{ "cells": [ { "cell_type": "markdown", "id": "9b1401a1-37ea-4058-a2a4-482a39106fce", "metadata": {}, "source": [ "# LHEH5/HDF5 example" ] }, { "cell_type": "code", "execution_count": 12, "id": "37b55429-a72e-47b2-8bf1-240abd77f259", "metadata": {}, "outputs": [], "source": [ "import shutil\n", "from tempfile import NamedTemporaryFile\n", "from urllib.request import urlopen\n", "\n", "import hist\n", "\n", "import pylhe" ] }, { "cell_type": "code", "execution_count": 29, "id": "6785dc2c-a9a1-448e-a42d-33f750118873", "metadata": {}, "outputs": [], "source": [ "url = \"https://zenodo.org/records/7751000/files/l2_1.hdf5?download=1\"\n", "with urlopen(url) as response, NamedTemporaryFile(suffix=\".hdf5\") as tmp:\n", " shutil.copyfileobj(response, tmp)\n", " tmp.flush()\n", "\n", " lhe = pylhe.LHEFile.fromfile(tmp.name)" ] }, { "cell_type": "code", "execution_count": 30, "id": "4bf821bf-e0c1-49cb-a9c0-c7949151da61", "metadata": {}, "outputs": [], "source": [ "arr = pylhe.to_awkward(lhe)" ] }, { "cell_type": "code", "execution_count": 31, "id": "b696fe38-4983-427c-b09f-289e7df8895c", "metadata": {}, "outputs": [], "source": [ "higgs = arr.particles[(abs(arr.particles.id) == 25) & (arr.particles.status == 1)]" ] }, { "cell_type": "code", "execution_count": 32, "id": "b9f55fb0-84f7-4124-98ce-5d8db074a10c", "metadata": {}, "outputs": [], "source": [ "mass_hist = hist.Hist.new.Reg(30, 0, 50).Int64()" ] }, { "cell_type": "code", "execution_count": 33, "id": "09760f25-8893-41f1-9540-47452042ab19", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "\n", "\n", "\n", "0\n", "\n", "\n", "50\n", "\n", "\n", "Axis 0\n", "\n", "\n", "\n", "
\n", "
\n", "Regular(30, 0, 50, label='Axis 0')
\n", "
\n", "Int64() Σ=7047002756.0 (22394099927.0 with flow)\n", "\n", "
\n", "
\n", "" ], "text/plain": [ "Hist(Regular(30, 0, 50, label='Axis 0'), storage=Int64()) # Sum: 7047002756.0 (22394099927.0 with flow)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# only take first higgs per event\n", "mass_hist.fill(\n", " higgs.vector[:, 0].pt,\n", " weight=arr.eventinfo.weight,\n", ")" ] }, { "cell_type": "code", "execution_count": 34, "id": "277a5042-afe0-4bf2-8755-6543fabdd8e7", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "artists = mass_hist.plot1d()\n", "ax = artists[0].stairs.axes\n", "# ax.set_yscale(\"log\")\n", "ax.set_xlabel(\"P_T [GeV]\")\n", "ax.set_ylabel(\"Count\");" ] }, { "cell_type": "code", "execution_count": null, "id": "a5e15bc8-88e3-463e-a8ed-4b5e2e0eba24", "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": "3.13.14" } }, "nbformat": 4, "nbformat_minor": 5 }