LHCb Tracks Extrapolator

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import uproot\n",
    "from matplotlib.image import NonUniformImage\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the root file\n",
    "tracks = uproot.open('/share/lazy/tboettch/BsPhiPhi_VeloUT_MCMatch_TrueTraj.root')['tracks']\n",
    "\n",
    "#extract the different arrays from the file\n",
    "arr = tracks.arrays([b'pt',b'p',b'eta',b'tx',b'ty',b'ox',b'oy',b'oz',b'rec_scifi',b'ut_p',b'scifi_p',b'q',b'long',b'velo_x',b'velo_y',b'velo_z',\n",
    "                     b'ut_x0',b'ut_z0',b'ut_dxdy',b'ut_ymin',b'ut_ymax',b'scifi_x0',b'scifi_z0',b'scifi_dxdy',b'scifi_ymin',b'scifi_ymax',b'true_traj_x',\n",
    "                     b'true_traj_y',b'true_traj_z'])\n",
    "\n",
    "#create individual Python arrays for each set of information \n",
    "p = arr[b'p']\n",
    "charge = arr[b'q']\n",
    "rec_scifi = arr [b'rec_scifi']\n",
    "pt = arr[b'pt']\n",
    "tx_i = arr[b'tx']\n",
    "ty_i = arr[b'ty']\n",
    "x_traj = arr[b'true_traj_x']\n",
    "y_traj = arr[b'true_traj_y']\n",
    "z_traj = arr[b'true_traj_z']\n",
    "velo_x = arr[b'velo_x']\n",
    "velo_y = arr[b'velo_y']\n",
    "velo_z = arr[b'velo_z']\n",
    "ut_x0 = arr[b'ut_x0']\n",
    "ut_z0 = arr[b'ut_z0']\n",
    "scifi_x0 = arr[b'scifi_x0']\n",
    "scifi_z0 = arr[b'scifi_z0']\n",
    "ut_ymin = arr[b'ut_ymin']\n",
    "ut_ymax = arr[b'ut_ymax']\n",
    "scifi_ymax = arr[b'scifi_ymax']\n",
    "scifi_ymin = arr[b'scifi_ymin']\n",
    "ut_dxdy = arr[b'ut_dxdy']\n",
    "scifi_dxdy = arr[b'scifi_dxdy']\n",
    "long = arr[b'long']\n",
    "scifi_p =  arr[b'scifi_p']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#select tracks based on specific conditions. Add or remove filters by changing parameters inside the if commands below\n",
    "\n",
    "p_l = 3000.\n",
    "p_u = 5000.\n",
    "\n",
    "\n",
    "p1 = [p[x] for x in range(len(p)) if (p_l < p[x] < p_u) ]\n",
    "rec_scifi1 = [rec_scifi[x] for x in range(len(p)) if ( p_l < p[x] < p_u) ]\n",
    "pt1 = [pt[x] for x in range(len(p)) if (p_l < p[x] < p_u) ]\n",
    "tx_i1 = [tx_i[x] for x in range(len(p)) if (p_l < p[x] < p_u) ]\n",
    "ty_i1 = [ty_i[x] for x in range(len(p)) if (p_l < p[x] < p_u) ]\n",
    "x_traj1 = [x_traj[x] for x in range(len(p)) if (p_l < p[x] < p_u) ]\n",
    "y_traj1 = [y_traj[x] for x in range(len(p)) if (p_l < p[x] < p_u) ]\n",
    "z_traj1 = [z_traj[x] for x in range(len(p)) if (p_l < p[x] < p_u) ]\n",
    "velo_x1 = [velo_x[x] for x in range(len(p)) if (p_l < p[x] < p_u) ]\n",
    "velo_y1 = [velo_y[x] for x in range(len(p)) if (p_l < p[x] < p_u) ]\n",
    "velo_z1 = [velo_z for x in range(len(p)) if (p_l< p[x] < p_u) ]\n",
    "ut_x01 = [ut_x0[x] for x in range(len(p)) if (p_l < p[x] < p_u) ]\n",
    "ut_z01 = [ut_z0[x] for x in range(len(p)) if ( p_l < p[x] < p_u) ]\n",
