diff --git a/.vscode/settings.json b/.vscode/settings.json
index b6b8aa0..6ee9c4a 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -6,4 +6,5 @@
 	"notebook.formatOnCellExecution": true,
 	"notebook.formatOnSave.enabled": true,
 	"editor.defaultFormatter": "ms-python.black-formatter",
+	"editor.formatOnSave": true,
 }
\ No newline at end of file
diff --git a/tracking_losses_ntuple_B_default.root b/tracking_losses_ntuple_B_default.root
new file mode 100644
index 0000000..ec3fd46
Binary files /dev/null and b/tracking_losses_ntuple_B_default.root differ
diff --git a/trackinglosses_B_photon_cuts.root b/trackinglosses_B_photon_cuts.root
new file mode 100644
index 0000000..d9e8b69
Binary files /dev/null and b/trackinglosses_B_photon_cuts.root differ
diff --git a/trackinglosses_energy.ipynb b/trackinglosses_energy.ipynb
index bf2cf82..f3d727f 100644
--- a/trackinglosses_energy.ipynb
+++ b/trackinglosses_energy.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,23 +17,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10923 2390\n",
-      "13313\n"
+      "40402 10099\n",
+      "50501\n"
      ]
     }
    ],
    "source": [
-    "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
-    "#file = uproot.open(\n",
-    "#    \"tracking_losses_ntuple_B_default.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
-    "#)\n",
+    "#file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
+    "file = uproot.open(\n",
+    "    \"tracking_losses_ntuple_B_default.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
+    ")\n",
     "\n",
     "# selektiere nur elektronen von B->K*ee\n",
     "allcolumns = file.arrays()\n",
@@ -53,18 +53,192 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 67,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>{all_endvtx_types_length: 7,\n",
+       " all_endvtx_types: [101, 101, 101, 101, 101, 101, 0],\n",
+       " all_endvtx_x_length: 7,\n",
+       " all_endvtx_x: [-6.97, -52.9, -52.9, ..., -1.71e+03, -2.14e+03, -3.57e+03],\n",
+       " all_endvtx_y_length: 7,\n",
+       " all_endvtx_y: [-0.89, -6.75, -6.75, -7.08, -66.1, -72.6, -39.5],\n",
+       " all_endvtx_z_length: 7,\n",
+       " all_endvtx_z: [112, 859, 859, 895, 8.7e+03, 9.68e+03, 1.26e+04],\n",
+       " brem_photons_pe_length: 6,\n",
+       " brem_photons_pe: [2.62e+03, 812, 2.54e+03, 1.86e+03, 3.12e+03, 241],\n",
+       " brem_photons_px_length: 6,\n",
+       " brem_photons_px: [-161, -49.7, -156, -114, -1.18e+03, -101],\n",
+       " brem_photons_py_length: 6,\n",
+       " brem_photons_py: [-18.9, -6.92, -21.6, -16.8, -20.9, -0.26],\n",
+       " brem_photons_pz_length: 6,\n",
+       " brem_photons_pz: [2.61e+03, 810, 2.54e+03, 1.86e+03, 2.89e+03, 219],\n",
+       " brem_vtx_x_length: 6,\n",
+       " brem_vtx_x: [-6.97, -52.9, -52.9, -55.2, -1.71e+03, -2.14e+03],\n",
+       " brem_vtx_y_length: 6,\n",
+       " ...}\n",
+       "---------------------------------------------------------------------------\n",
+       "type: {\n",
+       "    all_endvtx_types_length: int32,\n",
+       "    all_endvtx_types: var * float32,\n",
+       "    all_endvtx_x_length: int32,\n",
+       "    all_endvtx_x: var * float32,\n",
+       "    all_endvtx_y_length: int32,\n",
+       "    all_endvtx_y: var * float32,\n",
+       "    all_endvtx_z_length: int32,\n",
+       "    all_endvtx_z: var * float32,\n",
+       "    brem_photons_pe_length: int32,\n",
+       "    brem_photons_pe: var * float32,\n",
+       "    brem_photons_px_length: int32,\n",
+       "    brem_photons_px: var * float32,\n",
+       "    brem_photons_py_length: int32,\n",
+       "    brem_photons_py: var * float32,\n",
+       "    brem_photons_pz_length: int32,\n",
+       "    brem_photons_pz: var * float32,\n",
+       "    brem_vtx_x_length: int32,\n",
+       "    brem_vtx_x: var * float32,\n",
+       "    brem_vtx_y_length: int32,\n",
+       "    brem_vtx_y: var * float32,\n",
+       "    brem_vtx_z_length: int32,\n",
+       "    brem_vtx_z: var * float32,\n",
+       "    endvtx_type: int32,\n",
