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PyHEP.dev 2024 - "Python in HEP" Developer's Workshop

26–30 Aug 2024
Aachen, Germany
Europe/Brussels timezone

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Contribution List

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  1. 8. Welcome slides
    26/08/2024, 09:00
  2. 25. Self-introduction: Eduardo Rodrigues
    Eduardo Rodrigues (University of Liverpool (GB))
    26/08/2024, 09:30
  3. 26. Self-introduction: Juraj Smiesko
    Juraj Smiesko (CERN)
    26/08/2024, 09:37
  4. 27. Self-introduction: Jan Bürger
    Jan Bürger (ErUM-Data-Hub)
    26/08/2024, 09:44
  5. 48. Self-introduction: Jim Pivarski
    Jim Pivarski (Princeton University)
    26/08/2024, 09:51
  6. 49. Self-introduction: Lino Oscar Gerlach
    Lino Oscar Gerlach (Princeton University (US))
    26/08/2024, 09:58
  7. 50. Self-introduction: Josue Molina
    Josue Molina
    26/08/2024, 10:05
  8. 51. Self-introduction: Ianna Osborne
    Ianna Osborne (Princeton University)
    26/08/2024, 10:12
  9. 52. Self-introduction: Máté Farkas
    Mate Farkas (Rheinisch Westfaelische Tech. Hoch. (DE))
    26/08/2024, 10:19
  10. 53. Self-introduction: Yaroslav Nikitenko
    Yaroslav Nikitenko
    26/08/2024, 10:26
  11. 23. Fast end-to-end analysis pipelines for the HL-LHC
    Alexander Held (University of Wisconsin Madison (US)), Oksana Shadura (University of Nebraska Lincoln (US))
    26/08/2024, 10:40

    We provide an overview of two ongoing projects that aim to ensure the availability of fast and user-friendly solutions for physics analysis pipelines towards the HL-LHC. The Analysis Grand Challenge (AGC) defines an analysis task that captures relevant physics analysis workflow aspects. A variety of implementations have been developed for this task, allowing to probe user experience and...

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  12. 59. 200 Gbps notebook links
    26/08/2024, 13:30
  13. 29. Self-introduction: Peter Fackeldey
    Manfred Peter Fackeldey (RWTH Aachen University (DE))
    27/08/2024, 09:00
  14. 40. Self-introduction: Stefan Fröse
    Stefan Fröse (ErUM-Data-Hub)
    27/08/2024, 09:01
  15. 30. Self-introduction: Matthew Feickert
    Matthew Feickert (University of Wisconsin Madison (US))
    27/08/2024, 09:07
  16. 31. Self-introduction: Jonas Eschle
    Jonas Eschle (Syracuse University (US))
    27/08/2024, 09:14
  17. 33. Self-introduction: Alexander Held
    Alexander Held (University of Wisconsin Madison (US))
    27/08/2024, 09:21
  18. 35. Self-introduction: Giordon Holtsberg Stark
    Dr Giordon Holtsberg Stark (University of California,Santa Cruz (US))
    27/08/2024, 09:28
  19. 32. Self-introduction: Marcel Rieger
    Marcel Rieger (Hamburg University (DE))
    27/08/2024, 09:35
  20. 56. Self-introduction: Jonas Eppelt
    Jonas Eppelt (Karlsruher Insititute of Technology (KIT))
    27/08/2024, 09:42
  21. 57. Self-introduction: Alexander Heidelbach
    Alexander Heidelbach
    27/08/2024, 09:49
  22. 55. Self-introduction: Vincenzo Eduardo Padulano
    Dr Vincenzo Eduardo Padulano (CERN)
    27/08/2024, 09:55
  23. 17. An overview of the fitting ecosystem
    Jonas Eschle (Syracuse University (US))
    27/08/2024, 10:02

    This talk will give a broad overview on the fitting that we're doing in
    HEP. On one hand, the talk will cover the variety of fits in HEP, the
    different needs and types of inference as well as efforts for
    serialization and standardization. On the other hand, the relevant
    libraries will be covered, that is zfit, pyhf, hepstats iminuit and
    Python packages like SciPy and how they work...

