diff --git a/B_rework.ipynb b/B_rework.ipynb
index 110c371..b3fb3ac 100644
--- a/B_rework.ipynb
+++ b/B_rework.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -31,7 +31,7 @@
        "9056"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -59,7 +59,7 @@
        "0.8606728758791105"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -134,7 +134,7 @@
        "0.9533333333333334"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -190,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -199,7 +199,7 @@
        "0.8593328191284226"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -224,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -280,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -317,7 +317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -346,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -392,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -416,7 +416,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -428,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -487,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -511,7 +511,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -522,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -532,14 +532,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 2000x600 with 4 Axes>"
+       "<Figure size 1800x600 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -572,7 +572,7 @@
     "\n",
     "#Erste Annahme ist Bremsstrahlung\n",
     "\n",
-    "fig = plt.figure(figsize=(20,6))\n",
+    "fig = plt.figure(figsize=(18,6))\n",
     "axes = ImageGrid(fig, 111,  # similar to subplot(111)\n",
     "                 nrows_ncols=(1, 2),  # creates 2x2 grid of axes\n",
     "                 axes_pad=1,  # pad between axes in inch.\n",
@@ -605,27 +605,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.08902698999847938   101.0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1800x600 with 3 Axes>"
       ]
@@ -637,19 +629,26 @@
    "source": [
     "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n",
     "\n",
-    "a0=ax[0].hist2d(b_found, vtx_types_found, bins=100, cmap=plt.cm.jet, cmin=1)\n",
+    "a0=ax[0].hist2d(bs_found, vtx_types_found, bins=110, density=True, cmap=plt.cm.jet, cmin=1e-20,vmax=2)\n",
+    "ax[0].set_ylim(0,110)\n",
+    "ax[0].set_xlim(-1,1)\n",
     "ax[0].set_xlabel(\"b\")\n",
     "ax[0].set_ylabel(\"endvtx id\")\n",
     "ax[0].set_title(\"found endvtx id wrt b parameter\")\n",
+    "ax[0].set_yticks(np.arange(0,110,1),minor=True)\n",
     "\n",
-    "a1=ax[1].hist2d(b_lost, vtx_types_lost, bins=100, cmap=plt.cm.jet, cmin=1) \n",
+    "a1=ax[1].hist2d(bs_lost, vtx_types_lost, bins=110, density=True, cmap=plt.cm.jet, cmin=1e-20,vmax=2)\n",
+    "ax[1].set_ylim(0,110)\n",
+    "ax[1].set_xlim(-1,1)\n",
     "ax[1].set_xlabel(\"b\")\n",
     "ax[1].set_ylabel(\"endvtx id\")\n",
     "ax[1].set_title(\"lost endvtx id wrt b paraneter\")\n",
+    "ax[1].set_yticks(np.arange(0,110,1), minor=True)\n",
     "\n",
     "\"\"\"\n",
+    "vtx_id: 101 - Bremsstrahlung\n",
     "B:\n",
-    "\n",
+    "wir können nicht wirklich sagen dass bei den lost teilchen jegliche endvertex types überwiegen\n",
     "\"\"\"\n",
     "fig.colorbar(a0[3], ax=ax, orientation='vertical')\n",
     "plt.show()"
@@ -664,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {