Projektpraktikum/B_updown.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\t\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",
    "from scipy.optimize import curve_fit\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10522"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "\n",
    "#selektiere nur elektronen von B->K*ee und nur solche mit einem momentum von ueber 5 GeV \n",
    "allcolumns = file.arrays()\n",
    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 9056\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 1466\n",
    "\n",
    "ak.num(found, axis=0) + ak.num(lost, axis=0)\n",
    "#ak.count(found, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eff all =  0.8606728758791105 +/- 0.003375885792719708\n"
     ]
    }
   ],
   "source": [
    "def t_eff(found, lost, axis = 0):\n",
    "    sel = ak.num(found, axis=axis)\n",
    "    des = ak.num(lost, axis=axis)\n",
    "    return sel/(sel + des)\n",
    "\n",
    "def eff_err(found, lost):\n",
    "    n_f = ak.num(found, axis=0)\n",
    "    n_all = ak.num(found, axis=0) + ak.num(lost,axis=0)\n",
    "    return 1/n_all * np.sqrt(np.abs(n_f*(1-n_f/n_all)))\n",
    "\n",
    "\n",
    "print(\"eff all = \", t_eff(found, lost), \"+/-\", eff_err(found, lost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#try excluding all photons that originate from a vtx @ z>9500mm\n",
    "#ignore all brem vertices @ z>9500mm \n",
    "\n",
    "#found\n",
    "\n",
    "brem_e_f = found[\"brem_photons_pe\"]\n",
    "brem_z_f = found[\"brem_vtx_z\"]\n",
    "e_f = found[\"energy\"]\n",
    "length_f = found[\"brem_vtx_z_length\"]\n",
    "\n",
    "brem_f = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(found,axis=0)):\n",
    "    brem_f.begin_record()\n",
    "    #[:,\"energy\"] energy\n",
    "    brem_f.field(\"energy\").append(e_f[itr])\n",
    "    #[:,\"photon_length\"] number of vertices\n",
    "    brem_f.field(\"photon_length\").integer(length_f[itr])\n",
    "    #[:,\"brem_photons_pe\",:] photon energy \n",
    "    brem_f.field(\"brem_photons_pe\").append(brem_e_f[itr])\n",
    "    #[:,\"brem_vtx_z\",:] brem vtx z\n",
    "    brem_f.field(\"brem_vtx_z\").append(brem_z_f[itr])\n",
    "    brem_f.end_record()\n",
    "\n",
    "brem_f = ak.Array(brem_f)\n",
    "\n",
    "#lost\n",
    "\n",
    "brem_e_l = lost[\"brem_photons_pe\"]\n",
    "brem_z_l = lost[\"brem_vtx_z\"]\n",
    "e_l = lost[\"energy\"]\n",
    "length_l = lost[\"brem_vtx_z_length\"]\n",
    "\n",
    "brem_l = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(lost,axis=0)):\n",
    "    brem_l.begin_record()\n",
    "    #[:,\"energy\"] energy\n",
    "    brem_l.field(\"energy\").append(e_l[itr])\n",
    "    #[:,\"photon_length\"] number of vertices\n",
    "    brem_l.field(\"photon_length\").integer(length_l[itr])\n",
    "    #[:,\"brem_photons_pe\",:] photon energy \n",
    "    brem_l.field(\"brem_photons_pe\").append(brem_e_l[itr])\n",
    "    #[:,\"brem_vtx_z\",:] brem vtx z\n",
    "    brem_l.field(\"brem_vtx_z\").append(brem_z_l[itr])\n",
    "    brem_l.end_record()\n",
    "\n",
    "brem_l = ak.Array(brem_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cut_brem_found = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(brem_f, axis=0)):\n",
    "    cut_brem_found.begin_record()\n",
    "    cut_brem_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
    "    \n",
    "    cut_brem_found.field(\"brem_photons_pe\")\n",
    "    cut_brem_found.begin_list()\n",
    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
    "        if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n",
    "            continue\n",
    "        else:\n",
    "            cut_brem_found.real(brem_f[itr,\"brem_photons_pe\", jentry])\n",
    "            \n",
    "            #cut_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
    "    cut_brem_found.end_list()\n",
    "    \n",
    "    cut_brem_found.field(\"brem_vtx_z\")\n",
    "    cut_brem_found.begin_list()\n",
    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
