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{ "cells": [ { "cell_type": "code", "execution_count": 19, "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", "from scipy.optimize import curve_fit\n", "import pandas as pd\n", "import seaborn as sns\n", "from matplotlib import colormaps\n", "\n", "mplhep.style.use([\"LHCbTex2\"])\n", "plt.rcParams[\"savefig.dpi\"] = 600\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "file = uproot.open(\n", " \"/work/cetin/LHCb/reco_tuner/data_matching/resolutions_and_effs_B_default_thesis.root:Track/MatchTrackChecker_8319528f/Match;1\",\n", ")\n", "\n", "P_recoed = file[\"07_long_electrons_P_reconstructed;1\"].to_numpy()\n", "P_recoable = file[\"07_long_electrons_P_reconstructible;1\"].to_numpy()\n", "\n", "Pt_recoed = file[\"07_long_electrons_Pt_reconstructed;1\"].to_numpy()\n", "Pt_recoable = file[\"07_long_electrons_Pt_reconstructible;1\"].to_numpy()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "control eff: 0.6145546589237905\n", "new eff: 0.6155293168395326\n", "control eff: 0.6173168233870217\n", "new eff: 0.6176270902698983\n", "212152\n", "130379.0\n", "213171.0\n", "131213.0\n" ] } ], "source": [ "P_Velo_recoed = file[\"07_long_electrons_EndVelo_P_reconstructed;1\"].to_numpy()\n", "P_Velo_recoable = file[\n", " \"07_long_electrons_EndVelo_P_reconstructible;1\"].to_numpy()\n", "\n", "print(\"control eff: \", np.sum(P_recoed[0]) / np.sum(P_recoable[0]))\n", "print(\"new eff: \", np.sum(P_Velo_recoed[0]) / np.sum(P_Velo_recoable[0]))\n", "\n", "Pt_Velo_recoed = file[\"07_long_electrons_EndVelo_Pt_reconstructed;1\"].to_numpy(\n", ")\n", "Pt_Velo_recoable = file[\n", " \"07_long_electrons_EndVelo_Pt_reconstructible;1\"].to_numpy()\n", "\n", "print(\"control eff: \", np.sum(Pt_recoed[0]) / np.sum(Pt_recoable[0]))\n", "print(\"new eff: \", np.sum(Pt_Velo_recoed[0]) / np.sum(Pt_Velo_recoable[0]))\n", "\n", "print(np.sum(P_recoable[0], dtype=int))\n", "print(np.sum(P_recoed[0]))\n", "print(np.sum(P_Velo_recoable[0]))\n", "print(np.sum(P_Velo_recoed[0]))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n", " 0.0000e+00],\n", " [4.1620e+03, 2.4200e+02, 8.7000e+01, ..., 0.0000e+00, 0.0000e+00,\n", " 0.0000e+00],\n", " [2.3471e+04, 2.7160e+03, 1.4720e+03, ..., 0.0000e+00, 0.0000e+00,\n", " 0.0000e+00],\n", " ...,\n", " [1.3000e+01, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 1.0000e+00,\n", " 0.0000e+00],\n", " [1.3000e+01, 1.0000e+00, 1.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n", " 0.0000e+00],\n", " [9.0000e+00, 3.0000e+00, 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file[\"07_long_electrons_EndVelo_PdP_reconstructible;1\"].to_numpy()\n", "\n", "PdP_Velo_recoable" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_733517/2776815717.py:1: RuntimeWarning: invalid value encountered in divide\n", " effs = np.divide(PdP_Velo_recoed[0], PdP_Velo_recoable[0])\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>...</th>\n", " 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NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "1 NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "2 0.328205 0.208333 0.237500 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "3 0.444846 0.426966 0.346939 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "4 0.496689 0.591224 0.518625 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", ".. ... ... ... ... .. .. ... .. ... .. .. .. ... \n", "95 NaN 1.000000 1.000000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "96 NaN NaN NaN ... NaN NaN NaN NaN 1.0 NaN NaN NaN NaN \n", "97 1.000000 1.000000 0.000000 ... NaN NaN NaN NaN NaN NaN NaN NaN 1.0 \n", "98 NaN 1.000000 1.000000 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "99 NaN 1.000000 1.000000 ... NaN NaN 1.0 NaN NaN NaN NaN NaN NaN \n", "\n", " 99 \n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", ".. .. \n", "95 NaN \n", "96 NaN \n", "97 NaN \n", "98 NaN \n", "99 NaN \n", "\n", "[100 rows x 100 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "effs = np.divide(PdP_Velo_recoed[0], PdP_Velo_recoable[0])\n", "\n", "df = pd.DataFrame(effs)\n", "df" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1200x900 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# fig = plt.figure(figsize=(15, 7))\n", "ax = sns.heatmap(\n", " effs,\n", " robust=True,\n", " square=False,\n", " cmap=colormaps[\"rainbow\"],\n", " xticklabels=False,\n", " yticklabels=False,\n", " vmax=1,\n", " cbar_kws={\n", " \"label\": \"Efficiency\",\n", " \"pad\": 0.005,\n", " \"shrink\": 1,\n", " \"ticks\": [0.2, 0.4, 0.6, 0.8, 1.0],\n", " \"aspect\": 15,\n", " },\n", ")\n", "ax.set_ylabel(f\"$P$ [MeV]\")\n", "ax.set_xlabel(f\"$\\Delta P$ [MeV]\")\n", "ax.patch.set_edgecolor(\"black\")\n", "\n", "ax.set_yticks([0, 19, 39, 59, 79, 99], [0, 20000, 40000, 60000, 80000, 100000])\n", "ax.set_xticks([0, 19, 39, 59, 79, 99], [0, 2000, 4000, 6000, 8000, 10000])\n", "ax.invert_yaxis()\n", "# ax.set_ylim(0, 59)\n", "# ax.set_xlim(0, 59)\n", "\n", "ax.patch.set_linewidth(2)\n", "# ax.set_yticklabels([])\n", "# ax.set_title(\"EndVELO to EndUT $x/X_0$\", size=35)\n", "mplhep.lhcb.text(\"Simulation\", loc=0)\n", "plt.show()\n", "# plt.savefig(\n", "# \"/work/cetin/Projektpraktikum/thesis/Efficiency_PdP_Velo_hist2d.pdf\",\n", "# format=\"PDF\")" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([6.4927e+04, 8.7210e+03, 5.4020e+03, 4.0560e+03, 3.3500e+03,\n", " 2.8240e+03, 2.3990e+03, 2.0600e+03, 1.8280e+03, 1.5610e+03,\n", " 1.5130e+03, 1.3480e+03, 1.2090e+03, 1.1250e+03, 1.0270e+03,\n", " 9.3000e+02, 9.0200e+02, 8.2300e+02, 7.8100e+02, 6.7500e+02,\n", " 6.8700e+02, 6.5400e+02, 6.6000e+02, 6.2300e+02, 5.3100e+02,\n", " 5.1900e+02, 5.1200e+02, 4.4000e+02, 4.7200e+02, 4.6700e+02,\n", " 4.1100e+02, 4.1300e+02, 3.7700e+02, 3.4600e+02, 3.5400e+02,\n", " 3.3100e+02, 3.3400e+02, 2.8600e+02, 3.0600e+02, 2.8800e+02,\n", " 2.7000e+02, 2.8100e+02, 2.5100e+02, 2.2500e+02, 2.7100e+02,\n", " 2.3000e+02, 2.4100e+02, 2.1700e+02, 2.2900e+02, 2.0700e+02,\n", " 2.2500e+02, 1.8500e+02, 1.9000e+02, 1.8600e+02, 1.9100e+02,\n", " 1.9900e+02, 1.8400e+02, 1.8500e+02, 1.7200e+02, 1.5700e+02,\n", " 1.5300e+02, 1.7100e+02, 1.3700e+02, 1.5200e+02, 1.4500e+02,\n", " 1.4600e+02, 1.2900e+02, 1.3900e+02, 1.4300e+02, 1.3700e+02,\n", " 1.1600e+02, 1.2200e+02, 1.2600e+02, 1.1900e+02, 1.1800e+02,\n", " 1.3100e+02, 1.1400e+02, 1.0700e+02, 1.1700e+02, 1.1700e+02,\n", " 1.1500e+02, 1.3000e+02, 8.7000e+01, 1.0300e+02, 9.9000e+01,\n", " 1.0100e+02, 7.7000e+01, 8.6000e+01, 1.0500e+02, 7.6000e+01,\n", " 8.5000e+01, 8.6000e+01, 7.4000e+01, 8.6000e+01, 7.9000e+01,\n", " 6.8000e+01, 7.1000e+01, 7.9000e+01, 6.9000e+01, 6.3000e+01]),\n", " array([ 0., 100., 200., 300., 400., 500., 600., 700.,\n", " 800., 900., 1000., 1100., 1200., 1300., 1400., 1500.,\n", " 1600., 1700., 1800., 1900., 2000., 2100., 2200., 2300.,\n", " 2400., 2500., 2600., 2700., 2800., 2900., 3000., 3100.,\n", " 3200., 3300., 3400., 3500., 3600., 3700., 3800., 3900.,\n", " 4000., 4100., 4200., 4300., 4400., 4500., 4600., 4700.,\n", " 4800., 4900., 5000., 5100., 5200., 5300., 5400., 5500.,\n", " 5600., 5700., 5800., 5900., 6000., 6100., 6200., 6300.,\n", " 6400., 6500., 6600., 6700., 6800., 6900., 7000., 7100.,\n", " 7200., 7300., 7400., 7500., 7600., 7700., 7800., 7900.,\n", " 8000., 8100., 8200., 8300., 8400., 8500., 8600., 8700.,\n", " 8800., 8900., 9000., 9100., 9200., 9300., 9400., 9500.,\n", " 9600., 9700., 9800., 9900., 10000.]))" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dP_Velo_recoed = file[\"07_long_electrons_EndVelo_dP_reconstructed;1\"].to_numpy()\n", "dP_Velo_recoable = file[\"07_long_electrons_EndVelo_dP_reconstructible;1\"].to_numpy()\n", "\n", "dP_Velo_recoed" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1200x900 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "# plt.bar(\n", "# dP_Velo_recoable[1][1:],\n", "# dP_Velo_recoable[0] / np.max(dP_Velo_recoable[0]),\n", "# alpha=0.5,\n", "# color=\"#107E7D\",\n", "# label=\"p distribution, e\",\n", "# )\n", "plt.errorbar(\n", " dP_Velo_recoable[1][1:],\n", " dP_Velo_recoed[0] / dP_Velo_recoable[0],\n", " color=\"#107E7D\",\n", " label=\"Efficiency\",\n", " fmt=\"^\",\n", " ms=10,\n", ")\n", "plt.ylim(0, 1)\n", "plt.xlabel(r\"$dp$ [MeV]\")\n", "plt.ylabel(\"Efficiency of Long Tracks\")\n", "plt.legend(loc=\"best\")\n", "mplhep.lhcb.text(\"Simulation\", loc=0)\n", "plt.show()\n", "# plt.savefig(\"/work/cetin/Projektpraktikum/thesis/Efficiency_dP_Velo.pdf\",\n", "# format=\"PDF\")" ] }, { "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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