The AnalysisToolbox set of Python tools for statistically analyzing correlated data. This includes aspects of lattice QCD applications related to QCD phenomenology.
We advertise briefly here some features of the AnalysisToolbox:
- General mathematics: Numerical differentiation, convenience wrappers for SciPy numerical integration and solving IVPs.
- General statistics: Jackknife, bootstrap, Gaussian bootstrap, error propagation, various information criteria, estimation of integrated autocorrelation time, error ellipses, Kolmogorov-Smirnov tests, and curve fitting with and without Bayesian priors. We stress that our math and statistics methods are generally useful, independent of physics contexts.
- General physics: Unit conversions, critical exponents for various universality classes, physical constants, framework for spin models.
- QCD physics: Hadron resonance gas model, HotQCD equation of state, and the QCD beta function. These methods are useful for QCD phenomenology, independent of lattice contexts.
- Lattice QCD: Continuum-limit extrapolation, Polyakov loop observables, SU(3) gauge fields, reading in gauge fields, and the static quark-antiquark potential. These methods rather target lattice QCD.
In any of the above cases, after installing the AnalysisToolbox, you can easily incorporate its features in your own Python scripts like any other library. Some simple examples are in the tutorial. A realistic use-case that weaves the AnalysisToolbox into a lattice QCD workflow can be found in this data publication. More information can be found in the documentation.
To use the AnalysisToolbox, make sure you have Python 3.9+. You should then be able to conveniently install it using
pip install latqcdtoolsBesides this, there is a latexify() command you can use when plotting to make your
plot font match typical LaTeX documents. In order for this command to work, you need
to have LaTeX installed on your system. The easiest is to install texlive-full, but
if that is not possible, it may be enough to install texlive-mathscience in addition
to the basic stuff.
To acquaint yourself with the AnalysisToolbox, you can start by
having a look at the tutorial,
which walks through some scripts in the examples directory.
You can also look at some of the scripts in the applications and testing directories.
To learn about the code in more detail, especially learning how to contribute, please have a look the documentation.
Open an issue, if...
- you have troubles running the code.
- you have questions on how to implement your own routine.
- you have found a bug.
- you have a feature request.
If none of the above cases apply, you may also send an email to clarke(dot)davida(at)gmail(dot)com.
D. A. Clarke, L. Altenkort, H. Dick, J. Goswami, O. Kaczmarek, L. Mazur, H. Sandmeyer, M. Sarkar, C. Schmidt, H.-T. Shu, T. Ueding
If you used this code in your research, your teaching, or found it generally useful, please help us out by citing
@inproceedings{Altenkort:2023xxi,
author = "Altenkort, Luis and Clarke, David Anthony and Goswami, Jishnu and Sandmeyer, Hauke",
title = "{Streamlined data analysis in Python}",
eprint = "2308.06652",
archivePrefix = "arXiv",
primaryClass = "hep-lat",
month = "8",
year = "2023"
}
- We acknowledge support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the CRC-TR 211 'Strong-interaction matter under extreme conditions'– project number 315477589 – TRR 211.
- This work was partly performed in the framework of the PUNCH4NFDI consortium supported by DFG fund "NFDI 39/1", Proj.No. 460248186 (PUNCH4NFDI).
- DAC acknowledges helpful discussions with C. DeTar, S. Lahert, and G. P. LePage.