PyHEP 2019 Workshop

Abingdon, U.K.

Abingdon, U.K.

The Cosener's House Abingdon, U.K.
Benjamin Krikler (University of Bristol (GB)), Eduardo Rodrigues (University of Cincinnati (US))

The PyHEP workshops are a series of workshops initiated and supported by the HEP Software Foundation (HSF) with the aim to provide an environment to discuss and promote the usage of Python in the HEP community at large. Further information is given here.

PyHEP 2019 will be held at The Cosener's House, in Abingdon, near Oxford, United Kingdom, from 16-18 October 2019.

The workshop will be a forum for the participants and the community at large to discuss developments of Python packages and tools, exchange experiences, and steer where the community needs and wants to go. There will be ample time for discussion.
The agenda will be composed of plenary sessions, a highlight of which is the following:
1) A keynote presentation on the PyViz - open source visualization tools for Python - project, given by Philipp Rudiger, a member of the developers team.
2) Topical sessions on e.g. histogramming and statistics, including a talk and a hands-on tutorial.
3) Lightning talks from participants.
4) Presentations following up from topics discussed at PyHEP 2018.
Partial travel support for some U.S. participants (in particular, students and early-career postdocs) may be available from the IRIS-HEP institute. Please contact Peter Elmer ( to enquire about details.


Organising Committee

Eduardo Rodrigues - University of Cincinnati (Chair)
Ben Krikler - University of Bristol (Co-chair)



The event is kindly sponsored by

          Python Software Foundation   STFC PPGP

  • Adrian Oeftiger
  • Andrzej Novak
  • Bart Pelssers
  • Ben Smart
  • Benjamin Krikler
  • Beojan Stanislaus
  • Brett Mayes
  • Carl Gwilliam
  • Chris Burr
  • Diogo Castro
  • Eduardo Rodrigues
  • Emma Buchanan
  • Emma Torro Pastor
  • Emmanuel Olaiya
  • Gordon Watts
  • Hans Peter Dembinski
  • Helena Brandao Malbouisson
  • Henry Day-Hall
  • Henry Fredrick Schreiner
  • Igor Volobouev
  • Ilektra Christidi
  • Jacob Julian Kempster
  • James Pillow
  • James Walder
  • Jim Pivarski
  • Jonas Eschle
  • Joosep Pata
  • Jordan Palmer
  • Katerina Hladka
  • Lindsey Gray
  • Lukasz Kreczko
  • Martha Hilton
  • Martin Ritter
  • Martin Schwinzerl
  • Mason Proffitt
  • Massimiliano Galli
  • Matthew Feickert
  • Matthew Tan
  • Migle Stankaityte
  • Miroslav Kubu
  • Monika Wielers
  • Philipp Rudiger
  • Pieter David
  • Pratyush Das
  • Ralf Kliemt
  • Rob Taylor
  • Sam Mangham
  • Simone Bologna
  • Tim Adye
  • Tim Martin
  • Todd Brian Huffman
  • Victor Daussy-Renaudin
  • Will Turner
  • William Vinning
    • Visualisation
      Convener: Benjamin Krikler (University of Bristol (GB))
      • 11
        Panel: Turn your existing Python analysis code into deployable dashboards


        Over the last decade the Python scientific ecosystem have become an incredibly powerful toolkit for performing analyses and visualizing data. Once an analysis is done it often has to be shared with a wider audience, either within an organization or with the wider public, but this step often requires an entirely different set of tools and skillset. Panel is a new, open-source Python library built to easily wrap the outputs of an analysis, combine them with widgets and then lay them out as an interactive app or dashboard. This enables faster iteration cycles within organizations and ensures users without in-depth familiarity with web programming can develop and deploy complex dashboards with minimal code.

        Panel natively supports a wide range of plotting tools and many other types of data making it trivial to work with the tools users are already familiar with. A complex interactive Panel-based dashboard is typically many times shorter than the equivalent Dash or Bokeh code, focusing on expressing relationships between widgets, computation, and visualizations directly. At the same time Panel is not limited to building simple apps and can be used to visualize complex, multi-stage analysis pipelines, provides full styling flexibility using CSS and Bokeh themes and has the ability to dynamically resize to the size of the browser window.

        Once an application is built either in a Python script or notebook it can trivially be deployed as a standalone app using Bokeh Server without any change in behavior. Alternatively, Panel apps can be exported to static HTML files by defining Javascript based interactions or even recording and embedding the app's state space, making it possible to share interactive visualizations as self-contained files.

        In the talk we will discover some of the core ideas behind Panel, go through the process of making an existing Jupyter notebook deployable as a dashboard and finally look at a number of case studies of real world dashboards analyzing large volumes of scientific data. With Panel, your analyses and visualizations can now very easily leap from your notebook or custom scripts into the real world!

        Speaker: Philipp Rudiger (Anaconda)
      • 12
        mpl-hep - HEP needs for visualization
        Speaker: Andrzej Novak (RWTH Aachen (DE))
    • 10:10 AM
      Coffee/tea break
    • Analysis platforms
      Convener: Eduardo Rodrigues (University of Cincinnati (US))
    • High-level Analysis Tools
      Convener: Eduardo Rodrigues (University of Cincinnati (US))
    • 12:50 PM
    • Analysis fundamentals
      Convener: Benjamin Krikler (University of Bristol (GB))
    • 3:30 PM
      Coffee/tea break
    • Histogramming & news on Python 3.8
      Convener: Henry Fredrick Schreiner (Princeton University)
      • 19
        Aside: quick news on Python 3.8
        Speaker: Henry Fredrick Schreiner (Princeton University)
      • 20
        Python histogramming packages
        Speaker: Henry Fredrick Schreiner (Princeton University)
      • 21
        Speaker: Henry Fredrick Schreiner (Princeton University)
    • 7:00 PM
    • Statistics
      Convener: Hans Peter Dembinski (Max-Planck-Institute for Nuclear Physics, Heidelberg)
      • 22
        Speaker: Jonas Eschle (Universitaet Zuerich (CH))

        zfit: scalable pythonic fitting

        zfit brings together the model fitting efforts for HEP in Python by defining an API & Workflow and an implementation on top of TensorFlow and scikit-hep packages.

        It offers an extensive model building part, currently focused on unbinned likelihoods.

      • 23
        Speaker: Matthew Feickert (Univ. Illinois at Urbana Champaign (US))
      • 24
        Fast likelihood analysis in more dimensions for Xenon TPCs
        Speaker: Bart Pelssers (Stockholm University)
    • 10:30 AM
      Coffee/tea break
    • Statistics
      Convener: Eduardo Rodrigues (University of Cincinnati (US))
      • 25
        Introduction to iminuit
        Speaker: Dr Hans Peter Dembinski (Max-Planck-Institute for Nuclear Physics, Heidelberg)
      • 26
        Statistical Methods in the NPStat Package
        Speaker: Igor Volobouev (Texas Tech University (US))
      • 27
        Computing coverage with NumPy and Numba of Bayesian and Frequentist intervals
        Speaker: Hans Peter Dembinski (Max-Planck-Institute for Nuclear Physics, Heidelberg)
    • PyROOT
      Convener: Benjamin Krikler (University of Bristol (GB))
    • Closeout
    • 1:05 PM