diff --git a/B_rework.ipynb b/B_rework.ipynb
index 900c609..2099931 100644
--- a/B_rework.ipynb
+++ b/B_rework.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -31,7 +31,7 @@
        "10522"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +100,7 @@
        "<Record {energy: 4.62e+04, ...} type='{energy: float64, photon_length: int6...'>"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,6 +193,7 @@
     "            acc_brem_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
     "    acc_brem_found.end_list()\n",
     "    \n",
+    "\n",
     "    \n",
     "    acc_brem_found.end_record()\n",
     "\n",
@@ -233,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -242,7 +243,7 @@
        "9056"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -267,9 +268,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nph_e = found[\"brem_photons_pe\"]\\nevent_cut = ak.all(ph_e<cutoff_energy,axis=1)\\nph_e = ph_e[event_cut]\\n'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "\"\"\"\n",
@@ -289,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -299,45 +311,87 @@
       "sample size:  322\n",
       "eff (cutoff =  0 ) =  0.9379 +/- 0.0135\n",
       "sample size:  322\n",
+      "eff (cutoff =  25 ) =  0.9379 +/- 0.0135\n",
+      "sample size:  322\n",
       "eff (cutoff =  50 ) =  0.9379 +/- 0.0135\n",
       "sample size:  322\n",
+      "eff (cutoff =  75 ) =  0.9379 +/- 0.0135\n",
+      "sample size:  322\n",
       "eff (cutoff =  100 ) =  0.9379 +/- 0.0135\n",
+      "sample size:  384\n",
+      "eff (cutoff =  125 ) =  0.9453 +/- 0.0116\n",
       "sample size:  433\n",
       "eff (cutoff =  150 ) =  0.9423 +/- 0.0112\n",
+      "sample size:  485\n",
+      "eff (cutoff =  175 ) =  0.9443 +/- 0.0104\n",
       "sample size:  529\n",
       "eff (cutoff =  200 ) =  0.949 +/- 0.0096\n",
+      "sample size:  581\n",
+      "eff (cutoff =  225 ) =  0.9501 +/- 0.009\n",
       "sample size:  644\n",
       "eff (cutoff =  250 ) =  0.9519 +/- 0.0084\n",
+      "sample size:  705\n",
+      "eff (cutoff =  275 ) =  0.9475 +/- 0.0084\n",
       "sample size:  757\n",
       "eff (cutoff =  300 ) =  0.9498 +/- 0.0079\n",
+      "sample size:  802\n",
+      "eff (cutoff =  325 ) =  0.9451 +/- 0.008\n",
       "sample size:  846\n",
       "eff (cutoff =  350 ) =  0.9433 +/- 0.008\n",
+      "sample size:  876\n",
+      "eff (cutoff =  375 ) =  0.9452 +/- 0.0077\n",
       "sample size:  919\n",
       "eff (cutoff =  400 ) =  0.9467 +/- 0.0074\n",
+      "sample size:  972\n",
+      "eff (cutoff =  425 ) =  0.9475 +/- 0.0072\n",
       "sample size:  1019\n",
       "eff (cutoff =  450 ) =  0.949 +/- 0.0069\n",
+      "sample size:  1067\n",
+      "eff (cutoff =  475 ) =  0.9475 +/- 0.0068\n",
       "sample size:  1117\n",
       "eff (cutoff =  500 ) =  0.9418 +/- 0.007\n",
+      "sample size:  1144\n",
+      "eff (cutoff =  525 ) =  0.9423 +/- 0.0069\n",
       "sample size:  1184\n",
       "eff (cutoff =  550 ) =  0.9409 +/- 0.0069\n",
+      "sample size:  1234\n",
+      "eff (cutoff =  575 ) =  0.9408 +/- 0.0067\n",
       "sample size:  1268\n",
       "eff (cutoff =  600 ) =  0.9416 +/- 0.0066\n",
+      "sample size:  1303\n",
+      "eff (cutoff =  625 ) =  0.9417 +/- 0.0065\n",
       "sample size:  1342\n",
       "eff (cutoff =  650 ) =  0.9404 +/- 0.0065\n",
+      "sample size:  1381\n",
+      "eff (cutoff =  675 ) =  0.9399 +/- 0.0064\n",
       "sample size:  1416\n",
       "eff (cutoff =  700 ) =  0.9407 +/- 0.0063\n",
+      "sample size:  1444\n",
+      "eff (cutoff =  725 ) =  0.9418 +/- 0.0062\n",
       "sample size:  1484\n",
       "eff (cutoff =  750 ) =  0.942 +/- 0.0061\n",
+      "sample size:  1523\n",
+      "eff (cutoff =  775 ) =  0.9402 +/- 0.0061\n",
       "sample size:  1557\n",
       "eff (cutoff =  800 ) =  0.939 +/- 0.0061\n",
+      "sample size:  1593\n",
+      "eff (cutoff =  825 ) =  0.9379 +/- 0.006\n",
       "sample size:  1628\n",
       "eff (cutoff =  850 ) =  0.9373 +/- 0.006\n",
+      "sample size:  1656\n",
+      "eff (cutoff =  875 ) =  0.9384 +/- 0.0059\n",
       "sample size:  1690\n",
       "eff (cutoff =  900 ) =  0.9385 +/- 0.0058\n",
+      "sample size:  1721\n",
+      "eff (cutoff =  925 ) =  0.9378 +/- 0.0058\n",
       "sample size:  1745\n",
       "eff (cutoff =  950 ) =  0.9381 +/- 0.0058\n",
+      "sample size:  1769\n",
+      "eff (cutoff =  975 ) =  0.9378 +/- 0.0057\n",
       "sample size:  1796\n",
       "eff (cutoff =  1000 ) =  0.9365 +/- 0.0058\n",
+      "sample size:  1838\n",
+      "eff (cutoff =  1025 ) =  0.9374 +/- 0.0056\n",
       "\n",
       "cutoff energy = 350MeV, sample size: 846\n",
       "eff =  0.9433 +/- 0.008\n"
@@ -397,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -441,7 +495,7 @@
        "<Array [{energy: 2.58e+04, ...}, ..., {...}] type='1418 * {energy: float64,...'>"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -490,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -525,7 +579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -568,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -614,12 +668,79 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#try to find a split between energy lost before and after the magnet"
+    "#try to find a split between energy lost before and after the magnet (z~5000mm)\n",
+    "\n",
+    "upstream_found = ak.ArrayBuilder()\n",
+    "downstream_found = ak.ArrayBuilder()\n",
+    "\n",
+    "for itr in range(ak.num(brem_f, axis=0)):\n",
+    "    upstream_found.begin_record()\n",
+    "    upstream_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
+    "    \n",
+    "    downstream_found.begin_record()\n",
+    "    downstream_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
+    "    \n",
+    "    upstream_found.field(\"brem_photons_pe\")\n",
+    "    downstream_found.field(\"brem_photons_pe\")\n",
+    "    upstream_found.begin_list()\n",
+    "    downstream_found.begin_list()\n",
+    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
+    "        if (brem_f[itr, \"brem_vtx_z\", jentry]>5000):\n",
+    "            if brem_f[itr, \"brem_vtx_z\", jentry]<9500:\n",
+    "                downstream_found.real(brem_f[itr,\"brem_photons_pe\",jentry])\n",
+    "            else:\n",
+    "                continue\n",
+    "        else:\n",
+    "            upstream_found.real(brem_f[itr,\"brem_photons_pe\", jentry])            \n",
+    "    upstream_found.end_list()\n",
+    "    downstream_found.end_list()\n",
+    "    \n",
+    "    upstream_found.field(\"brem_vtx_z\")\n",
+    "    downstream_found.field(\"brem_vtx_z\")\n",
+    "    upstream_found.begin_list()\n",
+    "    downstream_found.begin_list()\n",
+    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
+    "        if brem_f[itr, \"brem_vtx_z\", jentry]>5000:\n",
+    "            if brem_f[itr,\"brem_vtx_z\",jentry]<9500:\n",
+    "                downstream_found.real(brem_f[itr,\"brem_vtx_z\",jentry])\n",
+    "            else:\n",
+    "                continue\n",
+    "        else:\n",
+    "            upstream_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
+    "    upstream_found.end_list()\n",
+    "    downstream_found.end_list()\n",
+    "    upstream_found.end_record()\n",
+    "    downstream_found.end_record()\n",
+    "    \n",
+    "\n",
+    "upstream_found = ak.Array(upstream_found)\n",
+    "downstream_found = ak.Array(downstream_found)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "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": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -657,7 +778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -688,12 +809,9 @@
     "sf_vtx_type_found = scifi_found[\"all_endvtx_types\"]\n",
     "\n",
     "\n",
-    "brem_vtx_type_found = scifi_found[scifi_found[\"endvtx_type\"]==101]\n",
-    "\n",
     "sf_energy_lost = ak.to_numpy(scifi_lost[\"energy\"])\n",
     "sf_eph_lost = ak.to_numpy(ak.sum(scifi_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
     "sf_vtx_type_lost = scifi_lost[\"all_endvtx_types\"]\n",
-    "brem_vtx_type_lost = scifi_lost[scifi_lost[\"endvtx_type\"]==101]\n",
     "\n",
     "\n",
     "\n",
@@ -703,9 +821,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>[101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 0]\n",
+       "------------------\n",
+       "type: 11 * float32</pre>"
+      ],
+      "text/plain": [
+       "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ak.num(scifi_found[\"energy\"], axis=0)\n",
     "scifi_found[\"all_endvtx_types\"][1,:]"
@@ -713,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -772,9 +916,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>[101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 101,\n",
+       " 0]\n",
+       "------------------\n",
