{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "70fa233c",
   "metadata": {},
   "source": [
    "# Inverse problem: Biology-informed assimilation of pheromone sensors data in the pheromone propagation model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c52d2d6f-a976-4a5c-80bb-605c2171218f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "from pathlib import Path\n",
    "import os\n",
    "import time\n",
    "\n",
    "import pherosensor\n",
    "\n",
    "from pheromone_dispersion.geom import MeshRect2D\n",
    "from pheromone_dispersion.diffusion_tensor import DiffusionTensor\n",
    "from pheromone_dispersion.velocity import Velocity\n",
    "from pheromone_dispersion.source_term import Source\n",
    "from pheromone_dispersion.convection_diffusion_2D import DiffusionConvectionReaction2DEquation\n",
    "\n",
    "from source_localization.obs import Obs\n",
    "from source_localization.control import Control\n",
    "from source_localization.cost import Cost\n",
    "from source_localization.population_dynamique import PopulationDynamicModel\n",
    "from source_localization.adjoint_convection_diffusion_2D import AdjointDiffusionConvectionReaction2DEquation\n",
    "\n",
    "from utils.plot_colormap import plot_colormap\n",
    "from utils.plot_obs import plot_obs\n",
    "from utils.plot_ctrl import plot_ctrl\n",
    "from utils.plot_cost import plot_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6133d41d-e3cc-41ff-bc15-bba0fa6731d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = os.getcwd()\n",
    "path_data = path + '/data'\n",
    "path_output = path + '/output'\n",
    "if not os.path.isdir(Path(path_output)):\n",
    "    os.makedirs(Path(path_output))\n",
    "path_plot = path_output + '/plot'\n",
    "if not os.path.isdir(Path(path_plot)):\n",
    "    os.makedirs(Path(path_plot))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b884208f-ca7c-482a-8f52-3b3dd5ca5aea",
   "metadata": {},
   "source": [
    "This notebook aims to present the biology-informed data assimilation (BI-DA) problem solved to infer the quantity of pheromone emitted from pheromone sensors data, the solvers implemented in the module `source_localization` and the different submodules on which the solvers are based. \n",
    "\n",
    "In a first section, the data assimilation problem is presented.\n",
    "Then, the different submodules are presented and illustrated over a simple but representative case, in line with the case used in the notebook dedicated to [modelling pheromone propagation](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/pheromone_propagation_model.ipynb).\n",
    "Finally, the numerical solvers of the BI-DA problem are presented and used to infer the quantity of pheromone emitted in different scenario of biological information addition. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f2f58d3-f3aa-44fe-99bf-9fd7103405e7",
   "metadata": {},
   "source": [
    "## Pheromone sensors data assimilation for the inference of the quantity of emitted pheromone"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85da972b-e2f7-4cf7-bcb6-92438d5f38b3",
   "metadata": {},
   "source": [
    "The goal of data assimilation (DA) is to estimate the quantity of pheromone emitted by the insects, also referred to as the control variable, using time-series of pheromone sensors spatially distributed over the landscape of interest.\n",
    "\n",
    "To do so, we solve an inverse problem corresponding to the following direct problem describing pheromone propagation over the landscape (see equation (1) in the notebook [modelling pheromone propagation](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/pheromone_propagation_model.ipynb) for details)\n",
    "\n",
    "$$\n",
    "\\frac{\\partial c}{\\partial t}-\\nabla\\cdot(\\mathbf{K}\\nabla c)+\\nabla\\cdot(\\vec{u}c)+\\tau_{loss}c=s~\\forall (x,y,t)\\in\\Omega\\times[0;T] \\quad \\quad \\quad \\text{(1)}\n",
    "$$\n",
    "\n",
    "In this equation, the unknown is the pheromone density $c$, and $\\mathbf{K}$, $\\vec{u}$ and $\\tau_{loss}$ are known parameters describing respectively the diffusion tensor, the advection by the wind and a deposition parameter. The term $s$ is the emission rate of pheromone, which is the target to be inferred by the inverse problem. \n",
    "\n",
    "The method consists in finding the quantity of pheromone emitted $s$ that enables the prediction of the pheromone propagation model (1) that predicts the closest outputs to the data.\n",
    "\n",
    "Therefore, the DA problem is defined as a minimization problem over a cost function $j$:\n",
    "\n",
    "$$\n",
    "    \\begin{cases}\n",
    "        \\text{find }s_a(x,y,t)\\text{ such that} \\\\ \n",
    "        s_a(x,y,t)=\\text{arg}\\min_{s(x,y,t)}j(s)\\text{ with } j(s)=j_{obs}(s)+j_{reg}(s)\n",
    "    \\end{cases}\n",
    "$$\n",
    "\n",
    "The term $j_{obs}$ aims to minimize the gap between model predictions and data. It reads \n",
    "\n",
    "$$\n",
    "j_{obs}(s)=\\|m\\left(c(s)\\right)-m_{obs}\\|_{\\mathbf{R}^{-1}}^2\n",
    "$$\n",
    "\n",
    "where $m_{obs}$ are the observations, $c(s)$ is the pheromone concentration map predicted by the pheromone propagation model (1) from the given pheromone emission $s$, $m(c)$ is the observation operator that models the observed variable from a given pheromone concentration map and $\\mathbf{R}$ is the covariance operator of the observation error. The observation operator depends on the properties of the sensor. For instance, pheromone sensors commonly use a pre-concentrator that actively filters air during a time window $\\delta_t$ and releases the accumulated quantity into the sensor for the measure. In this case, the observation operator is $(m(c))_{i,k}= \\int_{t_{obs,k}-\\delta_t}^{t_{obs,k}} c(\\tau,x_{obs, i},y_{obs, i}) d\\tau$ where $(x_{obs, i},y_{obs, i})$ is the position of the $i^{th}$ sensor and $t_{obs,k}$ the $k^{th}$ time of observation.\n",
    "\n",
    "The regularization term $j_{reg}$ is commonly added in order to ensure the well-posedness of the optimization problem. In the BI-DA framework, this regularization term is based on prior biological information describing biological knowledge of the emitter insects. A short introduction of this biology-informed regularization term can be found below and a more extensive one can be found in [Malou et al (2024)](https://hal.science/hal-04572831/)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fcf0a98-985d-447f-a927-3c8cb20da42b",
   "metadata": {},
   "source": [
    "## Biology-informed regularization terms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a9b1db-fbab-4a10-835d-2c40b9388d63",
   "metadata": {},
   "source": [
    "The BI-DA framework is based on regularization terms modeling biological information that enable to drive the DA method toward an optimum $s_a$ consistent with the biological knowledge introduced in $j_{reg}$. In general terms, the regularization term reads:\n",
    "\n",
    "$$\n",
    "j_{reg} = \\sum_{i \\in \\mathcal{L}} \\alpha_{reg,i} j_{reg,i} \\quad \\quad \\quad \\quad \\text{ (2)}\n",
    "$$\n",
    "\n",
    "with $\\mathcal{L}$ the list of considered BI regularization terms $j_{reg,i}$ and $\\alpha_{reg,i}$ the associated weight coefficient. This allows for composite regularization terms that take into account several biological informations.\n",
    "\n",
    "Several biology-informed regularization terms are shortly presented here. \n",
    "\n",
    "- The Tikhonov regularization $j_{reg,T}(s)=\\|s-s_b\\|^2_{\\mathbf{C}^{-1}}$ enables to take into account information on the insects life habits, such as their preferred habitat and insect life cycle, through $s_b$ a prior value of $s$ and $\\mathbf{C}$ the background error covariance operator.\n",
    "- The LASSO regularization $j_{reg,LASSO}(s)=\\|s\\|_{1}$ enables to select important local pheromones emissions at the beginning of the infestation, when only few insects are expected. The LASSO term enables to discard sporious emissions.\n",
    "- The population dynamics informed regularization $j_{reg,PD}(s)=\\|\\partial_ts-\\frac{\\partial_tq}{q}s+q\\nabla \\left(\\sum_i \\mathcal{F}_i(s)\\right)-q\\sum_i\\mathcal{R}_i(s)\\|^2_{L^2}$  (with $q$ the quantity of pheromone emitted per insect) enables to take into account the spatio-temporal evolution of the density of insects $p=s/q$ described by the flux $F_i$ and reaction $R_i$ terms that model the behavior of the insect of interest. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a893042b-d1a5-4ba9-a0c4-bfc8bf1a5291",
   "metadata": {},
   "source": [
    "## Solving the optimization problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6692c12-9040-4c3d-adb7-a8b98eed159e",
   "metadata": {},
   "source": [
    "To solve the minimization problem, gradient-based methods are used. Thus, at each descent step, the gradient $\\nabla_sj$ must be computed and reads:\n",
    "\n",
    "$$\n",
    "\\nabla_s j(s) = \\nabla_s j_{obs}(s) + \\nabla_s j_{reg}(s)\n",
    "$$\n",
    "\n",
    "Note that we denote $\\nabla_s$ the gradient with respect to the control $s$ to avoid confusion with $\\nabla$ the classical gradient with respect to the space variables. Since $s$ is defined in a high-dimensional space and $c(s)$ involves the computation of a PDE model, the numerical computation of its gradient is challenging. We use a variational method and compute the gradient by using the adjoint model of the CTM that reads:\n",
    "\n",
    "$$\n",
    "\\partial_tc^* + \\nabla\\cdot(\\mathbf{K}^T\\nabla c^*)+\\nabla(\\vec{u}c^*)-(\\nabla.\\vec{u})c^*-\\tau_{loss}c^* = \\left(\\frac{dm}{dc}(c(s))\\right)^*\\cdot2\\mathbf{R}^{-1}\\left(m(c(s))-m_{obs}\\right) \\quad \\quad \\quad \\quad \\text{ (3)}\n",
    "$$\n",
    "\n",
    "with $c^*$ the adjoint state.\\\n",
    "The adjoint model is closed by the following final and boundary conditions:\n",
    "\n",
    "- a null final condition $c^*(x,y,t=T)=0$ $\\forall (x,y)\\in\\Omega$,\n",
    "- a null diffusive flux $\\mathbf{K}^T\\nabla c^*\\cdot\\vec{n}=0$ $\\forall (x,y)\\in\\partial\\Omega$,\n",
    "- a null outgoing convective flux $\\vec{u}c^*\\cdot\\vec{n}=0$ $\\forall (x,y)\\in\\partial\\Omega\\cap \\{(x,y)|\\vec{u}(x,y,t)\\cdot\\vec{n}>0\\}~\\forall t\\in]0;T]$ with $\\vec{n}$ the outgoing normal vector.\n",
    "\n",
    "Once the adjoint state is computed, the gradient $\\nabla_s j$ is computed using the expression:\n",
    "\n",
    "$$\n",
    "    \\nabla_s j(s) = \\nabla_s j_{reg}(s)-c^*(s)\n",
    "$$\n",
    "\n",
    "The adjoint model is solved using an implicit downwind finite volume scheme on the same Cartesian grid as the direct problem. \n",
    "More details can be found in the notebooks located in the folder `./test_numerical_scheme/` that introduce and test the numerical scheme, including convergence, and the solvers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "382aa5a2-d731-456b-ae0e-8f3b35954221",
   "metadata": {},
   "source": [
    "# Construction of the solver of the data assimilation problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3d4391-e49c-4ee8-bec5-d3795918afee",
   "metadata": {},
   "source": [
    "## Specifying the geometry using the `geom` submodule "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a5c6841-70b3-4c5e-b3aa-7bdf33ffd18b",
   "metadata": {},
   "source": [
    "As mentionned in the notebook on [modelling pheromone propagation](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/pheromone_propagation_model.ipynb), for now, the numerical solver of the pheromone propagation model has been implemented for a rectangular domain $\\Omega=[0;L_x]\\times[0;L_y]$.\n",
    "Moreover, the mesh is supposed to be cartesian (rectangular cells) with constant space steps $\\Delta x$ and $\\Delta y$. \\\n",
    "Such geometry can be generated as an object of the `MeshRect2D`class of the `geom` [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/pheromone_dispersion/geom.py)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4bd0301-fdbb-4313-aa5f-8b5eeb2b6a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Lx = 30 # the length along the x-axis\n",
    "Ly = 40 # the length along the y-axis\n",
    "Delta_x = 0.5 # the space step along the x-axis\n",
    "Delta_y = 0.5 # the space step along the y-axis\n",
    "T_final = 20 # the final time of the simulation\n",
    "msh = MeshRect2D(Lx, Ly, Delta_x, Delta_y, T_final)\n",
    "msh.calc_dt_implicit_solver(.1) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c30c5d9-0aec-4304-b573-55a5020c2d52",
   "metadata": {},
   "source": [
    "## Specifying the pheromone propagation model and its solver using the `convection_diffusion_2D` submodule"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bac8d9c-41a2-410e-9d28-fcbf6906180f",
   "metadata": {},
   "source": [
    "The first step is specifying the pheromone propagation model using the `convection_diffusion_2D` [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/pheromone_dispersion/convection_diffusion_2D.py) of the `pheromone_propagation` module.\n",
    "This requires to specify the wind velocity field, the diffusion tensor, the loss coefficient and to initialize a source term (potentially by filling it with 0 or a prior estimate).\\\n",
    "The reader should refer to the [dedicated tutorial notebook](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/pheromone_propagation_model.ipynb) for detailed information on that topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1232cca0-2397-41ad-951a-85044b6e88e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "U_vi = np.load(Path(path_data) / f'U_vi.npy')\n",
    "U_hi = np.load(Path(path_data) / f'U_hi.npy')\n",
    "U = Velocity(msh,U_vi, U_hi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf62311d-c7e7-4c06-8a29-3320e73c0890",
   "metadata": {},
   "outputs": [],
   "source": [
    "K = DiffusionTensor(U, 10., 10.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a73c2e39-2e80-4420-8d50-992de320e257",
   "metadata": {},
   "outputs": [],
   "source": [
    "deposition_coeff = 0.01*np.ones((msh.y.size, msh.x.size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c0cd1f4c-eda8-46c2-8186-867328496eb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "amp = 0.5\n",
    "xc = 6.\n",
    "yc = 6.\n",
    "r = 1.5\n",
    "S_value = np.zeros((msh.t_array.size, msh.y.size, msh.x.size))\n",
    "xx, yy= np.meshgrid(msh.x, msh.y)\n",
    "S_value[:, (xx-xc)**2 + (yy-yc)**2 < r**2] = amp\n",
    "    \n",
    "S_target = Source(msh, S_value, t=msh.t_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "727aed9e-5b94-456f-8958-ef284c951860",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Load of the inverse of the matrix of the implicit part of the implicit with stationnary matrix inversion scheme ===\n"
     ]
    }
   ],
   "source": [
    "solver = 'implicit with stationnary matrix inversion'\n",
    "tol = 1e-10\n",
    "direct_model = DiffusionConvectionReaction2DEquation(\n",
    "    U, \n",
    "    K, \n",
    "    deposition_coeff, \n",
    "    S_target, \n",
    "    msh, \n",
    "    tol_inversion=tol, \n",
    "    time_discretization=solver\n",
    ")\n",
    "fname = 'inv_matrix_implicit_solver'\n",
    "direct_model.init_inverse_matrix(path_to_matrix=path_data, matrix_file_name=fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d5df038-1ce7-436d-ba7d-f0d41bf9a78e",
   "metadata": {},
   "source": [
    "## Specifying the pheromone sensors data and properties using the `obs` submodule"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f018db3-b87a-4130-a649-b600d2c3a8e5",
   "metadata": {},
   "source": [
    "The sensors data and the observation operator are contained in an object of the `Obs` class of the `obs` [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/obs.py).\\\n",
    "The user should provide the data array, that is of shape (nb data, 1), and arrays, resp. of shape (nb data, 1) and (nb data, 2), that contains the time and location of each data. \n",
    "The user can also specify the length of the time window over which the pheromone accumulates in the pre-concentration chamber of the sensor before measuring. If this time window length is not specified, it is assumed that the observation is instantaneous .\n",
    "\n",
    "The `Obs` class also contains useful attributes and methods for pheromone sensors data and properties.\n",
    "For instance, the attribute `c_est` contains the estimate of the concentration in pheromone at the positions and times required to compute the estimate of the observed variable that is containes in the attribute `d_est`. Initially, these attributes are filled with NaN. The attribute `c_est` can be updated using the `solver_est_at_obs_times` method of the `DiffusionConvectionReaction2DEquation` class and the attribute `d_est` can be updated from the attribute `c_est` using the method `obs_operator`.\n",
    "\n",
    "In the test case used here, the data are generated as following at random sensors location using the pheromone propagation model and the target source term introduced above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16369bff-2703-493c-be7f-fa09d6c81005",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- generating synthetic data from the target source term and random sensors location---\n",
      "t = 20.000 / 20.000 s"
     ]
    }
   ],
   "source": [
    "if not (Path(path_data) / 'd_obs_noised.npy').exists():\n",
    "    from random import choices, seed\n",
    "    print(\"--- generating synthetic data from the target source term and random sensors location---\")\n",
    "    dt_sensor = 2.\n",
    "    nb_sensor = 30\n",
    "    seed(2)\n",
    "    random_index_sensor_loc = choices(range(0, msh.x.size*msh.y.size), k=nb_sensor)\n",
    "    X_sensor = np.zeros((nb_sensor, 2))\n",
    "    index_sensor = 0\n",
    "    for i, (xi,yi) in enumerate(zip(np.repeat(msh.x,msh.y.size),np.tile(msh.y,msh.x.size))):\n",
    "        if i in random_index_sensor_loc:\n",
    "            X_sensor[index_sensor,:]=[xi,yi]\n",
    "            index_sensor += 1\n",
    "    nb_obs_per_sensor = int(msh.T_final // dt_sensor)\n",
    "    t_obs_per_sensor = np.array([dt_sensor*(i+1) for i in range(nb_obs_per_sensor)])\n",
    "    X_obs = np.array([[x,y] for x,y in zip(np.tile(X_sensor[:,0],nb_obs_per_sensor),np.tile(X_sensor[:,1],nb_obs_per_sensor))])\n",
    "    t_obs = np.repeat(t_obs_per_sensor, nb_sensor)\n",
    "    d_obs = np.zeros_like(t_obs)\n",
    "    obs_prov = Obs(t_obs, X_obs, d_obs, msh, dt_obs_operator=dt_sensor)\n",
    "    direct_model.solver_est_at_obs_times(obs_prov)\n",
    "    obs_prov.obs_operator()    \n",
    "    noise = np.random.normal(0., 0.01*np.max(obs_prov.d_est), size=obs_prov.d_est.size)\n",
    "    noise_level = msh.mass_cell * msh.dt * (noise).dot(noise)\n",
    "    np.save(Path(path_data) / 't_obs.npy', obs_prov.t_obs)\n",
    "    np.save(Path(path_data) / 'X_obs.npy', obs_prov.X_obs)\n",
    "    np.save(Path(path_data) / 'd_obs_no_noise.npy', obs_prov.d_est)\n",
    "    np.save(Path(path_data) / 'd_obs_noised.npy', obs_prov.d_est+noise)\n",
    "    np.save(Path(path_data) / 'noise_level.npy', noise_level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b50803e4-5b9e-4da6-9d0b-3a8288b7c1e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "||epsilon||_2^2 =  1.67083409330364e-05\n"
     ]
    }
   ],
   "source": [
    "dt_sensor = 2.\n",
    "t_obs = np.load(Path(path_data) / 't_obs.npy')\n",
    "X_obs = np.load(Path(path_data) / 'X_obs.npy')\n",
    "d_obs = np.load(Path(path_data) / 'd_obs_noised.npy')\n",
    "obs = Obs(t_obs, X_obs, d_obs, msh, dt_obs_operator=dt_sensor)\n",
    "\n",
    "noise_level = np.load(Path(path_data) / 'noise_level.npy')\n",
    "print(\"||epsilon||_2^2 = \", noise_level) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27bfed37-ac48-4d9a-a47e-24a6580531dc",
   "metadata": {},
   "source": [
    "Moreover, the data can be plotted at a given time using the `plot_obs` [method](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/utils/plot_obs.py) of the `utils` submodule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f8fbe4b-250f-4bd3-88a5-42c4855b6274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_obs(msh, t_obs[50], obs, figsize=(20, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dc829ec-3bbd-4e7d-8578-27c06218d95d",
   "metadata": {},
   "source": [
    "The `Obs` class also contains the method `adjoint_derivative_obs_operator` that computes the spatial map of $(d_cm)^*\\cdot\\delta c$, i.e. the adjoint of the derivative of the observation operator with respect to the state variable evaluated along $\\delta c$ at a given time. This method is needed to compute the source function of the adjoint model (see eq. (3) ).\n",
    "\n",
    "It also contains the method `onesensor_adjoint_derivative_obs_operator` that is similar to the `adjoint_derivative_obs_operator` method but for the one-sensor observation operator, i.e. the observation operator obtained with only one sensor, and that can be used to compute the one-sensor-adjoint state. \n",
    "\n",
    "The `Obs` class also have the attribute `R_inv` that contains $\\mathbf{R}^{-1}$, the inverse of the observation error covariance error. In the absence of dedicated data, this attribute is set to the identity matrix. However, it will be possible in the future to specify this covariance matrix. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d7ba14e-1cb9-4703-91f2-2097d18cc18d",
   "metadata": {},
   "source": [
    "## Specifying the control variable and some prior knowledge about it using the `control` submodule"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a47e0cc0-2710-4fca-91e0-9cb5496cafcb",
   "metadata": {},
   "source": [
    "The control variable to be optimized (the quantity of emitted pheromones) and all the tools to model prior knowledge about it are contained in the `control` [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/control.py).\n",
    "\n",
    "The user should provide an object of the class `Source` that will be used as background/prior value of the control. When initialized, the control value is set to 0. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "28cfe1db-512c-4262-9734-d1218eb25049",
   "metadata": {},
   "outputs": [],
   "source": [
    "amp = 0.5\n",
    "xc = 10.\n",
    "yc = 10.\n",
    "c = 10.\n",
    "S_value_prior = np.zeros((msh.t_array.size, msh.y.size, msh.x.size))\n",
    "xx, yy= np.meshgrid(msh.x, msh.y)\n",
    "S_value_prior[:, np.maximum(np.abs(xx-xc), np.abs(yy-yc) ) < c] = amp\n",
    "    \n",
    "S_prior = Source(msh, S_value_prior, t=msh.t_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "59acb742-69cc-4cbf-9536-aa72ce2e7770",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABbgAAAUJCAYAAACiymXyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAACwdUlEQVR4nOzdeZiVdfk/8Pdhhn0ZJCVTQHEBxIVB0p87uOcumpa5pLm1q6llX3crs/yapW3uaJaWplbuS4obfnEBd0VBUMBIXGbYl+H8/lBOEjvMMHOG1+u6nstnzvM5n+c+M17X2Hvu7qdQLBaLAQAAAACAMtOisQsAAAAAAIAVIeAGAAAAAKAsCbgBAAAAAChLAm4AAAAAAMqSgBsAAAAAgLIk4AYAAAAAoCwJuAEAAAAAKEsCbgAAAAAAypKAGwAAAACAsiTgBgAAAACgLAm4AQAAAAAoSwJuAFiNFYvFvPbaa/nDH/6Qk046Kdttt13atWuXQqFQOlalJ598MieccEL69u2bTp06pVOnTunbt29OOOGEPPnkk01uXwAAABpXoVgsFhu7CABY1dZff/2MGzcuSfLwww9n0KBBjVtQI7jyyitz2mmnZcqUKUtctyr+U2HatGn57ne/m2uvvXaJ6772ta/lsssuS/v27Rt1XwAAAJqGysYuAABoHBMnTlxquL0q1NXV5aCDDsr9999feq1t27bZdNNNU1lZmVdeeSW1tbVJkmuvvTYTJkzIXXfdlYqKikbZFwAAgKbDiBIAWM0VCoX07t07RxxxRH75y1/m9NNPX6X3P/vssxcIoY8//viMHz8+Tz/9dIYNG5aJEyfmrLPOKl2/7777cs455zTavgAAADQdRpQAsFoyoiR59tlnM2XKlAwYMCAdO3YsvT5kyJAcc8wxpa8b8j8VJkyYkI022igzZ85Mkhx55JG54YYbFrn27LPPzo9//OMkH3div/nmm1lnnXVW6b4AAAA0LTq4AWA1NWDAgAwaNGiBcHtVu+yyy0ohdLt27fLLX/5ysWvPPvvsdO/ePUkyY8aM/OpXv1rl+wIAANC0CLgBWG2MHTs2hUIhhUKh1L2dJDvvvHPp9U8fq2NX96p22223lc4PPfTQdOnSZbFrW7VqtUBn+e23377K911es2bNyh/+8IccdNBB2WCDDdKhQ4dUVlamU6dO2XjjjbP33nvnvPPOy1NPPbVKHuYJAADQ3Ai4AYBG8frrr+fNN98sff2FL3xhqe/Za6+9SudvvPFGRo0atcr2XV7PP/98tthiixx11FG5/fbb89Zbb2XatGmpq6vLlClT8uabb+aee+7J+eefn2233Ta33HLLSt8TAABgdVPZ2AUAwKrStm3b7LnnnkmSoUOHlkZYbLXVVovs8N1iiy1WaX2rm+eff36Br7fddtulvmfLLbdMq1atMnv27NIevXr1WiX7Lo933303u+66a95///3Sa507d07v3r3TsWPHTJ8+Pe+++27Gjh1b6tyeN2/eCt8PAABgdSXgBmC18dnPfjb33ntvkgUfMvnzn//cOJJG8Oqrr5bOW7VqVZqDvSTz140ePXqhPRp63+Vx4YUXlsLtddZZJ1deeWX22muvtGix4P957qOPPsq9996ba6+9NoVCYaXuCQAAsDoyogQAGsigQYMWOdt7ZY/m4tNz0Lt167bMn61Hjx6l87Fjx66yfZfH3XffXTq/4YYbss8++ywUbicfd3V/+ctfzv33358vfvGLK3VPAACA1ZGAGwBoFLW1taXzqqqqZX5fp06dSudTpkxZZfsuj/Hjx5fOt99++2V6T0VFxUrdEwAAYHVkRAkANJCtt946bdq0aewymqxp06aVzpfn+9S2bdtF7tHQ+y6PNm3alOZ5v/DCC9l6661Xaj8AAAAWTcANAA3k5z//eWOX0KTNmTOndF5Zuez/SfLptfND5FWx7/IYMGBAHn744STJEUcckeuuu26ZO7kBAABYdkaUAACNol27dqXzmTNnLvP7Pr22ffv2q2zf5XHSSSeVzt94443ssMMO6dWrV7773e/m1ltvzaRJk1ZqfwAAAD4m4AYAGkWHDh1K5zNmzFjm902fPn2RezT0vsvjgAMOyIUXXrjAgyXfeOONXH755TnkkEOy9tprZ4sttsiPfvSjvPvuuyt1LwAAgNWZgBsAaBRrrrlm6Xx5Qt5//etfpfPPfOYzq2zf5fXDH/4wzz33XI444ohFBuYvvvhizjnnnGy00Ua55JJLVvp+AAAAqyMzuAGggXz/+9/PCy+8UO/73nvvvfW+Z2Po3bt36fz999/P9OnTFxgvsjjvvPNO6bxPnz6rbN8V0a9fv/zhD3/InDlz8vTTT+eRRx7J0KFD8+ijj5ZGokyfPj2nnXZaWrRokVNOOaVe7gsAALC6EHADQAMZPnx4hg4d2thlNFmbbLLJAl+PHDky22233RLfM2HChLz33nuL3aMh910ZLVu2zHbbbZftttsu//M//5Np06blj3/8Y84888xMnjw5SXLuuefmhBNOWOn53wAAAKsTI0oAWC19ejZysVhsxEpWX1tvvXVat25d+vrxxx9f6nsee+yx0nmbNm2y9dZbr7J961P79u1zwgkn5JZbbim9NmXKlDz11FMNel8AAIDmRsANwGrp012yy/MgwuXxyCOPpFgs1vvRXHTo0CG77rpr6es//vGPS33Pp9fsuuuui+x2bqh9G8KgQYNSVVVV+nrSpEmr5L4AAADNhYAbgNXS2muvXTp/8803G7GS1dvRRx9dOn/hhRfyj3/8Y7Frn3vuudxzzz2LfO+q2ndZLM8fIWbNmpU5c+aUvu7SpctK3RsAAGB1I+AGYLW05ZZbls6vu+661NTUNGI1zcuQIUNSKBRKxyOPPLLYtV/84hfTr1+/0tcnnnhiXnvttYXWvfvuuzniiCNSV1eXJKmurs7BBx+8yvddFuPGjcuOO+6Yf/zjH5k7d+4S15577rmZPn16kqRVq1bZZpttVureAAAAq5tCsTn9f50BYBmNHDkyW265Zanbtn379tlyyy2zxhprpFAoJEk222yz/PjHP27MMhtcmzZtFnqtrq5ugWD20/Os5zvyyCNz1VVXLXLPIUOG5Jhjjil9/fDDD2fQoEGLreGZZ57JTjvtVBoV06lTp3zjG9/ITjvtlMrKygwfPjy//vWvS+M72rZtm0cffTSf//znl/jZGmrfpRk7dmx69uyZJFlzzTWzzz77ZKuttkrPnj1TVVWVGTNm5NVXX81NN92UYcOGld53yimn5Be/+MVK3RsAAGB1I+AGYLV11lln5Sc/+clirw8cOHCJ3cfNwfwwf3l99atfzZAhQxZ5bXkD7iS5/fbbc/jhhy91Hnrbtm3zxz/+MYMHD16mOhtq3yX5dMC9rPbbb7/ccssti/xjAgAAAItnRAkAq60f//jH+ec//5kjjjgivXv3TocOHVY48GXlDB48OM8++2x23XXXRf4MCoVCdttttzz33HPLFUI31L5L8tnPfjY///nPs+OOO6ZVq1ZLXNu7d+9cddVV+dvf/ibcBgAAWAE6uAGAJuWdd97Jk08+mQkTJiRJ1l133Wy33Xbp3r17k9x3SWbOnJnnn38+b7zxRv71r39lxowZad++fdZee+30798/m2yySYPdGwAAYHUg4AYAAAAAoCwZUQIAAAAAQFkScAMAAAAAUJYE3AAAAAAAlCUBNwAAAAAAZamysQvgY/PmzcvEiRPTsWPHFAqFxi4HAACg2SkWi5kyZUrWWWedtGih3wsAmgMBdxMxceLEdO/evbHLAAAAaPbeeeeddOvWrbHLAADqgYC7iejYseMnZ6ckad2YpQAAADRTs5Jc+qn//QUAlDsBdxPxn7EkrZO0acxSAAAAmjVjIQGg+TB0DAAAAACAsiTgBgAAAACgLAm4AQAAAAAoSwJuAAAAAADKkoAbAAAAAICyJOAGAAAAAKAsCbgBAAAAAChLAm4AAAAAAMqSgBsAAAAAgLIk4AYAAAAAoCwJuAEAAAAAKEsCbgAAAAAAypKAGwAAAACAsiTgBgAAAACgLAm4AQAAAAAoSwJuAAAAAADKkoAbAAAAAICyJOAGAAAAAKAsCbgBAAAAAChLAm4AAAAAAMqSgBsAAAAAgLIk4AYAAAAAoCwJuAEAAAAAKEsCbgAAAAAAypKAGwAAAACAsiTgBgAAAACgLAm4AQAAAAAoSwJuAAAAAIAGcOONN+bEE0/M5z//+bRu3TqFQiFDhgxp7LKalcrGLgAAAAAAoDk666yzMm7cuKy55pr53Oc+l3HjxjV2Sc2ODm4AAAAAgAZw9dVXZ+zYsXnvvffy9a9/vbHLaZZ0cAMAAAAANIDddtutsUto9nRwAwAAAABNxr///e/ceeedOeecc7LXXntlzTXXTKFQSKFQyNFHH71ce40bNy6nnnpq+vTpk/bt26dLly7ZaqutcvHFF2f69OkN8wFYpXRwAwAAAABNxmc/+9l62ecf//hHjjjiiNTW1pZemz59ep555pk888wzufrqq3PXXXdlo402qpf70Th0cAMAAAAATVKPHj2yxx57LPf7RowYkS996Uupra1Nhw4d8pOf/CRPPvlkHnrooRx//PFJklGjRmWfffbJlClT6rtsViEd3AAAAABAk3HOOedkq622ylZbbZXPfvazGTt2bHr27Llce5x00kmZMWNGKisrc//992fbbbctXdtll12y8cYb5/vf/35GjRqVSy65JOedd95Ce5x66qmZNWvWct1z4403Xq46WXkCbgAAAABgub388sv5xje+kb/+9a9Za621lrq+rq4uRx55ZPbZZ58cfvjhi113/vnnr1Rdw4cPz2OPPZYkOfbYYxcIt+c79dRTc9111+XVV1/Nr371q5x55plp2bLlAmuuuOKKTJs2bZnv+8UvflHA3QiMKAEAAAAAlsuMGTOy55575rHHHstuu+2WDz74YInr582bl2OOOSY33XRTjj766DzzzDMNVtsdd9xROj/mmGMWuaZFixY56qijkiQfffRRHn744YXWTJ06NcVicZmPQYMGNcTHYSkE3AAAAADAcmnbtm0uuOCCFAqFvPDCC9ljjz1SU1OzyLXFYjEnnnhi/vCHPyRJvvKVr2TAgAENVtvjjz+eJGnfvv0S7zNw4MDS+RNPPNFg9dCwBNwAAAAAwHL72te+ll//+tdJkmeffTZf+MIXFvnAxm9/+9u5+uqrkyRf+tKXcu2116ZQKDRYXa+++mqSZKONNkpl5eInNPfp02eh91B+zOAGAAAAAFbIN7/5zcyYMSOnnXZannrqqeyzzz659957065duyTJ9773vfz2t79Nkhx44IG58cYbU1FR0WD1zJw5M5MnT06SdOvWbYlr11hjjbRv3z7Tpk3LO++80yD1XH311aWO8hdffLH02iOPPJIk2WGHHXLcccc1yL1XFwJuAAAAAGCFnXrqqZkxY0bOPvvsPPbYY9lvv/1y11135YILLsill16aJNlrr73y5z//eYkd1fXh0x3kHTp0WOr6+QH31KlTG6Sexx9/PNdff/0Crz3xxBMLjEQRcK8cATcAAAAAsFLOOuusTJ8+PT/96U/zz3/+M5tttllGjx6dJNlll11y2223pVWrVg1ex8yZM0vny3K/1q1bJ/n4oZkNYciQIRkyZEiD7M3HzOAGAAAAAFbahRdemJNPPjlJSuH2DjvskL///e9p06bNKqnh0/eZPXv2UtfPmjUryccPzaQ8CbgBAAAAgHrRo0ePBb7u2rXrKgu3k6Rjx46l82UZOzJt2rQkyzbOhKZJwA0AAAAArLTf/e53+d73vpck+cxnPpMkue2223LUUUdl3rx5q6SGNm3alO49fvz4Ja798MMPSwF39+7dG7w2GoaAGwAAAABYKddee22+9a1vJUm23HLLvPHGG/na176WJPnTn/6U4447LsVicZXU0rdv3yTJm2++mblz5y523WuvvVY632STTRq8LhqGgBsAAAAAWGF/+tOfcvzxx6dYLGbzzTfP/fffnzXWWCNXXXVVDj/88CTJddddl2984xurpJ4ddtghycfjR5599tnFrhs6dGjpfPvtt2/wumgYAm4AAAAAYIX89a9/LY0g2WSTTfLggw+WRoS0aNEi119/fQ455JAkyRVXXJGTTjqpwWs68MADS+fXXXfdItfMmzcvN9xwQ5Kkc+fO2XnnnRu8roZQKBSW6Rg0aFBjl9pgBNwAAAAAwHL7xz/+kcMOOyx1dXXZeOON89BDD6Vr164LrKmoqMif/vSnHHDAAUmSyy67LN///vcbtK6tt946O+64Y5LkmmuuybBhwxZac8kll+TVV19Nkpx00klp2bJlg9ZEwykUV9XwG5aotrY2VVVVSc5IsuqeLAsAALD6mJnkotTU1KRTp06NXQxAWZsxY0Z69uyZSZMmpWfPnhk6dOgSH9Q4e/bsHHjggbnnnnuSJP/3f/+XrbfeepFrH3/88bz55pulrydPnpzTTz89ycejRI477rgF1h999NEL7TFixIhsv/32mTFjRjp06JD/+Z//yc4775wZM2bk5ptvzpVXXpkk6dWrV5555pl07NhxuT5/U1EoFJIk3/jGN/LNb35zsevat2+fnj17rqqyVikBdxMh4AYAAGhoAm6A+vTkk0/m2GOPzT333JP1119/qetnzpyZ/fbbL/vss09OPvnkxa47+uijc/311y9zHYuLN//xj3/kiCOOSG1t7SKv9+rVK3fddVc22mijZb5XUzM/4D733HNz3nnnNW4xjaSysQsAAAAAAMrPdtttl5deeikVFRXLtL5Nmza59957l3n9ytpvv/3ywgsv5Fe/+lXuuuuujB8/Pq1atcpGG22UQw45JN/+9rfTrl27VVILDUcHdxOhgxsAAKCh6eAGoHnRwe0hkwAAAAAAlCkBNwAAAABAGbvlllvSt2/ftGvXLh07dszGG2+cr371q3n44Ycbu7QGZ0RJE2FECQAAQEMzogSA5mX+iJIlOfDAAzNkyJBPssfmx0MmAQAAAIBmbebMmZk9e3Zjl7FYxWJxobC6devWad269RLf165du+y///7Zdddd06dPn3To0CHvvfdehg4dmt///vd5//33c8cdd+SAAw7IAw88kJYtWzbkx2gUOribCB3cAAAADU0HN8DqaObMmVmrbdtMbexClqBDhw6ZOnXBCpflwZEfffRROnfuvMhrkyZNyl577ZURI0YkSX71q1/lu9/9bn2U26QIuJsIATcAAEBDE3ADrI7m526nJFlyP3TjmJXk0iTvvPPOAr+flqWDe2nGjBmTPn36ZM6cOdloo43yxhtvrFyxTZARJQAAAABAs9c6TbuttFOnTvX+B9gNNtggu+++e+6+++68+eabmThxYtZZZ516vUdja9HYBQAAAAAA0DD69u1bOp8wYUIjVtIwdHADAAAAAM1eZZpmGNrQNf33wyubGx3cAAAAAADN1CuvvFI6b27jSRIBNwAAAABAs/TWW2/lgQceSJJsuOGGWXfddRu5ovon4AYAAAAAKDP/+Mc/Mnfu3MVenzRpUg4++ODMnj07SfLNb35zVZW2SjXFsTMAAAAAAPWq5SdHU1O3gu/7zne+kzlz5uTggw/Otttum/XXXz9t27bN5MmT88gjj+SKK67I5MmTkyQ77LBDvvWtb9Vf0U2IgBsAAAAAoAxNnDgxl19+eS6//PLFrjn44INz9dVXp3Xr1quwslVHwA0AAAAAUGauv/76DB06NMOGDcuYMWMyefLk1NbWpkOHDunevXu22267fPWrX822227b2KU2KAE3AAAAAECZGThwYAYOHNjYZTQ6ATcAAAAA0OxVpmmGoU2xpnLSorELAAAAAACAFSHgBgAAAACgLAm4AQAAAAAoS0a8AAAAAADNXmWSlo1dxCLMbewCypwObgAAAAAAypKAGwAAAACAsiTgBgAAAACgLJnBDQAAAAA0e5VpmmFoU6ypnOjgBgAAAACgLAm4AQAAAAAoSwJuAAAAAADKkhEvAAAAAECz1/KTo6mZ29gFlDkd3AAAAAAAlCUBNwAAAAAAZUnADQAAAABAWTKDGwAAAABo9irTNMPQplhTOdHBDQAAAABAWRJwAwAAAABQlgTcAAAAAACUJSNeAAAAAIBmrzJJy8YuYhHmNHYBZU4HNwAAAAAAZUnADQAAAABAWRJwAwAAAABQlszgBgAAAACavco0zTC0KdZUTnRwAwAAAABQlgTcAAAAAACUJQE3AAAAAABlyYgXAAAAAKDZa/nJ0dQ0xZrKiQ5uAAAAAADKkoAbAAAAAICyJOAGAAAAAKAsmcENAAAAADR7ZnA3Tzq4AQAAAAAoSwJuAAAAAADKkoAbAAAAAICyZAY3AAAAANDsVaZphqFNsaZyooMbAAAAAICyJOAGAAAAAKAsCbgBAAAAAChLRrwAAAAAAM1eZZKWjV3EIghoV44ObgAAAAAAypKAGwAAAACAsiTgBgAAAACgLBnxAgAAAAA0e5VpmmFoU6ypnOjgBgAAAACgLAm4AQAAAAAoSwJuAAAAAADKkhEvAAAAAECz1/KTo6lpijWVEx3cAAAAAACUJQE3AAAAAABlScANAAAAAEBZMoMbAAAAAGj2KtM0w9CmWFM50cENAAAAAEBZEnADAAAAAFCWBNwAAAAAAJQlI14AAAAAgGavMknLxi5iEQS0K6fZd3DX1tbm5ptvzqmnnpqBAwdmo402SlVVVVq1apWuXbtm0KBB+fnPf573339/sXs88sgjKRQKy3Scd955q+7DAQAAAACsxpr9HwiGDx+eww47bJHX3nvvvQwdOjRDhw7NxRdfnBtvvDF77rnnKq4QAAAAAIAV0ewD7iTp3r17dt555wwYMCDdu3fP5z73ucybNy/jx4/Prbfemttuuy2TJ0/O/vvvn+HDh6dfv36L3evaa6/NVltttdjrXbt2bYiPAAAAAADAf2n2AffOO++ct99+e7HXDz300Nxxxx0ZPHhwZs+enfPPPz+33XbbYtf37Nkzm222WUOUCgAAAAA0kMo0zTC0KdZUTpr9DO6KioqlrjnwwAPTu3fvJMljjz3W0CUBAAAAAFAPmn3Avaw6duyYJJk5c2YjVwIAAAAAwLIQcCd5/fXXM3LkyCRJnz59GrcYAAAAAACWyWobcE+fPj1vvPFGfvGLX2TgwIGZO3dukuTkk09e4vvOPPPMrLfeemndunXWWGON9O/fP6ecckpGjRq1CqoGAAAAAGC+1WqG+ZAhQ3LMMccs9voZZ5yRr3zlK0vc48knnyydz549OyNHjszIkSNz2WWX5eyzz865556bQqFQbzUDAAAAACuv5SdHU9MUayonq1XAvTjV1dW58sors9VWWy12zec+97kcdNBB2WGHHbLBBhuksrIyb7/9du68887ccMMNmTNnTs4///zMnj07F1544VLvOWvWrMyaNav0dW1tbb18FgAAAACA1UWhWCwWG7uIVeWjjz7K+PHjkyQzZszI6NGj85e//CW33357Ntxww/zyl7/Mvvvuu9D7pk2bllatWqVly0X/PWX48OHZY489UlNTk0KhkBEjRqRfv35LrOW8887L+eefv4grZyRps7wfDQAAgKWameSi1NTUpFOnTo1dDACrSG1tbaqqqjI8SYfGLmYRpibZOvH7aQWtVgH34vzhD3/IV7/61RQKhVxzzTU5+uijl3uPG2+8MUceeWSS5LjjjstVV121xPWL6uDu3r17BNwAAAANRcANsDoScDdvq+1DJj/tyCOPzCGHHJJ58+bl29/+dj744IPl3uPLX/5y6V/AoUOHLnV969at06lTpwUOAAAAAKBhVDbhgxUn4P7EAQcckOTjcST33nvvcr+/srIyvXr1SpJMmDChXmsDAAAAAGBhAu5PrLXWWqXzcePGrdAehUKhvsoBAAAAAGApBNyf+HTXdYcOyz+NZ+7cuRk1alSSZJ111qm3ugAAAAAAWDQjXj5xyy23lM4333zz5X7/n//859TU1CRJBg4cWG91AQAAAAArrzJJy8YuYhEEtCun2XdwDxkyJDNnzlzimksvvTR33313kqRnz57ZcccdS9c+/PDDPPLII0t8//Dhw/Ptb387ycdjSr7xjW+sXNEAAAAAACxVs/8DwXnnnZdTTz01Bx98cHbYYYdsuOGG6dChQ6ZMmZIXX3wxf/zjH/PEE08kSVq1apUrr7wyFRUVpffX1NRk5513zhZbbJEDDzwwAwYMyOc+97lUVFTk7bffzp133pk//OEPmT17dpLktNNOy4ABAxrlswIAAAAArE6afcCdJB988EGuuuqqXHXVVYtd061bt1x77bXZbbfdFnn9hRdeyAsvvLDY91dUVOTss8/OOeecs9L1AgAAAACwdM0+4L7vvvty11135Yknnsibb76ZSZMm5f3330/btm3TtWvXVFdXZ999982hhx6adu3aLfT+ddZZJ7fcckuGDRuW4cOHZ8KECZk8eXJmzpyZqqqq9O7dO4MGDcpxxx2X9ddff9V/QAAAAABgqVqmac7gboo1lZNCsVgsNnYRJLW1tamqqkpyRpI2jV0OAABAMzQzyUWpqalJp06dGrsYAFaR+bnbq0k6NnYxizAlySaJ308rqNk/ZBIAAAAAgOZJwA0AAAAAQFlq9jO4AQAAAAAq0zTD0KZYUznRwQ0AAAAAQFkScAMAAAAAUJYE3AAAAAAAlCUjXgAAAACAZq+yImlZaOwqFlZZTFLX2FWULx3cAAAAAACUJQE3AAAAAABlScANAAAAAEBZMoMbAAAAAGj2KiuTSjO4mx0d3AAAAAAAlCUBNwAAAAAAZUnADQAAAABAWTKDGwAAAABo9lpWJC2b4AzulsXGrqC86eAGAAAAAKAsCbgBAAAAAChLAm4AAAAAAMqSGdwAAAAAQLNXWZlUNsEZ3JVmcK8UHdwAAAAAAJQlATcAAAAAAGVJwA0AAAAAQFkygxsAAAAAaPZaViQtm2C7b8t5jV1BeWuCP1IAAAAAAFg6ATcAAAAAAGVJwA0AAAAAQFkygxsAAAAAaP4q0jTbfQuNXUB5a4o/UgAAAAAAWCoBNwAAAAAAZUnADQAAAABAWTKDGwAAAABo/irTNNt95zV2AeWtKf5IAQAAAABgqQTcAAAAAACUJQE3AAAAAABlyQxuAAAAAKD5M4O7WWqKP1IAAAAAAFgqATcAAAAAAGVJwA0AAAAAQFkygxsAAAAAaP7M4G6WmuKPFAAAAAAAlkrADQAAAABAWRJwAwAAAABQlszgBgAAAACavxZJKhq7COqbDm4AAAAAAMqSgBsAAAAAgLIk4AYAAAAAoCyZwQ0AAAAANH+VaZozuAuNXUB508ENAAAAAEBZEnADAAAAAFCWBNwAAAAAAJQlM7gBAAAAgObPDO5mSQc3AAAAAABlScANAAAAAEBZEnADAAAAAFCWzOAGAAAAAJq/ijTNGdysFB3cAAAAAACUJQE3AAAAAABlScANAAAAAEBZMoMbAAAAAGj+KtM0Z3AXGruA8qaDGwAAAACAsiTgBgAAAACgLAm4AQAAAAAoS2ZwAwAAAADNX0Wkoc2QDm4AAAAAAMqSgBsAAAAAgLIk4AYAAAAAoCyZOgMAAAAANH8VnxxNTbGxCyhvOrgBAAAAAChLAm4AAAAAAMqSgBsAAAAAgLJkBjcAAAAA0PxVRhraDOngBgAAAACgLAm4AQAAAAAoSwJuAAAAAADKkqkzAAAAAEDzZwZ3s6SDGwAAAACAsiTgBgAAAACgLAm4AQAAAAAoSwJuAAAAAADKkrHqAAAAAEDz5yGTzZIObgAAAAAAypKAGwAAAACAsiTgBgAAAACgLJk6AwAAAAA0fy2SVDR2EYswr7ELKG86uAEAAAAAKEsCbgAAAAAAypKAGwAAAACAsmQGNwAAAADQ/FWmaaahxcYuoLzp4AYAAAAAoCwJuAEAAAAAKEsCbgAAAAAAylJTnDoDAAAAAFC/zOBulnRwAwAAAABQlgTcAAAAAACUJQE3AAAAAABlqSlOnQEAAAAAqF8VnxxNzbzGLqC86eAGAAAAAKAsCbgBAAAAAChLAm4AAAAAAMqSGdwAAAAAQPNXmaaZhhYbu4DypoMbAAAAAICyJOAGAAAAAKAsCbgBAAAAAChLTXHqDAAAAABA/apI00xD5zV2AeVNBzcAAAAAAGVJwA0AAAAAQFkScAMAAAAANCM/+MEPUigUSscjjzzS2CU1mKY4dQYAAAAAoH5VfHI0NfVc08iRI/OLX/yifjdtwnRwAwAAAAA0A/PmzcsJJ5yQuXPnpmvXro1dzioh4AYAAAAAaAYuu+yyPP300+nTp0+OPfbYxi5nlRBwAwAAAACUubfffjtnn312kuT3v/99WrVq1cgVrRoCbgAAAACg+atswkc9+Na3vpWpU6fmq1/9agYOHFg/m5YBATcAAAAAQBn7y1/+kjvvvDNdunTJ//7v/zZ2OauUgBsAAAAAoEx99NFHOemkk5IkP/vZz7Lmmms2ckWrloAbAAAAAKBMff/738+//vWvbL/99qvNgyU/rZ4mvAAAAAAANGH1OO+6Xs37+B+1tbULvNy6deu0bt16iW997LHHcvXVV6eysjK///3vUygUGqrKJksHNwAAAABAI+vevXuqqqpKx09/+tMlrp89e3ZOOOGEFIvFnHLKKdlss81WUaVNS1P8mwUAAAAAwGrlnXfeSadOnUpfL617+8ILL8xrr72WHj165Nxzz23o8posATcAAAAAQCPr1KnTAgH3krz22mulDu/LL7887du3b8jSmjQBNwAAAADQ/DXxGdzL49JLL83s2bOzwQYbZPr06bn55psXWvPSSy+Vzv/5z3/mX//6V5Jkv/32a1aBeFP8kQIAAAAAsBizZs1KkowZMyaHHXbYUtf/6Ec/Kp2/9dZbzSrg9pBJAAAAAADKkoAbAAAAAKCMDBkyJMVicYnHpx88+fDDD5deX3/99Ruv8AZgRAkAAAAA0Py1SFLR2EUsghbkldLsv321tbW5+eabc+qpp2bgwIHZaKONUlVVlVatWqVr164ZNGhQfv7zn+f9999fpv2efPLJHHHEEVlvvfXSpk2brL322tlzzz1z0003NfAnAQAAAADg05p9B/fw4cMXO2j9vffey9ChQzN06NBcfPHFufHGG7Pnnnsudq/zzjsvP/rRjzJv3n8ebTpp0qTcf//9uf/++/PHP/4xt956a9q0aVPvnwMAAAAAgAU1+w7uJOnevXuOOuqo/OpXv8ptt92WYcOG5Yknnsif//znHHLIIamoqMjkyZOz//775/nnn1/kHldccUXOP//8zJs3LxtuuGGuueaaDB8+PHfccUd23nnnJMldd92Vr33ta6vyowEAAAAArLYKxWKx2NhFNKS6urpUVCx5uM4dd9yRwYMHJ0kGDx6c2267bYHrH3zwQTbYYIPU1NSkR48eefbZZ7PmmmsucI/BgwfnH//4R5KPh7YPGjRoueqsra1NVVVVkjOS6AAHAACofzOTXJSampp06tSpsYsBYBWZn7vVfDvp1Lqxq1lY7ayk6tfx+2kFNfsO7qWF20ly4IEHpnfv3kmSxx57bKHrV199dWpqapIkP/vZzxYIt+ff47e//W3pXhdffPHKlg0AAAAAwFI0+4B7WXXs2DFJMnPmzIWu3XHHHUmSTp065aCDDlrk+7t165bddtstSfLQQw9lypQpDVMoAAAAAABJBNxJktdffz0jR45MkvTp02eBa7Nnz87w4cOTJNtuu21atWq12H0GDhyYJJk1a1aeeeaZhikWAAAAAIAkq3HAPX369Lzxxhv5xS9+kYEDB2bu3LlJkpNPPnmBdaNGjUpdXV2ShcPv//bp66+++mr9FgwAAAAArLjKJnywwlarb9+QIUNyzDHHLPb6GWecka985SsLvDZ+/PjSebdu3Za4f/fu3Uvn77zzzgpWCQAAAADAslitAu7Fqa6uzpVXXpmtttpqoWufnqXdoUOHJe7Tvn370vnUqVOXuHbWrFmZNWtW6eva2tplLRcAAAAAgKxmI0oOPPDAvPjii3nxxRczfPjw3HTTTRk8eHBGjhyZww47LHfeeedC7/n0QyeXNH87SVq3bl06nzFjxhLX/vSnP01VVVXp+HT3NwAAAAAAS7daBdydO3fOZpttls022yxbbbVVvvzlL+e2227LDTfckDFjxuSAAw7IkCFDFnhPmzZtSuezZ89e4v6f7shu27btEtf+8Ic/TE1NTekw0gQAAAAAGlBFEz5YYatVwL04Rx55ZA455JDMmzcv3/72t/PBBx+UrnXs2LF0vrSxI9OmTSudL22cSevWrdOpU6cFDgAAAAAAlp2A+xMHHHBAko9D6nvvvbf0+qcfLPnpB04uyqe7sI0cAQAAAABoWALuT6y11lql83HjxpXOe/XqlYqKj/9/Aq+99toS9/j09U022aSeKwQAAAAA4NME3J+YMGFC6fzT40VatWqVrbfeOkkybNiwJc7hHjp0aJKPx498/vOfb6BKAQAAAIDlVtmED1aYgPsTt9xyS+l88803X+DagQcemCSpra3Nbbfdtsj3jx8/Pg8++GCSZNddd11gdjcAAAAAAPWv2QfcQ4YMycyZM5e45tJLL83dd9+dJOnZs2d23HHHBa4fd9xxqaqqSpKcccYZef/99xe4XldXl29+85upq6tLkpx++un1VT4AAAAAAIvR7BvgzzvvvJx66qk5+OCDs8MOO2TDDTdMhw4dMmXKlLz44ov54x//mCeeeCLJx+NIrrzyytLM7fm6dOmSn/3sZ/n617+ecePG5f/9v/+XM888M5tvvnkmTpyYX/7yl3n44YeTJIcddlgGDRq0qj8mAAAAAMBqp9kH3EnywQcf5KqrrspVV1212DXdunXLtddem912222R10888cRMnDgxP/rRjzJ69Oh87WtfW2jN3nvvnWuvvbbe6gYAAAAA6klFmmYaOrexCyhvTfFHWq/uu+++3HXXXXniiSfy5ptvZtKkSXn//ffTtm3bdO3aNdXV1dl3331z6KGHpl27dkvc6/zzz8+ee+6Z3/zmN3nssccyadKkdO7cOf369csxxxyTww47bBV9KgAAAAAAmn3A3bt37/Tu3Tvf+9736mW/7bbbLtttt1297AUAAAAAwIpr9g+ZBAAAAACgeWr2HdwAAAAAAKlM00xDm2JNZUQHNwAAAAAAZUnADQAAAABAWRJwAwAAAABQlkx4AQAAAACav4pPjqamKdZURnRwAwAAAABQlgTcAAAAAACUJQE3AAAAAABlyQxuAAAAAKD5q0zTTEObYk1lRAc3AAAAAABlScANAAAAAEBZEnADAAAAAFCWTHgBAAAAAJo/M7ibJR3cAAAAAACUJQE3AAAAAABlScANAAAAAEBZMuEFAAAAAGj+WiSpaOwiFkEL8krx7QMAAAAAoCwJuAEAAAAAKEsCbgAAAAAAypIZ3AAAAABA81eZppmGNsWayogObgAAAAAAypKAGwAAAACAsiTgBgAAAACgLAm4AQAAAAAoS0aYAwAAAADNn4dMNks6uAEAAAAAKEsCbgAAAAAAypKAGwAAAACAsmTCCwAAAADQ/FV8cjQ1TbGmMqKDGwAAAACAsiTgBgAAAACgLAm4AQAAAAAoS2ZwAwAAAADNX2WaZhraFGsqIzq4AQAAAAAoSwJuAAAAAADKkoAbAAAAAICyZMILAAAAAND8VaRppqEVjV1AedPBDQAAAABAWRJwAwAAAABQlgTcAAAAAACUpaY4dQYAAAAAoH5VpmmmoU2xpjKigxsAAAAAgLIk4AYAAAAAoCwJuAEAAAAAKEsmvAAAAAAAzV/FJ0dT0xRrKiM6uAEAAAAAKEsCbgAAAAAAypKAGwAAAACAsmQGNwAAAADQ/FWmaaahTbGmMqKDGwAAAACAsiTgBgAAAACgLAm4AQAAAAAoSya8AAAAAADNnxnczZIObgAAAAAAypKAGwAAAACAsiTgBgAAAACgLJnwAgAAAAA0fy2SVDR2EYugBXml+PYBAAAAAFCWBNwAAAAAAJQlATcAAAAAAGXJDG4AAAAAoPmrTNNMQ5tiTWVEBzcAAAAAAGVJwA0AAAAAQFkScAMAAAAAUJZMeAEAAAAAmj8zuJslHdwAAAAAAJQlATcAAAAAAGVJwA0AAAAAQFky4QUAAAAAaP4qPjmamqZYUxnRwQ0AAAAAQFkScAMAAAAAUJYE3AAAAAAAlCUzuAEAAACA5q8yTTMNbYo1lREd3AAAAAAAlCUBNwAAAAAAZUnADQAAAABAWTLhBQAAAABo/irSNNPQisYuoLzp4AYAAAAAoCwJuAEAAAAAKEsCbgAAAAAAylJTnDoDAAAAAFC/KtM009CmWFMZ0cENAAAAAEBZEnADAAAAAFCWBNwAAAAAAJQlE14AAAAAgOav4pOjqWmKNZURHdwAAAAAAJQlATcAAAAAAGVJwA0AAAAAQFkygxsAAAAAaP4q0zTT0KZYUxnRwQ0AAAAAQFkScAMAAAAAUJYE3AAAAAAAlCUTXgAAAACA5q8iTTMNrWjsAsqbDm4AAAAAAMqSgBsAAAAAgLIk4AYAAAAAoCw1xakzAAAAAAD1qyJNc951U6ypjOjgBgAAAACgLAm4AQAAAAAoSwJuAAAAAADKkhncAAAAAEDzV5mmmYY2xZrKiA5uAAAAAADKkoAbAAAAAICyJOAGAAAAAKAsmfACAAAAADR/ZnA3Szq4AQAAAAAoSwJuAAAAAADKkoAbAAAAAICyZMILAAAAAND8mcHdLOngBgAAAACgLAm4AQAAAAAoSwJuAAAAAADKkgkvAAAAAECzV2yRFCsau4qFFbUgrxTfPgAAAAAAypKAGwAAAACAsiTgBgAAAACgLAm4AQAAAAAoSx4yCQAAAAA0e3WVHx9NTVOsqZzo4AYAAAAAoCwJuAEAAAAAKEsCbgAAAAAAypIJLwAAAABAs2cGd/OkgxsAAAAAgLK0WgTczzzzTC644ILsscce6datW1q3bp0OHTqkV69eOeaYY/L4448vdY8hQ4akUCgs0zFkyJCG/1AAAAAAAKu5Zt8Av9NOO+Wxxx5b6PXZs2fnjTfeyBtvvJEhQ4bkqKOOylVXXZVWrVo1QpUAAAAAACyvZh9wT5w4MUmyzjrr5JBDDsmOO+6YHj16pK6uLsOGDcsll1ySCRMm5IYbbsicOXPypz/9aal73nfffVlnnXUWe71bt271Vj8AAAAAsPLmVhQyt6LQ2GUsZG5FMUmxscsoW80+4O7Tp08uvPDCHHzwwamoqFjg2jbbbJMjjzwy22+/fUaNGpWbbropX//617PTTjstcc9evXpl/fXXb8CqAQAAAABYmmY/g/vOO+/MoYceulC4Pd+aa66ZSy65pPT1rbfeuqpKAwAAAABgJTT7gHtZ7LzzzqXz0aNHN2IlAAAAAAAsq2Y/omRZzJo1q3S+uE5vAAAAAKB81VVWpq6y6c3grqssJpnT2GUsZOrUqTnssMNSXV2dAQMGZLvttkvXrl0bu6yF6OBOMnTo0NL5JptsstT1xxxzTNZZZ520atUqa665ZrbZZpucddZZmTBhQkOWCQAAAACwSvztb3/L3XffnZ/85Cc56KCDMnLkyMYuaZFW+4B73rx5ueiii0pfH3rooUt9zyOPPJJ33303c+bMyfvvv5//+7//y09+8pNstNFGueKKK5bpvrNmzUptbe0CBwAAAABAU3DHHXeUznfffffssccejVfMEqz2I0ouvfTSDB8+PEly0EEHZcCAAYtdu8EGG+Sggw7Ktttum+7duydJxowZk7/+9a+59dZbM3PmzHz9619PoVDICSecsMT7/vSnP835559ffx8EAAAAAKCePPbYYykWi0mSU089dZnfN2PGjNx4440ZNmxYZsyYkc9+9rPZcssts//++6dz5871XmehOL/K1dDQoUOz2267Ze7cuenatWtefPHFxc6RqampSadOnVIoLHpOz5133pmDDjooc+bMSbt27TJ69Oisvfbai733rFmzFpj9XVtb+0lofkaSNivzsQAAAFikmUkuKv3vOwBWD7W1tamqqsrYmjbp1KnpzeCurS1m/aqZTer301tvvZWNNtooxWIxPXr0yNixY5fpfePHj88uu+ySN998c6FrrVu3zumnn54zzzwzrVu3rrdaV9sRJS+//HIGDx6cuXPnpk2bNrnllluWOCS9qqpqseF2kuy7774555xzkiTTp0/PNddcs8T7t27dOp06dVrgAAAAAABobKNGjSqd77bbbsv8viOOOCKjR49OoVBYKEudPXt2fvzjH2f33XfPtGnT6q3W1TLgfuutt7LHHnvkww8/TEVFRW6++ebstNNOK73vCSecUPrBffrBlQAAAAAA5WLcuHGl82222WaZ3vPQQw8tMNYkSQqFQjp06FDKTAuFQh5//PEcdNBB9VbrahdwT5w4MbvttlsmTpyYQqGQa6+9NgcccEC97N21a9d85jOfSZJMmDChXvYEAAAAAFiVamtrS+frrLPOMr3nqquuSvJxiF0sFvONb3wjH330UWpra/Puu+/mvPPOS6tWrVIoFPLAAw/kN7/5Tb3UuloF3JMnT87uu++eMWPGJEkuv/zyHHXUUfV6jyWNMQEAAAAAGse8VKSuCR7zUtHY35qFzJ49u3ReVVW1TOvvuuuuUvf2xhtvnMsvvzwdO3ZM8nFj8Nlnn52//OUvpfz0Jz/5SebOnbvSta42AXdNTU323HPPvPLKK0mSiy66KN/61rfq9R7vvfdeJk+enGTZ/7IBAAAAANCUfPp5gcsyL3vo0KGZPn16kqRYLOZrX/vaIhuB99tvv3z1q19NoVDIv/71r9x+++0rXetqEXBPnz49++yzT5577rkkyZlnnpkf/OAH9X6fK6+8svRXioEDB9b7/gAAAAAADW3+GOYkpWkYS/LAAw8k+c90iwMPPHCxa7/zne+Uzh966KEVrPA/mn3APXv27AwePDhPPPFEkuSkk07Kj3/84+XaY+zYsRkxYsQS19x555254IILkiRt27bNMcccs2IFAwAAAAA0oi222KJ0/vjjjy91/QMPPFBq/O3evXt69+692LXV1dVZY401kiTDhw9fyUqTypXeoYk77LDDcv/99ydJdtlllxx77LF56aWXFru+VatW6dWr1wKvjR07NjvvvHO23Xbb7LfffunXr1+6du2a5OO/YNx666259dZbSz/E//3f/826667bQJ8IAAAAAFhec1ORuWl6z8+bm2Jjl7CQTTfdNJ06dUpNTU3uuOOOfPDBB+nSpcsi106cODEvvPBCko/HkwwaNGip+2+88cYZPnx43n333ZWutdkH3Lfddlvp/J///OcCf31YlPXWWy9jx45d5LVhw4Zl2LBhi31vu3btcumll+aEE05YoVoBAAAAAJqCQw89NFdffXWmT5+e733vexkyZMgi111//fVJPh5PUiwWs+eeey517/kPn/zwww9Xus5mH3DXhwEDBuTGG2/MsGHD8swzz+Tdd9/N5MmTM3fu3KyxxhrZdNNNs+uuu+a4444rdXYDAAAAAJSrb37zm7nmmmtSLBZzww03ZM0118xFF12Uysr/RMqjRo3KJZdckmKxmEKhkJYtW2bvvfde6t5TpkxJkrRosfITtAvF+XM1aFS1tbWpqqpKckaSNo1dDgAAQDM0M8lFqampSadOnRq7GABWkfm526iaTunYqemNKJlSW0yvqtom+fvpO9/5Tn7729+WRjP36NEj++67b9Zaa62MHj06t956a2bOnFnq3j7kkEPy5z//ean7rrvuunn33Xez1lprZdKkSStVow5uAAAAAKDZq0tF6rLyHcP1rS7zGruExbrooovy9NNP5+mnn06xWMw777yT3/72t6XrhUIhhcLHfzRo0aJFzjjjjKXuOX78+PzrX/9Kkqy//vorXWPT+4kCAAAAANDo2rdvn/vvvz+DBg0qdWl/WrFYLL32gx/8IP3791/qnnfeeWfpvHfv3itdo4AbAAAAAIBF6tSpUx566KFcc801GTBgQKlre/7RtWvX/OpXv8pPfvKTZdrvT3/6U+n8//2//7fS9RlRAgAAAADAEh199NE5+uij8+9//zvjxo1LbW1tPvvZz2bTTTctjSlZmuHDh+eJJ54ofT1o0KCVrkvADQAAAAA0e013BnfTe/DlknTt2jVdu3Zdofe2adMmZ511Vp577rl8+OGH2XTTTVe6HgE3AAAAAMBqoK6uLhUVFY12/y222CJbbLFFve4p4AYAAAAAWA107Ngxffv2Tf/+/VNdXZ3+/funX79+ad++fWOXtsIE3AAAAAAAq4HZs2dnxIgRGTFiRIrFYpKkUChkww03THV1dSn0rq6uzuc+97lGrnbZCLgBAAAAgGbPDO7k6aefzgsvvJDnn3++dHz44YcZPXp03nzzzdx6662ltV27dk2/fv0WCL179+69zA+UXFUE3AAAAAAAq4H+/funf//+C7w2YcKEPPjggznllFNSU1NTev29997Lgw8+mAceeKD0Wrt27bLZZpuluro6v//971dZ3Usi4AYAAAAAWE3NnTs3//M//5MZM2bkpJNOyh577JE111wzkydPzjPPPJO//vWveeGFF5IkM2bMyNNPP53hw4cLuAEAAAAAaFxnnnlm3n333VxzzTU55phjFrj2hS98IWeddVYefPDBfP3rX8+YMWNy/PHHp02bNo1U7cIE3AAAAABAs2cG96I99NBDqayszOGHH77YNbvttluGDRuWbbbZJjfffHNeeeWVVVjhkjW9nygAAAAAAKtETU1NWrdunVatWi1x3VprrZXLLrssU6dOzc9+9rNVVN3SCbgBAAAAAFZTPXv2zLRp0/L8888vde3ee++dVq1a5cEHH1wFlS0bATcAAAAAwGrqi1/8YgqFQk477bTMmzdvqetbtGiRt99+exVUtmwE3AAAAABAs1eXisxtgkddKhr1+3LaaaelW7dueeihh7L//vvn3XffXeza+++/PzNnzkyh0Lhzwz9NwA0AAAAAsJrq2LFj7rnnnqyzzjq5++6706tXrxx//PG5//7789FHHyVJJk2alGuvvTZHHnlkisVittpqq8Yt+lMqG7sAAAAAAAAaT9++ffPcc8/lqKOOygMPPJBrrrkm11xzTZKPR5LMH11SKBTSqlWrnH/++Y1Z7gJ0cAMAAAAArOa6du2ae++9Nw899FC++MUvplOnTikUCikWiykUCikUCunXr1/uu+++7Ljjjo1dbokObgAAAACg2atLZeqaYL9vXZb+YMdFqa2tzd13352nn346zzzzTCZMmJD33nsvM2bMSOfOndO3b9/svffeOfbYY/OZz3xmmfcdNGhQBg0alLq6urz11luZNGlSisVievbsmXXXXXeFam1IhWKxWGzsIvj4X8iqqqokZyRp09jlAAAANEMzk1yUmpqadOrUqbGLAWAVmZ+7PVXTMx06Nb2Ae2rtvGxT9dZy/3568MEHs/vuuy913Zprrpkbb7wxe+6556LvP3VqOnTosMz3bWp0cAMAAAAAlKHu3btn5513zoABA9K9e/d87nOfy7x58zJ+/Pjceuutue222zJ58uTsv//+GT58ePr167fQHlVVVVl//fVTXV2d6urq9OvXL9XV1enRo0cjfKLlp4O7idDBDQAA0NB0cAOsjpprB3ddXV0qKiqWuOaOO+7I4MGDkySDBw/ObbfdttCaysrKzI+IPx0Vd+7cOf369Ssd1dXV2XTTTdOqVatlrnFV0MENAAAAADR7dWmRuiw5EG4MdSv4vqWF20ly4IEHpnfv3nn99dfz2GOPLXLNmDFjMnLkyIwYMaJ0jB8/PjU1NXn00UczdOjQ0trKysr06dMnW2yxRW688cYVrLx+CbgBAAAAAJqpjh07Jklmzpy5yOs9evRIjx49sv/++5de++CDDzJixIgMHTo0d9xxR15++eUkH3eNv/zyy3nppZcE3AAAAAAANJzXX389I0eOTJL06dNnmd/XpUuX7Lrrrtl1111zwQUX5P7778/xxx+fDz74IGeeeWZqamoaqOLlJ+AGAAAAAGgmpk+fngkTJuQf//hHfv7zn2fu3LlJkpNPPnmF99xjjz0yYsSIbLPNNrn++utLoXlTIOAGAAAAAJq9ulQ0qxncnzZkyJAcc8wxi71+xhln5Ctf+cpK3aNLly65/PLLs/fee+eyyy7L6aefvlL71Zem99hQAAAAAIDVTG1t7QLHrFmzVnrP6urqDB8+PD/96U9TKBRWer899tgjrVu3zk033bTSe9UXATcAAAAAQCPr3r17qqqqSsdPf/rTZX7vgQcemBdffDEvvvhihg8fnptuuimDBw/OyJEjc9hhh+XOO++slxoLhUJatmyZUaNG1ct+9cGIEgAAAACARvbOO++kU6dOpa9bt269zO/t3LlzOnfuXPp6q622ype//OX84Q9/yFe/+tUccMABueaaa3L00Ucv9N6pU6emQ4cOy3Sft99+Oy1atMiUKVOWubaGJuAGAAAAAJq9uanI3CY4g3vuJ//s1KnTAgF3fTjyyCNz55135i9/+Uu+/e1vZ//990+XLl0WWFNVVZX1118/1dXVqa6uTr9+/VJdXZ0ePXostF+PHj3y4YcfZty4cfVa58oQcAMAAAAANFMHHHBA/vKXv2TatGm59957F3rYZKFQyNixYzN27Njcfvvtpdc7d+6cfv36lY7q6upsuummadWqVdZbb71V/TEWS8ANAAAAANBMrbXWWqXzRXVev/XWW3n++eczcuTI0j/feuut1NTU5NFHH83QoUNLaysrK9OnT59sscUWufHGG5Mkc+bMScuWLRv+gyyGgBsAAAAAoJmaMGFC6XxRs7a7d++e7t27Z9999y29Nm3atDz//POlY+TIkXnppZcyY8aMvPzyy3nppZdKAXf//v1TV1eXfv365eabb274D/RfBNwAAAAAQLM3L5Wpa4IzuOel0KD733LLLaXzzTfffJne0759+2y33XbZbrvtSq8Vi8WMGjWqFHj/9+uvv/66gBsAAAAAgKUbMmRIvvzlL6dNmzaLXXPppZfm7rvvTpL07NkzO+644wrfr1AopHfv3undu3cOPfTQ0ut///vf8+yzzy4Qeq9KhWKxWGyUO7OA2traVFVVJTkjyeL/pQQAAGBFzUxyUWpqatKpU6fGLgaAVWR+7vZgzeZp36npdXBPq63LblUvLvfvp/XXXz9TpkzJwQcfnB122CEbbrhhOnTokClTpuTFF1/MH//4xzzxxBNJklatWuWuu+7Kbrvt1lAfo9Ho4AYAAAAAKEMffPBBrrrqqlx11VWLXdOtW7dce+21Sw23H3300VxzzTUZMWJEpk6dmi5dumTzzTfP7rvvnsGDB6dt27b1XX69EHADAAAAAM1eXSqa5AzuuhV833333Ze77rorTzzxRN58881MmjQp77//ftq2bZuuXbumuro6++67bw499NC0a9duiXudccYZ+fnPf57k41EkSTJu3Lg899xzuf7667PGGmvk3HPPzXe/+90VrLbhCLgBAAAAAMrM/HnY3/ve91Zqn1tvvTUXX3xxCoVCtttuu3zhC1/I2muvnSlTpuSll17K3XffnUmTJuXkk0/OU089leuvvz4tW7asp0+x8gTcAAAAAACrqV//+tcpFos5/fTT87Of/Wyh68ViMddff31OOeWU3HzzzenYsWOuuOKKRqh00Vo0dgEAAAAAADSOkSNHJknOPPPMRV4vFAo5+uij83//93/p0qVLrrrqqjz22GOrsMIlE3ADAAAAAM3e/BncTfFoTHPnzk1VVVU6deq0xHW9evXKL37xixQKhVx55ZWrqLqlE3ADAAAAAKymunXrlpqamnz44YdLXfulL30phUIhTz755CqobNkIuAEAAAAAVlO77rprkuT3v//9Ute2bt06HTt2zMSJExu6rGUm4AYAAAAAWE2deOKJadmyZS644ILcd999S1w7adKk1NbWpkOHDquouqUTcAMAAAAAzV5dWjT6rO1FH40b0W6xxRY555xzMnv27Oy333457bTTMmnSpIXWzZo1KyeffHKKxWK23377Rqh00SobuwAAAAAAABrPmWeembq6upx//vn5xS9+kcsuuywDBgzIZpttlqqqqkyaNClDhw7N+PHjU1FRkTPOOKOxSy4RcAMAAAAArObOOeecbL/99jnzzDPz9NNPZ/jw4fm///u/0vVCoZA2bdrkqquuyjbbbNOIlS5IwA0AAAAAQHbdddfsuuuuee211/LPf/4zI0eOzOTJk9OqVatsscUWOeqoo9KtW7fGLnMBAm4AAAAAoNmbm4rMTUVjl7GQuSk2dgkL6dOnT/r06dPYZSwTD5kEAAAAAFhNTZ06tbFLWCk6uAEAAAAAVlNVVVVZf/31U11dnerq6vTr1y/V1dXp0aNHY5e2TATcAAAAAACrqUKhkLFjx2bs2LG5/fbbS6937tw5/fr1Kx3V1dXZdNNN06pVq0asdmECbgAAAACg2atLZeqaYBxa18j3HzNmTEaOHJkRI0aUjvHjx6empiaPPvpohg4dWlpbWVmZPn36ZIsttsiNN97YiFX/R9P7iQIAAAAAsEr06NEjPXr0yP7771967YMPPsiIESMydOjQ3HHHHXn55ZeTJHV1dXn55Zfz0ksvCbgBAAAAAGh6unTpkl133TW77rprLrjggtx///05/vjj88EHH+TMM89MTU1NY5dYIuAGAAAAAGCx9thjj4wYMSLbbLNNrr/++owcObKxSypp0dgFAAAAAAA0tHmpSF0TPOalorG/NcukS5cuufzyyzNq1KhcdtlljV1OiYAbAAAAAICl2mOPPdK6devcdNNNjV1KiYAbAAAAAIClKhQKadmyZUaNGtXYpZQIuAEAAAAAWKq33347LVq0yPTp0xu7lBIPmQQAAAAAmr35M6+bmroUG7uEZdajR498+OGHGTduXGOXUqKDGwAAAACAZbbeeus1dgklAm4AAAAAAMqSgBsAAAAAgLIk4AYAAAAAoCx5yCQAAAAA0OzNTYvMbYIPmZybeY1dQlnTwQ0AAAAAQFkScAMAAAAAUJYE3AAAAAAAlCUzuAEAAACAZq8ulalrgnFoXYqNXUJZ08ENAAAAAEBZEnADAAAAAFCWBNwAAAAAAJSlpjd0BgAAAACgntWlInWpaOwyFlKXeY1dQlkTcAMAAAAAsERjxozJo48+mmeffTavvvpqxo0bl8mTJ2fatGlJkvbt22fNNdfMeuutl759+2bAgAEZOHBg1l9//QatS8ANAAAAAMBCXnzxxdx444257bbbMnr06EWuKRQKSZKamprU1NRk9OjR+ec//1m6vvHGG+fggw/O4Ycfnk033bTeazSDGwAAAACAJMm8efPy5z//Odtuu2369euXiy++uBRuFwqFhY75FnftjTfeyEUXXZTNN98822+/ff785z9n3rz6G8uigxsAAAAAaPbM4F6yYrGYP/3pT7ngggvyxhtvJMkCAXZFRUX69OmTzTbbLBtttFHWXXfdrLHGGmnTpk0KhUJmzJiRDz/8MBMnTszo0aPz8ssv55VXXsncuXNLewwbNizDhg3Lueeem7PPPjtf+cpXFrjHihBwAwAAAACsxp588sl85zvfyYgRI5L8J9ju2bNnDjzwwOy5557Zfvvt065du+Xad8aMGRk2bFjuvffe3HHHHaVO8FGjRuXII4/ML37xi1x22WXZfvvtV7j2QrFYLK7wu6k3tbW1qaqqSnJGkjaNXQ4AAEAzNDPJRampqUmnTp0auxgAVpH5uduVNfunXaeWjV3OQqbXzskJVX9v1N9PFRUVKRaLKRQKad++fQ477LAce+yx2Xrrrev1Pk8//XSuvfba/PGPfyw9nLJYLK7UyBIzuAEAAAAAVnNrrbVWfvzjH+ftt9/OFVdcUe/hdpJstdVW+d3vfpe33347P/nJT7LWWmut9J5GlAAAAAAAzV5dKjLXDO5FOvfcc3Pqqaemffv2q+R+nTt3zhlnnJHvfOc7ueSSS1ZqLwE3AAAAAMBq7JxzzmmU+7Zv336l721ECQAAAAAAZUnADQAAAABAWTKiBAAAAABo9upSmbomGIfWpdho9/73v/+d/fffP9XV1RkwYECOP/74xa6dNm1annnmmUyYMCGzZs3KWmutlbXXXjubbLLJKpvdvSgN9hOdMGFCXnnllYwbNy7vvfdepk2bluTjuSprrbVW1ltvvfTt2zfrrrtuQ5UAAAAAAMBi/OUvf8nTTz+d4cOH59BDD11kwP3666/n7LPPzh133JG5c+cudL1Vq1bZcccds99+++VrX/taOnTosCpKL6m3gPvDDz/M3/72t9x333155JFH8u9//3uZ3rfWWmtl4MCB2XPPPXPAAQfkM5/5TH2VBAAAAADAYjz88MOl8xNPPHGh63feeWcOPfTQzJw5M4VCofR6oVBIsfhx5/ns2bPz0EMP5aGHHsr555+fk046KaeddlratWvX8B8gSaE4v5IVdM899+SKK67Ivffemzlz5iRJlnfL+d+cysrK7LnnnjnxxBOzzz77rExZZae2tjZVVVVJzkjSprHLAQAAaIZmJrkoNTU16dSpU2MXA8AqMj93+3XNIWnbqWVjl7OQGbVz8u2qWxrl99Mmm2ySUaNGpXXr1pkyZUoqKipK18aNG5e+fftmxowZSf6T4Xbu3Dmf+cxnMmXKlLz//vupq6srvWd+LtyrV6/ccsst2XzzzRv8M6xQB/e8efNy/fXX56KLLsqbb76ZZNGhduvWrbPOOutkjTXWSNu2bVMsFjNjxox8+OGHmThxYmbPnr3Ae+fMmZO77rord911VzbYYIOcccYZOfrooxf4xgIAAAAALK+6tEhdml7OWJe6pS9qIO+++26KxWJ69+69UAb785//PDNnzkzycbh97LHH5rTTTkuvXr1Ka4rFYp577rk8+OCDGTJkSEaNGpVisZg33ngj22yzTW688cYMHjy4QT/Dcndw33LLLfmf//mfjBkzJsl/wunWrVtn++23z8CBA7PVVltl8803X+p87QkTJuTFF1/MM888k6FDh+aJJ55Y4JuWJOuvv35++tOf5tBDD13uD1dOdHADAAA0NB3cAKuj+bnbr2q+lLadWjV2OQuZUTs7J1X9uVF+P7Vq1Spz587NLrvskgcffHCBaxtvvHHGjBmTYrGYM888Mz/60Y+Wut/f//73fPe7380777xT2v/BBx/M9ttv3yD1J8sZcO+000554oknknwcbFdWVmafffbJV77yley9994r/bTM6dOn55577smf/vSn3HnnnaWRJ4VCITvssEOGDh26Uvs3ZQJuAACAhibgBlgdCbgXr1OnTpk6dWp23HHHhbLXdu3aZebMmenYsWP+/e9/p3Xr1su0Z21tbY466qj84x//SPLxuJIXX3wxlZX19jjIBbRYnsWPP/54isViPvOZz+Tcc8/N+PHjc/vtt+eQQw5Z6XA7+fibdvDBB+evf/1rxo8fn/PPPz9du3ZNsVjM448/vtL7AwAAAADwsc985jNJPp608d/m571bbLHFMofbyceh+S233JIBAwYkSV5//fXcdNNN9VDtoi1XwL3mmmvmkksuydtvv51zzz03Xbt2bai6stZaa+Xss8/OuHHjcskll5S+2QAAAAAAy6suFU32aCybbrppCoVCxowZk4kTJy5wrXv37iu8b8uWLXPxxReXvr711ltXeK+lWa6Ae8yYMTnllFPSps2qG6HRunXrnHLKKaWZ3wAAAAAArLxBgwaVzq+88soFru25554pFAp54403spyPcUySDBw4MGuvvXaSZOTIkStT5hItV8DdoUOHhqqjSd8bAAAAAKC5OfLII9Oq1cdzyf/3f/83r7/+eunaUUcdlRYtWuTf//537r333hXaf+ONN06hUMi///3veql3UZYr4AYAAAAAoHn47Gc/mxNPPDGFQiHTp0/PnnvumVdffTVJsskmm+TrX/96CoVCvvvd72bq1KnLvf/s2bOTpNTJ3RAE3AAAAABAs9fYc7ab4gzuJPnZz36WzTffPEny9ttvZ8CAATnrrLPyr3/9KxdddFG22mqrjB49OnvuuWc++OCDZd53ypQpef7551MsFrPllls2VPkNE3CPHDky1113XS699NL87ne/y5133pk333yzIW4FAAAAAMAKat26dR544IFsvfXWKRQKmTlzZi688MKsu+66GThwYDbaaKNUVlZm2LBh6devX2666aZl2vfkk0/OzJkzS+cNpbI+N3v++edzzDHH5Pnnn1/k9Q4dOmSLLbZIdXV16dh8881Lc14AAAAAAFi11lprrQwdOjQXXnhhLr744syaNStJ8txzz+W5555LkhQKhUycODGHH354zjzzzBx++OHZfvvts/nmm6dLly5p27Zt3n///Tz11FO55JJLMnTo0CTJGWeckR133LHBai8UV+QRmIvw1ltvZcCAAampqVniUzULhcICX1dUVKRPnz4LhN7V1dXp0qVLfZRVNmpra1NVVZXkjCRtGrscAACAZmhmkotSU1OTTp06NXYxAKwi83O3i2uOTNtOTa/Rdkbt7Jxe9Ycm8/tpwoQJufbaa3Pddddl3LhxpdeXN0ZeY4018qMf/Sjf/OY367vEBdRbwH3cccfl2muvTaFQSLFYTMeOHbP55punU6dOefvtt/Pmm2+WhoovVMR/hd6FQiFz586tj7LKhoAbAACgoQm4AVZH83O3i2qOTpsmGHDPrJ2dM6qGNMnfT6NHj87QoUMzfPjwjBgxIi+99FJp7Mh88+Pl/85427Rpk969e6d3797p06dPzj333Aapsd4C7vXWWy/jx49Pkuy7774ZMmRI1lhjjdL1uXPn5pVXXsnzzz+fkSNHZuTIkXn++ecXOZi8UCikrq6uPsoqGwJuAACAhibgBlgdCbjrT7FYzGuvvZYRI0aUMt6RI0fm/fffX2jdf5s3b16D1FRvAXfr1q0zZ86cdOjQIe+8884nYe3SvfPOOwuE3iNHjszYsWN1cAMAAFDPBNwAqyMBd8ObMGFCKdudH36PHTu2FHQXi8UGC7jr7SGTHTp0yEcffZRNN910mcPtJOnevXu6d++efffdt/Ta1KlT66ssAAAAAAAa0Lrrrpt11103++yzT+m1KVOmLNDU3FDqLeDeYostMnTo0IVmrayIDh061ENFAAAAAAAfq0tl6uovDq03dWmYzubG1rFjx+y4447ZcccdG/Q+Lepro8GDBydJXn755cU+TBIAAAAAAOpLvQXcxx57bNZee+1MnTo1Q4YMqa9tAQAAAABgkeot4G7fvn3+8Ic/pFAo5IwzzsioUaPqa2sAAAAAAFhIvQ6d2XXXXXPJJZfke9/7Xnbeeefcfvvt2XrrrevzFgAAAAAAy60uFalLRWOXsZCmWFOSDB06dIGvBw4c2EiVLFm9T1U/6aST8rnPfS6HH354tt9++xx33HH5zne+k759+9b3rQAAAAAAaAC77LJLisVi6et585rmwzDrNeCeMmVKzjrrrNxwww2ZN29eisVirrzyylx55ZVZb731st1226V///6lY4011qjP2wMAAAAAUI8KhcICQXdTU28B98yZMzNw4MA8//zzKRaLKRQKKRQKSZJisZhx48Zl3Lhxuemmm0rv6d69+wKBd//+/dOtW7f6KgkAAAAAgBU0P99tyuot4L744oszcuTIUrD936n+olL+d955J++8807+/ve/l17r0qVLttxyy9x33331VRoAAAAAsJqrS4smOe+6Li0au4RFuu666xq7hGVSbwH3n//859J5jx498otf/CKDBg1KVVVVJkyYkJdeeikjRozIiBEjMnLkyIwZM2aRoff777+fBx98sL7KAgAAAABgOR111FGNXcIyqbeAe/To0UmSioqK/P3vf8/mm29euta9e/d07949e+21V+m1KVOm5Pnnny8F3iNGjMgrr7yS2bNn11dJAAAAAAA0Y/UWcLdr1y6zZ89O7969Fwi3F6djx47ZYYcdssMOO5Remzt3bl5++eWMHDmyvsoCAAAAAKCZqreAe/3118/IkSOz5pprrngxlZXp169f+vXrV19lAQAAAABkbioytwnO4G6KNZWTeptgvvfee6dYLOaNN96ory0BAAAAAGCx6i3gPuaYY9KyZcu8++67GTp0aH1tCwAAAAAAi1RvAfcGG2yQs846K8ViMaecckpmzpxZX1uvtGeeeSYXXHBB9thjj3Tr1i2tW7dOhw4d0qtXrxxzzDF5/PHHl2u/e+65J4MHDy7t1a1btwwePDj33HNPA30CAAAAAAD+W6FYLBbra7O6uroceeSRufnmm7PXXnvllltuSbt27epr+xWy00475bHHHlvquqOOOipXXXVVWrVqtdg18+bNywknnJBrrrlmsWuOO+64XHHFFWnRYvn+dlBbW5uqqqokZyRps1zvBQAAYFnMTHJRampq0qlTp8YuBoBVZH7udkbNyWnTqXVjl7OQmbWzclHVL/1+WkH11sHdoUOHbLvttmnZsmW6dOmSe++9NwMGDMi9995bX7dYIRMnTkySrLPOOjnppJNy6623Zvjw4Rk2bFh+8YtfZN11102S3HDDDTn66KOXuNeZZ55ZCrf79++fm266KcOHD89NN92U/v37J0muvvrqnHXWWQ33gQAAAAAASFKPHdwtWrRIoVAofT1/20KhkA022CB77bVXtttuu/Tv3z+9e/euj1suk3333TdHHXVUDj744FRULPxE0smTJ2f77bfPqFGjkiRDhw7NTjvttNC6UaNGZdNNN83cuXPz+c9/Po8++mjatm1buj59+vQMHDgwzzzzTCorK/Pqq69mo402WuY6dXADAAA0NB3cAKsjHdzNW711cCcfh9rzj0KhkEKhkGKxmDFjxuQ3v/lNDj/88PTt2zcdO3bM9ttvn29/+9u55ppr8txzz2X27Nn1WUrJnXfemUMPPXSR4XaSrLnmmrnkkktKX996662LXPfLX/4yc+fOTZJcfvnlC4TbSdKuXbtcfvnlSZK5c+fm0ksvrY/yAQAAAABYjMr62ujVV1/NyJEj8/zzz5f++e677yb5Tzf3fNOmTctTTz2Vp5566j+FVFZmk002Sf/+/dO/f/9897vfra/SlmrnnXcunY8ePXqh68ViMX/729+SJH369Mk222yzyH222Wab9O7dO6+//nr+9re/5de//vUCXe0AAAAAQOOYl4rUZdFNsI1pXhOsKUkGDx6cfv36pbq6Ov369UvPnj0bu6RFqreAu3fv3undu3e+9KUvlV577733Fgq9X3/99cydO