{
"cells": [
{
"cell_type": "markdown",
"id": "d7492051",
"metadata": {},
"source": [
"# reading remote data: examples\n",
"\n",
"dataset to use: PP8 ttbar from 2015 CMS Open Data, converted to flat ntuple"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "10f550aa",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import awkward as ak\n",
"import numpy as np\n",
"\n",
"dataset_opendata = \"http://xrootd-local.unl.edu:1094//store/user/AGC/datasets/RunIIFall15MiniAODv2/TT_TuneCUETP8M1_13TeV-powheg-pythia8/MINIAODSIM//PU25nsData2015v1_76X_mcRun2_asymptotic_v12_ext3-v1/00000/00DF0A73-17C2-E511-B086-E41D2D08DE30.root\"\n",
"treename = \"events\"\n",
"bins = np.linspace(0, 250, 26)"
]
},
{
"cell_type": "markdown",
"id": "3b68c225",
"metadata": {},
"source": [
"#### using `uproot`"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "88ed6c38",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[96.6, 90.3, 66.4, 57.2, 35.4, 16.4, 16.1, ... 16, 14.9, 13.9, 13.6, 10.4, 10.1]]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import uproot\n",
"\n",
"f = uproot.open(dataset_opendata)\n",
"jet_pt = f[treename][\"jet_pt\"].array()\n",
"print(jet_pt)\n",
"plt.hist(ak.flatten(jet_pt), bins=bins);"
]
},
{
"cell_type": "markdown",
"id": "09818da0",
"metadata": {},
"source": [
"#### using `func_adl_uproot`"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f4206330",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[96.6, 90.3, 66.4, 57.2, 35.4, 16.4, 16.1, ... 16, 14.9, 13.9, 13.6, 10.4, 10.1]]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from func_adl_uproot import UprootDataset\n",
"\n",
"ds = UprootDataset(dataset_opendata, treename)\n",
"jet_pt_query = ds.Select(lambda event: event.jet_pt)\n",
"jet_pt = jet_pt_query.value()\n",
"print(jet_pt)\n",
"plt.hist(ak.flatten(jet_pt), bins=bins);"
]
},
{
"cell_type": "markdown",
"id": "69613ce0",
"metadata": {},
"source": [
"#### using `func_adl_servicex`\n",
"\n",
"querying e.g. https://uproot-atlas.servicex.af.uchicago.edu/"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4c09eb08",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"http://xrootd-local....: 0%| | 0/9000000000.0 [00:00]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" http://xrootd-local.... Downloaded: 0%| | 0/90000"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[96.6, 90.3, 66.4, 57.2, 35.4, 16.4, 16.1, ... 16, 14.9, 13.9, 13.6, 10.4, 10.1]]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from servicex import ServiceXDataset\n",
"from func_adl_servicex import ServiceXSourceUpROOT\n",
"\n",
"sx_dataset = ServiceXDataset(dataset_opendata, backend_name='uproot', ignore_cache=False)\n",
"ds = ServiceXSourceUpROOT(sx_dataset, treename)\n",
"jet_pt_query = ds.Select(lambda event: event.jet_pt)\n",
"\n",
"jet_pt = jet_pt_query.AsAwkwardArray().value()\n",
"print(jet_pt)\n",
"plt.hist(ak.flatten(jet_pt), bins=bins);"
]
},
{
"cell_type": "markdown",
"id": "3c8ba99f",
"metadata": {},
"source": [
"#### Streaming data from ROOT files"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9d3f3372",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"[https://xrootd-loca...: 0%| | 0/9000000000.0 [00:00]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"remote file content: ['event', 'nel_pt', 'el_pt']\n",
"downloading 0.root\n",
"local file content: ['event', 'nel_pt', 'el_pt']\n"
]
}
],
"source": [
"import urllib\n",
"\n",
"from servicex import ServiceXDataset\n",
"import uproot\n",
"\n",
"dummy_ds = ServiceXSourceUpROOT(\"cernopendata://dummy\", \"Events\", backend_name=\"uproot\")\n",
"dummy_ds.return_qastle = True\n",
"query = dummy_ds.Select(lambda e: {\"event\": e.event, \"el_pt\": e.Electron_pt}).value()\n",
"\n",
"dataset_name = [\"https://xrootd-local.unl.edu:1094//store/user/AGC/nanoAOD/nanoaod15.root\"]\n",
"sx_dataset = ServiceXDataset(dataset_name, backend_name='uproot', ignore_cache=False)\n",
"\n",
"transformed_files = sx_dataset.get_data_rootfiles_uri(query, as_signed_url=True)\n",
"\n",
"# open remote file\n",
"with uproot.open(f\"{transformed_files[0].url}:servicex\") as f:\n",
" print(f\"remote file content: {f.keys()}\")\n",
"\n",
"# download file\n",
"for i, output in enumerate(transformed_files):\n",
" output_fname = f\"{i}.root\"\n",
" print(f\"downloading {output_fname}\")\n",
" urllib.request.urlretrieve(output.url, filename=output_fname)\n",
"\n",
"# open local file\n",
"with uproot.open(\"0.root:servicex\") as f:\n",
" print(f\"local file content: {f.keys()}\")"
]
},
{
"cell_type": "markdown",
"id": "ad71b1fe",
"metadata": {},
"source": [
"#### xAOD backend\n",
"\n",
"taken from https://github.com/iris-hep/func_adl_servicex#using-the-xaod-backend"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "19e6ee1e",
"metadata": {},
"outputs": [],
"source": [
"from func_adl_servicex import ServiceXSourceXAOD\n",
"\n",
"dataset_xaod = \"mc15_13TeV:mc15_13TeV.361106.PowhegPythia8EvtGen_AZNLOCTEQ6L1_Zee.merge.DAOD_STDM3.e3601_s2576_s2132_r6630_r6264_p2363_tid05630052_00\"\n",
"ds = ServiceXSourceXAOD(dataset_xaod)\n",
"data = (\n",
" ds\n",
" .SelectMany('lambda e: (e.Jets(\"AntiKt4EMTopoJets\"))')\n",
" .Where('lambda j: (j.pt()/1000)>30')\n",
" .Select('lambda j: j.pt()')\n",
" .AsAwkwardArray([\"JetPt\"])\n",
" .value()\n",
")\n",
"\n",
"print(data['JetPt'])"
]
}
],
"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": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}