{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import mplhep\n",
    "from mpl_toolkits import mplot3d\n",
    "import itertools\n",
    "import awkward as ak\n",
    "from scipy.optimize import curve_fit\n",
    "from utils.components import unique_name_ext_re\n",
    "mplhep.style.use([\"LHCbTex2\"])\n",
    "plt.rcParams[\"savefig.dpi\"] = 600\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50501"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = uproot.open(\n",
    "    \"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_B_thesis.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
    ")\n",
    "\n",
    "# selektiere nur elektronen von B->K*ee\n",
    "allcolumns = file.arrays()\n",
    "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n",
    "\n",
    "ak.num(electrons, axis=0)\n",
    "# ak.count(found, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# electrons.type.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cut_prop: bool = electrons.p_end_velo > 0  # 3e3\n",
    "found = electrons[~electrons.lost]\n",
    "lost = electrons[electrons.lost]\n",
    "\n",
    "eloss_found = (found[\"p\"] - found[\"p_end_scifi\"]) / found[\"p\"]\n",
    "eloss_lost = (lost[\"p\"] - lost[\"p_end_scifi\"]) / lost[\"p\"]\n",
    "\n",
    "eloss = (electrons[\"p\"] - electrons[\"p_end_scifi\"]) / electrons[\"p\"]\n",
    "eloss_magnet_found = ak.to_numpy(\n",
    "    (found[\"p_end_velo\"] - found[\"p_end_scifi\"]) / found[\"p_end_velo\"]\n",
    ")\n",
    "eloss_magnet_lost = ak.to_numpy(\n",
    "    (lost[\"p_end_velo\"] - lost[\"p_end_scifi\"]) / lost[\"p_end_velo\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "eloss_velo_found = (found[\"p\"] - found[\"p_end_velo\"]) / found[\"p\"]\n",
    "eloss_velo_lost = (lost[\"p\"] - lost[\"p_end_velo\"]) / lost[\"p\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AAACQRTrCxtTpdKJcLkfE+9BrMAQbVK/XI+L90sTe8dP04sWLkY4rFos6wgAAAIBM0xE2pnK53F/ueFunVa8z7OjoKBqNRtRqtdje3p5KDbVaLTqdTuzt7d25/9fnn38+lTEBAAAeM1dnhMdNEDaGdrs9tAn+1tbWrcc/e/Ysjo6OIuJ9aDatIKxSqUQ+n49KpTKV8wEAAAA8ZpZGjmEweCoWi3fu+7W5udm/3el0+qHYJI6OjqLdbtv3CwAAAGBEgrAxDG5QXygURvqZwf25Xr58OXENL168iFwud2c3GgAAAADvCcLuqdVqDd3/4osvRvq5wcBs0o6wVqsVrVYrOp1OLC0txdraWuzs7Eyl0wwAAADgsbJH2D0N7g0WESNfifHqca1Wa+RusquuLodst9tRq9X6nWqbm5vx5Zdfjn1+AAAAJjfNjfeB6dARdk+vX78eun/X/mA9n3766dD9k5OTsca/ulH/TY6OjmJ9fT12dnbGGgMAAADgMdIRdk/tdnvo/rgdYaenp2ONn8/no1qtRqfTidPT02g0Gtdq6qnVanFychLNZnOssQAAAAAeE0HYPX0odLqvTqcz9s9ub29fO1etVosXL15cO2+r1YpSqRTHx8djjwcAAADwGFgaeU/jBlhXl1C+eze9teK5XC729vbi/Pw86vX6tbEajUYcHBxMbTwAAACANNIRlpBJOsJus7m5GcViMTY2NoaucPnixYvY29ub2jhnZ2exuro68Xl2d3djd3d3ChUBAAAAD+nw8DAODw8nPs/Z2dkUqhmPIOyecrncVEKsUTfZH/fczWYz1tfX+2FYp9OJRqMRxWJxKmNcXl7G27dvJz7PxcXFFKoBAAAAHtrFxcVUsoAkCcLuaXl5eSpB2PLy8uTF3OHrr7+O9fX1/v3j4+OpBWFPnjyJlZWVic+zuLg4hWoAAACAh7a4uBhPnz6d+DxnZ2dxeXk5hYruTxB2T+N2cl0Nzx6yI6ynUChEsViMRqMREdPb6D8iYmVlJd68eTO18wEAAADzbVrbG62uribWWWaz/Hv6/PPPh+6P2h12dXP8tbW1aZV0q1KpNJNxAAAAAOadIOyeBpcaRozeZXV6ejp0f1pLFO+Sz+f7t2exHBMAAABgXgnC7ulqR9ioQdhg51gulxsKqB7S4DizWI4JAAAAMK8EYfdUKBSGAqXXr1+P9HMnJyf921fDtIc0OK5lkgAAAECWCcLGsLW11b/darVG+pnB4/b396de04cMLsmc1XJMAAAAgHkkCBvDzs5O/3bvioy3GTwmn8/PNJA6OjqKiIi9vb2ZjQkAAAAwjwRhYygUCkNhVi9s+pB6vd6/fVc3WK1Wi/39/ZH3HrvN0dFRtNvtyOVy8eWXX058PgAAAIA0E4SNqVqt9m+/ePHig8d1Op2o1WoR8X5p4vb29gePLZVKsbOzEwcHB7G2tja0wX7E+86ypaWlWFhYiFKpdOuyzHa7Hc+fP4+IiG+++cZG+QAAAEDmCcLGlM/n+51erVYrDg4ObjxuY2MjIt5fsXGwM+wmV5dZvnr1auh+vV7vh2ONRiPW19eHlmkOnmd9fT2Wl5fj9PQ0CoXCSP8nAAAAgMdMEDaBzc3NOD4+jlwuF/v7+1Eul6PVakWn0+mHUa1WKwqFQnz77bd3dmVd/fd8Pj90v1wuX/uZWq0WS0tLUS6XY2dnJ9bX16NUKsX29nY0m81r5wAAAADIKkHYhIrFYpyfn0elUol2ux0bGxv9YGp5eTnq9Xo0m82RlibW6/XI5/ORy+Vib2/v2qb6xWIxTk9PY3t7u39cT6vVinfv3sWXX37Zr8dySAAAAIDfWuh2u92kiyA9VldX4+3bt/H06dN48+ZN0uXc2//+f/4fSZcAAACQqJ99tpx0CUzBx8/+a9IljC3JbEFHGAAAAACZ8FHSBQAAAACz89V376Z2Lt1lpI2OMAAAAAAyQRAGAAAAQCYIwgAAAADIBEEYAAAAAJlgs3wAAABgLDbeJ210hAEAAACQCYIwAAAAADJBEAYAAABAJgjCAAAAAMgEm+UzlrOzs1hdXb31mN3d3djd3Z1RRQAAAEDSDg8P4/Dw8NZjzs7OZlTNdYIwxnJ5eRlv37699ZiLi4sZVQMAAADMg4uLizvzgiQJwhjLkydPYmVl5dZjFhcXZ1QNAAAAMA8WFxfj6dOntx5zdnYWl5eXM6pomCCMsaysrMSbN2+SLgMAAACYI6Nsk7S6uppY15jN8gEAAADIBEEYAAAAAJkgCAMAAAAgEwRhAAAAAGSCIAwAAACATBCEAQAAAJAJgjAAAAAAMkEQBgAAAEAmCMIAAAAAyARBGAAAAACZIAgDAAAAIBMEYQAAAABkgiAMAAAAgEwQhAEAAACQCYIwAAAAADJBEAYAAABAJgjCAAAAAMiEj5IuAAAAAOCr795N7Vw/+2x5auficdERBgAAAEAmCMIAAAAAyARBGAAAAACZIAgDAAAAIBMEYQAAAABkgqtGMpazs7NYXV299Zjd3d3Y3d2dUUUAAABA0g4PD+Pw8PDWY87OzmZUzXWCMMZyeXkZb9++vfWYi4uLGVUDAAAAzIOLi4s784IkCcIYy5MnT2JlZeXWYxYXF2dUDQAAADAPFhcX4+nTp7cec3Z2FpeXlzOqaJggjLGsrKzEmzdvki4DAAAAmCOjbJO0urqaWNeYzfIBAAAAyARBGAAAAACZIAgDAAAAIBMEYQAAAABkgiAMAAAAgExw1UgAAADgUfnqu3dTO9fPPlue2rlIno4wAAAAADJBEAYAAABAJgjCAAAAAMgEQRgAAAAAmSAIAwAAACATBGEAAAAAZIIgDAAAAIBMEIQBAAAAkAmCMAAAAAAyQRAGAAAAQCYIwgAAAADIBEEYAAAAAJkgCAMAAAAgEwRhAAAAAGSCIAwAAACATBCEAQAAAJAJgjAAAAAAMuGjpAsgnc7OzmJ1dfXWY3Z3d2N3d3dGFQEAAABJOzw8jMPDw1uPOTs7m1E11wnCGMvl5WW8ffv21mMuLi5mVA0AAAAwDy4uLu7MC5IkCGMsT548iZWVlVuPWVxcnFE1AAAAwDxYXFyMp0+f3nrM2dlZXF5ezqiiYYIwxrKyshJv3rxJugwAAABgjoyyTdLq6mpiXWM2ywcAAAAgEwRhAAAAAGSCIAwAAACATBCEAQAAAJAJgjAAAAAAMkEQBgAAAEAmCMIAAAAAyARBGAAAAACZ8FHSBQAAAADMq6++eze1c/3ss+WpnYvx6AgDAAAAIBMEYQAAAABkgiAMAAAAgEwQhAEAAACQCYIwAAAAADJBEAYAAABAJgjCAAAAAMgEQRgAAAAAmSAIAwAAACATBGEAAAAAZIIgDAAAAIBMEIQBAAAAkAmCMAAAAAAyQRAGAAAAQCZ8lHQBpNPZ2Vmsrq7eeszu7m7s7u7OqCIAAAAgaYeHh3F4eHjrMWdnZzOq5jpBGGO5vLyMt2/f3nrMxcXFjKoBAAAA5sHFxcWdeUGSBGGM5cmTJ7GysnLrMYuLizOqBgAAAJgHi4uL8fTp01uPOTs7i8vLyxlVNEwQxlhWVlbizZs3SZcBAAAAzJFRtklaXV1NrGvMZvkAAAAAZIIgDAAAAIBMEIQBAAAAkAn2CAMAAACYga++eze1c/23qZ0pW3SEAQAAAJAJOsLIhMPDw7i4uIj/91f/X/znP/wvSZcDM/M3v/ir+J+/+U38h48/NvfJFHOfLDLvySpznyz6m1/8Vfxv3/8Qi4uLd16hkWGCMDLh8PAw3r59G4uffOLNkUz521/+dVz88IO5T+aY+2SReU9Wmftk0d/+8q/j//m//u94+vSpIOyeLI0EAAAAIBMEYQAAAABkgiBsSg4ODqJUKsXS0lIsLCzE2tpalMvlaDQaj3psAAAAgLQQhE2o0WjE0tJS7O/vR0REvV6P09PTqFQq0Wq1olQqRalUik6n86jGBgAAAEgbm+VPoNFoRKlUioiI7e3tqFar/X/L5/OxubkZ6+vr0Wg0Yn19PZrNZuRyudSPDQAAAJBGOsLG1Ol0olwuR8T74GkwiBpUr9cjIqLdbvePT/PYAAAAAGklCBtTuVzuLznsLU28Sa87K+J9F1etVkv12AAAAABpZWnkGNrt9tBG9FtbW7ce/+zZszg6OoqI98HV9vZ2KscmOX/zi7+K//mb38R/+Pjj+M9/+F+SLufe0lx/mmt/DNL8+Ke59oj0159maX/s1c+40v7Yp7n+NNeedml/7NVPGgnCxlCpVPq3i8XinXtv9bqyIt4vazw6Ohr6u7SMTXL+9pd/HRc//BCLn3ySyhfoNNef5tofgzQ//mmuPSL99adZ2h979TOutD/2aa4/zbWnXdofe/WTRpZGjmFwiWGhUBjpZ/L5fP/2y5cvUzk2AAAAQJoJwu6p1WoN3f/iiy9G+rnB0Kq3VDFNYwMAAACknSDsngb354oY7ra6zdXjroZa8z42AAAAQNoJwu7p9evXQ/fv2qOr59NPPx26f3JykqqxAQAAANJOEHZP7XZ76P64XVmnp6epGhsAAAAg7QRh93Q1jBpXp9NJ1dgAAAAAaScIu6dxQ6SryxjfvXuXqrEBAAAA0m6h2+12ky4iTRYWFobuj/rwNRqNKJVK/fvFYjGOj49TM3bP7/7u78a//uu/RkTEkyeT56i///u/H7/3e7838XnucnZ2FpeXl7GwsBC/v7j44ONN2z9cXES321V/AtJce4T6k5Tm2iPUn6Q01x6h/iSlufYI9ScpzbVHpLv+NNceof4k9Wp/8uRJrKyszGzcf/zHf4x/+Id/mPg8l5eXERHxO7/zO/Ev//IvE5/vPj6a6WiPQC6Xm8rSwlE3up+XsXv+/d//vX+7N3En8cMPP8QPP/ww8XlG1e1242KG402b+pOT5toj1J+kNNceof4kpbn2CPUnKc21R6g/SWmuPSLd9ae59gj1J+ny8jLevn2bdBljG8wYZkUQdk/Ly8tTCaOWl5dTNXbPxx9/HP/8z//cT54nNauOMAAAAGAy0+wIW1hYiI8//ngKVd2PIOyexu2muhpgjdsRltTYPf/0T/809s8CAAAAJMlm+ff0+eefD90ftUPr6gb1a2trqRobAAAAIO0EYfe0vr4+dL/dbo/0c6enp0P3i8ViqsYGAAAASDtB2D1d7coaNYwa7N7K5XKRz+dTNTYAAABA2gnC7qlQKAztsfX69euRfu7k5KR/+2qglYaxAQAAANJOEDaGra2t/u1WqzXSzwwet7+/n8qxAQAAANJsodvtdpMuIm1ardbQfl13PYSNRiNKpVJEROTz+Wt7dqVlbAAAAIA00xE2hkKhMLTh/NHR0a3H1+v1/u27OrJqtVrs7+9/cP+vhxwbAAAA4DHTETamdrsda2trEfE+nGo2mzce1+l0YmlpKSLeX63x+Pj4g+cslUrRaDT698/Pz4f2BHvIsQEAAAAeOx1hY8rn8/1uq1arFQcHBzcet7GxERHvr9Y42J11k8EQLCLi1atXMxsbAAAA4LEThE1gc3Mzjo+PI5fLxf7+fpTL5Wi1WtHpdKLRaMT6+nq0Wq0oFArx7bff3tjdNejqv+fz+ZmNDQAAAPDYCcImVCwW4/z8PCqVSrTb7djY2IilpaUol8uxvLwc9Xo9ms3mSEFUvV6PfD4fuVwu9vb2hvYCe+ix59XBwUGUSqVYWlqKhYWFWFtbi3K5fK177rGNDUnNv1arFTs7O7G2thYLCwv9sff396PT6Tzo2BAxn6+9vefDXftywrjmcd73tFqtODo6ioODg7moh8clqbnf6XRif38/SqVS/zW+N3atVnvQscm2drsdpVJpZp8p5vn9JVFdmEPHx8fdXC7XjYhusVjsHh8fd09PT7v1er2bz+f7f39+fv6oxoak5t/5+Xl3c3OzGxG3/qlWq1MdF3rm9bV3b2+vP//r9fpMx+bxm8d5f35+3q1UKt1CodCNiG4ul+vu7e11j4+PffZhapKc+5VKpT+3K5VK9/j4uNtsNrvVarU/7/P5fPf4+HjqY5NdVz9rP/Rn6nl8f5kngjDmzvHxcf8FYnt7+8ZjBt+kpvnkTXJsSGr+nZ+f998QR/nzodpgXPP62jtYlyCMaZu3eX9+ft7d3t7u11QoFAQBPIgk534viCgUCh88ZvB50Gw2pzY22XR+fj70S7VZBGHz9v4yjwRhzJXz8/N+cp3P5z943Onpaf/JXSwWUz82JDn/isVi/0NhvV7vnp6e9n9jdNMbt0CAaZrX197Busx7pm3e5n21Wh2a77p/eShJzv1eJ1hE3PnFv/cLwlwuN5WxyaZKpdLN5/P9z9qzCMLm7f1lXgnCmCuDLxJ3vThMu7U0ybEhqflXrVa7EdHd29v74DGnp6f93xr1/vhgyLTM62vv5uZmN5/PD4UDgjCmZZ7m/eAvPPL5fPf09HTqY0BPknN/sNvxLpbFM6lmsznUUdj7zP3Qn2Pm6f1lngnCmBuDqfQov6mp1+tT+1Ke5NiQ5Pzr/ZbqvjVGhCUzTGxeX3t7H1abzaYgjKmbp3k/+CUol8tlcnkMs5Pk3B9cKra5uXnn8YNj3/bLQhhVs9l88CBsnt5f5p2rRjI3KpVK/3axWLzzapebm5v9251OZ6IrbyQ5NiQ1/1qtVrTb7ajX63cem8/nh+rs/TxMYh5fe9vtduzs7MTe3l4UCoWpnx/mZd5fvWpZ2q80zvxLcu632+3+7ft+fvn000/HHhd6ZvH6Oi/vL2kgCGNuDF6qeNQvH/l8vn/75cuXqRwbkpp/L1++jO3t7ZHfmIvF4tD977//fqxxoWceX3vL5XIUCoVrwS9MyzzM+1qtFo1Go3+/Wq0OjQEPIcm5v7y83L/dbreHgrGbvH79+sYaYJ7Nw/tLWgjCmAtXfzPzxRdfjPRzg0/wSbpikhobkpx/z549u9eX/atvqGtra2ONCxHz+dq7v78frVZrpC5JGMc8zPte1+Pgube3tyc6J9wl6bl/NcwafA7cpDdWLpcb6pqBeZX0cyxtBGHMhcHfSkaM/puXq8eNs1QrybEhyflXKBTu1abd6XRurQHuY95ee1utVhwcHOiM4UHNw7wvl8tD97/88suxzwWjSnruFwqFoS/8jUbj2nOhp1ar9TvGdAeTFkk/x9JGEMZcGGw/jhh9DfXVNfsnJyepGhvSNP+uLiO4ulQS7mPe5v7GxkZsbm7qjOFBJT3v2+320Jcc3S7MStJzPyLi66+/Hrp/dHR0LQxrNBr9brFqteo9gdSYh+dYmgjCmAtXv2CPm2Cfnp6mamxI0/wbfGP0wZBJzdPc730RuvolCaYt6XlfrVaH7vd+odELBNbW1mJhYSGWlpZifX09Dg4OrnUDwziSnvsR77vCri59Pzo6irW1tX5XcKlUinw+H8fHxz7rkCrz8BxLk4+SLgAirj9xxzXOh7Ukx4Y0zb/BL1D7+/sPPh6P27zM/aOjozg6Oorj42NXzOPBJT3vBzdSjni/gfj6+vq1pTCdTidarVa0Wq3Y39+Per2uc4yJJD33ezY3N6NarQ7tEdZut2N9fT0iIvb29iyHJJXm5TmWFjrCmAvjPuGufml59+5dqsaGtMy/weU0lUrFHkpMbB7mfqfTiXK5HNvb25b6MhNJzvt2u31t/FevXsXOzk6cn59Ht9uNbrcbp6en1zphyuVypjZRZvrm4TW/Z3t7+1p3ZE+j0chMEMDjMk/PsTQQhPGoJPnG5U2TJD30/Ov9djSfz8fe3t6DjgX3Mcnc39jYiHw+/8EvRDCvptEBn8vl4ttvv43t7e2hL0K958TV58Xz58991iFx05qD29vbNy59bLVa8aMf/SgzG4bDVVl5nReEMRemtRxlnPMkOTakYf61Wq2o1WqRy+Xi+Pj4wcYhW5Ke+wcHB9Fqta7tFwMPKcl5fzUIuxqAXXU1KOh0OvHixYt7jwsRyb/mX7WzsxO1Wi22t7evdbl3Op1YX1+/dhU+mGfz9hybd4Iw5sLy8nJi50lybEjD/Hv+/HlERHzzzTeWRDI1Sc793r5HlUolCoXCVOqAUSQ5769ugPzFF1/c+TNX94M8ODi497gQMV+fd0qlUtRqtdjb24tqtXrjcuDeccIw0mKenmNpIAhjLoybPF9t3ZxlR9g0xoZ5n387Ozv9rhmBAdOU5Nwvl8tRKBQs82Xmkpz345wjn89fe+23ZIxxzMvnnV6nV7FYHNoUv1qt3tghXC6XM7NUjHSbl+dYWgjCmAuff/750P1R33Cubua3traWqrFhnudfrVaLWq0W1WrV1cKYuqTm/sHBQbTb7VheXo5yuXznn8G6Xrx4MfRvNg/nvpJ8zR/3feJqzdO6MhnZMg+fd8rlcj/IvWlvyM3NzWg2m0NBQKfTcaVsUmEenmNp8lHSBUBE9C9Z3NNut0fqPrna5j/OVb+SHBvmdf41Go3Y2dmJarV643IBmFRSc//777+PiBhruUur1Rrqhsnn80Ji7iXJ1/yrv+Uf9UvS1S9FWbmiGNOV9Oeddrvd/+VFoVD44FYPhUIhms3m0Lyv1WpRqVQy0ylDOiX9HEsbHWHMhXF/2zj4IS6Xy421f1GSY8M8zr9WqxWlUikqlYoQjAczj3MfHloaP+9c/fKflf1jmK6kX/MHO8Cu1nLVTVcTPjk5GWtcmJWkn2NpIwhjLhQKhaEPWq9fvx7p5wbflO56U5vHsWHe5l+73Y6NjY3Y29uzfxIPKqm5X6lUotvtjvxn8ANhvV4f+rfB/WVgFEl/3hl0tQtgVFn5ksR0Jf15Z/DL/ihLv67+ItCSYOZd0s+xtBGEMTe2trb6t0fdiHXwuEnW7yc5NszL/Gu327G+vh7b29sjf8Fvt9uuIsbY5mXuwywlOe8Hl7yMujz46hJKF05hXEnO/cElvaOGwOY6aeNz1egEYcyNnZ2d/u1RPpwNHpPP5ydaz5zk2DAP86/T6USpVIqtra17dbmUy2Xzn7HNw9yHWUty3g9+yWm32yPtEzYYGnjOMYkk5/5gJ+M43V06IUkDn6tGJwhjbhQKhaEn311X4xq8xPFd6XWtVov9/f0PvvE95NhwlyTnfsT7EGx9fT3y+Xz/2Lv+NBqN/qacfmPKuJKe+5CEJOd9sVgc+kL/4sWLO+sd/KJkOTCTSHLuP3v2rH/75ORkpBB48FxZCgh4GKNeoOQ2vtNOURfmyOnpaTciuhHRLRQKHzzu/Py8f1yxWLz1nMVisX9sRHTPz89nNjaMKsm5XygUho67z59qtTrJfxsSnfujyOfz/fPU6/WxzwODkpz3zWZz6LjT09MPnvP4+Lh/3Pb29kj/N7hNknN/8Li9vb1bzzk49yuVyp3/L7hLvV4fmqf3nVe+006XIIy5M/gi8aEXiN4X91wud+cXnPt8cZ/22HAfScz9SUIwv0thWpJ83b+LIIyHkuS8r1ar/ePy+fyN5z4/P+/P/9u+TMF9JTX3z8/Pu7lc7s7jTk9P+8dlMSBg+gZfT0cJqW7iO+10+RbDXDo+Pu6/AW1ubnabzWb3/Py8e3x83H/SFgqFkZ60g294EdE9Pj6e2dhwX7Oc+5ubmxOFYLoDmKYkX/dvIwjjIc3L551cLtetVCrdZrPZbTab3Uql0v83r/U8hKTm/vn5eXd7e3uoE6ZarXabzWb3+Pi4u7e3N3bHDgw6Pz/vbm5uXuvkuvqnWCz2nwO38Z12ugRhzLVKpdItFApDH9SKxeK9vowcHx938/l8N5fL3dkGPe2xYVxJzn1IkrlPFiU576vVardYLA6NXSgUunt7e7cum4RpSGruN5vN7vb29tAvO/L5fHdzc7O7t7eX2XCA+eU77XQtdLvdbgAAAADAI+eqkQAAAABkgiAMAAAAgEwQhAEAAACQCYIwAAAAADJBEAYAAABAJgjCAAAAAMgEQRgAAAAAmSAIAwAAACATBGEAAAAAZIIgDAAAAIBMEIQBAAAAkAmCMAAAAAAyQRAGAAAAkBKlUikWFhZiZ2cn6VJSSRAGAAAAPEqlUimWlpZiYWFhan+WlpYS/T81Go2IiCiXyyP/zGN8HMb1UdIFAAAAADyE4+PjiIhotVqxsbERnU5n6N9zuVxsbW3F2tpa/+++//77iIjodDrRbrfj5ORk6Oc+//zzB6/7Q46Ojvq3i8XiyD/32B6HSQjCAAAAgEetUCjE1tZW1Gq1ob9vNpuRz+fv/PlGoxHlcjk6nU4UCoWHKvNOL1++jIiIzc3NsX7+sTwOk7A0EgAAAHj03r17N3Q/n8+PFP5EvO++qlQqERHxxRdfTL22UfU6wp49ezb2OR7D4zAJQRgAAADw6LVaraH791laGPHbpYBJdUL19gaLGL8jLCL9j8OkBGEAAADAo9bb52rQfTab78nlciN3T01bvV6PiPsHV4Mew+MwKUEYAAAA8KgNdlP13DdQ6m0on5RXr15FxHjBVc9jeBwmJQgDAAAAHrXeVRN7xumqyufzUa1Wp1XSvbRarf4VGycJodL+OEyDIAwAAAB41HrdVD2lUunW429aQpik3tUiC4VC5HK5sc+T9sdhGgRhAAAAwKPVbrf73VQ9d202v7+/f+MywqT0rha5s7Mz9jkew+MwDR8lXQAAAADAQ+mFSD13bfTebrejVqvF+fn5Q5c2kna73e/KmmRZZNofh2nREQYAAAA8Wlf3xborTCqXyxMvQZymXoA1aU1pfxymRUcYAAAA8GhdXdrX2xert0zw3bt30el0otFoRLVajXa7HXt7e7Mu84N6+4M9e/ZsovOk/XGYloVut9tNuggAAACAaWs0GnduCH+TZrMZhULhASq6n06nE0tLSxERcXp6eutSxtuk/XGYJh1hAAAAwKN0dTlgPp+PnZ2d+P7774c6odrtdrRarYh4v3fWvIQ/tVotIt7XPW4IFpH+x2GaBGEAAADAo3R1g/idnZ0PLvdrtVqxvr4exWJxFqWNpLcs8q6rO95llo/D4JUmO51O5PP52N/fn5vH1Wb5AAAAwKPT6XT6V1vsuS2M6XU/TboX17R0Op1+d9YkNc3qcWi1WrG2thZHR0dRr9ej2WzG6elpFAqFKJVKsb+/f//iH4COMAAAAODRefXq1dD9u5b69ZYIzkvnUq/+SZcozuJx6HQ6sbGxEZ1O59peZpVKJVqtVhwcHMSnn36a+Ab8OsIAAACAR+fqvlh3BTvtdjsKhULkcrkHrGp09Xo9IiK2trYmOs8sHofnz59Hp9OJzc3NG/cyq1QqEfF+2eTV7rRZE4QBAAAAj05vn6qeu66aWCgUotlsPmRJ99Krv1wuT+U8PdN+HDqdTn8Psg8tpxwM1nqhWFIEYQAAAMCj0mq1+kv8eibtrJqlwc3tJ1mqOYvH4cWLF/3bty257P0/elfCTIogDAAAAHhUrnZB5fP5iZY8Xr3q4kOb1tUiZ/E4DI5x07LIni+++OKDdc2SIAwAAAB4VHpBUs8kXVW1Wu2DVzzsdDqxs7MTS0tLsba2FgcHB7eea39/P9bX1/tXg/yQu5YajmoWj0Pv/3JbCBYRQwHcXf//h+SqkQAAAMCj0el0rgUt4+6z1Wq1Ymdn58Z9rVqtVv9Kib1x9/f34/vvv7/x+EajEQcHB5HP529dQjjYLTVJR9gsHofBZZd3dZoNBmWnp6dj1TENOsIAAACAR+OmZXeff/75vc/TC7oiIra3t4f+rdPpRLlcji+//DKOj4+jXq/3g6CDg4Nr+3JF/HaT+Ls2i+9dLXKS7q2I2TwOg1eAXF5eHvmc7969u3cd06IjDAAAAHg0ri4HHGdfrKOjo373VLFYvPbzL168iEqlMtSxVSgUYm1tLSIiXr16NRQatVqtaDQakc/n7+zyevXqVURMfrXIWTwO9zEYlN0UFM6KjjAAAADgUeh0Otc2dB+1+6jT6UStVov19fWhEGpnZ+fasZ9++um1QGsw5Go2m0P/1ruyYrVavbWGwas8TnJ1x1k9DvcJxgbHnyRQm5SOMAAAACD1essVb/r7paWl2Nra6ndsRUR8//330el0ot1ux8nJyY1dSrlc7sYOrr29vRtrePbsWRwdHQ0tGWy323F0dBTFYvHO5Y69Lq5CoTB2WDTLx2Gwy+s+yx3vs4xy2gRhAAAAQKqtra0NhU9X9bqc7uu+XVm9TfAHa+l1UvX2/rpNr4vrpu6rUcz6cRgM6+5a7jj474NB3KwJwgAAAIBUS/IqhIN6V0bsdUc1Go1oNBpRrVbv7PBqt9v9EGvcZZFJPA7FYjEajcadHWGDAd2kFwKYhD3CAAAAAKYkn8/3u592dnaiWCxeu9riTXrdYJMsi0xCqVSKiPcdX7d1hb1+/bp/u9c5lwRBGAAAAMCU5HK5yOVycXBwEO12+84N8nt6+4M9e/bsIcubusGQ7+Tk5IPHtVqta8cnQRAGAAAAMCXLy8vR6XRif38/qtVqf7nkbTqdTj8oumlT+nk2uJH+h0K/wWWf+/v7M6vtJoIwAAAAgCkbdUlkRPQ3sM/n8yMFZ/Pm66+/joi4dsXMnl74ValUEv//CcIAAAAApqS3afwoV4ns6S2LTFs3WE8ul4vT09PI5/NRKpWGwrCDg4M4OjqKvb292NvbS7DK91w1EgAAAGCKKpXKyBveDy6LTNv+YIPy+Xycnp7G/v5+lEql/v8/n89Hs9lMdIP8QQvdbrebdBEAAAAAaXd0dBTlcjnOz8/vdeXH3tUW03S1yLQShAEAAABMqN1ux9raWhQKhWg2m0mXwwfYIwwAAABgQjs7OxHxfpN85pcgDAAAAGACR0dH0Wg0IiLd+3xlgaWRAAAAABNYW1uLdrvd3zCe+aUjDAAAAGBM7XY72u12RETs7+8nXA13+SjpAgAAAADSqtPpRC6Xi62trdje3k66HO5gaSQAAAAAmWBpJAAAAACZIAgDAAAAIBMEYQAAAABkgiAMAAAAgEwQhAEAAACQCYIwAAAAADJBEAYAAABAJgjCAAAAAMgEQRgAAAAAmSAIAwAAACATBGEAAAAAZIIgDAAAAIBMEIQBAAAAkAmCMAAAAAAyQRAGAAAAQCYIwgAAAADIBEEYAAAAAJkgCAMAAAAgEwRhAAAAAGSCIAwAAACATBCEAQAAAJAJgjAAAAAAMkEQBgAAAEAmCMIAAAAAyARBGAAAAACZIAgDAAAAIBMEYQAAAABkgiAMAAAAgEwQhAEAAACQCf8/n0TuruCgD5MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 50\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_lost),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#F05342\",\n",
    "    label=\"lost\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "# #2A9D8F another teal color\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_found),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#107E7D\",\n",
    "    label=\"found\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "plt.legend(loc=\"upper center\")\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/emitted_energy_beginVelo2endT.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 50\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_magnet_lost),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#F05342\",\n",
    "    label=\"lost\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "# #2A9D8F another teal color\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_magnet_found),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#107E7D\",\n",
    "    label=\"found\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "\n",
    "plt.xlabel(r\"$E_\\gamma/E_{VELO}$\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "# plt.title(r'$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
    "plt.legend(loc=\"best\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/emitted_energy_endVelo2endT.pdf\",\n",
    "#     format=\"PDF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 50\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_velo_lost),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#F05342\",\n",
    "    label=\"lost\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "# #2A9D8F another teal color\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_velo_found),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#107E7D\",\n",
    "    label=\"found\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "# plt.ylabel(\"a.u.\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "\n",
    "# plt.title(r'$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
    "plt.legend(loc=\"best\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/emitted_energy_beginVelo2endVelo.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "### --- ### above should be correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 50\n",
    "plt.hist(\n",
    "    ak.to_numpy(lost[\"eta\"]),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#F05342\",\n",
    "    label=\"lost\",\n",
    "    range=[2, 5],\n",
    ")\n",
    "# #2A9D8F another teal color\n",
    "plt.hist(\n",
    "    ak.to_numpy(found[\"eta\"]),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#107E7D\",\n",
    "    label=\"found\",\n",
    "    range=[2, 5],\n",
    ")\n",
    "# plt.xlim(2, 5)\n",
    "plt.xlabel(r\"$\\eta$\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "plt.legend(loc=\"best\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "# plt.show()\n",
    "plt.savefig(\n",
    "    \"/work/cetin/Projektpraktikum/thesis/eta_found_lost.pdf\",\n",
    "    format=\"PDF\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 50\n",
    "plt.hist(\n",
    "    ak.to_numpy(lost[\"phi\"]),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#F05342\",\n",
    "    label=\"lost\",\n",
    "    range=[-3.142, 3.142],\n",
    ")\n",
    "# #2A9D8F another teal color\n",
    "plt.hist(\n",
    "    ak.to_numpy(found[\"phi\"]),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#107E7D\",\n",
    "    label=\"found\",\n",
    "    range=[-3.142, 3.142],\n",
    ")\n",
    "# plt.xlim(2, 5)\n",
    "plt.xlabel(r\"$\\phi$ [rad]\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "plt.legend(loc=\"best\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/phi_found_lost.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24996287312509283"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# magnet kick position\n",
    "input_tree = uproot.open(\n",
    "    {\n",
    "        \"/work/cetin/Projektpraktikum/tracking_losses_ntuple_B_zmag.root\": \"PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
    "    }\n",
    ")\n",
    "array = input_tree.arrays()\n",
    "array = array[(array.isElectron) & (array.fromB)]\n",
    "stretch_factor = ak.num(array[array.lost], axis=0) / ak.num(array[~array.lost], axis=0)\n",
    "stretch_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xmin: float = 5200\n",
    "xmax: float = 5600\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.hist(\n",
    "    array[array.lost][\"match_zmag\"],\n",
    "    bins=80,\n",
    "    range=[xmin, xmax],\n",
    "    color=\"#F05342\",\n",
    "    alpha=0.5,\n",
    "    label=\"lost\",\n",
    "    density=True,\n",
    ")\n",
    "plt.hist(\n",
    "    array[~array.lost][\"match_zmag\"],\n",
    "    bins=80,\n",
    "    range=[xmin, xmax],\n",
    "    color=\"#107E7D\",\n",
    "    alpha=0.5,\n",
    "    label=\"found\",\n",
    "    density=True,\n",
    ")\n",
    "plt.xlabel(r\"z$_{Mag}$ [mm]\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "plt.legend(loc=\"best\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "# plt.show()\n",
    "plt.savefig(\"/work/cetin/Projektpraktikum/thesis/match_zmag_lost_found.pdf\",\n",
    "            format=\"PDF\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "brem vtx 2d hists in trackinglosses_energy notebook\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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