CAMB.ipynb

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   "source": [
    "# CAMB: **C**ode for **A**nisotropies in the **M**icrowave **B**ackground\n",
    "\n",
    "- Código ampliamente utilizado en cosmología desarrollado por [Antony Lewis](https://inspirehep.net/authors/1000394) y [Anthony Challinor](https://inspirehep.net/authors/1031077)\n",
    "- Resuelve las ecuaciones de Boltzmann y estima observables dado un set de parámetros cosmológicos\n",
    "- Cuenta con una versión en [Python](https://camb.readthedocs.io/en/latest/) y en [Fortran](https://camb.info/readme.html)\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Versión de CAMB: 1.5.2\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Importemos nuestras librerías\n",
    "\n",
    "En caso de no haber instalado CAMB, podemos descomentar una de estas líneas:\n",
    "\"\"\"\n",
    "# !pip install camb\n",
    "# !conda install -c conda-forge camb\n",
    "\n",
    "import camb\n",
    "from camb import model\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('dark_background')\n",
    "import numpy as np\n",
    "\n",
    "print(f\"Versión de CAMB: {camb.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configurar un set de parámetros en CAMB\n",
    "\n",
    "Consideremos las estimaciones de [Planck](https://arxiv.org/abs/1807.06209)\n",
    "\n",
    "| | |\n",
    "| - | - |\n",
    "| $H_0 = (67.4 ± 0.5) km/s/Mpc $ | Hubble constant |\n",
    "| $\\Omega_m = 0.315 ± 0.0071$ | matter density parameter |\n",
    "| $\\Omega_c h^2: 0.120 ± 0.0011$ | dark matter density |\n",
    "| $\\Omega_b h^2 = 0.0224 ± 0.00011$ | baryon density |\n",
    "| $ns = 0.965 ± 0.0041$ | scalar spectral index | \n",
    "| $\\tau = 0.054 ± 0.0071$ | optical depth | "
   ]
  },
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   "source": [
    "H0 = 67.4 # km/s/Mpc\n",
    "omega_m = 0.315\n",
    "omega_ch2 = 0.120\n",
    "omega_bh2 = 0.0224\n",
    "ns = 0.965\n",
    "tau = 0.054"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para configurar un set de parámetros veremos dos formas:\n",
    "`camb.CAMBparams()` y `camb.set_params()`"
   ]
  },
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   "source": [
    "### `camb.CAMBparams()`"
   ]
  },
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     "text": [
      "Help on class CAMBparams in module camb.model:\n",
      "\n",
      "class CAMBparams(camb.baseconfig.F2003Class)\n",
      " |  CAMBparams(**kwargs)\n",
      " |  \n",
      " |  Object storing the parameters for a CAMB calculation, including cosmological parameters and\n",
      " |  settings for what to calculate. When a new object is instantiated, default parameters are set automatically.\n",
      " |  \n",
      " |  To add a new parameter, add it to the CAMBparams type in model.f90, then  edit the _fields_ list in the CAMBparams\n",
      " |  class in model.py to add the new parameter in the corresponding location of the member list. After rebuilding the\n",
      " |  python version you can then access the parameter by using params.new_parameter_name where params is a CAMBparams\n",
      " |  instance. You could also modify the wrapper functions to set the field value less directly.\n",
      " |  \n",
      " |  You can view the set of underlying parameters used by the Fortran code by printing the CAMBparams instance.\n",
      " |  In python, to set cosmology parameters it is usually best to use :meth:`set_cosmology` and\n",
      " |  equivalent methods for most other parameters. Alternatively the convenience function :func:`.camb.set_params`\n",
      " |  can construct a complete instance from a dictionary of relevant parameters.\n",
      " |  \n",
      " |  \n",
      " |  \n",
      " |  :ivar WantCls: (*boolean*) Calculate C_L\n",
      " |  :ivar WantTransfer: (*boolean*) Calculate matter transfer functions and matter power spectrum\n",
      " |  :ivar WantScalars: (*boolean*) Calculates scalar modes\n",
      " |  :ivar WantTensors: (*boolean*) Calculate tensor modes\n",
      " |  :ivar WantVectors: (*boolean*) Calculate vector modes\n",
      " |  :ivar WantDerivedParameters: (*boolean*) Calculate derived parameters\n",
      " |  :ivar Want_cl_2D_array: (*boolean*) For the C_L, include NxN matrix of all possible cross-spectra between sources\n",
      " |  :ivar Want_CMB: (*boolean*) Calculate the temperature and polarization power spectra\n",
      " |  :ivar Want_CMB_lensing: (*boolean*) Calculate the lensing potential power spectrum\n",
      " |  :ivar DoLensing: (*boolean*) Include CMB lensing\n",
      " |  :ivar NonLinear: (integer/string, one of: NonLinear_none, NonLinear_pk, NonLinear_lens, NonLinear_both) \n",
      " |  :ivar Transfer: :class:`camb.model.TransferParams`\n",
      " |  :ivar want_zstar: (*boolean*)\n",
      " |  :ivar want_zdrag: (*boolean*)\n",
      " |  :ivar min_l: (*integer*) l_min for the scalar C_L (1 or 2, L=1 dipoles are Newtonian Gauge)\n",
      " |  :ivar max_l: (*integer*) l_max for the scalar C_L\n",
      " |  :ivar max_l_tensor: (*integer*) l_max for the tensor C_L\n",
      " |  :ivar max_eta_k: (*float64*) Maximum k*eta_0 for scalar C_L, where eta_0 is the conformal time today\n",
      " |  :ivar max_eta_k_tensor: (*float64*) Maximum k*eta_0 for tensor C_L, where eta_0 is the conformal time today\n",
      " |  :ivar ombh2: (*float64*) Omega_baryon h^2\n",
      " |  :ivar omch2: (*float64*) Omega_cdm h^2\n",
      " |  :ivar omk: (*float64*) Omega_K\n",
      " |  :ivar omnuh2: (*float64*) Omega_massive_neutrino h^2\n",
      " |  :ivar H0: (*float64*) Hubble parameter is km/s/Mpc units\n",
      " |  :ivar TCMB: (*float64*) CMB temperature today in Kelvin\n",
      " |  :ivar YHe: (*float64*) Helium mass fraction\n",
      " |  :ivar num_nu_massless: (*float64*) Effective number of massless neutrinos\n",
      " |  :ivar num_nu_massive: (*integer*) Total physical (integer) number of massive neutrino species\n",
      " |  :ivar nu_mass_eigenstates: (*integer*) Number of non-degenerate mass eigenstates\n",
      " |  :ivar share_delta_neff: (*boolean*) Share the non-integer part of num_nu_massless between the eigenstates \n",
      " |  :ivar nu_mass_degeneracies: (*float64 array*) Degeneracy of each distinct eigenstate\n",
      " |  :ivar nu_mass_fractions: (*float64 array*) Mass fraction in each distinct eigenstate\n",
      " |  :ivar nu_mass_numbers: (*integer array*) Number of physical neutrinos per distinct eigenstate\n",
      " |  :ivar InitPower: :class:`camb.initialpower.InitialPower`\n",
      " |  :ivar Recomb: :class:`camb.recombination.RecombinationModel`\n",
      " |  :ivar Reion: :class:`camb.reionization.ReionizationModel`\n",
      " |  :ivar DarkEnergy: :class:`camb.dark_energy.DarkEnergyModel`\n",
      " |  :ivar NonLinearModel: :class:`camb.nonlinear.NonLinearModel`\n",
      " |  :ivar Accuracy: :class:`camb.model.AccuracyParams`\n",
      " |  :ivar SourceTerms: :class:`camb.model.SourceTermParams`\n",
      " |  :ivar z_outputs: (*float64 array*) redshifts to always calculate BAO output parameters\n",
      " |  :ivar scalar_initial_condition: (integer/string, one of: initial_vector, initial_adiabatic, initial_iso_CDM, initial_iso_baryon, initial_iso_neutrino, initial_iso_neutrino_vel) \n",
      " |  :ivar InitialConditionVector: (*float64 array*) if scalar_initial_condition is initial_vector, the vector of initial condition amplitudes\n",
      " |  :ivar OutputNormalization: (*integer*) If non-zero, multipole to normalize the C_L at\n",
      " |  :ivar Alens: (*float64*) non-physical scaling amplitude for the CMB lensing spectrum power\n",
      " |  :ivar MassiveNuMethod: (integer/string, one of: Nu_int, Nu_trunc, Nu_approx, Nu_best) \n",
      " |  :ivar DoLateRadTruncation: (*boolean*) If true, use smooth approx to radiation perturbations after decoupling on small scales, saving evolution of irrelevant oscillatory multipole equations\n",
      " |  :ivar Evolve_baryon_cs: (*boolean*) Evolve a separate equation for the baryon sound speed rather than using background approximation\n",
      " |  :ivar Evolve_delta_xe: (*boolean*) Evolve ionization fraction perturbations\n",
      " |  :ivar Evolve_delta_Ts: (*boolean*) Evolve the spin temperature perturbation (for 21cm)\n",
      " |  :ivar Do21cm: (*boolean*) 21cm is not yet implemented via the python wrapper\n",
      " |  :ivar transfer_21cm_cl: (*boolean*) Get 21cm C_L at a given fixed redshift\n",
      " |  :ivar Log_lvalues: (*boolean*) Use log spacing for sampling in L\n",
      " |  :ivar use_cl_spline_template: (*boolean*) When interpolating use a fiducial spectrum shape to define ratio to spline\n",
      " |  :ivar min_l_logl_sampling: (*integer*) Minimum L to use log sampling for L\n",
      " |  :ivar SourceWindows: array of :class:`camb.sources.SourceWindow`\n",
      " |  :ivar CustomSources: :class:`camb.model.CustomSources`\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      CAMBparams\n",
      " |      camb.baseconfig.F2003Class\n",
      " |      camb.baseconfig.CAMB_Structure\n",
      " |      _ctypes.Structure\n",
      " |      _ctypes._CData\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  clear_custom_scalar_sources(self)\n",
      " |  \n",
      " |  diff(self, params)\n",
      " |      Print differences between this set of parameters and params\n",
      " |      \n",
      " |      :param params: another CAMBparams instance\n",
      " |  \n",
      " |  f_PrimordialPower(self, *args)\n",
      " |  \n",
      " |  f_SetCustomSourcesFunc(self, *args)\n",
      " |  \n",
      " |  f_SetNeutrinoHierarchy(self, *args)\n",
      " |  \n",
      " |  f_Validate(self, *args)\n",
      " |  \n",
      " |  get_DH(self, ombh2=None, delta_neff=None)\n",
      " |      Get deuterium ration D/H by intepolation using the\n",
      " |      :class:`.bbn.BBNPredictor` instance passed to :meth:`set_cosmology`\n",
      " |      (or the default one, if `Y_He` has not been set).\n",
      " |      \n",
      " |      :param ombh2: :math:`\\Omega_b h^2` (default: value passed to :meth:`set_cosmology`)\n",
      " |      :param delta_neff:  additional :math:`N_{\\rm eff}` relative to standard value (of 3.044)\n",
      " |                         (default: from values passed to :meth:`set_cosmology`)\n",
      " |      :return: BBN helium nucleon fraction D/H\n",
      " |  \n",
      " |  get_Y_p(self, ombh2=None, delta_neff=None)\n",
      " |      Get BBN helium nucleon fraction (NOT the same as the mass fraction Y_He) by intepolation using the\n",
      " |      :class:`.bbn.BBNPredictor` instance passed to :meth:`set_cosmology`\n",
      " |      (or the default one, if `Y_He` has not been set).\n",
      " |      \n",
      " |      :param ombh2: :math:`\\Omega_b h^2` (default: value passed to :meth:`set_cosmology`)\n",
      " |      :param delta_neff:  additional :math:`N_{\\rm eff}` relative to standard value (of 3.044)\n",
      " |                         (default: from values passed to :meth:`set_cosmology`)\n",
      " |      :return:  :math:`Y_p^{\\rm BBN}` helium nucleon fraction predicted by BBN.\n",
      " |  \n",
      " |  get_custom_source_names(self)\n",
      " |  \n",
      " |  get_zre(self)\n",
      " |  \n",
      " |  get_zrei = get_zre(self)\n",
      " |  \n",
      " |  primordial_power(self, k, ix)\n",
      " |  \n",
      " |  scalar_power(self, k)\n",
      " |      Get the primordial scalar curvature power spectrum at :math:`k`\n",
      " |      \n",
      " |      :param k: wavenumber :math:`k` (in :math:`{\\rm Mpc}^{-1}` units)\n",
      " |      :return: power spectrum at :math:`k`\n",
      " |  \n",
      " |  set_H0_for_theta(self, theta, cosmomc_approx=False, theta_H0_range=(10, 100), est_H0=67.0, iteration_threshold=8)\n",
      " |      Set H0 to give a specified value of the acoustic angular scale parameter theta.\n",
      " |      \n",
      " |      :param theta: value of :math:`r_s/D_M` at redshift :math:`z_\\star`\n",
      " |      :param cosmomc_approx: if true, use approximate fitting formula for :math:`z_\\star`,\n",
      " |                             if false do full numerical calculation\n",
      " |      :param theta_H0_range: min, max iterval to search for H0 (in km/s/Mpc)\n",
      " |      :param est_H0: an initial guess for H0 in km/s/Mpc, used in the case cosmomc_approx=False.\n",
      " |      :param iteration_threshold: difference in H0 from est_H0 for which to iterate, used for cosmomc_approx=False\n",
      " |  \n",
      " |  set_accuracy(self, AccuracyBoost=1.0, lSampleBoost=1.0, lAccuracyBoost=1.0, DoLateRadTruncation=True, min_l_logl_sampling=None)\n",
      " |      Set parameters determining overall calculation accuracy (large values may give big slow down).\n",
      " |      For finer control you can set individual accuracy parameters by changing CAMBParams.Accuracy\n",
      " |      (:class:`.model.AccuracyParams`) .\n",
      " |      \n",
      " |      :param AccuracyBoost: increase AccuracyBoost to decrease integration step size, increase density of k\n",
      " |                            sampling, etc.\n",
      " |      :param lSampleBoost: increase lSampleBoost to increase density of L sampling for CMB\n",
      " |      :param lAccuracyBoost: increase lAccuracyBoost to increase the maximum L included in the Boltzmann hierarchies\n",
      " |      :param DoLateRadTruncation: If True, use approximation to radiation perturbation evolution at late times\n",
      " |      :param min_l_logl_sampling: at L>min_l_logl_sampling uses sparser log sampling for L interpolation;\n",
      " |                                  increase above 5000 for better accuracy at L > 5000\n",
      " |      :return: self\n",
      " |  \n",
      " |  set_classes(self, dark_energy_model=None, initial_power_model=None, non_linear_model=None, recombination_model=None, reionization_model=None)\n",
      " |      Change the classes used to implement parts of the model.\n",
      " |      \n",
      " |      :param dark_energy_model: 'fluid', 'ppf', or name of a DarkEnergyModel class\n",
      " |      :param initial_power_model: name of an InitialPower class\n",
      " |      :param non_linear_model: name of a NonLinearModel class\n",
      " |      :param recombination_model: name of RecombinationModel class\n",
      " |      :param reionization_model: name of a ReionizationModel class\n",
      " |  \n",
      " |  set_cosmology(self, H0: Optional[float] = None, ombh2=0.022, omch2=0.12, omk=0.0, cosmomc_theta: Optional[float] = None, thetastar: Optional[float] = None, neutrino_hierarchy: Union[str, int] = 'degenerate', num_massive_neutrinos=1, mnu=0.06, nnu=3.044, YHe: Optional[float] = None, meffsterile=0.0, standard_neutrino_neff=3.044, TCMB=2.7255, tau: Optional[float] = None, zrei: Optional[float] = None, Alens=1.0, bbn_predictor: Union[NoneType, str, camb.bbn.BBNPredictor] = None, theta_H0_range=(10, 100))\n",
      " |      Sets cosmological parameters in terms of physical densities and parameters (e.g. as used in Planck analyses).\n",
      " |      Default settings give a single distinct neutrino mass eigenstate, by default one neutrino with mnu = 0.06eV.\n",
      " |      Set the neutrino_hierarchy parameter to normal or inverted to use a two-eigenstate model that is a good\n",
      " |      approximation to the known mass splittings seen in oscillation measurements.\n",
      " |      For more fine-grained control can set the neutrino parameters directly rather than using this function.\n",
      " |      \n",
      " |      Instead of setting the Hubble parameter directly, you can instead set the acoustic scale parameter\n",
      " |      (cosmomc_theta, which is based on a fitting forumula for simple models, or thetastar, which is numerically\n",
      " |      calculated more generally). Note that you must have already set the dark energy model, you can't use\n",
      " |      set_cosmology with theta and then change the background evolution (which would change theta at the calculated\n",
      " |      H0 value). Likewise the dark energy model cannot depend explicitly on H0.\n",
      " |      \n",
      " |      :param H0: Hubble parameter today in km/s/Mpc. Can leave unset and instead set thetastar or cosmomc_theta\n",
      " |                (which solves for the required H0).\n",
      " |      :param ombh2: physical density in baryons\n",
      " |      :param omch2:  physical density in cold dark matter\n",
      " |      :param omk: Omega_K curvature parameter\n",
      " |      :param cosmomc_theta: The approximate CosmoMC theta parameter :math:`\\theta_{\\rm MC}`. The angular\n",
      " |                            diamter distance is calculated numerically, but the redshift :math:`z_\\star`\n",
      " |                            is calculated using an approximate (quite accurate but non-general) fitting formula.\n",
      " |                            Leave unset to use H0 or thetastar.\n",
      " |      :param thetastar: The angular acoustic scale parameter :math:`\\theta_\\star = r_s(z_*)/D_M(z_*)`, defined as\n",
      " |                  the ratio of the photon-baryon sound horizon :math:`r_s` to the angular diameter\n",
      " |                  distance :math:`D_M`, where both quantities are evaluated at :math:`z_*`, the redshift at\n",
      " |                  which the optical depth (excluding reionization) is unity. Leave unset to use H0 or cosmomc_theta.\n",
      " |      :param neutrino_hierarchy: 'degenerate', 'normal', or 'inverted' (1 or 2 eigenstate approximation)\n",
      " |      :param num_massive_neutrinos:  number of massive neutrinos\n",
      " |      :param mnu: sum of neutrino masses (in eV). Omega_nu is calculated approximately from this assuming neutrinos\n",
      " |             non-relativistic today; i.e. here is defined as a direct proxy for Omega_nu. Internally the actual\n",
      " |             physical mass is calculated from the Omega_nu accounting for small mass-dependent velocity corrections\n",
      " |             but neglecting spectral distortions to the neutrino distribution.\n",
      " |             Set the neutrino field values directly if you need finer control or more complex neutrino models.\n",
      " |      :param nnu: N_eff, effective relativistic degrees of freedom\n",
      " |      :param YHe: Helium mass fraction. If None, set from BBN consistency.\n",
      " |      :param meffsterile: effective mass of sterile neutrinos\n",
      " |      :param standard_neutrino_neff:  default value for N_eff in standard cosmology (non-integer to allow for partial\n",
      " |              heating of neutrinos at electron-positron annihilation and QED effects)\n",
      " |      :param TCMB: CMB temperature (in Kelvin)\n",
      " |      :param tau: optical depth; if None and zrei is None, current Reion settings are not changed\n",
      " |      :param zrei: reionization mid-point optical depth (set tau=None to use this)\n",
      " |      :param Alens: (non-physical) scaling of the lensing potential compared to prediction\n",
      " |      :param bbn_predictor: :class:`.bbn.BBNPredictor` instance used to get YHe from BBN consistency if YHe is None,\n",
      " |       or name of a BBN predictor class, or file name of an interpolation table\n",
      " |      :param theta_H0_range: if thetastar or cosmomc_theta is specified, the min, max interval of H0 values to map to;\n",
      " |        if H0 is outside this range it will raise an exception.\n",
      " |  \n",
      " |  set_custom_scalar_sources(self, custom_sources, source_names=None, source_ell_scales=None, frame='CDM', code_path=None)\n",
      " |      Set custom sources for angular power spectrum using camb.symbolic sympy expressions.\n",
      " |      \n",
      " |      :param custom_sources: list of sympy expressions for the angular power spectrum sources\n",
      " |      :param source_names: optional list of string naes for the sources\n",
      " |      :param source_ell_scales: list or dictionary of scalings for each source name, where for integer entry n,\n",
      " |          the source for multipole :math:`\\ell` is scalled by :math:`\\sqrt{(\\ell+n)!/(\\ell-n)!}`,\n",
      " |          i.e. :math:`n=2` for a new polarization-like source.\n",
      " |      :param frame: if the source is not gauge invariant, frame in which to interpret result\n",
      " |      :param code_path: optional path for output of source code for CAMB f90 source function\n",
      " |  \n",
      " |  set_dark_energy(self, w=-1.0, cs2=1.0, wa=0, dark_energy_model='fluid')\n",
      " |      Set dark energy parameters (use set_dark_energy_w_a to set w(a) from numerical table instead)\n",
      " |      To use a custom dark energy model, assign the class instance to the DarkEnergy field instead.\n",
      " |      \n",
      " |      :param w: :math:`w\\equiv p_{\\rm de}/\\rho_{\\rm de}`, assumed constant\n",
      " |      :param wa: evolution of w (for dark_energy_model=ppf)\n",
      " |      :param cs2: rest-frame sound speed squared of dark energy fluid\n",
      " |      :param dark_energy_model: model to use ('fluid' or 'ppf'), default is 'fluid'\n",
      " |      :return: self\n",
      " |  \n",
      " |  set_dark_energy_w_a(self, a, w, dark_energy_model='fluid')\n",
      " |      Set the dark energy equation of state from tabulated values (which are cubic spline interpolated).\n",
      " |      \n",
      " |      :param a: array of sampled a = 1/(1+z) values\n",
      " |      :param w: array of w(a)\n",
      " |      :param dark_energy_model:  model to use ('fluid' or 'ppf'), default is 'fluid'\n",
      " |      :return: self\n",
      " |  \n",
      " |  set_for_lmax(self, lmax, max_eta_k=None, lens_potential_accuracy=0, lens_margin=150, k_eta_fac=2.5, lens_k_eta_reference=18000.0, nonlinear=None)\n",
      " |      Set parameters to get CMB power spectra accurate to specific a l_lmax.\n",
      " |      Note this does not fix the actual output L range, spectra may be calculated above l_max\n",
      " |      (but may not be accurate there). To fix the l_max for output arrays use the optional input argument\n",
      " |      to :meth:`.results.CAMBdata.get_cmb_power_spectra` etc.\n",
      " |      \n",
      " |      :param lmax: :math:`\\ell_{\\rm max}` you want\n",
      " |      :param max_eta_k: maximum value of :math:`k \\eta_0\\approx k\\chi_*` to use, which indirectly sets k_max.\n",
      " |                        If None, sensible value set automatically.\n",
      " |      :param lens_potential_accuracy: Set to 1 or higher if you want to get the lensing potential accurate\n",
      " |                                      (1 is only Planck-level accuracy)\n",
      " |      :param lens_margin: the :math:`\\Delta \\ell_{\\rm max}` to use to ensure lensed :math:`C_\\ell` are correct\n",
      " |                          at :math:`\\ell_{\\rm max}`\n",
      " |      :param k_eta_fac:  k_eta_fac default factor for setting max_eta_k = k_eta_fac*lmax if max_eta_k=None\n",
      " |      :param lens_k_eta_reference:  value of max_eta_k to use when lens_potential_accuracy>0; use\n",
      " |                                    k_eta_max = lens_k_eta_reference*lens_potential_accuracy\n",
      " |      :param nonlinear: use non-linear power spectrum; if None, sets nonlinear if lens_potential_accuracy>0 otherwise\n",
      " |                        preserves current setting\n",
      " |      :return: self\n",
      " |  \n",
      " |  set_initial_power(self, initial_power_params)\n",
      " |      Set the InitialPower primordial power spectrum parameters\n",
      " |      \n",
      " |      :param initial_power_params: :class:`.initialpower.InitialPowerLaw`\n",
      " |                                   or :class:`.initialpower.SplinedInitialPower` instance\n",
      " |      :return: self\n",
      " |  \n",
      " |  set_initial_power_function(self, P_scalar, P_tensor=None, kmin=1e-06, kmax=100.0, N_min=200, rtol=5e-05, effective_ns_for_nonlinear=None, args=())\n",
      " |      Set the initial power spectrum from a function P_scalar(k, \\*args), and optionally also the tensor spectrum.\n",
      " |      The function is called to make a pre-computed array which is then interpolated inside CAMB. The sampling in k\n",
      " |      is set automatically so that the spline is accurate, but you may also need to increase other\n",
      " |      accuracy parameters.\n",
      " |      \n",
      " |      :param P_scalar: function returning normalized initial scalar curvature power as function of k (in Mpc^{-1})\n",
      " |      :param P_tensor: optional function returning normalized initial tensor power spectrum\n",
      " |      :param kmin: minimum wavenumber to compute\n",
      " |      :param kmax: maximum wavenumber to compute\n",
      " |      :param N_min: minimum number of spline points for the pre-computation\n",
      " |      :param rtol: relative tolerance for deciding how many points are enough\n",
      " |      :param effective_ns_for_nonlinear: an effective n_s for use with approximate non-linear corrections\n",
      " |      :param args: optional list of arguments passed to P_scalar (and P_tensor)\n",
      " |      :return: self\n",
      " |  \n",
      " |  set_initial_power_table(self, k, pk=None, pk_tensor=None, effective_ns_for_nonlinear=None)\n",
      " |      Set a general intial power spectrum from tabulated values. It's up to you to ensure the sampling\n",
      " |      of the k values is high enough that it can be interpolated accurately.\n",
      " |      \n",
      " |      :param k: array of k values (Mpc^{-1})\n",
      " |      :param pk: array of primordial curvature perturbation power spectrum values P(k_i)\n",
      " |      :param pk_tensor: array of tensor spectrum values\n",
      " |      :param effective_ns_for_nonlinear: an effective n_s for use with approximate non-linear corrections\n",
      " |  \n",
      " |  set_matter_power(self, redshifts=(0.0,), kmax=1.2, k_per_logint=None, nonlinear=None, accurate_massive_neutrino_transfers=False, silent=False)\n",
      " |      Set parameters for calculating matter power spectra and transfer functions.\n",
      " |      \n",
      " |      :param redshifts: array of redshifts to calculate\n",
      " |      :param kmax: maximum k to calculate (where k is just k, not k/h)\n",
      " |      :param k_per_logint: minimum number of k steps per log k. Set to zero to use default optimized spacing.\n",
      " |      :param nonlinear: if None, uses existing setting, otherwise boolean for whether to use non-linear matter power.\n",
      " |      :param accurate_massive_neutrino_transfers: if you want the massive neutrino transfers accurately\n",
      " |      :param silent: if True, don't give warnings about sort order\n",
      " |      :return: self\n",
      " |  \n",
      " |  set_nonlinear_lensing(self, nonlinear)\n",
      " |      Settings for whether or not to use non-linear corrections for the CMB lensing potential.\n",
      " |      Note that set_for_lmax also sets lensing to be non-linear if lens_potential_accuracy>0\n",
      " |      \n",
      " |      :param nonlinear: true to use non-linear corrections\n",
      " |  \n",
      " |  tensor_power(self, k)\n",
      " |      Get the primordial tensor curvature power spectrum at :math:`k`\n",
      " |      \n",
      " |      :param k:  wavenumber :math:`k` (in :math:`{\\rm Mpc}^{-1}` units)\n",
      " |      :return: tensor power spectrum at :math:`k`\n",
      " |  \n",
      " |  validate(self)\n",
      " |      Do some quick tests for sanity\n",
      " |      \n",
      " |      :return: True if OK\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties defined here:\n",
      " |  \n",
      " |  N_eff\n",
      " |      :return: Effective number of degrees of freedom in relativistic species at early times.\n",
      " |  \n",
      " |  omegab\n",
      " |  \n",
      " |  omegac\n",
      " |  \n",
      " |  omegam\n",
      " |  \n",
      " |  omeganu\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  Accuracy\n",
      " |  \n",
      " |  Alens\n",
      " |  \n",
      " |  CustomSources\n",
      " |  \n",
      " |  DarkEnergy\n",
      " |  \n",
      " |  Do21cm\n",
      " |  \n",
      " |  DoLateRadTruncation\n",
      " |  \n",
      " |  DoLensing\n",
      " |  \n",
      " |  Evolve_baryon_cs\n",
      " |  \n",
      " |  Evolve_delta_Ts\n",
      " |  \n",
      " |  Evolve_delta_xe\n",
      " |  \n",
      " |  H0\n",
      " |  \n",
      " |  InitPower\n",
      " |  \n",
      " |  InitialConditionVector\n",
      " |  \n",
      " |  Log_lvalues\n",
      " |  \n",
      " |  MassiveNuMethod\n",
      " |  \n",
      " |  NonLinear\n",
      " |  \n",
      " |  NonLinearModel\n",
      " |  \n",
      " |  OutputNormalization\n",
      " |  \n",
      " |  Recomb\n",
      " |  \n",
      " |  Reion\n",
      " |  \n",
      " |  SourceTerms\n",
      " |  \n",
      " |  SourceWindows\n",
      " |  \n",
      " |  TCMB\n",
      " |  \n",
      " |  Transfer\n",
      " |  \n",
      " |  WantCls\n",
      " |  \n",
      " |  WantDerivedParameters\n",
      " |  \n",
      " |  WantScalars\n",
      " |  \n",
      " |  WantTensors\n",
      " |  \n",
      " |  WantTransfer\n",
      " |  \n",
      " |  WantVectors\n",
      " |  \n",
      " |  Want_CMB\n",
      " |  \n",
      " |  Want_CMB_lensing\n",
      " |  \n",
      " |  Want_cl_2D_array\n",
      " |  \n",
      " |  YHe\n",
      " |  \n",
      " |  h\n",
      " |  \n",
      " |  max_eta_k\n",
      " |  \n",
      " |  max_eta_k_tensor\n",
      " |  \n",
      " |  max_l\n",
      " |  \n",
      " |  max_l_tensor\n",
      " |  \n",
      " |  min_l\n",
      " |  \n",
      " |  min_l_logl_sampling\n",
      " |  \n",
      " |  nu_mass_degeneracies\n",
      " |  \n",
      " |  nu_mass_eigenstates\n",
      " |  \n",
      " |  nu_mass_fractions\n",
      " |  \n",
      " |  nu_mass_numbers\n",
      " |  \n",
      " |  num_nu_massive\n",
      " |  \n",
      " |  num_nu_massless\n",
      " |  \n",
      " |  ombh2\n",
      " |  \n",
      " |  omch2\n",
      " |  \n",
      " |  omk\n",
      " |  \n",
      " |  omnuh2\n",
      " |  \n",
      " |  scalar_initial_condition\n",
      " |  \n",
      " |  share_delta_neff\n",
      " |  \n",
      " |  transfer_21cm_cl\n",
      " |  \n",
      " |  use_cl_spline_template\n",
      " |  \n",
      " |  want_zdrag\n",
      " |  \n",
      " |  want_zstar\n",
      " |  \n",
      " |  z_outputs\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from camb.baseconfig.F2003Class:\n",
      " |  \n",
      " |  __copy__ = copy(self)\n",
      " |  \n",
      " |  __del__(self)\n",
      " |  \n",
      " |  call_method(self, tag, extra_args=(), args=(), restype=None, nopass=False, allow_inherit=True)\n",
      " |  \n",
      " |  copy(self)\n",
      " |      Make independent copy of this object.\n",
      " |      \n",
      " |      :return: deep copy of self\n",
      " |  \n",
      " |  fortran_self = None\n",
      " |  replace(self, instance)\n",
      " |      Replace the content of this class with another instance, doing a deep copy (in Fortran)\n",
      " |      \n",
      " |      :param instance: instance of the same class to replace this instance with\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods inherited from camb.baseconfig.F2003Class:\n",
      " |  \n",
      " |  import_method(tag, extra_args=(), restype=None, nopass=False, allow_inherit=True) from camb.baseconfig.CAMBStructureMeta\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods inherited from camb.baseconfig.F2003Class:\n",
      " |  \n",
      " |  __new__(cls, *args, **kwargs)\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  make_class_named(name, base_class=None)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from camb.baseconfig.CAMB_Structure:\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods inherited from camb.baseconfig.CAMB_Structure:\n",
      " |  \n",
      " |  get_all_fields() from camb.baseconfig.CAMBStructureMeta\n",
      " |      # noinspection PyUnresolvedReferences\n",
      " |  \n",
      " |  get_valid_field_names() from camb.baseconfig.CAMBStructureMeta\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from camb.baseconfig.CAMB_Structure:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from _ctypes._CData:\n",
      " |  \n",
      " |  __ctypes_from_outparam__(...)\n",
      " |  \n",
      " |  __hash__(self, /)\n",
      " |      Return hash(self).\n",
      " |  \n",
      " |  __reduce__(...)\n",
      " |      Helper for pickle.\n",
      " |  \n",
      " |  __setstate__(...)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(camb.CAMBparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class: <CAMBparams>\n",
       " WantCls = True\n",
       " WantTransfer = False\n",
       " WantScalars = True\n",
       " WantTensors = False\n",
       " WantVectors = False\n",
       " WantDerivedParameters = True\n",
       " Want_cl_2D_array = True\n",
       " Want_CMB = True\n",
       " Want_CMB_lensing = True\n",
       " DoLensing = True\n",
       " NonLinear = NonLinear_none\n",
       " Transfer: <TransferParams>\n",
       "   high_precision = False\n",
       "   accurate_massive_neutrinos = False\n",
       "   kmax = 0.9\n",
       "   k_per_logint = 0\n",
       "   PK_num_redshifts = 1\n",
       "   PK_redshifts = [0.0]\n",
       " want_zstar = False\n",
       " want_zdrag = False\n",
       " min_l = 2\n",
       " max_l = 2500\n",
       " max_l_tensor = 600\n",
       " max_eta_k = 5000.0\n",
       " max_eta_k_tensor = 1200.0\n",
       " ombh2 = 0.0224\n",
       " omch2 = 0.12\n",
       " omk = 0.0\n",
       " omnuh2 = 0.000644866570625114\n",
       " H0 = 67.4\n",
       " TCMB = 2.7255\n",
       " YHe = 0.24586829360147697\n",
       " num_nu_massless = 2.0293333333333337\n",
       " num_nu_massive = 1\n",
       " nu_mass_eigenstates = 1\n",
       " share_delta_neff = False\n",
       " nu_mass_degeneracies = [1.0146666666666666]\n",
       " nu_mass_fractions = [1.0]\n",
       " nu_mass_numbers = [1]\n",
       " InitPower: <InitialPowerLaw>\n",
       "   tensor_parameterization = tensor_param_rpivot\n",
       "   ns = 0.96\n",
       "   nrun = 0.0\n",
       "   nrunrun = 0.0\n",
       "   nt = -0.0\n",
       "   ntrun = -0.0\n",
       "   r = 0.0\n",
       "   pivot_scalar = 0.05\n",
       "   pivot_tensor = 0.05\n",
       "   As = 2e-09\n",
       "   At = 1.0\n",
       " Recomb: <Recfast>\n",
       "   min_a_evolve_Tm = 0.0011098779505118728\n",
       "   RECFAST_fudge = 1.125\n",
       "   RECFAST_fudge_He = 0.86\n",
       "   RECFAST_Heswitch = 6\n",
       "   RECFAST_Hswitch = True\n",
       "   AGauss1 = -0.14\n",
       "   AGauss2 = 0.079\n",
       "   zGauss1 = 7.28\n",
       "   zGauss2 = 6.73\n",
       "   wGauss1 = 0.18\n",
       "   wGauss2 = 0.33\n",
       " Reion: <TanhReionization>\n",
       "   Reionization = True\n",
       "   use_optical_depth = True\n",
       "   redshift = 10.0\n",
       "   optical_depth = 0.054\n",
       "   fraction = -1.0\n",
       "   include_helium_fullreion = True\n",
       "   helium_redshift = 3.5\n",
       "   helium_delta_redshift = 0.4\n",
       "   helium_redshiftstart = 5.5\n",
       "   tau_solve_accuracy_boost = 1.0\n",
       "   timestep_boost = 1.0\n",
       "   max_redshift = 50.0\n",
       "   delta_redshift = 0.5\n",
       " DarkEnergy: <DarkEnergyFluid>\n",
       "   w = -1.0\n",
       "   wa = 0.0\n",
       "   cs2 = 1.0\n",
       "   use_tabulated_w = False\n",
       " NonLinearModel: <Halofit>\n",
       "   Min_kh_nonlinear = 0.005\n",
       "   halofit_version = mead2020\n",
       "   HMCode_A_baryon = 3.13\n",
       "   HMCode_eta_baryon = 0.603\n",
       "   HMCode_logT_AGN = 7.8\n",
       " Accuracy: <AccuracyParams>\n",
       "   AccuracyBoost = 1.0\n",
       "   lSampleBoost = 1.0\n",
       "   lAccuracyBoost = 1.0\n",
       "   AccuratePolarization = True\n",
       "   AccurateBB = False\n",
       "   AccurateReionization = True\n",
       "   TimeStepBoost = 1.0\n",
       "   BackgroundTimeStepBoost = 1.0\n",
       "   IntTolBoost = 1.0\n",
       "   SourcekAccuracyBoost = 1.0\n",
       "   IntkAccuracyBoost = 1.0\n",
       "   TransferkBoost = 1.0\n",
       "   NonFlatIntAccuracyBoost = 1.0\n",
       "   BessIntBoost = 1.0\n",
       "   LensingBoost = 1.0\n",
       "   NonlinSourceBoost = 1.0\n",
       "   BesselBoost = 1.0\n",
       "   LimberBoost = 1.0\n",
       "   SourceLimberBoost = 1.0\n",
       "   KmaxBoost = 1.0\n",
       "   neutrino_q_boost = 1.0\n",
       " SourceTerms: <SourceTermParams>\n",
       "   limber_windows = True\n",
       "   limber_phi_lmin = 100\n",
       "   counts_density = True\n",
       "   counts_redshift = True\n",
       "   counts_lensing = False\n",
       "   counts_velocity = True\n",
       "   counts_radial = False\n",
       "   counts_timedelay = True\n",
       "   counts_ISW = True\n",
       "   counts_potential = True\n",
       "   counts_evolve = False\n",
       "   line_phot_dipole = False\n",
       "   line_phot_quadrupole = False\n",
       "   line_basic = True\n",
       "   line_distortions = True\n",
       "   line_extra = False\n",
       "   line_reionization = False\n",
       "   use_21cm_mK = True\n",
       " z_outputs = []\n",
       " scalar_initial_condition = initial_adiabatic\n",
       " InitialConditionVector = []\n",
       " OutputNormalization = 1\n",
       " Alens = 1.0\n",
       " MassiveNuMethod = Nu_best\n",
       " DoLateRadTruncation = True\n",
       " Evolve_baryon_cs = False\n",
       " Evolve_delta_xe = False\n",
       " Evolve_delta_Ts = False\n",
       " Do21cm = False\n",
       " transfer_21cm_cl = False\n",
       " Log_lvalues = False\n",
       " use_cl_spline_template = True\n",
       " min_l_logl_sampling = 5000\n",
       " SourceWindows = []\n",
       " CustomSources: <CustomSources>\n",
       "   num_custom_sources = 0\n",
       "   c_source_func = None\n",
       "   custom_source_ell_scales = []\n",
       " "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pars = camb.CAMBparams()  # objeto en donde guardaremos los parámetros\n",
    "\n",
    "# configuremos los parámetros\n",
    "# los parámetros no especificados mantiene los valores por default\n",
    "pars.set_cosmology(H0=H0, ombh2=omega_bh2, omch2=omega_ch2, tau=tau)\n",
    "\n",
    "# revisar el output para ver los distintos parámetros que se pueden configurar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "si quiero cambiar un parámetro dentro de una \"categoría\", debo especificar el \n",
    "nombre de la categoría y luego establecer el parámetro deseado\n",
    "\n",
    "A modo de ejemplo, si quiero cambiar el parámetro de la ecuación de estado de la\n",
    "energía oscura, w:\n",
    "\"\"\"\n",
    "\n",
    "pars.set_dark_energy(w=-1);  # ; para no imprimir el output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dado el set de parámetros, pedimos a CAMB obtener los resultados\n",
    "results = camb.get_results(pars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### `camb.set_params()`\n",
    "función útil que permite hacer cambios de forma más rápida\n",
    "\n",
    "Permite cambiar parámetros que estén dentro de categorías sin tener que acceder\n",
    "a ellas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pars2 = camb.set_params(H0=67.4, w=-0.1, cs2=0.1)\n",
    "results2 = camb.get_results(pars2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \n",
    "Una vez pedido `get_results`, podemos pedir a CAMB observables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Evolución de las densidades $\\Omega$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method get_Omega in module camb.results:\n",
      "\n",
      "get_Omega(var, z=0) method of camb.results.CAMBdata instance\n",
      "    Get density relative to critical density of variables var\n",
      "    \n",
      "    :param var: one of 'K', 'cdm', 'baryon', 'photon', 'neutrino' (massless), 'nu' (massive neutrinos), 'de'\n",
      "    :param z: redshift\n",
      "    :return:  :math:`\\Omega_i(a)`\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(results.get_Omega)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def a_to_z(a):\n",
    "    return 1/a - 1\n",
    "\n",
    "a_s = np.linspace(1, 1e-4, 10_000)\n",
    "zs = a_to_z(a_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dark energy\n",
    "Omega_de = results.get_Omega('de', z=zs)\n",
    "\n",
    "# matter\n",
    "Omega_cdm = results.get_Omega('cdm', z=zs)\n",
    "Omega_b = results.get_Omega('baryon', z=zs)\n",
    "Omega_n = results.get_Omega('neutrino', z=zs)\n",
    "Omega_mn = results.get_Omega('nu', z=zs)\n",
    "\n",
    "Omega_m = Omega_cdm + Omega_b + Omega_n + Omega_mn\n",
    "\n",
    "# radiation\n",
    "Omega_r = results.get_Omega('photon', z=zs)\n",
    "\n",
    "# curvature\n",
    "Omega_K = results.get_Omega('K', z=zs)\n",
    "\n",
    "# total\n",
    "Omega_tot = Omega_de + Omega_m + Omega_r + Omega_K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.semilogx(a_s, Omega_m, color='b', label=r'$\\Omega_m$')\n",
    "plt.semilogx(a_s, Omega_de, color='r', label=r'$\\Omega_{\\Lambda}$')\n",
    "plt.semilogx(a_s, Omega_r, color='orange', label=r'$\\Omega_r$')\n",
    "plt.semilogx(a_s, Omega_K, color='grey', label=r'$\\Omega_K$')\n",
    "plt.semilogx(a_s, Omega_tot, color='grey', ls='--', label=r'$\\Omega_{tot}$')\n",
    "\n",
    "plt.xlabel('a', fontsize=16)\n",
    "plt.ylabel(r'$\\Omega$', fontsize=16)\n",
    "plt.grid(alpha=0.5, ls=':')\n",
    "plt.legend(fontsize=16, loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Valores actuales\n",
      "\n",
      "Ωm: 3.15e-01\n",
      "Ωde: 6.85e-01\n",
      "Ωr: 5.44e-05\n",
      "ΩK: 0.00e+00\n",
      "Ωtot: 1.00e+00\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "f\"\"\"\n",
    "Valores actuales\n",
    "\n",
    "\\u03A9m: {Omega_m[0]:0.2e}\n",
    "\\u03A9de: {Omega_de[0]:0.2e}\n",
    "\\u03A9r: {Omega_r[0]:0.2e}\n",
    "\\u03A9K: {Omega_K[0]:0.2e}\n",
    "\\u03A9tot: {Omega_tot[0]:0.2e}\n",
    "\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Distancias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method angular_diameter_distance in module camb.results:\n",
      "\n",
      "angular_diameter_distance(z) method of camb.results.CAMBdata instance\n",
      "    Get (non-comoving) angular diameter distance to redshift z.\n",
      "    \n",
      "    Must have called :meth:`calc_background`, :meth:`calc_background_no_thermo` or calculated transfer\n",
      "    functions or power spectra.\n",
      "    \n",
      "    :param z: redshift or array of redshifts\n",
      "    :return: angular diameter distances, matching rank of z\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(results.angular_diameter_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method luminosity_distance in module camb.results:\n",
      "\n",
      "luminosity_distance(z) method of camb.results.CAMBdata instance\n",
      "    Get luminosity distance from to redshift z.\n",
      "    \n",
      "    Must have called :meth:`calc_background`, :meth:`calc_background_no_thermo` or calculated transfer functions or\n",
      "    power spectra.\n",
      "    \n",
      "    :param z: redshift or array of redshifts\n",
      "    :return: luminosity distance (matches rank of z)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(results.luminosity_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "zs = np.linspace(0, 10, 1_000)\n",
    "\n",
    "D_ang = results.angular_diameter_distance(zs)\n",
    "D_lum = results.luminosity_distance(zs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=1, nrows=1, sharey=True, figsize=(6, 4), constrained_layout = True)\n",
    "ax.loglog(zs, D_ang, color='purple', label='Angular Diameter Distance')\n",
    "ax.loglog(zs, D_lum, color='b', label='Luminosity Distance')\n",
    "\n",
    "ax.grid(alpha=0.2, ls=':')\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlabel(r'Redshift z', fontsize=16)\n",
    "ax.set_ylabel(r'$d$ [Mpc]', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Propuesto:**\n",
    "\n",
    "\n",
    "Comparar con la solución analítica\n",
    "\n",
    "$$ D_a(z) = \\frac{f_K(\\chi)}{1+z}, \\quad D_L(z) = (1+z)f_K(\\chi)$$ \n",
    "\n",
    "Donde:\n",
    "\\begin{align}\n",
    "    f_K(\\chi) =\n",
    "    \\begin{cases}\n",
    "        \\frac{c}{H_0\\sqrt{\\Omega_{K,0}}} \\sinh{\\left(\\sqrt{\\Omega_{K,0}} \\int_0^z \\frac{dx}{E(x)}\\right)} & \\Omega_{k,0}>0 \\\\\n",
    "        \\frac{c}{H_0}\\int_0^z \\frac{dx}{E(x)} & \\Omega_{k,0}=0\\\\\n",
    "        \\frac{c}{H_0\\sqrt{-\\Omega_{K,0}}} \\sin{\\left(\\sqrt{-\\Omega_{K,0}} \\int_0^z \\frac{dx}{E(x)}\\right)} & \\Omega_{k,0}<0\\\\\n",
    "    \\end{cases}\n",
    "\\end{align}\n",
    "\n",
    "\n",
    "\\begin{align}\n",
    "    E(z) = H(z) / H_0\n",
    "\\end{align}\n",
    "\n",
    "\n",
    "\\begin{align}\n",
    "    H^2(z) = H_0^2 \\left[\\Omega_{r,0}(1+z)^4 + \\Omega_{m,0}(1+z)^3 + \\Omega_{\\Lambda,0}(1+z)^{3(1+w)} + \\Omega_{K,0}(1+z)^2\\right]\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.integrate as integrate\n",
    "from astropy.cosmology import Planck18 as cosmo\n",
    "import astropy.constants as const\n",
    "import astropy.units as u\n",
    "\n",
    "H_0 = (cosmo.H0).value\n",
    "c = ((const.c).to(u.km/u.s)).value\n",
    "\n",
    "def H(z, omega_m, omega_DE, omega_r=0, w=-1):\n",
    "    \"\"\"\n",
    "    Función que calcula el parámetro de Hubble\n",
    "    en unidades km/s/Mpc\n",
    "\n",
    "    inputs\n",
    "    ======\n",
    "    - z: redshift\n",
    "    - omega_m: parámetro de densidad de materia\n",
    "    - omega_DE: parámetro de densidad de energía oscura\n",
    "    - omega_r: parámetro de densidad de radiación\n",
    "      (valor por default: 0)\n",
    "    - w: parámetro de ec. estado energía oscura\n",
    "      (valor por default: -1)  \n",
    "    \"\"\"\n",
    "\n",
    "    omega_k = 1 - omega_m - omega_DE - omega_r\n",
    "\n",
    "    radiation = omega_r * (1+z)**4\n",
    "    matter = omega_m * (1+z)**3\n",
    "    dark_energy = omega_DE * (1+z)**(3 * (1+w))\n",
    "    curvature = omega_k * (1+z)**2\n",
    "\n",
    "    H_square = H_0 **2 * (radiation + matter + \n",
    "                          dark_energy + curvature)\n",
    "    \n",
    "    if H_square <= 0:\n",
    "        return np.NaN\n",
    "        \n",
    "    return np.sqrt(H_square) \n",
    "\n",
    "\n",
    "def f_k(z, omega_m, omega_DE, omega_r=0, w=-1):\n",
    "    \"\"\"\n",
    "    Función que calcula la función f_k(Chi)\n",
    "    entrega distancias en Mpc\n",
    "\n",
    "    inputs\n",
    "    ======\n",
    "    - z: redshift\n",
    "    - omega_m: parámetro de densidad de materia\n",
    "    - omega_DE: parámetro de densidad de energía oscura\n",
    "    - omega_r: parámetro de densidad de radiación\n",
    "      (valor por default: 0)\n",
    "    - w: parámetro de ec. estado energía oscura\n",
    "      (valor por default: -1)  \n",
    "    \"\"\"\n",
    "\n",
    "    def E_inv(x):\n",
    "        return H_0 / H(x, omega_m, omega_DE, omega_r, w=w)\n",
    "\n",
    "    d_com = c/H_0 * integrate.quad(E_inv, 0, z, full_output=1)[0]  # comoving distance\n",
    "\n",
    "    omega_k = 1 - omega_m - omega_DE - omega_r\n",
    "\n",
    "    if omega_k > 0:\n",
    "        arg = np.sqrt(omega_k) * d_com * H0 / c\n",
    "        fk = np.sinh(arg) / np.sqrt(omega_k) * H0 / c\n",
    "    \n",
    "    elif omega_k < 0:\n",
    "        arg = np.sqrt(np.abs(omega_k)) * d_com * H0 / c\n",
    "        fk = np.sin(arg) / np.sqrt(np.abs(omega_k)) * H0 / c\n",
    "\n",
    "    elif omega_k == 0:\n",
    "        return d_com\n",
    "\n",
    "    return fk\n",
    "\n",
    "\n",
    "def D_A(z, omega_m, omega_DE, omega_r=0, w=-1):\n",
    "    \"\"\"\n",
    "    Función que calcula la distancia angular\n",
    "    entrega distancias en Mpc\n",
    "\n",
    "    inputs\n",
    "    ======\n",
    "    - z: redshift\n",
    "    - omega_m: parámetro de densidad de materia\n",
    "    - omega_DE: parámetro de densidad de energía oscura\n",
    "    - omega_r: parámetro de densidad de radiación\n",
    "      (valor por default: 0)\n",
    "    - w: parámetro de ec. estado energía oscura\n",
    "      (valor por default: -1)  \n",
    "    \"\"\"\n",
    "\n",
    "    return f_k(z, omega_m, omega_DE, omega_r, w=w) / (1+z)\n",
    "\n",
    "\n",
    "def D_L(z, omega_m, omega_DE, omega_r=0, w=-1):\n",
    "    \"\"\"\n",
    "    Función que calcula la distancia de luminosidad\n",
    "    entrega distancias en Mpc\n",
    "\n",
    "    inputs\n",
    "    ======\n",
    "    - z: redshift\n",
    "    - omega_m: parámetro de densidad de materia\n",
    "    - omega_DE: parámetro de densidad de energía oscura\n",
    "    - omega_r: parámetro de densidad de radiación\n",
    "      (valor por default: 0)\n",
    "    - w: parámetro de ec. estado energía oscura\n",
    "      (valor por default: -1)  \n",
    "    \"\"\"\n",
    "\n",
    "    return (1+z) * f_k(z, omega_m, omega_DE, omega_r, w=w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "omegas = [Omega_m[0], Omega_de[0], Omega_r[0], pars.DarkEnergy.w]\n",
    "\n",
    "D_ang_analytic = [D_A(z, *omegas) for z in zs]\n",
    "D_lum_analytic = [D_L(z, *omegas) for z in zs]\n",
    "\n",
    "plt.loglog(zs, D_ang_analytic, color='purple', label=r'$D_A$ analítico')\n",
    "plt.loglog(zs, D_ang, color='orchid', ls='--', label=r'$D_A$ CAMB')\n",
    "plt.loglog(zs, D_lum_analytic, color='b', label=r'$D_L$ analítico')\n",
    "plt.loglog(zs, D_lum, color='cornflowerblue', ls='--', label=r'$D_L$ CAMB')\n",
    "\n",
    "plt.xlabel(r'Redshift z', fontsize=16)\n",
    "plt.ylabel(r'$d$ [Mpc]', fontsize=16)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Evolución de los Contrastes de Densidad $\\delta$\n",
    "\n",
    "<img src=\"https://i.ibb.co/28cCD6B/image.png\" width=500px>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method get_redshift_evolution in module camb.results:\n",
      "\n",
      "get_redshift_evolution(q, z, vars=['k/h', 'delta_cdm', 'delta_baryon', 'delta_photon', 'delta_neutrino', 'delta_nu', 'delta_tot', 'delta_nonu', 'delta_tot_de', 'Weyl', 'v_newtonian_cdm', 'v_newtonian_baryon', 'v_baryon_cdm', 'a', 'etak', 'H', 'growth', 'v_photon', 'pi_photon', 'E_2', 'v_neutrino', 'T_source', 'E_source', 'lens_potential_source'], lAccuracyBoost=4) method of camb.results.CAMBdata instance\n",
      "    Get the mode evolution as a function of redshift for some k values.\n",
      "    \n",
      "    :param q: wavenumber values to calculate (or array of k values)\n",
      "    :param z: array of redshifts to output\n",
      "    :param vars: list of variable names or camb.symbolic sympy expressions to output\n",
      "    :param lAccuracyBoost: boost factor for ell accuracy (e.g. to get nice smooth curves for plotting)\n",
      "    :return: nd array, A_{qti}, size(q) x size(times) x len(vars), or 2d array if q is scalar\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(results.get_redshift_evolution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method get_time_evolution in module camb.results:\n",
      "\n",
      "get_time_evolution(q, eta, vars=['k/h', 'delta_cdm', 'delta_baryon', 'delta_photon', 'delta_neutrino', 'delta_nu', 'delta_tot', 'delta_nonu', 'delta_tot_de', 'Weyl', 'v_newtonian_cdm', 'v_newtonian_baryon', 'v_baryon_cdm', 'a', 'etak', 'H', 'growth', 'v_photon', 'pi_photon', 'E_2', 'v_neutrino', 'T_source', 'E_source', 'lens_potential_source'], lAccuracyBoost=4, frame='CDM') method of camb.results.CAMBdata instance\n",
      "    Get the mode evolution as a function of conformal time for some k values.\n",
      "    \n",
      "    Note that gravitational potentials (e.g. Weyl) are not integrated in the code and are\n",
      "    calculated as derived parameters; they may be numerically unstable far outside the horizon.\n",
      "    (use the series expansion result if needed far outside the horizon)\n",
      "    \n",
      "    :param q: wavenumber values to calculate (or array of k values)\n",
      "    :param eta: array of requested conformal times to output\n",
      "    :param vars: list of variable names or sympy symbolic expressions to output (using camb.symbolic)\n",
      "    :param lAccuracyBoost: factor by which to increase l_max in hierarchies compared to default - often\n",
      "      needed to get nice smooth curves of acoustic oscillations for plotting.\n",
      "    :param frame: for symbolic expressions, can specify frame name if the variable is not gauge invariant.\n",
      "        e.g. specifying Delta_g and frame='Newtonian' would give the Newtonian gauge photon density perturbation.\n",
      "    :return: nd array, A_{qti}, size(q) x size(times) x len(vars), or 2d array if q is scalar\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(results.get_time_evolution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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16tVcc801ZGZmUlamlfgHDRoUTEi7d+/Onj17gteSZFUIIYRoPcb0iOXWm19mkeVJvFiJK/mB794+r9knqtCMK6vR0dFs2bKFTz/9lAceeCDS4bQIZWVlnHfeedWeHzVqVI3n33XXXcE/79mzJ2xe66WXXtrwAQohhBCiyU07sz9l/WaxwtsLA17KNz3NL0vejHRY9dZsk9W//vWvLF++PNJhCCGEEEK0SMkxcP+UK1hseQKPL4rYwH5WfXcbB/etiXRoR6RZJqu9e/emf//+fPPNN8EeTCGEEEIIUT8n9oxmwqR/8qPnMlAhpvQX5n9+D15XUaRDO2IN3rM6btw45s6dS05ODqqqcuGFF1Y7Z9q0aezevRun08mqVas4+eSTw17/97//zcMPP9zQoQkhhBBCtGqKAnec1Zt+58/ld89lKPhxbH6W7+bc0CITVWiEZDU2Npb169dz++231/j6FVdcwcyZM3nyyScZPnw4S5YsYd68eXTp0gXQVr1v3749bJcmIYQQQghRt9RY+NdNF7Oz1zdkefsSE8hly/dXsXzxLECNdHhHrcHbAH744Qd++OGHWl+/9957efPNN3nzTa2x95577uGss85i2rRpPPLII5xwwglceeWVXH755cTFxWE2myktLeWJJ56o8XoWiwWr1Rp8bLPZGvYLEkIIIYRo5k7uZWXipMf53j0FVIgu/Y0FX9yF15kf6dCOWZOOrjKbzYwcOZIFCxaEPb9gwQJOOukkAB555BG6du1Kjx49uP/++3njjTdqTVQBHn74YUpLS4NHTk5Oo34NQgghhBDNhaLAPWd3p9/5X7LIPQWFAOVbXuD7D/7UKhJVaOJkNTU1FZPJFBw8XyE3N5cOHToc1TWffvpp4uPjg0enTp0aIlQhhBBCiGatXRzMvPlctvf8lt2eQUQF8tky70+sWDQT1ECkw2swEZkGoKrhfROKolR7DgjuWV8Xj8eDx+NpsNiEEEIIIZq78b3NnD3pEb5y3QgqWO3L+fmL2/E4DkU6tAbXpMlqfn4+Pp+vWhU1LS2tWrX1SE2fPp3bbrsNg0E25RJCCCFE62RQ4L6z0ynr9yoLXCMAKN82i0W//BtV9Uc4usbRpJmd1+tl9erVTJw4Mez5iRMnsnTp0mO69qxZsxg0aBBjxow5pusIIYQQQjRH7W0w65bT2N7ze7a6R2AJFLNtwY2sWPhsq01UoREqq7GxsfTu3Tv4uEePHgwdOpTCwkKys7OZMWMG7733HqtWrWLZsmXccsstdO3alddee+2YPlcqq0IIIYRorU7rY+SCSffysfN2CIC5bD2Lv5qOy74v0qE1CbUhj/Hjx6s1efvtt4PnTJs2Tc3MzFRdLpe6atUqddy4cQ32+TabTVVVVbXZbNVe69atm/q///1P7datW4N+zU11jB8/Xs3MzIz49Vr691EOOeSQQw45WsphUFAfOTdNvfuej9TTpmWpp03LUsdM/IeqGCwRj+1YjrrytapHg1dWFy9ejKIodZ4ze/ZsZs+e3dAf3WadeOKJLF26lB9++IFzzjkn0uEIIYQQogF0jIenrj6Bb4wvU+RKwxQoY/MvfyFn+7eRDq1JyT3zVuDGG2/kgw8+4LTTTgvuBCaEEEKIlmtiX4W/3Xob7/MBRf40TI6t/PbxpDaXqEKERlc1hrbasxoTE8PkyZOZMGECSUlJXH/99XVuoiCEEEKI5stogP87JxFX/5l8XHYaAOWZn7Dqp0cJ+FwRji4yWk1mdyzTAAym6IgcDWHy5MkcPHiQlStXMmfOHG644YYGua4QQgghmlanBPjfrcPZ2vN7VjhOw6C62Ln4AVb88ECbTVShFVVWj5bBFM34m7dG5LMXv9GfgM95TNeYOnUqc+bMAeCrr77i9ddfZ8KECfz888/Bc8477zzOOuss7rzzzmP6LCGEEEI0jrP7w1UX3MA79kfw+SwYnLv545vplBdsiXRoEddqKqvTp08nIyODFStWRDqUJtO3b1/Gjh3LBx98AEB5eTlff/01N954Y9h5xx13HGvXro1EiEIIIYSog8kAT54fx/Hnz+Id++P4sODI/o5FcyZJoqprNZXVWbNmMWvWLGw2G6WlpfV+X8DnZPEb/Rsxsro/+1hMnTqVFStWsGPHjuBzc+bM4fPPPycxMZHi4mJAS1aTkpJYtWoVUVFRXHzxxWHvEUIIIUTT65IIL1w9gK+Ms8kp74Gieti19EmyNrwT6dCalVZTWT0WAZ8zIsexMBqNXHvttcGqaoX58+djt9u5+uqrg88dd9xx7N27l1GjRvHyyy9z7733HtNnCyGEEOLYnD8Qnv/zZN5QvyLH2wODax+rvrhcEtUatJrKaltz/vnn06FDBzZt2sSgQYPCXluyZAlTp07l1VdfxWKxYLFYePXVVwHYsGEDZ5xxRiRCFkIIIdo8ixGemhSFr+8/mV1yOQCO/QtZ/cM9+NzFkQ2umZJktYWaOnUqAD/99FOt5wwfPhyAzZs3o6pq8LkNGzY0foBCCCGECNMnFV6a0otPlVlk2vuD6mfPyn+TuXo22mZNoiatJllta3NWL7jggnqdd91119GrVy9MJlNwDqvsciWEEEI0rWtGwFXnTOLlomdwqnHgPsTaebdTfGB5pENr9lpNsnq0C6xauyFDhvD555+zcuVKDAYD99xzDwUFBZEOSwghhGgTYi3w0iUW7N0e5d+F1wLgOLiMtT/cgceZF+HoWoZWk6yKmt1///0A/O1vf4twJEIIIUTbMiwdXr26C+95X2Vb6VAAsla/TObKF1BVf4SjazkkWW1B9uzZw8yZM5vt9YQQQgihuX0sXDZhAs8UvEBZIAHVU8SGH++mcO+iSIfW4kiy2oJkZWXx4osvNtvrCSGEEG1dcgz89wojeR0f4O950wAoz1vD+h9uw122P8LRtUytJlltawushBBCCNG8nNwDXrmyPW+5XmZj8fEAZK//L7v+eAY14I1wdC1Xq0lWZYGVEEIIISLBoMAjE+CCcWP5+6GXKA6konpLyVj4IHm750U6vBav1SSrQgghhBBNrWM8vD9FYV/yHTx08B5UDDgKMtjwwzScpVmRDq9VkGRVCCGEEOIonDsAXro8mdfLZrKqaDwA+zd/yI7f/kbA745wdK2HJKtCCCGEEEfAbISnz4Wzxozi4dxXyPd3JOBzsnXxI+Ru/yLS4bU6kqwKIYQQQtRTrxT44GrIir+Je/Y/hB8zjuKdbJo/jfLC7ZEOr1WSZFUIIYQQoh6uGg7/vjie10qf47eCswHI3fE12xY/jN9bHuHoWq9Wk6zK6CohhBBCNAabFV65GE4YMpgHDs7igK8bAb+bHb//g/0Z70c6vFav1WR2s2bNYtCgQYwZMybSoTSa8ePHk5mZ2WyvJ4QQQrQ2o7vAmrshqc8U7sj5nAO+brhKs1nz5aWSqDaRVpOstmUnnngiqqoyb57MchNCCCEagkGBh06Hn6bH8ElgJi/kP41XjSIvcwErPz0Xe97GSIfYZrSaNoC27MYbb+SDDz7g0ksvpUuXLmRnZ0c6JCGEEKLF6pQA/7sKenbtw10HZpPl7YMa8LFr+b/IXvd6pMNrc6Sy2sLFxMQwefJkZs6cycKFC7n++usjHZIQQgjRYl00GNbfC/72FzNt31yyvH1wlx1k7dwrJVGNEElWW7jJkydz8OBBVq5cyZw5c7jhhhsiHZIQQgjR4kSbYfal8PG1Vt51PMXTh2biVmMozF7Cys/OpeTAykiH2GZJGwAQHaGc3UngmK8xdepU5syZA8BXX33F66+/zoQJE/j555+P+dpCCCFEW3BcR/joGohP7srtObPZ6RmMqgbYs+pF9qx+CdRj/3ktjl6bT1ajMbAx9aSIfPaQ/KXHlLD27duXsWPHBqup5eXlfP3119x4442SrAohhBD1cOfJ8K/zYYXrLB7Ifg6HmoDHWcDmn+6iaN+SSIcnkDaAFm3q1KmsWLGCHTt2BJ+bM2cOl1xyCYmJiQAMGjSInTt30r59ewBSUlJYu3YtZrM5EiELIYQQzUJaHHw3FZ6/0MRbxX/lb7n/waEmUHxgJas+PVcS1Wak1VRWj3ZTACcBhuQvbaSoDv/ZR8toNHLttdfyzDPPhD0/f/587HY7V199Na+++ioZGRl89NFHnH766Xz44Yf87W9/49lnn8Xr9R5r+EIIIUSLdFY/ePdKMEancU/Oq2S4tRnte9e9zu7l/0IN+CIcoQjVapLVWbNmMWvWLGw2G6WlpUf03oboHW1q559/Ph06dGDTpk0MGjQo7LUlS5YwdepUXn31VQAyMjLo27cvvXr1YuTIkdx5552RCFkIIYSIKIsRnjkP7jkF1jpP5G9ZL1NGO3zuUrb8cj/5mfMjHaKoQatJVtuaqVOnAvDTTz/Ves7w4cNZu3YtO3bs4Pzzz+epp57ir3/9a1OFKIQQQjQbA9vDnClwXLrCnKLpvFlwHyhG7PkZZMyfhrM0K9IhilpIstpCXXDBBfU+d8eOHUycOJHly5ezaNGixgtKCCGEaGYUBW4fC/86D7yGeB7c9wJrPGeAAge2fML2Jf9HwO+OdJiiDpKstgElJSUAPPTQQxGORAghhGg6HePh7claj+p292AezHqNUqULfp+L7Use5eDWTyIdoqgHSVbbAJPJRGFhIRkZGZEORQghhGgSFw+GNy6H5Bj4ovAqXi34O6rBirMki00LplGWLz8TWwpJVluQPXv2MHPmzCN+X//+/dm2bVuDXU8IIYRoruKs8OKFcOMYcAWieGDPU6wJXAoGyMtcwNaF9+HzHNlCbBFZkqy2IFlZWbz44otH/L5NmzZx4YUXNtj1hBBCiOboxG7w3lXQKxX2untw2+7XKLf0Rw342b38Wfauez3SIYqjIMmqEEIIIVo0kwEemwiPTACjAT47eA6vljyHYrHhdhxi84LbKT6wPNJhiqMkyaoQQgghWqw+qfD+FBjTFXyqidszHmZz1E0oJije/wcZP96Bx3Eo0mGKYyDJqhBCCCFapJuPhxcuhFgL7LC357adr+KLHw3A3rWvabtRqf4IRymOVbNLVuPi4li4cCFmsxmj0chLL73Ef//730iHJYQQQohmol0cvHkFTBqoPf7v9pP4n+clTPHt8LpL2LrwfvL3LIhskKLBNLtk1eFwMH78eJxOJ9HR0WzatIkvvviCwsLCSIcmhBBCiAg7b4CWqLa3gdOrMHXldA60uw9TtLYb1ab5f8ZVujfSYYoG1OyS1UAggNPpBCAqKgqj0YiiKA1ybVVVAW3uqDh6Fd+/iu+nEEII0dhsVnjhAph6vPZ4WU4C9+95AWuHCSjA/i0fs2PJo7IbVStkaOgLjhs3jrlz55KTk4OqqjWOTJo2bRq7d+/G6XSyatUqTj755LDXExISWLduHfv27eNf//oXBQUFDRJbxXX69+/fINdrqyq+f/n5+RGORAghRFtwai/YcJ+WqAYC8H+/D+G+vO+wdpiA3+diyy8PsG3Rg5KotlINXmKMjY1l/fr1vP3223zxxRfVXr/iiiuYOXMm06dP5/fff+fWW29l3rx5DBw4kOzsbEDbHnTYsGGkpaXxxRdf8Nlnn3Ho0LGv5CsvL2fRokVcccUVAGzduhWfz3fM120rTCYT/fv354orrmDRokU4HI5IhySEEKIVizLB0+fC3adoj3flw41Lr0Lp/3eijPpuVPP/TFnB5sgGKhqVAjTavVxVVbnooov4+uuvg8/98ccfrFmzhunTpwef27x5M1999RWPPPJItWvMmjWLhQsX8tlnn9X4GRaLBavVGnxss9nIyckhPj4eu91e7XxFUbjhhhs49dRTj+Era9sWLVrE22+/LW0AQgghGs3oLvC/q6B/mvb41WVWXit+gnZ9JwOQlzmfrQvvl92oWiibzUZpaWmt+VqoJm3eNJvNjBw5kmeeeSbs+QULFnDSSScBkJaWhtPpxG63Y7PZOOWUU5g9e3at13z44Yd5/PHH6x2Dqqq89dZbfPTRR6SmpjZYP2xboKoq+fn5UlEVQgjRaMxGePQMePh0MBlhfwnc+E1nCnq/Rru+Q7TdqFY8x961tecGonVp0mQ1NTUVk8lEbm5u2PO5ubl06NABgM6dO/Pmm2+iKAqKovDKK6+wcePGWq/59NNPM2PGjODjisrq4TgcDvbuldWCQgghRHMxqIO2XerwTtrjD9bAw8vH0fWUl7FFJeFxFrD5xzsoyvk9soGKJhWRZfFVbx8rihJ8bs2aNQwfPrze1/J4PHg8ngaNTwghhBBNx6DAfePhibPBaoL8cvjz5wqrTLfRc+J9KIqB0kPr2TT/z7jL9kc6XNHEmjRZzc/Px+fzBauoFdLS0qpVW4/U9OnTue222zAYGnzAgRBCCCEaSa8UeOdKOLmH9vibzXDrVzZSxsygZ48zAdi/+QN2/Pa4rPZvo5o0s/N6vaxevZqJEyeGPT9x4kSWLl16TNeeNWsWgwYNYsyYMcd0HSGEEEI0jT+fCOvv0xLVUhfc+DFc9VVfup71De16nInf52LrLw+ybfHDkqi2YY0yuqp3797Bxz169GDo0KEUFhaSnZ3NjBkzeO+991i1ahXLli3jlltuoWvXrrz22mvH9LlSWRVCCCFahu7J8N/LYUIf7fEvO+GGj8GZMomRl/wLozkGl30fm+b/GXte7etWRNuhNuQxfvx4tSZvv/128Jxp06apmZmZqsvlUletWqWOGzeuwT7fZrOpqqqqNputQb8uOeSQQw455JDj2A5FQZ12Eqr9SVT136jlT6HeeTKqwWhSe5/0qHratCz1tGlZ6tDz31fNUUkRj1eOxjuOJF9r1DmrkXAkc7uEEEII0TR6JMObV8Bp+s3XX3drt/2zHe0YdOarJKZr+6hmrXmF3SueBzUQwWhFY2u2c1aFEEII0bYoCtx2EjxzHsRaoNwDD30Hry6F+PajGHX5LKyx7fF57Gz5+V7y9yyIdMiimWk1yar0rAohhBDNS68UeGsynNJTe/zLTpj6CWQWQqfB19P7pP/DYDRTXridjT/cgrMkM7IBi2ZJ2gCEEEII0aAMCtxxMjx1DsRYoMwND34Hry0DxWCl7/in6NjvMgByd37Dtl8exO+T3RHbEmkDEEIIIURE9EnVqqkVc1N/3gE3fQp7CsEa25HBZ79OfNpQAgEfu5c9TfaG/0Y2YNHstZpkVdoAhBBCiMgxKHD3KfDPsyHaDHYX3PcNvLFcez2h42gGnzkbS0w7vK4iMhbcJtuminqRNgAhhBBCHJNBHbS5qSd00x4v2AY3fwZ7i7TH6YOuoc/YxzEYzdjzM9j0w6247NmRC1hEnLQBCCGEEKLRWU3wyAR4+HQwG6HEqVVT31yhva4YLPQd9w/SB14FQO6OuWxd9CABnzOCUYuWRpJVIYQQQhyxsd3hjcthQHvt8Veb4LYvYH+p9tgSk8bgs14nocMIVDXA7j+eYe+61yMWr2i5JFkVQgghRL3FR8HT58L0k7THB0rh9i/hi5BdUePbD2fwWa9jjW2P113C5h/voDB7cWQCFi1eq0lWZYGVEEII0bgmDYRZl0DnRO3xG39oI6mKQ+7qdxxwJX3H/QOD0UpZ4TY2zbsZZ2lWROIVrYMssBJCCCFEndrb4KWL4Iqh2uMdeXDLZ7BoV+U5isFM77GP0XnwtQDk7f6BLQvvxe8tb/qARbMnC6yEEEII0SBuGA3PT4KkGPD54blF8I8fweWrPMccncLgM2eTmH48qhogc8UMsta8Qiurh4kIkWRVCCGEENX0bQezL4XTe2uPV2Vrw/3X7w8/Lza5P0PO+S/R8V3wuUvZ/PPdFGT93PQBi1ZLklUhhBBCBEWZ4OEJ8JfTtNFUDg88Oh9eXAL+QPi5qd0nMuCMFzGZY3EUZ7Jx3k04indGJnDRarWaZFUWWAkhhBDHZmJfbQFV71Tt8fdb4LYvta1Sq+o6fBo9j38QRTFQtO93Ni2Yhs9d0rQBizZBFlgJIYQQbVwHG7xwAVw5XHucUwJ3fhU+jqqCwWil3/in6dDvUu3cTe+x4/fHUQO+6icLUQtZYCWEEEKIwzIo8OcT4alzICFau83/0m/w2Hwoc1c/3xLdjsFn/4eEDiMIBHzs+O1x9me81/SBizZFklUhhBCiDRreCV6/DEZ30R6v2Au3fgbr9td8flzqIIac/QZRtk54XcVkLJhOUc7vTRewaLMkWRVCCCHaEJsVnjgbbh8LRgOUOOHhefD6MgjU0hiY2uNsBk54AaM5hvKinWycNxVnyZ4mjVu0XZKsCiGEEG3ENSPgX+dDx3jt8Qdr4N5vILeOlsFuI26n5/EPAFC4dzEZP96Oz1PaBNEKoZFkVQghhGjlhqbDKxfDyT20xzvytFX+P26v/T2KwUy/8U/Rsf8VAGRveJNdS59EVf1NELEQlVpNsiqjq4QQQohwidHaLf9pJ2q3/Ms98MSP8MKv4Kkj5zRZ4hl89uskdTpJW0i15DH2b57TdIELEUJGVwkhhBCtjKLAjaPh6XOhXZz23Edr4YFvYd9hRqFG2bpw3HnvEJvUG5/HTsaC2yjMXtz4QYs2RUZXCSGEEG3U6C7aLf8xXbXHGQfh9i9h0a7Dvze+/XCGnPNfLNGpuMr2s+G7Gygv3Nq4AQtxGJKsCiGEEK1Aaqw2L3XqGDDoq/z/tgBe/R18gcO/v13Pcxkw4QWMpijseZvY8P0NeByHGj9wIQ5DklUhhBCiBTMb4Y6T4dEztB5VgHdWwkPf173KP1TXYbfS68RHAMjf8yObf7wTv8/RSBELcWQkWRVCCCFaqAsHwXPnQ5922uM1++COr2Dpnvq9XzGY6HPyP+g06GoAsje8xc6lT4Baj1KsEE1EklUhhBCihTmuI7xwIZzeW3t8oBT+Og/eXVX7YP+qDKZoBp85i5Rup6MG/OxY+g9yNr7TaDELcbQkWRVCCCFaiLQ4+OfZlX2pLi88vxie+QXK3PW/jjkqmePOfYv49sPxe51k/HQ7BXt+arzAhTgGkqwKIYQQzZzVBHeNg79OgPgo7bmP1mp9qVlFR3atKFsXhp7/P2ISe+J1FbHh+xspzV3T8EEL0UAkWRVCCCGascuOg2fPg54p2uMVe+GeufXvSw0VlzqI4857B2tMGs7SbDZ8dx2O4nrMtBIiglpNsio7WAkhhGhNxvWEf50HJ3TTHueUwEPfwZy1oB7Fdj5JnU9m8FmvY7LEYc/PYMN318toKtEiyA5WQgghRDMysD08cx5MGqg9LnNrfan/WgQOz9Fds32fi+h/2r8xGM0U5Sxl4w+34PfIz0gRObKDlRBCCNHCpMfD38+CG0aD0QA+P7yxHP7+Y/3npdak83FT6TP2MQByd8xly8L7UANHmfUKEQGSrAohhBARFB8FfzkN7h4HMRbtuS82wsPfw/a8Y7t2j9H30n3UXQBkb3iTnb8/QSu7oSraAElWhRBCiAiwGOHPJ8KjE7WtUgF+y4QHv4VlWcd6dYU+Y/9G5+NuAGD38n+RtebVY72oEBEhyaoQQgjRhIwGuHYk/O1M6JakPbclVxtDNTfj2K+vKEb6nfYvOva7DIDtvz5KTsb/jv3CQkSIJKtCCCFEE1AUuGIo/P1M6JemPbe/BB5fAG+tBH8D7HBqMFoZeMZLtOt5NoGAj60L7yd3x5fHfmEhIkiSVSGEEKKRTRoIT5wNQ9O1x3ll2q5Ts34Hl69hPsNoimHwOW+Q3Plk/D4Xm3+8nfw9PzbMxYWIIElWhRBCiEYyoQ88eQ4c31V7XOKEfy+GmUuObHvUwzFZ4jnuvHdJ6DACn7ecjfNuojhnacN9gBARJMmqEEII0cBO6q4lqaf20h6Xe+DFJfDvRVDkbNjPMlkTGTZpDrZ2g/G6itnw3XWUHlrXsB8iRAQ1u2S1c+fOvPfee6SlpeHz+XjiiSf47LPPIh2WEEIIcVgn94C/TYQz+mqP3T6YvRSeXgiHyhr+88xRSQydNAdb6iA8znzWzZ1CeeG2hv8gISKo2SWrPp+Pu+++m/Xr19OuXTvWrFnD999/j8PhiHRoQgghRI3G99KS1NN6a4+9fnh7JTzxI+wraZzPNEenMGzSHOJSBuB2HGLd3Ck4inY0zocJEUHNLlk9ePAgBw8eBCAvL4/CwkKSk5MlWRVCCNHsnN5bG0F1Sk/tsccHb67QFk/tLWq8z7VEt2PYBR8Qm9wXd3ku6+ZehaN4V+N9oBARZGjoC44bN465c+eSk5ODqqpceOGF1c6ZNm0au3fvxul0smrVKk4++eQarzVy5EgMBgP79u1r6DCFEEKIo3ZmX/jtNvj5z1qi6vbBK79Dr6dh+heNnKjGpDHswo+ITe6Lq+wAa7++QhJV0ao1eLIaGxvL+vXruf3222t8/YorrmDmzJk8+eSTDB8+nCVLljBv3jy6dOkSdl5ycjL/+9//uOWWWxo6RCGEEOKonDcAlt0B82+BsT3A6dUWTvV8Cu74svFu+VewxLZn+IUfE5vUG5c9h7VfX4GzZE/jfqgQzYDaWIeqquqFF14Y9twff/yhzpo1K+y5zZs3q0899VTwscViURcvXqxec801h/0Mi8Wi2my24JGenq6qqqrabLZG+7rkkEMOOeRoO4fRgDplOOqG+1DVf2tH+VOo/56E2sHWdHFYotupx1+1UD1tWpZ6wtW/qVG2LhH/3sghx9EeNput3vlak/asms1mRo4cyTPPPBP2/IIFCzjppJOCj9955x0WLlzI+++/f9hrPvzwwzz++OMNHaoQQog2LtoMN4yG+0+FHsnac6UumL0MZixunNX9tdFW/b9PTGIvXPZ9rP16Mu6ynKYLQIgIatJkNTU1FZPJRG5ubtjzubm5dOjQAYCxY8cyefJkNmzYwEUXXQTAn/70JzZt2lTjNZ9++mlmzJgRfGyz2cjJkf8DCyGEODqJ0TD9JLhrHKTFac/l2rVB/rOXQomraeMxWeIZev77xKX0x112kLVzr5JEVbQpEZkGoKpq2GNFUYLP/f777xiNxnpfy+Px4PF4GjQ+IYQQbU/HeLh7HPz5RIiP0p7bXQDPLYJ3VjbctqhHwmiO5bjz3sXWbrA2R/WbKbhK9zZ9IEJEUJMmq/n5+fh8vmAVtUJaWlq1auuRmj59OrfddhsGQ4OvGRNCCNGKDekI95wCU4aDVf+puGG/Nn7qk/XgD0QmLoMpiuPOfYuEDiPwuopYN/dqWfUv2qQmzey8Xi+rV69m4sSJYc9PnDiRpUuPbQ/jWbNmMWjQIMaMGXNM1xFCCNH6KQqc3R8W3AIb7tN6U60mWLIbzv0vDJ0BH66NXKKqGCwMOes/JKafgM9dyvpv/0R54dbIBCNEhDV4ZTU2NpbevXsHH/fo0YOhQ4dSWFhIdnY2M2bM4L333mPVqlUsW7aMW265ha5du/Laa68d0+dKZVUIIcThRJngTyO1SuqA9tpzPj98vhFe+BWWN4c77IqBgWfMJLnreHzectZ/dz32vI2RjkqIiGrQUQTjx49Xa/L2228Hz5k2bZqamZmpulwuddWqVeq4ceMiMgpBDjnkkEOOtnG0t6H+4yzUQ49Xjp8qfgL1ufNRuyZFPr7Qo++4f6qnTctSx9+yXU3qNDbi8cghR2McR5KvKfofWg2bzUZpaSnx8fHY7fZIhyOEECKChneCO04O70fNLISZv8JbK6HMHdn4quo+6i56jL4XVQ2QseA28nZ/H+mQhGgUR5KvRWQagBBCCNFYLEa47Di4bSyc1L3y+d8zYcav8NUmCDTDMk36oGvoMfpeALYveUwSVSF0rSZZlZ5VIYRo2zonwK0nws3HQ3ub9pzHB59tgBd/gxXNoR+1Fu16nkvfcU8AkLlqJvsz3otwREI0H9IGIIQQokU7tRfcPhYuHAQmfUz3vmJ4bRn8d4U20L85S+x4PEMnvYfBaCUn4322//rXSIckRKOTNgAhhBCtms0K14yE206CQSGju3/ZCa/8DnMzwBehsVNHIjqxJ4PP/g8Go5VDu+exfcmjkQ5JiGan1SSr0gYghBCt35iucMvxcOVwiLVoz5W54X+rYdZSyDgY2fiOhDkqmaHnvoM5KpGS3DVs+ekuUFtAhi1EE5M2ACGEEM1aYjRcM0LrRT0uvfL5Lblagvq/1VDqilx8R8NgtDJs0gckdByFszSb1V9ciNdZEOmwhGgy0gYghBCixTu5h5agXj4Uos3ac04vfLoe/vMH/L4nouEdA4X+pz1HQsdReN0lbPjueklUhaiDJKtCCCGajZQYuHaUlqRW7DAFsGE//Gc5zFkDxc7IxdcQeoy+h/Z9LiTg97Jp/p9xFO+MdEhCNGuSrAohhIgosxHO7Q/XjYLzB2qPQetF/WgdvLG8eY+dOhLtep5L91F3AbBt8cMU5yyNcERCNH+tJlmVBVZCCNGyDO+kJahThkO7uMrnV2VrCeqHa8HezHaYOhaxyf0ZcPrzAOxd9x8Obvs0whEJ0TLIAishhBBNpr0Nrh4B140MXyx1oBTeWw3vroLNuZGLr7GYrImMuuwbouO7Upj9Kxu+ux5V9Uc6LCEiRhZYCSGEaDaizdrt/WtHwtn9Kgf3u7zwdQa8swp+3A7+Vjq1SVGMDDrzVaLju+IsySLjx9slURXiCEiyKoQQosGZDDCxL1w1HC4aBLaoyteW7dES1E/Wt/zFUvXR84SHSe58Mj5vORt/uBmfuyTSIQnRokiyKoQQokEoijZu6qph2rip1NjK1zIL4YM12kzU7XkRC7HJpfW+gK7DbgZg68L7KC/cFuGIhGh5Wk2yKgushBAiMoZ30iqoVw6DLomVz+fa4eN18OE6+CMrMrFFUnRiT/qNfxqArDWvkLd7XoQjEqJlkgVWQgghjtjA9lr19Kph0C+t8vkSJ3y+UVvJ/8uu1tuHejgGUxQjL/mKuJQBFOUsY903U2QrVSFCyAIrIYQQDW5YOlx2HFx6HPQPSVCdXvh2M3ywFuZtBbcvcjE2F31O/jtxKQNwOw6x+ac7JVEV4hhIsiqEEKJWY7rCpUO0o1dq5fNun7aC/5P18NWm1jUP9Vi173sx6QOuRFUDbPnpbjyOQ5EOSYgWTZJVIYQQQYoCJ3XTKqiXDIGuSZWvOb1a5fSzDfDdFih1RS7O5iomsRf9TnkKgD2rZlKU83uEIxKi5ZNkVQgh2jirCU7vDRcOggsGQcf4ytfK3Not/s83aolquSdycTZ3isHMwDNexGiOoXDfb+xZ/XKkQxKiVZBkVQgh2qDkGDhvgJagntUP4qyVrxU7YW6GlqAu2AYu6UGtlx6j78bWbgheVxFbfr5H+lSFaCCtJlmV0VVCCFG3nilwwUC4cDCc3L1yJymAfcUwd7OWpC7cCV7ZYOmIJHQcTdfh0wHYuugh6VMVogHJ6CohhGiljAY4oSuc019LUAd3CH99/X5tu9O5GbB6X2RibA2MFhujL59HdHwXDmz5hK2LHoh0SEI0ezK6Sggh2qi0ODi7P5zbH87sC0kxla/5/LB4t5agfrMZ9hRGLs7WpO/J/yA6vgvOkix2/P54pMMRotWRZFUIIVowgwLH69XTcwfAyM7hrxeUw4Lt2iKp77dq/aii4aT2OJsO/S5BDfjZ/PM9+L3lkQ5JiFZHklUhhGhh2sXB2f20BPWsftpiqVCrsrXEdN5WWLEXAq2q2av5MFkT6HvKEwBkrZ1Nae7qCEckROskyaoQQjRzBkUbzn9Of+0Y3SX89UKHtmr/+60wfxscKotMnG1N77GPYY1Jo7xwB3tWvRjpcIRotSRZFUKIZqhzAkzsqx1n9oWU2PDXV+/TKqfztsLyveCXKUlNKrnrqXTsdxmqGmDrogdRAzKAVojGIsmqEEI0AwlRcGovOKMvTOwD/dLCXy92alXTeVvhh22QK8NOIsZojgvuUrVvw1uU5q6JcERCtG6SrAohRASYjXBCNy0xPaOPdpvfGDIm2h/Q+k1/3KHd4v9DqqfNRs/jHyTK1glnSRa7VzwX6XCEaPUkWRVCiCYyuIOWmE7sC+N7Qawl/PVth7Tk9KftsGgXlLgiE6eoXVzqYDoN/hMAWxc/RMAnf0lCNLZWk6zKDlZCiOamSyJM0CunE3pDh/jw1w+VaYnpTzu0I7s4ElGK+lPod8o/URQDB7d/RXHO0kgHJESbIDtYCSFEA0mMhtP0vtMz+kDfduGvOzzw624tMf1xO2w8CGqr+he4des44Cr6n/oMPncpyz+aIFuqCnEMZAcrIYRoAlYTnNRdS0zP6KMN5K+p77SicvpHFnj8EQtXHANzVDK9TngIgMyVMyRRFaIJSbIqhBD1pCgwPF2rnE7oDeN6QrQ5/JwtuZXJ6WLpO201ep7wEOaoROz5GeRs+l+kwxGiTZFkVQgh6tArpTI5Pb139Xmn+0sqk9Ofd8D+0sjEKRpPXOpg0gdMBmD7r4+iqlIeF6IpSbIqhBAh0uK0pLRiYVT35PDXS13aSv2KBHVLbmTiFE2n90n/B8DB7V/KlqpCRIAkq0KINi3WAqf01Ffs94Gh6eGve3ywLKsyOV2VDT6Zd9pmpHQ/g6ROJ+L3udi9/F+RDkeINkmSVSFEmxJthhO7abtFndYbju+qDegPtS6nMjldkqmt4hdtj2Iw0euEhwHYt+FN3GX7IxyREG2TJKtCiFYtygQndteT015acmqp8i/fnkJtlNRPO+CXXZBXFpFQRTOTPmAKsUm98TjzyVozK9LhCNFmSbIqhGhVrCZtG9PTemkJ6gndtOdC7SvWktJF+rG7ICKhimbMaLHRffTdAGSunInfK7/BCBEpzTJZ/eKLLzj11FP5+eefufzyyyMdjhCiGTMbw2/rn9AVoqqMk8opgV92VianuyQ5FYfRdegtWKJTKC/ayYEtH0Y6HCHatGaZrL700ku89dZbXHfddZEORQjRDPVrB2f2gzP7aklqnDX89f0lWlJaUT3dmR+ZOEXLZLIm0vm4GwHIXPFv1IAvwhEJ0bY1y2R10aJFjB8/PtJhCCGaicRobbX+mX21JLVbUvjruXZYqFdOf9kJOyQ5Fceg67BbMVnisOdlkLf7h0iHI0Sb1+DJ6rhx43jggQcYOXIk6enpXHTRRXz99ddh50ybNo0HHniAjh07kpGRwd13381vv/3W0KEIIVqwbklw4SC4cDCc0gNMISv2XV5tlf6C7bBgG2w8CKoauVhF62GOTqHzkOsByFz5PCD/YQkRaQ2erMbGxrJ+/Xrefvttvvjii2qvX3HFFcycOZPp06fz+++/c+uttzJv3jwGDhxIdnZ2Q4cjhGhBhneCiwdrSepxVeadZhyEH7ZpyemSTHB6IxOjaN26DpuG0RxDae46CrJ+jnQ4QggaIVn94Ycf+OGH2m+b3Hvvvbz55pu8+eabANxzzz2cddZZTJs2jUceeeSIP89isWC1Vjas2Wy2Iw9aCBEx/dPgymHa0S+t8nl/QEtKv94EczfLin3R+CwxaXQa/CcAMlfOiHA0QogKTdqzajabGTlyJM8880zY8wsWLOCkk046qms+/PDDPP744w0QnRCiqXRKgKtHwFXDYFinyuedXvh+C3y1Cb7fCoWOiIUo2qDOx92I0RRFycE1FGYvjnQ4QghdkyarqampmEwmcnPDN9POzc2lQ4cOwcc//PADI0aMIDY2luzsbC6++GJWrVpV4zWffvppZsyo/A3YZrORk5PTOF+AEOKomY1wwSCYOkZbKGU0aM97/TB/G3y4VquglrkjG6dom4wWG50GXg1A1ppXIhyNECJURKYBqFVWQiiKEvbc2WefXe9reTwePB7ZC1GI5mpgey1B/dNIaBdX+fziXfD+Gvhio1RQReR1GnQNJms8ZYXbKMhaGOlwhBAhmjRZzc/Px+fzhVVRAdLS0qpVW4/U9OnTue222zAYDMd0HSHEsTMa4IKBcOc4bQ5qhf0l8M4qeGuFDOYXzYfBaA3OVd279jVkAoAQzUuTJqter5fVq1czceJEvvrqq+DzEydOrDbe6kjNmjWLWbNmYbPZKC0tPcZIhRBHIzkGbjoepp9UOQvV54dvNsObK7TV/P5AZGMUoqoO/S7FGpOGy57DoZ1zIx2OEKKKRhld1bt37+DjHj16MHToUAoLC8nOzmbGjBm89957rFq1imXLlnHLLbfQtWtXXnvttYYORQjRRHqnwv3j4dpREK1vdZpfDv/5A2YvhX0lkY1PiFopBroMuxWA7PVvyG5VQjRTakMe48ePV2vy9ttvB8+ZNm2ampmZqbpcLnXVqlXquHHjjvlzp0+frmZkZKhbtmxRVVVVbTZbg35dcsghR/VjeCfUj65B9f0LVf23dqy+G/X60ahRpsjHJ4cchztSup+hnjYtSz35hvWqwRQd8XjkkKOtHDabrd75mqL/odWoaAOIj4/HbrdHOhwhWqXxveDh0+GsfpXPfbsZ/rUIluyOWFhCHLGh579HcpdT2Lv2NXb98XSkwxGizTiSfC0i0wCEEC3TxL7w9zPhxO7aY58fPlqnJakbD0QwMCGOQkxiL5K7nIKqBsjJeC/S4QghatFqklWZBiBE4zmlJzxxtva/AC6vtmDq+cWQWRjZ2IQ4Wp0GXwtA/p6fcNn3RTgaIURtpA1ACFGrE7rBE2fBGX21xy4vvLYMnvkFcuX/XqIFM5rjOOna5ZgscaybO4WinN8jHZIQbYq0AQghjsmQjvD0uXDeAO2xxwf/XQFP/Qw5srJftAId+l6CyRJHedFOSVSFaOZaTbIqbQBCHLv0ePjHWXDDaDAYtJ7Ud1fBEz9BVlGkoxOi4VS0AORsfDfCkQghDkfaAIQQxFnhgVO1WakxFu25T9bDX+fBzvyIhiZEg4tvP4KRl3yJ3+vg93dH4/eWRTokIdocaQMQQtSL0QA3jtaqqR3ited+z4T7v4U/siIbmxCNpeOAyQAc2vWdJKpCtACSrArRRp3eG168CAZ30B7vyIOHvocvNkY0LCEaldEUQ1qv8wE4sPXjCEcjhKgPSVaFaGO6JMLzk+DyodrjgnL4+4/aKn+vP6KhCdHo2vU6D5MlDkdxJiUHVkY6HCFEPbSaZFUWWAlRN6tJ60l9ZILWl+oPwKyl8Nh8KHZGOjohmkbHAVcAcGDrJxGORAhRX7LASog24PyBMPMC6JWqPV68C+74SnadEm1LdHw3Trj6V9SAn6XvnYDHcSjSIQnRZskCKyEEAD1T4KWLKuel5pTA/d9oW6QK0da073MhAEX7fpNEVYgWRJJVIVohkwHuPxUemwjRZm2o/4xf4Z8/Qbkn0tEJERlperJ6cMdXkQ1ECHFEJFkVopU5oRv85zJtFyqAn3fA9C9ge15k4xIikuJSBxGb1Bu/z0V+5oJIhyOEOAKtJlmVBVairUuI0rZIvfUEbfepvDK4dy68vybSkQkRee17XwBAQdbPMltViBZGFlgJ0QpcdpzWm9pRH+z/9kp44BsocEQ0rKOWqpgZZ0mitzEaUNjld7DIU0ih6ot0aKJFUjjxT0uJiktn4w+3kp/5Q6QDEqLNkwVWQrQRnRNg9qXaan/QbvXf+hks2hXZuI5WO8XM/bHducDaDrMSfpfErQaY4zrAjPIsXAQiFKFoiRI6jiIqLh2fu5TCvb9EOhwhxBGSZFWIFurm4+HfkyA+SltA9fRC7XC30OLjeHMSM2z9SDBo/yxt8NpZ57OjAqPM8QwyxXFjdCdOMidyXckmClRvg322ETjLkspxpjj2B9x85T5EqXr0OyQMNsUx3GRjpbeUrf7yBotTHJ12Pc8FIC9zPgG/O8LRCCGOlCSrQrQw3ZLgv5fDGX21x8v2wI2fwNYWPInnMmt7norrjUFR2OQr429lO1nvC+8rHG9O4hlbH/qbYpmTMITLStZTdgwJZYU4xcgb8QMZbU4IPndrdGemlm4+qkRzanQnHo7tAUBAVfm/sp184s6t13stKFwe1Z4oxcjHroMN8vUJaNfjTADy5Pa/EC2SrEYSooVQFJh+Emy6X0tUnV5tAdXJr7bsRPUcSwrP2PpgUBQ+cR3kiuL11RJVgMXeIq4s3sBBv5vephiejetzzJ9tQuEVW39GmxOwB3y859zPbp+D9kYrb8YPJFk5st/nR5ri+UtMdwB2+MoxKAqPx/WipzH6sO9VgNnxA/h7XG8eju3BO/GDMaEc9n1mlHqd11bFpQ4mytYZv9dBUfaSSIcjhDgKkqwK0QL0TIGFf4ZXL4E4K/y6G457Hl74FQIteInkIGMs/7JpJeL3nft5pGwnnjrWfGYFXEyzb8GjBjjLmsoF1nbH9PnXRnXkZEsS5aqfa0o28vfy3Vxasp6desL6gF4hra8HY7tjUBQ+deVyTvFafvEUYlEM3Bbd5bDvPduSwnhLMqBVZIeZbVwW1b7O90y0pLA8+Xh+Tx7NMJPtiGJtK9r1OAuAguzF0gIgRAvVapLV6dOnk5GRwYoVKyIdihANRlHgzpNhw31wai9toP8dX8Kps2FnfqSjOzZxipFZ8QOIVows9hTyj/Ld9XrfRl8ZLzv2AvBQbA/iFONRfX6KYuaOmK4APFm2mwz9lr9d9fNQ2Q4ALrWm0d0QVa/rDTTGMtIcj1sNMKN8DwAzy7MAONeaSrJirvP910d3AuBlx16eKs8EYEpUh1rPj1OMPBPXh3iDiRSDhedtfWutrxrQ2hNujErH3MaqsKk9tWQ1f/f8CEcihDharaZnddasWcyaNSs4CkGIlq5XCrw9Gcb11B4v3Ak3fQKZhZXnGIAOBiudDFYSDCaiMBClGHEToFz1cyjgYY/f2Sx7Hx+M6U4nYxR7/U7utm8LW98flzqI1G4TiEnqDSg4ineSt/sHygu3AfCmM4dLo9rT3RjN1VEded2574g///rodGwGExu9dj6t0lO6zmdnoaeQ0y3JXBudXq9E+ny9yrvQU0ievvgrw1/ORq+dIWYbZ1pT+Mh1sMb3djBYGGmOJ6CqfOA8gBeVB2O7M9AURw9jNJl+Z7X3XGhtR4LBxKGAhzjFSDdjNCeaE1jqLal27vToLtwd2w2AjkYrT+rJcGsXndCduOR+BPxeCrJ+jnQ4Qoij1GqSVSFak1tPhOcnQawF7C544Dv47x8w2BjHDVHxDDbFMcgURzdjVLURTzU54HezwlvCUm8xP3kKKYnwvNLjzQlMida22Hq4bCd2PZmOTe5Hn5P/TlKnE6u9p8foezm0ex7bf/0/PM58XnHs5d+2flwfnc7bzpw62weqsqAwWa9aznLuq/Gd7zv3c7olmXOtqTxZvpu60n0FON+aCsC37vCtwr735DPEbOMMS3Ktyerp+u3/Nb7SYKK70lvKWEsiJ5kTakxWJ1hSAHjLmUM3QxRXRXdkgiWlWrJqRuHa6PTg46ujOvKSY2/we96apeotAMX7l+HzSBFDiJZKklUhmpEONnjzCjh3gPZ4UYaJT75OYYg3ieVJCSQaqt9K9qgBDgTcFAW8ONUALgJYMRCnGOlotNLOYKGj0cqFxjQujErDowaY7yngP459bInAWCUD8FisVi6e4zzAcj256jjgKvqO+zsGo5WA30P+np8ozV0LqCR0GEVK9zNI63kO8WnDWDf3Kr4t2cO9Md1IN0ZxgTWNz+q54h7gHGsqyQYz+/0uFnoKMJpjSR90DfFpQ3GXHWDfpndZWppNYcBLqsHCCeZEfvcW13q9IaY40o1RlAV8LPIUkdrjbBI6jKR4/x8s2fcHf4mF0eYEzCh4a0iNx+iTCJZ4Kj/jD28xYy2JnGhOZE6VJNeCwvH6exZ5CultjOGq6I7B50Idb04g2WAmL+ChXPXT3RjNieZEFngKws6LU4xMi+7C/oCr2ue1VKndJgCQn/ljhCMRQhwLSVaFaCYuHwqzL4FEg4mC1SlsWJpKelki9xkUsGrnlAZ8rPSWsN5Xxiafne1+B7mBumuKcYqRwaY4TjAnMMGSwgBTLJOs7Zhkbcf37jyeKs/kYMDTJF8jwCRrO/qZYikJ+HjesQeA7iPvpMeY+wDI3/MT25c8irtsf/A92evfIDZlAIMmvkJsUm+GX/gxqz47n/ddB3gwtgeXRB1Zslpxy/4TVy7GmDSGX/gRMYm9gq+nD5zCxnk3Ma/4IFdHd+R8a2qdyWrF2Kvl3hK6nPRXugy9CYCuw24ha+VMCncvJdlgZrApjrW+6ju1jDZpW4+tVQL0OvH/MJqj2bD2LUBLhKvqZ4rFqhgoDHjZ6XdSGNAq5f1NscQpxrC2j5Fm7dq/eYopUX1cHx3N2BqS1X/E9uKCqDQAcgMefvIU0pIZLTbiO4wEoEA2AhCiRWs1C6yEaKkSo+H9q+DdUxLw/dSPzJfHUPxjH7qWJ2FSFDZ67bxQnsVlxesZXfgHt9q3MMuZza/eYg4eJlEFKFP9/OEtYaZjL5OK13JB0Vq+dh3Cr6qca23HD4kjuNia1iRfqxmFu2K03sn/OPdRqvrpNPi6YKKaueJ5Ns6bGpaoVigv2MLary6nvHA71tj2DDrzVb7xFBJQVcaYE+hksNYrhjjFyFhzIgDz/WUcd+7bxCT2wlW2n51Ln6R4/x8YzTEMPus1Fhm1fyLHmZPqvOYIfSX+lrg0ugy9CVUNUJClJUjdRt9NhkFb1FRT4tlOMdPeaMWvqqhnvkTXYTfTadA1WM55HYBOxigSqozQOk6/zkafHXN0CvaoeA7qK937GGPCzq2YErDWV8par3YrfIApNuycBMXE2XobA8Cl1rqnELQESZ3GYjCYKC/aicueHelwhBDHQJJVISLo/N5G1p6RzrjVI9j/4RDKtrRDCRjY4ivn+fI9TChcxcUl63nVmc06n73Ovsn62uwv576y7VxQvJY13lLiDCaes/XlH7G9Gn1e50XWNLoaozgU8PCucz9JnU+m99jHANi9/F/sWf1Sne/3ugrZ+MPN+NylJHYcg2HAZJbpbQT1HWN1ijkJi2Ig0+/EN/RGbO0G43HksfarK8he/x/WfXMNJQdWYbLGU3rifbjVAB2MVnrUMSu1onqZN+QaQKsEb/j+eg5s/RSAnE6jATiuhvFSA/TEM9tkJqrjKPxeJz53KaT0Yb/Jop8TW+N79iR0Y+y1KznxmqVkWrT4+lZJViveu8FXxna/o8ZzTjAnYAnpfT7JklhtasBAYyyv2QZwTVTHWr8PzUlK11MBKMxeHNlAhBDHTJJVISKgm8XC/KHdeck5Gv+ynngLY3Di4wPnAS4sWsuk4rXMdu4jK+BqtBi2+R1MLtnAzPIsAqrKlOiOvGjr16ijja7TF/q86cwhEJ3CoImvYDCYOLDtM7LWvFqvazhL9rDrj6cB6DHmXuar2uKj0/RFSodTsZhpoeqhy7BbANj261+D1Tc14GXrogcJBHzE9zyHDYr2K8Io/VZ9Vd0MUaQaLHhQOdT9VHyeMrJWvwJA1uqXUdUAed1PB6onnQAD9edyUrR5s5krZ7B9iZbAH0zSWhOqbipQkTi7Bl6BYjBiNEVR1O1UAPqEfIZNMZJq0BJey9hHiTr1KbyqSpzBFFaJHqwnv5+4DlIS8BGrGKt95qvxAzjDmsJjsT3rXcWOpOSu4wEo3CvJqhAtnfSsCtGEehmjeSSlM6eo7VByDASAsmgHzxfm8IUjn/IjWqGtYI3tQFR8F6JsnTBZEzCaojGYogj43Pi95Xgch3AUZ+IsyaxxILoKvOLMZqu/nJds/TnLmspLKNxm3xI2SqohjDHF098Ui0P184nrIH0mvoI5Kgl7XgbbFz9yRNfav+Uj0gdOwdZuCLv6ng9ZqxlqspGgmA476WC0XgXd2f00Lcnb9zv5meEzOB3Fuzi0Yy4d+l3CnnaDGJ23nWFmW7URVwDHmbVq6c7oRHxGC/k7vwmuPHeWZlG8fzn79X7YHsZojBBWIe+lVzkPpQ0i4PdyYOvHBHwufJ4nyE/qDnlb6FYlcayY/Zqf2I3ywh2YLDYKErsD0CUkkaxIagtMFhIGXglA7rYf6ewqoasxipyA9t/E4GBbQRk9jdGMMiTQxxjDNr0SO8QURxej9pkGReE8ayr/cebU+X2OpJikPkTFpeP3uSje/0ekwxFCHCNJVoVoAj2M0dwR04VJ1nYoAa1yqaSX8ErpPmbuKarX0CWTNYGkTmNJ6DASW9pxxKUOwmSuXqmrScDvofTQOor2/c6hnd/iKN4Z9vpPnkJuLt3M6/EDmGhN4QF/d57VFz81lIrxSV+6DhHV40zSep1LwO9l6y/3H/nOQmqAPateZMg5/8U8dCo7dv9GH6O2cOh7T+27JXQ0WOlkjMKnqhQOvQ6ArLWzajx3/+Y5dOh3CQd6nA552xlayw5RvfSEMLfdQO1/d84Nez1v1/ckj/s7boMJa8BHZ0NUWMW8u54E5sZ1pHj/H/jcWltDUc5SDsVp47VCNyaIVYy0N1r193Rg/8rnscakUdDxJEDrca1Qkawe0hNZgJKErnR2bSQ9JKmtqKJu9ZUzyBTHKHMCfUwxoK+7G1mlqlxTO0NzktL1NACK9/8hu1YJ0Qq0mmR1+vTp3HbbbRgM0tkgmo+uhihuj+nChdY0jIqWpMb2zWdVyj6u/amMourjM8PEpQykXa9zSe5yCrZ2Q1CqzFQN+L24ynJw23PwuAoJ+JwEfG4MRitGSxxRcR2JTuiBOSqRxI5jSOw4hh6j76Hk4Bqy1r5KwZ6fgtf63VvMg/YdvBTfn5tjOrPVX87XVWaGHq00g4WJ+lzQ99159DrxXQD2rnuNsoLNR3XN/D0/UV64g9jkPqyJ70if8mLGWZLqTFYrqqo7zFYCMamUF+2kaN/vNZ5bcnA1rrL97NOT0F7G6BpHT1VURvOSeuJ1FVG077ew1wv3/YqqGDhoS6dbyV76mGLI8lQmqxVV00O29hRt+TD4fNG+pRzqe3nYOQCd9STTbonDaYmlYO8vRNs6U9DrAoCwW/QVCWlhXAdc9n24yvZTEKMtpKpIak0odNDPizr+QYrytkDhXnqG9LX2M2l//sNTzAmWxFoT9+aisgVgUWQDEUI0iFaTrMoOVqI5SVXM3BXTlcujOmDSk9SY3gVEHb+Xe5eW8843tb83JqkP7XtfQFrv88LGKQGUFW6jOGcZpYfWYT+0EUfJblAPf8M+Kr4rSZ1OIrX7GSR3OZWEDiM47pw3Kcldw/Zf/4+y/AxAG2Dfx7GXO2K68nhsL5Z7SxpkrNX5llSMisJqbymO/pfQOb4L7vJcsta8cgxXVcnZ9D/6nvIEe3qeARs/C67Kr01FhXCHnoAe2PIx1FrXVjm08xuiht5CmcFMXMBLT2N08NZ4hYrq5QFbOkU5y1AD4W0IzpI9uMr2k2frSLeSvXQOqZLaFCPJ+uzcQ3EdKD6wMvhaae4aCoZNA7QdripUJJZFMSl4nPk4i3fjKTtIfozWi2szmIhXjJSqftrr7yuOTqJo31KcpXsotPUBtCpzxbWNioJHMRA95BrIWQXLXgy+F6CPUavgf+3O4wRLIh2NVmIw4NCbRWIwcGVUB+Z58jnQhGPQamIwWknsqC1oK8z+NaKxCCEaRqtJVoVoDqIwcGN0J26J7kScQfu/V0zPQpLG7WWjr4wpc2BnDYU/gymKtF7nkz7wahI6jAg+7/e5KNj7C/mZCyja9xsex6GjistVupcDpXs5sOUjzNGpdDnuBjoNvp6E9iMYdek37Fk1kz2rXwZUXnbsZZw5kWHmeP4Z15ubSo+u8hnqQn1+51xvMd1H3g7AnlUvEvAd2wKyQ7u+offYxzjYYwJs/IxephhsirHW3ZmG6/2l+7qNAyBv97w6r5+3ez5dh91KTmJX+hXuor8pNixZNVCZrB60pVOye26N1ynNXUt+bEVFs7Ly2UlPXO0WG07FSFn+puBr5YXbKLRq8cYZTMH5qRWJa1F0MvZDGwHw+xyUlu3HbrFh89jpaLBS6nfQviKxjU6mJPsn3GU5FHTQdgerqLp21iushbHtUBUDxdHamK7QBLmiTWCDEqAk4CPBYKKTMYod+vfi4dgeXBXdkcm+DkwqXntEu4k1tPj2IzAYrbjLc3EU74pYHEKIhiP3zIVoAAbgEmsaPyWN5N7YbsQZTKipdtKv3kD7yzbzwuYyxr5SPVGNsnWh99i/cdK1Kxhw+vMkdBhBwO8lf8+PbP7pLn5/ZwQZ8/9M7vYvjjpRrcrrzGf38udY/uFp5O78BsVgpMeY+zju3LcwmmIIAH8p24FbDXCqJZlzLamHvWZdehqjGWSKw6sGWNvzdCwx7XCU7OHA1o+P/WtxFVGw9xfKrPEcMGnJV02zTAGMVN6yz07uiT0/47DzN+156/F5yjiYqM2GrTq+Kt1gxaoY8BpMFMSmUnJwVc3XObSeghhttFZoZbUiISyMScFRvDOsvzLgd1Ns34vDrMVcUensYKxMQO0hyW1Z4TZKo7TNCSqqtWkVldWoROx5GynL30KpVTsnxWgJfg0ABbHtCPjdFEVrFdp2BisKWtU0Xv/Fq/uUXyi0dQx7XwwGrtK3zu1lignuxhUpiZ1OAKAoZ1lE4xBCNBxJVoU4RqNN8XyVOIx/2frSwWjFbnaRcv5Wek1dT4GtlAmvwyPzwBtS7LO1G8LAia9wwpTFdDnuRszWBJyle9n1x7Mse+9ENs67idwdX+H3Nt52qB7HITb/eDtbFt6H3+cipdvpDD3/PYwWG7v8Tl5zaInc/bHdsBzDOKsz9FFRS70lxB93IwB7175W7Xb50crb9R0AmcnaFq619VN2N0ZjVQw4DSYKYlLJz1xw2GurAR8lB1eRqy90qpqsVvR15sZ1wOdzB9spqrLnbSI/VktWQyurwcQzJpmy/C3V3ldetDNY6Qwmq4bK9ziKKhfKlRduw27V2hxSFLN+fS0xLo5OxlmSiceZR5F+6z5Zfy1FT2xLoxI5tPNbDnnLCaBgVhRSFHMw4XWarHisNooTugLQWY+9X5VxXMMj3M+alK4lq8X7JVkVorWQNgAhjlI7xcxfYntwkX6L26768AzJZtTZ+zGYVD7fADd/StgiqqTO4+g24jaSOp0YfK5g7yL2bXiTwuwl1N4/2XgObvuM8qIdDD3/PRI6jmLoef9j3dwredOZw1XRHelqjOayqPZ8cJT7xZ+hL6xaFteO6IRueF3F5O74ssHiL8z+FVUNsK/jCDi0hSG1JEsVg/Bz4juhKoZqC6FqU5q7hkOdtAU73aslq/okAFtH7Hnra03Ay4t2UBijfR/SQyqrHUNu6ZcXba/2PmfJHoqik0gvzSFNT1LT9OSyOCoJR0lmyLlZlMZqCXuKft0UvSKaF/AGf/HJcxcDkKTXKpL1xNZutVGWvxlzVBKlUQkkuoppb7AE21mK9YprxQKtiq9jUJVK9lCzDQ6zcLCxGIxW4tsPB5CRVUK0IlJZFeIIGYHro9L5MWkkF0WlEVBVlkYfoNPNqxhzfg7OgMpNn8Bl/6tMVBM7ncTwiz5j2KT3Sep0ojZPc9tnrPj4TDZ8d52+ECRyfX72Q+tZ9/WVeF3FJHQYQb9T/4WDALP16upN0Z0xHsV1UxRzcLvPXYO1OZ/7t3x4zL2qobyuQux5G9mTpCVqg2sYvA/QV3/+QFIPAn439ryN9bp+ae7aYGW1W0iiCZVV0vzYNMoLd9R6DY/jEPn6pJJEgylYqe4Q0lPqLNlT7X3Okj2URGmV1XZ6kpqsv6fMasNZXJmsuuz7KiurBjNxijG4I1leSAtJnrsIADPa1rNJ+nXLLDbKCrZQVriNMr1XNslgDlmklUzA78FepdWgYnvXiq1cu1T5Ho01JzLIWL8Ra8cqvsNIDEYrrrIDNX4/hRAtk1RWhTgCo03xPB7XK3jrc6PPjmPMLv50ZhkAq/fBlDmwXZ/4lNBxDD1G3xuspPp9LvZnvE/2+jdwlx9dpbKxlBVsZtP8PzP0/Pfo0PciyvI28umGt7gjpitdjVGcbUnluzrGQtVknCUJg6KQEXBDr3NQA35yNr3X4LEXZv9KzhCtxSDdGEWsYqy2wUKwsprQhdJD6+s9f9Oev5lCvZoYF7LSHiqTzcKYZMpz677tnFd2EJ9ixKT6STGYORDw0E5PBEuiE3GEJJ4VnCV7KE/WKoWJegW04vZ9sUJw8wGonqwmKto/726jhVJH5X9r9vJc3EYrVr+bZMUcTDrt1nicpXuJKs6kXF+IFq+YwvpeD2z5GHtMJwCSgm0J2v+u8JYy3BxPekibw2hTPO8mDAZgSvEGVvgad1JLZQuAVFWFaE2ksipEPSQrZv4d15cPE4+jnymWwoCXl5Ud9LpxPVP0RPXZX+DEl7VENTa5H8ed9y4jLvpUr6S62bfxHf6YM46dS59odolqheL9y9jx++MA9Dj+AQyJPXnfdQCAKdFHvif8ifpim/WJWp9j0b7fcJc1/M5HxTnLcFhiKdIrgr2q3K6HyspqTnxnSg6sqPe1vc58yjyl2C3atTuGzDGtXJmfgrN4d53Xcdr3UhKVCBBMUlP0xFNLFLOqvcdVtp8yq3abvaICmqBoNe6CKv3MnvJcSvVNItqZ40jQk1WHORZ3eeXOW077PuzByqmJFP17ZbfG4S4/iKtsPw6L9pkJBhNJ+nXKrPEUH1hOqf75KfpnVWxQsNPWHoBoxRh8z7nWysV551nb1fn9aQiJ+i+FkqwK0bpIsirEYVxsTWN+0ojgLf85zgO80301T92Ty5B0OFgKE1+Hh74DxZpGv1OfZfTl80jpeioBv4ecjPf4Y854dvz2twZb0d+Y9me8T0HWLxhNUfQ//Tk+defhV1WONyeE7aRUHxXJalbvswA42IC9qqFK8zYAcEBf/NMnZKA9aP/QVQzTz7V1pOTA6iO6flnB1pCe09BktXKxU3lR3WOSXPZ9wdX6FclqckWyq/oJ+Ko3enochyjXpwEkm2KwoFDRIVrgLQs7V1X9lOgbuSYYo0jUk9tySyyekGTVXX4geJs/WTGTrCebhQEfasCLu/wg5RYtEU1UTNgqkl5LDOVFOynwaUlyRWW1ovIaf9qzFOlJbsX36ISQyQDHN/KUAIMpivi0YYC2+5cQovVolsnqeeedx9atW9m+fTtTp06NdDiijepisPJO/CCes/UlyWBms6+MPznW0+vCXbw6xUeMBRZsg6EzYGFmNN1H3cUJUxaTPuBKFIORQzu/ZflHE9j+6//hLj8Q6S/niGxb/BBedwkJ7Ueg9LuYX71an+PlUe3rfY1uhijSjVF4UdnX7VT8Xme9VuAfDb/HTnnRTnL1sUrdqlRW2xksmBUDfn2O6JHumuUo3kWRPnS/o57cGahM1ApMMbjL9td5DVdpdrCymqrf0k/Sq5T5NSSqoE0jKNZ7mZNMlQmoXzFQ6Cyodr5db22IU8zBymq5JQ63ozJZ9ToLceoJcJzBFFxoVZH8usv247BoryeaoknQR1w5zDG47TnkebTtYBMxYAz5WoqikymOTQt+jxSgS8jWrz31HcAaS3zaMAxGC66yA7hK9zba5wghml6zS1aNRiMzZszg9NNPZ8SIEfzlL38hKSkp0mGJNsQITI3uxPdJIzjZkoRL9fOv8kz+EbOe9+4s4+oR4PPDw9/D2f8FpePFnDBlMT1G34vRHEPJwTWs/uISMn68rcX+0HSXHyRz5QwAuo++hy+9WoJyzhHcyq2oqm2OisdjslKQvahRR3GVHlpHXpyWTHc1hleA00MWMnk89iNuw3CUZFIYrVVWK9oAkhQzRkUhgMIBdz6HWyDnsu/DHlXRU2ohGgMVUVYkgDUp1neESlQswT7UckscrpAEtEKpvnDNphhIMFRUROPCKqteZwEuk/bJsYqRihp0kR6D31tOmf7eJLONBH2r1TKjBZ+nlHw9SbapAdIMVgyKgl8xUGa1BXtmExUz7QwWohQjAVXFp6oYFCVso4GGFt9e20yj9OCRVc2FEM1fs0tWx4wZQ0ZGBvv376esrIzvv/+es846K9JhiTZikDGWzxOH8XBsD6IVI8s8xZxXtBbryBx+u1OlTzvYWwTjZ8PL6/ox7IJPGHjGTKyx7XGW7mXT/Gms+fJiSnNb/g/M/RlzcBRnYo1JY2ffSThVP12NUQyo58ruYfoind1pQwAo2PNjo8UKYM9dzyG9sld11X5w8H1MKuWF24742s7iTEqiE4HKuaQpwdvscZTXY+W5q2w/Dr3PM8FgCi5s8hrMlDhqX7hWcds9UVGC7ymvkoBWKNHPjYWQxDYWd0j7icdVGExW2xmjgz8E7B575XX0xDvRGEWC/r0rDXgByNeTWiPQXf+loMxiQ1UMwfaBBIMp2HaxP+Bhn96e0Ml4ZG0kR6Ji57eS3DWN9hlCiMho8GR13LhxzJ07l5ycHFRV5cILL6x2zrRp09i9ezdOp5NVq1Zx8sknB19LT08nJ6dyAca+ffvo1KlTQ4cpRBgLCvfFdOPzxGEMNsVRHPDyF/t27vRuYua1Ll66CKwm+HIjjHjZxqH0Rxl1+fckph+P3+tg1x/PsvzDCeTt/j7SX0qDUQNe9qx+CYDUYVNZoo8mOsuaUq/3V8w7zelyAmrAT0HWwsYJVGfP2xBSWQ1vA6gYMVUYk3JUyaqjJLPawP3KVfQ2XKV174QF4HHk4dCTuXiDNbhgym614XbUXukt9GpbmsYHVOL0tgGnORqPq3obQKl+Kz8mECDRpH0PHJYYvK6i4Dl+jx2Xfmu/vf4LRQCFMndldbdU1TYOSDCYiDdon1mityq4XAW49O9nRZXZaY7BUbwr+PUlKKZgC0Bpu4E42g8LO78xBCurkqwK0eo0eLIaGxvL+vXruf3222t8/YorrmDmzJk8+eSTDB8+nCVLljBv3jy6dOkCgKJU72lS1cjNnxSt3yBjLF8mDmNaTBdMisJ37jzOKlrDwY6HWHcvXDQY3D64/UuYtvRi+l20kC5Db8JgMHFo1/cs/2gCe9fOQtVv17Ymh3bOxWXPwRqTxvKUXgBMsBw+WY3BEFzktCepJyW5q8MSpsZQXrQjOLA+wWAiVqmcDFtRWS2MTaW8oPrw/cNxlx2gVF+UlKrfFq9IWu1RCfVqK/B7y7Ab9V5Vc0zlan1LbJ0L70r0BNSMSpL+mS5TND5XcbVzi/WB/2ZUkvTvv8tgrrZ4y6FXTtvrCa3bZMXjrvz7setV1FjFiE3/MVGsV209zkI8+ta2FTNYneZoCvYuolxfYJVojKrcbCA2jYJY7e+lUyMlq9EJ3bFEp+jzc2veRUwI0XI1eLL6ww8/8Oijj/LllzWv+r333nt58803efPNN9m6dSv33HMP2dnZTJs2DYCcnJywSmrnzp05cKD2xSkWiwWbzRZ2CFEfZhTuiunK54nD6GeKpSDgYVrpFu4p28atp3tZNA26JsGOPBj7di+Wpn6s3/JPw1G8i3XfXEPGgmmHXVjTkqkBH/s2vg3AviFXAzDAFBscTVSbQaY4jIpCnjmakuikeu8WdSz83nJKXAU49MVDof2R6XqV72jbANSAl0L9l+YUY8VK/sr5pO6y+i2gK9V3uEo0WkOqpDF4nIW1vif09nyq/pluUxQeV/X3lIT8QpCmVz8d+vaqoSpm0LZTKq8X+suEw6/1vkZhIC6gvb9IT5pDe16DW7GaYygv3E6pXoVNNtuCPbPllrjgFITkRupZraiq2vM2tcpfGoVo65q0Z9VsNjNy5EgWLAhfEbxgwQJOOukkAFasWMHgwYNJT08nLi6Oc889l/nz59d6zYcffpjS0tLgEdpCIERt+htj+TxxKHfEdMWkKHzvzuOcojVssBbww83w5DlgMsK7q82c98udxI2fR2L6Cfot/2dY8fHZFO1bEukvo0kc3PYZAb+bQKcT2KH3Hh5uDNFx+u3lrOQ+QNONEiov3EGRvi1oh5AqXrCyGpMatkXpkcjXK4sp+l5eKaG38es57aFET6TiFXNlsmqKxltD4lnB7SrCrSfI7fSk22WOwldDpdrnLcNZ0Y+qx+cK+KudV1FZTdVX57tM0XhDEuZyn956oAaw6OeW6tVZj7MgpLJa0QYQjceRR7FPm0aQZIoOWwxWUXFN1mfVWlCYFt2ZP0Ud+ezemgT7VWVxlRCtUpMmq6mpqZhMJnJzwxcG5Obm0qGDtp2h3+/nvvvu45dffmHt2rU899xzFBbW/g/5008/TXx8fPCQ/lZRFxMKt0V34cvEoQw0xVEY8HJn6VbutG9jRG8f6++FiX2h3ANTvh3Bq4Hv6DzyPgxGKwVZC1n+0RnsXTu7TVVvvK4i8nZrvzBmJPUA4ERzYp3vGagvwspu1w+/10lp7rrGDDHIUZIZkqxWr6zmRcXjceQd1bUP6btFRQPRGEjRr69VVus3XaDIryV88RiI05M5lzk8UazK6y7CrSeg7fUWBJfRitddfYKAz2PHZdZu7afq/7w71OrJakVlNVWvFrtN1vDKqj61IcHvq3yP/pzXVYBbr9q217+vTnMMHkceJQEtWU1QTCSEzHkt05PtJLOWtE6L6cJ9sd35W1yvYCvBsZB+VSFat4hst1q1B1VRlLDnvvnmG7755pt6Xcvj8eDxtJ3EQRy9PsYY/hXXhyF61W+Bu4BHy3ZSrHh58hx46DQwGGDl/lhu3/ggMf2vJU4x4HHms+O3xzm0s37/TbZGB7d/Qfs+F7Cn5xmw+q2wYe816a0nVTnxnSk5uBJV74FsbM7SLApjOgOVi3liMBCvVzEP+N0cbsRUbYodh/AaTJgDPpIM5uBIp3JTzYudaryGvljKpqrY9ITPZYrCW8PM1Ao+VzEei9bTX1FZdSgKqNVv7/s8ZThtXUmiiBT931RHDdvKluvvrfgB4DJH49P7XQHK9TiN+vfKpxhx6+0IAZ+rcoGWMbyyWlRROQaSjBULvOLILdY2TEjUzz++ymYBc91H9wsEgNEcS1xyf0Aqq0K0Vk1aWc3Pz8fn8wWrqBXS0tKqVVuP1PTp08nIyGDFivpvoyjaBgVtburXicMYYrZRHPByj30b0+1bsNq8LPwzPDJBS1QfWz2Bu/N+Irb39SiKgQNbP2H5hxPadKIKULTvd3zuUvZ0GgNAL1NM8DZ2VQYqtzs9EN+ZkgMrmypMnCVZlERpc5lTq4yYchutlNYzqayJx1UQHKZvU4zEV4xtUtQaE8ealOiV1VjVH6wyOoxm/Ppt95p4XUW49NvuaXqPaU19qAA+T2mwClvBUcNdAIca/suD22QNm4Fb5rWHve4xWvCH9M669N7UdmF9twWU6V9HNJCox1Ec8JHv1BaQxes/cvqFjD8baYqv8WupL1vaUBSDEZd9X4vYIU4IceSaNFn1er2sXr2aiRMnhj0/ceJEli49tp62WbNmMWjQIMaMGXNM1xGtS0eDlffiB/NwbA8sioGFnkLOLV7LN+48JvaFtffCKT0hx5HAOX+8yG+Jb2GNS8dZksW6uVPY+ssDYRWntkoNeMjP+plyq42DeuI02BRX47mdDFaiFCMeg4n82HaUHlrfZHE6S7OCW4mmGKouhLLhsu876mt7HPkhyaqJeP26FYum6qM0ZIvUNH0l/uG2SfC6i4MJaIreY+qoZUKK31OGT584UKFc3yggVFmVSrfLFI3fW5kwOzzhW7n6jGZ8ehsEgEvRfnQY9XjKFAU14KFcT2itaoCEip2tfOUU6l93fMBPe4MluPgKIN14bBMCKrZYLclde0zXEUI0Xw3eBhAbG0vv3r2Dj3v06MHQoUMpLCwkOzubGTNm8N5777Fq1SqWLVvGLbfcQteuXXnttdcaOhTRxp1vSeUfcb2JN5goV/38s2w3n7pzMSjw97Pg//Rq6vt7J/BG6TMY26WhBvxkr3+DzFUvEKjhh3xblp85nw59L2Zvci86HNrMIFMcf3ir90321kcm5drSCRiMTZqsuu05lOq7RKXqlc+KpLXMGo/rGCY3eJ0FOK1ai4FNMWo9p6of+xH0LzvcpQRQMKCSarBAAMqVutsS/J6yYLJaMT22vJZdS32eMryG8GS1pspquT/8OZfRQiCkXUBLcCsr5x6jBZ+7srLqNIQH4NAfllUkq4EACYoCqBR5yynUNxKI83uDi90qdDjGcVa2dtqmE/a8jcd0HSFE89XgyeqoUaNYtGhR8PELL7wAwDvvvMMNN9zAJ598QkpKCo899hgdO3Zk06ZNnHvuuezde2zbUk6fPp3bbrsNg6HZbcolmphNMfJ4bC8ujNJ2M1rnLeU++3ayAi7a2+CDq+H03lDmj+fOTY+xJ/pyjNFQXrSTLQvvxd6EyVVLUrTvd9SAn5y0wXBoM4Nq2cmqol/1QHwnnCVZTVqZDvjdFOrVvmQ9WQ3O+7TacNmPflqIx5mPI64vAPEGExV15ZI6buFX5fPY8ZisRPlc+gKoAGWHaaEN+N24TOGLkBy1tB2oAQ9eJfzfwHJ/9X5htz/8FzFXlfnWLp+DAPEY9J5Vr9GMzx1SWSX8fGdFf2xwdyuVOP25Mp+LAn0mrBE1uFmAS/UTpRiPeQtWW7vBgCSrQrRmDZ6sLl68uMbB/qFmz57N7NmzG/RzZ82axaxZs7DZbJSWlh7+DaJVOt6cwHNxfUg3RuFTVWY5s3nVsRc/cGovLVHtGA+Li0/lif3PEIjuiKoGyF73HzJXzgirLolwPk8p9vxNZOkTAWprA6iorO6P70TpoXVNFV5Qgd4XmqKvtk/WbznbrfG4i44+Ho+zAKe+0t6mmILJWIn3CJJVdwkeYwJRPldwAVR5Lf2noVxV+oPLaljhH4yzyj+/zhoqq25v+CYB3iqLzgJ+Jx5zKlH6/x98BjO+kJ7V8HeDW28rsLtKQE9kK1Jmh7+cclcBPiUZk+oPbgywxVfOcHM8SQYzURhw1eP7UJXJEk90fFcAyvI2HfH7hRAtQ0SmAQjR0Cwo3BXTjZujO2FQFLL8Tu63b2etz46iwF9P1279u4jjkaxH+cN3JVjBUbybLQvvpzRXVhHXR1HOUrIGXgNAT1MMMRiqLfbpplfOcm0dse9r3C1Wa5KvLxRKRLuRHVZZLTv6yqrXWdmzmmSKJUavbpZ46//LsdddgidGq/jHVySr9ZiU4DSEJ6uuOt5TNTUtD1T/BcxdpRrsrVKp9XudeKKtwWTVY7SE9axW/Tt369Vbh6eEAEnBiixAuadMm81q64jJ6wi2AZR2GIGzaA/RAR/tDRayAkfedlPRAuAsyQqLTwjRurSae+YyDaDt6mOM4bPEodwa0xmDovCx6yCTitay1mcnNRa+nwr/PAfWucYyeecC/vBdqVVT1/+XlZ+eI4nqESjOWUqZ1UaJPuS9hzG62jkVycjR7hZ1rPLdRQT06l6iYiapYsSUJa7eO03VJOB3U66PbEo3V7ZAHHFltcotfVc9el6dVW7tu/21v6fqK84a7ha4vHUnqwGfC4+xMk6v0UzAV3mdqm0Ibv1r8PscYe8DLVn1OguCz3fSv3ee5N449J2tQhdcHYm4ihaAfKmqCtGatZrKqrQBtE1/iurIX2K7E6UYKQx4eaRsBz95tAHrY7vDR9dAanwUz+c+wndl14FRq8Js+eV+Sg7ILzZHqmLF9cH4ziTkb6W3KYYMf+V6diOVuxoVxqTgKNrZ5DG6nHm4zNHEeB3EG0wk6IuTygymY27zKNN74jvp1WOPwYzLW1bXW8JobQDhC4qcvsPH5K5ym75qz2moqjXXcm/Vm/YVldXKEVe+Km0FVZNOj8FE6HxaT5XKrluv3ga8TrwxlmBF1msw4fWWEvC78ehTCjoa9L8Pq02vVBcQf5jte2tjS63oV5VkVYjWrNUkq6JtSVZMPGvry2kWbbeixZ5CHrLvIE/1oihw/3h46hzY7RvMDVkvkhvQJlTs2/Q/di97us65lqJ2fo8dR/FuDsan0y9/K730/tQK7Q1WjIqC12CiyGg+pgVNR8vjLMSpJ6s2xUi8njzb6+jzrK9yvR+/vWICVR+m77Ef5l2V/D5HcOvUCnUlnhW8VWJ315F0e0LWDPgUI94arq99ZmWy6qly/YDPhTe0slqlZ9ZbpRrs9oVUVk2WYHnXa7Tg03fa8ujV0456YlpmsYXNrT0asrhKiLah1SSrMg2g7TjRnMC/4/rS3mjFrQZ4pjyT91za7d3kGHj3SjhngIGPi2/lzYL7UBUz7vJctiy8j6J9SyIcfctnz9tIrk3b072iP7VCRQtAUXQKZcW7Odrdoo6F11mA05wAFGBTTNgMJgiolDXAFrnOgJbUJehtBl6jGZ+nuN7vVwO+asmqw1e98lmVN1AlWa1jrJon5HvuNxgJ+KpPcnX7PaGTqfBWmRWrqn7cIfNa3VV6Zr1VqsEuPSGu2j6gjbzS7nS49YQ0Tm9pcFhicITMrQU4x5JCvGLiY/fhN4kxWmzEJPYEoEzaAIRo1VpNsiptAK2fCYW7Yrpya7TWm7rT5+Au+1a2+bUq6Ziu8MmfwBrXmbtzZpDhPh4UOLR7HtsWPSTD/RuIPW8Ted3OAqCzoUqyagxtAdjR5LGBNmLKmaAl09o8VCPgo6QBJj049Z2f4vV80GO04HMc2b83VRM/V32SVTU8mXTX8Z7QlDygGPDXkNh6AuHJqqeGjQ18IX2y3ioTXqq2AVR8Ddrt/vBe14rvj6dKIcFjtAYrqwkWG/18dl6OHwDA5qh4NpbU/d+PLWWg9tn2fXhdRXWeK4Ro2aQMKVqEzgYrHyUMYVpMFwyKwkeug1xUvC6YqN41Dn6dBttMF3Pj3nlkuI/H5yljy8L7yZj/Z0lUG5A9byP5se0AgjMzK1SMJSqITaU8QsmqV28DAIhXTMTqiWVpA7R+OPUkrSId81bZhrQ+qiarTu/h9rCqXvmsK8ENrawGFGONLS+eKrNXqybDAIGQRVSeKgu8fFXOd4YkxO6QxVJeoyW4M5ar6iIxxUCZngQnWhO4yJoWfO3yITdhNNc8x7dC5WYAUlUVorVrNZVV0Xqda0nlybje2AwmSgM+/lq2g3kebY/3hCh4azKcMTCBZ/OeZFH5JABKDq5m88934yo9ts0mRHX2/E3BZDXZYCZOMQbnfnYMWVzl3BeZ7S89znwceqJjM1qIC46YOnxSeDhVF0N5q8wfrQ931QH8/sNXVqstaKorWQ1JMgOKgUANC6xUvwefYsSk/715ati+1R+S9LqrJJpV2xJCvwaPIXznq4rKbtW6tsPnoEz/jARjFAkhv/gMKD1Acpfx5O3+vsavEUImAUiyKkSrJ8mqaLaiMfBYXE8uj+oAwGpvKffYt7FfX3k8ohN8ei0UR4/lpuznyfd3JBDwsWfVi+xd8ypqAyyoEdX5PXaK3SWUWeKI85SRbrCyXa9wV7YBpOKy74tIfD53CQ59AkA7sw2LnrwVH8Gq/dq4qrQSeI2WI05Wq+7+5KrHNICqldU6F1hV6Vn115DYBgIe/AYTJr/2/xFfDTtiBUIS2KrdvlWnB4TG5w5ZLOUN2cbVVWWzApffS5n+ufEGK53MlZtMJDkLSOg4uu5kNaU/AGUFm2s9RwjROrSaZFUWWLUuA4yxzLT1o5cphoC+E9XL+k5UANNOgmcnWXi/5AE+PXALoA343/zz3bJdahNwFO+iKDqZOE8ZHQyWymRV72EtiEmJyCQAjUq5Xt1LD5kDa2+IymqVKqjbYEStx1D/UFVvh7vqmJlawVtlCH/V5DUspqqV1RqSVdXvxW8wUvF/KF8Nu0cFwiqr4a9VTVZdYclq5ckeoxlV//pcVaq3zoCbUj0Am8EU/G8HINlRQEKHy2v8+gAUg4mYxF4AEZnlK4RoWq0mWZUFVq3HtVEdeSi2BxbFwEG/m/vLtvOHVxt/E2uB/1wG4wb34L6Dr7DTo90KzMl4n51L/1njD2bR8BxFOymOTqJLyd7gXFWAVL2yWmSOxusqjFR4lOsJYbq+77zTFIWnASqrjiqLlar2ctZHaOIZQKk2BqomVSufHrWOHaxCEsmAYqhxtmwg4MUX2ltaY2W18rmq27FWXZAV2qYQGllYZbXKNRx+N2V6IpuEQmrIa/HuUuJtXarFVCEmsScGvaodqQq+EKLptJpkVbR8Sfrs1NP12ak/uwt4qGwHRfpijv5p8Nm1kB1zGbfu+wcuNRaPs5Ctix6gYM9PkQy9zXEUZ1IU3RmA9npCaAAS9eQtv4ZxSU2pXNESo1TFCCq4TVFHfLu+Js4qVVD3UdzJ8YUkel6jhYCnHm0AVZPVOqqx7pBk2F9LMq0G9MpqxfVrGDHmD2lXCFTpUfXXkawGQpJlT0hLgKtKNdbhc+HUf7lsV9GOoBgJoGJRA6QGfFii2+Fx5lWLLTa5HwDlhdtr/PqEEK2LJKuiWRhtiucFWz86GK141ABPh8xOBZg8DF66NI7/lD7FwrwLASjKWcbmn+/CU374mYyiYbnseylJ1lZjp+nJaqJixoBCAIX8CI8Scumr3eNRABWv0Yzf3QDJqhqeJHqq9J/WR2iy6qvnrlqht929BhOqt442gEB4ZbXWa4Ykkp4aKquh6au/yuvaJgWGYDyBkO9L2BQBQ2iyWnWCgBOXPqkgQU92XeZoSvweOvk9JDqLiE7sUUuyWtGvurXWr08I0XpIsioiSgH+HN2Zu2O6YdRnp95t38ZWfQtPsxGenwRnjBrKPbkvc8DXDTXgI3PlC2StnQU1/JAVjc9Zmk1RtFYB76Df+k8xaEPky6w2HOUHIxYbgFNPjOL1PsmjWQhVE5evSmX1KNoA/KGJZ0hPZ11Ck0m/wVRnn6w7JBmurbIK4clq1R5UCLazan+utqDKC2h/7z6DCdVf2WLhD+tfrfx8Z5XqbLnPgUPvI47SX3OZoigJuOkExHjKiU7oXuO2yHEpUlkVoi1pNcmqLLBqeZIVMzNsfTnZkgTA565cHi/bhVO/jdklET66RmFvwi3cmfMAfsy47Nlk/HgXpbmrIxi5cNmzKY1KACBFHzkUmqy6I7a4SlN10ZKngZJVp79qz+qRV1a9YZVV8xFXVrVqbB1tACEVzEAd8flC/q30BmroWVUqa6u+QE2r/yuSVTOqL+QzQ2qyvtDFViHtCT7FiMdThqtKD7DbFEWpW6vKx3jLscZ2qDH2ispqeaFUVoVoC1pNsioLrFqW480JzNC3THWqfv5Wtosv3IeCr5/ZF169sh2vl81gdeEpABza+S3bFj+MzyN/v5EW8Lko0hOY5IrKqqIlq6XWeFxlkU1WnVVHTB3FPNQar+tzAyHbkB7FNXyhi5GMdSeewfOqJKtqHdMAQhc/1ZWshlZdPTVUVgOhPatVbuGHbgrgM5gIhPashl4jtCIcCK8o+7z2aqPAXKYoSgMewEiMx4E1No2qjOZYouO1xVfSBiBE29BqklXRMhiAadFduDOmK0ZFYYevnDvt29ihjz5SFHj0DDhn7Kk8mPc8xYFUAj4n2397nANbPops8CJMoT7GKUnftzM5pLLqysuIWFxQQ7JqNOP3HPs0AE/AhU+JCg7Td9ewMOlwfNUqq/VpA6hM9PwGY1hyWJW33m0Ala/V1AYQViGt8rrH7w1u1+ozhrclhM5nDUtWw9oftJ2/tGS18seQ2xSlbYtriCHa68AS075aXLHJfbVzy3NlZzoh2ghJVkWTSVHMPB9y2/9TVy7/CLntnxID704xszf1LzySezMA5QWb2bTgDhzFOyMWt6hZvj4KKg4wo2BTtH9OHOZYPI7qi2KakqPKCDOtd/LIE8uqAj4XXosZk0/vsTyKa4TuDOU9TP9phdDb8FqPaO0JridkFJa/jh21fYetrFbyV2kD8AVCklWDmYA/tLIasilB6MzX0IVXRgt+rxOX34X2X5DGZY6ixOcES4zWBpDaq1pcwUkAUlUVos2QZFU0iRPMCcyw9SPNYMGh3/b/MuS2/5iu8PKU7rzlepntJccBsG/jO+xa9lS9evpE0yt0FeJXDBjVAEkGM/F6ZdVhicHrzI9obNV6S0NWpR+LgN+N12ghOriF6JEv8AtN4LyG+v0THFYtPUwbgDc0cayjDcAbkqx6D5ushn+et8pEg9B4wtsAQirCgfDKqhoo15JVQ0iyaoqm2FcOlhS9DaB6ZTWuYhKAbAYgRJshyapoVAZgenQX7tBv+2/3lXOnfSs7Q3YCum0sTDr9Qv5e8BRONY6Ap4iMnx8gf8+PkQtcHJbLcYhySxzx7lKSFBOJ5lgAHKZovK7iiMbmrFKtPJoRUzUJ+Fx4jSE9q0cxjSK8slq/BaGhlU+fYqxzK2Gtsqron1VXz2rla4dNVmucBqD/2WgmEFLNra2yGnoNj9FCwOPF6XVWrNMCwGUw6T2raG0A0akoVb7e2OAkAKmsCtFWSLIqGk2qYmaGrR8nWRIB+NR1kL+X7cal/xiMtcDsy60c6vQ4z+ZPAcB+YBkbf7wbd4RHH4nDc5cfpMxqI95dSrLBTII+FcCOSkPccj8WVacBHM3w/pr4fS78SuU/m746Kpy1XkMNXS1fv7h8fi8VCajvMFViLVnVMsA6K6shiWzVHakgPKkOVEnKfSHJqcdoCa+sqrW1AVRtZfBq0wBCklWn0USpfq0YTzmKwYg5JjVslnJFz2pZgVRWhWgrWk2yKqOrmpcTzAm8YOtHO/22/2NlO/nKXdnH2D8NXv1TLz7wvcpu+wBQA2Sufok9q16U2akthNeZT3lUdwDiFRMJBguoUFpH1a+pOAPhrSPuhqqs+l2oIZeqa6FTbUK3Tq3vd8qr+qiYQnC4BNcbcrvdX0eyGloB9arVk9WQnBMfVXfQCl8kpvrrMQ0gZCOBgGIgEPDi0TcFqOBUjNj1/36i3NoWy9aY9sFk1WRNxBKtbczqKN5V69cmhGhdWk1mN2vWLAYNGsSYMWMiHUqbZgDujOnK/+IH085gYZuvnIuL14UlqpOHwXM3X8S/nd+w2zMA1Z3Hum+uYc/KFyRRbUG8riJc5mgAYhUjNr3/srQee903Nm3EVCVPw+SqBHxuCEl8/UeRrIZWLOubrHoC4W0AdZ9bGVNd9W01NFn111BZVUIrpOFX8ob0kVedThCg5lv//rCRWgbUgBeXP3whnNNgwKm/x+LVXgudtRqTqC24ctlzCFRZRCeEaL1aTWVVRF6qYuYFWz9O1G/7f+w6yBMht/3NRnh2UhT+vn9nRuGVAJQdXMr6+XfhcRyq7bKimfI4C3DqyWqcYsSGAfBTUo9RTI3N6XMBUcHHDbdETw1L8o6mshqawPnqWfHVbu1rX09d1VLQ+1srTlFrT1ezoxI4Qf+zt4Y2gNC31tWzGlCMYRMNantf6J9VRSHg96IGfHgNJsz653tUtfLfCz0htoQssopJ7AmAo3h3rV+XEKL1kWRVNIgT9dX+7QwWylU/j5btZG5INbVzgnbb/2vjLDLt/UENsGf1i2SuekmqqS2U11WEy6QlqzajFZv+91gSOJqBTg3L73fhMdiw6ElUQ86TCE3/6lqVX5uwntV69vZ6VF8wAT3c/1u8IWOllDr+v7XG1p7LK95T43ardfSsVpn7Gj4NIKRPNVBzlbWisgqEJat+1Y9LrUhWtddDJwLEJGmVVWkBEKJtkWRVHBMFuE0f8m9QFLb6yrnDvpXMkNt7E/vCny+5mNdKn8TljwXPIdb9cDdFOb9HLnBxzPweO06jBYAEUywxemJi90U+WQ343XjN5mCy6jqKEVO1UUOH3h9FFdkfuu1oPd/jDfiCCajdHFXvc5U6kuGMqAR+6HceblMU3i3vVHs9UEdlNTTJ9ldZ8OUPfV9oS4C/Mi5V71kFbbFV8LqqPzhhwaonsJaYdsHXpbIqRNskyao4asmKiedt/RinD/n/SL/tXzF7UlHgkYlRxA/9B6+UTAbAefA31vxwNx5nZIfGi4ZRrlf7Ek3RmPUEpqzKoplI0EZMWcCrxXI0I6ZqE5r+HVXP6tEssPK7g4leqV7Nrk3ohIK6KqsBv5vPj9OmcKgZ/63+ei0jqKryV+mhDaus1jK6KqAYgouyQmfg+gP+4C8WFv29lujQZFUqq0K0RZKsiqMy3GTjZVt/OhitOFU/j5bt4quQIf8pMTBzSh8WxrzK0rJ+KKqfrNUz2bXqFbnt34qU6X+X7YxRwfvT5Q2wremx0ob3H9s81NqEtQEcxXVDpwHUuw0gJAEtNVnrODO8NaGuZNXntYe8p3rS7a9vsmqomqyGtg+EXqMyLlVRKtsAQpJdr+oLtgGYAGPAhzU2TftaDCai47sB4CiSZFWItkSSVXHEro9K5y+x3TErBnb5HNxu38oOf2U1bXQXuGfyJbzneBKXNwaD5xCr591J8f5lEYxaNIbyiq1yMaDV1BScntLIBoVWWVVDhp1U3YHpWIQnq0f+fr965G0AoRXcwyarobfs6wjQH/JLRaCG70+gjp7VsOtUrazWMmfV5/dVTN8Kjq6C8I0RfIHKZBXA7PcEK6tRti4YjGb8XofMYRaijZFkVdRbnGLkmbg+nG3V5hx+687jr2U7KQ/54fjnk6PpesI/eKP8CgA8h5aw4vu7I779pmgcZfrffYr+2GOy4vPYa39DE/H7jn0eam3CktWj2PwgLIGr50it0Mpn6WF6VkPPNdQRny8kWQ2dk1ohNOn01TE7t3plNVDzn2tpAwitrPoD/rAtbM1+L+aYVEAJ6VfNJNKbTgghmpYkq6Je+htjeSW+P92N0XjUAE+VZ/K+60Dw9VgLPHtFH1Ylz+LH8r4oqp99a15g+8pX5bZ/K1bxi0qKvrjKY7Tg8xREMiQA1IAHNWQsVEMmq4SMjjqaZDVsJX09S7OBgI9ycyyx3nK22DrVea4aCK2s1hFHSAW8pqkG4dutHkEbgBr655DEPGz+qxL8O/GHbHLg01sFXKqfKMWI2efCEJWAOTqZmKTegPSrCtEWtZpkVXawajyXWdvzeFxPohQjOX4Xd9i3ssFXWZXpnwYPTLmUzzz/xOWNweTNZeV3d1B8YHkEoxZNoUxPOCr+IfEYrfjKI98GAOF52tFsi1qf66pH0QfgO4o2ANXv4aHzZmLxe7AfZiV8aOJZV2U1tA1ArXF0Vc2Lo6oK1LHAKny71eqbAkD43FivXm11qgGiFCOKIw/i2mOJbldZWS2RSQBCtDWtJlmdNWsWs2bNwmazUVraPH5YtnRRGHg8rheXRWlzDn/xFPKAfTvFIT90Lh9uZdjpT/C+Q1vtH8j/lUXf3o3XGfnqmmh8Tn+VnaJMFnzeyC+wAo55eH/t1635z/UVNii/nldQAz5c5hhc5pjDnltT/2mNcYRMbahpgVVY72ngCNoAaulZ9furJqva49CNEXyqPhdXf5+ibxZiiWlXOQmgaGetsQghWqdWk6yKhtXdEMUr8QPob4rFr6rMdGTxmnNf8Eer2QiPX9id3V1m85NjIIrq5+D6F9i87BWkn6ztcFaZqeoxWgl4m8c2mOHzUBuwDSD0M47iPdqtfy1B89WzMhta+XQ7cg9zbj0rqyF/dzUvsAr5c109q0rVOav1G11V+eeQZNUfPhdXKddG3Flj04itaAOQSQBCtDmSrIpqzrGk8HRcH+IMJvICHu6xb+MPb0nw9fR4ePxP5/C1+hwOjw2zP49V391BYY6s9m9r3FVmqrqNFvzNZM/20DStvtXG+l03tGf1yPuxtRFO2rL4+i6wCmXPXVf3CSEJolJHMhwISVZrrKyGbl5QR8/qys1zqryv5spq6BXUkGQ1tA2goq+1YiKAot+hiU3ujzkqCVUNSM+qEG2QJKsiyIzCg7HduSFaW8CxwlvCXaVbyVP/n737jq+qvv84/ror+2YRQiBM2Us2Kgi4EOvC0YqjThwFrKv6a9VqtdZtcQetta5irVZcrSAuEBCBsPeQFQIJkJA9bu695/fHTcK9JIGMm9ybm/fz8TiP3HPuued+bnIJ73zvdxz9j+yMXjbOv/gP/Kv0ZgDMectY8NntOEoO1npNCW2lx7SiOiy2WoNPIBheIeh4Uy81+Lret5s4ddXxBi7V5dCuefU+93hZ2Des1gzzLp9uADXvvzl/I6Nscfy7ZPcxj6t9BgCf5Vu9w6p3NwB31QArzzUsZbkAJKSO8Rwv3Ifb5c/Fc0WkNVBYFQA6msN5yd6XYbZYAF4vyWBmyR6fFXbuPKcjFYPSmFs6HID8zWmsXvhcrYMzpG0oPbZl1Rw8v1KaOsVUndf1SoBNnQ2gogGP/2n2eCxhdkrzd9f7MccLq1UDldyuijpmA/AKq7W0IC+oOMKCiiM1H1dHNwCflm7v1lSf2QA851dNX2Uu9YRVe/tBnprVBUCkTQqe/1kkYMbb4vmrvS8JZhv5bif3FW3jO0du9f32cHj06vEsjHqRgvJEwtz5rPv6Hg7s/CaAVUswKD1mMFV5EM3G4ROOmqkfdaPCqlcwbEjLamnBngY/l+k49bmdZSx6c3Cdf2z6tJA2oBtFnQOsvGrx6Qbg/ZyVIbWssiZTse8nNsUaXCXSJimstmFm4I6orkyP7ILZZGJ9RSG/LdzCPvfRj9kGdDBz45V38l/HHRhuM+El61kwZxplhRmBK1yCRmlFCXB0hHr5MYNtAsm7b2mjPq+vx3UbNXWVVytlQ1pWG8N0gss7j7PamM+iAMeZDeBYRh1TXnlfz11Xn9XKc6oeVXFM96KSPIVVkbZIYbWNSjTZeN7el7Fh8QDMLj3A48U7cXj953nFyHaknv4iX5aPA8Cx+z0Wzn9MfcakWrnbgW9YbcSIoRbgz0jY5Kmr3C6qVoKt76IAjXW8ltUTcddzntVj1dVntc4BVt6LN1SeVfV9Mcry8VaQvaredYhI6FBYbYNGWGN50d6XFEs4JYaLB4t28EX5oer7bRa4/7KRbOrwKmvKU7AZJfy86H5+3vhp4IqWoOR2lVNhs2GrWuc9iFYr8xlgFUzdALwC3PGWMfWHpvzp4KrnPKs1H1fHogB1DbDyKvJoy6rnq8UEuXsXkth1AuXFBynO3Vb/FyAiISN4Oph5mTNnDrm5uXz00UeBLiXk3ByZyuy4waRYwtnhLOGyvDU+QbVTLPzltltYkvRvclwpRDl28OOHFyuoSq3crnIqvAZVHW+Ko5bWXAOsfJ+jMX1WvT8ab97vV1NaVn3CqtGAPqs+izHU3rLqM8DK5zkrW1Yrr3GqLY63fppFv0VPsPnbu+pdg4iElqAMqy+99BLXXXddoMsIKXaThVn2/vwhugdWk4nPyg5yWd4adriOTj00oW8s19z4OvOMP+LGiiX7M+a9cxFFudsDWLkEM8PlwGmxVe835OPi5tbUKabqc93GXNg7+B1vsv2mWNx9AgD/6Tqm0ddobDcAt9t7UFXtLaulhZle53j3WXVVPp/n3PPD2xNrsvC7rI0cyVzSgOpFJJQEZTeABQsWMGHChECXETL6W6J5NbYfXS2ROAw3jxXv5F9lWT7nTJ80kLw+s0h3dMNCOQdX/pl1y/8ZoIqltfC0rB4NqxVBGlbdjZi8v17XbcwAK5+W1eb5fr0z8hY+HfQrMhoxg0AV3wFWDWlZdVPVDuKqo2U1e9dX1bddtXTXCJ53kYgEgwa3rI4bN47PP/+czMxMDMNg8uTJNc6ZNm0aO3fupLS0lPT0dE4//XS/FCsNd1l4Mh/Fn0xXSyT7XGVckb/OJ6jaw+HRm65kR885HHB2I9qVwZpPL1dQlXpxuxw4LUf/5m1I38bm5zMhqt8YTRxE5t1KabibqRuAyUR+ZAI0oVa310NrWxSgLi7vllWfVuTaw6/3lauOu475gZUG0R9BItLyGtyyGh0dzdq1a3nrrbeYM2dOjfuvuOIKXnjhBaZPn86SJUu47bbbmDt3LgMGDCAjwzPdUXp6OuHh4TUee+6553LgwIFGvAw5VhgmHo7pyZURKQB878jl3sJt5Hv95zGgUyQXX/44C5yXgwHR+d8w7+N7cJbn13VZER9ul8OnZdXZgL6Nzc07bBknmsOpkZo6z2rzL6jRhLDq9dgGdQOg9j65Pn1ZfRYcOMp5TJ/VKpEmC1GYKfFjC7mItB4NDqvz5s1j3ry6l/u75557ePPNN3nzzTcBuPvuu5k0aRLTpk3jgQceAGDkyJGNLLemsLAwn+Brt9v9du3WKtUcziv2fgy22XEbBi+W7CWtNMPn1/8vT+1J9KhZLHP2xYSLss3P8v2C1/DvJD8S6gx3hW+f1SBtWW3Mx/V1aepiA8465h5tDiY/TSXmasASuj6zAXgvgOB1jnfkrG1RgNqm9OpoCednV2mN4yIS+vw6wMpmszFixAjmz5/vc3z+/PmMGdP4jv7Hc//991NQUFC9ZWZmnvhBIWy8LYHP4ocy2GYn113BTQUbedUrqNoscN+UiygZ/jl7nX2JNg6yY95V/LhgFgqq0hgVXgsBOIPoPdRsy616325E2PReDaq5Zik4yj9htSFTbLmN2gdmeb9W7zDqHfgrKv/YObZlFSDWFJRDLESkBfg1rCYlJWG1WsnOzvY5np2dTUpKSr2vM2/ePD766CPOP/98MjIyjtsS++STTxIbG1u9paamNrr+1swM3BnVlb/HDiDebGNtRSGX5K1hcUVe9Tmp8WH87tZHSU98hVIjhvjSpXz97vns3bUsYHVL61dhPhpWa1tDPlCaazaApjLqCHDNo/Fh1fuRDWkx91kIwOtxvi2r3mHV+7F1D7CKCqLV0USkZTXLn6rHtjaYTKYGtUCcd9559T7X4XDgcDjqfX4oijdZmWnvy/iwBKD21agmDEyl91lpLHMOBcC25xU+nTuzBfrMSair8J7gPYhSoe+o/eaZDaAxLas+YTV4syomn59r/b9/rjqWafVZbtX7Ad7nV35vauteobAq0nb5NawePnwYp9NZoxU1OTm5Rmurv02fPp0ZM2