diff --git a/.vscode/settings.json b/.vscode/settings.json
index 6ee9c4a..7336d7e 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -7,4 +7,5 @@
 	"notebook.formatOnSave.enabled": true,
 	"editor.defaultFormatter": "ms-python.black-formatter",
 	"editor.formatOnSave": true,
+	"python.formatting.provider": "none"
 }
\ No newline at end of file
diff --git a/trackinglosses_energy.ipynb b/trackinglosses_energy.ipynb
index f3d727f..39d9320 100644
--- a/trackinglosses_energy.ipynb
+++ b/trackinglosses_energy.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -195,7 +195,7 @@
        "<Record {all_endvtx_types_length: 7, ...} type='{all_endvtx_types_length: i...'>"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -234,7 +234,7 @@
        "<Record {lost: True, rad_length_frac: ..., ...} type='{lost: bool, rad_leng...'>"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -349,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -387,7 +387,7 @@
        "<Record {event_id: 0, lost: True, ...} type='{event_id: int64, lost: bool, ...'>"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -407,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -497,7 +497,7 @@
        "<Record {event_id: 1, lost: False, ...} type='{event_id: int64, lost: bool,...'>"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -513,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -527,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -573,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -614,7 +614,7 @@
        "<Array [-3.61, -33.8, -133, ..., 8.82, 12.8, -17.8] type='32898 * float64'>"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -639,12 +639,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 2000x800 with 3 Axes>"
       ]
@@ -694,6 +694,7 @@
     "ax1.set_title(r\"$e^\\pm$ lost brem vertices\")\n",
     "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
     "\n",
+    "plt.suptitle(\"brem vtx of photons w/ $E>0.1E_0$\")\n",
     "plt.colorbar(a0[3], ax=ax1)\n",
     "\n",
     "plt.show()"
@@ -701,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -765,7 +766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -799,7 +800,7 @@
        "<Record {lost: False, energy: 5.09e+04, ...} type='{lost: bool, energy: flo...'>"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -810,7 +811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -908,7 +909,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -930,12 +931,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -954,8 +955,8 @@
     "plt.hist(diff_lost,bins=100,density=True,alpha=0.5,histtype=\"bar\",color=\"darkorange\",label=\"lost\",range=[-xlim,xlim])\n",
     "plt.hist(diff_found,bins=100,density=True,alpha=0.5,histtype=\"bar\",color=\"blue\",label=\"found\", range=[-xlim,xlim])\n",
     "plt.xlim(-20000,20000)\n",
-    "plt.title(\"emitted energy difference velo - rich\")\n",
-    "plt.xlabel(r\"$E_{velo} - E_{rich}$\")\n",
