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AnalysisToolbox\n\n[![](https://img.shields.io/badge/docs-dev-blue.svg)](https://latticeqcd.github.io/AnalysisToolbox)\n[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/LatticeQCD/AnalysisToolbox/commits/main)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8259640.svg)](https://doi.org/10.5281/zenodo.8241876)\n\n\n\nThe AnalysisToolbox set of Python tools for statistically analyzing correlated data. \nThis includes aspects of lattice QCD applications related to QCD phenomenology. \n\n\nWe advertise briefly here some features of the AnalysisToolbox:\n- **General mathematics:** Numerical differentiation, convenience wrappers for\nSciPy numerical integration and solving IVPs.\n- **General statistics:** Jackknife, bootstrap, Gaussian bootstrap, error propagation,\nvarious information criteria, Bayesian model averaging, \nestimation of integrated autocorrelation time,\nerror ellipses, Kolmogorov-Smirnov tests, \nand curve fitting with and without Bayesian priors.\nWe stress that our math and statistics methods are generally useful, independent of physics contexts.\n- **General physics:** Unit conversions, critical exponents and temperatures for various universality\nclasses, physical constants, framework for spin models. \n- **QCD physics:** Hadron resonance gas model, ideal fermion gas, HotQCD equation of state, \nand the QCD beta function. \nThese methods are useful for QCD phenomenology, independent of lattice contexts.\n- **Lattice QCD:** Continuum-limit extrapolation, SU(3) gauge\nfields, reading in gauge fields, and the static quark-antiquark potential. These methods\nrather target lattice QCD. \n\n\nIn any of the above cases, after installing the AnalysisToolbox, you can easily incorporate\nits features in your own Python scripts like any other library.\nSome simple examples are in the [tutorial](https://latticeqcd.github.io/AnalysisToolbox/tutorial.html).\nA realistic use-case that weaves the AnalysisToolbox into a lattice\nQCD workflow can be found in [this data publication](https://pub.uni-bielefeld.de/record/2979080).\nMore information can be found in [the documentation](https://latticeqcd.github.io/AnalysisToolbox).\n\n\nTo use the AnalysisToolbox, make sure you have Python 3.9+. You should then be able to\nconveniently install it using\n```bash\npip install latqcdtools\n```\nIf you would like alternative ways to set up the AnalysisToolbox, please see\n[the documentation](https://latticeqcd.github.io/AnalysisToolbox).\n\nBesides this basic install, there is a `latexify()` command you can use when plotting to \nmake your plot font match typical LaTeX documents. In order for this command to work, you need\nto have LaTeX installed on your system. The easiest is to install `texlive-full`, but\nif that is not possible, it may be enough to install `texlive-mathscience` in addition\nto the basic stuff.\n\n\n## Getting started and documentation\n\nTo acquaint yourself with the AnalysisToolbox, you can start by\nhaving a look at the [tutorial](https://latticeqcd.github.io/AnalysisToolbox/tutorial.html),\nwhich walks through some scripts in the `examples` directory.\nYou can also look at some of the scripts in the `applications` and `testing` directories.\n\nTo learn about the code in more detail, especially learning how to contribute, please have\na look [the documentation](https://latticeqcd.github.io/AnalysisToolbox).\n\n\n## Getting help and bug reports\n\nOpen an [issue](https://github.com/LatticeQCD/AnalysisToolbox/issues), if...\n- you have troubles running the code.\n- you have questions on how to implement your own routine.\n- you have found a bug.\n- you have a feature request.\n\nIf none of the above cases apply, you may also send an email to clarke(dot)davida(at)gmail(dot)com.\n\n\n## Contributors\n\n[D. A. Clarke](https://github.com/clarkedavida), \n[L. Altenkort](https://github.com/luhuhis), \n[H. Dick](https://github.com/redweasel),\n[K. Ebira](https://github.com/kaiebira),\n[J. Goswami](https://github.com/jishnuxx),\n[O. Kaczmarek](https://github.com/olaf-kaczmarek),\n[G. Liu](https://github.com/iXhong),\n[L. Mazur](https://github.com/lukas-mazur),\n[H. Sandmeyer](https://github.com/hsandmeyer),\n[M. Sarkar](https://github.com/mugdhasarkar),\n[C. Schmidt](https://github.com/schmidt74), \n[H.-T. Shu](https://github.com/haitaoshu), \n[T. Ueding](https://github.com/SiggiUphues)\n\n## Crediting AnalysisToolbox\n\nIf you used this code in your research, your teaching, or found it generally useful, please help\nus out by citing\n```\n@inproceedings{Altenkort:2023xxi,\n    author = \"Altenkort, Luis and Clarke, David Anthony and Goswami, Jishnu and Sandmeyer, Hauke\",\n    title = \"{Streamlined data analysis in Python}\",\n    eprint = \"2308.06652\",\n    archivePrefix = \"arXiv\",\n    primaryClass = \"hep-lat\",\n    month = \"8\",\n    year = \"2023\"\n}\n```\n\n## Acknowledgments\n\n- 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.\n- This work was partly performed in the framework of the PUNCH4NFDI consortium supported by DFG fund \"NFDI 39/1\", Proj.No. 460248186 (PUNCH4NFDI).\n- DAC acknowledges helpful discussions with \n[C. DeTar](https://github.com/detar),\n[X.-Y. Jin](https://github.com/jxy),\n[S. Lahert](https://github.com/Shaun252), \n[G. P. LePage](https://github.com/gplepage), \nand [C. Rohde](https://github.com/rohdog2003).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flatticeqcd%2Fanalysistoolbox","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flatticeqcd%2Fanalysistoolbox","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flatticeqcd%2Fanalysistoolbox/lists"}