    "scifi_x01 = [scifi_x0[x] for x in range(len(p)) if ( p_l < p[x] < p_u) ]\n",
    "scifi_z01 = [scifi_z0[x] for x in range(len(p)) if ( p_l < p[x] < p_u) ]\n",
    "ut_ymin1 = [ut_ymin[x] for x in range(len(p)) if ( p_l < p[x] < p_u) ]\n",
    "ut_ymax1 = [ut_ymax[x] for x in range(len(p)) if ( p_l < p[x] < p_u) ]\n",
    "scifi_ymax1 = [scifi_ymax[x] for x in range(len(p)) if (p_l < p[x] < p_u) ]\n",
    "scifi_ymin1 = [scifi_ymin[x] for x in range(len(p)) if ( p_l < p[x] < p_u) ]\n",
    "ut_dxdy1 = [ut_dxdy[x] for x in range(len(p)) if ( p_l < p[x] < p_u) ]\n",
    "scifi_dxdy1 = [scifi_dxdy[x] for x in range(len(p)) if (p_l < p[x] < p_u) ]\n",
    "long1 = [long[x] for x in range(len(p)) if ( p_l < p[x] < p_u) ]\n",
    "scifi_p1 = [scifi_p[x] for x in range(len(p)) if ( p_l < p[x] < p_u) ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#open the magnetic field map file and read it\n",
    "file = open(\"smooth_v5r0.map\",'r')\n",
    "rows=file.readlines()\n",
    "data=[]\n",
    "for x in rows:\n",
    "    data.append(x.split())\n",
    "file.close()\n",
    "\n",
    "#creating empty arrays to store respective components of magnetic field\n",
    "B_x = np.zeros((81,71,121))\n",
    "B_y = np.zeros((81,71,121))\n",
    "B_z = np.zeros((81,71,121))\n",
    "\n",
    "#create unique indexes for every 3-dimensional coordinate. Much faster to retrieve magnetic field from indices\n",
    "# than reading the file every single time\n",
    "# the origin of the code assigning indices is in the file called field instructions\n",
    "for x in data:\n",
    "    \n",
    "    if abs(int(x[0]))>4000 or abs(int(x[1]))>3500:\n",
    "        continue\n",
    "        \n",
    "    ix = (int(x[0])+4001)//100\n",
    "    iy = (int(x[1])+3501)//100\n",
    "    iz = (int(x[2])+ 501)//100\n",
    "    \n",
    "    if ix<0 or ix>80 or iy<0 or iy>70:\n",
    "        continue\n",
    "    \n",
    "    if iz>120:\n",
    "        break\n",
    "    \n",
    "        \n",
    "    B_x[ix][iy][iz] = x[3]\n",
    "    B_y[ix][iy][iz] = x[4]\n",
    "    B_z[ix][iy][iz] = x[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#everything here is in millimeters and MeV/c. Below's is the correct value of c for our units\n",
    "# these are the initial conditions for the extrapolator. Make changes here. r is the index within the root file. \n",
    "r = 1\n",
    "p_m = float(p1[r])\n",
    "tx = float(tx_i1[r])\n",
    "ty = float(ty_i1[r])\n",
    "q = int(charge1[r])\n",
    "x1 = x_traj1[r][0]\n",
    "y1 = y_traj1[r][0]\n",
    "z1 = z_traj1[r][0]\n",
    "s = np.array([x1,y1,z1])\n",
    "c = 299.792"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# retrieve x-coordinates of the UT and SciFi strips from the y-coordinates and slope.\n",
    "def x_coordinate(y, dxdy, x0):\n",
    "    x = y*dxdy + x0\n",
    "    return x\n",