+    "plt.title(\"emitted energy difference\")\n",
+    "plt.xlabel(r\"$E_{velo} - E_{rich}$ [MeV]\")\n",
     "plt.ylabel(\"a.u.\")\n",
     "plt.legend()\n",
     "plt.show()"
@@ -963,7 +964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1003,7 +1004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1012,7 +1013,7 @@
        "' \\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": 84,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/trackinglosses_photons.ipynb b/trackinglosses_photons.ipynb
new file mode 100644
index 0000000..a0ac189
--- /dev/null
+++ b/trackinglosses_photons.ipynb
@@ -0,0 +1,366 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uproot\t\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits import mplot3d\n",
+    "import awkward as ak\n",
+    "from scipy.optimize import curve_fit\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = uproot.open(\n",
+    "    \"trackinglosses_B_photon_cuts.root\"\n",
+    ")\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())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>{oneCut_event_id: 1,\n",
+       " oneCut_lost: False,\n",
+       " oneCut_rad_length_frac: 0.148,\n",
+       " oneCut_energy: 1.28e+04,\n",
+       " noneCut_brem_photons_pe: 1,\n",
+       " oneCut_brem_photons_pe: [7.42e+03],\n",
+       " noneCut_brem_vtx_x: 1,\n",
+       " oneCut_brem_vtx_x: [-3.61],\n",
+       " noneCut_brem_vtx_z: 1,\n",
+       " oneCut_brem_vtx_z: [35.6],\n",
+       " oneCut_photon_length: 1}\n",
+       "------------------------------------------\n",
+       "type: {\n",
+       "    oneCut_event_id: int64,\n",
+       "    oneCut_lost: bool,\n",
+       "    oneCut_rad_length_frac: float64,\n",
+       "    oneCut_energy: float64,\n",
+       "    noneCut_brem_photons_pe: int32,\n",
+       "    oneCut_brem_photons_pe: var * float64,\n",
+       "    noneCut_brem_vtx_x: int32,\n",
+       "    oneCut_brem_vtx_x: var * float64,\n",
+       "    noneCut_brem_vtx_z: int32,\n",
+       "    oneCut_brem_vtx_z: var * float64,\n",
+       "    oneCut_photon_length: int64\n",
+       "}</pre>"
+      ],
+      "text/plain": [
+       "<Record {oneCut_event_id: 1, ...} type='{oneCut_event_id: int64, oneCut_los...'>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "allcolumns[1][1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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",
+    "    basedict[\"0\"] = \"no\"\n",
+    "    basedict[\"1\"] = \"one\"\n",
+    "    basedict[\"2\"] = \"two\"\n",
+    "    basedict[\"3\"] = \"three\"\n",
+    "    basedict[\"4\"] = \"four\"\n",
+    "    basedict[\"5\"] = \"five\"\n",
+    "    basedict[\"6\"] = \"six\"\n",
+    "    basedict[\"7\"] = \"seven\"\n",
+    "    basedict[\"8\"] = \"eight\"\n",
+    "    basedict[\"9\"] = \"nine\"\n",
+    "    basedict[\"10\"] = \"ten\"\n",
+    "    \n",
+    "    return basedict\n",
+    "\n",
+    "Cuts = cutdict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "electrons = []\n",