+       "    endvtx_x: float64,\n",
+       "    endvtx_y: float64,\n",
+       "    endvtx_z: float64,\n",
+       "    energy: float64,\n",
+       "    eta: float64,\n",
+       "    event_count: int32,\n",
+       "    fromB: bool,\n",
+       "    fromD: bool,\n",
+       "    fromDecay: bool,\n",
+       "    fromHadInt: bool,\n",
+       "    fromPV: bool,\n",
+       "    fromPairProd: bool,\n",
+       "    fromSignal: bool,\n",
+       "    fromStrange: bool,\n",
+       "    ideal_state_770_qop: float64,\n",
+       "    ideal_state_770_tx: float64,\n",
+       "    ideal_state_770_ty: float64,\n",
+       "    ideal_state_770_x: float64,\n",
+       "    ideal_state_770_y: float64,\n",
+       "    ideal_state_770_z: float64,\n",
+       "    ideal_state_9410_qop: float64,\n",
+       "    ideal_state_9410_tx: float64,\n",
+       "    ideal_state_9410_ty: float64,\n",
+       "    ideal_state_9410_x: float64,\n",
+       "    ideal_state_9410_y: float64,\n",
+       "    ideal_state_9410_z: float64,\n",
+       "    isElectron: bool,\n",
+       "    isKaon: bool,\n",
+       "    isMuon: bool,\n",
+       "    isPion: bool,\n",
+       "    isProton: bool,\n",
+       "    lost: bool,\n",
+       "    lost_in_track_fit: bool,\n",
+       "    match_fraction: float32,\n",
+       "    mc_chi2: float32,\n",
+       "    mc_dSlope: float32,\n",
+       "    mc_dSlopeY: float32,\n",
+       "    mc_distX: float32,\n",
+       "    mc_distY: float32,\n",
+       "    mc_teta2: float32,\n",
+       "    mc_zMag: float32,\n",
+       "    mcp_idx: int32,\n",
+       "    mother_id: int32,\n",
+       "    mother_key: int32,\n",
+       "    originvtx_type: int32,\n",
+       "    originvtx_x: float64,\n",
+       "    originvtx_y: float64,\n",
+       "    originvtx_z: float64,\n",
+       "    p: float64,\n",
+       "    phi: float64,\n",
+       "    pid: int32,\n",
+       "    pt: float64,\n",
+       "    px: float64,\n",
+       "    py: float64,\n",
+       "    pz: float64,\n",
+       "    quality: int32,\n",
+       "    rad_length_frac: float64,\n",
+       "    scifi_hit_pos_x_length: int32,\n",
+       "    scifi_hit_pos_x: var * float32,\n",
+       "    scifi_hit_pos_y_length: int32,\n",
+       "    scifi_hit_pos_y: var * float32,\n",
+       "    scifi_hit_pos_z_length: int32,\n",
+       "    scifi_hit_pos_z: var * float32,\n",
+       "    track_p: float64,\n",
+       "    track_pt: float64,\n",
+       "    tx: float64,\n",
+       "    ty: float64,\n",
+       "    ut_hit_pos_x_length: int32,\n",
+       "    ut_hit_pos_x: var * float32,\n",
+       "    ut_hit_pos_y_length: int32,\n",
+       "    ut_hit_pos_y: var * float32,\n",
+       "    ut_hit_pos_z_length: int32,\n",
+       "    ut_hit_pos_z: var * float32,\n",
+       "    velo_hit_pos_x_length: int32,\n",
+       "    velo_hit_pos_x: var * float32,\n",
+       "    velo_hit_pos_y_length: int32,\n",
+       "    velo_hit_pos_y: var * float32,\n",
+       "    velo_hit_pos_z_length: int32,\n",
+       "    velo_hit_pos_z: var * float32,\n",
+       "    velo_track_idx: int32,\n",
+       "    velo_track_tx: float64,\n",
+       "    velo_track_ty: float64,\n",
+       "    velo_track_x: float64,\n",
+       "    velo_track_y: float64,\n",
+       "    velo_track_z: float64\n",
+       "}</pre>"
+      ],
+      "text/plain": [
+       "<Record {all_endvtx_types_length: 7, ...} type='{all_endvtx_types_length: i...'>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#electrons[0]"
+    "electrons[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 68,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>{lost: True,\n",
+       " rad_length_frac: 0.129,\n",
+       " energy: 1.17e+04,\n",
+       " photon_length: 6,\n",
+       " brem_photons_pe: [2.62e+03, 812, 2.54e+03, 1.86e+03, 3.12e+03, 241],\n",
+       " brem_vtx_x: [-6.97, -52.9, -52.9, -55.2, -1.71e+03, -2.14e+03],\n",
+       " brem_vtx_z: [112, 859, 859, 895, 8.7e+03, 9.68e+03]}\n",
+       "---------------------------------------------------------------------\n",
+       "type: {\n",
+       "    lost: bool,\n",
+       "    rad_length_frac: float64,\n",
+       "    energy: float64,\n",
+       "    photon_length: int64,\n",
+       "    brem_photons_pe: var * float64,\n",
+       "    brem_vtx_x: var * float64,\n",
+       "    brem_vtx_z: var * float64\n",
+       "}</pre>"
+      ],
+      "text/plain": [
+       "<Record {lost: True, rad_length_frac: ..., ...} type='{lost: bool, rad_leng...'>"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "lost_e = electrons[\"lost\"]\n",
     "e = electrons[\"energy\"]\n",
@@ -72,7 +246,7 @@
     "brem_z = electrons[\"brem_vtx_z\"]\n",
     "brem_x = electrons[\"brem_vtx_x\"]\n",
     "length = electrons[\"brem_vtx_z_length\"]\n",
-    "\n",
+    "rad_length = electrons[\"rad_length_frac\"]\n",
     "\n",
     "\n",
     "brem = ak.ArrayBuilder()\n",
@@ -80,6 +254,7 @@
     "for itr in range(ak.num(electrons, axis=0)):\n",
     "    brem.begin_record()\n",
     "    brem.field(\"lost\").boolean(lost_e[itr])\n",
+    "    brem.field(\"rad_length_frac\").append(rad_length[itr])\n",
     "    # [:,\"energy\"] energy\n",
     "    brem.field(\"energy\").append(e[itr])\n",
     "    # [:,\"photon_length\"] number of vertices\n",
@@ -91,23 +266,26 @@
     "    brem.field(\"brem_vtx_z\").append(brem_z[itr])\n",
     "    brem.end_record()\n",
     "\n",
-    "brem = ak.Array(brem)"
+    "brem = ak.Array(brem)\n",
+    "brem[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
     "photon_cut = 0\n",
-    "photon_cut_ratio = 0.2\n",
+    "photon_cut_ratio = 0.1\n",
     "\n",
     "cut_brem = ak.ArrayBuilder()\n",
     "\n",
     "for itr in range(ak.num(brem, axis=0)):\n",
     "    cut_brem.begin_record()\n",
+    "    cut_brem.field(\"event_id\").integer(itr)\n",
     "    cut_brem.field(\"lost\").boolean(brem[itr, \"lost\"])\n",
+    "    cut_brem.field(\"rad_length_frac\").real(brem[itr, \"rad_length_frac\"])\n",
     "    cut_brem.field(\"energy\").real(brem[itr, \"energy\"])\n",
     "\n",
     "    ph_length = brem[itr, \"photon_length\"]\n",
@@ -171,28 +349,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "13313\n"
+      "44163\n",
+      "50501\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<pre>{lost: False,\n",
-       " energy: 3.63e+03,\n",
-       " brem_photons_pe: [],\n",
-       " brem_vtx_x: [],\n",
-       " brem_vtx_z: [],\n",
-       " photon_length: 0}\n",
-       "-----------------------------------\n",
+       "<pre>{event_id: 0,\n",
+       " lost: True,\n",
+       " rad_length_frac: 0.129,\n",
+       " energy: 1.17e+04,\n",
+       " brem_photons_pe: [2.62e+03, 2.54e+03, 1.86e+03],\n",
+       " brem_vtx_x: [-6.97, -52.9, -55.2],\n",
+       " brem_vtx_z: [112, 859, 895],\n",
+       " photon_length: 3}\n",
+       "-------------------------------------------------\n",
        "type: {\n",
+       "    event_id: int64,\n",
        "    lost: bool,\n",
+       "    rad_length_frac: float64,\n",
        "    energy: float64,\n",
        "    brem_photons_pe: var * float64,\n",
        "    brem_vtx_x: var * float64,\n",
@@ -201,10 +384,120 @@
        "}</pre>"
       ],
       "text/plain": [
-       "<Record {lost: False, energy: 3.63e+03, ...} type='{lost: bool, energy: flo...'>"
+       "<Record {event_id: 0, lost: True, ...} type='{event_id: int64, lost: bool, ...'>"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(ak.sum(ak.num(ntuple[\"brem_photons_pe\"], axis=1)))\n",
+    "print(ak.num(ntuple,axis=0))\n",
+    "ntuple[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# nulltuple = ntuple[:7000]\n",
+    "# onetuple = ntuple[7000:14000]\n",
+    "# twotuple = ntuple[14000:21000]\n",
+    "# threetuple = ntuple[21000:28000]\n",
+    "# fourtuple = ntuple[28000:35000]\n",
+    "# fivetuple = ntuple[35000:42000]\n",
+    "# sixtuple = ntuple[42000:49000]\n",
+    "# seventuple = ntuple[49000:]\n",
+    "\n",
+    "# ntuple.nbytes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cut = \"tenCut\"\n",
