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172 lines
5.7 KiB
172 lines
5.7 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import uproot\n",
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"import numpy as np\n",
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"import sys\n",
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"import os\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"import mplhep\n",
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"from mpl_toolkits import mplot3d\n",
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"import itertools\n",
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"import awkward as ak\n",
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"from scipy.optimize import curve_fit\n",
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"from utils.components import unique_name_ext_re\n",
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"mplhep.style.use([\"LHCbTex2\"])\n",
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"plt.rcParams[\"savefig.dpi\"] = 600\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"file = uproot.open(\n",
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" \"/work/cetin/LHCb/reco_tuner/data/resolutions_and_effs_B_thesis.root:Track/MatchTrackChecker_8319528f/Match;1\",\n",
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")\n",
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"\n",
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"P_recoed = file[\"01_long_P_reconstructed;1\"].to_numpy()\n",
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"P_recoable = file[\"01_long_P_reconstructible;1\"].to_numpy()\n",
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"\n",
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"Pt_recoed = file[\"01_long_Pt_reconstructed;1\"].to_numpy()\n",
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"Pt_recoable = file[\"01_long_Pt_reconstructible;1\"].to_numpy()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"control eff: 0.8626619913200968\n",
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"new eff: 0.8626619913200968\n",
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"control eff: 0.8629752409817771\n",
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"new eff: 0.8629752409817771\n"
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]
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}
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],
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"source": [
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"P_Velo_recoed = file[\"01_long_EndVelo_P_reconstructed;1\"].to_numpy()\n",
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"P_Velo_recoable = file[\"01_long_EndVelo_P_reconstructible;1\"].to_numpy()\n",
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"\n",
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"print(\"control eff: \", np.sum(P_recoed[0]) / np.sum(P_recoable[0]))\n",
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"print(\"new eff: \", np.sum(P_Velo_recoed[0]) / np.sum(P_Velo_recoable[0]))\n",
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"\n",
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"Pt_Velo_recoed = file[\"01_long_EndVelo_Pt_reconstructed;1\"].to_numpy()\n",
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"Pt_Velo_recoable = file[\"01_long_EndVelo_Pt_reconstructible;1\"].to_numpy()\n",
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"\n",
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"print(\"control eff: \", np.sum(Pt_recoed[0]) / np.sum(Pt_recoable[0]))\n",
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"print(\"new eff: \", np.sum(Pt_Velo_recoed[0]) / np.sum(Pt_Velo_recoable[0]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"P_recoed"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "tuner",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.1.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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