From 9ff46dbd1517f97cdfeaee8490a0bc688f264dfd Mon Sep 17 00:00:00 2001
From: cetin <cetin@physi.uni-heidelberg.de>
Date: Thu, 26 Oct 2023 10:59:21 +0200
Subject: [PATCH] rework

---
 .vscode/settings.json |   2 +-
 B_rework.ipynb        | 164 +++++++++++++++++++++++++++---------------
 2 files changed, 107 insertions(+), 59 deletions(-)

diff --git a/.vscode/settings.json b/.vscode/settings.json
index 644e69c..60739cd 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -1,3 +1,3 @@
 {
-	"python.envFile": "/auto/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/"
+	"python.envFile": "/auto/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/",
 }
\ No newline at end of file
diff --git a/B_rework.ipynb b/B_rework.ipynb
index 918c353..56b7907 100644
--- a/B_rework.ipynb
+++ b/B_rework.ipynb
@@ -2,9 +2,17 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Bad value in file '/home/lhcb/cetin/.config/matplotlib/matplotlibrc', line 267 ('font.size:    large'): Key font.size: Could not convert 'large' to float\n"
+     ]
+    }
+   ],
    "source": [
     "import uproot\t\n",
     "import numpy as np\n",
@@ -22,7 +30,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/lhcb/cetin/.config/matplotlib/matplotlibrc'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "matplotlib.matplotlib_fname()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -31,7 +59,7 @@
        "10522"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -50,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -78,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +128,7 @@
        "<Record {energy: 4.62e+04, ...} type='{energy: float64, photon_length: int6...'>"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -163,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -234,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -243,7 +271,7 @@
        "9056"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -254,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +291,7 @@
        "'\\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": 56,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -280,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -313,7 +341,7 @@
     "deff_found = []\n",
     "\n",
     "\n",
-    "for cutoff_energy in range(0,10050,200):\n",
+    "for cutoff_energy in range(0,100050,1000):\n",
     "\tnobrem_f = acc_brem_found[ak.sum(acc_brem_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
     "\tnobrem_l = acc_brem_lost[ak.sum(acc_brem_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
     "\n",
@@ -346,12 +374,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -361,17 +389,26 @@
     }
    ],
    "source": [
-    "x_ = np.arange(0,10050,step=200)\n",
+    "x_ = np.arange(0,100050,step=1000)\n",
     "\n",
-    "plt.errorbar(x_,efficiencies_found, yerr=deff_found, ls=\"\", capsize=1,fmt=\".\")\t\n",
+    "plt.errorbar(x_,efficiencies_found, yerr=deff_found, ls=\"\", fmt=\".\", label=\"$\\epsilon(E_{\\gamma,tot})$ \") #, capsize=1)\t\n",
     "plt.xlabel(\"cutoff energy [MeV]\")\n",
     "plt.ylabel(r\"$\\epsilon$\")\n",
     "plt.title(r'$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
-    "plt.ylim([0.8,1])\n",
-    "plt.xlim([0,10100])\n",
-    "plt.yticks(np.arange(0.8,1.01,step=0.02),minor=False)\n",
-    "plt.xticks(np.arange(0,10100,step=200),minor=True)\n",
-    "plt.grid()\n",
+    "plt.ylim([0,1.1])\n",
+    "#plt.xlim([0,10100])\n",
+    "\"\"\"\n",
+    "plt.yticks(np.arange(0,1.04,step=0.02),minor=True)\n",
+    "plt.xticks(np.arange(-200,10400,step=200),minor=True)\n",
+    "\n",
+    "plt.tick_params(left=True, bottom=True, top=True, right=True,\n",
+    "\t\t\t\tlabelleft=True, labelbottom=True, labeltop=False, labelright=False,\n",
+    "\t\t\t\twhich=\"major\",direction=\"in\", length=12, labelsize=\"large\")\n",
+    "plt.tick_params(left=True, bottom=True, top=True, right=True,\n",
+    "\t\t\t\tlabelleft=True, labelbottom=True, labeltop=False, labelright=False,\n",
+    "\t\t\t\twhich=\"minor\",direction=\"in\", length=6)\n",
+    "\"\"\"\n",
+    "plt.legend()\n",
