{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "file not found\n\n    'tracking_losses_ntuple_Dst0ToD0EE.root'\n\nFiles may be specified as:\n   * str/bytes: relative or absolute filesystem path or URL, without any colons\n         other than Windows drive letter or URL schema.\n         Examples: \"rel/file.root\", \"C:\\abs\\file.root\", \"http://where/what.root\"\n   * str/bytes: same with an object-within-ROOT path, separated by a colon.\n         Example: \"rel/file.root:tdirectory/ttree\"\n   * pathlib.Path: always interpreted as a filesystem path or URL only (no\n         object-within-ROOT path), regardless of whether there are any colons.\n         Examples: Path(\"rel:/file.root\"), Path(\"/abs/path:stuff.root\")\n\nFunctions that accept many files (uproot.iterate, etc.) also allow:\n   * glob syntax in str/bytes and pathlib.Path.\n         Examples: Path(\"rel/*.root\"), \"/abs/*.root:tdirectory/ttree\"\n   * dict: keys are filesystem paths, values are objects-within-ROOT paths.\n         Example: {\"/data_v1/*.root\": \"ttree_v1\", \"/data_v2/*.root\": \"ttree_v2\"}\n   * already-open TTree objects.\n   * iterables of the above.\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "File \u001b[0;32m/work/cetin/software/miniconda3/envs/env1/lib/python3.11/site-packages/uproot/source/file.py:112\u001b[0m, in \u001b[0;36mMemmapSource._open\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 112\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_file \u001b[39m=\u001b[39m numpy\u001b[39m.\u001b[39;49mmemmap(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_file_path, dtype\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dtype, mode\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mr\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m    113\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fallback \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[0;32m/work/cetin/software/miniconda3/envs/env1/lib/python3.11/site-packages/numpy/core/memmap.py:229\u001b[0m, in \u001b[0;36mmemmap.__new__\u001b[0;34m(subtype, filename, dtype, mode, offset, shape, order)\u001b[0m\n\u001b[1;32m    228\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 229\u001b[0m     f_ctx \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(os_fspath(filename), (\u001b[39m'\u001b[39;49m\u001b[39mr\u001b[39;49m\u001b[39m'\u001b[39;49m \u001b[39mif\u001b[39;49;00m mode \u001b[39m==\u001b[39;49m \u001b[39m'\u001b[39;49m\u001b[39mc\u001b[39;49m\u001b[39m'\u001b[39;49m \u001b[39melse\u001b[39;49;00m mode)\u001b[39m+\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mb\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m    231\u001b[0m \u001b[39mwith\u001b[39;00m f_ctx \u001b[39mas\u001b[39;00m fid:\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'tracking_losses_ntuple_Dst0ToD0EE.root'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "File \u001b[0;32m/work/cetin/software/miniconda3/envs/env1/lib/python3.11/site-packages/uproot/source/file.py:36\u001b[0m, in \u001b[0;36mFileResource.__init__\u001b[0;34m(self, file_path)\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 36\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_file \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_file_path, \u001b[39m\"\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m     37\u001b[0m \u001b[39mexcept\u001b[39;00m uproot\u001b[39m.\u001b[39m_util\u001b[39m.\u001b[39m_FileNotFoundError \u001b[39mas\u001b[39;00m err:\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'tracking_losses_ntuple_Dst0ToD0EE.root'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/work/cetin/Projektpraktikum/wetest.ipynb Cell 2\u001b[0m line \u001b[0;36m2\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Blhcba1.physi.uni-heidelberg.de/work/cetin/Projektpraktikum/wetest.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m#file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Blhcba1.physi.uni-heidelberg.de/work/cetin/Projektpraktikum/wetest.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m file \u001b[39m=\u001b[39m uproot\u001b[39m.\u001b[39;49mopen(\u001b[39m\"\u001b[39;49m\u001b[39mtracking_losses_ntuple_Dst0ToD0EE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Blhcba1.physi.uni-heidelberg.de/work/cetin/Projektpraktikum/wetest.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39m#file.keys()\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Blhcba1.physi.uni-heidelberg.de/work/cetin/Projektpraktikum/wetest.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39m#file.show()\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Blhcba1.physi.uni-heidelberg.de/work/cetin/Projektpraktikum/wetest.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Blhcba1.physi.uni-heidelberg.de/work/cetin/Projektpraktikum/wetest.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39mvertices = file.arrays([\"all_endvtx_x\", \"all_endvtx_y\", \"all_endvtx_z\"])\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Blhcba1.physi.uni-heidelberg.de/work/cetin/Projektpraktikum/wetest.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=7'>8</a>\u001b[0m \u001b[39mvt_length = file.arrays([\"all_endvtx_x_length\", \"all_endvtx_y_length\", \"all_endvtx_z_length\"])\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Blhcba1.physi.uni-heidelberg.de/work/cetin/Projektpraktikum/wetest.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=17'>18</a>\u001b[0m \u001b[39mfromPairProd = file[\"fromPairProd\"].array()\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Blhcba1.physi.uni-heidelberg.de/work/cetin/Projektpraktikum/wetest.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=18'>19</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n",
      "File \u001b[0;32m/work/cetin/software/miniconda3/envs/env1/lib/python3.11/site-packages/uproot/reading.py:142\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(path, object_cache, array_cache, custom_classes, decompression_executor, interpretation_executor, **options)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m uproot\u001b[39m.\u001b[39m_util\u001b[39m.\u001b[39misstr(file_path) \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m (\n\u001b[1;32m    134\u001b[0m     \u001b[39mhasattr\u001b[39m(file_path, \u001b[39m\"\u001b[39m\u001b[39mread\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39mhasattr\u001b[39m(file_path, \u001b[39m\"\u001b[39m\u001b[39mseek\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    135\u001b[0m ):\n\u001b[1;32m    136\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    137\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39mpath\u001b[39m\u001b[39m'\u001b[39m\u001b[39m must be a string, pathlib.Path, an object with \u001b[39m\u001b[39m'\u001b[39m\u001b[39mread\u001b[39m\u001b[39m'\u001b[39m\u001b[39m and \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    138\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39mseek\u001b[39m\u001b[39m'\u001b[39m\u001b[39m methods, or a length-1 dict of \u001b[39m\u001b[39m{\u001b[39m\u001b[39mfile_path: object_path}, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    139\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mnot \u001b[39m\u001b[39m{\u001b[39;00mpath\u001b[39m!r}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m    140\u001b[0m     )\n\u001b[0;32m--> 142\u001b[0m file \u001b[39m=\u001b[39m ReadOnlyFile(\n\u001b[1;32m    143\u001b[0m     file_path,\n\u001b[1;32m    144\u001b[0m     object_cache\u001b[39m=\u001b[39;49mobject_cache,\n\u001b[1;32m    145\u001b[0m     array_cache\u001b[39m=\u001b[39;49marray_cache,\n\u001b[1;32m    146\u001b[0m     custom_classes\u001b[39m=\u001b[39;49mcustom_classes,\n\u001b[1;32m    147\u001b[0m     decompression_executor\u001b[39m=\u001b[39;49mdecompression_executor,\n\u001b[1;32m    148\u001b[0m     interpretation_executor\u001b[39m=\u001b[39;49minterpretation_executor,\n\u001b[1;32m    149\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49moptions,  \u001b[39m# NOTE: a comma after **options breaks Python 2\u001b[39;49;00m\n\u001b[1;32m    150\u001b[0m )\n\u001b[1;32m    152\u001b[0m \u001b[39mif\u001b[39;00m object_path \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    153\u001b[0m     \u001b[39mreturn\u001b[39;00m file\u001b[39m.\u001b[39mroot_directory\n",
      "File \u001b[0;32m/work/cetin/software/miniconda3/envs/env1/lib/python3.11/site-packages/uproot/reading.py:582\u001b[0m, in \u001b[0;36mReadOnlyFile.__init__\u001b[0;34m(self, file_path, object_cache, array_cache, custom_classes, decompression_executor, interpretation_executor, **options)\u001b[0m\n\u001b[1;32m    577\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhook_before_create_source()\n\u001b[1;32m    579\u001b[0m Source, file_path \u001b[39m=\u001b[39m uproot\u001b[39m.\u001b[39m_util\u001b[39m.\u001b[39mfile_path_to_source_class(\n\u001b[1;32m    580\u001b[0m     file_path, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_options\n\u001b[1;32m    581\u001b[0m )\n\u001b[0;32m--> 582\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_source \u001b[39m=\u001b[39m Source(\n\u001b[1;32m    583\u001b[0m     file_path, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_options  \u001b[39m# NOTE: a comma after **options breaks Python 2\u001b[39;49;00m\n\u001b[1;32m    584\u001b[0m )\n\u001b[1;32m    586\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhook_before_get_chunks()\n\u001b[1;32m    588\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_options[\u001b[39m\"\u001b[39m\u001b[39mbegin_chunk_size\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m<\u001b[39m _file_header_fields_big\u001b[39m.\u001b[39msize:\n",
      "File \u001b[0;32m/work/cetin/software/miniconda3/envs/env1/lib/python3.11/site-packages/uproot/source/file.py:108\u001b[0m, in \u001b[0;36mMemmapSource.__init__\u001b[0;34m(self, file_path, **options)\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_requested_bytes \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m    107\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_file_path \u001b[39m=\u001b[39m file_path\n\u001b[0;32m--> 108\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_open()\n",
      "File \u001b[0;32m/work/cetin/software/miniconda3/envs/env1/lib/python3.11/site-packages/uproot/source/file.py:118\u001b[0m, in \u001b[0;36mMemmapSource._open\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    116\u001b[0m opts \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fallback_opts)\n\u001b[1;32m    117\u001b[0m opts[\u001b[39m\"\u001b[39m\u001b[39mnum_workers\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_fallback_workers\n\u001b[0;32m--> 118\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fallback \u001b[39m=\u001b[39m uproot\u001b[39m.\u001b[39;49msource\u001b[39m.\u001b[39;49mfile\u001b[39m.\u001b[39;49mMultithreadedFileSource(\n\u001b[1;32m    119\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_file_path, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mopts  \u001b[39m# NOTE: a comma after **opts breaks Python 2\u001b[39;49;00m\n\u001b[1;32m    120\u001b[0m )\n",
      "File \u001b[0;32m/work/cetin/software/miniconda3/envs/env1/lib/python3.11/site-packages/uproot/source/file.py:248\u001b[0m, in \u001b[0;36mMultithreadedFileSource.__init__\u001b[0;34m(self, file_path, **options)\u001b[0m\n\u001b[1;32m    245\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_requested_bytes \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m    247\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_file_path \u001b[39m=\u001b[39m file_path\n\u001b[0;32m--> 248\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_open()\n",
      "File \u001b[0;32m/work/cetin/software/miniconda3/envs/env1/lib/python3.11/site-packages/uproot/source/file.py:252\u001b[0m, in \u001b[0;36mMultithreadedFileSource._open\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_open\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    251\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_executor \u001b[39m=\u001b[39m uproot\u001b[39m.\u001b[39msource\u001b[39m.\u001b[39mfutures\u001b[39m.\u001b[39mResourceThreadPoolExecutor(\n\u001b[0;32m--> 252\u001b[0m         [FileResource(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_file_path) \u001b[39mfor\u001b[39;49;00m x \u001b[39min\u001b[39;49;00m \u001b[39mrange\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_num_workers)]\n\u001b[1;32m    253\u001b[0m     )\n\u001b[1;32m    254\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_bytes \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mgetsize(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_file_path)\n",
      "File \u001b[0;32m/work/cetin/software/miniconda3/envs/env1/lib/python3.11/site-packages/uproot/source/file.py:252\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_open\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    251\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_executor \u001b[39m=\u001b[39m uproot\u001b[39m.\u001b[39msource\u001b[39m.\u001b[39mfutures\u001b[39m.\u001b[39mResourceThreadPoolExecutor(\n\u001b[0;32m--> 252\u001b[0m         [FileResource(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_file_path) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_workers)]\n\u001b[1;32m    253\u001b[0m     )\n\u001b[1;32m    254\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_bytes \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mgetsize(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_file_path)\n",
      "File \u001b[0;32m/work/cetin/software/miniconda3/envs/env1/lib/python3.11/site-packages/uproot/source/file.py:38\u001b[0m, in \u001b[0;36mFileResource.__init__\u001b[0;34m(self, file_path)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_file \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_file_path, \u001b[39m\"\u001b[39m\u001b[39mrb\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m     37\u001b[0m \u001b[39mexcept\u001b[39;00m uproot\u001b[39m.\u001b[39m_util\u001b[39m.\u001b[39m_FileNotFoundError \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m---> 38\u001b[0m     \u001b[39mraise\u001b[39;00m uproot\u001b[39m.\u001b[39m_util\u001b[39m.\u001b[39m_file_not_found(file_path) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: file not found\n\n    'tracking_losses_ntuple_Dst0ToD0EE.root'\n\nFiles may be specified as:\n   * str/bytes: relative or absolute filesystem path or URL, without any colons\n         other than Windows drive letter or URL schema.\n         Examples: \"rel/file.root\", \"C:\\abs\\file.root\", \"http://where/what.root\"\n   * str/bytes: same with an object-within-ROOT path, separated by a colon.\n         Example: \"rel/file.root:tdirectory/ttree\"\n   * pathlib.Path: always interpreted as a filesystem path or URL only (no\n         object-within-ROOT path), regardless of whether there are any colons.\n         Examples: Path(\"rel:/file.root\"), Path(\"/abs/path:stuff.root\")\n\nFunctions that accept many files (uproot.iterate, etc.) also allow:\n   * glob syntax in str/bytes and pathlib.Path.\n         Examples: Path(\"rel/*.root\"), \"/abs/*.root:tdirectory/ttree\"\n   * dict: keys are filesystem paths, values are objects-within-ROOT paths.\n         Example: {\"/data_v1/*.root\": \"ttree_v1\", \"/data_v2/*.root\": \"ttree_v2\"}\n   * already-open TTree objects.\n   * iterables of the above.\n"
     ]
    }
   ],
   "source": [
    "#file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "file = uproot.open(\"tracking_losses_ntuple_Dst0ToD0EE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "\n",
    "#file.keys()\n",
    "#file.show()\n",
    "\"\"\"\n",
    "vertices = file.arrays([\"all_endvtx_x\", \"all_endvtx_y\", \"all_endvtx_z\"])\n",
    "vt_length = file.arrays([\"all_endvtx_x_length\", \"all_endvtx_y_length\", \"all_endvtx_z_length\"])\n",
    "vert_len = vt_length[\"all_endvtx_x_length\"]\n",
    "\n",
    "vtx = vertices[\"all_endvtx_x\"]\n",
    "vty = vertices[\"all_endvtx_y\"]\n",
    "vtz = vertices[\"all_endvtx_z\"]\n",
    "\n",
    "isElectron = file[\"isElectron\"].array()\n",
    "lost = file[\"lost_in_track_fit\"].array()\n",
    "\n",
    "fromPairProd = file[\"fromPairProd\"].array()\n",
    "\"\"\"\n",
    "\n",
    "#vt_length[\"all_endvtx_y_length\"]\n",
    "#vertices\n",
    "\n",
    "#array[array.isElectron]\n",
    "allcolumns = file.arrays()\n",
    "tracked = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost)]\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost)]\n",
    "\n",
    "\n",
    "#~ := logical not \n",
    "\n",
    "#allc_isE= allcolumns[(~allcolumns.isElectron) & (bool 2)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#vtx[particle][index of vertex]\n",
    "\n",
    "vtx_x = ak.ravel(allcolumns.all_endvtx_x)\n",
    "vtx_y = ak.ravel(allcolumns.all_endvtx_y)\n",
    "vtx_z = ak.ravel(allcolumns.all_endvtx_z)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "\n",
    "ax.scatter(vtx_x, vtx_y, vtx_z, marker=\".\", s=1)\n",
    "\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "ax.view_init(15, 35)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create an array with all electron indices\n",
    "electron = allcolumns[(allcolumns.isElectron)]\n",
    "#electron.show()\n",
    "#electron_ind = electron[electron.]\n",
    "#electron_ind = electron_ind.to_numpy()\n",
    "e_vtx_x = ak.ravel(electron.all_endvtx_x)\n",
    "e_vtx_y = ak.ravel(electron.all_endvtx_y)\n",
    "e_vtx_z = ak.ravel(electron.all_endvtx_z)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "\n",
    "ax.scatter(e_vtx_x, e_vtx_y, e_vtx_z, marker=\".\", s=1)\n",
    "\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "ax.view_init(15, 35)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x2000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1, ax2), (ax3, ax4, ax5)) = plt.subplots(nrows=2, ncols=3, figsize=(40,20))\n",
    "\n",
    "colors = ['blue', 'darkorange']\n",
    "labels = [\"tracked\", \"lost\"]\n",
    "\n",
    "ax0.hist([ak.ravel(tracked[\"energy\"]),ak.ravel(lost[\"energy\"])], 1000, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax0.legend()\n",
