Estimating_pi_.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/fdeloscogna/Estimating_pi/blob/main/Estimating_pi_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Probability Project 1: estimating π**"
      ],
      "metadata": {
        "id": "fWTMOzShzcAQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook, $\\pi$ is estimated by using Monte Carlo simulation. The existstence of the constant called $\\pi$ is commonly been taken for granted and is usually computed by using the area of the circle formula $\\pi$*$r^2$. Instead, the project's aim is show how to get a precise value of $\\pi$ by using simulations tools.\n",
        "\n",
        "The french mathematician Laplace proposed to estimate the value of $\\pi$ using stochastic simulation. The following notebook writte in Python aims to recall the scientist's experiment by considering a square which sides are of length 1 and inscribe a circle in it, so that the radius, $r$, of the circle which length is 1/2.\n",
        "  \n",
        "Since locations of the points needles are truly random, we can write the $\\pi$ estimator considered for the following project:\n",
        "\n",
        "$$\\text{π̂} = \\frac{\\text{4 * points in circle}}{\\text{points in square}}$$\n",
        "\n",
        "Hence goal's of the project is to figure out at which amount of lambda the simulation reach a good approximation of $\\pi$'s value close to 3.14."
      ],
      "metadata": {
        "id": "7P0xh-GQuBKF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "REuHzwBPCWRJ"
      },
      "outputs": [],
      "source": [
        "####################################\n",
        "#Import fundamental packages\n",
        "####################################\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import math as mt\n",
        "import seaborn as sns\n",
        "import matplotlib.ticker as ticker\n",
        "from prettytable import PrettyTable\n",
        "\n",
        "####################################\n",
        "#Section 1, points generation and parameters setting\n",
        "####################################\n",
        "\n",
        "'''\n",
        "Generate Poisson process to produce random points within a discrete square region.\n",
        "In the following section: \"N\", \"U\" and \"V\" points are randomly generated within a two-dimensional square domain.\n",
        "Poisson has been considered since its complete randomness.\n",
        "'''\n",
        "\n",
        "def poisson_points(λ, T):\n",
        "    #Define the distribution of the points on the squared domain, in this case it follow a Poisson process on the plane\n",
        "    N = np.random.poisson(λ * T**2) #This line determines the generation of inputs by drawing samples from a Poisson distribution.\n",
        "    '''\n",
        "    N: eneration by drawing samples from a Poisson distribution with parameters (λ * T**2).\n",
        "    λ: called here as λ represent the expected number of events occurring in a fixed-time interval.\n",
        "    T: side length of the square.\n",
        "    This first snippet answer to the project question that the number of points in Λ have distribution Poi(λ*T^2)\n",
        "    '''\n",
        "    U = np.random.uniform(0, T, N)\n",
        "    '''\n",
        "    Points are generated uniformly, where 0: Lower boundary, T: Upper boundary , N: as seen before is the number of points generated.\n",
        "    U represent an array containing N random values that are uniformly distributed between 0 and T.\n",
        "    These random values represent the coordinates of the points randomly placed within the square domain.\n",
        "    '''\n",
        "    V = np.random.uniform(0, T, N)\n",
        "    return U, V\n",
        "\n",
        "\n",
        "####################################\n",
        "#Sub section 1.2, parameters setting\n",
        "####################################\n",
        "\n",
        "#M => Variable that represent the number of independent realizations of a Poisson proces\n",
        "T = 1 #length of the square and given by the project\n",
        "M = 90 #amount of simulations for each value of λ from 5^1 to 5^10\n",
        "\n",
        "####################################\n",
        "#Section 2, estimator function\n",
        "####################################\n",
        "\n",
        "'''\n",
        "The following function estimator and compute an estimate of π based on the provided inputs.\n",
        "Circle center which represent a list of tuple containing x and y coordinates of the center of the circle.\n",
        "The circle_radius representing the radius of a quarter circle\n",
        "Checking for empty U array, if it is, means that there are no points generated in the simulation and return np.nan, since there are no points to work with.\n",
        "'''\n",
        "def estimator(U, V, circle_center, circle_radius):\n",
        "    if len(U) == 0:\n",
        "        return np.nan\n",
        "    inside = np.sqrt((U - circle_center[0])**2 + (V - circle_center[1])**2) < circle_radius\n",
        "    '''\n",
        "    This part the code compute the Euclidean distance between each generated point and the center of the quarter circle as sowed by Simulation, Ross S., example 3a p.11\n",
        "    Then it checks if the calculated distance is less than the circle's radius.\n",
        "    For each point U[i], V[i], in the arrays u and v compute the euclidean distance between the point and the quarter circle\n",
        "    Then check if this calculated distance is less than the circle radius. If it is less the \"in circle\" becomes TRUE\n",
        "    If the distance is less than the radius, the point falls within the quarter circle, so the corresponding element in the inside array is True, otherwise it's False.\n",
        "    This False/True is decided by the \"< operator\", if it is < will be True\n",
        "    '''\n",
        "    return 4 * np.sum(inside) / len(U)\n",
        "    '''\n",
        "    np.sum has been used becuse the algorithms take in account if the point is in the circle, count it.\n",
        "    The last line represent the π estimator. It does this by dividing the count of points that fall within the quarter circle (sum of inside) by the total number of generated points (len(X)).\n",
        "    The factor of 4 is used to account for the fact that we are estimating π based on a quarter circle within a square, so the result of this division is an estimation of π.\n",
        "    '''\n",
        "\n",
        "####################################\n",
        "#Section 3, simulation on action\n",
        "####################################\n",
        "\n",
        "'''\n",
        "The current function include the function defined before with the aim to estimate the value of π,\n",
        "based on the generated random variables U and V.\n",
        "'''\n",
        "def find_pi(λ, T, M):\n",
        "    pi_estimates = np.zeros(M) #This line creates an array called pi_estimates with M elements, initialized to zero. Store the values of π obtained from the simulation.\n",
        "    for i in range(M): #Here a loop starts and will run M times. The loop will repeat the simulation M times to get \"M different estimates of π\".\n",
        "        U, V = poisson_points(λ, T) #That line then generates two sets of N independent random variables (N for U and N for V) using a Poisson process.\n",
        "        '''\n",
        "        As previous mentioned, λ parameter is the rate of the Poisson process, and T represents the time interval over which the process is generated.\n",
        "        This line calls the poisson_points function to generate two sets of random points, represented by the arrays U and V.\n",
        "        '''\n",
        "        pi_estimates[i] = estimator(U, V, [1/2, 1/2], 1/2) # since Γ is the circle with center with 1/2, 1/2 and the last 1/2 is the radius\n",
        "        '''\n",
        "        The [1/2, 1/2] represents the center of a unit square (a square with sides of length 1), and 1/2 represents the radius of a circle with the same center as the square and half the side length.\n",
        "        The estimator function likely uses some geometric calculations to compute an estimate of π based on the number of points that fall inside the circle compared to the total number of points generated inside the square.\n",
        "        Here, the estimator function is called with the generated X and Y points, along with the center [1/2, 1/2] and radius 1/2 parameters.\n",
        "        The estimated values are then stored in the pi_estimates array at index called for simplicity as i.\n",
        "        '''\n",
        "    return pi_estimates\n",
        "\n",
        "#Finally, the function returns the arrays U and V, which together represent the random points distributed within the square domain.\n",
        "#In formal terms, as seen in the paper, it gives us the Euclidean coordinates of N points of the Poisson process;\n",
        "\n",
        "'''\n",
        "#As seen from the project guidelines the k value is goes from 1 up to 10\n",
        "#Lambda's density will be defined as values of 5^k as defined here and are 10 different\n",
        "'''\n",
        "lambdas = [5 ** k for k in range(1, 11)] # 5 to k-th with k 1,2,..,10\n",
        "\n",
        "# Run the simulation for different values of lambda\n",
        "'''\n",
        "#Run with lambdas values including T, M index\n",
        "#Where pi_estimates will be made by 10 arrays of M simulations, the variable that will hold the list of estimated π values.\n",
        "'''\n",
        "pi_estimates = [find_pi(λ, T, M) for λ in lambdas]\n",
        "\n",
        "# Computation of the standard deviation of the pi estimates for each value of lambda\n",
        "std_dev = [np.std(estimates) for estimates in pi_estimates]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Problem 1, how the approximate value of π depends on λ"
      ],
      "metadata": {
        "id": "6Cuf-lIiQ5dY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Which is the lambda's density?\n",
        "print(f\"The density of lambda amounts to: {lambdas}\")"
      ],
      "metadata": {
        "id": "DpLL__L6KANO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2915e0f7-2b7a-42ab-af90-3f3f72b5b02e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The density of lambda amounts to: [5, 25, 125, 625, 3125, 15625, 78125, 390625, 1953125, 9765625]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#To draw the plot, simulations results are stored into a DataFrame with 2 columns: Lambda and Pi Estimate\n",
        "results_df = pd.DataFrame({\n",
        "    \"Lambda\": np.repeat(lambdas, M),  #by using numpy repeat original lambda values for M times\n",
        "    \"Pi Estimate\": np.concatenate(pi_estimates)  #concatenating here all pi estimates\n",
        "})\n",
        "plt.figure(figsize=(14, 8))\n",
        "#To repressent the relationship a boxplot has been selected\n",
        "sns.boxplot(data=results_df, x=\"Lambda\", y=\"Pi Estimate\", palette=\"Set3\")\n",
        "plt.xlabel(\"Density of λ\")\n",
        "plt.ylabel(\"π's estimated by the simulation\")\n",
        "plt.title(\"How the approximate value of π depends on the density λ\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "cN_1k831NSxe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 758
        },
        "outputId": "b3c11012-6bf8-45b1-a0c7-afeaf8762e1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "• Problem 2, how the standard deviation depends on Lambda 𝛌"
      ],
      "metadata": {
        "id": "s10yHhCqQOyv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot of the relationship between standard deviation and density of lambda\n",
        "plt.figure(figsize=(14, 10))\n",
        "sns.violinplot(x=lambdas, y=std_dev, split=True, inner='quart')\n",
        "plt.xlabel(\"Density of λ\")\n",
        "plt.ylabel(\"Standard Deviation\")\n",
        "plt.title(\"Standard Deviation of π estimates in relation to lambda\")\n",
        "plt.grid(True)\n",
        "ax = plt.gca()\n",
        "ax.xaxis.set_major_locator(ticker.MaxNLocator(nbins=100))\n",
        "plt.show()\n",
        "#K = 1 is the power of 5, at density 5 e.g. we observe a 0.8 of standard deviation, at K = 2, 5^2 => density of 125 instead 0.1 of standard deviation,"
      ],
      "metadata": {
        "id": "gaEadVgKFzBD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "outputId": "b7c40325-b033-4fa2-abca-dc88d16f0586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Further curiosity about the absolute error"
      ],
      "metadata": {
        "id": "A6pGYJ15JHZM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results_df = pd.DataFrame(columns=[\"Simulation runs, M\", \"Value of Pi\", \"Absolute error\", \"% Error\"])\n",
        "for λ in lambdas:\n",
        "    pi_estimates = find_pi(λ, T, M)\n",
        "\n",
        "    for i, pi_estimate in enumerate(pi_estimates):\n",
        "        absolute_error = abs(np.pi - pi_estimate) #The formula considered take the true value of pi Vs the estimated pi by the algorithm\n",
        "        percent_error = (absolute_error / np.pi) * 100\n",
        "\n",
        "        results_df = results_df.append({\n",
        "            \"Simulation runs, M\": i + 1,\n",
        "            \"Value of Pi\": pi_estimate,\n",
        "            \"Absolute error\": absolute_error,\n",
        "            \"% Error\": percent_error,\n",
        "        }, ignore_index=True)"
      ],
      "metadata": {
        "id": "H5TnjKsVUNZg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table = PrettyTable()\n",
        "table.field_names = results_df.columns\n",
        "for _, row in results_df.iterrows():\n",
        "    table.add_row(row)\n",
        "\n",
        "print(table)"
      ],
      "metadata": {
        "id": "zHKMEzL2UyD_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1611e19b-af5d-4f3c-df8b-0318c637534e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+--------------------+------------------------+------------------------+\n",
            "| Simulation runs, M |    Value of Pi     |     Absolute error     |        % Error         |\n",
            "+--------------------+--------------------+------------------------+------------------------+\n",
            "|        1.0         |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        2.0         | 1.1428571428571428 |   1.9987355107326503   |   63.62172729328107    |\n",
            "|        3.0         | 2.857142857142857  |   0.2844497964469359   |    9.05431823320266    |\n",
            "|        4.0         |        2.4         |   0.7415926535897932   |   23.60562731589024    |\n",
            "|        5.0         |        2.5         |   0.6415926535897931   |   20.42252845405233    |\n",
            "|        6.0         |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        7.0         |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        8.0         | 3.4285714285714284 |  0.28697877498163527   |    9.1348181201568     |\n",
            "|        9.0         |        2.4         |   0.7415926535897932   |   23.60562731589024    |\n",
            "|        10.0        |        3.2         |  0.05840734641020706   |   1.8591635788130243   |\n",
            "|        11.0        | 2.6666666666666665 |   0.4749259869231266   |   15.117363684322488   |\n",
            "|        12.0        | 3.4285714285714284 |  0.28697877498163527   |    9.1348181201568     |\n",
            "|        13.0        |        2.4         |   0.7415926535897932   |   23.60562731589024    |\n",
            "|        14.0        |        2.0         |   1.1415926535897931   |   36.33802276324186    |\n",
            "|        15.0        |        3.2         |  0.05840734641020706   |   1.8591635788130243   |\n",
            "|        16.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        17.0        |        2.5         |   0.6415926535897931   |   20.42252845405233    |\n",
            "|        18.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        19.0        | 3.111111111111111  |  0.030481542478681956  |   0.9702576317095634   |\n",
            "|        20.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        21.0        |        nan         |          nan           |          nan           |\n",
            "|        22.0        | 3.3333333333333335 |  0.19174067974354037   |   6.103295394596899    |\n",
            "|        23.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        24.0        |        2.0         |   1.1415926535897931   |   36.33802276324186    |\n",
            "|        25.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        26.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        27.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        28.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        29.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        30.0        | 3.3333333333333335 |  0.19174067974354037   |   6.103295394596899    |\n",
            "|        31.0        | 2.6666666666666665 |   0.4749259869231266   |   15.117363684322488   |\n",
            "|        32.0        | 3.3333333333333335 |  0.19174067974354037   |   6.103295394596899    |\n",
            "|        33.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        34.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        35.0        | 1.3333333333333333 |   1.8082593202564599   |   57.55868184216124    |\n",
            "|        36.0        |        3.2         |  0.05840734641020706   |   1.8591635788130243   |\n",
            "|        37.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        38.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        39.0        | 2.1818181818181817 |   0.9597744717716115   |   30.550570287172945   |\n",
            "|        40.0        |        3.5         |   0.3584073464102069   |   11.408460164326739   |\n",
            "|        41.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        42.0        |        2.0         |   1.1415926535897931   |   36.33802276324186    |\n",
            "|        43.0        |        2.0         |   1.1415926535897931   |   36.33802276324186    |\n",
            "|        44.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        45.0        |        3.5         |   0.3584073464102069   |   11.408460164326739   |\n",
            "|        46.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        47.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        48.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        49.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        50.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        51.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        52.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        53.0        | 1.3333333333333333 |   1.8082593202564599   |   57.55868184216124    |\n",
            "|        54.0        | 2.6666666666666665 |   0.4749259869231266   |   15.117363684322488   |\n",
            "|        55.0        |        2.4         |   0.7415926535897932   |   23.60562731589024    |\n",
            "|        56.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        57.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        58.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        59.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        60.0        | 3.4285714285714284 |  0.28697877498163527   |    9.1348181201568     |\n",
            "|        61.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        62.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        63.0        |        3.2         |  0.05840734641020706   |   1.8591635788130243   |\n",
            "|        64.0        | 3.3333333333333335 |  0.19174067974354037   |   6.103295394596899    |\n",
            "|        65.0        | 3.4285714285714284 |  0.28697877498163527   |    9.1348181201568     |\n",
            "|        66.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        67.0        | 3.6363636363636362 |   0.4947709827738431   |   15.749049521378428   |\n",
            "|        68.0        | 2.6666666666666665 |   0.4749259869231266   |   15.117363684322488   |\n",
            "|        69.0        | 2.6666666666666665 |   0.4749259869231266   |   15.117363684322488   |\n",
            "|        70.0        | 3.5555555555555554 |  0.41396290196576224   |   13.176848420903347   |\n",
            "|        71.0        | 2.6666666666666665 |   0.4749259869231266   |   15.117363684322488   |\n",
            "|        72.0        |        2.0         |   1.1415926535897931   |   36.33802276324186    |\n",
            "|        73.0        | 3.3846153846153846 |  0.24302273102559147   |   7.735653785282999    |\n",
            "|        74.0        |        3.2         |  0.05840734641020706   |   1.8591635788130243   |\n",
            "|        75.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        76.0        | 3.4285714285714284 |  0.28697877498163527   |    9.1348181201568     |\n",
            "|        77.0        | 1.7142857142857142 |   1.427306939304079    |    45.4325909399216    |\n",
            "|        78.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        79.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        80.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        81.0        |        0.0         |   3.141592653589793    |         100.0          |\n",
            "|        82.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        83.0        | 2.6666666666666665 |   0.4749259869231266   |   15.117363684322488   |\n",
            "|        84.0        |        2.0         |   1.1415926535897931   |   36.33802276324186    |\n",
            "|        85.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        86.0        |        2.4         |   0.7415926535897932   |   23.60562731589024    |\n",
            "|        87.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        88.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        89.0        | 3.3333333333333335 |  0.19174067974354037   |   6.103295394596899    |\n",
            "|        90.0        |        2.0         |   1.1415926535897931   |   36.33802276324186    |\n",
            "|        1.0         | 3.130434782608696  |  0.011157870981097329  |   0.3551660642046512   |\n",
            "|        2.0         |        2.4         |   0.7415926535897932   |   23.60562731589024    |\n",
            "|        3.0         | 3.1515151515151514 |  0.009922497925358265  |   0.3158429185279688   |\n",
            "|        4.0         | 2.909090909090909  |  0.23250174449888394   |   7.4007603828972535   |\n",
            "|        5.0         | 3.230769230769231  |  0.08917657717943772   |   2.8385786132246844   |\n",
            "|        6.0         | 3.8181818181818183 |   0.6765891645920252   |   21.536501997447356   |\n",
            "|        7.0         | 2.727272727272727  |  0.41431992631706605   |   13.188212858966184   |\n",
            "|        8.0         | 2.608695652173913  |    0.53289700141588    |   16.962638386837213   |\n",
            "|        9.0         |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        10.0        | 2.9473684210526314 |   0.1942242325371617   |   6.182349335303804    |\n",
            "|        11.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        12.0        | 3.3333333333333335 |  0.19174067974354037   |   6.103295394596899    |\n",
            "|        13.0        | 2.782608695652174  |   0.3589839579376193   |   11.42681427929303    |\n",
            "|        14.0        | 3.5714285714285716 |   0.4298359178387785   |   13.68210220849668    |\n",
            "|        15.0        | 3.5384615384615383 |   0.3968688848717452   |   12.632728957341316   |\n",
            "|        16.0        | 3.8461538461538463 |   0.7045611925640531   |   22.42687930145796    |\n",
            "|        17.0        |        3.36        |  0.21840734641020676   |   6.952121757753665    |\n",
            "|        18.0        |        3.52        |   0.3784073464102069   |   12.045079936694322   |\n",
            "|        19.0        | 2.6666666666666665 |   0.4749259869231266   |   15.117363684322488   |\n",
            "|        20.0        | 3.619047619047619  |  0.47745496545782595   |   15.19786357127663    |\n",
            "|        21.0        | 3.142857142857143  | 0.0012644892673496777  |  0.04024994347707008   |\n",
            "|        22.0        | 3.6129032258064515 |   0.4713105722166584   |   15.00228145995018    |\n",
            "|        23.0        | 3.066666666666667  |  0.07492598692312624   |   2.3849682369708503   |\n",
            "|        24.0        | 2.823529411764706  |  0.31806324182508705   |   10.124267430459096   |\n",
            "|        25.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        26.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        27.0        | 3.2857142857142856 |  0.14412163212449247   |   4.5875340318169355   |\n",
            "|        28.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        29.0        |        3.6         |   0.458407346410207    |   14.59155902616465    |\n",