3eh0HvOnDl58cUX8+KLL+aGG25YpQH3rFmzSueL6vR+6623SrO8Bw4cuMS9Bg4cmNdffz0TJkzI2LFjm+wPHgAAAABgcf7+97+Xmn6TpKqqKltsscUCofdmm22W1q0bd+xLvQXci7LWWmtl9913z+677156bdasWXnppZcWCL1feOGF1NTULDC3e1UaOnRo6XyTTTZZ6Porr7xSOu/Tp88S9/r09VdffVXADQAAAACUpfk5bbFYTE1NTR577LE89thjpeuVlZXp3bv3AqF3dXV11lprrVVWY4MG3IvSunXrDBgwIAMGDFjg9bfeemuB0HtVmTdvXi666KLS14ceeuhCa8aPH18679at2xL36969e+n8nXfeWey6WbNmLdA5Xltbu0z1AgAAAAA0tEsvvTQjRozIiBEj8uqrr5aeTzhfsVjM3Llz8/LLL+fll1/On/70p9K1z33ucwuF3r17926QOld5wL04PXv2TM+ePXPggQeu0vteeumlGT58eJLkoIMOWih4T5IpU6aUzjt06LDE/dq3b186nzp16mLX/fSnP83555+/0Otn5KK0WWrVAADNx3k5r7FLAABgNVDXRGdwN8WakiwwQnr27Nl56aWXSoH3iBEj8sILL2T69OkLvKdYLKZQKORf//pX3n333dx7772la/PmzWuQOptMwN0Yhg4dmjPOOCNJ0rVr1/zud79b5LqZM2eWzlu1arXEPT89c2bGjBmLXffDH/4w3/ve90pf19bWLtD9DQAAAADQFLRq1SpbbrllttxyywVef/311zNixIg8+eSTueeeezJmzJhFjqH+7+cx1qfVNuB++eWXM3jw4MydOzdt2rTJLbfckq5duy5ybZs2/+mpnj179hL3/fTYkbZt2y52XevWrRt9ADsAAAAAwIrq3bt3evfunS9/+ctJknvvvTennHJKRo0alTXWWCP77LNPXn311QWecVjfWjTYzk3YW2+9lT322CMffvhhKioqcvPNN2ennXZa7PqOHTuWzpc0diRJpk2bVjpf2jgTAAAAAIDm4gtf+EKeffbZfOELX8gHH3yQOXPmZNiwYUvNVFfGcgXcF1xwwQIB7qoybdq0XHDBBfWy18SJE7Pbbrtl4sSJKRQKufbaa3PAAQcs8T2ffrDkpx84uSiffrCkkSMAAAAA0DTUpUVpDnfTOppXD3K7du3y17/+NdXV1bn55ptz4YUXNuj9luu7d95552XDDTfMRRddlI8++qiBSvqPjz76KD/96U+zwQYbLPKBjMtr8uTJ2X333TNmzJgkyeWXX56jjjpqqe/r27dv6fy1115b4tpPX99kk01WsFIAAAAAgPLUpk2bXHrppSkUCvnxj39cymMbwnL/eeC9997LmWeemR49euQb3/hGnn766Xovavjw4TnxxBPTo0ePnHXWWXnvvfdWes+amprsueeepXkvF110Ub71rW8t03t79uyZddZZJ8nHD6ZckkcffTRJsu6662b99ddf8YIBAAAAAMrUwIED87nPfS5z5szJdddd12D3Wa6A+7HHHkt1dXWKxWKmTp2aK6+8Mttss0169eqV73//+/nnP/+ZGTNmLHcR06dPzwMPPJDTTjstG220UbbddttcffXVmTp1aorFYvr375/HHntsuff99P777LNPnnvuuSTJmWeemR/84AfL/P5CoVAaY/Laa6/lqaeeWuS6p556qtTBfcABByzwpFAAAAAAgNXJxhtvnEKhkLvvvrvB7lEoFovF5XlDsVjMn/70p/zoRz/KqFGjPt7kU0FuZWVl+vbtm0033TQbbrhh1llnnayxxhpp27ZtisViZs6cmQ8//DATJkzIm2++mZdeeimvvfZa6urqFrhH8vE34JxzzslXvvKVFQ6LZ8+enf322y/3339/kuSkk07KL3/5y+XeZ9SoUenbt2/q6ury+c9/Po8++mjatm1buj5jxozstNNOeeaZZ1JZWZlXXnklG2+88TLvX1tbm6qqqpyRpM1yVwcAUL7Oy3mNXQKw2piZ5KLU1NSkU6dOjV0MAKvI/Nzt2zVnpnWnppe8zaqdmV9X/aTJ/X66/vrrU11dnU033TSVlZUrtMfmm2+eV155JZ07d877779fzxV+bLkrKxQKOfzww3PYYYfllltuyWWXXZZhw4aVrs+ZMycvvPBCXnjhhWXe878z9v/3//5fTj755BxyyCFp0WLlhqwfdthhpXB7l112ybHHHpuXXnppsetbtWqVXr16LfR6r169cvrpp+eiiy7KM888k+233z4/+MEPsuGGG2b06NH52c9+lhEjRiRJTj/99OUKtwEAAAAAmpKvfe1rKRaLadWqVfr27Zv+/funuro6/fv3T79+/dKxY8clvv/FF1/M66+/nmKxmOnTpzdYncvdwb0oL7/8cv74xz/mr3/9a954441F3+iTDuzF3W7DDTfMQQcdlCOOOCKbb775ypa00H2X1XrrrZexY8cu8tq8efNy/PHH59prr13s+4899thceeWVyx3M6+AGAFZXOriBVUcHN8DqSAf3iqmoqFhsllsoFLLBBhuUAu/+/ftns802y7rrrpuampo88sgj+d73vpe33347xWIxffv2XWLT8cpYsd7y/7LpppvmwgsvzIUXXpixY8dm6NChefbZZ/PKK69k3LhxmTx5cqZNm5Ykad++fdZcc82st9562WSTTTJgwIDstNNO2WCDDeqjlAbVokWLXHPNNTn44INz5ZVX5umnn87kyZOz5pprZquttsqJJ56Yvfbaq7HLBAAAAABYKbvttluee+65fPDBBwtdKxaLGT16dEaPHp2//vWvi3z/pxuPv/SlLzVYnfUScH/a+uuvn/XXXz9f/epX63vrFVIPDeoL2XvvvbP33nvX+74AAAAAQMOoS2Xq6j8OXWlNsaYkue+++5Ik48ePz4gRIxY43nnnnYXWF4vFBULt+V9XV1fn9NNPb7A6m+Z3DwAAAACARtetW7d069Yt++23X+m1Dz/8cIHA+8knn8y4ceOS/CfYrqqqylFHHZUf//jHadOm4UbDCLgBAAAAAFhma6yxRnbZZZfssssupddeeeWVXH755bn66quTJFdccUUOPfTQBq9l+Z6ECAAAAAAA/6Vv37753e9+lyeeeCJdu3bNYYcdll/96lcNfl8BNwAAAADQ7M1LReqa4DEvFY39ralXW2+9dR5++OFUVVXl1FNPzUMPPdSg9xNwAwAAAABQb3r16pWLLrooxWIxRx55ZKZNm9Zg9xJwAwAAAABQr44++uh06NAh//rXv3LNNdc02H0E3AAAAAAA1KtWrVpl0003TZLcfvvtDXafygbbGQAAAACgiZg/87qpaYo1JcnUqVPToUOHldqjWCymUCjkhRdeqKeqFibgBgAAAABgAZ07d87666+f6urq0tGvX7907959md5fU1OTl19+OcViMVOnTm2wOgXcAAAAAAAsoFgsZsyYMRkzZkxuu+220utdunRJv379Fgi9+/btm8rK/0TNH374YY4++uhMmzYthUIha6+9doPVKeAGAAAAAGABhUJhodeKxWI++OCDPPzww3n44YdLr7dq1SobbbRRevTokdra2rz44ouZMmVKCoVCisVidt999warU8ANAAAAADR7c1ORFk1w3vXcJlhTkvz73//OyJEjFzhee+211NXVLbCuWCxm9uzZeeWVV/LKK68stE/nzp1z5plnNlidAm4AAAAAABbQpUuX7LLLLtlll11Kr82ePTsvvvjiAqH3Cy+8sNgZ2/369ct1112Xnj17Nlid9RZwf/nLX87//u//plu3bvW1JQAAAAAATUSrVq0yYMCADBgwYIHX33zzzbz22mt55513MnPmzHTu3Dn9+/dPdXV1g9dUbwH3X/7yl9x55505/fTT8/3vfz9t27atr60BAAAAAGiiNtpoo2y00UaNcu8W9bnZjBkzcsEFF6R37965+eab63NrAAAAAABYQL0F3Mcee2zpqZjjx4/P4Ycfnh133DHPPfdcfd0CAAAAAGCF1KUidalsgkfTfMhkuai3gPuqq67K8OHDs9122yX5+OmZTz75ZLbeeusce+yxmTRpUn3dCgAAAAAA6ndEyZZbbpnHH388N954Y9Zdd90Ui8XMmzcvQ4YMSa9evfLzn/88c+bMqc9bAgAAAACwmqrXgHu+r3zlK3n99dfzwx/+MK1bt06xWMyUKVPywx/+MH379s3f//73hrgtAAAAAACrkQYJuJOkXbt2+clPfpJXXnklBxxwQJKPx5aMHj06gwcPzh577JFXXnmloW4PAAAAAFDy8QzupnmsLn7zm9/kd7/7Xb3u2WAB93w9e/bM7bffnvvvvz+bbLJJko+D7oceeijV1dX5zne+kw8++KChywAAAAAAoBF997vfzXe+85163bPBA+75dtttt7zwwgu59NJL07lz5xSLxcydOze//e1vs/HGG+fXv/515s2bt6rKAQAAAABgFSsUCvW63yoLuJOkoqIiJ510Ut54440cf/zxadGiRYrFYj788MOcdNJJ6devXx544IFVWRIAAAAAAGVqlQbc833mM5/JFVdckWeeeSY77LBDko/Hlrz88sv5whe+kAMOOCBvvvlmY5QGAAAAADRDjT1n2wzuhrHKA+45c+Zk+PDh+c1vfpNLL700kyZNSvJxa3qhUEixWMydd96ZzTbbLGeccUamT5++qksEAAAAAKAMVDb0DV577bUMHz68dLzwwguZM2fOAmvmB9vzFYvFzJ49OxdffHFuuumm/P73v89ee+3V0KUCAAAAAFBG6jXgnjhx4gJh9rPPPpva2toF1nw6yJ6vRYsW2WyzzbLt/2/v3uOsquv98b82IMMdVIRCUPGCiKWZkSAl3tC8JCEnu4tIeclj2sNvF9NMPaZ2TmZ96WQaFto5aY+og9+8m+mUHMzISxl6lFsieELMGEQuMzC/P5T9gxxggBn2XjPP5+OxHo/P3vuz1npvWY+FvOYz7zVyZA477LD8+te/zm233ZZ169Zl4cKFOfnkk/PP//zPuf7669OhQ0W6qgAAAAAAUGVaLOAeOHBgXnrppY3eayrMTpKdd945I0aMyMiRIzNy5Mi8973vTc+ePcufT5w4MRdffHH+z//5P7n33nvT2NiY7373u3nxxRfz85//vKVKBgAAAADaiXVV2u96XRXWVCQtFnAvXrz4La1GkjfajwwbNqwcZh9++OHZf//9t3i8YcOG5e67786Pf/zjnHPOOVm5cmWmT5+en//85xk/fnxLlQ0AAAAAQEG1aIuSxsbG9OnTJ4cddlg50D7ssMPSq1evbT7mpz71qdTU1OSjH/1okuTGG28UcAMAAAAA0HIB95QpUzJy5MgccMABLXXIstNOOy2XXHJJ5s6dm2eeeabFjw8AAAAAQPG0WMB95plnttShmrT//vtn7ty5+d///d9WPQ8AAAAA0PY0pGNKVdjvuqEKayqSDpUuoLl22mmnJMm6desqXAkAAAAAANWgRXtwt6ZzzjkngwYNypNPPlnpUgAAAAAAqAKFCbiPP/74HH/88ZUuAwAAAACAbXDkkUemQ4eWbSpSmIAbAAAAAGBbrU3HdKjCOHRtO+rB/eCDD7b4MQvTgxsAAAAAADYk4AYAAAAAoJCqb00+AAAAAACFVFtbmw4dOmTUqFFb1W+7rq4ur776ajp27JiBAwc2ez8BNwAAAADQ5r3Rg7v6+l23tR7cRx99dBobG/NP//RP+clPfpJOnZoXQS9YsCDvfve7s27durzwwgvNDrm1KAEAAAAAoEVNmzYt48ePz5o1a5o1/6CDDsoHPvCBJMkvfvGLZp9HwA0AAAAAQIupqalJt27d8stf/jKnnHJKVq9e3az9JkyYkFKplF//+tfNPpeAGwAAAACAFtOnT5/cdddd6dmzZ+6///6ceOKJWbly5Rb3O+aYY5IkTz31VLPPJeAGAAAAANq8telYtVtbNHr06Nx3333p3bt3HnrooXzgAx/IihUrNrvPLrvskl69euWll15q9nkE3AAAAAAAtLgRI0bkV7/6VXbdddf89re/zbHHHptly5Ztcv66deuycuXK7LTTTs0+h4AbAAAAAIBWceihh+ahhx5K//7987vf/S5HHHFEXnjhhSbn/vd//3fq6+uz5557Nvv4Am4AAAAAAFrNO97xjtTW1mbgwIH505/+lOHDh2f69OkbzVmyZEnOP//8NDY25thjj232sTu1cK0AAAAAAFWnIR1TqsJ+1w1VWFNrGDJkSH77299mzJgxmTt3bk499dS85z3vyejRo7N06dLcfffdefnll9O1a9dccMEFzT6ugBsAAAAAgFa355575rHHHsukSZMyffr0/OEPf8isWbOSJKVSKZ07d87UqVMzePDgZh9TwA0AAAAAQIsolUqb/bxPnz75+c9/ngceeCA//OEP8/TTT6dTp0459NBD8/nPfz4HHnjgVp1PwA0AAAAAQItoaGho1rwxY8ZkzJgx230+ATcAAAAA0OatS6esrcI4dF0V1lQkHSpdAAAAAAAAbAsBNwAAAAAAhSTgBgAAAACgkDR4AQAAAADavLXpmFI6VrqMt1hbhTUViYAbAAAAAICN1NbWbvR69OjRFapk8wTcAAAAAABs5Oijj05jY2P59bp16ypYzabpwQ0AAAAAQJNKpVKlS9gsK7gBAAAAgDZvbTpUaQ/u6l2DXO3hdiLgBgAAAADgH/zoRz+qdAnNIuAGAAAAAGAjp59+eqVLaJbqXf8OAAAAAACbYQU3AAAAANDmNaRjUoU9uBuqsKYisYIbAAAAAIBCEnADAAAAAFBIAm4AAAAAAApJD24AAAAAoM1bm04pVWEcurYKayoSK7gBAAAAACgkATcAAAAAAIUk4AYAAAAAoJA0eAEAAAAA2rx16Zi16VjpMt5iXRXWlCSzZs3Kgw8+mIMPPjgHH3xw3v72t1e6pCYJuAEAAAAA2MiPfvSj3HDDDUmSd77znXnqqacqXFHTtCgBAAAAAGAjDzzwQEqlUpLkC1/4QoWr2TQBNwAAAAAAZa+88krmzp2bxsbG9OjRI//0T/+0ybnLly/P2WefnV133TWdO3fOnnvumTPPPDOzZ8/eIbUKuAEAAACANm/tmz24q3GrNhu2I/nABz6QLl26NDmvsbExJ598cn7wgx/k73//exoaGrJw4cJMnTo1hxxySL7//e+3eq0CbgAAAAAAyubPn18eH3744Zucd8stt+SRRx4pvy6VSuWtoaEh5513XqZPn96apQq4AQAAAAD4//3tb38rj/fbb79Nzps8eXJ5vP/+++exxx7Liy++mK9+9avl988777ysXr26dQqNgBsAAAAAgA00NDSUx3369Glyzl/+8pc8+eSTaWxsTIcOHXLbbbfl0EMPzdvf/vZcfvnlOfvss5MkL730UqZNm9ZqtQq4AQAAAIA2r9J9tovUg3vDUHvFihVNznnwwQfL42OOOSYHH3zwRp9/8YtfLI/vuuuuli1wAwJuAAAAAIACmjVrVq688socd9xxGThwYGpqatKjR48MGTIkEydO3Kg/9tbYe++9y+M//elPTc7ZsLf2Rz7ykbd8vtdee2WPPfZIkjz++OPbVEdzdGq1IwMAAAAA0CqOOOKI/Pa3v33L+2vWrMnzzz+f559/PlOnTs3pp5+eH/zgB+ncuXOzjz1ixIh06NAha9euzW233ZaLLrpoo8//9re/5YEHHii3J/ngBz/Y5HH23HPPvPDCC/nrX/+6dV9uK1jBDQAAAABQMIsXL06SDBgwIBdccEGmTZuWxx57LDNnzsy3vvWt7L777kmSW2+9NWecccZWHbt379456aSTUiqV8vjjj+e73/3uRp9fddVVWbNmTZJk1KhR2W233Zo8zvpQ/bXXXtuq828NK7gBAAAAgDavIR3SWIX9rtdu4xrkoUOH5uqrr8748ePTsePG32vEiBH51Kc+lVGjRuW5557LbbfdlnPOOSdHHHFEs49/ySWX5K677sratWtzwQUX5E9/+lNOOumkPPLII/nOd75TnvfRj350k8eoq6tLknTp0mUrv13zWcENAAAAAFAwd955Z0477bS3hNvr9e3bN9ddd1359bRp07bq+MOHD89XvvKVlEqlNDY25gc/+EE+9KEP5Zvf/GaSpLGxMX369MknPvGJTR7j+eefT2NjY/r3779V594aVnADAAAAALRBRx11VHk8d+7crd7/iiuuSGNjY6655pqsW7cuyRvBdpKUSqVcc8016dWrV5P7PvPMM/n73/+eJNlvv/22+tzNZQU3AAAAAEAbtHr16vJ4Uyu9t+TKK6/M73//+5x99tk56KCDMmDAgLz3ve/Nj3/845x99tmb3O/2228vjw877LBtOndzWMENAAAAALR5a9Mp1RiHrm3Fmmpra8vjAw44YJuP8653vSvf+973mj2/sbExd9xxR/n18ccfv83n3pLq+xMFAAAAAGC7rFu3Ltdee2359WmnnbbDzl0qlfLkk0/m5ZdfzhNPPJERI0a02rkE3AAAAAAAFVZXV7fR65qamtTU1Gzz8a6//vo89thjSZJTTz01hx566HbVty122223HHfcca16Dj24AQAAAAAqbNCgQendu3d5u+aaa7b5WLW1tfnyl7+cJOnXr19uuOGGliqz6ljBDQAAAAC0eWvTMcm2PWixNa19s6aFCxemV69e5fe3dfX2n//854wbNy4NDQ3p0qVLfvazn6Vfv34tUms1EnADAAAAAFRYr169Ngq4t8X8+fNz3HHH5dVXX03Hjh1z++2354gjjmihCquTFiUAAAAAAAW3ePHiHHvssVm8eHFKpVJ++MMfZuzYsZUuayP//u//3uLtUqzgBgAAAAAosKVLl2bMmDGZN29ekmTy5Mk5/fTTK1zVW33uc59LqVTKueee22LHFHADAAAAAG3euirtwb1uO2tatmxZjj/++MyePTtJcu211+a8885ridJaRalUatHjaVECAAAAAFBAr7/+ek466aQ8/vjjSZJLLrkkX/rSlypc1Y4l4AYAAAAAKJg1a9Zk3LhxmTFjRpLkggsuyFVXXVXhqnY8LUoAAAAAAArmYx/7WO6///4kydFHH51Jkybl6aef3uT8zp07Z8iQITuqvB1GwA0AAAAAtHkN6ZgObagH9y9+8Yvy+Ne//nUOOuigzc7fc889s2DBgm06VzXTogQAAAAAgEKyghsAAAAAoGAaGxsrXUJVsIIbAAAAAIBCsoIbAAAAAGjz1qZjGqswDt3WHty8wQpuAAAAAAAKScANAAAAAEAhCbgBAAAAACgkATcAAAAAAIVUfV3VAQAAAABa2BsPmay+Bzq2p4dMHnnkkenQoWXXXAu4AQAAAABodQ8++GCLH1OLEgAAAAAACknADQAAAABAIWlRAgAAAAC0eXpwt00CbgAAAAAAKqqxsTH/7//9vyTJMccckx49ejRrPy1KAAAAAABoER07dkyHDh3yyiuvbNV+pVIpZ599dsaNG5eHH3642fsJuAEAAAAAqLhDDz00STJr1qxm76NFCQAAAADQ5q1d1zGN66qv3/W6KqypUt7znvfk3nvvFXADAAAAAFA5r732Wrp06bJV+wwbNiyJFdwAAAAAAFTQ4MGDt2m/UqmUJUuWNHu+gBsAAAAAgBZVKpV2yHkE3AAAAABAm7e2oWPWNVRfv+vGKqypJUyfPj29e/du9fMIuAEAAAAAaFGHH354dt1111Y/T4dWPwMAAAAAALSCdhFwL1myJHfeeWcuu+yynHDCCenbt29KpVJKpVLOOOOMZh1j6tSp5X22tE2dOrVVvw8AAAAAQDXaUb2312sXLUr69+9f6RIAAAAAgApa29AppYbqi0Mbq7Cm7TFv3rwkyS677LJDzte2/us1wx577JGhQ4fm/vvv3+Zj3HfffRkwYMAmPx84cOA2HxsAAAAAoKj22GOPHXq+dhFwX3bZZRk+fHiGDx+e/v37Z8GCBRk8ePA2H2/IkCHZa6+9Wq5AAAAAAAC2WrsIuK+44opKlwAAAAAAQAtrFwE3AAAAANC+rW3okFJDx0qX8RaNDR0qXUKh+a8HAAAAAEAhCbi3wcSJEzNgwIB07tw5ffv2zYgRI3LppZdm0aJFlS4NAAAAAGCrvPbaa4U9t4B7Gzz88MN56aWXUl9fn1deeSW/+93v8vWvfz377rtvbrzxxmYdY/Xq1amrq9toAwAAAADY0fbee+9cf/31Wb169Q4756pVq3L99ddn77333q7j6MG9Ffbee++ceuqpGTlyZAYNGpQkmTdvXn7+859n2rRpWbVqVc4555yUSqWcddZZmz3WNddc4+GXAAAAALCDrG3oWKU9uCtf0yuvvJKLLroo3/jGN3LeeeflnHPOyW677dYq51qyZEluuOGG/Pu//3uWLl263ccrNTY2NrZAXYWyYMGCDB48OEkyYcKETJ06dYv7LFu2LL169UqpVGry8zvvvDOnnnpq6uvr061bt8ydOzdve9vbNnm81atXb/QTkbq6ugwaNChfTtJlq74NAECxXZ7LK10C0G6sSnJt+d93ALQPdXV16d27d3aavzClKrz/N9bVpX7woIr+/TR69Og88sgjWR8V77TTTjn55JPz8Y9/PCeccEK6deu2XcdfsWJF7r777vzkJz/JXXfdlYaGhnLOOmrUqPzmN7/Z5mNrUdJMvXv33mS4nSQnn3xyLrvssiTJ66+/nptvvnmzx6upqUmvXr022gAAAAAAdrTa2trcdtttGTx4cEqlUhoaGvJf//Vf+fCHP5y+fftmzJgxueqqq3Lvvfc26zmEixYtyr333pt/+Zd/ybHHHpu+ffvmIx/5SO64446sXbs2pVIpe++9d26//fbtCrcTLUpa1FlnnZXLLrssjY2Nqa2tzSWXXFLpkgAAAAAAtui0007L+PHjM3Xq1Fx77bWZN29ekjd6ZT/44IN58MEHy3M7d+6cAQMGZOedd07Xrl1TKpWycuXKvPrqq1m8eHGTvbzXLx7ed9998+UvfzkTJkxIhw7bv/5awN2C+vXrl1133TVLly5t1k8yAAAAAIAdo6GhY0r1le93/Y+qoQf3eh07dsykSZMyadKk3HXXXbnxxhtz3333paGhIUnKLUzWrFmTBQsWZMGCBZs93vpQe6eddsoHPvCBnH322TnhhBNatGYBdwvbXBsTAAAAAIAiOOmkk3LSSSfllVdeyR133JH77rsvtbW1efnll5u1f79+/XLkkUfm+OOPz9ixY7Pzzju3Sp0C7hb08ssvl5/8OWDAgApXAwAAAACwfXbdddeceeaZOfPMM5O80V979uzZ+ctf/pKXX345K1asSJJ07949u+22W/bcc88MGzYsu++++w6pT8Ddgm666abyMv3Ro0dXuBoAAAAAgJa1++6777DwujkE3M2wYMGCvPrqqznkkEM2OefOO+/MlVdemSTp2rVrJk6cuKPKAwAAAAC2oHFtpzSurcI4tBprKpB28V/vkUceyZw5c8qv17cRSZI5c+Zk6tSpG80/44wzNnq9YMGCHHXUURk5cmQ++MEP5uCDD06/fv2SJPPmzcu0adMybdq08urtb37zm1X1UwwAAAAAgLaoXQTcU6ZMyS233NLkZzNmzMiMGTM2eu8fA+71Zs6cmZkzZ27yPN26dcv111+fs846a5trBQAAAACgedpFwL29Dj300PzHf/xHZs6cmVmzZuWll17K0qVL09DQkJ133jkHHnhgjjnmmHz6058ur+wGAAAAAKB1lRrX99Wgourq6tK7d+98OUmXShcDALADXZ7LK10C0G6sSnJtli1bll69elW6GAB2kPW5W55+JelZhff/5XXJO3b199M26lDpAgAAAAAAYFsIuAEAAAAAKCQBNwAAAAAAheQhkwAAAABA29fQ8Y2t2lRjTQViBTcAAAAAAIUk4AYAAAAAoJAE3AAAAAAAFJIe3AAAAABA27e2lDSUKl3FW62twpoKxApuAAAAAAAKScANAAAAAEAhCbgBAAAAACgkPbgBAAAAgLav4c2t2lRjTQViBTcAAAAAAIUk4AYAAAAAoJAE3AAAAAAAFJIe3AAAAABA26cHd5tkBTcAAAAAAIUk4AYAAAAAoJAE3AAAAAAAFJIe3AAAAABA26cHd5tkBTcAAAAAAIUk4AYAAAAAoJAE3AAAAAAAFJIe3AAAAABA29eQpL7SRTRBD+7tYgU3AAAAAACFJOAGAAAAAKCQBNwAAAAAABSSHtwAAAAAQNu39s2t2lRjTQViBTcAAAAAAIUk4AYAAAAAoJAE3AAAAAAAFJIe3AAAAABA29fw5lZtqrGmArGCGwAAAACAQhJwAwAAAABQSAJuAAAAAAAKSQ9uAAAAAKDt04O7TbKCGwAAAACAQhJwAwAAAABQSAJuAAAAAAAKSQ9uAAAAAKDt04O7TbKCGwAAAACAQhJwAwAAAABQSAJuAAAAAAAKSQ9uAAAAAKDtW5vq7He9ttIFFJsV3AAAAAAAFJKAGwAAAACAQhJwAwAAAABQSHpwAwAAAABtX0Oqswd3NdZUIFZwAwAAAABQSAJuAAAAAAAKScANAAAAAEAh6cENAAAAALR9enC3SVZwAwAAAABQSAJuAAAAAAAKScANAAAAAEAh6cENAAAAALR99W9u1aYaayoQK7gBAAAAACgkATcAAAAAAIUk4AYAAAAAoJAE3AAAAAAAFJKHTAIAAAAAbd/aN7dqU401FYgV3AAAAAAAFJKAGwAAAACAQhJwAwAAAABQSHpwAwAAAABt39okDZUuogl6cG8XK7gBAAAAACgkATcAAAAAAIUk4AYAAAAAoJD04AYAAAAA2r6GVGcP7mqsqUCs4AYAAAAAoJAE3AAAAAAAFJKAGwAAAACAQtKDGwAAAABo+/TgbpOs4AYAAAAAoJAE3AAAAAAAFJKAGwAAAACAQtKDGwAAAABo+/TgbpOs4AYAAAAAoJAE3AAAAAAAFJKAGwAAAACAQtKDGwAAAABo+9amOvtdr610AcVmBTcAAAAAAIUk4AYAAAAAoJAE3AAAAAAAFJIe3AAAAABA29eQ6uzBXY01FYgV3AAAAAAAFJKAGwAAAACAQhJwAwAAAABQSHpwAwAAAABtX32SjpUuogn1lS6g2KzgBgAAAACgkATcAAAAAAAUkoAbAAAAAIBC0oMbAAAAAGj71r65VZtqrKlArOAGAAAAAKCQBNwAAAAAABSSgBsAAAAAgELSgxsAAAAAaPsa3tyqTTXWVCBWcAMAAAAAUEgCbgAAAAAACknADQAAAABAIenBDQAAAAC0fWtTnf2u11a6gGKzghsAAAAAgEIScAMAAAAAUEgCbgAAAAAACkkPbgAAAACg7WtI0rHSRTShGvuCF4gV3AAAAAAAFJKAGwAAAACAQhJwAwAAAABQSHpwAwAAAABtX32qc7lvfaULKLZq/CMFAAAAAIAtEnADAAAAAFBIAm4AAAAAAApJD24AAAAAoO1b++ZWbaqxpgKxghsAAAAAgEIScAMAAAAAUEgCbgAAAAAACkkPbgAAAACg7VubpKHSRTRBD+7tYgU3AAAAAACFJOAGAAAAAKCQBNwAAAAAABSSHtwAAAAAQNvXkOpc7luNfcELpBr/SAEAAAAAYIsE3AAAAAAAFJKAGwAAAACAQmoXAfeSJUty55135rLLLssJJ5yQvn37plQqpVQq5Ywzztjq491zzz0ZN25cBg4cmJqamgwcODDjxo3LPffc0/LFAwAAAADbr76KN7ZZu3jIZP/+/VvkOOvWrctZZ52Vm2++eaP3Fy1alEWLFmX69On59Kc/nRtvvDEdOrSLnx0AAAAAAFRMu0th99hjjxx33HHbtO8ll1xSDrcPOeSQ3HbbbXnsscdy22235ZBDDkmSTJkyJZdeemmL1QsAAAAAQNPaxQruyy67LMOHD8/w4cPTv3//LFiwIIMHD96qYzz33HP55je/mSR5z3vek9/85jfp2rVrkmT48OE55ZRTMnr06MyaNSv/9m//ljPPPDP77rtvi38XAAAAAADe0C5WcF9xxRU5+eSTt6tVybe//e00NDQkSSZPnlwOt9fr1q1bJk+enCRpaGjI9ddfv+0FAwAAAAAta20Vb2yzdhFwb6/GxsbccccdSZKhQ4dmxIgRTc4bMWJE9t9//yTJHXfckcbGxh1WIwAAAABAeyPgbob58+dn8eLFSZLRo0dvdu76zxctWpQFCxa0dmkAAAAAAO2WgLsZZs+eXR4PHTp0s3M3/PyZZ55ptZoAAAAAANq7dvGQye314osvlscDBw7c7NxBgwaVxwsXLmy1mgAAAACArdCQ6lzu21DpAopNwN0My5cvL4979Oix2bndu3cvj1977bVNzlu9enVWr15dfl1XV7cdFQIAAAAAtD/V+DOLqrNq1aryuHPnzpudW1NTUx6vXLlyk/Ouueaa9O7du7xtuPIbAAAAAIAtE3A3Q5cuXcrjNWvWbHbuhquyu3btusl5F198cZYtW1betDMBAAAAANg6WpQ0Q8+ePcvjzbUdSZIVK1aUx5trZ1JTU7PRam8AAAAAoBWtTXX2u15b6QKKzQruZtjwwZIbPnCyKRuuxNZ2BAAAAACg9Qi4m2HYsGHl8bPPPrvZuRt+fsABB7RaTQAAAAAA7Z2AuxkGDx6cAQMGJElqa2s3O/c3v/lNkmT33XfPXnvt1dqlAQAAAAC0WwLuZiiVShk7dmySN1ZoP/roo03Oe/TRR8sruMeOHZtSqbTDagQAAAAANqO+ije2mYC7mS688MJ07NgxSXL++edn5cqVG32+cuXKnH/++UmSTp065cILL9zRJQIAAAAAtCudKl3AjvDII49kzpw55ddLly4tj+fMmZOpU6duNP+MM854yzGGDBmSL3zhC7n22msza9asjBo1Kl/60peyzz77ZO7cufnGN76RJ554IknyhS98Ifvtt1+rfBcAAAAAgCRZsmRJHnvssTz22GP5/e9/n9///vd55ZVXkiQTJkx4S+7ZFrWLgHvKlCm55ZZbmvxsxowZmTFjxkbvNRVwJ8nXv/71LFmyJD/84Q/zxBNP5KMf/ehb5kyaNClXXXXVdtcMAAAAALA5/fv3r3QJFadFyVbo0KFDbr755tx1110ZO3ZsBgwYkM6dO2fAgAEZO3Zs7r777kyZMiUdOvjPCgAAAABVZW0Vby1gjz32yHHHHdcyByuQdrGCe+rUqS26HP/EE0/MiSee2GLHAwAAAADYWpdddlmGDx+e4cOHp3///lmwYEEGDx5c6bJ2qHYRcAMAAAAAtDVXXHFFpUuoOL00AAAAAAAoJCu4AQAAAIC2ryFJqdJFNKGh0gUUmxXcAAAAAAAUkoAbAAAAAIBC0qIEAAAAAKDC6urqNnpdU1OTmpqaClVTHFZwAwAAAABU2KBBg9K7d+/yds0111S6pEKwghsAAAAAaPuq/CGTCxcuTK9evcpvW73dPAJuAAAAAIAK69Wr10YBN82jRQkAAAAAAIUk4AYAAAAAoJC0KAEAAAAA2r6GShewCdVaV0FYwQ0AAAAAQCEJuAEAAAAAKCQtSgAAAAAACuiRRx7JnDlzyq+XLl1aHs+ZMydTp07daP4ZZ5yxgyrbcQTcAAAAAEDbtzZJqdJFNGHttu86ZcqU3HLLLU1+NmPGjMyYMWOj99piwK1FCQAAAAAAhSTgBgAAAAAooKlTp6axsbHZW1sk4AYAAAAAoJD04AYAAAAA2r6GShewCdVaV0FYwQ0AAAAAQCEJuAEAAAAAKCQBNwAAAAAAhaQHNwAAAADQ9lVrr+tqrasgrOAGAAAAAKCQBNwAAAAAABSSgBsAAAAAgELSgxsAAAAAaPsakjRWuogmrK10AcVmBTcAAAAAAIUk4AYAAAAAoJAE3AAAAAAAFJIe3AAAAABA21etva6rta6CsIIbAAAAAIBCEnADAAAAAFBIAm4AAAAAAApJD24AAAAAoO1rSNJY6SKaoAf3drGCGwAAAACAQhJwAwAAAABQSAJuAAAAAAAKSQ9uAAAAAKDt04O7TbKCGwAAAACAQhJwAwAAAABQSAJuAAAAAAAKSQ9uAAAAAKDta0iyrtJFNKEaayoQK7gBAAAAACgkATcAAAAAAIUk4AYAAAAAoJD04AYAAAAA2r61SRorXUQT9ODeLlZwAwAAAABQSAJuAAAAAAAKScANAAAAAEAh6cENAAAAALR9DanO5b56cG+XavwjBQAAAACALRJwAwAAAABQSAJuAAAAAAAKSQ9uAAAAAKDt04O7TarGP1IAAAAAANgiATcAAAAAAIUk4AYAAAAAoJD04AYAAAAA2r76VOdyXz24t0s1/pECAAAAAMAWCbgBAAAAACgkATcAAAAAAIWkBzcAAAAA0PatS9JY6SKaUI01FYgV3AAAAAAAFJKAGwAAAACAQhJwAwAAAABQSHpwAwAAAABtX0OSUqWLaIIe3NvFCm4AAAAAAApJwA0AAAAAQCEJuAEAAAAAKCQ9uAEAAACAtk8P7jbJCm4AAAAAAApJwA0AAAAAQCEJuAEAAAAAKCQ9uAEAAACAtq8+enC3QVZwAwAAAABQSAJuAAAAAAAKScANAAAAAEAh6cENAAAAALR9a6MHdxtkBTcAAAAAAIUk4AYAAAAAoJAE3AAAAAAAFJIe3AAAAABA+6DfdZtjBTcAAAAAAIUk4AYAAAAAoJAE3AAAAAAAFJKAGwAAAACAQhJwAwAAAABQSAJuAAAAAAAKScANAAAAAEAhCbgBAAAAACgkATcAAAAAAIUk4AYAAAAAoJAE3AAAAAAAFJKAGwAAAACAQhJwAwAAAABQSAJuAAAAAAAKScANAAAAAEAhCbgBAAAAACikTpUuAAAAAACg9dW/uVWbaqypOKzgBgAAAACgkATcAAAAAAAUkoAbAAAAAIBC0oMbAAAAAGgHGt7cqk011lQcVnADAAAAAFBIAm4AAAAAAApJwA0AAAAAQCHpwQ0AAAAAtAP1b27VphprKg4ruAEAAAAAKCQBNwAAAAAAhSTgBgAAAACgkPTgBgAAAADagYY3t2pTjTUVhxXcAAAAAAAUkoAbAAAAAIBCEnADAAAAAFBIenADAAAAAO1AQ5L6ShfRBD24t4cV3AAAAAAAFJKAGwAAAACAQhJwAwAAAABQSHpwAwAAAADtQH2qswd3NdZUHFZwAwAAAABQSAJuAAAAAAAKScANAAAAAEAhCbi3QqlUatZ25JFHVrpUAAAAAGAjDVW8sa0E3AAAAAAAFFKnShdQROeee24++9nPbvLz7t2778BqAAAAAADaJwH3NujXr1/e8Y53VLoMAAAAAIB2TcANAAAAALQDDUnqK11EE/Tg3h56cAMAAAAAUEgCbgAAAAAACknAvQ1+9rOfZdiwYenWrVt69uyZ/fbbLxMmTMhDDz1U6dIAAAAAANoNPbi3wezZszd6PWfOnMyZMye33nprPvShD2Xq1Knp3bt3haoDAAAAAN6qIdXZ77oaayoOAfdW6NatW0455ZQcc8wxGTp0aHr06JGXX345tbW1+f73v59XXnkl06dPz9ixY/PAAw9kp5122uSxVq9endWrV5df19XV7YivAAAAAADQZgi4t8KiRYvSp0+ft7w/ZsyYnH/++TnhhBPyxBNPpLa2NjfccEM+97nPbfJY11xzTa644opWrBYAAAAAoG3Tg3srNBVur9e/f/9MmzatvGp78uTJmz3WxRdfnGXLlpW3hQsXtmSpAAAAAABtnhXcLWjvvffOmDFjcvfdd2fOnDlZvHhxBgwY0OTcmpqa1NTU7OAKAQAAAKC9qn9zqzbVWFNxWMHdwoYNG1YeL1q0qIKVAAAAAAC0bQLuFlYqlSpdAgAAAABAuyDgbmGzZ88ujzfVngQAAAAAgO2nB3cLmj9/fh544IEkyT777JPdd9+9whUBAAAAAG9oeHOrNtVYU3FYwd1Mv/zlL9PQsOmL7a9//WvGjx+fNWvWJEk++9nP7qjSAAAAAADaJSu4m+n8889PfX19xo8fn5EjR2avvfZK165ds3Tp0jz88MO58cYbs3Tp0iTJ+973vpx33nkVrhgAAAAAoG0TcG+FxYsXZ/LkyZk8efIm54wfPz5TpkxJTU3NDqwMAAAAAKD9EXA30y233JLa2trMnDkz8+bNy9KlS1NXV5cePXpk0KBBOfzwwzNhwoSMHDmy0qUCAAAAAG/RkKS+0kU0QQ/u7SHgbqbRo0dn9OjRlS4DAAAAAIA3ecgkAAAAAACFJOAGAAAAAKCQtCgBAAAAANqBhlRnv+tqrKk4rOAGAAAAAKCQBNwAAAAAABSSgBsAAAAAgELSgxsAAAAAaAfq39yqTTXWVBxWcAMAAAAAUEgCbgAAAAAACknADQAAAABAIenBDQAAAAC0Aw1vbtWmGmsqDiu4AQAAAAAoJAE3AAAAAACFJOAGAAAAAKCQ9OAGAAAAANqBhiT1lS6iCXpwbw8ruAEAAAAAKCQBNwAAAAAAhSTgBgAAAACgkPTgBgAAAADagYZUZ7/raqypOKzgBgAAAACgkATcAAAAAAAUkoAbAAAAAIBC0oMbAAAAAGgH6t/cqk011lQcVnADAAAAAFBIAm4AAAAAAApJwA0AAAAAQCHpwQ0AAAAAtAN6cLdFVnADAAAAAFBIAm4AAAAAAApJwA0AAAAAQCHpwQ0AAAAAtAMNb27VphprKg4ruAEAAAAAKCQBNwAAAAAAhSTgBgAAAACgkPTgBgAAAADagYYk9ZUuogl6cG8PK7gBAAAAACgkATcAAAAAAIUk4AYAAAAAoJD04AYAAAAA2oGGVGe/62qsqTis4AYAAAAAoJAE3AAAAAAAFJKAGwAAAACAQtKDGwAAAABoB+pTnXFofaULKDQruAEAAAAAKCQBNwAAAAAAhSTgBgAAAACgkATcAAAAAAAUUjV2VQcAAAAAaGENb27VphprKg4ruAEAAAAAKCQBNwAAAAAAhSTgBgAAAACgkPTgBgAAAADagYYk9ZUuogl6cG8PK7gBAAAAACgkATcAAAAAAIUk4AYAAAAAoJD04AYAAAAA2oGGVGe/62qsqTis4AYAAAAAoJAE3AAAAAAABfaXv/wlF110UYYOHZru3btnl112yfDhw/Nv//Zvef311ytdXqvSogQAAAAAoKB++ctf5pOf/GTq6urK773++uuZNWtWZs2alSlTpuSuu+7KvvvuW8EqW48V3AAAAABAO1Bfxdu2eeKJJ/KRj3wkdXV16dGjR77+9a/nv//7v/Pggw/mM5/5TJLkueeey0knnZTly5dv83mqmRXcAAAAAAAFdMEFF2TlypXp1KlT7r///owcObL82dFHH5399tsvX/ziF/Pcc8/luuuuy+WXX165YluJFdwAAAAAAAXz2GOP5be//W2SZNKkSRuF2+tddNFFOeCAA5Ik3/nOd1Jfv+2rxauVgBsAAAAAoGCmT59eHk+cOLHJOR06dMjpp5+eJPn73/+ehx56aEeUtkMJuAEAAACAdqChiret98gjjyRJunfvnkMPPXST80aPHl0ez5gxY5vOVc0E3AAAAAAABfPMM88kSfbdd9906rTpRy0OHTr0Lfu0JQJuAAAAAIACWbVqVZYuXZokGThw4Gbn7rzzzunevXuSZOHCha1e24626WgfAAAAAKDNWF3pAjbhjbrq6uo2erempiY1NTVN7rF8+fLyuEePHls8Q/fu3bNixYq89tpr21FndRJwAwAAAABtVufOnfO2t70t//u/11e6lE3q0aNHBg0atNF7X/va13L55Zc3OX/VqlXlcefOnbd4/PVB+cqVK7e9yCol4AYAAAAA2qwuXbpk/vz5WbNmTaVL2aTGxsaUSqWN3tvU6u3kje+0XnO+1+rVb6wS79q16zZWWL0E3AAAAABAm9alS5eNQuGi69mzZ3ncnLYjK1asSNK8diZF4yGTAAAAAAAF0qVLl+y6665JkhdffHGzc1999dVywP2PbVDaAgE3AAAAAEDBDBs2LEkyZ86cNDQ0bHLes88+Wx4fcMABrV7XjibgBgAAAAAomPe9731J3mg/8oc//GGT82pra8vjUaNGtXpdO5qAGwAAAACgYD70oQ+Vxz/60Y+anLNu3brceuutSZI+ffrkqKOO2hGl7VACbgAAAACAgnnve9+b97///UmSm2++OTNnznzLnOuuuy7PPPNMkuSCCy7ITjvttENr3BE6VboAAAAAAAC23ne+852MGjUqK1euzHHHHZevfOUrOeqoo7Jy5crcfvvtuemmm5IkQ4YMyUUXXVThaluHgBsAAAAAoIAOOeSQ/PSnP80nP/nJ1NXV5Stf+cpb5gwZMiR33XVXevbsWYEKW58WJQAAAAAABfXBD34wf/zjH/P5z38+Q4YMSbdu3dKnT5+85z3vyTe+8Y088cQT2XfffStdZqspNTY2Nla6CJK6urr07t07X07SpdLFAADsQJfn8kqXALQbq5Jcm2XLlqVXr16VLgYAaAFWcAMAAAAAUEgCbgAAAAAACknADQAAAABAIQm4AQAAAAAoJAE3AAAAAACFJOAGAAAAAKCQBNwAAAAAABSSgBsAAAAAgEIScAMAAAAAUEgCbgAAAAAACknADQAAAABAIQm4AQAAAAAoJAE3AAAAAACFJOAGAAAAAKCQBNwAAAAAABSSgBsAAAAAgEIScAMAAAAAUEgCbgAAAAAACknADQAAAABAIQm4AQAAAAAoJAE3AAAAAACFJOAGAAAAAKCQBNwAAAAAABSSgBsAAAAAgEIScAMAAAAAUEgCbgAAAAAACknADQAAAABAIQm4AQAAAAAoJAE3AAAAAACFJOAGAAAAAKCQBNwAAAAAABSSgBsAAAAAgEIScAMAAAAAUEgCbgAAAAAACknADQAAAABAIQm4AQAAAAAoJAE3AAAAAACFJOAGAAAAAKCQBNwAAAAAABSSgBsAAAAAgEIScAMAAAAAUEgCbgAAAAAACknADQAAAABAIQm4AQAAAAAoJAE3AAAAAACFJOAGAAAAAKCQBNwAAAAAABRSp0oXwBsaGxuTJKsrXAcAwI63qtIFAO3GG//iWv/vLwCg+EqN/mavCi+++GIGDRpU6TIAAADavIULF2bgwIGVLgMAaAEC7iqxbt26LF68OD179kypVKp0OVSxurq6DBo0KAsXLkyvXr0qXQ4F5TqiJbiOaAmuI1qC64jmamxszPLlyzNgwIB06KBjJwC0BVqUVIkOHTpYQcBW6dWrl3/Asd1cR7QE1xEtwXVES3Ad0Ry9e/eudAkAQAvyI2sAAAAAAApJwA0AAAAAQCEJuKFgampq8rWvfS01NTWVLoUCcx3RElxHtATXES3BdQQA0H55yCQAAAAAAIVkBTcAAAAAAIUk4AYAAAAAoJAE3AAAAAAAFJKAGwAAAACAQhJwQxUrlUrN2o488shKl0qFLFmyJHfeeWcuu+yynHDCCenbt2/5ujjjjDO2+nj33HNPxo0bl4EDB6ampiYDBw7MuHHjcs8997R88VSNlriOpk6d2ux71tSpU1v1+1A5s2bNypVXXpnjjjuufB/p0aNHhgwZkokTJ+aRRx7ZquO5J7VPLXEduScBALQfnSpdAADbrn///i1ynHXr1uWss87KzTffvNH7ixYtyqJFizJ9+vR8+tOfzo033pgOHfxstK1pqeuI9u2II47Ib3/727e8v2bNmjz//PN5/vnnM3Xq1Jx++un5wQ9+kM6dO2/yWO5J7VdLXkcAALQPAm4ogHPPPTef/exnN/l59+7dd2A1VKs99tgjQ4cOzf3337/V+15yySXlIOmQQw7JF7/4xeyzzz6ZO3du/vVf/zVPPPFEpkyZkt122y1XX311S5dOFdme62i9++67LwMGDNjk5wMHDtzmY1O9Fi9enCQZMGBAPvzhD+f9739/9thjj6xduzYzZ87Mddddl0WLFuXWW29NfX19fvKTn2zyWO5J7VdLXkfruScBALRtpcbGxsZKFwE0rVQqJUm+9rWv5fLLL69sMVSlr33taxk+fHiGDx+e/v37Z8GCBRk8eHCSZMKECc36tevnnnsuBx54YBoaGvKe97wnv/nNb9K1a9fy56+//npGjx6dWbNmpVOnTnnmmWey7777ttZXogJa4jqaOnVqJk6cmCSZP39+9tprr1asmGp08skn5/TTT8/48ePTsWPHt3y+dOnSjBo1Ks8991ySpLa2NkccccRb5rkntW8tdR25JwEAtB9+pxOgwK644oqcfPLJ29Vi4tvf/nYaGhqSJJMnT94oSEqSbt26ZfLkyUmShoaGXH/99dteMFWpJa4juPPOO3Paaac1GUomSd++fXPdddeVX0+bNq3Jee5J7VtLXUcAALQfAm6AdqyxsTF33HFHkmTo0KEZMWJEk/NGjBiR/fffP0lyxx13xC//ANviqKOOKo/nzp37ls/dk2iOLV1HAAC0LwJugHZs/vz55X6no0eP3uzc9Z8vWrQoCxYsaO3SgDZo9erV5XFTK3Tdk2iOLV1HAAC0LwJuKICf/exnGTZsWLp165aePXtmv/32y4QJE/LQQw9VujQKbvbs2eXx0KFDNzt3w8+feeaZVquJ4ps4cWIGDBiQzp07p2/fvhkxYkQuvfTSLFq0qNKlUWG1tbXl8QEHHPCWz92TaI4tXUf/yD0JAKBtE3BDAcyePTvPPPNMVq5cmddeey1z5szJrbfemqOPPjrjxo3LsmXLKl0iBfXiiy+WxwMHDtzs3EGDBpXHCxcubLWaKL6HH344L730Uurr6/PKK6/kd7/7Xb7+9a9n3333zY033ljp8qiQdevW5dprry2/Pu20094yxz2JLWnOdfSP3JMAANq2TpUuANi0bt265ZRTTskxxxyToUOHpkePHnn55ZdTW1ub73//+3nllVcyffr0jB07Ng888EB22mmnSpdMwSxfvrw87tGjx2bndu/evTx+7bXXWq0mimvvvffOqaeempEjR5bDx3nz5uXnP/95pk2bllWrVuWcc85JqVTKWWedVeFq2dGuv/76PPbYY0mSU089NYceeuhb5rgnsSXNuY7Wc08CAGgfBNxQxRYtWpQ+ffq85f0xY8bk/PPPzwknnJAnnngitbW1ueGGG/K5z31uxxdJoa1atao87ty582bn1tTUlMcrV65stZoopnHjxmXChAkplUobvT98+PB85CMfyZ133plTTz019fX1+fznP59TTjklb3vb2ypULTtabW1tvvzlLydJ+vXrlxtuuKHJee5JbE5zr6PEPQkAoD3RogSqWFPh9nr9+/fPtGnTyqu2J0+evIOqoi3p0qVLebxmzZrNzt3woV5du3ZttZoopt69e78lSNrQySefnMsuuyxJ8vrrr+fmm2/eUaVRYX/+858zbty4NDQ0pEuXLvnZz36Wfv36NTnXPYlN2ZrrKHFPAgBoTwTcUGB77713xowZkySZM2dOFi9eXOGKKJqePXuWx1v6Ff8VK1aUx1tqHQBNOeuss8qB04YPiaPtmj9/fo477ri8+uqr6dixY26//fYcccQRm5zvnkRTtvY6ai73JACAtkHADQU3bNiw8njRokUVrIQi2vAhbhs+3K0pGz7EbcOHu0Fz9evXL7vuumsS96v2YPHixTn22GOzePHilEql/PCHP8zYsWM3u497Ev9oW66j5nJPAgBoGwTcUHCb+/Vb2JINf0Dy7LPPbnbuhp8fcMABrVYTbZt7VvuwdOnSjBkzJvPmzUvyRhut008/fYv7uSexoW29jraGexIAQPEJuKHgZs+eXR4PGDCggpVQRIMHDy5fN1v69ezf/OY3SZLdd989e+21V2uXRhv08ssvZ+nSpUncr9qyZcuW5fjjjy///XTttdfmvPPOa9a+7kmstz3XUXO5JwEAtA0Cbiiw+fPn54EHHkiS7LPPPtl9990rXBFFUyqVyr/q/eyzz+bRRx9tct6jjz5aXi05duxYK97YJjfddFMaGxuTJKNHj65wNbSG119/PSeddFIef/zxJMkll1ySL33pS83e3z2JZPuvo+ZyTwIAaBsE3FClfvnLX6ahoWGTn//1r3/N+PHjs2bNmiTJZz/72R1VGm3MhRdemI4dOyZJzj///KxcuXKjz1euXJnzzz8/SdKpU6dceOGFO7pEqtyCBQvyxBNPbHbOnXfemSuvvDJJ0rVr10ycOHFHlMYOtGbNmowbNy4zZsxIklxwwQW56qqrtvo47kntW0tcR+5JAADtS6dKFwA07fzzz099fX3Gjx+fkSNHZq+99krXrl2zdOnSPPzww7nxxhvLv1b7vve9r8V/bZdieOSRRzJnzpzy6/XXRJLMmTMnU6dO3Wj+GWec8ZZjDBkyJF/4whdy7bXXZtasWRk1alS+9KUvZZ999sncuXPzjW98oxwUfOELX8h+++3XKt+Fytne62jBggU56qijMnLkyHzwgx/MwQcfnH79+iVJ5s2bl2nTpmXatGnllZLf/OY3/cZJG/Sxj30s999/f5Lk6KOPzqRJk/L0009vcn7nzp0zZMiQt7zvntS+tcR15J4EANC+lBrX/58dUFX22muv/OUvf9nivPHjx2fKlCnp06dP6xdF1TnjjDNyyy23NHv+pm7569aty2c+85n88Ic/3OS+kyZNyk033ZQOHfzyT1uzvdfRww8/nKOOOmqL+3Xr1i3XX399zjrrrK2ukeq3tW1C9txzzyxYsKDJz9yT2q+WuI7ckwAA2hcruKFK3XLLLamtrc3MmTMzb968LF26NHV1denRo0cGDRqUww8/PBMmTMjIkSMrXSptQIcOHXLzzTdn/Pjxuemmm/L73/8+S5cuTd++fTN8+PCcffbZOeGEEypdJlXq0EMPzX/8x39k5syZmTVrVl566aUsXbo0DQ0N2XnnnXPggQfmmGOOyac//enyKkrYHPcktod7EgBA+2IFNwAAAAAAheR3OgEAAAAAKCQBNwAAAAAAhSTgBgAAAACgkATcAAAAAAAUkoAbAAAAAIBCEnADAAAAAFBIAm4AAAAAAApJwA0AAAAAQCEJuAEAAAAAKCQBNwAAAAAAhSTgBgAAAACgkATcAAAAAAAUkoAbAAAAAIBCEnADAFs0Y8aMlEqllEqlXH755ZUuZ5OWL1+e3XbbLaVSKe973/sqXQ4AAACtTMANAGzWunXr8rnPfS5J0rdv31x00UUVrmjTevbsmS9/+ctJ3gjlf/rTn1a4IgAAAFqTgBsA2Kzbbrstjz/+eJLkwgsvTM+ePStc0eZ99rOfTd++fZMkX/nKV9LQ0FDhigAAAGgtAm4AYJPWrl2bK6+8MknSvXv3nHvuuRWuaMu6du2a8847L0kyb9683HLLLRWuCAAAgNYi4AYANmnatGl57rnnkiQf//jHs8suu1S4ouY599xz06lTpyTJtddem8bGxgpXBAAAQGsQcAMAm/Sd73ynPJ40aVIFK9k6/fv3z0knnZQkmTNnTu6+++4KVwQAAEBrEHADAE16+umnM3PmzCTJPvvsk8MOO6zCFW2dj3/84+XxjTfeWMFKAAAAaC0CbgCoQg899FBKpVJ5mz59+hb3+fGPf7zRPt/73ve2q4b//M//LI8/9KEPNXu/Z599tlxDt27dsm7duiTJ3Llzc+mll+bd7353dtlll3Tp0iUHHnhgrrzyyqxcufItx3nttdfyf//v/82RRx6ZXXbZJZ07d84ee+yRSZMmZd68eVus48QTT0xNTU2S5N57780rr7zS7O8AAABAMZQaNaUEgKo0ZsyY/OpXv0qSvOtd78rjjz+eUqnU5Nxf/epXOfHEE1NfX58kufjii3P11Vdv1/mHDBmS559/PknywAMP5Nhjj23Wfrfffns+9rGPJUne/e5353e/+12uueaa/Mu//Eu5vn/0rne9Kw8//HB69+6dJHnwwQfzqU99Ki+99FKT8/v06ZNf//rXOeSQQzZbyzHHHJNf//rXSZKbb745Z555ZrO+AwAAAMVgBTcAVKkNA+onn3xyk6u4//jHP2b8+PHl8Pj000/f7nB7/vz55XC7U6dOGTlyZLP3feqpp8rjYcOG5VOf+lQuu+yy1NfX5+1vf3uOOOKIDBs2LB07dizPe/LJJ3PppZcmeaPv93HHHZeXXnopPXr0yGGHHZb3vve96d69e3n+3//+90ycOHGLtYwePbo8vu+++5r9HQAAACgGATcAVKnhw4dn3Lhx5ddXXHFF/vEXrxYuXJgTTzwxdXV1SZLjjz8+U6ZM2e5z19bWlsfDhg3bKFzekieffLI8vvvuu3P77bfnoIMOyoMPPphFixaltrY2f/7zn/OHP/whe+65Z3nurbfemp/+9Kf5/Oc/n549e+Z73/teXn755Tz66KP53e9+l4ULF+bkk08uz3/qqafy+OOPb7aW4cOHl8cPP/xws78DAAAAxSDgBoAqdtVVV6VDhzf+un7qqafyi1/8ovzZsmXLcuKJJ2bRokVJ3mgHMm3atOy0007bfd4//OEP5fGBBx64VftuuIL7b3/7W44//vjMnDkzRx999EYtVg4++OB861vfKr+uq6vLJz7xifTr1y8zZ87Mueeemy5dupQ/33nnnXPLLbekZ8+e5feeeOKJzdbyzne+szxesmRJFi5cuFXfBQAAgOom4AaAKjZs2LB88pOfLL9ev4p7zZo1GTduXJ5++ukkyeDBg3P33XenR48eLXLe2bNnl8f77LNPs/dbsmTJRn2z99tvv0ybNi3dunVrcv6RRx650evGxsb813/9Vw444IAm5++yyy4bhdZ///vfN1vPwIEDyw+aTDb+XgAAABSfgBsAqtzll19eXpX9pz/9KdOmTcvEiRPz0EMPJUl23XXX3Hvvvenfv3+LnfMvf/lLeTxgwIBm77dhe5Ikuf766zcbuq9/qOR6kyZN2mK/7169epXHG67w3pQN69/wewEAAFB8Am4AqHKDBw/OZz7zmfLrCRMm5Cc/+UmSpGvXrrnzzjszZMiQFj3n0qVLy+Odd9652ftt2J5k7733zoknnrjZ+evbq6z3uc99bovneOGFF8rjQYMGbXH+hvW//PLLW5wPAABAcQi4AaAAvvrVr5bbfKxcuTJJ0rFjx9x+++0ZMWJEi59vxYoV5XHXrl2bvd+GK7hPOOGEjXpuN2V9i5Uk2XPPPfOOd7xjs/Pr6+szZ86c8uuhQ4dusaYN69/wewEAAFB8Am4AKIC3ve1tGTVq1Ebvfetb38opp5zS6udubGxs9twNV3AffvjhW5y/4UMimzN/9uzZWbNmTZKkR48e2Xfffbe4z9bUDwAAQLEIuAGgAL7xjW/kgQce2Oi9DduItLTu3buXx6tWrWrWPqtWrcr//M//lF8fcsghW9xnw4C7OfM3DNAPOuigdOiw5f+VWb/iPdn4ewEAAFB8Am4AqHL/+Z//mYsvvvgt73/7299utZC7b9++5fGrr77arH2efvrpNDQ0JEm6deuW/ffff4v7bNjSpDkB94bz3/WudzWrrg3r32233Zq1DwAAAMUg4AaAKvbggw9m4sSJ5TYbF198cfnBisuXL8/VV1/dKufdc889y+N/fBDkpmzt6uq6urrMmzev/Lq1Au7FixeXxxt+LwAAAIpPwA0AVeqpp57Kqaeemvr6+iTJxIkTc/XVV+crX/lKec4NN9yQF198scXPfcABB5THGz7UcXO2djX2U089VQ7uBw0alF133bVZ+6zXnID7xRdfLPfsTpJhw4ZtcR8AAACKQ8ANAFXohRdeyIknnpi6urokyfHHH5+bbropSTJp0qTySuRVq1blyiuvbPHzH3rooeXx008/3ax9NgyfW6P/9gsvvJC//e1vSZKOHTvmne985xb3+dOf/lQe77bbbuXV7wAAALQNAm4AqDKvvvpqPvCBD5RbaxxyyCGZNm1aOnXqlCTZaaedcumll5bn/+hHP2r2KuvmOuKII8rjZ599Nq+99tpm5zc2NuaPf/xj+fXWthvZ2gdMDh06NF26dNniPrNmzSqPR48evcX5AAAAFIuAGwCqyKpVq3LKKafkmWeeSfJGz+i77rorPXr02GjeGWeckcGDBydJGhoactlll7VoHfvss0/22WefJMnatWszY8aMzc6fP39+li1bliTp1KlT3vGOd2zxHFu7gntb+m/X1taWx8cff3yz9gEAAKA4BNwAUCXWrVuXT3ziE3nkkUeSJDvvvHPuueeevP3tb3/L3E6dOuWrX/1q+fXtt9++UTuOlnDqqaeWx/fdd99m5264uvqAAw7Y4urq+vr6zJ49u/y6NQLuFStWlIP5Tp06ZezYsVvcBwAAgGIRcANAlbjwwgvzi1/8IklSU1OTO+64Y6OHPf6jT33qU9l3332TvNEi5JJLLmnRej75yU+Wx9OnT9/s3K1tN/LnP/+5/PDHXXbZJXvssccW99nagPuee+7JqlWrkiTHHXdcdtttty3uAwAAQLEIuAGgCvzrv/5rJk+enCQplUq59dZb8/73v3+z+3Tq1Gmj1iS//OUv8+ijj7ZYTQcddFAOO+ywJG+0INncsbf2AZNbG4gvX7488+fPL79uTsD9k5/8pDw+++yztzgfAACA4ik1NjY2VroIAKA63X777fnYxz6WJPn0pz+dH/zgBxWuqHmWLFmSgQMHpr6+PnvvvXeef/75dOjg5/oAAABtjX/pAQCb9OEPfzj77bdfkuS2227L3/72twpX1Dw33nhj6uvrkyRf+tKXhNsAAABtlH/tAQCb1LFjx3IblBUrVuSGG26ocEVbtmrVqnz3u99Nkuy1116ZOHFihSsCAACgtQi4AYDN+vjHP17uk/3tb387y5cvr3BFm3fDDTdkyZIlSZKrr746O+20U4UrAgAAoLUIuAGAzerQoUP5AZhLly7NddddV+GKNm358uW5+uqrkySjRo0q9w8HAACgbfKQSQAAAAAACskKbgAAAAAACknADQAAAABAIQm4AQAAAAAoJAE3AAAAAACFJOAGAAAAAKCQBNwAAAAAABSSgBsAAAAAgEIScAMAAAAAUEgCbgAAAAAACknADQAAAABAIf1/My5AFrHYQ/oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_on = 1.\n",
    "S_prior.at_current_time(t_on)\n",
    "plot_colormap(msh,S_prior.value,'S_{prior}', 'g.m^{-2}.s^{-1}', cmap=\"jet\",title=f't = {\"{:.2f}\".format(t_on)} s')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a9d7a1-e09e-42af-a58b-9f0b47205187",
   "metadata": {},
   "source": [
    "The user can include more prior knowledge of the control variable such as the population dynamic model. \n",
    "In such case, the user should specify the matrix or linear operator (by means of its matrix-vector product and of its transpose matrix-vector product) used to compute the residual of the population dynamic model. \n",
    "\n",
    "Up to now, the [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/population_dynamique.py) `population_dynamic` contains a stationnary population dynamique model $\\partial_t p = 0$ and a population dynamique model with linear reaction term $\\partial_t p = \\gamma p$ modelling that a given proportion $\\gamma$ of the population stops to emit pheromone at each time step. The user can also easily specify its own population dynamic model using the different differential operators included in the toolbox (see [the different notebooks to use differential operators](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/test_numerical_scheme/)). \n",
    "\n",
    "In the following, for sake of simplicity, a population dynamic model with linear reaction term is used with a null reaction term, which is then equivalent to a stationary population dynamic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f62c90fa-faea-4f53-b0ad-bac7333f0785",
   "metadata": {},
   "outputs": [],
   "source": [
    "death_rate = np.zeros((msh.t_array.size, msh.y.size, msh.x.size))\n",
    "population_dynamic_model = PopulationDynamicModel(msh, death_rate=death_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4d73d305-03fe-42ec-86ff-ca1b9d21369b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ctrl = Control(S_prior, msh, population_dynamique_model=population_dynamic_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb5ccea-b6d2-474e-a88a-dd5d2c203881",
   "metadata": {},
   "source": [
    "For sake of illustration, the target source term that has been used to generate the data is stored in a dedicated object of the `Control`class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "677213d0-6d45-4844-9db4-d3454f1661fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "ctrl_target = Control(S_target, msh)\n",
    "ctrl_target.value = ctrl_target.background_value "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ac8062d-8675-49c6-bf3e-fcfb6292facb",
   "metadata": {},
   "source": [
    "## Specifying the adjoint model and its solver using the `adjoint_convection_diffusion_2D` submodule"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22a04ce2-8cd2-496b-8892-1550fbac2ca9",
   "metadata": {},
   "source": [
    "The adjoint model and its solver are here contained in the `AdjointDiffusionConvectionReaction2DEquation` class of the [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/adjoint_convection_diffusion_2D.py) `adjoint_convection_diffusion_2D`.\n",
    "\n",
    "Similarly to the direct model, building an object of this class requires to specify the wind velocity field, the diffusion tensor and the loss coefficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2d1b5692-504e-4941-9fb7-dc52d3e630de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Load of the inverse of the matrix of the implicit part of the implicit with stationnary matrix inversion scheme ===\n"
     ]
    }
   ],
   "source": [
    "solver = 'implicit with stationnary matrix inversion'\n",
    "tol = 1e-10\n",
    "adjoint_model =  AdjointDiffusionConvectionReaction2DEquation(U, K, deposition_coeff, msh, tol_inversion=tol, time_discretization=solver)\n",
    "fname = 'inv_matrix_implicit_solver.npy'\n",
    "adjoint_model.init_inverse_matrix(path_to_matrix=path_data, matrix_file_name=fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97cb464c-39bf-4fb4-98e2-aa1e22ca7f77",
   "metadata": {},
   "source": [
    "A special care has been put on the time discretization so that the discretized adjoint problem is effectively the adjoint (i.e. the matrix transposition) of the discretized direct problem. \n",
    "\n",
    "The reader can find more details in the notebooks located in the folder `./test_numerical_scheme/` that introduces and test the numerical schemes and their convergence. These notebooks also include test of the [adjoint operators](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/test_numerical_scheme/test_adjoint.ipynb) and of the [gradient](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/test_numerical_scheme/test_gradient.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "239cd006-3cd4-4d9f-9d54-8e45661a3a6e",
   "metadata": {},
   "source": [
    "## Specifying the cost function using the `cost` submodule"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f8fd839-b0e1-412d-ab03-fe1543fd07a7",
   "metadata": {},
   "source": [
    "The cost function, its different terms and its gradient are contained in the `Cost` class of the `cost` [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/cost.py). \n",
    "The user should provide an object of the class `Obs` that will be used to compute the observation term of the cost function, and an object of the class `Control` that will be used to compute the regularization terms based on biological prior knowledge on the control variable and updated during the optimization process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "15378b49-a6b7-441a-9059-bda4f2203305",
   "metadata": {},
   "outputs": [],
   "source": [
    "cost = Cost(msh, obs, ctrl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a93d93f8-24bd-4f87-bd87-656f763c4471",
   "metadata": {},
   "source": [
    "The class `Cost` contains several useful attributes and methods that will be presented below. \n",
    "This class contains the attribute `j_obs` that is the observation term of the cost function, the attribute `j_reg` that is the dictionnary containing the different regularization terms, the attribute `alpha` that is the dictionnary of weight coefficient of the different regularization terms (see eq. (2)). \n",
    "The attribute `implemented_regularization_types` contains the list of keywords of the implemented regularization terms. The keywords of the attributes `j_reg` and `alpha` corresponds to keywords present in the attribute `implemented_regularization_types`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "14934178-0734-40dd-a360-a34cc0e6f51e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Tikhonov', 'Population dynamic', 'LASSO', 'time group LASSO', 'logarithm barrier']\n"
     ]
    }
   ],
   "source": [
    "print(cost.implemented_regularization_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b19c3863-7d60-47d2-9423-585b61ed6399",
   "metadata": {},
   "source": [
    "The list of keyword of regularization terms to be considered and the associated dictionnary of weight coefficient can be provided as input of the instanciation of the class `Cost` or can be added and modified afterwards as in the following. If no regularization terms and its weigth coefficient are specified, than the inference will be made without regularization terms by default.\n",
    "\n",
    "The class `Cost` also contains the methods `obs_cost` and `reg_cost` that computes resp. the observation and regularization terms and store the result in the associated attribute, as well as a method `cost_and_gradient` that brings together several methods and returns the evaluation of the cost function and its gradient from an array of shape (nb time point * nb cell in y axis * nb cell in x axis, 1) containing a quantity of pheromone emitted. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb361c5-e6d1-4fbe-ac7c-5ba424c26b1a",
   "metadata": {},
   "source": [
    "# Inference of the quantity of emitted pheromones "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b679233f-0360-47d6-a01a-9110c3dc5615",
   "metadata": {},
   "source": [
    "The inference is performed using the `minimize` method of the class `Cost`. \n",
    "This method takes as inputs: \n",
    "\n",
    "- the direct model that has been instanciated previously,\n",
    "- the adjoint model that has been instanciated previously,\n",
    "- the keyword of the optimization algorithm (see below).\n",
    "  \n",
    "The user can also provide the following optional intputs.\n",
    "\n",
    "- The input `options` is the dictionnary of optional inputs of the optimization algorithm, such as tolerance of the termination criteria, the maximal number of iteration or the step size of the gradient descent.\n",
    "- The input `path_save` is the string of the path to the folder in which the outputs will be saved. If not specified, the results of the optimization will not be save, just returned as outputs of the method.\n",
    "- The input `j_obs_threshold` is the value of the threshold of the observation term of the cost function under which the optimization algorithm is stopped. This is particularly useful when some properties of the observation error are known and that we want to satisfy the Morozov's principle. For instance, one do not want the observation term of the cost function to be less than the square of the norm of the noise. \n",
    "- The input `s_init` enables to specify the initial value of the control variable. If not, the initial value is 0.\n",
    "- The input `restart_flag` is a boolean flag that is False by default. It the flag is True, then the algorithm will load the outputs that has been previously saved in the folder given by the input `path_save` and start again where the algorithm stopped.\n",
    "\n",
    "When the optimization algorithm reached the termination criteria, the method `minimize` returns: \n",
    "\n",
    "- the direct model with the quantity of pheromone emitted resulting from the optimization algorithm as source term,\n",
    "- the array containing the observation term of the cost function at each iteration of the optimization algorithm,\n",
    "- the dictionnary containing the arrays containg the regularization terms of the cost function at each iteration of the optimization algorithm,\n",
    "- the array containg the resulting quantity of pheromone emitted.\n",
    "\n",
    "When the input `path_save` is specified, in addition to the outputs detailled above, the method saves: \n",
    "\n",
    "- the weight coefficients of each regularization terms, in order to keep track of the test case,\n",
    "- the array containg the resulting quantity of pheromone emitted in a file which name includes the number of the last iteration, to keep track of the different runs in case of restart,\n",
    "- the array containing the step size at each iteration of the optimization algorithm, to keep track of the different runs in case of restart,\n",
    "- the sum of the step size over the iterations of the optimization algorithm and the trajectory integrated quantity of pheromone emitted, to compute the trajectory-integrated one-sensor adjoint states. (*This feature will be improved in a future update of the module*)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a2a617-384a-4856-b271-3e2d6a5b8726",
   "metadata": {},
   "source": [
    "## Inference without regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4496bf60-7d61-4b52-8770-3fcfaf78aab8",
   "metadata": {},
   "source": [
    "In a first time, the inference is illustrated without regularization. In this situation, the user should use a differentiable optimization algorithm such as the gradient descent algorithm. This algorithm has been implemented in the [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/gradient_descent.py) `gradient_descent`. \n",
    "\n",
    "*Warning: the values of hyperparameter used here, especially the values of the step size and the maximal number of iterations, are not ideal and more attention should be given in real cases but they allow fast computations and illustrative results in this notebook.* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "29f95f9a-a049-4683-ad9b-3e6d1d92e28e",
   "metadata": {},
   "outputs": [],
   "source": [
    "options = {\n",
    "    \"nit_max\": 20, \n",
    "    \"step size\": 5. * 1.0e+1\n",
    "} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a78fc9bb-be7d-4b2d-ab77-0cc806913e17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initialization of the optimization process, computing the initial cost\n",
      "Optimizing using the gradient descent algorithm \n",
      "iteration 1: j = 1.167344e-02, j_obs = 1.167344e-02, ite comp time = 3.655e+00s -\n",
      "iteration 2: j = 4.660842e-03, j_obs = 4.660842e-03, ite comp time = 6.494e+00s -\n",
      "iteration 3: j = 3.549630e-03, j_obs = 3.549630e-03, ite comp time = 6.414e+00s -\n",
      "iteration 4: j = 3.090900e-03, j_obs = 3.090900e-03, ite comp time = 6.297e+00s -\n",
      "iteration 5: j = 2.786551e-03, j_obs = 2.786551e-03, ite comp time = 5.623e+00s -\n",
      "iteration 6: j = 2.553989e-03, j_obs = 2.553989e-03, ite comp time = 5.277e+00s -\n",
      "iteration 7: j = 2.365947e-03, j_obs = 2.365947e-03, ite comp time = 5.333e+00s -\n",
      "iteration 8: j = 2.208759e-03, j_obs = 2.208759e-03, ite comp time = 5.149e+00s -\n",
      "iteration 9: j = 2.074275e-03, j_obs = 2.074275e-03, ite comp time = 5.373e+00s -\n",
      "iteration 10: j = 1.957173e-03, j_obs = 1.957173e-03, ite comp time = 5.246e+00s -\n",
      "iteration 11: j = 1.853772e-03, j_obs = 1.853772e-03, ite comp time = 5.280e+00s -\n",
      "iteration 12: j = 1.761407e-03, j_obs = 1.761407e-03, ite comp time = 5.952e+00s -\n",
      "iteration 13: j = 1.678092e-03, j_obs = 1.678092e-03, ite comp time = 6.220e+00s -\n",
      "iteration 14: j = 1.602304e-03, j_obs = 1.602304e-03, ite comp time = 6.819e+00s -\n",
      "iteration 15: j = 1.532857e-03, j_obs = 1.532857e-03, ite comp time = 6.523e+00s -\n",
      "iteration 16: j = 1.468809e-03, j_obs = 1.468809e-03, ite comp time = 6.525e+00s -\n",
      "iteration 17: j = 1.409406e-03, j_obs = 1.409406e-03, ite comp time = 6.429e+00s -\n",
      "iteration 18: j = 1.354034e-03, j_obs = 1.354034e-03, ite comp time = 6.441e+00s -\n",
      "iteration 19: j = 1.302192e-03, j_obs = 1.302192e-03, ite comp time = 6.730e+00s -\n",
      "iteration 20: j = 1.253466e-03, j_obs = 1.253466e-03, ite comp time = 6.252e+00s -\n",
      "\n",
      "final iteration:\n",
      "number of iteration = 20\n",
      "j = 0.10737760430141632 % of the initial cost\n",
      "j_obs = 0.10737760430141632 % of the initial cost\n"
     ]
    }
   ],
   "source": [
    "direct_model_no_reg, j_obs_no_reg, _, S_a_no_reg = cost.minimize(\n",
    "    direct_model,\n",
    "    adjoint_model,\n",
    "    \"gradient descent\", \n",
    "    options=options,\n",
    "    path_save=path_output,\n",
    "    j_obs_threshold = noise_level\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fbb76e3-ac5f-45fa-9e05-ad1d108c83fc",
   "metadata": {},
   "source": [
    "The resulting optimal value of the quantity of emitted pheromones can be plotted at the a given time using the `plot_ctrl` [method](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/utils/plot_ctrl.py) of the `utils` module. The cost function and its different terms can also be plotted using the `plot_cost` [method](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/utils/plot_cost.py) of the same module (the empty inputs correspond to the regularization terms, see below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "33b85251-f7c3-4f35-bedf-210af6e6e998",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/slabarthe/Documents/SVN_GIT/PHEROSENSOR/pherosensor-toolbox/src/utils/plot_ctrl.py:106: UserWarning: No contour levels were found within the data range.\n",
      "  contour = ax.contour(msh.x, msh.y, ctrl_target_current_t, colors='k', linestyles='dashed', levels=0, linewidths=4)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABeQAAAUJCAYAAAAB4gM0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAD9uUlEQVR4nOzdeZiVdd0/8Pdx2FcXxBVFRURzS8RHRUVcMzU1UjM3XMrUCssWy1zLpfyZWU+PuaSouZRmllKmmZILT0iBmlIKCCKYissMyrAMzO8P4zxM7MPMOWdmXq/rOtd1n3N/7+/9OcyZOcP7fOdzF+rr6+sDAAAAAAA0q7XKXQAAAAAAALQFAnkAAAAAACgBgTwAAAAAAJSAQB4AAAAAAEpAIA8AAAAAACUgkAcAAAAAgBIQyAMAAAAAQAkI5AEAAAAAoAQE8gAAAAAAUAICeQAAAAAAKAGBPAAAAAAAlIBAHgBoVu+9915+8pOfZN99982mm26ajh07ZtNNN82+++6bn/zkJ3nvvfea7dz19fX5xz/+kdtvvz0jRozInnvumS5duqRQKBRva+qhhx7K8ccfn2222SbdunXLuuuumx133DFf+cpX8vzzzzfBswAAAKC1KNTX19eXuwgAKJXhw4fn1ltvTZJcdNFFufjii5v8HEuGvK+88kr69u3b5OdoKR599NGcdNJJmTlz5nLHbLzxxrntttuy//77N+m5b7jhhnz1q1/N7NmzVziusb8KvfXWWzn11FPz4IMPLnfMWmutlW984xu59NJL065du0adBwAAgNbDCnkAoFn86U9/ysc+9rEGYXzfvn0zZMiQ9O/fv/jYzJkzc8ghh+Sxxx5r0vPPnDlzpWF8Y73//vs58MADG4TxPXr0yO67755dd901nTt3TpIsWrQoV1xxRT7/+c83Sx0AAAC0LAJ5AKDJvfHGG/nUpz6Vurq6JMmGG26YRx55JK+88koef/zx/POf/8wzzzxTDOYXLFiQYcOG5Y033mjyWgqFQrbZZpuccMIJ+eEPf5ivfe1razzn5z73uTz77LPF+xdccEFmzpyZMWPG5Jlnnslrr72Wz372s8X9P/vZz3LjjTeu8XkBAABo2QTyAECTu+yyy/Luu+8mSTp27JhHH300BxxwQIMxu+66a/785z+nV69eSZJ33303l19+eZPVcPjhh+exxx5LdXV1gz7y22233RrN+9e//jV33XVX8f4FF1yQSy+9NF27di0+tu666+aGG27ICSecUHzswgsvzJw5c9bo3AAAALRsAnkAoEm99957ueGGG4r3v/zlLy83BN9ggw3yne98p3j/+uuvb7KLvA4cODD77rtvunfv3iTzLfb973+/uL3ZZpvl29/+9nLHXnvttenSpUuS5F//+lfx+gUAAAC0TQJ5ACpabW1tfvvb32bEiBHZe++9s+GGG6Zjx47p2rVrNttssxx22GH50Y9+lPfff3+F8/Tt2zeFQqFBIHrJJZekUCgs87a6Hn/88WUeu8UWWyxz/uHDh6/2OVqKBx98MPPmzUvyYbuYz33ucyscf8IJJxRXl8+bNy+jRo1q9hoba+7cufnd735XvH/qqaemQ4cOyx2/7rrr5lOf+lTx/n333ddktcyePTvXXXddDj300PTp0yddunRJ+/bts/baa2e77bbLkUcemSuuuCLPP/98k50TAACANSOQB6Bi3XXXXdlggw1yxBFH5Ec/+lGefPLJvPHGG5k/f37mzJmT6dOnZ9SoURkxYkQ233zzPPDAA+UumaRBoL7NNttkiy22WOH4bt26Za+99lrm8ZVm9OjRDT78+djHPrbSYw455JDlHt9Yf/rTn7LNNtvkrLPOyu9+97u89tprqa2tTV1dXaqrqzNx4sT85je/ybe+9a3suOOOGTt27BqfEwAAgDXXrtwFAMDyTJ48ObNnzy7e7927d/r27Zvu3buntrY2L730UmbNmpUkeeedd3LkkUfm/vvvz+GHH77UXEOGDMkbb7yR559/PjNnzkySbLXVVunXr1+T1Lruuuvm4IMPTpL84Q9/KD6+zz77pHPnzkuN32GHHZrkvJVoyYud7rHHHqt0zB577FH8d1vy+EqzZG0dO3bMLrvsstJjlvw3WLBgQSZOnJhBgwY1uobnnnsuhx56aObOnVt8bP3118/WW2+dLl265P3338/06dMzY8aM4v5FixY1+nwAAAA0HYE8ABWrUChk8ODBOfnkk3PooYdm4403XmrMk08+ma9+9av5y1/+kkWLFuXUU0/NlClTluobvrhVzfDhw4vbJ5xwQi6++OImqXXHHXfMQw89VKx7yfP27du3Sc7REtTV1WXSpEnF+1tttdUqHbfkuEmTJqWuri7t2lXerykTJ04sbvfp0yft27df6TF9+vRJhw4dMn/+/OIcaxLIn3/++cUwftttt82NN96YwYMHLzXuzTffzIMPPtignz8AAADlpWUNABXrnHPOyZNPPpnPfvazywzjk2SvvfbK448/nt133z1JMmvWrNx2222lLLMsFvfEb8pbU3xwMHPmzCxYsKB4f7PNNlul45YcN3/+/OJfMVSaadOmFbdX9bmttdZa2WSTTYr3p06d2ujz19XV5ZFHHkny4Qc/v/nNb5YZxicf/kXJqaeemv/93/9dow8AAAAAaDoCeQAq1uILfa5Mp06dctlllxXv/+Y3v2mukliJmpqaBvd79uy5Ssf16NGjwf0lWxVVkiWf36o+t6Th81uT5/bWW28VL5jbu3fvbL311qt0XFVVVaPPCQAAQNOpvL8FB4BG+K//+q/i9rhx48pYSWks7onflDbYYIM1nuODDz5ocL9Tp06rdNx/9tn/z3kqxZJ1repzSxo+vzV5bkue880338y//vWvbLjhho2eDwAAgNISyAPQIrzyyit59NFH89xzz+Wtt97K7NmzU1dXt8yx7777bubMmZMuXbqUuMrSWdwHv9Is2a4mySr3gf/PcYv7rVeaJZ/f6vS4X3Lsmjy3ddZZJ1tssUVeeeWV1NfX58gjj8yNN97Yqi8SDAAA0JoI5AGoaP/4xz8yYsSIPPLII6mvr1/l46qrq1t1IF+p/vPffPHFR1fmP8etaruiUlvy+a3qc/vPsWv63M4555yMGDEiSfKXv/wlO+64Y3baaaccdNBB2WeffTJ48OCss846a3QOAAAAmoce8gBUrD//+c8ZOHBgHn744dUK45MU+2xTWt26dWtwv7a2dpWOmzNnzgrnqRRL1rWqzy1p+PzW9Ll98YtfzFlnndXgsWeffTZXXXVVDj/88PTq1Sv/9V//lWuuuSbV1dVrdC4AAACalkAegIpUU1OTo48+uhhkdu/ePSNGjMioUaPy8ssvF1vW1NfXF2+UX69evRrcf/3111fpuH/9618N7q+33npNVlNTWvL5repzSxo+vzV9boVCIT/5yU8yevToHHXUUenYsWOD/YsWLcrYsWPzla98JVtssUXuvPPONTofAAAATUfLGgAq0s0335w333wzyYd9s//yl79k6623Xu742bNnl6q0inDyySc3y0Vd17Q3/brrrptevXpl1qxZSZJXX311lY6bPn16cXv99dfPuuuuu0Z1NJdtttkmDzzwQJJVf24ffPBB3nnnneL9AQMGNEkt++yzT/bZZ5/MnTs3Tz31VEaPHp3Ro0fn6aefLl5f4d13383xxx+fTp065ZOf/GSTnBcAAIDGE8gDUJEeeeSR4vaIESNWGMYnyYwZM5q7pIoyevToTJs2rUnn3HzzzZtknm233TZPPPFEkmTChAmrdMz48eMbHF+plqztrbfeyuuvv56NNtpohcf8579BUz+/Tp06Zf/998/++++fJHnnnXdy00035ZJLLin+hcnXvvY1gTwAAEAF0LIGgIq05OrjXXfddaXjx4wZs0rzrrXW/731NVebm0Kh0OznqGT77LNPcfsvf/lLcbX2iiwO8P/z+Erzn7UtWffyLDmmT58+2WKLLZq8riWtu+66+frXv56f/OQnxcemTJmSyZMnN+t5AQAAWDmBPAAVacGCBas1fuTIkas0rmvXrsXt1bko5+ooxTmmTp3aoH9+U9ymTp3aJLUdccQRxe2amppii5fl+etf/5p//vOfyzy+0vTr1y/bbbdd8f4dd9yx0mOW7OH+iU98olnqWpYjjzyywf2mbnEEAADA6hPIA1CRlmwD8tRTT61w7D333JM///nPqzTvhhtuWNyeNGlS44qrgHNUsl133TUf+chHivevuOKKLFy4cLnjL7vssuL29ttvn4EDBzZrfWtq+PDhxe1Ro0Y1aLfzn37729/m+eefL94/+eST1+jcq/MXF/95XYVK7csPAADQlgjkAahIQ4YMKW7/93//d4NQc0l/+MMfGgSkK7PLLrsUtx9++OG8+OKLja5xVc5x3XXXZd68eU1+jkpWKBTyne98p3j/mWeeyde+9rVlhsnXXHNNfv3rXxfvf+c732nQ8uc/7bvvvikUCikUCunbt2+T1r2qzj777OKHLgsXLszxxx+f119/falxEydOzBlnnFG8f8QRR2TQoEFrdO4///nPOeyww/L444+vMJxfuHBhzjvvvOL9DTfcMP3791+jcwMAALDmCvVtsbktABVv5syZ6devX7HlS7du3XLmmWdm6NCh6dq1a1599dX8+te/zn333ZckOf3003PTTTcVj3/llVeWGdjOnz8/ffr0yZtvvpkkqaqqykc/+tFsuOGGqaqqKo67//77G137b37zmwbtQtZZZ53svPPO6dGjR/Gx/fbbL1/60pcafY6W4Jhjjsk999xTvL/XXnvl9NNPz+abb55//etfueOOO/Lggw8W9x999NH55S9/ucI5991334wePTrJhxehXVmbnU6dOi312MKFCxv0te/YseNSY0488cTceOONy533gQceyJFHHplFixYlSTbYYIN88YtfzKBBg1JXV5c///nPue6661JTU5MkWX/99TN27Ng1/hDh8ccfz9ChQ5N82I/+kEMOya677po+ffqke/fumT17dp5//vnceuuteeGFF4rHXXvtta3+9QYAANASCOQBqFi33357Tj755JW26dh7773z8MMPp3PnzsXHlhfIJ8mDDz6Yo48+OnPnzl3unGv69njiiSfm5z//+XL3n3zyyavc976lqq2tzaGHHprHHntspWOHDh2aUaNGNfgaLsvqBvIrWm2/Iqvy9fnJT36SESNGrLAdT/Jhq5gHHngge+65Z6NqWdKSgfyq+vznP5//+Z//afS/BQAAAE1HyxoAKtaJJ56Y3/zmN9liiy2WuX+dddbJ+eefnz/96U/LXAm9PIcddlieffbZnHPOOdlll12y9tprN1gd3xRuv/323HffffnUpz6VLbfcMl27dm1zgWjnzp3zxz/+MVdeeWV69+69zDG9e/fOlVdemT/+8Y8rDeMrzdlnn50///nP2W233Za5v127dhk2bFiee+65Jgnjk2SHHXbIxRdfnF133XWlr9lddtkl9913X6677ro299oDAACoVFbIA1Dx6urqMmbMmDz77LOpqalJr1690rdv3+y7777p0KFDuctjFSxu4zJ58uTMmjUrvXr1ylZbbZV99tkn7dq1K3d5a+yll17KM888k5kzZ6ZDhw7ZdNNNs88++2T99ddvtnO+//77mTBhQiZNmpS33nor8+bNS7du3bLJJptk1113Xe4HWQAAAJSPQB4AAAAAAEpAyxoAAAAAACgBgTwAAAAAAJSAQB4AAAAAAEpAIA8AAAAAACXQrtwF8KFFixZl5syZ6d69ewqFQrnLAQAAaHXq6+sze/bsbLzxxllrLevTAIDSE8hXiJkzZ6ZPnz7lLgMAAKDVmz59ejbddNNylwEAtEEC+QrRvXv3f29dlqRTOUsBAABopeYmOX+J/38BAJSWQL5C/F+bmk5JOpezFAAAgFZNm1AAoFw0zQMAAAAAgBIQyAMAAAAAQAkI5AEAAAAAoAQE8gAAAAAAUAICeQAAAAAAKAGBPAAAAAAAlIBAHgAAAAAASkAgDwAAAAAAJSCQBwAAAACAEhDIAwAAAABACQjkAQAAAACgBATyAAAAAABQAgJ5AAAAAAAoAYE8AAAAAACUgEAeAAAAAABKQCAPAAAAAAAlIJAHAAAAAIASEMgDAAAAAEAJCOQBAAAAAKAE2pW7AAAAACpbff2ZjT62ULiuCSsBAGjZrJAHAAAAAIASEMgDAAAAAEAJCOQBAAAAAKAEBPIAAAAAAFACAnkAAAAAACgBgTwAAAAAAJSAQB4AAAAAAEpAIA8AAAAA0Ax+/vOf54wzzsiuu+6ajh07plAoZOTIkeUuizJqV+4CAAAAAABao29/+9uZNm1aevXqlY022ijTpk0rd0mUmRXyAAAAAADN4KabbsrUqVPz1ltv5fOf/3y5y6ECWCEPAAAAANAMDjjggHKXQIWxQh4AAAAAqBhvvvlmHnzwwVx44YU55JBD0qtXrxQKhRQKhQwfPny15po2bVrOPffcDBgwIF27ds26666bQYMG5aqrrsqcOXOa5wnAClghDwAAAABUjA022KBJ5nnggQdywgknpKampvjYnDlzMm7cuIwbNy433XRTRo0alX79+jXJ+WBVCOQBAABYoULhunKXAEAbtdlmm2XAgAF5+OGHV+u48ePH59hjj01tbW26deuWb37zmxk6dGhqa2tz991358Ybb8xLL72UQw89NOPGjUv37t2b6RlAQwJ5AAAAAKBiXHjhhRk0aFAGDRqUDTbYIFOnTs0WW2yxWnOMGDEitbW1adeuXR5++OHssccexX377bdftt5663z961/PSy+9lKuvvjoXX3zxUnOce+65mTdv3mqdc+utt16tOml7BPIAAAAAwGp74YUXcuaZZ+ZXv/pV1l9//ZWOX7hwYU488cQceuihOf7445c77pJLLlmjusaOHZsnnngiSXLaaac1COMXO/fcc3PLLbdk4sSJufbaa3P++eenffv2DcZcf/31+eCDD1b5vJ/61KcE8qyUi7oCAAAAAKultrY2Bx98cJ544okccMABeeedd1Y4ftGiRTnllFNy1113Zfjw4Rk3blyz1Xb//fcXt0855ZRljllrrbVy0kknJUnee++9PPbYY0uNef/991NfX7/Kt3333bc5ng6tjEAeAAAAAFgtnTt3zqWXXppCoZDnnnsuBx10UKqrq5c5tr6+PmeccUZuv/32JMlnPvOZDBw4sNlqe/LJJ5MkXbt2XeF5hgwZUtx+6qmnmq0eWJJAHgAAAABYbaeeemr++7//O0ny17/+NR/72Mcye/bspcZ94QtfyE033ZQkOfbYY3PzzTenUCg0W10TJ05MkvTr1y/t2i2/Y/eAAQOWOgaamx7yAAAAAECjnHXWWamtrc1Xv/rV/O///m8OPfTQPPTQQ+nSpUuS5Ctf+Ur+53/+J0ly5JFH5uc//3mqqqqarZ65c+dm1qxZSZJNN910hWPXWWeddO3aNR988EGmT5/eLPXcdNNNxRX7zz//fPGxxx9/PEmy11575fTTT2+Wc1OZBPIAAAAAQKOde+65qa2tzQUXXJAnnngihx9+eEaNGpVLL70011xzTZLkkEMOyS9+8YsVrlhvCkuu0O/WrdtKxy8O5N9///1mqefJJ5/Mrbfe2uCxp556qkGLHIF82yKQBwAAAADWyLe//e3MmTMnV1xxRf70pz9l++23z+TJk5Mk++23X+6777506NCh2euYO3ducXtVztexY8ckH16ktjmMHDkyI0eObJa5aZn0kAcAAAAA1tjll1+ec845J0mKYfxee+2V3/72t+nUqVNJaljyPPPnz1/p+Hnz5iX58CK1UAoCeQAAAACgSWy22WYN7vfu3btkYXySdO/evbi9Km1oPvjggySr1t4GmoJAHgAAAABYY9ddd12+8pWvJEnWW2+9JMl9992Xk046KYsWLSpJDZ06dSqe+7XXXlvh2HfffbcYyPfp06fZa4NEIA8AAAAArKGbb745Z599dpJkl112ycsvv5xTTz01SXLnnXfm9NNPT319fUlq2W677ZIkkyZNSl1d3XLH/eMf/yhub7vtts1eFyQCeQAAAABgDdx555357Gc/m/r6+uywww55+OGHs8466+TGG2/M8ccfnyS55ZZbcuaZZ5aknr322ivJh+1o/vrXvy533OjRo4vbgwcPbva6IBHIAwAAAACN9Ktf/arYkmbbbbfNH//4x2LLmLXWWiu33nprjj766CTJ9ddfnxEjRjR7TUceeWRx+5ZbblnmmEWLFuW2225Lkqy99toZOnRos9cFiUAeAABagPZuFXsDgLbrgQceyHHHHZeFCxdm6623zqOPPprevXs3GFNVVZU777wzRxxxRJLkRz/6Ub7+9a83a1277bZb9t577yTJz372s4wZM2apMVdffXUmTpyYJBkxYkTat/e+TmkU6kvVvIkVqqmpSc+ePZNcnaRzucsBAKCi+A9i5VpQ7gJYLbVJzk11dXV69OhR7mIAWrTa2tpsscUWeeONN7LFFltk9OjRK7ww6vz583PkkUfm97//fZLkL3/5S3bbbbdljn3yySczadKk4v1Zs2bla1/7WpIPW8ucfvrpDcYPHz58qTnGjx+fwYMHp7a2Nt26dcu3vvWtDB06NLW1tbn77rtzww03JEn69++fcePGpXv37qv1/KGxBPIVQiAPAMDyCeQrl0C+ZRHIAzSlp59+Oqeddlp+//vfp2/fvisdP3fu3Bx++OE59NBDc8455yx33PDhw3Prrbeuch3LizcfeOCBnHDCCampqVnm/v79+2fUqFHp16/fKp8L1lS7chcAAAAAALQ8e+65Z/7+97+nqqpqlcZ36tQpDz300CqPX1OHH354nnvuuVx77bUZNWpUXnvttXTo0CH9+vXL0UcfnS984Qvp0qVLSWqBxayQrxBWyAMAsHxWyFcuK+RbFivkAYDyclFXAAAAAAAoAYE8AAAAAACUgEAeAAAAAABKwEVdAQAAAIBWbe7cuZk/f365y1iuDh06pFOnTuUugxIQyAMAAAAArdbcuXOzfufOeb/chazAhhtumFdeeUUo3wYI5AEAAACAVmv+/Pl5P8mXk3QsdzHLMC/JNf/6V+bPny+QbwME8gAAAABAq9cxibibcnNRVwAAAAAAKAEr5AEAAACAVq9dKjMMrcSaaD5WyAMAAAAAQAn4AIZWpK7cBawm334AUF7ty10ArUJbeR0tKHcBAACtghXyAAAAAABQApboAgAAAACtXvtU5t+2LSx3AZSUFfIAAAAAAFACAnkAAAAAgBZo3LhxufTSS3PQQQdl0003TceOHdOtW7f0798/p5xySp588skmPd+0adNy7rnnZsCAAenatWvWXXfdDBo0KFdddVXmzJlT8nlaokJ9fX19uYsgqampSc+ePZNcnaRzuctpoVzUFQBYHZX4B8tQqVrLRV1rk5yb6urq9OjRo9zFAFAii3O3S5J0KncxyzA3yUXJar8/7bPPPnniiSdWOu6kk07KjTfemA4dOjS+yCQPPPBATjjhhNTU1Cxzf//+/TNq1Kj069evJPO0VFbIAwAAAACtXrsKvjXGzJkzkyQbb7xxRowYkXvvvTdjx47NmDFj8oMf/CCbbLJJkuS2227L8OHDG3mWD40fPz7HHntsampq0q1bt1x22WV5+umn8+ijj+azn/1skuSll17KoYcemtmzZzf7PC2ZJboAAAAAAC3MgAEDcvnll2fYsGGpqqpqsG/33XfPiSeemMGDB+ell17KXXfdlc9//vPZZ599GnWuESNGpLa2Nu3atcvDDz+cPfbYo7hvv/32y9Zbb52vf/3reemll3L11Vfn4osvbtZ5WjIr5AEAAAAAWpgHH3wwxxxzzFJh/GK9evXK1VdfXbx/7733Nuo8Y8eOLbbGOe200xqE6Iude+652XbbbZMk1157bRYsWLrdXVPN09IJ5AEAAAAAWqGhQ4cWtydPntyoOe6///7i9imnnLLMMWuttVZOOumkJMl7772Xxx57rNnmaekE8gAAAABAq9cuSfsKvDVnT/F58+YVt5e3kn5lnnzyySRJ165dM3DgwOWOGzJkSHH7qaeearZ5WjqBPAAAAABAKzR69Oji9uJWMKtr4sSJSZJ+/fqlXbvlf3wwYMCApY5pjnlaOhd1BQAAAAAos5qamgb3O3bsmI4dOzZ6vkWLFuXKK68s3j/mmGNWe465c+dm1qxZSZJNN910hWPXWWeddO3aNR988EGmT5/eLPO0BlbIAwAAAACUWZ8+fdKzZ8/i7Yorrlij+a655pqMHTs2SfLJT35yhW1ilmf27NnF7W7duq10fNeuXZMk77//frPM0xpYIV9x6v59o/VrS19nP2oAWJH25S4AWKmW9n26oNwFAFCB2qUyE4rFNU2fPj09evQoPr4mq+NHjx6d8847L0nSu3fvXHfddY2aZ+7cucXtDh06rHT84ppra2ubZZ7WoBJfgwAAAAAAbUqPHj0aBPKN9cILL+Soo45KXV1dOnXqlHvuuSe9e/du1FydOnUqbs+fP3+l4xdfRLZz587NMk9roGUNAAAAAEAr8Morr+Sggw7Ku+++m6qqqtx9993ZZ599Gj1f9+7di9ur0j7mgw8+SLJ0W5qmmqc1EMgDAAAAALRwM2fOzAEHHJCZM2emUCjk5ptvzhFHHLFGc3bq1CnrrbdekuS1115b4dh33323GKT36dOnWeZpDQTyAAAAAECr176Cb2tq1qxZOfDAAzNlypQkyY9//OOcdNJJTTBzst122yVJJk2alLq65V8T8R//+Edxe9ttt222eVo6gTwAAAAAQAtVXV2dgw8+OC+++GKS5Morr8zZZ5/dZPPvtddeST5sI/PXv/51ueNGjx5d3B48eHCzzdPSCeQBAAAAAFqgOXPm5NBDD83f/va3JMn555+fb3zjG016jiOPPLK4fcsttyxzzKJFi3LbbbclSdZee+0MHTq02eZp6QTyAAAAAAAtzPz583PUUUflqaeeSpKMGDEi3/3ud1d7nkKhkEKhkL59+y5z/2677Za99947SfKzn/0sY8aMWWrM1VdfnYkTJxbraN9+6UY8TTVPS1eor6+vL3cRJDU1NenZs2eS7yXpXO5yoIm1K3cBAFS01vdLNlBuC5bzeG2Sc1NdXZ0ePXqUsiAAymhx7vY/qczUrTbJWclqvz8NGzYs9913X5Jkv/32yw9/+MMUCoXlju/QoUP69++/1OOLj9l8880zderUZR47fvz4DB48OLW1tenWrVu+9a1vZejQoamtrc3dd9+dG264IUnSv3//jBs3Lt27d2/WeVoygXyFEMjTugnkAVgRgTzQ1ATyAPyf1hrIryh8X5blBe6rEsgnyQMPPJATTjghNTU1y9zfv3//jBo1Kv369VthHU01T0ulZQ0AAAAAACt0+OGH57nnnsuXv/zl9O/fP126dMnaa6+dXXfdNd/73vcyfvz4VQrRm2qelsoK+QphhTytmxXyAKyIFfJAU7NCHoD/01pXyNMySckAAAAAgFavXSpzKcjyPkamddKyBgAAAAAASkAgDwAAAAAAJaBlDVACdWU4px9vAKVXiX8ADKvC7w3Nqxy/Cy7v51E5agEA+D9+8wQAAAAAWr12qcwwtBJrovloWQMAAAAAACUgkAcAAAAAgBIQyAMAAAAAQAloUQQAAAAAtHrts/zLfpdTJdZE87FCHgAAAAAASkAgDwAAAAAAJSCQBwAAAACAEtBDHgAAAABo9fSQpxJYIQ8AAAAAACUgkAcAAAAAgBIQyAMAAAAAQAnoIQ8AAAAAtHrtUplhaCXWRPPx9QZaqboynNOPVNZcff2ZjT62ULiuCSuh7XJJKZqC98Tm1Va+T8vx+xwAQPPSsgYAAAAAAEpAIA8AAAAAACXgb0kBAAAAgFavXSqz8ZuAtm2xQh4AAAAAAEpAIA8AAAAAACUgkAcAAAAAgBLQoggAAAAAaPXapTLD0EqsieZjhTwAAAAAAJSAQB4AAAAAAEpAIA8AAAAAACWgRREAAAAA0Oq1//et0lRiTTQfK+QBAAAAAKAEBPIAAAAAAFACAnkAAAAAACgBPeQBmkzdGhzrxzEfKhSuK3cJtAq6UNJUvD+tXDm+38rxdWlpP1fW5PcyAFqrdqnM324qsSaajxXyAAAAAABQAgJ5AAAAAAAoAYE8AAAAAACUgBZFAAAAAECr1y6VeVUUAW3b0upXyNfU1OTuu+/OueeemyFDhqRfv37p2bNnOnTokN69e2fffffN97///bz99tvLnePxxx9PoVBYpdvFF19cuicHAAAAAECL0eo/gBk7dmyOO+64Ze576623Mnr06IwePTpXXXVVfv7zn+fggw8ucYUAAAAAALQFrT6QT5I+ffpk6NChGThwYPr06ZONNtooixYtymuvvZZ777039913X2bNmpVPfOITGTt2bHbaaaflznXzzTdn0KBBy93fu3fv5ngKAAAAAAC0cK0+kB86dGheffXV5e4/5phjcv/99+eoo47K/Pnzc8kll+S+++5b7vgtttgi22+/fXOUCgAAAAA0k3apzDC0Emui+bT6HvJVVVUrHXPkkUdmm222SZI88cQTzV0SAAAAAABtUKsP5FdV9+7dkyRz584tcyUAAAAAALRGAvkk//znPzNhwoQkyYABA8pbDAAAAAAArVKbDeTnzJmTl19+OT/4wQ8yZMiQ1NXVJUnOOeecFR53/vnnZ/PNN0/Hjh2zzjrr5KMf/Wi+/OUv56WXXipB1QAAAAAAtFRt6poBI0eOzCmnnLLc/eedd14+85nPrHCOp59+urg9f/78TJgwIRMmTMiPfvSjXHDBBbnoootSKBSarGYAAAAAYM21//et0lRiTTSfNhXIL8/OO++cG264IYMGDVrumI022iif/OQns9dee2XLLbdMu3bt8uqrr+bBBx/MbbfdlgULFuSSSy7J/Pnzc/nll6/0nPPmzcu8efOK92tqaprkuQAAAAAAUJkK9fX19eUuolTee++9vPbaa0mS2traTJ48Ob/85S/z61//OltttVV++MMf5rDDDlvquA8++CAdOnRI+/bL/rxq7NixOeigg1JdXZ1CoZDx48dnp512WmEtF198cS655JJl7Pleks6r+9QAGsFnsrA66uvPbNRxhcJ1a3BWa2VYzM/s5lWO77U1+ZquSb2NO7a+/vBGn7FQ+EUjj6xt9DlXPOdZqa6uTo8ePZphfgAqUU1NTXr27JmxSbqVu5hleD/Jbon3pzaiTfWQX3vttbP99ttn++23z6BBg/LpT3869913X2677bZMmTIlRxxxREaOHLnUcV27dl1uGJ8ku+22W/77v/87SVJfX1/cXpFvfvObqa6uLt6mT5/e6OcFAAAAAEDla1OB/PKceOKJOfroo7No0aJ84QtfyDvvvLPac3z6058ufoI1evTolY7v2LFjevTo0eAGAAAAADSPdhV8o+0QyP/bEUcckeTD9jQPPfTQah/frl279O/fP0kyY8aMJq0NAAAAAICWTyD/b+uvv35xe9q0aY2ao1AoNFU5AAAAAAC0MgL5f1tyVXu3bqt/eYe6urq89NJLSZKNN964yeoCAAAAAKB10KLo3+65557i9g477LDax//iF79IdXV1kmTIkCFNVhcAAAAAsObaJWlf7iKWQUDbtrT6FfIjR47M3LlzVzjmmmuuye9+97skyRZbbJG99967uO/dd9/N448/vsLjx44dmy984QtJPmxbc+aZZ65Z0QAAAAAAtDqt/gOYiy++OOeee26GDRuWvfbaK1tttVW6deuW2bNn5/nnn88dd9yRp556KknSoUOH3HDDDamqqioeX11dnaFDh2bHHXfMkUcemYEDB2ajjTZKVVVVXn311Tz44IO5/fbbM3/+/CTJV7/61QwcOLAszxUAAAAAgMrV6gP5JHnnnXdy44035sYbb1zumE033TQ333xzDjjggGXuf+655/Lcc88t9/iqqqpccMEFufDCC9e4XgAAAAAAWp9WH8j/4Q9/yKhRo/LUU09l0qRJeeONN/L222+nc+fO6d27d3beeeccdthhOeaYY9KlS5eljt94441zzz33ZMyYMRk7dmxmzJiRWbNmZe7cuenZs2e22Wab7Lvvvjn99NPTt2/f0j9BAAAAAGCl2qcye8hXYk00n1YfyG+zzTbZZptt8pWvfKVRx3fo0CGf+tSn8qlPfaqJKwMAAAAAoC1p9Rd1BQAAAACASiCQBwAAAACAEmj1LWsAWJ66Rh7Xlt461qST34Imq4LKUChcV+4SgBVqS91XS/8eUyjcV/JzAkBTa5fK/B9tJdZE87FCHgAAAAAASkAgDwAAAAAAJSCQBwAAAACAEtCiCAAAAABo9dpVJe0L5a5iae3qkywsdxWUihXyAAAAAABQAgJ5AAAAAAAoAYE8AAAAAACUgB7yAAAAAECr165d0k4PecrMCnkAAAAAACgBgTwAAAAAAJSAQB4AAAAAAEpAD3kAAAAAoNVrX5W0r8Ae8u3ry10BpWSFPAAAAAAAlIBAHgAAAAAASkDLGgBWU12ZztvYt6z2jT5jff3pjT62ULipkUcuaPQ5qVSNfw3SGvn1m8XW5P10TV5H5XifKcfvDss7Z7l+jwEA+JD/EQAAAAAArV67dkm7Cuwh304P+TZFyxoAAAAAACgBgTwAAAAAAJSAQB4AAAAAAEpAD3kAAAAAoNVrX5W0r8Dlye0XlbsCSqkCX4IAAAAAAND6COQBAAAAAKAEBPIAAAAAAFACesgDAAAAAK1fVSpzeXKh3AVQSpX4EgQAAAAAgFZHIA8AAAAAACUgkAcAAAAAgBLQQx4AAAAAaP3apTKXJy8qdwGUkkAegBairuRnLBSuK/k5AWDlSv+euGYWlLsAAICKUYmfCQEAAAAAQKsjkAcAAAAAgBLQsgYAAAAAaP30kKcCVOJLEAAAAAAAWh2BPAAAAAAAlIBAHgAAAAAASkAPeQAAAACg9dNDngpQiS9BAAAAAABodQTyAAAAAABQAgJ5AAAAAAAoAT3kAQAAAIDWb60kVeUugrbOCnkAAAAAgBbozTffzIMPPpgLL7wwhxxySHr16pVCoZBCoZDhw4ev0dz77rtvca5VvT3++OPLnGtVj993333XqOaWwAp5AAAAAIAWaIMNNih3CUVrrbVWtt5663KXUfEE8gAAAAAALdxmm22WAQMG5OGHH26S+W655ZZ88MEHKxzz4osv5thjj02S7L///tlkk01WOP7MM8/MWWedtdz9Xbt2Xf1CWxiBPEBFWFDuAlqA9o08rm4NzultkiU19jVI6+NnQ2Vak/fStvT93ZJ+51iT93AAWIZ2qcwe8oXGH3rhhRdm0KBBGTRoUDbYYINMnTo1W2yxRZOUtSrz3H777cXtk046aaXje/fune23336N6mrp/G8CAAAAAKAFuuSSS8p27kWLFuWOO+5IknTr1i2f/OQny1ZLS+KirgAAAAAArJZHH300M2bMSJJ86lOfSpcuXcpcUcsgkAcAAAAAYLXcdtttxe1VaVfDhwTyAAAAAEDr166Cby3M+++/n1//+tdJks033zz77rvvKh13zz33ZLvttkuXLl3SvXv3bL311jn55JPz2GOPNWO1lUUgDwAAAADAKvvVr36VDz74IElywgknpFBYtSvTvvjii5k4cWJqa2vz/vvvZ9KkSbntttuy33775aijjkp1dXVzll0RWuDnLwAAAAAArUtNTU2D+x07dkzHjh3LVM2KrW67mi5duuQTn/hE9t9//wwYMCDdunXLW2+9ldGjR+enP/1p3n777dx///054ogj8sgjj6R9+/bNWX5ZCeQBAAAAAMqsT58+De5fdNFFufjii8tTzAq89tprefzxx5Mku+++e/r377/SY2bMmJG11157qccPPPDAfPGLX8whhxyS8ePHZ/To0bnuuuvypS99qYmrrhwCeQAAAACg9av6961CTZ8+PT169Cjer9TV8T//+c+zaNGiJMnJJ5+8SscsK4xfbIMNNsi9996bAQMGZMGCBfnxj3/cqgN5PeQBAAAAAMqsR48eDW6VGsjffvvtST78wODYY49tkjm33HLLHHjggUmSSZMmZebMmU0ybyUSyAMAAAAAsFLjxo3Liy++mCQ57LDDss466zTZ3Nttt11xe8aMGU02b6URyAMAAAAAsFJLXsx1VdvVrKpCodCk81UqPeQBAAAAgNavXSqzh3wLyaEXLFiQu+++O0my/vrr55BDDmnS+RevvE+SjTfeuEnnriRWyAMAAAAAsEK///3v89ZbbyVJPvOZz6Rdu6Zb6/3KK6/kkUceSZJstdVW2WSTTZps7kojkAcAAAAAaKMKhUIKhUL69u27wnFLtqs56aSTVnn+Bx54IHV1dcvd/8Ybb2TYsGGZP39+kuSss85a5blbIi1rAJrMgnIXsJqW/2bYXOrrv97oYwuFHzTyyPaNPicAbUVLew9vaUr/OwcAtBVPPvlkJk2aVLw/a9as4vakSZMycuTIBuOHDx/eqPO8++67efDBB5Mk22+/fXbZZZdVPvaLX/xiFixYkGHDhmWPPfZI375907lz58yaNSuPP/54rr/++mLde+21V84+++xG1dhSCOQBAAAAgNavKq0uDb3pppty6623LnPfU089laeeeqrBY40N5H/xi19k3rx5SVZvdfxiM2fOzI9//OP8+Mc/Xu6YYcOG5aabbkrHjh0bVWNL0cpeggAAAAAANKXbb789SVJVVZXjjz9+tY699dZbM3r06IwZMyZTpkzJrFmzUlNTk27duqVPnz7Zc889c/LJJ2ePPfZojtIrTqG+vr6+3EWQ1NTUpGfPnkm+l6RzucsBGqWl/bm7ljUr53NrlqT9EYv52QCrp5Ja1tQm+VKqq6vTo0ePchcDQIkszt2qd016VOCvcjV1Sc9x8f7URrioKwAAAAAAlEAFfiYEAAAAANDEqv59qzT6l7QpVsgDAAAAAEAJCOQBAAAAAKAEBPIAAAAAAFACesgDAAAAAK1fu0hDKTsr5AEAAAAAoAQE8gAAAAAAUAICeQAAAAAAKAFdkwAAAACA1k8PeSqAlyBAAwvKXUAJNfYtoK7RZywUvt/oY71lAZWj8T8H/Syj5VqT1z0AAItpWQMAAAAAACUgkAcAAAAAgBIQyAMAAAAAQAloYgkAAAAAtH4u6koFsEIeAAAAAABKQCAPAAAAAAAlIJAHAAAAAIAS0DUJAAAAAGj91kpSVe4ilmFRuQuglKyQBwAAAACAEhDIAwAAAABACQjkAQAAAACgBPSQBwAAAABav3apzDS0vtwFUEpWyAMAAAAAQAkI5AEAAAAAoAQq8Y80AKho5XrraF+m89K6eB0BrUFduQtoARas5uMAAKUhkAcAAAAAWj895KkAWtYAAAAAAEAJCOQBAAAAAKAEBPIAAAAAAFACldg1CQAAAACgaVX9+1ZpFpW7AErJCnkAAAAAACgBgTwAAAAAAJSAQB4AAAAAAEpAD3kAAAAAoPVrl8pMQ+vLXQClZIU8AAAAAACUgEAeAAAAAABKQCAPAAAAAAAlUIldkwAAAAAAmlZVKjMNXVTuAiglK+QBAAAAAKAEKvEzIYAyar8Gxy5osiparzX59wVoDerKcE6/8jevcnxNAQBoqayQBwAAAACAErBcBgAAAABo/ar+fas0lVgTzcYKeQAAAAAAKAGBPAAAAAAAlIBAHgAAAAAASkAPeQAAAACg9WuXykxDF5W7AErJCnkAAAAAACgBgTwAAAAAAJSAQB4AAAAAAEqgErsmAQAAAAA0LT3kqQBWyAMAAAAAQAkI5AEAAAAAoAQE8gAAAAAAUAKV2DUJoIVqX+4CWCZvda2P7zVYPXXlLgAaaUG5CwCgtdFDngpghTwAAAAAAJSAQB4AAAAAAEpAIA8AAAAAACVQiV2TAAAAAACa1lpJqspdxDJYMt2mtPovd01NTe6+++6ce+65GTJkSPr165eePXumQ4cO6d27d/bdd998//vfz9tvv71K8z399NM54YQTsvnmm6dTp07ZcMMNc/DBB+euu+5q5mcCAAAAAEBLVqivr68vdxHN6Y9//GMOPPDAlY7r1atXfv7zn+fggw9e7piLL7443/nOd7Jo0bIvfXzooYfm3nvvTadOnVa7zpqamvTs2TPJ95J0Xu3jAVgefwzW+rQvdwEAlMSCZpizNsm5qa6uTo8ePZphfgAq0eLcrfrzSY+O5a5maTXzkp4/jfenNqLVr5BPkj59+uSkk07Ktddem/vuuy9jxozJU089lV/84hc5+uijU1VVlVmzZuUTn/hEnn322WXOcf311+eSSy7JokWLstVWW+VnP/tZxo4dm/vvvz9Dhw5NkowaNSqnnnpqKZ8aAAAAAAAtRKtfIb9w4cJUVa24OdT999+fo446Kkly1FFH5b777muw/5133smWW26Z6urqbLbZZvnrX/+aXr16NTjHUUcdlQceeCBJ8thjj2XfffddrTqtkAdoLlbItz5WyAO0DVbIA9A0iivkv1DBK+T/2wr5tqLVr5BfWRifJEceeWS22WabJMkTTzyx1P6bbrop1dXVSZLvfe97DcL4xef4n//5n+K5rrrqqjUtGwAAAACAVqbVB/Krqnv37kmSuXPnLrXv/vvvT5L06NEjn/zkJ5d5/KabbpoDDjggSfLoo49m9uzZzVMoAAAAAAAtkkA+yT//+c9MmDAhSTJgwIAG++bPn5+xY8cmSfbYY4906NBhufMMGTIkSTJv3ryMGzeueYoFAAAAAKBFarOB/Jw5c/Lyyy/nBz/4QYYMGZK6urokyTnnnNNg3EsvvZSFCxcmWTqs/09L7p84cWLTFgwAAAAANF67Cr7RZrSpL/fIkSNzyimnLHf/eeedl8985jMNHnvttdeK25tuuukK5+/Tp09xe/r06Y2sEgAAAACA1qhNBfLLs/POO+eGG27IoEGDltq3ZC/4bt26rXCerl27Frfff//9FY6dN29e5s2bV7xfU1OzquUCAAAAANACtalA/sgjj8yuu+6aJKmtrc3kyZPzy1/+Mr/+9a9z3HHH5Yc//GEOO+ywBscseZHXFfWPT5KOHTsWt2tra1c49oorrsgll1yyuk8BoIVrU287jdS+3AUAwDIsKHcBAACtQpvqIb/22mtn++23z/bbb59Bgwbl05/+dO67777cdtttmTJlSo444oiMHDmywTGdOnUqbs+fP3+F8y+54r1z584rHPvNb34z1dXVxZsWNwAAAADQjKoq+Eab0aYC+eU58cQTc/TRR2fRokX5whe+kHfeeae4r3v37sXtlbWh+eCDD4rbK2tv07Fjx/To0aPBDQAAAACA1ksg/29HHHFEkg9D9Yceeqj4+JIXcl3yAq/LsuQq9yUv8AoAAAAAAAL5f1t//fWL29OmTStu9+/fP1VVH/7dyD/+8Y8VzrHk/m233baJKwQAAAAAoCUTyP/bjBkzittLtpvp0KFDdttttyTJmDFjVthHfvTo0Uk+bEez+OKxAAAAAEAFaFfBN9oMgfy/3XPPPcXtHXbYocG+I488MklSU1OT++67b5nHv/baa/njH/+YJNl///0b9J4HAAAAAIBWH8iPHDkyc+fOXeGYa665Jr/73e+SJFtssUX23nvvBvtPP/309OzZM0ly3nnn5e23326wf+HChTnrrLOycOHCJMnXvva1piofAAAAAIBWotX/QcTFF1+cc889N8OGDctee+2VrbbaKt26dcvs2bPz/PPP54477shTTz2V5MP2NDfccEOxZ/xi6667br73ve/l85//fKZNm5b/+q//yvnnn58ddtghM2fOzA9/+MM89thjSZLjjjsu++67b6mfJgAAAAAAFa7VB/JJ8s477+TGG2/MjTfeuNwxm266aW6++eYccMABy9x/xhlnZObMmfnOd76TyZMn59RTT11qzMc//vHcfPPNTVY3AAAAANBEqlKZaWhduQuglCrxJdik/vCHP2TUqFF56qmnMmnSpLzxxht5++2307lz5/Tu3Ts777xzDjvssBxzzDHp0qXLCue65JJLcvDBB+cnP/lJnnjiibzxxhtZe+21s9NOO+WUU07JcccdV6JnBQAAAABAS9PqA/ltttkm22yzTb7yla80yXx77rln9txzzyaZCwAAAACAtqPVX9QVAAAAAAAqQatfIQ8AAAAAkHapzDS0Emui2fhyA7RZbeUtoH25CwCACrKg3AUAALRpWtYAAAAAAEAJCOQBAAAAAKAE2kq/AgAAAACgLav6963SVGJNNBsr5AEAAAAAoAQE8gAAAAAAUAICeQAAAAAAKAE95AEAAACA1q9dKjMNrcSaaDZWyAMAAAAAQAkI5AEAAAAAoAQE8gAAAAAAUAI6FAEAAAAArZ8e8lQAK+QBAAAAAKAEBPIAAAAAAFACAnkAAAAAACgBHYoAKoIfxyvXvtwFrKbGfk3rmrQKAFqjBeUuAABaprWSVJW7iGWwZLpN8eUGAAAAAIASEMgDAAAAAEAJCOQBAAAAAKAEBPIAAAAAQOvXroJvjfTmm2/mwQcfzIUXXphDDjkkvXr1SqFQSKFQyPDhwxs/8X9YPOfKbvvuu+8qzTdt2rSce+65GTBgQLp27Zp11103gwYNylVXXZU5c+Y0Wd2VyFUEAQAAAABaoA022KDcJay2Bx54ICeccEJqamqKj82ZMyfjxo3LuHHjctNNN2XUqFHp169fGatsPgJ5AAAAAIAWbrPNNsuAAQPy8MMPN9s5zjzzzJx11lnL3d+1a9cVHj9+/Pgce+yxqa2tTbdu3fLNb34zQ4cOTW1tbe6+++7ceOONeemll3LooYdm3Lhx6d69e1M/hbITyAMAAAAAtEAXXnhhBg0alEGDBmWDDTbI1KlTs8UWWzTb+Xr37p3tt9++0cePGDEitbW1adeuXR5++OHssccexX377bdftt5663z961/PSy+9lKuvvjoXX3xxE1RdWfSQBwAAAABogS655JIcdthhLaJ1zdixY/PEE08kSU477bQGYfxi5557brbddtskybXXXpsFCxaUtMZSEMgDAAAAAK1fuS/c2gwXdW1J7r///uL2Kaecsswxa621Vk466aQkyXvvvZfHHnusFKWVlEAeAAAAAIBm9eSTTyb5sM/8wIEDlztuyJAhxe2nnnqq2esqNYE8AAAAAAArdc8992S77bZLly5d0r1792y99dY5+eSTV2kl+8SJE5Mk/fr1S7t2y/+zgAEDBix1TGsikAcAAAAAYKVefPHFTJw4MbW1tXn//fczadKk3Hbbbdlvv/1y1FFHpbq6epnHzZ07N7NmzUqSbLrppis8xzrrrJOuXbsmSaZPn960T6ACtJEORQAAAABAm1b171ul+XdNNTU1DR7u2LFjOnbsWIaCltalS5d84hOfyP77758BAwakW7dueeuttzJ69Oj89Kc/zdtvv537778/RxxxRB555JG0b9++wfGzZ88ubnfr1m2l5+vatWs++OCDvP/++03+XMpNIA8AAAAAUGZ9+vRpcP+iiy7KxRdfXJ5i/sOMGTOy9tprL/X4gQcemC9+8Ys55JBDMn78+IwePTrXXXddvvSlLzUYN3fu3OJ2hw4dVnq+xR9E1NbWrlnhFUggD9Bk2sqP1PYrH1JRyvV1aUn/TnXlLgCgBVtQ7gIAgFZi+vTp6dGjR/F+payOT7LMMH6xDTbYIPfee28GDBiQBQsW5Mc//vFSgXynTp2K2/Pnz1/p+ebNm5ck6dy5c+MKrmB6yAMAAAAAlFmPHj0a3CopkF+ZLbfcMgceeGCSZNKkSZk5c2aD/d27dy9ur0obmg8++CDJqrW3aWkE8gAAAABA69eugm+twHbbbVfcnjFjRoN9nTp1ynrrrZckee2111Y4z7vvvlsM5P+zjU9rIJAHAAAAAGCNFAqFFe5fHNhPmjQpdXXLb536j3/8o7i97bbbNk1xFUQgDwAAAADAGnnxxReL2xtvvPFS+/faa68kH7aj+etf/7rceUaPHl3cHjx4cBNWWBkE8gAAAAAANNorr7ySRx55JEmy1VZbZZNNNllqzJFHHlncvuWWW5Y5z6JFi3Lbbbcl+fBCskOHDm36YstMIA8AAAAAtH5VKX+v+GXdqprzSa9coVBIoVBI3759l7n/gQceWGGLmTfeeCPDhg3L/PnzkyRnnXXWMsfttttu2XvvvZMkP/vZzzJmzJilxlx99dWZOHFikmTEiBFp37796jyVFqGVXDIAAAAAAKBtefLJJzNp0qTi/VmzZhW3J02alJEjRzYYP3z48NU+xxe/+MUsWLAgw4YNyx577JG+ffumc+fOmTVrVh5//PFcf/31xfPutddeOfvss5c717XXXpvBgwentrY2Bx10UL71rW9l6NChqa2tzd13350bbrghSdK/f/+ce+65q11rS1Cor6+vL3cRJDU1NenZs2eS7yXpXO5ygEZpK59xtrRPp8v1dWnsv9OCJq1i1Sx/pQMAK1OOn9s0Xm2Sc1NdXZ0ePXqUuxgASmRx7lb9s6RHl3JXs7SaOUnP09Ko96fhw4fn1ltvXeXxy4qCF1+MdfPNN8/UqVOX2t+3b99MmzZtpXMPGzYsN910U9Zee+0VjnvggQdywgknpKamZpn7+/fvn1GjRqVfv34rPWdL1FbSIwAAAAAAVtOtt96a0aNHZ8yYMZkyZUpmzZqVmpqadOvWLX369Mmee+6Zk08+OXvssccqzXf44Yfnueeey7XXXptRo0bltddeS4cOHdKvX78cffTR+cIXvpAuXSrwk5MmYoV8hbBCHlqDtvIZpxXyq8YKeYC2wQr5lsUKeYC2qLhC/tYKXiF/cuNWyNPyuKgrAAAAAACUgEAeAAAAAABKQCAPAAAAAAAl0FYaHgNtjh9vK6cX/Mo1/t+ovv74Rh1XKNzR6HM2vo/xmvzb6j8PtAb6wLc29fVnLvPxD3sIn1viagCoGFX/vlWaSqyJZmOFPAAAAAAAlIBAHgAAAAAASkAgDwAAAAAAJaDJMgAAAADQ+rVLZaahlVgTzcYKeQAAAAAAKAGBPAAAAAAAlIBAHgAAAAAASkCHIgAAAACg9dNDngpghTwAAAAAAJSAQB4AAAAAAEpAIA8AAAAAACWgQxEAAAAA0PqtlaSq3EUsgyXTbYovNwAAAAAAlIBAHgAAAAAASkDLGqAE/KhpPu3LXcBqKsdroTz/RoXCL8py3tJrad/fdeUuAFipBeUugFagUPjxcvbUlrQOAID/1NL+Fw0AAAAAsPrapTLT0EqsiWajZQ0AAAAAAJSAQB4AAAAAAEpAIA8AAAAAACWgQxEAAAAA0PrpIU8FsEIeAAAAAABKQCAPAAAAAAAlIJAHAAAAAIAS0KEIAAAAAGj9qv59qzSVWBPNxgp5AAAAAAAoAYE8AAAAAACUgEAeAAAAAABKQA95AAAAAKD1a5fKTEMrsSaajRXyAAAAAABQAj5/AVaRHxfNq325C6DJ1ZW7AJZpTX6W+ZrSFi0odwHQSMv7ee93WgCgvKyQBwAAAACAErA8AAAAAABo/apSmWloVbkLoJSskAcAAAAAgBIQyAMAAAAAQAkI5AEAAAAAoAQqsWsSAAAAAEDTapfKTEMrsSaajRXyAAAAAABQAgJ5AAAAAAAoAYE8AAAAAACUgA5FAAAAAEDrV/XvW6WpxJpoNlbIAwAAAABACQjkAQAAAACgBATyAAAAAABQAnrIV5y6JAuaeM72TTwf5edbtzK1le81r7/K1TZeg/X1xzf62ELhjiasZFXVleGcVK6m/j0PAIBV1i6V+V/aSqyJZmOFPAAAAAAAlIBAHgAAAAAASkAgDwAAAAAAJaBDEQAAAADQ+lWlMtPQqnIXQClZIQ8AAAAAACUgkAcAAAAAgBIQyAMAAAAAQAlUYtckAAAAAICmVZXK7NdeiTXRbKyQBwAAAACAEhDIAwAAAABACQjkAQAAAACgBPSQBwAAAABav3apzDS0Emui2VghDwAAAAAAJSCQBwAAAACAEvAHES1Eff1XGn1sofCDRh7ZvtHnZFX49qtMbel131ZegwvW4Ni29HpoOQqFO8pdAq3Gmvx8AAAAWH1tJY0BAAAAANoyPeSpAFrWAAAAAABACQjkAQAAAACgBATyAAAAAABQAjoUAQAAAACtnx7yVAAr5AEAAAAAoAQE8gAAAAAAUAICeQAAAAAAKAEdigAAAACAVq9+raS+qtxVLK3ekuk2xZcbAAAAAABKQCAPAAAAAAAlIJAHAAAAAIASEMgDAAAAAEAJuKhrC1Eo/GANjm7fZHXwn3wLVS6ve2i9Gv+zt77+2EYfWyiMbOSRa/JeUbcGx7YVC8pdAAAALcTCdh/eKk0l1kTzsUIeAAAAAABKQCAPAAAAAAAlIJAHAAAAAIAS0KEIAAAAAGj19JCnElghDwAAAAAAJdAmAvlx48bl0ksvzUEHHZRNN900HTt2TLdu3dK/f/+ccsopefLJJ1c6x8iRI1MoFFbpNnLkyOZ/UgAAAAAAtCit/g8i9tlnnzzxxBNLPT5//vy8/PLLefnllzNy5MicdNJJufHGG9OhQ4cyVAkAAAAAQGvX6gP5mTNnJkk23njjHH300dl7772z2WabZeHChRkzZkyuvvrqzJgxI7fddlsWLFiQO++8c6Vz/uEPf8jGG2+83P2bbrppk9UPAAAAAKy5uqpC6qoK5S5jKXVV9Unqy10GJdLqA/kBAwbk8ssvz7Bhw1JVVdVg3+67754TTzwxgwcPzksvvZS77rorn//857PPPvuscM7+/funb9++zVg1AAAAAACtTavvIf/ggw/mmGOOWSqMX6xXr165+uqri/fvvffeUpUGAAAAAEAb0uoD+VUxdOjQ4vbkyZPLWAkAAAAAAK1Vq29ZsyrmzZtX3F7eSnoAAAAAoOVa2K5dFrarvB7yC9vVJ1lQ7jIoESvkk4wePbq4ve222650/CmnnJKNN944HTp0SK9evbL77rvn29/+dmbMmNGcZQIAAAAA0IK1+UB+0aJFufLKK4v3jznmmJUe8/jjj+f111/PggUL8vbbb+cvf/lLLrvssvTr1y/XX3/9Kp133rx5qampaXADAAAAAKD1avMta6655pqMHTs2SfLJT34yAwcOXO7YLbfcMp/85Cezxx57pE+fPkmSKVOm5Fe/+lXuvffezJ07N5///OdTKBTyuc99boXnveKKK3LJJZesRqXtV2Msq6/NfytUKK/7lfPaXTmvo9anrtFHFgp3NGEdNB1/ngsAALQNhfr6+vpyF1Euo0ePzgEHHJC6urr07t07zz//fHr37r3MsdXV1enRo0cKhWX3mXrwwQfzyU9+MgsWLEiXLl0yefLkbLjhhss997x58xr0rq+pqfl3yH9Zkk7LOEKg1LyEmpXJ637lvHZXzuuIplKO0LjxHz60LAJ5oFRqk5xb/P8dAG1DTU1NevbsmanVndKjR+X1kK+pqU/fnnO9P7URbbZlzQsvvJCjjjoqdXV16dSpU+65557lhvFJ0rNnz+WG8Uly2GGH5cILL0ySzJkzJz/72c9WeP6OHTumR48eDW4AAAAAALRebTKQf+WVV3LQQQfl3XffTVVVVe6+++7ss88+azzv5z73uWJov+SFYgEAAAAAoM0F8jNnzswBBxyQmTNnplAo5Oabb84RRxzRJHP37t076623XpJkxowZTTInAAAAAACtQ5tqPjxr1qwceOCBmTJlSpLkxz/+cU466aQmPceK2toAAAAAAOWxKFVZmMrL7halzV7is01qMyvkq6urc/DBB+fFF19Mklx55ZU5++yzm/Qcb731VmbNmpUk2XjjjZt0bgAAAAAAWrY2EcjPmTMnhx56aP72t78lSc4///x84xvfaPLz3HDDDamv//ATrSFDhjT5/AAAAAAAtFytPpCfP39+jjrqqDz11FNJkhEjRuS73/3uas0xderUjB8/foVjHnzwwVx66aVJks6dO+eUU05pXMEAAAAAALRKrb6H/HHHHZeHH344SbLffvvltNNOy9///vflju/QoUP69+/f4LGpU6dm6NCh2WOPPXL44Ydnp512Su/evZMkU6ZMyb333pt77723uDr+//2//5dNNtmkmZ4RAAAAALC66lKVugrsIV+nh3yb0uoD+fvuu6+4/ac//Sk77rjjCsdvvvnmmTp16jL3jRkzJmPGjFnusV26dMk111yTz33uc42qFQAAAACA1qvVB/JNYeDAgfn5z3+eMWPGZNy4cXn99dcza9as1NXVZZ111slHPvKR7L///jn99NOLK+cBAAAAAGBJrT6QX9xGZk107949xx9/fI4//vgmqAgAAAAAYM29+eabGTt2bMaOHZtnnnkmzzzzTN5+++0kycknn5yRI0eu0fzjxo3L7373uzz55JN58cUX89Zbb6V9+/bZeOONM3jw4Jx22mnZa6+9VjpPobBqrYKGDBmSxx9/fI1qrnStPpAHAAAAAFiYqizMWuUuYykLs6jRx26wwQZNWElD++yzT5544omlHp8/f35efvnlvPzyyxk5cmROOumk3HjjjenQoUOz1dKaCOQBAAAAAFq4zTbbLAMGDMjDDz/cJPPNnDkzSbLxxhvn6KOPzt57753NNtssCxcuzJgxY3L11VdnxowZue2227JgwYLceeedK53zzDPPzFlnnbXc/V27dm2S2iuZQL7itEvSvtxFAFSc+vrhjTquULijaQuhhVtQhnPWleGcAABAW3DhhRdm0KBBGTRoUDbYYINMnTo1W2yxRZPMPWDAgFx++eUZNmxYqqqqGuzbfffdc+KJJ2bw4MF56aWXctddd+Xzn/989tlnnxXO2bt372y//fZNUl9LJZAHAAAAAGiBLrnkkmab+8EHH1zh/l69euXqq6/O4YcfniS59957VxrII5AHAAAAANqAyu0hv2oXPK1EQ4cOLW5Pnjy5jJW0HJX3CgQAAAAAoOLNmzevuP2fbW1YNoE8AAAAAACrbfTo0cXtbbfddqXj77nnnmy33Xbp0qVLunfvnq233jonn3xyHnvsseYss6JoWQMAAAAAUGY1NTUN7nfs2DEdO3YsUzUrt2jRolx55ZXF+8ccc8xKj3nxxRcb3J80aVImTZqU2267LUceeWRGjhyZnj17NqqecePG5dFHH81OO+2UnXbaKRtttFGj5mluAnkAAAAAoNWr9B7yffr0afD4RRddlIsvvrgMFa2aa665JmPHjk2SfPKTn8zAgQOXO7ZLly75xCc+kf333z8DBgxIt27d8tZbb2X06NH56U9/mrfffjv3339/jjjiiDzyyCNp3779atdzyy235LrrrkuS7LDDDnn22Wcb98SamUAeAAAAAKDMpk+fnh49ehTvV/Lq+NGjR+e8885LkvTu3bsYhC/PjBkzsvbaay/1+IEHHpgvfvGLOeSQQzJ+/PiMHj061113Xb70pS+tdk2PPPJICoVC6uvr87WvfW21jy+VyvtICAAAAACgjenRo0eDW6UG8i+88EKOOuqo1NXVpVOnTrnnnnvSu3fvFR6zrDB+sQ022CD33ntvcVX8j3/849Wu6e23387kyZNTX1+fbt265VOf+tRyx86ePTtnnHFG1ltvvXTo0CGbb755Tj311KXa6TQXgTwAAAAAACv1yiuv5KCDDsq7776bqqqq3H333dlnn33WeN4tt9wyBx54YJIP+8rPnDlztY5fsj3Nxz72sXTq1GmZ4+rr63PYYYflxhtvzHvvvZe6urpMnz49I0eOzEc/+tH89Kc/bfyTWEUCeQAAAACg1fuwh3xl3lqCmTNn5oADDsjMmTNTKBRy880354gjjmiy+bfbbrvi9owZM1br2FdeeaW4veeeey533K233ponn3yyeL9QKBRvdXV1Ofvss3P//fev1rlXl0AeAAAAAIDlmjVrVg488MBMmTIlyYdtZU466aQmPUehUGj0se+8805xe+utt17uuCXb4WyzzTYZO3ZsXnvttVxwwQXFx88+++zMmzev0bWsjEAeAAAAAIBlqq6uzsEHH1zssX7llVfm7LPPbvLzLNnDfeONN16tY+vq6orby+tXP23atEyYMCH19fVZa621ctddd2XgwIHZaKONcvHFF+eMM85Ikrz++uu59957V/8JrCKBPAAAAAAAS5kzZ04OPfTQ/O1vf0uSnH/++fnGN77R5Od55ZVX8sgjjyRJttpqq2yyySardfySIfwHH3ywzDGPPvpocXv//ffPTjvt1GD/17/+9eL2qFGjVuv8q6Nds80MJeflXLnal7uAFsDrd2UKhTvKXQIVY0G5C2C5fG0AAKhcC1OVugpcn7wwjW/V0hQWt4rZfPPNM3Xq1OLj8+fPz1FHHZWnnnoqSTJixIh897vfXe35H3jggRxyyCFp127Z2ccbb7yRYcOGZf78+UmSs846a7XPseWWWxa3n3/++Rx00EFLjVmyN/yxxx671P6+fftms802y7Rp04ofQDQHCRAAAAAAQAv05JNPZtKkScX7s2bNKm5PmjQpI0eObDB++PDhqzz3cccdl4cffjhJst9+++W0007L3//+9+WO79ChQ/r377/U41/84hezYMGCDBs2LHvssUf69u2bzp07Z9asWXn88cdz/fXXF+vea6+9GtUOZ/fdd89aa62VhQsX5q677sq5557bYP8777yTRx55pNiu5vDDD1/mPJtvvnleffXVvPHGG6tdw6oq1NfX1zfb7Kyympqa9OzZM8n3knQudzktlM+XKpcV8ivn9btyXkcs1tJWYdetfEir0dK+NkDbU5vk3FRXV6dHjx7lLgaAElmcuz1T3SfdelTeCvn3axZlUM/pjXp/Gj58eG699dZVHr+sKHh5K+RX9yKr/3n8Yn379s20adNWevywYcNy0003LbcH/MoceeSReeCBB1JfX58f/ehH+cIXvlDc95WvfCXXXntt6uvrs/fee2f06NHLnOOggw7Ko48+mrXWWisLFjTP/28kQAAAAAAANItbb701o0ePzpgxYzJlypTMmjUrNTU16datW/r06ZM999wzJ598cvbYY481Os/555+fUaNGZeHChRkxYkSef/75HHrooXnyySdz7bXXFsd9+tOfXu4cNTU1SZJOnTqtUS0rYoV8hbBCvin4fKlyWdm8cl6/K+d1xGItbRW2FfIAlcMKeYC2aHHu9r/VW1TsCvnde77i/akJXHTRRfnud7+71F8CFAqF1NfXZ+21187UqVOX+++83nrr5d13382WW27ZoBVQU5IAAQAAAADQ4l1yySWpr6/PFVdckUWLFiX5vzY9hUIhV1xxxXLD+IkTJ+a9995Lkmy99dbNVmPlfSQEAAAAAACNcOmll+aZZ57JGWeckR133DEbb7xxdtttt9x+++0544wzlnvc3XffXdz+r//6r2arzwp5AAAAAABajZ133jn/8z//s8rj6+vr85vf/KZ4/+CDD26OspII5AEAAACANmBh1srCVJW7jKUsLHcBpFAoZMKECXnrrbcyfvz47L777s12LoE8AAAAAABt3vrrr5+DDjqoWc8hkAcAAAAAoEX585//nJ/97GcZP3583n///ay77rrZYYcdcuCBB+aoo45K586dy13iMgnkAQAAAABoMc4777x8//vfT/Jhu5kkmTZtWv72t7/l1ltvzTrrrJOLLrooX/rSl8pZ5jIJ5AEAAACAVm9hqvSQbwXuvffeXHXVVSkUCtlzzz3zsY99LBtuuGFmz56dv//97/nd736XN954I+ecc07+93//N7feemvat29f7rKLBPIAAAAAALQI//3f/536+vp87Wtfy/e+972l9tfX1+fWW2/Nl7/85dx9993p3r17rr/++jJUumxrlbsAAAAAAABYFRMmTEiSnH/++cvcXygUMnz48PzlL3/JuuuumxtvvDFPPPFECStcMYE8sIrar8ENmsKCMtxoXr4uAAAArJ66urr07NkzPXr0WOG4/v375wc/+EEKhUJuuOGGElW3cgJ5AAAAAKDVq0tVxd5YdZtuummqq6vz7rvvrnTssccem0KhkKeffroEla0agTwAAAAAAC3C/vvvnyT56U9/utKxHTt2TPfu3TNz5szmLmuVCeQBAAAAAGgRzjjjjLRv3z6XXnpp/vCHP6xw7BtvvJGampp069atRNWtnEAeAAAAAIAWYccdd8yFF16Y+fPn5/DDD89Xv/rVvPHGG0uNmzdvXs4555zU19dn8ODBZah02dqVuwAAAAAAgOa2KO2ysAL7tS9KodwltDjnn39+Fi5cmEsuuSQ/+MEP8qMf/SgDBw7M9ttvn549e+aNN97I6NGj89prr6WqqirnnXdeuUsuEsgDAAAAANCiXHjhhRk8eHDOP//8PPPMMxk7dmz+8pe/FPcXCoV06tQpN954Y3bfffcyVtqQQB4AAAAAgBZn//33z/77759//OMf+dOf/pQJEyZk1qxZ6dChQ3bcccecdNJJ2XTTTctdZgMCeQAAAAAAWqwBAwZkwIAB5S5jlQjkAQAAAIBWb2GqKrKH/MJyF0BJrVXuAgAAAAAAoC0QyAMAAAAAQAkI5AEAAAAAoAT0kAcAAAAAWj095KkEVsgDAAAAAEAJWCEP0GT8SIXV076Rxy1o0ipWXV2ZzgsAAEBrYYU8AAAAAACUgOWcAAAAAECrtzBrVWgP+fpyl0AJWSEPAAAAAAAlIJAHAAAAAIASEMgDAAAAAEAJ6CEPAAAAALR6dalKXQX2kK/TQ75NsUIeAAAAAABKQCAPAAAAAAAloGUNAAAAAACtypQpU/LnP/85f/3rXzNx4sRMmzYts2bNygcffJAk6dq1a3r16pXNN9882223XQYOHJghQ4akb9++zVqXQB4AAAAAaPUWpl0WVmAcurDcBbQizz//fH7+85/nvvvuy+TJk5c5plAoJEmqq6tTXV2dyZMn509/+lNx/9Zbb51hw4bl+OOPz0c+8pEmr1HLGgAAAAAAWqRFixblF7/4RfbYY4/stNNOueqqq4phfKFQWOq22PL2vfzyy7nyyiuzww47ZPDgwfnFL36RRYsWNVm9lfeREAAAAAAArEB9fX3uvPPOXHrppXn55ZeTpEHgXlVVlQEDBmT77bdPv379sskmm2SdddZJp06dUigUUltbm3fffTczZ87M5MmT88ILL+TFF19MXV1dcY4xY8ZkzJgxueiii3LBBRfkM5/5TINzNIZAHgAAAACAFuPpp5/OF7/4xYwfPz7J/wXxW2yxRY488sgcfPDBGTx4cLp06bJa89bW1mbMmDF56KGHcv/99xdX2r/00ks58cQT84Mf/CA/+tGPMnjw4EbXXqivr69v9NE0mZqamvTs2TPJ95J0Lnc5LZTPl5pX+3IXUCJeRyyprbzuW5oFZTpv3cqHtHnl+toArKraJOemuro6PXr0KHcxAJTI4tztl9VD0qVH5f2/f05NXY7pOdr702qoqqpKfX19CoVCunbtmuOOOy6nnXZadttttyY9zzPPPJObb745d9xxR/FisPX19WvUwkYPeQAAAAAAWpT1118/3/3ud/Pqq6/m+uuvb/IwPkkGDRqU6667Lq+++mouu+yyrL/++ms8p0AeAAAAAIAW46KLLsqUKVPyzW9+M2uvvXazn2/ttdfOeeedl8mTJ+fiiy9eo7kq7280AAAAAABgOS688MKynLdr165rfG6BPAAAAADQ6i1MVRamqtxlLGVhXOKzLdGyBgAAAAAASsAKeQAAAAAAKt6bb76ZT3ziE9l5550zcODAfPazn13u2A8++CDjxo3LjBkzMm/evKy//vrZcMMNs+2226Zr164lrLohgTwAAAAAABXvl7/8ZZ555pmMHTs2xxxzzDID+X/+85+54IILcv/996eurm6p/R06dMjee++dww8/PKeeemq6detWitKLtKwBAAAAAKDiPfbYY8XtM844Y6n9Dz74YD760Y/m3nvvzcKFC4uPFwqF4vb8+fPz6KOP5pxzzsnmm2+eSy+9NHPmzGnewpcgkAcAAAAAWr26rJW6VFXgTUS7ql588cUkSadOnbLPPvs02Ddt2rQce+yxmTt3bvGxQqGQddZZJ1tttVU22GCDtGvXLoVCoXh79913c/HFF2eXXXbJ888/X5Ln4KsNAAAAAEDFe/3111NfX59tttkmVVVVDfZ9//vfL4bxhUIhp512WiZOnJi33347L730Ul5//fXMmzcvY8eOzeWXX57+/fsXV86//PLL2X333fPrX/+62Z+DQB4AAAAAgIq3uLXMeuutt9S+hx9+uLj9rW99KzfccEP69+/fYEyhUMjAgQPzjW98IxMnTsyvf/3rbLbZZkmSuXPn5jOf+UyeeuqpZnwGAnkAAAAAAFqATp06JUkWLFiw1L4ZM2akvr4+3bt3z7e//e1Vmu8Tn/hEnnvuuRx++OFJPuwvf/rppy/zYrBNRSAPAAAAALR6C9OuYm+smsUr42fMmLHUvq5duyZJdtxxx3Ts2HGV5+zRo0fuueeeDBw4MEnyz3/+M3fddVcTVLtsAnkAAAAAACreRz7ykRQKhUyZMiUzZ85ssK9Pnz6Nnrd9+/a56qqrivfvvffeRs+1Mj5+oRVZkz8l8a3Q+viaQmk0/nutvv7YRh1XKNzR6HMmS/9ZI//JvxEAAFCZ9t133/z+979Pktxwww25+OKLi/sOPvjgPPvss3n55ZdTX19fvGDrqhoyZEg23HDDvP7665kwYUITVt2QFfIAAAAAAFS8E088MR06dEiS/L//9//yz3/+s7jvpJNOylprrZU333wzDz30UKPm33rrrVMoFPLmm282Sb3LIpAHAAAAAFq9hamq2BurZoMNNsgZZ5yRQqGQOXPm5OCDD87EiROTJNtuu20+//nPp1Ao5Etf+lLef//91Z5//vz5SZINN9ywSetekkAeAAAAAIAW4Xvf+1522GGHJMmrr76agQMH5tvf/nb+9a9/5corr8ygQYMyefLkHHzwwXnnnXdWed7Zs2fn2WefTX19fXbZZZfmKl8gDwAAAABAy9CxY8c88sgj2W233VIoFDJ37txcfvnl2WSTTTJkyJD069cv7dq1y5gxY7LTTjvlrrvuWqV5zznnnMydO7e43VwE8gAAAAAAtBjrr79+Ro8enW9/+9vp1KlT8QKuf/vb33LnnXemrq4uhUIhM2fOzPHHH58tt9wyF1xwQR566KHMmDEjtbW1SZK33347o0aNyn777ZeRI0cmSc4777zsvffezVZ7u2abGQAAAACgQlRqv/aFWVTuElqkjh075pJLLsnnPve53Hzzzbnlllsybdq04v76+vri9tSpU3PZZZetcL511lkn3/nOd3LWWWc1W82JFfIAAAAAALRQm2yySS644IJMmTIlL730Um688cZ89rOfzaBBg9K5c+cUCoUGt8X+8/G5c+fmpptuynHHHZdLLrmk2eq1Qh4AAAAAgBZvq622ylZbbZVTTz01yYer5P/xj39k/PjxmTBhQvH29ttvNziuvr4+tbW1xf1JctFFFzVLjQJ5AAAAAABanUKhkG233TbbbrttPvOZzxQfnzFjRjF8XxzWT506tdjmZsl2N01NIA8AAAAAtHoLU5U6PeTJh21uNtlkkxx66KHFx2bPnt1gFX1zEcgDAAAAANCmde/ePXvvvXf23nvvZj2Pi7oCAAAAAEAJCOQBAAAAAKAEtKyBJElduQtYTb51oTQWrMGx7ZusisrW+J+fhcIdTVgHAADAii1MuyyswExlYZrvAqJUnmZ7Bc6YMSMvvvhipk2blrfeeisffPBBkqRr165Zf/31s/nmm2e77bbLJpts0lwlAAAAAADQBowePbrB/SFDhpSpkhVrskD+3XffzW9+85v84Q9/yOOPP54333xzlY5bf/31M2TIkBx88ME54ogjst566zVVSQAAAAAAtAH77bdf6uv/768NFi1aVMZqlm+Ne8j//ve/z5FHHpmNNtoop512Wn75y1/mjTfeSH19/Srd3nzzzdx777357Gc/m4033jif+MQnMmrUqKZ4bgAAAAAAtCGFQqHcJaxQo1bIL1q0KLfeemuuvPLKTJo0KUkafPqwWMeOHbPxxhtnnXXWSefOnVNfX5/a2tq8++67mTlzZubPn9/g2AULFmTUqFEZNWpUttxyy5x33nkZPnx4qqqqGvv8AAAAAACyMGtlYSovZ1yYheUuodWo9DA+aUQgf8899+Rb3/pWpkyZkuT/wvSOHTtm8ODBGTJkSAYNGpQddthhpf3hZ8yYkeeffz7jxo3L6NGj89RTT2Xu3LlJkilTpuRzn/tcLr/88lxxxRU55phjVrdUAAAAAADagFtuuaXcJaySQv2ylrYvxz777JOnnnoqyYdBfLt27XLooYfmM5/5TD7+8Y+na9eua1TMnDlz8vvf/z533nlnHnzwwSxYsODDIguF7LXXXks15m9Nampq0rNnzyTfS9K53OVQ8cpxRfD2ZTjnmqi8q6bT1rS075m2YsEaHFvXZFVUtjX5NwKodLVJzk11dXV69OhR7mIAKJHFudu11cemc48O5S5nKbU18zOi5y+8P7URq5VYPfnkk0mSXr165eyzz86ZZ56Z3r17N1kxXbp0ybBhwzJs2LC89dZb+elPf5qf/OQnefPNN4vnBgAAAACAlmi1Luraq1evXH311Xn11Vdz0UUXNWkY/5/WX3/9XHDBBZk2bVquvvrqrLfees12LgAAAACgdVuYqoq90Xas1gr5KVOmpFu3bs1VyzJ17NgxX/7yl/PZz362pOcFAAAAAICmtFor5EsdxlfKuQEAAAAAYE2tViAPAAAAAAA0zmq1rAEAAAAAaIkqtV97JdZE82mWQH7ChAkZP3583nvvvXTq1Cl9+vTJgAED0q9fv+Y4HQAAAAAAVLwmDeSfffbZnHLKKXn22WeXub9bt27Zcccds/POOxdvO+ywQzp06NCUZQAAAAAAQMVpskD+lVdeydChQ1NdXZ36+vpljpk9e3aefvrpPP3008XHqqqqMmDAgAYh/c4775x11123qUoDWqyW1lWrfbkLKJEF5S6ghWjsv1NbeR2tCa9BoKm1pZ+9foYCAJRTk6Vdl112Wd57770UCoUkSffu3bPDDjukR48eefXVVzNp0qTMnz9/qbC+rq4uL7zwQl544YXccccdSZJCoZC6urqmKg0AAAAAaOMWpip1FdivXQ/5tqXJAvlHHnmkGMYffvjhGTlyZNZZZ53i/rq6urz44ot59tlnM2HChEyYMCHPPvts3nnnneWuqAcAAAAAgJU56qijstNOO2XnnXfOTjvtlC222KLcJS1Tob6J0vCOHTtmwYIF6datW6ZPn56ePXuu0nHTp09vENJPmDAhU6dObXMr5Gtqav79b/a9JJ3LXQ4VrxytXMrxp9xa1lQmf+revNrK62hNlOs12FZ+N/E9TlvUln72tvXv8dok56a6ujo9evQodzEAlMji3O3K6uHp1KPyrmU5t2Z+zus50vvTGqqqqmqw8Ltnz57ZcccdG4T022+/fTp27FjGKpsw7erWrVvee++9fOQjH1nlMD5J+vTpkz59+uSwww4rPvb+++83VVkAAAAAALQBizu41NfXp7q6Ok888USeeOKJ4v527dplm222aRDS77zzzll//fVLVmOTBfI77rhjRo8eXXzSa6Jbt25NUBEAAAAAwIcWpl0WVuBf4y/MonKX0Cpcc801GT9+fMaPH5+JEycu1YGlvr6+wfVM77zzzuK+jTbaaKmQfptttmmWOpvsFXjUUUdl9OjReeGFFzJ//vx06FB5f/4BAAAAAEDr86Uvfam4PX/+/Pz9738vBvTjx4/Pc889lzlz5jQ4pr6+PoVCIf/617/y+uuv56GHHiruW7SoeT4oabJA/rTTTsuVV16ZN954IyNHjsznPve5ppoaAAAAAABWSYcOHbLLLrtkl112afD4P//5z4wfPz5PP/10fv/732fKlCnFvvNLdn5posuuLtNaTTVR165dc/vtt6dQKOS8887LSy+91FRTAwAAAADAGtlmm23y6U9/Oj/60Y/y8ssvZ9SoUdlmm21SKBSyzjrr5IQTTsjAgQPTpUuXZquhyQL5JNl///1z9dVXp7q6OkOHDs3YsWObcnoAAAAAgEZZmKqKvVEeH/vYx/LXv/41H/vYx/