Projektpraktikum/electron_lost_found.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 73,
   "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": 74,
   "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": 76,
   "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": 77,
   "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": 78,
   "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": 79,
   "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": 81,
   "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=0, range=[[0,200],[0,200]])\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=0, range= [[0,200],[0,200]]) #[[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": 82,
   "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": 83,
   "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",
    "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": 84,
   "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": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/work/cetin/software/miniconda3/envs/env1/lib/python3.11/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": 86,
   "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": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABkAAAAIlCAYAAACNejRdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAADUd0lEQVR4nOz9f3wU5b3//z+XBEKksBIwCSlRaYtURa0HWwR9Fyi/pCK12mJLD5XW+uOgUgpoi5xWbAUULdJCtWo9xiMqfvs9pUeqRbFVPBb8FaWV1mJtUbESYzUmoDGQsJ8/yO7OZGfnx+7M7kzyuN9uuSWZveaaa2Y3u9eVuV6vK5ZIJBICAAAAAAAAAADoRnoVuwEAAAAAAAAAAAB+4wYIAAAAAAAAAADodrgBAgAAAAAAAAAAuh1ugAAAAAAAAAAAgG6HGyAAAAAAAAAAAKDb4QYIAAAAAAAAAADodrgBAgAAAAAAAAAAuh1ugAAAAAAAAAAAgG6HGyAAAAAAAAAAAKDb4QYIAAAAAAAAAADodrgBAiA05syZo7q6umI3AwAAAAAii3EVAABp3AABAAAAAAAAAADdDjdAAHQb999/v44//niVl5crFotp+/btxW6SpaVLlyoWixW7GSlha48kbd26VUuXLtV7772X8VhdXZ1isZheffXVgrcLAAAA6M6C7Gvb9fGLLUpjIsZDAOANN0AAFNX06dN1+OGH6/DDD9e9996ruXPnpn6/7rrrXNfz9ttva/bs2fr4xz+uTZs2adu2bTrmmGMCbDmCtHXrVl1zzTWWg6MzzzxT27Zt05AhQwrfMAAAACCE/BpXBcmuj49M2a4X4yEA8Ka02A0A0LP95je/Sf08Z84cjR8/XnPmzPFcz8svv6wDBw7o3//93zVu3DgfW4hC+uCDD3TYYYfZljniiCN0xBFHFKhFAAAAQPj5Na5C8TmNiRgPAYA3RIAA8N2TTz6pKVOmKB6Pa+DAgTrzzDP1t7/9LbDjzZkzR6effrok6bzzzlMsFtP48eNTbZk4caL69++vww47TGPHjtWDDz5oWcfRRx+dsb1rKHTy9z//+c/66le/qng8rqqqKn3zm99Uc3Nzxv4PPvigPvWpT6msrEzDhg3TjTfe6Onc/va3v2nWrFmqrKxUWVmZjj32WP3sZz+zbKObNuXanl//+teKxWL63e9+l/HYLbfcolgspj/96U85tfv555/Xl770JQ0cOFAf//jHtXTpUl1xxRWSpGHDhikWiykWi+nxxx+XZB3y/de//lVf/epXVVVVpbKyMh155JH6+te/rra2Ns/X8+2339ZFF12k2tpalZWV6YgjjtBpp52mRx991NW1AgAAAPxQ6HGVXTucxlROfWinPn42fo+HpMKMidy029h2L2OibCmw3IyJGA8B6ImIAAHgq6VLl+pHP/qR5syZo/nz56u1tVXXXHONJk6cqL/85S/6yEc+knXfurq6nI75/e9/X5/5zGd06aWXavny5ZowYYIGDBigLVu2aPLkyTrxxBN1xx13qKysTDfffLPOOuss3XfffTrvvPNyPEvp3HPP1XnnnacLLrhAL774ohYvXixJ+q//+q9Umd/97nf6whe+oDFjxmj9+vXq6OjQypUr9dZbb7k6xl/+8heNHTtWRx55pH784x+rurpaDz/8sObNm6d//etfuvrqqz21KZ/2TJ8+XZWVlbrzzjs1ceJE02N1dXX6t3/7N5144ok5tfucc87RV77yFV1yySV6//33NWrUKL377rtas2aNfvWrX6VCu4877jjLtv3xj3/U6aefrsGDB+uHP/yhhg8frj179uiBBx7Q/v37VVZW5qlds2fP1vPPP69ly5bpmGOO0Xvvvafnn39e77zzjuN1AgAAAPxQjHGVFbdjKqc+9Le+9S1PfXzJ//GQVJgxkdd2S97GRFZrf7gZEzEeAtBjJQDAJxs3bkxISqxcudK0/eWXX05ISqxbty5jnzPOOCPRr18/y69ly5a5PvZjjz2WkJT45S9/mdp26qmnJiorKxN79+5NbWtvb0+MHDkyMXTo0MTBgwdT288///zEUUcdlVHv1VdfnTC+VSZ/73qOc+fOTfTt29dU5+jRoxM1NTWJ1tbW1LaWlpZERUVFws3b79SpUxNDhw5NNDc3m7Zfdtllib59+ybeffddT23Ktz0LFixIlJeXJ957773Utr/85S8JSYk1a9bk3O4f/OAHGce64YYbEpISu3btynjszjvvND32uc99LnH44YcnGhsbbdvvtl0f+chHEvPnz7etCwAAAAhKMcdVXfvabsdUbvrQdn18K36PhxKJwoyJ3Lbb2HYvY6Kuz1Ei4W5MxHgIQE9FCiwAvvnBD36gj3/84/r2t7+t9vb21NewYcNUXl6uf/zjHxn7/Pa3v9W+ffssv6666qqc2/L+++/r6aef1pe+9CXT7KiSkhLNnj1bb7zxhnbu3Jlz/TNmzDD9fuKJJ+rDDz9UY2Nj6vjPPvuszjnnHPXt2zdVrn///jrrrLMc6//www/1u9/9Tl/84hd12GGHma7n5z//eX344Yd66qmnXLcp3/ZI0je/+U21trbq/vvvT2278847VVZWplmzZuXc7nPPPdfV8a188MEH2rJli2bOnGmbB9dLuz7zmc+orq5O1157rZ566ikdOHAg5/YBAAAAXoVlXOVlTOV3H9rv8VDyfIIeE+XSbin4MRHjIQA9GTdAAPiioaFBL7zwgv7+97+rrKxMvXv3Nn21trbq8MMPL1h7mpqalEgkUqHCRjU1NZKUVwjvoEGDTL8n0yy1tramjn/w4EFVV1dn7Gu1rat33nlH7e3tWrNmTca1/PznPy9J+te//uW6Tfm2R5KOP/54ffrTn9add94pSero6NC6dev0hS98QRUVFTm32+o5cqupqUkdHR0aOnSobTkv7br//vt1/vnn6xe/+IXGjBmjiooKff3rX1dDQ0PO7QQAAADcCNO4ysuYyu8+tN/joeT5BD0myqXdUvBjIsZDAHoy1gAB4Ivdu3dLkm666abUguRdffzjHy9YewYOHKhevXppz549GY+9+eabkqTBgwentvXt2zdjwWzJunPq9vixWMyyk+im4zhw4MDUzKpLL73UssywYcMK1p6kb3zjG5o7d65eeukl/eMf/9CePXv0jW98I692GxeZ96qiokIlJSV64403bMt5adfgwYO1evVqrV69Wq+//roeeOABfe9731NjY6M2bdqUc1sBAAAAJ2EaV3kZU/ndh/Z7PJSsM+gxUa7tDnpMxHgIQE/GDRAAvkhGAMRiMZ1yyilFbo3Ur18/jR49Wr/61a904403qry8XJJ08OBBrVu3TkOHDtUxxxyTKn/00UersbFRb731lqqqqiRJ+/fv18MPP5zz8T/zmc/oV7/6lW644YZUiPXevXu1ceNGx/0PO+wwTZgwQS+88IJOPPFE9enTJ6d2+NWepK9+9atasGCB6urq9I9//EMf/ehHNWXKFN/b3XW2Vjbl5eUaN26cfvnLX2rZsmWmm1pGubbryCOP1GWXXabf/e53+sMf/uDtJAAAAACPwjSu8jqmSsrWh3bbx5f8Hw8lzyfoMZGf7fZzTMR4CEBPxg0QAL74+Mc/rgkTJug///M/tW/fPo0ePVqJREJ79uzRY489pvPPP1/jx48vaJtWrFihyZMna8KECVq0aJH69Omjm2++WTt27NB9991nmmVz3nnn6Qc/+IG+8pWv6IorrtCHH36on/70p+ro6Mj5+D/60Y90xhlnaPLkyVq4cKE6Ojp0/fXXq1+/fnr33Xcd9//JT36i008/Xf/v//0//cd//IeOPvpo7d27V6+88oo2btyo3//+9wVtjyQdfvjh+uIXv6i6ujq99957WrRokXr1MmdT9KPdJ5xwQqqu888/X71799aIESPUv3//jLKrVq3S6aefrtGjR+t73/uePvGJT+itt97SAw88oFtvvTW1j5t2NTc3a8KECZo1a5Y++clPqn///nr22We1adMmnXPOOa6uEQAAAJCrsI2r3Iyp3PahvfTxk+X8HA9JhRkT+dXubNfLips
      "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": 88,
   "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": 89,
   "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\"$E>10$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": 90,
   "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": 92,
   "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=[[0,1e5],[0,1e4]]) #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=[[0,1e5],[0,1e4]]) #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": 93,
   "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(eta_found, p_found/(1e3), bins=200, cmap=plt.cm.jet, cmin=0, range=[[0,7],[0,2e2]]) #50]])\n",
    "ax0.set_xlabel(r\"$\\eta$\")\n",
    "ax0.set_ylabel(r\"$p$ [GeV]\")\n",
    "ax0.set_title(\"found eta over 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],[0,2e2]]) #50]])\n",
    "ax1.set_xlabel(r\"$\\eta$\")\n",
    "ax1.set_ylabel(r\"$p$ [GeV]\")\n",
    "ax1.set_title(\"lost eta over 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",
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  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
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     "execution_count": 16,
     "metadata": {},
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   ],
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    "tracked[1].tolist()"
   ]
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  {
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