{ "cells": [ { "cell_type": "markdown", "id": "1baac5d8", "metadata": {}, "source": [ "# Probabilistic analysis of SUV" ] }, { "cell_type": "markdown", "id": "6ccab8f6", "metadata": {}, "source": [ "Author: Martin Horvat, October 2022" ] }, { "cell_type": "markdown", "id": "5da46295", "metadata": {}, "source": [ "## Reading matlab files and getting data" ] }, { "cell_type": "code", "execution_count": 1, "id": "b6bd26cc", "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", " Filenames of matlab files\n", " CWD = \"Optimisation Group/Retrospective data/Python\"\n", "\"\"\" \n", "suv_filename = \"../data/suv_percentilesSLOthenUWM.mat\"\n", "flags_filename=\"../data/flags_combined.mat\"\n", "normal_range_filename =\"../data/normal_range.mat\"" ] }, { "cell_type": "code", "execution_count": 2, "id": "02eb5b93", "metadata": {}, "outputs": [], "source": [ "from scipy import io\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "id": "a8a2b1fa", "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "Loading all data\n", "\"\"\"\n", "suv_dict = io.loadmat(suv_filename)\n", "flags_dict = io.loadmat(flags_filename)\n", "normal_range_dict = io.loadmat(normal_range_filename)" ] }, { "cell_type": "code", "execution_count": 4, "id": "767eadff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "suv: dict_keys(['__header__', '__version__', '__globals__', 'bowel_SUVperc_COMBINED', 'lung_SUVperc_COMBINED', 'thyroid_SUVperc_COMBINED'])\n", "flags: dict_keys(['__header__', '__version__', '__globals__', 'days', 'flags', 'patients'])\n" ] } ], "source": [ "# tables in data\n", "print(\"suv:\", suv_dict.keys())\n", "print(\"flags:\",flags_dict.keys())" ] }, { "cell_type": "markdown", "id": "00c25424", "metadata": {}, "source": [ "## Short analysis of max SUV at certain percentiles for lungs" ] }, { "cell_type": "code", "execution_count": 5, "id": "eabead83", "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", " Extracting only data associated to lungs\n", "\"\"\"\n", "lung_suv = suv_dict['lung_SUVperc_COMBINED']\n", "flags = flags_dict['flags']" ] }, { "cell_type": "code", "execution_count": 6, "id": "b7c8f0ff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(71, 17, 100)\n", "(71, 7)\n" ] } ], "source": [ "print(lung_suv.shape)\n", "print(flags.shape)" ] }, { "cell_type": "code", "execution_count": 7, "id": "62381e04", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1 0 0 1 187 0 0]\n", " [ 1 0 0 0 0 1 17]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 1 191 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 1 299 0 0]\n", " [ 1 1 196 0 0 0 0]\n", " [ 1 0 0 0 0 1 42]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 1 65 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 1 122 1 122]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 1 637]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 1 154]\n", " [ 1 0 0 0 0 1 721]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 1 195 1 273 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 1 63]\n", " [ 1 1 32 0 0 0 0]\n", " [ 9 1 323 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 2 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 1 27]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 1 231 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 1 28]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 2 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 2 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 3 0 0 0 0 0 0]\n", " [ 2 0 0 0 0 0 0]\n", " [ 5 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 0 0]\n", " [ 1 0 0 0 0 1 0]\n", " [ 1 0 0 0 0 1 0]]\n" ] } ], "source": [ "\"\"\"\n", "Flags: columns\n", " 0 base : line exist \n", " 1 bowel : status, 0 ~ NC, 1 ~ AE\n", " 2 bowel : time\n", " 3 lung : status\n", " 4 lung : time \n", " 5 thyroid : status\n", " 6 thyroid : time \n", "\"\"\"\n", "print(flags)" ] }, { "cell_type": "code", "execution_count": 8, "id": "4c04d7fa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0\n", " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", "NC: 66\n", "AE: 5\n" ] } ], "source": [ "\"\"\"\n", " Focus on lungs\n", "\"\"\"\n", "lung_flags = flags[:,3]\n", "print(lung_flags)\n", "print(\"NC:\", np.count_nonzero(lung_flags == 0))\n", "print(\"AE:\", np.count_nonzero(lung_flags == 1))" ] }, { "cell_type": "code", "execution_count": 9, "id": "20d1c213", "metadata": {}, "outputs": [], "source": [ "p = 95 # percentile of interest\n", "X = np.nanmax(lung_suv[:,:,p], axis=1) # taking SUV max and ignoring NaNs" ] }, { "cell_type": "code", "execution_count": 10, "id": "8c50b417", "metadata": {}, "outputs": [], "source": [ "# extracting Xs associated to NC and AE\n", "X_NC = X[lung_flags == 0]\n", "X_AE = X[lung_flags == 1]" ] }, { "cell_type": "code", "execution_count": 37, "id": "d2df81aa", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.rcParams['font.size']=16" ] }, { "cell_type": "code", "execution_count": 38, "id": "55fb09a9", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\"\"\"\n", " Plotting stuff\n", "\"\"\"\n", "plt.hist(X_NC,density=True,label=\"NC\")\n", "plt.hist(X_AE,density=True,label=\"AE\")\n", "plt.legend()\n", "plt.savefig(\"p_risk.pdf\")" ] }, { "cell_type": "markdown", "id": "ecd96505", "metadata": {}, "source": [ "Conclusion:\n", "The statistics of AE is not very good, but for NC is reasonable." ] }, { "cell_type": "markdown", "id": "f8cbe96f", "metadata": {}, "source": [ "## Distributions of max SUV in patients" ] }, { "cell_type": "code", "execution_count": 27, "id": "5df2cf8e", "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", " Extracting only data associated to lungs\n", "\"\"\"\n", "lung_suv = suv_dict['lung_SUVperc_COMBINED']\n", "lung_flags = flags_dict['flags'][:,3] # 0 == NC, 1 == AE" ] }, { "cell_type": "code", "execution_count": 28, "id": "d88b747a", "metadata": {}, "outputs": [], "source": [ "# generate percentiles\n", "P = np.arange(0,100)/100\n", "\n", "# calculate max SUV of all visits per patient and percentile (i)\n", "X = np.array([np.nanmax(lung_suv[:,:,i], axis=1) for i in range(100)], dtype=float).T" ] }, { "cell_type": "code", "execution_count": 29, "id": "dc54dff6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(71, 100)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.shape" ] }, { "cell_type": "code", "execution_count": 30, "id": "c9c9e21f", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 31, "id": "a2ce731c", "metadata": {}, "outputs": [], "source": [ "plt.rcParams['text.usetex'] = True" ] }, { "cell_type": "code", "execution_count": null, "id": "497ae324", "metadata": {}, "outputs": [], "source": [ "plt.xlim(0.1,20)\n", "plt.ylim(0.01,1)\n", "\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.xlabel(r\"$x = \\max_\\textrm{over visits}(SUV)$\")\n", "plt.ylabel(r\"$1 - P(X < x)$\")\n", "\n", "plt.title(\"AE cases\")\n", "for x in X[lung_flags == 1]: plt.plot(x,1-P)\n", "plt.tight_layout()\n", "plt.savefig(\"P_risk_AE.pdf\",bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 50, "id": "3f6154fd", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlim(0.1,20)\n", "plt.ylim(0.01,1)\n", "\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.xlabel(r\"$x = \\max_\\textrm{over visits}(SUV)$\")\n", "plt.ylabel(r\"$1 - P(X < x)$\")\n", "plt.title(\"NC cases\")\n", "\n", "for x in X[lung_flags == 0]: plt.plot(x,1-P)\n", "plt.tight_layout()\n", "plt.savefig(\"P_risk_NC.pdf\",bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 41, "id": "3caf02fd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LEydORCnFggULyMvLC/kkSdnoSzb6ighdXZqPPfIPdh89Q/W3bmXk0JSBnhCevA+2V8Hdv4fZnwpOoCKs7Nq1i6lTp9odRkTq7b2Tjb5ExIuLU/x86UwudXTx4/U7g3FC+PDDkHcHrPuqzNAXIogksYiIkTtiCF+6LZ9nth3ltfdPDvyECUlwz59h5FRjIuXxICQsIYQkFhFZym/NZcKwVH60bgeXOzoHfsLkofCJNZCYCn9ZDueCkLCEiHGSWERESUmM50dLptN46jx/3LQ/OCdNz4GP/xXOn4A1n4aOPq6sLIS4iiQWEXFumzySkqmj+N3Lezhx5lJwTppTZHTiH9wML/hfOVYI4Z8kFhGRfrBwKm2dXax68b3gnXTmMnOOyx/hnceCd14hYkzMJhaZIBnZJgwfzOdvnMgTdYfZfiSIf8M7/g0m3AzPfhOOvRu88woRQ2I2scgEycj3xdvzcQxK5KfP7hrYjHx38Qmw7I+Q4oA1n4HLZ4NzXiFiSMwmFhH50gcl8rX5k9jceJpX3gviaK4hI43k0rIPnv1W8M4rRIyQxCIi2ieuG8/4YalUvLCbrr5uZezPhBvh1pWw7XGo/1vwzitEDJDEIiJaUkIc3/rAZHYfO8vT9UeCe/Kbvw3j5hm1lmbf28AKYYcFCxb43KNo5cqVKKW83lavXh3y2CSxiIi3aGYW07PT+OWG92nrCOI+K/EJsHQ1qHh4sgw6O4J3biEGwOl0UlNTg8Ph8JoonE4nRUVFaK173MrKykIenyQWEfHi4hTf/sBkDjVfZE3NoeCe3DEWFv0SDm+BTb8M7rmF6KfVq1dzzz33UFZW5nPfeztJYhFR4bbJIyga5+B3L+/lUnsQlnpxN3MZzPgovFoBR+uDe24h+qGyspLS0lKWL19OXV2d12Xz7SR73ouooJRRa/nEo2/x17cP8rkbJwa3gA89BPvfgKe+AGV/h4Tk4J5fWOv579o/T2n0TLjr531+WV1dHc3NzZSUlADGpl5VVVWsWLEi2BH2m9RYRNS4IW8Y103M5OFXGoJfa0nNhCW/hRM74NVVwT23EH1QWVnJPffcc+X+Pffc47U5rK6urkfHfUZGhiUxxmyNRSm1GFicn59vdygiSJRSfL3kGj7+yD/4y1sH+fxNQa61XHMnzPqksevktCWQVRjc8wvr9KOmEC7WrFnD2rVrr9wvLy9n9erV1NXVUVRUdOXxoqIiamtr7QgxdmssMvM+Ot2QN4xrJ2ZS+VoIai0Ad/4UUocZm4N1heD8QvhRXV2N0+mktLSUjIwMMjIymD9/PkBYdeLHbGIR0eurd0zi+JnLrA32CDGAQRnwwQfg6FZ4O/TzAYRwV1lZyYoVK2hpabnqtmLFCtasWWN3eFdIYhFR58b8YRSNc/CHVxtp7wzivBaXGR+F/AWw8SfgDEHyEsILp9NJVVUV5eXlPZ4rLy/H6XRSXV3t9/VOpzOEEXaTxCKijlKKL9+RzxHnRZ7e2hSKAoy5LWh4PnxG4ojotmbNGoqKisjNze3xXG5uLkVFRVc1h3l23ruazrwlpmBTQVsVNkIVFxfrmpoau8MQQaa15kO/3URbRycbvnErcXHel74YkE2/huofwsf+ClM+FPzziwHbtWsXU6dOtTuMiNTbe6eUqtVaF3t7TmosIioppfjCbXk0nDzPhl3HQ1PIDV+CEVONWkvbhdCUIUQEksQiotaHZoxmbOYgKl9tCE0B8YlGk1jrIXj9odCUIUQEksQiolZCfBz33pRL3UEnNfubQ1PI+HlQ8DF48z/g1N7QlCFEhJHEIqJaafEYHKmJVL4WwrWUFvwYElLghZUQ432WQoAkFhHlUpMS+PT146nedZx9p86HppCho4xNwfZWw/svhqYMISKIJBYR9f7phgkkxsXx32/sC10h15bBsEnw4vegoy105Yg+i/WRr/0x0PdMEouIeiOGJrNkVjZraw7TeqE9NIUkJMEHf27sNPnWH0JThuizhIQEOjpkg7a+6ujoICGh/0tJSmIRMeHzN07kYnsnf9tyMHSFTCoxZuS/9hCcPx26ckTAUlJSOHfunN1hRJyzZ8+SkpLS79fHbGJRSi1WSq1ubW21OxRhgWnZaVw3MZM/bz5AZ1cIm0Y+8P+g7Ry8Grmr50aTESNGcPLkSS5cuCBNYgHQWnPhwgVOnTrFiBEj+n2emF02X2u9HlhfXFx8n92xCGt8dt4EvvBYHdW7jnPn9NGhKWTkFCj+HGz5L5h7H4y4JjTliICkpKQwatQojh07xuXLl+0OJyIkJyczatSoAdVYYjaxiNizYNoostNT+NOb+0OXWABu+x7UP24s9/Lxv4auHBGQ9PR0ZHsMa8VsU5iIPQnxcXzy+vG82XCaPcfPhq6gwcPh5m/Ae8/B/k2hK0eIMCWJRcSUj80dS1JCHH/efCC0BV3/RUjLgRf/BbpCsHS/EGFMEouIKcOGJLOoIIsn6w5z7nIIh6EmDoI7/tXYEGz7E6ErR4gw1OfEopSapZRaqpT6tlLqXvPnWSGITYiQ+PT14znf1slTdYdDW1DBchg9Ezb+GDqk41jEjoASi1JqglLqD0qpF4FyIA9oBZT58/1KqZeUUg8rpSaELFohgmDWWAfTs9N47K2DoR2CGhcHC34CrQdhy6OhK0eIMNPrqDCl1HeATGCl1trvpA+lVDpQppRq0VrL/yQRlpRSfPK68Xz/qXepO9jCnPGZoSss73bIuwNeexBmfwpSZHSSiH5+ayxmUqnSWn+vt6QCoLVu1Vo/CGxUSn07WEEKEWx3z8pmSHICj70Vwpn4LvP/DS62wOb/DH1ZQoQBv4lFa/2g1rrPK/dprfdprWXnIxG2BicncPesbJ7ddjR064e5ZM+GqYth8+9lqRcRE/o1KkwplRbsQISw2ieuG8flji6eeifEnfgAt/8A2s/D678IfVlC2Kw/o8J+Duxzuz9bRoWJSDQ9O52CMen89e1DoV9HauQUKPwEbHkEnIdCW5YQNutPjWULkOu6o7V+BximlLojaFEJYZGPzR3He8fPsvWQM/SF3bbS+Pe1VaEvSwgb9SexNAKl7s1hWuuNwQtJCOssLsxiUGI8j2+xoBbhGAfFn4d3HoNTe0NfnhA26U9iKQHuB/YrpbYopR5QSt0LlAY3NCFCb2hKIgsLslhf38T5UM7Ed7n5W5CQLMvqi6jWn8Ti1FoXa60zgTKMSZL3AyuDGpkQFlk+dyzn2zp59t2joS9syEhjG+N3q+D4ztCXJ4QNBrRWmNb6Ha31d7XWxUBxkGISwlLF4zPIHT6YqhoLRocB3Pg1SBoCrzxgTXlCWKzPiUVr/YhS6j73kWBKqceBomAGJoRVlFJ8dM4Y3t7fzP5T50NfYGomXP8F2LUOjtaHvjwhLNavGovW+hGt9Va3h9YAEdWBL1sTC3cfLRpDnIInQr0wpcsNXzKWd/m71FpE9AnKsvla6yfMYccRQ2u9XmtdJjvLCYDR6SncNGkET9QepqvLgr3RBzlg3lfg/efhcE3oyxPCQrIfixCm0jljaGq9xOZGi5Zdue5+SB0Gf/+pNeUJYRFJLEKYFkwbxdCUBJ6otag5LHmo0ZHf8DIcetuaMoWwgCQWIUwpifEsKsjm+e3HQru7pLu59xq1FhkhJqKIJBYh3Hy0KIeL7Z28sP2YNQUmDZZai4g6kliEcDNnfAbjMlN50qrRYeBWa5HZ+CI6BJxYlFIT/S00qZSaJdsSi0inlGJpUQ6bG0/T5LxoTaFJg2HeV6FhIxzaYk2ZQoRQwInF3PDL33pg5Vrr/QOOSAibfWR2DlrD01ubrCvUVWuRNcREFOhrU1idt71XzJpKYzACEsJu44cNpmicg6feORz6fVpckofADV+GvdVwuNaaMoUIkT4lFq31I0C5l6dWmnvdCxEVPlI0hvePn2Pn0TPWFXrtfTAoU2otIuL1p/O+1b0vRSmVDsi6KCKqLJqZRUKcsrY5LHkozPsy7HkJjtRZV64QQdafxPIAV9davgv8LDjhCBEeMgYncdvkETy99QidVizx4jL3PkhxwKuyy6SIXP1Z3bgVyHDbQTJDa21he4EQ1vjw7ByOn7nMW1Yt8QKQkmb0tbz/vKx8LCJWf+exVABl5s6RlUGMR4iwUTJ1FIOT4q1tDgO4rgyS0+D1X1hbrhBB0t9l8/cB+cCcSFvVWIhApSTGc+eM0Ty3/SiXOzotLDjd6MjfuQ5OvmdduUIEyUBm3lcCq4MViBDh6O5ZOZy91MHfd5+0tuDrvwiJg+D1X1pbrhBB0O/EYm5LLLUVEdVuzBvG8CFJrK+3uDls8HCY81l4dy20HLC2bCEGSNYKE8KPhPg4Fs7MonrXcc5eare28Bu+DCoONv/O2nKFGCBJLEL0YsmsHC53dPHSjuPWFpyeA4XLoe7PcM7ipjghBkASixC9KBrnYGzmIJ62ujkM4MZvQMdleOth68sWop8ksQjRC6UUiwuyeWPvKU6fu2xt4cPzYdoSePtRuCTTxURkkMQiRAAWF2bT2aV5zqoNwNzd9A243Ao1f7S+bCH6odfE4jbDXoiYNWX0UCaNHGL96DCA7NmQezts/j20X7K+fCH6KJAaS0XIoxAizCmlWFyYzdv7mjnaatEGYO5u/iacPwH1f7G+bCH6KJDEco9SamhvB4XL7pFKKYdSqkIpVWR3LCK6LCrIAuDZbUetL3zCzZAzB974DXR2WF++EH0QSGKpA74fQJOYt31a7FAM5NodhIg+uSOGMCMnjfV2JBaljL6Wlv2w62nryxeiD3pNLFrrBVrr7wHl3pKLUuoOpdQaYEUoAuwrrXU10Gx3HCI6LSnMpv6Qk4OnL1hf+OSFMGwSbPoVWLWzpRD90Jc97x8ElruavJRS9yql9gAbAAewL5DzmE1VK5RSXvtuzOeWKaXKlFJlgcYnhBUWFmQDsH6bDZ34cXFw09fh2Luwd6P15QsRoL4ON64G1iqlOjEWodwI5GutP0AATWFKqRKgBMjDSEaez1cAjVrrKq31aiBPKbWsjzEKETI5jkHMGZ9hz+gwgJn3wNBseOPX9pQvRAACGW78sNnc9RKwF8gAHgQ+oLW+31xCH611r1+htNbVWusqwOnjkDLzeZfHcUtYZi1mhZebdNQLyywqyGL3sbPsPXHW+sITkuCGL8L+1+FwrfXlCxGAQGos5RjNXU6gWGudr7X+Lhj9K8EKxEdycGLUcADQWq/WWq/ycpMNwoVlFs7MQilYX29DJz4Yqx6npMMbv7KnfCF6EUhiqQYytdb3uC+Tb9ZQlFJqaZBiyaRnp3ufO+HN5rZijP4gqcmIoBuZlsJ1EzNZv60JbUcnevJQmHsv7HoGTu21vnwhehFIYqk097nvwUwu+5RS9ymlHhhgLA5fTyilfD7nJaZqrfUcrfVKXzUZs0mtRilVc/KkrBor+m5RQTaNJ8+z+5gNzWEA190P8Umw+T/sKV8IPwIZbvxEL8+/A9Qy8OHGToxaizvP+0FhNqkVa62LR4wYEYoiRJS7a8Zo4hQ8Y8foMIAhI2HWJ2DrX+Gsxcv5C9GLoCxCadYM/CagADTTs9biMM/vHOC5hQiqYUOSmZc3nGe3HbWnOQxg3legsw3errSnfCF8CObqxgP6dJvJyenxcCZGH48QYWdhQRb7T19gR5NNy9kPy4Opi2DLo3DZpiY5IbwIWmIJZLhxANZ4zFtZwAATlhCh8sHpo4mPU/ZMlnS58etwqRVq/2RfDEJ48JtYlFLf6c/ikkqpiUqpb3t5vEgptQJYBpR4zkHRWpcDuUqpEnPWfYPHvJagUUotVkqtbm31Oi5BiF5lDE7ixvzhPPeujc1hY4qNBSrf/A9ZUl+EDdXbfwil1H0YizpWaq3393JsGvB94JTW+qFgBRlKxcXFuqamxu4wRIRaU3OIFVXbePpLN1I41mFPEI2vwp+XwMJfGMOQhbCAUqpWa13s7bmE3l6stX5EKTURuF8pNRtoxOgLacDoXB9m/ptnPrbKNRtfiGh357TR/Ev8uzz77lH7EsvEW2DMtbDp11D0GYhPtCcOIUwB9bForfdprb+rtb4TWIXRoa6AVqAGWK21/oDW+guSVEQsSU9N5OZJI+wdHaYU3PIdaD0E9X+zJwYh3PRaY/FkJo59GAtQChHzFs7M4uXdJ3jnkJOicRn2BDFpAWQVwqZfQuHHIb7P/7WFCJqAaixKqQeUUi8qpb4V6oCsIp33IlgWTB9FUnwcz9i1dhh011qaG+HdNfbFIQSBrW78c2AORi3lC0qpLSGPygJa6/Va67L09HS7QxERLi0lkVuuGcFz7x6lq8vGDbimLIKsWfDKA9DRZl8cIuYFUmPJNftP7tda5wOrlVIy9EQIN4sKsjh25hJ1B1vsC0IpuONfwXkQ6mRei7BPIIml0f2O1voRjBFgQghTybRRJCXE8cw2G5vDAPLnw7h58NqD0GbD9slCEFhiOe3lMdlTXgg3Q5ITuH2y0RzWaWdzmFIw/1/h3HF4e7V9cYiYFkhi8bbCcKOXx4SIaQsLsjlx9jI1+23+3jV+HuSXwKZfGcu9CGGxQBLLSqXUFnNk2O3mYzZ+JRMiPM2fMpKUxDBoDgO44wdwyQlv/NbuSEQMCiSxVAFrMEaGPaGU6gQeUUo9rJRaai7jQqR16MtwYxFsg5MTuGPKSJ7ffpSOzi57g8meDdOXwubfwxkbF8kUMSmQxPKA1vpBc2RYJjAJ+C7GUi6PAi1KqT3AyhDGGXQy3FiEwqKCbE6da+PtfWHQDVnyQ9Cd8PJP7Y5ExJhAdpB8x+N+o9b6Ea31PWaiyQceDFWAQkSS2yePJDUpnvXh0ByWMQGuLYOtj8Gx7XZHI2LIgPdjMdcRWw3IEBQR8wYlxVMydRQvbD9Ku93NYQA3fwtS0mHDv9kdiYghwdxBUhKLEBiTJVsutPNmg7eR+hZLzYRbV0DDRtgry/sJawRzB0npBRcCuHXyCIYmJ/BMfZh0ms+9FxzjjVpLV6fd0YgYEMwaixACSE6IZ8H0Uby44xhtHWHQHJaQDCU/guPbof6vdkcjYkDMJhYZbixCaXFBNmcudfDa+yftDsUw/SMwZi5s/Am0nbc7GhHlYjaxyHBjEUo3TRqOIzWRdeHSHKYU3PkzOHdMJk2KkIvZxCJEKCXGx3HXjCw27DzOhbYOu8MxjL3WmDT5xm9k0qQIqQElFqXUw8EKRIhos6Qwm4vtnWzcdcLuULqV/Ah0l9EkJkSIDLTGMiwoUQgRha6dmMmotOTwaQ4DyBgP138B6v8CTe/0frwQ/TDQxCKLUQrhQ3ycYuHMbF597yStF9vtDqfbzd+E1OHw4g9Ay39hEXzSxyJECC2ZlU1bZxcvbj9mdyjdUtLh9u/DgU2w+1m7oxFRSBKLECFUOCadcZmprN8WRs1hAEWfgRFTYMO/Qkeb3dGIKCOJRYgQUkqxuDCLN/ae4sTZS3aH0y0+AT7wU2huhC2P2h2NiDIxm1hkgqSwykdmj6FLw9PvhFmtZVIJ5N4Grz0IF512RyOiSMwmFpkgKaySP3IIhWMdPFF3GB1uneULfgwXm+GNX9sdiYgiA00sKihRCBHllhXlsPvYWXY0nbE7lKtlFULBcvjHw9B6xO5oRJQYaGJ5PChRCBHlFhdmkxQfxxN1h+0Opafb/8WYNPn3n9kdiYgSA0osWusnghWIENHMkZrE/KkjWbe1KTw2AHOXMb57p8njO+yORkSBmO1jEcJqHy0aw+nzbbz6XpiseOzu5m9BShpU/8juSEQUkMQihEVunTyCYYOTwrM5LDUTbv427HkJGl+1OxoR4SSxCGGRxPg47p6Vw8ZdJ3BeCMNJideWQfpYc6fJMGuuExFFEosQFvronBzaOrtYH04LU7okpsAdP4CjW2HHk3ZHIyJY0BKLUmpWsM4lRLSalpXGlNFDqaoL06G9M++BUTNh449lqRfRb8GssZQE8VwhJzPvhR2UUiyfO5b6Q05qD7TYHU5PcXGw4EfgPGCMEhOiH1RvM4GVUj8HApmeXqK1nhSUqCxUXFysa2pq7A5DxJALbR3c+POXmTM+g0c/M9fucHrSGv5rAZw9Dl+phYQkuyMSYUgpVau1Lvb2XCA1ltMYM+xbe7kJIQKQmpTA526cSPWuE+w+FmYz8QGUgltXQutB2PY3u6MRESghgGNWY9RG/E6GVEqdDk5IQkS/z9wwgcpXG3j4lQZ+87HZdofTU34JZBfBaw9B4cchPtHuiEQE6bXGorVuBZwBnKt6wNEIESPSUxP55PXjWV/fxIHT5+0OpydXrcV5ALbJyk2ibwLqvNdabwzgGNlAW4g+uPemiSTExVH5WqPdoXh3zZ2QNcuotXR22B2NiCAyj0UIm4xMS2FZ8Riqag5z4kwYbQLm4qq1tOyDd9faHY2IIJJYhLDR/bfk0dHVxaOb9tkdineT74LRM43NwLo67Y5GRAhJLELYaNywVBYXZvPYPw7QeqHd7nB6ctVamhtguyxmLgIjiUUIm33htjzOt3Xyp8377Q7Fu8kLYeR0qbWIgEliEcJmU0anUTJ1JP/9xj4utIVhJ3lcHNy6Ak69DzuesjsaEQEksQgRBr5wWz4tF9r569uH7A7Fu6lLYMRUs9YiKx8L/ySxCBEG5ozP4PrcTB55rZHLHWHY3BQXB7d+B07uhl1P2x2NCHOSWIQIE1+8LZ9jZy7x6OthOkJs2odh+DXw6iqptQi/YjaxyOrGItzcPGk4iwqyeOil96jeedzucHqKi4dbvgMndsJ7z9kdjQhjMZtYtNbrtdZl6emBLNwsROgppXhwWSEzc9L52t/eCc8FKqcvhcxceG2VsQqyEF7EbGIRIhwNSornkX8qZkhKAv/8PzWcOnfZ7pCuFp8AN38LjtbDng12RyPClCQWIcLMqLQUHvmnYk6fv8z9/1sbfp35BcshfZzUWoRPkliECEMFYxw8VFpIzYEWvv/kdnrbkM9S8Ylw09fh8BZofMXuaEQYksQiRJhaVJDN10sm8UTdYVaH2wrIsz8FQ7ONlY+F8CCJRYgw9rX5k1hYkMXPX9gdXiPFEpLhxq/BgU1w4E27oxFhRhKLEGFMKcVD4TpSbM5nYPAIY16LEG4ksQgR5sJ2pFjiIJj3FWj8OxyusTsaEUYksQgRAcJ2pFjxP8OgTKm1iKtIYhEiQhSMcfCL0lnhNVIseQjc8EXY8yI0bbU7GhEmJLEIEUEWFmSF30ixa8sgJR1elxFiwiCJRYgI87X5k1hkjhTbEA4jxVLS4br7Ydd6OL7T7mhEGJDEIkSEUUrxUKkxUuzr4TJS7Lr7IWmI1FoEIIlFiIiUkhhmI8VSM2HuvbD9STi1x95YhO0ksQgRocJupNgNX4aEFHj9F/bGIWwniUWICBZWI8WGjIDiz8O2NdAcppuVCUtIYhEiwi0syOIbJdeEx0ixeV+BuATY9Ct74xC2SrA7ACHEwH11fj57Tpzl5y/s5r1jZ1k8K5ub8oeTGG/xd8e0LCj6NNT+yWgWm7EUxlwLcfIdNpaosJhkZQOl1GJgcX5+/n179khno4h8l9o7+ckzO1lX38TZSx1kDk7irhmjWVKYzdwJmcTFKWsCOX8Knv0mvPcCdF6GtByY/hFj98mcIlAWxSFCSilVq7Uu9vpcrCYWl+LiYl1TI+sciehxuaOT194/xbr6Jqp3HudieydZ6SksKshiSWEOM3LSUFZc3C+dgfeehx1Pwt6N0NUOjvFGLWb6Uhg9U5JMBJPE4ockFhHNzl/uoHrXcdbXN/Hq+ydp79RMHD6YxYXZLCnMJn/kEGsCudgCu581hiM3vgK6E4blGwlmxkdh5BRr4hBBI4nFD0ksIlY4L7TxwvZjrKtvYnPjabSGaVlpLJmVzeLCbHIcg6wJ5Pxp2LUOtj8B+zcBGkZOM5PMUhiWZ00cYkAksfghiUXEohNnLvHMtqOsq29i6yEnAMXjM1gyK5sPzcxi+JBkawI5exx2Pm00lx3cbDw2uqC7uSxjvDVxiD6TxOKHJBYR6w6evsD6bU2s29rEe8fPEh+nmJc3jCWF2dw5YzRpKYnWBNJ6GHb8n5FkjtQaj+UUG0lm2ochPceaOERAJLH4IYlFiG7vHTvLuvojrKtv4lDzRZLi47ht8gjunpXD/KkjSUmMtyaQlv2w4ymjT+bYNuOxcTcY/THT7oYhI62JQ/gkicUPSSxC9KS1ZushJ+vqm3hm21FOnr3M4KR4FkwbxZJZ2dw8aYR1c2RO7TVqMdufhJO7QMXBhJuMprKpS2DwMGviEFeRxOKHJBYh/Ovs0rzVeJp19U08v/0YrRfbcaQmcteMLJYUZnPdRAvnyJzYZSSY7U9AcwOoeMi9zajJTFkIgxzWxCEksfgjiUWIwLV1dPH6npOsq29iw87jXGjrZFRaMosKjOHLBWPSrZkjo7XRRLb9SaM24zwI8UmQN9/ok5l8FyQPDX0cMUwSix+SWITonwttHWzcdYJ19U28+t5J2jq7mDAs9cocmUmjLLqwaw1H6oxazI6n4GyTsZzMpA8YSWbSnZCUak0sMUQSix+SWIQYuNYL7by4w5gj82bDKbo0TBk91JgjU5DN2EyLLuxdXXDoLaMWs+P/4PwJSBwMkz9o9Mnkl0BiijWxRDlJLH5IYhEiuE6cvcRz5hyZuoNOAIrGOVhSmM3CgmxGDLVojkxXpzEBc8eTsHMdXGyG5DSjL2b6UqNvJiHJmliikCQWPySxCBE6h5q758jsPnaWOAXz8oazuDCLD07PIj3Vojkyne2w71XY/hTsXg+XWiHFAVMXG81lE26BeFnsvS8ksfghiUUIa+w5fpZ19U2sq2/iwOkLJMXHccs1I1gyK5uSqSNJTbLowt5xGRpeNjr+33sO2s5B6nBjfsyMpcZ8mTiL5utEMEksfkhiEcJaWmu2HW4158g0cfzMZVKT4imZOoolhdnccs0IkhIsmiPTfhH2bDCay957ATouwpDRMP3DxhDmMXNlBWYfJLH4IYlFCPt0dmne3tfM+m1NPPfuUZwX2kkflHhlH5nrcocRb9Ucmcvn4P0XjJFlezYYe8mkjzWSzPSlkD1bkowbSSx+SGIRIjy0d3axaY+xj8yLO45xoa2TkUOTWVhgTMScNdZhzRwZMPpgdj9n1GQaXoauDsiY2L045qjpMZ9kJLH4IYlFiPBzsa2Tl3efYF39Ef6+25gjMy4zlcWFxmZlk0dbOPnxQjPsfsbok9n3mrGXzPBrupf5HzHZuljCiCQWPySxCBHezlxq58Xtrjkyp+ns0kwe1T1HZtwwCyc/njsJu542RpcdeAPQMGqGsfXyjKWQmWtdLDaTxOKHJBYhIsepc5d57t2jrNvaRM2BFgBmjTXmyCwqyGJkmoWTH88c7d5L5tBbxmNZs8zmso+AY5x1sdhAEosfkliEiEyHWy4Ym5VtbWLn0TMoBddPHMaSWdncNWM0jlQLJz86Dxmd/juehKZ3jMfGXNu9l0xalnWxWEQSix+SWISIfHtPnGNdfRPr65vYd+o8ifGKWya55siMYnCyhZMfmxvNvWSeguPvAgrGzzNqMdM+DENGWBdLCEli8UMSixDRQ2vN9iNnWFd/hGe2HeVo6yUGJcYzf+pIlhRmc+vkESQnWDj58eT73XvJnHrP2Etm4i3mXjKLITXTuliCLKYSi1KqzPxxDlChtW70d7wkFiGiU1eXpuZAC+vqj/Dcu8doPt9GWkoCH5wxmiWFOdyQZ+EcGa3hxM7uvWRa9kFcAuTebjSXTVkIKenWxBIkMZNYlFJFAFrrOqVUCbBSa73A32sksQgR/do7u3hjrzFH5qUdxzl3uYPhQ5JZVJDF4sJsisZZOEdGazi61dxL5iloPWTsJZO/wEgy13wQkodYE8sAxFJiWQYs0FqXK6UcwD6tdYa/10hiESK2XGrv5O+7jX1kNu4+QVtHF2MyBl3ZR2bK6KHWJpnDNUYtZuf/wdmjkDAIrrnT3EvmA5A4yJpY+iisEot5wS8DhmmtV3p5fgXQCGQCaK1X97McqbEIIfw6e6mdl3YcZ119E5v2nqKzSzNp5BCWFGazuDCbCcMHWxdMVxcc3Gwu8/80nD8JSUOM3TCnL4X8+ZBg0ZYDAQibxGJe7B3AAgCtdbnH8xXAFq11lbf7fSxrLXCf1trp7zhJLEIIgNPnLvPc9mOs39rE2/ubASgck87iwmwWFWQzOt3COTKdHbD/dSPJ7FoPF1sgOR2mLjL3krkV4i3acsCHsEksVwo1EobDS2JpcW+6MvtMKly1DrNj3uHllNVa6zq315WZj/ntuAdJLEKInpqcF3lmm7HE//YjxhyZaydksmRWNh+akUXGYAvnyHS2Q+MrRp/M7mfg8hkYlOm2l8zNtizzHxGJxUwiGz0SSy7QoLUOuMHTrBU1aq0blVIlWutqf8dLYhFC+NN48tyVfWQaT54nIU5x86ThLJmVzYJpoxli5RyZ9kvQsNHcS+Z5aD8Pg0d27yUz9nqIs2bLgUhJLCVApdY6z+0xB9ASaGJxJSeg2XyoTmtd6u81kliEEIHQWrOj6QzrzYmYTa2XSEmMY/6UUSwuzOa2ySNISbSw5tB2Afa8ZHT873kJOi7B0OzuvWRy5oR0BeZISSzLMJq9eiQWIKO3vpL+ksQihOirri5N3cEW1tU38ey2o5w+38by4rFULCuwJ6DLZ42NynY8CXurobMN7vlfmLYkZEX6SyzhtMmzE3MkmJuQTEs1+2BcEykvK6W2h6KcIEoHWsP43P05R19eE8ixvR3j73lfzw0HTvUanb1C+dkI1vlD+fkIm8/GKvMWNv797lB/Nib5fEZrbfkNqMBo9nJ/rMgIx/9jIYilxo73oI8xrg7nc/fnHH15TSDH9naMv+d9PRfrn41I+HzIZyM8PxsWbSzdO22M6nJ6PJwJ+O18jxHrw/zc/TlHX14TyLG9HePv+VC+v6EW6tjD/fMhnw3fbPtshE0fi/l4JbBBB2EeSx9iqdE+2glFbJPPhvBFPhv+WVpjUUoVmTPrlwElSqkVrvW94MqEyVylVInZD9IQyqRiCmhmv1LKoZSqcI9XRD2vnw2lVJnrM2oOiRexx9dnQ64TRNlaYaFkDocuBx7QbpMxRWwxE0m5NpcjUkqt1b0MaRexQ64ThrDpYwl32pho2dzrgSLaLQMa3O7H9DdTcTW5ThjCabjxgFi1uKWIDgP4vAwzHxdRSq4lAxcVNRaz+lkC5OFlLTFzEECj1rrK/BDkmRMyRQySz4vwRT4bwREVNRaz+olSai7eF6ks8/jm8TjGXBrX6LOAFrcU0WGAn5fThGjirrDfQK8lwhAVicUfH6MznBjfSgCpyopuAXxeqjA6Z13ki0eMCORaIgxRn1gwvl16dqb1uXPNrCIXA06lFFKTiVp+Py/aWDV7i/l5yAV6tMGLqNXrtUSuE4ZYSCwOX08opRw6wMUtzSrynCDFJMKXw9cTrs+LBXOrRHhy+HrC7bMh1wmipPO+F04sWtxSRAUn8nkR3jmRz0ZAYiGxNNPzm4YDINDaiogp8nkRvshnI0BRn1hkcUvRF/J5Eb7IZyNwUZ9YTGs8xpovACrtCkaEPfm8CF/ksxGAqFgrzBwG6FqjB4w/9FVzUMzZsnUYI3lkiHEMk8+L8EU+G8ERFYlFCCFE+IiVpjAhhBAWkcQihBAiqCSxCCGECCpJLEIIIYJKEosQQoigksQihBAiqCSxCCGCxpzjEXbCNa5oJYlFCBEU5oZ5Xld+VkoVKaVWKKXKzH9L3C/25vOVSimtlKowl593PVeilKpVSm1wzXo3z6HNx3vs4KiUWquUanAro0qSi3VkgqQQYsCUUrlAuY894kuAIq31KrfHyszj57g9tgyo0FrneTnHCvfXm4/VAo97Pu52/mqtdaPbYxVApftjIjSkxiKECIZyfK+ZVeHl4r+Gnos3LvfymD+NGHvTX8VMcs1eEsgDyMZslpDEIoQIhiJvNQGllANzTS0vHve4XwJs8HGs08tjjT7OvczbZmzm0va+YhFBFAs7SAoLmE0PYOxPUY3xH3iut6aRIJVXBFRgXHBc35SLzPvV5s+ZwBytdbnH6zLNOOcCG8xd/1xNMeUYW8vO11rXmc0tYDShyGKDXpg1BK/NS1prp1KqWSm1ge4FHZ3mRb7O4xwOvNRYzKY0bzWZBmCZx7HL8NHPY2pUShXF6pbBVpHEIgZMKVXmuuiaF2InRlPHI0qpB7xtgqSUCnSp8ZXeXm9e9CsxkkujuRd9DdACLHB9Y3V1+Lp9g30EeMC8X6WUagEyzHO6HmtwK8prG344c7237gl1IMcFIBfvNQqXORh/pwogVylVrbVe4HFMCcbf0dt5ilzJ38NVNRazdpTZSx9Kg/kaSSwhJIlFBEON28+5wBrzApHh6wVBuJiBeTFzXUjMb8dw9bdnJ1dvH1vqeeFx7Vfu9tACYIPZ2RuJtZS1/T3OWyd5AHIxLtheme9tuXn+XGCtl3IW4Lt/xenj8UbznK6/X1kAsTuR5rCQk8QiBszVrGA2M/n61hkq3r6dNvv4GaDZHHbqdHttJm4XL7P2U4WRhCIusfj4dh/ocXP7WazD8wFvTU7me1tJz073Irx0rLs64r0VaJ4LoNj8118TmDtngMeJfpLEIoLpqlE9/tqyB9oUNgC1GAnDlQxd8VyptZhNKlswmm368w0+Ipk1tP5oxsvoLIzPg7e/fx49O+7B+5eEZb28/07MvrUAE6rDRzkiiCSxiAExO0uXa61LMdrJK83HS/DxTROC1hQGVzdz+X3e1XHvllQcbseVYPSvOIDvaa1XKqWqgVqzT8D9NWV0X5xytdarXHMwzMdKzT6gZRh9Omu01uWuc2MkrSsDB8z3qgKjSXEDxgX5cW8jm3orB+P9rwBw9WO47XjowGhyqjB/vnKcGUMuUOSq0bn1m/V4vUdzYqP5uKdlSqkt7r+H+TdwePnCUWX+Ddw79H1OuHRTg/H5m9PLcS55yB71oae1lpvc+n3D+La4AuOi4MC4sJUAJRaUuwGjs76M7gulNmPINR9vwKillJivq3SLtwRjVFGl2+/RgHHBd5VTa5axwu2+w+35ZRgjxjDPV+sRZ5nbzw0er71yLrdYHWYsRX5+997KKXH9DuZ5l3nEW+R5nNtzaz3P6+v1Hsdt8LjvMF9bYv5bZr6/ZX5+rwrzGNctN4DPQaW/96q3OOUWmpvtAchNbpFyMy+qtV4e124JosXjwu3w9Vrzoljm9nxDH2LxWo7b/Q1uPzeYF/Zcj3MEklh8vt7L7+IINH6b/n4O15cAuYX2JhMkhQicr45kJ90jjR7AHAGFcSF2ur3Wte5Vidn0tJarm2X6MgTWVzlX0Ua/QzlGU1WtubaWI9BC+vD6CoxmvnBWRnczogghSSxCBK4R7306Drr7XFYD93iZNNiI8Y2+2uPW345kX+VcRSlVYpZTqrXOwOiTKPN1vNvrcs1bQK83f4/TZjxhx4zLOYD3W/SBJBYhAqSNTmiH+8XTNdPbVWMw/63BaHKp9nit0+O1ue6r+PYxFq/leFHkUYa/0XiNdA8bzjUvwgG/Xhujt3qsNBwmlukIHDoeqWR1YyH6wGNkVyZGLcRz1d0eq/l6eS2YQ2TN41diNJdVAqt9NW31Vo456up7GH0j7vNCXE14rlFsVx2nu0eAVWIMKkBrvdptqZ6rXt9bbCK2SWIRQggRVNIUJoQQIqgksQghhAgqSSxCCCGCShKLEEKIoJLEIoQQIqgksQghhAiq/w93/7M8+LH4qAAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlim(0.1,30)\n", "plt.ylim(0.01,1)\n", "\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.xlabel(r\"$x = \\max_\\textrm{over visits}(SUV)$\")\n", "plt.ylabel(r\"$1 - P(X < x)$\")\n", "plt.title(\"average distributions\")\n", "\n", "plt.plot(np.average(X[lung_flags == 0],axis=0),1-P,label='NC')\n", "plt.plot(np.average(X[lung_flags == 1],axis=0),1-P,label='AE')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 42, "id": "92373a7d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlim(0.1,30)\n", "plt.ylim(0.01,1)\n", "\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.xlabel(r\"$x = \\max_\\textrm{over visits}(SUV)$\")\n", "plt.ylabel(r\"$1 - P(X < x)$\")\n", "plt.title(\"average distributions +- std. of average\")\n", "\n", "for i,l in enumerate(['NC','AE']):\n", " T = X[lung_flags == i]\n", " plt.errorbar(np.average(T,axis=0), 1-P, xerr = np.std(T, axis=0), label=l)\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 49, "id": "ac9c21f8", "metadata": {}, "outputs": [ { "data": { "image/png": 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0tidEEpisdVNTU0Nra2vigyKDk41IPU6FWl5OFhp47WgMrpH662OmcEBlmfM437th+tsUwkWTVaY1NjbS3t5OS0tLwuOSZCNSyraGNeRlm/q0wVEfV051hIFg7t4FCz9ofj+13xQQPPaJaUYqhHtCtW48Hg87duxgw4YNPPjggxce7+zspKGhIeS5nliQZCNSjv8IA+eHx6K74DPQXU9B3izz++iAKRwQIkU5rZtgZdB1dXV0dHSwd+9eLr/8cpRSrF+/nqqqqrhe2Cnz2ch8NilpU9PzvHrUy+CIj7ycLK5fFOEcOOE8ehsce8lMyjbvavj8s9Pfpkg6+/fvZ8WKFW6HkZLCvXYyn41IK9sa1nDNghJyshTDo77pl0M77n7KDuKZBSdfh6YPxWa7QmQ4STYiZW2/dy35ueYQdsqhY+Lu3ePX4bz/CnxjgZzDEWKaMjbZyKjP6eGqipLYDGcTaP514wN4Dp+D91+NzXaFyFAZm2zkOpv0sK1hDasuKyU7S6GBN2JRLADm4s6vHB9v4Yz0y+CdQkxDxiYbkT6237uWAtudNjjim/7Fnv4qqsdn/jy6F77/8dhtW4gMIslGpIWrKkrIz8lizKd5LVbFAmBaOAtqIDvPjBZ9vE3O3wgRBUk2Ii1sa1hDbrYiS8HQiI+6h2LYutm8ExasMvPhjA7KNThCREGSjUgbV1aUkJ+bbYey8cauWADM9AQ5haYkemQAHlkXu20LkQEk2Yi0cdG1N2OaN2LZnQYw/1qTcNCmdSMFA0JETJKNSCvbGtZcuPZmaNQX29bN5p1Qcb1JONpnCgbk/I0QEZFkI9LOVRUl5OVkMerTvHbUG9uNb95pJl9zCgaO7ZOEI5LK+vXrUUoFfWzr1q0opYLempub4xpXxiYbuagzfTkjQytlWjcxLYWG8YIBlW0LBl6O7faFiJLX62Xfvn14PJ6gycPr9VJdXY3WesKtvr4+rrFlbLKRizrT25W2FNqn4dWjvbHtTgNTMJBbCCgzSrScvxFJoLm5mY0bN1JfXx90pk43ZWyyEenNKRbIVjA86ovNNASB5l9nL/j0SXeaSApNTU1s2LCBTZs20d7eHnSKAbfkuB2AEPGy/d61XPG3T9I/PMawLRaIyTQEjs07TYI58iKMDZsKtcc+YZaL1PSLL8GJ1ydf58Rr09vHJdeEefxq+Ng3p7zZ9vZ2uru7qa2tBcxEaS0tLWzZsiWaKGNOWjYirV3tXwodj9aNM8KAc/3N+69IC0e4oqmpiY0bN164v3HjxqBdae3t7ROKA0pLS+Me35RbNkqp64BKe/MC3UCn1vqVWAYmRCxsa1jDp777LK8d7aUoL04N+bueNPPevP+qqVLL8AkJU1oULYpksX37dnbs2HHhfkNDA83NzbS3t1NdXX1heXV1NW1tbQmPL6KWjVLqMqXUw0qpp4AGoAroBZT9/V6l1C6l1ENKqcviFq0QUfjJF26ifFY+p84O8anvxmnmzYZfgedSGOiG813x2YcQIbS2tuL1etmwYQOlpaWUlpaybp0Z5SJZCgXCftVTSv0VUAZs1VpP2g+hlCoB6pVSPVrrR2MUoxDTdm54FA28ebwv9uduHP1nTDn0mYNmOJt79sR+H0IE0dTUxJYtW2hsbLxo+datW2lubk6KhDNpy8Ymmhat9ZfDJRoArXWv1vpbwB6l1F/GKkghpuuqihLyshWjPh27KaQDzb/WlkNjrr2R6QhEAni9XlpaWmhoaJjwWENDA16vl9bW1kmf7/V64xihMWmy0Vp/S2t9aKob1Vof0lr/j+jDEiK2tjWs4eoF5pqq4VgPY+PYvNOvHHpMpiMQCbF9+3aqq6uprKyc8FhlZSXV1dUXtWwCCwScbrdgySqWlI7iZKZSqlhr3ReHeBKupqZG79u3z+0wRILc+M2nOeYd4KqKYn5+383x2cn3Pw5db8LQWTOWmnSnJZ39+/ezYsUKt8NISeFeO6VUm9a6JnD5lEuflVLfBA753b/eVqgJkfQuKc4nO0txzDsQn9YNmNEFZi8BNJx5R7rThCC662z2YsqeAdBavwzMVkp9NGZRJYCMjZaZcrKzyM5S9PSPxO/cDZiutOw8GPRKd5oQRJdsOoENSqliZ4HWOuX6CWRstMy0rWENV1cUk60U+TlxvKZ5806oWAn5xeAbNSMMCJHBonm31QL3Au8qpfYqpR5QSt0NbIhtaELEx+N/eiPzSvLp6R/hju/8On47uvMJKFsCvjHoPiStG5HRokk2Xq11jda6DKjHXNh5L7A1ppEJEUd9AyMA7H//bPzO3QDkzYDsXOg/BcekO01krmn1I2itX9Zaf8lWHkyoPhAiWV1ZUcL8kgJGfZrBkbH47cgZOy3Xzu7pi+O+xJREU4mb6abzmk052WitH1FK3eNfgaaU2gZUh36WEMllW8MaLikuQCk47h2Mb+vmzl9A2VIYGwLvYWndJIHc3FwGBgbcDiPlDAwMkJ+fH9Vzo2rZaK0fCRh4czuQckUCIrPl5WSRk6U4dW4oPiNC+ysohqxcOHvcjC4gCcdV5eXlHDt2jP7+fmnhhKG1ZmRkhO7ubo4ePcrs2bOj2k5MhsHVWj8ei+0IkUjOiNCvHu2luCA3vjvbvBP+9TZz3kYpGRnaZcXFppj2+PHjjIyMuBxN8svJyaGgoIDFixdTUFAQ1TaiGkEgncgIAmLl13fT0z/M9Ys8PP6nN8Z3Z99ZBWcOmCq1WZfIRGsi7cRsBAEh0s38kgJ8GrrODsV/Z0VzzbU33ndhTL5Ri8whyUZkvJ/fdzPFBTmc6Btkw8PPxXdndz4BZVWmKq3nXTl3IzKGJBshgPklhYyMac6cS8CV/nlFUFwB50/CYFqMZytEWBEnG6XU5ZONf6aUuk5m6RSpqqQwh8LcbN7vHWRjvFs3m3dCyWIzdlp3hwzUKTJCxMnGzmsz2ZA0DVrrd6cdkRAuUEoxv6SAgZEx+gZH47/DrGworYSR83DuRPz3J4TLptqN1h5sOgHboumMRUBCuGFbwxpmz8wjN1vxfm+cL/IE07qZMRsKSkyxwKO3xnd/QrhsSslGa/0IEGw6t612OmghUlaWUsybVUDvwAgDwwkYVkYpKLXFAt7D8d+fEC6KpkCg1//cjFKqBJBJYUTK29awhvLifJSCE32D8d/h5p1moM5ZFaYr7eEPSXWaSFvRJJsHuLh18yXgG7EJJ3Fk8jQRTG52FnOK8jh9boje/gRdB+NZbIay6e6QkQVE2opmIM5eoNRv8rRSrXXK1W/K5GkimG0Na5hXbC7y/GQ857pxbN4JWTlQehkMn4XzXdK6EWkp2rHRGoF6pZQXaIpdOEK4ryg/h1kFOXSdHWLMp8nOUgnYaTmcfd8UC8yIbqBDIZJZtKM+HwKWACu11i/HNiQh3OVMPzA06uP2f/7P+O9w8047skClGcKm96i0bkTamc6oz9KiEWmrdEYuedlZnExEoYAjv9iMndZ3DGZekrj9CpEAUQ9XY2fplFaNSEvb711LeXE+fYOj/M53nk3MTjfvBM9lpiTae8i0bqSFI9KEjI0mRAjls/JRwMmzCWzd5ORD8ULoPwOD3sTtV4g4k2QjRAj/909v5HevX8D5oTHODSVgCBswrZsvvGTGTkPBH/80MfsVIs4k2QgxibdPnOXc0Cif/HYCyqAdP6qD3EI4+Tp894PSlSbSgiQbISYxMz+bGXnZnOwbSuxc9TPm2EnWDoMvQa0qIeIobLLxu3hTiIyz/d61fPWTVzAwMkbb4Z7E7NQphf6jn4BvBBavTsx+hYijSFo2jXGPQogk9njbUbKVouGHbYnd8a6vmos9f/NtaF4n3WkipUWSbDYqpWaFW0kmThPpKjtLMWdWHt3nh+k+n4CZPP15LrWl0O8mdr9CxFgkyaYd+OsIutOCTT0gRMrb1rCGH9y5Gg20tB1J3I4374R7WuHmv4T+07D+/sTtW4gYC5tstNbrtdZfBhqCJRyl1EeVUtuBLfEIUIhk8IFLZjErP4f/uetA/KeN9vfYJ+DQf5pRoX/4aZlCWqSsqUwL/S1gk9NdppS6Wyl1ENgNeIBD8QhQiGRRXpzP0KgvMdNG+8vKMdMQDPXBQHdi9y1EjEx1bLRWYIdSqtrefwS4VWt9SCm1LrahCZFcdt53Mzc8sIfKuUWJ2+nmnebn2Aj8yxrQPhgbhezpDGsoROJFUvr8kO0q2wW8A5QC38IkmXvtCNBorffEN1Qh3FWQm01BTja/eP0En/7ebxK78x/8LmRlw+m34XurpTJNpJxIutEaMF1lXqBGa71Ea/0lMOdr4hibEEmnfFY+Gjh1bijxOy+cDfmzoPc98I0lfv9CTEMkyaYVKNNab/Qf5dm2ZJRS6jNxi06IJPPTP7uJGyrLUAp8vgSOKOBc6Pm5FhgbhqXSay1SSyTJpslOBT2BTTiHlFL3KKUeiG1oQiSnU2eHONI9wMf+VwLHS3Ps+ToUlsEzD8Cjt0p3mkgZkZQ+Px7m8ZeBNpKk9Fkp5VFKNfoVMQgRU2VFeeRkKU4lcuoBf6WXgR6D3gRe8yPENMVkIE6tdTswaVJKoBqg0u0gRPrace9a/njtZfQNjnLqbILP3WzeCfXPQPUfwflT8KnvJnb/QkQplqM+J8U00VrrVkAuRhBx9XsfXMSoT/N4+1F3AvjwX5vrb575B3f2L8QURVWsr5S6DtN66NRavwKRlz4rpTxAPTBba701yONbgE6gzG63OZoYhYinJeVmRIF/2n2Ap/efZPu9axMbwON3w4zZ8PoOOHUQ8meOX5MjRBKacstGKfVN4EHgXuBppdRBpdS1ET63FqgFqjCjDgQ+3ohJYC02yVQppeqmGqMQiTB3lhlR4GyiRxRwlCw0rRsZpFOkgGhaNh3OdTZwIYF8Sym1xWnlhGK7uFBKrSJIsgHqA1o72zBTHLTY59WHeF6rPW8kRMLsvO9mPviNVpZdEnZQ9NhzWjHPfRd2fQU+lBT1OUKEFDbZKKUewlSbtWqt3wXO+D9uE0irUuovgVeiDSRE9ZgX0xJy9iVdaiJpFOZl8+nrF/Afe49wf/8wnhl5iQ9i1d3wwkPQ+jW45xkzHYEQSSiSbrRVwK1Au1LqDGb05weCjADtnWYsZUw8sT/lE/22pVWDGTRUyp9FXLUf7mF41Menvpvg4WscP/ovkFsIx1+G790g192IpBVJN9pW5+S/UupyTEtjPfCuTT5O99XeacbiCfWAUsqjtfZGshHb0lo52Tq2O64eYPHixZFHKESAovwcivKy6To7hNYa5UbLoqgc+o6B97ApGhAiCYVNNv5VZnbQzUfsDaVUJXA55qT+dKcY8GIr0PwE3o8J2x3XDFBTU5PAMUdEutnWsIZ/f/EwX/l/b/Da0V6uXeRJbADOuZv9P4dtn4PrPpvY/QsRoaivs1FKXae17tRa74lBogHTZeYJWOYBiLRVI4QbfufaCgpzs/mPvS5e0b/8E7CgBn75TRgZcC8OIUKIpvR5nVKqG2hRSo0ppbZFMGV0WLaazBuwuAwzEKgQSWtWQS5F+dls2/sedQ8lcBZPf//2SRgdNN1pcu5GJKFoWjbrtdZlWuslwGxMpdqeWCQcYHvAdTXridPIBEqpO5RSzb29QccYFWJKymfl49PQfX7YvSAKPFBQCn1HzGRrQiSRaJLNhUIArbVXa/0gJinUh3uiUqrajhBQB9Qqpbb4V4xprRuASqVUrT2J36G1bokixrC01j/TWteXlJTEY/Miw+y872Yq5xZRVuRC+TOYczebd8LmJ0BrWCCFmCK5RHNR54RBLrXWXqVU2PM2tqusHTMCQah1Qj4mRLJSSvHZVYv4xhNv8U7XWZaUu3ChJ8C8K8wgnXsfMdfgzFniThxCBIimZdOslNqllPp0wPLLYxGQEKnqM9ULyclSbN/n0uCcjo98BXIKzIWeQiSJKScbO5HavcBXbIHAXqXUQczgmUJkrDkz85lVkMP3nz3EhoddKhQA2LHZXG/z1s/hoRulWEAkhahGfdZadwI19iJPZ/TnWJQ/J4xS6g7gjiVLpJtBxE75rHx6+kfw9rt8gn5WBZw9Ad2dMP86d2MRgmnOZ6O1PhTD62wSSgoERDw88V9v4ZLiAhaUFroXxOadcNeTsOHfYKTfXIMjhMuiuc7mHjs4pxAiQHaWYkPNQn514BTHvS5fXPmBj8HS2+CXD0Df++7GIjJetC0bOT8jRAgbVi5Ca2hpc7lQQCn42DfNNTe7/sbdWETGi/aczbdiHYgQ6WLx7BkUF+TwvWfe4dmDpxI/i6e/n/wZzCyHN1rg1Fvmwk+QWT1FwkXTstmulHpKKfWRmEeTQDKCgIgnZxbPPrdm8fRXvNCUQp/pAO1zOxqRoaJJNo3Ay8CXlVJnbOL5S6XUZbENLb6kQEDE0877bqakMJcl5TPdDcQpFvjsj2F0AC67SVo1whXRJJvdwDe01rdqrWdjRgOYg0lCQgigINfM4rnrzZPujpfmWLIOrtkEz/4TdO13OxqRgaK5qPNxoEopdZ29v0dr/SWt9aZYBydEKvvsBxcxPObj/718zO1QjNu+Afmz4Kf3gU+600RiRZRs7DTQTymlvgigtX5Za/1KXCMTIsUtv6SY6xZ52Lb3PbROgjn6iubA7Q/A0Zdg37+6HY3IMGGTjVLqm5hplg8Bn1dKTXf6ZyEyxmdXLeLAyXO8fMTrdijGNZug8iPQej/0JkmLS2SESFo2lfb8zL12DptmpdTd8Q5MiHTwyWsryFLQ8IN9bodi/NsnYfAsjJyHh2+C739cxk4TCRFJsrnoAk6t9SNAVXzCSRwpfRaJMDM/h9lF+Zw5P0zfYJJMaJZbAJ7FMNAN/afdjkZkiEiSzZkgy7pjHUiiSemzSJRH/7gGn4afvHLc7VDGJ1n7QhtUVEP/Gdj4A7ejEhkgkmRTFmSZDFcjRISuWVjCFfOL+fGLSVIoAJCdA5/6Lgx64akvux2NyACRJJutds6aB/xGDUiSd4wQyU8pxe+tXsz+9/t47WgSddvOuxJu/iK8tg0O7HI7GpHmIkk2LcB2TEXa40qpMeARpdRDSqnPKKWKAaRoQIjQfve6Cgpzs/nxi++5HcrFbv4izF0OP/9vMNjndjQijUWSbB7QWn/LVqSVAUuBLwGzgUeBHjtT59Y4xilESptVkMvvXFvBT189ztlkKRQAyMmH3/ku9B2DPfe7HY1IY2GTjdb65YD7nVrrR7TWG23yWQLIKNBChPF7qxczMDKWHIUC/hatghs+D3sfhcMuTmct0lpUUwz4s7N0NiulpKxLiElcu7CEGXnZfOOJ/Xxu9WKUUm6HZDz2CfCNmVbODz8DFdeDypIBO0VMTWta6ADNMdxW3Ml1NiLRlFLMK86nf3iMlw4l2dUDWdlQtsSMDN17xO1oRBqKWbLRWqfUp7ZcZyPc8MR9t1BSmMv/fv5dt0MZ51x786fPwdUb4NxJ+OQ/uR2VSDOxbNkIIcIozMvms6sW8dSbJznuHXA7nIluewByZ8DP/1xGhhYxJclGiAT7gxsuRWvNj1447HYoE82cC7d+HQ7/Bl75kdvRiDQiyUaIBFtUNoPaFfP4j71HGBwZczucia7/Q7j0Rtj1VTh3yu1oRJqQZCOEC/5k7WV0nx/mZ68mWRk0gFLwyX+GkX4ZykbEzLSSjVLqoVgFIkQmWVM1m2XzZvJvz72bPOOl+Zu7DG76C3h9B7zT6nY0Ig1Mt2UzOyZRCJFhlFL88drLePN4H22He9wOJ7ib/wJmL4Wf/wUM97sdjUhx0002SfiVTIjU8OnrF5CdpbgnWSZWC/TDz0BOAXgPw3dXySRrYloy9pyNXNQp3DYjL4dLivPp6R/h5feStHVTUAJFc83YaSODbkcjUljGJhu5qFMkg6f+24eYMzOfB37xVvKdu3Eu9qz/FeQWgmeB2xGJFJaxyUaIZDAzP4c/r13KS4e62bO/y+1wgitZADf+Ofz2J/Dub9yORqQoSTZCuGzTqkVUzi3im0++xehYkl61v/bPoHiBKYWWkQVEFCTZCOGy3Owstt6+nHe6zrGj7ajb4QSXNwNq74f3X4VX/4/b0YgUNN1kkyRjpAuR2m69Yh41l5byj7sP0D886nY4wV1dBwtXmUnWhs65HY1IMdNNNttiEoUQGU4pxZc/voJTZ4d49NeH3A4nOKXMQJ3nTsKzMiq0mJppJRut9eOxCkSITLfy0lI+dtUlNP2qg1Nnh9wOJ7hFq+DqjfDcd8D7ntvRiBQi52yESCJ/ddsHGBr18e09B90OJbTar5mZPHd/ze1IRAqRZCNEEqmcO5PfX72YH7/0Hh2nkvS8SMlCuPE+ePP/wnsvuB2NSBGSbIRIMvetW0pBThbfevJtt0MJ7cb/CrPmw5NfklJoERFJNkIkmTkz8ymdkceTb55g/T/+yu1wgvv3jZA3C46/DN/7oIybJsKKWbJRSl0Xq20lgoyNJpLZJSUFFOVn807XOX51IEknMCuaC3kzzUCdviScBE4klVi2bGpjuK24k7HRRDJr+fxantu6jhXzi6n/wT6e7zjjdkgX27wT7nwCPrcDxoZhyUfdjkgkORVu8D+l1DeBSD6Ra7XWS2MSVQLV1NToffuSdIh3kfHOnBtiU/MLHPcO8MO7VrPy0lK3Q5po2x+aCdb+rB2K57sdjXCZUqpNa10TuDySls0ZzEgBvWFuQogYmz0znx/fvZq5s/L5k8de4o1jSfhWW38/jI3AM//d7UhEEsuJYJ1mTKtl0gs4lVJJ1s4XIj2UFxfw73evZlPTC/zhv77ItoY1LJs3y+2wxpVVwuoGeP57sPpeuORqtyMSSShsy0Zr3Qt4I9iWTFQuRJwsLJ3Bv9+9mtzsLH7/kRfpTLZrcG75Kygshaf+GpJtXh6RFCIqENBa74lgnZenH44QIpTL5hTx43tWo7Xmc4++yJHufrdDGlfogQ9/GQ79Jxx4yu1oRBKS62yESCFLymfxw7tWc35olN9/9AVO9CbRVM01m2H2Utj1N+YcjhB+JNkIkWKuqCjmB3etpuf8CL//6AvJM2hndi7c+nU4cxD2PeZ2NCLJSLIRIgVdt8jDY5tX8b53kD/81xfpOT/sdkjGstvh8lvglw/AgNftaEQSkWQjRIpadVkZj/xRDZ2nz/NH33+JvsEk6LpSCm79BxjogV//D7ejEUlEko0QKeympXN4+A+qeetEH5sf28v5oSSY5XP+NXD95+DFJujudDsakSQk2QiR4j66fB7f/uz1vPxeD/f8YB+DI0kwTtlH/gaycqH179yORCQJSTZCpIGPXT2f/7nxWp7vPMO9P2pjaNTlhFM8H276c/jtT+Dw8+7GIpKCJBsh0sSnr1/INz59Nb98+xT3/Z+XGR1zeZ6ZNV+AWRXmQk+Z8ybjSbIRIo383gcX87U7ruCpN0/yxR2vMuZz8Wr+vBlmCunj7fBGi3txiKQQdtTndCejPot0sqnJdFkd9w5wpGeA/JwsPDNyKZ2Rx0+/cBN5OQn8fvnYJ8zQNSdehZF+KPBAYRn8yc+guCJxcYiECjXqcyQDcaYlpdQdwB1LlixxOxQhYq7CU0h+Thanzw3TdXaIk31DrPz6bm75wFxqV5Tz4WXllBblxT8QpWDucug7Bv3dMNAN/7jCDNa57HZYehssqIas7PjHIlwlLRtp2Yg0NzA8xrPvnGbP/pPseauLU2eHyFJQc2kZ61aUU3vFPKrmzox/IFrDqbfhwJNm/LQjL4IegxlzYOmtsOxWqPooFMiEhqksVMtGko0kG5FBfD7Na8d62bP/JK37u9j/fh8Al88pYt3yctatmMeqy0rJyU5Ad1t/N3Q8bRLPO7vNhaBZOXDpWtPiWXY7zJGeh1QjySYESTYikx3zDvC0TTzPd5xheMxHcUEOH/6AafF8aNlcSgpz4x/I2Cgc3QsHnzLJp+u3ZnlZFSy7zdwWr4WcBHT9iWmRZBOCJBshjHNDozx78BSt+7t45q0uzpwfJidLseoy2922Yh6XzSlKTDDe90zSOfCUmbZgbAjyZkHVR+y5nvUwszwxsYgpkWQTgiQbISYa82leOeK13W0nOXDSTNa2pHzmhcRTvbiU7CwV/2CGz9t5cuy5nrPvA8oUFiy73bR6LrnGFCMI10myCUGSjRDhHenup3X/Sfbs7+LFQ2cYGdOUzsjlIx8w53luWTaHWQUJ6G7TGk68bls9T8KxNkDDrPm2yOB2qPwQ5CWoBSYmkGQTgiQbIaamb3CE/zxwij37u3jm7S68/SPkZituqJx9ochgUdmMxARz7pQpLjjwJHQ8A0N9kJ0Pl99su9tuhdJLExOLACTZhCTJRojojY75aH9vvLut49R5AD4wb9aFsurrFnrISkR32+gwvPe8afUcfArOvGOWz10xXmSw8IOQnbGXFyaEJJsQJNkIETuHTp+/kHj2vtvDmE8zZ2behe62m5fOoSg/QR/2p98Zr247/BvwjZpRDJauN6XVS9bBjLLExJJBJNmEIMlGiPjo7R/hlwe6aN3fxS/f7uLs4Ch5OVmsrZrNuhXzWLe8nApPYWKCGew13WwHd5nk038aVBYsWm1bPbebkQ6kyGDaJNmEIMlGiPgbGfOx991u9uzvYs/+k7x7ph+AK+YXU2u7266qKElMd5vPZwYHdYoMTrxmlpcsHk88l90EuQXxjyUNSbIJQZKNEImltabj1Hlb3XaStsM9+DSUz8pn3Ypy1i2fx41L5lCYl6Dx0vqOj7d4On9pBg3NnQGVHzbJZ+mtMnDoFEiyCUGSjRDu6j4/zC/f7mLP/i5+deAU54ZGyc/J4qYlc0x324py5hUnqJUxMgjvPjt+TU/ve2b5JdeMX9NTUQ1ZMjtLKJJsQpBkI0TyGB718eKhM+zZ30Xr/pMc7RkA4JqFJaxbbhLPlRXFqEScW9EaTr1lE88uOPICaB8UzTWtnaXOwKHF8Y8lhUiyCUGSjRDJSWvNgZPnaLXVba8c8aI1zC8pMN1tK+axpnI2BbkJ6m7r74Z39pgKt4O7YdALWblm4FDnXM/sqsTEksQk2YQgyUaI1HD63BBPv2UKDH598DT9w2PMyMvmpiVzqF0xj48sL2furPzEBDM2CkdfGh+/7dR+s3z2Ejti9W2weE1GDhwqySYESTZCpJ7BkTFe6Bzvbnu/dxCl4LpFHmrteZ4PzJuVmO42gJ7DtsjgSTj0azNwaH7x+MChS9bDzLmJicVlkmxCkGQjRGrTWvPb9/sulFW/erQXgIWlhaxbbsqqV18+O3FTYg+fh85fmcRzcJffwKErbZHBrWk9cKgkmxAk2QiRXrr6Btlju9uefec0gyM+ZubncMuyOaxbbrrbyhIxJTbYgUNf8xs4tB0zcGiFSTpLb0u7gUMl2YQgyUaI9DUwPMZzHadpta2eLjsldvXiUmqvmEftinKq5s5MXHfbuS5TXOAMHDp81g4cesv4+G2exYmJJU4yJtkopertryuBRq1152TrS7IRIjP4fJo3jvdeSDxvHjdTYl86ewbrlpvEs+ryMnITMSU22IFDnzNl1Qd+Ad32o6r8Cnsx6W2wcFXKDRyaEclGKVUNoLVuV0rVAlu11usne44kGyEy0/u9AxcKDJ7rOMPwqI9ZzpTYK8r58LJySmYkYI4ex4WBQ5+Ew8+ZgUMLS01xwTI7cGhhaeLiiVKmJJs6YL3WukEp5QEOaa0n/e9IshFC9A+P8uuDp9mz/yRPv9XF6XPDZGcpai4tpXbFPGqvmMfliZoSG+zAoU+bVs/BXXbg0OyAgUM/kJRFBkmTbGwSqAdma623Bnl8C9AJlAForZuj3I+0bIQQU+bzaV496r0wM+lbJ84CUDm3yJRVLy9n5aWl5CSqu803ZgoLnFbPidfNcs/i8SF0Lk2egUOTItnYBOAB1gNorRsCHm8E9mqtW4Ldn+K+dgD3aK29k60nyUYIMZkj3f08/Zbpbnuh00yJ7bkwJXY5tyybS3EipsR29B67eODQ0QE7cOhH/AYOnZ+4eAIkRbLxC6YR8ARJNj3+3V72HEyj0zqxJ/89QTbZqrVu93tevV02aXEASLIRQkTu7OAIvz54mtb9J3nmrS56+kfIyVKsriyzRQbzWDw7QVNiA4wMBAwcesQsn3+tnRb7Nqi4PqEDhyZ9srGJZU9AsqkEOrTWEXdM2tZTp9a6UylVq7VunWx9STZCiGiM+TQvv9dDqy0yeKfrHADL5s1k3QpT3XbdolKyEzFHD5hrerr2j19MeuRFO3BouWntLLvVtH7iPHBoKiSbWqBJa13lt8wD9ESabJyEBXTbRe1a6w2TPUeSjRAiGpuanr/o/uDIGD39I3j7hzk7OIoGLp8zg/JZwc+lbGtYE5/AHvuE+Tk2AgM95jbYbc79FHhg3lUTn7N5Z8x2HyrZJFMBtyfUA0opT7hzL2BKnoGwtYG2m825HmdIKfVGhDG6pQToTYF9RLONqTwnknXDrTPZ45M9Ngc4HWbfbov3ceLWMTLV57l1nEw4Rg5PsoPt904WXrz0lcB7E2O/M6atr6VBl2qtE34DGjGtGP9ltZhWjP+ySkBjWkHximWfG6/BFGNsToV9RLONqTwnknXDrTPZ42Eey/jjxK1jZKrPc+s4kWNk8n0k03Rz3Uxs3XgAdAStmjT3sxTZRzTbmMpzIlk33DqTPZ6I1zme4h2/W8fIVJ8nx0lorn2WJM05G7u8R19cIBDRtTLTjGWfDtK/KIQ/OU5EOHKMTC6ZWjYA2+0oAI71QFOc9xnRRaNKKY9SqtEZEkdknAnHiVKqXilVa39WuhGUSCrBjhH53LASWiBgX/BaoM7e34LfNTLaDDOzxbZonLLnKV/QORU68hEKajAxiQwUeJzY5FLlLLcXEU9a+SjSW4jPEvncsBKabGxSaQcenGSdkI+5SWvdqpSSDxPhqAM6/O5n/DdXMZF8boxLptLnaUnUmGsivUzjuJltl4s0J58tsZFs52yiYrvdaoEqglyvYwsSOrXWLfZAqAo4NyQykBw3Ihw5RmInLVo22g5Jo5RaRfCLQ+sDvpFsw1zr4wz4GdGYayK9TPO4OYP9JivS13Q/W8S4tEg2kwlRBeLFfFsBpNkrJorguGkB/Ev35UtJhonks0WMS/tkg/n22R2wLPB+WLY5XQN4lVJIiyftTXrcaDPQ616/yskJffki7YX9bJHPjXGZkGw8oR6IdMw1uNCcXhmjmETy84R6wDlu4l2WL5KeJ9QDfseIfG5YaVEgEIaXiX3r0tcuwvEix42YnBc5RiKWCclGxlwT0ZDjRoQjx8gUpH2ysX2k3oDFZcCkk6qJzCbHjQhHjpGpSftkY7kx5ppIfXLciHDkGImQK6M+x5rfmGtOKWoTAdfI2Kt827HjFEm5s5DjRoQjx0jspEWyEUIIkdwypRtNCCGEiyTZCCGEiDtJNkIIIeJOko0QQoi4k2QjhBAi7iTZCCGEiDtJNkIIIeJOko0QIi7sxY5JJ1njSneSbIQQMWdnvw06BYNSqloptUUpVW9/1vonAPt4k1JKK6Ua7ZwwzmO1Sqk2pdRuZ5gYuw1tl0+YklkptUMp1eG3jxZJOIknIwgIIWJKKVUJNARMl+w8VgtUa60f9FtWb9df6besDmjUWlcF2cYW/+fbZW3AtsDlfttv1Vp3+i1rBJr8l4n4kpaNECLWGgg9GGVjkISwnYkjJW8KsmwynUCwxFQJdAdJKg8gs6smlCQbIUSsVQdrMSilPNjBKoPYFnC/FtgdYl1vkGWdIbZdF2xGVTvfTKhYRBxkwrTQIsFstwWYiaRaMW/qVcG6VWK0v2qgEfMh5Hyjrrb3W+3vZcBKrXVDwPPKbJyrgN12Gl+nG6cBM3/8Oq11u+2qAdP9IiP7BmFbEkG7prTWXqVUt1JqN+OjJ3vtB397wDY8BGnZ2G64YC2eDqAuYN06Qpw3sjqVUtX+IziL+JFkI2JKKVXvfBDbD2cvppvkEaXUA8FmMFRKRTr/x9Zgz7eJoAmTcDq11p1KqX1AD7De+WbrnFT2+6b7CPCAvd+ilOoBSu02nWUdfrsKek4gmTmvrX+Snc56EagkeMvDsRLzf2oEKpVSrVrr9QHr1GL+j8G2U+18IQhwUcvGtqLKwpyT6bDPkWSTAJJsRKzt8/u9EthuPzRKQz0hBh9wYD/gnA8X+y0aLv6W7eXiOeI3BH4YKaU8AR9y64Hd9oRyKrZmdkS7XrAT8RGoxHyIB2Vf2wa7/UpgR5D9rCf0+RpviOWddpvO/68+gti9SFdawkiyETHldEnYLqpQ307jJdi32O4QvwN02xJYr99zy/D7QLOtpBZMYkq5ZBOiFRDpequi3K0ncEGw7ir72jYx8cR+NUFO3jsn+4Pt0G4LoMb+nKz7zJ83wvXENEmyEfFyUTXRZH3j0+1Gm4Y2TBJxEqQTz4XWje2O2Yvp8onmm35Ksi25aHQTpCoMczwE+/9XMbE4AIJ/cagL8/p7sefqIkyynhD7EXEgyUbEjD0hu0lrvQHT795kl9cS4hspxKwbDS7uIpv0cac4wC/RePzWq8Wcr/EAX9Zab1VKtQJt9hyD/3PqGf/AqtRaP+hcI2KXbbDnlOow54i2a60bnG1jEtmF4gT7WjViuiN3Yz6ktwWrqAq3H8zr3wjgnBfxm8LYg+muarS/X1jPxlAJVDstP7/zcBOeH9AV2WmXB6pTSu31/zvs/8AT5EtIi/0f+BcNhLxI1M8+zPG3Msx6jiqmVl4tpkNrLTe5xeSG+Va5BfNB4cF82NUCtQnY725MQUA94x+e2sZQaZd3YFoztfZ5TX7x1mKqmZr8/o4OTBJw9tNm97HF777H7/E6TKUadnttAXHW+/3eEfDcC9vyi9VjY6me5G8Pt59a52+w260LiLc6cD2/x3YEbjfU8wPW2x1w32OfW2t/1tvXt36Sv6vRruPcKiM4Dpome63CxSm3+N5cD0BuckvFm/2gbQuyXPsljZ6AD3NPqOfaD8p6v8c7phBL0P343d/t93uH/bCvDNhGJMkm5POD/C2eSON36f/ncb4YyC0xN7moU4johDpZ7WW8wukBbOUV5sPZ6/dcZ5yvWttttYOLu3SmUo4baj8X0eY8RgOmm6vNjiXmiXQnU3h+I6aLMJnVM94FKRJAko0Q0ekk+DkiD+PncJqBjUEudOzEfPNvDbhFe7I61H4uopSqtfvZoLUuxZzjqA+1vt/zKu0toufbv+OMjSfp2Li803i9RRQk2QgRBW1OdHv8P1CdK9adloX9uQ/TXdMa8FxvwHMr/Uc3nmIsQfcTRHXAPiarAuxkvIS50n4wR/x8barGJozAnCTqdAqWsac6GfVZiCgFVJSVYVorgaMRTxjlOMhzwZbr2vW3YrramoDmUN1i4fZjq72+jDnX4n/ditP951TPXbSeHq88a8IULqC1bvYbhuii54eLTQiQZCOEECIBpBtNCCFE3EmyEUIIEXeSbIQQQsSdJBshhBBxJ8lGCCFE3P1/BJNhlI2cyZEAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlim(0.1,30)\n", "plt.ylim(0.01,1)\n", "\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.xlabel(r\"$x = \\max_\\textrm{over visits}(SUV)$\")\n", "plt.ylabel(r\"$1 - ProbX < x)$\")\n", "plt.title(\"average distributions +- std. of average\")\n", "\n", "for i,l in enumerate(['NC','AE']):\n", " T = X[lung_flags == i]\n", " n = np.count_nonzero(lung_flags == 0)\n", " plt.errorbar(np.average(T,axis=0), 1-P, xerr = np.std(T, axis=0)/np.sqrt(n), label=l)\n", " \n", "plt.legend()\n", "plt.tight_layout()\n", "plt.savefig(\"Pave_risk.pdf\",bbox_inches='tight')" ] }, { "cell_type": "markdown", "id": "4af369ab", "metadata": {}, "source": [ "## Logistic/logit regression" ] }, { "cell_type": "code", "execution_count": 34, "id": "10fc7388", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "id": "32864413", "metadata": {}, "source": [ "On ubuntu:\n", "```\n", "apt install python3-sklearn\n", "```\n", "Ref:\n", " * https://en.wikipedia.org/wiki/Logistic_regression\n", " * https://www.w3schools.com/python/python_ml_logistic_regression.asp\n", " * https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " * https://stackoverflow.com/questions/36681449/scikit-learn-return-value-of-logisticregression-predict-proba" ] }, { "cell_type": "code", "execution_count": 35, "id": "014247a5", "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", " Extracting only data associated to lungs\n", "\"\"\"\n", "lung_suv = suv_dict['lung_SUVperc_COMBINED']\n", "lung_flags = flags_dict['flags'][:,3] # 0 == NC, 1 == AE" ] }, { "cell_type": "code", "execution_count": 36, "id": "3d813361", "metadata": {}, "outputs": [], "source": [ "p = 95 # percentile of interest\n", "X = np.nanmax(lung_suv[:,:,p], axis=1).reshape(-1,1) # taking SUV max and ignoring NaNs, shape [71,1]" ] }, { "cell_type": "code", "execution_count": 37, "id": "9751c867", "metadata": {}, "outputs": [], "source": [ "from sklearn import linear_model\n", "logr = linear_model.LogisticRegression()\n", "clf = logr.fit(X, lung_flags)" ] }, { "cell_type": "code", "execution_count": 38, "id": "1cc45208", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "w0: [-5.9605805]\n", "w1: [[2.04920735]]\n" ] } ], "source": [ "# underlaying linear model coefficients\n", "print(\"w0:\", logr.intercept_)\n", "print(\"w1:\", logr.coef_)" ] }, { "cell_type": "code", "execution_count": 39, "id": "19901943", "metadata": {}, "outputs": [], "source": [ "x = np.linspace(0, 6, 100)\n", "y = logr.predict_proba(x.reshape(-1,1))" ] }, { "cell_type": "code", "execution_count": 40, "id": "845ca499", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EF6TEYv3nOc0yeRaDn8jLSCmx5cV9kFLiFzkroQvieH3yLAY/kZcpqmjAu4dPYct1SzA3NlzrcsgPMfiJvMjxDgt+tv0ALkyJxe0XcPlEUgeDn8hLSCnx0Mv7YLU7kH/zCgSxi4dUwuAn8hKv7j2GsgMn8cBVi3h1LqmKwU/kBTp6rPjJq/uxIjEGX7mUkw+SungBF5EXeHzHQbR19eIvX8ngKB5SHVv8RBqrPtqO//nwKL58STKWJcRoXQ4FAAY/kYZsdgcefOljzIwKxfeuWqR1ORQgGPxEGvrrB59h/7HTePjGpYgMYc8rTQ0GP5FGWjp78VRpLS4/Lx7XLJuldTkUQBj8RBrZ+vpBWGx2/PDGpVxGkaYUg59IA7sbzCiubMQ9l6Zgfnyk1uVQgGHwE00xh0Pi4Vc+QXxUCL65ZqHW5VAAYvATTbEXqhqxp8GMLdcu5gld0gSDn2gKdfXasHVHDVYnGXHTqgSty6EAxeAnmkIFb9WhpbMXP7hhKSdhI80w+ImmSLO5B4Xv1OPGlXOQmjRN63IogDH4iabIEztq4JDAxqt5hS5pi8FPNAX2NprxYnUT7rk0hatqkeYY/EQqk1Like0HEBcRjK9dMV/rcogY/ERq21VzEh8eacO3MxciOtSgdTlEDH4iNdkdEvmv1SA5Lhy3cQ1d8hKjXj0ihFgPIAfANABtAOIAtAIollL+Ud3yiHzbi1WNqDnRid/cvhoGHdtZ5B1GDH4hxB8AFEkprxpi3xohxO+llPepVh2RD7NY7fhlaS1WJsbg+uWztS6HqN+wwS+EiJZS3jvcfinlTgA7leNOq1IdkQ/76/uf4liHBb9ct4qzb5JXGfazp3uYCyGi3fcJIVYNdRwROXX0WPG7N+tw+XnxuHh+nNblEA0w1k7HPwohrgT6+/zz1CuJyPc983Y9Onqs2MCLtcgLjSn4pZTrAFwlhCgH0DqWfn0hRLYQIlMIkTvM/lTlmOzxlUzk3Vo6e/Gn947ghhWzuXg6eaUxBb/S2jcB2AzgViHEvFGOzwYAKWWZ8jhziMO2SClLAMQKIUzjqprIi/1212H02hz4btZ5WpdCNKSxdvVcJaVcJ6XcqbT+t4xyfAaAeuV+PYBU953Kp4ByIYRJSlkopawf/AREvqixvRt///AoctISYeLKWuSlxtrVs3nQ42FH+yiMgx4PPrs1X9nWJoQoEEIMPh5CiFwhRIUQoqKlpWUsZRJp7lc7DwEAvsWVtciLjfmKEiHEfym3a8dwuBlA7CjH1EkpzQAqAZxzHkD5JJAupUyPj48fa5lEmqlvOYOSykbccVES5hjDtC6HaFgTuZRwLGPTynG21W8CUDrEfhcjnH8oiHza0zsPIUSvw9e+sEDrUohGpMo15MpJW5NyUtfodpK31G2/0XXSV0pZqEYdRFOl5ngnXtnTjC9fkoz4qBCtyyEakWorPUsptyp3y9y2ZY20n8hXPVVWi4hgPfIu4wA18n4TafFLj1dB5MM+burAax8fxz2fS8G0iGCtyyEa1XiCXwy6JSIAT5bWIjpUj69+LkXrUojGZDzBXzTolijg7W4wY+fBk8i9zISYMC6yQr5hzMEvpexw3QohklWriMiHPFVWC2O4AXdfytY++Y4Rg18IsUMIETVo2804d3gmUcCp/Kwdb9a0IPcyEyJDVBsnQeRxo7X4HwewSwhxBdC/MMs6KSUvS6SA91RZLWIjgvHli5O1LoVoXEYMfmX8/RoAW4QQhwBUSClvmZLKiLxYxadteOfQKeRdZkIEW/vkY8bSx78OzukXtgJYN9rMnESB4MmyWkyPDMZdF/PXgXzPaGvuFsE5/3668rgYQJEQ4g0p5RNTUSCRt/noSBveO9yKh65fgvBgtvbJ94zW4t/mvuiKlNKsLLw+Xd2yiLzXU2W1mB4ZgjsuZGuffNNoffwvuD8WQvxe2b556K8g8m8f1rfi/bpW3Hu5CWHBOq3LIZqQ8U7ZwKt2KaA9vfMQ4qNCcOdFbO2T7xpv8HOeHgpYZ1v78xFqYGuffJcq0zIT+aOnypyt/TsuTNK6FKJJYfATjcG/61vxQT1b++QfGPxEY/BUWS1msLVPfoLBTzSKD+pa8e/6Ntz3Bbb2yT8w+IlG4Wrt33YBW/vkHxj8RCP4oK4VHx5ha5/8C8fxE42ArX3yR6PNx7/K/bGU8l5l+1oVayLyCu/XncKHR9rwNbb2yc+M1uLf4rrjmq5BkadOOUTeQUqJJ0trMSs6FLeytU9+ZrTgF2O4T+R33jvcivJP2/H1K9jaJ/8zWvDLYe63q1ALkVeQUuKXpTWYExOKdRlztS6HyONGm0w8SwhRDmcL3ySESFfur1a9MiKNvH3oFKqOmvHIl5YhRM/WPvmf0YI/ZUqqIPISztZ+LRKMYchJY2uf/NOIwS+l7BhquxAiGcCQ+4h82a6ak9jTYMaja5cjWM/LXMg/jfknWwgRLYT4LyFEBYBiFWsi0oSrtZ8UG47stEStyyFSzagLhgoh1sA5fDMVzhO8V0kpj6hdGNFU2/HJCXzcdBpP5KyEQcfWPvmv0S7gagOQDeDnUsoFAF5g6JM/cjic4/ZN8RG4adUcrcshUtVozZpcAHEAtgghrgQQo35JRFPvn/uOoeZEJ76TeR70bO2TnxttsfUSKeU6OP8AzAeQIYT4/eCpHIYihMgWQmQKIXJHOS5/PAUTeZrN7sBTZbVYNDMKNyyfrXU5RKobU9NGStkhpXxGSpkOYCuAW0c6XgiRrXxdmfI4c5jjMgGYxlUxkYe9vLsZ9S1duD9rIYKCeFE6+b9xf6aVUh6RUm4e5bAMAPXK/Xo4TwwPIIQwuR1DpIlemx1PltZieUIMrj5/ltblEE2JYYNfCBGjdOskD7M/RdkfPcRu46DHcUMcY5JSDhv8QohcIUSFEKKipaVluMOIJuX5jxrQZO7BhqsXQQi29ikwDDucU0rZIYTYDOeJ3VQAdcquODiDvQ7AZinl6SG+3AwgdrjnFkJkurqBRnj9QgCFAJCeni5HOpZoIrr7bPj1vw7jIlMsPr9wutblEE2ZsVy5uxlwtvDhDPz64a7odVOOs61+E4DSQfvblP59I5xzAKVKKavGVTnRJP35vU9x6kwvCu5KZWufAspo4/hjhBBFQohtAFqllNVjCH1IKUvgDPRMAEa3k7ylyv4qZVsszu0WIlJdR7cVBW/VYc3iGUibN+yHUyK/NNqVu4UACuCckbMYwNVjfWIp5VblbpnbtqxBx/R35xBNpd+/VYfOXhu+d9UirUshmnKjLsQipdyptM45KRv5hWMdPfjze0dw06oELJ0z1NgEIv82WvDXDXOfyGc9WVoLKYHvZp2ndSlEmhitqydP6ad3LcSSrdyXUsqFqldH5GG1JzpRUtmIey5NwdzYcK3LIdLEaKN6eNaL/MrW1w8iIkSPr1+xQOtSiDTD2agoYHx0pA1lB07ivi/Mx7SIYK3LIdIMg58CgsMh8cj2/ZgVHYqvXMIVRSmwMfgpILyypxl7Gjuw4epFCAvmAuoU2Bj85PcsVju2vn4QyxKi8aXVCVqXQ6Q5Bj/5vWffPYLmDgseun4pp10mAoOf/NzJTgt+t+swrlo6ExeZhpoklijwMPjJr/1iRy16bQ5svnax1qUQeQ0GP/mtPQ1mFFU24CuXJsMUH6l1OUReg8FPfsnhkPjRq58gLiIE31rDi8yJ3DH4yS+9VN2E6qNmbL52MaJCDVqXQ+RVGPzkdzotVjz62kGsmmvEWg7fJDrHaJO0Efmcp8sOobWrF89+OZ3DN4mGwBY/+ZX9zafx5/c/xa0Zc7FyrlHrcoi8EoOf/IbDIfHgy/tgDDNg0zUcvkk0HAY/+Y3nyxtQfdSMB69fAmM4Z98kGg6Dn/xCS2cvHnvtAC4yxXI+HqJRMPjJL/xs+370WO342U3LIQRP6BKNhMFPPm/ngRP4393N+NoXFmDBDF6hSzQaBj/5tNMWKx586WMsmhnF5RSJxojj+MmnPfp/B3Cy04KCu9IQrGc7hmgs+JtCPuu9w6fwj48asP4yE8fsE40Dg5980mmLFRtL9iJlegTuzzxP63KIfAq7esgn/fiV/TjW0YOS+y5BqIFr6BKNB1v85HNe23cML1Q14htXLEBq0jStyyHyOQx+8iknT1vw/Zf2YXlCDL7JefaJJoTBTz7D4ZB4oGQvuvvsePKWVTDo+ONLNBH8zSGfUfB2Pd6ubcFDNyzlhVpEk8DgJ59Q+VkbnnijBtctn4U7L0zSuhwin6baqB4hRDYAMwCTlLJw0D4jAJPyL0NKuUmtOsj3tXf14Zt/r0aCMQyP3byCc/EQTZIqLX4l9CGlLFMeZw46ZB2AdCllibI/V406yPc5HBIPFO9By5le/Ob21Yjm+rlEk6ZWV08GgHrlfj2AVPedUspCt08BJrdjiQZ4euch7Dx4Eg9dvxQrEo1al0PkF9QKfuOgx3FDHSSEMAFoc30yGLQvVwhRIYSoaGlpUaFE8nZvfHIcT+88hJtTE/GfF8/Tuhwiv6FW8JsBxI7huGwpZd5QO5RPBelSyvT4+HiPFkfe7/DJTty/bTdWJsbgkS8tY78+kQepFfzlONvqNwEoHXyAECJbSrlVuZ86eD8FrvauPqz/WyXCgnX4w11pnJKByMNUCX7lpK1JOalrdDvJW6rcZgLIF0JUCiEqMbZPBxQALFY7cv+7Ak3tPfj9nWmYHROmdUlEfke14Zyu1jyAMrdtWcptGYD5ar02+SaHQ2JDyV6Uf9qOX922GhnJbA8QqYEXcJHXeOKNGry6pxkbr1mE/1g5R+tyiPwWg5+8wrPvHsHv3qzDbRfMxX2X88MgkZoY/KS5ovIG/PSf+3Htsln46Rc5godIbQx+0tT2vcew+cW9uOy8eDx16yroOeMmker4W0aaef3jY/j289VImzcNBXemIUTPYZtEU4HBT5p4ZU8zvv73aqxIjMGzd2cgLJihTzRVuOYuTbkXKhuxoWQPMpJj8ezdGYgM4Y8h0VTibxxNqT+9ewQ/3b4fl86fjmf+M50tfSINMPhpSjgcEo+9fhCFb9fjmvNn4albV3EqBiKNMPhJdRarHRtL9uKVPc348sXz8MMbz4cuiEM2ibTC4CdVnThtQd5/V2J3gxmbr12MvMtMHKdPpDEGP6mm8rN23PtcJbp6bfjDnam4ZtlsrUsiIjD4SQVSSvztg8/wyPYDmBUTiue+eiEWzYrSuiwiUjD4yaPM3X3YULIXpftP4IpF8XjyllUwhgdrXRYRuWHwk8e8X3cKDxQ5F0Z/6PoluOfSFATxJC6R12Hw06Sd6bXhsdcO4Ll/H0VyXDheuO8SLoxO5MUY/DQpu2pO4qGXPkZzRw+++rkUPHDVIl6UReTlGPw0IQ1t3fjpP/fjjf0nYIqPQMm9FyNtHlfMIvIFDH4al06LFc+8cwQFb9VBFySw6ZrF+OrnUhCs53x/RL6CwU9j0mdz4O8ffoZf/+swWrv6cOPKOfj+dYu5GDqRD2Lw04gsVju2lTeg4K06NHdYcMn8OGy+djFP3hL5MAY/Dcnc3Yd/fNSAZ989glNnepE+bxoeu3kFPr9wOqdcIPJxDH4aoOZ4J/7y/qd4qboRFqsDn1swHd+4cjUuTIll4BP5CQY/4UyvDa/uaca28gbsbjAjRB+EL61OwN2XJmPxrGityyMiD2PwByiL1Y43a1rw6p5m7Dx4AharA+fNjMRD1y/B2tRExEZwmgUif8XgDyAdPVa8WXMSb3xyAm/VtuBMrw1xEcFYlz4XN61OwOq5RnbnEAUABr8fs9kd2NfUgXcOncLbtS2objDD7pCYHhmCG1fOxrXLZuOS+XHQ6zgGnyiQMPj9SFevDXsbO1B1tB0fHmlD5adt6OqzQwhgeUIM7rt8Pq5YPAOr5xo5eRpRAGPw+6hOixW1JzrxcdNpfNLcgX1Np1Fz/DQc0rn/vJmRWJuaiAtNsbhk/nT22RNRPwa/F5NSoqWzF5+2dqO+5QzqT3Xh8MkzqDneiSZzT/9xsRHBOH9ONLKuXIjVSUasSjRiGoOeiIbB4NdQr82Ok6d7cfy0Bcc7LDjW0YOm9h40mXvQ0NaDo23d6LHa+48P1gfBND0CafOm4fYLk7BoZhTOT4jGrOhQnpQlojFTLfiFENkAzABMUsrC8e73NVa7A50WGzp6rP3/zN19MHdb0d7dh/auPpzq6kPbmT60nOlFS2cvOnqs5zxPqCEI08KD0d7VB4vNgZgwA27NmIsTpy14eXczDh7vxMHjncAeDd4kgIUzIlD63S/g5eomPL6jBs3mHswxhmHD1YsAAI/vqEGTuQc6IWCXEgnKvptWJ2hTsGKoerWuiUgrQkrp+Sd1hjqklCVCiFwA9VLKsrHuHyw9PV1WVFSMuw6L1Y5emwNWu/LPJtGn3O+zOdDnurU50GtzHmux2mGxnr3tttpg6bOju8+Obqsd3b02dPXZ0dVrQ1evDWd67ei0WNFrc4xYS3SoHtMjQxAXGYy4iBDMiA7Bqc5elB44Aav97P+BQScACVgdZ7cFCcDh+f+mCZsZFYzTFvuATyND1e0SZtDh0bXLNQval6ubsOXFfQPq1bomoqkghKiUUqYP3q5Wiz8DwDblfj2AVABl49jvEZte2Iv/3d08qecI1gchzKBDmEGH8BAdIoL1CAvWYWZ0KCJD9IgI0SMqVI8o5TY6zIAY5Z8x3ABjeDBiwgwwDDFk8tLH/jUg9AGc8xjwrtAHgBOdfedsG6pulx6rHY/vqNEsZB/fUTMg9L2hJiItqRX8xkGP48a5H8ongVwASEpKmlARN61OwIpEI4J1AnpdEPRBAsH6IITog2DQBSFYH4Rg5TbUoEOIPgghBh1ClcehBh10Kg57bHY7QevvtHyvw712IH3/idypFfxmACMtxzTafij9/oWAs6tnIkVcsWgGrlg0ka+cGnOMYQNG5/izOUbt5u0f7vusZU1EWlLrks1ynG3VmwCUjnN/QNhw9SKEGQauT2vQCRgGfcrwtmutZkYFj6lulzCDrv/krxaG+j5rXRORllQJfillCQCTECITgNF14lYIUTrS/kBz0+oEPLp2ORKMYRAAEoxheDx7JR7PWTlg2y/XrcKdFyXBG0ZsLpwRgQ8fzBqxbgDQKcUmGMM0P4k61PdZ65qItKTKqB5Pm+ioHiKiQDbcqB7OzkVEFGAY/EREAYbBT0QUYBj8REQBhsFPRBRgfGJUjxCiBcBnE/zy6QBOebAcLfG9eB9/eR8A34s3muz7mCeljB+80SeCfzKEEBVDDWfyRXwv3sdf3gfA9+KN1Hof7OohIgowDH4iogATCMHv84u8uOF78T7+8j4AvhdvpMr78Ps+fiIiGigQWvxEROSGwU9EFGD8OviFENlCiExlNS+fpbwPn1+zQAhhFEKkKu8nX+t6Jkv52cr0h/fi4uvvRQjRLoQoFUJs1LqWyXL7Xcn29HP7bfC7LejuWgsgU9uKJk5Zv8AfrAOQ7no/vvwHWQiRCiBV+flKFUKYtK5pspTfEV9/HzlSyiwp5VatC/GALcrvSqynf77UWnrRG0zJgu40dspymi4+vfKalLIKQJUQwgigXkpZr3FJk6IEi0+/B4VRCGHyg/+PXADlynvx+Mgev23xYwwLupM2lJBp85OV19IB1GldhAf4fFgqYgG0CSEKtC5kkubDmVltQogCpYHhMf4c/GaMsqA7aSZbSpmndRGeoPzxmq9GP+xUEUJk+skfYUgpC6WUZgBmX/4/UdQp76USgEe7Rf05+LmguxcSQmS7+l+VfnKfJITIdztHYYZvNzLalJPU2XCuhe2T/y9CiFxfrX0I5W73jXD+jHmM3wa/Py3orryHdF9vwSjvI18IUSmEqIRvh2UBgHq3ny+fvVJUSlml/H7E4twuUl9SBAwY2OGzgyKU2o2uQSme/vnilbtERAHGb1v8REQ0NAY/EVGAYfATEQUYBj8RUYBh8BNNEeVCnFLlNlvZZlRGOLkfV6ncFg8eniiE2OjL04+Qd2DwE00BJejrlHlk8jC26UMKAGwZtO0WXx6aTN6BwU9+R7m4KlO5XzyZy92VGRJLleesVC4SKhBCFCv7Ta7Hbq34oV7fDCDLVYtyReaIXBPAudWSCc43RR7gz5O0UYCSUm5SAjcLQMHgkFXCd90wXzvkhTLKc+bDebFWnmuabGV+mzzleUsBlAzz+mXK6xYLIWIBrFcmehtNiRAiV6krD8CmMXwN0YgY/OSvCgDkSynPCUoliMdzJaRr8rJWt/tmIYRRSmlW+uHTMXBK43NeX7kas0SZpK4YQJry9cZR3kexUq/RTyZSI40x+Mlf5QF41K213G+EFn/beC/zV+brMcIZzO4Tzw14/UFTBbcNehr3PyIDpkeWUtYLIdqUTxu+PuMkeQkGP/kdZUreTUpoFgghUt27VSbQ4h9JPZzdL/3Tfg/1+sr2Ujj7+k0A1rs9Rw6AZ4QQbXDOl+O+D3AG/jNDfXohmgjO1UNEFGA4qoeIKMAw+ImIAgyDn4gowDD4iYgCDIOfiCjAMPiJiAIMg5+IKMAw+ImIAsz/A2qLqOCPgs2YAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"x = maxSUV\")\n", "plt.ylabel(\"P(AE|X=x)\")\n", "\n", "plt.scatter(X[lung_flags==0], lung_flags[lung_flags==0],label=\"NC\") \n", "plt.scatter(X[lung_flags==1], lung_flags[lung_flags==1],label=\"AE\")\n", "#plt.plot(x, y[:,1], label=\"probability\",color=\"red\")\n", "plt.plot(x, 1/(1 + np.exp(-logr.intercept_[0] - logr.coef_[0,0]*x)), label=\"logit model\")\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 41, "id": "1f7a8948", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Covariance matrix: \n", " [[ 3.13832521 -1.68557139]\n", " [-1.68557139 0.99280095]]\n", "Standard errors: [1.77153188 0.99639397]\n", "Params: [-5.9605805 2.04920735]\n", "Wald statistics: [11.32085346 4.22970058]\n" ] } ], "source": [ "# Ref:\n", "# https://stats.stackexchange.com/questions/89484/how-to-compute-the-standard-errors-of-a-logistic-regressions-coefficients\n", "# https://web.stanford.edu/class/archive/stats/stats200/stats200.1172/Lecture26.pdf\n", "\n", "# Calculate matrix of predicted class probabilities.\n", "predProbs = logr.predict_proba(X)\n", "\n", "# Design matrix -- add column of 1's at the beginning of your X_train matrix\n", "X_design = np.hstack([np.ones((X.shape[0], 1)), X])\n", "\n", "# Initiate matrix of 0's, fill diagonal with each predicted observation's variance\n", "V = np.diagflat(np.product(predProbs, axis=1))\n", "\n", "# Covariance matrix C_params = (X^T V X)^-1, params = (b0,b1) \n", "covLogit = np.linalg.inv(np.dot(np.dot(X_design.T, V), X_design))\n", "print(\"Covariance matrix: \\n\", covLogit)\n", "\n", "# Standard errors\n", "print(\"Standard errors: \", np.sqrt(np.diag(covLogit)))\n", "\n", "# Wald statistic (coefficient / s.e.) ^ 2\n", "logitParams = np.insert(logr.coef_, 0, logr.intercept_)\n", "print(\"Params:\", logitParams)\n", "print(\"Wald statistics: \", (logitParams / np.sqrt(np.diag(covLogit))) ** 2)" ] }, { "cell_type": "code", "execution_count": 127, "id": "4b1e2ee4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Linear approach to variance in logit regression:\n", "#\n", "# f(x|p*) + df(x|p*).dp dp ~ N(0,C_p)\n", "# V_f = E[(df(x|p*).dp)^2] = df(x|p*)^T C_p df(x|p*)\n", "\n", "f = lambda x: 1/(1 + np.exp(-logitParams[0] - logitParams[1]*x))\n", "\n", "def df(x): \n", " t = f(x)\n", " return -t*(1-t)*np.array((1,x))\n", "\n", "def Vf(x):\n", " t = df(x)\n", " return np.dot(t,np.dot(covLogit,t))\n", "\n", "x = np.linspace(0, 6, 100)\n", "y = f(x)\n", "dy = np.sqrt([Vf(t) for t in x])\n", "\n", "plt.scatter(X[lung_flags==0], lung_flags[lung_flags==0],label=\"NC\") \n", "plt.scatter(X[lung_flags==1], lung_flags[lung_flags==1],label=\"AE\")\n", "plt.plot(x, y, label=\"fit logitmodel \",color=\"red\")\n", "plt.errorbar(x,y,dy, label=\"linear variance\", fmt=' ')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 67, "id": "f5cd2ccf-6321-4211-ab61-08d68d8c5219", "metadata": {}, "outputs": [], "source": [ "# Direct approach to variance in logit regression:\n", "#\n", "# f(x|p*) p ~ N(p*,C_p)\n", "#\n", "\n", "f = lambda x,p: 1/(1 + np.exp(-p[0] - p[1]*x))\n", "fopt = lambda x: 1/(1 + np.exp(-logitParams[0] - logitParams[1]*x))\n", "\n", "#generate parameters and points in x axis\n", "ps = np.random.multivariate_normal(logitParams, covLogit, 1000)\n", "xs = np.linspace(0, 6, 100)" ] }, { "cell_type": "code", "execution_count": 97, "id": "290689a3-f3b4-44c8-a5e0-f3b1333ecd95", "metadata": {}, "outputs": [], "source": [ "# generate values in y axis\n", "yopts = fopt(xs)\n", "ys = np.array([np.fromiter(map(lambda p: f(x,p), ps),float) for x in xs])" ] }, { "cell_type": "code", "execution_count": 142, "id": "ca4438c7-24c4-4d02-bebc-be3137250652", "metadata": {}, "outputs": [], "source": [ "# doing statistics on values\n", "mys = np.median(ys,axis=1)\n", "yerr_min = np.quantile(ys, 0.25, axis=1)\n", "yerr_max = np.quantile(ys, 0.75, axis=1)" ] }, { "cell_type": "code", "execution_count": 141, "id": "03fdb9cc-b0b3-4d50-b59f-1d88efa5cef2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(X[lung_flags==0], lung_flags[lung_flags==0],label=\"NC\") \n", "plt.scatter(X[lung_flags==1], lung_flags[lung_flags==1],label=\"AE\")\n", "plt.plot(xs, yopts, label=\"fit logit model\",color=\"red\")\n", "plt.errorbar(xs, mys, yerr = [mys-yerr_min, yerr_max-mys], label=\"direct variance\",fmt=' ')\n", "plt.legend()" ] }, { "cell_type": "markdown", "id": "3fa88da2", "metadata": {}, "source": [ "## Bayesian Gaussian modeling" ] }, { "cell_type": "code", "execution_count": 42, "id": "21058bc8", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 43, "id": "c3e4dd89", "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", " Extracting only data associated to lungs\n", "\"\"\"\n", "lung_suv = suv_dict['lung_SUVperc_COMBINED']\n", "lung_flags = flags_dict['flags'][:,3] # 0 == NC, 1 == AE" ] }, { "cell_type": "code", "execution_count": 44, "id": "f9e6e879", "metadata": {}, "outputs": [], "source": [ "p = 95 # percentile of interest\n", "X = np.nanmax(lung_suv[:,:,p], axis=1) # taking SUV max and ignoring NaNs" ] }, { "cell_type": "code", "execution_count": 45, "id": "57558f7a", "metadata": {}, "outputs": [], "source": [ "# first central moments of X associated to NC and AE \n", "param_NC = [np.mean(X[lung_flags==0]), np.std(X[lung_flags==0])]\n", "param_AE = [np.mean(X[lung_flags==1]), np.std(X[lung_flags==1])]" ] }, { "cell_type": "code", "execution_count": 46, "id": "fc9def49", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.3857508112083783, 0.25838646551499894]\n", "[2.662019371986389, 0.9351373779777512]\n" ] } ], "source": [ "print(param_NC)\n", "print(param_AE)" ] }, { "cell_type": "code", "execution_count": 47, "id": "559eb809", "metadata": {}, "outputs": [], "source": [ "# define Gaussian distribution\n", "g = lambda x, mu, sigma: np.exp(-(x-mu)**2/(2*sigma**2))/(np.sqrt(2*np.pi)*sigma)" ] }, { "cell_type": "code", "execution_count": 48, "id": "df3ffcfe", "metadata": {}, "outputs": [], "source": [ "# probability for NC and AE \n", "P_NC = np.count_nonzero(lung_flags == 0)/len(lung_flags) # Prob(Y = NC)\n", "P_AE = np.count_nonzero(lung_flags == 1)/len(lung_flags) # Prob(Y = AE)\n", "\n", "\n", "# defined conditional probability densities for X at a class \n", "f_XatNC = lambda x: g(x, param_NC[0], param_NC[1]) # f(x | NC)\n", "f_XatAE = lambda x: g(x, param_AE[0], param_AE[1]) # f(x | AE)\n", "\n", "# probability density for X\n", "f_X = lambda x: P_NC*f_XatNC(x) + P_AE*f_XatAE(x)\n", "\n", "\n", "# conditional probability for AE at X\n", "P_AEatX = lambda x: P_AE*f_XatAE(x)/f_X(x)" ] }, { "cell_type": "code", "execution_count": 49, "id": "dbdf301c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = np.linspace(0, 6, 100)\n", "\n", "plt.xlabel(\"x = maxSUV\")\n", "plt.ylabel(\"P(AE|X=x)\")\n", "\n", "plt.scatter(X[lung_flags==0], lung_flags[lung_flags==0],label=\"NC\") \n", "plt.scatter(X[lung_flags==1], lung_flags[lung_flags==1],label=\"AE\")\n", "\n", "plt.plot(x, P_AEatX(x), label=\"Bayesian model with Gaussians\")\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 50, "id": "dabe8878", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"x = maxSUV\")\n", "plt.ylabel(\"f_X(x|case)\")\n", "\n", "plt.plot(x,f_XatNC(x),label=\"case=NC\")\n", "plt.plot(x,f_XatAE(x),label=\"case=AE\")\n", "plt.scatter(X[lung_flags==0], lung_flags[lung_flags==0],label=\"point NC\") \n", "plt.scatter(X[lung_flags==1], lung_flags[lung_flags==1],label=\"points AE\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "id": "3d0f363d", "metadata": {}, "source": [ "## Bayesian Lognormal modeling\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "2a60d334", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 11, "id": "0dea6bca", "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", " Extracting only data associated to lungs\n", "\"\"\"\n", "lung_suv = suv_dict['lung_SUVperc_COMBINED']\n", "lung_flags = flags_dict['flags'][:,3] # 0 == NC, 1 == AE" ] }, { "cell_type": "code", "execution_count": 31, "id": "f69c3512", "metadata": {}, "outputs": [], "source": [ "p = 95 # percentile of interest\n", "X = np.nanmax(lung_suv[:,:,p], axis=1) # taking SUV max and ignoring NaNs" ] }, { "cell_type": "code", "execution_count": 32, "id": "3219d4ee", "metadata": {}, "outputs": [], "source": [ "# first central moments of X associated to NC and AE \n", "param_NC = [np.mean(np.log(X[lung_flags==0])), np.std(np.log(X[lung_flags==0]))]\n", "param_AE = [np.mean(np.log(X[lung_flags==1])), np.std(np.log(X[lung_flags==1]))]" ] }, { "cell_type": "code", "execution_count": 33, "id": "0a62b5e1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.3099913036929419, 0.17899981637999948]\n", "[0.927665922391502, 0.3065334767927164]\n" ] } ], "source": [ "print(param_NC)\n", "print(param_AE)" ] }, { "cell_type": "code", "execution_count": 34, "id": "92d64787", "metadata": {}, "outputs": [], "source": [ "# define lognormal distribution\n", "lg = lambda x, mu, sigma: np.exp(-(np.log(x)-mu)**2/(2*sigma**2))/(np.sqrt(2*np.pi)*sigma*x)\n", "# define Gaussian distribution\n", "g = lambda x, mu, sigma: np.exp(-(x-mu)**2/(2*sigma**2))/(np.sqrt(2*np.pi)*sigma)" ] }, { "cell_type": "code", "execution_count": 35, "id": "493344e7", "metadata": {}, "outputs": [], "source": [ "# probability for NC and AE \n", "P_NC = np.count_nonzero(lung_flags == 0)/len(lung_flags) # Prob(Y = NC)\n", "P_AE = np.count_nonzero(lung_flags == 1)/len(lung_flags) # Prob(Y = AE)\n", "\n", "\n", "# defined conditional probability densities for X at a class \n", "f_XatNC = lambda x: lg(x, param_NC[0], param_NC[1]) # f(x | NC)\n", "f_XatAE = lambda x: lg(x, param_AE[0], param_AE[1]) # f(x | AE)\n", "\n", "# probability density for X\n", "f_X = lambda x: P_NC*f_XatNC(x) + P_AE*f_XatAE(x)\n", "\n", "\n", "# conditional probability for AE at X\n", "P_AEatX = lambda x: P_AE*f_XatAE(x)/f_X(x)" ] }, { "cell_type": "code", "execution_count": 41, "id": "b707afde", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = np.linspace(0.5, 6, 100)\n", "\n", "plt.xlabel(\"x = maxSUV\")\n", "plt.ylabel(\"P(AE|X=x)\")\n", "\n", "plt.scatter(X[lung_flags==0], lung_flags[lung_flags==0],label=\"NC\") \n", "plt.scatter(X[lung_flags==1], lung_flags[lung_flags==1],label=\"AE\")\n", "\n", "plt.plot(x, P_AEatX(x), label=\"Bayesian model with Lognormals\")\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 40, "id": "2a6cc81e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"x = maxSUV\")\n", "plt.ylabel(\"f_X(x|case)\")\n", "\n", "plt.plot(x,f_XatNC(x),label=\"case=NC\")\n", "plt.plot(x,f_XatAE(x),label=\"case=AE\")\n", "plt.scatter(X[lung_flags==0], lung_flags[lung_flags==0],label=\"point NC\") \n", "plt.scatter(X[lung_flags==1], lung_flags[lung_flags==1],label=\"points AE\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "id": "4c982d82", "metadata": {}, "source": [ "## Bayesian custom distribution modeling via Fitter package\n" ] }, { "cell_type": "code", "execution_count": 42, "id": "623911f7", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 43, "id": "87e42b85", "metadata": {}, "outputs": [], "source": [ "## fitter package provides a simple class to identify the distribution from which a data samples is generated from\n", "## pip3 install fitter --user\n", "from fitter import Fitter" ] }, { "cell_type": "code", "execution_count": 44, "id": "3b34915b", "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", " Extracting only data associated to lungs\n", "\"\"\"\n", "lung_suv = suv_dict['lung_SUVperc_COMBINED']\n", "lung_flags = flags_dict['flags'][:,3] # 0 == NC, 1 == AE" ] }, { "cell_type": "code", "execution_count": 45, "id": "4d08ebd0", "metadata": {}, "outputs": [], "source": [ "p = 95 # percentile of interest\n", "X = np.nanmax(lung_suv[:,:,p], axis=1) # taking SUV max and ignoring NaNs" ] }, { "cell_type": "code", "execution_count": 84, "id": "e14a1285", "metadata": {}, "outputs": [], "source": [ "X_NC = X[lung_flags==0]\n", "X_AE = X[lung_flags==1]\n", "\n", "flags_NC = lung_flags[lung_flags==0]\n", "flags_AE = lung_flags[lung_flags==1]" ] }, { "cell_type": "code", "execution_count": 47, "id": "ecc1366d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Fitting 106 distributions: 25%|███▊ | 27/106 [00:01<00:06, 12.87it/s]/usr/lib/python3/dist-packages/scipy/stats/_continuous_distns.py:3102: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", " the requested tolerance from being achieved. The error may be \n", " underestimated.\n", " t1 = integrate.quad(llc, -np.inf, x)[0]\n", "Fitting 106 distributions: 27%|████ | 29/106 [00:02<00:07, 10.94it/s]/usr/lib/python3/dist-packages/scipy/stats/_continuous_distns.py:3102: IntegrationWarning: The algorithm does not converge. Roundoff error is detected\n", " in the extrapolation table. It is assumed that the requested tolerance\n", " cannot be achieved, and that the returned result (if full_output = 1) is \n", " the best which can be obtained.\n", " t1 = integrate.quad(llc, -np.inf, x)[0]\n", "/usr/lib/python3/dist-packages/scipy/stats/_continuous_distns.py:3102: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n", " t1 = integrate.quad(llc, -np.inf, x)[0]\n", "Fitting 106 distributions: 44%|██████▋ | 47/106 [00:04<00:06, 8.78it/s]WARNING:root:SKIPPED kstwo distribution (taking more than 30 seconds)\n", "Fitting 106 distributions: 75%|███████████▏ | 79/106 [00:08<00:04, 6.09it/s]WARNING:root:SKIPPED rv_continuous distribution (taking more than 30 seconds)\n", "WARNING:root:SKIPPED rv_histogram distribution (taking more than 30 seconds)\n", "Fitting 106 distributions: 98%|█████████████▋| 104/106 [00:22<00:03, 1.92s/it]/usr/lib/python3/dist-packages/scipy/integrate/_quadpack_py.py:879: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n", " If increasing the limit yields no improvement it is advised to analyze \n", " the integrand in order to determine the difficulties. If the position of a \n", " local difficulty can be determined (singularity, discontinuity) one will \n", " probably gain from splitting up the interval and calling the integrator \n", " on the subranges. Perhaps a special-purpose integrator should be used.\n", " quad_r = quad(f, low, high, args=args, full_output=self.full_output,\n", "/usr/lib/python3/dist-packages/scipy/integrate/_quadpack_py.py:879: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n", " quad_r = quad(f, low, high, args=args, full_output=self.full_output,\n", "WARNING:root:SKIPPED levy_stable distribution (taking more than 30 seconds)\n", "Fitting 106 distributions: 99%|█████████████▊| 105/106 [00:34<00:04, 4.58s/it]WARNING:root:SKIPPED studentized_range distribution (taking more than 30 seconds)\n", "Fitting 106 distributions: 100%|██████████████| 106/106 [00:38<00:00, 2.74it/s]\n" ] } ], "source": [ "f_NC = Fitter(X_NC)\n", "f_NC.fit()" ] }, { "cell_type": "code", "execution_count": 22, "id": "1f426be8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sumsquare_erroraicbickl_divks_statisticks_pvalue
dgamma50.441287420.953150-5.174787inf0.1103200.370769
dweibull53.350977416.219346-1.473358inf0.1144250.327907
burr57.746991378.5039847.942108inf0.0867320.671072
mielke57.747002378.5028377.942121inf0.0867310.671079
logistic58.086717405.057087-0.050060inf0.0878420.655816
\n", "
" ], "text/plain": [ " sumsquare_error aic bic kl_div ks_statistic \\\n", "dgamma 50.441287 420.953150 -5.174787 inf 0.110320 \n", "dweibull 53.350977 416.219346 -1.473358 inf 0.114425 \n", "burr 57.746991 378.503984 7.942108 inf 0.086732 \n", "mielke 57.747002 378.502837 7.942121 inf 0.086731 \n", "logistic 58.086717 405.057087 -0.050060 inf 0.087842 \n", "\n", " ks_pvalue \n", "dgamma 0.370769 \n", "dweibull 0.327907 \n", "burr 0.671072 \n", "mielke 0.671079 \n", "logistic 0.655816 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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qDz74YCMchWekyUUIIXyEJHQhhPARktCFEMJHSEIXQggfIQldCCF8hCdvLLIopTYrpXYopfYopf5WQxmllJqtlDqslNqplBrUOOEKIYSojSc19DLgcq31AGAgMEEpdWm1MlcBPdyfe4C3GjJIIYQ4V8HBwee87l133XXaMADVvf/++6SkpHhcvrF58sYiDRS5J83uT/XXy00FPnCX3aSUClNKddBapzZotEII0YTefvvtsy5///336devHx07dvSofGPzqA1dKWVUSm0HMnC9U/SnakWigJNVppPd84QQolnQWvPoo4/Sr18/YmNjKx8ecjqd3HffffTt25fJkyczceLEypdXJCQksHXrVhwOBzNmzKhc9/XXX+fLL79k69atTJ8+nYEDB2K1WivLA/zwww8MGjSIAQMGcMUVVzTJMXr0pKjW2gEMVEqFAd8opfpprXdXKaJqWq36DKXUPbiaZIiMjGTVqlWAa1Syiu/NnS/E+nDsL4MRNYdjeTjWTmSA66cn8dQWf1Mdly9cA00lNDS0cqTCuXfd0ij7uOftT+ssU1hYyHfffUdiYiLr1q0jOzubhIQEBg0axKZNmzh8+DAbNmwgMzOT+Ph4brnlFgoLC3E4HBQXF7N+/XpOnDjBxo0bASoH5Lrkkkt49tlnGTRoEHa7vbL8sWPHuOuuu1i8eDExMTHk5OTUOGJjXUpLS+v1+6vXo/9a6zyl1CpgAlA1oScDnatMdwJSqEZrPReYCxAXF6cTEhIA1/98Fd+bO1+IdcbjCyu/J00/c3lTm/H4Qh6OtfPvXSaP4qkt/qY6Ll+4BprKvn37Gm0ExQqebD8kJITExERuu+02wsLCCAsLIyEhgX379pGYmMgtt9xCaGgooaGhjBkzhoCAAEJCQjAajQQFBdGtWzeOHz/OE088waRJkxg2bNhpyytiqJjevXs3l112WeWQved6DiwWS+VwvJ6oM6ErpdoCNncyDwDGAv+sVmw+8IBSah4wFMiX9nMhRFUPf7bgrMsbc/hccDW51Gd+VeHh4ezYsYMlS5bwxhtv8PHHH/Phhx+edV9K1dRw0bg8aUPvAKxUSu0EtuBqQ1+glJqplJrpLrMIOAocBv4L3Nco0QohxDkaPXo0n332GQ6Hg8zMTNasWcOQIUMYOXIkX331FU6nk/T09BqbOLKysnA6nUybNo1nnnmGHTt2AK6ad01NKcOGDWP16tUcO3YMcDXRNAVPernsBM6o82ut51T5roH7GzY0IYRoONdeey0bN25kwIABKKX417/+Rfv27Zk2bRo//vgj/fr1o2fPngwdOpTQ0NDT1j116hR33HEHTqcToPJ9oTNmzGDmzJkEBARUtq8DtG3blrlz53LdddfhdDpp164dy5Yta/RjlOFzhRA+reKFE0opXnjhBV544YXTlhsMBl588UWCg4PJzs5myJAhlW3fVWvr27Ztq/xeUSufNm0a06ZNq5xftfxVV13FVVdd1dCHc1aS0IUQF7zJkyeTl5dHeXk5f/3rX2nfvr23QzonktCFEBe8ltINtS4yOJcQolF50otEnOlczpskdCFEo7FYLGRnZ0tSryetNdnZ2VgslnqtJ00uQohG06lTJ5KTk8nMzKyzbGlpab0TmLc0RawWi4VOnTrVax1J6EKIRmM2m+nSpYtHZVetWlWvpyK9qbnGKk0uQgjhIyShCyGEj5CELoQQPkISuhBC+AhJ6EII4SMkoQshhI+QhC6EED5CEroQQvgISehCCOEjPHkFXWfgA6A94ATmaq1frVYmAfgOOOae9bXW+u8NGmkzFFP1HZazJjWrfcW439NZ8Z7Nxo6vsVU9fiFEzTx59N8OPKy13qaUCgESlVLLtNZ7q5Vbq7We3PAhCiGE8ESdTS5a61St9Tb390JgHxDV2IEJIYSoH1WfYS2VUjHAGqCf1rqgyvwE4CsgGUgBHtFa76lh/XuAewAiIyMHz5s3D3C9Iio4OPhcj6FJVY1116n8yvmxUaG1rdIg6ruvXafyiQyAdOuZ6zRl3J6oGmtt8VSNuSpvHFdLvV6bO4nVM2PGjEnUWsfVtMzjhK6UCgZWA89prb+utqwV4NRaFymlJgKvaq17nG17cXFxeuvWrYBr5LKEhASP4vC2qrG2hDb0f+8ynbFOU8btiaqx1hZPbW3o3jiulnq9NncSq2eUUrUmdI96uSilzLhq4B9XT+YAWusCrXWR+/siwKyUijiPmIUQQtRTnQldKaWAd4B9WuuXainT3l0OpdQQ93azGzJQIYQQZ+dJL5cRwK+AXUqp7e55TwAXAWit5wDXA/cqpeyAFbhZyzunhBCiSdWZ0LXW6wBVR5nXgdcbKighhBD1J0+KCiGEj5CELoQQPkISuhBC+AhJ6EII4SMkoQshhI+QhC6EED5CEroQQvgISehCCOEjJKELIYSPkIQuhBA+QhK6EEL4CEnoQgjhIyShCyGEj5CELoQQPkISuhBC+AhP3ljUWSm1Uim1Tym1Ryn1YA1llFJqtlLqsFJqp1JqUOOEK4QQojaevLHIDjystd6mlAoBEpVSy7TWe6uUuQro4f4MBd5y/xRCCNFE6qyha61Ttdbb3N8LgX1AVLViU4EPtMsmIEwp1aHBoxVCCFErVZ9XfyqlYoA1QD+tdUGV+QuAWe7X1aGU+hF4TGu9tdr69wD3AERGRg6eN28eAEVFRQQHB5/fkTSRqrHuOpVfOT82KrRR91vffe06lU9kAKRbz1ynKeP2RNVYa4unasxVeeO4Wur12txJrJ4ZM2ZMotY6rqZlnjS5AKCUCga+Av5QNZlXLK5hlTP+pdBazwXmAsTFxemEhAQAVq1aRcX35q5qrDMeX1g5P2l6QqPut777mvH4Qh6OtfPvXaYz1mnKuD1RNdba4qkac1XeOK6Wer02dxLr+fOol4tSyowrmX+stf66hiLJQOcq052AlPMPTwghhKc86eWigHeAfVrrl2opNh/4tbu3y6VAvtY6tQHjFEIIUQdPmlxGAL8CdimltrvnPQFcBKC1ngMsAiYCh4ES4I4Gj1Q0DqcDbFbw90J7oNMJBnkUQoiGUmdCd9/orKmNvGoZDdzfUEGJpqCZaPgJXn4YitIhZiT0uQb6TIWgiNNKluTnkXHsCGVWK+WlJQC0i+5KxEXRGE1mz3dpK4XE9+DkT5DyM+SfggE3EcQYoB7bEULUyOObosJ3dFbpPGN6nwTjDih0zzy2xvVZ8Qzcs4pCHcy+tas4svUnUg7thxp6QxlNJtp17c6AsVdx8YjRdSf37x+EnfNOn/fzRyzyW8pm62+B3g1xeEJcsCShX2jyTvCN31NEqALydSChVz/nqpkf/AF++g9lybvY/PKjbDvqwG4rB8BoNtOhey+MTiNluVasRWVYy3MotxeSenA/qQf3s/p/7zD46msYPOkaTH5+Z+73yEpXMjdZYMIs6BTvmv/tTKLTdtE5+Vm2Gu8CJjXZqRDC10hCv5CUF8OntxKhCtjg6MPvbb9ja9ytrmUDb2VPqpnVq9/G6nB1Xu8eP4zO0X1I31TAqZOhlJlbucr6g8Ef/HUZzvLD2MsSsRZnsW7eB2z56jvG/+Y+eowZ8ct+bVZY8JDr+2V/hLgqt1juWsFbT9/Jvabvecb0Lpy8EToPaYKTIYTvkYR+odAavr0P0ndx1NmembY/UIDrRqi9vJwf353D7pVLATMdA/KJbd+WI8ei2XQoGK1CwQxmezER5nzadvTH7G/CaDZQXtKBjFOXk1GcR3H5Fspsucyf80/afNiXKY/dR+tenWH1vyD3GLTrA8N/f3pcJj/+ab+FYRFlDMxbCp/fDr9dA8Ftm/4cCdHCSUK/UKx9EfZ+C34h3F34cGUyz89I5/uXnyf96GFMZj8uu3k6WZ9vZE3OeLTBiMJBJ1MKl1zTh04Jl2EwGWvdxbElm/nxk0/JLz1EdvEuPvz7P7lsyFAG5s8GFFz9KhhrbmdfH3Eztpwk4gsPwpd3wK++BaNcnkLUh/wfcwHoq5Jg5fOAgmlvc+Q9BwDh5Tl8+tdHKM7LRYUFkBbhx5avbJQHTgTtJMa6juGP3kp4vys92k+X8UO4a/wQdn67iOWf/he7PYkVm23sMPyOvQnrOb7jJYq2FjE+Zjwz+s4gxC+kcl2nMnFf+YNsafM3SFoLq/8Jl/+5Ec6GEL5LOgH7OANOnjO/DdoBQ2dCrwkAtC7P5rrU7yjOyyW1tZUSIuiUNYNy/3b4laayNfJV/jPyExxqR7332f+aidz2r5fxMwei7afIth+j6+prSD12lOMFx5m7cy4TvprAu7vfpdxRXrleJuFww3uuiXUvQ+aBBjkHQlwoJKH7uOnG5Qw0HIWQjpU13oiyLK5L/ZZAZylp4aV0K7mScHUDKCMxwccpm5nFyR7pHPMzM2v3f89pv+GdothztYESfyfamU2xbTO/3vYAb/V8gcGRgykoL+DlxJeZuXwmGMp+WTFmJAz6NThtsPDhGrtLCiFqJk0uLVxMLYNWAQTZc/mj6TMAfpt9E0ueWkMrWz43pn9HgLOcjLByepRMxxYYjcFeyl77CV4I6wzftmPD9Ie5ZuvfWVJ6iqtPruayzpedsb+kWb90Maw6/9jzE/nbxr+xvnQPHYZpJq2LwW7PJr9sLXv+cwXJwWMpieyPpcOXbEnbQuBFaZTpXwMhxDy+kDBGsML/a1onrYVdX0L/Gxr4rAnhm6SG7sNGZX5MiLKyzDGYJc44LI5SpmTMJ8BRTk4rO13LZmALjMZcms1Ccxbft/9lfLWOPa7igTzXoJrPbnqGEluJx/v94uAXfHfkOwJQvJybTkanLhQbg9COVEpsa7mh0EREdidKjs/EWR6GMeAk3zveQRmLAMgjhOft7u6US56A0pqHzhVCnE4Suo8aovbRo2gzJdqfp2y3Y3Q6mJSxgPDyIootTjrb78BuaY+5JJV3wpzsDm1z+gYsrbg1qBt9yspIK0nntZ9f83jfXx36CoA/5xbTt7yc1YZBfN1hClaDBaf9OFbnz9yZVUpgSQglx+/FUdaWHNII6Pw/UDYAvnSMhs6XQnEGrPxHg50XIXyZJHQfpHDyhPljAP5jn0yKbsMV2SvoWJpJuUkTYfgVDv+2mIpO8kaEhXRLzQNzmWJG8XRWDkYUn+z/hKT8pLr3bc5hb/ZeAoz+jM/PJskZSbJuR545jPntJ2HHiKN8D6WmTB5Iy8ZQHoL1+D0E46qpW9p/DWg0Bpj0b1AG2PI20SqtAc+QEL5JEroPmmzYxEDDUYqNofzXMYkBBTvpVXQEh0ETap6G9o/EWHiMl9uHku9nqX1DMaPoXW5jvNMfp3ayPmV9nfs2hewBYLR/eyxas87Zr3JZhn87lrcdA4Ddugqbxcx9p5LRjhDGG6ejnWbMYT9jbr3WtUL7fjDgVnDaedR9L0AIUTtJ6D7GD1tl8vupzXWEluYyImcDAEF+Y9GWGIxFSbzSIYLSugbTuuhSUAaG5rpqx1vSttS5f1PIbgDGFrlG/VrrjD1t+aHgHiSGDgQ0tuIF+Ad2YHJ6Mm1UB0pTbgTAv91ijEEHXSuMeQJMFiYbf2KgOuzJKRDigiUJ3cf8yriMiwyZHHRGscc/nqsyF2MEzOZ+EDAAU9FxXmnfmlKTBx2cLK2gwwDiSlw3RBPTEwFnrcWVqQBjwAn8DH6MSt4DysBGZ98zym0MH8rxgM6grZQXL6CvoQP+KXnYC2Mpy7wCpTSWjp+TZc2C0Ci49D4A/mT+hBrebCiEcPPkjUXvKqUylFK7a1meoJTKV0ptd3+ebPgwhSdaUcwDpm8BeN52C+WJKwiyl2E0tMMQNBZz0UlmR4bVXTOvKmYkne122hkDyCvLw+CfUWtRU/BelNIMD+1OkMMGHQdRQNAZ5bQysKTtWAqNwWhHGmW2bZj2+BFgt1GedQX24m4YTEX8df1f0VrDyD+Qo4MZatjPFYZt9T0tQlwwPKmhvw9MqKPMWq31QPfn7+cfljgXd5kWEq6K2OTsTWGOE7LTUVgwhVyLnzWDN9sFU2yuYWjbs4keiQLiXJ1PMAYeq7VoZXOL072PbmNqLVtmtLCk3VicKBxlWykyljAzNQ0wUJpyI9oeyLpT6/hk/ydgCeU1+7UA/NH0GYaz/JUgxIWszoSutV4D5DRBLOI8tCGf3xgXA/BW4QSG5rnau03Bk/ArL+P9cCN5Z7sBWht3O3pcrusVscbAozWXMxZjDDqK1gYSil3D79K+/1k3nWrpwE/hrnHRbcWLMQZ1ZHxGMtoeSmnadQC8tPUlDuYe5GPHWJJ1BL0MyUwxbKj/cQhxAVDag0erlVIxwAKtdb8aliUAXwHJQArwiNZ6Ty3buQe4ByAyMnLwvHmut9cUFRURHOyFd1qeg6qx7jr1ywMvsVGhjbrf2vZVMX9k5sdckreEg5aBzN/fHmUtxug/mADzIAy98yjtEFav/VXdx+Ct/0dW2XGmdOpIAEHcZnwcpdRpZb48uZLVzq+JUt34Ku0QQSXJbIl7lU354Wfdj9Ya+8bF6KxTGEwXEeg/DudgK/awQHZbFrGhaANR5igmOO+mX8EGxma8Tb65HR9Fz8KpTDWei+rxN7SWer02dxKrZ8aMGZOotY6raVlDPPq/DYjWWhcppSYC3wI9aiqotZ4LzAWIi4vTCQkJAKxatYqK781d1VhnVH0MfnpCo+63tn3NeHwhHcniHv8fQcF/DvWiozUFZWyHn98QfjSmszkrErLqt7/Tjid7MMG7jtDGFES2vZhXDubiLG93WpmZr76HOQyOpvYhqNzVvTF+3A3c8OSqOvcVGDCW35jn4bSdoNR8GL8NYfw7KoS9z73MtPnTSC5K5s3MtTiyLmep30K62VJJ3reeTx1XnHEuaoy/gbXU67W5k1jP33n3ctFaF2iti9zfFwFmpVREHauJBvQ70zf4Kzsf5w2nY1EKYMIcOAG/1ofYHB55/jsIj3a1o5tbAzW3oyuza5iA8HI/cJRDcCT4nXlDtCYlpkBMA0cBYLeuxR4Yyo1pJwk0B/L3Ea5bMn4RK9H+6bxkd43r8nvTN/hTXus2hbgQnXdCV0q1V0op9/ch7m1mn+92hWdiVCo3GFeTb/PnVJo/AKbAy8hxHMI6oEvD7CQsGoA4p+vlFjW1oyuTK6F3dLpHTmzdtV67MHTowt7giwEHtuLFdDFHkXfoFPHt47n14ltRyoml4xcscg5irzOaDiqH24zLz/2YhPBBnnRb/BTYCPRSSiUrpX6jlJqplJrpLnI9sFsptQOYDdysPWmYFw3iD6avMOLko5PDMGo7BlM0OK281/bsNyTrJTwGgLhi1+BZrhr66b9ig8n1IFFXu+tnfRM6wJo2I8g3hqAdGZTZd/Djv1fidDp5cNCDOMtbY7SkYo5YzQt21wNI95rmQ1nhuR2TED7Ik14ut2itO2itzVrrTlrrd7TWc7TWc9zLX9da99VaD9BaX6q1li4ITSV9L1MMG9mSdRGlZQ5Q/liMXXmt40VgaMBnxsJdNfRuuSk47UEYzAUo8y9/hJU5ylBGK1ob6a7dHaLC6//Xgc3gx/J2l6MBR+lmUuwGdr+7jEBzIKWp1wPgF7GK1eb2JDp7EKEKYNOc8z48IXyFPCnakq18jvwyC+uyXLXhANMg3uuSh12fvWdJvbWKAoMJVZQG1k4AGC0plYszSzIB0PZguij3g0etz625J8XSke2tBuAaGmAJGzcWU5iciaOkK+W5Q1HKgaXD17xgdyV4NrwG1txzPjQhfIkk9Jbq1Db0vgV8ljQajQOjMYZF3XeTUTa04fdlMEKoK5EH2Vx92ZX5ly6CWVZXFxptb0W0SnfNPIcmlwobw4eQawpFO7Ox2nax4p8/AFCWcRVOWyjGgJMkhuWxztEXyvJh/exz3pcQvkQSeku14lkWHL2cYl0CKoD9nU6xx3EVoBpnf+4bo23srkmDOa9yUabVVUN32kOqJPRzvyHrMJhY1vZylHI9RXrCqrgiMwWcFkrTrgHAv+1SZin3A8w/zYGi2ockEOJCIQm9JUpaz9716Rx099qzBlv4MaIX2tbm7OudD3c7ege7AwBlyqtcVNHkYrH7E6jKICDc9TkP6Zb2xF19Ha6ml6UMsYcQYLfhKOqNLX8AymDjSPuf0T3Gg60E1r50XvsTwhdIQm9xNDmfzWJpfl/AhjKG80lcFracEY27W3dPlxi767H+mmroYe7a+/k0t1Q1/IbptO7YCe3Mwar3c0eqq92+LP1qnPZATEGH+fZiV/91tr5DFJkNsl8hWipJ6C3MBMdWvtw5AIczBYUfn4/YQ2nqNBr9V+lucunpcLWdq6oJ3V1Db+9wj+DVQAnd5OfHhPseAqVwlCUS6B9C/7wstCOYsvSrAXjh4Kdk9ZkCjnL+YPqqQfYrREslCb0FMTgdXHbKQKHdNVTO5oszySkdibOsQ+Pv3F1D7+PMRmsjBlMxpfZS4Jebop0d7kG5zqHLYm069OhFvLvppdy6jElFGuV0Yi8YiL2oJ4XlhfwjLBAMJq4zrqWHSm6wfQvR0khCb0EeP7WTNJUG2MgOdVA8MIzyrMubZufuGnq0ykTbXANfpRW73mRU0eTS3eF6WrShaugVht8wnXxTqKvpxXCcW1NPAorS1GsJMAWwLHUDP/abiFFpeVWduKBJQm8hJqefwGoOwmk/gcMAy+NSXeOc6Hq8rOJ8BEWAOZBQVYLB5hplLrXYNaRuRZPLxU73Q0UNnNBNfn4saet+4KhsKx1NFjpai9D2cB4c9CAAz9lOkq4sjDMmMkgdbND9C9FSSEJvAVI27KGPao3d6np58rr+mVw74CYuaXdJ0wWhVGUtPdDuGjMmrTgNm8NGblkuWiv6O8+/D3pt0i3t+dn9wFFZ6SpuzXT943Fzr5sZ0HYAmaXZ3B/eG4DHzPNARp8QFyBJ6M1cUXImi9/eR1nZaqCc45HFlHYPrayZNil3O3pru+uySS1OJbvUNQSAsgcRrkoo0hZXbb4RbAofQpGxFdqZg92UwdiMFIwGI38f8Xf8DH4cCM1miSWMoYb9cGBRo8QgRHMmCb0Zs5eWseBvSynSyTjtJygzO9jYL4enhz9NoDmw6QNy90Vv7+6LnlacVtnc4m93PUF6XEe6avONwGEwsajdFZVNL4Ptdsryi+ga2pV7B94LwJMR7ShRCpY9CRW9boS4QEhCb6acTidL//QFmQRiL1kFwMZ+ORRaL2F41HDvBOVucolx925JLU4lw+p6QjPY4RpaN0k3wPjrZ5Fuac+OkP6AxmrbyOp/fAPA7X1vx2GNosRcyt/CO0L2Ydj6XqPGIkRzIwm9mdr8wnccLemAvXgJYOdYh2KORpgoTZ/svaDcNfSedldvlrTiNLJKXF0W29hdL24+0cgJHWBD66FYDSFoZw77Ug+Ssm43ZoOZ0tRpaG1gUaiRzRZ/WPU8lObXvUEhfIQk9GbowOdrSTwagqPsZ5yOZKx+Djb1zXE9QOQM8F5g7hp6rMPVbl61ht7e4RqH4IRu1+hhOAwm5rcfByjsZT/zw9vLcdjsOMs6VnbjfLJ9R4pLc2Htvxs9HiGaC09ecPGuUipDKbW7luVKKTVbKXVYKbVTKTWo4cO8cKRv2c+qpQU4nbk4rKsB2Ngvm8KSQTiKe3k3OHcNvQdZaEcAZY4yDuUeAqCdw/Xcf45u1SShZPi3Y0fIAADyyraw8QXXU6LlWWNwWKM4pRy82DoMNr0F2UeaJCYhvM2TGvr7wISzLL8K10uhewD3AG+df1gXpsLkTBbO2YPN6AdF36LRHIoqoqxLCGXebGqp4B9Ctg7BX9kw2EIA2Jm5E4AO7huQ+Xj2HtGGsK7NEEJatUU780ncm0iX4nzASGnKjZgNZr5sFcJ6PwP88Kcmi0kIb/LkjUVrgJyzFJkKfKBdNgFhSqkmeBbdt9iKS/n+6WVYzeEYi1dQ7synKMDO5j45/H3kM+C0eDtEAE7qtsAvfdErui1GOVw3SvN10yV0pzJy3V+fAgzYbXuZln0UnBpneST3D7wfgCfbRpB/ZBkc+KHJ4hLCW5Qnr/9USsUAC7TW/WpYtgCYpbVe557+EXhMa721hrL34KrFExkZOXjevHkAFBUVERwcfB6H0XSqxrrr1C833GKjQs95m9rpJOOTI2SZumG0HqKk9HucaJYMTadX9KVc3+b6WvdVdX51kQGQbj23mGo7ns5b/k634kTu6TyajaakyvnfnSqia3kO78W8RJeYbh7FV1esnhxnbFQoqevXkbJzE6gA2rQbRuHQbvTtGMIr6a9wrOwY44qKeabQzNb413Aa/TyK52xa6vXa3EmsnhkzZkyi1jqupmWmBth+TZ2Oa/xXQms9F5gLEBcXpxMSEgBYtWoVFd+bu6qxznh8YeX8pOkJ577NJz8jy9QNg70QTKvRwJ4uhQRc1IYXp76IxWSpdV9V51f3cKydf+86t19xbcczb/nLdDNBXq4JXJV1FIq2ZUWg4MX9oeyZ4Vl8dcXqyXEmTU9Ajx7Ne/feS27eKfKyDvHZTx3ZOHsqPQp7cP3861kaDJdZs5li3gmjH/EonrNpqddrcyexnr+G6OWSDHSuMt0JSKmlrKhm2+sL2ZPRFrST8NB1FBcVkN2qnJ0983g+4SUspubR1FIhD1fbeRv7L/+Oh1vCCVHl2LWBYpo+XmUwcP1zzwJmHI6T3JK5A4DOIZ3501BX+/k/2oSTvP4lyE1q8viEaCoNkdDnA79293a5FMjXWqc2wHZ93uFv1rNpp6sJ4KLw7ZxK3ofd4GTNwEx+O+Au+kb09XKEZ8pzt5FHup8WBWjr73o7keuGaCO9Aq8OrSLaktjK9T5Vm3Mv6/79EQBTu03lyugrKTYY+FPrYOzfPyTjvAif5Um3xU+BjUAvpVSyUuo3SqmZSqmZ7iKLgKPAYeC/wH2NFq0PSdu4lx8X5qENRqIDDpOUtgmAzX1y6dWhM7+55H4vR1izXHcNPcr+y2P1EX6ueU15Q7QmG9rEUmroDDjYvPUHsvYdRSnFk5c+STtLG7Zb/HkzNxF2feHVOIVoLJ70crlFa91Ba23WWnfSWr+jtZ6jtZ7jXq611vdrrbtprWNruhkqTpd74CQL3j6A3RRAe06S67+HcmspxyNLyOxs5fnx/8VoMHo7zBpV1NA7OawYlSvGdibXzaGCJuyyWJv3osahCEE78/jyHy/jdDoJs4Qx67IXMKB4O7QVG1b8GUrO1nFLiJZJnhRtYiXpOcz/10bKzKGE29NoNbCAjGNHKAqwsb5/Ns9e+hfaBTX+4/PnKk+729BVMe0CXU+FRhhcXRi9XUMHsJv8WN7mMsBIcfkxFj/5MgDx7eO5d+B9aKX4UyszmYsf9W6gQjSChujl0uRiqvb4mDWpydeva5u1OfiXy/nuLz9QZG6P2ZrJ5wEpjFq5CqfSrLwki5vaxfLr9wOAhWeNzZN9NaSq++ulXEk7jCI6BPUmtTiVtrhestHQDxWd63HubdWZ+yNGsf/AKvYfWkvPpUPoMW4Ud8fezdaTa/gpexePZa5h7v6FmC6u+/d/PteLJ+s2xvUomp+m+D1LDb2JGB0OFjz6FTnG9vjbCvguMI+heesB2Nw7l4tCFQ9NfMfLUdYt111DD1dFXNz6YgB6GF3jyzSHGnqFCU89RLB/NGBn4btvUpydg9FgZNYVs2ljDGBLgIXZKx6B4mxvhypEg5GE3hScmgdTUkjVHTHZrVw5vTMDCtfhp20ktS8mo1M+L47/D2aTv7cjrVNFLTyUIv5v8EN8PeVrBhN42rLmwGg0cvOzf8FgCMehC/n0kSfQWhMREMGLV7yBUcN7QSaWfDdDer0InyEJvQk8cOoExpCeGBzlXHltBNu3/UAbWy65weVsjM3ixe43067DYG+H6ZEy/CjR/vgpB/4OGz3Ce6DKXE9x5unm9ZRf6EUdGDv1NsCP/JJkFj/7IgBxHeJ5JPZuAP5adpTDP73hxSiFaDiS0BvZb5KTCAi5GOV0MOZyCxmlxzi8ZSPlJicrBmcyIT+A+NF/8XaY9ZJXURO35rp/5gHNq4ZeIfbm8XRzvxBk3+7V7FzgGtNl+qDfMTG0N1aDgQf3vEl+2k5vhilEg5CE3oh+feo4rYN7g3ZypOwIph4BrP/iYzSa1QMzudRZwuLMBxvtlW2NpaKnS2XXv9I8AAqaURt6VVf/8w+E+McCsPzDOaQfPoRSiqcnvc/FysIJk5H/W3w7trIiL0cqxPmRhN5Ibk45QWSQ66bhKetB1rS2sHD2v0BrtvXMI6JVPqdSZ5BNmHcDPQcVfdFbQg0dwGg2cdPffofZ1BWNnc+e/AslBfkEmAN5bdKHRDhhs8HOs9/egCeD1QnRXElCbwTXpp2kc0APADKL9jO/XRsmpy/CXlbG4agiMqNz6JsyhJ8c/b0c6bnJxd1Wbj29ht6cerlUF9qlI5NuvhlljMTmKObTRx/BXl5O+zYX89rQJ7E4NV+XJvP+CumfLlouSegN7Nq0E3T37w7KQH7Rfj7u2IGr0xYS7CghtXUp2/tm8rqpM/8tu8nboZ6z/Iqbn9Vr6M04oQN0u3o4g/uMARVMXl4qXz31V5xOB/363MBznVzvcHkpeQmLdjT/7qNC1EQSegO6Ju0k3f17gDJQULSfdzpGMSl9MRG2HPKDbKy9JIOXizW9pn2IswWf+soaeok7oVfU0Jtpk0tVo564lejwkaD8ST66hx9eeRmtNePGvsDDpo4A/PnnV9hwdImXIxWi/lpuVmlmrkk7SQ//bqAMFBbuZ25UZyZkLqNzaQol/naWxWfwZFEel17/MYQ030f7PZFXtYZuLwdbideGzq0vg8HA1Bfvok3ASMDIvp9WseHTj0ApZtzwNbfb/LAr+MPaR9mVvt3b4QpRL5LQG8CNqSfo4d8dlJGiwv3M6diZy7NW0bUkiTKTk6XxGfzWmsmkK1+Cjpd4O9zzlle1Df202nnL6K1jDrIw7ZnbCLKMBmDTd5+RuPBb8Avi/6Z9zdVWO1Y09y25k4O5B70brBD1IAn9PN166jjRAb0q28zf6nQRl+Wup0/RAWxGJ8vj07nZkUVJzhiIvd7b4TaI02roLaT9vLqQTm259oFJ+PtfBsCqD95m+9JFGMKj+duEtxltLSVP27h74XQMfulejlYIz0hCPw93JB8nyt01MbtoP3OjLuKy7LX0L9yNw6BZOSiTySqLDtl9ecF+o5ejbTi5VRN6RR/0FtB+Xl1k/MVMvHEUZndN/cd33mT3ymWYY0bw0rBnGFFiJcdRSnj0Gxj8MrwcrRB18yihK6UmKKUOKKUOK6Uer2F5glIqXym13f15suFDbT6cTic/PvEpEcGuZJ5WvJ93oy4iIXsN/Qv34DBoVgzOIMEvk8FZnXjMNhPtQ/92Vja5lOS02Bp6ha6TL+XK8fGYLCMBWDLnVXYsW4z/gJt5dcAfGGa1Um4qJzT6TQz+aV6OVoizq3P4XKWUEXgDuBLX+0O3KKXma633Viu6Vms9uRFibFa03cni//uEpNKOoB0cKz3M1x07MTZrJb2LDmA3uB7pH+uXwbisMG61PYStZY5SXKv8GmroLaGHS2163zqGsoJSVm8Ee+k6lr/9BmUlxQyZ+gCzSzL53eGP2RQAraLfZHtGyxhzR1yYPKk2DgEOa62Paq3LgXnA1MYNq3kqLygm7ZNjJJV2xOC0sbv8KN9GdmBi+hJ6u9vMfxycyRRTOldnBXNn+WMUE+DtsBvcLzdFcyv7orfUGnqFgTOvYvjA3pgCLgdg7Sfvs27eB/hf/hQjT/Xj8uISHMZy7lnyG/ZZ93k5WiFq5klCjwJOVplOds+rbphSaodSarFSqvm93fg8FZ7I4Iv/m0+OXxdMdivjJ7diRUQEU9O+p4v1OKVmB0uHpHOzMZWxOSFML/8z2YR6O+xGYcNEkbaAdkDeCaBl19ArxD80lZGDemIOHA8ofvrmcxa/+TJ/L59BXEp/phQWYXWW85+MOXx3+DtvhyvEGVRdY1copW4Axmut73JP/woYorX+XZUyrQCn1rpIKTUReFVr3aOGbd0D3AMQGRk5eN68eQAUFRURHOz50Ku7TuVXfo+Nqn/SrO/69qQsjq/WlPq3wb88l+hhDhwRRvZ8/yUUF1JksbMiPp1HStMYXh7ON1F/wmpqdcZ2qu6rthg8me+pyABIt9Z7NY/2ffux/6OVPYvMiKG0zfqJdRE383P4RI/WbchYPTl39VX842GOnHJiK14E2FGt22OKv5LR+fNYZtzGB6Gu3+34VuOZGDYRg6q9XuRJTA0Vd03q+/+WN/l6rA31ex4zZkyi1jqupmWeJPRhwNNa6/Hu6T8BaK2fP8s6SUCc1jqrtjJxcXF661bX+6RXrVpFQkLC2Y+iiqZ8Bd3hbzewYkEONlMgIbZMoq600PXiTnz/yixsVivZrcrZeEk6swpSsZREcWf5o7XWzKvuq7YYPJnvqYdj7fx717m139e17wV+T9DPkATt+kDGXv5ou5vPHWM8WrchY/Xk3J2L3e8vY/XaNEqLF4AuJt8UyveRE/h53F7m7fmAWW3CcSjFuOhxPDPiGQLNgTVux9uvoKvv/1ve5OuxNtTvWSlVa0L3pMllC9BDKdVFKeUH3AzMr7aD9kq5xoBVSg1xb7fFv9try8vfsXRRMTZTIJE6heufH09m6gG+nvU0NquVpPbFbI9L5r+5pygt7sUt5X/x2WaW6iq7LuYcA1p+G3p1/WZcyaTrLyYocCrKGEGoPZ+bU77iYNgk4ttO4430TIKdTpYeX8r0RdNJyk/ydshC1J3QtdZ24AFgCbAP+FxrvUcpNVMpNdNd7Hpgt1JqBzAbuFm34HFI7SWl/PDwx2w+EII2GOkZmsaE5yfyw7uvcGrTGtCa7d3zKO91kk/TU9lReim/sT1CSQt49L2h5FfcGLW72klaYj/0usRcFc+0B+IJMo/DYO6FSdv5/uVZbExuzaXjZvNRWjYx5TYO5x3m5gU3sfz4cm+HLC5wHnWO1lov0lr31Fp301o/5543R2s9x/39da11X631AK31pVrrDY0ZdGPKO3SKz3//DUeKO6Ccdi7tW0Lv2/vz4RMPcuznrZSZHSwfnEF8uxO8np7Ff8qu52HbTOw+1jWxLrnVXjfnazX0Cm0H9eC/rc1YdF9MAQmAgfQdW/n0842EXzGHeXnljCsqpthewkOrHuKZjc9QYivxdtjiAuU7T7s0gKMLNvHFrK3kmiLxtxUw4ZoQCkNT+ezvj1OSm0tGWBlrhqXwhD7O70vgbtsjvOm4hpYyhklDquy66OarCR0gyz+QF6PaUmoLxC/kBjC0Iv3oYT6c/QGHez7DC6bOPJadi1lrPj/4OTctuJHdWbu9Hba4AElCB5x2B2v+9jk/fF9IuTmENo5ULvtVG35c8g6JC7/DiWZHt3zyByTxUfYJRrXqDvesZJWz5Q+0da6qvxDaF7otno3DYOCtztGMHRNFcMB1GPx6Y7eVs/yTT/ky9VImtZvMpylpdC8vJ6ngOLctms4ria+Asnk7dHEBueATekFSKl/cN49dqRFoZaRbSBKhA9OZ/9Y/KUhLIy+4nB8vTeeqNkd5MyMDa8fJcPcKaNPN26F71WkJXRkp8sEHqGrSe/rldL/Kn85BAzAHTQIVwMl9e3h/URp5re/n48wyfpVfgNPp4J3d7xDU9WWMgYe8Hba4QFxYDb/VTExPZt6zDmymDphsBcR0S+LAofXYTlhxKM3urgUERaXzbm4WHS1t4bb/cSTZRGeTv7dD97rcqk0ullCwXjjNTsa2IVz35iQSZy8gcedNWG2bcZbvZd3KRHZFJHDtRVmMK/qJv7VtzWG/HAKj38FWEEty4QA6hXTydvjCh12QNfTQ8lIePpFMX/8elBuDCCzdjPb7ht07lmMrsZLSxsq6ESncFH6YNzPT6TjwdnhgM3Qf6+3Qm43TaugBYV6Lw1sMBgPxf5jCrX8ZTtdW/TEHT0MZWpOflcX8bXAgZzKzT8Dvc/Lwd2rMrXYx9dupvLrtVQrLC70dvvBRF1wN/cBnq7k/F8pDuuMsO4BJbyLHmg1WyA+0sb1XHmMsqTyTU0BgxMVw48sQPczbYTc7p90UtYR5LQ5va9W1I1PfuI2DX6xlw+IA8p2nsJduJPlkJp/Tg8BAJ//rsIuPoswsCIa3d73NZwc+Y0bfGUzvPZ0gs2/fexBN64JJ6IXJmaz45w+cLG+PU6XjyP8Gp86jHLD6OdjRI4+eYWm8WZBPp/JwmPQSXPJrMF4wp6heLvQaenU9bxhFt2vsbH9rMYnboyhxHMRR+jMlJTZ+ODKAnqllzO5wlA87G9gCvPbza3y090Nu7T0djK3BIYldnD+fz1YOm51try8kcVc5ZY5TOMoWoHUxACX+dnZ3LSCqTQb/KMijR5E/DHsIRjwIljPHYhG/OK1XywVcQ6/KaDYx+PdX0y+/iMS3lrD7QE9KHPtwlO0gpwRyjvRmdIqdqW1P8HUXB9vI443tbxDc3YwtL57ynOHePgTRwvl0Qk/6YTM/frqOAkcyTtsRwAFAXpCN/TEF9AxL5/nCfLoWBsClD8Kw+yGwtXeDbiEcGME/FMryISDc2+E0K/6hwQx/fBpxRVb+dm8OF1mup5ST2Mt+JttaQvaJrgw6CeNap7M52sqKSBt+rTfg13oDdy9dx429biShUwJmo9nbhyJaGJ9L6Fpr9i9ewbrPFlJQdgrctXENJLctIfmifEb5ZfJaYRH27HA6jX8aLvmVNBuci8Bwd0IP83YkzZJfcAAfR0WjnE6uzC5hrP8k0mwF2Mt2YnekkpYdyUXZcK/RQW5UNsujy9jEJjalbiLML5QJXa7i6m5XExsRi3uoJCHOyicSusNuI3nvHnb/uIxDmzfjcFYZh1WZ2R2TjX/7TCbZ8hhXYmVTYSxPOy5nmXMwR4ZP8V7gLV1AOOQmSZNLHbTBwNK2UcydNYns3Uns+qIdB48XUOI8jqP8AFaHFcuJdkw+AWYjJEXlsa2TlXll85h3YB5Rge0ZGzOesdFj6d+2/1mH6xUXtpaZ0LUm3JbLRdaTfPLsWtL27UPbHb8sV0EYDa040m0fvVsf59niYqJM3eCSO2DALdw+a6f3YvclAe7mKamhe6xNvxgS+sUw2unk+A+JHFxxgCMZ2ZTpZJy2o9gcZUSdCCPqRBgKA0XBTnZflMNXWR/zvz3/o7VfK0Z0Hs3IqFFc2vFSWlukiVD8okUm9Nb6JNNPucYWTs1xzVOGNhj8umFx2jFH/chlIQfoHt4Det0Nfa+DyL5Q+WerJPQGEdkHjvwIbXsDGd6OpkUxGAx0mRhPl4nxxPxxAfH5rRhUeBGRfuUUOLJw2I+jnQUEFcHQva0ZuhdQZkotilNttvJC56XkhJbTtVUn4qNGMKh9HAPbDqR9UHtvH5rwohaZ0C+2lmBQrcDcEaMpGoOpM61LDzKw7Xf0GdQJuv4Kek2A8Bhvh+rbxv4Nhs6E0E5A/V/AIdwMii3h7dgS3o6kWZMoSs7k2NKfObr9CKm5GVh1Dk57MugyLFboluxPt+QOgEKpAMr9drEyZDP/a1tAXudyekVdTJ/28fSO6Ee+PR+ttbTBXyBaZEI/aO7M6JDeoKGL/pnBl+0nYvQYiHoIpGdA0zEY3clcNKTgTm2JvXMcse7pkoxcTqzcxdHEPaRnplNYnofdmY125qJ1CeYyaFcG7bKCYR8o8ilW69hi3Eipn4Ot33wEbQ0Ed4sgsncfukbGEh0aTcegjtKTxse0yISe5RfEPn2QtSFRbHv5z94OR4hGFdgunItvGs3FN42unFd4PI2Tm/ZwctcBMlJTKCzNp8xZjNOZi6YMrcvADv528C8BsoB9xWQvOEWuWs825Y/CCEaFMoHJYsAvxJ+g1q1o1akjkd16ERnThbZtO2IxXzgvbmnpPEroSqkJwKuAEXhbaz2r2nLlXj4RKAFmaK23NXCsp/ku4ox3UAtxwQiJbk+f6Pb0uemK0+YXncrk1NY9pOzZT3byCfLy8yjT5dicpTh0CVCOUxe4+vECOAEb2KxgzYX8E5CyfTv7WQzKH6UsKPwwYEJhxKAMKGXAqBQGo8JkMmIyGTH7m/GzWPAPsGAJCiIgJJTA0HCCW4URGBaKX6A/pkB/TIEWTIH+mAP9MVj8MBikx05DqjOhK6WMwBvAlUAysEUpNV9rvbdKsauAHu7PUOAt908hRBMKjmpLr6gEek1NAM58mXFBciqp234m5/AhclPTyM3Pp6S0DJvdjt1px4EdrcvR2ECXonUpGlfeP0O9hno3gTIBRpT7J8oIKBRGFAZAsWPuZ+55yv3amNN/KlV12n1vQFUppdyvm1HK9V25y1Z8VxXbwbWeMqCUds8/vZxrG7pyfVd5V7mSUitffr8MKpe746jceJVY3f+5Oy0fJ7A2tHt9Tly9eFJDHwIc1lofdQc5D5gKVE3oU4EP3O8R3aSUClNKddBapzZ4xEKIc9aqUwdadepQZzlHWSllOSnknzhC+rFDZKWmUphbQEmRlTJrOXabA7vdicOh0U7QTo3WGo0TjRO0+ycOXH8O2EHbAaj+tuGW+vLhrJz6la9ouLrU3HgvPVF1vctZKXU9MEFrfZd7+lfAUK31A1XKLABmaa3Xuad/BB7TWm+ttq17gHvck72AA+7vEbha+VoCibVxSKyNQ2JtHN6MNVpr3bamBZ7U0Gvq71T9XwFPyqC1ngvMPWMHSm3VWsd5EIvXSayNQ2JtHBJr42iusXpyRyIZ6FxluhOQcg5lhBBCNCJPEvoWoIdSqotSyg+4GZhfrcx84NfK5VIgX9rPhRCiadXZ5KK1tiulHgCW4Oq2+K7Weo9SaqZ7+RxgEa4ui4dxdVu8o55xnNEM04xJrI1DYm0cEmvjaJax1nlTVAghRMsgvfqFEMJHSEIXQggf0agJXSk1QSl1QCl1WCn1eA3LH1VKbXd/diulHEqp1u5lSUqpXe5lW8/ceoPH+q5SKkMptbuW5UopNdt9LDuVUoOqLDvrcXoh1unuGHcqpTYopQZUWdbczmuCUiq/ynXwZJVlze28NovrVSnVWSm1Uim1Tym1Ryn1YA1lmsX16mGszeJ69TDWZnO91khr3SgfXDdQjwBdAT9gB9DnLOWvBlZUmU4CIhorvhr2PxoYBOyuZflEYDGuPveXAj+dy3E2UazDgXD396sqYm2m5zUBWHC+109TxFqtrNeuV6ADMMj9PQQ4WP3cNJfr1cNYm8X16mGszeZ6renTmDX0yiEDtNblQMWQAbW5Bfi0EeM5K631GuBsD/NWDm+gtd4EhCmlOlD/42z0WLXWG7TWue7JTbieC/AKD85rbZrdea3Ga9er1jpVuwe/01oXAvuAqGrFmsX16kmszeV69fC81qbJr9eaNGZCjwJOVplOppaTo5QKBCYAX1WZrYGlSqlE5RoywNtqOx6Pj9NLfoOrplahuZ1XgGFKqR1KqcVKqb7uec32vDan61UpFQNcAvxUbVGzu17PEmtVzeJ6rSPWZnu9NuZ46B4NB+B2NbBea121djRCa52ilGoHLFNK7XfXoLyltuOpz3E2KaXUGFz/g4ysMru5nddtuMamKFJKTQS+xTVqZ7M9rzST61UpFYzrH5U/aK0Lqi+uYRWvXa91xFpRpllcr3XE2qyv18asoddnOICbqfbnq9Y6xf0zA/gG15803lTb8TTLYQ+UUv2Bt4GpWuvsivnN7bxqrQu01kXu74sAs1IqgmZ6Xt28fr0qpcy4ks7HWuuvayjSbK5XD2JtNtdrXbE2++u1sRrncdX+jwJd+OUmQd8ayoXiarcMqjIvCAip8n0DrhEfG/umSAy137ybxOk3mTbX5zibONaLcD21O7za/OZ4XtvzywNuQ4AT7nPc7M5rc7le3efnA+CVs5RpFterh7E2i+vVw1ib1fVa/dNoTS7asyEDAK4Flmqti6usHgl8o1wDxZuAT7TWPzRWrABKqU9x3cGOUEolA08B5iqx1ji8QW3H6eVYnwTaAG+6z6Fdu0aGa47n9XrgXqWUHbACN2vX/y3N8bxC87heRwC/AnYppba75z2BKzE2t+vVk1iby/XqSazN5nqtiTz6L4QQPkKeFBVCCB8hCV0IIXyEJHQhhPARktCFEMJHSEIXQggfIQldCCF8hCR0IYTwEf8PYtH4zF6XR3MAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f_NC.summary()" ] }, { "cell_type": "code", "execution_count": 64, "id": "f66ffca5", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Fitting 106 distributions: 43%|██████▌ | 46/106 [00:07<00:04, 12.11it/s]WARNING:root:SKIPPED kstwo distribution (taking more than 30 seconds)\n", "Fitting 106 distributions: 74%|███████████ | 78/106 [00:14<00:11, 2.37it/s]WARNING:root:SKIPPED rv_continuous distribution (taking more than 30 seconds)\n", "WARNING:root:SKIPPED rv_histogram distribution (taking more than 30 seconds)\n", "Fitting 106 distributions: 96%|█████████████▍| 102/106 [00:31<00:09, 2.36s/it]WARNING:root:SKIPPED levy_stable distribution (taking more than 30 seconds)\n", "Fitting 106 distributions: 99%|█████████████▊| 105/106 [00:43<00:03, 3.44s/it]WARNING:root:SKIPPED studentized_range distribution (taking more than 30 seconds)\n", "Fitting 106 distributions: 100%|██████████████| 106/106 [00:45<00:00, 2.31it/s]\n" ] } ], "source": [ "f_AE = Fitter(X_AE)\n", "f_AE.fit()" ] }, { "cell_type": "code", "execution_count": 63, "id": "fd5c81fb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sumsquare_erroraicbickl_divks_statisticks_pvalue
exponweib228.925939440.01400525.557555inf0.4038420.298813
exponpow229.805636394.72102623.967294inf0.3884850.340837
fisk231.478144503.28730324.003551inf0.3673440.408087
gamma234.918930449.22934924.077327inf0.5306320.076795
powerlognorm235.582727876.61543825.700873inf0.7615590.001558
\n", "
" ], "text/plain": [ " sumsquare_error aic bic kl_div ks_statistic \\\n", "exponweib 228.925939 440.014005 25.557555 inf 0.403842 \n", "exponpow 229.805636 394.721026 23.967294 inf 0.388485 \n", "fisk 231.478144 503.287303 24.003551 inf 0.367344 \n", "gamma 234.918930 449.229349 24.077327 inf 0.530632 \n", "powerlognorm 235.582727 876.615438 25.700873 inf 0.761559 \n", "\n", " ks_pvalue \n", "exponweib 0.298813 \n", "exponpow 0.340837 \n", "fisk 0.408087 \n", "gamma 0.076795 \n", "powerlognorm 0.001558 " ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f_AE.summary()" ] }, { "cell_type": "code", "execution_count": 49, "id": "5ec7ddad", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'dgamma': {'a': 1.7679446613668826, 'loc': 1.3985560931689764, 'scale': 0.10908557770910016}}\n", "{'exponweib': {'a': 0.6969758080792576, 'c': 0.40664217557825433, 'loc': 1.7872766256332395, 'scale': 1.132668555489615}}\n" ] } ], "source": [ "print(f_NC.get_best())\n", "print(f_AE.get_best())" ] }, { "cell_type": "code", "execution_count": 50, "id": "cd262429", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1.3766861489939175, 0.134050348144684)\n", "(2.4707774407516867, 0.48732718406256026)\n" ] } ], "source": [ "# see tutorial: https://fitter.readthedocs.io/en/latest/tuto.html\n", "print(f_NC.fitted_param['logistic'])\n", "print(f_AE.fitted_param['logistic'])" ] }, { "cell_type": "code", "execution_count": 77, "id": "d14d775f", "metadata": {}, "outputs": [], "source": [ "import scipy.stats as ss\n", "\n", "# choose distributions\n", "dist_name_NC = 'logistic'\n", "dist_name_AE = 'logistic'\n", "\n", "par_NC = f_NC.fitted_param[dist_name_NC]\n", "par_AE = f_AE.fitted_param[dist_name_AE]\n", "\n", "dist_NC = ss.logistic.pdf\n", "dist_AE = ss.logistic.pdf" ] }, { "cell_type": "code", "execution_count": 82, "id": "91b734ad", "metadata": {}, "outputs": [], "source": [ "# probability for NC and AE \n", "P_NC = np.count_nonzero(lung_flags == 0)/len(lung_flags) # Prob(Y = NC)\n", "P_AE = np.count_nonzero(lung_flags == 1)/len(lung_flags) # Prob(Y = AE)\n", "\n", "# defined conditional probability densities for X at a class \n", "f_XatNC = lambda x: dist_NC(x, *par_NC) # f(x | NC)\n", "f_XatAE = lambda x: dist_AE(x, *par_AE) # f(x | AE)\n", "\n", "# probability density for X\n", "f_X = lambda x: P_NC*f_XatNC(x) + P_AE*f_XatAE(x)\n", "\n", "# conditional probability for AE at X\n", "P_AEatX = lambda x: P_AE*f_XatAE(x)/f_X(x)" ] }, { "cell_type": "code", "execution_count": 83, "id": "c3de7e50", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = np.linspace(0.5, 6, 100)\n", "\n", "plt.xlabel(\"x = maxSUV\")\n", "plt.ylabel(\"P(AE|X=x)\")\n", "\n", "plt.scatter(X_NC, lung_flags_NC,label=\"NC\") \n", "plt.scatter(X_AE, lung_flags_AE,label=\"AE\")\n", "\n", "plt.plot(x, P_AEatX(x), label = f\"Bayesian model with ({dist_name_NC}, {dist_name_AE})\")\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 58, "id": "b0f3b623", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"x = maxSUV\")\n", "plt.ylabel(\"f_X(x|case)\")\n", "\n", "plt.plot(x,f_XatNC(x),label=\"case=NC\")\n", "plt.plot(x,f_XatAE(x),label=\"case=AE\")\n", "\n", "plt.scatter(X_NC, lung_flags_NC,label=\"point NC\") \n", "plt.scatter(X_AE, lung_flags_AE,label=\"points AE\")\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "id": "67f408e7", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3", "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": "3.10.12" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 5 }