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{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import uproot\t\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits import mplot3d\n", "import awkward as ak\n", "from scipy.optimize import curve_fit\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n", "file = uproot.open(\n", " \"tracking_losses_ntuple_B_rad_length_beginVelo2endVelo.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n", ")\n", "\n", "# selektiere nur elektronen von B->K*ee\n", "allcolumns = file.arrays()\n", "\n", "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "p = ak.to_numpy(electrons[\"p\"])\n", "p_velo = ak.to_numpy(electrons[\"p_end_velo\"])\n", "p_ut = ak.to_numpy(electrons[\"p_end_ut\"])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xlim = 20000\n", "ax = plt.hist2d(p, p_velo, bins=80, range=[[0, xlim], [0, xlim]], cmin=1, vmax=250)\n", "plt.colorbar(ax[3])\n", "plt.xlabel(f\"True$P$ [MeV]\")\n", "plt.ylabel(f\"EndVELO$P$ [MeV]\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xlim = 20000\n", "ax = plt.hist2d(p,\n", " p_ut,\n", " bins=80,\n", " range=[[0, xlim], [0, xlim]],\n", " cmin=1,\n", " vmax=250)\n", "plt.colorbar(ax[3])\n", "plt.xlabel(f\"True$P$ [MeV]\")\n", "plt.ylabel(f\"EndUT$P$ [MeV]\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dvelo = p - p_velo\n", "dut = p - p_ut - dvelo\n", "xlim = 1\n", "nbins = 80\n", "\n", "plt.hist(\n", " dvelo / p,\n", " bins=nbins,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"darkorange\",\n", " label=\"VELO\",\n", " range=[-xlim, xlim],\n", ")\n", "plt.hist(\n", " dut / p,\n", " bins=nbins,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"blue\",\n", " label=\"UT\",\n", " range=[-xlim, xlim],\n", ")\n", "plt.xlim(0, xlim)\n", "# plt.yscale(\"log\")\n", "plt.title(\"emitted energy difference ratio\")\n", "plt.xlabel(r\"$(E_0 - (E_{VELO} , E_{RICH1+UT}))/E_0$\")\n", "plt.ylabel(\"a.u.\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(\n", " (dut - dvelo) / p,\n", " bins=nbins,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"blue\",\n", " label=\"UT\",\n", " range=[-xlim, xlim],\n", ")\n", "# plt.xlim(0, xlim)\n", "# plt.yscale(\"log\")\n", "plt.title(\"emitted energy difference ratio\")\n", "plt.xlabel(r\"$(E_{VELO} - E_{RICH1+UT})$ [MeV]\")\n", "plt.ylabel(\"a.u.\")\n", "plt.legend()\n", "plt.show()" ] }, { "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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