HEP Tables

Deployed

hep-tables presents the physicist with a uniform interface for data query and histogramming. It coordinates access to services from data delivery to local distributed clusters, removing the need for the user to code this boiler and interface code.

  • Data is fetched from ServiceX
  • That data is processed by coffea and similar tools using awkward array.
  • The data arriving back from ServiceX is distributed to DASK for faster processing.
  • Fits into the same ecosystem that tools like pyhf and (cabinetry)[https://iris-hep.org/projects/cabinetry.html] inhabit.

Further, it does this with a fairly straightforward array-like syntax:

  • Initial dataset to histogram are specified in a coherent and unified way.
  • Syntax is inspired by pandas and numpy array syntax.
  • Supports awkward array usage as well. Awkward array is the standard language for manipulating

Features

  • Basic array processing features
  • Lambda variable capture to allow for multi-object relationships
  • Basic histogramming
  • Uses ServiceX and awkward and coffea as back ends

Road map

It is best to check the repositories mentioned above for the most recent status. But some future projects

  • Integration of coffea as a backend processor to default to multi-CPU/processor work.
  • Ability to run numba (or numba-like) code
  • Ability to run C++ code
  • Running in a facility with the user having a very simple light-weight python front-end package.
  • Add skyhook as a backend for caching and fast processing close to the data.

Status

At the moment this is a prototype package. It’s development is being driven by the requirements of an analysis in ATLAS. The first version has been implemented, and we are now taking a step back to understand how best to drive this work forward.

  • Some initial documentation exists in the form of a tour to show off what it can do.
  • Three packages make up this project currently:
    1. dataframe_expressions - User facing API, converts array expressions into AST’s. Other packages then interpret this in order to execute or act on the user’s desire. Includes support for leaf referencing, slicing, lambda functions, and numpy integration.
    2. hep_tables - Interprets a dataframe expression and converts it to func_adl to be executed on a ServiceXDatasetSource. It can only interpret as much as what func_adl (or ServiceX can do: return data from the service.
    3. hl_tables - Plug-in architecture allows multiple back-ends for execution. Currently supports hep_tables to run data fetch and basic queries and also an immediate awkward array processor. The array processor can generate histograms among other things.
  • The three packages are being changed such that hep_tables will be the high level package, hl_tables will be retired, and plug-ins will be build in separate repositories.

Team

Presentations

Publications