Accelerate your Analysis with ServiceX

Select and transform large datasets into
analysis-ready data using Python.

Simplify Analysis Code

Analyze data without writing or building C++ code, reducing tech debt and startup time. A single Python query produces analysis-ready histogram data with minimal environment setup.

NTuples
ATLAS xAOD


uproot_raw_query = query.UprootRaw([{
    'treename':'reco',
    'filter_name':
      [ "truth_alp_decayVtxX",
        "truth_alp_decayVtxY",
        "truth_alp_pt",
        "jet_pt_NOSYS" ],
    'cut':
        '(num(jet_pt_NOSYS)<2) & any((truth_alp_pt>20)'
}])


query = FuncADLQueryPHYSLITE()
jets_per_event = (query
    .Select(lambda e: e.Jets().Where(lambda j: (j.pt() / 1000 > 30)))
    .Select(lambda jets: {
        'pt': jets.Select(lambda j: j.pt() / 1000),
    })
)

jet_data = get_data(jets_per_event, dataset)
plt.hist(ak.flatten(data.jet), bins=50, range=(0, 100))