    "# using the x-coordinate function to get UT and SciFi strips coordinates to plot\n",
    "ut_xmin = []\n",
    "ut_xmax = []\n",
    "scifi_xmax = []\n",
    "scifi_xmin = []\n",
    "for y, dxdy, x0 in zip(ut_ymin1[r], ut_dxdy1[r], ut_x01[r]):\n",
    "    x = x_coordinate(y, dxdy, x0)\n",
    "    ut_xmin.append(x)\n",
    "    \n",
    "for y, dxdy, x0 in zip(ut_ymax1[r], ut_dxdy1[r], ut_x01[r]):\n",
    "    x = x_coordinate(y, dxdy, x0)\n",
    "    ut_xmax.append(x)\n",
    "    \n",
    "for y, dxdy, x0 in zip(scifi_ymin1[r], scifi_dxdy1[r], scifi_x01[r]):\n",
    "    x = x_coordinate(y, dxdy, x0)\n",
    "    scifi_xmin.append(x)\n",
    "    \n",
    "for y, dxdy, x0 in zip(scifi_ymax1[r], scifi_dxdy1[r], scifi_x01[r]):\n",
    "    x = x_coordinate(y, dxdy, x0)\n",
    "    scifi_xmax.append(x)\n",
    "       \n",
    "ut_xmin = np.array(ut_xmin).T\n",
    "ut_xmax = np.array(ut_xmax).T\n",
    "scifi_xmin = np.array(scifi_xmin).T \n",
    "scifi_xmax = np.array(scifi_xmax).T "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the extrapolator. Here's more information about the code: https://gitlab.cern.ch/lhcb/Rec/-/blob/2018-patches/Tr/TrackExtrapolators/src/TrackParabolicExtrapolator.cpp\n",
    "# and the equations used here: \n",
    "def propagator(s,tx,ty,q,z):\n",
    "    \n",
    "    #dz0 is the change in z\n",
    "    dz0 = (z - s[2])\n",
    "    \n",
    "    x_mid = s[0] + (0.5*tx*dz0)\n",
    "    y_mid = s[1] + (0.5*ty*dz0)\n",
    "    m = np.array([x_mid, y_mid,s[2]+(0.5*dz0)])\n",
    "    \n",
    "    \n",
    "    ix = (int(m[0])+4001)//100\n",
    "    iy = (int(m[1])+3501)//100\n",
    "    iz = (int(m[2])+ 501)//100\n",
    "    \n",
    "    if (iz>119):\n",
    "        iz = 119\n",
    "    \n",
    "    #dx,dy,dz here are only used to calculate magnetic field. They are not the displacement\n",
    "    dx = (m[0]+4000)/100-ix\n",
    "    dy = (m[1]+3500)/100-iy\n",
    "    dz = (m[2]+ 500)/100-iz\n",
    "    \n",
    "    ex = 1-dx\n",
    "    ey = 1-dy\n",
    "    ez = 1-dz\n",
    "    \n",
    "    if abs(m[0])>3999 or abs(m[1])>3499 or (m[2]) < -499 or (m[2]) >= 11399:\n",
    "        m_B = np.array([0,0,0])\n",
    "    else:\n",
    "    #the commmented out code is the interpolator. Use that code for even more precise magnetic field information. \n",
    "    #if you use the interpolator, comment out the current m_B line. \n",
    "        #bx = dx*(dy*(dz*B_x[ix+1][iy+1][iz+1]+ez*B_x[ix+1][iy+1][iz])+ey*(dz*B_x[ix+1][iy][iz+1]+ez*B_x[ix+1][iy][iz]))+ ex*(dy*(dz*B_x[ix][iy+1][iz+1]+ez*B_x[ix][iy+1][iz])+ey*(dz*B_x[ix][iy][iz+1]+ez*B_x[ix][iy][iz]))\n",
    "        #by = dx*(dy*(dz*B_y[ix+1][iy+1][iz+1]+ez*B_y[ix+1][iy+1][iz])+ey*(dz*B_y[ix+1][iy][iz+1]+ez*B_y[ix+1][iy][iz]))+ ex*(dy*(dz*B_y[ix][iy+1][iz+1]+ez*B_y[ix][iy+1][iz])+ey*(dz*B_y[ix][iy][iz+1]+ez*B_y[ix][iy][iz]))\n",
