{ "cells": [ { "cell_type": "markdown", "id": "2fc8355a-7044-47cc-853d-92edfc098652", "metadata": {}, "source": [ "# Example with simulation data\n", "\n", "On this page, we walk through how we might apply the functions from this package to data from a snapshot of a wind-tunnel numerical simulation. \n", "\n", "First, we load in the data" ] }, { "cell_type": "code", "execution_count": 1, "id": "c01cc11d-b183-4fad-86b1-119026c2569a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "yt : [INFO ] 2024-09-14 13:04:42,436 Parameters: current_time = 7.5\n", "yt : [INFO ] 2024-09-14 13:04:42,436 Parameters: domain_dimensions = [960 160 160]\n", "yt : [INFO ] 2024-09-14 13:04:42,437 Parameters: domain_left_edge = [-60. -10. -10.]\n", "yt : [INFO ] 2024-09-14 13:04:42,437 Parameters: domain_right_edge = [60. 10. 10.]\n", "yt : [INFO ] 2024-09-14 13:04:42,437 Parameters: cosmological_simulation = 0\n", "Parsing Hierarchy: 100%|███████████████████| 256/256 [00:00<00:00, 25694.98it/s]\n", "yt : [INFO ] 2024-09-14 13:04:43,439 Projection completed\n", "yt : [INFO ] 2024-09-14 13:04:43,441 xlim = -60.000000 60.000000\n", "yt : [INFO ] 2024-09-14 13:04:43,442 ylim = -10.000000 10.000000\n", "yt : [INFO ] 2024-09-14 13:04:43,443 xlim = -60.000000 60.000000\n", "yt : [INFO ] 2024-09-14 13:04:43,443 ylim = -10.000000 10.000000\n", "yt : [INFO ] 2024-09-14 13:04:43,445 Making a fixed resolution buffer of (('gas', 'density')) 800 by 800\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from pairstat import vsf_props, twopoint_correlation\n", "import yt\n", "\n", "path = (\n", " \"../../../test_data/X100_M1.5_HD_CDdftCstr_R56.38_logP3_Res8/\"\n", " \"cloud_07.5000/cloud_07.5000.block_list\"\n", ")\n", "ds = yt.load(path)\n", "proj = yt.ProjectionPlot(ds, \"z\", (\"gas\", \"density\"))\n", "proj.set_width([(6, \"kpc\"), (1, \"kpc\")])\n", "proj" ] }, { "cell_type": "markdown", "id": "fb09340a-988c-467f-8350-66f121dd8b67", "metadata": {}, "source": [ "## Part 1: When the number of points is relatively small\n", "First, we consider a scenario where we compute the VSF and 2PCF when the number of points is relatively small. We will compute properties for the cold phase of the gas. We might select information about the cold phase using logic like the following:" ] }, { "cell_type": "code", "execution_count": 2, "id": "2a5dba36-e176-49da-8c88-d0d1e7b26e96", "metadata": {}, "outputs": [], "source": [ "data = ds.all_data()\n", "w = data[\"enzoe\", \"temperature\"] <= 2e4\n", "\n", "colder_pos = np.array(\n", " [data[\"index\", ii][w].to(\"pc\").ndarray_view() for ii in [\"x\", \"y\", \"z\"]]\n", ")\n", "colder_vel = np.array(\n", " [\n", " data[\"gas\", \"velocity_\" + ii][w].to(\"km/s\").ndarray_view()\n", " for ii in [\"x\", \"y\", \"z\"]\n", " ]\n", ")\n", "colder_dens = data[\"gas\", \"density\"][w].to(\"g/cm**3\").ndarray_view()" ] }, { "cell_type": "markdown", "id": "2429dbde-b637-44ae-b898-bfed6d35c27a", "metadata": {}, "source": [ "Down below, we plot the velocity structure function of this gas. In each panel, the vertical lines show the pixel scale and initial cloud radius.\n", "\n", "The limited resolution (and the fact that we make no attempt to remove large scale velocity gradients) limits what we can actually say about turbulence from these figures, but this illustrates how the program can be used for analyzing simulations.\n", "[Abruzzo et al. 2024](https://ui.adsabs.harvard.edu/abs/2024ApJ...966..181A/abstract) illustrates that with some additional care (and higher resolution) you can capture more information about the simulations with these types of statistics." ] }, { "cell_type": "code", "execution_count": 3, "id": "652ae76d-c2c5-47c1-96ab-2be8590a3b39", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dist_bin_edges = np.geomspace(7.0, 2000.0, num=21)\n", "\n", "result_dict = vsf_props(\n", " pos_a=colder_pos,\n", " pos_b=None,\n", " val_a=colder_vel,\n", " val_b=None,\n", " dist_bin_edges=dist_bin_edges,\n", " stat_kw_pairs=[(\"omoment3\", {})],\n", " nproc=1,\n", ")[0]\n", "\n", "\n", "fig, ax_arr = plt.subplots(1, 4, figsize=(15, 4), sharex=True)\n", "\n", "ax_arr[0].stairs(result_dict[\"counts\"], edges=dist_bin_edges)\n", "ax_arr[0].set_ylabel(\"Number of pairs\")\n", "ax_arr[1].stairs(result_dict[\"mean\"], edges=dist_bin_edges)\n", "ax_arr[1].set_ylabel(r\"$\\langle|\\delta{\\bf v}|\\rangle$ [${\\rm km}/{\\rm s}$]\")\n", "ax_arr[2].stairs(result_dict[\"omoment2\"], edges=dist_bin_edges)\n", "ax_arr[2].set_ylabel(r\"$\\langle|\\delta{\\bf v}|^2\\rangle$ [${\\rm km}^2/{\\rm s}^2$]\")\n", "ax_arr[3].stairs(result_dict[\"omoment3\"], edges=dist_bin_edges)\n", "ax_arr[3].set_ylabel(r\"$\\langle|\\delta{\\bf v}|^3\\rangle$ [${\\rm km}^3/{\\rm s}^3$]\")\n", "\n", "for ax in ax_arr:\n", " ax.set_xlabel(r\"$\\ell\\ [{\\rm pc}]$\")\n", " ax.set_xscale(\"log\")\n", " ax.set_yscale(\"log\")\n", " ax.axvline(56, ls=\":\", color=\"k\", label=\"initial radius\")\n", " ax.axvline(56 / 8, ls=\":\", color=\"k\", label=\"pixel scale\")\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "0a0cd61c-16e8-4d3f-ba45-ec6a132bbd88", "metadata": {}, "source": [ "And now, we plot the two-point correlation function for the density field" ] }, { "cell_type": "code", "execution_count": 4, "id": "dc739b55-d0d4-4d5a-a24b-4a0ae09a109d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corr_result_dict = twopoint_correlation(\n", " pos_a=colder_pos,\n", " val_a=colder_dens,\n", " pos_b=None,\n", " val_b=None,\n", " dist_bin_edges=dist_bin_edges,\n", " stat_kw_pairs=[(\"mean\", {})],\n", " nproc=1,\n", ")[0]\n", "\n", "fig, ax_arr = plt.subplots(1, 2, figsize=(8, 4), sharex=True)\n", "\n", "ax_arr[0].stairs(corr_result_dict[\"counts\"], edges=dist_bin_edges)\n", "ax_arr[0].set_ylabel(\"Number of pairs\")\n", "ax_arr[0].set_yscale(\"log\")\n", "ax_arr[1].stairs(corr_result_dict[\"mean\"], edges=dist_bin_edges)\n", "ax_arr[1].set_ylabel(r\"$\\xi\\ [{\\rm g}^2\\ {\\rm cm}^{-6}]$ \")\n", "\n", "for ax in ax_arr:\n", " ax.set_xlabel(r\"$\\ell\\ [{\\rm pc}]$\")\n", " ax.set_xscale(\"log\")\n", " ax.axvline(56, ls=\":\", color=\"k\", label=\"initial radius\")\n", " ax.axvline(56 / 8, ls=\":\", color=\"k\", label=\"pixel scale\")\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "id": "ca90bfd4-cdbe-4473-bf4f-80ce2fea04f7", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "fdf77234-c864-4760-9f35-63e4ec28ff09", "metadata": {}, "source": [ "## Part 2: When the number of points is large (random sampling)\n", "\n", "Now, we show an example of how you might use the functionality provided by this module with random sampling in a scenario where the number of pairs of points is very high. In this case, we consider the vsf and for all gas in the simulation." ] }, { "cell_type": "code", "execution_count": 5, "id": "a0eaea31-2aa2-42df-9a42-66c209a46a25", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3, 100000)\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def randomly_select(\n", " data, maxpoints=1000, pos_units=\"pc\", vel_units=\"cm/s\", dens_units=\"g/cm**3\"\n", "):\n", " \"\"\"\n", " Returns arrays of positions and velocities that can be\n", " used with velocity structure function\n", "\n", " Parameters\n", " ----------\n", " data\n", " This is the data source. This is like one of the objects you\n", " would get with yt from calling ds.all_data() or ds.cut_region()\n", " \"\"\"\n", " # initial number of points (without randomly sampling)\n", " npoints = data[\"index\", \"x\"].size\n", " # print(npoints)\n", "\n", " if maxpoints >= npoints:\n", " idx = slice(None)\n", " else:\n", " idx = np.random.permutation(npoints)[: int(maxpoints)]\n", "\n", " pos = np.array(\n", " [data[\"index\", ii][idx].to(\"pc\").ndarray_view() for ii in [\"x\", \"y\", \"z\"]]\n", " )\n", " vel = np.array(\n", " [\n", " data[\"gas\", \"velocity_\" + ii][idx].to(vel_units).ndarray_view()\n", " for ii in [\"x\", \"y\", \"z\"]\n", " ]\n", " )\n", " dens = data[\"gas\", \"density\"][idx].to(\"g/cm**3\").ndarray_view()\n", " return pos, vel, dens\n", "\n", "\n", "pos, vel, dens = randomly_select(\n", " ds.all_data(),\n", " maxpoints=100000,\n", " pos_units=\"pc\",\n", " vel_units=\"km/s\",\n", " dens_units=\"g/cm**3\",\n", ")\n", "print(pos.shape)\n", "\n", "# you might want to do this to avoid running out of memory\n", "# ds.index.clear_all_data()\n", "\n", "dist_bin_edges = np.geomspace(7.0, 2000.0, num=21)\n", "\n", "vsf_result_dict = vsf_props(\n", " pos_a=pos,\n", " pos_b=None,\n", " val_a=vel,\n", " val_b=None,\n", " dist_bin_edges=dist_bin_edges,\n", " stat_kw_pairs=[(\"omoment3\", {})],\n", " nproc=10,\n", ")[0]\n", "\n", "fig, ax_arr = plt.subplots(1, 4, figsize=(15, 4), sharex=True)\n", "\n", "ax_arr[0].stairs(result_dict[\"counts\"], edges=dist_bin_edges)\n", "ax_arr[0].set_ylabel(\"Number of pairs\")\n", "ax_arr[1].stairs(result_dict[\"mean\"], edges=dist_bin_edges)\n", "ax_arr[1].set_ylabel(r\"$\\langle|\\delta{\\bf v}|\\rangle$ [${\\rm km}/{\\rm s}$]\")\n", "ax_arr[2].stairs(result_dict[\"omoment2\"], edges=dist_bin_edges)\n", "ax_arr[2].set_ylabel(r\"$\\langle|\\delta{\\bf v}|^2\\rangle$ [${\\rm km}^2/{\\rm s}^2$]\")\n", "ax_arr[3].stairs(result_dict[\"omoment3\"], edges=dist_bin_edges)\n", "ax_arr[3].set_ylabel(r\"$\\langle|\\delta{\\bf v}|^3\\rangle$ [${\\rm km}^3/{\\rm s}^3$]\")\n", "\n", "for ax in ax_arr:\n", " ax.set_xlabel(r\"$\\ell\\ [{\\rm pc}]$\")\n", " ax.set_xscale(\"log\")\n", " ax.set_yscale(\"log\")\n", " ax.axvline(56, ls=\":\", color=\"k\", label=\"initial radius\")\n", " ax.axvline(56 / 8, ls=\":\", color=\"k\", label=\"pixel scale\")\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "code", "execution_count": 6, "id": "fb2dac5e-ff5c-466e-8e71-cfc0ff5d35cb", "metadata": {}, "outputs": [ { "data": { "image/png": 