ZgNgfl1LHNZrA1hlfs/Ui1RFBquHioaAefek3yD3DLBRPI7v4iPzsTiCSPPYvuZuOG7wJUsYQan7AaTN0AvPtr+nEVWIOmrYxluFuyZbXxGts313eJVa/ZAOoYYOXbPcDzWGctKT7a1LZ+t4vIUX79119RUcHKlSuZOHGiz/GJEyfy448/+vOpakhLS2PgwIGMHj26WZ8nmFwZkcK/404m1RLBblcpv8xb6xNU7eEm/m/qnfzc9W0K3Qm0c67lxw8uUFAVv6oweY8aD57w1dQW0HpdtxHdHlqyG4DJT90AGvIqvVthnXXMs1pXEK6onPZKLasi4q1RU1f16tWrer9Hjx4MGTKE3NxcMjIymDlzJu+99x7p6eksXbqUW2+9la5du/Laa6/5tfC2LAIzf47pyWURHQCYX57D/xVto8jrP8GBXeI5Y/KLrHCdAUDs4X/y8cePYrjbdpcJ8T+feTKDqKXQNxA1z3UbdwE325P60uXIbtItzRzAmtIa6f1HSANetc/33V17y6rvA7xaXKtaVhVWRcRLg8PqyJEjWbBgQfX+888/D8Dbb7/NjTfeyIcffki7du14+OGH6dixIxs2bOD8889n7969fiu6Nm2lG0A3cwSvxPanvzUal2HwXMlu3ij1nQHhsrGDsQ2bxSZXF2yUUbjmAb5f+nGAKpZQ5/RpWQ2eAVbejGaqy93I5PrsGX/E4nZR8s0d/i2oUn7WSuJSRnBgy4dNuMrRn2tDXqZhqn1+1rp+ArV1A6jt3GiFVZE2q8FhdeHChSecu2/WrFnMmjWr0UU1RlpaGmlpadjtdgoKClr0uVvKOWGJPBvTB7vZymG3g7sKt/JTxdEJ/G0WmHHFVWyKf5QKVzjxxm6Wf/EbMjM3B7BqCXXerWBB1WfV53ZzdQNo3HUNkxmnxYwf2mlrtfaLX2NvP5i8rBWNvoaroqRxj/O+7a59Bau6umhUdauorc+qWlZF2i5NXNcKWIC7o7rxm6guAKysKOCOwi1ke32k3ykhgiuv/Aur+RUAScXz+fTfv6OiPDSDuwQPp1euCK4+q14tg/5cbtVnp2ktts3V4utylpB3oGlT0hXnbAJ73wY/zuksq/6fxV3HogDevAN/1eCzWvus+neIhYi0IiETVkO1G0A7k40X7H05LSwegLdKM3m6eLdPa9b4gV056czXWO0aiBkXlp3P8NFXr6NJ/qUleL8XnUHUDcC3z6ofp67y+mCpMbMB+F4s9P6NOsqOQITnD2vvbiF1tkJ7n2PUXBTAYbgJM5nVsirShoVMsgvF2QCGWe18Fj+U08LiKTZc3FGwhceLd/mEg6kXnEPshP+yxzWQGA5zYMGvmf+Vlk2VlhO0YdUrVDbXqPvGZs3yoiwA8pvwMX1za+w8Au46uoXU1UXEZ8lZo+Zyq7mVS72qz6pI2xUyLauh5rqIjtwf3QObycwOZwm3F25mh9e62PYIC7decw+rwm4HA9o7VzLvw2kU5DfvfLYix6qoY/7MQPPJQP68rs/txl156exxWKwROB2h102nru9PnX/GeP9RUd2yetQRw0kK4WpZFWnDFFaDTBRmHrf35qLw9gD8r/wQDxTtoNh7Wqqu7Rh38cusco0FIP7QP/hozhMYlS0QIi3Ju89qQ9aQb24+H9c32zyrjRxg5XbgDNGV91x1BNS6vlOG26sbgLtmN4BCw9PvNVyLAoi0WSETVkOhz+pJlkhetfejtzWaCsPN08W7ebtsv885l44djmVYGltdHQmnmPxVv+eTZV8EqGKRY2YDCKZuAD4fZPszrDbPdUOF9zugPgPufAZY1dJntbTymNWfy5CJSKsSMmG1tU9d9YuwdjwZ05sYs5VsVzl3FG5lpfPo67CaYdqUG9gc/0dcLhuJxg5++vw3ZO7fHsCqRY7ps+rX6febprlWsMInXIVuWG1sa7RRxxRVdZ9TS1g1vMOq5z1lO8GUiSISukImrLZWVkz8X3R3bopMBeAnRx53Fm4lxzj6kX6nxCh+OeUp1jMZgPbF/+OTD+6jwlEckJpFvHl3PnG5gzOs+nP+17qWCg01Xzty+dlZ4vNHc324fW7Xo2XVOxRXvn+8f14lalkVafMUVgOovcnGS7H9GGWLA+D1kn3MLNntM7hg/OCedJ3wGutdfbBQgWXHE3z49T8CU7BILbxnAAimllUfzdQ9IZRbVstxMylvVYMf5zsbwInttdiqb9c2wEphVURCJqy2tj6ro62xvBjbj/bmMArdTv6vaDtfO3J8zrnpwvPJ6vosma4YYslm+3fT2bQ1PUAVi9TOpxuAO4gGWHnd9mcLqHGcPTl2UNWJvz8/hkfxr6HXsSehO8bnnkVNfFtWPVe0aoCVSJsVMmG1NfVZnRqZyn1R3bGaTGx1FjO9YDN73GXV98dGWLnp179nre1WMKBDxVK+/PC3FBQcCmDVIrWrMLxb0oInvNVnJHpj+ITgEG5ZbSzvPwzqas/2+a6ZTHzXe5LneC3zrFYNsLKpZVWkzQqZsNoaxJgsPBXTm/PCkwD4tOwgDxXtoNTrV/qgbsmcdtErrHWdAkBC9mv8+5Nnqj8eEwk23h/9O72W1wwm/vy43qjjtng0dDYA3wfXnA2gpPKKFg2wEmmzFFZbSG9LFGmx/elhicRhuPlL8U7eL8vyOeeScaMxn/wqO1zJRJgKyUu/lznL5wWoYpH68e6zGkwtjf6YD/WE1w2iqbqCRV0j/etU66IAGmAlIkcprLaAi8Pb85eYXkSZLOx3lXF74RbWOYuq77ea4bYrb2FL3B9wu620N7aw+JPfsD9rVwCrFqmfCu8BVkbwtKw216IAPs+httXjamiUr60bQIlXN4AxtjgqDIMVDZyhQERat5AJq8E4wMqGiQeie3BtZCcAFjuOcHfhVo54/YeemhjDJVOeZRPnA5BS9AkffXA/zorSWq8pEmwqvCJJUC0K4LPcqv/qar75W0NPXX2Y655/tbbZADw/O7vZyrtxgwEYePhHyoN15gkR8buQCavBNsCqozmMl+39GGqLBeDlkr28XLLX59fr+MG96TzhdTa5emLFgWX7n/nXN+8FpmCRRvJeYjW4lls92rTafH1WFVaPpz7fHZNPPwDPb0jvQVqltbynTrJEstmleaZF2oqQCavBZIwtjhfs/Ug028h3O/ld4VYWVBzxOeeGiyaT1eUpDriiiDftZ8vX09i8fU1gChZpAp9uAEE0wKq5BkL5zgag1r1jmbz+SGhs9wvv6dCKawmrvaxRCqsibYjCqh+ZgN9EdubuqG6YTSY2OouYUbCZfe7y6nPskTZuuPqPbAi7AQzoWLGIL/59B4WFuQGrW6QpKrxbVoPoo9mW+bheLavHU1c7e8kJWuDdtSy36q2XJbIpZYlIK6Ow6iexJgvP2ftyVlgiAB+WZfFI0c84vP4zG9g9hVMumMUG93AAErNe5v1PZzbb6joiLcF7IYBgbVl1+TFEN9f8raHo2G4STxTt5LSweD4tP3j0YC1TUnlH2dq6AXQyR/irRBFpBRRW/WCAJZpXYvvT1RJBueHmkaKf+ag82+ecS8aPxRj8Mrvc7Ygy5ZO7/C4+Tv8uQBWL+I/bp89q8Pzh5ROUNBtAQBz73flH2X7+Ubb/mKPHn5Kqtm4ASWZbLWeKSKhSWG2iy8OTeTSmJxEmC3tdZdxesJlNXn2prGYTt1w5nW1xv8PtttDB2MjCObdxIDsjgFWL+I87SFtWvTXXPKvBNK9ssDjodlTfdjby+27xul1aS6t4kjmsUdcVkdYpZMJqoKauOjMskQiThe8cudxbuJUCr1aATomxXDxlJluY6Nkv/Df//uAhnM7yui4n0uq4vKZicwXRbABuvAf6aOqqllJsuDg7Nx1HE/5EMHt1DSg33LgMw2cFq/ZqWRVpU0ImrAZq6qo/FG1nWUU+/yw74POLedyQAXQ6/TW2urthowzztoeZ/e2/W6wukZYSrC2rzRUqjePsicced1m9zzXV0g3A4nXMiYETw+dYosmGBRhliyPf7dTMACIhLmTCaqAUGS7eKzvgc+z6i39JVufHOeiOINGUwcb5v2HLjg0BqlCkebndTqr6HQbTClbemm8uAIXV5mA5Zt+JQbjXvtlk4sqIFB6N6cURdwWn5S5vdJcDEQl+Cqt+ZI8K59qrH2Gz7WowoHPFd3zywV0UFeUHujSRZuM2nIDnY1m3OzgHWKkbQOtSdszPy2kYNcZh3RXVDYAEs43B1hhWOwtbqjwRaWEKq34ysHtnRl4wi83ukzHhJvHA87z36cvoY0IJdbsNF2Cj3BKOEUQtq8210pTCqp/VMnXVCmcBn5UdZLurBKh9/t4Er36ro21xCqsiIUxh1Q8mTzgD98AX2euOJ8aUy+Gf7uA/qxYFuiyRFlGKm99e8gYusxXj7RGBLqeaz3yofu2z6tViqz9Gm4UB/K5oW/V+hdfP74CrnI6WcJ/zu2uRAJGQ1rJD50PQrdfcReGAtygy4ulorCH9PxewVEFV2hSDMlsUFZYwjCCaDcD7c+Pm6luqPqtNd2jnPACKc7fVeY53f9TMWgZvdTGH1zgWCDZMROi/VRG/U8tqE5WZkzAw07ngPd7/4M+4XI4TP0gkhBhe/QuDKaz6zofaPH1Wm2uxgbakrDCDxW8Nw+moexYXn7DqKmdkZQ+Aw24HSeYwUi2BX9HKAnwRP4xok4XJeavJDaIuMSKtnf4EbKL3//Vn3Ktv5r3Zf1RQlbbJK7AZ7mAKq151+fXKzTN/a1tWUZaLcZxpz5xe77F9Xi2r3zlyAehoDq8xg0BLO9lqp5c1io6WcKZGdg5wNSKhRWG1iZxOBwt/+jrQZYgEkFcoDNKW1eaaZ1XdAFqG939UyyqOzq6y2JGHw3BjNZnobI7g1shUzgpLbPkCgfFhCdW3B1tjAlKDSKgKmW4AgVrBSqSt8wmCQdXS6D0QSlNXtWbeK1qtqCjg4aIdjLXF850jl0x3OT0skbwU24+B1hiKDRejcn7C0cJ/SHgH1G5B0C1BJJSETLJLS0tj4MCBjB49OtCliLQpJXk7AKgoD675hJurb6laVlue2avrRQUG75dlMaNwC2W42efydAsYWBkWo00WhttiW7zGfpbo6tsdzeGE1bIyl4g0Tsi0rIpIYLidZfzw9wHH7XMYaH4NlV6tfEZQtSSHrtLjdC+pCqveRtvi+Kmi5f54SjBZSamcTsttGJhNJrpYIvjZVdpiNYiEspBpWRWRwHFVFON2lQe6DB/e7VouP/alVctqy9t1nNC3z13zfderct7VaFPLDLsaa4sHYLuzmJ2VtSaZw1rkuUXaAoVVEQl5zTbASn1WW8SOypWsarPcqwV1h9NzXk9LFI/H9GJtu9N4NLpns9d3QXh7wDM7QZ5RAUC8SR9civiL/jWJSMjzZ6T0mb9VLastYnbpAW6MTPWZCaDKamchm5xF9LFE82zJbl6PHUAvSxR9rZ4+pNdEduQvxTuxmyx0skSwwVnk19p6W6KYGN4Ot2HwafkhelmiAIVVqelUWxw3RaYy0BKNyWSiyO0k013OXlcZ+9xlZLg82153GYVBNLNKMNC/JhEJSc33cb36rLa0Q0YFp+Qsw1HHrA435G+kndnGDlcJpYaLyGM+/j87LJE/xfSkvTmMS/LWsMlZRC9LFDtdpT4LDjRG1VRZCyqOsN1VwpHKxQDizbYmXVdCy62Rqdwb1d1nZotkcxgnEVXr+fluJ3tdpWRUhti9lSE2w1XGAXc5bS3KKqyKSMjzZ6g06rgtzavsONOP5RoV5Lo8H7/vcpUy4Jh5Tv8c04vEyvB4YVgS90R1Y3xYAq+XZPBjRT63RnbmuZLdbHUWc7LVzg5XCXmGk0jMlJ5g2rPTbHEALHIcASC/MqzGqWVV8ATS/4vqziURyQB8WJbFh2XZlBlu4sxWUs3hdLFE0NUcQWdLBF0sESSbw4gzWxlstjMYe41rVhhu9rvLyXCVscdV5hNoM9xlFIVgq6z+NYlISPIeYNVcK02pz2rw2eEsqQ6rRW4nMWZrdVAFuCkytbp16+LwZCaGteMkaxRjw4ay0JHLhLBE1lQUstlZxFWRHXmtJIPBVjsJZitvlmbyYPRJfFJ+kBLDxaXhyXSunFP1x4o8APLcntCcYD7+f69mIAwzYSYTNsyEmcyEYfL9Wnnb5nOfGZvJVP1YKyYsmLCYKr9iwgJYarnPWn2bY/Yr7zeZMOOZKswzoMWEqbJWk8lUPcjFVHm/iaPTipnwzIdbdb7PY+s4rymTezX2X15T/sWaqjeTzz6Vx0zHHjOZqgf5OQ2Dx4t38l7ZgaMXrCNTRmKuDq5dzZVfLRF0qbwdbjLTzRJJN0skp9fy+Fx3RXVw3esqZauzhNXOQg7UMhixtVBYFZGQ13x9SxVWg80mVzEXV95+q2w/v43q6nO/98ewHSunm6oyofIj/aE2O0Ntnhat30R1qb7/r/a+AEyNTPV5XLarvHqaqryqbgAmG49G92RsWDw2r6BZFU6tJs3D2ha4DYO1zkKeLd7NcmdBvR5TipvtrhK21zKw0AR0MIdVB9eqENu18nY7cxiJZhuJZhtDj2mVzXKVs8ZZyNKKPBY4jpDZisJr0IXVzp07895775GcnIzT6eSxxx7jP//5T6DLEpFWzK99SzXPalB7v/QAbsMg13CysiLfJ6zud5XRqRlWl5rvyKm+nV8533BnSwQTw9vV+xrlhhuH4caBmwrDwIEbR42vbhwYVHh9dWLgNAxcGLgAV+W+GwMnBq7q+zz313Wf5zjV++D5U8xteHp8uyuPuKuOe/UE9z7HqDxuVN6uOt845pzm+pfTkD8B6nuuCVP1q616bZ7b3q/16Gs8ep7BEcPp14/lDSDL7SDL7WBFLeE32mShszmcrpZIuloi6GaOYLA1hv7WGFIs4ZxnCee88CTA8ynE/8oP8Un5wVqngAsmQRdWnU4nd911F2vXrqV9+/asWrWKL7/8kpKSuqcuERE5Vkv0LdVsAMGnBDf/KNtfvZ/hKqOLJYL/lh/CYbi5rDKszi49wDWRHQFYUZHPqMq+p6sqCqpXwNrnKqv+mD/bVU4HSziH3Q7iTFYcGPyucCv5bicrvUJDSWUwsXsN8rosb40njGLgMNxUVH6tCp9NHeQlUqXYcLHVVcLWY1plIzAz2BrDSFss48ISGG6NpZc1ijut3bgzuhvLKvJ5qzSTbx25QfluDLqwmpWVRVZWFgCHDh0iNzeXxMREhVURaTT/zrN69FpqWQ1+V+Wv47qITrxXth8XntWtdrpKmVmyh1RLOGZM3Fu4lb/a+9LZEsG0gs38OaYneYaTJ4t38XD0SSxwHGFpRR5XRqTwafkhIk1m8txOcivnVPVWXtlmGFUZVl2GwTo/T5cl0lBluFnhLGCFs4BZpfuwmyycFZbIpeHJjLHFc4otjlNscexwlnBP4VY2uYoDXbKPBi8KMG7cOD7//HMyMzMxDIPJkyfXOGfatGns3LmT0tJS0tPTOf302roAn9iIESMwm83s27evUY8XkbbLZ4BVM030ohWsgl+W28EzJbs54HZw0O3gzCPp3FSwkXzDyc0Fm7ipYCO5hpMbCzYy8chKcowKZhRu4cGiHRQZLv6vaDtfOg5zxHAyq3QfB9zl7HSV1hpUwfNxPkCUyfPfq0vvEQlChYaLz8oPcUPBRiYcWcGskgwK3E46WcKDsi9rg1tWo6OjWbt2LW+99RZz5sypcf8VV1zBCy+8wPTp01myZAm33XYbc+fOZcCAAWRkZACQnp5OeHh4jceee+65HDjgGSmXmJjIu+++y80339zQEkVEfPizZdXkFYMVQ1qf5v6ZVYXViMqWVXUVkWB3wO3gryV7eL10H4OsMdXTrwWTBofVefPmMW/evDrvv+eee3jzzTd58803Abj77ruZNGkS06ZN44EHHgBg5MiRx32OsLAwPvnkE5588kmWLl16wnO9g6/dXnNOMhFp2xQYpKU4jnmvOTW9mbQSRYaLn2pZJS4YNLgbwPHYbDZGjBjB/PnzfY7Pnz+fMWPG1Ps6b7/9Nt999x3//Oc/T3ju/fffT0FBQfWWmZnZ4LpFJPT4DLDyZ2DwupZiiByr/Jh+zOrVLNJ0fg2rSUlJWK1WsrOzfY5nZ2eTkpJSr2uMHTuWKVOmcMkll7B69WpWr17NoEGD6jz/ySefJDY2tnpLTU2t81wRaauaJ1ZqUQA51rErbWmkv0jTNctsAMf+AjeZTPX+pb5kyRIsFsuJT6zkcDhwOBwNqk9ExB8UQ+RYNVtW9S4RaSq/tqwePnwYp9NZoxU1OTm5Rmurv02fPp2NGzeyfPnyZn0eEWkdWmJ9IMUQOdaxYVV9VkWazq9htaKigpUrVzJx4kSf4xMnTuTHH3/051PVkJaWxsCBAxk9enSzPo+ISBXFEDmWA7Wsivhbo6au6tWrV/V+jx49GDJkCLm5uWRkZDBz5kzee+890tPTWbp0Kbfeeitdu3bltdde82vhIiLH0xIRQfOsyrHcgMNwE1Y5z2rwTQIk0vo0OKyOHDmSBQsWVO8///zzgGcE/4033siHH35Iu3btePjhh+nYsSMbNmzg/PPPZ+/evX4rujbTp09nxowZmM1+bSwWERFpEO+wqpZVkaZrcFhduHAhJtPxe4PNmjWLWbNmNbqoxkhLSyMtLQ273U5BQcGJHyAi0kSKIVKbctzEVN52qc+qSJOpGVJEQpIGWEmgeA+y0nKrIk0XMmFVswGISEtTn1WpTbnX+8IVwDpEQkXIhFXNBiAi3lpmgJVITWpZFfGvkAmrIiIiwaDMO6yqz6pIkymsiog0gOHVGVYxRGrjPdeqWlZFmi5kwqr6rIqINw2wkkBxG959VvUuEWmqkAmr6rMqIi3B1CIxWFozdx23RaRxQiasioh4U3uWBIr3QgBO9VkVaTKFVRGRBtB0VXIivi2rer+INFXIhFX1WRURkWDg3U/VqbAq0mQhE1bVZ1VEvKlnqQSKdzx1qxuASJOFTFgVEREJBt5zq6plVaTpFFZFJCQpIkigePdT1WwAIk2nsCoi0gAmpWA5Ae+AqnlWRZouZMKqBliJiEgw8G5Z1XKrIk0XMmFVA6xERCQYuLSClYhfhUxYFRHxptkAJFC846nCqkjTKayKiIj4kXdAdQWwDpFQobAqIiFJ7VkSKD4DrNRnVaTJFFZFRET8yGeAlf5sEmkyhVURERE/0tRVIv4VMmFVU1eJiLfmGmBlaOSWnIDPbADqBiDSZCETVjV1lYiIBANDA6xE/CpkwqqIiDe1Z0mgqBuAiH8prIqIiPiRSwOsRPxKYVVERMSPfBYFUJ9VkSZTWBWRkKRxUBIoWm5VxL8UVkVERPxIfVZF/EthVURCkiKCBIpbswGI+JXCqoiIiB95h1Wn+qyKNJnCqoiIiB+pG4CIf4VMWNUKViIiEgzcXq2pToVVkSYLmbCqFaxEpCW4nWWBLkGCnHc/VU1dJdJ0IRNWRURaQtHhTYEuQYKc93KralkVaTqFVRGRhnA7A12BBDmXwqqIXymsikhIKs3fG+gSpI3yGWClbgAiTaawKiIhqbw4K9AlSBulAVYi/qWwKiIi4keaukrEvxRWRSQk5RoVgS5B2iiXFgUQ8StroAsQEWkOb5Tso6clknnlOX69rqGWMjkB73eIugGINJ3CqoiEpBLc3FG4NdBlSBvk3bKqbgAiTRd03QBiYmJYvnw5q1evZt26ddx8882BLklERKTefAZYqRuASJMFXctqSUkJEyZMoLS0lMjISDZs2MCcOXPIzc0NdGkiIiInpAFWIv4VdC2rbreb0tJSACIiIrBYLJhMpgBXJSIiUj9uLQog4lcNDqvjxo3j888/JzMzE8MwmDx5co1zpk2bxs6dOyktLSU9PZ3TTz+9Qc8RFxfHmjVr2LdvH8888ww5Of4dICEiItJcXF631Q1ApOkaHFajo6NZu3Ytt99+e633X3HFFbzwwgs8/vjjDBs2jEWLFjF37ly6dOlSfU56ejrr16+vsXXs2BGA/Px8hg4dSo8ePbj66qtJTk5u5MsTERFpWYZaVkX8qsF9VufNm8e8efPqvP+ee+7hzTff5M033wTg7rvvZtKkSUybNo0HHngAgJEjR9bruQ4ePMi6desYP348//nPf2o9JywsjPDw8Op9u91e35ciIiLid95LrKrPqkjT+bXPqs1mY8SIEcyfP9/n+Pz58xkzZky9rpGcnFwdOO12O+PHj2fr1rqnn7n//vspKCio3jIzMxv/AkRERJrIZ4CVugGINJlfw2pSUhJWq5Xs7Gyf49nZ2aSkpNTrGp07d+aHH35gzZo1LF68mFdeeYX169fXef6TTz5JbGxs9Zaamtqk1yAiItIU3gOsKtSyKtJkzTJ1lXHMX5Imk6nGsbqsWrWKYcOG1fu5HA4HDoejQfWJiIg0F01dJeJffm1ZPXz4ME6ns0YranJyco3WVn+bPn06GzduZPny5c36PCIiIsfjM3WVugGINJlfw2pFRQUrV65k4sSJPscnTpzIjz/+6M+nqiEtLY2BAwcyevToZn0eERGR+lLLqkjTNbgbQHR0NL169are79GjB0OGDCE3N5eMjAxmzpzJe++9R3p6OkuXLuXWW2+la9euvPbaa34tXEREJBiZOLqQjaauEmm6BofVkSNHsmDBgur9559/HoC3336bG2+8kQ8//JB27drx8MMP07FjRzZs2MD555/P3r17/VZ0baZPn86MGTMwm4NuUS4REWlDvP8XUjcAkaYzQWj92We32ykoKCA2NpbCwsJAlyMiIebR6J5cE+lZwKTX4cUBrkaC0S/C2vFybH8A+h9eohkBRGrRkLymZkgRERE/UjcAEf8KmbCq2QBERCQYmLxuK6qKNF3IhFXNBiAiLcFQ/JATMJtMJz5JROotZMKqiIhIMNB/rCL+pX9TIiIifqR2VRH/Cpmwqj6rIiISDEyKqyJ+FTJhVX1WRUQkGITMf6wiQUL/pkREGmBxRR4ALk32LnXY6SoNdAkiIaXBK1iJiLRl3zhyuT5/A9ucxYEuRYLUamchdxduZbdCq4hfhExY1XKrItJSllS2rorU5YvyQ4EuQSRkaLlVEREREWlRWm5VREREREKCwqqIiIiIBC2FVREREREJWiETVrUogIiIiEjo0QArEREREWlRGmAlIiIiIiFBYVVEREREgpbCqoiIiIgELYVVEREREQlaCqsiIiIiErRCJqxq6ioRERGR0KOpq0RERESkRWnqKhEREREJCQqrIiIiIhK0FFZFREREJGgprIqIiIhI0LIGuoDmYrfbA12CiIiIiNSiITkt5MJq1YvPzMwMcCUiIiIicjx2u/2EswGE3NRVAJ06darzhS9fvpzRo0fX+djj3V/bfXa7nczMTFJTU1vFVFknev3B8hyNvUZDHlffc/We0XumIec29Ry9Z1ru+q3lPdOY+/WeaZ7r6z3jX3a7nf3795/wvJBrWQWO+8LdbvdxfwjHu/949xUWFraKXwgnev3B8hyNvUZDHlffc/We0XumIec29Ry9Z1ru+q3lPdOU+/We8e/19Z7xr/o+T5sbYPXqq682+v4TPbY1aInX4I/naOw1GvK4+p6r94zeMw05t6nn6D3TctdvLe+Zpt7fGug907Bz29p7JiS7AbQkrZglDaX3jDSU3jPSUHrPSEMF83umzbWs+lt5eTmPPPII5eXlgS5FWgm9Z6Sh9J6RhtJ7RhoqmN8zalkVERERkaClllURERERCVoKqyIiIiIStBRWRURERCRoKayKiIiISNBSWBURERGRoKWwKiIiIiJBS2FVRCSA3nrrLZ588snq/YULF2IYBn/84x9rnPvTTz9hGAaPPvpoS5YoIhJQ1kAXICLSVplMJi644AIuvvji6mNDhw5l9+7dDB482Ofc66+/nk6dOgGwatWqFq1TRCSQ1LIqItJM1q9fz4MPPsisWbPIycnhwIED3HXXXdX3jx07FrfbzbJlywDo3bs3sbGxvP322z5hNSYmhieffJK3334bgJUrVwKQnJyMYRjccccdrFq1itLSUjZs2MDYsWN96khKSuL1118nKyuLkpIS1qxZw7hx45r3xYuI+InCqohIMwgLC6Nv375cd911LFy4kNGjRzN79myeeuopoqOjAbj44ov54osvMAzPQoIjRoygtLSUf/3rX/Tu3ZuwsDAAHnroIdasWcOBAwc4dOgQ+/btA2DYsGEATJ8+nbvvvpshQ4awe/duZs+ejclkAqBr166sW7eOhIQEJk+ezMknn8zLL78cdGt/i4gcj6FNmzZt2vy7DR8+3DAMw5g0aVL1sQEDBhiGYRjt27c3AGPLli3GhRdeWH3/M888Y/z0008GYOTl5RlDhgwxevXqZRQWFhp9+/Y13njjDWPu3LnV5//f//2fUV5ebnTv3r3G83bu3NkAjP/973/Gd999F/DvhzZt2rQ1dlOfVRGRZjB06FAOHDjAV199VX2sQ4cOlJeXk5OTQ79+/ejcuTPffPNN9f0jRoyo/oh/3bp1DBo0iCuvvJK//e1vbN26lREjRjB37lyf55gzZw67d++uPlZeXl59u0uXLpx//vnVLbAiIq2RugGIiDSDIUOGkJ6eXuPYhg0bcLvdXHzxxXz99deUlZVV3z9s2LDqwVNr167lzjvvZPTo0Tz66KPYbDYGDhzoM7hq6NChrFmzxuc5hg8fzqFDh8jMzGTYsGGUl5fXOEdEpDVRWBURaQa1BUnvY5MnT+bzzz+vvq9Hjx4kJCRUt6yuWbOGUaNG8eCDD1JQUMDgwYMJCwurvj8iIoLevXtjsViqr2Eymbjzzjt55513MAyDiooKrFYrkZGRzftiRUSaWcD7ImjTpk1bqG1HjhwxLrvsMp9ja9asMW6//Xajffv2hsPhqO67Chi//OUvjbKyMsNqtRqAYbVajXbt2lXff/PNNxs5OTnV+6NGjTIcDoexadMm49RTTzX69etnfPjhh8aOHTuMuLg4AzASExONnJwc4+233zb69etn9O/f37jtttuMvn37Bvz7o02bNm313dSyKiLiZ926dSM+Pt6nZdVms9G/f39Wr17NRRddxLJlyzh06FD1/cOHD2fDhg04nU4AnE4nOTk5PvevXr26en/o0KFs2bKFv/zlL/znP/8hPT0dt9vNaaedRn5+PgC5ublcdNFF9O7dmxUrVrB48WIuueQSsrOzm/k7ICLiPyY8qVVERJrR0KFDWblyJXFxccyePZvFixfz7LPPNvp6r7zyCgkJCVxzzTV+rFJEJPioZVVEpAUMHTqUnTt3UlRUxOLFi/nXv/7V5OutW7fOT9WJiAQvhVURkRYwZMiQ6m4Bzz77bPXE/o01ePBghVURaRPUDUBEREREgpZaVkVEREQkaCmsioiIiEjQUlgVERERkaClsCoiIiIiQUthVURERESClsKqiIiIiAQthVURERERCVoKqyIiIiIStBRWRURERCRoKayKiIiISNBSWBURERGRoKWwKiIiIiJBS2FVRERERIKWwqqIiIiIBC2FVREREREJWgqrIiIiIhK0FFZFREREJGgprIqIiIhI0FJYFREREZGgpbAqIiIiIkFLYVVEREREgpbCqoiIiIgELYVVEREREQlaCqsiIiIiErQUVkVEREQkaCmsioiIiEjQUlgVERERkaClsCoiIiIiQUthVURERESClsKqiIiIiAQthVURERERCVoKqyIiIiIStBRWRURERCRoKayKiIiISNBSWBURERGRoKWwKiIiIiJBS2FVRERERIKWwqqIiIiIBC2FVREREREJWgqrIiIiIhK0FFZFREREJGgprIqIiIhI0FJYFREREZGgpbAqIiIiIkFLYVVE2pTY2FhcLhd33nlniz1nTEwMTz/9NF999RUHDx7EMAz+9Kc/Nega0dHRPP/882RmZlJaWsrq1auZMmVKreeazWays7O56667qo/94he/wDAMDMPg2muvrfEYm83Gtm3bMAyDjRs3Nqg2EZHmpLAqIm3KyJEjMZvNrFixosWes127dtx6662Eh4fz6aefNuoac+bM4frrr+fRRx/lF7/4BStWrOCDDz7gqquuqnHu+PHjSU5OZs6cOdXHhg8fDkB+fj6DBw+u8Zh77rmHlJQUAFauXNmoGkVEmouhTZs2bW1l+7//+z/D4XAYERERAXn+du3aGYZhGH/605/q/Zhf/OIXhmEYxpVXXulz/KuvvjL27dtnmM1mn+OvvPKKsXz5cp9jH3/8sZGdnW18/PHHxty5c33uS0lJMQoKCow33njDMAzDuPPOOwP+c9KmTZu2qk0tqyLSpowaNYoNGzZQVlZWfeyuu+6itLSUJ598ErM5+H4tXnrppRQWFvLRRx/5HH/rrbdITU3llFNOqXH+xx9/7HNsxIgRrFq1ijVr1tRoWX366afZt28fP/74IwCrVq2qvu/zzz9nxYoV3HzzzaxZs4aSkhL27t3LI488gslkqlHr6aefzpw5c8jOzqa0tJQdO3bw9NNPN+n1i4gEPDFr06ZNW0ttu3btMl5//XUDPK2cX3zxhXHo0CHjvPPOq/V8i8VSr62+z9+YltUff/zRWLZsWY3jAwYMMAzDMG655ZbqY2PGjDEMwzB69epVfSwxMdEwDMN4/PHHjYsuusgwDMNISEgwAOOUU04xXC6Xce655xrPP/+84XK5jJiYmOrHZmZmGoWFhcbGjRuNa665xjjnnHOM999/3zAMw5g6dapPPXfffbfhcrmM//znP8aUKVOMs88+27jnnnuMv/3tbwH/uWvTpq1VbwEvQJs2bdpaZKsKilOnTjXGjRtnZGRkGIsWLTJSU1NrPX/ChAlGfXXr1q1BNTQkrG7durXGR/fg+fjeMAzjD3/4Q/WxmTNnGmvXrvU575xzzjEMwzAuu+wyo0uXLoZhGMb48eMNwFi2bJnx2WefGYCxcOFCY/PmzdWP69Spk2EYhrFjxw4jNja2+rjVajX2799vfP7559XHJk2aZBiGYfzud78L+M9ZmzZtobVZERFpI0aNGgXAxIkTmTVrFs8//zwPPPAALper1vNXrlzJyJEj63Xt/fv3+63O2hiGUa/7LrvsMv7xj3/43D9ixAjA8/F+RkYGOTk5DB48mJNOOokhQ4Zw9dVXAzB06FD++9//Vj+u6vv1yCOPUFBQUH3c6XSyY8cOkpKSqo898cQTrFixgr/+9a9NeJUiIjUprIpImzFy5EhKS0s5//zz+eGHH/j9739/3POLiopYs2ZNva5dV+D1h5ycHNq1a1fjeGJiIgC5ubmAJ1x269atRn/V4cOHk5uby+7duwFYu3YtY8eO5cwzz+T555/n559/pnfv3sTGxvrMBDBy5EgcDkeNvrIAnTp1qj43JSWF4cOHc8899/jl9YqIeAu+kQQiIs1k5MiRrF69ml/+8peMHz+eJ5544rjnT5gwAafTWa+tW7duzVb3+vXr6d+/PxaLxed41UCpDRs2AHD55ZezdevWGvOkDh8+3GfQ1Jo1a7jqqqswDIPHH38cONr6emxYPXz4MOXl5T7XGz16ND179uTLL78EIDU1FYDMzMwmv1YRkWOpZVVE2oxRo0bx8ccfM3/+fG655Rbefvtt9u3bR1paWq3nB0s3gE8++YRbb72Vyy+/nA8//LD6+PXXX09mZibLli0DqHE/eBZBOOmkk3xaW+fOnctJJ53EO++8Q1FREeAJtG63m9WrV1efN3LkSOLi4oiLiyM/Px/wLDjw9NNPs2vXLt5//33gaEg9+eSTazy/iIg/BLzjrDZt2rQ191Y1GOn666+vPvbggw8aTqfTuOyyy5r9+c877zzj8ssvN2644QbDMAzj3//+t3H55Zcbl19+uREZGWkAxvjx442KigrjoYceqvH4r776ysjJyTFuvvlm44wzzjBef/11wzAM4+qrrzYAY8iQIYZhGMbw4cN9HnfGGWcYhmEYV1xxxXHr++abb4xt27ZV73fv3t0wDMPYvXu3MX/+fGPSpEnG5MmTjW+//dYoKCio8Txz5841SkpKjAcffNA488wzjYsvvth48sknjeuuuy7gP3tt2rS1+i3gBWjTpk1bs29VUzYNGjTI5/hrr71mlJSUGKeffnqzPv+uXbtOOJNA1ewDtc0UEB0dbbzwwgvG/v37jbKyMmPNmjXGlClTqu//85//bOzatavG4+65554aU1nVtuXk5BgffPBB9f4vf/lLwzAM49RTTzXeeecdIy8vz8jPzzc++eQTo1+/fjUeHx8fbzz//PPGzz//bJSVlRnZ2dnGl19+aQwYMCDgP3tt2rS17s1UeUNERFqxjRs3MnfuXO69916/XO/pp59m+vTpxMXF4Xa7/XJNEZHGUFgVEZEavv32W6xWKxMmTAh0KSLSxmk2ABERqWH48OGkp6cHugwREbWsioiIiEjwUsuqiIiIiAQthVURERERCVoKqyIiIiIStEJyBatOnTpRWFgY6DJEREREpA52u71eq/+FXFjt1KmT1qcWERERaQVSU1NPGFhDJqxOnz6dGTNmYDZ7ejakpqaqdVVEREQkCNntdjIzM+uV1UJu6iq73U5BQQGxsbEKqyIiIiJBqCF5TQOsRERERCRoKayKiIiISNAKmbA6ffp0Nm7cyPLlywNdioiIiIj4SZvss2oymYiPj8dut2MymVq4wtbLMAwKCwvJy8vDMELqbSMiIiItqCF9VkNmNoD6at++Pbfccgv9+vULdCmt1pYtW3jjjTc4dOhQoEsRERGRENemWlatVitpaWkUFRXx4YcfcvDgQVwuV4AqbX0sFgvJyclcccUVxMTEMH36dJxOZ6DLEhERkVZGLat16NixIxERETz33HNs27Yt0OW0Sjt37iQ3N5c//vGPpKSksG/fvkCXJCIiIiGsTQ2wqlowoLy8vKXKCklV3z+LxRLgSkRERCTUhUxYTUtLY+DAgYwePTrQpYiIiIiIn4RMWBURERGR0KOwKiIiIiJBS2G1FTrttNMwDIO5c+cGuhQRERGRZqWw2grddNNNvP/++5x55pl06dIl0OWIiIiINBuF1VYmKiqKKVOm8MILL/Ddd99xww03BLokERERkWYTMmG1PlNX1SUqLDBbY0yZMoWsrCxWrFjB7NmzufHGGxt3IREREZFWIGQWBUhLSyMtLa16RYT6igqD4ieasbDjiH4AShwNe8zUqVOZPXs2AJ9++imvv/46Z599Nt9++229r7FgwQKuv/569uzZ07AnFxERkZAVEw5FQTgVfci0rLYFffr0YezYsbz//vsAFBcX89lnn3HTTTc16Drdu3dXUBUREREA4iJM/OGGm7jt5plYgzAZhkzLamOVODwtnIF67oaYOnUqy5cvZ/v27dXHZs+ezccff0x8fDx5eXl07dqVV199lc6dO2Oz2Tj33HOJj4/nzTffJDIyknfeeYeMjIzqx3/55Zds3ryZMWPGEB8fz7XXXstDDz3EkCFDePzxx3njjTf89XJFREQkyIzrl8Swc//KsoozwA3jRn3K98t+CHRZNRihtNntdsMwDMNut9e4r1u3bsa7775rdOvWLeB1NnSzWCzGgQMHjDvvvLPG8YMHDxozZswwbDabsX79emPcuHEGYCQkJBgxMTHGunXrjP79+xuA8dlnnxmvvvpq9eP37t1rXH/99QZgvPPOO8a3335rREVFGUOGDDF+/PHHWmtpzd9Hbdq0adOmTRuGzYJx92VnGZfOWGmcOW2Pce60rcbYcde22PMfL68du7X5ltXW4sILLyQlJYUNGzYwcOBAn/sWLVrE1KlTOXToED/99BOLFi0C4MiRI1x11VUsWLCAzZs3A7B161Z+/vlnAGJjY3E4HLzzzjsAlJWV8eKLL1JSUkJ5eTn5+fkt+ApFRESkJfTtEM6vfvkAS9w3gBvinZtY9PkdHM7efsLHBoLCaisxdepUAL755ps6z7n++uv57LPPfI4NHDiQdevWVe8PHz6cTz75BIBBgwaxYsWK6vsGDx7Mww8/XH17w4YNfqtfREREAu/GM/tiDH6JJRX9AAjP/Duf/e8Z3K4gHFlVSWG1lbj44otPeM7tt9/OoEGDADCbzcTFxZGTk8OAAQMAmDhxImeccQbr168HPGG16jZAhw4dyM7OrvU+ERERab2SY+D2K29gcdj9VFREEG0cZMv397Jj68JAl3ZCQTjmSxrr7bffpmfPnqxfv5709HR69erFP//5T8aPH8/KlSv59a9/za5duygqKgI8ra5VgbR79+7s3r27+loKqyIiIqHh/JPb8eupb/G97VEqjAjshd8x/53zWkVQhRBqWZ0+fTozZszAbG67+buoqIgLLrigxvGRI0fWev6dd95ZfXv37t2cffbZ1fuXX365/wsUERGRFhNpg3svP4ONSc+x2tEeK2UcWfsE3//4TqBLa5CQCauNXRRAREREJNQM7xLOJZf+nu8rpoIL7BVbWPTZbzlyaFugS2uwkAmrIiIiIm2d2QS3T+pNUZ+X+L7CM2aFvW/x33lPBvUgquNRWBUREREJAd0S4PYrr+Mb04M4KiKINA6x8dv72LP9+0CX1iQKqyIiIiKt3JWjE+ky5lm+LD8HDIjI+56vP72XitLDgS6tyRRWRURERFqp+Eh44FfjWBY7k/TyZCyUc3DVE6xb9nagS/MbhVURERGRVmhCrzB+ceHvmVt+M7ggsnwbiz//LQWHtwS6NL9SWBURERFpRcIs8PsLe3Og+4t8Ve5Zgr1i9zssnP94qx1EdTwKqyIiIiKtRP8OMOOKa/jC9TAORwTh7hzWf3MvmT9/F+jSmo3CqoiIiEgrcNv4BBJHPs3HpZMAsB5ZyHef34uj5GCAK2teQbvcU2RkJLt37+bZZ58NdCkiIiIiAdPBDmm3jCXn5K/4qXQSFqOc7JV/5usPrg/5oApB3LL64IMPsmzZskCXISIiIhIwFw0K44xJ9/FRya3ggvCy7Sz54g4KD28KdGktJihbVnv16kW/fv348ssvA11KUJkwYQK7du0K2uuJiIiIf0SFwbNTepJy9if8t+RWAEp2vsf89y5sU0EVmiGsjhs3js8//5zMzEwMw2Dy5Mk1zpk2bRo7d+6ktLSU9PR0Tj/9dJ/7n3vuOe6//35/lxayTjvtNAzDYO7cuYEuRURERJpodFd4acZVfNvuf+xwDMLmymXzVzez7Ks/4naWBbq8Fuf3sBodHc3atWu5/fbba73/iiuu4IUXXuDxxx9n2LBhLFq0iLlz59KlSxcALr74YrZt28b27dv9XVrIuummm3j//fc588wzq7+PIiIi0rpYzfDAefGc+6vXmV32FOVGJOQsYsE/J5G18+tAlxcwfu+zOm/ePObNm1fn/ffccw9vvvkmb775JgB33303kyZNYtq0aTzwwAOceuqpXHnllfzqV78iJiYGm81GQUEBjz32WK3XCwsLIzw8vHrfbrc3uGazNbLBj/EHt7O0ydeIiopiypQpnH322SQkJHDDDTfU+b0SERGR4NQ7CR65ciyfMJOckhTMhoOM9GfYlv53wAh0eQHVogOsbDYbI0aM4KmnnvI5Pn/+fMaMGQPAAw88wAMPPADA9ddfz6BBg44bvu6//34eeeSRRtdktkYy4ZbArPSw8I1+TQ6sU6ZMISsrixUrVjB79mwee+wxhVUREZFW5LYxNgaf/jveLLwNAzOW0h389N87KTq8IdClBYUWHWCVlJSE1WolOzvb53h2djYpKSmNuuaTTz5JbGxs9ZaamuqPUluNqVOnMnv2bAA+/fRTkpOTOfvss33OueCCC3jppZcCUZ6IiIjUIcUO703tAaPn8HHhNAzM5G+fzXf/vFBB1UtApq4yDN/mbJPJVOMYwDvvvHPCazkcDhwOB9OnT2fGjBmYzQ3L325nKQvf6Negx/hLU1tV+/Tpw9ixY7nxxhsBKC4u5rPPPuOmm27i22+/rT7v5JNPZvXq1U16LhEREfGfSwbBVRdcwduFj1JWHoXFeYS13/yeQ7u+CnRpQadFw+rhw4dxOp01WlGTk5NrtLY2VFpaGmlpadjtdgoKChr0WH/0HQ2EqVOnsnz5cp/BaLNnz+bjjz8mPj6evLw8wBNWExISSE9PJyIigksvvVQD2ERERALAHg7PXBpLducnea3gQgDKDy4hfd7dOIqbloVCVYt2A6ioqGDlypVMnDjR5/jEiRP58ccfW7KUVs9isXDdddfx/vvv+xz/6quvKCws5Jprrqk+dvLJJ7N3715GjhzJyy+/zD333NPS5YqIiLR5p/eAj+4YxYL281hYfCEmo4Jdy57gx4+vUVA9Dr+3rEZHR9OrV6/q/R49ejBkyBByc3PJyMhg5syZvPfee6Snp7N06VJuvfVWunbtymuvvdak521sN4DW6sILLyQlJYUNGzYwcOBAn/sWLVrE1KlTefXVVwkLCyMsLIxXX30VgHXr1nHOOecEomQREZE2yWaBP51roePQO3k273bcWDCKd5E+9w4KD60LdHlBz+9hdeTIkSxYsKB6//nnnwfg7bff5sYbb+TDDz+kXbt2PPzww3Ts2JENGzZw/vnns3fv3iY9b1O6AbRGU6dOBeCbb76p85xhw4YBsGnTpuo+wcOGDWPdOv3DEBERaQkDOsCLV3bhU9OLfJs3AoDD2z5k08I/4XKWBLi61sHvYXXhwoWYTKbjnjNr1ixmzZrl76duUy6++OJ6nXf99dfTs2dPrFZr9Tysv/jFL5q5OhERkbbNZII7TodJEybz4pG/UOyOBWc+G79/kIM7vgh0ea1KQGYDaA5trRtAfQ0ePJiPP/6YFStWYDabufvuu8nJyQl0WSIiIiErNQ5enxLDurg/82zO5QCUHFzB2vl3UVa4L8DVtT4hE1bbWjeA+rr33nsB+NOf/hTgSkRERELflUPh7guH8GL+yxwo6gaGi13pL7Jn5SsYhivQ5bVKIRNW24Ldu3fzwgsvBO31RERE2qr4SHj5UjPmHr/hwZx7cGHDWZzBuvl3kZ+VHujyWrWQCattoRvAnj17ePHFF4P2eiIiIm3RWb3gr7/qyDtlz7M29zQADu34nC0LH8Tp0Ke9TRUyYVXdAERERKQlRdrg6QtgyNDz+NOhpyl0x2M4i9nyw0Nkbf040OWFjJAJqyIiIiIt5dRu8LcpkcznYR7JvhqAokNr2fD1HZTm7w5scSFGYVVERESknsIs8Kdz4fIxA3ni0EtkVPQCw82e1bPYteJ5DHdFoEsMOSETVttCn1UREREJnMEd4Z0rTeyIvok79v+eCsJxFGex6dt7OJK5JNDlhayQCavqsyoiIiLNwWKG+86AO89uz/M5z7Ei5wwADu36iq0Lfk9F2ZGA1hfqQiasioiIiPhbryR490owtT+T6fufI8+dhNtZxvYlf2b/ptmBLq9NUFgVEREROYbJBNPHwF/OD+efhX9gTtZNABQd3sTGb+6g5Mj2AFfYdiisioiIiHjpHAf/mAK9uvfm3uyX2enoD0DG2r+zc9kzuF3lAa6wbQmZ0UjTp09n48aNLF++PNClNJsJEyawa9euoL2eiIhIa3ftCFj3Oyjp8Gum7fsvOx39cZQcYu3/rmfHj48pqAZAyITVtLQ0Bg4cyOjRowNdSos77bTTMAyDuXPnBroUERGRVql9DMy5Hl6+IoG/5r/Bi4cfx2FEkLN3Acs/PI/cvQsCXWKbpW4AIeCmm27i/fff5/LLL6dLly5kZGQEuiQREZFW45JB8PovYZ95LFMznifX1QG3q5yff3qKfeveAoxAl9imKay2clFRUUyZMoWzzz6bhIQEbrjhBh577LFAlyUiIhL04iPhxclw1Qgbb+X+jn/n3YaBmeIjO9j09W8pytkU6BIFhVUAIgPUG6IUd5OvMWXKFLKyslixYgWzZ8/mscceU1gVERE5gQv6w99+Ca7IHty+7yW2O04GIHPjPz19U51lAa5QqrT5sBqJmfVJYwLy3IMP/9jkwDp16lRmz/bM8/bpp5/y+uuvc/bZZ/Ptt9/6o0QREZGQEh8JL0yG60bAvMJf8VLGo5QTTUXZEbYs+D2Hd30V6BLlGG0+rLZmffr0YezYsdx4440AFBcX89lnn3HTTTcprIqIiBzj/MrW1LiYWP6c/QQLiy8C4EjmUjZ/exflxVkBrlBqEzJhdfr06cyYMQOzuWEf6ZfiZvDhH5upqhM/d1NMnTqV5cuXs3370YmJZ8+ezccff0x8fDx5eXkMHDiQzz77jLFjx5KdnU27du345ptv2uSsCSIi0jbFRcDzk+HGUbC+dCR37nmRXKMzblcFu1b8lb1rXgej6V3zpHlo6io8oTEQW1NYLBauu+463n//fZ/jX331FYWFhVxzzTUAbNy4kQ8++ICzzjoLgD/96U88/fTTVFRUNOn5RUREWoNf9IMN98J1Iy28nXMnd2Z+SK7RmZL83az69HL2rp6loBrkQqZlta258MILSUlJYcOGDQwcONDnvkWLFjF16lReffVVwBNY+/TpQ8+ePRkxYgR33HFHIEoWERFpMd6tqQedHZm250V2uE4BE2Rt/Zhtix7CVVEc6DKlHhRWW6mpU6cC8M0339R5zrBhw1i9ejXbt2/nwgsv5IknnuDBBx9sqRJFREQC4rx+8MYvoXM8/FA4iceznqHCHI/TUci2H/5I9vZPA12iNIDCait18cUX1/vc7du3M3HiRJYtW8aCBQuarygREZEAio2AmRfB1FOg3B3Oo3sfYmHFtWCGguw1bPzmt5QV7A10mdJACqttQH5+PgB/+MMfAlyJiIhI8zi3D/z9CugSDzvLenP37lcotPUDYO/q19i5/DkMt8ZrtEYKq22A1WolNzeXjRs3BroUERERv4qLgOcugptPAcOAf+y/iveK/oTJFkl5yUE2f3sPR/YtCnSZ0gQKq63I7t27eeGFFxr8uH79+rF161a/XU9ERCQYXDQAZl0OqXFQ6IrlN5uf5kDk+ZgskLN3AZu/+x0VpYcDXaY0kcJqK7Jnzx5efPHFBj9uw4YNTJ482W/XExERCaSkaHjpErhqmGd/7oGRPJPzIqbIzrhdDnYue4aMtX8HjECWKX4SMmG1sYsCiIiISOtx1TBPUE2KBofLzO1rZrA99i5MkVZK8nez6evbKTy0PtBlih+FTFhNS0sjLS0Nu91OQUFBoMsRERERP0qN83zkf9EAz/4PmR34474XsSadhgnI2jqHbYv+qLlTQ1DIhFUREREJTbecAs9eCHGR4HDCXUvPYX3ic9iSEnBWFLPthwfJ3vZJoMuUZqKwKiIiIkHppHbwxq/grF6e/UW7w7lv2wNE9bwBG1B4aD0bv76d0vzdgSxTmpnCqoiIiAQVswnuHAd/OQ+iwqDEAb/7rhcrE14mpqenH8DeNW+wc9nTmju1DWhTYdXtdgMQHh4e4Epat6rvn8vlCnAlIiISagZ0gDevgFO7efa/3QH3pE8hfugjxNiicJQeZvN3vyN374KA1iktp02F1QMHDlBWVsZvfvMbPvzwQw4ePKjA1QAWi4Xk5GSuuOIKysrKyMrKCnRJIiISImwW+MNZ8MezIcwK+aVwz5cx/BT9BB1GeaZfzM34gc3f3oOj9FCAq5WWZCLEJiGrmg0gNjaWwsLCGve3b9+eW265hX79+gWgutCwZcsW3njjDQ4d0i8LERFputO6efqmDkzx7H+xCe76bhDtx7xKVFx33K4Kdi1/lr1r/kaIxZY260R5zVubC6sAJpOJuLg4YmNjMZlMLVxh62UYBgUFBeTn52MYIfW2ERGRAIiLgCfPh2ljPPsHi+COT2Gx8wZ6jXkAsyWc0oIMNn3zWwqyVwe0VvGvhoTVNtUNoIphGOTl5ZGXlxfoUkRERNqkywbDy5dApzjP/pvL4A/zY0k59Vn6nHQeAId2zmPL9/fhdGj+9LYs6MJqTEwM3333HTabDYvFwksvvcTf//73QJclIiIiftA5Dl69DC4e6NnfehBu+xhWFw5lwIWvEBnbBbernB0/PkHmhrcDWqsEh6ALqyUlJUyYMIHS0lIiIyPZsGEDc+bMITc3N9CliYiISCOZTXD7WM90VPYIz+T+T30PT3xrInngzQw7+/eYLTZK8/ewYf50ig5vCHTJEiSCLqy63W5KS0sBiIiIwGKxqF+piIhIKzakE/ztlzC6q2d/8S649T+wIz+BfhP/SlL3swHI3vEFWxfej8tx/D6M0raY/X3BcePG8fnnn5OZmYlhGEyePLnGOdOmTWPnzp2UlpaSnp7O6aef7nN/XFwca9asYd++fTzzzDPk5OT4u0wRERFpZpE2ePoCSL/TE1TzSuG2/8D4NNhvHsXIX80lqfvZuJxlbF14P5u+vl1BVWrwe1iNjo5m7dq13H777bXef8UVV/DCCy/w+OOPM2zYMBYtWsTcuXPp0qVL9Tn5+fkMHTqUHj16cPXVV5OcnOzvMkVERKQZndsHNtwL/3cmWC3w4Vro/wz87ScTXYfNYOjFHxAR05HiIztYOecS9m96P9AlSxAzmmszDMOYPHmyz7GffvrJSEtL8zm2adMm44knnqj1GmlpacYvf/nLOp8jLCzMsNvt1VunTp0MwzAMu93ebK9LmzZt2rRp01b71jEW44NfYxjPebY9D2Jc0N9zny0yyRhy4XvGmdP2GGdO22P0P2umYbFGBbxmbS2/2e32euc1v7esHo/NZmPEiBHMnz/f5/j8+fMZM8YzyVpycjJ2ux3wzME1fvx4tm7dWuc177//fgoKCqq3zMzM5nsBIiIiUiuLGe4cB1v+D6YMBZcbnv8BBjwL/9sM8Z1OY9Sv5pLYZTyuilI2f3cvm7+7B5ezJNClS5Br0QFWSUlJWK1WsrOzfY5nZ2eTkuJZtqJz5868+eabmEwmTCYTr7zyCuvXr6/zmk8++SQzZ86s3rfb7QqsIiIiLei0bjDrcs9AKoClu2HaHFi7H8BEt+HT6THqd5jMFopyt7Jx/gxKjmwPYMXSmgRkNoBjVz8ymUzVx1atWsWwYcPqfS2Hw4HD4WD69OnMmDEDs7lFG4tFRETarMQoeOp8uOVUz35uCfz+f/DmcjAMsIbHM+Ds52nX7SwADmz5kG2LHsLtLAtg1dLatGhYPXz4ME6ns7oVtUpycnKN1taGSktLIy0trXr5LhEREWkeJhPcOMoz0j8p2nPszWXwhy/hcLFn3548hEHnphFh74zLWca2RQ+RteXDwBUtrVaLhtWKigpWrlzJxIkT+fTTT6uPT5w4kc8++6wlSxEREZFGOLmj5yP/Md09++v2ez7y/3H30XNSB15Hr7EPYbaEUZK3iw3zp1GcszkQ5UoI8HtYjY6OplevXtX7PXr0YMiQIeTm5pKRkcHMmTN57733SE9PZ+nSpdx666107dqV1157rUnPq24AIiIizScmHB49F+443TMVVWEZPPwVvLzEM5gKwGKLpu+EJ+nQezIAB3fOZcv392nuVGkyv05FMGHCBKM2b731VvU506ZNM3bt2mWUlZUZ6enpxrhx4wIyFYI2bdq0adOm7cTb1cMw9j10dDqqf1+LkRrne05UQm9j9JXfGmdO22NMuHWH0fnkqQGvW1vwbg3Ja6bKGyGjqs9qbGwshYX6S05ERKSxhnaCly+F03t49rcfgts/gfnbfM/r0OdS+o5/AostirKiA2ycP4OC7JUtX7C0Gg3JawGZDaA5qBuAiIiIfyRGwV/Og9tOBbMZih3w+Dcw8wcodx49z2wJp9fYh0kd+GsAcjN+YNM3d1JRlhugyiUUqWVVREREAM/E/ree6gmqiVGeY/9aDff9FzLzfc8Nj0ll0KTXiE0+GcNwszv9RXavfAkMd8sXLq1Om2xZFRERkcYbdxK8fMnRif3X7Yfffgo/7Kx5bkLqWAZMfIWwyEQcpbls/vZOcjN+aMlypQ0JmbCqbgAiIiINlxoHz14IV1Wux5NbAg/Ng9d/OjrK31vXobdx0im/x2S2UHBwLRu++g3lRftbtmhpU9QNQEREpA0Kt8I94+HBcyA6DNxu+Nsy+ONcyCmpeb7FFk2/M58juef5ABzY/CHbFv0Rt6u8hSuXUKBuACIiIlKnXw3xrD7VI9Gzv2SX5yP/1Zm1nx8ZfxKDJ/2N6MTeuF0Oti9+hP2bZrdYvdK2KayKiIi0EaO6wPMXw9jKqagy8+EP/4N/rqr7Me26n8OAs57HGh5LeVEWG+ZPoyD7OA8Q8bOQCavqsyoiIlK7LvHwxC/g1yM8+yUOeGYBPLvAc7tWJjM9Rt5F95F3ApC3fxkb58/AUXqoBSoWOUp9VkVEREJUdBj8/ky49wyItHmOvb0CHpwL+wvqfpw1LJYB57xEu25nApCx7h/8vPRxDLez7geJNID6rIqIiLRhZhNcPxIe/wV0jPUc+2En3P0ZrKqjX2qVqPheDP7F34mK74GropStC+8ne/snzV+0SB0UVkVERELIGT1h5sUwLNWz//Nhz6T+n2w48WMTu57JwHNewhoeS2lBBhvm3UpRzqbmLVjkBBRWRUREQsDgjvDU+XB+f89+Xik89g28shgcrhM/vsvQ2+h56h8wmczk7V/Ghq9+o2VTJSiETFjVACsREWmLusTDnyfBdSPAbIYKl2dC/0fnw+HiEz/ebAmn74QnSel7OQD7N73PtkUPY7grmrdwkXrSACsREZFWKCES7j8bfjsWIioHT3241jN4asfh+l0jLCqZQee9TlyH4bjdTnYs+TOZG95pvqJFKmmAlYiISIiKsMJvT4f7z4KEKM+x73fA7/8HKzLqfx17+8EMPu/vhMekUFGWx8b50zmSuaR5ihZpAoVVERGRVsBsgutGej7y7xLvObZuP/z+S5i3pWHXSu51Mf3OfBaLNYLi3O2sm3sTZQV7/V6ziD8orIqIiAS5SwbBY+fBoBTP/p4j8NA8mL0K3A3szNd95J30GHUPAId3f8umb+7AVVHk54pF/EdhVUREJEhN6gt/OQ9GdvHs55bA49/Cq0ugvIHz85vMYfQ742lS+l4GwN7Vr/HzsqfBcPu5ahH/UlgVEREJMhN6ekLq6T08+4Vl8MIi+OtCyC9r+PWs4fEMPu9vxHc6BbfbybYfHuTA5g/8W7RIMwmZsKqpq0REpLU7pasnpJ7Tx7NfWgGvLIFnvq/fNFS1iYzrzsnnv0VU/Ek4ywvYMH8aR/Yt9l/RIs1MU1eJiIgE2NBOnj6pFw7w7Duc8Ldl8MS3cKCg8deN6ziKwee9gS0igdKCDNZ9eSMlR7b7p2iRJtDUVSIiIq3AoBR4eCL8aohn3+mCt9M9K0/tPdK0a3fofQn9znwGsyWcguw1rJ97M47SQ00vWqSFKayKiIi0sKGd4KGJcNlgz77bDe+vhke/rv+E/sfTbfgMTjrl/wA4uHMum7+9C7ezEZ1dRYKAwqqIiEgLGdkFHjoHLh7o2Xe74aN1npbUjVl+eAKTmd5jH6Hz4OsB2LvmdX5e+iQh1uNP2hiFVRERkWZ2ajdPSD2/v2ff5YYP1nimodqc7Z/nMFvC6X/28yT3vADDcLN9yaNkrn/bPxcXCSCFVRERkWZyeg9Pn9SJlaP7nS6YvRoe/wa2++Hj/iqWMDuDz3uDhNTTcLscbP72bg7+/F//PYFIACmsioiI+NnZveHBs+HMXp79Che8kw5Pfgc7c/z7XGFRyQy54B1ikgbgdBSyft6t5GX+6N8nEQkghVURERE/MJvg8pPh92fCiM6eYw4n/GMFPPWdZ4lUf4uMP4khF7xLZGwXyosPsu5/11OUs8n/TyQSQCETVrUogIiIBEK4Fa4fCfdOgN7tPcdKHPD3ZfDsAtiX3zzPa28/mJMveJewyERK8nay9r/XUVaY0TxPJhJAWhRARESkEWIjYNppcNc4SIn1HMsphpeXwCuLIaek+Z47ruMoTj7/LaxhdgoOrmXd/26goiy3+Z5QxM+0KICIiEgzSbHDXeM9QTU2wnNs7xH460L4+3JPq2pzSug8jsHnvYHFFsmRzKWsnzsVV0Uj12IVaQUUVkVEROphYArcPQ5+PcLz0T/Ahix45nv412pwupu/hqTu5zLw3FcwW8LJ2fM9G766DbervPmfWCSAFFZFRETqYDLBeX3h7vFHp58CWLwLnv4e/rcZjBbqTNeh9yX0O+uvmM1WDv78JZu+uQPDXdEyTy4SQAqrIiIix4i0wXUj4c7ToX8HzzGXG+ash+d/gKV7Wraejv2vou+EJzCZzBzY8hFbF/wew3C1bBEiAaKwKiIiUqlTLMwYC7edCu2iPcfySz19UV9e3DzTT51I6qAb6DPuUQD2bXiX7YseJsTGRoscl8KqiIi0eSM6e0b1TxkKNovn2M4ceHERvLUCCgPULTR10PXVQXXP6lns/OmpwBQiEkAKqyIi0iaFWz3hdPoYOKXr0eM/7PR81P/5RnAHsAEzdeB19Bn3ZwD2rHqVncueCVwxIgGksCoiIm1K90TPtFM3jYakyo/6y53w4Vp44QdYlRnY+gA6DbyWPuMfA2DPqjQFVWnTgi6sdu7cmffee4/k5GScTiePPfYY//nPfwJdloiItGJVo/qnj4Hz+0HVYod7jsCsHz1Loh4qCmyNVToNuIa+4/8CwN7Vr7Fz2dMBrkgksIIurDqdTu666y7Wrl1L+/btWbVqFV9++SUlJc24FIiIiISkxCi4cZSnJbVn0tHjX22FV5d4pp4K5Ef9x+o04Gr6TngCgL1rXufnn54McEUigRd0YTUrK4usrCwADh06RG5uLomJiQqrIiJSLyYTTDgJbjkFLhsMETbP8SMlnsFSs5bCjsOBrbE2HfpcSt8JnnC6d80b/Lz0iQBXJBIczP6+4Lhx4/j888/JzMzEMAwmT55c45xp06axc+dOSktLSU9P5/TTT6/1WiNGjMBsNrNv3z5/lykiIiEmxQ6/PxO2/R6+nwZXD/cE1VX7YOqHkPoY/O6L4AyqSd0n0u/M5wDIWPcPfl76lwBXJBI8/N6yGh0dzdq1a3nrrbeYM2dOjfuvuOIKXnjhBaZPn86SJUu47bbbmDt3LgMGDCAjI6P6vMTERN59911uvvlmf5coIiIhwmyC8/rBzaPhogFgrZx2qqAM3l8Nb/wUHAOmjic+dQwDz30Vs9nKgS0fsmPJnwNdkkjQMZprMwzDmDx5ss+xn376yUhLS/M5tmnTJuOJJ56o3g8LCzMWLlxo/PrXvz7hc4SFhRl2u71669Spk2EYhmG325vtdWnTpk2btsBu3RIwHp2EkfFHDOO5o9viGRg3jMKICgt8jfXZYpOHGuNu3mScOW2PMWjS64bJZAl4Tdq0tcRmt9vrnddatM+qzWZjxIgRPPWU76TG8+fPZ8yYMdX7b7/9Nt999x3//Oc/T3jN+++/n0ceecTfpYqISJCJCYfLB8P1I+HMXkePHy6Gd9PhzeWwKTtw9TVUdGIfTr7gHay2aHIzFrHx699qCVWRWrRoWE1KSsJqtZKd7fvbJDs7m5SUFADGjh3LlClTWLduHZdccgkA1157LRs2bKj1mk8++SQzZ86s3rfb7WRmBvlnPiIiUi9mE5zVC64b6RksFR129L5vtsEby+DTDeBoZRkvwt6ZIRf+E1tEPPlZq9gw71YMtyPQZYkEpYDMBmAYhs++yWSqPrZkyRIsFku9r+VwOHA4HEyfPp0ZM2ZgNvt9zJiIiLSwfsmeFtRfD4fO8UePbz0I766Ef66CvUcCVl6TWMPjOPmCdwiP7kBRzmbW/e96XE7NeCNSlxYNq4cPH8bpdFa3olZJTk6u0draUGlpaaSlpWG32ykoKGjStUREpOW1j4ErhsB1I2C01/KnuSXwwRp4Jx2W7w1YeX5htoQz+Lw3iE7oRVnRftb+73qcDv2fJXI8LRpWKyoqWLlyJRMnTuTTTz+tPj5x4kQ+++yzlixFRESCQFwEXDoYrhoKZ/cGS+WHYxUu+HKzpxX1v5ta38f8tTPR76y/Et/pFJzlBaz73w04iltRJ1uRAGmWqat69Tra871Hjx4MGTKE3NxcMjIymDlzJu+99x7p6eksXbqUW2+9la5du/Laa6816XnVDUBEpHWICvNMM3XlUPhFPwj3+p9o+V7PR/wfrAme5U/9pedp99Oh10W4XQ7Wf3UbxblbA12SSKvh16kIJkyYYNTmrbfeqj5n2rRpxq5du4yysjIjPT3dGDduXECmQtCmTZs2bS2zhVkwLhqA8f41GEVP+E43te53GA+cjXFSu8DX2Vxbp4HXGmdO22OcOW2P0aH3pQGvR5u2QG8NyWumyhsho6rPamxsLIWFhYEuR0SkzYqwwsQ+cPnJcPEASIg6et/Ph+FfazwtqBuzAlVhy0hIHcvJF76L2Wxl57Jn2LPq1UCXJBJwDclrAZkNoDmoG4CISODFhHs+2r98MFzQ37NfZV8e/HutJ6CmZ9R1hdASGduNgeemYTZbydr6sYKqSCOoZVVERJokPtLTB/XywTCpL0TYjt639wh8vB7mrIclu8EIqf9xjs9ii2HEZZ8Sndib/OxVrPnsStyu8kCXJRIU2mTLqoiItJzOcXDhALhkkGfSfpvX9NjbD3kC6sfr204Lag0mMwPOeYnoxN6UF2WxYd5tCqoijRQyYVXdAEREmo/JBCM7e1pQLxoAQ1N9719/oDKgroMNId4HtT5OGn0fSd3PxuUsY/28m3GUHAx0SSKtlroBiIhIraLC4JzennB6QX/oGHv0Ppcblu6BLzbBJ+th++HA1Rls2p90PoMmzQJg49e/5eCOzwNckUjwUTcAERFplG4JcF4/T0A9u5dv/9OCMvhqqyegfrkZcrRCaA2RcT3od+YzAOxd/ZqCqogfKKyKiLRhkTY4o6dnYNSkvtAv2ff+XbnwxUZPQF2407OylNTObI1g0KRZWMPs5O3/iZ3Lngl0SSIhIWTCqvqsiojUz6CUo+F0/Em+K0g5XZ6P97/c4gmooT4Hqj/1GfcXYtr1p7zkIBu//i2GoWQv4g/qsyoiEuLax3hG7J/bxxNQU+N879+d6/l4/6ut8O0Oz8f90jAd+02h35nPYLhdrPniGvL2Lw10SSJBTX1WRUTasLgImNDTE1DP6gWDO/reX+KABT8fDahbDwWmzlARndiH3uP+DMDO5c8pqIr4mcKqiEgrFxUGp3eHs3p7wunwVLAc0yNqTaan1XTeFli0C8qdASk15Jgt4Qw4+0Us1ghy9i5g7+pZgS5JJOQorIqItDKRNhjdFc6sbD09pSuEHfPbfMtB+G6HZ1uwQyP3m8tJp/wfMUkDcJQeZvN3vyPEetaJBIWQCasaYCUioSohEsb2gHGV24jONcPpniPw7XZPOP1+B+wvCEytbUlC59PpMuRmALZ8fx8VpZpsVqQ5aICViEiQ6RwH406qDKcneUbvHyszH37Y6Qmn3273TDElLccWkcCoK74iPLoD+za8y/ZFDwW6JJFWRQOsRERaCbMJBnSA07odDajdE2uet+UgLNrp6W+6eJfCaaD1nfAU4dEdKM7dzs9LHw90OSIhTWFVRKQFJUV7+pie1g1O7Qaju4A9wvccpwtW7/eE0kU7YfFuOFQUkHKlFsm9J9P+pPNwuxxs+vZO3E7N9SXSnBRWRUSaidUMJ3f0hNKqcNorqeZ5hWWwPAOW7PaE05/2QlF5i5cr9WCLTKLP6Y8CsDv9RYoObwxwRSKhT2FVRMRPeiTCyC4wqguc2tVzO9JW87xN2fDTHs+2dI9n3x1SowdCV5/TH8UWkUDhoY3sXfNaoMsRaRMUVkVEGqFrAoxI9QTSkZ09XxOjap53pMTTUloVTpfthXx9atwqJfU4j+ReF+J2O9my4D4MtyarFWkJIRNWNXWViDSXTrG+oXRkZ88Spscqd8La/bBynyeU/rQHth0GQ62mrZ41PI4+4x8DYO/qWfr4X6QFaeoqEREvHeyeeUy9g2nH2JrnVbhg/QFI3wfpGZ6vG7I8xyX09J3wFJ0GXEVx7nZWfHQ+htsR6JJEWjVNXSUicgIWM/RtD0M6wdBOnq9DOkJKLcHU5YaNWZXBtDKcrjugJUvbitgOw+g04CoAti78g4KqSAtTWBWRkBcXASdXhtGqYDooBSJqGfzkdsPmg56P8quC6Zr9UFrR8nVLEDCZ6TPuLwAc2PIh+VnpAS5IpO1RWBWRkNIj8ZjW0k6eY7UpKvf0MV17ANZker5uyIISNZxJpdQBv8befhAV5fn8/NNTgS5HpE1SWBWRVinCCoM6VraWpnq+ntwR4iJrP3/Pkcpgut/TUrp2P+zM1eAnqZstMokep9wLwM5lz1JRmhPgikTaJoVVEQl6KfaaraV923v6nR6r3OnpX+rdWrpuPxwpbfm6pXXreeofsIXHUXhoPfs3zQ50OSJtlsKqiAQNqxn6Jvu2lg7p5BmhX5uDRb4tpWv3w5aD4HS3bN0SemKSBtGx368A2LboITD0phIJFIVVEQmIuIiaraWDUiC8lt9KLjdsO+QVTA94bh8oaPm6pW3oNeaPAGRt+4SC7NUBrkakbQuZsKpFAUSCk8lUOejpmNbS7nUMeioo80wL5d1iuiFLo/Gl5bTrdjYJqafhcpaxc9mzgS5HpM3TogAi4jeRNk/rqHdr6ZCOYI+o/fzducd8jH8AdmnQkwSQyWxl1BVfEZ3Qiz2rXmXnsmcCXZJISNKiACLS7DrGen2MX9lq2jup9kFPZRWwMfvogKe1+z2tp3ka9CRBpmP/q4hO6IWjNIc9q2cFuhwRQWFVROqhWwIMT/UsQzq8s+d2XYOesgt9W0vX7Pf0N9WgJwl2ZmsE3UfcAcDu9BdxOfTpnEgwUFgVER8ntasMpalHv7aLrnmeyw1bD/m2lq7Z7wmrIq1R6sBrCY9OprQgg/2b3g90OSJSSWFVpA3rHAejuhzdRnSGhKia5zmcnkFOK/fBqkzPtm4/lDlbvmaR5mCxRtF12G8A2L3yJQy3RvSJBAuFVZE2ol0UjOrqFU47Q0pszfPKKjz9SVdlVobTfZ6g6nC1fM0iLSV18PWERSZRkr+b7G1zAl2OiHhRWBUJQTHhnlZS71bTHrVMFeV0eYLoioyj24Ys9S+VtsVii6Hr0NsA2J3+AoZbHxmIBBOFVZFWLjoMhlX2L63a+rWH2qYc3nrQN5iu2a/5S0U6D74BW0QCxUd2kL39s0CXIyLHCMqwOmfOHM444wy+/fZbfvWrXwW6HJGgcWwwHdkZ+tYRTPce8Q2mK/dBflnL1ywSzMyWcDqffCMAe1a+rGVVRYJQUIbVl156iX/84x9cf/31gS5FJGBsFk8wPa2bJ5SOOE4w3ZcH6fs8gbRqO1jU4iWLtDop/a4gLDKJ0oIMDu74ItDliEgtgjKsLliwgAkTJgS6DJEWlWL3BNMx3T1fR3SGCFvN8xRMRfzDZLLQdeitAGSsfQPD0ChCkWDk97A6btw47rvvPkaMGEGnTp245JJL+Owz3z5A06ZN47777qNjx45s3LiRu+66i8WLF/u7FJGg1sEOZ/aEs3p5tp5JNc85XAxL98DyvQqmIv7WvucFRMZ2xVGaw4Et/w50OSJSB7+H1ejoaNauXctbb73FnDk1p/+44ooreOGFF5g+fTpLlizhtttuY+7cuQwYMICMjAx/lyMSNGLC4execHZvTzgdmOJ7v8vtGYn/425PQF26B3YcDkipIm1C1byq+9a/jdupDt0iwcrvYXXevHnMmzevzvvvuece3nzzTd58800A7r77biZNmsS0adN44IEHGvx8YWFhhIeHV+/b7XWsASkSAN0T4cL+cOEAOKMnhHv9i3O7YfV++G6HZ1uyCwrLA1erSFuS0Hkc9qSBOCuKydzwTqDLEZHjaNE+qzabjREjRvDUU0/5HJ8/fz5jxoxp1DXvv/9+HnnkET9UJ+IfgzvCFUPgkkEw6JjW0x2HYd5W+HY7LPwZjpQGpkaRtq7zYM8A3qzNH+Iszw9wNSJyPC0aVpOSkrBarWRnZ/scz87OJiXl6P/q8+bNY/jw4URHR5ORkcGll15Kenp6rdd88sknmTlzZvW+3W4nMzOzeV6ASB36JcOUoTBlCPTvcPS40wWLd8N/N8EXm2DboUBVKCJVIuxdaNftbAD2bXw3wNWIyIkEZDYAwzB89k0mk8+x8847r97XcjgcOBwOv9UmUl/JMXDtCLhuBJzc6ejxsgqYuwU+Wuf5mqfWU5Gg0mngNZhMZnIzfqA0b2egyxGRE2jRsHr48GGcTqdPKypAcnJyjdbWhpo+fTozZszAXNsklCJ+YjXDL/rBTaPhgv6euVABHE6Yvw3+vQY+26i+pyLBymwJp1P/KwHYp76qIq1Ci4bViooKVq5cycSJE/n000+rj0+cOLHG9FYNlZaWRlpaGna7nYKCgiZWKuKreyL85jS4fgSkxB49vnQ3vLXC04qqFlSR4Jfc62JsEQmUFmSQs+e7QJcjIvXQLFNX9erVq3q/R48eDBkyhNzcXDIyMpg5cybvvfce6enpLF26lFtvvZWuXbvy2muv+bsUkSY7uzf8dixcNODoylHZhfDuSk9I3dy0DwREpIVVDazav/GfWlpVpBUx/LlNmDDBqM1bb71Vfc60adOMXbt2GWVlZUZ6eroxbty4Jj/v9OnTjY0bNxqbN282DMMw7Ha7X1+XtrazRYdhTBuDsfE+DOO5o9tXt2BMHohhNQe+Rm3atDV8s7cfbJw5bY8x/pathi0iIeD1aNPWlje73V7vvGaqvBEyqroBxMbGUlhYGOhypBVJioY7TocZYyExynOssAzeTodXl8BWjeQXadX6jPsLqYOuJWvbp2z+9s5AlyPSpjUkrwVkNgCRYNI9EX43AW4aBVFhnmPbDsHLi+GddA2WEgkFZks4yb0vBiBry4cBrkZEGiJkwqpmA5CGGpQCfzjLMzeqtXJU/4oMeOo7+HQDuEPqMweRti2pxyRs4XGUFe7jSOaPgS5HRBogZMKqZgOQ+hqYAn+aCL8acvTYV1vh6e/h+x2Bq0tEmk/HflcAcGDLR4RY7zeRkBcyYVXkRPole0LqFUM8I/vdbvjPek9L6moteiYSssJjUknoPBaArK0fB7gaEWmokAmr6gYgdemdBA9PhKuHHZ1+6qO18OjXsDErsLWJSPNL6XMpJpOZI5k/UlaYEehyRKSBNBuAhKwUOzxyLtx8ClgqQ+on6+GR+bDuQGBrE5GWM2rKfGIS+7L5+/s0uEokSGg2AGnT7OFw3xlwzwSIrhzd/99N8KevYJU+7hdpU6IT+xCT2Be3q5zDO+cFuhwRaQSFVQkZNgvcdio8NBGSYzzHlu6G+/4LS3YHsjIRCZTkXpMByNm7AKdDg29FWqOQCavqs9q2XX4yPHU+9Ery7G87BPd/CXPWB7YuEQmsDr0uAuDgji8CXImINJb6rEqrNqQTvDgZJvT07GcXevqk/n0ZOLXst0ibFps8lBGXf4arooTFbw/H7SwNdEkiUkl9ViXkJUXDY+fBLZWDp0or4NkFnq1IK06JCJBc2ap6ePfXCqoirZjCqrQqVjNMH+MZ5Z8Q5Tn27zWefqkZeYGsTESCTfuTzgPUBUCktVNYlVZjYh94YTIM6ODZX50Jd34Gi3YGti4RCT4xSYOIsHfGVVFCbsYPgS5HRJogZMKqBliFrq4Jnn6plwzy7B8qggfnwpvLwd2KelyHYWJCWCKn2+LpYonAicE2ZzFzyw+z0VUc6PJEQkr7HucCkJvxA26X+gaJtGYaYCVBy2aBu8d7lkiNCoMKF7yyBB6dD/llga6uYS4Jb8/vorrT0RJe6/3fOXJ5sHA7h4yKFq6s/sIwMcgawz53OQfdjkCXI3Jco674iph2/dj07V1kb/sk0OWIyDE0wEpavXEnwazLYGCKZ3/hzzB9DmzKDmxdDRWBmWftffhFuGdOrWxXOV86DrPFWUyYycwptjgmhbXjrLBEPk8YxnX5G9juKvHb87cz2bgwvD1xZiuLHUdY5WzcH3C9LVH8PXYAqZYInIbB48U7ea+sfsuAJZpsXBWRQonh4v2yLMo5/jQNI6yxnBmWwBflh9h6gu+FBXDV90VImxEZ242Ydv1wu53k7Pku0OWISBMprEpQSYqGZy+EG0Z59g8Vwb3/hXfTA1tXY8SaLPwtdgAjbXE4DDcvl+zlzdJMHF4fZvyrLItelkhetPejrzWaf8YN4tK8tex3N/1jyzG2OF6x9yfW7PlnfkdUV94vPcAjxT+fIC76isTM3yqDarnhJtxk5qHok9jmKmFZRf5xHxuFmffjBtPL6hkNN9oWx7TCzXWeP8Qawz/jBmEzmfl1REcuyltNRi3fi0STlb/FDqS3NYo7C7awoOJIjXO6myOYGpnKd45cvq/lfgldST0mAZC3/yec5cd/j4pI8FMHTwkKJpNnGqqtv/cEVbcbXlsKfZ9unUE1HDP/iB3ESFsc+W4nv85fz6zSfT5BtcoOVylX569nk7OIduYwXrb3IwxTk57/ZGsMf48dSKzZyiZnEZ+XHcRlGFwd2ZE7o7o26Fo3RabSxRLBPlcZY3KX82FZFmaTid9HdT/hY6+P7EQvaxQOw43bMJgY3o6xtvg6z58R1RWbyfNrKcZs5cbI1FrPmxbVhaE2O9EmC0/Ze2M95vtlBt6KG8RVkR15KbYfqWbf7hcxJguz4wbzZfww2plsNa6fYg7DbrKc8PVJcEqq7K96eNf8AFciIv6gsCoBNygFlsyAv/0KEqM8o/zHvALTPoYjrXRqxKfsvRlqs3PEXcFV+etO+PF7vuHkNwWbOeKuYIjNztQ6Qlp9RGHmeXtfwkxmvinP4Zd5a7mnaBu/L9oGwG8iu9DPEl2va1kxcXWEpy/GX0v2kG84ea54D6WGi5NtdoZYY+p8rBm4NrITAH8o2l7dbaDqesdKNNk4w5YAwCNFPwNwYXj7GrHdDFwW3qF6P8kcxunHBODTKgexAUSaLFxQ2Q2jys2RqZxii6OPNZq7jgnvV0eksDhxNLPjBtd47hRzmH5pBjlreDxxKSMAhVWRUKHfuxIw4Vb48yRYdTec1h0KyuDOT2HUi7Bsb6Cra7wbIjpxUXh7Kgw3txduYdsx/S7DopKJSxmJPXkIFtvR0LjfXc5jxZ55uKZHdaGjOaxRz399ZCe6WSLZ7yrjvqJt1a25n5Yf4svyQ1hMpnq3rp4ZlkgHSzg5bgfzyg8DkGtUMK88B4BLw5PrfOxoWxzJ5jDy3BV8WX6Yj8s8HY4nhCUQWcuvnrFh8ZhNJjY5i/igLIsit5NEs43+xwTrQdYY4sxWCtxO/lXqCcBjwuJ9zhljO/7++MpQ7HneBJ/7pkV2AWCANYZTbHHVx6tC7KfxQ7F5xdhIzCTW0jorgZHYZTwmk5minC2UF9evX7WIBLeQCavTp09n48aNLF++PNClSD2M7Q6r74aHJnpG/X+yHvo/Ay8tBlcrXia1uzmCe6O7AfCX4p3VfTpN5jBSB13PqCnzGXv9CoZf+jEjL/+ccTet5+Tz3yK2wzAAPi8/xIqKfCJNFn5TGZoaItpk4abKVtnnSvZQaPgOP3qhZG/1x/HdzBEnvN5llWH0P2XZVHh1Yfhv+SEAzgpLrPOxEyoD4TeOXJwYbHIVk+kqI8JkYbgttsb5p1UGw8WOPJwYLHcWAJ7Q621U5f5PFfmsrDzn5GNaeAdX7r9VmgnAUKu9+r4Yk4VBXud3tUSQXPmHQao53GfGBu+QW/W9GGCN8Xn8P+MG833iSPpaour8XkjLadf1DABy9y4IaB0i4j8hM8AqLS2NtLS06qkQJDjZw+GpCzyrUAEcKIAZc+CTDWA3WTjVFsNAaww9LJF0MofTyRxOrNlKJGbCTWYchptiw0WOUcEeVxk7XCWkVxSwqqKA0gYNG/I/E/C0vQ8RJguLHUeYXZYFgL39yQw45wWi4nsCAGPkOQABAABJREFUYLhdlBXtx2wJIzy6A+26nUVi1zPYs/IVdq34Ky+U7GV23GAuj0jmxZK95DZgOqtfhncgwWxjp7OkOlCCCZPZguF2stNVyqKKI0wIS+TyiA7MLNlT57WsmKoD5JflOaQOvI72PX9BSd4uVq6YSanhopMlgl6WSHa4avbXOLXysUsq8jBbwjEMNysqCki1RDDSFsuSijyf86sC4GpnIWZrJBucRZwVlkg/q2/LalUo3OAsYp2zCICB1hjMUP0OqLrW5+WHuDaiEzFmK8nmMA66HfSwRGI2mch2lZNnOOlrjaa/JZqDbgdDvEItwEmWSMDz3jzZ677htlhWOwvpb4lmiM1z/HfR3bm1YFP1z2FsWDwPFe2gyNB8BS3HRGKX8QDkKKyKhIyQCasS/C4c4JmOqnO8Z/+tRVb+920cw414pifE06MyGBxPmMlMDFY6EM4Ar9Yth+FmkeMIn5Qf5GtHTkCmM5oc3p4RtliK3E7uL9oBQPuTzmfAOS9gtoRTXnyQPStfJnv7pzgdnj+oIuN60H3Eb0npezndR96BLSKBZYv+yNqKQobY7FwTkcLLpRn1ruGyCE/r37tlBzDMVk4afS+dBlyNNcxO3v6f2LboYT4uPsiEsETOD086blgdbI0hxmzliLuC8lF30mfYrQAkpI4hvtMprJ13D6da7YywxtYIq1GYq38+Gb3OY9zYh3A5y9gy93aocDPsmFAYhonelSHUGP9nJvS+kMLlL8GeZTXDauX+gcSeGMmTcexdRaTJQoo5nP3uchJNNmLNVtyGwa5wz7yw3S0R9LBEctDtoHvl+2yPu4x8tyesdraEQwWer8Bht4Mkcxi9KmvqVhlwvb83AOO8uh9UnWvB02cZoMDt5E/FP9f5PRb/ikkaSFhUe5yOIvKzWuHITBGplcKqNLvkGHjpEpgyFJwFYexYkMSOFe043RnL+AjfISwZrjI2OovY7ioh01VOpruMI24npYYLB27CMBNtstDBHEY3SyQDrTGMtsWSaong7PB2nB3ejr2uUl4pyeCT8oMttuJFGCbujvJ8/J9WmsEBdzkpfX9FvzOfwWQyc2jXV2z5/r4a0+iU5u9i83f3cGT/T/Q742lSB11LSf5O3t36BX+19eWSiOR6h9XeligGWmNwGG6+dOZz8vlvk9hlXPX9CaljGH7pxyz/9ArKDTfdLZH0tkTVOa9r1aj9lRYzXSqD6p5Vr9Khz2VEJ/Ti5+SBnJq7lxG2WP5d7jsBbj9rNBaTiYMYJIz/CwBmSzgVp94Hi56mzzEB9CRLFDaTmQKzBVevCwAoHjAF9iyjtyWqevUSE9CzMmzaznqa7jEpHDo4jdTyArpaItjvLqdLZeDMsYYz6rpl5HzzAN2P7KGHJZJlFfnV3R8OhMdSZo0ARzmplcc6V35d6DjC5REd6GaJwAJ0PmY2gZTKbgNdvLpSdDaHE4aJwV5BfGxYPBR7Zod4KOYkig0XM4v3nHCuWWmcdl3PBODIvsUY7uBdYENEGkZhVZrVdSNh5iQL1t3t2fduMuX7YzEBvQFMsM1ZzJKKPH505LHaWUie4azXdTe5isFr7sxelkgmhyczJSKFrpZInrH34eqIFO4v2uHXSfbrcm1kJ1ItEWS5ynmn9AAJqWPpe8ZTmExmMjfOZtuiP4JRd0DJ2vIhFlsUfU5/lJ6nPsCS/cspNlx0s0QyzGpndT0m8784vD3gCVqJo39HYpdxOB1FbPn+PgoPb6D/WTOJ7ziKk859lZ++vJMJYfGcbouv8/tTNWhpRzfPx6r7N/2LncueIf9AOidf8BaH+lwCP73k03+zysDKY3vjPQO5srd/RlRCL7LbeVock81hJJisHKn8eXevHLl/MLYzmEyU5O8mx94Fp8lMONDBHEaW20GyOYwIkwUXJnKiPK83N6EHqVlr6WaO4CfyqwNnXkIPz9d2feHIHjpVBs5ulWHXNWAKhiUM1v6zukW1c2UdmSedi3v/WmwmM/EmG6mVxzNcZXSxRNCh8lpVxwHMJlPlH1BHg3hHczgm4NzwdlxZOQvC+opC/us4XNePUZqgur9qxsLAFiIifhUyA6wkuHSKhW8mx/JsTG/y3xzN4a96Ub4/FrdhsKIin8eKdnJ67nLOz1vN48W7+L7iSL2Dam12uEr5a8keJuSu4KniXRS5nQy1xTInfkj1wJjmEoaJWysHNb1YshdzbBcGTpqF2Wwla+sctv3wwHGDapXM9W9z8OcvMVtsdB33Z+ZXjrivCqEnMr5yVPvCCDtdhtwMwKZvfsuhnV9SVrCX9XNvprz4INEJvdiU1AeA044ZRe9tQOUo/AO9PS2de1anAZCz9zuKcrawv10vwNMqeuy8sFUf1WeleAaO7Vz+HBlr3qDcFsnBSE+dvb0GJFUFyEOxnakoz2fN51fjNls4EtUOoLrlsypw5kUl4jZbKMrdyqEYzzRWXSuDY9WUVYejPd+3ggjPa2xvtvl8PRKVSE7lOVXXr5qPtaL3BRSHeVpI25lt1S2rPyd6XnOHypbVTse0uHa1RFTfBxBuMpNkstHLq4tLz8oFEu6M6so3CSPqNdBNTswaFls9UFH9VUVCi8Kq+FU4Zl7o24HFHYbTfcnJFG7ogOG0sN1ZzONFOzn9yAquyl/PO2X7yWqG9eVLcfP30kzOzVvFIscRIk0WnrH3qTGXpj9NDk+mnTmM/a4y5jgO0f/s57GFx5GftZKtC//QoGttX/wnnI4i4lJGsKTdSQCccZwR91USTbbq1sz9wzxB9cDmD32WmnSW57Fz+bMAZA26BoDhx/QdrZJqDifGbKUCE9mxqeTuXUhZwdH5xLK2fMSRyEQKLeFYTSZ6HjMSvmpgUlZsZ/KzVlJWsJfDu7/G7SrnUGxn4GioBOhWeftQTAeO7FtCeVEmRblbq1tPq1o+O1aGw5yoJAoPbyR76xxyKgNtVWtn1Tm5Ue3Iz15FfoRnoFf7yhCZWBlWC8PjKKi8L7HyvqTKr3kRCRRGeGYsaGe2kVJ5zaweno+Zw0xmEkxWOlXWtc/meb3xJivJxwTYzpYIn+9PVZ/Z30Z1pbslkhdj+9X6M5CGiU89DZPZQvGRHZQX7Q90OSLiRwqr4heJJhsPJnRlbadRXJjTG/eRKAyLi/mmLH6Zt5Zf5K3mrbL9HKxXQDURHp1CXMpIkntdTKeBv6bLkFvoNvy3dBl6G50GXENSj0lEJ/bFbK29Veqg28FNBRt5qcQTsG6P6soD0T38+IqPurFy4vt3yw7Q6eSpxKWMwOkoZOP8GbhdDVs21VFykL2rXwPg8PBpOAw3XSwRdD9B69vYyhbSLbix9JyE21XB7pUv1jgve9snlBdlkd1xKOVAvNlW67WrBjXtj0nGZbZyYOtHPvcf3v01mEwciPf0061aTrVKVSDLtqdUB2ZXRREF2aurWzw7e4XVqr6fB2M6kJe5FID8/curz61uWbVUBdEk8g+soODgWgrDj4ZKOBpGC8LjyNzwLvnVLauex7avrC3PGk5eZa/mBItnsv+4yqVpi8OiKai6rslWfTw/Ip7CyhbXnpYooipXucpOHgSV38+qltsqqebw6vAO0MMSSZLXvKxVr92Cp7X52NW4pH4SUj1TjBzZtyTAlYiIv6nPqjRJF3M4t0Z25leRHbBiBgdYYstYHL2fGRuzKXCfeFx+WHQHEjqNwZ48GHv7wcS0G4A1rO6Vkby53U4KD60jL/MnDu38ksJD66vvM4CXSvaS43bwaEwvbopMJctVzj/K/NfqMqZyFaRiw8VnVgv9R98LwI4f/9LoCckzN7xD12G/wZYylHUWCyPdBuPCEthdVvf1qgZDbUjqC3hCaVnhvhrnGe4KDmz9iO4jfsvOmGT6Fx1kqM3O7vIyn/P6VnYB2J/YC8Nwk5uxyOf+0oI9lOTv5qC9I31yttHVK/DGmCzV85Zmx6RwpDJ8AuQdWMHhynloO3s9puqj8yORiRQe9vwMi3I2cSTJsxJR1YCmo62miRTlrKbw8Ibq1tGkyvBb1UpaGB7LkYzF5A68CYD2lghMQEJlwDzoKKDE4ekLHIPJZ9nV4rAYrxAcRnxl2CwOi6YwIha7o7C6ZdhpspBX2bUh3mStfi3FtmiiK4pJMNuqW2zB03Wgv1e/1hiTBTPwsr0/54a347/lh7ircCvSMNVhNfPHAFciIv4WMmF1+vTpzJgxA7NZjcUtoYs5nGlRXbgsPBlr5Vru4R0LyemZybVLD7N+Z92PNZmtxHc6laRu55DQ+XSiE3vXOMftqqC8OIuywn1UlOfhrijF7SrHbAnDYosmPDqFyLhu2CISiOswnLgOw+k2fDrFudvYu/YNsrd9Uj0aeHZZFmGYeTDmJP4Q3YPd7jK+c+T65ftwVURHAOaUZZN06j1YrBEcyfyRA5s/aPQ1nY4CDmz+gC5DbmZjx6GMzFzNuLCE6uVKa1M16X1Gr0kAHNjy7zrPPbjjc7qP+C37kwfRv+g7+liigUM+5/StbCndF9+VwkMbcJbn1bhO/oEVNfqLwtEQWhhmp9hipfDQuur7CrJXkzNoJACpXpPvd6h8/JHwOIpyNgNQlLOFglTPx+5VraXtvFpNi3O34XIUklPZOppUeb121qqWUxuO0kNkVdaeaLKQYLJV/9I7WHIQR0URbkyYMar7zZZYI8nNXnk0BJttxFU+b4ktmtLK61cF55KwaIor/7iKM1lJrnwtB2I70StnO3aTlVjT0V+1MSaLT19Xi8lEosnGueGe7gwXhrdXWG2gsMj2RCf2wTDc5O3/KdDliIifhUxY1aIALaMqpF4anoytMqRG9jiC/ZQMntxYwDOf174ClclsJSF1LO17nk/7HpOwRRxd4tJwuyg8tJ787JUUHlpP4aENlObtxKjHZOrhManEdzqFdl3PJKnHuUQn9qH/mc/SffjtbF/ySPVH0G+V7ae7JZJrIjvydExvLsxbTXYT+8zGmiycXdmf9L+RMaT0uRSAHT8+3qTrgidsdhlyM3t7XwiZq2vMS+ot2mSp/pg5o/0ASvP3HHeOyeLcbZTk7WJ/fHeAGvOYwtGW1cy4LhzZt7jW6+RnpXOwp+c1d/MKq1XLxOZGtaMoZ6vPFELFuVvJiU6qPC+8uv7oyvdSVkUBbmdZdZ01W009jykKj6XkiGcu24OV04ElYMYE1S2khyuK/5+9+w5vq7zbOP7V9HYSO3sSSCBkL2aAQGnKLLuE0pYVCi+BsgptgbLaskoLdBCglAKloQXKLgTCCoQkkL0M2ctxEifeQ1s67x+SZUm2Y9mWLdm+P9d1Wvvo6JxHRrFv/c4zACh2BBdnsFI/60CtLYvqmt34vbU4bZlkeWvDXRcc9mzKCxdSmxX8+fSyZJCDGTCosaThCvVPreuS4LRlUJMWGoxlyyYnVLktzerDiNLN9DPbsUbM0Wo3melnie7XekjMHMMW4NL0AYyzZnN/7TZqtbDAQfUcdBwANSXfNPrBSkQ6ty4TVqV99TXbuTFzKBfGhNS8E3ZRYFRz2n9g/b6Gz8vIHcaAI2fS/4gfkJZVPyrf4yyhZPt8Snd9TkXR4vAk+S3lrimieNMbFG96A4stm4Gjf8iQCdeQ0WMY4898PjQa/y78Pge/q93GeGs242w5PJg9glmh1YZa68y0PthNZr711eKd9nMgOEVTTcn6Np0XgkGtuqSAPXmH4wV6mW0MMadRGGjYB3a0JQuzyUSJLZPq9B4Ur3+x2fOX7V5IUf9jATg8ZnCUhfrwuSd3MBXLl8Q+HYDqA+s5MP5aIPqW/gBL/QCnmpKCqOe4qosoD/X/7B0KnnVdBhzWDCoiuk74vTWUh95r4bBaVzU1m8PvmWJXcBooK9DLZKNucdYDngoAah378Zht2APe8K37mrRsnFWFBHwOatKyyfLWMjzisZqyTThygiuO9bRmkBmq3hZXbAnOzUp9KHfasqi1B8P9AFs25tDkvuWhrgGDQlVqn8mC2QhgxgjPOlCnbhWsOn3NadybHby+3WRWpbUZ6gIg0rUprMpBZZss/DRjEFdlDCIjVDGyDi2n3/RdmPtV85uP4JHPwBdRTTWZrfQ59EwGjv5h+I8IBAPqga3z2L/1PSr2fh3XdE4t4ffWULjmWfYUzGXY1BsZMuGn9D/iAnL6jmfte1fgqi7k5zWb+F/PSUy35/E9ez7zPaWtvt73Q1NKvW82yB96MkbAz7alf0jUy6F489vkHHcnO7P6MKL2AOOsORR6GobVunlOd4bmMC3d9WmDY2KV717E3sMvBKC/JY0MzOHlavub07CZzHjNVioyelF9oPHw7SjfTHla/Uj7uuVO6yqm5Zn51JbFhiyDfc5gl4MMkym8wANAZUYvHJU7oo6uq47mhW6j54WqpqW++rlha6t3U2vLJMvr4FBLRnjU6H5HMMR6HAdw2TKwu73hUf0uawZeZwledyW1WSOB4nA3gFp7Fu6K1dQGglOpDYioGu8tWY/Tlhm132HLoCr0b2NAqAILUBm6ezAkVLmutWdjDfjI8tZGDS4DmBAzV+2oiA8QoywNK98STWFVpGtTB09plA0TP0kfwKe9pnJ95lAyTBaqe1Yy8MdrGHZpAd8Y1Uz9EzzwSX1QtdpzGTrxWo790ZeMmfEXeg06HsMIULrzM9Z9cC2L/3ksmxb+moo9SxIeVCP5fQ62ffUwq9+eiatmL1m9RjD5vP+S2Wsk2/xO/uYMDjy6M2t4g/lB49XLZGWqNTgAZ/2ocwE4sP3DqOmd2qp0x8cAFIZGmo9vZPJ9qA+rhfkj8borqd6/ttHjIlXtW4HDnkVNqK9lZJ/TuupjWWZvXI4SvK7G+/cG/G72uUrwm8xYTKbwIKLwIKiMPBwVDTsvVziKw9XJ3iZbOIBWpeXirIxe/rU4VB3NJTifbc/Q/hJP/SIJbsf+cICs6wfrsGbgDIViT8TjdVVcly0Dj+MAXmdZuCpa9zOotWfjcZZRG5rJoX8oKDusGdRUbsUZ6r86IFRNdtoyKXEEV/DqYwSDqsuajqMu1NY9356Jwx5qZ8wMDLFTf0215Ya/zgxVl3+ddShzco7EgkRKyx5ERo9hBAI+KvcuTXZzRKQdqLIqDZxhz+e2rEPClaZ9ZgdDvreDCRPK8AfgvvnwwMf1ITU9ZwiDx1/JgFEzw6P43Y797CmYy94NryZtzsPKfctZ8fo5TPj+v8jOO4JJ5/ybFW+cx9PVu7korR+DLen8IL0fc12N9F9oxin2PCwmE9/4XZjGXArA7rXPJbT9joqtuKp3syN/JGz/jHG2xvutjg5V7nb2Gk757kVx9fX1OA/grCpkf3Y/sstqGGbJYGNoJau6W/olWX2oLdtw0PPUVm6nKr0HvZzl9DPb2R/w0NcSmqu0ibDqqiqkKq0H6T4Xvc32+umi0rJxVUZXccscB/CZLFgNP4Mt6dSNqT/grl+9zOssxRWaDaEujLpt6XidZfWvNTs4l2ldFddlTcfjLCXgc+IOBef8uuda0/G5K6j1B/vO9gsFUIc9C6+7khqCP9+cUIh02jKpckQvN+uypocDcl0MddiysIX679bNG+u22Enze6IWEgAYGdGPON9sZ4A5jStCU6SNdmazzlfT4OfaXfUK9Vet3r8Wf6gSLyJdiyqrEjbKksXLPcbxl9wjGWbJoCTgYf2ILRx/20qGTixj4wE47q9w//xgUM3IHcaoUx7lmEsXMGT8LKz2bGpKN/Dtp7ex5KVp7Fj+RNIn5/Y49rPqrYupLinAntmHcWf+A689i6echQDMzhjSqurqDHtw5PbSvEOwWNOpKl590EFNrVVW+AU784Lzw45p5HawmfrVn/blDqR898IGxzSl+sDa8Gj+yAFSddXJkqw+1JQePKw6q3ZSnhEcZFYXuPJDVcIqWzqumqIGz3FVF1IVmnC/t9lGT1Pd3KbZuGuiPzi4nAdwhgY01U2P5bbYqXbWL1fqcRwIV2r7Rdzm97hKIx4PnqN/6HW6rel4naV4XeV4Qq+3rsLrNlvxe2upjuhqEDxnOj53JbFxqNZixREa5FXHbU2jIqYi7bBnhqutdcJTXsXMzToiotKaZjLz/bTe4e8Hx/R37e7UBUCk61NYFXqarNyXdShv95zI0bYeOA0/r1t30euK5Zx70T5MZvjLlzD5cVheWB9Sj/7hpwwYdTFms5Wywi9Y87+fsOzV09i38TWMdlidqrV87grWvX8V7tpisvOO4MhT/sBrrmL2+d30s6RxRkQQiIcVE8fZgn01N4+6AICign8mvN0QDKv7cgbiN5nINlvD843W6WdOw24y4zNZKMvIp6p4ddznrindQElohagBEQEocs7TummkmuKs3BEOXOGwGgpeJX53o909XNW7qQ71dc032+hRNy+pPQtPbXSF0ussCY++7x8efZ+Jx1nf19jjLAkH2n4R1VGvsy6s1j8+IBTsnRYbPk8VAb8bd6hCaguN2HeF+pzWeKOrlz6LHZ+7itrQ43VqTRZqPdER1m1NpzwmrDptWeFqa526oB8rdhnXKdb6bgHDLBn0Ndu5PXMYvUy6OdYzFFYrFFZFuiz9puvGzMAl6f25JXMYvUIB4333AdxTd3Dr6W6sFthdAVe+Ah9vhvTcoYw64Wf0O/wCzKFbt6U7P2X78ieo3r8meS8kDu7afaybN4vJ579Bn0NPp3TUxczduYifZx3C5ekDedt9oPmThIyzZpNttlKOwYEhx+Hz1nJg67x2aXflvhX4zVYOZPWjf80+DrNkRi1TW1cRLcnqizfgpbZ8U9znri3dQPnhEwDCA4+g/lZ6ZUavRgZIRXNW7qRmYHDUeq9QZbJnqFJd0sQtWXdtMY5+wdCWY7KSZwtWjGtsmeFqaB2PsxSXLXj7OyqIRgRBj7M0XDmtm6+17jY/BAfeOesqr6GBUJEtc8aET1doIYsaX/RCCV6LDZ+7kpqYbhYu/Dg8sZXVdCpi9nksdnzm6F+5lRm9aEzkVFcQPbXVMEs6z+eO4QhrFqOsWW2e1aIzS88ZQnr2AAJ+D5XFK5LdHBFpJylZWT3rrLPYsGEDmzZtYtasWcluTpd0lDWXt3pO5DfZI+hltrHBV8sdpnUcd9VGbjsrGFRfXgnj/gifF+Yxctp9HHNJfSW1dOenLH/9HNa+f2XKB9U61QfWsT00Wv+w4+/idby4jQDjbTmN3mJvSni1qJwBGCYzB7bNwx9zyzhRPI79uGv2sTd3EACHxczHOSximdKa0m8wQiPY41FbtjFc2YusrPatW440vSfOiu0HPYerZg81acF+yj3NNnJNlvAn4Mh+pVGvyVkSrjDmmm30Ck3tVGUYDSqxHkd9ZbVvRH/TyLDq91TjCPWT7RdquyuizyqAwxIdEh0RWdAVEz6dRvBnWBuzVK7XbAvOHhBzvMfvwxEz9ZrLmkaNM7qy6sXAExOM65ZureMzNT58aljEf/c+ZjtHhPq0Trc3XpntLnoMOAoI/tsOxHy4EJGuI+UqqxaLhccee4xTTjmFqqoqVq5cyRtvvEF5eeN/+KRl8kw27swaznnpwTlPKwJennDsIm/KXl48GzJsUOaA616H/xZkMGT8LMZN+j+soT+qpbsWsH3ZY50moMbateZZ+hx2Nrl9x5N37O18tPxZzk7rw7npfSmoPXgwq3O8vScAW4adCMD+zW+3V3MBqDqwhuKc4EpZQ2PCal3FbX92vyanmGqKq3o3ZaElRQdYI5Y+Dd1uP2A2NRvCPbXF1IbeG3mW9HC/T6c1nRrH/kaf43WV4whdr4c1i55mOwQMKgxvw2OdpeEVoyIHR3ljgqDDHAx5dZVTp8WOP+I2fq0p+nO5I6KfsjM2rIYGQdXEhFWP2ULA58QVs98d8FLjrgTqA7/bYsfpjO6v7TH8mI3osOqwR39Iqk7LpZer4e+6yEprj4hb/952nFWjM+jRP7gaWnv0FxeR1JFyldWjjz6agoIC9uzZQ01NDe+//z6nnXZaspvV6ZmAmWn9mN9rMuel9yVgGLzs3Mvl/hVcftle/nR+MKh+sAHG/dHCQs8lHHvp5xx6zO1Y7TlUH1jH6ncuZe17l3faoAqAEWDTwl8D0P+IC5lvC4ars+y94/rHYMcUXtp0y+Bj8bmrKC9qfNL8RKnev5aSrGDf0qEx83PWVdwOZPeLa8qqSIbhZ6/fCUBvkxUbJqyYyA/9JPZ5mh9x7vc5qAoFxTxrZnhZ1Oq03KYH1xkBqkMVxh62THqEgmRFoJGw6ioLV1b7hQYdxXYDAHCEwlxdjHPGVCidMed1UB/ynDHV6HBY9UU/q67zhccfXcHzBDx4fA78EYHYYbbhiunz6jH8eGKCcW1sWE3PpTmHRcwUYMaEDRPTbD0ZGTP9VXfQM1RZrdy7LMktEZH2lPCweuKJJ/LOO+9QVFSEYRice+65DY657rrr2LZtG06nk+XLl3PCCSeEHxs4cCBFRfUjiHfv3s2gQYMS3cxuZaQlk3/3GMcDOSPpabZR4Kvhwso1bDxiK1/e5uO7h4PDA7PfgEvnTWPQ6fMYdfIjpGX1w1lVSMFHP2P5f79PedGiZL+UhKjev4b9W9/DZDJTNOU6KgJe+lnSONrWo9nnjrJmYTeZqTBb2Z/dj9Jdn0UtJ9oeqvavoSS0+lfsSPC6PqvFOf2bnWaqMftDU0MB5Jlt4bAZwMSBiBH3B1Meev29LGnhqp/Dno3H0XQ/4OrQrfYcs51sIxg0q2MqlhCcy7Wualo3wMptTcMbM/q+JmZCB6c5+lebOyYk1kSG1Zhb83WVVqe3lkBEBdYT+tIT006X30PA58ZrqR/85jKbG4bVgB9vTDuqYr6vTms+rOZGBHGLycT37Pm82GMs7/ac1OxzuxJrWk+y8g4Hgn27RaTrSnhYzcrKYs2aNdxwww2NPn7xxRfzxBNP8MADDzBp0iQWLlzIvHnzGDJkCAAmU8NphIyYW2cSn3TM3JY5jHd6TmSqrQe1hp/f1Wxjlns1d11Sw78uhZ4Z8PUumPL0EBbmPMPEc14mO+8IvK5yNi/6DV//+zvs3/IO0LX+G2xf9jiGESBvxNl8TjBsnWxrfLBLpAmhqur2XsPBZOLA9g/btZ0ANaXfcKAurMZUVodEDLCKnVA/Hs7qQmpDc+PmmWzhsFlrz8YVsfTpwZSFqrM9TRayQwOIHLaMJhcTgPpgmmu2UfeKapvoclAXVnuFgqPDbGnQt9URe3s9Zjoyd0z11BGoD4mxQbauD6vf58Btjbi1HzqnO6bi6vK7CfhdeCOmn/IaAVye2LDqbdCOypjgWxVHWI31o4xgFxGrydStFgzo0X8KALXlWw76XhORzi/hYfWDDz7g7rvv5s0332z08VtvvZXnnnuO5557jg0bNnDLLbdQWFjIddddB0BRUVFUJXXw4MHs3dv0H0273U5OTk7UJnCSrRfv95rM/2UOwWYyM99dyunlK9gxaA8rfw4zJ4LXD3d+mM6lX91K39M+ps+hpxMI+Chc+w++mnsSu9c+l1JTUCWSo3wzJTs+AuCbwccCMN3efFit6wKwq/94DCNAeWH885q2ltdZSnGoUphpsoSXHs02WcgMVdmKTeCLGeQTD3fNHqrTgq+pl9kangqpJi0bd218iyWUe4Mhs4cRbBOEVohyljb5nMpQNTYHE+mh4Fnja3z2gNj+ps5GPtA6Y6rbjphjPDHvY7e//vuGfVaDgdLvdUSN3q+rrLq90aHaE66s1odVn+HH5a2OOs5t+PDEhNWKmIBelR5d3a+2N75qWaQjLdELCHQXPdQFQKTb6NA+qzabjSlTpjB//vyo/fPnz+f444Nz5S1dupSxY8cycOBAsrOzOfPMM/nww6arV3fccQdVVVXhLbILQXeUb7Lxp5wj+EePMQy1pLPH7+Laqm+4yfEtN53h4ZNrYUhP2LgfTnr9LBb0+5RhU27CYk2nfPcilr92BlsW3d+q4NPZ7F73AgBFE6/EbxiMtGZFjYpvzITQKlLb80ZQfWBdh/2cKqt2hqtufUNtrBtwVGvLpLImvipoLHftPmpCYTXPZAtPTl9rz8YdM+dpU+oqq9lGgNyISfm9Bwmr1aHwmGMY2OvCahN9ZGPrrQ5Tw19b7piwGhtAXf7osOqNqGjWjf4PHxsOq7UEIm65e0KVVVdMZdVt+PH7XVHdAHwBPz6fM7obgWFE9Vn1m8xUxrx/YrsB1Nqb//CdExGo+5rt9DJZu0WFtWf/UFjdp7Aq0tV1aFjt3bs3VquV4uLoP4LFxcX0798fAL/fz89//nM+++wzVq1axaOPPkpZWdO3eB566CFyc3PDW3fu33puWh8+6DWZs9L64DMM/u7YzenlK9ndo4wlP4NfngJmMzy6bCQ/2vQfMqbMIT1nEK7q3az/8P9Y/e6l1JbFP09nZ1dRtJjass24s/pSYAsGjZNC01I1JsdkCY++3553KOW7O64Pr6N8C5XpPYH6KZwi50N1VsY3k0Gs4NRTdZVVW7iyWmvPxtPEaP5Y1REhs18oXDkt9oMG+UpfMCz29teHzOomjo8cDAXgbGTBMXfsCP2YABpbWY0cJOVo0EUg2Ca/z0kgou+rO3Rdb+wsAQFfg8qq3/AR8HuiKrNew8Ab0Z3GZ7ZSG1NNLrdEV0brpgWL13fseSzLP5b7s0e06HmdjdmSRk7f8QBUqLIq0uUlZeqq2D6oJpMpat+7777Lu+++G9e5PB4PHk/XvFUdrwFmO7/JHsEpoTkXv/HVcEf1Zgr8tVx7HDz2fci0Q1FNOlcvvxHnwGvItdjw+1zsWjWHXauf6bZzFBZvfpNDj/kFm/qNY3zRSibZcnnF3XhFsW60dUl6Txz27A4dcFa3rOmQyl3himrd/1ek98JZuaNV53XX7KV6aLCal2e2hSuSLams1rgrCGDCjBGeo7W2kVv1kWr90fXSACYcrspGj42tmtY20oc9Nqx6YyqpsZVVly+ishpbla0Lq95a/FGV1dC1DD+Rn/M9hh8j4InusxrwYQS8+MxW7KHzefBjjWi732yl1hP9c6iICeaxU1s155qM4If1S9L782DNtgZBv6vI6Tses8WOu3Y/rqpdyW6OiLSzDq2slpSU4PP5wlXUOn379m1QbW2p2bNnU1BQwNKlS9t0ns7ERPCP0ryekznFnofHCPDH2h1cULGG/Rm1vH0lPH1hMKg+vWk6M7d8hHvI9ZgtNg5sn8/X//4OO5b/qdsGVYDize8AUHTIKQBMsjZ927UurO7rMRQj4O/QEciuqkIqMnoCDSurFRm9cFS1fHAVBBcdqAlVQ3tbs8gLTYtUkxZ/ZdXnrggPRKprU00zIckRs7qVx5qGt4nKqic2aDYyt6gn5j0cG3Bj+4p6/PW38h0xVVdHKPj6fc6obgDuUNCMDcKeUBXXE1FF9QV8BPxe/OaIsGsYeCN+Lj6zFa8nul9rbUwXg5qIbgBesy1qeqzGWCK6HUy2tXywVmfRI9wFQPOrinQHHRpWvV4vK1asYMaMGVH7Z8yYweLFbVvXec6cOYwZM4ajjz66TefpLIaa03kpdyy/yx5BttnKSm8V369YxVPO3Zx6hMHaW+GcMbDX1ZfzV/6VVy3/xJ4zFFfNHtbN+ynrP/gp7pru3b8XwFVdSOW+FezIHwnAYdbMqEnXI40IrbS0p8cgaso2EPDFzt7ZfpzVheGlOesqqr1D/1+VntvqymrA76Y6tLpTD2sGeaHXWG2xE2hkKqnGeF3luEOT/PcN/UqpbWb2iNiw6rak4W9qedbYcNjIVGHO2FvzTQTK8PcR4Ta2Khue9N8IEIgoELtDQdMbE3zrRvh7IoKpL1RZ9UcO0DICeAPRYTW2q4Q7ZrqryHlY/WZL+OfcFFtEmM1t4n3cFYTnV1V/VZFuIeG/zbKyshgxor6/1PDhw5kwYQJlZWUUFhby2GOP8dJLL7F8+XKWLFnCNddcw9ChQ3n66afbdN3Zs2dz/fXXYzZ3aP7ucGbgivSB3JI1jAyTBYfh54+1O3nJtQe7FZ44F246EfyGmTmFP+bV2tsx98jFCPjZve55ti97rMlQ0F2V7vyEHv2nUJSWyyB3FROs2XzhrWhwXF1ldW/uYKqKV3doG11Vu8IDrPJD8432rKti2nNwVbf+g0dtaBqnHHMatlAga345gHpedwXu7ODdkt6h6mNtMysrudwxI+WtaU2uluUKxPYRbRhW3bET9TeYuD8mYEaEWb8vtgtB/ff+iEqlO/SaPDErbXlD7fFGBEU/BoFQN4DwcYYfjOg+qx5PCZE1A1fM/LHVEX1Y/SYLHoudTG/jP6dYWU0s3dr5mcgNTVulmQBEuoeEh9WpU6eyYMGC8PePP/44AC+88AJXXnklr776Kvn5+dxzzz0MGDCA9evXc+aZZ7JrV9v6Hc2ZM4c5c+aQk5NDVVXXHMl+mCWDR7JHMjF0e2+Rp4Jf12ymMOBm3AB4+Ucwtj9sdR/JL7Y9RLl9EmYbVBWvYuMXd1FTUpDkV5CaSnd9zqHH/IIdfUczqPArJtlyGw2rI0JhdU/uIKp2vNehbfS6yqkKVdXqqp/hKqjt4CPvm1MTCl/ZZhsWDAgY1MaMpm+ube6ewbbV1f1qYiqZsTzeanymLKyh63gs9ia7o7h9B6+aQiOV0wbV2OjX440IvA3CakQ7Im+711dWvVH3pOpmD3BHfFD2GQYYgeg+r4afyPmK/WZrqOtDz/pjPNFhNbLPqd9swWM9+GwVkXJMFm7KHEqmycJDcS4l3Blk5Y3EltYDn7eWmpJvkt0cEekACQ+rn3/+eaMT+0d66qmneOqppxJ96S7LDFyZMYhbM4eRZjJTHfDxUO12XnUXYzLBzSfCw2eByZzGn/beyNs1/wd2Kz53FVu//j17vpnbYBJ1qVdTUoDbsZ/CvBFQ+FWjy1bmmCzhFZT25g6iqnhVRzeT8lDfyl6himpeaEGAypgQ1FI1oWCXZbJgNhkQ8FHbgvl1/Z6aBrena5sJqz5PFV57T6y+UFi1puFvomIYWzV1Bxp2T/D4Y8NqdPUzdnYAb8T3Pr8LIiZ78vubCKuhW/huf3RYdYd+VpFXCIT+e/jMTYdVn9mCz+uAiBko3K7oD9qRixf4TRa8kf1iTZZw2G/M5RkDGRR6j7zg3MPeRn5unVFdf9Wq4lUYLfhQJSKdV5fp1NRVuwEMM6fzSM5IpoaWAl3gKeOumi0UBzwMyIUXZsL3joAC12TuLXyUMvMIMMP+re+x+cv74h4k070ZlBcuZF+PYL/VEY2E1eGhUe7l6b2owYSjYmuHthCgPNRHtkeoWtfLZAXDoLSNIaQqdPs9GxOm0G3vmjj7q0JwpSdXTMXP4Tt42PV5avBm2MkIVTHd1vQmuwG4Y4Knx9dIWI0dQBUTYhoOsIqsrLqA+r6hvojKasAUOU9q8By+2HOFvo/c6wuF0qiwa/ijPlN4TZbgtesnEcAVW1mNqAAHzGaIaE9NWg49XRU0ZVDEamc9TVb20kXCqhYDEOl2ukxY7WrdAEzAj9MHcHvWIWSaLNQEfDxQu53XQtMqnTsG/n4xZGVk8ETx7bxTfSWYzbhr97Np4a8p6YBlQLuSyn3L2TtkOgDDLOlYMYUDB8DA0GT3pVm9cVRsSUqlujTU17hnqN9jD0yAQbm3bQO96sJqJgaBugFSLZghwgj4cEdM2wTg9h+8TQFf9PKkbrO1yZ9pg+DZSJAOVjfrg5zPiB0EFTs7QP33Pr8LzBEDmZqqrIbaEWhiNazI94s/9N/IF/F8T8AfNaLVZ7YQiHnN3pjqcm1k/1mTOWqRAact46BhNVKu2QpdpAjZo/9UQGFVpDvpMmG1KxloTuPh7JEcb+8JwGJPBb+q2cyegJtMOzx+DlxzLKx0TOPB7Q9TxlAwwd4Nr7Jl0W+7xepTiVZZvIqyjHxcljTS/W6GWdLZGhG46qpUZZm9k7ZwQmmo6mYj2C0hOxR0Srxt++9dFRqBnhHwEzAHw1B1E1XOpjjN0YN5Yld5ihXwu/FZIsNq04OBDhY0o4+pH4wUG3DdMQEzshuA3++Ouq3vi2h75ACruusaDWYDCIXVyDlUG+sGEPAR2UHKZzI3CKvBgWH1r8MR8R70mywYRuRsAtEfEA4mp4sMtkrL6k9G7hCMgJ+q/auT3RwR6SAKqynmB2n9uCtrONlmKw7Dz+9rdzDXtRcDmDwoOIhqUH4ujxbfybyaHwLgqt7Nxs/voKzwi+Q2vhOrLduIz+dgX+5ADinfzmGWzKiwWrcMa2lmb2rLNyeljVWuUjxmG/aAl2GWjHC+KouZq7PF5w0F0zQjgCkUxGpaOGOEq0FYPXhlNuCPXvHJd5A+t7Gj72Nvw0PdgKr6kOdtsIKVj8hfd5GB1x8z+Mrni6xm1sfLukFZRmy3hNB+X+RgqFCo9EXNJuCNrqyaTOG+rfWvLbotkfOuBiur9QLNzLkaqZ/Zzl9yRrHBV8uTzsK4n5dq6qqqNaXfalYTkW6ky4TVzt5nta/ZzgMRq1At91byy+rN7Ay4MJng5yfBg2fAcvep3LnzQcqN4FRBu9e9yLavH9Ev7rYyAlTvX8PenLqwmhH1cF03gLLMfGp3z09GC/E6y6i1Z2N3lTMo1B63JQ23u/GVn+JVHdGNwB4KWdXN3MaP5YwZVOls5v1oBHxR3QD8B+lW0aDPaiODatyx00n5G5uqKmIaqajqbHRgjKxeRobVulAaiKnsBvwNK6u+cGU1shuAF0tEXPXGVEoBfAEvUP/ec0YsZes3maOOb0lYvSvrUOwmM2ek9e4SYVVdAES6ly4TVjtzn9Vz0vpwT9ah9DTbcBsBHnPs5HlnEQGgXw68eAkcPyKXP5Tcz8c1FwDgqNjGhgW/0C/tBKoqXsm+3CFAw0FWAy0RYTVJlVWvuxyXrRe46leKclvT8LrK2nRej682XLGtU9PCVc18gejQ5fI1/+EpchJ9fyNLqIaPC/iIHK0fWzWFUBiNKO423g2g/r+pP6I6G3vtQGSFNLIbgL+uG0B0WK373hs50r+usho1m4AvKqz6TCYCMe30Gf7Irre4YvrPBloZVu0Rx1rovN1Xw4OrtHKVSLfSZcJqZ5RnsvKb7BGcntYbgLXean5Rs4ktoarW6aOCQXW76WSuLHyEMn9/jICfXWv+xo5lj8e9wpDEp7ZsM8VHBP8YDrZET8U0MFRpPWDPxlW9u8PbBuB1luPOHQhAn1BYdVkz8LraNuODz1ODy9YLuztUOcTUYIWpZtsWE7pccQz68kb0o/QdZHlWl99DZLXRG2gYtQJ+Dz5r/VROnkDsPKueqH6pkdXR2OpmVCCMbG/onLF9VsOV1Yh9dQE4EBGEXQEv1qjKqgkjJih7A96o0O2K+DceiKleH6wafTA5JisVzUwtloostiyy848EFFZFuhuF1SSZbuvFwzkj6WO24zUC/MVRyN+cu/FhYLcE50295oRsnir5Ne9XB/umOiq28u0nt2pgQTupLd9CWWY+UH/bHyADM3mhYFXkdyZtzlqvuzw8RVQ4rNrS8daWt+m8fm8N3oy+4e891jR8cVRGo9oWE+BcjUzcH8sTcYs8tt9o9Lmjw2rsAgAQDJ8BkxlCYTW2Pb6YuVEjA2cgJihHBsiobgD+hgO7oImwGjpnbsRt/Eq/B7vJQt1cVT5MDWcDCPiiwqo7YqCbz2TGHJFX/REfEAKYMMc5125uJw2ruX0nYjJbcFYV4q7dl+zmiEgH6jJhtbP0Wc3AzK+yhvOjjAEAbPLVclv1Jr7xB8PBEX3g3z8Go9fxzCp8lP2+wRhGgN1r/8G2pY82ucqPtJ2jYiulWcEqd1+zPTx91YBQFwCnNZ1yZ0nS2udzleOyZoTaF+qzak3H62pbWPV5avBlRYzMt6Th97SsWht7az529H2jz4m4Ne07yAcAV0xV1NvIbAABvwejkf6lkY9HzmcaOUgqtroZ3Q2gXuzCAvXnCr7WyO4Eda+nZ2SfU8MbWrI12BCPqWFYjR085vK5qesX4DeZomYTiGyb12InLc47LZ11ZoD6LgArktwSEeloXSasdoY+q+Ot2fwh+3AODS2T+byziEdrd+AJVUSuOhp+f04Gc6t/xVt7rwDAWbWLbz/9OZV7lyar2d1GwOdkv7sSr9mGLeCln9lOUcBdP8dqZm9ctXuS1j6vuxJ3qLLaP/QeclnT8bUxrPq9tdFr2FvsTa4m1ZTIkOUzWfDHEVY9EWHVf5BuAD6/h4C5vnIYO8E/1AXGpsOqEVPpjewGEIjtL9tEZdXXSLXYaU0Pn6uxeVZ7eeor1AG/B4fJQl3f2a97HYIR87r9sdNi+RzULVhgAEbEa4x8psdiizusjrflkGO28pW3bQPzOlp4cJW6AIh0O10mrKYyC3BdxhBuyByK1WRin9/NL2o2sTj0x6JHOjxzEYw5Yio37/8DRd7hABQVvMTWxQ82ubKPJF5t5XbKMvPoV1PMQHMaRQE3vUO33CszeuKqTl5YxQjgCAW8um4ATmtam98fPk91VFj1my0tnl0iMkD6LDYMVzxhtT54+Q9yB9sIeINV01AAjL3FD8HwaURNMxXTrzQmPMfbDSDy68au67BnNT7AKlT3tEVUTg2/F6fZz5wTbgZglb+2YTeA2L6/AQ91YdVkRE90FdmH1WuxE6/fZo8A4Inanfy1k8wMYDJZyO03CdBMACLdUWrfM+8ChpnT+U+P8dycNQyrycT/3Ac4q2JVOKgefwgsuyWN8sF3ctOe1yjyDsdVs4fV7/6YTV/8WkG1gznKt1CR3guoD4T5oSmWqtN64K5JYlilfoqovLrvzW3/vBnwOaMmr29NWI1cMcprtob7cR6MJ6JK6Dc1fVzA74mqKHobqdoafk9UkPPGVlL9sWG16QFWUTXLiLAaOz0WgMMWsUxr5IT9oa/n9R0FwMcjTyfgdxPwe1k16ChWDToKI+BtpBtA7MCw+msGw2pTldXo5W7j8ZNQV6TOICv/SKz2bHzuKmrLk7Moh4gkjyqr7WhmWj/uzD6ULJOFqoCPe2u38q77AABmE9x5Kvxw+ngePPAYO73Bden3bniNLYt+o1WokqS2fAvV6cER971DITXPVBdWc3DtLUpa2wAcsfOZtmD6ooOJ6j9qtuJvZgWqBs+PvAVutsY1U4W3kdvmjQn3Rw0d0mg3gJiKZOyIfSPgxW8yY2mkb2zsqPrI8GqKiISN9ZUtz8gLfx09G0DweX8fNIl1oy9iW/5IjLV/JXJO14C/sbAas5hBxMAwsxGI6qca3Wc1/tWsOqNwF4DilUkb4CgiydNlKquzZ8+moKCApUuT37czz2Tj6ZwjeSBnJFkmC195KjirYlU4qA7uAR/9n5XDjr6ZG/e8yU7vSLyO/aydN4sNn92moJpErurdVKb3AAjf/s8LhdaatNzkdgMAYiNk7GT8reWNqNj5TOYGc4k2J2pCfLOtwSpPjV8zcnnSpjXXHzV8jojA3TCs+nDaMmOfEnwsNvxEfh/xdeTiBHMnXcH+rH78e9Ll4X1eGlZWfSYTm/scGQrwnqgKrxHwYsQuSNBIu+uYjABGxH/uQOTPpMuH1SkAVO5Vf1WR7qjLVFZTZYDVKbZePJQzkt5mOx4jwB8dO/mHsyj8J+n8sfDbCw7hyaon2FAe7INVvOVdNn3xa3zuiqS1W4JcNUVUhcJqn1BI7RO61Vtlz8aT5ClzamPCjSMxWRVfZP9RWn7SyFvgXou1QR/R5p7jP8i0S8FpqerbFBvo6tTas8kODQxr7PouawbZEaPzw+c/SBtNkW2MuO6CETNYMGJG1LGRFdq6r9Oy62+1e13l2NJ7RrwOf4MqoREzhVfk6zAZRkxYreduZXeQM+z5OI0AC7xtG6TX3rQYgEj31mXCarJlYObOrOH8MNQPbKOvllurN7LRH/zjmWGDP54Dg0dfyu0ld+MyMgl4Kvn2i7vZv/ntZDZdIriri6gKBYo+oVWs8kMLApQZ/ga3mzuaIyZaOQ9y+7wloroBtCIAR07q7zNbmwyUUc8x4qysBrwYkVXTJqq21WnZ9KvdH3pOw+s7bRkN9kFjfVbrmSLa2FwA90aF1eAryuxxaOSV8EWEZUsjlV4jZnqsyNca7LNa37c4crhV5Gpg8co32/lLbnCS/dEli/DEOU9rR0vLHkR69gACAZ/mmBbpprpMN4BkmmDN5t1ek8JB9TlnEedXrA4H1XED4JMbe1N4yHM8XvIQLiOTiqLFfPXK6QqqKSbgd1MaCmt9QtND5ZmCn+lKA8lfMcwZcwu8qVviLeU1RXcDaKmoymqcVb6o2+bNBCVXxACiQBOvucqeU39MI4HW0UQ3gNglTyNFVlYbC8CeiHl3vVEDrEKzAURUUoPn8LL+w+twVu1i74ZXG16wQaU1ohsARP2UIgelNZh+q4UGmFs+QKuj9AxVVWtKCgi0sC+1iHQNqqy20c8yhnB9aEqqPX4Xv6jZHDV/4Q3T4MLvfJdHyx6hItAbAm62fPUohWv+DilayejuykILL/QMha6cUEyoaOHco+3B648Nq4lZiSgqrLaiG4AnMqzGGXZ9kSswNRO2nLYMerqD/66aqtpWpdWH1cb63G7uM4ojSjY02B84yL/DyJ9EYxVdr7Ms/HVUv91A8OdR8NENjDr59xR8dEP4sQPb3ufAtvebvGZU2yKquWYjuodrwIj8um2DjgZZ0tgZSM0FR3oOOg6Aij1fJbklIpIsCqtt1Ntsx2oy8bZrP/fVbqU69Ac4PxOenpnJjj738JuS4HKpzvJvWTf/JmrLNiazydKMytB0YbmhqJIVCgKVLVyCtD24Y6q7iaqs+qIGWLWmz2p98PTFuUJSvH1WIfoWflODv4qz+kR81/B87x15LgGTmdUDp8B/pof3HyzoRf4kGu0HGzGVWeQcqXXzrO7f8i77t77X6hHs0ZVVI2pQVSCqstq2sDrYnA6k5iIBPQceC0BFkcKqSHelsNpGD9du50tvBR95SsP7vjMC7rloMs/WPs6e6kPACLBrzbNs+/oPGHEMPJHkKvcG+xVmGQbZJku4l2BFCszS4ImZLzSeZU3jOm/kAKs2hlVvnEsee1sQVh0RYbWpbgAfD5/O4RW7WDdgInz4ZYPHXQEv7465sMH+2OVWI0VVViOC48Yv7mLI+Fls+uLX4X2Rq3D5I7sWtCFIHrQbQMQ3be1LPagV87R2hLSsAWT2OIRAwEeFVvET6ba6TFidPXs2119/PeY4/1AmipNAOKhazXDfaVYGTLyR31bcQAALfkcRaz+6VbewOpFyTwUQ7NBd15fPbzJT3cZlTRPB7Yu+VRsbXlvLF/V1K8JqoOXdACLjVbN9Vq0Rg6OaCH8uv4c5025t+nqeGqwRk/hHnLDJ5zQVVvcU/Is9Bf+KOjaqshpoW6WzMQ26AZgiBn+1sc9qD1Nq/inoOShYVa05sA6/t+FMDiLSPXSZAVZz5sxhzJgxHH300Um5/qH58Ob1h7L7iNf5V8VNBLBwYMubLP736QqqnUytswx3aPnKQaGw6rRl4nGnXlh1x7kefHMiR4K3phtAZFBzxzkyPbIbQHNhy2FNb74NzXyYaDLsxNkNoNnrR4XVxPQljm6L0WRlNXbJ2Jaym+NfrrUj9Rp4PADl+h0q0q2l5sfpTuaHk+Dc037Mk1W/xu3LwOSrZP2nd7J/6/+S3TRpBY/jAA5bFml+D4MtwZDktGXgdexPcssa67OamKm0Im+styZmeSLDapx9VlsyG8Du7L7Nn89VdtDHfY3MsQrNTF3V7FUjrh+I7LPaPpXVqH6qET+ztg7VzEjLheo2nqQd1FVWK4qWJLklIpJMCqtt9NeZfdg9+Pc8U/kdABz7vmT1/J/jTvLk8dJ6HmcpzvSh9HKVMzCisuqtrUhuw2g4G4A3QfO+Rq4m1ZqwGjlhvivu2QAarvjUlA8HTWaAu5r1/SfA2w37o0IcldUmwmq83QCa42/nyqoZA5qYm7atYXWkJZN3ek7kQ3cpTzoL23i2xEjLHkRG7lACAZ8WAxDp5hRW2+jDzL9T45iIKeBi85KHKVz7ApqSqnPzuSupzQn2bRxsqQ+rPncKDLCKCaceIzGhyNPGsBpZJXXH2Y3A18iKT01xe528PPnKg7fBefDK6p5v/02vwdOoPrAurvZBy8JqILKymoAPEYGYq5sMolbyiqystrWOO8rvA2s2o63ZKRNWe4WqqtX71+L3Jn8mDhFJHoXVNvryg98x9uR7WfHBLTjKNye7OZIAXnclTntwAvlBodWrHLZMfKkwG0DAS2SE8iZqntWIip23FStYef0+6trlijOsRs8GcHB+b/P3qMuLFjF4fNOBdv+Wd3FV76a2LP5/p+YWfPCMHJGfiLAaK7bPamRANbrgB+Reg6YBULFncZJbIiLJ1mUGWCVL+d5lLPz39xVUuxCfpwp3qKLaO9QNwGVNP8ht5I4TO6+qO0Fh1RPZf7QVuccXUeGNu7La1FRPjR3rab6yVrLjI775+GaWvvK9Jo+pKl7VolHlra6sJmj+W4Avhp8CwH8PnR4zwCqyz2rjLY2tznYeJnoNORGAssKFSW6LiCSbKqsJ0fWqGt2Z31ODJzQbQN0qVm6Tuc1zWSZCsJJaP3Lbm6BuAJGV1VYNsAr4ABsArjj/PUT3WT34c+INmMWb34zruHhVRk6Z1YyAUf8zaK5bQ0u8NGUW/xt9PoWuEibsWVF/vYiw31Rl1W+2YG6H/rPtLbv3aNIy++Lz1FC5b0XzTxCRLk2VVZEGDFyhglRWqDKVmAmi2i52EQB3gip4nqjVl1r+4StyoJczzlAfveLTwcOdz5OcoeovDj2Wb/qOZc7xNzd7rCPQTgOsTCbKM/MxYYqqlEZXVuHbvmMAqEzrEd4fiBjs5jXb4rrcWGs2k6w5zR/YjvKGBFcYKy9a3OSKZSLSfXSZsDp79mwKCgpYulSrnEjbuWIqfe52mIqoNbwxf7g9CQpFzoiA6m1VWK1vR7zTaUXPs3rwn+/+Le8C0cubdoTytCwen34HqwYd1eyxu/Hy/qjv899xlxx0OqxWM5mjVtvyxwywevaY63l/1Dk8esrd9fsjwqrHEl9YfavnRF7rOYHcOKcgaw/5Q4Nhtazw86S1QURSR5fpBjBnzhzmzJlDTk4OVVXJHwgjnZszJmy42yN8tIIn4Iv6iOnxJ6bqVBkRxlsTVn0Bb7hdsYG6KZGDw5qbuqpiz1cse+0sXFW7Wty2tjC1oM+nEfDx5rhLgl8vuadd2mNENMcf84GqOr0Hb46biSnyQ0BE4PSbW/brPt9sp8rvbF1D28Biyya33xQAynYprIpIFwqrIonkirmV7UpQ39C28vk9Uf9qvYHELLdaEXmNVizd6YkIq7GDwJoSNRtAHF0HakrWt7hdbdaS1bwib8u3xwpWJnPMbACNT11lRFRT/WZLo1/HIztJldW8ISditthwVGzDVZ0a02iJSHJ1mW4AIonkjJlMyZmglaLaKrb/XqL6rJZHvN5AKwaSRVZJYxcuaErkDAKtCcgdIs4FDiB6kFO7dAPAFJmHo/usNvHz80cETl8Lw2euKTm1jN6HBGdzKNnxcVKuLyKpR2FVpBEN+qymwEwAAIGYil28t9ybUxURxjNacc7IAUXxBmhfxDXjqawmQ23phvgPjgqP7VCJN5miKqj+iO+aisaRfVb9LQjeQFL6rJrMVvIPORWAku0fdvj1RSQ1qRuASCMadANIkQFWhj/6tr/Pn5huAD6jPmBmtaIfbORtb2ecXRO8AT91M5k212c1WbYvewzD8LN/y//iODqyG0DiX0+wG0DEAKvID1RN9FbwmVsfVvMz+4KntEXPaaueA4/FltYDj+MAlcUrO/TaIpK6FFZFGhE7/ZIrRboBBGKqloma1icQEXozWhVWvXw84jR6Ow6wzrEjrud4I+YlbW7qqmTxe2vZuuTBOI9un8pqdUkBOb3HULz5nZgBVpGzKTTVoojVzloYVntaM1t0fCL0Hn4aEFzggRT9ACMiHU9hVaQRzpiwkSrdAGIH7sSG19byuSvDX++Ic4qjSEbAzyuTLgu26cPr4nqON2IhAX+KfBhoC4+jpP6bBAQtIzS4a/U7P6TXoOMp2fExxvALwo9HTl1lYGAYAUwNAmnEYg8tDKs9WrAgQmKY6BPqr3pg+/wOvraIpDKFVZFGxA6oSpXZAGIrqUaCpq7yuiq4f8YcjtxfwLvFX7f4+ZGhOd6R8JGLAiRyxadk8brKWPX2JQR8iZ3uyeeu5MC2eUB039TIfr4BYPfafzBkwtUc2PZB/UGRA7IiwqrPZMHazAewQeY0JlpzWO3rmAUZeg48lrTs/njdlZTvXtQh1xSRziElB1i98cYblJWV8dprryW7KdJNxfa7dKXokpWJqqx6XWXs7jmMjw4/MypExisyoMYfViOekyKV67aq2LOEqv2r2+38RhPTVYHB1q8eZvW7P+abT25q9HhfRBcCbxzV8+953Py35wS+Z89vQ4vj1+/w8wA4sPV9jARNySYiXUNKhtU///nPXHbZZcluhnRjjgZhNTWXfExUn1Wvqzz8tdlib0U76oNnIM6g4Ytou6kLVFY7QmTX1MjKqoEJI+ClfPdCAj5Xo8+N7AbgbcF/4/PT+ra4nS1ltqTR99AzASje9Ga7X09EOpeUDKsLFiygujo5a4GLALj97qjvnQkadZ9Ie3MGJmzy+chb11Z7divO0PIJ8SPnZqUL9FntCFGLAhiRVdbm56mN/K/iNbe8X3J7yh/2Haxpubiqi6jYqyWzRSRai8PqiSeeyDvvvENRURGGYXDuuec2OOa6665j27ZtOJ1Oli9fzgknnJCQxop0FG9MOHWmUGV1U+8jAHhnzAXNHNk6Vntum55fW745ruOiVrrqIt0A2lsgauqqiMpqHGsqRIZVnyW1hiv0P+IiAIo3vw2tWO5XRLq2Fv/GysrKYs2aNTz//PO88cYbDR6/+OKLeeKJJ5g9ezaLFi3i2muvZd68eYwePZrCwuDSecuXLyctLa3Bc7/3ve+xd+/eVrwMkcTyxdxKdaVQZfXJabcyqHI3m/uMapfzW2xZrXre4n8eg8WWhdcZ39yckYsC1CisNmA0MnlqdDeAyEUBWlZZ9ZlTJ6ymZQ8if9h3ANi7UeMURKShFv/G+uCDD/jggw+afPzWW2/lueee47nnngPglltu4bTTTuO6667jzjvvBGDq1KmtbG5Ddrs9Kvjm5OQk7NzSffn8biJvPHgCjfcDTAaHPbtdgmrhmr8z4MhL2LX66VY93127r0XHGwEvL079KZmeGopW/7lV1+xuohcFCEQ90vjx9SIHWHlasDpVe/cVGzj6UkwmM+W7F+Gs2NbOVxORziihv4dsNhtTpkxh/vzoOfLmz5/P8ccfn8hLhd1xxx1UVVWFt6Kiona5jnQv/pjKqs+XOpXV9rJl8W/58vmJOCt3dMj1jICPL4efzPwjzsbQAKu4RN7uj66sNq/EWv+h3muuD6svTb6KyrQeTT5vgCWNS9P7k29KfD9Xk9nGwCNnAlBU8FLCzy8iXUNCw2rv3r2xWq0UFxdH7S8uLqZ///5xn+eDDz7gtdde48wzz6SwsPCgldiHHnqI3Nzc8DZo0KBWt1+kjjdmrkyvP3Uqq+0pUbMLxHet1JwOLJVFzbMa8d3BOgE8e8z1fDH8ZOb3rP/d6DHVl1l39hrObefM4ZMR32v0+aOt2fwmewTP5B7ZylY3rc9hZ2LP7IO7tji4apWISCPapeOSEdPb32QyNdh3MKeffnrcx3o8Hjyerl/1ko7l87ujPsr5UzCs+jw1yW5Cm0SGVZOpicXtJUrUcqsBPxCskB6sz+rSocezdOjxeFc9G94XufRq3UpZgWa6Bky0tW3gXWOGTfw/AIrWv6QPLyLSpIRWVktKSvD5fA2qqH379m1QbU202bNnU1BQwNKlmvZE2i52+U9vzFRWqaBkx8fJbkKbBKLCicJqfOp/TtGzATRfDHBFLKkb+fE+EAqufnPHzmSYN/RksnuPxuetpajgnx16bRHpXBL628nr9bJixQpmzJgRtX/GjBksXrw4kZdqYM6cOYwZM4ajjz66Xa8j3YM/ZnnV2D6syfTNxzdRumsBm7+8J9lNaRPDUFhtqchuAL6oRQGatumLuynZ8TF7Nr0e3hdZWa0Lq81VVhNt2KTrANhT8DK+iCAtIhKrVVNXjRgxIvz98OHDmTBhAmVlZRQWFvLYY4/x0ksvsXz5cpYsWcI111zD0KFDefrp1o0wjtfs2bO5/vrrMXdwdUC6pkDMgB9fCk1dVbz5LYo3v5XsZrRdxM/YXasp6+IRvShAIJzxmwqrhgmKCv5JUcE/6Z3WM7zfG3VM8CT+DgyrvQafQM+BxxLwe9i99u8ddl0R6ZxaHFanTp3KggULwt8//vjjALzwwgtceeWVvPrqq+Tn53PPPfcwYMAA1q9fz5lnnsmuXbsS1ujGzJkzhzlz5pCTk0NVVVW7Xku6vgZhVf3p2sVXc0/CbE1XZS1OkXOv+gL+ui6rcc2z6g/Uv6cjO7mEK6sd9UHfZOaw44LTGBYVvNTiKc9EpPtpcVj9/PPPmx0M8dRTT/HUU0+1ulEiyeaLCauGVtVpF86qncluQqcSOcAqEM9sABEP+CIGCUbObtGSbgCZ6fk4XPEt+tCU/oefT07vMXjdlexYrvl1RaR5XeaeuQZYSSIZsSsqae16SQFRK1gFIvusNrEoQERhIfI97TUig25ogJWp+T8HvTP7xNvURpktaQw/+jYAdq2cg89d0abziUj30GXCqgZYSSL5G1RWNWm9pJbI2QACcRT+aww/H7lL+cJTzv6IBQICdVNXmZuvrOaa7S1vaITB468iPXsgruoidq97vk3nEpHuo8uEVZFEahBWAwqr0rGMZrpbRfajbrKbSsyUVtdVf8tVVQVRfVYNU/yVVZu59atY2dLzGDZpNgDblj5KIAWngxOR1KSwKtKIhst/KqxKx3JZ0w/6eOQKVvEMsIoU+W6u77Pa/J8Dq7n168gcMuVGrGm5VB8ooHjTW60+j4h0P10mrKrPqiRUg8qq+qxKx7ij72HsyR3ELRlpBz0usvrf5JoATVRn/RHhtkUDrFpZWc0bchKDxl4GwNYlD3DwmWFFRKJ1mbCqPquSSA0GWOmPq3SQ1755ke8UfciCrW8f9LioFaxa+P6MPL5sb/ADfjx9Vp/zwBM5R2C152JL7xXXtTJ6DGf0jL9iMlvY8+0rlBctalFbRURaf09HpAuLZ/lKkfYSzyh5f8R71Giye2sTswREPHfHyjmY9i3jSOIbPHV2Wh9eu/BlwMxX/5qG3+c46PFHnvIHbGk9qNy7nE1f/Dqua4iIROoylVWRxFIfVUltUd0Amliu1l27v4ln14dVr89B4epncDZ5bEP2jN7YM/LI6Dn8oMf1GnwCPQZMxe91UPDR9RiB1FkJTkQ6jy4TVtVnVRJJfVQl1fnjWBTA73M2/kBEZbVuAQxfKz6g2SKWcG3MwNGXArB3w6taqUpEWq3LdAPQcquSWOoGIKktnspqUyL7rNbNfBG7atvBmIwAhsmMLSO/0cfzhp5CevYAeg8/DYA93/y7Re0TEYnUZcKqiEh3EhlWW1wTjaqs+hucrzl2nxu3LQN7KKzm9BmPyWyhqngVvQZNY8JZL4SPrSpeTW3Zhpa2UEQkTGFVRKQT8rdhoYrIeYQDraisZntqMEwmbBm9sdiymHrRuwB8+fxEBo27or6NXgebF93X6naKiIDCqohIJ1J/uz9yIYCWx9b65/pbEVYffv9mqtJyufzQafQYUD9dYM+Bx5HbbyIAa/73E6oPrMfrKmtx60REImmAlYhIJxTdZzWaO/TYUm9l40+OnPYq9OxAC8IqQK67ir6WNHpGhNX+R1xAWmZfAn4vFXu+VlAVkYToMmFViwKISHcSiFgUIHZ41RnlK3m4dju/r93RxLMjwmpo5otAg4UwmpduspCW1T/8fe9DZgDgqNhKwO9u8flERBqjbgAiIp3QwQZE7Qq4+LuzqMnHo2YDqOtE0MLKKkC62YYtLavBfkfF1hafS0SkKV2msioi0p0E4phntUmRK7SFvg60Ym7hNJMZa1qPBvsdFdtafC4RkaYorIqIdEJtmgk4sr9r6GuD1nUDaDysqrIqIomjsCoi0gm1JaxGPTfUV9VoxVRYdszYQmHV73WE91ftW9mG1omIRFNYFRHphIyDfNf8cxuuYGW0ps+qyRSurJbu+iy831m1s8XnEhFpSpcZYDV79myuv/56zGblbxHp+ozIfqctXG7VFHm80foabbo5DbPFDsDmRfdjGAH2b3m31ecTEWlMlwmrc+bMYc6cOeTk5FBVVZXs5oiItKvWr18Vrb7PasvZDV+wLX43ntpivvnohgS1SkSknsqQIiKdUKL6rBqhPqsbTMG9PpMl7vPY/J7gczw1bWiNiMjBdZnKqohId2K0Ia6aIp8bqqzWmuCmc/+GAfz57WviOo/d7wWiB1eJiCSawqqISCfUlspqlbm+ehrZ99Vhz8IS8MV9nrrKasDnakNrREQOTmFVRKST8Hmqw18H2hBXF6RlsXD4GLbkHw7zv4p6LGCKv3eYNRCqrPqcrW6LiEhzFFZFRDqJmtJvIWMQ0LapqwImE/+c+tPgM0MrV9V1KzBaMLNAXTeAgF+VVRFpPxpgJSLSWUTMhdq22QCMhl/XdQcwxR9Ww90AvKqsikj7UVgVEel2GgbS2rINLT7LhD0r6eUoVTcAEWlXXSaszp49m4KCApYuXZrspoiIdDpeVzmL/nk0C58bF/dzeroq+P17N3L8gU1ckNa3HVsnIt1Zlwmrc+bMYcyYMRx99NHJboqISIprvI+rp7YYn6fli6r8YtfX/D7ncLJaMEeriEi8ukxYFRHpvlq23Gp7yVZYFZF2oLAqItLttE+4zdCfFBFpB/rNIiLSyfncFcluAgCZqqyKSDtQWBUR6eRqyza28BltWf+qaRkKqyLSDhRWRUQkITJbsPqViEi89JtFRKTbaac+q6qsikg7UFgVkW5vgd8BwGveiuQ2pJNTZVVE2kPK/WYZPHgwn332GQUFBaxZs4aLLroo2U0SkS7uxvLVXFv1DfdWFiS7KR3k4H1W76nZwnvuAy0+qyqrItIerMluQCyfz8fNN9/MmjVr6NOnDytXruT999/H4XAku2ki0kU5CPCJpyzZzUgZL7v28bJrH2el9WnR8zQbgIi0h5QLq/v27WPfvn0AHDhwgLKyMvLy8hRWRURSnOZZFZH20OLfLCeeeCLvvPMORUVFGIbBueee2+CY6667jm3btuF0Olm+fDknnHBCqxo3ZcoUzGYzu3fvbtXzRURSXd3t9v+14rZ7a9UcWN8u51U3ABFpDy2urGZlZbFmzRqef/553njjjQaPX3zxxTzxxBPMnj2bRYsWce211zJv3jxGjx5NYWEhAMuXLyctLa3Bc7/3ve+xd+9eAPLy8vjnP//J1Vdf3dImioh0Gr+q3sy77gN86anosGtW7V/N2veuxFm1M6Hn1QArEWkPLQ6rH3zwAR988EGTj996660899xzPPfccwDccsstnHbaaVx33XXceeedAEydOvWg17Db7bz55ps89NBDLFmypNljI4NvTk5OvC9FRCTpnAT4OAn9ZUt3fZrwc6qyKiLtIaEfg202G1OmTGH+/PlR++fPn8/xxx8f93leeOEFPv30U/71r381e+wdd9xBVVVVeCsqKmpxu0VEJD5vjr2YuSNmNPqYBROHWjK4PfMQshVcRSRBEhpWe/fujdVqpbi4OGp/cXEx/fv3j+sc06ZNY+bMmZx33nmsWrWKVatWMXbs2CaPf+ihh8jNzQ1vgwYNatNrEBFJVfsDnmQ3gfePPJf5g49u9DEz8ETOEVybOZhnc0d3bMNEpMtql9kADCN6Dj+TydRgX1MWLVqExRL/J3KPx4PHk/xf4CIi7e0V1z4Ot2SyMMmLF/jMjf+ONgGjrdkAHGXr0YEtEpGuLKFhtaSkBJ/P16CK2rdv3wbV1kSbPXs2119/PWazOviLSNfkxeCe2q3JbgaGqfHlWs0mE1t8DkZYMwGwY8LTzAIEIiLNSWiy83q9rFixghkzovszzZgxg8WLFyfyUg3MmTOHMWPGcPTRjd+eEhGRRGkirBIM1HWy1G9VRBKgVVNXjRgxIvz98OHDmTBhAmVlZRQWFvLYY4/x0ksvsXz5cpYsWcI111zD0KFDefrppxPa8FiqrEp7qbVlJrsJIklVlDOAv+/9Eu9FDacrjGTGRFpEkM0yWSg3fO3dPBHp4lqc7KZOncrq1atZvXo1AI8//jirV6/mN7/5DQCvvvoqN998M/fccw+rV6/mpJNO4swzz2TXrl0JbXgsVVYl0X5/8t1szRvBY9PvTHZTRBrlMPwALGqnPqwPfec+Vg+czBNH/x8vufZSnDMw9Ejjt/bNmKKqqdmmlFskUUQ6oRb/Jvn8888xNdFfqc5TTz3FU0891epGiaSCzX1G8fCp9ye7GSJNOr5sKXkmG7sCrnY5/7b8kTw57ed43ZUxjzTdDSAyrKobgIgkQpe5Zz579mwKCgpYunRpspsiItIhagx/uwXVxuzd8BoAO1b8qdHHLSYT2eb6GojCqogkQpcJq+oGICLSvjZ8djuL/3ks+7e8yxuuhjO8xIbTbJOFH6T1440eExhsbrjEtohIPLpMWBURkfZm4K7dC8Avajbzm5roabRyYvqojrJm8VDOSMbbcrggvV+HtVJEuhaFVRERaRWXEYj6PiemsjrFlhv+epg5vUPaJCJdT5cZqqmpqyTR9m16k/6Hn0/h2ueS3RSRlOSNmRUgthvA0IiAOsKayXftefw8cxgfeEr5h7OI79jzsGDCbQRwE8BjBDgQ8LLRX0t0DBaR7sxEU3OQdFI5OTlUVVWRm5tLdXV1spsjnZjJbCe330SqildiBDRXpHQ/p1y3EwCvu5Iv/zG+wePfT+vD4zlHhL+vCfiiBlhF8hjBMNrU45FqAj4+8ZTxD2cRBf7aVrZeRFJZS/Jal6msiiSaEfBQuVezS4g0xRfTDcBmavrOlt1kxh7zeKHfxTa/Aztm0kzBbag5nRyzlXPT+/L9tD7Mde3lodrtWrZVpBtTWBURkVaJjY+WJuZfjfSOaz9feyuxmcy87irGGXPD3wyMs2ZzefpAzknvy08yBjLams3VVQVUhxZBEJHupct08NQ8qyIiHSs2rFqbWTAG4HNvOa+4i/mXa2+DoAoQANb4ari1ZhNXVq6nMuBjii2Xv+eOIb3r/MkSkRboMv/yNc+qiEhq+ptjN14jgNPws9BTHvfzFnor+FHlunBgvT/7sHZspYikqi4TVkVEJDXN95RyRvlKLqxYQ5nRssGKG/y1zK7+Fr9hcGF6P85L69tOrRSRVKWwKiIirRLvkKdaw8+OgItNfkerrvO1t5InHMGZCe7KGk6eydaq84hI56SwKiIirRJvWK1pYTW1Mc86i/jGV0Mvs427soa3+Xwi0nl0mbCqAVYiIh3LiDOu1iRgFL8Pg7tqtuA3DM5N78tR1tzmnyQiXUKXCasaYCUikho8MfOvOhI05dQ6Xw3/ce0D4Jeqrop0G10mrIqISMdyG40vilobEU4dhp9Ezo76F8cuag0/E205nGHPT+CZRSRVKayKiEirLPZW8KWnnLXe6KUSIyuptQmeyL/E8PJ3x24Abss6BFscCxGISOemsCoiIq3iB66oKuCh2u1R+yMDamyXgER4zlnEgYCHYZYMLknvn/Dzi0hqUVgVEZE2iY2jkZXV9lgg1UGAPzl2AfCzzKHkmiztcBURSRVdJqxqNgARkeQIxMwK4In4PvaxRHnNtY/NvlryzDZ+ljm0Xa4hIqmhy4RVzQYgIpIcsXE0YBjsD3gAWBPTnzVR/MADoe4HP04fwKGWjHa5jogkX5cJqyIikhyx1dMAcEHFam6s2sBvare123W/9FbwibsUm8nMHZrKSqTLUlgVEZE28RuxYdVgX8DD+54SKhOwetXBPFi7HY8R4BR7Hmfae7frtUQkORRWRUSkTRp0A+jAa+8MuHjGGZzK6nfZIxhoTuvAq4tIR1BYFRGRNokNp+01qKopTzoKWeWtItds5U85R5CuP20iXYo12Q0QEZHOrbE+qx3Jh8Gt1Rt5q+ckJtlyeTJ3FDdUbcDZ4S0RadpISyY/Su/PWGs2NpOZkoCHAwEvewNu9vrdFAXc7Al97dJ7N4rCqoiItElsHdUwOrayClAYcPPTqgJe7DGW6fY8Xuk5nrtqtrDOV9PhbRGJ9eP0AdyVNRybKb6qf2nAw55QgN0bcIe/3hNws8fvoqyd+4KnGoVVERFpk2RXVuus9FVzWeV6ns49ktHWbN7sOZFvfbXs8DvJMJnJN9nobbZjIrhs67e+GlZ7q1nuq2Kr39ngfCbgMEsG46059DBZKTd8bPE72Oxz4I55lRmYmWzLZbw1Gz8GO/0u1vqq2Ruawkvanyni/+u/NkXtp4n94a9NpkaObfp8poijGtvf02zj/zIGc256XwA+9ZTxhqsYpxEg32yjn9lOf3MaAy1pDDKnMdCcRrbZSr7ZTr7ZzjhyGn2tLsPPHn8wvBYF3BT53ewJuCjyu9nud1JieA/+w+pkFFZFRKRNYsOpv4P7rEZa5avmrPJV/DJrON9P68OR1iyOtGY1OK4/aYy1ZvOD0HKtZQEvy71VFAZcuAw/Qy0ZTLP1JM9sa/Bcn2Gwze+g0O/CRYBB5nTGWLMaVM28RoBbqjey0FuBFRM2TNhMJmyYsZlMwX0mc3A/JqwRj9lCj9U9z173tcmENeYYG9HnsphMWAAzJsyEvjaZMAOW0D5zaJ8l9Lgp9Dxz6Hl1z4/eFxPIIoJd/RYZ3xqGvOiAFzxHbMgzRZ0l+ryRzzebIo9KbT7D4FHHDp5zFjV7bK7JwkBzOoMswfA6MBRm677uZ0kj3WThUGsmh5LZ6DnKA142hz5YbfE7KPDVsN5XE7VgR2fSZcLq7Nmzuf766zGb1bFeRKQjBWJu+yf7z2GJ4eX2mk08WLuNo2096Ge2U2P4KQ94KTG8+A2DwZZgwJxizWWiLYc8s43vpeU3OJfD8LPeV8M+v5s+ZjuHWzPJN9s53JrF4TEhuMjvYrm3CgMYY81ipDWLv+Ye2UGvWlJFwDDC/wb8GHzlreRJRyErfFVxPb/K8FPlr2WDv7bRx+2Yoqqxgyzp4SA72JLGIHM6vcw2jjb34Ghbj/DzPEaA9b4aVnqr+MpbyRJvZYM7BKnKRPJ/ryRUTk4OVVVV5ObmUl3dPiuniIh0B6dctxMAr7uSL/8xvsnjhpjT+CzvqPD377kPcFP1xnZvX6LYMDHGms0kaw59zHYyTWaKAx6WeatY7avGF/Nnsq/ZzmhLFn3NdjJNFvYHPKz1VbM74A4fk4GZt3tO5FBrdOXLYwTwGAG8GPgw8BoBfBh4DCO4L+Kx8HGGgZcA3tAxdc8Jfl3/mC/0PJ9hEMDAj0GAYHgKf42B34j4utF9wbDlb+R5dT8JI7wZUd/TyL7w/pjn1x0b/VzCz25wLaOxa0X/b/RzGu6PunYT5zMijm7ufKkYoNIwc5glg5HWTEZaMjncmsV4aza9zfao4xyGn0WeCt5zH+AjT1mHB9eW5LUuU1kVEZHkaDh1VefixWC1r5rVvvgKHPsDnvBysk1xEuD0ipVkmSx4DAMfAfyJaKxIM9wE+MZfyzcxldmh5nQm23KYauvBdFsvBljSmJGWz4y0fKoCPt507+dF5x52BVxJannTFFZFROSgPI4DB328QVhNwmwAqSgAVBuKqJIadgVc7HK7eMsd/Pd8pCWL76Xlc0FaXwZZ0rk8YyA/SR/AjyrXsSzOLgsdRR08RUSkUavevoTyoiWs/+Dagx5nxNwM7WyVVZHu6Ft/LX9y7OLk8uVcUbmeBZ4y9gbcrIrzDkNHUmVVREQaVbFnCavfWdLscclewUpEWs8AvvRW8KW3gmyTpUEf7VSgyqqIiLRJ7G1/VVZFOqeaFO22orAqIiJtEltJje0WICLSFgqrIiLSJp19NgARSW0pF1azs7NZunQpq1atYu3atVx99dXJbpKIiBxEg+VWNRuAiCRQyg2wcjgcTJ8+HafTSUZGBuvXr+eNN96grKws2U0TEZFGaICViLSnlKusBgIBnE4nAOnp6VgslvD6wyIiknpio6miqogkUovD6oknnsg777xDUVERhmFw7rnnNjjmuuuuY9u2bTidTpYvX84JJ5zQomv06NGD1atXs3v3bn7/+99TWlra0maKiEgH0W1/EWlPLQ6rWVlZrFmzhhtuuKHRxy+++GKeeOIJHnjgASZNmsTChQuZN28eQ4YMCR+zfPly1q1b12AbMGAAAJWVlUycOJHhw4dz6aWX0rdv31a+PBERaW+x3QB0L0xEEs1o7WYYhnHuuedG7fvqq6+MOXPmRO375ptvjAcffLBV15gzZ45x0UUXNfm43W43cnJywtvAgQMNwzCMnJycVr8ubdq0adMW/2bDZGzpfUJ4uy/r0KS3SZs2bam95eTkxJ3XEtpn1WazMWXKFObPnx+1f/78+Rx//PFxnaNv377k5OQAkJOTw0knncTGjRubPP6OO+6gqqoqvBUVFbX+BYiISItpQJWItKeEhtXevXtjtVopLi6O2l9cXEz//v3jOsfgwYP54osvWL16NV9++SV//etfWbduXZPHP/TQQ+Tm5oa3QYMGtek1iIhIyzTsBqCOACKSOO0ydZUR09neZDI12NeUlStXMmnSpLiv5fF48Hg8LWqfiIgkjuqqItKeElpZLSkpwefzNaii9u3bt0G1NdFmz55NQUEBS5cubdfriIiIiEjHSWhY9Xq9rFixghkzZkTtnzFjBosXL07kpRqYM2cOY8aM4eijj27X64iIyMGpE4CIJFKLuwFkZWUxYsSI8PfDhw9nwoQJlJWVUVhYyGOPPcZLL73E8uXLWbJkCddccw1Dhw7l6aefTmjDY82ePZvrr78esznl1jkQERERkTZo0VQD06dPNxrz/PPPh4+57rrrjO3btxsul8tYvny5ceKJJ6bkVAjatGnTpi0xW+TUVb/JOizp7dGmTVtqby3Ja6bQF11GTk4OVVVV5ObmUl1dnezmiIh0C1t6169U+G/nXu6u3ZrE1ohIqmtJXusy98w1wEpERESk6+kyYVUDrEREUoPJpCFWIpI4XSasioiIiEjXo7AqIiIiIimry4RV9VkVEUkN6gQgIonUZcKq+qyKiIiIdD1dJqyKiEhqUGVVRBJJYVVERNrsD7U7wl8rrIpIInWZsKo+qyIiyfO0c3eymyAiXVSXCavqsyoiIiLS9XSZsCoiIqlCHQFEJHEUVkVEREQkZSmsiohIQqmuKiKJ1GXCqgZYiYiIiHQ9XSasaoCViIiISNfTZcKqiIikBnUDEJFEUlgVERERkZSlsCoiIgmlyqqIJJLCqoiIiIikrC4TVjUbgIhIcv3FsYuygJc/OXYluyki0oWYACPZjUiknJwcqqqqyM3Npbq6OtnNERHpVrrcHxURaRctyWtdprIqIiLJp6AqIommsCoiIiIiKUthVURERERSlsKqiIiIiKQshVURERERSVkKqyIiIiKSshRWRURERCRlKayKiIiISMrqMmFVK1iJiIiIdD1dbrERrWAlIiIiktq0gpWIiIiIdAkKqyIiIiKSshRWRURERCRlKayKiIiISMpSWBURERGRlKWwKiIiIiIpS2FVRERERFKWwqqIiIiIpCxrshvQXnJycpLdBBERERFpREtyWpcLq3UvvqioKMktEREREZGDycnJaXYFqy633CrAwIEDm3zhS5cu5eijj27yuQd7vLHHcnJyKCoqYtCgQZ1iedfmXn+qXKO152jJ8+I9Vu8ZvWdacmxbj9F7puPO31neM615XO+Z9jm/3jOJlZOTw549e5o9rstVVoGDvvBAIHDQ/wgHe/xgj1VXV3eKXwjNvf5UuUZrz9GS58V7rN4zes+05Ni2HqP3TMedv7O8Z9ryuN4ziT2/3jOJFe91ut0AqyeffLLVjzf33M6gI15DIq7R2nO05HnxHqv3jN4zLTm2rcfoPdNx5+8s75m2Pt4Z6D3TsmO723umS3YD6Eg5OTlUVVWRm5vbKT69SvLpPSMtpfeMtJTeM9JSqfye6XaV1URzu93cd999uN3uZDdFOgm9Z6Sl9J6RltJ7Rloqld8zqqyKiIiISMpSZVVEREREUpbCqoiIiIikLIVVEREREUlZCqsiIiIikrIUVkVEREQkZSmsiogk0fPPP89DDz0U/v7zzz/HMAx+/etfNzj2q6++wjAM7r///o5soohIUnXJ5VZFRDoDk8nEWWedxTnnnBPeN3HiRHbs2MG4ceOijr388ssZOHAgACtXruzQdoqIJJMqqyIi7WTdunXcddddPPXUU5SWlrJ3715uvvnm8OPTpk0jEAjw9ddfAzBy5Ehyc3N54YUXosJqdnY2Dz30EC+88AIAK1asAKBv374YhsGNN97IypUrcTqdrF+/nmnTpkW1o3fv3jzzzDPs27cPh8PB6tWrOfHEE9v3xYuIJIjCqohIO7Db7RxxxBFcdtllfP755xx99NHMnTuXhx9+mKysLADOOecc3n33XQwjuDbLlClTcDqd/Pvf/2bkyJHY7XYA7r77blavXs3evXs5cOAAu3fvBmDSpEkAzJ49m1tuuYUJEyawY8cO5s6di8lkAmDo0KGsXbuWXr16ce655zJ+/Hj+8pe/pNxyiiIiB2No06ZNm7bEbpMnTzYMwzBOO+208L7Ro0cbhmEYffr0MQBjw4YNxtlnnx1+/Pe//73x1VdfGYBRUVFhTJgwwRgxYoRRXV1tHHHEEcazzz5rzJs3L3z8L37xC8PtdhuHHHJIg+sOHjzYAIz33nvP+PTTT5P+89CmTZu21m7qsyoi0g4mTpzI3r17+fDDD8P7+vXrh9vtprS0lFGjRjF48GA+/vjj8ONTpkwJ3+Jfu3YtY8eO5ZJLLuFvf/sbGzduZMqUKcybNy/qGm+88QY7duwI74tc13vIkCGceeaZ4QqsiEhnpG4AIiLtYMKECSxfvrzBvvXr1xMIBDjnnHP46KOPcLlc4ccnTZoUHjy1Zs0abrrpJo4++mjuv/9+bDYbY8aMiRpcNXHiRFavXh11jcmTJ3PgwAGKioqYNGkSbre7wTEiIp2JwqqISDtoLEhG7jv33HN55513wo8NHz6cXr16hSurq1ev5qijjuKuu+6iqqqKcePGYbfbw4+np6czcuRILBZL+Bwmk4mbbrqJF198EcMw8Hq9WK1WMjIy2vfFioi0s6T3RdCmTZu2rraVl5cbF1xwQdS+1atXGzfccIPRp08fw+PxhPuuAsZFF11kuFwuw2q1GoBhtVqN/Pz88ONXX321UVpaGv7+qKOOMjwej/HNN98Yxx57rDFq1Cjj1VdfNbZs2WL06NHDAIy8vDyjtLTUeOGFF4xRo0YZRx55pHHttdcaRxxxRNJ/Ptq0adMW76bKqohIgg0bNoyePXtGVVZtNhtHHnkkq1at4vvf/z5ff/01Bw4cCD8+efJk1q9fj8/nA8Dn81FaWhr1+KpVq8LfT5w4kQ0bNvC73/2O//73vyxfvpxAIMBxxx1HZWUlAGVlZXz/+99n5MiRLFu2jC+//JLzzjuP4uLidv4JiIgkjolgahURkXY0ceJEVqxYQY8ePZg7dy5ffvkljz76aKvP99e//pVevXrxox/9KIGtFBFJPaqsioh0gIkTJ7Jt2zZqamr48ssv+fe//93m861duzZBrRMRSV0KqyIiHWDChAnhbgGPPvpoeGL/1ho3bpzCqoh0C+oGICIiIiIpS5VVEREREUlZCqsiIiIikrIUVkVEREQkZSmsioiIiEjKUlgVERERkZSlsCoiIiIiKUthVURERERSlsKqiIiIiKQshVURERERSVkKqyIiIiKSshRWRURERCRlKayKiIiISMpSWBURERGRlKWwKiIiIiIpS2FVRERERFKWwqqIiIiIpCyFVRERERFJWQqrIiIiIpKyFFZFREREJGUprIqIiIhIylJYFREREZGUpbAqIiIiIilLYVVEREREUpbCqoiIiIikLIVVEREREUlZCqsiIiIikrIUVkVEREQkZSmsioiIiEjKUlgVERERkZSlsCoiIiIiKUthVURERERSlsKqiIiIiKQshVURERERSVkKqyIiIiKSshRWRURERCRlKayKiIiISMpSWBURERGRlKWwKiIiIiIpS2FVRERERFKWwqqIiIiIpCyFVRERERFJWQqrIiIiIpKyFFZFREREJGUprIqIiIhIylJYFZEuKzc3F7/fz0033dRh18zOzuaRRx7hww8/ZP/+/RiGwb333tvosVlZWTz++OMUFRXhdDpZtWoVM2fOjPtaLX2+2WymuLiYm2++GYAzzjgDwzAwDIOf/OQnDY632Wxs2rQJwzAoKCiIu10iIomksCoiXdbUqVMxm80sW7asw66Zn5/PNddcQ1paGm+99dZBj33jjTe4/PLLuf/++znjjDNYtmwZ//nPf/jhD38Y17Va+vyTTjqJvn378sYbbwAwefJkACorKxk3blyD42+99Vb69+8PwIoVK+Jqk4hIezC0adOmrStuv/jFLwyPx2Okp6cn5fr5+fmGYRjGvffe2+CxM844wzAMw7jkkkui9n/44YfG7t27DbPZfNBzt+b5f/3rX42lS5eGv3/99deN4uJi4/XXXzfmzZsXdWz//v2Nqqoq49lnnzUMwzBuuummpP/31KZNW/fcVFkVkS7rqKOOYv369bhcrvC+m2++GafTyUMPPYTZnLxfgeeffz7V1dW89tprUfuff/55Bg0axDHHHJPw559//vm8/vrr4e+nTJnCypUrWb16dYPK6iOPPMLu3btZvHgxACtXrox6/J133mHZsmVcffXVrF69GofDwa5du7jvvvswmUxRx55wwgm88cYbFBcX43Q62bJlC4888shBX5+ISB2FVRHpsqZOnRruApCfn8+7777LXXfdxfnnn88dd9xBIBCIOt5iscS1JcLYsWP59ttv8fv9UfvXrl0bfjyRzz/++OMZOHBgOKzm5eUxbNiwcFgdNGgQvXr1AuCYY47hxz/+MTfffDPjx48nEAiwatWqqPNNmTKFUaNGccstt/Doo49yzjnn8OWXX3Lvvfdy1VVXhY+75ZZb+PzzzwkEAtx4442cffbZzJkzJ3wtEZF4JL28q02bNm2J3upuwc+aNcs48cQTjcLCQmPhwoXGoEGDGj1++vTpRryGDRvWojY01g1g48aNDW69Q/D2u2EYxq9+9auDnrulz3/ssceMNWvWhL//7ne/axiGYVxwwQXGkCFDDMMwjJNOOskAjK+//tp4++23DcD4/PPPjW+//TbqXAMHDjQMwzC2bNli5ObmhvdbrVZjz549xjvvvGMAxmmnnWYYhmH8/Oc/T/r7QZs2bZ13syIi0gUdddRRAMyYMYOnnnqKxx9/nDvvvLNBJbLOihUrmDp1alzn3rNnT0LaaBhGqx5rzfMvuOAC/vGPf4S/nzJlChC8vV9YWEhpaSnjxo3j0EMPZcKECVx66aUATJw4kf/9739R56r72d53331UVVWF9/t8PrZs2ULv3r0BePDBB1m2bBl//OMfm30tIiJNUVgVkS5p6tSpOJ1OzjzzTL744gt++ctfHvT4mpoaVq9eHde5mwq8LVFaWkp+fn6D/Xl5eQCUlZUl7PlHHXUUw4YNi+qvOnnyZMrKytixYwcAa9asYdq0aZxyyik8/vjjbN26lZEjR5Kbm9tgJoCpU6fi8Xga9JcFGDhwICtWrKB///5MnjyZW2+99aCvQ0SkOeqzKiJd0tSpU1m1ahUXXXQRJ510Eg8++OBBj58+fTo+ny+ubdiwYW1u37p16zjyyCMb9IGtG+i0fv36hD3/wgsvZOPGjVFzpU6ePDlq0NTq1av54Q9/iGEYPPDAA0B99bWxsFpSUoLb7Y7af/TRR3PYYYfx/vvvM2jQIACKiooO+jpEROKR9L4I2rRp05boraioyPjzn/9sAMbll19uGIZhzJ49u8njs7OzjSlTpsS12Wy2uNpwsD6rp59+umEYhnHxxRdH7X///ffjmrqqJc/fvHmz8cADD4S/z83NNfx+v/Hwww+H9333u9813nzzTeO8884L7/v9739v+P3+qH6pgHHgwAHD4/EYPXr0CO8zm83GZ599Zmzbts2w2WzhvrO/+93vkv5e0KZNW6ffkt4Abdq0aUvoVheULr/88vC+u+66y/D5fMYFF1zQ7tc//fTTjQsvvNC44oorDMMwjFdeecW48MILjQsvvNDIyMgIH/fhhx8apaWlxtVXX22cfPLJxjPPPGMYhmFceumlUec76aSTDK/Xa9x9991R++N5/oQJEwzDMIzJkyeH95188smNBt3Y7eOPPzY2bdoUte+QQw4xDMMwduzYYcyfP9847bTTjHPPPdf45JNPjKqqqqjrzJs3z3A4HMZdd91lnHLKKcY555xjPPTQQ8Zll12W9PeINm3aOtWW9AZo06ZNW0K373//+4ZhGMbYsWOj9j/99NOGw+EwTjjhhHa9/vbt2+OaSSArK8t44oknjD179hgul8tYvXq1MXPmzAbnq5upILZCG8/zf/Ob3xjbt2+P2nfrrbcahmEYI0aMOOjrKC0tNf7zn/9E7bvooosMwzCMY4891njxxReNiooKo7Ky0njzzTeNUaNGRR3bs2dP4/HHHze2bt1quFwuo7i42Hj//feN0aNHJ/09ok2bts6zmUJfiIhIF1RQUMC8efO47bbbEnK+Rx55hNmzZ9OjR48G89SKiLQHhVUREYnbJ598gtVqZfr06cluioh0E5oNQERE4jZ58mSWL1+e7GaISDeiyqqIiIiIpCxVVkVEREQkZSmsioiIiEjKUlgVERERkZRlTXYD2sPAgQOprq5OdjNEREREpAk5OTns2bOn2eO6XFgdOHCg1qIWERER6QQGDRrUbGDtcmG1rqI6aNAgVVdFREREUlBOTg5FRUVxZbUuF1brVFdXK6yKiIiIdHIaYCUiIiIiKUthVURERERSlsKqiIiIiKSsLttntTmZmZn07t0bk8mU7KZ0GoZhUFJSgsPhSHZTREREpJvodmHVZDJx5ZVXcvLJJye7KZ3WggULeP755zEMI9lNERERkS6u24XVK6+8kunTp/PKK6+wYcMGfD5fspvUaVitVkaNGsXFF18MwD/+8Y8kt0hERES6um4VVrOysjj55JN55ZVXeO+995LdnE5p69atAMycOZP//Oc/6hIgIiIi7apbDbDKz88HYMOGDUluSedW9/Pr3bt3klsiIiIiXV23Cqt1g6l0679t6n5+GpwmIiIi7a1bhVURERER6VwUVkVEREQkZSmsdkLHHXcchmEwb968ZDdFREREpF0prHZCV111FS+//DKnnHIKQ4YMSXZzRERERNqNwmonk5mZycyZM3niiSf49NNPueKKK5LdJBEREZF2o7DaycycOZN9+/axbNky5s6dy5VXXpnsJomIiIi0G4VVINOenK01Zs2axdy5cwF466236Nu3L6eeemqLzrFgwQKGDRvWugaIiIiIdKButYJVYzLtUPtgcq6ddSc4PPEff/jhhzNt2rRwNbW2tpa3336bq666ik8++STu8xxyyCHs3Lmzpc0VERGRLuqwfllMOuJI/vvF8mQ3pQFVVjuRWbNmsXTpUjZv3hzeN3fuXC644AJ69uwJwNChQ3n33XdZtWoV69evZ+DAgYwePZolS5awevVqbrnlFgoLC8PPf//99/njH//IkiVL+Pbbb5k6dSpvv/02O3bs4Kc//WlHv0QRERHpQL0yzdw0cyYTfrAA19h/cFj/XsluUgPdvrLq8AQrnMm6drwsFguXXXYZDz/8cNT+Dz/8kOrqan70ox/xt7/9jffee4/Zs2ezcOFCevXqhdfr5YMPPmDmzJl8++23vP3226xduzb8/LFjx/LKK6/w85//nBdffJFHHnmE73//+4wcOZKnnnqKZ599NlEvV0RERFKEzQKXzTiOwMi7WesbA37oaWzHljUQKE9286J0+7AKLQuNyXL22WfTv39/1q9fz5gxY6IeW7hwIbNmzeLAgQN89dVXLFy4EIDy8nJ++MMfsmDBAr799lsANm7cyNatWwHIzc3F4/Hw4osvAuByufjTn/6Ew+HA7XZTWVnZga9QREREOsK5Rw3j0GPvZLXvdPBBBpW4N/2Ztz57ESPgTXbzGlBY7SRmzZoFwMcff9zkMZdffjlvv/121L4xY8ZEVVInT57Mm2++CQSrqsuWLQs/Nm7cOO65557w1+vXr09Y+0VERCS5jhqey4zTb2QZl7PaZ8eMj+z9c5n3/uN4nKlVTY2ksNpJnHPOOc0ec8MNNzB27FgAzGYzPXr0oLS0lNGjRwMwY8YMTj75ZNatWwcEw2rd1wD9+vWjuLi40cdERESkcxqaZ+En5/yI9Vm3sCSQB0CeYwEL5v2O0v2bm3l28imsdiEvvPACr7zyCuvWrcPr9XLttdfyr3/9i3nz5rFixQrWr1/P9u3bqampAYJV17pK7SGHHMKOHTvC5xo7dixvvfVWEl6FiIiIJEJuOvz0zOkcGHQ3i30jIQA9fJsoWPg7PtvwebKbFzeF1S6kpqaGs846q8H+qVOnNnr8TTfdFP56x44dUfO1XnjhhYlvoIiIiLQ7qxkumz6SHuN/zUrPycF+qUYZpWsfY8GSlzEMf7Kb2CIKqyIiIiJdxJnje3HUybfypfdSAh4rFjyYCl9g/vy/4PNUJbt5raKwKiIiItLJTRxk46Kzr+Ar68/4wtsDgOyKD/hs3oPUVHTuhYAUVkVEREQ6qYG5cO05p7Gp15184jsEApDjKWD5J79hz46vkt28hFBYFREREelksuxw3Wlj8Bx2NwvcxwX7pQb2s2vZo3y26r9gBJLdxIRRWBURERHpJMwm+MlxfRl+zG184foBhtuMFReOzc/yxedP4ffWJruJCaewKiIiItIJfHdUGqfN+Cmf+mezy5UFgO3A23z+wSO4a4qS3Lr2o7AqIiIiksLG9Iervn8OS9N/yfu+wQBkOlayZP5vKdu7Msmta38KqyIiIiIpqF8O3HjWJIr638P/3JPBB+m+IjYseoTt37wDGMluYodQWBURERFJIRk2uP47A8ka80s+dp4HbrAatZSsf4o1Xz1LwOdKdhM7lMKqiIiISAowm+AnR2cyadp1zHNdg8eZjokAvt3/ZdEnj+Jx7E92E5NCYVVEREQkyWYcbuIHp1/E/MDtvO3sB4Ct6iuWzP8t1QfWJ7l1yWVOdgMkftOnT2f79u0pez4RERFpmQkD4dmfHsMhZ/yPf3v/QKm/H3bPTr6dfy3z587s9kEVFFa7hOOOOw7DMJg3b16ymyIiIiJxGNQD/vzDoZzzw6d52foqWzxjsQaqKFr2APOf/y77tn6Q7CamDHUD6AKuuuoqXn75ZS688EKGDBlCYWFhspskIiIijchJg9tOzSFv7M94t/oKvI40TIafyq0vs3bhY3hdZcluYspRWO3kMjMzmTlzJqeeeiq9evXiiiuu4Le//W2ymyUiIiIRrGb46bEWpp3wQ/5beyuV1fkA+PZ/zsrPfkdt2aYktzB1pWw3gIyMDHbs2MGjjz7a7tcyWzOSsiXCzJkz2bdvH8uWLWPu3LlceeWVCTmviIiIJMZ5Y+HVm0/ENXUez1U/QGUgH7NjC2veu4KFr1+moNqMlK2s3nXXXXz99dftfh2zNYPpP93Q7tdpzOfPjiLgc7bpHLNmzWLu3LkAvPXWWzzzzDOceuqpfPLJJ+FjzjrrLE477TRuvPHGNl1LRERE4nfMUPjl2YexLOMu/uI4FQCzr5xNXz3O7oK5GAFfklvYOaRkWB0xYgSjRo3i3XffZezYscluTso6/PDDmTZtWriaWltby9tvv81VV10VFVbHjx/PqlWrktVMERGRbuXQfLj3zB5UDbqZOZU/we+wYTK87C14kc1f/wmfpyrZTexUEh5WTzzxRG6//XamTJnCwIEDOe+883j77bejjrnuuuu4/fbbGTBgAAUFBdx88818+eWX4cf/8Ic/cPvtt3P88ccnunkNBHxOPn92VLtfp6lrt8WsWbNYunQpmzdvDu+bO3cur7/+Oj179qSiogIIhtVevXqxfPly0tPTOf/886OeIyIiIm2Xlwl3ftfK0LE/5l+Vt1Bd2ROAmt0fsf6LB3BWarrI1kh4n9WsrCzWrFnDDTfc0OjjF198MU888QQPPPAAkyZNYuHChcybN48hQ4YAcM4557Bp06YODVMBnzMpW1tYLBYuu+wyXn755aj9H374IdXV1fzoRz8K7xs/fjy7du1i6tSp/OUvf+HWW29t07VFRESkXpoVbjsZXrvlZNYd8iFPld9PdaAnvsoNrH73Ryx792oF1TZIeGX1gw8+4IMPmp4b7NZbb+W5557jueeeA+CWW27htNNO47rrruPOO+/k2GOP5ZJLLuEHP/gB2dnZ2Gw2qqqqmhzhbrfbSUtLC3+fk5OT2BeUos4++2z69+/P+vXrGTNmTNRjCxcuZNasWTz55JPY7XbsdjtPPvkkAGvXruW73/1uMposIiLSpZhMcMlEuP57I3nX+2t+V3Zy8AFPKRuX/IG9376CYfiT2cQuoUP7rNpsNqZMmcLDDz8ctX/+/PnhW/533nknd955JwCXX345Y8eOPehUTHfccQf33Xdfu7U5Vc2aNQuAjz/+uMljJk2aBMA333yDYRjhfWvXrm3/BoqIiHRh3x0J95zZi5Xpt3Bv1Y8IYIWAh8K1z7NjxV/VLzWBOjSs9u7dG6vVSnFxcdT+4uJi+vfv36pzPvTQQzz22GPh73NycigqKmpTOzuDc845J67jLr/8cg477DCsVmt4HtYzzjijnVsnIiLSNU0eBA+cZaOm7094tPxmaqp6AFC24wM2LXoQZ9XOJLew60nKbAB1Vb46JpOpwT6AF198sdlzeTwePB5PwtrW1YwbN47XX3+dZcuWYTabueWWWygtLU12s0RERDqVQ/Pht6fD8MO/w1Olv2Z36WEAOMsK2LDwt1TsWZLkFnZdHRpWS0pK8Pl8Daqoffv2bVBtlcS47bbbALj33nuT3BIREZHOp082/PpUOH3K4TxbcTd/33cSAH7XATZ/9Qf2bngVjECSW9m1degKVl6vlxUrVjBjxoyo/TNmzGDx4sUd2ZROaceOHTzxxBMpez4REZGuIssOd8+Albfn4R/1O2bv/YAVzpMw/G52rpzDon+dzN5v/6Og2gESXlnNyspixIgR4e+HDx/OhAkTKCsro7CwkMcee4yXXnqJ5cuXs2TJEq655hqGDh3K008/neimdDk7d+7kT3/6U8qeT0REpLOzmuGnx8Kdp9pYbFzBdftvpDaQC8D+re+zdcmDuKoLk9zK7iXhYXXq1KksWLAg/P3jjz8OwAsvvMCVV17Jq6++Sn5+Pvfccw8DBgxg/fr1nHnmmezatSvRTRERERGJ20Xj4YHT4UDWDO4ovYsi73AAqg+sZ8ui31Cxt/2XgZeGEh5WP//8c0wm00GPeeqpp3jqqacSfWkRERGRFjv5MHjkLMjvdyRPltzD6n3B6TQ9jv1s/er37Nv0um73J1FSZgMQERERSbbxA+Dhs+CYEb15vuznvL/7EgzMBHwudq35O7tWzcHvrU12M7s9hVURERHpVob1gt+cBhdPsvNW1ZVctutnOIzgCpjFW95l21cP46reneRWSh2FVREREekW+mbDXafCtcfC1+4zmLX7Tvb6hgJQtX8NWxb9hsp9y5PcSomlsCoiIiJdWo90uP1kuPkk2GOM4Zf772Gt61gA3DX72Pr1IxRvehNouECRJJ/CqoiIiHRJGTa48QT45SkQsPdlTtltfFD9AwzM+H0uClc/w65VT+P3OZLdVDkIhVURERHpUmwWuPoYuPu7kJ+dxn8rZ/HS3utxkw3Avk1vse3rR3DX7ElySyUeHbqClbTN9OnT2b59e8qeT0REJJnMJvjxZNjwC3jyfNhkPpMf7/yEv5f9EjfZVBavZMUb5/PtJzcpqHYiCqtdwHHHHYdhGMybNy/ZTREREUmKc8fAmp/DS5eCkX0kNxT+h/uLn6IkMARXzV6++fgmVr5xAVXFK5PdVGkhdQPoAq666ipefvllLrzwQoYMGUJhoZaBExGR7uGUEfDgGXDsMKjw5/HQ3p/zUe0PwWTB73Oxa9VT7Fr9DAGfM9lNlVZSWO3kMjMzmTlzJqeeeiq9evXiiiuu4Le//W2ymyUiItKujhoSDKnfPRx8hpWXSy/jhdKb8Vl6gAmKN7/D1q8e0u3+LkDdADq5mTNnsm/fPpYtW8bcuXO58sork90kERGRdjO2P7x+OSy9KRhUF1efxAUbP+TvFffis/Sg+sB6Vr51Ed98/DMF1S5ClVUgI0mZ3Unb1xmeNWsWc+fOBeCtt97imWee4dRTT+WTTz5p87lFRERSxai+cN/3YObE4Pc73cO5e9uv2W3/LtjA4yxh29ePsnfDq2C0/e+rpI5uH1YzMLOu9/FJufa4ksVtCqyHH34406ZNC1dTa2trefvtt7nqqqsUVkVEpEs4LB/u/R5cOgksZqjx53D3pp+x2nolJrudgN/L7nUvsHPFn/F5qpLdXGkH6gbQic2aNYulS5eyefPm8L65c+dywQUX0LNnTwDGjBnDli1b6NevHwD5+fmsWrUKm82WjCaLiIjE5ZA8eO7i4DRUP5kCmMz87puZnL3hM9bYr8VktlO681OWvvo9ti75nYJqF9btK6tOAowrWZy0a7eWxWLhsssu4+GHH47a/+GHH1JdXc2PfvQjnnzySQoKCvjPf/7Dd77zHf79739z77338sgjj+D1etvafBERkYQb3APu+i7MOjo4uT/AM99O5WXHfVh6jsMM1JZvYcvi31G267OktlU6RrcPq5CYvqMd7eyzz6Z///6sX7+eMWPGRD22cOFCZs2axZNPPglAQUEBhx9+OIcddhhTpkzhxhtvTEaTRUREmtQ/B+48Fa45FtJC6eS1jQP4S/EdWAedi8UOPncV25c/QdH6f2IEVHTpLhRWO6lZs2YB8PHHHzd5zKRJk1i1ahWbN2/m7LPP5sEHH+Suu+7qqCaKiIg0q082/PIUmH08ZIR6qM3fks4DW66BQ2ZjHZSBYQTY882/2b7sj3idpcltsHQ4hdVO6pxzzon72M2bNzNjxgy+/vprFixY0H6NEhERiVNeJtx+MvzsBMiyB/d9uQ3uWX0mrkPvIn3kYAAq9nzN5kX3U1NSkLzGSlIprHYDlZWVAPzqV79KcktERKS7650Ft54EN0yDnPTgvqW74NcLR7J/8H3kTTiBdMBVvZstSx7kwNb3ktpeST6F1W7AarVSVlZGQYE+lYqISHL0zYbbTg7e7q+rpK7cDfd8ks2G7JsZfNQV5FlsEUukPk3A50pqmyU1KKx2Ijt27OCJJ55o8fNGjRrFxo0bE3Y+ERGRePXPCd7u/7/jIDMUUpcVwv0fwXLf+Rx23J0MzewLwIFtH7Bl8e9wVRcmr8GSchRWO5GdO3fypz/9qcXPW79+Peeee27CziciItKcgbnBgVPXHAvpoYFTX+0MhtQvS8Yw8oT7GT3gKAAcFdvY/OV9lBV+nsQWS6pSWBUREZGEGdwDfvUduPqY+imoFm0PhtTPdvbg0KNvZ+r0SzGZLfi8texc/mcK1/4DI+BJbsMlZSmsioiISJsN6xUMqVcdBfZQuvhiG9w/Hz7dambgkZdwzA9vx56RB0Dx5rfZsuQBPLXFSWy1dAYKqyIiItJqh+UHb/dfcVT9ilOfbQlWUj/fCrn9JjP1wt+Q02ccADWlG9j85b1U7Pkqia2WzkRhVURERFps3AC44ztw8QSwmIP7PtoEv/0YFm4DW0ZvRp3ySwaMuhgAr7uS7cseY8/6lzAMfxJbLp2NwqqIiIjE7bhhwWVRzx5dv++9b+GBj2HJTsBkZuCYH3HoMbdjS+sBwN5vX2Xr1w9r9SlplW4VVg3DAILzjkrr1f386n6eIiLS9X3vcLjjVDj5sOD3gQC8ugYe/gzW7Anuy+49liNOeoDcfhMBqD6wjk0L76aqeFVyGi1dQrdKbaWlwU90o0aNYuvWrUluTec1atQoAEpKSpLcEhERaU8mE5w/NlhJnRJc/RSPD15cDr9fAFtCfwYs9hwOPfo2Bo35SXCUv7uKbUv/QFHBS2AEktZ+6Rq6VVitra1lwYIFXHxxsP/Mhg0b8Pl8SW5V52G1Whk1ahQXX3wxCxYswOFwJLtJIiLSDqxm+NHk4Oj+UcH5+qn1wN++gj9+DkWV9cf2HXkuI47/NWmhif33bXqLrYt/h8d5IAktl66oW4VVgOeffx6AmTNnJrklndeCBQvCP0cREek6suxw1dHw8+nBqagAyh3wl0Xw54VQGlGjyOx5GIef+Ft6DZ4GgKNiK5u+uJvyokVJaLl0ZSagS3U8zMnJoaqqitzcXKqrq5s8LjMzk969e2MymTqwdZ2bYRiUlJSooioi0sX0y4GfTYPrjoe8zOC+fVXwxy/gmSVQ7a4/1mxJY9iUnzF04rWYLXb8Phc7V/yVXauf0cT+Erd48xp0w8pqHYfDwa5du5LdDBERkaQZ1TdYRf3JlPrVpjYfCIbUF5aBO6anXN7QUzj8xN+QkTsUgNKdn7Jp4T24qgs7uOXSnXTbsCoiItJdnXgo3DYdzhlTv2/xDnh0AbxTAIGYe662jN6MnHYv/UaeA4CrZg+bv7yPku0fdlibpftSWBUREekGzCY4fxzcfjIcEyyMEgjA2wXwh8+DYbUxA0bN5LDj78KW1gMj4Kdw7T/Ysewx/D51CZOOobAqIiLShWXa4YqpcOtJcFjv4D6XNzj91GNfwKYmBu1n9BjOEdMfoteg4wCoPrCeDQt+SU3J+g5quUiQwqqIiEgXNKQnXD8NfnpM/aCp0lp4cjH8dREcqGn8eSazjaETr2XYlJ9hsabj9zrZvuyP7F77Dy2TKkmhsCoiItKFHH8I3HQiXDAWrJbgvq0l8PhCeH4ZOA4yYD+33ySOmP4w2fnBxV/Kdn3Oxi/u0gAqSSqFVRERkU7OZoGLJwRD6lFD6vd/shn+tBDe+7bhoKlIFlsWhx7zCwaNvQyTyYzHWcqWRb+hePNb7d52keYorIqIiHRSfbLh2mNh9vEwIDe4z+WFf62EP38J6/Y2f45eg6Yx6pTfk54TXE9178b/snXx7/C6ytux5SLxU1gVERHpZCYMhBtPgEsnQbotuK+oEuYsDi6JWlLb/DkstmxGHH8XA0dfCoCzqpCNC36pFagk5SisioiIdAJpVrhofLCKevwh9fu/3gVPfAGvrwNvnOOf8oacxBHTHyY9ZxAAu9e9wLavHtF0VJKSFFZFRERS2CF5wVv9s44O3vaHYCh9fS08sTAYVuNlsecw4vhfM/DISwBwVu5kw2e3U7H363ZouUhiKKyKiIikGJMJTjsiWEU9axSYzcH9hRXwzBL4+1IoPvhy6g3kDT2ZI6Y/RHr2wOC51v6DbV//noDPmdjGiySYwqqIiEiKyM+EK4+G646DQ/Pr98/fGOyP+r9vwR9o2TkttmxGTruXAUdeDICjYjsbFtxO5d5lCWy5SPtRWBUREUmyE4YHJ++/eEL9gKlyR3Be1KeXwOaS1p23x4CjOPI7j5OROwTDCLB77T/YtvRRAj5X4hov0s4UVkVERJKgdxZcPhWuPgZG9a3fv2I3PLkI/rManN7WndtktjP8qFsYOun/MJnMOKsK+fbTW1RNlU5JYVVERKSDmEzw3ZHBgHreGLCH/grXeuA/q+BvX8PSFgyYakxW3uEceeoT5PQeA8Ceb19hy6Lf4Pc2sb6qSIpTWBUREWlng3rAlUcFR/Qfkle/f+ku+PvXwSpqtbutVzExePxVHHrML7BY0/E4S9n4+R2UbP+wrScWSSqFVRERkXZgs8CZo2DWMcH/t4RG9Jc7gitM/f1rWBvHClPxSMvqz5HfeYxeg6cBULrzUzZ89gs8zgOJuYBIEimsioiIJNDkQcG+qJdODvZLrfP5Vnj26+D8qC5f4q6XP+w7jDrlj9gz8vB7HWxZ/Dv2fDM3cRcQSTKFVRERkTbqnwM/nhIMqWP71+/fU1lfRW3tiP6mmMx2Djv2lwyZcDUA1QfWU/DRDTgrtyf2QiJJprAqIiLSCulWOHdsMKB+7/D62/wuL7y5Hl5cDh9vbvm8qPHI6HEIY2b8lZw+4wAoXPscW5c8jBHwJP5iIkmmsCoiIhInkwmmHQI/ngwzJ0LPjPrHFm0PBtRX10BlO05j2nfkuRxx0oNY7dl4XeV8+9ltlO74uP0uKJJkCqsiIiLNmDAQfjgJfjgRhvaq37+zHP65HP65ArYk+DZ/LLM1ncNP+G14JaqKPV/zzcc34q7d174XFkkyhVUREZFGHJofDKeXTobR/er3V7ngjXXBgLpgKxhG+7clPXco4057huzeozGMADuW/4mdK/6CYfjb/+IiSaawKiIiEtI/J7jk6aWT4Zih9ftdXvjft/DvVfD+t4kdzd+c/GGncuSpj2NL64HHcYCCj2+komhxxzVAJMkUVkVEpFvrkx1cTeriCXDKiPqBUv5AcIDUv1cFB0xVtWM/1EaZzAyfeguHTL0RgMp9K1g//zo8tcUd3BCR5FJYFRGRbqdfDlwwDi4aD9MPrQ+oAIt3BAPqq2tgf5JWKLWm9WTMd/9M3tDpAOxe9wJbFv8OI+BNToNEkkhhVUREuoWBufUB9cThYI4IqMsK4b9r4bU1sL0seW0EyM4fzdjT/0ZG7hD8XicbP7+D4s1vJrdRIkmksCoiIl3W4B5w4fhgQD1hePRjX+0MBtTX18GOJAfUOr2Hn87oUx/HYsvEUbmD9R9cS23ZhmQ3SySpFFZFRKRLGTcAzhkdnLD/qCHRjy3aDq+tDY7mL6xISvOaNGzyzzj0mNsAKNv1OQUf3YDPU5XkVokkn8KqiIh0alZzsGp67phgQB2eV/9YIAALtwcrqG+sgz0pmP3MljRGnfIo/UaeC0Dh2n+wdfHvNC2VSIjCqoiIdDrZaXD6EcGAeuaRkJdZ/5jTCx9tgrcL4H/fJG+QVDzsmX0Zd/rfyO03iYDfy6aFd7P3238nu1kiKUVhVUREOoXheXDGKPj+6OAUU2kRf8EO1MC738A7BfDRZnB4ktfOeGXlHc74s14kPXsgXlc56z+8joo9S5LdLJGUo7AqIiIpKc0KJx0aDKhnjIJRfaMf37g/WD19pwCW7IRAB6wklSg9BxzD2DOexZbWg9ryLax7/yqcVTuT3SyRlKSwKiIiKeOQvPpw+p0RkGWvf8znh0U74P0N8PZ62Hggac1skz6HncXoUx/HbEmjYu8y1s2bhc9dmexmiaQshVUREUmadCuceGiw/+mZRzasnu6pDIbTeRuCq0l1+CpSCTZ4/CxGTrsHgP3b5vHtxzcR8LuT3CqR1JZyYXXw4MG89NJL9O3bF5/Px29/+1v++9//JrtZIiKSAGYTTB4M3x0Z3KYdAum2+sd9fli8E97/NhhQ1+5NWlMTzMRhx93F0Ik/BYIrUm1edD8YgSS3SyT1pVxY9fl83HzzzaxZs4Y+ffqwcuVK3n//fRwOR7KbJiIirTCyN3z38GA4PeUw6JUZ/fjuCpi/KRhOP9oElZ28etqAycwR0x9i4JGXALB1yYPsWv1Mkhsl0nmkXFjdt28f+/btA+DAgQOUlZWRl5ensCoi0kkMyIWTD6uvng7tFf14hRM+2xK8rf/J5s7b9zQeJrOVI7/zGP1GnosR8LNhwS/Yt1F3C0VaIuFh9cQTT+T2229nypQpDBw4kPPOO4+333476pjrrruO22+/nQEDBlBQUMDNN9/Ml19+2eBcU6ZMwWw2s3v37kQ3U0REEmRYL5h+GJw0PPj/I3pHP+72weIdwXD68WZYsRv83eDut9mSxugZf6XP8O8R8Hv55uMbObDt/WQ3S6TTSXhYzcrKYs2aNTz//PO88cYbDR6/+OKLeeKJJ5g9ezaLFi3i2muvZd68eYwePZrCwsLwcXl5efzzn//k6quvTnQTRUSkDUb2DoXTQ2H6oQ0rp/4ArN4TrJp+vBm+3B6cqL87MVvTGXf6s+QNOQm/z0XBh9dRuuvTZDdLpFMyAe02M51hGA0qq1999RUrV65k9uzZ4X3ffPMNb731FnfeeScAdrudjz76iGeffZZ//etfB72G3W4nLS0t/H1OTg5FRUXk5uZSXV2d4FckItK9WM0wYSAcf0hwMNRJhwZv80fy+mF5IXy+Db7YFpxeqrOP2m8LszWd8Wc+T69Bx+Pz1rJu3tVUFC1OdrNEUkpOTg5VVVVx5bUO7bNqs9mYMmUKDz/8cNT++fPnc/zxx4e/f+GFF/j000+bDaoAd9xxB/fdd1+imyoi0i31yYbjhgW34w+BqYMh0x59jMsLX+8KBtPPtwUn5O8MK0Z1BLMljXGnPxsMqp5q1vzvcqqKVyS7WSKdWoeG1d69e2O1WikuLo7aX1xcTP/+/QGYNm0aM2fOZO3atZx33nkA/OQnP2H9+vWNnvOhhx7iscceC39fV1kVEZGDs5hhbP/6YHrcsIb9TQHKHcFAumRnMJwu3RXshyrRTGY7Y097mrwhJ+Hz1rL2vSsUVEUSICmzARhGdM8Dk8kU3rdo0SIsFkvc5/J4PHg8+kgvInIwJhOMyIejhsDUIcH/nzQoeoWoOgX7gsF08Y7g/288AEYnWso0GUxmK2O+91fyh30Hv9fJuvevonLf8mQ3S6RL6NCwWlJSgs/nC1dR6/Tt27dBtVVERFrvkLzgLfy6cDplEPTIaHhclQu+2llfOf16V3BqKWkJE0d+5zH6DD8Nv8/FunmzqNjzVbIbJdJldGhY9Xq9rFixghkzZvDWW2+F98+YMaPB9FYiItI8syl4637iwOBAqMmDguG0d1bDY51eWFUUHAy1fDcsK4RNByCgqmmbjJh2N/1GnkvA72X9h/9HedGiZDdJpEtpl6mrRowYEf5++PDhTJgwgbKyMgoLC3nsscd46aWXWL58OUuWLOGaa65h6NChPP3004luiohIl5KdBuP6B0NpXTgdN6DxW/keX3Cp0rpQurwQvikGXzeY37QjDZ10HUPGzwJgw2e3UbbrsyS3SKTrSXhYnTp1KgsWLAh///jjjwPBEf5XXnklr776Kvn5+dxzzz0MGDCA9evXc+aZZ7Jr165EN0VEpFMymeCQXjCmP0wYABMHBf9/ZJ/Gj3d4YN0+WF0UnN90+W5Yuwc8/o5td3fT/4gfcNixvwJg86L7Kd78VnIbJNJFtes8q8nQknm7RESSKTKUju4HY/oFvz6yb8PpouoUVcKaPcFQuia0bS7RrfyOljf0FMad8XfMZis7V85h29ePJLtJIp1Kys6zKiLSHZlNwQFPo/vFH0pd3uAo/LV7o4NpSW3Htl0ayso7gjEz/orZbGXvhtcUVEXamcKqiEiC9MmGI/rA4XVbbziiLxyWD2lN/LZ1eWHD/mB/0oLi4LRR3xTDtrLgsqWSWmwZ+Yw74zms9mzKi5aw8fNfJbtJIl2ewqqISAtk2WFk7/pAGhlOezYyNVSdukppwb5gKP0mFEwVSjsPsyWNcaf9jYzcITgqtrP+w/9v777j26jv/4G/7rQsy/LeSZxlZ5HEGRDCpkDYqy2ldNFSWlpaSgtdP+ALXVDa0lK6gJZSoEBpgUKBkkCg7BCGswfZyzvetrZu/P7Q8J0s2bIt+2Tr9Xw89LB8Op0+MsJ5+X3vz+e+BlXh1RGIxhrDKhGRhiAAlbnArCJgVuHAr+W5iZ+rKMDh7tByULuPAnvaQ/f3tAH13ewrnejmnPZz5FUci6C/B1vXfBmSv8foIRFlBIZVIso4DiswMzaIFoVO188oALIsgz+/zRWqku7R3Ha3Afs7eBnSyarymC+gYu5lUBQJO9Z+A97uA0YPiShjMKwS0aRTmgNUFQBV+cB07deC0Nd4C+ZrSTJwuCt0in5/B3CgI3T/QAdwsJNXeMo0uaVLUHPSbQCAA+/9Al0N7xg8IqLMwrBKRBOK1QRMze8PoVUFwPT8/iBalT90ZRQAOj36EKoNpfXd7COlEEtWIY455z6IJiuOHliD+i0PGD0koozDsEpEacNpA6bkAVPzQl+jt9z++2U5gCgOfhxFAZr7gCNdoQrpke7+r5FtPb7xeEc0oQkiFpz1O2TlVMLTfQC7Xvue0SMiykgMq0Q05kQBKHPqg2e8QOrMSu54noA+eB7p1gTSLqChBwjy6k00StOXXovCaadCDnqw/eWvQQ66jB4SUUZiWCWiEcsyh0JoefimvV/uBCrDIbTcCZhNyR2z2xu6SlNjTyh0NvYAjb36bW3MDDTGnCWLMOPYGwAAe96+De7OPQaPiChzMawSkY5ZDE1Qig2e8b4fbF3RWLICNPfqg2e8UOoJjN17I0qGaLZjwVm/g2iy4Oj+F9Gy+ymjh0SU0RhWiTKAIABF2YMHz8j9kpzhHdsXBFr6gFYX0NIbuh/5vlkTTFtdnLREE0P1ibciO382fK5m7H7zJqOHQ5TxGFaJJrC8rIHBM14YLc1J/jQ8EFq66airP3i29AGtffG/50QlmkyKZpyFKcd8DgCw67XvcuF/ojTAsEqUZuyWxKEzdlsySzRptbkGD56RW4cHUHm1JcowZmsu5p56JwDgyOa/oKtxncEjIiKAYZVoXFhMoermUBXQMieQm+SM+Ihu79Dhs7UvVCmVeBqeKKHZJ/4fbI5SuLv24eAHvzZ6OEQUxrBKNEImEShx6ENn9GtO6BrykftFQ1wxKZY3GOr3HKoK2toH+Hh5T6JRK5hyEirnfxqqqmD3Gz+EIvuNHhIRhTGsEmkIQuhSnGU5A6ufsUG0OHvoxem1gvLQFdDItj7+O0k0bkSzHXNP/wUAoHH7o+hpqTN4RESkxbBKGcFiAipygQonUJkbWv+zwtn/NRJESxzDm4gkK6HT65HQ2RqnJ7Q1fOv0sg+UKB3NOPbbsOdWwdfXiAPv/9Lo4RBRDIZVmtAspvDi87mhMFqZG//+cJdjijcRKbI0kzaQtrsBhQGUaMKy583EtMVXAwD2vH0r5KDb4BERUSyGVUpLZrH/CkiDBdHhhNCABDT1hnpBm3r77zdHAmk4iLZxIhJRxqg56UcQTVZ0HH4NHYf/Z/RwiCgOhlUaV5EQGg2cefFPzZcOM4Q29wFNPeGvvZr7Pf2htMMzdu+LiCaeoulnomj6x6DIAexd91Ojh0NECTCsUkoVO4CqfGBaPlBVEP6a3/+1Ijc0iz4ZQbm/Cqqthuq+72EvKBENn2iyoeakHwEA6rf8Fd6egwaPiIgSYVilpDmsMeEzJoxOyw8taD+UoBw67R63Aqq5z4XpiWisTFl4Jex50+F3teDwhj8YPRwiGgTDKgEIVTun5OmroNFAmhf6Wpid3LGae4H6buBId/hrF1DfE/7aHeoLZQglIqOYrE5MX/ZNAMCBD38DWWKPEFE6Y1jNICYRmF4A1BQD1cX6rzMLQzPrh9Lj1YTQ7oFhtLEHCMhj+z6IiEajqvYaWLIK4O7ci9bd/zZ6OEQ0BIbVSWY0gdQvAQ3dccJo+Gt9N9DrG/v3QEQ0Viz2Ykyr/QoA4MAHd0FV+dc1UbpjWJ2ARhNIvUFgfwewtw3Y2w7sa+//2tjL0/NENLnNWH49TJZs9LZuQvvBl40eDhElgWE1TUUCaXURUFMy8kC6LyaYMpASUaay5UxB5YLPAgD2v8crVRFNFAyrBhKF/gppTYk+mI4kkEa+MpASEQ1UtfTrEE0WdDa8g+6m9UYPh4iSxLA6DrKtwJxiYH4ZMK80fCsB5pQAWYMs9eQLhgJo5FQ9AykR0chYs0tRMe9yAOBSVUQTDMNqCgkCMLsIWFIZvk0BFpaHqqeJ+CIV0vaBPaQNPQykRESpMK32qzCZs9DTXIfupveMHg4RDQPD6ihduhA4qyYUTmsrgRxb/P3aXMCuo+FbG/BRa+j+4S5AYSAlIhozZls+Ko/5HADg0MY/GjwaIhouhtVR+vhC4Mpj+7/3BoFtzcDmptBta1MolPK69ERExpiy8EqYLQ70te1A55HXjR4OEQ0Tw+oo/Wd76BKhmxtD4XRvOyArRo+KiIgAQBCtmHLMFwAARzbfb/BoiGgkGFZH6dntoRsREaWf0uoLYHOUwu9qQduB1UYPh4hGQDR6AERERGNl2uLQ1aoatj8CVZEMHg0RjQTDKhERTUp5FcfBWbIQsuRD085/GD0cIhohhlUiIpqUpi66CgDQsvvfkPzdxg6GiEaMYZWIiCYdi70YxTPOBgA0bv+7waMhotFgWCUiokmnYu5lEE0W9LRshLtzl9HDIaJRYFglIqJJRkDF/CsAgL2qRJMAwyoREU0q+VNOQHb+TEj+Xhzd/1+jh0NEo8SwSkREk0rl/M8AAFr3PgdF8ho8GiIaLYZVIiKaNMzWXJTMOgcA0PTREwaPhohSgWGViIgmjZLZ50M02eDq+Aiu9h1GD4eIUoBhlYiIJo3yOR8HALTuedbgkRBRqjCsEhHRpGDLmYL8ypVQVQWte583ejhElCIMq0RENCmU1VwCAOhueg9+d7PBoyGiVGFYJSKiSaF8zicAAK17/mPsQIgopcxGD2Ci+3xWBVZa8uBXFRyQvXg72IWtksvoYRERZRRH0Xw4CmsgSz60HVhj9HCIKIUYVkdpoTkH59qKo9/fgOnYEuzD7e4D2CT1GTiyxOwQ8Vl7BWaZ7Fgf6MZ/A+1JP1cAUCJacVQJDLmvCcDHrIXoViTUSb2D7lsh2tCq+KEkeHyeyYF2JYB2NRh3TAIQ97lFggUdcZ5DRJNL6azzAACd9W9CCgz++4aIJha2AYzSs/6juM21D790H8Rqfxu8qoxaixP/zFuMq+1TUvY6WRBxnX0a/p1Xi2fyavGd7CrkCKZhHydPMOOp/Frc5JiJT2eV457cebgjpzqp504TbViTvwzvFq7A8/lLUC5aB93/D875uD93Af6ZvxjX2qfG3UcE8JfcBXi78Dg8nLsQVggD9vmKfQr+W7AUrxceixlilu6xAsGMNwqOxcbClTjZkq977DJbGd4tXIH7nfN1R73MVoY1+Uvxdc2YcgQTXs5fhqfzamEO722GgNtzqnFnTnX0+fNNDjyWuxDHmnMBAFYIOMNaiCLBEj1WmWiNvo8cwYRTLflx3hURpVJJOKyyqko0+TCsjtL7wR78w9eCB7yNuL5vN07vrMNzvqMwCQJucszEN+3TRv0aU0UbXixYiu84pqPW4sRiixPXZVfhP/lLUCHahnWsn+dUY57ZgTYlgMe8TZBUFZ/OKseltpJBnycC+GPufFSbswEAC8w5+I1zbsL9z7AW4mxbUfT7b2VXoSJOuP2krQxnWAsBACda8/FxW6nu8RzBhG9nVwEA7IIJ3wrfj7jaPgVTTFnIEc26x0wAbs2ZBZMg4CxbEU4MB1kBwPcd01FjduB7jhnRwP/FrErMNmdjicWJi8M/iy/ZK3FFVjk+lVWOheYcAMC/8hdjpTUfv3LWRF//L7kL8L+C5bBDxBeyKrCucAVWFyyDCcAtjpn4W95CfN0+FbmCCc/nL8GPHLMgArgyqwK/d86FQzBhlsmOx/MWYanZiULBjLOtRQy4REnKzq+Go3AOFDmAjkP/M3o4RJRiDKsp1qEG8V3XHvzCfRAAcINjOi60Fg/xrMQKBTMeyVuI6SY7mmU/vt+3Bzf27Uaj7MMMkx1/zp0frQQO5ThzLs6xFSOoKvhyzw782H0Av/McBgB8N3sGLIMc53xrMY4x56BHkfC5nm3wqTKOt+RhpSUv7v5fyKoAAPzZ04C6YA+sgoiL4gTij2eFwmmnEjpVf3lWue7xkyz5sGsqyKdbC3Uf2jPDQRcAlltyURYOxPPNOXBonrfCEqqELjLnoEgTmk8Ih1jt+4gE2xWabR8LV0+zw8esMtnhEExYFj5ujmjGLJMdJ4WfO8Nkx1QxC58Kv5/vOmbgeEs+Fphz8AV7Jc6wFuK2nNk431aCH2TPwIO5x+B4Sx4eyVuIn+ZU497c+bgxezp+kD0Da/KXolS04j7nfDyYuwBmCLjFMTPuz5MoE0Wqql0N69gCQDQJMayOUsnsCzB75U2YcdwNKK25BBZ7KJj+1duI+zz1AIDbc6oxbZgV0Ihbc2ZjusmOI7IPn+zZgmf9R/G8vw1X9GxFpxLEAnMOvmivTOpYkbaEp3yt+Eh2AwAe9DbiqBJAhcmGVdaihM/9jD0UPh/2NuL9YA/+7TsKAPiUrWzAvrmCKRr+nvK14NnwvufGhPYCwRw9nf6V3tCVZhaZc+DUhMzIqf1HvE3oVSTkiWYcE65y5glm1JgdABDtoY1UQI8LHzfi2PB45pscuu214f1rwhVjAJhlsgMAqk392+abHKgy6VsQZohZmKvZZ4bJrttnnln/WjM1j0XeAwAcb8nDtPBj2YIp2gN9bfY0XJM9FTVmB+51zscqWxFOsxbixuzpuMo+Bb91zsUvcmrwdN5i2CFiupiFUtGKYsGCBTHvk2gyYwsA0eTGsDpKRdNOR9XSr2Pmsd/BMWf9Hide+T4WnPV7ZDmn4h7PYXwQ7EGOaMYtjlnDPvaplnxcZCuBpKr4Vu9HuklNzUoAvwxXb6/PrkKeMPhcueliVvR0+0O+ZhTPOBtVS69FVmkt/uVrAQBcmlUa97mlohXHW/KgqCqe9rcCAJ7zhwLoGdbCARXZlZZ8WAQR+yQP2vKnY70lVMlcYM5BtuYjV2t2QhQE7Jc82Cq5cEj2QhSEaIAFQhVSANik+qMT1iJBbHH4sQOSB28HugD0h8BI4Hwt0Kn7fnr4a8QUUxYKBDOKNdXWWSY77BB1wbNMtGJqTFidZc5GpWbbzJiwerq1QLf/Qk1ArTU7o/eT6T1eYunf/wpN9fmyrDIsseTi1pxZ+F/hsXg0dyHeLDwOzxcsxeW2Mjyce0z0vRNNRlnOaXCWHANFkdB+aK3RwyGiMcCwOkod9W/gyOa/oHHH4+hr2wZRNKOs5hIcd/nLKJh5Lm517UNQVXCWrSh6ijhZ12dPBwD83deEHbIHZTWXYsFZv8eCs/6A8rmfxDOBDnwkueEQTPikLX7QjDjXVgxREPB2sAfOc+/DovMewOyV/w/LP/kcNs1aBSBUxXTECU6nWkKha5viRfE59+PUr+5G8Iy70KlIcIpmXQgDQqfjAWB3UTVWfHotZn3uTbSIIsyCgFpN6IoEsP2Fs3HaNfuwvyjUBxoJqCKAueGKZ84nnoZ3zqUAgNnhbZHg2V56DNwLPwcAqAlXOiOVytYlVwMIrWBghYAZ4edsc4YC31TRFj1Op8UOGQJyRLMuHALhsBpTHT8mpnp5nCUPWZqf30Kz/hjan9Nizf1y0/Cq7rniwD9MIu0Ts83ZsAmh/61/7qzBydYC/NQxG1/OqsS37NOwOn8plpmd+JFj1oD/bkQTUcns8wEAPU3vI+jrMng0RDQWGFZHqW3/i9i//g7seetm1D19IT588jx0N38AszUHC8+5D67Z5+If4cplohnx8RxvycMSixM+VcaDci+WXvJPLDjrdyiruQRlNRdj/hl3Y8nFT+CfwdAv58/YKwbtXI1UVbdMOwFFVadDDnrQeeRNAIDp5FvRIABWQRxw+hwATgyfQt9TdTKKZ5wJkzkLxbPPw56CGQD6w2nEknBIa5l7MQBAEE04Ur4EQKi6GhE5Jd82+2yIJgu6p50EAJgdDo/TxCzYBRMCogVHc8pxtCi0akGkUhipYnYXz4OrZCEARCdxRR5rKD0GvnAYrBRtmB7evnf6qQCAqSY7isTQTP5uZyVcttB7ibQAeMyh1yoRraiKqVBqWweA/hAd/T5mf+3z80ULxstKaz5uzpmFbzumY47ZgSfza/EFeyX+k78EV2SVQ/vnCX8h0ERTMutcAMBRtgAQTVr8tynFXB07sfm5K9C441EIgoj5H/sNnswtQ1BVsNKaP6Aal0hkgtK/Ax2Ycv5fkV+5ElLQjUN1v8fBD++G5O9FfuXx2HfKrXApEmaa7FgWJ2gCod7QpeEAeWT51wEA2166BltevBINWx8CBAF7KpYBAE6w5g94/tJwGK2ffQ5kyYe97/wYiiKhPhz4lsRUEOeEg96R/Blo+uhfcHV8hJb8GQCAak1gi1Q5W5yVuq+R0Bc57d6WUwZVENHqDP1MomE1vIxVm6MMXdmhMF5hssMEoCL6WCk6s0O9uFNMWSgJP3df8RwAQIloia6o4LI54bbl6MeWWwlJMEEUBNSEq6191pzwe9H/t4xdyssqpP//XrfnVONe53yszl+K1flLUVe4EkvNTuQP0VZClA6s2aXIKwv97mo/+LLBoyGisZL+/5pOQKoqY89bt6Jl978hiCYUrfo91sihfssrk5gM5RRM+Fi4EvrOnAuRV7YMQV8XNvz7Ehz88Dc4VPc7bHru05CDHmTPPAvrskKh6SzNzHitU60FEAUBB7Ly0JVdhNZ9L6Cr4W0AwIEPf4OgvwcHp64AgGiojSgSLJhmyoIC4GDhLDTteAwN2x5C2/4XUZ8falOYo5lkVCFa4RTNkAQTWp0VaNz+CJp3PYXm3HAQDe9rQn+lsdVZgS0vfim6z8zw9spwiOzMLkLzrifRbgm9z5JwEJ0WDrVtOaVos2SHH7OgVLTCIghQIKDbXoAOR2jC0lTRhrxwgDyaUw45fD/yen22XLis+p7XPlsuerPydNsioXnKME/fp6szbUWYY3ZgjtmBXDG0Dm9d0UrMMtkHXSGCyGiFVacDAHqPbkHAc9TYwRDRmGFYHTMqdr91M1ydu2FzlOLtmlBf1dnWorgL32udbS2CTRCxHzKCx14HANjxyrfg6dob3cfVsROH6u4BAOxb+BkAwGkxE3oiIpN59k1dCQCo3/yX6GNyoA8tu5/GoYLQBLD5ZofuQ7EgPKO9xVkBnyUbTTv/Efp+99NozAu1NUw32WELPyty+vxoThlcrma42neg49CraHaGViKYFX58qpgFiyDAb7KiOehC55HX0RyeQOYQTMgRTKg09YfVroZ3Ud8bmlBmFwTkCCYUhyuvRyUfDja/H6qAApgTrni6rQ50Nr0XPbVfZcqKnvJ22ZzwhAPuzHBrQp/NCbc1UlnNju7ns4RCam74v1tntn5Vg057/D8SJrq1Bcvx7/xafNxWytBKaalo+hkAgI7Drxk8EiIaSwyrY0iRfNj9xv8DAHQvuwYtUOAUzTgtQQU04rzw0kXvTT0egmhCy+5nopVQrfqtD8LbczgaQueYHSgUBvZCRibSHCmcjZ6WDehr26p7vHXvc2h1VsBnssEumKLVT6B/wlJz7lS4OnfD070fANDV+C46BRF91hyYBCF6en+6pvrY3fQeAMDbexjNQijs5ItmZENERTSIFqPn6GYAQNvRrXCHA2S5aI2enu/MLoarYye6eg5Fe0jLRSvywgGq1XsUfR070WPPB9A/KcttdaKrYR3c4Yrs9HDw9pjt6GzbCm94+8zw1x7RApc1dH9aeHwuaw58Zv0qAD1Z+rVlO2LCa+z+E9kCcw7ucs7BR8Un4e+5C7HQnDOiK6cRpZogWlA49WQADKtEkx3D6hjrbd2Ilj3/AUQz1peFJgFdYEt8kQALhOhi9HsXfBKqIuPgh3fH3VdVJDTufBwumxOHHaEF4o+LmexkQqhaCgCHC2aibf/qAcfpO7oFPvdRNOaFrrZVow2r4eDXlDcVnUfe0L12T+tGHM0Jz6oPVzkjp+47HCXoaamL7t/a+RG84RBXZrKhNNzf2W0vQN/RUHj2dO1Fjz1UHS4VragMv5cOWy483Qfg6doXDaSzTdmIdFUe9RyFt68BnnDojLQXuGw56G3dBLc59FrTw6HdbctBV/3b8ESCabje2qn40Rsz077PbIc/Jnz2ZuXrvu906P97dmfpK9yRsDzRnWjNx3/yl+D5/CVGD4UIeRXHwWx1wu85ir62bUYPh4jGEMPqODi84Q8AgF3zPwkgdFWmRCdVa81OZAsmdJusaMydhqP7/wtfX33CY7fsehKy5MOhskUA9EsiAaHT7nbBBJ85C63OcrQfjn8pwq6m9dFezBmaSVCR0/ZNuVPQ3fie7jk9zXXRftAp4ZAa6ePsyC5GX9v26L6erj3oCp8uLxet/WE1Kx/u7n0AAHfXvmjQKxVtKAyHxA7ZB1UJwtO9Hz3hoBhZcN9jyUZfXz38rma4w+EzMvHKZXXC23sEvYoEAJgenoHvsjrR3fxBtA0goluV0Svq/5foktzwmfQTp7rM+l7V2MpqJFBH9GbFn/g2UVWZ7LgrZ070DxMiIxRVhVoAQn9Eq4aOhYjGFsPqOPB070P7oVdxuKgaHsGEAtGCeQlWBTghXFXdXb4YEAQ0bv/7oMcO+rrQeeSNaM/popgJUpFF8o/kT4e75xC8PQfjHqe76T20xMy2B0JLOwFAu6MUvUc36Z7T174dHdmhiu6UcGV1avj1OrIL4QmHUCAURCMz9stEG8rCQafHng9P9wEAgKd7P7rDldUy0Yr88OnmTil0tS2fqzE62SlS/e2zhQKp39UUPa0fOd3vtuXA725Gj+QFAGSH/0Rw2ZzwdO2Hx6JfWsoV9KA35h89t+SFV9D/aXE06NZ932XXV1K7YyqvkT7YyeTjWaW4M6cG5aIVp1vi90oTjaWi6R8DAHQcft3gkRDRWGNYHSeNOx6FLJqxp3gugP5QGiuydNTu8lp4e+vR07JhyGO3H3oFhwtmAuifEBUR+f5IwUx0NryT8Bh9R7dGl46KVFatEFAWrkY2KP4BC267O3f3z7SPzOAPh9ZGVYIi+aL7err2ocseWkKqQrSi3BqqNnbZcuHrawAA+PoaomG02JyN3HBu7PD3AAACnjZ4w9XQqsgEKKsTfncr/J6jcFtCrz0lPOZe0RJqV1D8unF7LdkI+jrhiqmYuoNueOWAfl/ZBy9iw2qffh/JF21xABAN3BHaNgKfyQZlkkxWmm7KwusFx+KvecfgnEEu1UuUalm5VXAUVEORg+iM089PRJMLw+o46Wp4BwFvJ/ZWLAEQWqg9loj+yVB7i+fi6L7nkczprc6Gt9GSWwkFAvJFC4o0k6wiwbPZWRntDY3H3bUHR8Onq6eE+1QjE5z8Jhuaewe2IvhdTdEloyotoXEXhINYi7dDt6+39whctlDVt9CUhdLwa7SrMqAqAAA56IZLDFVTiy05yA5v7/B1RB/3hHtKIz2yLpsTQW8HoCpwxVRAI6f0e5SgftyCAEX2R18rwhXsgy9chY2OO+jRVVaDogVdMWHVF+iNrhgAAN0xPaoBzRJXAbMNQdP4XRBgLE01ZcESXv7rdGvBJIngNBFEWgB6WuogB/qG2JuIJjqG1XGiKhLaDryI3SXzAQDL4yzgXyVmIVswwW+yoNVZgbYkF7kOuFvR09eAdkfokqvVmispRfo3j+aUoa99e9znR8bX5A9VTosEEywQooGww1GsO6Wv1Rb+h6JQtCBXMEUnPbXFhFXJ34O+cGjLt+QgLxyou2Sfbr++cLCcFg7ZCgS0a9ZPdKkyACA3HJJ85iwEfZ2hx2LCqkcN9ar2SPrX8If/AHDFpCt3oA++mH09kgcezXF95iy4wq8X3ebv0QXSHigIaiZq+TTPD5iskDWP+TXPkybwLPtPZZVjb/HJuNc5n6GVxlx/CwBXASDKBAyr46ht/xo05FchKJiQJ5oxLWaCSmTSUFPuNAQCfXC1JQ6XsXpaNkQX1Y8sIyWif2Z8iz1ft05rPC2uZgRFM0SEZuOXhSdBddkL4U7w3LZA6BR9gYrosllecxb6PM0Dx6iGgmihORs54QDXLXl0+/SG11qdEv5oeqwOBDTB1xVTJfWJJihy6DR/bH0lckq/JyYQ+5RQ4PWHJ15Fj+3vgVfS96N6Ai54NdXtoMmCvtiqsa8LiiaQeoMu/al/KNH7AZMVkiasejUVWV9MD+1EdLatCEvMTv5ioTEjmu3Irwwt18ewSpQZ0vLflAsuuAC7du3Cnj17cPXVVxs9nJTpaalDQJHQmF8FoP+Uf0QkrDbkV6G76X2o4SpiMvratuFoThmA0IL7QGgik1UQIAkm1HtaocaEs1ie3oOaGfs2FEV6P7Py4O05HPc5reEqoxWhRfeB0FWfIn2oWt3hUJlvssKphgJgt79Xt09POLyWhB93Wx0I+rv736cc03+q9gfBPk0oBABveF93bI9pOPD6FX1/qlvywhvUh2ef7IdHc1xFENHnbdft4/F3Q9XUE4P+Xt2pfp9mjAGzDUGx/zFdqDVP/LAKAN/KrsKe4pPxl9wFRg+FJqGCqSfBZM6Ct7c+4RkfIppc0i6smkwm3H333TjjjDOwbNky/PCHP0RBweSYbazIfvS2bsSRyGVKYyZDzQ+vENCQV4WuxnXDOnZf21Z0hNdajZy+L4+uZVqI3o6dQx7D030gGlYrRCuKwpXf3gThEwB63M3RU9nVmqs++fsaB+zbFZ5Fnw8THOHqZpcmiAJAT8xMe49FH1ZjK6teTaCPfcwT7j/1BVy67b5we4Bfjg2rPvhitvmUINyqNqya4PG2607ZB5SgLiYHAr2QNY/7NJVZv2iGZIpfWfVOgsoqELq8LwCcMcTFL4hGoqiKLQBEmSbtwuqKFSuwY8cONDU1weVyYfXq1TjnnHOMHlbKdDWuR3Nu6DKl2sX3AWBuOLzW51ehp+mDYR3X3bknut5n7ASpLnsB3J2DtwAAoUlQfeFJVoWiBaXhGfs9FgcCnra4z/G5GtAXnjg12xxZTioX/jjX6e4KV03L5P5Q2eXTn1LvkfSV04DZBkmzCoErpjoc6UsFAF/McyNh1RtTvY2Ex4CmP1WBAL/sRUCNaTNQAvBqXlMRBEi+bl3lNKBIULStAv4eKAnDqkkXdLVXu9Ku+zqZroJFlErRS6weYVglyhQpD6unnHIKnn/+eTQ2NkJVVVxyySUD9rn22mtx4MABeL1e1NXV4eSTT44+VllZicbG/qpcQ0MDpkyZkuphGqa78d1ob+lszXqmdojRimiDoyxhj2gictCNpnAoiiwjVaa5SpS359CQxwgtrB+e1S9aURIOz52QkGhVgqC3Ay5bKNRWR64QZc0JzdCP0S2HwmNWuFIZEC3w+PXLYfXI+tn4AZMFkiZs9sUuQ6X0V1b9Mc+NtAH4gvqw6o+8vqalIGC2QZa88MWEYZ8cQEDTLiADkCUPgpplryRV1ldWg25ImosLDAirmlUIvJoJVpGLGgCTJ6yKAFaYc7HA5EB2+v1tTBOMo3AesnIqIQe96G5cb/RwiGicpPxfD4fDgS1btuC6666L+/jll1+Oe+65B3fccQeWLl2Kt99+G2vWrMG0aaFLfQrCwLnEqjp5rk7S174dTY7QJUpnmLIRiS2RoOq2ZKPN0wI15pR2Mur9oYBYCCG8Rmp/G0AyYTXgbUdfuLpXaHVGe1Y7Yk6N65/TAVc44EYuDOA2WaKTnrQ6Y07He6wOSDHLzvQE9fv4Ieh6d10xY/FogmBsv2mkN9Xn79WtbRrpcw1ogmnAZIUieQe2Ach+BDUVW1kQIAe9+uWoFFkX5WVFgqx7PU1YFUzRpboAwK1pCejRtAHEXuI1GbEXI0gHHxQej3/kL8bzBUvxTuEKo4dDE1ykqtrVuC7u7xgimpzMQ+8yPC+99BJeeumlhI/feOONePDBB/Hggw8CAG644Qacc845uPbaa3HzzTejsbFRV0mdOnUq3n///YTHs1qtsNn6g4PT6Uy4bzpQJB8a/J2QBBMskFEm2tCk+DEtHPTaHaVwdXw0omO3uZohCSaYVRlFogUV4dP4nVl58LkG9pAOoCroCceuQksOCsIZqz1mNr1W0NsBf/jyo8Xh09ueBFXY3oC+wum1ZENy6bf1DgirMceIqZ76NG0A/tjJUeGwqkg+yHYzxEh4DYffAPpDcMAUqqz6lSAATeVT9iOo+UdRgQBVCSCgaQMIqpIuDCtQdZVW7QQrvyDAqgm/fZrjdGuqtdrKqt9khW2QPxjemHUmDhfMRI89H9e/8+uE+xkhXzOZLFc0QwAvjEkj198CwKtWEWWScT0vZ7FYsHz5cqxdu1a3fe3atTjxxBMBAB988AEWLlyIyspK5OTk4Pzzz8fLLydeb/Smm25Cb29v9KZtIUhXvR07+q/8FO4rnSZGwmrJiMOqz9UU7TktEq0oDveQdgoYciWAiO5woCsQbciOzNiPCZBactANbzhwmcOBzZNgFYNgoEc3E95rsetO8QOAFHDpZ8vHRBtP7AQsbc/qgDaAUMBTlIDu1Htkyaqgop2lb4Ui+QZMuvIrfgQ0rQeRJaoCmuWngooEVXNCQFFVfRuAdoyCAJsm/EqaINtl7g+rkT8AAP1FBSKT87QOF87CO7M+NuRqAjtLFw76OKBf93UsPJu/ZEyPT5OX2ZaHvLJlADi5iijTjGtYLS4uhtlsRmtrq257a2srystDp8ZlWcZ3v/tdvP7669i0aRPuuusudHZ2xjscAODOO+9Ebm5u9DYR+ls9nXvRHjNzf2o4JIymsurra0CvLXS50iLBgvzIOqmDVOVidYb7MwsEExzh0NkzxBVivDHfu+X4LQySv0c/MclkgxSIDat9+mAZ0xUSiFmX1SNrTuUPuFRq6HtVDurXNg2Hx4C2KmuyQpH98Mb0xAYlP6SYNgAgNKs/uo8i6yurqgJZ87+WdsUCH6CrkgY17R5dmoALzXMCmhD7ryVfwIYpx2FH2SLNmELP0wbceH572k2DPg4Azy78FH521u1D7jdSC805sLN3lUagcNppEEQTXB274Hc1GT0cIhpHhvyrEduDKgiCbtsLL7yAuXPnoqamBg888MCgxwoEAujr69Pd0p27ay86svVhtSw8KarLXgB3x+4RHdfX14A+W6SyakFe+D9vV8xC94PpDofVcs3Epe6YSVCxPKp+fVOXEr+XLOjr1lVNg4I4oO9MCvTqgmXskQKB2Mpqf9gLxK4UEB6HogR0V42KtAcENYEwEj79MeNRZD+CmlAbCaUBTaAOqEHdOquq0B9qgf7eWSAU7G2aMC9pfs59mv1EzT7aaqfL6sT9J34HG6b2939GVh4IaNoIkvHI8q8M2CaZLOgZ495Xm8CwSsMXbQFgVZUo44zrvxrt7e2QJClaRY0oLS0dUG2dzDxd+9CZXQSgf3mpsvD15Dut2Qh44y8TNRSfqxG9WaHKarEpC3nhENkeU70cTFd4uae8cIiSBBPcMYvgx4o97e9OMDkstrIajHNdTsnfqw+WMY/7Yxb4d2uqlIHYq1uFl6ZSFUkXgH3RNgBN9TL8eCBOWNUGymhlVXsJ1Zg2AFlVEoZVH1SYNeFeW1n1ayrYJu370lRM5XBIVjSBTw5XZIcbVt+Z9bEB25pypw56nGRaCYZyRVb50DsRaQiCCUVVpwMAOg7/z9jBENG4G9ewGgwGsWHDBqxatUq3fdWqVXj33XfHcyiG8vU1oDu8NmmJJTSTviQcWo8OMplpKEFvJ3ojM/MtTtjDoajTn7iNIlZfTE+o35yFgG/w53tirhzlSdAfKwX6dEs+BeJMtZGCrpjKqn6fQGzPqiZcxl4+1av5WeqWiwpXj/26XtLQ/wpqTCuBIvkhacJ4tLKqCYuhiwJoKquqql99QFtZjalCa8OxT9O/G9Qc36+pRscLq12tW3Bowx+w5a1bEKvNUYJOeyHuPfE7Ax6L9bfjvoa9JfN04TjWH07+Hv47/9IhjzWY7zlmYE7MGsNEg8ktXwZLVgGCvi70tm4yejhENM5SvhqAw+FAdXV19PuZM2eitrYWnZ2dqK+vx913341HH30UdXV1WL9+Pa655hpUVVXh/vvvT/VQ0paqyugIB6CS8On/EjU0T7plGFXQOEdGX7iiVxUOwQoEdPoGP42v5RowY98OyTP4mGLbABJVVgEVQW2vZ8zzQrso0R5MAPCrsWHVA2iylDaQhtZDDf08ZUHU9ZpqF+KPrL8a1IRbfyQExoZV2R+eBCWGjxv6+WrfYUAJ6peugqKL7x7NMX0xVWhZ8zPwBXrx9KIrsPLwO3i+shbf620Mj63/Z+b2tAGOUt1FB3o6duLggedQVn48gP5gCwAHimrw1+O/GRpH934MZv2MU0NjEhP/WpBMFjTmTR30OMmYbbJjj+wZekciAEXTzwQAdBx+fViXoSaiySHlYfXYY4/FG2+8Ef3+t7/9LQDg4YcfxlVXXYUnn3wSRUVFuO2221BRUYHt27fj/PPPx5EjR1I9lLR2NHy6vVgwI0cwwR6OO81DVDGH4gpXC6cIFkANrWUaiLmk6WDcMfv6zFmQA4lXAwDitQEkXv9Q1+uZYB9tFdQXU7VVlNAyUWL45xXUhdVg9FyBIoiQNVeo0gbgSDVW2+MaOa0f20OrqjIkVQIQqghH1k+VtFesUmKWrlJVQDNur+aYQUXCuqkrcFLDB3hz1hmQew70j8Hfg5fnXYSX512E0l3P9v8MND+Pve/fhfwTfgi3uyW6LVL59UoeAHm68QuasP/+E2cAxSdjMPvf+wV8Q0xe0QblkbKn4BiUOYqnnwWALQBEmSrlYfXNN9+Mu7C/1n333Yf77rsv1S89oRwNh8JCVUWREKqG+cxZ6B1hv2qEK1zVrAhnFLfVgaB34KVPE3H7uqHAHg2DPosd0iBLVwGAW9WffncPslh3UBNSEtVfdZcjjam+qqoMRRAhRtZKDfavReBXArqwqmiCrElT7e2LVlaDiFQifYiE1f4I3RHuK5Y0S1xFlq7SVkQDSmDA0lXQhES3ZhxBJYhHl38ZH848HbtL5mPhGz/uH3/QhcjJccnUv85qQPP/k8vViL1PnovqWZdGt0nh15JkH+45/eewST5cu/53AAAhXvU67MjmgZMXpUAfeprexw8uW43PbHoES5s2AAD6rE48ePw3Qu9dHH3QvDZ7GlZY8vBmoBNrAgOvdkYUkZVbBUdhDRQ5iM76t4weDhEZgNNyDdISvtqUBWr0ggBuqwN+T/LBMp7ecDCKrLjpM9sRHEZlNejv1k2w8ZmzoEixi1PpxZ72d0mJ+24DmuWZ4k2wAgBJUwX1xQTh2PViA9ogqJlBL4tmKJpxWDTP84a3a3tcfULk+P3H2F80J3Qs3WoA4eNrK6uyvmdVhqJrC/BK+rAqW53YUV4LyWSF9kKt2glfinYimuZYkRUMtGE5UllVJC92lC/GRs1KAeIgYXX/+oFLVPl6j0AOetCVXYTGvGnR7TdefB92lC+Gu3Mv5BRURWea7Lgsqwx/yJ2P72UPXDuWKKI43ALQ0/LhgKXuiCgzMKwaxO1thyd8adOZ4b5Vj8WBgHt0YdUVcxWngNkGaRg9q3LQrZtg4zFZBtk7xB3T5+kaJNz6hWTaALRhVd9ioKoKBE0U1F61SlL6jyjFhlVtkA0HXO3qAdqJXK/PPgt9VieeXvyZ0LE0Y4i0AWj7bRVFgqIJ3qqq6pZi8wzoq9W8V23o1YZizX8DSRtow+9Duy0SXOU4P/dDeVUAgH3vDr126oH3f4XO+rei7RPvTT8ptL1wNhCu7m78zyfRVv/2kMcajq9nT0MNJ1xRAtF+1UNsASDKVClvA6DkBDzt8FrsyA56MCW8EoDH6oC/Y3RLePXEBJaAyTrgKlGDkSWvrrLqx+AtHYA+jAEDL3uqFdSc0k7YBpBgJj0weGVV1a5fKpp1EzHMmudFQqy2x1W75uo/ll2FJ5Z+EWq4wiupCiJDksNfFU0YVZRAzAQr/aQwWfJF/0+TYi6YIKna3lcZjTseQ1nNpajf9WT/dt0+wf4xRY8RrqxqWiJ+surnWNS8GY8JQez86zGQh2jlAIDDG/8EAFDDgbrVWYkbLr4PXkt/kJT8Pehu3zbksYZrTcEyrOh4H51qok8FZSKTJQf5lccDANoPv2rwaIjIKKysGiTgbYuGgKnhy6KmpLIaExx9gjis2bNKTFgNIvFp5AhZjr3qU+KLEGj7L4MxM/0jdG0AA5bBUnWThrQz+hVNsA3GzGiPH1Y1lVglpoKrGYPuNaJj7B+DGjPBSlUV/bqrmj8g6qGgq2EdAKD90Ku6NgBJCWLPW7fgnYeWwtXXP8lJW8WVwmPRtQGExy5r/ts35E/HmvmXQDZZkgqqibhsuQNWB5AGaS0YjYttJWNyXJq4CqedCtFkhad7P7w9h4weDhEZhGHVIKHKaiikTjOHLgjgMttGFSwAoDemX3S4q7Yqsl/XBhB3eakYUkw1N/Z7LW2lNt46q4B+jVGPkqhZIETRBEntGqmxk4AsmjAamTDm1172VB24NmzAE5rsJsXrWdWkUUWOXbpK/74UyYufnXU77jnlB2gQVOx45ZvY/dYt+Oh/N+iCX6S6qyoBXZDWtxzEqawi/N5UBbvfvBn73/tF/4uP8GpRG5/95IClrva+8+Pwa4/N0kH/lzML+QJP9lC/klnnAgDa2QJAlNEYVg0SqqyGelUrxVAlsy8Fs6z7Ytau9A2xMkM82nU9YyuO8UhBX8z3g1VWNfcTVG0lzZhj+2EBRFcqAPRtAbrKaswkIIt2Yf9wmJY0z5XiXMigp3XjwMci/aG6yqq+/qyq+glWiuTDkYKZ2FFeC1WVEfR1oWnHY5ACvbp2Au3VrAKagKytvkaq5NrAqF2toGnn4ziyqX+lDWGEYbWnpQ5H970Y/X7dI8ehYdtD4XGOPqzuDU9ei/Wd7KpRH5smB9FkQ9GM0JJVbftfHGJvIprMGFYNokg+uMOn2wvC1UZXCq6Z3hezJmriRaQS82tCc7yKYyxJ0gdkaZCeVe14ErYBaO7HC6taapwgCQDBJIK/NtxqA2nbgZcAAAc/+A0AQNaGs/CYFd3QVaja1QBUBdq0qj09H9tzC03o1PbNBjS9rarmfUVWK5C1YXWQ/0Z+V3PCx4bS1fBO/3g0q1QMp7L6VHiSWqw/nvy9uNsrNUt2UWYrnHYazBYHfH2N6D262ejhEJGBeM7NQO6YQNUnxA9vwyFJbvhMhcgK95GOrLKqDatJVFZlHyA6+r8f5NS99tR/IEGLgah5zcHWbAX0bQC6MSWxvJIqxz/VvuOVb8Jsy0PQ2xF+TPsaofH7Y36u2lqqGhvCdWEzZiku7el8TUANan6G27NCC/132guhdoaer52YFa+HdMt/r0RZzaU4WHfPgMeS1d38Pjb953J4eg7qtmuv6DWYJmclXqs+G5/a+oRu+/7CanisjrjPOcNaiGyIAy7hS5mnZPYFAIC2A2sMHgkRGY2VVQO5YyqprgTBazikgEs3IcaXoC90MNrlpeQE1U8tJTYsDfIcbcNAorBq0vwcPEOEVTVBVTGYRJVaW1mVY1oLIkEViF9ZfcFZimZnJdbMvSh0LO2EqpgJVlp+t77SKWgrq9o2AM39XkHAty/5C2457+5o2NW3AQz8GXTWv4mPXrsBcqAv/kCS1N38vq6qCgystj+z8PK4z+2xF8S92tUfT/7uoK/5/xwzhzlKmmxEkw3FM0JLVh1lCwBRxmNYNVDsVCrfEMEsGXLApZtcNLKw2p+0pCQKs7plnCDoqoWxujVrqAYSBE3tzP3BVhYAoKtaagVF/Uf7j8d/Ez5zFv544g2ap/Yv5r/VYkUiUpzKqlsUcdu5d+GZxVdotoaEekz1P7itq6/Crjf+H9yde/TDjzPBCgBkXXuDBI/VAUmz5q22lziZVo1U0r72Lef+BmvmX5JwXyXmj4ZeWy5ctlwAwPrp8S/9+ll7BaaKNpxuKUjBaGkiKph2CsxWJ3yuJvS2bjJ6OERkMLYBGMgVEyS9SgrCatCtu8KQdwTLDGlHIQ2zsqoKAjBIQO7UBGF/ghYDk/ZKToP0vw4mGHOafkvVibh+2krdklSqIuHGi+9DTsCFps2JL/+rasYTPWrM2LVLVylQB/wEOg6/luDg2vVaNX2qShBH8qejwNOJfeYsTIl5WigQh/47j9VSUonoJqYN1RucoA1FCvThsWVfxgmH34n7+BuFxwEAfuM+hPu8DSMbKE1YpbMvBAC07V+NwX6fEFFmYGXVQN6Yiph3kMuUJksKuqAMcgWoZAR1/ZBD/0OhjWbqEJXVdu1qAAnbADQVxiEu9Rrr1ZrQUjdPVa0cOM44rQFumxOtzoqBE5+0z4vzWOzatdp3MqAtIknaUKzIQdx+1u34/kV/RFAcGPi0rQlBZXwX0tdWmmPXYI11eOMf426v+/fFCJhtaM0pG/T533XMwKdtg+9Dk4vJ4kDJzHMAAEf3/9fg0RBROmBYNZA/5rS/Z4RVRB1V0S2q7xlJWNUuVJ/EpC9ZV1kVE56aB4B2TbCVE4Qss3afYf5M/rXkC/jGJx7C4fCkpGQNduGEeI+piv496paqUhUkWw0StBXZmMqqKoiQRTNc7TsHPE/WBMZkJsGlku6ysIP0BityAIc2/GHAdlfnbni7DwAAnl346SFf78v22LoyTWYls86HyZINd9c+tgAQEQCGVUPF9qh65dFXVgFAwWBXgBqatuI53DYAZYjVB7o1wSpHjj82k+Y1ZWn4AT5oskIZYsmrWMOtrDbvDl0Otf3QKwAGVlbVJC5TC+g7W3WVVU1w9bka8eGT5+Hdv6/U7hy9G2+C1VjSBnVVENHd9H7c/ZSgJ+7Pbut/r4ze3zDteDxy7FdTP0iasCrmXQYAaNn9b4NHQkTpgj2rBgrEhFVfik7nSto2gBEcUx9Whz6lPdhp/wHH9vdE77ckmBhk0QbkEZ5Sjw2r+9b9DNUn3Yp9634Wd/++tu0JjxWvshpwt+LNB+ZGL92qD6sqRtJnp2r/W2netwABfR366qqqytGkm8yFG1JJG6S3v/otHGpYBxSuiLtvbFhVoMLvbgEA9LVtg7NkEd6ZcRqanRVoyymD25qD+//9Rd1zZpuzMc/kwC55iMl2NOFlOachv3IlVFVB655njB4OEaUJhlUD+YL6fkz/MKuBiWhPzfpHEFa1p5WlJEKXEtOzOhi/5yjuOPOnKOtrxradD8Xdx5yCNTbVmPddv/WvaN37HALeNt329584AzZHOdydu4b9Goqmx1jfBiAnXVn9yKz9XzD+z9oTPmWuf3E5Mr8Kkjq+PasdShCbK5ZBFQQ0HXga6hCXxE1k25qv4sQr3wMEAfuL5/ZvL1+MRS1bdfv+K38xajvWj2rclP4q5odW1+hqeCf6Rw0REcOqgfwx/8inYjUAAJA1QSk4guWwtAv3J1NZ1U0oGqINIOA5ikOFs3GocDbUbX+Ju8/bJfPw2SPv4XD+DKA9/mzxRLoa16Ngyglo3P73ga8dE1QBwNO9H57u/UkfP9G7G+kEqyMmM24/82fozcoDHlmme+ztv9XCZM6CFOgd8DyPpiqdisqqkmS4BkJr2/4pvFaquu+JIfZOzO9uhqf7ILLzh15X1ZHERR5oYhNEKyoXhMJq085/GDwaIkonDKsGCsRU/7xSaiqr2glWvhFUvaRhtgHowtkQPa4BT39gTNQn+sLUFeiYdhL2Fc8F9j025OtrbfnvF2BzlMPXVz+s542W9qckq8qwmgAOF86Ku13yd0NK8LdGqyrj2YWfgs+cBfntHw7j1UZP21s72MS0ZLTufRYzj7sxZuvwr7pGE1/p7PNhtRfD52pG+8G1Rg+HiNIIJ1gZKBDzD30qLgoAALKgrawOf9KWtmc1mETsGk4wC3jbo/fN4cXhYylmG7ZWLkt4Sc5Bx6IExzSoJopR2uW7FKjwmpL9O3DkwWz1/EvxWs25ow6MozLsnmL9+z288V50HnkzdeOhCWvKwlCvctOOx439TBNR2mFl1UCx/aQpWw1AE1YD0ggmWGmXrkriHw1Z1wYwxM7aqq1/4OltABBES9ztE4WsKnh0yjLUSBJer14FvDxIK0MKioiDrWSQLHFYf3KMfJH22GeqShBbV1+FabVfga+vET0tdfjWzMviPnemyY7DsjcFHc2UbnLLliGvfBkUOYCmj0beWkJEkxPDqoGC2isBCaYR9ZfGI2kSUGAEk2+GvxqA5rRwEsffuuZqOIuPQVfjuriPC0lXJdOHqml/UFUFXRY7fnr2nUM+z925d/SvPc6rAQS9Heg4/DpUVUHQ1zXg8TZHCUrcbXivaDYQZ25YLFWVcWTzn/uP7+uIu98rBcsBAOd3bcQeOQVrElPamL7smwCAlj3PIqg5+0JEBDCsGipUWQ39J5BMFt3s8tHQtgEERrTOqmY1gCQqq8O9YlPHoVfRcejVhI93NaxDTuHcuEEofWnWhh3Gz8PVvh1b11wNX1/jsF5NtzTXME6Z7ipZgHltO7GneK5u8tpwJlgBwNbVX0r42B1n/gwzug7g3WD8yvlQPsifjuXdiVs5bnPMwud7Ey81RhOLo2g+imecBVWRcWTTvUYPh4jSEMOqgfxyf1gNmKxQUtWzqq2sjmTpKu2lPJOZYJXia3cfeP9X8PUeiS64n06SiXQjCe/D5Xc1on7r3yAHXMPq77v/hOtx/JF1+KDqJGDXg8N+3WS4bU7sKK+F0hC/cj6U/5XMwSfb9qLU3Rr3cWGIFSdoYolUVY/ufxHenkPGDoaI0hInWBlIGyRlwZSysKqt8o0krAaGeSlPRbeI/7BfbuDxJC8atj0EX1/D6A82XlTtBKvx6arct+4nOPjhb4b1HLfNiddqzoXL5hyjUQ10/8rro/f/svizQ+6vCCLqpsW/yABNLs6SRSirvgiqquDwxj8ZPRwiSlOsrBrIrwSify4ogpiyNgDtJKbguLQB9O8z2Wteybw/WZFx4INfo2j6GajfOjbVywkhXAHdMO14fHXa4zDLQbiTau1QIQv81ZQJZq+8CQDQuufZEV2Yg4gyA/9FMFBsWE3Vci2CJqz6R3JRAE3ADSYxeUdNcRtAOpOSOAWtqAq8XXvx1l/np2Sm/kQVe0pXMlmQbOldFhNfBEA02UYxKkoXhVWno2DqSVBkPw58MLwzBESUWdgGYKCA3H+K3m9K4XJNmlPSI6qsKsNrA9BOKFInaWn1ydrPoc1Rgr9XLhly30gbQDoG1e7mDwAAPS0bx+w1Nv7nU2j66J/Yv37o1RDiaT/0CmQx8d/RxTmVIx0apQnRZEPNST8GADRsexh+1/AmGBJRZmFl1UDaHtWUhlXtAvWj7FmVk6ms6q5gNeyXmxBemXM+XplzPtr3/XfIfYezGsB42/7y11E+9zK07P73mL1GT/MH6AmH4ljJBPimj/4JX8HChI/P9HTii1mVeNbfil4uHj8hVS39OrLzZ8LvasGhut8bPRwiSnOsrBpIVcamsipo1/wcQVjVXqwgqA4dLrRrjI5HYbXDXgQA8Jjt4/Bqwzfc1QDGU9DbgfrNfx7XtSy1wXjHK98c+gmqgt72nbpNwZhK6605s7Cx6ASUidaUjJHGT3Z+NaqWfgMAsPfdn0IOugweERGlO4ZVAynaNoAxumqTKo+gsqpZwzOZntXxLqfec+r/Q93UFfjVGbeN6+smLY3DqhE+eu1GvPXXBXj9vunobd2U1HPUmJ9hIEGf6hnWwlGPj8aPIJox/8zfwmTOQsfh19G2/0Wjh0REEwDbAAykXdg9pZVV7WuMoG/Sr+tZTeL52p7VYb/a8LXkVuLPJ3x7HF4pjiQmWGkrzRQiB93D2j/2qlwBsxWOOMcwT/r1JyaX6cuuQ27pYgR93dj1xg+MHg4RTRCsrBpI1wYgpO4/hfY67yNpAwhqnqMm1QYw+SuJzbueAgAc3pjMFXYm/89jzMX0ovpNWXF3MzGsThi5pUswffm3AAB73roFAc9Rg0dERBMFK6uG6g+VKVphFUBs3+jwq3wBJQCEVw4SkqrMal5jkmaHXa9/D3vf+VFSFcJMCO9jLfZn6DfHbwMw8WpWE4IlqwDHnH0vRNGM1r3P4ej+oScqEhFFsLKaJlqsDqOHEOXTrFLQmYbLL8mpunjCcF832VPZDKspoP8jK9G6qzc5ZuICa/F4DIhGShAx/8x7kOWcAk/3Aex+82ajR0REEwzDqsHuX3k93p1+Cl4smZOyY4621iTJQfzfub/Gbef8Em4lMPQTtMahXXPzc59GX9s2bPzPZWP/YpQWBvuT6Xe588ZtHDR8M5Z/C0VVp0MOerH95a9z9j8RDRvbAAy2Ydrx2DDtePhTOCtWGGViVJQgWp0VAAZOdBn6tcde79HNqHv6wnF4JUoX6Vffp2QUTjsVM479DgBg91s3w92529gBEdGExMpqmpAlb8qOJYyyuqnK2moqZ7aT8bj0/8Rjy6nE/DN/B0EQ0bjjMbTuecboIRHRBMWwmiaUYOrC6mhpr3olDHOVAkbbzPTOjNMAAO9VnZSS48VW6GV+siYUQbTgmLP/BKu9EL1Ht2Lfup8aPSQimsAYVtNEIIVXFBrtqXjtcleSv3eUR6N09lr12QCAZxZePqrjPL7sS7jnlB/i78d+JRXDwkcW/VJV0mRdZmKSqj7x/5BXtgxBXzd2rL1Wd2lpIqLhYs+qwfau+wlKZp2H+i1/TdkxR9uzqioSNj13BUSTBVJgmGGVmWJC+eeSL+C16rPR4igF3vjWiI8jmazYUb44ZePabsvGCws+jot2PgsAkLlE1YRRWn0Rpi76EgDgo//dAF9fg7EDIqIJj5VVgzVs/Rs2/edTKZ0huzW/CgDgT3CJymR0N61HZ/1bqRoSpSlVEEOT6dIwDG6tWBq9Lw/xV9D6whU4k5deNVx2QQ3mnv5LAMChDX9Ax5HXDB4REU0GrKxOQv+Zshz+slrsLD0G+Pux4/vibC2kFJEFk+b+4PuWiFZ80z4N/wt0jvGoKBGTxYGF59wPs8WBzoZ3cPDDu40eEhFNEgyrk5AkmvDOzJONHgZNJGr6/ZWhaCb3JdOzelgx5mIRFDL3tF/AUVANn6sZO1+9nhfHIKKUYRvAJCQYeUo3/c4m0yB2/u8GKHIAW9dcParjbH7+s/C5mrHlv1emZFyd9W/pPksttpwhn+NTucCVUaYs+hLKai6GIgexY+03EfR2GD0kIppEWFmdlJgYKTmte57B0b3PQR1l0OtqXIf1j65M0agAT9deHPrwt9HvXyypQYm9BDvKF+OLdQ8g39c94Dkmfu4NkV9xPKpPvBUAsH/9Heht3WDwiIhosmFldRLqa99h3Iun39lkGsJog+pY8fYcit73QcC/ll6J7RVLEu5vs+WP+ZhIz+aowDHn3AtRNKNl9zNo2PaQ0UMiokmIldVJ6OAHv4YieXB0/+pxf23WtihlNJf6lZII1BcINuTkLsD1fbvhTtMAPpmIJhsWnvtnWO3F6Gvbgd1v3WT0kIhokmJldRKSg24ceP8uuIyssBKNkqSZoCMnOVnnNGshlpmdYzUk0phz6u3ILa1F0NeF7S9fA0XiBDciGhusrFJKsQuAUmWfGsTWiiXozcqDq+Oj6HZhiJULzKzvj7nKY76AinmXQ1Vk7Fj7TS78T0RjimGViNLWH075PgCg+W+LQxcuSKLCakrDCxxMJnnlx6LmpB8BAPa/9wt0Na4zeERENNkxrFJqMSfQGFDkAOo3/xkAoE45b9B9WVkdO1ZHGY455z6IJgta9z6P+i1/MXpIRJQB2LNKROlJUyFVleQnTFWINuRqrn5FqSGIViw8+37Yskvh6vgIu974gdFDIqIMwbBKqcWmVRoDqipF7x/OnTLovrfkzMK/85eM8Ygyz9zTfo688mUI+rqx7aVroEheo4dERBmCYZVSKlNPwO7L4gz0VJP8vf3faHpV/7r4M3hj1pn4yaqfJ3zuTJN9LIeWcaYt+Roq5n0KiiJhxyvfhK/3iNFDIqIMwp5VohR4Na8SZRUrsLd4LvDEKUYPZ1Lwuxqx5+3bIPl7dNt7bLl4fPmXh3y+CQBXWx29oulnYPbK/wcA2Lfup+hqeMfgERFRpmFYpZTK1C4AVRDwRvUqo4cx6TRuf2TgxiTL91aI8CK59VkpPkfhHCw46w8QBBGNOx6L/9+DiGiMsQ2AiCYlq8Bfb6NhySrEovP+BrM1B12N67H3nR8ZPSQiylD8bU5EE0rD1tD159sP/W/Q/awZ20E9eoJowcJz7oc9dxo8PYew/eWvQ1WkoZ9IRDQG2AZAlALaKyzR2Dqy+X70NH+AvrZtQMFxCff7f46ZuNdbj/0yZ60P15xTb0d+5fGQ/L3YtvpqSP5uo4dERBmMYZUoBZp3PQmTxYHupveNHsrkpyroaakbcrdLskpxnq0YCzreHYdBTR5TF1+NyvlXhC6l+uq34OneZ/SQiCjDMawSpYKqoGHrg0aPgmKwb3V4CqtOR/UJtwAA9q2/A51H3jB2QEREYM8qEREByM6vxjFn/QGCaELTR//kH19ElDYYVomIMpzFXoTFFzwEsy0X3U3vY89b/2f0kIiIohhWiWjC+v3J3zN6CBOeaLJh0bl/hT23Ct6ew+GZ/0Gjh0VEFMWwSinFxYJoPG2rWIqfnXW70cOYwATMP+Nu5JUvQ9DXjS2rv4Sgr9PoQRER6TCsEtGEpgwxiSqLv+YSmnX891FafSEUOYDtL10Db/cBo4dERDQAf4tTSmXq5VbJOEOF1VyRi57EUzH/Ckxf9k0AwK43foDuZi67RkTpiWGViCY0RTAN+njuEI9nooKpJ2POqXcAAA5++Fu07nnW4BERESWWdmF16tSpeP3117Fjxw5s2bIFl112mdFDouFg0yqNo6P7XxyysuoUWFnVchTOwcKz74MomtGy+xkcqrvH6CEREQ0q7X6LS5KE73znO9iyZQtKSkqwceNGrF69Gh6Px+ihEVGa2fnqt5FV8/FB97kjpxqPeJvwL3/rOI0qfVntJVh8fv8SVbve+KHRQyIiGlLaVVZbWlqwZcsWAEBbWxs6OztRWFho8KgoaWxapXGkKkH0dOwcdJ85ZgducswcpxGlL9GchUXnP4gs51R4ug9g20vXQFUCRg+LiGhIww6rp5xyCp5//nk0NjZCVVVccsklA/a59tprceDAAXi9XtTV1eHkk08e0eCWL18OURTR0NAwoucT0eQnK/KQ++Rk+iQrQcSCM3+H3NJaBLyd2PrilyD5u40eFRFRUob9G9zhcGDLli146KGH8Mwzzwx4/PLLL8c999yDb3zjG1i3bh2+9rWvYc2aNViwYAHq6+sBAHV1dbDZbAOee/bZZ6O5uRkAUFhYiL///e/4yle+MtwhElEGkVQlqf1EAMntOfnMXnkzSmadC0X2Y/tLX4W397DRQyIiStqww+pLL72El156KeHjN954Ix588EE8+GDoutI33HADzjnnHFx77bW4+eabAQDHHnvsoK9htVrx7LPP4s4778T69euH3FcbfJ1OZ7JvhYgmAUkdurIKAHbBBHeS+04mUxd/GVVLvgoA+Oi176Gnpc7gERERDU9Ke1YtFguWL1+OtWvX6ravXbsWJ554YtLHefjhh/Haa6/hscceG3Lfm266Cb29vdFbY2PjsMdNRBOXHOfSoD86+5fYX1it22ZPvxb9MVcy63xUn3grAGD/+p/j6L7nDR4REdHwpfS3d3FxMcxmM1pb9bNuW1tbUV5entQxTjrpJHz605/GpZdeik2bNmHTpk1YuHBhwv3vvPNO5ObmRm9TpkwZ1XsgoolFjtMG0JQ7BVJMn6p9iCWuJpu8ihVYcNY9EAQRDdsexpHNfzZ6SEREIzImsw5UVT8lXBCEAdsSWbduHUym5BfxDgQCCAQ4o5UoU0nxJlgJAhRR/3vEnkEXB8guqMGi8/4K0WRD24GXsHfdT4weEhHRiKW01NDe3g5JkgZUUUtLSwdUW4mIUiFRz6oi6K9QkZ0hKwJYs0tRe8HDsNjy0NNch52vXg8kOQmNiCgdpTSsBoNBbNiwAatWrdJtX7VqFd59991UvhQREQBAUqW422Mvw+owZY3HcAxlsuSg9oJHkOWcCnfXPmxdczUU2W/0sIiIRmVES1dVV/dPXJg5cyZqa2vR2dmJ+vp63H333Xj00UdRV1eH9evX45prrkFVVRXuv//+lA6ciAiIv87qh09dAH/hMt227EkeVgXRgoXn3I+c4gXwe45yLVUimjSGHVaPPfZYvPHGG9Hvf/vb3wIIzeC/6qqr8OSTT6KoqAi33XYbKioqsH37dpx//vk4cuRIygZNRBShxmkDcLVvhxwTVk+35KPd3IVNUt94DW1czTv9lyicdgqkgAtbX7wKvr56o4dERJQSww6rb775JoSYXrBY9913H+67774RD4qIKGkJrmAVu/XT5lx8Km8xTun6EK2T7DKjs47/PsrnfhKKImHH2mvhat9u9JCIiFIms9ZyoTE3+J8xRKkXr7IKAPE6WUVBQIU48Op5E1nlMZ/H9GXXAQB2v/FDdNa/ZfCIiIhSi2GViCa0RGFVTvCXk2MSLWFVPGMV5pz8UwDAgQ9+g5bdTxs8IiKi1MuMtVxo3CS3mi5R6qiKjN3F8zC15wj+fML1aMybBjxUO6ANICJnkoTV3LKlWHDWHyCIJjTt/AcOb/i90UMiIhoTDKuUUj7z5J5xTelIxW9OvwWiqkDWrKWaKKxOhspqdv5sLDrvbzBZ7Gg/9Cr2vPV/Rg+JiGjMsA2AUuL3J38PTc5K/O6E64weCmUgVRB1QRUA5ARXzZvoYdXmKEfthY/Cai9Eb+sm7HjluoStEEREkwErq5QS2yqWYlvFUkhBt9FDIQIAJLpmU54tH/A1j+dQUsZsy0PthY8iyzkltOj/6qugSF6jh0VENKZYWaXUSlDNIhpvaoK4mmfNHeeRpIZozsLi8/4GR+Ec+FzN2PLfKxH0dRk9LCKiMcfKKhFNGl2N7+Lwxj+Fvknwh5PTNPGWrhJEM45ZdS/yKo5F0N+DrS9eCb+r0ehhERGNC4ZVIprwdr91C7LzZ2Hfup9Gt6mJelZFy3gNK2XmnvYLFM84E7Lkw7bVX4a7c4/RQyIiGjdsA6CU2PP2jwAAO//3bYNHQpmoacdjuqAaEj+s5ggT62/02StvQsW8T0WvTtXTUmf0kIiIxtXE+q1Naatx+8No2vkPqJPsMpY0+TiEifM3+rTar6Jq6dcBhK5O1XH4NYNHREQ0/ibOb21KewyqNBGI4sRYuqpszidQfWJo/dT963/Oq1MRUcZiWCWiSSr+9VaXeLrx3/ylqDFlj/N4kldY9THM+9hdAIAjmx/Akc1/NnhERETGYVglokkpflQNmWd24I6c6nEby3Dkli3DwrPvgyia0bL7Gexff4fRQyIiMhTDKhFNSkKCCVYRVab0uzRwdkENFp//EEwWOzoOv45db3wfiSaKERFlCoZVIspI1jRbwsqWU4naCx+FJSsfPS0bsX3ttVAVyehhEREZjmGViCapwRoBgKw0CqtmW37oMqo5FXB37uVlVImINBhWiSgjWRUJliEC7XgwWRyoveAROAqq4XM1Yct/vwDJ3230sIiI0gbDKhFlrGzB2GWsRJMNi859ALllSxDwdmLLC1+A391s6JiIiNINwyoRZaxsAy8QIAgmLDjr9yiYehKkgAtbX7wSnu59ho2HiChdMawS0aTXmlMWd7vdsMqqgLmn/xIls86FLPmwbc1X0Ne2zaCxEBGlN4ZVIpqcNO2ot51zF+455YcDdjGqDaD6pFtRMe9TUBQJO1+5Dt1N6w0ZBxHRRMCwSkSTniKaIMW5zGq2Ab8Cpy+/HtMWXw0A2PX699F+6JVxHwMR0UTCsEpEk1K32ab7Xo3TnzrebQBTFn4Rs1Z8FwCw550foXXPM+P6+kREE5HZ6AEQEY2FR1Uf5k09HhumrgAAqHGWqXKMY1gtq7kUc075KQDg4Id3o3Hbw+P22kREExkrq0Q0KR3Y/RS+r/bib1v/DABQhIFhNdtkG7BtLBRNPxPzzvgNAKB+699wqO534/K6RESTASurRDQpqaqM+i0PwJJVGPo+ThtAblYh4G0c03HkV67EMWffC1E0o2X3v7Fv3U/H9PWIiCYbVlaJaFKTw5ctjdcGkJtVMKavnVO8EIvO+ytM5iy0HVyLXW/8AIA6pq9JRDTZsLJKRJOaInmx7aVrYLZXDHjMac0bs9fNzp+N2gv/DrPVia7G9dj5ynVQFWnMXo+IaLJiWCWiSa/94MvozKsGLOW67TmW7DF5vSznVNRe9Bis9iL0Ht2CbWuuhiL7x+S1iIgmO7YBEFFGkFVlwDa7vRjlcz+V0texOSqw5OJ/IiunEu6ufdj64hchB90pfQ0iokzCsEpEGUFR5AHb7NklmH/Gr1E84+yUvIbVUYYll/wT9txp8HQfxObnP4OgryslxyYiylRsAyCijCCrA8PqkqYNkEQLWormo/3Q2lEd32ovwdKLn0B23gx4e49g8/OfQcBzdFTHJCIihlUiyhBKnLBa4O3Cqr1rYHIU4ZpRHNtiL8KSi59Adv5s+PoasPn5z8Dvbh7FEYmIKIJtAESUEeJVViNOc3eO+LiWrAIsuehxOApr4HM1Y9NzV8DX1zDi4xERkR7DKhFlBCXOBKsIVRBRMOWkYR/TbMtD7YWPI6doPvzuVmx+/gr4+upHM0wiIorBsEpEGUEZZI1Tsyqjeum1wzpeKKg+BmfJMQh42rD5+c/A23NolKMkIqJY7FkloowwWGUVAHJ9vUkfy5JVgNoLHw8FVW8HNr/wWXi69492iEREFAfDKhFlhMEqqwCQ608urFrsxVhy0ePIKZoHv+cotrzwObg796RiiEREFAfDKhFlhMEmWAFAXmDohfst9mIsvfifcBTWhHtUP8OKKhHRGGNYJaKMMHgTAJA7xFWmtEHV52rG5uevYI8qEdE4YFglooygQh30cbvkT/iY1V6CJRf/oz+oPvdpeHsPp3qIREQUB8MqEWWEoSqrdskPQTBBtGRDDvT1b8+bgaUX/wu2nHIGVSIiAzCsElFGUNShK6tLL30KeeXLAQAH3v8VTJZsTF92HQDA030AW1/8EoMqEdE4Y1gloowwVGU1Ww5EgyoAzDr+B9H7UtCNLf+9kgv+ExEZgBcFIKKMoO1ZlQVhwOMOJfFqARuevphBlYjIIAyrRJQRFN39/l99PpMNAFDgKI37vI9e+y483fvGcmhERDQIhlUiyghKgsqq25oDALAHvXGf17r3ubEdGBERDYphlYgygnZ6laIJqx6rA0D8sPreP06DqgTHemhERDQIhlUiygja1QAUaCurobCaFSesctF/IiLjMawSUUbQ9azGawOQ9GG1u+n98RgWERENgWGViDKCtmdVW1n1m7MAABZZf7p/8wufHZ+BERHRoBhWiSgjaHtWtROsAiYrAMCkSICmVUBVpPEaGhERDYJhlYgygm41AM32gDkUVkWoMKmJ11olIiJjMKwSUUZQEtwPhNdZBcLVVQD73r1jfAZFRERD4uVWiSgj6JauUvvjqt/cH1bNioSNL1+LtgOrx3FkREQ0GFZWiSjj+P090fuSaIlOuNr7yncYVImI0gzDKhFlHFlTWVUEEZIYOslk4aQqIqK0w7BKRBlH1i5jpQmrJl6tiogo7TCsElHG0YZVWTRBMlkAsLJKRJSOGFaJKOPI2vVUBRGyaAIAmLl0FRFR2mFYJaKMo42k9bueQlAMVVbNrKwSEaUdhlUiyjjaCwQEJS/kcM+qWVUSPYWIiAzCsEpEGUfS9qyqcnSClVmWYIGQ6GlERGQAhlUiyjiKpmdVUpVoWP2jmI+3C4/DFNGW6KlERDTOGFaJKONoe1ZlVelfDUAQUCxa8UPHDEPGRUREAzGsElHGURG/shqxwJwz3kMiIqIEGFaJKOPo1lmNE1arxCzY+euRiCgtpO1vY7vdjkOHDuGuu+4yeihENMlo2wAUYEBYFQUBNebscR0TERHFl7Zh9ZZbbsH7779v9DCIaBLSXxRAGBBWAWCmyT6eQyIiogTSMqxWV1dj3rx5WL16tdFDIaJJSNsGAEGITrDSKhat4zgiIiJKZNhh9ZRTTsHzzz+PxsZGqKqKSy65ZMA+1157LQ4cOACv14u6ujqcfPLJw3qNX//617jpppuGOzQioqRoLwoAxK+slggDAywREY2/YYdVh8OBLVu24Lrrrov7+OWXX4577rkHd9xxB5YuXYq3334ba9aswbRp06L71NXVYdu2bQNuFRUVuPjii7Fnzx7s3bt35O+KiGgQsu47fVjtC19ytYSVVSKitDCwnDCEl156CS+99FLCx2+88UY8+OCDePDBBwEAN9xwA8455xxce+21uPnmmwEAxx57bMLnr1y5EldccQU+9alPIScnBxaLBb29vfjZz34Wd3+r1QqbrX8Bb6fTOdy3REQZRntRABVAwNQfTBsUP+aLZrYBpFCZaEWNKRvTTVmoEG2oEG2oNNlQIFiQLYiwCyZIUNGnSDik+LBDcuFlfwd2yW6jh05EaWDYYXUwFosFy5cvxy9+8Qvd9rVr1+LEE09M6hg333xzNNR+8YtfxMKFCxMGVQC46aab8OMf/3jEYyaizKPtWQ142xEw9//B2yj7MN/sQInINoDhKhTMmGN2oMaUjRpTNuaYs1FjciAvTptFPCWiFbOQjTOshfhWdhW2Sy7c5T6EdcHusR04EaW1lIbV4uJimM1mtLa26ra3traivLw8lS8Vdeedd+Luu++Ofu90OtHY2Dgmr0VEk4O2DcDnbsbhff+Nfl+v+AAAFbzkakJOwRQOo6FgOiccTIsSVKMlVcUh2YuDshdNih9Nih/Nsh+dahBuVYZXlWGGiHzRjGpTNk6w5OF0ayEWmnPwSN5C/NPXgp+69iOg6zUmokyR0rAaoar6XyiCIAzYloxHHnlkyH0CgQACgcCwj01EmUuNCT0dnbuAnNkAgAOyFwDgFM1wCib0qfKA52eKbIiYbY6E0f5gWm6KH+QVVUW94sNeyYM9sgd7ZQ/2SG4clL3JBU0ZeC/Yg8d8zSgQzPhmdhWuzKrAFVnlmG2y46u9O+HK4P8eRJkqpWG1vb0dkiQNqKKWlpYOqLYSEaUDFYBHE4B6FAntSgDFohXTxCzszIC+STMEzDTZMd/siAbTalM2qkxZCZ/TJPtCgVQTTPdLHnihpGRMXaqE290H8FqgA390zsdxljz8JXcBrurZAX+KXoOIJoaUhtVgMIgNGzZg1apV+M9//hPdvmrVKjz33HOpfCkiopRQVRVetT/8+KGgQfajWLRiimnyhdU8wYz5ZgfmmRyYZ3ZgfrhiahXiLw7TpgSwR/Jgj+zGPk0wHa8K57vBHny+Zxsez1uEFZY8/NY5F9/s+4gNAUQZZNhh1eFwoLq6Ovr9zJkzUVtbi87OTtTX1+Puu+/Go48+irq6Oqxfvx7XXHMNqqqqcP/996d04EREo7FTcqFKzMJWyYWVlrzo9oCqoFHxYQmcmDqB+1YtEDDLZI/2ls4zOzDf5EBFglP4LkXCLtmN3VL/6fu9sgddqjTOIx9op+zG13p34qG8hTjbVoSvSVNxv7fB6GER0TgZdlg99thj8cYbb0S//+1vfwsAePjhh3HVVVfhySefRFFREW677TZUVFRg+/btOP/883HkyJGUDZqIaLQu7d4MMwQEoMKnOa3sVxV0h9dazU1yFns6KBWt+JilAKWiFcdZ8rDM4kSWYIq77xHZh12SG7tkNz6S3NgludCg+NO6WvmB1Isfu/bjTmcNbsiejo3BXnwg9Ro9LCIaB8P+Tfzmm29CEIRB97nvvvtw3333jXhQRERjSQWgANFJP9qeVT+U6CluR4Kwlw7yBDM+Zi3AsZY8LDc7UWN2DNinV5GwR3Zjr+TBbtmDjyQ3dsvuCTtJ6Sl/K46z5OITWWX4rXMuLurejE41aPSwiGiMTZyyARHRGNH1rKoq3GkYVnMEE0625OMcWzHmmxyYYbLDHFM4OCR7sU1yoS7Yg/eCPdgfXtlgMvmRaz8WmXNQY3bgV84afLV3Z1pXhIlo9BhWiSjjHFX0y91pK6sBKGkRVi0QcJa1EDVmB06zFKDWMvDqfB9Jbrwd6MJGqRcbg73oTIP+0rHmhYLr+3bj2fxanG4txFVZlfibr8noYRHRGGJYJaKM8fXenTjPWowHPPrJOV5NWA2qClzh0GdEWK015+B8WwnOsxahMmbpqCOyD2v8bVgf7MFe2YNWJTPXmN4re3CH+yB+llON7zlm4P1gD3ZMslUbiKgfwyoRZYxXA514NdA5YLu2DUCEEK2s5oxTWC0Trfi4rRSXZZVhhske3e5SJLwT7MZbgS68HuhEG/szo57wteAkSz7OtRXj97nz8InuLejJgMoyUSZiWCWijKddDaBFCaBKDVU0x7qyeqqlAJ+3V+A0SwFM4f5Ttyrjf/4OrA604+1ANxfAH8Qtrn1YZM7BdJMd9+bOx1U923lJVqJJiGGViDKeCmBJx3qYIehWAxiLyqoA4GJbCb5mn4o5mhn8HwZ78JSvFWv87Sm7CtRk16NK+GrvTjyZtxjHW/Lwa+cc3Ni3BxIDK9GkwrBKRATolnMaiwlWIoBLbaW4xj4V1ebs0GsqEv7lb8UT3mYcUnwpe61Mskf24Lq+XfhL7gKcbyuBUzDjxr7daXExAyJKDYZVIqIYqV5n9QRLHm5yzMQCcw4AoE+R8BdvAx71NU/YNU/TyTvBbny9dyf+lDsfp1gLsLpgGX7nOYKnfC3gT5fSRY5gwseshagxZcMEAd1qEF2KhE41iDYlgDYlgA4liCDPDAzAsEpEFKMvXJXLEkwoFMwjXhLKKZhwm2M2Pp5VCiC0SP993nr809eCPobUlHor2I3Lurfg9855mG3Oxu051fh2dhVe9LfjvWA3Ngf70M4JamSQy2xluMkxE3lJXBWvUwmiXQmgLfo1gDZVcz+8vVvNnIYXAZhc79XpdKK3txe5ubno6+szejhENEE9m1eLRRYnfuY6gEdGsI7nCZY8/CpnDipMNsiqisd8zfij5whPT48xCwR8Jqsc38iehmLRqnusT5HQqPjRrPjRq0joUyW4VBluVUYAKoKqAgkqJFVFECoUqBAgQEDoH0tRc18AIAhCeDt0+5kgQBQE+FQZT/uOjuskORGAGQJMEGAShOh9sxAZnxAeb8z7Efq39++j2aZ5ryKE8Gtp3r+g/zmJSYx18GthJr9XMscZ7j6xV+oUEtwfeAxBt1+ZaMWlttLoOskHJA/eDXYjCBV5ghmFogVFggUlohVFogUWIZmfXEhAVdChBHE0HGK1X48oPhyRfWhW0neK5nDyGiurRERxvBHswiKLE9WapaSSYYWAHzhm4kv2SgDAYdmL7/XtwSaJfzyPhyBU/N3XjH/4WnCKJR9n2oqw1OxEjSkbTtGMeaIZ8zDw0rRj5ZvZVXjF3wGzEA6NcUKkCRjkMf392H1MQPSx4QQdGl9eVcY9niN42NuYsDVFAJAvmFEsWlEihgJssWhFiaC5L1pQLFpRKFpgFURUmGyoMFmXfPcAABFjSURBVNkSvm5AVdAg+3BY8WG35MZOyY2PZDcOyxNrGifDKhFRHJELBZiHEQDyBTPuzZ2PFZY8AMDj3mb80n0Qngn1z8LkIEHF68EuvB7sAgBkQcQUkw2Vog3log25ohk5gil6s0CEORwCLZpwqABQoYa/hiiqCjX8vRLeqmr2k1UVZaIVK635KBWt+Jy9YtzffyxJDVWKteNUAajh96J/n5ptMe81+rzIPprHI8+PHGMog+0x6GPq4Mce6XEHPp74u9jj6B/r/65XlbEp2It/+VrROUQbigqgS5XQJUvYO0SXkAUCisOBtlS06r6Wi1ZME7MwzZQFqyBiljkbs5CNj1kLo893KRI2SL1YH+zB+kA3PpLdaf1bimGViCiOYPgfRGuSJyvLRSseyV2I2eZs9CkSbujbjTfCQYmM54OC/bIX+2XvuL3m2dYiLDA7IKsqJKiQoUJGKDjKCG/TPBbZrr0ffx8M+vzI9sj9dA4hNDJBqGhWAmge5Cp2IoBy0YYqUxZmmuyYb3JggdmBuWYHckQzTrMW4jRrIeAIXYL6JX87VvvbsUHqTbv+UIZVIqI4IjNyLcLQYbVCtOHxvIWoMtnRJPtwde9O7JU9Yz1ESnNrAx1YG+gwehiUoRQATYofTYof7wV7ottFAHNMDqy05OEESx6Ot+ShVLTiSnslrrRX4vLuLdiYZm1LDKtERHEEw5dgtQwxVaRCtOLxvEWoMmXhiOzD53u2oUnxj8cQiYiGTQGwS3Zjl+zGw74mWCDgREs+zrcVY5E5Jy376xlWiYjiCCRRWc0TzHgodyGqTFk4LHvx+Z5tg56WIyJKN0GoeDPYhTfTuG2JUweJiOIYqrIqArg9pxrV5my0yH58vmc7gyoR0RhgWCUiikMaorL6rewqnGcrhqKq+GbfR2jmqX8iojHBsEpEFEdkglW81QA+bSvDt7KrAAC/9BzCFsk1rmMjIsokDKtERHFE2gCWWHJxia0kut0hmHCHswZAaK3Ch7yNhoyPiChTMKwSEcUR0Kw0+Bvn3Oj9F/KXRO+f172Ja1gSEY0xhlUiojiCca6Sc4IlD1Xhy69+JLnZp0pENA4YVomI4gjG1ExFAI/mLYp+/5mereM8IiKizMSwSkQUR2xldU/xydH7eyU3XOoQF+8mIqKUYFglIoojtrKq9b2+PeM4EiKizMawSkQUR7ye1YgdsnscR0JElNkYVomI4tCuBqC1g2uqEhGNK4ZVIqI4Iuusxrq+d9c4j4SIKLMxrBIRxRGMU1ndKblwWPEZMBoioszFsEpEFEe8yqoQ59KrREQ0thhWiYjiiFdZJSKi8cewSkQUR7ywyitWERGNP4ZVIqIk/di13+ghEBFlHIZVIqIkPO1rRRMrq0RE445hlYgoCTJ7WImIDMGwSkSUwJmdddH7jKpERMZgWCUiSqBFCUTvq4NcfpWIiMYOwyoRUQIS+tdaZVQlIjIGwyoRUQKy5j7DKhGRMRhWiYiIiChtMawSESVBZW2ViMgQDKtERElgVCUiMgbDKhFREhhWiYiMwbBKRJQEhlUiImMwrBIRJYFhlYjIGAyrRERJ4AQrIiJjMKwSERERUdpiWCUiSsIuyW30EIiIMpLZ6AEQEaWzS7s3Y4U5F8/6jxo9FCKijMSwSkQ0iO2SC9sll9HDICLKWGwDICIiIqK0xbBKRERERGmLYZWIiIiI0hbDKhERERGlLYZVIiIiIkpbDKtERERElLYYVomIiIgobTGsEhEREVHaYlglIiIiorTFsEpEREREaYthlYiIiIjSFsMqEREREaUthlUiIiIiSlsMq0RERESUthhWiYiIiChtMawSERERUdpiWCUiIiKitGU2egBjxel0Gj0EIiIiIopjODlt0oXVyJtvbGw0eCRERERENBin04m+vr5B9xEAqOMznPFTWVmZ8I1/8MEHWLFiRcLnDvZ4vMecTicaGxsxZcqUIX/Y6WCo958urzHSYwznecnuy88MPzPD2Xe0+/AzM37HnyifmZE8zs/M2Byfn5nUcjqdaGpqGnK/SVdZBTDoG1cUZdD/CIM9PthjfX19E+IXwlDvP11eY6THGM7zkt2Xnxl+Zoaz72j34Wdm/I4/UT4zo3mcn5nUHp+fmdRK9nUyboLVn/70pxE/PtRzJ4LxeA+peI2RHmM4z0t2X35m+JkZzr6j3YefmfE7/kT5zIz28YmAn5nh7Ztpn5lJ2QYwnpxOJ3p7e5Gbmzsh/nol4/EzQ8PFzwwNFz8zNFzp/JnJuMpqqvn9fvz4xz+G3+83eig0QfAzQ8PFzwwNFz8zNFzp/JlhZZWIiIiI0hYrq0RERESUthhWiYiIiChtMawSERERUdpiWCUiIiKitMWwSkRERERpi2F1jF1wwQXYtWsX9uzZg6uvvtro4dAE8Mwzz6CzsxNPPfWU0UOhCWDq1Kl4/fXXsWPHDmzZsgWXXXaZ0UOiNJeTk4MPPvgAmzZtwtatW/GVr3zF6CHRBGC323Ho0CHcddddhry+ytvY3Ewmk7p79261srJSzcnJUffs2aMWFBQYPi7e0vt2+umnqxdeeKH61FNPGT4W3tL/Vl5ertbW1qoA1JKSErW+vl7Nzs42fFy8pe9NFEXVbrerAFS73a7u379fLSwsNHxcvKX37fbbb1f/9a9/qXfddde4vzYrq2NoxYoV2LFjB5qamuByubB69Wqcc845Rg+L0twbb7yRdlcPofTV0tKCLVu2AADa2trQ2dmJwsJCg0dF6UxRFHi9XgBAVlYWTCYTBEEweFSUzqqrqzFv3jysXr3akNdnWB3EKaecgueffx6NjY1QVRWXXHLJgH2uvfZaHDhwAF6vF3V1dTj55JOjj1VWVqKxsTH6fUNDA6ZMmTIuYydjjPYzQ5knlZ+Z5cuXQxRFNDQ0jPWwyUCp+Mzk5eVh8+bNaGhowK9+9St0dHSM1/BpnKXi8/LrX/8aN91003gNeQCG1UE4HA5s2bIF1113XdzHL7/8ctxzzz244447sHTpUrz99ttYs2YNpk2bBgBx/1JVVXVMx0zGGu1nhjJPqj4zhYWF+Pvf/45rrrlmPIZNBkrFZ6anpwdLlizBzJkz8dnPfhalpaXjNXwaZ6P9vFx88cXYs2cP9u7dO57DHsDwPoiJcFNVVb3kkkt029577z313nvv1W3buXOn+vOf/1wFoJ5wwgnqM888E33snnvuUT/zmc8Y/l54S9/PTOR22mmnsWc1A28j/cxYrVb1zTffVD//+c8b/h54mxifGe3t3nvvVS+77DLD3wtv6fl5+fnPf64eOXJEPXjwoNrW1qZ2d3ert95667iOm5XVEbJYLFi+fDnWrl2r27527VqceOKJAIAPPvgACxcuRGVlJXJycnD++efj5ZdfNmK4lAaS+cwQaSX7mXn44Yfx2muv4bHHHhvvIVKaSeYzU1paCqfTCQBwOp049dRTsXv37nEfKxkvmc/LzTffjKqqKsycORPf+9738MADD+BnP/vZuI7TPK6vNokUFxfDbDajtbVVt721tRXl5eUAAFmW8d3vfhevv/46RFHEr371K3R2dhoxXEoDyXxmAOCll17CsmXL4HA4UF9fj49//OOoq6sb7+FSGkjmM3PSSSfh05/+NLZu3YpLL70UAPCFL3wB27dvH+/hUhpI5jMzdepUPPjggxAEAYIg4I9//CO2bdtmxHDJYMn+u2Q0htVRiu1BFQRBt+2FF17ACy+8MN7DojQ21Gfm3HPPHe8hUZob7DOzbt06mEwmI4ZFaWywz8zGjRuxdOlSI4ZFaWqof5ciHnnkkfEakg7bAEaovb0dkiQN+MujtLR0wF8oRAA/MzR8/MzQcPEzQ8MxUT4vDKsjFAwGsWHDBqxatUq3fdWqVXj33XcNGhWlM35maLj4maHh4meGhmOifF7YBjAIh8OB6urq6PczZ85EbW0tOjs7UV9fj7vvvhuPPvoo6urqsH79elxzzTWoqqrC/fffb+CoyUj8zNBw8TNDw8XPDA3HZPm8GL6UQrreTjvtNDWehx56KLrPtddeqx48eFD1+XxqXV2desoppxg+bt74meFt4tz4meFtuDd+ZnjLtM+LEL5DRERERJR22LNKRERERGmLYZWIiIiI0hbDKhERERGlLYZVIiIiIkpbDKtERERElLYYVomIiIgobTGsEhEREVHaYlglIiIiorTFsEpEREREaYthlYiIiIjSFsMqEZGBHnroIdx5553R7998802oqor/+7//G7Dve++9B1VV8ZOf/GQ8h0hEZCiz0QMgIspUgiDgggsuwMUXXxzdtmTJEhw6dAiLFi3S7fvFL34RlZWVAICNGzeO6ziJiIzEyioR0RjZtm0bbrnlFtx3333o6OhAc3MzvvOd70QfP+mkk6AoCt5//30AQE1NDXJzc/Hwww/rwmpOTg7uvPNOPPzwwwCADRs2AABKS0uhqiquv/56bNy4EV6vF9u3b8dJJ52kG0dxcTH+/Oc/o6WlBR6PB5s3b8Ypp5wytm+eiChFGFaJiMaA1WrF3LlzceWVV+LNN9/EihUr8Pjjj+MXv/gFHA4HAODiiy/GCy+8AFVVAQDLly+H1+vFE088gZqaGlitVgDArbfeis2bN6O5uRltbW1oaGgAACxduhQA8I1vfAM33HADamtrcejQITz++OMQBAEAUFVVha1bt6KgoACXXHIJFi9ejD/84Q/o6+sb7x8JEdGIqbzxxhtvvKX2tmzZMlVVVfWcc86JbluwYIGqqqpaUlKiAlB37dqlXnjhhdHHf/WrX6nvvfeeCkDt7u5Wa2tr1erqarWvr0+dO3eu+sADD6hr1qyJ7v+DH/xA9fv96owZMwa87tSpU1UA6osvvqi+9tprhv88eOONN95GemPPKhHRGFiyZAmam5vx8ssvR7eVlZXB7/ejo6MD8+bNw9SpU/Hqq69GH1++fHn0FP/WrVuxcOFCXHHFFfjLX/6C3bt3Y/ny5VizZo3uNZ555hkcOnQous3v90fvT5s2Deeff360AktENBGxDYCIaAzU1tairq5uwLbt27dDURRcfPHFeOWVV+Dz+aKPL126NDp5asuWLfj2t7+NFStW4Cc/+QksFguOOeYY3eSqJUuWYPPmzbrXWLZsGdra2tDY2IilS5fC7/cP2IeIaCJhWCUiGgPxgqR22yWXXILnn38++tjMmTNRUFAQraxu3rwZxx13HG655Rb09vZi0aJFsFqt0cezsrJQU1MDk8kUPYYgCPj2t7+NRx55BKqqIhgMwmw2w263j+2bJSIaY4b3IvDGG2+8TbZbV1eX+olPfEK3bfPmzep1112nlpSUqIFAINq7CkC97LLLVJ/Pp5rNZhWAajab1aKioujjX/nKV9SOjo7o98cdd5waCATUnTt3qitXrlTnzZunPvnkk+q+ffvUvLw8FYBaWFiodnR0qA8//LA6b948df78+erXvvY1de7cuYb/fHjjjTfekr2xskpElGLTp09Hfn6+rrJqsVgwf/58bNq0CRdddBHef/99tLW1RR9ftmwZtm/fDkmSAACSJKGjo0P3+KZNm6LfL1myBLt27cLtt9+Op59+GnV1dVAUBSeccAJ6enoAAJ2dnbjoootQU1ODDz/8EO+88w4uvfRStLa2jvFPgIgodQSEUisREY2hJUuWYMOGDcjLy8Pjjz+Od955B3fdddeIj/fHP/4RBQUF+NznPpfCURIRpR9WVomIxsGSJUtw4MABuFwuvPPOO3jiiSdGfbytW7emaHREROmLYZWIaBzU1tZG2wLuuuuu6ML+I7Vo0SKGVSLKCGwDICIiIqK0xcoqEREREaUthlUiIiIiSlsMq0RERESUthhWiYiIiChtMawSERERUdpiWCUiIiKitMWwSkRERERpi2GViIiIiNIWwyoRERERpS2GVSIiIiJKWwyrRERERJS2/j9HZuFYGZT5yQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x2000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eta = np.logspace(0, 4, 1_000)\n",
