Projektpraktikum/notebooks/thesis_electrons.ipynb

{
 "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",
    "import mplhep\n",
    "from mpl_toolkits import mplot3d\n",
    "import itertools\n",
    "import awkward as ak\n",
    "import seaborn as sns\n",
    "from scipy.optimize import curve_fit\n",
    "from utils.components import unique_name_ext_re\n",
    "\n",
    "mplhep.style.use([\"LHCbTex2\"])\n",
    "plt.rcParams[\"savefig.dpi\"] = 600\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38525"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = uproot.open(\n",
    "    \"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_B_upstream.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
    "    # \"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_B_def.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
    ")\n",
    "\n",
    "# selektiere nur elektronen von B->K*ee\n",
    "allcolumns = file.arrays()\n",
    "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n",
    "\n",
    "ak.num(electrons, axis=0)\n",
    "# ak.count(found, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# electrons.type.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cut_prop: bool = electrons.p_end_velo > 0  # 3e3\n",
    "found = electrons[~electrons.lost]\n",
    "lost = electrons[electrons.lost]\n",
    "\n",
    "eloss_found = (found[\"p\"] - found[\"p_upstream\"]) / found[\"p\"]\n",
    "eloss_lost = (lost[\"p\"] - lost[\"p_upstream\"]) / lost[\"p\"]\n",
    "\n",
    "eloss = (electrons[\"p\"] - electrons[\"p_upstream\"]) / electrons[\"p\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "eloss_velo_found = (found[\"p\"] - found[\"p_end_velo\"]) / found[\"p\"]\n",
    "eloss_velo_lost = (lost[\"p\"] - lost[\"p_end_velo\"]) / lost[\"p\"]\n",
    "\n",
    "eloss_velo = (electrons[\"p\"] - electrons[\"p_end_velo\"]) / electrons[\"p\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 50\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_lost),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#F05342\",\n",
    "    label=\"lost\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "# #2A9D8F another teal color\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_found),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#107E7D\",\n",
    "    label=\"found\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "plt.legend(loc=\"upper center\")\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/emitted_energy_beginVelo2endT.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_eloss_found = ak.to_numpy(ak.sort(eloss_found))\n",
    "sorted_eloss_lost = ak.to_numpy(ak.sort(eloss_lost))\n",
    "sorted_eloss = ak.to_numpy(ak.sort(eloss))\n",
    "sorted_eloss_velo = ak.to_numpy(ak.sort(eloss_velo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 50\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_velo),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.6,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#2A9D8F\",\n",
    "    label=\"electrons\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "mean_eloss = np.mean(ak.to_numpy(eloss_velo))\n",
    "plt.vlines(\n",
    "    mean_eloss,\n",
    "    ymin=0,\n",
    "    ymax=3.5,\n",
    "    colors=\"#F05342\",\n",
    "    label=f\"mean: {np.round(mean_eloss,2)} $E_0$\",\n",
    ")\n",
    "median_eloss = np.median(sorted_eloss_velo)\n",
    "plt.vlines(\n",
    "    median_eloss,\n",
    "    ymin=0,\n",
    "    ymax=3.5,\n",
    "    colors=\"#0B3954\",\n",
    "    label=f\"median: {np.round(median_eloss,2)} $E_0$\",\n",
    ")\n",
    "\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.legend()  # loc=\"upper center\")\n",
    "# plt.show()\n",
    "plt.savefig(\n",
    "    \"/work/cetin/Projektpraktikum/thesis/emitted_energy_velo_mean.pdf\",\n",
    "    format=\"PDF\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 50\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_magnet_lost),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#F05342\",\n",
    "    label=\"lost\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "# #2A9D8F another teal color\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_magnet_found),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#107E7D\",\n",
    "    label=\"found\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "\n",
    "plt.xlabel(r\"$E_\\gamma/E_{VELO}$\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "# plt.title(r'$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
    "plt.legend(loc=\"best\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/emitted_energy_endVelo2endT.pdf\",\n",
    "#     format=\"PDF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 50\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_velo_lost),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#F05342\",\n",
    "    label=\"lost\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "# #2A9D8F another teal color\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_velo_found),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#107E7D\",\n",
    "    label=\"found\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "# plt.ylabel(\"a.u.\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "\n",
    "# plt.title(r'$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
    "plt.legend(loc=\"best\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/emitted_energy_beginVelo2endVelo.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "### --- ### above should be correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 50\n",
    "plt.hist(\n",
    "    ak.to_numpy(lost[\"eta\"]),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#F05342\",\n",
    "    label=\"lost\",\n",
    "    range=[2, 5],\n",
    ")\n",
    "# #2A9D8F another teal color\n",
    "plt.hist(\n",
    "    ak.to_numpy(found[\"eta\"]),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#107E7D\",\n",
    "    label=\"found\",\n",
    "    range=[2, 5],\n",
    ")\n",
    "# plt.xlim(2, 5)\n",
    "plt.xlabel(r\"$\\eta$\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "plt.legend(loc=\"best\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "# plt.show()\n",
    "plt.savefig(\n",
    "    \"/work/cetin/Projektpraktikum/thesis/eta_found_lost.pdf\",\n",
    "    format=\"PDF\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 50\n",
    "plt.hist(\n",
    "    ak.to_numpy(lost[\"phi\"]),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#F05342\",\n",
    "    label=\"lost\",\n",
    "    range=[-3.142, 3.142],\n",
    ")\n",
    "# #2A9D8F another teal color\n",
    "plt.hist(\n",
    "    ak.to_numpy(found[\"phi\"]),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#107E7D\",\n",
    "    label=\"found\",\n",
    "    range=[-3.142, 3.142],\n",
    ")\n",
    "# plt.xlim(2, 5)\n",
    "plt.xlabel(r\"$\\phi$ [rad]\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "plt.legend(loc=\"best\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/phi_found_lost.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# magnet kick position\n",
    "input_tree = uproot.open({\n",
    "    \"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_B_upstream.root\":\n",
    "    \"PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
    "})\n",
    "array = input_tree.arrays()\n",
    "\n",
    "array[\"dSlope_yEndT\"] = array[\"ideal_state_9410_ty\"] - array[\n",
    "    \"ideal_state_770_ty\"]\n",
    "array[\"dSlope_yEndT_abs\"] = abs(array[\"dSlope_yEndT\"])\n",
    "\n",
    "array[\"dSlope_xEndT\"] = array[\"ideal_state_9410_tx\"] - array[\n",
    "    \"ideal_state_770_tx\"]\n",
    "array[\"dSlope_xEndT_abs\"] = abs(array[\"dSlope_xEndT\"])\n",
    "array[\"x_EndT_abs\"] = abs(array[\"ideal_state_9410_x\"])\n",
    "array[\"x_EndVelo_abs\"] = abs(array[\"ideal_state_770_x\"])\n",
    "\n",
    "array[\"y_EndT_abs\"] = abs(array[\"ideal_state_9410_y\"])\n",
    "array[\"y_EndVelo_abs\"] = abs(array[\"ideal_state_770_y\"])\n",
    "\n",
    "array[\"z_mag_xEndT\"] = (\n",
    "    array[\"ideal_state_770_x\"] - array[\"ideal_state_9410_x\"] -\n",
    "    array[\"ideal_state_770_tx\"] * array[\"ideal_state_770_z\"] +\n",
    "    array[\"ideal_state_9410_tx\"] *\n",
    "    array[\"ideal_state_9410_z\"]) / array[\"dSlope_xEndT\"]\n",
    "\n",
    "# array[\"yStraightOut\"] = array[\n",
    "#     \"ideal_state_770_y\"] + array[\"ideal_state_770_ty\"] * (\n",
    "#         array[\"ideal_state_10000_z\"] - array[\"ideal_state_770_z\"])\n",
    "# array[\"yDiffOut\"] = array[\"ideal_state_10000_y\"] - array[\"yStraightOut\"]\n",
    "\n",
    "not_e = array[(array.isProton)]\n",
    "\n",
    "array = array[(array.isElectron) & (array.fromB)]\n",
    "stretch_factor = ak.num(array[array.lost], axis=0) / ak.num(array[~array.lost],\n",
    "                                                            axis=0)\n",
    "# stretch_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# not_e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xmin: float = 5100\n",
    "xmax: float = 5800\n",
    "nbins = 100\n",
    "\n",
    "fig = plt.figure()\n",
    "# plt.hist(\n",
    "#     array[\"match_zMag\"],\n",
    "#     bins=nbins,\n",
    "#     range=[xmin, xmax],\n",
    "#     color=\"#712F79\",\n",
    "#     alpha=0.5,\n",
    "#     label=\"e pred\",\n",
    "#     density=True,\n",
    "# )\n",
    "# plt.hist(\n",
    "#     not_e[\"match_zMag_def\"],\n",
    "#     bins=nbins,\n",
    "#     range=[xmin, xmax],\n",
    "#     color=\"#107E7D\",\n",
    "#     alpha=0.5,\n",
    "#     label=\"K pred\",\n",
    "#     density=True,\n",
    "# )\n",
    "# 87A330\n",
    "plt.hist(\n",