     "plt.show()"
    ]
   },
@@ -384,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -428,7 +465,7 @@
        "<Array [{energy: 2.58e+04, ...}, ..., {...}] type='1430 * {energy: float64,...'>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -456,7 +493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -477,12 +514,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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IKgAAwLZa/VU/YbxWOCzQXwAIY/UX2NrwTrWtdkalbnVyVVWVxZW0bnX9ZdU9ACAYWu2MSnR0tNq1a+f5LpvExESf29CfjdvtVk1NjU6fPs3lyWdhGIaqqqpUUlKidu3aKTo62uqSAACtUKsNKpLUpUsXSfKEleYyDENffvmlEhISmh1uIlW7du08fQYAINBadVBxOBxKT09XWlpag1/SdzYul0vr1q3TxRdfzCmNRjidTmZSAABB1aqDSp3o6Gi/PlCjo6N15swZxcfHE1QAALAQCzAAAIBtEVQAAIBtEVQAAIBtEVQAAIBtEVQAAIBtRcRVPwAARJz6t8cPU8yoAAAA2yKoAAAA2yKoAAAA2yKoAAAA2yKoAAAA2yKoAAAA2yKoAAAA2yKoAAAA27JNUJk3b54cDoemTZtmdSkAAMAmbBFUtm3bphdffFGDBw+2uhQAAGAjlgeVyspK3XTTTVq4cKHat29vdTkAAMBGLP+un5ycHF199dW6/PLL9eijjza6b3V1taqrqz2Py8vLJUkul0sulytgNdWNFcgx4Ys+hw69Dg36HDr0uhncJuYi6vUzWH32ZzyHYRhGQI/uh9dee01z587Vtm3bFB8frzFjxmjIkCH67W9/2+D+ubm5mjNnjs/2JUuWKDExMcjVAgCAQKiqqtLEiRNVVlam5OTkRve1LKgUFxdr2LBhWrNmjbKysiSpyaDS0IxKRkaGSktLm3yj/nC5XMrLy9PYsWPldDoDNi680efQodehQZ9Dh143w5Z5/r9m5Cyvh8Hqc3l5uTp27NisoGLZqZ/CwkKVlJRo6NChnm21tbVat26dfve736m6ulrR0dFer4mLi1NcXJzPWE6nMyi/qMEaF97oc+jQ69Cgz6FDrxsR5fb/NWfpZaD77M9YlgWVb33rW/roo4+8tt1yyy0699xzdd999/mEFAAAEHksCypt27bVwIEDvba1adNGqampPtsBAEBksvzyZAAAgLOx/PLkr8vPz7e6BAAAYCPMqAAAANsiqAAAANsiqAAAANsiqAAAANsiqAAAANsiqAAAANsiqAAAANsiqAAAANsiqAAAANsiqAAAANsiqAAAANsiqAAAANsiqAAAANsiqAAAANsiqAAAANsiqAAAANuKsboAAADQiE25vttGNbCtlWJGBQAA2BZBBQAA2BZBBQAA2BZBBQAA2BZBBQAA2BZBBQAA2BZBBQAA2BZBBQAA2BZBBQAA2BZBBQAA2Ba30AcAINw1dJv9VoIZFQAAYFsEFQAAYFthGVQWLFigzMxMDR8+3OpSAABAEIVlUMnJyVFRUZG2bdtmdSkAACCIwjKoAACAyEBQAQAAtkVQAQAAtkVQAQAAtkVQAQAAtsWdaQEAsJPm3GW2Fd+Jtj5mVAAAgG0RVAAAgG0RVAAAgG0RVAAAgG0RVAAAgG0RVAAAgG0RVAAAgG0RVAAAgG0RVAAAgG0RVAAAgG0RVAAAgG35/V0/n3zyif785z9r/fr1+te//qWqqip16tRJ3/zmN3XllVfquuuuU1xcXDBqBQAAEabZMyo7d+7U2LFjlZWVpXXr1mn48OGaNm2aHnnkEf3whz+UYRiaPXu2unbtqvnz56u6ujqYdQMAgAjQ7BmV73znO5oxY4aWLl2qDh06nHW/9957T08++aSeeOIJPfDAAwEpEgAARKZmB5V9+/YpNja2yf2ys7OVnZ2tmpqaFhUGAADQ7FM/zQkpLdkfAACgvmbPqDz99NPNHnTq1KmmigEAAPi6ZgeVJ5980uvx0aNHVVVVpXbt2kmSTp48qcTERKWlpRFUAABAQDT71M+BAwc8P3PnztWQIUO0Z88eHT9+XMePH9eePXt0/vnn65FHHglmvQAAIIKYuuHbQw89pGeeeUb9+/f3bOvfv7+efPJJPfjggwErDgAARDZTQeXw4cNyuVw+22tra/XFF1+0uCgAAADJZFD51re+pdtvv13bt2+XYRiSpO3bt+vOO+/U5ZdfHtACAQBA5DIVVF5++WV169ZNI0aMUHx8vOLi4jRy5Eilp6frpZdeCnSNAAAgQvn9XT+S1KlTJ61evVp79+7Vxx9/LMMwdN5556lfv36Brg8AAEQwU0GlzjnnnCPDMNS7d2/FxLRoKAAAAB+mTv1UVVVpypQpSkxM1IABA3Tw4EFJX93o7bHHHmv2OM8995wGDx6s5ORkJScnKzs7W2+99ZaZkgAAQCtkKqjMmjVLH3zwgfLz8xUfH+/Zfvnll2vp0qXNHqd79+567LHHtH37dm3fvl2XXXaZrr32Wu3evdtMWQAAoJUxdb5m5cqVWrp0qS644AI5HA7P9szMTO3fv7/Z41xzzTVej+fOnavnnntOmzdv1oABA8yUBgAAWhFTQeXo0aNKS0vz2X7q1Cmv4OKP2tpa/fWvf9WpU6eUnZ3d4D7V1dWqrq72PC4vL5ckuVyuBu/rYlbdWIEcE77oc+jQ69Cgz6HTqnvtNnWyIzDq9TNYffZnPIdRdyMUP1xyySX63ve+p7vvvltt27bVhx9+qJ49e+qnP/2pPv30U7399tvNHuujjz5Sdna2Tp8+raSkJC1ZskTjx49vcN/c3FzNmTPHZ/uSJUuUmJjo79sAAAAWqKqq0sSJE1VWVqbk5ORG9zUVVDZt2qSrrrpKN910kxYvXqw777xTu3fv1nvvvaeCggINHTq02WPV1NTo4MGDOnnypJYtW6aXXnpJBQUFyszM9Nm3oRmVjIwMlZaWNvlG/eFyuZSXl6exY8fK6XQGbFx4o8+hQ69Dgz6HTqvu9ZZ51h175Cyvh8Hqc3l5uTp27NisoGLq1M+oUaO0ceNG/frXv1bv3r21Zs0anX/++Xrvvfc0aNAgv8aKjY1Vnz59JEnDhg3Ttm3b9NRTT+mFF17w2TcuLk5xcXE+251OZ1B+UYM1LrzR59Ch16FBn0Mn7Hq9Kdf78ahc332i3KGopGFn6WWg++zPWKZvfjJo0CD9/ve/N/vyszIMw2vWBAAARK4W3aWtpKREJSUlcru909/gwYOb9foHHnhA48aNU0ZGhioqKvTaa68pPz/frzUuAACg9TIVVAoLCzV58mTt2bNH9Ze4OBwO1dbWNmucL774QpMmTdLhw4eVkpKiwYMH6+2339bYsWPNlAUAAFoZU0HllltuUb9+/bRo0SJ17tzZ9CXJixYtMvU6AAAQGUwFlQMHDmj58uWeRbAAAADBYOquMt/61rf0wQcfBLoWAAAAL6ZmVF566SVNnjxZu3bt0sCBA30uM5owYUJAigMAAJHNVFDZtGmTNmzY0OA3HfuzmBYAAKAxpk79TJ061XO1jtvt9vohpAAAgEAxFVSOHTumn/3sZ+rcuXOg6wEAAPAwFVS++93vau3atYGuBQAAwIupNSr9+vXTrFmztGHDBg0aNMhnMe3UqVMDUhwAAIhspq/6SUpKUkFBgQoKCryeczgcBBUAABAQfgcVwzC0du1apaWlKTExMRg1AQAASDKxRsUwDPXr10+ff/55MOoBAADw8DuoREVFqW/fvjp27Fgw6gEAAPAwddXPr371K82YMUO7du0KdD0AAAAephbT/vCHP1RVVZWysrIUGxurhIQEr+ePHz8ekOIAAEBkMxVUfvvb3wa4DAAAAF+mgsrkyZMDXQcAAIAPU0FFkmpra7Vy5Urt2bNHDodDmZmZmjBhgqKjowNZHwAAiGCmgsqnn36q8ePH6/PPP1f//v1lGIb27t2rjIwM/f3vf1fv3r0DXScAAIhApr89uXfv3iouLtaOHTu0c+dOHTx4UD179uSutAAAIGBMzagUFBRo8+bN6tChg2dbamqqHnvsMY0ePTpgxQEAgMhmakYlLi5OFRUVPtsrKysVGxvb4qIAAAAkkzMq3/72t3XHHXdo0aJFGjFihCRpy5YtuuuuuzRhwoSAFggAgO1syvXdNqqBbWgxUzMqTz/9tHr37q3s7GzFx8crPj5eo0ePVp8+ffTUU08FukYAABChTM2otGvXTm+88Yb27dunjz/+WIZhKDMzU3369Al0fQAAIIKZvo+KJPXt21d9+/YNVC0AAABeTAWV2tpaLV68WO+++65KSkrkdru9nv/nP/8ZkOIAAEBkMxVU7rnnHi1evFhXX321Bg4cKIfDEei6AAAAzAWV1157TX/5y180fvz4QNcDAADgYeqqn9jYWBbOAgCAoDMVVO6991499dRTMgwj0PUAAAB4mDr1s2HDBq1du1ZvvfWWBgwYIKfT6fX88uXLA1Lc2SxYsEALFixQbW1tUI8DAACsZfo+Kv/zP/8T6FqaLScnRzk5OSovL1dKSopldQAAgOAyFVReeeWVQNcBAADgw9QaFQAAgFBodlC56qqrtGnTpib3q6io0Pz587VgwYIWFQYAANDsUz/XX3+9brjhBrVt21YTJkzQsGHD1LVrV8XHx+vEiRMqKirShg0btHr1an3729/W448/Hsy6AQBABGh2UJkyZYomTZqk119/XUuXLtXChQt18uRJSZLD4VBmZqauvPJKFRYWqn///sGqFwAABMumXO/H7ihJWVZU4uHXYtrY2FhNnDhREydOlCSVlZXpyy+/VGpqqs8lygAAAC3Vom9PTklJ4fJgAAAQNFz1AwAAbIugAgAAbIugAgAAbKtFa1QAAAh79a90kaRRDWwL1bHhxdSMSnFxsQ4dOuR5vHXrVk2bNk0vvvhiwAoDAAAwFVQmTpyotWvXSpKOHDmisWPHauvWrXrggQf0y1/+MqAFAgCAyGUqqOzatUsjRoyQJP3lL3/RwIEDtWnTJi1ZskSLFy8OZH0AACCCmQoqLpdLcXFxkqR//OMfmjBhgiTp3HPP1eHDhwNXHQAAiGimFtMOGDBAzz//vK6++mrl5eXpkUcekST95z//UWpqakALBAAgLNRfGBuqBbmtnKkZlfnz5+uFF17QmDFj9IMf/EBZWV99D8CqVas8p4QAAABaytSMypgxY1RaWqry8nK1b9/es/2OO+5QmzZtAlYcAACIbKZmVC677DJVVFR4hRRJ6tChg2688caAFAYAAGAqqOTn56umpsZn++nTp7V+/foWFwUAACD5eernww8/9Px7UVGRjhw54nlcW1urt99+W926dQtcdQAA2AF3kLWMX0FlyJAhcjgccjgcuuyyy3yeT0hI0DPPPBOw4gAAQGTzK6gcOHBAhmGoV69e2rp1qzp16uR5LjY2VmlpaYqOjg54kQAAIDL5FVR69OghSXK73UEpBgAA4OtMf3vy3r17lZ+fr5KSEp/g8otf/KLFhQEAAJgKKgsXLtSPf/xjdezYUV26dJHD4fA853A4CCoAgPAWiMWzLMANCFNB5dFHH9XcuXN13333BboeAAAAD1P3UTlx4oSuv/76QNcCAADgxVRQuf7667VmzZpA1wIAAODF1KmfPn366KGHHtLmzZs1aNAgOZ1Or+enTp0akOIAAEBkMxVUXnzxRSUlJamgoEAFBQVezzkcjmYHlXnz5mn58uX6+OOPlZCQoFGjRmn+/Pnq37+/mbIAAEArYyqoHDhwICAHLygoUE5OjoYPH64zZ85o9uzZuuKKK1RUVMS3MAMAAPP3UQmEt99+2+vxK6+8orS0NBUWFuriiy+2qCoAAGAXpoLKrbfe2ujzL7/8sqliysrKJEkdOnQw9XoAANC6mAoqJ06c8Hrscrm0a9cunTx5ssEvK2wOwzA0ffp0XXjhhRo4cGCD+1RXV6u6utrzuLy83HN8l8tl6rgNqRsrkGPCF30OHXodGvQ5dALaa7epC2Ajguv/exPo32l/xnMYhmEE4qBut1s/+clP1KtXL82cOdPv1+fk5Ojvf/+7NmzYoO7duze4T25urubMmeOzfcmSJUpMTPT7mAAAIPSqqqo0ceJElZWVKTk5udF9AxZUJOmTTz7RmDFjdPjwYb9ed/fdd2vlypVat26devbsedb9GppRycjIUGlpaZNv1B8ul0t5eXkaO3asz6XXCBz6HDr0OjToc5Btmef5V5c7SnknBgWm118bF94C2uevKS8vV8eOHZsVVAK6mHb//v06c+ZMs/c3DEN33323VqxYofz8/EZDiiTFxcUpLi7OZ7vT6QzKXwrBGhfe6HPo0OvQoM9BEuX22RSQXjcwLrwF+nfan7FMBZXp06d7PTYMQ4cPH9bf//53TZ48udnj5OTkaMmSJXrjjTfUtm1bHTlyRJKUkpKihIQEM6XZUm5u448BAEDDTAWVnTt3ej2OiopSp06d9MQTTzR5RdDXPffcc5KkMWPGeG1/5ZVXdPPNN5spDQAAtCKmgsratWsDcvAALo8BAACtUIvWqBw9elSffPKJHA6H+vXrp06dOgWqrojD6SEAYWlTrvfjUbkN7WUv9WuGrZm6ePzUqVO69dZblZ6erosvvlgXXXSRunbtqilTpqiqqirQNQIAgAhlejFtQUGB/va3v2n06NGSpA0bNmjq1Km69957PWtPYF5DMyrMsgAAIo2poLJs2TK9/vrrXotgx48fr4SEBN1www0EFQAAEBCmTv1UVVWpc+fOPtvT0tI49QMAAALG1IxKdna2Hn74Yf3hD39QfHy8JOnLL7/UnDlzlJ2dHdACAQAwjYWzYc9UUHnqqad01VVXqXv37srKypLD4dD777+v+Ph4vfPOO4GuEQAARChTQWXgwIHat2+f/vSnP+njjz+WYRj6/ve/r5tuuqlV3VEWAABYy/R9VBISEnT77bcHshYAAAAvphbTzps3Ty+//LLP9pdfflnz589vcVEAAACSyaDywgsv6Nxzz/XZPmDAAD3//PMtLgoAAEAyeernyJEjSk9P99neqVMnHT58uMVFhRMzt77nxm0AADSPqRmVjIwMbdy40Wf7xo0b1bVr1xYXBQAAIJmcUbnttts0bdo0uVwuXXbZZZKkd999VzNnztS9994b0ALDDbMlAAAEjqmgMnPmTB0/flw/+clPVFNTI0mKj4/Xfffdp1mzZgW0QAAAELlMBRWHw6H58+froYce0p49e5SQkKC+ffsqLi4u0PUBAIAIZvo+KpKUlJSk4cOHB6oWBICZxb0AANiVqcW0AAAAodCiGRWEJ2ZdAADhghkVAABgWwQVAABgW5z6CWOcsgEAtHbMqAAAANsiqAAAANsiqAAAANsiqAAAANtiMW0YYfEsANvblOu7bVQD24BmIqi0coQbAEA4I6ggqLgLLgCgJVijAgAAbIugAgAAbCssg8qCBQuUmZmp4cOHW10KAAAIorAMKjk5OSoqKtK2bdusLgUAAAQRi2nR4AJXFr0CAOyAoIIGNedqHa7oAQAEG0EFzUIIAQBYISzXqAAAgMhAUAEAALZFUAEAALZFUAEAALZFUAEAALZFUAEAALbF5ckIGC5hBhAwm3KtrgA2QVABQoQ7AAOA/wgqfuBDBQCA0GKNCgAAsC1mVGA7nCIBANQhqAAAgqv+wthRuQ3tBTSIoIKwwDc1A0BkIqjAcvPmSW631VUAAOyIoIKw1Jx1LKx1AYDwR1BBSH09KERFSVlZlpUCAAgDBBUAQGg1dNdZswtst8yTor527piFuq0O91EBAAC2xYwKWo1IWX/C2hsAkYSggojGh7698d8HAEEFEYUPOQAILwQVoJ5A3VyOUAQALUdQAWAJTuvI2lvLB/LKGyCICCqAhSLpqwFa83sDEDwEFaAJofw/f6tnGcweP5ICF4DQ4j4