C3/L6_density_estimation.ipynb

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      "name": "L6_density_estimation.ipynb",
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        },
        "outputId": "eb115cb0-2826-4f8c-a203-4bc491b7726a"
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      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "import arviz as az\n",
        "from statsmodels.tsa import stattools\n",
        "import statsmodels.api as sm\n",
        "import pymc3 as pm\n",
        "import pymc\n",
        "\n",
        "import sys\n",
        "sys.path.insert(0, '..')\n",
        "from utils.plot_lib import set_default\n",
        "\n",
        "set_default(figsize=(6, 4))"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WBG3Vsj2S3xV"
      },
      "source": [
        "### Mixture models for density estimation \n",
        "\n",
        "The galaxies dataset. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7VdfuBLShRW",
        "outputId": "df4dcdf3-0ad0-4702-a075-2ae02a5d4e04"
      },
      "source": [
        "!wget http://www.key2stats.com/Velocities_for_82_Galaxies_1152_58.csv -O galaxy.csv --no-check-certificate"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-05-24 11:30:43--  http://www.key2stats.com/Velocities_for_82_Galaxies_1152_58.csv\n",
            "Resolving www.key2stats.com (www.key2stats.com)... 52.207.128.24\n",
            "Connecting to www.key2stats.com (www.key2stats.com)|52.207.128.24|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1221 (1,2K) [application/csv]\n",
            "Saving to: ‘galaxy.csv’\n",
            "\n",
            "galaxy.csv          100%[===================>]   1,19K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-05-24 11:30:44 (3,63 MB/s) - ‘galaxy.csv’ saved [1221/1221]\n",
            "\n"
          ]
        }
      ]
    },
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      "source": [
        "# Read dataset - galaxies\n",
        "galaxy = pd.read_csv('galaxy.csv')\n",
        "galaxy.pop('Unnamed: 0')\n",
        "galaxy.pop('X')\n",
        "# Checking dataframe\n",
        "print(\"The shape of the dataset is: {}\".format(galaxy.shape))\n",
        "galaxy.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The shape of the dataset is: (82, 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    dat\n",
              "0  9172\n",
              "1  9350\n",
              "2  9483\n",
              "3  9558\n",
              "4  9775"
            ],
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      "source": [
        "These are velocities of galaxies measured in six well-separated conic sections of the Corona Borealis.\n",
        "So we want to understand whether this distribution is  multimodal, how many modes are present, and in general provide\n",
        "a density estimator for this different velocities.  "
      ]
    },
    {
      "cell_type": "code",
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          "height": 381
        },
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        "outputId": "eeea79d3-b421-4c7d-bd33-b42bcd7fa994"
      },
      "source": [
        "from scipy.stats import norm\n",
        "\n",
        "## Clear the environment and load required libraries\n",
        "np.random.seed(1)\n",
        "\n",
        "### Loading data and setting up global variables\n",
        "KK         = 6       # We're going to be using six components in our density estimators using mixture models. \n",
        "x          = galaxy['dat'].to_numpy() # Data\n",
        "n          = len(x)\n",
        "\n",
        "sns.histplot(x, stat= 'density', bins = 6)\n",
        "plt.title('Approximate density of the data')\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zE6mgAt8VwfV",
        "outputId": "a25b5ff2-bfc3-4070-bd91-832ca9d77181"
      },
      "source": [
        "### First, compute the \"Maximum Likelihood\" density estimate associated with a location mixture of 6 Gaussian distributions using the EM algorithm\n",
        "## Initialize the parameters\n",
        "\n",
        "## Initialize the parameters\n",
        "w     = np.ones(KK) / KK                         # Assign equal weight to each component to start with\n",
        "mu    = norm.rvs(loc = np.mean(x), scale = np.std(x), size = KK)   # Random cluster centers randomly spread over the support of the data\n",
        "sigma = np.std(x) / KK                  # Initial standard deviation \n",
        "\n",
        "print('The initial guess for mu are: \\n{}'.format(mu))\n",
        "print('The initial guess for sigma are: {}'.format(sigma))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The initial guess for mu are: \n[28195.94926738 18053.3385597  18432.46561473 15961.35154374\n 24753.52546142 10388.74831008]\nThe initial guess for sigma are: 755.9741399507708\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FqZi45dOUNKC"
      },
      "source": [
        "### MLE EM algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eH5GGs5RAl4o",
        "outputId": "bab4817a-27f8-4f80-87fa-297b909ebb6d"
      },
      "source": [
        "from scipy.stats import lognorm\n",
        "\n",
        "s  = 0     # Counter of the current iteration\n",
        "sw = False # Decide when the algorithm will end\n",
        "KL = np.NINF  # Value of Q to monitor convergence\n",
        "KL_out = []\n",
        "epsilon = 10e-6  # Stopping criteria\n",
        "\n",
        "## Checking convergence of the algorithm\n",
        "while(not sw):\n",
        "  ## Expectation (E) step\n",
        "  v = np.zeros((n, KK))\n",
        "  for k in range(KK):\n",
        "    v[:, k] = np.log(w[k]) + norm.logpdf(loc = mu[k], scale = sigma, x = x)\n",
        "  for i in range(n):\n",
        "    v[i, :] = np.exp(v[i, :] - max(v[i, : ])) / np.sum(np.exp(v[i, :] - max(v[i, :])))  # Go from logs to actual weights in a numerically stable manner\n",
        "  \n",
        "  ## Maximization (M) step\n",
        "  # Weights\n",
        "  w = np.mean(v, axis = 0)\n",
        "  # Mus\n",
        "  mu = np.zeros(KK)\n",
        "  for k in range(KK):\n",
        "    for i in range(n):\n",
        "      # Update Mu\n",
        "      mu[k] = mu[k] + (v[i,k] * x[i])\n",
        "    mu[k] = mu[k] / np.sum(v[:, k])\n",
        "\n",
        "  # Standard deviations\n",
        "  sigma = 0\n",
        "  for i in range(n):\n",
        "    for k in range(KK):\n",
        "      sigma = sigma + (v[i,k] * ((x[i] - mu[k])**2))\n",
        "  sigma = np.sqrt(sigma / np.sum(v))\n",
        "\n",
        "  ##Check convergence\n",
        "  KLn = 0 # Value of Q function at current iteration\n",
        "  for i in range(n):\n",
        "    for k in range(k):\n",
        "      KLn = KLn + v[i, k]*(np.log(w[k]) + norm.logpdf(loc = mu[k], scale =sigma, x = x[i]))\n",
        "  \n",
        "  # If relative error is less than epsilon stop\n",
        "  if(np.abs(KLn - KL) / np.abs(KLn) < epsilon):\n",
        "    sw = True\n",
        "\n",
        "  KL = KLn\n",
        "  KL_out.append(KL)\n",
        "  s = s + 1\n",
        "  print('The iteration {} has a Q value of {}'.format(s, KLn))\n",
        "\n",
        "fig, axs = plt.subplots(2)\n",
        "axs[0].plot(KL_out[0 : s], c = 'red')\n",
        "axs[0].set_ylabel('Q')\n",
        "axs[0].axis(xmin = 1, xmax = max(10, s))\n",
        "axs[1].set_title(\"Q-value over iterations\")\n",
        "\n",
        "# Estimate of the density\n",
        "xx = np.linspace(5000, 37000, num = 300)\n",
        "nxx = len(xx)\n",
        "density_EM = np.zeros(nxx)\n",
        "for s_ in range(nxx):\n",
        "  for k in range(KK):\n",
        "    density_EM[s_] = density_EM[s_] +  w[k] * norm.pdf(loc = mu[k], scale = sigma, x = xx[s_])\n",
        "\n",
        "# Plot the initial guess for the density\n",
        "axs[1].plot(xx, density_EM, 'blue', label = 'Density estimated')\n",
        "axs[1] = sns.scatterplot(np.array(x), np.zeros(len(x)))\n",
        "axs[1].set_ylabel('x')\n",
        "axs[1].set_ylabel('Density')\n",
        "axs[1].set_title(\"s = {}, Q = {}\".format(s, np.round(KL_out[s - 1], 4)))\n",
        "fig.subplots_adjust(hspace=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The iteration 1 has a Q value of -2.273882724488057e-15\n",
            "The iteration 2 has a Q value of -9.695342114157089e-08\n",
            "The iteration 3 has a Q value of -7.373874301861718e-08\n",
            "The iteration 4 has a Q value of -2.8723033738928338e-08\n",
            "The iteration 5 has a Q value of -2.0978876550599337e-08\n",
            "The iteration 6 has a Q value of -1.9896787060702164e-08\n",
            "The iteration 7 has a Q value of -2.0520085583274648e-08\n",
            "The iteration 8 has a Q value of -2.1480242425242425e-08\n",
            "The iteration 9 has a Q value of -2.2364050638849752e-08\n",
            "The iteration 10 has a Q value of -2.3071142483565767e-08\n",
            "The iteration 11 has a Q value of -2.360434359700346e-08\n",
            "The iteration 12 has a Q value of -2.3994848055055625e-08\n",
            "The iteration 13 has a Q value of -2.427639503358452e-08\n",
            "The iteration 14 has a Q value of -2.447757511567058e-08\n",
            "The iteration 15 has a Q value of -2.4620553493403924e-08\n",
            "The iteration 16 has a Q value of -2.4721818215931134e-08\n",
            "The iteration 17 has a Q value of -2.4793372367146036e-08\n",
            "The iteration 18 has a Q value of -2.4843848453080708e-08\n",
            "The iteration 19 has a Q value of -2.4879409988954325e-08\n",
            "The iteration 20 has a Q value of -2.4904437638435036e-08\n",
            "The iteration 21 has a Q value of -2.49220355847386e-08\n",
            "The iteration 22 has a Q value of -2.493439892959901e-08\n",
            "The iteration 23 has a Q value of -2.494307759240784e-08\n",
            "The iteration 24 has a Q value of -2.4949164683621548e-08\n",
            "The iteration 25 has a Q value of -2.4953430422193186e-08\n",
            "The iteration 26 has a Q value of -2.4956417079261802e-08\n",
            "The iteration 27 has a Q value of -2.495850616192297e-08\n",
            "The iteration 28 has a Q value of -2.495996588756689e-08\n",
            "The iteration 29 has a Q value of -2.4960984695479998e-08\n",
            "The iteration 30 has a Q value of -2.4961694882347604e-08\n",
            "The iteration 31 has a Q value of -2.496218926041248e-08\n",
            "The iteration 32 has a Q value of -2.4962532890600394e-08\n",
            "The iteration 33 has a Q value of -2.4962771341811166e-08\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HTL9WA6WaX8_"
      },
      "source": [
        "### Bayesian EM algorithm\n",
        "\n",
        "This is the same model used in L5A."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZEy-XZ2yaZbG"
      },
      "source": [
        "### Get a \"Bayesian\" kernel density estimator based on the same location mixture of 6 normals\n",
        "## Priors set up using an \"empirical Bayes\" approach\n",
        "aa  = np.ones(KK)  # Uniform prior on w\n",
        "eta = np.mean(x)          # Mean for the prior on mu_k\n",
        "tau = np.sqrt(np.var(x))  # Standard deviation for the prior for mu_l\n",
        "dd  = 2          # Inverse gamma prior for sigma_2, parameter d\n",
        "qq  = np.var(x) / KK          # Inverse gamma prior for sigma_2, parameter q\n",
        "\n",
        "## Initialize the parameters\n",
        "## Initialize the parameters\n",
        "w     = np.ones(KK) / KK                        # Assign equal weight to each component to start with\n",
        "mu    = norm.rvs(loc = np.mean(x), scale = np.std(x), size = KK, random_state = 1) # Random cluster centers randomly spread over the support of the data\n",
        "sigma = np.std(x) / KK                   # Initial standard deviation\n",
        "cc    = np.random.choice(np.arange(KK), n, p = w)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "LmDKH9ktacHX",
        "outputId": "2da5e229-830f-4bcd-f24e-3219270b60e9"
      },
      "source": [
        "from scipy.stats import dirichlet\n",
        "from scipy.stats import invgamma\n",
        "from scipy.stats import gamma\n",
        "\n",
        "# Number of iterations of the sampler\n",
        "rrr   = 12000 # Number of iterations\n",
        "burn  = 3000 # Burning period\n",
        "\n",
        "# Storing the samples\n",
        "cc_out    = np.zeros((rrr, n))    # Store indicators\n",
        "w_out     = np.zeros((rrr, KK))         # Sample of the weights\n",
        "mu_out    = np.zeros((rrr, KK))   # Sample of mus\n",
        "sigma_out = np.zeros(rrr)         # Sample of sigmas\n",
        "logpost   = np.zeros(rrr)         # Used to monitor convergence\n",
        "\n",
        "for s in range(rrr):\n",
        "  # Sample the indicators\n",
        "  cc = np.zeros(n)\n",
        "  for i in range(n):\n",
        "    v = np.zeros(KK)\n",
        "    for k in range(KK):\n",
        "      v[k] = np.log(w[k]) + norm.logpdf(loc = mu[k], scale = sigma, x = x[i])    # Compute the log of the weights\n",
        "    v     = np.exp(v - max(v)) / np.sum(np.exp(v - max(v)))  # Go from logs to actual weights in a numerically stable manner\n",
        "    cc[i] = np.random.choice(np.arange(KK), 1, p = v) # C_i sample \n",
        "\n",
        "  # Sample the weights\n",
        "  counts = np.zeros(KK)\n",
        "  for k in range(KK):\n",
        "    sum_k = np.sum(cc == k)\n",
        "    counts[k] = int(sum_k)\n",
        "  w = dirichlet.rvs(alpha = aa + counts, size = 1)[0]\n",
        "\n",
        "  # Sample the means\n",
        "  for k in range(KK):\n",
        "    nk    = np.sum(cc == k)\n",
        "    xsumk = np.sum(x[cc == k])\n",
        "    tau2_hat = 1 / (nk / sigma**2 + 1 / tau**2)\n",
        "    mu_hat  = tau2_hat * (xsumk / sigma**2 + eta / tau**2)\n",
        "    mu[k]   = norm.rvs(loc = mu_hat, scale = np.sqrt(tau2_hat), size = 1)\n",
        "\n",
        "  # Sample the variances\n",
        "  dd_star = dd + n / 2\n",
        "  mu_temp = [mu[int(c_i)] for c_i in cc] # Create vector of mus\n",
        "  qq_star = qq + np.sum((x - mu_temp)**2) / 2\n",
        "  sigma = np.sqrt(1 / gamma.rvs(a = dd_star, scale = 1 / qq_star, size = 1)) \n",
        "\n",
        "  # Store samples\n",
        "  cc_out[s, :]   = cc\n",
        "  w_out[s]       = w\n",
        "  mu_out[s, :]   = mu\n",
        "  sigma_out[s]   = sigma\n",
        "\n",
        "  for i in range(n):\n",
        "    # Computing logposterior likelihood term\n",
        "    logpost[s] = logpost[s] + np.log(w[int(cc[i])]) + norm.logpdf(loc = mu[int(cc[i])], scale = sigma, x = x[i])\n",
        "  # W term\n",
        "  logpost[s] = logpost[s] + dirichlet.logpdf(alpha = aa, x = w)\n",
        "  # Mu term\n",
        "  for k in range(KK):\n",
        "    logpost[s] = logpost[s] + norm.logpdf(loc = eta, scale = tau, x = mu[k])\n",
        "  # Sigma term\n",
        "  logpost[s] = logpost[s] + gamma.logpdf(a = dd, scale = 1 / qq, x = 1 / sigma**2) - 4 * np.log(sigma)\n",
        "  if s / 500 == np.floor(s / 500):\n",
        "    print('Current iteration is: {}'.format(s))\n",
        "\n",
        "## Plot the logposterior distribution for various samples\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "ax.plot(np.arange(len(logpost)), logpost, 'r-', lw=1, alpha=0.6, label='Trace plot') # Trace plot of data\n",
        "ax.legend(loc='best', frameon=False)\n",
        "# plot density estimate of the posterior\n",
        "plt.title('Trace plot of Logposterior')\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current iteration is: 0\n",
            "Current iteration is: 500\n",
            "Current iteration is: 1000\n",
            "Current iteration is: 1500\n",
            "Current iteration is: 2000\n",
            "Current iteration is: 2500\n",
            "Current iteration is: 3000\n",
            "Current iteration is: 3500\n",
            "Current iteration is: 4000\n",
            "Current iteration is: 4500\n",
            "Current iteration is: 5000\n",
            "Current iteration is: 5500\n",
            "Current iteration is: 6000\n",
            "Current iteration is: 6500\n",
            "Current iteration is: 7000\n",
            "Current iteration is: 7500\n",
            "Current iteration is: 8000\n",
            "Current iteration is: 8500\n",
            "Current iteration is: 9000\n",
            "Current iteration is: 9500\n",
            "Current iteration is: 10000\n",
            "Current iteration is: 10500\n",
            "Current iteration is: 11000\n",
            "Current iteration is: 11500\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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143.782502 \nL 80.171602 149.061617 \nL 80.196967 125.582702 \nL 80.29843 130.690537 \nL 80.526722 204.62962 \nL 80.349162 126.420155 \nL 80.577454 195.647965 \nL 80.653551 165.002767 \nL 80.755014 169.353139 \nL 80.805745 193.031352 \nL 80.831111 168.339099 \nL 80.856477 173.412428 \nL 81.034037 120.655533 \nL 81.059403 132.441374 \nL 81.1355 121.966623 \nL 81.186232 121.990461 \nL 81.211597 110.787973 \nL 81.236963 138.703745 \nL 81.338426 197.289855 \nL 81.363792 186.562462 \nL 81.389158 183.883828 \nL 81.414523 184.577012 \nL 81.490621 153.253953 \nL 81.541352 156.384591 \nL 81.617449 148.581613 \nL 81.592084 164.221897 \nL 81.668181 148.729512 \nL 81.693547 168.392724 \nL 81.744278 143.300874 \nL 81.769644 157.886534 \nL 81.997936 74.179879 \nL 82.023301 93.407099 \nL 82.048667 91.235767 \nL 82.226227 144.151921 \nL 82.251593 143.691806 \nL 82.276959 129.531555 \nL 82.32769 148.745221 \nL 82.353056 145.319678 \nL 82.454519 189.680328 \nL 82.479885 170.883557 \nL 82.505251 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143.361585 \nL 84.8389 140.298225 \nL 84.914997 119.820399 \nL 84.965728 123.099976 \nL 85.041826 146.896034 \nL 85.01646 120.672176 \nL 85.117923 134.830187 \nL 85.244752 116.42141 \nL 85.168654 139.098192 \nL 85.270117 116.922433 \nL 85.396946 175.578598 \nL 85.422312 174.399637 \nL 85.447678 172.944511 \nL 85.574506 142.868611 \nL 85.599872 148.36528 \nL 85.625238 143.421718 \nL 85.675969 168.415456 \nL 85.726701 148.213688 \nL 85.752067 115.152857 \nL 85.85353 126.955788 \nL 86.056456 73.438098 \nL 86.081821 88.850968 \nL 86.107187 93.828787 \nL 86.132553 81.095598 \nL 86.157919 80.39359 \nL 86.284747 110.703784 \nL 86.310113 111.596137 \nL 86.360845 127.812427 \nL 86.411576 119.976281 \nL 86.436942 107.128974 \nL 86.487673 127.599806 \nL 86.513039 116.911044 \nL 86.538405 116.475135 \nL 86.589136 129.85388 \nL 86.614502 115.590586 \nL 86.639868 121.448344 \nL 86.690599 95.198868 \nL 86.741331 113.517194 \nL 86.766697 115.117537 \nL 86.817428 138.941595 \nL 86.86816 111.518426 \nL 86.994988 77.013484 \nL 87.04572 85.975498 \nL 87.071086 73.609949 \nL 87.121817 48.190243 \nL 87.22328 52.997591 \nL 87.299377 94.84147 \nL 87.350109 85.193439 \nL 87.527669 45.842531 \nL 87.705229 96.735602 \nL 87.781327 47.812464 \nL 87.832058 50.237972 \nL 87.857424 49.685871 \nL 87.958887 63.608517 \nL 87.908155 49.459676 \nL 87.984253 59.126313 \nL 88.034984 53.163114 \nL 88.06035 73.090674 \nL 88.111081 46.563774 \nL 88.136447 55.868262 \nL 88.161813 38.106556 \nL 88.23791 45.6627 \nL 88.288642 65.40377 \nL 88.339373 57.722419 \nL 88.364739 45.39588 \nL 88.440836 54.751318 \nL 88.491568 76.024589 \nL 88.567665 66.793286 \nL 88.618396 55.344473 \nL 88.669128 62.003111 \nL 88.719859 67.955688 \nL 88.745225 55.526293 \nL 88.821322 58.713081 \nL 88.846688 62.318973 \nL 88.872054 52.666097 \nL 88.922785 39.403079 \nL 88.948151 59.530563 \nL 88.973517 51.297471 \nL 89.049614 41.675245 \nL 89.07498 46.998204 \nL 89.100346 60.980459 \nL 89.125711 36.334133 \nL 89.151077 46.867747 \nL 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107.820269 52.207724 \nL 107.871001 69.759632 \nL 107.997829 97.687905 \nL 107.947098 62.842937 \nL 108.023195 87.368478 \nL 108.073927 87.790227 \nL 108.099292 90.593922 \nL 108.150024 76.608429 \nL 108.200755 86.641885 \nL 108.403681 150.597699 \nL 108.479779 134.12173 \nL 108.454413 160.588964 \nL 108.53051 136.372744 \nL 108.631973 200.28721 \nL 108.657339 185.977324 \nL 108.834899 134.381192 \nL 108.860265 141.571394 \nL 108.885631 132.823321 \nL 108.910996 134.692074 \nL 108.936362 130.251507 \nL 108.987094 156.048221 \nL 109.037825 152.967009 \nL 109.164654 94.085234 \nL 109.19002 104.659664 \nL 109.215385 114.332054 \nL 109.291483 102.782739 \nL 109.316848 97.909796 \nL 109.342214 103.19041 \nL 109.469043 154.940156 \nL 109.494409 143.760811 \nL 109.646603 89.545802 \nL 109.849529 43.184539 \nL 109.697335 95.077037 \nL 109.900261 43.741851 \nL 109.950992 82.715522 \nL 110.001724 58.713597 \nL 110.027089 52.433597 \nL 110.052455 62.287673 \nL 110.103187 54.4024 \nL 110.153918 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118.600713 58.262505 \nL 118.626079 77.350678 \nL 118.702176 67.66577 \nL 118.778273 54.221138 \nL 118.803639 64.381327 \nL 118.981199 126.886522 \nL 119.006565 111.389587 \nL 119.057297 121.880013 \nL 119.082662 135.18465 \nL 119.133394 114.233441 \nL 119.209491 99.303243 \nL 119.234857 104.683531 \nL 119.361686 123.610367 \nL 119.387051 121.189998 \nL 119.412417 133.221095 \nL 119.437783 112.246786 \nL 119.463149 120.87312 \nL 119.488514 114.957624 \nL 119.51388 121.711951 \nL 119.640709 147.289827 \nL 119.666075 145.346708 \nL 119.69144 145.631643 \nL 119.716806 136.317203 \nL 119.742172 152.451771 \nL 119.767538 167.53944 \nL 119.818269 137.874866 \nL 119.843635 166.4825 \nL 119.894366 135.405161 \nL 119.970464 147.046444 \nL 120.071927 163.466906 \nL 120.148024 169.522942 \nL 120.249487 112.299017 \nL 120.300218 110.496782 \nL 120.503144 152.228042 \nL 120.553876 122.870953 \nL 120.604607 145.578002 \nL 120.832899 88.772668 \nL 120.908996 122.268619 \nL 120.959728 102.482337 \nL 120.985094 102.191935 \nL 121.035825 126.084196 \nL 121.086557 116.940847 \nL 121.111922 103.724675 \nL 121.137288 123.402493 \nL 121.162654 122.112517 \nL 121.18802 122.659462 \nL 121.238751 126.4445 \nL 121.264117 111.293528 \nL 121.314848 126.150628 \nL 121.36558 124.170994 \nL 121.467043 69.124801 \nL 121.517774 90.561262 \nL 121.54314 87.779899 \nL 121.7207 132.889009 \nL 121.771432 155.431787 \nL 121.822163 134.896512 \nL 121.872895 130.742417 \nL 121.898261 142.219889 \nL 121.923626 113.80034 \nL 121.999724 129.673929 \nL 122.025089 145.98578 \nL 122.101187 125.008104 \nL 122.151918 112.358628 \nL 122.228015 151.615594 \nL 122.278747 131.327633 \nL 122.354844 110.681787 \nL 122.38021 125.880976 \nL 122.507039 176.218467 \nL 122.55777 156.452432 \nL 122.583136 182.121799 \nL 122.608502 196.21553 \nL 122.633867 170.211886 \nL 122.659233 194.081675 \nL 122.786062 153.817925 \nL 122.912891 185.919428 \nL 122.963622 151.192447 \nL 123.039719 153.172544 \nL 123.090451 154.930472 \nL 123.115817 146.922473 \nL 123.166548 161.628093 \nL 123.191914 170.603264 \nL 123.242645 149.355548 \nL 123.344108 115.833922 \nL 123.521669 165.234793 \nL 123.673863 106.920467 \nL 123.699229 123.091683 \nL 123.74996 90.765569 \nL 123.775326 88.582113 \nL 124.003618 171.814355 \nL 124.028984 151.112368 \nL 124.105081 156.926208 \nL 124.130447 149.773284 \nL 124.155812 148.629087 \nL 124.282641 206.8929 \nL 124.308007 200.435007 \nL 124.333373 204.382809 \nL 124.40947 152.18732 \nL 124.460201 157.293906 \nL 124.485567 157.221477 \nL 124.510933 154.263419 \nL 124.536299 160.165106 \nL 124.561664 185.051007 \nL 124.663127 183.834879 \nL 124.942151 96.103374 \nL 124.713859 188.385258 \nL 124.967516 118.527247 \nL 125.094345 186.211936 \nL 125.195808 96.428861 \nL 125.221174 118.278501 \nL 125.271905 167.504586 \nL 125.297271 123.608095 \nL 125.322637 98.679724 \nL 125.4241 102.37827 \nL 125.525563 144.87501 \nL 125.550929 157.254516 \nL 125.627026 155.162991 \nL 125.652392 128.701062 \nL 125.703123 170.887312 \nL 125.728489 151.151478 \nL 125.753855 165.196684 \nL 125.77922 155.879073 \nL 125.906049 88.936327 \nL 125.931415 105.825038 \nL 125.956781 116.941652 \nL 125.982146 92.921669 \nL 126.083609 76.357679 \nL 126.108975 83.595016 \nL 126.26117 116.537656 \nL 126.387998 65.676725 \nL 126.413364 72.258691 \nL 126.43873 61.529046 \nL 126.464096 85.079229 \nL 126.489461 91.814372 \nL 126.540193 85.19309 \nL 126.590924 55.419284 \nL 126.692387 56.279617 \nL 126.844582 84.571449 \nL 126.869948 81.397486 \nL 126.946045 46.70957 \nL 126.996776 64.044423 \nL 127.022142 65.200025 \nL 127.072874 44.345526 \nL 127.174337 49.283507 \nL 127.250434 91.300405 \nL 127.301165 59.966397 \nL 127.377263 46.557377 \nL 127.427994 48.151425 \nL 127.478726 60.952314 \nL 127.504091 57.370638 \nL 127.605554 62.025056 \nL 127.63092 39.32754 \nL 127.757749 62.091247 \nL 127.783115 57.356385 \nL 127.833846 64.968508 \nL 127.909943 40.011882 \nL 127.935309 51.07416 \nL 128.087504 77.240277 \nL 128.112869 51.42349 \nL 128.188967 60.116381 \nL 128.239698 81.662011 \nL 128.29043 58.261551 \nL 128.315795 57.32817 \nL 128.417258 42.756906 \nL 128.442624 46.535146 \nL 128.493356 43.920926 \nL 128.569453 67.759434 \nL 128.64555 47.942287 \nL 128.670916 52.793743 \nL 128.721647 47.360475 \nL 128.747013 61.186216 \nL 128.772379 38.334805 \nL 128.82311 64.208583 \nL 128.873842 45.061638 \nL 128.975305 67.776863 \nL 129.000671 51.30889 \nL 129.026036 51.140159 \nL 129.127499 67.460771 \nL 129.076768 44.663831 \nL 129.152865 61.600562 \nL 129.178231 57.432148 \nL 129.203597 61.387403 \nL 129.254328 85.484245 \nL 129.279694 48.46941 \nL 129.330425 39.238269 \nL 129.355791 51.170965 \nL 129.381157 48.388301 \nL 129.584083 81.53845 \nL 129.66018 59.048232 \nL 129.710912 72.536198 \nL 129.736277 73.376953 \nL 129.787009 45.926736 \nL 129.83774 54.396486 \nL 129.913838 110.435348 \nL 129.964569 80.034676 \nL 130.091398 49.369557 \nL 130.167495 40.975042 \nL 130.294324 89.057957 \nL 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143.357685 77.798732 \nL 143.383051 84.20588 \nL 143.459148 106.761338 \nL 143.50988 100.531274 \nL 143.560611 109.637487 \nL 143.611343 83.993443 \nL 143.662074 116.051375 \nL 143.712806 96.536651 \nL 143.890366 49.709624 \nL 144.017195 56.265564 \nL 144.067926 81.465213 \nL 144.118658 54.672323 \nL 144.169389 59.227448 \nL 144.194755 48.555527 \nL 144.270852 57.10116 \nL 144.296218 61.68148 \nL 144.346949 54.394895 \nL 144.423047 37.654945 \nL 144.448412 54.405399 \nL 144.549875 81.109089 \nL 144.575241 76.821516 \nL 144.600607 54.79312 \nL 144.676704 68.576085 \nL 144.70207 92.163901 \nL 144.752801 59.951346 \nL 144.778167 73.969038 \nL 144.803533 73.542612 \nL 144.828899 76.457198 \nL 144.854264 71.423476 \nL 144.87963 67.712781 \nL 144.904996 70.619668 \nL 144.930362 83.714815 \nL 145.006459 66.563214 \nL 145.082556 42.404494 \nL 145.133288 53.582868 \nL 145.234751 75.61215 \nL 145.260116 75.140358 \nL 145.310848 64.397611 \nL 145.361579 67.40522 \nL 145.386945 52.333444 \nL 145.437677 56.904113 \nL 145.463042 87.975141 \nL 145.53914 59.582289 \nL 145.564505 48.705268 \nL 145.615237 66.58458 \nL 145.691334 92.608433 \nL 145.742066 87.058693 \nL 145.767431 71.727387 \nL 145.843529 78.805407 \nL 145.868894 84.946276 \nL 145.919626 72.278883 \nL 145.944992 60.004897 \nL 146.021089 75.956768 \nL 146.097186 77.931819 \nL 146.147918 61.422986 \nL 146.173283 67.907315 \nL 146.224015 54.948003 \nL 146.249381 57.359752 \nL 146.325478 48.1516 \nL 146.300112 57.74514 \nL 146.376209 49.968791 \nL 146.401575 61.858527 \nL 146.452307 47.695879 \nL 146.477672 59.049755 \nL 146.579135 42.628537 \nL 146.629867 45.369403 \nL 146.782061 84.583532 \nL 146.832793 71.942966 \nL 146.858159 77.443344 \nL 146.883524 89.552414 \nL 146.934256 66.890345 \nL 146.984987 70.846676 \nL 147.010353 66.316693 \nL 147.061085 55.182198 \nL 147.08645 59.111724 \nL 147.162548 98.418955 \nL 147.238645 78.145267 \nL 147.264011 61.045212 \nL 147.340108 75.359268 \nL 147.416205 50.570949 \nL 147.492302 57.285039 \nL 147.543034 54.017336 \nL 147.593765 76.247162 \nL 147.695228 41.414044 \nL 147.720594 53.250338 \nL 147.74596 47.868436 \nL 147.771326 53.460033 \nL 147.822057 53.455812 \nL 147.847423 53.754974 \nL 147.872789 61.077153 \nL 147.898154 45.136403 \nL 147.948886 55.263482 \nL 147.974252 45.887786 \nL 147.999617 62.684908 \nL 148.075715 84.786972 \nL 148.10108 69.400462 \nL 148.126446 60.982162 \nL 148.151812 82.582097 \nL 148.177178 97.074405 \nL 148.227909 65.441507 \nL 148.380104 40.567131 \nL 148.405469 59.23309 \nL 148.481567 58.24729 \nL 148.506932 51.318802 \nL 148.532298 61.831392 \nL 148.557664 53.230989 \nL 148.735224 88.441173 \nL 148.887419 54.837049 \nL 148.912784 72.096629 \nL 148.93815 45.631621 \nL 148.988882 58.262791 \nL 149.039613 42.277635 \nL 149.090345 61.201548 \nL 149.11571 45.447619 \nL 149.166442 66.212885 \nL 149.191808 39.500388 \nL 149.267905 59.267291 \nL 149.318636 51.853629 \nL 149.344002 59.674196 \nL 149.369368 41.791724 \nL 149.470831 42.572211 \nL 149.521562 51.864134 \nL 149.546928 64.075592 \nL 149.59766 44.338314 \nL 149.623025 44.675525 \nL 149.673757 44.21651 \nL 149.724488 54.940946 \nL 149.749854 69.714387 \nL 149.800586 45.199805 \nL 149.825951 37.969697 \nL 149.876683 39.823257 \nL 149.902049 59.508685 \nL 149.978146 54.638593 \nL 150.003512 49.79666 \nL 150.028877 56.874044 \nL 150.054243 56.07277 \nL 150.104975 79.892454 \nL 150.13034 49.835 \nL 150.155706 52.372848 \nL 150.181072 47.79864 \nL 150.206438 59.789959 \nL 150.231803 53.886102 \nL 150.333266 100.585095 \nL 150.434729 35.881192 \nL 150.460095 62.048446 \nL 150.536192 39.915527 \nL 150.561558 58.976457 \nL 150.586924 80.83547 \nL 150.663021 74.637358 \nL 