    "    not_e[\"z_mag_xEndT\"],\n",
    "    bins=nbins,\n",
    "    range=[xmin, xmax],\n",
    "    color=\"#F05342\",\n",
    "    alpha=0.5,\n",
    "    label=\"proton\",\n",
    "    density=True,\n",
    ")\n",
    "plt.hist(\n",
    "    array[\"z_mag_xEndT\"],\n",
    "    bins=nbins,\n",
    "    range=[xmin, xmax],\n",
    "    color=\"#87A330\",\n",
    "    alpha=0.5,\n",
    "    label=\"electron\",\n",
    "    density=True,\n",
    ")\n",
    "plt.xlabel(r\"$z_{\\mathrm{Mag}}$ [mm]\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "plt.legend(loc=\"best\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/match_true_zmag_e_kaon.pdf\", format=\"PDF\"\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xmin: float = 5200\n",
    "xmax: float = 5600\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.hist(\n",
    "    array[\"match_zMag\"],\n",
    "    bins=80,\n",
    "    range=[xmin, xmax],\n",
    "    color=\"#F05342\",\n",
    "    alpha=0.5,\n",
    "    label=\"new\",\n",
    "    density=True,\n",
    ")\n",
    "plt.hist(\n",
    "    array[\"match_zMag_def\"],\n",
    "    bins=80,\n",
    "    range=[xmin, xmax],\n",
    "    color=\"#107E7D\",\n",
    "    alpha=0.5,\n",
    "    label=\"default\",\n",
    "    density=True,\n",
    ")\n",
    "plt.hist(\n",
    "    array[\"z_mag_xEndT\"],\n",
    "    bins=80,\n",
    "    range=[xmin, xmax],\n",
    "    color=\"#712F79\",\n",
    "    alpha=0.5,\n",
    "    label=\"true\",\n",
    "    density=True,\n",
    ")\n",
    "plt.xlabel(r\"$z_{\\mathrm{Mag}}$ [mm]\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "plt.legend(loc=\"best\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/match_zmag_lost_found.pdf\", format=\"PDF\"\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(5150, 5700, 50)\n",
    "ax = sns.regplot(\n",
    "    x=ak.to_numpy(array[\"z_mag_xEndT\"]),\n",
    "    y=ak.to_numpy(array[\"z_mag_xEndT\"]) - ak.to_numpy(array[\"match_zMag\"]),\n",
    "    x_bins=bins,\n",
    "    fit_reg=None,\n",
    "    x_estimator=np.mean,\n",
    "    label=\"bla\",\n",
    ")\n",
    "ax.set_ylim(-150, 175)\n",
    "ax2 = ax.twinx()\n",
    "ax2.hist(\n",
    "    ak.to_numpy(array[\"z_mag_xEndT\"]),\n",
    "    bins=50,\n",
    "    range=[5150, 5700],\n",
    "    color=\"#2A9D8F\",\n",
    "    alpha=0.5,\n",
    "    align=\"mid\",\n",
    "    density=True,\n",
    ")\n",
    "ax.set_xlabel(r\"$z_{\\mathrm{Mag}}$ [mm]\")\n",
    "ax.set_ylabel(\n",
    "    r\"$\\left\\langle z_{\\mathrm{Mag}}-z_{\\mathrm{Mag}}^{\\mathrm{pred}}\\right\\rangle$ [mm]\"\n",
    ")\n",
    "ax2.set_ylabel(\"Number of Tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/magnet_kink_regression_plot.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(-40, 40, 41)\n",
    "ax = sns.regplot(\n",
    "    x=ak.to_numpy(array[\"yDiffOut\"]),\n",
    "    y=ak.to_numpy(array[\"yDiffOut\"]) - ak.to_numpy(array[\"match_yCorr\"]),\n",
    "    x_bins=bins,\n",
    "    fit_reg=None,\n",
    "    x_estimator=np.mean,\n",
    "    label=\"bla\",\n",
    ")\n",
    "ax2 = ax.twinx()\n",
    "ax2.hist(\n",
    "    ak.to_numpy(array[\"yDiffOut\"]),\n",
    "    bins=30,\n",
    "    range=[-40, 40],\n",
    "    color=\"#2A9D8F\",\n",
    "    alpha=0.5,\n",
    "    align=\"mid\",\n",
    "    density=True,\n",
    ")\n",
    "ax.set_xlabel(r\"$y_{\\mathrm{corr}}$ [mm]\")\n",
    "ax.set_ylabel(\n",
    "    r\"$\\left\\langle y_{\\mathrm{corr}}-y_{\\mathrm{corr}}^{\\mathrm{pred}}\\right\\rangle$ [mm]\"\n",
    ")\n",
    "ax2.set_ylabel(\"Number of Tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/bend_y_regression_plot.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xmin: float = -30\n",
    "xmax: float = 30\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.hist(\n",
    "    array[\"match_yCorr\"],\n",
    "    bins=80,\n",
    "    range=[xmin, xmax],\n",
    "    color=\"#F05342\",\n",
    "    alpha=0.5,\n",
    "    label=\"new\",\n",
    "    density=True,\n",
    ")\n",
    "plt.hist(\n",
    "    array[\"match_yCorr_def\"],\n",
    "    bins=80,\n",
    "    range=[xmin, xmax],\n",
    "    color=\"#107E7D\",\n",
    "    alpha=0.5,\n",
    "    label=\"default\",\n",
    "    density=True,\n",
    ")\n",
    "plt.hist(\n",
    "    array[\"yDiffOut\"],\n",
    "    bins=80,\n",
    "    range=[xmin, xmax],\n",
    "    color=\"#712F79\",\n",
    "    alpha=0.5,\n",
    "    label=\"true\",\n",
    "    density=True,\n",
    ")\n",
    "plt.xlabel(r\"$y_{\\mathrm{corr}}$ [mm]\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "plt.legend(loc=\"best\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/match_zmag_lost_found.pdf\", format=\"PDF\"\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tuner",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}