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        "author": "Chitsanu Khurewathanakul",
        "author_email": "chitsanu.khurewathanakul@gmail.com",
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            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Information Technology",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Topic :: Utilities"
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        "description": "PyrootCK\n========\n\n[![package version](https://img.shields.io/pypi/v/PyrootCK.svg)](https://pypi.org/project/PyrootCK)\n[![pipeline status](https://gitlab.com/ckhurewa/PyrootCK/badges/master/pipeline.svg)](https://gitlab.com/ckhurewa/PyrootCK/commits/master)\n[![coverage report](https://gitlab.com/ckhurewa/PyrootCK/badges/master/coverage.svg)](https://ckhurewa.gitlab.io/PyrootCK)\n[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)\n[![Documentation Status](https://readthedocs.org/projects/PyrootCK/badge/?version=latest)](http://PyrootCK.readthedocs.io/en/latest/?badge=latest)\n[![python version](https://img.shields.io/pypi/pyversions/PyrootCK.svg)](https://img.shields.io/pypi/pyversions/PyrootCK.svg)\n\nImproving ``PyROOT`` for better productivity.\n\nCollection of utilities are organized into subpackages::\n\n- `mathutils`:\n  - `asymvar`: class for variable with asymmetric-error, inspired from `uncertainties.ufloat`.\n  - `Eff`, `EffU`, `EffU_unguard`: functions to compute efficiencies with Clopper-Pearson uncertainty.\n  - `weighted_average`, `weighted_harmonic_average`: when a simple average is not enough.\n  - `combine_fully_correlated`, `combine_uncorrelated`, `combine_BLUE`: for combining multiple observables with uncertainty into one, given choices of correlation. For BLUE (Best Linear Unbiased Estimator), see Valassi, 2013.\n\n- `iouils`\n  - `import_tree` to quickly load `TTree` from (multiple) `TFile` overloaded for different source types (local, ganga, eos, xrootd, ...).\n\n- `tmvautils`\n  - `TMVA_Adapter` to help setup `TMVA.Reader` variables, and return `TTree` of mva-response weights.\n\nAs well as miscellaneous monkey-patching on `ROOT` and `uncertainties`\nfor more methods::\n\n- `ROOT`: \n  - Misc conversion to/from `ROOT` (`TH`,`TGraph`,`RooWorkspace`,`RooFitResult`,...) and `pandas` (`Series`, `DataFrame`).\n  - `TFile.slice_tree` to extract `TTree` into smaller one.\n  - `TTree.drop` to make index-unique TTree.\n  - `TH1.vlookup`, `TH2.vlookup`: like in Microsoft Excel, to retrive value in a bin given point(s) on the axis.\n  - `TMultiGraph.brazillian`: for the upper limits plot.\n\n- `uncertainties`:\n  - class `var`, based on ufloat but ready-made for statistical (Poisson) error.\n  - Additional methods on `ufloat`: `rerr`, `upperlim`, `low`, `high`, `interval`, `rounding_PDG`\n  - More methods involving error tag: `tags`, `get_error`, `get_rerr`\n\nSee the docstring from module index for more details.\n\n\nInstallation\n------------\n\nIt's available on pip: `pip install pyrootck`\n\nDependency: `uncertainties`, `pandas`, `root_numpy`, `pyroot_zen`, `PythonCK`\n\n\nDisclaimer\n----------\n\nThis packacge was written and used during my PhD in 2013-2017 at EPFL (Lausanne) \nand LHCb collaboration (CERN), for the work in *Z->tau tau* cross-section measurement \nand *H->mu tau* searches at LHCb (8TeV).\n\nI hope it can be of a good use for future analysis...\n\n\n\n",
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