    "ax0.set_xlim(0,40000)\n",
    "ax0.set_title('energy')\n",
    "\n",
    "ax1.hist(tracked[\"eta\"], bins=100, label=\"tracked\", edgecolor=\"blue\", fill=False, density=True)\n",
    "ax1.hist(lost[\"eta\"], bins=100, label=\"lost\", edgecolor=\"orange\", fill=False, density=True)\n",
    "ax1.legend()\n",
    "ax1.set_title('eta')\n",
    "\n",
    "ax2.hist([ak.ravel(tracked[\"p\"]),ak.ravel(lost[\"p\"])], 500, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax2.legend()\n",
    "ax2.set_xlim(0,50000)\n",
    "ax2.set_title('p')\n",
    "\n",
    "ax3.hist([ak.ravel(tracked[\"pt\"]),ak.ravel(lost[\"pt\"])], 500, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax3.legend()\n",
    "ax3.set_xlim(0,3000)\n",
    "ax3.set_title('pt')\n",
    "\n",
    "ax4.hist([ak.ravel(tracked[\"tx\"]),ak.ravel(lost[\"tx\"])], 100, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax4.legend()\n",
    "ax4.set_title('brem vtx y')\n",
    "\n",
    "ax5.hist([ak.ravel(tracked[\"ty\"]),ak.ravel(lost[\"ty\"])], 100, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax5.legend()\n",
    "ax5.set_title('ty')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 4500x3000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1, ax2), (ax3, ax4, ax5), (ax6, ax7, ax8)) = plt.subplots(nrows=3, ncols=3, figsize=(45,30))\n",
    "\n",
    "colors = ['blue', 'darkorange']\n",
    "labels = [\"tracked\", \"lost\"]\n",
    "n_bins=75\n",
    "\n",
    "ax0.hist([ak.ravel(tracked[\"velo_hit_pos_x\"]),ak.ravel(lost[\"velo_hit_pos_x\"])], n_bins, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax0.legend()\n",
    "ax0.set_title('velo x')\n",
    "\n",
    "ax1.hist([ak.ravel(tracked[\"velo_hit_pos_y\"]),ak.ravel(lost[\"velo_hit_pos_y\"])], n_bins, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax1.legend()\n",
    "ax1.set_title('velo y')\n",
    "\n",
    "ax2.hist([ak.ravel(tracked[\"velo_hit_pos_z\"]),ak.ravel(lost[\"velo_hit_pos_z\"])], n_bins, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax2.legend()\n",
    "ax2.set_title('velo z')\n",
    "\n",
    "ax3.hist([ak.ravel(tracked[\"ut_hit_pos_x\"]),ak.ravel(lost[\"ut_hit_pos_x\"])], n_bins, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax3.legend()\n",
    "ax3.set_title('ut x')\n",
    "\n",
    "ax4.hist([ak.ravel(tracked[\"ut_hit_pos_y\"]),ak.ravel(lost[\"ut_hit_pos_y\"])], n_bins, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax4.legend()\n",
    "ax4.set_title('ut y')\n",
    "\n",
    "ax5.hist([ak.ravel(tracked[\"ut_hit_pos_z\"]),ak.ravel(lost[\"ut_hit_pos_z\"])], n_bins, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax5.legend()\n",
    "ax5.set_title('ut z')\n",
    "\n",
    "ax6.hist([ak.ravel(tracked[\"scifi_hit_pos_x\"]),ak.ravel(lost[\"scifi_hit_pos_x\"])], n_bins, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax6.legend()\n",
    "ax6.set_title('scifi x')\n",
    "\n",
    "ax7.hist([ak.ravel(tracked[\"scifi_hit_pos_y\"]),ak.ravel(lost[\"scifi_hit_pos_y\"])], n_bins, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax7.legend()\n",
    "ax7.set_title('scifi y')\n",
    "\n",
    "ax8.hist([ak.ravel(tracked[\"scifi_hit_pos_z\"]),ak.ravel(lost[\"scifi_hit_pos_z\"])], n_bins, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax8.legend()\n",
    "ax8.set_title('scifi z')\n",
    "\n",
    "\n",
    "#fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.hist([ak.ravel(tracked[\"brem_photons_pe\"]), ak.ravel(lost[\"brem_photons_pe\"])], 5000, density=True, histtype=\"bar\", color=colors, label=labels)\n",
    "plt.xlim(0,8000)\n",
    "plt.title(\"brem photons abs(p)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 4000x2000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1, ax2), (ax3, ax4, ax5)) = plt.subplots(nrows=2, ncols=3, figsize=(40,20))\n",
    "\n",
    "colors = ['blue', 'darkorange']\n",
    "labels = [\"tracked\", \"lost\"]\n",
    "n_bins=200\n",
    "\n",
    "ax0.hist([ak.ravel(tracked[\"brem_photons_px\"]),ak.ravel(lost[\"brem_photons_px\"])], 500, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax0.legend()\n",
    "ax0.set_xlim(-1500,1500)\n",
    "ax0.set_title('brem photon px')\n",
    "\n",
    "ax1.hist([ak.ravel(tracked[\"brem_photons_py\"]),ak.ravel(lost[\"brem_photons_py\"])], 1000, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax1.legend()\n",
    "ax1.set_xlim(-500,500)\n",
    "ax1.set_title('brem photon py')\n",
    "\n",
    "ax2.hist([ak.ravel(tracked[\"brem_photons_pz\"]),ak.ravel(lost[\"brem_photons_pz\"])], 1000, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax2.legend()\n",
    "ax2.set_xlim(0,10000)\n",
    "ax2.set_title('brem photon pz')\n",
    "\n",
    "ax3.hist([ak.ravel(tracked[\"brem_vtx_x\"]),ak.ravel(lost[\"brem_vtx_x\"])], n_bins, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax3.legend()\n",
    "ax3.set_title('brem vtx x')\n",
    "\n",
    "ax4.hist([ak.ravel(tracked[\"brem_vtx_y\"]),ak.ravel(lost[\"brem_vtx_y\"])], n_bins, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax4.legend()\n",
    "ax4.set_title('brem vtx y')\n",
    "\n",
    "ax5.hist([ak.ravel(tracked[\"brem_vtx_z\"]),ak.ravel(lost[\"brem_vtx_z\"])], n_bins, density=True, histtype='bar', color=colors, label=labels)\n",
    "ax5.legend()\n",
    "ax5.set_title('brem vtx z')\n",
    "\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": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'velo x')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['blue', 'darkorange']\n",
    "labels = [\"tracked\", \"lost\"]\n",
    "n_bins=50\n",
    "\n",
    "plt.hist([ak.ravel(tracked[\"velo_hit_pos_x\"]),ak.ravel(lost[\"velo_hit_pos_x\"])], n_bins, density=True, histtype='bar', color=colors, label=labels)\n",
    "plt.legend()\n",
    "plt.title('velo x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'all_endvtx_types_length': 11,\n",
       " 'all_endvtx_types': [101.0,\n",
       "  101.0,\n",
       "  101.0,\n",
       "  101.0,\n",
       "  101.0,\n",
