Getting Started

• Introduce the client with a minimal example, working through line by line (pydantic version)

• Top-level explanation DataBinder configuration yaml

First request

Once you’ve been approved, you’re ready to go!

You can interact with ServiceX by making a transformation request. A transformation request includes the following information:

• An input dataset

• Filters to be applied

• Computation of new columns (if any)

• Columns to be returned to the user

Below are some basic examples which you can run to confirm that ServiceX is working for you.

xAOD

from servicex import ServiceXClient, RucioDatasetIdentifier, ResultFormat
from func_adl_servicex_xaodr22.event_collection import Event
from func_adl_servicex_xaodr22 import calib_tools

# A Z to ee sample - Release 21
ds_name = (
    r"mc16_13TeV:mc16_13TeV.361106.PowhegPythia8EvtGen_AZNLOCTEQ6L1_Zee"
    r".deriv.DAOD_PHYS.e3601_e5984_s3126_r10201_r10210_p5313")

sx = ServiceXClient(backend="uc-af")
did = RucioDatasetIdentifier(ds_name, num_files=10)

ds_raw = sx.func_adl_dataset(
    did, codegen="atlasr21", title="Zee", result_format=ResultFormat.parquet, item_type=Event)

# ds = calib_tools.apply_calibration(ds_raw, "PHYS") <this is what we should have>
ds = calib_tools.query_update(ds_raw, calib_tools.default_config("PHYSLITE"))

good_ele = ds.Select(
    lambda e: {
        "run": e.EventInfo("EventInfo").runNumber(),
        "event": e.EventInfo("EventInfo").eventNumber(),
        "good_ele": e.Electrons("Electrons")
                    .Where(lambda e: (e.pt() / 1000 > 25.0) and (abs(e.eta()) < 2.5)),
    }
)

electron_pt = good_ele.Select(lambda e: {
    "run": e.run,
    "event": e.event,
    "pt": e.good_ele.Select(lambda ele: ele.pt() / 1000.0),
})

r = electron_pt.as_signed_urls()
print(f"number of URLs: {len(r.signed_url_list)}")

Expected output:

            JetPt
entry
0       36.319766
1       34.331914
2       16.590844
3       11.389335
4        9.441805
...           ...
857133   6.211655
857134  47.653145
857135  32.738951
857136   6.260789
857137   5.394783

[11355980 rows x 1 columns]

uproot

Instead of a rucio dataset, here we will use a file directly available over https, and a slightly more complex query, and we’ll ask for the data to be locally downloaded so we can access the files directly.

import ast

import qastle

from servicex import ServiceXSpec, General, Sample
from servicex.func_adl.func_adl_dataset import FuncADLQuery
from servicex.servicex_client import deliver

query = FuncADLQuery().Select(lambda e: {'lep_pt': e['lep_pt']}). \
    Where(lambda e: e['lep_pt'] > 1000)

qstr = """
FuncADLDataset().Select(lambda e: {'lep_pt': e['lep_pt']}). \
        Where(lambda e: e['lep_pt'] > 1000)
"""
query_ast = ast.parse(qstr)
qastle_query = qastle.python_ast_to_text_ast(qastle.insert_linq_nodes(query_ast))
print("From str", qastle_query)
q2 = FuncADLQuery()
q2.set_provided_qastle(qastle_query)
print(q2.generate_selection_string())
print("From python", query.generate_selection_string())
spec = ServiceXSpec(
    General=General(
        ServiceX="testing1",
        Codegen="uproot",
        OutputFormat="parquet",
        Delivery="LocalCache"
    ),
    Sample=[
        Sample(
            Name="mc_345060.ggH125_ZZ4lep.4lep",
            XRootDFiles="root://eospublic.cern.ch//eos/opendata/atlas/OutreachDatasets/2020-01-22/4lep/MC/mc_345060.ggH125_ZZ4lep.4lep.root", # NOQA E501
            Query=query
        )
    ]
)

print(deliver(spec))

Expected output:

[{pt: [36.3, 24.7], eta: [2.87, 3.13], phi: [, ... -2.15], mass: [12.3, 6.51, 3.98]}]
349

Next steps

Check out the [requests guide](requests.md) to learn more about specifying transformation requests using func-ADL.