Projektpraktikum/notebooks/electron_lost_found.ipynb

{
 "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",
    "from mpl_toolkits import mplot3d\n",
    "import itertools\n",
    "import awkward as ak\n",
    "import seaborn as sns\n",
    "from scipy.optimize import curve_fit\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "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",
    "#look at particles only from Signal\n",
    "allcolumns = file.arrays()\n",
    "tracked = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)]\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] \n",
    "\n",
    "#ak.num(tracked, axis=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lost\n",
    "l_eph = lost[\"brem_photons_pe\"]\n",
    "ak.nan_to_num(l_eph)\n",
    "l_pT = lost[\"pt\"]\n",
    "l_sci_x = lost[\"scifi_hit_pos_x\"]\n",
    "ak.nan_to_num(l_sci_x)\n",
    "\n",
    "#found\n",
    "f_eph = tracked[\"brem_photons_pe\"]\n",
    "ak.nan_to_num(f_eph)\n",
    "f_pT = tracked[\"pt\"]\n",
    "f_sci_x = tracked[\"scifi_hit_pos_x\"]\n",
    "ak.nan_to_num(f_sci_x)\n",
    "\n",
    "l_sci_x, l_pT = ak.broadcast_arrays(l_sci_x, l_pT)\n",
    "f_sci_x, f_pT = ak.broadcast_arrays(f_sci_x, f_pT)\n",
    "\n",
    "l_sci_x = ak.to_numpy(ak.flatten(l_sci_x))\n",
    "l_pT = ak.to_numpy(ak.flatten(l_pT))\n",
    "f_sci_x = ak.to_numpy(ak.flatten(f_sci_x))\n",
    "f_pT = ak.to_numpy(ak.flatten(f_pT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0=ax0.hist2d(l_sci_x, l_pT, bins=200,  cmap=plt.cm.jet, cmin=0, range=[[-3000,3000],[0,2000]])\n",
    "ax0.set_xlabel(\"scifi x\")\n",
    "ax0.set_ylabel(r\"$p_T$\")\n",
    "ax0.set_title(\"lost electron positions in the scifi in regard to their transverse momentum\")\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1=ax1.hist2d(f_sci_x, f_pT, bins=200,  cmap=plt.cm.jet, cmin=0, range=[[-3000,3000],[0,2000]])\n",
    "ax1.set_xlabel(\"scifi x\")\n",
    "ax1.set_ylabel(r\"$p_T$\")\n",
    "ax1.set_title(\"found electron positions in the scifi in regard to their transverse momentum\")\n",
    "plt.colorbar(a1[3],ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "we can see that the lost electrons cover a wider spread in the x direction of the scifi tracker, most widely scattered electrons have low pt\n",
    "\n",
    "D:\n",
    "heatmaps look fairly similar. lost e are more densely located between x \\in [1000,2000]. found e between x \\in [200,1500].\n",
    "we can see a near empty space around the x origin in both. lost seem to have less pt.\n",
    "\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.09e+04], [8.66e+03], [8.07e+04], ..., [5.63e+03], [6.29e+03], [2.26e+03]]\n",
      "[4.62e+04, 9.36e+03, 1.34e+05, 5.63e+04, ..., 2.01e+04, 6.94e+03, 7.83e+03]\n",
      "8657.132\n",
      "9355.866625028413\n"
     ]
    }
   ],
   "source": [
    "energy_found = tracked[\"energy\"]\n",
    "energy_found = energy_found[tracked[\"brem_photons_pe_length\"]!=0]\n",
    "#ak.nan_to_num(energy_found)\n",
    "\n",
    "e_ph_found = tracked[\"brem_photons_pe\"]\n",
    "e_ph_found = e_ph_found[tracked[\"brem_photons_pe_length\"]!=0]\n",
    "#ak.nan_to_num(e_ph_found, nan=[0])\n",
    "e_ph_found = ak.sum(e_ph_found, axis=-1, keepdims=True)\n",
    "print(e_ph_found)\n",