LOO+9kwYIFGTNmTN5///1mO2eTBvJJMmLEiNx111158803M3jw4Jx55pl58cUXm/o0AAAAAACwRrp06ZJf/epX2XnnnXP33Xfn8ssvb9bzNWkgP3v27IwYMSJnnHFGFi1alIULF+aGG27IDjvskC233DInnHBCrr766vzpT3/Ku+++25SnBgAAAACA1dapU6dcc801KRQK+e53v5spU6Y027ma7KKuc+fOzZAhQ/Lss88Wr067uBF+fX19pk2blmnTpuWuu+4qHtOnT5989KMfbXDbdNNNm6okAAAAAABYqSFDhmSjjTbKzJkzc8stt+Q73/lOs5ynyQL5q666KhMmTCgG8f95JdplXZl2+vTpmT59en77298WH1t33XWzyy675A9/+ENTlQYAAAAAtHELs1ZF9mtf2PRdxWmkrbfeOq+//np+97vfNVsg32Rf7V/84hfF7c022yy/+tWv8vbbb6euri7Tpk3LqFGj8t3vfjfDhg3LVlttVQzt//P29ttv549//GNTlQUAAAAAQCt366235tlnn01dXV2j55g1a1aSZOrUqU1U1dKabIX85MmTkyRVVVX57W9/mx122KG4r0+fPunTp08OOeSQ4mOzZ8/Os88+m/Hjx2fChAkZP358XnzxxcyfP7+pSoISWNDoI+vrv9LoYwuFHzfyyCb7lm/F2pfpvI392jT+Tabx1uTfqPHfM22Hf6PmVY7vmXLwOqItKtd7eOPU15/e6GMLhZuasBIAgNbh1FNPTX19fTp06JDtttsuH/3oR7Pzzjvnox/9aHbaaad07959hcc///zz+ec//5n6+vrMmTOn2epssnSuS5cumT9/frbZZpsGYfzydO/ePXvttVf22muv4mN1dXV54YUXMmHChKYqCwAAAACANmL+/PmZMGFCg4y5UChkyy23LAb0H/3oR7P99ttnk002SXV1dR5//PF85StfycKFC5MkW221VbPV12SBfN++fTNhwoT06tWr8cW0a5eddtopO+20U1OVBQAAAACQulSlrgJ7yFdiTS3RAQcckL/97W955513ltpXX1+fyZMnZ/LkyfnVr361zOMLhUJx+9hjj222OpsskP/4xz+e8ePH5+WXX26qKQEAAAAAYKX+8Ic/JElee+21jB8/vsFt+vTpS42vr69vEMIvvr/zzjvna1/7WrPV2WQXdT3llFPSvn37vP766xk9enRTTQsAAAAAwDK8+eabefDBB3PhhRfmkEMOSa9evVIoFFIoFDJ8+PAmP9+0adNy7rnnZsCAAenatWvWXXfdDBo0KFddddVq9V1vqnmWZdNNN83hhx+eCy+8ML/+9a8zderUzJo1K4888ki+//3v57jjjsvmm29eDOPr6+uTJD179swXvvCFjB49Op06dVqjGlakyVbIb7nllvn2t7+diy66KF/+8pfz9NNPN2vhq2PcuHH53e9+lyeffDIvvvhi3nrrrbRv3z4bb7xxBg8enNNOO61BL/uV+f3vf58bbrghzzzzTN56662sv/76GTRoUD73uc81uHAtAAAAAEBz2WCDDUp2rgceeCAnnHBCampqio/NmTMn48aNy7hx43LTTTdl1KhR6devX0nmWR3rrLNO9ttvv+y3337Fx1588cX8+Mc/zk033ZQkuf7663PMMcc02TmXp8lWyCfJt771rXz605/OhAkTMmzYsGa9Gu2q2meffTJo0KBcdNFFeeSRRzJjxozMnz8/H3zwQV5++eWMHDkye++9d04++eTMnz9/hXMtWrQop59+ej7+8Y/n/vvvL841Y8aM3H///fn4xz+ez372s1m0aFGJnh0AAAAAsCoWpl3F3prCZpttloMOOqhJ5vpP48ePz7HHHpuampp069Ytl132/9m79zA5y/p+/O/ZzfnMGYFAgBBDxANFDiFICIpKUQFTD1XkIB5RpC3f2gL+EOxBW4tI01ZBEMS2YEXFIqhQxAgIYjQWNEEIEBoSBAIhm8Mm2cP8/pjskpDdZHdmd2Z29/W6rrny7MxzP/dnZ2ZnN++55/P8XX7+85/nzjvvzIc//OEkySOPPJKTTjopa9as6ffj9IUZM2bkK1/5Su69997svvvu+dM//dNcccUV/Tpn0oeB/Lhx4zJz5swMHz48O++8c370ox/lsMMOy49+9KO+mqIsK1asSJLstddeOe+883LTTTflgQceyH333ZcvfelL2XvvvZMk119//Q4/xnHRRRflmmuuSZIceuihueGGG/LAAw/khhtuyKGHHpokufrqq/OZz3ym/74hAAAAAIAkF198cW655Zb84Q9/yJNPPpkrr7yyX+Y577zz0tzcnGHDhuX222/PhRdemJkzZ+b444/PVVddlX/8x39MUgrTL7vssn4/Tl864ogjctddd2XixIk5//zzc+edd/brfIViR5OcCjU0NGzTBD8pnZ32gAMOyIknnpijjz46hx56aF75ylf2xZQ98ra3vS2nn3565s6dm8bGbc9YvHLlysyaNSuPPPJIkmT+/Pk59thjt9nvkUceyate9aq0trbm9a9/fX72s59l9OjRnbevX78+s2fPzoIFCzJs2LAsXry4Vx+raGpqysSJE5P8Q5LRO9qdutFS9shi8S/KHlsozCtzZCXvuA6vYGy5+qyrVi/U4vtMyv9eW/u0iv5X/s8M9I2B9jNTLj9rDEW1+h1enmLxQ2WPLRSurmDmof760Jzk/KxevToTJkyodTEAVElH7vbXq/8soyaMrHU529jQtDFfmPjlPvn9tHTp0uy///5JkjPOOCPXXXddxfU98MADOfLII5MkH/3oR/PVr351m33a29tzyCGHZPHixZk0aVKeffbZDB8+vF+O01+uuuqqfPzjH88ee+yRRx99NGPHju2Xefq0ZU2xWOy8dJw8oFgs5vHHH8+//uu/5v3vf39mzJiR8ePHZ9asWfnkJz+Za665Jr/+9a932C6mXD/4wQ/y7ne/u8swPkl23XXXrd5tuemmm7rc78tf/nJaW0v/iZ83b95WYXySjBkzJvPmlULS1tbWXH755X1RPgAAAABAzdx8882d22eddVaX+zQ0NOT0009Pkrz44ou56667+u04/eXMM8/MuHHj8oc//KGzS0p/6LNAfvHixbnhhhvy13/913nrW9+aPffcs3OV/JZBfbFYzLp163L//ffnK1/5Sj7ykY/k8MMPz/jx4/O6170uZ511Vv75n/+5r8rqkTlz5nRuP/bYY9vcXiwW8/3vfz9JMn369Bx11FFdHueoo47qXP3//e9/P3304QMAAAAAoELtaUxbHV7a0/VC4npxzz33JEnGjh2bww47rNv9Zs+e3bl977339ttx+suIESPyqle9Kknyve99r9/m6bN+EK985Svzyle+Mu95z3s6r3vuuefym9/8Jv/7v//b+e/vf//7tLa2bhNWt7S05KGHHspDDz2U66+/Pp/61Kf6qrQd2rhxY+d2Vyvpn3jiic5e9Fs+Iboye/bs/P73v8/y5cu3+ogIAAAAAMBAs3jx4iTJ1KlTM2xY93Hy9OnTtxnTH8fpztq1azNu3Lge79+Vjs4vDz74YEXH2Z5+bdC822675YQTTsgJJ5zQed3GjRvz29/+dquQ/sEHH8zq1au36jtfTfPnz+/cPvjgg7e5fdGiRZ3bWz4huvLyJ4xAHgAAAADYkaampq2+HjlyZEaOrG3P+w0bNmTlypVJkn322We7++60004ZO3Zs1q1bl2XLlvXLcbZn0qRJmTJlSl73utd1Xl772tdm8uTJPRq/evXq/O53v0uxWMzatWt7PG9vVf2MiSNHjsxhhx22zccSnnjiia1C+mppb2/PF77whc6v3/3ud2+zz1NPPdW5vaMnzJYP8PaeMBs3btxqZf7Lf+AAAAAAgKHj5cHxZz/72VxyySW1KWazNWvWdG73ZPV5R5D+8kC7r46zPR3nMn388cfz3e9+t/P6nXfeOa997Wu3CulnzJix1Sr9VatW5cwzz8y6detSKBSy55579nje3qp6IN+d/fffP/vvv39OOeWUqs57+eWX54EHHkiSvPOd7+yyf1FvnjBbnn13e0+Yz3/+87n00kt7Wy6DSKHwpQpGV+fs0n2nFi81tbiPyv8+i8X37HinLhQK3yp7zqS1grEA0F8G2t855SkUrq51CQAw5HT0bK83HTUtW7YsEyZM6Ly+1qvjk9LK9g4jRozY4f4dNTc3N/fLcbanq64rxWIxL7zwQu66666tThA7YsSITJ06Nfvuu2+ampry0EMPZc2aNSkUCikWi1t1fOlrdRPI18L8+fPz13/910mS3XffPV/5yle63K83T5gtf1C294S54IIL8hd/8RedXzc1NfX44xMAAAAAwOAyYcKErQL5ejBq1KjO7U2bNu1w/46OIKNHj+6X42zPs88+m9/85jdbXR5++OG0tbVttV+xWMymTZuyaNGirVqVd5g0aVIuuuiiHs/bW0M2kP/d736XU089Na2trRk1alS+/e1vZ/fdd+9y3948YbZsQ7O9J0w99IACAAAAAOjO+PHjO7d70j5m3bp1SbbtMtJXx9menXfeOccff3yOP/74zus2bdqUhx56aKuQ/sEHH+y2hte+9rW59tpr+/W8oEMykH/iiSfy5je/OatWrUpjY2NuvPHGHHvssd3u35snTMeTJendEwYAAAAAoJ6MGjUqu+yyS55//vmtzrPZlVWrVnVmoy/vBNJXx+mtESNGdHk+0yVLluThhx/OsmXLsmHDhkyaNCmHHnpoXve611U0X0/0KpD/3Oc+l/PPP3+rPunVsG7dulx22WW5+OKLKz7WihUr8qY3vSkrVqxIoVDI17/+9Zx88snbHbPliVx39ITZ8kSuWtAAAAAAQH1oS0Od9pBvqHUJ2zVjxozcfffdWbJkSVpbW7c6GeqWHn744c7tgw8+uN+O0xemTp2aqVOn9suxd6RXj/Yll1ySAw88MF/4whfy4osv9lNJL3nxxRfz+c9/PgcccECfnAB15cqVOeGEE/L4448nSebNm5fTTz99h+NmzJjRub3lE6Ir1XjCAAAAAABUwzHHHJOktGj6V7/6Vbf7zZ8/v3N71qxZ/Xacga7Xb78899xzueiii7Lvvvvm4x//eH75y1/2eVEPPPBAPvrRj2bffffNZz7zmTz33HMVH3P16tV5y1ve0tmo/wtf+EI+8YlP9Gjs/vvvn7322ivJ1k+IrvzsZz9Lkuy9996ZMmVK+QUDAAAAANTYKaec0rl97bXXdrlPe3t7rr/++iSlk6LOmTOn344z0PUqkL/77rvzute9LsViMWvXrs1VV12Vo446KtOmTcunP/3p/OQnP0lzc3Ovi1i/fn3uuOOO/L//9/8yderUzJw5M1dffXXWrl2bYrGYQw89NHfffXevj7vl8U866aT8+te/TpJcdNFF+au/+qsejy8UCp1tbR5++OHcf//9Xe53//33d66QP/nkk1MoFMquGQAAAACgvxUKhRQKhW4XFx9xxBF5wxvekCS55pprct99922zz2WXXZbFixcnSc4777wMHz68344z0BWKxWKxNwOKxWL+8z//M3/zN3+TRx55pHSQLYLnYcOGZcaMGXnVq16VAw88MHvttVd22mmnjB49OsViMRs2bMiqVauyfPnyLFmyJL/97W/z8MMPp62tbas5kuSggw7KxRdfnPe9731lh9ubNm3K29/+9tx+++1JSg/kl7/85V4f55FHHsmMGTPS1taW17/+9fnZz36W0aNHd97e3NycY489NgsWLMiwYcOyaNGiHHTQQT0+flNTUyZOnJjkH5KM3tHu1I2WGs1b7otRJedxruQFsBbnj67FC3b532ex+J6yxhUK3yp7zqS1grHlqtXPDHSoxfO+FvysMZANvv901Zeh/vrQnOT8rF69OhMmTKh1MQBUSUfu9snVF2XkhFG1LmcbG5s25F8m/l1Zv5/uueeeLFmypPPrlStX5i//8i+TlNq9fOhDH9pq/zPPPHObY3Tkrvvtt1+WLl3a5TwLFy7MrFmz0tzcnHHjxuXCCy/MnDlz0tzcnBtvvDFXXXVVkmTatGlZsGBBxo8f36/HGch6Hch3aG9vz7e//e388z//8zbvZvQ2PH95CUceeWT+7M/+LO9617vS0FDZSQ3mzp2b7373u0mS448/Pl/+8pe3W9+IESMybdq0Lm+74IIL8oUvfCFJcuihh+av/uqvcuCBB+axxx7LP/zDP2ThwoWd+/393/99r+oUyA9UAvn+n7dcAvkdE8gzFAnkof4J5PvXUH99EMgDDEWDOZA/88wz841vfKPH+3cVBfckkE+SW265Jaeddlqampq6vH3atGm59dZbd3iy1L46TjX867/+axoaGvLxj3+8z45ZdiC/pd/97nf5j//4j3znO9/Jo48+2vVEmx/Y7qY78MAD8853vjOnnXZaXv3qV1da0jbz9tT2nnjt7e358Ic/nK9//evdjj/77LNz1VVX9fqNBIH8QCWQ7/95yyWQ3zGBPEORQB7qn0C+fw311weBPMBQJJB/SSWBfJI8+eSTueKKK3LrrbfmqaeeyogRIzJ16tS8613vyic/+cmMGTOmR3X01XH6W2NjYwqFQlpb++7/kn0SyG9p6dKlmT9/fn71q19l0aJFefLJJ7Ny5cqsW7cuSTJ27Njsuuuu2W+//XLwwQfnsMMOy7HHHpsDDjigL8vo1JeBfIfbbrstV111VX75y19m5cqV2XXXXXP44Yfnox/9aE488cSy6hTID1QC+f6ft1wC+R0TyDMUCeSh/gnk+9dQf30QyAMMRYM5kKd/NTY2pqGhIS0tffc3VJ+nZFOmTMmUKVNyxhln9PWhy9LH7zckSf74j/84f/zHf9znxwUAAAAA+kdbhqWtJosGt68ea6L/VNagHQAAAAAA6BGBPAAAAAAAVIHPQ0BFatXj1I/uYFN+L/ih0g8btjSUnvdDvdczAADA4CLVAwAAAAAGvfY0pi2NtS5jG+11WBP9R8saAAAAAACoAoE8AAAAAABUgUAeAAAAAACqQA95AAAAAGDQa6vTHvL1WBP9xwp5AAAAAACoAoE8AAAAAABUgUAeAAAAAACqQA95AAAAAGDQa01jGuqwX3trHdZEyXHHHZeGhr5d0y6QBwAAAACAl7nzzjv7/Jh9Fu+/973vzVNPPdVXhwMAAAAAgEGlzwL5//qv/8r06dNz6aWXprm5ua8OCwAAAAAAg0KfNsBpbm7O5z73ubzyla/MjTfe2JeHBgAAAACAAa3PesifffbZufbaa9Pe3p6nnnoq73//+/Ov//qvueKKK/JHf/RHfTUNDCK1OIXD8BrMOZS01roAqAHP+x1rqXUBwKDjdQUAytGWxrTV4Sk125zUtabmz5+fhoaGzJo1q1cncG1qasqqVavS2NiYffbZp8fj+myF/Ne+9rU88MADOfroo5MkxWIxP//5z3PEEUfk7LPPzjPPPNNXUwEAAAAAQMWOP/74zJ49O+9973vT2trzRWdLly7NgQcemH333bdX51bt05Y1f/RHf5R77rkn//7v/5699947xWIx7e3tue666zJt2rT84z/+Y1parOYAAAAAAKB+3HTTTZk7d242bdrUo/1f85rX5K1vfWuS5Lvf/W6P5+nTQL7D+973vvz+97/PBRdckJEjR6ZYLGbNmjW54IILMmPGjPz3f/93f0wLAAAAAAC9MnLkyIwZMya33HJL3vGOd2Tjxo09GnfGGWekUCjkJz/5SY/n6pdAPknGjBmTv/u7v8uiRYty8sknJym1sXnsscdy6qmn5s1vfnMWLVrUX9MDAAAAAHQq9ZCvzwu1NWnSpNx6660ZP358br/99vzxH/9xmpubdzjujW98Y5Lkf//3f3s8V78F8h3233//fO9738vtt9+egw8+OEkpmL/zzjvzute9Lueee25eeOGF/i4DAAAAAAC6NHv27Pz4xz/OxIkTc9ddd+Wtb31r1q1bt90xO++8cyZMmJCnn366x/P0eyDf4U1velMefPDBXH755Zk0aVKKxWJaW1vzb//2bznooIPyL//yL2lvb69WOQAAAAAA0Omoo47K//zP/2SXXXbJ3XffnTe96U1ZvXp1t/u3t7enubk5w4cP7/EcVQvkk6SxsTHnnXdeHn300Xz4wx9OQ0NDisViVq1alfPOOy+vfe1rc8cdd1SzJAAAAAAASJIcdthhueuuu7LHHnvkF7/4RY499tj83//9X5f7/vznP09LS0v222+/Hh+/qoF8h1122SVXXnllFixYkGOOOSZJqY3N7373u7z1rW/NySefnCVLltSiNAAAAABgEKp1n3g95AeOQw45JPPnz88+++yThx56KIcffnhuvvnmrfZ59tlnc+6556ZYLOZNb3pTj489rI9r3aGWlpYsXLgwv/zlL/PAAw/kmWeeSZIUCoUkpWD+Bz/4QX784x/nz/7sz3LxxRdnzJgx1S4TAAAAAIAhatq0abn77rtzwgkn5LHHHss73/nOvP71r8/s2bOzcuXK3HbbbXnuuecyevTonHfeeT0+br8H8g8//HAeeOCBzsuDDz6YlpaWrfYpFAopFoudXxeLxWzatClf/OIXc8MNN+SrX/1qTjzxxP4uFQAAAAAAkiT77bdfHnjggZx99tm5+eab86tf/SoLFixIUsq0R4wYkeuuuy77779/j4/Zp4H8ihUrtgrff/WrX6WpqWmrfbYM3js0NDTkkEMOycyZM3PkkUfmJz/5SW644Ya0t7dn2bJledvb3pZPfvKTufzyy9PQUJMuOwAAAAAADDIdnVu6M2nSpHznO9/JHXfcka9//ev57W9/m2HDhuWwww7Ln//5n+dVr3pV7+YrdpWQl2GfffbJ008/vdV13R16p512ylFHHZWZM2dm5syZOeKIIzJ+/Pit9lm0aFH+3//7f/nRj35UKrRQyCmnnJLvfOc7fVFu3WlqasrEiROT/EOS0bUuh6qoeseoJD0/43PfGkrfK9vXsuNdGGBaa13AAOB5D73jd/iOeV0pX3OS87N69epMmDCh1sUAUCUdudu7Vv9Lhk+ov9ytpak53574Sb+fhog+S8lWrFixTeuZpBSkz5gxozN8P/roo/PKV75yh8ebMWNGbrvttnzzm9/Mxz72sTQ3N+fmm2/Od77zncydO7evygYAAAAAgKro02WrxWIxkyZNypFHHtkZwB955JEVvbPzgQ98ICNHjsx73/veJMmVV14pkAcAAAAAYMDps0D+6quvzsyZM3PwwQf31SE7vfvd785FF12Uxx57LIsXL+7z41NvatHeBOg5H5MffLSd6RnPfaiOcn/WBlqrG68pAABDUZ8lnx/84Af76lBdeuUrX5nHHnssf/jDH/p1HgAAAABg8GlNYwpprHUZ22itw5roPw21LqCnhg8vrXhpb2+vcSUAAAAAANB7A6Y3yMc+9rFMnjw5v/nNb2pdCgAAAAAAdWT+/PlbfT179uwaVbJ9AyaQf8tb3pK3vOUttS4DAAAAAIA6c/zxx6dYLHZ+Xa+dVgZMIA8AAAAAUK62NKahDuPQNj3k+1ShUNgqmK83A6aHPAAAAAAAdKdQKNS6hB2qv7eEAAAAAACgF6699tpal9AjAnkAAAAAAAa0008/vdYl9IhAHgAAAAAY9Eo95OuvX7se8kOLHvIAAAAAAFAFAnkAAAAAAKgCgTwAAAAAAFSBHvIAAAAAwKCnhzz1wAp5AAAAAACoAivk2Q5Pj/o1vAZzDrTnQ0uZ42px39ZKufcR9au11gUMAJ73MHj5+QYAoP5ZIQ8AAAAAAFUw0Ja8AgAAAAD0WmsaU6jDfu2tdVgT/UcgDwAAAADAgLZgwYLceeedee1rX5vXvva1ecUrXlHrkrokkAcAAAAAYEC79tpr85WvfCVJ8upXvzr/+7//W+OKuqaHPAAAAAAAA9odd9yRQqGQJPnLv/zLGlfTPYE8AAAAADDotWdY2urw0q6JScWef/75PPbYYykWixk3blz+5E/+pNt916xZk49+9KPZZZddMmLEiOy333754Ac/mEWLFlWlVoE8AAAAAAAD1pbtad761rdm1KhRXe5XLBbztre9LV/72tfy4osvprW1NcuWLct1112XQw89NF/96lf7vVaBPAAAAAAAA9YTTzzRuX300Ud3u983vvGN3HPPPZ1fFwqFzktra2s+8YlP5Oabb+7PUgXyAAAAAAAMXC+88ELn9kEHHdTtfvPmzevcfuUrX5kHHnggTz31VP6//+//67z+E5/4RDZu3Ng/hUYgDwAAAAAMAW1prNsLlWltbe3cnjRpUpf7PPnkk/nNb36TYrGYhoaG3HDDDTnssMPyile8Ipdcckk++tGPJkmefvrp3HTTTf1Wq0AeAAAAAIABa8sQft26dV3uc+edd3Zuv/GNb8xrX/varW7/9Kc/3bl966239m2BWxDIAwAAAAAwYB1wwAGd2w899FCX+2zZG/4973nPNrdPmTIl++67b5Lk17/+dd8WuAWBPAAAAAAAA9ZRRx2VhoZS1H3DDTdsc/sLL7yQO+64o7Ndzdvf/vYuj7PffvulUCjkmWee6bdah/XbkelTxeK5ZY8tFObteCdqYHitC2BQaKl1AXSrdce7UCbPewAAoPfa0pBCHfZrb7NmumITJ07MSSedlFtuuSW//vWv8y//8i/55Cc/2Xn73/7t32bTpk1JklmzZmW33Xbr8jgjRoxIkqxdu7bfahXIAwAAAAAwoF100UW59dZb09bWlvPOOy8PPfRQTjrppNxzzz254oorOvd773vf2+0xmpqakiSjRo3qtzq9/QIAAAAAwIB2+OGH58ILL0yhUEixWMzXvva1nHLKKfmnf/qnJEmxWMykSZPy/ve/v9tjPProoykWi9ljjz36rU4r5AEAAAAAGPAuvfTSFIvFfP7zn097e3uSUhCfJIVCIZ///OczYcKELscuXrw4L774YpLkoIMO6rcaBfIAAAAAwKDXmsakDnvIt9ZhTQPZ5z73ubzzne/MVVddlfvuuy/PPfdcJk+enHPPPTfve9/7uh134403dm4feeSR/VafQB4AAAAAgEHjda97Xf7t3/6tx/sXi8V8//vf7/z6LW95S3+UlUQgDwAAAADAEFYoFPKb3/wmzz33XBYuXJijjjqq3+YSyAMAAAAAMOTttttuefOb39yvcwjkAQAAAIBBry3DUqjDOLStDmui/zTUugAAAAAAABgKBPIAAAAAAFAFAnkAAAAAAHiZf/3Xf81XvvKVPj2mBkUAAAAAwKDXnsa0pbHWZWyjvQ5rouRTn/pUCoVCPv7xj/fZMa2QBwAAAACALhQKhT49nkAeAAAAAACqQMuaAaJQmFfB6HIf5uEVzAkDVUutC6CutNa6gEHOz9uOeQ4OPv78BgCAocz/CAAAAACAQa/UP77++rXXY197+o+WNQAAAAAAUAUCeQAAAAAAqAKBPAAAAAAAVIEe8gAAAADAoKeHPPXACnkAAAAAAKgCgTwAAAAAAFSBQB4AAAAAAKpAD3kAAAAAYNBrTUOKddivvc2a6SHFow0AAAAAAFVghTwAAAAAALzMcccdl4aGvl3TLpAHAAAAAICXufPOO/v8mAJ5AAAAAGDQa8uw1GMc2laHNdF/9JAHAAAAAIAq8PZL3enunbrh1S4ktXt6tNZo3qHAjzxDkdeU/tVS6wIGgPKfg8XiuWWNKxTmlT0n/a3c54Pf4QAAMBj4yx4AAAAAAHqhWCzmv//7v5Mkb3zjGzNu3LgejRPIAwAAAACDXlsakzTWuoxttNVhTUNJY2NjisVinnvuueyyyy49HlcoFPLRj340zz77bP77v/87b3vb23o0Tg95AAAAAADopcMOOyxJsmDBgh6PEcgDAAAAAEAvvf71r0+hUOhVIK9lDQAAAAAAQ9ratWszatSoXo2ZMWNGkt6tkBfIAwAAAACDXnud9pBvr8OahqL999+/rHGFQiHPPvtsj/cXyAMAAAAAMKQVCoWqzCOQBwAAAABgSLv55pszceLEfp9HIA8AAAAAwJB29NFHZ5dddun3eQTyAAAAAMCg15rGNNRhv3Y95IeWhloXAAAAAABA+Z588smcf/75mT59esaOHZudd945hx9+eL74xS9m/fr1ZR3zuOOOS6FQ6NXlpz/9aZfH6un44447rvw7oUzV6h3fwQp5AAAAAIAB6pZbbslpp52WpqamzuvWr1+fBQsWZMGCBbn66qtz6623ZurUqf1aR0NDQw466KB+naM/PP7440mSnXfeuSrzCeQBAAAAAAaghQsX5j3veU+am5szbty4XHDBBZkzZ06am5tz44035mtf+1oeeeSRnHTSSVmwYEHGjx/f42Nfe+21Wbdu3Xb3WbRoUd7znvckSd74xjdm77333u7+H//4x3POOed0e/vYsWN7XF9f2Xfffas6n0C+7gzffHm5Sh6qro63Y8Xi+8uesVD4j7LH1kZrrQvoJT+6QF9qqXUBA0T1f1cUCvOqPufAM9Cev+X9XVbZ88/fDQAASdKWxhTr8G+jSnrIn3feeWlubs6wYcNy++23Z+bMmZ23HX/88TnooIPy6U9/Oo888kguu+yyXHLJJT0+9v7777/Dfb75zW92bp9++uk73H/33XfPIYcc0uMaBiM95AEAAAAABpgHHnggd999d5Lk7LPP3iqM73D++efn4IMPTpJcccUVaWnpuwUt7e3t+Y//KC3KHTduXN75znf22bEHM4E8AAAAAMAAc/PNN3dun3XWWV3u09DQ0Lly/cUXX8xdd93VZ/PfeeedWb58eZLkT/7kTzJmzJg+O/ZgJpAHAAAAABhg7rnnniSlvuuHHXZYt/vNnj27c/vee+/ts/mvv/76zu2etKuhRCAPAAAAADDALF68OEkyderUDBvWfW/86dOnbzOmUmvXrs33vve9JMl+++2X4447rkfjvv3tb2fGjBkZM2ZMxo8fn4MOOihnnHFGr1fur127trcl95lK5xbIAwAAAACDXlsa6/bSWxs2bMjKlSuTJPvss892991pp50yduzYJMmyZct6f8d14Tvf+U7WrVuXJDnttNNSKBR6NG7RokVZvHhxmpubs3bt2ixZsiTXX399jj/++Jx66qlZvXp1j45zwAEH5PLLL8/GjRvL/h56a8OGDbn88stzwAEHVHQcgTwAAAAAQI01NTVtddle2LxmzZrO7XHjxu3w2B2BfF+tLO9tu5oxY8bkve99b772ta/l7rvvzsKFC3P77bfnoosuyi677JKk1BP/5JNP7tGJZ59//vmcf/752W+//fI3f/M3ee6558r/Znbg2WefzaWXXpp99903559/fucbIeUSyAMAAAAA1NjkyZMzceLEzsvnP//5bvfdsGFD5/aIESN2eOyRI0cmSZqbmyuu86mnnspPf/rTJMlRRx2VadOm7XDM8uXLc8MNN+RDH/pQjjnmmLzuda/LCSeckL/927/N7373uxx66KFJkvnz5+crX/nKDo93zDHHpFAo5Nlnn81nP/vZ7LPPPpk7d26+853vZP369RV9f0mybt26fPvb386pp56affbZJ5deemmef/75FAqFHHPMMRUdu/vmQgAAAAAAVMWyZcsyYcKEzq87QvSujBo1qnN706ZNOzx2x2r70aNHV1Bhyb//+7+nvb09SXLGGWf0aMykSZO6vW2PPfbITTfdlOnTp6elpSXz5s3Lpz71qe0eb/78+fmv//qvXHDBBVm6dGlaW1vzve99L9/73vcyatSozJo1K7Nnz87rX//6vPrVr87ee++93eMtX748Dz30UH75y19m/vz5uffeezvvs0KhkEKhkAMOOCB///d/n3e96109+p67I5AHAAAAAAa9tjSmWEa/9v7WvrmmCRMmbBXIb8/48eM7t3vShqaj33tP2tvsyDe/+c0kpTcM3vOe91R8vKTUE/6EE07IbbfdliVLlmTFihXZa6+9tjvm3e9+d+bOnZvrrrsuX/jCF/L4448nKX164M4778ydd97Zue+IESOy1157Zaeddsro0aNTKBTS3NycVatWZcWKFV22B+roiz916tT89V//dc4444w0NFTecEbLGgAAAACAAWTUqFGdvdefeuqp7e67atWqzkB+8uTJFc27YMGCLFq0KEnytre9LTvttFNFx9vSjBkzOreXL1/eozGNjY05++yz8+ijj+a///u/c9JJJ2X48OGdq9o7bNq0KUuXLs3ChQvz85//PPfee29+/etf54knntgqjO8YN2LEiLz97W/PD37wg/z+97/PWWed1SdhfGKFPAAAAADAgDNjxozcfffdWbJkSVpbWzNsWNdR78MPP9y5ffDBB1c055Ync+1pu5qe2jJAL8dJJ52Uk046Kc8//3y+//3v58c//nHmz5/f4xO+7r777jnuuOPylre8JSeffHKfvtmwJYE8AAAAAMAAc8wxx+Tuu+/OunXr8qtf/SpHHnlkl/vNnz+/c3vWrFllz9fS0pIbb7wxSbLbbrvlxBNPLPtYXelYeZ9kh+1qtmeXXXbJBz/4wXzwgx9MUlptv2jRojz55JN57rnnOj8tMHbs2Oy2227Zb7/9MmPGjB32me8rAnkAAAAAYNBra29Msb0Oe8iXWdMpp5ySz3/+80mSa6+9tstAvr29vXNV+6RJkzJnzpyy6/zhD3/Yudr8fe97X7cr8svxxBNP5I477kiSHHjggX0aju+9995VC9t7Qg95AAAAAIAB5ogjjsgb3vCGJMk111yT++67b5t9LrvssixevDhJct5552X48OHb7NPRN33KlCnbnW/LdjWnn356j+u85ZZb0tra2u3tzzzzTObOnZtNmzYlSc4555weH3sgKhSLxWKtiyBpamrKxIkTk/xbktF9fPRtf9B6ppJ3ubr/IduxlgrGAoNXJa8rbN9Qet31PKpPtXoOlvd8KBY/XfaMhcKXyhxZ7t9zlfKB2vpUq+dDtfXHa0NzkvOzevXqTJgwoR+OD0A96sjddlq1OA0Txte6nG20N63Jqp0OLuv308KFCzNr1qw0Nzdn3LhxufDCCzNnzpw0NzfnxhtvzFVXXZUkmTZtWhYsWJDx47f9/jt6t++3335ZunRpl/OsWrUqr3jFK7Jx48Yccsgheeihh3pc45QpU9LS0pK5c+dm5syZmTJlSkaPHp2VK1fmpz/9aa688sqsXLkySakNz//8z/9k5MiRvbofBhJ/YQMAAAAADECHHnpovvWtb+W0005LU1NTLrzwwm32mTZtWm699dYuw/ie+ta3vpWNGzcm6d3q+A4rVqzIvHnzMm/evG73mTt3bq6++upBHcYnAnkAAAAAYAhoa21Me2v99ZAvVljT29/+9jz44IO54oorcuutt+app57KiBEjMnXq1LzrXe/KJz/5yYwZM6aiOb75zW8mSRobG/P+97+/V2O/8Y1vZP78+bnvvvvy+OOPZ+XKlWlqasq4ceMyefLkHH300TnjjDMyc+bMimocKLSsqRNa1mxpKLVOAHpOq5H+M5Redz2P6pOWNTumZQ1b0rKmfFrWAAxFHbnbhOceSaEOW9YUm9akabdpfj8NEU7qCgAAAAAAVTAkAvlnn302P/jBD3LxxRfnxBNPzK677tp59uAzzzyzR8e47rrrOsfs6HLdddf16/cDAAAAAMDAMyQ+g7rHHnvUugQAAAAAoIbaWoel0Fp/cWixDmui/wy5R3vffffN9OnTc/vtt5d9jB//+MfZa6+9ur19n332KfvYAAAAAAAMTkMikL/44otz+OGH5/DDD88ee+yRpUuXZv/99y/7eNOmTcuUKVP6rkAAAAAAAAa9IRHIX3rppbUuAQAAAACAIW5IBPIAAAAAwNDW1tqQQmtjrcvYRrG1odYlUEUebQAAAAAAqAKBfBnOOuus7LXXXhkxYkR23XXXHHXUUfnMZz6T5cuX17o0AAAAAADqlEC+DD/96U/z9NNPp6WlJc8//3x+8Ytf5O/+7u8yderUXHnllT06xsaNG9PU1LTVBQAAAACAwUsP+V444IAD8s53vjMzZ87M5MmTkySPP/54vvOd7+Smm27Khg0b8rGPfSyFQiEf+chHtnusz3/+87082ezwCiovV2sN5gQGN68r/aul1gUw5A2N52Ch8I8VjPbnNx1q8ff9QFPJfTQ0Xo8A6J221sY67SFffzXRfwrFYrFY6yKqbenSpdl///2TJGeccUauu+66HY5ZvXp1JkyYkEKh0OXtP/jBD/LOd74zLS0tGTNmTB577LHsueee3R5v48aN2bhxY+fXTU1Nm0P+f0syuosRQ+kPdn88w+AlkO9fXj93zHOwfw2052Atng/lBvK1+lvQGwj9Zyj9fV8L3b0eNSc5v/P/dwAMDU1NTZk4cWKGP7EshTp8/S82NaVl/8l+Pw0RWtb00MSJE7sN45PkbW97Wy6++OIkyfr163PNNdds93gjR47MhAkTtroAAAAAADB4CeT70Ec+8pHO0H7+/Pk1rgYAAAAAgHriM6h9aPfdd88uu+ySlStXZvny5bUuBwAAAADYrLW1MYWW+uvXrof80GKFfB/bXlsbAAAAAACGLoF8H3ruueeycuXKJMlee+1V42oAAAAAAKgnAvk+dNVVV6VYLCZJZs+eXeNqAAAAAACoJ3rI98DSpUuzatWqHHrood3u84Mf/CCf+9znkiSjR4/OWWedVa3yAAAAAIAdKLYNS7GtDuPQeqyJfjMkHu177rknS5Ys6fy6o61MkixZsiTXXXfdVvufeeaZW329dOnSzJkzJzNnzszb3/72vPa1r83uu++eJHn88cdz00035aabbupcHf9P//RP2XvvvfvnmwEAAAAAYEAaEoH81VdfnW984xtd3nbvvffm3nvv3eq6lwfyHe67777cd9993c4zZsyYXH755fnIRz5Sdq0AAAAAAAxOQyKQr9Rhhx2Wf//3f899992XBQsW5Omnn87KlSvT2tqanXbaKa961avyxje+MR/60Ic6V84DAAAAAMCWCsWOPivUVFNTUyZOnJjk35KM7mKP4VWuqJZaal0A0G9aa13AIOf1c8c8B/vXQHsO1uL5UO56mFr9LWj9Tv8ZSn/f10J3r0fNSc7P6tWrM2HChGoWBEANdeZuv30+GV+Hr/9rmpJDdvH7aYhoqHUBAAAAAAAwFAjkAQAAAACgCnwGte4Mi4+vlvv9D7SPyQN0xWvZjg2dtjPF4rlljSsU5lUw61B6DvpTmL4w1P9237Fi8UNljSsUrq5g1u4el6HzOwQAqE/+FwIAAAAADH6tjaVLvanHmug3WtYAAAAAAEAVCOQBAAAAAKAKBPIAAAAAAFAFesgDAAAAAINfWyFpLdS6im211WFN9Bsr5AEAAAAAoAoE8gAAAAAAUAUCeQAAAAAAqAI95AEAAACAwa9186Xe1GNN9Bsr5AEAAAAAoAoE8gAAAAAAUAUCeQAAAAAAqAI95AEAAACAwU8PeeqAFfIAAAAAAFAFVsgziAyvYGxLn1UBAH2lUJhX6xLoc5X8vQIDU6Fwda1LAACoG1bIAwAAAABAFVghDwAAAAAMfnrIUweskAcAAAAAgCoQyAMAAAAAQBUI5AEAAAAAoAr0kAcAAAAABr/WJC21LqILesgPKVbIAwAAAABAFQjkAQAAAACgCgTyAAAAAABQBXrIAwAAAACDX9vmS72px5roN1bIAwAAAABAFQjkAQAAAACgCgTyAAAAAABQBXrIQ5JkeK0LoG601LoAGEJaa10AlMnfDQAAA1Jr6vO/IfVYE/3GCnkAAAAAAKgCgTwAAAAAAFSBQB4AAAAAAKpAD3kAAAAAYPDTQ546YIU8AAAAAABUgUAeAAAAAACqQCAPAAAAAABVoIc8AAAAADD46SFPHbBCHgAAAAAAqkAgDwAAAAAAVSCQBwAAAACAKtBDHgAAAAAY/NpSn/3a22pdANVkhTwAAAAAAFSBQB4AAAAAAKpAyxqArQyvYGxLn1XBYOD5APWvktf8gcSf/AxF3f0e9vsZAKgtf50DAAAAAINfa+qzh3w91kS/0bIGAAAAAACqQCAPAAAAAABVIJAHAAAAAIAq0EMeAAAAABj89JCnDlghDwAAAAAAVSCQBwAAAACAKhDIAwAAAABAFeghDwAAAAAMfi2bL/WmHmui31ghDwAAAAAAVSCQBwAAAACAKhDIAwAAAABAFQjkAQAAAACgCpzUFQAAAAAY/No2X+pNPdZEvxHI151h8bBAh9ZaF9BLw2swp1OxA32tFq9lMJCV+7vYzxoAwFCkZQ0AAAAAAFSBQB4AAAAAAKpAbxQAAAAAYPBrS312x9VDfkixQh4AAAAAAKpAIA8AAAAAAFUgkAcAAAAAgCrQQx4AAAAAGPxaU5895OuxJvqNFfIAAAAAAFAFAnkAAAAAAKgCgTwAAAAAAFSBHvIAAAAAwOCnhzx1wAp5AAAAAACoAoE8AAAAAABUgZY1MCANr8GcLTWYc6C9RNXiM2aVPBcG2mPqM3wAMLTU4m8VAID+NdDSLgAAAACA3tNDnjqgZQ0AAAAAAFSBQB4AAAAAAKpAIA8AAAAAMIA9+eSTOf/88zN9+vSMHTs2O++8cw4//PB88YtfzPr16ys+fqFQ6NHluOOOq4t665ke8gAAAADA4NeW+uzX3lbZ8FtuuSWnnXZampqaOq9bv359FixYkAULFuTqq6/OrbfemqlTp1ZYaN8YaPX2NYE8AAAAAMAAtHDhwrznPe9Jc3Nzxo0blwsuuCBz5sxJc3Nzbrzxxnzta1/LI488kpNOOikLFizI+PHjK5rv4x//eM4555xubx87dmxd1VuPBPIAAAAAAAPQeeedl+bm5gwbNiy33357Zs6c2Xnb8ccfn4MOOiif/vSn88gjj+Syyy7LJZdcUtF8u+++ew455JABU2890kMeAAAAAGCAeeCBB3L33XcnSc4+++ytwu0O559/fg4++OAkyRVXXJGWlpaq1rilgVZvfxHIAwAAAACDX2sdX8pw8803d26fddZZXe7T0NCQ008/PUny4osv5q677ipvsj4w0OrtLwJ5AAAAAIAB5p577klS6tt+2GGHdbvf7NmzO7fvvffefq+rOwOt3v4ikAcAAAAAGGAWL16cJJk6dWqGDev+VKHTp0/fZky5vv3tb2fGjBkZM2ZMxo8fn4MOOihnnHFGj1ay16LeeiSQBwAAAAAYQDZs2JCVK1cmSfbZZ5/t7rvTTjtl7NixSZJly5ZVNO+iRYuyePHiNDc3Z+3atVmyZEmuv/76HH/88Tn11FOzevXquqq3HnX/VgQAAAAAwGDRkqSx1kV0YfN5S5uamra6euTIkRk5cmSXQ9asWdO5PW7cuB1OMXbs2Kxbty5r164tq8QxY8bkHe94R974xjdm+vTpGTduXJ577rnMnz8/X/3qV/P888/n5ptvzsknn5w77rgjw4cPr2m99UwgDwAAAABQY5MnT97q689+9rO55JJLutx3w4YNndsjRozY4bE7gv3m5uayalu+fHkmTZq0zfUnnHBCzj333Jx44olZuHBh5s+fn6985Sv51Kc+VdN665lAfkgYvuNdGGBq8ZhWMmdLn1XR/yqptZKX1DJPqV6Rch/TgfR4AkC98vsUANjasmXLMmHChM6vu1sdnySjRo3q3N60adMOj71x48YkyejRo8uqraswvsMee+yRm266KdOnT09LS0vmzZu3TSBf7XrrmR7yAAAAAAA1NmHChK0u2wvkx48f37ndk7Yu69atS9KzdjHlOOCAA3LCCSckSZYsWZIVK1ZsdXu91VtLAnkAAAAAYPBrq+NLL40aNSq77LJLkuSpp57a7r6rVq3qDLhf3hanL82YMaNze/ny5VvdVo/11opAHgAAAABggOkIwJcsWZLW1u5b4T788MOd2wcffHC/1VMoFLZ7e73VWysCeQAAAACAAeaYY45JUmrv8qtf/arb/ebPn9+5PWvWrH6rZ9GiRZ3be+211za311u9tSKQBwAAAAAYYE455ZTO7WuvvbbLfdrb23P99dcnKZ2Ydc6cOf1SyxNPPJE77rgjSXLggQdm77333mafeqq3lgTyAAAAAMDg11rHlzIcccQRecMb3pAkueaaa3Lfffdts89ll12WxYsXJ0nOO++8DB8+fJt9CoVCCoVCpkyZ0uU8t9xyy3ZbzDzzzDOZO3duNm3alCQ555xz+rXega5QLBaLtS6CpKmpKRMnTkzy9SRj+vjog++Jy0B7TFtqXUAv1KrWMn/71oT7qGcG0vO+VgbaYwoD1bBaFwB1pDnJ+Vm9enUmTJhQ62IAqJLO3O2C1cmoOnz939CUfH5iWb+fFi5cmFmzZqW5uTnjxo3LhRdemDlz5qS5uTk33nhjrrrqqiTJtGnTsmDBgowfP36bY3T0ft9vv/2ydOnSbW6fMmVKWlpaMnfu3MycOTNTpkzJ6NGjs3Llyvz0pz/NlVdemZUrVyYptaX5n//5n4wcObLf6h3oBPJ1QiBP7wy0x3QgBZPC5h1zH/XMQHre18pAe0xhoBLIw0sE8gBD0WAO5JPSCvbTTjstTU1NXd4+bdq03HrrrZk6dWqXt/ckkH/yySd3WMfcuXNz9dVXZ9KkSf1a70Dnr3MAAAAAgAHq7W9/ex588MFcccUVufXWW/PUU09lxIgRmTp1at71rnflk5/8ZMaMKX8B8De+8Y3Mnz8/9913Xx5//PGsXLkyTU1NGTduXCZPnpyjjz46Z5xxRmbOnFkX9dY7K+TrhBXy9M5Ae0wH0kphq793zH3UMwPpeV8rA+0xhYHKGhx4iRXyAENRZ+726dXJyDp8/d/YlPxj+SvkGVic1BUAAAAAAKpAIA8AAAAAAFUgkAcAAAAAgCrQULLuDM/A6w/e1wbS9+9HqGdqcT+V25u6kuffQOsZPtDuo3KfR/qU9y/3LwAAMEC0JmmsdRFd8N+qIcUKeQAAAAAAqAKBPAAAAAAAVIFAHgAAAAAAqkADbAAAAABg8GtJfS5PHminpKMi9fgUBAAAAACAQUcgDwAAAAAAVSCQBwAAAACAKtBDHgAAAAAY/No2X+pNPdZEv7FCHgAAAAAAqkAgDwAAAAAAVSCQBwAAAACAKtBDHgAAAAAY/NqStNa6iC7oIT+kWCEPAAAAAABVIJAHAAAAAIAq0LKm7gzffBnKavG0rMV9PlS+z0q0VDC2Fp9Bq+QxLfexqeQ+Klc9fr6vHg2kx7QSlTzvPZcAAAAYWgTyAAAAAMDg15r67BdirdKQUo9PQQAAAAAAGHQE8gAAAAAAUAUCeQAAAAAAqIIhEcg/++yz+cEPfpCLL744J554YnbdddcUCoUUCoWceeaZvT7eD3/4w5x66qnZZ599MnLkyOyzzz459dRT88Mf/rDviwcAAAAAKtdSxxeGjCFxUtc99tijT47T3t6ej3zkI7nmmmu2un758uVZvnx5br755nzoQx/KlVdemYaGIfFeBwAAAAAAPTTkUuN99903b37zm8sae9FFF3WG8YceemhuuOGGPPDAA7nhhhty6KGHJkmuvvrqfOYzn+mzegEAAAAAGByGxAr5iy++OIcffngOP/zw7LHHHlm6dGn233//Xh3jkUceyT/90z8lSV7/+tfnZz/7WUaPHp0kOfzww/OOd7wjs2fPzoIFC/LFL34xH/zgBzN16tQ+/14AAAAAABiYhsQK+UsvvTRve9vbKmpd8+Uvfzmtra1Jknnz5nWG8R3GjBmTefPmJUlaW1tz+eWXl18wAAAAANC32ur4wpAxJAL5ShWLxXz/+99PkkyfPj1HHXVUl/sdddRReeUrX5kk+f73v59isVi1GgEAAAAAqG8C+R544oknsmLFiiTJ7Nmzt7tvx+3Lly/P0qVL+7s0AAAAAAAGCIF8DyxatKhze/r06dvdd8vbFy9e3G81AQAAAAAwsAyJk7pW6qmnnurc3meffba77+TJkzu3ly1b1m81AQAAAAC90Jr6XJ7cWusCqCaBfA+sWbOmc3vcuHHb3Xfs2LGd22vXru12v40bN2bjxo2dXzc1NVVQIQAAAAAA9U4g3wMbNmzo3B4xYsR29x05cmTndnNzc7f7ff7zn8+ll15aeXE9UouHeXgN5qzk+xwq9Q6lH/ly317u/ud2xyq5fyuZd6hoKXNcJY+LZQoAAABA36nHD2nUnVGjRnVub9q0abv7brnqffTo0d3ud8EFF2T16tWdF+1tAAAAAAAGt6G0XLZs48eP79zeXhuaJFm3bl3n9vba24wcOXKr1fQAAAAAQD9qS31+ELqt1gVQTVbI98CWJ3Ld8gSvXdlypfuWJ3gFAAAAAGBoE8j3wIwZMzq3H3744e3uu+XtBx98cL/VBAAAAADAwCKQ74H9998/e+21V5Jk/vz52933Zz/7WZJk7733zpQpU/q7NAAAAAAABgiBfA8UCoWcfPLJSUor4O+///4u97v//vs7V8iffPLJKRQKVasRAAAAANiOljq+MGQI5Hvoz/7sz9LY2JgkOffcc9Pc3LzV7c3NzTn33HOTJMOGDcuf/dmfVbtEAAAAAADq2LBaF1AN99xzT5YsWdL59cqVKzu3lyxZkuuuu26r/c8888xtjjFt2rT85V/+Zb7whS9kwYIFmTVrVv7qr/4qBx54YB577LH8wz/8QxYuXJgk+cu//MscdNBB/fK9AAAAAAAwMBWKxWKx1kX0tzPPPDPf+MY3erx/d3dJe3t7PvzhD+frX/96t2PPPvvsXHXVVWlo6N2HD5qamjJx4sQkNyYZ06uxO1aL912G12DOSr7PoVLvkHgPbrPWMsc173iXblXyGbNy5y33+0zKr7eS77MW9VaiknrLNZQ+q1iL+xeGoqH0+x92pDnJ+Vm9enUmTJhQ62IAqJLO3O3k1cnwOnz9b2lKvj/R76chQsuaXmhoaMg111yTW2+9NSeffHL22muvjBgxInvttVdOPvnk3Hbbbbn66qt7HcYDAAAAAP2srY4vDBlDYoX8QGCFfF+wQr5/5xxorJDfMSvkd8wK+f5lhTxUx1D6/Q87YoU8wFDUmbu9rY5XyP/ACvmhwlJuAAAAAACoAoE8AAAAAABUgc+v1p1h6fuHRTuW+pxzdAVjy623FnNWopLWHeWOraRlzZoKxpZ7/1ZS71Ax0FrAVPKzNtC+VwAAgCpqTVKodRFd0M1zSLFCHgAAAAAAqkAgDwAAAAAAVSCQBwAAAACAKhDIAwAAAABAFTipKwAAAAAw+DmpK3XACnkAAAAAAKgCgTwAAAAAAFSBQB4AAAAAAKpAD3kAAAAAYPCr117t9VoX/cIKeQAAAAAAqAKBPAAAAAAAVIFAHgAAAAAAqkAPeQAAAABg8GtLUqh1EV1oq3UBVJMV8gAAAAAAUAVWyNedYUmG17qIzSp5elTyPYwuc1wl9ZY7Z7njkmRMBWPHlzmuRs+tch+aSt4hLpY7sLmCSZ+vYOwLFYwdKso97XytXlPLrRcAAAAYrKyQBwAAAACAKrBCHgAAAAAY/Or1g8z1Whf9wgp5AAAAAACoAoE8AAAAAABUgUAeAAAAAACqQA95AAAAAGDwq9de7fVaF/3CCnkAAAAAAKgCgTwAAAAAAFSBQB4AAAAAAKpAD3kAAAAAYPBrTVKsdRFdaKt1AVSTFfIwYL38N0hbvIIDAAAAQP0SyMOA8fIAvjnJg0mWb/66cfMlSdo3XwAAAACAeqFlDQwYhSR/SHJnkhuSrEsyPMkLScYmOTLJ25LMSTKmRjUCAAAAAN0RyA8YtXiohlcwdnQNxtZizglVnHNhko8l+WU3t/8uydeT7Jk0zk0a35s0HJEUtngcdy2nzs0mVTB2XJnjKnnaryxz3FMVPI/W7FP+2LJ/3ir5OX2hgrG10FLmuNY+rQIGv3J/1mqlktdBAACGlHrt9FuvddEvBPJQ19pSakMzP8n/l1IYPzzJmKThuFLY3v5/SZqS4gspBazPJG3fSIq/SRo/kgz7QI1qBwAAAAC2JJCHulbY/O+/JvnF5u23JyP+PGk4LCmMKl3VvjRpvz9pvzlpuynJ2qT93qT990nDoUnDIdUuHAAAAAB4GSd1hbrWkGRDku/lpRYCf580ziqF8cXNrTgapiTD3puMuDEZuSRp/GTp+mEfEMYDAAAAQJ2wQh7qVntKgfzPU2pd05BktySNSbFYur2w+Ue44+sUNofzf1m6vvGDL91eKAQAAABgyGpNUqx1EV3QQ35IsUIe6lbHj+emJDun9BvjmST/XArXC40v7drxdWHzmIbJyYh/Thpe9dLtAAAAAEBNCeSh7r01ySEpBfKFJPOSTR9N2n9XurlYTIrtWw8ptm9eNQ8AAAAA1AuBPAwI85Icm87PVbX9R9Ly2aT9V5tXx7/sR7nQYFU8AAAAANQZgTwMCK9OcmmSYzZ/vT5p/26y8fBk0zlJ+5KeH8rKeQAAAGAoaq3jC0OGQB4GjNlJfpbkhqThmJTa1yRp+/ek5a+T9odLX+8ocO+LlfPF9qTYum2rHAAAAACgWwJ5qEtdBd0dp9yemwz7fNJw8uav15ZWy7d8MCmu2DZw7wjoi6uTDT9LNj2YFDeWV1bb6tKl0JAUhr3UKqfYtvli9T0AAAAAdEcgD3Wpqx/Nxs3/DksaZyUjv5s0/tlL17Xfn7R9r4txm8P3tm8nL5yerLksaX2yvLLW3Jos3i155FXJHz6TrH+gdH2hcfOlsPkkszVYPW+1PgAAAAB1blitCwBe7sNJZiU5s/tdiq2lFerD/yJpn58Uf5OkobSdT2y9b2FU6d/Wf02K/5c07l669Fb7hqRlWWnujYuT5xYnz/190jA2GXdCMuGUZPxbk2G7Z6uXlmLb5nPR9uOJZovF0mr9Tc8km1Yk6xclL76YjHpNMvqopDC8f+YFAAAABo7WdN2UoNbqsSb6jUAe6sr3k/xnkp8nGZ5kbpJRXezX0RpmZFKYkhQXbr5uj803F0vhd9v8pP2XSWFSUvzfpGG3ZOLflwLqYnuS9lKw31PD9knGHJO0vZC0Ppu0r03a1yVNN5cuSTJyejL+7cmEk5OxR29eOd9RdjGl1jsNL7W76QuFQvLUZcnK7yRr7k8axyXtLaXWPA1jk3FvTyaekYydkxRG9N28AAAAANALAnmoG6uS3JakOcniJB9IKaA/I8lRSXZOZ7LdseK77ftJcXPbmExMGt+x+fZC0r4saf1c0n7XS1M0TEo2/SYZPqMUVPema1XDqGSn9yeT3pNsWJQ035+sv7+03fp0qbd8+7pk48Oly8ovllbnjzs+2e3UZKcTkxF7ZZvV8yn27k2BzrGb33RY/0iy7O+TZ6/vKDRpW7v5fhi++Q2DbyUbf5vs8lfJxPf3fi4AAAAA6AMC+bozLH3/sJTbrmN0BXPWYuyECuYcX+a4Clqh7Pqyr4ujk03HJevvSdoWb77ypqTh18nw2cnwOUnj5KS4IdlpU7LunuT5eUlxU2nXnU5N9nrDSxl7cc+k+QvJ03+RNP+iFH63Ppo8e2QpAB//qmT63yZ7nNSzeg8sllbVNwxL8prNl48k659Lnv1VsuK+5NkFyeonkg3PJ5vWJq3NyZrbkrW3JU8k2e2g5JCTkteckhx0bF7qi9+NSd1c3xHGr1mZXPuZ5LmbXlqFv/uU5Ig/SdbslLzwSLLsZ8mqx0qB/IoPJK9uSv7o493PeX/P7o4uPVTB2JV7VDC4XC0DbM6h0nqotYKx5d5HtXgu0L+G0mNai+91oL0eVfK64r8LAADQl/yFDfWiMCoZ+afJsJnJxuuTjTcmbQ8n7Y8nGx9PNl5baj1TGJ80Ldt67Lg3Jnt8vrSKvfN4w5Mxh5dWwhdbk50+lOz9mmTlT5IX7k2a/jfZ9Hwv6iuU2s8Ui0n75v/YNw5PxuyWTHlr6VJsT55flPzhF8nT9yfP/y5ZtyJpWZ1sXJs892hy15dLl+Gjk8mHJSd8OnnViUnDDsL5l9eSJN+6MFn4g5eun3VacurFyZ5Tk99uvm7jmuTezyULv5psWpf88svJ5Dckux3S8/kAAACAgW/zB/Xrjh7yQ4pAHupN45RkzMXJyPclm36UtNydtC1K2p9Mii+WLmlM0pYMn5Ls/OFk0vuT4Xu8tHK8Q9NtydrbkxEHJruck+z/umT/TyZtzcmLv0x2ObZnNW16PnnuqWTC/snICaUgvkN7aymILzSWQvVdDyldDjk7aX4+eXZhsum+5MlfJisfT9auLIXkLc3J4/ckD01Pph6bjO7lJxx+f0/y06tL2w2NydSjko9f/9Lt7W2lPvUjxydHfTr5v58lT/8yeeHR5PEflQL5Ynvf9rIHAAAAgO0QyEO96QjVG6cmoz6WjHh70rYkaX86Ka5Nis8mY5uTcSckIw5IRkx5aeyWYXzb6uTFb5S2J85NRr9uc8/2JI2jS2H8ywP87qx6IPn1BcmwsckehyV7HpnseUSy00GbW9hsUXvH6vmGYcnoXZL93pRMfVPpuj88nCz9RfLE/cny3yTrnk+mn9DzML6j3tXPJnd+9aXr935VaWV8krS3Jw0NW6+4H7Nb8kfnJLd9sHSMRTeWvh4+pmfzAgAAAEAfEMhDvdkyIC8MSxr3K122tOcW2x2rvNs3lPrPt/yhdP36+0v929OQjD5i884NLzt+D8L4tubk+buS5x4sfb3yf5NHvl0KuSceWArmXzEz2f3QZOTErlfPtzUkjcOSPaeXLkedkaxflTz7aLLT5B7eMVvUu+iu0qXDMaclBx+3+Vt82Yr3jvtn91eXwvgkWft00rapbwL5nr6pAQAAAMCQJ5CHetcRIneEvsW2bHUy1EJDsvHR5Ml3JBsf7voYzQuTie8sLzhuGJHsMjtZ+9Pkud8kLetLl/XPJC88nDw1Pxk5KRm3d7Lrq0vh/J5HJDu/8qXV843ZdvX8mJ2SKUd0Pef2bFyf/O9tyYtPp3Qm12Jy7JnJsG5OsNfRkqZYTHY9OFm5uDRm+X3JgSf2fv6X27A0WfyBZPguycb3JcNPSBp2rvy4AAAAQN9qTVKPnWv1kB9SBPJQ714eohdedvLTjY8mj89JWpdv/zjF9pT1W6fQmOxxUjLrpFKgvuynyWM3J0/cmjQ9mWx8sXRpWpo8tzBZ8t1k9K6l1fN7vD7Z6+jkFYcmY3