            "|        30.0        | 2.787878787878788  |  0.35371386571100505   |   11.259062033609863   |\n",
            "|        31.0        | 2.5714285714285716 |   0.5701640821612215   |   18.14888640988239    |\n",
            "|        32.0        | 3.111111111111111  |  0.030481542478681956  |   0.9702576317095634   |\n",
            "|        33.0        |        3.2         |  0.05840734641020706   |   1.8591635788130243   |\n",
            "|        34.0        | 2.962962962962963  |  0.17862969062683032   |    5.68595964924721    |\n",
            "|        35.0        |       3.125        |  0.016592653589793116  |   0.5281605675654113   |\n",
            "|        36.0        | 3.1666666666666665 |  0.025074013076873403  |   0.7981306248670452   |\n",
            "|        37.0        | 3.4285714285714284 |  0.28697877498163527   |    9.1348181201568     |\n",
            "|        38.0        | 3.5294117647058822 |  0.38781911111608913   |   12.34466571192612    |\n",
            "|        39.0        | 2.857142857142857  |   0.2844497964469359   |    9.05431823320266    |\n",
            "|        40.0        | 3.8260869565217392 |   0.6844943029319461   |   21.78813036597209    |\n",
            "|        41.0        | 3.111111111111111  |  0.030481542478681956  |   0.9702576317095634   |\n",
            "|        42.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        43.0        | 3.310344827586207  |  0.16875217399641373   |    5.37154852980657    |\n",
            "|        44.0        | 3.7037037037037037 |   0.5621110501139106   |   17.892550438440992   |\n",
            "|        45.0        |        2.75        |   0.3915926535897931   |   12.464781299457561   |\n",
            "|        46.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        47.0        |        3.04        |  0.10159265358979308   |   3.233794600127631    |\n",
            "|        48.0        | 3.259259259259259  |  0.11766660566946596   |   3.7454443858280695   |\n",
            "|        49.0        | 3.4074074074074074 |   0.2658147538176143   |   8.461146403365715    |\n",
            "|        50.0        | 2.6666666666666665 |   0.4749259869231266   |   15.117363684322488   |\n",
            "|        51.0        |        2.0         |   1.1415926535897931   |   36.33802276324186    |\n",
            "|        52.0        |        2.72        |   0.4215926535897929   |   13.41971095800893    |\n",
            "|        53.0        | 3.1666666666666665 |  0.025074013076873403  |   0.7981306248670452   |\n",
            "|        54.0        | 3.1724137931034484 |  0.030821139513655282  |   0.9810673410646348   |\n",
            "|        55.0        | 3.3043478260869565 |  0.16275517249716343   |   5.180658043339531    |\n",
            "|        56.0        | 3.5384615384615383 |   0.3968688848717452   |   12.632728957341316   |\n",
            "|        57.0        | 2.1538461538461537 |   0.9877464997436394   |   31.440947591183548   |\n",
            "|        58.0        | 3.130434782608696  |  0.011157870981097329  |   0.3551660642046512   |\n",
            "|        59.0        | 3.466666666666667  |  0.32507401307687367   |   10.347427210380774   |\n",
            "|        60.0        | 3.230769230769231  |  0.08917657717943772   |   2.8385786132246844   |\n",
            "|        61.0        | 3.3846153846153846 |  0.24302273102559147   |   7.735653785282999    |\n",
            "|        62.0        | 3.259259259259259  |  0.11766660566946596   |   3.7454443858280695   |\n",
            "|        63.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        64.0        | 3.6129032258064515 |   0.4713105722166584   |   15.00228145995018    |\n",
            "|        65.0        |        2.72        |   0.4215926535897929   |   13.41971095800893    |\n",
            "|        66.0        |        4.0         |   0.8584073464102069   |   27.32395447351627    |\n",
            "|        67.0        | 3.225806451612903  |  0.08421379802310991   |   2.680608446384085    |\n",
            "|        68.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        69.0        | 3.7142857142857144 |   0.5726930606959213   |   18.229386296836545   |\n",
            "|        70.0        | 3.3043478260869565 |  0.16275517249716343   |   5.180658043339531    |\n",
            "|        71.0        | 3.657142857142857  |   0.5155502035530639   |   16.410472661500588   |\n",
            "|        72.0        | 3.0588235294117645 |  0.08276912417802862   |   2.634623049664033    |\n",
            "|        73.0        |        3.68        |   0.538407346410207    |   17.138038115634977   |\n",
            "|        74.0        | 3.1724137931034484 |  0.030821139513655282  |   0.9810673410646348   |\n",
            "|        75.0        | 3.111111111111111  |  0.030481542478681956  |   0.9702576317095634   |\n",
            "|        76.0        | 3.259259259259259  |  0.11766660566946596   |   3.7454443858280695   |\n",
            "|        77.0        | 3.130434782608696  |  0.011157870981097329  |   0.3551660642046512   |\n",
            "|        78.0        | 3.3043478260869565 |  0.16275517249716343   |   5.180658043339531    |\n",
            "|        79.0        | 2.933333333333333  |    0.20825932025646    |   6.6291000527547395   |\n",
            "|        80.0        | 2.923076923076923  |   0.2185157305128702   |   6.9555717308919585   |\n",
            "|        81.0        | 2.857142857142857  |   0.2844497964469359   |    9.05431823320266    |\n",
            "|        82.0        | 2.8333333333333335 |  0.30825932025645963   |   9.812198914592635    |\n",
            "|        83.0        | 2.962962962962963  |  0.17862969062683032   |    5.68595964924721    |\n",
            "|        84.0        | 2.742857142857143  |  0.39873551073265023   |   12.692145503874555   |\n",
            "|        85.0        | 3.2941176470588234 |  0.15252499346903026   |   4.855021331131044    |\n",
            "|        86.0        | 3.130434782608696  |  0.011157870981097329  |   0.3551660642046512   |\n",
            "|        87.0        |        3.2         |  0.05840734641020706   |   1.8591635788130243   |\n",
            "|        88.0        |        3.68        |   0.538407346410207    |   17.138038115634977   |\n",
            "|        89.0        | 2.9565217391304346 |  0.18507091445935853   |   5.8909901717488475   |\n",
            "|        90.0        | 3.3333333333333335 |  0.19174067974354037   |   6.103295394596899    |\n",
            "|        1.0         | 3.1940298507462686 |  0.052437197156475435  |   1.6691278258674689   |\n",
            "|        2.0         |       2.944        |  0.19759265358979317   |   6.289569507492024    |\n",
            "|        3.0         | 3.079136690647482  |  0.06245596294231115   |   1.9880350455666111   |\n",
            "|        4.0         | 3.1225806451612903 |  0.019012008428502813  |    0.6051710239002     |\n",
            "|        5.0         | 3.044776119402985  |  0.09681653418680813   |   3.081765997771198    |\n",
            "|        6.0         | 3.1181102362204722 |  0.023482417369320885  |    0.74746856001488    |\n",
            "|        7.0         | 3.4015748031496065 |  0.25998214955981336   |   8.275488843620144    |\n",
            "|        8.0         | 3.411764705882353  |   0.2701720522925597   |   8.599843521528582    |\n",
            "|        9.0         | 3.2203389830508473 |   0.0787463294610542   |   2.5065735168139445   |\n",
            "|        10.0        | 2.9514563106796117 |  0.19013634291018144   |   6.052227767114205    |\n",
            "|        11.0        | 2.9606299212598426 |  0.18096273232995053   |   5.760222673145433    |\n",
            "|        12.0        | 3.1940298507462686 |  0.052437197156475435  |   1.6691278258674689   |\n",
            "|        13.0        | 3.0458715596330275 |  0.09572109395676565   |   3.0468970522766003   |\n",
            "|        14.0        | 3.338345864661654  |  0.19675321107186106   |   6.262849222257944    |\n",
            "|        15.0        | 3.2459016393442623 |  0.10430898575446923   |   3.3202581383451744   |\n",
            "|        16.0        |      3.40625       |   0.2646573464102069   |   8.424304981353702    |\n",
            "|        17.0        |       3.296        |   0.1544073464102067   |   4.9149384861774035   |\n",
            "|        18.0        | 3.211267605633803  |  0.06967495204400986   |   2.2178226054989856   |\n",
            "|        19.0        | 3.186991869918699  |  0.04539921632890609   |   1.445101938248739    |\n",
            "|        20.0        | 3.1652173913043478 |  0.023624737714554644  |   0.7519987573041796   |\n",
            "|        21.0        | 3.154471544715447  |  0.01287889112565388   |   0.4099478368380319   |\n",
            "|        22.0        | 3.0588235294117645 |  0.08276912417802862   |   2.634623049664033    |\n",
            "|        23.0        | 3.096774193548387  |  0.044818460041406105  |   1.4266158914712748   |\n",
            "|        24.0        |       3.168        |  0.026407346410207033  |   0.8405719430248935   |\n",
            "|        25.0        | 3.2108843537414966 |   0.0692917001517035   |   2.2056233188770094   |\n",
            "|        26.0        |       3.104        |  0.037592653589793024  |   1.1966113285513689   |\n",
            "|        27.0        |       3.456        |  0.31440734641020684   |   10.007896665118059   |\n",
            "|        28.0        | 3.033333333333333  |   0.1082593202564599   |   3.4460011909168293   |\n",
            "|        29.0        |        3.2         |  0.05840734641020706   |   1.8591635788130243   |\n",
            "|        30.0        | 3.074074074074074  |  0.06751857951571916   |   2.1491831360939786   |\n",
            "|        31.0        | 3.207547169811321  |  0.06595451622152781   |   2.0993974551781496   |\n",
            "|        32.0        | 3.1884057971014492 |  0.04681314351165611   |   1.4901086383100715   |\n",
            "|        33.0        | 3.0654205607476634 |  0.07617209284212967   |   2.424633020295943    |\n",
            "|        34.0        | 3.1666666666666665 |  0.025074013076873403  |   0.7981306248670452   |\n",
            "|        35.0        | 3.0977443609022557 |  0.04384829268753743   |   1.395734505472358    |\n",
            "|        36.0        | 3.386861313868613  |  0.24526866027881988   |   7.807143933780197    |\n",
            "|        37.0        | 3.007751937984496  |  0.13384071560529698   |   4.260282295107918    |\n",
            "|        38.0        | 3.135135135135135  |  0.006457518454658029  |   0.2055491964331925   |\n",
            "|        39.0        |        3.0         |  0.14159265358979312   |   4.507034144862795    |\n",
            "|        40.0        |      3.40625       |   0.2646573464102069   |   8.424304981353702    |\n",
            "|        41.0        | 3.114503816793893  |  0.02708883679590013   |   0.8622644557354252   |\n",
            "|        42.0        | 3.2063492063492065 |  0.06475655275941339   |   2.061265093850351    |\n",
            "|        43.0        | 3.1074380165289255 |  0.03415463706086763   |   1.0871758635493456   |\n",
            "|        44.0        |       3.136        |  0.005592653589792995  |  0.17801969276323767   |\n",
            "|        45.0        | 3.1884057971014492 |  0.04681314351165611   |   1.4901086383100715   |\n",
            "|        46.0        | 2.924137931034483  |   0.2174547225553103   |    6.92179879867086    |\n",
            "|        47.0        | 3.2755905511811023 |   0.1339978975913092   |   4.265285553115687    |\n",
            "|        48.0        | 2.945736434108527  |  0.19585621948126608   |   6.2342970931469335   |\n",
            "|        49.0        | 3.129032258064516  |  0.012560395525277102  |  0.39980980700743474   |\n",
            "|        50.0        | 3.015873015873016  |   0.1257196377167773   |   4.001780357269478    |\n",
            "|        51.0        | 3.1379310344827585 | 0.0036616191070346638  |  0.11655295612085972   |\n",
            "|        52.0        |       3.168        |  0.026407346410207033  |   0.8405719430248935   |\n",
            "|        53.0        |        3.2         |  0.05840734641020706   |   1.8591635788130243   |\n",
            "|        54.0        | 2.9565217391304346 |  0.18507091445935853   |   5.8909901717488475   |\n",
            "|        55.0        | 3.0839694656488548 |  0.05762318794093835   |   1.8342030395027267   |\n",
            "|        56.0        | 3.238095238095238  |  0.09650258450544502   |   3.071772669036985    |\n",
            "|        57.0        | 2.909090909090909  |  0.23250174449888394   |   7.4007603828972535   |\n",
            "|        58.0        |       3.1875       |  0.045907346410206884  |   1.4612762210832806   |\n",
            "|        59.0        | 2.8976377952755907 |  0.24395485831420238   |   7.765324318397654    |\n",
            "|        60.0        | 2.8846153846153846 |  0.25697726897440853   |   8.179840523906535    |\n",
            "|        61.0        | 3.3134328358208953 |   0.1718401822311022   |   5.469842884778399    |\n",
            "|        62.0        | 3.1549295774647885 |  0.01333692387499541   |  0.42452747206916697   |\n",
            "|        63.0        | 3.180327868852459  |  0.03873521526266588   |   1.2329801961563807   |\n",
            "|        64.0        | 3.1714285714285713 |  0.029835917838778148  |   0.9497067611450403   |\n",
            "|        65.0        | 2.896551724137931  |  0.24504092945186207   |   7.799895036419249    |\n",
            "|        66.0        | 2.857142857142857  |   0.2844497964469359   |    9.05431823320266    |\n",
            "|        67.0        | 3.4857142857142858 |  0.34412163212449265   |   10.953731755492754   |\n",
            "|        68.0        |      3.09375       |  0.047842653589793116  |   1.5228789618897571   |\n",
            "|        69.0        | 3.267175572519084  |  0.12558291892929097   |   3.9974284631010817   |\n",
            "|        70.0        | 3.0285714285714285 |  0.11302122501836465   |   3.5975773271948244   |\n",
            "|        71.0        | 3.3956834532374103 |  0.25409079964761716   |   8.087961351618137    |\n",
            "|        72.0        | 3.0679611650485437 |  0.07363148854124946   |    2.34376307371082    |\n",
            "|        73.0        | 3.357142857142857  |   0.2155502035530641   |   6.861176075986874    |\n",
            "|        74.0        | 2.852713178294574  |   0.2888794752952193   |   9.195319290205441    |\n",
            "|        75.0        | 3.2125984251968505 |  0.07100577160705734   |   2.260183907863466    |\n",
            "|        76.0        | 3.3333333333333335 |  0.19174067974354037   |   6.103295394596899    |\n",
            "|        77.0        | 3.0943396226415096 |  0.04725303094828348   |   1.5041106902987254   |\n",
            "|        78.0        | 2.9743589743589745 |  0.16723367923081867   |   5.323213340205845    |\n",
            "|        79.0        | 3.0793650793650795 |  0.06222757422471359   |   1.980765206896197    |\n",
            "|        80.0        | 3.1636363636363636 |  0.022043710046570464  |   0.7016730835992327   |\n",
            "|        81.0        | 3.2624113475177303 |   0.1208186939279372   |   3.8457784713075935   |\n",
            "|        82.0        | 2.878504672897196  |   0.263087980692597    |   8.374350519058387    |\n",
            "|        83.0        | 3.027027027027027  |  0.11456562656276592   |   3.6467371551768686   |\n",
            "|        84.0        | 3.089430894308943  |  0.052161759280850095  |   1.6603603659833683   |\n",
            "|        85.0        | 3.303030303030303  |   0.1614376494405101   |   5.138719981918747    |\n",
            "|        86.0        | 3.215686274509804  |  0.07409362092001093   |   2.358473204199361    |\n",
            "|        87.0        | 3.225806451612903  |  0.08421379802310991   |   2.680608446384085    |\n",
            "|        88.0        | 3.0956521739130434 |  0.045940479676749746  |   1.462330885713496    |\n",
            "|        89.0        | 3.2072072072072073 |   0.0656145536174142   |   2.0885761093959343   |\n",
            "|        90.0        |       3.104        |  0.037592653589793024  |   1.1966113285513689   |\n",
            "|        1.0         | 3.143769968051118  |  0.002177314461325075  |  0.06930607183707062   |\n",
            "|        2.0         | 3.1296296296296298 |  0.011963023960163355  |  0.38079487951735586   |\n",
            "|        3.0         | 3.104575163398693  |  0.03701749019110023   |   1.1783033089538704   |\n",
            "|        4.0         | 3.1335504885993486 |  0.008042164990444522  |   0.2559900622779662   |\n",
            "|        5.0         | 3.1986644407345577 |  0.057071787144764574  |   1.816651407035554    |\n",
            "|        6.0         | 3.126365054602184  |  0.01522759898760917   |  0.48470953005982814   |\n",
            "|        7.0         | 3.0638297872340425 |  0.07776286635575058   |   2.4752689139024295   |\n",
            "|        8.0         | 3.197278911564626  |  0.05568625797483273   |   1.7725486437970215   |\n",
            "|        9.0         | 3.1789819376026274 |  0.037389284012834256  |   1.1901378738618695   |\n",
            "|        10.0        | 3.0751104565537557 |  0.06648219703603742   |   2.1161940571789417   |\n",
            "|        11.0        | 3.128526645768025  |  0.013066007821767922  |   0.4159039462623466   |\n",
            "|        12.0        | 3.201192250372578  |  0.059599596782784925  |   1.8971140868528087   |\n",
            "|        13.0        | 3.1614906832298137 |  0.01989802964002063   |   0.6333739549996661   |\n",
            "|        14.0        | 3.2865853658536586 |  0.14499271226386545   |   4.6152613738190125   |\n",
            "|        15.0        | 3.109717868338558  |  0.03187478525123533   |   1.0146059265453486   |\n",
            "|        16.0        | 3.1090342679127727 |  0.03255838567702041   |   1.0363656039180327   |\n",
            "|        17.0        | 3.1491875923190547 |  0.007594938729261536  |   0.2417544082484103   |\n",
            "|        18.0        | 3.125645438898451  |  0.015947214691342193  |   0.5076156093349609   |\n",
            "|        19.0        | 3.1589403973509933 |  0.017347743761200185  |   0.5521958342173195   |\n",
            "|        20.0        | 3.122923588039867  |  0.01866906554992598   |   0.5942548130354666   |\n",
            "|        21.0        | 3.1608832807570977 |  0.019290627167304564  |   0.6140397338038656   |\n",
            "|        22.0        | 3.0971993410214167 |  0.04439331256837642   |   1.4130830270961343   |\n",
            "|        23.0        | 3.090909090909091  |  0.05068356268070229   |   1.6133079068283367   |\n",
            "|        24.0        | 3.2241653418124008 |  0.08257268822260766   |   2.6283702990027877   |\n",
            "|        25.0        |  3.14785373608903  | 0.0062610824992370695  |  0.19929644577194755   |\n",
            "|        26.0        | 3.2288401253918497 |  0.08724747180205661   |   2.777173281913613    |\n",
            "|        27.0        | 3.127625201938611  |  0.013967451651182294  |   0.4445977945365436   |\n",
            "|        28.0        |  3.13015873015873  |  0.011433923431062976  |  0.36395308659758335   |\n",
            "|        29.0        | 3.113237639553429  |  0.02835501403636398   |   0.9025681290654805   |\n",
            "|        30.0        | 3.1182108626198084 |  0.023381790969984717  |   0.744265522242902    |\n",
            "|        31.0        | 3.1221238938053095 |  0.01946875978448359   |   0.6197098711138533   |\n",
            "|        32.0        | 3.133437990580848  |  0.008154663008945295  |   0.2595709854244545   |\n",
            "|        33.0        | 3.1452991452991452 |  0.003706491709352111  |  0.11798129541450342   |\n",
            "|        34.0        | 3.289308176100629  |  0.14771552251083575   |    4.70193111580033    |\n",
            "|        35.0        | 3.115919629057187  |  0.025673024532606004  |   0.8171977516967484   |\n",
            "|        36.0        | 3.1706539074960127 |  0.02906125390621961   |   0.9250484423247006   |\n",
            "|        37.0        | 3.179968701095462  |   0.0383760475056687   |   1.2215475313713147   |\n",
            "|        38.0        | 3.180722891566265  |  0.03913023797647197   |   1.2455541596635438   |\n",
            "|        39.0        | 3.1239388794567065 |  0.017653774133086664  |   0.5619370835017165   |\n",
            "|        40.0        | 3.1901081916537866 |  0.048515538063993446  |   1.5442975399295118   |\n",
            "|        41.0        | 3.1743119266055047 |  0.032719273015711536  |   1.0414868069647512   |\n",
            "|        42.0        | 3.115919629057187  |  0.025673024532606004  |   0.8171977516967484   |\n",
            "|        43.0        | 3.0865671641791046 |  0.05502548941068852   |   1.7515157271523645   |\n",
            "|        44.0        | 3.1019108280254777 |  0.03968182556431543   |   1.263111737894228    |\n",
            "|        45.0        | 3.1877133105802047 |  0.04612065699041157   |   1.4680661077339556   |\n",
            "|        46.0        | 3.198776758409786  |  0.05718410481999303   |   1.820226589677394    |\n",
            "|        47.0        |  3.12540192926045  |  0.016190724329343098  |   0.5153667618506332   |\n",
            "|        48.0        | 3.0642504118616145 |   0.0773422417281786   |   2.4618800161695757   |\n",
            "|        49.0        | 3.1279620853080567 |  0.013630568281736366  |   0.433874463837989    |\n",
            "|        50.0        | 3.0617283950617282 |  0.07986425852806489   |   2.542158304222117    |\n",
            "|        51.0        | 3.1133004926108376 |  0.028292160978955483  |   0.9005674541104803   |\n",
            "|        52.0        | 3.0592105263157894 |  0.08238212727400374   |   2.6223045556166684   |\n",
            "|        53.0        | 3.0792682926829267 |  0.06232436090686644   |   1.9838460226742147   |\n",
            "|        54.0        | 3.0320699708454812 |  0.10952268274431187   |   3.486215267888533    |\n",
            "|        55.0        | 3.151702786377709  |  0.010110132787915838  |   0.3218155217024501   |\n",
            "|        56.0        | 3.176470588235294  |  0.03487793464550082   |   1.1101991407335055   |\n",
            "|        57.0        | 3.1810089020771515 |  0.03941624848735836   |   1.2546581569803053   |\n",
            "|        58.0        | 3.1125198098256734 |  0.02907284376411967   |   0.9254173589596062   |\n",
            "|        59.0        | 3.0759493670886076 |  0.06564328650118556   |   2.089490705492234    |\n",
            "|        60.0        |  3.09697933227345  |  0.044613321316343324  |   1.4200861230486128   |\n",
            "|        61.0        | 3.1650485436893203 |   0.0234558900995272   |   0.7466241707920006   |\n",
            "|        62.0        | 3.0288315629742035 |  0.11276109061558959   |   3.589296991980843    |\n",
            "|        63.0        | 3.163763066202091  |  0.022170412612297685  |   0.7057061515268154   |\n",
            "|        64.0        | 3.1076443057722307 |  0.033948347817562397  |   1.0806094729936024   |\n",
            "|        65.0        | 3.1438127090301005 | 0.0022200554403073447  |  0.07066655945259362   |\n",
            "|        66.0        | 3.1061093247588425 |  0.03548332883095062   |   1.1294694361601911   |\n",
            "|        67.0        | 3.0980392156862746 |  0.04355343790351851   |   1.3863489861981773   |\n",
            "|        68.0        | 3.151702786377709  |  0.010110132787915838  |   0.3218155217024501   |\n",
            "|        69.0        | 3.1465798045602607 |  0.004987150970467535  |  0.15874594577909024   |\n",
            "|        70.0        | 3.2120200333889817 |  0.07042737979918856   |   2.241773124810231    |\n",
            "|        71.0        | 3.2343234323432344 |  0.09273077875344127   |   2.9517123630742166   |\n",
            "|        72.0        |       3.0464       |   0.0951926535897929   |   3.030076272969999    |\n",
            "|        73.0        | 3.1617873651771955 |  0.02019471158740238   |   0.642817634690053    |\n",
            "|        74.0        | 3.2732732732732734 |  0.13168061968348033   |   4.191524306405965    |\n",
            "|        75.0        | 3.084019769357496  |  0.05757288423229712   |   1.8326018227255052   |\n",
            "|        76.0        | 3.0823909531502425 |  0.05920170043955064   |   1.8844486528800237   |\n",
            "|        77.0        | 3.0170648464163823 |  0.12452780717341083   |   3.963843212808543    |\n",
            "|        78.0        | 3.1336717428087986 |  0.00792091078099455   |   0.2521304209170336   |\n",
            "|        79.0        | 3.147335423197492  |  0.005742769607699039  |  0.18279803402064132   |\n",
            "|        80.0        | 3.1318327974276525 |  0.00975985616214059   |   0.310665870414114    |\n",
            "|        81.0        | 3.1082802547770703 |  0.033312398812722854  |   1.0603665874586856   |\n",
            "|        82.0        | 3.135135135135135  |  0.006457518454658029  |   0.2055491964331925   |\n",
            "|        83.0        | 3.1269035532994924 |  0.014689100290300683  |   0.4675685841547897   |\n",
            "|        84.0        | 3.0713101160862353 |  0.07028253750355784   |   2.2371626513465497   |\n",
            "|        85.0        | 3.0879478827361564 |  0.053644770853636725  |   1.707566090477664    |\n",
            "|        86.0        | 3.1296296296296298 |  0.011963023960163355  |  0.38079487951735586   |\n",
            "|        87.0        | 3.174193548387097  |  0.03260089479730377   |   1.0377187112419497   |\n",
            "|        88.0        | 3.2564543889845097 |  0.11486173539471656   |   3.656162592036491    |\n",
            "|        89.0        | 3.009771986970684  |  0.13182066661910907   |   4.1959821388200025   |\n",
            "|        90.0        | 3.0033444816053514 |  0.13824817198444173   |   4.400575988948476    |\n",
            "|        1.0         | 3.125696594427245  |  0.015896059162548326  |   0.5059872782801561   |\n",
            "|        2.0         | 3.1501240694789083 |  0.008531415889115213  |  0.27156340206508467   |\n",
            "|        3.0         | 3.1491472868217056 |  0.007554633231912522  |  0.24047144442103577   |\n",
            "|        4.0         | 3.1073446327683616 |  0.034248020821431524  |   1.0901483609689961   |\n",
            "|        5.0         | 3.141855275741154  | 0.0002626221513608584  |  0.00835952271090171   |\n",
            "|        6.0         | 3.1014765944077913 |  0.040116059182001784  |    1.27693382323652    |\n",
            "|        7.0         | 3.108641975308642  |  0.03295067828115128   |   1.0488526653351968   |\n",
            "|        8.0         | 3.1246882793017456 |  0.016904374288047475  |   0.538082945563659    |\n",
            "|        9.0         | 3.140913508260447  | 0.0006791453293462091  |  0.021617867248644486  |\n",
            "|        10.0        | 3.1205018157807856 |  0.021090837809007557  |   0.6713422182505985   |\n",
            "|        11.0        | 3.181353767560664  |   0.0397611139708709   |   1.2656355662608647   |\n",
            "|        12.0        | 3.126137841352406  |  0.015454812237387205  |  0.49194195242745764   |\n",
            "|        13.0        | 3.1246753246753247 |   0.0169173289144684   |   0.5384953041298187   |\n",
            "|        14.0        | 3.160525472604934  |  0.01893281901514099   |   0.6026503465847836   |\n",
            "|        15.0        | 3.134232498394348  |  0.007360155195445195  |   0.2342810162557196   |\n",
            "|        16.0        | 3.158176943699732  |  0.01658429010993867   |   0.5278943497333544   |\n",
            "|        17.0        | 3.108481262327416  |  0.033111391262377055  |   1.0539683184114201   |\n",
            "|        18.0        | 3.1559278350515463 |  0.014335181461753166  |   0.4563029979514637   |\n",
            "|        19.0        | 3.121221969450764  |  0.020370684139029205  |   0.6484190149780337   |\n",
            "|        20.0        | 3.1405371642723297 |  0.001055489317463465  |  0.03359726845100025   |\n",
            "|        21.0        | 3.1743002544529264 |  0.03270760086313329   |   1.041115270808881    |\n",
            "|        22.0        | 3.0834157599736236 |  0.05817689361616951   |   1.8518280385489416   |\n",
            "|        23.0        | 3.116383456235973  |  0.025209197353820212  |   0.8024336740479229   |\n",
            "|        24.0        | 3.080282594733462  |  0.06131005885633112   |   1.9515597856480267   |\n",
            "|        25.0        | 3.1870550161812297 |  0.045462362591436545  |   1.447111946212639    |\n",
            "|        26.0        | 3.1412213740458017 | 0.0003712795439914096  |  0.01181819493902753   |\n",
            "|        27.0        | 3.151670951156812  |  0.010078297567019057  |   0.3208021751484211   |\n",
            "|        28.0        | 3.182894736842105  |  0.041302083252312105  |   1.3146861419196914   |\n",
            "|        29.0        | 3.168982229402262  |  0.027389575812468703  |   0.8718372759489218   |\n",
            "|        30.0        | 3.1195862960568843 |  0.022006357532908805  |   0.7004841161620006   |\n",
            "|        31.0        |  3.0986632718014   |  0.042929381788392895  |   1.366484663100384    |\n",
            "|        32.0        | 3.047803617571059  |   0.0937890360187339   |   2.9853977380410632   |\n",
            "|        33.0        | 3.1291989664082687 |  0.012393687181524449  |   0.3945033156148553   |\n",
            "|        34.0        | 3.130603031280232  |  0.010989622309561131  |  0.34981054265592504   |\n",
            "|        35.0        | 3.176881549698317  |  0.035288896108523815  |   1.1232804503855829   |\n",
            "|        36.0        | 3.125906394199077  |  0.015686259390716106  |  0.49930914413082617   |\n",
            "|        37.0        | 3.221442246330568  |  0.07984959274077497   |   2.541691477713812    |\n",
            "|        38.0        | 3.151553184813304  |  0.009960531223510927  |  0.31705355600858565   |\n",
            "|        39.0        | 3.1626717801214443 |  0.021079126531651227  |   0.6709694367143624   |\n",
            "|        40.0        | 3.2064343163538873 |   0.0648416627640942   |   2.0639742294406562   |\n",
            "|        41.0        | 3.1825273010920436 |  0.04093464750225051   |   1.3029902987414952   |\n",
            "|        42.0        | 3.171845275840203  |  0.03025262225040981   |   0.962970874528916    |\n",
            "|        43.0        | 3.1239935587761676 |  0.017599094813625538  |   0.5601965867062886   |\n",
            "|        44.0        | 3.150554675118859  |   0.008962021529066    |   0.285270005289368    |\n",
            "|        45.0        | 3.1156680551105413 |  0.02592459847925177   |   0.8252055991291104   |\n",
            "|        46.0        | 3.100834938985228  |  0.04075771460456501   |   1.297358349689051    |\n",
            "|        47.0        | 3.160574412532637  |  0.018981758942843818  |   0.6042081528664767   |\n",
            "|        48.0        | 3.1777848707309286 |  0.03619221714113552   |   1.1520340518933887   |\n",
            "|        49.0        | 3.1369819677317303 |  0.004610685858062791  |  0.14676268907091802   |\n",
            "|        50.0        | 3.147693328908065  |  0.006100675318271964  |  0.19419052662034098   |\n",
            "|        51.0        | 3.1614123581336697 |  0.019819704543876604  |   0.630880789755772    |\n",
            "|        52.0        | 3.1570195075151903 |  0.015426853925397221  |   0.4910520117167154   |\n",
            "|        53.0        | 3.1011673151750974 |  0.040425338414695755  |   1.2867784869723027   |\n",
            "|        54.0        | 3.1485841552656697 |  0.006991501675876588  |  0.22254641027020586   |\n",
            "|        55.0        | 3.139035769828927  | 0.0025568837608660466  |   0.0813881378906454   |\n",
            "|        56.0        | 3.1332706177485106 |  0.008322035841282549  |   0.2648986281456075   |\n",
            "|        57.0        | 3.1618204804045513 |  0.02022782681475821   |   0.6438717251151115   |\n",
            "|        58.0        | 3.115923566878981  |  0.025669086710812028  |   0.8170724069360431   |\n",
            "|        59.0        | 3.1389236545682104 | 0.0026689990215826853  |  0.08495687747846332   |\n",
            "|        60.0        | 3.1382007822685787 | 0.0033918713212144347  |  0.10796661742058306   |\n",
            "|        61.0        | 3.123448726322665  |  0.018143927267128213  |   0.5775391423326557   |\n",
            "|        62.0        | 3.1512717536813923 |  0.009679100091599224  |   0.3080953248518467   |\n",
            "|        63.0        | 3.1741449639159085 |  0.032552310326115386  |   1.0361722194925223   |\n",
            "|        64.0        | 3.1436655139893115 | 0.0020728603995183725  |  0.06598119578455801   |\n",
            "|        65.0        | 3.1262262916939174 |  0.015366361895875702  |  0.48912649061351327   |\n",
            "|        66.0        | 3.1493892890698403 |  0.007796635480047165  |  0.24817461522703174   |\n",
            "|        67.0        | 3.1361138053669575 | 0.0054788482228356195  |  0.17439715542290699   |\n",
            "|        68.0        | 3.139646869983949  | 0.0019457836058442979  |  0.06193621581145843   |\n",
            "|        69.0        | 3.128124011388801  |  0.013468642200991976  |   0.4287201966047956   |\n",
            "|        70.0        | 3.1635320369780047 |  0.021939383388211553  |   0.6983522629244168   |\n",
            "|        71.0        | 3.129559748427673  |  0.012032905162119967  |   0.3830192672614754   |\n",
            "|        72.0        | 3.0941286113699906 |  0.047464042219802494  |   1.5108273876807967   |\n",
            "|        73.0        | 3.1724581724581724 |  0.030865518868379294  |   0.9824799797997457   |\n",
            "|        74.0        | 3.162196679438059  |  0.020604025848265817  |   0.6558465122689374   |\n",
            "|        75.0        | 3.1778501628664495 |  0.036257509276656386  |   1.1541123651160228   |\n",
            "|        76.0        | 3.138029964934651  |  0.00356268865514231   |   0.1134039020326631   |\n",
            "|        77.0        | 3.1330554664956716 |  0.008537187094121546  |   0.2717471052259556   |\n",
            "|        78.0        | 3.133899412148922  |  0.007693241440871024  |  0.24488348074280775   |\n",
            "|        79.0        | 3.109380071405388  |  0.032212582184405036  |   1.0253583368803971   |\n",
            "|        80.0        | 3.142483660130719  | 0.0008910065409257761  |  0.028361619063109682  |\n",
            "|        81.0        | 3.071334214002642  |  0.07025843958715106   |   2.2363955908436783   |\n",
            "|        82.0        | 3.192566485100929  |  0.05097383151113588   |   1.6225474506661386   |\n",
            "|        83.0        | 3.1529263687853994 |  0.011333715195606331  |   0.360763359395295    |\n",
            "|        84.0        | 3.142025234551925  | 0.00043258096213172337 |  0.013769479682142354  |\n",
            "|        85.0        | 3.1976245463543385 |  0.056031892764545344  |   1.7835505408544794   |\n",
            "|        86.0        | 3.1308290155440415 |  0.010763638045751645  |   0.3426172401266725   |\n",
            "|        87.0        | 3.144197685329997  |  0.002605031740203767  |  0.08292073567294232   |\n",
            "|        88.0        | 3.1665104654795377 |  0.024917811889744623  |   0.7931585866573717   |\n",
            "|        89.0        | 3.1438250692947336 | 0.0022324157049404825  |  0.07105999889545118   |\n",
            "|        90.0        | 3.1363636363636362 | 0.0052290172261568735  |  0.16644478781110753   |\n",
            "|        1.0         | 3.144912641315519  |  0.003319987725725948  |  0.10567849151074085   |\n",
            "|        2.0         | 3.155609475767831  |  0.014016822178037902  |   0.4461693072149678   |\n",
            "|        3.0         | 3.1514838709677417 |  0.009891217377948625  |   0.3148472277793959   |\n",
            "|        4.0         | 3.1330593372720266 |  0.008533316317766548  |  0.27162389458785535   |\n",
            "|        5.0         | 3.1373834131585583 |  0.004209240431234829  |  0.13398428425865683   |\n",
            "|        6.0         | 3.135589978963473  | 0.0060026746263202035  |  0.19107106771023122   |\n",
            "|        7.0         | 3.133409062223905  |  0.008183591365888088  |   0.2604918036250489   |\n",
            "|        8.0         | 3.1325301204819276 |  0.009062533107865534  |   0.2884693882101513   |\n",
            "|        9.0         | 3.1436432273338024 | 0.0020505737440092986  |  0.06527178950670694   |\n",
            "|        10.0        | 3.132523961661342  |  0.009068691928451056  |  0.28866542955811164   |\n",
            "|        11.0        |  3.14356656267906  | 0.0019739090892669786  |   0.0628314777541722   |\n",
            "|        12.0        | 3.1382628350016146 | 0.0033298185881784903  |   0.1059914175815766   |\n",
            "|        13.0        | 3.164789910559166  |  0.023197256969373026  |   0.7383916225697273   |\n",
            "|        14.0        | 3.1459858744249334 |  0.004393220835140266  |   0.1398405624013756   |\n",
            "|        15.0        | 3.141676730439204  |   8.407684941103e-05   |  0.002676249236671667  |\n",
            "|        16.0        | 3.1588038154163445 |  0.017211161826551358  |   0.5478482962100365   |\n",
            "|        17.0        | 3.121337827326938  |  0.02025482626285502   |   0.6447311442401835   |\n",
            "|        18.0        | 3.1472817987429536 |  0.005689145153160435  |  0.18109111461855626   |\n",
            "|        19.0        | 3.134071661237785  | 0.0075209923520080935  |  0.23940062195568565   |\n",
            "|        20.0        | 3.138204169571003  | 0.0033884840187901055  |  0.10785879623566721   |\n",
            "|        21.0        | 3.132099553286535  |  0.009493100303258117  |    0.30217476770614    |\n",
            "|        22.0        | 3.1358660656793305 |  0.005726587910462655  |  0.18228295460008392   |\n",
            "|        23.0        | 3.166178530497899  |  0.024585876908105764  |   0.7825927680347834   |\n",
            "|        24.0        | 3.1434861434861436 | 0.0018934898963505198  |  0.06027165533974915   |\n",
            "|        25.0        | 3.1528150134048256 |  0.011222359815032501  |  0.35721880754365415   |\n",
            "|        26.0        | 3.1266687959118493 |  0.014923857677943797  |   0.4750411438889381   |\n",
            "|        27.0        | 3.124431636247198  |  0.01716101734259512   |   0.5462521477119511   |\n",
            "|        28.0        | 3.139760554505356  | 0.0018320990844369334  |  0.05831752510445474   |\n",
            "|        29.0        | 3.1379000128518184 |  0.003692640737974706  |  0.11754040530223575   |\n",
            "|        30.0        | 3.1379112104758864 |  0.003681443113906724  |  0.11718397385797492   |\n",
            "|        31.0        | 3.143205284422497  | 0.0016126308327040206  |  0.05133163368144884   |\n",
            "|        32.0        | 3.1323872595395774 |  0.009205394050215698  |   0.2930167932401103   |\n",
            "|        33.0        |  3.14650502966211  |  0.004912376072316871  |  0.15636578684711597   |\n",
            "|        34.0        | 3.1447610585771404 |  0.003168404987347273  |  0.10085346309066652   |\n",
            "|        35.0        | 3.1509566775452744 |  0.009364023955481304  |   0.2980661399491544   |\n",
            "|        36.0        | 3.1496366793132275 |  0.008044025723434434  |   0.2560492912485899   |\n",
            "|        37.0        | 3.1320418047412693 |  0.009550848848523863  |  0.30401296099322195   |\n",
            "|        38.0        |  3.1521086303695   |  0.010515976779706726  |   0.3347339371859834   |\n",
            "|        39.0        | 3.139110968979853  |  0.002481684609940249  |  0.07899447457341456   |\n",
            "|        40.0        | 3.1521019108280255 |  0.010509257238232372  |   0.3345200475377925   |\n",
            "|        41.0        | 3.146256123163051  |  0.004663469573257917  |  0.14844284690852985   |\n",
            "|        42.0        | 3.1375728401317455 |  0.004019813458047583  |  0.12795463643111962   |\n",
            "|        43.0        | 3.142766133656113  | 0.0011734800663196765  |  0.03735303063491634   |\n",
            "|        44.0        | 3.145720894371627  |  0.004128240781833714  |  0.13140598534047726   |\n",
            "|        45.0        | 3.153875720691864  |  0.012283067102071055  |   0.3909821691248101   |\n",
            "|        46.0        |  3.15519671921056  |  0.013604065620766992  |   0.4330308579383161   |\n",
            "|        47.0        | 3.129683729201067  |  0.011908924388726039  |  0.37907283667467545   |\n",
            "|        48.0        | 3.1269952305246425 |  0.014597423065150661  |  0.46465040744447483   |\n",
            "|        49.0        |  3.14177523907323  |  0.00018258548343697   |  0.005811876445163432  |\n",
            "|        50.0        | 3.1419040287400564 | 0.0003113751502632489  |  0.009911378864075547  |\n",
            "|        51.0        | 3.1477066597564134 |  0.006114006166620278  |   0.1946148607023895   |\n",
            "|        52.0        | 3.1641325536062377 |  0.02253990001644457   |   0.7174673008828493   |\n",
            "|        53.0        | 3.1196564763186565 |  0.021936177271136614  |   0.6982502090482952   |\n",
            "|        54.0        | 3.154238491832914  |  0.01264583824312071   |   0.4025295331866381   |\n",
            "|        55.0        | 3.1454021147068247 | 0.0038094611170316206  |  0.12125891345839113   |\n",
            "|        56.0        | 3.100712619032604  |  0.04088003455718914   |   1.3012519147088306   |\n",
            "|        57.0        | 3.157152924594785  |  0.015560271004992021  |   0.4952988092587949   |\n",
            "|        58.0        | 3.144696189495366  |  0.003103535905572663  |  0.09878861608701422   |\n",
            "|        59.0        | 3.1587402779548643 |  0.017147624365071223  |   0.5458258359968218   |\n",
            "|        60.0        | 3.134631134631135  |  0.006961518958658264  |   0.2215920307396813   |\n",
            "|        61.0        | 3.130166087292391  |  0.011426566297402019  |   0.3637189017597575   |\n",
            "|        62.0        | 3.1508821094197823 |  0.00928945582998919   |  0.29569256279532097   |\n",
            "|        63.0        | 3.1544197310560196 |  0.012827077466226466  |  0.40829855683452126   |\n",
            "|        64.0        | 3.1481859989780276 |  0.006593345388234528  |  0.20987270200993538   |\n",
            "|        65.0        | 3.1386151286317676 | 0.0029775249580255014  |  0.09477756304984934   |\n",
            "|        66.0        | 3.1471036585365852 |  0.005511004946792131  |  0.17542073573717107   |\n",
            "|        67.0        | 3.1297543454557117 |  0.011838308134081377  |  0.37682505147680867   |\n",
            "|        68.0        | 3.1492290047151776 |  0.007636351125384522  |   0.2430726057580609   |\n",
            "|        69.0        | 3.136328823078887  |  0.005263830510906065  |  0.16755292908172742   |\n",
            "|        70.0        | 3.1445852167823265 | 0.0029925631925333818  |   0.0952562449213102   |\n",
            "|        71.0        | 3.1373842445769378 |  0.004208409012855352  |   0.1339578193896826   |\n",
            "|        72.0        | 3.1156711540975035 |  0.025921499492289612  |   0.8251069553103894   |\n",
            "|        73.0        | 3.1140039956177095 |  0.027588657972083652  |   0.8781742579057478   |\n",
            "|        74.0        | 3.132539328705178  |  0.009053324884614966  |   0.288176281360667    |\n",
            "|        75.0        | 3.1249840418741224 |  0.01660861171567074   |   0.5286685304885925   |\n",
            "|        76.0        | 3.155825087775295  |  0.014232434185502019  |   0.4530324505705439   |\n",
            "|        77.0        | 3.142311122435979  | 0.0007184688461858713  |  0.022869573665602407  |\n",
            "|        78.0        |  3.15022335673261  |  0.008630703142816998  |   0.2747238135076163   |\n",
            "|        79.0        | 3.1172151898734177 |  0.02437746371637539   |   0.7759587701008938   |\n",
            "|        80.0        | 3.144258327495064  |  0.00266567390527106   |  0.08485103573899319   |\n",
            "|        81.0        |  3.13264012173472  |  0.008952531855073165  |  0.28496793958450994   |\n",
            "|        82.0        | 3.118080243778568  |  0.023512409811225154  |   0.7484232490917723   |\n",
            "|        83.0        | 3.1476411790921244 |  0.006048525502331259  |  0.19253054642268183   |\n",
            "|        84.0        | 3.1444701052091353 |  0.002877451619342164  |  0.09159212974521684   |\n",
            "|        85.0        | 3.1384969031351764 | 0.0030957504546167414  |  0.09854079748624733   |\n",
            "|        86.0        | 3.128153124603879  |  0.013439528985914162  |   0.4277934941870093   |\n",
            "|        87.0        | 3.142122847597019  | 0.0005301940072257594  |  0.016876599409535935  |\n",
            "|        88.0        | 3.155011360767483  |  0.013418707177689893  |   0.4271307154464085   |\n",
            "|        89.0        | 3.1422516216042644 |  0.000658968014471295  |  0.020975603368511644  |\n",
            "|        90.0        | 3.1319387041393782 |  0.009653949450414867  |   0.3072947550785625   |\n",
            "|        1.0         | 3.1476329512565187 |  0.006040297666725536  |  0.19226864628116216   |\n",
            "|        2.0         | 3.1456420981964186 |  0.004049444606625485  |  0.12889782518425233   |\n",
            "|        3.0         | 3.140592791754689  | 0.0009998618351039212  |  0.03182659069314453   |\n",
            "|        4.0         |  3.13719173090937  |  0.004400922680423136  |  0.14008571975091516   |\n",
            "|        5.0         | 3.141120588385515  | 0.00047206520427822696 |  0.015026302144513032  |\n",
            "|        6.0         |  3.15699902897736  |  0.01540637538756684   |  0.49040015961211547   |\n",
            "|        7.0         |  3.13959031752304  |  0.002002336066753152  |  0.06373633655098948   |\n",
            "|        8.0         | 3.1417168230175943 | 0.00012416942780113516 |  0.003952435643088574  |\n",
            "|        9.0         | 3.1440446745034043 |  0.002452020913611186  |   0.0780502497931851   |\n",
            "|        10.0        | 3.1457699874798783 |  0.004177333890085233  |   0.1329686675104722   |\n",
            "|        11.0        | 3.1423981080421064 | 0.0008054544523132812  |   0.0256384115042068   |\n",
            "|        12.0        | 3.1467110741049127 | 0.0051184205151195705  |  0.16292438516084898   |\n",
            "|        13.0        | 3.1400783709128284 |  0.00151428267696474   |  0.04820111465547322   |\n",
            "|        14.0        |  3.14647554459515  |  0.004882891005356882  |  0.15542724801630045   |\n",
            "|        15.0        | 3.1358213911666284 |  0.005771262423164725  |  0.18370498850543515   |\n",
            "|        16.0        | 3.1430185675030176 | 0.0014259139132244414  |  0.04538824954263555   |\n",
            "|        17.0        | 3.135175179778008  |  0.006417473811785257  |  0.20427453586168223   |\n",
            "|        18.0        | 3.142688981012334  | 0.0010963274225410657  |  0.03489718570892152   |\n",
            "|        19.0        | 3.139069444801274  | 0.0025232087885189713  |  0.08031623022914142   |\n",
            "|        20.0        | 3.145673323092678  |  0.004080669502884771  |  0.12989174450169172   |\n",
            "|        21.0        | 3.1310025219873774 |  0.010590131602415731  |   0.3370943585036316   |\n",
            "|        22.0        | 3.1407502734701755 | 0.0008423801196175695  |  0.026813791999895652  |\n",
            "|        23.0        | 3.140933778718413  | 0.0006588748713802772  |  0.020972638531841576  |\n",
            "|        24.0        | 3.140784188715333  | 0.0008084648744599576  |  0.025734236217294173  |\n",
            "|        25.0        | 3.141788367922111  | 0.0001957143323179089  | 0.0062297806844650175  |\n",
            "|        26.0        | 3.140838412102163  | 0.0007542414876300363  |  0.024008252208260982  |\n",
            "|        27.0        | 3.1415118480986743 | 8.080549111877389e-05  | 0.0025721186681042225  |\n",
            "|        28.0        | 3.1467280752254845 |  0.005135421635691362  |  0.16346554763626936   |\n",
            "|        29.0        | 3.1450892569854694 | 0.0034966033956762566  |  0.11130034289075652   |\n",
            "|        30.0        | 3.1350267895000576 |  0.006565864089735474  |   0.2089979451101937   |\n",
            "|        31.0        | 3.1458530472366437 |  0.004260393646850602  |  0.13561254168271603   |\n",
            "|        32.0        | 3.134729234475512  |  0.006863419114281122  |  0.21846941570984774   |\n",
            "|        33.0        | 3.1362036076864976 |  0.005389045903295475  |  0.17153865881172062   |\n",
            "|        34.0        | 3.1425537087507838 | 0.0009610551609906537  |  0.03059133589112796   |\n",
            "|        35.0        | 3.1344086021505375 |  0.007184051439255601  |   0.2286754595967948   |\n",
            "|        36.0        | 3.130901699075672  |  0.010690954514120943  |   0.3403036514585921   |\n",
            "|        37.0        | 3.1386571315057608 | 0.0029355220840323604  |  0.09344057004583446   |\n",
            "|        38.0        | 3.145114813723858  | 0.0035221601340649222  |  0.11211383913952903   |\n",
            "|        39.0        | 3.1421470539503704 | 0.0005544003605773007  |  0.01764711156756131   |\n",
            "|        40.0        | 3.146055286391734  | 0.0044626328019408135  |  0.14205001392658312   |\n",
            "|        41.0        | 3.1526561475251724 |  0.01106349393537931   |  0.35216194953656466   |\n",
            "|        42.0        | 3.1473249333968263 |  0.005732279807033169  |  0.18246413329503697   |\n",
            "|        43.0        | 3.1396893534334955 | 0.0019033001562975826  |  0.060583925612467456  |\n",
            "|        44.0        | 3.1504673016358744 |  0.00887464804608129   |  0.28248882094693356   |\n",