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  24. 20. evermore: differentiable (binned) likelihoods in JAX
    Manfred Peter Fackeldey (RWTH Aachen University (DE))
    27/08/2024, 10:32

    I'd like to present evermore (https://github.com/pfackeldey/evermore) that focusses in efficiently building and evaluating likelihoods typically for HEP. Currently, it focusses on binned template fits.
    It supports autodiff, JIT-compilation and vectorization of full fits (even on GPUs).

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  25. 7. Group photo
    27/08/2024, 10:52
  26. 54. Plothist - integrating with other histogram libraries (Remote talk)
    Cyrille Praz, Tristan Fillinger (KEK / IPNS)
    28/08/2024, 09:00
  27. 34. Self-introduction: Saransh Copra
    Saransh Chopra (Princeton University (US))
    28/08/2024, 09:20
  28. 37. Self-introduction: Oksana Shadura
    Oksana Shadura (University of Nebraska Lincoln (US))
    28/08/2024, 09:27
  29. 38. Self-introduction: Nikolai Hartmann
    Nikolai Hartmann (Ludwig Maximilians Universitat (DE))
    28/08/2024, 09:33
  30. 18. b2luigi — bringing batch 2 luigi!
    Alexander Heidelbach, Jonas Eppelt (Karlsruher Insititute of Technology (KIT))
    28/08/2024, 09:40

    Workflow managers help structure the code of pipelined jobs by defining and managing dependencies between tasks in a clear and easy-to-understand fashion. This abstraction allows independent tasks to be automatically parallelised more independently of computing systems. Additionally, workflow managers help keep track of different tasks’ outputs and inputs.

    b2luigi is an extension of the...

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  31. 22. End-to-end workflow automation: updates of the luigi analysis workflow package
    Marcel Rieger (Hamburg University (DE))
    28/08/2024, 10:00

    Physicists performing data analyses are usually required to steer their individual, complex workflows manually, frequently involving job submission in several stages and interaction with distributed storage systems by hand. This process is not only time-consuming and error-prone, but also leads to undocumented relations between particular workloads, rendering the steering of an analysis a...

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  32. 19. waluigi - Beyond luigi
    Benjamin Fischer (RWTH Aachen University (DE))
    28/08/2024, 10:20

    Workflows for research in HEP experiments are not only quite complex but also require sufficient flexibility to adapt to changes in structuring, conditions, methodologies, and research interests. This holds especially true in the physics analyses extracting the results and measurements.
    Here, the use of workflows systems, specifically Luigi, have shown to be of great use to manage and...

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  33. 21. offloading @ coffea
    Benjamin Fischer (RWTH Aachen University (DE))
    28/08/2024, 10:40

    Offloading resource intensive tasks, i.e.:
    - histograms (accumulation) - memory intensive
    - DL algorithms - compute intensive

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  34. 58. Sustainability in computing
    Martin Erdmann (Rheinisch Westfaelische Tech. Hoch. (DE))
    28/08/2024, 12:15
  35. 5. Workshop Dinner
    28/08/2024, 18:30

    Ratskeller Aachen, Markt 40, 52062 Aachen

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  36. 60. Job openings in the ROOT team
    Dr Vincenzo Eduardo Padulano (CERN)
    29/08/2024, 09:00
  37. 36. Self-introduction: Benjamin Fischer
    Benjamin Fischer (RWTH Aachen University (DE))
    29/08/2024, 09:05
  38. 39. Self-introduction: Azzah Alshehri
    Azzah Aziz Alshehri (University of Glasgow (GB))
    29/08/2024, 09:12
  39. 14. File synchronization between Linux systems in Python with yarsync
    Yaroslav Nikitenko
    29/08/2024, 09:25

    Yet Another Rsync is a Python wrapper around a well-established Linux tool rsync with a simple and familiar interface of git. Python allows us to create a higher-level instrument, which is safer and sometimes more efficient than the original binary.

    While many data analysts today heavily use databases and rely on cloud computing, other approaches have also their benefits. Many...