    "print(energy_found)\n",
    "\n",
    "energy_lost = lost[\"energy\"]\n",
    "energy_lost = energy_lost[lost[\"brem_photons_pe_length\"]!=0]\n",
    "#ak.nan_to_num(energy_lost)\n",
    "\n",
    "e_ph_lost = lost[\"brem_photons_pe\"]\n",
    "e_ph_lost = e_ph_lost[lost[\"brem_photons_pe_length\"]!=0]\n",
    "#ak.nan_to_num(e_ph_lost)\n",
    "e_ph_lost = ak.sum(e_ph_lost, axis=-1,keepdims=True)\n",
    "\n",
    "#e_ph_found, energy_found = ak.broadcast_arrays(e_ph_found, energy_found)\n",
    "#e_ph_lost, energy_lost = ak.broadcast_arrays(e_ph_lost, energy_lost)\n",
    "\n",
    "e_ph_found = ak.to_numpy(ak.flatten(e_ph_found))\n",
    "energy_found = ak.to_numpy(energy_found)\n",
    "\n",
    "e_ph_lost = ak.to_numpy(ak.flatten(e_ph_lost))\n",
    "energy_lost = ak.to_numpy(energy_lost)\n",
    "\n",
    "print(e_ph_found[1])\n",
    "print(energy_found[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_e_found = e_ph_found/energy_found\n",
    "q_e_lost = e_ph_lost/energy_lost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(q_e_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
    "plt.hist(q_e_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"counts (normed)\")\n",
    "plt.title(r'$E_{ph}/E_0$')\n",
    "plt.legend()\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "we can clearly see that lost electrons are responsible for higher energy photons\n",
    "D:\n",
    "still able to see a trend that most electrons that give up all of their energy to photons are lost e. but nowhere near as extreme as for the B decay\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0 = ax0.hist2d(e_ph_found/(1e3), energy_found/(1e3), density=False, bins=200,  cmap=plt.cm.jet, cmin=1, range=[[0,50],[0,50]])\n",
    "ax0.set_xlabel(r\"$E_\\gamma$ [GeV]\")\n",
    "ax0.set_ylabel(r\"$E_e$ [GeV]\")\n",
    "ax0.set_title(\"found electron energy against photon energy\")\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1 = ax1.hist2d(e_ph_lost/(1e3), energy_lost/(1e3), density=False, bins=200, cmap=plt.cm.jet, cmin=1, range= [[0,50],[0,50]])\n",
    "ax1.set_xlabel(r\"$E_\\gamma$ [GeV]\")\n",
    "ax1.set_ylabel(r\"$E_e$ [GeV]\")\n",
    "ax1.set_title(\"lost electron energy against photon energy\")\n",
    "plt.colorbar(a1[3],ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "concentrated at the E_ph/E_0~1 line especially at lower energies.\n",
    "lost E_ph to E_0: fewer entries at lower q_e\n",
    "D:\n",
    "both energies are much smaller than in the B decay. otherwise similar pattern.\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "brem_vtx_x_found = tracked[\"brem_vtx_x\"]\n",
    "brem_vtx_x_found = brem_vtx_x_found[tracked[\"brem_vtx_x_length\"]!=0]\n",
    "brem_vtx_x_found = ak.to_numpy(ak.flatten(brem_vtx_x_found))\n",
    "\n",
    "brem_vtx_z_found = tracked[\"brem_vtx_z\"]\n",
    "brem_vtx_z_found = brem_vtx_z_found[tracked[\"brem_vtx_z_length\"]!=0]\n",
    "#print(ak.to_numpy(brem_vtx_z_found))\n",
    "brem_vtx_z_found = ak.to_numpy(ak.flatten(brem_vtx_z_found))\n",
    "\n",
    "brem_vtx_x_lost = lost[\"brem_vtx_x\"]\n",
    "brem_vtx_x_lost = brem_vtx_x_lost[lost[\"brem_vtx_x_length\"]!=0]\n",
    "brem_vtx_x_lost = ak.to_numpy(ak.flatten(brem_vtx_x_lost))\n",
    "\n",
    "brem_vtx_z_lost = lost[\"brem_vtx_z\"]\n",
