZ17wcZi3JuclXixrZWgRlDj9weuGFkElD0eQ91ybniDr5Q0ArAlZpnkAkG6pMLQISth91dbQIqjkoSc5jv11OH19w9AiKMLpfsRAyhlBWjvcIzVACLI63L9/H//5z3+wa9cu3LhxAyEhIdi/fz+CgoL4qQMiIiIwcuRI3Sspq1atwtOnTxvc7+7ujrw88iato1AoFAqlpVAFI4g4o9p1spUUR0dHdOvWDVOmTEFERAQ6dOigdIyHhwdeffXVZtUjSElxd298QjpjY2M4O5M1YyVJHB/WDUYMOXPA3I14xdAiKOF4i8w5TVwSyJvnSG5BjlWuDoZAH79bmeSFanf/65GhRVCJ9YWGO6GG4lFwL0OLoIC8uhL4Vrd1SDkxjHhLCnlW3PqkpKTg9dcbt4BbWVkhNTW1WfWo/bYrLS2FRCIBANjb28PamrzEUhQKhUKhtDQqWWMwrFHtOtmWlKYUFG0hWEnZuXMnNm7ciJycHIXtbm5u+OijjzB16lStC/eikPuFM0QW5DiGWv6XPA292qmdoUVQiXHGVUOLoER1AHmWMLkpec+Upz9hPg0A/s7rbmgRVNLxKHnh2mYlZJnnZFLdy1PFGYOpG+4h1CfFy8uL9zlpCCMjI9jb22P48OGYMWNGg5HBQhCkpMTExGDFihWYN28egoKC+PjnwsJCnDp1Cv/6179QXFyMjz/+WGNBXmSsz5pDbEKOktL2L/JMu5V22pnZU9s8DfE0tAhKFISQF7Vi/hd5L9Q7O8hTCNrnkufMCwAyR/I6CcRNMKiHrNhSTsTnSZFyZF1/HWPHjm3yGJZlcf/+fXz66af466+/8NVXX2lcnyAl5d///jd27dqlFN3Tq1cvDBo0CJ6enliwYAFVUigUCoVC0ZBq1hgMa1y7bmBhGmD58uWCj3333Xcxfvx43Ssp9+/fR58+fRrc36dPHzx8+FBjIV50HvnIITInx3TZJpe8+HuTUvLCagHgsSN5Fp7uX5HXVpXtyZOp1a+5hhZBiaIg8qw7ACCzIM8SVvQaWUlC2QoAOp79RcqK+TwpUjXT4pOIt7c33nvvvWaVIehr9eqrr+Kzzz6DTEUyG7lcjs8//7zZYUYUCoVCobzMVHHGCgtpeHt7o7i4WPDxI0eOxEcffdSsOgUP9wQFBcHe3h4DBw5U8ElJS0uDiYlJo9M7v+yIWkkhsiAnWRKJ1gHjp+Q5XgJkZpx96GNhaBGUcFtGnoPxnRm9DS2CEuXuVYYWQSVdDpJnXbVbWmhoERSQsVW4o+M6quVGgNyodp28d2JWVhb+/PNP2NjYCD6+qqp5z7wgJcXDwwPXr1/Hnj178Ouvv+Lvv/8GUBOC/Omnn+K9996DlZVVswR5kbE/agIjY3LypFgSmKuBMyEv9wcAiCtbG1oEJSptyFF4eeyVEzkZGseTRYYWQQnuLHm9YwC4MZG8LNTcu7aGFkEBtqISmKHbOqQcA4YT8eskMnToUMEZ5puKAhKC4C+DpaUlZs2ahVmzZjW7UgqFQqFQKIpUsUbgavOkVBPok6JJZnlHx+YlVdRK91UqlaKgoACdO3dW+9ytW7ciJiYGEokEnp6e2LJlC/r376/y2MTEREyePFlhm6mpKSorn03aVV5ejujoaBw5cgSPHj2Ci4sL5s2bh5kzZwIAioqKsHz5cpw6dQr5+fno0KEDxo4di9WrVyskplOlAe7fvx8TJkxQ+xpJ4+5I8nq90tfKDC2CSrrMumVoEZSwaGNpaBGUyHvX3tAiKOGUQl6oPakwUvJ67T2XkxWMoY/hHhn3zHFWxpFnMTVEZnmtKClXr16Ft7c35HL1IlgOHjyIqKgoxMXFwdfXF7GxsQgKCkJOTg5sbVWb+qysrBQSyj2vTERFReHMmTPYs2cPunTpglOnTmH27NlwcHBAaGgo7t27h3v37mHDhg1wd3fH7du3MXPmTNy7dw/ff/+9Qlm7du3CyJEj+d9t2rRR6/ooFAqFQhGKlDUCai0pL0J0jzYwqCPAxo0bERkZyVtH4uLicPz4cSQkJCA6OlrlOQzDwN6+4V5beno6wsPDMWjQIADA9OnTsX37dly4cAGhoaHo3bs3fvjhB/74bt26Yc2aNXj//fchk8lgZPSsSdq0adNoXc9TVVWl4CRUVlZrHeBqF0Kwuk1OOHQd5hfI9El5PKCroUVQorQreT0sl+03DS2CErnzuhlaBCW6fldqaBFU4raVPP8deQeyplyRyyuB27qtQ8aJeJ8UGUeeM7MhEPRl8Pb2bnR/RYX6WRSrq6uRmZmJRYsW8dtEIhGGDRuGjIyMBs8rLy+Hs7MzWJaFt7c31q5di1deeZYmPCAgAElJSZgyZQocHBxw9uxZXL9+HZs2bWqwzNLSUlhZWSkoKAAwZ84cTJs2DV27dsXMmTMxefLkRh2B1q1bh5UrVyptNy2VwsiInA+L6S3yHGev/aujoUVQSY/F2YYWQQmrDuRlBy0aSp4yZ9yLvCFETguOhLqgsit5Q8DFbuQEGwCAvNoIyNRtHdVyMTh5bZ4UOTnfDEMiSEm5evUqJkyYABcXF5X7CwoKcP36dbUqfvjwIeRyOR/OXIednR2uXbum8hw3NzckJCTAw8MDpaWl2LBhAwICAnDlyhXeOWfLli2YPn06HB0dYWRkBJFIhB07dmDgwIENyrF69WpMnz5dYfuqVaswZMgQWFhY8ENG5eXlmDdvXoPXtGjRIkRFRfG/y8rK4OTkJKg9KBQKhfJyI2VF4Op8UlhqSQEEKim9e/eGr69vg5E9WVlZ2LFjh1YFU4W/vz/8/f353wEBAejVqxe2b9+O1atXA6hRUn799VckJSXB2dkZaWlpmDNnDhwcHDBs2DCF8srKyhAcHAx3d3esWLFCYd/SpUv5dS8vLzx58gQxMTGNKimmpqYwNVXOQWLyTwmMxOTkJqnoTl6vySGNoPGweojaCcsHoE8eDG6et7wuaPdHiaFFUKJtJnlzHJX0bW9oEVRi9Td5EwwWe5P1kWYrdJ9VWcaKgFrlpCUoKSUlJfj++++Rm5uLBQsWwMbGBhcvXoSdnR06deqklToEKSmvvfaa0uzH9bG0tGzQUtEQ7du3h1gsRmGhYsKewsJCwX4gxsbG8PLyws2bNePhFRUVWLx4MQ4fPozg4GAANTlesrKysGHDBgUl5fHjxxg5ciQsLS1x+PBhGBs3nr/A19cXq1evRlVVlUpFhEKhUCiU5iBlxfUsKWQP92RnZ2PYsGGwtrbGrVu3EBkZCRsbGxw6dAj5+fnYvXu3VuoRpKR8+eWXje7v1q0bUlNT1arYxMQEPj4+SElJ4WdVZFkWKSkpmDt3rqAy5HI5Ll26hNGjRwOoCYWWSqUQiRQ1ULFYDLaep3RZWRmCgoJgamqKpKQkmJk1PUNxVlYW2rZtq5GCwnAcGJYcS0FJV/ISSrW5SdY8HXXcH0becJ3NNxcMLYISImfyrDsyO7IcLwE+cIM89DDDr7r03EqWdUcmr8I/Oq5DzorA1FpQ5GpaUtLS0hATE4PMzEwUFBTg8OHDTc5YfPbsWURFReHKlStwcnLCkiVLEBERIai+qKgoREREYP369bC0fJYWYfTo0c2er6c+Bv2XiYqKQnh4OPr164f+/fsjNjYWT5484aN9wsLC0KlTJ6xbtw5AjZ+In58fXF1dUVJSgpiYGNy+fRvTpk0DUBOeHBgYiAULFsDc3BzOzs44d+4cdu/ejY0bNwKoUVBGjBiBp0+fYs+ePSgrK+OjcDp06ACxWIyjR4+isLAQfn5+MDMzw+nTp7F27VqNZ3kOPHAFZq3JeTsd/Jw8J1WzS7rOQKAZ5Y7kRYiIzMiz5OW/pR3TrjbpdIa8SBqzYvIi6wCgyIO8jOEP+5GlOLEVxsAC3dYhq+c4K1fTcfbJkyfw9PTElClTMG7cuCaPz8vLQ3BwMGbOnIm9e/ciJSUF06ZNQ8eOHREUFNTk+b/99ip6gnMAADKGSURBVBu2b9+utL1Tp06QSCRqyd4Ygr+c9+/fx+XLl+Hj4wNra2sUFhbim2++AcuyCA4ObnSW5IZ455138ODBAyxbtgwSiQR9+/ZFcnIy70ybn5+vYBUpLi5GZGQkJBIJ2rZtCx8fH6Snp8Pd3Z0/5sCBA1i0aBEmTpyIoqIiODs7Y82aNXwyt4sXL+L8+fMAAFdXVwV58vLy0KVLFxgbG2Pr1q2YP38+OI6Dq6srHy5NoVAoFIoukIMBatPhy1Hzl09lUUtDvo+jRo3CqFGjBNcVFxcHFxcXfPHFFwCAXr164ZdffsGmTZsEKSmmpqZKsgHA9evX0aGD9vweGU5AEv6zZ89izJgxePr0Kezs7JCcnIwxY8bA3NwcIpEIt27dQlJSEkaMGKE1wV4EysrKYG1tjb4T10Bs0vSQkr54MIA8h0L31WRNJlbH1f8lL5Pqv4f8x9AiKLHl7TcNLYISzPVbhhZBiVsfeRpaBJXYXCPLagEA94aQJRNbUYl/PlzGp6zQJnXfilcOLoDYokYBkT+twpV3YpSOXb58uVKgx/MwDNPkcM/AgQPh7e2N2NhYftuuXbvw4YcforS0aSvktGnT8OjRI3z77bewsbFBdnY2xGIxxo4di4EDByqU2xwEDXotXboUERERKCsrw0cffYTg4GC88cYbuH79Oq5du4YPPvhAZX4QCoVCoVAowpDJxQoLANy5cwelpaX8Uj+3WHOQSCQqU4CUlZUJyn32xRdfoLy8HLa2tqioqEBgYCBcXV1haWmJNWvWaEVGQOBwT3Z2NhITE9G6dWt8+OGHWLRoEe8HAtRkddVHCHJLxfyBDEbGug9fE4qJhDzH2TIfB0OLoBLnH8nqzQHA5p1vG1oEJQqGkDefkNPDtoYWQQmXbTcMLYJKHoS4Nn2QnmEsyHlnAgDD6F4elgUYluHXgRpfS21bbrSBtbU1Tp8+jV9++QXZ2dkoLy+Ht7e3UqqP5iJISTExMeEn8auurgbLsgqT+lVUVDQZwvsyU91GDLkxOeFknZPVzxCsa3LfJmc4rD4WBeQ9144plU0fpGdInGoBYvLyTDAEOj0DAEOeLg7nfeS8MwFAJhXrfIJBeb08KepG96iLvb29yhQgVlZWMDc3F1zOgAEDMGDAAG2LxyM4T0p0dDSio6Oxe/dueHt749NPP8XBgwfBMAxWr16Nfv366UxICoVCoVBeeFgGXK0lBaxup1Dw9/fHiRMnFLadPn1aIWHq82zevFlw+Y0lPlUHQUpKTEwMgoOD8frrr6Nnz544ffo0Zs+ezc8K3LZtWyQnJ2tFoBcR68ulMBIT1Ps1Iq+H6XyCnBDt+twab2gJlKm4LLyXoy8sb5A3T06ZF3mh9uJqAk0WANqff2hoEZS4/xpZ2Xnl1bp/R7FyESAXPVtXg/Lycj6xKVATrZqVlQUbGxt07twZixYtwt27d/kkazNnzsS///1vfPLJJ5gyZQrOnDmDb7/9FsePH2+wjufnwHvw4AGePn3K6wIlJSWwsLCAra2tfpWU7t274/r163j06BHatauZ3OzHH39ESkoKKioq4O/vz2+nUCgUCoWiPhxbs9Stq8Pvv/+OwYMH87/r5pELDw9HYmIiCgoKkJ+fz+93cXHB8ePHMX/+fHz55ZdwdHTEzp07Gw0/zsvL49f37duHr776CvHx8XBzcwMA5OTkIDIyEjNmzFBP+EYQFIJM0Yy6sLKh3ecTNXdP4QbyLClFheQ5hgFA5x/Ja6tWv+Y1fZCeKR1MXtI7yzyyMpYCwKPerQ0tgkoqQsizhIl+IStjsLyqEtf+vVinIchO25dDZF7jn8dWVOLOjJU6qU8bdOvWDd9//z28vLwUtmdmZuKtt95SUGiag0b2K47jcPbsWdy8eRMODg4YMWIEdZxthIe+7YnKkyJNJW+6eJtyMnXlMmfy2qrakjyFoO2fxYYWQZl79w0tgRKP5nQ1tAgq6TWfPCVFTtjcnjJ5Fa7puhKWeeaLomOflOZSUFAAmUw54kkulys55DYHQd3E0aNH88ldioqK4O/vj6FDh+J///d/ERoaCg8PDzx48EBrQlEoFAqF8tJRp6TUV1YIZejQoZgxYwYuXrzIb8vMzMSsWbO0GoYsyJKSnJyMqqoqAMCSJUvw+PFj5ObmwsXFBf/88w/Gjh2LZcuWYdu2bVoT7EXC6nYVjIzIeeCMf3tqaBGUuDmJvJwWAGD7O3mhtU/syQrNBACmN3n3z9yevKGV9qkmhhZBJXdDyZtI0yFZe/O/aAORvErndXByBpyc4ddJJiEhgZ97r24kRSaTISgoCDt37tRaPWoP95w5cwbr16+Hi4sLAMDR0RGff/45ndeGQqFQKJRmwLDPctaQmLumPh06dMCJEyf4zPMA0LNnT/To0UOr9QhWUhimRqsrLi5Gt26KY+Kurq64d++eVgV7kSh1MYXYhBzH2eKx5PXmrG6S2WswLifvTdEhq9rQIihxZzg5Pld1MHJy/ufqcDpNnjMvAHAi8v7/CgfbNX2QHpFXVwK5uq6EqVnq1lsAPXr00LpiUh/BSkpERARMTU0hlUqRl5eHV155hd8nkUj4OGmKMm1znsLIiJyPndUd8pycjcrJm/QQAAr7tzK0CErIXidvuK7NIfIUgiedyHvJG5USlC+pHlV25A2NkWZJ0Ic8jJwBU6ucMIQrKVOmTGl0f0JCglbqEaSkhIeH8+tvvPEGnj5VfEn+8MMP6Nu3r1YEolAoFArlpYSrXVDvL6EUFytG9EmlUly+fBklJSUYMmSI1uoRpKTs2rWr0f3Lly+HWEyeMx8pFL7aCmJTcszhT7zJm7vH5Dp5FgsA6HKkyNAiKFH0iDwnVcvb5D1T5o/IsxgWDCIz6SVLXlPB7gJZQ2Myme6tYAxbz5JCeHTP4cOHlbaxLItZs2YpuYQ0h2ZlqsrLy4NMJkOrVq1gZkbOR5hCoVAolJZGneNsfQfaloRIJEJUVJRS+vzm0KzJCNzc3PDnn3+iV69e2pLnhaRVAQsjY3KeOMdksnooAFA4gEwlN2caWVkvAaDnv8lLUsaZk+eMfb+fhaFFUKLjL+QlTQOAgtfIy2j6qDdZ909eLQLO67YORl6z1K23RHJzc1UmedMUQUrKuHHjVG6Xy+WYN28eLC0tAQCHDh3SmmAvEg9GVkFkQY7prtiNrIm7AKDLIfKGVQDA4gF5SsrNVeQNjbF3yfqgAECPhEeGFkEJjsDJPQGg/WXd5wBRF5ML1w0tggIyTg9RdS0o42zd3EB1cByHgoICHD9+XMGPtbkIUlKOHDmCgQMH8rlR6tO6dWtYW5P3IqdQKBQKpSXByAFRrQWFI9yS8scffyj8FolE6NChA7744osmI3/UQZCSsm/fPixYsADh4eGYPHkyv33Pnj1Ys2YN3N3dtSbQi0iri+ZEOc6W9SWv11Teg0xF1yr9lqFFUKLcgby5eywekDOcyVNFXj6ZSifyrJgAYPREe+Z5bfH3wt6GFkEBtrISWK3bOlrScE9qaqpe6hFke5wwYQL++9//Ij4+Hm+++aZS6BGFQqFQKJRmwj23EMyQIUNQUlKitL2srEz/IcgA0KVLF6SlpWHlypXw9PTEjh07+Cy0lMbp+H+lMBKTk8TJ8h9LQ4ughNyY0GfJwtzQEihh8xd5lrD7/chL5madUmpoEZQo7tHR0CKoxOYqeZYw1513DS2CAjK2Cn/ruA6GrWdJIe+WKHD27FlUVytbKysrK/Hf//5Xa/WoFd0jEomwcuVKDB8+HGFhYZDLm2+P2rp1K2JiYiCRSODp6YktW7agf//+Ko9NTExUGG4CAFNTU1RWPlMAysvLER0djSNHjuDRo0dwcXHBvHnzMHPmTP6YQYMG4dy5cwrlzJgxA3Fxcfzv/Px8zJo1C6mpqWjdujXCw8Oxbt06GBmpHxDFGYvAEZRHpv28W4YWQQlpBDnDYfW5tpqw+eIBuK0rN7QISjjF3TC0CMrYkpeT5IkDmd1jMwJzykBka2gJFJDJKoHbuq2jJczdk52dza9fvXoVEsmziSDlcjmSk5PRqVMnrdWnUQjygAEDkJ2djdzcXLi6umpc+cGDBxEVFYW4uDj4+voiNjYWQUFByMnJga2t6gfUysoKOTk5/O/nrTlRUVE4c+YM9uzZgy5duuDUqVOYPXs2HBwcEBoayh8XGRmJVatW8b8tLJ5FJ8jlcgQHB8Pe3h7p6ekoKChAWFgYjI2NsXbtWo2vl0KhUCiUhmgJPil9+/YFwzBgGEblsI65uTm2bNmitfo0zpPSunVreHp6NqvyjRs3IjIykreOxMXF4fjx40hISEB0dLTKcxiGgb29fYNlpqenIzw8HIMGDQIATJ8+Hdu3b8eFCxcUlBQLC4sGyzl16hSuXr2Kn3/+GXZ2dujbty9Wr16NhQsXYsWKFTAxUS8nxBMHCxgZk2MpyLne2dAiKOFeecfQIqhEfIec+1ZHhVOz0hvphHJf8ixOHIHRvtYEGpwAoN2ZW4YWQQlOxVCCIZGxupenJVhS8vLywHEcunbtigsXLqBDhw78PhMTE9ja2mo1A73gf+MTJ05g2rRp+OSTT/DXX38p7CsuLlbbUaa6uhqZmZkYNmzYM2FEIgwbNgwZGRkNnldeXg5nZ2c4OTnhjTfewJUrVxT2BwQEICkpCXfv3gXHcUhNTcX169cxYsQIheP27t2L9u3bo3fv3li0aJHCfEQZGRno06cP7OyezcIZFBSEsrIypfrqU1VVhbKyMoWFQqFQKBQh1FlS6ltUSMPZ2RldunQBy7Lo168fnJ2d+aVjx45anyJHcAhyWFgYRo4ciZycHGzZsgU7d+7ExIkTAdQoHM/7eDTFw4cPIZfLFRQBALCzs8O1a9dUnuPm5oaEhAR4eHigtLQUGzZsQEBAAK5cuQJHR0cAwJYtWzB9+nQ4OjrCyMgIIpEIO3bswMCBA/ly3nvvPTg7O8PBwQHZ2dlYuHAhcnJy+GR0EolEpVx1+xpi3bp1WLlypdL2wrFkJXNz/+SBoUVQhiNzrF5cRc59q6NwGnnz5IjPk5ex1CmJvMy8MCLHN60+OR91MbQISpA2nxBbUQks1G0dpA/3JCUlYdSoUTA2NkZSUlKjx9YfuWgOgpSUmJgYbNy4EfPmzQMAfPvtt5gyZQoqKysxdepUrQgiBH9/f/j7+/O/AwIC0KtXL2zfvh2rV9cEsG/ZsgW//vorkpKS4OzsjLS0NMyZMwcODg681Wb69Ol8GX369EHHjh0xdOhQ5ObmNmtipEWLFilk4SsrK4OTkxO4B2bgSJrbiMCorCfe5A1BAUCXwwSG2yvP62Vw7owytATK/DOGLMdLALC5KjW0CCrp8Rl541CMFVlRiDK2CroelGa4mqVunTTGjh0LiUQCW1tbjB07tsHjGIbRSmANIHC458aNGwgJCeF/jx8/HkePHsWHH36oEBGjDu3bt4dYLEZhYaHC9sLCwkZ9TupjbGwMLy8v3Lx5EwBQUVGBxYsXY+PGjQgJCYGHhwfmzp2Ld955Bxs2bGiwHF9fXwDgy7G3t1cpV92+hjA1NYWVlZXCQqFQKBSKEOpCkBm5Zj4pW7duRZcuXWBmZgZfX19cuHChwWMTExN5B9i6pamJglmW5YNaWJZtcNGWggIItKRYWVmhsLBQIS3+4MGDcezYMYwZMwb//POP2hWbmJjAx8cHKSkpvEbGsixSUlIwd+5cQWXI5XJcunQJo0ePBgBIpVJIpVKIRIq6l1gsBss2fMezsrIAAB071uQw8Pf3x5o1a3D//n3+hpw+fRpWVlYaZdft/vV9GInJySPB1fO/IYV/BpPnDAoAr7+u/rOta/77yyuGFkEJkxJDS6DM5XlfGVoEJUZ7Dje0CCopG6R5lKauKH6XrFB7+VMj4H3d1tEcx1ldRMuSgKAvQ//+/XHy5En4+fkpbA8MDMTRo0cxZswYjSqPiopCeHg4+vXrh/79+yM2NhZPnjzho33CwsLQqVMnrFu3DgCwatUq+Pn5wdXVFSUlJYiJicHt27cxbdo0ADUNHhgYiAULFsDc3BzOzs44d+4cdu/ejY0bNwKomaFx3759GD16NNq1a4fs7GzMnz8fAwcOhIeHBwBgxIgRcHd3x6RJk7B+/XpIJBIsWbIEc+bMgakpOcoGhUKhUF4cRM2Yu0cX0bLPs3nzZsHH1rmHNBdBSsr8+fORnp6uct+gQYNw9OhR7N69W+3K33nnHTx48ADLli2DRCJB3759kZyczDup5ufnK1hFiouLERkZCYlEgrZt28LHxwfp6ekK1o0DBw5g0aJFmDhxIoqKiuDs7Iw1a9bwydxMTEzw888/8wqRk5MT3nzzTSxZsoQvQywW49ixY5g1axb8/f3RqlUrhIeHK+RVUQfJUHuITcjxSel4Tr0Qan3Q9TB51h0AuHSdrPlDAMA5l6zQTAB40pEwL0cAXp/ONrQISth4kJctGAAKXiOvB93+CFnD5fJqPWQNZ2sXPPv7fJSoqampUme5Llp20aJF/DZ1omVZloW3tzfWrl2LV15p2FK7adMmQZfBMIzWlBSG4wgNq3gBKCsrg7W1NQYMWg4jI3KUFNN88pxB/1rc1tAiqGShb7KhRVAi6e0BhhZBCdaMvOG6MleyHC8BoO35e4YWQSVc6WNDi6BE2ZAehhZBAZm0Er/9uBSlpaVa9zes+1b0nbSG79DKqyuR9Z//VTp2+fLlWLFihcK2e/fuoVOnTkhPT1cILvnkk09w7tw5nD9/XqmcjIwM3LhxQyFaNi0tTSFalgTIe7NQKBQKhfISoioE+c6dOwpKkbZcDoREywqlztahC58WqqTogXuvmUBsRs4Qi2NqG0OLoITFdTJ9ffYdCTa0CEoUjSHv3/aJs8zQIijhvibf0CIoIe1CXlg0ADzp72BoEZQwe0hWuLZYpvtnXJXjrJBIUV1EywohPj4emzZtwo0bNSHs3bt3x4cffsj7iWoDAhNHUygUCoXy8sGwz5xn1YnuqR8tW0ddtGx9a0lj1EXL1kW5NsWyZcvwr3/9CyEhIfjuu+/w3XffISQkBPPnz8eyZcuEC98E5HXJXkDM7zMQm5DjmFbWmTyrhf0FMh0KTe+RN7VBmXOHpg/SM06nDC2BMgWhzoYWQQn70w1nrDYk1g/JCvcFgLwJZFmd5JUcoF5idbVhWA4My/Hr6qDtaNmm2LZtG3bs2IF3332X3xYaGgoPDw988MEHGgeaPI/GSsr+/fsRGhqKVq1aaUWQF5n2l57CyIic2aKMrt81tAhK5M3ubmgRVNIhizyH3iedyPN1t08nLzrL8nfyHMQregoP99Qn1W3I66923U3We0rGViFXx3UwsmcJwRk1R5d0ES3bGFKpFP369VPa7uPjA5kWh8Y0fjJnzJgBX19fdO3aVWvCUCgUCoXysiKScxCJOH5dXebOndtgMtSzZ88q/N60aZPgkGJVTJo0Cdu2beNzkNXx9ddf8/P6aQONlRQauSwc438ewUhEzhCLvAt5vTmXrTlNH2QA8ua6GVoEJVw/vWRoEZSQ+pBnCbv7HnnDPTILQ0ugmq67dD0rjfr8M9bJ0CIoIK+qBLbpto7mZJw1BPHx8Th16hSf6PX8+fPIz89HWFiYwjx2zysy6kCejY9CoVAolJcQRs6BqbWkMBpYUvTJ5cuX4e3tDaAmkztQE2XUvn17XL58mT+uuWHJGispJ0+eRKdOnZpV+ctCyasOMDImJ5mb1Q3yEjfJ3MjqNdVhcY+8F4WoHXl+MjJzsaFFUMLuNz1kCFUTcQV5odoAwJmSkyKhjo5bG54czxDIOCn+0nEdLUlJSU1N1Us9GispAwaQl/WSIpBGJls0FPdeJ9MObkdg1FHhMPI6B08cyIleq6PLF1cNLYISuf/rYWgRVOKUQt60BkY3bxlaBAX04eLAsByvnKgb3fOiQod7KBQKhUIhgJbkk1JZWYktW7YgNTUV9+/fB/tc5/fixYtaqYcqKXqA4WoWUrgZTc7QUx3dNpE3BAUATBVZWS8BgO1Bnmm+a8JtQ4ugRP4sT0OLoESb6wS9COrxwJMcx/46rK2Uw1sNiUxaCRw9pNM6RDIWotqZBUUysrWUqVOn4tSpU3jrrbfQv39/naTEB6iSQqFQKBQKETByDgzTMnxSjh07hhMnTuC1117TaT1USdEDT23FEJuQ41jI5JsbWgQl/hlCnk8DALS/RJ4lxfZ38qxOVd3tDC2CEkYVhpZAGfNHckOLoJIqApO5WRSQdQNlMt07YrckJaVTp06wtNT9TOOCn0yWZZGYmIhDhw7h1q1bYBgGLi4ueOuttzBp0iSdmXpeBOz/WwQjMTnmVPkV8nKSVI161dAiqKSkO3kOhWZtyVF467DMJ8/BuONPBYYWQYnCIeTlKAIAEPg9FJVXG1oEBURy3XdYGDkLptYZhZGTPdzzxRdfYOHChYiLi4Ozs+5yEglSUjiOQ2hoKE6cOAFPT0/06dMHHMfhr7/+QkREBA4dOoQjR47oTEgKhUKhUF54WA6os6AQHt3Tr18/VFZWomvXrrCwsICxsWKHrqioSCv1CFJSEhMTkZaWhpSUFAwePFhh35kzZzB27Fjs3r0bYWFhWhHqRaPKrhXkRuQ4q+Yt8DG0CEr02EpeTgsAaGVJoNXib/Imgyvt3trQIihR7idsNld9YvaIzA/P467k9drLCtoYWgQFZNJKQMdR7YycBQM5v04y7777Lu7evYu1a9fCzs7OsI6z+/fvx+LFi5UUFAAYMmQIoqOjsXfvXqqkUCgUCoWiIYycA4OW4ZOSnp6OjIwMeHrqNopOkJKSnZ2N9evXN7h/1KhR2Lx5s9aEetEw+e06jBhywkYtXiUvNFNUQt6MtQBglXTD0CIowXQjb04ai0LyHIzb/EWW4yUAsAQ50NfH6jY576c6TO+VGVoEBWRyPfhdyeVArSWlZp1cevbsiYoK3f+PCVJSioqK+OmeVWFnZ4fiYjI/MiTAuruAJWi4p/Ph+4YWQYlKZ/JSvQOAtE8HQ4ughNkjshwKAaCyHXnRIWbXSw0tghL33iRPwQSADtnkDbeWu5H1TpBJKwEdxxwwMhYM1zIcZz/77DN89NFHWLNmDfr06aPkk2JlZaWVegS9WeRyOYyMGj5ULBZDJiNzTgoKhUKhUFoEchaoTeYGwpWUkSNHAgCGDh2qsJ3jODAMA7mWLEGCo3siIiJgaqo6jLaqirzwQ5J44NUaYhNyLCn2v5Bnmr83kDxzMwBY3zS0BMoYl5M3ZGBRQN47gLMg53+ujiobQ0vQAAS6PzzsTZZ1Tl5lBBzTdSVygKv9uLNkD/cQNcFgWFhYk5671GmWQqFQKJRmwNbzSSFcSQkMDGxw3+XLl7VWj+AQZF2xdetWxMTEQCKRwNPTE1u2bEH//v0blGPy5MkK20xNTVFZ+Ww8tby8HNHR0Thy5AgePXoEFxcXzJs3DzNnzgQA3Lp1Cy4uLirL//bbb/H2228DgEqlbP/+/ZgwYYLa19ju0lMYGZFjuivroZ2xQm3i8nm2oUVQCeNIXhgrd/sfQ4ugjJvq/ylDwkjJG4Ju/Q+BJgsAj9zJszp1OkeW47NMVgmdG1ZlLCCqU1LI+WYI4fHjx9i/fz927tyJzMxM/Q73jBs3rumCjIxgb2+P4cOHIyQkRFDlBw8eRFRUFOLi4uDr64vY2FgEBQUhJycHtra2Ks+xsrJCTs4z76XnlYmoqCicOXMGe/bsQZcuXXDq1CnMnj0bDg4OCA0NhZOTEwoKFDNRfv3114iJicGoUaMUtu/atYsfdwOANm3aCLqu53ngZQGxKTkvAbvzTwwtghKlY/oYWoQWg+SDdoYWQQnXfeQ5XrLm5GULtrn81NAiqKSsK3lTZRj/mWtoERRgOD04rLeg4Z460tLSEB8fjx9++AEODg4YN24ctm7dqrXyBSkp1tbWTR7Dsixu3LiBnTt34uOPP8aqVauaPGfjxo2IjIzkrSNxcXE4fvw4EhISEB0drfIchmFgb99waun09HSEh4dj0KBBAIDp06dj+/btuHDhAkJDQyEWi5XOP3z4MMaPH4/WrRUTUrVp06bRuigUCoVC0RoyeT1LCrlKikQiQWJiIuLj41FWVobx48ejqqoKR44cgbu7u1brEqSk7Nq1S3CBx44dw+zZs5tUUqqrq5GZmYlFixbx20QiEYYNG4aMjIwGzysvL4ezszNYloW3tzfWrl2LV155hd8fEBCApKQkTJkyBQ4ODjh79iyuX7+OTZs2qSwvMzMTWVlZKjW/OXPmYNq0aejatStmzpyJyZMnN+qbU1VVpeBEXFZWE+dv/JSDWEaOmbfahjwn1ba/SQwtgmqqyAv3NXtE3hBU3hsWhhZBiS4nyLPuPHYkZw6v+rDGBM69JiJMJk738nByGThWVLPOkTdcCQAhISFIS0tDcHAwYmNjMXLkSIjFYsTFxemkPq27Tw8YMAD9+vVr8riHDx9CLpcr5V+xs7PDtWvXVJ7j5uaGhIQEeHh4oLS0FBs2bEBAQACuXLkCR0dHAMCWLVswffp0ODo6wsjICCKRCDt27MDAgQNVlhkfH49evXohICBAYfuqVaswZMgQWFhY8ENG5eXlmDdvXoPXtG7dOqxcubLJa6dQKBQKRQm5HGBqLSgcmZaUkydPYt68eZg1axa6d++u8/q0rqS0adMGhw4d0naxAAB/f3/4+/vzvwMCAtCrVy9s374dq1evBlCjpPz6669ISkqCs7Mz0tLSMGfOHDg4OGDYsGEK5VVUVGDfvn1YunSpUl31t3l5eeHJkyeIiYlpVElZtGgRoqKi+N9lZWVwcnJC+99LiJoF+fYb5MVBWluROawmNyGsNwdAOo68xIndPyEvcRqJGTvb/U1e+D8ASDu3N7QIStyNeKXpg/SIvKoS+Eq3dXBSGTiGbEvKL7/8gvj4ePj4+KBXr16YNGmSRgElQjFYIHr79u0hFotRWFiosL2wsFCwH4ixsTG8vLxw82aNz3VFRQUWL16Mw4cPIzg4GADg4eGBrKwsbNiwQUlJ+f777/H06VNB4dO+vr5YvXo1qqqqGswXY2pqqnLfP8PbEuU465KYb2gRlLg9sbOhRVCJ+X1yhunqKPu7jaFFUMLOkrxJDys6kjcE1fp38v73AODvceS8n+pw3UOW4iuTV+EvHdfByVlwtZYUjiMzusfPzw9+fn6IjY3FwYMHkZCQgKioKLAsi9OnT8PJyQmWlpZaq89gSoqJiQl8fHyQkpKCsWPHAqhxvk1JScHcuXMFlSGXy3Hp0iWMHj0aACCVSiGVSiESiRSOE4vFYFWEc8XHxyM0NBQdOjSd+jwrKwtt27ZtUEFRBcfVfODkVWSNjctY8hJvkdZGdciryVNS2EryZJLJybt/Mqmo6YP0jIwlz8cJANhKAu+fPubKUYM6eere67pAKnsKDjVRaTKQaXWro1WrVpgyZQqmTJmCnJwcxMfH47PPPkN0dDSGDx+OpKQk7VTEGZADBw5wpqamXGJiInf16lVu+vTpXJs2bTiJRMJxHMdNmjSJi46O5o9fuXIl99NPP3G5ublcZmYmN2HCBM7MzIy7cuUKf0xgYCD3yiuvcKmpqdzff//N7dq1izMzM+O++uorhbpv3LjBMQzDnTx5UkmupKQkbseOHdylS5e4GzducF999RVnYWHBLVu2TK3ry83N5VCTy5EudKELXejyAix37txR6zsghIqKCs7e3l6pLnt7e66iokLr9ekKmUzGHT58mAsJCdFamQbNO/zOO+/gwYMHWLZsGSQSCfr27Yvk5GTemTY/P1/BKlJcXIzIyEhIJBK0bdsWPj4+SE9PVwh5OnDgABYtWoSJEyeiqKgIzs7OWLNmDZ/MrY6EhAQ4OjpixIgRSnIZGxtj69atmD9/PjiOg6urKx8urQ42Njb8dQgJ46Y0Tp2Pz507d7Q2edXLDm1T7ULbU/uQ0qYcx+Hx48dwcHDQetlmZmbIy8tDdbWipc3ExARmZuQNxTWEWCzG2LFj+dERbcBwnA5tVy85ZWVlsLa2RmlpKX1haQHantqHtql2oe2pfWibvtyQN2hLoVAoFAqFAqqkUCgUCoVCIRSqpOgQU1NTLF++XK2IIErD0PbUPrRNtQttT+1D2/TlhvqkUCgUCoVCIRJqSaFQKBQKhUIkVEmhUCgUCoVCJFRJoVAoFAqFQiRUSaFQKBQKhUIkVElRQVpaGkJCQuDg4ACGYXDkyBGF/RzHYdmyZejYsSPMzc0xbNgw3LhxQ+GYoqIiTJw4EVZWVmjTpg2mTp2K8vLGJ2GrrKzEnDlz0K5dO7Ru3Rpvvvmm0gSMLRVDtGlRURE++OADuLm5wdzcHJ07d8a8efNQWkrWxGWaYqjntH75o0aNUll3S8SQ7ZmRkYEhQ4agVatWsLKywsCBA1FRUaHNyzMIhmpTiUSCSZMmwd7eHq1atYK3tzd++OEHbV8eRQ9QJUUFT548gaenJ7Zu3apy//r167F582bExcXh/PnzaNWqFYKCglBZb5KuiRMn4sqVKzh9+jSOHTuGtLQ0TJ8+vdF658+fj6NHj+K7777DuXPncO/ePYwbN06r12YoDNGm9+7dw71797BhwwZcvnwZiYmJSE5OxtSpU7V+fYbAUM9pHbGxsWAYRivXQgKGas+MjAyMHDkSI0aMwIULF/Dbb79h7ty5ShOltkQM1aZhYWHIyclBUlISLl26hHHjxmH8+PH4448/tHp9FD2gtVmAXlAAcIcPH+Z/syzL2dvbczExMfy2kpISztTUlNu/fz/HcRx39epVDgD322+/8cecPHmSYxiGu3v3rsp6SkpKOGNjY+67777jt/31118cAC4jI0PLV2VY9NWmqvj22285ExMTTiqVNv9CCELfbfrHH39wnTp14goKCpTqfhHQZ3v6+vpyS5Ys0f5FEIY+27RVq1bc7t27FbbZ2NhwO3bs0NLVUPRFy1fV9UxeXh4kEgmGDRvGb7O2toavry8yMjIA1PSM2rRpg379+vHHDBs2DCKRCOfPn1dZbmZmJqRSqUK5PXv2ROfOnflyX1R01aaqqJv/w8jIoHNr6hxdtunTp0/x3nvvYevWrbC3t9fdRRCErtrz/v37OH/+PGxtbREQEAA7OzsEBgbil19+0e0FEYAun9GAgAAcPHgQRUVFYFkWBw4cQGVlJQYNGqSz66HoBqqkqIlEIgEAfqbmOuzs7Ph9EokEtra2CvuNjIxgY2PDH6OqXBMTE7Rp06bBcl9UdNWmz/Pw4UOsXr1a8HBGS0aXbTp//nwEBATgjTfe0LLU5KKr9vz7778BACtWrEBkZCSSk5Ph7e2NoUOHKvlmvGjo8hn99ttvIZVK0a5dO5iammLGjBk4fPgwXF1dtXwVFF3zYncnKZRaysrKEBwcDHd3d6xYscLQ4rRYkpKScObMGTq2ryVYlgUAzJgxA5MnTwYAeHl5ISUlBQkJCVi3bp0hxWuxLF26FCUlJfj555/Rvn17HDlyBOPHj8d///tf9OnTx9DiUdSAWlLUpM68/XzUTWFhIb/P3t4e9+/fV9gvk8lQVFTUoHnc3t4e1dXVKCkpabDcFxVdtWkdjx8/xsiRI2FpaYnDhw/D2NhYi9KTia7a9MyZM8jNzUWbNm1gZGTED5u9+eabL7QpXVft2bFjRwCAu7u7wvZevXohPz9fK7KTiq7aNDc3F//+97+RkJCAoUOHwtPTE8uXL0e/fv0adOClkAtVUtTExcUF9vb2SElJ4beVlZXh/Pnz8Pf3BwD4+/ujpKQEmZmZ/DFnzpwBy7Lw9fVVWa6Pjw+MjY0Vys3JyUF+fj5f7ouKrtq0rpwRI0bAxMQESUlJMDMz092FEISu2jQ6OhrZ2dnIysriFwDYtGkTdu3apbsLMjC6as8uXbrAwcEBOTk5CtuvX78OZ2dnHVwJOeiqTZ8+fQoAStFRYrGYt1xRWhCG9twlkcePH3N//PEH98cff3AAuI0bN3J//PEHd/v2bY7jOO6zzz7j2rRpw/34449cdnY298Ybb3AuLi5cRUUFX8bIkSM5Ly8v7vz589wvv/zCde/enXv33Xf5/f/88w/n5ubGnT9/nt82c+ZMrnPnztyZM2e433//nfP39+f8/f31d+E6xBBtWlpa+v/t3XtIU+8fB/D3vivN7WiWli6yjIyKMCojKbLsQmkXLMmii25qQVSUfxQWFHSDqAwjAuuP0oqwC1JRURY0cwhZ0oWEsGZGFiu6CVa2lX5+f4Tn55r19dLc+db7BQc8z3n2nM95mOzD5zlnk9jYWImOjha73S4Oh0Pdvn371rUT4AW+ep/+CH/I0z2+ms/c3FwJCgqSs2fPypMnT2Tz5s3So0cPsdvtXXfxXuKLOXW5XBIVFSVxcXFSXl4udrtdcnJyRKfTyeXLl7t2AqjTmKS0wmq1CgCPzWw2i8j3R+e2bNkiYWFh4u/vL9OmTZOqqiq3Md69eyeLFy8WRVEkKChI0tPTpb6+Xj1eU1MjAMRqtaptDQ0NsmrVKunVq5cYDAaZP3++OByOrrhkr/PFnP7snACkpqami67ce3z1Pv3Rn5Kk+HI+d+3aJf379xeDwSDjx48Xm83m7cvtEr6a08ePH0tycrL07dtXDAaDjBw50uORZPpv0ImIeKlIQ0RERNRhvCeFiIiINIlJChEREWkSkxQiIiLSJCYpREREpElMUoiIiEiTmKQQERGRJjFJISIiIk1ikkJERESaxCSFiDTDYrFAp9NBp9Ph/PnzPo2lpKREjWXevHk+jYXob8UkhciLWn7ottzsdruvQ9OshIQEOBwOJCYmqm3N83br1i23vk6nEyEhIdDpdCgpKXHr31qSY7FY2pxwTJgwAQ6HAwsXLuzIZRDRb8AkhcjLmj90W26DBg3y6OdyuXwQnfb4+/sjPDwc/v7+bu0REREev7R87tw5KIrilTj8/PwQHh6OgIAAr4xPRP+OSQqRlzV/6Lbc9Ho94uPjsWbNGmRlZSE0NBQzZ84EAFRWViIxMRGKoiAsLAypqal4+/atOt6nT5+QlpYGRVFgMpmwb98+xMfHIysrS+3TWiUhODgYBQUF6n5tbS0WLlyI4OBg9O7dG0lJSXj27Jl6vLnqkJOTA5PJhJCQEKxevRpfv35V+zidTmRnZyMiIgL+/v6IiorCkSNHICKIiopCTk6OWwz379/vcCXJbDbj1KlTaGhoUNuOHj0Ks9nc7rEA4NmzZ61WueLj4zs0HhH9fkxSiHzo2LFj8PPzQ1lZGQ4dOoS6ujpMnToVo0ePRkVFBa5evYrXr1+7LTls2LABN2/exIULF3Dt2jWUlJTg7t277Trv169fMXPmTAQGBsJms6GsrAyKoiAhIcGtomO1WlFdXQ2r1Ypjx46hoKDALdFJS0tDYWEhDhw4gEePHuHw4cNQFAU6nQ4ZGRkelY/8/HxMmjQJUVFR7Z6rmJgYREZGoqioCADw/PlzlJaWIjU1td1jAd8rMy2rW/fu3UNISAgmTZrUofGIyAt8/CvMRH80s9kser1ejEajui1YsEBERCZPniyjR492679jxw6ZMWOGW1ttba0AkKqqKqmvrxc/Pz85c+aMevzdu3cSEBAg69atU9sAyLlz59zG6dmzp+Tn54uIyIkTJ2To0KHS1NSkHnc6nRIQECDFxcVq7AMHDpRv376pfVJSUmTRokUiIlJVVSUA5Pr1661e+8uXL0Wv10t5ebmIiLhcLgkNDZWCgoJfzldSUpJHe/P17N+/X6ZMmSIiItu2bZP58+fLhw8fBIBYrVa3/j169HCbd6PRKN26dWt1/IaGBomNjZU5c+ZIY2Njm2IiIu/r5ssEiehvMGXKFOTl5an7RqNR/TsmJsat74MHD2C1Wlu9z6K6uhoNDQ1wuVyIjY1V23v37o2hQ4e2K6YHDx7AbrcjMDDQrf3Lly+orq5W90eMGAG9Xq/um0wmPHz4EMD3pRu9Xo/Jkye3eo5+/fph9uzZOHr0KMaNG4eLFy/C6XQiJSWlXbG2tGzZMmzcuBFPnz5FQUEBDhw48NO+ubm5mD59ultbdnY2GhsbPfpmZGSgvr4e169fxz//sMBMpBVMUoi8zGg0/nR5o2XCAgAfP37E3LlzsXv3bo++JpOpzfdy6HQ6iIhbW8t7ST5+/IiYmBicPHnS47V9+vRR/+7evbvHuE1NTQDQphtKly9fjtTUVOTm5iI/Px+LFi2CwWBo0zW0JiQkBHPmzEFmZia+fPmCxMRE1NfXt9o3PDzcY94DAwNRV1fn1rZz504UFxfj9u3bHkkbEfkWkxQiDRkzZgyKiooQGRmJbt08/z0HDx6M7t27o7y8HAMGDAAAfPjwAY8fP3araPTp0wcOh0Pdf/LkCT5//ux2ntOnT6Nv374ICgrqUKzR0dFoamrCzZs3PSoWzWbNmgWj0Yi8vDxcvXoVpaWlHTpXSxkZGZg1axays7PdqjwdUVRUhO3bt+PKlSsYPHhwp2Mjot+LdU0iDVm9ejXev3+PxYsX486dO6iurkZxcTHS09PR2NgIRVGQmZmJDRs24MaNG6isrITFYvFYopg6dSoOHjyIe/fuoaKiAitXrnSriixduhShoaFISkqCzWZDTU0NSkpKsHbtWrx48aJNsUZGRsJsNiMjIwPnz59Xxzhz5ozaR6/Xw2KxYNOmTRgyZAjGjx/f6TlKSEjAmzdvsH379k6NU1lZibS0NGRnZ2PEiBF49eoVXr16hffv33c6RiL6PZikEGlIv379UFZWhsbGRsyYMQPR0dHIyspCcHCwmojs3bsXcXFxmDt3LqZPn46JEyd63Nuyb98+REREIC4uDkuWLMH69evdllkMBgNKS0sxYMAAJCcnY/jw4eoSSnsqK3l5eViwYAFWrVqFYcOGYcWKFfj06ZNbn8zMTLhcLqSnp3diZv5Pp9MhNDQUfn5+nRqnoqICnz9/xs6dO2EymdQtOTn5t8RJRJ2nkx8XronoPyc+Ph6jRo3C/v37fR2KB5vNhmnTpqG2thZhYWG/7GuxWFBXV+fzr8RvSYsxEf0tWEkhIq9wOp148eIFtm7dipSUlH9NUJpdunQJiqLg0qVLXo7w12w2GxRFafXmYiLqGrxxloi8orCwEJmZmRg1ahSOHz/eptfs2bMHmzdvBvD9aSZfGjt2LO7fvw8AXvvqfSL6NS73EBERkSZxuYeIiIg0iUkKERERaRKTFCIiItIkJilERESkSUxSiIiISJOYpBAREZEmMUkhIiIiTWKSQkRERJr0P0HIKMmNcfGvAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x800 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define some labels for all the cases that we want to compare\n",
    "waterfall_labels = [\n",
    "    \"A: Source SkyModel\",\n",
    "    \"B: Diffuse SkyModel\",\n",
    "    \"C: Composite SkyModel\",\n",
    "    \"A + B\",\n",
    "    \"C - (A + B)\",\n",
    "]\n",
    "\n",
    "# Get waterfalls of the complex visibility,\n",
    "# same baseline (0, 1, 'xx') as in Example 1.\n",
    "waterfall_list = [\n",
    "    sim.data_model.uvdata.get_data(bls)\n",
    "    for sim in [source_sim, diffuse_sim, composite_sim]\n",
    "]\n",
    "\n",
    "# Make A + B, and C - (A + B), add them to the list\n",
    "waterfall_list.append(waterfall_list[0] + waterfall_list[1])\n",
    "waterfall_list.append(waterfall_list[2] - waterfall_list[3])\n",
    "\n",
    "# Extent for the waterfall plot, same for all three simulations\n",
    "extent = [\n",
    "    source_sim.uvdata.freq_array[0] / 1e6,\n",
    "    source_sim.uvdata.freq_array[-1] / 1e6,\n",