+    "for jcut in range(11):\n",
+    "\tenergy_emissions = ak.ArrayBuilder()\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",
+    "\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",
+    "\n",
+    "\t\tenergy_emissions.field(\"velo_length\").integer(tmp_velo_length)\n",
+    "\t\tenergy_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",
+    "\n",
+    "\t\tenergy_emissions.end_record()\n",
+    "\n",
+    "\tenergy_emissions = ak.Array(energy_emissions)\n",
+    "\telectrons.append(energy_emissions)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x600 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x600 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x600 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nbins = 6\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",
+    "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",
+    "plt.suptitle(\"number of photons in velo and rich\")\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "### found\n",
+    "\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",
+    "plt.suptitle(\"number of photons of found electrons\")\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "### lost \n",
+    "\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",
+    "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": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x600 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "quality_cut = electrons[jcut].quality != -1\n",
+    "### all split in lost and found\n",
+    "\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",
+    "plt.suptitle(\"number of photons in lost and found\")\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "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": []
+  }
+ ],
+ "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/trackinglosses_rad_length.ipynb b/trackinglosses_rad_length.ipynb
index c08e3ed..565381f 100644
--- a/trackinglosses_rad_length.ipynb
+++ b/trackinglosses_rad_length.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,9 +17,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "40402 10099\n",
+      "50501\n"
+     ]
+    }
+   ],
    "source": [
     "file = uproot.open(\n",
     "    \"tracking_losses_ntuple_B_default.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
@@ -43,58 +52,114 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stretch factor:  0.24996287312509283\n"
+     ]
+    }
+   ],
    "source": [
-    "lost_e = electrons[\"lost\"]\n",
-    "e = electrons[\"energy\"]\n",
-    "brem_pe = electrons[\"brem_photons_pe\"]\n",
-    "brem_z = electrons[\"brem_vtx_z\"]\n",
-    "brem_x = electrons[\"brem_vtx_x\"]\n",
-    "length = electrons[\"brem_vtx_z_length\"]\n",
-    "\n",
-    "\n",
-    "\n",
-    "brem = ak.ArrayBuilder()\n",
-    "\n",
-    "for itr in range(ak.num(electrons, axis=0)):\n",
-    "    brem.begin_record()\n",
