diff --git a/B_photon_eta_phi.ipynb b/B_photon_eta_phi.ipynb
new file mode 100644
index 0000000..40ff207
--- /dev/null
+++ b/B_photon_eta_phi.ipynb
@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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",
+    "from mpl_toolkits import mplot3d\n",
+    "import itertools\n",
+    "import awkward as ak\n",
+    "from scipy.optimize import curve_fit\n",
+    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10522"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
+    "\n",
+    "#selektiere nur elektronen von B->K*ee und nur solche mit einem momentum von ueber 5 GeV \n",
+    "allcolumns = file.arrays()\n",
+    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 9056\n",
+    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 1466\n",
+    "\n",
+    "ak.num(found, axis=0) + ak.num(lost, axis=0)\n",
+    "#ak.count(found, axis=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plot minimal energy of photon abhängigkeit von eta und phi\n",
+    "#materialpeak (beampipe)\n",
+    "\n",
+    "#minimal photon energy\n",
+    "energy_found = ak.to_numpy(ak.min(found[\"brem_photons_pe\"],axis=-1))\n",
+    "energy_lost = ak.to_numpy(ak.min(lost[\"brem_photons_pe\"],axis=-1))\n",
+    "\n",
+    "eta_found = ak.to_numpy(found[\"eta\"])\n",
+    "eta_lost = ak.to_numpy(lost[\"eta\"])\n",
+    "\n",
+    "phi_found = ak.to_numpy(found[\"phi\"])\n",
+    "phi_lost = ak.to_numpy(lost[\"phi\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n",
+    "\n",
+    "a0=ax[0].hist2d(energy_found, eta_found, bins=200, cmap=plt.cm.jet, cmin=1, range=[[0,0.3e4],[1,6]], vmax=20)\n",
+    "ax[0].set_xlabel(\"minimal photon energy\")\n",
+    "ax[0].set_ylabel(r\"$E_{ph}$\")\n",
+    "ax[0].set_title(\"found eta wrt photon energy\")\n",
+    "\n",
+    "a1=ax[1].hist2d(energy_lost, eta_lost, bins=200, cmap=plt.cm.jet, cmin=1, range=[[0,0.3e4],[1,6]], vmax=20)\n",
+    "ax[1].set_xlabel(\"minimal photon energy\")\n",
+    "ax[1].set_ylabel(r\"$E_{ph}$\")\n",
+    "ax[1].set_title(\"lost eta wrt photon energy\")\n",
+    "\n",
+    "\"\"\"\n",
+    "\"\"\"\n",
+    "fig.colorbar(a0[3],ax=ax[1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 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": "env1",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/B_rework.ipynb b/B_rework.ipynb
index 93bb6d3..2eb7e8c 100644
--- a/B_rework.ipynb
+++ b/B_rework.ipynb
@@ -2,11 +2,11 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import uproot\n",
+    "import uproot\t\n",
     "import numpy as np\n",
     "import sys\n",
     "import os\n",
@@ -22,16 +22,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "9056"
+       "10522"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -44,24 +44,21 @@
     "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 9056\n",
     "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 1466\n",
     "\n",
-    "ak.num(found, axis=0)\n",
+    "ak.num(found, axis=0) + ak.num(lost, axis=0)\n",
     "#ak.count(found, axis=None)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.8606728758791105"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eff all =  0.8606728758791105 +/- 0.003375885792719708\n"
+     ]
     }
    ],
    "source": [
@@ -70,12 +67,18 @@
     "    des = ak.num(lost, axis=axis)\n",
     "    return sel/(sel + des)\n",
     "\n",
-    "t_eff(found, lost)"
+    "def eff_err(found, lost):\n",
+    "    n_f = ak.num(found, axis=0)\n",
+    "    n_all = ak.num(found, axis=0) + ak.num(lost,axis=0)\n",
+    "    return 1/n_all * np.sqrt(np.abs(n_f*(1-n_f/n_all)))\n",
+    "\n",
+    "\n",
+    "print(\"eff all = \", t_eff(found, lost), \"+/-\", eff_err(found, lost))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -83,20 +86,20 @@
      "output_type": "stream",
      "text": [
       "sample size:  32\n",
-      "eff (cutoff =  0 ) =  0.96875\n",
+      "eff (cutoff =  0 ) =  0.96875 +/- 0.030757843257858637\n",
       "sample size:  32\n",
-      "eff (cutoff =  100 ) =  0.96875\n",
+      "eff (cutoff =  100 ) =  0.96875 +/- 0.030757843257858637\n",
       "sample size:  65\n",
-      "eff (cutoff =  200 ) =  0.9692307692307692\n",
+      "eff (cutoff =  200 ) =  0.9692307692307692 +/- 0.021419791425796485\n",
       "sample size:  129\n",
-      "eff (cutoff =  300 ) =  0.9457364341085271\n",
+      "eff (cutoff =  300 ) =  0.9457364341085271 +/- 0.019945474377053428\n",
       "sample size:  169\n",
-      "eff (cutoff =  400 ) =  0.9408284023668639\n",
+      "eff (cutoff =  400 ) =  0.9408284023668639 +/- 0.018149660480088193\n",
       "sample size:  227\n",
-      "eff (cutoff =  500 ) =  0.920704845814978\n",
-      "cutoff energy = 350MeV\n",
-      "sample size: 150\n",
-      "eff =  0.9533333333333334\n"
+      "eff (cutoff =  500 ) =  0.920704845814978 +/- 0.017933729291194522\n",
+      "\n",
+      "cutoff energy = 350MeV, sample size: 150\n",
+      "eff =  0.9533333333333334 +/- 0.017221863795553384\n"
      ]
     }
    ],
@@ -123,7 +126,7 @@
     "\tnobrem_f = found[ak.all(found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
     "\tnobrem_l = lost[ak.all(lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
     "\tprint(\"sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
-    "\tprint(\"eff (cutoff = \",str(cutoff_energy),\") = \",str(t_eff(nobrem_f,nobrem_l)))\n",
+    "\tprint(\"eff (cutoff = \",str(cutoff_energy),\") = \",str(t_eff(nobrem_f,nobrem_l)), \"+/-\", eff_err(nobrem_f, nobrem_l))\n",
     "\n",
     "\"\"\"\n",
     "we see that a cutoff energy of 350MeV is ideal because the efficiency drops significantly for higher values\n",
@@ -136,8 +139,8 @@
     "nobrem_found = found[ak.all(found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
     "nobrem_lost = lost[ak.all(lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
     "\n",
-    "print(\"cutoff energy = 350MeV\\nsample size:\",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n",
-    "print(\"eff = \",t_eff(nobrem_found, nobrem_lost))"
+    "print(\"\\ncutoff energy = 350MeV, sample size:\",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n",
+    "print(\"eff = \",t_eff(nobrem_found, nobrem_lost), \"+/-\", eff_err(nobrem_found, nobrem_lost))"
    ]
   },
   {
@@ -149,18 +152,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.8593328191284226"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eff =  0.8593328191284226 +/- 0.003413861022128076\n"
+     ]
     }
    ],
    "source": [
@@ -178,12 +178,12 @@
     "eph_lost = ak.to_numpy(ak.sum(brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
     "energyloss_lost = eph_lost/energy_lost\n",
     "\n",
-    "t_eff(brem_found,brem_lost)"
+    "print(\"eff = \", t_eff(brem_found,brem_lost), \"+/-\", eff_err(brem_found, brem_lost))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -239,12 +239,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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v0aNHTWhoqLn11ltdBr7OVVsud77vSY8ePUx4eLjL53z69GkTExNjrrzySut4VT9zVYMtVaZNm2YkmcLCwvO2y5i65y8XOs6+ffuMv7+/efjhh13ijh49aiIjI10GuoYNG2Ykmb/+9a8usStWrDCSzJw5c1zWT5kypdrgR1W7tmzZYq2rytO+P3AJwHMw7RXgIXbt2qWDBw8qNTVVTZp896PZvHlz/fKXv1ReXt4lz0fZp08fhYSEWK8jIiIUHh6uL7/8UpJ0/Phx5efn6/bbb1ezZs2suJCQEA0aNOiC+1+3bp2+/fZbDRs2TKdPn7aWM2fOqH///srPz69Wlj148GCX1127dtXJkydVXFwsSXrnnXdks9k0dOhQl31GRkbqhhtu0EcffeTy/rCwMN12220u61avXq2QkBD179/fZf3dd99d7RweeughSdLcuXOtdbNnz1aXLl30k5/85Lzn/sEHH0g6W0L7fXfeeaeCg4OtqZtuvPFGBQQE6MEHH9SCBQtqLF++5ZZbdOTIEd199936f//v/+nrr78+73G/r1evXmrWrJlWrlwpSdaURf3799e6dev0n//8R/v379dnn32mfv36VXv/L3/5y4s6zvlcyudf1/f+5z//0erVqzVkyBC1bt36ktta0/fkgw8+UOfOnXXLLbe4rE9LS5MxxvqMqwwcOFB+fn7W665du0qS9fN0Pu+884769Okjp9Ppcq4DBgyQdPb7WpfjfP755/rXv/6le+65R5Jc9vnTn/5UhYWF2rVrl8s+z/2s8/LyVF5eriFDhris79GjR7Wy/quvvlrJycl64YUXrGkClixZom+++Ua//e1vaz13AACAy2H9+vU6ceJEtb55VFSUbrvtNqtv3qJFC11zzTV69tlnlZWVpS1btlSbnqmu6pq/REZGVut/du3a1aVP+c477ygmJkY33nijyz6TkpJks9mq7fO2225TWFiYy7rVq1frtttuU6tWrax1TZo0qdb/k6Tf/e532rp1q/7xj39IOjud1MKFCzVs2DA1b978vOd+sf3pi8l3brnlFr333nt6/PHH9dFHH1308yj69u2rEydOaN26dZLOTqubkJCgfv36KTc311onqVpOVFOOUFd1/fwv5b3r1q1TWVmZRo4cWW2667o493ty/PhxbdiwQXfccYfL5+zn56fU1FQdOHCgWl5RUz4v1Z4TXUr+cqHjvP/++zp9+rTuvfdel/01a9ZM8fHxNV73c3Oiqjzs3J+Jmv5ucPfddys8PNyaWkySZs2apdatW+tXv/rVec8dQMNh8APwEN98840kqU2bNtW2OZ1OnTlzRiUlJZe075YtW1ZbZ7fbrY5kSUmJzpw5o8jIyGpxNa0716FDhyRJd9xxh5o2beqyPPPMMzLG6Ntvv621TXa7XZKsNh06dEjGGEVERFTbZ15eXrWOck3X7ZtvvlFERES19edb96tf/UovvfSSKisr9c9//lMff/zxBf+o+80338jf