    "\n",
    "ks = np.array([0.3, 1, 10])\n",
    "evolution = results.get_time_evolution(ks, eta, ['delta_cdm', 'delta_baryon',\n",
    "                                                 'delta_photon'])\n",
    "\n",
    "fig, axs= plt.subplots(nrows=len(ks), figsize=(8,20), sharex=True)\n",
    "\n",
    "for i, ax in enumerate(axs):\n",
    "    ax.loglog(eta, evolution[i, :, 0], color='darkorange', label=r'$\\Delta_{cdm}$')\n",
    "    ax.loglog(eta, np.abs(evolution[i, :, 1]), color='royalblue', label=r'|$\\Delta_{b}$|')\n",
    "    ax.loglog(eta, np.abs(evolution[i, :, 2]), color='crimson', label=r'$|\\Delta_{\\gamma}|$')\n",
    "\n",
    "    ax.legend()\n",
    "    ax.set_title(f'$k = {ks[i]}/Mpc$')\n",
    "    ax.set_xlabel(r'$\\eta/Mpc$')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Matter Power Spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method get_linear_matter_power_spectrum in module camb.results:\n",
      "\n",
      "get_linear_matter_power_spectrum(var1=None, var2=None, hubble_units=True, k_hunit=True, have_power_spectra=True, params=None, nonlinear=False) method of camb.results.CAMBdata instance\n",
      "    Calculates :math:`P_{xy}(k)`, where x, y are one of model.Transfer_cdm, model.Transfer_xx etc.\n",
      "    The output k values are not regularly spaced, and not interpolated. They are either k or k/h depending on the\n",
      "    value of k_hunit (default True gives k/h).\n",
      "    \n",
      "    For a description of outputs for different var1, var2 see :ref:`transfer-variables`.\n",
      "    \n",
      "    :param var1: variable i (index, or name of variable; default delta_tot)\n",
      "    :param var2: variable j (index, or name of variable; default delta_tot)\n",
      "    :param hubble_units: if true, output power spectrum in (Mpc/h) units, otherwise Mpc\n",
      "    :param k_hunit: if true, matter power is a function of k/h, if false, just k (both :math:`{\\rm Mpc}^{-1}` units)\n",
      "    :param have_power_spectra: set to False if not already computed power spectra\n",
      "    :param params: if have_power_spectra=False, optional :class:`~.model.CAMBparams` instance\n",
      "                   to specify new parameters\n",
      "    :param nonlinear: include non-linear correction from halo model\n",
      "    :return: k/h or k, z, PK, where kz and z are arrays of k/h or k and z respectively,\n",
      "             and PK[i,j] is the value at z[i], k[j]/h or k[j]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(results.get_linear_matter_power_spectrum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method get_matter_power_spectrum in module camb.results:\n",
      "\n",
      "get_matter_power_spectrum(minkh=0.0001, maxkh=1.0, npoints=100, var1=None, var2=None, have_power_spectra=False, params=None) method of camb.results.CAMBdata instance\n",
      "    Calculates :math:`P_{xy}(k/h)`, where x, y are one of Transfer_cdm, Transfer_xx etc.\n",
      "    The output k values are regularly log spaced and interpolated. If NonLinear is set, the result is non-linear.\n",
      "    \n",
      "    For a description of outputs for different var1, var2 see :ref:`transfer-variables`.\n",
      "    \n",
      "    :param minkh: minimum value of k/h for output grid (very low values < 1e-4 may not be calculated)\n",
      "    :param maxkh: maximum value of k/h (check consistent with input params.Transfer.kmax)\n",
      "    :param npoints: number of points equally spaced in log k\n",
      "    :param var1: variable i (index, or name of variable; default delta_tot)\n",
      "    :param var2: variable j (index, or name of variable; default delta_tot)\n",
      "    :param have_power_spectra: set to True if already computed power spectra\n",
      "    :param params: if have_power_spectra=False and want to specify new parameters,\n",
      "                   a :class:`~.model.CAMBparams` instance\n",
      "    :return: kh, z, PK, where kz and z are arrays of k/h and z respectively, and PK[i,j] is value at z[i], k/h[j]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(results.get_matter_power_spectrum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24c3d91e4f0>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAGhCAYAAAC6URSFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAQ0ElEQVR4nO3dz2tUZ/vA4Xts7CaeWYlIgoLgsiBWsFQMaRfB0i5CFxW6aRcFIYO7/gHtRgtdSChU3LlwKWShUGs2LQgqw1hwMVK60EU6QkBSmClo1fa8m9fwzWv6bcbO5NyTXBc84JyMc+5E4flw5kdqEVEGAEASO6oeAADg/xInAEAq4gQASEWcAACpiBMAIBVxAgCkIk4AgFTGqh7gVUxMTESv16t6DACgD0VRxMOHD//xfiMXJxMTE9HpdKoeAwB4BZOTk/8YKCMXJy+umExOTrp6AgAjoiiK6HQ6G9q7Ry5OXuj1euIEALYgL4gFAFIRJwBAKuIEAEhFnAAAqYgTACAVcQIApCJOAIBUxAkAkIo4AQBSqSROFhYWYmVlJS5fvlzF6QGAxCqJk2+++SY++eSTKk4NACRXSZz8+OOPfi8OALCuvuNkamoqrly5Ep1OJ8qyjNnZ2ZfuMzc3F/fv34/Hjx9Hq9WK48ePD2RYAGDr6ztOxsfH4+7du3H69Ol1v37y5MmYn5+PM2fOxOHDh+PGjRtx7dq12Ldv3ysN+Prrr0dRFGsWALC1la+6yrIsZ2dn1xy7fft2ef78+TXH7t27V549e3bNsenp6fLy5cv/eI4vvviiXE9RFK88t2VZlmVZm7uKotjw/j3Q15zs3Lkzjhw5EouLi2uOLy4uxrFjx17pMb/66quo1+ura3JychCjAgBJjQ3ywXbv3h1jY2OxvLy85vjy8nLs3bt39fb3338fb775ZoyPj8fS0lJ8+OGH0Wq11n3Mp0+fxtOnTwc5JgCQ2EDj5IWyLNfcrtVqa4699957wzgtALAFDPRpnUePHsXz58/XXCWJiNizZ89LV1MAANYz0Dh59uxZ3LlzJ2ZmZtYcn5mZiZs3bw7yVADAFtX30zrj4+Nx8ODB1dsHDhyIQ4cOxcrKSiwtLcW5c+fi0qVL0Wq14tatW3Hq1KnYv39/XLhwYaCDAwBbV19vBZqenl73rb0XL15cvc/c3Fz54MGD8smTJ2Wr1SqnpqYqeSuSZVmWZVk5Vj/7d+2/fxgZRVFEt9uNer3uI/ABYET0s39X8rt1AAD+jjgBAFIRJwBAKuIEAEhFnAAAqYgTACCVkYmTRqMR7XY7ms1m1aMAAEPkc04AgKHzOScAwMgSJwBAKuIEAEhFnAAAqYgTACAVcQIApCJOAIBUxAkAkIo4AQBSEScAQCriBABIRZwAAKmIEwAgFXECAKQiTgCAVEYmThqNRrTb7Wg2m1WPAgAMUS0iyqqH6EdRFNHtdqNer0ev16t6HABgA/rZv0fmygkAsD2IEwAgFXECAKQiTgCAVMQJAJCKOAEAUhEnAEAq4gQASEWcAACpiBMAIBVxAgCkIk4AgFTECQCQijgBAFIRJwBAKuIEAEhFnAAAqYgTACCVkYmTRqMR7XY7ms1m1aMAAENUi4iy6iH6URRFdLvdqNfr0ev1qh4HANiAfvbvkblyAgBsD+IEAEhFnAAAqYgTACAVcQIApCJOAIBUxAkAkIo4AQBSEScAQCriBABIRZwAAKmIEwAgFXECAKQiTgCAVMQJAJCKOAEAUhEnAEAq4gQASEWcAACpiBMAIJWRiZNGoxHtdjuazWbVowAAQ1SLiLLqIfpRFEV0u92o1+vR6/WqHgcA2IB+9u+RuXICAGwP4gQASEWcAACpiBMAIBVxAgCkIk4AgFTECQCQijgBAFIRJwBAKuIEAEhFnAAAqYgTACAVcQIApCJOAIBUxAkAkIo4AQBSEScAQCriBABIRZwAAKmIEwAgFXECAKQiTgCAVMQJAJCKOAEAUhEnAEAqIxMnjUYj2u12NJvNqkcBAIaoFhFl1UP0oyiK6Ha7Ua/Xo9frVT0OALAB/ezfI3PlBADYHsQJAJCKOAEAUhEnAEAq4gQASEWcAACpiBMAIBVxAgCkIk4AgFTECQCQijgBAFIRJwBAKuIEAEhFnAAAqYgTACAVcQIApCJOAIBUxAkAkIo4AQBSEScAQCriBABIRZwAAKmIEwAgFXECAKQiTgCAVMQJAJCKOAEAUhEnAEAq4gQASEWcAACpiBMAIJWRiZNGoxHtdjuazWbVowAAQ1SLiLLqIfpRFEV0u92o1+vR6/WqHgcA2IB+9u+RuXICAGwP4gQASEWcAACpiBMAIBVxAgCkIk4AgFTECQCQijgBAFIRJwBAKuIEAEhFnAAAqYgTACAVcQIApCJOAIBUxAkAkIo4AQBSEScAQCriBABIRZwAAKmIEwAgFXECAKQiTgCAVMQJAJCKOAEAUhEnAEAq4gQASEWcAACpiBMAIBVxAgCkIk4AgFTECQCQijgBAFIRJwBAKuIEAEhFnAAAqYgTACAVcQIApCJOAIBUxAkAkIo4AQBSGZk4aTQa0W63o9lsVj0KADBEtYgoqx6iH0VRRLfbjXq9Hr1er+pxAIAN6Gf/HpkrJwDA9iBOAIBUxAkAkIo4AQBSEScAQCriBABIRZwAAKmIEwAgFXECAKQiTgCAVMQJAJCKOAEAUhEnAEAq4gQASEWcAACpiBMAIBVxAgCkIk4AgFTECQCQijgBAFIRJwBAKuIEAEhFnAAAqYgTACAVcQIApCJOAIBUxAkAkIo4AQBSEScAQCriBABIRZwAAKmIEwAgFXECAKQiTgCAVMQJAJCKOAEAUhEnAEAq4gQASEWcAACpiBMAIBVxAgCkIk4AgFTECQCQijgBAFIRJwBAKuIEAEhFnAAAqYgTACAVcQIApCJOAIBUxAkAkIo4AQBSEScAQCriBABIRZwAAKmIEwAgFXECAKQyMnHSaDSi3W5Hs9msehQAYIhqEVFWPUQ/iqKIbrcb9Xo9er1e1eMAABvQz/49MldOAIDtQZwAAKmIEwAgFXECAKQiTgCAVMQJAJCKOAEAUhEnAEAq4gQASEWcAACpiBMAIBVxAgCkIk4AgFTECQCQijgBAFIRJwBAKuIEAEhFnAAAqYgTACAVcQIApCJOAIBUxAkAkIo4AQBSEScAQCriBABIRZwAAKmIEwAgFXECAKQiTgCAVMQJAJCKOAEAUhEnAEAq4gQASEWcAACpiBMAIBVxAgCkIk4AgFTECQCQijgBAFIRJwBAKuIEAEhFnAAAqYgTACAVcQIApCJOAIBUxAkAkIo4AQBSEScAQCriBABIRZwAAKmIEwAgFXECAKQiTgCAVMQJAJCKOAEAUhEnAEAq4gQASEWcAACpiBMAIBVxAgCkIk4AgFTECQCQijgBAFIRJwBAKuIEAEhFnAAAqYgTACAVcQIApCJOAIBUxAkAkIo4AQBSEScAQCriBABIRZwAAKmIEwAgFXECAKQiTgCAVMQJAJCKOAEAUhEnAEAq4gQASEWcAACpiBMAIBVxAgCkIk4AgFTECQCQijgBAFIRJwBAKuIEAEhFnAAAqYgTACAVcQIApCJOAIBUxAkAkEolcfLBBx/Ezz//HL/88kt89tlnVYwAACQ1ttknfO211+LcuXPx7rvvRrfbjZ9++ikWFhbit99+2+xRAICENv3KydGjR6PdbsfDhw/j999/j++++y5OnDix2WMAAEn1HSdTU1Nx5cqV6HQ6UZZlzM7OvnSfubm5uH//fjx+/DharVYcP3589WsTExPR6XRWb//6668xOTn5iuMDAFtN33EyPj4ed+/ejdOnT6/79ZMnT8b8/HycOXMmDh8+HDdu3Ihr167Fvn37IiKiVqu99HfKsvzb873++utRFMWaBQBsbeWrrrIsy9nZ2TXHbt++XZ4/f37NsXv37pVnz54tI6J8++23y4WFhdWvzc/Plx9//PHfnuOLL74o11MUxSvPbVmWZVnW5q6iKDa8fw/0NSc7d+6MI0eOxOLi4prji4uLcezYsYiIaDab8cYbb8TExETs2rUr3n///bh+/frfPuZXX30V9Xp9dXkKCAC2toG+W2f37t0xNjYWy8vLa44vLy/H3r17IyLizz//jM8//zx++OGH2LFjR3z99dexsrLyt4/59OnTePr06SDHBAASG8pbif/3NSS1Wm3NsatXr8bVq1eHcWoAYMQN9GmdR48exfPnz1evkrywZ8+el66mAACsZ6Bx8uzZs7hz507MzMysOT4zMxM3b94c5KkAgC2q76d1xsfH4+DBg6u3Dxw4EIcOHYqVlZVYWlqKc+fOxaVLl6LVasWtW7fi1KlTsX///rhw4cJABwcAtq6+3go0PT297lt7L168uHqfubm58sGDB+WTJ0/KVqtVTk1NVfJWJMuyLMuycqx+9u/af/8wMoqiiG63G/V6PXq9XtXjAAAb0M/+XclvJQYA+DviBABIRZwAAKmIEwAgFXECAKQiTgCAVEYmThqNRrTb7Wg2m1WPAgAM0ch+zsnk5KTPOQGAEVEURXQ6nQ19zslQfivxMBVFERERnU6n4kkAgH4VRfGPcTJyV04iIiYmJqLX60Wz2YyjR48O7HFfVJ2rMqxn0P/ftput/vMbte8v07xVzrJZ5x7meQb52MPeB4uiiIcPH/7j/UbuyklErH5jf/3111B+eL1eT5zwkmH9f9sutvrPb9S+v0zzVjnLZp17mOcZxmMPax/c6GOOzAti1/Ptt99WPQLbiP9v/85W//mN2veXad4qZ9mscw/zPJn+LQdlJJ/WGRa/VBCA7SzLPjjSV04G7Y8//ogvv/wy/vjjj6pHAYBNl2UfdOUEAEjFlRMAIBVxAgCkIk4AgFTECQCQijgBAFIRJxv0wQcfxM8//xy//PJLfPbZZ1WPAwCbbmFhIVZWVuLy5ctDPY+3Em/Aa6+9Fvfu3Yt33303ut1u/PTTT/HWW2/Fb7/9VvVoALBp3nnnndi1a1d8+umn8dFHHw3tPK6cbMDRo0ej3W7Hw4cP4/fff4/vvvsuTpw4UfVYALCpfvzxx0355NhtESdTU1Nx5cqV6HQ6UZZlzM7OvnSfubm5uH//fjx+/DharVYcP3589WsTExPR6XRWb//6668xOTm5KbMDwCD8271wM22LOBkfH4+7d+/G6dOn1/36yZMnY35+Ps6cOROHDx+OGzduxLVr12Lfvn0REVGr1V76O2Xp2TAARse/3Qs3W7mdVlmW5ezs7Jpjt2/fLs+fP7/m2L1798qzZ8+WEVG+/fbb5cLCwurX5ufny48//rjy78WyLMuyXmW9yl74Yk1PT5eXL18e6nzb4srJ/2fnzp1x5MiRWFxcXHN8cXExjh07FhERzWYz3njjjZiYmIhdu3bF+++/H9evX69iXAAYuI3shZtpbNPPmMzu3btjbGwslpeX1xxfXl6OvXv3RkTEn3/+GZ9//nn88MMPsWPHjvj6669jZWWlinEBYOA2shdGRHz//ffx5ptvxvj4eCwtLcWHH34YrVZr4PNs+zh54X9fQ1Kr1dYcu3r1aly9enWzxwKATfNPe+F77723KXNs+6d1Hj16FM+fP19ThhERe/bseakgAWAryrYXbvs4efbsWdy5cydmZmbWHJ+ZmYmbN29WNBUAbJ5se+G2eFpnfHw8Dh48uHr7wIEDcejQoVhZWYmlpaU4d+5cXLp0KVqtVty6dStOnToV+/fvjwsXLlQ4NQAMzqjthZW/pWnYa3p6ulzPxYsXV+8zNzdXPnjwoHzy5EnZarXKqampyue2LMuyrEGtUdoL/W4dACCVbf+aEwAgF3ECAKQiTgCAVMQJAJCKOAEAUhEnAEAq4gQASEWcAACpiBMAIBVxAgCkIk4AgFTECQCQyn8AN+xrV6J616QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kh, z, PK = results.get_linear_matter_power_spectrum(hubble_units=True, \n",
    "                                                     k_hunit=True)\n",
    "\n",
    "plt.figure()\n",
    "plt.loglog(kh, PK[0, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- no aparece nada en la figura anterior porque no le hemos dado a CAMB información sobre el Matter Power Spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: redshifts have been re-sorted (earliest first)\n",
      "Note: redshifts have been re-sorted (earliest first)\n"
     ]
    }
   ],
   "source": [
    "z_max = 250\n",
    "redshifts = np.arange(0, z_max, int(z_max / 5))\n",
    "\n",
    "# set Matter Power Spectrum\n",
    "pars.set_matter_power(redshifts=redshifts, kmax=1)\n",
    "pars2.set_matter_power(redshifts=redshifts, kmax=1)\n",
    "\n",
    "results = camb.get_results(pars)\n",
    "results2 = camb.get_results(pars2)\n",
    "\n",
    "# get Matter Power Spectrum\n",
    "kh, z, PK = results.get_linear_matter_power_spectrum(hubble_units=True,\n",
    "                                                     k_hunit=True)\n",
    "kh2, z2, PK2 = results2.get_linear_matter_power_spectrum(hubble_units=True,\n",
    "                                                         k_hunit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = plt.cm.Reds(np.linspace(0, 0.9, len(redshifts)))\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 5), sharey=True)\n",
    "for iz, redshift in enumerate(redshifts):\n",
    "    ax[0].loglog(kh, PK[iz, :], color=colors[iz], label=f\"z={redshift}\")\n",
    "    ax[1].loglog(kh2, PK2[iz, :], color=colors[iz])\n",
    "\n",
    "\n",
    "fig.legend(loc='upper right')\n",
    "ax[0].set_ylabel(r\"$\\mathcal{P}(k, z)$\", fontsize=15)\n",
    "ax[0].set_xlabel(r\"$k$ [h/Mpc]\", fontsize=15)\n",
    "ax[0].set_title(f\"$H_0$: {pars.H0} $\\\\frac{{km/s}}{{Mpc}}, $\" + \n",
    "                f\"$w$: {pars.DarkEnergy.w}, $c_s^2$: {pars.DarkEnergy.cs2}\")\n",
    "ax[0].grid(alpha=0.5, ls=':')\n",
    "\n",
    "ax[1].set_xlabel(r\"$k$ [h/Mpc]\", fontsize=15)\n",
    "ax[1].set_title(f\"$H_0$: {pars2.H0} $\\\\frac{{km/s}}{{Mpc}}, $\" +\n",
    "                f\"$w$: {pars2.DarkEnergy.w}, $c_s^2$: {pars2.DarkEnergy.cs2}\")\n",
    "ax[1].grid(alpha=0.5, ls=':')\n",
    "fig.suptitle(\"Matter Power Spectrum\", fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- notar que, a medida que $z$ aumenta, el Matter PS disminuye\n",
    "\n",
    "### Términos no lineales en el MPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "redshifts = [10, 5, 1, 0]\n",
    "\n",
    "# set non-linear Matter Power Spectrum\n",
    "pars.NonLinear = model.NonLinear_pk\n",
    "pars.set_matter_power(redshifts=redshifts, kmax=1)\n",
    "results = camb.get_results(pars)\n",
    "kh_nl, z_nl, PK_nl = results.get_matter_power_spectrum(minkh=1e-4)\n",
    "\n",
    "\n",
    "# o de forma alternativa, de la celda anterior\n",
    "# kh_nl, z_nl, PK_nl = results.get_linear_matter_power_spectrum(hubble_units=True,\n",
    "#                                                               k_hunit=True\n",
    "#                                                               nonlinear=True)\n",
    "\n",
    "\n",
    "# set linear Matter Power Spectrum\n",
    "pars.NonLinear = model.NonLinear_none\n",
    "pars.set_matter_power(redshifts=redshifts, kmax=1)\n",
    "results = camb.get_results(pars)\n",
    "kh, z, PK = results.get_matter_power_spectrum(minkh=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 5), sharey=True)\n",
    "\n",
    "for iz, redshift in enumerate(redshifts):\n",
    "    ax.loglog(kh_nl, PK_nl[iz, :], color='r', ls='--')\n",
    "    ax.loglog(kh, PK[iz, :], color='b')\n",
    "\n",
    "\n",
    "ax.legend(['non-linear', 'linear'], loc='upper right')\n",
    "ax.set_ylabel(r\"$\\mathcal{P}(k, z)$\", fontsize=15)\n",
    "ax.set_xlabel(r\"$k$ [h/Mpc]\", fontsize=15)\n",
    "ax.set_title(f\"$H_0$: {pars.H0} $\\\\frac{{km/s}}{{Mpc}}, $\" + \n",
    "                f\"$w$: {pars.DarkEnergy.w}, $c_s^2$: {pars.DarkEnergy.cs2}\")\n",
    "ax.grid(alpha=0.5, ls=':')\n",
    "fig.suptitle(\"Matter Power Spectrum\", fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matter PS utilizando función de Transferencia\n",
    "\n",
    "$$ \\mathcal{P}(k) \\propto T(k)^2 P_{\\zeta}(k)$$\n",
    "\n",
    "<image src=\"https://i.ibb.co/JsDTkLT/image.png\" width=500px>\n",
    "\n",
    "<image src=\"https://i.ibb.co/rwdKrpY/image.png\" width=500px>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pars.set_matter_power(redshifts=[0], kmax=1)\n",
    "results = camb.get_results(pars)\n",
    "\n",
    "transf = results.get_matter_transfer_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get transfer function\n",
    "kh = transf.transfer_data[0, :, 0]\n",
    "k = kh * results.Params.h  # transfer data gives the results in units of h/MPC, so we transform\n",
    "\n",
    "transfer = transf.transfer_data[model.Transfer_tot-1,:,0]\n",
    "\n",
    "# Get primordial power spectrum\n",
    "primordial_PK = results.Params.scalar_power(k)\n",
    "\n",
    "# All together\n",
    "matter_power = primordial_PK * transfer**2 * k**4 / (k**3/(2*np.pi**2))\n",
    "\n",
    "kh2, zs, PK = results.get_linear_matter_power_spectrum(hubble_units=False, k_hunit=False)\n",
    "\n",
    "plt.loglog(k, matter_power, c='r', label='Transfer+Primordial')\n",
    "plt.loglog(kh2, PK[0, :], c='b', ls='--', label='Matter power spectrum')\n",
    "plt.legend()\n",
    "plt.ylabel(r\"$\\mathcal{P}(k, z)$\", fontsize=15)\n",
    "plt.xlabel(r'$k\\, [h/Mpc]$', fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Angular Power Spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method get_cmb_power_spectra in module camb.results:\n",
      "\n",
      "get_cmb_power_spectra(params=None, lmax=None, spectra=('total', 'unlensed_scalar', 'unlensed_total', 'lensed_scalar', 'tensor', 'lens_potential'), CMB_unit=None, raw_cl=False) method of camb.results.CAMBdata instance\n",
      "    Get CMB power spectra, as requested by the 'spectra' argument. All power spectra are\n",
      "    :math:`\\ell(\\ell+1)C_\\ell/2\\pi` self-owned numpy arrays (0..lmax, 0..3), where 0..3 index\n",
      "    are TT, EE, BB, TE, unless raw_cl is True in which case return just :math:`C_\\ell`.\n",
      "    For the lens_potential the power spectrum returned is that of the deflection.\n",
      "    \n",
      "    Note that even if lmax is None, all spectra a returned to the same lmax, appropriate\n",
      "    for lensed spectra. Use the individual functions instead if you want to the full unlensed\n",
      "    and lensing potential power spectra to the higher lmax actually computed.\n",
      "    \n",
      "    :param params: optional :class:`~.model.CAMBparams` instance with parameters to use. If None, must have\n",
      "      previously set parameters and called `calc_power_spectra` (e.g. if you got this instance\n",
      "      using :func:`.camb.get_results`),\n",
      "    :param lmax: maximum L\n",
      "    :param spectra: list of names of spectra to get\n",
      "    :param CMB_unit: scale results from dimensionless. Use 'muK' for :math:`\\mu K^2` units for CMB :math:`C_\\ell`\n",
      "      and :math:`\\mu K` units for lensing cross.\n",
      "    :param raw_cl: return :math:`C_\\ell` rather than :math:`\\ell(\\ell+1)C_\\ell/2\\pi`\n",
      "    :return: dictionary of power spectrum arrays, indexed by names of requested spectra\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(results.get_cmb_power_spectra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total\n",
      "unlensed_scalar\n",
      "unlensed_total\n",
      "lensed_scalar\n",
      "tensor\n",
      "lens_potential\n"
     ]
    }
   ],
   "source": [
    "powers = results.get_cmb_power_spectra(pars, CMB_unit='muK')\n",
    "\n",
    "for name in powers: print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unlensedCL=powers['unlensed_scalar']\n",
    "\n",
    "ells = np.arange(unlensedCL[:, 0].shape[0])\n",
    "\n",
    "plt.figure(figsize=(7, 5))\n",
    "plt.plot(ells, unlensedCL[:, 0])  # 0: TT, 1: EE, 2: BB, 3: TE\n",
    "plt.xlabel(r'Momento multipolar $\\ell$', fontsize=16)\n",
    "plt.ylabel(r'$\\ell(\\ell+1)C_{\\ell}^{TT} / 2\\pi$ [$\\mu K^2$]', fontsize=16)\n",
    "\n",
    "plt.xlim(2, 2500)\n",
    "plt.grid(alpha=0.5, ls=':')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://i.ibb.co/cTSL91J/image.png\" width=\"750px\" alt=\"Image\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notación simbólica\n",
    "guía: [Notebook](https://camb.readthedocs.io/en/latest/ScalEqs.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'background_eqs'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/latex": [
       "$\\displaystyle \\left[ \\frac{d}{d t} a{\\left(t \\right)} = H{\\left(t \\right)} a{\\left(t \\right)}, \\  \\frac{d}{d t} H{\\left(t \\right)} = - \\frac{\\kappa \\left(3 P{\\left(t \\right)} + \\rho{\\left(t \\right)}\\right) a^{2}{\\left(t \\right)}}{6}, \\  \\frac{d}{d t} \\operatorname{exptau}{\\left(t \\right)} = \\operatorname{visibility}{\\left(t \\right)}\\right]$"
      ],
      "text/plain": [
       "⎡                                                     2                       \n",
       "⎢d                     d          -κ⋅(3⋅P(t) + ρ(t))⋅a (t)   d                \n",
       "⎢──(a(t)) = H(t)⋅a(t), ──(H(t)) = ─────────────────────────, ──(exptau(t)) = v\n",
       "⎣dt                    dt                     6              dt               \n",
       "\n",
       "            ⎤\n",
       "            ⎥\n",
       "isibility(t)⎥\n",
       "            ⎦"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'density_eqs'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/latex": [
       "$\\displaystyle \\left[ \\frac{d}{d t} \\rho_{b}{\\left(t \\right)} = - 3 \\left(\\operatorname{p_{b}}{\\left(t \\right)} + \\rho_{b}{\\left(t \\right)}\\right) H{\\left(t \\right)}, \\  \\frac{d}{d t} \\rho_{c}{\\left(t \\right)} = - 3 H{\\left(t \\right)} \\rho_{c}{\\left(t \\right)}, \\  \\frac{d}{d t} \\rho_{g}{\\left(t \\right)} = - 4 H{\\left(t \\right)} \\rho_{g}{\\left(t \\right)}, \\  \\frac{d}{d t} \\rho_{r}{\\left(t \\right)} = - 4 H{\\left(t \\right)} \\rho_{r}{\\left(t \\right)}, \\  \\frac{d}{d t} \\rho_{\\nu}{\\left(t \\right)} = - 3 \\left(\\operatorname{p_{\\nu}}{\\left(t \\right)} + \\rho_{\\nu}{\\left(t \\right)}\\right) H{\\left(t \\right)}, \\  \\frac{d}{d t} \\rho_{de}{\\left(t \\right)} = - 3 \\left(\\operatorname{w_{de}}{\\left(t \\right)} + 1\\right) H{\\left(t \\right)} \\rho_{de}{\\left(t \\right)}\\right]$"
      ],
      "text/plain": [
       "⎡d                                       d                            d       \n",
       "⎢──(ρ_b(t)) = -3⋅(p_b(t) + ρ_b(t))⋅H(t), ──(ρ_c(t)) = -3⋅H(t)⋅ρ_c(t), ──(ρ_g(t\n",
       "⎣dt                                      dt                           dt      \n",
       "\n",
       "                     d                          d                             \n",
       ")) = -4⋅H(t)⋅ρ_g(t), ──(ρᵣ(t)) = -4⋅H(t)⋅ρᵣ(t), ──(ρₙᵤ(t)) = -3⋅(pₙᵤ(t) + ρₙᵤ(\n",
       "                     dt                         dt                            \n",
       "\n",
       "          d                                          ⎤\n",
       "t))⋅H(t), ──(ρ_de(t)) = -3⋅(w_de(t) + 1)⋅H(t)⋅ρ_de(t)⎥\n",
       "          dt                                         ⎦"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'delta_eqs'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/latex": [
       "$\\displaystyle \\left[ \\frac{d}{d t} \\Delta_{r}{\\left(t \\right)} = - k \\operatorname{q_{r}}{\\left(t \\right)} - 4 \\dot{h}{\\left(t \\right)}, \\  \\frac{d}{d t} \\Delta_{g}{\\left(t \\right)} = - k \\operatorname{q_{g}}{\\left(t \\right)} - 4 \\dot{h}{\\left(t \\right)}, \\  \\frac{d}{d t} \\Delta_{b}{\\left(t \\right)} = \\left(k \\operatorname{v_{b}}{\\left(t \\right)} + 3 \\dot{h}{\\left(t \\right)}\\right) \\left(- \\frac{\\operatorname{p_{b}}{\\left(t \\right)}}{\\rho_{b}{\\left(t \\right)}} - 1\\right) + \\left(- 3 \\operatorname{c^{2}_{sb}}{\\left(t \\right)} + \\frac{3 \\operatorname{p_{b}}{\\left(t \\right)}}{\\rho_{b}{\\left(t \\right)}}\\right) \\Delta_{b}{\\left(t \\right)} H{\\left(t \\right)}, \\  \\frac{d}{d t} \\Delta_{c}{\\left(t \\right)} = - k \\operatorname{v_{c}}{\\left(t \\right)} - 3 \\dot{h}{\\left(t \\right)}, \\  \\frac{d}{d t} \\Delta_{\\nu}{\\left(t \\right)} = - k \\operatorname{q_{\\nu}}{\\left(t \\right)} + \\left(- \\frac{3 \\operatorname{p_{\\nu}}{\\left(t \\right)}}{\\rho_{\\nu}{\\left(t \\right)}} - 3\\right) \\dot{h}{\\left(t \\right)} + 3 \\left(- \\Delta_{P \\nu}{\\left(t \\right)} + \\frac{\\Delta_{\\nu}{\\left(t \\right)} \\operatorname{p_{\\nu}}{\\left(t \\right)}}{\\rho_{\\nu}{\\left(t \\right)}}\\right) H{\\left(t \\right)}, \\  \\frac{d}{d t} \\Delta_{de}{\\left(t \\right)} = - k \\left(\\operatorname{w_{de}}{\\left(t \\right)} + 1\\right) \\operatorname{v_{de}}{\\left(t \\right)} - 3 \\left(\\Delta_{de}{\\left(t \\right)} + \\frac{3 \\left(\\operatorname{w_{de}}{\\left(t \\right)} + 1\\right) H{\\left(t \\right)} \\operatorname{v_{de}}{\\left(t \\right)}}{k}\\right) \\left(\\hat{c}^{2}_{sde}{\\left(t \\right)} - \\operatorname{w_{de}}{\\left(t \\right)}\\right) H{\\left(t \\right)} + \\left(- 3 \\operatorname{w_{de}}{\\left(t \\right)} - 3\\right) \\dot{h}{\\left(t \\right)} - \\frac{3 H{\\left(t \\right)} \\operatorname{v_{de}}{\\left(t \\right)} \\frac{d}{d t} \\operatorname{w_{de}}{\\left(t \\right)}}{k}\\right]$"
      ],
      "text/plain": [
       "⎡                                                                             \n",
       "⎢                                                                             \n",
       "⎢d                              d                                d            \n",
       "⎢──(Δᵣ(t)) = -k⋅qᵣ(t) - 4⋅ḣ(t), ──(Δ_g(t)) = -k⋅q_g(t) - 4⋅ḣ(t), ──(Δ_b(t)) \n",
       "⎣dt                             dt                               dt           \n",
       "\n",
       "                                                                              \n",
       "                                                                              \n",
       "                    ⎛  p_b(t)    ⎞   ⎛                3⋅p_b(t)⎞              d\n",
       "= (k⋅v_b(t) + 3⋅ḣ(t))⋅⎜- ────── - 1⎟ + ⎜-3⋅c_sb__2(t) + ────────⎟⋅Δ_b(t)⋅H(t)\n",
       "                    ⎝  ρ_b(t)    ⎠   ⎝                 ρ_b(t) ⎠              d\n",
       "\n",
       "                                                                              \n",
       "                                                                              \n",
       "                                d                        ⎛  3⋅pₙᵤ(t)    ⎞     \n",
       ", ──(Δ_c(t)) = -k⋅v_c(t) - 3⋅ḣ(t), ──(Δₙᵤ(t)) = -k⋅qₙᵤ(t) + ⎜- ──────── - 3⎟⋅\n",
       "t                               dt                       ⎝   ρₙᵤ(t)     ⎠     \n",
       "\n",
       "                                                                              \n",
       "                                                                              \n",
       "     ⎛            Δₙᵤ(t)⋅pₙᵤ(t)⎞       d                                      \n",
       "ḣ(t) + 3⋅⎜-Δ_P_ν(t) + ─────────────⎟⋅H(t), ──(Δ_de(t)) = -k⋅(w_de(t) + 1)⋅v_d\n",
       "     ⎝                ρₙᵤ(t)   ⎠       dt                                     \n",
       "\n",
       "                                                                              \n",
       "                                                                              \n",
       "    ⎛          3⋅(w_de(t) + 1)⋅H(t)⋅v_de(t)⎞                                  \n",
       "e(t) - 3⋅⎜Δ_de(t) + ────────────────────────────⎟⋅(ĉ_sde__2(t) - w_de(t))⋅H(t\n",
       "    ⎝                       k              ⎠                                  \n",
       "\n",
       "                                     d          ⎤\n",
       "                      3⋅H(t)⋅v_de(t)⋅──(w_de(t))⎥\n",
       "                                     dt         ⎥\n",
       ") + (-3⋅w_de(t) - 3)⋅ḣ(t) - ──────────────────────────⎥\n",
       "                                  k             ⎦"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'vel_eqs'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/latex": [
       "$\\displaystyle \\left[ \\frac{d}{d t} \\operatorname{q_{r}}{\\left(t \\right)} = - \\frac{2 Kf_{1} k \\pi_{r}{\\left(t \\right)}}{3} - \\frac{4 k A{\\left(t \\right)}}{3} + \\frac{k \\Delta_{r}{\\left(t \\right)}}{3}, \\  \\frac{d}{d t} \\operatorname{q_{g}}{\\left(t \\right)} = - \\frac{2 Kf_{1} k \\pi_{g}{\\left(t \\right)}}{3} - \\frac{4 k A{\\left(t \\right)}}{3} + \\frac{k \\Delta_{g}{\\left(t \\right)}}{3} + \\left(- \\operatorname{q_{g}}{\\left(t \\right)} + \\frac{4 \\operatorname{v_{b}}{\\left(t \\right)}}{3}\\right) \\operatorname{opacity}{\\left(t \\right)}, \\  \\frac{d}{d t} \\operatorname{v_{b}}{\\left(t \\right)} = - k A{\\left(t \\right)} + \\frac{k \\Delta_{b}{\\left(t \\right)} \\operatorname{c^{2}_{sb}}{\\left(t \\right)} \\rho_{b}{\\left(t \\right)} - \\left(- \\operatorname{q_{g}}{\\left(t \\right)} + \\frac{4 \\operatorname{v_{b}}{\\left(t \\right)}}{3}\\right) \\operatorname{opacity}{\\left(t \\right)} \\rho_{g}{\\left(t \\right)}}{\\operatorname{p_{b}}{\\left(t \\right)} + \\rho_{b}{\\left(t \\right)}} - H{\\left(t \\right)} \\operatorname{v_{b}}{\\left(t \\right)} - \\frac{\\operatorname{v_{b}}{\\left(t \\right)} \\frac{d}{d t} \\operatorname{p_{b}}{\\left(t \\right)}}{\\operatorname{p_{b}}{\\left(t \\right)} + \\rho_{b}{\\left(t \\right)}}, \\  \\frac{d}{d t} \\operatorname{v_{c}}{\\left(t \\right)} = - k A{\\left(t \\right)} - H{\\left(t \\right)} \\operatorname{v_{c}}{\\left(t \\right)}, \\  \\frac{d}{d t} \\operatorname{q_{\\nu}}{\\left(t \\right)} = - \\frac{k \\left(2 Kf_{1} \\pi_{\\nu}{\\left(t \\right)} - 3 \\Delta_{P \\nu}{\\left(t \\right)}\\right)}{3} - k \\left(\\frac{\\operatorname{p_{\\nu}}{\\left(t \\right)}}{\\rho_{\\nu}{\\left(t \\right)}} + 1\\right) A{\\left(t \\right)} - \\left(- \\frac{3 \\operatorname{p_{\\nu}}{\\left(t \\right)}}{\\rho_{\\nu}{\\left(t \\right)}} + 1\\right) H{\\left(t \\right)} \\operatorname{q_{\\nu}}{\\left(t \\right)}, \\  \\frac{d}{d t} \\operatorname{v_{de}}{\\left(t \\right)} = - k A{\\left(t \\right)} + \\frac{k \\Delta_{de}{\\left(t \\right)} \\hat{c}^{2}_{sde}{\\left(t \\right)}}{\\operatorname{w_{de}}{\\left(t \\right)} + 1} - \\left(1 - 3 \\hat{c}^{2}_{sde}{\\left(t \\right)}\\right) H{\\left(t \\right)} \\operatorname{v_{de}}{\\left(t \\right)}\\right]$"
      ],
      "text/plain": [
       "⎡                                                                             \n",
       "⎢                                                                             \n",
       "⎢d             2⋅Kf₁⋅k⋅πᵣ(t)   4⋅k⋅A(t)   k⋅Δᵣ(t)  d              2⋅Kf₁⋅k⋅π_g(\n",
       "⎢──(qᵣ(t)) = - ───────────── - ──────── + ───────, ──(q_g(t)) = - ────────────\n",
       "⎣dt                  3            3          3     dt                   3     \n",
       "\n",
       "                                                                              \n",
       "                                                                              \n",
       "t)   4⋅k⋅A(t)   k⋅Δ_g(t)   ⎛          4⋅v_b(t)⎞             d                 \n",
       "── - ──────── + ──────── + ⎜-q_g(t) + ────────⎟⋅opacity(t), ──(v_b(t)) = -k⋅A(\n",
       "        3          3       ⎝             3    ⎠             dt                \n",
       "\n",
       "                                  ⎛          4⋅v_b(t)⎞                        \n",
       "     k⋅Δ_b(t)⋅c_sb__2(t)⋅ρ_b(t) - ⎜-q_g(t) + ────────⎟⋅opacity(t)⋅ρ_g(t)      \n",
       "                                  ⎝             3    ⎠                        \n",
       "t) + ─────────────────────────────────────────────────────────────────── - H(t\n",
       "                               p_b(t) + ρ_b(t)                                \n",
       "\n",
       "                  d                                                           \n",
       "           v_b(t)⋅──(p_b(t))                                                  \n",
       "                  dt          d                                   d           \n",
       ")⋅v_b(t) - ─────────────────, ──(v_c(t)) = -k⋅A(t) - H(t)⋅v_c(t), ──(qₙᵤ(t)) =\n",
       "            p_b(t) + ρ_b(t)   dt                                  dt          \n",
       "\n",
       "                                                                              \n",
       "                                                                              \n",
       "   k⋅(2⋅Kf₁⋅πₙᵤ(t) - 3⋅Δ_P_ν(t))     ⎛pₙᵤ(t)    ⎞        ⎛  3⋅pₙᵤ(t)    ⎞     \n",
       " - ───────────────────────────── - k⋅⎜────── + 1⎟⋅A(t) - ⎜- ──────── + 1⎟⋅H(t)\n",
       "                 3                   ⎝ρₙᵤ(t)    ⎠        ⎝   ρₙᵤ(t)     ⎠     \n",
       "\n",
       "                                                                              \n",
       "                                                                              \n",
       "         d                       k⋅Δ_de(t)⋅ĉ_sde__2(t)                       \n",
       "⋅qₙᵤ(t), ──(v_de(t)) = -k⋅A(t) + ───────────────────── - (1 - 3⋅ĉ_sde__2(t))⋅\n",
       "         dt                           w_de(t) + 1                             \n",
       "\n",
       "           ⎤\n",
       "           ⎥\n",
       "            ⎥\n",
       "H(t)⋅v_de(t)⎥\n",
       "           ⎦"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from camb.symbolic import *\n",
    "sympy.init_printing()\n",
    "\n",
    "display('background_eqs', background_eqs)\n",
    "\n",
    "#Fluid components\n",
    "display('density_eqs',density_eqs)\n",
    "display('delta_eqs', delta_eqs)\n",
    "display('vel_eqs',vel_eqs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Referencias\n",
    "- [Github CAMB](https://github.com/cmbant/CAMB)\n",
    "- https://camb.info\n",
    "- [CAMB demo](https://hub.ovh2.mybinder.org/user/cmbant-camb-5fgbcrkq/notebooks/docs/CAMBdemo.ipynb)\n",
    "- http://icg.port.ac.uk/~jschewts/cantata/CAMB/CAMB_lecture.pdf\n",
    "- [Tutorial Dani Grandón](https://www.u-cursos.cl/ingenieria/2020/2/FI7015/1/material_docente/bajar?id=3386200&bajar=1)"
   ]
  }
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