    "        if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n",
    "            continue\n",
    "        else:\n",
    "            cut_brem_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
    "    cut_brem_found.end_list()\n",
    "    \n",
    "\n",
    "    \n",
    "    cut_brem_found.end_record()\n",
    "\n",
    "cut_brem_found = ak.Array(cut_brem_found)\n",
    "\n",
    "\n",
    "\n",
    "cut_brem_lost = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(brem_l, axis=0)):\n",
    "    cut_brem_lost.begin_record()\n",
    "    cut_brem_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n",
    "    \n",
    "    \n",
    "    cut_brem_lost.field(\"brem_photons_pe\")\n",
    "    cut_brem_lost.begin_list()\n",
    "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
    "        if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n",
    "            continue\n",
    "        else:\n",
    "            cut_brem_lost.real(brem_l[itr,\"brem_photons_pe\", jentry])\n",
    "            \n",
    "            #cut_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
    "    cut_brem_lost.end_list()\n",
    "    \n",
    "    cut_brem_lost.field(\"brem_vtx_z\")\n",
    "    cut_brem_lost.begin_list()\n",
    "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
    "        if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n",
    "            continue\n",
    "        else:\n",
    "            cut_brem_lost.real(brem_l[itr, \"brem_vtx_z\",jentry])\n",
    "    cut_brem_lost.end_list()\n",
    "        \n",
    "    cut_brem_lost.end_record()\n",
    "\n",
    "cut_brem_lost = ak.Array(cut_brem_lost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>{energy: 9.36e+03,\n",
       " brem_photons_pe: [2.47e+03, 170, 224, 388, 3.23e+03, 809, 172, 224],\n",
       " brem_vtx_z: [400, 501, 638, 667, 677, 709, 8.58e+03, 9.28e+03]}\n",
       "---------------------------------------------------------------------\n",
       "type: {\n",
       "    energy: float64,\n",
       "    brem_photons_pe: var * float64,\n",
       "    brem_vtx_z: var * float64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Record {energy: 9.36e+03, ...} type='{energy: float64, brem_photons_pe: va...'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data in cut_brem_found and cut_brem_lost\n",
    "\n",
    "cut_length_found = ak.num(cut_brem_found[\"brem_photons_pe\"],axis=-1)\n",
    "cut_length_lost = ak.num(cut_brem_lost[\"brem_photons_pe\"], axis=-1)\n",
    "\n",
    "cut_brem_found[1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### in magnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "inmagnet_found = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(cut_brem_found, axis=0)):\n",
    "    \n",
    "    inmagnet_found.begin_record()\n",
    "    inmagnet_found.field(\"energy\").real(cut_brem_found[itr,\"energy\"])\n",
    "    \n",
    "    inmagnet_found.field(\"brem_photons_pe\")\n",
    "    inmagnet_found.begin_list()\n",
    "    for jentry in range(cut_length_found[itr]):\n",
    "        if (cut_brem_found[itr, \"brem_vtx_z\", jentry]>1500):\n",
    "            if cut_brem_found[itr, \"brem_vtx_z\", jentry]<=9500:\n",
    "                inmagnet_found.real(cut_brem_found[itr,\"brem_photons_pe\",jentry])\n",
    "            else:\n",
    "                continue\n",
    "    inmagnet_found.end_list()\n",
    "    \n",
    "    inmagnet_found.field(\"brem_vtx_z\")\n",
    "    inmagnet_found.begin_list()\n",
    "    for jentry in range(cut_length_found[itr]):\n",
    "        if cut_brem_found[itr, \"brem_vtx_z\", jentry]>1500:\n",
    "            if cut_brem_found[itr,\"brem_vtx_z\",jentry]<=9500:\n",
    "                inmagnet_found.real(cut_brem_found[itr,\"brem_vtx_z\",jentry])\n",
    "            else:\n",
    "                continue\n",
    "    inmagnet_found.end_list()\n",
    "    inmagnet_found.end_record()\n",
    "    \n",
    "\n",
    "inmagnet_found = ak.Array(inmagnet_found)\n",
    "\n",
    "\n",
    "inmagnet_lost = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(cut_brem_lost, axis=0)):\n",
    "    \n",
    "    inmagnet_lost.begin_record()\n",
    "    inmagnet_lost.field(\"energy\").real(cut_brem_lost[itr,\"energy\"])\n",
    "    \n",
    "    inmagnet_lost.field(\"brem_photons_pe\")\n",
    "    inmagnet_lost.begin_list()\n",
    "    for jentry in range(cut_length_lost[itr]):\n",
    "        if (cut_brem_lost[itr, \"brem_vtx_z\", jentry]>1500):\n",
    "            if cut_brem_lost[itr, \"brem_vtx_z\", jentry]<=9500:\n",
    "                inmagnet_lost.real(cut_brem_lost[itr,\"brem_photons_pe\",jentry])\n",
    "            else:\n",
    "                continue\n",
    "    inmagnet_lost.end_list()\n",
    "    \n",
    "    inmagnet_lost.field(\"brem_vtx_z\")\n",
    "    inmagnet_lost.begin_list()\n",
    "    for jentry in range(cut_length_lost[itr]):\n",
    "        if cut_brem_lost[itr, \"brem_vtx_z\", jentry]>1500:\n",
    "            if cut_brem_lost[itr,\"brem_vtx_z\",jentry]<=9500:\n",
    "                inmagnet_lost.real(cut_brem_lost[itr,\"brem_vtx_z\",jentry])\n",
    "            else:\n",
    "                continue\n",
    "    inmagnet_lost.end_list()\n",
    "    inmagnet_lost.end_record()\n",
    "    \n",
    "\n",
    "inmagnet_lost = ak.Array(inmagnet_lost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff_energy=350\n",
    "#possibly: instead of checking if any photons exceed the cutoff, use the sum of all photon energies to separate nobrem and brem\n",
    "\n",
    "inmagnet_brem_found = inmagnet_found[ak.sum(inmagnet_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n",
    "magnet_energy_found = ak.to_numpy(inmagnet_brem_found[\"energy\"])\n",
    "magnet_eph_found = ak.to_numpy(ak.sum(inmagnet_brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "magnet_residual_found = magnet_energy_found - magnet_eph_found\n",
    "magnet_energyloss_found = magnet_eph_found/magnet_energy_found\n",
    "\n",
    "\n",
    "inmagnet_brem_lost = inmagnet_lost[ak.sum(inmagnet_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n",
    "magnet_energy_lost = ak.to_numpy(inmagnet_brem_lost[\"energy\"])\n",
    "magnet_eph_lost = ak.to_numpy(ak.sum(inmagnet_brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "magnet_residual_lost = magnet_energy_lost - magnet_eph_lost\n",
    "magnet_energyloss_lost = magnet_eph_lost/magnet_energy_lost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24784.620206013704"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.mean(magnet_eph_lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.hist(magnet_energyloss_found, alpha=0.5, bins=80, density=True, histtype='bar', color=\"blue\", label=\"found\")\n",
    "plt.hist(magnet_energyloss_lost, alpha=0.5, bins=80, density=True, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
    "\n",
    "#plt.vlines(ak.mean(both_eloss),0,3,colors=\"red\", label=\"mean\")\n",
    "plt.xlabel(r\"Energyloss Ratio $E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"counts (normed)\")\n",
    "plt.title(r'$B^0\\rightarrow K^{\\ast 0} e^+e^-$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
    "plt.legend(title=\"LHCb Simulation\", title_fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nstart = 0\n",
    "nend = 5e4\n",
    "plt.hist(magnet_residual_found, alpha=0.5, bins=70, density=True, histtype='bar', color=\"blue\", label=\"found\", range=[nstart, nend])\n",
    "plt.hist(magnet_residual_lost, alpha=0.5, bins=70, density=True, histtype='bar', color=\"darkorange\", label=\"lost\", range=[nstart, nend])\n",
    "\n",
    "#plt.vlines(ak.mean(both_eloss),0,3,colors=\"red\", label=\"mean\")\n",
    "#plt.xlim(0,50000)\n",
    "plt.xlabel(r\"Residual Energy in magnet $E_\\gamma$\")\n",
    "plt.ylabel(\"counts (normed)\")\n",
    "plt.title(r'$B^0\\rightarrow K^{\\ast 0} e^+e^-$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
    "plt.legend(title=\"LHCb Simulation\", title_fontsize=15)\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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cut_length_found[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split in Upstream and Downstream Events and analyse separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#try to find a split between energy lost before and after the magnet (z~5000mm)\n",
    "\n",
    "upstream_found = ak.ArrayBuilder()\n",
    "downstream_found = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(cut_brem_found, axis=0)):\n",
    "    upstream_found.begin_record()\n",
    "    upstream_found.field(\"energy\").real(cut_brem_found[itr,\"energy\"])\n",
    "    \n",
    "    downstream_found.begin_record()\n",
    "    downstream_found.field(\"energy\").real(cut_brem_found[itr,\"energy\"])\n",
    "    \n",
    "    upstream_found.field(\"brem_photons_pe\")\n",
    "    downstream_found.field(\"brem_photons_pe\")\n",
    "    upstream_found.begin_list()\n",
    "    downstream_found.begin_list()\n",
    "    for jentry in range(cut_length_found[itr]):\n",
    "        if (cut_brem_found[itr, \"brem_vtx_z\", jentry]>5000):\n",
    "            if cut_brem_found[itr, \"brem_vtx_z\", jentry]<=9500:\n",
    "                downstream_found.real(cut_brem_found[itr,\"brem_photons_pe\",jentry])\n",
    "            else:\n",
    "                continue\n",
    "        else:\n",
    "            upstream_found.real(cut_brem_found[itr,\"brem_photons_pe\", jentry])            \n",
    "    upstream_found.end_list()\n",
    "    downstream_found.end_list()\n",
    "    \n",
    "    upstream_found.field(\"brem_vtx_z\")\n",
    "    downstream_found.field(\"brem_vtx_z\")\n",
    "    upstream_found.begin_list()\n",
    "    downstream_found.begin_list()\n",
    "    for jentry in range(cut_length_found[itr]):\n",
    "        if cut_brem_found[itr, \"brem_vtx_z\", jentry]>5000:\n",
    "            if cut_brem_found[itr,\"brem_vtx_z\",jentry]<=9500:\n",
    "                downstream_found.real(cut_brem_found[itr,\"brem_vtx_z\",jentry])\n",
    "            else:\n",
    "                continue\n",
    "        else:\n",
    "            upstream_found.real(cut_brem_found[itr, \"brem_vtx_z\",jentry])\n",
    "    upstream_found.end_list()\n",
    "    downstream_found.end_list()\n",
    "    upstream_found.end_record()\n",
    "    downstream_found.end_record()\n",
    "    \n",
    "\n",
    "upstream_found = ak.Array(upstream_found)\n",
    "downstream_found = ak.Array(downstream_found)\n",
    "\n",
    "\n",
    "upstream_lost = ak.ArrayBuilder()\n",
    "downstream_lost = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(cut_brem_lost, axis=0)):\n",
    "    upstream_lost.begin_record()\n",
    "    upstream_lost.field(\"energy\").real(cut_brem_lost[itr,\"energy\"])\n",
    "    \n",
    "    downstream_lost.begin_record()\n",
    "    downstream_lost.field(\"energy\").real(cut_brem_lost[itr,\"energy\"])\n",
    "    \n",
    "    upstream_lost.field(\"brem_photons_pe\")\n",
    "    downstream_lost.field(\"brem_photons_pe\")\n",
    "    upstream_lost.begin_list()\n",
    "    downstream_lost.begin_list()\n",
    "    for jentry in range(cut_length_lost[itr]):\n",
    "        if (cut_brem_lost[itr, \"brem_vtx_z\", jentry]>5000):\n",
    "            if cut_brem_lost[itr, \"brem_vtx_z\", jentry]<=9500:\n",
    "                downstream_lost.real(cut_brem_lost[itr,\"brem_photons_pe\",jentry])\n",
    "            else:\n",
    "                continue\n",
    "        else:\n",
    "            upstream_lost.real(cut_brem_lost[itr,\"brem_photons_pe\", jentry])            \n",
    "    upstream_lost.end_list()\n",
    "    downstream_lost.end_list()\n",
    "    \n",
    "    upstream_lost.field(\"brem_vtx_z\")\n",
    "    downstream_lost.field(\"brem_vtx_z\")\n",
    "    upstream_lost.begin_list()\n",
    "    downstream_lost.begin_list()\n",
    "    for jentry in range(cut_length_lost[itr]):\n",
    "        if cut_brem_lost[itr, \"brem_vtx_z\", jentry]>5000:\n",
    "            if cut_brem_lost[itr,\"brem_vtx_z\",jentry]<=9500:\n",
    "                downstream_lost.real(cut_brem_lost[itr,\"brem_vtx_z\",jentry])\n",
    "            else:\n",
    "                continue\n",
    "        else:\n",
    "            upstream_lost.real(cut_brem_lost[itr, \"brem_vtx_z\",jentry])\n",
    "    upstream_lost.end_list()\n",
    "    downstream_lost.end_list()\n",
    "    upstream_lost.end_record()\n",
    "    downstream_lost.end_record()\n",
    "    \n",
    "\n",
    "upstream_lost = ak.Array(upstream_lost)\n",
    "downstream_lost = ak.Array(downstream_lost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>{energy: 4.62e+04,\n",
       " brem_photons_pe: [3.26e+03, 4.45e+03, 178, 1.45e+04, 1.1e+03, 3.79e+03],\n",
       " brem_vtx_z: [162, 187, 387, 487, 1.34e+03, 2.32e+03]}\n",
       "-------------------------------------------------------------------------\n",
       "type: {\n",
       "    energy: float64,\n",
       "    brem_photons_pe: var * float64,\n",
       "    brem_vtx_z: var * float64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Record {energy: 4.62e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upstream_found[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "upstream: cutoff energy = 350MeV, sample size: 1562\n",
      "eff =  0.9181 +/- 0.007\n"
     ]
    }
   ],
   "source": [
    "#plot efficiency against cutoff energy \n",
    "up_efficiencies = []\n",
    "up_deff = []\n",
    "\n",
    "\n",
    "for cutoff_energy in range(0,10050,200):\n",
    "\tup_nobrem_f = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
    "\tup_nobrem_l = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
    "\t\n",
    "\tif ak.num(up_nobrem_f,axis=0)+ak.num(up_nobrem_l,axis=0)==0:\n",
    "\t\tup_efficiencies.append(0)\n",
    "\t\tup_deff.append(0)\n",
    "\t\tcontinue\n",
    "\n",
    "\teff = t_eff(up_nobrem_f,up_nobrem_l)\n",
    "\tdeff = eff_err(up_nobrem_f,up_nobrem_l)\n",
    "\tup_efficiencies.append(eff)\n",
    "\tup_deff.append(deff)\n",
    "\n",
    "\t#print(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(up_nobrem_f,axis=0)+ak.num(up_nobrem_l,axis=0))\n",
    "\t#print(\"eff = \",np.round(eff,4), \"+/-\", np.round(eff_err(up_nobrem_f, up_nobrem_l),4))\n",
    "\n",
    "\"\"\"\n",
    "we see that a cutoff energy of xxxMeV is ideal because the efficiency drops significantly for higher values\n",
    "\"\"\"\n",
    "cutoff_energy = 350.0 #MeV\n",
    "\n",
    "\"\"\"\n",
    "better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n",
    "\"\"\"\n",
    "up_nobrem_found = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
    "up_nobrem_lost = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
    "\n",
    "print(\"\\nupstream: cutoff energy = 350MeV, sample size:\",ak.num(up_nobrem_found,axis=0)+ak.num(up_nobrem_lost,axis=0))\n",
    "print(\"eff = \",np.round(t_eff(up_nobrem_found, up_nobrem_lost),4), \"+/-\", np.round(eff_err(up_nobrem_found, up_nobrem_lost),3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nobrem_vertices\n",
      "upstream: cutoff energy = 350MeV, sample size: 1562\n",
      "eff =  0.9181 +/- 0.007\n",
      "\n",
      "downstream: cutoff energy = 350MeV, sample size: 5131\n",
      "eff =  0.8864 +/- 0.004\n"
     ]
    }
   ],
   "source": [
    "down_efficiencies = []\n",
    "down_deff = []\n",
    "\n",
    "for cutoff_energy in range(0,10050,200):\n",
    "\tdown_nobrem_f = downstream_found[ak.sum(downstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
    "\tdown_nobrem_l = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
    "\n",
    "\tif ak.num(down_nobrem_f,axis=0)+ak.num(down_nobrem_l,axis=0)==0:\n",
    "\t\tdown_efficiencies.append(0)\n",
    "\t\tdown_deff.append(0)\n",
    "\t\tcontinue\n",
    "\teff = t_eff(down_nobrem_f,down_nobrem_l)\n",
    "\tdeff = eff_err(down_nobrem_f,down_nobrem_l)\n",
    "\tdown_efficiencies.append(eff)\n",
    "\tdown_deff.append(deff)\n",
    "\n",
    "\n",
    "\t#print(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(down_nobrem_f,axis=0)+ak.num(down_nobrem_l,axis=0))\n",
    "\t#print(\"eff = \",np.round(eff,4), \"+/-\", np.round(eff_err(down_nobrem_f, down_nobrem_l),4))\n",
    "\n",
    "\"\"\"\n",
    "we see that a cutoff energy of xxxMeV is ideal because the efficiency drops significantly for higher values\n",
    "\"\"\"\n",
    "cutoff_energy = 350.0 #MeV\n",
    "\n",
    "\"\"\"\n",
    "better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n",
    "\"\"\"\n",
    "down_nobrem_found = downstream_found[ak.sum(downstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
    "down_nobrem_lost = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
    "\n",
    "\n",
    "print(\"nobrem_vertices\\nupstream: cutoff energy = 350MeV, sample size:\",ak.num(up_nobrem_found,axis=0)+ak.num(up_nobrem_lost,axis=0))\n",
    "print(\"eff = \",np.round(t_eff(up_nobrem_found, up_nobrem_lost),4), \"+/-\", np.round(eff_err(up_nobrem_found, up_nobrem_lost),3))\n",
    "\n",
    "print(\"\\ndownstream: cutoff energy = 350MeV, sample size:\",ak.num(down_nobrem_found,axis=0)+ak.num(down_nobrem_lost,axis=0))\n",
    "print(\"eff = \",np.round(t_eff(down_nobrem_found, down_nobrem_lost),4), \"+/-\", np.round(eff_err(down_nobrem_found, down_nobrem_lost),3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot efficiencies wrt cutoff energy\n",
    "fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(18,6))\n",
    "x_ = np.arange(0,10050,step=200)\n",
    "\n",
    "ax[0].errorbar(x_,up_efficiencies,yerr=up_deff, ls=\"\", capsize=1,fmt=\".\")\n",
    "ax[0].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"upstream\", ylim=[0.8,1.0])\n",
    "#ax[0].set_yticks(np.arange(0.8,1.01,step=0.02),minor=False)\n",
    "#ax[0].set_xticks(np.arange(0,10100,step=200),minor=True)\n",
    "#ax[0].grid()\n",
    "\n",
    "ax[1].errorbar(x_,down_efficiencies,yerr=down_deff, ls=\"\", capsize=1,fmt=\".\")\n",
    "ax[1].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"downstream\", ylim=[0.8,1.0])\n",
    "#ax[1].set_yticks(np.arange(0.8,1.01,step=0.02),minor=False)\n",
    "#ax[1].set_xticks(np.arange(0,10100,step=200),minor=True)\n",
    "#ax[1].grid(True)\n",
    "\n",
    "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, nobrem electrons\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brem vertices\n",
      "upstream eff =  0.851 +/- 0.004\n",
      "downstream eff =  0.836 +/- 0.005\n"
     ]
    }
   ],
   "source": [
    "cutoff_energy=350\n",
    "#possibly: instead of checking if any photons exceed the cutoff, use the sum of all photon energies to separate nobrem and brem\n",
    "\n",
    "upstream_brem_found = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n",
    "up_energy_found = ak.to_numpy(upstream_brem_found[\"energy\"])\n",
    "up_eph_found = ak.to_numpy(ak.sum(upstream_brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "up_residual_found = up_energy_found - up_eph_found\n",
    "up_energyloss_found = up_eph_found/up_energy_found\n",
    "\n",
    "\n",
    "upstream_brem_lost = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n",
    "up_energy_lost = ak.to_numpy(upstream_brem_lost[\"energy\"])\n",
    "up_eph_lost = ak.to_numpy(ak.sum(upstream_brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "up_residual_lost = up_energy_lost - up_eph_lost\n",
    "up_energyloss_lost = up_eph_lost/up_energy_lost\n",
    "\n",
    "\n",
    "print(\"brem vertices\\nupstream eff = \", np.round(t_eff(upstream_brem_found,upstream_brem_lost),3), \"+/-\", np.round(eff_err(upstream_brem_found, upstream_brem_lost),3))\n",
    "\n",
    "\n",
    "downstream_brem_found = downstream_found[ak.sum(downstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n",
    "down_energy_found = ak.to_numpy(downstream_brem_found[\"energy\"])\n",
    "down_eph_found = ak.to_numpy(ak.sum(downstream_brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "down_residual_found = down_energy_found - down_eph_found\n",
    "down_energyloss_found = down_eph_found/down_energy_found\n",
    "\n",
    "\n",
    "downstream_brem_lost = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n",
    "down_energy_lost = ak.to_numpy(downstream_brem_lost[\"energy\"])\n",
