{ "cells": [ { "cell_type": "markdown", "id": "908099da-3ae1-4cb5-86c6-b123eb16a067", "metadata": {}, "source": [ "# Asymmetric errors\n", "\n", "Sherpa provides *minimal* support for asymmetric errors - where the low and high error values do not match. The approach taken is currently designed to work best wih the Astronomy UI interface, so that will be used in this notebook." ] }, { "cell_type": "code", "execution_count": 1, "id": "5c570ab6-a1c8-4b0d-a7f7-1fe18297804e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: failed to import sherpa.astro.xspec; XSPEC models will not be available\n" ] } ], "source": [ "from sherpa.astro import ui\n", "from sherpa.utils.testing import get_datadir" ] }, { "cell_type": "markdown", "id": "c715c4ec-fdd1-4867-b2ea-35c3a6740ba5", "metadata": {}, "source": [ "For this example we shall use one of the test datasets, which contains `x` and `y` values along with the error range on `y`:" ] }, { "cell_type": "code", "execution_count": 2, "id": "9608cbc6-3dac-4c4c-9164-2612754f6ad3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.00310 13.383 12.379 15.002\n", "0.00284 13.352 12.437 14.323\n", "0.00413 14.717 12.992 15.693\n", "0.00469 14.275 13.518 15.093\n", "0.00451 13.169 12.321 14.319\n" ] } ], "source": [ "from pathlib import Path\n", "inpath = Path(get_datadir()) / 'gro.txt'\n", "\n", "# Display the first 5 lines\n", "with open(inpath) as fh:\n", " for inval in fh.readlines()[:5]:\n", " print(inval.strip())" ] }, { "cell_type": "markdown", "id": "8dd93e25-b3a5-4425-af15-601efb2148aa", "metadata": {}, "source": [ "This can be read in with the `load_ascii_with_errors` command (it can also files where the errors are given as relative values by setting `delta=True` in the call):" ] }, { "cell_type": "code", "execution_count": 3, "id": "74546cad-9017-4924-8b4f-659179a8d48e", "metadata": {}, "outputs": [], "source": [ "ui.load_ascii_with_errors(str(inpath))" ] }, { "cell_type": "markdown", "id": "0d4437e3-3044-4c83-90cc-11e0ff2ff39e", "metadata": {}, "source": [ "Let's change the name field to avoid the full path:" ] }, { "cell_type": "code", "execution_count": 4, "id": "f7d2ddce-606d-4c5b-bf59-cc1a1fd85e8c", "metadata": {}, "outputs": [], "source": [ "ui.get_data().name = 'gro.txt'" ] }, { "cell_type": "markdown", "id": "9ba44f03-f7c6-471c-9b54-c40420be93a6", "metadata": {}, "source": [ "If we display the object we can see there's a `staterror` field as well as `elo` and `ehi`. The `staterror` field is set to be the average of the `elo` and `ehi` fields, which give the low and high errors:" ] }, { "cell_type": "code", "execution_count": 5, "id": "7534f5a6-a8c9-4d2f-b757-93eb1823ccd9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "name = gro.txt\n", "x = Float64[61]\n", "y = Float64[61]\n", "staterror = Float64[61]\n", "syserror = None\n", "elo = Float64[61]\n", "ehi = Float64[61]\n" ] } ], "source": [ "print(ui.get_data())" ] }, { "cell_type": "markdown", "id": "c33c3ab9-4b44-4264-af01-4f776ee78071", "metadata": {}, "source": [ "If we access the fields we can see how they are related:" ] }, { "cell_type": "code", "execution_count": 6, "id": "7fa77dae-e6e9-4103-a9fc-d94435cbf0ab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 1.312 1.004 - 1.619\n", " 0.943 0.915 - 0.971\n", " 1.350 1.725 - 0.976\n", " 0.787 0.757 - 0.818\n", " 0.999 0.848 - 1.150\n" ] } ], "source": [ "d = ui.get_data()\n", "\n", "for idx, (err, elo, ehi) in enumerate(zip(d.staterror, d.elo, d.ehi)):\n", " if idx == 5:\n", " break\n", " \n", " print(f\" {err:.3f} {elo:.3f} - {ehi:.3f}\")" ] }, { "cell_type": "markdown", "id": "eb1426de-d6a8-4f57-934d-1a66aa273155", "metadata": {}, "source": [ "When the data is plotted the asymmetric errors are shown:" ] }, { "cell_type": "code", "execution_count": 7, "id": "c8444cf3-9c50-4411-a0a1-d80ca0bc5437", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ui.plot_data()" ] }, { "cell_type": "markdown", "id": "aac4691f-b4e8-4f5e-b985-683e13aa0399", "metadata": {}, "source": [ "However, the `staterror` field is used when a statistic is needed - e.g. with `calc_stat` or when fitting:" ] }, { "cell_type": "code", "execution_count": 8, "id": "acc68f36-7d2c-45d9-9f33-ec021593d6c6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "248263792.37785548\n" ] } ], "source": [ "ui.set_stat(\"chi2\")\n", "ui.set_source(ui.powlaw1d.pl)\n", "\n", "print(ui.calc_stat())" ] }, { "cell_type": "code", "execution_count": 9, "id": "45d1b8ae-9c60-45b8-badc-3d25c4a777e7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset = 1\n", "Method = levmar\n", "Statistic = chi2\n", "Initial fit statistic = 2.48264e+08\n", "Final fit statistic = 84.7331 at function evaluation 61\n", "Data points = 61\n", "Degrees of freedom = 59\n", "Probability [Q-value] = 0.0157157\n", "Reduced statistic = 1.43615\n", "Change in statistic = 2.48264e+08\n", " pl.gamma -0.598396 +/- 0.0207753 \n", " pl.ampl 332.533 +/- 38.2647 \n" ] } ], "source": [ "ui.fit()" ] }, { "cell_type": "code", "execution_count": 10, "id": "aa1292fb-7c9a-447d-803e-45a6a4848ebe", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ui.plot_fit(xlog=True, ylog=True)" ] }, { "cell_type": "markdown", "id": "667c9d5c-aef8-41cc-96d9-e9f32da7c108", "metadata": {}, "source": [ "As with the `fit` call, `covar` and `conf` use the \"average\" error bar:" ] }, { "cell_type": "code", "execution_count": 11, "id": "b2320d4a-b2f3-4ecd-8b50-b419e095c480", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pl.gamma lower bound:\t-0.02077\n", "pl.ampl lower bound:\t-35.0532\n", "pl.gamma upper bound:\t0.0201346\n", "pl.ampl upper bound:\t40.4317\n", "Dataset = 1\n", "Confidence Method = confidence\n", "Iterative Fit Method = None\n", "Fitting Method = levmar\n", "Statistic = chi2\n", "confidence 1-sigma (68.2689%) bounds:\n", " Param Best-Fit Lower Bound Upper Bound\n", " ----- -------- ----------- -----------\n", " pl.gamma -0.598396 -0.02077 0.0201346\n", " pl.ampl 332.533 -35.0532 40.4317\n" ] } ], "source": [ "ui.conf()" ] }, { "cell_type": "markdown", "id": "f1015787-8bac-4c2e-9038-3b0f51195598", "metadata": {}, "source": [ "We will use the `confidence` results below, so store them:" ] }, { "cell_type": "code", "execution_count": 12, "id": "3f12d1bf-d8cd-4571-9b83-d1fa1462d73d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "datasets = (1,)\n", "methodname = confidence\n", "iterfitname = none\n", "fitname = levmar\n", "statname = chi2\n", "sigma = 1\n", "percent = 68.26894921370858\n", "parnames = ('pl.gamma', 'pl.ampl')\n", "parvals = (-0.5983957573984634, 332.53266628336416)\n", "parmins = (-0.020770017510067684, -35.053209709661644)\n", "parmaxes = (0.020134631729182173, 40.43171458331858)\n", "nfits = 18\n" ] } ], "source": [ "conf = ui.get_conf_results()\n", "\n", "print(conf)" ] }, { "cell_type": "markdown", "id": "01fc6b66-72b9-4cbf-84fb-455be43c366f", "metadata": {}, "source": [ "We can draw a vertical rectangle on a plot to show the gamma range (from `parvals + parmins` to `parvals + parmaxes`, since `parmins < 0`). As I plan to use this more-than once I make a helper routine:" ] }, { "cell_type": "code", "execution_count": 13, "id": "f9641ac8-66c7-4d04-b6fa-ab5c96ec71c2", "metadata": {}, "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", "\n", "def add_conf_gamma():\n", " \"\"\"Draw a rectangle representing the gamma conf results on the plot.