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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", "from methods.fit_linear_regression_model import fit_linear_regression_model\n", "import sklearn\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "41978 8523\n", "92337\n" ] } ], "source": [ "file = uproot.open(\n", " \"tracking_losses_ntuple_B_EndVeloP.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n", ")\n", "\n", "# selektiere nur elektronen von B->K*ee\n", "allcolumns = file.arrays()\n", "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) &\n", " (allcolumns.fromB)] # B: 9056\n", "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) &\n", " (allcolumns.fromB)] # B: 1466\n", "\n", "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n", "\n", "notelectrons = allcolumns[(~allcolumns.isElectron) & (allcolumns.fromB) &\n", " (~allcolumns.lost)]\n", "\n", "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n", "print(ak.num(notelectrons, axis=0))\n", "# ak.count(found, axis=None)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "rad_length_found = ak.to_numpy(found[\"rad_length_frac\"])\n", "eta_found = ak.to_numpy(found[\"eta\"])\n", "rad_length_lost = ak.to_numpy(lost[\"rad_length_frac\"])\n", "eta_lost = ak.to_numpy(lost[\"eta\"])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 2000x800 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "endVeloP_found = ak.to_numpy(found[\"p_end_velo\"])\n", "trueP_found = ak.to_numpy(found[\"p\"])\n", "\n", "endVeloP_lost = ak.to_numpy(lost[\"p_end_velo\"])\n", "trueP_lost = ak.to_numpy(lost[\"p\"])\n", "\n", "endUTP_found = ak.to_numpy(found[\"p_end_ut\"])\n", "endUTP_lost = ak.to_numpy(lost[\"p_end_ut\"])\n", "\n", "stretch_factor = ak.num(trueP_lost, axis=0) / ak.num(trueP_found, axis=0)\n", "\n", "nbins = 80\n", "vmax = 150\n", "\n", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n", "\n", "a0 = ax0.hist2d(\n", " trueP_found,\n", " endVeloP_found,\n", " density=False,\n", " bins=nbins,\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=vmax,\n", " range=[[0, 20000], [0, 20000]],\n", ")\n", "ax0.set_xlabel(f\"True $P$ [MeV]\")\n", "ax0.set_ylabel(f\"EndVelo $P$ [MeV]\")\n", "ax0.set_title(f\"found P\")\n", "\n", "a1 = ax1.hist2d(\n", " # trueP_notelectrons,\n", " # endVeloP_notelectrons,\n", " trueP_lost,\n", " endVeloP_lost,\n", " density=False,\n", " bins=nbins,\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=vmax * stretch_factor,\n", " range=[[0, 20000], [0, 20000]],\n", ")\n", "ax1.set_xlabel(f\"True $P$ [MeV]\")\n", "ax1.set_ylabel(f\"EndVelo $P$ [MeV]\")\n", "ax1.set_title(f\"lost P\")\n", "\n", "plt.suptitle(\"Momentum at Creation and EndVelo\")\n", "plt.colorbar(a0[3], ax=ax1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 2000x800 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbins = 80\n", "vmax = 150\n", "\n", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n", "\n", "a0 = ax0.hist2d(\n", " # endVeloP_found,\n", " trueP_found,\n", " endUTP_found,\n", " density=False,\n", " bins=nbins,\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=vmax,\n", " range=[[0, 20000], [0, 20000]],\n", ")\n", "ax0.set_ylabel(f\"EndUT $P$ [MeV]\")\n", "ax0.set_xlabel(f\"EndVelo $P$ [MeV]\")\n", "ax0.set_title(f\"found P\")\n", "\n", "a1 = ax1.hist2d(\n", " # endVeloP_lost,\n", " trueP_lost,\n", " endUTP_lost,\n", " density=False,\n", " bins=nbins,\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=vmax * stretch_factor,\n", " range=[[0, 20000], [0, 20000]],\n", ")\n", "ax1.set_ylabel(f\"EndUT $P$ [MeV]\")\n", "ax1.set_xlabel(f\"EndVelo $P$ [MeV]\")\n", "ax1.set_title(f\"lost P\")\n", "\n", "plt.suptitle(\"Momentum at EndVelo and EndUT\")\n", "plt.colorbar(a0[3], ax=ax1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a0 = plt.hist2d(\n", " trueP_found,\n", " endVeloP_found,\n", " # endUTP_found,\n", " density=False,\n", " bins=nbins,\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=150,\n", " range=[[0, 20000], [0, 20000]],\n", ")\n", "plt.xlabel(f\"True $P$ [MeV]\")\n", "plt.ylabel(f\"EndVelo $P$ [MeV]\")\n", "plt.title(f\"found P\")\n", "plt.colorbar(a0[3])\n", "plt.show()" ] }, { "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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