    "down_eph_lost = ak.to_numpy(ak.sum(downstream_brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "down_residual_lost = down_energy_lost - down_eph_lost\n",
    "down_energyloss_lost = down_eph_lost/down_energy_lost\n",
    "\n",
    "\n",
    "print(\"downstream eff = \", np.round(t_eff(downstream_brem_found,downstream_brem_lost),3), \"+/-\", np.round(eff_err(downstream_brem_found, downstream_brem_lost),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upstream:\n",
      "mean energyloss relative to initial energy (found):  0.33078325542598164\n",
      "mean energyloss relative to initial energy (lost):  0.5708618852236069\n",
      "downstream:\n",
      "mean energyloss relative to initial energy (found):  0.19104090843883118\n",
      "mean energyloss relative to initial energy (lost):  0.3051594568487781\n"
     ]
    }
   ],
   "source": [
    "print(\"upstream:\\nmean energyloss relative to initial energy (found): \",ak.mean(up_energyloss_found))\n",
    "print(\"mean energyloss relative to initial energy (lost): \", ak.mean(up_energyloss_lost))\n",
    "\n",
    "print(\"downstream:\\nmean energyloss relative to initial energy (found): \",ak.mean(down_energyloss_found))\n",
    "print(\"mean energyloss relative to initial energy (lost): \", ak.mean(down_energyloss_lost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#in abhängigkeit von der energie der elektronen\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n",
    "\n",
    "\n",
    "ax[0].hist(up_energyloss_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
    "ax[0].hist(up_energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
    "#ax[0].set_xticks(np.arange(0,1.1,0.1), minor=True,)\n",
    "#ax[0].set_yticks(np.arange(0,11,1), minor=True)\n",
    "ax[0].set_xlabel(r\"$E_\\gamma/E_0$\")\n",
    "ax[0].set_ylabel(\"counts (normed)\")\n",
    "ax[0].set_title(\"Upstream\")\n",
    "ax[0].legend()\n",
    "#ax[0].grid()\n",
    "\n",
    "ax[1].hist(down_energyloss_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
    "ax[1].hist(down_energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
    "#ax[1].set_xticks(np.arange(0,1.1,0.1), minor=True,)\n",
    "#ax[1].set_yticks(np.arange(0,11,1), minor=True)\n",
    "ax[1].set_xlabel(r\"$E_\\gamma/E_0$\")\n",
    "ax[1].set_ylabel(\"counts (normed)\")\n",
    "ax[1].set_title(\"Downstream\")\n",
    "ax[1].legend()\n",
    "#ax[1].grid()\n",
    "\n",
    "\"\"\"\n",
    "most electrons lose little energy relative to their initial energy downstream\n",
    "\"\"\"\n",
    "fig.suptitle(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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(up_energyloss_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
    "plt.hist(up_energyloss_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\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\")\n",
    "plt.legend(title=\"LHCb Simulation\", title_fontsize=15)\n",
    "plt.text(0.35,2.0, \"Upstream\", size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "both_eloss = np.append(up_energyloss_found,up_energyloss_lost)\n",
    "plt.hist(both_eloss, bins=100, density=True, histtype='bar', color=\"cornflowerblue\", label=\"Upstream\")\n",
    "plt.vlines(ak.mean(both_eloss),0,3,colors=\"red\", label=\"mean\")\n",
    "plt.xlabel(r\"Energyloss Ratio $E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"counts (normed)\")\n",
    "plt.title(r'$B^0\\rightarrow K^{\\ast 0} e^+e^-$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
    "plt.legend(title=\"LHCb Simulation\", title_fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#energyloss in abh von der energie der elektronen\n",
    "#upstream\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0=ax0.hist2d(up_energyloss_found, up_energy_found, bins=(np.linspace(0,1,80), np.linspace(0,5e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
    "ax0.set_ylim(0,5e4)\n",
    "ax0.set_xlim(0,1)\n",
    "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
    "ax0.set_ylabel(r\"$E_0$\")\n",
    "ax0.set_title(\"found energyloss wrt electron energy\")\n",