            "|        45.0        | 3.1427144558411126 | 0.0011218022513195258  |  0.035708074693823845  |\n",
            "|        46.0        | 3.1549769004619908 |  0.013384246872197636  |   0.4260338098544986   |\n",
            "|        47.0        | 3.143904439659592  | 0.0023117860697987602  |  0.07358643607589162   |\n",
            "|        48.0        | 3.1417882815592244 | 0.00019562796943128902 |  0.006227031668403969  |\n",
            "|        49.0        | 3.1370550576184377 |  0.00453759597135539   |  0.14443616571901613   |\n",
            "|        50.0        | 3.136813207958642  |  0.00477944563115118   |  0.15213447948733477   |\n",
            "|        51.0        | 3.1384773135093567 | 0.0031153400804364573  |  0.09916435464275301   |\n",
            "|        52.0        | 3.148292084270812  |  0.006699430681019081  |  0.21324950175713786   |\n",
            "|        53.0        | 3.1456439927293207 |  0.004051339139527599  |  0.12895813003949663   |\n",
            "|        54.0        | 3.1402189921239674 | 0.0013736614658257373  |  0.04372500248420495   |\n",
            "|        55.0        | 3.1473586654309544 |  0.005766011841161323  |   0.183537857289445    |\n",
            "|        56.0        | 3.1477084590012447 |  0.006115805411451625  |  0.19467213244413778   |\n",
            "|        57.0        | 3.1329386643480928 |  0.008653989241700355  |   0.2754650330561389   |\n",
            "|        58.0        | 3.1431555351775433 | 0.0015628815877501978  |  0.049748066031550756  |\n",
            "|        59.0        | 3.1517152252632656 |  0.010122571673472525  |   0.3222114637270303   |\n",
            "|        60.0        | 3.1394756707652545 |  0.002116982824538649  |  0.06738565619319371   |\n",
            "|        61.0        | 3.1472773846661086 |  0.005684731076315508  |  0.18095061018874473   |\n",
            "|        62.0        | 3.1411341433747744 | 0.0004585102150187481  |  0.01459483343567231   |\n",
            "|        63.0        | 3.1539490609724723 |  0.01235640738267918   |   0.3933166627621161   |\n",
            "|        64.0        |  3.13386614668189  |  0.007726506907903108  |  0.24594235344529108   |\n",
            "|        65.0        | 3.146843444076158  |  0.005250790486365009  |  0.16713785220897767   |\n",
            "|        66.0        | 3.142900848991663  | 0.0013081954018701047  |  0.041641152947543135  |\n",
            "|        67.0        | 3.144001740666317  | 0.0024090870765238748  |  0.07668362331351557   |\n",
            "|        68.0        | 3.133107286801868  |  0.00848536678792522   |   0.2700976136492194   |\n",
            "|        69.0        | 3.1381769126999424 |  0.003415740889850749  |  0.10872640938817117   |\n",
            "|        70.0        | 3.131146489461328  |  0.010446164128464996  |   0.332511731478889    |\n",
            "|        71.0        | 3.1445293989574927 | 0.0029367453676996114  |  0.09347950837432378   |\n",
            "|        72.0        | 3.1535438127777633 |  0.011951159187970184  |   0.3804172120887153   |\n",
            "|        73.0        | 3.139797693447587  | 0.0017949601422060368  |  0.05713535585700443   |\n",
            "|        74.0        | 3.133791902035379  |  0.007800751554414287  |   0.248305633943364    |\n",
            "|        75.0        | 3.1385143794535293 | 0.0030782741362638255  |  0.09798450899566448   |\n",
            "|        76.0        | 3.1473378310908555 |  0.005745177501062404  |   0.1828746796468849   |\n",
            "|        77.0        | 3.1377650312403973 | 0.0038276223493958206  |  0.12183700343907171   |\n",
            "|        78.0        | 3.139078094848628  | 0.0025145587411650183  |  0.08004089067026929   |\n",
            "|        79.0        | 3.1408396849269673 | 0.0007529686628258148  |  0.023967736936404613  |\n",
            "|        80.0        | 3.132128813385078  |  0.009463840204714913  |  0.30124338984243865   |\n",
            "|        81.0        | 3.141386463161669  | 0.00020619042812430877 |  0.00656324517084358   |\n",
            "|        82.0        | 3.133796420667511  |  0.007796232922282265  |  0.24816180141539892   |\n",
            "|        83.0        | 3.1517658697020696 |  0.010173216112276506  |   0.323823526282184    |\n",
            "|        84.0        | 3.1478932692061705 |  0.006300615616377403  |  0.20055482397369054   |\n",
            "|        85.0        | 3.143395114703283  |  0.001802461113489695  |  0.05737411918856134   |\n",
            "|        86.0        | 3.1465860624767856 |  0.004993408886992512  |  0.15894514144877153   |\n",
            "|        87.0        | 3.1401998285721597 | 0.0013928250176333812  |  0.04433499728368178   |\n",
            "|        88.0        | 3.1430663121797093 | 0.0014736585899162158  |  0.04690800980299961   |\n",
            "|        89.0        | 3.1418353351364514 | 0.00024268154665829655 |  0.007724793549570866  |\n",
            "|        90.0        | 3.143315494295358  |  0.001722840705564721  |   0.0548397228901108   |\n",
            "|        1.0         | 3.1400483728548942 |  0.001544280734898873  |  0.04915598249614809   |\n",
            "|        2.0         | 3.1366806136680614 |  0.004912039921731726  |  0.15635508684166616   |\n",
            "|        3.0         | 3.139356438179341  |  0.002236215410452047  |   0.071180947278343    |\n",
            "|        4.0         | 3.1396596768032077 | 0.0019329767865854386  |  0.06152856209339204   |\n",
            "|        5.0         | 3.1397096209253714 |  0.001883032664421691  |  0.05993879130924285   |\n",
            "|        6.0         | 3.143067665479553  | 0.0014750118897599584  |  0.046951086674923044  |\n",
            "|        7.0         | 3.1438505998572315 |  0.00225794626743836   |  0.07187266193974194   |\n",
            "|        8.0         | 3.143087918042676  | 0.0014952644528829495  |  0.047595745781183973  |\n",
            "|        9.0         | 3.1402621333559178 | 0.0013305202338753652  |  0.042351774421009805  |\n",
            "|        10.0        | 3.1372215635714635 |  0.004371090018329582  |  0.13913611662335926   |\n",
            "|        11.0        | 3.143920252158983  | 0.0023275985691899592  |   0.074089763564041    |\n",
            "|        12.0        | 3.1396436360475253 |  0.001949017542267839  |  0.06203915520494872   |\n",
            "|        13.0        | 3.145097718454295  |  0.003505064864501861  |  0.11156967980863912   |\n",
            "|        14.0        | 3.143929616962538  | 0.0023369633727448047  |  0.07438785451940864   |\n",
            "|        15.0        | 3.142446470800741  | 0.0008538172109480158  |  0.027177845923862447  |\n",
            "|        16.0        | 3.141646155260282  |  5.3501670489009e-05   | 0.0017030110643999129  |\n",
            "|        17.0        | 3.141096244967621  | 0.0004964086221721509  |  0.015801177202426967  |\n",
            "|        18.0        | 3.136670380187725  |  0.004922273402068189  |  0.15668082863778254   |\n",
            "|        19.0        | 3.143635707688017  | 0.0020430540982236955  |  0.06503243174729116   |\n",
            "|        20.0        |  3.14384170969886  | 0.0022490561090666716  |  0.07158967940979713   |\n",
            "|        21.0        | 3.1412971525044515 | 0.00029550108534159847 |  0.009406091684227083  |\n",
            "|        22.0        | 3.1424663369868426 | 0.0008736833970495184  |  0.02781020626754998   |\n",
            "|        23.0        | 3.144005354903404  |  0.002412701313610821  |  0.07679866805309427   |\n",
            "|        24.0        | 3.1400668485151177 | 0.0015258050746753682  |  0.04856788396585667   |\n",
            "|        25.0        | 3.141224807830814  | 0.00036784575897907956 |  0.011708894167382092  |\n",
            "|        26.0        | 3.144202876267208  |  0.002610222677414953  |   0.0830859683362303   |\n",
            "|        27.0        | 3.1421127075113526 | 0.0005200539215595157  |  0.016553830458104345  |\n",
            "|        28.0        | 3.1484266949946242 |  0.006834041404831126  |  0.21753429417471085   |\n",
            "|        29.0        | 3.143535973057691  | 0.0019433194678977195  |  0.06185777986452678   |\n",
            "|        30.0        | 3.137174488321849  |  0.00441816526794403   |   0.1406345683580441   |\n",
            "|        31.0        | 3.143880196669115  | 0.0022875430793218854  |  0.07281475772194673   |\n",
            "|        32.0        |  3.13978252295904  | 0.0018101306307531928  |  0.05761824750528421   |\n",
            "|        33.0        |  3.14307172939539  | 0.0014790758055966968  |  0.04708044513366831   |\n",
            "|        34.0        | 3.1474493719559087 |  0.005856718366115565  |  0.18642513565287622   |\n",
            "|        35.0        | 3.1448931116389547 |  0.00330045804916157   |  0.10505684259829952   |\n",
            "|        36.0        | 3.145314427736156  | 0.0037217741463630993  |  0.11846775049306128   |\n",
            "|        37.0        | 3.1403957209126196 | 0.0011969326771734679  |  0.038099550424074646  |\n",
            "|        38.0        | 3.141715551375838  | 0.0001228977860447955  |  0.003911958028815872  |\n",
            "|        39.0        | 3.139977305950543  | 0.0016153476392499755  |  0.05141811231969146   |\n",
            "|        40.0        | 3.146590862611968  |  0.004998209022174738  |  0.15909793449712364   |\n",
            "|        41.0        | 3.1434695422643286 |  0.001876888674535504  |   0.0597432220371042   |\n",
            "|        42.0        | 3.1399176533245936 |  0.001675000265199511  |  0.053316914377347556  |\n",
            "|        43.0        | 3.1404461722457406 | 0.0011464813440524857  |  0.03649363461371861   |\n",
            "|        44.0        | 3.1437853237616507 |  0.002192670171857536  |   0.0697948592842565   |\n",
            "|        45.0        | 3.142099760478736  | 0.0005071068889428432  |  0.01614171361024126   |\n",
            "|        46.0        | 3.143985880517988  | 0.0023932269281949914  |  0.07617877911257306   |\n",
            "|        47.0        | 3.1415878475207797 | 4.806069013429948e-06  | 0.00015298192806563297 |\n",
            "|        48.0        | 3.140320060954251  | 0.0012725926355421358  |  0.04050788169777474   |\n",
            "|        49.0        | 3.140892222782039  |  0.00070043080775406   |  0.02229540506958154   |\n",
            "|        50.0        | 3.139365154105207  |  0.002227499484586115  |  0.07090351074130587   |\n",
            "|        51.0        | 3.1456458879182065 |  0.004053234328413424  |  0.12901845577535104   |\n",
            "|        52.0        | 3.139113548033845  | 0.0024791055559481023  |  0.07891238073514437   |\n",
            "|        53.0        | 3.143740458994522  | 0.0021478054047290307  |  0.06836676939242282   |\n",
            "|        54.0        | 3.1375402795438645 | 0.0040523740459286195  |   0.1289910721333686   |\n",
            "|        55.0        |  3.14098357298988  | 0.0006090805999132698  |  0.019387637643514786  |\n",
            "|        56.0        | 3.138940321709089  | 0.0026523318807041285  |  0.08442634590685706   |\n",
            "|        57.0        |  3.14390462287604  | 0.0023119692862469954  |   0.0735922680365701   |\n",
            "|        58.0        | 3.1409876903914316 | 0.0006049631983615633  |  0.01925657668158512   |\n",
            "|        59.0        | 3.1429940033863835 | 0.0014013497965903454  |  0.04460634942563511   |\n",
            "|        60.0        | 3.1379570616033714 | 0.0036355919864217157  |  0.11572448714085978   |\n",
            "|        61.0        | 3.145245939466971  | 0.0036532858771778365  |   0.1162877011761327   |\n",
            "|        62.0        | 3.140458972750893  | 0.0011336808389001973  |  0.036086181879906615  |\n",
            "|        63.0        | 3.1434959047005466 | 0.0019032511107535122  |  0.060582364444312364  |\n",
            "|        64.0        | 3.1415785399761122 | 1.4113613680866166e-05 |  0.00044925027643985   |\n",
            "|        65.0        | 3.141484511265925  | 0.0001081423238682433  | 0.0034422770802151155  |\n",
            "|        66.0        | 3.1376555833399453 |  0.003937070249847796  |  0.12532083831266402   |\n",
            "|        67.0        | 3.1419975102058553 | 0.00040485661606215473 |  0.012886986337949912  |\n",
            "|        68.0        | 3.141775368630304  | 0.0001827150405109279  |  0.005816000374910016  |\n",
            "|        69.0        | 3.1387691724275766 | 0.0028234811622165346  |  0.08987419673872221   |\n",
            "|        70.0        | 3.142835199606777  | 0.0012425460169840363  |  0.039551468124431104  |\n",
            "|        71.0        | 3.1443364306377584 | 0.0027437770479652457  |  0.08733713598515146   |\n",
            "|        72.0        | 3.143946284581345  |  0.002353630991551725  |   0.0749184013039472   |\n",
            "|        73.0        |  3.13973718683478  | 0.0018554667550132464  |  0.05906134116060739   |\n",
            "|        74.0        | 3.144391261659303  | 0.0027986080695097115  |  0.08908246160786744   |\n",
            "|        75.0        | 3.1394261336833686 |  0.002166519906424469  |  0.06896247048288895   |\n",
            "|        76.0        | 3.1416870911884245 | 9.443759863136947e-05  |  0.003006042127182172  |\n",
            "|        77.0        | 3.139420479135456  |  0.002172174454337039  |  0.06914246033313606   |\n",
            "|        78.0        | 3.1442310898183936 | 0.0026384362286004936  |  0.08398403356290131   |\n",
            "|        79.0        | 3.1402047277981002 |  0.00138792579169289   |  0.04417905007853114   |\n",
            "|        80.0        | 3.144790451036996  | 0.0031977974472030546  |  0.10178905414580208   |\n",
            "|        81.0        | 3.141232765653812  | 0.0003598879359811846  |  0.011455588794109023  |\n",
            "|        82.0        | 3.1419385038022667 | 0.00034585021247357517 |  0.011008754176910353  |\n",
            "|        83.0        | 3.1413343568093137 | 0.00025829678047939453 |  0.008221841879603564  |\n",
            "|        84.0        | 3.1404007062255315 | 0.0011919473642616474  |  0.03794086298551943   |\n",
            "|        85.0        | 3.1447395016464244 |  0.003146848056631235  |  0.10016728467439713   |\n",
            "|        86.0        | 3.1447225189089782 | 0.0031298653191851322  |  0.09962670735204132   |\n",
            "|        87.0        | 3.1441132515814685 | 0.0025205979916753662  |  0.08023312598452773   |\n",
            "|        88.0        | 3.138040506926387  |  0.003552146663406308  |  0.11306834001369938   |\n",
            "|        89.0        | 3.143365623695646  | 0.0017729701058528669  |  0.056435391260128934  |\n",
            "|        90.0        | 3.1427458896705076 | 0.0011532360807144926  |   0.0367086445595271   |\n",
            "|        1.0         | 3.1426849654529767 | 0.0010923118631835926  |  0.03476936648471737   |\n",
            "|        2.0         | 3.1431284767397787 | 0.0015358231499855712  |   0.0488867692070338   |\n",
            "|        3.0         | 3.1398602934827977 | 0.0017323601069954009  |  0.05514273484870455   |\n",
            "|        4.0         | 3.1417752610023535 | 0.00018260741256037605 |  0.005812574470840981  |\n",
            "|        5.0         | 3.1404839403058404 |  0.001108713283952678  |  0.035291439922543376  |\n",
            "|        6.0         | 3.140793695693206  | 0.0007989578965870159  |  0.02543161971282538   |\n",
            "|        7.0         | 3.140458241853848  | 0.0011344117359453065  |   0.0361094470554307   |\n",
            "|        8.0         | 3.1404429632146873 | 0.0011496903751058163  |   0.0365957812446532   |\n",
            "|        9.0         | 3.1423950075345606 | 0.0008023539447674644  |  0.025539719283804704  |\n",
            "|        10.0        | 3.1410453369342104 | 0.0005473166555827191  |  0.017421630234502827  |\n",
            "|        11.0        | 3.1435902556421778 | 0.0019976020523846394  |  0.06358564819350612   |\n",
            "|        12.0        | 3.1416075213503807 | 1.4867760587566892e-05 | 0.00047325551804362656 |\n",
            "|        13.0        | 3.1412518558796987 | 0.0003407977100944315  |  0.010847928031185498  |\n",
            "|        14.0        | 3.140302532563097  | 0.0012901210266962337  |  0.04106582771709934   |\n",
            "|        15.0        | 3.141029471349232  | 0.0005631822405609554  |  0.017926647489368997  |\n",
            "|        16.0        | 3.140409348857125  | 0.0011833047326681445  |  0.037665759477633794  |\n",
            "|        17.0        | 3.1422356843133934 | 0.0006430307236002797  |  0.020468303644188557  |\n",
            "|        18.0        | 3.1415232393695516 |  6.94142202415371e-05  | 0.0022095232544620254  |\n",
            "|        19.0        | 3.1406860687706657 | 0.0009065848191274206  |  0.02885749105924017   |\n",
            "|        20.0        | 3.1417290169717984 | 0.00013636338200528897 |  0.00434058126057403   |\n",
            "|        21.0        | 3.140208919014171  | 0.0013837345756222419  |  0.044045639527489175  |\n",
            "|        22.0        |  3.14190532684293  | 0.0003126732531368859  |  0.009952698761871772  |\n",
            "|        23.0        | 3.1422691008184986 | 0.0006764472287055234  |  0.02153198403785958   |\n",
            "|        24.0        | 3.144918422015438  |  0.003325768425644693  |  0.10586249690406069   |\n",
            "|        25.0        | 3.140032508671112  | 0.0015601449186810612  |  0.049660955149558796  |\n",
            "|        26.0        | 3.1413991439409212 | 0.00019350964887188127 |  0.006159603430787383  |\n",
            "|        27.0        | 3.1420393918975655 | 0.00044673830777242074 |  0.01422012199009785   |\n",
            "|        28.0        | 3.1398899615479015 | 0.0017026920418916625  |  0.05419837100605812   |\n",
            "|        29.0        | 3.1405622531012956 | 0.0010304004884975626  |  0.032798666221738146  |\n",
            "|        30.0        | 3.140431671324837  | 0.0011609822649560897  |  0.03695521326195724   |\n",
            "|        31.0        | 3.142068989531694  |  0.000476335941900885  |  0.015162243945171945  |\n",
            "|        32.0        | 3.1413190101794557 | 0.0002736434103374208  |  0.008710340279944873  |\n",
            "|        33.0        | 3.139219772226684  | 0.0023728813631089807  |  0.07553115966188578   |\n",
            "|        34.0        | 3.139784924221873  | 0.0018077293679201745  |  0.057541812935376656  |\n",
            "|        35.0        | 3.1421500825843176 | 0.0005574289945244892  |  0.017743515980263507  |\n",
            "|        36.0        | 3.141011193442234  | 0.0005814601475591097  |  0.018508451338995032  |\n",
            "|        37.0        | 3.1416430870355785 | 5.043344578536946e-05  | 0.0016053464387797332  |\n",
            "|        38.0        | 3.1414373394248143 | 0.00015531416497882589 |  0.004943803417714056  |\n",
            "|        39.0        | 3.142136536133739  | 0.0005438825439458839  |  0.017312319066076484  |\n",
            "|        40.0        | 3.1441054649400306 | 0.0025128113502375093  |   0.0799852694895439   |\n",
            "|        41.0        | 3.1421977122718685 | 0.0006050586820753878  |  0.019259616022593108  |\n",
            "|        42.0        | 3.141449859008517  | 0.0001427945812761955  |  0.004545292691368783  |\n",
            "|        43.0        | 3.138976090630217  | 0.0026165629595760187  |   0.0832877857855365   |\n",
            "|        44.0        | 3.143119820207389  | 0.0015271666175959986  |  0.04861122322306669   |\n",
            "|        45.0        | 3.1409795699536494 |  0.000613083636143763  |  0.019515058244206573  |\n",
            "|        46.0        | 3.1433575355375596 | 0.0017648819477664723  |  0.05617793719213726   |\n",
            "|        47.0        | 3.1409259237434175 | 0.0006667298463756133  |  0.02122267015151577   |\n",
            "|        48.0        | 3.142575702859075  | 0.0009830492692817216  |  0.031291430101812345  |\n",
            "|        49.0        |  3.14010146156624  | 0.0014911920235531895  |  0.047466116329539225  |\n",
            "|        50.0        | 3.1429049622245278 |  0.001312308634734638  |  0.041772081216038835  |\n",
            "|        51.0        | 3.1448212756832885 | 0.0032286220934953924  |  0.10277023311109904   |\n",
            "|        52.0        | 3.139879425412011  |  0.001713228177782078  |  0.054533746627667634  |\n",
            "|        53.0        | 3.141928551720325  | 0.00033589813053191975 |  0.010691969569896344  |\n",
            "|        54.0        | 3.1433857833193364 | 0.0017931297295432636  |  0.05707709201236876   |\n",
            "|        55.0        | 3.141632939177953  | 4.028558816004946e-05  | 0.0012823300982072409  |\n",
            "|        56.0        | 3.141901105522292  | 0.0003084519324989543  |  0.009818329952691243  |\n",
            "|        57.0        | 3.140789091059622  | 0.0008035625301712201  |  0.025578189752035997  |\n",
            "|        58.0        | 3.142246415118092  | 0.0006537615282988796  |  0.020809875766415742  |\n",
            "|        59.0        | 3.1424403495732847 | 0.0008476959834915476  |  0.026983001202365103  |\n",
            "|        60.0        | 3.1417488020290216 | 0.00015614843922850952 |  0.004970359191860342  |\n",
            "|        61.0        | 3.142198178434059  | 0.0006055248442660144  |  0.019274454425977264  |\n",
            "|        62.0        | 3.1435579178059747 | 0.0019652642161815947  |  0.06255630289738401   |\n",
            "|        63.0        | 3.142597396883443  | 0.0010047432936497458  |  0.031981972344557755  |\n",
            "|        64.0        | 3.140692271655569  | 0.0009003819342239261  |  0.02866004710047592   |\n",
            "|        65.0        | 3.1426717532661765 | 0.0010790996763834215  |  0.034348809517057224  |\n",
            "|        66.0        | 3.1406815638738883 | 0.0009110897159048115  |  0.029000886377288274  |\n",
            "|        67.0        | 3.140810150693559  | 0.0007825028962340497  |  0.024907840783874695  |\n",
            "|        68.0        | 3.144102042611875  |  0.002509389022081976  |  0.07987633340097675   |\n",
            "|        69.0        | 3.142689093713015  | 0.0010964401232218535  |  0.034900773083008964  |\n",
            "|        70.0        | 3.143160955041504  | 0.0015683014517109406  |  0.049920585659598324  |\n",
            "|        71.0        | 3.1397751455777745 | 0.0018175080120186315  |  0.05785307684437783   |\n",
            "|        72.0        |  3.13927789559489  |  0.002314757994902994  |  0.07368103539005916   |\n",
            "|        73.0        | 3.141201016847619  | 0.00039163674217412847 |  0.012466184682683741  |\n",
            "|        74.0        | 3.142131472316694  | 0.0005388187269010913  |  0.017151132763358137  |\n",
            "|        75.0        | 3.1423304019811074 | 0.0007377483913142413  |  0.02348326064715108   |\n",
            "|        76.0        | 3.1417412215837444 | 0.00014856799395124298 | 0.0047290661245174255  |\n",
            "|        77.0        | 3.139739452574701  | 0.0018532010150922318  |  0.05898922041896937   |\n",
            "|        78.0        | 3.1446670290533825 | 0.0030743754635893517  |  0.09786041039013653   |\n",
            "|        79.0        | 3.1415833079955733 | 9.345594219833941e-06  | 0.0002974795032435234  |\n",
            "|        80.0        | 3.1456927636668404 |  0.004100110077047248  |  0.13051055719659227   |\n",
            "|        81.0        | 3.141521761803194  | 7.089178659924755e-05  | 0.0022565556523772064  |\n",
            "|        82.0        | 3.1404907454663165 | 0.0011019081234766581  |  0.03507482493688494   |\n",
            "|        83.0        | 3.145546155161451  |  0.00395350157165808   |   0.1258438635301921   |\n",
            "|        84.0        | 3.142445220470698  | 0.0008525668809049591  |  0.027138046682492697  |\n",
            "|        85.0        | 3.1397337693097054 | 0.0018588842800877359  |  0.059170124362356497  |\n",
            "|        86.0        |  3.14280494152171  | 0.0012122879319167446  |  0.03858832336304019   |\n",
            "|        87.0        | 3.141247985442936  | 0.0003446681468570745  |  0.010971127859725344  |\n",
            "|        88.0        | 3.139405507082246  |  0.002187146507547144  |  0.06961903558846068   |\n",
            "|        89.0        | 3.141411063523823  | 0.00018159006597029403 |  0.005780191323111134  |\n",
            "|        90.0        | 3.1435409687650737 | 0.0019483151752806016  |  0.06201679816937205   |\n",
            "|        1.0         | 3.1413544493930377 | 0.0002382041967554649  | 0.0075822750757733315  |\n",
            "|        2.0         | 3.141408228538154  |  0.000184425051639181  |  0.005870431719670742  |\n",
            "|        3.0         | 3.142349189449276  | 0.0007565358594829874  |  0.024081284332598596  |\n",
            "|        4.0         | 3.141952956685854  | 0.0003603030960608322  |  0.01146880374987909   |\n",
            "|        5.0         | 3.141569244422434  | 2.3409167359211835e-05 | 0.0007451369397768027  |\n",
            "|        6.0         | 3.1419434799086337 | 0.00035082631884053583 |  0.011167148562040922  |\n",
