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    "info": {
        "author": "Ant\u00f3nio Ferreira",
        "author_email": "aeferreira@fc.ul.pt",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Information Analysis"
        ],
        "description": "\n***********\nMetabolinks\n***********\n\n``Metabolinks`` is a Python package that provides a set of tools for high-resolution\nMS metabolomics data analysis.\n\nMetabolinks aims at providing several tools that streamline most of\nthe metabolomics workflow. These tools were written having ultra-high\nresolution MS based metabolomics in mind.\n\nFeatures are a bit scarce right now:\n\n- peak list alignment\n- data matrix filtering, conversion and sample similarity measures\n- compound taxonomy retrieval\n\nBut our road map is clear and we expect to stabilize in a beta version pretty soon.\n\nStay tuned, and check out the examples folder (examples are provided as\njupyter notebooks).\n\nInstalling\n==========\n\n``Metabolinks`` is distributed on PyPI_ and can be installed with pip on\na Python 3.4+ installation::\n\n   pip install metabolinks\n\n.. _PyPI: https://pypi.org/project/metabolinks\n\n\nHowever, even if ``Metabolinks`` is written in Python, it requires some of the powerful scientific\npackages that are pre-installed on \"Scientific/Data Science Python\" distributions.\n\nOne of these two products is highly recommended:\n\n- `Anaconda <https://store.continuum.io/cshop/anaconda/>`_ (or `Miniconda <http://conda.pydata.org/miniconda.html>`_ followed by the necessary ``conda install``'s)\n- `Enthought Canopy <https://www.enthought.com/products/canopy/>`_\n\nThe formal requirements are:\n\n- Python 3.4 and above (it runs on Python 2.7 too)\n- ``setuptools``, ``pip``, ``six``, ``requests`` and ``pytest``\n- ``numpy``, ``matplotlib`` and ``pandas``\n\nThe installation of the ``Jupyter`` platform is also recommended since\nthe examples are provided as *Jupyter notebooks*.\n\n\n\n",
        "description_content_type": "",
        "docs_url": null,
        "download_url": "https://github.com/aeferreira/metabolinks",
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        "home_page": "https://github.com/aeferreira/metabolinks",
        "keywords": "Metabolomics,Mass Spectrometry,Data Analysis,Ultra-high resolution MS",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "metabolinks",
        "package_url": "https://pypi.org/project/metabolinks/",
        "platform": "",
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        "summary": "A set of tools for high-resolution MS metabolomics data analysis",
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