d+2er5tiSFbVe0d6ejFc2yh5InftXxTSWHnZyM3/Wl27szfq9kY1Npe+0fklE7bT5EhX3kNyxNVv988xe3lP5pfE0y4h2ly/DXl39sAAAAAAYVgTwMZD0N45PKQuf21qS9UFrZvt+bSpf8S9L0f6Vg/rGbk2V3bbF6/tlk1SPJ8p8lv7sm+fVeyV6HJPvPTKYcmex58NY93nvj9/ckLzxV2h4xOvmjkzffsIPTpBeLyZot7qeRm/vWV3S/tCRND5S2G0Yl7cUkLUnbg0nzg0nz3yaFcaVV8yNOTUa8JWnYrfz5AAAAABjQBPIwUPUmjK9Uw7CXFte3tyUplq6bsG/y2o+XLsX2ZPndyZKbkyd+kLz4WLJxdemy5v+S5f+bPPj9ZPweybhdkyNOTw57dzKih33cO1a/P3JPsnFdaXvSK5KDZ5e2uwvWO1bAP/3Ll67baWqph3yl2tYkq+8tbbdvSIbPTgrjk7ZHkvYXNp+Ed22y6XulS5I0TktGvCcZc1FSGFF5DQAAAAAMGAJ5GIh6G8YX27ftPV+uLVe2F4ubj1soXb/P7NLluMuTNU8lS39YWj3/1E+SlubSZe1zpbH7z9wc7vek/s0nTn3uyWTlk6VxhYZk71clu+9f2mdH/fGfeygZNipp3ZCM2fWl1j+VnJR14/Jkzeagv2FkMu7KUuDe+quk5ZdJ6y+StkVJ+9NJsSkpbiiF9Zu+XwrkAQAAgOrRQ546IJCHgaZtB2H88AOTlseTxl2TXf8iGf/mZNie2/ae7wuFQlLY4mWk2L55RXpjMn6f5NUfLl2mJnns3tIK+Qe/nzz3aDJpn2TU+J7N0xGaL/9dsvaF0nUNjcn+h5W2t9c/vmPl/MrFpRYzSTJiQjJuz5e+h3IUi8m63yabnklSSEbumzRMLd027LDSJR9L2p/dHNDfl7QuTFofSEZ90Op4AAAAgCFIIA8DSdujyeo5SXs3Yfzoo5IptyZ/+Ktk1TXJMxcmq65ORr0mecVlSfbv3/oKDS8F4Fuunk9jcuCs0uXUf0zWruzdcTvC9mefSNavfmmuabO2P64jyF/9ZOkEtO1tSeOIZOJ+yZgKe7m3N29xMtdiMumYpKUhKW7xdnuhIWnYPRlxYulSbE/afps0vKKyuQEAAAAYkOrxQxpAV3oSxu//42TYzsnuFyd7/lMy+o+STY8nTTcnhVFVLTeFzSeB7Whx096etLWWQvJxu5YuvdHenrRsSJpXpxTyJznwiNK/3a2O7zjR67MPJmtXlLaHjUp2e/Xmmyv4TFjL81sE8kl2evPmjcLWb0x0zNPRy37Ya5zYFQAAAOhTTz75ZM4///xMnz49Y8eOzc4775zDDz88X/ziF7N+/fqyj7tgwYJ87nOfy5vf/Obss88+GTlyZMaNG5dp06blrLPOyj333NOj4xQKhR5djjvuuLJrHSiskIeBoKdhfOOE0tcjJie7/UXp0vpssvH3yfBXvBQK10JDQyp6D7BQ2BzGJ0kx2WNqMnr89tvVdAT3T92brN/cu370zskrDi+/jg7NjyfrHiptD9852eWPk2eTpH3z+wANL7XDqdV9DgAAALykJfW5PLnCHvK33HJLTjvttDQ1NXVet379+ixYsCALFizI1VdfnVtvvTVTp07t1XGPPfbY3H333dtcv2nTpjz66KN59NFHc9111+X000/P1772tYwYoT1vTwjkod71NoxPSqvQs/kkrsN2L12KxYEbDLe3lVbar3zypesmvzppbUmGDe9+XKGQbGxKlt+XbFpX+nrXQ5I9Xrf59jLvj/bWZM2CUnuaQkMyYu+kdVVSHLltb/hKThoLAAAAsB0LFy7Me97znjQ3N2fcuHG54IILMmfOnDQ3N+fGG2/M1772tTzyyCM56aSTsmDBgowf38Pz+SVZsaLUbWCvvfbKu971rrzhDW/Ivvvum7a2ttx333257LLLsnz58lx//fVpaWnJf/7nf+7wmB//+MdzzjnndHv72LFje1zfQCWQHzC2EzruULkPcyVPj0rGji5zXM9fULZV5v3by64rW5ncg302PJos2U4Yv/NRyRt+nAyf8LIbCtn2MSgkh/a+zE5vLH/oIUf+sqxxb8mPt/r6B7cszCOFUsY946DFmfOOSzJqUtfPl6+u+XAKjY1p/dEd2XD1wtJdMmlCRpz+uow4f02KxaYUugjK187rwYPavDb59t2b7+YRSZ5OHn1Tsvr5ZMLhya6nJruclIzaJ0mhZ6H8gh1P26U1O5c5MEmayxzXWsGcLRWMrWTegTTnUFLu74qh9LhU8jMzcBSLf1H22ELhS31YSU/V4nGp5G9BAAAGq/POOy/Nzc0ZNmxYbr/99sycObPztuOPPz4HHXRQPv3pT+eRRx7JZZddlksuuaTHx54+fXr+/u//PnPnzk1jY+NWtx111FH5wAc+kFmzZuWRRx7JDTfckI997GM59thjt3vM3XffPYccckivvsfBZoAul4UhovXZpO3Frm/rNowfnIrt7dn1kN1Li/+TrPzts92G8Uk629i0/td3kzVrS1dNn5bGNxy9+YDF8otZtTRZ/uvSduuGZP3K5PklpVXyL9yePPLx5DfHJ0v/Jml50Qp5AAAAoM898MADnS1lzj777K3C+A7nn39+Dj744CTJFVdckZaWni8u+cEPfpB3v/vd24TxHXbddddcdtllnV/fdNNNvSl/yBLIQz0bNys58IdJw8s+rjMEw/hCQ0PG7zPxpeta27Jp7cYUuwnWC4VC2n7zYFpvua3zumEnviUNh8wo3d5t3/keGDUxOeyM5P3fSc76YXLiF5M/OiOZcGQybFJpn+YlybIvJUvOSzb8X/lzAQAAAH2jPaUOv/V2KbOH/M0339y5fdZZZ3W5T0NDQ04//fQkyYsvvpi77rqrvMm6MWfOnM7txx57rE+PPVgJ5KHejXvD1qH8mKEVxicvheevOGqfjN2jdD8899CzeeKHj27VdqZYLKa9rfRbrH3ZU2n50j8nzaW2LIXdd8uw9707hTFjKi9o5/2TN/9NcsipybS3JMeen7zr2uS1P05m/Huyy9uShhFJ6+rkD98srZSvZEU+AAAAwMvcc889SUp91w877LBu95s9e3bn9r333tunNWzcuLFzu7uV9GxNIA8DQUcoP/6EZOrQCuO3tPMrd8t+JxyYpNQF5tdfvi/PLFyRYnv75usKaWhsSEtzS1r++Stp/e5/lwZOnJDhHzs7Da/YM8W2tsoLaW9/KWBvby+ddLZYTIZNSHb54+TVNyev+Ejp9kJD8vQ1ybLLK58XAAAAYLPFixcnSaZOnZphw7o/R9f06dO3GdNX5s+f37nd0Rpne7797W9nxowZGTNmTMaPH5+DDjooZ5xxRp+v3K9nTuoKA8W4NyQH/njI9yP/oz8/Okt/vCTNK9dnxX3Lcu9n7syrzjw0ux6ye9o2tqVl3ab8/LM/ScvPlnWOGf6+92TYB95X+qKSVjUdtjzGy49XbEsKjcm0fy61rXnhR6Xrn78lecUZyfBdKp8fAAAAGHSampq2+nrkyJEZOXJkl/tu2LAhK1euTJLss88+2z3uTjvtlLFjx2bdunVZtmzZdvftjfb29nzhC1/o/Prd7373DscsWrRoq6+XLFmSJUuW5Prrr88pp5yS6667LhMnTuxm9OAgkIeBZIiH8Umyx6F75U1XviM//MB30rKuJU/88NE88cNHs9PUndM4alhW/vbZ0t1UKAXljcfPzojPfSaF0aUTwBa6uA87Vs0X+uKjVYXG0mr5QiHZ60PJqjtL1784P1m/JJm4y0u3AwAAANXTmqQe/zu++UP4kydP3urqz372s7nkkku6HLJmzZrO7XHjxu1wio5Afu3atWWX+XKXX355HnjggSTJO9/5zu22zRkzZkze8Y535I1vfGOmT5+ecePG5bnnnsv8+fPz1a9+Nc8//3xuvvnmnHzyybnjjjsyfPjwPquz3gjkodY2PJpsejKZ8KZaVzIgFIvFHHTKjEy8e6cs/Jdf5KmfPpHVT6zKqiUvbLFPkkkTMvzsMzLi03+ewujRKRaLXYbxSR8F8VsdcPM8E44uBfTtm/uptTyz9e0AAAAAmy1btiwTJrzUpri71fFJaYV8hxEjRuzw2B3Hat58rr1KzZ8/P3/913+dJNl9993zla98Zbv7L1++PJMmTdrm+hNOOCHnnntuTjzxxCxcuDDz58/PV77ylXzqU5/qkzrrkUAeamnDo8mSOUnryuSA7ycT3lLriupeR6i+++tekTf8/Zvyh18uz3MPPZN1K9Zk7fKmbFjVnGnvOiQ/P+z/peFVB6dQKHQbxhdbW9P6zRvS9rN7M+zd70zjnGP7psiOFfAtK5PRBybrfpc0jknWLUp2fUffzAEAAAAMKhMmTNgqkN+eUaNGdW5v2rRph/t3nHx19OYOApX43e9+l1NPPTWtra0ZNWpUvv3tb2f33Xff7piuwvgOe+yxR2666aZMnz49LS0tmTdvnkAe6AcdYXzL8tLXj58slO+lMbuPywEnvTL7n3hQWta1ZMT4l945/sX6GZ3b3a2Mz9p1ab3tx2n74e1p/fZ303D4Yclx/5XsvH/X+/e01UzHPm1NSbF98/b6ZMQevTsOAAAAQBfGjx/fud2TNjTr1q1L0rP2NtvzxBNP5M1vfnNWrVqVxsbG3HjjjTn22MoXOB5wwAE54YQTctttt2XJkiVZsWJF9tprr4qPW4/64OyGQK+1vSyMT5LixlIo3/Tj2tU1wBSLpSZrhYaGjBg/MsX29t6Nf/oPaX/498nmvmSNh74mGdnFO9Gtm5K21lKI3t7W8wmaH082PvnS1xOOKv0rjAcAAIDqa63jSy+NGjUqu+yyS5Lkqaee2u6+q1at6gzkX96nvjdWrFiRN73pTVmxYkUKhUK+/vWv5+STTy77eC83Y8ZLiyuXL1++nT0HNoE8VFvbo8nql4XxHYobk2f+YXMTdHbk5SvfCw29e0lrf3RJik88mbS0JEkaXvvqZMzO2+747KLkR3+dPP9Y0rCDfvMdK+I3PZOs+klpZXySjJycjD24V/UBAAAAdKcjwF6yZElaW7tP9R9++OHO7YMPLi+bWLlyZU444YQ8/vjjSZJ58+bl9NNPL+tY3em2w8EgI5CHauoI49u7eZdvzFHJATdbQV0lw95xUkbd+t0M//iH03jsMWk4YP+u7/sXHk9+/s/JDX+a/Pa7yYamrg/YvikpbH5Zfe7mZOX3Xrptt1NK/xZ7t4ofAAAAoCvHHHNMklI7ml/96lfd7jd//vzO7VmzZvV6ntWrV+ctb3lLFi1alCT5whe+kE984hO9Ps6OdBw/yaBtV5MI5KF6ehLGT/1x0tizk3fQN4bNPiYjv/h3GX3bd9Mwa+a2O7RuSp5dnLS3JssXJD/48+SOi5PH5yfrnt+6hU3DiKR9Y7L8q8mTf5O0ri5dP/bVye5/unknb7YAAAAAlTvllFM6t6+99tou92lvb8/111+fpHRi1Tlz5vRqjvXr1+ekk07Kr3/96yTJRRddlL/6q78qr+DteOKJJ3LHHXckSQ488MDsvffefT5HvXBS1wGjpdYF9FIlZ2wud+zw8qfcqcxxu/Vwv02PJk9uJ4zf6ajkyB8nw3sYxpfb+eSoMscled2R95c99oqcV9a4Y/OLsuecPOajPdqvvb2YYnsxjcNK70/+2YlXbr1DW2My7IBk4SuSlU8nq5eVVss/8u3kNUclrz062e0VSdOqpHltsvynybIfl1baFwql9kOvPzeZfvjmt0C7CeT/UOY3uriC533KffOnuYI5Kxlb7q+sSl4/B9qvyTIaD0IdKBT+sYLRA+3ntFyVvJZV8rsCAGAQaUl9rpMrs3PxEUcckTe84Q25++67c8011+SMM87IzJlbLza87LLLsnjx4iTJeeedl+HDt/3bsKNVzH777ZelS5d2Xr9p06aceuqpuffeezvH/+3f/m2v67zlllty4oknZtiwrv92f+aZZzJ37txs2rQpSXLOOef0eo6BZKj8DwZqpyOMb+2jMJ4+1dBQSBq289u4sTE58U+TWW9Nbvlmcvu3kocXlsL5n3yvdGlsTEaOTtZtPqt5Q2Np5fzwccn0DyYzPlSdbwYAAAAYUq644orMmjUrzc3NefOb35wLL7wwc+bMSXNzc2688cZcddVVSZJp06bl/PPP79Wx//RP/zS33357kuT444/P2Wefnd/+9rfd7j9ixIhMmzZtm+vPPffctLS0ZO7cuZk5c2amTJmS0aNHZ+XKlfnpT3+aK6+8MitXrkxSasPTH+1w6olAHvqTMH5wKBaTCTsl7/9U8oY/Tn59d/Kbe5PHfpesWJo8/0yyfnMY3zgiaduU7H188upPJfu/Y/Mx2l/qLw8AAADQBw499NB861vfymmnnZampqZceOGF2+wzbdq03HrrrRk/fnyvjv3d7363c/snP/lJXvOa12x3/5evsN/SihUrMm/evMybN6/b8XPnzs3VV1+dkSNH9qrOgUYgD/1FGD94bHmi132nli7Hvi1Z/kTy3IrkxeeT9WuSHz2RTDwo2XNWMmlaMnzsFscQxgMAAAB97+1vf3sefPDBXHHFFbn11lvz1FNPZcSIEZk6dWre9a535ZOf/GTGjBlTs/q+8Y1vZP78+bnvvvvy+OOPZ+XKlWlqasq4ceMyefLkHH300V222xmsBPLQH3YUxo8Wxg94O+9WuiSlFfSFQrK2tiUBAAAA29GWQdVDfkv77bdfvvSlL+VLX/pS76cvdl1Ad9f31uzZszN79uw+OdZgYMkm9LVNS3ccxk8Wxg8KHb+YCoWkvb3UlgYAAAAAumGFPPS1P3xkx2F8ozB+UNiylU1DQ32+yw4AAABA3bBCHvpSsSVZd1fXtwnjAQAAAGBIs0Ie+lKxJUnrttePer0wHgAAAKDW+qYtOpTNCnmohrFvFsYDAAAAwBBnhTz0qYZSa5qXGz65+qUAAAAAAHVFIA99qWFUMuW+WlcBAAAAANQhLWsAAAAAAKAKBPIAAAAAAFAFAnkAAAAAAKgCPeSHgGJxVlnjCoUHK5h1eAVjR5c3rJJn87gyx+1awZx7VDB2zzLH7VP+lJPyYtljl6XMk9o+XSh7zqdfcUl5Aye1lj1ndq3gSTipzHFl/rgkSZrLHVzJD1slY8t9XangMU1LBWMHkkpes4fKfUT/qtWfpJW8PpSnWPx0WeMKhS/1cSWDUSWvZZXwOggAQM8J5IEh4ed/Oz9PP7B8q+vGvmJ83nrl22tUEQAAAABDjUAeGBKefmB5ltzyyFbXTTpwpxpVAwAAAMBQpIc8AAAAAABUgRXy0JeKrcnK7257/ZiDk7Gvrn49AAAAAEDdEMhDX2rflDz8nm2vn3yhQB4AAAAAhjgtawAAAAAAoAoE8gAAAAAAUAUCeQAAAAAAqAKBPAAAAAAAVIGTugIAAAAAQ0DL5ku9qcea6C9WyAMAAAAAQBUI5AEAAAAAoAoE8gAAAAAAUAV6yAMAAAAAQ0Dr5ku9qcea6C8C+bozfPOl7xQK95Y5cucKZq3keyhz7OgKphzXR+PautlvRBf7TipzzkrGjir/BX59xpQ99hc5sqxxK16xV9lzLsyhW329Ordus8/GjNxmv6yt4GWxklfUUVUelyTN5f7QVPLDVsnY5jLHVfLA9O3rcc9UcjKfcr9Xf/xBtRQK/1jmyKH0Z3stXnsrUW69Tt4GADAUaVkDAAAAAABVIJAHAAAAAIAqGEqffQWGsAMvfV8mf/Kkra5rHD2iRtUAAAAA1deS+mwbV4810V8E8sCQMOHQA2pdAgAAAABDnJY1AAAAAABQBQJ5AAAAAACoAi1rAAAAAIAhoHXzpd7UY030FyvkAQAAAACgCgTyAAAAAABQBVrWQF9qGJkcdd+214/aq/q1AAAAAAB1RSAPfanQmOx0VK2rAAAAAGAbrUlaal1EF/SQH0oE8sCQsPgTX80Ldz201XWj9t0th/3oktoUBAAAAMCQI5AHhoQNy1Zm3eKntrqufZN3oAEAAACoHoF83RmWrh+W4RUesxyjazBnjaYcVea4sRXMWcnd21jmuNZyBybPZPeyxz6UV5c1blFmlD3n7/PKrb5ek/Hb7LMpI7adY2XZUyYbKhg7oFTyelSLeSt546WSseV+DLKS+7ceP3rZHyp5wfdGHFvyp3D/qf7frsXimWXPWChcV/bY8l9XvN4DAAxF/hcCAAAAAAwBLanPN7brsSb6S0OtCwAAAAAAgKFAIA8AAAAAAFUgkAcAAAAAgCrQQ74XCoVCj/abPXt2fvrTn/ZvMdSntubkRwdtfV3LmuSVf5UcfGFtagIAAAAgpZOxl3tC9v5UjzXRX6yQh75ULCbNy7e+tDYlv7soKbbVurohq71pTVofWVrrMgAAAAAY4qyQL8PHP/7xnHPOOd3ePnbs2CpWQ10pNHZ/2y/PSA7/xvb3oc+1N63Jyrd8MK2/f3yb2wojhtegIgAAAACGKoF8GXbfffcccsghtS6DetQ4Mpl0aPLiwm1v+7//KP0rlK+ajjB+0/2/6fL2EUe8proFAQAAADCkCeShrx36leRnc0r95F9uy1A+Qvn+tKMwvnHvPTLx78+vblEAAABADbUmaal1EV3QQ34o0UMe+touRyazbksaR3d9+//9R6l9Tbue8v2ltWn9DsP4Xe/69zTutUd1CwMAAABgSBPIQ3/Y/bgdh/LfF8r3h9am9Vn4lv9vh2H88IOmVLUuAAAAABDIl+Hb3/52ZsyYkTFjxmT8+PE56KCDcsYZZ+Suu+6qdWnUkx2F8r8Vyve1jjC+6f7fd3m7MB4AAACAWtJDvgyLFi3a6uslS5ZkyZIluf7663PKKafkuuuuy8SJE2tUHXWlI5S/94+77in/28095U/+RtKgp3ylnrj0P4XxAAAAQDdaU5/92uuxJvqLFfK9MGbMmLz3ve/N1772tdx9991ZuHBhbr/99lx00UXZZZddkiQ333xzTj755LS0bP8EERs3bkxTU9NWFwYpK+Wr5oC/OS07Hf+aba4XxgMAAABQDwrFYrFY6yIGihdffDGTJk3q8rZnnnkmJ554YhYuXJgkueKKK/KpT32q22NdcsklufTSS7u45d4k4yovdivDyxw3voI5J1R/7E4VTHlgmeMO6sW+z/w0md/NSvkkOeT9PV8pP70X8/bFuCSjp64qe+ykCS+WNa4tvf/UQHF9c158+wfT8pOfl67YY+/k+ruSKT18sO7v9ZR9M/aXZY77XQVzdvNU3LFnKpj0hQrGlvvG5ZoK5iz7TkqyvsxxlayM2P6bwf0zZyXKrbcSA23lSS3uI+pTuX/PVarcD9TWot5KPvzrtWHoaE5yflavXp0JEyr5PwsAA0lTU9PmbhYPpO9zt76wNskRfj8NEVbI90J3YXyS7LHHHrnpppsyfHjpPx/z5s3b7rEuuOCCrF69uvOybNmyviyVerTHcclsK+X7W2HM6Ey65evJUcf3PowHAAAAgH6kh3wfOuCAA3LCCSfktttuy5IlS7JixYrstddeXe47cuTIjBw5ssoVUnMdoXx3K+X1lO8ThTGjk6/ekjz/bLLPlFqXAwAAANSFltTnJ83qsSb6ixXyfWzGjBmd28uXL69hJdStjlB+2HZWyj/xP1UtaVAaPUYYDwAAAEBdEcj3sUKhUOsSGAj2OC75025C+bfOSw58S9VLGija165LsXWg9XkFAAAAAIF8n1u0aFHndnftaiBJMuW4bUP5t85LDv9kzUqqd+1Na/LiCe9P0xl/IZQHAAAAYMDRQ74PPfHEE7njjjuSJAceeGD23nvvGldE3esI5W88KXnjPwjjt6O9aU1efMsH0nr/wrTevzBNSSZ840spDPMyBgAAAPRE6+ZLvanHmugvVsj30C233JLW7azIfeaZZzJ37txs2rQpSXLOOedUqzQGuinHJZ9YIozfji3D+A4b//P7VsoDAAAAMKBYWtpD5557blpaWjJ37tzMnDkzU6ZMyejRo7Ny5cr89Kc/zZVXXpmVK1cmSY455ph84hOfqHHFDCjjX1HrCupWV2F8h43/+f2s3X3XjL/84hpUBgAAAAC9I5DvhRUrVmTevHmZN29et/vMnTs3V199dUaOHFnFyhgy1jydjN09SWOtK6mK7YXxSdKw954Zfc4HqlwVAAAAAJRHIN9D3/jGNzJ//vzcd999efzxx7Ny5co0NTVl3LhxmTx5co4++uicccYZmTlzZq1LZbBa9XjyzTnJ5Dck07+RNAzuUL4Uxp+53TB+0l03ZthB+1e5MgAAAGBgak3SUusiuqAd71AikO+h2bNnZ/bs2bUug6GqI4xf/X/J6v9IvprkY4M3lG9vWpMXhPEAAAAADDJO6gr1bsswvsO9/5F89Yykva12dfWTjjC+RRgPAAAAwCAjkId61t6a3HDS1mF8h0EYygvjAQAAABjMtKypO8M3X/pSLR7m5upP2Tqh/LHlZtqVtB3rUXuwYclb/i35r5OS1i7u03v/o1T72T1sX/Ni70rcUvNTO5U/dtz4He+0pin5wJ8mC7sO47PH3mn/2l15oeWgZFEPJl3SqxJfsrTMcUnyXAVjXyxvWHF9+VMWCjX4Oa2JSl4DKxnb16/lPVHui1Il36deh/2r3OdRPfbFpKQWrw1D5U9+r0cAwI60pj7/ZqjHmugvVshDvdtvTvLuW5Nho7u+/f7/SK4Z4Cvl1zQlH/jjZOEvur59j72Ta+9K9juounUBAAAAQB8SyMNAMJhDeWE8AAAAAEOEQB4GisEYygvjAQAAABhChkpDSRgcOkL5m05KNnXR//v+/yj9e+bVSeFlPeUbGrPD9+Bat9NzuJx2xIVCMqybl5kdhfF77p18XRgPAAAA9JWW1Of5luqxJvqLQB4Gmv3mJOfdmlyxnVC+I5jf0oX3JQcctf1jv29K8vyKPikzSfLWdyeXfavr2352+/bD+BvvSHYTxgMAAAAweGhZAwPR9M2h/Ihu2tcMBCf9SfK5K7a9viOM318YDwAAAMDgIpCHgWowhPJnnLN1KC+MBwAAAGAQ07IGBrLpO2hfMxCccU7p3698MbnhdmE8AAAA0E9aN1/qTT3WRH+xQh4Guo5QfvSEWldSvjPOSf7nQWE8AAAAAIOaFfIwGEyfk3zud8kv/ytp+kPX+0x8xY6P846PJ+uaur5tbBl1vfI1Pd933PgyJgAAAACAgUMgD4PFzvskb/mLyo5x2me6v23Pyg4NAAAAAEOdQB4AAAAAGAJak7TUuogu6CE/lOghDwAAAAAAVSCQBwAAAACAKtCypu4MS98/LMPLHFfJx2Wayx5ZLO5R1rhCoewpy/9W2yqYs5JPSG0oc9yLFcxZiWFlPqdfrGDOJeUNK15c/pSF48sfW+5zqaLnfU1+BVQyZ7mvZeW/Hg085d5H9fiRTahn5f6sDSWVvK64fwEAGLwE8gAAAADAENCa+uzXXo810V+0rAEAAAAAgCoQyAMAAAAAQBUI5AEAAAAAoAr0kAcAAAAAhoCWVHby+f5SjzXRX6yQBwAAAACAKhDIAwAAAABAFQjkAQAAAACgCvSQBwAAAACGAD3kqT0r5AEAAAAAoAoE8gAAAAAAUAUCeQAAAAAAqAI95AEAAACAIaB186Xe1GNN9Bcr5AEAAAAAoAqskK87w5IM74djVlv5cxYKz5Q55R5lz1m2St7A3FjB2DVljhtVwZy1OOH36grGvljesMK7KpizkudDuWMLFcxZLPe1pq9fo/pbJfXW4kGtxQ9brZT72FRyH1XyO3EgrVqp5HnvOQjVMZR+1gAA6GCFPAAAAAAAVIEV8gAAAADAENCa+vyU2kD6NC6VskIeAAAAAACqQCAPAAAAADCAPfnkkzn//PMzffr0jB07NjvvvHMOP/zwfPGLX8z69evrbp5q1VuPCsVisVjrIkiampoyceLEJA8mGd/HRx9onYnKPMFaJSd1PbjMcVPKn7KisXuXOW63CuYcW8HYclVyUtenyhy3uII5yzwfcZJkaZnjyv0+k6TsV/8XKpj0+QrGlns24+YK5qzF2Er+8KjFiWRr8dHKWn3EdKh8jLQeP8LbX4bKSV2HyN+CA85Q+lmrJ81Jzs/q1aszYcKEWhcDQJW8lLv9W5LRtS6nC81Jzin799Mtt9yS0047LU1NTV3ePm3atNx6662ZOnVqRVX21TzVqrdeWSEPAAAAAAwBrXV8Kc/ChQvznve8J01NTRk3blz+7u/+Lj//+c9z55135sMf/nCS5JFHHslJJ52UNWvKXejWd/NUq956NtCWywAAAAAAkOS8885Lc3Nzhg0blttvvz0zZ87svO3444/PQQcdlE9/+tN55JFHctlll+WSSy6p6TzVqreeWSEPAAAAADDAPPDAA7n77ruTJGefffZW4XaH888/PwcfXOrVfMUVV6Slpfdt8/pqnmrVW+8E8gAAAAAAA8zNN9/cuX3WWWd1uU9DQ0NOP/30JMmLL76Yu+66q2bzVKveeieQBwAAAACGgJY6vvTePffckyQZO3ZsDjvssG73mz17duf2vffeW7N5qlVvvRPIAwAAAAAMMIsXL06STJ06NcOGdX+q0OnTp28zphbzVKveeuekrgAAAAAANdbU1LTV1yNHjszIkSO73HfDhg1ZuXJlkmSfffbZ7nF32mmnjB07NuvWrcuyZct6VVNfzVOtegcCK+QBAAAAAGps8uTJmThxYufl85//fLf7rlmzpnN73LhxOzz22LFjkyRr167tVU19NU+16h0IrJCvO8PS9w/L8CqPSyr7HiaUN2x0BVPWQmsFY9eVOa6Sh3RDBWPL9WIFY8t9vW6rYM5KHtNyNVYwthb11ux1ZSCp5D6qyYPKoFPJc7C83peVqaRe6lO5z6NaPRdq8bwHAAajZcuWZcKEl3Kx7lbHJ6UV5x1GjBixw2N3HKu5ublXNfXVPNWqdyAYKukGAAAAADCktaY+FzKVapowYcJWgfz2jBo1qnN706ZNO9x/48aNSZLRo3u3orWv5qlWvQOBljUAAAAAAAPI+PHjO7d70tZl3bpSu4WetIvpj3mqVe9AIJAHAAAAABhARo0alV122SVJ8tRTT21331WrVnUG3JMnT67JPNWqdyAQyAMAAAAADDAzZsxIkixZsiStrd234nn44Yc7tw8++OCazVOteuudQB4AAAAAGAJaUzohe71dyutrf8wxxyQptXf51a9+1e1+8+fP79yeNWtWzeapVr31TiAPAAAAADDAnHLKKZ3b1157bZf7tLe35/rrr0+STJo0KXPmzKnZPNWqt94J5AEAAAAABpgjjjgib3jDG5Ik11xzTe67775t9rnsssuyePHiJMl5552X4cOHb7NPoVBIoVDIlClT+nWevjrOQFcoFovFWhdB0tTUlIkTJyZZlGT8jnbvpXKfuJU84YdVMHZCecMqudumVHlckuxTwdhdyxw3qYI5R1UwtlwvVjB2ZZnjllYw5x8qGLv985l07+kK5izvE3FJXqhg0qYajF1TwZzNNRjbMsDmLPuJVIFK6q1ELb7XgaYWj83g+wO971Xyd9lAUqvnQq1ekyhPc5Lzs3r16kyYUOb/OwAYcF7K3f4utQk4dmRDkovK+v20cOHCzJo1K83NzRk3blwuvPDCzJkzJ83Nzbnxxhtz1VVXJUmmTZuWBQsWZPz4bQO0QqGQJNlvv/2ydOnSfpunL48zkA2Vv84BAAAAgCGtNfW50Kb8mg499NB861vfymmnnZampqZceOGF2+wzbdq03HrrrRWF2301T7XqrWda1gAAAAAADFBvf/vb8+CDD+bP//zPM23atIwZMyaTJk3K61//+vzDP/xDFi5cmKlTp9bNPNWqt15pWVMntKzZkpY1O6RlzY5pWbNjWtb0gJY1O6ZlDVvSsqY+DZUPxWpZQ09oWQMwFL2Uu12a+m1Z81m/n4YIK+QBAAAAAKAKhspyGQAAAABgSGtJ0ljrIrrgE3dDiRXyAAAAAABQBVbI153h6fv+l+Ueb3QFc1YytkwjKxhb7k9CJe2EN1Qwttw20ZX8xFdSb7nWVjC23LbhlTymA611ctnf60D71THQ6q3EQPte9WQffPRzp5asLAMAoP5ZIQ8AAAAAAFUw0JbSAQAAAACUoTX1+UndeqyJ/mKFPAAAAAAAVIFAHgAAAAAAqkDLGgAAAABgCNhY6wK6Ua910R8E8gAAAADAoDVixIjsueee+cMfLq91Kd3ac889M2LEiFqXQRUI5AEAAACAQWvUqFF54oknsmnTplqX0q0RI0Zk1KhRtS6DKhDIAwAAAACD2qhRowTe1AUndQUAAAAAgCoQyAMAAAAAQBUI5AEAAAAAoAoE8gAAAAAAUAUCeQAAAAAAqAKBPAAAAAAAVMGwWhfAyw1LMryb68vV1fF6WksNlDttud9mrbRWMHZjn1XRc7W4f9dWMLbc+6iSx6WtgrFDRi1ey5ormLOSessd21LBnPSvch/TSl5YAAAAGEyskAcAAAAAgCoQyAMAAAAAQBUI5AEAAAAAoAoE8gAAAAAAUAUCeQAAAAAAqAKBPAAAAAAAVIFAHgAAAAAAqkAgDwAAAAAAVSCQBwAAAACAKhDIAwAAAABAFQjkAQAAAACgCgTyAAAAAABQBQJ5AAAAAACogmG1LoCXG5auH5bhFR6zHJXMWYHRZY6rxbO5pYKxzRWMHVnB2HK1VTC2tcxxldxH5Y6t5DEdMmr02gCdKnkO+iFnoPJnOwAADAZWyAMAAAAAQBUI5AEAAAAAoAoE8gAAAAAAUAUCeQAAAAAAqAKBPAAAAAAAVIFAHgAAAAAAqkAgDwAAAAAAVSCQBwAAAACAKhDIAwAAAABAFQjkAQAAAACgCgTyAAAAAABQBQJ5AAAAAACoAoE8AAAAAABUgUAeAAAAAACqYFitC+Dlhm++9PUxq6xQwdhyn5VD6dncVua41grmrGRsS5njNlYwZ7n3UbnjksruI/pRJa+BzX1WRc9VUm+5P2wAAABANVghDwAAAAAAVSCQBwAAAACAKhDIAwAAAABAFQjkAQAAAACgCgTyAAAAAABQBQJ5AAAAAACoAoE8AAAAAABUgUAeAAAAAACqQCAPAAAAAABVIJAH/v/27j84qur+//hrk4WQkPAzgMbwM4AhKII0BUQJPwRKpNCQ0Q4oP9IgFhwoTscigoDUpugUoaUjBcECrRjH1MJIQUSEWJhAi/xQCBRCgkDCFAJIAiTNhuz3D765n40ku5vsj7sLz8cMM+fuvs/e9zXHk+Sdc88FAAAAAAAA4AcU5AEAAAAAAAAA8AMK8gAAAAAAAAAA+AEFeQAAAAAAAAAA/MBqdgJwV5B9qULNTsBPbpnUt7KB/WwenNMMDb1OT/p6ck64oZHZCQAAAAAAAJiGFfIAAAAAAAAAAPgBBXkAAAAAAAAAAPyAgjwAAAAAAAAAAH5AQR4AAAAAAAAAAD+gIA8AAAAAAAAAgB9QkAcAAAAAAAAAwA+sZieA2+x2+/9vldYR4cmXqpEHfRvI7jrE632rPDjnrQb2q/TgnDYP+lY0sJ/Fg3OawZP/Rg3t68nXtKHjSGr4+PXk/7UGK/Ogb11znDuuN7CfJ/ne8KBveQP7eTIIb5pwTrP6NpQnE0tDmXGduPvwYzvgHbe/P//f718AAAD+xU/2AaK0tLpI1c3UPLzGk8Lkd37uBwAAAOCeUlpaqubNm5udBgAAuAdZ7CwNCAhVVVUqKipSVFSULJZgW8YMfyopKVH79u117tw5NWvWzOx0EKQYR/AGxhG8gXEEb2AcwV12u12lpaWKiYlRSAg7uAIAAP9jhXyACAkJUWxsrNlpIIg0a9aMXzjhMcYRvIFxBG9gHMEbGEdwByvjAQCAmVgSAAAAAAAAAACAH1CQBwAAAAAAAADADyjIA0EmLCxMCxcuVFhYmNmpIIgxjuANjCN4A+MI3sA4AgAAQLDgoa4AAAAAAAAAAPgBK+QBAAAAAAAAAPADCvIAAAAAAAAAAPgBBXkAAAAAAAAAAPyAgjwAAAAAAAAAAH5AQR4IYBaLxa1/gwcPNjtVmOTixYvasmWLFixYoFGjRik6OtoYF1OmTKn3523btk0pKSmKjY1VWFiYYmNjlZKSom3btnk/eQQMb4yjdevWuT1nrVu3zqfXA/McOHBAixcv1ogRI4x5JDIyUt27d1daWpr27NlTr89jTro3eWMcMScBAAAgUFnNTgAA0HDt2rXzyudUVVVp2rRpWrt2bY3XCwsLVVhYqE2bNmnq1KlatWqVQkL4W+7dxlvjCPe2QYMG6Z///Ocdr1dUVOjUqVM6deqU1q1bp0mTJundd99V48aN6/ws5qR7lzfHEQAAABCIKMgDQWD69OmaMWNGne83bdrUj9kgUHXo0EHx8fH67LPP6t133rx5RuGrT58++tWvfqW4uDidPn1ab731lg4dOqQ1a9aoTZs2ysjI8HbqCCCejKNq27dvV0xMTJ3vx8bGNvizEbiKiookSTExMXr66af1xBNPqEOHDrp165ZycnK0dOlSFRYWasOGDbLZbNq4cWOdn8WcdO/y5jiqxpwEAACAQGKx2+12s5MAUDuLxSJJWrhwoRYtWmRuMghICxcuVGJiohITE9WuXTudOXNGnTt3liRNnjzZrdvwT548qZ49e6qyslI/+MEP9OWXXyo8PNx4/+bNm0pKStKBAwdktVp1/Phxde3a1VeXBBN4YxytW7dOaWlpkqSCggJ16tTJhxkjEI0ePVqTJk1SamqqQkND73i/uLhYAwcO1MmTJyVJ2dnZGjRo0B1xzEn3Nm+NI+YkAAAABCru8QWAIPb6669r9OjRHm05snz5clVWVkqSVqxYUaPwJUkRERFasWKFJKmyslLLli1reMIISN4YR8CWLVv0zDPP1FpElaTo6GgtXbrUOM7Kyqo1jjnp3uatcQQAAAAEKgryAHAPs9vt2rx5syQpPj5e/fv3rzWuf//+evDBByVJmzdvFjdXAWiIIUOGGO3Tp0/f8T5zEtzhahwBAAAAgYyCPADcwwoKCoz9epOSkpzGVr9fWFioM2fO+Do1AHeh//3vf0a7thXQzElwh6txBAAAAAQyCvJAEPjoo4+UkJCgiIgIRUVFqVu3bpo8ebJ27dpldmoIcrm5uUY7Pj7eaazj+8ePH/dZTgh+aWlpiomJUePGjRUdHa3+/ftr/vz5KiwsNDs1mCw7O9to9+jR4473mZPgDlfj6PuYkwAAABBIKMgDQSA3N1fHjx9XWVmZrl+/rry8PG3YsEFDhw5VSkqKrl27ZnaKCFLnz5832rGxsU5j27dvb7TPnTvns5wQ/Hbv3q0LFy7IZrPp8uXL2r9/v37zm9+oa9euWrVqldnpwSRVVVVasmSJcfzMM8/cEcOcBFfcGUffx5wEAACAQGI1OwEAdYuIiNCYMWM0bNgwxcfHKzIyUpcuXVJ2drb+9Kc/6fLly9q0aZPGjh2rHTt2qFGjRmanjCBTWlpqtCMjI53GNm3a1Ghfv37dZzkheHXp0kXjxo3TgAEDjGJpfn6+/va3vykrK0vl5eX6+c9/LovFomnTppmcLfxt2bJl+te//iVJGjdunPr27XtHDHMSXHFnHFVjTgIAAEAgoiAPBLDCwkK1aNHijteHDx+umTNnatSoUTp06JCys7O1cuVKzZo1y/9JIqiVl5cb7caNGzuNDQsLM9plZWU+ywnBKSUlRZMnT5bFYqnxemJion76059qy5YtGjdunGw2m1566SWNGTNG9913n0nZwt+ys7P1yiuvSJLatm2rlStX1hrHnARn3B1HEnMSAAAAAhdb1gABrLZifLV27dopKyvLWBW/YsUKP2WFu0mTJk2MdkVFhdNYx4fohYeH+ywnBKfmzZvfUfhyNHr0aC1YsECSdPPmTa1du9ZfqcFkx44dU0pKiiorK9WkSRN99NFHatu2ba2xzEmoS33GkcScBAAAgMBFQR4IYl26dNHw4cMlSXl5eSoqKjI5IwSbqKgoo+1qy4cbN24YbVdbSQC1mTZtmlEgc3woI+5eBQUFGjFihK5evarQ0FBlZmZq0KBBdcYzJ6E29R1H7mJOAgAAgBkoyANBLiEhwWgXFhaamAmCkeNDEx0fplgbx4cmOj5MEXBX27Zt1bp1a0nMV/eCoqIiPfnkkyoqKpLFYtF7772nsWPHOu3DnITva8g4chdzEgAAAMxAQR4Ics5uxwZccfyDzokTJ5zGOr7fo0cPn+WEuxtz1r2huLhYw4cPV35+vqTb26pNmjTJZT/mJDhq6DiqD+YkAAAA+BsFeSDI5ebmGu2YmBgTM0Ew6ty5szFuXN2u/+WXX0qSHnjgAXXq1MnXqeEudOnSJRUXF0tivrqbXbt2TSNHjjS+Py1ZskQvvviiW32Zk1DNk3HkLuYkAAAAmIGCPBDECgoKtGPHDklSXFycHnjgAZMzQrCxWCzGrf8nTpzQvn37ao3bt2+fsRp17NixrChEg6xevVp2u12SlJSUZHI28IWbN2/qqaee0sGDByVJ8+bN05w5c9zuz5wEyfNx5C7mJAAAAJiBgjwQoD755BNVVlbW+f5///tfpaamqqKiQpI0Y8YMf6WGu8zs2bMVGhoqSZo5c6bKyspqvF9WVqaZM2dKkqxWq2bPnu3vFBHgzpw5o0OHDjmN2bJlixYvXixJCg8PV1pamj9Sgx9VVFQoJSVFe/fulST94he/0BtvvFHvz2FOurd5YxwxJwEAACCQWc1OAEDtZs6cKZvNptTUVA0YMECdOnVSeHi4iouLtXv3bq1atcq4zfrxxx/3+m3cCA579uxRXl6ecVw9JiQpLy9P69atqxE/ZcqUOz6je/fuevnll7VkyRIdOHBAAwcO1Jw5cxQXF6fTp0/rzTffNAobL7/8srp16+aTa4F5PB1HZ86c0ZAhQzRgwAD9+Mc/1iOPPKK2bdtKkvLz85WVlaWsrCxjJervfvc77ui5C40fP16fffaZJGno0KFKT0/X0aNH64xv3LixunfvfsfrzEn3Nm+MI+YkAAAABDKLvfonUQABpVOnTvr2229dxqWmpmrNmjVq0aKF75NCwJkyZYrWr1/vdnxdU35VVZWef/55vffee3X2TU9P1+rVqxUSws1VdxtPx9Hu3bs1ZMgQl/0iIiK0bNkyTZs2rd45IvDVd9uYjh076syZM7W+x5x07/LGOGJOAgAAQCBjhTwQoNavX6/s7Gzl5OQoPz9fxcXFKikpUWRkpNq3b6/HHntMkydP1oABA8xOFXeBkJAQrV27VqmpqVq9erX+/e9/q7i4WNHR0UpMTNQLL7ygUaNGmZ0mAlTfvn3117/+VTk5OTpw4IAuXLig4uJiVVZWqmXLlurZs6eGDRumqVOnGqtUAWeYk+AJ5iQAAAAEMlbIAwAAAAAAAADgB9zjCwAAAAAAAACAH1CQBwAAAAAAAADADyjIAwAAAAAAAADgBxTkAQAAAAAAAADwAwryAAAAAAAAAAD4AQV5AAAAAAAAAAD8gII8AAAAAAAAAAB+QEEeAAAAAAAAAAA/oCAPAAAAAAAAAIAfUJAHAAAAAAAAAMAPKMgDAAAAAAAAAOAHFOQBAAAAAAAAAPADCvIAAAAAAAAAAPgBBXkAAODS3r17ZbFYZLFYtGjRIrPTqVNpaanatGkji8Wixx9/3Ox0AAAAAACogYI8AABwqqqqSrNmzZIkRUdH65e//KXJGdUtKipKr7zyiqTbf0T48MMPTc4IAAAAAID/Q0EeAAA49cEHH+jgwYOSpNmzZysqKsrkjJybMWOGoqOjJUmvvvqqKisrTc4IAAAAAIDbKMgDAIA63bp1S4sXL5YkNW3aVNOnTzc5I9fCw8P14osvSpLy8/O1fv16kzMCAAAAAOA2CvIAAKBOWVlZOnnypCRpwoQJatWqlckZuWf69OmyWq2SpCVLlshut5ucEQAAAAAAFOQBAIATv//97412enq6iZnUT7t27fTUU09JkvLy8rR161aTMwIAAAAAgII8AACow9GjR5WTkyNJiouLU79+/UzOqH4mTJhgtFetWmViJgAAAAAA3EZBHgCAALRr1y5ZLBbj36ZNm1z2+ctf/lKjzzvvvONRDu+//77R/slPfuJ2vxMnThg5REREqKqqSpJ0+vRpzZ8/X48++qhatWqlJk2aqGfPnlq8eLHKysru+Jzr16/rD3/4gwYPHqxWrVqpcePG6tChg9LT05Wfn+8yj+TkZIWFhUmSPv30U12+fNntawAAAAAAwBcsdjZVBQAgIA0fPlyff/65JKl37946ePCgLBZLrbGff/65kpOTZbPZJElz585VRkaGR+fv3r27Tp06JUnasWOHnnzySbf6ZWZmavz48ZKkRx99VPv379dvf/tb/frXvzby+77evXtr9+7dat68uSRp586dmjhxoi5cuFBrfIsWLfTFF1+oT58+TnMZNmyYvvjiC0nS2rVr9bOf/cytawAAAAAAwBdYIQ8AQIByLKgfPny4zlXyX3/9tVJTU41i96RJkzwuxhcUFBjFeKvVqgEDBrjd98iRI0Y7ISFBEydO1IIFC2Sz2XT//fdr0KBBSkhIUGhoqBF3+PBhzZ8/X9LtfetHjBihCxcuKDIyUv369dMPf/hDNW3a1Ij/7rvvlJaW5jKXpKQko719+3a3rwEAAAAAAF+gIA8AQIBKTExUSkqKcfz666/r+ze2nTt3TsnJySopKZEkjRw5UmvWrPH43NnZ2UY7ISGhRjHclcOHDxvtrVu3KjMzU7169dLOnTtVWFio7OxsHTt2TF999ZU6duxoxG7YsEEffvihXnrpJUVFRemdd97RpUuXtG/fPu3fv1/nzp3T6NGjjfgjR47o4MGDTnNJTEw02rt373b7GgAAAAAA8AUK8gAABLA33nhDISG3v10fOXJEH3/8sfHetWvXlJycrMLCQkm3t4fJyspSo0aNPD7vV199ZbR79uxZr76OK+SvXLmikSNHKicnR0OHDq2x5c4jjzyit99+2zguKSnRs88+q7Zt2yonJ0fTp09XkyZNjPdbtmyp9evXKyoqynjt0KFDTnN5+OGHjfbFixd17ty5el0LAAAAAADeREEeAIAAlpCQoOeee844rl4lX1FRoZSUFB09elSS1LlzZ23dulWRkZFeOW9ubq7RjouLc7vfxYsXa+z73q1bN2VlZSkiIqLW+MGDB9c4ttvt+vvf/64ePXrUGt+qVasaRfbvvvvOaT6xsbHGg12lmtcFAAAAAIC/UZAHACDALVq0yFj1/s033ygrK0tpaWnatWuXJKl169b69NNP1a5dO6+d89tvvzXaMTExbvdz3K5GkpYtW+b0jwTVD3Gtlp6e7nK/+mbNmhltxxX0dXHM3/G6AAAAAADwNwryAAAEuM6dO+v55583jidPnqyNGzdKksLDw7VlyxZ1797dq+csLi422i1btnS7n+N2NV26dFFycrLT+OrtdqrNmjXL5TnOnj1rtNu3b+8y3jH/S5cuuYwHAAAAAMBXKMgDABAEXnvtNWPbl7KyMklSaGioMjMz1b9/f6+f78aNG0Y7PDzc7X6OK+RHjRpVY8/42lRvuSNJHTt21EMPPeQ03mazKS8vzziOj493mZNj/o7XBQAAAACAv1GQBwAgCNx3330aOHBgjdfefvttjRkzxufnttvtbsc6rpB/7LHHXMY7PpTVnfjc3FxVVFRIkiIjI9W1a1eXfeqTPwAAAAAAvkRBHgCAIPDmm29qx44dNV5z3FbG25o2bWq0y8vL3epTXl6u//znP8Zxnz59XPZxLMi7E+9Y8O/Vq5dCQlz/KFN9R4FU87oAAAAAAPA3CvIAAAS4999/X3Pnzr3j9eXLl/usKB8dHW20r1696lafo0ePqrKyUpIUERGhBx980GUfxy1u3CnIO8b37t3brbwc82/Tpo1bfQAAAAAA8AUK8gAABLCdO3cqLS3N2HZl7ty5xoNMS0tLlZGR4ZPzduzY0Wh//8Grdanv6vWSkhLl5+cbx74qyBcVFRltx+sCAAAAAMDfKMgDABCgjhw5onHjxslms0mS0tLSlJGRoVdffdWIWblypc6fP+/1c/fo0cNoOz5E1Zn6rnY/cuSI8YeG9u3bq3Xr1m71qeZOQf78+fPGnvOSlJCQ4LIPAAAAAAC+QkEeAIAAdPbsWSUnJ6ukpESSNHLkSK1evVqSlJ6ebqz0Li8v1+LFi71+/r59+xrto0ePutXHsVjui/3jz549qytXrkiSQkND9fDDD7vs88033xjtNm3aGHcXAAAAAABgBgryAAAEmKtXr+pHP/qRsdVKnz59lJWVJavVKklq1KiR5s+fb8T/+c9/dnsVu7sGDRpktE+cOKHr1687jbfb7fr666+N4/puP1PfB7rGx8erSZMmLvscOHDAaCclJbmMBwAAAADAlyjIAwAQQMrLyzVmzBgdP35c0u09z//xj38oMjKyRtyUKVPUuXNnSVJlZaUWLFjg1Tzi4uIUFxcnSbp165b27t3rNL6goEDXrl2TJFmtVj300EMuz1HfFfIN2T8+OzvbaI8cOdKtPgAAAAAA+AoFeQAAAkRVVZWeffZZ7dmzR5LUsmVLbdu2Tffff/8dsVarVa+99ppxnJmZWWN7Fm8YN26c0d6+fbvTWMfV6z169HC5et1msyk3N9c49kVB/saNG8YfEqxWq8aOHeuyDwAAAAAAvkRBHgCAADF79mx9/PHHkqSwsDBt3ry5xsNVv2/ixInq2rWrpNtbxsybN8+r+Tz33HNGe9OmTU5j67v9zLFjx4yHrbZq1UodOnRw2ae+Bflt27apvLxckjRixAi1adPGZR8AAAAAAHyJgjwAAAHgrbfe0ooVKyRJFotFGzZs0BNPPOG0j9VqrbFVzSeffKJ9+/Z5LadevXqpX79+km5vSePss+v7QNf6FvBLS0tVUFBgHLtTkN+4caPRfuGFF1zGAwAAAADgaxa73W43OwkAABCYMjMzNX78eEnS1KlT9e6775qckXsuXryo2NhY2Ww2denSRadOnVJICOsQAAAAAADm4jdTAABQp6efflrdunWTJH3wwQe6cuWKyRm5Z9WqVbLZbJKkOXPmUIwHAAAAAAQEfjsFAAB1Cg0NNbbFuXHjhlauXGlyRq6Vl5frj3/8oySpU6dOSktLMzkjAAAAAABuoyAPAACcmjBhgrHP+/Lly1VaWmpyRs6tXLlSFy9elCRlZGSoUaNGJmcEAAAAAMBtFOQBAIBTISEhxgNni4uLtXTpUpMzqltpaakyMjIkSQMHDjT2vwcAAAAAIBDwUFcAAAAAAAAAAPyAFfIAAAAAAAAAAPgBBXkAAAAAAAAAAPyAgjwAAAAAAAAAAH5AQR4AAAAAAAAAAD+gIA8AAAAAAAAAgB9QkAcAAAAAAAAAwA8oyAMAAAAAAAAA4AcU5AEAAAAAAAAA8AMK8gAAAAAAAAAA+AEFeQAAAAAAAAAA/OD/AU1/ETMXtUlZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ctrl(\n",
    "    msh,\n",
    "    msh.t_array[1],\n",
    "    cost.ctrl,\n",
    "    ctrl_target=ctrl_target.value,\n",
    "    cmap=\"jet\",\n",
    "    label='s_o',\n",
    "    obs=cost.obs,\n",
    "    figsize=(20, 15)\n",
    ")\n",
    "plot_ctrl(\n",
    "    msh,\n",
    "    msh.t_array[-1],\n",
    "    cost.ctrl,\n",
    "    ctrl_target=ctrl_target.value,\n",
    "    cmap=\"jet\",\n",
    "    label='s_o',\n",
    "    obs=cost.obs,\n",
    "    figsize=(20, 15)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "438702dc-d5d8-4579-803f-90f08d14974c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cost(\n",
    "    j_obs_no_reg, \n",
    "    {}, \n",
    "    noise_level, \n",
    "    {}, \n",
    "    {}, \n",
    "    figsize=(20, 15)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b22d9e2f-0a88-41ea-acb5-e9e09ec26707",
   "metadata": {},
   "source": [
    "## Inference with a population dynamic-informed regularization term"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac3e9c5-df03-4c3c-b846-ce53af5766f4",
   "metadata": {},
   "source": [
    "Now, the inference is illustrated with the population dynamic-informed regularization.\n",
    "\n",
    "One can add a regularization term to the cost function by using the `add_reg` method of the object of the class `Cost`. It takes as input the keyword of the regularization term and the value of the weight coefficient associated. If the regularization term is already in the cost function, this method will only update the value of the weight coefficient.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "15844b55-bc2f-4c9f-be07-5008e6a3dede",
   "metadata": {},
   "outputs": [],
   "source": [
    "cost.add_reg(\"Population dynamic\", 5. * 1e-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e031f6d6-eb1c-40b8-a22b-34ac42eef232",
   "metadata": {},
   "source": [
    "As the population dynamic-informed regularization term is differentiable, the gradient descent algorithm is used. \n",
    "\n",
    "One may want to start from where the optimization stopped in the case without regularization (which is referred to as \"hot start\" in the litterature). To do so, the input 'restart_flag` is set to True. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f274843a-aa01-4494-bfea-7e99dc42b3d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "options = { \n",
    "    \"nit_max\": 100, \n",
    "    \"step size\": 5 * 1.0e-2\n",
    "} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2349377e-8df6-4780-b2c5-32622f53dbce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "restarting the optimization process from the data contained in the dir /home/slabarthe/Documents/SVN_GIT/PHEROSENSOR/pherosensor-toolbox/tutorials/output\n",
      "Warning: the regularization Population dynamic term has not been found in the given directory. It is assumed that this regularization term was not used previously and thus is set to 0.\n",
      "Optimizing using the gradient descent algorithm \n",
      "iteration 21: j = 1.639804e-02, j_obs = 1.207513e-03, ite comp time = 6.609e+00s -\n",
      "iteration 22: j = 9.358158e-03, j_obs = 1.208904e-03, ite comp time = 6.403e+00s -\n",
      "iteration 23: j = 7.672829e-03, j_obs = 1.209167e-03, ite comp time = 6.625e+00s -\n",
      "iteration 24: j = 6.808572e-03, j_obs = 1.209675e-03, ite comp time = 6.597e+00s -\n",
      "iteration 25: j = 6.243985e-03, j_obs = 1.209887e-03, ite comp time = 6.350e+00s -\n",
      "iteration 26: j = 5.829556e-03, j_obs = 1.210224e-03, ite comp time = 6.179e+00s -\n",