    "brem_vtx_z_lost = brem_vtx_z_lost[lost[\"brem_vtx_z_length\"]!=0]\n",
    "brem_vtx_z_lost = ak.to_numpy(ak.flatten(brem_vtx_z_lost))\n",
    "\n",
    "#vtx_x_fit= ak.to_numpy(vtx_x_found)\n",
    "#vtx_z_fit = ak.to_numpy(vtx_z_found)\n",
    "\n",
    "#brem_x_lost=np.array([])\n",
    "#brem_z_lost=np.array([])\n",
    "\n",
    "#for i in range(5):\n",
    "#    brem_x_lost = np.append(brem_x_lost, brem_vtx_x_lost)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62968"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ak.num(brem_vtx_z_found, axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0 = ax0.hist2d(brem_vtx_z_found[:11000], brem_vtx_x_found[:11000], density=False, bins=300,  cmap=plt.cm.jet, cmin=1, vmax=30)\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(r\"$e^\\pm$ found brem vertices\")\n",
    "\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1 = ax1.hist2d(brem_vtx_z_lost, brem_vtx_x_lost, density=False, bins=300, cmap=plt.cm.jet, cmin=1, vmax=30)\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(r\"$e^\\pm$ lost brem vertices\")\n",
    "#ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
    "\n",
    "plt.colorbar(a1[3], ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "z: VeLo - RICH1 - TT - Magnet - T1,T2,T3 - RICH2 - M1\n",
    "B:\n",
    "vertices of lost e photons are more densely concentrated around the beampipe, especially in the z range of the magnet\n",
    "found: vertices are densely located @ or around the detectors, while there are no clusters in the z range of the magnet\n",
    "D:\n",
    "lost brem vertices: we can very clearly see the concentration of vertices @ the beampipe\n",
    "both: less statistics in general, can still make out the tracking stations but not as well as in the B decay\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot singular tracks by fitting brem vertices\n",
    "vtx_z_found = tracked[\"brem_vtx_z\"]\n",
    "vtx_z_found = vtx_z_found[tracked[\"brem_vtx_z_length\"]>3]\n",
    "\n",
    "vtx_x_found = tracked[\"brem_vtx_x\"]\n",
    "vtx_x_found = vtx_x_found[tracked[\"brem_vtx_x_length\"]>3]\n",
    "\n",
    "vtx_z_lost = lost[\"brem_vtx_z\"]\n",
    "vtx_z_lost = vtx_z_lost[lost[\"brem_vtx_z_length\"]>3]\n",
    "\n",
    "vtx_x_lost = lost[\"brem_vtx_x\"]\n",
    "vtx_x_lost = vtx_x_lost[lost[\"brem_vtx_x_length\"]>3]\n",
    "\n",
    "def cubic_fit(x, a, b, c, d):\n",
    "    return (a + b*x + c*x**2 + d*x**3)\n",
    "\n",
    "def quint_fit(x, a, b, c, d, e, f):\n",
    "    return (a + b*x + c*x**2 + d*x**3 + e*x**4 + f*x**5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:1010: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "n_end=100\n",
    "\n",
    "for i in range(0,n_end):\n",
    "    popt, pcov = curve_fit(cubic_fit,ak.to_numpy(vtx_z_found[i,:]),ak.to_numpy(vtx_x_found[i,:]))\n",
    "    z_coord = np.linspace(vtx_z_found[i,0],12000,1000)\n",
    "    fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
    "    ax0.plot(z_coord, fit, \"-\", label=\"fit\"+str(i), lw=0.5)\n",
    "    ax0.errorbar(ak.to_numpy(vtx_z_found[i,:]),ak.to_numpy(vtx_x_found[i,:]),fmt=\".\",ms=2)\n",
    "\n",
    "#ax0.legend()\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(\"found tracks of brem vertices from few signals\")\n",
