diff --git a/methods/__pycache__/adashof.cpython-310.pyc b/methods/__pycache__/adashof.cpython-310.pyc
deleted file mode 100644
index 0eae274..0000000
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diff --git a/methods/__pycache__/fit_linear_regression_model.cpython-310.pyc b/methods/__pycache__/fit_linear_regression_model.cpython-310.pyc
deleted file mode 100644
index 7d3d19e..0000000
Binary files a/methods/__pycache__/fit_linear_regression_model.cpython-310.pyc and /dev/null differ
diff --git a/notebooks/B_updown.ipynb b/notebooks/B_updown.ipynb
index 480d54f..b8bad6c 100644
--- a/notebooks/B_updown.ipynb
+++ b/notebooks/B_updown.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,55 +22,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "10522"
+       "44804"
       ]
      },
-     "execution_count": 9,
+     "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(\"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",
+    "# 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",
+    "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)"
+    "# ak.count(found, axis=None)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "eff all =  0.8606728758791105 +/- 0.003375885792719708\n"
+      "eff all =  0.8343228283189001 +/- 0.0017564670414882176\n"
      ]
     }
    ],
    "source": [
-    "def t_eff(found, lost, axis = 0):\n",
+    "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",
+    "    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_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))"
@@ -78,14 +88,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "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",
+    "# try excluding all photons that originate from a vtx @ z>9500mm\n",
+    "# ignore all brem vertices @ z>9500mm\n",
     "\n",
-    "#found\n",
+    "# found\n",
     "\n",
     "brem_e_f = found[\"brem_photons_pe\"]\n",
     "brem_z_f = found[\"brem_vtx_z\"]\n",
@@ -94,21 +104,21 @@
     "\n",
     "brem_f = ak.ArrayBuilder()\n",
     "\n",
-    "for itr in range(ak.num(found,axis=0)):\n",
+    "for itr in range(ak.num(found, axis=0)):\n",
     "    brem_f.begin_record()\n",
-    "    #[:,\"energy\"] energy\n",
+    "    # [:,\"energy\"] energy\n",
     "    brem_f.field(\"energy\").append(e_f[itr])\n",
-    "    #[:,\"photon_length\"] number of vertices\n",
+    "    # [:,\"photon_length\"] number of vertices\n",
     "    brem_f.field(\"photon_length\").integer(length_f[itr])\n",
-    "    #[:,\"brem_photons_pe\",:] photon energy \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_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",
+    "# lost\n",
     "\n",
     "brem_e_l = lost[\"brem_photons_pe\"]\n",
     "brem_z_l = lost[\"brem_vtx_z\"]\n",
@@ -117,15 +127,15 @@
     "\n",
     "brem_l = ak.ArrayBuilder()\n",
     "\n",
-    "for itr in range(ak.num(lost,axis=0)):\n",
+    "for itr in range(ak.num(lost, axis=0)):\n",
     "    brem_l.begin_record()\n",
-    "    #[:,\"energy\"] energy\n",
+    "    # [:,\"energy\"] energy\n",
     "    brem_l.field(\"energy\").append(e_l[itr])\n",
-    "    #[:,\"photon_length\"] number of vertices\n",
+    "    # [:,\"photon_length\"] number of vertices\n",
     "    brem_l.field(\"photon_length\").integer(length_l[itr])\n",
-    "    #[:,\"brem_photons_pe\",:] photon energy \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_vtx_z\",:] brem vtx z\n",
     "    brem_l.field(\"brem_vtx_z\").append(brem_z_l[itr])\n",
     "    brem_l.end_record()\n",
     "\n",
@@ -134,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -142,80 +152,75 @@
     "\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(\"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",
+    "        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.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",
+    "\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",
+    "        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.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(\"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",
+    "        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.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",
+    "\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",
+    "        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.real(brem_l[itr, \"brem_vtx_z\", jentry])\n",
     "    cut_brem_lost.end_list()\n",
-    "        \n",
+    "\n",
     "    cut_brem_lost.end_record()\n",
     "\n",
-    "cut_brem_lost = ak.Array(cut_brem_lost)\n"
+    "cut_brem_lost = ak.Array(cut_brem_lost)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "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",
+       "<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",
@@ -223,135 +228,139 @@
        "}</pre>"
       ],
       "text/plain": [
-       "<Record {energy: 9.36e+03, ...} type='{energy: float64, brem_photons_pe: va...'>"
+       "<Record {energy: 3.26e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "#data in cut_brem_found and cut_brem_lost\n",
+    "# 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_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"
+    "cut_brem_found[1]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### in magnet"
+    "#### in magnet\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "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",
+    "\n",
     "    inmagnet_found.begin_record()\n",
-    "    inmagnet_found.field(\"energy\").real(cut_brem_found[itr,\"energy\"])\n",
-    "    \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",
+    "        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",
+    "\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",
+    "        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",
+    "\n",
     "    inmagnet_lost.begin_record()\n",
-    "    inmagnet_lost.field(\"energy\").real(cut_brem_lost[itr,\"energy\"])\n",
-    "    \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",
+    "        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",
+    "\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",
+    "        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"
+    "inmagnet_lost = ak.Array(inmagnet_lost)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "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",
+    "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",
+    "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(ak.sum(inmagnet_brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\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",
+    "magnet_energyloss_found = magnet_eph_found / magnet_energy_found\n",
     "\n",
-    "inmagnet_brem_lost = inmagnet_lost[ak.sum(inmagnet_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\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(ak.sum(inmagnet_brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\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"
+    "magnet_energyloss_lost = magnet_eph_lost / magnet_energy_lost"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "24784.620206013704"
+       "20316.361014308728"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -362,12 +371,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -377,26 +386,43 @@
     }
    ],
    "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.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(r'$B^0\\rightarrow K^{\\ast 0} e^+e^-$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\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": 25,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -408,14 +434,34 @@
    "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.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(r'$B^0\\rightarrow K^{\\ast 0} e^+e^-$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\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()"
    ]
@@ -436,16 +482,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "8"
+       "6"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -458,123 +504,123 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Split in Upstream and Downstream Events and analyse separately"
+    "### Split in Upstream and Downstream Events and analyse separately\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#try to find a split between energy lost before and after the magnet (z~5000mm)\n",
+    "# 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",
+    "    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",
+    "    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",
+    "        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.real(cut_brem_found[itr, \"brem_photons_pe\", jentry])\n",
     "    upstream_found.end_list()\n",
     "    downstream_found.end_list()\n",
-    "    \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",
+    "        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.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",
+    "    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",
+    "    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",
+    "        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.real(cut_brem_lost[itr, \"brem_photons_pe\", jentry])\n",
     "    upstream_lost.end_list()\n",
     "    downstream_lost.end_list()\n",
-    "    \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",
+    "        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.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"
+    "downstream_lost = ak.Array(downstream_lost)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "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",
+       "<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",
@@ -582,10 +628,10 @@
        "}</pre>"
       ],
       "text/plain": [
-       "<Record {energy: 4.62e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
+       "<Record {energy: 1.28e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -603,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -611,52 +657,64 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "upstream: cutoff energy = 350MeV, sample size: 1562\n",
-      "eff =  0.9181 +/- 0.007\n"
+      "upstream: cutoff energy = 350MeV, sample size: 6604\n",
+      "eff =  0.8798 +/- 0.004\n"
      ]
     }
    ],
    "source": [
-    "#plot efficiency against cutoff energy \n",
+    "# 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",
+    "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",
+    "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\"],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"
+    "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": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -664,11 +722,11 @@
      "output_type": "stream",
      "text": [
       "nobrem_vertices\n",
-      "upstream: cutoff energy = 350MeV, sample size: 1562\n",
-      "eff =  0.9181 +/- 0.007\n",
+      "upstream: cutoff energy = 350MeV, sample size: 6604\n",
+      "eff =  0.8798 +/- 0.004\n",
       "\n",
-      "downstream: cutoff energy = 350MeV, sample size: 5131\n",
-      "eff =  0.8864 +/- 0.004\n"
+      "downstream: cutoff energy = 350MeV, sample size: 21986\n",
+      "eff =  0.8739 +/- 0.002\n"
      ]
     }
    ],
@@ -676,50 +734,70 @@
     "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",
+    "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",
+    "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(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"
+    "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": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1800x600 with 2 Axes>"
       ]
@@ -729,24 +807,39 @@
     }
    ],
    "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",
+    "# 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()"
    ]
@@ -760,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -768,52 +861,69 @@
      "output_type": "stream",
      "text": [
       "brem vertices\n",
-      "upstream eff =  0.851 +/- 0.004\n",
-      "downstream eff =  0.836 +/- 0.005\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",
+    "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",
+    "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(ak.sum(upstream_brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\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",
+    "up_energyloss_found = up_eph_found / up_energy_found\n",
     "\n",
-    "upstream_brem_lost = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\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(ak.sum(upstream_brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\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",
-    "\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",
+    "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(ak.sum(downstream_brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\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",
+    "down_energyloss_found = down_eph_found / down_energy_found\n",
     "\n",
-    "downstream_brem_lost = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\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(ak.sum(downstream_brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\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",
-    "\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))"
+    "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": 14,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -821,30 +931,38 @@
      "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",
+      "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):  0.19104090843883118\n",
-      "mean energyloss relative to initial energy (lost):  0.3051594568487781\n"
+      "mean energyloss relative to initial energy (found):  0.18366915850891907\n",
+      "mean energyloss relative to initial energy (lost):  0.32907909342930114\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))"
+    "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": 15,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 1800x600 with 2 Axes>"
       ]
@@ -854,34 +972,65 @@
     }
    ],
    "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",
+    "# 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(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[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",
+    "# ax[1].grid()\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",
+    "fig.suptitle(\n",
+    "    r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\")\n",
     "plt.show()"
    ]
   },
@@ -894,12 +1043,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -909,24 +1058,41 @@
     }
    ],
    "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.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(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\")\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.text(0.35, 2.0, \"Upstream\", size=15)\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -936,12 +1102,21 @@
     }
    ],
    "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",
+    "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(r'$B^0\\rightarrow K^{\\ast 0} e^+e^-$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\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()"
    ]
@@ -955,12 +1130,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x600 with 3 Axes>"
       ]
@@ -970,39 +1145,54 @@
     }
    ],
    "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",
+    "# 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(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",
+    "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(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, Upstream photons w/ brem_vtx_z$<9500$mm\")\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": 18,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x600 with 3 Axes>"
       ]
@@ -1012,38 +1202,53 @@
     }
    ],
    "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",
+    "# 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(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",
+    "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(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, Downstream photons w/ brem_vtx_z$<9500$mm\")\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": 19,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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qTft+jXU7OK7aaXdeHpZ+1dlhHwsAgM4YDAEADNXMzIwk6cyZM/ULh+Dlg8EN03HjchET3Lzo9LJai25frtx4073xhkEkElE2m62/zHVhYcF0ARvcLAnjk06nlU6nNT093dWgTLVare+X4OZzsF2FQiHU/dDIWr+HcTwE+7TxZlKjoNz68YL4WCymfD6vlZUVra2taXp6ul4GvWi8edV4I3DrMeJ6w6GxXBKJxLbjN7h5vvXj35qFLd/32x6fZ86cqf9/8PLZ4P9H9cXproI2pbGs0+l0/Wbf0tLSphdRh63bfdRY34Nj0HJD3qIf+7dZuXerX+e0QZ8rw6p3kUikfv5ovPEevNg9KPvG79rdkA/OwS4vcbc4fPiwJFvZhdWmWYSVTlB+lUpl0zZns9lNbWw3MplMvS724wc6wY8yotFoy4GWQqGg/fv3K5PJKJPJ1H+w0g+pVGpT3292drb+o4zZ2dlNPyJxLY9W5+XGAYJ2P7poHHyLRCJdLxeGfp2Tx/lcDwDjhMEQAMDQLC0t1W+GpNNpFQoFJZNJLS4u6vz582M/ENLu4qwfv7bvVuOMiWY3vE+dOlX/f9dfQwYX7WF+gkGQhYWFlr9o7bSN0tsDDufPn6//rR+zQ6z1e1jHQ5Buq5sqjTfH+qnTLyatksmkpO2zQRrruOuv5lvdoAiO315vOKdSqXq+CoVC/VfllUql55t4WzWWs3WmTDfuuOOOpn8vl8v1m23ValXJZFIzMzOh56mXfRT8AnttbU35fD70fdFPrcrdql/ntGGdK8Oqd8GN92AWhXSrvqRSqfp58uLFi/X4TCbTsv7kcjlFIpHQb7J3M4AdVps2DLFYrF6GwUBUtVpVrVYLpWwbf0AR5uDt0tJS/Ty7dVZLM4O6YZ7P55XNZpVIJHTx4kXNzMwok8loZmZmU7/Q0jdodl5urKftjsOgbgbb3+1yAAAwGAIAGJrgoi8ajdYfWZHNZjfdrBg3jRdnweMymml2MTcMW2dMNNN4E8H1Bkkqldr0C9IwPvPz84pEIspkMtseX9NJs9kvQVrSrRv+wSNbwmKt38M6HoIbdo03NwKVSqW+z5t936tqtaqZmZn6DeeVlZVN+6oXwbG19RhrLMteB5jCfKRF42N7MpmMstnspscdhaXxhpDrDea1tbWebyQFv1JvlM/nlc/nNw0EWQc6O+l2HyWTSc3OzqpYLI70Y8o6aVbuFv06pw3zXBlGvUskEvW8BW3o4uKizpw5U3+8V61W2zTzsNmxHJx3+lHHgvxZ2pBxf0xPMEgVPHIqm82GMuOmWq0ql8vVyzKsWTy1Wq3e9qdSqbYDC6lUSuvr6/WZGcHjG/spnU6rWCxqfX1d6+vrKhaLSiQS9f6Ktd42Oy9HIpH6v//bf/tvLZdtfIxhL8sBAMBgCABgaIKL7eDGX+Ovo8dZsA3tfjkYbHuvN6p61WzGxFaNN0WG9cu6hYUFFQoFlcvlrp6NHWzn1ptC8/Pz9f0V9uwQa/0exvEQ/Gq21a+SG2/ShHlTPng3yvT0tA4cOKD19fVNNyfDWoe0/bn4jb/O7vURaVsfpdRK8Picdhqf8R+8n2Trew3C0uqdCq2Uy+Wu26rgRvfWd+MEUqmUVlZW6sd1cNMxLN3so+CRgOl0emzPSc3KvVv9OqcN+lwZdr1rfEdFoVDQ4cOHt90sz2azbW/IB49e69dgs9T6nSzNhNmmDUOzdjSMgaZgVkQwo7RarYbyHrWg3xWJRDbNVm0nnU5rfX29/risxpmzg1Aqlep1y3qOanVeDn6k0O58HMzkDGaX9LIcAGBnYzAEADAU/XiG76hofG51q5uNwYVbv252umr1vpBWMWE/09xFoVBQLpdTuVzu6sZk4422ZjecGl8UGtb+sNbvYR0PQdk0e3FvpVJRqVRSNBp1vknjsr54PK7jx48rFotpfX1d2Wy2L9tcLBYViUS2bVvwLpxAL4Ngjb9Mfeihh1o+qiN450snWx+j08/nwUu36p3LYNDFixe7PvaDOta4fDab3dY2ZjKZer5azRTo5lFJ3eyj4Cawy4uBR1Wzcu9WGOe0Zvtu0OfKbutdK435f+ihhzblMTh2S6VS2xvyhUKhLzPAgny1ehdUK2G3acMQtPkLCwubZvC0065tCQYagnfOBANoCwsLPc2gaXzc1vnz55vms90gRzCgl8/ndeHCBe3fv18LCwt9ffxhrVarD+AEsxctWp2Xg2Op1cBo4w83Gutzt8sBAHY2BkMAAEMRiUTqF37NHotTqVSGcuM9jIvIxtkGzd6xEdz4aYwL9PJc9CDv7R4XsFWrGRON3we//kylUkO5mHzooYda3ihw0fgopGY3bxpnYDS+t6MX1vrd7fEwOzureDze9SO+gnU1e0TMzMyMIpFI/eZFLwqFQv3l6KdOnar/qrWXdJeWllrejKpUKsrlcsrn803XkU6n6wMilUpF8Xi87X5v913jYFo8Ht80uBCkHTw2p5PGWU+djrVe9n1j2R8/frzt9s3MzGz6xXsrzdrO4BfEzdq6Zs/GD3652/hL9sb911i2luPUuo+CdnjrC9MbX26/dXsH3Xa3yofUvty70e05rdO+6+Vc2U67feFa71xEo9H6OeXAgQObzi+RSKT+71Qq1bQdCo7dfvR1gjrQzbmzmzZtWPW/mWBQqlartS1bl7alWq1qYWFh03m5sf3sZd8FA/GJRKLpozALhYLTvkskEiqXy8rn86pUKn0dFDl+/LhqtZrS6XTTc1S35+VoNFpPr9k5LaiTW9uJbpeT+ldnR+lYAAC04AMAMCSZTMaX5Evy0+m0XywW/Ww266dSKT+dTg8lT/l8vp6nfD7fNKYxz62srKz40WjUl+RnMpltf2+1bCKR8CX5iUSibd6KxeK27yORiC/JT6VSnTbT933fz2az9fQikYi/vr6+KZ/pdLr+vWuaYctms34sFuspjaBcJPkrKytNY+bn5+sxzcq+G9b6bY1vrA+SNu0/V83q0/r6uh+LxfxEItFVmo2y2awfjUb9aDTqZ7PZntLaqnHb0+m0v7Ky4q+srNTrTLlc7phGuVz2Y7HYtnJfWVnx19fX/XK57GcymXodapVuKpXalJ+tebMI6mK7/Iex78vlcr2NikQifjqd9rPZrF8ul/1isejPz8/7kUikYz1oPL4a93GxWKynu1XQtmxtxxKJRNPjPWgXI5GIn8lk/EQisWlZl7K27KPGti8ajfqpVMqPRqObyj34e9CmDLLtblzGUu7r6+v1ZRrPS4HGct6q13Naq33Xbbrtyiw4prfWJWu9cxHs22b9heC7Zvs8WG+781InQbklEolNaeTzeT8SibRsc8M+Xhq3ZVD1v5NEIuFHo1GnuHb1MxaLdTz/NjuWOmlsY5rt/6D+t6o77ZTL5fr+C86NvVpZWakfV/Pz8y3jej0vp1IpPxKJbMpzsVjsuN5ulutXnR21YwEAsB2DIQCAoQpulgY3l9LptNNNzLCVy+X6zb/GAYL5+fn6TY5MJlPPa+PFXqtBk2CZ4KI8uMHc7AKoXC5vujgO0g7KovGGbFBWwQV4sVisX3xtvQhtZn19fdu6GtMN/ptIJEK7kO5WIpHo6kaD7789kNK4fbFYbNOFcVDujWXbLK5b1vptiQ8GLYI8t6uHzZTL5Xo9T6VS/vz8vJ9Op/1UKtXVDZhGQX0NbiD3Qz6f37T9wY37bgZdguM/FovV60KQ/1Qq5WcymY7HQTab9ROJhB+JROp56aYcg/3qEtPtvm8U5LuxbQu22yX/QXlls1k/nU770WjUj0QifiwWa7kvMplM/eZV0M4kEomWx1zjjbhYLFbPl7VNtuyjoF0IBhaCAaFg0DK4UTnItruXct96vAQ3CdfX1/2VlZVtN78TiUTTtsf1nBZote+6TbdcLrcss2bb0ThgZa13rtoNoLf6LhiY6nWwP5PJ1NutxjarWR3qx/HS7/rfakCm8dNMPp93Ohe0qp/FYrH+963HwsrKyrZ8p1Kpehvhkuet/a7GT2N59dIfbvxRSyqV6jqt4IczkUik4zkhjPNyUO9SqZSfSCRatkXdLtevOjuscwEAwM7zfd8XAADACFpYWNCZM2cm7r0yYZuenlY+nzc9d35hYUFLS0ubHhkVhtnZWV25ckWZTGZknyk/SbrZ92Hav3+/arWastksz2QfIModgItarabFxUXlcjmtrq469acqlUr9cVexWEynTp1q+igvAADG0W3DzgAAAEArzZ7zjM2q1aoikYj5ZnjwvpjgeflhCXNgBe11u+8BADtDJBJRJpMx9adisdim960BADBJeIE6AADAmApeENvspeudBC+MZfbGeOpl3wMAAADATsTMEAAAgDG1uLiofD5vfoxYMCskEonwCLIx1e2+D1utVhvq+ncqyh0AAACwY2YIAADAmMpkMuab4dVqVQsLC5Ju3VANZohgvHSz78OWy+Xq/5/NZlWtVoeYm52DcgcAAAC6wwvUAQAAdpBCobDtb9FolPdOwGR6errpTfhYLKZyuTyEHO0MlDsAAADQPQZDAAAAAAAAAADAROMxWQAAAAAAAAAAYKIxGAIAAAAAAAAAACYagyEAAAAAAAAAAGCiMRgCAAAAAAAAAAAmGoMhAAAAAAAAAABgojEYAgAAAAAAAAAAJhqDIQAAAAAAAAAAYKIxGAJg5FQqFeVyuWFnA3BGnQUAAAAmH/1+ABhvDIYAO0ylUtHCwoJmZmY0PT2tpaWlYWeprlqtamZmRvF4XNlsdtjZGapgP8XjccXj8WFnZ8epVqvav39/xwudftfZftQD6hYAAHBBn2EwKOfhot8PADsLgyHADlKpVHT8+HFlMhnl83mlUimtrKwMO1t10WhU+Xx+2NkYCbFYTKdOnVKlUhl2VgaqWq2qVqsNOxuq1Wqq1Wodj49+19l+1INB1a1R2ZcAAKA79BkGY6f2+6XR2Pf0+3s3CvsRAFwxGALsIIuLizpw4ED935lMZsfPwBhlsVhs2FkYuJmZGa2trQ07G4rFYvJ9X5lMZthZ6Us9GETdGpV9CQAAukefYTB2Yr9fGo19T7+/d6OwHwHAFYMhwA6yE39thPExMzNDHZ0Q7EsAAOCCPsPOxb6fDOxHAOOGwRBgB8jlcpqZmVG1Wq0/63RmZkalUqkeU6vVNDs7q4WFBSWTSSWTyU3fFwoF7d+/X57n1Ts7pVJJMzMz8jxPMzMz9XRyuZzi8bgKhYJKpZLi8fimmEbBeoOP9R0mlUpFMzMzSiaTmp6e1sLCwqa0LXlpl1ZQBkG55HI57d+/X7Ozs9vSCbZlenp6W+ewVCrVyzGZTG76LpfLyfM8pzLotL+2xgT5aXwWbqfvmymVSvI8b1MZVioVTU9Py/M8xePxTdsU1I9qtdq2DAuFQn252dnZjp3qTvui077stO2FQqF+nLQq+1Z11vVYCSwtLdXzEo/HQ3khY6ft73XZxjII6l+wre32Zaf91qleW4/pbrZxGO3GINoMAADC1mt/1Nr/k3bmOXxY/f5gG4bd96ff3x79/u63cZLvFwBowwewY0SjUT8ajW77e7lc9iORiF8ul+t/y2azviQ/k8nU/5ZOp31Jm+JWVlZ8SX4qlar/O5VK+ZL8RCLhz8/P++Vyub5sY3orKyt+JBLxi8Vi/W+ZTMaX5MdisY7bUy6X/UQiUf93Pp/3JfnpdNqcl05p5fN5PxqN1v82Pz/vx2KxbfmMxWL+/Pz8prKR5Eej0XpawTY2rj+I35pes7Jw3V+pVKqelyDG8n0r8/PzvqRN+61YLPqSNqUXpBn8rVMZBumurKy0XX+ndDrty07bvrKyUt9HjekE37nUWZdjpXGbt5ZjY/q+37wetOKy/a3SdFl2ZWXFj0ajm/ZTJBLxI5HItu1qjHHZb53qteWY7rZ8Bt1uhN1mAAAQtn72R137f8E6J/kcPor9ft8fbt+ffn979Pt7K59JuF8AwI7BEGAHaTUYEovFtnX+gr83dmyCjk5jp2V9fX1bRy/oGGw9eW/tZKZSqabrde0AxmKxTXnx/VudM0n++vq6KS8uaQWdkq2d/kCwrsZ0go7U1k5+0OFplMlk/Gw2uy2uWQfKZX9FIpFteW0sh07ft9Kscx+kt3WbUqnUpm1vV4aWi+F26bjsS5dtb3ZR5FpnXY+VRCKx6WIiiNmaN8tFkcv2t0rTZdlYLLatrIL9EcS02ped9ptLvXY9plsZpXajH20GAABh62d/1NL/m/Rz+Cj2+31/+H1/+v2t0e9vb5TajMZ10fcHhovHZAE7XLVaVaVSafpitWBKZ7cvWY9EItv+FrxYrVqt1qeRdiPI9+LiYn1ac+NU1itXrpjy4pJWkMZHPvKRpnn65je/ue1vjdPJG6XTaVWr1U1TgS9cuKCTJ0+22uRNeXXZX9FoVEtLS5um0c7Pz9f/v9P3rUSjUcViMRUKhab5C6bF12o11Wo1RaPR+vedytBVq3Rc92U3295rnW0mn8+rXC5vy1+tVusqPetxYV02iEkkEpuWnZ+fl+/7TY+zRp32m6UdandMtzJq7cYg2gwAAMIWZn/Uus6ddA4fhX5/sOww+/70+5uj3z9e/X6Jvj8wKm4bdgYADFe75/IePnxYkuod3DAFaTZ2li2CfOfz+Z7zYk2rVccv6PSUSqVtnbut/15YWFAul1Mmk1EikVCtVtOBAwc6diot+yufzysej2thYUHZbFb5fH5TPjp9307wnNNCoaBUKqVcLqeTJ08ql8spm80qk8no4sWLLZ/n2mk7XW1Nx3VfdrPtvdbZZiKRiCKRiAqFgi5cuNDzIFEvx4XLssFFcK/7r9V+aybMdmjU2o1BtBkAAIQtzP6odZ076Rw+Kv1+aTT6/vT7N6Pf396otRkSfX9gVDAzBICk5r9ICU60Bw4cCH19QQen0y86Oi0fRkcprLRSqZQSiYQWFxdVKpVUq9WUyWQ0Pz+/rSMdjUaVSCRUKpVUrVbrL7l35bK/otGoVldXlUgkVK1Wt72kr9P37aTTaUlv/2onuAhKJBL1NLLZbD1uUFz3ZTfb3mudbZVmPB5XtVpVPp/v6peSW9Nr/G/Yy4Z53DXT73Zo1NqNQbYZAACELYz+qKudfA4fdr9fGs2+P/1++v3tjFqbIdH3B0YFgyHADhf8AqFx+mUg6KBMT0+Hvt7gZN84Vbib5ZtN15aab88g0srn80okEqpUKvVfcmQymaaxCwsLkm5dOBSLRaeLB8v+qlarikQiKhaL9V+xBFOPXb7vJJVKqVQqqVAo6PDhw4pEIpqdnVWtVtPS0lL9lz2D5Lovu9n2XutsM8lkUgcOHOj5YijQS112WTaof61+FdXtBcKg2qFRbDf63WYAABC2MPujrnbiOXyU+v3S6PX96ffT729nFNsMib4/MAoYDAF2kLW1tW2/bgmeAdv4vNfAlStXFIlE6ifdO+64Q9Lmjk/jM2Itgs5yLpdrumyn9IJnly4sLGybamv9tVmYac3MzNR/6TM/P992CnYikag/w9Z1uqtlfzV2qlKpVP2XXMFynb7vJLiImJmZqXfUUqmUpFtl2c2FrtT9c3Ml933ZzbZb6qzLsRLsw8Z9H3zX7a/QeqnLLss2lsHWTv/CwsK2X3G57ktLve7FKLYb/W4zAAAIW5j90cA49v13Ur9fGr2+P/1++v3tjGKbIdH3B0YBgyEAlM/n67/sCQRTNs+fP18/8QYn6oWFBZVKpfozYqVbv4YIXjDn0qGLRCL1X8XE4/H69M+gY12tVje95K7T8jMzM1paWlIymdTKykq9w9JNXlqlFXTwWnX0go5isHwul1OhUGj7XNRge0+dOtUxnwHX/XXx4sVNHczghYbBL1s6fd9JIpFQJBKpd9ICqVSq3sndql0ZBr8Aymaz9ZcWttIqHdd92Wnbm12cWOqsy7ESXEAUCgXlcjnlcrl6WpVKRYVCwXxx6Lr93S4biUTqF5TJZLJ+MRyPxzU9PV2ve632Zbv971qve3lcwai1G4NqMwAACFtY/VHX/t9OPYePSr9fGl7fn35/c/T7wymfSbxfAKADH8DEK5fLfjqd9iX5kvx0Ou0Xi8VNMevr634qlfITiYSfTqf9dDrtl8vlbWllMhk/Eon4kUjEn5+f933f96PRqD8/P++Xy2W/XC77sVjMl+RHo1G/WCz66+vrm9afyWTq6WWzWT8ajfqS/Fgs5q+srNTTW1lZ6bhtmUymvnw0GvWz2eym7bbkpV1a+Xy+5XeBIO9B2o2faDTqr6+vb1tmfX3db9YUb91nmUxm0/Iu+yuRSNTLcn5+3k+lUpvS6PS9i0wms2295XK5afm4lGEsFvMjkYifTqdbrtMlnXb70vfbb3u5XPZTqVTLsnets52OlSCtSCRS/7vv+346na4v06ketNLpuGiXZqeyC/ZBcGzFYrFt7Ynvb9+XLvutU722HtPdls+g2o0w2wwAAMI2iP6o77v1/wKTeA4fl35/UGaD7PvT7387L63Q7++tfMb1fgGA7nm+7/sugyYAgPYqlYouXLigM2fOaG1tTbVarf5Lk3w+r+np6W3PiC2VSsrn8/VfDwHYOWgzAAAYT5zDAVjRbgCj4bZhZ8CiWq2apnECwKBUq1XF43Gtr68rEolse6ZnNBpt+mK1bDarM2fODCiXAEYFbQaAfuK6CegfzuEArGg3gNEx0oMhnudt+ncsFlO5XK7/u1KpaHFxUdFoVLVaTclksv4Cr2HEANi5gufQPvTQQzpz5kz92bHValWlUkkrKyv1Z66WSiVFo9H6S+PavTQNwGSizQAQJq6bgMHhHA7AinYDO0WhUNDi4qIqlYpisZgymcy2dxh121cMrY857Od0tZLNZv10Ou1nMpn6p/H5gSsrK76kTX/b+ly+QcYAQPCsWDU8+3Prc1WLxeKm713eiwJgMtFmAAgD103A4HEOB2BFu4FJl8lk/EQi4WezWX9+fr5ejxvreLd9xTD7mCP7zpBkMqlisdj2e0mbYnK5nGZnZxVs0iBjACAQPPuz1eMpFhYWVK1WN/0iBMDORZsBoBdcNwHDwzkcgBXtBibVzMyM8vl8/d+VSkXxeFyJRKLeN+y2rxhmH3OXKbqDr371q6GkUygUdOXKFc3MzCiXy237vlarqVQq1QsicPjwYUm3CmOQMQDQKBKJtH1OdyaTUT6fp2MDQBJtBrDThHXNJHHdBAwb53AAVrQbmESlUqn+qLdALBZTLBarPyau275i2H3MUAdD/sk/+Sd673vfq+eee66ndIrFomq1mgqFgmZnZ7V///5NLxK6cuWKJG1rPIKGolgsDjQGAAAAAFyEdc0kcd0EAACA4UskEi0H+YK/d9tXDLuPGepgyDPPPKNPfOITOn78uE6dOqXXXnutq3Sy2ax831e5XFY6na6/FCUYSQr+G4lEmi4fvGRoUDEAAAAA4CKsayaJ6yYAAACMrmq1qpmZmfr/S/a+Yth9zNtM0R1MTU0pk8no137t17S4uKh4PK6lpSV9/OMf7yq9WCymbDarZDKpmZkZLSwsKJ/Pa2VlRZJ04MCBpsvVarWBxrTy/e9/X7//+7+vO++8U+985ztbxnWyZ88e7dmzp+vlAQAAABfXr1/X9evXu17+hz/8ob73ve/pb//tv613v/vdIeZscoR9zSSN93UT10wAAGDYJq0P/Gd/9mf6/ve/v+3vvW6nJN15551673vf2zGuUCgoGo0qnU5LUtd9xV7uzTcT6mCIJD377LOq1Wqanp7WiRMndOLECSWTSSWTyfpz8e6//35TmqlUSqlUSpVKRZI0PT0tSVpbW2saH41GBxrTyu///u/rgQceaPk9AAAAMIm+9KUv6e///b8/7GyMrH5cM0njed3ENRMAAJgUo9AH/rM/+zP9H/7aX9OP+pT+O97xDv3pn/6pfuInfqJt3OLi4qYXqnfbV+zl3nwzoQ6GfOQjH1GtVtPs7Kw+/OEP6/Dhw5qdnVWtVtOFCxc0Pz8vz/M0PT2tK1eu6F3vepd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       "text/plain": [
