Universal HEP plots¶
Universal HEP introduces an exchange format for high-energy physics plots that couples the raw data with the representation into a single file. However, the two parts are still separated, such that you can change its appearance and composition.
What is Uhepp?¶
The term Uhepp – universal high-energy physics plot – refers to three distinct projects:
Uhepp is a storage format for high-energy physics plots, usually in JSON or YAML syntax. The format couples the raw data with its visual representation in a single file while keeping them separate. This allows changing the visual appearance, binning, content, labels, color scheme, etc. Since the two components (data and visual style) are coupled, it’s easy to reproduce the same plot or a modified version months or years after the initial creation. Read more about the semantic of the file format.
Uhepp is a Python package, installed with
pip install uhepp. The package provides useful tools to work with the storage format. It can, for example, can translate the semantic JSON into an object-oriented representation and vice versa. With the package, it’s also easy to build new plots and store them in a JSON file. Besides interfacing with the storage format, the package provides methods to render a plot as a graphics format and interact with a central hub.
Uhepp might refer to the central hub at https://uhepp.org. The hub provides a central storage facility for uhepp plots exposed via a REST API. Plots can be uploaded, download, and shared via the API. The hub also exposes the plots via a web interface, which makes sharing and discussing plots with colleagues easy and convenient.
You can easily install the package with pip.
pip install uhepp
To dive right into the topic, download a publicly available histogram from https://uhepp.org specified by a unique identifier.
>>> import uhepp >>> hist = uhepp.pull("a378d2b0-cde2-4266-be9b-85945d94880d")
To view the download histogram, call the
Alternatively, you can change the binning of the histogram.
>>> hist.rebin_edges = [0, 40, 60, 80, 100, 120, 200] >>> hist.show()
If you need to know the total Data yield, extract the
from the histogram.
>>> data_yield = hist.yields["data"] >>> data_yield.sum() 608.0
The Getting Started guide walks you through the creation of the above example histogram and how to save it locally and push it to a remote hub.