{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import mplhep\n",
    "\n",
    "mplhep.style.use([\"LHCbTex2\"])\n",
    "plt.rcParams[\"savefig.dpi\"] = 600\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_tree = uproot.open({\n",
    "    \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_def_bak/matching_ghost_mlp_training.root\":\n",
    "    \"MatchNNDataSet/TrainTree\"\n",
    "})\n",
    "test_tree = uproot.open({\n",
    "    \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_def_bak/matching_ghost_mlp_training.root\":\n",
    "    \"MatchNNDataSet/TestTree\"\n",
    "})\n",
    "train_array = train_tree.arrays()\n",
    "test_array = test_tree.arrays()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AaltdX9cjBVeKrGTu5LP6VI2usLt27tSenTvrWj8dK0Dr8P4C0DJXnnmq+uMnqj8OAAAAOKFWN1iOla6wvt5ebavzq6+3164fBUAJOsIA2OLy0W8X3TZNU+bym1WfYy6/qUvf+xNOcwQAAIBrWO0Gy7HSFQbAPXinAmgJwzCuXd2x2jxgN9xICAYAAABXeOiZE5Kk+LlzlrrBcl7PZvVPvvdd7d21q1WlAbARQRiAlum9bVRX//qVKo/f3sZqAAAA0E0ur60p8uMXJEmf+40PWu7YYk4vYGsjCAPQMr137tFG6qzMTHrTY4ZnSL13/poDVQEAAKAbPB6P60vRqCTppm3X6bMf+ICl5zE/F7C1EVwDaBmjv1/9H75Pve+/69ppkpJ0w43qff9d6v/wx2T09ztbIAAAALak8xcvaq5gnq+Hn/2Rli9WmbIDQNegIwxASxn9/erbc7f69twt0zSZEwwAAAAtd3v4oaLbb6ysaDT8kNJf/opDFQFwC4Iw5KVSKY2MjFRdZmpqSlNTU22qCFsNIRgAAADslJvgvtD61asVl//q8Zj6ejgxCmilubk5zc3NVV0mlUq1qZrNCMKQt7GxoXPnzlVdJlvH1VMAAAAAoB6NTnBf6OU33qj8WColH1d3BFoqm83WzBacRBCGvJ6eHg0PD1ddZmBgoE3VAO3DKZsAAADu0OgE9znrGxt6/tVXKz7+wmuv6a7hYbrCgBYaGBjQrhqBcyqV0sbGRpsqKkYQhrzh4WGdPXvW6TKAtjAvXdTqnx7VlWeekrn8poyhW7XtI/fpun94QEZuYn8AAADUZEcXl1R+gvuP79mjoRut/232ciqlC1euVHx8ZXVVr6RS2ktXGNAyVqZUGhkZcaxrjBgcQNcxL13Uhf/1/6vVJ74jc/nNa/ctv6nVJ76jC1+cknmJKwoBAABYlevi+lI0qsfj8YbHuT38kN5YWcnfzk1wb1WtbrCc5197TesOdaIAcB4dYQC6zuqfHtXGmUTZxzaSP9XqD47q+k/+0zZXBQAA0Hkur63pj587mb/9yMln9Wmfr2JXWLnJ7SV7Jriv1Q2WQ1cY0N3oCAPQda4881T1x09Uf7wW0zSbej4AANgaLq+t6RvPndQ3njup1fV1p8tpiW+9+KJeL7ig1uvZrL71l39Z9zi1JrivxWo3WA5dYUD3oiMMQFcxTTN/OmTFZZbfrHsCfeYcAwAApZqd+L2V7JjX6/Lamv7lDzf/B+If/PAp/e4HPmB5TDsmuLfaDZZDVxjQvQjCAGxpl49+e/OdN9woVZsH7IYbtXrs3266+/oDnym7eG7OscLTLXNzjq299GPd/OU5wjAAALqMHRO/F7JrQvocO0K6avOBPR6PWx7Tjgnu79q5U3t27rS0vhxOjwK6E0EYgK7Te9uorv71K1Uev72u8ZhzDAAAlLq9ZJL33MTv6S9/paHx7OwuqzekKzev1/rGhiIvvFBxHV+JRfXGSrbm3F71THD/61W6wvp6e2uOAQASITiALtR75x4ZnsGyjxmeIfXe+Wt1jdfKOceYbwwAgNrsnIvLjrEur61VfKyRMctNSN/Mz9ns1Rkl611czY5T73gAUAsdYQC6jtHfr/4P36er//W/6OqZn107TfKGG9V72+3qvfPXZPT3l31eudMsrc45dul7f1I051il0ywl5hsDAGx9bjzNz86x7DplMKfShPSf/83frLu2WiGdldfCri6uRia4rzYeAFhBEAagKxn9/erbc7f69txd98T4ReMYhqU5x6yO38r5xpr5OQEAsJOdwVW5bqlP+3wNT/7e7Fir6+tFY5Sqd8xGJ6QvdzqjJMXPnau4rk9/77vyWZg8vt4urkpzezHBPQAnEIQB6HrNhkN2zjlm93xjdJdtTYSaADqZncGVZG+3VL1jlQub4ufOFY1R6vVsVv/ke9+1HObYEVzl2HF1Rju7uJjgHoATCMLQ8ebm5rT8n57VLTfeoH/x9+9zuhx0od4792gjdVZmJr3psUpzjpW9mqWk1f/wZNV1rf75k5JR/CfgN/78KV3y3qmBgQFNTU3l7+dqlu7SbHhFqFne3Nycstnspv0f6Abt3v/tOp3R7tP8GumWatVYdp0yaHU8K8FVITuuzmhnF1ezE9yfXFjQ6tsXdd1NN+pDwWBTYwGd5uTCgi7/6Fn+BmqAYTITc9cbGRnRuXPntGvXLp09e9bpcuqWq/+9Q4NK/JtHnC4HXcpcW6t7zrFNY5imriw+XnO5bYFPFwUqo5//X/T6cnrTe/jyd/93rT7xnYrjXBf4VMNXs7SzI8mtY9kxnl3hVblQM6fHe0dXh5qd/jsMaEa79/8jP/6xpv/Dn0uSvv73f7uh0xkvr61p+Kv/W9nH3vhf/391h2uFNZWqVGO1UwajP/1p2ccm7rjDUudVtTFKffSOO2p2hVkZz8o40jtXeawVYt1y3XUK/d2/W3ler6tXtVFzbcV61JqrOh7ef0DZN9/UwK236tCxo7aPD7hZbv/v1L+BnPwbjo4wALCBHXOO2T3fmJWrWTp1mqVbx5KkjfR5rf7ZE1p7NiYzfV7G4A71/5Zf1wc+1XR41WhHnt2nzBbV6eLTLN1cG9BKdk4kb9dYdp3O2Ogk8uXCq1ywU8lXYlG9sZK11C3ltlMG6xnPaneZXfN6tSLQAoB2IggDAJs1c+DezHxj5qWL+VMuG72apVT+ipZ2hjpuHSs33srB/cX3pc/ryg+Oav2VuGPhlZtDTbu5uTagXdx2BUTJntMZ7Z5E3o7T/Owcy+6J3+0KriSuzgiHmKbEf2jBhQjCAMBFrs03dk5mZnnTY5XmGyvH7u4yO0Mdt46VG68Sp8Irq6Gm1e4pN1+ZtFPmtbOrU+3y2poiLzwvGUbTXT92s7MjyU5u7JSyezy3XQExN44d83A9Ho/XnET+O/G4ftdCWGdnt5RdY9k58bvdwRVXZ0S79F9Z1d0vPa9f+euXdfPbK7pw0y36m/ffpZ/s/aDWtl3X3OAEa7CJO/6qQVmzs7OKRqM6deqUMpmMvF6vfD6fJicn5ff7nS4PQAsY/f3q//DHmp5vTGq8u6zcRP6NTOJfcdkGxyrXqWZ3p5QbwyvDMGQM3Vp1PGPoVkdCTcneDq5WnQJqR+DUitN50z/8d/qdC1ml+rYp/uP/h37j81NNBX12nkr6eDyuLz39tGQYTXck2Rn4ubFTyu7xnLwCYiVFpzOWHIhaPZ2x1mmMOV+ORZWycDqjnd1SLT9lsIGDd7uDK67O6EJ2hjouCYj6r6zqH3z/cb3rzZ/n77v57RXtO/UXet+rCf3gH36q7jCsZcHaFtz+sI4gzIVisZiCwaAymYz8fr8WFhbk9XoVj8c1MzOjiYmJ/P0ej8fpcoG2u7yxoW8tvyVJ+uyQR9f1bK1fPHbMNybZ111mmmb1zjJJunTRUq3NjFUa0DVz+qe0OVhzc3i17SP3Vb3wwbZ7rV8x187w0O4OrladAtps4NSq03lzzxhev6Lh55/Ryhv/Xbd85ZG6a7PzVFLz0kW9/eT/Xx/+8yd15sqqUn3b9FTmDV1+/6/o+lsG6h7LzsAv3930ywMON3RK2T1ey66AWHCQVmus0rm41jc29PhfPKepn7+qYOZvNbx+Ram+bVrwvFuRW3dZnoer5af5VTkQrdYtZedYhZo9eLc7uGr5vF5uDRXsDiiaHM/OUMeNAdHdLz1fFIIVetcv3tDdL72gF//u37M8nt3BWkdsf7QFQZjLxGIxTUxMSJJCoZAikUj+Ma/Xq0AgoLGxMcViMY2NjWlpaYkwrMO5NdRxa12S9N3Mir76t9fCnZt6evQ7Q9sdrqh1munusKu7zM7TLN00VrnONyvjrR77tzVrk6Se9wzrapUgrOc97y1bQ7nOt+v+4QGtvfRjbSQ3Xzmsx3uHrvsHB8quw87wsFxddnZwtfIU0GYDp0o/pyn7Tuc1Jen0z+rbZi2YIy833rt/ed/w+hX903MJvT7z/9HuhyOOBn4v/Zs5PfHiXxQFMd99/nn9zoc+ZHmcHDu7ruwcL995VeZAtFrn1crq6qYA67+cOVMxvPr0975r6QqIkvRfX3tN3/pvcf365bfz9w2vX9E/f/OsPnIhrU/etqfx+akqHHBbPc3vpqvrmnzzXNmf8e3edw5zqoVrdo6VY8fBu53dZRVt0VDH7oDCrvHsDHXcGhD9yl+/XOPx/1xXEGZnsObm7Y/2IwhzkUwmo2AwKOla6FUYghVaWFjQ6OioksmkgsGgor9sw0dnsjPUsTO8cmvYdHljQ//6zXT+9r96M61/5Blo+Ge1c5u5MTw0+vu1/qt36Vs73yeZpj67Y1B9DdTVzCT+VscyJRkOjlVtvHcer6O2BjvyygZ0kvru2qer192gleR/042rl5Xq26bzI7fJd9eYVv/dgqWamgkPW33KbG7d9QaRZoXl7QycKnWq5bZSPZ1qtcZarWMsOwO6WuPt+NvX9fb3v6ub//HvNj1WI4Hfyr/8F/rVV5P5+3JBzN88NqfLd/+durrVGu2UqjleSahQbbxyV0A0Ll+S/uwJPX8+VTaIKdd5tbK6WrYm4/Ilff7ED/Srly7k7ysMrz573XU1r4AoXQuw9px6rigEyzEl/frltxU6f06PvXaT5fmprAROVk7z2zs0qMAP/g+9+/zfbvoZD2hNT/y//nHRgWiln9TOsXJa0RXjtpCo/8qq/sGffkfv+sUb+fvcECq0IiCyazw79wu3BET7/vLZd26Ypm5+e6Xqum5+e0X7fvyjigHsqQ/8VtFtO4M1t25/OIMgzEVyp0NK0szMTMXlcp1hi4uLisVimp+fVygUalOVncmtYUdpqPO1s6mmQh27wqt6w6Zv/PlTWrl4SbfceIP+xd/ffIqWndvs2+msUutX87dT61f17fRbCu3wNDRe4Tb7y//rb/Tt3/pgw7V1RKjZ29tQXXadZlltLMPhsaqNpyrjVdr/CzvyNoVXd4/VNd9bbrz1X71L49s8Sq2tS4ah4b5ePd/bp3oOg3JhXy4szKk3PLTzlNn8mO/zyvibv2qotsKrpkqVQ7p84GRxLjo7O9WsjKU6ut7sDOisjLfy9J9tCsLm5uaUzWY1MDCgqakpy2PVG/iZBSFYjinpV97O6qX5f60P/t4flH1uucDJrk4p6doB5M4fPa3nk39TNtSxOl7/lVVNLP6J3vfWO589pV1XK6uyPHn5yMlYUQiWkwuv/vHrSb2S8tYc6+VUSl85nyr7WO613J/+Wz3y7vdZmp+q/8qq5cCp1ml+97z8l0XjFHrPmz/XB15+0dKBaLNjnVxY0OrbF3XdTTfqQ7/8D207D97bFRLt+MkpHf+n/6y+UKcgBMsx5WyoYHdAYed4du4XbgmIFk8+r4uXV3Xj9dcp8KEP6kr/Nm1bq3wK9JX+bda7EC0Ga1Y7G926/TdhvrG2IAhziWQyqVgslr+9f//+qssfOHBAi4uLkq6FZgRh1dkZUNg5Vmmos9LX33CoY2en1HczK5vCpu9mshV/1n/15z/U68tpvXdosGwQZmdA96Wfn990/xd/fl6fGdxe989aus2i19+o1Q2zoW1md6eaU6FmJXaGOrmxVv/mr/SLxH/Vu9d+efB406D++W99xJax/s8dO/X//ru/2VDYlBvv4umf6qbVyzVPJ622/9sVXuXkPzN++QdSI0FwLuxTmfDw/C3bNezQlUklaeE9/6Pef+b0pu4TQ9LyLdu1sw3z2rX6lNlaY/1t/zbpe9+u+R51Yo68Gy9kdXltTdcXvA/m5uZ07tw57dq1Kx+ENRP4lQuuPv3UD3RzmSFyz9xx6i/01eOxmt1Nkn2dUtIvQ4Un/63eVSbUyYVXL7z2mqXx7or/p6IQLKew6+qRd7/P8hUQ953+r2UfKwyvPlpjrPWNDT1/5oyG16vP6zW8fkUyzaq15U7zsyu8ktxzUPvcwqKyb76pgVtvvRaE2Xzw3q6QaPf6qi2hjpF/3JlQwe6Awrbxmtwv7O68KnTXf36xxuN/KbPCOA+ffF5vZld068AtCnzog3rz1vfovan/XvE/tH7xrvdUXVfRzylZCtb2vXiy7GNF3WV2vi9tfo9LzDfmBIIwlwiHw/nv/X5/zXm/AoFA/vtMJqPFxcWi+/AOOwMKu8eyM9SpN7yqVte/KvgZc77R4M9qd0BX+bH6f9bSbWbefHND40j2dqo5GWpWY2eoY/T369vv3a0v9Q4U/aGw/cJFha7bZstYvQ2MlRvvu7u8+sIvxzs8/C57Au8mwivJvs8Mo79fG781oX/1/F9of/qdjphjg+/W/I5d+i91vJ52dZdJ0uqGqUcyF5S9bY9C589tqu3fv+d/VMxibVZCur/t36Z3m9J1JZvtuTOnNy27y7ND7710seLPeXJgh64v87xyLg/s0IeqjPVdz7u1kjitj/Vvfj3/n7ftzn/fbBAZC39p03139m/TQJUDjlTfNn0r/NWi2lZXsvl/C8e8q3+brqsy1s/7tuk/hb+k/pL69pUuaOGA4z1rq3rrPz2rj22rHV5dfu1MzU6pt57fKLv9S0/Zuful54tCsNKxQufP6ZHe8p1SpQd7d758qmy9pV1XK6ureuv5k/n6tl3ZfGrky6+/rn+2Vv6UyZzh9StauXx5U22Fdf1wzdSFNVOpvm1Vw7BU37UOj9LaymnmgLuInUFAk2PlXoNtV1bz26/Rg/fSfUxyaUjkolCnaPv/+Ee2BkR2B07NhDpFDMO+zivTrDqOpGuPWwx1UsMj2v5WWjddLP6cNSS9fePNemPnSO2aCjQTrNkZqpVb1paATsw35hSCMJeYn5/Pf+/z+Sw9x+v1Kpm8dqrA0aNHCcIqsDOgsDNUsDPUsTO8+m5mRW8U/Iw5bzT4szayzcodiK6Zpv7oslnxOX/08zf1vrfObzqoKjxwLGTnNuuWUFNqUahT8Jo1ss3sHCs3Xj6INAzXBN52fmZ89+3LeuTd79Mj737fpj9u6xmr9849Wk+dk1Gmu+yV62/Sr9/xqxX/0Ch9n/9wzVRq3ZR6+8rXZkpfLRMQXVlfz/9bOGYzgVOp13bu0lvpZb2/TKfaK9ffpC8MvVdh09z0+VNqzTR1aOi9+uZby2W73l65/ibN79il69dN3dunTeOVbjM7A7o109RRz7t18BdnK453bPDd+n6F2kr95dB79KGfVz54+d7gu7WyLn2sRtPmmq6FZu+pEcR8/6p0b43XYM009T+dL98NUxg4/fZ73lf2Zyw9qPr1CqFOaXi19LOf6nfO/qxibWsbG1UDSOmdrisZRtnXIBfCrJmmjly2Hl5Vq+0jfdLf6zO09q73SFUORNfe9R49fsO1R6pGkXYecNsZBNg51i81evBeuo+5KfAr5cpQx+7X0ubxKu4Xv+yOrdUtZWksWeu8yrP5Z9zo7dPf/Mqva+cbZ/WuX/xc29au6Er/Nv3iXe/RGztHtNFbX/xgZ7Dmyu0v5htzCkGYC8RzVwj6pXvuucfS83w+Xz4Iy50miWJ2HoTaGSqsbphFHT+l/nWFMcsFRJL01JqpN9Y3h0RvrF8te+BYSaNhU+5AtJSd2+zEurRcuTSdN68tU+ugKsfOwK9bQk3Xhjo2dwraHZ7XW5udQXA5m8YqeU49Y0nSif/hf9L1/WfLdpf9o9fOWvr8WTNNPVnmM6y0NqshTLOBU6mY2afvVuhUm9+xS2/39Fn6/DmxLp3t6dMnq43V26e3LX6e2RXQ5Wr77o5d+o2VdNVtZqU2O7f/iXXplOfd+udvVg/orPwOOLFm6jctnOZ3fsPUiXWj+vY3zaodb7mxZJo6L6NqbSeuGtptMbiSqv++y/2uXLCwzWqNte2X6/vF8Ih2VDkQ/cXwiK53IKSw80DUzrEkGw/eXRwSuXX72/1a2jlexf3CMPRX2be16rMe6rQlIFJj22yjt0+v77pNr++6ren5ruwM1ty6/Vs63xgqIghzgcK5waRrnV5WlC4Xj8ctd5N1C7sPthsJFcod1OY7Hyp4vY4Aa8009f0qY1k9cJSaD5tKOzLsDOjKHiSXsPqzNhpEtnosqbnwqnD7NxOcVOqi64hQp4mxJOmKaepLlzaP98Wfn9cd2eWy41TqSLKzNjuDYDvHWjNNHTV7tVyhu8zqe7JWTfXWZmfglPv8ebtSp5rFn7Xwc6zWWFbGk+wL6Ap/xlrbzEptdm3/XF2rt+7SRy5UD+hq1bVmmnryqvS7FgOnmq+nrHWq5V7bauN9pE91dV1JlTuvcl1cvf/DiFYupnXLxc3bbOXGm3TPyIge77XQxSV7D0Tt7Miw80DU7tO52rLNZGNItIW2v92vpZ3jldsv3rh8Rf/29Dn9H6llPTa+eZ7desayPSBSY9useJDGQ7Acu4I1V27/Fsw3BmsIwlzgxReLW+trzQ+Ws2PHjqLbp06dIggr0ExAUXrwbveBu52hjl0HtXbX1c6ALqfcz9rqINLuUNOujrxm9otWh1duDXVy41V7zOo4dtZm53uz5Z8/Jctb+Rmt1mS1NrsDp7KvY5lla/2sFfeHCuutNZ5dAV1pbVa2WbXa7Nz++bosBnTt6pTKjWelU83KNttmGLZ1XeW6uNTXr//2K3dVPEjr7+1TPZcSsetAtFpHhh2hQqNBgN2nc+XGbOk2k40hUQMdMW7d/na/lq0Yr3C/+EeHH8lPNl+vVgZEze7/LdNkGOS67W8YunDTLVXDsAs33UII1gIu2qu7V+70xpxGO8ISiYRtNXWi0m4MOwOKdh641zOmnQe1rejIaGcIkFPPQXIz49g9luTO4MTNtbk5vHXre9ONnz9Wa7Jam52Bk52fP7lOnVK/8/V/rfMrF7Tjlpv1Jw/8s6LHqnXq2BXQVautON4pVqk2O7d/cV390s236Wfvu00/++UBh0/SoxbrsrtT6iN9klHHWLXGc3MQs4nNpzmdu3hZT75xXr8ZCDQdKjRbW0u2l+SaU8Ps7IjJjefG7W/3a+nG95LdY7V0/3crh7Z/6VyA2QGPbn57peJ/qry13bN5/sBfKnfBFFhDEOYCpUFYozKZjC3jbAVuPdi2O9Sx66DWzWFT2w6S6xzH7rHcGpy4uTa3hrd21mbne9Otnz+VQ5jKagUUdo1n5+fPtgrb0Vhfl9bWZKyvW5tnSfa/lpVqa4Sd279iXQ3UW61TqjCIsdoptc0wXNt1VZaLDmoLf85/9LW5fDfGbzTbdbJFt5fU2pComY6YIm7d/na/li7bN1qiG35GF2npqamoyDBNs44/8dAKpZc2t/qSxGIxTUxM5G8HAgEtLCzUvf5t27ZpbW1NktTTU/vS47Xccsstuvnmm5sex6pUKqWNjQ31GIaGbrm23suS3ra4Z99kSNdXedypsWqNaUrKmNKGxXF6JHmM8v+/32xdyysXtGGa+dfAzm3W6AdUuZ/Tzm1m51hSc/tZ4fYfvOVmW+tqtrZCbt7+VscrN07p/u+m92arxrJ7+zejdPvbyc7Pn0oaqd/O17JZrdz+7UD9zur0+qXO/xmo31nU7yw31d979ap6Nq7K0LW/PzZ6enW1t7fqc/L19/RoeHi4LXVK0oULF7SyUn1uMys2Nq79Jdnf368rV6pfgMZuBGEuYFcQ5vf7FY1G615/b29vficEAAAAAABoh56eHl29uvliYa3EqZEu4PF4bDmt0eok+6Wuv/56Xbp0SeYv0+RmtbsjDAAAAAAAtJ6dHWGGYej661vRs14dQZgLDA0N2RKEDQ0NNfS8t99+u/ZCAAAAAAAAHa759h80rdFOrtLwrNFxAAAAAAAAugFBmAvs27ev6LbV7rDl5eWi26Ojo3aVBAAAAAAAsOUQhLnA2NhY0e1kMmnpeYlEoui23++3rSYAAAAAAICthiDMBUo7wqwGYYWdYx6PR16v186yAAAAAAAAthSCMBfw+XxF83u9+OKLlp536tSp/PelYRoAAAAAAACKEYS5xP79+/Pfx+NxS88pXG5mZsb2mgAAAAAAALYSwzRN0+kicC3UKpwrrNbLEovFNDExIUnyer2b5gsDAAAAAABAMTrCXMLn8xVNdr+4uFh1+YWFhfz3dIMBAAAAAADURkeYiySTSY2Ojkq6FowtLS2VXS6TyWhwcFDStStFRqPRttUIAAAAAADQqegIcxGv15vv9IrH45qdnS273Pj4uKRrV4os7AwDAAAAAABAZQRhLhMIBBSNRuXxeDQzM6NgMKh4PK5MJqNYLKaxsTHF43H5fD6dPn266GqTAAAAAAAAqIwgzIX8fr/S6bTC4bCSyaTGx8c1ODioYDCooaEhLSwsaGlpaUuEYLOzs5qYmNDg4KAMw9Do6KiCwaBisdiWXjcgObcPxuNxTU5OanR0VIZh5Nc9MzOjTCbT0nUDOW78DM69J2rN0wk0y437f048Htfi4qJmZ2ddUQ+2JqfeA5lMRjMzM5qYmMh/5ufWPT8/39J1A9K16ZAmJiba9reGm3/fOMoEHBCNRk2Px2NKMv1+vxmNRs1EImEuLCyYXq83f386nd5S6wZM07l9MJ1Om4FAwJRU9SsSidi6XqCQWz+Dp6en8++BhYWFtq4b3cON+386nTbD4bDp8/lMSabH4zGnp6fNaDTK30KwnZPvgXA4nN/Hw+GwGY1GzaWlJTMSieT3f6/Xa0ajUdvXDZT+Hd7qv7fd+PvGTQjC0HbRaDT/ARAKhcouU/jLyM43p5PrBkzTuX0wnU7nf+lZ+apUG9AMt34GF9ZFEIZWcdv+n06nzVAolK/J5/MRAKClnHwP5AIIn89XcZnC98PS0pJt60Z3S6fTRf/Z1o4gzG2/b9yIIAxtlU6n88m01+utuFwikci/ef1+f8evGzBNZ/dBv9+f/wNwYWHBTCQS+f8VKvfLmTAAdnPrZ3BhXez7aBW37f+RSKRov6cTGK3m5Hsg1wkmqeYBf+4/DT0ejy3rRncLh8Om1+vN/x3ejiDMbb9v3IogDG1V+CFQ681vd+uok+sGTNO5fTASiZiSzOnp6YrLJBKJ/P8M5b74IxB2cutncCAQML1eb1EoQBAGu7lp/y/8zw+v12smEgnb1wGUcvI9UNj1WAunycMuS0tLRZ2Fub/HW/33jZt+37gZQRjapjB1tvI/MgsLC7YdkDu5bsA0nd0Hc/8TVW+NkjhNBrZw62dw7o/SpaUlgjC0jJv2/8KDHo/H05Wnw6D9nHwPFJ4iFggEai5fuO5q/4EI1GtpaanlQZibft+4HVeNRNuEw+H8936/v+ZVLwOBQP77TCbT1JU1nFw3IDm3D8bjcSWTSS0sLNRc1uv1FtWZez7QLDd+BieTSU1OTmp6elo+n8/28YEct+z/pVcp2ypXIIf7OfkeSCaT+e/r/Ztmx44dDa8XKNWOz1u3/L7pBARhaJvCSxJbPejwer35748ePdqR6wYk5/bBo0ePKhQKWf7l6/f7i26fP3++ofUChdz4GRwMBuXz+TaFv4Dd3LD/z8/PKxaL5W9HIpGidQCt5OR7YGhoKP99MpksCsbKefHFF8vWAHQCN/y+6RQEYWiL0v+Bueeeeyw9r/AN3ExHjFPrBiRn98EDBw7UdaBf+ktzdHS0ofUCOW78DJ6ZmVE8HrfUKQk0ww37f677sXDsUCjU1JiAVU6/B0rDrML3Qjm5dXk8nqJuGcDtnH6vdRqCMLRF4f9CStb/h6V0uUZO03Jy3YDk7D7o8/nqasXOZDJVawDq5bbP4Hg8rtnZWTpi0BZu2P+DwWDR7UOHDjU8FlAvp98DPp+v6EA/Fottek/kzM/P5zvG6BZGp3H6vdZpCMLQFoVtxpL1c6RLz80/depUR60bkDprHyw9ZaD0VEmgXm7b/8fHxxUIBOiIQVs4vf8nk8migxq6XNBuTr8HJOnIkSNFtxcXFzeFYbFYLN8tFolE+B2BjuOG91onIQhDW5QeXDeaUCcSiY5aNyB11j5Y+MuPPwJhBzft/7kDn9KDIqBVnN7/I5FI0e3cf27kgoDR0VEZhqHBwUGNjY1pdnZ2U2cw0Ayn3wPSta6w0lPhFxcXNTo6mu8SnpiYkNfrVTQa5e8fdCQ3vNc6SZ/TBaA71JqY0qpG/jhzct2A1Fn7YOFB08zMTMvXh63PLfv/4uKiFhcXFY1GuVIe2sbp/b9w4mTp2sThY2Njm059yWQyisfjisfjmpmZ0cLCAp1jsIXT74GcQCCgSCRSNEdYMpnU2NiYJGl6eprTIdHR3PJe6xR0hKEtGn1DlR6sLC8vd9S6Aalz9sHCU2jC4TDzJ8EWbtj/M5mMgsGgQqEQp/uirZzc/5PJ5Kb1Hzt2TJOTk0qn0zJNU6ZpKpFIbOqACQaDXTVpMlrHDb8DckKh0KYuyZxYLNY1AQC2Jje91zoBQRg6ipO/oPjlCKe1eh/M/U+o1+vV9PR0S9cF1KuZ/X98fFxer7fiARDgdnZ0xHs8Hp0+fVqhUKjowCf33ih9fxw8eJC/feAadu2LoVCo7KmP8Xhcu3fv7pqJwoFKuuVznyAMbWHXaSiNjOPkugGpM/bBeDyu+fl5eTweRaPRlq0H3cfp/X92dlbxeHzT/DBAOzi5/5cGYaUBWKnSgCCTyejw4cN1rxco5PTvgFKTk5Oan59XKBTa1PmeyWQ0Nja26ep7QCdw23vN7QjC0BZDQ0OOjePkugGpM/bBgwcPSpKOHz/OKZGwlZP7f26+o3A4LJ/PZ0sdQD2c3P9LJzy+5557aj6ndG7I2dnZutcLFHLT30ATExOan5/X9PS0IpFI2dOCc8sRhqHTuOm91gkIwtAWjSbLpa2Z7ewIs2PdgOT+fXBycjLfMUNYALs5uf8Hg0H5fD5O9YVjnNz/GxnD6/Vu+j3AqWJohlv+Bsp1evn9/qJJ8SORSNmO4WAw2DWniGFrcMt7rVMQhKEt9u3bV3Tb6i+W0sn6RkdHO2rdgOTufXB+fl7z8/OKRCJcIQwt4dT+Pzs7q2QyqaGhIQWDwZpfhXUdPny46DEmDUejnPz8b/R3RmnNdl2JDN3JDX8DBYPBfKBbbq7IQCCgpaWlogAgk8lw9Wx0FDe81zpJn9MFoDvkLk2ck0wmLXWelLb1N3K1LyfXDUju3QdjsZgmJycViUTKnhoA2MGp/f/8+fOS1NDpLfF4vKgLxuv1EhSjIU5+/pf+r77Vg6LSg6BuuYIYWsPpv4GSyWT+PzN8Pl/F6R98Pp+WlpaK9v/5+XmFw+Gu6ZBBZ3P6vdZp6AhDWzT6v4uFf7R5PJ6G5i5yct2A5M59MB6Pa2JiQuFwmBAMLeXG/R9ol078+6f0oL9b5otBazj9O6CwA6y0llLlri586tSphtYLtJvT77VOQxCGtvD5fEV/WL344ouWnlf4y6fWLy83rhuQ3LcPJpNJjY+Pa3p6mrmT0HJO7f/hcFimaVr+KvzDb2FhoeixwvlkgHo4/fdPodL/9beqWw6K0BpO/w1UeJBv5ZSv0v8c5NRgdAqn32udhiAMbbN///7891YnXi1crpnz9J1cNyC5Zx9MJpMaGxtTKBSyfHCfTCa5chia4pb9H3CCk/t/4SkuVk8TLj2FkouooFlOvgcKT+21Ggazz6NT8feWdQRhaJvJycn891b+GCtcxuv1NnW+spPrBiR37IOZTEYTExPav39/XR0uwWCQ9wCa4ob9H3CKk/t/4UFNMpm0NE9YYVjAew92cPI9UNjR2Eh3Fx2R6CT8vWUdQRjaxufzFb25al2Fq/BSxrXS6fn5ec3MzFT8BdfKdQNWOLn/S9dCsLGxMXm93vyytb5isVh+4k3+dxTNcHr/B5zk5P7v9/uLDuQPHz5cs97CAyNOC4YdnHwPHDhwIP/9qVOnLIXBhWN1UzCA1rJ6wZJqOOa1kQm0USKRMCWZkkyfz1dxuXQ6nV/O7/dXHdPv9+eXlWSm0+m2rRuoh5P7v8/nK1qunq9IJNLMjw2Ypuns/m+F1+vNj7OwsNDwOEA5Tu7/S0tLRcslEomKY0aj0fxyoVDI0s8GWOHke6Bwuenp6apjFr4HwuFwzZ8LsGphYaFof613/+KY114EYWi7wg+BSh8AuYN2j8dT88CmnoN2u9cN1MuJ/b+ZEIz/L4GdnPz8r4UgDK3m5P4fiUTyy3m93rJjp9Pp/Pug2sET0Cin3gPpdNr0eDw1l0skEvnlujEYQOsUfr5aCanK4ZjXXhzhwBHRaDT/iyYQCJhLS0tmOp02o9Fo/k3p8/ksvSkLf7FJMqPRaNvWDTSinft/IBBoKgSjIwB2c/LzvxqCMLSDW/7+8Xg8ZjgcNpeWlsylpSUzHA7nH+NzH63k1HsgnU6boVCoqAMmEomYS0tLZjQaNaenpxvu1AHKSafTZiAQ2NTJVfrl9/vz74VqOOa1F0EYHBUOh02fz1f0h5nf76/rICQajZper9f0eDw1253tXjfQDCf3f8Bp7P/oZk7u/5FIxPT7/UXr9vl85vT0dNXTJgE7OfUeWFpaMkOhUNF/fni9XjMQCJjT09NdGwrA/TjmtZdhmqYpAAAAAAAAYIvjqpEAAAAAAADoCgRhAAAAAAAA6AoEYQAAAAAAAOgKBGEAAAAAAADoCgRhAAAAAAAA6AoEYQAAAAAAAOgKBGEAAAAAAADoCgRhAAAAAAAA6AoEYQAAAAAAAOgKBGEAAAAAAADoCgRhAAAAAAAA6AoEYQAAAADQYplMxukS0MEymQz7EGATgjAAAAAAaIF4PK7Z2VmNjY1pcHDQ6XLQYeLxuGZmZvL7TywWc7okYEvoc7oAAAAAANgq5ufnFY1GFYvF6OBB3RYXF3X06FH2H6CF6AgDAAAAABvkgouJiQn5/X5ni0FH8nq9mpycVDgcdroUYMsyTNM0nS4CAAAAALaa0dFRJZPJ/G0OvVCPmZkZzc7O5m8vLCwoEAg4WBGwNdARBgAAAAAt4PV6nS7BFRYXF50uoSONjo46XQKwJRGEAQAAAEALeDwep0tw3OLiog4fPux0GR1paGjI6RKALYkgDAAAAABgu2QyqWAw6HQZAFCEIAwAAAAAYLuJiQmnSwCATQjCAAAAAAC2mpiYKLpQAAC4BUEYAAAAsIXE43HNzMxocHBQmUwmf38ymdTk5KQGBwdlGIbGxsY0OTlZ9/iZTEYzMzMaGxvLjzU4OKhgMKhYLGbjT9KYTCaj2dnZTRON5+4zDEMTExNF26bU4uKigsGgxsbGZBiGDMPQ6OioJicnFY/HW/wT1Ob219gt+0IzcvtAbp/J7QPBYLDuyf/t2CedZEf9dn5uLC4uamJiIr/u0dHRoqtr1pJMJvPvn3Lj5urLvecbCXTt3H9a+X5vdlvmuP33wiYmAAAAgI62tLRkTk9Pm16v15SU/0qn06ZpmmYkEim6v/DL6/WaiUTC0nqmp6dNSabH4zH9fr8ZCAQ2rdPv9+fXWygSiWxaNrf+cst6PJ6y9UYikbK1RaNRMxAIFC2b4/f7N40TCoU2jbGwsJBf7/T0tJlIJMx0Om1Go9Gi2sPhsKXtVameRnTCa5xOp8u+xuW+otGoaZpm1eUDgUB+7Gg0WnGfyNVbbt8IhUKWXvvC9eRq8vv9ZiQSMaPRqBmJREyfz1e0TRcWFqpuy2b3yYWFhaLHy61venq67Fi5Gv1+v+XX3u76C2tsdJ8qlE6n8+vNvT9N89p7IzdmIBAw/X5/2eeWvoa5nyedTm+6v/TL6nverv2n1e/3ZrZlKbte33YiCAMAAAA6WO7ApdyBaSKRyAcBgUDADIfDZigU2hQoWDnYyY1f7oCw9KCsXLiVEw6HLS+bSCQ2HVCVhh3hcLhiQJJOpzcdyFf6mQtDh0oHvYXrqRWCmKZ9QVinvMaJRMKMRCKbls2FVIVfhQfF5UKISgfN5YKtWq9FIpEoG65V+xmnp6fLLlO6/5bbVq3YJ2v9nIVjhkKhpkIHu+o3Tfs+Nwp/xkqvTS6Q8fl8Ze8v95VIJPI/ay44rBSK1QrD7Np/2vF+b3RblrLz9W0ngjAAAABgiyg9cPL5fKbX6zWXlpaKlivXuVO6TKHcgVelg6bCZawcNNZzcFR6MFUahOUO+JeWljYdNOa6MHLdDoUH8IX1pdPpTQeb5RQeUFsJluzsCMvplNe48MC81sF06Wvn8XiqLl960G9FbltUem0LQ6dqHWOmuXk7tGKfLK2pWhCWW4/H48l32jXDrvrt3Kei0WhRGFeJx+PZ9JlSLjwt/Pzx+/2b3htLS0tluxXbsf8UasX7vZltWe7nsOszo50IwgAAAIAtorTzwefzVTzQKT3IrnSAUngwXO2gqbDrplaYURhk1ArCCg/a6j1o9Hq9m2qORqObAoXSsK1SmFC6XC2tCMI68TWuFYSZ5uausGp1lHbVVAv4TPOdoLNSeJlOp4vqtXIqYWkYV+k5je6TpmktCMu9dtX2g2Y0Wr/d+1Thfl9r36j0mVIu2KvWIVi6X1RavpX7Tyve73ZvS7s+M9qJyfIBAACALWLHjh1Ft8PhsDweT9ll/X5/0e1EIlF2uZmZmfzylcaSJK/XW/R4JpOpOCn00NBQxXGaUVrfzMzMpvv8fr8CgUDRfVYnKfd6vUW3nbgqYqe8xvU6dOhQ0e35+fmKy4ZCoaLbkUik6ti5yborTSR++PDh/D7g9/s3vc7lhMPhotu5bViq0X3SisXFRY2NjSkQCGhpaanqa9eoRuu3e58qvEhFtcnXA4GAlpeXyz7m8/nk8/mK7it9HQt5PJ5Njy8uLm76vGjl/tOK97sd29INnxnNIAgDAAAAtqhqgVPpwUu5UCeZTOYPlCYmJmqub9++fUW3o9GohSpbZ//+/ZaWC4VC+e3h8/k2HVBW4oar/G2V17g0SKkWbnk8nqJA49ixY1XHjkQi8ng8FcOmwqvklQYllZTuW+UCEivPa9T8/LyCwaCmp6e1sLBgy5hWWKm/FftU4b48MzNTcVt7vd6q79/S90ut0Ko0dJU2h0ft3H+afb+XLtfItnTLZ0YzCMIAAAAAlFV4wDczMyPDMKp+lR4gnjp1qt0lF7HaIePxeJROp7W0tKSlpaWKy5UeMFbqlugkbnqNC0OHZDJZ1LlSqvDgOpPJVOxsyR20lws0pM2hxujoqKVaS8O4cmNVel6zZmZmNDk5qUgkUrWjqRWs1N+Kfeqee+7Jf59MJjU2NlZx/6gWDDay/Uv3ncIgp937jx2a3ZZu+sxoVJ/TBQAAAABwp8IDvlAopLGxsbqe36pTIFulUjdHMplUOByuerpep3LTazw5OVm0jSORSMXOsNLXIhKJlO1eyT2/0mmRpQGAldPacvbt21f0/Gg02tApjvUIBoNaXFxUKBSqGO45rRX7VCgUKjoFMRfghEKhmqfGNmtiYqJofysMwDtt/5Ga35Zu+sxoFEEYAAAAgLIKT62ZmJhoy0Gam8RiMYXD4XxHg8fjccXpkHZy02vs8/nk9XrzNR07dqzsgXnuVLTCZXOnlpV2/MzPz+fHLafSPEpWlHb/tLpD8ODBg/n9b35+XpOTk5ZPxWunVuxTHo9HR44cUTAYLLp/fn5ex44d05EjR1q271Y75bCT9p+cZrelmz4zGsWpkQAAAADKKgx9tsJpgFblJiGfmJjIn1aXSCTaOhdTu7jtNS6cNLzSxNqHDx+Wx+PZNNdQ6VxhsVhMmUymYjeYtPlnrifotDonk11KO8DGx8ddGcy2ap8KBAJlg9FMJqNgMKhgMNiS7VEaohZ2NHXS/lOomW3pts+MRhCEAQAAACir8CCnmc6HTpFMJjUxMaFgMKh4PK5QKKR0Oq1IJFLXKU+dxG2vcekk4qUH67nOr0OHDm2azLt02dz8WfWcQljPgX21gKQV7rnnnqI5wTKZjMbHx1u6zka0cp8KhUJaWloq+35cXFzU7t27q84t14jSdVWbZ8zN+0+pRrel2z4zGkEQBgAAAKCswgOzdk3k7JRYLKbR0dH8z7m0tJS/2uBW5rbXuPTqjrmurpzDhw9LeifcKuz2isfj+a6a3AT6tUKw0vCh2sUSaj23HWHp9PR0UfgXj8eLuujcoNX7lM/nUyKRKPva5sJBO7urSjujCl/nTtt/SjWyLd32mdEIgjAAAAAAZRWGQPF43JWnYdkh1wmWs7Cw4Mq5l1rBja9x6amMuVMe4/F4vlMvV3fp/ES5rrDc5ObVTouUNs/TVM8V7UoDAqtXDGzWwsJC0es2Oztb9hRSp7Rrn4pEIlpaWtr0Xs2d3meX0te58KqLnbj/lFPPtnTjZ0a9CMIAAAAAlFXaoVA6B1Mti4uLrutWKacwLPH5fB05+XOj3Pga+/3+ooPtXLiV6wYrXV9hN0suEDp8+LC8Xm/NQLP0SpP1nFZXehpcu/Ybj8ej48ePF90XDAbbOsdUNa3YpypdsdXn82lpaanolFGpuDuwWaXjFO4znbj/NLst3fiZUS+CMAAAAABlFXZJSdp0gFRNJpPRwYMHa3bkOC13Cl3Ovn37HKym/dz6GheGW/F4XLFYTIuLiwoEApsOxAvXn0wmNTMzU3OS/Byfz7fp9Ferp3sVzo/k9Xrbemqbz+fb9FqVvpZOacU+tbCwUDVkmp6eLhvg2KEwBCoNaTtx/2l2W7r1M6MeBGEAAAAAyiqduDwXMlgxPj4uv99f9uCu8MCxVtdG6eP1nIZjZdl6TmXqxFOAamnVa1yoke1WeqCcO/g+dOjQpmV9Pl9RDbOzs5KsT5JfOma5q+mVU3g6otVtZuc+VDpfWDKZtPWUwHKs1N+qfero0aNVnzs9PV302WLX/H6F+0O5AKed+49dmtmW7fjMaDWCMAAAAGCLOH/+fNHteq5gVm5Zj8ez6dSf2dnZfNBQTiaT0djYmOLxeMVOgdKDoGrdCaVjtPoqZdW6OaLRaF1jlYYGdoQgnfgaN3KKWukVIaVrgVelUx1LA4pAIGA5CCk96M9dmbKawtPFfD5fXVemtMrKa1s6X5gbTjtr1T5lZR60wo5OK92d9b7O5U5fbOX+Y/f7vbDGWipty1a9vu1EEAYAAABsEfUEL6XhU6Vly3U3zMzMaGxsTIuLi0omk8pkMvmr1w0ODioej2thYaHi//oXTjYtXZvfqHT9uas4lgYf8/PzmpmZKXsgVxq4WOn2Kq0xmUxuOqBLJpOanJzcNLdO4TYst/1K66nnILaSTnmNS+8vDRgnJydrhgWl4Va1A+jSgKLeU69K59w6ePBg1eVz85VJ18KoShrZJ3NKt0+lILO09lqhRD0arb8V+1Qymaw4v1VpveVOWSyn1niF+0G117lV+08r3u9S89uyFa9vW5kAAAAAOl4ikTAlFX35/f6KywcCgU3LLywslF02EolsWrbaVyQSqVmvx+Mpeo7H4zEDgYAZCARMr9drSjIDgYCZTqcrrieRSFSt0efzFS1Tic/nK/vcUCiUf8zv95tLS0ubas4tMz09XXObBQKBmrVU00mv8cLCwqZtFYlEzEgkYvp8PsvbIrefeL3emsv6/f78uhpRWnPpa5oTDofz61laWqo4XjP7ZCKRyL8PCp9bSbnXOhAIWFpXK+qv9PxG96nC17bSNo9Go2U/GwqV206VXufCZau9zjl27z+ter/btS1b8XuhXQjCAAAAgA6WSCTKBjmlB6/pdNo0TdOcnp7eFEIVfnm93rIHT1YOerxerxmNRi3VXXigVe4rHA7nly39Waanp/MHcKFQyFJdoVCo6jastk0KaykNJ0oDCr/fX3Usj8dj+nw+SwfWhfV14mtcblvVOpgvlXt9rRxE54KIwterXktLS0Xb2ufzmZFIxIxGo+bCwkI+RPD7/RUDgmb2yenp6YrbLbf/+P3+/M+4tLRUNgAp3Tfq2d/seE/l2LVPFYY3Ho/HDIfD+Z8pnU6bkUgk/1i1sUq31fT0dH7cUChkhsPhoveP1+utK0y0Y/9p9fvdrm1pmvZ/ZrQLQRgAAAAAS9LptBkOh02fz1d0oBgIBBr63/50Om2GQqH8gX+uC6v0ANHr9ZrT09NNdbdYqaWwGy23ztzBZk7hga7H46nY9dGp7HyNc9s0N04uFKh3jHqCs8KAoBnRaLR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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_bkg = train_array[train_array.classID == 1]\n",
    "train_sig = train_array[train_array.classID == 0]\n",
    "test_bkg = test_array[test_array.classID == 1]\n",
    "test_sig = test_array[test_array.classID == 0]\n",
    "plt.hist(train_sig.matching_mlp,\n",
    "         bins=50,\n",
    "         alpha=0.5,\n",
    "         density=True,\n",
    "         color=\"#107E7D\",\n",
    "         label=\"training sample, true pairs\")\n",
    "plt.hist(train_bkg.matching_mlp,\n",
    "         bins=50,\n",
    "         alpha=0.5,\n",
    "         density=True,\n",
    "         color=\"#F05342\",\n",
    "         label=\"training sample, wrong pairs\")\n",
    "mplhep.histplot(np.histogram(np.array(test_sig.matching_mlp), 50),\n",
    "                histtype=\"errorbar\",\n",
    "                density=True,\n",
    "                yerr=True,\n",
    "                color=\"#107E7D\",\n",
    "                marker=\"^\",\n",
    "                markersize=7,\n",
    "                label=\"test sample, true pairs\")\n",
    "mplhep.histplot(np.histogram(np.array(test_bkg.matching_mlp), 50),\n",
    "                histtype=\"errorbar\",\n",
    "                density=True,\n",
    "                yerr=True,\n",
    "                color=\"#F05342\",\n",
    "                label=\"test sample, wrong pairs\")\n",
    "plt.xlabel(\"neural network response\")\n",
    "plt.ylabel(\"Number of tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.legend(loc=\"upper center\")\n",
    "plt.savefig(\"/work/cetin/LHCb/reco_tuner/thesis/default_NN_elec_response.pdf\",\n",
    "            format=\"PDF\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 2, figsize=(14, 15), sharey=False)\n",
    "# 0,0\n",
    "axes[0, 0].hist(train_sig.chi2,\n",
    "                bins=50,\n",
    "                alpha=0.5,\n",
    "                density=True,\n",
    "                log=False,\n",
    "                color=\"#107E7D\",\n",
    "                label=\"training sample, true pairs\")\n",
    "axes[0, 0].hist(train_bkg.chi2,\n",
    "                bins=50,\n",
    "                alpha=0.5,\n",
    "                density=True,\n",
    "                log=False,\n",
    "                color=\"#F05342\",\n",
    "                label=\"training sample, wrong pairs\")\n",
    "axes[0, 0].set_xlabel(r\"$\\chi^{2}_{\\mathrm{match}}$\")\n",
    "axes[0, 0].legend(prop={'size': 20})\n",
    "# 1,0\n",
    "axes[1, 0].hist(train_sig.distX,\n",
    "                bins=50,\n",
    "                range=(0, 100),\n",
    "                alpha=0.5,\n",
    "                density=True,\n",
    "                log=False,\n",
    "                color=\"#107E7D\",\n",
    "                label=\"training sample, true pairs\")\n",
    "axes[1, 0].hist(train_bkg.distX,\n",
    "                bins=50,\n",
    "                range=(0, 100),\n",
    "                alpha=0.5,\n",
    "                density=True,\n",
    "                log=False,\n",
    "                color=\"#F05342\",\n",
    "                label=\"training sample, wrong pairs\")\n",
    "axes[1, 0].set_xlabel(r\"$D_{x}$ [mm]\")\n",
    "axes[1, 0].set_ylabel(\"Number of tracks (normalised)\",\n",
    "                      va=\"bottom\",\n",
    "                      ha=\"center\")\n",
    "# 0,1\n",
    "axes[0, 1].hist(train_sig.teta2,\n",
    "                bins=50,\n",
    "                range=(0.0, 0.02),\n",
    "                alpha=0.5,\n",
    "                density=True,\n",
    "                log=False,\n",
    "                color=\"#107E7D\",\n",
    "                label=\"training sample, true pairs\")\n",
    "axes[0, 1].hist(train_bkg.teta2,\n",
    "                bins=50,\n",
    "                range=(0.0, 0.02),\n",
    "                alpha=0.5,\n",
    "                density=True,\n",
    "                log=False,\n",
    "                color=\"#F05342\",\n",
    "                label=\"training sample, wrong pairs\")\n",
    "axes[0, 1].set_xlabel(r\"$t_{x}^{2}+t_{y}^{2}$\")\n",
    "# 1,1\n",
    "axes[1, 1].hist(train_sig.distY,\n",
    "                bins=50,\n",
    "                range=(0, 100),\n",
    "                alpha=0.5,\n",
    "                density=True,\n",
    "                log=False,\n",
    "                color=\"#107E7D\",\n",
    "                label=\"training sample, true pairs\")\n",
    "axes[1, 1].hist(train_bkg.distY,\n",
    "                bins=50,\n",
    "                range=(0, 100),\n",
    "                alpha=0.5,\n",
    "                density=True,\n",
    "                log=False,\n",
    "                color=\"#F05342\",\n",
    "                label=\"training sample, wrong pairs\")\n",
    "axes[1, 1].set_xlabel(r\"$D_{y}$ [mm]\")\n",
    "# 2,0\n",
    "axes[2, 0].hist(train_sig.dSlope,\n",
    "                bins=50,\n",
    "                alpha=0.5,\n",
    "                density=True,\n",
    "                log=False,\n",
    "                color=\"#107E7D\",\n",
    "                label=\"training sample, true pairs\")\n",
    "axes[2, 0].hist(train_bkg.dSlope,\n",
    "                bins=50,\n",
    "                alpha=0.5,\n",
    "                density=True,\n",
    "                log=False,\n",
    "                color=\"#F05342\",\n",
    "                label=\"training sample, wrong pairs\")\n",
    "axes[2, 0].set_xlabel(r\"$|\\Delta t_{x}^{\\mathrm{match}}|$\")\n",
    "# 2,1\n",
    "axes[2, 1].hist(train_sig.dSlopeY,\n",
    "                bins=50,\n",
    "                range=(0, 0.02),\n",
    "                alpha=0.5,\n",
    "                density=True,\n",
    "                log=False,\n",
    "                color=\"#107E7D\",\n",
    "                label=\"training sample, true pairs\")\n",
    "axes[2, 1].hist(train_bkg.dSlopeY,\n",
    "                bins=50,\n",
    "                range=(0, 0.02),\n",
    "                alpha=0.5,\n",
    "                density=True,\n",
    "                log=False,\n",
    "                color=\"#F05342\",\n",
    "                label=\"training sample, wrong pairs\")\n",
    "axes[2, 1].set_xlabel(r\"$|\\Delta t_{y}^{\\mathrm{match}}|$\")\n",
    "plt.savefig(\"/work/cetin/LHCb/reco_tuner/thesis/default_NN_elec_variables.pdf\",\n",
    "            format=\"PDF\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tuner",
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}