37/aH+RtNpsiIyOtz/Waa67RypUrFR4erlGjRumaa67RNddcoz//+c/We1JTU/XXv/5VX375pX75y18qPDxcsbGxVmf9fJo1a6ZevXpZnflVq1YpISFBvXv3VmVlpT7++GNrHzUNftR07eriUj7/ur63pKRElZWVuvLKK39QW8/3PTnfz13V9u+70Hf3fA4dOqS333672nlWzZ987nf6Yn5GJGns2LHV9jly5Mga93nueVad28X+nPzud7/TZ599Zn2fnn/+ecXFxenHP/5xrecOAABwOVwop6rabrPZtGrVKiUlJWnatGn68Y9/rNatW2v06NE6evToJR27rvnLhXK0qn3+85//rLa/kJAQGWPcnhP97Gc/U/v27a0/7M6fP1/Hjx/XqFGjaj33i+1PX0y+87//+7967LHHtHz5cvXp00ctWrTQz3/+c3322We1tqHqeR0rV67U559/rr1791qDHxs2bNCxY8e0cuVKXX311erQoYPLe39oPiTV/fO/lPcePnxYktyeE5WUlMgYU+850aXkLxebE918883V9vn6669X219QUJBCQ0Nd1lXl9C1atHBZX9PPiN1u14gRI7RkyRIdOXJEhw8f1t/+9jf9+te/ttoGwLP4N3QDAJxV9T/1wsLCatsOHjyoJk2aWHdnNGvWTOXl5dXiLrZS4FxhYWGy2WwqKiqqtq2mdeequoto1qxZ6tGjR40xNXUcLrRPm82mjz/+uMZOxLnrarrzpWXLltq4cWO19ec7p9/97ndauHCh/t//+3/Kzs62HpBem5YtW+r06dM6fPiwywCIMUZFRUW6+eabrXX/8z//o//5n/9RZWWlPvnkE82aNUtjxoxRRESE7rrrLknSfffdp/vuu0/Hjx/XmjVrNH78eCUnJ2v37t1q167dedvRt29fPfXUU9q4caMOHDighIQEhYSE6Oabb1Zubq4OHjyoa6+9VlFRUdXe+0PuGpJ+2Od/se+trKyUn5+fDhw48IPaer7vyfl+7r7fxh+qVatW6tq1qyZPnlzj9qrEoi77k6Rx48bp9ttvrzEmOjra5fW551/1e6cqafi+oqKiatUft912m2JiYjR79mw1b95cmzdv1qJFi+rUbgAAgPpyoZzq+/26du3aad68eZKk3bt3629/+5smTJigiooKvfjii3U+dl3zl4vdZ2BgoP7617+ed/v3na+ve76+3rmaNGmiUaNG6YknntCMGTP0wgsvqG/fvtX6lDUd42L70xfKd4KDgzVx4kRNnDhRhw4dsqpABg0apH/961/nbUNAQIBuvfVWrVy5UldeeaUiIyPVpUsXXX311ZKkjz76SKtWrVJycnK19/7QfKjqHC/187/Y91blm+7OicLCwtSkSZN6z4kuJX+52H3+3//9X635cpXz/YycPn1a3377rcsAyPn+bvDQQw9p6tSp+utf/6qTJ0/q9OnT+s1vflOndgO4fBj8ADxEdHS02rZtqyVLlmjs2LHW/5SPHz+uN998U3FxcQoKCpIktW/fXsXFxTp06JD1R+WKigq9//77l3Ts4OBg3XLLLfr73/+uZ5991pr66ujRo3r77bcv+P5evXrpiiuu0I4dO9w2/U1ycrKmTp2qr776qsaS7IsRHx+vv/3tb3rvvfesqYUkaenSpTXGd+vWTT179tQzzzyjgoICPfjggwoODq71GH379tW0adO0aNEiPfLII9b6N998U8ePH1ffvn2rvcfPz0+xsbG67rrrtHjxYm3evNka/KgSHBysAQMGqKKiQj//+c+1ffv2Wjtz/fr10xNPPKE//vGPuvLKK3XddddZ69966y0VFRXVaXqr799RExgYWGvsD/n86/Le+Ph4vfHGG5o8efJ5O98XW4XxfX379tWUKVO0efNmlwqGV199VTabTX369LnofdUmOTlZ7777rq655ppq0xFciujoaHXs2FGffvqpMjMzL2kfsbGxstvtev31110SkLy8PH355ZfVBj8kafTo0frNb36j0tJSRURE6M4777zUUwAAAHCruLg4BQYGatGiRS59lAMHDuiDDz7QHXfcUeP7rr32Wv3hD3/Qm2++qc2bN1vrz63EqI078pea9pmZmamWLVtWq1a4WPHx8Xr33Xf19ddfW33oM2fO6I033qgx/te//rUmTJige+65R7t27dIzzzxzwWNcSn/6YvKdiIgIpaWl6dNPP9XMmTP1n//8x8qJa9KvXz+NGzdOISEhVsV7cHCwevTooVmzZungwYM1VsKfz+X6/C/2vT179pTD4dCLL76ou+6667yDNnXJ5aSz1yg2NlZ///vfNX36dOs9Z86c0aJFi3TllVfq2muvrdM51cQd+cu5kpKS5O/vr3//+9+XPJ1zfHy8pk2bptdff92aDls6/98N2rRpozvvvFMvvPCCKioqNGjQIF111VWXdGwA9Y/BD8BDNGnSRNOmTdM999yj5ORkjRgxQuXl5Xr22Wd15MgRTZ061Yr91a9+paeeekp33XWXfv/73+vkyZP63//9X1VWVl7y8f/0pz+pf//+SkhIUEZGhiorK/XMM88oODj4vFMWVWnevLlmzZqlYcOG6dtvv9Udd9yh8PBwHT58WJ9++qkOHz6sOXPm1Kk9vXr10oMPPqj77rtPn3zyiX7yk58oODhYhYWFWrt2rbp06eLSManJsGHD9Nxzz2no0KF6+umn9aMf/UjvvfeeNUj0/WerVPnd736nX/3qV7LZbFbpbW0SEhKUlJSkxx57TGVlZerVq5f++c9/avz48brpppuUmpoqSXrxxRf1wQcfaODAgbrqqqt08uRJ6w6uqg748OHDFRgYqF69eqlNmzYqKirSlClT5HA4XCpIatKtWzeFhYUpJydH9913n7W+X79++tOf/uRynIvRpUsXSdIzzzyjAQMGyM/PT127dlVAQEC12B/y+dflvVlZWbr11lsVGxurxx9/XD/60Y906NAhvfXWW3rppZcUEhKimJgYSdLLL7+skJAQNWvWTB06dKhxWoEqjzzyiF599VUNHDhQkyZNUrt27bRixQq98MILeuihh9zS0ZekSZMmKTc3Vz179tTo0aMVHR2tkydPau/evXr33Xf14osv1rmE/aWXXtKAAQOUlJSktLQ0tW3bVt9++6127typzZs3nzeprdKiRQulp6drypQpCgsL0y9+8QsdOHBAEydOVJs2bWr8GRk6dKjGjRunNWvW6A9/+EON3wkAAICGcMUVV+iPf/yjnnjiCd177726++679c0332jixIlq1qyZxo8fL0n65z//qd/+9re688471bFjRwUEBOiDDz7QP//5Tz3++OPW/rp06aKlS5fq9ddf19VXX61mzZpZ/eRzuSN/OdeYMWP05ptv6ic/+YkeeeQRde3aVWfOnNG+ffuUk5OjjIwMxcbG1rqPJ598Um+//bb69u2rJ598UoGBgXrxxRetZ/Kd29+74oordO+992rOnDlq167dRT0D8mL70xeT78T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",
       "text/plain": [
        "<Figure size 2000x600 with 4 Axes>"
       ]
@@ -257,13 +257,13 @@
     "#energyloss in abh von der energie der elektronen\n",
     "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
     "\n",
-    "a0=ax0.hist2d(energyloss_found, energy_found, bins=200, cmap=plt.cm.jet, cmin=1)\n",
+    "a0=ax0.hist2d(energyloss_found, energy_found, bins=200, cmap=plt.cm.jet, cmin=1, vmax=15)\n",
     "ax0.set_xlabel(\"energyloss\")\n",
     "ax0.set_ylabel(r\"$E_0$\")\n",
     "ax0.set_title(\"found energyloss wrt electron energy\")\n",
     "plt.colorbar(a0[3],ax=ax0)\n",
     "\n",
-    "a1=ax1.hist2d(energyloss_lost, energy_lost, bins=200, cmap=plt.cm.jet, cmin=1) \n",
+    "a1=ax1.hist2d(energyloss_lost, energy_lost, bins=200, cmap=plt.cm.jet, cmin=1, vmax=15) \n",
     "ax1.set_xlabel(\"energyloss\")\n",
     "ax1.set_ylabel(r\"$E_0$\")\n",
     "ax1.set_title(\"lost energyloss wrt electron energy\")\n",
@@ -276,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -351,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -375,7 +375,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -387,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -430,7 +430,7 @@
     "    scifi_fitpars_lost.end_list()\n",
     "    \n",
     "    vtx_types_lost.begin_list()\n",
-    "    #[:,6] -> endvtx_type\n",
+    "    #endvtx_type\n",
     "    vtx_types_lost.extend(sf_vtx_type_lost[i,:])\n",
     "    vtx_types_lost.end_list()\n",
     "    \n",
@@ -446,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -470,7 +470,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -491,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -571,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -622,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -703,6 +703,13 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/Projektpraktikum.code-workspace b/Projektpraktikum.code-workspace
new file mode 100644
index 0000000..362d7c2
--- /dev/null
+++ b/Projektpraktikum.code-workspace
@@ -0,0 +1,7 @@
+{
+	"folders": [
+		{
+			"path": "."
+		}
+	]
+}
\ No newline at end of file