{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Ideal Gas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "In this notebook, we create a GP model for the temperature-dependence of the average position of a 1D ideal gas in an external field. The system is described in [this paper](https://doi.org/10.1063/5.0014282), and is available as a helpful test module in {mod}`thermoextrap` with GP and active-learning specific features defined in {mod}`thermoextrap.gpr_active.ig_active`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gpflow\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from scipy import linalg\n",
    "\n",
    "import thermoextrap\n",
    "from thermoextrap import idealgas\n",
    "from thermoextrap.gpr_active import active_utils, gp_models, ig_active"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.5.2'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpflow.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {},
   "source": [
    "## Creating a GP model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5",
   "metadata": {},
   "source": [
    "```{eval-rst}\n",
    ".. currentmodule:: thermoextrap.gpr_active\n",
    "```\n",
    "\n",
    "To start, we need to create some data. We will create data for this analytically tractable system at $\\beta = \\frac{1}{k_\\mathrm{B}T}$ values of 0.1 (high temperature) and 9.6 (low temperature). We do this in two steps: first, we create {class}`~ig_active.IG_DataWrapper` class objects, then we use the associated {meth}`ig_active.IG_DataWrapper.get_data` function of these objects to generate data. All {class}`~active_utils.DataWrapper` objects must be able to generate new data to be useful for active learning. Since we will be comparing to methods like interpolation, and want all models to use the same data, it is important to have the separate data list here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define beta values\n",
    "beta_list = [0.1, 9.6]\n",
    "\n",
    "# Create DataWrapper objects that can also be used to build states\n",
    "wrap_list = [ig_active.IG_DataWrapper(b, seed=None) for b in beta_list]\n",
    "\n",
    "# Create actual data so consistent when build states with different orders\n",
    "data_list = [wrap.get_data() for wrap in wrap_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {},
   "source": [
    "Next, we will require `State` objects for creating our GP model. We make sure that each {class}`~ig_active.IG_DataWrapper` object uses the previously generated data when creating a state rather than using new data, and we set the maximum derivative order to 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8",
   "metadata": {},
   "outputs": [],
   "source": [
    "state_list = [\n",
    "    wrap.build_state(all_data=dat, max_order=1)\n",
    "    for (wrap, dat) in zip(wrap_list, data_list)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "The quickest and easiest way to create a GP model from a list of states is to call {func}`active_utils.create_GPR`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M2\n"
     ]
    }
   ],
   "source": [
    "gp_model = active_utils.create_GPR(state_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11",
   "metadata": {},
   "source": [
    "The above creates a GPR model using the default RBF kernel and other default settings, then trains it, again with the default initial values, etc. To get a better sense of what is happening, we will break down the specific information needed to train a GP model and show how to change some default values. As a convenience, most of these can be changed through keyword arguments to {func}`active_utils.create_GPR`, in particular the `base_kwargs` argument is a dictionary of arguments passed on to the {func}`active_utils.create_base_GP_model` function we will discuss below.\n",
    "\n",
    "Essential to a GP model are the input locations, here $\\beta$ values, output values (average ideal gas particle positions and derivatives of this with respect to $\\beta$), and the noise covariance matrix (covariance matrix at each $\\beta$ for values and derivatives). The function {func}`active_utils.input_GP_from_state` is helpful for generating this data from a given `State` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = []\n",
    "y_data = []\n",
    "cov_data = []\n",
    "\n",
    "for s in state_list:\n",
    "    this_x_data, this_y_data, this_cov_data = active_utils.input_GP_from_state(s)\n",
    "    x_data.append(this_x_data)\n",
    "    y_data.append(this_y_data)\n",
    "    cov_data.append(this_cov_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13",
   "metadata": {},
   "source": [
    "Some manipulation is required to properly format this data, in particular making sure that the covariance matrix is block diagonal. In other words, we expect that the calculated values and derivatives at different temperatures are from indpependent simulations and therefore should have zero covariance - at least from the associated noise. We will, of course, employ a kernel with adjustable parameters that learns covariances between different temperatures as part of our GP model. At the same temperature, however, average values and derivatives will clearly have some noise covariance associated with the specific random sample drawn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = np.vstack(x_data)\n",
    "y_data = np.vstack(y_data)\n",
    "noise_cov_mat = linalg.block_diag(*[dat[0, ...] for dat in cov_data])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15",
   "metadata": {},
   "source": [
    "We can see that the location data (`x_data`) includes the $\\beta$ values, but also the associated derivative order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.1 0. ]\n",
      " [0.1 1. ]\n",
      " [9.6 0. ]\n",
      " [9.6 1. ]]\n"
     ]
    }
   ],
   "source": [
    "print(x_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17",
   "metadata": {},
   "source": [
    "The output data (`y_data`) includes the average particle position or derivative at each temperature. So from the `x_data`, we an see that the 3rd entry in the `y_data` list is the 2nd derivative at $\\beta=0.1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.49158797]\n",
      " [-0.08323265]\n",
      " [ 0.10408915]\n",
      " [-0.01060673]]\n"
     ]
    }
   ],
   "source": [
    "print(y_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19",
   "metadata": {},
   "source": [
    "As mentioned earlier, the covariance matrix is block-diagonal in structure with the $i^\\mathrm{th}$ entry in `x_data` corresponding to the $i\\mathrm{th}$ row or column in `noise_cov_mat`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8.37603984e-09 -2.19201971e-08  0.00000000e+00  0.00000000e+00]\n",
      " [-2.19201971e-08  1.28356006e-06  0.00000000e+00  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  9.95237242e-10 -7.64728604e-10]\n",
      " [ 0.00000000e+00  0.00000000e+00 -7.64728604e-10  2.31726047e-08]]\n"
     ]
    }
   ],
   "source": [
    "print(noise_cov_mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21",
   "metadata": {},
   "source": [
    "These these inputs, as a tuple or list, are the required input to {func}`active_utils.create_base_GP_model`. A number of keyword arguments allow adjustment of the model further.\n",
    "\n",
    "A specific kernel satisfying the {class}`~gp_models.DerivativeKernel` class can be specified, with the default being the RBF kernel.\n",
    "\n",
    "A mean function can be provided, which should be a callable that takes input locations and produces mean function values. By default, a constant mean function set to the average of the zeroth-order output is used if there are two data points, while more data points are fit to a linear mean function, again fit to the zeroth-order data.\n",
    "\n",
    "Keyword arguments to the likelihood model can also be specified. The likelihood model used by the {class}`~gp_models.HeteroscedasticGPR` class, which is the output of {func}`active_utils.create_base_GP_model`, is described in detail in the manuscript. Briefly, we model the logarithm of each entry in the covariance matrix as $\\ln \\Sigma_{\\mathrm{n}, i, j} = \\ln \\Sigma_{\\mathrm{n, raw}, i, j} + p (d_i + d_j + 2) + s$, where $\\Sigma_\\mathrm{n, raw}$ is the raw input noise covariance matrix, $d_i$ is the derivative order of data point $i$, and $p$ and $s$ are tunable parameters. Allow the noise do be scaled by a constant or scaled according to the derivative order to account for inaccurate uncertainty estimates. This is most useful in the case that some derivative orders are much more uncertain than others, with the accuracy of the uncertainty estimates changing in a correlated fashion. This is typical with increasing derivative order for molecular simulation data, so by default $p>0$ but able to be optimized and $s=0$. In what is below, however, we also constrain $p$ to be zero to only look at the behavior of the kernel without considering the likelihood model, which allows better comparisons of different kernels and makes GPR more comparable to polynomial interpolation (we will do these things shortly)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_gp = active_utils.create_base_GP_model(\n",
    "    (x_data, y_data, noise_cov_mat),\n",
    "    kernel=active_utils.RBFDerivKernel,\n",
    "    mean_func=None,\n",
    "    likelihood_kwargs={\"p\": 0.0, \"transform_p\": None, \"constrain_p\": True},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23",
   "metadata": {},
   "source": [
    "We can take a look at our GP model taking advantage of automated printing in a Jupyter notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<thermoextrap.gpr_active.gp_models.HeteroscedasticGPR object at 0x29fa3eb60>\n",
       "<table>\n",
       "<thead>\n",
       "<tr><th>name                                     </th><th>class    </th><th>transform  </th><th>prior  </th><th>trainable  </th><th>shape  </th><th>dtype  </th><th style=\"text-align: right;\">  value</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>HeteroscedasticGPR.kernel.kernel.var     </td><td>Parameter</td><td>Softplus   </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td style=\"text-align: right;\">      1</td></tr>\n",
       "<tr><td>HeteroscedasticGPR.kernel.kernel.l       </td><td>Parameter</td><td>Softplus   </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td style=\"text-align: right;\">      1</td></tr>\n",
       "<tr><td>HeteroscedasticGPR.likelihood.power_scale</td><td>Parameter</td><td>Identity   </td><td>       </td><td>False      </td><td>()     </td><td>float64</td><td style=\"text-align: right;\">      0</td></tr>\n",
       "<tr><td>HeteroscedasticGPR.likelihood.power_add  </td><td>Parameter</td><td>Identity   </td><td>       </td><td>False      </td><td>()     </td><td>float64</td><td style=\"text-align: right;\">      0</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<thermoextrap.gpr_active.gp_models.HeteroscedasticGPR object at 0x29fa3eb60>\n",
       "╒═══════════════════════════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤═════════╕\n",
       "│ name                                      │ class     │ transform   │ prior   │ trainable   │ shape   │ dtype   │   value │\n",
       "╞═══════════════════════════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪═════════╡\n",
       "│ HeteroscedasticGPR.kernel.kernel.var      │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │       1 │\n",
       "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────┤\n",
       "│ HeteroscedasticGPR.kernel.kernel.l        │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │       1 │\n",
       "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────┤\n",
       "│ HeteroscedasticGPR.likelihood.power_scale │ Parameter │ Identity    │         │ False       │ ()      │ float64 │       0 │\n",
       "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────┤\n",
       "│ HeteroscedasticGPR.likelihood.power_add   │ Parameter │ Identity    │         │ False       │ ()      │ float64 │       0 │\n",
       "╘═══════════════════════════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧═════════╛"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_gp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25",
   "metadata": {},
   "source": [
    "Two parameters are adjustable (the kernel variance and length scale) and start with default values of 1, while the other two parameters ($p$ and $s$) are set to zero and not trainable. Note that no training has yet taken place. We now must train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26",
   "metadata": {},
   "outputs": [],
   "source": [
    "active_utils.train_GPR(this_gp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27",
   "metadata": {},
   "source": [
    "And we can examine the model now that it's trained:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;thermoextrap.gpr_active.gp_models.HeteroscedasticGPR object at 0x29fa3eb60&gt;\n",
       "<table>\n",
       "<thead>\n",
       "<tr><th>name                                     </th><th>class    </th><th>transform  </th><th>prior  </th><th>trainable  </th><th>shape  </th><th>dtype  </th><th style=\"text-align: right;\">  value</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>HeteroscedasticGPR.kernel.kernel.var     </td><td>Parameter</td><td>Softplus   </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td style=\"text-align: right;\">1.48656</td></tr>\n",
       "<tr><td>HeteroscedasticGPR.kernel.kernel.l       </td><td>Parameter</td><td>Softplus   </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td style=\"text-align: right;\">4.59991</td></tr>\n",
       "<tr><td>HeteroscedasticGPR.likelihood.power_scale</td><td>Parameter</td><td>Identity   </td><td>       </td><td>False      </td><td>()     </td><td>float64</td><td style=\"text-align: right;\">0      </td></tr>\n",
       "<tr><td>HeteroscedasticGPR.likelihood.power_add  </td><td>Parameter</td><td>Identity   </td><td>       </td><td>False      </td><td>()     </td><td>float64</td><td style=\"text-align: right;\">0      </td></tr>\n",
       "</tbody>\n",
       "</table>"
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      "text/plain": [
       "<thermoextrap.gpr_active.gp_models.HeteroscedasticGPR object at 0x29fa3eb60>\n",
       "╒═══════════════════════════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤═════════╕\n",
       "│ name                                      │ class     │ transform   │ prior   │ trainable   │ shape   │ dtype   │   value │\n",
       "╞═══════════════════════════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪═════════╡\n",
       "│ HeteroscedasticGPR.kernel.kernel.var      │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 1.48656 │\n",
       "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────┤\n",
       "│ HeteroscedasticGPR.kernel.kernel.l        │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 4.59991 │\n",
       "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────┤\n",
       "│ HeteroscedasticGPR.likelihood.power_scale │ Parameter │ Identity    │         │ False       │ ()      │ float64 │ 0       │\n",
       "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────┤\n",
       "│ HeteroscedasticGPR.likelihood.power_add   │ Parameter │ Identity    │         │ False       │ ()      │ float64 │ 0       │\n",
       "╘═══════════════════════════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧═════════╛"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_gp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29",
   "metadata": {},
   "source": [
    "By specifying some $\\beta$ values to make predictions at, we can look at the behavior of the GP model. Note that we are making predictions for the zeroth-order derivatives, or the values of the average particle location. We accomplish this by appending a column of zeros to our $\\beta$ values, though any derivative order could be predicted, even those not used for training, by appending a different value, or set of values, in this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_betas = np.linspace(beta_list[0], beta_list[-1], 100)\n",
    "\n",
    "# Make predictions using predict_f (see GPflow)\n",
    "gp_mu, gp_var = this_gp.predict_f(np.vstack([test_betas, np.zeros_like(test_betas)]).T)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31",
   "metadata": {},
   "source": [
    "All predictions will be Tensorflow `Tensor` objects, so it's a bit easier for plotting to convert to numpy arrays. Further, we want to plot 95\\% confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32",
   "metadata": {},
   "outputs": [],
   "source": [
    "gp_mu = gp_mu.numpy()\n",
    "gp_std = np.sqrt(gp_var.numpy())\n",
    "gp_qs = np.concatenate([gp_mu - 2.0 * gp_std, gp_mu + 2.0 * gp_std], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 345x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(1.15, 1.8), dpi=300)\n",
    "\n",
    "ax.plot(test_betas, gp_mu, label=\"GPR, RBF\")\n",
    "ax.fill_between(test_betas, gp_qs[:, 0], gp_qs[:, 1], alpha=0.2)\n",
    "\n",