      "iteration 27: j = 5.503943e-03, j_obs = 1.210407e-03, ite comp time = 5.965e+00s -\n",
      "iteration 28: j = 5.236778e-03, j_obs = 1.210670e-03, ite comp time = 6.208e+00s -\n",
      "iteration 29: j = 5.010742e-03, j_obs = 1.210833e-03, ite comp time = 6.005e+00s -\n",
      "iteration 30: j = 4.815256e-03, j_obs = 1.211054e-03, ite comp time = 5.956e+00s -\n",
      "iteration 31: j = 4.643237e-03, j_obs = 1.211202e-03, ite comp time = 5.847e+00s -\n",
      "iteration 32: j = 4.489867e-03, j_obs = 1.211395e-03, ite comp time = 6.055e+00s -\n",
      "iteration 33: j = 4.351593e-03, j_obs = 1.211532e-03, ite comp time = 6.156e+00s -\n",
      "iteration 34: j = 4.225846e-03, j_obs = 1.211705e-03, ite comp time = 6.253e+00s -\n",
      "iteration 35: j = 4.110601e-03, j_obs = 1.211832e-03, ite comp time = 6.062e+00s -\n",
      "iteration 36: j = 4.004340e-03, j_obs = 1.211990e-03, ite comp time = 6.161e+00s -\n",
      "iteration 37: j = 3.905805e-03, j_obs = 1.212109e-03, ite comp time = 6.047e+00s -\n",
      "iteration 38: j = 3.814033e-03, j_obs = 1.212254e-03, ite comp time = 6.016e+00s -\n",
      "iteration 39: j = 3.728191e-03, j_obs = 1.212366e-03, ite comp time = 6.040e+00s -\n",
      "iteration 40: j = 3.647632e-03, j_obs = 1.212501e-03, ite comp time = 5.930e+00s -\n",
      "iteration 41: j = 3.571777e-03, j_obs = 1.212607e-03, ite comp time = 5.921e+00s -\n",
      "iteration 42: j = 3.500174e-03, j_obs = 1.212734e-03, ite comp time = 5.973e+00s -\n",
      "iteration 43: j = 3.432403e-03, j_obs = 1.212834e-03, ite comp time = 6.018e+00s -\n",
      "iteration 44: j = 3.368137e-03, j_obs = 1.212953e-03, ite comp time = 6.147e+00s -\n",
      "iteration 45: j = 3.307059e-03, j_obs = 1.213049e-03, ite comp time = 5.965e+00s -\n",
      "iteration 46: j = 3.248926e-03, j_obs = 1.213161e-03, ite comp time = 6.175e+00s -\n",
      "iteration 47: j = 3.193494e-03, j_obs = 1.213252e-03, ite comp time = 6.408e+00s -\n",
      "iteration 48: j = 3.140574e-03, j_obs = 1.213359e-03, ite comp time = 6.200e+00s -\n",
      "iteration 49: j = 3.089975e-03, j_obs = 1.213446e-03, ite comp time = 6.268e+00s -\n",
      "iteration 50: j = 3.041549e-03, j_obs = 1.213547e-03, ite comp time = 6.073e+00s -\n",
      "iteration 51: j = 2.995140e-03, j_obs = 1.213630e-03, ite comp time = 6.012e+00s -\n",
      "iteration 52: j = 2.950630e-03, j_obs = 1.213726e-03, ite comp time = 6.154e+00s -\n",
      "iteration 53: j = 2.907892e-03, j_obs = 1.213805e-03, ite comp time = 6.078e+00s -\n",
      "iteration 54: j = 2.866828e-03, j_obs = 1.213897e-03, ite comp time = 6.034e+00s -\n",
      "iteration 55: j = 2.827331e-03, j_obs = 1.213973e-03, ite comp time = 6.107e+00s -\n",
      "iteration 56: j = 2.789322e-03, j_obs = 1.214061e-03, ite comp time = 6.053e+00s -\n",
      "iteration 57: j = 2.752711e-03, j_obs = 1.214134e-03, ite comp time = 1.217e+01s -\n",
      "iteration 58: j = 2.717430e-03, j_obs = 1.214217e-03, ite comp time = 1.644e+01s -\n",
      "iteration 59: j = 2.683402e-03, j_obs = 1.214287e-03, ite comp time = 1.548e+01s -\n",
      "iteration 60: j = 2.650569e-03, j_obs = 1.214367e-03, ite comp time = 1.702e+01s -\n",
      "iteration 61: j = 2.618866e-03, j_obs = 1.214434e-03, ite comp time = 1.623e+01s -\n",
      "iteration 62: j = 2.588243e-03, j_obs = 1.214511e-03, ite comp time = 1.625e+01s -\n",
      "iteration 63: j = 2.558642e-03, j_obs = 1.214575e-03, ite comp time = 1.736e+01s -\n",
      "iteration 64: j = 2.530021e-03, j_obs = 1.214648e-03, ite comp time = 1.618e+01s -\n",
      "iteration 65: j = 2.502329e-03, j_obs = 1.214710e-03, ite comp time = 1.597e+01s -\n",
      "iteration 66: j = 2.475528e-03, j_obs = 1.214780e-03, ite comp time = 1.760e+01s -\n",
      "iteration 67: j = 2.449575e-03, j_obs = 1.214839e-03, ite comp time = 1.871e+01s -\n",
      "iteration 68: j = 2.424436e-03, j_obs = 1.214906e-03, ite comp time = 1.772e+01s -\n",
      "iteration 69: j = 2.400072e-03, j_obs = 1.214963e-03, ite comp time = 1.663e+01s -\n",
      "iteration 70: j = 2.376454e-03, j_obs = 1.215027e-03, ite comp time = 1.875e+01s -\n",
      "iteration 71: j = 2.353546e-03, j_obs = 1.215081e-03, ite comp time = 1.816e+01s -\n",
      "iteration 72: j = 2.331323e-03, j_obs = 1.215143e-03, ite comp time = 1.987e+01s -\n",
      "iteration 73: j = 2.309754e-03, j_obs = 1.215195e-03, ite comp time = 1.843e+01s -\n",
      "iteration 74: j = 2.288815e-03, j_obs = 1.215254e-03, ite comp time = 1.875e+01s -\n",
      "iteration 75: j = 2.268479e-03, j_obs = 1.215304e-03, ite comp time = 1.750e+01s -\n",
      "iteration 76: j = 2.248724e-03, j_obs = 1.215360e-03, ite comp time = 1.820e+01s -\n",
      "iteration 77: j = 2.229525e-03, j_obs = 1.215408e-03, ite comp time = 1.797e+01s -\n",
      "iteration 78: j = 2.210865e-03, j_obs = 1.215462e-03, ite comp time = 1.759e+01s -\n",
      "iteration 79: j = 2.192718e-03, j_obs = 1.215507e-03, ite comp time = 1.860e+01s -\n",
      "iteration 80: j = 2.175070e-03, j_obs = 1.215560e-03, ite comp time = 1.895e+01s -\n",
      "iteration 81: j = 2.157899e-03, j_obs = 1.215603e-03, ite comp time = 1.905e+01s -\n",
      "iteration 82: j = 2.141190e-03, j_obs = 1.215653e-03, ite comp time = 1.654e+01s -\n",
      "iteration 83: j = 2.124924e-03, j_obs = 1.215694e-03, ite comp time = 1.599e+01s -\n",
      "iteration 84: j = 2.109087e-03, j_obs = 1.215742e-03, ite comp time = 1.632e+01s -\n",
      "iteration 85: j = 2.093662e-03, j_obs = 1.215781e-03, ite comp time = 1.585e+01s -\n",
      "iteration 86: j = 2.078638e-03, j_obs = 1.215827e-03, ite comp time = 1.517e+01s -\n",
      "iteration 87: j = 2.063996e-03, j_obs = 1.215865e-03, ite comp time = 1.691e+01s -\n",
      "iteration 88: j = 2.049728e-03, j_obs = 1.215909e-03, ite comp time = 1.589e+01s -\n",
      "iteration 89: j = 2.035817e-03, j_obs = 1.215944e-03, ite comp time = 1.634e+01s -\n",
      "iteration 90: j = 2.022255e-03, j_obs = 1.215986e-03, ite comp time = 1.512e+01s -\n",
      "iteration 91: j = 2.009026e-03, j_obs = 1.216020e-03, ite comp time = 1.650e+01s -\n",
      "iteration 92: j = 1.996123e-03, j_obs = 1.216060e-03, ite comp time = 1.619e+01s -\n",
      "iteration 93: j = 1.983532e-03, j_obs = 1.216093e-03, ite comp time = 1.619e+01s -\n",
      "iteration 94: j = 1.971245e-03, j_obs = 1.216131e-03, ite comp time = 1.560e+01s -\n",
      "iteration 95: j = 1.959251e-03, j_obs = 1.216162e-03, ite comp time = 1.680e+01s -\n",
      "iteration 96: j = 1.947543e-03, j_obs = 1.216198e-03, ite comp time = 1.657e+01s -\n",
      "iteration 97: j = 1.936108e-03, j_obs = 1.216227e-03, ite comp time = 1.686e+01s -\n",
      "iteration 98: j = 1.924942e-03, j_obs = 1.216262e-03, ite comp time = 1.796e+01s -\n",
      "iteration 99: j = 1.914032e-03, j_obs = 1.216290e-03, ite comp time = 1.752e+01s -\n",
      "iteration 100: j = 1.903374e-03, j_obs = 1.216323e-03, ite comp time = 1.253e+03s -\n",
      "iteration 101: j = 1.892958e-03, j_obs = 1.216349e-03, ite comp time = 1.628e+01s -\n",
      "iteration 102: j = 1.882778e-03, j_obs = 1.216380e-03, ite comp time = 1.474e+01s -\n",
      "iteration 103: j = 1.872825e-03, j_obs = 1.216405e-03, ite comp time = 8.805e+01s -\n",
      "iteration 104: j = 1.863095e-03, j_obs = 1.216435e-03, ite comp time = 6.269e+00s -\n",
      "iteration 105: j = 1.853578e-03, j_obs = 1.216458e-03, ite comp time = 5.585e+00s -\n",
      "iteration 106: j = 1.844272e-03, j_obs = 1.216487e-03, ite comp time = 6.424e+00s -\n",
      "iteration 107: j = 1.835166e-03, j_obs = 1.216509e-03, ite comp time = 6.427e+00s -\n",
      "iteration 108: j = 1.826259e-03, j_obs = 1.216536e-03, ite comp time = 6.527e+00s -\n",
      "iteration 109: j = 1.817541e-03, j_obs = 1.216556e-03, ite comp time = 6.285e+00s -\n",
      "iteration 110: j = 1.809010e-03, j_obs = 1.216582e-03, ite comp time = 6.368e+00s -\n",
      "iteration 111: j = 1.800658e-03, j_obs = 1.216601e-03, ite comp time = 6.228e+00s -\n",
      "iteration 112: j = 1.792483e-03, j_obs = 1.216625e-03, ite comp time = 6.170e+00s -\n",
      "iteration 113: j = 1.784477e-03, j_obs = 1.216643e-03, ite comp time = 6.180e+00s -\n",
      "iteration 114: j = 1.776637e-03, j_obs = 1.216666e-03, ite comp time = 6.321e+00s -\n",
      "iteration 115: j = 1.768958e-03, j_obs = 1.216683e-03, ite comp time = 6.363e+00s -\n",
      "iteration 116: j = 1.761436e-03, j_obs = 1.216704e-03, ite comp time = 6.830e+00s -\n",
      "iteration 117: j = 1.754066e-03, j_obs = 1.216720e-03, ite comp time = 6.875e+00s -\n",
      "iteration 118: j = 1.746845e-03, j_obs = 1.216740e-03, ite comp time = 7.198e+00s -\n",
      "iteration 119: j = 1.739767e-03, j_obs = 1.216754e-03, ite comp time = 7.369e+00s -\n",
      "iteration 120: j = 1.732832e-03, j_obs = 1.216773e-03, ite comp time = 6.777e+00s -\n",
      "\n",
      "final iteration:\n",
      "number of iteration = 120\n",
      "j = 0.1484422225954371 % of the initial cost\n",
      "j_obs = 0.10423431620529669 % of the initial cost\n",
      "j_Population dynamic = 0.04420790639014042 % of the initial cost\n"
     ]
    }
   ],
   "source": [
    "direct_model_pop_dyn_reg, j_obs_pop_dyn_reg, j_reg_pop_dyn_reg, S_a_pop_dyn_reg = cost.minimize(\n",
    "    direct_model,\n",
    "    adjoint_model,\n",
    "    \"gradient descent\", \n",
    "    options=options,\n",
    "    path_save=path_output,\n",
    "    restart_flag=True,\n",
    "    j_obs_threshold = noise_level\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9fb69db9-2395-4393-8e0b-370e573aed30",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/slabarthe/Documents/SVN_GIT/PHEROSENSOR/pherosensor-toolbox/src/utils/plot_ctrl.py:106: UserWarning: No contour levels were found within the data range.\n",
      "  contour = ax.contour(msh.x, msh.y, ctrl_target_current_t, colors='k', linestyles='dashed', levels=0, linewidths=4)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ctrl_pop_dyn_reg = Control(direct_model_pop_dyn_reg.S, msh)\n",
    "ctrl_pop_dyn_reg.value = ctrl_pop_dyn_reg.background_value\n",
    "\n",
    "plot_ctrl(\n",
    "    msh,\n",
    "    msh.t_array[1],\n",
    "    ctrl_pop_dyn_reg,\n",
    "    ctrl_target=ctrl_target.value,\n",
    "    cmap=\"jet\",\n",
    "    label='s_o',\n",
    "    obs=cost.obs,\n",
    "    figsize=(20, 15)\n",
    ")\n",
    "plot_ctrl(\n",
    "    msh,\n",
    "    msh.t_array[-1],\n",
    "    ctrl_pop_dyn_reg,\n",
    "    ctrl_target=ctrl_target.value,\n",
    "    cmap=\"jet\",\n",
    "    label='s_o',\n",
    "    obs=cost.obs,\n",
    "    figsize=(20, 15)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "578c61d9-6628-4593-999b-74cdfb502bef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cost(\n",
    "    j_obs_pop_dyn_reg, \n",
    "    j_reg_pop_dyn_reg, \n",
    "    noise_level, \n",
    "    {'Population dynamic': 'PD'}, \n",
    "    {'Population dynamic': 'g'}, \n",
    "    figsize=(20, 15)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63ed80b9-7fd2-4e20-83c5-d17ba863eaec",
   "metadata": {},
   "source": [
    "Let us note that if the optimization is started again and no regularization terms are found in the provided folder, the regularization terms are set to 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c8ffdcd-bcfc-4120-b31f-9149cdbd6d99",
   "metadata": {},
   "source": [
    "## Inference with the LASSO regularization term"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fadbdea-0492-452a-886d-007cd38fc5ed",
   "metadata": {},
   "source": [
    "Now, the inference is illustrated with both the population dynamic-informed and the LASSO regularizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "de8702e7-4a05-4c0f-9c7d-f024f8548af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "cost.add_reg('LASSO',  10 * 1e-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34582663-13ce-4c2f-b400-d4fd6d5b057d",
   "metadata": {},
   "source": [
    "In this case, the weight coefficient of the population dynamic-informed regularization term is modified using the `modify_weight_reg` method of the object of the class `Cost`.\n",
    "This method takes as input the keyword of the regularization term and the new value of the weight coefficient.\n",
    "If the regularization term was not already considered, then this method acts as the `add_reg` method. If the new weight coefficient is 0, then the regularization term will be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c036e9a7-a457-4d8c-a1e5-9de85857c79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cost.modify_weight_reg(\"Population dynamic\", 2. * 1e-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f9832b-497c-446e-af24-f02a0fca570a",
   "metadata": {},
   "source": [
    "As the cost function is no longer differentiable due to the LASSO regularization term, the user should use a convexe non-differentiable optimization algorithm such as the proximal gradient algorithm. \n",
    "This algorithm has been implemented in the [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/proximal_gradient.py) `proximal_gradient`. This type of algorithm should also be used when the group-LASSO regularization term is used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "65b17f3e-7919-4a47-96b0-63a79e84c011",
   "metadata": {},
   "outputs": [],
   "source": [
    "options = {\n",
    "    \"nit_max\": 40, \n",
    "    \"step size\": 10 * 1.0e-1\n",
    "} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "39e9a404-5486-4f9e-8e0d-a53d834b2006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "restarting the optimization process from the data contained in the dir /home/slabarthe/Documents/SVN_GIT/PHEROSENSOR/pherosensor-toolbox/tutorials/output\n",
      "Warning: the regularization LASSO term has not been found in the given directory. It is assumed that this regularization term was not used previously and thus is set to 0.\n",
      "Optimizing using the proximal gradient algorithm \n",
      "iteration 121: j = 9.183761e-02, j_obs = 1.216786e-03, ite comp time = 1.498e+01s -\n",
      "iteration 122: j = 9.125290e-02, j_obs = 1.216103e-03, ite comp time = 1.456e+01s -\n",
      "iteration 123: j = 9.069233e-02, j_obs = 1.215469e-03, ite comp time = 1.552e+01s -\n",
      "iteration 124: j = 9.015351e-02, j_obs = 1.214884e-03, ite comp time = 1.513e+01s -\n",
      "iteration 125: j = 8.963486e-02, j_obs = 1.214345e-03, ite comp time = 1.576e+01s -\n",
      "iteration 126: j = 8.913538e-02, j_obs = 1.213851e-03, ite comp time = 1.620e+01s -\n",
      "iteration 127: j = 8.865464e-02, j_obs = 1.213400e-03, ite comp time = 1.629e+01s -\n",
      "iteration 128: j = 8.819243e-02, j_obs = 1.212991e-03, ite comp time = 1.346e+01s -\n",
      "iteration 129: j = 8.774803e-02, j_obs = 1.212623e-03, ite comp time = 1.277e+01s -\n",
      "iteration 130: j = 8.732052e-02, j_obs = 1.212291e-03, ite comp time = 1.256e+01s -\n",
      "iteration 131: j = 8.690920e-02, j_obs = 1.211995e-03, ite comp time = 1.208e+01s -\n",
      "iteration 132: j = 8.651345e-02, j_obs = 1.211733e-03, ite comp time = 1.258e+01s -\n",
      "iteration 133: j = 8.613257e-02, j_obs = 1.211502e-03, ite comp time = 1.321e+01s -\n",
      "iteration 134: j = 8.576591e-02, j_obs = 1.211301e-03, ite comp time = 1.217e+01s -\n",
      "iteration 135: j = 8.541292e-02, j_obs = 1.211129e-03, ite comp time = 1.265e+01s -\n",
      "iteration 136: j = 8.507305e-02, j_obs = 1.210984e-03, ite comp time = 1.329e+01s -\n",
      "iteration 137: j = 8.474563e-02, j_obs = 1.210865e-03, ite comp time = 1.222e+01s -\n",
      "iteration 138: j = 8.443013e-02, j_obs = 1.210771e-03, ite comp time = 1.289e+01s -\n",
      "iteration 139: j = 8.412587e-02, j_obs = 1.210700e-03, ite comp time = 1.222e+01s -\n",
      "iteration 140: j = 8.383223e-02, j_obs = 1.210651e-03, ite comp time = 1.213e+01s -\n",
      "iteration 141: j = 8.354878e-02, j_obs = 1.210623e-03, ite comp time = 1.267e+01s -\n",
      "iteration 142: j = 8.327504e-02, j_obs = 1.210616e-03, ite comp time = 1.237e+01s -\n",
      "iteration 143: j = 8.301037e-02, j_obs = 1.210627e-03, ite comp time = 1.180e+01s -\n",
      "iteration 144: j = 8.275438e-02, j_obs = 1.210656e-03, ite comp time = 1.299e+01s -\n",
      "iteration 145: j = 8.250657e-02, j_obs = 1.210702e-03, ite comp time = 1.309e+01s -\n",
      "iteration 146: j = 8.226660e-02, j_obs = 1.210764e-03, ite comp time = 1.270e+01s -\n",
      "iteration 147: j = 8.203406e-02, j_obs = 1.210840e-03, ite comp time = 1.405e+01s -\n",
      "iteration 148: j = 8.180871e-02, j_obs = 1.210929e-03, ite comp time = 1.319e+01s -\n",
      "iteration 149: j = 8.159025e-02, j_obs = 1.211031e-03, ite comp time = 1.328e+01s -\n",
      "iteration 150: j = 8.137838e-02, j_obs = 1.211144e-03, ite comp time = 1.293e+01s -\n",
      "iteration 151: j = 8.117296e-02, j_obs = 1.211267e-03, ite comp time = 1.359e+01s -\n",
      "iteration 152: j = 8.097372e-02, j_obs = 1.211399e-03, ite comp time = 1.548e+01s -\n",
      "iteration 153: j = 8.078034e-02, j_obs = 1.211539e-03, ite comp time = 1.506e+01s -\n",
      "iteration 154: j = 8.059263e-02, j_obs = 1.211687e-03, ite comp time = 1.676e+01s -\n",
      "iteration 155: j = 8.041036e-02, j_obs = 1.211841e-03, ite comp time = 1.535e+01s -\n",
      "iteration 156: j = 8.023325e-02, j_obs = 1.212000e-03, ite comp time = 1.546e+01s -\n",
      "iteration 157: j = 8.006114e-02, j_obs = 1.212165e-03, ite comp time = 1.495e+01s -\n",
      "iteration 158: j = 7.989385e-02, j_obs = 1.212333e-03, ite comp time = 1.602e+01s -\n",
      "iteration 159: j = 7.973123e-02, j_obs = 1.212505e-03, ite comp time = 1.558e+01s -\n",
      "iteration 160: j = 7.957308e-02, j_obs = 1.212679e-03, ite comp time = 1.667e+01s -\n",
      "\n",
      "final iteration:\n",
      "number of iteration = 160\n",
      "j = 6.81659129779809 % of the initial cost\n",
      "j_obs = 0.10388358493708338 % of the initial cost\n",
      "j_Population dynamic = 0.0001659359187830108 % of the initial cost\n",
      "j_LASSO = 6.712541776942223 % of the initial cost\n"
     ]
    }
   ],
   "source": [
    "direct_model_LASSO_reg, j_obs_LASSO_reg, j_reg_LASSO_reg, S_a_LASSO_reg = cost.minimize(\n",
    "    direct_model,\n",
    "    adjoint_model,\n",
    "    \"proximal gradient\", \n",
    "    options=options,\n",
    "    path_save=path_output,\n",
    "    restart_flag=True,\n",
    "    j_obs_threshold = noise_level\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "62daaeb5-214b-402e-8263-6448c60d8590",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/slabarthe/Documents/SVN_GIT/PHEROSENSOR/pherosensor-toolbox/src/utils/plot_ctrl.py:106: UserWarning: No contour levels were found within the data range.\n",
      "  contour = ax.contour(msh.x, msh.y, ctrl_target_current_t, colors='k', linestyles='dashed', levels=0, linewidths=4)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ctrl_LASSO = Control(direct_model_LASSO_reg.S, msh)\n",
    "ctrl_LASSO.value = ctrl_LASSO.background_value\n",
    "plot_ctrl(\n",
    "    msh,\n",
    "    msh.t_array[1],\n",
    "    ctrl_LASSO,\n",
    "    ctrl_target=ctrl_target.value,\n",
    "    cmap=\"jet\",\n",
    "    label='s_o',\n",
    "    obs=cost.obs,\n",
    "    figsize=(20, 15)\n",
    ")\n",
    "plot_ctrl(\n",
    "    msh,\n",
    "    msh.t_array[-1],\n",
    "    ctrl_LASSO,\n",
    "    ctrl_target=ctrl_target.value,\n",
    "    cmap=\"jet\",\n",
    "    label='s_o',\n",
    "    obs=cost.obs,\n",
    "    figsize=(20, 15)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03240969-036d-4343-87aa-f8238e7a246e",
   "metadata": {},
   "source": [
    "## Inference with the Tikhonov regularization term"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9644060-585c-4f68-815d-0da927c10193",
   "metadata": {},
   "source": [
    "Now, the inference is illustrated with both the population dynamic-informed and the Tikhonov regularizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "044f71d6-bc53-4054-a627-dd56a096e98b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cost.add_reg('Tikhonov',5 * 1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c27879-1e04-4b60-b53c-5173ccfcd295",
   "metadata": {},
   "source": [
    "One can remove a regularization term from the cost function using the `remove_reg` method of the object of the class `Cost`. This method just take the keyword of the regularization term to remove. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "45f2b255-bce1-483d-96cf-0df96f99bac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "cost.remove_reg(\"LASSO\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f56e15de-015f-41ad-8bd0-8e50750ac33c",
   "metadata": {},
   "source": [
    "As the LASSO regularization was removed and as the Tikhonov regularization term is differentiable, the cost function is differentiable and thus, the gradient descent algorithm will be used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "801c2032-9668-4027-88df-b75dda52d67a",
   "metadata": {},
   "outputs": [],
   "source": [
    "options = {\n",
    "    \"nit_max\": 40, \n",
    "    \"step size\": 15 * 1.0e-1\n",
    "} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a49208f7-012a-4007-9427-6431d45790f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "restarting the optimization process from the data contained in the dir /home/slabarthe/Documents/SVN_GIT/PHEROSENSOR/pherosensor-toolbox/tutorials/output\n",
      "Warning: the regularization Tikhonov term has not been found in the given directory. It is assumed that this regularization term was not used previously and thus is set to 0.\n",
      "Optimizing using the gradient descent algorithm \n",
      "iteration 161: j = 9.809640e-02, j_obs = 1.212855e-03, ite comp time = 7.005e+00s -\n",
      "iteration 162: j = 9.805680e-02, j_obs = 1.206557e-03, ite comp time = 6.695e+00s -\n",
      "iteration 163: j = 9.801895e-02, j_obs = 1.201276e-03, ite comp time = 6.908e+00s -\n",
      "iteration 164: j = 9.798274e-02, j_obs = 1.196922e-03, ite comp time = 7.180e+00s -\n",
      "iteration 165: j = 9.794805e-02, j_obs = 1.193410e-03, ite comp time = 6.803e+00s -\n",
      "iteration 166: j = 9.791479e-02, j_obs = 1.190662e-03, ite comp time = 6.769e+00s -\n",
      "iteration 167: j = 9.788286e-02, j_obs = 1.188607e-03, ite comp time = 6.745e+00s -\n",
      "iteration 168: j = 9.785217e-02, j_obs = 1.187181e-03, ite comp time = 7.210e+00s -\n",
      "iteration 169: j = 9.782265e-02, j_obs = 1.186322e-03, ite comp time = 7.362e+00s -\n",
      "iteration 170: j = 9.779423e-02, j_obs = 1.185978e-03, ite comp time = 7.062e+00s -\n",
      "iteration 171: j = 9.776682e-02, j_obs = 1.186096e-03, ite comp time = 6.797e+00s -\n",
      "iteration 172: j = 9.774037e-02, j_obs = 1.186631e-03, ite comp time = 7.066e+00s -\n",
      "iteration 173: j = 9.771482e-02, j_obs = 1.187542e-03, ite comp time = 7.111e+00s -\n",
      "iteration 174: j = 9.769011e-02, j_obs = 1.188788e-03, ite comp time = 7.212e+00s -\n",
      "iteration 175: j = 9.766619e-02, j_obs = 1.190336e-03, ite comp time = 7.219e+00s -\n",
      "iteration 176: j = 9.764301e-02, j_obs = 1.192152e-03, ite comp time = 7.208e+00s -\n",
      "iteration 177: j = 9.762053e-02, j_obs = 1.194207e-03, ite comp time = 6.509e+00s -\n",
      "iteration 178: j = 9.759871e-02, j_obs = 1.196475e-03, ite comp time = 6.116e+00s -\n",
      "iteration 179: j = 9.757750e-02, j_obs = 1.198930e-03, ite comp time = 6.041e+00s -\n",
      "iteration 180: j = 9.755688e-02, j_obs = 1.201551e-03, ite comp time = 6.692e+00s -\n",
      "iteration 181: j = 9.753680e-02, j_obs = 1.204316e-03, ite comp time = 6.005e+00s -\n",
      "iteration 182: j = 9.751724e-02, j_obs = 1.207207e-03, ite comp time = 5.804e+00s -\n",
      "iteration 183: j = 9.749817e-02, j_obs = 1.210208e-03, ite comp time = 6.274e+00s -\n",
      "iteration 184: j = 9.747956e-02, j_obs = 1.213302e-03, ite comp time = 6.097e+00s -\n",
      "iteration 185: j = 9.746138e-02, j_obs = 1.216476e-03, ite comp time = 6.232e+00s -\n",
      "iteration 186: j = 9.744362e-02, j_obs = 1.219717e-03, ite comp time = 6.217e+00s -\n",
      "iteration 187: j = 9.742624e-02, j_obs = 1.223013e-03, ite comp time = 5.758e+00s -\n",
      "iteration 188: j = 9.740923e-02, j_obs = 1.226354e-03, ite comp time = 5.960e+00s -\n",
      "iteration 189: j = 9.739258e-02, j_obs = 1.229731e-03, ite comp time = 5.683e+00s -\n",
      "iteration 190: j = 9.737625e-02, j_obs = 1.233136e-03, ite comp time = 5.730e+00s -\n",
      "iteration 191: j = 9.736025e-02, j_obs = 1.236559e-03, ite comp time = 5.984e+00s -\n",
      "iteration 192: j = 9.734454e-02, j_obs = 1.239996e-03, ite comp time = 5.841e+00s -\n",
      "iteration 193: j = 9.732911e-02, j_obs = 1.243438e-03, ite comp time = 5.850e+00s -\n",
      "iteration 194: j = 9.731396e-02, j_obs = 1.246881e-03, ite comp time = 6.898e+00s -\n",
      "iteration 195: j = 9.729906e-02, j_obs = 1.250320e-03, ite comp time = 5.560e+00s -\n",
      "iteration 196: j = 9.728442e-02, j_obs = 1.253751e-03, ite comp time = 5.714e+00s -\n",
      "iteration 197: j = 9.727000e-02, j_obs = 1.257168e-03, ite comp time = 5.829e+00s -\n",
      "iteration 198: j = 9.725581e-02, j_obs = 1.260570e-03, ite comp time = 5.725e+00s -\n",
      "iteration 199: j = 9.724184e-02, j_obs = 1.263952e-03, ite comp time = 5.738e+00s -\n",
      "iteration 200: j = 9.722807e-02, j_obs = 1.267312e-03, ite comp time = 5.790e+00s -\n",
      "\n",
      "final iteration:\n",
      "number of iteration = 200\n",
      "j = 8.328998044207413 % of the initial cost\n",
      "j_obs = 0.10856372400338492 % of the initial cost\n",
      "j_Population dynamic = 0.00017074318343414518 % of the initial cost\n",
      "j_Tikhonov = 8.220263577020594 % of the initial cost\n"
     ]
    }
   ],
   "source": [
    "direct_model_Tikhonov_reg, j_obs_Tikhonov_reg, j_reg_Tikhonov_reg, S_a_Tikhonov_reg = cost.minimize(\n",
    "    direct_model,\n",
    "    adjoint_model,\n",
    "    \"gradient descent\", \n",
    "    options=options,\n",
    "    path_save=path_output,\n",
    "    restart_flag=True,\n",
    "    j_obs_threshold = noise_level\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d1880613-693a-4bd9-a26f-55602e1ece91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ctrl_Tikhonov = Control(direct_model_Tikhonov_reg.S, msh)\n",
    "ctrl_Tikhonov.value = ctrl_Tikhonov.background_value\n",
    "plot_ctrl(\n",
    "    msh,\n",
    "    msh.t_array[1],\n",
    "    ctrl_Tikhonov,\n",
    "    ctrl_target=ctrl_target.value,\n",
    "    cmap=\"jet\",\n",
    "    label='s_o',\n",
    "    obs=cost.obs,\n",
    "    figsize=(20, 15)\n",
    ")\n",
    "plot_ctrl(\n",
    "    msh,\n",
    "    msh.t_array[-1],\n",
    "    ctrl_Tikhonov,\n",
    "    ctrl_target=ctrl_target.value,\n",
    "    cmap=\"jet\",\n",
    "    label='s_o',\n",
    "    obs=cost.obs,\n",
    "    figsize=(20, 15)\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}