    "\n",
    "a1=ax1.hist2d(up_energyloss_lost, up_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,5e4,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
    "ax1.set_ylim(0,5e4)\n",
    "ax1.set_xlim(0,1)\n",
    "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
    "ax1.set_ylabel(r\"$E_0$\")\n",
    "ax1.set_title(\"lost energyloss wrt electron energy\")\n",
    "\n",
    "fig.colorbar(a1[3],ax=ax1)\n",
    "fig.suptitle(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, Upstream photons w/ brem_vtx_z$<9500$mm\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#downstream\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0=ax0.hist2d(down_energyloss_found, down_energy_found, bins=(np.linspace(0,1,80), np.linspace(0,5e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
    "ax0.set_ylim(0,5e4)\n",
    "ax0.set_xlim(0,1)\n",
    "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
    "ax0.set_ylabel(r\"$E_0$\")\n",
    "ax0.set_title(\"found energyloss wrt electron energy\")\n",
    "\n",
    "a1=ax1.hist2d(down_energyloss_lost, down_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,5e4,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
    "ax1.set_ylim(0,5e4)\n",
    "ax1.set_xlim(0,1)\n",
    "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
    "ax1.set_ylabel(r\"$E_0$\")\n",
    "ax1.set_title(\"lost energyloss wrt electron energy\")\n",
    "\n",
    "fig.colorbar(a1[3],ax=ax1)\n",
    "fig.suptitle(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, Downstream photons w/ brem_vtx_z$<9500$mm\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot residual energy against energyloss and try to find a good split (eg energyloss before and after the magnet)\n",
    "#upstream\n",
    "nbins=60\n",
    "\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0=ax0.hist2d(up_energyloss_found, up_residual_found, bins=(np.linspace(0,1,nbins), np.linspace(0,5e4,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20)\n",
    "ax0.set_ylim(0,5e4)\n",
    "ax0.set_xlim(0,1)\n",
    "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
    "ax0.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
    "ax0.set_title(\"found energyloss wrt residual electron energy\")\n",
    "\n",
    "a1=ax1.hist2d(up_energyloss_lost, up_residual_lost, bins=(np.linspace(0,1,nbins), np.linspace(0,5e4,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20) \n",
    "ax1.set_ylim(0,5e4)\n",
    "ax1.set_xlim(0,1)\n",
    "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
    "ax1.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
    "ax1.set_title(\"lost energyloss wrt residual electron energy\")\n",
    "\n",
    "fig.colorbar(a1[3],ax=ax1)\n",
    "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, Upstream photons w/ brem_vtx_z$<9500$mm\")\n",
    "\n",
    "\"\"\"\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#downstream\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0=ax0.hist2d(down_energyloss_found, down_residual_found, bins=(np.linspace(0,1,60), np.linspace(0,5e4,60)), cmap=plt.cm.jet, cmin=1, vmax=25)\n",
    "ax0.set_ylim(0,5e4)\n",
    "ax0.set_xlim(0,1)\n",
    "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
    "ax0.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
    "ax0.set_title(\"found energyloss wrt residual electron energy\")\n",
    "\n",
    "a1=ax1.hist2d(down_energyloss_lost, down_residual_lost, bins=(np.linspace(0,1,60), np.linspace(0,5e4,60)), cmap=plt.cm.jet, cmin=1, vmax=20) \n",
    "ax1.set_ylim(0,5e4)\n",
    "ax1.set_xlim(0,1)\n",
    "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
    "ax1.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
    "ax1.set_title(\"lost energyloss wrt residual electron energy\")\n",
    "\n",
    "fig.colorbar(a0[3],ax=ax1)\n",
    "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, Downstream photons w/ brem_vtx_z$<9500$mm\")\n",
    "\n",
    "\"\"\"\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env1",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}