    "    source_sim.uvdata.time_array[-1] - 2458101,\n",
    "    source_sim.uvdata.time_array[0] - 2458101,\n",
    "]\n",
    "\n",
    "# Plotting\n",
    "fig, ax = plt.subplots(5, 1, sharex=\"all\", sharey=\"all\", figsize=(6, 8))\n",
    "for i, waterfall in enumerate(waterfall_list):\n",
    "    im = ax[i].imshow(\n",
    "        np.abs(waterfall), aspect=\"auto\", interpolation=\"none\", extent=extent\n",
    "    )\n",
    "    fig.colorbar(im, ax=ax[i], label=\"Amplitude [Jy]\")\n",
    "    ax[i].set_title(waterfall_labels[i])\n",
    "    ax[i].set_ylabel(\"JD - 2458101\")\n",
    "ax[-1].set_xlabel(\"Frequency [MHz]\")\n",
    "fig.subplots_adjust(hspace=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulator Choices\n",
    "\n",
    "`hera_sim.visibilities` provide wraper classes for three visibility simulators, allowing them to be used in the new uniform interface. These classes are actually subclasses of the `VisibilitySimulator` class, a new abstract class that serves as a template for wrapping visibility simulators to be used with the uninorm interface, making it easy to create your own wrapper for a different simulator. \n",
    "\n",
    "Each of the simulator approaches the visibility calculation differently with its own merits.\n",
    "\n",
    "### MatVis\n",
    "`MatVis` is the wrapper for the [matvis](https://github.com/HERA-Team/matvis) package, a Python/numpy-based simulator for interferometer visibilities developed by members from HERA collaboration. It models the sky as an ensemble of point sources, each with their own frequency spectrum. The code is capable of modelling polarized visibilities and primary beams, but currently only a Stokes I sky model. \n",
    "\n",
    "[HERA Memo #98](http://reionization.org/manual_uploads/HERA098_vis_cpu_maths.pdf) describes the mathematical underlying of the `matvis` simulator although be warned that some information might have already been obsolete due to the recent rapid development of the simulator. In summary, `matvis` computes a geometric delay for each source and antenna, which is used to calculate the phase factor that is then multiplied by the source flux to obtain per-antenna visibility factor. The direction-dependent polarized antenna beam factor (Jones matrix) is computed by evaluating the complex (efield) antenna pattern at each source position on the azimutal-zenithal coordinates. This is usually done by calling the `interp` method of the UVBeam object, or by directly evaluating an analytic function in the case of using an analytic beam model. All of these are done per time at a given _single_ frequency. \n",
    "\n",
    "The `hera_sim` wrapper also adds a frequency loop on top of the internal time loop in `matvis` codes and interfaces between the `ModelData` to `matvis` input parameters, including transformation of a HEALPix SkyModel into a point source model. MPI parallelization over frequencies is also implemented on the `hera_sim` wrapper although shared memory is currently not supported.\n",
    "\n",
    "\n",
    "### UVSim\n",
    "The `UVSim` class provides a `hera_sim` wrapper to [pyuvsim](https://pyuvsim.readthedocs.io/en/latest/index.html), a comprehensive simulation package for radio interferometers that emphasizes accuracy and extensibility over speed. Under the hood, it execute `pyuvsim.uvsim.run_uvdata_uvsim` given the information in the `ModelData` object.\n",
    "\n",
    "### FFTVis\n",
    "\n",
    "`FFTVis` wraps the [fftvis](https://github.com/tyler-a-cox/fftvis) package, a Python/numpy-based simulator that has a very similar API to `matvis`, but uses the `finufft` package to perform the RIME computation. This is _very_ fast, especially for large-N arrays."