\"\"\"\n", "\n", " # Label on the conf result as a rectangle\n", " ymin, ymax = plt.ylim()\n", " xy = (conf.parvals[0] + conf.parmins[0], ymin)\n", " dx = conf.parmaxes[0] - conf.parmins[0]\n", " dy = ymax - ymin\n", " patch = plt.Rectangle(xy, dx, dy, color='k', alpha=0.2)\n", " ax = plt.gca()\n", " ax.add_patch(patch)" ] }, { "cell_type": "markdown", "id": "5316a58d-b3b0-414f-b9ea-f74d7d823c06", "metadata": {}, "source": [ "This routine can be used to overplot the `conf` results on a `region-projection` plot, showing that the 1D results (e.g. from `conf`) are slightly smaller than the 2D results (as expected):" ] }, { "cell_type": "code", "execution_count": 14, "id": "3973d17f-7040-4556-9731-8cad73a30585", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ui.reg_proj(pl.gamma, pl.ampl, nloop=[101, 101])\n", "add_conf_gamma()" ] }, { "cell_type": "markdown", "id": "2a9b27dd-afb9-44c0-83cf-8564d9ac52ba", "metadata": {}, "source": [ "So, **all** the above used the average error values when calculating statistic values. The question is, how much different would it be if the fit could use the asymmetric errors?\n", "\n", "The `resample_data` call allows us to estimate what the asymmetry means for any error estimation. It performs a parametric bootstrap assuming a skewed normal distribution centered on the observed data point with the variance given by the low and high measurement errors. The function simulates a number of realizations of the data and fits each realization with the assumed model to obtain the best fit parameters. It returns the best fit parameters for each realization, and displays the average and standard deviation for each parameter.\n", "\n", "In the following we explicitly ask for 1000 iterations (although that is the default value) and fix the random-number seed." ] }, { "cell_type": "code", "execution_count": 15, "id": "dfe66669-a317-45f0-9077-574183bbf9ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pl.gamma : avg = -0.4180195954679258 , std = 0.19844933475231044\n", "pl.ampl : avg = 163.0552687577543 , std = 94.50832743941801\n" ] } ], "source": [ "result = ui.resample_data(niter=1000, seed=123)" ] }, { "cell_type": "markdown", "id": "fc6f7ad8-b5d2-466e-8315-e687722b9f7e", "metadata": {}, "source": [ "The routine returns a dictionary with arrays as values; we can check what the keys are with:" ] }, { "cell_type": "code", "execution_count": 16, "id": "f5577f8f-ce02-4d24-9d64-4bb1878dd028", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['samples', 'statistic', 'pl.gamma', 'pl.ampl'])\n" ] } ], "source": [ "print(result.keys())" ] }, { "cell_type": "markdown", "id": "2470998c-2cbd-47de-86fa-9309e38d5d8c", "metadata": {}, "source": [ "A trace of the statistic value can be created (at present this is always calculated using the least-squares statistic):" ] }, { "cell_type": "code", "execution_count": 17, "id": "eb325127-62b6-4fef-a8ec-775d7dad6daa", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAHFCAYAAADv8c1wAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACSX0lEQVR4nO2deXwURdrHf7lJQhi5YxQEAREMKoJyqZxyyLEuu+uBRlxdXAVEFFYXXVd0FbwQD1xUdEUFjfsKqCsaLpH7MhDum0ACJCTkmNyTZKbeP0I63T19VPf0XJnn+/mgk+7qqqerq6uefuqpp8IYYwwEQRAEQRCEJuH+FoAgCIIgCCIYIKWJIAiCIAiCA1KaCIIgCIIgOCCliSAIgiAIggNSmgiCIAiCIDggpYkgCIIgCIIDUpoIgiAIgiA4IKWJIAiCIAiCA1KaCIIgCIIgOCCliSAIU4SFhXH9+/XXX/0tqtfYunUrZs+ejeLiYtN5/PTTT5g9e7biuQ4dOuChhx6yTKZBgwZh0KBBhmUkCKKOMNpGhSAIM2zfvl3y97/+9S+sX78ev/zyi+R49+7d0axZM1+K5jPeeust/O1vf0NmZiY6dOhgKo+pU6figw8+gFJXvGfPHjRr1gydOnWyRKZDhw4BqHsmBEEYJ9LfAhAEEZz07dtX8nfr1q0RHh7udlxORUUF4uLivClao6Fnz56W5kfKEkF4Bk3PEQThNQYNGoTk5GRs3LgR/fv3R1xcHB5++GEAwDfffIPhw4fj8ssvR2xsLLp164a///3vKC8vd8tnx44dGDt2LFq2bIkmTZqgU6dOmD59uiTN8ePHMWHCBLRp0wYxMTHo1q0bPvjgA9Oyu1wuvPLKK+jatStiY2Nx2WWX4frrr8e7774LAJg9ezb+9re/AQA6duzoNh3Jc38PPfSQIKN4SvP06dMA3KfnPJVJaXrO4XDg5ZdfRrdu3dCkSRO0bNkSgwcPxtatW03XHUE0VsjSRBCEV8nJycEDDzyAZ555BnPmzEF4eN232vHjx3HnnXdi+vTpiI+Px5EjR/D6669j586dkim+VatWYezYsejWrRvefvtttG/fHqdPn8bq1auFNIcOHUL//v3Rvn17zJs3D4mJiVi1ahWmTZuGixcv4sUXXxTSDho0CBs2bFCcDhPzxhtvYPbs2fjHP/6B22+/HTU1NThy5IjgK/SXv/wFhYWFeP/997F8+XJcfvnlABqsOTz398ILL6C8vBzffvsttm3bJpRdn5fVMsmpra3FqFGjsGnTJkyfPh1DhgxBbW0ttm/fjqysLPTv31+zjggi5GAEQRAWMHHiRBYfHy85NnDgQAaArVu3TvNal8vFampq2IYNGxgAtnfvXuFcp06dWKdOnVhlZaXq9SNGjGBXXnkls9vtkuNTp05lTZo0YYWFhcKxIUOGsIiICN37GTNmDLvxxhs107z55psMAMvMzNRMp3V/U6ZMYWpd8VVXXcUmTpxomUwDBw5kAwcOFP7+4osvGAC2aNEizTwJgqiDpucIgvAqzZs3x5AhQ9yOnzp1ChMmTEBiYiIiIiIQFRWFgQMHAgAOHz4MADh27BhOnjyJRx55BE2aNFHMv6qqCuvWrcPvf/97xMXFoba2Vvh35513oqqqSuK0vm7dOtTW1urKfcstt2Dv3r2YPHkyVq1ahZKSEkP3zXN/RvFUJjk///wzmjRpIkyZEgShDU3PEQThVZSmmsrKynDbbbehSZMmeOWVV3DNNdcgLi4O2dnZGD9+PCorKwEA+fn5AIArr7xSNf+CggLU1tbi/fffx/vvv6+Y5uLFi4blnjVrFuLj47FkyRJ8+OGHiIiIwO23347XX38dvXv31ryW9/58KZMS+fn5SEpKEqZMCYLQhpQmgiC8SlhYmNuxX375BefPn8evv/4qWF8AuMUWat26NQDg7Nmzqvk3b94cERERSElJwZQpUxTTdOzY0bDckZGRePrpp/H000+juLgYa9euxXPPPYcRI0YgOztbcwUg7/35UiYlWrdujc2bN8PlcpHiRBAc0FtCEITPqVekYmJiJMc/+ugjyd/XXHMNOnXqhP/85z9wOByKecXFxWHw4MHYs2cPrr/+evTu3dvtX8uWLT2S97LLLsMf//hHTJkyBYWFhcLqtnr55ZYj3vvTysNqmZQYNWoUqqqqsHjxYkNlE0SoQpYmgiB8Tv/+/dG8eXM89thjePHFFxEVFYWlS5di7969bmk/+OADjB07Fn379sVTTz2F9u3bIysrC6tWrcLSpUsBAO+++y5uvfVW3HbbbXj88cfRoUMHlJaW4sSJE/jf//4nWY03dOhQbNiwQdevaezYsUhOTkbv3r3RunVrnDlzBu+88w6uuuoqdOnSBQDQo0cPofyJEyciKioKXbt2NXR/9Xm8/vrrGDVqFCIiInD99dcjOjraUpkSEhLc8rvvvvvw2Wef4bHHHsPRo0cxePBguFwu7NixA926dcO9996rWUcEEXL42xOdIIjGgdrqueuuu04x/datW1m/fv1YXFwca926NfvLX/7Cdu/ezQCwzz77TJJ227ZtbNSoUcxms7GYmBjWqVMn9tRTT0nSZGZmsocffphdccUVLCoqirVu3Zr179+fvfLKK24y8XR98+bNY/3792etWrVi0dHRrH379uyRRx5hp0+flqSbNWsWS0pKYuHh4QwAW79+vaH7czgc7C9/+Qtr3bo1CwsLk6x8k6+e81Qm+eo5xhirrKxk//znP1mXLl1YdHQ0a9myJRsyZAjbunWrbh0RRKhB26gQBEEQBEFwQD5NBEEQBEEQHJDSRBAEQRAEwQEpTQRBEARBEByQ0kQQBEEQBMEBKU0EQRAEQRAckNJEEARBEATBAQW3tBCXy4Xz588jISFBcesIgiAIgiACD8YYSktLdfdiJKXJQs6fP4927dr5WwyCIAiCIEyQnZ2tuUE4KU0WUr9NQXZ2Npo1a+ZnaQiCIAiC4KGkpATt2rVT3G5IDClNFlI/JdesWTNSmgiCIAgiyNBzrSFHcIIgCIIgCA5IaSIIgiAIguCAlCaCIAiCIAgOSGkiCIIgCILggJQmgiAIgiAIDkhpIgiCIAiC4ICUJoIgCIIgCA5IaSIIgiAIguCAlCaCIAiCIAgOSGkiCIIgCILggJQmgiAIgiAIDkhpIgiCIAiC4ICUJoIgiBCiutaFWqfL32IQRFBCShNBEESIUOt04ZY5a3Hr6+vBGPO3OAQRdET6WwCCIAjCN+TYq1BcUQOgBo5aF5pERfhbJIIIKsjSRBAEQRAEwQEpTQRBEARBEByQ0kQQBEEQBMEBKU0EQRAEQRAckNJEEARBEATBASlNBEEQBEEQHPhVaZo9ezbCwsIk/xITE4XzjDHMnj0bSUlJiI2NxaBBg3Dw4EFJHg6HA0888QRatWqF+Ph4jBs3DmfPnpWkKSoqQkpKCmw2G2w2G1JSUlBcXCxJk5WVhbFjxyI+Ph6tWrXCtGnTUF1d7bV7JwiC8CcUpokgjON3S9N1112HnJwc4d/+/fuFc2+88QbefvttLFiwALt27UJiYiLuuOMOlJaWCmmmT5+OFStWIDU1FZs3b0ZZWRnGjBkDp9MppJkwYQIyMjKQlpaGtLQ0ZGRkICUlRTjvdDoxevRolJeXY/PmzUhNTcWyZcswY8YM31QCQRAEQRCBD/MjL774IrvhhhsUz7lcLpaYmMhee+014VhVVRWz2Wzsww8/ZIwxVlxczKKiolhqaqqQ5ty5cyw8PJylpaUxxhg7dOgQA8C2b98upNm2bRsDwI4cOcIYY+ynn35i4eHh7Ny5c0Kar7/+msXExDC73c59P3a7nQEwdA1BEISvyCooZ1c9+yO76tkfWWV1rb/FIYiAgXf89rul6fjx40hKSkLHjh1x77334tSpUwCAzMxM5ObmYvjw4ULamJgYDBw4EFu3bgUApKeno6amRpImKSkJycnJQppt27bBZrOhT58+Qpq+ffvCZrNJ0iQnJyMpKUlIM2LECDgcDqSnp6vK7nA4UFJSIvlHEAQRDND0HEEYx69KU58+ffDFF19g1apVWLRoEXJzc9G/f38UFBQgNzcXANC2bVvJNW3bthXO5ebmIjo6Gs2bN9dM06ZNG7ey27RpI0kjL6d58+aIjo4W0igxd+5cwU/KZrOhXbt2BmuAIAiCIIhgwa9K06hRo/CHP/wBPXr0wLBhw7By5UoAwOeffy6kCQsLk1zDGHM7JkeeRim9mTRyZs2aBbvdLvzLzs7WlIsgCCJQ0OlGCYJQwO/Tc2Li4+PRo0cPHD9+XFhFJ7f05OXlCVahxMREVFdXo6ioSDPNhQsX3MrKz8+XpJGXU1RUhJqaGjcLlJiYmBg0a9ZM8o8gCCIYoOk5gjBOQClNDocDhw8fxuWXX46OHTsiMTERa9asEc5XV1djw4YN6N+/PwCgV69eiIqKkqTJycnBgQMHhDT9+vWD3W7Hzp07hTQ7duyA3W6XpDlw4ABycnKENKtXr0ZMTAx69erl1XsmCIIgCCI4iPRn4TNnzsTYsWPRvn175OXl4ZVXXkFJSQkmTpyIsLAwTJ8+HXPmzEGXLl3QpUsXzJkzB3FxcZgwYQIAwGaz4ZFHHsGMGTPQsmVLtGjRAjNnzhSm+wCgW7duGDlyJCZNmoSPPvoIAPDoo49izJgx6Nq1KwBg+PDh6N69O1JSUvDmm2+isLAQM2fOxKRJk8h6RBBEo4SBTE0EYRS/Kk1nz57Ffffdh4sXL6J169bo27cvtm/fjquuugoA8Mwzz6CyshKTJ09GUVER+vTpg9WrVyMhIUHIY/78+YiMjMTdd9+NyspKDB06FIsXL0ZERISQZunSpZg2bZqwym7cuHFYsGCBcD4iIgIrV67E5MmTMWDAAMTGxmLChAl46623fFQTRDDhqHUiIiwMkREBZaglCIIgvEwYYzSzbRUlJSWw2Wyw2+1koWqkOGqduOnlNWiVEIMNfxvsb3EIwhDZhRW47Y31AIBDL49AXLRfv5sJImDgHb/pjSEIAxy/UIbyaifKCyr8LQpBeAR9LhOEcWh+gSAIgiAIggNSmgiCIAiCIDggpYkgCCIEodk5gjAOKU0EQRAEQRAckNJEEAQRgtDCaYIwDilNBEEQBEEQHJDSRBAEQRAEwQEpTQRhEpreIIIZar0EYRxSmgiCIAiCIDggpYkgTEKGJoIgiNCClCaCIIgQhJR+gjAOKU0EQRAEQRAckNJEECahD3UiqKEGTBCGIaWJIAiCIAiCA1KaCIIgCIIgOCCliSBMQnGaiGCG0fwcQRiGlCaCIAiCIAgOSGkiCIIgCILggJQmgjAJTW4QwQzNLhOEcUhpIgiCIAiC4ICUJoIwCX2pE8EMNV+CMA4pTQRBEARBEByQ0kQQBEEQBMEBKU0EYRKKc0MEMxRnjCCMQ0oTQRAEQRAEB6Q0EQRBhCBkZyII45DSRBAmodkNgiCI0IKUJoIgCIIgCA5IaSIIgghByFJKEMYhpYkgCCJEIEWJIDyDlCaCIAiCIAgOSGkiCJPQVzsRzFCcMYIwDilNBEEQIQIpSgThGaQ0EQRBhCKkPxGEYUhpIgiT0Fc7QRBEaEFKE0EQRIhAfngE4RmkNBEEQYQgpD8RhHFIaSIIk9BXO0EQRGhBShNBEESIQHo+QXgGKU0EQRAhCFlKCcI4pDQRhElozCEIgggtSGkiCIIIEZjIvEQhMwjCOKQ0EYRJGM1vEARBhBSkNBEEQRAEQXBAShNBEESIILaNkqGUIIxDShNBmITGHIIgiNCClCaCIIgQgaxLBOEZpDQRBEGEIKQ/EYRxSGkiCJPQVztBEERoQUoTQRBEyCCK00RaP0EYhpQmgiAIgiAIDkhpIgiz0Ic6QRBESEFKE0EQRIggnpGj2TmCME7AKE1z585FWFgYpk+fLhxjjGH27NlISkpCbGwsBg0ahIMHD0quczgceOKJJ9CqVSvEx8dj3LhxOHv2rCRNUVERUlJSYLPZYLPZkJKSguLiYkmarKwsjB07FvHx8WjVqhWmTZuG6upqb90uQRAEQRBBRkAoTbt27cLHH3+M66+/XnL8jTfewNtvv40FCxZg165dSExMxB133IHS0lIhzfTp07FixQqkpqZi8+bNKCsrw5gxY+B0OoU0EyZMQEZGBtLS0pCWloaMjAykpKQI551OJ0aPHo3y8nJs3rwZqampWLZsGWbMmOH9myeCFtrwlCAIIsRgfqa0tJR16dKFrVmzhg0cOJA9+eSTjDHGXC4XS0xMZK+99pqQtqqqitlsNvbhhx8yxhgrLi5mUVFRLDU1VUhz7tw5Fh4eztLS0hhjjB06dIgBYNu3bxfSbNu2jQFgR44cYYwx9tNPP7Hw8HB27tw5Ic3XX3/NYmJimN1u574Xu93OABi6hggu9p8tZlc9+yO76tkfWVG5w9/iEIQhjuaWCO03q6Dc3+IQRMDAO3773dI0ZcoUjB49GsOGDZMcz8zMRG5uLoYPHy4ci4mJwcCBA7F161YAQHp6OmpqaiRpkpKSkJycLKTZtm0bbDYb+vTpI6Tp27cvbDabJE1ycjKSkpKENCNGjIDD4UB6erqq7A6HAyUlJZJ/ROhAPiEEQRChRaQ/C09NTcXu3buxa9cut3O5ubkAgLZt20qOt23bFmfOnBHSREdHo3nz5m5p6q/Pzc1FmzZt3PJv06aNJI28nObNmyM6OlpIo8TcuXPx0ksv6d0mQRBEwEFKP0EYx2+WpuzsbDz55JNYsmQJmjRpopouLCxM8jdjzO2YHHkapfRm0siZNWsW7Ha78C87O1tTLoIgCH9CihJBeIbflKb09HTk5eWhV69eiIyMRGRkJDZs2ID33nsPkZGRguVHbunJy8sTziUmJqK6uhpFRUWaaS5cuOBWfn5+viSNvJyioiLU1NS4WaDExMTEoFmzZpJ/ROhA4w9BEERo4TelaejQodi/fz8yMjKEf71798b999+PjIwMXH311UhMTMSaNWuEa6qrq7Fhwwb0798fANCrVy9ERUVJ0uTk5ODAgQNCmn79+sFut2Pnzp1Cmh07dsBut0vSHDhwADk5OUKa1atXIyYmBr169fJqPRBEKJKRXYwvt5+hrTz8CK3+JAjj+M2nKSEhAcnJyZJj8fHxaNmypXB8+vTpmDNnDrp06YIuXbpgzpw5iIuLw4QJEwAANpsNjzzyCGbMmIGWLVuiRYsWmDlzJnr06CE4lnfr1g0jR47EpEmT8NFHHwEAHn30UYwZMwZdu3YFAAwfPhzdu3dHSkoK3nzzTRQWFmLmzJmYNGkSWY8Iwgvc9cEWAEBisya4o7u6NZewFlKUCMIz/OoIrsczzzyDyspKTJ48GUVFRejTpw9Wr16NhIQEIc38+fMRGRmJu+++G5WVlRg6dCgWL16MiIgIIc3SpUsxbdo0YZXduHHjsGDBAuF8REQEVq5cicmTJ2PAgAGIjY3FhAkT8NZbb/nuZomgg6wknnMir4yUJoIggoYwRj2/ZZSUlMBms8Fut5OFqpFy4JwdY97fDABI/8cwtGwa42eJgpMOf18JAHh25LV4fFAnP0sTOhzJLcHIdzYBAH6dOQgdWsX7WSKCCAx4x2+/x2kiCIIgfAN9IhOEZ5DSRBAmofGHCGao/RKEcUhpIgiCIAiC4ICUJoIwCU11EMEGtVmC8AxSmgiCIEIQWgNEEMYhpYkgDEDjjLXo7IhEWAzFaSIIzyCliSBMQgMQQRBEaEFKE0EQRAhCKj9BGIeUJoIwAFmXiGCGppcJwjNIaSIIs9AARAQxpEARhHFIaSIIA9BAQxAEEbqQ0kQQBEEQBMEBKU0EYRIyOnkORRzwJ9SCCcIopDQRhAFomCEIgghdSGkiCJOQfxMRbIjbLLVfgjAOKU0EQRAEQRAckNJEEAag/boIgiBCF1KaCMIkFOiSCDbEbZZaL0EYh5QmgjCAvwaaTzadwsA31yPHXuknCbwDbdhLEEQwQUoTQQQBr6w8jDMFFXgz7ai/RSEIgghZSGkiCJP4w72pxkWTKoR5aPUcQXgGKU0EYQB/DzQ0m0UQBOE/SGkiCIIIQWghA0EYh5QmgjAJDTlEsEFtliA8g5QmgjAEDTsEQRChCilNBGESCnRJBDPUfAnCOKQ0EUQQQXGNCE8gRZ8gPIOUJoIwAI05BEEQoQspTQRhElKgiGBD3GSp/RKEcUhpClLSDuTizVVHyNzuY6i2rSWMIk8RBBFERPpbAMIcjy1JBwDc2K457uje1s/SEL6CVAzCKihOE0EYhyxNQU5eaZW/RSAIIkggwzRBeAYpTQRhABp0CIIgQhdSmgiCIEIQ+gAgCOOQ0kQQJvHHoBPWyAI1NbLbCQJIUyIITyCliSAMQKsVCYIgQhdSmgjCJLT6iCCIxs7Wkxcx9avdyC91+FuUgIBCDhCEAUJFTWKM4eD5EnRoFY+mMdRNNBbIUEoYZcKiHQDq2s4H99/kZ2n8D1maCCKI8JUL0LrDeRjz/mbc+e4mH5VI+BpSoAgjnC2u9LcIAQEpTQRhksY86Pxv33kAQFZhhZ8lIQiCCBxIaSIIA/hdUaLVZoQH+Lv5EkSwQ0oTQRBECEILGQjCOKQ0EYRJaMghCIIILUhpIggD0Nc5Ecz4fXqZIIIcUpoIIogII6cmwiJIgSII45DSRBBGEA00FB2cIAgitCCliSAIIkQQK/qk8hOEcUhpIgiT0KBDEAQRWpDSRBAG8LeiFEYuTQRBEH6DlCaCIIgQQaz0k08eQRiHlCaCMAmNOQRBEKEFKU0EYQBSlKwljOYbfQq1X4LwDFKaCCKIIBWDsArSnwjCOKQ0EYRpaNghCIIIJfyqNC1cuBDXX389mjVrhmbNmqFfv374+eefhfOMMcyePRtJSUmIjY3FoEGDcPDgQUkeDocDTzzxBFq1aoX4+HiMGzcOZ8+elaQpKipCSkoKbDYbbDYbUlJSUFxcLEmTlZWFsWPHIj4+Hq1atcK0adNQXV3ttXsnghN/b6NCs1mEJ4jbL03VBQ+MMVRU1/pbDAJ+VpquvPJKvPbaa/jtt9/w22+/YciQIfjd734nKEZvvPEG3n77bSxYsAC7du1CYmIi7rjjDpSWlgp5TJ8+HStWrEBqaio2b96MsrIyjBkzBk6nU0gzYcIEZGRkIC0tDWlpacjIyEBKSopw3ul0YvTo0SgvL8fmzZuRmpqKZcuWYcaMGb6rDCJgqK514d21x5GRXexvUbxGfqkDLheNmgQRDDy8eBe6/3MVsgsr/C1KyONXpWns2LG48847cc011+Caa67Bq6++iqZNm2L79u1gjOGdd97B888/j/HjxyM5ORmff/45Kioq8NVXXwEA7HY7Pv30U8ybNw/Dhg1Dz549sWTJEuzfvx9r164FABw+fBhpaWn45JNP0K9fP/Tr1w+LFi3Cjz/+iKNHjwIAVq9ejUOHDmHJkiXo2bMnhg0bhnnz5mHRokUoKSnxW/0Q/uE/WzIxf+0x3PXBFrdzjCn/Dia2nSzAza+uxV+XpPtbFIIgOFh/NB8A8G36WZ2UhLcJGJ8mp9OJ1NRUlJeXo1+/fsjMzERubi6GDx8upImJicHAgQOxdetWAEB6ejpqamokaZKSkpCcnCyk2bZtG2w2G/r06SOk6du3L2w2myRNcnIykpKShDQjRoyAw+FAerr6wOJwOFBSUiL5RwQ/R3Ia93P8ZNMpAMCaQxf8LAnhc5jqHwRBcOB3pWn//v1o2rQpYmJi8Nhjj2HFihXo3r07cnNzAQBt27aVpG/btq1wLjc3F9HR0WjevLlmmjZt2riV26ZNG0kaeTnNmzdHdHS0kEaJuXPnCn5SNpsN7dq1M3j3RDDjjyEnjNbPEUTIQmqu//G70tS1a1dkZGRg+/btePzxxzFx4kQcOnRIOC+P48IY043tIk+jlN5MGjmzZs2C3W4X/mVnZ2vKRQQHWs+cOi2CIIjQxe9KU3R0NDp37ozevXtj7ty5uOGGG/Duu+8iMTERANwsPXl5eYJVKDExEdXV1SgqKtJMc+GC+zREfn6+JI28nKKiItTU1LhZoMTExMQIK//q//maYPWrCWRoewmisSLdRsVvYhBmoYfmd0wpTdOmTcN7773ndnzBggWYPn26RwIxxuBwONCxY0ckJiZizZo1wrnq6mps2LAB/fv3BwD06tULUVFRkjQ5OTk4cOCAkKZfv36w2+3YuXOnkGbHjh2w2+2SNAcOHEBOTo6QZvXq1YiJiUGvXr08uh+i8UL9l+fQZCNB8BOIXc6s5fvxZOqekPnYNKU0LVu2DAMGDHA73r9/f3z77bfc+Tz33HPYtGkTTp8+jf379+P555/Hr7/+ivvvvx9hYWGYPn065syZgxUrVuDAgQN46KGHEBcXhwkTJgAAbDYbHnnkEcyYMQPr1q3Dnj178MADD6BHjx4YNmwYAKBbt24YOXIkJk2ahO3bt2P79u2YNGkSxowZg65duwIAhg8fju7duyMlJQV79uzBunXrMHPmTEyaNMkv1iMicPF3x0Bxmgir2JFZSGEnCI+oqnHi651Z+D7jPM7bq/wtjk+INHNRQUEBbDab2/FmzZrh4sWL3PlcuHABKSkpyMnJgc1mw/XXX4+0tDTccccdAIBnnnkGlZWVmDx5MoqKitCnTx+sXr0aCQkJQh7z589HZGQk7r77blRWVmLo0KFYvHgxIiIihDRLly7FtGnThFV248aNw4IFC4TzERERWLlyJSZPnowBAwYgNjYWEyZMwFtvvWW4bgiC0MbfimcoI676N1cdRcv4aNx7S3v/CUQYIpBfHaczgIWzEFNKU+fOnZGWloapU6dKjv/888+4+uqrufP59NNPNc+HhYVh9uzZmD17tmqaJk2a4P3338f777+vmqZFixZYsmSJZlnt27fHjz/+qJmGICQ+IQFpLCcIfr7LOEdKE2EJodIfmlKann76aUydOhX5+fkYMmQIAGDdunWYN28e3nnnHSvlIwiikRHIX8sEEcgEmmISiu4CppSmhx9+GA6HA6+++ir+9a9/AQA6dOiAhQsX4sEHH7RUQEKbUGy0oUCOvRIn8spwW5fWkuON4XkHVrcfWgTaoEsYI5A/OAJZNisxpTQBwOOPP47HH38c+fn5iI2NRdOmTa2UiyD8hua776NtVPrN/QUA8PnDt2DgNa11UhMEQfieUFGUxHgcp6l169akMBGEl9h+qsDfIlgOOYIThDnozfE/3Jamm266CevWrUPz5s3Rs2dPzajJu3fvtkQ4gghkaOwngg1qs4S3CJWmxa00/e53v0NMTIzwW28rE4LwJrn2KlwWF4UmURH6iS3E/z4hwf/eiWuQuhGC4CfQlN5Ak8cXcCtNL774ovBbKwQAQXibE3mlGPb2RrRrEYtNzwzxtziabDqej83HL2LmiK6IivD7rkUBQSh2tIECVX1w4/+PNimBJo8vMNWLX3311SgocPe1KC4uNhSniSDMkHagbp/A7MJKr+TPO6jzdBgpn+7ERxtP4eudWR5KVQdZZgiCCERCxVfRlNJ0+vRpOJ1Ot+MOhwNnz571WCiCCFTM9gtni8wpeI2xHwrFr1OCsIQAe3UaY/+kh6GQAz/88IPwe9WqVZKtVJxOJ9atW4eOHTtaJx1B+AGy5hCNFbk1IBQHPcI7hEpTMqQ03XXXXQDqtjeZOHGi5FxUVBQ6dOiAefPmWSYcQfgDrYGE+ShOUz2NUYET11sjvD2C8BoMwLrDF/DxxlN46083oF2LOL/LI/wOEa3JkNLkcrkAAB07dsSuXbvQqlUrrwhFEIQypGQQVtIYlfLGDGMMj3z+GwDg2WX78NWkvn6XJ9Qw5dOUmZnppjAVFxdbIQ9BEAThJUJviGu8FJZXW5rfkdwSTF6ajhN5pSZzCI3WZUppev311/HNN98If//pT39CixYtcMUVV2Dv3r2WCUcQgUZodAveJQQ/TgnCErz57oz/91b8tD8X93+yg18e74kTsJhSmj766CO0a9cOALBmzRqsXbsWaWlpGDVqFP72t79ZKiBBNAZC0YxNEETwUFFdtyL+QonD1PWh0sWZ2rA3JydHUJp+/PFH3H333Rg+fDg6dOiAPn36WCogQQQq/ugkGoMPCoUc8CNU9UFNoD2+UFGUxJiyNDVv3hzZ2dkAgLS0NAwbNgxA3de0UvwmwnuEYqP1J2YtRma3HWqMz7cx3hNB+IKAe3cCTR4fYMrSNH78eEyYMAFdunRBQUEBRo0aBQDIyMhA586dLRWQIHyNN/oBmp4jCKIxEyo9nCmlaf78+ejQoQOys7PxxhtvoGnTpgDqpu0mT55sqYAEEUhI4pL4oJtoDNNxckKlcw1EaGo0uAm05xdo8vgCU0pTVFQUZs6c6XZ8+vTpnspDEH7HG3oKTc8RBNHY8HWw30CAW2n64YcfMGrUKERFRUm2U1Fi3LhxHgtGEP4ikKfnwhpBeEuaqiQIc9Cr43+4laa77roLubm5aNOmjbCdihJhYWHkDE40WmgbFc+hft9/0KBLWEkoNidupal+CxX5b4Ig9DE7PdfYoXrxL6REEVYRKv5NpkIOfPHFF3A43ANgVVdX44svvvBYKIJobNCUVANUFQRhjkDrR8TyBJhoXsOU0vTnP/8Zdrvd7XhpaSn+/Oc/eywUQQQuTOGXF0uTFUKGGcITqD0RViJZTUxKkzqMMUWz+tmzZ2Gz2TwWitAm0L425JwpKMe81Uct31DSV3ijfmkaSkRgN1+CCFisfnUctU78fdk+pB3I8TivUJmeMxRyoGfPnggLC0NYWBiGDh2KyMiGy51OJzIzMzFy5EjLhSSkiMf0QByL7/pgC4oqanDwfAn+89DN/hbHaxhRrsxHEjd1GUEQjRCrv+eWbM9C6q5spO7K1k2r1BVRyAEd6lfNZWRkYMSIEUJQSwCIjo5Ghw4d8Ic//MFSAYngo6iiBgCwK7PQz5JYj1bHcCKvDLtOF+Lu3u0QEW6NttMYOyLxFykphb5F3pwaY/si+MkrreJOS02lDkNK04svvggA6NChA+655x40adLEK0IR2lDjDUyGvb0BQN1ANKFPe8k5q6bnGoOOQQM1QZjD8ikwD7MTyxMq77Upn6aJEyeSwkSEJEzlt5iM7CL362h6jggA5O2Q2hfhEeLpuRD5nDe1jYrT6cT8+fPx3//+F1lZWaiuljr8FhY2vmmZQCLQHcFDHW8+nsbgUE6tN3CgriS4sPx5WdidhEpbMmVpeumll/D222/j7rvvht1ux9NPP43x48cjPDwcs2fPtlhEggh+GoOyQxCNndSdWRj7/mbklfD7+viSQNNLeCzvjQ1TStPSpUuxaNEizJw5E5GRkbjvvvvwySef4J///Ce2b99utYyEjFBpnP5Cq37NrhYxax1sjF9vZCn1H/KaJ11eyt+X78f+c3a8nnbU36IEBdL+MDTea1NKU25uLnr06AEAaNq0qRDocsyYMVi5cqV10hFEEEIDkTah0bUSwUxlTa2/RVAkkPWSABbNUkwpTVdeeSVycuqCYXXu3BmrV68GAOzatQsxMTHWSUcoEsgvTmPAU51H6fmYnZ4LCwudLziCIIIL6eq50OinTClNv//977Fu3ToAwJNPPokXXngBXbp0wYMPPoiHH37YUgEJwtdoTs+ZnMX/eOMpuFzGOxXGGp+SHIoB8QIFeX1T/QcbgRVyQJJViLQlU6vnXnvtNeH3H//4R7Rr1w5btmxB586dMW7cOMuEI5QJlaWdjY1fj+VhyLVtDV9HT5sgCCDwFBPJB5D/xPApppSmjRs3on///sI2Kn369EGfPn1QW1uLjRs34vbbb7dUSEKdQHuJGjueWElKq4z7SehNz9U4XaiodsIWG2U4b39BSn/gQP53gc3KfTlo2sTUMM2HgeevuI2K+HeIvNamnsbgwYORk5ODNm3aSI7b7XYMHjwYTqfTEuEIZUKlcQYrVj4evV3pR76zESfzy7HjuaFo2yxIAs6G4IqbwIHqO1i4UFKFKV/tlhwL5NclVN5lUz5NjDFFx9aCggLEx8d7LBRBEA1odUUn88sBAL8ezfONMAQRAgTC+F9UUa2fyFM83UZFVFEBUGU+wZClafz48QDqVgI99NBDkpVyTqcT+/btQ//+/a2VkCACCH8Ec+PpwMOCaFe6UAyIF6gEgnJAKBOuYJjw59S2Xsmh0pYMKU02mw1AnXaZkJCA2NhY4Vx0dDT69u2LSZMmWSshQYQ45ANEWEWoDGyeEgi+XuEKMgTaNipSR/DQaFyGlKbPPvsMANC6dWvMnj0bcXFxAIDTp0/ju+++Q7du3dCqVSvrpSQk+KvjyyqowMebTmLSbVfjqpaNeBo2gN79Okdw0d9BZFFSg0IOBA6BoBwQygTd1ksh8i6b8mnas2cPvvjiCwBAcXEx+vbti3nz5uGuu+7CwoULLRWQCBwe+HQHlmzPwgOf7vC3KH5DMofvg06iMSoVofJFGgw0xvZlBYFQLz5ZrWZlnCbrsgpoTCtNt912GwDg22+/Rdu2bXHmzBl88cUXeO+99ywVkHDHX4NOVmEFACC7sNIv5RPWEEgfsKHS0QYKVN/Bg5JPU6ARilZjU0pTRUUFEhISAACrV6/G+PHjER4ejr59++LMmTOWCkgQoYx8es4K/N25+bt8ooEgGJf9QiDUi6IjuHiq3goZPfVpEm+jEiIquSmlqXPnzvjuu++QnZ2NVatWYfjw4QCAvLw8NGvWzFIBCXeMDDouF8OBc3bUOl3eEyhE8UVcEsaknZFqRxkAnbwZQiW2C0EYReldl+71ZkEhBvLQ62JM7BIVlJhSmv75z39i5syZ6NChA/r06YN+/foBqLM69ezZ01IBCc94Z91xjHl/M2Yt3+9vUYIGT7+YSA/QhqrHf1Db5CMQ6ikQrF16SKfnAqDSfIAppemPf/wjsrKy8NtvvyEtLU04PnToUMyfP98y4QhljDTN99YdBwD8X/pZ7wgTYvhjryXp6rngJ1Q6V1+x8NeTuOPtDSgsNx4MkR5F4KL4bKyenrOQUGlKppQmAEhMTETPnj0RHt6QxS233IJrr73WEsEI31BZ7cQH60/g+IVSf4tCqGB5aJYA62wJz3g97QiO55Xhww0n/S1KoyEY3hFLFF6PfZrU/mi8mFaaCP9h5Zf6u+uO481VR3HH/I2W5RnqWN3hWm2Z8bd1wd/lN1aqa/X9FuVTz8GgHBANWP7qWLqNSmi82KQ0hTgZ2UX+FiGo4HHEtHy1G0caq8c+X42lpED5F6p/ZQKhXpRkECspgabw8tZZTZAvSiKlKQgJgPeZ8BOB1lESwUUgKAMEH3qWm0B4lkaDbb6edgRdnv8Zh3NKvCaTtyGlKQixPFYHEbB4I05TIBEqJn0iuAiEflXR0uR7MbjL5pFt4a91fndvrjrqsTz+wq9K09y5c3HzzTcjISEBbdq0wV133YWjR6WVyRjD7NmzkZSUhNjYWAwaNAgHDx6UpHE4HHjiiSfQqlUrxMfHY9y4cTh7VrparKioCCkpKbDZbLDZbEhJSUFxcbEkTVZWFsaOHYv4+Hi0atUK06ZNQ3W18RUpRHCjpaT4eoNKxmB5T8kzIHjzzhqzEkgQVhEMrwmFHPAxGzZswJQpU7B9+3asWbMGtbW1GD58OMrLy4U0b7zxBt5++20sWLAAu3btQmJiIu644w6Uljas9po+fTpWrFiB1NRUbN68GWVlZRgzZgycTqeQZsKECcjIyEBaWhrS0tKQkZGBlJQU4bzT6cTo0aNRXl6OzZs3IzU1FcuWLcOMGTN8UxlGCI22aYhAemGtVqYsz8/fjuBWB+gLQs4XV2Llvhy4LIwIaEYZDgSLSiASqO0y8ORiCr8aN5H+LFwc4wkAPvvsM7Rp0wbp6em4/fbbwRjDO++8g+effx7jx48HAHz++edo27YtvvrqK/z1r3+F3W7Hp59+ii+//BLDhg0DACxZsgTt2rXD2rVrMWLECBw+fBhpaWnYvn07+vTpAwBYtGgR+vXrh6NHj6Jr165YvXo1Dh06hOzsbCQlJQEA5s2bh4ceegivvvoqRToPYFbsOYtXVx7Gxw/2xk3tm3ucn9ZA4o9OSzod2/hGuYIyB347U4Sh17ZBZERoeAwMeP0XMAa8Nr4H7r2lvd/kCLxBmKhH6UMwkB9XqLSlgOqh7HY7AKBFixYAgMzMTOTm5grbtABATEwMBg4ciK1btwIA0tPTUVNTI0mTlJSE5ORkIc22bdtgs9kEhQkA+vbtC5vNJkmTnJwsKEwAMGLECDgcDqSnpyvK63A4UFJSIvnnC8gPRMpT3+zFxbJq/PVL5edkFO6XP4AeQzApU/IAob/7YAv++mU6Fm3K9JtMvqa+DracLLA8z8bEibxSfLLpFBy1Tv3EFhIIr1OgPU6lKpG2uUCT2DsEjNLEGMPTTz+NW2+9FcnJyQCA3NxcAEDbtm0ladu2bSucy83NRXR0NJo3b66Zpk2bNm5ltmnTRpJGXk7z5s0RHR0tpJEzd+5cwUfKZrOhXbt2Rm+bsJBAmqKzksZ5Vw2cLaoEAKQdVH7PCOuQvyP1ykGOvRJfbj+DiupaP0ilzrC3N+KVlYfx8YZT/hbF5+iFHAgExNLQ3nM+ZurUqdi3bx++/vprt3Pyr2jGmO6XtTyNUnozacTMmjULdrtd+Jedna0pk1UE2HsTUjCV314t0+IH7u+vaKPLlBszAWDQAAD8/oOteOG7A5jz02F/i6JIRnaxT8sL9XZphlCps4BQmp544gn88MMPWL9+Pa688krheGJiIgC4WXry8vIEq1BiYiKqq6tRVFSkmebChQtu5ebn50vSyMspKipCTU2NmwWqnpiYGDRr1kzyz9eESkMNZcSP2IpB1t9tJtC+lgkgt6QKALD+SL6fJSEaCHyfJl+vJg4E/Ko0McYwdepULF++HL/88gs6duwoOd+xY0ckJiZizZo1wrHq6mps2LAB/fv3BwD06tULUVFRkjQ5OTk4cOCAkKZfv36w2+3YuXOnkGbHjh2w2+2SNAcOHEBOTo6QZvXq1YiJiUGvXr2sv3kPCI2mSYQCodLReoMapwu1HkZXJv01cNHbsDfQCJW25NfVc1OmTMFXX32F77//HgkJCYKlx2azITY2FmFhYZg+fTrmzJmDLl26oEuXLpgzZw7i4uIwYcIEIe0jjzyCGTNmoGXLlmjRogVmzpyJHj16CKvpunXrhpEjR2LSpEn46KOPAACPPvooxowZg65duwIAhg8fju7duyMlJQVvvvkmCgsLMXPmTEyaNIlWzhECkr2WfNRJyB2nlQimbVRCpG/lwuxUaa3ThX5z1yFStGG6v6ddGxOBUJfB8J4wCjngWxYuXAgAGDRokOT4Z599hoceeggA8Mwzz6CyshKTJ09GUVER+vTpg9WrVyMhIUFIP3/+fERGRuLuu+9GZWUlhg4disWLFyMiIkJIs3TpUkybNk1YZTdu3DgsWLBAOB8REYGVK1di8uTJGDBgAGJjYzFhwgS89dZbXrp789D0RoCj8njKHU5U1TjRJCpCOYFqdvrPOxA6eTNQUzZHXqkDF8s8D7wrbzfUt9QRCNWgHBG84aCj1olvdmVh4DVtkGhr4kPJRPKYDG4ZzO3Mr0oTT8WFhYVh9uzZmD17tmqaJk2a4P3338f777+vmqZFixZYsmSJZlnt27fHjz/+qCsT0bjRjAjuQb7PrdiPOT8dxoGXRhgUSPTTgs7G3wpWEPeXluPrRyGv+2B5Fv5us4HI6YIKPLtsP1onxGDX88P8LU7IEBCO4IQxgqSfa/SY8ccpcxhf0m318+YZKL3bxqgFByr0ZAIHpf5F6d3NL3X4QBplpJYmv4nhU0hpCnJCpJ0SlwjFvZ7q2Xe2GO+tO+7zQIehRKA2KV/LFQiWLeU4Tb6Xg5dQWdTh1+k5whyB/OL4E5/Ui5+3UeE5HuiYlXvcgi0AgMiIMEwe1NlCifyHryO56w1soTLw6REI71YgyKBHKO4jSZamYCdUWqoK/rS2mC3aqMxqK1Qk8ZsC4MvYDGae39HcUv1EBBfujuD+kYNwR3F6LsCUWpqeI4KCQHtx/MmS7Wf8LYJhjHYu6pYmc+3A3woWtd4G/K3rhspAR3gfV4g0JlKagpzQaKbqvPD9QeG3L5QBK+KSGL1ObdsR0+X7PSK48u+QxMdak159h/rjqMffHxaAdT5NL/3vICYs2g6nlzeHC5W2Q0pTMBIqrbORYtWUojcVjgAYM0KeHHslfth73uOo31oEgnIQiASqMm9GrM+2nMbWkwXYdrLAkvwk1/tjM04/Q47gQU6gvtzBjKfTn3pXG7Y0iaOQQ/l3MKFnrXO5GMLDaTQf8tYGVNY4UTC2O/48oKP+BSbwdtwml4vBxRgiI+j73CiWPwsvDxbB2h8ZhVpyECJumoG6K3ljxYol/9b5NJkq3i8cu1CK332wBRuOaW8Iu+7wBfSYvQppB3I10zUWwjRsepU1daEV9OrMCPptxtpGNX7hVtz6+npU13rPWtZY4Y3TxEukwoeI1qfJsQul2H7K3Tolkcfk6rkg6rrcIKUpyHHUujxaTaTVaRPewZMvPrVLjTxHf0zJPL4kHXuzizHxPzs1fZoe+fw3lFc78diSdN8KGMDwNpdAfJczsouRW1IVdCseg3naUro/ZsNvo9bb4fM34t6Pt/OXayj34IWUpiBE3olWVBuPMi3kFTJN3Rr8UVuNwdJUVFEj/A4muQMBK6tLd+o4QJ+Nr5WYQKgHZRm0BXv2230YOm8Dqi5ZKWtFzt/hCpVopU9TINSZLyCliSBM4u/Va41B4W0M9+AJPMoA7zSwmboMZotKY0fpaeo1hW9+y8api+XC9Hats+ECb7iVSf3AQ+NdJqUpCJE3ztBoqvoEy5eOblRmJn++yuZ288E1zV3nCWrO7IQ+3nxebo7g3iuKMIgVq2xrXQ2+ZEqWJit15mDpfz2FlKYgJFQaZyBiRd3rxsrhXNHkzWZATSxwUI4MbTIvH3YeRsq6WOZAQZn/Np6VEwgWOEVLk8E8xJamyHDrh3vpx1BoQEoTQcjg7utN9hJGHcHVQqGIO6z6Tj7HXoktJy5q5ufvASEU/SDU4HkUykEOvVNxVuYrjqWo1eYctU70fmUter2y1qsxqYzgq3ZZUlWDR7/4DSv35Xgl/xqRpUnpGVh6myHyMpPSFISERtMMXvQGHqviOCkF+O039xfc/8kOXcXJ1/h6Y9rGBG9kaCtWz1nqdM45iBaVNywSqLjkwBwqLPjlBFYfuoApX+12O2eFsiyOAu6NOE0hGNuSlKZQJxCXKfsbrfHdiD+OWgenPz3HVP/mjcCrF18lUDDT0YbIB62AlT5g8py8uWGvtQqYhZkFEPmlWlOSnk/LiqfnlD6yPO39xc/F5eVtWgIFUpqCECtN6I3JKdcqYwZv9ZquO4OXqSU3Wz7P/XlTlW6sA6AZ+FbPea98d/85C/sWes66aNW3FfVXI5ruVCrLykcUIjoTKU2NjUPnS/DYl+k4kRdcweS8wYZj+ci8WG5pnoai3qqk1TOTy89KN+n1fPVcQNEobsK7mFl6Hgg0pg8yb2G0how+d+n0nMHCuGjI9OUfD/FfFcRNg5SmIESrwf1h4VakHczFA5/s9J1AAcjurCJM/M9ODH7rV6+VYXbHeOP9hco0n+zvvJKqhnMahZi1yJVU1WDW8v0eT/3RYNoA1/S4km+Ll6ycNKXmW7TqyIrqq3GKP7K84NMUgs+YlKZGgLjbrd+vKlc0gIYi+7KL/S2CKrqO4lodqWTlmTThsLc3eCKWRpl15cxbdRRf78wytLWCbt6W5dR4sXoPMp3CrMvKwrwa6zoCrSpSdAQ3mL/Y0qR0rb+qNZjfe1KaiEZJIIwpqo7gumXIHcH5ZCmpMr+dDg+nCyoMpd+ZWYg9WUVux1UH01D8bOXAisHTW6zYcxbrj+QpnjNjDQu1JqDt06SkLBurIHHIASVHbU+rO8QeFwAg0t8CEP6lMa2e83WHa3p6zgNHcMlvk1Nwhstnxr/07RU1uPujbQCAk3PulObngSyNDS5HcKVjJitONxq9gbyyCyvw1Dd7AQCnXxvtnhdnZpI6CLH2oGlpsiB/vdVzhHHI0hSE0NYHgY2uJcng9Jy6pSlwncILK6qF307e3rqxzsF4iLdXPZklT3O5PL+MagsdAhXGGDYcy8f54kpL8vIm4m1UrAiX4p7OsEhBD1maQpxg6KTM4Mnwq1kjBnoJq7Y/UVWOTA42Rq0bZlqIVhG0D50xvLl6zj1Ok3UDq5lgisEwCP96NB9/XrwLgLKFzQi8/otmqXWq9B06cIddCYYHZjFkaQpCaKDRxxc1ZNQ3SThukXBGnUj9hdXttTEZpMzHaTI5PadjxTTks2egLN5nFkDNVpVtFgaO1V495/kCAImlyYjSZKyYkIKUJoLwMbp+JZoDm/KXo9VKksTNxMPMtQbmQFLuAhVvWpo8QTcCtAkZQ81yodkXKC4AMFY/Up8mL0zPmbwumCGlKQgJgXYZMLhtaWLoWmPHG8qQlWmxT5Mnjuhm4PXRIlTwoU+TkWejrzPxDrzi6drQwttxmmp19p5TMwCadRoPBWdzUppCiOmpe1BQpu282VjwxcBs9qvKqK+HmnIkdaANLMTTMd7YKLTxoD9vxWtpMrsSzyzSPRHNTyV502Ia6Bj2bzQ8PaetkKplx63wun0Q8V4XvA+alKYgxGxz+y7jPF76nzTUfWMKOeCNvee8YSXRtTTxDjaSa6x1BFeSx2z9atlJgrfr9B3KcZq8U3NG8nXpKDu8yrKkHYdYi/CWI3j9u1qrs/ecp2W7WcW5SwheSGkKMbKLjAUoDFZ84SxsxBHWo3LUrEs+/lozUpxYGSdLkzp81iHPHYJ5MTK9IpZL6RnzZiVpxyHXVNRv2JPnXp9O4tPkUklsIaHwqpPSFIRYuhN56PVSuogHMk9qx+zqOf7Bxvg1ZjAV2VnD3yqUp2Pk8Oj28jrKtVdJBkO1dDx5eYJe++N9zuJzoeATI8aopcnou+iUhPdwR639cbcTWbpQ+EAipYlolHjy7mpZc3gUVqOO3u7Xy8vkuNaIhcDX/ZpcaVJNZ0ywqhonHl68C19uP2NKrGBBXC2/nS5E37nrcN8i6/b/kxbGn1Q8QFo1hRhqH3FaSgavLxtv/jP+m4HsQulMg8c+TcbEaRSQ0hSEhGJD5cHXyoBZ3ySjX9M8FitvDjZm6pV3es4TuZfuyMIvR/LwwncHTOehR1ZBBbacuOi1/HkQ19BXO7MAAGUOc/sMWtlOdNufCUvTzsxCzwULIow+DcPpRReUVNVi0he/Gb7ObHmNFVKaGgGh0FD9hWfTcyrHdR6Y1lm1gcqQv5GPff/lovHK+s2uLMXjRRU1WPjrSZzIK/VMMA5uf3M97v9kBzKyiz3Kx+liKCqvdjvOF9ySr8KseK6GttrQaX8uTqVefO7J1Azu8v2Fla+PtjKprYjypJcfOZLL986YcRGou67xD0akNAUhpCTp4y3FwIqq18uDd8Welk/JgvUnMOO/e42KpsiOzEJMWLQdpy6Wmbpe089BfEr20J5dtl/xko3H8vF62hF8vTPblDxm2H+22KPr7/14G3r+a41PFD1fIXYs1pue07Ku+t5CzDD1q9145ltz74eV4mp+ICke01A+rZjPEy4zd10o+KSR0tQoMN9SG1PIATHeWLmmcFbnWnOO4FqlqP1WYtnuszhnwaaiE/+zE1tPFiC7kD8vuRVCL6ZPwOOhFr7rdBEAYNnuc9JseeI0ecl529OyJD5NOiu9tJ65r52Hc+xV+HFfDv7721lUVjt9WrYczXdBSRE1lty0EsPvB27c5zPYIaUpKFF3FDaeU2A18nJHLe7/ZDu+3HZaNY0v30ve6NzK16ocN7h8Ti29niMuANTUuq8z9kX9aTrTq/y2mq0nLmLDsXxL8rJ2SsbYXUuUCosrzG3DXgPX6sVp4n3Ovu6BIsIbbrpa4f3Qw5+fmZr+gUrTc6YtTebSBdZo4h1IaWoEPLNsHz7fetrfYljC59tOY8uJArzw/UF/i+I1jHYsaoMTjyN4LcenJmMM5SYdi9WQWiF8T3WtCxM+2YGJ/9mJkqoaj/OrVy7eXXscQ976FYUK/klqVNVIrRmeWBqtxrNVpuLpN+0B21tBHM0gVpoctf62NGmcU7LeaeXFeYwLsxYqH8SC8jekNAUh8hftVH45XvyhcSgZFQ79ToznffasHzbe2RvZRsJ4SAJVm5Vunk4FpUluXXhuxX5c9+Iq7PXQ2VlZMvcBlXfaxhOqRZGQS6usUwjnrz2GUxfLsWjTKe5r7v1YGh5Aw41LEUkdeWjm0Pen438eepYkaVYNf+zMLMRdH2zBPsFPzLdak1guhwlLk5UY9VEyqnyan57ju9C9p2r8tiZSmoKQxtwsxV+Bavhz2lx9yk0hjZrSpOsLJf1bbRUSz6xNrU4YYMaY4FD9/i8nNNMagTew4aJNmZaV6U3CZdqNeHsKPeQr7/R8eL7cfgbvrTsu/K2ie/gd3ThNKm3g7o+2ISO7GPddUiZ97gguqsSAtjTpTHm6n/P99JxV1wUTkf4WgCDE8ChNaojHNV/4HZj1zTE8PaOqfOknUrI0qWHtikP1qRtff41acVu+WmYOwD3ulA+ry6xPk3KYJu0p2vJLTth8ZVr4BEQFVtX419JkNLilVuOxdMEAbzoNf0WzlFbV4L5F2zEq+XJMGdzZghythSxNQYiVL0egrZ7jsjT5cBTxhpO9nqVBflbNEZhHNj2lyRtfhk4Xw87MIq+W4WvcHKYtahdc26hIBDFfbl1m1rUHic+S7uo5a8q0AvErEciWJiWMhm4wbzEyNz1nxUrIz7eexoFzJXhz1VGP8/IGZGkiAgpPpue8uMhIVIZyzpb6NGn4AEmO63zJAwYtTdwptfnP5ky8+tNh4W83+X0wSFrtKyX/uPAkd+OWRh80bBOIxVJqZhJDlKZFxcc+TeLpOT9bmjSn25T6FC0fKIVzZpUYs0/EitfO335mepClKQixdCuEQOqFAUT4Oly1DryOmt6sRekXvUr5KgLorZ4Tn7Wq6r/eKY3k7Ra2QVUYK9s1R3lGsHJ2SNNaoDRQBiZS/zrtAVtbObBSKn3Er0SVny1NWhWjdMpKHygtuJ8Jk/8ZqK3VOkhpIgKKcK7pOXVcfgpJa6TDMhimic/53KRPky+C0fnjkVh9W/JWadn0nExTVbTYSDRb8+XWlW0dYl94vQHeyGDv7TYpzt9bliaXi+Gdtcew+bj2voWWrp7jzIMH/tVzblqTxwT6dD5NzwUhnjYqxhge/M9O2GKjrBHIQiI8GBQKyqtx0ytrMHlQJ0SEm/8e4O3sJdcYWLlidH8v6SolZT8StRx54jTV4y3/Nt7pRm+VaYkjuIIZ7tWVh9CqaQz+OrCToby0/VJ0pmQ0rvW1jVb8gaL8TJXbqnsq9/bhTYOzWFZvWZp+2Hse76ytWwF5+rXRqum0X09jVkdJmw+rz9+01mQK2kaFaJSczC/DpuMX8eO+nIBr5BERDU1S9YtToyMorqjBnJ+OWC2Wshgmexa9OpfcHjPvGwUAf/l8l3ZZ+ll4DHcbs3CktHrqVC7Z8bxSLNqUibk/G29r2luKKKU3XIRPcKoo8w3HxH+o5+NmaVJOZUQ0TeqVGcB7lqaswgqv5KvtG2ZhObzpfDg9V1Xj9IllXA9SmoIQT9uN3lJhfyL2aapxWtN9evKiuU2VcSgw9eWpT6vpTJlp+AAZnfJTq0MlvPd1L78f7zc6K0pQ+nKvp8KDPcs0rQU6q9A8np6zsOrFU7+KU0Mqv93SyU56ey+6ZbvPCr/l0dp9jaYSZHR6TjG98gVrD13Af39T3/Da23GajOZ/+mI5rn0hDTP/b59xoSyGlKYQRNzv+nqzTD1EhibFwIxVNU5sO1lgKE9PrGkeKVxqCpaRPKD+jKywpvji8cvr3zdlel6IWClwDzngQbvQMG7oZhtAr6tecEuevREB5ek5X1Hl94jgxs5pfnAYULL+8sVveObbfThTUK6SFd9D4LMSes6nmzMBSBVef0FKUxDi6Ze6eAAIPKWpoUkqWUmeTN2D384U6eYj3ffKO/f49ppjojL4rzM8MEqsWOLDnt+j1CnZVBb6ZfihiUmWw5vUmsWXWRVygDEd/x4D1gK3dCZlMovU0qRtITNyz77sk4yE5BDw2UpKg+kl/UHd//VuT20PRX5fTin+WojjS0hpCnEMzN74BImlSWGrilUHLxjO0/B0nsa14o7pVL7yV5pSWslx3ek5aVoeS5MVA423HMFdjEkcqX3R5KxQlMV1qqVQGi1LooyFyc8pKB+GctfGyg8Ip44jOPfqOY3rAhIL5TMav8rINKdaHmLU3vlAfwT+hJSmIMTzTkU0gAVYDyUWx8jKL948vYWxFXEG8mXq03Di3wa2QnPL39v4xdIk+m1WodTc4kKisPLnGRamN1BqlxVIiOtHWdkTW6LUcd+KQym1lxR6P1tGjJZudNsVs22Ht168sTI20GM9kdIU4gTa9Jz4Xa0xqwkAMsuGsXsUd88eOTaqXKuXJ+90BdMZtJTSaeKl6Tm3vecMtDkrNhw128SlPk3q03NG3yGt1HrKhxaGt2TxEEmcJl1LE/9g7009Ri6Hi1NGCX6cntNehag0Rap9TK1tme17A13hsQK/Kk0bN27E2LFjkZSUhLCwMHz33XeS84wxzJ49G0lJSYiNjcWgQYNw8OBBSRqHw4EnnngCrVq1Qnx8PMaNG4ezZ6XOYkVFRUhJSYHNZoPNZkNKSgqKi4slabKysjB27FjEx8ejVatWmDZtGqqrled7GxMKvtZ+RTxoGFn5JUfq02TwWtU/1POSH3a6GB5bkq4rm3Je0i90+XSdUplavhkBphcbtrSZgTcatXYeDb+1xkktGZWei5aDtFE/FiM4ap0oKLOuT9PdQ1HFQqqVru5v7zVY+eP4377zeDJ1j99W0WnHr+I7pnSuPl+z1ideXy+zCm8wK1d+VZrKy8txww03YMGCBYrn33jjDbz99ttYsGABdu3ahcTERNxxxx0oLS0V0kyfPh0rVqxAamoqNm/ejLKyMowZMwZOZ8NLMGHCBGRkZCAtLQ1paWnIyMhASkqKcN7pdGL06NEoLy/H5s2bkZqaimXLlmHGjBneu3kvwNvZiD+aA67piqfnPLA0SbL0w01uOJaH3VnFiueMT8+pWZoafmt1crzTTN6KOOCJNdOKvbNMT89prJ4TV5xW/vIvdibTghdvPY1vdom2nVFSmniE5eCOtzdiwfoTFuXG4dMkWcGglZO69cdq5O/SibwyfJ9xHou3nubvJyyUT+ujlddy1HDO/bfyc1FGvBkD7werLxXeQMGvEcFHjRqFUaNGKZ5jjOGdd97B888/j/HjxwMAPv/8c7Rt2xZfffUV/vrXv8Jut+PTTz/Fl19+iWHDhgEAlixZgnbt2mHt2rUYMWIEDh8+jLS0NGzfvh19+vQBACxatAj9+vXD0aNH0bVrV6xevRqHDh1CdnY2kpKSAADz5s3DQw89hFdffRXNmjXzQW3wo9YuzXQ2gdbIrbI0ifHkq4Z33zR5PVZWq/eGRr7Q5X+rSaNpadIsrQGlqNdW4Ha76pXofsh0me4D+tPfZCCzoBzfPtafa2NoiSO4SZVSyS9PfuTZZftxz83t3coU0lv0ilodcFHXp0nSbg1YQr3YJam9JoXl1X75gDRaprbFzr3NG9nENzwsTDhnalWhjnyNhYD1acrMzERubi6GDx8uHIuJicHAgQOxdetWAEB6ejpqamokaZKSkpCcnCyk2bZtG2w2m6AwAUDfvn1hs9kkaZKTkwWFCQBGjBgBh8OB9HTlKZZAxIwCFMg+TUpxmszgG0dw5d+6iXWTMtWOXmJp0rhJbedRsWLgHTxpY2YvVfKnWb7nHPZkFWN3VhFXHprbnUjSqSeUW0vDwow78wbqUCTZRkXhvOSd4KxLwLt9UiD7/vHA23bqfyu1YbXpYfE3E3/fK/uM46zDABt2DBGwe8/l5uYCANq2bSs53rZtW5w5c0ZIEx0djebNm7ulqb8+NzcXbdq0ccu/TZs2kjTycpo3b47o6GghjRIOhwMOh0P4u6SkhPf2PELtq417PplzWscfiO/NOkuTB9eaqFNAZ4m6Xl6yfNWmOSQDtwU+TV6L0+T2N/8TMT09pzEzxJulniWFJz95G66bbuUrkyd/f6K/jYry4CxH3nb98SFn5YbMSvmp4a2I4MKuBAbyqLOmGrM08fjkWUEg+UAFrKWpHrdVK4zpTiPI0yilN5NGzty5cwXncpvNhnbt2mnK5W34o7jydWb+QGJpssynyQNLh1tensnCkwfvMl7xYGPWEdwnVjjO+7ES3mjU3HnIzonz1LQ0KXyxa0+xKBzTSO9PJKvnFM5LVqZp5OOuVHsP41Y+c1iRl+6WOjJOi6J7M+H/2nmoZWc23EugtlUrCVilKTExEQDcLD15eXmCVSgxMRHV1dUoKirSTHPhgntAxPz8fEkaeTlFRUWoqalxs0CJmTVrFux2u/AvO1t9Lx8rUXtxuK0iot+BNj0nHmAti9NkSS7WFWL0q0k6eCsP5FrTc7wrdMJUjnuKljO7BMUPF5Nlin6bbeNSR2eZ4icqQauZ1sosTWFhelZBbYtNIKGvmGpbotSu9WafxPsB4b19GKWYtWAqce/H20UXq+ehtpOAWBZ5u1WVUf63lx6dtwLvmiFglaaOHTsiMTERa9asEY5VV1djw4YN6N+/PwCgV69eiIqKkqTJycnBgQMHhDT9+vWD3W7Hzp07hTQ7duyA3W6XpDlw4ABycnKENKtXr0ZMTAx69eqlKmNMTAyaNWsm+edPzE07eEkYk/BaT4ygtdeXYnqdaQfFa7hXCunXuZkpQa2B2NwCAePX8JZvJG/z03OeW5qk0x1aCdVPGY13o5RVgL2iAlpKZd0x0W+NfNyUei/eMK9/Hy+eKLRVNU4cu1DmlfKEkAMK6dUsgBKlyaQ/qTiPxVsysfaQ8R0cAh2/+jSVlZXhxImGJbCZmZnIyMhAixYt0L59e0yfPh1z5sxBly5d0KVLF8yZMwdxcXGYMGECAMBms+GRRx7BjBkz0LJlS7Ro0QIzZ85Ejx49hNV03bp1w8iRIzFp0iR89NFHAIBHH30UY8aMQdeuXQEAw4cPR/fu3ZGSkoI333wThYWFmDlzJiZNmuR3RUgJtdfGzCaLgTRXDHjHCqZ1jy4Xw7e7z6Jnu8vQpW2C6byMiGo0uKPaF71YFi2rHK/zq/dWz5l/jmavFFeHizE3GUqravDaz0cw9oYk9L26pWIeWnur8U/PKQ1axp6Vv6NWq+E05AiuZeKR/unN2zXj96mXTum1UXuT9p+1Y092EVL6XoVVB9X9ZdVkMCq/ch5q/UkDnvo07T9rx+z/HQIAnH5tNFdewYJflabffvsNgwcPFv5++umnAQATJ07E4sWL8cwzz6CyshKTJ09GUVER+vTpg9WrVyMhoWFwmz9/PiIjI3H33XejsrISQ4cOxeLFixERESGkWbp0KaZNmyasshs3bpwkNlRERARWrlyJyZMnY8CAAYiNjcWECRPw1ltvebsKLIX3hZJYmgIuuGXDb6v6Tq0O8Ie95/HMt/sAKL/cvDJI0unoHkbui0FDfs6B24zOYuW45WZpMiKHaYVLXckB6jZbXrojC0t3ZKl26rzviZaE8sFHzxE8mHya9KbneC11blM83rxjlaw/2ngKTw7rYlV2qoxdsBkA0DwuGlER2hM9yoqosWkzpTGBx71DSdlXUg7dQ7LU/X2hpIpHTG4C6ePer0rToEGDNDvFsLAwzJ49G7Nnz1ZN06RJE7z//vt4//33VdO0aNECS5Ys0ZSlffv2+PHHH3VlDgTUgx0a/zoQd3w/7D2PcTckKVzhO8xMjenmqXEuI7tYM71XVofo5OHu4+F+rriiGuuP5gnHtR3BtaYkGvCW14C8wzNiQTRrdZC3cXmZpy9qb7YsL9ttYNdRWEuralBYXq147vnv9muUGTxak5YlDuCPyq7V3q1Gq+2tPdzwPvEaXeveLQVfPFkauRX32IVS9OukbOGU5i0/xitXvRxKeeg/F6X+hKds7Q+CAG3IBgnYkAOEOmpNz9OQA9O+3oNBXVujWZMo88J5iNRPx6o8tRRzbRlMlaFzvRGlQc2Jevy/t+KUaODXcp3htmyII8Vb2MG5K54G8uZu09KBSa5omhmIpZYUdcVP6XZufnUtqmpceO++npLjh3NKsOVEgWqZQaQz6VrieCxNldVOd6Xai1qTVs6V1bWG8+MRVWkKLwxAhN4qcM5jytcyoWwleZT/aIDXF8/IR6UZX816yBGc8A6cjVLLEZx31YS34I2No4cBHUY7H4Pm8HrM7lUmL5OBKfoxnZJZSoz6yfgSTywJWg6tWmXIVxmaqQMtxUhLoQKAqpq6QWfriYuS45XV2nucefKofP2U9SxN8nasxKC31nttxZUSvO2Ae4DnqHW1Mr25QEMrHY8F0HxEcD6rdjBDSlMQovZCfLv7rPIJEQ3hy+rzkmbmbxOq1IHXfD7SZbVGrzVRnoJiYzZ/TQVA5WLT26iIToq/5qxsBfJBw8z0nL6iKbtO9KFc5wjOXaSAllLA207dfJp024bStIi1A71VSOI06ZhF1GS7UOJwf3aMT1E2g+W+fyrXiD+a1MKB6N3fWYVtb/hXz9WhuLBA0naV8+MN9yJPxTs9F8wzdaQ0NSL+9eMh3TTyr273Ac1qqYyhFkPEcD6SDls9HyWzr5GpNjN+B8ZWz/E9E7Mb9voC90FRLaG6wqB3B/J7lCuaZupA6rekLJe8LHe51PPUK7Mhf+Xf7ul8+5x191BU+e2Wzu3DTfp/q6iudeGTTZlcaXl9mlyM4ZHFu/DCdwdU06jdh957/d4v7psrc1eJoHgqnBLlouYCwb96Tv7eeYdAcgQnpSko8awBaTkC+n2AtcjS5I1VeFrlyAdprU43v8yBKV/txtaTFxXPa329mTGna379iXIM81J0SzeFxojSyHmNlm8FY+a6XGkcIum5k/kN06Na7dRdmdPGk21UvPXqHrtQqjitqOfXpTeFqZRO/LfVt7N0xxl8vPGU6nkz5e07a8e6I3n4cvsZ1bzUp+fU3wuPF/sI/9duT6qWJpO7MVgRcd1obDNfQ0pTCCJ5aQJg3ydJ+SJ5PPJp8mB6TpqP7G+1js5AGc+vOICV+3IwYdEO5TJlSi1PPZiddvDG43bLUnbA2PQc3wCqFUeJtw7FLN99FpuO56vmLy2LXyHQVf4Uj4kUW82rreeXIxcwfP5G/P7fW9zOOXXeVfGhJ1Mz8O9f3S0nAFDukCpkvIqyUY7mlnKn1SpabJ3mGeBfWXkY01P3aC4mkJepVj5vjdT3o3ohBzydnlPL2+wCm3JHLRZxWgP9Ba2eC0I87UuUlrArnfMHPPPtPPD6GOm93HoiNFiapOVZ1d8zJstbJV8rHMGlhibrGoL76ihDF9f9T0ccPT8wI1HhT+SV4en/7pUc03ovtGQzGqPKk8ja3mDZ7nMAgCMKCoeeQ7H82BtpRzF5UGe3dOUO6ao13ilZo8RFaw933BY98fSWyjXid+mrHVkAgKlDpHGg3NtsA+p+ULwyqqfn2RHCbHDLYxdKkWOvRGxUhChN3cpWnj5l03Fl63sgQUpTCCLumOUvp7+jD3tj7tqoAmNEBiWFZNXBC/jdjVcYK1RSvvyAvjxaX4ZaV6uds/IjX64kqSpxSnvPCf/XsdDIByCZ8m1EAc+1KwTmM2nJk79Pnvo0aV/r23dXfxsVPnnKZEoTr/O/UeKiIzTPq05Va10jm1LTiqofES49p2yFDFM8p3aNulzCL8081Pr7/2w2Z+2ZtbwuBtnDAzqKygMiwsw9z/o6DaSQA6Q0BQl7s4txscyBod3aeqxWaFlz/Dw7Z5mlSdwZaOWi9ypqTftoHV9/JE85IQfSvJjsa1BZAE1HcE4n8XBvbaMi/9vAY21YSaVXhkz5l/nb5CgpQgos3pKJz7edcTvOoK4EGLHy6d264qMSHdMMZaGTt9VIVs8pnOeVR640MQZsOp6PJ1MzhGNWNM24GB2liVNgtQG8XjlQQx6XSf6stWYA9I67pdNIL/6IUbNoned8X07lK++dd/C8vaE8xhBhUulhrO7ZB5IjOClNQcLvPqjzKfhlxkCP8gmDtPOXv7hqL5GvkMjmgT+g+C6MWs9MDeqyl/q8vdJQmVqy8Hyxm1UwxXUj7tMttTTp+HJowT1IaEx1/Ha6EPPWHBOlVc+0fr8sOS6X+pSroalRXZ8mBYuNeKpR61ovv7pHc0vRNbFhCys9R29eS5N8eq7MUYOUT3eqpObD6WL4ascZ9O7QAt0ur9s/NC5KW2kSY1Q5AaTKgdLl8k1wtVZ8em5pYqrpxXIYeRfliuvP+3MUV/jJ4f3wUSJwVKUGyBE8yMguqvTYsVnLcGF28M0urHDr/MzA46RoNB9PrtUajMXn5emMmJOrauQrk+RKhro89ZgNOeALJdkTvzmtL2ZpnuoWwS9kK5tMBRWEegeulZ1Rnya9yNpamHVW18lUYMQ7G1FQ5hD+1lpdqHZMiTKZI/jLPx42JKISy9LP4oXvD2LUu5uEY010lCYzNST9ANVWepT2IVT7W62NGnY1ULI0iQ5mFxr7uKuorsVP+3NQ5qjF59tOq5erIAOPtUiumPl7YZISpDSFIFovt5nO9WR+GW57Yz1uff0XS2Xz5H3hzUdvE0r96RSmmI5nOiEyPAzvrD2Ga19Iw67ThYppGOPrzLWsclqxgsTnJJYmKx3BPbA01VvCjAYM1Vq+baaNu5i63Jp7+xm8d2VLUwNmZ6msclXMEgVc1HUE5yxTvn3JXoX9II2y/5xdP5EckcD8Pk0Nv6UBVd3Tyv0Ojay6rIf3I6dBUXGnxoNdH/727T5MXrobT32ToSNAw0+PLE2BpzOR0hSMeOLwWTc91/C3FcEtNx6rW5pdVFFjWi6l8j0LOSD+7b03T3X1DEevGxYGvLP2OADgxe8PCsclli4wqQKgkpdWZ6rZOSvcwNYTF3Eop0T1GqNoKTTceehcomUR1LMW8pWvPj2nJZvb9LfOlLOyxYZPideWg/+uJc9H1ozFuehamjjL4xnDrRg89fo2c+1CnL+6og64W5o0P2ZUhOWP08TcZFKTgxfGgJX7cgAAaw5d4L5ObYUx37V16QLJEZyUphCDQXvJqRlFJSaS31dADy1/K4MZKf00eqmClUKeWC2dsUKrRaOpu5Khf73W6jmtKSJpBxqGs0UVmPDJDsNmey08Ucy1vpilCWVligpxWyFqcgQ2s4eYfIBy6jjqeaJ8mFWo5Ehk5rw3T3ya9OrEKqzypVPzPRL/VlJM5O+omk9Trr0Kvx7NhxLcU7X16RTSm1WajMDjnwXUtZFvdmUh45J1UU01IkdwwjTyvePMoPXimXmhoiOt073FpVtnaVJHySJkpFRPTM9hoqdZXas8cDCm/wUL6Dm7q5+TKxRZBe77XXmKu08Tf2Xx7kOmtUpN7wvfiBxKaG+jIleatAtXi+C8O6sIb6YdxbZTBerXWmRp4q0fvXg/vPn4apNwvTr4p8jaq4lKNrrTc04dR/BLf/adu061aO7pOQD5pQ4s33POXQ4fh5URPnwUZN94/CKeXVYXpuD0a6PdzgeiTxMpTUGA1fFXtB1G3Y+VO2pxrljd8hAlWmfraZwnl87XK3c+Ok6ZSghxViSWJv5yPEEcWdjMdJK2I7gsP3HdqKye00MvJo20fE6lRclaIfu/qjwaWXmitInzULU0ueRp1due3uuhdn78v7fqyqhVS0Zumbd+tDY01pNHLR9vYlUMOjVfLr2PG3efJul5HumMWJo+WK+8ss0nlibJ+3fpw0ch3fEL2lHaA1BnIqUpGNBbdWEUbadh98yHz9+oqTTFiCxNVbXue1QZko0p/zaK1qApRrIbuYshUivQCtwHB6veabGlSe6IrudwC2gPdFqds8QRXPiPPoyZj51jbMNifoVX7W/exQ5agyqDejvSWrknf9fky87dyvHEuqpwaZmjFpuPX8TNHZpz58M7qEoMRAqXKMmjVMe+snxYVYza9P15eyVm/t9eTOzfgcuXyIpFOPXskFkgGZjqFi96bdBqtIKVyo/JP8QCUGcipSkYsLpP0cpOqSwthQmQTs9V1Xj2Qkq3v/BgANH4Sw0nY4iEu2KkhSfTc2IFpVqlg2OML2/TIQfMOoUaSOtJnCbe6NBafltuliaVJqoXb4lXaqmSK73KqTMVZdWHQj1Tlu7GhmP5+H1P/gj12osKxL+1lXmlY0p5+8zSZERZ17LaiX6LFb7nlu/H3ksb+D5ya0e369x9mtTzNco9H2+X5qWRmVX1reWcrWjpV1KadO5aeU9Dfiu3NyBH8CBAKwiaFfnxnlNDfEmlW8wh83n5wtKkdI3WtW55Cf2BZ9ZArek5nny1BjpNS4hJxdSYtUguj6GSJP9TT8V/j2pZ6SkLvAEHtaylamU0WGCsVSA2XFrZukLBt0WMeIcPnmms/+7Kxr6z0qjPchQtTSaVJivGSMscwUXnxLKLPy6V7knu8O6v3RjMKk1GnoHSKmilcUt3RSOnBdOXkNIUBIgbuaedhzwiuBwz8/5i+SqrPZ2eU/hCMQHvMm3xx5KZzkTNEqJmOVJD1REcjOuZaMmuVY1uzry8CiZfMiFfJvlb5Wolp3wVpVRPIK04XerxljSyZ/whB7T29lJ7TvXKlEcfChp1FBmu3XGIt9DRakv1yZ5Ztk9aNqeCpGTl852lyap8lPsoaRBa98LkDu9WxA9TQysvX9S30vvHowB9uOGk7LyCMu6xdJ5B03NBgPtWDObzulDiwFPf7FU9b25lUcNv9+jWRvNSH+yMIL6UN5+GgUuscBk3HwNQ9SdQz6fhtyRLmcKhaiUxoDSJ/5ReZ82XuHtaaeKD5/ljQFkxPSe37qgNKHp1yLv3nJaVS81/x+liiIrwsM1rXBsXHYGSKr6I/Vp9gKriyHlM0dLkI9OBEWWBN6VYEdILl6I7PWdhNby1+pjqOV/4kCkplnqlXixzIP1MkeSYmjJudi87KyBLUxBgpd/eueJKzSk0M9Yd8TWeKk1WTB3V5WPcYlVvFdAK2ifPiakc51GaeGOS8Miv7cSsrjRIzOgG2pmRKWK5+PUB8oyUo1eaVp3xrmDTDivArzBI4vXITqotr6+/xqowG3KaxvB/H2vJsPDXk4rHeeM0KSlIvnIE92RKWS0ftdhMSnUo7xN4p42txheWJqdC+AVlq5H22ME77etLSGkKAty+lL1YlplOWzI957GlSfm3J/lwW5rqlSYDdSBs8yG7pqbWmqckH6xVrSSaPk3q+RuxqknkMmJpAjP9Xcg0OlxNeTSeP2/oAPk1ahJobZUiP6c2besU2pG6DHpoXRsbzb/vmlYogbWHL8ChsEKWV2ymOD3nq+CWRlLzvU+1Kh9YSmU9mZqhKU+uvQqfbDplREhuhnVrI/y2SknVWimtFNaESc5L/6+aj8Ixf8duoum5IMBtyopzBHoydY/hssy0R7F8FQZ9muyVNWgaE4mISz4XZixESkgUDc74NfWKh8TkzlmOm6XJg4FAriTxfCFrGbZ4fdiMTbnxp/VM+eW1NMn/5nvmSmWpXcMbEVzJCbYeta/8+ubi2YpR9WvjdSxN4nL1LBEVDgWlidMioLx6TrM4y7DKwiKuZzXrEo8forzO7v14G/cUqlEGX9sG208VosxRa1m8qj1Zxarn9NwseCXwt4KkBFmaggCX7MXkbUffZ5w3XJY5Z2ix0sT/0mcXVuCGl1Zj/MKGwH08VhUepKEL1NNJ6vZS561ZB25WP2ULgVGfJi14akF7RaT6deJB7P/Sz2LCJzs4ZfKOVUrtWr08isqrkVdaJfxtNBYZoL9/n+r0HHNPqyaHniO4lUOEeHFBbBS/pUlcP0rLyssc7u+4mu8JzzEeS5MVY6d103MNv6VKE0S/9cuSKy/eUpiAuudY78TvG58m8W/3PrL+WUiD6yrunq6QN1maCB3MTDWZL8t4AWL9oMzAi//jJd8W8a7mWlszKE0LqME9JSfxP2Fux/S371A+zjM9x7MSkoHvmci3aJDkcen69DNFCA+TTSOY1O2MWZo8b7R6StqY9zcDAA68NAJNYyI1U5uyNEG9LWiHHOC79/rB16O6kl0qDmIYrtLY6i9RUwSU6r1c4cNI0ZqgNOApLsXnuWfP25ARXUGz/Yh+q1qaOMrykSsXgLq+pr4NmJ0ONfTOK9SL3oesUttQqiNf1psSpDQFAU4mbYDe3LzQ0+m5UoWvUGN5qcsyZ+Vh7nwkIQc4vxpP5pWhsrpWNmjwliNN6Ymlyd1yoX6uHk3jGOqsA3+4ZNG7pUML4ZzZlUtGrvp44ykUVdSYKkcrMJ4SWQUV6J7UTHtKUsU/SWssYUxdBC2fKV7LrUdBUuvlkP1dqzKga16klfYSSh9GSlco1rHCQR7LhxUfi0baulb7UXMhMGol96XFJDysIRaX71fPXfohrh/3Q8pO36hzTygW9R9WbytmFFKaggDJju0u/uk5U2WZyFwsH6+lKa+0CvmlDrfjWj5NX24/wy0Tk/xWvyfxoPbgf3YCAFrGR3PlWyfjpeOyE0bjNEnLkHbEPH2cZkRwF0NJZUOn4xDJZta/QanjOnDODltslNvxrELOTYA1/GKMSqnt/M4/nSQ+px7fSW5pMq40CY7gXKmVcZODU/l3l1+7nFKFd1wtcrMcxdVzJjfsvVBShf9sycQDfa5CuxZxuumtUlLE2agppjxl+XLsD0OY4Ddq1reruKKaO62StV7SJyt8JNQotAMXA177+YgwK1F/zJ+Q0hQEGDX7elaW8WvEL0i5gqXp0S9+Q1FFNb55tB/Cw8NQ43ThlleVd/JWerHMIJmC0shGqRMvKG/oHPREUNuMki/kgP78HINMiVIZ/jT9ceR/G+zcefLMsVcKU2QdW8WbylPpHoQvUsNi8lkKJOVrhm1Ql8G9fht+837VWzE9p2VpUrvnui95ZVnUKKlytxoqTs8pXKs8BcOhYCgce3xJOnZnFePHvTnY8vch+nkYqFptJbPht9I0FOAeakK5DJ9qTYLPkFml6XQB58cPpFZbpQ/LBktTw0GlPpMxho82ylYUktJE6CGfN/dmm/EkKjbgPj3ndDGsPnQBAHDqYhk6t0lQ/FJtyMsaBVFr2bcYo4qZ29SZSjqlrybTZXBkpWUxkrcZ8Ze96b3nZP3bibwyU/mIOXDOPehlg1JqTE5TARp1rlE7rbVCTsvXTPEaD9q8lvKjpbzJz+gqTZVKU618liYlBYlves49ze5Lq7fE25eUO2pVVwoasapqtgUo16thR3Df6kzC9JyvI4Ir+TTtySrGDS+thl3UlpSVJu28/QGtngsCzDiWmsWMdUdrek7sjMo1+Ft0r9L4Q+rodyDa59V8USxb3sykIQfUqoR3k1VAPwgfl1w++txTm/7UQyu92dVzaoOue8gBPmVFTH06K99vqfKmYmli2tOLSiit8lK0NCkcU3ovuJbn66YA1hy6gOteXIV31x5XPG/Ip0njnFhctfc88HyawgRHcF/4NCn1MfLbtcuUb1KaCMuQW1+86QhnanpOdJHYilTqqMXR3FLh7zCOLx29rQh4EZuHtarL08gAQnwdC5UIJvvN87j1rCTigUkcQ6qqxvsraTyDif5r5Coty5vacX5rjOSc7DpPpuc8qVcti5HadDHTuU5JHiVLk9JtKt2KNx3BZy3fDwCYv1a6hYiS/4wniPPRi7ulmoeF8vAgXT3nW6WJ1y+xWmHFsdJ77F+ViZSmoECitXu5wXu6jYo8IvhzK/a7pefdJ80T5VD6VcmnpCmxbPc52MUrN2R5qcVp4kEt5ICWY66ZclyMSQYmcf0rxdzhQUuMWgsjPAsdLueN17fFZ77dp5vG7biOM73WRr+V1U58n1HXViSWJk6tvF6psWrrIACqz9z9OunfejLILQSAyuDGaSXgGcT5akXbEmikbjWn9MXBLdXakk5ZLg3LpTcICwPCL432Zh3vjSBW0hssTdrlKin2yiEHyNJE6CCfH/Zmk/FUaZIPmEoRwrWcpI2uQFHNR+druR49k/3CX0/isSXpquc9eX+5Arszz61YDNLYLOJO00gwUkmeGjfutLBTNjo9Vz8Aa0Wml2clBNrTstZpyMDAMPuHg3gyNQOTvvyNa/pGjiWr51TyBNStOYy5hzARv55Kij2vIzh/cEseS5N5a1R9mZZZWMTPV2cvQTVczLc+TeLpOV9skKzm66WFmiO4+0GzUlkDKU1BgFgP8bZp1W0TSY4XTNzW5V8xjhrxF8elNAr3oGRC9+RWpbGtgN1ZRfjzZztxMl/qsMxTxrZTBarnPImvo3aJ1EYmXd1kRoGSW5rEim2ZwpYYPGhJcd5epXHWYDkGK5ZnQFCy5DlqnXhl5SGNazQcwV3Ast1nAQA7Mws98mnyaOpddinP9FxdmdK/9bbAKKk0H6dJqf+ybnsTZRpWJhrISyMtz/PVWwiiFcLCW3ga3NII4nGAt4/ktzR5JJrH0Oq5IMCtE/Nio5G/TzwNVCyfvOGL4xXVn1N6aZ0uhsiIMN0OmxfplBbD+H9vAwBkF6Vj7dMDG9IZfAPlMjXM1xsX1j0vhrCwMMXjHsHUV8xVmJye81WHX1+KUUuTFvJn7mIM/9ubg03HL6pfozHIqVmueOUBGqbxPNOZ5BYj0eCu5giucEzvnVC2NClZBJQ+jtyT8Uzn8vn1qVnTtM8rXqM1pS/6rdYm9O6JMe/6pspxMebTbVSkC4D4yqtWbKMKbcjPpiayNAUB8ojg3oR3g1HJNZIvWml68f5X9XkpfYXVKnwNenKv/9vbsO/eb2eKhN9nCsol6Tz9yvXE0qS1/UY9jMkVQHPliO/zYllDHCqlLTG48FG/pbRcWYtap/4Gx0qr3apq9C1uetM/Svkbj9PEldxQnlpyZCnE3tGz1ik5gvPOoiiunuNRiDiev6qlycT03OmLFXh8STr2n7W7lyPKRi1PPUsTY8ynFhOXS2Rp8oFPk7Qfr/u/rqWp1l3RHP3eZs28/QEpTUGAJCK4gg+ClTy7TOpAy6O4iDtZueOrWGmq77iVvnobVg+JLU3W3Oebq44Kv7WWh/PgZlUwKRPg3ok0fJ1JT+w6XehBKXX3rDYQasXM0kKem9c6Ms4Otx6ni+kOju5T0MBlce6RzKVp1JUxo0v2lRCm5zxoUfXF1jpdmL/mGLaLppXVpmQKyquRIdr7sS6tnqVJaXqOz6pkOsQFl6VJ+biLMVRWOw1F6T9XXImfD+Ri7AL3QVtcjFpd6S0AqPNp8t3ozwBE+DDkgBgXZ9tWmp5zKChSvnSgV4Km54IAJa3dF2UBJixNsvTi1XT1eSmZrhsGDXVZrMDdKuBZIdat8FOu6wslVTgiCttgpjTG1BUJLYdp7Tylf/OuEjNcjuz/ejhl/ls8uBjTXVHkYho+aHJF3ERV1L8TnrT5ejm+3pmFd9dJYxVp3d8PIqtsnQzaQpRa7AjubUqranHb679Y0p8UlDkkVmxVpYlDcfeppUk0PefrZ8BtafJSH2I1ZGkKAuQhB7zd5uWWLd30oiRaDb++41bqUJS2kfDGyy3P0vPpufr/G89Hfk2ti7k937NFlZBjVFFzMfVB03zIAXfZvQHvcuV6nC6XbuerNJ2mdw2Duk+Tp9ZLoOH5eLR10KVncjK/3O1cjYYmJy9TL3aX0tQTp0uTaUXBE5+m9DNFlikoD3y6U/K3+ek53/o0McZ8GtxSXjag/+Gj7NPkDoUcIHSxYq8wI4g7WB5TqHR6Tj19g0+TkqXJJUkD+Gbu2mgZSo7g1bUujFuwxXDZ8rwe/HQnRr67UdLhui35ZmbqRd3SVGmRpclbqzobHO35qHXyWY2kfzOO1U7qA7db7C4TVWFF/WmVq72hs/TvyUt34711ylG1VfPgtCqZ3rbHoE+T5OPLwrZ5OEe61Y+aAqI/Pefb1XOMNcRp8oVPk5gGS5N2ubyWJj/rTKQ0BQNipcTpA0tTjcG9yaSO4OoNv0ZBMaqnwuFEVY1TsuHv1zuzAjKYp/z6HZnqIQmMkJFdjGMXynDwfIPzqRV+IS/+cFB1NY+j1pzSpGQl8wbC9Bxn9k4X07SqAAo+TS7e1U5q+UmVWzNtqn5a24oNe8MVgitpKYVKZb695phCSv2yxSj6o3jRp0ksxNIdWcJvb4ZpUbPE80zPKQUJ9RYM8JulqWExhzZKjuBa+fkL8mkKAvS2NbCamloXEHOpbK7pOZGlSeOFrKp2Ys5PhxW/OAa99aviNRuP52NQ1za6MpjFiE+P08Ww9aR0WbqLMYmzu9XIBx4GhgW/nDCUx4USh+rAoTSw8SB+hFU1ThSUOUzlo0dD2+KcnuPwT3LzQeKwNEFjeq7OSVyan1HqHbU9eb/r3yu1SPNqaI2h3PIopFNakWg2RBCPGGb2FPQUtY86vT6htKoW32ec10xjJS7R9Jwv4jTJywas82ny9+o5UpqCAHmH7O02I15lwvN+idu61lfdZ1tOY6fBlWBKK3Ws5Hyxu8+QGst2n8U++RJkpu+/4Anygae0qtbNyZcHVaXJ5N5zYga/9StyLAxoKUEw7fMlP5FXhvYt4rSzVFgMoDudonHaXQnTzEqRekXPkwFh7eE8jHxnI/p1amnoOrVpky+3nUbawVy+PBR6JaWpX9MKjAf14lVLk5rSpNOe9p9zD2XgTVwMCK/f+9PHSgfvI+f1afJZvBMVaHouCBC/mHN/PoITeWUaqT1H/JXE89XM+2V9PK9UP5EMbzpL1jhduFDCN9g7XUyyaqaef608jD1ZRZbKJa5PuSXIrGVIvidgQ36e+zR5TWFCw2DM2wreWXtc179MvhLOxfSnLLQdwZnEumOmzSqF3DDDkdxS5Jcas/qp3dcL3x/kzkMpC6U2Z3p6zoM4TQ6OGFxmUf8Y0S7T5zNMAWBp0nuLg8XSREpTECDvaF5PO+LV8sSNl8unibMH8MVGkUbItVdxv4BVNU5cl2RzO344pwQfbTxlqVxaliuzA0BxhbL/hKer57y9Aqi+f7eyGBeTxlxiHFOsWj5NVliaagSlyfi1cvRWv8mxomr/vnw/rnn+Z3wl8iVSUpq2nTTn/8e3ek75eKWH1lTNfRZVzul93Hh7Oyw5dZamSz5NfnME105HjuCEZfja8U0yPcdRNm8HoOegq4Q3b72oolo/0SUqa5w+iyOipRhVmbQ0qd2r2anF+udi1vLFXY7wf+sagjywYJ2lST8YoZoEVsT+ctZvo2LBfRq1Hlo1flc7XXhuxX7hbyWfpo9NfmDwiKhWd5Vmo95fQksJNesrmFXoHondmzCRNdT3ChuftZjf0kTTc4QOvo75VVPb0CjVXjCxAyRvIzb6BQxYO1jW89/fsgEYW25fWe1UneKyGq0O12pLk1nqn4oVPlGa5Xihg2SMSXyUGDicxzWm57adLDDkCN7jChvmju8hOaa0jZBZeKec6/HWIGQ2nIUSPO1A3dLkmRzd/pmGnZmF+GLbabdzgWY9V4MBiAj3c5wmnWKra/nk8reliRzBg4BAtDRV1jgRH1PXfLw5RS62hFhVC898uw93925nqDOtqnGiysJBQAstpclqS5NZ6tuFWZ8o/nKAWcv3W+o3xhQsTXzBCJXPLd9zTvL3wl9PauYVHRmO7pc3kxyrtXB67tgFgz6PXuhezhVXYv3RfMvy251VjLNFFbjisliEGVweaMXHzt0fbVM8zrPZcCAgnp7zW0Rwy3yayNJE6ODrRiL1aVJOI97o1ZtLesUKhNXF8GzSWk9ljRNVXlYQ6tFSRMya1gvLrVWa6p+FGeuhEapqnPh6Z5ZkKxlPcclDBLgYR5wm/Y2A69l0/KLm+YjwMNhipXvdKUXE9xXemHZeZLGfHwDc+vp6jFuwRfEdOJVfpm5pqvZeGzW7d6OvEU/PecM61jRG3f7CG3KgoJxvAYO/LU2kNAUBPrc01YqVJuWyKxwNA7s3A1B6c+WLkS/QymqnpdMNWnjDT8jq6bl684S3FckShX3OPEXu08QYzzYq1hlkoiLclSZhGxWLyjCC2f0HtVAKsGkF+8/ZcVEhJtjDi3epLvM38nFkFF8GqPQEaZwm61tZjytsuOKyWJWy6/6vN4xtOcG3SMD7QXe0IaUpCPC5TxPH9NzmExeFgIa+sjRZSXWty9AXaGWN73yalu8+p5/IIFZbmmqcDF9uO61rVfGUfxpY9s4LY0w2Pce3jYpVHwcR4eFoJleahOCWvh8QNp+w9hnOWr4fK/actTRPMUpt+XSBumO1N9/bYFGaGGvwacrXCERbn8YoLsbQu0NzxXOFZQ7UOF2WKTsUcoDQxdtbicjJyC4WYhJ9m67c+f3juwMY8PovsFfWeLURe8vS9NGGk5j782Hu9EdySz1aujysW1vT1wLA4K6tPbq+2GKfplHvbsIL3x/Ev348ZGm+vuCjjackbZYnuGVltdOyIKZR4WFug5OVPk3+5uudWSiy3LLZQJHBD4AKD1fPaeftmw8pT3ExCIF5My+6b+hcT1xUhKn8GVO3Ls7+3yG88uMh2C1qEwt+OW54sYOVkNIUBPh6tcP7v5zAE1/vwc2vrsXiradV01XVuHDDS6uRedF7wTZ3nS7yyhTNvDXHDPkjvPbzEezNLtZNF6nwpTaoa2u8e++N+Pf9N2HM9ZcbEVPg/j5XGUrfIj5a8nd5kHTu3kL+XMSO8S7WECepnuQrpI7aaw9fwAOf7rBEFqWv+YW/nkTmxXK/WJqCjX98fwCrOSOVA0BeiXe2+AkmGJjitKacmChzKoE8wKucz7edwYRP6t6f27q0MlVGPWsP56HUC2MCL6Q0BQE5dv6tPm7p2MJ0OVER0lbPG1n4wLkS/UQm2XaqAL1fWYsXvjvgtTJiIq17DeJlDpH33dIOHz7QC/Exkbizx+WIjza+YPXLR27BLVerP1clRS39H8MMl9OYuSxOqkSeEU3n/Ho0Dyv35UjO39bFM8uemFXTb5f8HRVR195e/4M07MDgt371+QeSEa5qGefWv/Rsf5nmNUpt01NO5Zfj0S/T+dNrWFZ8xYjrPLM0A8CiB3vjJlF9f/Jgb8l5rbpmDPjLrR09lkGNaqcLUwZ35kp7ua2Jx+Vd2Vx7qyRvQkqTjH//+9/o2LEjmjRpgl69emHTpk3+Fgmn8vlf+smDOqm+oL/veYXqdR1bxaN/J8++AMSMvSEJL4zpbuiav95+teLx6loXvtx+RvW69+7rqXquD4cS2bP9Zabn8uXIV5Fc2TwOTUQm73CD5YSF1Q3gzZpEqaa5s0eD9So2KgKfP3yL4WXZYv42oqvpa81y141JhtJ3ah3Pnfa2Lq1wd+8rVc+/stJ9mrZV0xhD8qjRqXU8uiYmYNusIcKxeivXPTe3xwN92+vKIkfexm5sd5nHcv5jdDfMv+cGzTSXxUZJ2vKtnVvpthVPF7G8ONZYH2KEYd3aYtrQLrgszv3duv5Km2Xl3NT+MnyU0ls/oQ53dG+Ljy8pSrFRERjWXdrP7/7nHarXXpfUDM/d2U23DLPKyFUt49GpdVMcenmEbtrLbcoO40ZoYnIa0QpIaRLxzTffYPr06Xj++eexZ88e3HbbbRg1ahSysrL0L/YiRr6UEppE4qVxyXj9Dz2QJNPo7+iu/rXzwphuiPWgIXZu01T4fV1SM7x/X088ovNls/bp2/HUsGuEv/t2aoknh3bRLSv10b5Y9nh/xEZF4OEBHTHuBvUB96OUXpK/Wye4D4Z9r26JbbOGSAbW9i3icHXreETLrFB6m8HGRkvrUD7APTm0i6IMamz822Dht9pALr7/dTMGYuA1dVaSl8Zdx12OmCmDO+NvI7qiS5um+H7KAMPXx1+qg9+JFKFbO9cp5F3bJgjH7r25nfC7Y6um+GnabfjwgZvwh5vUFZx65t9zI7c8C+67CdOHXaO6ukfOO/fciH5XG9v0Vo3/e6w/AOlAcVy0d+SferXTbVO3dWmFSbc1vEtbZw3BLzMG4si/RmLTM4MxrFsb4ZxcCePh9z2vwEP9O+CuG6/ArZ1boUlUONootNHuSTYkJzVMW15xWSz6d2qF9+7riR+fuBVXtXS/j3qrmpxWTWOQfEUzyTORWyCmDemMq1s39Ct/Haj8USXmti6tsPsFd+VBrAT9sdeV+H7KAHwysTeevuMatJMpCs3jovDD1Ftx3y3tJMfNfFitfXogvprUVzPNgM4t0apptGaaelo1jcH2WUOxfdZQAA3v0LBubdGsSRTm/alB8V379O1Y+pc+eO++nhh4TWuEh4fh6TuuUcy3HrXNnts2i9Fspw/2q3MfiFOwpH/4wE2Sv3mm565Laqabxl+EMZpEF+jTpw9uuukmLFy4UDjWrVs33HXXXZg7d67u9SUlJbDZbLDb7WjWzLqH/ufPdkoCxW35+xAMeO0XAMAzI7vihisvw/2X5otPzrlTeLlP5JXh8SXpuOfmdrir5xWIi45A93+uEvL58YlbMfWr3cgqrMCGvw1GVEQ4Fm06heZxUUjp1wHx0RE4lFOCD9afwC0dW2LuT4cl0wcDOrcUlonueeEOPL40HdtPFeKbR/uiz6VBZ9rXe/DD3vOIi47AZw/djEWbTmHt4Tysfup2XHNpAO3w95UA6pShvle3xPojefjz4l3o07EFbmh3mbD1QpOocDx3Zzc82K8DgLp4RjGRdQP05uMXsXz3Wbw49jq8s+4YPttyGn8deDVmjeqGr3Zk4Yttp/HsqGtxTdsEHDxnR/IVNvS/VIezx3bHQwM6Iq+kCnfM3wh7ZQ2+ntQX/Tq1xMUyB6Z9vQdbTxagf6eWmDWqG8LCgElf/IYOLePxwpjumL/2GH47XYiiihp89Zc+SN2VjY3H8zF1cGfcc3M7JMisRIwxXCyrxtB5v6JE5Fd1f5/2WHPoAv5yW0dEhoej11XNcYPIilBUXo2e/1ojyeuXGQNxdeum+M/mTMRGR+C+W6SDZq69Cne8vQGlCnvMjb0hCYdzSpD6aF8cv1CGCZ9sx9TBnTFjuNR68OvRPDy7bB8ulDjQ7fJmeHJoZ/Tv3Arf/nYWL19yBH/k1o74fc8r0P3yZhJr2g97zyMhJhKDr60b2F0uhvwyB/adtWPItW3wzLf7YK+sxlt/ukEyhbYs/SxeTzuCqhonSqpqkXxFM2EaOG36bejaNgEdZ/0kpJ816lpsOJaPCyVVWDDhJvx9+X7szS5G98ub4acnbwNQt/R8xZ5zOH2xHLHREaiqcaG0qgaZF8ux9dKeaK/clYwH+tYNAHuyipBoa4LHvkzH4ZxSjL0hCW2bxeDfl4JXju95hRDYcs8Ld6B5fDQ2H7+Id9cdw67TRRh7QxLeF1lBNx3Px+QluzF73HX4Qy93xfDjjScx56e6fSVtsVGwV9Zg+rAumD7sGtgranDDy6sBAKdfGy25Lv1MIf6wcBs6tIzD+pmDcK64Es3jovHjvvOwxUZh7eE85NqrhFVyTaLCkf6POxAVEY6oiDCJVbLW6UKNk6HUUYOV+3Iw/qYrMeC1X1DmqMW3j/VDTGQEfvfBZsRGRWD55AHompgguXbsgi2IDA9D+5ZxWHUgF6mP9sXdH21Dq6YxuP7Ky1BSWYO3/nQDki5rgojwurJP5ZchI7sYQ69tix/3n8fzKw5gyLVt8J+Hboa9sgYTFm3HmYIKrHn6dhy/UIZ//3oCo69PEqbsHx/UCYVl1cgrrcLUIZ3R66oW2HW6EH/6sC4g5ajkRDzYrwPuW7QdCTGR2P+S1Bpy+mI5Xvj+APZkFaPMUYs/D+iAF8deh6Lyajy7bB9WH7qAZ0Z2RfsWcZj61R7hulHJifj5gNS36vorbfjykT4Y9vYGXJuYgC8f6SOcm/PTYaEv69ymKb6bMgD7zhajT8e6vvJwTgkOnS/BJ5tP4XRBhRD65cZ2l6FPxxaYpWApqqpx4oeM8xjVIxEJTaJQXevCiz8chNPlwut/uF7R4ny+uBL//P4ATuSVSVYdDu7aGh+l9MY/vz+AzScuIjYqAh+m9EJsVAQutzWBiwGdnqt751Y/dTuGz98IoM59QDydvWjjKbz6U53F9Ld/DEOrpjFYfTAXH288hXtuboc/9W6HQ+dLsPF4Pl772X0f1bf+dAMGdG6J/+46i2Hd2+CXw3m4LC4Km09cxKqDF/D8nd0wSWVWwhO4x29GMMYYczgcLCIigi1fvlxyfNq0aez2229XvKaqqorZ7XbhX3Z2NgPA7Ha75fIdzrGzPy7cwradvNggc42TMcaYy+Vi3+zMYodz9MvddvIi+9PCrWzxlkzGGGMVjlp2rqiCS4bCMge7UFLJzlwsZ4wxVlDmYG+vPsqyCsqF8yfySt2uq3W6JDLnlVRJzm88lsf+vf4Ec7ka0mUVlLOaWidzuVzss82n2KZj+VwyMsZYTa2TZWQVsZpap2a6fdnF7K1VR1hRuUM45nK5WGGZQ+Mqdcqqagylr7/fonIH++10Adc1OcWV7Lnl+9iGo3kSuY3I+MAn29knm065nSutqpE8AzEFZQ62/sgF5hQ9y5paJ5u3+ij7ad951es8xel0sX3ZxcxR42S/nS5g54sb2uquzAI2478ZLL+0yu26sqoalrrzDLuocE6JQ+ftbOGvJ1hlda3bOUeNk1U4Go5fsFeyg+fsrKSymo16ZyN76YeDkvQul4tlZBWxUoX2oFdPX247zV74bj8rrqhmP+8/L3l3LtgrWXFFteJ1+7KLddtfSWU1O5JTwl0n9WQXlrMdpxra57HcEpZTXKmY1ul0Cf/qZb1QUmnoncoqKGfVonfX5XIpPpctJ/LZ9xnnVPNxOl1s9cFcoX1sPp7PTl8sU01fXetk+88WS9q4GJer7p4mL01nC389ITl+vriCfbThBDt03i7kVSvLx+VyMXtlNdt64qLqc6wn117Jnv4mg+3NLtJM5wlOp4udvlimWr9K5JdWCePFuaIKtuFonmK6/+7KYit2n9XN7z+bT7H/7a17hnpyOGqcrMBk38yD3W7nGr/J0nSJ8+fP44orrsCWLVvQv39/4ficOXPw+eef4+jRo27XzJ49Gy+99JLbcastTQRBEARBeA9eSxP5NMmQmzMZY6pOtbNmzYLdbhf+ZWdn+0JEgiAIgiD8AG3Ye4lWrVohIiICubnSOeq8vDy0bavsQB0TE4OYGGtW2RAEQRAEEdiQpekS0dHR6NWrF9askTrarlmzRjJdRxAEQRBEaEKWJhFPP/00UlJS0Lt3b/Tr1w8ff/wxsrKy8Nhjj/lbNIIgCIIg/AwpTSLuueceFBQU4OWXX0ZOTg6Sk5Px008/4aqrjG1hQRAEQRBE44NWz1mIt+I0EQRBEAThPWj1HEEQBEEQhIWQ0kQQBEEQBMEBKU0EQRAEQRAckNJEEARBEATBASlNBEEQBEEQHJDSRBAEQRAEwQEpTQRBEARBEByQ0kQQBEEQBMEBRQS3kPo4oSUlJX6WhCAIgiAIXurHbb1436Q0WUhpaSkAoF27dn6WhCAIgiAIo5SWlsJms6mep21ULMTlcuH8+fNISEhAWFiYZfmWlJSgXbt2yM7Opu1ZvAjVs++guvYNVM++gerZd3irrhljKC0tRVJSEsLD1T2XyNJkIeHh4bjyyiu9ln+zZs3ohfQBVM++g+raN1A9+waqZ9/hjbrWsjDVQ47gBEEQBEEQHJDSRBAEQRAEwQEpTUFATEwMXnzxRcTExPhblEYN1bPvoLr2DVTPvoHq2Xf4u67JEZwgCIIgCIIDsjQRBEEQBEFwQEoTQRAEQRAEB6Q0EQRBEARBcEBKE0EQBEEQBAekNAUB//73v9GxY0c0adIEvXr1wqZNm/wtUtAwd+5c3HzzzUhISECbNm1w11134ejRo5I0jDHMnj0bSUlJiI2NxaBBg3Dw4EFJGofDgSeeeAKtWrVCfHw8xo0bh7Nnz/ryVoKKuXPnIiwsDNOnTxeOUT1bx7lz5/DAAw+gZcuWiIuLw4033oj09HThPNW159TW1uIf//gHOnbsiNjYWFx99dV4+eWX4XK5hDRUz8bZuHEjxo4di6SkJISFheG7776TnLeqTouKipCSkgKbzQabzYaUlBQUFxd7fgOMCGhSU1NZVFQUW7RoETt06BB78sknWXx8PDtz5oy/RQsKRowYwT777DN24MABlpGRwUaPHs3at2/PysrKhDSvvfYaS0hIYMuWLWP79+9n99xzD7v88stZSUmJkOaxxx5jV1xxBVuzZg3bvXs3Gzx4MLvhhhtYbW2tP24roNm5cyfr0KEDu/7669mTTz4pHKd6tobCwkJ21VVXsYceeojt2LGDZWZmsrVr17ITJ04IaaiuPeeVV15hLVu2ZD/++CPLzMxk//d//8eaNm3K3nnnHSEN1bNxfvrpJ/b888+zZcuWMQBsxYoVkvNW1enIkSNZcnIy27p1K9u6dStLTk5mY8aM8Vh+UpoCnFtuuYU99thjkmPXXnst+/vf/+4niYKbvLw8BoBt2LCBMcaYy+ViiYmJ7LXXXhPSVFVVMZvNxj788EPGGGPFxcUsKiqKpaamCmnOnTvHwsPDWVpamm9vIMApLS1lXbp0YWvWrGEDBw4UlCaqZ+t49tln2a233qp6nuraGkaPHs0efvhhybHx48ezBx54gDFG9WwFcqXJqjo9dOgQA8C2b98upNm2bRsDwI4cOeKRzDQ9F8BUV1cjPT0dw4cPlxwfPnw4tm7d6iepghu73Q4AaNGiBQAgMzMTubm5kjqOiYnBwIEDhTpOT09HTU2NJE1SUhKSk5PpOciYMmUKRo8ejWHDhkmOUz1bxw8//IDevXvjT3/6E9q0aYOePXti0aJFwnmqa2u49dZbsW7dOhw7dgwAsHfvXmzevBl33nknAKpnb2BVnW7btg02mw19+vQR0vTt2xc2m83jeqcNewOYixcvwul0om3btpLjbdu2RW5urp+kCl4YY3j66adx6623Ijk5GQCEelSq4zNnzghpoqOj0bx5c7c09BwaSE1Nxe7du7Fr1y63c1TP1nHq1CksXLgQTz/9NJ577jns3LkT06ZNQ0xMDB588EGqa4t49tlnYbfbce211yIiIgJOpxOvvvoq7rvvPgDUpr2BVXWam5uLNm3auOXfpk0bj+udlKYgICwsTPI3Y8ztGKHP1KlTsW/fPmzevNntnJk6pufQQHZ2Np588kmsXr0aTZo0UU1H9ew5LpcLvXv3xpw5cwAAPXv2xMGDB7Fw4UI8+OCDQjqqa8/45ptvsGTJEnz11Ve47rrrkJGRgenTpyMpKQkTJ04U0lE9W48VdaqU3op6p+m5AKZVq1aIiIhw04zz8vLcNHFCmyeeeAI//PAD1q9fjyuvvFI4npiYCACadZyYmIjq6moUFRWppgl10tPTkZeXh169eiEyMhKRkZHYsGED3nvvPURGRgr1RPXsOZdffjm6d+8uOdatWzdkZWUBoDZtFX/729/w97//Hffeey969OiBlJQUPPXUU5g7dy4AqmdvYFWdJiYm4sKFC2755+fne1zvpDQFMNHR0ejVqxfWrFkjOb5mzRr079/fT1IFF4wxTJ06FcuXL8cvv/yCjh07Ss537NgRiYmJkjqurq7Ghg0bhDru1asXoqKiJGlycnJw4MABeg6XGDp0KPbv34+MjAzhX+/evXH//fcjIyMDV199NdWzRQwYMMAtbMaxY8dw1VVXAaA2bRUVFRUID5cOkREREULIAapn67GqTvv16we73Y6dO3cKaXbs2AG73e55vXvkRk54nfqQA59++ik7dOgQmz59OouPj2enT5/2t2hBweOPP85sNhv79ddfWU5OjvCvoqJCSPPaa68xm83Gli9fzvbv38/uu+8+xSWuV155JVu7di3bvXs3GzJkSEgvG+ZBvHqOMapnq9i5cyeLjIxkr776Kjt+/DhbunQpi4uLY0uWLBHSUF17zsSJE9kVV1whhBxYvnw5a9WqFXvmmWeENFTPxiktLWV79uxhe/bsYQDY22+/zfbs2SOE0bGqTkeOHMmuv/56tm3bNrZt2zbWo0cPCjkQKnzwwQfsqquuYtHR0eymm24SlssT+gBQ/PfZZ58JaVwuF3vxxRdZYmIii4mJYbfffjvbv3+/JJ/Kyko2depU1qJFCxYbG8vGjBnDsrKyfHw3wYVcaaJ6to7//e9/LDk5mcXExLBrr72Wffzxx5LzVNeeU1JSwp588knWvn171qRJE3b11Vez559/njkcDiEN1bNx1q9fr9gnT5w4kTFmXZ0WFBSw+++/nyUkJLCEhAR2//33s6KiIo/lD2OMMc9sVQRBEARBEI0f8mkiCIIgCILggJQmgiAIgiAIDkhpIgiCIAiC4ICUJoIgCIIgCA5IaSIIgiAIguCAlCaCIAiCIAgOSGkiCIIgCILggJQmgiCCkkGDBmH69On+FkNCWFgYvvvuO3+LQRCEl6DglgRBBCWFhYWIiopCQkICOnTogOnTp/tMiZo9eza+++47ZGRkSI7n5uaiefPmiImJ8YkcBEH4lkh/C0AQBGGGFi1aWJ5ndXU1oqOjTV9fv0s7QRCNE5qeIwgiKKmfnhs0aBDOnDmDp556CmFhYQgLCxPSbN26FbfffjtiY2PRrl07TJs2DeXl5cL5Dh064JVXXsFDDz0Em82GSZMmAQCeffZZXHPNNYiLi8PVV1+NF154ATU1NQCAxYsX46WXXsLevXuF8hYvXgzAfXpu//79GDJkCGJjY9GyZUs8+uijKCsrE84/9NBDuOuuu/DWW2/h8ssvR8uWLTFlyhShLIIgAgtSmgiCCGqWL1+OK6+8Ei+//DJycnKQk5MDoE5hGTFiBMaPH499+/bhm2++webNmzF16lTJ9W+++SaSk5ORnp6OF154AQCQkJCAxYsX49ChQ3j33XexaNEizJ8/HwBwzz33YMaMGbjuuuuE8u655x43uSoqKjBy5Eg0b94cu3btwv/93/9h7dq1buWvX78eJ0+exPr16/H5559j8eLFghJGEERgQdNzBEEENS1atEBERAQSEhIk02NvvvkmJkyYIPg5denSBe+99x4GDhyIhQsXokmTJgCAIUOGYObMmZI8//GPfwi/O3TogBkzZuCbb77BM888g9jYWDRt2hSRkZGa03FLly5FZWUlvvjiC8THxwMAFixYgLFjx+L1119H27ZtAQDNmzfHggULEBERgWuvvRajR4/GunXrBKsXQRCBAylNBEE0StLT03HixAksXbpUOMYYg8vlQmZmJrp16wYA6N27t9u13377Ld555x2cOHECZWVlqK2tRbNmzQyVf/jwYdxwww2CwgQAAwYMgMvlwtGjRwWl6brrrkNERISQ5vLLL8f+/fsNlUUQhG8gpYkgiEaJy+XCX//6V0ybNs3tXPv27YXfYqUGALZv3457770XL730EkaMGAGbzYbU1FTMmzfPUPmMMYl/lRjx8aioKLdzLpfLUFkEQfgGUpoIggh6oqOj4XQ6JcduuukmHDx4EJ07dzaU15YtW3DVVVfh+eefF46dOXNGtzw53bt3x+eff47y8nJBMduyZQvCw8NxzTXXGJKJIIjAgBzBCYIIejp06ICNGzfi3LlzuHjxIoC6FXDbtm3DlClTkJGRgePHj+OHH37AE088oZlX586dkZWVhdTUVJw8eRLvvfceVqxY4VZeZmYmMjIycPHiRTgcDrd87r//fjRp0gQTJ07EgQMHsH79ejzxxBNISUkRpuYIggguSGkiCCLoefnll3H69Gl06tQJrVu3BgBcf/312LBhA44fP47bbrsNPXv2xAsvvIDLL79cM6/f/e53eOqppzB16lTceOON2Lp1q7Cqrp4//OEPGDlyJAYPHozWrVvj66+/dssnLi4Oq1atQmFhIW6++Wb88Y9/xNChQ7FgwQLrbpwgCJ9CEcEJgiAIgiA4IEsTQRAEQRAEB6Q0EQRBEARBcEBKE0EQBEEQBAekNBEEQRAEQXBAShNBEARBEAQHpDQRBEEQBEFwQEoTQRAEQRAEB6Q0EQRBEARBcEBKE0EQBEEQBAekNBEEQRAEQXBAShNBEARBEAQHpDQRBEEQBEFw8P8geqKMXPvtNQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ui.plot_trace(result['statistic'], name='statistic')" ] }, { "cell_type": "markdown", "id": "9f9e8aa2-a2af-4c17-8215-80b4ccb8fa51", "metadata": {}, "source": [ "The other fields can be displayed, either with Sherpa commands like `plot_trace`, `plot_scatter`, `plot_cdf`, and `plot_pdf`, or directly with a plotting library:" ] }, { "cell_type": "code", "execution_count": 18, "id": "e1c08a8c-95ab-4cea-9ab2-a717d633cead", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ui.plot_scatter(result['pl.gamma'], result['pl.ampl'], xlabel=r'$\\Gamma$', ylabel='ampl')" ] }, { "cell_type": "markdown", "id": "70f782e9-c851-4432-bf3d-3dcc2f6c1a89", "metadata": {}, "source": [ "We can compare the fit result from earlier to that from the resampled data. In this particular case we can see that the `conf` result (the gray band) is both \n", "\n", "- too narrow\n", "- and biased\n", "\n", "due to the assumption of symmetric errors." ] }, { "cell_type": "code", "execution_count": 19, "id": "0ed4d923-782b-408b-80f7-c1065a86cc77", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ui.plot_cdf(result['pl.gamma'], xlabel=r'$\\Gamma$', name=r'$\\Gamma$')\n", "add_conf_gamma()" ] }, { "cell_type": "markdown", "id": "2c9c1c69-6841-48f2-94a9-24aa8f0734a6", "metadata": {}, "source": [ "We can use the parameter distribution to estimate errors. Sherpa has useful routines for this but other modules - such as NumPy - can be used. Here we use the `get_error_estimates` routine to return the median value along with the 1-sigma range calculated from the input distribution:" ] }, { "cell_type": "code", "execution_count": 20, "id": "c87f24ef-164a-4c95-9a75-7a2b4aa282ca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "median = -0.472\n", " range = -0.547 - -0.269\n" ] } ], "source": [ "from sherpa.utils import get_error_estimates\n", "\n", "mval, lval, uval = get_error_estimates(result['pl.gamma'])\n", "print(f\"median = {mval:.3f}\")\n", "print(f\" range = {lval:.3f} - {uval:.3f}\") " ] }, { "cell_type": "markdown", "id": "3075d30c-fffc-4063-a7ae-03a3769e41f5", "metadata": {}, "source": [ "Or use `quantile` to select a non-standard range:" ] }, { "cell_type": "code", "execution_count": 21, "id": "9030f422-b9c5-4b5b-b051-b3aefcfd4dca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.5890799324170864\n", "-0.10309411994420781\n" ] } ], "source": [ "from sherpa.utils import quantile\n", "\n", "print(quantile(result['pl.gamma'], 0.05))\n", "print(quantile(result['pl.gamma'], 0.95))" ] } ], "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }