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    "info": {
        "author": "Eduardo Rodrigues, Henry Fredrick Schreiner III",
        "author_email": "eduardo.rodrigues@cern.ch, henry.schreiner@cern.ch",
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        "description": "<img alt=\"DecayLanguage logo\" src=\"https://raw.githubusercontent.com/scikit-hep/decaylanguage/master/images/DecayLanguage.png\"/>\n\n# DecayLanguage: describe, manipulate and convert particle decays\n\n[![Scikit-HEP](https://scikit-hep.org/assets/images/Scikit--HEP-Project-blue.svg)](https://scikit-hep.org/)\n[![PyPI Package latest release](https://img.shields.io/pypi/v/decaylanguage.svg)](https://pypi.python.org/pypi/decaylanguage)\n[![Conda latest release](https://img.shields.io/conda/vn/conda-forge/decaylanguage.svg)](https://anaconda.org/conda-forge/decaylanguage)\n[![Supported versions](https://img.shields.io/pypi/pyversions/decaylanguage.svg)](https://pypi.python.org/pypi/decaylanguage)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3257423.svg)](https://doi.org/10.5281/zenodo.3257423)\n\n[![GitHub Actions Status: CI](https://github.com/scikit-hep/decaylanguage/workflows/CI/badge.svg)](https://github.com/scikit-hep/decaylanguage/actions)\n[![Code Coverage](https://codecov.io/gh/scikit-hep/decaylanguage/graph/badge.svg?branch=master)](https://codecov.io/gh/scikit-hep/decaylanguage?branch=master)\n[![Documentation Status](https://readthedocs.org/projects/decaylanguage/badge/?style=flat)](https://decaylanguage.readthedocs.io)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n\n[![Binder demo](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/scikit-hep/decaylanguage/master?urlpath=lab/tree/notebooks/DecayLanguageDemo.ipynb)\n\n\nDecayLanguage implements a language to describe and convert particle decays\nbetween digital representations, effectively making it possible to interoperate\nseveral fitting programs. Particular interest is given to programs dedicated\nto amplitude analyses.\n\nDecayLanguage provides tools to parse so-called .dec decay files,\nand describe, manipulate and visualize decay chains.\n\n\n## Installation\n\nJust run the following:\n\n```bash\npip install decaylanguage\n```\n\nYou can use a virtual environment through pipenv or with `--user` if you know\nwhat those are. [Python\n3.6+](http://docs.python-guide.org/en/latest/starting/installation) supported\n(see version 0.12 for Python 2 & 3.5 support).\n\n<details><summary>Dependencies: (click to expand)</summary><p>\n\nRequired and compatibility dependencies will be automatically installed by pip.\n\n### Required dependencies:\n\n-   [particle](https://github.com/scikit-hep/particle): PDG particle data and identification codes\n-   [NumPy](https://scipy.org/install.html): The numerical library for Python\n-   [pandas](https://pandas.pydata.org/): Tabular data in Python\n-   [attrs](https://github.com/python-attrs/attrs): DataClasses for Python\n-   [plumbum](https://github.com/tomerfiliba/plumbum): Command line tools\n-   [lark-parser](https://github.com/lark-parser/lark): A modern parsing library for Python\n-   [graphviz](https://gitlab.com/graphviz/graphviz/) to render (DOT language) graph\n    descriptions and visualizations of decay chains.\n\n### Python compatibility:\n-   [importlib_resources](http://importlib-resources.readthedocs.io/en/latest/) backport if using Python /< 3.7\n</p></details>\n\n\n## Getting started\n\nThe [Binder demo](https://mybinder.org/v2/gh/scikit-hep/decaylanguage/master?urlpath=lab/tree/notebooks/DecayLanguageDemo.ipynb)\nis an excellent way to get started with `DecayLanguage`.\n\nThis is a quick user guide. For a full API docs, go [here](https://decaylanguage.readthedocs.io)\n(note that it is presently work-in-progress).\n\n### What is DecayLanguage?\n\n`DecayLanguage` is a set of tools for building and transforming particle\ndecays:\n\n1. It provides tools to parse so-called `.dec` decay files,\nand describe, manipulate and visualize the resulting decay chains.\n\n2. It implements a language to describe and convert particle decays\nbetween digital representations, effectively making it possible to interoperate\nseveral fitting programs. Particular interest is given to programs dedicated\nto amplitude analyses.\n\n### Particles\n\nParticles are a key component when dealing with decays.\nRefer to the [particle package](https://github.com/scikit-hep/particle)\nfor how to deal with particles and Monte Carlo particle identification codes.\n\n### Parse decay files\n\nDecay `.dec` files can be parsed simply with\n\n```python\nfrom decaylanguage import DecFileParser\n\nparser = DecFileParser('my-decay-file.dec')\nparser.parse()\n\n# Inspect what decays are defined\nparser.list_decay_mother_names()\n\n# Print decay modes, etc. ...\n```\n\nA copy of the master DECAY.DEC file used by the LHCb experiment is provided\n[here](https://github.com/scikit-hep/decaylanguage/tree/master/decaylanguage/data)\nfor convenience.\n\nThe `DecFileParser` class implements a series of methods giving access to all\ninformation stored in decay files: the decays themselves, particle name aliases,\ndefinitions of charge-conjugate particles, variable and Pythia-specific\ndefinitions, etc.\n\nIt can be handy to parse from a multi-line string rather than a file:\n\n```python\ns = \"\"\"Decay pi0\n0.988228297   gamma   gamma                   PHSP;\n0.011738247   e+      e-      gamma           PI0_DALITZ;\n0.000033392   e+      e+      e-      e-      PHSP;\n0.000000065   e+      e-                      PHSP;\nEnddecay\n\"\"\"\n\ndfp = DecFileParser.from_string(s)\ndfp.parse()\n```\n\n#### Advanced usage\n\nThe list of `.dec` file decay models known to the package can be inspected via\n\n```python\nfrom decaylanguage.dec import known_decay_models\n```\n\nSay you have to deal with a decay file containing a new model not yet on the list above.\nRunning the parser as usual will result in a `UnexpectedToken` exception.\nIt is nevertheless easy to deal with this issue; no need to wait for a new release.\nIt is just a matter of adding the model name to the list in `decaylanguage/data/decfile.lark`\n(or your private copy), see the line\n`MODEL_NAME.2 : \"BaryonPCR\"|\"BTO3PI_CP\"|\"BTOSLLALI\"|...`,\nand then proceed as usual apart from adding an extra line to call to `load_grammar`\nto specify the Lark grammar to use:\n\n```python\ndfp = DecFileParser('my_decfile.dec')\ndfp.load_grammar('path/to/my_updated_decfile.lark')\ndfp.parse()\n...\n```\n\nThis being said, please do submit a pull request to add new models,\nif you spot missing ones ...\n\n### Visualize decay files\n\nThe class `DecayChainViewer` allows the visualization of parsed decay chains:\n\n```python\nfrom decaylanguage import DecayChainViewer\n\n# Build the (dictionary-like) D*+ decay chain representation setting the D+ and D0 mesons to stable,\n# to avoid too cluttered an image\nd = dfp.build_decay_chains('D*+', stable_particles=('D+', 'D0'))\nDecayChainViewer(d)  # works in a notebook\n```\n\n![DecayChain D*](https://raw.githubusercontent.com/scikit-hep/decaylanguage/master/images/DecayChain_Dst_stable-D0-and-D+.png)\n\nThe actual graph is available as\n\n```python\n# ...\ndcv = DecayChainViewer(d)\ndcv.graph\n```\n\nmaking all `graphviz.Digraph` class properties and methods available, such as\n\n```python\ndcv.graph.render(filename='mygraph', format='pdf', view=True, cleanup=True)\n```\n\nIn the same way, all `graphviz.Digraph` class attributes are settable\nupon instantiation of `DecayChainViewer`:\n\n```python\ndcv = DecayChainViewer(chain, name='TEST', format='pdf')\n```\n\n### Universal representation of decay chains\n\nA series of classes and methods have been designed to provide universal representations\nof particle decay chains of any complexity, and to provide the ability\nto convert between these representations.\nSpecifically, class- and dictionary-based representations have been implemented.\n\nAn example of a class-based representation of a decay chain is the following:\n\n```python\n>>> from decaylanguage import DaughtersDict, DecayMode, DecayChain\n>>>\n>>> dm1 = DecayMode(0.0124, 'K_S0 pi0', model='PHSP')\n>>> dm2 = DecayMode(0.692, 'pi+ pi-')\n>>> dm3 = DecayMode(0.98823, 'gamma gamma')\n>>> dc = DecayChain('D0', {'D0':dm1, 'K_S0':dm2, 'pi0':dm3})\n>>> dc\n<DecayChain: D0 -> K_S0 pi0 (2 sub-decays), BF=0.0124>\n```\n\nDecay chains can be visualised with the `DecayChainViewer` class making use\nof the dictionary representation `dc.to_dict()`, which is the simple\nrepresentation understood by `DecayChainViewer`, as see above:\n\n```python\nDecayChainViewer(dc.to_dict())\n```\n\nThe fact that 2 representations of particle decay chains are provided ensures\nthe following:\n\n1. Human-readable (class) and computer-efficient (dictionary) alternatives.\n2. Flexibility for parsing, manipulation and storage of decay chain information.\n\n### Decay modeling\n\nThe most common way to create a decay chain is to read in an [AmpGen]\nstyle syntax from a file or a string. You can use:\n\n```python\nfrom decaylanguage.modeling import AmplitudeChain\nlines, parameters, constants, states = AmplitudeChain.read_ampgen(text='''\nEventType D0 K- pi+ pi+ pi-\n\nD0[D]{K*(892)bar0{K-,pi+},rho(770)0{pi+,pi-}}                            0 1 0.1 0 1 0.1\n\nK(1460)bar-_mass  0 1460 1\nK(1460)bar-_width 0  250 1\n\na(1)(1260)+::Spline::Min 0.18412\na(1)(1260)+::Spline::Max 1.86869\na(1)(1260)+::Spline::N 34\n''')\n```\n\nHere, `lines` will be a list of AmplitudeChain lines (pretty print supported in Jupyter notebooks),\n`parameters` will be a table of parameters (ranged parameters not yet supported),\n`constants` will be a table of constants,\nand `states` will be the list of known states (EventType).\n\n#### Converters\n\nYou can output to a format (currently only [GooFit] supported, feel free\nto make a PR to add more). Use a subclass of DecayChain, in this case,\nGooFitChain. To use the [GooFit] output, type from the shell:\n\n```bash\npython -m decaylanguage -G goofit myinput.opts\n```\n\n## Acknowledgements\n\nThe UK Science and Technology Facilities Council (STFC) and the University of Liverpool\nprovide funding for Eduardo Rodrigues (2020-) to work on this project part-time.\n\nSupport for this work was provided by the National Science Foundation\ncooperative agreement OAC-1450377 (DIANA/HEP) in 2016-2019\nand has been provided by OAC-1836650 (IRIS-HEP) since 2019.\nAny opinions, findings, conclusions or recommendations expressed in this material\nare those of the authors and do not necessarily reflect the views of the National Science Foundation.\n\n\n[AmpGen]: https://gitlab.cern.ch/lhcb/Gauss/tree/LHCBGAUSS-1058.AmpGenDev/Gen/AmpGen\n[GooFit]: https://GooFit.github.io\n\n\n",
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