    "ax0.set(xlim=(0,12000), ylim=(-4000,4000))\n",
    "ax0.grid()\n",
    "\n",
    "for i in range(0,n_end):\n",
    "    popt, pcov = curve_fit(cubic_fit,ak.to_numpy(vtx_z_lost[i,:]),ak.to_numpy(vtx_x_lost[i,:]))\n",
    "    z_coord = np.linspace(vtx_z_lost[i,0],12000,1000)\n",
    "    fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
    "    ax1.plot(z_coord, fit, \"-\", label=\"fit\"+str(i), lw=0.5)\n",
    "    ax1.errorbar(ak.to_numpy(vtx_z_lost[i,:]),ak.to_numpy(vtx_x_lost[i,:]),fmt=\".\",ms=2)\n",
    "\n",
    "#ax1.legend()\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(\"lost tracks of brem vertices from few signals\")\n",
    "ax1.set(xlim=(0,12000), ylim=(-4000,4000))\n",
    "ax1.grid()\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "we can see that of the lost brem vertices, many trajectory fits seem illogical and not plausible\n",
    "found: most seem like reasonable tracks\n",
    "D:\n",
    "both: many tracks arent good fits and are unusable\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "endvtx_x_found = tracked[\"all_endvtx_x\"]\n",
    "endvtx_x_found = endvtx_x_found[tracked[\"all_endvtx_x_length\"]!=0]\n",
    "endvtx_x_found = ak.to_numpy(ak.flatten(endvtx_x_found))\n",
    "\n",
    "endvtx_z_found = tracked[\"all_endvtx_z\"]\n",
    "endvtx_z_found = endvtx_z_found[tracked[\"all_endvtx_z_length\"]!=0]\n",
    "#print(ak.to_numpy(brem_vtx_z_found))\n",
    "endvtx_z_found = ak.to_numpy(ak.flatten(endvtx_z_found))\n",
    "\n",
    "endvtx_x_lost = lost[\"all_endvtx_x\"]\n",
    "endvtx_x_lost = endvtx_x_lost[lost[\"all_endvtx_x_length\"]!=0]\n",
    "endvtx_x_lost = ak.to_numpy(ak.flatten(endvtx_x_lost))\n",
    "\n",
    "endvtx_z_lost = lost[\"all_endvtx_z\"]\n",
    "endvtx_z_lost = endvtx_z_lost[lost[\"all_endvtx_z_length\"]!=0]\n",
    "endvtx_z_lost = ak.to_numpy(ak.flatten(endvtx_z_lost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0 = ax0.hist2d(endvtx_z_found, endvtx_x_found, density=False, bins=500,  cmap=plt.cm.jet, cmin=1)\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(r\"$e^\\pm$ found end vertices\")\n",
    "ax0.set(xlim=(0,12000), ylim=(-4000,4000))\n",
    "\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1 = ax1.hist2d(endvtx_z_lost, endvtx_x_lost, density=False, bins=500, cmap=plt.cm.jet, cmin=1)\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(r\"$e^\\pm$ lost end vertices\")\n",
    "ax1.set(xlim=(0,12000), ylim=(-4000,4000))\n",
    "\n",
    "plt.colorbar(a1[3], ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "z: VeLo - RICH1 - TT - Magnet - T1,T2,T3 - RICH2 - M1\n",
    "B:\n",
    "vertices of lost e photons are more densely concentrated around the beampipe, especially in the z range of the magnet\n",
    "found: vertices are densely located @ or around the detectors, while there are no clusters in the z range of the magnet\n",
    "D:\n",
    "lost: densely located @ the beampipe.\n",
    "both: almost cant make out the velo or ut\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try to plot trajectories using all tracker hits (Velo, UT, SciFi)\n",
    "\n",
    "velo_x_found = tracked[\"velo_hit_pos_x\"]\n",
    "velo_z_found = tracked[\"velo_hit_pos_z\"]\n",
    "ut_x_found = tracked[\"ut_hit_pos_x\"]\n",
    "ut_z_found = tracked[\"ut_hit_pos_z\"]\n",
    "scifi_x_found = tracked[\"scifi_hit_pos_x\"]\n",
    "scifi_z_found = tracked[\"scifi_hit_pos_z\"]\n",
    "\n",
    "tracker_x_found = ak.concatenate([velo_x_found,ut_x_found,scifi_x_found], axis=1)\n",
    "tracker_z_found = ak.concatenate([velo_z_found,ut_z_found,scifi_z_found], axis=1)\n",
    "\n",
    "velo_x_lost = lost[\"velo_hit_pos_x\"]\n",
    "velo_z_lost = lost[\"velo_hit_pos_z\"]\n",
    "ut_x_lost = lost[\"ut_hit_pos_x\"]\n",
    "ut_z_lost = lost[\"ut_hit_pos_z\"]\n",
    "scifi_x_lost = lost[\"scifi_hit_pos_x\"]\n",
    "scifi_z_lost = lost[\"scifi_hit_pos_z\"]\n",
    "\n",
    "tracker_x_lost = ak.concatenate([velo_x_lost,ut_x_lost,scifi_x_lost], axis=1)\n",
    "tracker_z_lost = ak.concatenate([velo_z_lost,ut_z_lost,scifi_z_lost], axis=1)\n",
    "\n",
    "\n",
    "#tracker_x_found = tracker_x_found[tracked[\"energy\"]>1e4]\n",
    "#tracker_z_found = tracker_z_found[tracked[\"energy\"]>1e4]\n",
    "\n",
    "#tracker_x_lost = tracker_x_lost[lost[\"energy\"]>1e4]\n",
    "#tracker_z_lost = tracker_z_lost[lost[\"energy\"]>1e4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "nstart=0\n",
    "nend=130\n",
    "\n",
    "for i in range(nstart,nend):\n",
    "    popt, pcov = curve_fit(cubic_fit,ak.to_numpy(tracker_z_found[i,:]),ak.to_numpy(tracker_x_found[i,:]))\n",
    "    z_coord = np.linspace(tracker_z_found[i,0],14000,1000)\n",
    "    fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
    "    ax0.plot(z_coord, fit, \"-\", lw=0.5)\n",
    "    ax0.errorbar(ak.to_numpy(tracker_z_found[i,:]),ak.to_numpy(tracker_x_found[i,:]),fmt=\".\",ms=3)\n",
    "\n",
    "ax0.legend([r\"$E>5$GeV\"])\n",
    "ax0.vlines(3000, -4000,4000, lw=1, ls=\":\", color=\"red\")\n",
    "ax0.vlines(7500, -4000,4000, lw=1, ls=\":\", color=\"red\")\n",
    "ax0.set_xticks(np.arange(0,14000,1000) , minor=True)\n",
    "ax0.set_yticks(np.arange(-4000,4000,500), minor=True)\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(\"found tracks from detector hits from few signals\")\n",
    "ax0.set(xlim=(0,14000), ylim=(-4000,4000))\n",
    "ax0.grid()\n",
    "\n",
    "for i in range(nstart,nend):\n",
    "    popt, pcov = curve_fit(cubic_fit,ak.to_numpy(tracker_z_lost[i,:]),ak.to_numpy(tracker_x_lost[i,:]))\n",
    "    z_coord = np.linspace(tracker_z_lost[i,0],14000,1000)\n",
    "    fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
    "    ax1.plot(z_coord, fit, \"-\", lw=0.5)\n",
    "    ax1.errorbar(ak.to_numpy(tracker_z_lost[i,:]),ak.to_numpy(tracker_x_lost[i,:]),fmt=\".\",ms=3)\n",
    "\n",
    "ax1.vlines(3000, -4000,4000, lw=1, ls=\":\", color=\"red\")\n",
    "ax1.vlines(7500, -4000,4000, lw=1, ls=\":\", color=\"red\")\n",
    "ax1.set_xticks(np.arange(0,14000,1000) , minor=True)\n",
    "ax1.set_yticks(np.arange(-4000,4000,500), minor=True)\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(\"lost tracks from detector hits from few signals\")\n",
    "ax1.set(xlim=(0,14000), ylim=(-4000,4000))\n",
    "ax1.grid()\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "electrons and photons will be stopped by the ECAL which serves to measure the particles energy\n",
    "\n",
    "B:\n",
    "the trajectories between the velo and tt should be linear, which cannot be plotted accurately using a single fit.\n",
    "lost tracks diverge more severely.\n",
    "\n",
    "most higher energy particles maintain a trajectory closer to the beamdirection ie a larger pseudorapidity,\n",
    "and show less bending in their trajectory, especially upstream.\n",
    "found: higher energy: very compact trajectory, less bending wrt lower energy particles \n",
    "\n",
    "D:\n",
    "E<10GeV: almost all diverge from the x origin (almost no hit for x<1500)\n",
    "E>10GeV: much more densely clustered. however still a noticeable empty space around the x origin\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = 299792458 #m/s\n",
    "energy_found = tracked[\"energy\"]\n",
    "p_found = tracked[\"p\"]\n",
    "pt_found = tracked[\"pt\"]\n",
    "eta_found = tracked[\"eta\"]\n",
    "\n",
    "energy_lost = lost[\"energy\"]\n",
    "p_lost = lost[\"p\"]\n",
    "pt_lost = lost[\"pt\"]\n",
    "eta_lost = lost[\"eta\"]\n",
    "\n",
    "p_found = ak.to_numpy(p_found)\n",
    "pt_found = ak.to_numpy(pt_found)\n",
    "eta_found = ak.to_numpy(eta_found)\n",
    "\n",
    "p_lost = ak.to_numpy(p_lost)\n",
    "pt_lost = ak.to_numpy(pt_lost)\n",
    "eta_lost = ak.to_numpy(eta_lost)\n",
    "#print(np.sqrt(energy_found[0]**2 - p_found[0]**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0=ax0.hist2d(p_found, pt_found, bins=200, cmap=plt.cm.jet, cmin=0,range=[[0,1e4],[0,1e3]])\n",
    "ax0.set_xlabel(\"p\")\n",
    "ax0.set_ylabel(r\"$p_T$\")\n",
    "ax0.set_title(\"found electron momentum over transverse momentum\")\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1=ax1.hist2d(p_lost, pt_lost, bins=200, cmap=plt.cm.jet, cmin=0, range=[[0,1e4],[0,1e3]]) \n",
    "ax1.set_xlabel(\"p\")\n",
    "ax1.set_ylabel(r\"$p_T$\")\n",
    "ax1.set_title(\"lost electron momentum over transverse momentum\")\n",
    "plt.colorbar(a1[3],ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "\n",
    "D:\n",
    "both: clustered between 2000<p<6000 and 20<pt<400 (found a little more spread)\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0=ax0.hist2d(eta_found, p_found/(1e3), bins=200, cmap=plt.cm.jet, cmin=0, range=[[0,7],[0,50]])\n",
    "ax0.set_xlabel(r\"$\\eta$\")\n",
    "ax0.set_ylabel(r\"$p$ [GeV]\")\n",
    "ax0.set_title(\"found eta and electron momentum\")\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1=ax1.hist2d(eta_lost, p_lost/(1e3), bins=200, cmap=plt.cm.jet, cmin=0, range=[[0,7],[0,50]])\n",
    "ax1.set_xlabel(r\"$\\eta$\")\n",
    "ax1.set_ylabel(r\"$p$ [GeV]\")\n",
    "ax1.set_title(\"lost eta and electron momentum\")\n",
    "plt.colorbar(a1[3],ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "particles with lower momentum appear to have lower rapidity as well, ie a larger angle to the beam axis.\n",
    "D:\n",
    "both: clustered between 3<eta<5 and 0<p<10GeV. it seems that most particles had a higher rapidity \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": 36,
   "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",
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       "  101.0,\n",
       "  101.0,\n",
       "  0.0],\n",
       " 'all_endvtx_x_length': 11,\n",
       " 'all_endvtx_x': [19.496400833129883,\n",
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       "  -3819.826904296875],\n",
