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        "description": "=======\nhep_spt\n=======\n\n.. image:: https://img.shields.io/travis/mramospe/hep_spt.svg\n   :target: https://travis-ci.org/mramospe/hep_spt\n\n.. image:: https://img.shields.io/badge/documentation-link-blue.svg\n   :target: https://mramospe.github.io/hep_spt/\n\n.. inclusion-marker-do-not-remove\n\nThe **High Energy Physics Statistics and Plotting Tools** package provides tools to work in **High Energy Physics** using general python packages.\n\nMain points\n===========\n\n  * Functions needed on day-to-day work, like calculating errors, residuals, etc.\n  * Classes to create adaptive binned histograms, and some functions to represent them using `matplotlib <https://matplotlib.org/>`_.\n  * Statistical functions to work with Bayesian/Frequentist approaches.\n  * Utilities to handle poissonian and/or weighted histograms.\n  * Simple classes to work with the CLs method.\n  * A set of `matplotlib <https://matplotlib.org/>`_ styles.\n\nConsiderations:\n===============\n\n  * Inputs passed to the functions and classes are usually preferred as `numpy.ndarray <https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.html>`_ objects.\n  * Plotting functions and classes are designed to work with `matplotlib <https://matplotlib.org/>`_.\n  * Statistical tools are built on top of the standard `scipy <https://scipy.org/>`_ package.\n\nInstallation:\n=============\n\nThis package is available on `PyPi <https://pypi.org/>`_, so simply type\n\n.. code-block:: bash\n\n   pip install hep-spt\n\nto install the package in your current python environment.\nSince this package uses the Numpy C API, it is necessary to have Numpy already installed.\nIf you attempt to install \"hep_spt\" with no installation of Numpy, an error will be raised.\nTo use the **latest development version**, clone the repository and install with `pip`:\n\n.. code-block:: bash\n\n   git clone https://github.com/mramospe/hep_spt.git\n   pip install hep_spt",
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