150.78985 42.482292 \nL 150.96741 79.049509 \nL 151.119605 39.344664 \nL 151.170336 50.326814 \nL 151.195702 64.435586 \nL 151.271799 47.247293 \nL 151.322531 61.095606 \nL 151.373262 48.150862 \nL 151.398628 45.942159 \nL 151.525457 71.953141 \nL 151.576188 55.103142 \nL 151.62692 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140.857938 \nL 156.091292 132.43008 \nL 156.167389 137.077533 \nL 156.192755 108.198123 \nL 156.294218 163.954563 \nL 156.319584 158.959898 \nL 156.446412 129.420258 \nL 156.573241 142.630713 \nL 156.674704 143.817012 \nL 156.70007 108.885946 \nL 156.750801 150.012426 \nL 156.801533 117.150258 \nL 156.87763 122.805195 \nL 156.928362 84.325346 \nL 157.05519 131.976371 \nL 157.080556 104.059175 \nL 157.105922 94.83672 \nL 157.156653 97.968188 \nL 157.258116 151.713238 \nL 157.283482 126.231431 \nL 157.384945 111.353274 \nL 157.435677 146.115303 \nL 157.486408 104.166674 \nL 157.511774 132.607008 \nL 157.562505 148.528314 \nL 157.587871 163.242349 \nL 157.613237 144.605943 \nL 157.638603 119.665516 \nL 157.7147 138.122131 \nL 157.790797 186.542702 \nL 157.816163 180.959652 \nL 157.841529 145.111031 \nL 157.917626 148.560133 \nL 157.942992 159.647348 \nL 157.968357 136.651214 \nL 158.171283 87.92393 \nL 158.222015 119.947623 \nL 158.247381 99.360514 \nL 158.323478 62.663983 \nL 158.348844 85.573896 \nL 158.475672 157.993222 \nL 158.501038 140.956651 \nL 158.526404 140.295313 \nL 158.55177 130.41225 \nL 158.577135 141.136435 \nL 158.602501 133.859053 \nL 158.678598 162.41838 \nL 158.703964 145.527656 \nL 158.754696 134.59122 \nL 158.780061 170.428379 \nL 158.856159 144.556989 \nL 158.881524 144.130734 \nL 159.008353 196.576323 \nL 159.033719 188.154916 \nL 159.185913 123.758287 \nL 159.287376 176.287564 \nL 159.338108 172.834404 \nL 159.439571 158.191041 \nL 159.5664 183.095406 \nL 159.591765 179.976937 \nL 159.718594 119.3369 \nL 159.74396 141.403683 \nL 159.769326 113.452151 \nL 159.820057 123.718463 \nL 159.946886 148.167652 \nL 159.896154 118.037878 \nL 159.972252 133.140186 \nL 160.073715 66.206217 \nL 160.149812 78.832443 \nL 160.200543 101.72874 \nL 160.251275 85.308979 \nL 160.302006 70.845688 \nL 160.327372 87.138307 \nL 160.352738 96.651628 \nL 160.403469 88.163605 \nL 160.428835 76.416131 \nL 160.504932 83.684233 \nL 160.555664 111.083376 \nL 160.58103 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163.016142 88.536611 \nL 163.092239 107.507315 \nL 163.219068 63.962668 \nL 163.244433 68.643797 \nL 163.345896 37.376956 \nL 163.396628 42.96764 \nL 163.421994 59.840703 \nL 163.498091 40.183993 \nL 163.650285 61.230112 \nL 163.726383 45.854626 \nL 163.701017 61.515307 \nL 163.751748 54.857708 \nL 163.777114 69.001924 \nL 163.853211 49.599671 \nL 163.878577 49.94937 \nL 163.929309 38.972121 \nL 163.954674 53.455926 \nL 164.030772 51.809763 \nL 164.056137 40.995485 \nL 164.106869 41.549629 \nL 164.132235 67.432253 \nL 164.233698 66.400717 \nL 164.309795 48.118606 \nL 164.360526 62.102881 \nL 164.538087 118.546504 \nL 164.588818 93.893276 \nL 164.614184 87.24619 \nL 164.63955 100.012018 \nL 164.664915 99.177413 \nL 164.715647 96.834345 \nL 164.81711 133.362794 \nL 164.842476 107.769514 \nL 164.943939 114.492939 \nL 164.969304 113.350215 \nL 164.99467 116.824933 \nL 165.020036 129.950582 \nL 165.045402 108.271685 \nL 165.096133 68.877111 \nL 165.17223 80.369107 \nL 165.222962 92.211131 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177.953403 \nL 170.270746 179.664944 \nL 170.397575 138.485533 \nL 170.473672 151.904505 \nL 170.499038 147.387625 \nL 170.524404 145.467206 \nL 170.625867 175.07663 \nL 170.651232 155.486267 \nL 170.676598 155.636799 \nL 170.701964 184.621907 \nL 170.778061 175.051542 \nL 170.803427 172.884063 \nL 170.828793 159.883443 \nL 170.854158 188.014732 \nL 170.879524 171.406819 \nL 170.90489 180.774287 \nL 170.930256 172.642675 \nL 171.031719 148.32775 \nL 171.057084 169.664177 \nL 171.133182 147.816559 \nL 171.158547 126.325817 \nL 171.26001 131.913554 \nL 171.412205 85.995356 \nL 171.437571 86.416081 \nL 171.462936 81.044565 \nL 171.488302 92.495123 \nL 171.513668 105.480959 \nL 171.589765 102.874989 \nL 171.767325 71.254866 \nL 171.818057 122.373182 \nL 171.894154 89.703496 \nL 172.09708 137.220161 \nL 172.147812 169.534008 \nL 172.198543 152.681999 \nL 172.223909 141.564057 \nL 172.249275 170.837085 \nL 172.300006 147.536204 \nL 172.350738 140.820416 \nL 172.452201 158.832852 \nL 172.477566 142.248963 \nL 172.579029 146.635658 \nL 172.655127 164.802407 \nL 172.705858 159.988724 \nL 172.731224 158.11525 \nL 172.75659 164.210071 \nL 172.858053 184.57141 \nL 172.908784 164.228204 \nL 172.959516 171.528737 \nL 172.984881 190.772815 \nL 173.060979 172.341699 \nL 173.11171 189.400718 \nL 173.137076 183.253121 \nL 173.213173 186.87015 \nL 173.263905 136.584781 \nL 173.28927 160.637142 \nL 173.365368 138.178492 \nL 173.390733 135.891866 \nL 173.466831 164.770637 \nL 173.492196 144.286631 \nL 173.517562 141.728231 \nL 173.593659 155.824741 \nL 173.619025 141.62529 \nL 173.644391 151.955057 \nL 173.669757 152.355514 \nL 173.695122 147.452983 \nL 173.745854 176.718157 \nL 173.821951 164.750856 \nL 173.847317 155.614589 \nL 173.94878 92.226592 \nL 173.974146 137.578037 \nL 174.050243 121.71492 \nL 174.075609 107.83697 \nL 174.12634 133.370271 \nL 174.151706 116.735208 \nL 174.177072 118.493214 \nL 174.354632 169.996401 \nL 174.405363 176.218169 \nL 174.481461 181.722686 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139.500959 \nL 177.119499 121.238943 \nL 177.17023 163.018079 \nL 177.195596 178.83307 \nL 177.246327 160.758733 \nL 177.271693 171.705922 \nL 177.322425 145.774362 \nL 177.398522 162.761426 \nL 177.423888 175.906269 \nL 177.474619 148.769955 \nL 177.499985 148.773392 \nL 177.652179 162.618243 \nL 177.82974 90.87862 \nL 177.981934 135.87793 \nL 178.0073 118.19586 \nL 178.083397 139.441181 \nL 178.108763 122.19679 \nL 178.134129 126.763287 \nL 178.235592 98.26261 \nL 178.36242 127.361487 \nL 178.413152 124.637576 \nL 178.438518 143.758366 \nL 178.463883 165.307553 \nL 178.514615 146.30001 \nL 178.641444 111.934546 \nL 178.590712 151.398662 \nL 178.666809 122.048936 \nL 178.692175 135.578236 \nL 178.717541 115.009374 \nL 178.742907 97.042437 \nL 178.768272 113.994175 \nL 178.869735 164.833703 \nL 178.895101 159.629761 \nL 178.945833 137.915302 \nL 178.996564 152.957352 \nL 179.047296 193.848789 \nL 179.123393 174.040772 \nL 179.148759 151.609216 \nL 179.224856 166.36021 \nL 179.326319 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44.621804 \nL 184.120446 63.233118 \nL 184.171177 40.484518 \nL 184.374103 87.212518 \nL 184.4502 48.327261 \nL 184.526298 53.457409 \nL 184.627761 39.95588 \nL 184.602395 56.886223 \nL 184.653126 48.718432 \nL 184.678492 48.11733 \nL 184.703858 50.433427 \nL 184.754589 67.296927 \nL 184.805321 47.450955 \nL 184.830687 40.919676 \nL 184.856052 47.46539 \nL 184.906784 64.804668 \nL 184.957515 48.927841 \nL 185.008247 45.954783 \nL 185.033613 52.700095 \nL 185.084344 65.918075 \nL 185.160441 37.631634 \nL 185.338002 142.932005 \nL 185.46483 141.720811 \nL 185.490196 131.856087 \nL 185.515562 151.419059 \nL 185.667756 181.747765 \nL 185.693122 177.939836 \nL 185.718488 176.702268 \nL 185.743854 182.607222 \nL 185.870682 142.045511 \nL 185.896048 141.308741 \nL 186.048243 178.827668 \nL 186.200437 160.671093 \nL 186.225803 160.26901 \nL 186.3019 180.529102 \nL 186.276534 158.883775 \nL 186.327266 166.039709 \nL 186.377997 131.972371 \nL 186.428729 173.383328 \nL 186.454095 181.762624 \nL 186.504826 170.848967 \nL 186.530192 166.440144 \nL 186.657021 193.699756 \nL 186.682386 170.274933 \nL 186.758484 189.549712 \nL 186.783849 191.080132 \nL 186.809215 204.64404 \nL 186.834581 177.620762 \nL 186.859947 182.115073 \nL 187.037507 123.206613 \nL 187.062873 144.147935 \nL 187.113604 109.238564 \nL 187.164336 136.253205 \nL 187.189701 108.19845 \nL 187.265799 115.877099 \nL 187.367262 115.831648 \nL 187.392627 128.479497 \nL 187.544822 69.673767 \nL 187.595554 42.674394 \nL 187.697017 43.006535 \nL 187.747748 48.968767 \nL 187.899943 62.468424 \nL 187.925308 66.526111 \nL 188.052137 46.824966 \nL 188.102869 71.362399 \nL 188.178966 61.02971 \nL 188.204332 60.331643 \nL 188.381892 82.968298 \nL 188.483355 56.492837 \nL 188.534086 69.137915 \nL 188.584818 89.354244 \nL 188.610184 66.334293 \nL 188.635549 66.147755 \nL 188.660915 52.784857 \nL 188.737012 55.178821 \nL 188.787744 53.161782 \nL 188.863841 66.480522 \nL 188.965304 44.696577 \nL 188.99067 55.474586 \nL 189.041401 58.113413 \nL 189.066767 44.542954 \nL 189.092133 57.160317 \nL 189.117499 82.066749 \nL 189.16823 69.589514 \nL 189.193596 46.302312 \nL 189.269693 77.312277 \nL 189.295059 84.537126 \nL 189.320425 63.322386 \nL 189.421888 42.735938 \nL 189.447253 45.441505 \nL 189.497985 76.07341 \nL 189.574082 57.799478 \nL 189.700911 46.688966 \nL 189.82774 94.066205 \nL 189.954568 48.67169 \nL 189.979934 71.89844 \nL 190.056031 45.294849 \nL 190.106763 75.068363 \nL 190.18286 55.503592 \nL 190.208226 65.330253 \nL 190.258957 47.772012 \nL 190.284323 43.768859 \nL 190.335055 48.506086 \nL 190.36042 66.988881 \nL 190.436518 53.687317 \nL 190.461883 55.601505 \nL 190.512615 85.66705 \nL 190.537981 68.305385 \nL 190.563346 46.297207 \nL 190.614078 77.005692 \nL 190.639444 55.069429 \nL 190.664809 69.412387 \nL 190.715541 57.927034 \nL 190.740907 39.233328 \nL 190.817004 48.305377 \nL 190.943833 74.001481 \nL 191.096027 54.891719 \nL 191.19749 99.418481 \nL 191.248222 76.547107 \nL 191.298953 37.118058 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220.647126 42.685685 \nL 220.723223 44.268144 \nL 220.850052 101.666418 \nL 220.951515 81.063276 \nL 221.002246 83.96363 \nL 221.027612 76.130524 \nL 221.078344 87.584791 \nL 221.154441 102.455598 \nL 221.205172 98.318628 \nL 221.332001 68.079463 \nL 221.357367 85.955933 \nL 221.433464 61.802606 \nL 221.484196 46.505103 \nL 221.509561 53.248652 \nL 221.63639 96.813724 \nL 221.661756 93.15579 \nL 221.763219 49.709687 \nL 221.788585 62.980541 \nL 221.81395 89.183987 \nL 221.890048 73.650067 \nL 221.915413 71.258238 \nL 221.991511 85.016438 \nL 221.966145 65.764639 \nL 222.016876 73.682744 \nL 222.042242 49.742436 \nL 222.118339 74.670872 \nL 222.143705 80.678372 \nL 222.169071 67.338809 \nL 222.2959 55.222717 \nL 222.321265 62.896574 \nL 222.346631 46.318526 \nL 222.371997 59.809398 \nL 222.448094 38.36129 \nL 222.498826 44.973827 \nL 222.65102 71.04344 \nL 222.676386 71.830977 \nL 222.727117 92.764519 \nL 222.82858 83.801619 \nL 222.853946 66.886427 \nL 222.904678 93.271251 \nL 222.930043 92.495015 \nL 222.955409 99.010081 \nL 223.056872 68.79232 \nL 223.082238 69.23584 \nL 223.107604 70.497092 \nL 223.132969 44.215407 \nL 223.209067 72.939398 \nL 223.335895 97.74926 \nL 223.361261 81.348532 \nL 223.411993 85.132315 \nL 223.462724 139.132109 \nL 223.564187 124.714185 \nL 223.640285 101.184568 \nL 223.66565 81.572624 \nL 223.741748 97.910942 \nL 223.767113 92.819601 \nL 223.792479 111.955353 \nL 223.868576 98.958496 \nL 223.893942 101.692057 \nL 223.919308 120.658585 \nL 223.970039 84.212129 \nL 223.995405 72.869859 \nL 224.046137 86.562499 \nL 224.096868 85.888493 \nL 224.172965 105.153717 \nL 224.198331 85.327324 \nL 224.274428 108.689478 \nL 224.32516 148.37819 \nL 224.375891 137.308349 \nL 224.528086 85.856599 \nL 224.68028 150.572171 \nL 224.731012 120.672332 \nL 224.807109 107.366038 \nL 224.933938 137.180417 \nL 224.959304 140.340096 \nL 224.984669 126.633116 \nL 225.035401 150.555937 \nL 225.060767 136.744018 \nL 225.086132 144.884304 \nL 225.16223 140.933871 \nL 225.314424 99.892343 \nL 225.390521 97.312393 \nL 225.415887 117.451971 \nL 225.441253 106.341541 \nL 225.466619 130.000199 \nL 225.491984 113.212544 \nL 225.51735 126.270262 \nL 225.568082 98.143281 \nL 225.593447 93.824903 \nL 225.618813 102.958505 \nL 225.669545 106.427971 \nL 225.69491 99.553735 \nL 225.720276 113.611257 \nL 225.745642 106.117755 \nL 225.821739 127.849735 \nL 225.847105 125.17474 \nL 225.923202 167.434579 \nL 225.948568 139.101639 \nL 225.999299 115.414718 \nL 226.100762 121.304883 \nL 226.17686 162.254231 \nL 226.227591 144.481073 \nL 226.278323 156.071247 \nL 226.303688 135.115696 \nL 226.329054 116.434531 \nL 226.430517 118.915259 \nL 226.455883 107.119787 \nL 226.53198 111.643141 \nL 226.734906 159.049066 \nL 226.582712 108.478997 \nL 226.760272 144.373603 \nL 226.988564 80.323539 \nL 227.166124 155.879349 \nL 227.242221 93.802987 \nL 227.292953 107.600503 \nL 227.318318 111.256283 \nL 227.394416 175.026113 \nL 227.445147 148.914775 \nL 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236.399257 104.733749 \nL 236.449988 91.737589 \nL 236.50072 97.356774 \nL 236.551451 110.907131 \nL 236.627549 109.746566 \nL 236.729012 71.787706 \nL 236.754377 91.838223 \nL 236.779743 87.465736 \nL 236.805109 98.180212 \nL 236.830475 93.357397 \nL 236.85584 103.534184 \nL 236.881206 96.899569 \nL 236.906572 78.202761 \nL 236.957303 114.237301 \nL 237.008035 131.73356 \nL 237.033401 104.7817 \nL 237.058766 67.724921 \nL 237.134864 113.675149 \nL 237.160229 112.938498 \nL 237.210961 157.033124 \nL 237.287058 126.636264 \nL 237.312424 131.378735 \nL 237.33779 124.117351 \nL 237.363155 112.200095 \nL 237.413887 118.883341 \nL 237.464618 134.34867 \nL 237.489984 130.422608 \nL 237.540716 88.920354 \nL 237.616813 97.533273 \nL 237.642179 119.535269 \nL 237.69291 88.119175 \nL 237.819739 47.066727 \nL 237.845105 49.660949 \nL 237.87047 64.747257 \nL 237.921202 42.557158 \nL 237.946568 57.85589 \nL 237.997299 41.154194 \nL 238.048031 56.735074 \nL 238.149494 41.529766 \nL 238.098762 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240.609971 71.425424 \nL 240.686069 91.597758 \nL 240.711434 91.328599 \nL 240.7368 80.438217 \nL 240.787532 93.543136 \nL 240.812897 91.903148 \nL 240.838263 85.068991 \nL 240.863629 98.465721 \nL 240.91436 111.064491 \nL 241.066555 152.053267 \nL 241.193384 188.172156 \nL 241.218749 169.688254 \nL 241.269481 163.315433 \nL 241.320212 182.06694 \nL 241.472407 151.950603 \nL 241.497773 154.62786 \nL 241.548504 182.768153 \nL 241.57387 178.602581 \nL 241.624601 135.19751 \nL 241.700699 150.487633 \nL 241.75143 165.702532 \nL 241.802162 154.950525 \nL 241.827527 114.213475 \nL 241.903625 133.238176 \nL 241.979722 152.456742 \nL 242.081185 129.18491 \nL 242.106551 131.088856 \nL 242.182648 104.044919 \nL 242.208014 109.18577 \nL 242.233379 130.704165 \nL 242.258745 104.995755 \nL 242.284111 115.681451 \nL 242.385574 75.909536 \nL 242.41094 82.45798 \nL 242.436305 95.629936 \nL 242.461671 73.174619 \nL 242.512403 82.761195 \nL 242.5885 102.27043 \nL 242.639231 130.350514 \nL 242.689963 116.013935 \nL 242.892889 84.254963 \nL 242.918255 95.661645 \nL 242.94362 79.316964 \nL 242.968986 49.841764 \nL 243.070449 53.661108 \nL 243.146546 67.215031 \nL 243.121181 51.578298 \nL 243.171912 60.140932 \nL 243.273375 49.860081 \nL 243.298741 51.816085 \nL 243.324107 55.115228 \nL 243.374838 47.747763 \nL 243.400204 54.71944 \nL 243.42557 41.575756 \nL 243.476301 42.592839 \nL 243.527033 74.855556 \nL 243.60313 58.618269 \nL 243.653861 66.592209 \nL 243.729959 45.066918 \nL 243.856787 76.815056 \nL 243.882153 63.106328 \nL 243.907519 54.291942 \nL 243.95825 72.878921 \nL 244.085079 54.014076 \nL 244.110445 55.261511 \nL 244.135811 70.129647 \nL 244.186542 47.607206 \nL 244.211908 41.259644 \nL 244.262639 53.17459 \nL 244.288005 50.991948 \nL 244.338737 63.918652 \nL 244.389468 47.499942 \nL 244.414834 42.251274 \nL 244.465565 43.865082 \nL 244.567028 99.211804 \nL 244.592394 95.187648 \nL 244.643126 65.766203 \nL 244.668491 87.839248 \nL 244.744589 130.885399 \nL 244.79532 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256.61576 169.739238 \nL 256.666491 150.847923 \nL 256.742588 159.23036 \nL 256.818686 133.768251 \nL 256.844051 110.118901 \nL 256.945514 118.840614 \nL 257.021612 144.688638 \nL 257.046977 133.296302 \nL 257.097709 114.561995 \nL 257.123075 135.954107 \nL 257.173806 150.193916 \nL 257.199172 133.846483 \nL 257.224538 142.608653 \nL 257.376732 107.536209 \nL 257.402098 108.051441 \nL 257.478195 71.58596 \nL 257.605024 77.203949 \nL 257.655755 83.438351 \nL 257.681121 76.381382 \nL 257.757218 42.587538 \nL 257.858681 54.66798 \nL 257.884047 54.097717 \nL 257.934779 45.588797 \nL 257.960144 56.137289 \nL 257.98551 65.590394 \nL 258.036242 45.512566 \nL 258.061607 61.480748 \nL 258.112339 45.968517 \nL 258.137705 62.406027 \nL 258.188436 51.803938 \nL 258.289899 65.855893 \nL 258.315265 79.877392 \nL 258.365996 62.782032 \nL 258.391362 59.91837 \nL 258.416728 68.963599 \nL 258.442094 85.064786 \nL 258.467459 56.489302 \nL 258.518191 70.186939 \nL 258.619654 51.053591 \nL 258.64502 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319.446723 46.384504 \nL 319.548186 72.409644 \nL 319.598917 65.67491 \nL 319.624283 41.568515 \nL 319.725746 42.465991 \nL 319.801843 64.552743 \nL 319.827209 55.8063 \nL 319.877941 40.455365 \nL 319.903306 60.080552 \nL 319.928672 60.369068 \nL 320.004769 76.349918 \nL 320.030135 69.224481 \nL 320.055501 53.438958 \nL 320.106232 89.369982 \nL 320.131598 69.761943 \nL 320.156964 76.82848 \nL 320.18233 61.491695 \nL 320.283793 51.489392 \nL 320.233061 62.627246 \nL 320.309158 57.500578 \nL 320.461353 97.346113 \nL 320.486719 88.325437 \nL 320.664279 54.312399 \nL 320.689645 64.528787 \nL 320.71501 76.695499 \nL 320.740376 64.3189 \nL 320.765742 68.085326 \nL 320.791108 56.929337 \nL 320.841839 78.092297 \nL 320.867205 71.476426 \nL 320.917936 99.370352 \nL 320.994034 87.587308 \nL 321.070131 100.876653 \nL 321.171594 167.653232 \nL 321.222325 135.310629 \nL 321.247691 123.296214 \nL 321.273057 147.427027 \nL 321.298423 145.072833 \nL 321.399886 130.484157 \nL 321.475983 130.72114 \nL 321.526714 107.94406 \nL 321.577446 128.180405 \nL 321.628177 111.69858 \nL 321.678909 131.655552 \nL 321.72964 113.078987 \nL 321.780372 97.467004 \nL 321.805738 151.725875 \nL 321.881835 109.794543 \nL 321.907201 93.108615 \nL 321.932566 121.080788 \nL 322.034029 162.668494 \nL 322.084761 160.638469 \nL 322.236955 120.395719 \nL 322.262321 155.843345 \nL 322.338418 141.232667 \nL 322.490613 42.907824 \nL 322.592076 65.776338 \nL 322.541344 41.402542 \nL 322.617442 53.979799 \nL 322.642807 45.36193 \nL 322.668173 56.124795 \nL 322.74427 47.727016 \nL 322.820368 62.731351 \nL 322.845733 51.658554 \nL 322.871099 47.826087 \nL 322.896465 51.970809 \nL 322.947196 71.228013 \nL 322.972562 40.907089 \nL 323.074025 52.472811 \nL 323.048659 40.492179 \nL 323.099391 46.109028 \nL 323.124757 44.726533 \nL 323.150122 56.628108 \nL 323.22622 50.617965 \nL 323.251585 48.904999 \nL 323.276951 54.197227 \nL 323.302317 58.041547 \nL 323.378414 54.466923 \nL 323.454511 38.270202 \nL 323.505243 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4A6Qw8hLgzRu"
      },
      "source": [
        "# Values to sample\n",
        "xx = np.linspace(5000, 37000, num = 300)\n",
        "nxx = len(xx)\n",
        "density_mcmc = np.zeros((rrr-burn, len(xx)))\n",
        "for s in range(rrr-burn):\n",
        "  for k in range(KK):\n",
        "    density_mcmc[s, :] = density_mcmc[s, :] + \\\n",
        "                             w_out[s + burn, k] * norm.pdf(loc = mu_out[s + burn, k], scale = sigma_out[s + burn], x = xx)\n",
        "\n",
        "density_mcmc_m = np.mean(density_mcmc, axis = 0)\n",
        "density_mcmc_lq = np.quantile(density_mcmc, 0.025, axis = 0)\n",
        "density_mcmc_uq = np.quantile(density_mcmc, 0.975, axis = 0)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "CNMBd9txhfix",
        "outputId": "bdbdaaca-f7fb-4a3f-f8be-f5924c16066b"
      },
      "source": [
        "## Plot the final result distribution for various samples\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "# Bayesian plot\n",
        "ax.plot(xx, density_mcmc_m,  lw=2, alpha=0.6, label='MCMC') # Trace plot of data\n",
        "# EM algorithm\n",
        "ax.plot(xx, density_EM, 'blue', label = 'EM')\n",
        "# KDE plot\n",
        "ax = sns.kdeplot(x, bw_adjust = 0.5, label = 'KDE')\n",
        "# Plotting original data\n",
        "ax.scatter(np.array(x), np.zeros((x.shape[0])), label = 'Original data')\n",
        "# Plotting uncertainty\n",
        "plt.fill_between(xx, density_mcmc_uq, density_mcmc_lq, alpha=0.2,\n",
        "                 label='Uncertainty Interval')\n",
        "ax.legend(loc='best', frameon=False)\n",
        "# plot density estimate of the posterior\n",
        "plt.title('Trace plot of Logposterior')\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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tce7Ro4c4ffq0SEpKEomJieLAgQOidevWepdZq8Orr74qli9fLiIiIkRmZqaIj48XZ86c0dqvRB0qVqwo5s2bJ65cuSJSU1NFcnKyCAsLE9u2bRNDhw7V2hfEUFAvmw0MDBQ1a9YUu3btEgkJCSI1NVWcOHFCdO7cWeee/OwPCAgQO3bsEA8fPhSZmZnizp07YsWKFcLT01Nv+Z988om4du2ayMjIEEIInWXAhoKTk5OYPn26uHLlikhLSxNJSUni+PHjevcQghdbJp4fixcvfu5nr1mzpti+fbtITEwUKSkp4tSpU6J79+5i8uTJQgghevXqpVMXIyMjRZkyZcT3338vYmJiREZGhrh27Zr417/+ZfA5Bg4cKI4fPy6SkpJEenq6CA0NFTNmzNDZJ6lTp05i7dq14urVq+Lx48ciJSVF3LhxQyxdulRreb86DB06VFy4cEGkpaVp9Hj6uoODgxg/frw4efKkePz4scjIyBB37twRhw8fFhMnThSurq6atOpl4vktyde3TNzUuvB0fS/KdkMGswezGyCDDDKYOcgG3/LChg0bhBBC1KhRQyve0A+8DDLIoB3kHByJRCIxEwqFQu9qqQ4dOjBw4EBCQ0MJCwszg2USSfFHzsGRSCQSM+Ho6Mjdu3cJDg7mxo0b5OTkULduXTp16kRmZibjxo0zt4kSSbFFOjgSiURiJrKzs1mxYgXt27enSZMmlCpVivj4eLZt28bcuXO5fPmyuU2USIotClRjVRKJRCKRSCRWg5yDI5FIJBKJxOqwCAdn7NixREREkJ6eTkhISL47eAK0adOGkJAQ0tPTCQ8P19lbAqBv375cvXqVjIwMrl69qrMlfkHl2tvbM3/+fC5fvkxKSgr37t3jp59+0mz7rSY4OBghhFbYtGnTcyohkUgkEomkMDD7HJwBAwawZMkSxo0bx8mTJxkzZgz79++nTp06WrudqvH19WXfvn2sXr2aYcOG8frrr7NixQri4uLYsWMHoNq9dcuWLXz22Wfs3LmTt956i61bt9KqVSv+/PNPo8p1cXGhcePGfPnll1y6dIny5cuzZMkS9uzZQ5MmTbRs+uGHH7R2fy1oUzU3NzejN16TSCQSiUTyD87OzppdtPPD7HNwzpw5w19//aW1WuDatWvs2rWLGTNm6KSfP38+vXr1ok6dOpq4lStX0qBBA83ZLJs3b6ZMmTJ0795dk2b//v0kJiYyZMiQ5yoXICAggHPnzuHt7a1xvoKDg7l48SKffPKJUc/r5ubGli1bjEorkUgkEolEl4EDBxbo5Ji1B8fBwQF/f3/mz5+vFX/w4EG9B8mB6mC6gwcPasUdOHCA0aNHY29vT05ODi1atGDx4sU6aT7++OPnLhegbNmy5OXlaU58VjN06FCGDRtGbGws+/fv54svviAlJUVvHuqem9GjR2v14mRmZpKdnW2w7IJwd3cnNjb2ue+3FqQOKqQOKqQOUgM1UgcVxV0HZ2dntmzZYtQoiFkdnAoVKmBvb68jdmxsLB4eHnrv8fDw0JvewcGBChUq8ODBA4Np1Hk+T7lOTk7Mnz+fjRs3ap3HExQURGRkJA8ePKBevXrMmzePBg0a0Llz53yf/fbt29jb/yP/kSNHWLduHXv37mXs2LGAyuGyt7enQ4cOACxbtox+/frh5eXF/fv32bp1KxMnTgTA1dWVTZs2aU6h/s9//kPXrl3x9fUlPj6ewMBApkyZAsDJkydJSEjgzTffBGDNmjW0adMGPz8/njx5wooVK5g+fTqgOhE4JiaGvn37AvDzzz8TEBBAnTp1SE9PZ9GiRUyfPh2lUsmFCxcICwtj4MCBAGzatInatWvTsGFDcnJy+Oabb5g8eTIlSpTg6tWrXLhwgWHDhgGwfft2fHx8CAgIAFRn2UyYMIHSpUtz8+ZNTp48yahRowDVIZMVK1bUOKPffvst7733Hq6urnh5efH5559r5mXt378fFxcXzcndixcvZsiQIbi7uxMTE8Pu3bsZP348AIcPHwbgjTfeAGD58uX07t2bypUrExsby8aNGzU9dceOHSMtLU1zyOWqVavo1KkTVatWJSEhgR9//JGpU6cCcOrUKeLi4ujduzegOvTv9ddfp2bNmiQnJ7Ns2TJmzpwJQEhICHfu3KFfv34AbNiwgUaNGlG3bl0yMjJYuHAhn376Kfb29ly8eJHr168zePBgALZs2UKNGjVo1KgRfn5+jBgxgkmTJuHs7My1a9cICQnh3XffBWDHjh1UrlyZpk2bAqqDG8eNG0fZsmW5desWf/zxB6NHjwZUB4C6urry+uuvA7BgwQJGjhxJhQoViIqK4rfffuPDDz8EVP+RcHJy0pxcvXTpUgYMGICnpyf37t1j+/btTJgwQVPnc3JyNO/KypUr6dGjB97e3sTFxbF+/XomTZoEwPHjx0lKSqJHjx4ArF69mvbt21O9enUSExNZtWoV06ZNA1S9sw8ePKBPnz7UqFGDGTNm0KxZM2rXrk1qaipLlixhxowZKBQKzp8/T0REhOZMsI0bN1K/fn3q169PVlYWCxYsYMqUKTg6OnLlyhWuXLmi6QXetm0bVatWxd/fHyEEc+fO5eOPP6ZkyZJcv36ds2fPMmLECEB1EKeHhwfNmzcHVD3RY8aMoXz58ty+fZvg4GDef/99APbu3UuZMmU0p3AvWrSId955h4oVKxIdHW1yGzFnzhzCwsI4evQomZmZNttGzJgxg6ioKCIiIjh06JDNthETJkwgNjaWvLw85s2bV+zaCLVGxmK2bZQ9PT2FEEI0b95cK37GjBni+vXreu+5efOmmDZtmlZcy5YthRBCuLu7C0BkZmbqnD8yZMgQkZ6e/lzl2tvbi507d4rz58+L0qVL5/tMjRs3FkII0ahRI73XXVxcxOHDh0WlSpVE6dKlNcHR0fGFtJw5c6bZt8W2hCB1kDpIHaQGUgfr1UH9G+ri4lJgWrOuooqPjycnJ0en16RSpUoGu9DUPTTPps/OztaMxxlKo87TlHLt7e3ZunUrVapUoVOnTgWepvzXX3+RlZWFn59fvulSUlJITk7WhKysrHzTF8Svv/76QvdbC1IHFVIHFVIHqYEaqYMKW9LBrA5OdnY258+fp1OnTlrxnTp14tSpU3rvOX36tE76zp07ExISQk5OTr5p1HkaW67aufHz8+ONN94gISGhwGeqW7cujo6O3L9/v8C0hYmrq+tLLc9SkTqokDqokDpIDdRIHVTYmg5m7W4aMGCAyMzMFCNHjhS1atUSixYtEsnJycLb21sAYu7cueKnn37SpPf19RUpKSli4cKFolatWmLkyJEiMzNT9O3bV5OmRYsWIjs7W0ydOlXUrFlTTJ06VWRlZYmmTZsaXa6dnZ3YtWuXiI6OFq+99ppwd3fXBAcHBwGIqlWris8++0z4+/sLHx8f0a1bN3Ht2jVx/vx5oVQqX7h7zZRQ3LsdpQ5SB6mD1EDqIHUoKJjyG2r2fXC2bt2Km5sbn3/+OZ6enoSGhtK9e3eio6MB8PT0xNvbW5M+KiqK7t27s3jxYsaPH8/ff//NhAkTNHvggKoHZ9CgQXz11Vd8+eWXhIeHM3DgQM0eOMaUW7lyZc1kpkuXLmnZ3K5dO44dO0ZWVhYdO3Zk4sSJlCpVirt377J3716++OIL8vLyikwziUQikUgkBWN2j8yWQlH14LzoJGVrCVIHqYPUQWogdbBeHYrNJGNJ4TFy5Ehzm2ARSB1USB1USB2kBmqkDipsSQfp4FgJFSpUMLcJFoHUQYXUQYXUQWqgRuqgwpZ0kA6OlRAVFWVuEywCqYMKqYMKqYPUQI3UQYWt6WD2MTVbCkU1B8fNzc3sz2YJQeogdZA6SA2kDtarg5yDY4Oot8G2daQOKqQOKqQOUgM1UgcVtqSDdHAkEolV4ojC3CbYPIGBgQghWLlypc615cuXI4QgMDBQE+fu7s6yZcsIDw8nIyOD6Oho9uzZozlrCyAyMhIhhOY8q6cJDQ1FCMHw4cO14hs2bMigQYN48OAB6enp3Lx5kx9++KHAHeclxRvp4FgJBw4cMLcJFoHUQYWt61ABBzwVjhyycR3A/HUhOjqaQYMGUaJECU2ck5MTgwcP5s6dO5o4Hx8fzp8/T4cOHZg6dSr169ena9euBAcHs3z5cp08n10N1KxZMzw8PEhJSdGK79GjB2fOnCEhIYGhQ4dSu3Zt3nnnHZ48ecKXX35ZBE9s2Zi7PrxMzL7Rn6RwcHJyMrcJFoHUQYUt6+CAgtIK1f/d3JxczGyN+TF3Xfjrr7+oWrUqffv2ZePGjQD07duXu3fvEhERoUm3YsUKhBA0bdqUtLQ0Tfy1a9dYu3atVp5BQUF88sknVK5cmZiYGABGjRpFUFCQ5jRsAGdnZwIDA9m3bx8LFy7k5MmTgGqi7Z9//knZsmWL7LktFXPXh5eJ7MGxEtRHz9s6UgcVtqxDiaeatVbt29n8QJUl1IXAwECtHpdRo0ZpOS3ly5ena9euLF++XMu5UfPkyROt77GxsRw4cEAzFOXs7MzAgQN1HKEuXbpQsWJFvv32W706PJuvLWAJ9eFlIXtwJBKJVeH0zP/bXFCSivUdneLRfgh2JUoWmO5miWp4dXu/UMrMzUjlQfBGk+9bv3498+bNw8fHByEEr7/+OoMGDdL82FavXh2lUsmNGzeMznPt2rUsXLiQr7/+mrfffpvw8HCdY3XUc2xu3LhBx44dTbZbUryRDo6VsHTpUnObYBFIHVTYsg4lFP/02WxcuhwX7KzSwbErURJ751IFprsQet2odEXJo0eP2Lt3L8OHD0ehULB3714ePXqkua74399MCGF0nnv37mXVqlW0adNGp0fo2XzBtt+Jp7ElHeQQlZUwYMAAc5tgEUgdVNiqDkpUc3DUdB7QDxeFdTZzuRmp5KSnFBhq+L5qVDpjQm5G6nPbu3btWkaMGMHw4cN1nJFbt26Rl5dH7dq1jX/+3FzWr1/PF198QbNmzQgKCtJJExYWBkCtWrVs9p14FlvSQfbgWAmenp7mNsEikDqosFUdSjzzf7YKnh4oAScUZGJ870BxwNihohGNZ7J//+oitqZgfvvtNxwdHQHdlTyJiYkcOHCA8ePHs2zZMp15OGXLltU7X2bt2rVMmTKFzZs38/jxY53rBw8eJC4ujqlTp3L+/Hmd64bytWZsqW2QDo6VcO/ePXObYBFIHVTYqg6Ozzg4D+/9rYnPJNccJpkdS6kLT/fQ5OXpDhmOGzeOU6dO8eeff/L5559z+fJl7O3t6dSpE2PHjqVOnTo699y4cQM3Nze9E5MB0tLSeO+999i2bRu+vr6cOXOG27dvU6FCBQYMGIC3tzeDBw8u3Ae1cCylPrwMrLPv1gbZvn27uU2wCKQOKmxVh2c39/t9+y5A1YNjq1hSXUhOTiY5OVnvtaioKBo3bkxwcDALFy4kNDSUQ4cO0bFjR8aOHWswz4SEBDIyMgxe37NnDy1btiQ6OpqNGzdy48YNNm3aRNmyZZk1a9YLP1Nxw5Lqw8vA7GdL2FIoqrOoZs6cafZns4QgdbBtHSorHEUVhZMmfD3rc1FF4SRewdHstsm6IHWwhFDcdZBnUUkkEptDgfYE46dxUNhuD45EYqtIB8dKOHLkiLlNsAikDipsUQd9zs2fvwcDuqurbAlbrAv6kDqosCUdpINjJeTk5JjbBItA6qDCFnXQd7hmbm5uvtdtAVusC/qQOqiwJR2kg2MldO7c2dwmWARSBxW2qMOzK6gAWnR+I9/rtoAt1gV9SB1U2JIOtvnGSyQSq6OgHhpb7cGRSGwZs8+KtqVQVKuoXF1dzf5slhCkDrarw6tPrZ5ShwZuHprPlRW2uZLKFuuC1MF6dZCrqGyQHj16mNsEi0DqoMIWddC3a2mb7l2fum6bPTi2WBf0IXVQYUs6SAfHSvD29ja3CRaB1EGFrelgyHnx8PlHh/yWkVsztlYXDCF1UGFLOkgHx0qIi4sztwkWgdRBha3pYMhxSXwYZ1Q6a8bW6oIhpA4qbEkHBaqxKslLwsXFhT179tCrVy+D56c8D87OzqSnpxdafsUVqYMKW9OhNHZUUOgOUjk5lyAz/Z9t/BNEDk9s7EwqW3+bE8EAACAASURBVKsLhpA6qCjuOpjyGyp7cKyESZMmmdsEi0DqoMLWdDA0RPXOpIla3x1ssMkzZ10IDAxECKET9u/fD0BkZCRCCAYOHKhzb2hoKEIIhg8fXii22No7YQhb0sH23naJRGJ1GDv0JJeKv3z279+Ph4eHVnj6BO/o6GhGjhypdU+zZs3w8PAgJSXlZZsrsSKkg2MlHD9+3NwmWARSBxW2poOhHpy//jih9d0Wz6Qyd13IzMwkNjZWKzx+/FhzPSgoiLZt21K5cmVN3KhRowgKCirUXXfNrYOlYEs6SAfHSkhKSjK3CRaB1EGFrelgb8BxSU1O1vquBOxegj2WhKXXhdjYWA4cOKAZinJ2dmbgwIGsXbu2UMuxdB1eFrakg3RwrARb2tsgP6QOKmxJBwWGnZbWPbrpxFnPfjjngLsFhvDwo0alMy6cM9nKnj17kpycrBVmzZqllWbt2rWMGDECgLfffpvw8HAuXbpkcln5YUvvRH7Ykg769saSSCSSYoOpDosjSjKtYiWVB1C5wFSqTqyyRW2MQYKDgxk7dqxWXEJCgtb3vXv3smrVKtq0acOoUaMKvfdGYptIB8dKWL16tblNsAikDipsSYf8JhjvWK37Q2k9PTgPjEqlVCrJy8t7qWU+TWpqKuHh4fmmyc3NZf369XzxxRc0a9aMt95663kNNIgtvRP5YUs6yCEqK6F9+/bmNsEikDqosCUd8nNYmrRvqxNnPZv9NQFeLTD07z/JqHTGhSZF9jRr166lXbt27N69W2sScmFhS+9EftiSDrIHx0qoXr26uU2wCKQOKmxJh/wcnFerV9OJsx4HxzjMXRecnJxwd3fXisvJyeHRo0dacTdu3MDNza1QN0B9GnPrYCnYkg7SwbESEhMTzW2CRSB1UGFLOuTn4CQl6Opgr1DY1P7t5q4L3bp148ED7aGtGzduULt2bZ20z87NKUzMrYOlYEs6yKMaXjJFdVSDnZ0dubnWMHHyxZA6qLAlHV7BEScDy8SVdnbk6dEhWljHNGNjsKW6kB9SBxXFXQd5VIMNMm3aNHObYBFIHVTYkg52+WzeN2ra/9Mbb0tHNthSXcgPqYMKW9LBdt5yiURilTzPOLutzcORSGwR6eBYCWfOnDG3CRaB1EGFrehQ0JLvK2fO6o23JQfHVupCQUgdVNiSDtLBsRKencRnq0gdVNiKDgU5OPH3Y/XG29Khm7ZSFwpC6qDClnSQDo6V0KdPH3ObYBFIHVTYig4FDU+1f6uX3ngHhe00fbZSFwpC6qDClnSwnbdcIpFYHc+7K7E92FAfjkRim0gHx0pYt26duU2wCKQOKmxFh4IcnD3r1hu8ZivzcGylLhSE1EGFLekgHRwroVmzZuY2wSKQOqiwFR3sCnBS6jczfLSArTg4tlIXCkLqoMKWdJAOjpWgb1dQW0TqoMJWdCjIwalSu5bBa7ayF46t1IWCkDqosCUdbOMNtwFSU1PNbYJFIHVQYSs65LfJH0B6SorBa7aykspW6kJBSB1U2JoOQoaXF1xcXMThw4eFi4uL2W2RQYbiHnwVTqLKcwYvHM1uv7WHwMBAsXPnTq24fv36ifT0dDFlyhQxe/ZsoSY7O1vExcWJY8eOiYkTJwpHR+2/T3BwsNDHypUrzf6cMry8YMpvqOzBsRJmzJhhbhMsAqmDClvQQUnBK6FGT59i8JpDAb0/1oIl1YXRo0cTFBTERx99xIIFCwAIDQ3Fw8MDb29v2rdvz7Zt25g+fTqnTp2iVKlSWvf/8MMPeHh4aIWpU6caVbYl6WBObEkHi3Bwxo4dS0REBOnp6YSEhNCqVat807dp04aQkBDS09MJDw9nzJgxOmn69u3L1atXycjI4OrVq3rX/udXrr29PfPnz+fy5cukpKRw7949fvrpJzw9PbXycHR0ZNmyZcTFxZGSksLu3bvx8vJ6TiWeH4WNNNYFIXVQYQs6FDT/BkChNNzEKbCNicaWUhemTJnC999/z5AhQ1izZo0mPicnh9jYWO7fv09oaCjff/89bdu2pV69enz66adaeaSlpREbG6sVkpOTjSrfUnQwN7akg9kdnAEDBrBkyRK+/vprGjVqxPHjx9m/fz+vvvqq3vS+vr7s27eP48eP06hRI+bOncuyZcvo27evJk3z5s3ZsmUL69evp0GDBqxfv56tW7fStGlTo8t1cXGhcePGfPnllzRu3Ji+fftSo0YN9uzZo2XPkiVLeOuttxg0aBCtWrWiVKlS/Pe//0WZT8NaFJw/f/6llmepSB1U2IIOxuyBc/38X/let4V5OJZQF+bNm8dnn31Gz5492bFjR4Hpb968yf79+7Xa9RfFEnSwBGxJBwWqsSqzcebMGf766y/GjRunibt27Rq7du3S25U2f/58evXqRZ06dTRxK1eupEGDBrRs2RKAzZs3U6ZMGbp3765Js3//fhITExkyZMhzlQsQEBDAuXPn8Pb25u7du5QpU4a4uDjeeecdtm7dCoCnpyd3796le/fuHDx4UCcPU456N4UaNWoQFhZWaPkVV6QOKmxBh1IoqahwyDeNT43q3Am7bfB6osjlMTmFbdpL4St7H8oqCj5q1MHenuycwnnGJyKHWTl3jE4fGBjI4MGDcXJyokOHDgQHB2tdnz17Nn369KFRo0Y6986bN48JEyZQsmRJAIKDg2nZsiVZWVla6caPH8/PP/9coC228E4YQ3HXwZTfULP24Dg4OODv76/jCBw8eFDjrDxLixYtdNIfOHCAgIAA7O3t802jzvN5ygUoW7YseXl5PH78GAB/f38cHR218lF3s+aXD0CpUqUoXbq0Jjg6OuabviD69+//QvdbC1IHFbaggzFDVJ3698v3enHuwSmrsMdN4VBgKJOrMCqdMcEYh+pZLl++TGRkJP/3f/+nM6cmPxQKBUJo//87KCiIhg0baoWdO3calZ8tvBPGYEs6mF5bC5EKFSpgb29PbKz2gXixsbF4eHjovcfDw0NvegcHBypUqMCDBw8MplHn+TzlOjk5MX/+fDZu3KgZ8/Xw8CAzM1Pj8BiTj5p79+5pHDKAI0eOsG7dOvbu3cvYsWMBlcNlb29Phw4dAFi2bBn9+vXDy8uL+/fvs3XrViZOnAiAq6srAQEBdOnSBYD//Oc/dO3aFV9fX+Lj4wkMDGTKFNWEy5MnT5KQkMCbb74JwJo1a2jTpg1+fn48efKEFStWMH36dAD+/PNPYmJiNF3FP//8MwEBAdSpU4f09HQWLVrE9OnTUSqVXLhwgbCwMAYOHAjApk2bqF27Ng0bNiQnJ4dvvvmGyZMnU6JECa5evcqFCxcYNmwYANu3b8fHx4eAgAAAvv76ayZMmEDp0qW5efMmJ0+eZNSoUQDs3r2bihUrapzIb7/9lvfeew9XV1e8vLyoUKGCZl7W/v37cXFxoW3btgAsXryYIUOG4O7uTkxMDLt372b8+PEAHD58GIA33ngDgOXLl9O7d28qV65MbGwsGzdu5JNPPgHg2LFjpKWl0a1bNwBWrVpFp06dqFq1KgkJCfz444+ayY+nTp0iLi6O3r17A7B27Vpef/11atasSXJyMsuWLWPmzJkAhISEcOfOHfr1U/0wb9iwgUaNGlG3bl0yMjJYuHAhn376Kfb29ly8eJHr168zePBgALZs2UKNGjVo1KgRfn5+AEyaNAlnZ2euXbtGSEgI7777LgA7duygcuXKmmHbefPmMW7cOMqWLcutW7f4448/GD16NAC//vorrq6uvP766wAsWLCAkSNHUqFCBaKiovjtt9/48MMPAdV/JJycnGjXrh0AS5cuZcCAAXh6enLv3j22b9/OhAkTNHU+JyeHzp07A6qe2B49euDt7U1cXBzr169n0qRJABw/fpykpCR69OgBwOrVq+neviN+fn4kJSTyyw9rGDXt/wGqE8Tj78fS/q1e+PhVp5LXK9Rv1oQqtWuRnpJC0NLljJ4+BYVSSej5vzgfHqZp9Ddu3Ej9+vWpX78+WVlZLFiwgClTpuDo6MiVK1e4cuWKphd427ZtVK1aFX9/f4QQzJ07l48//piSJUty/fp1zp49y4gRIwDYtWsXHh4eNG/eHFD1RI8ZM4by5ctz+/ZtgoODef/99wHYu3cvZcqUoXXr1gAsWrSId955h4oVKxIdHa1pI1x+/p3khCcoACenEgCkpKTg7OyMnZ0duXm5pKelUa5ceXJycsjMzEQgKKFOm5pCiRIlsLezJy8vl9S0NEqXKg1AZlYmeXl5OJdwBlRLi52cnHApW46Penczuo2oW7cu9+7d48iRI7z//vucPXuWIUOG0LNnTwDKly9PmTJlmDlzpk4b0b59e2JiYjTvhouLC+XKlWPQoEHP1Ub4+voyc+ZMIiIiOHTokM22Ee7u7sycOZO8vDzmzZtX7NoIU8/SMttyL09PTyGEEM2bN9eKnzFjhrh+/bree27evCmmTZumFdeyZUshhBDu7u4CEJmZmWLQoEFaaYYMGSLS09Ofq1x7e3uxc+dOcf78eVG6dGlN/ODBg0VGRoZO+oMHDxpcuqhe4lapUiVRunRpTXh2SaSpoUqVKmZfvmcJQepgOzpUwqHApeCtqtYsMI3CAp7FWuvC08vEvby8RFhYmDh16pSmHZ09e7a4cOGCzn01a9YUmZmZYs6cOZq44OBgsXjx4mKpgyWF4q5DsVkmHh8fT05Ojk5vR6VKlXR6V9Soe2ieTZ+dnc2jR4/yTaPO05Ry7e3t2bp1K1WqVKFTp05aM/YfPHiAk5MT5cqVM9p+NSkpKSQnJ2vCs+PKplK/fv0Xut9akDqosAUdjBmiql6vToFprH0llaXUhXv37tGuXTvc3Nw4ePAgZcqUAVRtrLu7O56entSrV4+PPvqIY8eOcfHiRc1ScjUuLi64u7trhWfbX0NYig7mxpZ0MKuDk52dzfnz5+nUqZNWfKdOnTh16pTee06fPq2TvnPnzoSEhJDzv4l0htKo8zS2XLVz4+fnxxtvvEFCQoJW+vPnz5OVlaWVj4eHB/Xq1TNof1FhS5U2P6QOKmxBBzsj/BK/1wrWwcn8i0mLFEuqC3///Tdt27alXLlyHDp0iHLlylGvXj0ePHhAdHQ0R48eZcCAAcybN4/WrVvr7Lr7wQcf8ODBA62wadMmo8q2JB3MiS3pYNY5OKAaX16/fj0hISGcPn2aDz74AG9vb/7zn/8AMHfuXLy8vBg+fDigmlvy0UcfsXDhQlavXk2LFi0YPXq0ZowRVGN6f/zxB1OnTmX37t307t2bN954Q2ufm4LKtbOz45dffqFx48b07NkTOzs73N3dAUhISCA7O5ukpCTWrFnDwoULefToEQkJCXz33XdcuXJFM1b7snjRHiBrQeqgwhZ0MKYHJ9sIHYrzRGNjMGddGDlypE7cgwcPtM5DUs9ZKYj27du/kC228E4Yg63pYPYxtbFjx4rIyEiRkZEhQkJCROvWrTXXAgMDRXBwsFb6Nm3aiPPnz4uMjAwREREhxowZo5Nnv379xPXr10VmZqa4du2aeOutt0wq18fHR++24EII0bZtW006JycnsWzZMhEfHy9SU1PFnj17ROXKlQtl/FAGGWTQHxTw3Ec0PBtekUc2yCBDsQkm/oaa32BbCkXl4EyZMsXsz2YJQepgGzrYozDKeZkzdVqBaXytfKKxtdcFqYNt6VBsJhlLCo8X3UfHWpA6qLB2HeyMTOdghA4KrHuYytrrgrFIHVTYkg7SwbESrly5Ym4TLAKpgwpr18GY+TcAty4bp4OjFTeF1l4XjEXqoMKWdLDet9rGsKVKmx9SBxXWroOxDs7t0GtGpbPmlVTWXheMReqgwpZ0sN632sZQ765q60gdVFi7DsY6ON2GDDQqXQkrPmHZ2uuCsUgdVNiSDtLBkUgkxY7C3t/CAYXR83okEknxQDo4VsK2bdvMbYJFIHVQYe06GNuDc2jbdqPzLPESm0NnlJTBDvuXMLnZ2uuCsUgdVNiSDtLBsRKqVq1qbhMsAqmDCmvXwVgHp3LVKkbn6fySmkM37PFQOOCmsKfMS+g3sva6YCxSBxW2pIN0cKwEf39/c5tgEUgdVFi7DnZGzpmp1bgxmaI+GaI5QuSftoSi6JtDBVBK8Y9T8/TnoqI41wUfHx+EEDRo0MDoe4YPH05iYqJO/Ivo8Dx2AERGRjJx4sTnLrcoKM71wVSkg2MliIJabxtB6qDC2nUwxi3IEnVYvGICf3OO+xwlgW/yTf8y5uG4oNRqdO0o+p4jc9eFypUr8+OPP3Lv3j0yMzOJiopiyZIluLq6Fnjv3bt38fDwIDQ01OjytmzZQo0aNXTiza2DMRhyzgqT4qBDYSEdHCth7ty55jbBIpA6qLBmHRT/C/khhJI41vIo0U0Tl8REksR7+d7nUsQuTik9+euLK0zMWReqVKlCSEgINWrUYPDgwVSvXp0PP/yQjh07cvr0acqXL2/wXgcHB/Ly8oiNjSU3N9foMjMyMoiLi9OJt+Z3whRsSQfp4FgJH3/8sblNsAikDiqsWQdjJuam8C5ZNNSJT+BbckVZg/cVZW+KAnDWMwxW1ENj6rqgdHLm1V7jqT9tA6/N3Ez9aRt4tdd4lE7ORVb28uXLycrKonPnzvzxxx/cvXuX3377jTfeeAMvLy++/vprTdrIyEhmzpxJYGAgjx8/ZvXq1XqHht58803CwsJIS0vjyJEjvPvuuwghKFtW9Xd9thdk9uzZXLhwgQ0bNhAZGcnjx4/ZtGkTpUqV0qTp0qULx48fJzExkfj4eH799VeT56pUrFiRPXv2kJaWRkREhN7l2J988gmXL18mJSWF6Oholi9fTsmSJQFo27Yt69ato1y5cgghEEIwe/ZsAIYOHcq5c+dISkri/v37BAUFUbFiRZPsU2PNbcOzSAfHSlC/JLaO1EGFNetQ0ARjIZQkMkvz3YNOlGKd6houpDLA4L36HJDCogRKvZbboxoeKypKliyJ0smZ2uP/TaUWvXFy9cSxbEWcXD2p2KI3tcf/u0icnPLly9OlSxdWrFhBRkaG1rXY2FiCgoIYOFB7n6IpU6YQGhqKv78/X375pU6ePj4+/PLLL+zatYuGDRuyatUqLSfJENWqVaNhw4b07NmTnj170rZtW6ZNm6a5XrJkSRYtWkSTJk3o2LEjeXl57Ny5E4UJ+yOtW7cOX19fOnTowNtvv824ceOoVKmSVpq8vDwmTJhAvXr1GD58OB06dODbb78F4NSpU0ycOJEnT57g4eGBh4cH3333HaA6XuGzzz6jQYMG9OnThypVqrBu3TqjbXsaa24bnqWwt5OQmInr16+b2wSLQOqgwpp1KGhAJ5Nm5FIZgMruF3B4eByleEwKIwBIZgRlWK33XiXghIJMCn+eQn69Q84oycb4YRhTuH79Ol5dRlGiojcKO231lHZ2OFX0xqvzSO7+uqJQy/Xz80OpVBqsi9evX8fV1ZWKFStqhpSOHDnCwoULNWl8fHy07vnwww+5efMmU6dOBSAsLIx69eoxa9Ys8kOpVDJ37lyuXr0KwPr16+nYsaPmvh07dmilHz16NHFxcdSpU0dzT0HP2r17d5o1a8aff/6pyePGjRta6ZYuXar5HBUVxWeffcbKlSsZP3482dnZPHnyBCEEsbGxWvcFBgZqPkdGRjJhwgTOnTtHyZIlSU1NLdC+p7HmtuFZZA+OlXD27Flzm2ARSB1UWLMOBfXgpPGm5rNj1gYAnBRXcOQ8AFn4kynqG7zfuYjmxOTXO1SUe/CcPXuWcnVa6Dg3apR2dpSr07LIyjeEunfk6UmvISEh+d5Ts2ZNzp07pxWndijyIyoqimPHjmm+379/X6t3pWrVqgQFBREeHs6TJ0+IjIwEwNvbu+AHAWrXrk12draW/Tdv3tSZMNyuXTsOHjxITEwMSUlJ/Pzzz1SoUAEXF5d882/YsCG7du0iKiqKpKQkjh49apJ9T2PNbcOzSAfHShgxYoS5TbAIpA4qrFmH/BwcISCVXv/7lsPgkf/MUyjNT5rPafQ2mEfpIli6bUf+J5YX5dDY8BEjUNjl31lf0PXn4fbt2+Tl5VGnTh2912vVqkVCQgLx8fGauIJ6IxQKhc4qIGOGkbKzs7XeCSEESuU/mv/666+4ubnx/vvv06xZM5o1awYYf/K2PmftWby9vdm3bx+hoaH069cPf39/xo8fD6gmVBvCxcWFgwcPkpKSwrBhw2jSpAlvvfWWSfY9jTW3Dc8iHRyJRFKsyM/ByaYWOVQHoAQncXFJ11xzYb/mcwZtDeZhj2o5d2FS0ORlJUU3D0cBiNycfNOIvPyvPw8JCQkcOnSIcePGUaJECa1r7u7uDB06lC1btpiU540bN2jSpIlWXEBAwAvZ6erqSp06dfjqq684cuQIN27cyHd1lz6uX7+Og4ODli01atTQyicgIAB7e3smT57M2bNnuXXrFq+88opWPllZWdg909NWq1YtKlasyLRp0zhx4gQ3b97Umdsj0Y90cKyEXbt2mdsEi0DqoMKadcivfyWdNzSfXfiV4J17NN/tFXexJxyADJqSJwxPrC1dyMNUxgxBFdUw1a5du3h87TR5BpZa5+Xm8vjqqSIp+6OPPsLJyYkDBw7QunVrKleuTJcuXTh06BD37t1j5syZJuW3atUqatWqxfz58/Hz86N///6aHomC9ncx9E6oV0598MEHVKtWjfbt27No0SKT7AoLC2P//v2sXr2apk2b0rhxY3788UfS0tI0acLDw3FwcOBf//oXVapUYdiwYXz44Yda+URFRVG6dGk6dOiAm5sbzs7OREdHk5mZqbnvzTff5LPPPjPJvqex5rbhWaSDYyV4eHiY2wSLQOqgwpp1yK8HJ5Omms8l+IMKnu5a1505+r9PTmTSwmA+LgplvkNKpuJkxBCUUxH14Hh4eHDvwFoy46J1nJy83Fwy46K5dzDQwN0vxu3btwkICCA8PJwtW7YQHh7ODz/8QHBwMC1atDB5U7uoqCjefvtt+vbty+XLlxk7dqxmFVVmZma+9xp6J4QQDBo0CH9/f0JDQ1m8eDFTpkwxyS6AkSNHcvfuXY4dO8aOHTv44YcfePjwoeb6pUuX+OSTT/j0008JDQ1l6NChTJ8+XSuP06dPs3LlSrZs2UJ8fDxTp04lPj6eESNG0L9/f65du8a0adP4f//v/5lsnxprbhv0IWR4ecHFxUUcPnxYuLi4FGq+M2fONPuzWUKQOli/Dq8qnEQVA8GeKAFCKEgRvriIr2d9rnW9IsMECAFClGW+wXyqKJyEBw6FYq8S8i1HHV7BsUjrgtLJWbz65jhR/9MN4rUZm0X9TzeIV98cJ5ROzmb/m75ImDFjhoiOjjZaB1sPxV0HU35D5TJxiURSrDDUF5IjPMjBBwAnzqNQ6A7JlOAPzecM2uRbjrNCibNQkk7ec9uqssW4jnJHhQKFULXMRUFeZjp3f11R6MvBXzZjx47l3LlzPHr0iNdff50pU6bw/fffm9ssiQWioOjeJ4keXFxc2LNnD7169dIan31R7OzsTNrO3FqROqiwVh0UgK/CSe+1VPEmD9kGQFkW4Kr4DKWdnc6wzF1xhRz8UJCBDxVQKAxPsM1GcE9kvVAjWR57yhm5MutvkVXoe/BYW11YtGgRAwcOxNXVlejoaNavX8+8efMKfEZr0+F5Ke46mPIbKufgWAljxowxtwkWgdRBhbXqYOz8GydUe6O8/cFonXROXABAUIJsauVbngMKyr7gfqimTB42trfHFKytLkyaNAkvLy+cnZ2pWbMmX331lVE/2Namw/NiSzpIB8dKMHVZo7UidVBhrTrk1w+SSTPNZydUm8GVcdXVwYm/nrqncYFlllPYvdASbkcTtvsvzInNaqy1LpiK1EGFLekgHRwr4fbt2+Y2wSKQOqiwVh0M9eAIAVnU+1+ae9grHgBw93a4TlpHLmo+Z9KowDIVQEUMb8SWHw4oTGpkHYugSbbWumAqUgcVtqSDdHCshODgYHObYBFIHVRYqw6GHJxcPMnDFQBH/jk76FzwMZ20jv8bogL0njiuDyeFgjLPsTeOqXvbOJjQ22Ms1loXTEXqoMKWdJAOjpXw/vvvm9sEi0DqoMJadTDkYmRRV/PZ4SkHp+/7o3TzUDzRbPiXRQOEMM5xKa+wN9nFMXVvGyUFHyZqKtZaF0xF6qDClnSQDo5EIik2GOrByeaf844cuVZgPv9MNHYhm5pGla0E3EwcqnqeScNFMUwlkdgi8k2yEvbu3WtuEywCqYMKa9XBkIOTpeXg/NODc3zvfn3JceTSU/fWM7r8kgolJY1sNhWYNsFYTWGfSWWtdcFUpA4qbEkH6eBYCWXKlDG3CRaB1EGFtepgqMH6pwcnDwduaOJLli6tN73DU7082dQ2yQY3hYNRw0jPu+S7sFdSWWtdMJXC1iE4OJjFixcXap4vA0upD4GBgezcubNIy5AOjpXQunVrc5tgEUgdVFirDnZ6ekSEUGh6cOyJQqn4Z/Ovxm1a6c3Hkeuaz1kmOjh2gKsRQ1XPe3imKUNUVRROBYZOfvWMSldQeB4MOQG9e/cu8HDMwsbQOxEZGcnEiRNNzq9v374mHXrp4+ODEIIGDRqYXNazmGpz27ZtEUJQtmxZq20b9CEdHIlEUmzQN0SVgw+CUoD28BRKJclKZxQuur049kShIB0wvQcHoJRCiXMBzafLczavRbGSSqKLg8PzLf1Xk5iYSEpKSiFZU3ywsyvsafBFh3RwrIRFixaZ2wSLQOqgwlp10Ne0ZlND81k9PGXvUweX7qPZf+MxLp2GYedZVeseheKfoaxsqiGEo8m2uOazw/Hzzr8BVaNcmA1zUFBQIeZWNMyePZsLFy4wbNgwIiMjefz4MZs2baJUqVKaNAqFgqlTp3Lr1i0yMjK4c+cOM2bM0Fx/5ZVX2Lx5MwkJCcTHx7NrWbkznwAAIABJREFU1y58fHw012vWrMnOnTuZNm0a9+7dIywsjODgYHx9fVmyZAlCCE2vkqurKxs3buTu3bukpqZy+fJlBg0apGXzs71TkZGRTJ8+nTVr1pCUlMSdO3e0VixFRUUBcPHiRYQQBAcH07p1a7KysnB31z71/rvvvuPYMd0tDgwhhGD06NHs2LGD1NRUwsLCePPNNwFVz9HRo0cBePz4MV999RWBgYGae6dMmUJ4eDhpaWlcvHiRfv36aa6pe346d+7MuXPnyMzMZPTo0QghqFlTe3L+J598QmRkJABKpZIff/yRiIgI0tLSuHHjBhMmTDD6eQoL6eBYCe+88465TbAIpA4qrFEHQ/9vzKa65rMDt1CWq4RT4w4oHJ3w9qsOdnaUaN4dOw9frfv+matjr+UkGYujQmFwwnEJlC80k6YwJxp379690PIqSqpVq0afPn3o2bMnPXv2pG3btkybNk1zfd68eXz66ad8+eWX1KlThyFDhhAbGwuAs7MzwcHBpKSk0KZNG1q1akVKSgq//fabpqemWrVqdOzYkdq1a9OpUyd69uxJ3759uXv3Lp999hkeHh54eHgAUKJECc6fP0/Pnj2pV68eP/zwA+vXr6dp06a6hj/F5MmTCQkJoVGjRqxYsYKVK1dqHIEmTZoA0LFjRzw8POjbty/Hjx8nIiJC6321s7Nj2LBhWk6IMcyePZutW7fy2muvsW/fPoKCgihfvjx3796lb9++ANSoUYMpU6Zohre++uorRo4cydixY6lbty6LFy9mw4YNtGmjfRDtt99+y/Tp06lduza//PILISEhDB06VCvNkCFD2LhxI6BycGJiYhgwYAB16tTh//7v/5g7dy79+/c36ZleFOngWAkVK1Y0twkWgdRBhTXqYMhlyNFycG7j2LAd6qROziVUHxTgWL8VPNWr8vRyclPn4agpr7DXa9XzDk+pKUwHp7hsza9UKhkxYgRXr17lxIkTrF+/no4dOwJQqlQpJk6cyNSpU/n555+JiIjg5MmTrFmzBoBBgwaRl5fHe++9R2hoKDdu3GDkyJF4e3vTrl07QOW0pKam8t5773Ht2jWuXr1KYmIiubm5JCcnExsbq3GY/v77bxYuXMilS5eIjIzk+++/58CBAwX+QO/bt4+VK1cSHh7ON998Q3x8vKb8uLg4AB49ekRsbCyJiYkArFmzhpEjR2ry6NGjBy4uLmzdutUk/datW8fmzZsJDw9nxowZlCxZkqZNm5KXl0dCQgIADx8+xMnJiaSkJFxcXJg0aRKjRo3i4MGDREZG8tNPP7Fhwwad86o+//xzDh8+TEREBAkJCQQFBTFkyBDNdT8/PwICAtiwYQMAOTk5zJkzh5CQEKKioti4cSPr1q1jwIABJj3TiyIdHCshOjra3CZYBFIHFdaog+E9cP5xcJw8c7FzrQRAXlIiHjePkBt3DwBl6XLYeT3tDD29kuqfZeam4IAC92cmHDuhoLSRp4cbwr4Qm+YHDx4UWl5FSVRUlNaclvv371OpkupvWbt2bUqUKMHvv/+u915/f3+qV69OcnKyJiQkJFCiRAmqVasGQGpqKleuXCE7O7tAW5RKJTNmzODSpUvEx8eTnJxM586d8fb2zve+y5cva31/8OCB5hkMsW7dOqpXr06zZqqz1EaNGsXWrVsLPCk7v7LT0tJITk7WW7a6bahTpw7Ozs4cOnRIS7d3331Xo5makJAQre+bN2/Gx8dHY/PQoUO5cOEC16//M3l/zJgxnDt3jocPH5KcnMz7779foH6FzYsdkyuxGGxpb4P8kDqosEYdChqiUpCEk08FTXzWleMc/yuZLLuSOFf0AsCxVhPSY26pPmutpDJusz99OCuUVBQOxJGNHVBR8eL9L4W5VPzEiROFlpepJCUlUbZsWZ34cuXK8eTJE624Zx0PIQRKpcrRS09Pz7ccpVLJ+fPndYZN4J+ek5iYGJydnY2ye/LkyXzyySd8/PHHXLlyhdTUVJYsWYKjY/5ztfJ7BkPExcXx66+/MnLkSCIiIujevbum18cUjC1b3Taor/Xo0YN79+5ppcnMzNT6npqaqvX9wYMHBAcHM2TIEM6ePcvgwYNZtWqV5nr//v1ZvHgxkydP5vTp0yQnJzNlyhSNQ/SykD04VsLYsWPNbYJFIHVQYY066OvBEcKBHFQTSR0Ix8FD9VlkpJH78C4Dxn5AXlwMuY9UvRjKMq4oy6qcIHvuAKofhRyq6eRtCqUUSrxw5FWFU6EML+kf+Ho+3n777ULLy1Ru3LhBQECATnyTJk24efOm0fncunWLtLQ0zZDVs/z111/4+fnx8OFDwsPDtUJSUhKAzqRYNVlZWTorg1q3bs3u3bsJCgri8uXLRERE4OfnZ7S9hsoB/auQfvzxRwYNGsSYMWMIDw/n1KlTL1RWfmWr24Zr166RkZGBt7e3jmYxMTEF5hkUFMTAgQNp3rw51apVY/PmzZprrVu35tSpU6xcuZKLFy8SHh6u0yv0MpAOjkQiKRbom4OTjS/qvh1Hl1iwUzVpOfduq44Y/x850f9s/qceplIocnEg8n/5VONFt2VxVCgKzS2xlqXiK1asoFq1anz//fe89tpr+Pn5MW7cOEaPHs2CBQuMziczM5NvvvmGb7/9lnfeeYeqVavSrFkzRo1SnTUWFBREfHw8u3fvplWrVvj6+tKmTRuWLFmCl5dXvnlHRUXRpk0bXnnlFdzc3ADVidudOnWiRYsW1KpVi1WrVmkmID8vDx8+JC0tja5du1KpUiWtDfcOHDjAkydPmDVrlsmTi43hzp075OXl0bNnT1xcXChZsiQpKSn/n70zj4+quvv/+97ZsrKEbOwBZEdDCiKC4gZU9FEUWrRqrYiPGxZFLS78rFYL2FoFsXWrIIJVoUIftAqCgkpRi1FxYd+XbCQEQpbJrOf3x00mM5lJMjOZZLbzfr3uiztzz7335MO5M98557vwl7/8hYULF3LzzTfTt29fhg8fzt13383NN9/c4jXXrFlDhw4deOmll9i8eTOFhYWuY/v372fkyJFMnDiR/v378+STT7qcrNsTuUQVI2zYsCHcXYgIpA4asaiDryUqdwdjU1qD/4b92F4AvtzwMQCOwgNQ53ys734Wtp1fAaDnADYGIEjBQTZ6IsNfpb7opqOFdoeEpYUW8N4PX/vVri04cuQIF154IfPmzWPDhg0kJCSwd+9ebrnlFt59992ArvXUU09ht9t58skn6datG0VFRbz88suAtoQ1btw4/vSnP7FmzRpSU1MpKCjgk08+cc3guH8Bu/P73/+eV155hQMHDpCQkICiKDz11FP06dOHjz76iJqaGl599VX+7//+z+dym784HA5mzZrF73//e5588km2bNnCJZdcAmjLScuWLePRRx9l+fLlQd+jKQoLC3n88cd5+umnycrKolu3bkyfPp3HHnuMEydO8Mgjj9C3b19Onz7Nt99+y/z581u8ZmVlJe+//z7Tpk3zcJIGePnllxk+fDgrV65ECMHbb7/Niy++yKRJk0L+tzWHArRvOsk4Jykpiffee4+rr746YCey5jj//PP58ssvQ3a9aEXqoBGLOmRiIFnxnHSuEL+lHG0moHvem3Tuux1RW0PNh0sBOGf0KH74ahsACeOmoEvvBkDNxrcQleWcFM9wht8CkM14EpXw+as0plBYsYTg4zkWx0IwRLoOr776KllZWUyePLlN7xPpOrREIN+hcokqRrj00kvD3YWIQOqgEYs6+PLBsbn5zpg6amG3jrIGh8lRl13i2rcf3+/a13fTEv8ZONBwvJV+OKEmVFXFY3EsBEOk6tChQwcuu+wybrzxRl544YU2v1+k6tAWSANHIpFEBaoPtxQ7fVz7xuSTADjKfC9FOEqOuPZ1dVFVehqMHvdw80gglI7Gkshl7dq1vPfee7zyyit8/PHH4e5OTCF9cGKExYsXh7sLEYHUQSMWdfBdhyoHAEWtRWfSfHCcbgbO24tfdO2L6gpETRVKUgq6Lt1AVTE4DrqO2yJsBidUyf5icSwEQ6TqUO+H015Eqg5tgZzBiRHc64fEM1IHjVjUobGTsRBgR0scZkw6haKAsFpwnjnpanPZ1Gs8znGU1oW/6nSonbNDGioeakI1gxOLYyEYpA4a8aSDNHBihJZCIeMFqYNGrOngK4LKQTYCLXGbMaXO/+ZkkUebzO7dPM8pPdZwzcweKIoDPYeB0ISKh5JQhYrH2lgIFqmDRjzpIA2cGKGoqKjlRnGA1EEj1nTwlQOnfnkKwJCs1dpxlnuGeZcVeb6uL9sAoMvooZ1b54dTHyoeKYSqqnisjYVgkTpoxJMO0sCJEQItzBarSB00Yk2H5vxvQFuiAnCePuHRZsOq1R6vhbkKZ7WWF0XXOUvzw3GLpIo0R+NQ+OHE2lgIFqmDRjzpIA2cGOHee+8NdxciAqmDRqzp4DvJX2/XvqEugsp5upQ09Nyu68rv9D2Zk5DDL3UZHtW9nfXLWDodasd01wyOds3Y88OJtbEQLFIHjXjSQRo4Eokk4vGdAyfHtW9MLkfU1pBisTJdn01vNYFOih5DRTXD1RRu1WeTWPdx53BbxlLTukZ0qHioIqkkkngkIgycu+66i4MHD2I2m8nPz+eCCy5otv24cePIz8/HbDZz4MAB7rjjDq82U6ZMYceOHdTW1rJjxw6uueYarzYt3ffaa69l/fr1lJaWIoQgNzfX6xqbN29GCOGxvf322wEq0Ho+/fTTdr9nJCJ10Ig1HfxZotKfLOY3+izSFAMAFuHEUXdaV8XErfpsjCgefjq6tGwMuIeK922bPyBIDCH4iI61sRAsUgeNeNIh7AbOtGnTWLRoEfPmzSMvL48tW7awbt06evbs6bN9Tk4OH374IVu2bCEvL4/58+ezePFipkyZ4mozevRoVq5cyYoVK8jNzWXFihWsWrWKUaNGBXTf5ORktm7dysMPP9zs3/Dqq6+SnZ3t2nwZXG1N4/L28YrUQSPWdGjOwFENZnRGM2MOHaWrYgLglLCzyH6cD/p34IywA9BNMXGx2glnRRk4tCpPalrjUPHYm8GJtbEQLFIHjXjSIewGzv3338+SJUtYsmQJu3fvZvbs2Rw7dsxV0r0xd955J0ePHmX27Nns3r2bJUuWsHTpUh588EFXm/vuu4+NGzfy9NNPs2fPHp5++mk++eQT7rvvvoDu++abb/LUU0+1mF2ypqaGkpIS11Zf3K09+fnPf97u94xEpA4asaaDdw4cFTtaFJQxqRyD3c6I09UA2IVgmb2YMzg4+5rLWWovxl4X/z1G7UBHoeI4VQKAmpyKmmCM2FBxfQhCxWNtLASL1EEjnnQIq4FjMBgYMWKEV+XjDRs2MGbMGJ/nnH/++V7tP/roI0aOHIler2+2Tf01g7lvc9x4442Ulpby008/8cwzz5CSktLiOSkpKaSmpro2o9EY8H0lknih8QyOg26AthRlSDrFsOMFJNUZJj+KKsrqZmQASrHxpbNCa6uojNd1xnnS3Q8nO2JDxXVEwK9QiSRKCbpUg8FgwGaztdywGdLT09Hr9ZSUlHi8X1JSQna27w+Z7Oxsn+0NBgPp6ekUFxc32ab+msHctyn+8Y9/cOjQIYqLixk2bBgLFiwgNzeXiRMnNnteQUGByyAD2LRpE8uWLeODDz5wzSJt2LABvV7vKo62ePFipk6dSvfu3SkqKmLVqlUuj/j8/HxGjhzpss5ffvllLr/8cnJycigrK+P111/nd7/7HQBbt26lvLycq666CoAlS5Ywbtw4+vfvT0VFBS+++CKPPPIIANu2beP48eOuJcDly5czcuRIhgwZgtls5rnnnuORRx5BVVW+++479u7dy3XXXQfA22+/zeDBgxk+fDh2u50//elPPPDAAyQkJLBjxw6+++47brrpJgBWr15N7969GTlyJADz5s1j1qxZpKamsmfPHrZu3cqtt94KaLVbMjIyXMbon//8Z2677TbS0tIoLi4mPT3dtUy4bt06kpKSuOiiiwBYuHAhN9xwA1lZWRw/fpy1a9cyc+ZMANdM3fjx4wH429/+xuTJk+nRowclJSW89dZbzJ49G4DPPvuMmpoaJk2aBMArr7zChAkT6Nu3L+Xl5bz22mvMmTMHgC+++ILS0lJXleClS5cyduxYBg4cSGVlJYsXL2bu3Lmu/8cjR464so2++eab5OXlMXToUGpra3n22Wd56KGH0Ov1bN++nV27dvGrX/0KgJUrVzJgwADy8vJcY+v+++8nMTGRnTt3kp+fz8033wzAmjVr6NGjh2vZdsGCBdx999107NiRffv28fnnnzNjxgwA3n//fdLS0hg7diwAzzzzDNOnTyc9PZ3Dhw+zfv167rzzTkD7IWEymbj44osBeP7555k2bRpdu3aloKCA1atXM2vWLNeYt9vtrmflpZde4sorr6RXr16UlpayYsUK7r//fgB2bPmS2spKLrxS03v5334ALTKcjO6CUQcOuZ6lipED6FeZwSXXXo3BYCCzezdEXh6Oz/ajq7UyXEkhcey57Op5NqdLy6gdOpyM7p358mvt/KFjb+LCS3TYrFbeeGYhv/ndbAxGI/t++JH9P+1k0g3a+N74z9X06NuHwSN+hnA6WbLgGW68dyaJKSkc2rWbH//7NVff8msANv/rPdK7ZnH26PO0MfD0X/jF7TPokNaZY/sP8PXmz5jyv9r43vLBOpJTU/nZOM0n8M/P/oXrfn0TGRkZHD16NODPCKPRyNy5c/n000+xWCxx+xlRXV3N3LlzOXjwIBs3bozbz4i9e/cyd+5cnE4nCxYsiLrPCF/+tM0hgtlOnDgh5s+fL3r27BnU+YDo2rWrEEKI0aNHe7z/6KOPil27dvk8Z8+ePeLhhx/2eG/MmDFCCCGysrIEICwWi7j++us92txwww3CbDYHdd/evXsLIYTIzc1t8W/62c9+JoQQIi8vz+fxpKQk8fHHH4vMzEyRmprq2oxGY9A6AuLGG29s1fmxskkdYlOHHMUk+rhtGfxagBAgxMV9/izeMg4SbxkHiT/qe3u0m3njza79m3QZrnYPmnLEsKkPiGFTHxCDL5gmunCv63rp3O5xjXBvKahyLIRgkzrEhg7136FJSUkttg169vP9999n1qxZHDhwgDVr1gRVgr2srAy73e41a5KZmek1u1JP/QxN4/Y2m42TJ08226b+msHc11++/fZbrFYr/fv3b7ZdVVUVlZWVrs1qtbbqvjk5Oa06P1aQOmjEkg4qeLna1vvfAIys2eba/9Lp6f/WrU+Oa3+bsxKz0JyLz3YaSajU2qqdM9F7JPuLtFw4rVukiqWx0BqkDhrxpEPQT86MGTPo0aMHc+fOJTc3lw0bNrBz505mzpzplw8KgM1m45tvvmHChAke70+YMIEvvvjC5zlffvmlV/uJEyeSn5+P3W5vtk39NYO5r78MHToUo9HY7umwy8rK2vV+kYrUQSOWdPAdQaVFOyo4GFip+c/UCid7RI1Hu9NuOtgQfOvUKo4bFJWz9+/VrmE0YUgsdbt2Tkj731qMrYykiqWx0BqkDhrxpkNIpo2uuuoq8dFHHwm73S4qKirECy+8IAYOHNjiedOmTRMWi0VMnz5dDBo0SDz33HOisrJS9OrVSwBi/vz54o033nC1z8nJEVVVVeLZZ58VgwYNEtOnTxcWi0VMmTLF1eb8888XNptNzJkzRwwcOFDMmTNHWK1WMWrUKL/vC4jOnTuL3NxcMWnSJCGEENOmTRO5ubmupbC+ffuKxx57TIwYMUL07t1bTJo0SezcuVN88803QlV9TysHMr0WyNbaJa5Y2aQOsadDAqrXsk0S7wsQ4izlO9ey06P6nl7tBphSPV6fq6S42v81eYgYNuV+MWzqA6Jf9+ECHAKEMLIt7MtS7ls3Wvd/GUtjQeogdQjwOzR0Nx4+fLj45JNPhMPhEA6HQ9jtdvHuu++KjIyMZs+76667xKFDh0Rtba3Iz88XF154oevY66+/LjZv3uzRfty4ceKbb74RtbW14uDBg+KOO+7wuubUqVPFrl27hMViETt37hTXXnttQPcFxG9+8xvhi8cff1wAokePHuLTTz8VZWVlora2Vuzbt08sWrRIdO7cOVT/OX5vc+fODfvAi4RN6hB7OiT7MHCMbBcgxC91z7kMll+oXbzazft/v/d670l9b9c5V114sxg29QEx8JxLhY4jAoRQKQu7UeO+9VZMciyEYJM6xIYOgXyHBh1FVY9Op+OXv/wlM2fO5Pzzz+fYsWM89NBDrFy5ksmTJ/PYY4+xfPlylye5L1566SVeeukln8emT5/u9d7nn3/OiBEjmu3X6tWrWb16dbNtmrsvwBtvvMEbb7zR5PHjx4+7PMElEknb0NwSVZ7uU9d7e4XZr+v913mGs9REAM45fpxDmRmonTIxcAgHvXDSBYfoiE6paH3nQ4CKFi7uCHdHJJIoI2gDp1u3btxxxx387//+L1lZWWzZsoVp06bxr3/9C1GXKeuvf/0rBQUFvPnmmyHrsMQ3W7duDXcXIgKpg0Ys6dDYwHGKFJyk0YkT5KD50RQIC5U+TIDt//H2qdsjaqgRDpIUHQOKijHa7Vg6Z6LnIHARoPnh6Pg+9H9MkOhRcCCCOjeWxkJrkDpoxJMOQRs4hw8fxm6388477/D888/z/fe+PwwOHjzY6sgkScuUl5eHuwsRgdRBI5Z0aJzFuD6Carj6ueu9Pc4afHHm1Cmv9xzAj85qztN1wOB0MqComJ969sBgKgZL/T36YoogA8eIiiXIOZxYGgutQeqgEU86BB1F9Yc//IFevXpx6623NmncAHz//ff07RtZBexikfqEXPGO1EEjlnRoPINTvzw1VPmv672mlqfGXXWlz/e3iyrX/rCCAgBMqQ3GkI0+wXW2jdC3IpIqlsZCa5A6aMSTDkEbOEePHsXpdPo81rlzZ379618H3SmJRCKpx9vA6QVAf3U7AFZFW6IKhKPCQrnQMrHnlJaRXFuLMa1hFsgeYQZOKIpuSiTxRtAGzuuvv06/fr4TYvXp04fXX3896E5JAmfJkiXh7kJEIHXQiCUddErjOlTd6UgpmcpxAAp1At8/teBfS5Y1ed3tdTlxVGBIQSGmzIYlIBuRNevcmhmcWBoLrUHqoBFPOgRt4CjNVLlNSEjA4ZA+/+3JuHHjwt2FiEDqoBFLOjR2FLTTnf7Kdtfro83M3oy4cGyTx753Vrv2hxUUYOySiMrpuntE2AxOK6qKx9JYaA1SB4140iEgJ+OePXt6pHnOy8sjISHBo01iYiK33347R48eDUkHJf7RUmmIeEHqoBErOjR2MIY6A0f9zvX6qPWMj1YavQY0rUMZNo47LfRQTXStOEOGuZp9umNYHZ2w0wshdChKZPxQU+u2pmaqmiNWxkJrkTpoxJMOARk406dP5/HHH0cIgRCCF1980atN/cxOfZVrSftQUREZOTvCjdRBI1Z08LU046A7/ZVlrtdHnU3nv6lqQYftoooemAAYeryAfFMJ1pqzAT12emHgUFD9bgsMKFiCCBWPlbHQWqQOGvGkgwL+PzGDBg1iyJAhKIrCqlWrePTRR9m3b59HG4vFwk8//cSRI0dC3deYICkpiffee4+rr76amhrfoa3BoKpqk07f8YTUQSNWdEhCJUsxeLx3TBTwmuEyjIqFcpORZ6v2NHm+oqqIZnRIRuUhQy90KJxOTOQx00OcPHEdANlcQaKyKTR/SAg4IWxUBzGHEytjobVIHTSiXYdAvkMD8sHZvXs3a9asYfXq1UyfPp2///3vrFmzxmP74IMPpHETBh555JFwdyEikDpoxIoO3kn+UumlFGJUNL+b44kGX6e5mPHI75o9Xo2TA6IWgE5mMwMTG36wRZqjcbCRVLEyFlqL1EEjnnQI2sl4+fLlcZUwSCKRtD/eIeLdPByMj/ly0gmQ7Y5K1/659h/c7pXT+ouHEEPwH9cSSVwSkA/OY489xmuvvUZRURGPPfZYs22FEPzxj39sVeck/rNt27ZwdyEikDpoxIoOjT+gHPSgn/Kj6/VRZ22z5//0369bvMdOUYNVVTA6Beec2oMeK3aMEZfszxjkDE6sjIXWInXQiCcdAjJwnnjiCdavX09RURFPPPFEs22lgdO+HD9+PNxdiAikDhqxooP3DE53eiu76vZ1lFiajqACOFFQ2OI9bAh2Jajk1jhItFsZrmwmX/wce6QtUSlKAB6TDcTKWGgtUgeNeNIhoDlPnU7H119/7dpvbtPrW12oXBIAU6ZMCXcXIgKpg0as6OAdRZVNN0WLbDqR2AVbTfMRIZdOmezXfbY7GnLiXGhYA2g+OCK4+pZtgkJwCf9iZSy0FqmDRjzpIBd1JRJJxNI4i3GWYkRXl5umOLkD2AIr0dAU+2tKqDIZARjOFyRxBkFHnHQOyfVDRbDLVBJJPBK0gWMymUhNTfV475e//CULFizgsssua3XHJIGxfPnycHchIpA6aMSKDo19iLspdtf+icSWv+zfX/4Pv+7jdDrZ0UUzZgzYOU9dD9Duy1ROYeKMuI1KcQt20c3reDAGTqyMhdYiddCIJx2CNnBWrFjB4sWLXa9/+9vf8s477zBnzhw++ugjJk2aFJIOSvxj5MiR4e5CRCB10IgFHXwFSPVQG3xuSozNOxgDDB35M7/v931Cg/FwiboaaN+q4hZxNoV8xUn+Shkvc4y9nBG3ebQJJpIqFsZCKJA6aMSTDkEbOKNGjWL9+vWu17NmzeLNN9+kU6dOrFmzhgcffDAkHZT4x5AhQ8LdhYhA6qARCzr48jfpQZFrv1i0bOD0HTLY7/sVmE9T3KEDAGepP5Cj7Gi3GRyH6EgJa7Hh3l89J1mEWTTUDgpmBicWxkIokDpoxJMOQRs4GRkZFBQUAJCTk0Pfvn154YUXqKysZMmSJQxd+D1IAAAgAElEQVQbNixknZS0jNncdLr6eELqoBELOjQ2cJwigV7KAQBOKhnU1Fb6Os0DSwDZwp2nT/BtTm/X6/HqynabwTnFPBxoS1J6DpHA5rojekp5A6dIAoIruhkLYyEUSB004kmHoA2cmpoaOnbsCMCFF15IVVUV+fn5ANTW1pKSkhKaHkr84rnnngt3FyICqYNGLOjQ2MDpQFeSFc2oKTD0wFnVck2dFQtf8Pt+zspydmRlYtFpEaBj1A8wkB1Aj4PDIvKoRFuKUqikKxPI5koS+AQAB12p4qa644FnNI6FsRAKpA4a8aRD0AbOjz/+yMyZMxk2bBh33303mzdvdh3r1asXxcXFIemgxD/iKf12c0gdNGJBh8YGTpbSxbVfmJiNqG7ZwLn14QCWyoWgtuoUP/XQZlISlBpGqfv9Pz9IznCPa78zT6BXjqMoTtJo+D+s4LcIoekR6DJVLIyFUCB10IgnHYJOVvPUU0/x73//m+3bt2O1Whk/frzr2JVXXsm3334bkg5K/ENVZcQ/SB3qiQUdGhs4XZUE135xUmdEWXXjU7xQdYHVctCWqfow4shRACbp1rLPZkAotoCu4xO9AX3vIejSsnFWV+Ao2IfttEIVv9D6SjmpLHU1Nyk/kCA+pZaLsdMfM5NI4sM6R2P/iyXGwlgIBVIHjXjSIWgDZ/PmzQwePJgRI0awfft2Dh065Dq2adMmtm/f3szZklDz3XffhbsLEYHUQSMWdGhs4GSrDVn3TiT699G1+7vAPoec5SWUnpXLDuM5DLX+QKZSwNlqT34QBwO6TmPUThkkjL0axZTY8OaAEZze1AsqTACksgxV8fSP6MAL1HIxAFVcRxIfBjyDEwtjIRRIHTTiSYdWpRs+evQoR48e9Xr/1Vdfbc1lJUGwd+/ecHchIpA6aMSCDvpGDrXZihY1ZRUmTusr/fqaP7o3sCUmx6kSADZ0uYyhRVrhzUt0en60B1UlAQAlNY2EC65BMZo8D6gKVc7r6144SeXvXucm8REqp3DSmRouRwgDRsUaUGdiYSyEAqmDRjzp0Oq5qoyMDEaOHMmFF17otUnaj+uuuy7cXYgIpA4a0a6DgueHkw7I5DQAhaIvqvD+YeWLidf9IqD7iuoKhM1KQZdO7HCeB0CmYuMcJTmg67hQdSSMnuQybhwni6n9z1psh3ZQeyYLS2UWACblKwzKIa/TFcVOIuu0vtERMxdhQAloDifax0KokDpoxJMOQc/gZGdns2LFCi655BKvY4qiIISQ9agkEklQNF6e6oIBnaL5nRSIvuhs27D7OjEEOE+dwJh8kn857mKo+l8ALtelsdtegyXAeRzDoFGoqVqGZOfpMmq3rgW7DceJY1T2uNnVrkP2d9BEXEYy71HNDQDUcDVJfIwBBWvQc0oSSXwQtAXy17/+lby8PB566CF++OEHLJbQ1ISRBMfbb78d7i5EBFIHjWjXobGBk6kYXfuF+h4I80d+XWf926sCvrfzVAnGHuXsFKP4zjmOPPVzOih6JqppvO886fd1lJTOGAfUZVJ2Oqn9egPYG5yVzxSc7drvfM5J7KcSERbvHCWJbEDBjCCRGq5EiFmYULHi8Ksf0T4WQoXUQSOedAjawLnooot48MEHWbZsWQi7IwmWwYMHc/Bg6xwhYwGpg0a06+Bt4DT4r5QYsxA1LSf5A+gzeCDHD3ov/TSHo6wQ04DTKKqD1+2/Z4jhSkyKhVFqKt+LKo4K/37MGYeOBlX7O6x78hGV5a5jNpGDVWjJUBPTjmBMOQM5Q7Htyfe6jqrUkCC2YGYiDrpjpx8G/PejiPaxECqkDhrxpEPQPjhCCI4dOxbKvkhawfDhw8PdhYhA6qAR7To0TmaXQUNh39LENETNmcan+GTg8NyA7+0oK0ARTozJZZTRnX86ZgKgKgo36DLp7MfvQrVzJvru/QAQtTXY9nqmzTDTkFYjNXsHAIY+w6CJTMUJbHHt1zIOUwAf3dE+FkKF1EEjnnQI2sD55z//yf/8z/+Esi+SVmC3t5VHQnQhddCIdh0az+BkKAYA7MLAqWQTota/Egx2WxD5axx2HKdKMKaUAbDeOZ1DTs3fJVXR8xt9FsktfHQaB5/n2rfu2gYOz/8PMxNd+0n6TwFQklLQZef4vJ67gWNmHMYASjZE+1gIFVIHjXjSQSHI6MeLLrqIv//976xbt47333+fkye916bjKd7eX5KSknjvvfe4+uqrqQmgTo5EEk90w4ip7ktcBR7X90WvCI46+/Ncn9+QvP/GNr2/YfAoyu1/oGyvFkTRl8uZZdjn8gU6I+y86yjlgI+Cn2qnDBIv1SJVnNWVmDeuAGdDYj4h9ByhEEEHVE7QJ2sciRdoPxbtx/Zh+drbv0gIA0coRpCMjuP05CyOY8UuHY0lcUYg36FBz+Bs2rSJfv36cc8997B+/Xq+/vpr15afn8/XX38d7KUlQfDAAw+EuwsRgdRBI9p1cC8qmYYBvaJ9kReIfujVQr+vc/P99wZ1f8eJ464ZHIAKBvCGvYQzQvv120HRc6u+K7fqshmhpJCJwTXrZBh4rus8295vPIwbgFpGI9CqlifyCc7Sowir5tej65oDqnf2ZUWxYeIrrW/0wE4fTH4Gi0f7WAgVUgeNeNIhaCfj6dOnh7IfklaSkJDQcqM4QOqgEc06NM6Bk1m3PAVQIM5C5/QvBw6AMTE4HZynijEOaIjbtjGA09h5yV7IFF0G/VUtI3E/NZF+akN2YguC2p2HqN1/HLNOR8HJIo4qSewVZldYt7v/TSIbQThxFB5EnzMYRW9Al9kTR/Fhrz4lsIVaLgOgljEY2I8/JRuieSyEEqmDRjzpELSBs3z58lD2Q9JKduzYEe4uRARSB41o1qGxg7F7iHiB6IfO8bnf1zrwU5A6OJ3oahocg+3GYWCDMzh4w1HMuSKVsWpH0t2MLwATCqbaWjrWaktXOWoHxqodMAsHXzsr+Y+zgkLGudonskm7fuEB9DmDAdD36N+EgbPNtW9lBIms8OtPieaxEEqkDhrxpENIMvENGDCA9PR0tm/fLv1KwoT0d9KQOmhEsw7eBk6DEXGcfiRYD/ldanL39h+C70jp16g6C06HCZtyluttAWxzVrLNWUlPxcRZSiJdFAOddQkkJ3ciwWYj0WZD77Y0lajoGKfrRJ6aymK7gx0CDOxFr2izRI4TRxE2G4rBoDkaKwoIT/8aI9+49i2MpKOi+uVBGc1jIZRIHTTiSYdWlWr49a9/zbFjx9i5cyeff/45AwcOBGDlypXcdtttIemgxD9uuummcHchIpA6aESzDrpGBk79LIlTqJQZ0qG21O9rXXnTr3ACp4QdiwjMIddZchhjsuaHY7N2RQjv34PHhIXNztO86yhl+TmD+PslF/HCxPH8sXdH/mg7whv2YvKdldhFfRSWjkf0d3ClupQE3GainE4cJ7S0G4rRhNoxw+teOqXClf/GQi46YcCfWunRPBZCidRBI550CNrA+cUvfsGyZcv49ttvueeee1DcnAK//fZbpk2bFpIOSiSS+KLxDE46moFzgh4oiWa/k/zVUySsnMZBSaDFDRx2DIYj2r7Q4Uy/oMmmSnJH9D21H3jCasF24AfMONkrzPzLUcZf7MfY49Rmt1VFcKP+GS5VV3ve7kSDb5Euq5fP+xipTwRowsowjK0vJyiRxCxBPx2PPPIIr7/+OpMnT/aqHr5r1y6GDBnS6s5J/Gf16tUtN4oDpA4a0ayDu4GTig6Ton1MFYve6BMqELXVfl9rzbvvuswaB1Aj/F3c0tBbGqbznV286+7VYxw8ivpu2/Z955X3phIHKxwl/J/jKtd7v9QdYrCS5HrtKHEzcDJ7+ryPiYZMxxZG+pXwL5rHQiiROmjEkw5BGziDBw/mnXfe8XmsvLycLl26BN0pSeD07t073F2ICKQOGtGsg95tNtjdibdI5KA3lHr5pjSFVQjSe3saChUBlujUV3/VcD3d2ShJHbzaqGnZ6Hu5z9587/NaDpHIKscfed9xq3aeovBLXYYrM7KoOYOzSsvQrOvSFfQGr2uYGvnh+BMqHs1jIZRIHTTiSYegDZyamho6duzo81j37t05depU0J2SBM7IkSPD3YWIQOqgEc06uGcxrl+eAigSfdCrJX5f5yR2Lx0sCGwBLFQZxa6Gc6uyMA45z7OBomIafrHrpXXnfz0Kanre+zzAyDuOB/ivQ4uYMikq1+rSXW1cy1Sqiq5LN+/+8D3UGWlWhmNUWv4Ij+axEEqkDhrxpEPQBs7WrVu55557fB675ZZb+PTTT4O9tEQiiVN04DEn4TWDo/iX5K9aOKltItYqkGUqPfugrmq35UwW+l4DUTN6uI4bc8ehdtIMFOfpMuyHfmzyWrV14eECldWOJE7XJQ3spyZyrqrV2qp3NAbQpXf3uoaq1GJgDwBWBvvtaCyRxCNBGzhPPvkko0ePZtu2bcyaNQshBFOmTOG9995j3LhxzJs3L5T9lLSA1FtD6qARrToYGn0kNTZwdM6WC/wKoLxulsOXDma/g8xBVSzo0aqRWyozEUIh4bxJGM4ajulnl2Hoq1UEx+HE8t2mZpfPzG75bxS+5F+OhkzJl6tpJKHiPNlgwKnp3jM4AEZ+dO3ZGNiio3G0joVQI3XQiCcdgjZwvvnmGyZNmkRKSgrPPvssiqLw6KOPMmDAAK644oq4SiYUCcyaNSvcXYgIpA4a0apD4wiqLormn1IrEjlFFjr7oRavUSucrhpNvnSoxRmAiQNGdgMgHEZsNZ1QjCaM51zgSswHYPnuE5ynTjR5DadIwIJWwkHPAfRKAfuFmW+cWkRYgqIyTu2EsJhxVp4GQNc5C3TeoelGGnL7WDgHYwt+ONE6FkKN1EEjnnRoVaK/Tz/9lCFDhtC3b1+ysrIoKytj3759oeqbJABSU1PD3YWIQOqgEa06uPvfqGh1qECLoBKoqNaDLV6j2s188aWDAMzCSbIf/isABnYBWjFM8xEwugWICrsN649bsR/d0+w1NP8bE4BH/puPHac4R0nGoKiMVlP5wllBbVkBamonUBXUtGycpcc9ruVu4Fg5mwRU6pfRfBGtYyHUSB004kmHoAyc9PR07rjjDsaNG0e3bto0amFhIZs3b+bVV1+lvLw8pJ2UtMyePc1/wMYLUgeNaNXB3cDpjB5dXURVschB0VlRzMebOhXQjJdqty/7pnSw4CTZzwnsep8XgOpdZzBVrkftkAZOJ/Zje/zKy1PLRa79BLa49s/g4EvnGcbpOmFQVC5RO/FBWSH0GQqArks3HwZOg5+PlXNIVZRmMxpH61gINVIHjXjSIWAD59JLL2X16tV06NABh8NBWVkZiqIwcOBAxo8fz4MPPsi1117Lli1bWr6YJGRs3bo13F2ICKQOGtGqg/sSlYf/DTkYEirAXNXs+bXCc/mpKR0CSflnZKfbecNwFDyPo8Dv0wEwuxk4iXzmcexzZwWj1A4kKCoj1FQ+KyumPg5Ll96VxjFZeqUEVZTgJAsrZ2MQCgpN2zjROhZCjdRBI550CMgHJz09nZUrV1JRUcG0adPo2LEj3bp1o2vXrnTs2JHrr7+e6upq3n33XdLS0tqqzxIf3HrrreHuQkQgddCIVh2azYFjOtliDpzqRt41TelgDcALx8BOGkKzz/H7vHqcItHN/2Y/esXTOjLj5AtnBQA6RWGURUWY67Ied872ec36WRwnmTjIbtYPJ1rHQqiROmjEkw4BGTgzZsxAp9MxduxYVq9ejdlsdh0zm83885//5IILLsBgMDBjxoyQd1YikcQuCniEPHvmwMnBoG/aibcefyOkHOC3idM4NFsI7wR8zWFhDKBVRG88e1PPf93qVZ2rpqKWatFUisGAktrZq72Rn1z7NgbLkg0SiQ8CeiomTpzI0qVLKShoen722LFjvP7661x++eWt7pzEf9auXRvuLkQEUgeNaNRB30SRTdB8cHQtJPmzIlzRU/U0p4M1gOKbDY69RqwM8vs88FyeSmjCwKnCwQ9CW35LUnQMO+6WDyfNexanPrILwMqgZjMaR+NYaAukDhrxpENABs7gwYP5z3/+02K7LVu2MHjw4Bbb1XPXXXdx8OBBzGYz+fn5XHBB00XtAMaNG0d+fj5ms5kDBw5wxx13eLWZMmUKO3bsoLa2lh07dnDNNdcEfN9rr72W9evXU1paihCC3Nxcr2sYjUYWL15MaWkpVVVVrF27lu7dvRN0tTUZGd7Vh+MRqYNGNOrgVWSzzsCpEGlU0xE9zTsYm30k8GtOh0CWqUw0lF+w4v050By1bvlvPCqIN+JLxxnX/nmnql3LcWrnLK+2WmSXho3Bzdakisax0BZIHTTiSYeADJxOnTpx4kTL08QnTpygU6dOfl1z2rRpLFq0iHnz5pGXl8eWLVtYt24dPXv6LjaXk5PDhx9+yJYtW8jLy2P+/PksXryYKVOmuNqMHj2alStXsmLFCnJzc1mxYgWrVq1i1KhRAd03OTmZrVu38vDDDzfZ/0WLFnHttddy/fXXc8EFF5CSksK///1vVLV9p4zHjBnTrveLVKQOGtGog7uBY0ShQ10OnGKRA4DOcdTXaS58LU81p0NAJRs8QrP998NxihQsaKnxDexGrxQ32bYQK4edtQBkOhV6ntTK3fiawTG4zeDYGIxRUZqcw4nGsdAWSB004kmHgL6FTSYTNpvvOivu2O12jEajX9e8//77WbJkCUuWLGH37t3Mnj2bY8eOcdddd/lsf+edd3L06FFmz57N7t27WbJkCUuXLuXBBx90tbnvvvvYuHEjTz/9NHv27OHpp5/mk08+4b777gvovm+++SZPPfUUH3/8sc++dOjQgRkzZvDAAw/wySefsH37dm666SbOPvtsxo8f79ffL5FINAxN1KAqrDNw1GaS/DmhydIMTRFYJJW7geP/DE4tY6gPVm1qecqdbc6GWZyzD+4HQO3QxSvhn06pQEdBXX8G1fWx5cKbEkk8EXCY+MCBA7Hbm6/IO2iQf2vUBoOBESNG8PTTT3u8v2HDhiatzPPPP58NGzZ4vPfRRx8xY8YM9Ho9drud888/n4ULF3q1qTdwgrmvL0aMGIHRaPToT1FRET/99BNjxozx6qc7KSkp6HQNLpUWiwWr1er3vRvz5z//OehzYwmpg0Y06uBepqFLI/8bAF3t/ibPtQinT3OlOR1sARhEOqUMnSjAQXes5CKEgqK0bCDVNhMe7oudooZa4SRBURlcWsZGux2bXo/aKdOjjANo+XkcdMdJBg6RjoliLD4S/kXjWGgLpA4a8aRDwAbOsmXLWmyjKArCDwe+9PR09Ho9JSWezoMlJSVkZ/sOj8zOzvbZ3mAwkJ6eTnFxcZNt6q8ZzH2b6ovFYuH06dMBX6egoAC9vkH+TZs2sWzZMj744APXLNKGDRvQ6/VceumlACxevJipU6fSvXt3ioqKWLVqFffeey+g+QK9//77/PznPwfg5Zdf5vLLLycnJ4eysjJef/11fve73wFaHoTy8nKuuuoqAJYsWcK4cePo378/FRUVvPjiizzyyCMAbNu2jePHj7uWAJcvX87IkSMZMmQIZrOZ5557jkceeQRVVfnuu+/Yu3cv1113HQBvv/02gwcPZvjw4djtdv70pz/xwAMPkJCQwI4dO/juu++46aabAFi9ejW9e/d2VbqdN28es2bNIjU1lT179rB161ZXeOPatWvJyMhwGaN//vOfue2220hLSyMtLY0FCxa4/LLWrVtHUlISF12kfdEsXLiQG264gaysLI4fP87atWuZOXMmgGumrn727W9/+xuTJ0+mR48elJSU8NZbbzF79mwAPvvsM2pqapg0aRIAr7zyChMmTKBv376Ul5fz2muvMWfOHAC++OILSktLmTx5MgBLly5l7NixDBw4kMrKShYvXszcuXMByM/P58iRI0ydOhXQZhHz8vIYOnQotbW1PPvsszz00EPo9Xq2b9/Orl27+NWvfgXAypUrGTBgAHl5efTu3Zvbb7+d+++/n8TERHbu3El+fj4333wzAGvWrKFHjx6uZdsFCxZw991307FjR/bt28fnn3/uioR8//33SUtLY+zYsQA888wzTJ8+nfT0dA4fPsz69eu58847Ae2HhMlk4uKLLwbg+eefZ9q0aXTt2pWCggJWr17tShW/adMm7HY7EydO1P5fX17CJVdcTnbvXpg2bIPthwEtgkpRHQwa1IGLrnxI6//fl3LuJRfR86x+nCk/xauvvMLchzUNv/rqK4qLi7nmmmvIycnhiSee4LzzzmPw4MFUV1ezaNEiHn30URRF4dC331N48BATfqnpve6tlZw1bAj9zzkbm9XKG88s5De/m43BaOSV145xtLg7Tjpx9R3P8MOnL9Ojbx8Gj/gZwulkyYJnuPHemSSmpHBo125+/O/XWB3XU1GkPeNjxjk4d5zW/6VP/4Vf3D6DDmmdObb/AF9v/owp/6uN72Ov/ov+p22YnIKLTSY2oXDRjb+hX6qT4iNH+fzD9Uy763b+/VESX36tXXvir5+kV6/DPLN4kddnxMKFCzl8+DCffvopFoslbj8jFi5cSFFREQcPHmTjxo1x+xkxY8YMqqqqcDqdLFiwIKo+I1566SWf/rTNIfzdbr755oC2lq7XtWtXIYQQo0eP9nj/0UcfFbt27fJ5zp49e8TDDz/s8d6YMWOEEEJkZWUJQFgsFnH99dd7tLnhhhuE2WwO6r69e/cWQgiRm5vr8f6vfvUrUVtb69V+w4YN4qWXXvLZ/6SkJPHxxx+LzMxMkZqa6tqMRqPf/w++trlz57bq/FjZpA7RqYMCoo9icm1z9D3EW8ZB4i3jINGdfcKQcNLjeOPNgBKUDt0wNntd9y2NhwQIAUJ04e4W2/cmQ4BdgBAGfvT7PhepHVx/+7xuY8WwqQ+IQaOv8WrXhZlu/ZkpeiqmmBgL8pmQOjS31X+HJiUltdg2oBmc5cuXB9K8RcrKyrDb7V6zHZmZmV6zK/XUz9A0bm+z2Th58mSzbeqvGcx9m+qLyWSiU6dOHrM4mZmZfPHFF82eW1VVRU1Njd/3aomDB1uu0RMPSB00ok0HrwiqOh8cp1AooRcG42Gw+D7XhmjSYbglHeyIZkOs3THxlWvfwmhgSbPtzVxGfWYff5an6jkqLJQJG+mKgZyyk6SazZzxGUnl6Wisr7tb40WqaBsLbYXUQSOedAhrdiibzcY333zDhAkTPN6fMGFCkwbCl19+6dV+4sSJ5Ofnu3yDmmpTf81g7uuLb775BqvV6nGd7Oxshg0bFtB1QsHGjRvb9X6RitRBI9p0aCpEvIzu2DGi1zUdvVnrIzy8npZ0CCSSysS31FtZtZzfYvsaJrn2E1nn930Atju1nDgKMKiwCCUpBSUh2aON0S1U3Mrguve8P9KjbSy0FVIHjXjSIezpL5977jluu+02pk+fzqBBg3juuefo1asXL7/8MgDz58/njTfecLV/+eWX6d27N88++yyDBg1i+vTpzJgxg7/85S+uNs8//zwTJ05kzpw5DBw4kDlz5jB+/HgWLVrk930BOnfuTG5uLkOGaOWDBw4cSG5uLllZ2q+pM2fOsGTJEp599lkuvfRShg8fzptvvsmPP/7YZORVW+ErF1A8InXQiDYd3A2cFHQk1FX6LhR9ANAphT7Pg+azF7ekQyCOxopirTNywM5ZOETT+USEUKhBS3aqUOVRYNMffnRWu/YHF2lOPGqa5yyOTjmJimb42eoiqXzlw4m2sdBWSB004kmHoKqJh5JVq1bRpUsXfv/739O1a1d++uknrrjiCo4e1XJedO3alV69ernaHz58mCuuuIKFCxcyc+ZMCgsLmTVrFmvWrHG1+fLLL7n++uv54x//yFNPPcWBAwe47rrr2LZtm9/3Bbj66qs9nKpXrlwJwBNPPMEf/vAHAGbPno3dbmfVqlUkJibyySefcMstt+B0BhayKpHEM3q3L+bGGYwB9KLp7OmBhoe7E8gMDkACX2Cpm72pZSzJ/J/PdhZG4iQTgEQ+QVWaWF9rgjJsFAsr2YqRHqdOk2o2Y+ucjaPQc3nByB5qycRBNxyiEybKA7qPRBLLNFeEVtIGJCUl8d5773H11VeH1AfnZz/7Gd9++23IrhetSB00ok2Hbhgx1RXaHKmkcq0+HYDX7f+Pjc4byUy9l+SqV7zOsyE4LppOr9CSDjqgl2Lyu581YiIlvAdACm+Qofj+NVwu/kAFWsRUOneSqizz+x71XKJ2YrxOq0O1cchgvko1UvsfT4OqTCymktsB6MrFGJSvOCo8jaloGwtthdRBI9p1COQ7NOxLVJLQkJSUFO4uRARSB41o08HgUUW8YWK5qH6JyubbMbI5/xtoWYdAim6ClqxPQfOPqeFyhPB2UBZCoZrrXHcI1P+mnsbLVGrnTK82niUbBqHDs2ApRN9YaCukDhrxpIM0cGKE+hwO8Y7UQSOadNDh+UHkvkRVVJ/kz7LP57ktLU/5o4MtgKKbqmIhkU8AcJKFhXO92lgYg50cABLZhF7xPzLTnfplKoAep07Twe5ASfEsgdO46CZ4++FE01hoS6QOGvGkgzRwJBJJWGkc+VOfxdiKkXK0VA564dvJuDYEK+yNK5C3RBIfuvZruNLreBW/cu2n8FbwHQN2uM3i9C8pQdfJcxancdFN8O1oLJHEI/JJiBEal6aIV6QOGtGkg3sElQqk1eXAKaYXAhWdoQJF8fazcdCyceKPDoE6GiexjvqFrSpuQLilE3OIjlTzC0CLnkpibUDXbswuZ4OPQf/iE96RVBSjouXgsqJFeyY0+liPprHQlkgdNOJJB2ngxAg33HBDuLsQEUgdNKJJB/cikZ3Qo6/zxyly9gVAry/1eZ6lBf8b8E+HQELFAXTKCZdfjYOeVDPVdewMs3CiLSMlswpVaV0gQUxmQvcAACAASURBVBFWTqPl98opKyMhNd3juKI0zOI46IlTJLmcteuJprHQlkgdNOJJB2ngxAj1uXniHamDRjTpYGwiRLzewViv+vZhsfhhmPijQ6AzOAAdacipVcFshFBxiHQq+K3rqp0ITVHD3Q7NSNIJwVlWoVk1bhjY69q30R8FPLIzR9NYaEukDhrxpIM0cGKE48ePh7sLEYHUQSOadPCMoPJ2MNYrvnPgWPwwTPzRIRgDJ4EtGMkHwMpwCtlCIZ8h6ABAKm9gUA4HfF1f7BINs0ADyspQU9M8jnsaOAMBTz+caBoLbYnUQSOedJAGToywdm3r1vpjBamDRrTo4BVBhY8IKsdRfOFPgj9/dHASWKg4aJMoaTwK2ACwMgI7/QDNL6YT8wO8YtMcEmYsdTZgv5IT6L0cjfe49usNHHc/nGgZC22N1EEjnnSQBk6MMHPmzHB3ISKQOmhEiw6NI6h8hojbD3mdZ0X4Ne/irw6BhIrXk6h8Tga3ebynUkY2V6BvprREoDiAfXqthGaSzUYvU0eP454GzgAATEqDrtEyFtoaqYNGPOkQ9lINEokkfmlcZLM+RLxKTaa6zllXZztM44LfwRgkzWELoKq4OynKSnSiCAvnoVBNMquDznvTHLutFQxTtKWp/g6V/W7HDBxCm0kyuAwcPaBHCTgEXiKJJaSBEyO0d3HPSEXqoBEtOrjP4BhQ6FSXxbhE7eZ6X88Rr/P8cTAG/3UIxg+nnkTlcxL5POjz/WG/vQoMmoHT70w1qCrU1btTFDsGcQAbg7AxACEUFEWQiEoljqgZC22N1EEjnnSQS1QSiSRsuIeIe/jf1GUCBtDh7RRpDfHMhL0VBTvbg0ocFCcYAehacYbURuHi9Y7GgkTsaMWJZcI/Sbwjn4AYYfz48eHuQkQgddCIFh2MbhFUXdz8bwoddQ67+lOoSq3Xef7O4PirQ2tmcNqLfQ3ycFZC40gqbz+cxDo/nGgZC22N1EEjnnSQBo5EIgkLBhQPr5cMNwOnwKrVVdLrirzOsxN41FNLRIOvyj57pWu/v9PgccxXqLge78KbEkk8IQ2cGOFvf/tbuLsQEUgdNKJBB2MTDsYARdQl+VN8LE/5kcG4Hn91cEDEmziHq0qw6jSTpV+1xUM9XzM4oIWLR8NYaA+kDhrxpIM0cGKEyZMnh7sLEYHUQSMadDA0DhGvi3lwAiVC8yPRC18Oxv6bIoHoEOmzOA6ng8MdUwFIsdnI1iW6jvmawQHNwImGsdAeSB004kkHaeDECD169Ah3FyICqYNGNOjQeAanPgfOaV0qNhIA0Nn3e50XSO2oQHSwhzj0vC3Yb2r4yB6Y2MW1r1NOo6KFpzeewYmGsdAeSB004kkHaeDECCUloc+9EY1IHTSiQQd3B+NkVBIVbfmlxJDtel/v8E7yF8gMTiA6RPoMDsBe9+ridUZgPca6WRwHXXEKrWSEUVGiYiy0B1IHjXjSQRo4McJbb70V7i5EBFIHjUjXQcEzyZ97BuMSXS/Xvh7PMg1OAjNEAtEhGgyck5WllCclAdDT4vBITtiUH86at95pvw5GMJH+TLQX8aSDNHBihNmzZ4e7CxGB1EEj0nXwWp5yz4Hj7OPa13PMo12gGYwD0SEaQsWdZ8o5mKHlwNEBfRV3P5wGA8fq5odzb4SPhfYi0p+J9iKedJAGjkQiaXca16Byj6AqsGtfzopSi8pJj3b+5r8JhvaYwakWTgqFFXMAkWAeCCf7k4yul/31Ka79phyN5Ye8JF6RYz9G+Oyzz8LdhYhA6qAR6To0rkHlngPnWO1QAPT6QpRG5aECzWAciA5taeA4gVJh4wQ2LAiKsXFKOIK61kFhxlEnTH81yfW+p4HTsET17ef/CaLKVuwR6c9EexFPOkgDJ0aoqalpuVEcIHXQiHQdmsqBY1MUyhx1IeI+SzQENvMRiA5tlQvHLJwUCCtVjfp+GjunhD3g61kqSjmWpmUyTnMqdKkLr9dzBAUt67P7DE5tdQ0J8qM+4p+J9iKedJCjPkaYNGlSuLsQEUgdNCJdB6PS8NGjAF3qfHBOGlMQdfl39c6DXucFOoMTqA6h9sMxCyfF2JqcHTqNg+oAl6scp05wMDPD9bq/qvnhKIoTPfsAsNEPITQdL7ji5yTKj/qIfybai3jSQY56iUTSrujwLCHQCT36uiWXUmNmQzvHYY/z7LR9tuFAnZibww6cwNZiuzJsBLJYJapOc7BzJ9frs9wcjY0uR2MjdreCpdLAkcQjctTHCK+88kq4uxARSB00IlmHxlWu3UPETxgakpB5R1AF7pgbqA6h9MOpEP7VKHdCYEtVQlDkMFNt1JyN+6gN5ouhbgYHGpapVr+yBKOixH1dqkh+JtqTeNJBGjgxwoQJE8LdhYhA6qARyTo0jqBK96hB1du13zgHTqDLUxC4DqFaonIClQHMy1TiwBrA7JHzdCmH68LFE1DpqZgA36HioydcCshZnEh+JtqTeNIhvkd8DNG3b99wdyEikDpoRLIOpmZy4BQ6z3Lte83gBGF8BKpDoE7MTVEpHAH39jT+z+I4ThZxKD3d9bp+mcpXsr/ufbW8QolxPocTyc9EexJPOkgDJ0YoLy8PdxciAqmDRiTr4O5gDJ4zOAXWwXV7Tq8oqmCMj0B1CNUMTiCzN/VU4/T7/s7yIg5lNBg4/VwGjncunIqTmgaJSnx/3EfyM9GexJMO8T3iY4jXXnst3F2ICKQOGpGqgw7qgpobqDdwzKpKeY0WIq5TS1AUzxmNYJaoAtXBWbe1BhsiaEPpjJ+5cYTFTIXDSlmKluivh2IiARVVqUZXZxjWz+D8a8kyQNO+8exZPBGpz0R7E086SAMnRpgzZ064uxARSB00IlWHxv43JhQ6KZrJU5qUiMOqFYnUC8/lqWAjqILRobWRVIGGfbtTicNvA8tZXuyaxdEpCn0Vrfhm/SyOk3Qcogu3zLnfdU48++FE6jPR3sSTDvE72iUSSbvTOILKPYPxicSG3C56cdijXTARVMHS2mWqmiCWp+oR+G8gOU56LlP59sMZ6HFOUpz74UjiC2ngxAhffPFFuLsQEUgdNCJVh4TGJRpoqKtUkth0FfFgjY5gdLC1YpHKDlhaaSBV+uls7DxZxJEuXVxlG/rVJfwzNnI0/v6LL12vTYoSt4tUkfpMtDfxpIM0cGKE0tLScHchIpA6aESqDo0djDPdZnAKlaZDxIM1cILRoTUzOOYg60u5Y0Fg8WOZzFlZjlUICjp3BjRfps7ovWpSnSot8zgvKU4/9iP1mWhv4kmH+BzpMcjkyZPD3YWIQOqgEYk66PFONue+RHXc0d+trWeZhmCNjmB0CMaZuZ6aUIWZ+7PMJYSHHw5oy1SNc+FcPPkqj9Pi1Q8nEp+JcBBPOsTnSJdIJO2OrwieDEVborIqcMLSz/W+gUMe7dqy0ndjbIig7iYAc4gMnCo/nY0dJ4s4mNHgu9RPTURHAQpVgLcPDkCiIv1wJPGBNHBihKVLl4a7CxGB1EEjEnVo7GCsRyGtLmi8zGTEWlP/Re0M2RJVsDoEc79a4QyZGaY5G7c8i+MsL6K4U0fMBm0mrJ+SgKo0RFLZ6cO/lrztcY4eMMShJ04kPhPhIJ50kAZOjDB27NhwdyEikDpoRKIOjQ2cLuhR6xxky1KSsVanAWgzEEpDkcoAqjR5EawOwYSKh2r2ph5/lrsc5cUIFI6kdwEgSdHRXTG6+eHoyDn7Kq/z4tEPJxKfiXAQTzrE3yiPUQYO9J6KjmUUfA/eeNOhKSJRB6PiOWuQqTREUJWmZuCwJgNgaOx/04oQ8WB1CGYGJ9QGjhk/ZoTsNpxnTnqVbXD3wzF2ONfrtHgMF4/EZyIcxJMOjZOKSqKUysrKcHeh3UhGpYtiQIc2le8ArMJJBY640qE5Ik0HE4qXQeoeQVWc6B5BFTr/m2B10ELF/TcCnLTOOdkXAm3Zq6USC47S4xzs1uCg3U9J9IikKixI9TrHpCgoIrjkidFKpD0T4SKedJAzODHC4sWLw92FdiEZlcw64wa0mRw9kKSodFUMLFv8tzD2LnKItPHQeHkKGhyMAQrp49pv7GDcmrDtYHUI9J7mNkpE6M+skOPEMSqSkihPSgKgl5JACrtdx/O/PO11jkL8LVNF2jMRLuJJh/ga4THM3Llzw92FNseI4lGY0RcP/b+5cRsG606kjYcEH/8n9TM4dqDY1lDhWM9hj3atMXCC1SHQ2ZjaEC9P1eOXH05ZATgFh+vCxfWKQn/lOPVVtTp1u8jneSlxtkwVac9EuIgnHeQ3gSQqUNBypvgzYDOUeIwRiWxMjZZZdEA6moFz0qTHYnYr0xDCGZxgEeBXsr16Qu1/U48N0XJGHLsNx+kTHuHi/VUdeo4AUHoyHV9/SqKiyudEEtNIAydGyM/PD3cX2pTO6DH68XG84+tv0NW1j2ciaTzoUbz+NzIwoqtzOj6RnIK1qovrWChz4LRGB4ufRktrqof7gz/LX44TxzmS3sXV47PUBkdjiyUBB9le5yhoS77xQiQ9E+EknnSIn9Ed4xw5ciTcXWgzdEAHP5OTFR3V8qd0UHRxNgHvSSSNB19LhlluS42lnTtjqdJmHxQqUGlIJR9sFfF6WqODv8tONW1cCNTsR1ZjR+kxLAYDhZ07AZClGMngW9dxXwn/IL6WqSLpmQgn8aSDNHBihKlTp4a7C21GR/R+T6WPn3otoP06jedZnEgaD778b7LdHIxLOmdiq9G+mA3sxz2avLVVxFujg78GTnUrqof7gz/LX86TReBwctgtXHyY2vBL3cYAn+clKmrcmDiR9EyEk3jSQRo4kohGBVKDTC2fougCMI0kbUWCjzBndwOnQN+b+o8iAwc82rVniYbGOGg5yaCD1lcP96cfLTo9Ox04yos4mNnghzNUOezab8rAAUiOGxNHEm9IAydGePPNN8PdhTahA/qABukHbzakpY/nWZxIGQ8GH/43oC2hAJgVKLP2cmu/z6Nda3PLtFYHSwszSG29PFWPf344xyjo1MlVtmGQWoNaZ6I1tUQFkBonBk6kPBPhJp50kAZOjJCXlxfuLoQcBf99b+oZNPwcj9cpihqXdXciZTz48r9JRKWjopk9JYkmrNWZrmMG9nu0be0MTmt1qGlh+aml46HCr3w4pccRquqqLp6oKAxUtgDNGzhGRfHLgT/aiZRnItzEkw4RYeDcddddHDx4ELPZTH5+PhdccEGz7ceNG0d+fj5ms5kDBw5wxx13eLWZMmUKO3bsoLa2lh07dnDNNdcEdd/HH3+cgoICampq2Lx5M0OGDPE4vnnzZoQQHtvbb7/tdZ22ZujQoe1+z7YmhcAdhfsN89YhHmdxImU8+PK/yfIo0ZCKparBb0TfyMBpbXRSa3VoLg9NKKuHt0StH2UbnKdOIGw29mc2GIx56gcA2OmNUyQ3eW6HOJjFiZRnItzEkw5hN3CmTZvGokWLmDdvHnl5eWzZsoV169bRs2dPn+1zcnL48MMP2bJlC3l5ecyfP5/FixczZcoUV5vRo0ezcuVKVqxYQW5uLitWrGDVqlWMGjUqoPvOmTOH+++/n3vuuYdzzz2X4uJiNm7cSEpKikefXn31VbKzs12bL4OrramtrW33e7Y1wUydW83eOiTH4SxOpIwHX2UGst0jqLpkYK1q8BsJ9QxOa3Vw0nQ+HHMIq4e3hF95eYQTR8kRDz+cXGWba9/KEF9nAZCs6ML/ZdDGRMozEW7iTQcRzu2rr74SL774osd7O3fuFPPnz/fZ/umnnxY7d+70eO+ll14SX3zxhev1O++8Iz788EOPNuvWrRNvvfVWQPctLCwUc+bMcb02Go3i1KlT4vbbb3e9t3nzZrFw4UK//96kpCTx8ccfi6SkpLDqHumbEUX0UUwh29LQh/1virfN1MT/4Wx9N/GWcZB4yzhI/PySW4U+8ZQAIVRKPdr1VExh/xsA0Qmdz78jFV1E9MN969/rHDFs6gPiLxnnuTTuTLEAIbpwV7Pndmjnv0ducgtmC+Q7NKxGu8FgYMSIEWzYsMHj/Q0bNjBmzBif55x//vle7T/66CNGjhyJXq9vtk39Nf25b58+fejatatHG6vVymeffebVtxtvvJHS0lJ++uknnnnmGa8ZHl+kpKSQmprq2oxGY4vnNMdDDz3UqvMjjWAdH2+Zc7/v6ym6uJrDiYTxkNjE/2EWbiHiSZ2xmxtCxN1pbYg4hEaHah/LUDYEle3kf1NPLaLFNvbiw+AUHMhqWKbKVT8HwMawZs8N1N8t2oiEZyISiCcdwuqckJ6ejl6vp6SkxOP9kpISsrO9M28CZGdn+2xvMBhIT0+nuLi4yTb11/TnvvX/+mrTu3dv1+t//OMfHDp0iOLiYoYNG8aCBQvIzc1l4sSJzf7tBQUFLoMMYNOmTSxbtowPPviAu+66C9AMLr1ez6WXXgr/n70zD5Oiuhr3e6uqt5np2XeGYZEdQfZdwAXFlaCfoMbdRA0mahaJkeTz52dcohGjSTRqEqImGo2oYBRwBwQEBhBk35cZGJh9n+6urvv7o3uaWXpmumd69n6fp56ZrrpVdfv0ubdO3XvuOXiSpF177bX06tWLU6dO8c4773D//fcDEB8fz7hx47j00ksB+Mtf/sLs2bPp27cv+fn5LFmyhAcffBCAdevWUVhYyFVXXQXA3/72N6ZPn87AgQMpKSnhxRdf5Fe/+hUAmzZtIjs72zcF+PrrrzNu3DiGDRtGVVUVixcv5le/+hWKorBt2zb279/P/PnzAXjrrbcYOnQoo0aNQtd1fve73/Hzn/8cq9XKrl272LZtGzfddBMAS5cupU+fPowbNw4BvPHE77nxvgVE2O0c3buP7eu/Yc4dtwLw1bIPiUtK5LwpkwH4x9OLmXvnbcQkxJPcK524xESuvftOAL7+eBW2yAjGzjgfpzR45rnF3HjjjaSkpJCdnc2yZcu49957Afjss88AuPjiiwH485//zJw5c8jIyOD06dO8+eab/PSnPwVg9erVVFZWctlllwHw8ssvM2vWLPr3709hYSF//etfWbhwIQDr168nLy+POXPmAPD3v/+dqVOnMnjwYMrKynjhhRd8+WGysrI4duyYL1bFP//5T0aPHs3w4cOprq7m2Wef5Ze//CWapvHtt9+yZ88ebrjhBgDefvttBg0axOjRoxk40JNd+mc/+xk2m43du3eTlZXFLbfcAsB7771HRkaGb9r2ySefZMGCBcTExHDgwAHWrFnDnXd6ZPjhhx8SHx/P1KlTAXjmmWe4/fbbSUxM5OjRo6xcuZJ77rkH8LxIWCwWZs6ciRWFf7/wIpfMu5bEtFTO5Jzki3c/IMOIApdOmc2CsJ31B4iwHOfKeTeQ2ieTojN5/PuNN1j0M48M165dS2lpKVdccQUAr776KhdccAEDBgygqKiIl19+mYceegiAb775htzcXL73ve8xaNAgevXqxcSJExk6dCgVFRX84Q9/4OGHH0YIwZYtWzh8+DDXXXcdAG+++SYjRoxgxIgROJ1OnnnmGR548BdEma0c+W4nB3fu5rIb5+OUBm/95x369+/P2LFjkVLyxBNP8MADDxAZGcmePXvYuHEjt912GwAffPABqampTJo0CYCnnnqKu+++m7i4OA4ePMiXX37JD3/4QwA++ugjoqOjOf/88wFYvHgxN998M8lJSRQfz2HtxyuZ96O7ANjwyWeoqsqEiy7wtLkXXmRI/3T01GTY71mRNlb5kq+M6zBFT2HYoDFMmT0LgP/85VWmXjqL9H59Kc7PZ9mSN/jfXyzEjez0fQTA448/zn333Yfdbmffvn2sW7eOO+64A4Bly5aRlJTkexl9+umnGTBgAIsWLeLw4cN8+umnPleCFStWEBERwYwZMwB47rnnunUf0atXLxYtWoRhGDz55JMd2kcAPP/888ybN4+0tDRycnJYunQp9913H+B5Luq67nuevvTSS379aZuiw4aa0tLSpJRSTpo0qc7+hx9+WO7Zs8fvOfv27ZMPPfRQnX1TpkyRUkqZkpIiAelwOOT1119fp8yNN94oq6qqAr7v5MmTpZRSpqam1inzyiuvyBUrVjT6ncaMGSOllHL06NFNDq8lJydLu93u28xmc6tkecUVV3T40GGoNnsAQ/GNbbdcOafRYxmidTLuSltH64MCfn+DCSLKN3Xy6+TxMmPCGxKkBCnjWFSnbEwIpkxCJQcNIft665WKqcPkmo652TYwaOBEee41P5Mv28+Tb5qHyH+YR0ozlVKhUPal6XM78ru19dbRbaKzbF1dDl1miio/Px9d1xuM1iQnJzcYOamhZoSmfnmXy0VBQUGTZWquGch9c3NzAYKqG8DWrVtxOp2+N+jGKC8vp6yszLc5nc4myzfHnj17WnV+Z6I1KzqO7NnX6DETgohu70rpoaP1obGM7r2Exff/6YREHKUpvs8m6tY5FEH+QiUHHUmudOGQkjO4QnLNlhBIdGX91GEQgoMpHtmacTJCrMcgDjcZTZ5rE0q3XTLe0W2is9CT5NChvb3L5WLLli3MmjWrzv5Zs2axfv16v+ds2LChQflLLrmErKwsdF1vskzNNQO575EjRzh16lSdMiaTiRkzZjRaN/AswTObzZw6daqprx5yaoYguzoWBGbR8g529g3zmjwe00OWjHe0PkQ0YqT2qrVE/FRSUh0Dx8zeOmVDkcAylHKoxuAkznZaGO6fQBKAyopSjNIi9qee9cMZq3wBgLMZPxzovmEVOrpNdBZ6khw6XJMXL17MG2+8QVZWFhs2bOCuu+4iMzOTv/zlLwA88cQT9OrVi1tvvRXw+Jb8+Mc/5tlnn+XVV19l8uTJ3HnnnXV+tOeff541a9awcOFCli1bxpw5c7j44ovrxLlp7r6Ab67+wIEDHDhwgIcffpjKykrefPNNAPr378/3v/99Pv74Y/Lz8xk2bBjPPvssW7duZd26de0hvm5HW0dVtQqBSYo2zf4cxv/ycID02iM4MTE4yjwGjqAajaN1yoZ/o4YEmh/LfeowRweMwqmqmN1uRitfIdxunIwggpVNnhshFCxStHkKijBh2poON3DeeecdEhIS+N///V/S0tLYuXMnl19+Oce9WaHT0tLIzDwbyv3o0aNcfvnlPPfcc9x7772cPHmS++67j/fee89XZsOGDVx//fX89re/5bHHHuPQoUPMnz+fTZs2BXxf8Dim2Ww2XnzxReLi4ti4cSOXXHIJ5eXlgGdV1UUXXcT9999PVFQUJ06c4KOPPuLRRx/FMNr3Pe/tt99u1/u1BQJPPI7W8Mnb7zZbJhqVgmazDHVtOlIfLAi/ZqoA0r0jOEWaQqVm9QX50ziAEGdXJRkQksdrd2gXtXHjMfyai+uknzqCe/BYDiclMSQ3lxhRyECxnRw5IqD7xKGR24FTcW1Bd9OFltKT5NDhBg54PKNfeuklv8duv/32BvvWrFnD2LFjm7zm0qVLWbp0aYvvW8Ojjz7Ko48+6vdYdna2zxO8oxk0aBAHDx5svmAnJhKl1XOmmYMGcPzgoSbLRAmVQtmRaRzbno7Uh8ampxIxYfaO7JyKivQYN9JT1lzP/8bVXFC7AOkO7aI+1VJiamYa1yjMRVaWcyA1hSFef8LxyqcccV8Z0D1sQsEmlXaL1NwedEddaAk9SQ49w+OyB9Ad8otEhWB6asjoUc2WUUJ0r85MR+pDZCPTUw0cjMtqOxjXdQ4PlfnZHdpFfQLxwwHQsw9wICUZQ/H8HhOVVbgYhJSBxdzqbr443VEXWkJPkkPYwOkmtPeUWKhRAGsjD8ZgMNyBBV/r7hmUO0ofTIhGp08aOhifdYJtMIITIgOnq7cLfwQ6qqJn76fabOZM/34AJIpTDBC7cDIkoPMtQnSrHFXdURdaQk+SQ9jA6SY8+eSTHV2FVhFJaCIN//2p3wdUztLNMyh3lD5ENtGl1Hcwri5J9302sbtOWVeIpka6ervwh44MyIPMKM7DKCtmc9TZJJuTlJU4CcwPByBOaN3GxOmOutASepIcwgZON+FnP/OfoqCr0NSDMRhu/ulPAi7bnUdxOkofIhuRqUItB2OTSpXZ7DNwBFWYOFCnfKhGcLp6u2gMR4BpLPTje3FfcAG6qD1NFXg2aQWIw9Rsua5Ad9WFYOlJcggbON0Em83W0VVoMSqNLysOFktERMBlo7pxfqqO0IemYhilCbPPwTgnNhZDN+MsTwDAxK46K6ggdAZOV24XTRHocnH96C6kPYrDCZ6ApQkilwxhD+pedqFg6QYtpbvqQrD0JDmEDZxuwu7du5sv1EkJpcPv4d2BR+lUCN3IUWejI/ShqRGxPsLq+z87OZXqklRquh8z39Upa3i3UNCV20VTBGrgSEcVzuyD7M1I8u2boBwm2EVqCd1gFKe76kKw9CQ5dM/evQeSlZXV0VVoMVEhzGK8K2trUOXt3WylSA3trQ/NxTCqbeCcSIyv439jZkedsqFaIg5du100hRMZsBGY+83n7E9NwenN4j5Z+RJJZjNn1cUiRJef0u2uuhAsPUkOYQOnm1CTAbarYSG0zr5X3fL9oMpbReOrfroy7a0PdtQmO5M+XgfjakWQb7fXM3DqjuCEMoJxV20XgRCoH85VV16Iw+VgR9RIAKJECYNE/6DvFye0Lv3A6M66EAw9SQ5dWV/DdAM6QzyanpKfqi2JbmL0Jh4Nu/DIODvajhSC6pI033FL/RGcbhRcri2pDtAQFIDr6G6+Tevn2zdODV7GKp5gjWHCdBXCBk43oXaqiq6CILTTUwBfvLcs6HOihNIJzKzQ0p76EIHS5ChY7empnORUpBRUF3sMHI2jKKK0TvlQjuB0xXYRKIEG/PvivWXox3ZzsrdCvvTIfZjIadHLRaRQuqzfWmfSBQ3PlF80arv3PZ1JDm1N19TUMA3IyMjo6CoETUQIMz2dqQAAIABJREFUUjPUJ7lXevOF6iHofqM47akPzcmuj3I2/s2JhHgcpSkYbo/RY2ZLg/KhNHC6YrsIlGqMgCSV3CsdWVUOxdtZJy4FQBUGo0RUi+4bK7pmW+kMuhCFQjpmegsziUIjQWj0FhbSMBOL2i6r1TqDHNqLsIHTTZgwYUJHVyFo2sJp8dyJ41t0nl20/5tUW9Je+mBDwdpMXqSaERw3cDI2lqqi3r5jFho6PIbSwOmK7SJQJOAMwCG7pk249m9hc+zZHH4T1PgWPU7NCCK64KOjI3VBAMmYSBImLPXai8DjCxgnNNKFmT7CQjxam5k63blN1KfraWmYboEJ0eLYNzYURopIpijRzFBi6C+sre4MFCC2m43itAfN5Suyo5LsDfB3yh6FrqlUFp5dwWNhc53yOqHJIt5TCCYZplF0hlJrIduNaQAkCBgsAo8bVZtwWwkcDUE65kZztNVHAWKESoawYA0/oluFINyftCsREREsX76cq6++msrKypBdV1GULpVjJB6NmCD9byJQmK7EMEGJxlKvsyiVOmuNEr6hvMVykMAJ6SCwbFadm/bQBzsqic1MV4wWUfyP5onB8vXAAawZMpgDq36Mo7wv4KYPySiiwle+Shrk4gpZHbtauwgWGwqpomnHX6EoSK8MHDHXk1xyGwtNPwLgoFHFEndui+5dIHVKu1Br6QhdiEAhSZhaZaYUSp2SEMq5q7eJYJ6hYfOwm7BgwYKOrkLACDxTQsGQjIkFWi/OV2MbGDcA0ULjCjWBB/uObfHweXfyxWlrfVCB+AB8MQYpZ6OmHkpOwtBNOMo9PgAmdtcxbsAT3yWUdKV20RIC8cOZv+Au3/+m4o/YLqeRKz2jaAMUG0ktXBnV1fJUtbcu2FFJaaVxA552Fh/Cfqm7t4nahA2cbkJMTExHVyFgopqJmVKfvsLKXVoacd4HqlOa+cR9A3/Sn+El/UmyjIswpGeSKvpEPvdY+rbYv8cugqtbZ6Ut9UEFUoW5WTkJYIDwGDjVquLxvynuBd7Ouq39b6BrtYuWEIgfTlQtGSiiDJPpMJ+4b/Ttm6q2TEYKXSvCcXvqQgRKs6ObwRAj1JD5LHb3NlGb7tCXhwEOHDjQfKFOQnQQDTUZEzerKdi8Iz5HjGH8zLWSf7j/ly2R48iKGcMf5LM8pb9KifTkNkowJLdGDsHagiXoChDdDUZx2kofolBIF5aAgjP2EhYian63pGSkolBxsnb8m80NznGGOAZOV2oXLaU5P5zj++vKwOJaw2rjGiqlZxXVGMXeYp+aSKFg6yKPkfbSBRVIambasCUkCC0kQVF7QpuooWtoZphmWbNmTUdXISCaSshYHxsKN2mpWL1TUt8a5/N/+usYmTkMnPlrMpOvJk2fQKZ5ILnRX/Ib4w3ypGeZeJqzkpujR6KowXfcMd0gCWco9UHgMUp7CwtJwhTwo3CQODs9dTjF44dTkXt2BZWNhnUM9RRVV2kXraG5vFRb1q6r89nCRqqws9K4GfA8kGeosS2+fyBTlZ2B9tKFJFo/LeUPgcdwam3f1BPaRA1hA6ebcOedd3Z0FQIiUB8XAcxXk0nwdp5HjKE8b/ye5AnvkZryKPrXf0A/vANZXoyoPoO99LdY3bN50f57SmQ8AH0ri5mVMhWCXK2lADFdyrugIaHQBxsKSZjIFBYShBb0O/6A2gZOUhLSUKgsGwaAyik0DtYp3xYrqLpKu2gNzRk4c++8rc5nKxsAWOG+hSo8v9EYxd5inTcjghqV7SjaQxcSMbV4dWggmBEkt3JasCe0iRrCBk6YdsOECHip5CQlmoFeB9Vimchi43nSpr6JrfxVHFmfgltveH1xjOtuX8nz8hfo0vM4npZ3gsHnnB90XWO6mANlqKg9WpMqTESJlgVjtHuvAZAXFUWZzUblyRgknmkRK2upP5DnCjC3Upi6SMARRIJSjYOonKKCWFbJ73v3wUVafIvr0NUcjtuCWFTsbWjc1BAhlBY7hvc0wgZON+HDDz/s6Co0S3MxU2pIQOMSJdH3+c/674ic+BmWordx7dnU5LmbP1tKpX4f/zFuBUBBMudECVF9zg2qrl3NgbI+weqDimfUqncLR2vqc64SieK1YPalpQJQdiTOd9zqZ3rK0QYRK7pCuwgFTY3irPnwozqfhfAYmAAfue6kSvXEKRorosjwxiwKFgU6/UO3LXUhEsW3CKI9iBJKi52Oe0qbgLCB022Ij2/521d7EOjojQDmqr0wC8/D7hP3jeQPLyLC+ADnrg3Nnh8dF4cq8tjm/ifbjSkAxBglXFx1DkpcclB1jhRKl4zYCoHrgw2FVO80VHwI38JHikjf/7u96TMqCgb79vkzcELtYAydv12EiqYcjaPj4hrsq5F/BbGsjJvt23+lKaXFPh42oXTqqaq20gWbN9ZNe5MgtCZzwDVGT2kTEDZwug1Tp07t6Co0SaCjN+eJKPopHrXMlZl8mDyb+JR3cWz5PKDzR03zGDUmsZeleplvpcj4oh0MzJwFJktTpzcgIQRxLDqCpvShZhoqQ5hJFaH3GYhDI1PxpGc4HW0n327HlZtHtdvz26jkYGJ/g/PaYgSns7eLUFGF0aiJU9MmamPjK9//q0rmkGf3tJPeUmNMC3NUQeeeqmoLXbAgSAmB429LEEByC+7dU9oEhA2cMO2AOcDRGzOCS9Revs+vqT8ncdR7ODat8Otz0xxusZyl7hm+z5ftOUTk6IuDuoZG156qqo8FQYZ3GqqtuuWRSq3Rm3TP6E3p3iik16E1gpUN/G8MQA8HVW8VVUH4MHn8cE4CUFYxkpXnDPAdu9yUFPALSX0UPI62PQGPgRGKhdstx4wgoRuEtWgrwgZON+GZZ57p6Co0SqBROGcoGcQIJwCbjYsoGXcEY+8nyPLigO/12jPP1fm823iDvcZIAJLkacYdT0XrPyLg60Hr5rs7Cn/6YEaQJsxt3h2ep5wdAdjdKx1ZXUl54SjfPhsrGpwTSNLIltCZ20WoqWwknH/9NgE1fjirAZBEsf2wme29PRGmrRKu05Jb/HCI6KTtJdS6kETgIRPaErtQiQri1+pJbSJs4HQTbr/99o6ugl9sKAFNgcRLjWmKZ/rIKc0sT5mNTX6OfnRXUPebc/vNdT4rooQP9Hx06XmrvCB/LQmpFyDsDf0SmiJUQbbai/r6oAAp7fC22VdYSfE6qmbHxVISEYHz4A4q5WUACKqx8WWD8xxt4H8DnbddtAWVjciwfpuoIYJPfP9X5I9gZZKdogjPKFsfYeFCJbg2Upv4NhwhbCmh1IVY1IBXhLYHCcIUsEnZk9pE5/mFwrSKxMTE5gt1AIGO3szWhqMJzxvoCvF9tBFf49j2RdD3i/UjhzKxjs8Mz6iNSbi4cFs2ljGzQAlc/Vs6391R1NYHM4JewtIub5vTlGjf/1v69kG6nFQcVHHjHR1gTYP8U9A2DsbQedtFW2Dgf5rKX5sAsPGp9yyo5FLKvlvLspEjfL/EBWos59VyFg+GzriqKlS60N4rpgIhGHn3pDYRNnC6CUePHu3oKjQgBjWgqMUDSGS4UgpAgUxl88h4jD0rkVXlQd/z5JGjfvdvcK+iQHoa9rn6NjKz+2EeOjGoa5sQpHSyTrsxavTBitIu01Lg8b0YLCIAKLVa2ZOejuvQDsrd1/rKRLDM77lt4WAMnbNdtCVlfqapGmsTqsjHzBYAXIzAVRHNkRM7+WLYUF+Za7Qk+gpri+piEaLFvjxtQSh0oaNWTAWCLcCpwZ7UJsIGTjdh5cqVHV2FOpgQAb3lqMDlWqbv83v26zGZVqIf39ui+65b9anf/booZ7l+tmO64thqzL2moiT28lu+MWxCaXUk0fZg5cqVvhUe7dXIpyrRvtg3m/v1xa27cO7fQQXzvCUcRLK0wXk6oU+yWUNnaxdtTaWf1VSNtQmoO01VyaXoR3exIUJlax9Pm9QQ3KKm0K+FRk6sUDtNrqrW6kIUSoetmAqUQKYGe1Kb6ByaF6bV3HPPPR1dhTokBTgDP16MJkUUAbBHjiFn9HFc3wY/NVXDdff8sNFjB1nPLuMcABLFaUZt1LGOC37peKRQiO2ETpS1WXDPjwLK+B0q4tAY7XUudmga3/bJxLlrA1WuKbjxBPqL4GNU0dBh3NGGEYw7W7toayRQJuuO4jTVJiL42Pd/JXMAcGz7glWDBnAw2ZM/zCIUblFTGFgr9UYwJAXhH9KWtEYX7KghyQPV1ih4EhQ3Vc+e1CbCBk6