Skip to content

torkjellsdatter/pisa

 
 

Repository files navigation

PISA

Introduction | Installation | Documentation | Terminology | License | Contributors | Others' work

PISA (PINGU Simulation and Analysis) is software written to analyze the results (or expected results) of an experiment based on Monte Carlo simulation.

In particular, PISA was written by and for the IceCube Collaboration for analyses employing the IceCube Neutrino Observatory, including the DeepCore and the proposed PINGU low-energy in-fill arrays. However, any such experiment—or any experiment at all—can make use of PISA for analyzing expected and actual results.

PISA was originally developed to cope with low-statistics Monte Carlo (MC) for PINGU when iterating on multiple proposed geometries by using parameterizations of the MC and operate on histograms of the data rather than directly reweighting the MC (as is traditionally done in high-energy Physics experiments). However, PISA's methods apply equally well to high-MC situations, and PISA also performs traditional reweighted-MC analysis as well.

Directory listing

File/directory Description
docs/ Sphinx auto-generated documentation
images/ Images to include in documentation
pisa/ Source code
pisa_examples/ Example resources for PISA from data to settings, notebooks with examples of how to use PISA, etc.
pisa_tests/ Scripts for running physics and unit tests
.gitattributes Used with versioneer
.gitignore GIT ignores files matching these specifications
CONTRIBUTORS.md Listing of individuals who contributed code to PISA
EXTERNAL_ATTRIBUTION.md Authors, references, and/or copyrights on external code used within PISA
INSTALL.md How to install PISA
LICENSE Apache 2.0 license; applicable unless noted otherwise
MANIFEST.in Extra files to distribute with PISA package
README.md Brief overview of PISA
pylintrc PISA coding conventions for use with pylint
requirements.txt Hard dependencies, use with pip install -r requirements.txt
setup.cfg Setup file for versioneer
setup.py Python setup file, allowing e.g. pip installation
versioneer.py Automatic versioning

About

Monte Carlo-based data analysis

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • D 51.6%
  • Python 46.8%
  • Makefile 1.4%
  • Shell 0.2%