    "        #bz = dx*(dy*(dz*B_z[ix+1][iy+1][iz+1]+ez*B_z[ix+1][iy+1][iz])+ey*(dz*B_z[ix+1][iy][iz+1]+ez*B_z[ix+1][iy][iz]))+ ex*(dy*(dz*B_z[ix][iy+1][iz+1]+ez*B_z[ix][iy+1][iz])+ey*(dz*B_z[ix][iy][iz+1]+ez*B_z[ix][iy][iz]))\n",
    "        #m_B = np.array([bx,by,bz]) \n",
    "        m_B = np.array([B_x[ix][iy][iz],B_y[ix][iy][iz],B_z[ix][iy][iz]])/1000\n",
    "    \n",
    "    \n",
    "    nTx2 = 1 + tx**2\n",
    "    nTy2 = 1 + ty**2\n",
    "    norm = np.sqrt(nTx2 + nTy2 -1)\n",
    "    \n",
    "    m_ax = norm*(ty*(tx*m_B[0]+m_B[2])-(nTx2*m_B[1]))\n",
    "    m_ay = norm*(-tx*(ty*m_B[1]+m_B[2])+(nTy2*m_B[0]))\n",
    "    \n",
    "    fac = c*dz0\n",
    "    fact = fac*q/p_m\n",
    "    \n",
    "    s_1 = s[0]+dz0*(tx+(0.5*m_ax*fact))\n",
    "    s_2 = s[1]+dz0*(ty+(0.5*m_ay*fact))\n",
    "    \n",
    "    s_new = np.array([float(s_1), float(s_2), z])\n",
    "    tx_new = tx + (m_ax * fact)\n",
    "    ty_new = ty + (m_ay * fact)\n",
    "    \n",
    "    return s_new, tx_new, ty_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the tracks after we have used the propagtor to obtain the trajectory\n",
    "def plot_tracks(z_traj, x_traj, y_traj,xc, ut_z0, ut_xmin, ut_xmax, ut_ymin, ut_ymax, scifi_xmax, scifi_xmin, scifi_z0, scifi_ymin,\n",
    "             scifi_ymax):\n",
    "    fig = plt.figure(figsize=(20,22))\n",
    "    axs = fig.subplots(2, 1)\n",
    "    axs[0].set_title(\"Real Track\")\n",
    "    axs[0].set_xlabel(\"z (mm)\")\n",
    "    axs[0].set_ylabel(\"x (mm)\")\n",
    "    axs[1].set_title(\"Real Track\")\n",
    "    axs[1].set_xlabel(\"z (mm)\")\n",
    "    axs[1].set_ylabel(\"y (mm)\")\n",
    "  \n",
    "    \n",
    "    axs[0].set_xlim(0,12000)\n",
    "    axs[0].set_ylim(-4000,4000)\n",
    "    axs[1].set_xlim(0,12000)\n",
    "    axs[1].set_ylim(-4000,4000)\n",
    "\n",
    "    \n",
    "    axs[0].plot(z_traj[r], x_traj[r],'b-', label = 'MC Track', alpha = 0.3, linewidth = 4 )\n",
    "    axs[1].plot(z_traj[r], y_traj[r],'b-', label = 'MC Track', alpha = 0.3, linewidth = 4 )\n",
    "    axs[0].plot(xc[2], xc[0], '--', color = \"#002d04\", label = 'Extrapolated Track', alpha = 1 )\n",
    "    axs[1].plot(xc[2], xc[1], '--', color = \"#002d04\", label = 'Extrapolated Track', alpha = 1)\n",
    "    axs[0].plot(velo_z1[r], velo_x1[r], 'o', color = 'purple', label = 'Vertex Locator Hits')\n",
    "    axs[1].plot(velo_z1[r], velo_y1[r], 'o', color = 'purple', label = 'Vertex Locator Hits')\n",
    "    \n",
    "    for i in range(len(ut_z0[r])):\n",
    "        axs[0].plot([ut_z0[r][i],ut_z0[r][i]],[ut_xmin[i],ut_xmax[i]],'r-o', label = 'Upstream Tracker Hits Strips')\n",
    "    for i in range(len(ut_ymin[r])):\n",
    "        axs[1].plot([ut_z0[r][i],ut_z0[r][i]],[ut_ymin[r][i],ut_ymax[r][i]], 'r-o', label = 'Upstream Tracker Hits Strips')\n",