+    "# tree = \"Tree10\"\n",
+    "# with uproot.update(\"trackinglosses_B_photon_cuts.root\") as outFile:\n",
+    "#     #outFile[\"README\"] = \"The Cuts are placed on the photons. noCut: 0*E, first: 0.05*E, second: 0.1*E, etc.\"\n",
+    "#     outFile.mktree(tree, {cut + \"_event_id\": ntuple[\"event_id\"].type, cut + \"_lost\": ntuple[\"lost\"].type, cut + \"_rad_length_frac\": ntuple[\"rad_length_frac\"].type, cut + \"_energy\": ntuple[\"energy\"].type, cut + \"_brem_photons_pe\": ntuple[\"brem_photons_pe\"].type, cut + \"_brem_vtx_x\": ntuple[\"brem_vtx_x\"].type, cut + \"_brem_vtx_z\": ntuple[\"brem_vtx_z\"].type, cut + \"_photon_length\": ntuple[\"photon_length\"].type} )\n",
+    "#     outFile[tree].extend( {cut + \"_event_id\": nulltuple[\"event_id\"], cut + \"_lost\": nulltuple[\"lost\"], cut + \"_rad_length_frac\": nulltuple[\"rad_length_frac\"], cut + \"_energy\": nulltuple[\"energy\"], cut + \"_brem_photons_pe\": nulltuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": nulltuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": nulltuple[\"brem_vtx_z\"], cut + \"_photon_length\": nulltuple[\"photon_length\"]} )\n",
+    "#     outFile[tree].extend( {cut + \"_event_id\": onetuple[\"event_id\"], cut + \"_lost\": onetuple[\"lost\"], cut + \"_rad_length_frac\": onetuple[\"rad_length_frac\"], cut + \"_energy\": onetuple[\"energy\"], cut + \"_brem_photons_pe\": onetuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": onetuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": onetuple[\"brem_vtx_z\"], cut + \"_photon_length\": onetuple[\"photon_length\"]} )\n",
+    "#     outFile[tree].extend( {cut + \"_event_id\": twotuple[\"event_id\"], cut + \"_lost\": twotuple[\"lost\"], cut + \"_rad_length_frac\": twotuple[\"rad_length_frac\"], cut + \"_energy\": twotuple[\"energy\"], cut + \"_brem_photons_pe\": twotuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": twotuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": twotuple[\"brem_vtx_z\"], cut + \"_photon_length\": twotuple[\"photon_length\"]} )\n",
+    "#     outFile[tree].extend( {cut + \"_event_id\": threetuple[\"event_id\"], cut + \"_lost\": threetuple[\"lost\"], cut + \"_rad_length_frac\": threetuple[\"rad_length_frac\"], cut + \"_energy\": threetuple[\"energy\"], cut + \"_brem_photons_pe\": threetuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": threetuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": threetuple[\"brem_vtx_z\"], cut + \"_photon_length\": threetuple[\"photon_length\"]} )\n",
+    "#     outFile[tree].extend( {cut + \"_event_id\": fourtuple[\"event_id\"], cut + \"_lost\": fourtuple[\"lost\"], cut + \"_rad_length_frac\": fourtuple[\"rad_length_frac\"], cut + \"_energy\": fourtuple[\"energy\"], cut + \"_brem_photons_pe\": fourtuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": fourtuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": fourtuple[\"brem_vtx_z\"], cut + \"_photon_length\": fourtuple[\"photon_length\"]} )\n",
+    "#     outFile[tree].extend( {cut + \"_event_id\": fivetuple[\"event_id\"], cut + \"_lost\": fivetuple[\"lost\"], cut + \"_rad_length_frac\": fivetuple[\"rad_length_frac\"], cut + \"_energy\": fivetuple[\"energy\"], cut + \"_brem_photons_pe\": fivetuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": fivetuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": fivetuple[\"brem_vtx_z\"], cut + \"_photon_length\": fivetuple[\"photon_length\"]} )\n",
+    "#     outFile[tree].extend( {cut + \"_event_id\": sixtuple[\"event_id\"], cut + \"_lost\": sixtuple[\"lost\"], cut + \"_rad_length_frac\": sixtuple[\"rad_length_frac\"], cut + \"_energy\": sixtuple[\"energy\"], cut + \"_brem_photons_pe\": sixtuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": sixtuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": sixtuple[\"brem_vtx_z\"], cut + \"_photon_length\": sixtuple[\"photon_length\"]} )\n",