       " 'all_endvtx_y_length': 11,\n",
       " 'all_endvtx_y': [-20.370500564575195,\n",
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       " 'all_endvtx_z_length': 11,\n",
       " 'all_endvtx_z': [399.7018127441406,\n",
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       " 'brem_photons_pe_length': 10,\n",
       " 'brem_photons_pe': [2469.182861328125,\n",
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       " 'brem_photons_px_length': 10,\n",
       " 'brem_photons_px': [133.6999969482422,\n",
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       " 'brem_photons_py_length': 10,\n",
       " 'brem_photons_py': [-137.9199981689453,\n",
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       "  -5.800000190734863,\n",
       "  -36.66999816894531],\n",
       " 'brem_photons_pz_length': 10,\n",
       " 'brem_photons_pz': [2461.699951171875,\n",
       "  169.36000061035156,\n",
       "  223.00999450683594,\n",
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       "  143.85000610351562,\n",
       "  642.2899780273438],\n",
       " 'brem_vtx_x_length': 10,\n",
       " 'brem_vtx_x': [19.496400833129883,\n",
       "  24.957000732421875,\n",
       "  32.490699768066406,\n",
       "  34.14419937133789,\n",
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       " 'brem_vtx_y_length': 10,\n",
       " 'brem_vtx_y': [-20.370500564575195,\n",
       "  -26.043100357055664,\n",
       "  -33.85060119628906,\n",
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       "  -533.6621704101562,\n",
       "  -598.8317260742188,\n",
       "  -599.1124877929688],\n",
       " 'brem_vtx_z_length': 10,\n",
       " 'brem_vtx_z': [399.7018127441406,\n",
       "  500.7049865722656,\n",
       "  637.5723266601562,\n",
       "  667.4852905273438,\n",
       "  677.0195922851562,\n",
       "  709.4874877929688,\n",
       "  8576.556640625,\n",
       "  9281.2119140625,\n",
       "  11183.1640625,\n",
       "  11188.4541015625],\n",
       " 'endvtx_type': 0,\n",
       " 'endvtx_x': nan,\n",
       " 'endvtx_y': nan,\n",
       " 'endvtx_z': nan,\n",
       " 'energy': 9355.866625028413,\n",
       " 'eta': 3.237728027535365,\n",
       " 'event_count': 2,\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': 5488,\n",
       " 'mother_id': 511,\n",
       " 'mother_key': 5479,\n",
       " 'originvtx_type': 2,\n",
       " 'originvtx_x': -0.0663,\n",
       " 'originvtx_y': -0.0023,\n",
       " 'originvtx_z': 40.3966,\n",
       " 'p': 9355.866611073503,\n",
       " 'phi': -0.8090232566094933,\n",
       " 'pid': -11,\n",
       " 'pt': 733.3612464536151,\n",
       " 'px': 506.17,\n",
       " 'py': -530.67,\n",
       " 'pz': 9327.08,\n",
       " 'scifi_hit_pos_x_length': 13,\n",
       " 'scifi_hit_pos_x': [-1402.2215576171875,\n",
       "  -1448.5460205078125,\n",
       "  -1495.479736328125,\n",
       "  -1542.3804931640625,\n",
       "  -1865.9482421875,\n",
       "  -1914.599853515625,\n",
       "  -1915.0545654296875,\n",
       "  -1963.8216552734375,\n",
       "  -2012.78173828125,\n",
       "  -2349.04052734375,\n",
       "  -2398.658935546875,\n",
       "  -2448.573486328125,\n",
       "  -2498.376220703125],\n",
       " 'scifi_hit_pos_y_length': 13,\n",
       " 'scifi_hit_pos_y': [-470.2232666015625,\n",
       "  -473.55914306640625,\n",
       "  -476.8390197753906,\n",
       "  -480.12762451171875,\n",
       "  -501.0274963378906,\n",
       "  -504.04315185546875,\n",
       "  -504.0709228515625,\n",
       "  -507.0843200683594,\n",
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       "  -530.0570068359375,\n",
       "  -532.8589477539062,\n",
       "  -535.61572265625,\n",
       "  -538.2548217773438],\n",
       " 'scifi_hit_pos_z_length': 13,\n",
       " 'scifi_hit_pos_z': [7824.40576171875,\n",
       "  7894.1943359375,\n",
       "  7964.38330078125,\n",
       "  8034.171875,\n",
       "  8506.294921875,\n",
       "  8575.99609375,\n",
       "  8576.6455078125,\n",
       "  8646.2744140625,\n",
       "  8716.0634765625,\n",
       "  9191.1904296875,\n",
       "  9260.98046875,\n",
       "  9331.171875,\n",
       "  9400.962890625],\n",
       " 'track_p': 1931.9397828451663,\n",
       " 'track_pt': 151.36962154532284,\n",
       " 'tx': 0.05426886013629132,\n",
       " 'ty': -0.056895620065443846,\n",
       " 'ut_hit_pos_x_length': 4,\n",
       " 'ut_hit_pos_x': [112.31356048583984,\n",
       "  114.4996337890625,\n",
       "  122.83889770507812,\n",
       "  124.72588348388672],\n",
       " 'ut_hit_pos_y_length': 4,\n",
       " 'ut_hit_pos_y': [-135.26077270507812,\n",
       "  -138.64544677734375,\n",
       "  -152.51470947265625,\n",
       "  -155.91305541992188],\n",
       " 'ut_hit_pos_z_length': 4,\n",
       " 'ut_hit_pos_z': [2313.153564453125,\n",
       "  2368.153564453125,\n",
       "  2593.153564453125,\n",
       "  2648.153564453125],\n",
       " 'velo_hit_pos_x_length': 10,\n",
       " 'velo_hit_pos_x': [3.2025206089019775,\n",
       "  4.559732437133789,\n",
       "  5.917426109313965,\n",
       "  7.274953365325928,\n",
       "  8.638668060302734,\n",
       "  10.008487701416016,\n",
       "  11.378168106079102,\n",
       "  12.745656967163086,\n",
       "  15.399646759033203,\n",
       "  19.47773551940918],\n",
       " 'velo_hit_pos_y_length': 10,\n",
       " 'velo_hit_pos_y': [-3.429784059524536,\n",
       "  -4.8510894775390625,\n",
       "  -6.275496482849121,\n",
       "  -7.6981940269470215,\n",
       "  -9.124002456665039,\n",
       "  -10.54817008972168,\n",
       "  -11.963949203491211,\n",
       "  -13.374946594238281,\n",
       "  -16.1297550201416,\n",
       "  -20.351228713989258],\n",
       " 'velo_hit_pos_z_length': 10,\n",
       " 'velo_hit_pos_z': [100.64099884033203,\n",
       "  125.64099884033203,\n",
       "  150.64100646972656,\n",
       "  175.64100646972656,\n",
       "  200.64100646972656,\n",
       "  225.64100646972656,\n",
       "  250.64100646972656,\n",
       "  275.6409912109375,\n",
       "  324.3590087890625,\n",
       "  399.3590087890625],\n",
       " 'velo_track_idx': 143,\n",
       " 'velo_track_tx': 0.054571494460105896,\n",
       " 'velo_track_ty': -0.056447889655828476,\n",
       " 'velo_track_x': 39.710758209228516,\n",
       " 'velo_track_y': -41.2618293762207,\n",
       " 'velo_track_z': 770.0}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracked[1].tolist()"
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