qAADAtphRAUxgBgEAQsPSoLJu3To9/vjjKiws1OHDh7VixQp95zvfsbIkwBSCCmwlUAtlrVzsC/w/S0/9nDp1SllZWfrd735nZRkAAMCmLJ1RGTdunMaNG2dlCUBEmTdPcn/t+9uYCQJgd6xRAYAAIxACgRNWQaW6ulrV1dWex+Xl5ZIkl8sll8sVsOPUjVV/zCiukQqoqCiX1z/RsPq/2g39Hjb161/3u1y/12bGnjfPd5/6r2uonub8+QnEe7VSc/vs4W5G48yoP25DY5vZp6H6GhrHjObU9/Xd//95V1M1Bqq+COXpc4D/4PkznsMwDCOgRzfJ4XA0uZg2NzdXc+bM8dm+ZMkSJSYmBrE6AAAQKFVVVZo4caLKysqUnJzc6L5hFVQamlHJyMhQaWlpk2/UHy6XS3l5eRo7dqycTqdne0P/JwnzoqJcGjQoTx99NFZut7PpF0SoWbO8H5v5PTxbr5sztpnj13+N2dc1px6z6o8diHHr/u5oqs8eW+oVMTJAb67+uA2NbWafhupraJwQcLmjlHdikMa2/0jOKHfTL4Apnj7X+zxsqfLycnXs2LFZQSWsTv3ExcUpLi7OZ7vT6QxoA882rps/C0HhdjsJKo2o/6vdkt/D+r1uzthmjj93roniTNZjVv2xA/lXSFN99qj/ARuoIhr64K4/tpl9GqrP4pDgjHITVEIg0J+z/oxlaVCprKzUp59+6nl84MABvf/+++rQoYO+8Y1vWFgZYB8sxPQfN+T7r9xFY776lzVf25ZrRSWAOZYGle3bt+vSSy/1PJ4+fbokafLkyVq8eLFFVQEAALuwNKiMGTNGNlkiAwAh45nRKB7z321T8s0P2NCdaFsoN1e+9QXhOEBTuG4LAADYVlgtpgWAYGlo3UZz1nJ8fZ+oKCkrK0AFNUPAZ2YAGyKoAAgrkbJQ1mxwCiXPQt26x1PyrSgDrRynfgAAgG0xowKgUXb7v/hIxn8LRCKCChDB+OAz6etXvxSPkTLGWFRIgNV/X36qfypI4nQQWo6gAiCsBTNsnXVsEx/ikYp1LGgpggoAtCINzWoA4YygAsA2OBUFyfpTSMwC2QtBBQCCrDUEMGZqYBWCSrgqzvfd1loW9AF2Upxvr+MH4c85IQR2RlABAMAinGZqGkEFAIAgIIQEBnemBQAAtsWMCgDYQO6iMdKaEB0rV7a6FwxrZNAYggpgF8X53o9ZHB0wQb/qpjj/q3/GGFKWpM83SOmXBvmgfirOt7qCgJr3x4uUNb5c8/54kdxnHJI4tdJaEVQAAK2S1fdjQWAQVAAAaASBx1oEFQBAxOBKnPBDUAEAhExrXTjbWt+XHRBUAq043/txa1gQWZzvuy1U78vKYyO8FOf7brPT70pxfmD2iVD1g0BUjGFNIQg5gkoj5s2T3G6rqwAAtAbNmXVhPYwvbvgGAABsi6ACAABsi1M/AIBWwcyCVhbB2h8zKgAAwLaYUQm14nzfbcG6MiGUx0LgFec3vY/dfncael2wxmmO+uPw+48AYSYmdJhRAQAAtkVQAQAAtkVQAQAAthWWQWXBggXKzMzU8OHDrS4FAAAEUVgups3JyVFOTo7Ky8uVkpJidTk4m+J878ehXEQZqYrzfbfZfQFpcb7VFXgrzre6gsYV51tdAWyoNX/ZYljOqAAAgMhAUAEAALZFUAEAALZFUAEAALYVlotpbaM4PzjjWL34sTjf2uPbWXG+7zYr/3sV51t37HBUnO/9OJj/7eofC4ApBBUAAFqZhm7xH65XAnHqBwAA2BYzKgAA2FhrvkdKcxBUAAAII5H2zc0EFX8U57eu47REcb59jm32jrf1X9fQPq1Rcb7VFdhHcb7VFQBoAmtUAACAbRFUAACAbRFUAACAbRFUAACAbbGYNtiK8+11rPr7hPKuqvWPHWNIWaE7vI/i/OCMY3Zxr90V53s/Nvu7U38cAGgEMyoAAMC2mFEBACAChOuN45hRAQAAtkVQAQAAtsWpHwAAIlC4fMMyQSXSFedbXUFwFOdH5rFDqTjf6goABFj98DL7lnXWFPI1nPoBAAC2RVABAAC2RVABAAC2RVABAAC2xWJaWO/zDdIZh/nXF+cHqpLWqTj/v19X8PkGSS3oNQCEGDMqAADAtggqAADAtggqAADAtggqAADAtggqAADAtiwPKs8++6x69uyp+Ph4DR06VOvXr7e6JAAAYBOWBpWlS5dq2rRpmj17tnbu3KmLLrpI48aN08GDB60sCwAA2ISlQeU3v/mNpkyZottuu03nnXeefvvb3yojI0PPPfeclWUBAACbsOyGbzU1NSosLNT999/vtf2KK67Qpk2bGnxNdXW1qqurPY/LysokScePH5fL5QpYbS6XS1VVVaqpOSa32/nfJ9yVATsGpCi38VWf3VFyu7kJWTDR69Cgz6FDr0PjWEWNqqqqdOzYMTmdzqZf0EwVFRWSJMMwmtzXsqBSWlqq2tpade7c2Wt7586ddeTIkQZfM2/ePM2ZM8dne8+ePYNSI0LgJasLiCD0OjToc+jQ66Cbtzi441dUVCglJaXRfSy/hb7D4Z2EDcPw2VZn1qxZmj59uuex2+3W8ePHlZqaetbXmFFeXq6MjAwVFxcrOTk5YOMG2/Dhw7Vt2zary2g2+hw69Do06HPo0OvQCFafDcNQRUWFunbt2uS+lgWVjh07Kjo62mf2pKSkxGeWpU5cXJzi4uK8trVr1y5YJSo5OTms/gBER0eHVb116HPo0OvQoM+hQ69DIxh9bmompY5li2ljY2M1dOhQ5eXleW3Py8vTqFGjLKoqvOXk5FhdQkSgz6FDr0ODPocOvfafw2jOSpYgWbp0qSZNmqTnn39e2dnZevHFF7Vw4ULt3r1bPXr0sKoslZeXKyUlRWVlZWGZfMMFfQ4deh0a9Dl06HVo2KHPlq5RufHGG3Xs2DH98pe/1OHDhzVw4ECtXr3a0pAifXWK6eGHH/Y5zYTAos+hQ69Dgz6HDr0ODTv02dIZFQAAgMZYfgt9AACAsyGoAAAA2yKoAAAA2yKoAAAA2yKoAAAA24rIoPLss8+qZ8+eio+P19ChQ7V+/fpG9y8oKNDQoUMVHx+vXr166fnnnw9RpeHPn14vX75cY8eOVadOnZScnKzs7Gy98847Iaw2fPn7O11n48aNiomJ0ZAhQ4JbYCvib6+rq6s1e/Zs9ejRQ3Fxcerdu7defvnlEFUbvvzt86uvvqqsrCwlJiYqPT1dt9xyi44dOxaiasPXunXrdM0116hr165yOBxauXJlk68J+WeiEWFee+01w+l0GgsXLjSKioqMe+65x2jTpo3x73//u8H9P/vsMyMxMdG45557jKKiImPhwoWG0+k0Xn/99RBXHn787fU999xjzJ8/39i6dauxd+9eY9asWYbT6TR27NgR4srDi799rnPy5EmjV69exhVXXGFkZWWFptgwZ6bXEyZMMEaOHGnk5eUZBw4cMLZs2WJs3LgxhFWHH3/7vH79eiMqKsp46qmnjM8++8xYv369MWDAAOM73/lOiCsPP6tXrzZmz55tLFu2zJBkrFixotH9rfhMjLigMmLECOOuu+7y2nbuueca999/f4P7z5w50zj33HO9tt15553GBRdcELQaWwt/e92QzMxMY86cOYEurVUx2+cbb7zRePDBB42HH36YoNJM/vb6rbfeMlJSUoxjx46ForxWw98+P/7440avXr28tj399NNG9+7dg1Zja9ScoGLFZ2JEnfqpqalRYWGhrrjiCq/tV1xxhTZt2tTga9577z2f/a+88kpt375dLpcraLWGOzO9rs/tdquiokIdOnQIRomtgtk+v/LKK9q/f78efvjhYJfYapjp9apVqzRs2DD96le/Urdu3dSvXz/9/Oc/15dffhmKksOSmT6PGjVKhw4d0urVq2UYhr744gu9/vrruvrqq0NRckSx4jPR0lvoh1ppaalqa2t9vp25c+fOPt/iXOfIkSMN7n/mzBmVlpYqPT09aPWGMzO9ru+JJ57QqVOndMMNNwSjxFbBTJ/37dun+++/X+vXr1dMTET9FdAiZnr92WefacOGDYqPj9eKFStUWlqqn/zkJzp+/DjrVM7CTJ9HjRqlV199VTfeeKNOnz6tM2fOaMKECXrmmWdCUXJEseIzMaJmVOo4HA6vx4Zh+Gxrav+GtsOXv72u8+c//1m5ublaunSp0tLSglVeq9HcPtfW1mrixImaM2eO+vXrF6ryWhV/fqfdbrccDodeffVVjRgxQuPHj9dvfvMbLV68mFmVJvjT56KiIk2dOlW/+MUvVFhYqLffflsHDhzQXXfdFYpSI06oPxMj6n+nOnbsqOjoaJ9UXlJS4pMQ63Tp0qXB/WNiYpSamhq0WsOdmV7XWbp0qaZMmaK//vWvuvzyy4NZZtjzt88VFRXavn27du7cqZ/+9KeSvvowNQxDMTExWrNmjS677LKQ1B5uzPxOp6enq1u3bkpJSfFsO++882QYhg4dOqS+ffsGteZwZKbP8+bN0+jRozVjxgxJ0uDBg9WmTRtddNFFevTRR5n5DiArPhMjakYlNjZWQ4cOVV5entf2vLw8jRo1qsHXZGdn++y/Zs0aDRs2TE6nM2i1hjszvZa+mkm5+eabtWTJEs4vN4O/fU5OTtZHH32k999/3/Nz1113qX///nr//fc1cuTIUJUedsz8To8ePVr/+c9/VFlZ6dm2d+9eRUVFqXv37kGtN1yZ6XNVVZWiorw/zqKjoyX99//2ERiWfCYGbZmuTdVd9rZo0SKjqKjImDZtmtGmTRvjX//6l2EYhnH//fcbkyZN8uxfdynWz372M6OoqMhYtGgRlyc3k7+9XrJkiRETE2MsWLDAOHz4sOfn5MmTVr2FsOBvn+vjqp/m87fXFRUVRvfu3Y3vfe97xu7du42CggKjb9++xm233WbVWwgL/vb5lVdeMWJiYoxnn33W2L9/v7FhwwZj2LBhxogRI6x6C2GjoqLC2Llzp7Fz505DkvGb3/zG2Llzp+dScDt8JkZcUDEMw1iwYIHRo0cPIzY21jj//PONgoICz3OTJ082LrnkEq/98/PzjW9+85tGbGyscc455xjPPfdciCsOX/70+pJLLjEk+fxMnjw59IWHGX9/p7+OoOIff3u9Z88e4/LLLzcSEhKM7t27G9OnTzeqqqpCXHX48bfPTz/9tJGZmWkkJCQY6enpxk033WQcOnQoxFWHn7Vr1zb6964dPhMdhsG8GAAAsKeIWqMCAADCC0EFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFQFBdfPHFcjgcPj833XRTk6+9+eabdf/99wdkLADhiTvTAggawzCUkpKihx9+2CdMJCUlKSkp6ayvdbvd6ty5s1atWqXs7OwWjQUgfMVYXQCA1mvfvn2qqKjQxRdfrC5duvj12o0bNyoqKsrzjc4tGQtA+OLUD4CgKSwsVExMjAYPHuz3a1etWqVrrrlGUVFRLR4LQPgiqAAImh07dqi2tlapqame0zNJSUm6/fbbm3ztqlWrdO211/o11ptvvqn+/furb9++eumll4LyngCEFmtUAATNZZddpk6dOmnu3Lle29u3b6/U1NSzvm7Pnj0aNmyYSktLlZCQ0Kyxzpw5o8zMTK1du1bJyck6//zztWXLFnXo0CHwbwxAyDCjAiBodu7cqQsvvFB9+vTx+klNTdWuXbvUu3dvHTlyRJJUWlqqIUOGqKamRqtWrdLYsWM9IaWpsSRp69atGjBggLp166a2bdtq/Pjxeueddyx53wACh6ACICg+++wznTx5Ut/85jcbfH7gwIH6/ve/r3/+85+SpDlz5ui+++5TbGys3njjDU2YMKHZY0nSf/7zH3Xr1s3zuHv37vr8888D9G4AWIWrfgAERWFhoSSpc+fOnlmTOmlpaYqKitKAAQO0d+9effrppyosLNTTTz+tkpISbdu2TStXrvRrrIbOYjscjgC/KwChRlABEBQ7duyQJPXr189ru9PpVEVFheLi4tS3b1+9+eabeuCBBzR37lw5HA797W9/08iRI5WWlubXWN26dfOaQTl06JDn0mYA4YvFtAAsc/LkSfXt21cjR47Um2++KUmaMGGCLrzwQs2cOdOvsc6cOaPzzjtP+fn5nsW0mzdvbnTRLgD7Y0YFgGXatWsnSXrsscc82y688EL94Ac/8HusmJgYPfHEE7r00kvldrs1c+ZMQgrQCjCjAsAyLpdLAwcO1CeffGJ1KQBsiqt+AFjm448/Vv/+/a0uA4CNMaMCAABsixkVAABgWwQVAABgWwQVAABgWwQVAABgWwQVAABgWwQVAABgWwQVAABgWwQVAABgWwQVAABgWwQVAABgWwQVAABgWwQVAABgW/8HLN4O6IqnNRYAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -512,12 +549,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x600 with 3 Axes>"
       ]
@@ -555,12 +592,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x600 with 3 Axes>"
       ]
@@ -604,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -633,7 +670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -676,7 +713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -700,7 +737,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -712,7 +749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -771,7 +808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -795,7 +832,7 @@
        "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -816,12 +853,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1800x600 with 3 Axes>"
       ]
@@ -892,14 +929,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "cannot convert to RegularArray because subarray lengths are not regular (in compiled code: https://github.com/scikit-hep/awkward/blob/awkward-cpp-24/awkward-cpp/src/cpu-kernels/awkward_ListOffsetArray_toRegularArray.cpp#L22)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m/work/cetin/Projektpraktikum/B_rework.ipynb Cell 26\u001b[0m line \u001b[0;36m3\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Blhcba2/work/cetin/Projektpraktikum/B_rework.ipynb#X33sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m fig, ax \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplots(nrows\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m, ncols\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m, figsize\u001b[39m=\u001b[39m(\u001b[39m18\u001b[39m,\u001b[39m6\u001b[39m))\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Blhcba2/work/cetin/Projektpraktikum/B_rework.ipynb#X33sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m a0\u001b[39m=\u001b[39max[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39;49mhist2d(bs_found, vtx_types_found, bins\u001b[39m=\u001b[39;49m\u001b[39m110\u001b[39;49m, density\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, cmap\u001b[39m=\u001b[39;49mplt\u001b[39m.\u001b[39;49mcm\u001b[39m.\u001b[39;49mjet, cmin\u001b[39m=\u001b[39;49m\u001b[39m1e-20\u001b[39;49m,vmax\u001b[39m=\u001b[39;49m\u001b[39m2\u001b[39;49m)\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Blhcba2/work/cetin/Projektpraktikum/B_rework.ipynb#X33sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m ax[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mset_ylim(\u001b[39m0\u001b[39m,\u001b[39m110\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Blhcba2/work/cetin/Projektpraktikum/B_rework.ipynb#X33sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m ax[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mset_xlim(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m,\u001b[39m1\u001b[39m)\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1462\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m   1463\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(ax, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m   1464\u001b[0m     \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m         \u001b[39mreturn\u001b[39;00m func(ax, \u001b[39m*\u001b[39;49m\u001b[39mmap\u001b[39;49m(sanitize_sequence, args), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1467\u001b[0m     bound \u001b[39m=\u001b[39m new_sig\u001b[39m.\u001b[39mbind(ax, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m   1468\u001b[0m     auto_label \u001b[39m=\u001b[39m (bound\u001b[39m.\u001b[39marguments\u001b[39m.\u001b[39mget(label_namer)\n\u001b[1;32m   1469\u001b[0m                   \u001b[39mor\u001b[39;00m bound\u001b[39m.\u001b[39mkwargs\u001b[39m.\u001b[39mget(label_namer))\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/axes/_axes.py:7191\u001b[0m, in \u001b[0;36mAxes.hist2d\u001b[0;34m(self, x, y, bins, range, density, weights, cmin, cmax, **kwargs)\u001b[0m\n\u001b[1;32m   7098\u001b[0m \u001b[39m@_preprocess_data\u001b[39m(replace_names\u001b[39m=\u001b[39m[\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mweights\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[1;32m   7099\u001b[0m \u001b[39m@_docstring\u001b[39m\u001b[39m.\u001b[39mdedent_interpd\n\u001b[1;32m   7100\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mhist2d\u001b[39m(\u001b[39mself\u001b[39m, x, y, bins\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m, \u001b[39mrange\u001b[39m\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, density\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, weights\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m   7101\u001b[0m            cmin\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, cmax\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m   7102\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   7103\u001b[0m \u001b[39m    Make a 2D histogram plot.\u001b[39;00m\n\u001b[1;32m   7104\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   7188\u001b[0m \u001b[39m      `.colors.PowerNorm`.\u001b[39;00m\n\u001b[1;32m   7189\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 7191\u001b[0m     h, xedges, yedges \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mhistogram2d(x, y, bins\u001b[39m=\u001b[39;49mbins, \u001b[39mrange\u001b[39;49m\u001b[39m=\u001b[39;49m\u001b[39mrange\u001b[39;49m,\n\u001b[1;32m   7192\u001b[0m                                        density\u001b[39m=\u001b[39;49mdensity, weights\u001b[39m=\u001b[39;49mweights)\n\u001b[1;32m   7194\u001b[0m     \u001b[39mif\u001b[39;00m cmin \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   7195\u001b[0m         h[h \u001b[39m<\u001b[39m cmin] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
+      "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mhistogram2d\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/highlevel.py:1445\u001b[0m, in \u001b[0;36mArray.__array_function__\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m   1431\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__array_function__\u001b[39m(\u001b[39mself\u001b[39m, func, types, args, kwargs):\n\u001b[1;32m   1432\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1433\u001b[0m \u001b[39m    Intercepts attempts to pass this Array to those NumPy functions other\u001b[39;00m\n\u001b[1;32m   1434\u001b[0m \u001b[39m    than universal functions that have an Awkward equivalent.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1443\u001b[0m \u001b[39m    See also #__array_ufunc__.\u001b[39;00m\n\u001b[1;32m   1444\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1445\u001b[0m     \u001b[39mreturn\u001b[39;00m ak\u001b[39m.\u001b[39;49m_connect\u001b[39m.\u001b[39;49mnumpy\u001b[39m.\u001b[39;49marray_function(\n\u001b[1;32m   1446\u001b[0m         func, types, args, kwargs, behavior\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_behavior\n\u001b[1;32m   1447\u001b[0m     )\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/_connect/numpy.py:94\u001b[0m, in \u001b[0;36marray_function\u001b[0;34m(func, types, args, kwargs, behavior)\u001b[0m\n\u001b[1;32m     91\u001b[0m unique_backends \u001b[39m=\u001b[39m \u001b[39mfrozenset\u001b[39m(_find_backends(all_arguments))\n\u001b[1;32m     92\u001b[0m backend \u001b[39m=\u001b[39m common_backend(unique_backends)\n\u001b[0;32m---> 94\u001b[0m rectilinear_args \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39;49m(_to_rectilinear(x, backend) \u001b[39mfor\u001b[39;49;00m x \u001b[39min\u001b[39;49;00m args)\n\u001b[1;32m     95\u001b[0m rectilinear_kwargs \u001b[39m=\u001b[39m {k: _to_rectilinear(v, backend) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems()}\n\u001b[1;32m     96\u001b[0m result \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39mrectilinear_args, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mrectilinear_kwargs)\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/_connect/numpy.py:94\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     91\u001b[0m unique_backends \u001b[39m=\u001b[39m \u001b[39mfrozenset\u001b[39m(_find_backends(all_arguments))\n\u001b[1;32m     92\u001b[0m backend \u001b[39m=\u001b[39m common_backend(unique_backends)\n\u001b[0;32m---> 94\u001b[0m rectilinear_args \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39m(_to_rectilinear(x, backend) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m args)\n\u001b[1;32m     95\u001b[0m rectilinear_kwargs \u001b[39m=\u001b[39m {k: _to_rectilinear(v, backend) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems()}\n\u001b[1;32m     96\u001b[0m result \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39mrectilinear_args, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mrectilinear_kwargs)\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/_connect/numpy.py:70\u001b[0m, in \u001b[0;36m_to_rectilinear\u001b[0;34m(arg, backend)\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[39m# Otherwise, cast to layout and convert\u001b[39;00m\n\u001b[1;32m     68\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     69\u001b[0m         layout \u001b[39m=\u001b[39m ak\u001b[39m.\u001b[39mto_layout(arg, allow_record\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, allow_other\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m---> 70\u001b[0m         \u001b[39mreturn\u001b[39;00m layout\u001b[39m.\u001b[39;49mto_backend(backend)\u001b[39m.\u001b[39;49mto_backend_array(allow_missing\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m     71\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(arg, \u001b[39mtuple\u001b[39m):\n\u001b[1;32m     72\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mtuple\u001b[39m(_to_rectilinear(x, backend) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m arg)\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/contents/content.py:1077\u001b[0m, in \u001b[0;36mContent.to_backend_array\u001b[0;34m(self, allow_missing, backend)\u001b[0m\n\u001b[1;32m   1075\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1076\u001b[0m     backend \u001b[39m=\u001b[39m regularize_backend(backend)\n\u001b[0;32m-> 1077\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_to_backend_array(allow_missing, backend)\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/contents/listoffsetarray.py:2060\u001b[0m, in \u001b[0;36mListOffsetArray._to_backend_array\u001b[0;34m(self, allow_missing, backend)\u001b[0m\n\u001b[1;32m   2058\u001b[0m     \u001b[39mreturn\u001b[39;00m buffer\u001b[39m.\u001b[39mview(np\u001b[39m.\u001b[39mdtype((\u001b[39m\"\u001b[39m\u001b[39mS\u001b[39m\u001b[39m\"\u001b[39m, max_count)))\n\u001b[1;32m   2059\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 2060\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mto_RegularArray()\u001b[39m.\u001b[39m_to_backend_array(allow_missing, backend)\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/contents/listoffsetarray.py:277\u001b[0m, in \u001b[0;36mListOffsetArray.to_RegularArray\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    272\u001b[0m _size \u001b[39m=\u001b[39m Index64\u001b[39m.\u001b[39mempty(\u001b[39m1\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mindex_nplike)\n\u001b[1;32m    273\u001b[0m \u001b[39massert\u001b[39;00m (\n\u001b[1;32m    274\u001b[0m     _size\u001b[39m.\u001b[39mnplike \u001b[39mis\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mindex_nplike\n\u001b[1;32m    275\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_offsets\u001b[39m.\u001b[39mnplike \u001b[39mis\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mindex_nplike\n\u001b[1;32m    276\u001b[0m )\n\u001b[0;32m--> 277\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_backend\u001b[39m.\u001b[39;49mmaybe_kernel_error(\n\u001b[1;32m    278\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_backend[\n\u001b[1;32m    279\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39mawkward_ListOffsetArray_toRegularArray\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    280\u001b[0m         _size\u001b[39m.\u001b[39;49mdtype\u001b[39m.\u001b[39;49mtype,\n\u001b[1;32m    281\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_offsets\u001b[39m.\u001b[39;49mdtype\u001b[39m.\u001b[39;49mtype,\n\u001b[1;32m    282\u001b[0m     ](\n\u001b[1;32m    283\u001b[0m         _size\u001b[39m.\u001b[39;49mdata,\n\u001b[1;32m    284\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_offsets\u001b[39m.\u001b[39;49mdata,\n\u001b[1;32m    285\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_offsets\u001b[39m.\u001b[39;49mlength,\n\u001b[1;32m    286\u001b[0m     )\n\u001b[1;32m    287\u001b[0m )\n\u001b[1;32m    288\u001b[0m size \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mindex_nplike\u001b[39m.\u001b[39mindex_as_shape_item(_size[\u001b[39m0\u001b[39m])\n\u001b[1;32m    289\u001b[0m length \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_offsets\u001b[39m.\u001b[39mlength \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/_backends/backend.py:72\u001b[0m, in \u001b[0;36mBackend.maybe_kernel_error\u001b[0;34m(self, error)\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[39mreturn\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 72\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mformat_kernel_error(error))\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot convert to RegularArray because subarray lengths are not regular (in compiled code: https://github.com/scikit-hep/awkward/blob/awkward-cpp-24/awkward-cpp/src/cpu-kernels/awkward_ListOffsetArray_toRegularArray.cpp#L22)"
+     ]
+    },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 1800x600 with 3 Axes>"
+       "<Figure size 1800x600 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -943,20 +1002,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, figsize=(15,10))\n",
     "\n",
@@ -1055,7 +1103,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.10.12"
   },
   "orig_nbformat": 4
  },