-    "    brem.field(\"lost\").boolean(lost_e[itr])\n",
-    "    # [:,\"energy\"] energy\n",
-    "    brem.field(\"energy\").append(e[itr])\n",
-    "    # [:,\"photon_length\"] number of vertices\n",
-    "    brem.field(\"photon_length\").integer(length[itr])\n",
-    "    # [:,\"brem_photons_pe\",:] photon energy\n",
-    "    brem.field(\"brem_photons_pe\").append(brem_pe[itr])\n",
-    "    # [:,\"brem_vtx_z\",:] brem vtx z\n",
-    "    brem.field(\"brem_vtx_x\").append(brem_x[itr])\n",
-    "    brem.field(\"brem_vtx_z\").append(brem_z[itr])\n",
-    "    brem.end_record()\n",
+    "rad_length_found = ak.to_numpy(found[\"rad_length_frac\"])\n",
+    "eta_found = ak.to_numpy(found[\"eta\"])\n",
+    "rad_length_lost = ak.to_numpy(lost[\"rad_length_frac\"])\n",
+    "eta_lost = ak.to_numpy(lost[\"eta\"])\n",
     "\n",
-    "brem = ak.Array(brem)"
+    "stretch_factor = ak.num(eta_lost,axis=0)/ak.num(eta_found,axis=0)\n",
+    "print(\"stretch factor: \", stretch_factor)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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On+KybVulUokxPSkRfgAAWACjhnrEPSDxMlKmaer169eqVCoKw1DlcjkZ8IzxEX4AAJijuLvr9Hge6X0gKpVKkt61ABmGoW63m0z8G3eNYXyEHwAA5sg0TVmWpTAMBybzlaSXL1/KMIxkYPPJ9S03NjaSsUCn9+OBofMVKvysr6/rj//4j0f+7O7uzrt6AICCisfynGzFiaJIjuPoyZMnSffX3t7eQNCJokimaSaPtcctRK7rKgxDeZ43uw+R0u7u7pn35vX19UzKvNbv9/uZHHmBBEGgcrmsXq83k6nFr5pRa7myvisAXE4QBHJdNxmj4ziO6vV6EmyiKNLm5mYSaKR3XVon71vxUk8bGxuS3rX4nAxHklQulxWGoe7du5e0DOVVVvfvhZrkEACAqyqe0uWsQGIYxoULeHe73QvLYdWDixWq2wsAAICWHwygOwsAcNXR8gMAAAqF8AMAAAqF8AMAAAqF8AMAAAqF8AMAAAqF8AMAAAqF8AMAAAqlUPP8rK+v6/r16yNf29ra0tbW1oxrBABAse3u7p65vubbt28zKbNQ4efp06es7QUAwAI5r/EhXttr2uj2AgAAhUL4AQAAhVKobi8AwOLJw5qCeagjxkfLDwAAGBIEgdrt9ryrkQnCDwAAMxYEgWzbVq1WU6lU0s7OzryrlAjDULVaTeVyWa7rzrs6maDbCwCAGQqCQHfv3tXh4aEkybZt7e/vz7lW75mmqU6no2vXrs27Kpkh/AAAMEOtVkurq6vJnx3HmWNtioluLwAAZigIgnlXofAIPwAAzEC73VatVlMYhsm4mlqtJt/3k/dEUaRGoyHbtlWtVlWtVgde9zxPKysrunbtWhKifN9XrVbTtWvXVKvVkuO0222Vy2V5niff91Uulwfec1JcbvyzSGOQskD4AQBgBur1ujqdjkzTTMbVdDodVSoVSe9ahG7duqVGoyHHcdTtdlWr1VStVpMwsrGxoXv37g0ct1KpDHWdHRwcqNvtKggCua6rbrerJ0+eqF6vy/O8gXAThqFu3bqlWq0m13Wv7CDnkwo15qfIa3uNmqOCeSsAYHFsbm5qbW1tYBmmer0u13Vl27Y2NjZkmqYMwxja9+QYIundoOX79+/L8zxVq1U1m01Jkuu6arfb6na7yTbbtrW2tpaEMElqNpuybTuDTzmMtb0yxtpeAIBFFIahgiBIAslJcVeU67qpBkePCksHBwdJuZ7nzXXQNWt7AQBQQOcNgl5bW5P0LqhMW3xM0zSnfuxFRvgBAGBBRFE0tC1uuTndtTUNcfiJW4KKgvADAMCcxUMyTj7ZFYsDUalUmnq5cYtPr9eb+rEXGeEHAIAZOjg4GGppMU1TlmUlj8Gf9PLlSxmGoXq9Lkm6efOmpMFusPj/R7UcnSfuUmu32yP3vezx8oLwAwDAAuh0OjIMQ41GI9kWRZEcx9GTJ0+S7q+4lci2bfm+r3a7nTye7vu+qtWqpPG6sgzDSAZZl8tl+b6vMAyTJ73CMLySc/4s1NNeURSp1WpJGj3dt+d5arVaCoJAlmXJcZyBR/NwOTzqDmARFOW7KJ5zJ25NaTQaqtVqyX3MNE29fv1am5ubqlarSZdUp9MZeFI5nten1WqpVqslj8P7vq+NjQ3dv38/KUt6dz81TVNra2tJqAmCQDs7O2o2m3IcR6VSSY7jqFqtyrIsdTodeZ6njY0NbWxszPAszca1fr/fn3clpHdp1XVdeZ6XXMiTdnZ2kgmf9vf3kyTa7XYvDEDxo3K9Xq+wj7pP+8ulKF9WAID5yer+vTAtP5VKRZVK5cxVZF+8eKFut5v8+f79+yqXy7T+AACAS8nFmB/f94e6wSzLSgaHAQAAjGthWn7Oc17LzmUmZjo6OtKbN29S12NpaUlLS0up9wcAoEiOj491fHycev+jo6Mp1ua9XISfs4RhODAq/iJ37tyZqLxHjx5pm8EuAACMpdVq6fHjx/OuxpDchh/P82SaZjLvwTieP3+u27dvpy6TVh8AAMb38OFDPXjwIPX+r169mrjhYpTchp9Wq6VOp3OpfZaXl3Xjxo2MagQAAE6adLjI8vLyFGvzXi4GPJ9m27aePHlSuIXYAADA5HIXftrtdjIJEwAAwGXlKvx4nidp+OmvIAjmUR0AAJBDCzXm57wF1HzfV6vVUqPRULvdTrb3ej2Vy2VaggAAwFgWJvycXIdkb29P1WpVlUpFhmEoCIJkobZRj7YfHh7OtK4AACC/Fib8WJYl13WH1vSKX1uQJcgAAEDO5WrMDwAAwKQIPwAAoFAIPwAAoFAWZszPLKyvr+v69esjX9va2tLW1taMawQAQLHt7u5qd3d35Gtv377NpMxChZ+nT5/ySDwAAAvkvMaHIAhULpenXibdXgAAoFAIPwAAoFAIPwAAoFAIPwAAoFAKNeAZ07O9Pd42AAAWDS0/AACgUAg/AACgUAg/AACgUAg/AACgUAg/AACgUAg/AACgUAr1qDsLmwIAsFhY2DRjLGwKAMBiYWFTAACAjBF+AABAoRB+AABAoRB+AABAoRB+AABAoRB+AABAoRB+AABAoRB+AABAoRB+AABAoRB+AABAoRRqeQvW9gIAYLGwtlfGWNsLAIDFwtpeAAAAGStUyw9S+vlnw9v+5UczrgQAANNByw8AACgUwg8AACiUher2iqJIrVZLkuQ4ztDrQRCo1WrJNE1FUaRqtaqNjY1ZVxMAAOTYwoQf3/fluq48z1O9Xh96PQxDlctl9Xq95ImtUqmkg4ODke8HAAAYZWG6vSqVijqdzpmvNxoNVSqVgUfVbdtWo9GYRfUAAMAVsTDh5zxRFMn3fVWr1YHta2trkqR2uz2PagEAgBzKRfh5+fKlJMk0zYHtcStQt9udeZ0AAEA+LcyYn/OEYShJMgzj3NcvcnR0pDdv3qSux9LSkpaWllLvDwBAkRwfH+v4+Dj1/kdHR1OszXu5CD/7+/uSpNXV1ZGvR1E01nHu3LkzUT0ePXqk7e3tiY4BAEBRtFotPX78eN7VGJKL8FMqlSRJBwcHI18/3R12lufPn+v27dup60GrDwAA43v48KEePHiQev9Xr15N3HAxSi7CTxxuzmrhGTf8LC8v68aNG9OqFgAAOMekw0WWl5enWJv3cjHgOX6q6/TYnvjPWaz4CgAArqZchB/DMGRZ1tBTXb7vS5Lu3bs3j2oBAIAcWqjwc97A5SdPnsj3/YHWH8dx5DjOmU+BAQAAnLYwY36CIJDrupKkvb09VatVVSqVJNhYlqVeryfbtmWapsIwlG3bLG0BAAAuZWHCj2VZcl03CUBnvee8JTAAAAAuslDdXgAAAFkj/AAAgEIh/AAAgEIh/AAAgEJZmAHPs7C+vq7r16+PfG1ra0tbW1szrhEAAMW2u7ur3d3dka+9ffs2kzILFX6ePn0qy7LmXQ0AAPB75zU+BEGQySoOdHsBAIBCKVTLD075+WfD2/7lRzOuBAAAs0XLDwAAKBTCDwAAKBTCDwAAKBTCDwAAKBTCDwAAKBTCDwAAKBTCDwAAKBTCDwAAKJRCTXLI2l5j+Pln864BAKBAWNsrY6ztBQDAYmFtLwAAgIwRfgAAQKEQfgAAQKEQfgAAQKEUasAzpujnn43Y+NFs6wAAQAq0/AAAgEIh/AAAgEIh/AAAgEIh/AAAgEJhwHNR/PyzedcAAICFUKjww9peAAAsFtb2yhhrewEAsFhY2wsAACBjhB8AAFAohB8AAFAohB8AAFAohB8AAFAouXvay/M8dbtdGYahMAxlmqYcx5l3tQAAQE7kKvx4nqdWq6Ver5dsq1arsm2bAAQAAMaSq24v13W1trY2sK1arcrzvDnVCAAA5E2uWn4ODg4UhuHAtv39fZmmOacaAQCAvMlV+Gk0Gmo0GqrVaup0OgqCQHt7e3r27NlY+x8dHenNmzepy19aWtLS0lLq/QEAKJLj42MdHx+n3v/o6GiKtXkvV+GnXq+r1+up3W6rVCrJNE29fv1ahmGMtf+dO3cmKv/Ro0fa3t6e6BgAABRFq9XS48eP512NIbkKP9K7cT8vX75UEAQKw1C+72tjY2OsfZ8/f67bt2+nLptWHwAAxvfw4UM9ePAg9f6vXr2auOFilNyFn2q1qkajIdM0VavVki6wcQLQ8vKybty4MYNaAgCASYeLLC8vT7E27+Xqaa9GoyHpXfdXpVJJurw2NzfnXDMAAJAXuQo/e3t7siwr+bNhGHIcR1EUKQiCOdYMAADkRa7Cz+rqqqIoGthWqVQkaexBzwAAoNhyFX4ajYb29vYGApDnebIsi7l+AADAWHI14LnZbMowDNVqtaT7K4qisef5AQAAyFX4kd4Ndq7X6/OuBgAAyKlcdXsBAABMivADAAAKhfADAAAKJXdjfiaxvr6u69evj3xta2tLW1tbM64RAADFtru7q93d3ZGvvX37NpMyCxV+nj59OjBJIgAAmK/zGh+CIFC5XJ56mXR7AQCAQiH8AACAQiH8AACAQiH8AACAQiH8AACAQiH8AACAQiH8AACAQiH8AACAQiH8AACAQiH8AACAQiH8AACAQinU2l4sbAoAwGJhYdOMsbApAACLhYVNAQAAMkb4AQAAhUL4AQAAhUL4AQAAhUL4AQAAhUL4AQAAhUL4AQAAhUL4AQAAhUL4AQAAhUL4AQAAhVKo5S1Y2wsAgMXC2l4ZY20vAAAWC2t7AQAAZIzwAwAACoXwAwAACoXwAwAACiX3A57DMJTneZKker0uwzDmWyEAALDQcht+wjCUbduKokiu68o0zXlXCQAA5EAuu73iR99WV1fV7XYJPgAAYGy5Cz9RFOnu3bsyTVOu6867OgAAIGdyF37iri7HceZdFQAAkEO5G/PTbrclSd1uV7ZtKwxDra2tjTXu5+joSG/evEld9tLSkpaWllLvDwBAkRwfH+v4+Dj1/kdHR1OszXu5Cj9BEEiSLMtSo9GQ4zgKw1DValWlUkmHh4fnPu11586dicp/9OiRtre3JzoGAABF0Wq19Pjx43lXY0iuwk8YhpKkRqORtPLEY3+q1aparda53WHPnz/X7du3U5dPqw8AAON7+PChHjx4kHr/V69eTdxwMUquws9ZrTqVSkXS+3B0luXlZd24cWPa1QIAACNMOlxkeXl5irV5L1cDntfW1iRJ+/v7I19fXV2dZXUAAEAO5Sr8GIahSqUi3/cHtkdRJEmZLHsPAACullyFH0lyHEdBEAwEoHa7LcuyVK/X51gzAACQB7ka8yO9e9Kr1+vJtm11Oh0ZhqEoitTr9eZdNQAAkAO5Cz/SuwDU7XbnXQ0AAJBDuev2AgAAmAThBwAAFArhBwAAFArhBwAAFEouBzyntb6+ruvXr498bWtrS1tbWzOuEQAAxba7u6vd3d2Rr719+zaTMgsVfp4+fSrLsuZdDQAA8HvnNT4EQZDJBMZ0ewEAgEIh/AAAgEIh/AAAgEIh/AAAgEIh/AAAgEIh/AAAgEIh/AAAgEIh/AAAgEIh/AAAgEIh/AAAgEIp1PIWrO0FAMBiYW2vjLG2FwAAi2Uea3sVKvzkyt9vD2/71ohtAADgUgg/mJrt7fG2AQAwTwx4BgAAhUL4AQAAhUK31xU0t66mn382YuNHs60DAAAXoOUHAAAUCuEHAAAUCuEHAAAUCuEHAAAUCuEHAAAUSqGe9mJtLwAAFgtre2WMtb0AAFgs81jbi24vAABQKIQfAABQKIXq9sIVwGr3AIAJEX6uop9/Nu8aAACwsHLf7eX7vlZWVuZdDQAAkBO5Dz+NRmPeVQAAADmS6/Bj27ZM05x3NQAAQI7kNvz4vq+bN28ybw8AALiU3IYf13XVbDbnXQ0AAJAzuXzay7ZtOY5z6f2Ojo705s2b1OUuLS1paWkp9f4AABTJ8fGxjo+PU+9/dHQ0xdq8l7vwEwSBbt68mWqsz507dyYq+9GjR9re3p7oGAAAFEWr1dLjx4/nXY0huQs/rVZLnU4n1b7Pnz/X7du3U5dNqw8AAON7+PChHjx4kHr/V69eTdxwMUquwo9t26pWqwrDMNkW/3/83/NahJaXl3Xjxo1sKwkAACRNPlxkeXl5irV5L1fhx/d97ezsjHytVCrJsiz1er0Z1woAAORJrp726vV66vf7Az/NZlOGYajf7xN8AADAhXIVfgAAACZF+AEAAIWS+/DjOI4ODw/nXQ0AAJATuQ8/AAAAl0H4AQAAhUL4AQAAhUL4AQAAhZKrSQ4ntb6+ruvXr498bWtrS1tbWzOuEQAAxba7u6vd3d2Rr719+zaTMgsVfp4+fSrLsuZdDUxg+y8/Gt72rdnXAwAwHec1PgRBoHK5PPUy6fYCAACFQvgBAACFQvgBAACFUqgxP7n399vD2741YhsAADgTLT8AAKBQCD8AAKBQCD8AAKBQCD8AAKBQGPCMxTVqgLc+mnElAABXDS0/AACgUArV8sPaXgAALBbW9soYa3sBALBYWNsLAAAgY4QfAABQKIQfAABQKIQfAABQKIQfAABQKIQfAABQKIQfAABQKIQfAABQKIQfAABQKIQfAABQKIVa3gKzt7093rbRK7gDADB9hQo/C7uwKTd+AEBBsbBpxljYFACAxTKPhU0LFX4wBz//bMTGj6ZbxqiWs2+N2AYAgBjwDAAACoaWHyys7b/8aN5VAABcQbT8AACAQsll+PE8T+VyWdeuXVO5XJbv+/OuEgAAyInchZ+dnR25rqtGo6Fms6kgCFStVglAAABgLLkb8/PixQt1u93kz/fv31e5XJbjOKpUKnOs2XSNGu+y/fFns64GAABXTq7Cj+/7chxnYJtlWbIsS2EYzqlWmAYGNwMAZiVX4ee8lh3TNGdYEwAAkFe5Cj9nCcNQjUbjwvcdHR3pzZs3qctZWlrS0tJS6v0BACiS4+NjHR8fp97/6OhoirV5L/fhx/M8maaper1+4Xvv3LkzUVmPHj3S9shVOQEAwGmtVkuPHz+edzWG5D78tFotdTqdsd77/Plz3b59O3VZtPoAADC+hw8f6sGDB6n3f/Xq1cQNF6PkOvzYtq0nT56MPd5neXlZN27cyLhWAABAmny4yPLy8hRr815uw0+73Va1WmWVdoyeFuBbs68HACAfchl+PM+TNPz0VxAEVzoMcZMHAGByuQs/vu+r1Wqp0Wio3W4n23u9nsrl8pUOPwAAYHK5Cj/xUhaSRj7afnh4OOsqAQCAnMlV+LEsS/1+f97VAAAAOZa7hU0BAAAmkauWH2Bh/P328LZvjdgGAFg4hJ+8G3UT1kczrsQl5bHOAIArg24vAABQKIVq+VlfX9f169dHvra1taWtra0Z1wgAgGLb3d3V7u7uyNfevn2bSZmFCj9Pnz5lHiAAABbIeY0PQRCoXC5PvUy6vQAAQKEUquUHi2HUMh0AAMwKLT8AAKBQCD8AAKBQCD8AAKBQGPMDjGPkxIwAgDwi/OQcg4cBALgcur0AAECh0PKDq4mFRwEAZ6DlBwAAFEqhWn5Y2wsAgMXC2l4ZY20vAAAWC2t7AQAAZIzwAwAACoXwAwAACqVQY36AIczcDACFQ/hZAMzSjCuD+ZUA5EAhws8XX3wx8N+5GtnS8NGMKzE/v/3nL/Sffvq/69/8yb/Xl7/0QWbljAqU29/KrLhcOj4+VqvV0sOHD7W0tDTv6hQa12JxcC0WS1b3b8IPZuqf//mf9PzV/6b/6V/VMg0/I9HFNeD4+FiPHz/WgwcPsv2SpzXoQjO7FrgQ12KxZHX/ZsAzAAAolEK0/ADTNna3Gq0eALBwCD/AtBStW61onxfAlVGo8PO9731PX/3qV0e+xtpeAADM3nlre/3617/OpMxChZ8f//jH+va3vz3vaiCHmI5gyugOBPB75zU+/OQnP9GdO3emXmahwg9w2sixOx9/NutqzMbpwPHfjtPvCwA5RvhBYYzbepN5K88VbvUoVJgEkFuEnynZ3d3NfMzQf/m//4P+9R//u9yXMQtZf45Rxx87XH382fDGEYFo9x/+h9mMQ/vPLekPs5vP5H/+Xw+Hz9XHn021jKz//s3i7/cszOJzXJUysnZVzlNerwXz/EzJWYO1punF//N/XIkyZiHrzzHJ8bf/8qOhn1F2f7j9LhSd/BnX6f3m2G01i9+prP/+TXT8BboWs/ieuiplZO2qnKe8XotctvwEQaBWqyXTNBVFkarVqjY2NuZdLSB7CzT2Zurdg1ehO3CBrg+As+Uu/IRhqHK5rF6vJ8uyJEmlUkkHBweq1+tzrh2AcTA2CMA85S78NBoNVSqVJPhIkm3bajQa2YWftP8iHbEfj0xjlNG/F/lsTgaARZer8BNFkXzfl+M4A9vX1tYkSe12m9YfXBn/f/SHmYbl4y/+29jvnUVoH3vJkKtgkmkHAEwsV+Hn5cuXkiTTNAe2x61A3W53duGHvn1cEa0f/xstffCH867GaIs8DojvACC3chV+wjCUJBmGce7rp/3mN7+RJP30pz+9uJD/66+GNv3V/3lbkvQHX/qyvvwH/0KS1Pi3vYH3vI1+oeCvGgPb3P9YHlHAf724Dmf47W+P9Ytfpt9/Ecr44p/eSpL+8fP/Tx/8i+uZlZP15+BajG+Sz9Fw/sfhbY+CoW2j/v7pTxpD70tr5PGn6OjtF5KkV69eaXl5+f0LP3XTH3TE53/79q2C4NT5G7eMUedzxL4jy5jELMo44ejoSNKIazHKqHM37nma9u/suOdp3DqP6aIyvvin3+qL3/5O+lf/y3gHPHUP/un/+4+S3t/Hp6afI81msy+p3+v1hl6T1DdNc+R+n3zySV8SP/zwww8//PCTw59PPvlkqnkiVy0/pVJJknRwcDDy9dPdYbHvfve7+uSTT/T1r39dX/nKV1KX/8EHH+iDDz5IvT8AAEXyxRdf6Isvvki9/29+8xv94he/0He/+90p1ipn3V5xuImi6NzXT/va176m73//+1lVCwAA5EiuZniOn+o6PbYn/nO5PGqMDQAAwHu5Cj+GYciyLHW73YHtvu9Lku7duzePagEAgBy59vvBwrkRBIHK5bL29/eTbq5SqaRGo6Fmsznn2gEAgEWXu/AjDa7t1ev1dHh4qEqlcql1vlgfbPrSnlPP89RqtRQEgSzLkuM4qlQqM6jx1TWN32/f91Wr1XR4eJhRLYthGtciDEN5nidJqtfrZ073gYtN8j3V7XZlGIbCMJRpmkMT7mJ8URSp1WpJ0tjncar37ak+OzZj+/v7fWnw0XfTNPuu62ayH86W9pw6jtOvVCp913WTqQwk9bvdbtZVvrKm9fttmmbfMIxpV69QJr0W+/v7/Y2NjX6lUunv7+9nVc3CSHs9Op1O37KsgW2VSqXfbDYzqedV1+12+xsbG31J/Xq9PtY+075v5zr8VCqVfqVSGdjmum7/okyXdj+cLe053djYGPhzr9frSxo6FsY3jd/vZrPZr1QqhJ8JTXIter1e3zCMsW8OuNgk94zT18FxnDPnlsN4LhN+pn3fztWA55Pidb6q1erA9pPrfE1zP5wt7TkdtU6bZVmyLOvM2bpxvmn8fvu+r5s3bw4sHozLm+RaRFGku3fvyjRNue4EszwjMcn1ODg4SB6siZ0cd4psZXHfzm34GWedr2nuh7OlPaeVSuXMLw++VNKZxu+367o8PDAFk1wL27YVRRFjSqZokuvRaDQUhqFqtZqkd2NP9vb2uD4zksV9O7fhJ+06X2n3w9mmfU5Pfsngcia9FrZt84U+JZNci/hfst1uV+VyWSsrK6pWq3w/TWCS61Gv11Wv1+V5nkqlkmzb1uvXr2kdnZEs7tu5DT/7+/uSpNXV1ZGvnzULdNr9cLZpnlPP82Sapur1+jSqVjiTXIsgCHTz5k1a3aYk7bWIF4m0LEuNRkO9Xk+9Xk9hGKpUKvEdldKk31Ou6yZd8r7vD3WDITtZ3LdzG37SrvOVdj+cbZrntNVqqdPpTKVeRTTJtWi1WnR3TVHaaxH/K7bRaCTvOTn2J348GJcz6fdUtVpVo9FIHnev1WrJ9APIVhb37Vyt7XVS2nW+0u6Hs03rnNq2rSdPnnANJpD2Wti2PdStEv9//F+uy+WkvRZnNe3Hc1/R9ZXOJN9TjUZDkpIW6devX+vWrVva3NxkfrgZyOK+nduWn7TrfLE+2PRN45y2221Vq1X60CeU9lr4vq9Go6FSqZT8eJ6nKIpUKpUYg5XCpN9RcVP/aWc1/eN8k3xP7e3tDXw3GYYhx3EURVHSTYnsZHHfzm34SbvOF+uDTd+k5zRuOj49qzNfKpeX9lr0ej313837lfw0m00ZhqF+v69er5d53a+aSb6jKpXK0JiS+F+9/AMtnUm+p1ZXV4daHeLvK2bbzl4m9+1UswMtiHhCvJMzn5qm2XccJ/nz/v5+3zTNgRmDx9kPl5P2WnS73b5lWX3XdQd+6vU6M26nlPZanNZsNpnkcEKTfked3OY4ztAsw7ictNfDcZy+YRj9w8PDgW1cj/QODw/PnORwFvft3I75kd49DdHr9WTbtkzTVBiGsm174EmhKIp0cHAwkNrH2Q+Xk+ZaBEGQTFoV96mfxJpS6aT9e4Hpm8Z3VKfTkWEYiqKIFrgJpb0ecStorVZLur+iKNKzZ89m/RGuhCAIkgH8e3t7qlarqlQqSSvaLO7buVzYFAAAIK3cjvkBAABIg/ADAAAKhfADAAAKhfADAAAKhfADAAAKhfADAAAKhfADAAAKhfADAAAKhfADAAAmdnrh0UVG