Projektpraktikum/trackinglosses_endVelo_momEff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\t\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "import awkward as ak\n",
    "from scipy.optimize import curve_fit\n",
    "from methods.fit_linear_regression_model import fit_linear_regression_model\n",
    "import sklearn\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41978 8523\n",
      "92337\n"
     ]
    }
   ],
   "source": [
    "file = uproot.open(\n",
    "    \"tracking_losses_ntuple_B_EndVeloP.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
    ")\n",
    "\n",
    "# selektiere nur elektronen von B->K*ee\n",
    "allcolumns = file.arrays()\n",
    "found = allcolumns[\n",
    "    (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)\n",
    "]  # B: 9056\n",
    "lost = allcolumns[\n",
    "    (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n",
    "]  # B: 1466\n",
    "\n",
    "notelectrons = allcolumns[\n",
    "    (~allcolumns.isElectron) & (allcolumns.fromB) & (~allcolumns.lost)\n",
    "]\n",
    "\n",
    "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n",
    "print(ak.num(notelectrons, axis=0))\n",
    "# ak.count(found, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rad_length_found = ak.to_numpy(found[\"rad_length_frac\"])\n",
    "eta_found = ak.to_numpy(found[\"eta\"])\n",
    "rad_length_lost = ak.to_numpy(lost[\"rad_length_frac\"])\n",
    "eta_lost = ak.to_numpy(lost[\"eta\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "endVeloP_found = ak.to_numpy(found[\"ideal_state_770_p\"])\n",
    "trueP_found = ak.to_numpy(found[\"p\"])\n",
    "\n",
    "endVeloP_lost = ak.to_numpy(lost[\"ideal_state_770_p\"])\n",
    "trueP_lost = ak.to_numpy(lost[\"p\"])\n",
    "\n",
    "endVeloP_notelectrons = ak.to_numpy(notelectrons[\"ideal_state_770_p\"])\n",
    "trueP_notelectrons = ak.to_numpy(notelectrons[\"p\"])\n",
    "\n",
    "stretch_factor = ak.num(trueP_lost, axis=0) / ak.num(trueP_found, axis=0)\n",
    "\n",
    "nbins = 100\n",
    "vmax = 100\n",
    "\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
    "\n",
    "a0 = ax0.hist2d(\n",
    "    trueP_found,\n",
    "    endVeloP_found,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=vmax,\n",
    "    range=[[0, 30000], [0, 30000]],\n",
    ")\n",
    "ax0.set_xlabel(f\"True $P$\")\n",
    "ax0.set_ylabel(f\"EndVelo $P$\")\n",
    "ax0.set_title(f\"found P\")\n",
    "\n",
    "a1 = ax1.hist2d(\n",
    "    # trueP_notelectrons,\n",
    "    # endVeloP_notelectrons,\n",
    "    trueP_lost,\n",
    "    endVeloP_lost,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=vmax * stretch_factor,\n",
    "    range=[[0, 30000], [0, 30000]],\n",
    ")\n",
    "ax1.set_xlabel(f\"True $P$\")\n",
    "ax1.set_ylabel(f\"EndVelo $P$\")\n",
    "ax1.set_title(f\"lost P\")\n",
    "\n",
    "plt.suptitle(\"Momentum at Creation and EndVelo\")\n",
    "plt.colorbar(a0[3], ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a0 = plt.hist2d(\n",
    "    trueP_found,\n",
    "    endVeloP_found,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=vmax,\n",
    "    range=[[0, 30000], [0, 30000]],\n",
    ")\n",
    "plt.xlabel(f\"True $P$\")\n",
    "plt.ylabel(f\"EndVelo $P$\")\n",
    "plt.title(f\"found P\")\n",
    "plt.colorbar(a0[3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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