        "<Figure size 2000x600 with 3 Axes>"
       ]
@@ -1053,29 +1258,44 @@
     }
    ],
    "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",
+    "# 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(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",
+    "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(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, Upstream photons w/ brem_vtx_z$<9500$mm\")\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()"
@@ -1083,12 +1303,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x600 with 3 Axes>"
       ]
@@ -1098,26 +1318,41 @@
     }
    ],
    "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",
+    "# 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(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",
+    "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(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, Downstream photons w/ brem_vtx_z$<9500$mm\")\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()"
diff --git a/notebooks/thesis_electrons.ipynb b/notebooks/thesis_electrons.ipynb
new file mode 100644
index 0000000..e8d1d16
--- /dev/null
+++ b/notebooks/thesis_electrons.ipynb
@@ -0,0 +1,246 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uproot\n",
+    "import numpy as np\n",
+    "import sys\n",
+    "import os\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep\n",
+    "from mpl_toolkits import mplot3d\n",
+    "import itertools\n",
+    "import awkward as ak\n",
+    "from scipy.optimize import curve_fit\n",
+    "from utils.components import unique_name_ext_re\n",
+    "mplhep.style.use([\"LHCbTex2\"])\n",
+    "plt.rcParams[\"savefig.dpi\"] = 600\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50501"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file = uproot.open(\n",
+    "    \"/work/cetin/Projektpraktikum/tracking_losses_ntuple_B_rad_length_endVelo2endUT.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
+    ")\n",
+    "\n",
+    "# selektiere nur elektronen von B->K*ee\n",
+    "allcolumns = file.arrays()\n",
+    "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n",
+    "\n",
+    "ak.num(electrons, axis=0)\n",
+    "# ak.count(found, axis=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# electrons.type.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "found = electrons[~electrons.lost]\n",
+    "lost = electrons[electrons.lost]\n",
+    "\n",
+    "eloss_found = (found[\"p\"] - found[\"p_end_scifi\"]) / found[\"p\"]\n",
+    "eloss_lost = (lost[\"p\"] - lost[\"p_end_scifi\"]) / lost[\"p\"]\n",
+    "\n",
+    "eloss = (electrons[\"p\"] - electrons[\"p_end_scifi\"]) / electrons[\"p\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# eloss_upstream_found = (found[\"p\"] - found[\"p_end_ut\"]) / found[\"p\"]\n",
+    "# eloss_upstream_lost = (lost[\"p\"] - lost[\"p_end_ut\"]) / lost[\"p\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZAADwtC3sHmEX/cmf/MmVF/PXGW6K/Yc//GF0pchsNhtffvnlY5cIC6nb7Y4tiYz4YxA0rWKxuBBXGRxecGAYaPX7/SgUCnF0dHTnfoCXu0eZj36/f2WvOgAAgHla+I6wafzJn/xJvHjxIl68eCEEI9UuLxfMZDIzuWjExSswTlJDqVSK9fX1WFlZiVwuN3H31n3r6Ha7V66MOa12ux2VSiVyudyt4/r9fjQajSgUCmPHHS7/u/h57+3tXXl8t9uNWq0WuVxuNO6mrrvhcYbLvi8u/76oVCqN5rv4NssuuEajMXac9fX1KBQK136OEZ+fzy+//PJKMHuxvpue60m/FkP9fj/29vbGzrthfbVabaIgrtvtxt7eXuRyubGaL34NcrlclMtl3YUAALBEnmQQBnx2dHQ0dn94RdV5KZfLUSqVotVqjcKCYcBwXShyXzs7O1EsFq+8v1KpRKFQuPf8w8BlGJo0Go1r90MbBi2FQiHW19ejUqmMHWsYbDUajbHPu1arjS1NHY7b29sbhTPdbncUtlwOWHZ2duL4+Dh2dnZu/TyOjo6i0+ncK7Cc1OHhYaysrIw+l+FFSd6/fx/9fn/0OV2uPZvNxvv376/UdHx8PHq7eDGGSb8WlzUajVhfX49arRalUinev38fnU5ntEfkMNwql8tXHnux/mFgO/y6DDsOL36tu91uHB4expdffqnLDQAAloQgDJ6wyy/OL19t8jFVKpU4PDyM7e3tqFarsb29PXb8YbDw0DDs6Ojo2uWe7XY7CoXCxB073W433r17N3HXUT6fv7Lf2vHx8ehzajab0el04vT0dGwpaaPRiHa7PQoIj46O4vT0NE5PT+Po6Gj0HHW73Xjz5s21x74uxLnOQ5fBXtZqtUbH/uabb8bmz+fzoyCr2+1eWc6eyWQin89fuVrp8H0XP3bfr8XQMJzLZDLR6XSiWq1GPp+PbDYb29vbcXx8PKr58PDwSmD36dOnKJVKV8LVDx8+xJdffjlaft/pdOLg4GD0ter3+zb/BwCAJbEUe4RBGqyvr898zssB0LNnz2Z+jJuO++HDhzg9Pb0SvlUqlbElhK9fv47j4+MHHa/ZbMbe3l7UarUrHzs8PIzDw8M4ODi4tZMqm82OdStdN9dQJpMZhSUHBwejMK/RaMTR0dGVIKVer0e73R4tVS0UCrGzs3OlY69YLMbbt29HYVOj0XhQV9esv94HBwe3fvxiyHXxSqX3dZ+vxVCj0Rgty6zX6zcuAW42m5HL5aLb7Ua3241yuTz6OmSz2chms1EsFsfO0VqtFs1mc+zrurOzE8+ePRt9rYZdj/MMmwEAgPvTEQYLolgsTvx2uavmJpMsJXss79+/vzYUODg4GOskarfbM9nTq1qtxunp6Y1dUJVKJUql0kTdYffZR+1i2HTTUs2IuHKlxJtCpYv19/v9hd1/6qbA5+L7Z7FccNKvxTAsy2Qydy4dvRistVqtK3vpDecZevv27bVf18vn2vAiLQAAwOLSEQYLotlsTtxNMtwU/i7Pnj0bC1LmtY/R8IqON6nX62MdQ3d1a93nuM1mM9rtdrx+/frKsstWqxUvXry4MaSb9piTuBjo3OfY3W534uDzsb19+za++uqr0VLDu8wrxLu4F9skwdnOzs7YBRvq9fqNAeZdstns2P5uAADAYtMRBk/Y5VAgyQ6xi4bLz4Yeuk/YZfl8Po6Pj6/tuhqGZPM2bfC2KF+ziM+fw3C/t8va7fbMv46TurjEdNIOsovn33UdYQAAwNMkCIMn7HInUVJBxXUu1/YY3UM7OzvR6XSuhFCHh4cLHX4syz5TwytctlqtazfCn2cd93U5MFvUJagAAMBsCcLgCbu8L1XE4oRhl5d2Plb4k81m4/j4+Mr8d238zs2GV70cLmmddlnhrEyz/Pfy+bdInXcAAMDjEYTBE1YsFq8EQO/evUummEseckXDVqsVhUJh4vHZbDbevn079r5FCQSXTaVSiUKhcOXKjkmaZoP+y+fffS6QAAAALC9BGDyy67qy5umbb74Zu7+3t7cQy8AuduDct6Mom81Gu92+1/LGy/ta2dj8/iqVyugKn4sSgkWMh1iTXmnz4vknBAMAgPQQhMEj2tvbS3zJ1c7OzpWusDdv3jxozll0U3U6ndHtSqVyr8cOg4v7Pu6iRbkS42Oa5bnXbrdHIVg2m12ofcxevXo1dn9Y520unn+TXAETAAB4GgRhTKXX68XW1tatb/v7+0mXmahutxu1Wu3GbqeHhBSXO15umyuTycT79+/H3re3txeHh4dTHbvRaMSLFy8eHIYNu7my2exUQUQ2mx09x5O43AF2OTxZJpeX9d3UATXLrreL3Xe3zZtEt+H29vZYMDfJ/m8XP5/LXZMAAMD09vf378wLer1eYvUJwpjK+fl5nJyc3Pp2dnaWdJlTe+iL+X6/P1oSeVPgcp8w67L7hmj5fD6azebY+8rlcuzt7U08R7fbjXK5HJVKJd6/f/+gjqpWqzUK0i7XNalhV9je3t5EHUAXw5FsNhvVanWq41426blycdxdj7nr45ef++s+/+tCwll2iF0XpE5yvMvLEC+HatN+713cA67b7d66bLbdbo/Ov3q9vlDdbQAAsOzOzs7uzAvOz88Tq08QxlRWV1djc3Pz1re1tbWky5zahw8fxu7f58V5o9GIL7/8cvQC/7rA6Lr57tO9c3nsJI/d3t6+cvXEWq0WhULh1u6wdrsdlUolcrlcdLvdOD4+vjME6/f7N9bUbrejXC5HxOcQbNpA7WKgUqlUolKp3Ph1ajQaY6Hf0dHRrXNfDHCS3k/tpuNf7DSs1WpRqVSi1WrF4eHh6Ot1OeS56WtyObC67piXv07DILXVasXe3t7o/Lj4dTk4OIhWqxW1Wm005+UgbPh163a7Y3uQXVfbbV+L7e3tsX3LyuXyteP7/X68fv06Ij4vG74pEJ30637xOU36XAEAgEWwtrZ2Z16wuppgHDWAe9jc3BxExGBzczPpUh5Np9MZZLPZQUSMveXz+cHOzs6gWq0O6vX6oF6vD6rV6qBarQ52dnYG+Xz+ymOKxeK1x9jZ2bl2/k6nM3V91Wp14s/x4OBgkMlkrsyRzWYHxWJxUCwWB/l8fjQmk8kMDg4O7pz38ue1s7MzaDabg6Ojo8HBwcHo49lsdnB8fDxxvTd9Dpfrj4jB9vb2oFqtjo538bkqFosTPceXv5bNZvPacaenp1e+hjfZ3t4eG3vT83l0dHTlObzp2Nd9DYdfr6Ojo8FgMBgbMzzHTk9Pp6rt8riLb/V6fTAYXH9ub29vj81TrVavneO6z3XSr8VQs9m8ct52Op1Bp9MZHBwcjM6HYb3XmfRrcHncTd/vAADM37/9Z//vmb0xe0lmC4Iw7uUpB2E3hUMPebsYKBwfH18bYF0XRl33Yr9er0/8+MtBx02Ojo4G1Wp1kM/nx+YeBmLVavXegVWn0xnU6/VBsVi8Muf29vadQcakjo6OBplMZtBsNgfNZnOws7NzJcAbhiHb29ujYOgmx8fHg+3t7RvPgXw+PwpPbhs7/DyHX4PLYdzl4GQ459HR0aBYLN74Nb0ujDk9PR2bP5/PXwlEh++7HACenp7e+fled8yL52E2mx1Uq9Wx8+309HT0eVx8zi6rVqtjdV88L+7ztbjJwcHBoFgsjoViw8fd9P1xfHw80deg0+ncOu6u2gAAeHyCsMWWZLawMhgMBgET2traipOTk9jc3IyPHz8mXQ4AAABc8cO7X81srh+/+sXM5uKzJLMFe4QBAAAAkAqCMAAAAABSQRAGAAAAQCoIwgAAAABIBUEYAAAAAKkgCAMAAAAgFQRhD9Rut6NSqUQul4uVlZVYWVmJXC4XtVot+v1+0uVFRES3241SqZR0GQAAAACJEoRNqd/vR7lcjkKhEI1GI7rd7uhj3W439vb2Yn19PRqNxqPWMAzfbnvL5XKRzWYfrQ4AAACAZSAIm0K/349CoRCHh4d3jq1UKlGpVB6ljvuEbLVa7VFqAAAAAFgWgrAplMvl6Ha7kc/no9lsRqfTiU6nE81mM6rV6pXxjUZjotDsvt68eTPRuGKxqCMMAAAASL0vki5g2TQajWi1WlGtVqNer499LJvNxvb2dlQqlSiXy9Fut0cfe/36dWxvb8+0jn6/H9Vq9c79v54/fz6z4wIAAAAsq5XBYDBIuohlMtxv6+jo6NZx3W43crnc2PuOjo6iWCzOrI6IiE6nM5P5JrW1tRUnJyexubkZHz9+nOuxAQAAYBI/vPvVzOb68atfzGwuPksyW7A08h7a7XZ0u91oNpt3js1ms1c6xi52iD3E4eFhdLtd+34BAAAA3IMg7B7evXsXOzs7kclkJhp/ufvr97///UzqePPmTWQymXj58uVM5gMAAABIA3uE3cOrV6/utel8Pp8fu395qeQ02u32qLNsfX09stlsFIvFKJVKM92DDAAAAOCpEYTdw+Vg6y79fn/s/iyu3Hh5OWS3241GoxGNRiMiIra3t+Obb765d60svlmucU+TRVrP32q1ZrZPIAAAAPdnaeQj6na7Y/cf+gK42+1Gq9W6dczh4WEUCoWoVCoPOhbwcMOgulQqxcrKyp1XeAUAAOBx6Qh7RB8+fBjd3tnZefB82Ww2Dg4Oot/vR6fTiVardSVsG2o0GvHhw4c4Pj5+8HGB++l2u1Eul6Pb7V7pDAUAACA5OsIe0cHBwej2rK7wuLOzE9VqNQ4ODqLT6cTp6WnU6/VrN/Bvt9s6UCAB2Ww2jo+P4/T0NKrVatLlAAAA8P8QhD2Sbrc72tS+Xq/PZH+w62QymahWq3F6ehrNZvNKINZqtWJvb+9Rjg3cTRgNAACwOCyNfCT1ej0iPneGzKsjZHt7O4rFYrx48WIUwkVEvHnzZuY19Hq92NraevA8u7u7sbu7O4OKYDE9e/Ys6RKmVqlUxjpbAQCAdNvf34/9/f0Hz9Pr9WZQzXQEYY+g3W5Ho9GITCYTR0dHcz12JpOJ4+PjKBQKozCs3+/P/Gp15+fncXJy8uB5zs7OZlANMGvDq9EKwgAAgKGzs7OZZAFJEoQ9gtevX0dExPv37x9tSeRd3r59G4VCYXT/6OhopkHY6upqbGxsPHietbW1GVQDzFK323XlWQAA4Iq1tbXY3Nx88Dy9Xi/Oz89nUNH9CcJmrFKpRLvdjmazGfl8PrE68vl8FIvFaLVaERE3Xl1yWhsbG/Hx48eZzgkkr9/v29cMAAAu+OHdr2Y2149f/WJmcyVhVtsbbW1tJdZZZrP8Gbq4lGh7ezvpcryYBe6l3+/HixcvZh6cAwAALApB2Iy0Wq3RxtI7OztJlxMRMbYsc5k37Ia06ff7sbe3F6VSKdbX12NlZSXW19ejUChErVabOKhqtVpRKpUil8vF+vr66G34vova7XZ8+eWXYxfaiIhYWVkZvV1+DAAAwLIRhM1Au92OUqkU9Xp9YUKwiPEgLJPJJFcIMLFGoxHr6+tRq9WiVCrF+/fvo9PpxNu3byMiYm9vL3K5XJTL5VvnKZfLUSqVolwuR6fTidPT0zg9PY2XL19Gq9WKbrcb/X5/ND6bzcb79+9HV7wdOj4+Hr01m82Zf74AAADzZI+wB+p2u/HixYuoVqtRrVaTLmfMhw8fRrctk4TFV6vVYm9vb3T114thdjabje3t7SiXy3F4eBiHh4eRy+Xi+Pj4StC9t7cXh4eHUa1Wr4Tzw6tANhqNsfdnMpnI5/Px6dOnsfcnudchAADArOkIe4ButxuFQiF2dnaudFHc9pi9vb1HruyzTqczuj3LK0YCs9doNEY/G+r1+o1XnG02m6OPdbvdazvDhmHXT37yk2vnGP68uhx6AQAAPHWCsCkNr6z28uXLiUOwiM/LleYVSh0eHkZELFynGnBVrVaLiM+dWXctsR6Ojfi8D9jw6rBDwz3E3r17d+3jM5nMQlzQAwAAYN4EYVPo9/tRKBQim82ONq6+663VakWhUIiIm5caNRqNe22EfZvDw8PodruRyWTim2++efB8wONpNBqj/bpu6gS7aGdnZ2w55OUwfjjHcP/Ci3uBXXzMJMcCAAB4SuwRNoUXL16MAq77XkVtuGTpslKpNOrq2Nvbi9PT07EXuq1WK8rlcvT7/SgWi1Gv128M1Lrdbrx+/ToiIt6/f2+jfFhwR0dHo9uThlPFYnHU9Xm5I6xWq0WlUhl97Msvv7xyMQ8hGAAAkEY6wu6pUChEu92e+vE3LXm6/EL222+/HbvfbDZHXR3D7rLhC93L8xQKhXj27Fl0Oh0bXcMSmKYL9HKQdbHra2dnZ2zpY7/fj0qlErlcbhSeAQAApJEg7B7K5fKjhGARcaVr6/KL3Os2xG40GrG+vh7lcjkqlUoUCoUolUqxs7Nz5YpzwOK6GGJNGopd7ka9vPF9s9m8smRyuLl+qVSayRJsAACAZSMIu4dmsxmDwWDqt5uWRQ7nzmazkclkolqtXtlQv1gsRqfTiZ2dndG4oXa7HZ8+fYpvvvkmTk9Po16vWw4JS+Ti9+ukAdWzZ8/G7l8XfFer1Tg9Pb2yMX6r1YpcLvegYB8AAGAZ2SNsQQyDrttks9lbwzRgOWWz2VEo1e/3o9/v3xlmX+wAu637M5PJRLPZjHa7HbVabWwZ9osXL+L09PRhxQMAACwRHWEACXv16tXY/UajcedjLgbnlzu+rltKnc/n4+joaCxM7/f79gwDAABSRRAGkLDt7e2xDrBJOj8vdnZ98803Yx+7Ldza2dmJarU6un95bzEAAICnTBAGsADevn07ut3tdq9cSfaidrs9Wkp5056At3WVlUql0e3L+xFeXmZ5ec+yixv7AwAALBtBGMAjutxxdVOQtL29PXaVx3K5fO3Yfr8fr1+/joir3V0X1Wq1G481DNGKxeKV4Ovy/UqlEv1+P7rdblQqlYmWbQIAACwqQRjAI7q8zPHbb7+9cWy1Wo1msxmZTCb6/X58+eWX0Wg0otvtRrfbjUajEYVCIdrtdtTr9VuXUA4ff3mZ5OHhYdRqtcjn89FsNm+sY6jVasX6+nrkcrkrHwMAAFg2gjCAGev3+1Eul2N9ff1KEFWpVKJQKESlUrn2sdvb23F6ehoHBwfx/PnzqNVqkcvlolAoxMHBQVQqlTg9Pb01kNre3o6Dg4N49uxZlMvlWFlZiVwuF+vr63FwcBAHBwdxfHx845Up6/V6VKvVUXfYMDRz1VoAAGDZrQwGg0HSRbA8tra24uTkJDY3N+Pjx49JlwMAAABX/PDuV0mXcK0fv/pF0iUshCSzBR1hAAAAAKSCIAwAAACAVBCEAQAAAJAKXyRdAMup1+vF1tbWrWN2d3djd3d3ThUBAAAASdvf34/9/f1bx/R6vTlVc5UgjKmcn5/HycnJrWPOzs7mVA0AAACwCM7Ozu7MC5IkCGMqq6ursbGxceuYtbW1OVUDAAAALIK1tbXY3Ny8dUyv14vz8/M5VTROEMZUNjY25n6JUwAAAGCxTbJN0tbWVmJdY4IwAAAAIHE/vPtV0iWQAq4aCQAAAEAqCMIAAAAASAVBGAAAAACpIAgDAAAAIBUEYQAAAACkgiAMAAAAgFQQhAEAAACQCl8kXQAwmV/++rukS1hKX//s50mXAAAAwILQEQYAAABAKgjCAOao3W5HpVKJXC4XKysrsb6+HoVCIQ4PD5MubWH1+/1oNBrRbreTLgUAAFhygjCAOanValEoFKJUKsXR0VHs7OxEv9+Pdrsd5XI59vb2ki5xYXS73djb24tSqRTr6+tRqVTiw4cPSZcFAAAsOXuEAcxBo9GIvb29qFarsb29HRERBwcH8enTp1E32NHRUVSr1STLXAiHh4fx7t27aLfb0e12ky4HAAB4QgRhAHNQq9UiIqJUKo29v9lsRqlUim63OxqTdtvb26OwcGVlJeFqAACAp0QQBvDIDg8Po9/vR0TEs2fPrnz86OhozhUtj2w2qysMAACYGXuEATyyg4OD0e1MJpNcIQAAACknCAN4ZDqaAAAAFoMgDOCRCcIAAAAWgyAMAAAAgFQQhAHMWLfbjZWVldHbRblcbuxjt10Vsd/vx97eXpRKpVhfX4+VlZVYX1+PQqEQtVrtxk6zRqMRhULhzuOUSqVr6xlu7H/d57W3txe5XC7a7fa1x8vlclEul2+c46bPsVAoxPr6eqyvr0epVIq9vb2JHg8AAHAfgjCAGctms3F8fBzHx8dXrgh5cHAw+tjw7TqNRiPW19ejVqtFqVSK9+/fR6fTibdv30ZEjAKpcrl85bE7OztxfHwcOzs7t9Z5dHQUnU4n6vX6jWP6/X7UarXI5XKRy+XGArh+vx+FQiEqlcooGOt2u3F4eBhffvnlnUtCh+NqtVoUi8VoNptxfHwclUolDg4OIpfLxadPn26dAwAA4D4EYQCPIJ/PRz6fj2KxOPb+58+fjz42fLusVqtFpVKJTCYTnU4nqtVq5PP5yGazsb29HcfHx7G9vR0Rn8OkXC53bQfWdSHZdYZzXefTp09RKpWufB4fPnyIL7/8MrLZ7ChQOzg4GF0Vs9/vR6VSuXHeRqMxqu/4+Djq9XoUi8XR59jpdCKTyUzcWQYAADCJL5IugOXU6/Via2vr1jG7u7uxu7s7p4rgaWg0GqNlgfV6PbLZ7LXjms1m5HK56Ha70e12o1wuX+k+m9SzZ89u/Fg2m41sNhvFYjEajcbo/bVaLZrN5lhAtrOzE8+ePRsFXK1WK/r9/igcG2q326OQrF6vXxsGXvwcAQCA5bG/vx/7+/u3jun1enOq5ipBGFM5Pz+Pk5OTW8ecnZ3NqRp4Omq1WkREZDKZO5c2DjvHIj6HTq1W60rn1ixd7NB6+/bttce63F324cOHK+Nev349mu+2zzGbzeoKAwCAJXN2dnZnXpAkQRhTWV1djY2NjVvHrK2tzakaeBoajcYo9LmpE+yinZ2dqNVqo8cMlxcmLZvNjvYHu7xPWKvVGu0nNkmtz549E4QBAMASWVtbi83NzVvH9Hq9OD8/n1NF4wRhTGVjYyM+fvyYdBnwpFxc2jhJEBbxOUw6PDyMiM8h06JrNpuj25N+jgAAwPKYZJukra2txLrGbJYPsCDuusridS6HSYvePXUxrLP/FwAAMG+CMIAFcTHEmjQUuxwmffr0aZYlzdzF+hY9tAMAAJ4eQRjAgrh4dcVJg7DLV3xc9OWGF8OvTqeTXCEAAEAqCcIAFsTFEKvf70/UMXWxw2rRQ7CI8RqnWQoKAADwEIIwgAXx6tWrsfuNRuPOx1zsqtre3p7quPNcTpnP50e3W62W5ZEAAMBcCcIAFsT29vbY8siDg4M7H3Nx8/lvvvlm7GOXl03eFDrNszPrcthXq9XmdmwAAABBGMAc3dV99fbt29Htbrc7FnRd1m63o91uR0REvV4fC9EixruvIq7vMOt2u1fCqMfsENve3h6rq9Fo3Nj51m63x0I6e4oBAAAPJQgDeESXu63uWgq4vb0d9Xp9dL9cLl/7mH6/H69fv46IiJ2dnahWq9fOVywWR7drtVpUKpVotVpxeHgYlUolcrnclRDtpg6xSZcxXnz8dY9pNptj9yuVSlQqldHjut1u7O3txYsXL8bq2tvbi0qloosMAACYmiAM4BFdXt44yXLHarUazWYzMplM9Pv9+PLLL6PRaES3241utxuNRiMKhUK02+2o1+u3zjmcZ6jRaESpVIpyuRzffvttHB0djYVlEZ8Ds263OxZiXe5MOzo6uvZ4k4zLZrPR6XTGNs5vNBqRy+ViZWUlcrlcvHnzJt6/fz+2vDObzUYmk4lKpXLj5wsAAHCblcFgMEi6CJbH1tZWnJycxObmZnz8+DHpcmAhDZcbXl7aN5TJZOL58+eRz+fHur+u02g0otlsxocPH6Lf70cmk4lsNhuvXr2KnZ2dK8shr9Pv96NWq0Wr1Yputxv5fD6KxeLYsQuFQhSLxahUKmMBVbvdHj32smw2G8ViMQ4ODqLb7Y66za4bV6lUru1a29vbi3fv3o2Ct+Gcwy61Uqk0evzlpZ4AADwtP7z7VdIlPLofv/pF0iUshCSzBUEY9yIIAwAA4DEIwtIjyWzB0kgAAAAAUkEQBgAAAEAqCMIAAAAASAVBGAAAAACpIAgDAAAAIBUEYQAAAACkgiAMAAAAgFQQhAEAAACQCoIwAAAAAFJBEAYAAABAKgjCAAAAAEgFQRgAAAAAqfBF0gWwnHq9Xmxtbd06Znd3N3Z3d+dUEQAAAJC0/f392N/fv3VMr9ebUzVXCcKYyvn5eZycnNw65uzsbE7VAAAAAIvg7OzszrwgSYIwprK6uhobGxu3jllbW5tTNQAAAMAiWFtbi83NzVvH9Hq9OD8/n1NF4wRhTGVjYyM+fvyYdBkAAADAAplkm6Stra3EusZslg8AAABAKgjCAAAAAEgFQRgAAAAAqSAIAwAAACAVBGEAAAAApIIgDAAAAIBUEIQBAAAAkAqCMAAAAABSQRAGAAAAQCoIwgAAAABIhS+SLgAAAABYTj+8+1XSJcC96AgDAAAAIBUEYQAAAACkgiAMAAAAgFQQhAEAAACQCoIwAAAAAFJBEAYAAABAKgjCAAAAAEgFQRgAAAAAqSAIAwAAACAVvki6AFhWv/z1dzOb6+uf/XxmcwEAAADX0xEGAAAAQCroCGMqvV4vtra2bh2zu7sbu7u7c6oIAAAASNr+/n7s7+/fOqbX682pmqsEYUzl/Pw8Tk5Obh1zdnY2p2oAAACARXB2dnZnXpAkQRhTWV1djY2NjVvHrK2tzakaAAAAYBGsra3F5ubmrWN6vV6cn5/PqaJxgjCmsrGxER8/fky6DAAAAGCBTLJN0tbWVmJdYzbLBwAAACAVBGEAAAAApIIgDAAAAIBUEIQBAAAAkAqCMAAAAABSQRAGAAAAQCoIwgAAAABIBUEYAAAAAKkgCAMAAAAgFQRhD9Rut6NSqUQul4uVlZVYWVmJXC4XtVot+v3+ox9/b28vSqVSrK+vj45dLpej1Wo9+rEBAAAAlokgbEr9fj/K5XIUCoVoNBrR7XZHH+t2u7G3txfr6+vRaDQe5fitVivW19ejVqtFRESz2YxOpxP1ej3a7XaUSqUolUpzCeMAAAAAlsEXSRewjPr9fhQKhbHw6yaVSiWOj4/j4OBgZsdvtVpRKpUiImJnZ2ds7mw2G9vb21EoFKLVakWhUIjj4+PIZDIzOz4AAADAMhKETaFcLke32418Ph/ffPNN5PP5iPi8TPL777+Pvb29sfGNRiNKpVJsb28/+NjDTrSIz6HXTQFbs9mMXC4X3W43yuVyHB0dPfjYPJ5f/vq7mc739c9+PtP5AAAA4CmwNPKeGo1GtFqtqFarcXx8HNvb25HNZkedWPV6PTqdzigcG3r9+vVMjl8ul0fLHYfLIq8zrCficwfZYy3RBAAAAFgWgrB7qtfrUSwWo16v3zgmm81Gs9kce1+/33/wBvbdbndsjpcvX946/tWrV6Pbt4VmAAAAAGkgCLuHdrsd3W73Ssh1nWw2eyUsa7fbDzr+xfmKxeKd+35dXIrZ7/fj8PDwQccHAAAAWGaCsHt49+5d7OzsTLzxfLFYHLv/+9///kHHv7i88fLSy5tks9nR7Xfv3j3o+AAAAADLzGb59/Dq1auxYOkul8OqXC439bEvd5N99dVXE9cwvLqljjAAAAAgzXSE3UM+n5+4GywiRpvaD90nRLvs8v5ik851edxDl2cCAAAALCtB2CMadmINXV4qeR/ff//92P1JA7mf/OQnY/c/fPgwdQ0AAAAAy0wQ9oguhk47OzsPmutyqDZtR1in03lQHQAAAADLShD2iA4ODka3a7Xag+a6HIRN6/JyTQAAAIC0sFn+I+l2u6P9uOr1+oP2B4uYPsC6vITy06dPD6pjqNfrxdbW1oPn2d3djd3d3RlUBAAAADym/f392N/ff/A8vV5vBtVMRxD2SOr1ekR8XppYrVYTruaPZtURdn5+HicnJw+e5+zsbAbVAAAAwOL74d2vZjbXj1/9YmZzTers7GwmWUCSBGGPoN1uR6PRiEwmE0dHRzOZM5PJzCTEus9VL2+zuroaGxsbD55nbW1tBtUAAAAAj21tbS02NzcfPE+v14vz8/MZVHR/grBH8Pr164iIeP/+/YOXRA49e/ZsJkHYs2fPHl5MRGxsbMTHjx9nMhcAAACw+Ga1vdHW1lZinWU2y5+xSqUS7XY7ms1m5PP5mc07bSfX5fBsVh1hAAAAAMtGEDZDjUYjGo1GHBwcxPb29kznfv78+dj9SbvDLm+On8vlZlUSAAAAwFIRhM1Iq9WKSqUSBwcHsbOzM/P5C4XC2P1utzvR4zqdztj9YrE4s5oAAAAAlokgbAba7XaUSqWo1+uPEoJFXO0ImzQIu9g5lslkZrZnGQAAAMCyEYQ9ULfbjRcvXkS1Wo1qtfpox8nn82P7e33//fcTPe7Dhw+j25fDNAAAAIA0EYQ9QLfbjUKhEDs7O1Gv1yd+zN7e3lTHe/ny5eh2u92e6DEXx9VqtamOCwAAAPAUCMKm1O/3o1QqxcuXLycOwSIiyuXy1Pt0VSqV0e1Wq3Xn+Itjstms/cEAAACAVPsi6QKWUb/fj0KhENlsNmq12kT7dXW73VFHVj6fv3ZMo9GITqcTlUrl2r288vl8FIvFUcB1eHh469Upm83m6LZuMAAAACDtBGFTePHiRXS73eh2u5HL5e712IODg2vfXyqVRgHX3t5enJ6eju0JdvHxw2O+efPmxiCs3+9Ho9GIiM9XinysTfwBAAAAloWlkfdUKBQm3p/rOjcFUpeXOn777bfXjstms6NOr3a7feN+Yy9evIiIz1eKvNgZBgAAAJBWgrB7KJfLjxKCRcSV7q/rlkYObW9vx9HRUWQymajVaqO6+v1+tFqtUViXz+fjN7/5zbWdZQAAAABpY2nkPTxmZ1Wz2YxKpRKfPn2KnZ2dOze2LxaLcXp6Gnt7e/Hu3bt48eJF9Pv9yGQy8fz582g2m7fuHwYAAACQNoKwBVEsFqPT6dz7cdVqNarV6iNUBAAAAPC0WBoJAAAAQCoIwgAAAABIBUEYAAAAAKkgCAMAAAAgFWyWT6r88tffJV0CAAAAkBAdYQAAAACkgiAMAAAAgFQQhAEAAACQCoIwAAAAAFLBZvlMpdfrxdbW1q1jdnd3Y3d3d04VAQAAAEnb39+P/f39W8f0er05VXOVIIypnJ+fx8nJya1jzs7O5lQNAAAAsAjOzs7uzAuSJAhjKqurq7GxsXHrmLW1tTlVAwAAACyCtbW12NzcvHVMr9eL8/PzOVU0ThDGVDY2NuLjx49JlwEAAAAskEm2Sdra2kqsa8xm+QAAAACkgiAMAAAAgFQQhAEAAACQCvYIgyfol7/+bmZzff2zn89sLgAAAEiSjjAAAAAAUkEQBgAAAEAqCMIAAAAASAVBGAAAAACpIAgDAAAAIBUEYQAAAACkgiAMAAAAgFQQhAEAAACQCoIwAAAAAFJBEAYAAABAKgjCAAAAAEgFQRgAAAAAqSAIAwAAACAVBGEAAAAApIIgDAAAAIBUEIQBAAAAkAqCMAAAAABSQRAGAAAAQCp8kXQBLKderxdbW1u3jtnd3Y3d3d05VQQAALBYfnj3q5nN9eNXv5jZXPCY9vf3Y39//9YxvV5vTtVcJQhjKufn53FycnLrmLOzszlVAwAAACyCs7OzO/OCJAnCmMrq6mpsbGzcOmZtbW1O1QAAAACLYG1tLTY3N28d0+v14vz8fE4VjROEMZWNjY34+PFj0mUAAAAAC2SSbZK2trYS6xqzWT4AAAAAqSAIAwAAACAVBGEAAAAApIIgDAAAAIBUEIQBAAAAkAqCMAAAAABSQRAGAAAAQCoIwgAAAABIBUEYAAAAAKkgCAMAAAAgFQRhAAAAAKSCIAwAAACAVBCEAQAAAJAKgjAAAAAAUkEQBgAAAEAqCMIAAAAASAVBGAAAAACp8EXSBQCL7Ze//m5mc339s5/PbC4AAAC4Lx1hAAAAAKSCIAwAAACAVBCEAQAAAJAKgjAAAAAAUkEQBgAAAEAquGokU+n1erG1tXXrmN3d3djd3Z1TRQAAAEDS9vf3Y39//9YxvV5vTtVcJQhjKufn53FycnLrmLOzszlVAwAAACyCs7OzO/OCJAnCmMrq6mpsbGzcOmZtbW1O1QAAADCpH979KukSeMLW1tZic3Pz1jG9Xi/Oz8/nVNE4QRhT2djYiI8fPyZdBgAAALBAJtkmaWtrK7GuMZvlAwAAAJAKgjAAAAAAUkEQBgAAAEAqCMIAAAAASAVBGAAAAACpIAgDAAAAIBUEYQAAAACkgiAMAAAAgFQQhAEAAACQCoIwAAAAAFJBEAYAAABAKgjCAAAAAEgFQRgAAAAAqSAIAwAAACAVBGEAAAAApIIgDAAAAIBUEIQBAAAAkAqCMAAAAABSQRAGAAAAQCoIwgAAAABIBUEYAAAAAKkgCAMAAAAgFb5IugCWU6/Xi62trVvH7O7uxu7u7pwqAgAAAJK2v78f+/v7t47p9XpzquYqQRhTOT8/j5OTk1vHnJ2dzakaAAAAYBGcnZ3dmRckSRDGVFZXV2NjY+PWMWtra3OqBgAASLMf3v1qZnP9+NUvZjYXpNHa2lpsbm7eOqbX68X5+fmcKhonCGMqGxsb8fHjx6TLAAAAABbIJNskbW1tJdY1ZrN8AAAAAFJBEAYAAABAKgjCAAAAAEgFQRgAAAAAqSAIAwAAACAVBGEAAAAApIIgDAAAAIBUEIQBAAAAkAqCMAAAAABS4YukC3gKut1uVCqVqFQqsb29nXQ5EfHHmo6OjpIuBQAAgAf64d2vki4BngQdYQ/Q7/ejXC5HLpeLVqsVnz59mssxV1ZW7nzL5XKRzWYfvR4AAACAZaEjbAr9fj/evHkTe3t7cz92o9GYeGytVnvESuD+fvnr72Y219c/+/nM5gIAACAddITd097eXhQKhWi324kc/82bNxONKxaLOsIAAAAALtARdg/tdjuKxWJUq9WI+NydValU5nb8RqMR/X4/qtVqlEqlW8c+f/58TlUBAAAALAdB2D3k8/mx+/MOm+r1emSz2ajX63M9Ljx1lmwCAACkgyDsATKZzNyOdXh4GN1uNw4ODuZ2TAAAAICnxB5hS+LNmzeRyWTi5cuXSZcCAAAAsJQEYUug3W5Hu92Ofr8f6+vrkcvlolKpxOHhYdKlAQAAACwNQdgSqNVqY/e73W40Go0ol8uxsrIS5XI5satYAgAAACwLQdiC63a70Wq1bh1zeHgYhUJhrlewBAAAAFg2NstfcNlsNg4ODqLf70en04lWqxXdbvfasY1GIz58+BDHx8dzrhIAAABg8QnClsDOzs7Y/X6/H41GI968eRP9fn/sY+12O0qlUhwdHc2xQgAAAIDFJwhbQplMJqrValSr1Tg8PIzXr1+PBWKtViv29vaiWq0+Wg29Xi+2trYePM/u7m7s7u7OoCIAAADgMe3v78f+/v6D5+n1ejOoZjqCsCW3vb0dxWIxXrx4MbZh/ps3bx41CDs/P4+Tk5MHz3N2djaDagAAAIDHdnZ2NpMsIEmCsCcgk8nE8fFxFAqFURjW7/ej1WpFsVh8lGOurq7GxsbGg+dZW1ubQTUAAADAY1tbW4vNzc0Hz9Pr9eL8/HwGFd2fIOwJefv2bRQKhdH9o6OjRwvCNjY24uPHj48yNwAAALB4ZrW90dbWVmKdZauJHJVHkc/nx4Kvm64uCQAAAJBGgrAnplQqJV0CAAAAwEIShD0x2Wx2dPvZs2cJVgIAAACwWARhT8zFICyTySRXCAAAAMCCEYQ9MR8+fBjdtkwSAAAA4I8EYU9Mp9MZ3X6sK0YCAAAALCNB2BNzeHgYERHVajXhSgAAAAAWiyDsAfr9/oPnaDQaUavVotvtPniuw8PD6Ha7kclk4ptvvnnwfAAAAABPiSDsAS6HV/cNxkqlUlQqldjb24tcLnfl8a1WK9bX12NlZSVKpVK02+1ba3n9+nVERLx//95G+QAAAACXCMKm1O/3o1arjb3v3bt395qj1WqN3f/222/H7jebzVE41mq1olAoRKVSuXaeQqEQz549i06nE/l8/l51AAAAAKSBIOwe+v1+lMvlKJVKsb6+fqUjrN1uj7q3yuXyrR1cEXGlayubzY7dL5fLVx7TaDRifX09yuVyVCqVKBQKUSqVYmdnJ46Pj6/MAQAAAMBnXyRdwDLJZDLRbDZnNl+z2YxKpRKfPn2KnZ2dK1d5LBaL0el0ol6vR6vVik+fPo06xNrtduTz+fjmm2+iWCxaCgkAAABwB0FYgoZB122y2WwcHBzMqSIAAACAp0sQBiylX/76u6RLAAAAYMkIwgBmaJYB3dc/+/nM5gIAAMBm+QAAAACkhCAMAAAAgFQQhAEAAACQCoIwAAAAAFJBEAYAAABAKgjCAAAAAEgFQRgAAAAAqfBF0gWwnHq9Xmxtbd06Znd3N3Z3d+dUEQAAAJC0/f392N/fv3VMr9ebUzVXCcKYyvn5eZycnNw65uzsbE7VAAAAAIvg7OzszrwgSYIwprK6uhobGxu3jllbW5tTNfA0/fLX381srq9/9vOZzQUAAHCTtbW12NzcvHVMr9eL8/PzOVU0ThDGVDY2NuLjx49JlwEAAAAskEm2Sdra2kqsa8xm+QAAAACkgiAMAAAAgFQQhAEAAACQCoIwAAAAAFJBEAYAAABAKgjCAAAAAEgFQRgAAAAAqSAIAwAAACAVBGEAAAAApMIXSRcAAABA+vzw7ldJlwCkkI4wAAAAAFJBEAYAAABAKgjCAAAAAEgFQRgAAAAAqWCzfAAAAPh/2MQfnjYdYQAAAACkgiAMAAAAgFSwNBKAe/nlr7+b2Vxf/+znM5sLAADgLjrCAAAAAEgFQRgAAAAAqSAIAwAAACAVBGEAAAAApIIgDAAAAIBUcNVIptLr9WJra+vWMbu7u7G7uzunigAAAICk7e/vx/7+/q1jer3enKq5ShDGVM7Pz+Pk5OTWMWdnZ3OqBgAAAFgEZ2dnd+YFSRKEMZXV1dXY2Ni4dcza2tqcqgEAAAAWwdraWmxubt46ptfrxfn5+ZwqGicIYyobGxvx8ePHpMsAAAAAFsgk2yRtbW0l1jUmCAMgMb/89Xczm+vrn/18ZnMBAABPk6tGAgAAAJAKgjAAAAAAUkEQBgAAAEAqCMIAAAAASAVBGAAAAACpIAgDAAAAIBW+SLoAAJiFX/76u5nN9fXPfj6zuQAAgMWhIwwAAACAVBCEAQAAAJAKgjAAAAAAUkEQBgAAAEAq2CwfIAVmuZE8AADAstIRBgAAAEAqCMIAAAAASAVBGAAAAACpIAgDAAAAIBUEYQAAAACkgiAMAAAAgFQQhAEAAACQCoIwAAAAAFLhi6QLAIBF88tffzezub7+2c9nNhcAAPAwgjCm0uv1Ymtr69Yxu7u7sbu7O6eKAAAAgKTt7+/H/v7+rWN6vd6cqrlKEMZUzs/P4+Tk5NYxZ2dnc6oGAAAAWARnZ2d35gVJEoQxldXV1djY2Lh1zNra2pyqAQAAABbB2tpabG5u3jqm1+vF+fn5nCoaJwhjKhsbG/Hx48ekywAAAAAWyCTbJG1tbSXWNeaqkQAAAACkgiAMAAAAgFQQhAEAAACQCoIwAAAAAFJBEAYAAABAKgjCAAAAAEgFQRgAAAAAqSAIAwAAACAVBGEAAAAApIIgDAAAAIBUEIQBAAAAkApfJF0AAAAAy+GHd79KugSAB9ERBgAAAEAqCMIAAAAASAVBGAAAAACpIAgDAAAAIBUEYQAAAACkgqtGAsAj+uWvv5vZXF//7Oczm2tR6wIAgMekIwwAAACAVBCEAQAAAJAKgjAAAAAAUsEeYQAAd7CnGgDA0yAIYyq9Xi+2trZuHbO7uxu7u7tzqggAAABI2v7+fuzv7986ptfrzamaqwRhTOX8/DxOTk5uHXN2djanagAAAIBFcHZ2dmdekCRBGFNZXV2NjY2NW8esra3NqRoAAABgEaytrcXm5uatY3q9Xpyfn8+ponGCMKaysbERHz9+TLoMAAAAYIFMsk3S1tZWYl1jgjAAAIAF88O7X81srh+/+sXM5gJYdoIwAACAJ2yWoRrAsltNugAAAAAAmAdBGAAAAACpIAgDAAAAIBUEYQAAAACkgiAMAAAAgFQQhAEAAACQCoIwAAAAAFJBEDYD3W43SqVSHB4ezuV4e3t7USqVYn19PVZWViKXy0W5XI5WqzWX4wMAAAAsI0HYA/T7/SiXy5HL5aLVasWnT58e9XitVivW19ejVqtFRESz2YxOpxP1ej3a7XaUSqUolUrR7/cftQ4AAACAZfRF0gUso36/H2/evIm9vb25HbPVakWpVIqIiJ2dnTg4OBh9LJvNxvb2dhQKhWi1WlEoFOL4+Dgymczc6gMAAABYdIKwe9rb24uDg4PIZrNzO+aw8yzic+h1MQS7qNlsRi6Xi263G+VyOY6OjuZWIwDp9ctffzezub7+2c9nNhcAAFxmaeQ9tNvtKBaL0el04ujo6MZAatbK5fJoueNwWeR1hp1hEZ87yBqNxjzKAwAAAFgKOsLuIZ/Pj91//vz5ox+z2+2ObYL/8uXLW8e/evVqtGl/rVaLnZ2dR60PAGZJdxkAAI9JR9gDzGMPrnq9PrpdLBbvPOawIyzi85LKeV3JEgAAAGDR6QhbcBeXN17uSLtJNpuNbrcbERHv3r0bC8cAWF6z7JYCAIA00hG2wNrt9tj9r776aqLHXQzMdIQBAAAAfKYjbIFd3BssIia+UuXlce12e+JuMgB4KnTQAQBwmY6wBfb999+P3Z90T7Kf/OQnY/c/fPgwq5IAAAAAlpYgbIEN9/kamrYjrNPpzKwmAAAAgGUlCFtgl4OwafX7/ZnMAwAAALDM7BG2wKYNsC4vofz06dPDi7mk1+vF1tbWg+fZ3d2N3d3dGVQEAAAAPKb9/f3Y399/8Dy9Xm8G1UxHEJYCj9ERdn5+HicnJw+e5+zsbAbVAAAAAI/t7OxsJllAkgRhCyyTycwkxJp0k/37WF1djY2NjQfPs7a2NoNqAAAAgMe2trYWm5ubD56n1+vF+fn5DCq6P0HYAnv27NlMgrBnz549vJhLNjY24uPHjzOfFwAAAFhMs9reaGtrK7HOMpvlL7BpO7kuh2eP0REGAAAAsGwEYQvs+fPnY/cn7Q67vDl+LpebVUkAAAAAS0sQtsAKhcLY/W63O9HjOp3O2P1isTizmgAAAACWlSBsgV3uCJs0CLvYOZbJZCKbzc6yLAAAAIClJAhbYPl8fmx/r++//36ix3348GF0+3KYBgAAAJBWrhq54F6+fBmNRiMiItrt9kSPuTiuVqs9Sl0AQPJ++evvZjbX1z/7+czmAgBYVDrCFlylUhndbrVad46/OCabzdofDAAAAOD/IQh7gEmv4nibRqMRtVrtxv2/8vn8WJh1eHh463zNZnN0WzcYAAAAwB9ZGvkAl8Or+wZjpVJp1MG1t7cXp6enY3uCDR0cHEQul4uIiDdv3sT29va18/X7/dEyymKxGDs7O/eqBwDgKZvlUtIIy0kBYBnpCJtSv9+/0nH17t27e81xeanjt99+e+24bDY76vRqt9uxt7d37bgXL15ExOcrRV7sDAMAAABAEHYv/X4/yuVylEqlWF9fv9IR1m63Y2VlJUqlUpTL5Ts3t7/c/ZXNZm8cu729HUdHR5HJZKJWq43m7/f70Wq1olAoRLvdjnw+H7/5zW+u7SwDAAAASDNLI+9h1p1WzWYzKpVKfPr0KXZ2du7c2L5YLMbp6Wns7e3Fu3fv4sWLF9Hv9yOTycTz58+j2WzeuGwSAAAAIO0EYQkqFovR6XTu/bhqtRrVavURKgIAWCyz3tcLAEg3SyMBAAAASAUdYQAAwLV+ePermc3141e/mNlcADAtHWEAAAAApIIgDAAAAIBUsDQSAABILcs/AdJFEAYAMEeugggAkBxLIwEAAABIBUEYAAAAAKkgCAMAAAAgFQRhAAAAAKSCzfKZSq/Xi62trVvH7O7uxu7u7pwqAgAAAJK2v78f+/v7t47p9XpzquYqQRhTOT8/j5OTk1vHnJ2dzakaAAAAYBGcnZ3dmRckSRDGVFZXV2NjY+PWMWtra3OqBgB4qF/++ruZzfX1z34+s7kAgOWytrYWm5ubt47p9Xpxfn4+p4rGCcKYysbGRnz8+DHpMgCABTTLUA0AWC6TbJO0tbWVWNeYIAwAALjWX/zu08zm+m9mNhMATM9VIwEAAABIBUEYAAAAAKkgCAMAAAAgFQRhAAAAAKSCzfJhAfy7f/m3M53vi//0P5vZXLOsbZZ1AQAAwH3pCAMAAAAgFXSEAQAAj+6Hd7+a2Vw/fvWLmc0FQLroCAMAAAAgFXSEAQDAFH756+9mNtfXP/v5zOYCAG6mIwwAAACAVBCEAQAAAJAKgjAAAAAAUkEQBgAAAEAqCMIAAAAASAVBGAAAAACp8EXSBQAAQNr98tffzWyur3/285nNBQBPjSAMAABgBn5496ukSwDgDpZGAgAAAJAKgjAAAAAAUkEQBgAAAEAq2CMMAICZ+nf/8m9nNtcX/+l/NrO5AAAEYUyl1+vF1tbWrWN2d3djd3d3ThUBACyvWYaH4aqRACRof38/9vf3bx3T6/XmVM1VgjCmcn5+HicnJ7eOOTs7m1M1AAAM/fLX3yVdAgApdnZ2dmdekCRBGFNZXV2NjY2NW8esra3NqRoum+l/lQEAAGBCa2trsbm5eeuYXq8X5+fnc6ponCCMqWxsbMTHjx+TLgOYkP16AEjaX/zu08zm+mKGXW//r5nNBEDEZNskbW1tJdY15qqRAAAAAKSCIAwAAACAVBCEAQAAAJAK9ggDACAVZn0xGXsmAsDy0REGAAAAQCroCAMAAFJrllez/LOfPpvZXAA8Dh1hAAAAAKSCjjAAAIAZ0F0GsPh0hAEAAACQCjrCAACApTLrK4ACkB6CMAAAmIIwBgCWj6WRAAAAAKSCIAwAAACAVBCEAQAAAJAK9giDKdkXBAAAAJaLjjAAAAAAUkEQBgAAAEAqWBoJAJfMcunzF//pfzazuQAAgIfREQYAAABAKgjCAAAAAEgFQRgAAAAAqWCPMGBu7LsELCs/vwAAngZBGFPp9XqxtbV165jd3d3Y3d2dU0UAAABA0vb392N/f//WMb1eb07VXCUIYyrn5+dxcnJy65izs7M5VQMAwNAsOxhJzl/87tPM5vqznz6b2VwAdzk7O7szL0iSIIyprK6uxsbGxq1j1tbW5lQNAAAAsAjW1tZic3Pz1jG9Xi/Oz8/nVNE4QRhT2djYiI8fPyZdBgAAALBAJtkmaWtrK7GuMUEYAADAE2aZJcAfCcKA1HM1OGCe/MwBAEjOatIFAAAAAMA8CMIAAAAASAVBGAAAAACpYI8wgAU1y32EgKfJfmMAAPcjCAOAFBKgAPCUuDImMClLIwEAAABIBR1hADOUhuWMOomSk4bzCy5z3sNi0XkFLDsdYQAAAACkgiAMAAAAgFQQhAEAAACQCvYIA4BHZH8jALjeLPcbA5iUIAxYSsIF4C5+TgAAcJmlkQAAAACkgiAMAAAAgFSwNBIAAEtJAYBU0BEGAAAAQCoIwgAAAABIBUsjAXgSLOt6GnwdAQB4TDrCAAAAAEgFQRgAAAAAqWBpJFPp9XqxtbV165jd3d3Y3d2dU0UAAAAP9xe/+zSzuf7sp89mNhcsi/39/djf3791TK/Xm1M1VwnCmMr5+XmcnJzcOubs7GxO1QAAAACL4Ozs7M68IEmCMKayuroaGxsbt45ZW1ubUzUAAADAIlhbW4vNzc1bx/R6vTg/P59TReMEYUxlY2MjPn78mHQZAAAAwAKZZJukra2txLrGBGEAAACw4OxdBrPhqpEAAAAApIIgDAAAAIBUEIQBAAAAkAqCMAAAAABSQRAGAAAAQCoIwgAAAABIBUEYAAAAAKkgCAMAAAAgFQRhM7K3txelUinW19djZWUlcrlclMvlaLVaSZcWERHdbjdKpVLSZQAAAAAk5oukC1h2rVYryuVy9Pv9KBaL0Ww2I5vNRrvdjlqtFqVSafT+TCYz8+P3+/1YX1+faOzOzs7Mjw8A/+5f/m3SJQDAQvqL331KugTgEkHYA7RarVGX1c7OThwcHIw+ls1mY3t7OwqFQrRarSgUCnF8fDzzMKzRaEw8tlarzfTYAAAAAMvE0sgp9fv9KJfLEfE59LoYgl3UbDYj4vPSxOH4WXrz5s1E44rFYmSz2ZkfHwAAAGBZ6Aib0nA5ZMTtnVbDzrDDw8NotVrRaDRmtkSx0WhEv9+ParV65/5fz58/n8kxAQAAAJaVIGwK3W53bBP8ly9f3jr+1atXcXh4GBGfQ7NZBWH1ej2y2WzU6/WZzAcAAADwlAnCpnAxeCoWi3fu+7W9vT263e/34/DwcOx90zg8PIxut3vjkkyAZWCTdQAAYJ7sETaFixvU5/P5iR5zcX+ud+/ePbiGN2/eRCaTubMbDQAAAIDPBGH31G63x+5/9dVXEz3uYmA2XCb5kBra7Xb0+/1YX1+PXC4XlUrlwfMCAAAAPGWCsHu6uDdYREx8JcbL4y4HavdxeXP+brcbjUYjyuVyrKysRLlcftD8AAAAAE+RIOyevv/++7H7d+0PNvSTn/xk7P6HDx+mOv7ljfqvc3h4GIVCISqVylTHAAAAAHiKbJZ/T91ud+z+tB1hnU5nquNns9k4ODiIfr8fnU4nWq3WlZqGGo1GfPjwIY6Pj6c6FgAAAMBTIgi7p5tCp/vq9/tTP3ZnZ+fKXI1GI968eXNl3na7HaVSKY6OjqY+HgAAAMBTIAi7p2kDrMtLKD99+vTwYi7MXa1Wo1qtxuHhYbx+/XqszlarFXt7e1GtVmd2zF6vF1tbWw+eZ3d3N3Z3d2dQEQAAAPCY9vf3Y39//8Hz9Hq9GVQzHUFYQh7SEXab7e3tKBaL8eLFi7EN89+8eTPTIOz8/DxOTk4ePM/Z2dkMqgEAAGBSf/G72TVm/NlPn81sLhbf2dnZTLKAJAnC7imTycwkxJp0k/1p5z4+Po5CoTAKw/r9frRarSgWizM5xurqamxsbDx4nrW1tRlUAwAAADy2tbW12NzcfPA8vV4vzs/PZ1DR/QnC7unZs2czCcKePXv81Pzt27dRKBRG94