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{ "cells": [ { "cell_type": "code", "execution_count": 1, "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", "from utils.components import unique_name_ext_re\n", "mplhep.style.use([\"LHCbTex2\"])\n", "plt.rcParams[\"savefig.dpi\"] = 600\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "50501" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file = uproot.open(\n", " \"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_B_thesis.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n", ")\n", "\n", "# selektiere nur elektronen von B->K*ee\n", "allcolumns = file.arrays()\n", "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n", "\n", "ak.num(electrons, axis=0)\n", "# ak.count(found, axis=None)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# electrons.type.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "cut_prop: bool = electrons.p_end_velo > 0 # 3e3\n", "found = electrons[~electrons.lost & cut_prop]\n", "lost = electrons[electrons.lost & cut_prop]\n", "\n", "eloss_found = (found[\"p\"] - found[\"p_end_scifi\"]) / found[\"p\"]\n", "eloss_lost = (lost[\"p\"] - lost[\"p_end_scifi\"]) / lost[\"p\"]\n", "\n", "eloss = (electrons[\"p\"] - electrons[\"p_end_scifi\"]) / electrons[\"p\"]\n", "eloss_magnet_found = ak.to_numpy(\n", " (found[\"p_end_velo\"] - found[\"p_end_scifi\"]) / found[\"p_end_velo\"]\n", ")\n", "eloss_magnet_lost = ak.to_numpy(\n", " (lost[\"p_end_velo\"] - lost[\"p_end_scifi\"]) / lost[\"p_end_velo\"]\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "eloss_velo_found = (found[\"p\"] - found[\"p_end_velo\"]) / found[\"p\"]\n", "eloss_velo_lost = (lost[\"p\"] - lost[\"p_end_velo\"]) / lost[\"p\"]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1200x900 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbins = 50\n", "plt.hist(\n", " ak.to_numpy(eloss_lost),\n", " bins=nbins,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"#F05342\",\n", " label=\"lost\",\n", " range=[0.001, 1],\n", ")\n", "# #2A9D8F another teal color\n", "plt.hist(\n", " ak.to_numpy(eloss_found),\n", " bins=nbins,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"#107E7D\",\n", " label=\"found\",\n", " range=[0.001, 1],\n", ")\n", "\n", "plt.xlabel(r\"$E_\\gamma/E_0$\")\n", "plt.ylabel(\"a.u.\")\n", "# plt.title(r'$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n", "plt.legend(title=\"LHCb Simulation\")\n", "plt.show()\n", "# plt.savefig(\n", "# \"/work/cetin/Projektpraktikum/thesis/emitted_energy_beginVelo2endT.pdf\",\n", "# format=\"PDF\",\n", "# )" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 1200x900 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbins = 50\n", "plt.hist(\n", " ak.to_numpy(eloss_magnet_lost),\n", " bins=nbins,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"#F05342\",\n", " label=\"lost\",\n", " range=[0.001, 1],\n", ")\n", "# #2A9D8F another teal color\n", "plt.hist(\n", " ak.to_numpy(eloss_magnet_found),\n", " bins=nbins,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"#107E7D\",\n", " label=\"found\",\n", " range=[0.001, 1],\n", ")\n", "\n", "plt.xlabel(r\"$E_\\gamma/E_{VELO}$\")\n", "plt.ylabel(\"a.u.\")\n", "# plt.title(r'$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n", "plt.legend(title=\"LHCb Simulation\")\n", "plt.show()\n", "# plt.savefig(\n", "# \"/work/cetin/Projektpraktikum/thesis/emitted_energy_endVelo2endT.pdf\", format=\"PDF\"\n", "# )" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1200x900 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbins = 50\n", "plt.hist(\n", " ak.to_numpy(eloss_velo_lost),\n", " bins=nbins,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"#F05342\",\n", " label=\"lost\",\n", " range=[0.001, 1],\n", ")\n", "# #2A9D8F another teal color\n", "plt.hist(\n", " ak.to_numpy(eloss_velo_found),\n", " bins=nbins,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"#107E7D\",\n", " label=\"found\",\n", " range=[0.001, 1],\n", ")\n", "\n", "plt.xlabel(r\"$E_\\gamma/E_0$\")\n", "plt.ylabel(\"a.u.\")\n", "# plt.title(r'$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n", "plt.legend(title=\"LHCb Simulation\")\n", "plt.show()\n", "# plt.savefig(\n", "# \"/work/cetin/Projektpraktikum/thesis/emitted_energy_beginVelo2endVelo.pdf\",\n", "# format=\"PDF\",\n", "# )" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "### --- ### above should be correct" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# magnet kick position\n", "input_tree = uproot.open({\n", " \"/work/cetin/Projektpraktikum/param_data_B_default_thesis.root\":\n", " \"PrParameterisationData_2ece6184.PrDebugTrackingTool/Tuple;1\"\n", "})\n", "array = input_tree.arrays()\n", "array = array[array.isElectron & (array.fromB)]\n", "array[\"dSlope_fringe\"] = array[\"tx_ref\"] - array[\"tx\"]\n", "array[\"z_mag_x_fringe\"] = (\n", " array[\"x\"] - array[\"x_ref\"] - array[\"tx\"] * array[\"z\"] +\n", " array[\"tx_ref\"] * array[\"z_ref\"]) / array[\"dSlope_fringe\"]" ] }, { "cell_type": "code", "execution_count": 21, "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.hist(array[\"z_mag_x_fringe\"],\n", " bins=50,\n", " range=[5150, 5300],\n", " color=\"#2A9D8F\",\n", " density=True)\n", "plt.xlabel(r\"z$_{Mag}$ [mm]\")\n", "plt.ylabel(\"Number of Tracks (normalised)\")\n", "mplhep.lhcb.text(\"Simulation\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "50501" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ak.num(array, axis=0)" ] }, { "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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