    "ax.plot(test_betas, idealgas.x_ave(test_betas), \"k--\", linewidth=0.75)\n",
    "\n",
    "ax.xaxis.set_major_locator(MaxNLocator(prune=\"both\", nbins=3))\n",
    "ax.tick_params(axis=\"both\", labelsize=8)\n",
    "\n",
    "ax.set_ylabel(r\"$\\langle x \\rangle$\", fontsize=8)\n",
    "ax.set_xlabel(r\"$\\beta$\", fontsize=8)\n",
    "\n",
    "ax.set_xlim((-0.3, 10.0))\n",
    "ax.set_ylim((0.05, 0.55))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34",
   "metadata": {},
   "source": [
    "## Compare kernels and polynomial interpolation\n",
    "\n",
    "There are two key differences to take not of when comparing GPR to polynomial interpolation:\n",
    "  1. No specific functional form is assumed. With polynomial interpolation, you assume (at first derivative order) that a 3rd order polynomial is the exactly correct functional form and compute uncertainty within that assumption. A GP model with an RBF kernel is a universal function approximator, so no specific form is assumed, other than the idea that the function should be smooth on an inferred lengthscale. This leads to greater uncertainty with lower order derivatives because the model is not restricted to any specific order polynomial at any order.\n",
    "  2. Uncertainties are taken into account when fitting the model. Polynomial interpolation does not weight input data, such as different orders of derivatives, by their uncertainty when using that data for model fitting. GP models can be used for polynomial interpolation and weight information by its uncertainty by using a polynomial kernel. Using an RBF kernel, however, takes us a step further and saves us from needing to select a specific polynomial order, and is infinitely differentiable. Due to its weighting of provided training information by uncertainty, a GP can utilize much higher derivative order information, even becoming more certain in its model fit with high derivative order data.\n",
    "\n",
    "To examine these differences, we plot, as in Figure 1 of the paper, the results for GPR and polynomial interpolation using only zeroth, first, and third order derivative information. The code here is long because we want to use the same `State` objects, and thus the exact same data, for all models at every order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fq84N+em+ffr06WyFyh5++GGXF08TuZOkWggPUb16dV588UXb8uDBg/nkk09yjCU6fvw4Dz74IMePHwcgODiY8ePHOy2O4cOHEx4eDli7Jj344IMcO3Ys2zZGo5HXXnuN2bNn4+Pjc8t9tmzZkueee8623LdvX0aOHOnwApSens6KFSt44oknePzxxwvx0xTO5MmTbWPWNm3aRO/evXMUWFm/fj3du3e3LT/77LPUrVu3WOMU3k3ODZnc5dwghDuQc0Mmdzg3dOjQgX79+rFp0yaHD+FNJhPffvst99xzDzExMYB1/vDJkycXZ6iiAKT6txAe5P3332f79u1s27aNjIwMBg8ezDvvvMO9995LYGAgx48f56+//sJsNgOg0+n44osvqFq1qtNiKF26NHPnzuXJJ5/EbDaza9cu6tSpQ8uWLalWrRoJCQmsX7+ey5cvo9frmTJlSran347MmTOH6OhoVq9ejaqqvP/++8ycOZOmTZtSvXp1DAYD8fHxHD9+nH379pGWlgbAnXfe6bSfLb/q1KnDZ599Rv/+/QH46quv+PXXX2nTpg1BQUHs378/2/i0hg0b8tFHH7kqXOHF5NzgXueG7du3284LWWWdX/eNN97gww8/zLa+SZMmzJs3r6jDEyWInBvc59xgMpmYP38+8+fPJyQkhNtvv50KFSoQHBxMWloaZ8+eZceOHcTFxdm+o9PpWLx4sRQ39QSqEMKjJCYmqt26dVOBXF9ly5ZVf//991z3NWHCBNv2EyZMyFcc33zzjRoYGOjw+EFBQeqPP/6orl+/3vZZy5Ytc91nRkaGOn78eNXf3/+WPx+g6vV6ddCgQXb31bJlS9t269evz9fPll9z585VAwICco31gQceUC9cuFCkcYiSTc4N7nNuyPqz5ed1q9+DEAUh5wb3ODfcdddd+TofNGrUSP3nn3+cHocoGtJSLYSHCQwMZOnSpQwdOpRFixaxYcMGLly4QGpqKhEREdSvX59HH32Uvn375mlOyoLq3r079957LzNnzuS3337jzJkz6HQ6KlasyMMPP8yAAQOoWrVqnipj36DVannzzTcZMmQIX331FWvXruXAgQNcuXIFk8lEcHAwlStXpkGDBrRu3ZqHH34423QdrtK/f38efPBBvvjiC3755RfOnDlDUlISZcuWpXHjxvTs2ZNOnTqhKIqrQxVeTM4N7nduEMIdyLnBPc4Nf//9N//73//YuHEj27dv5+jRo0RHR5OcnIyvry8hISHUqFGDJk2a0LlzZ+69916XxCkKRlHVW5QCFEIIIYQQQgghhF1SqEwIIYQQQgghhCggSaqFEEIIIYQQQogCkqRaCCGEEEIIIYQoIEmqhRBCCCGEEEKIApKkWgghhBBCCCGEKCBJqoUQQgghhBBCiAKSpFoIIYQQQgghhCggSaqFEEIIIYQQQogCkqRaCCGEEEIIIYQoIEmqhRBCCCGEEEKIApKkWgghhBBCCCGEKCBJqoUQQgghhBBCiAKSpFoIIYQQQgghhCggSaqFEEIIIYQQQogCkqRaCCGEEEIIIYQoIEmqhRBCCCGEEEKIApKkWgghhBBCCCGEKCBJqoUQQgghhBBCiAKSpFoIIYQQQgghhCggSaqFEEIIIYQQQogCkqRaCCGEEEIIIYQoIEmqhRBCCCGEEEKIAtK5OgBRssXFxbFx40bbcsWKFfH19XVhREIId3D58mVWr15tW27fvj0REREujEgI4Q7k3CCEsCc9PZ2zZ8/allu2bEloaGixHV9RVVUttqMJcZMVK1bQqVMnV4chhBBCCCGE8BLLly+nY8eOxXY86f4thBBCCCGEEEIUkCTVQgghhBBCCCFEAcmYauFSFStWzLa8fPlyatSokfcdJJ0Gc4qToxKiJNBASG1XB+HQqlWrePXVV23L+T43CCG8kjecG4xXYjBevezqMISTKRotATXquDqMEuvYsWPZhpTenGMUNUmqhUvdXJSsRo0a1KtXL+87iAMsRqfGJESJEVIDtO5ZGPDYsWPZlvN9bhBCeCVvODckH9dhLhXs6jBEEQisXRuNTtIrd1DchY+l+7fwXKoqCbUQhWFOdXUEQghRoqgWC5bUZFeHIYqIJT3N1SEIF5GkWnguSaiFKBxJqoUQoliZk5OQiXe8lyTVJZck1cJzWdJdHYEQni1D6hEIIURxykhKcHUIoghJUl1ySVItPJfF5OoIhPBsZrn4CyFEcTInJ7o6BFGEJKkuuSSpFp7LLC3VQhSKxQiWDFdHIYQQJYIlIwNzqvQQ8maSVJdcklQLzyVjqoUoPJmSTgghioW0Uns/1WSUMfMllCTVwnNJUi1E4UkXcCGEKBbmJEmqvZ2qqtJaXUJJUi08lyTVQhSeFCsTQohiIUXKSgZJqksmSaqFZ1ItUqhMCGeQKvpCCFHkLEYjFqOcb0sCSapLJkmqhWeSVmohnEO6fwshRJGTVuqSQ5LqkkmSauGZJKkWwjlUi1TSF0KIImaWpLrEkKS6ZJKkWngm6fothPOYU10dgRBCeC1VVclIlKS6pLCkSVJdEklSLTyTtFQL4TzSBVwIIYqMOSUZ1WJ2dRiimKiqBYtR7lNLGkmqhWeSpFoI55GkWgghiox0/S55pAt4ySNJtfBM0v1bCOeRpFoIIYpMRmK8q0MQxUyS6pJHkmrhmaSwkhDOY0kDVXV1FEII4XUsGRmYU1NcHYYoZpJUlzySVAvPpEpLtRBOo6oyX7UQQhQBs7RSl0iSVJc8klQLz2M2SquaEM4mFcCFEMLpZH7qkkmS6pJHkmrheaRImRDOJ0MqhBDCqWQqrZLLkmFCNUvF95JEkmrheSSpFsL5pKVaCAF8+eWXKIpClSpVXB2KxzMnJaKaM1wdhnARaa0uWSSpFp5HKn8L4XzSUi3cjMVi4aeffqJv377UrVuX8PBw9Ho9YWFh1K9fn169erF48WISEuy3BG7YsAFFUXK89Ho9pUuXplWrVkyfPp2kpKQc3z116pTd72q1WkJDQ2nSpAmjR4/mzJkzRfo7aNWqld04AgMDqVWrFr1792bLli1FGoMoOFP8NVeHIFzInCYPq0sSSaqF55GWaiGcTyqACzeydetW6tatS+fOnVmwYAEHDx4kPj6e4OBgUlNT2b9/P19//TU9e/akYsWKzJgxI9f9hYWFERkZSWRkJP7+/ly5coWNGzfy6quv0qhRI06dOuXwu8HBwbbvhoaGEh8fz44dO5g2bRp169bljz/+cPJPn5Ner7fFEBkZSVpaGkePHmXhwoU0b96cSZMmFXkMIn9UVSUjIc7VYQgXkpbqkkWSauF5JKkWwvmkArhwE8uXL+f+++/n8OHDhIeHM3nyZP777z9MJhOxsbGkpaVx6dIlfvjhBzp27EhSUhJLly7NdZ/Lli3j4sWLXLx4kfj4eKKjoxk6dCgAx48fp1u3bg6/+9FHH9m+GxsbS3JyMgsWLCA0NJTk5GS6d+/O1atXnfkryKF58+a2GC5evEhKSgqrV6+mRo0aqKrKxIkT+e2334o0BpE/5uQk6fpdwklSXbJIUi08jyTVQhQNs9wACNc6dOgQvXr1wmg00rBhQ/bu3cu4ceOoV68eiqLYtitTpgxdunRh+fLl7N27l3vuuSdfx4mKimLGjBn07NkTgG3btrF169Y8fdff35/evXszc+ZMAOLj4/nhhx/ydfzC8vHxoV27dqxYsQIfHx8AZs2aVawxiNxlSNfvEk+S6pJFkmrhMVRVJSbRJEm1EEXFw8dVpyYUbWuhKHrjxo0jKSmJgIAAfvrpJ8qVK3fL79SrV++W3b8d6dWrl+39tm3b8vXdDh062N7v37+/QMcvrLp163LnnXcC9uPfsGEDTz75JOXLl8fX15eIiAgeeOABFixYgDmflYmbNWuGoigMHDgw1+3+/PNPFEVBo9Fw4sSJfB2jqLji3CBdv4XFmI5qsbg6DFFMdK4OQIhbUi2w/23+jq3K5wcr8cq9QTS59X2WECK/PLT797ULpzgwsTvmzduo98tewqvWdXVIxSrDonI5yXXdTEsH6tBplFtveAvR0dEsW7YMsCa71apVK/Q+b6VChQq2944KnuVFfhNUZ7rxM9wc//Dhw20PGxRFISQkhLi4ONatW8e6dev4+uuvWb58OUFBQXk6zoABA9i6dSuLFy/m/fffx9/f3+52c+fOBaBt27bF8v8wNzFH93B0Ug8sB47SZEM0huBSxXLcjOQkLBlSVFVYE2utn8HVYYhiIEm1cH9JJ+DANO4D7isF1/aV4sqpOkRE1YGy7cBQ1tURCuEdPKylOi0xjt1vPUv6z79jTrG2Bhya1IMWX+5ycWTF63JSBt0XnHLZ8b/tU4WywfpC72f9+vWo14vlPf7444XeX15kLVBWqlT+Eq6sBcpcmTze+Bmyxj9r1ixbQv3CCy8wadIkoqKiSE5OZu7cuYwcOZJ169bx/PPPs2TJkjwd56mnnmL48OFcu3aNpUuX0qdPnxzbXLlyhZ9++gmAF198sZA/WcElXr7Avjd7YFz1Fxaj9d/U7jd7cs/7vxfL8U3XYovlOML9WdJSJakuIaT7t3B7Rw//k205THuViJTNcGI+bO4Jxz6XOXaFcAaL54z/2jlrONvuL0PKkl9tCTWAcdMezm5f58LIREEdOHDA9r5Ro0bFcsxPP/3U9r5Zs2Z5+k5KSgoLFy7klVdeAcDX15fu3bsXSXy38u+//7Jjxw4gM/7U1FQmTJgAQPfu3ZkzZw5RUVEABAQEMHToUKZPnw7A0qVL2b59e56OZTAYeO655wD4/PPP7W6zcOFCjEYjkZGRxfZg5Gb/vvUcu9pUJu2XjbaEGsD46yqunj5c5MdXLRYZTy1sZFx1ySFJtXBr+6NT+e+//9mWE1NVpvyYztWk6xdK1QynvoF/esO1va4JUghvYcmwvjxA2okDmK5lxhpvVlkYp5KeoXLmrb4ujEwUVGxsZuueo1bjY8eOERUVZfe1efPmPB0nLS2NPXv28Mwzz9gqZrdu3dphIv/KK6/YjhEeHk5AQAC9e/cmLi4OvV7PwoULKVu2eHtMXbhwgUWLFtGxY0csFguKotiqma9Zs8ZWjXzixIl2vz9w4EBbzN9++22ejztgwAAAtmzZwr59+3KsnzdvHgB9+/ZFry9874WCMB3ZTUZiZnf8yxkqX8WpmFItHJz4TJEfPyMhDtXiuuEAwr1IUl1ySPdv4dZWHkjgQZ21AMz89UZe+yadywkqCakq03r6ZW6Ydgl2vQoNp0DE3S6KVggvYE4DTaCro7ilRhO+Zsef5TDFZ7A0XmVxPCSrEKiBLrvOcOjXL6nzaG9XhymcLCMjg0uXLtldZzQ6LmLZunVrh+vuuOOOXBPLhIQEu+OtK1WqxKpVq6hTp04uETvHxo0bs1U/z0qv1zN9+nRatWoFYGt5rlixIrVq1bL7Ha1WS5s2bVi8eHGeW6oBateuTevWrVm/fj1z5861VUAH2LRpE4cOHUJRFPr375/nfTpb7Te+Yf/DDTGlWZgfBz8mglGFSnpo/dcuzu1cT4XGjv89FJZ0/RZZSVJdckhLtXBrw1qVoo7PEQAuxqlcTrC2UM9caeTMlZsqKlpMsHccXMnbtChCCDs8pLq+f0gEvk/1AOCS2ZpQA3wdD0kWlSvTX8UiVVc9Snh4uO29o3mf69Spg6qqttfJkyfztO+wsDAiIyOJjIykfPny1K1bl65du7Jo0SK2bt1KZGSkw+8uWLDAdrz4+HjWr19PixYtOHPmDH369CEpKSl/P2gB6PV6W/xRUVFUrVqVFi1aMHLkSP777z8GDx5s2zYmJgaA8uXL57rPGwXObmyfVzdaqxctWkRqaubQqxtdwtu1a+fSMeYR1euhf6g1GkXhXIY1oQb4Ig6MGSqnJxddTxaL0UhGUsEL3gnvY0lPs9WKEN5Nkmrh1jQZiegqPkKCrgpDH/ahbJj1SX26CSZ8Z6eo0o3EWrqCC1EwHjRXdeMRs/GJ8qNXCBiuN+IlWGBpPBhPXGPP/MmuDVDkS926mVXbd+/e7dR9L1u2jIsXL3Lx4kXOnTvH/v37+f777+nZs2e+uikHBwfTqlUrVq9eTb169diyZUu2hLaoNG/e3BZ/dHQ0J06c4O+//2batGkOW6MdtWwXdLsbnnjiCaKiooiLi+P7778HIC4uzjZX9wsvvJCv/RWF29/4Bl2wln6hmTe6FzLgtyRI33Waw78vLJLjmuKklVpkp6oqlnTPKgIqCka6fwv35lsK5Z75BAOr9pym/sNvEr14PgAL/zIx/FEfGlTSZv+OxQT/vQl3zwOf0GIPWQiP5kHFynQ+vgT1H4FxyhS6BassjLd+/mMiPB6kop87DVOv19D7+ro20CJWOlDHt32quPT4ztC6dWsURUFVVX7++Wceeughp+y3KPj7+/Pxxx/Tpk0bFi5cyAsvvEDz5s1dHRYAZcqUAeDs2bO5bnfu3DkASpcuna/96/V6+vbty9SpU5k7dy7PPvssixYtIi0tjaioKJcVKMsqoFQZfLs9Q8V5i3g4UOXX650JFsXBgwEql98bRs32PdFotbnuJz9UVcV09YrT9ie8hyU9Da2f3603FB5NWqqFx2h/e2VeH/8BAZHWbmWqCq994+DpX/oV2P+OdSMhRN552LRaDfpMwLd6KE8GQ9j1K1q6Cl/Fg+lSKtvfG+jaAIuBTqNQNljvspcz5qgGKFu2LJ07dwasXYvz2rXbVVq3bk3Lli0BGD16tIujydSkSRPAmjQfOXLE7jZms5n169cD0LRp03wf44UXXkCj0fD3339z8OBB29zUffr0cVmBsps1HjkHn0g/ng0Bv+v/ROMs8F0CGE/Gsefz8U49XkbcVSwmzxg+I4qXjKsuGSSpFh6lZe1Q3npjqG35910ZbNjvoFpx7BY480PxBCaEt7Cke9TDKI1WS8SwaRg0Cs+GZn6+MglOGVVMSxeReFW6ZHqKKVOmEBAQQHJyMp06deLChQuuDilXr7/+OgB///03a9ascXE0Vu3atbONT3dU/XvOnDm2321BpgOrXLmyrSfBgAED2LdvH4qi8Pzzzxcs6CKg9/Uj4PlXCdcpdA3O/Pz7BLhqVkmaNx1jsvPGw6dfvui0fQnvIkl1ySBJtfA4L/doz52Nb7ctv7jI4LAIhOXoHEg+U1yhCeH5VNVjipXdUPvR/hjuqMDDgVDhek9kC9bCRBmJGeya2NOV4Yl8qFOnDl9//TU+Pj7s3buXhg0bMmXKFPbv35/tPJ+QkMDKlSsZMmSIC6O1JrA3WnrHj8/Z8vnll1+iKAqKorBhw4ZiiclgMNiS6W+//ZYBAwbYKqanpKTw8ccf26bfeuqpp7jzzjsLdJwbBcv++usvwPq7qFq1auGCd7Lb+0zEt0YY3YIh5Podb5oKX8WBKdbIrrd6O+U4prirkjgJhyzpqbfeSHg8SaqFx1G0Psx8a6ht+cjJWD7+O8LuthoyuLxrlke1vAnhch5UrOyGSuPmodcp9AvN/GxzKuxLU7GsXE300UMui03kT6dOndi4cSO1a9cmNjaW8ePHU79+ffR6PREREYSEhBASEsJDDz3Er7/+SlBQEJMnT6ZZs2YuiXfs2LEAbN261TbvtasNHjyYYcOGAdZW6bJly1KqVClCQkJ4+eWXMZlMtG7d2tZtuyAefvhhKleubFt2hwJlN9NotUSM+ogArUKvkMzPf0uCsyaVtGXLiT93otDHMUortciFJc3zrqki/ySpFp5Ho6f5XbfT6eHMeSbHLUkn3mi/GFHptH/ZsH2L5NVC5JXFs8ZVA5S/80H8WzXgPn+4zSfz8znXwJxu4dAbPVwXnMi3Zs2aceDAAX788Ud69+5NnTp1CA4OJj4+Ho1Gw2233UaPHj1YuHAh0dHRjBs3Dj8XFQLq2LEj9evXB+CNN97Itu78+fMABAYGUq9evWKNa/r06axbt44uXboQGRlJUlISQUFBtG7dmvnz57NmzRqCgoIKvH+NRmMbA+8uBcrsqf1gT/zuqs6jQVDupp4s5jQL+17rWqj9m+KuYk6TlkjhmKpasBg9qweYyD+p/i08j8Z6x/z2+MH8suovzGYzqtnEpGO9mF53nt2v1I79mE//bcZLdyk4qaaOEN7Lw4qV3VB7wtfs3dyIF8IsDLP2diXOArFmiNiyi4N//clt9z/g2iBFnt1I2m4kbvnVqlWrAs8PW6VKlTx/V1EU9u3bZ3fdjWJgQ4cOzXeVbaDQXcZbt25N69atb71hFr1796Z379552nbt2rWAexUos6fyG4s49kRz+oaqTLleoPucCVItKvxvN2c3/UbF+x7J935Vs5n06HNOjlZ4I0t6Ghofn1tvKDyWtFQLz6OxXrjr1KrKsJeeYcrYgZzbtZwyd/Zln/F2u18pq7uA34WlTN7ki9FcnMEK4YE8sKUaILRSAwIeb0VDP4UOATA4DBaUgwidAipET+6LxWJxdZiihEhPT2fz5s2UKlWKESNGuDocp9uwYQP79u1Do9G4ZdfvrMrXa4ZPh3tp6Q/3GmBkOMwpCwaN9dxwcnxf1AKcG9IvnseSYSqCiIW3sUhvBq8nSbXwPJrMJ33vTRrG66/2JyTIwOjmRo5EDMWi2m+Kfirga7adSeW1dX4kSy8cIRzz0JZqgPpjF+MbrmVkhMITwQp6Jcv54OhZtn79heuCEyXKli1bSE1NZdSoUYSEhNz6Cx4kJibGVuysa9euVKlSxaXx5EXdCUswROiZVEahQ6CCNsu5IePUZf6b/Wa+9mdOScZ49bKzwxReyiyF7LyeJNXC82jsd59RFOhyV1VOBna0uz5Qk8STAd+w86KOoWsMXE2VfuBC2GUxemxxP9+QKEJ7PeVwferHo0hPk6dqoui1bNkSVVXdag7rwnr66aepWLEi5cuXZ8+ePQQFBfHOO++4Oqw8CQkvh77nsw7Xx37yNsarV/K0L0tGBqnnTjkpMlESSEu195OkWngejQ4Ux/90y9frgUkTaHdd14BvCVbiOHpVy+CVBs4nSmIthF0e2gUc4LaBswmsmrNolVFV4XI8f00b54KohPB8Fy9e5Ny5cwQEBNC2bVs2bNjgdtNo5eaOQZ8QUCvn/YFRVbEkm9gx6plb7kO1WEg9dUym0BL5Iv9evJ8k1cIzaXIWRElLS+e9jxdSsclTHFPb2v1agCaZboGLAbiQpGHwSgNHYuXPQIgcPLgLuMYnkDIvvWJbTraozL+m0v0cXDWraJfMJPZi3lqkhBCZNmzYgKqqxMXFsWbNGho3buzqkPJF7+OLYchbKFrrcpxZZdZVld7nrUXLUteuJXbrBoffV1WV1DMnMKcmF0/AwmuoFjMWk4y/92aSTQjPZKcL+KPdX2HUxI+4EhvHmPknQB9s96td/JcQrMQBcC1NwyurDeyI1hZltEJ4Hotnd5Gu0vVNwhqHYFFVhlyExQnWSuCL4oAUI/+MG+DqEIUQLtDgkUEEtaqCUVXpfwF+SoRLZvgxwbp+3/Bn7E5/ZDEaSTlxhIzE+GKOWHgL6QLu3SSpFp7JTkv1gD6Zc02uWPk3m662sPtVgyaVjgE/2JZTMxRGr/Nj/SmZYU4IG7OHd1XT6Kn46lR0PgqPZunt+WsSnDWp+K/7iSM7/3NdfEIIl1A0GiJGfEVwhIYOWc4NSxLgmlnFcv4iB98eaasGbjEaMV2LJfnoAcwpSS6KWngD6QLu3SSpFp7JTkt1l8ce4O4769uWR366D1Vnv7X6Cf/v8CGze2uGReHNTb4sO+S+82wKUaw8vKUaIKL5i5RuU57HgqDs9WdmFmDuNcCicnhc7wLPYyyE8FxVbrsXn65teToEgq/fCaeq8FWc9f2lhbOI+fV7Eg/sIenwPlLPnUK1yHyconAkqfZuklQLz2QnqVYUhfcmDbUtb915gB+ONrL79VLaq7Qz/JHtMxWFmdt8mbfLx1MLHwvhPB5cqMxG0VBtxCcEhGjoH5r58f9SYV+aSsCBnfy9dLmrohNCuFC1F+ZRuZEfPbPMdvbb9Z4sWFSOfDBWpkESTmWW7t9eTZJq4ZnsdP8GuO+exnR8qJVteczsnRgtvna37Rb4NQqWHJ9//Z8P7/3jS0bOVUKUHB48rVZWhhqPUbZTXVr6Q+0sz+LmXLMWHUr8YLBMsSVECVQqvCLmZ4fQJVKx9WQxA1/EWd+bTp8keul8V4UnvJC0VHs3SaqFZ3IwVzXAO28MQau1Fh47fuoCs7dWs7tdZd0pmvn+z+6634/reWOjH2kZhQ9VCI+kql7RBRxFoeqgOYRU0jIgLPPjg0b4KwX8Lkezatr7rotPCOEy9R6bQMWOUfQLzfxsUwr8l2Z9oBj93RckHdrrmuCE11HNGVgy5MbSW0lSLTyT4njsc51aVXm+1xO25TcXHiQ+xf581F0CvnW4n83ndIxYayDBC3rBClEg3tAFHNBENqdCr/tp6KfQ3JD5+bw4MKkqft+8xaWz0S6LTwjhGr6+ARifeJeuTXTZe7LEWXuyYLFw4v03MKfIFFrCOaQCuPeSpFp4Jo0W20STdkwY9QIBAda759hrCbyzpozd7Zr4/ktl3UmH+/nvspaXVxmISbaflAvh1cxe0FJ9XVS3j4lsrKd/WOaF70IG/JoIurRUNrw+0qXxiZwsFgs//fQTffv2pW7duoSHh6PX6wkLC6N+/fr06tWLxYsXk5CQYPf7GzZsQFGUHC+9Xk/p0qVp1aoV06dPJykpZ0XnU6dO2f2uVqslNDSUJk2aMHr0aM6cOVOkv4NWrVrZjSMgIIDq1avz9NNPs2rVqlz38eWXX9rdh6IoGAwGqlSpQrdu3fjjjz8c7sPR78Pe68svv3Tyb6Fo1W3aA//OjRkYlfnZgXRrizWA8dJ5zsx5T4oaCqeQLuDeS5Jq4bkcjKsGiIqMYNSQ52zLH/54irNX7A+SfrvmEny0ji+Wp+K1DFpp4FScJNaihLF40cU/tB4V+3Smqr/Cw1mm0fkqHpIsKhF/fcPev7a6Lj6RzdatW6lbty6dO3dmwYIFHDx4kPj4eIKDg0lNTWX//v18/fXX9OzZk4oVKzJjxoxc9xcWFkZkZCSRkZH4+/tz5coVNm7cyKuvvkqjRo04deqUw+8GBwfbvhsaGkp8fDw7duxg2rRp1K1bN9dk1Fn0er0thsjISIxGIydOnGDp0qV06NCB559/Pk9JX0REhG0fZcqUISMjg9OnT/P999/z8MMP07dv31vuI+vvw97LYDDcch/uRNFo8G09g6d7GbgnS+hz46w9WQBi1/3GlTU/uyZA4VWkpdp7SVItPFcu46oBXh3Yi7KREZQKC+HtN16mTIU6drcrl/gH01vHEujj+IbkcoqGIav8+S9G/mRECWL2ju7fNwS3fptKDxh4LhT8FAjXwgthYFBAAQ5PGIzZIhUKXW358uXcf//9HD58mPDwcCZPnsx///2HyWQiNjaWtLQ0Ll26xA8//EDHjh1JSkpi6dKlue5z2bJlXLx4kYsXLxIfH090dDRDhw4F4Pjx43Tr1s3hdz/66CPbd2NjY0lOTmbBggWEhoaSnJxM9+7duXr1qjN/BTk0b97cFsPFixdJS0tj+/bt3HfffQDMmzeP+fNvXVRr27Zttn1cunSJ9PR0du/eTbt27QBYsGAB337reFgUZP992Hs99dRThf+Bi1mlGs253PRJxrTQoQHK6aB/KOiybHNm9jRSThx2UYTCW0hLtfeSDEF4rlxaqgECAgz89NUHHNu+gqEDeuBbrYv9Dc0p1DevYuaDqUT4O76hTjQqvLrWwOazjrudC+FVvKFQWVaBVaj47POUK63h7TKwsBw8FKigVay9UMJP7mDtZwtcHGTJdujQIXr16oXRaKRhw4bs3buXcePGUa9ePRQls7dQmTJl6NKlC8uXL2fv3r3cc889+TpOVFQUM2bMoGfPnoA12dy6NW89Ffz9/enduzczZ84EID4+nh9++CFfxy8srVbLnXfeyYoVKwgPDwfgiy++yPd+NBoNt99+Oz/99BMhIda5pZYvX+7MUD1G+fum0KpnGB/WUJhfDloGKNn+zakmI8ffeY2MxHgXRik8nUyr5b0kqRae6xZJNcDdTRoQFhpsXYhsBfoQ+xue/YlqoWY+6ZBKpWDHiXW6WWHcRj/+OKZzuI0QXsNLCpVlpb9jDNUfD6ahn4JBk3NIh/GzscTHxhV/YAKAcePGkZSUREBAAD/99BPlypW75Xfq1at3y+7fjvTq1cv2ftu2bfn6bocOHWzv9+/fX6DjF1ZYWBh33313oWMICAigRo0aAHbHmJcEoaUqcqr8yzz7oj8+OvvDvdKjz3H83TFSwVkUmFQA916SVAvPdYvu3zlofaH8I/bXJZ+C+H1EBqh83CGFuhFmh7uxqArv/uPH4n16b5jGVwjHVBUsJldH4VyGSMo88QphNe33OAlIusyq8W8Uc1CFZMmA5NOue1mcc4MYHR3NsmXLAGuyW62a/ekQnalChQq2944KnuWF2ez4mlHUboylLkwMKSkpHDt2DIDatWs7JS5PVOf+V0mvUIkaj/o63CZxzzbOfv6+FC4TBSbjqr2TNLcJz5WHluqbJYa0Zd5vXzKkgx6d9qYn0ed+hdCGhPjCB21TmbTJjy3nHf+JzN3ty9U0hUFNjNhp8BLCO5jTC/S35s6U24ZSo/PnbH/vIur1jinxZpU/k+GJIAhbOZvD25+ndpMGrg00r1LPw28NXXf8R/ZCQOVC72b9+vW2ROXxxx8v9P7yImuBslKlSuXru1kLlBXHAwB7rl27xr///lvgGFRV5b///mPUqFHEx8djMBgYNGiQs8P0GHofA/G136SG0pf4k2Zi9mZwOUNlayo8GpR5ob/8x4/4lqtIVKceLoxWeCpLehoEBrk6DOFk0lItPFcuc1XbM/erZdS47wWGf5XG/PV2Wt9i1oPJ2lJh0MOUVmm0r5Z7K92Ph3yY8rcvRtc1UghRtLxtXDWATyhBrUZS4V49FlVlabxKr/PwyTXYkgpaSwa7xg7BIkXLitWBAwds7xs1alQsx/z0009t75s1a5an76SkpLBw4UJeeeUVAHx9fenevXuRxOeI2Wxmx44ddOzYkdjYWAD69Olzy+81bdqUqKgo28vX15eGDRuyYcMGOnXqxJYtW6hevXqu+3jllVey7SPr6403PKyXhx01G3XhuLY5VR73ZZERnrsAM67C4fTsLdPnvviQK3/+6qIohSeTlmrvJEm18Fz57P69+d89xFy2Vmgd/106iak3dd2ymCB6jW1Rp4HXmqfTvV7uScW6U3peW+dHipf1khUC8M6kGqDmi1R5pCJ+wRp2pkHy9dPB59cgQ1Upc+Qv1s1f7NoYS5gbySE4bjU+duyYw4Ru8+bNeTpOWloae/bs4ZlnnuG3334DoHXr1g4T+axJZHh4OAEBAfTu3Zu4uDj0ej0LFy6kbNmy+fth82nz5s3ZflY/Pz+aNGnCpk2bAOjSpQuDBw++5X6uXLnCpUuXbC+TyXrhSk9PJy4ujgsXLtxyHwkJCdn2kfVVmC70bkNRCL7nfXwCdGzXKNzIpWdfI0eX71Mzp3Bty0YXBCk8mRQr806SVAvPpdGCkvdK3JPHDMRg8AMgJl7l3RV2ijCd/4WsA6UVBV5sbGTQnbkXbNp5UcfQ1Qaupko/cOFlvLBYGQA6f/R3vkb1h315Icw6pRbAmQz47XqdpuSZr5FwzQuSBC+SkZHhMKEzGh0/AGrdujWKYq3mbDAYaNSokW3qqDvuuCPXaaSyJpFZp86qVKkSe/fuLZYppEwmU7afNeN6oSNFUfjkk0/44Ycf0Otv3Xvr5MmTqKpqe6Wnp3Pw4EFGjhzJX3/9xSOPPJKt9d6eBQsWZNtH1teHH37ojB/X5cqUr8+R4GeZ3tvP9tnedPjfzbmQxcyJd1+TxFrki0yr5Z0kqRaeLR9jPSuUj+TVgT1tyx/8auTslZu6dyafgvicFVSfrGti3L1p6DSOC5Mcuapl8CoDFxIlsRZexOylLdUAVZ8l8r4aNK6lo0Ng5scL4yDJohKUEM1KTyta5sFuTA0FOJz3uU6dOtmSuJMnT+Zp32FhYURGRhIZGUn58uWpW7cuXbt2ZdGiRWzdupXIyEiH382aRMbHx7N+/XpatGjBmTNn6NOnT7FUy27ZsqUtBqPRyNGjRxk5ciQAo0aNYuPGgiV1Pj4+1KlTh3fffZehQ4disVh45ZVXOH78uDPD90jVW03krvqleahRZm2Vz6+B6abWajUjgxPvjObqpjU370IIu1RzBhaTdG/0NpJUC8+Wzy7go4Y8R2QZ641bmgleX2Kvtdr+GKm2VTN4p3UafjrHifWFRA2DVho4elX+tISX8Nbu3wBaHzQNxlGrky99QsHv+vOweAt8e30q2rDfP+PQtn0uC7EkqVu3ru397t27nbrvZcuWcfHiRS5evMi5c+fYv38/33//PT179sxTC+8NwcHBtGrVitWrV1OvXj22bNmSp27XzqTX66lRowbTpk1jwoQJJCcn061bN2JiYgq13/79+wPW3gDff/+9M0L1aIaAMC6UfZX3evraipGez4BfEnNuq5rNnHh/HDG/fle8QQqPJa3V3keqfwvPpsnfP+GgoAAmj3mJF4ZNAWDRJhOvPOzDndWydCO/tAFqDwFdQI7vNyln5sMHUxn9p4H4dPst0tfSNLyy2sCUlmk0LisVzISHsxitQyIUL+2BUakrQbd9yO337+Cp34wsvJ5M/5gAjwWpROlM7B4zkJqrN6LVuOnDMkN5awVuVx7fCW500VZVlZ9//pmHHnrIKfstCv7+/nz88ce0adOGhQsX8sILL9C8efNij2Ps2LEsWrSI48ePM378eObMmVPgfVWunFnBPa89ALxd7RYDOBv9Dc8/sIM5a60ti1/FQ7sAlaCbZxCxWDgz5z3Sos9Sse9QFG3eh6eJkseSlioVwL2Mm94hCJFH+Z2rGujboyP1b6thW351UVr24iOWNLi03uH364Rb+KRDClEBjisDp5gURq/zY/0peW4lvIC3jqsGUDTQYDxVH/TlmXIK4dfvg03AF9es78sc+x9rZ3/pqghvTaOzTmnlqlc+H246UrZsWTp37gzAokWL3D6xa926NS1btgRg9OjRLolBr9czbtw4AL744guOHDlS4H2dO3fO9j4gIOdD5ZJIo9Oj1BjDpCd9Cbw+vDrRAotzKbUQ8/MSjkx8GdO1WMcbiRJPWqq9jyTVwrMVIKnWarV8MHmYbXnjATPLt2Vk3+j8b7nuo0KwyicdUqke5rgl2mRReHOTL8sOedccv6IE8uYu4ABlO6CvcDcNHvOjX2jmx+tS4MD10r/GT8ZwNcb+OF/hPFOmTCEgIIDk5GQ6deqUp2rUrvT6668D8Pfff7NmjWvG1Pbs2ZPKlStjNpuZNGlSgffzzTff2N43adLEGaF5hXK1WnIl7HHGdvK1fbY8Cc6bHA8FS9z9Lwde6UHCnm3FEaLwQFIB3PtIUi08m1KwFpIHW9/DQ21b2JZHfp2GMSPLBTLhICTl3koS7q/y0YOp3B7pOLFWUZi5zZd5u3xQHV9/hXBv3lysDKxd2xtMIKqxjifra6mZ5Vndp1et0+gEJF9m1RjXtEaWJHXq1OHrr7/Gx8eHvXv30rBhQ6ZMmcL+/fuz9ShKSEhg5cqVDBkyxIXRQrt27WjatCkA48ePz7H+yy+/tFUd37BhQ5HEoNPpbEXLlixZkm2+77y4du0aH374IVOnTgWgSpUqth4DArRBwZSp/gL9HwqnUoS1y7fJAot9cr+FNl2L5ci4gZz+5G0ykou+mJ3wLDJXtfeRpFp4tgK0VN/w/qRhaLXWP4FUIxyNvqk794Xfb7mPQB+Y9kAqLStl5Lrd1//58N4WXzIc9xgXwn15c/fvG8rci1K2LXU6+/FSlimSEyxw5fpzs9LrFrBzXd7mQhYF16lTJzZu3Ejt2rWJjY1l/Pjx1K9fH71eT0REBCEhIYSEhPDQQw/x66+/EhQUxOTJk2nWrJlL4h07diwAW7dutc17Xdz69etHVFQUFouFCRMmONyuadOm2ea7Dg8PJzw8nGHDhmEymahUqRK//vorfn5+DvdR0iiKQlBkVWLC+/POM5m/l0tAxcd8UG5xJ3155TL2D+zGlbW/oJqlzoqwUi1mqQDuZSSpFp6tEEl13TrVGPZSDyb2qsiRDwOpV/GmoiLRq8By6xOerxbeuC+NjrVy3/b3Y3ombPQjPff8Wwj34+3dv29o8AaBUVoea+dDhwB4KQy+KAelddbWKY1q4dj4gZiM8kdc1Jo1a8aBAwf48ccf6d27N3Xq1CE4OJj4+Hg0Gg233XYbPXr0YOHChURHRzNu3DiXJYIdO3akfv36ALzxRvYp2M6fPw9AYGAg9erVK7IY/Pz8GD58OAA//vgje/bssbvdlStXss13nZCQQHh4OK1atWL69Ons37+/SOP0VLrgUCpUeIR7m9XliaY6vhjgx/a3A6h5ny+39zOgu8U/PdPVy5z66E0ODO3J1f/9Kcm1AKS12tsoqiqdUouC0Whk6dKlfPvtt+zfv59Lly4RFhZG1apV6dy5M7179yYiIqLY4uncuTM//fSTbblly5ZF1hUtP/bv32+7GQH477//8n9Bv7YH1EI0AV/4Aw68a39dg0kQ2TJPu1FV+GqfngV7fHPdrn5pM2+3TiUo982EcB86fwiuXayHXLFiBZ06dbItF+jcUBD/9Cbj2DK2fpBMepz9y2NCv3fpNH5k0cciPF7btm35888/GTduHJMnT3Z1OF7BFecGVVVJOriXq5f3UvlSzinUUq5Y2Lc4jeTzeUuWfaPKU/qRboS36oA+tNStvyC8kl9UBXxKR7o6DK/hlJyiEKSluggcOnSIZs2a8eyzz/LHH39w5swZ0tPTuXjxIv/88w8jR46kXr16/P77rbsXO8OPP/6YLaH2OoVorQYgshVo/e2vy0MX8BsUBZ5raOLVu9PQKI6fVf13WcuQVQZikr10iiLhfcwloPv3DfXHofPTUetxx0+9fBdN5syx08UYlPBE6enpbN68mVKlSjFixAhXhyMKQVEUdEEhhJWqyyHNIznW+0doaDrIgG+zSnnaX/rF85z7YgZ7ez/M0UnDuLxyGcYrl5wdtnBzUqzMu0hS7WTnzp3jgQceYNeuXYD1RNyyZUv69evHY489hsFgACAmJoZOnTrx559/Fmk8cXFxDB6c86mqV9EUsrq21gBRbWyLcclZEuLYbZAWk6/dPVYrg0n3p+GjdZxYn4rXMnilgdPxklgLD6CawVJCuisG1YAqPYiopyPituxDQpIs1r9pX2MSf48YgnT0ErnZsmULqampjBo1ipCQEFeHIwpJHxIGQESVfiRZgrOti0tW0egUWnS+Bi8NwqdMuTztUzWbid/+N6c/eZu9fR5l3wtPcHLGRC79/C0Je/7FGBuDapFiLN7Kki5JtTeRSXSdrEePHrYpQCpXrszPP/9Mw4YNbeuvXLnC008/zZ9//onJZKJbt24cP36c0NDQIolnxIgRXLx4Eb1eT8eOHfnhhx+K5DguVdikGqDcQ6Sf+oWPVxqZ/GM6S14x8NAdesAC0auhas987e6+SmbefyCVMesNJJvsJ84xKRqGrPTn7Tap1CstF03h5izpoHHQo8Pb1HsN5fRSanZSuXosmeR0lSUJ8EMCzIhUqeWrELX7V9Z//SNtenV1dbTCTbVs2VIevHgRbVAwikaLj28o50Oep3biB8QmWpj0g5EvNxrZ914glUtrqF9pFQnvfUn6T18S8+t3qBl5r8GQHn2O9OhzxK7LLHin6HTow8ugDw1HFxyCNiAIrcEfjZ8Bjd4HxccHRatD0WpRNBrQWP+raLUoOh2KzgeNjw+Kjy9agz9a/wC0AcHoQ0LR+EpBOleypMlc1d5Ekmon+v333/nrr78A8PHx4ZdffqFBgwbZtomIiGDFihU0bNiQEydOcPXqVaZNm2abysKZ1q1bxxdffAHAqFGj0Ol0XppUF7L7N0BwXV6Yr+WrddZursO/SqdtAx16nWLtAl6lh7V/dz40jLTwcftURv3px5VU+51CEowKw9cYmHh/GvdUKCEtgcIzWYxACUmq/ctDzRcwWD6majtfOi9IY+f1e59Pr1kTa0VRSHh/GHGPPkhoWHDu+xNCeDxFUdAFh2CKu0rZ8h04c+A3Hp64nf3nrA/FRy9OY8lQf8poL3HgwjIa9xtG6Yef5PyiT7m2qeBzmKsZGRgvXcB4yflztmsM/vhEROJTOgrfshUwVKyKX6Vq+FerjS4g0OnHE9mpqgVLejoaXymy4w2k+7cTffLJJ7b3zz33XI6E+oaAgADefPNN2/KcOXPIyMeTzLxITU3lhRdeAKBGjRqMGzfOqft3K4oTWqoVhWHPd7LlzYcuWPhszfWKx6kXIM5+JdVbqRZmYVaHVCoGO26JTjcrvL7Bj5XH5RmXcGMlpQL4DXWGgy6Iivfpea5K5qVyXzr8lWJ9Hxx/npWjZe5qIUoKXbC1CziKFm3FoQx7NDMZWvpPBn8fst7L3atZwn/Rp/ErW4Hqo6ZSd+Y3hLd+GEWrtbdbl7GkppB29iQJO//h8m/fc2b2NI6MHcDu7m34b2A3Ts2cTOyGlZjirro6VK8l46q9hyTVTpKUlJRtfHSfPn1y3b5r164EBQUBcPXqVVsLt7NMmDCB48ePA/DZZ59595yTzuj+DTRq2ZP+bTJbvSd8n86VhOvJ8PmCF5WLClSZ1SGF2yIct0RbVIV3Nvvx7X490ltQuKWSllT7loI6r6DRKjzXz8DdhsxVc65B+vXx1RFr5rLzT5m7WoiSQBcUjHJ9Yuqg4Frc0/Zp7qyWeSs9dGEaFouKTjFT6vIMUkzWewj/qjWpOnwSDeatoFyPF/EtW8El8eeZqpJ29iRX1vzMyQ/Gs6dXew6O6MPFZYtIL4IW85JMxlV7D0mqnWTz5s2kp1u7DgcEBNC0adNct/f19aVZs2a25XXr1jktlh07djB9+nQAevbsSdu2bZ22b7fkpKQa31JMGXgfwddvnuOSrYk1ADEbISOpwLsO8YXpbVO5u1zuPRLm7PTlk+0+WCSxFu6mpCXVADVfAt/ShFTSMqGNnhttTJfM8EOi9b1GtXB83Iukp5XA348QJYyi0aANDLItl632HJN7ZU6JtOOEhYUbTQDU0e9lz/Hs3b59IiIp93R/6s9ZRp1p84jq+hyGytWLJ/hCSj78H+cWzGRf/44cfv0lrv61GotJznuFJXNVew9Jqp3k4MGDtvcNGjRAp7t1V97GjRvb/X5hZGRk0L9/f8xmM6VKlbIl117NGWOqrytT7wnGd8nszjV7jYl9Z8zWIk0XC1ep3aCHt1qn0b6aKdftfjjkw1t/+2KSIdbCnZhL4M2TPhDqWrt3t3vSjy7hmau+iYfLGdanX+HR+/l1kvPrYggh3I8uKLOSu1YXSJ0Ww3nqnsx7vjHfppOQYj033GP+jBPX4nPsQ1EUAm+7nQrPDaberCU0/PJ3qr/2LpEdnyGoYRN0bj53deLe7Zx473X29Xuc6O+/JCO54I0OJZ10//YeMojTSQ4fPmx7X7ly5Tx9p1KlzPkMDx065JQ43n//fXbv3g3Ae++9R+nSpZ2yX7em0VmLiDmj33T4Xbz8eFk+//MMR6MtWFR45cs0/hzvj3L+N6jQsVC712ngtebplDKofLvf8cOAP0/piU9XeLNlGv5OaogXolBKYks1QLXn4PBMdJzhrb5+rPogjXgLpKkwNw7GRlg3C/z+XQ5360btO+q6NFwhRNHKmlQDhEe2YkivZqzY/jdpJrgUrzJlWTrTevoRrEkg5ewcMkJGoculGcsnvDQ+LdoQ1iJzes+MhDjSYy5ivByN6eoVMhLiyEiIx5yShDk1BUt6GmqGydpabDaj3nhZzNZliwXVnIFqMmIxGm3bO5PpWiznv/qE6O+/JOqJHkR2fAatf4BTj+HtVGM6qmotfik8myTVThIbG2t7HxkZmcuWmaKiomzvr14tfBGIo0ePMmnSJMA6lcetxnU7W0xMDJcvX87Xd44dO+acg2t8wJzuhP3o8Kn8ENN7fclj06xPD9fvN/PTvxl0vvsIJB6zzmNbCIoCLzY2Euan8ukOxxUft0frGLbGwDut0wgzSH9w4WI35qrW5L/QTkHODWfOnMn3cYqE1gcajIetz1OjiQ8v1zUx+T9rN5I/k6FjoEo9PwV9Rjq7RjxP9dWb0GmlE5gQeeGJ5waNjw8aXz8s6denBFAUajceyYjHdzDlR+t9w4e/G3n+AT01y2pprl/JypPtaV799nwdRxccii44lIAadZwWu8VkwpySfD1Bv4Yp9jLpMdEYL10g9exJ0s6cICMxZ8v6LfebmsyFbz4n5rfvKdf9eUp36Ox2RdnclaqqWNLT0PoZbr2xcGuSVDtJUlJm1xeDIW9/GFm3y/r9glBVleeff560tDR8fHyYPXt2sT/1+vTTT21JfbFT9IATkmqAcg/xSOOv6dBIy8rd1pvnVxel8dAdOgwXfofaLzvlMN3qmgjzU3lnsy9m1f7/q8OxWgavMvDeA6mUC5LEWriYxQia/F/4XXpucIZKXeHgB5BwiNcG+/HtK8kcu366+eQazIpS0SgKZY7/w+/TP+HxkUNcG68QHsJTzw26oBCM6ZlzDPv4l+epp/uxcMMnnI1VMZmtU3P+Mto6DWHDlPeJTppH2UDXTp2k0evRhISiDwkFquRYr6oqxphoko/8R+K+nSTs/pf06LN53n9G/DXOzJ7GlTU/U2ngawTWque02L2ZJS1VkmovII/TnSQtywTuPj55G+Prm2VeutTUwo2pmDt3Lhs3bgRgzJgx1KnjvCebHsFZxcoA/CuglLqDGc/6obv+oPXUZZUv1pkgerVzWsSva1ctg7fbpOGnc5wwn0/UMGilgaNX5c9VuFhJ7QKuaKCh9cbfP0zLu49nnuMPG+F/WU7funnjOH30dHFHKIQoRjd3AQeoVPMZxvWoaFv+dWcGW45Yi5OW053n9Ilv3X52D0VR8I0sR6n7HqTywNdo8Pky6s/5kfLPDsRQtWae95Ny/BCHRvThzNwPMlv0hUNSrMw7yF26k2SdsspozNuN541q4ZD31m17Lly4wKhRowCoVasWY8aMKfC+PJYzk2qAcg9Tp7yWIR18CPGHj3r78mJbvbUCeMxGpx7qrnJmPmyXSoiv46vttTQNr6w2sOuidKcSLlRSk2qAsu2hVBMAnujsS7vSCmEaGBUOLbKcvv3SE/nfsBexWBzPTS+E8GzagECUm4fCaHx4tMtE7r9NS5XSCj+NMHB3zcxt2ugW8+85z3vg5leuEmWf7EO9md9w24xFlO7QGY1PHlrcVZWYn5dw4JWeJB3ZX/SBejApVuYdpPu3kwQGBtre57XVOet2Wb+fX4MGDSI+3joGZs6cOdlawIvTwIEDefLJJ/P1nWPHjtGpU6fCH9yJFcABKNMSDs9kYleV1zr6UCYky/On879C2Qederg6ERZmdUhh5FoDF5PtP+tKMSmM+tOP1+9No1VlKQ0uXKCASXVBzg3r1q3j5ZedM9TCKRQFGr4JGx5G0SjMGWZg/+cp+NsZuhH132pWzV7IQwOLt66FN5g4caKtO7DqxGa93bt3s3z5ckJDQxk6dKjT9isKx1PPDYqioAsKxhR/LdvngWENmTrscRob1mLwyX5u0ClmKl17j9jSMwn388w2rYAadQioMYZyPV/i8u/fc+nnJZiTEnL9Ttr50xwe1Y/yvV8msmN3Kchlh7RUewdJqp0kPDxzrpVLly7l6TsXL160vS9VqmDTJ6xYsYLly5cD0Lt3b1q1alWg/ThDmTJlKFOmjGsOrnHyP2WtL5R9kOCMZQRz0wUgbi8kn4aAvFV5z6uKwSqfdEhl1Do/jl+z3yJtsihM+suPuLvS6VQ79zmvhXC6AibVBTk3OK2IoTOVaQGRbeDSOqpW16G09+XESvu/E/OskVx4pD3lKpcr5iCFPbt372bSpElUrlxZkmo34snnBm1gzqQaoH6TlzEd2YWBnAVoa+sP8MuxX7i/Xkc8ObfUh4RSrvvzlHnsaS7+uJCYn5dgMToeGqeazZz7YgZJB3ZT5ZU30AUUvCHJG1lMRlSzWYq7eTjPfFTmhmrXrm17f/p03rr3ZK1gWdAx0Lt27bK9//fff2nWrJnD17x582zb7ty5M9u6nTt3Fuj4bsPZLdUA5R91vO78r84/HhDur/LRg6ncHum4JVpF4cN//Zi/28ftx2cJL2Nx7nQsHqnBeG5cOiu19CGwnP3LqH/KVdYPG+jU1lYhhPvQBQbb/VzRBZEQMcjh99oqn7Pj4pWiCqtY6QKDrHNtf/YDofe0uuX2cf+s59DIvqRFnyv64DyMdAH3fJJUO8ltt91me79v3z4yMm7dipg1kc36/YI6cOAAW7dudfg6f/68bdvExMRs6xIScu++4/aUIpjMObAahGSvXJmYqjLq6zQ+nf+jUwuWZTusD0x7IJX7K+X+b+irfT58sMWXDBm6KYqLpWj+zXuUUo1tD9w0WoXbnvRD0UC8WWV6rMpfyZlJdNmdP7N67iJXRSqEKEI3ptayJ7h0K85q7gHgXKyFpz9M4e9D1mu6QZNKSMwMErzodOpbJooaY9+jxhsz0Jcqneu2aWdPcujV3iT+5+GNOU4mXcA9nyTVTtK8eXPbWObk5GS2b9+e6/bp6els2bLFttymTZsijc/rafQUSV+qLK3V/xzJoM6wJN77xciYr+O4dKBoWqsBfLUw4b40HquZe8vgr8f0TNjoR7r0BBfFwZIBqjzFoe5oW++YoPJaTlTX8twF+C0JPrsGqZbMxNr40Qiiz1xwVaReY8OGDSiKYhuPeezYMfr27UvFihXx9fWlQoUKPP/889keHt+gKAp9+ljHt58+fdq2nxuviRMn5vhOfHw8b731FnfffTdhYWH4+vpSsWJFunfvnu3andWpU6ds+zx16hTHjx/nhRdeoGrVqvj6+lKlShXbtq1atbId22g08s4779CwYUMCAgIICwujXbt2/PHHH4X/xYki5ai1GkUhqMrLzN8ItYclsfSfDAbPT8N8/dxwp88/bD22vhgjLR6hTe+l3idLKNWyfa7bZSTGc2T8IK7+vbaYInN/5tQUV4cgCkmSaicJDAzkgQcesC1/+eWXuW6/bNkyEhMTAQgLC+P+++8v0HEnTpyIqqp5ek2YMMH2vZYtW2Zb58qx2E6hKKAUQYmAyFagDQCgeqSG5HTrBTEhFcZM/dz5x8tCq4Hhd6fzXMPcx7H+75yOEX8aSPSip97CjZXkCuA3hNaFSpnFlZq39yX1eh4dY4Zv4zM3DUi+wp9DX0ItwmrglowMUs+ectnLkoeeWc60fv167rjjDhYsWEB8fDwWi4Xz588zb9487rrrrhyJdWRkJMHB1uRHo9EQGRmZ7XVzodCtW7dSu3Ztxo0bx7///ktiYiK+vr6cO3eOJUuW0Lx5c95+++1cY9y8eTONGjVi7ty5xMTEoNfb701lNBpp27YtY8aM4eDBg/j4+BAXF8fatWt5+OGH7Sb8wn04TKoBjW8kYdU7kXL92rzntIU5azIflLdlJrtjEos6xGKnCwym2ogpVBk6Idcq4WpGBiemjeXyH8uKMTr3JS3Vnk+SaicaOHCg7f2CBQvYv9/+FAIpKSm88cYbtuUXX3wRnU5qxhVaUYyr1hqgbDsAyoRomNwts6vXgtWX+WfDb84/ZhaKAn1uNzLs7jQUHI/N3Bej5eXVBi6neHDlE+EZJKm2zltdezDoggC4rZKWIa0yk6bvEuCcKfPvtdzOX1j9+dwiCyc9+hz/3FfNZa/0Yh4f2aVLF9q0acPBgwdJSEggOTmZpUuXEhQUxIULF3JMK3nx4kU++ugjACpWrMjFixezvUaMGGHb9tSpU3To0IFLly7RtWtXduzYQVpaGgkJCVy6dInx48ej1WoZO3asrUioPS+++CL16tVj27ZtJCcnk5SUxOrVq3Ns9+mnn/Lvv/8ye/ZsEhMTuXbtGmfOnKFr164ATJo0iZ9//tkJvzVRFLSBQblWs36g7SCevD/Ctvz60jQuJ1gfsIVo4tFcmEWSl5aqiHjgUep88CW+5Ss53khVOf3p20T/sLD4AnNTMp+355Ok2okeeeQR7rvvPsD69PnRRx9l37592baJjY2lU6dOtuqVpUqVYvTo0Xb3l7UrmaIobNiwoUjj93jOrgB+Q4WOtrcvPainYaXMP5uBr32A2Vz001t1rJXBpJZp6DWOE+uTcVoGrzRwOl4Sa1GEzJJUA9aaC1V72Bbf7O1HpL/1vQmYdTX7lFDGmeO4cFTmanWGRo0a8dNPP9kKfPr4+NCtWzfeeustAH744Yc81TWxZ+TIkcTFxdGrVy++//57GjdubHvoXaZMGd58802mTZsGkGsrcnh4OGvXrqVJkya2z2rVqpVju/j4eD799FNefPFF/PysD20rVqzI0qVLbT3Ybn5IINyHotGg9c+lkrWi5c3RUwk2WK/Lccnw2jeZ3cru9V3DpmP/FnWYLuNfpQa3vb+AoNub5rrd+YWziP5uQTFF5Z5UixlLunQ59GSSVDvZN998Q9myZQFrUtyoUSPatGlD//796dixI5UqVWLNmjUA6HQ6vvvuO0JDQ10YsRcpipZqgMCqEHo7ADqtwqy+ma3Vu4/GMXve4qI57k3ur2Tm/bapBOgdJ9aXkjUMWeXPgcvypy2KiLRUW2n9oOpz4BcJQKCfwodZzg3b0uB/WXrzBaTEsm7kKNS02OKO1OuMHTsWjSbnOa5jR+sD0NTUVI4ePZrv/V69epVly6xdUV977TWH2z377LMA7Nmzx+EUmoMHD87RrdyeihUr2sZ7Z6XRaBg3bhxgLUJ68wN64T60uXQBByhXsTHDet1nW56/3sQ/RzIf+jxgfp89l723668uMJiaE2dSukPnXLc7v+hTLiz9opiick9SAdyzyZ23k1WoUIF169bRqFEjACwWC+vXr+eLL77g559/JiXFWoigdOnSLF++PNs4bFFIRVEB/IaKnWxv77tNR6/7Mo/1+tTPiLmccz7KonB7pIWP26cSbnA8PjMhXWH4GgNbz8t8h6IISFKdyb8cVOtrW3zqXj2tamZeVj+5mr1oWbndf/DH7IVg8vDZFlzs7rvvtvt5uXKZc4JfvZr/c/I///yD5frY9zZt2hAVFWX3Va9e5qwQjqbQbNGiRZ6OeaNgmT3333+/rZX8VsVPhevognJPqgFefvEt6lbKfOg28IvMomUR2isYz8/22m7gABqdjkoDX6PcMy/kut2Fr2dzcXnxNFS4I0u6JNWeTJLqIlCnTh22bt3KwoUL6dChAxUrVsTHx4cyZcrQrFkz3n33XQ4cOMAjjzzi6lC9i6YIk+rS94FPKdviez19CTZY38cnpTNq4odFd+ybVAuz8EmHVCoGO06s08wKY9b7seq4jNUXTiZzVWfyCYXyD0NgdcBaZfqzlwzor19ZY8ywOD77VyxzpnD2vx2QIZVeCyooKMju51lrk5hM+f93euFCZpX2S5cu5fq64caD8puVKVMmT8csX768w3W+vr6Eh4cDEBMTk6f9ieKnNfijaHJ/iK3TG3jvtcyx+7tPWfh0Vea/0Qd8f2HDsd1FFaJbUBSFct2fp9KAUbnO1nLuiw+5sqZk1hGQYmWeTZLqIuLj48Ozzz7LH3/8wZkzZ0hPT+fSpUv8888/jBo1ioiIiFvuo0qVKk6t0J21UrhXjs8uqu7fYB2vXf4x22JkqIYpT2U+dV645Fc2/VN8cy5GBap83D6F28Idj+e2qApvb/ZjyX49quMe40Lkj7RUZ+cXCTUyW1/qlNcy7JHMc9H3CXA6S9Ey/9Q4/ho7BXPCsSKb614UzI36GAaDIc+zaji6Lmu1eesplFuRK+E5tIH2H/Rkde+9nejapqZtedzSNC7GZT4cb2t+l50x3l+sqswjT1Ll5XG5JtanZr3Ftc3rijEq9yDdvz2bNGMJ71GULdVgnbP61NegWm+8XnpQz/wNRnafsl4UXxrxNrs2fONw6hRnC/WDD9qlMuEvP7ZdcPynPHunL7GpCi/daUQj92+isFQTqGrRzAvviXzDIeJuKNUYrlofrL3R1ZdvN5k4G6eSAcyMhfcjVVsCVfbgen797Gc6DnoCgmqCtnAPBH3LVuCeTScK+5MU6vjeICoqCrCOyT527Bg1atQo8mOeO+e4cnp6ejqxsdYx+Hlt+RauoQsIIiMh7pbbvTNuOmu2diQ+2UJCKoxYlM7XQ6zd3spoL7Hn/BwSw14hqHhuI1wmou3jgMKpmZOx+9TfYuHEB29Qu1QEgXUaFnt8rmJJT0O1WFDs1IwQ7k/+rwnvUZQt1QB+paFMK9uiTqvwWT8/FAXKhim8MfixYp8azV8Pb7dOo13V3Ls6fn/Qh6l/+2Iq+kLlwtupqnQBz0qjA99SUGOA7aMAP4WP+1t7spTXwdMhOVskfRa+y5EDZyDpOFgKN8+zRqfDULGKy14aD5gS8kZhMzWXbjvNmze3/X9asmRJscS1ceNGhzFt2rTJVsU8axVx4X7y0lINUDqiLONfegaA2ytrGNw+e/bczm8Ffx7Z6/T43FFE28eoPPh1h+tVYzrH3hxO2oUzxRiV60kXcM8lSbXwHopSdNNq3VCpa7bFZrV0LHnFwKHpgXRreN4lXfl0GhjTIp1udXPvlrv2lJ6x6/1IkXxIFJZ0Ac/ONwKCa0FUO9tHjzfR8dVAP769U0NTQ87zgq8xmR1jJmBMTYbEY2CRJ15FKTjYWkwqLi7O4TZlypSxVRB/7733OHLkSK77LEgxtJudOXOGhQtzztFrsViYOnUqALfddhsNGjQo9LFE0dH6GVC0ebv/6NtjCDOH3cf2twNoVivnd9qrb/PvRe/vBg5Q+sGOVOg31OH6jMR4jk4cmqdeAN7CnCr1NjyVJNXCuxR1a3XIbRBSP9tH3e7RE+yvwMV1kO6a6XI0Cgy808iAxrmP0dwWrWP4GgNxJeN6LYqKJNXZ6QJA5w/V+9lmIVAUhV4tfbijuwHFwfDayNPbWfHBUjCnXm+xlsS6qNSvbz1vJyQk8N133znc7oMPPiA8PJyEhATuvfde5s+fT3x8ZrW5K1eusGzZMjp37kz37t0LHVdISAgvvfQSc+fOJS3NemI+e/Ys3bt3Z/369QC2+beFe9PlsbVaq9Xy3HNTSdLaL1IXqb2EJXo214wlY4hNVKcelH2qr8P16dFnOf7uGCwFnHve00hLteeSpFp4F6UYuiHe1Fpto5rgzA9Ff/xcPF3PxNgWaWgVx10cD8VqGbzSn+jEknHBFkVAun/n5FsaDFFQKftcrIFltVRr7/hhX8iymezacgwykiHphCTWRaRGjRq2KSyfeuopgoODqVKlClWqVOHDDz+0bVetWjXWrFlDlSpVuHz5Mv369SMsLIxSpUoRFBRE6dKl6dKlCz/99JNt+q3CGDhwIE2aNOGFF14gODiYUqVKUalSJVviP27cOJ544olCH0cUPW1A3pJqADQGqDDC4eoH/X7mz8M7S0yR0XI9BhD+wKMO1yfu3c65L2YUY0SuI8XKPJck1cK7FHVLNUDpe60Vf+05t4KL508xZPS7JCW5pgvPg9UymNo6DT+t46vxuUQNg1YZOHZVTgGiAKSlOiefUOvwkyo9QZ993tpK9/sQUlXLBZPKrKsq5ix3yjqziWMTx5OYmA4ZSZJYF6EffviBYcOGUatWLUwmE6dPn+b06dM5uoTfcccdHDhwgFmzZtG2bVsiIiJITEzEYrFQs2ZNnnnmGZYsWcKyZcsKHZOPjw9//vknU6dOpXbt2qSnpxMSEsIDDzzAb7/9xuTJkwt9DFE8dIG3nq86K01QIy75Wx+Y7DtjZtTXadnG1z+mvMNfF0pGtzJFUag8+HWCG9mfgx4g5tfvuLxqefEF5SIW6f7tseSOWniXoq4ADtYbZwet1fNWx1GneXdmzVvKhHdnF30sDtxd3sz0dqkE+zpOrK+manhltYHdl+Q0IPJJkuqcFI11Lnt9EFTtddM6+D1UQ79o+CkRfkzMvjo85gg/T5pjXSjhiXXWqR+zatWqld3Pb5bbVFehoaFMnz6dw4cPk5qaatt24sSJObY1GAwMGjSINWvWEBMTg8lkIjk5mSNHjrB48WKeeuqpHPNlZ50Gs0qVKnn+mX18fBgzZgz79u0jOTmZuLg41q5dy8MPP5znfQjX0/j6otHn78G+vmxvhi7W0fi1ZN77xciSzZldnEtrLxMQM5OLqSWjV5lGp6P6mHcwVK7ucJszs6eRfPRAMUZV/FTVgiVdplv0RHI3LbxLcbRUg3V6LX3Op9KnLluIT7KeDD+a8y279h4qnnjsqFvawqz2KUQGOO6imGxSGLnWwF9n8janqhCAdP92xDfC+t8KncBQzvaxoiicjFMxXs8HF8bBpYzsyWHZdQtZ/8t260JGkoyxFsIDaQMC87e9PpD/LpUj4/qf+tCFaVxLyjw3tPFbyYbD/2AuId3Atf6B1Bj3AbqgELvr1QwTx995zesLl5nTpLXaE0lSLbxLUVf/vkFrgIpdcnz8emdfakRZ/6zMZjMvDJuC2ey6G+NKISqfdEilWqjjGEwWhQkb/VhxxP2nxRFuQlqq7dP6Wh+2afRQ44Vsq97r6UtEkLXFKU2Fj67mnN4p8b3xXLhwvShWRjIkHi30dFtCiOKj9c9fUq0oCh9MmILB1/pgOyZeZdTi7F2+n9K/yx+nkpwWo7vzjSpPtTHvomjtP+w3xkRz4oPxqE6oaeCupFiZZ5KkWniX4mqpBqj4hDW5zsLgozD7+vy0ANt3H2DWvKXFF5MdEf4qH7VP5fYyjhNrFYUZW/1YsMenxBRGEYWgmqUV1ZEbrdVlWmabKSA8SMP0Z31ty1tT4a+bGiMCk2JYN3oqGTeapcyp1sTaLA8xhPAE+W2pBqhaoQKjBzxvW563zsSmg5kP00I08VSJe4/jCSWjGzhAcIM7qfj8cIfrE3Zu4eIPOaei8xYyrZZnkqRaeBelGMZU36APhvKP5fj4gQY6et2XGcfrb33CmXPRxReXHUE+MK1tKvdVzL3Va+FeH6Zv9cXsvQ+AhbNIa7V9PiHWh3uKArVeyraq5316Hqif2foy6xok3tSvs9z+tfz86c+ZH5jTrifWJaNgkRCeLD/zVWc1+Nne1K9R0bb8wudppJsyzw3N/Tax/eha0kvQs8zSDz9JqVYPOVx/fvEcEg/sKcaIio+0VHsmSaqFd9FocTgpbFGo3M1u6/gHz/oSHmT980pOTuWlEW/fssBOUfPVwsT703isZu7jYX85qmfiX34l6uItCkCVcdUO3WitDqkHkW1sHyuKwuznDfhdf+Z21Qxz43J+3X/Re+zbeTrzA4vRmlhnSOuFt9iwYYPDImnCsxWktVqv1/HRxMko1xujD12wMPWn7MWqevjNYNnRWGeE6BEURaHyoLEYqtSwv4HFzMn3XicjMd7+eg9mMRlRXTh0UBSMJNXC+xRHBfAbfCOgQsccH5cO1jC9V2ay/fuav1n60+rii8sBrQaG353Ocw1zb2XcdFbHqLUGEqUxUjgixcoc8w3Hdndc4/ls56QaURomdM3sBv5bEuxOy/7AzScjlUOvj7VOs3WDJcOaWJsSijR0IUTh6PI5rvqGJg3r88LTT9qW315uZP/ZzMTKX5NC8/QpbL9ccrqBa/38qD5mGhpDgN31xiuXOPXxFJc3WhQF6QLueSSpFt6nOMdVA1R5BjR+OT7udb+edrdn3jy/PGYasVfjijEw+xQF+txuZNjdaSg4vhDtidHyyioDV1JKzgVc5IN0/3ZMowN9qPW9oSxUzD4F36uP+nB75czL7/RYSLdk/1sMv3SIFW98mn2/qsU63Vb61aKIWgjhBAVpqb5h/MuDqRAZDoDJDP3npGHOcm5o4LOH86e/45qx5FyX/cpVpMrgsQ7Xx/2zgStrfna43lPJfNWeR5Jq4X2KqwL4DT5hUKlzjo8VRWF2P72tquflK9d4dfyM4o0tFx1rZTDx/jT0GseJ9Yk4LYNWGjgTX3Iu4CKPJKnOnV/pzPdVe1rPE9fpdQrzXjSguf5ndT4DFtnpwVhu49es/m5z9g9VFZJPQ6pr6zQIIezTGPxRlILdXgcFBDB9/Hjb8pajZj5bnb1XUHf/z1l26GSJKipa6v4HiWjfyeH6s3M/IO3C2eILqBjItFqeR5Jq4X2Ku6UaoPLToM3ZPalapIbJ3axdP+vWrsaA3jmn4XKllpXNvNc2lQC946vzpWQNg1f5c+CynC5EFtL9O3e6AND5Z76v1jfb6ibVtQx92Hquqheipa393o0YPxrPiWOXc65IvWhNrkvSnbUQHkBRFLT+Dv6g86D9/ffS9aEHAWjXUMujjbM3FOgUM12Uifx+tmSNua3Y/1X8KlWzu86SlsrJ6W+gmr1nCkLp/u155C5ZeJ/irAB+gz4YKj9ld9UrD+mZNaQOO9d/Q7OmDYs5sFtrFGnhowdTKWVwXPI7IV1h+BoDW88XYxE44d6kpfrWbhQsAyj/MARmvyF8s5svn/X3Y9v7flQoFWx3F/6pcWwdNZ40o50b6PSrkHhM5rIWws0Upgs4wLujR/LZxGH8PLYMVcrkvFWvpDtD4OXPOJFYcm7jtX5+VBsxBUVn/x4v+fB/RP/wVTFHVXRUY7pXz8XtjUrOX6MoOYqzUFlWlbuBb5kcH+u0CoPuvYBv4i4XBJU3NUpZ+KR9KhWCHJ/A08wKY9f7sep4MXevF+5Jkupb8wnLnI1A0ULNgdlWB/gpDGjngyFYS6Oe/qjYH2YReWoby6Z8af8YGUmQcBgyZAoWIdyFtoDFym6IKBXGM517YCo31OE2j/kvY93hf0krQQ3W/lVrUv65wQ7XRy+ZS8rJI8UYUdFRVVWm1vIwklQL7+OqpFrrBzVfcLz+8IduPdds2SCVWR1SqBPu+AptVhXe3uzHkv0u+h0L96Gq0gX8VhQN+JbKXA5vAhEt7G5aploKAQ/e4XBXZX6bzYbfdtpfaTFC4hEwXitMtEIIJylM9++s1NAHiPd/wOH6foa3+PZIolOO5SkiH3+aoEZ32V2nZmRwcvpELCbvuDZJF3DPIkm18D6uGFN9Q+QDEHyb/XWp0XBiIQAHDp2g+/NjSElxr6eQoX4wvV0qTcvm3p109k5fPt3ug0WGc5Zs0lp9a1m7gAPUegkU+709mj5wkp1hVXjnior5prHSGtVCwjtjOXPaQeVv1QJJp6SAmRBuQNFq0foZnLOvCq+QopRh7d4MBs5LzTZ9VIgmnhbpb/HXxZJzO69oNFQdOgFtQJDd9amnjhK9dF4xR1U0JKn2LCXnr1CUHBpd5hyxxU1RoLbjrkmcWcp773/AHa27s2TZKsZN/dTxti7ir4eprdNoWyX3J73fHfRh6v98MZWgrmfiJpJU35rWD/RZbv78K0DFnLMFWCwqYxYnMXrPSdYkww92pqMOTL7M3yPeIN3e+OobUi9C4nGwyB+mEK7kaG7l/Eo3+zBocTnavZXCZ2tMfPN39ofeTXz/5eLZZUSXoOkvfcLLUOnFEQ7XX/xhISknDhdjREXDIhXAPYok1cI7ubK1OqQelH/M/jrVQuyxtRiN1oT1w9nf8L+tu4svtjzSa2Hsvek8eVvuSdPak3rGbvAjxTt6Won8kgJZeeMbnn252rOZ81hfp9EoXIxTbb0/FsTBWVPOriBRx/9h2VsLcz+eKQESD0OG3JAJ4SrO6gLuo9dzOibdtjxkQSoX47LXP+kd8AnfHzyFqQTVtSrV6iFC72lld51qNnPqo8lYMjz7GmVJy94zQbg3SaqFd3JFBfCsarwAPqXsrpr4eAp1KocC1kIUfYZMdLtu4AAaBQY1MTKgcXqu2227oGP4GgNx7jtcXBQVaanOG31o9loPukCo0T/HZh/29iMq1NraZALevUKObuAAZX75lPW/bM/9mOZ06zjrtCuFCFwIUVCFLVZ2g6IozJwwjkB/6xR915Lhxblp2ZItnWKmj894lhzz7CQyPxRFofLAMeiCQ+2uTzlxmEs/LSreoJxMVVUs6XJz5Sk8IqlOT0/nl19+oX///kRFRdGoUSMmTJjArl3uW01ZuJiripXdoA+C2i/bXeXno7CgvxGNxnrzfPT4GcZOmVWc0eXL0/VMjG2RhlZx/LT0UKyWwSv9iU4qOd3PBJJU55Wi5GytLvcQBNXK9lGpQIU5z/vZlg8a4Xs73cAVVJLeHcvJk7dImFUVUs5ax1pLd3AhipXWzw9F45xpKCuVK8ubwzPvKX7ensHXm7J3ESuvO0etpOlsiSk5U1/qQ0tR6cWRDtdf+GYuaedOFV9ARcAi46o9htsm1VevXmXhwoV07tyZ8PBwOnXqxIIFC4iJiWHfvn1MmTKFJk2aULlyZYYMGcLatWvJ8PBuHsKJXJ1UA5Rp6bDSb7NaOkZ2zJyX9qM537LxfzuKK7J8e7BaBlNbp+GndZxYn0vUMHilgePX3Pa0IpxNqn/nnW9E9loPihZqD8mx2eNN9PS6L/P8tSAeThlz/t0FpMSyZfjrpKbl4bpnvCbdwYVwAWd1AQfo+2QXWjXLrHo9ZEEa569m7+/dzrCS/SfXcim15DzgDruvHaF3t7S7Ts0wcWrWVI+e71mKlXkOt7r7PXnyJDNmzKBVq1ZERUXRt29fVqxYQUpKCqqqoqoqtWvXJiwszLZ89uxZPv30U9q3b0/p0qXp0aMH3333HYmJJWuKAXETV46pvkFR4LZXrXPV2jGxs4W6lTKrg/YZMpHExOTiii7f7i5vZnq7VIJ9HSfWsakaXl5lYPcltzq1iKIiSXXeafSgD8n+WWgDiGqbY9OPevtRLsx6U5yhwruxkGGnG3jk6e38+MYc8jTkztYdPKYg0QshCsCZSbWiKMya9AZBAdZ9xqdA/zk5x9wOCHyPRQculZjx1YqiUOml0WgD7He3T9q/iytrfynmqJxHkmrP4fI73x07djB+/HgaNmxIjRo1GDFiBJs2bSIjIwNVVVEUhRYtWjBt2jSOHDnCwYMHiYmJYd26dbzyyitUrVrVlmDHx8ezZMkSunfvTunSpenQoQOzZ8/mwoULrv4xRXFzh5ZqsM5RW3e03VV+PgoLB2jQaq03zydPn2fEhBnFGV2+1S1t4eP2KZTxd3y1TjYpjFxr4K8zJacLWomlmshbRieAnNNrAdR40VohPIuwQIV5L2Z+dsQIi+Pt77Lc2vn88c1feTu+qkLK+evVweWBiBBFzVnjqm+oWDaKqSOH2ZZX7jYz98/sf8sGTRq99a/z9dGSc272CS9NxX7DHK4/N/8jTNc8s76EdP/2HC5LqgcNGkTFihW56667mDp1Kvv377clx35+fjz22GPMmzeP6OhoNm3axIgRI6hRo4Y1aI2GVq1aMWPGDI4fP86ePXt48803ady4sW0fRqORNWvWZDvO4sWLXfXjiuLmLkk1QEQzu1PoADSpruX1Tpmxfr5wGb+v+bu4IiuQyiEqnzyUStVQx2M0TRaFiX/5seKI/fl4hZdQVUnO8kMflCOBxq80VH02x6YP3aGnf5vMc8PX8XA43cFN8sfj+W/P2bzHYUqAhENgjMv7d4QQ+ebMluobej3Rkfb332tbHv5VGscvZn/QXU1/nOpJH/O/mJJzDQ5v+xhBDe60u86cnMjZLz4s3oCcRFUtmNOkWJkncFlS/dlnn3HhwgVbElyqVCmee+45li1bxpUrV1ixYgV9+/aldOnSt9xXgwYNGDduHNu3b+fs2bPMmjWLdu3aodPpbPvfsWMHK1asKIafTLgFd+j+nVWNF3MUJbphXGdfGlfN/FMc+vr7mM3uXVSotL/KzAdTaVDGcZwWVWHGVj++3KOXxkxvpkpSnS/2WqsrdbXOX32T6c/6UaW0tSeLBfgk3n7vDz9jEgdHjST2Wj5mEbBkQNJJSD4jRcyEKCKKVovG1+/WG+Znn9ergYeFWIeTJKfD2CU5k65H/Zez78QGLqS4vFNqsVAUhcqDxqDo7d//Xd24ioTdW4s5KuewpLrv0ECRyaV/aVWrVmXYsGFs2LCBS5cusWDBAjp16oTBYLj1lx0oX748AwcOZNWqVVy+fJlvvvmGp556iqCgICdGLtyeRp+9KJCraX3h9il2x1frdQqLBhvw1cO9javwx9KP0Wrdv+t0kC+8/0AqLSrkXijpy72+TN/qi7mEjO8qcaQCeP74lALlpkuvxgdq5SxaFmRQWDjQgKLAI22bMX3aZFTsn9fCLx/l91ffJsOczydY6bHWVmuT1CERoihoDc5vrY4qHcH0ca8B0L11OeY8b/++eUjQ23y5/xLpJeS5mV/5ypR9qp/D9ac/m4bFmPs0oe5IxlV7Bpcl1Xv37uXYsWN88MEH3H///Wg0zg8lODiYp59+mm+//ZYrV67w1ltvOf0Ywo25eq7qm/mVgYZT7MZVt4KWvycFsGHkVarrbjH/rBvx1cGklmk8WiP31spfjuqZtMmvxFzYSxTp/p0/Gq394oURd0PpnLMF3F9Xx5YpAfwyoSoPPtMBY+cXHO66/O7f+PGDH/Ifk8UIiccg5Ryo8vRLCGcqii7gAJ3bt+PPr79kzgeLMASVsbuNQZPKC75jmXu45JS/iOrcC79K1eyuS79whugfFhZzRIUnSbVncFlSXb9+/WI9nk6no2bNmsV6TOFi7jSu+obQelDX/pyKTapr0WqAwx/BsXkec3Or08CrzdJ5tkHuLZZ/ndExaq2BRGnY9C7SUp1/9rqAA9QabHfoyl01tCgXfoP4/bR/rR9xdexP1QcQvvQ9Nvy2q2BxpV2GhMNgSirY94UQORRVUg3QpGF9VF0IGRXHY8F+D7dq+uPckTqDVRfc8J6oCGj0eioPfM3h+os/LCTtQj5qULgBS2pKjirvwv243UCLUaNGuToE4S3cMakGKPug3a6e2Zz6Gva8TnzsBbcfXw3WnvZ9GxkZelcaCo5P/HtitAxdbeBKiht1zReFIy3V+afzB52dG21DWajS0/H3Ds5Ao1FpN/NNLgaWtXuTpVHNJL49iqNHCjh1ljkNEo9Kq7UQTqLxM6AU8XA0s3990kv3BeBaUs7zwkP+v3Dm7CqOJLjdbX+RCKp3B+FtH7O7TjUZOfP5+x6VpKqqBUu6FCtzd2731/X+++/z7LPPekQiIdycuybVAJW6QHXH434A/v57E7ff24m33367mIIqvE61M5hwfxp6jeOL1fFrWgatNHA2QRJrryBJdcE4aq2u/BQYytlfl3QMzi1n4469DDmXxO8p9iv7BqRcZefwkcQnFmLsYNplGWsthBMoioLGz7/Ij2Ms1Y0vtlSkyuBE/tiV87w8JPg9lh44S7yxZFx7K/R+GW1QiN11CTs2E7dlYzFHVDgytZb7c7ukGmDx4sU88sgjJCdLtTtRCO5WAfxmVXpCtb52V/241UTLiSmcjslg4kfL2PrDGI+5uW1V2cy0B9Lw1ztOrC8laxi80p+DV9zyFCTyQ7p/F4xPGGjsJMVaX6j9isOvffrJLB57ZihX4xP4LEHDOZP9v7PSF/7j5+Hv5L9wWVbmdOtYa6kQLkShFGUX8BumfjaX/jP2k5AKvT9L41Jc9p4mvko6QwNeY9Z+U4koHKoPCaVCb8e9As/O/cCjpqqScdXuz+3uaMPDwwFYs2YNrVq14vLly/neR3JyMm+++aazQxOexp1bqsHaZ7ras3DbCG7+U2zbQEfFCOvTZLMFnnljNYl/dofTS603um7ujigzHz2YSpif4yt3fLrCsDUG/r3g/pXORS4sppJTAceZFAV8wu2vi7gbytxvd9XjjS2EBVnPbalGI1OMpTA5+P2X3/EzP77/XeFjTY+FhIMyr7UQBVQcSXXn9u3w8/UFICZepe/stBxdnMvrztORyXx13M3vj5wkou1jBNS2X8PJePkiF39YUMwRFZwk1e7P7ZLqzZs3U6VKFVRVZefOnTRv3pwTJ07k6bsmk4mPPvqIatWqMWnSpCKOVLg9d6v+7Uj5R+H2yaDNnBIjxF/h68EGNNd7aZ24pDJo9mU4+hn87xk48ZXb3+DWLGXh0w6plA9ynFinZSiMWefH6hP2u7EKDyFdwAvG10FSDdaiZdqc89tWCNcw9/nMv5ejV64yW410uJvw7z5g7XInzChgMVnntU46If+/hcin4uj+fVuN6kwe/rJt+fddGcz8I2dPouZ+mwi8tpi/Lnn/dVfRaKg0YDQ4mGHo4rKvSb94rpijKhgpVub+3C6prlmzJps3b6Zx48aoqsrx48dp3rw5O3fudPgdVVX58ssvqVmzJsOHDy9Q67bwQu7e/Tur0i3grtkQUMX20b11dIzrnPkzLNpkYtFfRjDGwon5sOlJ2DMeLm0Ac2rxx5wHZYNUZrVPpXa4466jZlVh6v/8WHrAQx6CiJykC3jBaH1BH2x/nV8ZqNbH7qoud+vp3y7ze8vPXuJvxf7YQY1qJn3aKP7b66QbR2M8xB+EtCvO2Z8QJYDWzw9FU/S9sp5/uhvt77/XtjxqcTq7Tua8/vYJnMPm47s4meh2aYDTBdSoQ+mHuthdp5qMnJ03o5gjKhgpVub+3PKvKTIyko0bN9K+fXsAYmJiaNWqFatXr86x7U8//UT9+vXp168fZ8+etT3FKVfOQaEXUXK4e/fvmwVUhrs+g7IP2T4a38WXFrUzL8QDv0jjaPT1C6RqgsubYN9E2PA47BwOpxbDtd1ulWSHGVRmtEulSdmMXLf7bIcvn+3wwSIPYj2PKi2XBeaoYBlAxS4QaH++1Q97qtSpHGpbnhWvcgX75zxDWjyHRgwnJsZJU2WpZkg5CwlHrdXChRC3VBxdwBVF4dM3JxAZYe0FY8yApz9KJSkt+4VVo6i8FjKez/dfI6EEFC4r33MAuuBQu+vitv5F/I7NxRtQAZlTpNaUO3PLpBogICCAX3/9ld69ewOQlJTEY489xtdffw3AunXruPvuu+natSuHDh1CVVVUVaVMmTJMnz6dY8eOuTB64RYUxfMSa60B6o2GO94DQ1l0WoXFQwyEXr8WJ6VZL5DpNxcnUk1wdSccmws7hsL6R2BzL9j7hvWz87/B1R2QfBoyUop9DKy/Ht5unUbbKrknX0sP+PDOZl8ySkARFa8i3YELzifE2mJtj0YHdV4Fct70BvgpLBlkxsfH2oXz8rV4PjBUwuLgb7vUleOseXk8aUYnFhzLSLJWCE+Nlum3hLiF4kiqASJKhfH51Mm2abyORFsYMj/nw69gTQJD/Ucz/T/F66+5usBgyj87yOH6M3M/wGJy/+uYJNXuza0HVGi1WubPn0/58uV56623MJlMPPfcc8yYMYPdu3cD2Fqmw8PDGTlyJIMHD8bfv+jHrggPodF75g1/eFNoNh9OfUtl7XfMe9FM1+nW1uedJy2MXpzOh71zjrfMZLG2JKWctb9aowd9iLXrqS7A+tIarC+Nj/UmX+NjHZeu0YKiu/7SgqK5/tICivU9XH+vWP97IwlQMt/rgbE1oYlWx+bz1ocdqp1kIeW8wuJ1Zp6uZ8I3T73lsuxDufnYmuwx2WLXANrrP5v2+s+ps/5eND6guf7z26vOLHKS7t+F4xsBKeftrwutBxUeh3Mrcqy6vbLC+33L8fLsMwD8e+gYX9/VmGcv7bK7q7JH/uL7sZ/S870hOG3aXFWF1ItgvAb+FUEf5KQdC+FdimNc9Q2tmt3FsL69mf6FtRDXlxtNtKmvpdf92YfF1dQf4aGMd1hwdBzP1/bu83hEu8e5vHIZKccO5liXfv4MMb8uJeqJni6ILO8saVKszJ15xB3j5MmT0ev1TJw4EVVV2bUr84YhJCSE4cOHM2zYMAIDA10YpXBLntZSnZXWANX7QsUn6FLua17671s+W2192vzRH0Za1dPSqWkBfz6LCdKvWF/FSAN0ADqE5WHj/UUcTF5ofKz/H3QBoAu0Jgz6EPAJtRaZ8okAQ6R1XmHfiCwPGEoYSaoLx6dU7q291ftDzCYwXs2xanCra/x5qCYrNhwF4Osde6h1Zz2aXbL/B1R+3Zcs+7QqXQY96rTwgczpt3xLgaG8PJAS4iZaQ/E2+Iwd+CL/27GDrbv3AvDSvDTuqqGldrnsT6vbGVZyJK4Oqy904cFyHtgIkUfWomWjODTCfq2K6CXzCG/1EPqwXApIupglLRXVYkFxUHhNuJbb/1+JiYlh+PDhvPvuuyiKku115513curUKcaPHy8JtbDPk4qVOeITBrWHMP3zFTSsWRqAqFCFEIP3j4NyOYsRTPGQegESj1i70F9aB2eXWbvVH3jb2t3+726wvgNsfR72vw1nvof4AyUn2bTkPl5e3IJGZ31Q44g+CGrbn29VURTmP3eNSuWt54bKFcvScuzLJIZXcri74AVTWP/77kIEnIv0q9bpt9Jji2b/QngojY8Pirb4Hjbp9Tq+eHcqocHWoobVywXYZhS52YCgmfx7cg8H4rx7isvA2vUJf8D+A0VzSjLnF31WzBHlj6qqMrWWG3PbpDo2NpbRo0dTrVo1PvroI9LSrPPtabI8ndm5cydTp051YZTC7XnKtFp54BdUmu8WzeGJR1qze+UMWrfvbE24hXuwGCHxKESvgiOfwLaBsOER2DkCTn8HyWdcHWHRKSkPD4qSb+nc15dpBRHN7K4q5W9kyejKPNOlAzvXL6bFfU24c+YM0n3tP2zWWkykTBnOvj1FNJWMJcP67z3xmBQyEyKL4m6trlg2ik8nv0G/bl1Z+833VKlgv4ivVrEwLnQsC/ZfISbVux/YV3huEBqD/fHtV9b+TLKd7uHuxCJJtdtyu6Q6Li6O119/napVq/L++++TkpJiK0L2xBNPsG/fPr766it0OuvTvvfff5+ePXuSkSEtJcIOT+7+bUftmlVY9tUHRNa8H24bDvf9CHd9bu0eGn6XtYuycB8WE1zdDkc/hX+ehS394dQS72vFs5iKvfid19H5W4cZOKIoUGdYtvnss7on8giLJ7UiJNg6prnCbVUoO+FdLA6GJBjS4jny6iucj04obOSOmRKzFDKTfx9CFFexsqwead2K6eNewzegDOkVJ2HGfg++YE0CrwWN5L29KilefEutD4ug3NP97K9UVc7One7W80GbU6VYmbtyu6S6SpUqvPPOOyQlJdn+Ubdp04atW7fy448/UqdOHXr06MGvv/5q6/L97bff8tBDD5GYmOjK0IU78obu37lRNBBcC6r2hDumQcuf4Z6voMFEqPocRLaB4DqO58IVxSvpGBybDX8/Bf+9BQmHXR2R83hiQUB3k9v0WgB+kdYHaI4cmWXtfn1dow7N0PYb6XDzsKun2DhoNInJRfj/7kYhs4RD1iRbiBKsOIuV2WMx1MJYbrjD9VX1J3jWZwIzDvhgdt+8stDKPPYUvmUr2l2XdGA31/5eW8wR5Z1UAHdfbldJJCEhwTYNQJMmTZg6dSpt27bNsV27du3YsGEDDz/8MJcuXWLdunXcf//9/PHHH0RFRRV32MJdeVlLtT3nL8TQ75VJfPjWCOrUqgoBlayvyFbZNzSnW1tIjVet44SN8dYpcTKSrS9LmrWrpjnd2p3XYgQ143orpNnapVM1AxZrQaUbL1TrS1Uz30OWZbL8N4ubngRbVEjNwOE81cr1ffhqQa+9cYxsO8wSy41ly/X31+N0l2l/1Ay4uMb6imgBNfo5nI/YY1iMoPXyh1hFzScMUs/nPka9Yie4uNY6bvlmpgQ4PBMaTgTgyLHTvLVmHT2aPUblLb/Y3V3UqX/5aehUus9+A722CLt9mtOuFzILv17IzLvHbgphT3F3/7bHFPIgu/dsYeLnv/H9MH+C/bP/3bfw28SppHl8dfx5+tRId1GURUuj96Fi/2Ecm2z/AcO5BTMJves+NL65zbLiGhZjOpaMDDQ6t0vhSjy3/D9Sq1YtJk+eTNeuXXPd7o477mDz5s20b9+eY8eOsWfPHu655x5WrlxJ7dq1iyla4da8vKV6w9/bear/a8Rcvkrn50bw75pFBAY6uGhrfcG/nPXlhjSAJR3GbjCwLyb3G+4+t6fzbANTwaYFsj0QMF9/ZVx/YJCR+TDBbMx8yJCRcv3BQ6I1aTHGWacPSr8CaZesDyYK48r/4MpmKPcQ1Bzgub0KVGmpLjRFAZ9w678rh9tooe4oa1E81U7yHbMBLm3gp20Wnhs0gcSkZFKbNuSVWs2IOLLF7i4r7PiZpRMq0GNyP+dNteVIeqz178i/vNSEECXOjWJlqtl1/asX/ricEVP/wGgy0+ezVH4YbrA1Zt3QI/BLpsRUZ7V/K6+tCB7S9F6C72hGwq6c50Xj5YtcXL6Yck856CbuYpbUFDRBHnqv4MXcrvv3559/zv79+2+ZUN9QtWpV/vnnH5o2bQrA6dOnadGiBZs3by7KMIWnUBSvntolPiGJmMvW7p4Hj5yk/9A33Xos0K0E+cL7D6TSokLuNxwL9vjy4b++mAvS8KxorP8mtL7Wcaz6YOs0QH5lwL+CtcU4pA6ENbIWhopqAxUegyrPWJPeeq/BHe9Csy+g1a/Q8he4azbUHQOVn7J+T5Pfp9sqXPgd/nkOLq7zzPGn0v3bOW7VBRwgsCpU7eV4/aEPuRR9jsQkazfBzdv28leFCiSUqe7wK+V+/5SfZv+R32gLxmKCpFOQdML6AEuIEsTVrdXnL17CaLKer5f9m8H7v9j/GxwVOpmNJ46w56p39ipRFIWK/Yc57DVz8fsvMcZeLuao8kbGVbsnt0uq+/fvn63Cd16Eh4ezfv16HnroIQCuXr1Ku3btiiI84Ym8uLW648OteO2VzDkXl/60mhmfLXZhRIXnq4NJLdN4pEbuSdqKI3ombfIj3VxMgTmiD7KOWy/XHmq+BHd+aE22m34G1fpCSN2878t4Df57E/ZPBXNqkYVcJCSpdg6tD/iE3Hq7Ks84HjJgiuPFu0/w7FOZU8d8suB7LnbsRKp/KYe7DPpiImuWb89vxAVnjLeOtU67UnzHFMLFXJ1Ujx7Qnwea32Nbfu2bdNb9l/NBto9iZFLoSL44EM/ZZLdLF5zCUKkaZR7uYnedJT2N84s+LeaI8kbGVbsnr/kr8ff35+eff6Z3794ApKXJNB7iOi8fVz157Eu0ua+pbXnUxI9Yv2mbCyMqPJ0GRjRLp1eD3Fux/jqjY/SffiS5W2OXRgcht0G1Z6Hpp9BiCVR/HvzyWO/h4hr49yVIPl20cTqTTKvlPHlprdbord3AHVT3VmI28Nmoe7m9fi3bZ8PfmoVl0DAydL52v6O1ZJDxznC2bT5eoLALRDVDytnr02955/hNIbLSuDip1mq1zHtnMhXLWq9HFhWe+jCVM1dydv0qpb3KG8HDmLbHwjWjd061Va7782gD7Xeljv3zV7ecYsucXMhhZ6JIeE1SDdYTxfz58xk7dqyrQxHuxItbqgF0Oh1L5r1DxfLWC6TZbKZbv9GcORft4sgKR1GgXyMjr9yVbitSZs/uSzpeWW0gNsWNL/iGKKjaA1oshoaTIajWrb+TfMqaWF/dWeThOYW0VDuPPhi0eRhCEFwHKj/jcLX/6U9YNm88YaHWG8bU1DSGvj8bzZDXUbH/9+JrTObCa4M5fCiXcd1F4cb0W2kxxXtcIYqZ1sEcycWpVGgoi2a8h6+P9f7oSqJK5w9SSDXmvNZW0x9noGEcb+/1cX3PsCKgCw6lXPfnHa53xym2VIsZszQeuh2vSqpvmDJlCp988omrwxDuQvHulmqA0hFhLFv4Pr6+1y+QsXE88eyrpKR4WBdiO56obWLC/WnoNY4vasevaRm00sC5BDdOrMFaZKrMfXDXHGgwCQy3KBpnToFdo6zjrN2dJNXOlZfWarD2hgioYn+dKZ5qKd/yzZy3bIWITpw6x3srfsPy7DCHuwxKimHfkCFFO4e1PaoFUs5DwhFrkUAhvNCNYmWudkfd2/hwfGYj1I4TFl78PM1uAnm332ba8yEf7Pfzyqm2Sj/cFb/yle2uSzqwm2ub3e8abE6R1mp345VJNcCAAQNcHYJwF17e/fuGJnfU5bP3xtiWd+45xPPDprjdE9aCaFXZzLQH0vDXO/5ZLiZrGLzSwKFYDzitKQpEtoRmC6BKD2uy7YiaAf9NhnP2p0RyG1L927l8SuX+7+IGjQ/Ufc1hN3Au/02HeslMGTvQ9tHq9VtYdTWGlAcdt3KXij3OXwNeJS7eBcltRrJ1DndptRZeSutncHUIADzT8VGef/pJ2/KiTSY+/N3+UJ4nAr6nQur3zDvq55G1NHOj0emo0G+ow/XnFszEYnKvIU7SBdz9uOzuc9++fcV6PJPJxJEjR4r1mMJNeHn376z69OjIkOefti1/88MfvD/rKxdG5Dx3RJn56MFUwvwcl/yOS9cwdLWBbRc8pFqp1hdqPA9NP7nFeGsVDk2H6DXFFlq+qaq0VjuTRpv3KadCcu8GzuGPGPPSI3R9vK3tow8+WcTVRrWIb/SAw69Fnt3JrwPHk5rmgul/bK3WR2WstfA6rh5XndXbI1+lxZ2Nbcsjvk5nzV77f/MDgz4k4crfLD/rffdVIU1aENzobrvrjJcuEPPL0mKOKHdSAdz9uCypvv3226lWrRrDhg1jw4YNWCwFmRsnd/Hx8XzzzTc89dRTREREMG7cOKcfQ3iAEpRUA3wweRit720CgF6vo3S498wFW7OUhU86pFI+yPH5Ii1D4bV1fqw96frudXkWXAfungsR9+SykQoH3oaYv4strHyTYmXO5Vc679tWew4CHUyZlZGMcuBdFsx8g4b1agIQGOBPWGgwj8x6k7gqjRzuttzBdXw/dBomV/X5zEi6PtbaPae2EaIgXF0BPCu9XsdXH7xrK1xWKiQYH1/78WkUlXGh49l6+igbL3rQNTYPrFNsDQUHMxBFL/0CU/y14g0qF5b0NCwZrpvvXOTk0n6Sp0+fZubMmTzwwAOUKVOG5557jmXLlpGSklLgfZ49e5ZZs2bRrl07ypQpQ69evfjhhx9ITEx0YuTCo5SQ7t836PV6vpv/LnffWZ/1Kz6n9zOPuzokpyoXpDKrfSq1SjmumGJWFab87cf3Bzzo/70+CG6fAhU6Od5GtcC+SRC3v9jCyhdpqXYurZ+1aFleaPRQbwwoDm50r+0i8OrvLF80naZ31OOfVV/yWIeW+Pj78eDc6SSUdjA9F1Dh3x9ZMnY2Fld1+VQtkHIOEo/LvzHhFTR+7pNUA0SUCmPxh+9z1+3/b+++w5uq3jiAf292uhellFH2XrJlT0FRRAQUB+AAFBDUHyIuUFBABAcIiAsQRRRBQVQ2MmRTZplllwIt3SM79/dHILQ06Uxy0/b7eZ4+JLk3uW9oc3Lfe855T1Ns+/kntOo1E1Y4bkvUggHTg1/DyrO3ytwa1tqo2qjwQH+H2yzZWYj/aZFnAyoAh4B7F8mS6pEjR6JSpUoQRRGiKCI5ORk//vgjBg0ahLCwMDzyyCP49ttvkZBQ8Jyqo0ePYurUqWjRogWqV6+O8ePHY+vWrTCZTPbXb9GiBR599FEPvDPyOoJQ7hLrsNBg7NmwFB3aNpc6FLcI1or4/AEdWkbkf5V2/iE1FkWrSs/8L0EO1BtvW9/aGdEEHHvXO+ebMuFxPXVo4ff1rw3UHO58e+w3qBGix75NP6Bxg9r2h/1CA9Fh0Vxk+4c7fWrlTd9ixYyfpf0smdKBtFO29dyJSjG5RgPBWR0EiTRrUB8bf/gO1SIrweLbHIZKE5zuGyRPxYzgcZh/Qo/zGd71Pkoq8ulRkDmp0J644XfoLntwycECWDkE3KtI9kn46quvEBcXh3379uHtt99Go0aN7AmwXq/H33//jVGjRiEyMhIdOnTArFmz7HOiLRYLtm7divHjx6NGjRpo0aIFPvjgAxw9etT+GkqlEg888ADmz5+Pq1ev4uDBg3j66aelersktXI2BByAvdpvTqIowmQqG4mPjxKY2V2P7tXzfz8/x6gwc7caZtfPMHEPQbBVdK71ovN9jCnA0Xe9b64pk2rXUwXZ5t4XVtSTQGBjx9tEE3BiGgQHw/RDqoaj8RdfwKD2d/rSEb/NxqoFfxc+FncQLUDmJdsa7tYyuL4PlRsyLylWllPO8wZT0AMwhA2FySw6LHhaWRGHKYGvYeZRAde9eUnLIlIGhaDS4Occb7RacXXxXM8GlA8ze6q9iuSXl1q3bo0PP/wQx48fR2xsLObMmYPOnTtDJpNBFEVYrVbs3bsXb731Fho0aIC6desiPDwcvXr1wpdffonLly/bE+mAgAAMGTIEK1asQGJiItavX4+XX34ZlStXlvptktRkZWvuT3EYDEYMGz0ZL46fViYqggOAUg6829GAx+vnP5d3wwUl3v1XA11pyvmqPw1EDXG+PeOsrXiZN+Gcavco7PJagK2ta/Q2IHdywp51CYjNPYQxPT0Tjzz1Khat3YDqH38Gs8J5Eh/0/RT8+eOOwsfjLoZkIOOMrVI4USnkTfOqnYkTHkHnD1WYt95x295AdRKv+r6NqUfUSDGUncS6Yr8noQp3vORl+qHdSIve4+GIHLPqssvM+VxZIHlSnVPOwmU3b97EkiVL8Nhjj8HHx8eeOMfGxiIlJcV+v1q1ahg7diw2bdqExMRE/PTTTxg8eDD8/Z1fbadyqBz2VOeUkpqOngNewrJf/8IPv6zDR3O+kzokl5EJwNhWRoy4L/9e273XFHh9sxZpXta565QgALVHApEPOd/n+gbgxhbPxVQQ9lS7hyrU+ZJZjvhEAnXHOt9+dTVway8A4Fp8Ajo89Dz+2fwfZn6xGHuuXkboOzNgdXI8mWiF+ouJ2PT7gaK8A/ewGICMc4DuhtSREBWZN1UAd+TcxUvo/sxz2HsqCa/9YMC6Q47b9/s1/+FZ9Ud4/6gWmWXkK0CmUqPKcOdt6NXvPodokb5ImChaYcnmhUVv4VVJdU4hISEYOnQoVq1ahVu3buHPP//Eiy++iIiICDRv3hxTpkxBdHQ0Ll26ZC92plCwN5KcKOdJta+PFnL53YIi781YgOW//SNhRK4lCMDTjU14s70eMsH5VdtTt+R4Zb0PbmaVkivqggDUf9X5cF7A1lvtLUkF16p2D5nctm51UUQ+BFTo5Hx7zAzAcAv+frlP7Ee9Ph0pgRpoxk12+lSFxQTLjNewfcOxosXkDqII6K4DGbG8qEOlirf3VAf4+0EUbfOmrFbgybkGHLrgeMpFH5+/0EtYgA+P+cBQRmZlBHfsCd8GTR1u01+5gMSNazwckWMsVuY9vDapzkmtVqNv3774+uuvER8fj+joaEyZMgXNmzeXOjQqLZxVxC0nVColVi35BHVqVbM/9twr72P7f4ckjMr1Hqxlxkdd9VDLnSfWV9JlGPOPFhdSSkXzZ7sg1HQqoHayvJI5CzjxIWCV/qo5h3+7UVGW1wJsF2QaTHA+dNyUBpyYjgB/Ldb9/DkqhtsKopnNZjw+/A0Etq4PcdjrTl9eZdYha8or2L39TNHichdThm3pLWOa1JEQFYpMo3VY+8RbVAwLwy/zPoe/r61oV5beioc/1uPKLccFSp7y+wFNzD/j4xPa0lPDJB+CIKDqC6853R7/0yKvmNNsyZY+BrIpJWeVRCVUznuqASA0JAh/r5iLsNAgAIDRaEL/Z1/HqTMXpA3Mxe6vYsGnvXQIUDlPrG/pZBi3UYtjN0tJE6gOAZp+4HwIcNoJIO53z8bkiCh6R3JfFhVlea07VIG2Zbbg5MQ9JRq4tBxRVSOxbvkX8PHRAADSMzLx0BPjUH9QLxj6Oy+YpzFmIuWtl3Fw78WixeUuVjOQeQHIjkfpKflP5ZUgCJCpNVKHka9GdWtj6ZyP7SPdbqRa8NBMPVIyHX++Xg74AmHZ/+Czk1pItbS9K/nVa4yQLr0dbjOnpeDGysUejigv9lR7j1JyRklUQkyqAQC1a1bD2p8+g0ZjK0SUmpaBPoPHIv56osSRuVajClbM65ONcB/nl8szjQImbNFi19VSss5mYEOgppOKpABw/jvvGAbO3mr3KUrBsjtCWtoqgjtzfjGQcgyt7muIFd/MgExmOy2Ii7+JB58Yi/vHP42sns4L5mn1aYif8BKOHLpc9NjcRX+Tw8GpVPC29aod6dG+HT5/7y37/ZirZjw2Rw+90XHW/EbghxDTdmHhaU2ZuLZVeehYCCrHxRtvrvkZhhvXPBxRbqLVAosuW9IYyIZJNZUP5Wyd6vzc37oZflz4oX3Y2ZW4G3jwibFIS8+QODLXigoU8WUfHaoHOp/gZbQImLxdg3XnSsn0gOpPAUHNHG+z6IHTn0nfQ8dExn1UgUVbXuuOWi8AAQ2cbLQCJ6YCxhQ80qcL5s54w77l+MlYDBg6Ab0+GIuM9v2cvrxf9i1cfu0lHD8aV/TY3MWcCaSfsQ0LJ/JS3j6v+o6hA/pjwojn7fe3nzRh2AI9rNa83zdywYrJwW8jKekwvo9VS/6VVFLq8AhE9He8JK9oNiFuyZcejigv9lZ7BybVVD4IAhPrHB7v1wOffzTBfv9YzDk89uz/YDCUrV7GcF8R83rr0LiC88TaKgqYvVeDH44pvf/LX5ADjfNZLilpH3Bzq2djuheTavdyNrc+PzIF0GQyIPd1vN1wCzgxHRCtGPPiE5g0/u6IiG27DmLY2Cl4aM5bSGvRy+kh/DMTcH7cSzh54nrR43MXqwnIPA/obkodCZFD3rhWtTPvjn0ZT/V72H7/1z0mvPaDweGSTirBhA+D/4dzN87gp4vFuBDoZSIeHwplcKjDbSn/bUZGzBHPBnQPcxYvHnoDJtVUfnAIeC7jRg3BG68Mtd/ftusgFi5eKWFE7uGvBmb31KF9lfzn+n5/VI0vDqhg8fYCK5qKQK0RzrefWwhYdJ6L516sAO5eqhDbxZWi0lYCGk5wvj35AHBxGQBg+ntj8ezgvvZNv/6xCb//8y8eWTANqY07O32JgIzrODN2JE6d9IJpCHeIIqCLt821tpaRssRUZpSmpFoQBMyd8i56tL/f/tjcf4zYddrx50or02NWyDhEx13Er5dK9/mX3McXkc+87HT71W8/hWiV7uSBPdXegUk1lR/sqc5j5uRxeGaQbR3kl4YPxCsj8pl7WYppFMDULno8VDv/hO+PMypM3amB0dvPvas+6nw4r+EWcGmFZ+PJiXOq3UsmtxWuK46K3YDKzodx48ISIGk/BEHAt19MRu/utpPn998chUGP9oJCpcQji2YitW47py8RmB6P02NG4tQpL0qsAVtV8IyztmkSRF5CplBApiw9CadSqcDSOTNxX6OGEAQBc96ZhDYdhzrd30+WidkhY7HzSjx+v1J63qcjYT0ehrZGHYfbsmNPIfnf9R6O6C7RYoZFz7ZNakyqqfxgT3UeMpkM38+bgmULp2HB7LdyrWVd1ihkwBvtDHimcf5J3/YrCry5RYNMb84NBbltuSRnPZaXVwD6BM/GdAerf7tfcYaA31F3DODv+MQQEG3Ls+luQKVS4rfFn+CX72ZiysRR9hoMSq0afb+bjdSaLZ0eIjDtGk6P9sLE2qIH0s8CxlSpIyGyK0291QDg7+uL3+Z/gRVzP8WLTwyEocILMAY5v1gXJE/FnJDR2HgxEX9eLb3nYYJcnu8SW3E/zIdFL90oMQuHgEuOSTWVH0yqHVIqlXhmcF+vXi/TVQQBePE+I8a1NkCA8wnUh28qMH6jFknZXvx/4l8LqDrA8TarAYj91rPx2I/tzVcjygi52la0rLjPbTIFkDspkGRKB45PASwG+Pn5YHD/B/LsovbV4qHFnyE1qrnTwwSmXcPpl0fgZIyXJdaiBci86B2V8olQeoqV5RQWEow+XTrZ7ggC9BHjYAzo6Xx/+S18GvoS/ryQhL/jSu+owYBmrRHU1vEUGFNSAm6sXubhiO5iUi09JtVUfnD4d5Gs/nMLvl3mBWsfu8GA+iZM7mSAQuY8sT6fIsfYDVrEpXtxYl1jqPO1i29stPXKeRoLlXlGSXqrfaoAjSY5355+Jt9K8l8vXYX1O/ajz5LPkVqtqdOXCUyPx9kxI3DiRHzxY3UX3XVbci16exEFKutKW0+1Q4IMukpvYNpfEdh12vFopYrym/gs5GWsjk0r1Yl1lefGQXAyqu/mqh9gTJTmgp05k0m11JhUU/nBnupCW7J8LQY9/yZGvvYhfvz1L6nDcYtu1c34uLsePkrnifX1TBnGrtfidJKXNpVK/wLWrv7ec7HcIVpYEMoTlP6AogQ9XOGdgagnnG+/vh6IW5Pn4ZmfL8ao1z/CEy9Ows5Dx9Fn6VykVW3i9GUC0uNxfvSLOHr4avFjdRdjKpBxDrBwdAVJR14K1qouiNVqxVufzMXkH87ioZkGHDzv+DugkiIen4WOwqpSnFhrKkch/GHHbafVaEDcUmmW2LLNq5awSCkxqaZyhEl1oWRmZuPd6QtgtVohiiKGj30fq9ZukTost2hZyYLPe+kQrHHeW5VqkOHVjVocjPfS+eaVHwF8oxxvS9oLpMZ4Nh6AFcA9pSS91YCtiryzdc8B4Ow8IOWo/W789UTM/GIxAMBoNOGxoRNwMOYsHlg6D2lVGzt9Gf/Mm7g8bgSi918sWbzuYM62FTAzZ0kdCZVTMrUaglC6T8fPXLiI71euAgBk6CzoPcOA41ccJ9aVFdfweehLWBWbjr9KaWJd6ckXoPB3PAUnefsGZJ4+5uGIbCzsrZZU6f4UExWFTAGU8i8uT/Dz88Hm1QtRISwYAGCxWDBk5FtYt2GHxJG5R91QK77so0Okv/PEWm8WMGmbBlsuKjwYWSHJFEDtUc63X/jOc7HcwXnVnqEKLtm0ljvrV6vDHG8XLcCxybah0gAiK1XA+l+/hJ+vrWdNp9Oj75PjcfTsBfRe+mW+PdZ+WYmIf20E9u06V/x43cVqAjJiAUOy1JFQOSUrhfOqc2pQuxaWfToLSoXtOzI5w4weHxpwMs5ZYh2Hz0JfwurYdKwthcXLFH4BiHzmJafbr3w9R5IltrhetbSYYVD5wnnVhVK/bg1sXr0QwUG2+bomkxmPD38D67f8J3Fk7lHZX8SXvXWoE+J82LLZKmDaLg1WnvLCv6Gw+4HAho63JUcDyYc9Gw8rgHuGIJS8t1odCjT9ABCc/F2b0oCj79p6dAG0a90U637+AhqNGgCQmZWNPoPG4njsJfT+YR7Sopz3fPvoUnDrjZHYuVmC0RMFEa1A1mUWMCNJyMvAvOrenTviu48/gkxmSy0S08zoPs2AM/GOv1erKK7i89CR+PNCSqlcbqtC7/7QVKvpcFv2uZNI2va3hyNiT7XUmFRT+cIh4IXWtFFdbPxtAQID/ADYhnv2f/Z/2LB1t8SRuUeIVsTnvXRoEZF/Qjj/oBpfR6uc1XCShiAANZ93vv3CEo+FAoA91Z6kDi35CJzARkD98c63Z54HYqbbi3p16dASa378FGq1rT3NyMxC70FjEHP+CvosmYvUGi2cvpTWkI6sd17ClrWHShazu+iu25Jrr/qAU1lX2nuq73i0Vw8s+ugD+2oiN1PN6DbViLNOEuvKimv4ImQk/rmYhF8ulq7zM0GuQNUXX3e6/doP82HJ9uy0EtFqgUWX7dFj0l1Mqql8YU91kbS6ryE2rJwPfz9fAIDBYMSjz7xeZnusfVXAzO56dIvKf07w8hgVPt6jhtmbCgeHtHQ+Pzb1KJB6wnOxsAK458gUgCqk5K9T+WGgsvO1ZpG4C4j9xn73gW73Y/XS2VCpbG1qWnomej3+Mo7HXkLfpV8gtU4bpy+lMmXDMm0s/lq+s+Rxu4Mh2XYhgQX3yEPKQk/1HYP7PogFU6fYE+vrKSZ0nWp02mMdobiOuaEj8e+VBPxwXl2qrmcF3tfW+RJbybdwfeViD0fE3mopMamm8oU91UXWtlUT/PPrPPs8yjuJ9d+bdkkcmXuo5MB7nQwYUC//3tb155V4918N9N4y0lkQgFovON9+6SfPxcKeas/ShLvmdeqNy79w2eWfgWvr7Hcf6tURK7+fBaXSNo/SlliPxpHT5/Hw4k+RWr+905dSWIxQz/kfVi9a750n0aYMWwEzVgYnDygTy2rl8NSjD2PulHfs922JtQGnrzlOrMPlNzE3dAQOXLuGb86pYfXGNsGJKs+Ph6BwXG/l5h/Lob8e59F4OK9aOkyqqXxhUl0sHdo2x/qVdwsUGY0m/O+9T2E2e0tG6VoyAXiltREjmhvy3W/vNQVe36RFWv67eU5wUyC4ueNtt/YAmRc8Ewd7qj1LrgZUjivRFolMYZtfrYlwvs/pz4Ckg/a7/R7sgt8Wf2JPrNMzMvHG+59DqdWg3+I5SGvW3fnhRAuCFr2DFbN+9c7E2qK/XRmcwynJvQSZDDK1RuowXGrogP6Y9/679h7rGylmTFnt/BwsVJ6EL0JG4vSNi5h3SgOLN40Ey4cmshrC+w1xuE00mxD33WcejceSmQHRKxvUso9JNZUvHP5dbB3aNseG32xDwatWjsA/v34JhZOrs2WBIABPNzFh4v16yATnX1Anb8nxynof3MwSPBhdPqo/7XzbpeWeiYFLanme2kW91aogoPl0QO5kjuediuAZ5+0P9Xuwi30oeMN6NbFqyWwIggCFWoV+i6Yjvc2D+R6y4i8f48d3vobJ4oUngncqg5vSpY6EyriyNAT8jpyJdbv7muGzj3+CRVPP6f5B8lR8HvoSbiSdwqwYLUylJLGOfOJ5KIIcT8NJ3bcDaYc8V4tGFK2wZGV67Hh0F5NqKl/YU10i7ds0w+bVC7Hl969QvVqk1OF4xEO1zfiwix4qufMT/ivpMoxZr8XFVC9oUkNaAf51HW+7sdW+PJJbWc0s9ORpSj9A4eua1/KrCTSZAqenCJZs4MibgD7B/tDDvTvj7xVzseX3r+zL8QGAXKVEvy8/QFa3QfkesvL6Rfh57EzovGY+RQ6ixTbKg0tukRvJNGWjWNm9nn3sUfz8xRysnP8F/PzDkVXtE5i1TlarAOAny8TskDEwph3C1KM+0Hlhk3AvuY8fqgwb43T71W8+hdXkuYvNlkxeBJSCF5wBEnkQk+oSa9OyMerUqpbn8awsnQTReEb7qhZ82lMHf5XzRPFWtgyvbNDiWILEzaog5NNbbQWurvZMHJxX7XmaEi6vlVNYW6DeWOfbDbeAw2/a5h7f1qNLW0RUzLvmtc5gxMOz34T+kXwq1AOosu83rHrhLaSk6YsdttuIoq0qeI4LCUSuVBZ7qu94sGtnBPjZVhKB3A/ZVWfB7NMMmXrH36lamR4zQ15FkO5fvHfEF+lGLxkJlo/Q7g/Dp47jiwX6a5eR8OcKj8Vizkjz2LHoLibVVL4IMtu8QXKpGzdvoXnXJ/HRnG/L7FyexuFWzO2tQwUf5+PRMo0CJmzW4r+rcg9G5kB4J8CnquNt1/4CzB5Y5oPzqj1PFWybX+0qVQcAVR93vj3rInD0bcDivKjAufNXULdNf3z34x946IMxsDydz9JdACJPbcX6oWNx9ZqXnhRmX7P9ELlYWVlWq1DkPtiZNhQ1x+mx5oDj7wqlYMaUoLdQ17QGb0b7IEHn3Ym1IJOh2qg3nG6PX/EtjEmJHonFotd5tGecbJhUU/nD3mqXSk5JQ6/HRyP2wlW8O30B/vfep7BaS8lEqCKqEWTF/D46RAU6f39Gi4D3tmuw7pyEF28EGRD1hONtlmwg/m/3x8Ceamm4am71HXVHAxU6Od+eehw4PtU25P8eV+Kuo+eAlxB/IxEjXp2GWXOXoNf/hkI5djKs+aytXfHqYewf9gJOxXhgqkJx6BO4ljW5nEyphCAvHxf9T56LxeNjJiAxzYTHP9Vjyb+Ovy9kgogJQdPRXbYYEw/54FKmd6ctfvUaI7THww63WXXZiPv+C4/FwiHgnufdf51E7sBiZS6Vla2DwXj3C/GzhT/hubHvw1RGr5KG+4qY1zsbjSo4X8PWKgqYvVeDH44ppTvvjugFKAMcb7u62jZP1J3YUy0NdYhrR+MIcqDxu0Cg8zmQuPUfcHIWIOa+2JSZqYPJdDfZfvODuXhjymfoMPwRBL37Ccxy5xc4g5MvInbUcOz+93SJ34JbGJJtPfVi2byASNKQl5Pe6oysLFhvrwNvsYp4bqEec/50PuLlBf+v8KxmNt6J1uB4isQjwQpQZdgYyH0c17dI3rEB6ccPeSQODgH3PCbVVP4ITKpdqWrlCOz663vc17S+/bEfflmHR595vczOsw5QA3N66tCucv4VVL4/qsYXB1TSLA0iVwOV+zneprsOJLq5GikrgEtDkAFqF86tBmx/S82mO59SAAA3NgJn5uXqvW1YvyZ2/f09atWoYn9s9pfLMHzMFDR7uAOqzF4Ag9rf6Uv6Zt9C+sQX8feK/1zyNlzOmGYrYGZ18wUqKjfK2nrVzrRt3gzrvluE8NBQ+2MTfjRgwjI9rE4WqX7MdyUm+L2ND4/Iseum9/boK4PDEPnUSKfbr3w1C1YPLEdq5tJaHsek2k2MRiOWLVuGhx56CFFRUdBoNKhUqRLat2+P2bNn49atWy4/5qlTpzB37lwMHjwYDRs2RGBgIJRKJcLCwtCqVSuMHz8ehw8fdvlxSx0O/3a58Aoh+HfN1+jasZX9sX82/4duj45E4q0UCSNzH40C+LCrHn1q5Z88/nFGhWm71DBKcd5d9TFAcHLyceU39x6bw7+low6zJdeupAoC7vvE9trOxP0OnP8mV2Jds3oV7PrrezRrfLci/bJf/8LDQ8ajaou6aLToW2T5V3R+WLMOqk9exYpPVsLJuba0TBlAZqzD4e9ERVVeeqoBoGn9etjww3eIqlzZ/ticdUY8+6UORrPjD3sX7VbMDH4FC0+a8PsVldfOwKjQdzA01Wo63Ka/cgEJa392ewyixQyrLtvtx6G7mFS7wenTp9GuXTsMHToU//zzD65cuQKDwYAbN25gz549eOONN9CoUSP8/bdr5jVu2rQJjRs3RsOGDTF+/HisXLkSp06dQnp6OsxmM5KSknDo0CHMnTsXLVq0wODBg5GcXI6XBslnyCEVX0CAH/75ZR4G9utpf+zA4Rjc32cYzp2/ImFk7qOQAW/eb8DTjfNPIP+9rMSkrRpkeTrPVIcCFbs73pZ6FMi86L5jM8mQjkxh+927mjYCuG8WoPBzvs+l5cDFpbkeiqgYhu1/foNuOS66bdy2F10fHQFleCDuX/Y90ivWdvqSMtGK8J9n4sfxc7xzyS1zNpBxjlMeqMTKS0/1HTWrVsGmZd+hSf27F92W/2fGgzOykZrlOGNupj6ML8NewD8Xk7DorEaakWAFkCkUqPbSRKfb43/+BsbEG26Pg0PAPYtJtYvFxcWhR48e9h5hQRDQpUsXvPDCC3jkkUeg1doazISEBPTv3x9btmwp8TEPHTqEmJgY+31BENCsWTMMGjQII0eOxGOPPYYKFe4OB1y5ciU6d+6MpKSkEh+7VGJPtdtoNGqs+HYGxrww2P7Y+YtxuL/PcOzef1TCyNxHEIAR9xkxtpXz+WAAEH1DgfEbtUjydAXTagOdb4tb477jsqdaWupw2x+nq/nVBJrPBGQa5/tcWAJc/CnXQ4EB/vjn1y8xuH8v+2PRR0+jXe9huJmdhV7Lv0FqrVbIT5Vdy7Fq+AQkJGSW5B24h0VvS6zzqYROVBCZWgPB1aNMvFzFsDD8/f3X6NK2jf2xrScs6Dg5G1duOc6YoxSXsCD0OZxPOIePjmuR7YXX2gKatERI594Ot1n1Olz99jO3x2BOT3X7Meiu8vXJ9YCnn34a8fHxAICoqCgcOXIE//77L7799lusXbsWV65cQY8ePQAAJpMJgwcPRmpqqkuO3bx5c8yfPx+JiYk4cuQIfv31VyxatAirV69GXFwcZsyYAbncVuAhJiYGo0ePdslxSx0m1W4ll8sx7+M3MXPyOPtjScmp6DNoLJKSU6ULzM0GNjDhvY56KGTOx6PFpsjxynot4tI9mFgH1HVeZOr6RlsvmzuIZlZHlpJcZVtiyx2CGgPNpjmfWgDYhoHfk1ir1Sr8/M0MvPby3XXUr8TdQO+BY6Dw0aDfj/OQ3uahfA8deXYndjzzvHdWBrcYbifWXrjONpUKgiBApsnnglUZFeDnh98WfIHBfR+0PxYTZ0G/WXqn84JD5Mn4InQkNJm78Fa0L27pvW/JrSovvAqZ1nHRspTdW5F60L31Iix6HaxGXuD2FCbVLvT3339jx44dAACVSoU///wTTZs2zbVPWFgY1qxZg5o1bXMtkpOTMWvWrBIdt27duvj9999x+PBhjB49GqGheYf9qVQqTJo0CZ988on9sV9//RWnT3tpZVV3kind04NDdoIg4M3xw/HzNzOgUtkKw3320f8QGhIkbWBu1qOGGR9310OrcJ5MxmfK8MoGLc4mebD5rdLf8eOWbODGJvccUxQ5HFZqGudzlUsstDXQZHL+c7fPfwNcXJbrIZlMhk8//B++mP6GLYGQybDo03egVqugUKvw6IIPYOj/Yr6HDrl1HudHPot//z7igjfiYlaTLbF218UqKvNkmvIzrzonlVKJr6dPxYQRzwMANGo1Pp08BaLC+VQWjWDA1OCJaCMux4SDPjiX7l1pjSokDJWfecnp9isLP4ZF796Cruyt9hzv+usr5ebPn2+/PWzYMDRp0sThfr6+vpg6dar9/qJFi2AuQSXAAQMGoH///oXad9y4cYiMjLTfd9W87lKHvdUe8eSA3ti0agE+fHs0Xnimv9TheETLShZ88YAOwRrnE71S9DKM36jFweseWhokvAugDHS87erv7utRZgVwack1gMrJ790VwjsDjd5BvqcS578Dzi/O8zc2btQQ/P7DHMybORF9H7i7DrYgk+HByS9DNe59WPJZGsxHlwLT5FFY+cUa7ytgZjUDGbGAOUvqSKgUkpezedU5CYKA914ZjXnvv4uFH76PVq0fRFaNL2FRVXP6HJkg4uWAL/CCdjrei1Z5XWXw8L4Doa1Z1+E2Y8J1XF/xrVuPz3nVnsOk2kUyMzNzzY9+7rnn8t1/4MCB8Pe3LSWSnJxs7+F2N7lcjrZt29rvX7p0ySPH9ToytdQRlBud27fEO//L2/NkNJpwM6FszuuvG2rFl310iPRznljrzAImbdVgy0UPnADI1UDlvo63ZV0CUo+757icVy09d/ZWA0BED6DhRAD5jP65uBQ4tzBPYv3oQ10xOkf9hTvS0zPRZEBXVJq1AAaNk7XWAcitZoQunYqfXpmFzGwvu4AjWoCM84DJC+d/k1eTlaMK4M4MHdAfA3rb6i+IyghkVZ8Hs08zJKZboTc6vorW12cNZgSNwaJTBiy/oPaai22CXIGolyc5HSF54/efkH3xnNuOb8nKgGjhsn+ewKTaRXbv3g2DwVagxNfXF61bt853f7VajXbt2tnvb9261a3x5STk+GBbyusHTca1qqUkiiJenjAdrXo8g4OHT0odjltU9hcxr48OtYOdf8bMVgHTdmnw2ykP/D1WfgROE59r69xzTFYAl57CF1A6XwvaJSL7AA3eQL6J9ZVfgdOf2pLNfFgsFgwZ+Tba9hoGZdVQNP1+CTJCo/J9TuXdv+DPp8bg8mUvW9VCtACZ5wFTutSRUClSnnuqnZL7I6nCVPSdrUSXD7JwLdnxBetm6sP4KnQY9sddwcwTWui85CvIr34TVOgzwPFGqwWX5093W+IriiLMmWyDPIFJtYucOnXKfrtJkyZQKArufWrRooXD57vb8eN3e6WqVq3qseN6FQ7/ltRnC3/C9z+tQVz8TXR6+AX8+OtfUofkFqFaEV88oEOLiPy/2b88qMaiaDevuamtBIS1c7wtYZt7TvzZU+0d3N1bDQCVHyq4x/ran8Dxqfn+Xbz5wVz8vWkXzsReQpteQ3H06lX0WLEYqXXaOn0OAFS8cghHhj6L3du87CKdaAUyLwBGDsGkwhHkcshUHE2XkyiKGPvBTBw4k4L9sVa0eisLu884/l6tpIjH/NDnoczYiYmHfHHD0ytuOFF56Bgogx3PD886cwIJf61027E5r9ozmFS7yJkzZ+y3o6Lyv6p+R7Vqd+eIeKpg2O7du3Hu3N1hJj179sxn7zKMSbWk1CqlvRK9Xm/Asy+/h1ff/gQmk5cN4XQBXxUws7se3aLyf28/x6gwa48aZneuuVn5EcePW03AdTcULGOhMu+g9M9/bWlXiXwQaDgJ+Z5aJGwHjrzlcL6xKIrw9blb+Tg9IxMPDxmPzxevxCNLP0NmjyfyPbx/xg1kTnwev81b6zVDPwHYhr1nXQSMqVJHQqWEnEPAc7FYLAjwu9uG3UgV0fWDbCzaZHRYHVwr02Fa8ER0F77B/w744HCyh+qX5EPh54+qI153uv3asgUwJLhnVQNzeprTKurkOt41m78Uy7nmc8WKhesViIiIsN9OTnb/sDWr1YrXXnvNfr9t27Zo1Sr/dUGLIiEhAYmJiUV6TmxsrMuOXyRMqiU15sUn0KBuDQx+YZJ9ma0vFv2Mw8fP4JdvZyKiYpi0AbqYSg6818mAII2I3884/9v757wSqXoBUzrroXFH6xzaBlCHAYZbebdd+xOoOsC1lfFvJ9XFaRuuXLniujgI0IQDmR6Y3xvZ2zaH/8Q050O9kw8BB8cD9820/T3eJggCPpj0Mpo2rIthYycjK0sHURTx3owFOHA4Bj8snIrDdWrD9PXHkDuZWqCwmBCy+AP8FHMC/T5+HYGBXrI8kSja6hcgyn1LnZVCbBsck2m0QFqK1GF4DYVCgc/eewtN6tfFxBmfwGQ2w2QBXvpWj/3nLZj/vAYaVd7vrmH+36Ku8jSmH52Kx2qo8HiUUdLFX4I79kLAlr+Qfmh3nm1WvQ6X589Anfe/yDVN0xVEqwWWjHQoAtxYuJKYVLtKZo6TFa22cPNhcu6X6YGTnWnTpmH//v0AbMuazJ4926Wvv2DBAnzwwQcufU23YVItue6d2+DA5mV4bOj/cPTEWQDAjt3RaNHtKfzy3Ux0ur9FAa9QusgEYFxrI0K1Ir494nxo355rCvxvsxYzuukQ4OoRgDIFENnXVjjqXlmXgLQY2xrErnK7+nepahvKKlUgoPDxzFJPFbvaEutjk52PVsiMBQ6MAZp/DPhVz7Xp8X49ULd2NTw29H84fzEOALB2/Xa06vEMflv8CZR1F+Liu29Cm+38YnTl/auwYchJNJk1Cw0aRzrdz6NEEci6bPtXHSJ1NF6BbYNj8nK6rFZBnh/0OBrUqoVhE97EzVu2zqzvt5lw+KIFv73ug5oV846SuV+zC1+FDcV7Vz7B2YzqeLWBDj4SZT+CICBq9CTEjHkCVgdLaaVH70HStr8R1t1JYdESMKWlMKl2Mw7/dhG9Xm+/rVIVLmFTq++eMet07l2n7s8//8z1xfXGG2+gY8eObj2mV+Na1V6hRlRl7P5nMZ4dfPcL5PrNW+j26Ch8Mm9pmRuuJAjAM01MeKOdHjLB+XuLSZTjlQ0+SMhyw99o5YfgsYJlnFPtXTwxt/qOsPuB5rMAeT7Jgf4mcHAMkHQwz6YmDevgwOYf0feBu99TsReuol3vYdh37Sra/PQD0io3yj+EG6dwYcTT+Gf5TvfWKyiKO4m1wcuKqpFXkbFYmVP3t2iO7St+RNvmTe2PHb5kRYtJmVhzwPFFvMqKOCwIGw7/zE14/YAvLmVKl/6owyuh8rOjnW6/+vUcGJMdjCYrIXMGh4C7G5NqF9Fo7g4xMxoLdyJ5p1o4UPje7eI4cOAAhgwZYv8w9ejRA9OmTXPb8UoFQQAEVgD3Bj4+WixdMBXzZ02CUmm7fGyxWDDx/S/Q/5nXS7SGu7fqW8eMD7vooZI7/4K7nCbDmPVaXEx1cTOtqQiEOin6dPNf166tK4qsAO5NVEGA3IMn6yH3AS0/B5RBzvcxZwFHJgJxf+bZFBwUgLU/fY4PJr1kHw6p1xvw/LgP8PZn3+KhFYuQ3r5fviFoDelQz34VP70xF5k6L5rjn3UZMJTNJQWp5GQqFQQ5B5M6Uym8AtZ9twijnrpbZyEtG+g/W4fJv+odPkcjGPBO0GQ8qfwYbx1UYNt16c4Bw/sOgm89x6PCLFkZuLJghssTYNFihiUzw6WvSbnxE+sifjkKKBS21znnfjmf70onT57Egw8+iKws24ly69at8ccff0CpdH1jMnr0aAwaNKhIz4mNjUX//v1dHkuhyFTsSfMSgiBg9AuD0ap5Qwx6fiKuxN0AANSqUaVQlfRLo/ZVLZjTU4e3t2mRYXTcc5yYLcO4Dbah4I3DXVjBrPLDQNLevI9b9cDNbbbtrmI1Fqtt2Lp1K8aNG+e6OMhGWxHIvOS54wXUBVp/CRyeCOjiHe8jWoHTc2xVsuuOsU1TuE0mk2HyGyNxf6umeGrU27iVlAoAqFc7CmpfLR6dNxn/LmoE87efOJ1nDQCRW5di3dkjaDFrOurWj3C6n0dl3Z4brHZcEbg8YNvgnFzrw6WQ8qFSKjFr0hto27wZxr3/ITKzbVNb6lXWAnCekPbzXY36qhi8f/ZjnEgNx8i6eqg9XMdMkMtR/ZV3cPLVZyE66DhI3bcDydvXI7Trgy49riktBQr/AJe+Jt1VNs9WJRAaevdL8ebNm4V6zo0bN+y3Q0JcP7/q4sWL6NWrl72IWsOGDfHPP/+4LYEPDw9HeHi4W17bLeQqgJ1oXqVNy8aI3rYcQ0dPxq2kVMycXLZPnJqEWzG3tw4Tt2iQmO24RzrDKOD1zVq830mP9lVdtI5lWDtbsSSjg0I41/5ycVJtKlbbIFkRw7JOFQzIbwIW9045ysWnCtDqS+DoW0D6Gef7xf1um9vfZIqtVz2HXt3aIXrbcjz54lsIDvLH66OfAWC7INftpYE406Quzr49Cb4Zzr9/w+OOIva5IYgd8z4efLqLd8wAyrpiG9GhKVvFGQuLbYNzMo0WYFJdoMf7PIAm9epi2P/eRKumTfDos0NhiZsMudF5Qbu6yjP4OuxpfJLyHiYc7IaJjXWo6uvOpTfy0kbVRqUnRyD+x4UOt1/56hP4N2kJVajrzqvN6akQxWouL4RGNhz+7SL16tWz3758+XKhnpOzgmX9+vVdGs+1a9fQo0cPxMfbegZq1aqFTZs25Ur+yz0WK/NKoSFB+HP55/jn13lQqXKPqLBYLLh+o2iVYr1djSAr5vfRISrQ+Re60SLg3e0a/B3rouugMgVQqY/jbemnbD2GrsJltbyP1oNzq+9QhwAtPrPNtc5PymFg/yggPe8yk1UrR+DftV9j+aLpkMlyn75Ua1EXjb+eX+B61hpDOjSfvo4fx81CWprjYaIel30V0Jetdo1KjstqFV7dGtWx5aelmDVpAqzqasiqPh8m/84AgNQsEZn6vD3XfrIsfBA8CQPkszDpgBxbris9Xnsh4vGh8KlZz+E2S1YGLs2d5tJh4BwC7l5Mql2kQYMG9tvHjx8v1DzQ6Ohoh88vqYSEBPTo0QMXL14EAFSpUgWbN29GZKSXVED1FkyqvZZMJkNIcN4qlbO/XIZGHQbht7WbJYjKfcJ9RczrnY1GFZz3RFtFAbP2aPDTcRd98UfmM6zs2t8uOMBtIpNqr6MK9uzc6jsUPkCzD4GqA/PfT38TOPiKrXDePX/sSqUSAQF5R1tNmjoPHQe8BL+Rz0D/2EiIzorx3Vb5v1+wcfBwRO9z4QWkksiOY2JNuchYAbxIfLQaaO/UN5L7Qld5CnQVRmHEIj1aTMrCgVjH36+P+q7CFyHDsfbcNXx6UoNsD45glCkUqP7qFAhyx+PP06P3InH9apce05TKIonuwqTaRdq3b2+v5p2VlYWDB/NWM83JYDBg7967cxq7d+/ukjiSkpLQs2dPnDljG2IXHh6OzZs3o3r16i55/TJFxkJlpcmhIyfx7vQFSElNx6DnJmL4mClISy87V1wD1MCcnjq0q5z/N/o3R9T48qAK1pIm1r7VgKCmjrfd2Oi6egOsW+CdpOitBgBBDtQbC9R/zXbbGasJODUbiJle4DJg67f8h7lf/4z4G4noPXgsNukz4Pv+p9D55L8edGjiOdwa+zRWzl4Jk8ULquIysaYcZGo1BIGn6cUmCFjynw9+22fCuetWtJ+chWmrDDA7+KzXVJ7HV2HDEJixFuP3++J0mucmWfvUqINKQ0Y43R733efQx11y2fHM6akQrZ4d6l5e8NPqIn5+fujRo4f9/pIlS/Ldf/Xq1cjIsCUEwcHB6Ny5c4ljSE9PR58+fXD8+HH7627atCnX0HTKQebqRYDJnS5fvQ6N+u7ogqUr/kTTTk/g3135X8AqTTQK4MOuevSumX/v7qrTKkzbqYaxpFOsIx9y/LgpHUjcXcIXv43Dv72TVL3Vd1R5FLhvNqAsoGjOjU3A/peADOfzaK9eu5lrqsjnXy3HCzPnQjP5A6TWapXvyyssRoQun4mVT72KSxdcv4xNkWXHAfoEqaMgLyAIAmQ5VpahoruekGifP2y2AJN/NaDj5Gycjc/75akWDHg9cCbGaydg5hE9VlxUweKh3LPSwGHwret4iUCrQY8LcybDanLNd6lotcCcnuqS16LcmFS70OjRd9edW7x4MWJiYhzul52djcmTJ9vvjxo1qsQVjrOzs9G3b197D7m/vz/Wr1+Ppk2d9EQRh3+XMgMe6YEj239Gu1ZN7I9dibuBbo+OxLhJs5CV5cHCS26kkAGT2hvwVKP8e3i3XVZi0lYNskrSEVyxi/N1hONdNAScSbX30kpcBTvkPqDNIsCvVv77ZV8B9r8MXFmVZzg4AIwYOgD7Ny1Dw3o17Y+dPHMBvZ95FYebN0NGvxcLHA4ecW4XYp4ZjL9+2Cb9mtbZ15hYEwAOAS+pCSOex9/ff42qle62dftiLWj+Zha++NsAq4MhX+01O/Ft6BBcjN+Pt6J9cD3b/UW9BLkCNV57H4LKcWdPduwpxC9f5LLjcQi4ezCpdqG+ffuiU6dOAGxrVT/88MP2XuM7kpKS0L9/f3v1ypCQELz55psOX+/SpUsQBMH+8++//zrcz2AwoH///ti1axcA25rX69atQ5s2bVz0zsooQeAQ8FKmVo2q2PnXd/jw7dG5LkTN+2YFmnd9Ejv3ROfz7NJDEICRLYwY28qQ737RNxR4bZMWybpifunLtUBED8fbkg645sSew7+9lyoIUPhKG4O2EtB6PhDRK//9RBNwdh5w5E3AkLdHuVnjuji09Se8PvoZe8+UxWLBh59+i/f/3YGUMe8iyz//KrpafRq0n0/ATy9OwY0bEk8tyb4G6Aq3kgiVXXKNhKNJyoj2Le/Df7+twNOPPmJ/TGcEXl1qQLep2Yi9kbc7OkSejBkhr+MRYQbeOgisv+b+ImaaKtVRZfgrTrffWPUD0o+5ZmSeJTMd1kLUfqKiEURXry5ezsXFxaFNmza4fv06AFvBpS5duqBmzZpITEzE5s2bkX17LT2FQoH169fnGjae06VLl1CjRg37/W3btqFr16559ps4cSI++eQT+/0WLVrg/vsLqLB6W506dTB+/PjCvj2Xi4mJQePGje33T5w4gUaNHA+BcYv0c4A503PHI5eJPnoKQ0dPRszp87keH/viE5jx3ivw8ysbV/i3XFRgxm41zFbniXOkvxWf9NChsn8xmvO0k8CB0Y631XweqDm06K95r6CmgKxoc9TWrFmTaw17j7cN5YUpHcg4X/B+7iaKtiW1zi4AxAJO9pQBQP3XgYpdHW7+d9dBPPfK+7h05e662AqFAq+NeBItL19DhZh/Cwwnw68ifMZPQc8BbaVdeksbKd38dy9VntoGc1Ymsi/kswwdFcmfW7bh1anTcSvl7nKSWhXw4RNqjH9IBbks74f9hrkSPk6bDLl/c4ypr0eo2n1pkyiKOPf+OKRH73W4XRkShoZf/ARlUMmX4dVUqgpVWClaBrcQpM4pmFS7wenTpzFkyBAcOXLE6T4VKlTA4sWL0bdvX6f7FDapHj58OJYuXVqsWLt06eK0B9wTpP4AIPOS47V6qVQwGIz4YNYifDx3Kaw5Cm8sWzgNzwx2/tkqbQ7Gy/Hedg10Zudn98EaK2b10KNOSBEngYkisPc52/rA99JGAu1/BEpaLCegPqAoWo9LeTpxlpw3XVxMOw0cfx/Q3yh43/CuQP3xtvnh98jIyMLE97/AV0t+y/X4ptULoTwbD/03s6E0F7yk1rU2j6P3tHEIr5C34rjHaCtJP1Tfi5SntkG0WJBx8ojUYZQpt5JTMGHGLPy+YZP9MZkAHJnliybVnF/8/T1rEH7MHouhdWToGmFy28U2Y/ItnHxliNN5zwEt2qHOlC8gyEr2vSzX+sC3tutWHvIGUucUHP7tBvXr18e+ffuwdOlS9OnTB1WrVoVKpUJ4eDjatWuHjz/+GCdPnsw3oSYPkbNYWWmmVqsw/b1XsHfDUjSqb5uX2a1jKzw9yEkBrlKqVaQFnz+gQ5DaecKcopdh/EYtDl0vYtVSQXBesEwXD6QcLdrrOcJltbybtpLUEdwVWB9o+w0QXojinQn/AnuGA9c35Zlr7e/vi4Vz3samVQtRvZptOckhj/dBzy5t0WXEY2j0w3KkVin4ZKvy/lXYM/AJbPptj3RzrXXXAV0hLjJQmSPI5ZA5mWdLxRMWEowln8zA0tkzUSHE1uM7vl94vgk1ADzmuxJfBg/B9vPH8dFxLZIM7smqVSFhqD7uXafb06P34sbqZSU+jkWXDUt2Volfh+5iTzVJSuqrSjAkA1mXPXc8chuDwYgZn3+Ppwc+hDq1quXalp2tg0ajhqyEV3alFpcu4I0tWlzPdP4+FDIR73QwoFv1IsyXMqYAOwcCooNy4hG9gMbvFCPaHHyrAerQIj2lPPVGeYWM87ah4N5CFIH4v4Az8wBr/rUFAAAhrW3LdPlE5tmUmZmNqbO/xoQxQxFe4e6wSbPRhLUffg3/dYshR8GnQvEt+6H7B68iMjKwSG/FZbQR3nUBRCLlrW3Ivnye1ZrdJDk1FbO/+R7vjH4BIVm/QJX0C4TbbUGWXoSvxnHi/Ff2o/ghexwG1VKhVyX39Fpf/moWEv9a6XijTIa6Hy5AQJOWJTqGMjgM2ipRJXoNbyJ1TlG6zzCJSoqFysoMtVqF9998KU9CDQCvTJqFTn1fwNETZyWIzHWqBIj4so8OtYOdr6VltgqYulON1aeL8LetCgbC2jvelrAdMJWwaBOLlXk/bd5kVFKCAFR+GGj7NRBQiGUhkw8Ae4cDF37I8/fm5+eDWe+/miuhBgC5UoFvTp/CtLDGOONXucBDRB5ai0ODB2Ld9xthKfFC8cWguwFkxxe8H5Upcm3ZqA/ijUKCgjD9jdfh6xsIQ/hIZEd9BouyEswWEZ3fz8KQL7IRn5x3hFhfnzVYEDwYxy7txntHtG6pEF71+fHQ1qzreKPViguz3oYxqWTr2ptTkyFaSro2J93BpJrKN65VXebt3BON739ag937j6Jl96fx6tufIC1d4sq+JRCqFfHFAzrcV9F5T7QIAXMPqPHtYVXhh6xWdjIE3GoEbm4teqC5XoPDv72eQutwbrLkfKOAVvOBGsMKnttvNQIXvgf2Pg/c2lPgS69cswnrt+zGzkPH8FpsAr4OaQxDAcmyb3YyfL58C7889RrOnLxelHfiGvqbtsrgVG7I1KwA7ikWn6bIqvENPt9eF9EXrVix24z6r2fis78MMJlztw2h8iRMDX4TT2Aiph7MxG+XVDC7cF1rmUqNWhOnQ+bkooo5NRnnP55UovWrRdHK5bVciEk1lW9yFaQt7Uru9tfGXfbbFosFXyz6GfXaDsCS5WtzFTcrTXxVwMc99OhSLf8h3j+eUOGTPerCfdGHtAbUYY63Xfur6EHmxKS6dNBW8s72UKYAaj0HtF4I+NYoeP/sOODIW8DhN/Od3rNuw077bYPBiF8OH8cIQxg2CaEoaGZcxNmduDR8IH796Edk6z38961PsL1HKhfYU+1ZokyLv47enV+doQNe/8GA+97MwtYTeb9zO2h24tvQQTDc/BUTDqhxOq2IdU3yoakchagxbzvdnnXqGK5+M6dExzAll6y3m+5iUk0kU0kdAbnRzCnjsH7ll6hds6r9sZsJSXjulfdxf+/h2HvgmITRFZ9KDkzupEf/evkPrf77vBKTt2ugL2iKtUwBVOrjeFvGWSAjtniBAkyqSwu52vmFFW8QUM82HLzGUEAoxIlr0j5bZfvTnwPG1Dybly6YihXfzkBkRAX7Y9cSbmHmpVv4nzkCFwqYtaA06xG26jP8/eiz2LXRw+2IPhHIuurZY5IkZCoVhCIuSUjFJwgCfv/qS3w++W0EBfjbH4+Js6LHtGwM/DQbFxNyX6nWyvR4OWAu3tM+ix+Pn8GXpzRIN7nmAmVol96o0HeQ0+2J/6xCwj+riv36Fr0O5iwvWf2hlGNSTcQh4GVe7+7tcXznr5j21mhotRr74/ujT+D+PsPxzKh3cPVa6auuK5cB41sb8Xyz/As57Y5TYMJmLdILqvcU+aDzbfF/Fz3AOzinuvTQRBQuYZWKTAnUet5WITywEAVoRCsQ9wfw31PAhaWAOdu+SRAEPPFYb5zeuxoTxj4LhUJh33Y0/gZG3QQ+yfZHqiX/XuuQxHMwTnoOP46ciqtXPDiU0nALyLriueORZNhb7VkymQzPDRyA6D9/x7DHH4OQYwTPqn1mNHg9E2//rEd6du62oYbyAuaGjkRr3VS8sz8bG64p4YryC1VfeA2+9Zo43X510SfIOBFd7Nc33rpZ7OfSXUyqiVisrFzQaNR4d8KLOL13FQY92ivXtp9++wd12zyGb5f9LlF0xScIwNCmJkxop4dMcP7tfSJRjnEbtEjIyufquU9lILi5423XNwGWQlRhdkS0AFYWQykVZApAU1HqKArmVxNoNQ+o/zqg8C94f0s2cGExsPtp4MrKXH/L/v6++OSD13B85y/o1bWd/XGrVcT6xHQ8m6DCdl3Bp0uRB9fg+BMDsGr2L9AXODTERQxJQOalPEuKUdki03BetRRCg4Mwd8o72Lp8KVo1aWh/3GACZvxhRJ1XM7HjZN7Pei/teiwMGoTkuF/w1kElzqaXLN2SKZWoNWkGFIGO616IFgtip0+EPr54F9nM6amwGor5/U52TKqJ2FNdrlSrUgm/fv8x/l37DZo1vltZU683oK6DyuGlxcN1zJjaRQ+V3PnJ9aU0Ocau1+JyWj6JtbM1q80ZtkrgxcW1qksPTYXSMS1GkAFV+gHtlzn/u72XMQU4O9/Wc31lda7kun7dGtjw23ys+fGzXNNFDBYLOn08G6nVmhb48hpDBoKXz8K6R5/G9j8PeibXNaYAWZeYWJdhTKql1aJRQ2xatgRfT5+KShXuJrbZBhH1Ih2nUj6ybIwK+BLvaobg9+MH8cVJNVJKsLa1Kqwiar31MQS545FElow0nHv/1WIvv2ZMSih2bGTDpJpIXgpOHsnlunRoiUNbf8I3n7+HiIpheKxvN3Run3fNx4IKFnmTjlUtmN1TBz+V85gTsmV4Zb0PYhKdNP/hXQCFr+NtJSlYxiHgpYcgK13rIauCgIYTgdZfAYENC9wdAGBMAs7OBf4bAlz+xT4sXBAE9HuwC07sWok5015HUKA/Xhr+OHr264L+v30LccQ7MKj9IIpivm1DSGIsLFNGYfnQN3EmxgPVuo2pQOYF23B3KnPkTKolJ5PJ8MTDD+Hgn2sw6aUXoVUrMKm/PyoG5f4uvbddqKyIw0chE9DXOB6zDl7Db5dUMBZz4JZ/o/tQbdREp9sN168i9qM3YDUWvdfZlHyLy2uVEJNqotLQI0NuIZfL8eKzj+Hc/j+w4JO38mw/cvwM2j0wDBu37Sk1yXXTcCvmPqBDmNb5yXW6UcDrm7TYE+fgirdcDUQ84PiJqUfzraacLxYrK13UIc4vrnirwPpAqy+Bxu8Wfgi7MRk4txDY9QRw/jt7QTO1WoXXRz+D2INrMO3t0QAAmUKOHi8PQJvffsO/Vdtgwk3gpCH/dqFSzGZcHv44fp74JRIS3FwMyJTOxLqMYk+19/Dz8cFbo19C9Lq1GPHKTzAFdM+1fcm/Jjz6STaOX8mdoLZQH8S84GcRmTQT7+7Pws6bimINLqnw4ABUePBxp9szTx7BhdnvFjlBFkUrTMm3ih4Q2TGpJuLw73LPz88HERXzVj1++8MvsT/6BHoPHIOu/UZg197DEkRXdDWDrfiyjw5VA5yfXBssAt75V4N/zivybqzyiPMXL25vNZPq0senstQRFJ0gAyJ6Avf/ANR+CVD4Fe555gzg4jJg1yDg1Gz7xaPQkCAEBwXk2jW4UijWpafhiAF45QbwToKIWKPzs2OFxYSKmxdj/2OP4vdPf4POnUtwmTJslfpZw6BMEWQyyFQ8V/EmkRXDofGPhK7yu8iqNgdmVRT0RhFTVhqw9qAZzSZm4am52Th3/e5nUSaI6OOzDl8EDkTale8w5ZAZJ1OLXhiy6sgJCLivndPtqXv+xeWFHxe5M8B46ybEUrrUqDdgUk0kU3h3tVuSRMzp8/hn83/2+zt2R6NT3xfwwOOjsefAUQkjK5wIPxHzemejQZjzk2urKODj3RosP6HMfcXcr6bzYbTX1xevYBmHf5c+Cl9A5bgwjteTq4HqTwIdfgaqP134i6dWE3BtHbBnGBD9BnBrT56e3607DuDI8TP2+3t1wKjrwPuJIi7kk1z76FIR+OMM/PPwE9jw4zaYC6gqXmzmLCDjHGD1ULE08gj2Vnsvi+99yK75DX6K6YyrSbbPtSgCP/9nRoPXszB8gQ7nb9xtR9SCAU/5LcVU7UAcOfMbPj4m4EpW4VMymUKBmm/OgKZaTaf73NrwO679ML9IibXVbGJvdQkwqSYCOASc8mhUvxb2bliKnl3a5np807970b7Pc+g9cDT+23dEmuAKKUgDfNpTh3aV8z+5/vqwGl8eVOVe+qOyk95qUzqQsKPowbCnunTSRtp6f0srpT9QewTQYTlQdWDRVntIPgAceQvY/Qxw6Wf70PCeXdti06qFaNOica7dd2YDI24n1+fzSa6Dki9D+ekErO7/HHb+Fe2e+mIWnW19eQsvZpUVXFbLywkKDBzyPn78ZDIaVLs7usViBZZuN6Hea5kYvkCXq+c6QJaOlwLm4TXZIGw+/jfmnZQjQVe4YmYKXz/UmfI5lMGhTve58dtSXP/1+yK9DWPiDfZWF1Mp/qYkciEWKyMH2rZqgk2rF2Lbmq/RoW3zXNs2btuLjg89jx79R2HbzgNeO+daqwQ+7KpH75r5J7WrTqvw0S41THe+7yt2BeRO5tTG/VH0QJhUl05yVelYYqsg6lCg3lhbz3W1QUWb9qOLB2IXATsHAcenAsmH0bNLG+zduBRrfvws1yoCgC25HnkdeC9BxOl85lyHXTsO03sjsOLJcTi081Rx35lzFoOtx9qid/1rk8fJ1Oyp9naCIOCR3v3w35pN+HbqWNSOvPs7u5Nc138tC8/M0+Fk3N3kOkx+C68GzsILliew5uhmfHNGgeRCVApXh1dCnQ/mQu7jvP5F/I9f4cbqZYV+D+ytLj4m1UQA51VTvrp2bIWdf32Hjb8tQLtWTXJt27rzALr3H4WY0+cliq5gChkwqb0BQxrl32u15ZISk7ZqkG0CINcClXo53jEtBkg/V7QgOPy79NJUtA2nLgvUYUDdMUDHFbZh4UUpxiaagJtbgejXgN1PQ7i4DP26NUD0tuVYteQTNG5QO9fuu3XAuBtAcgHDvCue+w9p45/B8qFv4tiBC8V5V85ZjbbE2pzl2tclj2MF8NJDLpdjUP/h2PfnVix652nUrHh3hIxVBH7aZUKHyVnQ3TOiJUJxA/8L/AhDjEOw+vAWLDkrR6ox/+Tap0Zd1H53DgSl886huMVzcX3l4kLHz97q4mFSTQRw+DcVSBAE9OrWDrvXL8HG3xbk6rl+oFu7PCfU3kYQgFEtjBjTMv/50IduKPDqRi1SdAJQ5VHnO8b9XrQARAurEpdWggBoS2HRsvyogm3Dwjv+CtR5yZZsF4UuHrjwPbDrCcgOT8CAliYc3fodVi35JFfPddcG9eBfyB7GiBObkThqMJY//y5ioi8VLZ78WM224mWmdNe9JnmcTK2GUJqnYpRDCqUSTz7xGvb/tQ1fv9UPtSPuFgYd/YAKWpXjhDlScQ2vB36IwYYn8cfhDVh6VkBKPsm1f5OWqPnGh4DMeX2gaz8swLXlXxdqVJ3VbILx1s0C96Pc+OkkAphUU6HdSa53/vUdtq35Gj06t8E7r7+QZ799B4/j0wU/Ij3dzcvoFNGghia800EPueD8i/VsshxjN2gRL9YEgpo53unGlqKfpLO3uvRSBQLKgIL3K20UvkDUk0CHFUCjdwD/ugU/JxcRSIkGTs6EbNfjGFB9Fw7/OgZrf/wE97duivmLZ+C+31YjpcNjsN5OiPbrRPyZIcJgzfsZFCAi4sg/uPHiQCx//l2cOHTRBW8StgtamRcAQ7JrXo8kwWJlpZNSpcETQyZj37pNWPJOH7SurcCrD+U971zxnwk/7TTBZLa1DRGK6xgXMBNPGgdj/ZE1WHLGgiQnw8KD7++GGq+9b7sI6sT1n7/B1W8/K1QvtDHhBqwmTtsqCkH01omAVC7ExMSgceO7xV5OnDiBRo0aeT4QiwFIO+n541KZ1e/pV/Hn+h3w9/PFiKGPYdzIJxFVNVLqsOwOxMvx3nYN9GbnX8DBGisWtPgHlS5OcbxDnZeBqCcKf1D/2rbCUYWwZs0a9O/f335fsraB7rIYgfRTZXvEgSjavguurgIStttGWBSH3BcI72irTRDSCpApcfHoeRycNQ/vbt2Bs0YgUAb087f9hMgdfw5FCEho0hMNXnoeze8vasLvhDYS0JbeefLluW3QxV2GKYXzXUs7wZwK1a2foUhZAzlsF5tNZhG1xmXiapKIyiECxvVRYUQPFYL97rYN6dYArMkehCS/x9AnyheRPnlTuFub1+LSF9PyPX5Il96oPn4KZMr8Czcqg0KhrVq96G9QIlLnFOypJgJsPdX5XN0jKoqTpy/gz/W2CtkZmVn4dMGPqNmiHwY9NxG79h72iqJmrSMt+PwBHYLUzhOkFL0MIw88AKPCydDYq78XbdkeFisr3eQqQBMhdRTuJQhAUCOgyWTb0PBaLxSvUJslC7i+wVY9fMdjQMwM1Kh0HaEjBuHs7QEbaVZgWRrwVBzw8S0R5xxUDBcgouLxTUgeMwQ/D3kNezYeLXm1cF08kHUV7ik7Tu7EedVlg6gIgiHiZWTXXgZD4COwQoFf9pjsy3FdSxbx5nIDqozOwJjvdDgTb7u4FyBLx7N+3+Fl8XGcPfklvj2RiNj03KlcWM9+iHrl3XzPaZO3b8C5D8bDnJn/aDNTahIs2azHUFhMqokAW+PDIeDkIqEhgZj4yjAEBvjZH7Narfht7WZ06vsCWnR7Ct//9Ad0Ommr8tYPtWJeHx0ifJ0n1hkmJX5OHeB4o/4GkLir8Afk8O/STxMOyDVSR+EZ6lCgxrO25biafwxU6AQIzucsOmXOtCXYx95FrYT3MfrRKPho7/YQmQBszAJeug6MvyHi3ywRZgcJb8UzO6Cb9Dx+fWwEtv66CyZzCUYMGG7ZhoOX5VEHZZCMy2qVKaKyAgyRryGr1g9o3KQbhnZRQZmjick2AAs22iqG95mehb+iTbBYRagFA/r5rsKrsiehO/8elhw5hQOJcvuymBUeeBTVX50CyJyneRlHD+D0Gy9Afz0u3xj18Ve8oiOgNGBSTXQHK4CTi1QMD8XH749H3PH1+GL6G6hVo0qu7UeOn8EL46aiSpMH8caUz3D9RqJEkQJVA0TM76NDrWDnw1zXZA2ASVQ43nh5ReF7vNhTXfoJAuBTVeooPEuQA2FtgWbTgE6/AXXGAH41i/VSUSEmzH8qBVe/1OCjJzWIDM19MfeEAZh2C3jqGrA0VUSGg8rhFa5EAzPHY+2DT2DdwrXIzMy/+KBTpnRbZXB+LksN9lSXTaIqAjXvn4Yv56zG6R/6Y9JjGoT65+5p3nDUgoc/1qHO+Ex8staAbIMImSCig2YnxqnHIOr6i1gZvQWb4qwwWICw7n1Rc8I0CHLnFwL1cZdw6n/DkRa91+k+Fl02jIksWlYYTKqJ7mBPNbmYn58Pxo0agjP7fseaHz9Dt46tcm1PTknD7C+XIT1D2uFVoT4ivnhAh2YVHSfWydYwbNH1cfzk9NNA6vHCHYg91WWD0q/o1bLLClUwEDUIaPc90PZb25rXqtAiv0yIn4C3H1Ph0lw1lo/Tok2t3KdjSRbg5zQgv37k4KQL8PnmA/zb52GsfP87xF8tRhEyczaQfsb2L3k9QS6HTJH/PFgqvURVBMKavIu3312NMz89ha9G+qFx1dxtw8UEEbPWGiG/J4OrrTyHET4foWfa49hxZDFWn0uCuVVv1J78eb4F7iwZaTj3/jhc/3Wx0wJmxoR4WPRc774gTKqJ7igr67CS15HL5ej3YBdsXfM1ju/6FaOGPw5fX9uXXPdOrVGvTvVc+1utVly8fM2jMfqpgFk9dOhczfEc6V+ynnH+5MsrCncQ9oiVHdpIQFbOT+79a9vWvO70K9BiDhD5EKDwK/h5OSgVAoZ0UGLfdD/s+8gXz3RUQHW7Y6mLLxB4TwEzvVXELXPu3muf7GSErl2AEwMews8vTcWRPWeLNl3aarL1WBtTixQ7SYNDwMs+URkOZfXX8NRLq7H3u5HY8F4oHmutsCfSz3dTQq3M3TakZolIyrAiUJaGx32WYahpMJJOv4MtSgUwcQEUAUH5HFDEtWULcG7qazCl5r04J4oi9HGXOAy8AEyqie5gTzV5QOMGtfHVnHdw7cR6zJ0xEW+OH55nn03/7kXNFo+gR/9RWP7bPx6be62WA1M66dG/bt4e5Yvm2tivv9/xE2/tBjIvFXwA9lSXHTI54FOl4P3KA0EOhLQEGk4EOv8ONJsORDxgqwBeBG1qy7HsFR/EfeWH6U+q8MaTagTc00v1bzYw5BrwToKIXdm5514rLCZU3L8GyWOG4NfHXsSGpZug0xXyQpZoBTIvArobRYqZPI9DwMsPURECS8SLaDdgNX78+DWcmh+FdweoMLJH3vPV+RuMiHwpE098no2NR80QRSvaanZjuGIiGmo/QtLzj8FSMf+pO+mHduPkuKccDge36LJgvBnvsvdWFnFJLZKU1OXvc7HogbRT0hybKIfHh03A6nVb7fcDA/wwZEAfDHvyYbRt1QSCmyvViyKw7LgS3x/NPXqjhWo/Pg0d7fhJFbsBTZwsvZVTUFNbQlaA8rxsTqmSeQkwpkgdhXeyGoHkQ8DNHcCt/4q+rjtsPUQp5y24vM2IlHMWjL0u4lSOa1NBMqCHL9DHD6ipytsuZPmEQt91AFo9+yhq1KtUuIOqAgGfqEJ9TqVQ3tsGU2oydFddtH45lS6iCfK0bbAm/AZ/S6z9YavVthzXpcS7KV3VUAFDOysxrIsSdSrZPsuGbGDvMiUs5wueKlKhzwBUeW4c5D65Lw76RNWGIiDQRW/ItaTOKdhTTXQHC5WRFzCZTDh7/kqux9LSM/HVkt9wf5/haNDucUz/9Dtcvuq+K8aCAAxtasKEdnrIhLtf0tHG1jhncrJW7s1/bdWECyJyCHiZ4lOFw8CdkamAsPuBRm8CnVYDLT4Dqg4o0hJdgiAgpLYC943wQa0XtUi7J89NtQKrMoAR14FR8SJ+SxeRnKO4mW92EkL//gYXhjyCFU+Ox9ZfdsBgKOAzaEwDMs7aLjST18lvfiyVcYISlqAHINZZhKxqs5Gibg+rKOBiggjdPQPBriaJ+Oh3I+q+moUO72Vh0SYjsq0iOr1oRNXOBbfZietXI2bsk0jdvzPX47q4S7AaOerMESbVRHdwWS3yAkqlEsd2/oJdf3+P557qZ597fceZ2Et456P5qN78YXTtNwIr12xyWywP1zHjg856KGV3TtIF/JT5nJO9ReDCkoJflEPAyxaZgsPAC0OmAELuA+qNAzqssBU5q/k8EFC/0C8RVVeBq9/7489xGjwUJcM9UyoRawIWpgBPxAGTborYr7ubXAsQEX52F/Dxa9jY82GsfHs+zh6/6vxgFj2QfpajELyQTK1x+2gl8nKCAItvC8hrfois2j8gtM4gxC6IwB8TtHikpSJPEbPdZy146Vs9IkZlYOBnOiTWUaDRUxrICzjlNSbeQOy01xE7/Q0YEmxTQ0SLGbrLsRAtzlcMKa+YVBPlxGJl5AUEQUCHts3x/bz3cePkJiye9z66dGiZZ7/t/x3CgcMxbo2lUzULZvfUwVdpO0Hfru+BC6ZajndO2GEreJQfFisre1RBgDpE6ihKD0GwFTmrORRo8xXQaRXQYCJQoTMgz78XUiYT8HAHFf6a5Ye4L/0wrbMCDe+pW2UFcEAPXHHyUfPNuoXQ9d8jblh//Np/BP7+ai1SkjLy7ihabMP7s+MKv2weuZ0gCJCpy8la8VQgUVUZsipjYG30K7r1nYilk5rg2kI/zH5GnadyuNEM/H7AjKtJIio2V6LVOF/4RRacCqbu+RcnXh6IuB8WwJKdBYteB93l8yxcdg8m1UQ5saeavIyfnw+GP9UP/679Bhei/8QHk15CnVrV7NufevzBPM9ZsXoDfl+3FXp9MdevvUezilbM661DmNYKETIsyRzpdN/UE1/nfwLOnuqySVuF7WdxqUOByg8BzaYCXdbaholXGwz4Vs/3aeFhMrw7xgfHv/XHzpfUGF5NQPjt4eECgK4OikT/lWHrwb5T4CzsSjQ0X3+A/Q/2xooX3sWO33fnHR6uT+RwcC/DIeCUh0wLhD4Ced2v4dv0Szwz6GEcmhWMQzN8Me5BFcIDbaMb/DTAIy0VAADfcBlajfVBlY5KrEoXcUQvwuLk+1s0GnBj5WIcH/kYbvyxHMaUW9BfvcjEOgcWKiNJSV1UIA/dTUDH6obk3URRxP5DJ7Bh2x68N2FErqGAoiiiZotHcOlKPPx8ffBIn84Y+EgP9OnRHj4+JTsRu5Ep4I0tWsSlA9+EPYPayrMO94sOn44WTds7fhF1COAbVeCxynsxolLJlFnwSAUqGv1NIOkAkLTfVvTMnP+a9unXzPhjrRF7T5gx0Df3EGGjKOLxq0C2CPjLgA5aoLMv0EIDKHO0IdnaYGS16YPa/R5A8y5NIJfd3ibIbEP91UVfl9uV2DYAhoQbMNz07LKLVApZ0mFK2gQh+R8EmM9j0zEzLt8S8VKv3BdAE9KsqDQqE1YRCJEDHW+3DU3VgNzJVANlaDgiHnsalQY9B9+6jbxiSoLUOQWTapKU1B+APIyptmVFiEqpA9ExaNPr2TyP+/ho8GCPDhjwcHf0faAjAgP8i/X6qXrgrW1ahGTsxEchExzuc90ciZ1VfsSgRjLk+Z5V+tuGvhaAJ86lVHa8LREk17OagfTTtiQ7+QCQdhq2wd55mfUibkSbcG2vCVk3bPv8ly1icmLefX0FoJ0P0MkHaKUBtLK7H9r0wEiY2vZG3X490KRdfchkwu3q4NVs88QlwLYBMKenIftybME7EgG20WO608i4uQGhui3QCLkvzn21yYiXv807EiVQBnTwATr6APdpAJWDxFnuH4iIAc8gavRb0FSMdNtbKAypcwppWkQib8UK4FTKaTQqPDXwQaz9Zzsys7Ltj2dn67Hqzy1Y9ecWKJUKdO/UGo8+2BWPP9ID4RUKPx82SAN82lOH97d3wHFjMzRRHc2zTyVFPLLOrcB83XCMbmWELOf3MOdUl23aSoA5s8AeVSoGmQIIamz7qfUcYMoAUg4DSQeB5IO5RlkpNAKqtFeh8v1KpF+x4to+I4L3mtBRC+zTATk/hVkisCXL9qMWgJYa0X4iHZAWD2xcjKSNi/FnUCSMbXujzkPd0KRjBuT+1QBVsOf/H4jDv6loBAHwaQD/Gg1gtL6MjJRdMCZtQiXzQcgEK2pUkOHB5gpsOm6GOUf9sTQr8Hem7cdHANpobW3D/dq7F98sGWm4tnQ+rv24CBUeGoiqz76MwNYdvaLn2tPYU02SkvqqUh6iFUjJmyQQlTY6nR4btu7ByrWb8ef6HcjIdJzk/Ln8czzcu3ORX99sBZb8dxnP6Z+DXMjbW2YUlRh9azGiqtTCpPYGKO8sBSTIgOBmBb4+e6NKMYvR1qMqsjqsR+mu24aIJx0EUqLzrItt1ou4ecSE2L1GbDpnxY5sYL8OMDg5C1wYAdRVOz4xNvgHI6tFL1R5sC/u6zcA6nvWsnUntg02GTFHIFr5GaMSMN5CeuJWKNO3oCLOITlTxJoDJqzca8bm42aYnPx5/VIZCFM4T5pllSuj0qARiBr8HDSRVd0UfF5S5xRMqklSUn8AHEo9wd40KlMMBiM2b9+H1eu2Yu367biVlArANiT81tmt0GpzV5L9aM63aNqoDrp3apNnSa+crCJwctdcNDasdrj9irkaRt36EY0iVJjaRQ+fO0tjBjUpcOgoT5xLOU6lkZZoBTLO23qwk6OB1GOA9W7hwqybFlw/ZMbFg0b8d1PErmxgr87Waw0A4XJgeWXkqdfwfSrQTAM0zTEU1KpRQ9eoNXx6PI0Wjz+O4Iphbn1rbBtsss6fgSU7U+owqIyw6i4i+ea/CMzeilDhGlKzRKyLNmHVHjM2HDVDZ7btV18FzK+UO6HWW0X8mAa00QIN1YDiTrshALK6tRDS92nUe/IlqMMj3PoepM4pmFSTpKT+ADiUEWsbVkdUBpnNZvy37yjW/PMvLBYrvpjxRq7t8dcTUblxbwCAWq1Cl/Yt8GDPDniwRwfUrR2Vd0iXKR2Gnc9CbU1zeLz12Q9jZtr7qBtiwcfd9QjWikBAPUDhoDRxDjxxLgOyrwH6BKmjIMBWdT81xtaTnRJ9eySBFaJVRHKsBTejTYg/bsKhdGB3NhAsB4YG5f6snzWIeNm2VC00gm2OZRut7SfiTq+VTIAlqirQ9gFU6fci6rVrBZnMtQvNsG2w0V+7AmOyg0nyRCUhijBmnUFKwg6E6bchWLiJbIOIv/eZ8PM/RlRJt+LRe4og7skW8e7tP0VfAWh5u11orcnRoy0A8pqVoOnQC9UHjUXFJq1cHrrUOQXnVBPdS6YGwKSayiaFQoEuHVo6XPcaAP7evMt+22AwYuO2vdi4bS9ee2cOqleLRO/u96N3t/vRrVNrBAX6A8oAqOu/BJz82OHr9fFZh2uWKliW/CLGbtDikx46RPoZAeSfVFMZoI0EzNm2OdYkLZkKCLnP9gPY5rynHIWQHI3QgGiE1r2AegM0aBxjxoNHTEg6a8kzen+v7u5tvQjs0dl+AKCqQkQrLdBaK6Lp+cvQXvwW11d8ixsBaogNG0LbsR/qPjYCoZWlLWRUlnBeNbmFIEDlVx8V/eoD4otIyDiDlMQd6NZxBwZ2vg5DhhVXd5pwbY8RltuDX3K2DVkisCPb9gMANZW32wYN0Dg2HpbzPyDmhx9wJlQDWdPG8O/SH3X6j4JPkLSrCrgCk2qie8k1Be9DVEbVjKqMgf16YuO2vUjPyJ0MXboSj0VLVmHRklWQy+Vo06IRvv18MhrW62Nb8ufmNoev+YL/VwCAZRkvYsx6LWb11aNOFbe/FZKaIAB+1YH0M5xS420UvkCF9rYfADAkQ55yGBHVoxHR/jBMSdeQGGNGwlETUmItEK1ALZWtQNFhvS2pzumqGbiaAfyeYTuxbKQW8XYYEJZuAPYehm7vYRyd8wEUkQEQGjWFf6d+qPPI8/ANKnyRRMqNSTW5nSCDJqABKgU0AMSRuJl5HsmJ/yGoz0506HYe8QdMiNttRKNMETfMwFF97iKIAHDBZPv5Nd1WCLGpWsT7FQBNkh7YdhBJ2w4iedq7UFYPhqxxCwR1eRy1+wyFyqf0XXhnUk10LzkrgFP51b1zG3Tv3AYmkwm79x/DP5v/wz9b/sOxmNzrD1ssFuw9eBwVw0NsyVOD/wFppwD9DZyJt6BOhMy2/M5tL/h/hZqKWMxPfx3j18rw0SMBuK9q6fvSpCKSKQG/Grb1qznbzHupQ4CIHrYfAErddUS2PIzIlGgYrh5E0pFEhBwzoWOsBQaziGN6W5Gz/TpbQp2TGcA5IxAkz/24aBVx4VIaqsbtRPLGXdj//kQoqwVBaNAEfu0fRu2+w+EbXMEz77cMkDOpJk8SBGj9a6Oyf20Aw5CcHYfMCgcR0XEnXj99CE/tNyDupAnR2cCB223DjXtGuxhE4JoZ0MhyDx83mkXEnU5GZOxm6NdsQcKkMVBWD4XQqDkCOz6K2n2GQu1bvGVAPYlJNdG9ZOypJlIqlfZh4jOnjEP89URs3LYHG7btwebt+3ArKRUtmtZHaEiQ7QkKP6DJZKTvGItG/0tHoI+ALg3k6NpIjq4NFWhcVYZu2s3opt2M48ZmOLOlMdQN2qFhww6AX03kXdCaygyFr21d46zLUkdChaWtBFSuBFR+COpGIiI7X0FkcjT0V/cjafcBVDmRgbanzbAYgRtmEQd0wEGdrRc7S7QVM1Pc85m+bgaeiweCZUAzjYjmGqCZPgVVL+yA4e+dSJ7yJlSV/SHUqw9t656o/tBzCK5S8Jr25ZUgl0OmVMFqMkodCpUTMoUSioAgKPwD4e/bHOHyRwEAxuwkZJz6B1Exq1Bn7zb0is5E5nUL4sy2BPug3taLrReBFg5OsU8ZgNdu2gok2toGEc2yEhFxZhP0v29G4puv2C7A1W8I3za9UfOh5+Af7n3D3ViojCQldVEBp1KO2qqnElEeVqsVh4+dRkZmNrp2zF1s5K8Vc/HwmCV5nhPiJ6BTfTm6NJSjcwMFmkXJoJDfPumuPRJo8Umu/VmMqAzKjgf0N6WOgkrqdmVx440DuL5zG7KiTyHllB6GVBEWUcRpAyATgAb3LMf1V4aIT5PzvlywzFZNvKkGaKoGqisBmSAAAqB5uAvaz8s9rYRtw13Zl2JhznBcJJLIFQRBgNw/EKrQcCj8Cu4tFi0mXL+0Fwnbl0L8bwP0R+OhuyXCKIqIMQCBMqCmKnfb8EOqiKUO/ozD5XfbhaYaoIri9ooEMsB32JNoO2V5rv2lzinYU03kiEwNWHQF70dUDslkMrRs3tDhtnOpoZDLZbBYcl+USs4UseagGWsOmgEYsH2KDzo3tH0F7Uqqjg6imLeyOJUtPpG2ZZ2MqVJHQiUhyICgBlCF3IeoJmMByGBNPYX4PX8iZfsmBJ+4iIzLpjzXpePNgADg3p6cFCuwPdv2AwA/RAKVlbYdlVH13f9+SjG5RsukmtxGGRgMdcXKkKkLPy1SkCsRWasTImt1Ap4HMtOu4+KWpdBt+RUdjp1CdpwhTyMQb3b8WgkWYHOW7UcG4I+qturisAI+dR0XW5USk2oiR+RMqomK49WXnsbzTz2KnX9/i3/X/4JtMSYcvmiFNceXqFoJtKl9d8LlstgoVGlhRPVQ1jMo83yrA9ZYVgQvTWQKQO5jG8av8AHkWttc+Zy7+FZFlYEPoMpAABYj0i/8iyt/LoHuwC4YT8fDkGLFiGABTwSIOGYAjuhtPxfvqWoUKgcic5yZVu491P3vrxRjsTJyB7lGC03lKMh9fEv8Wn6BldBkwCRgwCSIViuundiB+LWLYDm0E6az12HOEjEpTMDIYBFH9MCx223DvbUa6qgA39tzsQU5UOuhYSWOzdWYVBM5wgrgRMUWEOCHvk++ir59egGn5yAt4Tz+O2PG9pMW7DxtgY8K0Nwe/mUQ1Xiye3sm1OWFINwtXGbRSx0NOSLIAaUfoAiw/VvU70O5CgF1HkDj1x8AAFjNelzb8ztubloB7ZH96BqbgI7ZtqtsaRYRxw22E+njBqCqEvYRK8pgBSKb3O/St1bWMKkmV1OFhkMdURmCi9eXBwBBJkOVpl1RpWlXAIDJqMeFTT8heduvqHQsGmGXktD9domAZIutIOKx2+1D0xzNkKqKP3wCw1weX0kxqSZyRMYTfKISC2oEtPkagVdX48HAn/HQfSkAAGuObutMbUN0q8dldcoVmQLwrw2knwWsLLLkFeQaQBkAKANtPdIunIohU2hQtdMQVO00BABgMelxcesvSNq2EpoT0Qg5fxMddbY2wZqjzI+sVlWXxVBWydQaCIIAlkeikhIEGTRVq0MZGOyxYypVGtTr+wLQ9wUAgDE7E7Hrf0Dqjt9R6dRRhF1MQldj3rZBqOud00KYVBM5wmW1iFxDpgCiBkOoOgDIOIdjsWcRH38O9RQnUV15EaFV2kgdIUlBprQl1hnnuIa1VOQaQBUEKIMAhed6POVKDWr3HobavW3DNy1mEy7t+B23tq2E+cQBWM5fgzndAmXTdh6LqbQSBAEytQYWPaerUfEJcgV8qtd2yXDvklD5+KHhgNHAgNEAAJNehwtbf0XKjtWwxByC5cINmLOs0LbuKWmczjCpJnKEy2oRuZZMAQQ2QNOWDZAaGYo/bmrwaictYDFIHRlJRa6+nVjHMrH2FJkSUAXbfhTesU68XKFEre6DUav7YACA1WLBtcPb4VuRPdWFIdNomVRTscmUKvjUqFukYmSeotRoUe+hYcDt+dNWiwWX961HhXreV6QMYFJN5JhMbjv54Ikekct1ru2Lzo1CbXeUBS/RQWWYXMPE2t0EwTa0WxVq+9fLq+zL5HJUbdVd6jBKDZma86qpeGQqtS2hVqmkDqVQZHI5arTvK3UYTjGpJnJGruFJHpE73FM5mMo5JtbuIVfbEml1qG2kCJVJcq13jDig0qW0JdSlAVtZImdkagAZUkdBVPbI+CVO95BrAP+6QOZ5VgUviTu90uoKHAVSTrACOBWVbQ51HSbULsakmsgZLqtF5B5MqskRuQrwrwNkXgDMWVJHU7rIlLYeaXUYR4KUMzKlEoJcAdFiLnhnKvcEQQaf6rW9cg51acekmsgZVgAncj2ZAhBcv/4llRF3ltvKugwYU6WOxvspfG2JtCrY6+dKk/vINVqYsziyjgqmqVpD8irfZRWTaiJn5BxSReRy7KWmgggywK8GoLsO6G5IHY33EQTbMliaCrakmso9mUYLMKmmAqgrREAZGCR1GGUWk2oiZ2RKW6+JlUOqiFyGQ1OpsLSVbBc3s64AokXqaKQnU9p6pdWh/BxRLpxXTQVR+AVAHVFZ6jDKNCbVRPmRaQBrptRREJUd7KmmolAF3U6sLwHmbKmjkQaHeFMB5EyqKR8yhRKaqjWkDqPMY1JNlB+5BjAzqSZyGSbVVFRyta0yuP4GoL8JiKLUEbmfILNdUFBXABRcMonyx55qyo+mag3IFEz53I3/w0T5YQVwItdiUk3FIQi24eDKANtw8LK67JZcfbtXOoRrS1OhCTIZZCo1rEaD1KGQl1GFhkPhx+X1PIEtNlF+mFQTuRaTaioJhS8QUB/QJ9h6rkWr1BGVnCAAykDbXGllgNTRUCkl02iZVFMuMrWG86g9iEk1UX5YAZzItZhUU0kJAqCtaJtjrL8OGJKljqh45FpAHcJeaXIJudYH5vRUqcMgL6KtUh2CjEtYegpbcaL8yBSsAE7kKoKMyQO5jlwF+EYB6nBbr3VpWNdaprRdDFAFc640uZRMzU4AuksVUoHrUXsYz26ICiLXAlau/0hUYuylJndQaG3rWlv0tmHhxhTvGhYuU9qGd6uCACXnNpJ7sAI43SFTKDnsWwJMqokKIlMDYFJNVGJMqsmd5BrAtxqgrWxLrI3JgDlLmlgUPrb50coA2zxwIjeTqdUQBBlEb7qgRJJQR1aFIJdLHUa5w6SaqCCcV03kGjKl1BFQeSCTA5ow24/FCJhSAVOGbXlEdyUcCh9A7gMo/QCFP6c5kCRkGi0sOokuJJFXUPgFQBkYLHUY5RJbfaKCsAI4kWuwp5o8Ta4C5OGAJty2vrUlGzBn2/61GGxDxkVL4V5LEABBaVv2Sqa2fTfItbaEWmAxIJIek+ryTRAEqCtVkTqMcotJNVFB2FNN5BpMqklKgmAbin3vcGzRClhNtuRatAK405st2JJlQQ4ICvY+k9eTa31gSpE6CpKKMjiMc+slxG8IooLI5LZkwGqUOhKi0o1JNXkjQWbrfSYq5WRMqMotQSaHqmKk1GGUaxyvRFQYCn5REZUYExciIrdhL2X5paoQAZmCfaVSYlJNVBgcAk5UMoKMhcqIiNxIkMshU3JEUHkjUyihCguXOoxyj0k1UWEwqSYqGQ79JiJyOw4BL39UFSIgyJjSSY2/AaLCkPtIHQFR6cakmojI7eRanq+UJzKlCsrQClKHQWBSTVQ4cpWtAiwRFQ+TaiIit2NPdfmiqhABQRCkDoPA6t8kMYPBkOt+bGysRJEUQtY1wMz1H4mKRZ0OaNILvfv58+dz3ffqtoGIPIZtQ/4sBgN0l88XvCOVejKFElpBDeFGgtSheIV724J7cwx3E0RRFD16RKIc1qxZg/79+0sdBhERERERlRF//PEHHn30UY8dj8O/iYiIiIiIiIqJSTURERERERFRMXH4N0kqNTUV27dvt9+vWrUq1Gq1/X5sbGyu4eF//PEHateu7ckQyQH+XrxTWfq9JCYmYuPGjfb7vXv3RlhYmP1+WXqvZQl/L96pLP1e2DaUTvy9eKey9HsxGAy4evWq/X6XLl0QFBTkseOzUBlJKigoqEjzHWrXro1GjRq5MSIqDv5evFNp/7107dq10PuW9vdaVvH34p1K+++FbUPpx9+Ldyrtv5cWLVpIdmwO/yYiIiIiIiIqJibVRERERERERMXEpJqIiIiIiIiomJhUExERERERERUTk2oiIiIiIiKiYmJSTURERERERFRMTKqJiIiIiIiIiolJNREREREREVExMakmIiIiIiIiKiYm1URERERERETFxKSaiIiIiIiIqJgUUgdAlJ8KFSpgypQpue6T9Ph78U7l6fdSnt5racLfi3cqT7+X8vReSxP+XrwTfy+uI4iiKEodBBEREREREVFpxOHfRERERERERMXEpJqIiIiIiIiomJhUExERERERERUTk2oiIiIiIiKiYmJSTURERERERFRMTKqJiIiIiIiIiolJNREREREREVExMakmIiIiIiIiKiYm1URERERERETFxKSaiIiIiIiIqJiYVBMREREREREVE5NqIiIiIiIiomJiUk1ERERERERUTEyqySsZjUYsW7YMDz30EKKioqDRaFCpUiW0b98es2fPxq1bt6QOkW577bXXIAiC/ad69epSh1Tu7N27F2PGjEGLFi0QEhICpVKJgIAA1KlTB4MHD8by5cthMBikDrPE2C6ULmwbpMe2gW2DN2LbIL3y0jZ4lEjkZU6dOiXed999IgCnP+Hh4eJff/0ldajl3r59+0SZTJbrdxMVFSV1WOVGcnKyOGDAgHw/K3d+atWqJf73339Sh1xsbBdKF7YN0mLbwLbBW7FtkFZ5ahs8TRBFUXRZhk5UQnFxcWjbti3i4+MBAIIgoHPnzqhduzYSEhKwefNm6HQ6AIBSqcQ///yDHj16SBlyuWUymdCyZUscP3481+NRUVG4dOmSNEGVIzqdDp06dcKhQ4fsj1WoUAH33XcfqlSpgsTERMTExODChQv27T4+Pti6dSvatm0rRcjFxnahdGHbIC22DWwbvBXbBmmVp7ZBElJn9UQ5de7cOdeVy6NHj+banpiYKPbo0cO+T0hIiJiSkiJNsOXctGnT7L+Hp556ilecPeyDDz6w/5/LZDLxww8/FLOzs3PtY7VaxZ9//lkMDAy079u0aVOJIi4+tgulC9sGabFtuIttg3dh2yCt8tQ2SIFJNXmNv/76y/4BVqlU4rFjxxzul5mZKdasWdO+71tvveXhSOnUqVOiWq0WAYhPP/20uHjxYn45elj16tXt/+evvvpqvvv+9ttvuYZ0OftseSO2C6UL2wbpsW3IjW2Dd2DbIL3y0jZIhYXKyGvMnz/ffnvYsGFo0qSJw/18fX0xdepU+/1FixbBbDa7PT6yEUURL774IgwGA4KDg/Hpp59KHVK5k56enmuo3JAhQ/Ldv3///vDx8bHfP3v2rLtCczm2C6UH2wbpsW3Ii22D9Ng2SK88tQ1SYVJNXiEzMxNbtmyx33/uuefy3X/gwIHw9/cHACQnJ2PHjh1ujY/uWrhwIf777z8AwCeffILw8HCJIyp/MjMzc90PCgrKd3+5XI6AgAD7favV6o6wXI7tQunCtkF6bBscY9sgLbYN0isvbYOUmFSTV9i9e7e9dL+vry9at26d7/5qtRrt2rWz39+6datb4yObuLg4TJo0CQDQqVMnPP/88xJHVD5VqFABGo3Gfj8mJibf/RMSEpCQkGC/36xZM7fF5kpsF0oPtg3egW2DY2wbpMO2wTuUl7ZBSkyqySucOnXKfrtJkyZQKBQFPqdFixYOn0/u8/LLLyMjIwMqlQqLFi2CIAhSh1QuKZVKPPjgg/b706ZNQ3Z2ttP9J02aZL/K3KNHD9StW9ftMboC24XSg22Dd2Db4BzbBmmwbfAO5aVtkBKTavIKZ86csd+Oiooq1HOqVatmv3369GmXx0S5rVixjk+p8gAADhJJREFUAuvWrQMAvPnmm2jQoIHEEZVv06dPh5+fHwDg8OHDaNq0KZYuXYrY2Fjo9XpcvXoVf/31Fzp16oTFixcDABo0aGC/XRqwXSgd2DZ4F7YNjrFt8Dy2Dd6lPLQNUir40h6RByQlJdlvV6xYsVDPiYiIsN9OTk52eUx0V1JSEsaPHw8AqFOnDt555x2JI6L69etj165deOSRR3D16lWcP38ew4cPd7hvUFAQnn76aUyfPj3XHClvx3bB+7Ft8D5sGxxj2+BZbBu8T3loG6TEnmryCjkLKGi12kI9J+d+9xZgINd67bXX7HNrFi1aBLVaLXFEBNjmOJ09exbz5s2Dr6+v0/169+6Np59+utR9MbJd8H5sG7wT24a82DZ4FtsG71TW2wYpsaeavIJer7ffVqlUhXpOzgZap9O5PCay2bhxI5YtWwbAtmxJt27dJI6I7khMTMTEiRPx008/wWQyISIiAh06dEBoaCjS0tKwb98+XLp0Cb/88gt++eUXjBw5EgsWLIBcLpc69EJhu+Dd2DZ4L7YNebFt8By2Dd6rrLcNUmJSTV4hZ0VCo9FYqOfcqfwJFP5KNRVNVlYWRo0aBQAIDQ3F7NmzJY6I7jh37hy6deuGa9euQa1WY+HChRgxYkSuLz5RFLFy5UqMHDkSaWlp+PrrryGXy7FgwQIJIy88tgvei22D92Lb4BjbBs9g2+C9ykPbICUO/yavcKdwAlD4K8g598v5fHKdd955B5cuXQIAzJkzB2FhYdIGRAAAs9mMAQMG4Nq1awCAr7/+Gi+99FKeK8mCIGDw4MFYvXq1/bGFCxdi//79Ho23uNgueC+2Dd6JbYNzbBs8g22DdyovbYOUmFSTVwgNDbXfvnnzZqGec+PGDfvtkJAQl8dU3kVHR2PevHkAgG7dumHYsGESR0R3rFq1CidOnABgKzwydOjQfPfv3r07evXqZb9fWip5sl3wTmwbvBfbBufYNrgf2wbvVV7aBilx+Dd5hXr16tlvX758uVDPuXLliv12/fr1XR5TeXfs2DH7GoVXrlxBu3btnO6bmJhov339+vVc+7733nvo27ev+wIth9avX2+/3bVr10I9p3v37ti0aRMA4ODBg+4Iy+XYLngntg3ei22Dc2wb3I9tg/cqL22DlJhUk1fIuXbh8ePHYTaboVDk/+cZHR3t8PnkeufPn8f58+cLta/RaMS+ffvs93N+cZJr3Bm+BeTusclPzv3S0tJcHpM7sF3wfmwbvAvbBufYNngW2wbvUl7aBilx+Dd5hfbt29src2ZlZRV4RcxgMGDv3r32+927d3drfETeJGeRncKut5pzXdegoCBXh+QWbBeIioZtg2NsG6i8Ky9tg5SYVJNX8PPzQ48ePez3lyxZku/+q1evRkZGBgAgODgYnTt3dmd45dLw4cMhimKhfnLOtYmKisq1bfjw4dK9iTKqWrVq9tvbtm0r1HO2bt1qv127dm2Xx+QObBe8E9sG78W2wTG2DZ7BtsF7lZe2QUpMqslrjB492n578eLFiImJcbhfdnY2Jk+ebL8/atSoAod9EZUlPXv2tN8+ffq0fT1QZ7Zu3WqfFwUAvXv3dltsrsZ2gajw