examining_power_law_fitting_methods.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An ipython notebook that implements the MLE (and other) powerlaw fits\n",
    "\n",
    "Accompanies *Frequency-dependent activity in FRB 20201124A* by Bilous et al., https://arxiv.org/abs/2407.05366\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contents:\n",
    "    -- A) Defining necessary functions and classes\n",
    "    -- B) Calculations and plots the results of power-law index estimation against sample size (Fig. A.1).\n",
    "    -- C) Calculations and plots the distribution of fit accuracy statistic against the sample size (Fig. A.3-A.4).\n",
    "    -- D) Demonstration that power-law indices measured with MLE method are described by gamma distribution.    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A) Defining necessary functions and classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import pareto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_power_law_rv(alpha: float, num_points: int = 100) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Simulates power-law distributed random variables.\n",
    "\n",
    "    Parameters:\n",
    "    alpha (float): The shape parameter of the power-law distribution.\n",
    "    num_points (int): The number of data points to generate. Default is 100.\n",
    "\n",
    "    Returns:\n",
    "    np.ndarray: Sorted array of power-law distributed random variables.\n",
    "    \"\"\"\n",
    "    y = pareto.rvs(-alpha, size=num_points)\n",
    "    y = np.sort(y)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_survival_function(y: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Computes the survival function for the given data sample. Survival function is defined\n",
    "    as number of data points that are not smaller than the threshold value.\n",
    "\n",
    "    Parameters:\n",
    "    y (np.ndarray): Array of data points.\n",
    "\n",
    "    Returns:\n",
    "    np.ndarray: Survival function values corresponding to the data points.\n",
    "    \"\"\"\n",
    "    # Compute the survival function by counting the number of values greater than or equal to each y_i\n",
    "    SF = np.array([len(y[y >= y_i]) for y_i in y])\n",
    "    return SF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_power_law_lstsq(y: np.ndarray) -> tuple:\n",
    "    \"\"\"\n",
    "    Fits a power-law distribution (linear function in log-log space) to data using a least squares method.\n",
    "\n",
    "    Parameters:\n",
    "    y (np.ndarray): Array of data points.\n",
    "\n",
    "    Returns:\n",
    "    tuple: Slope and slope error of the fitted power-law.\n",
    "    \"\"\"\n",
    "    SF = get_survival_function(y)\n",
    "    log_SF = np.log(SF)\n",
    "    log_y = np.log(y) - np.mean(np.log(y))\n",
    "\n",
    "    coeffs, cov_matrix = np.polyfit(log_y, log_SF, 1, cov=True)\n",
    "    slope, _ = coeffs\n",
    "\n",
    "    # Calculate the standard error of the slope from the covariance matrix\n",
    "    slope_err = np.sqrt(np.diag(cov_matrix))[0]\n",
    "    return slope, slope_err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bootstrap_slope_errors(\n",
    "    y: np.ndarray, num_bootstrap: int = 20, plot_slopes: bool = False\n",
    ") -> float:\n",
    "    \"\"\"\n",
    "    Estimates the error of the slope of the fitted power-law using bootstrap method.\n",
    "\n",
    "    Parameters:\n",
    "    y (np.ndarray): Array of PL-distributed data points.\n",
    "    num_bootstrap (int): Number of bootstrap samples. Default is 20.\n",
    "    plot_slopes (bool): If True, plots the bootstrap samples and histogram of slopes. Default is False.\n",
    "\n",
    "    Returns:\n",
    "    float: Standard deviation of the slopes from bootstrapped samples.\n",
    "    \"\"\"\n",
    "    slopes = np.zeros(num_bootstrap)\n",
    "\n",
    "    # Determine the size of each bootstrap sample\n",
    "    if len(y) <= 10:\n",
    "        size = len(y) - 1\n",
    "    else:\n",
    "        size = int(0.9 * len(y))\n",
    "\n",
    "    SF_bootstrap = np.zeros((size, num_bootstrap), dtype=float)\n",
    "    y_bootstrap = np.zeros((size, num_bootstrap), dtype=float)\n",
    "\n",
    "    for i in range(num_bootstrap):\n",
    "        # Shuffle the data to create a bootstrap sample\n",
    "        np.random.shuffle(y)\n",
    "        y_bootstrap[:, i] = np.sort(y[:size][::-1])\n",
    "        SF_bootstrap[:, i] = get_survival_function(y_bootstrap[:, i])\n",
    "        slopes[i], _ = fit_power_law_lstsq(y_bootstrap[:, i])\n",
    "\n",
    "    if plot_slopes:\n",
    "        # Plot the survival functions of bootstrap samples if requested\n",
    "        plt.figure()\n",
    "        plt.subplot(121)\n",
    "        for i in range(num_bootstrap):\n",
    "            plt.plot(y_bootstrap[:, i], SF_bootstrap[:, i], \"k-\", lw=0.1)\n",
    "        plt.loglog()\n",
    "        plt.xlabel(\"Bootstrapped random variable\")\n",
    "        plt.ylabel(\"Survival function\")\n",
    "        plt.subplot(122)\n",
    "        # Plot a histogram of the slopes from bootstrap samples if requested\n",
    "        plt.hist(slopes, bins=50)\n",
    "        plt.xlabel(\"Fitted bootstrapped PLI\")\n",
    "        plt.show()\n",
    "\n",
    "    # Compute the standard deviation of the slopes as the bootstrap error\n",
    "    bootstrap_slope_error = np.std(slopes)\n",
    "\n",
    "    return bootstrap_slope_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# The following formulae for calculating power-law index and its uncertainty can\n",
    "# be directly implemented in power-law package v.1.5.\n",
    "# In the file powerlaw.py\n",
    "# in the definition of class Power_Law(Distribution):\n",
    "# around line 1160, substitute\n",
    "# self.alpha = 1 + (self.n / sum(log(data/self.xmin)))\n",
    "# with\n",
    "# self.alpha = 1 + ((self.n-1) / sum(log(data/self.xmin))\n",
    "# For alpha uncertainty, substitute around line 1140\n",
    "# return (self.alpha - 1) / sqrt(self.n)\n",
    "# with\n",
    "# return (self.alpha - 1) / sqrt(self.n-2)\n",
    "\n",
    "def fit_power_law_MLE(y: np.ndarray, ymin: float = None) -> tuple:\n",
    "    \"\"\"\n",
    "    Fits a power-law distribution to data using Maximum Likelihood Estimation (MLE).\n",
    "    Unbiased estimates of slope and its error are used, see James et al. (2019),\n",
    "    arXiv:1810.04357.\n",
    "\n",
    "    Parameters:\n",
    "    y (np.ndarray): Array of PL-distributed data points.\n",
    "    ymin (float): Minimum value for fitting. Default is the minimum value in y.\n",
    "\n",
    "    Returns:\n",
    "    tuple: Slope and slope error of the fitted power-law.\n",
    "    \"\"\"\n",
    "    if ymin is None:\n",
    "        # Set ymin to the minimum value in y if not provided\n",
    "        ymin = np.min(y)\n",
    "    N = len(y)\n",
    "    a_inv = -1.0 / (N - 1) * np.sum(np.log(y / ymin))\n",
    "    a = a_inv**-1\n",
    "    a_err = -a / np.sqrt(N - 2)\n",
    "    return a, a_err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_power_law_powerlaw(\n",
    "    y: np.ndarray, xmin: float = None, rescale: bool = False\n",
    ") -> tuple:\n",
    "    \"\"\"\n",
    "    Fits a power-law distribution to data using the powerlaw package.\n",
    "\n",
    "    Parameters:\n",
    "    y (np.ndarray): Array of data points.\n",
    "    xmin (float): Minimum value for fitting. Default is None.\n",
    "    rescale (bool): If True, rescales the slope. Default is False.\n",
    "\n",
    "    Returns:\n",
    "    tuple: Slope and slope error of the fitted power-law.\n",
    "    \"\"\"\n",
    "    import powerlaw as pl\n",
    "\n",
    "    N = len(y)  # Number of data points\n",
    "    # Use the powerlaw package to fit the data\n",
    "    fit = pl.Fit(y, xmin=xmin)\n",
    "    a_pl = fit.power_law.alpha\n",
    "    a_err = fit.power_law.sigma\n",
    "\n",
    "    if rescale:\n",
    "        # Rescale the slope and its error if requested\n",
    "        a_pl = (a_pl - 1) / N * (N - 1) + 1\n",
    "        a_err = a_err * (N - 1) / (N * np.sqrt(N - 2))\n",
    "\n",
    "    a = -(a_pl - 1)\n",
    "    return a, a_err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ExaminePowerLawFits:\n",
    "    \"\"\"\n",
    "    A class to examine power-law fits using different fitting methods and calculate deviations from the true slope.\n",
    "\n",
    "    Attributes:\n",
    "    alpha (float): The true slope of the power-law distribution.\n",
    "    sample_size_grid (np.ndarray): Array of sample sizes to consider.\n",
    "    num_trials (int): Number of trials to perform for each sample size.\n",
    "    percentiles (list): Percentiles to compute for the deviation distribution.\n",
    "    label (str): Label describing fitting method.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, alpha=-2, sample_size_grid=None, num_trials=100):\n",
    "        \"\"\"\n",
    "        Initializes the ExaminePowerLawFits class with the given parameters.\n",
    "\n",
    "        Parameters:\n",
    "        alpha (float): The true slope of the power-law distribution. Default is -2.\n",
    "        sample_size_grid (np.ndarray): Array of sample sizes to consider. Default is None, which sets a default grid.\n",
    "        num_trials (int): Number of trials to perform for each sample size. Default is 100.\n",
    "        \"\"\"\n",
    "        \n",
    "        if sample_size_grid is None:\n",
    "            sample_size_grid = np.round(\n",
    "                np.logspace(np.log10(5), np.log10(500), 20)\n",
    "            ).astype(int)\n",
    "        self.sample_size_grid = sample_size_grid\n",
    "        self.alpha = alpha\n",
    "        self.num_trials = num_trials\n",
    "        self.percentiles = [0.1, 2.3, 16, 50, 84, 97.7, 99.9]\n",
    "\n",
    "    def calculate_fit_deviation(self, method=\"lstsq\"):\n",
    "        \"\"\"\n",
    "        Calculates the deviation of the fitted power-law slope from the true slope using different fitting methods.\n",
    "\n",
    "        Parameters:\n",
    "        method (str): The fitting method to use. Options are \"lstsq\", \"lstsq_bootstrap_err\", \"direct_MLE\", \n",
    "                      \"powerlaw\", \"powerlaw_rescaled\", \"powerlaw_xmin_fit\". Default is \"lstsq\".\n",
    "\n",
    "        Returns:\n",
    "        dict: A dictionary containing the method, label, deviation percentiles, percentiles used, and sample sizes.\n",
    "        \"\"\"\n",
    "\n",
    "        num_sample_size = len(self.sample_size_grid)\n",
    "        num_trials = self.num_trials\n",
    "        sample_size_grid = self.sample_size_grid\n",
    "\n",
    "        alpha = self.alpha\n",
    "        dev = np.zeros((num_sample_size, num_trials), dtype=float)\n",
    "\n",
    "        for i_ss, n in enumerate(sample_size_grid):\n",
    "\n",
    "            for i_tr in range(num_trials):\n",
    "                y = simulate_power_law_rv(alpha, num_points=n)\n",
    "\n",
    "                if method == \"lstsq\":\n",
    "                    slope, slope_err = fit_power_law_lstsq(y)\n",
    "                    self.label = \"Least Squares\\n\"\n",
    "                elif method == \"lstsq_bootstrap_err\":\n",
    "                    slope, _ = fit_power_law_lstsq(y)\n",
    "                    slope_err = bootstrap_slope_errors(y, num_bootstrap=20)\n",
    "                    self.label = \"Least Squares,\\nbootstrap error\"\n",
    "                elif method == \"direct_MLE\":\n",
    "                    slope, slope_err = fit_power_law_MLE(y)\n",
    "                    self.label = \"Maximum Likelihood\\nEstimator\"\n",
