{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44804"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "file = uproot.open(\n",
    "    \"/work/cetin/Projektpraktikum/tracking_losses_ntuple_B_rad_length_beginVelo2endUT.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
    ")\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)\n",
    "                   & (~allcolumns.lost)\n",
    "                   & (allcolumns.fromB)\n",
    "                   & (allcolumns.p > 5e3)]  # B: 9056\n",
    "lost = allcolumns[(allcolumns.isElectron)\n",
    "                  & (allcolumns.lost)\n",
    "                  & (allcolumns.fromB)\n",
    "                  & (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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eff all =  0.8343228283189001 +/- 0.0017564670414882176\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",
    "\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": 4,
   "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": 5,
   "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",
    "    cut_brem_found.end_record()\n",
    "\n",
    "cut_brem_found = ak.Array(cut_brem_found)\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",
    "    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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>{energy: 3.26e+04,\n",
       " brem_photons_pe: [824, 287, 1.26e+04, 4.49e+03, 3.59e+03, 111],\n",
       " brem_vtx_z: [157, 158, 601, 2.33e+03, 8.65e+03, 8.67e+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: 3.26e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
      ]
     },
     "execution_count": 6,
     "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]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### in magnet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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\",\n",
    "                                                   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",
    "inmagnet_found = ak.Array(inmagnet_found)\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\",\n",
    "                                                 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",
    "inmagnet_lost = ak.Array(inmagnet_lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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\"],\n",
    "                                            axis=-1,\n",
    "                                            keepdims=False) >= cutoff_energy]\n",
    "magnet_energy_found = ak.to_numpy(inmagnet_brem_found[\"energy\"])\n",
    "magnet_eph_found = ak.to_numpy(\n",
    "    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",
    "inmagnet_brem_lost = inmagnet_lost[ak.sum(inmagnet_lost[\"brem_photons_pe\"],\n",
    "                                          axis=-1,\n",
    "                                          keepdims=False) >= cutoff_energy]\n",
    "magnet_energy_lost = ak.to_numpy(inmagnet_brem_lost[\"energy\"])\n",
    "magnet_eph_lost = ak.to_numpy(\n",
    "    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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20316.361014308728"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.mean(magnet_eph_lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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(\n",
    "    magnet_energyloss_found,\n",
    "    alpha=0.5,\n",
    "    bins=80,\n",
    "    density=True,\n",
    "    histtype=\"bar\",\n",
    "    color=\"blue\",\n",
    "    label=\"found\",\n",
    ")\n",
    "plt.hist(\n",
    "    magnet_energyloss_lost,\n",
    "    alpha=0.5,\n",
    "    bins=80,\n",
    "    density=True,\n",
    "    histtype=\"bar\",\n",
    "    color=\"darkorange\",\n",
    "    label=\"lost\",\n",
    ")\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(\n",
    "    r\"$B^0\\rightarrow K^{\\ast 0} e^+e^-$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\"\n",
    ")\n",
    "plt.legend(title=\"LHCb Simulation\", title_fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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(\n",
    "    magnet_residual_found,\n",
    "    alpha=0.5,\n",
    "    bins=70,\n",
    "    density=True,\n",
    "    histtype=\"bar\",\n",
    "    color=\"blue\",\n",
    "    label=\"found\",\n",
    "    range=[nstart, nend],\n",
    ")\n",
    "plt.hist(\n",
    "    magnet_residual_lost,\n",
    "    alpha=0.5,\n",
    "    bins=70,\n",
    "    density=True,\n",
    "    histtype=\"bar\",\n",
    "    color=\"darkorange\",\n",
    "    label=\"lost\",\n",
    "    range=[nstart, nend],\n",
    ")\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(\n",
    "    r\"$B^0\\rightarrow K^{\\ast 0} e^+e^-$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\"\n",
    ")\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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cut_length_found[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split in Upstream and Downstream Events and analyse separately\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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\",\n",
    "                                                     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\",\n",
    "                                                     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",
    "upstream_found = ak.Array(upstream_found)\n",
    "downstream_found = ak.Array(downstream_found)\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\",\n",
    "                                                   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",
    "upstream_lost = ak.Array(upstream_lost)\n",
    "downstream_lost = ak.Array(downstream_lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>{energy: 1.28e+04,\n",
       " brem_photons_pe: [7.42e+03],\n",
       " brem_vtx_z: [35.6]}\n",
       "-----------------------------------\n",
       "type: {\n",
       "    energy: float64,\n",
       "    brem_photons_pe: var * float64,\n",
       "    brem_vtx_z: var * float64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Record {energy: 1.28e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upstream_found[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "upstream: cutoff energy = 350MeV, sample size: 6604\n",
      "eff =  0.8798 +/- 0.004\n"
     ]
    }
   ],
   "source": [
    "# plot efficiency against cutoff energy\n",
    "up_efficiencies = []\n",
    "up_deff = []\n",
    "\n",
    "for cutoff_energy in range(0, 10050, 200):\n",
    "    up_nobrem_f = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],\n",
    "                                        axis=-1,\n",
    "                                        keepdims=False) < cutoff_energy]\n",
    "    up_nobrem_l = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],\n",
    "                                       axis=-1,\n",
    "                                       keepdims=False) < cutoff_energy]\n",
    "\n",
    "    if ak.num(up_nobrem_f, axis=0) + ak.num(up_nobrem_l, axis=0) == 0:\n",
    "        up_efficiencies.append(0)\n",
    "        up_deff.append(0)\n",
    "        continue\n",
    "\n",
    "    eff = t_eff(up_nobrem_f, up_nobrem_l)\n",
    "    deff = eff_err(up_nobrem_f, up_nobrem_l)\n",
    "    up_efficiencies.append(eff)\n",
    "    up_deff.append(deff)\n",
    "\n",
    "    # print(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(up_nobrem_f,axis=0)+ak.num(up_nobrem_l,axis=0))\n",
    "    # print(\"eff = \",np.round(eff,4), \"+/-\", np.round(eff_err(up_nobrem_f, up_nobrem_l),4))\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",
    "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\"],\n",
    "                                        axis=-1,\n",
    "                                        keepdims=False) < cutoff_energy]\n",
    "up_nobrem_lost = upstream_lost[ak.sum(\n",
    "    upstream_lost[\"brem_photons_pe\"], axis=-1, keepdims=False) < cutoff_energy]\n",
    "\n",
    "print(\n",
    "    \"\\nupstream: cutoff energy = 350MeV, sample size:\",\n",
    "    ak.num(up_nobrem_found, axis=0) + ak.num(up_nobrem_lost, axis=0),\n",
    ")\n",
    "print(\n",
    "    \"eff = \",\n",
    "    np.round(t_eff(up_nobrem_found, up_nobrem_lost), 4),\n",
    "    \"+/-\",\n",
    "    np.round(eff_err(up_nobrem_found, up_nobrem_lost), 3),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nobrem_vertices\n",
      "upstream: cutoff energy = 350MeV, sample size: 6604\n",
      "eff =  0.8798 +/- 0.004\n",
      "\n",
      "downstream: cutoff energy = 350MeV, sample size: 21986\n",
      "eff =  0.8739 +/- 0.002\n"
     ]
    }
   ],
   "source": [
    "down_efficiencies = []\n",
    "down_deff = []\n",
    "\n",
    "for cutoff_energy in range(0, 10050, 200):\n",
    "    down_nobrem_f = downstream_found[ak.sum(\n",
    "        downstream_found[\"brem_photons_pe\"], axis=-1, keepdims=False) <\n",
    "                                     cutoff_energy]\n",
    "    down_nobrem_l = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],\n",
    "                                           axis=-1,\n",
    "                                           keepdims=False) < cutoff_energy]\n",
    "\n",
    "    if ak.num(down_nobrem_f, axis=0) + ak.num(down_nobrem_l, axis=0) == 0:\n",
    "        down_efficiencies.append(0)\n",
    "        down_deff.append(0)\n",
    "        continue\n",
    "    eff = t_eff(down_nobrem_f, down_nobrem_l)\n",
    "    deff = eff_err(down_nobrem_f, down_nobrem_l)\n",
    "    down_efficiencies.append(eff)\n",
    "    down_deff.append(deff)\n",
    "\n",
    "    # print(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(down_nobrem_f,axis=0)+ak.num(down_nobrem_l,axis=0))\n",
    "    # print(\"eff = \",np.round(eff,4), \"+/-\", np.round(eff_err(down_nobrem_f, down_nobrem_l),4))\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",
    "better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n",
    "\"\"\"\n",
    "down_nobrem_found = downstream_found[ak.sum(\n",
    "    downstream_found[\"brem_photons_pe\"], axis=-1, keepdims=False) <\n",
    "                                     cutoff_energy]\n",
    "down_nobrem_lost = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],\n",
    "                                          axis=-1,\n",
    "                                          keepdims=False) < cutoff_energy]\n",
    "\n",
    "print(\n",
    "    \"nobrem_vertices\\nupstream: cutoff energy = 350MeV, sample size:\",\n",
    "    ak.num(up_nobrem_found, axis=0) + ak.num(up_nobrem_lost, axis=0),\n",
    ")\n",
    "print(\n",
    "    \"eff = \",\n",
    "    np.round(t_eff(up_nobrem_found, up_nobrem_lost), 4),\n",
    "    \"+/-\",\n",
    "    np.round(eff_err(up_nobrem_found, up_nobrem_lost), 3),\n",
    ")\n",
    "\n",
    "print(\n",
    "    \"\\ndownstream: cutoff energy = 350MeV, sample size:\",\n",
    "    ak.num(down_nobrem_found, axis=0) + ak.num(down_nobrem_lost, axis=0),\n",
    ")\n",
    "print(\n",
    "    \"eff = \",\n",
    "    np.round(t_eff(down_nobrem_found, down_nobrem_lost), 4),\n",
    "    \"+/-\",\n",
    "    np.round(eff_err(down_nobrem_found, down_nobrem_lost), 3),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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(\n",
    "    xlabel=\"cutoff energy [MeV]\",\n",
    "    ylabel=r\"$\\epsilon$\",\n",
    "    title=\"upstream\",\n",
    "    ylim=[0.8, 1.0],\n",
    ")\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_,\n",
    "               down_efficiencies,\n",
    "               yerr=down_deff,\n",
    "               ls=\"\",\n",
    "               capsize=1,\n",
    "               fmt=\".\")\n",
    "ax[1].set(\n",
    "    xlabel=\"cutoff energy [MeV]\",\n",
    "    ylabel=r\"$\\epsilon$\",\n",
    "    title=\"downstream\",\n",
    "    ylim=[0.8, 1.0],\n",
    ")\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(\n",
    "    r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, nobrem electrons\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brem vertices\n",
      "upstream eff =  0.826 +/- 0.002\n",
      "downstream eff =  0.796 +/- 0.003\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\"],\n",
    "                                            axis=-1,\n",
    "                                            keepdims=False) >= cutoff_energy]\n",
    "up_energy_found = ak.to_numpy(upstream_brem_found[\"energy\"])\n",
    "up_eph_found = ak.to_numpy(\n",
    "    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",
    "upstream_brem_lost = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],\n",
    "                                          axis=-1,\n",
    "                                          keepdims=False) >= cutoff_energy]\n",
    "up_energy_lost = ak.to_numpy(upstream_brem_lost[\"energy\"])\n",
    "up_eph_lost = ak.to_numpy(\n",
    "    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",
    "print(\n",
    "    \"brem vertices\\nupstream eff = \",\n",
    "    np.round(t_eff(upstream_brem_found, upstream_brem_lost), 3),\n",
    "    \"+/-\",\n",
    "    np.round(eff_err(upstream_brem_found, upstream_brem_lost), 3),\n",
    ")\n",
    "\n",
    "downstream_brem_found = downstream_found[ak.sum(\n",
    "    downstream_found[\"brem_photons_pe\"], axis=-1, keepdims=False) >=\n",
    "                                         cutoff_energy]\n",
    "down_energy_found = ak.to_numpy(downstream_brem_found[\"energy\"])\n",
    "down_eph_found = ak.to_numpy(\n",
    "    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",
    "downstream_brem_lost = downstream_lost[ak.sum(\n",
    "    downstream_lost[\"brem_photons_pe\"], axis=-1, keepdims=False) >=\n",
    "                                       cutoff_energy]\n",
    "down_energy_lost = ak.to_numpy(downstream_brem_lost[\"energy\"])\n",
    "down_eph_lost = ak.to_numpy(\n",
    "    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",
    "print(\n",
    "    \"downstream eff = \",\n",
    "    np.round(t_eff(downstream_brem_found, downstream_brem_lost), 3),\n",
    "    \"+/-\",\n",
    "    np.round(eff_err(downstream_brem_found, downstream_brem_lost), 3),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upstream:\n",
      "mean energyloss relative to initial energy (found):  0.3289231319498724\n",
      "mean energyloss relative to initial energy (lost):  0.530926023218989\n",
      "downstream:\n",
      "mean energyloss relative to initial energy (found):  0.18366915850891907\n",
      "mean energyloss relative to initial energy (lost):  0.32907909342930114\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"upstream:\\nmean energyloss relative to initial energy (found): \",\n",
    "    ak.mean(up_energyloss_found),\n",
    ")\n",
