PyHEP 2022 (virtual) Workshop

Europe/Zurich
Eduardo Rodrigues (University of Liverpool (GB)), Graeme A Stewart (CERN), Jim Pivarski (Princeton University), Matthew Feickert (Univ. Illinois at Urbana Champaign (US)), Nikolai Hartmann (Ludwig Maximilians Universitat (DE)), Oksana Shadura (University of Nebraska Lincoln (US))
Description

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 on the PyHEP Working Group website.

PyHEP 2022 will be a virtual workshopIt will be a forum for the participants and the community at large to discuss developments of Python packages and tools, exchange experiences, and inform the future evolution of community activities. There will be ample time for discussion.

The agenda is composed of plenary sessions:

1) Hands-on tutorials.
2) Topical sessions.
3) Presentations following up from topics discussed at PyHEP 2021.
 
Registration is open until September 16th. There are no workshop fees.
 
You are encouraged to register to the PyHEP WG Gitter channel and/or to the HSF forum to receive further information concerning the organisation of the workshop. Workshop updates and information will also be shared on the workshop Twitter in addition to email. Follow the workshop @PyHEPConf.
 
Live stream on YouTube: click here!

Organising Committee

Eduardo Rodrigues - University of Liverpool (Chair)                         
Graeme A. Stewart - CERN                         
Jim Pivarski - Princeton University                         
Matthew Feickert - University of Wisconsin-Madison                         
Nikolai Hartmann - Ludwig-Maximilians-Universität Munich                         
Oksana Shadura - University of Nebraska-Lincoln

 

Sponsors

The event is kindly sponsored by

   
 