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pbQrL9B6KhoiIiP7Ob5sKrVYLrVYLnU6H0FDnnrar0Wh4KttHMFe+r/iiEUMXZ8Nocf4PAGqFHM00vEGWxKHRaPDzmYsYsSwXwcH8y7eUsVYQCee3TQUR+YYKgxlGixUZY3qiQ0SIU9s20yjROkztociIiIjoKjYVROQTOkSEIK61c2cViYiIyDs4pawLTCYTZs6ciZkzZ8JkMokdDjWAuSIisZhMJrz24lxc+GYNzBx/JI21gkg4NhUuqK6uxhtvvIE33ngD1dXVYodDDWCuiEgs1dXV2Lh2FS4f/ALVVo4/UsZaQSQcL39ygUKhwLPPPmtfJulirohILAqFAlOf/Cc25Z2GIpDjj5SxVhAJx6bCBUqlEq+88orYYZADmCsiEotSqcSMZ57H18tyoVByFjIpY60gEo6XPxERERERkSCNoqlYunQpYmNjERMTgxkzZnh8rmmbzQaDwQCDwcB5rSWOuSIisdhsNlRWGlBjruL4I3GsFUTC+fzlT+fOncPy5ctx9OhRKBQKDBw4EPv27UNiYqLHjllZWYmQkCvz5ev1emg0fKiRVDFXRCSWyspK3NqpNQDA+H/FAMJEjYfqx1pBJJzPNxXAlVkbqqqqAAAWiwUREREiR0RERERE5D9Ev/wpJycHI0eORFRUFGQyGbZu3VrnMytWrED79u0RFBSE+Ph47N692/5ey5YtMXv2bERHRyMqKgpDhw7FzTff7NGYg4ODodfrodfrERwc7NFjkTDMFRGJJTg4GD/8Wow2sz6FWs3xR8pYK4iEE72pMBgM6NGjB5YvX37N9zdt2oSZM2di3rx5OHz4MAYMGIDk5GScOnUKAFBRUYEvv/wSJ0+eRHFxMfbu3YucnByPxiyTyaDRaKDRaCCTyTx6LBKGuSIischkMgQHaxCgDOL4I3GsFUTCiX75U3JyMpKTk+t9f8mSJZg8eTKmTJkCAMjIyMCOHTuwcuVKpKen45tvvkGHDh0QHh4OALj77ruxb98+DBw48Jr7M5lMtZ6WqdPp3PjdEFF9ii8aUWEwO71dYZneA9EQERGRO4neVDTEbDbj4MGDmDt3bq31SUlJ2Lt3LwCgTZs22Lt3L6qqqqBQKLBr1y5Mmzat3n2mp6dj/vz5guO6uo8XX3wRSs4/LlnMlTQUXzRi6OJsGC1Wl7ZXK+RopmHuyLeYzWa8+dq/UJF3GpbHbxE7HGoAawWRcJJuKsrLy2G1WhEZGVlrfWRkJEpLSwEAffv2xfDhw9GrVy8EBARgyJAhuOeee+rdZ1paGlJTU+2vdTod2rRp41RcFosFr776KgDg2Wef5eAjYcyVNFQYzDBarMgY0xMdIkKc3r6ZRonWYWoPREbkORaLBW8tW3xluXqpyNFQQ1griISTdFNx1d+vb7TZbLXWvfLKKw4/CVOlUkGlUiEzMxOZmZmwWp3/y2lgYCCeeuop+zJJF3MlLR0iQhDXOlTsMIi8IjAwEI9OTsHn+X8iUM7xR8pYK4iEk/T/nBYtWkAul9vPSlxVVlZW5+yFs7RaLbRaLXQ6HUJDnfslR6VSISMjQ9DxyTuYKyISi0qlwjPz/43dy3KhVKnEDocawFpBJJzosz81RKlUIj4+HllZWbXWZ2VloV+/foL2nZmZiZiYGCQkJAjaDxERERGRvxP9TIVer0dhYaH9dVFREfLz8xEeHo7o6GikpqZi3Lhx6NOnDxITE7FmzRqcOnUKKSkpgo4r5EwFERERERH9j+hNRV5eHgYPHmx/ffUm6gkTJmD9+vUYM2YMzp8/j5dffhklJSWIi4vD9u3b0bZtW7FChsFgQEjIlZtN9Xo9NBqNaLFQw5grIhKLwWBAtxvDAACVk4sB8A9YUsVaQSSc6E3FoEGDYLPZGvzM9OnTMX36dLceV8iN2kRERERE9D+iNxViEXL5U3BwMMrKyuzLJF3MFRGJJTg4GNk/FmLs2z9Areb4I2WsFUTC+W1TIYRMJkPLli3FDoMcwFwRkVhkMhnCm7eAPDi0ztToJC2sFUTCSXr2JyIiIiIikj6/bSqETClrNpvtD9wzm80eiI7chbkiIrGYzWaseXMRLu3dBAvHH0ljrSASTma73l3SjdzVeyouXbqEpk2bOrQNZ4nwHcyVNBwpvoQRy3Lx5ZP9+URtD3D139eV8c8fufrv9Nfx54dfi3FLxyiHt+X/Ge9irSCqn6NjIO+pcEFgYCCmTJliXybpYq6ISCyBgYG4/x/jseNoKQLlHH+kjLWCSDi//Z8jZEpZlUqFt956ywNRkbsxV+5XfNGICoNzlwcUluk9FA2RdKlUKry08E3kLcuFUqUSOxxqAGsFkXB+21TwidpEziu+aMTQxdkwWpxvxtUKOZpplB6IivxFeno6PvvsM/zyyy9Qq9Xo168fXnvtNXTu3LnB7bKzs5GamoqjR48iKioKc+bMQUpKipeiJiLyD37bVBCR8yoMZhgtVmSM6YkOESFObdtMo0TrMLWHIiN/kJ2dDa1Wi4SEBFRXV2PevHlISkrCsWPH6r0GvqioCMOHD8fUqVOxceNG7NmzB9OnT0fLli1x//33e/k7ICJqvNhUuMBgMCAiIgIAUFZWxhu6JIy58owOESG8eZS87quvvqr1et26dYiIiMDBgwcxcODAa26zatUqREdHIyMjAwDQtWtX5OXlYdGiRR5vKgwGA27pGIWqaisqJ/8OgP9npIq1gkg4NhUuqqysFDsEchBzRdQ4Xbp0CQAQHh5e72e+//57JCUl1Vp35513Yu3atbBYLFAoFHW2MZlMMJlM9tc6nc7lGI1Gjj++grWCSBi/bSqE3KitVqtRVFRkXybpYq6IGiebzYbU1FT0798fcXFx9X6utLQUkZGRtdZFRkaiuroa5eXlaNWqVZ1t0tPTMX/+fMExqtVqfPX9j5j8bh6Cgjj+SBlrBZFwfttUCLlROyAgAO3atfNMYORWzBVR4/TEE0/gp59+Qm5u7nU/K5PJar2++nimv6+/Ki0tDampqfbXOp0Obdq0cTrGgIAAtG7TFoGhpxEQ4LfPmvUJrBVEwvltU0FERL7pySefxBdffIGcnBzceOONDX72hhtuQGlpaa11ZWVlCAwMRPPmza+5jUqlgopTwBIROYVNhQssFgsyMzMBXDnjca1rckkamCuixsNms+HJJ5/Eli1bsGvXLrRv3/662yQmJuI///lPrXVff/01+vTp4/HxwGKxYMPbK6A7UASL5VaPHouEYa0gEo7nY11gNpsxa9YszJo1C2azcw8BI+9irogaD61Wi40bN+KDDz5AkyZNUFpaitLSUhiNRvtn0tLSMH78ePvrlJQU/PHHH0hNTUVBQQHeeecdrF27FrNnz/Z4vGazGa+/9CwqvnsLFgvHHyljrSASjmcqXCCXy/HII4/Yl0m6mCuixmPlypUAgEGDBtVav27dOkycOBEAUFJSglOnTtnfa9++PbZv345Zs2YhMzMTUVFRePPNN73yjAq5XI7hox/Erl/LIA/g+CNlrBVEwvltUyFk9qegoCC8//77HoiK3I25Imo8rt5g3ZD169fXWXf77bfj0KFDHoioYUFBQXht+VsYsSwXqqAgrx+fHMdaQSSc317+pNVqcezYMRw4cEDsUIiIiIiIfJrfNhVEREREROQebCpcYDAY0LJlS7Rs2RIGg0HscKgBzBURicVgMGBg95tx+s1HUFnJ8UfKWCuIhPPbeyqEKi8vFzsEchBzRURiqbhwXuwQyEGsFUTCsKlwgVqtxpEjR+zLJF3M1bUVXzSiwuD8tImFZXoPREPUOKnVamz59ntMf/8QgoI4/kgZawWRcGwqXBAQEIDY2FixwyAHMFd1FV80YujibBgtzs98BgBqhRzNNEo3R0XU+AQEBKBD565QtjyPgABebSxlrBVEwrGpIPIzFQYzjBYrMsb0RIeIEKe3b6ZRonUY/5JHRERE/8OmwgUWi8U+F/rEiROhUCjEDYjqxVzVr0NECOJah4odBlGjZbFY8On77+Jy/glYLLeKHQ41gLWCSDi/bSqEPPzObDZj2rRpAIBHHnmEg4+EMVdEJBaz2Yz5zzwFALBY0kSOhhrCWkEknN82FVqtFlqtFjqdDqGhzv21Vi6XY9SoUfZlki7miojEIpfLMfjO4dj3+wXIAzj+SBlrBZFwfttUCBEUFIStW7eKHQY5gLkiIrEEBQXhzbUfYMSyXKiCgsQOhxrAWkEkHKejICIiIiIiQdhUEBERERGRIGwqXFBZWYl27dqhXbt2qKysFDscagBzRURiqaysxJ19u+HMysdgNHL8kTLWCiLheE+FC2w2G/744w/7MkkXc0VEYrHZbPjzzGn7MkkXawWRcGwqXBAUFIT9+/fbl0m6mCsiEktQUBA+/PI7zPo4HyoVxx8pY60gEs7nm4rjx49jzJgxtV5/+OGHGD16tMeOKZfLkZCQ4LH9k/swV0QkFrlcjrievaHaXclpSiWOtYJIOJ9vKjp37oz8/HwAgF6vR7t27TBs2DBxgyIiIiIi8iM+31T81RdffIEhQ4ZAo9F49DjV1dXYtGkTAGDMmDEIDGxU/4yNCnNFRGKprq7Gl599DP3R46iu7it2ONQA1goi4Zye/en06dM4c+aM/fX+/fsxc+ZMrFmzxqUAcnJyMHLkSERFRUEmk13z4TMrVqxA+/btERQUhPj4eOzevfua+/r4449rXQrlKSaTCY8++igeffRRmEwmjx+PXMdcEZFYTCYT0mZMw/kvF8Ns5vgjZawVRMI53Yo/8sgjmDZtGsaNG4fS0lIMGzYMsbGx2LhxI0pLS/HCCy84tT+DwYAePXpg0qRJuP/+++u8v2nTJsycORMrVqzAbbfdhtWrVyM5ORnHjh1DdHS0/XM6nQ579uzBRx995Oy35LSAgAAMHTrUvkzS1dhzVXzRiAqD2altCsv0HoqGiP4qICAAfQcMQv6piwiQNb7xpzFp7LWCyBucbiqOHDmCW265BcCVMwNxcXHYs2cPvv76a6SkpDjdVCQnJyM5Obne95csWYLJkydjypQpAICMjAzs2LEDK1euRHp6uv1zn3/+Oe68887rztpgMplq/RVCp9M5FS8AqNVqZGVlOb0deV9jzlXxRSOGLs6G0WJ1elu1Qo5mGqUHoiKiq9RqNd76cCtGLMtFkFotdjjUgMZcK4i8xemmwmKxQKVSAQC++eYb3HPPPQCALl26oKSkxK3Bmc1mHDx4EHPnzq21PikpCXv37q217uOPP8a0adOuu8/09HTMnz/frXESiaHCYIbRYkXGmJ7oEBHi1LbNNEq0DuMvOUREROQeTjcVsbGxWLVqFe6++25kZWXhX//6FwDgzz//RPPmzd0aXHl5OaxWKyIjI2utj4yMRGlpqf31pUuXsH//fmzevPm6+0xLS0Nqaqr9tU6nQ5s2bdwXNJGXdYgIQVzrULHDICIiIj/m9IWDr732GlavXo1BgwbhH//4B3r06AHgysxLVy+LcjeZTFbrtc1mq7UuNDQUZ8+ehVJ5/cs5VCoVmjZtig0bNqBv374YMmSI0/FUVlYiNjYWsbGxqKysdHp78h7miojEUllZidF39MWfb0+H0cjxR8pYK4iEc+pMhc1mQ/v27fHHH3/AarWiWbNm9vemTZuG4OBgtwbXokULyOXyWmclAKCsrKzO2QtnabVaaLVa6HQ6hIY691dem82GY8eO2ZdJupgrIhKLzWbDb7/+Yl8m6WKtIBLO6aaiY8eOOHr0KDp27FjrvXbt2rkzLgCAUqlEfHw8srKycO+999rXZ2VlYdSoUW4/nqOCgoKwc+dO+zJJF3NFRGIJCgrCOx//B2lbfoZKxfFHylgriIRzqqkICAhAx44dcf78+TpNhav0ej0KCwvtr4uKipCfn4/w8HBER0cjNTUV48aNQ58+fZCYmIg1a9bg1KlTSElJEXTczMxMZGZmwmp1fuYcuVyOQYMGCTo+eQdzRURikcvlSOg3AEEHZZDL5WKHQw1grSASzukbtV9//XU8/fTTWLlyJeLi4gQHkJeXh8GDB9tfX72JesKECVi/fj3GjBmD8+fP4+WXX0ZJSQni4uKwfft2tG3bVtBxhVz+RERERERE/+N0U/Hoo4+isrISPXr0gFKphPpvc29fuHDBqf0NGjToutcvTp8+HdOnT3c2VI+prq7Gl19+CQAYMWIEAgOd/mckL2GuiEgs1dXV+ParL1H5awGqq/uKHQ41gLWCSDin/9dkZGR4IAzvE3L5k8lkst/jodfrOfhIGHNFRGIxmUyYOeVRAID5Nen8YYzqYq0gEs7p/zUTJkzwRBxeJ+Typ4CAAPTr18++TNLFXBGRWAICAtCzz60oKNEhQMbxR8pYK4iEc6ip0Ol0aNq0qX25IVc/15ip1Wrs2bNH7DDIAcwVEYlFrVZjw9YdGLEsF0FqPsFeylgriIRzqKlo1qwZSkpKEBERgbCwsDoPowP+90A6Vy4nEoOQy5+IiIiIiOh/HGoqvvvuO4SHhwOAfR5nX8fZn4iIiIiI3MOhpuL222+/5rK/MhqNGDhwIAAgJyenzgxYJB3MFRGJxWg04uG7B6PkrB5VU3IA8A9YUsVaQSScy9MbVFZW4tSpUzCbzbXWd+/eXXBQUldTU4O8vDz7MkkXc0XkHV988YXT2wwbNqxR//JWU1ODoz8evrJs4/gjZawVRMI53VScO3cOkyZNwn//+99rvu8r9ygIuadCpVLZ57NWqVTuDo3cyBdyVXzRiAqD+fof/JvCMr0HoiFyzejRo536vEwmw4kTJ3DTTTd5JiAJUKlUyHx3E176zzEoldIcf+gKX6gVRFLndFMxc+ZMVFRUYN++fRg8eDC2bNmCs2fPYsGCBVi8eLEnYvQIIfdUBAYG4u677/ZQZOROUs9V8UUjhi7OhtHiWjOuVsjRTKN0c1REriktLUVERIRDn23SpImHoxFfYGAgBg65E8HHNHzugcRJvVYQ+QKnR7nvvvsOn3/+ORISEhAQEIC2bdti2LBhaNq0KdLT0/mfksgJFQYzjBYrMsb0RIeIEKe3b6ZRonVY4718hHzHhAkTnLqU6dFHH/WLKciJiPyF002FwWCw/yUqPDwc586dQ6dOndCtWzccOnTI7QFKkdVqxXfffQcAuOOOOyCXy0WOiOrjK7nqEBGCuNa8iZN817p165z6/MqVKz0UiXRYrVbszdkJY9ERWK2JYodDDfCVWkEkZU43FZ07d8bx48fRrl079OzZE6tXr0a7du2watUqtGrVyhMxSk5VVRWSkpIAAHq9HhqNRuSIqD7MFZFnGY1GqFQq+1OIv/32W+Tn56Nr164YPny4yNGJq6qqCo8/ci8AwLTgMZGjoYawVhAJ59I9FSUlJQCAF198EXfeeSfef/99KJVKrF+/3t3xeYyQG7UDAgLQo0cP+zJJF3NF5Fl9+/ZFdnY2wsLCkJ6ejqysLAwfPhyrVq3Cd999h0WLFokdomgCAgLQOSYOv5cbECDj+CNlrBVEwjndVIwdO9a+3KtXL5w8eRK//PILoqOj0aJFC7cG50lCbtRWq9XIz8/3TGDkVswVkWdVV1cjLCwMALB582bs3bsXSqUSqamp6Nmzp6ixiU2tVuPTr3MxYlkughrx1LmNAWsFkXCC2nGbzQa1Wo3evXv7VENBRETuERkZib179wIAWrdujfLycgDA5cuXxQyLiIi8zKWmYu3atYiLi0NQUBCCgoIQFxeHt99+292xERGRxL3zzjt49tlnkZSUZL+EZPjw4bjjjjuwcOFCscMjIiIvcfryp+effx5Lly7Fk08+icTEK7NZfP/995g1axZOnjyJBQsWuD1IqTEajUhOTgYA/Pe//23UT4T1dcwVkWe1a9cOu3btwrFjx3DixAmMHTsWN954IxISEvx+Bh2j0YhJD9yN0uJLqJqSBYAzvEkVawWRcE43FStXrsRbb72Ff/zjH/Z199xzD7p3744nn3zSL5qKmpoaZGdn25dJupgrIu+IiYlBTEwMSktLccMNN3jsODk5OVi4cCEOHjyIkpISbNmypcGnee/atQuDBw+us76goABdunTxWJzAlTEnb9+eK8s2jj9SxlpBJJzTTYXVakWfPn3qrI+Pj0d1dbVbgvIGIbM/qVQqfPzxx/Zlki7misi7kpKS8NNPP3ls/waDAT169MCkSZNw//33O7zd8ePHaz1sr2XLlp4IrxaVSoVFq9bj3//9BUolxx8pY60gEs7ppuLRRx/FypUrsWTJklrr16xZU2tmKKkTMvtTYGAgHnzwQQ9FRu7EXBF5l81m8+j+k5OT7ZepOCMiIsI+S5W3BAYG4s4Ro7GsKBeBgU6XW/Ii1goi4Vwa5dauXYuvv/4affv2BQDs27cPp0+fxvjx45Gammr/3N8bDyIiatxkMpnYIVxTr169UFVVhZiYGDz33HPXvCTqKpPJBJPJZH+t0+m8ESIRkU9zuqk4cuQIevfuDQD47bffAFw5jdyyZUscOXLE/jmpFhZ3sFqt2LdvH4ArD37y95sRpYy5IvJvrVq1wpo1axAfHw+TyYQNGzZgyJAh2LVrFwYOHHjNbdLT0zF//nzBx7ZarTh8YB+qzhyD1ZooeH/kOawVRMI53VTs3LnTE3H4lKqqKvTv3x8AoNfrodFoRI6I6sNcEfm3zp07o3PnzvbXiYmJOH36NBYtWlRvU5GWllbrrLtOp0ObNm2cPnZVVRXG33sXAMD0ou9cHuyPWCuIhONFni6QyWTo0KGDfZmki7ki8i6lUil2CNfVt29fbNy4sd73VSqVW27WlclkiG53E/68ZOT4I3GsFUTCsalwQXBwME6cOCF2GOQAb+Wq+KIRFQaz09sVluk9EA2RePLy8sQO4boOHz6MVq1aefw4wcHB2JZ7CCOW5UKtDvb48ch1rOtEwrGpIBKo+KIRQxdnw2hxfnpiAFAr5Gimkf5fd4mkQK/Xo7Cw0P66qKgI+fn5CA8PR3R0NNLS0lBcXIz33nsPAJCRkYF27dohNjYWZrMZGzduxObNm7F582axvgUiokbJb5sKIc+pIPqrCoMZRosVGWN6okNEiNPbN9Mo0TqMT2+lxqWqqgo//fQTysrK6jxM7J577nF5v3l5ebVmbrp678OECROwfv16lJSU4NSpU/b3zWYzZs+ejeLiYqjVasTGxmLbtm0YPny4yzEQEVFdDjUVvXv3xrfffotmzZrh5ZdfxuzZsxEc7NuncoU8p6Kqqsr+0KXNmzcjKCjIEyGSG3gzVx0iQhDX2rmfJaLG6KuvvsL48eNRXl5e5z2ZTCbojzmDBg1q8FkY69evr/V6zpw5mDNnjsvHE6KqqgrTxz+EspMXYJr6JQCOD1LFuk4knENNRUFBAQwGA5o1a4b58+cjJSXF55sKIaxWK7Zv325fJuliroi874knnsCDDz6IF154AZGRkWKHIxqr1Yrd3319ZbmG44+UsVYQCedQU9GzZ09MmjQJ/fv3h81mw6JFixAScu3LPF544QW3BihFSqUS69atsy+TdDFXRN5XVlaG1NRUv24ogCtjzr+WZCLjmxNQKDj+SBlrBZFwDjUV69evx4svvogvv/wSMpkM//3vfxEYWHdTmUzmF02FQqHAxIkTxQ6DHMBcEXnfAw88gF27duHmm28WOxRRKRQKjH5oLN4+mwuFQiF2ONQA1goi4RxqKjp37oyPPvoIABAQEIBvv/0WERERHg2MiIh80/Lly/Hggw9i9+7d6NatW51fqGfMmCFSZERE5ClOz/7091k8/JHVasXPP/8MAOjWrRvkcrnIEVF9mCsi7/vggw+wY8cOqNVq7Nq1q9bDxGQymd80FVarFb8c/Qnms7/Dak0UOxxqAGsFkXAuTSn722+/ISMjAwUFBZDJZOjatSueeuopvznVXVVVhV69egG4Mme6RqMROSKqD3NF5H3PPfccXn75ZcydOxcBAQFihyOaqqoqPHjnQACA6dn7RY6GGsJaQSSc06P9jh07EBMTg/3796N79+6Ii4vDDz/8gNjYWGRlZXkiRsmRyWSIiopCVFRUrb/AkfQwV0TeZzabMWbMGL9uKIAr409EZCvIQ8I5/kgcawWRcE6fqZg7dy5mzZqFf//733XWP/PMMxg2bJjbgnNUUVERHnvsMZw9exZyuRz79u3z6F8ZgoODUVxc7LH9k/swV0TeN2HCBGzatAnPPvus2KGIKjg4GN8eLMCIZblQq/13GnZfwFpBJJzTTUVBQQE+/vjjOusfe+wxZGRkuCMmp02cOBELFizAgAEDcOHCBahUKlHiICKiK9env/7669ixYwe6d+9e50btJUuWiBQZERF5itNNRcuWLZGfn4+OHTvWWp+fny/KjFBHjx6FQqHAgAEDAADh4eFej4GIiP7n559/tl+ffuTIkVrv8dISIqLGyemmYurUqZg2bRp+//139OvXDzKZDLm5uXjttdfwz3/+0+kAcnJysHDhQhw8eBAlJSXYsmULRo8eXeszK1aswMKFC1FSUoLY2FhkZGTYm4gTJ04gJCQE99xzD86cOYMHHnjA46fcq6qqMG7cOADAhg0bEBQU5NHjkeuYKyLv27lzp9ghSEJVVRVSH5+Ac4XlME3dDCBU7JCoHqwVRMI53VQ8//zzaNKkCRYvXoy0tDQAQFRUFF566SWXpgk0GAzo0aMHJk2ahPvvrzs7xqZNmzBz5kysWLECt912G1avXo3k5GQcO3YM0dHRsFgs2L17t/1MyV133YWEhASP3tthtVrx6aefArjyYECSLuaKiMRitVqRte3zK8s1VpGjoYawVhAJ53RTIZPJMGvWLMyaNQuXL18GADRp0sTlAJKTk5GcnFzv+0uWLMHkyZMxZcoUAEBGRgZ27NiBlStXIj09HTfeeCMSEhLQpk0bAMDw4cORn59fb1NhMplgMpnsr3U6ndMxK5VKLF++3L5M0sVcEXlfeno6IiMj8dhjj9Va/8477+DcuXN45plnRIrMu5RKJZ5dsBCrsn+DQsHxR8pYK4iEc+k5FVcJaSYcYTabcfDgQcydO7fW+qSkJOzduxcAkJCQgLNnz6KiogKhoaHIycnB448/Xu8+09PTMX/+fEFxKRQKaLVaQfsg72CuiLxv9erV+OCDD+qsj42NxcMPP+w3TYVCocA/Jk7F+5dz69ysTtLCWkEknKCmwtPKy8thtVoRGRlZa31kZCRKS0sBAIGBgXj11VcxcOBA2Gw2JCUlYcSIEfXuMy0tDampqfbXOp3OfpaDqPiiERUGs1PbFJbpPRQNkW8qLS1Fq1at6qxv2bIlSkpKRIiIiIg8TdJNxVV/ny3EZrPVWne9S6j+SqVSQaVSITMzE5mZmbBanb/OtaamBr/99hsA4Oabb/b7BzxJmTO5Kr5oxNDF2TBanP+ZUCvkaKbhKXMiAGjTpg327NmD9u3b11q/Z88eREVFiRSV99XU1OCP33+D5UIxampqxA6HGsC6TiScpJuKFi1aQC6X289KXFVWVlbn7IWztFottFotdDodQkOdm5HDaDSiU6dOAAC9Xu/RB+2RMM7kqsJghtFiRcaYnugQEeLUcZpplGgdphYUK1FjMWXKFMycORMWiwV33HEHAODbb7/FnDlzXJol0FcZjUaMGBgPAKh6egSAZuIGRPViXScSzqmmwmKxICkpCatXr7b/5/MkpVKJ+Ph4ZGVl4d5777Wvz8rKwqhRowTtW8iZCgBONyIkHmdz1SEiBHGtmV8iV82ZMwcXLlzA9OnTYTZfuZwwKCgIzzzzjH3WQH/RpGlT6E2c+ckXsK4TCeNUU6FQKHDkyBG3PrxIr9ejsLDQ/rqoqAj5+fkIDw9HdHQ0UlNTMW7cOPTp0weJiYlYs2YNTp06hZSUFEHHFXKmQqPR4OLFi4KOT97BXBF5n0wmw2uvvYbnn38eBQUFUKvV6NixI1QqldiheZVGo8HeY6cwYlkugoP5l28pY60gEs7py5/Gjx+PtWvX4t///rdbAsjLy8PgwYPtr6/eRD1hwgSsX78eY8aMwfnz5/Hyyy+jpKQEcXFx2L59O9q2beuW4xMRkWeEhIQgISFB7DCIiMgLnG4qzGYz3n77bWRlZaFPnz51rjtcsmSJU/sbNGgQbDZbg5+ZPn06pk+f7myoDRJ6+RMREREREV3hdFNx5MgR9O7dGwDw66+/1nrPnZdFeZqQy59MJpP9WRirV6/2u1P6voS5IiKxmEwmzJv1fygvKIN5Gs/YSBlrBZFwTjcVO3fu9EQcPqW6uhrvvvsugCtnPDj4SBdzRURiqa6uxheffHhl2VotcjTUENYKIuFcnlK2sLAQv/32GwYOHAi1Wl3n2RFSJ+TyJ4VCgddff92+TNLFXBGRWBQKBVLnvYx39hRBEcjxR8pYK4iEc7qpOH/+PB566CHs3LkTMpkMJ06cwE033YQpU6YgLCwMixcv9kScbifk8ielUomnn37aQ5GROzFXRJ5nNBpx4cIFtG7dutb6o0ePIjY2VqSoxKdUKjHp/2Zgc3UuFEo+HFPKWCuIhHP6kZGzZs2CQqHAqVOnEBwcbF8/ZswYfPXVV24NjoiIpO3TTz9Fp06dMHz4cHTv3h0//PCD/b1x48aJGBkREXmT02cqvv76a+zYsQM33nhjrfUdO3bEH3/84bbApKympgYlJSUAgFatWiEgwOnejLyEuSLyrAULFuDQoUNo2bIl8vLyMGHCBMybNw+PPPLIdWf2a+xqampwtuRPVF8uR01NjdjhUANYK4iEc7qpMBgMtc5QXFVeXu5TNzYJuafCaDTamyq9Xl9nWl2SDuaKyLMsFgtatmwJAOjTpw9ycnJw3333obCw0Kfus/MEo9GIoQkxAICqmcUAmokbENWLtYJIOKdb8YEDB+K9996zv5bJZKipqcHChQtrPcRO6rRaLY4dO4YDBw64tH1gYCACA12+z528iLki8pyIiAj89NNP9tfNmzdHVlYWCgoKaq33V4GBgUCAXOwwyAGsFUTCOP2/Z+HChRg0aBDy8vJgNpsxZ84cHD16FBcuXMCePXs8EaPkaDQaWCwWscMgBzBXRJ61YcOGOr+IKZVKfPjhh3jiiSdEikoaNBoNDp8sx4hluQgO5l++pYy1gkg4p5uKmJgY/PTTT1i5ciXkcjkMBgPuu+8+aLVatGrVyhMxEjml+KIRFQaz09sVluk9EA1R4/b3++v+6rbbbvNiJEREJCaXzvPdcMMNmD9/vrtjIRKs+KIRQxdnw2hx/l4ZAFAr5Gim4dSPREKkpqZec71MJkNQUBA6dOiAUaNGITw83MuRERGRp7jUVFRUVGDt2rUoKCiATCZD165dMWnSJJ8qEEJu1DaZTPaiuWTJEp+6Qb2xqzCYYbRYkTGmJzpEhMBsMuH1l+cBAOa88AqU18lVM40SrcPU3giVqNE6fPgwDh06BKvVis6dO8Nms+HEiROQy+Xo0qULVqxYgX/+85/Izc1FTEyM2OF6jMlkwoJ5s3H+pxKYpyWIHQ41gHWdSDiZzck5/7KzszFq1Cg0bdoUffr0AQAcPHgQFy9exBdffIHbb7/dI4F6ytWH3126dAlNmzZ1aBuDwYCQkBAAnCVCao4UX8KIZbn48sn+iGsdylyRX/j7z72jXBn/HJGRkYHdu3dj3bp19v3qdDpMnjwZ/fv3x9SpU/HII4/AaDRix44dbjuup7j67/TX8eeHX4txS8coh7d1NafkGtYKovo5OgY6faZCq9XioYcest9TAQBWqxXTp0+HVqvFkSNHXI/aRygUCrz44ov2ZZIu5orI+xYuXIisrKxaxadp06Z46aWXkJSUhKeeegovvPACkpKSRIzS8xQKBf5v1jP4YP8pKAI5/niCq/fQAbXPTLNWEAnndFPx22+/YfPmzfaGAgDkcjlSU1NrTTXbmCmVSrz00ktih0EOYK6IvO/SpUsoKyurc2nTuXPnoNPpAABhYWEwm137ZdBXKJVKTP9nGrYvy4VCyXu13M0d99B988/b0TpMzVpB5AZONxW9e/dGQUEBOnfuXGt9QUEBevbs6a64iIjIR40aNQqPPfYYFi9ejISEBMhkMuzfvx+zZ8/G6NGjAQD79+9Hp06dxA2UfNrf76FzRmGZHjM35aPCYOZ9dERu4lBT8dcHGM2YMQNPPfUUCgsL0bdvXwDAvn37kJmZiX//+9+eiVJibDYbLl26BAAIDQ31+6fGShlzReR9q1evxqxZs/Dwww+juroawJUHi02YMAFLly4FAHTp0gVvv/22mGF6nM1mg+7SRdRU6eHk7Ys+yV2XIjmrQ0SI4PtOWCuIhHOoqejZsydkMlmtQXHOnDl1PvfII49gzJgx7ovOg4TM/lRZWYlmzZoB4A1dUsdcEXlfSEgI3nrrLSxduhS///47bDYbbr75ZvuNsAD84sx2ZWUlbottBwAwaosBhIkZjke541KkVePi0dyJKb3d8Wyhq/uorDTg1k6tAVy5qf56DyvkTIFEdTnUVBQVFXk6Dq/TarXQarX2O9qJiMh1P/30E+Li4hAQEGBfFxISgu7du1/z80ePHkXnzp3rPI2bfJOQS5HOG8xI2XAQE97Z7/RxXX22UDONEmqFHDM35QMAasxV9vceWPk9ApRB1z3u1fsxiOgKh0bztm3bejoOnxIcHGy/wZAFUdqYKyLv6NWrF0pLS9GyZUuHPp+YmIj8/HzcdNNNHo5MPMHBwThUdA6jM/dArQ4WOxyvcPVSpG/+ebtLl065esagdZi61jFtNhuqZ5wDcKVWNHT509X7MQ4UXUCFkw0Uz3BQY+bSb1nFxcXYs2cPysrKUFNTU+u9GTNmuCUwKZPJZJxyzkcwV0TeYbPZ8PzzzyM42LFfnhv7zE/A/8YfmbzhX1Lpyi/53v5l29Vj/v0shzN4hoMaM6ebinXr1iElJQVKpRLNmzevNVDKZDK/aCqIiKi2gQMH4vjx4w5/PjExEWo1f7Ei3/P3sxyOEnKGA+BZDpI+p5uKF154AS+88ALS0tJqXTvrT8xmM+bNmwcAeOWVV6Dk/OOSxVwReceuXbvEDkFyzGYzFv/reVQcLobl8Vu8emyxZmLyVc7WClfOcgg5wwG4djO7UGL8LAj52XWVP/7Me4LTTUVlZSUefvhhv20oAMBisWDRokUAgJdeeom/qEoYc0VEYrFYLFi/etmV5eplXjuur87EJCZv1ApXz3AAwm5mF8LbjczV79PVn11XCfk+fanx8nSsTjcVkydPxieffIK5c+d6Ih6foFAoMHv2bPsySRdzRURiUSgUmPj4k/jscDEUgd4bf3xtJiYp8FatEHLviKsNiavEbGTefewWrzcyrn6fvtR4efqeHqebivT0dIwYMQJfffUVunXrVuc/35IlS9wWnFQplUosXLhQ7DDIAcwVEYlFqVTin8//CzuX5UIhwllSX5mJSQp8oVaIcTO7txsZQJyfI188g+Rs4+WNp8g73VS8+uqr2LFjBzp37gwAdW7U