+AEAABOr1Wq5WTKH8AMAACYSBIFM00zW54rX3rp27ZquXbumlZUV7ezsJO/3fV+lUil5LV6hfVZY2wsAAEyk0WioVqupUqkMbK/VavI8TxsbG+p0OkOvhWE4lwV7CT8AAGAipVJJ+/v7Q9uDIFC5XJZhGDo8PEy2e54n27ZH7jMLdHsBAIDUPM8bavGJWZYly7IURVHStRUEgWzbVrfbnWU1BxB+AABAap9++qkajcaZr8evua6rKIpUq9XU6XRkmuasqjiE8AMAAJIWmZWVlYEwU6vVtLKyMvJR9iiKFIahLMs687j37t2T9K6F6O7du3Ic59z3zwJjfgAAQKJarcr3ffX7/WQgcxiGqtfrQ+9tt9uKokjNZvPcY5438HkevjzvCgAAgMVRq9Xk+74ajYZs2z63e8p1XT179uzCY8bHCILgzPc0Gg2VSiV9/vnn+uY3v6mNjY3LV35MhB8AAJCIBy8bhnFu8AnDUKurq8ncPmfxPE++78s0TYVhqCAIhrq9arWaTNNMWpDiJ8TOGkg9Kcb8AACARBx4LlquwnXdcwc6S+/HET179mxg4PNJYRjK87yBY92/f1+O46Sp/lgY8wMAABK2bcv3fUVRdO48PGfN7ROLokjlclmdTid53H1lZUWSdDJ6eJ6nWq02sM33fVWrVR0eHl7YspQGLT8AAEDSuyBSrVbVaDQUhmHS+nO6Fcj3/Qu7pO7evSvXdZMuLsMwknE8nucl73vx4sVQwFldXZUkHRwcTPR5zkL4AQCgwMIw1M7OjjzP08HBgSqVShJsXNfVzs5OEkZiF3V5VatVmaY5FJCq1aokqdVqJduiKBo6/sm6ZYHwAwBAgQVBoFarpRcvXiSPs5umqY2NDbXbbVUqlaGWmVGDlqX3C5b6vq8gCAZaeDzPS8b7BEGQPFVWKpXObOHJaiJExvwAAICxjTu3z7jOG/OTVUThUXcAADC2cef2GVfcghSG4cB8QFnOAk23FwAAGMu4c/tcRtzFdrKL7NNPP+VRdwAAMH+2bWc2+/LJGZ5LpdLI5TSmhfADAADGEq/InneEHwAAUCiM+QEAAIVC+AEAAIVC+AEAAIVC+AEAAIVC+AEAAIVC+AEAAIVC+AEAAIVC+AEAAIXy3wFLozekq2UejAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "plt.hist(rad_length_lost,bins=100,density=True,alpha=0.5,color=\"darkorange\",histtype=\"bar\",label=\"lost\",range=[0,1])\n",
+    "plt.hist(rad_length_found,bins=100,density=True,alpha=0.5,color=\"blue\",histtype=\"bar\",label=\"found\",range=[0,1])\n",
+    "plt.xlim(0,1)\n",
+    "#plt.yscale(\"log\")\n",
+    "plt.title(\"radiation length fraction\")\n",
+    "plt.xlabel(f\"$x/X_0$\")\n",
+    "plt.ylabel(\"a.u.\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nbins=100\n",
+    "vmax=80\n",
+    "\n",
+    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
+    "\n",
+    "a0 = ax0.hist2d(\n",
+    "    rad_length_found,\n",
+    "    eta_found,\n",
+    "    density=False,\n",
+    "    bins=nbins,\n",
+    "    cmap=plt.cm.jet,\n",
+    "    cmin=1,\n",
+    "    vmax=vmax,\n",
+    "    range=[[0,0.5],[2,5]],\n",
+    ")\n",
+    "ax0.set_xlabel(f\"$x/X_0$\")\n",
+    "ax0.set_ylabel(f\"$\\eta$\")\n",
+    "ax0.set_title(f\"found $\\eta$ rad_length_frac\")\n",
+    "\n",
+    "a1 = ax1.hist2d(\n",
+    "    rad_length_lost,\n",
+    "    eta_lost,\n",
+    "    density=False,\n",
+    "    bins=nbins,\n",
+    "    cmap=plt.cm.jet,\n",
+    "    cmin=1,\n",
+    "    vmax=vmax * stretch_factor,\n",
+    "    range=[[0,0.5],[2,5]],\n",
+    ")\n",
+    "ax1.set_xlabel(f\"$x/X_0$\")\n",
+    "ax1.set_ylabel(f\"$\\eta$\")\n",
+    "ax1.set_title(f\"lost $\\eta$ rad_length_frac\")\n",
+    "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
+    "\n",
+    "plt.suptitle(\"radiation length fraction and eta\")\n",
+    "plt.colorbar(a0[3], ax=ax1)\n",
+    "\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "code",
@@ -118,7 +183,15 @@
    "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"
   }
  },