            "|        7.0         | 3.1423694892234955 | 0.0007768356337023441  |  0.024727446214730605  |\n",
            "|        8.0         | 3.1416388553713346 | 4.620178154146615e-05  |  0.001470648382395245  |\n",
            "|        9.0         | 3.1417277994619788 | 0.0001351458721856602  |  0.004301826719362663  |\n",
            "|        10.0        | 3.142794503274249  | 0.0012018496844556914  |  0.03825606362691159   |\n",
            "|        11.0        | 3.1419101664311655 | 0.00031751284137238045 |  0.010106747639913441  |\n",
            "|        12.0        | 3.1408177882471153 | 0.0007748653426777885  |  0.024664729903553083  |\n",
            "|        13.0        | 3.1413453428179414 | 0.00024731077185169426 |  0.007872146364013823  |\n",
            "|        14.0        | 3.1410579737155238 | 0.0005346798742693615  |  0.017019388992344394  |\n",
            "|        15.0        | 3.141260202643131  | 0.0003324509466620107  |  0.010582242299367809  |\n",
            "|        16.0        | 3.1424012190738355 | 0.0008085654840424006  |  0.025737438719767816  |\n",
            "|        17.0        | 3.1418660006496695 |  0.000273347059876361  |  0.008700907151791828  |\n",
            "|        18.0        | 3.1414504365564575 | 0.00014221703333561209 |  0.004526908769445504  |\n",
            "|        19.0        | 3.1409947304660615 | 0.0005979231237316363  |  0.019032484146167372  |\n",
            "|        20.0        | 3.1428803637764653 | 0.0012877101866721752  |   0.0409890882957328   |\n",
            "|        21.0        | 3.1423710768398405 | 0.0007784232500473998  |  0.024777981612540422  |\n",
            "|        22.0        | 3.142270066644737  | 0.0006774130549440649  |  0.021562727241865923  |\n",
            "|        23.0        | 3.1409738827175673 | 0.0006187708722258378  |  0.019696088591205134  |\n",
            "|        24.0        | 3.1424603568878786 | 0.0008677032980854982  |  0.02761985380548947   |\n",
            "|        25.0        | 3.1414760074807524 | 0.00011664610904071893 |  0.003712960969253328  |\n",
            "|        26.0        | 3.141544338621695  | 4.831496809831748e-05  | 0.0015379131996348916  |\n",
            "|        27.0        | 3.141103638449217  | 0.0004890151405763099  |  0.015565835373899559  |\n",
            "|        28.0        | 3.141274827022514  | 0.0003178265672789671  |  0.01011673384567529   |\n",
            "|        29.0        | 3.1413193092883387 | 0.0002733443014544257  |   0.0087008193484946   |\n",
            "|        30.0        | 3.1416578632326906 |  6.5209642897468e-05   | 0.0020756874008778675  |\n",
            "|        31.0        |  3.14207434138472  |  0.000481687794926966  |  0.015332598717932366  |\n",
            "|        32.0        | 3.1414130919763785 | 0.00017956161341459165 |  0.005715623672897649  |\n",
            "|        33.0        | 3.142138832344078  | 0.0005461787542850871  |  0.017385409711249068  |\n",
            "|        34.0        | 3.1421983754304854 | 0.0006057218406922971  |  0.019280725016980128  |\n",
            "|        35.0        | 3.1416634046190595 | 7.075102926634713e-05  | 0.0022520752073157003  |\n",
            "|        36.0        | 3.1419507972708063 | 0.00035814368101316774 |  0.011400067434074526  |\n",
            "|        37.0        | 3.1415751609742895 | 1.7492615503655884e-05 | 0.0005568072450025516  |\n",
            "|        38.0        | 3.1411287260907947 | 0.00046392749899837327 |  0.014767270940370286  |\n",
            "|        39.0        | 3.141667896932839  | 7.524334304598312e-05  | 0.0023950699961054803  |\n",
            "|        40.0        | 3.1404669300547052 | 0.0011257235350878858  |  0.03583289303282394   |\n",
            "|        41.0        | 3.1404408342947647 |  0.001151819295028389  |  0.036663546870478056  |\n",
            "|        42.0        | 3.1422444035558414 | 0.0006517499660483317  |  0.02074584575131339   |\n",
            "|        43.0        | 3.141223197044783  | 0.0003694565450103049  |  0.01176016707920867   |\n",
            "|        44.0        | 3.140903628015061  | 0.0006890255747320495  |  0.021932365227067964  |\n",
            "|        45.0        | 3.141617552681156  | 2.4899091362939885e-05 |  0.00079256269378172   |\n",
            "|        46.0        | 3.141760568609227  | 0.00016791501943380638 |  0.005344901072452391  |\n",
            "|        47.0        |  3.14188431828626  |  0.00029166469646702   |   0.0092839756336247   |\n",
            "|        48.0        | 3.142093764752387  | 0.0005011111625941034  |  0.015950863713075608  |\n",
            "|        49.0        | 3.141357433355062  | 0.00023522023473132592 | 0.0074872926145452875  |\n",
            "|        50.0        | 3.1416447379743215 | 5.2084384528416905e-05 | 0.0016578974511193172  |\n",
            "|        51.0        | 3.1425460171199373 | 0.0009533635301441912  |  0.03034650367719744   |\n",
            "|        52.0        | 3.1417434801324013 | 0.00015082654260822892 | 0.0048009579611120005  |\n",
            "|        53.0        | 3.140842729562179  | 0.0007499240276143126  |  0.023870823187640174  |\n",
            "|        54.0        | 3.1407409834554825 | 0.0008516701343106448  |  0.027109502351855507  |\n",
            "|        55.0        |  3.14198531995567  | 0.0003926663658768703  |  0.01249895862304693   |\n",
            "|        56.0        | 3.141853999315166  | 0.0002613457253728413  |  0.008318892809804933  |\n",
            "|        57.0        | 3.1409848306217087 | 0.0006078229680843705  |  0.01934760597908298   |\n",
            "|        58.0        | 3.1421409757153262 | 0.0005483221255331294  |  0.01745363533705046   |\n",
            "|        59.0        | 3.1417428797020257 | 0.00015022611223258764 |  0.004781845668658834  |\n",
            "|        60.0        | 3.1421945446844175 |  0.000601891094624385  |  0.019158788582492516  |\n",
            "|        61.0        | 3.1415591698118854 | 3.348377790768353e-05  |  0.001065821753479807  |\n",
            "|        62.0        | 3.141318560446135  | 0.00027409314365822723 |  0.008724655736160769  |\n",
            "|        63.0        | 3.1411079829434603 | 0.00048467064633284096 |  0.015427545827083087  |\n",
            "|        64.0        | 3.1410172150545455 | 0.0005754385352476632  |  0.018316777466045087  |\n",
            "|        65.0        | 3.141844903318632  | 0.00025224972883908237 |  0.008029358247666037  |\n",
            "|        66.0        | 3.1408883407111143 | 0.0007043128786787811  |  0.022418975225002077  |\n",
            "|        67.0        | 3.141482019838061  | 0.00011063375173225865 | 0.0035215816921981002  |\n",
            "|        68.0        | 3.141861261000365  | 0.00026860741057177506 |  0.008550039428722445  |\n",
            "|        69.0        | 3.141875571380455  | 0.0002829177906620295  |  0.009005552974500012  |\n",
            "|        70.0        | 3.1412275882489413 |  0.000365065340851789  |  0.01162039070961797   |\n",
            "|        71.0        | 3.1421374179366515 | 0.0005447643468583507  |  0.01734038772454687   |\n",
            "|        72.0        | 3.1411906964093634 | 0.00040195718042967954 |  0.01279469443533287   |\n",
            "|        73.0        | 3.1419572023823537 | 0.0003645487925605373  |  0.011603948466838295  |\n",
            "|        74.0        | 3.1415081275130885 | 8.452607670461632e-05  |  0.002690548585540878  |\n",
            "|        75.0        | 3.1417820917195476 | 0.00018943812975447116 |  0.006030002952101589  |\n",
            "|        76.0        | 3.142101194541137  | 0.0005085409513436723  |  0.016187361234200094  |\n",
            "|        77.0        | 3.1426044639328503 | 0.0010118103430571956  |  0.03220692351381182   |\n",
            "|        78.0        | 3.140990738310175  | 0.0006019152796179483  |  0.01915955841474737   |\n",
            "|        79.0        | 3.141798230184177  | 0.0002055765943840271  |  0.006543706236043096  |\n",
            "|        80.0        | 3.1418919354777604 | 0.00029928188796723987 |  0.009526438369572211  |\n",
            "|        81.0        | 3.142033991777646  | 0.0004413381878527467  |  0.014048230834396824  |\n",
            "|        82.0        | 3.1419608813261792 | 0.0003682277363861175  |  0.011721052885877993  |\n",
            "|        83.0        | 3.142799747060645  | 0.0012070934708519054  |  0.038422978532006685  |\n",
            "|        84.0        | 3.1410430984787068 | 0.0005495551110863417  |  0.01749288248616139   |\n",
            "|        85.0        | 3.141543362877516  |  4.92907122771058e-05  |  0.001568972101484352  |\n",
            "|        86.0        | 3.141643607609361  | 5.095401956767276e-05  |  0.001621916816919256  |\n",
            "|        87.0        | 3.1411520094149967 | 0.0004406441747963896  |  0.014026139712698915  |\n",
            "|        88.0        | 3.1410844620320333 | 0.0005081915577598295  |  0.01617623969100946   |\n",
            "|        89.0        | 3.1424157765938947 | 0.0008231230041015891  |  0.02620081897508367   |\n",
            "|        90.0        |  3.14211649172034  | 0.0005238381305470163  |  0.016674285571315044  |\n",
            "+--------------------+--------------------+------------------------+------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(pi_estimates, marker=\"x\", linestyle=\"--\")\n",
        "plt.xlabel(\"Amount of K of Lambda\")\n",
        "plt.ylabel(\"Value of the estimated π\")\n",
        "plt.title(\"Speed of approximation of π using Monte carlo simulation\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "okUR0qfrQmD-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "b615398f-b9f8-45f1-b4eb-9d305a4bcf03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#test section and trial section\n"
      ],
      "metadata": {
        "id": "8nzW4q2UCal3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define values of k\n",
        "k_values = list(range(1, 11))\n",
        "\n",
        "# Calculate corresponding λ values\n",
        "lambda_values = [5 ** k for k in k_values]\n",
        "\n",
        "# Create a 2D array to store π estimates for each λ\n",
        "pi_estimates_2d = []\n",
        "\n",
        "# Perform simulations for different values of λ\n",
        "for lambda_ in lambda_values:\n",
        "    pi_estimates = run_simulation(lambda_, T, M)\n",
        "    pi_estimates_2d.append(pi_estimates)\n",
        "\n",
        "# Plot the relationship between λ and π estimates\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i, k in enumerate(k_values):\n",
        "    plt.plot([lambda_values[i]] * M, pi_estimates_2d[i], marker=\"x\", linestyle=\"--\", label=f\"k = {k}\")\n",
        "\n",
        "plt.xlabel(\"λ (Lambda)\")\n",
        "plt.ylabel(\"Value of the estimated π\")\n",
        "plt.title(\"Approximation of π using Poisson process\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "CF2mlx14JDTp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# ... (Previous code for simulations and data collection)\n",
        "\n",
        "# Combine the simulation results into a DataFrame\n",
        "results_df = pd.DataFrame({\n",
        "    \"Lambda\": np.repeat(lambda_values, M),  # Repeat lambda values for M times\n",
        "    \"Pi Estimate\": np.concatenate(pi_estimates_2d)  # Concatenate all pi estimates\n",
        "})\n",
        "\n",
        "# Plot using Seaborn\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(data=results_df, x=\"Lambda\", y=\"Pi Estimate\", palette=\"Set3\")\n",
        "plt.xlabel(\"λ (Lambda)\")\n",
        "plt.ylabel(\"Value of the estimated π\")\n",
        "plt.title(\"Approximation of π using Poisson process\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "id": "uxEEwGhqLuMU",
        "outputId": "46039e63-c3ab-4ad1-855d-b99c7c2bb823"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cgKbdDnAEo2X"
      },
      "outputs": [],
      "source": [
        "#Ogni linea rappresenta il numero di \"M\", quindi se setto M=1 avrò 1 sola linea.\n",
        "import math as mt\n",
        "\n",
        "plt.plot(pi_estimates)\n",
        "plt.axhline(y=mt.pi, color='r', linestyle='-')\n",
        "plt.ylabel(\"Value of pi\")\n",
        "plt.title(\"Standard Deviation and k lambda of Pi Estimates\")\n",
        "plt.xlabel(\"Numbers of K lambda\")\n",
        "\n",
        "plt.show()\n",
        "#You can see that the variation along each curve (which is what your first method uses) is smaller than the variation between the curves.\n",
        "#That's because points on the same curve are correlated: the estimates at 800 and 900 throws share the first 800 throws and so have a correlation of 800/900, or nearly 0.9.\n",
        "\n",
        "#The bootstrap idea is a slightly more general version of this, where the estimate_sd\n",
        "#is based on empirical standard deviations over intervals of more than one time point, so it would work for, eg, autocorrelation estimates as well as for means."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create a violin plot\n",
        "plt.violinplot(std_dev, )\n",
        "plt.xlabel(\"Density (λ)\")\n",
        "plt.ylabel(\"Standard Deviation of π Estimates\")\n",
        "plt.title(\"Standard Deviation of π Estimates vs. N of lambda\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "SvACLo-BCYwQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ErvdWabECY_Y"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import math as mt\n",
        "\n",
        "\n",
        "def generate_poisson_process(lambda_, T):\n",
        "    N = np.random.poisson(lambda_ * T**2) #T**2 for a squared domain in our case could just be T\n",
        "    X = np.random.uniform(0, T, N) #points are generated uniformly\n",
        "    Y = np.random.uniform(0, T, N)\n",
        "    return X, Y\n",
        "\n",
        "def estimate_pi(X, Y, circle_center, circle_radius):\n",
        "    if len(X) == 0:\n",
        "        return np.nan #if N= 0 we stop the algorithm x,y same lenght\n",
        "    in_circle = np.sqrt((X - circle_center[0])**2 + (Y - circle_center[1])**2) < circle_radius #eukledian formula to see if poits are in circle or not\n",
        "    return 4 * np.sum(in_circle) / len(X)\n",
        "\n",
        "def run_simulation(lambda_, T, M):\n",
        "    pi_estimates = np.zeros(M)\n",
        "    for i in range(M):\n",
        "        X, Y = generate_poisson_process(lambda_, T) #generate 2N independet random Variables\n",
        "        pi_estimates[i] = estimate_pi(X, Y, [1/2, 1/2], 1/2) #use generated parameters to estimate the pi M times #1/2 cricle center and radius\n",
        "    return pi_estimates\n",
        "\n",
        "# Parameters to choose\n",
        "T = 1 #length of the square\n",
        "M = 150 #numebers of simulations\n",
        "lambdas = [5 ** k for k in range(1, 11)] # 5 to k-th with k 1,2,..,10\n",
        "\n",
        "# Run the simulation for different values of lambda\n",
        "pi_estimates = [run_simulation(lambda_, T, M) for lambda_ in lambdas]\n",
        "\n",
        "# Plot the histograms of the pi estimates\n",
        "fig, ax = plt.subplots(5, 2, figsize=(10,10), sharex=True, sharey=True)\n",
        "for i, (lambda_, estimates) in enumerate(zip(lambdas, pi_estimates)):\n",
        "    row, col = i // 2, i % 2\n",
        "    ax[row, col].hist(estimates, bins=int(np.sqrt(len(estimates))))\n",
        "    ax[row, col].set_title(f\"lambda = {lambda_}\")\n",
        "    ax[row, col].axvline(np.pi, color='red', linestyle='--')\n",
        "    # Zoom out on the histogram\n",
        "    ax[row, col].set_xlim(left=1.5, right=5)\n",
        "\n",
        "# Plot the standard deviation of the pi estimates for each value of lambda\n",
        "std_dev = [np.std(estimates) for estimates in pi_estimates]\n",
        "plt.figure()\n",
        "plt.plot(lambdas, std_dev, marker='o')\n",
        "plt.xlabel(\"lambda\")\n",
        "plt.ylabel(\"Standard Deviation\")\n",
        "plt.title(\"Standard Deviation of Pi Estimates\")\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(5, 2, figsize=(10,10), sharex=True, sharey=True)\n",
        "for i, (lambda_, estimates) in enumerate(zip(lambdas, pi_estimates)):\n",
        "    row, col = i // 2, i % 2\n",
        "    ax[row, col].hist(estimates, bins=int(np.sqrt(len(estimates))))\n",
        "    ax[row, col].set_title(f\"lambda = {lambda_}\")\n",
        "    ax[row, col].axvline(np.pi, color='red', linestyle='--')\n",
        "    # Zoom out on the histogram\n",
        "    ax[row, col].set_xlim(left=1.5, right=5)\n",
        "\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.suptitle(\"Approximation of π using Poisson process\\nStandard Deviations for Different Densities\",\n",
        "             fontsize=16)\n",
        "plt.subplots_adjust(top=0.9)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "IcUK3gThi5I7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.interpolate import make_interp_spline\n",
        "# Smooth curves using smoothing spline\n",
        "spline = make_interp_spline(lambdas, std_dev)\n",
        "densities_smooth = np.linspace(min(lambdas), max(lambdas))\n",
        "standard_deviations_smooth = spline(densities_smooth)\n",
        "\n",
        "# Line plot with smooth curves\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(lambdas, std_dev, color='b', label='Smooth Curve')\n",
        "plt.scatter(lambdas, std_dev, marker='o', color='r', label='Data Points')\n",
        "plt.xlabel(\"Density (λ)\")\n",
        "plt.ylabel(\"Standard Deviation of π Estimates\")\n",
        "plt.title(\"Standard Deviation of π Estimates vs. N of lambda\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Ge45rFhOjiRL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Parameters to choose\n",
        "T = 1  # Length of the square\n",
        "M = 90  # Number of simulations\n",
        "\n",
        "# Generate a list of lambdas in the form λ = 5^k for k in {1, 2, ..., 10}\n",
        "lambdas = [5**k for k in range(1, 11)]\n",
        "\n",
        "# Create lists to store the average π estimates for each λ\n",
        "average_pi_estimates = []\n",
        "\n",
        "# Perform simulations for different values of lambda\n",
        "for lambda_ in lambdas:\n",
        "    pi_estimates = run_simulation(lambda_, T, M)\n",
        "    average_pi_estimate = np.mean(pi_estimates)\n",
        "\n",
        "    average_pi_estimates.append(average_pi_estimate)\n",
        "\n",
        "# Plot the average π estimates vs. lambda\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(lambdas, average_pi_estimates, marker=\"o\", linestyle=\"-\")\n",
        "plt.xlabel(\"Lambda (λ)\")\n",
        "plt.ylabel(\"Average Estimated π\")\n",
        "plt.title(\"Average Approximation of π vs. Lambda (λ = 5^k)\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "slfd9WHnFzKc",
        "outputId": "26673a23-8c7a-47e2-f4b3-6cc40269bb08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define values of k\n",
        "k_values = list(range(1, 11))\n",
        "\n",
        "# Calculate corresponding λ values\n",
        "lambda_values = [5 ** k for k in k_values]\n",
        "\n",
        "# Create a 2D array to store π estimates for each λ\n",
        "pi_estimates_2d = []\n",
        "\n",
        "# Perform simulations for different values of λ\n",
        "for lambda_ in lambda_values:\n",
        "    pi_estimates = run_simulation(lambda_, T, M)\n",
        "    pi_estimates_2d.append(pi_estimates)\n",
        "\n",
        "# Plot the relationship between λ and π estimates\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i, k in enumerate(k_values):\n",
        "    plt.plot([lambda_values[i]] * M, pi_estimates_2d[i], marker=\"x\", linestyle=\"--\", label=f\"k = {k}\")\n",
        "\n",
        "plt.xticks(lambda_values)  # Set the x-axis ticks to match lambda values\n",
        "plt.xlabel(\"λ (Lambda)\")\n",
        "plt.ylabel(\"Value of the estimated π\")\n",
        "plt.title(\"Approximation of π using Poisson process\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "P9c57eOPKOgA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#To draw the plot, simulations results are stored into a DataFrame with 2 columns: Lambda and Pi Estimate\n",
        "'''\n",
        "results = pd.DataFrame({\n",
        "    \"Lambda\": np.repeat(lambdas, M),  #by using numpy repeat original lambda values for M times\n",
        "    \"std\": np.concatenate(std_dev)  #concatenating here all pi estimates\n",
        "})\n",
        "'''\n",
        "\n",
        "filtered_lambdas = [l for l, s in zip(lambdas, std_dev) if not np.isnan(s)]\n",
        "filtered_std_dev = [s for s in std_dev if not np.isnan(s)]\n",
        "\n",
        "plt.figure(figsize=(14, 8))\n",
        "#To repressent the relationship a boxplot has been selected\n",
        "sns.boxplot(x=filtered_lambdas, y=filtered_std_dev, palette=\"Set3\")\n",
        "plt.xlabel(\"Density of λ\")\n",
        "plt.ylabel(\"π's estimated by the simulation\")\n",
        "plt.title(\"How the approximate value of π depends on the density λ\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 758
        },
        "id": "YXh89mEzEQxq",
        "outputId": "91dc4704-f3d8-4bd0-a817-8db40ab0bbd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize lists to store total errors and corresponding values of M\n",
        "total_errors = []\n",
        "M_values = []\n",
        "\n",
        "# Group the DataFrame by the number of simulations (M) and calculate total error\n",
        "for M, group in results_df.groupby(\"Simulation runs, M\"):\n",
        "    total_error = group[\"Absolute error\"].sum()\n",
        "    total_errors.append(total_error)\n",
        "    M_values.append(M)\n",
        "\n",
        "# Create a line plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(M_values, total_errors, marker=\"o\", linestyle=\"-\")\n",
        "plt.xlabel(\"K value from 1 to 10\")\n",
        "plt.ylabel(\"Total Error\")\n",
        "plt.title(\"Total Error vs. K\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "OSDCn6PukLqN",