    "                elif method == \"powerlaw\":\n",
    "                    slope, slope_err = fit_power_law_powerlaw(y, xmin=np.min(y))\n",
    "                    self.label = \"Original powerlaw\"\n",
    "                elif method == \"powerlaw_rescaled\":\n",
    "                    slope, slope_err = fit_power_law_powerlaw(\n",
    "                        y, xmin=np.min(y), rescale=True\n",
    "                    )\n",
    "                    self.label = \"Rescaled powerlaw\"\n",
    "                elif method == \"powerlaw_xmin_fit\":\n",
    "                    slope, slope_err = fit_power_law_powerlaw(\n",
    "                        y, xmin=None, rescale=False\n",
    "                    )\n",
    "                    self.label = \"powerlaw with\\nxmin fit\"\n",
    "                else:\n",
    "                    print(\"Unsupported method\")\n",
    "                    exit(1)\n",
    "\n",
    "                dev[i_ss, i_tr] = (slope - alpha) / slope_err\n",
    "\n",
    "        dev_perc = np.percentile(dev, self.percentiles, axis=1)\n",
    "\n",
    "        dev_dict_corr = {\n",
    "            \"method\": method,\n",
    "            \"label\": self.label,\n",
    "            \"deviation\": dev_perc,\n",
    "            \"percentiles\": self.percentiles,\n",
    "            \"samples\": self.sample_size_grid,\n",
    "        }\n",
    "\n",
    "        return dev_dict_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_figure_PLI_lstsq_vs_sample_size(\n",
    "    alpha: float,\n",
    "    num_points: float,\n",
    "    num_realizations: float,\n",
    "    ys: np.ndarray,\n",
    "    SFs: np.ndarray,\n",
    "    y_theory: np.ndarray,\n",
    "    SF_theory: np.ndarray,\n",
    "    slope: np.ndarray,\n",
    "    slope_err: np.ndarray,\n",
    "    slope_bootstrap_err: np.ndarray,\n",
    ") -> None:\n",
    "    \"\"\"\n",
    "    Plots the results of power-law index estimation against sample size (Fig. A.1).\n",
    "\n",
    "    Parameters:\n",
    "    alpha (float): The true power-law index.\n",
    "    num_points (float): Number of PL-distributed data points.\n",
    "    num_realizations (float): Number of sample realizations.\n",
    "    ys (np.ndarray): Simulated data points.\n",
    "    SFs (np.ndarray): Survival functions of the data points.\n",
    "    y_theory (np.ndarray): Grid for computing analytical survival function.\n",
    "    SF_theory (np.ndarray): Analytical survival function.\n",
    "    slope (np.ndarray): Estimated PL indices.\n",
    "    slope_err (np.ndarray): Errors of the estimated PL indices.\n",
    "    slope_bootstrap_err (np.ndarray): Bootstrap errors of the estimated PL indices.\n",
    "    \"\"\"\n",
    "\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    ax_facecolor = \"0.93\"\n",
    "    cls = [\"DarkMagenta\", \"DarkOrange\", \"DarkGreen\"]\n",
    "    ax = plt.subplot2grid(\n",
    "        (1, 2), (0, 0), title=\"Power-law distributed\\nrandom variable\"\n",
    "    )\n",
    "    ax.set_facecolor(ax_facecolor)\n",
    "    for i in range(num_realizations):\n",
    "        plt.plot(ys[i, :], SFs[i, :], \".-\", markersize=5, lw=1, zorder=10, color=cls[i])\n",
    "    plt.plot(y_theory, SF_theory, \"k\", lw=1, ls=\":\", zorder=9)\n",
    "    plt.annotate(\n",
    "        r\"$\\alpha_\\mathrm{true} = -2$\", (8, 20), color=\"k\", horizontalalignment=\"center\"\n",
    "    )\n",
    "    plt.loglog()\n",
    "    plt.xlim(0.9, np.max(ys) * 1.1)\n",
    "    plt.ylim(0.92, 1.2 * num_points)\n",
    "    plt.grid(which=\"both\", color=\"w\")\n",
    "    plt.xlabel(\"$X_0$\")\n",
    "    plt.ylabel(\"$N(X x X_0)$\")\n",
    "\n",
    "    ax = plt.subplot2grid((1, 2), (0, 1), title=\"Least-squares\\npower-law index\")\n",
    "    ax.set_facecolor(ax_facecolor)\n",
    "    for i in range(num_realizations):\n",
    "\n",
    "        y = ys[i, :]\n",
    "        SF = SFs[i, : -min_num_points + 1]\n",
    "        slope = slopes[i, : -min_num_points + 1]\n",
    "        slope_err = slopes_err[i, : -min_num_points + 1]\n",
    "        slope_bootstrap_err = slopes_bootstrap_err[i, : -min_num_points + 1]\n",
    "\n",
    "        plt.plot(slope, SF, \".-\", markersize=2, lw=0.8, color=cls[i], zorder=5)\n",
    "        plt.fill_betweenx(\n",
    "            SF,\n",
    "            slope - slope_bootstrap_err,\n",
    "            slope + slope_bootstrap_err,\n",
    "            lw=0.8,\n",
    "            color=cls[i],\n",
    "            zorder=4,\n",
    "            alpha=0.2,\n",
    "        )\n",
    "        plt.fill_betweenx(\n",
    "            SF,\n",
    "            slope - slope_err,\n",
    "            slope + slope_err,\n",
    "            lw=0.8,\n",
    "            color=cls[i],\n",
    "            zorder=3,\n",
    "            alpha=0.5,\n",
    "        )\n",
    "\n",
    "    plt.axvline(alpha, lw=1, ls=\":\", color=\"k\", zorder=3)\n",
    "    plt.semilogy()\n",
    "    plt.xlim(alpha - 2, alpha + 2)\n",
    "    plt.xticks(np.arange(-4, 0, 0.5))\n",
    "    plt.ylim(0.92, 1.2 * num_points)\n",
    "\n",
    "    plt.annotate(\n",
    "        \"darker shade: least squares error\",\n",
    "        (-3.9, 1.1),\n",
    "        color=\"k\",\n",
    "        horizontalalignment=\"left\",\n",
    "        fontsize=8,\n",
    "    )  # , fontweight='bold')\n",
    "    plt.annotate(\n",
    "        \"lighter shade: bootstrap error\",\n",
    "        (-3.9, 1.5),\n",
    "        color=\"k\",\n",
    "        horizontalalignment=\"left\",\n",
    "        fontsize=8,\n",
    "    )\n",
    "\n",
    "    plt.grid(which=\"both\", color=\"w\")\n",
    "    plt.xlabel(r\"$\\alpha_\\mathrm{meas}$\")\n",
    "    plt.ylabel(\"$N(X x X_0)$\")\n",
    "    ax.yaxis.tick_right()\n",
    "    ax.yaxis.set_label_position(\"right\")\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B) The following code performs calculations and plots the results of power-law index estimation against sample size (Fig. A.1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting up simulation parameters\n",
    "alpha = -2.0\n",
    "num_realizations = 3\n",
    "num_points = 100\n",
    "num_bootstrap = 100\n",
    "min_num_points = 4\n",
    "\n",
    "SFs = np.zeros((num_realizations, num_points), dtype=float)\n",
    "ys = np.zeros((num_realizations, num_points), dtype=float)\n",
    "slopes = np.zeros((num_realizations, num_points), dtype=float)\n",
    "slopes_err = np.zeros((num_realizations, num_points), dtype=float)\n",
    "slopes_bootstrap_err = np.zeros((num_realizations, num_points), dtype=float)\n",
    "\n",
    "for i in range(num_realizations):\n",
    "    ys[i, :] = simulate_power_law_rv(alpha, num_points)\n",
    "    SFs[i, :] = get_survival_function(ys[i, :])\n",
    "    for j in np.arange(num_points - min_num_points, -1, -1):\n",
    "        y_trunc = np.copy(ys[i, j:])\n",
    "        slopes[i, j], slopes_err[i, j] = fit_power_law_lstsq(y_trunc)\n",
    "        slopes_bootstrap_err[i, j] = bootstrap_slope_errors(\n",
    "            y_trunc, num_bootstrap, plot_slopes=False\n",
    "        )\n",
    "\n",
    "y_theory = np.linspace(1, np.max(ys), 100)\n",
    "SF_theory = num_points * y_theory**alpha\n",
    "\n",
    "# Making figure\n",
    "plot_figure_PLI_lstsq_vs_sample_size(\n",
    "    alpha,\n",
    "    num_points,\n",
    "    num_realizations,\n",
    "    ys,\n",
    "    SFs,\n",
    "    y_theory,\n",
    "    SF_theory,\n",
    "    slopes,\n",
    "    slopes_err,\n",
    "    slopes_bootstrap_err,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C) The following code performs calculations and plots the distribution of fit accuracy statistic against the sample size (Fig. A.3-A.4)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
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      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "Calculating best minimal value for power law fit\n",
      "xmin progress: 99%\r"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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bhbYNpe3BRs49iqDTDj9hM21VcJhDRIbCBPZt9LPwF/u7u0DTrcDRIvKfZRwvLiInun+weRkuE5HtOb+fgSa2KgaFFuhXFtietP83sWWvUuof6Of9VREpWu+7ollXMCQcqmCLQudXGsWea4e0v8GeOD45QPuDjTPQOdQ3oBn4zkSvzh8vRKTijIHSxVGOBLzTvjcnopm0vli8eWk4gsZnWHGo3+EyMB/YPMLn+Ad6TlAAylWZLwePozklsNtNRHN6vNGxTtj+7yXFTiYiF9lBbkUFtdO48jdK/9AEKl9EExP0Ab8C6lzbP4rWwnrRJDGzXNvOQtMFBu3/z7J//zp6ZZdAay4/Rq8Ivwd0ojW5jWj2sI+hCTlSdttHXP26EU3KkkSvEr+AJl8I2/19j6svV6NJXn5s92cbcEGR656FJm3oQlOTfsq17VY0U9rv7L5+BG2u+rp9jji6Alfe9dv9vtCj/floQoUgmsr1D/a5nkGzqkXt6/8PNA1ic849usEeiyjalDYdXRUtDPwLmOhq/wCa1CRoH/84+3dnrMW+N85YH4sOfrHQRD+XusZgt31vNtj//8t1ng+gyXF60P61fWhfmjOGv7M/L7DPeRXaFNiNLpLjHOd0NKlMAE2i8WM0naOzXdAajdd9fApNOrHavld/ASa5tl+KnnwDdttj7X7e6br+b9nj+oD9/Uz0MxdG58t/AD1x9tr967C37bXH9240sVLSHo80+rk6GU1D6dQVEHu/j9jnOQ9N9vGc/f1x9HvhnCsG3Om6lsnodzBkX+//OPt6jIsz5h9Dk9y0AZ9zba8Fvm9va7U/19rbnkZrfqA5uwW4xP5+AbDOdZwP2dfXhxZC83Pu23XogkF7c+8lmqN+rT0mJvBT17uZAO6yv79g77cH/Y6sss/3f/ZYvAIs8Hpe0MV1foImRgkDLwNHeYxXrX1s513c7Xr3LkQTRKXsexsB1pc4v0Y8fqtHEzJtRD+bj7rmsB+72jUAv0bTQq8H/s3+/Xz7mjfYf5e6+jrF/nwq8FTZ8uBgCJ3K3+D+7Bu8Cc24NQktXL7meii60ZNOLfAj4Bl72yT7hXk/WpBeaX+fbG9/CntSsr9fhPajTEAL7mOBmfa2e51z5vRrnd2vevu3K+wX2YcWalHXMa5Gc3V/Gqi2twdxTdyuY/vsvnwFzWa2CD0RXGRvv9V+Kd9tt623r+cA2ixWhRaWXtd/gH5h7W5/PzqwxIf2UZ3t6k+WMMJbWL9kn3M2esGzBq0R1qGZym5xtf8QmmXJmZDXubY9jhbKVfb3avRi7CY0A94q9KT2I3sMnrDH8Tw0K9R99n7L0JPW2fYYfttuX0xY/8weyxPQgu1Ye/spaAFZZbfdClxfaHxy7uVT6AXGcjSl7kOu8y5BPyNvsa/z8/a17kNzRQfQz9dW9KTfbI9vnz0GfjTjnAl8Cs1YF7G3L0MXlfizPT5fsvvxRvoFxHy0gP02+hkbh16UGPa+9faYvWT3r8Pedg16ok6gF0LL7Ou5H82xP9a+3hYGFta/t9sfj15AOPfnq/Z5p6GL9rwA/I9r24/szzehF2x3uLb9wP78Lns8j7Xv3ZeAF3Lu2+PouaI+916in/Pj0e/Erfa1/zta6D9G/2Jqiz1Oj6ItQa32fbjQPu9vgF95PS/2vehBLwir0AL+/iLzYe67uA+PZ/pI/TvkHaj8Fbk5+mH8L9f3t9O/qvwF8C3Xtgb7pVmAFlKrc471InC1/fkpsoX1+eiiGmcCvpz97sVbWH9ogL6vA95lf77afomVa/tq4P0e+50BHMj57YvOC2+/lM/kbH8K+Krre6Hr77YnEqfMaABtnfgNcA8wx6M/pQjr97m+PwTc7fr+SeDPBcZogn388fb3Z3Ct9tE85+3oCfN29OT6e/t6n7HvzW9c4+IIwq8Av3cdZwxa8ygmrOe42q8G/l+BPl8P/KnQ+Hjcl9td35fZ/fCjtf0/urb50AKu3b5Hln3sTfZ1rkYL1heAv9r7/AdaOF+FFnoBNMWmI9jute/tP4D/LvQ8u/pwov1sOM/tdvRi4Uy09tSF1uLOQ2tNP0VTrfrR795S17G+wcDC2t3+W8Av7M+7gbe7tl2ELpEKWnveYH9+DG1Zesn+/jT9Gt7fgQ/njG8MW7u2z39+sWc9Z9tu9PuzAb0A6LOP2YpeFDXb7TYDW137vZPsBWmusP65a9vbgW1F5pTXtbAeTb66CrxRdqm73G2ufWd7nUBEnkCbN38CdCql7lFKjSujX6WU+GwR+63yuBY35gOzVHb5vZvQmqvnuT1+K3T9frRG/hzwSdHVgn6G1uoUsNqOMP2Q1wUXQW6pTM/SmWrg8q1JYIzLhzgLzYVuobW9bvs6Gl3X6zUWuaVKYwxcrtWzJKBSaolS6lGlVLvd52+QfV8HQu7zW23vn/v8WnZb5x7dh34eF6AFxFNo8+EZwFvs5+Je9ILnf9GCvgq98Py8UmolWtNrQmvou3M7ppQao5R6QimVsgMJ19rnn2o32W4f483YlcbQgunv6CBDp0zpVPvcudda7th4vts5215EF1iZjl5c/AaYa6cNuStjzQd+4HqHetHPuHsOKFbG8gyl1JNKqS6lVBA9hpPRWv1W+stRjke/T045ytlojd7BQKVjR10pytGKirAe/RhMqbti5Q3BoyyciPxQRE5Baz9L6K+Ek9c293dVWonP2U7Qhse1uNGE9qFNcP01ioi7sL1Xn9y/Fbp+dxR4pr2ItIvIR0VkFvBx4K4RSmEpWr6VflPrv9nfW9ETcSM6D3SVfR1hV/+9xqINXXVMH1xX7prs0a4U3I2OMVgsupzhTWTf14GQ+/ym0YuO3OdX2W2de/S03T6GTqd5Gi2Yo8Cb7GfsQ2g/fb30lw2tstttQ8cpCPqZOsqjb19FW0ouRwtbJ5jImRfdwvoltHa/Bi0w3yf9ZUq70Pct91rLHRvPd9u9zV54vYYuPLNJNH3uC+h83t3SX2ilCfh4zntULyIvuI5b6N0GvVj6q93H2ej7sBu4VelKXk45SoW2aDjlKJ2qeQcbxa4lD0qpCUqpa+3Ps5RSD9qfz1VKPToSHRwqKsJ69GMwpe4Klje0980qC6d0ucUzlFLV6MkwQXbZzIHyIgcq8Qna//Yppcs7XoE2pf3N41irgbBS6kalVL2tjS5XSp02QB/cKHT9nry/SqkrVH95vz77Wsq5/lJRsHyrjWa0GfFHSpfpfA1tNn7F3rYfbVbcNMB5HkSXMDzLjiC/lfIEbG6fQ0BEadalcmto/6dSapnStc2/CjwounLSH4FLlFIX2M/dZ9Fjk7D3c8okRtDugD704qYBmGpHdj8OHK+U+pRSarZS6t+UUm9CV15zApIAfg58Til1iv19or3AnGi32Wt//kVO33eifdenAz9EC62laO3+Bfu9Oda+nofRgmyMUmoZWnANhC/b7Y9Dl3x1v9tfUrr07xS0W+N3rv2cUpBObeencr6DtjZ80T42Sqnx9ntXKhqBXhFJoH3JdegFy0r72E4fgjl9aOLQ1IjuABaUkdkxAbgWQERaReTykerYcKEirEc/yi51JwOXN/wBcLlSqk8p9UN0cM3P0BOiE0F8p932F8Ay25z2Z68OysAlPkFHei5Ga1VfRzMB5Zlm7YnvHWgT2167/c/RGk5JKHT9aAH8CHry/7HS5Ap/Ak4DXlZKRdDaxH+LyB77cLcCv7av/99L7UMBDFS+9RdoodqIFmaOUB+Hvr8/QEc/d1MEokuVfhId9NSGFlyd9Od5loPPoS0CYfQz8ofizfPwW7S5uh094X/K7uN2dCDZj9DX805cNbGlv0zi0+hx+yzavPoSOvOgC+1P3oa2Vmyw+/Yk+lk7B/08IiIPoJ+5+9Cm6yvQgVVfRi+CNqCf21yN0MmC2CyaDOOt6DGsQUcLu8uUfgK9kGi3r/dXJYzN0/Y1rQK+LSL/tH//GpqoY4N9njX2b+793KUgc78jIn+y+3e/7b7YhLbOlIprga8qpeLo6/6t/ftn0EGt4+xzBuzfnXKUB8o4x3DiAfv/HqXUmhLa3w4cZbvuHlBK5S2AVWHq0ePs39YpTTW6eBivoyAqJTIrGHEopa5GB7Sdfaj78nqE0vzEAbQpe+9BPO9TlEi+83qCUmoBeiFaLSLGIe7O6xL2PXhURJbnfD4XnUb3DqXUN4AtIvI7pdQEtNXvJLSgf0lE/s+2XPlFJK6U+ht6nvNy7w0Zh0syfAUVVFAGlFLvRGtsCh1FvZH+gLYKKqhgYLwVuFQp5fB4O9SjLwI3266zh0VkJ0BOXM2wY1SZwW2n/4NKqW1Kqa1KqTcopSYppR5XSu20/594qPs50lBK1dlmlvVKRyffZv++UCn1slJql1LqD6oAo9XrCbZPe+1oDQo5mMgZi3fRT6qxGJ2K9boxo1Wei35UxmLQcKhHT7T/5onIVhG5D03oEwf+ppQ6/2B0ZlQJa7RP7jERWYomZ9iK9k+tEpHFaE3hC4ewfwcLSXQO5Alo3+3FSqkz0T6o74nI0Wj/8ocPXRdLh4jcO4Im8P9GPycVuMZCRD5iRwCPF5ELJIfr+GBARM49hCbwUftciMg+EVEH0QQ+asfiECKM9rkXgyf1qNKFSPaIyA/RrHwrRrKjDkaNsFZKOSkSvwAQkZTowgXvQtO6Yf//7kPRv4MJ0XAKF1Tbf4LOIX3Q/v11MRbFYJuhLkEHoL2uURmLflTGoh+VsfCGHYT6vB1YdmeBZv+Dnns3KKU2299BM7ltUrqy1nJ0ACRKqb8ppby4I4YFo8lnvRAd4fkrpdQJ9OcSTheRNrtNO9nkGEcs7NSU19A81z9BR4IHXKtxh37x9YzvoyO9B1ohvx7wfSpj4eD7VMbCwfepjIUnROS9Hr89hU5DQ0TiaN6F3Da3o4PMcn8fUZ/1qIkGV0qdil21REReVkr9AJ3f+UmbAMFp1ycieX5rpdTH0MT4jB079pQlS5aglEJEPP93UOz6nfZe370+O8cd7DmWn7KAi//tVGrrqjPbo5E43/rKr7niA2/hm1+8l+OOO441a9ZE0KlcS9B5ufeIyD2ljMWHP3MxM2ZnD18sEGDPSy9xzLnnUl1XRzKRzuqDZVrcefODGOl+TpFiY7H85PlcdNmp1NbqY4SDMX571xN8+DMXZX4D2LGpmQd+9WxJ4507zoFAgFAoRHd3dwTtl52OToMpeSyK3bfheC7ed835zD9qGg/d+xw7NrcM27M3WsYit0/FxmK43pGBzhEIBNizZ08anfbUaI/F593j4DUW733vm5kzxUDM/me8qzXIrg1tLD1lDhOn9hNr7VjXQk97uGB/DzV8PsXUo+fy3JrOQY3FkiVLuP7W9zCmoZZv3vCHkp4LpRQ33n4Fyufjji/8Ecu0yDnHIX0uRIS1a9dG0EQ3Du7JHYvRjNEkrGegw+EX2N/fhPZPHw2cKyJtSqmZ6GolResJn3zyybJy5UoaGhqIRCKe/zuIRAqXyXXae333+uwcd7Dn2LT/n4TinXltHvjN49TU1vDoH19g9+7dTJo06TV0Hu2tInJROWOxYf/fiMSz00kjPT3se+UVFr/pTdSOHYvfV4Vp9bvTFIrzTvoQiXh/mm6xsdjdvJa9Ha9h2UaA6qo6TjvmXby89WFMK505xvgxMzhu3oWexyz2vaGhgZtuuomHH36Y1tbWFNp/Pw4dmVmwnm2x5wLI+1wIpT4Xeqz7WL7gXGZNOWbYnr3RMha5fSo2FsP1jgx0jptuuokf//jHafSipa7UsfjmN69j9dN/xDL7n/uW3T0c2NnFzAUTWbC036C35dUmgt0DlTY+dPD5FG9651tp7Rs7qLFYuXIlr+x6kLSR4MKTP0osFh/wuUikwqzbuxIQzj7+SoxU9nEP9XMRiUSYNWvWayJyauGRG90YNT5rEWkHmpTmlwVNWL8FTVLhsAFdhXboH3EwzBThhOa6CAYiRCNxAJLJNOtf28mc+dN5w1lv4M9//rOzy6DGwmtxZtnahGU4E1UO2ZVSaOrm0mCJU4PBOaeFiJlHoWWJyWBx0003sW3bNtApSf8PeKLYJHQokDb04sYwUwO0HBoOh7E4WLjppptAF7pYwBDHIh7V96+vK1swm+nBP7cHE8MxFu5FezHEUkGUUijlI5EavQuZwxmjyWcNWlt0Es33oCn4fMAflVIfRrM/DZVFalSiN9KMwodg0dcT5sd3/AHLshBLOOvcFZz6hmW8/c1X89+fuh50UMNe8ukRB4R4MAE6pj/LLDQJlSesRays84iIFuAqW1wPRVgfDjAsXZ56pIV1BSODeEzft2QshWmY+Kv8AJhG6e/C4Y5S39F4MohpGfh9VSRSERpqxo5wz15/GFXCWkTWoSvr5OKCg9yVg46OwK6M2XjBUTP59j3X57WZP38eTz31FOPGjdskIuXw/PajJM06GwrKF9aSrVlblpnHGCzW8Ex67qCQ0QIRwTS1yT9lJAZoPaznfYpRNhaHCkMdi1RCvw8+n49QX5yJUxuIhhMk4+kB9hx9GOxYWJZBKQbYSKIHECzLJJmO0vC6Z4AYfowaM/jrGSJCJN51cM7loVk7wtosollbZQrrfDO4lWddH02atWWZZS1IBjyeGCj7gtMHUVhXMDwQEdJJ572wCPZEERF2rm/FskZHnM/BgGWV9o5GEwEABIt4cvQG3x3OGFWa9esVaTMOygcHQXiV5rP22A+h1MJNuYJdEE9hX84CYKSxvekF/KqOxsZyinsVhmGmUD4fYlmkzcHUz6jgUMJIZz+bfV1Rauv7DkutevBQmGLio7poKxEhafQHesWSwSKtKxgsKsJ6FCCRiuJTPsyDomkWEdaFNGvlaMulGWIsj6AUyzLyFgoyijTrWDIIUjjytFwYZtKlWVeE9eGGZDyNz68wDf3MJmMpDuzowjJfP1q1QmFZxoBvfcqIoVCZmSWRGr73qIJ+VMzgowDJdMTTPD0S8NKszbTWFhzNOi93tswAM4tcIazsYKvcvowezTqZjhNLBobteGkzmckXNY4wzTqWDNDSs+VQd2NEkUyks9a1SqnXlfkbAFWaGTyeCuIuI51Me5aN90Qo2sXW5qcG07vXHSrCehQgkQ57aqMjAU+ftS2kTaPfz5q3XznC2sonRHCCrbL7MnqEddpI6EVTAd6BeCpMc3deyduCMMxU5liGx7UfzgjHu+kO7zvU3RhRpOLpLDeNZYmXUeqIRympW7FkMEuop41kUTITN+KpMNFEXll7TyRSEeKpUEltj0RUhPUowEH18Xj5rB1hnU5TyC9dXoBZ9mpc2Zp17kLBGiWEPKC1X0vMgpHb0UQPPeEDZR3Pud5Sc1UPF6SNBCkjfqi7MaKIx1KMIsPPIUMpQaCRRE/WwtunfCXHaaSNJCkjUZIy0Nq3lf2daz2O8foI4KwI61GAxEFcLXpp1k4UuJVOa/+0Vy52GYI17wV3NOs8n/XomA1FLDtHtJpoos+zTcpIYFilm7MNM5Wxlhwsq8nBQiIdwTzCc8cT0SP7+kpFKc9uLOedUcpHyijNFK6FupRkOo8megnHu7N+iyR6eXX3w68LP3lFWI8CJEt8sIcFRaLB+83gXsK6XAazfijIohl1HbWsRcBwYd2uf2S99GkjiU/5EbGI2SkouUgZ0bI05JTZr3laMrxpYYcayXQES8yS03oOR7y+or4LQMAc4B6LCIl0tqBUKFIl+q0drbgUYZtIhUmb8SytPRhtR8Rid/tLJZ3vcEZFWB9iWJZ5UBmuiuVZa3O48vTNlUuKkg2tWeefu7zAteFCyohnrfxTRhylfFhiEo55+8+S6WhZGrLbTKyUb9T6rXc0vUgyXR49pHNtB5Ps5WAjlTyyrCGDgwxoBk+bCQ/3llmyZu0I9YFysy3LJG0m8fuqiLgW2oFoCwDheBddgf0lnfNwRUVYH2Ik0lF8yn9I+5BLN+oV+FWeZp3/ghtW2jPK/FAQo4hkU4Cm0vFM5HYo1u25TzIdszXk0iwBhkuQ+ZRv1FKO9oZbyg7aMcwEoI5Yv7VpWq+/yG8PCAObwePJYN78ZYlZ8gIwVaJmHU+F7AJDJiGbQEpECMd7MufcvO+pI9raUxHWhxjxZCirJNxIw5MUxY7etkwzl767v80QNOv+aPCcc6vymNGGC9pH7RLWRiIzLoWC/dJmIrNvKcgOsFGjVljnLlxKa5/Gp/yk0iMjrC0xC7hNDg5S8TQ+X2VqdOhDiyEY6/BsE0+XtgB0zODRAu6nzPFSIfsdFYLRdnufbF+5YaZp69tW0nkPR1SeyEOMeDJ8UE3BAxbyKKBQlJMHnieABc88a805fgg0a4S0W7M24pl7YBhJzzQzJ1e6VCHiXgyo0S6srdL7Zlr9i66R0KzjqRDr9jzCrrYX87YdrCCiZDxdcNH6eoPXe+tGd2ifpyUuWWLlLYcwaKCMmFgykKmdEEv2ISL0RdpxT1iWGPRFWko67+GIirA+xIglgiNuCk6mo2zd/6z+4qFZOwU1xLIKiuTyfNb51+MlALVmfWjMVu50j1Q6numHz1eVN3FoTVxrk6UL6/52MoorbwlWWZHdaSOO8ulgvPQwa9Y94QOs3/s3EulIHkFNyoizds9fD4olJpkwkIoZHCjw3tpIGTGSRr9QDnd20rFzJ2BTKJcA570YaCEWSfS6viniqRDdgf1580c06Z3NcSTgiBbWe9vX5YX6jzYUShUaTqSNBF1BHXzhGWDmmMELVsGSoQWYKe+azprO8NAIa8PsF9a5E0UkxySXNlMo5UcpX0kR4SKS127UCmuRjIm/FKTMhE0taZXFVDXgcY0EO1qfy2hPuceOp0L2OUdeu07EUhWftY1iz3tfpAXlEiHxUIhorxaqpRKjOIvagVwq8VQg63s43m1r1tnQwW1HZizFES2sg5GOUU8q7/Sve+/eYSsZ6YVCkbsi0q9tiyAewlM3Gbyw9hJeDg4VYYjbp+wOhjGtNJF4b3ZbI47PplMsRbO2rGwmuNGsWUN53OVpI45jeowPo1k6luzNClQSyc6ScLgI4gfhfY5XcqwzKPZ+doX2ZRZXAKlYDCNpP0tKDbgI1POCE9QqBZ9DEclKBbPEoCu0xzP4zaf8drnOIw9HtLDWdZRHZ8qMg0QqimVZtG/fTjoxcqkwlmXoFy+XmMSycBx0OhDM++Usx/zoxUxWSMiZZfhL3UgkEpx77rkAy5RSm5VSt5Wzv1sQJHNW9eGciHC3Sa8UYa15wfsFz0in5w11LMrSrI14xgJTbspXMUQTgSwri89XRSLdn87jLGoHilxP6HfoWKXU+sGMBUAyNrrnjFIxHGNR+L01COeU9U3F4xgp/Zz7lI9EaqB0rP5FrU/5C5rCk+loFvc4QCjWkfeb069IvCKsDzs4zFSjFaZlYFkG6bgWBkZ6ZCcJnROZDR0BbmuBPl+mqEc2yiUvyRfsntzgZUYiu1FbW8ujjz4KsAU4EbhYKXVmqfu7J6FcusJIPNs14USLF7MQuGFYyZwI/8Jaw3BgqGNRTqGRZDqWCSgazmjwcLwzK1BJRLIme8cXGU325u3rRm1tLcB2ETmBQYwFHDk51sMxFoWe995QS17KVjqRwDKMzLsykLA2LF1G1kEhYZ1bKERDFSRaCsU6i573cMWRLayRsigiDzaS6Qg+X1VGWJvJke2rFko5BAam2a9ZQ0HNeigMZmL/yzsmVlmRyG4opWhoaHC+Vtt/Ja8o3JNQrrBKpqNZi5O04QSgSUmWAK8FSMqlvfaGWkiUGC1bCoY6FuUQtrh9xsNZpzs3dsMSg7hrsncm/liiuBncXiQ5D2DZYyGWYKSPjFzdoY4FFK661d63O09YmrZWbabT9v0rbgUxzFRGsxaxsu63G/FU0KMfhS/jSA0yO6KFNYzuikeJdASUIuVo1qmR9ZV5mTst0+z3rirlWdNar5TL8VlL7g8F2llFo00Hgs1pvgzoBB4XkZdL3dfxtWnrS07hkRxuYx3xWoZmbfMdu+HW3ne2vJwJ+PNCW9/2guQshTCUsSjHFeEeF/eixBJr0MGcluXNeBWzJ10Ry/aVk0dtWQhKqXUMYixSSQOf78jK2xrsWIC3Zi0ieWxhYllYponP78/MYwMJTcNVRtYSk3jSW7hHEr1lVeizxBjW4MfRgqpD3YGRxujWrKOIZZKK6QdrxM3gHlGS4tKsob8CV167slJmctsWXgWX4y/Nhd/vB236vRD4k1JquYhk1bFUSn0M+BjA3LlzaWhoQClNc1pVXUVtXTU+m2rUgU/5wJ/OaKtmW9K+Cgt/laKmpsatyWZ9r6mpwV+l8hYsppmioaGBmpoaookAqXSEhqkNeKF7/x7i6V5WLHqL5zmc72PHjiUQbWVCwyxqampAT8h9wDVKqUuAH4rIPQONhSVm5vj2cfI+O8h6n0Soq6+lyl9NZ2AfG/c/RltgLsvmn0NdTUPeuBQ6fijajc9XlaepJY0IDQ0NGFZCp4tZhp60fVbecXM+TwG6gZ32WMRF5Fb3sXPHorqmGpSdY+1TcJhHg/v6rS1lj0VDQ0OWxuvcq53Nr9DcuRURKy89M51MapO2UjrIrKEhc//A+z5F09lm9HgqlPVcgH5OEiUSrPRfexWxVICJDbNyzz1FKfWqq+k97vdjtOOIF9ZD0dxGEiLat2KJSTKqTaLGCAaYgTeJRa4m7alZI2UymOVW1yrcdijC2nW+gFLqSeBiYFPOtnuAewBOPvlkiUT6a1bHYmECoR7tD3NNPpZl0hvsoNY3QbdLuAKd4hFSqRSRSL+G19DQkPne0NBAJBbKy/9MGQkikQh19TUYZopQtCfrGG4kUjEi8V4Ck3up8vcLt9xz9gW7eW3XI7xh6fucyahbRE5VSn0FiOVORIXGwrTSJJNJotFoZkKNRCJZnzPXkXbRqPr8BII91NU00NGzD4CeUAvPbfw986edzOK5p2SPS84xnc+9oXbPmIh4MkwkEiFp9mUFIgVCXVQx1nvs9eduETkVwBmL3GPnjkU6lQaBZCJd9Hk9XGCJDHosIpGIq7xrmkgkwtixY9jV/EpBy5KRSGhhLZKJCE+kooTD4YybJvc+RaLBrHklGg9mPRegn5NYojxhbVppeoOtVMu4rPO5x+JwxBFvBj+UtIWFYJgptjY/mWHbcTTrYtHg4a7OIVeo0qahbBNflnAWKaBZe/ucCyFPWBcxYZUT3ORGT08PgUAAAKVUPfAWoCyuQcNMkTYSeXSvlphZpBzuBUWh/rpdBV6LIscd49AqxgqY/MAJUPPRFdxbtP8693h4xqIU874lZlaqjlK+zNgEom2AnYEhJvs717C96cWSntlQtCvruJnzWQamlSaaDGbGVkSKUlP29PQA+HX/yh+LVMIowjdweGGoYwH98SfBaCeFat2D1qwRwbIslztPir7fhpnKsth5ma7TRqIsE7hz3t5wa5n7jH68DoT16IrsTBkx1u19hGCsPTNBOULaKBBgFu7qYt+rr5JKDS36NpWOZuX/gkMxqidUKSisy8yzLkOwDzYavKOjg3e84x2g/bSvoP1xj5ZzjLSZJG16kze4S2W6J5xC9IuBaBuv7voTsUTQ093gLBpjiQA+5S+Y9mRaOprWEpO2vu1F++8E8AzHWJSyaEobiewIYNELGcsy8yJ/LTFp6trMrraBBXYg0uH5u8+n03kisZ6MpcISg2i8sC+0o6MD4Bil1AYGMRaJWKrMEKzRi6GOBZDhXejs21t0Lk0nEhnOBmc+8yl/VvpdLgwzmTWvGGYqz4IXSwYGVeioUPW8wxmjzgyudILqq0CLiLxDKbUQuB+YDLwGvF9ESp7hR5tm3Rdpy1pRWpaVEZDOirS7M8CPbr+fYJ82G604ZhKnnziLQCDAxz56HcBypdTjwL+LSMmhjykzTm65ass0M19FxNMMrvtZWoSsQ7ZfKgYrrJctW8Zzzz3HuHHjtgzWtGWYKZ3O5rEQcSYZrU2aWft4IZ4KkjYTvLT1Iap8tXnbnYkukgjoY5oWlmXi82VPRCkjjk/5scQgZUSJJvoYWzfR85xa+5dhGYu0mUJRU7SNU0rUcRlYWKSNJJFEDz6fHzNHI7Usg57wAWZMXExjY2PB4xZj8Uukw4RzSGpCsS5meQ8Jy5YtAxj0OCSOkBxrGPpYQL9m3d63h6KxJ/F4htTJyW4REeKpMI31U733yXGB+X1+m/xkXOa3aDIwKEpi00qTMuLUVNWXve9oxWjUrP8b2Or6fgfwPRE5Gh088+FyDjaS3NPRRF/Zmns02ZvVp3Q8jk8HSmVynP1+H1f91zv4/q8+x/XXX8TLrxygqyfKT3/6c8455xzQftlVwBfKOXduPjHYAWYuBjNziJp1uUVJDt1iSpE2kqSNBJaYdOzYQW9zc2Zr2kzqGro52mSh/uooZZ037qVNWLbG7BCu+H1VJD0ioNNG3BUha9Ee2KEnvWQo7/5FE715+w8WRgl54LkWA11jOKFjL4os5gpF+YKdt13gmTFtjT13/9gAFZqGglTiyBHWwwERi2Q6msUg5gXHlQf9lkJLjAzznBdyn2elfFlc46DrVA+m0JFP+TPVuY4UjCphrZSaA1wC/Nz+roDzgQftJr8G3l3OMUeSe3pPx2p6I01l7ZPLrpOOxzPR2I6GPXHyOBYtmUMyGiXa0caUyWMIh5Os+tcTvPe973V2LWsslPLpVAkPM7jbTOlNigJmiYseS6y8cxRvf+jcFIaZyqQMxQIBwp39ZArahBexfdr9r0khRrxikxL01+52TLhKKU+ea10BLGProCOwi5e238dzm+5ne8uzOecsTjpROlRJOdPu6mRO/1JGlEC0taBf0RIjryiHG+FYT551wX38SKInb1J3C/h4MjSs73g6dWTkWA8XLLF0bM0AZcjc8TbuFNRokXuf+8yJWKRy3EPuBamRSpWc3mpaaTqDu0tqe7hgtJnBvw98HnBsZpOBgEhmRm8GZnvt6JV+4PP5kJy0FK/UgEIYKEUnZcSwSBVNS8k9R67WlYr3T84CmVxFgM5du+jti9LWEWb2rPH09Oxm3rx5oNMxHgGOslMRslIQcseifky9rhgl6Twx6vZZQ+HULaUoOhaZbT6bvrTEYDjTMhk7dixKFU+Jcn/PGe9BpmPo4BdHWKcTiSwXgFJKC+CcSSo3J9vBQNSberGUImG3czSWXHgJRNE7EEn0ICI63coyh42URGFr1h6zgVsQpox4nqUqmY4NyMVcLN82Eu8pap0Kxjry0rp8Pu3zF4T1O/5Gta+WpXPOKfoulwLLEkzjyAguGz4IXaG9njzcbrjjbUyXQC1mVcnN79eWlP4FrCXZhVvat20DpZhz/PEl9TwU7xx1MUtDwagR1kqpdwCdIvKaUurccvf3Sj+wLAtBSCTixGLxvPQRoGD6DHiny7jTD9JGgkC4M5PK45WW4j6HYabyyhGmYrFMPWnl82GkUtTUaz9LsLuPP/55ExdfsJi62ipEIKVfBCdFp8/LH5U7FvGYNq0qD0NKrtm7kGZtGOni6Ur25+paH8WiRr0QCgfx+6qKjrf7e859HHQ6RspIZAqcGMkklmliGgb+qiosyySeClHlr8kSnoUsAYUKpThQyqdpE1EI2oftRfCRSkeLRL8KyXSUupoG4slQXn74YCGQVd/bQV+4jfW7/8nJi96dEZC5iMS7Geh+ezFZiQjheC/tvbuLmjkNM4nfV531m8JHJNHL/q61WJZJ0oqycf8/iBunM6XhqKJ9KYZ0Mo3PpyoVt1xQ+Ox7XBgiksUR4VjrlFKkXGbteCqEafXzF+QTVgl90f4o7ngiiFJ+RLQLKdTZmZkbHaQTCQ6sXcuiM8/My+pQykcg2sr4cRMGvM6HnrxzFnA3cB4QBr522Xk33G1vW2pvOx1oB2647LwbHi5wnHegXbeLgHXAhy8774YtDz1552zgz8BS4GeXnXfDZ+z2nwaOvey8Gz42UB9Hkxn8jcClSql96ICy84EfABOUUs6iYg5QdnXxkfCLWpYOPBqIUs+NeCqIz5e9PnJyrEFrc46ZxzBMfnvfyxy/bDrLlkwDYMqUybS3tzttZ6JJMEqGUr48TTFXkzYLBJiVukIVscoS1T7lG3Qxj6EilY5jmFpIOxaNRFhbPgSLaLIvo02m4nF6m5o8Ta6WXe+6GBSKSLwny//tVUGqOEOXIpLQE2c0EcibnAYP8Yxn2NO2hpQRpydyAPC2HuggoeLCTY9hv0AORjt5bfefeHnrwyVE7ebXPDfFYHf7y3aKnD63JSa7WlYPcKziSCUMTYhyCFDrq2FW3bRDcu5iUErlzVm5sAwj651XPl9Gu9YlWHXGxbbmpznQtb5/P8l/Z6LJ3sxcE3a5DGOBAGJZWb5xgHgwSDwYJBXNfzYtyxgw/dGFnwLvBG4DNgJ3PfTknafY2x4ATgNuAHqB+2zhnoWHnrxzjt0W4HpgCfD7h568UwHXoS3F3wU+/dCTdy5+6Mk7p6BjtG4upYOjRliLyBdFZI6ILAD+H/CEiLwPeBK43G52FfCXco89EpSjTk5gro+lGGLJQJ4WkfvwmakUIsJd3/ojUyaN4azT5mW2nX/Bedx3333O17LHQmvX2cjVrAuZwXtCTSURo+g2+iyJcJjuffsG6tQho4RNGwkdEZ5I4PP7dfGBYL8AjSUDGQEV7emhZ/9+BCsvFSmVjmVKaBZDJNGdJXi8AtG8aDcdWGIQjOk0J12lavhMtrm54cl0jD47V7W1Z4tnm/5+FdfufcqfZQ5t7tqiXUgOI1nRfX0eJVd1Lncui9ZQcSgJUa4/+v388pSvMad+xqHpQBEMtBBNO4QoNpTPp/OucdLvwrT37iKRCmUWqO7ymG74lJ++sM7ZD0W7M5asYFsbYllIVh63rqENEO721v4D0dZSYxrOAXZfdt4N3wG+Z/921UNP3jkJWA68eNl5N9yFjqeqRcuoXJwO1AH3XXbeDT8F/gmsAE4CxqK18lV22wbgf4AfXXbeDV0ex8rDqBHWRXAj8Bml1C70yuQX5R5gJDTrlBHH76vCsNIlB7hEEr15E5s7MENEMFIptm3axzP/Wsu+A33cfe9q7r53NTt2d/Pxj3+UJ598EvTDcyFwe6n9bdmyiVigjzxSlBKFtYjQF2n23JbVzjIzp4gFAgRai5MTKNSh06yNOJYY+h4ohVgW0b5+/2oyHc0I60QkoiclfHnPUyIV8SzX54YghOM9WfffK8K2kEB04FQUCsd6BkEWURhuZjKA9r7t9NetDhFLBjEGyTanlMoqwNETGvg5clBoITDcghp0JPhI1pQvhmWN2nw/q847zelQIXeBbqRSdO7aRdu2fm4V5/3JbachxJIBtjU9jyB6kWYvthR6Qd+8YUNmP9NK0xXYB0DAjuYWEYJtWoArv5+kyzUWs8mAQu3ekd9K+egJl2SM7QJmPfTknSuAc+3fFqJN4glg6UNP3rkYONu1zesYAGc89OSdi4ATXG1/jxbaT6NTk0EvEH5YSudglAprEXlKRN5hf94jIqeLyNEicoWIlB1VM3KatdJaQ4lRubmpNu4ca9Bk+GYqxbHHL+RXf7iB6z56FtdcfTrXXH06S46awsSJE