Skip to main content

Python class for reading UCN runs converted to ROOT with midas2root

Project description

ucndata

This repository defines the ucndata package and a few scripts which utilize this package to analyze UCN ROOT files.

The ucndata package contained within has been installed system-wide on daq04. You will therefore be able to import it from any directory.

ucndata quick links

Quick API Reference

These are the main workhorses of the ucndata project:

  • ucnbase - base class for the following:
  • ucnrun - workhorse object representing a single run
  • ucncycle - workhorse object representing a single cycle within a run
  • ucnperiod - workhorse object representing a single period within a cycle

But these can also be useful:

  • the chopper module - redefinition for working with chopper data
  • applylist - for working with sets of runs or cycles or periods. Does element-wise attribute access and operators
  • datetime - convert timestamps to datetime objects and back

rootloader API

rootloader is a custom package which facilitates the reading of ROOT files in a more pythonic way than is provided by generic PyROOT. In general it attempts to make the various ROOT objects (histograms, trees) look like pandas dataframes. When you are looking at the contents of ucnrun.tfile you are looking at the rootloader.tfile object. Here are the reference for some of the more commonly found rootloader objects:

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ucndata-4.4.0.tar.gz (57.9 kB view details)

Uploaded Source

File details

Details for the file ucndata-4.4.0.tar.gz.

File metadata

  • Download URL: ucndata-4.4.0.tar.gz
  • Upload date:
  • Size: 57.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for ucndata-4.4.0.tar.gz
Algorithm Hash digest
SHA256 220ac67458fc7a276e9134e19b2b47e1ab52a32ae8acb27365959bf9af50be3e
MD5 f49c2ab068e9f950be105c180db35d71
BLAKE2b-256 f4abd01dd907895e7c1b753829f581e49217e16fc06e700c86dac40b2e9d9c9d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page