+OjmYWhG1sbMTHjx9nMhcAAACw+Ga1vdHW1lZinWWriRx1iU3byXU5PHvMjrChfD4/FnzNaqN/AAAAgGUkCLun58+fj92ftDvs8ub4uVxuViXdqlQqzeU4AAAAAItOEHZPF5caRkzeZdXpdMbuz2qJ4l2y2ezo9jyWYwIAAAAsKkHYPV3uCJs0CLvYOZbJZMYCqsd08TjzWI4JAAAAsKgEYfeUz+fHAqXvv/9+osd9+PBhdPtymPaYLh7XMkkAAAAgzQRhU3j58uXodrvdnugxF8fVarWZ13STi0sy57UcEwAAAGARfZF0AcuoUqlEo9GIiIhWq3Xn+ItjstnsXAOpw8PDiIioVqtzOyYAAADp8Be/+3T3oAn92U/ta83j0xE2hXw+PxZmDcOmmzSbzdHtu7rBGo1G1Gq1ifceu83h4WF0u93IZDLxzTffPHg+AAAAgGUmCJvSwcHB6PabN29uHNfv90fdY8ViMXZ2dm4cWyqVolKpxN7eXuRyubEN9iM+d5atr6/HyspKlEqlW5dldrvdeP36dUREvH//3kb5AAAAQOoJwqaUzWZHnV7tdjv29vauHffixYuI+HzFxoudYde5vMzy22+/HbvfbDZH4Vir1YpCoRCVSuXaeQqFQjx79iw6nU7k8/mJPicAAACAp0wQ9gDb29txdHQUmUwmarValMvlaLfb0e/3R2FUu92OfD4fv/nNb+7syrr88Ww2O3a/XC5feUyj0Yj19fUol8tRqVSiUChEqVSKnZ2dOD4+vjIHAAAAQFoJwh6oWCzG6elp1Ov16Ha78eLFi1Ew9ezZs2g2m3F8fDzR0sRmsxnZbDYymUxUq9Urm+oXi8XodDqxs7MzGjfUbrfj06dP8c0334zqsRwSAAAA4I9WBoPBIOkiWB5bW1txcnISm5ub8fHjx6TLubdf/vq7mc317/7l385sLgAAgLRz1cj7+fGrXyRdwtSSzBZ0hAEAAACQCoIwAAAAAFJBEAYAAABAKgjCAAAAAEgFQRgAAAAAqSAIAwAAACAVBGEAAAAApIIgDAAAAIBUEIQBAAAAkAqCMAAAAABS4YukC2A59Xq92NraunXM7u5u7O7uzqkiAAAAIGn7+/uxv79/65herzenaq4ShDGV8/PzODk5uXXM2dnZnKoBAAAAFsHZ2dmdeUGSBGFMZXV1NTY2Nm4ds7a2NqdqAAAAgEWwtrYWm5ubt47p9Xpxfn4+p4rGCcKYysbGRnz8+DHpMgAAAIAFMsk2SVtbW4l1jQnCSJV/9y//NukSAAAAuMZf/O7TzOb6s58+m9lcPC2uGgkAAABAKgjCAAAAAEgFQRgAAAAAqSAIAwAAACAVBGEAAAAApIIgDAAAAIBUEIQBAAAAkAqCMAAAAABSQRAGAAAAQCoIwgAAAABIBUEYAAAAAKkgCAMAAAAgFb5IugAAAACAWfqL332a2Vx/9tNnM5uL5OkIAwAAACAVBGEAAAAApIIgDAAAAIBUEIQBAAAAkAqCMAAAAABSQRAGAAAAQCoIwgAAAABIBUEYAAAAAKnwRdIFsJx6vV5sbW3dOmZ3dzd2d3fnVBEAAACQtP39/djf3791TK/Xm1M1VwnCmMr5+XmcnJzcOubs7GxO1QAAAACL4Ozs7M68IEmCMKayuroaGxsbt45ZW1ubUzUAAADAIlhbW4vNzc1bx/R6vTg/P59TReMEYUxlY2MjPn78mHQZAAAAwAKZZJukra2txLrGbJYPAAAAQCoIwgAAAABIBUEYAAAAAKkgCAMAAAAgFQRhAAAAAKSCIAwAAACAVBCEAQAAAJAKgjAAAAAAUkEQBgAAAEAqCMIAAAAASAVBGAAAAACpIAgDAAAAIBUEYQAAAACkgiAMAAAAgFQQhAEAAACQCoIwAAAAAFLhi6QLAAAAAFhUf/G7TzOb689++mxmczEdHWEAAAAApIIgDAAAAIBUEIQBAAAAkAqCMAAAAABSQRAGAAAAQCq4aiRT6fV6sbW1deuY3d3d2N3dnVNFAAAAQNL29/djf3//1jG9Xm9O1VwlCGMq5+fncXJycuuYs7OzOVUDAAAALIKzs7M784IkCcKYyurqamxsbNw6Zm1tbU7VAAAAAItgbW0tNjc3bx3T6/Xi/Px8ThWNE4QxlY2Njfj48WPSZQAAAAALZJJtkra2thLrGrNZPgAAAACpIAgDAAAAIBUEYQAAAACkgiAMAAAAgFQQhAEAAACQCoIwAAAAAFJBEAYAAABAKgjCAAAAAEgFQRgAAAAAqSAIAwAAACAVBGEAAAAApIIgDAAAAIBUEIQBAAAAkAqCMAAAAABSQRAGAAAAQCoIwgAAAABIBUEYAAAAAKkgCAMAAAAgFQRhAAAAAKTCF0kXAAAAAJAGf/G7TzOb67+Z2UzpoiMMAAAAgFQQhAEAAACQCpZGMpVerxdbW1u3jtnd3Y3d3d05VQQAAAAkbX9/P/b3928d0+v15lTNVYIwpnJ+fh4nJye3jjk7O5tTNQAAAMAiODs7uzMvSJIgjKmsrq7GxsbGrWPW1tbmVA0AAACwCNbW1mJzc/PWMb1eL87Pz+dU0ThBGFPZ2NiIjx8/Jl0GAAAAsEAm2SZpa2srsa4xm+UDAAAAkAqCMAAAAABSQRAGAAAAQCoIwgAAAABIBUEYAAAAAKngqpGkwv7+fpydncX/72//RfznP/8vki4H5uZ/+e5/iv/zhx/iP/jxj537pIpznzRy3pNWzn3S6H/57n+K//b3f4i1tbU7r9DIOEEYqbC/vx8nJyex9id/4pcjqfK//vp/jrM//MG5T+o490kj5z1p5dwnjf7XX//P8c//P//f2NzcFITdk6WRAAAAAKSCIAwAAACAVBCEzcje3l6USqVYX1+PlZWVyOVyUS6Xo9VqPeljAwAAACwLQdgDtVqtWF9fj1qtFhERzWYzOp1O1Ov1aLfbUSqVolQqRb/ff1LHBgAAAFg2Nst/gFarFaVSKSIidnZ24uDgYPSxbDYb29vbUSgUotVqRaFQiOPj48hkMkt/bAAAAIBlpCNsSv1+P8rlckR8Dp4uBlEXNZvNiIjodruj8ct8bAAAAIBlJQibUrlcHi05HC5NvM6wOyvicxdXo9FY6mMDAAAALCtLI6fQ7XbHNqJ/+fLlreNfvXoVh4eHEfE5uNrZ2VnKY5Oc/+W7/yn+zx9+iP/gxz+O//zn/0XS5dzbMte/zLU/Bcv8/C9z7RHLX/8yW/bnXv1Ma9mf+2Wuf5lrX3bL/tyrn2UkCJtCvV4f3S4Wi3fuvTXsyor4vKzx8PBw7H3LcmyS87/++n+Osz/8Idb+5E+W8gf0Mte/zLU/Bcv8/C9z7RHLX/8yW/bnXv1Ma9mf+2Wuf5lrX3bL/tyrn2VkaeQULi4xzOfzEz0mm82Obr97924pjw0AAACwzARh99Rut8fuf/XVVxM97mJoNVyquEzHBgAAAFh2grB7urg/V8R4t9VtLo+7HGot+rEBAAAAlp0g7J6+//77sft37dE19JOf/GTs/ocPH5bq2AAAAADLThB2T91ud+z+tF1ZnU5nqY4NAAAAsOwEYfd0OYyaVr/fX6pjAwAAACw7Qdg9TRsiXV7G+OnTp6U6NgAAAMCyWxkMBoOki1gmKysrY/cnffparVaUSqXR/WKxGEdHR0tz7KF//9//9+P//r//74iIWF19eI769/7e34v/6D/6jx48z116vV6cn5/HyspK/L21tUc/3qz967OzGAwG6k/AMtceof4kLXPtEepP0jLXHqH+JC1z7RHqT9Iy1x6x3PUvc+0R6k/SsPbV1dXY2NiY23H/j//j/4h//a//9YPnOT8/j4iIf+/f+/fi//q//q8Hz3cfX8z1aE9AJpOZydLCSTe6X5RjD/3d3/3d6PbwxH2IP/zhD/GHP/zhwfNMajAYxNkcjzdr6k/OMtceof4kLXPtEepP0jLXHqH+JC1z7RHqT9Iy1x6x3PUvc+0R6k/S+fl5nJycJF3G1C5mDPMiCLunZ8+ezSSMevbs2VIde+jHP/5x/Nt/+29HyfNDzasjDAAAAHiYWXaEraysxI9//OMZVHU/grB7mrab6nKANW1HWFLHHvo3/+bfTP1YAAAAgCTZLP+enj9/PnZ/0g6tyxvU53K5pTo2AAAAwLIThN1ToVAYu9/tdid6XKfTGbtfLBaX6tgAAAAAy04Qdk+Xu7ImDaMudm9lMpnIZrNLdWwAAACAZScIu6d8Pj+2x9b3338/0eM+fPgwun050FqGYwMAAAAsO0HYFF6+fDm63W63J3rMxXG1Wm0pjw0AAACwzFYGg8Eg6SKWTbvdHtuv666nsNVqRalUioiIbDZ7Zc+uZTk2AAAAwDLTETaFfD4/tuH84eHhreObzebo9l0dWY1GI2q12o37fz3msQEAAACeMh1hU+p2u5HL5SLiczh1fHx87bh+vx/r6+sR8flqjUdHRzfOWSqVotVqje6fnp6O7Qn2mMcGAAAAeOp0hE0pm82Ouq3a7Xbs7e1dO+7FixcR8flqjRe7s65zMQSLiPj222/ndmwAAACAp04Q9gDb29txdHQUmUwmarValMvlaLfb0e/3o9VqRaFQiHa7Hfl8Pn7zm99c29110eWPZ7PZuR0bAAAA4KkThD1QsViM09PTqNfr0e1248WLF7G+vh7lcjmePXsWzWYzjo+PJwqims1mZLPZyGQyUa1Wx/YCe+xjL6q9vb0olUqxvr4eKysrkcvlolwuX+mee2rHhqTOv3a7HZVKJXK5XKysrIyOXavVot/vP+qxIWIxf/YOvx/u2pcTprWI5/1Qu92Ow8PD2NvbW4h6eFqSOvf7/X7UarUolUqjn/HDYzcajUc9NunW7XajVCrN7W+KRf79kqgBLKCjo6NBJpMZRMSgWCwOjo6OBp1OZ9BsNgfZbHb0/tPT0yd1bEjq/Ds9PR1sb28PIuLWt4ODg5keF4YW9WdvtVodnf/NZnOux+bpW8Tz/vT0dFCv1wf5fH4QEYNMJjOoVquDo6Mjf/swM0me+/V6fXRu1+v1wdHR0eD4+HhwcHAwOu+z2ezg6Oho5scmvS7/rf3Yf1Mv4u+XRSIIY+EcHR2NfkDs7OxcO+biL6lZfvMmeWxI6vw7PT0d/UKc5O2m2mBai/qz92JdgjBmbdHO+9PT08HOzs6opnw+LwjgUSR57g+DiHw+f+OYi98Hx8fHMzs26XR6ejr2T7V5BGGL9vtlEQnCWCinp6ej5Dqbzd44rtPpjL65i8Xi0h8bkjz/isXi6I/CZrM56HQ6o/8YXfeLWyDALC3qz96LdTnvmbVFO+8PDg7GznfdvzyWJM/9YSdYRNz5wn/4D8JMJjOTY5NO9Xp9kM1mR39rzyMIW7TfL4tKEMZCufhD4q4fDrNuLU3y2JDU+XdwcDCIiEG1Wr1xTKfTGf3XaPjmD0NmZVF/9m5vbw+y2exYOCAIY1YW6by/+A+PbDY76HQ6Mz8GDCV57l/sdryLZfE81PHx8VhH4fBv7sf+O2aRfr8sMkEYC+NiKj3Jf2qazebMXpQneWxI8vwb/pfqvjVGhCUzPNii/uwd/rF6fHwsCGPmFum8v/giKJPJpHJ5DPOT5Ll/canY9vb2neMvHvu2fxbCpI6Pjx89CFuk3y+LzlUjWRj1en10u1gs3nm1y+3t7dHtfr//oCtvJHlsSOr8a7fb0e12o9ls3jk2m82O1Tl8PDzEIv7s7Xa7UalUolqtRj6fn/n8sCjn/eWrli37lcZZfEme+91ud3T7vn+//OQnP5n6uDA0j5+vi/L7ZRkIwlgYFy9VPOmLj2w2O7r97t27pTw2JHX+vXv3LnZ2dib+xVwsFsfu//73v5/quDC0iD97y+Vy5PP5K8EvzMoinPeNRiNardbo/sHBwdgx4DEkee4/e/ZsdLvb7Y4FY9f5/vvvr60BFtki/H5ZFoIwFsLl/8x89dVXEz3u4jf4Q7pikjo2JHn+vXr16l4v9i//Qs3lclMdFyIW82dvrVaLdrs9UZckTGMRzvth1+PFuXd2dh40J9wl6XP/cph18XvgOsNjZTKZsa4ZWFRJf48tG0EYC+HifyUjJv/Py+Vx0yzVSvLYkOT5l8/n79Wm3e/3b60B7mPRfva22+3Y29vTGcOjWoTzvlwuj93/5ptvpp4LJpX0uZ/P58de8LdarSvfC0ONRmPUMaY7mGWR9PfYshGEsRAuth9HTL6G+vKa/Q8fPizVsWGZzr/LywguL5WE+1i0c//Fixexvb2tM4ZHlfR53+12x17k6HZhXpI+9yMi3r59O3b/8PDwShjWarVG3WIHBwd+J7A0FuF7bJkIwlgIl19gT5tgdzqdpTo2LNP5d/EXoz8MeahFOveHL4Quv0iCWUv6vD84OBi7P/yHxjAQyOVysbKyEuvr61EoFGJvb+9KNzBMI+lzP+JzV9jlpe+Hh4eRy+VGXcGlUimy2WwcHR35W4elsgjfY8vki6QLgIir37jTmuaPtSSPDct0/l18AVWr1R79eDxti3LuHx4exuHhYRwdHbliHo8u6fP+4kbKEZ83EC8UCleWwvT7/Wi329Fut6NWq0Wz2dQ5xoMkfe4PbW9vx8HBwdgeYd1uNwqFQkREVKtVyyFZSovyPbYsdISxEKb9hrv8ouXTp09LdWxYlvPv4nKaer1uDyUebBHO/X6/H+VyOXZ2diz1ZS6SPO+73e6V43/77bdRqVTi9PQ0BoNBDAaD6HQ6VzphyuVyqjZRZvYW4Wf+0M7OzpXuyKFWq5WaIICnZZG+x5aBIIwnJclfXH5pkqTHPv+G/x3NZrNRrVYf9VhwHw8591+8eBHZbPbGF0SwqGbRAZ/JZOI3v/lN7OzsjL0QGn5PXP6+eP36tb91SNyszsGdnZ1rlz622+348ssvU7NhOFyWlp/zgjAWwqyWo0wzT5LHhmU4/9rtdjQajchkMnF0dPRoxyFdkj739/b2ot1uX9kvBh5Tkuf95SDscgB22eWgoN/vx5s3b+59XIhI/mf+ZZVKJRqNRuzs7Fzpcu/3+1EoFK5chQ8W2aJ9jy06QRgL4dmzZ4nNk+SxYRnOv9evX0dExPv37y2JZGaSPPeH+x7V6/XI5/MzqQMmkeR5f3kD5K+++urOx1zeD3Jvb+/ex4WIxfp7p1QqRaPRiGq1GgcHB9cuBx6OE4axLBbpe2wZCMJYCNMmz5dbN+fZETaLY8Oin3+VSmXUNSMwYJaSPPfL5XLk83nLfJm7JM/7aebIZrNXfvZbMsY0FuXvnWGnV7FYHNsU/+Dg4NoO4XK5nJqlYiy3RfkeWxaCMBbC8+fPx+5P+gvn8mZ+uVxuqY4Ni3z+NRqNaDQacXBw4GphzFxS5/7e3l50u9149uxZlMvlO98u1vXmzZuxj9k8nPtK8mf+tL8nLtc8qyuTkS6L8PdOuVweBbnX7Q25vb0dx8fHY0FAv993pWyWwiJ8jy2TL5IuACJidMnioW63O1H3yeU2/2mu+pXksWFRz79WqxWVSiUODg6uXS4AD5XUuf/73/8+ImKq5S7tdnusGyabzQqJuZckf+Zf/i//pC+SLr8oSssVxZitpP/e6Xa7o39e5PP5G7d6yOfzcXx8PHbeNxqNqNfrqemUYTkl/T22bHSEsRCm/W/jxT/iMpnMVPsXJXlsWMTzr91uR6lUinq9LgTj0SziuQ+PbRn/3rn84j8t+8cwW0n/zL/YAXa5lsuuu5rwhw8fpjouzEvS32PLRhDGQsjn82N/aH3//fcTPe7iL6W7fqkt4rFh0c6/brcbL168iGq1av8kHlVS5369Xo/BYDDx28U/CJvN5tjHLu4vA5NI+u+diy53AUwqLS+SmK2k/965+GJ/kqVfl/8RaEkwiy7p77FlIwhjYbx8+XJ0e9KNWC+Oe8j6/SSPDYty/nW73SgUCrGzszPxC/xut+sqYkxtUc59mKckz/uLS14mXR58eQmlC6cwrSTP/YtLeicNgZ3rLBt/V01OEMbCqFQqo9uT/HF2cUw2m33QeuYkjw2LcP71+/0olUrx8uXLe3W5lMtl5z9TW4RzH+YtyfP+4oucbrc70T5hF0MD33M8RJLn/sVOxmm6u3RCsgz8XTU5QRgLI5/Pj33z3XU1rouXOL4rvW40GlGr1W78xfeYx4a7JHnuR3wOwQqFQmSz2dHYu95ardZoU07/MWVaSZ/7kIQkz/tisTj2gv7Nmzd31nvxhZLlwDxEkuf+q1evRrc/fPgwUQh8ca40BQQ8jkkvUHIbr2lnaAALpNPpDCJiEBGDfD5/47jT09PRuGKxeOucxWJxNDYiBqenp3M7NkwqyXM/n8+PjbvP28HBwUM+bUj03J9ENpsdzdNsNqeeBy5K8rw/Pj4eG9fpdG6c8+joaDRuZ2dnos8NbpPkuX9xXLVavXXOi+d+vV6/8/OCuzSbzbHz9L7nlde0syUIY+Fc/CFx0w+I4Qv3TCZz5wuc+7xwn/Wx4T6SOPcfEoL5XwqzkuTP/bsIwngsSZ73BwcHo3HZbPbauU9PT0fn/20vpuC+kjr3T09PB5lM5s5xnU5nNC6NAQGzd/Hn6SQh1XW8pp0tr2JYSEdHR6NfQNvb24Pj4+PB6enp4OjoaPRNm8/nJ/qmvfgLLyIGR0dHczs23Nc8z/3t7e0HhWC6A5ilJH/u30YQxmNalL93MpnMoF6vD46PjwfHx8eDer0++pif9TyGpM7909PTwc7OzlgnzMHBweD4+HhwdHQ0qFarU3fswEWnp6eD7e3tK51cl9+KxeLoe+A2XtPOliCMhVav1wf5fH7sD7VisXivFyNHR0eDbDY7yGQyd7ZBz/rYMK0kz31IknOfNEryvD84OBgUi8WxY+fz+UG1Wr112STMQlLn/vHx8WBnZ2fsnx3ZbHawvb09qFarqQ0HWFxe087WymAwGAQAAAAAPHGuGgkAAABAKgjCAAAAAEgFQRgAAAAAqSAIAwAAACAVBGEAAAAApIIgDAAAAIBUEIQBAAAAkAqCMAAAAABSQRAGAAAAQCoIwgAAAABIBUEYAAAAAKkgCAMAAAAgFQRhAAAAAEuiVCrFyspKVCqVpEtZSoIwAAAA4EkqlUqxvr4eKysrM3tbX19P9HNqtVoREVEulyd+zFN8Hqb1RdIFAAAAADyGo6OjiIhot9vx4sWL6Pf7Yx/PZDLx8uXLyOVyo/f9/ve/j4iIfr8f3W43Pnz4MPa458+fP3rdNzk8PBzdLhaLEz/uqT0PDyEIAwAAAJ60fD4fL1++jEajMfb+4+PjyGazdz6+1WpFuVyOfr8f+Xz+scq807t37yIiYnt7e6rHP5Xn4SEsjQQAAACevE+fPo3dz2azE4U/EZ+7r+r1ekREfPXVVzOvbVLDjrBXr15NPcdTeB4eQhAGAAAAPHntdnvs/n2WFkb8cSlgUp1Qw73BIqbvCItY/ufhoQRhAAAAwJM23OfqovtsNj+UyWQm7p6atWazGRH3D64uegrPw0MJwgAAAIAn7WI31dB9A6XhhvJJ+fbbbyNiuuBq6Ck8Dw8lCAMAAACetOFVE4em6arKZrNxcHAwq5Lupd1uj67Y+JAQatmfh1kQhAEAAABP2rCbaqhUKt06/rolhEkaXi0yn89HJpOZep5lfx5mQRAGAAAAPFndbnfUTTV012bztVrt2mWESRleLbJSqUw9x1N4Hmbhi6QLAAAAAHgswxBp6K6N3rvdbjQajTg9PX3s0ibS7XZHXVkPWRa57M/DrOgIAwAAAJ6sy/ti3RUmlcvlBy9BnKVhgPXQmpb9eZgVHWEAAADAk3V5ad9wX6zhMsFPnz5Fv9+PVqsVBwcH0e12o1qtzrvMGw33B3v16tWD5ln252FWVgaDwSDpIgAAAABmrdVq3bkh/HWOj48jn88/QkX30+/3Y319PSIiOp3OrUsZb7Psz8Ms6QgDAAAAnqTLywGz2WxUKpX4/e9/P9YJ1e12o91uR8TnvbMWJfxpNBoR8bnuaUOwiOV/HmZJEAYAAAA8SZc3iK9UKjcu92u321EoFKJYLM6jtIkMl0XedXXHu8zzebh4pcl+vx/ZbDZqtdrCPK82ywcAAACenH6/P7ra4tBtYcyw++mhe3HNSr/fH3VnPaSmeT0P7XY7crlcHB4eRrPZjOPj4+h0OpHP56NUKkWtVrt/8Y9ARxgAAADw5Hz77bdj9+9a6jdcIrgonUvD+h+6RHEez0O/348XL15Ev9+/spdZvV6Pdrsde3t78ZOf/CTxDfh1hAEAAABPzuV9se4KdrrdbuTz+chkMo9Y1eSazWZERLx8+fJB88zjeXj9+nX0+/3Y3t6+di+zer0eEZ+XTV7uTps3QRgAAADw5Az3qRq666qJ+Xw+jo+PH7OkexnWXy6XZzLP0Kyfh36/P9qD7KbllBeDtWEolhRBGAAAAPCktNvt0RK/oYd2Vs3Txc3tH7JUcx7Pw5s3b0a3b1tyOfw8hlfCTIogDAAAAHhSLndBZbPZBy15vHzVxcc2q6tFzuN5uHiM65ZFDn311Vc31jVPgjAAAADgSRkGSUMP6apqNBo3XvGw3+9H5f/f3t0dp61FYQDdd+YWgJ0OpA5wSoASbHdgOkCTCjLQAUkHjjqIOoihA9SBE3Xg+5ARVwRswBAw1lpPHiPjo/P4zf4ZDOLi4iLSNI3xePzid2VZFldXV4ttkM/Z1Gq4rWPcQ/0uL4VgEbEUwG16/7/J1kgAAADg3aiqaiVoee2crdlsFoPBYO1cq9lsttiUWP/fLMvi8fFx7fNFUcR4PI4kSV5sIWxWS+1TEXaMe2i2XW6qNGsGZfP5/FXnOAQVYQAAAMC7sa7t7uPHjzt/Tx10RUTc3d0tfVZVVVxfX8enT5/i+/fv8e3bt0UQNB6PV+ZyRfw/JH7TsPh6W+Q+1VsRx7mH5gbIy8vLrb/z58+fO5/jUFSEAQAAAO/Gn+2Ar5mLlef5onqq1+ut/P3nz59jNBotVWx1u91I0zQiIu7v75dCo9lsFkVRRJIkG6u87u/vI2L/bZHHuIddNIOydUHhsagIAwAAAN6FqqpWBrpvW31UVVV8+fIlrq6ulkKowWCw8uyHDx9WAq1myDWdTpc+qzcrTiaTF8/Q3PK4z3bHY93DLsFY8//vE6jtS0UYAAAAcPbqdsV1v7+4uIibm5tFxVZExOPjY1RVFWVZxsPDw9oqpU6ns7aCazgcrj3D7e1t5Hm+1DJYlmXkeR69Xm9ju2NdxdXtdl8dFh3zHppVXru0O+7SRnlogjAAAADgrKVpuhQ+/amuctrVrlVZ9RD85lnqSqp69tdL6iquddVX2zj2PTTDuk3tjs3Pm0HcsQnCAAAAgLN2yi2ETfVmxLo6qiiKKIoiJpPJxgqvsiwXIdZr2yJPcQ+9Xi+KothYEdYM6PZdBLAPM8IAAAAADiRJkkX102AwiF6vt7JtcZ26GmyftshT6Pf7EfG74uulqrAfP34sfq4r505BEAYAAABwIJ1OJzqdTozH4yjLcuOA/Fo9H+z29vZvHu/gmiHfw8PDs8/NZrOV509BEAYAAABwIJeXl1FVVWRZFpPJZNEu+ZKqqhZB0bqh9G9Zc5D+c6Ffs+0zy7KjnW0dQRgAAADAgW3bEhkRiwH2SZJsFZy9NV+/fo2IWNmYWavDr9FodPL3E4QBAAAAHEg9NH6bLZG1ui3y3KrBap1OJ+bzeSRJEv1+fykMG4/Hked5DIfDGA6HJzzlb7ZGAgAAABzQaDTaeuB9sy3y3OaDNSVJEvP5PLIsi36/v3j/JEliOp2edEB+0z9PT09Ppz4EAAAAwLnL8zyur6/j169fO21+rLctntO2yHMlCAMAAADYU1mWkaZpdLvdmE6npz4OzzAjDAAAAGBPg8EgIn4PyeftEoQBAAAA7CHP8yiKIiLOe85XG2iNBAAAANhDmqZRluViYDxvl4owAAAAgFcqyzLKsoyIiCzLTnwaNvn31AcAAAAAOFdVVUWn04mbm5u4u7s79XHYQGskAAAAAK2gNRIAAACAVhCEAQAAANAKgjAAAAAAWkEQBgAAAEArCMIAAAAAaAVBGAAAAACtIAgDAAAAoBUEYQAAAAC0giAMAAAAgFYQhAEAAADQCoIwAAAAAFpBEAYAAABAKwjCAAAAAGgFQRgAAAAArSAIAwAAAKAVBGEAAAAAtIIgDAAAAIBWEIQBAAAA0AqCMAAAAABaQRAGAAAAQCsIwgAAAABoBUEYAAAAAK0gCAMAAACgFQRhAAAAALSCIAwAAACAVhCEAQAAANAKgjAAAAAAWkEQBgAAAEArCMIAAAAAaIX/AMuQOTKRBZoVAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nbins = 50\n",
+    "plt.hist(\n",
+    "    ak.to_numpy(eloss_lost),\n",
+    "    bins=nbins,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    histtype=\"bar\",\n",
+    "    color=\"#F05342\",\n",
+    "    label=\"lost\",\n",
+    "    range=[0.001, 1],\n",
+    ")\n",
+    "# #2A9D8F another teal color\n",
+    "plt.hist(\n",
+    "    ak.to_numpy(eloss_found),\n",
+    "    bins=nbins,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    histtype=\"bar\",\n",
+    "    color=\"#107E7D\",\n",
+    "    label=\"found\",\n",
+    "    range=[0.001, 1],\n",
+    ")\n",
+    "\n",
+    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
+    "plt.ylabel(\"a.u.\")\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\")\n",
+    "\n",
