{ "cells": [ { "cell_type": "markdown", "id": "de8616bc", "metadata": {}, "source": [ "# Filter LHE Events" ] }, { "cell_type": "code", "execution_count": null, "id": "a69095ea-7aff-4efa-bcde-928be2559aba", "metadata": {}, "outputs": [], "source": [ "import gzip\n", "\n", "import hist\n", "from skhep_testdata import data_path\n", "\n", "import pylhe" ] }, { "cell_type": "code", "execution_count": 2, "id": "1e6bce3e-b076-4862-8a30-7dd9a08c3284", "metadata": {}, "outputs": [], "source": [ "def plot(data):\n", " lheevents = pylhe.read_lhe_file(data).events\n", " events = pylhe.to_awkward(lheevents)\n", " mass_hist = hist.Hist.new.Reg(30, 50, 150).Weight()\n", " mass_hist.fill(\n", " (events.particles.vector[:, -1] + events.particles.vector[:, -2]).mass,\n", " weight=events.eventinfo.weight,\n", " )\n", " artists = mass_hist.plot()\n", " ax = artists[0].stairs.axes\n", " ax.set_yscale(\"log\")\n", " ax.set_xlabel(\"Mass [GeV]\")\n", " ax.set_ylabel(\"Count\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "7649faa3-9ecc-4761-96ce-857c6ee92868", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b'\\n'\n", "b'
\\n'\n", "b'\\n'\n", "b'\\n'\n", "b'3.1.0\\n'\n", "b'\\n'\n", "b'\\n'\n", "b' l+ l-\\n'\n", "b'define p = g u c d s u~ c~ d~ s~\\n'\n", "b'define j = g u c d s u~ c~ d~ s~\\n'\n", "b'define l+ = e+ mu+\\n'\n", "b'define l- = e- mu-\\n'\n", "b'define vl = ve vm vt\\n'\n", "b'define vl~ = ve~ vm~ vt~\\n'\n", "b'output drell-yan_output\\n'\n", "b']]>\\n'\n", "b'\\n'\n", "b'\\n'\n", "b'#*********************************************************************\\n'\n", "b'# MadGraph/MadEvent *\\n'\n", "b'# http://madgraph.hep.uiuc.edu *\\n'\n", "b'# *\\n'\n", "b'# proc_card.dat *\\n'\n", "b'#*********************************************************************\\n'\n", "b'# *\\n'\n", "b'# This Files is generated by MADGRAPH 5 *\\n'\n", "b'# *\\n'\n", "b'# WARNING: This Files is generated for MADEVENT (compatibility issue)*\\n'\n", "b'# This files is NOT a valid MG4 proc_card.dat *\\n'\n", "b'# Running this in MG4 will NEVER reproduce the result of MG5*\\n'\n", "b'# *\\n'\n", "b'#*********************************************************************\\n'\n", "b'#*********************************************************************\\n'\n", "b'# Process(es) requested : mg2 input *\\n'\n", "b'#*********************************************************************\\n'\n", "b'# Begin PROCESS # This is TAG. Do not modify this line\\n'\n" ] } ], "source": [ "lhe_data = data_path(\"pylhe-drell-yan-ll-lhe.gz\")\n", "with gzip.open(lhe_data) as f:\n", " for _ in range(100):\n", " print(f.readline())" ] }, { "cell_type": "code", "execution_count": 4, "id": "1b0954bf-4d67-4ddc-8496-052f508135d5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", " 2212 2212 6.5000000e+03 6.5000000e+03 0 0 247000 247000 -4 1\n", " 1.6782100e+03 5.1789220e+00 1.6782100e+03 1\n", "\n", " \n", " MUR=0.5 MUF=0.5\n", " MUR=0.5 MUF=0.5 dyn_scale_choice=sum pt\n", " MUR=0.5 MUF=0.5 dyn_scale_choice=HT\n", " MUR=0.5 MUF=0.5 dyn_scale_choice=HT/2\n", " MUR=0.5 MUF=0.5 dyn_scale_choice=sqrts\n", " MUR=0.5\n", " MUR=0.5 