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{ "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", "import awkward as ak\n", "\n", "mplhep.style.use([\"LHCbTex2\"])\n", "plt.rcParams[\"savefig.dpi\"] = 600\n", "# %matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "train_tree = uproot.open({\n", " \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_bestprecuts_NozMagCut/matching_ghost_mlp_training.root\":\n", " \"MatchNNDataSet/TrainTree\"\n", "})\n", "test_tree = uproot.open({\n", " \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_bestprecuts_NozMagCut/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": 4, "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(\n", "# \"/work/cetin/LHCb/reco_tuner/thesis/new_electron_NN_response.pdf\",\n", "# format=\"PDF\",\n", "# )\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The cut is set to 0.5, thereby rejecting 0.901 of fake tracks, while keeping 0.935 of the true matches.\n" ] } ], "source": [ "response = 0.5\n", "\n", "n_sig_keep = ak.num(train_sig[train_sig.matching_mlp >= response], axis=0)\n", "n_bkg_rej = ak.num(train_bkg[train_bkg.matching_mlp < response], axis=0)\n", "nevents = 115e3\n", "\n", "print(\n", " f\"The cut is set to {response}, thereby rejecting {np.round(n_bkg_rej/nevents,3)} of fake tracks, while keeping {np.round(n_sig_keep/nevents,3)} of the true matches.\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "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, 150),\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, 150),\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, 150),\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, 150),\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, 0.03),\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, 0.03),\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/new_electron_NN_variables.pdf\",\n", " format=\"PDF\",\n", ")\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, axes = plt.subplots(2, 3, figsize=(25, 13), 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", "\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", "# 0,2\n", "axes[0, 2].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[0, 2].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[0, 2].set_xlabel(r\"$D_{x}$ [mm]\")\n", "axes[0, 0].set_ylabel(\"Number of tracks (normalised)\", va=\"bottom\", ha=\"center\")\n", "# 1,0\n", "axes[1, 0].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, 0].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, 0].set_xlabel(r\"$D_{y}$ [mm]\")\n", "# 2,0\n", "axes[1, 1].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[1, 1].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[1, 1].set_xlabel(r\"$|\\Delta t_{x}^{\\mathrm{match}}|$\")\n", "# 2,1\n", "axes[1, 2].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[1, 2].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[1, 2].set_xlabel(r\"$|\\Delta t_{y}^{\\mathrm{match}}|$\")\n", "plt.savefig(\n", " \"/work/cetin/LHCb/reco_tuner/thesis/newparams_filtered_NN_elec_variables_landscape.pdf\",\n", " format=\"PDF\",\n", ")\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": [] } ], "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}
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