9hVCHn5HRETiyMnJwcKFC3Hw4EGUlJRgy5YtGD16dIPbZGdnIzU1FUePHkVUVBTmzJmDlJQU7wTsg8T45ZWkyV9+FnzpDBIg3Qbe6aZiyZIleOeddzBx4kQPhOM9Qh5+Z7PZUF1dDeD681mTuJgrIu8YMWIE3n//fY8/TNRgMKBHjx6YNGkS7r///ut+vqioCMOHD8fUqVOxceNG7NmzB9OnT0fLli0d2l4Im80Gi8UCm7UaNpvNpX24cq+Cr9/fIAbWCnKVvzRejnC6qVCpVLjttts8EYvPqKysREjIletU9Xo9NBqNyBE1Tq7ciPT3YspcEXnH9u3bcfr06VpNRWFhITp06FDnszabzeVf2pKTk5GcnOzw51etWoXo6GhkZGQAALp27Yq8vDwsWrTI401FZWUlere/8jBA47RiAGEOb+uOmYJ89f4GMbBWEAnndFPx1FNPYdmyZXjzzTc9EQ8RAGGzl7CYEonjt99+Q1xcHIArjUOXLl2Qn59vXwcAEydOxIYNGxAfH4+NGzeiU6dOHo3p+++/R1JSUq11d955J9auXQuLxXLNm3JNJhNMJpP9tU6n82iM1yJkpiBAupdHEFHj5XRTsX//fnz33Xf48ssvERsbW2dA/uyzz9wWnFQFBwejoqLCvkzuJ2T2kr8WU+aKyHs+++wzjBo1CgBQXFyMmpoalJWV2d+/dOkSNmzYgK1bt2L37t147LHHkJub69GYSktLERkZWWtdZGQkqqurUV5ejlatWtXZJj09HfPnzxd87ODgYOw5ehIPr9kHtdr58YeXVXgPawWRcE43FWFhYbjvvvs8EYvPkMlkCAsLEzsMv+Dq7CVXMVdE3pOdnY1Vq1ZhypQpWLt2LVQqFbKzs3HHHXcAAP7880+oVCqMHDkSQ4YMwfvvv++VuP5+qdXV+xvquwQrLS0Nqamp9tc6nQ5t2rRx6bhNQ8MQEBTCa/QljrWCSDinm4p169Z5Ig4iIvJhY8eOxWOPPYbx48fjqaeeQnV1NTIzM/HSSy/h0UcfRceOHbFt2zbcdNNNAK78NXjq1Kkej+uGG25AaWlprXVlZWUIDAxE8+bNr7mNSqWCSqXyeGxERI2J000FAWazGa+++ioA4Nlnn+VTmiWMuSLyjg0bNgAAfv/9d+Tn5yMsLAwdO3bE5cuXERcXh+7du+PHH390y2VFzkhMTMR//vOfWuu+/vpr9OnTx+MPxDSbzVixOB0X95+C5fFbPHosEoa1gkg4p5uK9u3bN3ga9/fffxcUkCsCAwPtNwL26dMHb7/9tkePZ7FY7IXx6aef5uAjYcwVkXcpFAokJCTYXz/99NPo168fduzYgccffxyTJ08WtH+9Xo/CwkL766KiIuTn5yM8PBzR0dFIS0tDcXEx3nvvPQBASkoKli9fjtTUVEydOhXff/891q5diw8//FBQHI6wWCxYufS1K8vVizx+PHIdawWRcE43FTNnzqz12mKx4PDhw/jqq6/w9NNPuysup4SFhSE/P99rxwsMDMT06dPtyyRdzBWR+G677Ta3TUWel5eHwYMH219fvfdhwoQJWL9+PUpKSnDq1Cn7++3bt8f27dsxa9YsZGZmIioqCm+++abHp5MFrow5YyZMwbafShAo5/gjZawVRMK5NKXstWRmZiIvL09wQL5ApVIhMzNT7DDIAcwVUeMyaNCgBh8kt379+jrrbr/9dhw6dMiDUV2bSqXCc68swr5luVDyHg1JY60gEi7AXTtKTk7G5s2bnd4uJycHI0eORFRUFGQyGbZu3VrnMytWrED79u0RFBSE+Ph47N69u9b7Op0O8fHx6N+/P7Kzs139FoiIiIiIyAVuayo+/fRThIeHO72dwWBAjx49sHz58mu+v2nTJsycORPz5s3D4cOHMWDAACQnJ9c6vX3y5EkcPHgQq1atwvjx40V5UBERERERkb9y+vKnXr161bpR22azobS0FOfOncOKFSucDiA5ORnJycn1vr9kyRJMnjwZU6ZMAQBkZGRgx44dWLlyJdLT0wEAUVFRAIC4uDjExMTg119/RZ8+fa65P3c8KdVgMNjns7548SI0Go3T+yDvYK6ISCwGgwG92rVAdY0NlZNPAXD9mTvkWawVRMI53VSMHj261uuAgAC0bNkSgwYNQpcuXdwVF4ArU7wdPHgQc+fOrbU+KSkJe/fuBQBUVFQgODgYKpUKZ86cwbFjx+zzoF+Lu56UWl1dLXgf5B3MFRGJheOP72CuiIRxuql48cUXPRHHNZWXl8NqtSIyMrLW+sjISPvDjAoKCvD4448jICAAMpkMb7zxRoOXYbnjSalqtRpnzpyxL5N0MVdEJBa1Wo1vDhzDhHX7ERTE8UfKWCuIhPOJedP+/lwMm81mX9evXz/8/PPPDu/r6pNSMzMzkZmZCavV6nQ8AQEBaN26tdPbkfcxV0QkloCAAES2ikJgkxYICHDbLYzkAawVRMI53FRcPRPQEJlM5tbThy1atIBcLreflbiqrKysztkLZ2m1Wmi1Wuh0OoSG8jpXTym+aESFwez0doVleg9EQ0RERESe4HBTsWXLlnrf27t3L5YtW9bg3OGuUCqViI+PR1ZWFu699177+qysLIwaNcqtx3KG2WzGG2+8AeDKczv45M1rK75oxNDF2TBanD8bBABqhRzNNML+bZkrIhKL2WzGupVv4tIPRbA8fovY4VADWCuIhHO4qbjWL/G//PIL0tLS8J///Adjx47Fv/71L6cD0Ov1KCwstL8uKipCfn4+wsPDER0djdTUVIwbNw59+vRBYmIi1qxZg1OnTiElJcXpY/2VkMufLBYL5syZAwCYPn06B596VBjMMFqsyBjTEx0iQpzevplGidZhwq5tZa6ISCwWiwVLXnnhynL1ApGjoYawVhAJ59I9FX/++SdefPFFvPvuu7jzzjuRn5+PuLg4lwLIy8vD4MGD7a+v3kQ9YcIErF+/HmPGjMH58+fx8ssvo6SkBHFxcdi+fTvatm3r0vGuEnL5U2BgICZMmGBfpoZ1iAhBXGtxLjFjrohILIGBgbjnwX/g24IyBMo5/kgZawWRcE79z7l06RJeffVVLFu2DD179sS3336LAQMGCApg0KBB171savr06Zg+fbqg47iTSqXC+vXrxQ6DHMBcEZFYVCoVXlm6Ej8uy4VSpRI7HGoAawWRcA5PR/H666/jpptuwpdffokPP/wQe/fuFdxQiCkzMxMxMTFISEgQOxQiIiIiIp/m8JmKuXPnQq1Wo0OHDnj33Xfx7rvvXvNzn332mduC8yTO/kRERERE5B4ONxXjx4+/7pSy/sJgMNjnsy4uLoZGoxE5IqoPc0VEYjEYDOgXEw29yYrKyb8C4B+wpIq1gkg4h5uKxnatoZDZn4Ar95eQb2CuiEgsl3U6sUMgB7FWEAnjt1McCLn8Sa1W49dff7Uvk3QxV0QkFrVajS9zDmLahjwEBXH8kTLWCiLh/LapECIgIAAdO3YUOwxyAHNFRGIJCAhA25tuhiK8BAEBDs+LQiJgrSASjqMcEREREREJ4rdnKoQ+UXvNmjUAgGnTpkGhULg7PHIT5oqIxGKxWPDh+rdw+dBvsFhuFTscagBrBZFwfttUCLmnwmw244knngAATJw4kYOPhDFXRCQWs9mMV597GgBgsTwvcjTUENYKIuH8tqkQQi6X44EHHrAvk3QxV0QkFrlcjmF3j8KewnLIAzj+SBlrBZFwbCpcEBQUhE8++UTsMMgBzBURiSUoKAhLVr+LEctyoQoKEjscagBrBZFwfnujdmZmJmJiYpCQkCB2KEREREREPs1vmwqtVotjx47hwIEDYodCREREROTT/LapEKKyshKtW7dG69atUVlZKXY41ADmiojEUllZiSHxXXEmczyMRo4/UsZaQSQc76lwgc1mw59//mlfJulirohILDabDWVnS+zLJF2sFUTCsalwQVBQEA4fPmxfJulirohILEFBQfhkRw5mfJgPlYrjj5SxVhAJx6bCBXK5HD179hQ7DHIAc0VEYpHL5egS2x3KSB2nKZU41goi4XhPBRERERERCeK3ZyoyMzORmZkJq9Xq9LYWiwXvv/8+AGDs2LF88qaEMVdEJBaLxYKtH78P/c8nYLHcKnY41ADWCiLh/Lap0Gq10Gq10Ol0CA0NdWpbs9mMSZMmAQAefPBBvxh8ii8aUWEwO7VNYZneQ9E4zh9zRUTSYDab8XyqFgBgsTwtcjTUENYKIuH8tqkQQi6XY/jw4fblxq74ohFDF2fDaHH+rI5aIUczjdIDUTnG33JFRNIhl8sx4I4k5J28AHkAxx8pY60gEo5NhQuCgoKwbds2scPwmgqDGUaLFRljeqJDRIhT2zbTKNE6TO2hyK7P33JFRNIRFBSEFe99jBHLcqHijEKSxlpBJBybCnJYh4gQxLV27lIxIiIiImr8OPsTEREREREJwqbCBZWVlejYsSM6duyIyspKscOhBjBXRCSWyspK3N2/N4rXTIXRyPFHylgriITj5U8usNlsKCwstC+TdDFXRCQWm82GUyd/ty+TdLFWEAnnt02FkOdUBAUFITc3175M0sVcEZFYgoKC8N6Wr/D0pz9BpeL4I2WsFUTC+W1TIeQ5FXK5HLfddpuHIiN3Yq6ISCxyuRy9EvoiaF81pymVONYKIuF4TwUREREREQnit2cqhKiursaWLVsAAPfeey8CA/nPKFXMFRGJpbq6Gju+3ArDL7+gurqv2OFQA1griITj/xoXmEwmPPTQQwAAvV7PwUfCmCsiEovJZMLslIkAAPPCJ8UNhhrEWkEkHP/XuCAgIAC33367fZmki7kiIrEEBASgT9/b8HPxJQTIOP5IGWsFkXBsKlygVquxa9cuscMgBzBXRCQWtVqNdZ9uw4hluQhSq8UOhxrAWkEkHNtxIiIiIiISpNGcqaisrETXrl3x4IMPYtGiRWKHI0nFF42oMJid3q6wTO+BaIiIiIiosWg0TcUrr7yCW2+91SvHMhqNSExMBAB8//33UPvAae3ii0YMXZwNo8X5h/0BgFohRzON0s1ReZ4v5oqIGgej0YgHkvrjz3IDqqbsBeDcM5HIe1griIRrFE3FiRMn8Msvv2DkyJE4cuSIx49XU1ODH3/80b7sCyoMZhgtVmSM6YkOESFOb99Mo0TrMN8bZH0xV0TUsBUrVmDhwoUoKSlBbGwsMjIyMGDAgGt+dteuXRg8eHCd9QUFBejSpYtH46ypqcHxY1dqUo2N44+UsVYQCSd6U5GTk4OFCxfi4MGDKCkpwZYtWzB69Ohan7leAZk9ezYWLlyIvXv3eiXmoKAgfP311/ZlX9IhIgRxrf3nr2W+nCsiqmvTpk2YOXMmVqxYgdtuuw2rV69GcnIyjh07hujo6Hq3O378OJo2bWp/3bJlS4/HGhQUhNUfbMHzW49ApeL4I2WsFUTCiX6jtsFgQI8ePbB8+fJrvn+1gMybNw+HDx/GgAEDkJycjFOnTgEAPv/8c3Tq1AmdOnXyWsxyuRzDhg3DsGHDIJfLvXZcch5zRdS4LFmyBJMnT8aUKVPQtWtXZGRkoE2bNli5cmWD20VEROCGG26wf3ljPJDL5eg3cDDU7Xtx/JE41goi4UQ/U5GcnIzk5OR63/9rAQGAjIwM7NixAytXrkR6ejr27duHjz76CJ988gn0ej0sFguaNm2KF154wVvfAhEReYHZbMbBgwcxd+7cWuuTkpKue6a6V69eqKqqQkxMDJ577rlrXhJ1lclkgslksr/W6XTCAici8gOin6loyNUCkpSUVGv9XwtIeno6Tp8+jZMnT2LRokWYOnVqgw2FyWSCTqer9eWs6upqbNu2Ddu2bUN1dbXT25P3MFdEjUd5eTmsVisiIyNrrY+MjERpaek1t2nVqhXWrFmDzZs347PPPkPnzp0xZMgQ5OTk1Huc9PR0hIaG2r/atGnjUrzV1dXI+XYHKn87wPFH4lgriIQT/UxFQ1wpINeTnp6O+fPnC4rLZDJhxIgRAAC9Xo/AQEn/M/o15oqo8ZHJZLVe22y2Ouuu6ty5Mzp37mx/nZiYiNOnT2PRokUYOHDgNbdJS0tDamqq/bVOp3OpsTCZTNBOGAMAML86zentyXtYK4iE84n/NY4WkIkTJ153X+4oFgEBAejTp499maSLuSJqPFq0aAG5XF7nj0plZWV1/vjUkL59+2Ljxo31vq9SqaBSqVyO86qAgADE9uiFE2f1CJBx/JEy1goi4STdVLirgPzV1WKRmZmJzMxMWK3OP7dBrVbjwIEDLh2fvIu5Imo8lEol4uPjkZWVhXvvvde+PisrC6NGjXJ4P4cPH0arVq08EWItarUaH23biRHLchHE5x5IGmsFkXCSbircVUCuRavVQqvVQqfTITTUf6ZYJSLyZampqRg3bhz69OmDxMRErFmzBqdOnUJKSgqAK2eji4uL8d577wG4MrlHu3btEBsbC7PZjI0bN2Lz5s3YvHmzmN8GEVGjI3pTodfrUVhYaH9dVFSE/Px8hIeHIzo6+roFxFVCzlQQEZE4xowZg/Pnz+Pll19GSUkJ4uLisH37drRt2xYAUFJSYp9yHLgy4cfs2bNRXFwMtVqN2NhYbNu2DcOHDxfrWyAiapREbyry8vJqTe139X6HCRMmYP369dctIK4ScqbCaDRi6NChAIBvvvkGap7WlizmiqjxmT59OqZPn37N99avX1/r9Zw5czBnzhwvRFWX0WjEuNF3orREh6op3wHgWXGpYq0gEk70pmLQoEGw2WwNfqahAiKGmpoa+5S2NTU1IkdDDWGuiEgsNTU1yM/74cqyjeOPlLFWEAknelMhFiGXP6lUKmzZssW+TNLFXBGRWFQqFTLe3ohXthVAqeT4I2WsFUTC+W1TIeTyp8DAQIwePdozgZFbMVdEJJbAwEAMuWsElp4I43MPJI61gkg4jnI+pviiERUGs9PbFZbpPRANEREREZEfNxVCLn+yWq3YvXs3AGDAgAGQy+XuDu+aii8aMXRxNowW12asUivkaKZRujkqaRMrV0REVqsVB/buRtWpn2G1JoodDjWAtYJIOL9tKoRc/lRVVWWfsUqv10Oj0XgixDoqDGYYLVZkjOmJDhEhTm/fTKNE6zD/mtFCrFwREVVVVeGxh0YCAEzzJ4gcDTWEtYJIOL9tKoSQyWSIiYmxL3tbh4gQxLXm1ISOEDtXROS/ZDIZbu7UBafOV3L8kTjWCiLh2FS4IDg4GEePHhU7DHIAc0VEYgkODsbW7/ZhxLJcqNXBYodDDWCtIBIuQOwAxJKZmYmYmBgkJCSIHQoRERERkU/z26ZCq9Xi2LFjOHDggNihEBERERH5NL9tKoQwGo0YNmwYhg0bBqPRKHY41ADmiojEYjQaMfUfo3H2o+dQxfFH0lgriITjPRUuqKmpwTfffGNfJulirohILDU1Ndi3e9eVZRvHHyljrSASjk2FC1QqFTZu3GhfdoUrD7HjA+yc545cERG5QqVSIf3NNVj09XEolRx/pIy1gkg4v20qhDz8LjAwEGPHjnX52EIeYuePD7ATQmiuiIhcFRgYiBH3PYRVxbkIDPTbcusTWCuIhPPbUU7Iw++EEvIQO398gB0RERERSZvfNhVCWK1WHDp0CADQu3dvyOVyl/bDh9h5nrtyRUTkLKvViiP5h2Aq+RVWa6LY4VADWCuIhGNT4YKqqirccsstAAC9Xg+NRiNyRFQf5oqIxFJVVYV/jLgDAGB6bozI0VBDWCuIhGNT4QKZTIa2bdval0m6mCsiEotMJkPUjW1wVmfi+CNxrBVEwrGpcEFwcDBOnjwpdhjkAOaKiMQSHByMHft+xohluVCrg8UOhxrAWkEknN8+/C4zMxMxMTFISEgQOxQiIiIiIp/mt02FVqvFsWPHcODAAbFDISIiIiLyaX7bVAhRVVWF0aNHY/To0aiqqhI7HGoAc0VEYqmqqsKMyY+g7LMFMHH8kTTWCiLheE+FC6xWKz7//HP7MkkXc0VEYrFardi5Y/uV5RqOP1LGWkEkHJsKFyiVSqxZs8a+TNLFXBGRWJRKJV587Q0s++4EFAqOP1LGWkEkHJsKFygUCkydOlXsMMgBzBURiUWhUOCBsROw/kIuFAqF2OFQA1griITjPRVERERERCQImwoX1NTU4OjRozh69ChqamrEDocawFwRkVhqampQeLwA5nN/cPyRONYKIuF4+ZMLjEYj4uLiAAB6vR4ajUbkiKg+zBURicVoNOLeIYkAgKq5owE0EzUeqh9rBZFwfttUZGZmIjMz0+VZHlq0aOHmiMhTmCsiEkuz8Oa4ZLSIHQY5gLWCSBi/bSq0Wi20Wi10Oh1CQ0Od2laj0eDcuXMeiozcibkiIrFoNBrk/PQbRizLRXAw//ItZawVRMLxngoiIiIiIhKETQUREREREQnCpsIFVVVVGDt2LMaOHYuqqiqxw6EGMFdEJJaqqio888RUnPvPQpg4/kgaawWRcH57T4UQVqsVH3zwAQDYn8BJ0sRcEZFYrFYrtm/95MpyjWuTgpB3sFYQCcemwgVKpRJLly61L5N0MVdEJBalUok5L72Kt3KKoFBw/JEy1goi4dhUuEChUGDmzJlih0EOYK6ISCwKhQLjpkzHJmMuFAqF2OFQA1griITz+abi8uXLuOOOO2CxWGC1WjFjxgxMnTrVK8cuvmhEhcHs9HaFZXoPRENEREREJA6fbyqCg4ORnZ2N4OBgVFZWIi4uDvfddx+aN2/usWPW1NRg/5FfMfatH1AdHA6ZzPn73dUKOZppeIrV02pqanDq1CkAQHR0NAICODcBEXlHTU0Nik//gepLZ1FTUyN2ONQA1goi4Xy+qZDL5QgODgZwZfYGq9UKm83m0WMajUYk9ugKAPhwz6+Iaxvh9D6aaZRoHaZ2d2j0N0ajEe3btwcA6PV6aDR8ABUReYfRaMRdiT0AAFWpdwFoJm5AVC/WCiLhRG/Fc3JyMHLkSERFRUEmk2Hr1q11PrNixQq0b98eQUFBiI+Px+7du2u9f/HiRfTo0QM33ngj5syZgxYtWng8brU6GDKFCje11CCudajTX2wovCc4ONjeeBIRedPVWkHSx1pBJIzoTYXBYECPHj2wfPnya76/adMmzJw5E/PmzcPhw4cxYMAAJCcn209TAkBYWBh+/PFHFBUV4YMPPsDZs2c9GrNGo8H+E38iOnUzgoP51wwp02g0MBgMMBgM/MsTEXkVa4XvYK0gEk70piI5ORkLFizAfffdd833lyxZgsmTJ2PKlCno2rUrMjIy0KZNG6xcubLOZyMjI9G9e3fk5OTUezyTyQSdTlfri4iIiIiIXCd6U9EQs9mMgwcPIikpqdb6pKQk7N27FwBw9uxZe2Og0+mQk5ODzp0717vP9PR0hIaG2r/atGnjuW+AiIiIiMgPSLqpKC8vh9VqRWRkZK31kZGRKC0tBQCcOXMGAwcORI8ePdC/f3888cQT6N69e737TEtLw6VLl+xfp0+fdjouk8mEl56egfP/fRNmk8np7cl7TCYTpk6diqlTp8LEXBGRF7FW+A7WCiLhfGL2J5lMVuu1zWazr4uPj0d+fr7D+1KpVFCpVMjMzERmZiasVqvT8VRXV2Pzh+9dWbZWO709eU91dTXefvttAEBGRgZUKt4wSUTewVrhO1griISTdFPRokULyOVy+1mJq8rKyuqcvXCWVquFVquFTqdDaGioU9sqFAo8Oec5bPj+DygC+ZRUKVMoFFiwYIF9mYjIW1grfAdrBZFwkm4qlEol4uPjkZWVhXvvvde+PisrC6NGjRI1rmkzZuMLWS4USj7ATsqUSiXmzZsndhhE5IdYK3wHawWRcKLfU6HX65Gfn2+/hKmoqAj5+fn2KWNTU1Px9ttv45133kFBQQFmzZqFU6dOISUlRdBxMzMzERMTg4SEBKHfAhERedH1nl30d9nZ2YiPj0dQUBBuuukmrFq1ykuREhH5D9Gbiry8PPTq1Qu9evUCcKWJ6NWrF1544QUAwJgxY5CRkYGXX34ZPXv2RE5ODrZv3462bdsKOq5Wq8WxY8dw4MABp7e12Wy4cL4c1spLHn96Nwljs9lw7tw5nDt3jrkiagQceXbRXxUVFWH48OEYMGAADh8+jGeffRYzZszA5s2bPR4ra4XvYK0gEk5m8/P/PVfvqbh06RKaNm3q0DYGgwEhISEAgB9+LcYtHaM8GSIJ8Ndc6fV6PtSIGqUjxZcwYlkuvnyyP+JaO36PmCvjn9huvfVW9O7du9azirp27YrRo0cjPT29zuefeeYZfPHFFygoKLCvS0lJwY8//ojvv//eoWO6+u/EWuE7WCuosXO1TgCOj4GSvqfCk67O/lRdfWVGDmcegmcwGOzLev1lPkBPwv6aK51O59JsX0RSp7+sQ42pEvrLOuh0sutv8P9dHbt85W9LV59dNHfu3Frr//rsor/7/vvv6zzr6M4778TatWthsViueVOuyWSqNa3opUuXADhXJwDWCl/CWkGNnat1AnCiVtj83OnTp20A+MUvfvHLb79Onz4t9lDskOLiYhsA2549e2qtf+WVV2ydOnW65jYdO3a0vfLKK7XW7dmzxwbA9ueff15zmxdffFH0nPCLX/zil9S+rlcr/PZMxVVRUVE4ffo0mjRpUud5GAkJCQ3ec9HQ+/W9d631Op0Obdq0wenTpyVxCcL1vm9v79PZbR35vDdyK7W8Ar6dW0c/y9w6zmaz4fLly4iK8q3Lchp6dpGjn7/W+qvS0tKQmppqf11TU4MLFy6gefPm19yGP3PS2KeUxhNncs7cunc7qfwOAEgvt67mwNFa4fdNRUBAAG688cZrvieXyxv8IWjo/frea2ibpk2bSuKH7nrft7f36ey2jnzem7mVSl4B386to59lbp3j7HN6xOTKs4tuuOGGa34+MDAQzZs3v+Y2Vx+S+ldhYWH1xsWfOWnsU0rjiSs5Z27ds53UfgcApJNbIXl1pFaIPvuTlGm1Wpffr++96+1TCjwRo5B9OrutI59nbqWxT2e2dfSzzG3j9ddnF/1VVlYW+vXrd81tEhMT63z+66+/Rp8+fdz2kDP+zEljn1IaT1zJuZRIKbf8HcB9PB2j38/+JAW+OAMLXR/z2ngxt+LZtGkTxo0bh1WrViExMRFr1qzBW2+9haNHj6Jt27ZIS0tDcXEx3nvvPQBXppSNi4vD448/jqlTp+L7779HSkoKPvzwQ9x///0ifzeO489c48XcNl7+llu/v/xJClQqFV588cU6p9vJtzGvjRdzK54xY8bg/PnzePnll1FSUoK4uLhazy4qKSmp9cyK9u3bY/v27Zg1axYyMzMRFRWFN99806caCoA/c40Zc9t4+VtueaaCiIiIiIgE4T0VREREREQkCJsKIiIiIiIShE0FEREREREJwqaCiIiIiIgEYVMhcffeey+aNWuGBx54QOxQyI1Onz6NQYMGISYmBt27d8cnn3widkjkBpcvX0ZCQgJ69uyJbt264a233hI7JPITrBWND+tE49VYawVnf5K4nTt3Qq/X491338Wnn34qdjjkJiUlJTh79ix69uyJsrIy9O7dG8ePH4dGoxE7NBLAarXCZDIhODgYlZWViIuLw4EDB+p9cjORu7BWND6sE41XY60VPFMhcYMHD0aTJk3EDoPcrFWrVujZsycAICIiAuHh4bhw4YK4QZFgcrkcwcHBAICqqipYrVbw7zbkDawVjQ/rROPVWGsFmwoPysnJwciRIxEVFQWZTIatW7fW+cyKFSvQvn17BAUFIT4+Hrt37/Z+oOQ0d+Y2Ly8PNTU1aNOmjYejputxR14vXryIHj164MYbb8ScOXPQokULL0VPvoq1onFinWi8WCuujU2FBxkMBvTo0QPLly+/5vubNm3CzJkzMW/ePBw+fBgDBgxAcnJyrafBkjS5K7fnz5/H+PHjsWbNGm+ETdfhjryGhYXhxx9/RFFRET744AOcPXvWW+GTj2KtaJxYJxov1op62MgrANi2bNlSa90tt9xiS0lJqbWuS5cutrlz59Zat3PnTtv999/v6RDJRa7mtqqqyjZgwADbe++9540wyUlC/s9elZKSYvv44489FSI1QqwVjRPrROPFWvE/PFMhErPZjIMHDyIpKanW+qSkJOzdu1ekqMgdHMmtzWbDxIkTcccdd2DcuHFihElOciSvZ8+ehU6nAwDodDrk5OSgc+fOXo+VGg/WisaJdaLx8udaESh2AP6qvLwcVqsVkZGRtdZHRkaitLTU/vrOO+/EoUOHYDAYcOONN2LLli1ISEjwdrjkBEdyu2fPHmzatAndu3e3X4u5YcMGdOvWzdvhkoMcyeuZM2cwefJk2Gw22Gw2PPHEE+jevbsY4VIjwVrROLFONF7+XCvYVIhMJpPVem2z2Wqt27Fjh7dDIjdpKLf9+/dHTU2NGGGRQA3lNT4+Hvn5+SJERY0da0XjxDrRePljreDlTyJp0aIF5HJ5rb80AUBZWVmd7pZ8C3PbODGvJAb+3DVOzGvj5c+5ZVMhEqVSifj4eGRlZdVan5WVhX79+okUFbkDc9s4Ma8kBv7cNU7Ma+Plz7nl5U8epNfrUVhYaH9dVFSE/Px8hIeHIzo6GqmpqRg3bhz69OmDxMRErFmzBqdOnUJKSoqIUZMjmNvGiXklMfDnrnFiXhsv5rYe4kw65R927txpA1Dna8KECfbPZGZm2tq2bWtTKpW23r1727Kzs8ULmBzG3DZOzCuJgT93jRPz2ngxt9cms9kawXPBiYiIiIhINLyngoiIiIiIBGFTQUREREREgrCpICIiIiIiQdhUEBERERGRIGwqiIiIiIhIEDYVREREREQkCJsKIiIiIiIShE0FEREREREJwqaCiIiIiIgEYVNBRERERESCsKkgIiIiIiJB2FQQecGgQYMgk8kgk8mQn5/vtv1OnDjRvt+tW7e6bb9ERORdrBPk69hUEAmUm5uLzp07Y/jw4Q1+burUqSgpKUFcXJzbjv3GG2+gpKTEbfsjIiL3Y50gf8CmgkigGTNmYMaMGTh58mSDnwsODsYNN9yAwMBAtx07NDQUN9xwg9v2R0RE7sc6Qf6ATQWRAMePH8fZs2dxww03oFu3bk5tO2jQIDzxxBN44oknEBYWhubNm+O5556DzWazf6ampgavvfYaOnToAJVKhejoaLzyyivu/jaIiMhDWCfIX7ivFSbyQ1999RWGDRuGr7766rqnta/l3XffxeTJk/HDDz8gLy8P06ZNQ9u2bTF16lQAQFpaGt566y0sXboU/fv3R0lJCX755Rd3fxtEROQhrBPkL9hUEAmwe/duDBw4ECtXrsQbb7zh9PZt2rTB0qVLIZPJ0LlzZ/z8889YunQppk6disuXL+ONN97A8uXLMWHCBADAzTffjP79+7v72yAiIg9hnSB/wcufiAQ4cuQIjhw5gocffhjBwcFOb9+3b1/IZDL768TERJw4cQJWqxUFBQUwmUwYMmSIO0MmIiIvYp0gf8GmgkiAM2fO4MCBA0hNTXX7vtVqtdv3SURE3sU6Qf6CTQWRADabDc888wyaNGni0vb79u2r87pjx46Qy+Xo2LEj1Go1vv32W3eESkREImCdIH/BeyqIXPTFF1+gsrIS7dq1w48//ojz58/jjjvucGofp0+fRmpqKh5//HEcOnQIy5Ytw+LFiwEAQUFBeOaZZzBnzhwolUrcdtttOHfuHI4ePYrJkyd74lsiIiI3Yp0gf8KmgsgFJpMJ69evx9q1a/HYY48hOjoa69evd3o/48ePh9FoxC233AK5XI4nn3wS06ZNs7///PPPIzAwEC+88AL+/PNPtGrVCikpKW78ToiIyBNYJ8jfyGx/neyYiDxi0KBB6NmzJzIyMhpc5yqZTIYtW7Zg9OjRgvdFRETexzpBvo73VBB5yYoVKxASEoKff/7ZbftMSUlBSEiI2/ZHRETiYZ0gX8YzFUReUFxcDKPRCACIjo6GUql0y1+gysrKoNPpAACtWrWCRqNxR7hERORlrBPk69hUEBERERGRILz8iYiIiIiIBGFTQUREREREgrCpICIiIiIiQdhUEBERERGRIGwqiIiIiIhIEDYVREREREQkCJsKIiIiIiIShE0FEREREREJwqaCiIiIiIgEYVNBRERERESCsKkgIiIiIiJB/h8X+gwYi31ZegAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corr_result_dict = twopoint_correlation(\n", " pos_a=pos,\n", " val_a=dens,\n", " pos_b=None,\n", " val_b=None,\n", " dist_bin_edges=dist_bin_edges,\n", " stat_kw_pairs=[(\"mean\", {})],\n", " nproc=10,\n", ")[0]\n", "\n", "fig, ax_arr = plt.subplots(1, 2, figsize=(8, 4), sharex=True)\n", "\n", "ax_arr[0].stairs(corr_result_dict[\"counts\"], edges=dist_bin_edges)\n", "ax_arr[0].set_ylabel(\"Number of pairs\")\n", "ax_arr[0].set_yscale(\"log\")\n", "ax_arr[1].stairs(corr_result_dict[\"mean\"], edges=dist_bin_edges)\n", "ax_arr[1].set_ylabel(r\"$\\xi\\ [{\\rm g}^2\\ {\\rm cm}^{-6}]$ \")\n", "\n", "for ax in ax_arr:\n", " ax.set_xlabel(r\"$\\ell\\ [{\\rm pc}]$\")\n", " ax.set_xscale(\"log\")\n", " ax.axvline(56, ls=\":\", color=\"k\", label=\"initial radius\")\n", " ax.axvline(56 / 8, ls=\":\", color=\"k\", label=\"pixel scale\")\n", "\n", "fig.tight_layout()" ] } ], "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 }