PyHEP 2022 | Saransh Chopra

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.

Setup

There are two ways to follow along (or run this notebook after the talk) -

Locally

Clone this repository -

 git clone https://github.com/Saransh-cpp/PyHEP22-Constructing-HEP-vectors-and-analyzing-HEP-data-using-Vector.git

Change directory

 cd Constructing-HEP-vectors-and-analyzing-HEP-data-using-Vector

Launch the classic Jupyter notebook or Jupyter lab -
```
 jupyter notebook
 # or
 jupyter lab
```

On cloud (recommended)
- Binder (recommended)
- Google Colab

We will be directly importing vector, awkward, numpy, numba, and uproot in this tutorial. Hence, a user must install these packages if this notebook is being run locally or on Google Colab.

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