diff --git a/B_rework.ipynb b/B_rework.ipynb
index b3fb3ac..fe31740 100644
--- a/B_rework.ipynb
+++ b/B_rework.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -31,7 +31,7 @@
        "9056"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -59,7 +59,7 @@
        "0.8606728758791105"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -134,7 +134,7 @@
        "0.9533333333333334"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -157,6 +157,7 @@
     "\n",
     "\n",
     "\n",
+    "\n",
     "for cutoff_energy in range(0,1050,50):\n",
     "\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",
@@ -180,17 +181,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "def weird_div(n,d):\n",
-    "    return n/d if d else 0"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -199,7 +197,7 @@
        "0.8593328191284226"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -224,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -245,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -280,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -317,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -346,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -392,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -416,7 +414,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -428,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -487,7 +485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -511,7 +509,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -532,7 +530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -612,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -648,7 +646,7 @@
     "\"\"\"\n",
     "vtx_id: 101 - Bremsstrahlung\n",
     "B:\n",
-    "wir können nicht wirklich sagen dass bei den lost teilchen jegliche endvertex types überwiegen\n",
+    "wir können nicht wirklich sagen dass bei den lost teilchen jegliche endvertex types überwiegen, im gegensatz zu den found \n",
     "\"\"\"\n",
     "fig.colorbar(a0[3], ax=ax, orientation='vertical')\n",
     "plt.show()"
@@ -663,12 +661,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Lly/r1VdfVYsWLeTq6ipnZ2e5urrqyJEj+Z5jwIABVuvNmzeXJKsX5OZu/+uvvyxDLb766iuZTCY9/PDDunz5smXx9fVVmzZtFBsbe81n8tVXX6lmzZoaOHCg1Tnatm0rX1/fPOdo3bq1mjZtes3zbt++Xenp6Ro3blyx3x2zadMm9ezZ0+rXTScnJ91///1W7b755htdvnxZw4cPt4q9atWq6t69e773n9/nlZ9bb71VUVFRmjFjhuLi4vIM5b2e+ytt1/Od/Prrr9W0aVP16tXrmtfZs2ePOnfuLB8fH23btk0NGza06X0AKB+2bNmigQMHyt/fXyaTqUQT73zzzTfq3LmzPD09VbduXQ0dOlQJCQm2DxYVGnleXqWV5+3YsUMXL17UQw89ZLW9S5cuRcoHcj+Tq48fNmyY1frFixe1ceNG3XXXXapWrZrVPfTv318XL15