Metadata-Version: 2.1
Name: func-adl-servicex
Version: 1.1b6
Summary: Submit Functional Queries to a ServiceX endpoint.
Home-page: https://github.com/iris-hep/func_adl_servicex
Author: G. Watts (IRIS-HEP/UW Seattle)
Author-email: gwatts@uw.edu
Maintainer: Gordon Watts (IRIS-HEP/UW Seattle)
Maintainer-email: gwatts@uw.edu
License: TBD
Platform: Any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Software Development
Classifier: Topic :: Utilities
Requires-Python: >=3.6, <3.10
Description-Content-Type: text/markdown
Provides-Extra: test

# func_adl_servicex

Send func_adl expressions to a ServiceX endpoint

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## Introduction

This package contains the single object `ServiceXSourceXAOD` and ``ServiceXSourceUpROOT` which can be used as a root of a `func_adl` expression to query large LHC datasets from an active `ServiceX` instance located on the net.

See below for simple examples.

### Further Information

- `servicex` documentation
- `func_adl` documentation

## Usage

To use `func_adl` on `servicex`, the only `func_adl` package you only need to install this package. All others required will be pulled in as dependencies of this package.

## Using the xAOD backend

See the further information for documentation above to understand how this works. Here is a quick sample that will run against an ATLAS `xAOD` backend in `servicex` to get out jet pt's for those jets with `pt > 30` GeV.

```python
from func_adl_servicex import ServiceXSourceXAOD

dataset_xaod = "mc15_13TeV:mc15_13TeV.361106.PowhegPythia8EvtGen_AZNLOCTEQ6L1_Zee.merge.DAOD_STDM3.e3601_s2576_s2132_r6630_r6264_p2363_tid05630052_00"
ds = ServiceXSourceXAOD(dataset_xaod)
data = ds \
    .SelectMany('lambda e: (e.Jets("AntiKt4EMTopoJets"))') \
    .Where('lambda j: (j.pt()/1000)>30') \
    .Select('lambda j: j.pt()') \
    .AsAwkwardArray(["JetPt"]) \
    .value()

print(data['JetPt'])
```

## Using the CMS Run 1 AOD backend

See the further information for documentation above to understand how this works. Here is a quick sample that will run against an CMS Run 1 `AOD` backend in `servicex`. It turns against a 6 TB CMS Open Data dataset, selecting global muons with a pT greater than 30 GeV.

```python
from func_adl_servicex import ServiceXSourceCMSRun1AOD

dataset_xaod = "cernopendata://16"
ds = ServiceXSourceCMSRun1AOD(dataset_xaod)
data = ds \
data = ServiceXSourceCMSRun1AOD("cernopendata://16") \
    .SelectMany(lambda e: e.TrackMuons("globalMuons")) \
    .Where(lambda m: m.pt() > 30) \
    .Select(lambda m: m.pt()) \
    .AsAwkwardArray(['mu_pt']) \
    .value()

print(data['mu_pt'])
```

## Using the uproot backend

See the further information for documentation above to understand how this works. Here is a quick sample that will run against a ROOT file (TTree) in the `uproot` backend in `servicex` to get out jet pt's. Note that the image name tag is likely wrong here. See XXX to get the current one.

```python
from servicex import ServiceXDataset
from func_adl_servicex import ServiceXSourceUpROOT


dataset_uproot = "user.kchoi:user.kchoi.ttHML_80fb_ttbar"
uproot_transformer_image = "sslhep/servicex_func_adl_uproot_transformer:issue6"

sx_dataset = ServiceXDataset(dataset_uproot, image=uproot_transformer_image)
ds = ServiceXSourceUpROOT(sx_dataset, "nominal")
data = ds.Select("lambda e: {'lep_pt_1': e.lep_Pt_1, 'lep_pt_2': e.lep_Pt_2}") \
    .AsParquetFiles('junk.parquet') \
    .value()

print(data)
```

## Development

PR's are welcome! Feel free to add an issue for new features or questions.

The `master` branch is the most recent commits that both pass all tests and are slated for the next release. Releases are tagged. Modifications to any released versions are made off those tags.

## Qastle

This is for people working with the back-ends that run in `servicex`.

This is the `qastle` produced for an xAOD dataset:

```text
(call EventDataset 'ServiceXDatasetSource')
```

(the actual dataset name is passed in the `servicex` web API call.)

This is the `qastle` produced for a ROOT flat file:

```text
(call EventDataset 'ServiceXDatasetSource' 'tree_name')
```