+    "plt.savefig(\"/work/cetin/Projektpraktikum/thesis/emitted_energy.pdf\", format=\"PDF\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# magnet kick position\n",
+    "input_tree = uproot.open(\n",
+    "    {\n",
+    "        \"/work/cetin/Projektpraktikum/param_data_B_default.root\": \"PrParameterisationData_2ece6184.PrDebugTrackingTool/Tuple;1\"\n",
+    "    }\n",
+    ")\n",
+    "array = input_tree.arrays()\n",
+    "array = array[array.isElectron & (array.fromB)]\n",
+    "array[\"dSlope_fringe\"] = array[\"tx_ref\"] - array[\"tx\"]\n",
+    "array[\"z_mag_x_fringe\"] = (\n",
+    "    array[\"x\"]\n",
+    "    - array[\"x_ref\"]\n",
+    "    - array[\"tx\"] * array[\"z\"]\n",
+    "    + array[\"tx_ref\"] * array[\"z_ref\"]\n",
+    ") / array[\"dSlope_fringe\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(exptext: Custom Text(0.05, 0.95, 'LHCb'),\n",
+       " expsuffix: Custom Text(0.05, 0.955, 'Simulation'))"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "plt.hist(\n",
+    "    array[\"z_mag_x_fringe\"], bins=50, range=[5150, 5300], color=\"#2A9D8F\", density=True\n",
+    ")\n",
+    "plt.xlabel(r\"z$_{Mag}$ [mm]\")\n",
+    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
+    "mplhep.lhcb.text(\"Simulation\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50501"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ak.num(array, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tuner",
+   "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
+}
diff --git a/notebooks/trackinglosses_photons.ipynb b/notebooks/trackinglosses_photons.ipynb
index a0ac189..4467edc 100644
--- a/notebooks/trackinglosses_photons.ipynb
+++ b/notebooks/trackinglosses_photons.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,35 +12,30 @@
     "from mpl_toolkits import mplot3d\n",
     "import awkward as ak\n",
     "from scipy.optimize import curve_fit\n",
+    "import mplhep\n",
+    "mplhep.style.use([\"LHCbTex2\"])\n",
+    "\n",
+    "plt.rcParams[\"savefig.dpi\"] = 600\n",
     "%matplotlib inline"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "file = uproot.open(\n",
-    "    \"trackinglosses_B_photon_cuts.root\"\n",
-    ")\n",
+    "file = uproot.open(\"/work/cetin/Projektpraktikum/trackinglosses_B_photon_cuts.root\")\n",
     "\n",
     "# selektiere nur elektronen von B->K*ee\n",
     "allcolumns = []\n",
     "for i in range(11):\n",
-    "  allcolumns.append(file[\"Tree\"+str(i)].arrays())"
+    "    allcolumns.append(file[\"Tree\" + str(i)].arrays())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -76,7 +71,7 @@
        "<Record {oneCut_event_id: 1, ...} type='{oneCut_event_id: int64, oneCut_los...'>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -87,25 +82,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
     "def cutdict():\n",
     "    basedict = {\n",
-    "\t\t\"0\": {},\n",
-    "\t\t\"1\": {},\n",
-    "\t\t\"2\": {},\n",
-    "\t\t\"3\": {},\n",
-    "\t\t\"4\": {},\n",
-    "\t\t\"5\": {},\n",
-    "\t\t\"6\": {},\n",
-    "\t\t\"7\": {},\n",
-    "\t\t\"8\": {},\n",
-    "\t\t\"9\": {},\n",
-    "\t\t\"10\": {},\n",
-    "\t}\n",
-    "    \n",
+    "        \"0\": {},\n",
+    "        \"1\": {},\n",
+    "        \"2\": {},\n",
+    "        \"3\": {},\n",
+    "        \"4\": {},\n",
+    "        \"5\": {},\n",
+    "        \"6\": {},\n",
+    "        \"7\": {},\n",
+    "        \"8\": {},\n",
+    "        \"9\": {},\n",
+    "        \"10\": {},\n",
+    "    }\n",
+    "\n",
     "    basedict[\"0\"] = \"no\"\n",
     "    basedict[\"1\"] = \"one\"\n",
     "    basedict[\"2\"] = \"two\"\n",
@@ -117,122 +112,241 @@
     "    basedict[\"8\"] = \"eight\"\n",
     "    basedict[\"9\"] = \"nine\"\n",
     "    basedict[\"10\"] = \"ten\"\n",
-    "    \n",
+    "\n",
     "    return basedict\n",
     "\n",
+    "\n",
     "Cuts = cutdict()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "electrons = []\n",
-    "for jcut in range(11):\n",
-    "\tenergy_emissions = ak.ArrayBuilder()\n",
+    "# electrons = []\n",
+    "# for jcut in range(11):\n",
+    "\n",
+    "jcut = 4  # cut 0.2*E\n",
+    "\n",
+    "energy_emissions = ak.ArrayBuilder()\n",
+    "\n",
+    "for jelec in range(ak.num(allcolumns[jcut], axis=0)):\n",
+    "    energy_emissions.begin_record()\n",
+    "    energy_emissions.field(\"lost\").boolean(\n",
+    "        allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"lost\"])\n",
+    "    energy_emissions.field(\"rad_length_frac\").real(\n",
+    "        allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"rad_length_frac\"])\n",
+    "    energy_emissions.field(\"energy\").real(\n",
+    "        allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"energy\"])\n",
     "\n",
-    "\tfor jelec in range(ak.num(allcolumns[jcut], axis=0)):\n",
-    "\t\tenergy_emissions.begin_record()\n",
-    "\t\tenergy_emissions.field(\"lost\").boolean(allcolumns[jcut][jelec, Cuts[str(jcut)]+\"Cut_\"+\"lost\"])\n",
-    "\t\tenergy_emissions.field(\"rad_length_frac\").real(allcolumns[jcut][jelec, Cuts[str(jcut)]+\"Cut_\"+\"rad_length_frac\"])\n",
-    "\t\tenergy_emissions.field(\"energy\").real(allcolumns[jcut][jelec, Cuts[str(jcut)]+\"Cut_\"+\"energy\"])\n",
+    "    tmp_velo = 0\n",
+    "    tmp_richut = 0\n",
+    "    tmp_neither = 0\n",
+    "    tmp_velo_length = 0\n",
+    "    tmp_richut_length = 0\n",
+    "    tmp_neither_length = 0\n",
     "\n",
-    "\t\ttmp_velo = 0\n",
-    "\t\ttmp_richut = 0\n",
-    "\t\ttmp_neither = 0\n",
-    "\t\ttmp_velo_length = 0\n",
-    "\t\ttmp_richut_length = 0\n",
-    "\t\ttmp_neither_length = 0\n",
-    "\t\t\n",
-    "\t\tfor jphoton in range(ak.num(allcolumns[jcut][jelec][Cuts[str(jcut)]+\"Cut_\"+\"brem_photons_pe\"], axis=0)):\n",
-    "\t\t\tif allcolumns[jcut][jelec, Cuts[str(jcut)]+\"Cut_\"+\"brem_vtx_z\", jphoton] <= 770:\n",
-    "\t\t\t\ttmp_velo += allcolumns[jcut][jelec, Cuts[str(jcut)]+\"Cut_\"+\"brem_photons_pe\", jphoton]\n",
-    "\t\t\t\ttmp_velo_length += 1\n",
-    "\t\t\telif (allcolumns[jcut][jelec, Cuts[str(jcut)]+\"Cut_\"+\"brem_vtx_z\", jphoton] > 770) and (\n",
-    "\t\t\t\tallcolumns[jcut][jelec, Cuts[str(jcut)]+\"Cut_\"+\"brem_vtx_z\", jphoton] <= 2700\n",
-    "\t\t\t):\n",
-    "\t\t\t\ttmp_richut += allcolumns[jcut][jelec, Cuts[str(jcut)]+\"Cut_\"+\"brem_photons_pe\", jphoton]\n",
-    "\t\t\t\ttmp_richut_length += 1\n",
-    "\t\t\telse:\n",
-    "\t\t\t\ttmp_neither += allcolumns[jcut][jelec, Cuts[str(jcut)]+\"Cut_\"+\"brem_photons_pe\", jphoton]\n",
-    "\t\t\t\ttmp_neither_length += 1\n",
+    "    for jphoton in range(\n",
+    "            ak.num(\n",
+    "                allcolumns[jcut][jelec][Cuts[str(jcut)] + \"Cut_\" +\n",
+    "                                        \"brem_photons_pe\"],\n",
+    "                axis=0,\n",
+    "            )):\n",
+    "        if (allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"brem_vtx_z\",\n",
+    "                             jphoton] <= 770):\n",
+    "            tmp_velo += allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" +\n",
+    "                                         \"brem_photons_pe\", jphoton]\n",
+    "            tmp_velo_length += 1\n",
+    "        elif (allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"brem_vtx_z\",\n",
+    "                               jphoton]\n",
+    "              > 770) and (allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" +\n",
+    "                                           \"brem_vtx_z\", jphoton] <= 2700):\n",
+    "            tmp_richut += allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" +\n",
+    "                                           \"brem_photons_pe\", jphoton]\n",
+    "            tmp_richut_length += 1\n",
+    "        else:\n",
+    "            tmp_neither += allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" +\n",
+    "                                            \"brem_photons_pe\", jphoton]\n",
+    "            tmp_neither_length += 1\n",
     "\n",
-    "\t\tenergy_emissions.field(\"velo_length\").integer(tmp_velo_length)\n",
-    "\t\tenergy_emissions.field(\"velo\").real(tmp_velo)\n",
+    "    energy_emissions.field(\"velo_length\").integer(tmp_velo_length)\n",
+    "    energy_emissions.field(\"velo\").real(tmp_velo)\n",
     "\n",
-    "\t\tenergy_emissions.field(\"rich_length\").integer(tmp_richut_length)\n",
-    "\t\tenergy_emissions.field(\"rich\").real(tmp_richut)\n",
-    "\t\t\n",
-    "\t\tenergy_emissions.field(\"neither_length\").integer(tmp_neither_length)\n",
-    "\t\tenergy_emissions.field(\"downstream\").real(tmp_neither)\n",
-    "\t\t\n",
-    "\t\tenergy_emissions.field(\"photon_length\").integer(tmp_richut_length+tmp_velo_length)\n",
-    "\t\t\n",
-    "\t\tif (tmp_velo==0) and (tmp_richut==0):\n",
-    "\t\t\tenergy_emissions.field(\"quality\").integer(0)\n",
-    "\t\telse:\n",
-    "\t\t\tenergy_emissions.field(\"quality\").integer(1)\n",
+    "    energy_emissions.field(\"rich_length\").integer(tmp_richut_length)\n",
+    "    energy_emissions.field(\"rich\").real(tmp_richut)\n",
     "\n",
-    "\t\tenergy_emissions.end_record()\n",
+    "    energy_emissions.field(\"neither_length\").integer(tmp_neither_length)\n",
+    "    energy_emissions.field(\"downstream\").real(tmp_neither)\n",
     "\n",
-    "\tenergy_emissions = ak.Array(energy_emissions)\n",
-    "\telectrons.append(energy_emissions)\n"
+    "    energy_emissions.field(\"photon_length\").integer(tmp_richut_length +\n",
+    "                                                    tmp_velo_length)\n",
+    "\n",
+    "    if ((tmp_velo == 0) and (tmp_richut == 0)\n",
+    "            or (allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"energy\"] -\n",
+    "                tmp_velo < 3000)):\n",
+    "        energy_emissions.field(\"quality\").integer(0)\n",
+    "    else:\n",
+    "        energy_emissions.field(\"quality\").integer(1)\n",
+    "\n",
+    "    energy_emissions.end_record()\n",
+    "\n",
+    "energy_emissions = ak.Array(energy_emissions)\n",
+    "# electrons.append(energy_emissions)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-      "text/plain": [
-       "<Figure size 1500x600 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "<Figure size 1500x600 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1500x600 with 9 Axes>"
+       "<Figure size 1200x900 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
+   "source": [
+    "velo_found = ak.to_numpy(\n",
+    "    energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\"velo\"]\n",
+    ")\n",
+    "rich_found = ak.to_numpy(\n",
+    "    energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"]\n",
+    ")\n",
+    "energy_found = ak.to_numpy(\n",
+    "    energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
+    "        \"energy\"\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "velo_lost = ak.to_numpy(\n",
+    "    energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\"velo\"]\n",
+    ")\n",
+    "rich_lost = ak.to_numpy(\n",
+    "    energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"]\n",
+    ")\n",
+    "energy_lost = ak.to_numpy(\n",
+    "    energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
+    "        \"energy\"\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "diff_found = velo_found - rich_found\n",
+    "diff_lost = velo_lost - rich_lost\n",
+    "\n",
+    "xlim = 20000\n",
+    "nbins = 60\n",
+    "\n",
+    "plt.hist(\n",
+    "    diff_lost,\n",
+    "    bins=nbins,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    histtype=\"bar\",\n",
+    "    color=\"#F05342\",\n",
+    "    label=\"lost\",\n",
+    "    range=[-xlim, xlim],\n",
+    ")\n",
+    "plt.hist(\n",
+    "    diff_found,\n",
+    "    bins=nbins,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    histtype=\"bar\",\n",
+    "    color=\"#107E7D\",\n",
+    "    label=\"found\",\n",
+    "    range=[-xlim, xlim],\n",
+    ")\n",
+    "# plt.xlim(-20000, 20000)\n",
+    "# plt.yscale(\"log\")\n",
+    "# plt.title(\"emitted energy difference\")\n",
+    "plt.xlabel(r\"$E_{VELO} - E_{RICH1+UT}$ [MeV]\")\n",
+    "plt.ylabel(\"a.u.\")\n",
+    "plt.legend(title=\"LHCb Simulation\")\n",
+    "plt.show()\n",
+    "# plt.savefig(\n",
+    "#     \"/work/cetin/Projektpraktikum/thesis/emitted_energy_difference.pdf\",\n",
+    "#     format=\"PDF\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "nbins = 6\n",
-    "quality_cut = electrons[jcut].quality !=-1\n",
+    "quality_cut = electrons[jcut].quality != -1\n",
     "\n",
     "### all split in velo and rich\n",
     "\n",
-    "fig, axs = plt.subplots(3,3, figsize=(15, 6))\n",
+    "fig, axs = plt.subplots(3, 3, figsize=(15, 6))\n",
     "ax = axs.ravel()\n",
-    "for jcut,ax in enumerate(ax):\n",
-    "  ax.hist(ak.to_numpy(electrons[jcut][quality_cut][\"velo_length\"]),bins=nbins,density=True,alpha=0.5,color=\"darkorange\",histtype=\"bar\",label=\"velo\",range=[0,nbins])\n",
-    "  ax.hist(ak.to_numpy(electrons[jcut][quality_cut][\"rich_length\"]),bins=nbins,density=True,alpha=0.5,color=\"blue\",histtype=\"bar\",label=\"rich\",range=[0,nbins])\n",
-    "  ax.set_xlim(0,nbins)\n",
