tracking-parametrisation-tuner/thesis/TMVA_stuff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import mplhep\n",
    "\n",
    "mplhep.style.use([\"LHCbTex2\"])\n",
    "plt.rcParams[\"savefig.dpi\"] = 600\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_tree = uproot.open({\n",
    "    \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_filter_bkg/matching_ghost_mlp_training.root\":\n",
    "    \"MatchNNDataSet/TrainTree\"\n",
    "})\n",
    "test_tree = uproot.open({\n",
    "    \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_filter_bkg/matching_ghost_mlp_training.root\":\n",
    "    \"MatchNNDataSet/TestTree\"\n",
    "})\n",
    "train_array = train_tree.arrays()\n",
    "test_array = test_tree.arrays()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_bkg = train_array[train_array.classID == 1]\n",
    "train_sig = train_array[train_array.classID == 0]\n",
    "test_bkg = test_array[test_array.classID == 1]\n",
    "test_sig = test_array[test_array.classID == 0]\n",
    "plt.hist(\n",
    "    train_sig.matching_mlp,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "plt.hist(\n",
    "    train_bkg.matching_mlp,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "mplhep.histplot(\n",
    "    np.histogram(np.array(test_sig.matching_mlp), 50),\n",
    "    histtype=\"errorbar\",\n",
    "    density=True,\n",
    "    yerr=True,\n",
    "    color=\"#107E7D\",\n",
    "    marker=\"^\",\n",
    "    markersize=7,\n",
    "    label=\"test sample, true pairs\",\n",
    ")\n",
    "mplhep.histplot(\n",
    "    np.histogram(np.array(test_bkg.matching_mlp), 50),\n",
    "    histtype=\"errorbar\",\n",
    "    density=True,\n",
    "    yerr=True,\n",
    "    color=\"#F05342\",\n",
    "    label=\"test sample, wrong pairs\",\n",
    ")\n",
    "plt.xlabel(\"neural network response\")\n",
    "plt.ylabel(\"Number of tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.legend(loc=\"upper center\")\n",
    "plt.savefig(\"/work/cetin/LHCb/reco_tuner/thesis/filtered_NN_elec_response.pdf\",\n",
    "            format=\"PDF\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1400x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 2, figsize=(14, 15), sharey=False)\n",
    "# 0,0\n",
    "axes[0, 0].hist(\n",
    "    train_sig.chi2,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[0, 0].hist(\n",
    "    train_bkg.chi2,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[0, 0].set_xlabel(r\"$\\chi^{2}_{\\mathrm{match}}$\")\n",
    "axes[0, 0].legend(prop={\"size\": 20})\n",
    "# 1,0\n",
    "axes[1, 0].hist(\n",
    "    train_sig.distX,\n",
    "    bins=50,\n",
    "    range=(0, 100),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[1, 0].hist(\n",
    "    train_bkg.distX,\n",
    "    bins=50,\n",
    "    range=(0, 100),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[1, 0].set_xlabel(r\"$D_{x}$ [mm]\")\n",
    "axes[1, 0].set_ylabel(\"Number of tracks (normalised)\",\n",
    "                      va=\"bottom\",\n",
    "                      ha=\"center\")\n",
    "# 0,1\n",
    "axes[0, 1].hist(\n",
    "    train_sig.teta2,\n",
    "    bins=50,\n",
    "    range=(0.0, 0.02),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[0, 1].hist(\n",
    "    train_bkg.teta2,\n",
    "    bins=50,\n",
    "    range=(0.0, 0.02),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[0, 1].set_xlabel(r\"$t_{x}^{2}+t_{y}^{2}$\")\n",
    "# 1,1\n",
    "axes[1, 1].hist(\n",
    "    train_sig.distY,\n",
    "    bins=50,\n",
    "    range=(0, 100),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[1, 1].hist(\n",
    "    train_bkg.distY,\n",
    "    bins=50,\n",
    "    range=(0, 100),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[1, 1].set_xlabel(r\"$D_{y}$ [mm]\")\n",
    "# 2,0\n",
    "axes[2, 0].hist(\n",
    "    train_sig.dSlope,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[2, 0].hist(\n",
    "    train_bkg.dSlope,\n",
    "    bins=50,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[2, 0].set_xlabel(r\"$|\\Delta t_{x}^{\\mathrm{match}}|$\")\n",
    "# 2,1\n",
    "axes[2, 1].hist(\n",
    "    train_sig.dSlopeY,\n",
    "    bins=50,\n",
    "    range=(0, 0.02),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#107E7D\",\n",
    "    label=\"training sample, true pairs\",\n",
    ")\n",
    "axes[2, 1].hist(\n",
    "    train_bkg.dSlopeY,\n",
    "    bins=50,\n",
    "    range=(0, 0.02),\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    "    log=False,\n",
    "    color=\"#F05342\",\n",
    "    label=\"training sample, wrong pairs\",\n",
    ")\n",
    "axes[2, 1].set_xlabel(r\"$|\\Delta t_{y}^{\\mathrm{match}}|$\")\n",
    "plt.savefig(\n",
    "    \"/work/cetin/LHCb/reco_tuner/thesis/filtered_NN_elec_variables.pdf\",\n",
    "    format=\"PDF\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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