       "  101.0,\n",
       "  101.0,\n",
       "  101.0,\n",
       "  101.0,\n",
       "  101.0,\n",
       "  0.0],\n",
       " 'all_endvtx_x_length': 11,\n",
       " 'all_endvtx_x': [-5.370299816131592,\n",
       "  -6.995699882507324,\n",
       "  -20.013700485229492,\n",
       "  -26.470500946044922,\n",
       "  -82.10430145263672,\n",
       "  -146.57949829101562,\n",
       "  -894.7764282226562,\n",
       "  -1237.57470703125,\n",
       "  -1244.2630615234375,\n",
       "  -1245.8055419921875,\n",
       "  -1321.20361328125],\n",
       " 'all_endvtx_y_length': 11,\n",
       " 'all_endvtx_y': [-7.805699825286865,\n",
       "  -10.154500007629395,\n",
       "  -28.963199615478516,\n",
       "  -38.28559875488281,\n",
       "  -118.39790344238281,\n",
       "  -210.52830505371094,\n",
       "  -888.7171020507812,\n",
       "  -1131.568359375,\n",
       "  -1136.3031005859375,\n",
       "  -1137.3917236328125,\n",
       "  -1181.4761962890625],\n",
       " 'all_endvtx_z_length': 11,\n",
       " 'all_endvtx_z': [161.9427032470703,\n",
       "  186.9705047607422,\n",
       "  387.3406982421875,\n",
       "  486.6791076660156,\n",
       "  1340.39501953125,\n",
       "  2322.552490234375,\n",
       "  9494.216796875,\n",
       "  12068.0263671875,\n",
       "  12118.072265625,\n",
       "  12129.564453125,\n",
       "  12655.4697265625],\n",
       " 'brem_photons_pe_length': 10,\n",
       " 'brem_photons_pe': [3264.454345703125,\n",
       "  4453.86376953125,\n",
       "  178.029052734375,\n",
       "  14471.001953125,\n",
       "  1095.5640869140625,\n",
       "  3793.752685546875,\n",
       "  357.2669982910156,\n",
       "  825.275634765625,\n",
       "  8990.57421875,\n",
       "  3479.052490234375],\n",
       " 'brem_photons_px_length': 10,\n",
       " 'brem_photons_px': [-210.4199981689453,\n",
       "  -287.5400085449219,\n",
       "  -11.5,\n",
       "  -934.47998046875,\n",
       "  -71.08999633789062,\n",
       "  -248.42999267578125,\n",
       "  -46.75,\n",
       "  -108.80000305175781,\n",
       "  -1188.0799560546875,\n",
       "  -461.8500061035156],\n",
       " 'brem_photons_py_length': 10,\n",
       " 'brem_photons_py': [-304.42999267578125,\n",
       "  -415.2699890136719,\n",
       "  -16.600000381469727,\n",
       "  -1348.9000244140625,\n",
       "  -102.06999969482422,\n",
       "  -354.0199890136719,\n",
       "  -33.2599983215332,\n",
       "  -76.9000015258789,\n",
       "  -840.1199951171875,\n",
       "  -325.6400146484375],\n",
       " 'brem_photons_pz_length': 10,\n",
       " 'brem_photons_pz': [3243.409912109375,\n",
       "  4425.1298828125,\n",
       "  176.8800048828125,\n",
       "  14377.66015625,\n",
       "  1088.47998046875,\n",
       "  3769.02001953125,\n",
       "  352.6300048828125,\n",
       "  814.4500122070312,\n",
       "  8872.0400390625,\n",
       "  3432.85009765625],\n",
       " 'brem_vtx_x_length': 10,\n",
       " 'brem_vtx_x': [-5.370299816131592,\n",
       "  -6.995699882507324,\n",
       "  -20.013700485229492,\n",
       "  -26.470500946044922,\n",
       "  -82.10430145263672,\n",
       "  -146.57949829101562,\n",
       "  -894.7764282226562,\n",
       "  -1237.57470703125,\n",
       "  -1244.2630615234375,\n",
       "  -1245.8055419921875],\n",
       " 'brem_vtx_y_length': 10,\n",
       " 'brem_vtx_y': [-7.805699825286865,\n",
       "  -10.154500007629395,\n",
       "  -28.963199615478516,\n",
       "  -38.28559875488281,\n",
       "  -118.39790344238281,\n",
       "  -210.52830505371094,\n",
       "  -888.7171020507812,\n",
       "  -1131.568359375,\n",
       "  -1136.3031005859375,\n",
       "  -1137.3917236328125],\n",
       " 'brem_vtx_z_length': 10,\n",
       " 'brem_vtx_z': [161.9427032470703,\n",
       "  186.9705047607422,\n",
       "  387.3406982421875,\n",
       "  486.6791076660156,\n",
       "  1340.39501953125,\n",
       "  2322.552490234375,\n",
       "  9494.216796875,\n",
       "  12068.0263671875,\n",
       "  12118.072265625,\n",
       "  12129.564453125],\n",
       " 'endvtx_type': 0,\n",
       " 'endvtx_x': nan,\n",
       " 'endvtx_y': nan,\n",
       " 'endvtx_z': nan,\n",
       " 'energy': 46180.704276008204,\n",
       " 'eta': 2.866545652490217,\n",
       " 'event_count': 0,\n",
       " 'fromB': True,\n",
       " 'fromD': False,\n",
       " 'fromDecay': True,\n",
       " 'fromHadInt': False,\n",
       " 'fromPV': False,\n",
       " 'fromPairProd': False,\n",
       " 'fromSignal': True,\n",
       " 'fromStrange': False,\n",
       " 'isElectron': True,\n",
       " 'isKaon': False,\n",
       " 'isMuon': False,\n",
       " 'isPion': False,\n",
       " 'isProton': False,\n",
       " 'lost': False,\n",
       " 'lost_in_track_fit': False,\n",
       " 'match_fraction': 1.0,\n",
       " 'mcp_idx': 2298,\n",
       " 'mother_id': 511,\n",
       " 'mother_key': 2235,\n",
       " 'originvtx_type': 2,\n",
       " 'originvtx_x': -0.1652,\n",
       " 'originvtx_y': -0.2848,\n",
       " 'originvtx_z': 81.7996,\n",
       " 'p': 46180.70427318103,\n",
       " 'phi': -2.176283395191852,\n",
       " 'pid': -11,\n",
       " 'pt': 5237.958145203148,\n",
       " 'px': -2981.25,\n",
       " 'py': -4306.78,\n",
       " 'pz': 45882.69,\n",
       " 'scifi_hit_pos_x_length': 13,\n",
       " 'scifi_hit_pos_x': [-675.690185546875,\n",
       "  -684.6648559570312,\n",
       "  -693.7130737304688,\n",
       "  -702.7500610351562,\n",
       "  -764.2944946289062,\n",
       "  -773.4475708007812,\n",
       "  -782.661376953125,\n",
       "  -791.83447265625,\n",
       "  -854.4851684570312,\n",
       "  -863.6622314453125,\n",
       "  -863.7481689453125,\n",
       "  -873.00439453125,\n",
       "  -882.2459106445312],\n",
       " 'scifi_hit_pos_y_length': 13,\n",
       " 'scifi_hit_pos_y': [-730.9426879882812,\n",
       "  -737.5319213867188,\n",
       "  -744.1569213867188,\n",
       "  -750.76220703125,\n",
       "  -795.3870849609375,\n",
       "  -801.9813842773438,\n",
       "  -808.6095581054688,\n",
       "  -815.1967163085938,\n",
       "  -860.0216674804688,\n",
       "  -866.5638427734375,\n",
       "  -866.6251831054688,\n",
       "  -873.2205200195312,\n",
       "  -879.8038940429688],\n",
       " 'scifi_hit_pos_z_length': 13,\n",
       " 'scifi_hit_pos_z': [7823.46728515625,\n",
       "  7893.24365234375,\n",
       "  7963.42041015625,\n",
       "  8033.197265625,\n",
       "  8505.2353515625,\n",
       "  8575.01171875,\n",
       "  8645.1884765625,\n",
       "  8714.96484375,\n",
       "  9190.001953125,\n",
       "  9259.3623046875,\n",
       "  9260.01171875,\n",
       "  9329.9560546875,\n",
       "  9399.732421875],\n",
       " 'track_p': 18897.48523925218,\n",
       " 'track_pt': 2145.9785156918188,\n",
       " 'tx': -0.06497548421855824,\n",
       " 'ty': -0.09386502840177853,\n",
       " 'ut_hit_pos_x_length': 4,\n",
       " 'ut_hit_pos_x': [-146.94874572753906,\n",
       "  -150.57948303222656,\n",
       "  -165.50015258789062,\n",
       "  -169.16537475585938],\n",
       " 'ut_hit_pos_y_length': 4,\n",
       " 'ut_hit_pos_y': [-211.05450439453125,\n",
       "  -216.2235870361328,\n",
       "  -237.37039184570312,\n",
       "  -242.54531860351562],\n",
       " 'ut_hit_pos_z_length': 4,\n",
       " 'ut_hit_pos_z': [2328.153564453125,\n",
       "  2383.153564453125,\n",
       "  2608.153564453125,\n",
       "  2663.153564453125],\n",
       " 'velo_hit_pos_x_length': 8,\n",
       " 'velo_hit_pos_x': [-3.7419943809509277,\n",
       "  -5.3643646240234375,\n",
       "  -5.370828151702881,\n",
       "  -6.9883952140808105,\n",
       "  -8.61259651184082,\n",
       "  -10.237198829650879,\n",
       "  -11.94439697265625,\n",
       "  -15.19279670715332],\n",
       " 'velo_hit_pos_y_length': 8,\n",
       " 'velo_hit_pos_y': [-5.452081680297852,\n",
       "  -7.797121047973633,\n",
       "  -7.80646276473999,\n",
       "  -10.1439847946167,\n",
       "  -12.4910888671875,\n",
       "  -14.83889102935791,\n",
       "  -17.30618667602539,\n",
       "  -21.99948501586914],\n",
       " 'velo_hit_pos_z_length': 8,\n",
       " 'velo_hit_pos_z': [136.85899353027344,\n",
       "  161.85086059570312,\n",
       "  161.95083618164062,\n",
       "  186.85899353027344,\n",
       "  211.85899353027344,\n",
       "  236.85899353027344,\n",
       "  263.1409912109375,\n",
       "  313.1409912109375],\n",
       " 'velo_track_idx': 72,\n",
       " 'velo_track_tx': -0.06491152942180634,\n",
       " 'velo_track_ty': -0.09396103769540787,\n",
       " 'velo_track_x': -44.84230041503906,\n",
       " 'velo_track_y': -64.91973876953125,\n",
       " 'velo_track_z': 770.0}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracked[1].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'all_endvtx_types_length': 3,\n",
       " 'all_endvtx_types': [101.0, 101.0, 0.0],\n",
       " 'all_endvtx_x_length': 3,\n",
       " 'all_endvtx_x': [1643.8668212890625, 2936.032470703125, 3191.061767578125],\n",
       " 'all_endvtx_y_length': 3,\n",
       " 'all_endvtx_y': [-922.9655151367188, -1172.96728515625, -1219.758056640625],\n",
       " 'all_endvtx_z_length': 3,\n",
       " 'all_endvtx_z': [9210.24609375, 12095.1630859375, 12668.1044921875],\n",
       " 'brem_photons_pe_length': 2,\n",
       " 'brem_photons_pe': [307.6462097167969, 144.2554931640625],\n",
       " 'brem_photons_px_length': 2,\n",
       " 'brem_photons_px': [123.86000061035156, 58.97999954223633],\n",
       " 'brem_photons_py_length': 2,\n",
       " 'brem_photons_py': [-24.309999465942383, -11.199999809265137],\n",
       " 'brem_photons_pz_length': 2,\n",
       " 'brem_photons_pz': [280.55999755859375, 131.1699981689453],\n",
       " 'brem_vtx_x_length': 2,\n",
       " 'brem_vtx_x': [1643.8668212890625, 2936.032470703125],\n",
       " 'brem_vtx_y_length': 2,\n",
       " 'brem_vtx_y': [-922.9655151367188, -1172.96728515625],\n",
       " 'brem_vtx_z_length': 2,\n",
       " 'brem_vtx_z': [9210.24609375, 12095.1630859375],\n",
       " 'endvtx_type': 0,\n",
       " 'endvtx_x': nan,\n",
       " 'endvtx_y': nan,\n",
       " 'endvtx_z': nan,\n",
       " 'energy': 3157.966926619241,\n",
       " 'eta': 2.996801854154586,\n",
       " 'event_count': 0,\n",
       " 'fromB': False,\n",
       " 'fromD': False,\n",
       " 'fromDecay': False,\n",
       " 'fromHadInt': False,\n",
       " 'fromPV': False,\n",
       " 'fromPairProd': True,\n",
       " 'fromSignal': False,\n",
       " 'fromStrange': False,\n",
       " 'isElectron': True,\n",
       " 'isKaon': False,\n",
       " 'isMuon': False,\n",
       " 'isPion': False,\n",
       " 'isProton': False,\n",
       " 'lost': True,\n",
       " 'lost_in_track_fit': False,\n",
       " 'match_fraction': nan,\n",
       " 'mcp_idx': 2016,\n",
       " 'mother_id': 22,\n",
       " 'mother_key': 2013,\n",
       " 'originvtx_type': 102,\n",
       " 'originvtx_x': -0.6686000000000001,\n",
       " 'originvtx_y': -11.4908,\n",
       " 'originvtx_z': 192.3992,\n",
       " 'p': 3157.9668852760315,\n",
       " 'phi': -1.627965945231371,\n",
       " 'pid': 11,\n",
       " 'pt': 314.67409489819784,\n",
       " 'px': -17.98,\n",
       " 'py': -314.16,\n",
       " 'pz': 3142.25,\n",
       " 'scifi_hit_pos_x_length': 12,\n",
       " 'scifi_hit_pos_x': [1047.31494140625,\n",
       "  1075.65234375,\n",
       "  1104.3212890625,\n",
       "  1132.9744873046875,\n",
       "  1335.25,\n",
       "  1365.5250244140625,\n",
       "  1396.008544921875,\n",
       "  1426.3865966796875,\n",
       "  1634.8421630859375,\n",
       "  1665.6279296875,\n",
       "  1696.6456298828125,\n",
       "  1727.5711669921875],\n",
       " 'scifi_hit_pos_y_length': 12,\n",
       " 'scifi_hit_pos_y': [-799.5249633789062,\n",
       "  -806.191650390625,\n",
       "  -812.8439331054688,\n",
       "  -819.4522094726562,\n",
       "  -861.0640258789062,\n",
       "  -867.190673828125,\n",
       "  -873.3914794921875,\n",
       "  -879.5537109375,\n",
       "  -921.186279296875,\n",
       "  -927.24951171875,\n",
       "  -933.3619384765625,\n",
       "  -939.46533203125],\n",
       " 'scifi_hit_pos_z_length': 12,\n",
       " 'scifi_hit_pos_z': [7823.22021484375,\n",
       "  7892.99658203125,\n",
       "  7963.17333984375,\n",
       "  8032.94970703125,\n",
       "  8504.9990234375,\n",
       "  8574.77734375,\n",
       "  8644.955078125,\n",
       "  8714.7333984375,\n",
       "  9189.7822265625,\n",
       "  9259.560546875,\n",
       "  9329.7392578125,\n",
       "  9399.517578125],\n",
       " 'track_p': nan,\n",
       " 'track_pt': nan,\n",
       " 'tx': -0.005722014480070013,\n",
       " 'ty': -0.09997931418569497,\n",
       " 'ut_hit_pos_x_length': 4,\n",
       " 'ut_hit_pos_x': [-4.739322185516357,\n",
       "  -4.502962589263916,\n",
       "  -3.4144935607910156,\n",
       "  -3.0408475399017334],\n",
       " 'ut_hit_pos_y_length': 4,\n",
       " 'ut_hit_pos_y': [-228.3432159423828,\n",
       "  -235.0196075439453,\n",
       "  -257.5648498535156,\n",
       "  -263.3006591796875],\n",
       " 'ut_hit_pos_z_length': 4,\n",
       " 'ut_hit_pos_z': [2313.153564453125,\n",
       "  2376.846435546875,\n",
       "  2593.153564453125,\n",
       "  2648.153564453125],\n",
       " 'velo_hit_pos_x_length': 6,\n",
       " 'velo_hit_pos_x': [-0.7800574898719788,\n",
       "  -0.9077054858207703,\n",
       "  -1.0252660512924194,\n",
       "  -1.2704850435256958,\n",
       "  -1.606397032737732,\n",
       "  -2.0029914379119873],\n",
       " 'velo_hit_pos_y_length': 6,\n",
       " 'velo_hit_pos_y': [-13.436500549316406,\n",
       "  -15.927563667297363,\n",
       "  -18.418764114379883,\n",
       "  -23.419601440429688,\n",
       "  -31.038658142089844,\n",
       "  -40.98640441894531],\n",
       " 'velo_hit_pos_z_length': 6,\n",
       " 'velo_hit_pos_z': [211.85899353027344,\n",
       "  236.85899353027344,\n",
       "  261.8590087890625,\n",
       "  311.8590087890625,\n",
       "  388.1409912109375,\n",
       "  488.1409912109375],\n",
       " 'velo_track_idx': 48,\n",
       " 'velo_track_tx': -0.004504487384110689,\n",
       " 'velo_track_ty': -0.09966171532869339,\n",
       " 'velo_track_x': -3.2919559478759766,\n",
       " 'velo_track_y': -69.08153533935547,\n",
       " 'velo_track_z': 770.0}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lost[0].tolist()"
   ]
  },
  {
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name                 | typename                 | interpretation                \n",
      "---------------------+--------------------------+-------------------------------\n",
      "all_endvtx_types_... | int32_t                  | AsDtype('>i4')\n",
      "all_endvtx_types     | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "all_endvtx_x_length  | int32_t                  | AsDtype('>i4')\n",
      "all_endvtx_x         | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "all_endvtx_y_length  | int32_t                  | AsDtype('>i4')\n",
      "all_endvtx_y         | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "all_endvtx_z_length  | int32_t                  | AsDtype('>i4')\n",
      "all_endvtx_z         | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "brem_photons_pe_l... | int32_t                  | AsDtype('>i4')\n",
      "brem_photons_pe      | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "brem_photons_px_l... | int32_t                  | AsDtype('>i4')\n",
      "brem_photons_px      | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "brem_photons_py_l... | int32_t                  | AsDtype('>i4')\n",
      "brem_photons_py      | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "brem_photons_pz_l... | int32_t                  | AsDtype('>i4')\n",
      "brem_photons_pz      | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "brem_vtx_x_length    | int32_t                  | AsDtype('>i4')\n",
      "brem_vtx_x           | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "brem_vtx_y_length    | int32_t                  | AsDtype('>i4')\n",
      "brem_vtx_y           | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "brem_vtx_z_length    | int32_t                  | AsDtype('>i4')\n",
      "brem_vtx_z           | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "endvtx_type          | int32_t                  | AsDtype('>i4')\n",
      "endvtx_x             | double                   | AsDtype('>f8')\n",
      "endvtx_y             | double                   | AsDtype('>f8')\n",
      "endvtx_z             | double                   | AsDtype('>f8')\n",
      "energy               | double                   | AsDtype('>f8')\n",
      "eta                  | double                   | AsDtype('>f8')\n",
      "event_count          | int32_t                  | AsDtype('>i4')\n",
      "fromB                | bool                     | AsDtype('bool')\n",
      "fromD                | bool                     | AsDtype('bool')\n",
      "fromDecay            | bool                     | AsDtype('bool')\n",
      "fromHadInt           | bool                     | AsDtype('bool')\n",
      "fromPV               | bool                     | AsDtype('bool')\n",
      "fromPairProd         | bool                     | AsDtype('bool')\n",
      "fromSignal           | bool                     | AsDtype('bool')\n",
      "fromStrange          | bool                     | AsDtype('bool')\n",
      "isElectron           | bool                     | AsDtype('bool')\n",
      "isKaon               | bool                     | AsDtype('bool')\n",
      "isMuon               | bool                     | AsDtype('bool')\n",
      "isPion               | bool                     | AsDtype('bool')\n",
      "isProton             | bool                     | AsDtype('bool')\n",
      "lost                 | bool                     | AsDtype('bool')\n",
      "lost_in_track_fit    | bool                     | AsDtype('bool')\n",
      "match_fraction       | float                    | AsDtype('>f4')\n",
      "mcp_idx              | int32_t                  | AsDtype('>i4')\n",
      "mother_id            | int32_t                  | AsDtype('>i4')\n",
      "mother_key           | int32_t                  | AsDtype('>i4')\n",
      "originvtx_type       | int32_t                  | AsDtype('>i4')\n",
      "originvtx_x          | double                   | AsDtype('>f8')\n",
      "originvtx_y          | double                   | AsDtype('>f8')\n",
      "originvtx_z          | double                   | AsDtype('>f8')\n",
      "p                    | double                   | AsDtype('>f8')\n",
      "phi                  | double                   | AsDtype('>f8')\n",
      "pid                  | int32_t                  | AsDtype('>i4')\n",
      "pt                   | double                   | AsDtype('>f8')\n",
      "px                   | double                   | AsDtype('>f8')\n",
      "py                   | double                   | AsDtype('>f8')\n",
      "pz                   | double                   | AsDtype('>f8')\n",
      "scifi_hit_pos_x_l... | int32_t                  | AsDtype('>i4')\n",
      "scifi_hit_pos_x      | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "scifi_hit_pos_y_l... | int32_t                  | AsDtype('>i4')\n",
      "scifi_hit_pos_y      | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "scifi_hit_pos_z_l... | int32_t                  | AsDtype('>i4')\n",
      "scifi_hit_pos_z      | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "track_p              | double                   | AsDtype('>f8')\n",
      "track_pt             | double                   | AsDtype('>f8')\n",
      "tx                   | double                   | AsDtype('>f8')\n",
      "ty                   | double                   | AsDtype('>f8')\n",
      "ut_hit_pos_x_length  | int32_t                  | AsDtype('>i4')\n",
      "ut_hit_pos_x         | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "ut_hit_pos_y_length  | int32_t                  | AsDtype('>i4')\n",
      "ut_hit_pos_y         | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "ut_hit_pos_z_length  | int32_t                  | AsDtype('>i4')\n",
      "ut_hit_pos_z         | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "velo_hit_pos_x_le... | int32_t                  | AsDtype('>i4')\n",
      "velo_hit_pos_x       | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "velo_hit_pos_y_le... | int32_t                  | AsDtype('>i4')\n",
      "velo_hit_pos_y       | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "velo_hit_pos_z_le... | int32_t                  | AsDtype('>i4')\n",
      "velo_hit_pos_z       | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "velo_track_idx       | int32_t                  | AsDtype('>i4')\n",
      "velo_track_tx        | double                   | AsDtype('>f8')\n",
      "velo_track_ty        | double                   | AsDtype('>f8')\n",
      "velo_track_x         | double                   | AsDtype('>f8')\n",
      "velo_track_y         | double                   | AsDtype('>f8')\n",
      "velo_track_z         | double                   | AsDtype('>f8')\n"
     ]
    }
   ],
   "source": [
    "file.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "var=\"energy\"\n",
    "#plt.hist(tracked[var], bins=1000, label=\"tracked\",edgecolor=\"blue\", fill=False, density=True)\n",
    "#plt.hist(lost[var], bins=1000, label=\"lost\",edgecolor=\"darkorange\", fill=False, density=True)\n",
    "plt.hist([tracked[var], lost[var]],bins=1000,label=[\"tracked\", \"lost\"], density=True)\n",
    "plt.title(var)\n",
    "plt.xlim([0,40000])\n",
    "plt.xlabel(var+\" [MeV]\")\n",
    "plt.ylabel(\"scaled\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "var=\"eta\"\n",
    "plt.hist(tracked[var], bins=100, label=\"tracked\", edgecolor=\"blue\", fill=False, density=True)\n",
    "plt.hist(lost[var], bins=100, label=\"lost\", edgecolor=\"orange\", fill=False, density=True)\n",
    "#plt.hist([tracked[var], lost[var]],bins=150,label=[\"tracked\", \"lost\"], density=True)\n",
    "plt.title(var)\n",
    "plt.xlabel(var)\n",
    "plt.ylabel(\"scaled\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "var=\"p\"\n",
    "#plt.hist(tracked[var], bins=100, label=\"tracked\")\n",
    "#plt.hist(lost[var], bins=100, label=\"lost\")\n",
    "plt.hist([tracked[var], lost[var]],bins=200,label=[\"tracked\", \"lost\"], density=True)\n",
    "plt.title(var)\n",
    "plt.xlabel(var+f\" [MeV/$c^2$]\")\n",
    "plt.ylabel(\"scaled\")\n",
    "plt.xlim([0,150000])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "var=\"pt\"\n",
    "#plt.hist(tracked[var], bins=200, label=\"tracked\",density=True)\n",
    "#plt.hist(lost[var], bins=200, label=\"lost\", density=True)\n",
    "plt.hist([tracked[var], lost[var]],bins=200,label=[\"tracked\", \"lost\"], density=True)\n",
    "plt.title(var)\n",
    "plt.xlabel(f\"p transversal [MeV/$c^2$]\")\n",
    "plt.ylabel(\"counts\")\n",
    "plt.xlim([0,6000])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "var=\"tx\"\n",
    "#plt.hist(tracked[var], bins=100, label=\"tracked\")\n",
    "#plt.hist(lost[var], bins=100, label=\"lost\")\n",
    "plt.hist([tracked[var], lost[var]],bins=100,label=[\"tracked\", \"lost\"], density=True)\n",
    "plt.title(var)\n",
    "plt.xlabel(f\"tx [MeV/$c^2$]\")\n",
    "plt.ylabel(\"counts\")\n",
    "#plt.xlim([0,6000])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "var=\"ty\"\n",
    "#plt.hist(tracked[var], bins=100, label=\"tracked\")\n",
    "#plt.hist(lost[var], bins=100, label=\"lost\")\n",
    "plt.hist([tracked[var], lost[var]],bins=100,label=[\"tracked\", \"lost\"], density=True)\n",
    "plt.title(var)\n",
    "plt.xlabel(f\"ty [MeV/$c^2$]\")\n",
    "plt.ylabel(\"counts\")\n",
    "#plt.xlim([0,6000])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\"\"\""
   ]
  }
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