+       "type: 11 * float64</pre>"
+      ],
+      "text/plain": [
+       "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "vtx_types_found[0]"
    ]
@@ -791,9 +961,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#b parameter des fits [:,1] hat für lost eine breitere Verteilung. Warum?\n",
     "#evtl multiple scattering candidates (lost); findet man einen gewissen endvtx_type (mult scattering)\n",
@@ -860,9 +1041,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "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",
@@ -900,9 +1092,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WqOQCAwNttu3evdv8/J577tFXX32lmTNn6uabb1aNGjVkMpnUt29fqyKOJJt3l4qOjtbChQv15JNPqnv37qpVq5aqVKmisWPH2uyjdu3aFs89PT2LbL98+bKqVaumP/74Q4ZhKCAgwOb+5i8dKcoff/yhM2fOmPsuqOD16652N618eXl5ioyM1O+//66ZM2fqhhtukK+vr/Ly8tS5c2ebx+HvTp48qZCQEKv2gq/hH3/8IUkaMmRIoX2dOnXKYjmIvfuwYMECNWjQQKtWrdKLL74ob29v9e7dWy+//LKuv/568/JUV7vwtmR7H+fPn6/HHntM48eP1zPPPKO6devKzc1NM2fOtPhHx59//qng4GBVqXL1776+//57ZWRk6LnnnnPJ4wAAqDzI86yVVp6Xz9bc8l+Hv19+pKD8nxV8zdzd3VWnTh2Ltj/++EOffvppoYW5ku5Dt27d9NFHH2nBggUaOXKksrKy1KZNGz311FMaMWKEpL9yokaNGtnVX1mytY+pqanq2rWrWrRooVdffVVNmjSRt7e3vv/+ez388MPm92Zx8tfs7GytWrVKbdq0MRdNgbJEEQ+o5NLT02225ScLZ8+e1WeffaZZs2Zp2rRp5pisrCydOnXKZp+2vsHKv0bb888/b9GekZGhmjVrXsMeWKpbt65MJpO2bNli81ob9lx/o27duqpTp47Wr19v8+fVq1e3eG7vHdn27t2r3bt3a9myZRo1apS5/ddff7Vr+zp16hT6ev1d/rVrXnvtNas7g+UrmGDauw++vr6aPXu2Zs+erT/++MN8Vt6AAQN04MAB893FfvvtN7v6K0uFvS8jIiK0ePFii/aC156pV6+etm7dqry8vKsW8oYNG6bAwEA99dRTysvLM9+wAwCAskaeZ7uP0sjz8uV/mfp3+a9DwWLc3+X/LD09XfXr1ze35+bmWhX/6tatq3bt2um5556z2VdwcLDF8+Lsw8CBAzVw4EBlZWXp22+/1dy5c3XPPfeoSZMmCgsLU7169cpNnvfRRx/pwoULWrNmjXnliyTt2rXLIq44+Wv+jUF69+6t22+/XevXr1etWrWubfJAMbCcFqjgvLy8ijzDa8WKFTIMw/z86NGj2rZtm/mC/iaTSYZhWCVFb731ll132cpnMpms+vj8889tnqZ/Lfr37y/DMHT8+HF17NjR6nHDDTeYYws7Nv3799fJkyd15coVm30UvDizvfKTi4LH4Y033rBr+x49euinn36y+PZckj788EOL57feeqtq1qypffv22Zx/x44dC/32uTgCAgI0evRojRgxQgcPHtTFixfVvHlzNWvWTG+//baysrKueYziuNp73RZb78sff/xRycnJFm19+vTR5cuXtWzZMrv6nTFjhuLi4vT0008rJiamWHMCAMBe5Hmuk+flO3funD755BOLtg8//FBVqlRRt27dCt0u/zX54IMPLNr//e9/W9xxNn8f9u7dq2bNmtnch4JFvJLw8vJS9+7d9eKLL0qS+a7Gffr00c8//2xxs4uykP/+Kk6uZyv3NgxDb775pkVceHi4/Pz8tGTJEovfl8J06NBBSUlJ+u233xQREaETJ07YPSfgWnEmHlDB3XDDDVq5cqVWrVqlpk2bytvb2yLBOXHihAYNGqRx48bp7NmzmjVrlry9vc2Fhxo1aqhbt256+eWXVbduXTVp0kRJSUmKj48v1jer/fv317Jly9SyZUu1a9dOO3bs0Msvv+zw5Ya33nqrHnjgAd13333avn27unXrJl9fX6WlpWnr1q264YYb9NBDD5mPzZo1a7R48WKFhoaqSpUq6tixo4YPH64PPvhAffv21aOPPqpbbrlFHh4e+u2337Rp0yYNHDhQgwYNKvbcWrZsqWbNmmnatGkyDEO1a9fWp59+qoSEBLu2nzx5st5++23169dPzz77rPnutAcOHLCIq1atml577TWNGjVKp06d0pAhQ8x3Yt29e7f+/PNPqzPP7NWpUyf1799f7dq1U61atbR//369//77CgsLU9WqVSVJCxcu1IABA9S5c2dNmTJFjRo1Umpqqr788kurxNSRrvZet6V///565plnNGvWLHXv3l0HDx7UnDlzFBISYpEwjxgxQu+8847Gjx+vgwcPqkePHsrLy9N3332nVq1aafjw4VZ9P/roo6pWrZoeeOABnT9/XgsWLCj2t/kAABSFPM918rx8derU0UMPPaTU1FQ1b95c69at05tvvqmHHnqoyGWorVq10j//+U/FxcXJw8NDt99+u/bu3Wu+o+zfzZkzRwkJCQoPD9ekSZPUokULXb58WUeOHNG6deu0ZMmSEh37p59+Wr/99pt69uypBg0a6MyZM3r11Vfl4eGh7t27S/orH121apUGDhyoadOm6ZZbbtGlS5eUlJSk/v37q0ePHsUe1x757+tXX31Vo0aNkoeHh1q0aGF15uTf9erVS56enhoxYoSmTp2qy5cva/HixTp9+rRFXLVq1fTKK69o7Nixuv322zVu3DgFBATo119/1e7du23eVbhVq1basmWLbr/9dnXr1k0bN27kMiooG865nwaAa1Gcu5YdOXLEiIyMNKpXr25IMt+lK7+P999/35g0aZJRr149w8vLy+jatauxfft2iz5+++03Y/DgwUatWrWM6tWrG3fccYexd+9eo3HjxhZ3ysq/u9QPP/xgNefTp08bY8aMMfz9/Y2qVasaXbp0MbZs2WJ159HC9q2wvgu7o+nbb79tdOrUyfD19TV8fHyMZs2aGSNHjrTYt1OnThlDhgwxatasaZhMJotjl5OTY8ybN89o37694e3tbVSrVs1o2bKl8eCDDxq//PKLOa5x48ZGv379rPa3MPv27TN69eplVK9e3ahVq5bxj3/8w0hNTbW6g9fVtvf29jZq165tjBkzxvj4448t7k6bLykpyejXr59Ru3Ztw8PDw6hfv77Rr18/i2Nb2PErzLRp04yOHTsatWrVMry8vIymTZsaU6ZMMTIyMizikpOTjT59+hh+fn6Gl5eX0axZM4u7rZXG3Wmv9l4v+J4yDMPIysoyHn/8caN+/fqGt7e3cdNNNxkfffSRMWrUKKs72l26dMl4+umnjeuvv97w9PQ06tSpY9x2223Gtm3bzDH6/3en/bsVK1YY7u7uxn333WdcuXLlqvsBAKjcyPOs97m85Hndu3c32rRpYyQmJhodO3Y0vLy8jKCgIGP69OlGTk7OVbfPysoyHnvsMcPf39/w9vY2OnfubCQnJ1u9FoZhGH/++acxadIkIyQkxPDw8DBq165thIaGGk899ZRx/vx5wzAKvyNsYT777DOjT58+Rv369Q1PT0/D39/f6Nu3r7FlyxaLuNOnTxuPPvqo0ahRI8PDw8Pw9/c3+vXrZxw4cMAcUzC3vda70xqGYcTExBjBwcFGlSpVLHLfol6nTz/91Pw6169f33jiiSeML774wmbuvG7dOqN79+6Gr6+vUbVqVaN169YWd5/9+91p8/32229Gy5YtjSZNmhiHDh266j4A18pkGHacLwqgwklMTFSPHj30n//8p8gbIAAAAKB8Ic8DgIqJa+IBAAAAAAAALo4iHgAAAAAAAODiWE4LAAAAAAAAuDjOxAMAAAAAAABcHEU8AAAAAAAAwMVRxAMAAAAAAABcnLuzJ2DLokWL9PLLLystLU1t2rRRXFycunbtWmh8UlKSoqOj9dNPPyk4OFhTp07V+PHjLWJWr16tmTNn6tChQ2rWrJmee+45DRo0qFjjmkwmm+O/9NJLeuKJJ+zat7y8PP3++++qXr16of0BAAD8nWEYOnfunIKDg1WlCt/BuiryPAAAUFzFyvMMF7Ny5UrDw8PDePPNN419+/YZjz76qOHr62scPXrUZvz//vc/o2rVqsajjz5q7Nu3z3jzzTcNDw8P47///a85Ztu2bYabm5vx/PPPG/v37zeef/55w93d3fj222+LNW5aWprF4+233zZMJpNx6NAhu/fv2LFjhiQePHjw4MGDB49iP44dO1aC7AplhTyPBw8ePHjw4FHShz15nsvdnbZTp0666aabtHjxYnNbq1atdNddd2nu3LlW8U8++aQ++eQT7d+/39w2fvx47d69W8nJyZKkYcOGKTMzU1988YU55o477lCtWrW0YsWKEo0rSXfddZfOnTunr776qtD9ycrKUlZWlvn52bNn1ahRIx07dkw1atS42uEAAABQZmamGjZsqDNnzsjPz8/Z00Ehzp49q5o1a5LnAQAAuxUnz3Op5bTZ2dnasWOHpk2bZtEeGRmpbdu22dwmOTlZkZGRFm29e/dWfHy8cnJy5OHhoeTkZE2ZMsUqJi4ursTj/vHHH/r888/17rvvFrlPc+fO1ezZs63aa9SoQXIHAACKhSWari3/9SHPAwAAxWVPnudSF1XJyMjQlStXFBAQYNEeEBCg9PR0m9ukp6fbjM/NzVVGRkaRMfl9lmTcd999V9WrV9fdd99d5D7FxMTo7Nmz5sexY8eKjAcAAAAAAAAKcqkz8fIVrD4ahlFkRdJWfMF2e/oszrhvv/227r33Xnl7exc6L0ny8vKSl5dXkTEAAAAAAABAUVyqiFe3bl25ublZnf124sQJq7Pk8gUGBtqMd3d3V506dYqMye+zuONu2bJFBw8e1KpVq4q3gwAAAAAAAEAJuFQRz9PTU6GhoUpISNCgQYPM7QkJCRo4cKDNbcLCwvTpp59atG3YsEEdO3aUh4eHOSYhIcHiungbNmxQeHh4icaNj49XaGio2rdvX/KdBQCglBmGodzcXF25csXZU8FVuLm5yd3dnWveAQAAu5DnlR+OzPNcqognSdHR0YqKilLHjh0VFhampUuXKjU1VePHj5f01zXmjh8/rvfee0/SX3eiff311xUdHa1x48YpOTlZ8fHx5rvOStKjjz6qbt266cUXX9TAgQP18ccfa+PGjdq6davd4+bLzMzUf/7zH73yyitlcDQAACiZ7OxspaW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+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, figsize=(15,10))\n",
     "\n",
diff --git a/B_tasks.ipynb b/B_tasks.ipynb
index ae0f6a2..a36c251 100644
--- a/B_tasks.ipynb
+++ b/B_tasks.ipynb
@@ -669,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {