Projektpraktikum/electron_lost_found.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 99,
   "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": 100,
   "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)]\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal)] \n",
    "\n",
    "#ak.num(tracked, axis=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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": 102,
   "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=100,  cmap=plt.cm.jet)\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=100,  cmap=plt.cm.jet)\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,\n",
    "while most of those have low pT \n",
    "\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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": 104,
   "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": 105,
   "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",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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, energy_found, density=True, bins=200,  cmap=plt.cm.jet, range=[[0,100000],[0,100000]])\n",
    "ax0.set_xlabel(r\"$E_\\gamma$\")\n",
    "ax0.set_ylabel(r\"$E_e$\")\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, energy_lost, density=True, bins=200, cmap=plt.cm.jet, range=[[0,100000],[0,100000]])\n",
    "ax1.set_xlabel(r\"$E_\\gamma$\")\n",
    "ax1.set_ylabel(r\"$E_e$\")\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",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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, brem_vtx_x_found, density=False, bins=300,  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 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)\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",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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": 110,
   "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",
    "for i in range(3,6):\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))\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(3,6):\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))\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",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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": 112,
   "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",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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=200\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, \"-\", label=\"fit \"+str(i), 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.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, \"-\", label=\"fit \"+str(i), 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",
    "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",
    "electrons and photons will be stopped by the ECAL which serves to measure the particles energy\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'velo_hit_pos_y_length': 10,\n",
       " 'velo_hit_pos_y': [-3.429784059524536,\n",
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       " 'velo_hit_pos_z_length': 10,\n",
       " 'velo_hit_pos_z': [100.64099884033203,\n",
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       "  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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracked[1].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  }
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