UXFyc1THFyfXOnj2rBx98UF988YX+/PPPPG2+/vpr9ejRw/KZOYo77rhDXl5eebZ/99136tWrl8xms5ycnOTi4qIXX3xRZ86cUUpKiiQpJiZG2dnZGj9+/DWvc/HiRQ0ZMkQrVqzQhg0b8nxeQGmjiAdUcr6+vvlu++cQyWHDhmnBggV67LHH9M033+iHH37Qzp07VbduXV24cCHP8f/s2p4rLCxML7zwgoYMGaK1a9fq+++/186dO9WmTZt8z1GrVi2rdVdX10K3X7x4UZJ06tQpGYYhHx8fubi4WC1xcXH5JiNXO3XqlM6ePStXV9c850hOTs5zjvzuNz+571kpyct7z5w5U+BndXXsktSxY8c8sa9atSpP7NWqVSvycIZVq1ZpxIgR+uCDDxQUFKRatWpp+PDhSk5Ovu77K23X8508ffp0ke8pJiZGp06d0mOPPaaaNWvaKnwA5UxGRobatGmjBQsWlOj43377TYMHD9Ydd9yh+Ph4ffPNN/rzzz9199132zhSVHTkeXmVVp6X+0yLkq8VdLyzs7Nq165d6LFnzpzR5cuXNX/+/Dzx9+/fX5JKfA+hoaH68MMP9fvvv2vo0KHy9vZWp06dFBMTY2lTnLyoLOV3jz/88INCQkIkSe+//762bdumnTt36rnnnpMky3ezODlsSkqKvvnmGwUFBalLly62Ch8oMt6JB1RyuQWYq7flJhBpaWn66quv9NJLL+mZZ56xtMnMzNRff/2V7znz64W1fPlyDR8+XK+++qrV9j///NOmxY46derIZDLpf//7X573wkh53xVT0Dlq166t6OjofPd7enparRe111ndunUlSX/88UeR2v9T7dq1C/ys/in3vTX//e9/i/Srb3F6zNWpU0fz5s3TvHnzlJiYqC+//FLPPPOMUlJSFB0dfV33V9qu5ztZt27dIt/TlClTdPToUUtPyOHDh19X3ADKp379+uX7HqtcWVlZev7557VixQqdPXtWgYGBev311y0vet+zZ4+ys7M1Y8YMValy5Tf38PBwDR48WJcuXZKLi0tZ3AYqAPK8/M9RGnle7jMt6JnnTjpS2PGXL1/WmTNnrAp5V5/Py8tLTk5OCg0NLbDnWOPGja3Wi5PvPfLII3rkkUeUkZGhLVu26KWXXtKAAQP0yy+/qGHDhsXKi8pSfve4cuVKubi46KuvvlLVqlUt26/uHf3PHLZ+/fqFXqdBgwaaO3eu7rrrLt1999369NNPrc4NlDZ64gEVnJubW76/gOb65JNPrGZn+v3337V9+3bL/0iYTCYZhpEnKfrggw+UnZ1d5DhMJlOec6xbt04nTpwo8jmKYsCAATIMQydOnFCHDh3yLK1atbK0LejZDBgwQGfOnFF2dna+57jWi4kL0qVLF5nNZi1atCjfGbEK06NHD23cuNHS006SsrOztWrVKqt2ffr0kbOzs44ePZpv7B06dChR7Fdr0KCBnnzySfXu3Vt79uyRdH33d72u9T3PT1G/k/369dMvv/ximXSjMFWqVNG7776rf/3rXxo5cqQWLlxYrJgAVA6PPPKItm3bppUrV+qnn37Svffeq759++rIkSOSpA4dOsjJyUlLlixRdna20tLS9NFHHykkJIQCHqyQ5zlOnte5c2dVrVpVK1assNq+ffv2QidHyNWjRw9JynP81ZOYVatWTT169NDevXvVunXrfO/h6t58JeHh4aF+/frpueeeU1ZWlg4cOCDpSl60adMmHT58+LqvURy536/i5Hsmk0nOzs5ycnKybLtw4YI++ugjq3YhISFycnIqct4WEhKib775Rlu2bNGAAQOUkZFR5JiA60VPPKCCa9WqlVauXKlVq1bpxhtvVNWqVa0SnJSUFN11110aPXq00tLS9NJLL6lq1aqaNm2apCuzfN5+++2aNWuW6tSpo0aNGmnz5s1avHhxsX5ZHTBggKKiotSsWTO1bt1au3fv1qxZs2zeHb9r1656/PHH9cgjj2jXrl26/fbb5eHhoaSkJG3dulWtWrXS2LFjLc/ms88+08KFC9W+fXtVqVJFHTp00AMPPKAVK1aof//++te//qVbb71VLi4u+uOPP7Rp0yYNHjxYd911V7Fjq169uubMmaPHHntMvXr10ujRo+Xj46Nff/1VP/74Y6HDrp5//nl9+eWXuuOOO/Tiiy+qWrVq+ve//50naWjUqJGmT5+u5557Tr/99pv69u0rLy8vnTp1Sj/88IM8PDz08ssvFzv2tLQ09ejRQ8OGDVOzZs3k6empnTt3Kjo62jK863ru73q1atVKsbGxWrt2rfz8/OTp6XnNJLyo38lJkyZp1apVGjx4sJ555hndeuutunDhgjZv3qwBAwZYku5/mjNnjjw9PTVu3DidP39eU6ZMsen9Aii/jh49qk8++UR//PGH/P39JV3pZRcdHa0lS5bo1VdfVaNGjbRhwwbde++9euKJJ5Sdna2goCCtX7/eztHD0ZDnOU6e5+XlpfDwcM2YMUOPPfaY7r33Xh0/flwRERFFGk4bEhKi22+/XVOnTlVGRoY6dOigbdu25Sk4SdJbb72lbt266bbbbtPYsWPVqFEjnTt3Tr/++qvWrl1bpB8e8zN69Gi5u7ura9eu8vPzU3JysiIjI2U2m9WxY0dJ0vTp0/X111/r9ttv17PPPqtWrVrp7Nmzio6OVlhYmJo1a1aia19L7vf69ddfV79+/eTk5KTWrVtbhlzn584779TcuXM1bNgwPf744zpz5oxmz56dp+DcqFEjPfvss3rllVd04cIFPfjggzKbzTp48KD+/PPPfHPnbt26aePGjerbt69CQkK0fv16mc1m2940kB97zagBoOSKM7vTsWPHjJCQEMPT09OQZJmlK/ccH330kTFx4kSjbt26hpubm3HbbbcZu3btsjrHH3/8YQwdOtTw8vIyPD09jb59+xr79+/PM3No7qxlO3fuzBNzamqqMWrUKMPb29uoVq2a0a1bN+N///uf0b17d6vZugq6t4LOnXvPp0+fttr+4YcfGp06dTI8PDwMd3d346abbjKGDx9udW9//fWXcc899xg1a9Y0TCaT1bO7dOmSMXv2bKNNmzZG1apVjerVqxvNmjUznnjiCePIkSOWdg0bNjTuvPPOPPdbmPXr1xvdu3c3PDw8jGrVqhktWrQwXn/99Wset23bNqNz586Gm5ub4evra0yZMsV47733rGanzfX5558bPXr0MGrUqGG4ubkZDRs2NO655x7j22+/tbQpaEbY/Fy8eNEYM2aM0bp1a6NGjRqGu7u7ERAQYLz00ktGRkZGse6vNGanjY+PN7p27WpUq1bNagY4W3wnc9v+61//Mho0aGC4uLgY3t7exp133mn8/PPPhmFYz077T7NmzTIkGS+++OI17wFAxSTJWLNmjWX9P//5jyHJ8PDwsFqcnZ0tM4gnJSUZN998szFlyhRjz549xubNm43u3bsbPXv2LPbs5ih/yPPy3nN5yfNycnKMyMhIo379+oarq6vRunVrY+3atfnmFvk5e/as8eijjxo1a9Y0qlWrZvTu3dv4+eef88xOaxhXco9HH33UuOGGGwwXFxejbt26RpcuXYwZM2ZY2hT0vAuydOlSo0ePHoaPj4/h6upq+Pv7G/fdd5/x008/WbU7fvy48eijjxq+vr6Gi4uLpd2pU6csscnGs9NmZmYajz32mFG3bl3L55mb/0oyxo8fn+9xH374oREQEGC4ubkZN954oxEZGWksXrw43/x52bJlRseOHS3fiXbt2lndQ+7stP+0f/9+w9fX17jlllvyfE+B0mAyjDIe8wTAIcTGxqpHjx769NNPdc8999g7HAAAKiSTyaQ1a9ZoyJAhkq5MEvTQQw/pwIEDVkO8pCs9mn19ffXCCy/o66+/1q5duyz7ct/VtGPHDnXu3LksbwHlEHkeAFRMDKcFAAAAyki7du2UnZ2tlJQU3Xbbbfm2+fvvv/MU+HLXc3JySj1GAADgmJjYAgAAALCh8+fPKz4+XvHx8ZKkhIQExcfHKzExUU2bNtVDDz2k4cOH67PPPlNCQoJ27typ119/3fLOuzvvvFM7d+7U9OnTdeTIEe3Zs0ePPPKIGjZsqHbt2tnxzgAAgD0xnBYAAACwodyhjFcbMWKEoqKidOnSJc2YMUPLli3TiRMnVLt2bQUFBenll1+2vLx95cqVeuONN/TLL7+oWrVqCgoK0uuvv15qL40HAACOjyIeAAAAAAAA4OAYTgsAAAAAAAA4OIp4AAAAAAAAgINjdtoylpOTo5MnT8rT01Mmk8ne4QAAgHLAMAydO3dO/v7+qlKF32AdFXkeAAAoruLkeRTxytjJkydVv359e4cBAADKoePHj6tevXr2DgMFIM8DAAAlVZQ8jyJeGfP09JR05cOpUaOGnaMBAADlQXp6uurXr2/JI+CYyPMAAEBxFSfPs2sRLzIyUp999pl+/vlnubu7q0uXLnr99dcVEBBgaTNy5EgtXbrU6rhOnTopLi7Osp6Zmanw8HB98sknunDhgnr27Kl33nn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       "text/plain": [
        "<Figure size 1500x1000 with 4 Axes>"
       ]
@@ -689,6 +687,7 @@
     "\n",
     "ax1.hist(scifi_fitpars_found[:,1], bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$b_x$ found\")\n",
     "ax1.hist(scifi_fitpars_lost[:,1], bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$b_x$ lost\")\n",
+    "ax1.set_xticks(np.arange(-1,1,0.1),minor=True)\n",
     "ax1.set_xlabel(\"b\")\n",
     "ax1.set_ylabel(\"normed\")\n",
     "ax1.set_title(\"fitparameter b der scifi track\")\n",