 
Participants
  • Aashi Singh
  • Aayush Gautam
  • Aazhimukilan G
  • Abdualazem Mohammed
  • Abdul Basith Kaliyar
  • Abdullah Nayaz
  • Abhaya Kumar Swain
  • Abhipsa Acharya
  • Abhisek Praharaj
  • Abhishek Bohare
  • Abhishek Pandey
  • Abhishek Prashant
  • Abhishek Roy
  • Abhishek Singh
  • Abinash Pun
  • Abishek P
  • Achyut Khanal
  • Adam LaMee
  • Adam Lyon
  • Adhavan A
  • Adil Hussain
  • Adithya Sampath
  • Aditya Gupta
  • Aditya Kumar
  • Aditya Rana
  • Adrian Oeftiger
  • Ahmed Abdelmotteleb
  • Ahmed Elsayed
  • Ahmed Rockey Saikia
  • Aiham Al Musalhi
  • Aiqiang Guo
  • Aiswarya S Nair
  • Ajay Chaudhary
  • Ajay Kumar
  • Ajay Sharma
  • Ajay Sharma
  • Ajita Tiwari
  • Akhil Krishnan
  • Akshat Rawat
  • Alan Taylor
  • Alberto Amin Souissi Ayuso
  • alberto plebani
  • Alberto Saborido Patiño
  • Alec Peck
  • Alejandro Ramirez
  • Aleksandr Iniukhin
  • Alessandro Pascolini
  • Alessandro Reineri
  • Alex Goldsack
  • Alexander Borissov
  • Alexander Froch
  • Alexander Gio Veltman
  • Alexander Held
  • Alexander Kryukov
  • Alexander Moreno Briceño
  • Alexis Kalogeropoulos
  • Ali Al Kadhim
  • Ali Can Canbay
  • Ali Hassan
  • Alicia Postuma
  • Alireza Mohamaditabar
  • Allan Eduardo Flores Godoi Ferreira
  • Aman Desai
  • Aman Desai
  • Aman Goel
  • Aman Kumar
  • Aman Verma
  • Amardeep Chawla
  • Amartya Rej
  • Amirmohammad Chegeni
  • Amit Adhikary
  • Amit kumar Singh
  • AMLANA JYOTI BISWAL
  • Ammar Mureed
  • Amrutha Samalan
  • Amália Buchweitz Garcez
  • Ana Maria Rodriguez Vera
  • Ana Marin
  • Anand Kumar Verma
  • Anand Sharma
  • Ananthakrishna Panuganti
  • Ananya Paul
  • Anastasiia Riabchikova
  • Anay Jain
  • Andrea Piccinelli
  • Andrea Villa
  • Andreas Molander
  • Andreas Pfeiffer
  • Andrei Gribushin
  • Andrei Maltsev
  • Andrej Saibel
  • Andres Guillermo Delannoy Sotomayor
  • Andrew Naylor
  • Andrew Stevens
  • Andrew Wightman
  • Andrii Len
  • Andris Potrebko
  • Andy Wharton
  • Anfeng Li
  • Angel Fernando Campoverde Quezada
  • Angela Zaza
  • Aniket Raj
  • Anindya Ghosh
  • Anirudh Ralhan
  • Anisha Yadav
  • Anja Novosel
  • ANJALI P
  • Anjaly Sasikumar Menon
  • Anjana b s
  • Ankit .
  • Ankit Kumar
  • ANNA ZEENIA CYRIAC
  • Annalisa De Lorenzis
  • Annalisa Mastroserio
  • Anshika Agarwal
  • Anshika Bansal
  • Ansona Annat Joseph
  • Antoine Laudrain
  • Antoine Perus
  • Antonio Cota
  • Antonio Giannini
  • Antonio Paredes
  • Antonis Agapitos
  • Antony Cyriac Thekkedath
  • Antra Gaile
  • Anuj Chandra
  • ANUMITA SAHA
  • Anup Karekar
  • Anwesha Paul
  • Anza-Tshilidzi Mulaudzi
  • APARNA SEKHAR E R
  • Apeksha Singhal
  • Apoorv Tripathi
  • Apurva Narde
  • Aravindhan Venkateswaran
  • Aravinth Ram K
  • Arghyajit Datta
  • Aritra Sankar Brahmachari
  • Arjun K R
  • ARKADIP MUKHERJEE
  • Arnav Lakhdive
  • Arshad Mohammad
  • ARTEM BOLSHOV
  • Artur Lobanov
  • Artur Suarez
  • Arun Madhu
  • Arvind Kumar
  • Aryan Agarwal
  • Aryan Bhatia
  • Ashika A
  • Ashish Narayan
  • Asrar Ahmed
  • Atanu Debnath
  • Athul Dev Sudhakar Ponnu
  • Atul Ashutosh Samanta
  • Atul Prajapati
  • Auro Prasad Mohanty
  • Austin Townsend
  • Avijit Hazra
  • Avinash Mall
  • Aviral Verma
  • Avnika Tyagi
  • Ayanabha Das
  • Ayush Dhingra
  • Ayush Mishra
  • Ayush Shivkumar
  • Babu Pokhrel
  • Baidyanath Kundu
  • Baktash Amini
  • Balasubramaniam K. M.
  • Banajit Barman
  • Banee Ishaque K
  • Bar G
  • Bartosz Idzior
  • Batuhan Yılmazer
  • Beatrice Cervato
  • Ben Gayther
  • Ben Krikler
  • Benjamin James Wilson
  • Benjamin Lieberman
  • Benjamin Tovar Lopez
  • Bevan Mathew A
  • Bhavna Yadav
  • Bhavuk Rohilla
  • Bhoomika Pandya
  • BHUMIDHAR BARMAN
  • Bhuvaneshwari Kashi
  • Bibhuti Parida
  • Bill Loizos
  • Binesh Mohan
  • Binish Batool
  • Biswaranjan Behera
  • Blaise Delaney
  • Bogdan Wiederspan
  • Booshan Bharathi
  • Boyang Yu
  • Bruna Pascual Dias
  • Buddhadeb Mondal
  • Burak Hacisahinoglu
  • Burhani Taher Saifuddin
  • cagdas simsek
  • Cameron Michael Garvey
  • Camilo Torres
  • Carl Friedberg
  • Carlos Maltzahn
  • Carlos Moreno Martinez
  • Carsten Hensel
  • Cauê Evangelista de Sousa
  • Cecilia Uribe Estrada
  • Cesar Bernardes
  • Cesar Fernandez Ramirez
  • Chanchal Pal
  • Chandrasekhar Akondi
  • Chaoyi Lyu
  • Chara Kitsaki
  • Cheng-Han Wu
  • Chiara Maccani
  • Chiara Mancuso
  • Chris Steer
  • Christas Mony A
  • Christian Schmitt
  • Christopher Heinz
  • Christopher Wenzel
  • Churamani Paudel
  • Claire David
  • Clara Landesa Gomez
  • Corey Adams
  • Costanza Carrivale
  • Cristian Rodriguez
  • Cristina Mantilla Suarez
  • Cristina-Andreea Alexe
  • Dan Thompson
  • Daniel Cervenkov
  • Daniel Dorner
  • Daniel Felea
  • Daniel Ocampo-Henao
  • Daniele Dal Santo
  • Daniil Rastorguev
  • Danish Farooq Meer
  • Danny van Dyk
  • David Koch
  • David Lange
  • David Morrison
  • Davide Nicotra
  • Davide Valsecchi
  • Dayanand Mishra
  • Debjit Ghosh
  • Deepa Thomas
  • Deepanshi Singh
  • Derek Doyle
  • Derek Doyle
  • Dev Barevadiya
  • Dev Malik
  • Dev Patel
  • Dev Ranjan Das
  • Devashri Kulkarni
  • Devin Michael Aebi
  • Dexu Lin
  • Dhanya Rajeev
  • DHARITREE BEZBORUAH
  • Dharm Veer Singh
  • Dharmender Gaur
  • Dhiren Panda
  • Dhruv Aryan
  • Dhruv Verma
  • Dhruvi Agrawal
  • Diego Ciangottini
  • Dimbiniaina Rafanoharana
  • Dimitrios Kaminaris
  • Dinupa Nawarathne
  • Dirk Krucker
  • Divya Shaji
  • Divyansh Tripathi
  • Divyanshu Kumar
  • Diya Shaji
  • Doina Duma
  • Doug Benjamin
  • Doug Davis
  • Duc Bao Ta
  • Dvij Mankad
  • Dylan Jaide White
  • Dylan Temples
  • Ebru Uslan
  • Eden Mautner
  • Edgar Huayra Paitan
  • Eduardo Rodrigues
  • Edward Brash
  • Edward Khomotso Nkadimeng
  • Efstathios Logothetis Agaliotis
  • EL Abassi Abderrazaq
  • Elena Mazzeo
  • Eleni Skorda
  • Elham Khazaie
  • Eliana Marroquin
  • Eliot Jane Walton
  • Elisabeth Maria Niel
  • Elizabeth Sexton-Kennedy
  • Elrina Hartman
  • Elzbieta Richter-Was
  • Ema Puljak
  • Emily Filmer
  • Emmanuel Olaiya
  • Enric Tejedor Saavedra
  • Erick Jhordan Reategui Rojas
  • Erik Wallin
  • Essma Redouane Salah
  • Eswari Lekhya Jarajapu
  • Ethan Lewis Simpson
  • Evan Koenig
  • Fabian Becherer
  • Fabio Catalano
  • Fabio De Vellis
  • Fakhira Afzal
  • Fakhri Alam Khan
  • Fanqiang Meng
  • Farida Fassi
  • Fatemeh Irani
  • Fatma Boran
  • Federico Vazzoler
  • Fernando Vergara
  • Feyza Baspehlivan
  • Filiberto Bonini
  • Finn David Stevenson
  • Florian Bury
  • Francesca Del Corso
  • Francesco Curcio
  • Francois Gutherz
  • Frank Meier
  • Frank Sauerburger
  • Franz Glessgen
  • Franz Mauri Zapanta
  • Furkan Dolek
  • Gaadha S
  • Gabriel Nowak
  • Gabriel Rabanal Bolaños
  • Gabriele Benelli
  • Gabriele Martelli
  • Gaelle Khreich
  • Gagandeep Kaur
  • Gage DeZoort
  • Gamze Sokmen
  • Ganesh Parameshwar Bhat
  • Gaogalalwe Mokgatitswane
  • Garth Huber
  • Gatikrushna Mishra
  • Gaurang Kuksal
  • Gaurav Dadwal
  • Gaurav Dhir
  • Gauri Shankar H
  • Gayathri Varma
  • Gayatri Bhuyan
  • Gediminas Sarpis
  • Georgios Alexandris
  • Gerhard Hejc
  • Gherardo Vita
  • Ghnashyam Gupta
  • Gopal Yadav
  • Gordon Watts
  • Gourab Saha
  • Graeme A Stewart
  • Grigori Rybkin
  • Guanyue Wan
  • Guillaume Taillepied
  • Guillermo Antonio Fidalgo Rodriguez
  • Guillermo Palacio
  • Gunjan Jain
  • Gursharan singh
  • Géraldine Räuber
  • Hadi Hashamipour
  • Haesung Park
  • Halil Kolatan
  • Halil Saka
  • Hamid Basiri
  • Hamzeh Khanpour