    "print(\"mean energyloss relative to initial energy (lost): \",\n",
    "      ak.mean(up_energyloss_lost))\n",
    "\n",
    "print(\n",
    "    \"downstream:\\nmean energyloss relative to initial energy (found): \",\n",
    "    ak.mean(down_energyloss_found),\n",
    ")\n",
    "print(\"mean energyloss relative to initial energy (lost): \",\n",
    "      ak.mean(down_energyloss_lost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
    "ax[0].hist(\n",
    "    up_energyloss_lost,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"darkorange\",\n",
    "    label=\"lost\",\n",
    ")\n",
    "ax[0].hist(\n",
    "    up_energyloss_found,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"blue\",\n",
    "    label=\"found\",\n",
    ")\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(\n",
    "    down_energyloss_lost,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"darkorange\",\n",
    "    label=\"lost\",\n",
    ")\n",
    "ax[1].hist(\n",
    "    down_energyloss_found,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"blue\",\n",
    "    label=\"found\",\n",
    ")\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",
    "most electrons lose little energy relative to their initial energy downstream\n",
    "\"\"\"\n",
    "fig.suptitle(\n",
    "    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": 21,
   "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(\n",
    "    up_energyloss_lost,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"darkorange\",\n",
    "    label=\"lost\",\n",
    ")\n",
    "plt.hist(\n",
    "    up_energyloss_found,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"blue\",\n",
    "    label=\"found\",\n",
    ")\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"counts (normed)\")\n",
    "plt.title(\n",
    "    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": 22,
   "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(\n",
    "    both_eloss,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    histtype=\"bar\",\n",
    "    color=\"cornflowerblue\",\n",
    "    label=\"Upstream\",\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(\n",
    "    r\"$B^0\\rightarrow K^{\\ast 0} e^+e^-$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\"\n",
    ")\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": 23,
   "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(\n",
    "    up_energyloss_found,\n",
    "    up_energy_found,\n",
    "    bins=(np.linspace(0, 1, 80), np.linspace(0, 5e4, 80)),\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=15,\n",
    ")\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(\n",
    "    up_energyloss_lost,\n",
    "    up_energy_lost,\n",
    "    bins=(np.linspace(0, 1, 50), np.linspace(0, 5e4, 50)),\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=15,\n",
    ")\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(\n",
    "    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": 24,
   "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(\n",
    "    down_energyloss_found,\n",
    "    down_energy_found,\n",
    "    bins=(np.linspace(0, 1, 80), np.linspace(0, 5e4, 80)),\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=15,\n",
    ")\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(\n",
    "    down_energyloss_lost,\n",
    "    down_energy_lost,\n",
    "    bins=(np.linspace(0, 1, 50), np.linspace(0, 5e4, 50)),\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=15,\n",
    ")\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(\n",
    "    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": 25,
   "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(\n",
    "    up_energyloss_found,\n",
    "    up_residual_found,\n",
    "    bins=(np.linspace(0, 1, nbins), np.linspace(0, 5e4, nbins)),\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=20,\n",
    ")\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(\n",
    "    up_energyloss_lost,\n",
    "    up_residual_lost,\n",
    "    bins=(np.linspace(0, 1, nbins), np.linspace(0, 5e4, nbins)),\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=20,\n",
    ")\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(\n",
    "    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": 26,
   "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(\n",
    "    down_energyloss_found,\n",
    "    down_residual_found,\n",
    "    bins=(np.linspace(0, 1, 60), np.linspace(0, 5e4, 60)),\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=25,\n",
    ")\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(\n",
    "    down_energyloss_lost,\n",
    "    down_residual_lost,\n",
    "    bins=(np.linspace(0, 1, 60), np.linspace(0, 5e4, 60)),\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=20,\n",
    ")\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(\n",
    "    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": []
  }
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