-    "  ax.set_ylim(0,1)\n",
-    "  ax.set_title(\"Photon Cut: \"+str(np.round(jcut*0.05,2))+f\"$E_0$\")\n",
-    "  ax.set_xlabel(\"number of photons\")\n",
-    "  ax.set_ylabel(\"a.u.\")\n",
+    "for jcut, ax in enumerate(ax):\n",
+    "ax.hist(\n",
+    "ak.to_numpy(electrons[jcut][quality_cut][\"velo_length\"]),\n",
+    "bins=nbins,\n",
+    "density=True,\n",
+    "alpha=0.5,\n",
+    "color=\"darkorange\",\n",
+    "histtype=\"bar\",\n",
+    "label=\"velo\",\n",
+    "range=[0, nbins],\n",
+    ")\n",
+    "ax.hist(\n",
+    "ak.to_numpy(electrons[jcut][quality_cut][\"rich_length\"]),\n",
+    "bins=nbins,\n",
+    "density=True,\n",
+    "alpha=0.5,\n",
+    "color=\"blue\",\n",
+    "histtype=\"bar\",\n",
+    "label=\"rich\",\n",
+    "range=[0, nbins],\n",
+    ")\n",
+    "ax.set_xlim(0, nbins)\n",
+    "ax.set_ylim(0, 1)\n",
+    "ax.set_title(\"Photon Cut: \" + str(np.round(jcut \\* 0.05, 2)) + f\"$E_0$\")\n",
+    "ax.set_xlabel(\"number of photons\")\n",
+    "ax.set_ylabel(\"a.u.\")\n",
     "plt.suptitle(\"number of photons in velo and rich\")\n",
     "plt.legend()\n",
     "plt.tight_layout()\n",
@@ -240,70 +354,116 @@
     "\n",
     "### found\n",
     "\n",
-    "fig, axs = plt.subplots(3,3, figsize=(15, 6))\n",
+    "fig, axs = plt.subplots(3, 3, figsize=(15, 6))\n",
     "ax = axs.ravel()\n",
-    "for jcut,ax in enumerate(ax):\n",
-    "  ax.hist(ak.to_numpy(electrons[jcut][~(electrons[jcut].lost) & quality_cut][\"velo_length\"]),bins=nbins,density=True,alpha=0.5,color=\"darkorange\",histtype=\"bar\",label=\"velo\",range=[0,nbins])\n",
-    "  ax.hist(ak.to_numpy(electrons[jcut][~(electrons[jcut].lost) & quality_cut][\"rich_length\"]),bins=nbins,density=True,alpha=0.5,color=\"blue\",histtype=\"bar\",label=\"rich\",range=[0,nbins])\n",
-    "  ax.set_xlim(0,nbins)\n",
-    "  ax.set_ylim(0,1)\n",
-    "  ax.set_title(\"Photon Cut: \"+str(np.round(jcut*0.05,2))+f\"$E_0$\")\n",
-    "  ax.set_xlabel(\"number of photons\")\n",
-    "  ax.set_ylabel(\"a.u.\")\n",
+    "for jcut, ax in enumerate(ax):\n",
+    "ax.hist(\n",
+    "ak.to_numpy(\n",
+    "electrons[jcut]~(electrons[jcut].lost) & quality_cut][\"velo_length\"]\n",
+    "),\n",
+    "bins=nbins,\n",
+    "density=True,\n",
+    "alpha=0.5,\n",
+    "color=\"darkorange\",\n",
+    "histtype=\"bar\",\n",
+    "label=\"velo\",\n",
+    "range=[0, nbins],\n",
+    ")\n",
+    "ax.hist(\n",
+    "ak.to_numpy(\n",
+    "electrons[jcut]~(electrons[jcut].lost) & quality_cut][\"rich_length\"]\n",
+    "),\n",
+    "bins=nbins,\n",
+    "density=True,\n",
+    "alpha=0.5,\n",
+    "color=\"blue\",\n",
+    "histtype=\"bar\",\n",
+    "label=\"rich\",\n",
+    "range=[0, nbins],\n",
+    ")\n",
+    "ax.set_xlim(0, nbins)\n",
+    "ax.set_ylim(0, 1)\n",
+    "ax.set_title(\"Photon Cut: \" + str(np.round(jcut \\* 0.05, 2)) + f\"$E_0$\")\n",
+    "ax.set_xlabel(\"number of photons\")\n",
+    "ax.set_ylabel(\"a.u.\")\n",
     "plt.suptitle(\"number of photons of found electrons\")\n",
     "plt.legend()\n",
     "plt.tight_layout()\n",
     "plt.show()\n",
     "\n",
-    "### lost \n",
+    "### lost\n",
     "\n",
-    "fig, axs = plt.subplots(3,3, figsize=(15, 6))\n",
+    "fig, axs = plt.subplots(3, 3, figsize=(15, 6))\n",
     "ax = axs.ravel()\n",
-    "for jcut,ax in enumerate(ax):\n",
-    "  ax.hist(ak.to_numpy(electrons[jcut][(electrons[jcut].lost) & quality_cut][\"velo_length\"]),bins=nbins,density=True,alpha=0.5,color=\"darkorange\",histtype=\"bar\",label=\"velo\",range=[0,nbins])\n",
-    "  ax.hist(ak.to_numpy(electrons[jcut][(electrons[jcut].lost) & quality_cut][\"rich_length\"]),bins=nbins,density=True,alpha=0.5,color=\"blue\",histtype=\"bar\",label=\"rich\",range=[0,nbins])\n",
-    "  ax.set_xlim(0,nbins)\n",
-    "  ax.set_ylim(0,1)\n",
-    "  ax.set_title(\"Photon Cut: \"+str(np.round(jcut*0.05,2))+f\"$E_0$\")\n",
-    "  ax.set_xlabel(\"number of photons\")\n",
-    "  ax.set_ylabel(\"a.u.\")\n",
+    "for jcut, ax in enumerate(ax):\n",
+    "ax.hist(\n",
+    "ak.to_numpy(\n",
+    "electrons[jcut](electrons[jcut].lost) & quality_cut][\"velo_length\"]\n",
+    "),\n",
+    "bins=nbins,\n",
+    "density=True,\n",
+    "alpha=0.5,\n",
+    "color=\"darkorange\",\n",
+    "histtype=\"bar\",\n",
+    "label=\"velo\",\n",
+    "range=[0, nbins],\n",
+    ")\n",
+    "ax.hist(\n",
+    "ak.to_numpy(\n",
+    "electrons[jcut](electrons[jcut].lost) & quality_cut][\"rich_length\"]\n",
+    "),\n",
+    "bins=nbins,\n",
+    "density=True,\n",
+    "alpha=0.5,\n",
+    "color=\"blue\",\n",
+    "histtype=\"bar\",\n",
+    "label=\"rich\",\n",
+    "range=[0, nbins],\n",
+    ")\n",
+    "ax.set_xlim(0, nbins)\n",
+    "ax.set_ylim(0, 1)\n",
+    "ax.set_title(\"Photon Cut: \" + str(np.round(jcut \\* 0.05, 2)) + f\"$E_0$\")\n",
+    "ax.set_xlabel(\"number of photons\")\n",
+    "ax.set_ylabel(\"a.u.\")\n",
     "plt.suptitle(\"number of photons of lost electrons\")\n",
     "plt.legend()\n",
     "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1500x600 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
+    "plt.show()\n",
     "quality_cut = electrons[jcut].quality != -1\n",
+    "\n",
     "### all split in lost and found\n",
     "\n",
-    "fig, axs = plt.subplots(3,3, figsize=(15, 6))\n",
+    "fig, axs = plt.subplots(3, 3, figsize=(15, 6))\n",
     "ax = axs.ravel()\n",
-    "for jcut,ax in enumerate(ax):\n",
-    "\tax.hist(ak.to_numpy(electrons[jcut][(electrons[jcut].lost) & (quality_cut)][\"photon_length\"]),bins=10,density=True,alpha=0.5,color=\"darkorange\",histtype=\"bar\",label=\"lost\",range=[0,10])\n",
-    "\tax.hist(ak.to_numpy(electrons[jcut][(~electrons[jcut].lost) & (quality_cut)][\"photon_length\"]),bins=10,density=True,alpha=0.5,color=\"blue\",histtype=\"bar\",label=\"found\",range=[0,10])\n",
-    "\tax.set_xlim(0,10)\n",
-    "\t#ax.set_ylim(0,1)\n",
-    "\t#ax.set_yscale('log')\n",
-    "\tax.set_title(\"Photon Cut: \"+str(np.round(jcut*0.05,2))+f\"$E_0$\")\n",
-    "\tax.set_xlabel(\"number of photons\")\n",
-    "\tax.set_ylabel(\"a.u.\")\n",
+    "for jcut, ax in enumerate(ax):\n",
+    "ax.hist(\n",
+    "ak.to_numpy(\n",
+    "electrons[jcut](electrons[jcut].lost) & (quality_cut)][\"photon_length\"]\n",
+    "),\n",
+    "bins=10,\n",
+    "density=True,\n",
+    "alpha=0.5,\n",
+    "color=\"darkorange\",\n",
+    "histtype=\"bar\",\n",
+    "label=\"lost\",\n",
+    "range=[0, 10],\n",
+    ")\n",
+    "ax.hist(\n",
+    "ak.to_numpy(\n",
+    "electrons[jcut](~electrons[jcut].lost) & (quality_cut)][\"photon_length\"]\n",
+    "),\n",
+    "bins=10,\n",
+    "density=True,\n",
+    "alpha=0.5,\n",
+    "color=\"blue\",\n",
+    "histtype=\"bar\",\n",
+    "label=\"found\",\n",
+    "range=[0, 10],\n",
+    ")\n",
+    "ax.set_xlim(0, 10) # ax.set_ylim(0,1) # ax.set_yscale('log')\n",
+    "ax.set_title(\"Photon Cut: \" + str(np.round(jcut \\* 0.05, 2)) + f\"$E_0$\")\n",
+    "ax.set_xlabel(\"number of photons\")\n",
+    "ax.set_ylabel(\"a.u.\")\n",
     "plt.suptitle(\"number of photons in lost and found\")\n",
     "plt.legend()\n",
     "plt.tight_layout()\n",
@@ -315,10 +475,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "\n",
-    "\n"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
diff --git a/notebooks/trackinglosses_rad_length_beginVelo2endUT.ipynb b/notebooks/trackinglosses_rad_length_beginVelo2endUT.ipynb
index 4b92ef1..555da42 100644
--- a/notebooks/trackinglosses_rad_length_beginVelo2endUT.ipynb
+++ b/notebooks/trackinglosses_rad_length_beginVelo2endUT.ipynb
@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/auto/work\n"
+      "/auto/work/cetin/Projektpraktikum\n"
      ]
     }
    ],
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,14 +46,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "41978 8523\n",
+      "40402 10099\n",
       "49865\n"
      ]
     }
@@ -65,16 +65,16 @@
     "\n",
     "# selektiere nur elektronen von B->K*ee\n",
     "allcolumns = file.arrays()\n",
-    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)]\n",
-    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)]\n",
+    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) &\n",
+    "                   (allcolumns.fromB)]\n",
+    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) &\n",
+    "                  (allcolumns.fromB)]\n",
     "\n",
-    "electrons = allcolumns[\n",
-    "    (allcolumns.isElectron)\n",
-    "    & (allcolumns.fromB)\n",
-    "    & (allcolumns.eta <= 5.0)\n",
-    "    & (allcolumns.eta >= 1.5)\n",
-    "    & (np.abs(allcolumns.phi) < 3.142)\n",
-    "]\n",
+    "electrons = allcolumns[(allcolumns.isElectron)\n",
+    "                       & (allcolumns.fromB)\n",
+    "                       & (allcolumns.eta <= 5.0)\n",
+    "                       & (allcolumns.eta >= 1.5)\n",
+    "                       & (np.abs(allcolumns.phi) < 3.142)]\n",
     "\n",
     "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n",
     "print(ak.num(electrons, axis=0))\n",
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,12 +96,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -130,23 +130,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.DataFrame(\n",
-    "    {\n",
-    "        \"phi\": phi_a * 90.0 / np.pi,\n",
-    "        \"eta\": eta_a * 2.0,\n",
-    "        \"rad_length_frac\": rad_length_frac_a,\n",
-    "    }\n",
-    ")\n",
+    "df = pd.DataFrame({\n",
+    "    \"phi\": phi_a * 90.0 / np.pi,\n",
+    "    \"eta\": eta_a * 2.0,\n",
+    "    \"rad_length_frac\": rad_length_frac_a,\n",
+    "})\n",
     "df = df.round({\"phi\": 0, \"eta\": 1, \"rad_length_frac\": 4})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -161,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -284,17 +282,17 @@
        "    <tr>\n",
        "      <th>3.3</th>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.30455</td>\n",
+       "      <td>1.2679</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.526900</td>\n",
+       "      <td>1.2759</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.14755</td>\n",
+       "      <td>1.87600</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.135100</td>\n",
+       "      <td>0.516900</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.1247</td>\n",
+       "      <td>0.5076</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -310,47 +308,47 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.459800</td>\n",
-       "      <td>0.1691</td>\n",
-       "      <td>0.1710</td>\n",
-       "      <td>0.26650</td>\n",
+       "      <td>1.2055</td>\n",
+       "      <td>0.5502</td>\n",
+       "      <td>0.28410</td>\n",
+       "      <td>1.89590</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.291200</td>\n",
-       "      <td>0.1778</td>\n",
+       "      <td>0.673400</td>\n",
+       "      <td>0.2915</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.14020</td>\n",
-       "      <td>0.1775</td>\n",
-       "      <td>0.5234</td>\n",
-       "      <td>0.1498</td>\n",
+       "      <td>0.52245</td>\n",
+       "      <td>1.2931</td>\n",
+       "      <td>1.2685</td>\n",
+       "      <td>0.26290</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.14810</td>\n",
+       "      <td>0.5286</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3.5</th>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.45340</td>\n",
-       "      <td>0.35060</td>\n",
-       "      <td>0.447200</td>\n",
+       "      <td>0.8370</td>\n",
+       "      <td>0.6883</td>\n",
+       "      <td>0.5597</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.1885</td>\n",
+       "      <td>1.30930</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.1711</td>\n",
-       "      <td>0.243100</td>\n",
+       "      <td>0.40660</td>\n",
+       "      <td>1.724750</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.2161</td>\n",
-       "      <td>0.1313</td>\n",
+       "      <td>0.3628</td>\n",
+       "      <td>0.5306</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.16210</td>\n",
+       "      <td>0.56120</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.4284</td>\n",
-       "      <td>0.4008</td>\n",
+       "      <td>1.1481</td>\n",
+       "      <td>1.13330</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.24475</td>\n",
+       "      <td>0.4898</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -380,25 +378,25 @@
        "    <tr>\n",
        "      <th>9.6</th>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.06550</td>\n",
-       "      <td>0.09655</td>\n",
-       "      <td>0.181325</td>\n",
-       "      <td>0.1296</td>\n",
+       "      <td>0.1595</td>\n",
+       "      <td>0.1948</td>\n",
+       "      <td>0.2811</td>\n",
+       "      <td>0.2285</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.25035</td>\n",
-       "      <td>0.0707</td>\n",
+       "      <td>0.34385</td>\n",
+       "      <td>0.16810</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.2469</td>\n",
+       "      <td>0.3408</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0692</td>\n",
-       "      <td>0.2822</td>\n",
-       "      <td>0.26520</td>\n",
-       "      <td>0.1244</td>\n",
+       "      <td>0.1605</td>\n",
+       "      <td>0.3835</td>\n",
+       "      <td>0.35830</td>\n",
+       "      <td>0.2229</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.1084</td>\n",
-       "      <td>0.1665</td>\n",
-       "      <td>0.06750</td>\n",
+       "      <td>0.20530</td>\n",
+       "      <td>0.25955</td>\n",
+       "      <td>0.1618</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -406,95 +404,95 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.070000</td>\n",
-       "      <td>0.0816</td>\n",
-       "      <td>0.0878</td>\n",
-       "      <td>0.07350</td>\n",
-       "      <td>0.0864</td>\n",
+       "      <td>0.1688</td>\n",
+       "      <td>0.1762</td>\n",
+       "      <td>0.17905</td>\n",
+       "      <td>0.16470</td>\n",
+       "      <td>0.17470</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.1993</td>\n",
+       "      <td>0.2906</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.14480</td>\n",
-       "      <td>0.0652</td>\n",
-       "      <td>0.0755</td>\n",
+       "      <td>0.23890</td>\n",
+       "      <td>0.1618</td>\n",
+       "      <td>0.1730</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0796</td>\n",
+       "      <td>0.17520</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9.8</th>\n",
-       "      <td>0.0781</td>\n",
-       "      <td>0.09960</td>\n",
-       "      <td>0.08180</td>\n",
-       "      <td>0.083267</td>\n",
-       "      <td>0.0779</td>\n",
+       "      <td>0.1703</td>\n",
+       "      <td>0.1920</td>\n",
+       "      <td>0.1787</td>\n",
+       "      <td>0.1725</td>\n",
+       "      <td>0.1707</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.08810</td>\n",
-       "      <td>0.1684</td>\n",
-       "      <td>0.140867</td>\n",
+       "      <td>0.16860</td>\n",
+       "      <td>0.26055</td>\n",
+       "      <td>0.231933</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0977</td>\n",
+       "      <td>0.1747</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.13015</td>\n",
+       "      <td>0.21850</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0821</td>\n",
-       "      <td>0.0646</td>\n",
-       "      <td>0.08440</td>\n",
+       "      <td>0.17465</td>\n",
+       "      <td>0.14510</td>\n",
+       "      <td>0.1860</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9.9</th>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.09210</td>\n",
-       "      <td>0.08210</td>\n",
-       "      <td>0.071900</td>\n",
+       "      <td>0.1831</td>\n",
+       "      <td>0.1671</td>\n",
+       "      <td>0.1485</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0936</td>\n",
-       "      <td>0.32600</td>\n",
-       "      <td>0.0748</td>\n",
-       "      <td>0.193300</td>\n",
+       "      <td>0.17920</td>\n",
+       "      <td>0.41690</td>\n",
+       "      <td>0.17350</td>\n",
+       "      <td>0.284200</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.0727</td>\n",
-       "      <td>0.1494</td>\n",
-       "      <td>0.0353</td>\n",
+       "      <td>0.3746</td>\n",
+       "      <td>0.2385</td>\n",
+       "      <td>0.1257</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.3264</td>\n",
+       "      <td>0.4171</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0844</td>\n",
-       "      <td>0.07660</td>\n",
+       "      <td>0.16620</td>\n",
+       "      <td>0.1656</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10.0</th>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.05840</td>\n",
+       "      <td>0.2501</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.070600</td>\n",
+       "      <td>0.1900</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0578</td>\n",
+       "      <td>0.23640</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0785</td>\n",
-       "      <td>0.0915</td>\n",
-       "      <td>0.03070</td>\n",
+       "      <td>0.1828</td>\n",
+       "      <td>0.2026</td>\n",
+       "      <td>0.11630</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.07090</td>\n",
+       "      <td>0.1526</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -503,52 +501,52 @@
        "</div>"
       ],
       "text/plain": [
-       "phi    -90.0    -89.0    -88.0     -87.0   -86.0   -85.0    -84.0   -83.0  \\\n",
-       "eta                                                                         \n",
-       "3.1      NaN      NaN      NaN       NaN     NaN     NaN      NaN     NaN   \n",
-       "3.2      NaN      NaN      NaN       NaN     NaN     NaN      NaN     NaN   \n",
-       "3.3      NaN  0.30455      NaN  0.526900     NaN     NaN  0.14755     NaN   \n",
-       "3.4      NaN      NaN      NaN  0.459800  0.1691  0.1710  0.26650     NaN   \n",
-       "3.5      NaN  0.45340  0.35060  0.447200     NaN  0.1885      NaN  0.1711   \n",
-       "...      ...      ...      ...       ...     ...     ...      ...     ...   \n",
-       "9.6      NaN  0.06550  0.09655  0.181325  0.1296     NaN  0.25035  0.0707   \n",
-       "9.7      NaN      NaN      NaN  0.070000  0.0816  0.0878  0.07350  0.0864   \n",
-       "9.8   0.0781  0.09960  0.08180  0.083267  0.0779     NaN  0.08810  0.1684   \n",
-       "9.9      NaN  0.09210  0.08210  0.071900     NaN  0.0936  0.32600  0.0748   \n",
-       "10.0     NaN  0.05840      NaN  0.070600     NaN  0.0578      NaN     NaN   \n",
+       "phi    -90.0   -89.0   -88.0   -87.0   -86.0    -85.0    -84.0    -83.0  \\\n",
+       "eta                                                                       \n",
+       "3.1      NaN     NaN     NaN     NaN     NaN      NaN      NaN      NaN   \n",
+       "3.2      NaN     NaN     NaN     NaN     NaN      NaN      NaN      NaN   \n",
+       "3.3      NaN  1.2679     NaN  1.2759     NaN      NaN  1.87600      NaN   \n",
+       "3.4      NaN     NaN     NaN  1.2055  0.5502  0.28410  1.89590      NaN   \n",
+       "3.5      NaN  0.8370  0.6883  0.5597     NaN  1.30930      NaN  0.40660   \n",
+       "...      ...     ...     ...     ...     ...      ...      ...      ...   \n",
+       "9.6      NaN  0.1595  0.1948  0.2811  0.2285      NaN  0.34385  0.16810   \n",
+       "9.7      NaN     NaN     NaN  0.1688  0.1762  0.17905  0.16470  0.17470   \n",
+       "9.8   0.1703  0.1920  0.1787  0.1725  0.1707      NaN  0.16860  0.26055   \n",
+       "9.9      NaN  0.1831  0.1671  0.1485     NaN  0.17920  0.41690  0.17350   \n",
+       "10.0     NaN  0.2501     NaN  0.1900     NaN  0.23640      NaN      NaN   \n",
        "\n",
        "phi      -82.0   -81.0  ...    81.0    82.0    83.0     84.0    85.0    86.0  \\\n",
        "eta                     ...                                                    \n",
        "3.1        NaN     NaN  ...     NaN     NaN     NaN      NaN     NaN     NaN   \n",
        "3.2        NaN     NaN  ...     NaN     NaN     NaN      NaN     NaN     NaN   \n",
-       "3.3   0.135100     NaN  ...  0.1247     NaN     NaN      NaN     NaN     NaN   \n",
-       "3.4   0.291200  0.1778  ...     NaN     NaN     NaN  0.14020  0.1775  0.5234   \n",
-       "3.5   0.243100     NaN  ...  0.2161  0.1313     NaN  0.16210     NaN  0.4284   \n",
+       "3.3   0.516900     NaN  ...  0.5076     NaN     NaN      NaN     NaN     NaN   \n",
+       "3.4   0.673400  0.2915  ...     NaN     NaN     NaN  0.52245  1.2931  1.2685   \n",
+       "3.5   1.724750     NaN  ...  0.3628  0.5306     NaN  0.56120     NaN  1.1481   \n",
        "...        ...     ...  ...     ...     ...     ...      ...     ...     ...   \n",
-       "9.6        NaN  0.2469  ...     NaN  0.0692  0.2822  0.26520  0.1244     NaN   \n",
-       "9.7        NaN     NaN  ...  0.1993     NaN     NaN  0.14480  0.0652  0.0755   \n",
-       "9.8   0.140867     NaN  ...     NaN  0.0977     NaN  0.13015     NaN     NaN   \n",
-       "9.9   0.193300     NaN  ...  0.0727  0.1494  0.0353      NaN  0.3264     NaN   \n",
-       "10.0       NaN     NaN  ...     NaN  0.0785  0.0915  0.03070     NaN     NaN   \n",
+       "9.6        NaN  0.3408  ...     NaN  0.1605  0.3835  0.35830  0.2229     NaN   \n",
+       "9.7        NaN     NaN  ...  0.2906     NaN     NaN  0.23890  0.1618  0.1730   \n",
+       "9.8   0.231933     NaN  ...     NaN  0.1747     NaN  0.21850     NaN     NaN   \n",
+       "9.9   0.284200     NaN  ...  0.3746  0.2385  0.1257      NaN  0.4171     NaN   \n",
+       "10.0       NaN     NaN  ...     NaN  0.1828  0.2026  0.11630     NaN     NaN   \n",
        "\n",
-       "phi     87.0    88.0     89.0   90.0  \n",
-       "eta                                   \n",
-       "3.1      NaN     NaN      NaN    NaN  \n",
-       "3.2      NaN     NaN      NaN    NaN  \n",
-       "3.3      NaN     NaN      NaN    NaN  \n",
-       "3.4   0.1498     NaN  0.14810    NaN  \n",
-       "3.5   0.4008     NaN  0.24475    NaN  \n",
-       "...      ...     ...      ...    ...  \n",
-       "9.6   0.1084  0.1665  0.06750    NaN  \n",
-       "9.7      NaN  0.0796      NaN    NaN  \n",
-       "9.8   0.0821  0.0646  0.08440    NaN  \n",
-       "9.9      NaN  0.0844  0.07660    NaN  \n",
-       "10.0     NaN     NaN  0.07090    NaN  \n",
+       "phi      87.0     88.0    89.0   90.0  \n",
+       "eta                                    \n",
+       "3.1       NaN      NaN     NaN    NaN  \n",
+       "3.2       NaN      NaN     NaN    NaN  \n",
+       "3.3       NaN      NaN     NaN    NaN  \n",
+       "3.4   0.26290      NaN  0.5286    NaN  \n",
+       "3.5   1.13330      NaN  0.4898    NaN  \n",
+       "...       ...      ...     ...    ...  \n",
+       "9.6   0.20530  0.25955  0.1618    NaN  \n",
+       "9.7       NaN  0.17520     NaN    NaN  \n",
+       "9.8   0.17465  0.14510  0.1860    NaN  \n",
+       "9.9       NaN  0.16620  0.1656    NaN  \n",
+       "10.0      NaN      NaN  0.1526    NaN  \n",
        "\n",
        "[70 rows x 181 columns]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -571,7 +569,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2100x700 with 2 Axes>"
       ]
@@ -589,7 +587,8 @@
     "    cmap=colormaps[\"rainbow\"],\n",
     "    xticklabels=False,\n",
     "    yticklabels=False,\n",
-    "    vmax=0.7,\n",
+    "    vmax=1,\n",
+    "    vmin=0.1,\n",
     ")\n",
     "# ax.set_yticks([5, 15, 25, 35], [2, 3, 4, 5])\n",
     "ax.set_yticks([10, 30, 50, 70], [2, 3, 4, 5])\n",
diff --git a/notebooks/utils/__pycache__/components.cpython-310.pyc b/notebooks/utils/__pycache__/components.cpython-310.pyc
new file mode 100644
index 0000000..721832d
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diff --git a/methods/adashof.py b/notebooks/utils/adashof.py
similarity index 100%
rename from methods/adashof.py
rename to notebooks/utils/adashof.py
diff --git a/notebooks/utils/components.py b/notebooks/utils/components.py
new file mode 100644
index 0000000..a1a4315
--- /dev/null
+++ b/notebooks/utils/components.py
@@ -0,0 +1,48 @@
+# flake8: noqaq
+import inspect
+import re
+import importlib
+import warnings
+from collections import OrderedDict
+from functools import lru_cache
+from html import escape as html_escape
+
+# String that separates a name from its unique ID
+_UNIQUE_SEPARATOR = "_"
+_IDENTITY_LENGTH = 8
+_IDENTITY_TABLE = {}
+
+# If a property ends with this key, it is a pseudo-property which exists to
+# hold the data dependencies of another property value
+_DATA_DEPENDENCY_KEY_SUFFIX = "_PyConfDataDependencies"
+
+_FLOW_GRAPH_NODE_COLOUR = "aliceblue"
+_FLOW_GRAPH_INPUT_COLOUR = "deepskyblue1"
+_FLOW_GRAPH_OUTPUT_COLOUR = "coral1"
+
+
+def unique_name_ext_re():
+    return "(?:%s[1234567890abcdef]{%s})?" % (_UNIQUE_SEPARATOR, _IDENTITY_LENGTH)
+
+
+def findRootObjByName(rootFile, name):
+    """
+    Finds the object with given name in the given root file,
+    where name is a regular expression or a set of them separated by '/' in case directories are used
+    """
+    curObj = rootFile
+    for n in name.split("/"):
+        matches = [
+            k.GetName() for k in curObj.GetListOfKeys() if re.fullmatch(n, k.GetName())
+        ]
+        if len(matches) > 1:
+            raise Exception(
+                "Collision of names in Root : found several objects with name matching %s inside %s : %s"
+                % (n, curObj.GetName(), matches)
+            )
+        if len(matches) == 0:
+            raise Exception(
+                "Failed to find object with name %s inside %s" % (n, curObj.GetName())
+            )
+        curObj = curObj.Get(matches[0])
+    return curObj
diff --git a/methods/fit_linear_regression_model.py b/notebooks/utils/fit_linear_regression_model.py
similarity index 100%
rename from methods/fit_linear_regression_model.py
rename to notebooks/utils/fit_linear_regression_model.py
diff --git a/thesis/emitted_energy.pdf b/thesis/emitted_energy.pdf
new file mode 100644
index 0000000..bcee9ff
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diff --git a/thesis/emitted_energy_difference.pdf b/thesis/emitted_energy_difference.pdf
new file mode 100644
index 0000000..298cfd9
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