+    "#     outFile[tree].extend( {cut + \"_event_id\": seventuple[\"event_id\"], cut + \"_lost\": seventuple[\"lost\"], cut + \"_rad_length_frac\": seventuple[\"rad_length_frac\"], cut + \"_energy\": seventuple[\"energy\"], cut + \"_brem_photons_pe\": seventuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": seventuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": seventuple[\"brem_vtx_z\"], cut + \"_photon_length\": seventuple[\"photon_length\"]} )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50501\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre>{event_id: 1,\n",
+       " lost: False,\n",
+       " rad_length_frac: 0.148,\n",
+       " energy: 1.28e+04,\n",
+       " brem_photons_pe: [7.42e+03],\n",
+       " brem_vtx_x: [-3.61],\n",
+       " brem_vtx_z: [35.6],\n",
+       " photon_length: 1}\n",
+       "-----------------------------------\n",
+       "type: {\n",
+       "    event_id: int64,\n",
+       "    lost: bool,\n",
+       "    rad_length_frac: float64,\n",
+       "    energy: float64,\n",
+       "    brem_photons_pe: var * float64,\n",
+       "    brem_vtx_x: var * float64,\n",
+       "    brem_vtx_z: var * float64,\n",
+       "    photon_length: int64\n",
+       "}</pre>"
+      ],
+      "text/plain": [
+       "<Record {event_id: 1, lost: False, ...} type='{event_id: int64, lost: bool,...'>"
+      ]
+     },
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -220,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -234,12 +527,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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643itVtPm5qbW1tbiUOR5nnK5nNbW1pTL5eJyqtWqHMdRrVaT53lyHKfjnKui+0avQeu+LSvCDAAAc5DP53V8fCzLsuJ5KcfHx0qlUpIue2wePHigQqGgUqmker2uXC6ndDodh4tsNtuzploqleoZqmo2m6rX6/J9X5VKRfV6XQcHB8rn86rVah1hJQgCPXjwQLlcTpVKRZVKZcYtMX2EGQAAlsDTp0+1vb0t27bj9/L5vGzbluu68fpq/dZU696A2bIsPXnyRJKUTqdVKpVk23YcVK4uPuu6rra3t+NQJWlpnqwaFWEGAIAFix7RvhpkIoVCQZLG7jHpF36iDZuDIFCtVlM6nR6r7GWxUk8zZTIZbWxs9D22s7OjnZ2dOdcIAIDhk4K3t7cl9e5LOA3dGzkvQrlcVrlc7nus1WqNVMZKhZmTk5O+qRcAgGUQhmHPe1HPSvdQ0jREYSbqqVmEYZ0Jvu+PtIk0w0wAACxY9A/tq08uRaKAk0wmp37fqEem0WhMvex5IswAADBHzWazpyfEsizZth0/tn3V6empEolEvK3A/fv3JXUOO0W/7tezM0w0hFWtVvtee9PyFoUwAwDAEjg+PlYikYgn/EqXYaJUKung4CAebop6cVzXled5qlar8eRgz/PiybyjDB0lEon4ySXHceR5Xrw3k3QZkkxYc2al5swAAJbPquxIHa35EvV2FAoF5XK5+JFoy7L06tUrPX36VOl0Oh4COj4+7pjvGa0rUywWlcvllM/nValU5Hmestmsnjx5Et9LutwuwbIsbW9vxyHF933t7+9rd3dXpVJJyWRSpVJJ6XQ63sy5Vqspm80qm83OsZXGs9Zut9uLrsSsRROIGo3G1CcA9/tLuCp/MQEAmKVRv78ZZgIAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjMYKwJP6/NM+b/5gvnUAAGCFrVSYyWQy2tjY6Hts2BbkAABgNsrlssrlct9jrVZrpDJWKsycnJxMfTsDAAAwvmGdCdF2BtdhzgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0d5adAXmKZPJaGNjo++xYVuQAwCA2SiXyyqXy32PtVqtkcpYqTBzcnIi27YXXQ0AAPCNYZ0Jvu/LcZxry2CYCQAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBoby26AvOUyWS0sbHR99iwLcgBAMBslMtllcvlvsdardZIZaxUmDk5OZFt24uuBgAA+MawzgTf9+U4zrVlMMwEAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABht6cJMrVaT4zhaW1tTMpmU53k95/i+r1wuJ9d1VSgUVKvVFlBTAACwDJZqBeBqtapGo6FSqSRJcl1X6XRaZ2dnsixLkhQEgRzHUaPRiFfzTSaTajabyufzC6s7AABYjKXqmQnDUJVKRalUSqlUSgcHB5Iue2IihUJBqVSqY1uCqIcGAACsnqUKM7u7ux0/JxIJSYqDSxiG8jxP6XS647zt7W1Jlz07AABgtSxVmOlWq9VUKpXiIabT01NJin+ORGGnXq/Pt4IAAGDhlmrOzFWu66parcZDTdLlfBnp2x6bbtHxQc7Pz/XmzZux67S+vq719fWxrwcAYJVcXFzo4uJi7OvPz89HOm8pw8z+/r6CIFAYhsrlcqpUKsrn8zo7O5MkbW1t9b0uDMOh5b733nsT1evDDz/U3t7eRGUAALAqisWiPvroo5nfZynDTDR3xvM85XI5lUol5fN5JZNJSVKz2ex7XffwU7fPPvtMDx8+HLte9MoAADC6Z8+e6YMPPhj7+pcvX47UEbGUYSaSSqWUz+e1v78v6duwMqgH5rowc+fOHd29e3eqdQQAAP1NOj3jzp07I5231BOAJendd9+NQ0r01FL33JjoZ8dx5ls5AACwcEsfZoIgUCqVknQ58de27Z6nlqJVgh8/fjz3+gEAgMVamjATTfa9ujVBEASq1+uqVCrxewcHB/I8r6N3plQqqVQqDXzKCQAA3F5LM2cmkUgoDEM9ffpUlUpF6XRalmX19MLYtq1GoyHXdWVZloIgkOu6bGUAAMCKWpowI42+6J1t2zo+Pp5xbQAAgAmWZpgJAABgHIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRlmrRvFnLZDLa2Njoe2xnZ0c7OztzrhEAAKutXC6rXC73PdZqtUYqY6XCzMnJiWzbXnQ1AADAN4Z1Jvi+L8dxri2DYSYAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAo7216ArMUyaT0cbGRt9jw7YgBwAAs1Eul1Uul/sea7VaI5WxUmHm5OREtm0vuhoAAOAbwzoTfN+X4zjXlsEwEwAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjrdSu2ZlMRhsbG32PDdu1EwAAzEa5XFa5XO57rNVqjVTGSoWZk5MT2ba96GoAAIBvDOtM8H1fjuNcWwbDTAAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGe2vRFZinTCajjY2NvseGbUEOAABmo1wuq1wu9z3WarVGKmOlwszJyYls2150NQAAwDeGdSb4vi/Hca4tg2EmAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBoSxdmarWaHMfR2tqaHMeR53k95/i+r1wuJ9d1VSgUVKvVFlBTAACwDJZqBeD9/X3V63UVCgWdnZ1pf39f6XRa9XpdqVRKkhQEgRzHUaPRiFfzTSaTajabyufzi6w+AABYgKXqmXnx4oXq9bry+bxKpZIajYYkqVQqxecUCgWlUqmObQmiHhoAALB6libMeJ7XEVokybZt2batIAgkSWEYyvM8pdPpjvO2t7clSdVqdT6VBQAAS2NpwkwqlZJlWX2PRe+fnp52/ByJemnq9foMawgAAJbRUs2Z6ScIgngIKeqhSSQSA88d5vz8XG/evBm7Luvr61pfXx/7egAAVsnFxYUuLi7Gvv78/Hyk85Y6zNRqNVmWFU/sPTs7kyRtbW31PT8Mw6HlvffeexPV58MPP9Te3t5EZQAAsCqKxaI++uijmd9nqcNMsVjU8fFx/HMymZQkNZvNvucPGqaKfPbZZ3r48OHY9aFXBgCA0T179kwffPDB2Ne/fPlypI6IpQ0zruvq4OCgI6BEvx7UA3NdmLlz547u3r07tToCAIDBJp2ecefOnZHOW5oJwFdVq1Wl0+mOx6+lb59a6p4bE/3sOM58KggAAJbG0oWZaDXfaJG8iO/7SiQSsm2756mlaJXgx48fz6eSAABgaSzVMJPneSoWiyoUCh1rxjQaDTmOI9u2dXBwIMdxFARBPKxUKpVUKpUGPuUEAABur6UJM77vx4vh9VvN9/Xr15Iu15RpNBpyXVeWZSkIArmuy1YGAACsqKUJM7Ztq91uj3zu1aecAADA6lq6OTMAAAA3QZgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABhtaRbNm4dMJqONjY2+x3Z2drSzszPnGgEAsNrK5bLK5XLfY61Wa6QyVirMnJyc9OzEDQAAFmdYZ4Lv+3Ic59oyGGYCAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMNpbi67APGUyGW1sbPQ9NmwLcgAAMBvlclnlcrnvsVarNVIZKxVmTk5OZNv2oqsBAAC+Mawzwfd9OY5zbRkMMwEAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAY7a1FV2CeMpmMNjY2+h4btgU5AACYjXK5rHK53PdYq9UaqYyVCjMnJyeybXvR1QAAAN8Y1png+74cx7m2DIaZAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABhtpXbNzmQy2tjY6Hts2K6dAABgNsrlssrlct9jrVZrpDJWKsycnJzItu1FVwMAAHxjWGeC7/tyHOfaMhhmAgAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGW6oVgMMwVLFYlCSVSqWe477vq1gsyrIshWGodDqtbDY772oCAIAlsjRhxvM8VSoV1Wo15fP5nuNBEMhxHDUajXhLgmQyqWaz2fd8AACwGpZmmCmVSun4+Hjg8UKhoFQq1bG3kuu6KhQK86geAABYUksTZoYJw1Ce5ymdTne8v729LUmqVquLqBYAAFgCSzPMNMzp6akkybKsjvejXpp6vT7SUNP5+bnevHkzdj3W19e1vr4+9vUAAKySi4sLXVxcjH39+fn5SOcZEWaCIJAkJRKJocev8957701Ujw8//FB7e3sTlQEAwKooFov66KOPZn4fI8LM2dmZJGlra6vv8TAMRyrns88+08OHD8euB70yAACM7tmzZ/rggw/Gvv7ly5cjdUQYEWaSyaQkqdls9j3ePfw0yJ07d3T37t2p1QsAAAw26fSMO3fujHSeEROAo7AyqAdm1DADAABuHyPCTPTUUvfcmOhnx3HmXicAALAcjAgziURCtm2rXq93vO95niTp8ePHi6gWAABYAksVZoZN5D04OJDneR29M6VSSaVSaeBTTgAA4PZbmgnAvu+rUqlIko6OjpROp5VKpeKgYtu2Go2GXNeVZVkKgkCu67KVAQAAK25pwoxt26pUKnGgGXTOsC0PAADA6lmqYSYAAICbIswAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABhtadaZmYdMJqONjY2+x3Z2drSzszPnGgEAsNrK5bLK5XLfY61Wa6QyVirMnJycyLbtRVcDAAB8Y1hngu/7I20mzTATAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNHeWnQF5imTyWhjY6PvsWFbkAMAgNkol8sql8t9j7VarZHKWKkwc3JyItu2F10NAADwjWGdCb7vy3Gca8tgmAkAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaG8tugLzlMlktLGx0ffYsC3IAQDAbJTLZZXL5b7HWq3WSGWsVJg5OTmRbduLrgYAAPjGsM4E3/flOM61ZTDMBAAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohJkJXFxc6FP/Y/3m/3296KrcahcXF9rb29PFxcWiq3Lr0dbzQTvPB+08H8vQzmvtdru9sLvPSbSF+Ntvv62NjY2+5wzbgnyQN2/e6N69e/pPP/ofWv/u78Xv7/30B5NUd+b29kZ7b1lE7fzVV1/p7t27i67OrUZbzwftPB+083xM2s7lclnlcrnvsVarpV/84hdqNBqybXtgGW/d+K4GOzk5GdoYq6w7zCxzuAEA3B7DOhOizojrrFSYwehM670BAKwuwswq+vzT3vf+5Q+uvYzeGwDAMmICMAAAMBo9M7j0+aedP4/RUzPuOQAATIIwg/4+/7T3vRECTu91I1wDAMAECDMY3eef3viSvR+PcE2fkLT3R3vf/vDry7ULikVpff3KOXsdl4w9aZm5QABgNsLMEiuXyzde+2YUf/Pz/6Y/eOffTr3cscv+/NOet66u1XPx9a/7XtYTOvqVs/eDnve6/c3flPUHf3BNO/91183+sPvmvWb1/2/WZc+Kie1BO8+n7KmV2/339NezW8RtpDp310fis2NGVmrRvOsW3bmpWS+a98477+jnP//5VMq66p9vvq2df/fx1MuVpPJ//fdTL/vi61/rzz751z3t3NOj8/mnvRePcE75v/9H7ez8fOg5I+m6V7n8Tme5U3R01Ptno6cXrF+P197wckdd/KqnN+uP9vqd1uGd/3A0kz/P0uz+rsyq3Kku5tb1hfnOD8v6+X/p+rIa4Qt0FEvfzl1t8ebXF7r3R39283YeIYSMVOd+5fQzTtljmkXZs1yccNTvbyN7ZnzfV7FYlGVZCsNQ6XRa2Wx20dXCvH3+6XTO+c0/jh9ght2rX7mjBLAR/OqX/zjaEF6X666JesGKO/+zMzh2G2WCeFeo/9Wvjq4d0us7VDhCUOpuj76/z+4v9FG/aLqN0Us3sMfgfxWl31sfv5xRjXNdv/q0ftVZVr9zxmmffkYpZ9Tf19V2Hlf3vbrbYpr6lT2lQHpbGRdmgiDoSWnJZFLNZlP5fH7BtQOu8fmnt+NeXWWP2xs5SiAbreyPr7/mp933ur7cX/3y4z517Lqup9xeez++9pQRw+kPrj1D6r8s/EyM8mU+yjmjBINZBYdlME47zjJIGsi4MFMoFJRKpTq6m1zXVaFQIMwAy2xaPWAG6g5XcQ/Yf/5Xw3vAxvCr8PemM9Q9Skgb4T6jhLR+dR6n53GQq+08zXIXalpB8pYwKsyEYSjP81QqlTre397eliRVq1UCDQDMyDhBaZyetdGvG67fwwPTmtP4q/DjmQawmRmlF8zAHh2jwszp6akkybKsjvejXpp6vU6YAQAsxK0JSgYOVxkVZoIgkCQlEomhx7u1Wi1JlxOHz8/Px77/d7/7XX33u9+Nf47K+j9f/m99959txO/7/nRmc7daLfm+P5WyrvrNby70yy/+furlzqrsr//v5f+/7naellm1h2ntLM22rU1sD9p5PmXTzteXXSj9i6mU/csv3rpxWYV/Uxh6/Lz1tSTp5cuXunPnTsexr7/+Wl9//fXNKnnF3//9ZTtE3+MDtQ2yu7vbltRuNBo9xyS1Lcvqe90nn3zSlsSLFy9evHjxMvD1ySefDM0HRvXMJJNJSVKz2ex7vHv4KfL+++/r448/1u///u/rd3/3d8e+f3fPDAAAGGzSnpl/+qd/0i9/+Uu9//77Q88zKsxEYSUMw6HHu33ve9/TH//xH8+qWgAAYIG+s+gK3ET01FL33JjoZ8dx5l4nAACwWEaFmUQiIdu2Va/XO973PE+S9Pjx40VUCwAALJBxezNF+zScnZ3Fw0rJZFKFQkG7u7tTvc84Wyaw1cLNjNtetVpNxWJRvu/Ltm2VSiWlUqk51Nhc0/iz6XmecrmcXr9+PaNamm8a7RwEgWq1miQpn88PfIJzlU3y2VGv15VIJBQEgSzL6lm7DJfCMFSxWJSkkdtoYd+BU3jIaO4ajUY7m822d3d329lstl2pVKZa/tnZWVvqfGrKsqxr7zPudatq3PYqlUrtVCrVrlQq8RNuktr1en3WVTbWtP5sWpbVTiQS067erTFpO5+dnbWz2Ww7lUq1z87OZlVN443bzsfHx23btjveS6VS7d3d3ZnU02T1er2dzWbbktr5fH6kaxb5HWhkmJm1VCrVTqVSHe9VKpX2ddlv3OtW1bjtlc1mO35uNBptST1l4VvT+LO5u7vbTqVShJkhJmnnRqPRTiQSI39xrLJJPqO727dUKg1c1gPtG4WZRX4HGjVnZh6iLRPS6XTH+1e3TJjmdatq3Pbqt52FbduybXvgoomrbhp/Nj3P0/379zv2REOnSdo5DEM9evRIlmWpUqnMtJ6mm6Sdm81mPMcycnXKAsa36O9AwkyXUbZMmOZ1q2rc9kqlUgM/ePhA6m8afzYrlcpU56TdRpO0s+u6CsOQuRsjmKSdC4WCgiBQLpeTdDm/4+joiHafgkV/BxJmuoy7ZcK4162qabfX1Q8odJq0rV3X5cN+BJO0c/Sv1nq9LsdxtLm5qXQ6zedGH5O0cz6fVz6fV61WUzKZlOu6evXqFT2OU7Do70DCTJezszNJ0tbWVt/jgxbsG/e6VTXN9qrVarIsi01GB5ikrX3f1/379+n1GsG47Rztv2bbtgqFghqNhhqNhoIgUDKZ5LOjy6SfHZVKJR6W9jyvZ9gJ41n0dyBhpsu4WyaMe92qmmZ7FYtFHR8fT6Vet9EkbV0sFhleGtG47Rz9i7VQKMTnXJ07Ez0ai0uTfnak02kVCoX48excLhc/Bo/xLfo70KjtDOZh3C0Txr1uVU2rvVzX1cHBAe07xLht7bpuz1BH9Ovov7T7t8Zt50Hd8tG6SQw1dZrks6NQuNz9OerFffXqlR48eKCnT5+yHtiEFv0dSM9Ml3G3TGCrhZuZRntVq1Wl02nGu68xblt7nqdCoaBkMhm/arWawjBUMplkjlKXST87om76boO67VfVJJ8dR0dHHZ8XiURCpVJJYRjGw30Yz6K/AwkzXcbdMoGtFm5m0vaKuoW7V/3lA6nXuG3daDTUvlyLKn7t7u4qkUio3W6r0WjMvO4mmeSzI5VK9czdiP6Fyz+EOk3y2bG1tdXTcxB9hrDK8mQW/h0485VsDBQtwnZ1BU7LstqlUin++ezsrG1ZVseqs6Nch2+N2871er1t23a7Uql0vPL5PKstDzBuW3fb3d1l0bwhJv3suPpeqVTqWa0Wl8Zt51Kp1E4kEu3Xr193vEc79/f69euBi+Yt23cgc2b6sG1bjUZDruvKsiwFQSDXdTuelgnDUM1msyPlj3IdvjVOO/u+Hy/KFI1/X8WeQf2N+2caNzONz47j42MlEgmFYUjv1wDjtnPUs5jL5eLhpjAM9fz583n/Fpae7/vxJPSjoyOl02mlUqm4B2vZvgON22gSAADgKubMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAAATW+SmqIQZAAAwsVwut7AVxAkzAADcYvv7+9rc3NTa2prW1taUTqfjVzKZjN+fhO/7siyrZ8POedxbktibCQCAW2x3d1dnZ2eqVqva3d1VqVTqOB4EQbzn3bgqlUrf/fLmcW+JnhkAAG6909NTSeobHCzLUiqVmqh8z/MGljHre0tsNAkAwK0XDeUM+soPw7BniGhUtVpN9Xo93mV7nveO0DMDAMAt5nmeJPX0gNRqtfjXk4SJw8PDvkNM87h3hDADAMAtdnx8LKlzmCcMQx0eHk5cdhiGCoJAtm3P/d5XEWYAALjFot6Rw8NDOY6jZDKpzc1NvfvuuxOXfXR0pCdPnizk3lfxNBMAALdU1HOSSCTUaDTi9x49ejSVibeVSkXPnz+f2r0LhYKSyaS+/PJLvfvuu8pmsyPVgzADAMAtdXR0JKlzzkoikVAqlRo4NDSqIAi0tbU1cM7LTe+dy+VkWZZ2d3clSY7jxOdfh2EmAABuqXq9Lqn3sehnz55NXPagtWXGuXcQBKrVah3lPXnypGddmkEIMwAA3FLRnJXHjx93vH+1NyU6R7oMFclkUo7jxO+FYSjHcTqeQJIun0gaNgx0k3v7vi/pct2ZiG3b8jxvpC0SCDMAANxCQRAoDMO+2wxEqtVqxwaRruuqVCopDMM4aBSLRYVh2BFchi2SN869X7x40XPe1taWJKnZbF73W2XODAAAt1HUk3K1tyMShqFc11W1WtXr16/j9588eaJsNhuHkTAMtb+/Hw8ZRSqVytChqpveOwzDOLx0C4KgbzlXEWYAALhl9vf35bqupMteFMdxtLW1pWazGT9lJEnZbLajRyTqfbFtW0EQqFgsKpvN9vTC+L4/cALxOPdOJpPxhOFu1wUZie0MAABAF9/3dXh4qFqtpkaj0RF4qtWqwjCMnzqahlqtplwu17Hlged5SqfTA7dBuIqeGQAA0CGRSGh/f1/Hx8c9c1mGrS0zrqiX5+qQ0rDen25MAAYAAB3CMFQqlep5Wum6tWXGZVmWstlsxxNTh4eHIz+azTATAADo4Lqu7t+/3zOU5LrujVbmvamrKwAnk0nl8/mRriPMAACAWBiG2tzcVKVS6QkTuVwu3jxymTDMBAAAYtVqVVL/p4iWMchIhBkAAHDF2dmZLMuaykaU88IwEwAA6BCG4dQn+c4SYQYAABiNYSYAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGj/HyNhBgmm/u/dAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -280,48 +573,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "found:  4943 , lost:  1757\n",
-      "0.3554521545620069\n"
+      "found:  32898 , lost:  11265\n",
+      "0.34242203173445196\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<pre>[-26.5,\n",
-       " 19.5,\n",
-       " 34.7,\n",
-       " 36.4,\n",
-       " 532,\n",
-       " 95.5,\n",
-       " 3.25,\n",
-       " 4.11,\n",
-       " -3.04,\n",
-       " -112,\n",