YkBODiiWAqakIGcHFqgMAQwpWnDMZuf9DZHVlm9XtY307Lq/D8ayKj7HmnoNl1IxmzmpInNA6bRBAFc9DqT1rd6WagMnrdLm1TyZVlaXoR3dRxh2+MlG85ffc5rJhhwkOf9NUjWFmCyrHAajiQtwyFlldQdWONSwdN5YDyZ7gmGahcLOawiTFHnR9VCCZlkVI7gzYUUnsZD43TWEWoscs1W+OztlDhwmaVatWdXQVAE+clUAd8Gaq07CJKgDWmi+lLOZL9OyGAeCCYf3KxofjAYpFHl8Ynk5bEy4u3fUdImUEWsagoO+VLEydZvi9BgGkYGZDM3IIJUNFBEMUj+9NmdXCunP648j6FJfR1zcqoHKSCD/Lw6H5ZJGtobO0i/bEhaS61rL7ptqEEBDJ+95PJiq5EgB39gGqj+9l6fix7PWm2lCF4Co1kblqYtArpKydwB+nJboQ3cWMmxqaCm3Rk9pE5+qdw7QYiyW4aZa2QEDAw7hJDGOy4skmXSHtbDgvAmcrpqZqMFuaf1Nc715Hvjfj+LlsJn1zMubRFyAigns7rVlZZe0kzUgF0jFjESIgOYSCSBSu0pJ8nz8bPozyg9swSvIpZYG3VhDNXxDC1eB8N23nfwOdo110BKWcDYzZnC6cNXCggmt8/zu3r0EvK+K9sWNYP+Ac3/5xip17tXTSgxyV6Wh/nGB1IR6NhC5o3NTQmD9OT2oTnaNnDtNqZs6c2dFVIAlTQG92UipcoWag1OSbSryIyuPLwFnV6jqMu6D56SZdSP6r5/k+f6/gffTCvljGzaJBiN1m8MSX6XgjR0OQLiy+VWuByKG1mBDcbE4nxvvdDycm8p2sxrUvC12m+6anBJXY+avfa1S2cQbxztAuOoIKDJxew7E5XbCwEZVsAKqYhVt6DVbDTfXGFUi3zldDh7Bs9Hk4vc0jSZi5W0tnmhId1FhOR/rjBKoLnlFQEzGiM3gOtZyavql+z9ST2kTYwAkTEmKCCHw1UPkeA5UDAJwSvdmZvg93zsG2rF4DDnCcnYanI08QpxmfVYqIy8Q0cEzQ16rpSDrq7dTUThGKa2NGcIOlF729/kylVisfnpOJY4tnOqSI/4fEE0PFzt9RRaHf61QF4S8SJjgKZWDpTYSQtfyjTJRzg++YLC/GkfUZALsyMvjbjJnkqB7DSROCy9QEblNTA06yqeKZQu2szroCSMNMRBsF8RO2KLTegzEPn4Jl3Cwsk67AMmE25vOmo/UfgRIT2hg1JgSpnVjebY2AcAKY9iQiIoLly5dz9dVXU1kZOmfaqKgoysuDjxsTCmwopAYYG0LIPvzUlEaCOA3AawMvZf/Bf4CzOiR1iYiKojJAOcRJM/eb+mASOrrUeKb3QuR531L11TsYxXnNX6AeEiiQelBOnq3FhCDVj3ETjByCJQUTN1gzSPJ+TYem8dqo4RzbsgqcVVTLCZziK0BBoYgMhvs1cCRwTLZVBBwPHdkuOgOpmEiwRzerCy45gGx2AmBiF70YW2cw0zRkAuZhEwAQTgdTP/+IaS6zLyxAlXSzzF3Ad7JhEEd/lEuDPBpOWbYlzelCjfEVyAKJoDBZMPUdhpY5BCUmwbdbSoGzPJHq0hTcDjuGW0PRHJi0U2jlXyOPrUZWloakClXSINcr767eJoJ5hoZHcLoJ8+bNa75QG2BGkBygcSOlwnTTbJ9xs8s6gkNFq0Jm3ABcMu/a5gt5KRJOvvTOkGhC5+rjn1JdmoZl/KWgBj8eIoBEobVb8jsV/Bo3EJwcAkEAGcLCPFMqPzbVNW7+M+Qcjm/1GDduGUMer1PTtcTyRKOjN9XSaPO3q45qF52FfHRmBaALJnEQC+sBcDEcB3WDYLr2bkLP9oyySrOFtTMv5R9aKSXeUSKbULleS+Y6NSmg6doooRDbzk7HTemC2TvFG0rjRtiiMI+YRsRlt2EeMRUlJgEpoTzvHLKz5rHvo99w4JNfcuKb2zi57Vpyd8zh5NZ5HNt0P4d2/4cc9VvK0v+O2z661XWpHb+rJ7WJrutBFaYOaWlp7X7PmtGDQK3kGPUHXIxnuNuJmU9TynAfPBLSOiV6V3wEyjrjIKOVc0kS5QxXNnPOxqlkz4rHMvoCHFktW4kULVRMUnC6Dd1nBZBK49NSwcqhPhZvh58uzPQSFvoLG/Z6Pgmno6N5JyOe3B2fgVvHkBbyeAOdvt5rbCCalxq9R0U7LA/viHbRmdCRxKYmB1TWzhIcTAGghPuw8v06xx1bPkOJjEaJS0bYIjk5/Qr++NVSrnJH+hKsjlKi6CusLHXncVg2/eISJ1TcUrbbiGdjuhCNSrzQQjeNo6iYBo/FPGgcqJ7eUUooyR5F3q7pOCoym7mAB0dpOo7SG0HMJzruS6LLH8Lk2tniakUKhXip9ag2ETZwugk5OTntej/Na9wE6oanM5b5ynEU4XmofZ48jLxD/sP2t4YzOSeDKq8j+Ug/ym0mz9z3fMcrPL39QbTRBkZFKa49G1tUD5tQSJdmTuNCD7GZU2PcNJUGI1g5xKIxSLGRKaz0EmYSMfmmH+pTaTazsU9vvi7PoXq7J3GmWyZwhteo5mIAFIpI4laE8O8HYgDl7fBga+920Rk5mpNNhTSa9ZGL4m2KeAw3qVTyPVyyLyZx9GwBt07Vug+xzbgGxR6HEhWDcf4c3lnzHnv1Sq5WE7AJlVihcaeWxtfuEj4xCpv8lROFhlvKRhOFhpL6umDGEy8mlKM2ampfzOdNR4k8m5OtMi+Dk1mXUl05tE5ZQTlWVmNhKxonEFRjEI1LGYLDfCGO6iGAAlKltPBiStlEtP1jYssXoHKmRfWLESqFOada8xW7FGEfnHamrXxw7HY7ZWVlIbteU2hBOrUaMoqp5p9wJf8GINuUzivGdtzVrZsHloBTSgwkAoEqINYeTUUL5HClMpTJqueN84BxHkvGX0RU+j4cW79AP7q7xXU0gDzpClkHrtK8cQMQabc3KwcrCqOUKMYqUaSLppeOOlWVo4mJHEhN5lu9lMrdG8FZhSGtlHMTxTyMm3TA03GnMgerWNfo9cqkQX47+GC0Z7vorNjtdsrLykjFjLUZvSmSD1HM//Ocx0skip82KCNsUdhm/A8iwjNqY5QUUP31B0Q7nFyrJnGOcjbicY7h4N/uMxTSuMOzAeRKZ5vlI6uhRhcEHoM+RqghG7UREXbMI6ejpffz7XNXW6lYO5KM8gh6iwOkicPEUECUOI1GIcgyHLgpx02x1MnDxRnp4rR0UoWBLjKpiP0FJWXfx+06m/RU0cpJMD9GZOULCBG8zCLtdnJLi8lv4jfpzATzDA0bOO1MWxk4ixYt4vHHHw/Z9RqjMafWxpBSEGd7gZ/rz6EIiY7Gq1E62UUtm5pyAxXSTRUGVTT04fj1okU8/fgTRKBiF2rA9TQjWKANI0l4fpOP5M1svkhithVSvWkl7pOHWlTfGkqkm6JWjuUEI/sfLPolf338d36PxaAyVYlhvGLH7OetXkclx9KL4+Y+HDf15YS5D6e0NPTKcoyiPKSjCokFFwNwMAnJ2QeaQj4pzG/SuAE4JV1tGuCvhvZqF52ZGhnUjPw1ZeS4ZTwnOOBdAeckg/MwiYZtVUTFYpt+DcLqCfBolJdQvfZ9qCpnshLNpUo8mvc+DmmwzJ3P9iYckA2gQLoob0OdWLRoEX94/EniAsjXFDCKgmngGMxDxoPqGc9OKi2j/45ihhUWkiayW3TZUqlzXDo4Lqs5pkZwIOFhzuTfhOGK8JWxRXxHguP7mIzggqPW9A0V0uBMOzt6h4JgnqHhKaowAWMJ0ucGwG1/hjuq3/TFvPk0Iobsoqyg7+1EUiL1ZjtA6S3rRKdE6sSgEiO0ZuvsRPKufpAfahlowuAK8QbHv15E/kUVWCdehmPLp+jH9wVd7xpihEokKiXeVVbBGjp2r59Aa1YFmBHMUGKZpsT4Hj41HDKGs8W4iB1yGsfkYNzO4AMF2viIRH6MJpoeAndI2S7GTZi6SCAXJwlSa+BPVYMqComWf6SEhwAzRfwvydze8FrlxVSteQ/btO8hIqJQomKwzfgfqjd8yPqSAo4Y1VyvJZMoTFiEwjwtmQFGGR+6C3zxeWqj4ImREyUNCtBD7r0WgYIVJeAFEYGgJGVgGTUTxR4LUjIg9zQT9x+iT4knt15jNpRDGhhIFASWRqYNo4XGuULjXG+oBT1/MTnq8+y3zWJr2TXslWOpqhxBjpJFQtTTRJU9HvRoTqRQSJWmNvUV7GjCBk434YsvWh8FuCnsqCQE6YjnSFjIncXfkKDkAnDQFM+6kuCMGx0oDmLpdW05SKAYN2XSTRImbM34IGTj4CN3GXM0T6dyu+M5nln7W4wZX3mCAGpm9MPfBVX/2mhAgtCIQ6NceoammxuWV4BETAHHGKph0+df1vk8QkRymRpPTK3IrA5p5SvjWj435pEtg09VAaCSjY1PiOavWMTWgM5paroi1LR1u+gK1G8T+eg4pGy0PceymDJ+gEEiFdxAtfyr3xE5WV5M1ep3sZ4/FyUqBhERhXXGdTi2fM6pnAP8Wc/hKjWBMd78VWMUO72Fhbf1PE7h9FtXm1DohZkS6aYUvVVeWgJPvxUtVEwIsr74qhVXq3XdCDvmc6egZQxEdbsZcew4Ew8dJqGi7giVIRUOyT4ckwc5KfM4I12UoNcx7VUgEpV4YSJZmEjGRLLXsd9aq81rQtDHMOhTvYpZplXoqBwyzmOnnMSu8kvJibySGNcdmFzNT6fX7htsQiFNmsnF2S1fOcIGTjdB19vmoSGABEzYg3zAOnr9mCtyDYYqWwAoUSJ4t/LbgN8UJB7DpiTI0Q5/cnADubiIlWqzebI2yTNkuAcwVnVjFZXcV/Y4z25YhGXKF1hGzUCJTsC5Yy0YLe96FTwrraJR0fFMuVV7I8+6kSiABYUIFCKF2qJRG7fbU78UTFypJtFfOetj45ImVhi38JH7DsqIB+HGGpuN1X4Kk9yNKN6GKD+GoAyBG8+vUfMreP4XuNE4jkJ+UMGfq6TRrqM3bdUuuhL+ZFCGmyppEItGlFDqGDqKKCVWPk4hzwGQz0uky/EowtHgOrKqnOrV72KZfBVqfDJC07BOvBTXviSce75hqTufg0YVc9RELEIhSZi5R0tnhVHIN4b/GC8CT1qHWFQqpEExut9Rn8aw4cnDZKuXcLamTbQYVcM0aAzmQWMRisK5J04wfd8BYqrqRmA/KfvxmXsu+4wNSN5ssn24gVLclEo3R2utOhN4soJnKlYyhYVMYSWx1uiThpvBylYGs5Vr1Repdtr4Tk5mX+RYjpa/SZVo/LvWl4NFCNIxc0a6gpJzVyBs4HQTLrnkEjZv3hzSa1oQJInA0i/40Ey4+v6CmUesXKD8AwAnGm86DwQ8CuNEcka6WjRw2pQcinHjkJJkP+HLa7PcOES8GEg/xUWMKODHBYv546YHsE74ClP/c1HjU6ne+DGyovVBuDQ801cxIQ5gP3X6dPpuOckkxYpS6+fbaszgDf1X5GkpRPf5jt6p/yUydgfGiU3oR3Yhq71voW0Q+tSAdg/u1hbtoqvRmAx0JPm4KJI1Ix2aTwujeZkKrsfBRFwMopDfkcgDfq8vHVVUr1mKZcyFaJmDATANHoOa2gdH1qdsL8knW3cwX0uml7CgCcFVagIDhY1l7nxKm+gXIoVCJGbftKYTAxcSHek7y4LAgoIZQYRQG21Jky+5mF2btwQotVoI4Y0+PBlhjWBwbi4z9u4nsV6wvN3GeD5y385eqZPIAjRxosXNSAKncXHacLEZj5N8BAr9hJVzhI3+ipUkcXYa2SqqGC++YLwTDHNfjiL4zn2a74wKquq9UPiTgwlBujBT5H2p7C6EDZwwDRB4Es1FB5mLRYlLpirtUc7fW8CV6t98+9/TT5Et/Q9J16dMuikI+cLqs1RhcFI6m3TW1ZH8032QOxlIuuIkWWTz09NP86e1D6JMWY8Sm4jtohtx7cvCdWBbq0ZzQo0ARom+JP/5M/qoVt/+XJnJG/pD7IvvS1y/9Qzp9R2yNBv90HYcWQfAaPtRlQLp6kZdZ/fBjcf4L5FuYryrixRhkCjv4SQbkFgp4x4scgt28Yb/ixhuHFmf4i46g2XENFAESkwCtgvm49y/lYL9Wbysn+QSJZ5pagwAQ5QI+ooMPjEK2WSUNdnmLUJgQYV65oukTWxxD0JByxyMafA4lMho+p/JY/rmbaSXlNQp9q1xPkvdP+aw7Es8D5PCX4NNaRcQlRjskpXskpVgeOL39Bc2ztFiGCjisHuduBUM+gP91USuUBI4ICv51qhgr6xssmcVeBJ0RkqVAlxtvqqtPQivompn2moVVXx8PIWF/iPGBkM0njgWQZk2Ziva4KmUlv2Ma4+vYpr6oe/Q+3ohWbKkiZM9GEC+dLU6+FugcghkubUdldu1gaR4h+YrZDSvmn5O0fmHsEQVeOpdXopz9wZPLi3ZcU1JSugvxjJb1chQzsbIqJY2lrnvYm3qOGIHbSAi7gh6zkFcB7djFJ1ut/oVSTfFHbAsNVTtoisTrAxUIA6PI3KZvJV8XvYecZHMDUSK/zZ5vhKTiGXcrLppCRxVOPdsQj+6i8HSwlw1EXut6eJjRjUfuPPbdFVPdHwcpYVFzZYTFhtav3Mx9TsXYYukT34+0/fup3dR3XP3GaO+xHdrAAAYAklEQVR52/1T9srx2FhBIj9Ba+GqqdYigLTo2fStHsNoYwPpteMXeamWBrtkBQciBTtL85p98FdIg6I2cPhuLeFl4p2YtjJwbr75Zt54o5G3qwCwoxLjdcYLGJMFU/8R6MlzkFuncmf1Yvoru3yHP9AL2Cybn8aplpIzOEPydh+MHAQeB96oJvyLIlC4SR1EH+Ws/8EK4wbWDB1E1MAtvpULRkUZroPfoh/fC66GvgpthSGtJInLuEJzMFjsqXNsg3EpH6VfhDp0JyZxBP3ITvRje5CO1mdtD4Zi7xL5jqC17aI70FIZWFFIEBql8nnKuMe710kiP8EuXmv6ZEXx5K8aNAaUs+1LVlfiOrwT7fBOLnVHMN7rgAxgSMlWWc4X7qI2mSa58qYb+O8/3/J/UNXQ0vqj9h6ElpKJAgw5lcu4I0fIKCquU/SoMZT/uH/CNjkThQIS+AWR/LtNRm2CxVCiKYl5mZiiIUxVPmSK8hHxomFQwFKps8OoYLtRzslGHL5raIkfVFsSNnA6MW1h4JjNZlasWMFll12G0xnYVBB4nF1r5t4DnqsUAiU+Fa3PUGT8FEr2ns+0k9u5WvkrJuF5+3JIlXfdJ9ktm/5+NY7ExSHqzFoqh+aWYJsRXKP2Z4RydnSpQKbwrnYLR0Yo2DN2nV2iaUjcZ06g5xzEnZcdsmR5tZFS4FBn0N80hZnuAwwXdVemHWI42ybezN7YrRh5O9CP7MR95kTI69EcbqCwjWObNEVL9aE7EQoZ2DFhsISKWqkboniNeBaiiqZHZ0VkNObhk9EyBtY9YBi4z5wg4/A+riqoIrFWD6RLyXZZzjp3CadDNKJjNpt59eNl/PDyOR45CAUlOh41uTdqSiZqQi9QFSKrqznv+AnGHjuGvbrui0q2cQ7vun/CZjkLiYGdvxPHY6gi+MS8bY0zYhYF4hkc5QMZJjYxVfkvE5RVRIiGsYjypJPtRgW7jIomR9Ac3rQa5S0IcxFKupyB86Mf/YgHH3yQtLQ0du3axQMPPMDXX3/daPnp06ezePFihg8fzsmTJ3n66ad5+eWX65S55ppreOyxxzjnnHM4dOgQixYt4oMPPgj6vo888gh33XUXcXFxbNy4kXvvvZfdu88uxTObzfz+97/nhhtuwGaz8fnnn7NgwYJGQ8S3hYFjt9spLS0lOjq62aitAs8qgyhUIuqtnmj0nAg7anwaSnIGSvJAyovGoB7qw9jC7VyqvEVUrU4uTwre0k802zFVNRPvQrHY6HXpHcQOm4IWGY3QzCBB+N4GJdIwkLoTt6MK6XahH9nG7n//nmHX/wKt3xjfeVJ3oVeWULxrPTmr/o7hZwTD5A3b3lQQtAkikcvVWEy1Viiclr35VLma/efEomQewxxZdxhbVpbjLjyFUVKAUVqAUV6MrCoHPcCOW9UQ1khkRD8cYiZJjv6cW3mGyfLTBm9muSKDTzIncbLvGV555Ul+OGEU5fnNh3QXJisp0+YSPXgCiuZxXDR0J6X7NnH66/eRruCSoTqRVEg3pbibNW3O/s5TMUXHI1SNmt+26vQxqnOPEDdyhnc/ICVSGgil+UnUSLPK6gdmMuMPX1HhPPubGS4H0us3pWgWhFrvWlIipUTqTvTKUvTKMrSIKISioVg8QQ2FUFDMVppHYjiqMNw6qjWyTr2lYSDdOopmornXf72ihH2vPkjS+Mvqycp7LbdO4bdfYLgcxAwej1BNKBYbkWaNr395GZP/39vkbFtbR//Pyn4yQtWQbp3i3Rv8thGBioXnqeZe3z6TpZK+I7PoNXAHgiI0awQIBQw3ZYd3cGbDcp/uKHHJmAaNQ0vv38BpRtXdTNi9k8kncrAadfuDHMPBNlnOXqOyxaOAwhpJVHI6r6/7mlv/5xacVjtqbJIvMF9sRSXnnDnD4NxcMvMLGrzoHDcG8aFxJ+uNK5Co2PiIeBZhFnvr3sdkJXny1dj7jwRFrSMHt6sahzTQkUizlZSZ1xM9eBxCURG6k4p9WyhY8y6qyxH0qsnG7nt6/XIqtOspMh7B5UjDRDVjxFdMVf/LKLEazU8alWKps9+oZL+s4qisbuCgDB6DoVIaVOCm0k+w1bamSxk48+bN44033mDBggWsW7eOu+++mx/84AcMGzaMEycavnX27duXnTt38uqrr/Lyyy8zdepUXnzxRW644Qbee+89ACZNmsTatWv5zW9+w/vvv8/cuXP5v//7P6ZNm8amTZsCvu/ChQtZtGgRt912G/v37+fXv/4106dPZ/Dgwb508y+++CJXXXUVt912GwUFBTz77LPEx8czduxYDD+Omx1h4NSsMohAwVJv6aQPsxXFFoXwboo9DiU6HsOaidPZB5GXSPxpQd/SHM6VmxmkfFvndLcUrDVK+NIobNKRzSElReh+G04NisXG0Hv/iDUps+HDpwlsKqz52YXMWPwFlX4GhaTbTXXecfb8+Sd+jRyASBRihYa5EdMvAY3Z6gCGKXXf7gypsE+OYZ9pKNnx8RQmKuiJJZijClG0hsaM1F3gcnr+unWk7kIikDISXU/AWZWOuTyOhFI3KSWlnKPvZ4jIIlo09CE4raayLjWDnaaDOHMOEqnCjpI8RsYkUd6cwWuy0m/+QsxxaYh6D1kpJc6iUxx5+2mkq9oTQFFKXHjSY9T8ygYSA3DjORZoh9Lc7yy9Pk3+6lV/nz8izSpf3T+Dmc+vrmPgBEug92vJNQK9do0saMK48yev2jIoq3Li8Oo/4Ff2htvtK+OvjajqXaC8gNt1NuyAouokZp4kMfMkCb1PERlXikDHWZTLkXeeqWMgiwg7pn4j0DIHI2yRda5tczqZcOgwY44dw+Zq+PDNN6kct5o4aVbIV6FQFZSZNaSqgaIgFA0sVoTZhjBbERYbSkQ0qAomk5nrbpjPp6/+DXtJMWnFJaQWl5BWXEq0n+9pSMEWeSGr3DezW04A3ETyLjE8i0U0jIUlTFb6zXsQc1wqQlFxaxpOixWHqlFeepqdf/kphqOqUZ2vLXfhqMKMggmB1dtnNzbCXv++NUjD7ZO/4XRRoV1PqfpLHA5PvKtIipmgfMJU5b8MUxpfZVigQI6ik+2u5LReSaHhieVT05okHt+eam9keWcQ7b+ldCkD55tvvmHr1q0sWLDAt2/37t188MEHPPzwww3KP/XUU1x99dUMGzbMt++ll17ivPPOY8oUTybcf//730RHR3P55Zf7yqxYsYKioiJuvPHGgO978uRJ/vCHP/D0008DntGa06dP88tf/pJXXnmF6Oho8vLyuPnmm3nnnXcAT8baEydOcPnll/PJJ580qH+oDRxhTiY6ZQ7P/H4xDy18GN2powoNVdE8f4XJY9ULzfN2pZgQqhmpmkHYMIhEykj6VmST7MjH5nJgdelYdScRuoM4I59EkUOcyPd7f7cUfGtUs9o4Q0Ejb1g6UBlgYDuA3lffS/LkOUEZNxDYA81wu8lb/wEnPnyxyWvVHuXyZxD2x8pUtT9DlMb9WSpkNMUkUkocpWo05WoUhiIwhIJLmpCoaNKF2dDR3G4sbhcx5BMvzhDHGcx+4o7U4EZljyWdbZZ89pUeQtby+4my2wM2cFKmzyPuvJmIen5IUghcZjMuVePktk85uPLVkHcULf2dAyVUBk5noSWGVn0Z1Og/QjQq+6baSO+r7yV26DxyVg2k8NtU/K1hUlQ3ETFl2KJLkVXHcJ7eikIRKkUInIATgRMlKhItMRElJt6TnNJ7KbPbwZj83YzN30VGZdOO8C6hUmqy41DNOBQzDsWCLjQ0w40qDVTDwOp2EKOXEmk0niqihlyZyQbjcr5yX0seGZjYSxRvEsVbaKLxad6EmddjHz8bly0Cl8WCWzsbs6a2PJvS+abkrgJmFCwIzCiYhcCE8LTfkTP8Gr3ScFO0/StOr/2P57MEJeJips5bxr/eqMJwe3KJxXOKCcqnnKesZajYhFk0PZVpICjSoilVIqhULFQoZiqFhkNR0JE4kTiAKimpcpdTWbqBQ7IqZH5VXSZVg8lkYuzYsTz11FN19n/yySc+Y6U+kydPbmA4rFq1ijvvvBNN09B1ncmTJ/Pcc881KPPAAw8EfN9+/fqRlpZW515Op5PVq1czZcoUXnnlFcaOHYvZbK5T5tSpU+zcuZMpU6b4NXBqSEpKoqpWgCiHw4HLFfx8s8k+FVfBizxwO8BigBYN5E5RH2aC8qnngwBM3s1H3eH4XKmxy8jnO1nmDcZnQkXDkB7fCxcSp9ei94zoeOJTRNA8KSOmYDEpBGt7mxWJruuev0oj5yoKKSOmUvD5P5q9XoV3M3lHwEwINAQqcFwIsjlCCiaGKr0ZpthIpm4HGoWTKE4CfjJ7134uqNSTdU2BujKvwswhxcpBdzYH3MVUGTlQhUdWprP5oCw2G7que/66G2pDTdg+A4geNh4RGYlbVTA0DUPVcJnMuFXNV8eYMTOxrflXs/IKlpb+zoESkD50OVrZJrz6j6Bx2TfRRlJGTMES5yL6pt1UXXqUgqw0inYko1fUTe1hVEdRUR0FpEPEZP+VM4Az3q0eOcCHQJp2mFHKGs5Tvqaf2IVWr3czA4lUA9We6/kbGPatLm84pVhJJMfkUHYb4/lOTuGE7IeFzdj4K/35AhPfgfBeWlq9I5WeUBIub0weF5KEKZfgjEsCPA9VrbZca8mzSZ0PoG9yejfwNM+USRfhsCcg3G5Ut47iNlAMzyYwiBsyluIt//V1N5FRO/j7q5JdH4+isHw0VcrFVCsXsUa/kTXciJlKhogshimb6Sd2kSn2Ya7nhKwCiThIxPtSVbtD8Uf0AP6k57CtiVxkwWCz2Zov5KVDDZzExEQ0TeP06bpW+unTp0lNTfV7Tmpqqt/yJpOJxMREcnNzGy1Tc81A7lvz11+ZPn36+Mo4HA6Ki4sbvU59an6cv/3tb36Pt4zVIbjGpd4tMJKAEcD1Ibhz6HCzevVqfjmsuXIpcOHy9qhQyMkELgig3OrVq3nqX0tCdNeuKq9A9aE7408GKQGcF8hvXgmzDgGtS0TbPP28W9vQB5gOwGnvBnC+dwsVgbahULe1VJj3bp09q1ev5uk3a/usHvNutRnn3ULDoyG70llsNlvnHsGpQdaLHyKEaLCvufL19wdyzVCVqU9TZQoKCrjpppsa+Oe0dAQnTJgwYcKE6UnYbDYKCgqaLdehBk5+fj66rjcY7UhOTm4wclJDzQhN/fIul8v3hRsrU3PNQO6bm+tJEJmamur7318Zi8VCbGxsnVGc5ORk1q9f3+j3rn29MGHChAkTJkzgBOq/2pI8fiHD5XKxZcsWZs2aVWf/rFmzGjUQNmzY0KD8JZdcQlZWli+pXGNlaq4ZyH2PHDnCqVOn6pQxmUzMmDHDV2bLli04nc46ZVJTUzn33HObNHDChAkTJkyYMG2P7Mht3rx50uFwyNtvv10OGTJELl68WJaVlcnMzEwJyCeeeEK+9tprvvJ9+/aV5eXl8tlnn5VDhgyRt99+u3Q4HPKaa67xlZk8ebJ0uVxy4cKFcvDgwXLhwoXS6XTKCRMmBHxfQC5cuFAWFRXJ733ve3L48OHyX//6l8zJyZFRUVG+Mi+++KI8fvy4vPDCC+WoUaPkZ599Jrdt2yYVRelQuYa38Bbewlt4C289fOvwCsgf/ehH8siRI7K6ulpmZWXJ888/33dsyZIl8ssvv6xTfvr06XLLli2yurpaHj58WN59990NrnnttdfKPXv2SIfDIXfv3i3nzp0b1H1rtkceeUSePHlSVlVVya+++koOHz68znGLxSJfeOEFmZ+fLysqKuTy5ctlRkZGh8s0vIW38Bbewlt46+Fbh1egx2+PPPKIrM+pU6calMnJyZGVlZXyyy+/lMOGDatz3Gw2yxdeeEHm5eXJ8vJyuWzZMtmrV686ZWJjY+Xrr78ui4uLZXFxsXz99ddlTExMh33v888/Xy5fvlzm5ORIKaWcM2eOX9m0x/fu3bu3XL58uSwvL5d5eXny+eeflyaTqVPIYcmSJQ30Y8OGDd1KDg899JDctGmTLC0tladPn5bvv/++HDRoUI/Th0Dk0BP04Z577pHbt2+XJSUlsqSkRK5fv17Onj27R+lCIHLoCbrQyq3DK9Djt0ceeUR+9913MiUlxbclJib6ji9cuFCWlJTIuXPnyuHDh8u33nrL71TZiRMn5EUXXSRHjRolP//88wZTZR9//LHcsWOHnDRpkpw0aZLcsWOHXL58eYd979mzZ8vHHntMzp071++Dvb2+t6IocseOHfLzzz+Xo0aNkhdddJHMzs6WL7zwQqeQw5IlS+THH39cRz/i4uLqlOnqclixYoW89dZb5bBhw+TIkSPlhx9+KI8ePSojIiJ6lD4EIoeeoA9XXnmlvOyyy+TAgQPlwIED5W9/+1vpcDh8RkxP0IVA5NATdKGVW4dXoMdvjzzyiNy2bVujx0+ePCkXLlzo+2w2m2VRUZG86667JCCjo6Olw+GQ8+bN85VJS0uTuq7LSy65RAJyyJAhUkpZxw9p4sSJUkrp9025vTd/D/b2+t6zZ8+Wuq7LtLQ0X5n58+fLqqoqabfbO1wOS5Yske+//36j53RHOSQmJkopZZ1p456oD/7k0BP1AZAFBQXyjjvu6LG64E8OPVUXAt06dBVVmLMMHDiQnJwcDh8+zFtvvUW/fp7AVs1FVAaajagMngjQxcXFvlxcABs3bqS4uLjRqNEdSXt+78mTJ7Nz505OnTrlK7Nq1SqsVitjx45t0+8ZKDNnzuT06dPs27ePV155haSkJN+x7iiHmJgYAAoLC4Geqw/15VBDT9IHRVGYP38+kZGRbNiwocfqQn051NCTdCFYOkWgv57Oxo0bueWWW9i/fz8pKSn8+te/Zv369QwfPjxkEZVTU1M5c6ZhTPQzZ840GnW5I2nP7+0v8nVxcTEOh6NTyGbFihX85z//4dixY/Tr14/HHnuML774grFjx+J0OrulHBYvXszatWvZtWuXr27Q8/Shvhyg5+jDueeey4YNG7BarZSXlzN37lz27NnD5Mme1A89RRcakwP0HF1oKWEDpxOwcuVK3/87d+5kw4YNHDp0iFtvvZVvvvkGICQRlf2VD+Q6HUl7fe/OLJuaRK4Au3btIisri2PHjnHFFVfw/vvvN3peV5XDn/70J0aOHMm0adMaHOtJ+tCYHHqKPuzbt49Ro0YRGxvLtddey2uvvcaMGTMarVt31YXG5LBnz54eowstJTxF1QmprKzku+++Y+DAgXUiKtemsYjKTZVJSWmYgyYpKanRqNEdSXt+b3+Rr2NjY33Z4zsbubm5HDt2jIEDB/o+dxc5vPDCC1x99dVccMEF5OTk+Pb3NH1oTA7+6K764HK5OHToEFu2bOHhhx9m+/bt3H///T1OFxqTgz+6qy60hg53BApvdTez2SxPnDghf/Ob30jwONQ9+OCDvuMmk8mvQ911113nK5OamurXkWz8+PG+MhMmTOj0Tsbt8b1rHOhSU1N9ZebNm9dpnIzrb/Hx8bKqqkrefPPN3UoOf/zjH2V2drYcMGCA3+M9RR+ak0NP0Yf622effSaXLFnSo3ShOTn0VF0IYuvwCvT47ZlnnpHTp0+Xffv2lRMmTJDLly+XJSUlvqjKoYqo/PHHH8tvv/1WTpw4UU6cOFFu3779/7dz9ygKQ1EYhplGAqlcQEgtCNfSQgyI+3ALLkIrO8FOsEhrZyOkdh+iNgHByiIgfDZjGJkfCwczc+77wIUUKXIPJ/AFck+lx8TDMJRzTs45SdJwOJRzTlEUvXTftyOQWZap1Wqp1+tpt9u97AjkT3UIw1CTyUTtdltxHCtJEm02G+33e1N1mM1mOp1O6na7d0degyAo7/GhHx7VwZd+GI/H6nQ6iuNYzWZTo9FIl8tF/X7fm154VAdfeuHJVfkDeL9uMxyKotDhcNByuVSj0bi75zcmKtfrdaVpWg6NStO00kF/SZLoKx+/Tl617yiKtFqtdD6fdTweNZ1OVavVKq9DEARar9fK81xFUWi73WqxWHza43+vw3cGg0El78FfrYMv/TCfz8sp83meK8uyMtz40guP6uBLLzyz3t4vAAAAzOAnYwAAYA4BBwAAmEPAAQAA5hBwAACAOQQcAABgDgEHAACYQ8ABAADmEHAAAIA5BBwAAGAOAQcAAJhDwAEAAOYQcAAAgDlXDLRIXMnFZXsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}