dyn_scale_choice=sum pt\n", " MUR=0.5 dyn_scale_choice=HT\n", " MUR=0.5 dyn_scale_choice=HT/2\n", " MUR=0.5 dyn_scale_choice=sqrts\n", " MUR=0.5 MUF=2.0\n", " MUR=0.5 MUF=2.0 dyn_scale_choice=sum pt\n", " MUR=0.5 MUF=2.0 dyn_scale_choice=HT\n", " MUR=0.5 MUF=2.0 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1.7276e+03\n", " 1.6457e+03\n", " 1.7422e+03\n", " 1.6643e+03\n", " 1.7046e+03\n", " 1.7812e+03\n", " 1.7269e+03\n", " 1.6793e+03\n", " 1.7117e+03\n", " 1.6415e+03\n", " 1.7508e+03\n", " 1.6613e+03\n", " 1.5098e+03\n", " 1.6033e+03\n", " 1.6635e+03\n", " 1.7519e+03\n", " 1.7578e+03\n", " 1.7314e+03\n", " 1.6045e+03\n", " 1.5908e+03\n", " 1.7609e+03\n", " 1.7125e+03\n", " 1.7523e+03\n", " 1.7188e+03\n", " 1.7395e+03\n", " 1.6576e+03\n", " 1.7401e+03\n", " 1.5894e+03\n", " 1.6494e+03\n", " 1.6464e+03\n", " 1.6246e+03\n", " 1.7022e+03\n", " 1.6742e+03\n", " 1.6311e+03\n", " 1.6913e+03\n", " 1.6360e+03\n", " 1.7300e+03\n", " 1.6052e+03\n", " 1.6524e+03\n", " 1.6642e+03\n", " 1.6335e+03\n", " 1.7074e+03\n", " 1.6903e+03\n", " 1.7843e+03\n", " 1.7860e+03\n", " 1.5502e+03\n", " 1.5893e+03\n", " 1.5945e+03\n", " 1.6666e+03\n", " 1.6919e+03\n", " 1.7579e+03\n", " 1.7130e+03\n", " 1.7634e+03\n", " 1.7523e+03\n", " 1.7510e+03\n", " 1.7362e+03\n", " 1.7321e+03\n", " 1.6999e+03\n", " 1.7430e+03\n", " 1.6965e+03\n", " 1.6589e+03\n", " 1.6458e+03\n", " 1.6210e+03\n", " 1.6908e+03\n", " 1.6696e+03\n", " 1.7365e+03\n", " 1.7383e+03\n", " 1.7671e+03\n", " 1.5845e+03\n", " 1.5555e+03\n", " 1.6596e+03\n", " 1.6536e+03\n", " 1.6854e+03\n", " 1.6476e+03\n", " 1.6182e+03\n", " 1.6414e+03\n", " 1.5937e+03\n", " 1.6078e+03\n", " 1.6649e+03\n", " 1.6722e+03\n", " 1.6840e+03\n", " 1.7125e+03\n", " 1.6588e+03\n", " 1.5446e+03\n", " 1.6702e+03\n", " 1.6419e+03\n", " 1.8054e+03\n", " 1.7315e+03\n", " 1.7515e+03\n", " 1.6859e+03\n", "\n", "\n", "\n" ] } ], "source": [ "lhe_file = pylhe.read_lhe_file(lhe_data)\n", "print(\n", " pylhe.write_lhe_string(\n", " lhe_file.init, [lhe_file.events.__next__() for i in range(5)]\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "id": "b2a72c22-5caa-4505-8c6e-708f9114900f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(lhe_data)" ] }, { "cell_type": "code", "execution_count": 6, "id": "e81691f4-31b1-41a2-a503-1393e3019faa", "metadata": {}, "outputs": [], "source": [ "# events were consumed so reload here\n", "lhe_file = pylhe.read_lhe_file(lhe_data)\n", "\n", "\n", "def filtered(events):\n", " for e in events:\n", " for p in e.particles:\n", " # only keep Z bosons\n", " if p.id == 23:\n", " yield e\n", "\n", "\n", "pylhe.write_lhe_file(lhe_file.init, filtered(lhe_file.events), \"filtered.lhe.gz\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "ec72748b-b1c6-425b-b2e6-e485e3b2e39f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# continue with modified lhe, rivet, pythia ,...\n", "plot(\"filtered.lhe.gz\")" ] }, { "cell_type": "code", "execution_count": null, "id": "67370f77-f471-4901-8daf-9f8979fdb4bf", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.13.3" } }, "nbformat": 4, "nbformat_minor": 5 }