        "outputId": "fe637db9-df6a-4705-faab-5a1b22098f96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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BlmWsWLECt9xyi2mQBQDRaBTRaFT7e2hoCAAQj8cRj8fd+wAmiPcoxnsRIuC4I6WA446UCo49Ugqsxl00ntD+ezwW49gsY5z8NpKiKCUNg1955RWsWrUKkUgEDQ0NuOeee7B27VrDx27duhVvvPEGzjjjDAwODuLb3/42nnjiCezcuROzZ882fM7NN9+M9evXT7j9nnvuQV1dnaufhRBCCCGEkO/t8GP/sFpO8qElSZzRQtWpXBkbG8N73/teDA4OoqmpyfKxJQ+cYrEYDh06hMHBQdx33334xS9+gccffxxLly7N+dx4PI5TTz0V1157Lb72ta8ZPsZIcZozZw5OnDiR88txg3g8js2bN2P16tUIBoOevx8hAMcdKQ0cd6RUcOyRUmA17v7+9m3YcUzNcvr+u87A2tNZZlKuDA0NYdq0abYCp5Kn6oVCISxevBgAcPbZZ+PZZ5/F97//ffz0pz/N+dxgMIjly5djz549po8Jh8MIh8OGzy3m5Frs9yME4LgjpYHjjpQKjj1SCozGXUJO6xKyJHFcljFOfpuy6+Mky3KGQmRFMpnEK6+8go6ODo+PihBCCCGEEHtkuuoxTa9aKKnidMMNN+DKK6/E3LlzMTw8jHvuuQdbtmzBpk2bAADXXXcdZs2ahVtvvRUA8NWvfhXnn38+Fi9ejIGBAXzrW9/CwYMH8eEPf7iUH4MQQgghhBCNaIJ9nKqRkgZOPT09uO6669DZ2Ynm5macccYZ2LRpE1avXg0AOHToEHy+tCjW39+Pj3zkI+jq6sLUqVNx9tln46mnnrJVD0UIIYQQQkgx0AdLcQZOVUNJA6f/+Z//sbx/y5YtGX/fdtttuO222zw8IkIIIYQQQgojs48TA6dqoexqnAghhBBCCKlk4kk2wK1GGDgRQgghhBDiInrFSf/fpLJh4EQIIYQQQohLyLKSYUfOVL3qgYETIYQQQgghLpHtosfAqXpg4EQIIYQQQohLRBPZgRNrnKoFBk6EEEIIIYS4RLbCxD5O1QMDJ0IIIYQQQlwi2wwiTnOIqoGBEyGEEEIIIS4xIXCi4lQ1MHAihBBCCCHEJSaaQ7DGqVpg4EQIIYQQQohLZCtOrHGqHhg4EUIIIYQQ4hK0I69eGDgRQgghhBDiEhMUJ5pDVA0MnAghhBBCCHEJmkNULwycCCGEEEIIcYmJNU40h6gWGDgRQgghhBDiEhNqnJiqVzUwcCKEEEIIIcQlRGqeT8r8m1Q+DJwIIYQQQghxiWhKYaoPBQAwcKomGDgRQgghhBDiEqLGqT4sAifWOFULDJwIIYQQQghxiXTg5Ff/puJUNTBwIoQQQgghxCVEoNQQZqpetcHAiRBCCCGEEJeYkKpHV72qgYETIYQQQgghLiEUJtY4VR8MnAghhBBCCHEJTXEKpWucFIXBUzXAwIkQQgghhBCXiGal6gFAQmbgVA0wcCKEEEIIIcQlss0hABpEVAsMnAghhBBCCHGJbHMIAIgnqDhVAwycCCGEEEIIcQmhLtWlapwA9nKqFhg4EUIIIYQQ4hJCcQoFfAj51aU2U/WqAwZOhBBCCCGEuIQWOPl9CPolAAycqgUGToQQQgghhLiESMsLBXwIBqg4VRMMnAghhBBCCHGJqC5VL5hK1YvRHKIqYOBECCGEEEKISwh1KeRnjVO1wcCJEEIIIYQQlxA1TsEAa5yqDQZOhBBCCCGEuIQInMJ+XaoeA6eqgIETIYQQQgghLpFhDqGl6rHGqRpg4EQIIYQQQohLZPRxEq56CSpO1QADJ0IIIYQQQlwinmQD3GqFgRMhhBBCCCEuIezIg34fggHVHII1TtUBAydCCCGEEEJcQkvV87PGqdpg4EQIIYQQQogLKIqiqUvhDHMIKk7VAAMnQgghhBBCXCAhK1BS4hJrnKoPBk6EEEIIIYS4gD5ACuka4MboqlcVMHAihBBCCCHEBfQBUogNcKsOBk6EEEIIIYS4gAicJAnw+yQEtT5ONIeoBhg4EUIIIYQQ4gJRnaOeJEmscaoyGDgRQgghhBDiAjFd81sAWo0TA6fqgIETIYQQQgghLhDXWZEDYI1TlVHSwOn222/HGWecgaamJjQ1NWHVqlV4+OGHLZ/zhz/8Aaeccgpqampw+umnY8OGDUU6WkIIIYQQQszRN78FwD5OVUZJA6fZs2fjG9/4Bp5//nk899xzuPTSS/H2t78dO3fuNHz8U089hWuvvRYf+tCH8OKLL+Lqq6/G1VdfjR07dhT5yL0nKSvYurcXD2w/iq17e5GUWVRICCGEEFLOiMBJmEKEaA5RVQRK+ebr1q3L+PvrX/86br/9dmzbtg2nnXbahMd///vfx5o1a/Dv//7vAICvfe1r2Lx5M374wx/iJz/5SVGOuRhs3NGJ9Q/uQudgRLuto7kGN61bijXLOkp4ZIQQQgghxIyJihNrnKqJkgZOepLJJP7whz9gdHQUq1atMnzM1q1bcf3112fcdsUVV+D+++83fd1oNIpoNKr9PTQ0BACIx+OIx+OFH3gOxHvYfa9NO7vxqXtfQva+RNdgBB+7+wX84D1n4orTZrh8lKTacDruCHEDjjtSKjj2SCkwGndj0RgANWCKx+PwpVZ00XiS47NMcfK7lDxweuWVV7Bq1SpEIhE0NDTgj3/8I5YuXWr42K6uLsyYkRk0zJgxA11dXaavf+utt2L9+vUTbn/kkUdQV1dX2ME7YPPmzTkfIyvA+hf8qVNMyrhPSf3vV/53O+IHkvBJE55OyATsjDtC3IbjjpQKjj1SCvTj7uU+CYAfY8ND2LBhA3Z3qX8fPnYMGzYcKdkxEnPGxsZsP7bkgdOSJUuwfft2DA4O4r777sP73/9+PP7446bBk1NuuOGGDJVqaGgIc+bMweWXX46mpiZX3sOKeDyOzZs3Y/Xq1QgGg5aPfXp/Hwa2PWfxCAkDMWD60vNx3oIWdw+UVBVOxh0hbsFxR0oFxx4pBYbj7pUuYPfLaJvWgrVrz8Xo80fxh/070TKtDWvXrijtARNDRDaaHUoeOIVCISxevBgAcPbZZ+PZZ5/F97//ffz0pz+d8Nj29nZ0d3dn3Nbd3Y329nbT1w+HwwiHwxNuDwaDRZ1c7bxf71jC1mv1jiV4YSC2KPY4JwTguCOlg2OPlAL9uEumMobCQT+CwSBqw+pSO6mAY7NMcfK7lF0fJ1mWM2qS9KxatQp/+ctfMm7bvHmzaU1UpdHWWOPq4wghhBBCSPEwsyMXt5PKpqSK0w033IArr7wSc+fOxfDwMO655x5s2bIFmzZtAgBcd911mDVrFm699VYAwGc+8xlcdNFF+M53voO3vvWtuPfee/Hcc8/hZz/7WSk/hmusXNCCjuYadA1GJphDAGrVU3tzDVYyTY8QQgghpOwQjW5DAfZxqkZKqjj19PTguuuuw5IlS3DZZZfh2WefxaZNm7B69WoAwKFDh9DZ2ak9/oILLsA999yDn/3sZzjzzDNx33334f7778eyZctK9RFcxe+TcNM6tbYr2/tB/H3TuqXw0xmCEEIIIaTs0BQn0cdJC5zYx6kaKKni9D//8z+W92/ZsmXCbddccw2uueYaj46o9KxZ1oHb37diQh+ndvZxIoQQQggpazTFyU/FqRopuxonogZPT37xUiyYVg8AuOzUNjz5xUsZNBFCCCGElDFCcQoGMhvgxhg4VQUMnMoUv0/STrapdSGm5xFCCCGElDkTzCECVJyqCQZOZUwkrp5kdGIhhBBCCCl/xJotnF3jlGCNUzXAwKmMiSaSABg4EUIIIYRUAnTVq24YOJUx0VTAxJONEEIIIaT8iU8wh2CNUzXBwKmMicRTihNPNkIIIYSQsieaoOJUzTBwKlMURdFOvihT9QghhBBCyh7NVS8VMIUC7ONUTTBwKlNiSRlK6hxjjRMhhBBCSPmT3QBXBFBJWUFSZvBU6TBwKlP0KhPlXUIIIYSQ8ifbHEL8P8D1XDXAwKlMEfVNABUnQgghhJBKQARH4awGuPr7SOXCwKlMicbTJxfNIQghhBBCyp8JDXB9esWJqXqVDgOnMkX0cAKAOBUnQgghhJCyJ9scwueTEPCpqhMVp8qHgVOZEqHiRAghhBBSUWTbkQPpIIqlF5UPA6cyRa840Y6cEEIIIaT8yTaHANJ1TlScKh8GTmVKRo0TAydCCCGEkLInbhA4sZdT9cDAqUyJ6GucuENBCCGEEFL2ZJtDAOlUPa7nKh8GTmWKXnGSFSDBk40QQgghpKzJboALpAMnll5UPgycyhS94gTQIIIQQgghpNwxVpxY41QtMHAqU/SKEwDEE8yLJYQQQggpZ4zNIZiqVy0wcCpTIvFMxSmaTJo8khBCCCGElBpFUTQDCGNzCAZOlQ4DpzIlOw+WznqEEEIIIeWLvqzCuI8Ts4cqHQZOZUokzsCJEEIIIaRS0K/VWONUnTBwKlOiWeYQ9P4nhBBCCClfzAMnpupVCwycyhQqToQQQgghlYNI1Qv4JPh8knZ7iIFT1cDAqUzJVpxiNIcghBBCCClbjHo4AboaJ2YPVTwMnMqUiYoTTzZCCCGEkHIlbmBFDgBB4arH7KGKh4FTmTJRceLJRgghhBBSrkQNmt8CNIeoJhg4lSmscSKEEEIIqRzEWi2YFTixxql6YOBUpkxQnBg4EUIIIYSULWKtFmaNU9XCwKlMiWYpTtylIIQQQggpX2JmNU5UnKoGBk5lilCcpJSbJRUnQgghhJDyxdwcIlXjxLVcxcPAqUwRNU4NoQAAIMpdCkIIIYSQsiVmYg7BGqfqgYFTmSIUp6baIADuUhBCCCGElDNRE3MI1jhVDwycyhShODXWqIoT7cgJIYQQQsqXXA1wqThVPgycyhShOGmBExUnQgghhJCyxdwcgn2cqgUGTmVKWnFSU/UYOBFCCCGElC9xE8VJ/M3AqfJh4FSGKIoyQXHiyUYIIYQQUr4IxSlsVuOUYI1TpcPAqQxJyArk1LklAqcoFSdCCCGEkLIllsMcgpvglQ8DpzIkEk9q/90kUvV4shFCCCGElC3m5hCscaoWGDiVIXp1qUGk6lFxIoQQQggpW6Im5hBh1jhVDQycyhChOIUCPoQDfgBUnAghhBBCypl4qobJzI6cfZwqHwZOZYhQnGoCPoRS8i5d9QghhBBCypdYMrXxbVbjxLVcxcPAqQwRilM46Nd2LRg4EUIIIYSUL2yAW/0wcCpDNMUp6EsHTjzZCCGEEELKFi1w8mf3caI5RLXAwKkM0RSngF/n/c+TjRBCCCGkXImZmEOkFSfWOFU6DJzKkAzFyU/FiRBCCCGk3InlNIfgWq7SYeBUhkR1ilOIFpaEEEIIIWWPpjixAW7VwsCpDDGscWKqHiGEEEJI2RJLpNvJ6Amx7KJqKGngdOutt+Lcc89FY2Mj2tracPXVV2P37t2Wz7nzzjshSVLGv5qamiIdcXHQ1zjxZCOEEEIIKX/EWi2YrTjRHKJqKGng9Pjjj+MTn/gEtm3bhs2bNyMej+Pyyy/H6Oio5fOamprQ2dmp/Tt48GCRjrg4UHEihBBCCKksRKpe2MIcQlFoEFHJBEr55hs3bsz4+84770RbWxuef/55vPnNbzZ9niRJaG9v9/rwSkbEoMaJ3aYJIYQQQsqXXH2cADV4EvbkpPIoaeCUzeDgIACgpaXF8nEjIyOYN28eZFnGihUrcMstt+C0004zfGw0GkU0GtX+HhoaAgDE43HE43GXjtwc8R5O3mssmgAAhPyAJKsnYSyRLMrxkuogn3FHSKFw3JFSwbFHSkH2uBOBkw9yxliU5KT232ORKKRwWS2/Jz1O5g1JKRPNUJZlXHXVVRgYGMCTTz5p+ritW7fijTfewBlnnIHBwUF8+9vfxhNPPIGdO3di9uzZEx5/8803Y/369RNuv+eee1BXV+fqZ3CLhw75sPmoD29ul3Fxh4yvvhhA0Kfg2+clcz+ZEEIIIYQUnZuf96M/JuH60xOY15C+PakA129Tg6VbzkmgPliiAySGjI2N4b3vfS8GBwfR1NRk+diyCZw+9rGP4eGHH8aTTz5pGACZEY/Hceqpp+Laa6/F1772tQn3GylOc+bMwYkTJ3J+OW4Qj8exefNmrF69GsGgvTPl1od345dPHcRH3jQfH7hgHi7878fh90l4bf1qj4+WVAv5jDtCCoXjjpQKjj1SCrLH3apvbsGJkRj+9PFVOLWjUXucoihYctNmKArw1BcuwvTGcAmPmmQzNDSEadOm2QqcykIr/OQnP4mHHnoITzzxhKOgCQCCwSCWL1+OPXv2GN4fDocRDk8coMFgsKiTq5P3i8lqLFsbDqK+JgQASMoKfP4A/D7mxRL7FHucEwJw3JHSwbFHSoEYdyJVr64mNGEcBv0+xBIyFJ+fY7TMcPJ7lNRVT1EUfPKTn8Qf//hHPPbYY1iwYIHj10gmk3jllVfQ0dHhwRGWhmg87aqnLyiksx4hhBBCSHli5qoHpHs5xbmWq2hKqjh94hOfwD333IMHHngAjY2N6OrqAgA0NzejtrYWAHDddddh1qxZuPXWWwEAX/3qV3H++edj8eLFGBgYwLe+9S0cPHgQH/7wh0v2OdwmkhAnnj/DmSWWkFEb8pfqsAghhBBCiAnxlANytqseAAT97OVUDZQ0cLr99tsBABdffHHG7XfccQc+8IEPAAAOHToEny89APv7+/GRj3wEXV1dmDp1Ks4++2w89dRTWLp0abEO23OiKTvymqAPAZ8ESQIUJb2TQQghhBBCyoekrCCZKrUI+Y0CJ9Fehmu5SqakgZMdX4otW7Zk/H3bbbfhtttu8+iIygO94iRJkpYXy5ONEEIIIaT80JdTBA0Vp3QTXFK5lLTGiRijV5wAICx2KZgXSwghhBBSdujXaEaKk0jfY6peZcPAqQzRK05A+mRj4EQIIYQQUn5Ek+lem6KeSY9W48S1XEXDwKkMyVacuEtBCCGEEFK+6I0hJMkocGKNUzXAwKkMiWYpTuJki3KXghBCCCGk7BBZQWGDND2ANU7VAgOnMsRMcWKqHiGEEEJI+SHWaEbGEICujxMVp4qGgVMZMqHGiScbIYQQQkjZIgInI2MIAAgG2MepGmDgVIZkK05BKk6EEEIIIWVLLGUOYdT8FtDVOHEtV9EwcCpDshWnMAsKCSGEEELKllgibQ5hBGucqgMGTmVGIilrnadZ40QIIYQQUv6IzW2zVD2WXVQHDJzKjIguOJrQx4knGyGEEEJI2aHVOJkqTqxxqgYYOJUZor4JAMKpk0+cbFScCCGEEELKj5zmECy7qAoYOJUZUd2J5/OpAVMopTwxcCKEEEIIKT9ymUOI2+MJ1jhVMgycyoxISnEK60485sUSQgghhJQvcdvmEFzLVTIMnMoMoTiFg37ttlCAqXqEEEIIIeVKNJc5RICBUzXAwKnMsFKcmBdLCCGEEFJ+2DWH4FqusmHgVGYIxUlYkQO0IyeEEEIIKWfEGi2YwxyCilNlw8CpzEgrTvpUPSpOhBBCCCHlSm7FieYQ1QADpzLDSHHSLCypOBFCCCGElB1CSQqbuepRcaoKGDiVGZaKEwMnQgghhJCyQ2QFscapumHgVGYY1jhxl4IQQgghpGzJ2QCXrnpVAQOnMiPKGidCCCGEkIoiatMcgtlDlQ0DpzLDSnHiyUYIIYQQUn7kModIZw/RHKKSYeBUZljVOEUZOBFCCCGElB25a5yYPVQNMHAqM6z6ODEvlhBCCCGk/IjbbIDLtVxlw8CpzNAUp2BacWJeLCGEEEJI+SKUpDDNIaoaBk5lhqY4BSYqTpR3CSGEEELKD7G5HQxIhveH2AC3KmDgVGYYKU5hnmyEEEIIIWVL2o7cb3h/kK1lqoJAqQ+AZCIUJ33n6SAVJ0IIIRVKUlbwzP4+9AxH0NZYg5ULWuD3Ge/KE1KpRNkAd1LAwKnMMFKcaEdOCCGkEtm4oxPrH9yFzsGIdltHcw1uWrcUa5Z1lPDICHGX3OYQVJyqAabqlRlWNU60IyeEEFIpbNzRiY/d/UJG0AQAXYMRfOzuF7BxR2eJjowQ99HsyE3MIdIOySy7qGQYOJUZhooTnVgIIYRUEElZwfoHd8FoiShuW//gLiRlLiJJdZCrAa6mOHETvKJh4FRmGCpOTNUjhFQ5SVnB1r29eGD7UWzd28sFdYXzzP6+CUqTHgVA52AEz+zvK95BEeIhaXMI1jhVM6xxKjMi8ZQ5hIHixJONEFKNsA6m+ugZNg+a8nkcIeVOLIc5RIg1TlWBI8UpHo8jEAhgx44dXh3PpCeaUFP1jBSnpKxwF5YQUlWwDqY6aWuscfVxhJQ7ds0hZAVcy1UwjgKnYDCIuXPnIplMenU8k56ogeIU1J2E3KkghFQLrIOpXlYuaEFHcw3MTMclqKriygUtxTwsQjwjpx0513JVgeMapy9/+cv40pe+hL4+5iV7gaY4BScqTur9PNkIIdUB62CqF79Pwk3rlhreJ4Kpm9YtZT8nUhUoimK7xglg6UUl47jG6Yc//CH27NmDmTNnYt68eaivr8+4/4UXXnDt4CYjWo1TQKc46U82Bk6EkCqBdTDVzZplHbj9fSvwhftexlAkod3ezvo1UmXoLcZNAyefTnHiWq5icRw4XX311R4cBhEYKU6SJCEU8CGWkCnvEkKqBtbBVD9rlnVgz/ERfHvT6wCA2951Jq46axaVJlJV6BUks1Q9n09CwCchISvs5VTBOA6cbrrpJi+Og0DN9xcnk15xAtQdjFhCpuJECKkaRB1M12DEsM5JgqpOsA6msonE0tetRW0NDJpI1RG3ETgBqkFEQk5yE7yCyduO/Pnnn8err74KADjttNOwfPly1w5qsiLUJiBTcQJSJ2KUebGEkOpB1MF87O6JKd6sg0mTlBU8s78PPcMRtDWqgWQlfSejsXSa3oguZY+QakFsavt9kuW5GfRLGI9zLVfJOA6cenp68J73vAdbtmzBlClTAAADAwO45JJLcO+992L69OluH+OkQdQ3AcaKE8AaJ0JIdSHqYP7zgZ04PhzVbmcdjEo19Lgai6Y3BYcYOJEqROvhZFLfJBBqFBWnysWxq96nPvUpDA8PY+fOnejr60NfXx927NiBoaEhfPrTn/biGCcNQnEK+ifuWAQD7DhNCKlO1izrwN0fWqn9fcmS6Xjyi5dWTGDgFdXS4ypDcYoycCLVRyyhJhvrzbyMEL2c4gnWOFUqjgOnjRs34sc//jFOPfVU7balS5fiRz/6ER5++GFXD26yYeSoJ6DiRAipZmK6hYQkWae7TAaqqcfVWCytOA1H4iU8EkK8QbMiN1i/6RGKEzfBKxfHgZMsywgGgxNuDwaDkGUOhEIwctQTiJORgRMhpBqJ6Go89Sl7k5Vq6nE1GmWNE6luROpd2MIYAtApTgycKhbHgdOll16Kz3zmMzh27Jh229GjR/G5z30Ol112masHN9mIWilOzIslhFQx4zEGTnrc7HGVlBVs3duLB7Yfxda9vUVXqfSKE1P1SDWi1TgxcKp68mqAe9VVV2H+/PmYM2cOAODw4cNYtmwZ7r77btcPcDIRiasXl7CR4pTKm6XiRAipRsT8BwAnRqKQZQW+SZyu51aPq3Iwl9DXONEcglQjWqpeLnOI1FqOgVPl4jhwmjNnDl544QU8+uijeO211wAAp556Kt7ylre4fnCTjWgit+LEvFhCSDUS0W0KJWQF/WMxtDaES3hEpcWNHlfCXCL7+cJc4vb3rShK8KR31aPiRKoRsTYTRl5mBLV69fKvTSTGOAqc4vE4amtrsX37dqxevRqrV6/26rgmJWLH1bDGieYQhJAqJqJL5wKA4yPRSR04FdrjKpe5hATVXGL10nbPjTj0ihPNIUg1YldxYqpe5eOoxikYDGLu3LlIJpO5H2yDW2+9Feeeey4aGxvR1taGq6++Grt37875vD/84Q845ZRTUFNTg9NPPx0bNmxw5XhKTVpxmvizaLsUPNkIIVWI3hwCAHqGWOckelxNqc00ZGpvrsmpFpWLuYSiKJk1TkzVI1VI2lUvR+DEevWKx7E5xJe//GV86UtfQl9f4ZPt448/jk984hPYtm0bNm/ejHg8jssvvxyjo6Omz3nqqadw7bXX4kMf+hBefPFFXH311bj66quxY8eOgo+n1KQVJ4tUPSpOhJAqRF/jBNAgQrBmWQc+9HcLtL8DPgl/+8IlOVPs3DSXKIRoQs4woxhm4ESqkHhSHeM57chZ41Tx5GUOsWfPHsycORPz5s1DfX19xv0vvDAxrcCMjRs3Zvx95513oq2tDc8//zze/OY3Gz7n+9//PtasWYN///d/BwB87Wtfw+bNm/HDH/4QP/nJTxx+mvLCSnFi4EQIqWbGY5lz2/ERBk6Co/3j2n8nZAXDkQSm1ocsn+OWuUShjGWlYLLGiVQjmquezVS9WJI1TpWK48Dp6quv9uAwVAYHBwEALS3mxa5bt27F9ddfn3HbFVdcgfvvv9/w8dFoFNFo+gI8NDQEQK3Xise9z7UW72Hnvcai6mNCfmnC40XZUySWKMpxk8rGybgjxC0KGXdi/hN0DYxx/KY42JuZhXGkbwQNoUbL5yyf3Yj2pjC6h6IW5hJhLJ/d6On3PDiaqWgNRby59nLOI6VAjLfx1PwV9FmPwZTghPFocdagxB5OfgtHgVMikYAkSfjgBz+I2bNnOz4wK2RZxmc/+1lceOGFWLZsmenjurq6MGPGjIzbZsyYga6uLsPH33rrrVi/fv2E2x955BHU1dUVdtAO2Lx5c87HvHxUAuBHT+cxbNhwJOO+Y4d9AHzY9drr2DD2mjcHSaoOO+OOELfJZ9y9tl+d44I+BXFZwsuvH8AG7HP/4CqQ14/6AUiQoECBhAf/8iT2Tc29Y722XcIvh8QOuN4AQoEC4MoZY9i08WEPjjjNsTEACGjHPhKJ489/3gDJIz8KznmkFOx49TUAfvR0d2LDhqOmj+vpUue5HTt3YcPAzqIdH7FmbGzM9mMdBU6BQADf+ta3cN111zk+qFx84hOfwI4dO/Dkk0+6+ro33HBDhkI1NDSEOXPm4PLLL0dTU5Or72VEPB7H5s2bsXr1agSDQcvHvv6XPcChfVi8YB7Wrj01476XHt6Nv3UfxNwFC7H2ipO9PORJT1JW8NzBfvQMR9HWGMY586Z67jrlNk7GHakcyn1sFjLunnpgJ9B1FPNaG7Dn+CiCTa1Yu/Zcj460ckgkZVz/9F8AKDi1owm7Oocxd8npWHtO7s3LtQBW7OzGl+7fmdE/qaO5Bl++8hRccdoM8ye7xIuHB4CXnkFLfRi9ozEokHDRWy5HQ9hxwoslnPNIKRDjbv7CxcDB/Vgwdw7Wrj3N9PFPPbATzxw/ioUnLcHaixcW8UiJFSIbzQ6OZ65LL70Ujz/+OObPn+/0qaZ88pOfxEMPPYQnnngip5LV3t6O7u7ujNu6u7vR3t5u+PhwOIxweKKlbTAYLOrkauf9RPlSXTgw4bE1IfWnSioSLwoeUg7NIt2k2OOceEcljc18xp0ohZnXWo89x0dxfCTGsQuga3gMSVlBKODDmXOmYFfnMI6PxG1/N287azZ2dY3gx1v2AgDeuWI2vvnOM4oWcMeS6vu0NoQwOB5HQlYQTUqY6tFvyzmPlAI5pejWhPyW4y8cVNdysgKO0zLCyW/hOHC68sor8R//8R945ZVXcPbZZ08wh7jqqqtsv5aiKPjUpz6FP/7xj9iyZQsWLFiQ8zmrVq3CX/7yF3z2s5/Vbtu8eTNWrVpl+33LFeEqZd0A1x0reDKRcmkWSUg2k2Fsjqcipzktago1XfVUDvepKSRzptaivakWANA95MwJr0v3+Cl1waKqlKKHU304gIaaAAbG4hiOxNHe7K0pBSHFRDS0DfmtXfVoDlH5OA6cPv7xjwMAvvvd7064T5IkRz2ePvGJT+Cee+7BAw88gMbGRq1Oqbm5GbW16gXiuuuuw6xZs3DrrbcCAD7zmc/goosuwne+8x289a1vxb333ovnnnsOP/vZz5x+lLJDuOoZNcANsgGup5RTs0hC9EyWsSn6OInAaTiSQCSeNGzPMJk4JAKnljp0pIINq/5MRnQOpB9fbFe7MRE4hQJoFIETnfVIlaG56uXq48QGuBWP4z5Osiyb/nPaGPf222/H4OAgLr74YnR0dGj/fve732mPOXToEDo7O7W/L7jgAtxzzz342c9+hjPPPBP33Xcf7r//fktDiUrBSnEK047cU8qlWSQh2UyWsSnmv7bGsLb4oOoEHO4XilMdZqQCJ6eK07HBtJ15sYOW0aj6u9aF/GgIq+kw7OVEqg2tAa7fevOKfZwqH3erMx2iKLmlyi1btky47ZprrsE111zjwRGVFivFKaR1m6a86wXl0iySkGwmy9gcj6vzX23Qj+kNYRwdGEfPcFRToCYrh/rUoGduSx3am9TAqctB4CTLSkbgXeygZUyXqtdYoy45Rhg4kSqDitPkwbbitHbtWq3PEgB84xvfwMDAgPZ3b28vli5d6urBTTasFCdxskWpOHlCuTSLJCSbyTI2o6n5rybox/RG1dCHipOuxqmlVgucBsbi2vUiF72jsYxMhZFIcXvH6BWnxpST3nCRj4EQr4knbAZOWvYQN8ErFduB06ZNmzIayd5yyy3o60unhiQSCezevdvdo5tkiKAobKQ4aQWFDJy8YOWCFnQ018BMZJegOpitXGDenJkQL5gsY1MEArUhH9pE4DTCwOmwrsapqTaA2lTNV5fNOqdOXZoeUCaKE2ucSJWhKU5+Kk7Vju3AKTutzk6aHXGGLVe9BF31vMDvk3DTOlUxzV6gir9vWre0oovvSWWiH5vZVNPYHNfNf5ri5LCWp9oYjSbQOxoDoAZOkiRpbnR20/WOpYwhxIKu2EHLaExX45QKnIaYqkeqDK3GyWD9poc1TpWPY3MI4h2scSota5Z14Pb3rUBbU2bfr/bmmqqweyaVixibgazgqJrGZiQu5j9d4DTJFacj/apaNKUuiKYa1VhhRmp+sqs4HRtQX2NRWwOA4tcXjUX1rnrBkhwDIV4jFKdgDnMIKk6Vj21zCEmSIEnShNuIe1gqTrQjLwprlnXgpLZGXPbdxwEAZ8xuxh8/fmHF7+aTymfNsg40hF/GwHgC4YAPd/7zSqxc0FI1YzOdqufX6rUme42TZkU+NW2Q0dGstuqwqziJVL0lMxrwaucQRmIJyLICX5HGjaY4hf2IJVnjRKqTmN0aJ/ZxqnhsB06KouADH/gAwmF1tysSieBf//VftQa4+vonkh92FCcGTt4jUmMANWCtloUpqWxiCRkD4+pOfTwp4/yFLVWzeSXLSnr+C/g0xalnkgdOemMIwQzhrGdbcVIfd9KMRgCAoqhNaYX64zX6Pk6yrC4WWeNEqg2RDRS2aQ4R51quYrEdOL3//e/P+Pt973vfhMdcd911hR/RJEakqljVOFHe9R69rTMv8KRc6B1NBxGyom60VEtzWL1baG2IrnoCffNbQXsqVc9uLyfRw2nhtHoEfBISsoLhSPECJ72rnpJq48w+TqTasGtHzhqnysd24HTHHXd4eRwEQDQh7Hgnnni0Iy8ePUPpxdpojBd4Uh5kBxFjsWTVBE7jOmvtmoBfc9U7MRItalpZuXGkf2KqXnsqVc+qKbKezpTiNHNKLRprAugfixd1Q0jvqudLKaTFbsJLiNekG+Baz8mscap8aA5RRkSF4mSwGKIdefHQpweJ3VJCSs3EwKl6Fp+ivink98Hnk9DaEAKgpr8MjE/eehihOM3VK04pVz07ilM8KaM7paB3TKnRXO2KqfiMGbjqscaJVBsicLJrDsEap8qFgVOZIMuKFhTVGEi9rHEqHj1DTNUj5Ud24DQeq56gPhLPVNvDAT+m1KmpZJM1XU9RFBzuU9PsMlP11MCpZziKpGy9+OoeikBR1IB0Wn0YjWH1Oy1m4CICp4w+TkzVI1WG3VQ9Kk6VDwOnMkGfgmekOIVZ41Q09IpTLCHzOydlgVGqXrUwrgVO6bmvbZLXOfWOxjAeT0KSgFlT0uYQ0xvD8PskJGUFJ3LYtQtjiPbmGvh8kqb4FHNDaDT1XnUhvy5wY+BEqgthDpGzxinAGqdKh4FTmRBN6HP8zWucqDh5j94cAkhf+AkpJdk9jaopcNL3cBKknfUmZxNckabX0VSTsRjz+yRMb7DXy0lYkc+coqpUjeHipuolkrK2Kaj2cVLffzyeRIILR1JFiLVZTlc9P131Kh0GTmWCWDj4fRICfvNUvYSsaJauxBuyLZBHq2iBSiqXCal68eoJ6KOih5M+cGqY3IqTsCKfrUvTE8xI1Tnl6uUkFKeZKUOJYqfKjelMP+rC6RongGnQpLrQUvVsmkOwxqlyseWq96c//cn2C1511VV5H8xkRnPUM9mt0O84xpIyanzV4aZVbkQTSQyMqfn/Qb+EeFKh4kTKgsmRqpee59qaJncT3MMGxhCC9qYwXkJuxenYgFCc1MCp2OYMYylznYBPQsjvgyRJqAn6EInLGI4kMKUuVJTjIMRrnDbAZape5WIrcLr66qttvZgkSUgmq+diXkwiFo56QKZTSyxZPf1byg1hRR4K+NDWGMaR/nHujJKyQKTqNYYDGI4mqipwMpr/hOI0WZvgasYQUycGTh0pBSmX4iRS9TpEql6qd1Ox7MBFO4e6kF9r1twQDiISj7LOiVQNsqJmAwG5XfVYr1752ErVk2XZ1j8GTfmTU3HSpe+xzsk7xCJtekMYDal6ACpOpBwQysvcVnUhXU2ueuNGqXqT3BxCsyJvrZ1w34yUGtedQ3E6mpWqJ+a0oqXqRdOOeoKmEhhUEOIl+qw7Kk7VD2ucyoRcipMkSeleTgycPON4qhB9RlNYu9gzcCKlZkSnMM1LBU7VpTgZpOqJwCmHc1y1ctig+a2gvVn9bnI1wU2bQ6iBU1OR+zjpFScBezmRakO/JMsdOAlXPQWKwjqnSsRWql42o6OjePzxx3Ho0CHEYrGM+z796U+7cmCTDaE4WTmyhAI+xJK0x/YSoTi1NdZohc0jbIJLSoxQXepDfrTWq4vmamyAa+iqZ6PRa7URT8pafZJxjZMaCFk1wR2LJbR6TZGqV2w7cjFG9YpTIxUnUmUk9IqTgbmXnqBujRdPKpo9OakcHAdOL774ItauXYuxsTGMjo6ipaUFJ06cQF1dHdra2hg45Uk0h+IEpHcqqDh5h6hxamsKa4tVKk6k1IixOL0xrO3eV6PiZJSqNxRJIBJPTqq6zs6BCGRF3UgT34Oedp2rnqIoWv2QHuGo1xgOoClV29RQ5Aa4o6lNpwzFKRVEDbHGiVQJYkkmDFCs0AdW8aScU6Ei5YfjX+xzn/sc1q1bh/7+ftTW1mLbtm04ePAgzj77bHz729/24hgnBZEcNU5AWgKOMnDyDLGD29aYTtXjzigpNfrAqbYqA6eJfZyaa4PaIiNXo9dqQ6TpzZ5aa7gQa0/VOI3FkqZGD9nGEEBa7SmWOYSmOIXSe7QieCtWnRUhXiOWZLmMIdTHZAZOpPJwHDht374dn//85+Hz+eD3+xGNRjFnzhz893//N770pS95cYyTAjuKU4huLJ6jT9UTO6PVlBJFKhNReze9MawtQseraFwapepJkjRpDSIOWViRA0BtyI/mWjUAMbMkF6l+woEPKL45hKY4GaTqscaJVAsiVc+OeuT3SfCl4qsY13IViePAKRgMwudTn9bW1oZDhw4BAJqbm3H48GF3j24SYUdxCtIcwnO0wKkpjPqwuogbZY0TKTHCIGF6Q3UqTkZ9nABgWuPktCQXPZzmmAROQFp1Mg+cUo56U9KBk0jZK5Y5RFpxSgfErHEi1UbSQeAE6J31aA5RiTiucVq+fDmeffZZnHTSSbjoootw44034sSJE/j1r3+NZcuWeXGMkwJbipPWcZqBk1eInf22xhqm6pGy4bgW0Ndo9SIi2KgGjFL1AJ2z3iQLnHIpTgAwo7kGu7uHTXs5aY56zelUPWEOMR5PIpGUEchRyF4oozFR42RgDsFUPVIlaDVONgOnkN+HaEJGnJvgFYnjWfOWW25BR0cHAODrX/86pk6dio997GM4fvw4fvrTn7p+gJMFO4qTcNyj4uQN8aSMEyOqS2RbE/s4kfLhuK6/WFWaQyQmmkMAk7eX0+F+NeiZbWBFLujIQ3Fq0KXMFWNDaCwqXPX05hCq6kVzCFIt6M0h7BBk2UVF41hxOuecc7T/bmtrw8aNG109oMlKWnHKbQ7Bk80bRAF6wCehpS6k1ZJQcSKlRkvVawzDl0qQr6rAKWacqje9YXKn6uVSnACYKk7HDMwhQgEfwgF1t3s4ksCUupBbh2yIpeIUZY0TqQ4SijonB+0GTikTCRp9VSaOFadLL70UAwMDE24fGhrCpZde6sYxTUrSipOVHTld9bxEWJFPa1AXp2yAS8oFIzvyqjKHSEw0hwAmp+I0Gk2gb1RVvue01Jo+TtQ4dRsoToqiaOYQM5szX6OYNUbpPk5GDXCrZ/ySyY1Ykln14dSTrnHiWq4Scaw4bdmyZULTWwCIRCL429/+5spBTUacKE5M1fMGsas9o0ldrNEcgpQDsqxoKaTTG9P9xapJcRqPGQdOWo3TJLIjF1bkU+qCaEyZORjR3qx+N50GgdPAWFyrG2vX1TgBQGNNECdGYkUJXNJ9nNJLjSaaQ5Aqw4mrHpBO6SvUHCIpK3hmfx96hiNoa6zBygUt8PvYUNdrbAdOL7/8svbfu3btQldXl/Z3MpnExo0bMWvWLHePbhIRtaE4uXWyEWN6NMtndaGhKU5VtLNPKo/+sRiSsgJJAlrqQ5oCOl5FgZOZOYSmOJmko1Ujh3pzp+kBQHuTqiR1G3w3Ik1vWkNowneqWZIXIVXOyFUv3YSX8yqpDpyaQ7ihOG3c0Yn1D+7K2DjpaK7BTeuWYs2yjrxfl+TGduB01llnQZIkSJJkmJJXW1uLH/zgB64e3GTCjuIU1BSn0i+YqnGno3sobUUOgOYQpCwQaktLXQhBv0/bvR+NJaAoSs5O9ZWAmTnOdJ3iVC2fNRfCGGKOhTEEkFaSekdjiCaSCOs23YyMIQSNRUyVs+rjRFc9Ui1oipNtcwh1HsvXIXnjjk587O4XkL2F3jUYwcfufgG3v28FgycPsR047d+/H4qiYOHChXjmmWcwffp07b5QKIS2tjb4/eZqCbHGLMdfT7hM7MirdacjbUUuUvVE4FT6QJVMXvT1TQC0Pk6yotY7Ws0ZlYIwh6gNGStO8aSCwfG452YG5YCdHk4AMLUuiFDAh1hCRs9QNOPxwoq8IytND0hvCBUjcDJUnFKBUywpIxJPVsX4JZMboTjZN4dIKU55lF0kZQXrH9w1IWgCAAWABGD9g7uweml7xW9mlyu2A6d58+YBAGSZ9TVeoClOFlJvOdQ4VfNOhzCHmJEqum4IpS/wsYRsW4af7FSjGllKsgOnOt0idDxWHQvPSMI4VS8c8KO5NojB8Th6hqOTLHAyN4YAAEmS0N5Ug0N9Y+gaimQETkcHROA08TUailhjZOSq1xDKtESvhvFLJjdOa5wKaYD7zP4+w7pGgQK17vGZ/X1YtajV8euT3Dg2hwCAvXv34nvf+x5effVVAMDSpUvxmc98BosWLXL14CYTdhQnLXAqQo2T0eIXQFXvdAhziLTilP4tRqMJhALVv2grlGpVI0uJvocToF50Q34fYkkZY/Ekppby4FwiEjfu4wSoAePgeBzHh6M4eUZjsQ+t6NhpfivQAqeshVRnKlVvlkGqXlONqDEqQo2TQR8nn09CQziAkWgCw5EEpqXGNSGVSjKPBrhAfjVOohbbrccR5zgOnDZt2oSrrroKZ511Fi688EIAwP/93//htNNOw4MPPojVq1e7fpCTATuKk9il8FpxMlv8vufcOVW909GjpeqpilPAn+55MhJNYGo9AycrqlmNLCXZihOgprTFxuWqsCRXFAXjqcDJqMazrTGMPT0jk8KSXFEUHLFZ4wSkezllG0R0GvRwEmjmEB6n6smygrH4RMUJUOucRqIJ1jkRTylW9oNQnOzbkedf4yTWJ249jjjHceD0H//xH/jc5z6Hb3zjGxNu/+IXv8jAKU+E4hS2ctUrQqqe2eK3czCC2x59w9ZrVOJOR1JWtIWZMIcA1DqnaCJGZ70cMO/aO/TNbwV1IT8Gx+NVYUkeS8pQUgPHSHEXn7sS5xWnnBiJYTyehCQZGztkI2qYsje0ysEcIpJIar+rXnEC9HVWbIJLvKGY2Q8JWb2m2TaHKEBxWrmgBR3NNegajBhebyWoxjEiS4i4j+OijVdffRUf+tCHJtz+wQ9+ELt27XLloCYjtvo4edw0zWrx64RK3OnoHY1CVgBJAlp1ylK6lxMDJyuc5F0TZ5gpTkB19HISVuSASapew+RpgivS9GY219pK+xH1mF06xSkpK9rf2c1vAV0DWo/nNGGqI0kT22w0FukYyOREbABnX5NE9sPGHZ2uvp/jGqdA/uYQfp+Em9YtNbxPbEnetG4pNyg9xHHgNH36dGzfvn3C7du3b0dbW5sbxzQpKQfFKdfiNxcS1B2dStzpEMYQ0xrCCOh2jepDdNazA/OuvSO7xglIG0RUQy8nUd/k90mGrlRCAZ4MgdORVPPb2VNzq02AWuMEAN26ebtnOIKkrCDgkzKCbUFjkWqchKNeXdAPX9YirqGGvZyIN+TKfgDU7Iek7F6tuFNXvUJ7cq5Z1oHb37cCgazzqr25pmgp8UlZwda9vXhg+1Fs3dvr6vdZ7thO1fvqV7+Kf/u3f8NHPvIRfPSjH8W+fftwwQUXAFBrnL75zW/i+uuv9+xAq52o1gAyt+LklR25k0WtBGRMTJW+03E8yxhCwF5O9mDetXcYpuoF1XFZHYqTcQ8nQTpVr/oDJ7vNbwXtBql6Ik1vRlON4VzcGC6Oq55RDyftGLReTkzVI+5SCte5pGNXvcL6OAFq8NRc+wp6R9Vz6G1ndOD771lelPXXZDeBsq04rV+/HiMjI/jP//xP3HjjjfjBD36Aiy66CBdddBF++MMf4uabb8ZXvvIVL4+1qhGLh1IqTnYXtZ97y8naBVtQzJ0OL+jJ6uEkqC/SIqPSEXnXZlN2JauRpSSaSGJgTL0wGqfqVf64FMYQ2T2cBNMb1LlmMihOh/vt9XASiHm4ZzgCObXjK4whZhoYQwA6O3KP1R6jHk6CxiL2kiKTi1JkPyQcuuoVUuMkkGUFA+Pp82cokiha0FTMNMhyxHbgpKSqPCVJwuc+9zkcOXIEg4ODGBwcxJEjR/CZz3xmUnR194powobiFPBWcbK7+P3kpYvx5BcvxefecjIAYNG0ejz5xUsrNmgCgO4hoThlLjaoONmDedfe0DsSA6DuUDbXBrXb66qwxsls00hL1Rup/sDJiRU5oG70SJKa8tM3po6VYwMicDJO9yuWOYRRD6fsY+CGFHGbUmQ/aDVORTCHEAxHEhnpcbu7hvJ+LbuUIg2yHHFU45QdGDU2NqKxsfr7aniNoiha4GSlOHltR+5k8ev3Sbhi2QwAwInRWMUviDXFqSlbcUqZQ1TBAtVr1izrwI//ccWE2ytdjSwl+vom/fxbXeYQooedSapeqrZrYCyOaKLyP68Vh/tSVuQ5mt8Kgn4fWuvV70f0chKpekbNbwGdo53HQYtRD6f0MaibAENUnIjLlCL7QSzJ7NqRi03wfGucAGgbJeK1uoeiGEjd5hU0gVJxFDidfPLJaGlpsfxHnBPVBUK2FCcP7chF0WFTTeYuodHiV+yKDo7HPT9hvUaYQ7Q1Ze5CMVXPGfqLUVNtAL/9yPkVr0aWkh4DRz1Abw5R+eMyV6relLqgVhNwYqSy5xkzkrKCJ984nlaLTIIeI4QleTpwsk7VE+YQsYTsaSBKxYmUglJkPwjFya45hFbjVMBarm9UnQtnNIU1M5nXuobzfj070ARKxVEfp/Xr16O5udmrY5m0RHV2vJY1Th7bkQvWLOvAzmND+MFje3DBolZ86tKTDBvH1YUCaGsMo2c4ioO9Y5hSV7kNYntMzCHSrnq8wNvhaGrRBgDJpFKRjZDLCSMrciA9LqtBcYpq5hDGc58kSZjeEMaxwQiOD0cxy0Z/o0rCqND6729/CjfbLLSe0VSDV44Oahbk4nXMgq8GnVnDSCSBcIP5NacQtBonI8Wphn2ciHeIDeAv/XGHFmAA6gawFwYGyRLUOPWnPldLXQjTG8M40j+O3V3DOH+hd9dcmkCpOAqc3vOe99By3APErp9PSu9EGBEKFO7EYpfe1El5zvwWy8Xv/NZ69AxHcaB3FGfOmeL5cXmFmatefZh25E442p8OnEZjSSRlpeLTOEuJWeCkperFK39cihonM8UJUD+/CJyqCbOG492pQms7Ka7tzerY6NYCJ/Uc7DBRnPw+CXUhP8ZiSYxEE2htmGhZ7gaaq56B4tRUJIMKMnlZs6wDsqzg4/e8qN125z+fiyXtTa6/V0JJNcAtYuAkUvWm1oewpL0Rj77a47nixOa7KrZT9Wj84B364mir7znkVxcWXqbqCU5otRXWKtLcVjVd72DKRrcSURRFV+OUbQ7BBrhO0CtOADBaBalkpeT4iDoupzeYpepVfuA0bsNRNG1JXj0pIG4VWotaps7BCCLxpJbOaKXMFcMgwspVT9Q40VWPeEl2Dd2+46OevI/mqlekPk5ApuIkgkGvDSLcTIOs5D5QthUn4apH3EcoTlb1TUBxapwEJ0bSDWGtmF8FgVP/WFybwLIXqJrixADAFkf6MwOnkUgCTTVBk0eTXJgrTiJVr/LHZS5zCACY3lh9luRu9ZuZIZrgDkW0OqfaoD/DhTGbhnAA3Yh6GrjY6uNkc0MqKSt4Zn8feoYjaGusMUwdJySbgfHMVNA3ekZwpQfvkyhBHye94nRKu2rS9nr3CBRF8VToEGmQX7jv5YzA1EkaZKX3gbIdOMmy94v1yUouO16B13bkesSupVHneT3zWusBAAd7vdnJKQZiF7ulPjRh4qM5hDOyFSfuKBeGWeBUF6weVz3NHCKYW3GqpsDJrULr9qa0OYQwhuiYUmO5eBIGEV7WGFkqTqnAacjG+1f6IouUDtEDL+iXEE8qeL3bm1Q2p4pTULjqFbAJrilO9SEsmFaPoF/CSDSBI/3jtvvA5cuaZR147mA/fvG3/QCAVYtacfeHzrO1mWGWntzlID251Dhy1SPeYFdxcsOJxS52Fad5KcXpQAUrTpqjnkGQyD5OzjiarThFWfxdCKJ30fSsYttqStUTG0c1NgKnnioKnNwqtG7XueodSwUXuQw0iuFqZ9dVzyqbhc02SSEIt99ls1RTsz09I568j9MGuG4YffWNqtfWlvoQgn4fFk1vAADs9rjOSdA5kD4ng36f7fS8augDxcCpDLCrOIWLpDiNxRLaTva0XIpTi6o4nRiJVmxwYWb5DNAcwilCcRIXECpO+aMoiqlpSTX1cYraSNVrq0LFya1+MyJwGo4mtIWhsCg3oxiBk1Ufp8ZUjZOimPfIq5ZFFikdQnFaOV89h/YdH0XCg/WT01Q9N/o49YtUvZSbsUjX2+2RqpaNPrtkaNzeBmm19IEqaeD0xBNPYN26dZg5cyYkScL9999v+fgtW7ZAkqQJ/7q6uopzwB5hu8YpZQ5RiLxrhxPDMe14jNIs9DTXBTG1Tr0IVmqdk3CjMtrZFeYQTNXLzUg0gcHUBHpSW4N2G8mPkWhC21TJVn7FLv54FbjqTdZUPX2hdXbw5KTQuiEc0JTxFw71AwBm5lCctCa4XtY4pVL1jBSnmqAPgdTnMnPWq5ZFFikdA+PqWmbpzCbUBv2IJWUc7HN/neK8xqnwTXB9qh4AzSDCa2c9QT6BU7X0gSpp4DQ6OoozzzwTP/rRjxw9b/fu3ejs7NT+VbpFul3FKVgkO/LjujQ9O0WGcyu8zkksxmY0TVSc6nR9nGiQYo1I02uqCWhOX7Qbzh8xLhvDgQlW3SJVr1JVXj3CHCJsFTg1pAOnajoPRaF1e5ZCZNRw3Arx/JePDADI3UC3GK52Qg01UpwkScrZy6laFlnVTjm7ownFaWpdCItTm3lvdLufrpd0WuPkQqperxY4qeeypjh57KwHqHO2fhNr0GbgVC19oBz1cXKbK6+8Elde6dzjpK2tDVOmTHH/gEqEUJzCORWntLwrywp8HrkKiRMiV32TYH5rHV46PODJTk4x0KzILVL1ErKCWFLOGdxOZo4OqL//rKl1RUkFqnbMjCGAdKreZKtxiiVlDI0n0FxXPU6Na5Z1YPXSdiy9cSOiCRnfe/eZWHfmLEeuce1NNdjTM6J9l2Y9nASNRWhAK4J6I8VJHMPAWBzDJnNEtSyyqplyN+4QC/opdUGcNKMBrxwdxJ6eYQDtrr6PSAIK2g6c1HM738ApkZS1zyZS9ZakAqd9x0cRS8i21a986MpSggfH47bc/KqlD1RJA6d8OeussxCNRrFs2TLcfPPNuPDCC00fG41GEY2mI+OhITUaj8fjiMe9L1wX72H1XmOpAvqQX7J8nKSkT7KxSNRyh7YQugfVBXBrfdDWdzQndZHef3y4KN+p23SnJoGWusCE4w9J6e98YCSiyeLljp1x5zaHTqg7eTObw6hLbQIMjkUrckyUA12pQLS1ITThOwxK6mVnLJ5ELBYrmz57+Yw7bf7zmT/PD1XJHIokcKx/BHXBhoKPtZxIJGVEU6uvCxZOhZxMQHYQE09vzJyX2nLM3XVBdbwMjcc8Oz9F4BQ2+V3rUwHVwEjE8P7lsxvR3hRG91DUYpEVxvLZjRnXc843xWHTzm586t6XTN3RfvCeM3HFaTNKcmwCYQ5RH5SwMGVk9VrnkKtjJBaLaal6Psi2XtsH9VyPxe09PpveVFaQJAF1AXXMT6vzo7EmgOFIArs7BzQFygsOnlDTATuaa9A5GEFCVjA4GtE2mq348pVL8Kl7X5pwu6S73+n85wZOfoeKCpw6Ojrwk5/8BOeccw6i0Sh+8Ytf4OKLL8bTTz+NFStWGD7n1ltvxfr16yfc/sgjj6CuzlvLRj2bN282ve+FTgmAH33Hu7FhwwbTx6nXVfUn+/PDm1Dj0a+39bB6PGN91scj6D+uPv6F1w9jw4aD3hyUhxzo9gOQsHfHC9hwaOL9QZ8fcVnCnzc9itYK29y0Gndu87eDPgA+xAe60T0EAD688toebIi8XrRjqCYeT80LieHeCefhWAIAAkjKCh7888PwcHMxL5yMuyOd6rjZvesVbDj+sunjaiU/hiDhob/8DSc3l086kBuoBlnqhP7kY4/C5sa1xkiP+h0KXt72OF6z2Ffb362OrX2Hj2HDhiNOD9cWg6PqvPrstidxyCBzMJ66/4ltz2L4DePfc227hF8Oic+l3xxQoAC4csYYNm18OOM5xZzzJiuyAqx/wZ8KmjI3bZTU/37lf7cjfiCJUrXbisvAeFw9p559cgsGh9Qx/+LeTlfHfFIBlNS5+/hjf0G9DTF87xAABDAwNGxrjZVN15j6/Fq/gkc2bdRunx70Yzgi4febnsQ5072bI7f1qN9lM8bQI0lIKhLu3/AIptpLUsI/nyzhd/t8GE2kB0dzSME/zJeRPPg8SrGMHBuznzFVUYHTkiVLsGTJEu3vCy64AHv37sVtt92GX//614bPueGGG3D99ddrfw8NDWHOnDm4/PLL0dTU5Pkxx+NxbN68GatXr0YwaHxGHfnbfuDAG5g/dzbWrl1m+lqyrODzT6sXhYsuewtaPVI/nn5wF3DkCJafuhhr37I45+PbDw3gN3uewahUh7Vr3+zJMXmFoij4wrN/ASDjqssvxlyD/gdffXkLekdjOPeCv/N0F8dN7Iw7t3nkdy8Dx7pw4VmnIJaQ8eixPZjWMQdr155WlPevNl7d/AZwYD9OP2k+1q49JeO+eFLGDc8+CgB486WrMaUMUteSsoJte4/jsa3P49JVZ+P8RdNtpZv9+tgzwOAAVp69HFcuM0+h+W3Xs+je34+FS8/C2jNLnwbkJof6xoDnnkRdyI91b7vc8fP7nzmMzUdfBQBMrQvi6nXWryHt6MK9+15GbVML1q5dmdcxW6EoCj7/9KMAFFy5+lKt15Se+/tewN7dJ3DSqadj7TmzDV9nLYDlO7rwqd9lBtQdzTX48pWnZCgapZjzJitP7+/DwLbnLB4hYSAGTF96Ps4rUdpV91AEePoJ+H0S/mHdlTjcP46f734Sx2N+XLHmctcaKA+ORoBtTwAA3nrl5aapqXpeOjKI/7fzaYRqavNaMz1zoA946TnMaK7H2rVv0m5/OrkL+545gtqORVh7+cmOX9cuex7bA+zdhzNPmoPjrx5H72gM56z6Oy1dMBdrASx58Si++L87cXJbA2582yk4Z97Ukja1FtlodqiowMmIlStX4sknnzS9PxwOIxyeGAYHg8GiTq5W75dKS0ddKJDzmEQjN0XyeXb8faNqikX7lFpb77FohhqAdg5FkITPslah3Bgcj2spMrNaGhA0OPaGmgB6R2OIJlFxF+RijvNjKXfCua0NmsHIWDxZcd9ZudCb6tMxo3nieRgMpueCuCKV/DvOrHXw41dvbLdd6xBN5bk01oYtP0dbUy2AfvSPJ0r+ed1mPFXm01iT+xpgREdzesOnqSYInz9guQiZUq8GMqMx2ZPvMppIIpEyCWiurzF8j+ZadeNvPKFYHsO5C6dn/P2dd52Jq88yrwEr9rV9MtI7Zq92tXesdOfqaFy9HjXXBhEKhTB/ehA1QR8icRmdw3EsmFbvyvsoUjrFq74mjIANubg2Zc4Sl63HvhnDUXXN0tKQOWeeOnMKgCN4o2fU0++9c0hNgZzTUo/m2gH0jsYwGnf2WUZj6vxwcnsj3nRyaVM6AWdruzJL8HDO9u3b0dFR2buPYuFux3hAGER42QTXbvNbQWt9CPUhPxQFONJfWQYRx1PGEE01AdOAT+Ti0+jAGuGqN2tqbVHsjquddPNb4/NQ2HcXq5eTmXtWoU1K06561pejamyCKxhKmTQ01Thf7Gzc0Ykv3f+K9vfBvjG86ZuPWX7vuRztCmVM1/euzmReFccwlGOOyL6mzG2pK+nONKkM4w5R3zSlVj2n/D5JaxL7hou9joTLsU+CraAJKHwdJ5rfCmMIQdpZz1tLcnGtnzmlFk2p79eus55gYNz4M1QCJVWcRkZGsGfPHu3v/fv3Y/v27WhpacHcuXNxww034OjRo/jVr34FAPje976HBQsW4LTTTkMkEsEvfvELPPbYY3jkkUdK9RFcIWKjAaQgGPABsWRBNpa5cBo4SZKEea312NU5hIO9Y1jcVhnpbADQM5RqMGqQSiJoYBPcnEQTSW1BO2tKrea6w2Azf6xc9QBVoR6KJIrirGfmnvWfbz0VX/vzq6ZNSiWoTUpXL203Xeza6eMEqCYZAPDCwX5s3duLlQtaqmYBPZSSnMQixC4iaDUr0DezNG8Me7sZJHo4hQM+08WksETP1bJA3y8GAHpHYi4cISmElQtaMK0hhBMmv0U5uKOJhbnegfPkGY3YeWwIb/SM4HKXMshF8GPXUU//2Hwb4Irmty1ZBVUnz1DXXscGIxgcj6PZ4Xxil2ODqU3SKbXaezgOnERgWwZp5k4pqeL03HPPYfny5Vi+fDkA4Prrr8fy5ctx4403AgA6Oztx6FC6Wj8Wi+Hzn/88Tj/9dFx00UV46aWX8Oijj+Kyyy4ryfG7RT6KU9RTxUkd0NMa7O8EzJ+mpoocqLAmuGKxb2RFLqhL9SERiwEykc4BdUFdE/ShpT6k7Sazj1P+aIGTyQaG6OU0ZnNc5ttvxUpR+vg9LxbcpNSOHfnGHZ346eP7AADPHezHtT/fllNVqSSE4tTowPEnKStY/+Au06AVUINWo9+5sSbdx8mLvljpHk7mnyfdssB6wXWkPzNw6htl4FRqfJL5+sBJ82YvEQtzvaKR7uXkniIjNrGd2H8HA4U1wBXnwNSsOvfm2iBmpnq6ve7iZ9Qjy4p2vZ81tZDAKRXYehTceUlJFaeLL77YctK+8847M/7+whe+gC984QseH1XxcaI4iZPTq1S98VhS24WcZhFMZDO3pTKb4HYPmfdwEtRrihODADPErvCsKbWQJElLOaLilB9JWdEaHJqNTdHLaSyeW3HKt9+KncW5HayalEa1+c84cMpXVakkREqrk1S9Z/b32Q5aVy1qzbhPbGwkZQWRuDyhwXKhpHs4mb9uupdUrlS9zMBJ7LaT0rHl9eN4rWsEAZ+EqfWhjGao7WXSx0kszKfoFuYnicCpx70muGItZrf5LZDZx8lO/6Ns+kXzW4M0tyXtjTg2GMFrXcM4d777it/xkShiSRk+CZjRVKMFPkN5pupNqcBUvYqvcaoG0oqT/cApX4k3FyJNLxTwaekcdpif6pFwsEIVpxlWqXohBk65SNc3qeNApDdSccqP/rEYkrICSYJp7zBNccqRQlpIDVKuxbldrGodrFL1ClFVKgmx6HCiOFkFo7keVx/yQ6zVhnMoPvmgKU4WDmN2m2SLGiehcDBVr7QkkjJu3aA6OP7zhfOx7YbLsO7MmQCA1afOwJNfvLTkQRNgnqoHAHt6RlybM2J5KE4iyFIU5HUcfWPGihMALGlXzbp2d9l3iXOC2CRtb6pB0O9LB04Or/WDWTVolQQDpzIg146rHq/NIbSC9Iawo12Qea2VqTj15KgjAdKK00iF1DglZQVP7+/D8yckPL2/ryiLyiM6xQlI72iPxBKQK3xRWwrEDm5rfci0RqQ2tSi1StUrNPCwuzg3Q4KqbJnVOiSSsua+ZqS4O1FVKhlNcXKwiCikQF+SJE8NXDTFKWx+TRM1TrkWXGKhdvqsZgBA32j1mYNUEvc9fwSvd4+guTaIT15yEvw+CRefrDofDkXiZVN3mFac0sHFnJY6hAI+RBOya0ZW+SlO6cfmswlupTh5bRChN4ECgKZadR7J1xyCNU4kLzTFyUmqXtKbRfyJYWEM4Uw+nZdSnI70jyPhoXGF2/SIVD1Lc4hUjVMFKE4bd3TiTd98DO/75XP41Rt+vO+XzxWlFkRMprNTk6lYlCmKvVQyksnx4dwGLcKtbNzi+y008HDiipW9XLJT6xDRbQAZbRwVoqpUEvnUOK1c0IKO5poJ37sgV9Da6KEq7EhxsnD2UxRFm1tOnz0FALQUVlJ8xmIJfHez2tD8U5cu1tQcvZJTLgyOTzQfyHTWc+dYheIk0u/soA+c8qlzslac1N/ita5hT+oXxUbGzNQmaaE1TkzVI3mh1TiVgR25MIawUmCMaG+qQSjgQ0JWcGygchYxx22YQ1RKjVOhttCFcHRA3b0TilM44NMuJF5ZHlczuRz1gPRuvpUdeaGBh1icmyEW5z9+7wq0Zz2uvbkmZ/2R3hHQKFW5EmyP3WA4Dztyv0/CTeuWAsgvaNUbRLiNMNKxqnGyo3idGIkhmpAhScBpM9UUJNY4lY6fP7EfPcNRzGmpxT+tmqfdvritAZKkBrW9I+WhCKYX5tnOc2rg9HqPO4qMUIwcmUPogqx8HJL7U3bkRmnci6Y3IOCTMBxJuJJmnc2xrOySfAKnpKxom0VUnEheOFGcxE5FzOMaJ7tW5AKfT8K8FuGsVznpek7MIcrZ6KDUtSBHsuR7fSoQ65yck6uHE6B31TMPnAoNPPw+Cf9+xRLD+/SL87VndODJL16Kq89Ug6TLTplmq9ZBb4xjlBpcqKpSKQg7cieKEwCsWdaB29+XX9DaYNPVLh9E3Z2Vq54dAxl9PYWoQ+1jjVNREW6cv952AD/eoraP+cIVp2S4ANeG/Fq2wesuKTmF0m+iaAiDiD1uKU4J5zVOkiRlGEQ4IZpIG3gZBU6hgA8Lp6ulE16k601M1XMeOA2NxyHEMLrqkbxwpDh57KqXb+AEqOl6b/SMpOqcpud8fKkZjSYwmlp02unjVKxGo/lQiMNWoSRlRevbJHahAHVh1j8Wx3AZB5zliugvZh04qeNy3KLGSQQeXYMRUxe8XIHHzmNqkbHfJ2UE3tnuWX6fhFM7GnH/S52oCwVs1TpEE9b1nUJV+djdL0BCppNfudgeu4EwaHDaxwlQg6fVS9vxzP4+9AxH0NZYY6vHlV1Xu3ywpTjVpOfVRFI2rOUTdSizptSiNbVI7B2N5eVERpxj5MYZ9EsIGIytk9sacbhvHHt6hl2/xuSDmfmA6DPplrNePjVOgLoJHk8mEU8428wUSprfJ6HJZKNlSXsTXu8ewWtdw7jklDZHr58LN1L1RH1TQzjgqP9VucDAqQzIq8bJ88DJed5p2iCiMpz1hDFEfcivBUdGiIt/sRSnpKw4XgSVshakeyiChKwg4JMy3AnV4u9xKk55oDdpMUO40FkF9FaBh+Cjb15oOr5e7x7GnU8dAAD84rpzUBP0W45Lp4vx8Zic8VmMEKpK9gLObdvjfM47t9Aa4DpUnAR+n+R4seqlOYSdPk76OXc0mkRz3cTrn353W+yuRxMyxmJJy9cmhWPWBiCeVPDx30xsA3DSjEb85bWeslGczMwHRKrenp4RyLICX4HneD6ueoDIHko6rnESrpJT60KmmwentDfiwZe8cdYTgdPsrMDJiR256LFViWoTwMCpLBCKk60GuJoduUeueqIo3WGNE5C2JK+UJrh2jCGA9AW+GDVO+fbbKWUtiJZO01yTsdC0azdMJnI8FeDaSdUbz6GEmgUeoYAPsYSM/++FI/jH8+ZNuPArioKbHtiJpKzg8qUzbO1cNjhMa43kUJz0n2H10nZ85f5X8NtnDuNNi6fhrg+udC2wyfe8c4uhPGqcCqXRw15rdvo4hQI+hFMOZ0OReIZttEBbpE2tRV3Ir43ZvtGY7cCplAGxHcrx+KxSvwXrH9yF1UvbtWPVaoc8arzqhGgiqQXvelc9AJjbUoeQ34fxeBJHB8YxJ1VikC9iE9uJOYT6+PzWcqLGr6XefK5YMiNtEOEmQ5G4ttEiFCehkkcTMiLxpC136Ep21ANY41QWCMXJVgPcIplD5JOqNzelOB3qq4wap24bBfhA8cwhCjF3KGUtiLYrrEvTA7x17ap2bJlD2KhxEqxZptYgCUXjG/9wOv76+YsxpS6IHUeHNKcsPQ+93Imt+3oRDvjwn29bauu4nRoOiKDPzsXW75Nw3gJVVUnKiqtBU6lMVQA1QM3HjrxQ0upgafo4qcdgHbxptZNT6iBJkpau12fTWU+4jF778234zL3bce3PtxXFZdQu5Xp8+bhxntRWPs56Im1MkibWDQb86RqgN1wwiBCBj9NUvVCeNU5i7E+1cKMTznp7j4+4uskujCGm1AW1dVFDKAAxFdtVnQZNjDsqBQZOZYAjxUkzh/AocLJhg2yGvgluJfTuEYqTVfNboDjmEIWaO+gdtrLxuhZE7AqLYlGBqGFgjZNz7Lg9an2cbNq9+6S0dfnfnTwds6bW4hv/cAYA4KdP7MWTbxzH1r29eGD7Ufz1tR7810O7AAAfv3ix7V1Zp6l6enMIO4hzVZi6FEqpTVUANcgQr+/UHKIQnKqDTrDTxwnIPV6y2xy0OAicSh0Q56Kcjy+f1O9yctYTC/Pm2qBhKt5JKUXGjbTCWB6uegAQzDN7KK04mQdOs6fWoiEcQDypYP8J9zayjTZJfT7JsUGE+AyVaEUOMHAqOYqi5FXjFPVAcYrEk9oi16q2woxZU2oR8EmIJmR0V0BfFTuLU0CXqhdLetIXAXCn0adIyarPSo+x47BVCGJXeHaW4kRXvfyIxJNaU9DpDeZBfTpVz6a6E09q1rkit3zNsna859w5UBTgul8+o+18//Odz6J7OIrWhhD+5aKFto9dqIx2g2XRx8mOMQ4AzGhSz1W3AqdyaLArgoaAT7Ks9XIbL80h7CtO5s5+iqKkzSEcBk7lEBBbUe7Hl0/qd23IjzlT1Q2WUtc5CUc9M1VGOOu50cspH1c9QOeQ7NAcQlOcLAInSZK01Ek30/WODhhnlzg1iEg3J6biRPIglpQ1W0Y76Sr55sXaQRhDhPw+rRu0EwJ+n3aBqwSDiB6bgVN9atc0KSueBKzqsbhj7rBmWQcuXzpD+/uGNSfbsoUuhJyKE/s4OUI7DwPW52FtSDRmtqc4iYua3ydlBNfCVMBojdY7EsOW3T22Xh/QW1wnbG0yRFIL7FqLWhg9QnEajSVdGVdumqoI2+YHth/F1r29the9+ua3xXSK89Icwo6rXq5jGByPa66nYqFmN3Aqh4DYinI/vnxTv7WAxKUeSfmSy3xAsyR34TgLcdUD8lCcUmO/JYdas6Rd7XvmpkFEtqOewGngNMgaJ1II+oW4UQPIbLx01UvXN5m7teQi7axX/nVOYjHU1pQjcNLtmnqVruemuUPvWHrymttS53mh8VHNMjgznauxAvpflSNafVND2PI8rEtttIzbTNUTF6vm2qD2uklZwTcefs30ORKc7XyL31xRoC16rUibQ9i7FNWHA9p7dA8Vng7k1nlXSK2K1vy2yLuvXppD2OnjpB6DeeAklOxpDWFtU7FFZ0luRSldRt1831IdX76p3yIFzg0lpxBymQ9ox5ly1isEUTbh1FY77xonoaZZKE5A2qzj8d3HHW3kWJGdOisQpjZDNjezBjSreKbqkTwQOf6SZG/HwtPAqQBHPUElOeuJhVeuRZHPJ6UL8W3u7jvFTXMHfX6514YWiqKYK04O07aIih1jCCDdx2nMZqqePu9f4PbOd03QB5+kXqDtKEJOetgJZjS7V+fkxnlXaK1Kvs1vC8VLcwj7ipO5mUh2U20AOnMI66C5lC6jbr5vqY4PSKd+Z58bVqnf5eKsN5gjFWxeax2CfgljsSSODY4X9F7xguzIC1CcLFz1Nu7oxA8eU5sV7zg25JrpSM5UvTGbgZPYxKPiRPIhGk/VNwV8tlSesId25IU0vxXMTRWRH6qAwCltDpH784pFqlfqiZvmDqLPA+C92tM3GkMkNYZnTsm8yDeIHW3WODlC6+GUK3AK23fVA9KKk17ZcHvnW5Ik1KbWynZSwEQfpxqbqXqAu3VOVuedwOq8c6NWpRRW5IC3NYh2+jgB1jVOeityQUu9+tv3jVov0ErpMmqHcj8+wapF07Rx/K13noHffuR8y9Tvk3VKTikZGLc2Hwj6fVg4TaQVFnashabqCXMJu+Ry1RMbOdnprG6YjhwzSdVLm0PYm0tY40QKIpqw76gHpHsFeKE4aT2c8mh+K5ifStU7UOapehkF+DZ29RpSi9RRm7v7+bBmWQc++5aTJ9zuxNxBURT06nZjRzxSyARicdPWGJ4whr107apm7CtO9vo4CfSpegIvdr5rtMDJhuKUyENxStU5dblkELFmWQf+37XLJ9zeXBvMed65odiJeahkilOJ+jhlHIOh4qRuvulNZ8Quey7FSR8QmwUnXrmM2qGULqhOEAvllvoQrjlnDlYtarU8pkXTVWe9vhI76/UbqOvZLJ4hDCIKU8fyboArNsEdruWsXPW8NB2JJWStLjw7uyTfGqdc6YblCgOnEiN26+3m+ItdjWiZKk7zp6Utyb1yoCuUpKzgkZ1dAIBgVqG8GcWwJAeAZOo7E5em8xe2ODJ3GIokNOc0wNtAD9DZk2ZNpAC0nkFUnJyhr3Gyoi6ofr8JWbG1kWIUOHmx8y38LIZsKU7OapyAdODU40KNk2DpTLWQOuT34W1nqOfasplNOc87NxQ70fuk6IqTzsjDzfYRiaSs1e7adtUzGCtGc0taccptRy5SzYwyCr73nrOK0tjYijXLOvDjf1yB7ESTtqawpy6oThC/QXY2gRnl4qw3qLnqmZ9TbjnrpV31nAW5+dQ4KYpiqTh5aTrSOTgORVGznlqzAh6ngZNmR07FieSDU8UplHqct+YQ+QdOs6fWQZLUi7HdJoXFRBRxf/re7QCAuKzg7/77rznl62I1wd22txcAcMFi1elMUeBo1zH7O7fruJYvZjnPQObCjNjHruKkd6KzozoNaYFTejFrtTOf7853jV/UOOX+3cX858SGu10oThYLBKccSPU6WTi9Hl+44hQAwLb9fTnnMDcUu1I0vwXSgZqi2O8FZgf9a+Xq4yRqnIyCbONUPXvmEII1yzpw94fPB6AuVEU2hd3rrdecOWcKFEXtsSYWn9+55syyCJoA6/ndDFHnVEpnvVypeoB7aYXCTrwYrnrj8aS2KWGkOHlpOqIfC9llJU4CJ1lW0pt4rHEi+RB1qjh5WOMkaisKMYeoCfrRkVrYlJtBRCFF3A1FCJzGY0m8eLgfALDujJkA0iqgXbLTI7wOWowKuAVpq2HakTvBbo1TKOBDIBXQjMVz/85GihOQ3plvb85c3Ofb/6vWSaqeNv+VLlUPgNYkcsG0esxtrcOyWU1Iygo2pZRpM4RiZ4YdxU5vR15Mwrrx4+Y5Kgx0Aj4p52LSqsZJm1t0bp1ip3s4krB9DRQbEbOn1uHqs2YBQM7ftVjsOqZaRZ88oxHnL1THiJt9dwrl2MDE3yAXi9tK76wnamisFuZpS/KRgrJj8nbVCzivcRIbOaGAzzAN1kvTEcvsktRmnB1XveFIQmvBY5VKWc4wcCoxkTKqcRKL9Hya3+opR0vyQnN/06l63ik4zx/sRzypoKO5BmfPmwogfdG3y4mRbMXJ41Q9sSucQ3Eq17TNciMpKzjcp244dA9FcuaiC9XJjkGEWeAEqMHTk1+8FL/9yPn4/nvOylkEboVY/9szh0il6uVhDtHjYuAkajIXTFPnrrWnq5/7zy9bK9F+n4TPXHaS4X12FTtNcSpyqp4kSZapcvmid9TLZXjUYFLjNByJa+NVv1Brrg1CfJX9NlWnbs0EqAZXLGsHAPzl1W5PrqFO2dWpBk5LO5pw2sxm9bZj7vXdKZQjA85S9YDycNazYz4wr7UeAZ+EkWjCMr0tF3nXOOWhOPWnTFFa6oxbxnhpOmKlPoprypANxUmogXUhf9kov05h4FRinCpO4SLYkU9vLKxgb15rus6pXCg091eYQ4x5GIhs3XcCALBqYauWLjkUSWjpTHbozSqattNLpxCsdqEaU2k4smLf+W0ys3FHJy785mNa8HvjAztzWsg6MYiwCpwANQhYtagVbz9rVs4icCscKU6aOYT9S5FQxnqGo67V5hw4oc5V81OB01tTgdPWfb050/WOpeYVf9ZCxq5iJxYbxVacgHTgYqcezS5CcarLUd8E6Oogs+ZVsUibUhfUlGtAbQ0hajvspusJZbK9uQYr5k7FtIYwhiIJbNvXa+v5XiKCpKUzm3Baqs5uZxkFTscM0iVzUQ7OeukGq+ZrmVDAp22UFBLkxQttgOtgLdeXqg0yM1XwIvVaYOaoBzhL1at0Rz2AgVPJcao4eZWqF02kXeYKqXECylNxKjT3VxQ5j3hotrA1Vd90/qJWNNcGtTSa3hH7tWLisaIotliKk1EqR03Qp03QrHOyRqSRZtft5EojFYtTO79zrsDJLdKuermPSevj5CBVb1pDGJKkmmLYXTznQp+qB6hzmJ10vVhCxj1PHwIA3PIPy7Tb7/jAubYVu1I1wAXSNUZunp+a4pSjvkn//tljRduQMViktWi9nGwGToNpxcnvk7B66QwAwMYySNczUpz2HB/RzotSkzaHsB846Z31nKaau0EsIWvj2cocAgAWt6nn+/0vHs27SWy+ilM+5hB2ejiZpV63NoQKMh2xozjZCpxsBLXlDgOnEuNUcRK7FFGXFSex4A76pYIXVuXYBLfQ3N86j2ucRqMJvHxkEICqOPl8ElpThcxOLj6ixkn00/IyYBmJJgzTaQSSJOnqnBg4mVFIGqkwVbBT3G/Ux8kLagP2zSHGU/OfE3OIoN+nbe640cspEk83wRTtFIB0ut6GV8wVvw2vdOLESBQzmsL4hxWztXl8cVuD7V3dUtmR69/TzVQ90ZA5l6Oe1fuL+iYjpcNp4CQ2w9pTKZ5rUul6j+zszmuh7BZDkTgOpdJyT+1owoymMFrrQ0jKCnaXQZ1TNJFM2087CJz0znqlqHMS85wkAY0W6a8bd3Tib2+om5X3bz+Wd5PYtKue932cerXAyXpzW596LWq5vrDmlIJMR6yyS8SacSyWzBkIDghHvQo1hgAYOJUcsbNkW3HSTjZ3AydRS9NaH7bViNeKuanASVwUyoFCc3+1Pk4e1Tg9d7AfCVnBrCm1mJMKesTi0EngdCI1sc5LvYaXrnpiIm2uzUyn0cNeTrkpJI3UWaqe+hsUT3HKvfsYzUNxAtxtgnuobwyKoo5VfQ87ka731F7zdL27th4AAPzjefMQ9PvQktpFFXa7dhguUQNcAGj0wMBlVEvVs6E4pQKnWFLOUFmslOx8FSexA79qYSsaawI4MRLFi4f6bb2GF7zWqQZHM5trMLVerVlZ6kG6XlJWsHVvLx7Y7kxVEd9bTdBn6OBmRSmd9QZTNTRNNUHTzQuh8Gdfl/JpEisCJ1F/bpdgHtlDmuJkI+gQqdfnL1Qdevcdzz8DSJYVLSXZKIjWB6i5VCctVY+BE8kXoRyFHbrquV3jpPVwKrC+CUin6vWNxmz7+ntNobm/XvdxEml6qxa1ardpgdOw/UVYX0o5nNuiTm5e9nE6OqAGxla7kV7saFcbhaSRCiU0Vw2Zoig6O3KPFafUetlO3Uw6Vc/ZpajdRWc9fZqeftNoXms9Tptpnq738pEBvHhoACG/D9eunAsgXXvgpBXD0HhpzCEAvaudB4qTyWaKngadKqU/hqM2FCe7aZrdqX5fwo0xFPDhslPaAJTWXW/XMTXDQARL+v/embqvUET7jWt/vg2fuXe7I1VFn6bndDP1pFSdUykMInItzJ0o/HaCTqEYFcOOPFeNkxGLde6B+XJiNIpYQoZPwoQUQEBdX4lNmFwGEZrjYS1T9UieaIFTiWuc3Gh+K1B3btXXOVRG6Xoi97ctqyGinSJuJ3bk+ezwbU0VKq9amA6chB31cSepeilziDkicIomPXO0s5LuBVZ2w5VIvru3VhSSRloXFIqT9biMxGVNpfZccXLiqpe34qR+F90uNMEVPZyEMYQeq3S9O586AAB46xkd2rnqVA2JJ2XtO2iqrRJziJh9xcnnM07nPdKf2pQxmFtate84928vy0qGq55ApOtt3NlVMsdPfX2TQNQ5uaE4FdJ+A0g76jlJ0xO41Vw2H3KZD9hV+H/42B5bQWdpapycB057j+f/WxwbSJ9DZrbrTTbrnNI9tipXcSr+TE0ycLrjqqXqua44Fd78Vs/cllqcGIni/3vhMEaiCaxc0JK3S5ebrFnWgUXTG7D6ticQDvhw5z+vtHVs9TaL8Dfu6MT6B3dlTMwdzTW4ad1S08BsOBLHjqOp+iYjxclRjZNQnNQUl4SsIJqQHS9M7WDnwlrsGqekrOCZ/X3oGY6grbHG1XGXz29rB5FG2jUYMdwFlaAG90ZppHU27cjFxcyvW6h6Ra3WANebPk6ALnByoQmupji1TkwLe+vpHfjWpt1aup6mdoxE8dBL6gLqulXztMcLxze7gZP+vPD6dzFCpNi4W+OkjkU7NU7qMQQwEk1kHINVIbr4DYQ1sxW9ozEkZAWSlNkX7c0nT0c44MPhvnG82jmcofoUCy1wmqkPnNT/fq1rCElZyXvuyqWqSFBVldVL203fIx9HPUEpnfUGtOaqxsGFXYX/tkdfn3CbCDr1G62FuuqJBrp2EPPKVAfGCiJwOtg7imgimZcFuB2TkObaII4OjOcMnAbpqkcKJV/Fye3A6bhmRV544LRxRydeTV0U7nzqYN5Fl14hFJxZU2tt2y7bSdXLd4fv2QN9SMoK5rXWZUxM0zRzCHuLsKSsaFL+HN3Fzqv0Qqt0GkFDjfuuXWYUkpZi57UL2b21Qp9Gmk2uNFK7fZw0Y4iaQME1jLlw4qo3XkapekaK0/xp9VjaoabrPaJL67r32cOIJWWcObsZy+dO1W7XFvU2a5xEWkt9yI+Aw4WXG6RrEN2scbLvqqc/BhFoj8eS2pwnTAb0TNVS9XJvKAm1aVpDOGOnvC4UwEUnTwdQGne9eFLG611qULG0o1m7fUFrPepCfkTiMvYVoBAU2n4D0C2Wm50HTqV01hPmA2aOevk0fxUYmfUU2sfJSb26mFecKE5tjWE0hgOQlXTbBafYScu366wnAlsnwV+5wcCpxDhWnLRUPXfTC9xK1RMLTOGWJXBjgekW4mLa3mR/Am3IUUtSiDOaVt+kS9MD0kHsCZtNcPvHYlpH7tb6EMI+9Q+vnACtdoUFxVKcvAxsCm2ebAeRRprtLpcrjVQzh8jhqlcsK3IAEBlnuRofy7KibQA5cdUDoKXbumEOIZrfGgVOgJqKBwB/TqXrJZIy7t52EADw/gvmZzw2rTjZC0S05rcl2n1tNGlAWwj5KE4AMJyap8S80hAOGKYvtqYcxeyoepoxhMFcf8Vpwl2v+IHT3uMjiCVlNIYDGRtPPp+EUzsKN4gotP0GAM1p0ioV24zakF/Leih2ul6uVL1cRlG5yA46Y/kqTmIt56SP06jzoEOSJCwqsM5JpOpZjQW7TXBF8Ndcwal6DJxKjFPFSb9L4WZudjpwyn8XoBgLTDcQdRFOAqf61O6pmXJSyA6fVt+0KDNwcpqqp+/hFPD7IDZ8vVac7NU4eRc4eT3u3Ni9tcOaZR24YJGajvfuc+fgtx85P2cvoNqQCOitv99iBk5CcUrKiqUSpm+p4DRVTxQoFxo4jcUS2nyw0CxwynLX27yrG52DEbTWh7SgSiD6q/TbTNUbipSu+a3+fV3t4+RUcarJ7OV0VJciZqSOOqkj6zKobxJcdmobAj4Jr3UNa3VuxUI0vj21owm+LCVZpOuJVL58KLT9BpBfDyc9Wp1TkZ31RA2NWaqeHaMoO4igs1g1Toqi5KU4AYUbRByxmaoH5FacmKpHCiZfxQlw15JcpEZML0BxKtYCs1C0hogG7jBm1OvMIYwC1nx3+AbH4trO4vkLjQMnu+YQoodTa+p5YgHrhaOd3R4fThWnfMwXvB53buze2mUg5bB2yZLpttJIndY4FUPZCPmgHbfV765XyRzXOKUWfP1jcUQT+Vvui9SVKXVB04aM+nS927fswbcf2Q1ADW6zN7w0Vz2bqXqltCIH0g1ovTCHcKo4jaS+C80YwmReEf3t+sfikHPMDz0iu6B54nVtSl1Im3OL7a4nAiej2qrTXHDWK7T9hiwraZUh38CpRM56uRQnwLxJbHtzDT73lpNsvU9bYw0URdGyf0JO7cgdpuoNRRLa9dCpsYIWOOWZ/qltZliMBaEO55pLqqEBLs0hSozjGiedHBxLyHkV+hkhapymFVDjVMwFZiFoLksOPqsInGRFLWivzXKMyneH7+n9vVAUdbc7e1dUqH8DY3HEk7Kpm42gN8txR2z4emFJ3jlgr8eHkz5O+ZoveD3u3Ni9tUu6OaC9i4rdPk7FVJwkSe0PNDAex3AkbmhfC6Q3jUJ+n+Mi+Cl1QYQCPsQSMnqGolrvM6do9U2txmqT4KQZDdjVOYSf/22/dtt9zx/BGbObM8am1sfJruI0Xrrmt/r3HXGzj1NqvrHjqgfoe0mlFKccSrZYNCZlBUORuOW50pUjLfuKZe14cs8JbNzZhX+5aJGt43UDI0c9gd5ZT1GUvGoSharysbtfmHCfnfYbJ0ajiCXN7aftIHo5vV7kVL1BbWFuPdetWdaB1UvbJ5gJAWoNo5lZD6Bmdaxc0JKx+fPy0UFMb663PZc5tSMXc0p9yO94o2nx9EJT9XJnl2iK05j5XCLLChvgksLJ11UPcK/OKZaQtcmmkBqnYi4wC0G7mDq4INTpJiqjIMBO3rTRDp9I0zs/K00PUPOYxSTca8Mgojcr3bIm5W424kETXH19k9WFvcHmwqyQGiWvx12hu7dOcNocUNQGjZZR4ASkG0Zb7T5qzb8dGkMAat6+G01wRX3TApM0PUAdmw9sPzbh9uPD0Qljc6pTc4hI8ZRAI7xoUD2Wmm/s9HECJqYLHslhOhMO+LVgK1cvp65UGmabWeC0dAYA4MVDA/jV1gOutRiwQlEUQ0c9wUkzGhDwSRgYi2tNR/NBqCrZ63g77TdE8GplP52Lk9pUxamQ/kH50O9gYS6axL79rFmawm+VyicYGIvjqw/uxCXf3qLd9tG7tzsyI0oHTvbGWz49nARCcdp3fMTx+B6JJrTrR6GpeiOxBMTbF+ta5AUMnEqMU8XJ55MQSM2EbjnrCXciv08qKO+0mAvMQujJaohoB59PQn1qB9XIbMHKGU1w49sm7vBt26emj2UbQ4j3FGqOnTonsYgQxdOa4uRBfVF6V9h6p7/RxsKs0Bolr8ddIa53TlAUxbHjUF0qHSpXH6diNb8V2HFTFLu1To0hBG446+VSnMTYNMJobLbW208jA9KBZakVJzfNIZwqTtnpgulNGfO5paXBXp1Tt4U5BAC8cKgfwVSK1Y0P7CyKA2znYAQDY3EEfJK2oNUTDvi123ceLawR7kUnt0E/DGc0hXPWTQL2jH9yUSpnvfTmU/6pYGapfB3NNbhwUSsUAHdtPTihj5wTM6JQwFmNUz49nARzWuoQCvgQTchaKqxdxLW+uTZo2TLBTh8noUbVBH2etEgpFgycSkw0Dztety3JTwyLBXdoQqGqE+zs1LixwCwEfUNEpykIuSzJxWRrZoOaXffQPxrTbNuz65sETuqcRJ2aqAHwMnCy2xyxMavw24hCa5SKEdiI3zb7PLWze2uX4Wg6h91ugOO0xqlYgVN6QW5+Ec23h5PAjSa4whRgwXTjwMnp2BSLtaSs2ApGSl3jJM7PsVjSNaVFq3HKU3GyYzpj1yDCKrtAqNzZO/5eO8CK+qbFbQ2mY9+tRriHU4tkkanSMxy1tVAXqVn5GkMAmc56xaxzcst8YM2yDjz5xUvx24+cj++/5yzNrOeuD6403RRwYkYUdNiTM58eTgK/T9LMb5wqgMdsXuvtKE79Y/l/hnKCgVOJcao4AbrAySVzCLEb5EYPJ7OdmoBPwo//0Z0FZiHoGyI6TUvMZUkOqJ//s6tPBgAsm9WE337kfHxp7SkAgP966NUM96an96tpeie1NZh+904syc3MIbywArfTwwnQpepZBG9u1CiJcdeUtXPvZmCzZlkHTm1v1P5+x4pZtnZv7TKQspqtDdrPYS/HGidgYt2KEflsGulJB04upOqZKE5Ox2Yo4NM+ux2DiHSNU2lT9QD3TGQ0Vz27ipMunTeWkNGd+i6t5pYWG42GI/GkNu6zswtK6QBrVd8kSBtEFBY4HepVA6eTZjRgal0QiqJaoefCTvBqh2Kn68WTsmZr74b5gFEq37MH+i3XAHbNiBzXOOXpqCfI15L8iM0g2k7gJNTASk7TAxg4lRynNU6A852KXBx3qYeTQL9T8413nI6Q34eErLgSmBWKWUNEO+id9awQrn3nzGvBqkWt+PCbFmLVwlaMx5O4/vfbtYux1r/JoL5J4KQJrkjVm1Yvapxg63jzQTTEyxk42VhAu1WjtGZZB96zcm7Gbb/64EpXg/XD/emFdF0o4Kp6Kmx0zRRLI+pCuYN5oDwVJ9dS9fKsAxmKxLXzav4047SwfMam5qxno0FrusapNKl6oYAP4dRG3LBLTXAdu+rp5ojOwXEoino9bLVYINpRnMS4qA36J2yolNIB1spRT6BZkhfgrAekFad5rXVaEGOnr9LRAh31BCdpBhHFUZz0PYSyf3O3cMuMyHGNUx49nPSclGfgZHeTVKTqDVnM+QM2jTvKHQZOJSYvxSmPjtNWuNX8Vo/YqXnPuXNx9fKZAIB7nj7k2uvnSz7NbwW5ejkJ0mkO6nv4fBK+/a4z0RgO4IVDA/jxlj3YurdXs8A9b7557c10B72cshWncMocwm1XvaSsaLuW/aMxy11ZfRqOWd8xN2uUxHcvONSXX6d0I8ZiiYzfwU4fGSf0i904BxfGWi1Vr3z6OAH2amdEql44z8Cp0Ca4Qv2d1hAyVXzyGZvpwCl3IFLqVD3A3V5OiqKka5xs9nFq1NXDCWOIXKYzosbJyjRHn6aX/VqldIB9tSu34iSCqmODEdsOjUaI+W/O1DotiLHTV8mNGicAWJxKgd26t7coxhtiYd5YE0AgT1OLXLi10RfK01VP9IpzSr6W5E5T9YZ1tunZDArjjlqm6pECyEdxCntU4zSt0ZvB/N7z5gEAHnqlU7OiLBXphojOg0Sxg5pLwTHKD581pRY3XXUaAOA7j7yOa3++TXN8Wv/QLtN8eidNcHtNapzcdNXbuKMTF37jMRxPjZmbH9xlWUwtFKekrGgL5WzsNCS0W6MkFl5CxdjvYmPLw32ZQZnbBc8DY/koTqlUvbg9xalY7m12UvXGtbmvMMUp38DJjhV5PmOzpc5+E9xS25Gr7527DtEukbgMsT9iV3Fq0AXZdk1nWm24F3ZbzPWlcoAdjsRxMJU+d6pF4NRYE8S8VvU7KCRd77AInFrqNMXBjj34UdFLq4BUvY07OnHrw68BAPYeHy2K8cZAEWpo3NroCzo0hxAZJfm46gGZTXDNNjGNOOowVQ8wzzRw6hpbrjBwKjGF1DjZPeFyIVL1Cml+a8WZs5txakcTYgkZ/98LRz15D7sIlyUnjnqCXOYQAtE4MHuiqTfJ+TeyNRaIYDbXIj2aSGq53a1ZqXpu9WgRxdTZLmZWxdR1IT/EZq9VKpCoUcpO55zeGHZUoyQcg0T64z4XA6ds9SqXFbJT8rmoiMApnlQs54Oi25HXiGaIVuYQIlWv0BqnqKOFgEA0v7WyIgesm2UajU0nTXDFOVEqO3JAZ0nuQuCkV7ftpmDq0zpzNb8ViIWx1TmYDpwmzvWlcoB9rUtVe2Y21+RcALvRCFfMWXNb6rSGtLlStYYjcc3hMF9zCHGtyE4x99p4oxgLc7c2+pyWXGg1TnkGhQum1cMnqRsUx23UTAvs1rsF/T7temRW5yQUwWYGTiRfFEUpixonYTzgZqqeHkmS8N7z1NqT3z5zKK9FjlvkaohoRbrGyXx3P5GUtffQX/yTsoKvPmTf1ligKU7D1oswkTYW8Ela2k+6xqlwxSnfYmpJkmwvzNYs68AvP3Buxm3fe/dZtoOm8VhSu1C/+aRpAID9x90PnMTOXa/LilO/w+a3ADIaMZvVOUXiSW2uKNYFy16qXmGKk1gQj8eTObvVGyGMIebnCJwAc4cto7HppAmuUJy8qsewgzg/rYJcu4geTnUhv22HVn3Lglw9nAStmh25+TnYNajeZzTXu6lyO8FOfZNAOOsJMwmnKIqSFTip89bB3lHt3DNCbPzlsp82o5TGG8UyH3C6mWJEyGGNUyF25IC6OS9cDu3WOcWTabMWO2mbuQwi6KpHCiYhK1qPhXwUp6hF4JSUFWzd24sHth/NmVvsRY1TNlefNRN1IT/29Izg2QP9nr1PLoR18Yw8uqGLpp5WNUM9w1EkZQVBv5Sh4OVbjGw3VU+k6bXoLOXTqXqF7yQXUkxtp5eTIDv1Riyk7CAMKxprAjhzzhQAbqfqqa+/PPXaA+NxJFxSfYH0Rd9Jql7I79MWd2bOeuIi5pOABpvpU4WSTtXLrTjVOJj79NSG0kX/PXmk64mxkUtxEhg5bBkx1aZVtqIoVVfjlO7hZH+ciVRBWQHeSC3ocgVOLaledX0WNU5WihPgzuLXKVrgZJGmJ1haoLPe8ZEoInEZPklVjqY3hNFcG4SsAPssNpTEPJpvfVMpjTfS5gPeL8zFZsrdHzwH152UxN0fPMeRy6pTV72+Al31AOd1Tl2DESiKuua0MmsR5Aqc3LKKLzUMnEqIftcn7KSPU44TbuOOTrzpm4/h2p9vw2fu3Z4zt1gLnDyqcQLUi+NVZwqTiIOevU8ucl1MrbCTqifqm9qbazJ2XPMtRhaBU99YzHKRfiLLGAIAalw0hyikmDptN5z7OLJTCJyYO4gapNlT67TFcNdQxDVXQXEsZ8yZAkkCFCVt6OAGA3kUzkqShLqgdUCvr28qpE+bE5yYQ9TatK02Qix682mCa6fGKR9abNTfAMBoLKltnJXKjhzIrDEqFGFSUm/TGAJQsy1EECrc13IqTjbSIa16OAnE4veb7zgdgKr8udliIBvNityW4qQ+Zt/xkZztBowQGz0dzbUIBXyQJAkn2zCIOGqSam6XUhpvpM0HinM++X0SzlvQgrOnKThvQYsjhVI0Xk7ISs5m2YmkrM3j+dY4Ac4syZOygkdSBlYtdSFDBTEbsQEklPRs6KpHCkavGAnDBzsELcwhRG5x9o6PWW5xPClriz+vapwE16asojfs6CrIKagQCknV0/o4WSzEtULK5syLTr7FyC31IfhSi3SrHWyhOAn7cgAIp4aUG7ULhRRTa8XnNgKY7MDpoIPASdRHzJ5aiyl1IW0B65bqJAKnBa31uhoL99L1+vPMz6/N0cup2PVNgD3DAbFx5GTTKJt8m+D2j8a078XMijxfptroMQSk1bigX8q7l5UbiMWOKzVOWqqefcVJkiQt0BbXxFlTrH8TsXiMxGVTR8kum/Wsfp+ES5a0AVA3xbzaWognZexOBYZLO5pzPr6tsQbTGsKQlbQTnxPERpJIzwKAxTYsye3aT5tRKuMNoLIW5kHdmi8uW6tOg+NxzXSlkKBw8XR7gZPYfP/an18FoK6b7Bh7NOVQnMTmYDNd9Ui+aAuH1G6QXczsyPPJLRYXd79P8jzv9IzZzThtpjCJOOLpexkRiSe1dKhCapysXOqOmfS/yLcY2e+TtADguEW6nljA6+V0UTbhRgqOOH4zrIqp7fRyEojASVzsnShOIq1vTsqRS6hObgROsqxoO7hzW+rSO942+mvZJd80kzrNktwkcCpB00E7qXqF9nEC8m+Cuz9V39TeVONokW8HUX+TS43UN791Mv+7TYODVNpcaIqTQxVRX0sT9Etoy9Hzrz7k11LWjSzJZVnRFA0rxUkgAjFZSZ+HbrPv+ChiCRmN4YDtoKSQRriaFXlL+r1OttFXya79tBmlMt4A9JtP5b8wD+ns0nPVOfVrAUewIJv1xTYUJ6eb73pypupVUGBrBQOnEpJ21HP2M5jZkeeTWywWqvraGK/Qm0TcUwKTiJ7UrnRN0JdXw0k7DXCNrMiBwoqR03VOuRUnfaqeUJyiCbngWhz98WeT6/jTqXq5FyQi5fDseVMBpNNN7JBdWO5m4HR8JIpoQobfJ6FjSo0WzJ5wUTnNx44c0DfBNR6Xoui/mIGTPv3L7DwXqXr5mkMA+VuSix5ObqtNgHPFqZTGEIA9B0S7aIqTQ1MBfarizCm1Oa9FkiSlNy8Mvue+sRjiSQWShJxBGKDWm4jzw23TF8GuTtUd79SOJtvX2kIa4eqNIQSiCa7Vwtmu/bQZpTLeAPTpzuW/MA/qA6ccRl+iJ1wh9U1AOlWvZzhqGNwUauxhFTgpikI7clI4+bpKmdmR55NbXAxjCD1vP2sW6kJ+7Ds+ijv+b78t8wq36NLVN+Wzwyt2Ua1qhswCJyD/YmRh0X3CwkJU2PK26lL1anTDyg1nvTXLOjDTYPc21/E7MYcQqtqKuVMAqIsiK9VCz2Fdqh7gbuAkAriZU2oQ9Pu088XNRdZAnruldTZT9YppeS1SrxIW/bsiCWEOUUiqnvo7dFlsGBlxwKExhBPE4mZwPG5Z+C0ClVLWN6nv754deb6KU6Mu0LKrdGgBqkGdkwikW+vDGQtUK8Tc6XabAYEwhji1o9H2c4SzXmGKUzpwEorTAQtnPbv201aYXevampy1l3BKJSkafp8EETvmMogQmwNON9WyaaoJanOmUfBcqLGHVeA0Ek0gkVrnVbqrXmm3uiY5muLkML9dFBVmu+rlk1t8XLMiL85AbggHsGLuFDy5pxdffehV7faO5hrctG6pZxMqkBk45YMdc4j0bp25k9Pqpe14Zn8feoYjaGtUUxasdt/sOOuJBbw+Vc/vU4PsWELGcDResBX14b4xHBuMwCcBP/2nszEWS9o6fi1Vz0GN04JpDWipD6FvNIZDfWPaAsIKLVUvtVBYlOpa70YvJ20RkkoD1BZZLqXqJWVFW0jnW+NkmqpXghqn+pAfPklNfRqOxA0NICKp4y3EHCLfVL19HhlDAOr3LMxDBsbiE3qTCTQr8jzUbzdxkkqbi9GY8xonILMBsN00Ns2S3OActGp+a/p69SHsOz7q2jmdjRNjCIFQnF7rGkY8KdsOAgFkpBYLpjeG0VQTwFAkgf0nRic04dXbT5tdw+yiv9b9y6+fw1AkgZ+872wsnzu1oNe1otIUjaDfh2hCnlB2kU2/C456gsVtDegeimJvz4iW2SEo1NijOTWXDRkETuK3CQd8BWUZlAMlVZyeeOIJrFu3DjNnzoQkSbj//vtzPmfLli1YsWIFwuEwFi9ejDvvvNPz4/SKfO14QyapevnkFov0L6+NIQQbd3TiyT29E273ujEekG5+m099E5BeYNhJ1bPaNbVraywQQa1l4CQUp/rM31GzUHdBcfrr7h4AwDnzW7B6abvt48/HVW96Y1i74NtJ1xuNJrRduVma4qTuru4/7qxTuhHZaS/ie3Zrd7qQ4l+txslkB7kUgZO+f5dZjyVNcXKlxsmZ8id6OHmhOPl9kvYbWjnrlYMVuf79rTaE7La3EMY5Tlz1gPQcAeQ2hhC0WKTqWfVwMkOc01a9ofJFURSdFXnuTSDB3JY61If8iCVk/OJv+2xnZ0TiSW2jUB84qc56quJlVOekt5+eVl/4mkBc60TzXSs1ww0qzXzAbi+ntOLkQuAkDCIMLMkLNfYQWQ1Gab+VpAbmoqSB0+joKM4880z86Ec/svX4/fv3461vfSsuueQSbN++HZ/97Gfx4Q9/GJs2bfL4SL0hX8Up5FcvStm7FFZ1KILs3OK0Fbn3gZPInzXC68Z4QHoX0k6xsBG5GuDqO6535JkfboSzGqfMibU+5F7h96OvqoHTZae0OXqe3eJzvcPjtIaQdsE/2Js7cBJqU3NtUFsIzmutgySpC/dc9Sa5yE57adEUJ3cWWeKC3xgOOC7+Fbv74znsyIsZOAF6Zz3jVEuRWuikh1024lw+PhK1PW8oioIDJ1IOiR4EToC9Xk5irmgskxons/PTSXuLfBUnfaA1Hk/a+i1F4GS0eaFlFziY61u0DSp3FaekrGDDK53oH4vDJwELp9sfc4/s6tLSuL65cXfO1iKCowPjUBRV+c1WKUQjXKNULTGPzrJRZ+YEoSI6qVl1SiIpa+dUpSzOzcousim0+a0eK4OIQo09rFL1NDWwQoJaK0oaOF155ZX4r//6L/z93/+9rcf/5Cc/wYIFC/Cd73wHp556Kj75yU/ine98J2677TaPj9QbonkqTsGAOqyNCgpFbrHRxHHVWTMnpMKla5y8H8ylbIwHpC+mdoqFjajXNcA1UjDEZ8u347oZuVL1FEUxrVWzY2hhh9FoAtv2qkrhZafOcPRcuzUUIvgTDo/zWu076x3Jqm8CVCVD2MIXmq6XnfYyzWLRlg+aG1S98wt+rlS9oZIFTtYpYG70cZrWEIZPUhendoPYEyMx1XZayqz/cJOW1M6wVduFoTJRnNK/08TFjlOHrXxqnDbu6MQDLx7T/v7J43ttBQdW33E+2QVWZhP5IoLOT9zzIgA1dfUt333cVmaF+O5jWWqEneyMw7qNnux6XmEQYaQ4HcuRap4vIsVZ1KF6gV7ZrgRzCCBtEGHUWkaPqOMrpIeTwKqXk9h8N9q2sGPsYRk4jafUwAoJaq2oqBqnrVu34i1veUvGbVdccQU++9nPmj4nGo0iGk1fUIeGVLk8Ho8jHvfGdlSPeA+j9xpNXahCfsnRsaTiJkTiCcPnXbZkGj5x8UJ8fcNunD6rCRcsbMFP/3YAT+/rxXgkmrGjfTwVTLTUBjz/PjoH7C1eOwdGEY/bzwO3S9egelGYXh/M67OGfep0oijA0Fhkwq7qoRPqhaijucbV73JqrboIOT4UMXzdkWhCUy+bwlLG2K5LqZkDo8bPtcvjr/UglpQxt6UWc6eEHL1WbWrADkWsz7nOfnV8TKsPIZlMYGazGgQeODGa8/0OnlAvArOmZH7381vrcHRgHG90DeGsWfaLsrM5lFK9Zjapn7055bzROxJ15bfuHVbH5pRa52OzJlXzOGLy/Qo1qz7obJ7JB/18J9JEzcZeJK4udAKSXNBxTW8Io3s4iiN9I9q5YsWebtWhbGZzDfyQETcxryiEKalc/+PD46afbSC1QK8L+YpyLTJDmMgMRzKvJ0lZwc1/2mnqsCUBWP/gTlx8Ujpdd3hc1DHYG2ubdnbjU/e+NOE9RHDwg/eciStOM96oaU791idGIhlzXjweR+eger5OczDXp3+zwuZKQSGfLZ/vXs+B46lGwlMmXosWtKqbSW90D0+471CvOo92NLl7DZvZrC74D/eOeTbWTwypv3lDOABFTiIuF56ebgerNV4uAqm5ezwas3y+2BRqCvsL/v4WtKhB8eH+MQyPRSakSl+2ZBpOntGA17N6fbU3h/HlK0/BZUummR5DXVD9PINjE69Fvam6qOYa79ea+eBoDe7hcbhOV1cXZszInGhmzJiBoaEhjI+Po7Z2YnrUrbfeivXr10+4/ZFHHkFdnTe7jUZs3rx5wm1Pd0kA/DjYdQLf/+3DWNSkwI46fuCI+ry9+w9iw4b9ho/ZdtAHwIepyQGcHOtDQ8CPrqEo/vueTTirNT0d7+/0A5Cwd9dL2HBsez4fzTb7BtXjzvm4ndux4ciLrr///i71s+7b+QI2HHb+fEUBJPihQMKfNjyCpqzNn//rVj9fIDqIDRs2uHHIAIAjowAQwNG+YcPXPRFR7w/5FGx59JGM+8aH+wH4sPW5F6Ecyj8F8rd71fG0IDSKhx9+2NFzXxtQv5djPX2W38vOfvVxQTmCDRs2QHXgDWD3kRM5v8+/HVCPL9bflfFY34h6+1+eeQX13S85Om5BLAl0D6tT5e7n/w9HXga6x9Vj6x4YdeW3fua4+tnjIwOOX+/oIfUzvvrGPmxI7plw/5Eeddy/9kphY8AJmzdvxviQelxPPWv8vn1D6nE9/8w2nDDO4LVFWFZfZ8Nfn8Lhltyfb1uP+l3XK2Ounqd6RvrUz/70izvQfPwVw8e8vl99zJF9r2PD+G5PjsMOYwkACCCakPGnhzZAmBy+MSiha8h8vlYzBKL44e824qRm9Xs/cET9TPt278KGgZ2W7ysrwPoX/KngIPPCl9qiwlf+dzviB5KG18UDvanr4JGejN9x8+bNeOOoOiYO7X4ZG2ye94dOqK+353AXNmw4aus5ZhT62fL57vU8npoPE4PdE8b4YAwAAjhwYjTj9waAp1Pz/GjPIWzYcNDGJ7XHkdS1/7Ujxz075w4MA0AAIcQ9ew8rjNZ4uYiNq+P0b//3FI5Z7BUfOJZau7xqfzyboShAnd+PsaSEX9+/CbOyMkd7xoHXuwMAFLz/JBkKgKYgsKhpFMmDz8NqWIixNTgew0N/3pAxtp9JrVuHe7tK8vvkYmzMvhpaUYFTPtxwww24/vrrtb+HhoYwZ84cXH755Whqcl/VyCYej2Pz5s1YvXo1gsG0RLlpZzc2vrgTQAJHx3z44S6gvSmMr6w9xXQXSnDsyQP48+HX0T5zFtauPd3wMY/d9wpwrBPnnbEEV/3dAuypeQO3P74fO2PT8KW152qPW//yXwHEsfbSN+GU9vx35O2QlBXc950n0D0UNZWC25vD+OS73+x6jwdFUfDvz/4FgIy3X3GxljrglK+8+BhGogmc/3cXTXDkem3zG8C+/Tjr5HlYu/ZUF45apWc4im+9/DhGExKuWHPlhO/mxcMDwIvPoK2pFmvXvhlAetzNn9WOVwd6sGjJUqxdNS+v95dlBV/71uMAYvjnNefiwkWtjp7fcXgAt7/6DKRwHdau/TvTx40+fxR4bScWzZyOtWtXoHMwgh/segIDcR8uv2K1Ze3Pn3+7Hejswd+tWIq158/Vbj++9SD+tmE3fM3tWLv2LEfHLdjTMwI88xQawgG886rVkCQJA2Nx3LL9rxhPSrjs8jWOe7Fl0/3UQWDPbiyeOxNr157h6LmHn9iPTUffQFuH8Xxwy47HAURx+UUXai5dXqGf7x4dfRU7+7sw/+RTsfbC+RMee/NLfwVicbzlojdrdRf58GD/izj02nHMPnkZ1q6ck/Pxux55A9i7H+cucfc81bPzkdexrecAps9egLVrTzF8zH3Hnwd6e3HeijOwdvksT47DDklZwQ3Pqou+N13yFq2O4sGXO4FdxkGfnoWnnYW1Z6gp4L/tehbo78d5Z6dvM+Pp/X0Y2PacxSMkDMSA6UvPx3kGNRXTD/Tjl68/CyVUj7Vr35Qx9m5+6UkAcay77E2aGUIupu7rxV1vPA8l3IC1ay+09RwzCv1s+Xz3eh66ZzvQ2YM3Z82HgHot/NbOv2I4ksCSc/4OS3TX/d/f+TzQ04uLzj0Da1e4NyaX9Y3hx7uexEDcjyuvvNyThs9bXj8O7HgRHa1NWLt2leuvb4bZGs8OP973FHoiIzj73PNwgcV19duv/Q3AOFa/eZXWqqMQfnXsGbxwaAAdS5ZPGD//vel1AAdw0cnT8ZV/WuHodaPxJG58/i9QIOGiy1ZntFp46eHdwOGDWHbyQqy94uSCP4PbiGw0O1RU4NTe3o7u7u6M27q7u9HU1GSoNgFAOBxGODyxpiUYDDoe5IWgf7+NOzoNJfzuoSg+de9LOfsc1KRSxOIyTD9Dz7BwGKtHMBjEdRcswM/+dgDPHOjHnhPjOLWjCQldMX77lHrPv48ggJuvOg0fu/sFSEDG50/nz56GmrD79Vb9ozEtj3hWSwOCeRak14f9ampcUprwfXWlvvPZLe5+lzOa/ZCEtXNMwfTGzO9nMKJ+rtaG8IT3FRNXJKHkfUwvHR7AiZEYGsIBXLC4DUGHQcLUejU1YDSasDyG/pQ9c1tTDYLBIGa3BDQ79RNjScxpMa9NO5aqaZjX2pDxHotnqIHCwb6xvD9/Z+p3ndtSh1BI/e5bGwMI+CQkZAXDMRkNtYWZqwynDEdaDH7DXDSmim3NfuPBVEpwa2Nt0ea8YDCI5lQNylhMNnxfkV7aUOv8M+vpSLmw9Y7Gbb3OoVQB/MK2Rs++j2kp16nBSNL0PUZSNWlT62uKei3KJgjVmXEslsyY1zqm2DMx6NBdO8ZTaY9Ndbl/094xe3WXvWPG80Zbqn6xfyyWcb8Mn3Zdm9XSYPu7bWuuS72evXGU65jtPs7ovfL57vUcGVDnwwXTjcf4yTMa8fzBfuzvi2DZnHTgJmrZ5k6z/73ZYe60Rvgk9ZwfiMq23ducMBJTx95UG2PPC/JZU4ZS6xBZ8llfG1PjeXqTO3P4SW2NeOHQAPb3RTJeL5aQ8b+pesN/PG+e4/cKBoPaNXssAbTonj9UwDWuGDg5popqgLtq1Sr85S9/ybht8+bNWLWqeLsLhVJoZ2bA3I5cT3bPoo7mWqw5rR0A8KutBwCoRbCKAvgkd9xa7JBvE9hCEd9HS32oIBcvq15OhXZcNyPg92k2pEYGEVoPJwNL+XoHPZTM+Mtrqpvem0+epo09J+hdu6xswfVW5ADg80mYkzJ7yGUQkd3DSbBwmmj4OJa3W6Oob9Lb+vp8UtrVywUXLmFb7bT5LWBtDhFNJDUThmI2wAXSQbuRHbmiKBgX5jihwi5DTpvg7tea33qXqt2iWVtbmEOMl0cDXPUYhHV8Os9fOGyZYeSw5cRVr1DrY/EdD0USGa5k3al5JBzwOTJEEXbk/WOxgp1dC/1shbibKYqSYQ5hxEkpg4A3dAYRiqJo1zC7TYjtEvT70JEKdMVc7Tb9o5Vndy16choZfQmiiaS23nBrnSac9fZmGUQ8+mo3ekdjaGsM41KH7rkCM4MIuuq5xMjICLZv347t27cDUO3Gt2/fjkOHDgFQ0+yuu+467fH/+q//in379uELX/gCXnvtNfz4xz/G73//e3zuc58rxeHnhRvOcsL736xpmqIo2iJCH6C8/4L5AIA/vngUA2MxHE8tuFvqQ66nxlmxZlkHnvzipfjXNy8EACyb2YQnv3hpWTe/FVj1ckr3cHJ/N82ql1O6h9PECUm4WxXiqvfYa6rKe+kpztz0BOI7iyeVCU2b9WQHTgBsWZIPR+LapJzd7X7W1FqE/OoOmPh9nHKoT33e3NbMRYiVHbJTxPHn0xm+XrMjnxg4iYuXJKlW58Wk0cLmOpaUtb5VhTZDFOd0l40muIqiaGPJi+a3gpaUO6IdO/JSN8AFjFsGWLW3MHPYctLHqVDr4ym1Qa2GQu+sJ3p6tTfXOEoJE+eeolj337JDoZ9N/91nv0Yud7OBsbj2O5o1ExYL5zd0C+fe0RiiCRmSBC3IcZNZHluSD1Rgn6CgjT5O4trgk9xz4DSzJP/tM+ra+93nznHcFkNgFjgNjovNwcr5fcwoaeD03HPPYfny5Vi+fDkA4Prrr8fy5ctx4403AgA6Ozu1IAoAFixYgD//+c/YvHkzzjzzTHznO9/BL37xC1xxxRUlOf58KLQzM5Db+38oktB2c/V2rOfOn4pTO5oQicv4/XOHtX4V2RbWxcDvk7A6VcvVPxb3PHDryaOTvBFmfZGScjpYdVtxAtLBhFHgdMJCcRJqT74NcLsGI9hxdAiSBFy8ZHper1Gv23226uV03MBSfV5qcWulOIkdzKl1E23g/T5JszXfn6clubDQnZO1CBHH6UYvp0I63qcb4E78boWq0VQTdLUvix2s+jhFYum5y2k7hmxE4NRjowlu91AU4/Ek/D7JMytyIN2o0ipwKpcGuID+t8ocQxcvaTO0Fp/WGDbMEHCiOBUSHACq6iu+596MwCm/TTJV2c8d8Nohn6AzG7PsjLYm4+9eIObK9qYa000Joya4R1PzaFtjOK/MglyIumKvFKdBodpXkKJhp4+TvvmtW3O4CJz2nxhFIvXeh3rH8Lc3TkCSgHedk7tW1AwROA2ZKk6ln+8KpaSB08UXXwxFUSb8u/POOwEAd955J7Zs2TLhOS+++CKi0Sj27t2LD3zgA0U/7kIoVMIHdIqTye69uHA01wYzeqRIkoQPXKAaBPxq60EtmChF4AQAC1JpVEcHxhGJe2sdmk8neSNE6lt2WtTx4SgSsgK/T/Ikf1vr5TQ88YLeqwXARopTYQ1wH0ul6Z01Z0re48Tnk9I72ha9nE4YKE5icWu1S2mWpicQTU7zDpxM0l5aG8o/Va9UzW8BoMmij1MkoR6r3ydp6Sr5IhaWdhSnfSnb+jlTa7XdXi8QaqSZchFLyOkUyrIInMQ8kbnY+dP2YxiNJTGzuQZ3f2glTk6ZeHz84kWGC3etj5MNxQkoPHVb9LXJUJyG85/rxe9m1jPPCWuWdeCTly6ecLuTtHSRnfHbj5yP9tSm383rTrN8brpZt/kGnjBjOdA7pq0jjnmUai4Q6tcRj3o5VbLiZJY9BLjb/FYwa0otaoI+xJKyNl7ufVYVKd580vSCNpVMU/W036dyAlszSp8jMMkQEn7XYMTCWc5cwgdy1zh1WTT/e/tZs3Drw6/hSP84/udvaSvzZGrRX0ym1gXRVBPAUCSBA72jOKXdO8cv91L1jFPfRG54e1ONJ9+jVRPc3lGhOBkETqnjzdV81gyRpndZnvnOgoZwACPRhLXiZJWq12ce9Bg1v9WzYLoaOO07PrHhXy4URdEuLHOzA6dUTYSbqXr57MYJxckqVa8UgZNVA9yI1vzbV7DDljinB8fjiMSTlql/B06k0vSmeZemB6QX9GOxpOEx6VU4oQqXEqONDUVRcMdTBwCoad5vOmk6rl4+C/+9cTe27u3FP1+4IOM1YglZSzmyozgJ1izrwOql7Xhmfx96hiNoa1Svf3bmUaN0WX2qnlNaG8LYe3zUtSa4Yr67ZIn63Tn5bAK/T8KqRa245JQ2/PaZw3jx8ACuPN1O4GS++G1vqkFjOIDhaAL7T4xiSXujZ/VNgvQmmDeKk5hDSzHX5YvYNLKqV3ez+a3A55OwcFoDdnUOYU/PCOa01OH3zx0BAFy7cm6OZ1tjFDgpiqL1E6ykwNaMijKHqAYKTU8AdIGTSV6sCJxmGFw4aoJ+nDtfDcpeS8n0T+45YatTu9tIkoQF01M7X3mqAXYRKlw+F1M9ZuYQxzy+6IjA6bihOYSocTI3hxiNOQ+cIvEkntxzAgBw2an51TcJGiwW0eK9hIGFPnASaXaHLGqcxIV4tonF/MLUInlfHmOsdzSGsVgSkjSxfiqtOLmRqpdOx3BKXZkqTlapepoxRIH1TYCqbNWkGj13W6hOSVnBU3vV8RwO+Ao2ALCiMay6LgLGqpOob2oIB4q+YWVE2hwifX4+e6Afr3YOoSbow7vPVVN3Llw0DQCwbV/vhO9vTDfH1Bmk91khgoO3nzULqxYZN3U1QtR19rmQqqd/PTdUZAB4fPdxAGrNiNPPls3Z89Tr9nMHzOufgbRCnr3Ro0eSJCyeIeqc1HWAUO69uoYVT3GqHEUjXeNkQ3Fy+XNpdU7HR/CXV7txYiSK6Y1hXHZqYZukItNAHziNxZLapgoDJ5IXhaYnaPJuwji9Tagr7Qb1PBt3dGLzru4Jt4tu5sUOngpZ1Dqh26UaJzNziHSag/tpeoDeHMIgVU+YQxgoTkIhyydVb+veXkTiMmY21xTc4yutPhh35xZqUyjgyzAxEHnxQ5GEFlxkk1NxmpbO53aK2L3taKqZ4MbY6pI5RCwha7Uh+QROtVbmECXchbVWnNSFghuBkyRJmrrebVLntHFHJ970zcfw0Mvq/LZpZ7enm0WSJGk7xEbqRbq+qfRqEwA0hNXxoZ8n7nxKzUj4++WztMXoslnNWpbAjqODGa8hxnAo4PM0DVKPkeLUk5pL8pnrtc0QFxSnQ71j2HdiFAGfhAsXTyv49c6ZNxUAsOPokGVqu5lCns3JbaLOSVXitc0/k3m0UITidHRg3JNNi/TmU+UszEM2Aqe+lFugm4oTkGkQcc8zhwEA7zpndsHnbrrGKT2XiKA25Peh1oU5v9SUx6w9CSkkPSGtOJmk6g0Zp+oJK3QjFKiK1/oHd2H10vai7YIKZ6v9x4sVOBUW2NRpNUOZFy6v88OnCXOI4cyFoSwr2sLMUHEKmbsA5uLRV1Nueqe2FZxOZeTapUcoadMbwhnvVRvyo60xjJ7hKA71jRnuJmo1TiaKk6hxErV0ThbrVra+woyj0EXWQMptyCelgw0n1KU+TywpI56UMy58g+PCua2UipNFql7QnQV2W1MNDvSOGdY5bdzRiY/d/cKE1GixWeRVG4SWuhCOD0c1m2Q9YlFRDlbkwMSNjWMD49i0Uz3/hRsroCpD5y9sxSO7uvF/e0/gzDlTtPs0Rz2HalMhtBjUOHWJVL28apzcM3zZ8rpaH3r2vKmu/M7zWuswrSGEEyMxvHJ0UMscycZu4CTqnPakFCevU/Xam2oQ8EmIJxX0DEdcd+4rxGCnVNhx1ROKtXDqdAuxaf3Yqz1aYPPucwpL0wPS1xq94iSC2ua6oCfNj4sNFacSkm96Qk5zCM2KPHNicsMK3W1E/cmBXmeBU1JWsHVvLx7YfhRb905MG9ETS8iaUlO4OYRZjZN3jnqAGlAAE2ucBsfj2mc3Kh5NK2T2zTfU7/YE/pzanb/k5MKke8Damhowrm8SiAWAmbPe4RyK07SGEBrDAShK7n5Q2Rj1cBK4laqnz83PxzVJbwCTna5XDjVOsaQ8YYfczVQ9IH1e92QFTm70zcuXqcKS3EAp1RSnMrAiB3TnZyrIvXvbQSRlBecvbJlQeyrUk6f29Gbc7sRRzy1aslQ9RdErTvmn6rlR47QllaZ38ZLC509AVTHPTqlOzx3oN3xMPCmnm9jmDJyMFSevrmF+n6S9ttt1TklZ0XqQNVeQq14wYKPGSeeq5xYbd3Ti5gd3AtCpQQEfdnUOWj3NFkY1ToNV5KgHMHCqSNIWliY1Tlo9T+Yi1A0rdLdZmIfjmUi7ufbn2/CZe7fj2p9vs0y7EZ8n6JcKnnxypep5XePUOxqDrFvkCWOIppqAoYVsvS5VT7axOEx/t09rE+qX73+l4JQm8b2Z1TiJgNAqcDLq5TQ4Htde0yzFRK2lEwYRzgJ0q91bt+oh0jul+Y3NcMCn9bPJTtcrZeDUEApAbC5m/+7RVODkVtqGWRPcUm4WGakhArHIKxfFSX9+RuJJrZ/LBy5YMOGxFy5uBQA8e6AvIyB20sPJLdKpeur8MZpIL0LzCpxccsqMxJNaPV2+bRyMOCdV5/T8QePx2jkQQVJWEA74DOdSPaIJ7oEToxgci6PfpBeemwinP7frnIYjca0vXGWZQ9iocdIUJ3cCJ6HAZ6f9xxKyK+UaRoFTJToeWsHAqQLJpThp5hBZFw43rNDdRrhbnRiJZXStN0Oc9NmLIasaLVH30NZYU3AfBDOzhWOD3u7WiQt6UlYyis1z9eLS9zUay2H5bvbddg9FC55QjWoo9AjFyehziMazRpbk4gI8rSFkudOdT4AO6AKnVvNUvfF4MqMw3in9BboNSZKkffbs4yhl4OTzSWgIGde2uVnjBJg3wS3lZpFVLyeRqlcuNU5aWmU0gT9tP4b+sThmTanFWwwKxRdNb0BbYxjRhIwXDqaVj1IoTiI9WXzHgzFxeyivXkTZgVi+PLO/D5G4jPamwutD9Zw9X1Wcnj/YD0WZuBGmd9TLlRLV0VyDhnAACVnB/6WCvMZwwFN7/NlTvHHWE5tP9SG/Jz2ovMJejZN7rnpWCrygUAXeqI9TIe02ypHKGWFEw8qOPJpIajUX2WlphXYz94KGcEDbGcvlrJdv2o1bjnqAcerbaDShTdxemUME/T5tYa3fKdIc9QyMIQBVjRApoFZ1Tl6nNDVkpQJlYydVz0hx0pygTOqbBGmDCGeW5FY1TvUhP8Kpc7GQHepCHPUEZr2chkoYOAHmBhFup+qZNcEt5WaRVdrXcLkpTrrf6c6UBfk/rZqHgEGhuCSlzQ7Eghtw3sPJDbR0yFQd2UBMnevyrWWd5lLdokjTu+jk6a7WdCyb2YxwwIf+sTj2GqjnduubgJSzXkp12rJbrcfyUm0C0orTYZcVp0pdmNuqcXLRVa8YCrxxjRNT9UiJ0ZtDZO86iYVDyO+bIO26YYXuBXYblOZ70lv1tXJKvUGqXmdKbWqsCXi6EDLq5aT1cDIwhgDUi6Mo1rZy1vN6Qm3MZQ5hEThpluQGipMIbMzqmwT5pOpFE0l0DpnXC0iS5IqznhsXFfEbj8fLJ1UPMDeIcNscwqwJ7soFLZbnvZebRZqrnoUdebnUOAn78N1dQ9jVOYRwQMJ7UhbkRlywSE3X+z9dnZPYTCqF4tQ/pqYwC8Up300ycc0cGIsjYaEC5EIYQ7iZpgeo1/4zZ08BYJyu5yRwAqA1NBaBnlep5gLRMsLtVL1KTQWz0wC3z8VUvWIo8JriFIlr69PBCv19zGDgVIHoXbOydyo097jmsOFOV6FW6F6woNXeojbfk158J20FWpEDmTVDAmEM4fVFx8ggIpfiBBg3t8zG6wk1Vx+nEzpXvWyE2tM5OD5BZRWKU67AKZ9UvWMDESiKWofTanLR0pz1CjCI6C+wxglIW5KXkzkEYG5D75U5RPdQJGMzye+TcNHJxotXrzeL7NQ4eZkWZRc1Rfd5AIAQlH0+H7bt6zV9jlCcXj4yoH0WTXEqoqueUJxUc4AEBjXFKb+5fmpdSKvLMwp47XC4bwz7jqdsyE8q3IY8G5GuZ2QQYaWQG3FSypJcGGp4lWou0BQnl1P1BivQUQ9Im0PETcouRiIJLa35jZ6Rgk1siqHAi2tNPKlo8/xAhSqCZpTHdhdxRFiXwxtLyhk5vWZW5HoKsUL3ArvOevme9N02vhO7GJlDeO1GJBCW5MeHDRQnkxonwFgly8brCTVdfG7Sx0kzh5g4sU5vCKM26Md4PImjA+OaQgnktiIXiFq63tEYBsfiaLZxgdXv3pql27jR92VwvPCO6kIxGC+jGifAPFVPLAbcMocQSmU0IWNwPK5doLsGI/jzK2ptnug/JGhvrsFN65Z6tllkp8ap1Kl6Zlbt47GkpVX7zCm1WDCtHvtPjOLpfX1YvXRGWnEKF29ZEQ740RgOYDiaQP9YTFOc8k3V8/tUA6G+0Rj6RmN5zXci7W3FvKmeBMain9PzBycGTk4VJ2FJLvA6VU8oTl1DESSSsmEqaD5oC/MKctQDrGucNu7oxH8+sFP7+4N3PouOAucsUa7RNRgxTMuXoM6LhSjwdSE/Aj4JCVnB4HgcdaFAhnNsNUDFqQLRK07ZO/BmxhDZ5GuF7gV2U/XyrdHqcrHGSaShjMaSmkud181vBUZNcDXFyULGb8hhBQ54X/9mZUeuKEo6Va9h4ncoSZKpJXmu5reChnAAbanF9X6b1veHbOzetmp9X/IPnESfn0IaN9YZ1DjFErK241fqVL1s45eoy6l6NUG/9v3pm+B+9aGdGIkmcNacKXjuK6vx24+cj++/5yz89iPn48kvXuqpwq4pTmVqR15ooXg6XU+tcyqF4gQgo9GwlqpXwCZZoW6ZaRtyd9P0BMKSfN+J0QlKt6gdsh84ZRpXjEUTnljzC6Y3hBEK+JCUFcvUcKeIVD07G2LlhFh3Hegdy2irIjY0jmf1bbQywbL7fl6Xa0iSNMFZr1JTKc1g4FSB+H2SNrCzdyrcrOcpFlrgdHzU0ClIIE56s50SwPikFwupQpvfAsYudUf7i6Q4FZiql+0EqEc/oWbjxoRqFTiNRNPpCNMMFCcgHbzoAydFUXSperkXCukA3Z5BxGEbu7du9HJyo7BZKDf6wElctKQ8G+u6QbHMIYCJznqPvdaNDa90we+TcMvfn45QwFfUzaKpWqpefMK8JpSvUipOhdY1av2cUgYRYn4pZo0ToO/lFE+n6hWwSdZSQN2iakOupjhe7EL/OyOm1IU0Uwe96jQ4Htd29kVKXC5ePjyQsYD+f4/tsWztUSg+n4TZopeTi3VOlWg+sHFHJ27b/AYAYPvhAa2tyoaXj3lq1FSMcg0tcEr9Lm4YIJUTDJwqFDNLcjfVlWKhpkKpVrjZvQWyWbOsA//y5om9RcxOekVRbKtwdqgJpnvmiNQ3rzuuC4xqnE7kMIcA/v/27jy6jfrcG/h3JEuyLS+J7cR2VichITHZEwhOuIWWQFIoJfRe1lBC4HJ6KOkNpFBKIQSaQi5QoLTQsNwXaG9LQ2lLyxL8kgaSvoAhZIOYbGQhToLXeLdjSZbm/UP6jUayZMmWRiNpvp9zck4sy9ZYHs/MM8/zex7Abo28xgnwH1CDswDxOKAq7chDbIP4nefYMsJedCkNIlTZorbTLiUQi5RxAoDxw/wBejT8w2/Df+94NIeIx8JZf6le38Ap15YRcxv+wYrcHCL+gVN9ew+6nb1Y/XdvmcvN541D+Yi8/r5UE6ILltPtUVp1C0rGScd25LGua6wYXwhJ8g5QbejoQbevVC+RXfUAVffCbida45BxEjeomgdxM+TTr5px2uVGcZ4NU0rj14Y8WKhyPXGjJ9JoBqGyuhY//GPfMs1YsxqRjPLdiDoRx3VOrTGOdEg0kVEKzsTXtfXgh6/s0rzz3eKppfjg7m9ploHPDc44sVSPkoFY1+QICpzi2Xo7UTItZiXoiLTOyct7EXjBpCJk+t6HZ64LfWHf4ehV7m7HIwsnSVKfNUNaz3ASRDYmVMapqJ+Mk13paNf/HCfAe0CdPjIfALCsYmzcDqhKc4gQGSf/DKfwP0OoUj2RbRqWa4vqAlxknI5E2SCivxlOQmEc2he3xKUded+sYlsSlK+Eaw4R7zlOgH993Jb9Dbj7L5/jZOtpjByShdsXTozbawxEltWs3IQIbhAh2sTrmXGKdV3jULsVZ/kC0qrDp3TPONW396Cr13tuiOVYH0vGSas25MFEud72EIFTNI0htB4/0R9xkyuuGSfl5lPyZzSiee+jEevsOS2Xa/g76/VClmWW6lFyEIFTn1K9ODZCSCR1uV4ku2paAQCXTh+BC870lkN8cKgp5HPrfXdu8jIzlFk3sVLPcnJ7/BmthJXqdXhP6C63R7k47q85hLh47a85hJq4YFg0tSRuB1Txnjl7PXD0BgZw/bUiF0Twop7lFG0rcmG8Mssp8j4my3LCSvXicTcuVMZJ7xlOgD+jEr5ULz6noMrqWlRW1wEANlbX4c3PvXfLL585IuEX8moFIRpEeDyykinVc41TPNY1Lpjgm+d0qEkpE010xkkEOvvrvCW41gxTTBdosTR8EY0hxHlJK3PLvL+TPSfalOztQBpDJGKeTzijlZbk8cw4pU6pXqT3PlpazJ6LF/Uapx6XR6mMSoXANhoMnFJUqFI9WZZR3xa/9TyJFG02wOX24POTrQCAWWOG4nzfAtx/HWwM+XwtShftqplETZ0OuNwyTBJQ3M+Ffzz4hzM64PHIyl1sk9T/CSNUC/X+iAuGon6CsYFSrw3rcgQHTt7fUb+BU4GYON+trBcZyPomwN+98WhT/2vpAO+JuEMpA+yvOURsC8lPO91K1jiWyfChmkPo3VEPUGcagzNO3u2MR1c9UfYSKqO6fsthzUqOohFqllOXs1dp+61nO/J4LBSfLwbhHjqlHF/0yjjtq+sA4D0Ox5Lt8f9ND+xmyPHmbhxu7ILZ5B8QrJWywmwU2q1wuj2oPtkGYGCBUyLm+YTjb0kev4xTWwplnGJ9T7WcPRcv+b4bQm2nXWj1dY3NMEkJbxyjFQZOKUo9BFdo7nIqH6dq4PRVhMDpQF0Helwe5GVmYHyRHd/wzWjZWdOiLERUi+f6JkFdqifWN5XkZcattWo44k6oy+1t8ynWBhXYbf2uYbFHGD6r5nJ7lLt38QyczCZJubgPXufU1Bk5UBs1NAuS5O1mKO7ei456o6PMOI0emg2zSUK30x3QeS0UcRFSnNd/GaDI9DV3OSMGY6GIMj2LObaTSlaSBk65ttBrnBxxKtWLtTOc1kLNchKNIaxmU8BoCT3EulD87LKhsJglnGw9jUMN3oyPXafASdxIGewMJ0H9Nz0QW3w37+aMGar535wkSX3K9aLpAiokYp5POKM0yTilzhqngbynWnW+05pSqqdqWDIk26Jp+WoiMXBKUaEyTiK7Umi3Bsx2SgXRtiTfVeM9ScwcMxQmk4SRQ7JwxvAceGTgw8N9y/XEYL94Bk45vgxOl7M3YTOcAO/MElH61NTpUGY49bc2CAg9eyoccbFgNklxL3tQZjkFZR/8rcjDX/DYMswo9f0Oj/kuEI4PMONkzTApQdaRCJ31or17K+5OO92ekOu3IvGX6VljOqmIi9XTrhBrnPQMnDQu1dOz5CgaBfa+pXpivVduZkZSXEjEslA825qBWWO8F/Did5yd6OYQQce/4hgv9gsGmEV2e2RUHT6FV7fVAAD+bZK22SZhbtAgXBGIRJNx0nr8RH/EMbi+o6dP2fZgeHzzgoDUKNWL9r3/7XXadr7TkrpULx5dY5MNB+CmKDFxWp1xSsXGEIKScTrVBY9HDptBEeubZo0eojx2/qRhONTQiX8dbMQl0wIPKFq0ZxelKJ2OXiUYSUTgBHjL2dp7etHY6VBO7AURSrxyBpBxEo0nCuzWuHdiy8nMQEOHo89FtH/4bf93ikcXZOPrth4cb+7G7DFDo57hpDauyI6vTnXjaFMX5k8If4FzXMlm9X8Rkmkxw241o8vpxqlO54BLr/xtWmM74feXccrTNXDStqueniVH0Qg1BFcMv9Xz9xJMLBQfjAUTigIC00RnnIKbqsSccRpAc4jK6lo8+ObegOD9dx99hYnDczS/uJ0z1hvU7KxpQa/boxwPowmcRJnmrX/YCQmBTQm0zmoU2K3KQPOvW3sCBpoPRkePv/Q1FeY4RfveL55aikVTS7DtaDMaOnowPNcbyCZzpkkQ58G20y6lEigVgtpopVZaghQhM06+9U2p1hgC8LbytpglOHo9qG0Pf5Gz63grAGDWmCHKY6Jcb+vBxj7lUiILF+vJVE2dwfm6NTGNIQT/LCenEuT01xgCQJ8ugP1pimKg7mCJi+jgUr1omkMA/pbkx051B81wiv69H1voPUn/c299wMDBYAPpUKV01htEg4h4dRtK1jVO4brqxWuOk54lR9EINQQ3GVqRx9OCMwIDrgN17QktjQwexRCvUr22064+zZfUxNq64IznqU6npu28hakj82DNMKG5y4mqI6fgcsuwmKWoqysSMc8nFEmS4rrOSayhybaaYctIjTU00b73Wna+01JAqV6addQDmHFKWcoapxClerEM/9NLhtmEMQXZONzYhaONXSFnIrV0OZVSvpmqjNO8cQWwZZhQ29aDLxs6MUk1Db2+XYs1Tr5SPYdbNcMpMe95Ua7orOdQ7ohGCnIGknESF//xXN8k5IbZjqYoX1Pdkryl26UECSOjDJwqq2vxt50nAADvH2jE+wcaUZqfqdzdUxvIQuvCHCtqmrsH1YUrXmUM/c1x0rernve1Hb3ezkriuCXakcfaHEKUvdS19YQdjF2i40LqoSFK9dqVUr30uJCoa+8JuHP+Xxt2Y907+0P+XWmhILhUL8Zj/ZAsC0wS4JG955zhIb5fpJbSErxr6y4qL9HsYteWYcaMUfn49KsWvL7zJABv2fJAXm/x1FJcVJ74rMaoodk4WN8Zl3VOqdRRT02v9z4R1KV66nL0dMGMU4qy+u6sqO+I1bX5GxWkIv86p9DrT3b7sk3jh9kDLjQzLWacO95713PrgcDuelqUL9oDMk6JW+MEBA7B9Qc5/R+Q7Kr26ZE0Rfk9B8O/xskfOHk8svKakTJOY3zZoprmbuVOZXGeLaq7jP6Bg4FBW7hhj9HMcBLEHe/BdNYTJ5WYS/Us3ve2O9QcpyToqgcEZp0ccco4xaMznJZEO/KWLv/PLsoW9WxFHi+V1bX40Su7Ej5EVc1uNQes6Y0142QySUqmMNxA9mRZWyfK9Sq/8LbijyZDHkyPrMboOM5yalXm1aXehXmqZpQiyVMHTqdTp3FHtBg4pSir2bfGKSDjlLqleoA6cAp9MFUaQ6iyTcL5vnK9f33pD5x63R6lDCye70mOathoogMnEdA0dTqUu9iRSvUGlnGK7nsOhriIVpfqecthZN9r9n/iUzJOp7oH1Ip8oMMeXW6PUoIZVcZpkO2LAXU3qPhnnJJhjpNZ1YJWvc4pnnOc9Co5isZQu/e9V7cjV4bf2lL7QkLPIapqkiRhqGofr207HfNrhmrqoZYsa+vm+jrriey71Szp1kFyIMRxOy6leuIYmmIZp3Smzjil4xqn1L/lZVCh2pGLYa+p2BwCAMYpA0pDZ5z865uG9vmcWOf0yZFmdDt7kW3NQFOnEx7Ze/EWz0BAZHAaO5xo8R0U9Fjj1DzAUr0uh3eKd3+dvKJpDT5Y/gDOf/ddNIYYkm2JmDkSQUxdew8ON3r3kWhakQ/k7nDFhELUtvbA7ZFhyzD12+lPiGVgZkt3nNc4uUI0h9C5JCw304Iup1sJnHrdHvT6Lu7iMccJSN6yl1DtyNMl4zTQvyutVFbXBvztrXqtGo+9eyimUkFvFrlT6VwaLFnW1rV2Bx5z/rmvAec98l7CyiQHS6xximupXhplNFKdaNLh6PUoVT9DNFg3rRdmnFJUf+3IUzVwKivyXhiHaknu8cjYHaKjnjBhmB0jh2TB6fbgkyPe8gjxfgzLscX1AkoEAF82dCgfJ2qhd5G6VK8r2uYQ3ovTXo+sDFsNx99wQovmEH0zTk0d0a+pGpptUdZJVR0+BSC6jNNA7w6r56FE01lQaQ4xiMDJX6oX2/utdNVzJNcaJ6Bvg4ge1T4Ya6meWjKWvSilet1OeHzBYrqscUqGrIsowe0NyrLEWioo1k2FK7/Vs523UFldi7v+8nmfxxNZJjlY/llOsWWc3B4Ze054BwA7ej0pkW0zghxrBsT9WTE+JJ0yTgycUpTFHJhxOu10KxdKqTb8Vhjvyzgdbzndp5vR4cZOdDh6kWkxYXJJbp+vlSQJ55/p764HqBpDxDmQFHNKxAX2iCGZCZvHEtAcIsoOeOr2wJHK9UQwFk2mZaBCrXFSWpFH8XrebkzeE+4O39DHaDrqDeTusNsjY8uBBgDeZhbRnIiLcuJQqhfjSUW0yHe6Peh1e+Bye5TynWQJnMT6MnU5od4DYLUmSjA9sj9gUtqRp3hXPb2zLlqWChZFKNVTr60Lloi1dclSJjlYYsxDU6cz4HgwEJXVtTjvkffwF1/Dn/f2e7NtyRwwGoXJJCmVDieavVnFdMoIpvdZK40Fd9UT2ZUsizllT8jFeTZkWcxwe+Q+tc9iftP0UUOQYQ69235jom+dU1DgVBLHVuSAv1RPdD5PVJke4L9Ir+9wKBfGkbJDJpOklHJFakne1CHWOGnQHCLEMNRoW5ELoiW5uGEQTcYp0t1hwHuxI07E//PBUQDe0tBoTsQDHZipFu+ueoC3XE/cRAH0nxfkn+Xkyzip1jclwwBYLVkzTEqWVFyEp0vGSe+si5YNGgpEw5cwpXqAf21dcLlpItbWJUtzisHKy8pQ/i4Gk3UK1wo+FbJtRiFu2Ilz9RB21SO99QmcVOubUvViRJIklCkNIgLL9XYd92YY1PObgs0/oxAZJglHmrpwvLlbeU/inYETmRMhsYGT94Qu7iRaM0x9tieUaBpEyLIcdfnfYCjbEEPgFNysQdTK96e/zmuCDOB3VccGdSJWuuoNolSvLU4zLmwZJqU0Qp19zrVl6F6ylhsUMMdr+G2qCG5J3t6TfANwB0PvjoZalgoWRijVExZPLcXkEm+lxLKKsfjTLefig7u/pfn6omQok4yFJEkYVSDK9Qa2zinVs21GEVzpwIwT6U4ETqKkrV6DQa96GB8ucFLWN/VtDCHkZVow29dlaOvBRtXw2/gGTurSNwAhZ05pJdNiVu7UAd6SkmgC5VBBS7D2073+DncaLOQUqfvOEKV60TajULfblSSgND+69z5c57XS/Ew8fc2ssMFnNCdikQVs7nIo61iiIcty3NY4SZKk7JfdqsApGS7O/RknETh5j1mZKTKsMlbBHdrSaQCunh0NtSwVVDplRrgZIssyDjd6z1XXnDMmYWvr9C6TjIfBtiRP9WybUQQHTvlpFDil/pHboIKbQ4ggIdoLyWQ1LkTg1OnoxYF6byOG/jJOgLct+bajzdh6sFGpnY53e/a+GafEnpyG5dqUdULRZoaUWU7O8IFTky/blGvL0CQboLQjdww+46Tuojc02xKXYY/bjjb3n4lD/93BREbBI3tnihREGXR2OnqVRe3xuBuXZTWj09GLbmdv0jSGAPwBgr85hPfvMstqrMBJlGWKNU6pXqon6NXRUMvhx+K4Gm6Nk9DU6UR7Ty9Mkv/clQjJPvg5GoNtSZ7q2TajUJ97zCYp4IZvqmPGKUVZg5pDaFWWlmihAqfPj7dClr2ZnUg/n5jn9NGhJqV2Ou4ZJ1vgBd+IBAer6uxMtGuRxDZ39jMEV+lwF2UQM1BKc4hBlupVVtfiztf8XaSau1wDXgwcqvNarCdii9mknCQG0iBCZJsyLaa4BKrqWU7JMMNJCC7VEzc00r0xhCCyic2+IbhKxinF25Gr6dHRUMtSQf8A3P7/ng81+MYiFGQntPRU7zLJeBhsS/J0yLYZgfr4NiTLkrJLSEIxxpkrDYlSPUfwGqcUL9ULtcZJzG+aGSHbBADlpXkoyrGiy+nGV6e8gVNJvjbNIYRErnECgKJcf7Ak1tdEkuMbttlfc4hTUc6FGqxQc5yaouyqJxYDNwZdyMRjMXA8TsSDmeUkMhCxlukJYpG6ulQvOQInX6meI7A5hHEyTt6fv6XbCUevWzlmp0vGSU9alQqKY2BHT2/AyI9gh3zz5CYMyxnU68QimQc/R0PJOA2wVE/vpiQUHXWZeDqV6QEs1UtZ/jVO3kR9qs9wEsQap9q2Hpx2upFlNWNXja8xRIj5TcFMJgnfmDgMf9t1Unms5lQ3xhXlxO3umy3DhAyThF6PDJOU+PdcnXEqijLjlGOL3FXv1ADXGw2UyDz0uLztsk2SpJTCqIPBYJEWA0vwrkG6qLxkUL/jeJS9FNltONLYNaDOeiLjFK/gRhmC63Qr09qTI3AKag7Ra6w1TurmEOI9kCSkVemKnkSpYNWhBrz7/z7Bxf82DxVnDI/peJ+f5S0DdntkNHc5wx7jD/syTmcMT3zgBCTv4OdoDDbjJLJtt/5hZ5/PpUq2zQjU5550muEEMOOUspQ5Tr71AvUaNUJItKF2q7Le46tTXZBlGbt9GadZY8I3hlALvrtx0++2x3W+g0cGrGbvQXlItgWmBKeg1WtoOnpcUXUPsocokwvW2KldK3L1NgDeAO5UlwMeGTBJ/WfOtF4MHI+yF6UleT/ti4PFO+MkZjmddqnWOCXBnT6RWRHd5Hqc/nbkRqAMwe1yKiWUOdaMqIYrU3TMJgnzxhVgTpGMeXEIHEwmSfm77O9vWpTqnaFDxklIxsHP0RAZp9Zul1K+Gi2RbRPnYSFVsm1GEBA4xekclyyMceZKQ+p25G6PjAbfWpFUbw4BAGWF/nK9Ey2n0dTphMUs4awReRG/trK6Fi99+FWfx+M130HM+un2dQYbzDqbWF//xQ++Uj5+ZdvxqF5flMlFk3HSohU54A32xcVyR0+vsr6pwG7r92SfiMXAsZa9RNu+WE0EN0Pt8QluspRZXclWqmfs5hBKxqnbn3FKhm6H1L+iKP6mD4tSPZ0yTqksx5aBob4bOwPNOgHAReUlyPCdN366eHLCWsFTdNI548RagRRlU5Xqnep0wO0rG4u2dCuZjS+yY/fxVhxt6lLarZePyI+4+FaUdIUSj5Iusc4mOL8jgjKt73TF8vo50XTVU9YbabcP5dgs6HE5AgKnSI0hErUYOJayFxFsDijj1CWCm3hlnPzNIZKrHXmYOU4GKdVTuup1OVXDb3nqTXbBbeSDdTp6lUy4nhmnVDZqaDZauttwvLkbU0oj3xhVO9zYiW6XB9lWM275xviUybQZRX4ar3FixilFWVTtyMXBe1iuDRnm1P+Vqjvr+ec3DYn4dVqWdOk9dC/W17crjRnCd9U7pZTqaddgJFfVkrzJ93qRgv1ELgYebNlLNHeng/lL9TRY45RMGSebmOPk3abTTu/NEJtRBuBm+y/ARSvyPDaGSHriOBius55Y31SUY0u7C8NEGew6JwBKCf/UkfkMmpKQet5lZ09vWg0kTv2rbIMS7cgdbo+/MUSKr28S1J31dinrm4ZE/DotS7r0HroX6+v7B+CGryVv0rg5BKAOnFxRZ5xSofWussZpEKV68ZqonmXxDcB1Jdccp+CmIEqpnkECJ7FvtPf0otkXLDPjlPwKI2SclPVNwxM3vyndDLazHgB8JrrtRnFTlRKrsroWP/jfHcrHr+04kdAlDVpj4JSi1Guc0qUxhCAyTl/Wd2Dv120AgNlRNIbQsqRL76F7sb6+MgA3qoyTlqV6/rKtgcxwSvbWu6K5xWCaQ8Rr4ayY1ZVsc5xyVEFCR0+vMsfJKM0h8rMsEP1jak55xywkQwkl9a8wws0Qsb5Jr4566UAMND/ePPCM02cnWgEAM0YNieMWUay0HB2SLHjbK0X525F7lBlOpSneilwQgZPowlWUY8WooZGbXmg5TV3voXuxvn6OqkQulB6XGx0O8X5rl3Hyz3LqVQ6skWY4CcncerdoEHOcRDvyuM1xStJSPYvZhCyLGaddbnT0uODoFYGTMTJOZl+HtuYuJ475ZsvlMeOU9Aoi/E0nQ0e9VCdmIO79ug1Vh09FfTzvcbmxv7YDADBjdL6m20jR03p0SLIwxi2/NKRe4yRK9YrTJHCy2zICGhSMKbAjmvJYLUu69B66F+vrK3OcwjSHEBcHVrNJ04s6JYDr6UXTADJOQrK23hXlWK3dLqWhSSStSsYpTmucfIFIR48LXb6sTjIETkBgg4geX0dKo5TqAf51bCJw4vDb5CeyyM1hssiH2FEvJpXVtbj7r58DAL5u68G1L3wcdTnX3tp29HpkFOVYMTLBA+gpPL2XNCQKA6cUZVOV6omMU7qscaqsrlXumAPAzpqWqA+oWpV06b3OJtbXV5pDhJnj5G9FboWk4Vyq3BgyTslsSLYV4q0XJXiRtCgZp3g1h/C+t3Xt/gu9ZMlsqAMno5XqAf7A+lizKNVLjt8LhVfYT8bJ2etRgmCW6g2cKOdqCiqDjLacS6xvmjFqiKbnKxoYvZc0JIpxzlxpRlnjlGbNIcQB1ekOTDENpD528dRSfHD3t/CnW87FU9fMjNt8B73X2cTy+qLDTbhSvSZV4KSlnMzBrXFKdmaTNKAGEW6PrLSmjlc7clGqV9fmXS+QY8tImi6bIsPS0eNSmkMYpVQP8JdjimwbM07JT2kOEeLv+dipLrg9MnJsGWlx3k2keHSoFYHTdK5vSip6L2lIFN72SlGiVM/V60F9W3qU6sWzPlaUdMWb3utsBvv6Ym2Ro9eDXrenzwW1uPMnylO0kuNrTd3c5VSyilquqUqkQrsNTZ3OqAKn9tMuyL4dPW6ler7ASQzDTpYyPSC4VM94gZMIqgW2I09+4ljY4eiFo9cNm2rumDL4dpidGY8BGkg5V7hz+GcnvE2juL4puWi5zjyZMHBKUSLj1OnsVS7AUv3OVzwOqImgVVCm5euLUj3A21kvPzs4cNK+FTngzzh95esuZjFLSXWBHwsl4xRFZ71WX9CYa8tQboLESmScxPEgmTq35akyTqd9WRcjBU5DgwIntiNPfnlZGcgwSej1yGjucqI037+WRjSGmMDGEAMWazlXW7cLR5u85w921EsuYknBrX/YCQkICJ6SZXRIPCRHHQcNmJjjJC6ScjMzAi6OU5FR6mP1YM0wKcF2h6PvLKdTUQ6jjZVY43S0scv3ejaYUvwgKhQOYAiuWAcVz8GZ2dbAv//8JFpHo844OVzGmuMEAAVBnROTKail0CQpfPmtEjhxfdOAxVrO9fnJVgDA2MLsPjckSH96L2lIhKQInJ555hmUlZUhMzMT8+bNw7Zt28I+9+WXX4YkSQH/MjNTO9MyGOIiWEj1bBNgnPpYveT0M8vpVIIyTsoFtG+tVTqsbxLEexdVxskXOMWrFTngL9UTkimTp/69+0v1kuL0kxDMOKUmfxY5KHDiDKdBi7VDrLoxBCUnrdaZJwvdz1yvvvoqVq1ahTVr1mDnzp2YMWMGFi1ahIaGhrBfk5eXh9raWuXfsWPHErjFycEaVN4THN2nIr1bfqc7MSA1VIOIpgQMvwX8wZuQLuubgMgDM9XEDKd4rW8C+mZwkitwUpfqGXGNU+DvgmucUoNyM0Q1zNPjkXG4wZsxZ+A0cP11iBX6K+fafdy7vmn6KK5vSmbJOjokHnQPnJ544gnccsstWL58OcrLy/Hss88iOzsbL774YtivkSQJJSUlyr/i4uIEbnFyCM44FadBxknvlt/pTjRm6AoZOCV2jZOQDq3IBTEwM7jFbigtSuBkrIxTu2qOk5ECp+DMIjNOqUFknJpVGafa9h6cdrlhMUsYU5Ct16altHDlXJIE/Oa6WWEzE7Is47MTrQCAmaOHaLyVRKHpevR2Op3YsWMH7rnnHuUxk8mEhQsXoqqqKuzXdXZ2YuzYsfB4PJg9ezYefvhhnHXWWSGf63A44HD47xa1t7cDAFwuF1yuvms94k28hhavZTZJSsvO4TnWhPw8WrvwzCL85poZ+MXG/QHzaErybbj325Nx4ZlFafFzai3Ufme3eoPt1q6ePu+hCJzyM02avr+ZQdfKBfaMtPl9DvH9cKc6+76/wU751unlZ5rj9vNbTYF9jHKs8fve0Qp3vMu2eG92tHc7lYxThuRJm999JHk2/40ua4YJZnjgckU3KJmio8W5dmi29xKpof208n0PfN0KAN6gyeOGy9O39Jkiu/DMIlww8d+w/VgL6tp68PO396O9pxdWU/jfYW1bDxo7HDCbJEwalp0Uxw8tr/EocQby+9M1cGpqaoLb7e6TMSouLsb+/ftDfs2ZZ56JF198EdOnT0dbWxt++ctfYv78+fjiiy8watSoPs9ft24dHnzwwT6Pv/vuu8jOTtzdok2bNsX9e5pghtuXi2ms+RIbNx6M+2vo5e5y4HC7hHYXkGcBJuR1wX1sBzYaryozJur9rqvNBMCEqu27INf4L7I9MnCq0wxAwmeffICvNKzW63QB6sNOw7FD2LjxS+1eMIGOtANABo43tGLjxo39PvfzI97fReOJY9i48WhcXl+WAQlmyL5jwvEjB7CxO/RxVGvBx7uDzRIAM2rqmuDs9W7fB1vfR27yJMU05Z077d3vbZI74v5BgxfPc23TCe9++9n+I9jYewgAsKXW+5jd3cHfY5xYAMwaYsLWOhOeq9yB04dD31T47JT3vS/J9OD9f/7fhG5jJFpc41HidHd3R/3clKsXqKioQEVFhfLx/PnzMWXKFDz33HNYu3Ztn+ffc889WLVqlfJxe3s7Ro8ejYsvvhh5eXmab6/L5cKmTZtw0UUXwWKJ71XC6l3vweU9I+PC+XNw4eThcf3+lLpC7Xf/t+Mz7Gutx4Qzy3FJxVjluae6nJA/3gIA+I/LFsetPXYojl4P7t3+T+Xj8+fNwrenlmj2eol0tKkLT33xIU4jA5dcsqjf57776udAfR3mzpgS8LuI1b07N6PL6b0DPn/uTFwyPbGLccMd7wqONOP/HNiO3owsAN5s23cWX5zynUCjJcsy7tv5T7jcMory7LjkkvP03qS0o8W5tmP7Cbx9fC+yC4bjkktmAwCq3tgLfHUCC6ZNwCULJ8bldQgYeaINW5/7BHvbMnD+hReEPDZ88e5B4OBXOK98NC65pFyHrexLy2s8ShxRjRYNXc9aRUVFMJvNqK+vD3i8vr4eJSXRXUxZLBbMmjULhw4dCvl5m80Gm63vOgqLxZLQnVyL17NmmAF4A6dRBTn8o6U+1PtdXpY3ldTTKwfsK+0O74XskGwLsjO1XXNksXhLlZy93juKJUPsabPfFg+xA/B2LXTD1O8annbfOrPCnMy4/vxZ1gwlcCqI8/ceiODj3dAc71qGRtX6r9zsTMOsV3R7ZNitGWg97YLJJMFkzjDMz55o8TzXDsvzzm5q6e5VvueRJu+d6TNL8tPm2JUM5pQVYmxhNo6d6sbWQ824fObIPs/Zc7IDADBrzNCke+8TfU1J8TWQ352uzSGsVivmzJmDzZs3K495PB5s3rw5IKvUH7fbjT179qC0ND3aHA6ETdUgIh2aQ5C2xB28jqDmEIlqDCHkqu4kplM78rzMDFjM3ovh5q7+G0S0aNCOHAhsEJGMzSFEwGwxS4YJHCqra3HeI+8pQ48PN3bhvEfeQ2V1rc5bRpGIuXbqv+fDHH6rCUmScPmMEQCAf+z+us/nPR4Ze056O+rNYGMI0pHuXfVWrVqFF154Ab/73e+wb98+3Hrrrejq6sLy5csBADfccENA84if//znePfdd3HkyBHs3LkT119/PY4dO4b//M//1OtH0I24SLOYJaUVMlE4dmWOU3Dg5GtFnqB9SN1RLJ0CJ0mSUGgX7YsjBE5d8W9HDiRz4BS4LUbpqFdZXYtb/7ATtW2BQ7vr2npw6x92MnhKcgX2wHbkLV1OZabThOF23bYrXX3Xl2X618HGPjefjjR1otPRiyyLGRPZBp50pHuB+dVXX43Gxkbcf//9qKurw8yZM1FZWak0jKipqYHJ5I/vWlpacMstt6Curg5Dhw7FnDlz8NFHH6G8PDnqXRPF7ZHR6/Hevc3PtECO8HyiHN8cp+ABuMrw2wQEMW6PrPSWt5glZGbofu8mrgrsVtS196ApwhDcttPxb0cOAFlJGzgFnmqMEDi5PTIefHNvyGOzDO+fwYNv7sVF5SWGyb6lGjHXrsvpRo/LrQy+HTkkC9lW3S+f0s4Zw3MwdWQeqk+24+09tfj+uf71n2J+07SR+cjQcB0uUSRJsfetWLECx44dg8PhwCeffIJ58+Ypn9uyZQtefvll5eMnn3xSeW5dXR3efvttzJo1S4et1o8o/TjR4r2L2dTlZOkHRSTmOAUPwFVK9TTOOIn99ivfGgGXW8a/Pfp+Wu234kKruZ+Mk7PXo/wOhmqYccpLosDJYjYh0+I/3QQP601H244298k0qcnwtlfedrQ5cRtFA5Jr85ffnupy+sv0mPHQzOUzvFmnN3afDHj8s+OtAIAZozn4lvSVFIETRY+lHzRYdl/GqbMnMHASZWWFGq5xMsp+K9aJneon4ySyTZLUt4QtVpkZ3t+xzSxh+1ctypy3ZKD+WdVBVLpq6AgfNA3meZR46vLb5k4nDvkCpzO4vkkz35lRCkkCPv2qBSda/C2ixeDb6aOG6LNhRD7pf/ZKI5FKPwBv6UcyXSxR8sgRa5yciW0OYaT9VmSQPjnSjKrDp0L+TK2+xhD5WZa4lmhVVtfiw8NNAACHW8a1L3ycVJlodVMQI5TqDc+NrmFPtM8jfRT4MvFNXQ6lVI/rm7RTmp+FeeMKAABvfuY9djl63dhX620XPZONIUhnDJxSCEs/KBaiOUTfUj2RcdKmVM8o+21ldS1e234CALB5f0PYwKWl25tximdHPZHR63EFDo5Mpoyeep2TEQKnc8YVoDQ/E+FCYwlAaX4mzvFdJFJyUpffMuOUGEt8TSL+4SvX21fbAZdbRoHdilFDs/TcNCIGTqmEpR8Ui5ywXfW0zTgZYb8VgUtwq/dQgYs64xQPqZLRCyzVS//AyWySsOYyb9Oi4OBJfLzmsnI2hkhyotvoydbTONl6GoC3iQFp59tTS2ExS9hf14H9de3+9U2j8iFJ/HshfTFwSiEs/aBY+AOn4K563gv5Io0yTum+3w40cGlVMk7xCZxSJaMXkHFKs26K4SyeWor1189GSX7gvl2Sn4n118/G4qnGmz+YakRL8k+/aoYse/9utVwPSkB+tgUXnDkcAPDG7q9VjSGG6LdRRD7sp5lCROlHXVtPyIs0Cd4TMks/KBR1qZ7HI8NkktDt7MVplzeQ0irjlO777UACl4oJhWg97Q1U49WKPFUyeurASd02Pd0tnlqKi8pLsO1oMxo6ejA817uvM9OUGkSp3o5jLQA4+DZRLp85Apv21uPPnx6Hyfe3Mm0EO+qR/oxx2y9NsPSDYpGjWpzf7QuWmjq8F/GZFlNAK+t4Svf9dqCBi1jjFK/ht6mS0Qso1cswTuAEeP8GKiYU4vKZI1ExoTBl93UjEqV63U7vMZNleonh8ciQ4B230tDhLSe/5/U9SbFek4yNgVOKYekHDVamxQRxvSbWOYlBrUU5Nk1rx9N5vx1o4CLWOMWrOUSqNCEIbA7BUw+lhuCyPAZO2qusrsXKDbv7VCg0djiSptkNGRdL9VIQSz9oMCRJQo4tA+09veh09KIYQJPvTl4iavbTdb+NVIoIBAYurXHOOImM3q1/2AkJCNiGZMroBWScDFSqR6mtIGgwOIffaivSmlEJ3jWjF5WX6H5MI2Pibb8UxdIPGgxRrieG4J7q8jWGsGvTGCJYOu63/ZUiCvd8e7Lys7Z0x3eNE5AaGb3A5hAMnCg1BDdxGVfIGU5aSpVmN2RczDgRGYg9qCW5yDhp1RjCKETg8uCbewNO+iIDtOVgIy6bMQKSJPkzTnFqR67ehmTO6OUZtDkEpa7K6lqseeOLgMeuef5jPPDd8qS4GZGOUqXZDRkXAyciAwkegisyTloNvzWSUIFLr9uDG1/+FH/beRLlpXlYvmAc6tu9J/wTzd1we+S4BjYio5eMAptDsNiBkpuYzRZcMlbf7p3NliyZ3HSTKs1uyLh49iIyEFEu1eX0ZZw0Hn5rNMGliP82aRjuu3QKAOCht/dh7i/+qXTV+9nfq3HeI+8ZZqFzYHMIZpwoeaXKUOl0lCrNbsi4GDgRGYjdGrjGSQROzDhp58b5ZZg/oRAy/OubhLq2HsN0icq2+gOnEy3dvOikpMV1NvpJ9/EVlPoYOBEZiL9UzzuT5FSn90J+GDNOmvHIwJHGzpCfM8rd68rqWlz3wsfKx0+/f9hQ2TZKLVxno69UaHZDxsU1TkQGkmPzlkgpzSE6E9eO3Ki2HW1GXbsj7OfVd6+TdX1SLMKtFRHZNl4IUbLhOhv9JXuzGzIuBk5EBpKT6W8O0ev2KOttWKqnHSPfveZMFkpFkWazSfBmP7jORlvJ3OyGjIulekQGou6q1+zrqGeSgKFxnClEgYx895prRSgVcZ0NEYXDwInIQHJUc5yafOubCuxWXgBoyMhdooycbaPUxnU2RBQKS/WIDETpqufoxakutiJPBHH3+tY/7FQG4grpfvfayNk2Sn1cZ0NEwZhxIjIQscbJm3FiK/JEMerdayNn2yg9BM9mY9BEZGzMOBEZSI5qjZNoRc6MU2IY8e61kbNtRESUfhg4ERmIXVnj5EajyDjZGTglihG7RIls24Nv7g1oFFGSn4k1l5WnbbaNiIjSDwMnIgMRc5zUGSeW6pHWjJhtIyKi9MPAichA7La+a5yGsVSPEsCI2TYiIkovbA5BZCBijVOvR8bJltMAmHEiIiIiigYDJyIDEe3IAaCmuRsAm0MQERERRYOBE5GBmEwSsq3edU6OXg8AZpyIiIiIosHAichgxDongRknIiIiosgYOBEZTK4qcMqxZSDTYtZxa4iIiIhSAwMnIoNRZ5xYpkdEREQUHQZORAZjt/kzTCzTIyIiIooOAycig8lRZ5zszDgRERERRYOBE5HBqAOnolxmnIiIiIiiwcCJyGDUa5yKmHEiIiIiigoDJyKDYcaJiIiIaOAYOBEZTEBXPTsDJyIiIqJoMHAiMphsq7+rXn37abg9so5bQ0RERJQaGDgRGUhldS1+894h5eOfv7UP5z3yHiqra3XcKiIiIqLkx8CJyCAqq2tx6x92ou20K+DxurYe3PqHnQyeiIiIiPrBwInIANweGQ++uRehivLEYw++uZdle0RERERhMHAiMoBtR5tR29YT9vMygNq2Hmw72py4jSIiIiJKIQyciAygoSN80DSY5xEREREZDQMnIgMYnpsZ1+cRERERGQ0DJyIDOGdcAUrzMyGF+bwEoDQ/E+eMK0jkZhERERGlDAZORAZgNklYc1k5APQJnsTHay4rh9kULrQiIiIiMjYGTkQGsXhqKdZfPxsl+YHleCX5mVh//Wwsnlqq05YRERERJb8MvTeAiBJn8dRSXFRegm1Hm9HQ0YPhud7yPGaaiIiIiPqXFBmnZ555BmVlZcjMzMS8efOwbdu2fp//2muvYfLkycjMzMS0adOwcePGBG0pUeozmyRUTCjE5TNHomJCIYMmIiIioijoHji9+uqrWLVqFdasWYOdO3dixowZWLRoERoaGkI+/6OPPsK1116Lm2++Gbt27cKSJUuwZMkSVFdXJ3jLiYiIiIjIKHQPnJ544gnccsstWL58OcrLy/Hss88iOzsbL774YsjnP/XUU1i8eDHuuusuTJkyBWvXrsXs2bPx9NNPJ3jLiYiIiIjIKHRd4+R0OrFjxw7cc889ymMmkwkLFy5EVVVVyK+pqqrCqlWrAh5btGgR/v73v4d8vsPhgMPhUD5ub28HALhcLrhcrhh/gsjEayTitYgE7nekB+53pBfue6QH7nfpYSC/P10Dp6amJrjdbhQXFwc8XlxcjP3794f8mrq6upDPr6urC/n8devW4cEHH+zz+Lvvvovs7OxBbvnAbdq0KWGvRSRwvyM9cL8jvXDfIz1wv0tt3d3dUT837bvq3XPPPQEZqvb2dowePRoXX3wx8vLyNH99l8uFTZs24aKLLoLFYtH89YgA7nekD+53pBfue6QH7nfpQVSjRUPXwKmoqAhmsxn19fUBj9fX16OkpCTk15SUlAzo+TabDTabrc/jFosloTt5ol+PCOB+R/rgfkd64b5HeuB+l9oG8rvTtTmE1WrFnDlzsHnzZuUxj8eDzZs3o6KiIuTXVFRUBDwf8KZIwz2fiIiIiIgoVrqX6q1atQrLli3D3Llzcc455+BXv/oVurq6sHz5cgDADTfcgJEjR2LdunUAgJUrV+L888/H448/jksvvRQbNmzA9u3b8fzzz+v5YxARERERURrTPXC6+uqr0djYiPvvvx91dXWYOXMmKisrlQYQNTU1MJn8ibH58+fjlVdewX333Yef/exnmDhxIv7+979j6tSpev0IRERERESU5nQPnABgxYoVWLFiRcjPbdmypc9jV155Ja688kqNt4qIiIiIiMhL9wG4REREREREyY6BExERERERUQQMnIiIiIiIiCJIijVOiSTLMoCBDbuKhcvlQnd3N9rb29njnxKG+x3pgfsd6YX7HumB+116EDGBiBH6Y7jAqaOjAwAwevRonbeEiIiIiIiSQUdHB/Lz8/t9jiRHE16lEY/Hg6+//hq5ubmQJEnz12tvb8fo0aNx/Phx5OXlaf56RAD3O9IH9zvSC/c90gP3u/QgyzI6OjowYsSIgBFIoRgu42QymTBq1KiEv25eXh7/qCjhuN+RHrjfkV6475EeuN+lvkiZJoHNIYiIiIiIiCJg4ERERERERBQBAyeN2Ww2rFmzBjabTe9NIQPhfkd64H5HeuG+R3rgfmc8hmsOQURERERENFDMOBEREREREUXAwImIiIiIiCgCBk5EREREREQRMHAiIiIiIiKKgIGThp555hmUlZUhMzMT8+bNw7Zt2/TeJEoj69atw9lnn43c3FwMHz4cS5YswYEDBwKe09PTg9tuuw2FhYXIycnBv//7v6O+vl6nLaZ09N///d+QJAm333678hj3O9LKyZMncf3116OwsBBZWVmYNm0atm/frnxelmXcf//9KC0tRVZWFhYuXIgvv/xSxy2mVOd2u7F69WqMGzcOWVlZmDBhAtauXQt1bzXud8bBwEkjr776KlatWoU1a9Zg586dmDFjBhYtWoSGhga9N43SxNatW3Hbbbfh448/xqZNm+ByuXDxxRejq6tLec4dd9yBN998E6+99hq2bt2Kr7/+Gt/73vd03GpKJ59++imee+45TJ8+PeBx7nekhZaWFixYsAAWiwXvvPMO9u7di8cffxxDhw5VnvPoo4/i17/+NZ599ll88sknsNvtWLRoEXp6enTcckpljzzyCNavX4+nn34a+/btwyOPPIJHH30Uv/nNb5TncL8zEJk0cc4558i33Xab8rHb7ZZHjBghr1u3TsetonTW0NAgA5C3bt0qy7Ist7a2yhaLRX7ttdeU5+zbt08GIFdVVem1mZQmOjo65IkTJ8qbNm2Szz//fHnlypWyLHO/I+3cfffd8nnnnRf28x6PRy4pKZEfe+wx5bHW1lbZZrPJf/rTnxKxiZSGLr30Uvmmm24KeOx73/uevHTpUlmWud8ZDTNOGnA6ndixYwcWLlyoPGYymbBw4UJUVVXpuGWUztra2gAABQUFAIAdO3bA5XIF7IeTJ0/GmDFjuB9SzG677TZceumlAfsXwP2OtPPGG29g7ty5uPLKKzF8+HDMmjULL7zwgvL5o0ePoq6uLmDfy8/Px7x587jv0aDNnz8fmzdvxsGDBwEAn332GT744AN8+9vfBsD9zmgy9N6AdNTU1AS3243i4uKAx4uLi7F//36dtorSmcfjwe23344FCxZg6tSpAIC6ujpYrVYMGTIk4LnFxcWoq6vTYSspXWzYsAE7d+7Ep59+2udz3O9IK0eOHMH69euxatUq/OxnP8Onn36K//qv/4LVasWyZcuU/SvUuZf7Hg3WT3/6U7S3t2Py5Mkwm81wu9146KGHsHTpUgDgfmcwDJyI0sBtt92G6upqfPDBB3pvCqW548ePY+XKldi0aRMyMzP13hwyEI/Hg7lz5+Lhhx8GAMyaNQvV1dV49tlnsWzZMp23jtLVn//8Z/zxj3/EK6+8grPOOgu7d+/G7bffjhEjRnC/MyCW6mmgqKgIZrO5Txep+vp6lJSU6LRVlK5WrFiBt956C++//z5GjRqlPF5SUgKn04nW1taA53M/pFjs2LEDDQ0NmD17NjIyMpCRkYGtW7fi17/+NTIyMlBcXMz9jjRRWlqK8vLygMemTJmCmpoaAFD2L557KZ7uuusu/PSnP8U111yDadOm4fvf/z7uuOMOrFu3DgD3O6Nh4KQBq9WKOXPmYPPmzcpjHo8HmzdvRkVFhY5bRulElmWsWLECr7/+Ot577z2MGzcu4PNz5syBxWIJ2A8PHDiAmpoa7oc0aBdeeCH27NmD3bt3K//mzp2LpUuXKv/nfkdaWLBgQZ+RCwcPHsTYsWMBAOPGjUNJSUnAvtfe3o5PPvmE+x4NWnd3N0ymwMtls9kMj8cDgPud0bBUTyOrVq3CsmXLMHfuXJxzzjn41a9+ha6uLixfvlzvTaM0cdttt+GVV17BP/7xD+Tm5iq11Pn5+cjKykJ+fj5uvvlmrFq1CgUFBcjLy8OPfvQjVFRU4Nxzz9V56ylV5ebmKuvoBLvdjsLCQuVx7nekhTvuuAPz58/Hww8/jKuuugrbtm3D888/j+effx4AlHliv/jFLzBx4kSMGzcOq1evxogRI7BkyRJ9N55S1mWXXYaHHnoIY8aMwVlnnYVdu3bhiSeewE033QSA+53h6N3WL5395je/kceMGSNbrVb5nHPOkT/++GO9N4nSCICQ/1566SXlOadPn5Z/+MMfykOHDpWzs7PlK664Qq6trdVvoyktqduRyzL3O9LOm2++KU+dOlW22Wzy5MmT5eeffz7g8x6PR169erVcXFws22w2+cILL5QPHDig09ZSOmhvb5dXrlwpjxkzRs7MzJTHjx8v33vvvbLD4VCew/3OOCRZVo0+JiIiIiIioj64xomIiIiIiCgCBk5EREREREQRMHAiIiIiIiKKgIETERERERFRBAyciIiIiIiIImDgREREREREFAEDJyIiIiIioggYOBEREREREUXAwImIiDTzwAMPYObMmZq/Tl1dHS666CLY7XYMGTJE89cjIiLjYeBERGQAN954I5YsWRLw2F/+8hdkZmbi8ccf12ej4ujJJ59EbW0tdu/ejYMHD+q9OQCAhx56CPPnz0d2dnbUwVxZWRl+9atfxfzatbW1uO666zBp0iSYTCbcfvvtIZ/32muvYfLkycjMzMS0adOwcePGmF+biChdMXAiIjKg//mf/8HSpUuxfv16/PjHP9Z7c2J2+PBhzJkzBxMnTsTw4cNDPsflciV0m5xOJ6688krceuutCX1dAHA4HBg2bBjuu+8+zJgxI+RzPvroI1x77bW4+eabsWvXLixZsgRLlixBdXV1greWiCg1MHAiIjKYRx99FD/60Y+wYcMGLF++PORz2tvbkZWVhXfeeSfg8ddffx25ubno7u4GANx9992YNGkSsrOzMX78eKxevbrfAOWCCy7ok/1YsmQJbrzxRuVjh8OBO++8EyNHjoTdbse8efOwZcuWsN+zrKwMf/3rX/H73/8ekiQp30uSJKxfvx7f/e53Ybfb8dBDDwEA1q9fjwkTJsBqteLMM8/E//7v/wZ8P0mS8Nxzz+E73/kOsrOzMWXKFFRVVeHQoUO44IILYLfbMX/+fBw+fDjsNgHAgw8+iDvuuAPTpk3r93nq9+bYsWO44447IEkSJElSPvfXv/4VZ511Fmw2G8rKyiJmCcvKyvDUU0/hhhtuQH5+fsjnPPXUU1i8eDHuuusuTJkyBWvXrsXs2bPx9NNPR7W9RERGw8CJiMhA7r77bqxduxZvvfUWrrjiirDPy8vLw3e+8x288sorAY//8Y9/xJIlS5CdnQ0AyM3Nxcsvv4y9e/fiqaeewgsvvIAnn3wypm1csWIFqqqqsGHDBnz++ee48sorsXjxYnz55Zchn//pp59i8eLFuOqqq1BbW4unnnpK+dwDDzyAK664Anv27MFNN92E119/HStXrsSPf/xjVFdX4wc/+AGWL1+O999/P+B7rl27FjfccAN2796NyZMn47rrrsMPfvAD3HPPPdi+fTtkWcaKFSti+jmD/e1vf8OoUaPw85//HLW1taitrQUA7NixA1dddRWuueYa7NmzBw888ABWr16Nl19+OabXq6qqwsKFCwMeW7RoEaqqqmL6vkRE6SpD7w0gIqLEeOedd/CPf/wDmzdvxre+9a2Iz1+6dCm+//3vo7u7G9nZ2Whvb8fbb7+N119/XXnOfffdp/y/rKwMd955JzZs2ICf/OQng9rGmpoavPTSS6ipqcGIESMAAHfeeScqKyvx0ksv4eGHH+7zNcOGDYPNZkNWVhZKSkoCPnfdddcFZNWuvfZa3HjjjfjhD38IAFi1ahU+/vhj/PKXv8Q3v/lN5XnLly/HVVddBcAbbFZUVGD16tVYtGgRAGDlypVhs3WDVVBQALPZjNzc3ICf44knnsCFF16I1atXAwAmTZqEvXv34rHHHgvI1A1UXV0diouLAx4rLi5GXV3doL8nEVE6Y8aJiMggpk+fjrKyMqxZswadnZ0Rn3/JJZfAYrHgjTfeAOAtF8vLywvIUrz66qtYsGABSkpKkJOTg/vuuw81NTWD3sY9e/bA7XZj0qRJyMnJUf5t3bo1YmlcKHPnzg34eN++fViwYEHAYwsWLMC+ffsCHps+fbryfxFcqEvuiouL0dPTg/b29gFv00CF2+Yvv/wSbrdb89cnIiIvBk5ERAYxcuRIbNmyBSdPnsTixYvR0dHR7/OtViv+4z/+QynXe+WVV3D11VcjI8NbrFBVVYWlS5fikksuwVtvvYVdu3bh3nvvhdPpDPs9TSYTZFkOeEy9JqqzsxNmsxk7duzA7t27lX/79u0LKMGLlt1uH/DXAIDFYlH+L9YahXrM4/EM6vsng5KSEtTX1wc8Vl9f3ydrR0REXgyciIgMZOzYsdi6dSvq6uqiCp6WLl2KyspKfPHFF3jvvfewdOlS5XMfffQRxo4di3vvvRdz587FxIkTcezYsX6/37Bhw5S1OwDgdrsDurjNmjULbrcbDQ0NOOOMMwL+xeOCfsqUKfjwww8DHvvwww9RXl4e8/eOB6vV2ieLFG6bJ02aBLPZPOjXqqiowObNmwMe27RpEyoqKgb9PYmI0hnXOBERGczo0aOxZcsWfPOb38SiRYtQWVmJvLy8kM/9xje+gZKSEixduhTjxo3DvHnzlM9NnDgRNTU12LBhA84+++w+659C+da3voVVq1bh7bffxoQJE/DEE0+gtbVV+fykSZOwdOlS3HDDDXj88ccxa9YsNDY2YvPmzZg+fTouvfTSmH72u+66C1dddRVmzZqFhQsX4s0338Tf/vY3/POf/4zp+4ZSU1OD5uZm1NTUwO12Y/fu3QCAM844Azk5OSG/pqysDP/6179wzTXXwGazoaioCD/+8Y9x9tlnY+3atbj66qtRVVWFp59+Gr/97W/7fX3xep2dnWhsbMTu3bthtVqVIHHlypU4//zz8fjjj+PSSy/Fhg0bsH37djz//PNxew+IiNKKTEREaW/ZsmXy5ZdfHvDYiRMn5IkTJ8rnnnuu3NbWFvZrf/KTn8gA5Pvvv7/P5+666y65sLBQzsnJka+++mr5ySeflPPz85XPr1mzRp4xY4bysdPplG+99Va5oKBAHj58uLxu3Tr58ssvl5ctWxbwnPvvv18uKyuTLRaLXFpaKl9xxRXy559/HnYbg7+HLMsyAPn111/v89zf/va38vjx42WLxSJPmjRJ/v3vf9/v1x09elQGIO/atUt57P3335cByC0tLWG3admyZTKAPv/ef//9sF9TVVUlT58+XbbZbLL6FP2Xv/xFLi8vly0WizxmzBj5scceC/s91D9H8L+xY8cGPOfPf/6zPGnSJNlqtcpnnXWW/Pbbb0f8vkRERiXJclCxOREREREREQXgGiciIiIiIqIIGDgRERERERFFwMCJiIiIiIgoAgZOREREREREETBwIiIiIiIiioCBExERERERUQQMnIiIiIiIiCJg4ERERERERBQBAyciIiIiIqIIGDgRERERERFFwMCJiIiIiIgogv8PberjrDoJiGkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "8nzW4q2UCal3"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}