3jkkUcANonIhSJScu5OIhwmEfFIKcqlGy0wWZlWmhZbwyoGdzS4kUplci6LYSRrPRdDIqXr5GYIHdBmNQeWy6+cCIX68+Fz+ptIRfK07XxIVl1s0AQreZPhAEFjiVQYSywiw5i2Bdk5r5Zl0h7YkembJRZtfdsKEsIMBMsyM7nUaSM5aossJGKpQ6JZ1/iqmVo7CYB6f93B70BR9A9I5+7dbH/qKbr27KF3//7+9yGZzFrkiIty1LQMDnSty1TzU7aVRRfx8BHt6yPQ2pr13nXawtrJnEiEQpl5SSyLhEtYJ223VTwU8uSIMK00bT07S7nQG9HycD3aZA2QuOy8G9LoFNmZwA7gXc623ANcdt4Nz6Izl94J7Ea7bZ3jvIQW2meiBf630cHUH3voyTv3PPTknS8/9OSdy4p1cFQK6+FGblDXcCCV1lG7SiniydKEda5/251jDWSx/6RcJAPDAcuySCe9q265IQV81qCjegcsKJ+TQ26mB9JW5JDlWidt83U6Hs9cd9oONAPw+aoy0ahObIGVNvL45hOp6IC5oF4uBJ/yZWnXOg5iALOwz0cs2VfyM1cq3D7rjr49OYsPoTO4u2QfuWUYmUpyem8rU0UsEGnHp0ZNXGsW4tFDs2icXTct40ap9+cT6hTCkoYF/PbU23njpJNGqmtZ6D1wIGOKVj4fkV69YEwlEtnC2rKyosPTZiJjzlZKEU0EdHlM5csI6c7d/WlWaSNBONaTWRwG29szxxfLyuxjGkYmsE35fMT6vLMOekvQrC8774YHgXnAWfSbuLfY234AzAfeAHzGve2hJ++seejJO+tcx7kCzSR4CvBH++et9rb2y8674WXgbUAa7eL9AdqluQX4crE+vi6E9WA1gmJIpmO238wqmJ6wq2U1Ld3aXOSQbLjhzrHO/OYI62h0eE1yIqRTKTzrWRf5nnuMtr4dA5ymv8/OtaSL1OkWkUOmWeuxUFlxA+4gM2esHD+Zssv/5T5PsURwQJO0lzBXypeVmpIy4wP6vi0RApG2YSf7ce6BiLC79VXNoBaLseuFF3RkL76S/PIArVu30uIybQJEbBay3nDLoNweYlkDulSGikNFiOL2U48pQ7M+ecIyptZO4k1TThm48RBhGkaWr9gyzYzpOZ0Td+NWOvKOYxlE4n2ZOvYJ2+cc6e7O7KOUYl/7Ovz2oi7Q1paVCursk4xEMsqOZRiEOr3jbUshVXnoyTv/A7gSbbr+AbrgiRMNfg1aWz4T+Do6yPn39q7/BOJ2sBgPPXnnN+12l6OF8MOXnXfDHtd5aoBvAp9Gy18F/Lt93qKr2NeFsB6JyluOhmmJ6VlYQEQ40LmJ7U0vYFoG8WQwn1oyHs8zn2Yq1hRJKRssDI/z5WrSxRYIgkVncFdRQeHWDJ1rKVZNzBIz8+IeCohIlrDOWrlbBo4w9/l8KKVIJxN5bpWYR1S3F5xI8JbNmwl3dSEiWYxNaYfOs2h/TTqDu/APEKFbLpx3pCfU1B9U19dHIhQiGYlgiVFSbEYqFiPQ2koyN2rXtgR0B8sjEXLQ29xM84YNBVMLhwOp5KHJyZ3rEtblmMEnVGs6ioMRlJYIh/H5sp9N5xlOebi6Cru/hFC0C9PUgbTOcyIidO3VkdumZdDWq+eZVCyWlbcN/UG5iXA4az4LFxDWJWIs8CW0DzkEvP2y825wHOGT0QL2dnQhlLdddt4NhSa1FcCPgU8AfwA+krP9euCfl513w9bLzrshbJ/zA+iAs28W6+DotEcNM0ZCs3Yn70fi+f7DQESX/ROE1t6tVPtrPSPBc4Wls3rNjRIfDqRT+UT5Xj5qy7LyXkwHIsK6PY9QV9tIlaplwfRT0c+Zvd01oRvptM61TCQYU6RfpVBdjhyytQCxLGJ9fUyePx+wmcai0QzRi5FMareKa3jcz0LRM9ljH+3pQQGNU6dm7TtQcFnmfOnI8AtrO11mZ/PLmc+OWTHU0UFdY2NJBVrad2jLi5FKZcyloBdlqXSs4MIm3NVFb1MT8046Kb8KmqnpYJVSxEMhGiZPHtxFFoFYFkb60LCXzamfnvlcjhl8fHVDZn+dvz9yDvdEMJi/0LcsknbgZS6KWdMi8V7G1U3XbjkX1XFfUxPTFy/GX1UF6GI28VBI15/POa+RShEPBrPmTyOlMzuq68r3+1923g2/BH5ZYNvXgK8V2HZuzvdLBjjPt3K+f5MBhLSDimY9SLiDZLzM4O29uzAtA8syaOnZTDDWkaeRRnvzhbyjORSqwDUUmB5m8FxNWvl8BSPCweb7TUcIRNrojTQRinXkbHcRiKTTmnBjgDrdw1HTejDQBTPMPArDmCvYRUQHtFimafv9k3mLv1KFrDOZppPJzL2Pp/rP5bCXhbu6PLWVfqhhZ2ayxCQYbc8Spk5kfKnm52QkorUbEZTPl3UNfuWnM7Q3r4KSkUpxYN06DqxdS6Sry1Mj6963Tz+nSrlcFMOLeCw2jHnr5WHOoDVrXfCizl/L5JoJw92tLET7+vLmCsuyCHV05Gm+QFFa0EQqjGGlSIRDWSlfKEVfhp9f34tYIJDnmnMiwp1I8P7dFZECKVxHAl4fmvUIBZg50FzF/UJQRGjv3UXG54lFT3h/dp9SKc+JxzIMLbBHICzVy4SYZ/ZWqrjf2gVLrLyguUzdYcvKHCfpQVjgxnDRZpYNpTDTRr4rIpHASKWosiljE6FQ5n6kE/Gs58kSs6xsA9MwtEYSi2FZVlbAnk5rMWnfto2xU6Yw69hjvbvN8FVx7Ni7i/rJ46ipG8PejlcyiwDLRUCRTiRK0ljat2/PPE9KafdB7dixgF6odAZ2ZUzpIkJvUxMdO3ZkgpZ8fj+xYJCaMf12GDOdpnvPnsxxY319sGDBMF19P8KBED6fwjwE7GWzszTr8s3goE3p3amSC/ANiP3r1jBt8VFU1WpN3x2tnYGdUuUZhW3zeFe5aJfdiCYDxMOh7MA00yTU0cGUBQtwnvBcgQx2RHg4nGd9tEyTYHs7E+fMydtnINx24wV5DGa33LHqbnub10Nx2y13rLo15xjnooPGcnEesBMXg9ktd6z6jL3Pp4Fjb7lj1WHFYDZi8GJnGircE7YgWX7XSKInS8P0CnAItbd7kuOLCIlIBOX3520bKrwqauVp1hTOtc6H5Kej2dYDM53OrJq92IXcKKWW8khAxNKEKDkmf+X3Z1k93PEDqVgsy1KTSsfxFyxEkY90PI7P58Pn85EIhbJIUJwUsFQsRrC1tWA62HCaOzv37iLY3o5Sviz2tGQ4nJWp4A7eSUajedp2PBgk0tPPOCWWlbVI0/SqYQRtqdj5zDO0b9+OZS9eQE+2udamzt27s8bBU2gMAyKh0pkIhxOTasYz1lXGsZwAs/EuYe02pQ8Hwt2dGUFpmWZBs3YyFsvWjm2ICHtfftlzzvH5/IRjndqMnTP/xEOhrPud9IjdEcsqGEwW7e0dbGBuHoPZbTde4ETuXen6e8r+bY3HMbbktO0EUsBmchjMbrvxgsW33XjB4clgNpLw4j12I5mOsnbPIyXkymqYlpEljH3Kn6UhdYf2D2im7G1u9kyTUn4/8UBgRDRrMc2sB9kzp7oMzRoglsqePC2xMuQujllxIDP4YGIKWlpauOSSSwCOU0ptVkr9d7nHELE8I/Itw8gEq4hIlmlWB5j1P08pI+bJuV4IKft8IkIsoFNYnMVKyohpbcHn075zD63CC0MZC7EszQ5lE1U4iLkmUncktmWa7HvlFZo3bsxoNmJZNK1fn5e+42U5EhGaN2zQwZUez1nMJaxFhL7m5qzjulPrvNDS0gKwRCm1pZyxKOeZH044JnDTnk9K1awVKuOzdh/HjcGOBYBYknEHeQWXOfD5fJ7CGjvwbO/q1R5cDiaJdDgT1Z0LZ5GXzkkJcyPa2+t5XqVUFrFRGTgH2H3LHauyGMwAbrlj1f233LHqfuBPwPFAE7Ay9wC33LGq09V2FzANeOiWO1Z1UYTBzN4+IF4XwnogM3gsGSCeCub5XwshbSTytCknBUdE6A7tJddQ2dvUlFllphMJzxUj2GxPweCwUkm64TaFi2l6+ulK16ztwhNZLgDdbyOZzAjBQv4ryzRJxmKDyrOuqqri61//OuhV65nAdUqpoqQCXnATorjh+L5yYweMZDLLUuNUfyr5fLFYxuwb6elBKR+dNn9xyoiTCIe1dcM0CbSURoM/1LGIBYMYRvY9ivb0ZE2UiWAQ0zBo2bQpcz9bt+qy89379nlqXvnPuCLS01PULeK4B0ALidxFq8/nK/jugB4LoFlEljGE5+JgwdGIm2KaoaveV1qAWUPVGPwu/7+XsB7aWEhm4RT3CC5zYJlmQW4GJwBt57PP0rJpE30tLRiplJ3y6h1FjkgmsDERDheOIxDxnKcs25Q+CHQBs2678YJcBjM3Lkdrxz+75Y5VA63uPm7/f7f9/5HJYDbcGEhYO+a/jhLrn6bNeJY2ZVpGhsUsGO3MEz7JaJTWzZs5sHatps4rQI3nYKQ0ayCvOo6XKb4cLUNTCPa/dNrioDVr5xrc/ms3gu3ttG7cOGD5PS9Mnz6dE088UR9fJIwmHvAsn1oM6QIanmnoPONkNJq1gjfT6SxLTdKIZVlRTMMg1NFRcAyTrvz5eDCIJSZtvVvtfPOkZmuy93WTQRTDkMdCJM/8HMsxNyufj5ZNmwh1dOg+iRDt6SHQ2krnrl2eY5jrUxSxaNu6tSjxjs/ny2jk4c7OvEWriGT4oL0wffp00DmyQ3ouDhYcIbsjsg8oXbN2/NUpe67xMoMPdSwc90/MI7jMjWIWSbG0q6mvuZnWzZvZ96qWU4WsbWJZmYVy3MVclociwYBuYa3zr0sS3p4MZjltPg4YaH7wgrjtxgvGoc3gm2+5Y9WzALfcsaogg9ltN16w57YbL3j5thsvqDCYDSSsHXL53vCBglWV2gP9lHUpI5GjTWn+20Qqytpdf8+L+tY+QUWsr4/2bdvoa2kp+PBLEUKB4YCZQ2yQt3IV8fQzFYJP+bPyzMUyM2Zw50Xz+XyeUb7JSMTWpIYW2ayUWoBetb7sse1jSqlXlVKvdnd309DQkHXNxbQ8RwvMch2YJoaRpKGhgYaGBkzRlZ/SyST7X3uNbU88QdP69bRu8aZmdZ/PSqcxkkkMK4lBBNMysk3fHkLUCzm1vTcAFwN32HXNi46Fw+PunuBMw8izKDgkGLmm7uYNGwpO1qZhZC1aQh0dAz7bIqIXq+jiDbmL1oHcA3b5xSn2dTpjkReplzsWOl3o4MPJsd5pVzYrNXXLiQTfHTlA2jKYXjeZWl9/MJdPqUGPhVPC0jI0b37uwm2wcOIYIj09JCMRb/M5/SmDsd7ewkpLkQWCmU5nFort27ezf/3a3LFw/jLvxy13rCrIYAZgC9I3AX+55Y5Vba7fa2678YLcFdZ/os3e/+v+8ZY7VrXfcseqCoNZMQzkP44lA4BmlOqL5pseo4le9rS/nMmJTdspNtnH6OPV7X/1XBgE7GAhsSx6m5qKBlyNdCGBXBai3BWqiJSnWYtFzCWsHV9+Opnsf6GU8pyk46EQZiqFJVJyMRQP+ICHgOtFJE/lEpF7RORUETl1ypQpRCLZPN6FUqTETktJhLIjVpXPRywWJBKJEIlECEf1xNJ74ADh7u6MiTvY1uaZRuI+n/L5iAWDmJbB9gMv4lO+rGA2yzTpK2IKT8ZitG7eTCQSceqc96IngStF5GS7rnnRsXDWnOGufrdZPBjMpsEdCAUmTp8rfUusgbVqp12kpwcjmSzINVAsyMyuf96NNmU6Y5Fnaswdi3IWqMMJRyPeOUjNuicVpDWh4ytm1/eX2LREBj0WTg155fMRCwSGVXkQU2c7JO10SC+Y9iI2PoQ0vVCnDpDrbW4GsTJj4Vyn/Zd5P2678YKCDGY2HLN2lgDGZjCzg8XcbaPAb3P7dduNF1QYzIphIJ+oYwY3rTQdLg3aQSSho1y7g3r169Q4diOeChFPRfKJT+LxLK3SmcwLYoQrCbg1a6+JUwr4ggrBEjOLFMYSE8HCyLlmrxc+FdX+bjEGx2KW1ib9o4D/ExHPYvBe6Gtr7q8bXiSf3WHwckP5fMRj/ZOIw0CWqwU6QVdZMQKuAgeQHf0cindhmVZeel2ovZ39r71G1969OtfVPke0t5fdzz9Pb5NmBBvsWDgw02laN2+mZ/9+Aq2twxNwpVRmYRrq6Cj5uYoHArrkYQHNy3luip0ZvYAb1FgcLFSrKqbXTsYUkz0xnV9cqmbtBJcF0iGa49oqUoDJbNBjIZZFX3NzweCywSIZi9Fz4EDBuU75/YS7ugbNVucERDatX1/OfJrHYHbLHavaAW678YJ6NMvYTvoDxDxx240XnIlmMbv/ljtWea0qrwf+ecsdq7becseqCoNZLoppbSJC2uVzDcY6MMwUVf5+k5JTlrCtZycT5szL0DHmHyv/PIMJdlB2tPBIIM9nnYsyhTVAKNYDk5zdXZq1c0jLytNgRbTpWPl8GIlE2cVWRITrrrsOICEi3y1n3+Ztm2icOY1Jc+cWJW9Q4OkfTcb7td+0kcjkIefCMk1at2xh7gm6Up5lGHmThyOsfcpHLKI12qxKbCKEu7oId3frSVMpGiZP1lSPTrT2EMYicx7b6qNsWtXhWDQ6AYSgU7BKXQCY6bQuGlGovc+XlcPthv3ezAeeHexYHCzMqtcFPJrjHaSsNAkzRZ2/hlpfDckBFq+OGTxoRIja1dzm5gjr4RiLSHd3QXP1YCGmWbQan2UY9Bw4kPculINEKFRW+ustd6wqyGB2yx2r4sDEAtvOzfn+Erl1iLO3fyvne4XBzI1iXNYpI5bFx6zw0RM+kNUmErcrBkU7MC2DlFE8b9iNQBH/dCGMpG7tFioFzVBlviBu1iunoHyuxppr+k8nEiilNNNVIlFy+lYiFSaaCLB69Wruv/9+gEal1Dr77+2lHEMs7X810+nCbxXeqW0iQjIRs7ebWGIW5CQWy9LnscczFYvlTSDJSET7dsUkFuwrvEiz3ROWHcDmfqaGMhZefR62NCYREqEQ8WCwLPpc5fcXNXUrKJj2s3r1atARu+cPdSxGGo5wbY7rgNO4zeRXSq61k2MdSIUz++cGmQ3XWByKGt+57qfBYCCXy+GGsjVrpaNzPunl+xitELEKVl5JpCNaWNsC3RKDjsBOpk84GsAWznqi8Sk/wVg7qRJJVoxksmiaSZEOl79PiTByhbXHuco1P6XT2i3gU/5MsFjuMXIn65QdZS2WznX28vW39+6mvmpyFg92S+8W6qNjOeOMMwiFQowbN26LiJxaVofRzEipeFz3odBLLZLhBM/8ZFmk4vpaIokefMqvYxIKTCzK5yPa08O46dML+sd79u9n2lFH5XEdl4qhjsVIIhmJ0OViICsFlmForarAWFimSV9zM/6aGurHj88KDjvjjDMAXhtt4+CF6bWa47w1oeMF4maCiYyj3l9HX7o4SYvjsw6kQxnmslwz+OE0Fl44FIuE0YyyNWvRS//TlFJXKqXePlpXrW4o5SsYZJZIhfO0mWiir7+YQbIPny0sTCtNT+hAHpe1ZRh07d2bd5xQZ2dBv9uhQm6AmZcmV65m7fNVZYLvLDsaPPcYuXm4TpS1I/xyyyaalsGmvU/k5b7nMqYNFo7WWywFRDeUvO+peIxgtJ0tTU+QTieKaoHuvM90PJ5fP9yy6N6zB9MwRoyd61AiGYtlBa+VioG0+0hvLwfWrmXrqlU0bdhQ1J0xWtFQpWlVw3bMTNwmxylHsw6mwzQV0KwrOLIwWEnyL6AGmGr/jWpoYe2tLcZToUxhdAc+nz/jp44kerPYynojzXmFQVq3bqVj+/Y8LbqvpWVIphixBNOwhtV/nRcN7oFcP5FYFq1btxbUuBWKhM0RnjYTnsc1Uqms60iEw5mVcyISydOsg9F2u/xoNhtRPDX4CNFcFEuhK4Z0PM7W5iexxCjJp+eUEkxEIp6WDBGhe+/eATnUD0eMmHbkxFaIEGpvZ8czz9A3wvWuhxtjbWHt+JwdM3gpEeETXMI6YsQIpELU++uYMsIFPSo4dBisGXz84WQGV0WqFDlpW26YVppAtJW5LCEU68wKHBOb9MNBuKtLRwIrRbC9nbpG/RKZ6TSJIWpKOze20tOmhVNVtZ/LPzB0Du2s4KUc+lGvNgCdu3bRu38/NfX1Nsl+NjQpTIiWns10BfZjpFL4fL4soa3QY5IpjuFKy9CadfZCoDu8D4BIoj/9KW0k8zTwocCrWlApSCeTmTiIQoUM3LDs/NJCKXtiWXTbtXyPOIiMWLBk5hS2haZ543pdMuEwQYNfc4JH7QDXWEZYDxwR3m8G1+9RW7KbCTXjmFo7iV7jyLPQVPC6MYOrgpp1ooCm1hfR+a1OcJkDEQtl0/wZqVQ/J7IIQdfKPtTZOeQoykig39xuGiaGMfRSn6bRX2WqkOnQSKUyAWLxYJDuffsA7Vv1mngFi5beLTR1b8ASE9NFNepA+f1ZwW1uH7aRTGZV3hKRzPhHXYupXC37UMEZN7EsnWI0EOyI7tQA+aqHqkRjBYcGjmYdsWNi4lZpmrVf+RlX3YApFmE72DVpW6ZqfNUj1d0KDjEGm7r1L6AabQI/+DXlBoFCLFnuqkduJNNREqlI3nah3yzdunlzllaVtokcasaMIdDSMqSoWrGEVMItnIdvIhfTJB6JZHJ0c2Gm02x/+mnGTppEMhLJaN9OwfcxEybk72OmEfrbeZl70/E49ePGYZlmlk/bTKdJpfrHWee16/0197iFUj4i8dEhrM10mmQkQvPGjSW1d0hSzCI53WJZh8eLVMGwwfFZR0xbWJeoWY+r0ilroXQE56lxaEcrwvrIxWBVvwnAm0Tk1+g8vlENwbtmctrwLuIAOvL7QMfGTHBZ7hEt09SBMzn7Bzs6MA2j5IpJhRCPpVC+4de0lM29vP+11wpTnjqFJrq7s/OlTZOe/fu996H/WLn+acgOtErFYllEC8rnIxYNZL73hA9kUsCU8mf81OHY6CgsL6bJrhdeKCt6OxEODxzMVsFhhRpfNR+Ydynzx8wasG2tr4aJdm60g7E5ZvBSA8zc/moHabGFtaoI6yMVgxXWR6HLhAE0Fms4GmBZBjvbnue1HSvpi7Rk0rhiyRC+ArWITUnT1LWlYMpXuKsrz2wplkWgpUUHHQ1xYo5HkgynNu1AKcX+tWtLj/jOjXC3FyPFYKRS+QsBu4CJkUzqQCrX+Cifj2i0X2vuCe/H0ayVUpm4gmCs/KjikUK5gVMFSwlWcNji3Cmn89657+D/zXnbgG3/Z9mn+NUpX88IaPAwg5cYYOYQogRcwtrRrKsrmvURi8HOHgLUK6WWAwMvK0uAUmquUurJ3NqrSqlJSqnHlVI77f89mWSKdtbOs+4JNbG95Vk273sa0MK6WPCLaaULEqoUomRMxWJ07907JBP4n/++lZ/dv5Znt/RX50oZFh//+Cec6kqLBzMO4KITHWzQj1KZ2saFUIydqHvfPpLRaPb4iBCP2rVzU5GsEpSmlSaa1Olabg7yww1WgWC+Cg5fLG3UFRQnVo8fsO2ShvnU+WuZZudWA4ytsjVr2wweszXrgczg43OCywBStpuvYgY/cjFYYf0dtNr3fuCmYeqLAXzWo/bqF4BVIrIYzcv6haGcxBKD9t5dJFIRYolgUXazgsdwlXHLg1JDzpc9cfkM3nT8zKzf9naGOf3001i3bh1AmEGOQ6EI8HL2796zp2i97WLl73oPHNAuAtdiwbIs4pEga3f+nS1Nq/J8t+F4N4aZGrAgy2hHRVgfWVjSsADoF56F0OAfQ50tgBurtb/Zh48x/jpMsTLm71I1a4cXPOgiTklnfNavCwbp1yVKFtZKqVOczyJyQES+ICI3isiBYvuVChFpE5E19md37dV3Ab+2m/0aePeQz4Wwp3WNXYCifA0zWsTMLaY5ZHPngrkTMVPZi4iOUIJLL73E+drDMIzDYGGkUkVLIxYrjiFAJJckQ4RENExXcD+JdDiPYz2eDBJPBfGpMipBVVDBCKLWV8PCsbo0tCM8C2Fy7YTMZyc4zNGqY666BOWawYPpfl6HlM0VUTGDH7koR6pcpZR6UCl1pvODUmpEiPJz6hNPFxGnfmg74EnT41WTtVC1GBGL1t7tGeIT0NWxSs0H7RugKtFQNSjTtDCM7OOn0iYL5s116haPB47yqssK+WNRP6ae4YRYFuGuroK5wUYRutJCAVlOKpdlmhxYty4r19swk6SscKaGuN/vy63h7FmjtoIKRgqLxs7Bby8ex1UVF9ZTavo9Vo22sM6NBIf+POtSA8zcZvCMZq0qmvWRinLubCdwKfCwUiqMZjB7cbg7pJRqwFWf2K3BiogopTwlql2b9B6Ak08+WSKRSFFTLQLxpH7YIz097HvlFapqa5k4Zw4TZs+mdsyYTNNkJELbtm3Ujx+fqXg0VKRTBl0tQcZPHsvYcdkvZzyS8lxoRONxp25xN5qYxpPzN3cs4rHCPuTBQkyTzl27qK6vZ/z06VnWhMGUtnN4s/taWgi1txOeMYPxMzTXsc9XRUvXjkz6XfOeLUxrPJZGTUDTfbhyH1dw+OKYhoWZz1U+P2P89cRM7/dsam2+sM6NBAdIZHzWpQrrfjN4JcDsyEc5wvo/gWNEJKmUmoUu67V2ODujlKomv/Zqh1Jqpoi0KaVmohcNQ4b2VStAMqQfRjJJ1549dO/dy9wTTmDc9OnEQyH2rl6NZRhEenrosdsOFuFAnJbdPQR6oiDCmMYQK85amNUmFs5PKaup9tPV1c1C3bSaYRqHoUAsi9ZNm2jZuJG6xkYap01j7MSJg6JYNe10r67duwFdrcwR1iKSYTKLh0JseuExTjz+3cN2HRVUUC6OaVyQ9X18dUNBYV1UszbyNet632ACzCp51kc6yjGDNwELAUSkVUSuAj4+XB2xaUx/AWzNqb36V+Aq+/NVwF+G65wgGMkk0Z4e10+CWBZN69fTtm0be19+ud8ka5cpHIwwCvZE2fDCPrasPkBfVwSxBBGIR1MEe7JpKKOhJGJlC+tp4+r4619XOl8nM6zjMHg4Uc7xYJDOXbvY/9prg/LZW6apiUPssY709GRcDW7u9r7m5uHp+EGAZVlEgoWtGiJCX1dpVdkC3VF6O4aPF/1QItAVYef6Vjs9cfgQjw7v8YrBCS5z/MzFTOFT3D7r6hzN2mUGL91nnZ9nfaQJaxEhlTQI98UIB4bfMng4ohzN+r+Bh5RSa9Aa9WxgOCsPvBEdXb5RKbXO/u0m4Hbgj0qpDwP7gX8fxnMWnPydyOXhiOAN9kTZ9lozlpVvwbdMYf/2ziztetXL++jsi5M2LJ7a2s7R0xtZNK2Bl1562UndGocel9EFezEzGCi/n7atWzMLIaUUke5uxk3vD1EQyxowbWwkISIl588bhsnWV5qIhpOc8ZYlnvvt29ZB+/4Ay8+cT+ME77gC0zDZu6WD7rYQdWNrmDR99NEaiAgHdnbR2x5mxVkL8VcVX6zt39FFLJykpyPM+MljOPr4mVTXFJ+K0kmDbWuaWXb6PPz+/OPHI0k2vuRN2DPcaKgaw+z66STNFFvDezh5wrKiQWaTXZp1QybAzNGsvQLMytes06M0dSvYG6N9fx/HnDS75H1EhPXP7yURS+PzKcQSjjp+JlNm6sC6dMpg+5oWps4Zz/Q5EzL7tOzpIZ0ymbd4Cv6qIy8YtWRhLSJblFInAxcCJ6KDvd41XB0RkecozAJywXCdJ+ecmu+6CJPXUJFKGmxf2+IpqB042vX4yfpFXjF3IubM7NxNpRQ/+9ldLFy4lHHjxu0QkeGpFTlKoJTKGm/LNOlrackS1pGenkyN6c9+9nM88cQTAMcdjP51NAdo3dPDCWcvwjcAs1wqabD55f0kE2lN6hJO5sUltB/oo7NJp/i17Olh6clz8o4Tj6bY/PJ+DMNCBBKxtOeC4dOf/jSrVq2CYR6LdMpg75YOJs9oZMLUBk8haRoWO9a1EOqNIQh7t3ZwdE7aoRvRcIJE1OFWFwLdUdr29zFvcfHifV2tQSLBBD3tYabNzs9r7uuOIpbw6U9/GuAEpdQmEVle1gWXiGNsrXpX9AC9KX0Pi2rWnmZwR7PuF9axEnzW1aqKsVX1pC0ja99+n3X/lH4wxmIgdLeG6O0Ik04aVNeWJm4C3VGS8bSuOmjPm7s3tlFbX01dfTUbX9LvVjSUIBKIM/foqexY30I0lEAEuluDLDpuJpNnjL6F7VBQlr1SRJIislJEvi4id4pIy0h17GAg2ts7JPKSgSAi7FjbjGkWF/qWKezb1kkqaZBOmVgDtD8SYZlm3r2IdHdn/dbb1JT5fsUVV/Dwww9zMJBOmezf2kkyYdC2r/gaSUTYsvoAiXgap0Bbrhkv0B1l/7bOzAIu0B0llcjOIU/EUmx6aT/plJlxiSgFyXh+8N5//Md/jMhY9HZE6O2IsGtjO6+u2knT7tyiNsLGF/cR7I1hWYJY0NMWIlDEtN+2ry974SrQ2RwomokhIrQfCADQurfHs01vexhBjwWws8RLHBQcE/j2yD5CdvqUW7NeOGYODf7+AFW3GbwxR7OOunzWiRJIUZzzhIzsMU5Jvhn8YIzFQAh0R1CKgi6cdNIg1Jtdf6FpZzeWmUNXbAlbX21iw4v7SCbSIPq37tYQrz29i0ggjmUKYglG2mLXhlb6OktzMR0uGHCpo5R6A/CSjHSduxFAX2crHU07M0USlFIopaiuraV+/PghM40NhJY9PUTDyZJSuROxFGue3o0ClE8h5mE33EODx+OllCLS08O4adMwDSOLiObMM88gEDg4pQD3b+/EEguxoHl3N1NnjaemzvvViQQTmckE9IQS7IkyY16/drVva0eepaX9QC/zlkwD9LOw8cX9GOncZ1MRDSepG1OT9euZZ55JT4+3ECsVeza3M33eBMY29mt1XS1BRCTzLLbu6WHOoskZzT4ZT2c0IAeWJezY0MqKNyygrzNCV2uI+cdMZfzksZiGRU9biFyYhkUkmCjoCoiFk6STejGTjGmNym2pyMQGKMWZZ54JmmBpxLhdj7GZy3aE9zLdZiQbZwvR+WNm8ZMTv8SLvev5n213U+erzQhogEZbSDvlMd0BZhYWCTNFnb+GWl8NSY9ysI2ZIh7ZHsiMGdzFDT7cY5GMp6murRrQspRpn0jrxaZAR3OQ6a53AHTswo71rViWcOwpcxg/eSyRYLxgLINpWFqRcb06znuUO3tYllaAJkwdSym4+ITZC4Gfogm5QGc6ffSx9S0HXG0+hI6rApj62PqWPGasi0+YXQX8AB2QbQE/B258bH2LdfEJs78LfBTYBrz7sfUtLRefMHsO8Cxw4mPrW4pOaKXcxA8Aryml7ldKXa2UmlHCPqMCTdvW07V7N91799Kzdy/de/bQtXu3DhxbvVqbVUcI6ZRJy56evBViITirQsuSkvc50mGZJm1bt9K5ezdde/YMqhCGV/59oDuMaZRmvYgEE/S0hXAo4sXSpt5CaN/fl3f/3Jp1OmWQiGVrx2JpzdGyLHo7w2x4YZ+HoAbLtIgG89nhGhoaqK6uBvAXyzn3GguUjqnoaAqwd3P/dRlpk4jHuWLh/ok0Gkp4+uItw2LtM3s4sKOLaCjBtjXNRIJxulqDno4uyxQ6mwOZ76mkQTTcf249NnpMLRHa9mdXXwv3xTNFbxoaGgAmUYSHwGss/FWlh++4Neugka1ZL21YiE/5OGXCMvzKn9Gqe1L6+hyh3q9ZZ1td4gPkWjfa+4dzNescM7hPqUGPRUNDA5FgNu9EKmmw/vm9tOwpfc4M9cQyr2wsnCSV7Lce7d3awfa1LZiGhVjC9jUtxKMpmnZ1F3UZlqMyppJpAl16UWOPRTFOhq8Bb0ETb/0BeCtwi7Px4hNmTwe+DXiXaezHJ4Frgd8ADwKfA66++ITZS4BP28eYarcBHXv0zYEENZQgrEXkGhE5GbgVmAjcq5R6USn1DaXUm5UaxbRSBW6sWJbWqIfJWCCitSf3w92yp2e4Dv+6Rjoep3PXLnr37x9UFL6I3CMip4rIqVOmTCESibBz/f6CJjLLtGjb30tXS5BQb4xdG1uzJg8RrRF4RaiahkmPh7nPSJmkbUa6QHcU5fHWiQibXjrAznWtRRcS7vOKiPblRiKkdW676Vyr/XfPQGMhtgYC2p/sZCbofuZIViHLZBkOxD1dPOKyKoAWxlteaaJld+HFa3dbCMsSTMNi88v72fjifgJdEW3qbHPNY6JN7aaLNKivK5I5biQSAegFdhcaB6+xcLIQ3jjpJD6y4PIM4UkuptRMZFLNeMLpKG2JrowZfFyV9o/Oqde6TJ2/lkVj52T81S3xTlJWmhpfNbW+Gk9SFBg4ItzRrJ061g7SOdHglsigxyISibD55X10NAWc7excr5/L9v19JZNH9XaEM/fFbQrv7QjT2RzIeq9M02Lz6gMEewaShaXDcS9K/1h0F3k/nLfyWeAF+3PAtf1H9rZXBjjt1Wg66OvRgjsFfBBwVPxV6HivhotPmH0mOs7k56VcT8nmERHZJiLfE5GLgfOB54Ar0Cxjr2t0NAXY8koTTTu1VSSdNOg40JeXflXBIDGEKHMvWIZVMB3kwM5u9m/vYs+Wdratafb0EVuWsHdLe96k1d0W9lT+fT4fEft8Pe1hT4FlmUIsnCiqVQDEXCbCSDDBrg1tRVoPjN6OEIlYKtOHPVs6MouA3NgJyxJ6XYucUF/pKTWmYZH2sBb0QxHojrBjfQvJhKG1rbUt7HUVs3E1pdOliPR2DJ9v8tqjruTy2W/l3CmneW7PmMAj+wDyNOvZ9f0Bkcc1Hs3kmgkAdCd7MwK2sWoMYzxIUWBgFjPHjJ4rrIebFEUsiwPbuzAMk46mQOb5tSwrK90w3BeneXd33rsgIgRdCzvL0tYTwzDZvanN8x0w0kMoMFQAqWS61JTHL6DN038Afgmsw9asLz5h9juBi+jXhothIdD+2PoW87H1LQk0NfQiYAPwKlrgrwDuB74PXP/Y+paSzHyD8mWISFxE/iYin3y9s0clE2n2b9eaSdu+Xlr29tC0uztDjXk4IJ002Lm+dUBBcSQhHMhfwUdDicwiyzLF9pF5j0k8msqY2By07uv1bG+aFqG+WMYCUwilzFNm2sxQ0fa05/t/y8W+rS1ZfU4l0nS3hgpqOJGANo+KCPFweXnNxRavlmmxa0Objip3NHJL6GoJ5gcbmcKBHV2EA3FSSSMvOG+wmFk7NSNc3znzPM82SxrmA/3Cul+z1sJ6jltYjzuaKTZ7WVeqj3DaEdZjM9Hgbp81DMxiltGs09n7pUagnrVlWezZ1J4VDGmZQuseHWQZjyTZ8moTzbt72PJKU5a1Ix7Jr2kfi6TYvbENs8A7Jdawy2qdGrvNY8GXj/cCS4Ebgc+jM57+x952F/AToN7+A1h48QmzS7EqK4DH1reYwNlon/gi4GigGei6+ITZL198wuy9F58w+7piBypbWCulxo5q0/dBhIiwa0Nb1uTSvLObzqYgBcpgj0rs295Jd1uI9hxf4GjGddd9ggsvvBCgVinVbOfhl4xYzmQilrBjXfEUOzcsU/uuHcHV0x4m5aGFOwj2xGwf8NBqlPv8PmLhJCI6EhbgmmuuGfRYpFPZgs4yhd2b2gsHESlN2uNlcRgqLDN/cVRo8rZMYesrTbTv78tyK1xzzTWgJ91jyh2L5eMWZz4vbVyY8U27sTgjrHVOd0ZYVzfgw8fMuv4UtOPGHc1U2wzekwoQcjTr6gbG2tHiuaxnsQFyrQuZwb1KZA5lLECPfU97OO+diIQShPvibF59AMvUPudwIM765/dlrDSB7kieG1IBfV3Rg25xTCVKelb/EzAeW9/yrcfWt9yJDsx7q71tDvBFdGT96fZvq4GZF58w23/xCbPrXIJ7r/t3NIHVHoDH1rckH1vf8jLaTH4L2p/9FWAT2nz+g4tPmF0wIm5AYa2U8iml3quUWqmU6kSbCtrsutN3KqWOLmUkDmdoP2Zfxo8ZCycJ9cVo2dNjB2K42lpSsk9nNCAaStDbrs1ETbu6swKbxJKyJ2XTMGnb11s0/Ww4xucnP/kxO3fuBFgjInNE5BcD7ZPVB0uyAr1a9/WWraGlUwYHdnSx6aX9eb7tXMQiSfo6B+CrLwFiCdFQklgkmfFt33333UMai1woRUG/uYgOSCsUXDYUlPtYmKaVF8R59913A2wQkepyx+K4Rj2VObnTl3po146w3mlr1mEjhiUWjVVjmFU/lWpfFV3JXgLpMJNqxnP8+CUAdCf7XGbwsZ50owBxa6g+6/5AuaGMRTGICJtX789yazhzxfrn99LZHKC3I5L3Puj0vlE7N+4Bqi4+Yfa3Lj5h9h3oTKnt9rYrXH9b7N8+hDZxvx+Io4PHQAeoNaBN3D9EU0Pfm3OuLwB/eGx9yz7Ajy5adTl6PVNQJpcSAvkk8C/0ymKTiNYZlVKTgPOAO5RSfxKR35VwrMMOIsKujW30dup8QaWUXjEqPbkczpHbIqL9R076gwjNu3tYsHQapmmx7bVmIoE4J715ETV11Vn7aTOxic/vo6ran/l925oWwn0xOpoCHHvqXGrrs81y8WiKDS/sZdzEMcxaOIlxk8YM+6RfKqKhBPVja0glDJoHiEL1gmXqyORSJiClNBHKUL0jliVEAnHSKWPEFoVFo3Etoa8zQsOEugH5Aw43LG/UmvVP9vyem4/5GG+ecio/2/dghtZzZt1UGqvG0psK0m1Hd1tYRIwY46obWNqwCICmeDsJM8VZk09k/phZAHSn+ojYAnZ8dQNjq+qxxMpo0g4GYjFz6EpDI+yzLgop/Bg7FqfDcF68Hl2cyqHQ/ju2AH5sfcuDTqOLT5j9CfvjI4+tb4lffEIeM9uPgMXoLCoBvgf8yrX/fOBK4AT7p2+i/dcfAL702PqWgg72UoT1hSKSp17ZDFoPoSlIRxfH3TCibV9fP5c3MOTZdhShqzVEPNqfyymW0HGgj2mzx7NrYxuxiDa37tzQxrLT5qKUom1/L/vt6GGlFChYuGw6U2eNZ9+2zoylIR5Lsf75vSw9eQ7jJo3JHH/72mYsUzNXhfpi1I2p4dhT5mQtBopBRDIMWENFJBhnysxxenIZpOArWVMQhqxVO4gE44QD8WH375V8/lBi2K5ltGCsUcvMuqlEjTgv9qxldd9Gzpx0AhdPP5s/NP8dcJvA92XtGzIijKtu4LhxWjNvjnfQkejmrMknZtp0JwMZbdjJzY6ZibzYFofFrHCAWSHNevTQjR6GgprH1rfsRgeRDdTu3Jzv9+LSnB9b35IGrrH/vPbfj/ZXO9/XAEtK6WMpqVsD2kFLaTNaISJ0NPWxefWBvLzSYE+Upp1dh+XDVwxag+5m7+b2/OAdETa8sJdYJKEFkWjh0NUSZNfGNg7s6ELEtirYgVh7t3Sw8cX9dDYF+o8n2pS69dUm2vb16nPu6SHpMj1bphCPJFn3/N4ByfpFhN7OMGuf2cO6Z/dgGEMPKgr1aaEX6Mr3rw03hjN4LxFLZ0hCDgWUUkRDB69oxsHArMQEALaEd2Mh/LXtSQDeMeMcfPY0uSRjAs/mIA/afutl444CoCXewZbw7sx2wzIJpEMZIhPHrx018oP4EqWmbuWQojgBZtWVetZHLAZ1Z5VS/w0sF5GPKqW+LCL/M+BOhxAigpEyMQwr45P1V/kwUiZ7NreTTKSxTGHzy/uZNnc8M+ZNom1fL50twdHsYxkULEtH3fZ15fuUgH4Tl9sP7wo6KlSMJBrKJ9DQ59ORu8GeKMGeWN7+ImCmLbasPsC4yWNoGFdH3ZgakvE0kVCCRCyFaViaPEFchDHDoFbGw0l2b2w7aFHww6UJ+/zqkObwi2UVfBYOV8y0hfWmkGbnXBvYSlOsnbljZnDmpBW80LuOxXbAWSFh7Zi8m+Pt7IocIGmmqPXX0JsOYiEZbXhG3RQgu4iHg9gAlKMNA2jWtf6avH0qODIw2GXYUeiSmQCjmi092Btj57oWDMOy6Ub172JLJLdmaVlCZ1OQDpuHeLTEiZ0yYRlLGhcO3LAItry8i/b9XRlhPJhFyGAnZ8sSAj2x4qk7lhDoihLoiuLzq4NmzUiWFik6qnCoLT0iHFapiaVgdnwCAJuCWlgLwmMdz/LRhVdw/tQzebF3PUePnQfkC+uQke1mbI53kBaD7ZF9rBi/hO6kzrJwBOzMWluz9qh/PVhSFEEypCtVFe36iMRg76oA9Uqp5cCsYezPsGLP5v3sWNPk4o8deIIZTdrClJqJfHzhv/OmKadgicXurrRdUbx8REPxQzrJl7M4OFj9HE33+rDDETZ0uxo62b/vQJY/+qnuV/jwgss4fdLxLGlYwNiqerqSvfSls/PbHc0adKBXV1LnIW8J7dLCOpUtrBvtIDEvM3gxYV3jq6bOX0PKSnvyhqctgxpfdVZEeAVHDgZ7V7+DZnN5PzpKfFSiu7V31E3IxzQsZGbdVCbWjMOvfDzS9pTni3f6xOO56ZiPUeevJWEm+b+mRzl90n8dgh5XUMGRj1cm7uXvjz2A6SJI6EkF2BDcwYkTlvLB+e8B+vOr3Qi5hHVLvBPLXsk83vkCp086nie6NMljrjacSzUKxRnMCmnVDlJWmrHUj4ogswqGH4MV1nfY/48FfgecMzzdOTLhw8ebppzCFbMv4uiGeVnbJlWP5559D+S0V3x84b9T56/l+Z413L3nD/SkA5zm9wwwrKCCCkYIT3a9zIkTlnLihKVAvgkcsstVtsT7i6G0JDq5dl1/OE9upaxcqlEozmBWKLjMgZO+NZwsZhWMHgxKWIvIlc5npdT1w9abUQRlM00N1Td35qQT+MiCyzIE/72pIBuCO4iaMS6ZcQ7vmHkuf2l7go5kT9Y+s+un05bo4uvb7sHCOmS5yBVU8HrGcz1ruO6o92a01Z05aVuQbQZvjhemtszTrD3M4BkGM19+gNlAmnVaDmKudQUHHYONBn+7a/+Th687owNVys9Xl32SYxoW8te2J3io9fG8F8uHj5MmHMubppzMmZNOpMZXzYFYGwdirUTMOIhwdMM8Vow/BoDWeBcPtDzGvzpfJC06crPeV8v5087kqnnv4ls7f5k59mWzNcvdwy3/wuLwymedVD2ehJXMI3uooILDEVEzzurejZw9RU9zOyMH8tqEsoR14fKpSSuVCQJzjp2LYj7r/iIe3rztoynXuoLhx2DN4A4BbhJNfH5E4aMLLufkCcsAuHLuJVw683weaXuSP7WtIpgOM7tuGl845qMZkgQHSxsXsjQnajucjvK7pkdY2f40hmRXHfr1gb9w9pRTOH/amTzc+i92RQ9wbOMijht3NOF0lH92Pj+yF1oEU2omsmjsHHZFDtCb1vSLNb5qPrbgCtqTPTzY8o9MWx+KUycu5x0zzuXUicfRmwpy85YfsD/WWvD4CsXJE45lV+RApnJRBRUMFm+cdBJNyfYRSeF4sutlzp5yMu2J7iyTtwP389uSKCysQWvTk2rGZz7nwlnkTqhuxIcva7Feis8aqASYHaEo+64qbY8dLyI/HIH+HHKcN/V03jXrAtKWwU/23MebJp/CKROP4//NfTvvnnUhz/es4azJJ1Lvr6Mz2cs/Op7jue7XCKTDzB8zizljZlDrq0GhSFopnu5+xfOlBOhI9vBI25NcNvutfPKo9/H75r9x8fSzAXi03TvwbCRw9Nh5HDfuaGbVTWVW/XSOGjs3M6G0J7r55PqvEzaifOboqzl3qi4dOLd+Oj/Y9Tsm1DRy8zEfz7A3AUypnci3l9/Al7f8kG2RvXnnq1ZVfG7JBzlnymn0pALcsuXH7IrmaywVVFAK6ny13LT0YwB0pYffEvVi73rua3qUbeH8ZxmyzeBNRczgoLVw592Kefise1IBmuMdzKmfzqkTj2N138bMtka7DGe4wOI2Qzla8VkfkShbWIuIKKVOU0pdCQTt3/427D0bJrx5yqmcOuE4OpO97I+1si/W4vlCKRTvmHEOH1lwOQB3772fxzqe47GO51jasJD/N/ftnDnpBC6YdiYAT3Wt5oe7/y+ras6G0A42hHaU1b/fN/+Nt0w7i2MaF3LrsbpCWspKZxiURhIN/jF8ZOFlXDz9TXnbwukoSSvFjLop3HTMx9gS3s25U08jZibwobho+tlMrpnAIluw96QC/Kn1XzzVtZrrFr2XN0w+kduXf4anulcTNeKEjRj7Yi20xju57qj3coLtHphcM4FvH/95vrXj57zQu86znwrFpJrxhNKRjAuhgtGJidXjuGz2W3iyazW7o00D7zAMWNq4EL/ysyOyj8Ya7zrUQ4GFxW8O/LXg9pgZZ31wOykrXXBh7sCtFXtFgwM81vEsH1lwOW+f8eZsYe2YwQsGmOl3o/oI0qyXj1tMbypIa6JzwLYTqhsJpEuqXX1YYrB39V9o0vOpjNKMy6rOODfN+CjL6o/K2/Zq3yZ+tPv/MkFds+qmcf3R78/4lx9pe5K/tT+Tab8tspdbt/6EhWNm8/YZb2ZLeDdPdq0eln5GjBjXb7idC6edyYpxx3B0w3z+0rYqL5dzOLFwzBzeOPkk3j7jzUyqGU/KSvNk18vsj7XRluhiX6yFtkQXU2om8qMTbuakCcdy0oRjMcXi9u0/I2xE+eqyT3LqxOUArA9u5xvb78kUPPifbf/Lp4/+AG+ZfpbnQgC0BnHr1p/wzhnn8tbpb+RLS/+L+5pW8n9Nj2aC+iZVj+et09/I26afzXSb9SmQDtOTDOBLmJ7HfT3hjZNPztBTjgZMqhnPHcd9lrljZnD+1DP5+NpbC5pshxMOzeeW8G7OGPGzeePGTd8pqZ3b3+wVDQ7weOeLXDXv3Zw28Xim1EzIFA3pN4MX8Fk7Na2PEJ/1GyadyC3HXktnspcPvnYzphR+56+c83aumv9u/ty6iv/d+4eD2MuDh8EK6y5Hm1ZKXTqM/fGEUupi4AfocmI/F5HbB9pnwp/3MbX+KILpCA+3/JN6fx3zxsxixfglnDpxOT896Ta2hnczb8zMTMH5vlSIH++5j+d71ngec2+shZ/s+f3wXZiN1kRn0ZW7G48//jjAcqXULkoci7rWJG+b8CZmT5jG8vGLs2rubg7t4vu7fuNpbehO9fG17f/LHcd9liqfn5/vezCz0v/8xm/zyaP+k42hHfx6/1+yfGsWFt/d9Wue7F7N9NrJjK2qZ2L1OBaOncPCMXNoiXfwrZ2/oDPZy3d3/ZrmeAdXz383/znvnRzTuJDHOp7jwqlncvqk4/HbpdMjRow6Xy0TqhtprBpDU41vUGNxuKDGV83Xlv03ppjcvPkHeYGGp088ni8u+SgWFsGuOI+/+CIcwrGYUjORO5Z/htn107HEYlLNeD551Pv4xvZ7Bn3Mo8fO49SJy/lz6yoSVmEu8uPsalmbQ7scYT1OKbWdMuaLg4UszbqA0A2mw7zQu5ZzppzGW6e/kfuaVgLl+KyzhPWoHYtimFIzkc8cfRUA02oncebEFTzfu9az7YpxS3j/PC2G3j3rAg7E27KUrSMFg/FZXwJcaacSKeC9QGmSZhBQSvmBnwBvAZqBV5RSfxWRLcX2C100hzU//g0/3fqHrJdiQnUjH1/475w39QxOmnAsoIM6nu9Zw8/2PugZQDJaYJomn/3sZwF2AKdS4lhMfqaPq6e/O/O9LxXixd51PN+zljWBLUXT0zaHdnHjpu8wpXYiT3e/kvl9b6yFz2y8o+B+grAmULRbGfyx5TF2RQ9w45KPcNrE5Zxma+ymmDzfs4a/tT/DmsBWFDChehzjqxv5nPxuUGMxGIzx13P6xOPZHNpFV6q3aNvZddM4ccKxdCR6aIq3ZaXkOcda2riQzaFdRWMSrp7/HlbYtZDfNuNNrGx/OrPtxPFL+dLS/6LK5+fBllWcOfGUQY9Fo38McYZWlGOMv55vLf8cs+qnsitygB/s/i13LP8sb55yKi/2rhuUFWpSzXi+cdz1jKtu4Phxi7ll64/zAjRBBzc6QZ1bwrsxTRNgHrCMMuaLgwW3v7mQGRzgb+3PcM6U07h4+pu4v+lvWEjJwrpaVZOGUT8WbigUixvm05MK0JcK8fklH6axeizBdITx1Q1cMvOcjLA+ftwSVoxfwnM9azJtfcrHusA2TpywlGsXXklzrL1sl+RoR8nCWil1ioi8BkxBF9ueCpjAN0aobw5OB3aJyB67H/cD76K/CLgnUvMb+XXPX/JWr4F0mDt2/II/ta5iUs149kab6Uz2HhZcx5s2bWbRokXs27cvJSKpUsciungML256mq09u9kZOcDOyL4My1Ip2BzeBSPsCloT2MIn1n2Nzy35IOOqGljV9SKrOl/KcgcI0JsO0psOsn79+kGNRTmYVjuJd8+8gIumn83Yqnp2R5q4bn3hmjXHj1vCV5d9Iivt5oWedXx1212Z75866n2cO/V0wuko/+h8nj3RJubWz2BK7URe6dvEM92vcsL4Y/i3WRdm9nn/vEt5sms1MTPOceOO5tZjr6PGV80jbU/x830PUrvuwrLHwodi4v27+Pmyr/H+V7+QcWF4wa/8vHnKKWwO7aIzmb9YefuMNzOrfiq7I018YdN3iZgxfrrnD3x68VVct+i97Ik0sT/eVvD4uVAoPnv01YyzA6pOmXgcn118Nd/a8cu893TB2NmMraqnLdFFbzrI2rVrAZLlzhcHC25/c8wo7MLYENxBS7yD2fXTOXXiclb3bSxLs07DqB8LN/5t1oV8dOEVgFaexvjr6E0FuWHjnfzkxK9w8oRlzK6bhlIq8469f96lhNK6POnm0C5u2vx9PrTgPVw++yJuXvpffGTNlw+KG+ZgoRzN+iql1BeB74jIrwGUUt8Vkd+NTNcymE1/0RDQK8Qhu6Zya9IeDujs7GLOnDnun0oai8Ap47jnfx8g2D26gy+6Ur0l+/7a2zsGNRalYsX4Y/ifYz+ZqWKUtgyOapjLqROW82pgU177k8Yfyy3HXkedv4YNwe1YIhw37mjOmnwiM+um0pboYoy/nrMmnwRofujL7Xx6B2+ZdhbvnHFuxj//mwN/4cTxx7Ji/BKunPN2dkb387nFH6TGV83jHS9wl+2SaW9vL3ssnMVaja+at0x7Aw+2/NOz3Rh/PTcf83FOmbiM1ngnH1t7S5aG61d+3jXzPAB+tf9PGW3xH53Pc9rE4zl7ysl8Y/mn+dzGO2lLdBXrUgaXzjyPUyYeRzAd4Ts7f8UXjvko5009g9l10zHEpMrn57cH/sorfZsyJvAtod2ZsQDcJothfS6GCsffHDXiRTkUBOGfHc/zwQX/xhsnn8zqvo2MsznFc5nQHPTnWVcRZfSPhYNqVZXhlogaccZW1WOKybd2/IKWRCdPdb/MxdPfxLtnXcCyxqOp99exJ9rEtNrJjKtuIGxEuX37z7Gw+OW+hzmmYSHHj1/C+VPP4C9tTxziqxtGiEhJf8CXgH1AK7Ad2AvcV+r+g/0DLkf7Wpzv7wd+7NHuY8CrwKvf/e53xY3r/uMiue4/Lsr67bd3fVsuWjFLujvaMr/t2LxeLloxS1Y+8NustldecJJ85ZNXZf32/dtukItWzMr67cWn/ikXrZglLz71z6zfL1oxS75/2w1Zv33lk1fJlReclPXbygd+KxetmCU7Nq/P/Nbd0SYXrZglv737O/LAAw/Ihz/8YbHvw6vAHqAT+Njraizu+vaIjsVHzzhFzOv/LnLNoyJ3vyL7n3xFfva2q/X377yQPxYHAiKf/JvINY/Kps/8TN62YrZucO9akWselaYf/00uWjFLdv7KPuZ3XxDZ2ycr3/N52XH9r0T+uk3kH7tEbvin3n7NoyJ3PCdimCL7AyLXPCrGf/2lf9t9G6S7tXXoY7G+XR/vlic9x+Kh739f9nzgf/vPe82j0vl/z2U/F6+0iFzzqDR/6Fdyyyeyn4sf3XqjrL3y23rfm1eJ9MQGfi5cY3nfdV/Uz8W2rsxvzl/q838XSRkiv1gjcs2j8oO3/r/MO4K2A73qGosnZbTMF6+16mv44r8GHos9vbrtV5+Sr3zyKol//GH9PZ4WEY935KEtItc8Kr94+wfl2Sf+PLxjYZgi3dHhHQsHLxwQueZR2f2Bu+XFJ/8h0hPLnOuiFbPkvi/dlnXvu6+5Xz70ljNFkoZ+hltD2WPxqn4m0199Qi5aMUv+76f6mlzvh/OX9X6M9j8lJZIIKKW2ASeISFIpNQv4JrBWRL5f0gEGCaXUG4BbReQi+/sXAUTkm4X2Ofnkk2XlypXc8KG30t2abbbz+TRt52gr8DEQlFK897O3ce+99/HEE0+8JiKnljoWf3jwN3zpo5ePes26HHzsKz/gJ3fdXfZYPPbbh7n5U5fT1O1tlp1YPY7vr/gC0+um8HzPGr6+7adYCGP8dfzm1NtpqBrDpzfcztbwHkCbbL+34gssbVzIPzue53u7fpMx1Z44fim3L/8M7YluPvjazdx27Cc4fdLx/HD37zwDYMb663nf3HdwTONCvrPz3ky6ymcXX81bpp2FJRY/3/cgD7f+K2u/6772v3z/+98veyxWPvIoNbc+yeTqCdyw8dtsdPn4ptZM4nsrbmRK7UQOxNp4pO0prjvqSsLpKB987eaMBv29FV/g2MZFBa+p3l/LN5Zdz7HjjqIl3sHnN32HHju6ORcz66byneM/z6Sa8fy9/Vl+sPu3Wf2ZP2YWcTPBJ496HwvGzuYnu+/jijkXM612Eh9feysHEm1c+9W7ufTSS0MiMh5Kny8+9N5z+fvvH8A0RpYx0Hkm9kZbuGbdbUXbVqsqHjrzB1QpP1e+cgN/OP07pC2Dd754rWf7D8y7lPfOfQe/PfAXmk82WH7aewY1FitXruRjl55BOqUtKAqVeXb/3v4s/7v3DyVxQPhQvGXaWWwI7ShqVbn7xFtYOHY2d+74Jau6XvJs84MVX+SYxoWkLYPPbLzDk6PdQbWq4r7T76SxaiwfX3srrUYH9/z1ZWbNmvWaiJw6YMdHKcoxgzehCzRuE5FWtFl8K/D9keiYC68Ai5VSC4EW4P+hg9rKhs+nmLVoEpYltO/rO+wE9nHHLWPPnj0ANUqpGoYwFiMJpUa+FviKFSsGNRbj/tnEz465jfBRUXZFDvDn1lW83LcB0MLi5mM+zvS6KWwL7+VbO36ZMRfHzASPtD3FlXPfzn/MeRu3bv0JABdPP5uljQvpTvZx9977s3yqG4Lb6Ur2MqNuCm+YdAInT1img+a6vbMNomY8r6gLwM/3PkjSTPFy3wZe6cs3wZ944omDey58iid6X+KK6Rdz8fSzM8K6zlfLbcuuY0rtRDYFd3Lr1p8QMWO8cfJJnDhhKVfOfTs/2/cgxzYu4tjGRYTSEVZ1ek+ycTPJl7f8iNuXf4ajG+bxreWfzQjsMf46ZtRNoTcVxKd8fOO465lUM541gS0ZE7+DrlRvJrjvtwf+ypePvYYPzHsXjdVjCRtRDsTawKfHAqgbjvliJLA32kwoHclaGBVCWgz2RJtZ2riQUyccBxT2V4M7z7oaMIZtLC6b/RZOn3Q8oIMdjx+/mK9v+yl7Yy1F97tkxrlcd9SVRWM9TpmwjIVjZ9Od7MsKYM3FfU0ruXnpx/nfvX8oKqhBj9sz3a9yyYxzuHDqG/hN25+LXyDAtSuvBn6V8+t7uOuSP3Ptyip0NtJ/Ahbwc+BG7rokf2V37cpZwC+A84AO4GbuuuR3XLuyAfgzcCbwKPBe7rrE4tqV7wH+i7suuWigLpYjrP8beEgptQZYi/Ylj7j3XkQMpdQngH+g0w9+KSKbS9m3vqEOn19hmYLPp1h03AymztbsQTW1VRzY3nVYCeyqqiruvPNOrrjiiiXAVsoYi5KhdIh/dW0VdWNqsEyLWDiJZekxRGmrhMJbIPt8imlzJtDVEsQSKauOdTkY7FhIjZ+IEaWxamwmf3xX5ABrA1u5dOZ51PpraEt0cevWH+dpD39pW8W/zbqQMyedwKePvorfHvhLpnTiT/f+kbiZHVVtITzR9TL/MedtfOqo/6TK5+fVvk1l06sGjQg/3nPfsI8FwL9sYf2mKafQlw6yMbiTi6efzaKxc2mOt2cENcDP9j3Aj064mUtnns+BWBsXTnsDoCOXi2laETPGFzZ/l9uP0wL7zuU30J3qZVnj0VT5/Fltt4f38j/b7i5KfvN871p2RvZn6H63hvYgCApFVVUVwAEGMV8cDASNCFe+ckPRnGE3toX3aGE9sRRh7U7dig/LWCxpWMDV8/Qz/r97/sDF089mwdjZfOO4T/PJ9V/P1OrORa2vhivn6hISRzXM5eQJyzLZIdWqiim1E7HE4vLZWkb9te1Jz2h/By/3beDSF68rud//6nyRS2acw/nTzuB3bWUlK30KcMwAzurhk+iS0D8G6oDPYb9nHvvfDZyPLh39buDXXLvyJfu3k4FbgG8Bv+Dalc8AtwPvKaVjJQtrEdmilDoZuBA4EWhHRxaOOETndJfNkrb8zKVMmFxNT3uYidMaGDdxTGbbzPmT8PkU+7Z25glsR8APBJ9fZShhnNZ1Y6pJxNIjJqQuuugigE3lmnPqG+oI9oTx+Qpfm1KK2voqlp0+j9q6/lzNWCRJV0uQujE1jJs0Br9fcWBnF91tYRCtSQP4q3wce+pcxo6rY+7iKTTt6qazKVB0QVTqWHthMGMR+LeFfOqPV1MV9XPOlNP49zkXc3TDvEzp0n91vsg9ex/wTOELpMN8e+ev+OziD3LR9DdyzpRTqfPXsiawhWd7XvM836rOl/iPOW9jQs04gKLaw1Aw2OeiM9XDCz3rOGvyiVw++6LM5BlOR/nKlh9npRftjjaxqusl3jLtLD69WOfApi2DR9qfGvA8ESNbYM+qn4opFk2xdsZXNzCuuoHt4b18ecuP8hY9Xvj1/r/wteM+BdjZCtkIjmZzZ6mCGshQnJ7iaNYFgssA0t551oMeizH+Or6w5KNU+fw83Pov/ty2ir91PMOtx17HyROWccux1/LZjd/KLBLceNfM8zOESzW+ai6f/VbWBLZQrar44Qk3sXBsf0Bk3EwMe1701vCeDG3rieOWlrPr48Bu7rrEfVFXo33/1wPVwAeAD5IrrK9dOQl4J/B37rrku1y7ch2wCrgK6EUzfv4LXVOjwT7eP7nrkpKi88vKsxaRJLDS/hv1UErRML6ehvH1ntunz52IaQpNO7SG7fMramqrmTprHC17evq1SaBxUj3h3jgoEEsLmQVLpzF+ylhbYCmqa/0opdi2ppm+rsio4nZb/obFzFs8kUggzvY1LZhmtgXH51OMHV/HsafMwV+Vre2Maahl/jHTsn47+vhZzF6UItAVQfkUPp9i0vRGqqr1vlXVfhYeO536sTXs35a/IFI+qK6pYs6iybQ3BUhEUwfVytGXDmUmn4unn80pE47jr21P8NoAueHP9rxGc7yDLy+9hln1U0lbRp7J1o0D8TZ2hPexpHEBKSvNCz3rhvlKho5vbP9pJnf1+HFLmFo7ie/suteT4vFX+/7EpOrxpMVgf6yV1X0bC/qgcxExYnxh03d5x8xzaYl3sDawNbMYqPFVe076hfBqYBMbgttZMf6YkvP5D0dst7n1x2V4wQfWrIdKNzru703cuuQTzK6dzoy6KeyKHOBX+x7OnOMb2+/hhytuZnHDfP77qPdz585smTXWX88Vc/Si784dv+Qzi6/i5AnLOGrsXM6efAoLx84hasSJ2NfycOu/iuacDxarOl/kqvnv5vwpZ5az2xbA4tqVTwDv565LOtDu33buusQETK5d2QMs8th3Adow6fgHmu3/F6EF+5eBdcB+YDPwXeCUUjt25JDIDhKzFkzCMiyadnUza8EkZh81RQueGY3s3dLB+MljmDFvIlXVfkzDoqc9RDyaYvaiyRnBlIv5x0wj0B11Ii1HDfx+H+Mnj2XZ6fPYvPoAli2wfT7FlFnjWLRsBspXet3s+rE11I+dVLTNjHkTsaz+BRFKn69hfB3HnDSHqmo/0+dNJBpKsGNdC6mEUVRoKwU1ddX4/N5jXy4cHvZyuNj3xpr51Pqvc+Xct7MtvLdoWUSAxztfYEnjAl7p2+hZFvFQwxCTtcGtrA1uHbBtb1pXVBssImaM+5vzjWTlCGoHt2z9MTNqp7I31jxw48MUbYkuAukwE6obgQGEtUM3OsRCHrV7QpwyXmvyfakQ39x+T5ZbImLE+Oq2u/jeihu5YNqZNMfb+b3rnl4++600Vo1lXWAbz/a8xrHjjuLfZl3IdYveyzGNC7DE4stbfsiW8O4h9XMgrOp6iavmv5vTJhxPcOACL7vQJvC9wKXAR9EcIh/2aKsoTRXrn0zvumQv1648CjgG2IT2gX8HOJdrV96O1rav5a5Lni10sNedsFZKgVKI1X/z5hw9hRkLJlLl0ijHNNRy3Onzsvb1V/mYNmfCgOeoH1vDpGkN9HRoM7HPp5hz9BSad3WPCh95w/g6jjt9HptX60CNRcfNYOqs8SN2vlkLJuGv8hENJaitq6a2vprJMxpRqv9ZHjuujhPOXsi+rZ3a3+0xTsqnqB9bw7LT5+Hz+Uasv6UgYsb42b4HS2q7sv1pElbSMzisgsEjbiaPaEHtYEd4XybAq5iwHq561qELZ/Pjr32W9lgPLfFOz3iEfbEWvr3zXm4+5mNcNf/d+JWf3zU9wknjj+XfZr0FgF8f+DMAf279F++aeV6Gw/0vratGXFADdCZ7+cb2e9ga2823qh8v3viuS54DngPg2pVPo4X1MnvrXmAR1670o83gk4GX7bbV6HiAJDo1TADHxj/b/n+PfY4+4CWuXXkycBo6Za4FuAGtfX8LeEOhLg6GbvSdIvJIufuNGihF7dixJKPRLIFdVTU8mpqDeUum0tsZQSlYtFwLw0QsRVdraMT82eWgYXwdx79hAQqob6gd8fNNL2GR4/P5WHTcDKbPnUBHU4Du1lAmuloExk8aw5KTZueZ6Uc7LITHO1881N2o4DDFtvAel7AubC7uN4MPTVgnj5nAK8FNmdStQni+Z00mhuM/572TBWNmc/qk46nxVbOy/elMemNnspenu1/l/Kln0JHo5lf7/zyk/pWDZ7pfpbqmhPni2pU/QfuUdwBvs3992f7/12gt+PtALVpg32tv+xnaJ30ad13yKteufBS4iGtXfgYdYGYBv8k52/eBz3LXJaa9AHgbmhG0qDwejGb9deCwFdZ1jY0sPO00dr/8MslIZMRyjOrG1DBz3kT8Nf6M1rpg6XT6OiP9+YsHIcWpGMYcBCE9GIwdV8ei42aw4NjpJONpfD6VFRNQQQWvJ7hrwh8MzbocPNH1MikrzReWfJSzp5wMwJ9a/8U9e7NTEH+170/48PFw6z+LFmU5hNiMNoPPRwvte4Cb7W0/AhajA8sE+B75aV4OrkH7p7+BJib6IHddsjOz9dqV/w70cdcljqr/OfRCIIG3yT2DwQjrw2a2bJg4hb7O1oxE9Pn9TF24CH91DYtOP52dzz6LkRo4uX+wmL80OyjLX+VjyUmz2be1g/qxtVTX+OloDgw6GvpIh882e1dQwesZ28P9wjqULpz211/I4+B6N5/rWcNXt93FxxZeweMdL/CHlsfy2nSlerl9x88Oar/Kwl2X3AXcVWBbGi2Er/HYdjU6Wtz53gIUzpm+65I/An90ff81WnMfEIO5q4eNZJm56Biad2zsD/RSinHTp6OUD391NXNOOIH9r72WZQ4faYybOIYVZ+kqQSJCJJggHBh9QUcVVFDB6EDUjNMUa2fumBkFy2qCK8DsENSzXt23MVM+t4KRwaGN0hlh1DeMo7ZBpzwon4/J8+YxfeJiaqp0VaSGyZO18D5EwUpKKY5aPiOTHlZBBRVU4IXfNT3Cs92vFQ3MGq7UrQpGJ45oYQ0wef78jDCePG8Bcycfz8xJS1BK/zZr2bKCwlopNeKCvL6hlqmzx5eVMlVBBRW8vvB09yt8fftPizLFpbxJUSo4QjAYSVQ8qXSUYfyMGQCMmTCR+XNWUFM9hpmTj84Ia391NbOXL88TysrvZ8axxzJl4cLyBPYgBPy8JVM5mHFTh8qSUEEFFYwcMgxmQ8yzrmB0ouxZW0TeMhIdGSn4/H6mL1nCjGOWctQszbrXUD8pYwoHLdCnHn10lhDz+/1MmjOH6YsX520rBn9VFVW15UVZV1X7mTS9sax9BotZHguTwwnK58Pvr6pEhVdQQQ7SWYU8KjjScPjO2mVgyoKFzJt7AnU1DZnf3KZwgGmLFjFj6VKUz4fy+TKfnW1zVqzQrFlFhITy+Zh/yilMX7y4bIatabPHa67xkYJSjJ08mYmzZzP1qKNQw8QAdrDg8/vx+f1Mnj+f897zMXy+w6v/FVQw0ugPMKv4rI9EvC7uqt9XxcSG2Vm/zZx8NPs7NyDSHwk+ed48/FVV9DY1MX7mzKz242fMYMyECRxYu5Z4KER1bS2N06cT7+sjEdHpFP+/vTcPk+usDvTfc7e6te/V3dX7oq0lubVYtrxiIRMUEuLkZ4/DZCEOSQghCySxRzD2L7Yfk8GKwYGQMRmWGSAkYAcnIcSggBUlIQwGbGIRMN739qLFkqzW0t3q+uaPW/vSXVVd3V3duu/z9NNVt27d+9WpW/d853xnSa1ahS8SwQ6FeOmRxuoVh2K+rLW4MMH23aPr0SzHIo339XHoqacqzpSbnITTaQzLQkQ4/Mwz84qW13SdzEz9jQvKERG8kQjxvj6CqRSarmP7FscL4eKynJhagjxrl8WjKWUtIqZSalpEhoEjSqljrR1WaxEETUotMb8dRaqkjEfSaSLpdNXjmLbN0PbtnJ2awixydU+ePMmpo0eJdDsTAk3TSAwOOgqxTkUnIsQ7gxx88Xi9H6shfOEIZ2ec4BRN10mNjPDqE0+gsopUNI1IdzcdI6swPIXc5okjRzh97FhT55TseU4dO8aJQ4eqyiI3QbADAc6cPJkfT+61/i1bCCQShW2iY5nVG7O4NMPyW07IeaDc+gSlzKgZZlQGXXREze97DUR9HDs00RbVFl0cmnWD/w8R6QY+gNPjs+0pryUtIvi90YaP41TSKl2T9vj9RHt6StZRY729DR87WeYKz3WzWghivb35aHc7FGLkkkvoXr8e07aRossi1ttb4dKvtl5cbR3c4/cTHxigb/NmBs4/v8L1LppGMJlk9eWXM3TRRdiBQNGLgj8WI5hIlR1V0RGt1vBm4VmJy+ShmH856mt0/ZxYwWuYXJCZMc8VznVb+7C95rK8NlYqzX6jIZxe1h8EXmrdcBYIcSyycqKBzpadQhMDXTMBQRMDw7KIdnc3FMwVjHjzylnTnBzs3tVJNE1a3sFL03W6N26kc80ahi+6CI/fjyYG6ei6kvXgUEdHyblF09CrBdCVRcGLptG1bl1esftjMexgqftaNI3ujRudCYJo9Jx3Xv4YIkL/2JaKiYFtBtj71fu54oorCIfDAD4aRNOlYcWraQufxtcMrxw7PS9Z9K3uRFtGsxARp858qidc8R3mZAFsFZG27We9kOTSt374yLPzkoVu6Ky/oA/LY1Q1GNxU08Wn2bvPvwBRpdQPcAqftzcKNKn8qCFfEm2epfk0MfDZEYY6L2D76NXs2PQr+XXwzrVrsbzeuk0yESHRFQKgayBGMh0mPRCjd3XSOVeLlUW4o8PJQ8+OzzK8JMOD+eYZ4ES3+6OOB0I0jVAq5aS6FVvJIsR6eugqyln3hsP59+XoKgrak2yUvm7k5K/w+P0kh53OPInBQQa6xzD1QtS+Jjqp8Aijo6N86lOf4pJLLmnqc2+8aDWbLx9m7NLBmp6L4u2aJqQHY4xs7KwZBCiaOH/Cglsjmi75cQRsY16yCIR92GUlXZuZzFSMb6Fu5iJ09kdJpsMVE7mcLIDaNTlXODll3Z1IzFsWpsdg02VDdPZFS4wI22/hC1gr0tPUzjSrqZ4BLhWRXNfxT7VoPAuCQlW1rEO+RJW9Z0cTHY8ZIOCLoouHeLCPdHKIkydP4rcdN65l+picnkDTdQa2beOJf/93MmfPznFkh87+GLqh07uqMLb0QIx4Zwjb27DhVDe6ZtAT34BtBslkSgPCor29nHztNTRdJ71+PZphYJgm07n1bhHi/QNYPi+njx3j6Isv0rV2bcU5fJEIvkiEk6+9hunx5JcKNDHwmH5OTx0nOeiUYu0YWkU0mCaTEV48/EMUzgQoHuoj0d/JxETz92Pb72HmrBOE09kX5ZXnj+ZbcmqaMHpBHy89+xrHDk6gcIL/ekac78P79BFOvl7aiEBE8i1VdUPjzKkpHvneC0xNTqMWqJLt6rFunnv8ECLCyMjIvI7VPRTnqR++TGZGoelC3+ok408dKem6pOlCJqPQRGq2eRVNCMd8dPVHGX/mNU4cPTVro5rcMk9mJlOynzOvlqrrpcGIF9vnTC4s2+DMKUc5iUAq4Z+3LJY7uSCznkSiJbLQdY3+tSlSvRGe/fGrBCJeeobinD07w8PffIaz080Hj7o0RrOm2q8DR4FbcBR321PNsvbZ4ZJo8HJ0zUTXTDQx0ETHMrys7r6MzUNvZfPILoY6thH2dVTM8MO+jvxj07YZ2Lq1bheq129li6SUuX+9Cx3hKcRD/WiajlEWTRpMJhFdp+e889BNExEhNTKSX8u2g0EsnzORSI+O0r91K95w9f7YXevWOfutX5//jCJCb2IjumYimkZqeBg0CAc6SIYG854JryeIbQaqHrdZelYl0A0tJwKiqQDBiJfVY2l6ViXwBTys3tTtrO+LMLyxK9sFLPsWAW/AYv0Fvfnj2D6L8y4ewB+yZ7UwRWgqXS/WESSaCjB2yQBbrhhu+P3Vjqdpgjdgcd7Fg3T1x1h3fm+JNdXZF2XbG1eRHopVWM6iCZZtMHbJIOvO7yWSDLBmczeGWTlB1nTBMHWS3WEG1qRYvambrmy/c01zXksPxunsz1pzUvre7qF4/nmqN5J3x+qGxrrz+8pPd84xnU3fMlRrvXBev8W683vpHUkgmmBaBmu39rilkheRZi3rVwEbp1dnxxz7tgXl0eAAIhq2FeD01OsV22OBHgbTmzhz+gyT0xNk1AwD6Y2cPnVmznOF/Z0cOfE8M1mXlC8axQ4GOX18YSK954/QnViLns3P9Jh+zk4Wyhpqus7aN76xxA0f7uri5UcfRbKR77mUs1zQWDGa6GSUMwO3g0FWv+ENzvJAlligl7C/K78PwK3Xf4LpU58mk8lwZuoEGTWDZfq48f0G11xzTf2fTOSdOE3e6e3tJRAoVfa6rjG8sYvH/2McKHRKExG6B+N0D8ZL9vcHbdZf2Mfx105x4jXHcly1KV3RY9swddZf0M+PvvMcE6+fKcnIE83JQ0j2hIkmAzzzo1eYnpqpabF+7+nDTE1nJ5UCnueP8vUHX2DnZUOsXZUkEAjkJj4REXmw6K2fUEp9YlZZiKOMxy4ZxLAK65P+kM3wxk6e/uGrjIx1EUs58Qa9I0nSA3Feff4oLz59hExGEYx4WbOlu6QnvGHqrN3ay4++8xxKKSzbJJr0k0iHCYTtksloJOGnd1WS0ycn8QU8+de6+qO88MRhJk9NgQi2z+TL9z/OxCnn2lQZxZns452XD2PZZu77NYHPiUiu60WJHKrJorAcs3wouS6yvOm5m7njol/D3Gw0LYvy30gtghEv/WtTPPvoQSeupYYXRbTqXpLFJPuZErP9PtqdZq/QvwKmgP8G3N+64SwMjhu8+kwzYCfKlLXQFV3LQGoLgUCACZkAnBu4XmexgZA3VWGxhzo6OHPixKJ2+KoXXTeJh3ryz72eMCcnj5bsU75eruk68b4+jjz/PKHU7IF6hm4RC6Z59ejTKFSJotY1g0RoAFP34LWCnJp0JjR/+vFb2Lb2p5mYmGD8yCM8d+j7XLbxF5mZbmwmn/0xfgJgy5Ytqpr7PJoMEE748QU8eOy5PRiBsJdA2AtlirwcTRPWbunh4X9/mrPZm6pogi/oYfT83rzlGb5siGcfO8iRl08gGnlX89mzGVCwbaiwJKIbGtt2ripRdhMTE7kgwGNKqZrBRFVlkb2HWlU+d6IrTLwzVOHl0Q2N9FCczoEoJ46dIRT1Vs0QCIRtxi4dRDc0TGv2346mCf6gXbLNY5uMbCytdzC8ofT5Y99/kVhHkGS348nJfr/TwNuVUg9Sg3JZ+Px+lFJouobKZJa0z3y9FF8XOe7cuJvR0DCfU//StCwmJibwR2Mcf/WVOcfQ2RclkvDz0jOvcWj8OEqpEtlpmqAbWsmSykIh4hgLmZnKe2xWFodn+320O80q6zcBG5RSvyEi/z/w960b0gKgqq9ZAwS9CY6ceC5v1WmiOa7XeeAx/WiaUVIMJJBIVC1E0g4olSHgjTLjOALwe6Ic5jnmKtCSHBoi1NmJbhhkMtXX5DXROW/4TeiawaHjzzFTtp9SirDfcc4kw/08d/AHiOhE/IWbcjI8yNmZSbyeIBOz9POtF6lS/WzN5u4FKWFqegzWbunhke+9gELhD3oYvaCvJPVI0zWGRjsZGq2c9Lzy3FGee/xgPqc40VWpPBeS2c6laRrh2OxxFLn15YVizZaeuXeqg86+Hra/eZQTRyd47rFDTBw/vSzzuHMBZvN1g3etGeX1QwfrMi5sn8XQ+k56VyX5z28/y+SZaSeoVxd6hhP4gx4e+4/xmp6jajhLRAXFWz55ysVQFN+iTMugf02K46+d5MgrJ5g5236G0XxoVlkPAy9kH7d9OSmFqrpmDeC3Y47VnVXWhu7B54nM+5yRQAeHjz+ff16ettROKJXBtoKcnD4JgG0G0TW9QrGWoxkG3lAIDQ2KXN3510UnGR4iFkyjlMI0bGamSpVtxJ/OL1EkIn28ePjHKKUIeguudMvw0p/anH/+la98heuvv54jR44ArBKRf1JK1W74Xkaib5CTrx8tLcCygAowGPXRtybF0YMnWLOlp6Ec4Y6+CMcOn+TYkZOIkLcgc/z48UNs3bq1aVmsJH782EG2bt0K4AfuE5GHG5GFiBAIexnd1surLxzjuUcPNqRg2oGcsv7+409y/Txk0duzgefDD3Pq6Gt1n9u0nHSvH/zfZzg7ncEfskkPxhy5Rry8/lrtXtwlCHi8FoOjHfhDNidfP8Nj33+xJAi0qz/KscMnOX1yisyMwrB0Nmzvx+M1SaRDRJIBnvzBS3VPuHaNdW8F/hewCdCBbXsPjD9Y9PrlwIeB83DitT6498D4R6scxwA+CvwSzjLxp4Ddew+MZ3aNdd8J/AbwKPCzew+Mj+8a6+4Bvgls2ntgfNZ10manXwrwisgGoHq5rwYQkTtE5FER+YGI/J2IRIpee7+IPCkij4lIUzchpVTVNWsAnyeStwoFjVR4uCU37niot+Scki3yMRc/evQg//PT3+GWP/lnxl8uXUv/i49/grGxMYANzchCE51IoLMiXc1rBUs+s201OLEQ8Fqhqi/1xDc6u4jQk1xf4uHQxCjxYkQCnWTUDIoM/lkmTG9961t56KGHOHz4MMCBRpVTsm+wcqq+wHT1Rxnd1tdwMQ8RYWSsC8PU0XSNQLjUVbxudXJesjA99tw7LRPWrUnx0EMPAXxfKdXR7KRFxAmoG72gb2Hr9S8AuQCzi1atm5cs+js20rVmTcO1BTxek9FtffhDhcBMgKH1nXUHo1mWwYYL+4gk/JiWTiThzwezaZowONpB3+oUGy8aID0Yw7INNlzoKOocsVRgzqWXMrzAw8B/lL+wa6y7E/gaznroHwJ/AtQy238XeDfwOeBLwPXAdbvGulcDvw98CEhm9wG4HUfxzxnQ1Kyy/jBOnOYvA+9v8hjFfAPHrX4eTt72+wFEZBR4G7Ae2AXcJbX82bNSe81a1wzMbAcuESEZnp8LPEc00FlxzlBHx5wNPlJJPz//sxvo742UbD94eIL77vsa3/3ud8GRUcOyENGqVv8KeEvXXm0rUJG+VUt+4EyGfHaU8gRjhcIyCuvT3fHV5PxWmuhE/J1Eg4Wa7bpm4PdE8VnhWc83XwzTyrdOXQ4Yhs7otl5GNna13AOQ7B9suOnMuYIv4FnywKhGKbjB5/edhvxJosleJ6ujwWvOH7I57+JBLE9BWXr9Fh19EUfhzjIBMkyN9Rf2YXpKFW047mf0gj7Wbu0h1RMBslkkI0m2XjGCt6xWgIg01Hp474Hxf997YPzXgR9VefndOAWHfg349N4D43fuPTD+sRqHug44AbwXR3FPAb+K4+EA2Ae8AgR2jXVvx9FtdaU+N3tHfAn4IvC3QNcc+86JUurrSqmcz/UBILcQdRXwRaXUpFLqGeBJ4ILGzyCz3uT8HsfitQxfTQuxUQK+eIVbOJBIVFQDKycZ95OI+yu2P/rEYX7qp34Sj1M9bIqmZKGIBdMUL/QIGmF/afS2rploRcF0muizWqJKOZZwuffCMkoDjzyWn4AdB4Tu+HrWdL+h4j0dkVUkQwtfTjQ+MNCWFclq4Qt4iCZbm7YGkOgZbHl1vJWCpktbxpjMRi7Per7lRgG64+sY2LK1ZZO5/jUpzrtkkKH1nSTSobylDE7gZSThZ8P2gZpxDsGIl3CVe2Mt4p3BCqXfJKPZ/38GnNo11v3crrHuK2rsOwi8svfA+MzeA+NngCPAEPAD4EEcl/d5OPrzI8B79x4Yr2txvdlv9B4cS/fS7F8reQeOywGgm8LaOMCL2W0NMZc1klsfTYXnn7OaQxONYJnFato2uuVciKJpmB5P3T+EExOTdHWVWIMNyyKjMnisQMmavKbpBLyV7nmPUfqjCAc6qFWaK6Nm8FqRCjl7PZUTn6GObYz2vpHexHlVv5eOyAjp+Lo6Ps388IZCeAKBc96q7OkYJRBvvDjQuYCILLsa5NMtsqwBEqEBDMtD79hYSya2IoLXb5FMh1l1XpqtO0boXZUg0RXivIsGWHd+b4WFPN/z9a1OtqLyY66+8gHgvwIx4PP1DgNg74HxGRxduR1HeY/g3MMP7Rrr/s6use5ndo11//ZsB2p22vEjpdTtjbxBRO4Hqvkeb1RKfTm7z43AWZzUsIaoliuY+5I00QgEAliWlf+fIxAIkJjp5vnDD9PfNYrXU7BecvtXe17tce64ucedsSFOnD5SksYVTCT4yIe/wqnTZzG9XqZOncpbNrm82Vp4PJ7cORLAzwBXiMj7mCOf1uvzZvMgFX5vkESkl4lXjgCOqzoa6qj4rEF/jFNTxxz5aTqbV/0E//rwX1V4C8BxX0fCiYoq8SF/vELeHYnahStmk3fx8zJ5N507Obx9O6eOHeP1V1/l+Msvc3Z6etHXspeaztgwsd5eTr52ZF6tTFcquqEtq6jiqayDUmf+ylrXTFLRQTJqhmhPD0dffLGlqae5AjgLSaIrRDA6b49UrvDXZ/YeGP+nXWPd7wEu3DXWbeOkxpnAdFYhPwMM7Rrr1rPb48B3APYeGJ8EvrNrrNsP3AxcibNm/UOcNe59u8a6P7P3wPjJaoNoVllPi8g3gEMASqlfmOsNSqkrZ3tdRK4DfhrYqQp+uXGguH1VT3ZbteNX5ApmsheWiMbExISTN539n2NiYgKTAL2JMWamtZLUoNz+1Z5Xe5w7bu5xLNSL9tJDzBQp63BXF+98x6UMXnghhmVx6JlnOPjEE3P+CIIBD88++xxTU1MAh4HHgFuUUt+eSxanT50mkzmLoXuYnp7G1iPomslMZhqVyaCLh6mpqZLPaulBnEmhIuhNYeo+Qr4Ojp2s7NuiayZnp1SJItdEw2uFq8q7FrPJu/h5mbwbzp20zQAn5SgZzWkw4o/FiA8M8MQ3v3nOuYRtK0Cia5DnDzy81ENpSwxTZ+pMfaWC24HpFqVu5ehJjnLw6LN0rl3LyaNHmZyYcDxiIiXZFO1KLsJ/LnaNdXcBPwWsym66atdY9/DeA+N3A5/FWX/+vV1j3atwIsa/t/fA+JldY93XAf8HuAEneOyzODFdH8GxyE3gM2Wnex9w994D489mlfpm4BTODbfmF9fsN9qplHqTUuoX6lHUcyEiu3AKrPyMUqo4vv8fgLeJiEdEBnEE+d3Gjz/7x9R1k97ExkYPOydBb7xiTTYQj7PqssswstZmtLs+T/bakQT33fc1JicnASwalIVC5QPpAt5EXrF6TH9Vd7TXCqKJjiY6sYAzxr7kWNWoesPwYGhW6Xq86PiquMHbgbHhN9EVXVvyWSyvl3BXV13BNKJpaLqOpuvLat27Fl2xNQxuPZ/E4CDBVMrpgrZIn2sxc8abwbSW1zJJqwLMckQDXWiajqZpjFx8Met27mT15ZcTiMVWxLVfxBrgk8DF2ec3AXsA9h4YfwgnyGwTTqfJfwNq6b2PAX8BvB24FvhTHGUOwK6x7n4cV/r/yG76IBDI7n/T3gPjJ2oNsFnL2icibwNeB1BKfbXJ4+T4c5xZyDeyP94HlFLvUkr9SETuAR7BcY//tlJV/LBzUCvHeqEREZKRAV468ljNfQzLIphM8vqrrwJO3uxX73+cU6en+Ot7D9CZCvLL124ilQzwk2+5kG3btgGsBn6uUVlY2XVoy7AxdIvps2fw29V7etvZdC6lFGGfs3oRsOP4PBEmzhwpPa7ulI80dJOzM4UypV5PCNpw8q1pOv2pzfi8AZ566cH8xKVj1SqOv/zynNa1bhgMbt9OZnqapx54YDGGvKDEQ30E4gl8MedamDl7lsf/9V+ZWYRqewqcCVKbejQaTP9ZcqZa1M86h4iQCg/z8tFHgQy6YaAbBr2bNvHUt7/N5MmTbfvdNcLeA+P/wiz98vYeGP848PEq2z9DkeW898D4NPBb2b9qx3kOZ7069/z7OPfzOWn2StyPo1xrL7A2gFKqZnsYpdQfA388n+MvZBrQXHTEhnn16NP5OuHVSAwOcuLwYdTMDOtWJ1m3ulKsCnj3u9/Frbd8gFAo9EOl1NcqjzQ7tlVwR4d8SY68/kI2OrvKvmaQmcw0hmbhKWqeEfF3VSpr07HYTd3OK+tM5ixeT7CuWupLRU9yHU++VFjuNm2bSDrN0fHxWW9A/ngcT7ZxicfvZ3IeHcDaAY/pJ+hLcPzkQUChGwbdGzfywsMPL3x5XKXQDKPurnSLjWkvT2Wtt8iyBugID/PK0cdKIuM1XWdw2zae+Na3mJmaqvlel9ZRtxYTka25x0qpzxb/LczQWketgiiLQSyYrhqUVYw3HJ41YlF0nUA8gWXNvfYyG8Udq3K1wL2e6t2xDN1CE4NQWVcxrxWqKKriMXMWe1HNb92su5b6UmEaNolgH1I0oU6NjKAbRk0Xn6brThey7E8nkFgZkdSbR3bhMX35iW0olSIQj4OI4+7XdWd9Px7H30IXqCcQaOu1T8vSl1Xf5ukWpm7l8HrCeD2VhZIMj4e+zZvP+YyKxaKRb/RXRORLIrI9t0FE7lyAMbUcrUot6MVC1wxC3tSs+4hIzXKkoutE02kGzj9/3ut7llGo4xz2O2PyWZGa+3utILFAae1l26qsTe0xs1Zm0WSg2IpvZ9Kx0ZJa4aZts+qyyxyFXEUhKaUIJ7uIBZ0KdYF4fEXcrCzTy8b+N2MZNjlvYPeGDcR6e+ndtInRnTsZvOBCBrdtY/CCC5zOai3QYqFUCmlj+Rmmnm/DuRxo9Zp1jp7k+pLaCzl8kQiaWbv5jWjaSlvbXjIakeJB4Hzgb7OlP5+heipW27FUa9Y5EqGBCmu0HF+0cu1YdJ1Ef39J7+f5UKysQ/4ksWB33iquxqr0pSRCAyXbbCtYUt1MRM8HrhUfqxX11RcDvx3FX+ZdMCyLvs2bq+aX6oZBMtHPmu7LWJ2+lEA0ns86WO5Yho8L1v4cpuGklRoeD+nR0ZKJS86j0LVu3byvSc0w8IbDVSeq7XKDNyy97YPgipnOpW61uJ91V2ykopMgOIZGrLe3+vclguX3l3TZc2meRr7RXwLWKKXSwA6ciLiGI7OXgqV0gwNEA92omqVkHbyhEFp5T12liPa0pqsQONZTDl0zOH/NW2e9Efk84QqvhKl7SmIANNHyytoyfHnXd9A3dx30dmGwawu65lgHzrUiiGiEOjqI9/WV3Ij88QSxbInUaKAHzTCw7JVTX9vrCTKcPr+iS52umYx0XZRd6hBM2553FTiVyWCHQvjLJqqarhNJpxs+tm62vsOXabav1V+NQoBZa8dtmV6igerFKmtltBiWxeC2bXSsWdPW3pPlQiO/hhdwSqmhlHpJKfUrwG8uyKhaTDX3zWJiGV5igZ68VZJDEz0/NjsYrAjmERHMFs5Ki9eU50O5NV5Q1l5ENDQx2jZtqxqpyAAiGiI6HdFh3jD2dmKBHjTRSa1ahZ5184muE051EMqWZxURPKYff3x+hR1yqWDtsjgaC6ZL1vHBqVIXD/ayof8nMHRHKaaGhuZtdZq2jTcSKVlKUErRNTpK/9bapS6rKfLIAtR7N0x9WQU7L5QbHKAvtaFqHIpp23hDpb93TdfztSSCySTGLK5yl/poRFm/F7hXRP5SRP5ARD4MVK200m4s5Zp1juHO7eh64YIV0Yj40wx3bUUTvapS9obDLXPBmYbdMg9Def10K+s2NQ1vtne4VC012q5oms7a3ksY6tjGxqE34jG9rOm+nJGui9ENi56NGx3loBTeSJiQrxBU5rejBBKJhtatRdfRTRNfNEp8YIDO1avpXLu2bdytfjtaMW/wWU4hHY/pZ13PDmeiaRgEU7PHY8yGJxBwrpVQqCRdLpBIoGkagXicgW3bKhRzNVlruk64c55tCqqI37D0ZVUop9VFUYpJhPuold0U6+/Pfy+i63Rv2JDPmBARUqtWrYjYjqVkzm9UsncQpdSPgC04Bci9OJ1Drirep11Zajc4ONHVq9OX5sdiGT5WpS9hsGuzc/PTdDz+IotVZN4WWw5dNwnUyKduBn9Rhy2lMiWWdUZlnFrhNaLM25V0YjUdkdIMwkSon4AdI5BIEEwm0Q0DfzCatyzByT33R+ONpTgpxeo3vIGhCy+ka+1a4gMDxHp7sXy+ud/Lwq/nigghb0fhORrxYKGQYMCOY+jOBC2STs96Eza93pqv+yIRwFkbz91Cci7wnAXni0Tozk2WcD57//nnl/5WcserEvdRL8muIap17TBMfVl13looNzg4k9pUeLjCQwhOoKBSCkTwRSKEyrwcka6utolDWK7UI739IvK7ItKX7X51Xzb3+aPAGhH5LPArCzvM+aG3gWUNTo5yMjSIJjqjvTvzN6SwvxOfFXba0WXRdD1/M5svXivEtrVXteRYzvHC+bFnipS1qXvIqAwoslHFC8NNN93EZZddxkUXXQQwXNz/vNX0Js5DE4PuDRvo27y5Ii/d6wlje335Bi31EEgk0MvjE4BYX9+ca3uiaQRTqbwC/Pr+JxdEFtFgT36JRkQjEiisS4oIydAQIo71W0uVia6TGh6ujMWg9PoWETzZkrQqkyGYSJVYs5GuLhKDTuva9Ogo/miUWH9/iawCiQSf+1//yGWXXQYwKiJ/14gsLI/PCZork7+I1N2HuR2Yyvaz/sC/f7FpWcxGR2RVVQ+Q49nodPrWb9xYsY9oGp1r1xas7wVQ3LkUw5VKPRLbhVOH6gsi8pKIPCIiTwNP4JRN+4hS6jMLOMZ5s9Rr1sUMdm5j0+Bb8VqlEbABbwJfJJK/2DIzMyXKu52wi8YuSF5xi2gYuolZ1hqz1ezYsYP9+/fz7W9/G+AMrempXpWwrxPT8KCbJv5YnJCv1GLwWREyKPyxQkDdbDciTdeJ1QgaDHd2zlqMRTSN9OhoSZT60EB0QWQRLvqcIuD3lFqtyfCgs86vaYRqucIzGYLJJNGenqoysYPB/Nq4Lys/bySCYVoVtQlSIyMMX3RRPuAy3NGRl5Wm64S7uhjbupr9+/eDU/HwcRqURby3v2qwoG4sH4sw1yLzjb1j85JFLXyeMD67+n2pY/VqBrdtw6wRcBnt7mb0TW9i/ZvfTPeGDS1X2JlMpnYJshXAnNJSSp1RSt2llLoE6Ad2AluUUv1Kqd9QSv3Hgo9ynrSTstZEr5qDHLQTeEPR/MVm2nZV66sd8FpBMtmbQnnAiWnYC55jvXPnToyCbE5S6H/eckSE3rhjXWuiE7BLo9w9ph+lMvniKN5w2FHcs0xWahVSMSyrqitX052Yhr7Nmx3FJ0IiG4k9MhhfEFnYZiC/ZBPydVZMvnyecD5gsZYr3PT5MDyeqtHCmUwGj9+PytrlvuzENNLdnT1XmWUmUjJ51U0zv0ykMo78N21bUyyLB2hQFiJC55o1FZ4AfRlFhOfWrK/s3TQvWcxGf8d5Ve+ppm1XXL+CVrEMKSJE0mnCdbjG88GXdeALh1dMGmU1GpraKKWmlVIvK6WOLdB4FoR2r6QF4LOjeIOB/MXmj8XQtfasAqZrZj5YTtdL3b8e049vcderExT6n1cgIu8UkQdF5MHDhw9XtNjM/a/2OPc3kN6ApmnMZM6SiveWvB4MBvF5QkS6uli7YwfDF100aw5yqLNz1htUrLe3xFVoer30jI2x+vLLnUIkWaK9RWvIhXahaeCi3OfNtkptShbBYJBE2Lm/dydWlcgj99fXMeq0n63mChdxPAWA5fNVrMdbXm+JHHLRxKHsZywO4ptNVgB2KJSf2GY7sSVwmidcVE0O1WRhe5369p4qWRnLKX0rX8FM6U3LYq7fSH/neuKh7jlrR2SPXfO19OhowQrPVsqrRueaNVVfK75+NF0n2ttb06rPyaLot1FVFu1M+2mCBaAdFV45PisMAqbHw/TkJP5YjExmhnCgg2MTryz18CqwrSATp49gaKXKOuRLYOnzt6yvvfZajhw5UjJT1jSNG264gWuuuQaAO+64A5ywoJr9z6u1Tq3RarPicTFd0bUcPP40Z05PYuhmaetOb4yTZ45heJygK4/fj8fv58yJ0gY6mq7PmTcfTKVQODeiSDpN17p13Lb70xx7rbIZz5veuIa+hMHExAQf//jHASaBYVUjfLlRWcRDvbzy2lPYRqxqa9NUeIgnXnzQWUdPJnn9lcJ1qpW5x+N9fbz86KOIpqEyGadymWh4rSBnpk9i+XwMX3wxhsdDJnOWkLeTE6eOVNQnuPX6T5TIYnJiAsPj4ZcyIbZfel5unAZO/4L/r15ZnDl9BqWUc7Mve0u7dt763tOHmZouT/c8xHXTH+Cnd+3kkomLoQlZ1PMbGRt+Mw8//k+8NvFizXLKIhodkVWcmjzG8VOV9zBN1+k//3wOP/00ls+Hpuu88thjJZMly+cj0tPDy48+WjiuphHt6eHY+Hh+kqiUIpRKceLQIaZPn644V/a6aLidbjvR/lqsBbRDNPhcaJqOafiwQyGmDx7EFw5jWwHGht/EN//zr/Nu53Yh4I0ycfpIPrgsx9q+S2btWV0v99xzz6z9re+++2727t0L8Eytm1ArScdGiQf7qr4W9qc4ePTZEsUSHxjg5UceIVNU91rqCBrUNI3ONWswPR5CHU5E9s0fqm4AnD5+nKceeGDBZJEI95GOr6mZn++zHVf45PQEsZ4eThw8CDhuaUSwi3Jvw+k0Z6emsEMhvOEwpseDJjrpxBqefflhMlCUqysEvHE0Ta/o/FUui1PHjuEJBPKW9d133w0QAX6xGVnkahtMnSp06m3XZh7bhqp7Hw5qL/CE5yVenKcsZkMTjVXpS3nqlQc4/PqzVRW2IPQkNnL49Wc5cfpQ2T4CKDw+H90bNuS3vv7KK5w8etTZIzth1bJBlcWTwdTICIbHw6GnnkJlMnjDYWdpJBrlxMGDC9+AZglYPpET86Ad8qzrwW9H8UejiK5j+QNE/Gk8po/hjguXemgVBL3OeqGpL371rm984xvcdddduRvzovwqdc2oWULV741VXGPhzs7S/t6aRueq6pG05cT7+vKKuhq5yac3HOaknVowWXhMHxsGd8y6TzI0iKARSCQY2r6dzjVrCCSTzpq6SL7anW4YpEZGCKVSmFkPBCiS4f78unUOQ7fwWZGq5S3L8UUieUX9/e8+yl133QXwpFLq1KxvnIVcZHoOy1OqrNs7UdXh8SdfbIksZkNE6IlvrPGaTld0LabuIVwl5qFqnhw41c6K3Nu5FLBod3feFR7u7MSwLBL9/U7tcZG8x8oOBldsitjK/FRlLAfLGiDkTRGIJ4ik0xi6ScTvFHlIhAbYuvqn8yUx2wG/HQHAMuvLDW4l119/PRMTE1x11VXgpKb8xaIPooiAHa1QLLkIZXAUdc/GjSXrzM2giYFl+Al6k+QCsP7yk19fUlkkQv35G7E3FCLe38/A1q2kRpycdV0zav7+osE0thWosMoM3ZMN3GvMGPzUR/8u53lZLSIPNyKLks5yZdW4ypt5KAWa3t4a+x/2PtC0LBrBY/prtCBWdMfXA07qaGmJYh2vFa665u2LRPIeGcPjyRdWCWSDCUWE5NCQcxzDJDUygoL8kosdCpV4s1YS54SyXsp+1o3gt6P4I3G6168nk5nJ3pSdCzQe6mmbCldAPn1jKSzrAwcO8NBDD/Gtb30L4BGl1LsWfRBF2FagwjoEiPf3o5smA+efn1fc8yER6mfr8M+Sjo3mJ25//vndSyoLJ+e+eo65rhms6ro0a1mVl9o1SEUGSwrM5HBadUrDE8FP/80HeOihh8CRw6ZGZBH2d+XH6AkESgKazLJmHoGw3Va/xWpc/zvXNC2LRhARgnalO95nRfLfrYgQDaZLXh/uvLBmv4TO1asBJ8sgfx5NI9TZiS8axRsME/Z1omsGsb4+Brdty5cE1g1jxZY2XR5abJ4sFze4zxMlk+2a4zF9VW9k7UKunKjZxmNcLESkpFd4Dm8oxNo3vrEkB7vGEeY8h66ZRPzpbHWxZP46WWpEJNuZrfIz2FaQiL+Lka6LKqxrhcpPQE3dU/Kax3CqkzXauc2epYPcXOiaweqe7WhiVFRHM8xCT2vRhEQ6TM9wfFkVS1lIIoF0xWQs7C+tR5CKFDoP2laAkC9VMxbCH4sR6+0l0t1N8XWVXreOvi1bUEBXbC3gxHiU/75qtRte7pwbynqZuMEtw85Hrkf86Tn2Xlp0zcAyvPmyk+c65UVDcsxmgWli0Js4r65sBYXK53jrullRVGcpSYT6K35jmjjKT0QwDZv1A1c49cSz+5m6hdfjfAZPmQXtyebpO9Xi6leI861H351Yg2nYWH5/SRaCUZS6JUAk4aejL7qs+lwvJEFvsqQNsa6ZhLzJkn1iwW4UChGN7sQaZ1ugl1rfb3r9ejw+H5poRP3dTr62YaAbBqZuEfF31Qy69c1R52C50p5hji1muVjW4LjCj598lbB//m7ThSYdHc3WCXcJeOMcOfHCnK1Qi9E1g574BkLeFD9+cX/NFBgAlMJTZL3HQz2cOnR8PkNuGQE77lhW+ZUAwWP6SIT7OHnS6fXTGRtGx8vps6/x6mtPEbQLaV22J8iJ00cAR8mbumNx+TwRdM1gJlvoYy5qVdaqFxGN/uQmnnrlOximydmpKSDbzCNbH1wzNGyfiYiQHowz/tRhMsuodvhC4LdjJZMbpTIEvOVleYMYmsn0zCQdsREy0xAL9HDw+FOzfr9+O85w54U89PTf56+vaLYjnmX6mJyu7CXlDYXQdJ3M2fq9T7fu3nkTTtnsHuAQ8JGb9+y7s8a+HwV+EQgCLwIfunnPvo/funtnAPh7YDvwj8Av3LxnX+bW3Tt/DnjXzXv2vbnuAVXBtazbjEjAiQIO+ZrvZrRYpOPrsIzFDzBrR3yeaEOTQl0z6E2MIaIR9nfSl9w063Ua8EZLrPR4qKdtKvOJSL7Rh6YZhLwpRnuvrPAqeK0QfakNbFt7FT2JQhRxcTtVEcm7R32ecN1BZpoY83KDF8boKHyryBVumHpeIUeT/vzn6uqPNmL4r1g00Uoa92iaUfW+EPZ3YZvB/Pcd9CVnjfjXNYP+5CYs05f3NOqambXIC0GuOQQNQcMOhZpJ3boAR9H+HjAFfPjW3TvfUGPf54CbsvvawF237t7ZDfwCTrOrm4H/Auy8dfdOD3A78PuNDqicc0RZL5+PGfKnsM1gxTqeS3sT9CbIZOqPQtU0g1RkKP88HVtHT3xDTYUdCXSVPe9ENXC+hSYVHkYTg3W9l7K+78oK1/Zs2FawZM0z1wTGNgOorLdhrgm3iOTd5/PBMn1O17iiiHBd1xBxIsBjqcLyg25o2D43ZgPIZ64A+X7v5fTE1zPcVUhD1UQnHCjq7la0TALOskYuyLYnvh5NdDJqJm/IhP2lBo2m6U69Co+nmfSta27es++Gm/fs+yROkyqA9dV2zFrcXwT24XSfzM0M/MBx4H6cAkUBnNbSX795z75HGh1QOctHi82D5eQGT0UGWNtzxVIPw6VBdM2sW1nomsFQto95MT2JjdkuX5Xrv5FAad61ZXqd/uFtQsiX4sLV19KdbLwvt2358xNqp4ub87lENEzDh4hGV3QtG/vfXFNpK6Wwrflb1rmKfOX5upqukZlRhGJl6+velRl53CghXwpdM7PtVCtrwYOzrBH2lV7HnVGn5aauGfSlNtCf2kLY34EgrMrGPICzLu4x/fg9sXyMR8AbK0v/Uvl17PT69U6N9zqvxZv37JsqevoTOAr4W7O85V9wmlltAn7/5j37xnEs8zDwMHAQ+BHwLhxLe960hx9tgdFEr5WD33Y4xTfas9uWy+xE/F28MvX6nPsppUhG+qm2VNcdX8/k9ClePf5EkYtQEfIlKY+nCfs6OfT60/MfeItoNkXSY/op6Y9e5FXqT24iEkpgimPRDnVcwNMHv1cRXJRRM3hMP2fOTjY3+CyOG97G4/fnS6OC4wr32GZJsBmA7XeVNTjKNJOZQdP0Cot3NjpjI0xNnaW/azRfxndV71YyKoMmWkkFw5Gui0u+d2dpiMJadrCbidNHOTP1OpGuLgLxOC//+MdMNlBR8dbdOz8M/DTw/pv37Dswy66/CQwDtwH//dbdO7948559z9y6e+cwsAb4IY6F/mHgilt377wdx9p+98179n2z7gEV0VaWtYj8oYgoEUlkn4uI/JmIPCkiPxCRLU0ddxlZ1rlezTt37uQd73gHx44dy7/2sY99jLGxMYANIjKvYAWX1hPxddZVuEbT9Fmjubvjo/nWkTmqRTo7rsflv2hqW/78xETXjBKlnwwPEg0WXKypyDBdsZEqedtOe9ZW4DEDWH4/qqi4hmnpxDorvzPbttwULpxe9qbhYSYzTagBZW0aHjoiwxXfXbWly6A3UZIS5rMjzGRyyyQGHdHBEsvdsCx6x8ZY94addY0lGzj2B8BtN+/Zd3vRdvPW3TvtW3fvzH/RN+/Z952b9+z7a+DzQAdweXb70Zv37HsAWA1sAz4O3IWj1L8E/Eldg6lC2yhrEenFcT88X7T5J4FV2b934nzwhllOa9a5Xs379u1jaGiIO+90AhIfffRRvvzlL/Pd734XnP60d4kso8i5c4CgLzV7RHeWkC85q6vYY/qJhQquRK8Vrrp/yJdq++Ic9eCsE2ct2DpiNdLxNRXucKOFxXl8niCGZZW4wbuH46R6Kj1eltdwU7iyhHwpPIa/ZZOmudA1I++FUWSIh3oI+VJ1dQMr59bdOz+IEzD2XeCRW3fvfNutu3fmipZ/EjgNbL11987wrbt3fu3W3Tt/+9bdO38H+A0c2/7RskN+BPjDm/fsmwF0HF12KfPwZreTFvtT4L9R6rC+CviccngAiIhIwzlNyykavLhX89atWxkfHwfgvvvu46qrrsLj1FWeAp7EiWB0aRNM3YNnjuh4EY14eO6yo0NdW/I3nVqZAR7Tz/Z1Vzc+0DZDEy2/DlmrUEYxAW+sIpivnvfViy9XSreorWcsFcRjVyqhatvOVeLBfuKh/kU9p9dyPE6W7sW2AgTtJE2ueV6U/X8B8IXs3zVV9psGQsAHcazkw8Av37xn3w9zO9y6e+e1wNGb9+z7RnbT9TiG6DqcKPKmaIs1axG5ChhXSh0osxS6gReKnr+Y3fZylWO8E8f6pre3l0AggJadGdseL+IxSnqz5ggEagcF5fav9rza49xxW3WOv/mbv+Haa68lEAjw6quvsmXLltxxE0A/8HkROQp8ItvmrqYsqvWnLR9rO8ui/NhFx02IyINFhymRxWITC6V56cjjNV/XRCca6Kz5eo6wvwPL8HJmeoKgt3Zv56Av3pIuZ0uNqdvMZKbrSgU0DQ+aZjCTKcQE2S2IBM/h9QSdVLBgsKLNaTmWbZzzedY54sG+mp3pFoqAHef10weJBpxGHk6tcmlYX9+8Z98Vs7x2HXBd0aZL5jjWPcA9Rc8/C3y2sRFVsmjKWkTuB6rdpW4E/jvOzKNpqvVkzSXqnz07w+SZMxW9WaGyb3Exs7VorPa4Wl/kaue49tprOXToEOCUy8uNc/fu3VxzzTVMTEzw0Y862QNXX301ExMTzMzMMDk5yZRTqOEwTtrA15RSX6pHFs30cF4MWdR7jvJjFx23rXrUOj2gn65ZDtRJPame2lKMiNNe8MmX/y9+Oz7n/ssdy/BxZvpE3UrXZ4U5ceZQ/rlttq6im20FnBKyoRDyyiuz5uwapl7R/9pl8fBlizLFgo6yFhH8njivn351KYe1ICyaslZKXVltu4hsBAaBnFXdA3xfRC4AxoFin2FPdltDOKlb9VVBWgzuuSc/6apQSOD05L3//vu5++6782uS6XSal156qXi3pmThsrBEg135ph6aZqAyMyVNPnyecN2phIlgP6cmj1WtO77SsK0Ar59+te4iOwFvPK+sNTGyEeWtG4tCVUSEV0NEMEyd6an2yXk/l/BZYUQ0Qt7CUlHE38mJ04caqia4HFjyNWul1H8qpVJKqQGl1ACOq3uLUuoV4B+At2ejwrcDx5VSFS7wuVhOa9a5Xs2f+cxn8BWtmb3lLW/hy1/+MpOTkwAWTtDdd5domC41sK0AhmZiGT42j/wk3WWFTuKhnrqPpWk6A6ktKyKIbC5ylnG9a89+u5Bj66Rbta6Snm36yWRmsHy+uiqomZ62WE08J/HbMdb3vqlkAhz0JpdVbY16afer7KvAW3CCqU4Bv9rMQZbTze7666/nzJkz/PzP/zwA27dv5wMf+ADr1q3jrW99K9u2bQMnLeDnlKoj9Nhl0dnYvwvL8BIKhTGUj5ePPgpqBl0zKloFujhYpqOkc3XB58LniRTl2KqGKqbNha6baKJjWBbUUbbS4zU5dWJ++d0uzSEiFctKATteV1bGcqPtlHXWus49VsBvz+d4svTOg4Y4cOBAzXXb97znPdx4442EQqEfKqW+tlRjXGpuu+02/vEf/zEXNb9KRNJKqZfmet9iUbzuqusmQ+mtPPXSg2RUpqIS2Xxpd1nUS84yrtey9lnhfI5tJjODZfjysgBGReTrwHXNysI0nIC3eixrr9/iaDMnWUC+sf/7/OWXdkILZLHc0HUT2wpwenLuAkXLieWlyZpgOVnVLvXxnve8h3379vGtb30LnFq8f7TEQ5qVvtQGp2iHZrXUAoTlJ4ta5JS1adSXL61per6GOCLompmXBfAITtejpmXhMXyICKLP7U71eM22y7W+7OINLZPFcqQjOsRKKBhUzDmgrFf8RzznCIVKqnlptHkxWV0zWNN7yYLkoC43WdTCY/oJ+Trq6u2dw2tFACe/XUTKZeFnHrKwsxXm9HqUtW22XRUz21PSYGResliOdESHllWsUj20nRu81bjKemVy++23c++99wLEmcVqaJec88H0RroTaxYk5/zOO+/kC1/4AkAKuKgo73xB8+/Lx1SvLMqPDxAORdg++nMNySIeTnP81MvYVqBEFjjNFdYDj2VlUZF7P5cswoEEh44/g2aaMFXc46HK+Oz2u41qIi2TxVJeF83+RkzTaVmbmTmLILntbVWToVHa7yprMcttzdrF4dprr+XIkSMlTe01TeOGG27gmmuu4X3vex8f+MAHCIVCR4DfoUZnm5WQcz6XLP7gD/6AP/qjPyIUCh0EvqyUWhRZVEs7XExZzGSmmZw+ia6Z/NFNKi+LD33oQw8D9wJ2s7JAGWiajm7OXaHMsk0yM4tvuH7v6cNMTVcGwK1Oh8go1TJZLIfrotpxo4EeDh1/CoXKba9Zk+He/XcYOI03fgmn49angN1X77ihQsD37r/jV3Fqg4wAJ6/ecUOg6LU7cUqQPgr87NU7bhi/d/8dPcA3gU1X77jheM0PNAcrX1m7lvWy5J577pmzSEqW14CraVEbunbElUWBYlmcnnqd/3j6H+iOrac/tbl817/CySZpShYew6mEZVjWnPsa5tKsPmwbql7ZropLfl6yWK4kgv0cPv5Mvbv/LvBu4M8BG6dE6I+B/11lXxsnrfg6IF/M/t79d6wGfh+4BSdz6d04Rb9uBz44H0UN58Ca9XJq4uFSH08++WTx0wiVRfTPGc5lWdhmABEtXxClTBZXMQ9ZeEw/GZWpS1k7Sr291kcPHynRC/OSxXIl7OtoJMbsOuAE8F4cxT1FjVThq3fc8PGrd9zwh9n9i8lV5tkHvAIE7t1/x3acZYhPNTD0qriWtcuy45ZbbuGxxx7LpSuFgPcs8ZCWjHNZFiIaATuKlY2wz8kCGMUpX/yuZo9tGV4ymRkMz9xdwAAsj8H0ZPvk9v7TPz/Efft2QAtksVzRNJ2wr5OTk6/Vs/sg8MrVO26YAWbu3X/HEWCowVP+AHgQx+V9GsfK/gjw3mru9EZZ8cp6pUUEusDnP//5/NpWKBR6Uil1zpZdPddlsWnkzUxn479yskin048opd46n+OKaBi6lW+VOVvJUQCP1+Lk65PZ9y59ufBf/C9v5Lrf+uOWyGI5kwwNcvpwU97nfMmderl6xw0z9+6/41KcoL7ngDfhVOQ8dO/+O76DEwD6oat33PA/mxnQilfWrmXt4rJy8XpCzEwvTNcxj+lDN01EZM67dijmZeL4aYIRL6dPTrkVzdqEWLAnG1MwJ88AQ/fuv0MHTJwsk+8A3Lv/DhOnJ/Xk1TtumPVSuHrHDZPAd+7df4cfJ0bgSpw16x8CnwP23bv/js9cveOGk41+lhWvyVzL2sXFpRlsK+BEg9dRWKmrP8bWK0ZYvambZDqMayO0B7pm0ptaX8+unwUCOG7rP8NR2J/JvvZJHLf2VoB799+x5d79d/x6dn/j3v13/Pq9+++4vOx47wPuvnrHDc/iKPrNOP2xhSb17oq3rFdiQXcXF5eFx29H6krdqnhfyIOmaczUUVfcpW34GE5zpLfjuL//FPg/Nfb9GUoj6z+Jo+z/DeDe/Xf0A/8VGMu+/kHgi9lj33T1jhtmb5Jeg5WvrF3L2sXFpQm8nhCmZTe8AO0P2WRmXEW9nLh6xw3TwG9l/8pfuw4nWjz3/Bac9Kxax3oOJwc79/z7OM2X5sWKd9a4ytrFxaUZbCuAYXnqauZRjGHq6KZ733FpLStfWbtucBcXlybwWgEMy5ozErwa/mB9KV8uLvXiKmsXFxeXKpiGjULV1XmrnGDUt9KaPrksMStfWcuKX5Z3cXFZAAzdQqlMXZ23ygmEbHR9xd9eXRaRFX41SUMt91xcXFxy5JS1ZjR+D/GHPGQy51RXSpcFZkUraxFxi6K4uLg0hYigSX2dt8oxPQZaHfnZLi71suI1mdvIw8XFpVl0zairmUc5IoI32Pj7XFxqsaI1mSCIm7rl4uLSJLpu1t3Mo5xQ1Nfi0bicy6xsZS3iWtYuLi5NY+hW08o6EHaDzFxaxwq/klzL2sXFpXkM3cIwTURr/FbpCzqpXy4uraBtlLWI/K6IPCoiPxKRPyna/n4ReVJEHhORNzdyTNvyYxne1g92AbntttvYuXMnV155JW9729t4+eWXAVBKcdNNNzE2NgYwKiJblnSgbcDHPvYxgK0ikljqsSw1rixK6BAR1QpZmLoH3bKQJoLFbJ+55K0yaaEsXJaWtlDWIrIDuAoYU0qtBz6U3T4KvA1YD+wC7pIGTOW1fZcS8XctwIgXjve85z3s27eP+++/nyuvvJI9e/YA8PWvf51nnnmGhx9+GJxeqR9fwmEuOePj4+zbtw9gaqnHstS4sigwPj4OEAKeb8XxTMOuu/NWOSKC7W08krxVtFoWLktLWyhrnOLptyulJgGUUgez268CvqiUmlRKPQM8CVywRGNcFEKhUP7x6dOn8zP6r371q1xzzTW55yeBiIgsr5lIC7nlllu47bbblnoYbYEriwK33HILwIvQGv+zlVPWTeIL2q0YRlO0WhYuS0u7KOvVwGUi8h0R+VcR2Zbd3g28ULTfi9ltK5rbb7+drVu38rd/+7fceOONALz00kuk0+ni3c4JWVTjvvvuo7Ozk40bNy71UJYcVxYFcrLA6T3cEhzL2mq481aOQMRekt7Wjzz2XMtl4bK0SKMdZZo+kcj9QGeVl24E/hjYD/wesA24GxjC6TH6gFLq89ljfBr4mlLqS1WO/07gndmn64EfAWHgeI3/4DQYPzzLsHP7V3te7XHuuHOdY3V2OzgTplyngHGcWXDuuJ2AF3gGWIczcdGz2xWOwj4FfEIp9YncwRuQRfFYl0oWtc6xGvBQkA04snoB6AF+jNP8fRj4AXA2u48ri8WTRbkcyrcthixyv59xCrJYm93+YxxZlMgBXFkUH3CFyKKec+Q+W44KWbQ1Sqkl/wP2AjuKnj8FJIH3A+8v2v5PwEV1HO8Tc/3P/j1Yz3GqPa/2OHfcFp6jD3gt+/gRnIbmueM/BnTNRxZlj9taFkXH2YhjLTwLnMC5AT0PdLqyWFxZlI9piX4juWO5snBlMec55vr87fzXLoWz/x7YAewXkdWAhTOj+gfgr0XkTiANrAK+W8fxvlLn/7kiqr8yy/Nqj6tta/QcDxY9vgpnRgzwN8DbcYLstgPHlVIvz3HsamOrNdZ2lEXFc6XUf4rItUqpr4jIW3G8L+crpWabgdcamyuL+cmifEzV3l/rfMWP5y1vVxaFx64s6jrHsmPR3OCzDkLEAv43sAknovV6pdQ/Z1+7EXgHzuzwvUqpr7XwvA8qpc5v1fFacQ4RuRdYg+POeg54l1JqXJzIsj/HiYo/BfyqUurB2kda2HG2yzlE5FnqvxHVe0xXFoVjurIoHNOVReGYy1IWy5m2sKyVUlPAL9V47Y9x1rQXgsVYr2joHEqpq2tsV8Bvt2RE1Wk7WdSDUmqg1cfElUUxriwKuLIosCxlsZxpC8vaxcXFxcXFpTbtkrrl4uLi4uLiUgNXWbu4uLi4uLQ5rrJ2cXFxcXFpc1xlXYSIDInIp0WkouhKC8/xsyLySRG5W0R+YqHOM19cWRRwZVHAlYXDYsghex5XFoXztL0sFpSlTvRe6D+clLCDwA/Ltu/CKSzyJPC+ste+tAjniAKfdmXhysKVRXvIYjHk4Mpi+cmiXf6WfAAL/gHhcpzk+h8WbdNxqqQN4RRgOQCMFr3e6I2omXN8GNjiysKVhSuL9pDFYsjBlcXyk0W7/K14N7hS6t+A18o2XwA8qZR6Wjk53l/EqRa24OcQhz04Nc6/3+w5F3qci3EOVxauLKqdY6lksRhyaPQ8riyWXhbtwopX1jWo2s1LROIi8hfAZhF5/0KcA/hd4ErgGhF51zzP0QpcWRRwZVHAlYXDYsih5nlwZZE/D+0li0WnLSqYtQtKqSPAgl4ESqk/A/5sIc/RClxZFHBlUcCVhcNiyCF7HlcWhfO0vSwWknPVsh4Heoue92S3LbdztAJXFgVcWRRwZeGwWGN0ZbH451lWnKvK+nvAKhEZzDYReRtOh6/ldo5W4MqigCuLAq4sHBZrjK4sFv88y4uljnBb6D/gC8DLwDTO2sevZbe/BXgcJ+rwxnY/hysLVxauLJa/HFxZLD9ZtMuf28jDxcXFxcWlzTlX3eAuLi4uLi7LBldZu7i4uLi4tDmusnZxcXFxcWlzXGXt4uLi4uLS5rjK2sXFxcXFpc1xlbWLi4uLi0ub4yprFxcXFxeXNsdV1i4uLi4uLm2Oq6ybRER+U0SUiKwr2vZjERlcynEtBa4sCriyKODKooAriwKuLJrDVdbNsxF4GPgpABGxgQ7g2aUb0pLhyqKAK4sCriwKuLIo4MqiCVxl3TznAXvIXnDAKPCoOjfrt7qyKODKooAriwKuLAq4smgCV1k3zyjwZSAlImGc2eIPlnZIS4YriwKuLAq4sijgyqKAK4smcJV1E4hIL3BEKXUa+AbwZpzZ4n8u6cCWAFcWBVxZFHBlUcCVRQFXFs3jKuvm2Ejh4voqjjvnXJ0durIo4MqigCuLAq4sCriyaBJXWTdH8UzwX4HLOXdnh64sCriyKODKooAriwKuLJrEVdbNkZ8dKqUmcWaFU0qpY0s5qCXClUUBVxYFXFkUcGVRwJVFk4gbgOfi4uLi4tLeuJa1i4uLi4tLm+MqaxcXFxcXlzbHVdYuLi4uLi5tjqusXVxcXFxc2hxXWbu4uLi4uLQ5rrJ2cXFxcXFpc1xl7eLi4uLi0ua4ytrFxcXFxaXN+X/KFi2pEeWzKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x252 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comment out the methods which are not needed.\n",
    "methods = [\n",
    "    \"lstsq\",\n",
    "    \"lstsq_bootstrap_err\",\n",
    "    \"direct_MLE\",\n",
    "    \"powerlaw\",\n",
    "    \"powerlaw_rescaled\",\n",
    "    \"powerlaw_xmin_fit\",\n",
    "]\n",
    "num_methods = len(methods)\n",
    "alpha = -1\n",
    "\n",
    "plt.figure(figsize=(7, 3.5))\n",
    "\n",
    "# Colors taken from seaborn cubehelix\n",
    "colors = [[0.673, 0.746, 0.543], [0.500, 0.462, 0.259], [0.278, 0.179, 0.101]]\n",
    "\n",
    "for j, method_name in enumerate(methods):\n",
    "    # Use large number of trials (num_trials>1000) for good measurements of outlying percentiles.\n",
    "    # Bootstrap fit can take time, so saving / loading the calculation results may be useful.\n",
    "    fit = ExaminePowerLawFits(alpha=alpha, sample_size_grid=None, num_trials=100)  \n",
    "    dev_dict = fit.calculate_fit_deviation(method_name)\n",
    "    # np.save(method_name + \".npy\", dev_dict, allow_pickle=True)\n",
    "    # dev_dict = np.load(method_name+\".npy\",  allow_pickle=True)\n",
    "    dev = dev_dict[\"deviation\"]\n",
    "\n",
    "    ax = plt.subplot2grid((1, num_methods), (0, j), title=dev_dict[\"label\"])\n",
    "    ax.set_facecolor(\"0.9\")\n",
    "\n",
    "    for i in range(3):\n",
    "        plt.fill_between(\n",
    "            dev_dict[\"samples\"], dev[i, :], dev[6 - i, :], color=colors[i], zorder=7 + i\n",
    "        )\n",
    "\n",
    "    plt.plot(dev_dict[\"samples\"], dev[3, :], lw=2, color=\"HotPink\", zorder=11)\n",
    "    plt.axhline(0, lw=1, ls=\"--\", color=\"w\", zorder=10)\n",
    "    if j == (len(methods) - 1):\n",
    "        colors_label = [\n",
    "            colors[0],\n",
    "            colors[1],\n",
    "            colors[2],\n",
    "            \"HotPink\",\n",
    "            colors[2],\n",
    "            colors[1],\n",
    "            colors[0],\n",
    "        ]\n",
    "        plt.annotate(\n",
    "            \"Percen-\\ntiles:\", (770, 4.4), annotation_clip=False, color=\"k\", va=\"center\"\n",
    "        )\n",
    "        for i_k, k in enumerate(fit.percentiles):\n",
    "            plt.annotate(\n",
    "                \"% 2.1f%%\" % k,\n",
    "                (770, dev[i_k, -1]),\n",
    "                annotation_clip=False,\n",
    "                color=colors_label[i_k],\n",
    "                va=\"center\",\n",
    "                weight=\"bold\",\n",
    "            )\n",
    "    if j == 0:\n",
    "        plt.ylabel(\n",
    "            r\"$(\\alpha_\\mathrm{meas} - \\alpha_\\mathrm{true})\\,/\\,\\alpha_{\\mathrm{err}}$\"\n",
    "        )\n",
    "\n",
    "    plt.semilogx()\n",
    "    plt.xlabel(r\"$N$\")\n",
    "    if method_name == \"lstsq\":\n",
    "        ylim = 60\n",
    "    elif method_name == \"lstsq_bootstrap_err\":\n",
    "        ylim = 30\n",
    "    else:\n",
    "        ylim = 4\n",
    "    plt.ylim(-ylim, ylim)\n",
    "    plt.grid(color=\"w\", zorder=20, which=\"both\", lw=0.5)\n",
    "    plt.tick_params(\n",
    "        labelbottom=True,\n",
    "        labeltop=False,\n",
    "        labelleft=True,\n",
    "        labelright=False,\n",
    "        bottom=True,\n",
    "        top=False,\n",
    "        left=True,\n",
    "        right=True,\n",
    "    )  \n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "D) Demonstrating that power-law indices measured with MLE method are described by gamma distribution (James et al 2019)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "alpha_true = -3.0\n",
    "num_points = 8\n",
    "num_trials = 10000\n",
    "alpha_measured = np.zeros(num_trials)\n",
    "for i in range(num_trials):\n",
    "    y = simulate_power_law_rv(alpha_true, num_points=num_points)\n",
    "    alpha_measured[i], _ = fit_power_law_MLE(y)\n",
    "\n",
    "plt.figure()\n",
    "bins = np.linspace(-60, 0, 400)\n",
    "alpha_measured_hist, _ = np.histogram(alpha_measured, bins=bins)\n",
    "bin_centers = 0.5 * (bins[1:] + bins[:-1])\n",
    "M = num_points - 1\n",
    "# See Eq. 5 in James et al. (2019)\n",
    "p = 1.0 / (-bin_centers) ** (M + 1) * np.exp(-alpha_true * M / bin_centers)\n",
    "p = p / np.sum(p) * np.sum(alpha_measured_hist)\n",
    "\n",
    "plt.plot(bin_centers, alpha_measured_hist, \"go-\", label=\"Measured alpha\")\n",
    "plt.plot(bin_centers, p, \"k\", label=\"Gamma distribution\")\n",
    "plt.axvline(alpha_true, label=\"True alpha\")\n",
    "plt.legend(loc=0)\n",
    "plt.xlim(-15, 0)\n",
    "plt.xlabel(\"Measured alpha\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}