2DbkxbaBqHy1DZIRibxIp06dRAAiALF69erisWPHcm2/deuW2KtXL/s+ISEhYkpKijTBkt3ixYvtv5OoqCipwynzTCaTWK9ePfv/uUajERcuXCiazeZc+1mtVvGXX34RAwMD7ftWrVpV1Ov1EkVePGwXSi+2DZ7FtoFtQ2nBtsGzylvbIAVBFEXRE8k7UWHExcWhTZs2uH79OgBAJpOhS5cuqFmzJhITE7F582ZkZ2cDABQKBdavX59rCBhJY8mSJXjuuecA2IZx3Vmjktxn37596N69u/3zAACVKlVC+/btERYWhrS0NOzduzfX70KtVmPTpk3o1KmTBBEXH9uF0ottg+exbWDbUBqwbfC88tQ2SELqrJ7oXqdOnRKbN29uv0Lm6KdChQriunXrpA6VbuMVZ2ns27dPrFu3br6flTs/NWrUEHft2iV1yMXGdqF0YtsgDbYNbBu8HdsGaZSntsHTOKmEvE79+vWxb98+rFixAj///DNiYmJw8+ZNBAUFoWbNmnjsscfw/PPPIywsTOpQiSTVpk0bxMTEYO3atfjjjz9w8OBBxMfHIzMzE76+vqhYsSJatmyJfv36YeDAgVAqlVKHXGxsF4gKj20D2wYiR8pT2+BpHP5NREREREREVEys/k1ERERERERUTEyqiYiIiIiIiIqJSTURERERERFRMTGpJiIiIiIiIiomJtVERERERERExcSkmoiIiIiIiKiYmFQTERERERERFROTaiIiIiIiIqJiYlJNREREREREVExMqomIiIiIiIiKiUk1ERERERERUTExqSYiIiIiIiIqJibVRERERERERMXEpJqIiIiIiIiomJhUExERERERERUTk2oiIiIiIiKiYmJSTURERERERFRMTKqJiIiIiIiIiolJNREREREREVExMakmIiIiIiIiKiYm1URERERERETFxKSaiIiIiIiIqJiYVBMREREREREVE5NqIiIiIiIiomJiUk1ERERERERUTEyqiYiIyOtt3boVzz33HOrXr4+AgACoVCqEh4ejS5cumDlzJlJSUqQOkYiIyilBFEVR6iCIiIiIHLl58yaGDh2KjRs35rtf1apVsWnTJtSrV89DkREREdkwqSYiIiKvlJycjI4dO+LUqVP2x5o3b45mzZpBqVTi5MmT2L17t31bp06dsGPHDilCJSKicoxJNREREXmlIUOGYMWKFQCAatWqYfny5ejQoUOufdatW4d+/frhzunM9evXERER4fFYiYio/GJSTURERF7nwIEDaNOmDQDA19cX0dHRqFu3rsN9W7ZsiejoaADAwYMH0bJlS4/FSURExEJlRERE5HW++uor++1XX33VaUINAH5+fg5vExEReQJ7qomIiMjrRERE4ObNmwCACxcuoEaNGg73E0UR4eHhuHXrFlQqFdLS0qDRaDwZKhERlXPsqSYiIiKvcvr0aXtCXaNGDacJNQDs2bMHt27dAgC0a9eOCTUREXkck2oiIiLyKocOHbLfbt26db77zpgxw367X79+bouJiIjIGSbVRERE5FUOHz5sv33fffc53W/OnDlYt24dACAkJAQjR450e2xERET3YlJNREREXiVnUt2iRYs82y9cuIBhw4ZhwoQJ9sc+++wz+Pv7eyQ+IiKinBRSB0BERESU05EjR+y37yTVM2bMwNGjRxETE4OYmBj7utQymQzTpk3D0KFDpQiViIiI1b+JiIjIe1y+fBnVq1cHAFSpUgVXr16F1WpFYGAgMjMz8+w/adIkTJ8+HYIgeDhSIiIiGw7/JiIiIq/haOh3TEyMw4QaAGbOnIn7778fV65c8Uh8RERE92JSTURERF7D0dDvJk2aQKfTISEhAfv27cO8efPQsWNH+3779u1Dz549kZ2d7elwiYiImFQTERGR93BWpEyj0aBChQpo06YNxo4di507d2Lx4sX2Yd/nzp3D8uXLPR4vERERk2oiIiLyGoVdTgsAhg8fnmtt6oMHD7otLiIiImeYVBMREZFXSEpKwtWrVwEAFSpUQJUqVQp8Ts7ebBYrIyIiKTCpJiIiIq9Q0PrUjiQkJNhv161b1+UxERERFYRJNREREXmFogz9vmPbtm322127dnV1SERERAViUk1EREReIWdSXatWrQL337lzJ06ePGnfv3nz5u4KjYiIyCkm1UREROQVci6n9fnnn8NoNDrdNzMzE2PHjrXfHzduHOdUExGRJJhUExERkeSys7Nx9uxZ+/2YmBj06dMH165dy7NvTEwMevTogWPHjgEAmjVrhtGjR3ssViIiopwEURRFqYMgIiKi8m3v3r24//778zyuUCjQuXNnVK9eHRaLBSdOnEB0dDTunL5Uq1YN27dvR/Xq1T0cMRERkY1C6gCIiIiIcs6nrl27NmJjYwEAZrMZW7dudfichx9+GIsWLUJkZKRHYiQiInKESTURERFJLmdSPXnyZERGRuKrr77C/v37kZCQAIvFgsDAQNSqVQvt27fHM888U+hlt4iIiNyJw7+JiIhIcq1bt8bBgwcBAEePHkXTpk0ljoiIiKhwmFQTERGRpMxmM/z9/aHX66FSqZCZmQmlUil1WERERIXC6t9EREQkqdOnT0Ov1wMAGjRowISaiIhKFSbVREREJKmc86mbN28uXSBERETFwKSaiIiIJJUzqW7WrJmEkRARERUdk2oiIiKSFJNqIiIqzViojIiIiCQVHByM1NRUAMCtW7cQGhoqbUBERERFwKSaiIiIiIiIqJg4/JuIiIiIiIiomJhUExERERERERUTk2oiIiIiIiKiYmJSTURERERERFRMTKqJiIiIiIiIiolJNREREREREVExMakmIiIiIiIiKiYm1URERERERETFxKSaiIiIiIiIqJiYVBMREREREREVE5NqIiIiIiIiomJiUk1ERERERERUTEyqiYiIiIiIiIqJSTURERERERFRMTGpJiIiIiIiIiomJtVERERERERExcSkmoiIiIiIiKiYmFQTERERERERFROTaiIiIiIiIqJiYlJNREREREREVExMqomIiIiIiIiKiUk1ERERERERUTExqSYiIiIiIiIqJibVRERERERERMXEpJqIiIiIiIiomJhUExERERERERUTk2oiIiIiIiKiYmJSTURERERERFRMTKqJiIiIiIiIiolJNREREREREVExMakmIiIiIiIiKqb/A4SInX5qMfEkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1011x540 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results of making predictions with each model\n",
    "# Idea is to go to higher order and look at effects\n",
    "d_orders = [0, 1, 3]\n",
    "\n",
    "test_betas = np.linspace(beta_list[0], beta_list[-1], 100)\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    1, len(d_orders), sharex=True, sharey=True, figsize=(3.37, 1.8), dpi=300\n",
    ")\n",
    "\n",
    "for i, d_o in enumerate(d_orders):\n",
    "    m_order = 1 if d_o == 0 else d_o\n",
    "\n",
    "    # And create states (ExtrapModel objects), using up to max_order derivative orders\n",
    "    # Order will define what order is used in GP models as well\n",
    "    this_states = [\n",
    "        wrap.build_state(all_data=dat, max_order=m_order)\n",
    "        for (wrap, dat) in zip(wrap_list, data_list)\n",
    "    ]\n",
    "\n",
    "    # Create interpolating model\n",
    "    this_interp = thermoextrap.InterpModel(this_states)\n",
    "\n",
    "    # Will create two different types of GP models, with different kernel for each\n",
    "    x_data = []\n",
    "    y_data = []\n",
    "    cov_data = []\n",
    "    for s in this_states:\n",
    "        this_x_data, this_y_data, this_cov_data = active_utils.input_GP_from_state(s)\n",
    "        x_data.append(this_x_data)\n",
    "        y_data.append(this_y_data)\n",
    "        cov_data.append(this_cov_data)\n",
    "\n",
    "    x_data = np.vstack(x_data)\n",
    "    y_data = np.vstack(y_data)\n",
    "    noise_cov_mat = linalg.block_diag(*[dat[0, ...] for dat in cov_data])\n",
    "\n",
    "    if d_o == 0:\n",
    "        x_data = x_data[::2, :]\n",
    "        y_data = y_data[::2, :]\n",
    "        noise_cov_mat = noise_cov_mat[::2, ::2]\n",
    "\n",
    "    data_input = (x_data, y_data, noise_cov_mat)\n",
    "\n",
    "    # For purposes of comparing models and comparing to interpolation, best to fix likelihood behavior\n",
    "    # In other words, don't want to scale variances based on derivative order here\n",
    "    # Essentially saying that here we trust our uncertainty estimates, so no learning of likelihood params\n",
    "    # And no modification of covariance matrix\n",
    "    like_kws = {\"p\": 0.0, \"transform_p\": None, \"constrain_p\": True}\n",
    "\n",
    "    # Create GP model with default RBF kernel\n",
    "    this_gp = active_utils.create_base_GP_model(\n",
    "        data_input,\n",
    "        likelihood_kwargs=like_kws,\n",
    "    )\n",
    "    active_utils.train_GPR(this_gp)\n",
    "\n",
    "    # And a GP model with a polynomial kernel of order high enough for current derivative order usage\n",
    "    # Will match order used by interpolation, but will weight uncertainties in Bayesian framework\n",
    "    poly_kern_info = active_utils.make_poly_expr(2 * d_o + 1)\n",
    "    poly_kern = gp_models.DerivativeKernel(\n",
    "        poly_kern_info[0], 1, kernel_params=poly_kern_info[1]\n",
    "    )\n",
    "    this_gp_poly = active_utils.create_base_GP_model(\n",
    "        data_input,\n",
    "        kernel=poly_kern,\n",
    "        likelihood_kwargs=like_kws,\n",
    "    )\n",
    "    active_utils.train_GPR(this_gp_poly)\n",
    "\n",
    "    interp_mu = this_interp.predict(test_betas).values\n",
    "    interp_resamples = this_interp.resample(nrep=1000).predict(test_betas)\n",
    "    interp_qs = interp_resamples.quantile([0.05, 0.95], dim=\"rep\").values\n",
    "\n",
    "    gp_mu, gp_var = this_gp.predict_f(\n",
    "        np.vstack([test_betas, np.zeros_like(test_betas)]).T\n",
    "    )\n",
    "    gp_mu = gp_mu.numpy()\n",
    "    gp_std = np.sqrt(gp_var.numpy())\n",
    "    gp_qs = np.concatenate([gp_mu - 2.0 * gp_std, gp_mu + 2.0 * gp_std], axis=1)\n",
    "\n",
    "    gppoly_mu, gppoly_var = this_gp_poly.predict_f(\n",
    "        np.vstack([test_betas, np.zeros_like(test_betas)]).T\n",
    "    )\n",
    "    gppoly_mu = gppoly_mu.numpy()\n",
    "    gppoly_std = np.sqrt(gppoly_var.numpy())\n",
    "    gppoly_qs = np.concatenate(\n",
    "        [gppoly_mu - 2.0 * gppoly_std, gppoly_mu + 2.0 * gppoly_std], axis=1\n",
    "    )\n",
    "\n",
    "    ax[i].plot(test_betas, gppoly_mu, label=\"GPR, Poly\")\n",
    "    ax[i].fill_between(test_betas, gppoly_qs[:, 0], gppoly_qs[:, 1], alpha=0.2)\n",
    "\n",
    "    ax[i].plot(test_betas, gp_mu, label=\"GPR, RBF\")\n",
    "    ax[i].fill_between(test_betas, gp_qs[:, 0], gp_qs[:, 1], alpha=0.2)\n",
    "\n",
    "    if d_o > 0:\n",
    "        ax[i].plot(test_betas, interp_mu, label=\"Interp\")\n",
    "        ax[i].fill_between(\n",
    "            test_betas, interp_qs[0, :, 0], interp_qs[1, :, 0], alpha=0.2\n",
    "        )\n",
    "\n",
    "    ax[i].plot(test_betas, idealgas.x_ave(test_betas), \"k--\", linewidth=0.75)\n",
    "\n",
    "    ax[i].set_title(r\"order %i\" % d_o, fontsize=8)\n",
    "    ax[i].xaxis.set_major_locator(MaxNLocator(prune=\"both\", nbins=3))\n",
    "    ax[i].tick_params(axis=\"both\", labelsize=8)\n",
    "\n",
    "ax[0].set_ylabel(r\"$\\langle x \\rangle$\", fontsize=8)\n",
    "ax[1].set_xlabel(r\"$\\beta$\", fontsize=8)\n",
    "\n",
    "ax[0].set_xlim((-0.3, 10.0))\n",
    "ax[0].set_ylim((0.05, 0.55))\n",
    "\n",
    "ax[1].legend(frameon=False, handlelength=1.0, fontsize=6, labelspacing=0.3)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(wspace=0.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36",
   "metadata": {},
   "source": [
    "## Demonstration of active learning\n",
    "\n",
    "A brief demonstration of active learning is now shown, with two different active learning strategies. In both, we will two separate stopping criteria that must both satisfy their respective tolerances simultaneously. These will be the maximum relative variance predicted by the GP model ($\\mathrm{max}(\\mathbf{\\sigma}_{rel})$ in the paper and {class}`~active_utils.MaxRelGlobalVar` in the code) and the maximum relative deviation since the last update ($\\mathrm{max}(\\mathbf{\\delta}_{iter})$ in the paper and {class}`~active_utils.MaxAbsRelGlobalDeviation` in the code). You need both because the first can become very small if the model becomes prematurely certain of itself - you'll see a jump on the next iteration if this is the case. The second only becomes small when adding new points stops modifiying the function, but you also need the maximum relative variance criteria so that the uncertainty is ensured to reach a specified target. Otherwise, you could have the GP model not changing while the uncertainty remains high."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37",
   "metadata": {},
   "source": [
    "### ALM (Maximum variance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38",
   "metadata": {},
   "source": [
    "We start with updates that select new points based on where the GP model predicts the highest variance. The below code sets up and executes an active learning run. As we go, plots of the progress will be shown and compared to the, in this case known, ground truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Initial beta values:  [0.1, 9.6]\n",
      "\n",
      "Current GP info: \n",
      "╒═══════════════════════════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤════════════╕\n",
      "│ name                                      │ class     │ transform   │ prior   │ trainable   │ shape   │ dtype   │      value │\n",
      "╞═══════════════════════════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪════════════╡\n",
      "│ HeteroscedasticGPR.kernel.kernel.var      │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 1.48641    │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼────────────┤\n",
      "│ HeteroscedasticGPR.kernel.kernel.l        │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 4.59952    │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼────────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_scale │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 0.00113597 │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼────────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_add   │ Parameter │ Identity    │         │ False       │ ()      │ float64 │ 0          │\n",
      "╘═══════════════════════════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧════════════╛\n",
      "\n",
      "Current stopping metrics: \n",
      "{'MaxRelGlobalVar': 0.4371759046250532, 'MaxRelGlobalVar_tol': 0.01, 'MaxAbsRelGlobalDeviation': 4.411052897156387, 'MaxAbsRelGlobalDeviation_tol': 0.01}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After 1 updates, beta values are: \n",
      "[0.1, 9.6, 4.845245245245246]\n",
      "\n",
      "Current GP info: \n",
      "╒═══════════════════════════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤═════════════╕\n",
      "│ name                                      │ class     │ transform   │ prior   │ trainable   │ shape   │ dtype   │       value │\n",
      "╞═══════════════════════════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪═════════════╡\n",
      "│ HeteroscedasticGPR.kernel.kernel.var      │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 6.85509     │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────────┤\n",
      "│ HeteroscedasticGPR.kernel.kernel.l        │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 4.91396     │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_scale │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 2.82589e-06 │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_add   │ Parameter │ Identity    │         │ False       │ ()      │ float64 │ 0           │\n",
      "╘═══════════════════════════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧═════════════╛\n",
      "\n",
      "Current stopping metrics: \n",
      "{'MaxRelGlobalVar': 0.008402339332781852, 'MaxRelGlobalVar_tol': 0.01, 'MaxAbsRelGlobalDeviation': 0.18938427673320804, 'MaxAbsRelGlobalDeviation_tol': 0.01}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After 2 updates, beta values are: \n",
      "[0.1, 9.6, 4.845245245245246, 2.0684684684684687]\n",
      "\n",
      "Current GP info: \n",
      "╒═══════════════════════════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤═════════════╕\n",
      "│ name                                      │ class     │ transform   │ prior   │ trainable   │ shape   │ dtype   │       value │\n",
      "╞═══════════════════════════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪═════════════╡\n",
      "│ HeteroscedasticGPR.kernel.kernel.var      │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 3.55717     │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────────┤\n",
      "│ HeteroscedasticGPR.kernel.kernel.l        │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 3.80006     │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_scale │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 2.62155e-07 │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_add   │ Parameter │ Identity    │         │ False       │ ()      │ float64 │ 0           │\n",
      "╘═══════════════════════════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧═════════════╛\n",
      "\n",
      "Current stopping metrics: \n",
      "{'MaxRelGlobalVar': 0.011398021909848623, 'MaxRelGlobalVar_tol': 0.01, 'MaxAbsRelGlobalDeviation': 0.027179965607974112, 'MaxAbsRelGlobalDeviation_tol': 0.01}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After 3 updates, beta values are: \n",
      "[0.1, 9.6, 4.845245245245246, 2.0684684684684687, 7.251151151151151]\n",
      "\n",
      "Current GP info: \n",
      "╒═══════════════════════════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤══════════╕\n",
      "│ name                                      │ class     │ transform   │ prior   │ trainable   │ shape   │ dtype   │    value │\n",
      "╞═══════════════════════════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪══════════╡\n",
      "│ HeteroscedasticGPR.kernel.kernel.var      │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 8.55103  │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼──────────┤\n",
      "│ HeteroscedasticGPR.kernel.kernel.l        │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 4.80266  │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼──────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_scale │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 0.367004 │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼──────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_add   │ Parameter │ Identity    │         │ False       │ ()      │ float64 │ 0        │\n",
      "╘═══════════════════════════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧══════════╛\n",
      "\n",
      "Current stopping metrics: \n",
      "{'MaxRelGlobalVar': 0.003343668958617897, 'MaxRelGlobalVar_tol': 0.01, 'MaxAbsRelGlobalDeviation': 0.022727068293967773, 'MaxAbsRelGlobalDeviation_tol': 0.01}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After 4 updates, beta values are: \n",
      "[0.1, 9.6, 4.845245245245246, 2.0684684684684687, 7.251151151151151, 0.8702702702702703]\n",
      "\n",
      "Current GP info: \n",
      "╒═══════════════════════════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤══════════╕\n",
      "│ name                                      │ class     │ transform   │ prior   │ trainable   │ shape   │ dtype   │    value │\n",
      "╞═══════════════════════════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪══════════╡\n",
      "│ HeteroscedasticGPR.kernel.kernel.var      │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 9.47951  │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼──────────┤\n",
      "│ HeteroscedasticGPR.kernel.kernel.l        │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 4.84643  │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼──────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_scale │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 0.136959 │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼──────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_add   │ Parameter │ Identity    │         │ False       │ ()      │ float64 │ 0        │\n",
      "╘═══════════════════════════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧══════════╛\n",
      "\n",
      "Stopping criteria satisfied with stopping metrics of: \n",
      "{'MaxRelGlobalVar': 0.001268546362125952, 'MaxRelGlobalVar_tol': 0.01, 'MaxAbsRelGlobalDeviation': 0.001201886949586077, 'MaxAbsRelGlobalDeviation_tol': 0.01}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define beta values to start and find function between\n",
    "beta_list = [0.1, 9.6]\n",
    "\n",
    "# Set up active learning protocol\n",
    "deriv_order = 1\n",
    "sim_protocol = ig_active.SimulateIG()\n",
    "\n",
    "# Update by selecting point with maximum variance (uncertainty) predicted by model\n",
    "update_func = active_utils.UpdateALMbrute(\n",
    "    show_plot=True,\n",
    "    compare_func=idealgas.x_ave,\n",
    ")\n",
    "\n",
    "# Metrics for stopping criteria with tolerances of 0.01\n",
    "metrics = [\n",
    "    active_utils.MaxRelGlobalVar(1e-02),\n",
    "    active_utils.MaxAbsRelGlobalDeviation(1e-02),\n",
    "]\n",
    "\n",
    "# Define stopping criteria (will have to satisfy all metrics reaching tolerances)\n",
    "stop_func = active_utils.StopCriteria(metrics)\n",
    "\n",
    "# Run active learning\n",
    "active_list, train_history = active_utils.active_learning(\n",
    "    beta_list,\n",
    "    sim_protocol,\n",
    "    update_func,\n",
    "    stop_criteria=stop_func,\n",
    "    max_order=deriv_order,\n",
    "    alpha_name=\"beta\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40",
   "metadata": {},
   "source": [
    "To plot or examine the results aftwards, we can note that the returned `train_history` dictionary contains quite a bit of pertinent information, especially if we set `save_history=True`, though that will save a .npz file of this dictionary so we do not do it here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': [3.2507510705764253,\n",
       "  4.91000328731043,\n",
       "  1.399727354179742,\n",
       "  -2.4915684880846456,\n",
       "  -8.328047824694043],\n",
       " 'params': [[array(4.59952118), array(1.48640622), array(0.00113597)],\n",
       "  [array(4.91395956), array(6.85508863), array(2.82589304e-06)],\n",
       "  [array(3.80006472), array(3.55717488), array(2.62155079e-07)],\n",
       "  [array(4.80265527), array(8.55102948), array(0.36700411)],\n",
       "  [array(4.84643139), array(9.4795095), array(0.13695855)]],\n",
       " 'MaxRelGlobalVar': [0.4371759046250532,\n",
       "  0.008402339332781852,\n",
       "  0.011398021909848623,\n",
       "  0.003343668958617897,\n",
       "  0.001268546362125952],\n",
       " 'MaxAbsRelGlobalDeviation': [4.411052897156387,\n",
       "  0.18938427673320804,\n",
       "  0.027179965607974112,\n",
       "  0.022727068293967773,\n",
       "  0.001201886949586077]}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_history"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42",
   "metadata": {},
   "source": [
    "The \"loss\" is the value of the GPR loss function at each iteration, \"params\" are the optimal GP parameters (only the trainable ones, so here $p$, $l_\\mathrm{K}$, and $\\sigma_\\mathrm{K}$) at each iteration, then the stopping criteria metrics by name.\n",
    "\n",
    "Based on this, it is easy to plot our metrics as a function of iterations, for instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(train_history[\"MaxRelGlobalVar\"])\n",
    "ax.plot(train_history[\"MaxAbsRelGlobalDeviation\"])\n",
    "\n",
    "ax.axhline(y=metrics[0].tol, color=\"k\", linestyle=\"--\")\n",
    "ax.axhline(y=metrics[1].tol, color=\"k\", linestyle=\":\")\n",
    "\n",
    "ax.set_yscale(\"log\")\n",
    "\n",
    "ax.set_xlabel(\"Iteration\")\n",
    "ax.set_ylabel(\"Stopping Metrics\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44",
   "metadata": {},
   "source": [
    "With `save_history=True`, the GP mean and standard deviation predictions will also be returned. If this is not returned, we can still access these at each iteration through the {class}`active_utils.StopCriteria` class, which saves a history of these. To plot the function itself, with indications of where we sampled our data from, we can do the following, making use of the fact that the update classes also have a built-in plotting function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "update_func.do_plotting(\n",
    "    stop_func.create_alpha_grid(beta_list)[0],\n",
    "    stop_func.history[0][-1, ...],\n",
    "    [\n",
    "        stop_func.history[0][-1, ...] - 2.0 * stop_func.history[1][-1, ...],\n",
    "        stop_func.history[0][-1, ...] + 2.0 * stop_func.history[1][-1, ...],\n",
    "    ],\n",
    "    [dat.beta for dat in active_list],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46",
   "metadata": {},
   "source": [
    "### Space-filling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47",
   "metadata": {},
   "source": [
    "Next, we can also follow an active learning protocol where we select the next point as the point furthest away from any other points already chosen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Initial beta values:  [0.1, 9.6]\n",
      "\n",
      "Current GP info: \n",
      "╒═══════════════════════════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤════════════╕\n",
      "│ name                                      │ class     │ transform   │ prior   │ trainable   │ shape   │ dtype   │      value │\n",
      "╞═══════════════════════════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪════════════╡\n",
      "│ HeteroscedasticGPR.kernel.kernel.var      │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 1.48756    │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼────────────┤\n",
      "│ HeteroscedasticGPR.kernel.kernel.l        │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 4.60242    │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼────────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_scale │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 0.00128764 │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼────────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_add   │ Parameter │ Identity    │         │ False       │ ()      │ float64 │ 0          │\n",
      "╘═══════════════════════════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧════════════╛\n",
      "\n",
      "Current stopping metrics: \n",
      "{'MaxRelGlobalVar': 0.4364408549860364, 'MaxRelGlobalVar_tol': 0.01, 'MaxAbsRelGlobalDeviation': 4.4108036125354975, 'MaxAbsRelGlobalDeviation_tol': 0.01}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After 1 updates, beta values are: \n",
      "[0.1, 9.6, 4.85]\n",
      "\n",
      "Current GP info: \n",
      "╒═══════════════════════════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤═════════════╕\n",
      "│ name                                      │ class     │ transform   │ prior   │ trainable   │ shape   │ dtype   │       value │\n",
      "╞═══════════════════════════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪═════════════╡\n",
      "│ HeteroscedasticGPR.kernel.kernel.var      │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 6.85836     │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────────┤\n",
      "│ HeteroscedasticGPR.kernel.kernel.l        │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 4.91423     │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_scale │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 9.65745e-08 │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_add   │ Parameter │ Identity    │         │ False       │ ()      │ float64 │ 0           │\n",
      "╘═══════════════════════════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧═════════════╛\n",
      "\n",
      "Current stopping metrics: \n",
      "{'MaxRelGlobalVar': 0.008245426925784718, 'MaxRelGlobalVar_tol': 0.01, 'MaxAbsRelGlobalDeviation': 0.18894267674171195, 'MaxAbsRelGlobalDeviation_tol': 0.01}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After 2 updates, beta values are: \n",
      "[0.1, 9.6, 4.85, 2.475]\n",
      "\n",
      "Current GP info: \n",
      "╒═══════════════════════════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤════════════╕\n",
      "│ name                                      │ class     │ transform   │ prior   │ trainable   │ shape   │ dtype   │      value │\n",
      "╞═══════════════════════════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪════════════╡\n",
      "│ HeteroscedasticGPR.kernel.kernel.var      │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 3.39015    │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼────────────┤\n",
      "│ HeteroscedasticGPR.kernel.kernel.l        │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 3.83338    │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼────────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_scale │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 8.4969e-08 │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼────────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_add   │ Parameter │ Identity    │         │ False       │ ()      │ float64 │ 0          │\n",
      "╘═══════════════════════════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧════════════╛\n",
      "\n",
      "Current stopping metrics: \n",
      "{'MaxRelGlobalVar': 0.010579574558828677, 'MaxRelGlobalVar_tol': 0.01, 'MaxAbsRelGlobalDeviation': 0.02674250072026747, 'MaxAbsRelGlobalDeviation_tol': 0.01}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After 3 updates, beta values are: \n",
      "[0.1, 9.6, 4.85, 2.475, 7.225]\n",
      "\n",
      "Current GP info: \n",
      "╒═══════════════════════════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤══════════╕\n",
      "│ name                                      │ class     │ transform   │ prior   │ trainable   │ shape   │ dtype   │    value │\n",
      "╞═══════════════════════════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪══════════╡\n",
      "│ HeteroscedasticGPR.kernel.kernel.var      │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 8.1504   │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼──────────┤\n",
      "│ HeteroscedasticGPR.kernel.kernel.l        │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 4.83504  │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼──────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_scale │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 0.300765 │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼──────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_add   │ Parameter │ Identity    │         │ False       │ ()      │ float64 │ 0        │\n",
      "╘═══════════════════════════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧══════════╛\n",
      "\n",
      "Current stopping metrics: \n",
      "{'MaxRelGlobalVar': 0.003984179385637088, 'MaxRelGlobalVar_tol': 0.01, 'MaxAbsRelGlobalDeviation': 0.022187640111361196, 'MaxAbsRelGlobalDeviation_tol': 0.01}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After 4 updates, beta values are: \n",
      "[0.1, 9.6, 4.85, 2.475, 7.225, 3.6624999999999996]\n",
      "\n",
      "Current GP info: \n",
      "╒═══════════════════════════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤═══════════╕\n",
      "│ name                                      │ class     │ transform   │ prior   │ trainable   │ shape   │ dtype   │     value │\n",
      "╞═══════════════════════════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪═══════════╡\n",
      "│ HeteroscedasticGPR.kernel.kernel.var      │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 9.84103   │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼───────────┤\n",
      "│ HeteroscedasticGPR.kernel.kernel.l        │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 5.17591   │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼───────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_scale │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 0.0586081 │\n",
      "├───────────────────────────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼───────────┤\n",
      "│ HeteroscedasticGPR.likelihood.power_add   │ Parameter │ Identity    │         │ False       │ ()      │ float64 │ 0         │\n",
      "╘═══════════════════════════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧═══════════╛\n",
      "\n",
      "Stopping criteria satisfied with stopping metrics of: \n",
      "{'MaxRelGlobalVar': 0.002120754273169207, 'MaxRelGlobalVar_tol': 0.01, 'MaxAbsRelGlobalDeviation': 0.004120014396690739, 'MaxAbsRelGlobalDeviation_tol': 0.01}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define beta values to start and find function between\n",
    "beta_list = [0.1, 9.6]\n",
    "\n",
    "# Set up active learning protocol\n",
    "deriv_order = 1\n",
    "sim_protocol = ig_active.SimulateIG()\n",
    "# Update by selecting point with maximum variance (uncertainty) predicted by model\n",
    "update_func_space = active_utils.UpdateSpaceFill(\n",
    "    show_plot=True,\n",
    "    compare_func=idealgas.x_ave,\n",
    ")\n",
    "metrics = [\n",
    "    active_utils.MaxRelGlobalVar(1e-02),\n",
    "    active_utils.MaxAbsRelGlobalDeviation(1e-02),\n",
    "]\n",
    "stop_func_space = active_utils.StopCriteria(metrics)\n",
    "\n",
    "active_list_space, train_history_space = active_utils.active_learning(\n",
    "    beta_list,\n",
    "    sim_protocol,\n",
    "    update_func_space,\n",
    "    stop_criteria=stop_func_space,\n",
    "    max_order=deriv_order,\n",
    "    alpha_name=\"beta\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49",
   "metadata": {},
   "source": [
    "### Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50",
   "metadata": {},
   "source": [
    "In this simple system, the maximum-variance and space-filling updates yield nearly the same results. Note, though, that the chosen points are slightly different and that the uncertainties are more uniform for the maximum-variance strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1011x750 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Or want to plot series of updates to provide sense of how active learning is working\n",
    "plot_betas = np.linspace(beta_list[0], beta_list[-1], 1000)\n",
    "\n",
    "update_inds = [0, 1, -1]\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    2, len(update_inds), sharex=True, sharey=\"row\", figsize=(3.37, 2.5), dpi=300\n",
    ")\n",
    "\n",
    "update_colors = [\"#3f90da\", \"#ffa90e\"]\n",
    "update_labels = [\"MaxVar\", \"Space\"]\n",
    "\n",
    "for j, active_info in enumerate(\n",
    "    zip([stop_func, stop_func_space], [active_list, active_list_space])\n",
    "):\n",
    "    this_mu, this_std = active_info[0].history\n",
    "    this_mu = np.squeeze(this_mu)\n",
    "    this_std = np.squeeze(this_std)\n",
    "    this_alpha = np.array([dat.beta for dat in active_info[1]])\n",
    "    for i, ind in enumerate(update_inds):\n",
    "        if ind == 0 and j == 1:\n",
    "            continue\n",
    "        ax[0, i].plot(\n",
    "            plot_betas, this_mu[ind, :], color=update_colors[j], label=update_labels[j]\n",
    "        )\n",
    "        ax[0, i].fill_between(\n",
    "            plot_betas,\n",
    "            this_mu[ind, :] - 2.0 * this_std[ind, :],\n",
    "            this_mu[ind, :] + 2.0 * this_std[ind, :],\n",
    "            color=update_colors[j],\n",
    "            alpha=0.2,\n",
    "        )\n",
    "        ax[1, i].plot(\n",
    "            plot_betas, this_std[ind, :], color=update_colors[j], label=update_labels[j]\n",
    "        )\n",
    "        end_ind = -1 if ind == -1 else ind + 2\n",
    "        ax[0, i].plot(\n",
    "            this_alpha[:end_ind],\n",
    "            (j * 0.05) * np.ones_like(this_alpha[:end_ind]),\n",
    "            marker=\"^\",\n",
    "            markersize=2,\n",
    "            color=update_colors[j],\n",
    "            linestyle=\"\",\n",
    "        )\n",
    "        ax[0, i].xaxis.set_major_locator(MaxNLocator(prune=\"both\", nbins=3))\n",
    "        ax[0, i].tick_params(axis=\"both\", labelsize=10)\n",
    "        ax[1, i].tick_params(axis=\"both\", labelsize=10)\n",
    "\n",
    "ax[0, 1].legend(frameon=False, fontsize=6)\n",
    "\n",
    "ax[0, 0].set_ylabel(r\"GPR $\\mu_{\\langle x \\rangle}$\", fontsize=10)\n",
    "ax[1, 0].set_ylabel(r\"GPR $\\sigma_{\\langle x \\rangle}$\", fontsize=10)\n",
    "ax[1, 1].set_xlabel(r\"$\\beta$\", fontsize=10)\n",
    "\n",
    "ax[1, 0].set_yscale(\"log\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(hspace=0.0, wspace=0.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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