   ]
  },
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   "source": [
    "### Creating a Custom Simulator\n",
    "\n",
    "The `VisibilitySimulator` abstract base class defines the required class infrastructure for creating a custom simulator."
   ]
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     "text": [
      "Help on class VisibilitySimulator in module hera_sim.visibilities.simulators:\n",
      "\n",
      "class VisibilitySimulator(builtins.object)\n",
      " |  Base class for all hera_sim-compatible visibility simulators.\n",
      " |  \n",
      " |  To define a new simulator, make a subclass. The subclass should overwrite available\n",
      " |  class-attributes as necessary, and specify a ``__version__`` of the simulator code\n",
      " |  itself.\n",
      " |  \n",
      " |  The :meth:`simulate` abstract method *must* be overwritten in the subclass, to\n",
      " |  perform the actual simulation. The :meth:`validate` method *may* also be\n",
      " |  overwritten to validate the given `UVData` input for the particular simulator.\n",
      " |  \n",
      " |  The subclass may define any number of simulator-specific parameters as part of its\n",
      " |  init method.\n",
      " |  \n",
      " |  Finally, to enable constructing the simulator in command-line applications, a\n",
      " |  :meth:`from_yaml` method is provided. This will load a YAML file's contents as a\n",
      " |  dictionary, and then instantiate the subclass with the parameters in that dict.\n",
      " |  To enable some control over this process, the subclass can overwrite the\n",
      " |  :meth:`_from_yaml_dict` private method, which takes in the dictionary read from the\n",
      " |  YAML file, and transforms any necessary parameters before constructing the class.\n",
      " |  For example, if the class required a set of data from a file, the YAML might contain\n",
      " |  the filename itself, and in :meth:`_from_yaml_dict`, the file would be read and the\n",
      " |  data itself passed to the constructor.\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  compress_data_model(self, data_model)\n",
      " |      Temporarily delete/remove data from the model to reduce memory usage.\n",
      " |      \n",
      " |      Anything that is removed here should be restored after the simulation.\n",
      " |  \n",
      " |  estimate_memory(self, data_model: 'ModelData') -> 'float'\n",
      " |      Estimate the memory usage of the simulator in GB.\n",
      " |      \n",
      " |      This is used to estimate the amount of memory needed to run the simulator.\n",
      " |      \n",
      " |      .. note:: the default method is very much a lower bound -- just the size of the\n",
      " |        output visibilities. Each individual simulator may or may not implement a\n",
      " |        more accurate estimate.\n",
      " |  \n",
      " |  restore_data_model(self, data_model)\n",
      " |      Restore data from the model removed by :func:`compress_data_model`.\n",
      " |  \n",
      " |  simulate(self, data_model: 'ModelData') -> 'np.ndarray'\n",
      " |      Simulate the visibilities.\n",
      " |  \n",
      " |  validate(self, data_model: 'ModelData')\n",
      " |      Check that the data model complies with the assumptions of the simulator.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods defined here:\n",
      " |  \n",
      " |  from_yaml(yaml_config: 'dict | str | Path') -> 'VisibilitySimulator'\n",
      " |      Generate the simulator from a YAML file or dictionary.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset({'simulate'})\n",
      " |  \n",
      " |  diffuse_ability = False\n",
      " |  \n",
      " |  point_source_ability = True\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from hera_sim.visibilities import VisibilitySimulator\n",
    "\n",
    "# You may also try VisibilitySimulator?? which will likely open another window\n",
    "help(VisibilitySimulator)"
   ]
  },
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   "source": [
    "A custom simulator can be made by subclassing the `VisibilitySimulator` class and overriding the `simulate` method, which must take a `ModelData` object as an input and must return a Numpy array with the same shap as `UVData.data_array`. The `validate` method *may* be overriden, and `point_source_ability` and `diffuse_ability`atrributes may be set, as neccessary.\n",
    "\n",
    "Let's take a look at the codes of the `UVSim` class that wraps the pyuvsim simulator as an example. It defines a `simulate` method that calls `pyuvsim.uvsim.run_uvdata_uvsim`, passing in a UVData object from the ModelData input, and returning the `data_array` from the UVData output."
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     "text": [
      "Help on class UVSim in module hera_sim.visibilities.pyuvsim_wrapper:\n",
      "\n",
      "class UVSim(hera_sim.visibilities.simulators.VisibilitySimulator)\n",
      " |  UVSim(quiet: bool = True)\n",
      " |  \n",
      " |  A wrapper around the pyuvsim simulator.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  quiet\n",
      " |      If True, don't print anything.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      UVSim\n",
      " |      hera_sim.visibilities.simulators.VisibilitySimulator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, quiet: bool = True)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  simulate(self, data_model: hera_sim.visibilities.simulators.ModelData)\n",
      " |      Simulate the visibilities.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from hera_sim.visibilities.simulators.VisibilitySimulator:\n",
      " |  \n",
      " |  compress_data_model(self, data_model)\n",
      " |      Temporarily delete/remove data from the model to reduce memory usage.\n",
      " |      \n",
      " |      Anything that is removed here should be restored after the simulation.\n",
      " |  \n",
      " |  estimate_memory(self, data_model: 'ModelData') -> 'float'\n",
      " |      Estimate the memory usage of the simulator in GB.\n",
      " |      \n",
      " |      This is used to estimate the amount of memory needed to run the simulator.\n",
      " |      \n",
      " |      .. note:: the default method is very much a lower bound -- just the size of the\n",
      " |        output visibilities. Each individual simulator may or may not implement a\n",
      " |        more accurate estimate.\n",
      " |  \n",
      " |  restore_data_model(self, data_model)\n",
      " |      Restore data from the model removed by :func:`compress_data_model`.\n",
      " |  \n",
      " |  validate(self, data_model: 'ModelData')\n",
      " |      Check that the data model complies with the assumptions of the simulator.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods inherited from hera_sim.visibilities.simulators.VisibilitySimulator:\n",
      " |  \n",
      " |  from_yaml(yaml_config: 'dict | str | Path') -> 'VisibilitySimulator'\n",
      " |      Generate the simulator from a YAML file or dictionary.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from hera_sim.visibilities.simulators.VisibilitySimulator:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from hera_sim.visibilities.simulators.VisibilitySimulator:\n",
      " |  \n",
      " |  diffuse_ability = False\n",
      " |  \n",
      " |  point_source_ability = True\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from hera_sim.visibilities import UVSim\n",
    "\n",
    "help(UVSim)"
   ]
  }
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