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  40. 13. Architectural framework for data analysis Lena
    Yaroslav Nikitenko
    29/08/2024, 09:45

    The term «architecture» in software has numerous definitions. Ultimately it defines whether your analysis code will be extensible and maintainable. We propose an architecture based on the functional style and separation of data, logic and presentation. It is implemented in a free software framework Lena.

    Lena is a general data analysis framework in Python, named after a great...

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  41. 16. FCCAnalyses: A Framework for Future Circular Collider Physics Performance Studies
    Juraj Smiesko (CERN)
    29/08/2024, 10:05

    Physics performance analyses provide essential input for defining detector requirements in the Future Circular Collider (FCC) project. To streamline these analyses, we employ FCCAnalyses, a software framework built on top of the ROOT DataFrame.

    Among the functionalities offered by the FCCAnalyses are:
    * Standard set of RDataFrame EDM4hep functions: Events can be analysed directly in the...

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  42. 12. Bridging Python and Julia for Enhanced Data Analysis
    Ianna Osborne (Princeton University)
    29/08/2024, 10:25

    Let’s discuss the exciting world of combining Python and Julia for data analysis for high-energy physics (HEP) and other data-intensive fields.

    We'll kick things off with a quick overview of why Python is so popular for data analysis and introduce Julia, which is making waves with its incredible performance and suitability for scientific computing.

    Next, I'll show you how we can get the...

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  43. 24. A Deep Dive into PocketCoffea
    Mate Farkas (Rheinisch Westfaelische Tech. Hoch. (DE))
    29/08/2024, 10:45

    PocketCoffea is a python columnar analysis framework based on coffea for CMS NanoAOD events. It provides a workflow for HEP analyses using a combination of customizable abstractions and configuration files. The package features dataset query automatisation, jet calibration, data processing, histogramming and plotting. PocketCoffea also provides support for code execution on various remote...

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  44. 4. Sightseeing Tour
    29/08/2024, 14:30

    Starting and ending at Erholungsgesellschaft (workshop venue)

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  45. 9. Organizing the paper-writing
    30/08/2024, 09:00
  46. 11. Close-out
    30/08/2024, 12:15
  47. 3. Blazing Speed and Efficiency in Data Analytics with DuckDB and Python
    Aditya Mehra

    Join us for an in-depth exploration of how DuckDB, a state-of-the-art database management system, can be integrated with Python to revolutionize your data analytics workflow. This talk will demonstrate how DuckDB combines the power of traditional databases with the simplicity and efficiency of dataframes, providing a seamless experience for data scientists and analysts.

    In this technical...

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  48. 15. Case for awkward array - efficient I/O for highly hierarchical data structure
    Jerry 🦑 Ling (Harvard University (US))

    We present from our developer experience, a case for adopting awkward array abstraction in data I/O library for highly hierarchical data set commonly as seen in HEP.

    We will discuss both technical, performance-related lessons learned in implementing TTree/RNTuple reading, these are universal principles for all columnar data format I/O.

    Specifically, we discuss the the importance of...

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  49. 1. Introducing Immortal Objects: building block towards a multi-core Python runtime.
    Aditya Mehra

    In this presentation, I'll cover memory management in Python starting from the fundamentals. I'll explain the rationale behind the need for PEP 683: "– Immortal Objects, Using a Fixed Refcount" , discussing about this change may be unlocking exciting avenues for true parallelism in python.

    Objective of talk is to discuss on how and why we need pure immutable objects and fixing the old time...

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  50. 10. Organizing the paper-writing
  51. 6. Registration & Reception
  52. 41. Self-introduction:
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  59. 28. Self-introduction: Niclas Eich
    Niclas Eich (Rheinisch Westfaelische Tech. Hoch. (DE))
  60. 2. Unleashing the Power of Python Decorators: A Hands-On Workshop
    Aditya Mehra

    -Workshop Overview:
    Dive deep into the world of Python decorators with our hands-on workshop designed for intermediate Python developers. This session will demystify the concept of decorators, showcasing their power and versatility in enhancing the functionality of your code. By the end of this workshop, you'll have a solid understanding of how to create, apply, and leverage decorators to...

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