  • Han Miao
  • HANANE LAHRAICHI
  • Hangil Jang
  • Hannah Nelson
  • Hannah Wakeling
  • Hans Peter Dembinski
  • Hao-Kai Sun
  • Harish R
  • Harishankar Nath Tiwari
  • HARITHRA RAJARAJAN
  • Harleen Dahiya
  • Harsh Aggarwal
  • Harsh Raj
  • Harsh Vinchurkar
  • Harshit Chauhan
  • Harshita Pant
  • Hasan SANSAR
  • Hassan Abdalla
  • Hassane Hamdaoui
  • Hector Pillot
  • Helena Brandao Malbouisson
  • HEMANT K B N
  • Henry Fredrick Schreiner
  • Herbert Potrykus
  • Hichem Bouchamaoui
  • Hirak Koley
  • HISHAM THALTHODI
  • Homesh Bambal
  • HONEY RENJAN E
  • Hong Pham
  • HRISHABH BHARADWAJ
  • Hugo Chancay
  • Huw Haigh
  • Hyeonja Jhang
  • Ian Skillicorn
  • Ianna Osborne
  • Iason Krommydas
  • Ibrahim Chahrour
  • ibrahim TOPBAŞI
  • Ijaz Ahmad
  • Ilija Vukotic
  • Ilkay Turk Cakir
  • Imanol Corredoira
  • Imran Awan
  • Ioannis Michail Maniatis
  • Irena Veljanovic
  • Isabel Beth Carr
  • Isabel Dominguez
  • Isadora Stefanhak C. Arantes
  • Ishaan Utkarsh
  • Ishan Gupta
  • Ishan Kaushal
  • Ishant Yadav
  • Israr Israr
  • Itana Bubanja
  • itay gelber
  • Iza Veliscek
  • J Alexander Ward
  • Jack Dodson
  • Jackson Carl Burzynski
  • Jacob Linacre
  • Jaeyoung Kim
  • James Cochran
  • James Simone
  • Jan de Boer
  • Jan Ellbracht
  • Jan Hauke Voss
  • Jan-Marc Basels
  • Jannis Guido Speer
  • Jasmine Liu
  • javad shahrzad
  • Javier Cuevas
  • Javier Mariño Villadamigo
  • Javier Mauricio Duarte
  • Jay Gohil
  • Jayakrishnan K
  • Jayjeet Chakraborty
  • Jean Yves Beaucamp
  • Jelena Celic
  • Jennifer Smallwood
  • Jesus Eduardo Muñoz Mendez
  • Ji Zhang
  • jialin guo
  • Jialin Guo
  • Jie Xiao
  • Jim Pivarski
  • Jing-Ge Shiu
  • Jiri Chudoba
  • Jishant Talwar
  • Joanna Gao
  • Joanna Gao
  • Joergen Sjoelin
  • Johan Wulff
  • Johan Wulff
  • Johannes Heuel
  • Johannes Lange
  • Johnpaul Mbagwu
  • Jonas Eschle
  • Jonas Rübenach
  • Jonathan Davies
  • Jongho Lee
  • Jordan Correa
  • Jordy Butter
  • Jorge Alda Gallo
  • Joschka Birk
  • Jose Andres Monroy Montanez
  • Jose Miguel Munoz Arias
  • Joshua LaBounty
  • Jost Migenda
  • Josue Molina
  • Juan Macharé
  • Jubin George Mathew
  • Judita Mamuzic
  • Julien Beckers
  • Julius Hartmann
  • Junli Ma
  • Justin Skorupa
  • K.C. Kong
  • Ka Wa Ho
  • Kamal Lamichhane
  • kamal singh
  • Kamogelo Mogale
  • Karem Penalo Castillo
  • Karsten Köneke
  • Karthik Jayadevan
  • Kashinathan R S
  • Kate Whalen
  • Katharina Voß
  • Kaustav Kapil
  • Kaustuv Datta
  • Keerthana Rajan Lathika
  • Kevin Patrick Lannon
  • Khathutshelo Phadagi
  • Khawla MOKRANI
  • Khushi Sharma
  • Khushi Sharma
  • Kilian Lieret
  • Kim Siang Khaw
  • Kiran Prava Tripathy
  • Krishna Relekar
  • Ksenia Germanovich
  • Kumarjit Sen
  • Kunal Raj
  • Kunal Singh
  • Kuntal Pal
  • Kush Kothari
  • Kyle Shiells
  • Kyra Mossel
  • Kyungeon Choi
  • Kyungmin Lee
  • Labh Singh
  • lalit kumar
  • Lars Kolk
  • Laura Pereira Sanchez
  • Lawrence Ng
  • Laxmi Sharma
  • Layan AlSarayra
  • Leif Gellersen
  • Leon David Carus
  • Leonardo Barreto
  • Levi Evans
  • Lex Greeven
  • Leyna Bajaj
  • Lida Kalipoliti
  • Lindsey Gray
  • Lipshit Dash
  • Livia Maskos
  • Loick Marion
  • LOKESH KRISHNAN K
  • Lopamudra Nayak
  • Lorenz Gaertner
  • Love Preet
  • Loyr Nascimento
  • Luca Canali
  • Luca Fiorini
  • Lucas Foresti Mascarello
  • Lucas Kang
  • Lucas Wiens
  • Luciana de Oliveira Wilken Roderjan
  • Luciano Arellano
  • Luis Fariña
  • Luiz Eduardo Balabram Filho
  • Lukas Bierwirth
  • Lukas Holub
  • Lukas Kretschmann
  • Lukas Linzen
  • M. Gabriel Santiago
  • Maciej Mikolaj Glowacki
  • Maciej Pawel Szymanski
  • Maciej Szymkowski
  • Madan Singh
  • Mahdi Sanagostar
  • Mahesh Kumar Saini
  • Mahmoud Abbas
  • Makoto Uchida
  • Malihe Malekhosseini
  • Malte Wagner
  • Malusi Msweli
  • Manas Kumar Sinha
  • Manasvi Goyal
  • Mandar Tijare
  • Mangesh Sonawane
  • Manimala Mitra
  • Manish Das
  • Manish Rohilla
  • Manosh T. M.
  • Manuel Guth
  • Manul Patel
  • Maral Salajegheh
  • Marcel Hohmann
  • Marcela Garcia Hernandez
  • Marek Biros
  • Margaret Morris
  • Maria Myrto Pegioudi
  • Maria Novopriezzhaia
  • Maria Perganti
  • Maria-Myrto Pegioudi
  • Mariel Pettee
  • Marike Schwickardi
  • Mario Lamprea
  • Mark Waterlaat
  • Marta Felcini
  • Martina Koppitz
  • Martine Joy Irog
  • Maryna Borysova
  • Masaki Miyataki
  • Mason Proffitt
  • Massimiliano Galli
  • Mateus Hufnagel
  • Matias Senger
  • Matteo Barbetti
  • Matteo Presilla
  • Matthew Feickert
  • Matthew Knight
  • Mauricio Thiel
  • Maurizio Giorgio Bonesini
  • Maximilian Linkert
  • Maximilian Reininghaus
  • Mayank Bhandare
  • Mayank Jain
  • Mayuri Prabhakar Kawale
  • MD ALAM
  • Md Forhad Hossain
  • Md Rihan Haque
  • MEGHAMANI HALDAR
  • Mehek Arora
  • Mehmet Demirci
  • Meihong LIU
  • Melanie Cardona
  • Melanie Cardona
  • Mengchuan Du
  • Merle Graf-Schreiber
  • Merve Nazlim Agaras
  • Miao Yu
  • Michael Dolce
  • Michael Finger
  • Michael Grippo
  • Michael Wilkinson
  • Michael Wood
  • Michal Rigan
  • Michel Hernandez Villanueva
  • Michele Aversano
  • Michele Veronesi
  • Mihael Banozic
  • Mihail Bogdan Blidaru
  • Mihir Patel
  • Milos Dordevic
  • Ming-Yan Lee
  • Mingrui Zhao
  • Miriam Diamond
  • Miroslav Finger
  • Miroslav Kubu
  • Miroslav Saur
  • Mohamed Aly
  • Mohamed Elashri
  • Mohamed Krab
  • Mohamed Lamine Bellilet
  • Mohamed Naseem
  • Mohamed Zaazoua
  • Mohammad Kaish
  • Mohammas Reza Tousif
  • Mohammed Abdelrazek Aboelela
  • Mohemad El Arebi Gadja
  • Monal Kashav
  • Monika Bharti
  • Monika Mittal
  • Monu Kumar
  • Moritz Scham
  • Moritz Wiehe
  • MOSAM PANDYA
  • Mouhssine Majdoul
  • Mousam Maity
  • Mozhdeh Rashidazad
  • Mpho Gololo
  • Mridul Patel
  • Mrutyunjaya Sahoo
  • Muhammad Ahmad
  • Muhammad Attallah
  • Muhammad Farooq
  • Muhammad Irfan Asghar
  • Muhammad Junaid
  • Muhammad Naeem Anwar
  • Muhammad Waqas
  • Muhammad Zohaib Arshad Qureshi
  • Muhesh Kanna Ayyappan
  • Mukesh Kumar
  • Munera Alrashed
  • Murad Ali
  • Muralika M
  • Mushfiq Dijoo
  • Mykyta Kizilov
  • N Sushree Ipsita
  • Nachiket Jhala
  • Namita Pradhan
  • Namitha Chithirasreemadam
  • Nasir Mehdi Malik
  • Nataliia Zakharchuk
  • Natasha Sachdeva
  • Nathan Barry Heatley
  • Nathan Grieser
  • Nathan Prouvost
  • Nathan Simpson
  • Naveen Kumar Baghel
  • Navneet Kumar
  • Navya Jose
  • Neelima M
  • Negin Alizadehvandchali
  • Neha Chaudhary
  • Neha Verma
  • Neha Zeenat
  • Nevil S
  • Neza Ribaric
  • Nicholas Kyriacou
  • Nicholas Manganelli
  • nicholas Zachariou
  • Nick Smith
  • Nicole Michelle Hartman
  • Nicolás Gómez
  • Nidhi Tripathi
  • Nihal Brahimi
  • Nikhil Krishna
  • Niki Stratikopoulou
  • Nikolai Hartmann
  • Nilabh Jyoti Kalita
  • Niladri Sahoo
  • Nilima Akolkar
  • Nilima Nilesh Akolkar
  • Nimmitha Karunarathna
  • Ning Cao
  • Nishant Gaurav
  • Nishat Parveen
  • NITIKA GOTHWAL
  • Nitin Mishra
  • Noah Vaughan
  • Noreen Rauls
  • Normunds Ralfs Strautnieks
  • Nouhaila INNAN
  • Nowar Koning
  • Ntsoko Phuti Rapheeha
  • Oksana Shadura
  • Oleg Filatov
  • Olivier Couet
  • OMID BAGHCHEH SARAEI
  • Ondrej Theiner
  • Orhan Cakir
  • Oscar Ferrer Naval
  • Oskar Tittel
  • Otsile Tikologo
  • Pankaj Borah
  • Paola Rioseco
  • Paolo Mastrandrea
  • Paraskevas Deligiannis
  • Paraskevi Ganoti
  • Parth Mehrotra
  • Patricia Rebello Teles
  • Patrick Dougan
  • Patrik Adlarson
  • Paul Dervan
  • Paul Feichtinger
  • Pavel Larionov
  • Pedro Ladron De Guevara
  • Pedro Ventura
  • Pegah Farahi Shandiz
  • Pete Markowitz
  • Peter Elmer
  • Peter Johannes Falke
  • Petey Ridolfi
  • Petra Loncar
  • Philip Daniel Keicher
  • Philipp Gaggl
  • Philipp Haas
  • Philipp Horak
  • Phu Nguyen
  • Pilar Casado Lechuga
  • Piyush Patil
  • Potturu Sreenivasula Naidu
  • Prabhakar Palni
  • Pragati Patel
  • Pramod Sharma
  • Pranati Jana
  • Pranav PV
  • Pranav Sapatnekar
  • Pranjal Panwar
  • Prasant Kumar Rout
  • Prashant Gupta
  • Pratyasha Tripathy
  • Preeti Saini
  • Prerna Chauhan
  • Prerna Chauhan
  • Pritish Karmakar
  • Priya Sharma
  • Priyajit Jana
  • Priyanka Boora
  • Priyanka Sarangi
  • Punit Punit
  • Punit Sharma
  • Qi Shi
  • Quratulain zahoor
  • R Jothika
  • Rachik Soualah
  • Radi Radev
  • Rafael Silva Coutinho
  • Raghav Kansal
  • Rahul Musale
  • RAHUL SHAW
  • Rajan Mishra
  • RAJAT KUMAR SAHU
  • Rajeev Singh
  • Rajul Srivastava
  • Ram Vikas Mishra
  • Rameshwar Bankar
  • Raphael Souza
  • Rashmi Sarwal
  • Rashmi Sarwal
  • Raul Rabadan
  • Ravindra Kumar Verma
  • Razvan-Daniel Moise
  • Reetanshu kumar
  • Remco de Boer
  • Renu Siwach
  • Rian Fritz Jalandoni
  • Riccardo Salvatico
  • Riccardo Triozzi
  • Richard Trotta
  • Richeek Debnath
  • Richik Bhattacharya
  • RISHABH MEHTA
  • Rishabh Moolya
  • Rishabh Raturi
  • Rishi Mukherjee
  • Rishita Ray
  • Ritesh Kumar
  • Ritu Yadav
  • Rituparna Maji
  • Rizwaan Mohammed
  • Rob Calkins
  • Robert Hammann
  • Robert Kralik
  • Roberto Perrino
  • Roberto Salerno
  • Rohan AP
  • Rohith Gopinath
  • Roman Riutin
  • Roman Shorkin
  • Roshan Joshi
  • Rostyslav Lobov
  • Rouzbeh Rouzbehi
  • Rouzbeh Rouzbehi
  • Roy Cruz
  • Rudra Prasad Sahu
  • Rui Zhang
  • Ruiting Ma
  • Rupali Hatte
  • Rushikesh Bhutkar
  • Saad El Farkh
  • Saba None Sehrish
  • Saba Taj
  • Sabrina Maria Appel
  • Sachin Gupta
  • Sadayappan AL
  • Safa Naseem
  • Safoora Rana
  • Sagar Gowala
  • Sagar Hazra
  • Sahana Narasimha
  • Sahil Arora
  • Sai Vara Prasad Kotha
  • SAI WAGH
  • Saiva Huck
  • Saketha ram Belide
  • Salah-Eddine Dahbi
  • Saliha Bashir
  • Sambit Kumar Pusty
  • Samet Lezki
  • Samira Shoeibi Mohsenabadi
  • Samuel Van Stroud
  • Sandip Maiti
  • Sandra Amato
  • Sankalp Shinde
  • Sanmay Ganguly
  • Santosh Parajuli
  • Santu Mondal
  • Sara Sellam
  • Saransh Chopra
  • Sarthak Saini
  • Sascha Dreyer
  • Sascha Pascal Liechti
  • Saswat Subhankar
  • SATYAM VERMA
  • Saumya Saumya
  • SAURABH RAI
  • Saurabh Saini
  • Saurabh Shukla
  • Saurabh Shukla
  • Saurabh Singh
  • Savvas Kyriacou
  • Sayantan Dutta
  • Sayeed Akhter
  • Scott Demarest
  • Sebastian De Jesus Quiros Araya
  • Sebastian Montoya Hernández
  • Sebastian Schmitt
  • Sebastian Torres-Lara
  • Seema Choudhury
  • Sehar Ajmal
  • Sen Deng
  • Sergey Kalinovich Korjenevski
  • Sergey Korpachev
  • Sergi Castells
  • Sergio Sanchez Cruz
  • Serhii Cholak
  • Shabeeb Alalawi
  • Shafeeq Rahman Thottoli
  • Shahid Khan
  • Shailaja Mohanty
  • Shaowei Song
  • Sharal Deegoju
  • Sharry -
  • Shashank Mishra
  • SHASHI KUMAR SAMDARSHI
  • shashwat gaur
  • Shaza Fathima Azeez
  • Shihai Jia
  • Shilpi Jain
  • Shinichi Okamura
  • Shiva Sai
  • Shivam Chaudhary
  • Shivam Kulshrestha
  • Shivam Raj
  • Shivam Raj
  • SHIVAM TOMAR
  • Shivam Verma
  • Shivangi Singh
  • Shivani Ramachandran
  • Shivank .
  • Shivanshi Tiwari
  • Shouvik Mondal
  • Shreecheta Chowdhury
  • SHRIYA PANDEY
  • Shubham Bangalia
  • Shubham Sawant
  • Shuchong Ding
  • Shunan Zhang
  • Siannah Penaranda Rivas
  • Siavash Neshatpour
  • Siddharth Singh
  • Siddhi K
  • Simon Thor
  • Simon Thor
  • Simone Gasperini
  • Simone Gasperini
  • Simran Arora
  • Sirswa Kuldeep Shree Ram
  • Sivasish Paul
  • Sneha Borse
  • Snehankit Pattnaik
  • Somnath Roy
  • Sonali Borah
  • Soumyadeep Das
  • soumyadeep sar
  • Soumyajit Datta
  • Sourabh Chutia
  • Sourav Das
  • Souvik Maity
  • Sowmiya K
  • Spandan Bhattacharya
  • Sparsh Jain
  • SRAVAN GURUJU
  • Sree Ram Sathyan
  • Sreemanti Chakraborti
  • Srimoy Bhattacharya
  • Sruthi PV
  • Stefan Wallner
  • Suchismita Sarker
  • Sukriti Atrey
  • Suman Das Gupta
  • Suman Kumar Kundu
  • Sumiran Mishra
  • Sumit Biswas
  • Suneel Dutt
  • Sunil Kumar
  • Sunny Gupta
  • Surjakanta Kundu
  • Surya Prakash
  • Susanna Costanza
  • Sushil sharma
  • Susrestha Paul
  • Suvajit Dey
  • Suzanne Rosenzweig
  • Sviatoslav Bilokin
  • Swapnesh Khade
  • Swarna Prabha Maharana
  • Swastik Dewan
  • Syed Ahmed
  • Syed Faheem Andrabi
  • Syeda Eman Zahra
  • Sébastien Rettie
  • Tae Hyoun Park
  • Tamas Almos Vami
  • TANAY DEY
  • Tanish Nandre
  • Tanisha Kuhard
  • Tanvi Wamorkar
  • Tanya Wadhwa
  • Tao Huang
  • Tapasi Ghosh
  • Tasneem Jareen
  • Taylor Carnahan
  • Tej Chand
  • Tejin Cai
  • Tess Santhosh
  • Thanh Mai Vu
  • Thejus Mary S.
  • Thomas Klijnsma
  • Thomas Lenz
  • Thomas Oeser
  • Thomas Poeschl
  • Thorsten Lux
  • Tiago Lopes
  • Tianji Cai
  • Tobias Laimer
  • Todd Zenger
  • Toktam Rashidi
  • Tom Cheng
  • Tomas Atehortua Garces
  • Tomas Jakoubek
  • Tommaso Fulghesu
  • Tommaso Tedeschi
  • Tommy Martinov
  • Toni Mlinarevic
  • Towsifa Akhter
  • Tripti Bakshi
  • Tristan Schefke
  • Tuan Pham
  • Umesh Makode
  • Umit Sozbilir
  • UTSAB DEY DEY
  • Vaclav Kus
  • Vaibhav Sharma
  • Vaishnav Sankar K
  • Vaishnavi Raskar
  • Valentina Franco Velásquez
  • Valentina Guglielmi
  • Valeri Pozdniakov
  • Valeriia Zhovkovska
  • Valeriy Onuchin
  • Vanessa Cerrone
  • vani jain
  • Vaniya Ansari
  • Vansh Aggarwal
  • Vansh Teotia
  • Vansh Tomar
  • VARSHINI M
  • Vassil Vassilev
  • Vassil Verguilov
  • Vatsalya Sharan
  • Vengatesan Ganapathy
  • Veronika Kraus
  • Victor Hugo Ruelas Rivera
  • VIKAS ARYA
  • Vimal Kumar
  • Vincenzo Eduardo Padulano
  • Vincenzo Mastrapasqua
  • VISHAL DEWANGAN
  • Vishnu Kadam
  • Vishnu Santosh Kumar
  • Vishu Saini
  • Vishwanath Pratap singh
  • Vishwas Gaur
  • VISMAYA V S
  • Vivek Sharma
  • Volker Hejny
  • Vongani Chabalala
  • Wassef Karimeh
  • WENXING FANG
  • Werner Sun
  • William Briscoe
  • Winmariya P J
  • Wolfgang Korsch
  • Wolfgang Walkowiak
  • Xiaobin Ji
  • Xiaodong Shi
  • Xin Xiang
  • Xing-Fu Su
  • Xinyuan Lin
  • Xiqing Hao
  • Xiu-Lei Ren
  • Xu Liangjun
  • Yachika Sampat Yadav
  • Yacine Haddad
  • Yajing Wei
  • Yalcin Guler
  • Yannik Buch
  • Yanting Fan
  • Yasar Hicyilmaz
  • Yash Kumar
  • Yash Raj Sood
  • Yashraj Kumar singh
  • Yftach Moyal
  • Yi ZHANG
  • Yichao (Alex) Cai
  • Yifeng Wang
  • YingAo Tang
  • Yingao Tang
  • Yixiao Han
  • Yixiong Zhou
  • Yo Sato
  • Yonghao Zeng
  • Yongqi Wang
  • Yoran Yeh
  • Yotam Granov
  • Yu Nakazawa
  • Yuan-Tang Chou
  • Yunxuan Song
  • Yurii Kvasiuk
  • yuzhi shang
  • Zahra Mohebtash
  • Zekeriya Uysal
  • Zhefei TIAN
  • Zheng-Gang Chen
  • zhenxiong Xie
  • zhenxuan zhang
  • Zhihong Shen
  • Zhuolin Zhang
  • Ziyi Wang
  • Zoë Earnshaw
  • Zubair Dar
  • Zvi Citron
Surveys
PyHEP 2022 - post workshop survey
    • 2:00 PM 6:20 PM
      Plenary Session Monday
      Zoom Meeting ID
      61281099011
      Host
      Eduardo Rodrigues
      Alternative hosts
      Graeme A Stewart, Jim Pivarski, Oksana Shadura, Matthew Feickert, Nikolai Hartmann
      Useful links
      Join via phone
      Zoom URL
      • 2:00 PM
        Welcome and workshop overview 10m
        Speaker: Eduardo Rodrigues (University of Liverpool (GB))
      • 2:10 PM
        Level Up Your Python 1h

        This tutorial covers intermediate Python, dataclasses, errors, decorators, context managers, logging, debugging, profiling, and more. Participants are expected to have introductory Python knowledge, like basic syntax, function definitions, dicts, lists, and variables.

        Speaker: Henry Fredrick Schreiner (Princeton University)
      • 3:10 PM
        TheBureaucrat, a package to help you organize your work 10m

        In science we deal a lot with data acquisition and analysis. The process until we draw a conclusion is usually very complex, involving the acquisition of several different datasets, and the application of many analysis procedures that in turn create new data. Moreover, we often want to repeat this procedure many times. This creates a lot of information that we have to store and keep well organized, which can be a time consuming and very tedious process that we don't like but, sooner or later, we cannot avoid. In this talk I present The Bureaucrat, a simple yet powerful Python package that will help you with this organizational task transparently and easily. And of course, cross platform.

        Speaker: Matias Senger (University of Zurich (CH))
      • 3:20 PM
        Histograms as Objects: Tools for Efficient Analysis and Interactivity 30m

        Histograms are a pillar of analysis in High Energy Physics. Particle physicists utilise histograms in order to find new particles, measure characteristics, and understand data activities. An instance of an application is fitting bumps in histograms to find particular interactions by accumulating huge amounts of data given that the probability of occurrence is low. The Scikit-HEP ecosystem provides a coordinated set of tools for histogramming. This talk aims to discuss these histogramming packages built on the histogram-as-an-object concept with a focus on results, techniques, recent updates, and future directions. The boost-histogram library enables fast and efficient histogramming, while Hist adds useful features by using boost-histogram as a backend. A new library, uproot-browser, has been introduced which enables a user to browse and look inside a ROOT file, completely via the terminal.

        Speakers: Aman Goel (University of Delhi), Jay Gohil (IRIS HEP Fellow)
      • 3:50 PM
        PyHEP and the Climate Crisis [Cancelled] 10m

        What can we really do as high energy physicists in our day-to-day programming lives to reduce HEP carbon emissions? In my lightning talk, I will present the key challenges and potential solutions which would make a real difference to the climate crisis.

        Speaker: Hannah Wakeling (McGill University)
      • 4:00 PM
        BREAK 30m
      • 4:30 PM
        Uproot, Awkward Array, hist, Vector: from basics to combinatorics 30m

        This is an introduction to doing particle physics analysis with Scikit-HEP tools: Uproot, Awkward Array, hist, and Vector.

        It starts at a basic level—exploring files, making plots—and ramps up to resolving e⁺e⁻e⁺e⁻, μ⁺μ⁻μ⁺μ⁻, and e⁺e⁻μ⁺μ⁻ final states in Higgs decays.

        Speaker: Jim Pivarski (Princeton University)
      • 5:00 PM
        Python in the Belle II experiment 30m

        The Belle II software framework, basf2, is newly designed to process the data taken in the Belle II experiment. A single task of data-processing is performed by a single basf2 module, and the basf2 modules are configured in an ordered sequence, called the basf2 path. All configuration of the basf2 module and steering of the basf2 path is done via Python. Moreover, Python plugin packages have been developed to automate the calibration of the Belle II detector and build a pipeline of batch jobs. In this talk, the usage of Python in the Belle II experiment will be covered.

        Speaker: Yo Sato (KEK IPNS)
      • 5:30 PM
        Teaching Python the Sustainable Way: Lessons Learned at HSF Training 30m

        With thousands of new members joining the HEP community every year, it is of paramount importance to have training strategies that are efficient and sustainable at scale. The HEP Software Foundation (HSF) Training Working Group offers workshops with this goal in mind. We present our goals, technical setup, and experiences with regard to the most recent training events. The talk also aims to further improve the integration of HSF Training with the HEP community as a whole.

        Speakers: Alexander Moreno Briceño (Universidad Antonio Nariño), Aman Goel (University of Delhi), Guillermo Antonio Fidalgo Rodriguez (University of Puerto Rico at Mayagüez)
      • 6:00 PM
        Uhepp: Sharing plots in a self-contained format 10m

        The uhepp (universal high-energy physics plots) ecosystem defines a
        self-contained storage format that couples raw histogram data (like a TH1F) with the description of visual histogram stacks, styles, and labels. Since the raw histograms are retained and packaged in a single file, rebinning, recoloring, or merging of processes can be achieved easily at render time in a non-destructive way. The ecosystem is powered by the reference implementation in Python (uhepp on PyPI) that provides utility methods to create, save, and load uhepp plots, and can render plots with Matplotlib. This talk demonstrates how to build, save and load a histogram.

        Traditionally, plots are shared in a "plot book" containing many histograms in a graphics format that does not allow extracting numerical values or modifying the binning or the plot composition. This talk introduces the online hub uhepp.org. The hub exposes a REST API to facilitate collaboration and sharing of plots via push and pull operations provided by the reference implementation in Python. The online hub offers an interactive preview of uploaded plots in the browser, removing the need for "plot books." Besides the publicly hosted uhepp.org hub instance, the project allows self-hosted deployments of the hub infrastructure.

        Speaker: Frank Sauerburger (Albert Ludwigs Universitaet Freiburg (DE))
      • 6:10 PM
        jacobi: Error propagation made easy 10m

        In HEP, we often use Monte-Carlo simulation or bootstrapping to propagate errors in more complicated scenarios. However, standard error propagation could be done in most cases, if it was easy to compute the derivatives of the mapping function. Jacobi is a new library which offers a very powerful, fast, easy-to-use, and robust numerical derivative calculator. In contrast to libraries which do error propagation with with automatic differentiation, like the popular uncertainties library, Jacobi can compute derivatives for any analytical function, even if the function is opaque and calls into non-Python code. Jacobi is also completely non-intrusive, since it does not require one to replace the number and array types in the analysis with special number or array objects. In the talk, I show how to perform simple and more advanced error propagation with Jacobi.

        Speaker: Hans Peter Dembinski (TU Dortmund)
    • 2:00 PM 6:00 PM
      Plenary Session Tueday
      Zoom Meeting ID
      61281099011
      Host
      Eduardo Rodrigues
      Alternative hosts
      Graeme A Stewart, Jim Pivarski, Oksana Shadura, Matthew Feickert, Nikolai Hartmann
      Useful links
      Join via phone
      Zoom URL
      • 2:00 PM
        iminuit: fitting models to data 1h

        iminuit is a Pythonic wrapper to the MINUIT2 C++ library which is part of the ROOT framework, but does not require ROOT to be installed. iminuit is a rather simple low-level library to do fitting compared to zfit or pyhf, but its simplicity also makes it is very flexible and easy to learn. The tutorial will cover how to do typical HEP fits with iminuit, typical pitfalls, and how to resolve them. In particular, we will show to perform template fits with the new BarlowBeestonLite cost function implemented in iminuit.

        Speaker: Hans Peter Dembinski (TU Dortmund)
      • 3:00 PM
        Skyhook: Managing Columnar Data Within Storage 10m

        The advent of high-speed network and storage devices like RDMA-enabled networks and NVMe SSDs, the fundamental bottleneck in any data management system has shifted from the I/O layer to the CPU layer resulting in reduced scalability and performance. This issue is quite prominent in systems reading popular data formats like Parquet and ORC which involve CPU intensive tasks like decoding and decompression of data on the client. One solution to this problem is adopting computational storage, where CPU intensive tasks like decoding, decompression, and filtering are offloaded/distributed to often underutilized storage server CPUs, getting back scalability and accelerating performance. We build Skyhook, a programmable data management system based on Apache Arrow and Ceph that enables offloading of query processing tasks to storage servers. Skyhook does not require any modifications to Ceph nor assumes computational storage devices, rather its unique design embeds query processing in Ceph objects. This approach makes adding query offloading capabilities to the storage systems a breeze for practitioners. We use Skyhook to manage HEP datasets with storage, i.e., minimizing the creation of additional copies. The current release is deployed in University of Nebraska and University of Chicago and supports offloading of Nano Event filtering and projection queries. Our roadmap includes supporting joins with a distributed query execution framework that partitions Substrait query plans and distributes them for execution on clients, worker nodes, and storage objects. For the execution we plan to use the Acero (Arrow Compute Engine). For generating Substrait query plans we are planning to use Ibis. We are also collaborating with Argonne National Lab to extend Skyhook to other storage systems such as the Mochi software-defined storage system using RDMA for data transport to accelerate Skyhook query performance.

        Speaker: Jayjeet Chakraborty
      • 3:10 PM
        The Particle & DecayLanguage packages 10m

        An overview of the Particle & DecayLanguage packages is given. Particle provides a pythonic interface to the Particle Data Group particle data tables and particle identification codes, with extended particle information and extra goodies. DecayLanguage provides tools to parse so-called .dec decay files, and describe, manipulate and visualise decay chains. DecayLanguage also implements a language to describe and convert particle decays between digital representations, effectively making it possible to interoperate several fitting programs.

        Speaker: Eduardo Rodrigues (University of Liverpool (GB))
      • 3:20 PM
        PhaseSpace + DecayLanguage 10m

        PhaseSpace is a Python package used for simulations of n-body decays and uses TensorFlow as the computational backend.
        During this lightning talk, I will present a new feature in PhaseSpace: importing and simulating decays created using DecayLanguage, a package used for describing decays of particles. This improvement makes it possible to simulate complex decays in PhaseSpace while simultaneously improving the interface between various packages in the Scikit-HEP and zfit ecosystem.
        A brief overview of PhaseSpace and DecayLanguage will be given, as well as a demonstration of the new feature and the customizations that are available.

        Speaker: Simon Thor (CERN)
      • 3:30 PM
        Constructing HEP vectors and analyzing HEP data using Vector 30m

        Vector is a Python library for 2D, 3D, and Lorentz vectors, including arrays of vectors, designed to solve common physics problems in a NumPy-like way. Vector currently supports pure Python Object, NumPy, Awkward, and Numba-based (Numba-Object, Numba-Awkward) backends.

        This talk will focus on introducing Vector and its backends to the HEP community through a data analysis pipeline. The session will build up from pure Python Object based vectors to Awkward based vectors, ending with a demonstration of Numba support. Furthermore, we will discuss the latest developments in the library's API and showcase some recent enhancements.

        Speaker: Saransh Chopra (Cluster Innovation Centre, University of Delhi)
      • 4:00 PM
        BREAK 30m
      • 4:30 PM
        Matplotlib with HSF Training 1h

        Based on the Matplotlib for HEP workshop developed by HSF Training, we will present a short introduction to matplotlib and perform an HEP analysis using ATLAS and CMS open data in order to get publishable plots. We will also use mplhep, a matplotlib wrapper for easy plotting required in HEP. This tutorial aims to present a subset of the complete matplotlib training module and requires a beginner level understanding of Python. Some knowledge of Scikit-HEP modules is helpful but not essential.

        Speaker: Alexander Moreno Briceño (Universidad Antonio Nariño)
      • 5:30 PM
        Lessons learned converting a production-grade Python CMS analysis to distributed RDataFrame 30m

        The high-level and lazy programming model offered by RDataFrame has proven to be both user-friendly while at the same time providing satisfactory performance for many HEP analysis use cases. With the addition of a Pythonic layer for automatic distribution of workloads to multiple machines, RDataFrame can easily serve as a swiss knife tool for developing a full production-scale analysis with an ergonomic interface. This talk will present how a full-scale CMS analysis was translated from a traditional iterative approach with PyROOT to using RDataFrame and how such translation made it possible to automatically parallelize the analysis computations and merge their results. The analysis is benchmarked on a next generation analysis facility developed in the context of INFN R&D activities, which provides the users with a JupyterLab session and a built-in connection to HTCondor distributed resources accessible via the Dask Python library.

        Speakers: Tommaso Tedeschi (Universita e INFN, Perugia (IT)), Vincenzo Eduardo Padulano (Valencia Polytechnic University (ES))
    • 6:00 PM 6:30 PM
      Social time: Tuesday Meet and Mingle

      Get to know the other PyHEP participants better and help to reinforce our community.

      We will be using the RemotelyGreen platform. Click the link below to join. Here are some tips to help you join and participate:

      1. Use a laptop or desktop.
      2. Join early to sign-in and test your camera and mic. Make an account by connecting with LinkedIn or using an email and password (check for a verification email in this case).
      3. You can choose your networking topics and set up your business card before the event begins. Choose as many of the event's topics that interest you.
      4. You can also specify your preferred networking topics at the start of the event and in between encounters. Fill in your business card to make follow-ups easier by clicking on your avatar or the username in the top-right corner of the screen.
      5. Wait for the session to begin - you'll be shuffled with other participants automatically. If you arrive late, you will be able to join at the next shuffle.

      Attached is a short flyer with more details.

    • 2:00 PM 6:50 PM
      Plenary Session Wednesday
      Zoom Meeting ID
      61281099011
      Host
      Eduardo Rodrigues
      Alternative hosts
      Graeme A Stewart, Jim Pivarski, Oksana Shadura, Matthew Feickert, Nikolai Hartmann
      Useful links
      Join via phone
      Zoom URL
      • 2:00 PM
        The SuperNova Early Warning System & Software for Studying Supernova Neutrinos 30m

        The SuperNova Early Warning System (SNEWS) connects different neutrino experiments to quickly alert the astronomy community once the next galactic supernova happens. Since 2019, it has been completely redesigned for the new era of multimessenger astronomy.
        This talk will give an overview over SNEWS’ fully Python-based toolchain, covering communication of experiments with SNEWS, coincidence detection and combined analyses like supernova triangulation. I will also introduce physics simulation software that SNEWS is developing for use by the broader supernova neutrino community.

        Speaker: Jost Migenda (King’s College London)
      • 2:30 PM
        Python Usage Within the LHCb Experiment 30m

        Expanding HEP datasets and analysis challenges continues to give rise to advancing software within the ecosystem. The LHCb experiment has seen many analyzers make the change to Python-based analysis software tools, making use of many of the scikit and scikit-hep packages. Additional development of flavour-physics aimed packages is spearheaded by many users within the collaboration. A broad overview of the tools and specific applications of such tools is provided to encourage the discussion and growth within the PyHEP community.

        Speaker: Nathan Grieser (University of Cincinnati (US))
      • 3:00 PM
        Basic Physics Analyses Implemented Using Apache Spark. 30m

        Apache Spark is a very successful open-source tool for data processing. This talk will focus on the use of Spark and its DataFrame API in the context of HEP. We will go through a few demos of some simple and outreach-style analyses implemented using Jupyter notebooks and the Spark Python API (PySpark). We will wrap up with a short discussion of the key features in Spark and its ecosystem that can be useful for Physics analysis and what still needs improvements.

        Speaker: Luca Canali (CERN)
      • 3:30 PM
        Automatic Resource Management with Coffea and Work Queue for analysis workflows 30m

        In this notebook talk we will demonstrate the Coffea Work Queue executor for analysis workflows. Work Queue is a framework for building large scale manager-worker applications. When used together with Coffea, it can measure the resources, such as cores and memory, that chunks of events need and adapt their allocations to maximize throughput. Further, we will demonstrate how the executor can dynamically modify the size of chunks of events when the memory available is not enough, and adapt it to desired resource usage. We will introduce its basic use for small local runs, and how it can automatically export the needed python environments when working in a cluster with no previous setup.

        Speaker: Benjamin Tovar Lopez (University of Notre Dame)
      • 4:00 PM
        BREAK 30m
      • 4:30 PM
        Data Management Package for the novel data delivery system, ServiceX, and its application to an ATLAS Run-2 Physics Analysis Workflow 30m

        Recent developments of HEP software in the Python ecosystem allow novel approaches to physics analysis workflows. The novel data delivery system, ServiceX, can be very effective when accessing large datasets at remote grid sites. ServiceX can deliver user-selected columns with filtering and run at scale. I will introduce the ServiceX data management package, ServiceX DataBinder, for easy manipulations of ServiceX delivery requests and delivered data using a single configuration file. I will also introduce the effort to integrate ServiceX and Coffea into an ongoing ATLAS Run-2 physics analysis.

        Speaker: Kyungeon Choi (University of Texas at Austin (US))
      • 5:00 PM
        EOS -- A software for Flavor Physics Phenomenology 30m

        EOS is an open-source software for a variety of computational tasks in flavor physics. Its use cases include theory predictions within and beyond the Standard Model of particle physics, Bayesian inference of theory parameters from experimental and theoretical likelihoods, and simulation of pseudo events for a number of signal processes. EOS ensures high-performance computations through a C++ back-end and ease of usability through a Python front-end. To achieve this flexibility, EOS enables the user to select from a variety of implementations of the relevant decay processes and hadronic matrix elements at run time. We describe the general structure of the software framework and provide basic examples. Further details and in-depth interactive examples are provided as part of the EOS online documentation.

        Speaker: Danny van Dyk
      • 5:30 PM
        Correctionlib 30m

        Correctionlib provides a well-structured JSON data format for a wide variety of ad-hoc correction factors encountered in a typical HEP analysis and a companion evaluation tool suitable for use in C++ and python programs. The format is designed to be self-documenting and preservable, while the evaluator is designed to have good performance.

        Speaker: Nick Smith (Fermi National Accelerator Lab. (US))
      • 6:00 PM
        pyhepmc: a Pythonic interface to HepMC3 10m

        pyhepmc is a Pythonic frontend for the HepMC3 library and part of Scikit-HEP. It allows one to read/write HepMC3 records in various formats and to convert any other particle record to HepMC3. pyhepmc was originally proposed to become the official Python interface for HepMC3. HepMC3 eventually got an alternative Python interface which is an automatic translation of the C++ interface, while pyhepmc offers a hand-written interface with a Pythonic feel. Another advantage of pyhepmc is that it is listed on PyPI and can be easily installed with pip, thanks to Scikit-HEP releasing binary wheels for common platforms.

        Speaker: Hans Peter Dembinski (TU Dortmund)
      • 6:10 PM
        What's new in Python 3.11 10m

        Python 3.11 is coming out in October! We will look through the major features you can expect, such as results from the Faster CPython project, enhanced exceptions, new typing functionality, a very nice new asyncio feature, and several exciting enhancements sprinkled throughout the standard library. A couple of major new changes will be rolling in in future versions, as well, like the removal of distutils (3.12) and utf-8 by default (3.15); 3.11 provides some assistance for those, as well.

        Speaker: Henry Fredrick Schreiner (Princeton University)
    • 10:30 AM 11:00 AM
      Social time: Thursday Meet and Mingle

      Get to know the other PyHEP participants better and help to reinforce our community.

      We will be using the RemotelyGreen platform. Click the link below to join. Here are some tips to help you join and participate:

      1. Use a laptop or desktop.
      2. Join early to sign-in and test your camera and mic. Make an account by connecting with LinkedIn or using an email and password (check for a verification email in this case).
      3. You can choose your networking topics and set up your business card before the event begins. Choose as many of the event's topics that interest you.
      4. You can also specify your preferred networking topics at the start of the event and in between encounters. Fill in your business card to make follow-ups easier by clicking on your avatar or the username in the top-right corner of the screen.
      5. Wait for the session to begin - you'll be shuffled with other participants automatically. If you arrive late, you will be able to join at the next shuffle.

      Attached is a short flyer with more details.

    • 2:00 PM 6:10 PM
      Plenary Session Thursday
      Zoom Meeting ID
      61281099011
      Host
      Eduardo Rodrigues
      Alternative hosts
      Graeme A Stewart, Jim Pivarski, Oksana Shadura, Matthew Feickert, Nikolai Hartmann
      Useful links
      Join via phone
      Zoom URL
      • 2:00 PM
        Analysis Optimisation with Differentiable Programming 1h

        This tutorial will cover how to optimise various aspects of analyses -- such as cuts, binning, and learned observables like neural networks -- using gradient-based optimisation. This has been made possible due to work on the relaxed software package, which offers a set of standard HEP operations that have been made differentiable.

        In addition to targeting Asimov significance, we will also use the full analysis significance that incorporates systematic uncertainties as an optimisation objective. Finally, we will reproduce the neos method for learning systematic-aware observables, and you'll see how you can modify it for your use-case.

        Speaker: Mr Nathan Daniel Simpson (Lund University (SE))
      • 3:00 PM
        Speeding up differentiable programming with a Computer Algebra System 30m

        In the ideal world, we describe our models with recognizable mathematical expressions and directly fit those models to large data samples with high performance. It turns out that this can be done by formulating our models with SymPy (a Computer Algebra System) and using its symbolic expression trees as template to computational back-ends like JAX and TensorFlow. The CAS can in fact further simplify the expression tree, which results in speed-ups in the numerical back-end.
        In this talk, we have a look at amplitude analysis as a case study and use the Python libraries of the ComPWA project to formulate and fit large expressions to unbinned, multidimensional data sets.

        Speaker: Mr Remco de Boer (Ruhr University Bochum)
      • 3:30 PM
        The pythia8 python interface 30m

        Pythia is one of the most widely used general-purpose Monte Carlo event generators in HEP. It has included a python interface to the underlying C++ since v8.219, and it was redesigned to handle C++11 using pyBind11 since v8.301, allowing users to generate a custom python interface.

        This talk will showcase the power and flexibility of Pythia's default, simplified python interface by presenting its basic features and walking through a simple event-generation and analysis workflow.

        Speaker: Michael Kent Wilkinson (University of Cincinnati (US))
      • 4:00 PM
        BREAK 30m
      • 4:30 PM
        End-to-end physics analysis with Open Data: the Analysis Grand Challenge 1h

        The IRIS-HEP Analysis Grand Challenge (AGC) provides an environment for investigating analysis methods in the context of a realistic physics analysis. It features an analysis task that captures all relevant workflow aspects encountered in LHC analyses, reaching from data delivery to statistical inference. By using publicly available Open Data, the AGC allows anyone interested to test different analysis approaches and implementations at scale.

        This tutorial showcases a complete Python implementation of the AGC analysis task, making heavy use of Scikit-HEP libraries and coffea. It demonstrates how these libraries provide the required functionality and interfaces for an end-to-end analysis pipeline. This includes the organization of input datasets, columnar data processing, evaluation of systematic uncertainties, histogram creation, statistical model assembly and inference, alongside the relevant visualizations that a physicist running this pipeline requires.

        Speaker: Alexander Held (University of Wisconsin Madison (US))
      • 5:30 PM
        Awkward RDataFrame Tutorial 30m

        This Jupyter notebook tutorial will cover usage of Awkward Arrays within an RDataFrame.

        In Awkward Array version 2, the ak.to_rdataframe function presents a view of an Awkward Array as an RDataFrame source. This view is generated on demand and the data is not copied. The column readers are generated based on the run-time type of the views. The readers are passed to a generated source derived from ROOT::RDF::RDataSource.

        The ak.from_rdataframe function converts the selected columns as native Awkward Arrays.

        The tutorial demonstrates examples of the column definition, applying user-defined filters written in C++, and plotting or extracting the columnar data as Awkward Arrays.

        Speaker: Ianna Osborne (Princeton University)
      • 6:00 PM
        Developing implicitly-parallel Python analysis tools for NOvA 10m

        The NOvA collaboration together with a Dept. of Energy ASCR supported SciDAC-4 project, have been exploring Python-based analysis workflows for HPC platforms. This research has been focused on adapting machine-learning application workflows using highly-parallel computing environments for neutrino-nucleon cross section measurements. This work accelerates scientific analysis and lowers the learning barriers required to leverage leadership computing platforms.

        Users of these HPC workflows have often been required to have significant experience with parallel computing libraries and principles in addition to dedicated access to accounts and resource allocations at off-site HPC/Supercomputing centers such as NERSC and the Argonne Leadership Computing Facility. With the commissioning of the new Fermilab analysis cluster, which will provide dynamically provisioned pools of HPC resources, we are now exploring ways to improve the efficiency and approachability of Python-based analysis tools. These include data organizations using HDF5 and the PandAna analysis framework, both of which natively support highly data-parallel operations.

        Our research enables fully data-parallel exploration, selection, and aggregation of neutrino data, which are the fundamental operations required for neutrino cross section analysis work in NOvA. These operations are executed with the analysis cluster through Jupyter notebook interfaces and have been demonstrated to achieve low execution latencies, which are highly compatible with interactive analysis time-scale(s). We have developed a method for constructing large monolithic HDF5 based files, which each represent an entire NOvA dataset, and we have demonstrated a factor of more than $10\times$ speedup of basic event selection using this data representation, relative to equivalent multi-file composite representations of the datasets. We have developed a complete, implicitly-parallel analysis workflow with basic histogram operations and demonstrated its scalability using a realistic neutrino cross section measurement on the Perlmutter system at NERSC. These tools will enable real-time turnaround of more physics results for the NOvA collaboration and wider HEP community.

        Speaker: Derek Doyle (Colorado State University)
    • 6:30 PM 8:30 PM
      Hackashop
      Zoom Meeting ID
      61281099011
      Host
      Eduardo Rodrigues
      Alternative hosts
      Graeme A Stewart, Jim Pivarski, Oksana Shadura, Matthew Feickert, Nikolai Hartmann
      Useful links
      Join via phone
      Zoom URL
      • 6:30 PM
        Intro talk - how to get your set-up to "hack around" 30m
        Speaker: Aman Goel (University of Delhi)
    • 10:00 AM 12:00 PM
      Hackashop
      Zoom Meeting ID
      61281099011
      Host
      Eduardo Rodrigues
      Alternative hosts
      Graeme A Stewart, Jim Pivarski, Oksana Shadura, Matthew Feickert, Nikolai Hartmann
      Useful links
      Join via phone
      Zoom URL
    • 2:00 PM 6:30 PM
      Plenary Session Friday
      Zoom Meeting ID
      61281099011
      Host
      Eduardo Rodrigues
      Alternative hosts
      Graeme A Stewart, Jim Pivarski, Oksana Shadura, Matthew Feickert, Nikolai Hartmann
      Useful links
      Join via phone
      Zoom URL
      • 2:00 PM
        Dask Tutorial 1h

        Dask provides a foundation to natively scale Python libraries and applications. Dask collection libraries like dask.array and dask.dataframe mimic the ubiquitous APIs of NumPy and Pandas to parallelize and/or distribute NumPy-like and Pandas-like workflows. The dask.delayed collection supports parallalization of custom algorithms. In this tutorial we will introduce the core Dask collections, the concepts behind them (partitioned objects represented by task graphs), and Dask's distributed execution engine that is compatible with common HEP batch compute systems. Finally, we will introduce recently developed Dask collections that support partitioned and distributed representations of awkward arrays and boost-histogram objects.

        Speaker: Doug Davis
      • 3:00 PM
        Uproot + Dask 10m

        This lightning talk will focus on introducing the new features in Uproot v5, with most focus on the newly-introduced uproot.dask function. The dask integration will be demoed through example workflows that explore all the new features of the uproot.dask function. Important options of the function's API like delaying the opening of files and variable chunk sizes will be demoed in the Jupyter notebook. The talk will also include updates and brief demos of the dask-awkward and awkward-pandas projects showing how they tie in with the Uproot update.

        Speaker: Kush Kothari
      • 3:10 PM
        Enabling Dask Interoperability with XRootD Storage Systems 10m

        This lightning talk will introduce fsspec-xrootd, a newly published middleware software package that allows Dask and other popular data analysis packages to directly access XRootD storage systems. These packages use the fsspec api as their storage backends and fsspec-xrootd adds an XRootD implementation to fsspec. This means that when using fsspec-xrootd, the user will be able to access XRootD content within Dask itself without resorting to an external program. The talk will include an explanation of fsspec-xrootd as well as a demonstration in jupyter notebook.

        Speaker: Scott Demarest (Florida Institute of Technology)
      • 3:20 PM
        3D and VR Industrial Use Cases in Python 10m

        Why Python is a good choice for making digital twins for the industry/research?

        Through several examples of practical use cases the talk will present our experiences of 3D and Virtual Reality, all implemented in Python with the help of our 3D package "HARFANG 3D" :

        • Human factor study of a railway station in virtual reality
        • Using a aircraft simulation sandbox for AI training
        • Tele-operating a humanoid robot in VR
        • A 2-way operation robotic digital twin in 3D

        Use case #1, Railway station study

        Duplicating a railway station in virtual reality to build a behavioral study, all implemented in Python.
        - What are the benefits of the language here?
        - Let's dive into the production workflow
        - What is the protocol needed for a reliable scientific study?

        Use case #2, Flight simulator sandbox

        Using a Python aircraft simulation sandbox for trajectory visualization and AI reinforcement learning.
        - How the simulation sandbox works?
        - A network interface to harness the simulation easily
        - Demo

        Use case #3, Pollen Robotics Reachy, in VR

        The future of industry, tele-operating an humanoid robot in VR.
        - Best way to read and decode an URDF file with PyBullet
        - Inverse kinematic with PyBullet again :)
        - Teleoperation in VR
        - Limitations and challenge

        Use case #4, Poppy Ergo Jr, in 3D

        How many lines of codes of Python are needed to create a digital twin ? (spoiler : 150 loc)
        - From the STL files to a realistic realtime 3D digital twin
        - Piloting the robot in Python
        - Compliance mode driving the 3D digital twin in realtime

        Using simple diagrams, code snippets, photos and videos, the talk will demonstrate the inputs and outputs of these experiments, and how much Python is relevant when it comes to implement 3D and VR applications in the industrial and scientific fields.

        TLDR;

        Most of the talk is about virtual reality and 3D images, so the impact of visuals is quite relevant. We would like to share with the audience that Python combined with HARFANG makes an exellent toolkit to build first-class VR experiences.

        Speaker: Francois Gutherz
      • 3:30 PM
        Using C++ From Numba, Fast and Automatic 30m

        The scientific community using Python has developed several ways to accelerate Python codes. One popular technology is Numba, a Just-in-time (JIT) compiler that translates a subset of Python and NumPy code into fast machine code using LLVM. We have extended Numba’s integration with LLVM’s intermediate representation (IR) to enable the use of C++ kernels and connect them to Numba accelerated codes. Such a multilanguage setup is also commonly used to achieve performance or to interface with external bare-metal libraries. In addition, Numba users will be able to write the performance-critical codes in C++ and use them easily at native speed.

        This work relies on high-performance, dynamic, bindings between Python and C++. Cppyy, which is the basis of PyROOT's interfaces to C++ libraries. Cppyy uses Cling, an incremental C++ interpreter, to generate on-demand bindings of required entities and connect them with the Python interpreter. This environment is uniquely positioned to enable the use of C++ from Numba in a fast and automatic way.

        In this talk, we demonstrate using C++ from Numba through Cppyy. We show our approach which extends Cppyy to match the object typing and lowering models of Numba and the necessary additions to the reflection layers to generate IR from Python objects. The uniform LLVM runtime allows optimizations such as inlining which can in the future remove the C++ function call overhead. We discuss other optimizations such as lazily instantiated C++ templates based on input data. The talk also briefly outlines the non-negligible, Numba-introduced JIT overhead and possible ways to optimize it. Since this is built as a Cppyy extension Numba supports all bindings automatically without any user intervention.

        Speaker: Baidyanath Kundu (Princeton University (US))
      • 4:00 PM
        BREAK 30m
      • 4:30 PM
        zfit - binned fits and histograms 30m

        zfit is a scalable, pythonic model fitting library that aims at implementing likelihood fits in HEP. So far, the main functionality was focused on unbinned fits. With zfit 0.10, the concept of binning is introduced and allows for binned datasets, PDFs and losses such as Chi2 or Binned likelihoods. All of these elements are interchangeable and convertable to unbinned counterparts and allow for an arbitrary mixture of both.
        In this talk, we will introduce the binned part of zfit and its integration into the existing Scikit-HEP ecosystem.

        Speaker: Jonas Eschle (University of Zurich (CH))
      • 5:00 PM
        abcd_pyhf: Likelihood-based ABCD method for background estimation and hypothesis testing with pyhf 30m

        The ABCD method is a common background estimation method used by many physics searches in particle collider experiments and involves defining four regions based on two uncorrelated observables. The regions are defined such that there is a search region (where most signal events are expected to be) and three control regions. A likelihood-based version of the ABCD method, also referred to as the "modified ABCD method", can be used even when there may be significant contamination of the control regions by signal events. Code for applying this method in an individual analysis has generally been developed using the RooFit and RooStats packages within the ROOT software framework. abcd_pyhf is a standalone implementation of this method utilizing pyhf, a pure-Python package providing the functionality of the statistical analysis tools available in RooFit/RooStats. This implementation does not make any assumptions about the underlying analysis and can thus be used or adapted in any analysis using the ABCD method. This talk will provide an overview of the likelihood-based ABCD method and present an example of how to use abcd_pyhf to obtain statistical results in such an analysis.

        Speaker: Mason Proffitt (University of Washington (US))
      • 5:30 PM
        Pyhf to Combine Converter 10m

        The use of statistical models to accurately represent and interpret data from high-energy physics experiments is crucial to gaining a better understanding of the results of those experiments. However, there are many different methods and models that researchers are using for these representations, and although they often generate results that are useful for everyone in the field of HEP, they also often slightly deviate in results between different models, and that deviation is difficult to interpret. Fortunately, many statistical models use similar frameworks, as well as the same mathematics, so it is quite feasible to convert a model generated in one environment to a different environment. This will allow the results to be more consistently replicated across models, as well as give deeper insight into certain differences in results between models. In addition, both pyhf and Combine offer unique tools and nuances within their respective models, and an easy conversion would allow someone that is familiar with one environment to develop the model in that environment, and then transfer it to the other in order to take advantage of both sets of tools. The python script that I have developed successfully does this conversion, and I have also verified mathematical agreement between the models. Even with complex models, the inferences made by both pyhf and Combine agree within a 5% of the relative uncertainty of those inferences. Moreover, even for large models, the conversion takes less than 30 seconds.

        Speaker: Mr Petey Ridolfi
      • 5:40 PM
        Scalable, Sparse IO with larcv 10m

        At the intersection of high energy physics, deep learning, and high performance computing there is a challenge: how to efficiently handle data I/O of sparse and irregular datasets from high energy physics, and connect them to python and the deep learning frameworks? In this lightning talk we present larcv, an open source tool built on HDF5 that enables parallel, scalable IO for irregular datasets with simple python access. This tool is highly optimized for reading sparse and irregular datasets up to 10s of thousands of process, with latency of milliseconds when combined with GPU computing.

        Speaker: Corey Adams
      • 5:50 PM
        JetNet library for machine learning in high energy physics 10m

        Machine learning is becoming ubiquitous in high energy physics for many tasks, including classification, regression, reconstruction, and simulations. To facilitate development in this area, and to make such research more accessible and reproducible, we present the open source Python JetNet library with easy to access and standardised interfaces for particle cloud datasets, implementations for HEP evaluation and loss metrics, and more useful tools for ML in HEP.

        Speaker: Raghav Kansal (Univ. of California San Diego (US))
      • 6:00 PM
        Workshop close-out 5m
        Speaker: Eduardo Rodrigues (University of Liverpool (GB))