    "    for i in range(len(scifi_z0[r])):\n",
    "        axs[0].plot([scifi_z0[r][i],scifi_z0[r][i]], [scifi_xmin[i],scifi_xmax[i]], 'k-o', label = 'Scintillating Fiber Hits Strips')\n",
    "    for i in range(len(scifi_ymin[r])):\n",
    "        axs[1].plot([scifi_z0[r][i],scifi_z0[r][i]], [scifi_ymin[r][i],scifi_ymax[r][i]],'k-o', label = 'Scintillating Fiber Hits Strips')\n",
    "    \n",
    "    \n",
    "    handles, labels = axs[0].get_legend_handles_labels()\n",
    "    newLabels, newHandles = [], []\n",
    "    for handle, label in zip(handles, labels):\n",
    "        if label not in newLabels:\n",
    "            newLabels.append(label)\n",
    "            newHandles.append(handle)\n",
    "    axs[0].legend(newHandles, newLabels, prop={'size': 16})\n",
    "    axs[1].legend(newHandles, newLabels, prop={'size': 16})\n",
    "    \n",
    "    \n",
    "    for i in range(0,2):\n",
    "        for item in ([axs[i].title, axs[i].xaxis.label, axs[i].yaxis.label] +\n",
    "                 axs[i].get_xticklabels() + axs[i].get_yticklabels()):\n",
    "            item.set_fontsize(22)\n",
    "            \n",
    "    axs[0].text(0.7, 0.04, 'Momentum = ' + str(round(p_m,2)) + ' MeV',fontsize = 18, bbox=dict(facecolor='white', alpha=0.5),transform=axs[0].transAxes)\n",
    "    axs[1].text(0.7, 0.04, 'Momentum = ' + str(round(p_m,2)) + ' MeV',fontsize = 18, bbox=dict(facecolor='white', alpha=0.5),transform=axs[1].transAxes)\n",
    "            \n",
    "    plt.subplots_adjust(left=0.1,\n",
    "                    bottom=0.1, \n",
    "                    right=0.9, \n",
    "                    top=0.9, \n",
    "                    wspace=0.4, \n",
    "                    hspace=0.4)\n",
    "    \n",
    "    plt.show\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using the propagator to obtain the trajectory\n",
    "xc = [s[:,]]\n",
    "zs = np.linspace(0.,3000.,1000)\n",
    "zt = np.linspace(3000.,7000., 10000)\n",
    "zu = np.linspace(7000.,12000.,1000)\n",
    "        \n",
    "for z in zs:\n",
    "    s, tx, ty = propagator(s, tx, ty, q, z)\n",
    "    xc.append(s)\n",
    "    \n",
    "for z in zt:\n",
    "    s, tx, ty = propagator(s, tx, ty, q, z)\n",
    "    xc.append(s)\n",
    "    \n",
    "for z in zu:\n",
    "    s, tx, ty = propagator(s, tx, ty, q, z)\n",
    "    xc.append(s)\n",
    "    \n",
    "xc = np.array(xc).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1584 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the track along with the VeLo, UT, SciFi hits, and the true trajectory from Monte Carlo\n",
    "plot_tracks(z_traj1, x_traj1, y_traj1,xc, ut_z01, ut_xmin, ut_xmax, ut_ymin1, ut_ymax1, scifi_xmax, scifi_xmin, scifi_z01, scifi_ymin1,\n",
    "             scifi_ymax1)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}