-       " ...,\n",
-       " 3.61,\n",
-       " 8.6,\n",
-       " 54.6,\n",
-       " 26.1,\n",
+       "<pre>[-3.61,\n",
+       " -33.8,\n",
+       " -133,\n",
+       " 65.2,\n",
+       " -5.73,\n",
+       " -26.6,\n",
+       " -4.26,\n",
+       " 6.83,\n",
+       " 10.7,\n",
        " 26.2,\n",
-       " 23.6,\n",
-       " 31.5,\n",
-       " -67.7,\n",
-       " 25]\n",
-       "--------------------\n",
-       "type: 4943 * float64</pre>"
+       " ...,\n",
+       " -11.6,\n",
+       " -13.1,\n",
+       " -25.6,\n",
+       " -4.27,\n",
+       " -4.27,\n",
+       " -103,\n",
+       " 8.82,\n",
+       " 12.8,\n",
+       " -17.8]\n",
+       "---------------------\n",
+       "type: 32898 * float64</pre>"
       ],
       "text/plain": [
-       "<Array [-26.5, 19.5, 34.7, 36.4, ..., 31.5, -67.7, 25] type='4943 * float64'>"
+       "<Array [-3.61, -33.8, -133, ..., 8.82, 12.8, -17.8] type='32898 * float64'>"
       ]
      },
-     "execution_count": 181,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -346,12 +639,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x800 with 3 Axes>"
       ]
@@ -408,14 +701,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "13313\n"
+      "50501\n"
      ]
     }
    ],
@@ -472,22 +765,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<pre>{lost: False,\n",
-       " energy: 9.36e+03,\n",
-       " velo_length: 3,\n",
-       " velo: 6.51e+03,\n",
+       " energy: 5.09e+04,\n",
+       " velo_length: 0,\n",
+       " velo: 0,\n",
        " rich_length: 0,\n",
        " rich: 0,\n",
        " neither_length: 0,\n",
        " downstream: 0,\n",
-       " photon_length: 3,\n",
-       " quality: 1}\n",
+       " photon_length: 0,\n",
+       " quality: 0}\n",
        "--------------------------\n",
        "type: {\n",
        "    lost: bool,\n",
@@ -503,10 +796,10 @@
        "}</pre>"
       ],
       "text/plain": [
-       "<Record {lost: False, energy: 9.36e+03, ...} type='{lost: bool, energy: flo...'>"
+       "<Record {lost: False, energy: 5.09e+04, ...} type='{lost: bool, energy: flo...'>"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -517,26 +810,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "found:  10923\n",
-      "lost:  2390\n",
-      "13313\n",
-      "VELO energy emission, eff:  0.1835048448884549\n",
-      "RICH1+UT energy emission, eff:  0.1139487718771126\n",
-      "Neither, eff:  0.5230226094794562\n",
-      "total efficiency:  0.8204762262450237\n",
-      "efficiency:  0.8204762262450237\n",
+      "found:  40402\n",
+      "lost:  10099\n",
+      "50501\n",
+      "VELO energy emission, eff:  0.2624700500980179\n",
+      "RICH1+UT energy emission, eff:  0.17696679273677748\n",
+      "Neither, eff:  0.3605869190709095\n",
+      "total efficiency:  0.8000237619057049\n",
+      "efficiency:  0.8000237619057048\n",
       "\n",
       "found in velo/(found + lost in velo)\n",
-      "VELO energy emission, eff:  0.8151484818151484\n",
-      "RICH1+UT energy emission, eff:  0.737481769567331\n",
-      "eff von e die nicht strahlen:  0.8430802760624773\n"
+      "VELO energy emission, eff:  0.807739183424741\n",
+      "RICH1+UT energy emission, eff:  0.7549417131272175\n",
+      "eff von e die nicht strahlen:  0.8183166314654204\n"
      ]
     }
    ],
@@ -615,16 +908,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10923\n",
-      "2390\n",
-      "13313\n"
+      "40402\n",
+      "10099\n",
+      "50501\n"
      ]
     }
    ],
@@ -637,12 +930,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -670,12 +963,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -710,7 +1003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -719,7 +1012,7 @@
        "' \\nphoton cut = x*E_0\\neffs, all photons included: x=0\\nfound in velo/(found + lost in velo)\\nVELO energy emission, eff:  0.8446167611094543\\nRICH1+UT energy emission, eff:  0.7961586121437423\\neff von e die nicht strahlen:  0.7954674220963173\\n'"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }