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    "info": {
        "author": "Joachim Rambeau",
        "author_email": "joachim.rambeau@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
        ],
        "description": "===========\ntunacell\n===========\n\n|Build-Status| |Documentation-Status| |PyPI-Version| |License|\n\n.. |Build-Status| image:: https://travis-ci.com/LeBarbouze/tunacell.svg?branch=develop)\n   :target: https://travis-ci.com/LeBarbouze/tunacell\n   :alt: Travis CI Status\n\n.. |Documentation-Status| image:: https://readthedocs.org/projects/tunacell/badge/?version=latest\n   :target: https://tunacell.readthedocs.io/en/latest/?badge=latest\n   :alt: Documentation Status\n\n.. |PyPI-Version| image:: https://img.shields.io/pypi/v/tunacell?color=blue\n   :target: ttps://pypi.org/project/tunacell/\n\n.. |License| image:: https://img.shields.io/github/license/LeBarbouze/tunacell\n\n\n``tunacell`` is a Python package that provides tools to analyze data from time-lapse\nmovies of dividing micro-organisms.\n\nMain features\n=============\n\n- Analysis of time-series defined over many cell-cycles\n- Computation of average values, variance, number of samples over sampling time or generations\n  (the former is better used for dynamic quantities such as instantaneous growth rate,\n  the latter for cell-cycle observables such as birth size)\n- Computation of autocovariance for a single observable, and cross-covariance\n  for a couple of observables over time\n- Plotting functions of the computed statistics\n- Filtering-out cells\n- Conditional analysis: computation of statistics for subgroups of cells,\n  lineages, colonies, or containers (*aka* fields of view) where the subgroups are defined\n  respective to conditions (the term *gating* is employed elsewhere)\n- Data visualization of small samples\n- Export of computed data as text files in a comprehensive folder structure\n- Numerical simulations\n- Input type includes text files (comma- or tab-separated values) and output from\n  segmentation software (only Supersegger's output is supported)\n\n\nInstall\n=======\n\nInstall the package from PyPI:\n\n    $ pip install tunacell\n\nIt is adviced to run this command in a virtual environment.\n\n\nTesting the install\n===================\n\nUnit tests\n----------\n\ntunacell comes with unit tests that can be run with pytest. Run::\n\n\tmake test\n\nto execute those tests. Unit test coverage is getting better but is far from\nexhaustive.\n\nEnd-to-end tests\n----------------\n\nTo check whether most features works appropriately, clone the\nrepo and run::\n\n\tmake full-demo\n\nIt should not raise any Python error (though some warnings may show up).\n\n(note that these scripts writes files and folders in a new ``tmptunacell``\nfolder in your home directory--taking roughly 13 MB when all scripts have been\nlaunched)\n\n\n`Quick start`_\n==================\n\n.. _`quick start`: https://tunacell.readthedocs.io/en/latest/users/tutorial.html\n\nThis 10 minute tutorial gives an overview of tunacell and its API.\n\nFor a deeper, tutorial-like exploration of tunacell API,\nconsider running **sequentially** the scripts in\nthe ``scripts`` folder as it is done in the full-demo recipe in Makefile.\nJust change the ``--time .5`` option to interactive mode by adding ``-i``::\n\n   cd <your-location-for-tunacell-repo>/scripts\n   $ tunasimu -s 42  # makes the simutest numerically simulated experiment\n   $ python tutorial.py -i\n   $ ...\n\n(note that these scripts writes files and folders in a new ``tmptunacell``\nfolder in your home directory--taking roughly 13 MB when all scripts have been\nlaunched)\n\nOnce you've run, read, and understood the bits of code in these files, consider\nyourself as a tunacell expert.\n\nIf you got how it works, plug your data in\n(look at [how to format input files][tunadocs-data-structure])\nand use tunacell API to write your\nscripts and discover new things about the dynamical properties of your cells!\n\n\nDocumentation_\n==============\n\n.. _documentation: https://tunacell.readthedocs.io/en/latest/\n\nThere is an extensive documentation_. Start with the `introduction to tunacell\n<https://tunacell.readthedocs.io/en/latest/intro.html>`_.\n\n\nDependencies\n=============\n\nNumpy_, Scipy_, matplotlib_ are classic libraries,\nas well as pandas_ that is used\nto provide the user with DataFrame objects for some statistical analyses.\n\nThe tree-like structure arising from dividing cells\nhas been implemented using the treelib_ library.\n\nWe use pyYAML_ to parse yaml files such as metadata or other library-created\nfiles, and tqdm_ package for progress bars.\n\n.. _Scipy: http://www.scipy.org/\n.. _Numpy: https://docs.scipy.org/doc/numpy-dev/user/index.html\n.. _pandas: http://pandas.pydata.org/\n.. _matplotlib: http://matplotlib.org/\n.. _treelib: https://github.com/caesar0301/treelib\n.. _pyYAML: https://pypi.python.org/pypi/PyYAML\n.. _tqdm: https://pypi.python.org/pypi/tqdm\n\n\nContributing\n============\n\nAs bugs may come up quickly, we encourage users to report them with an Issue.\nWe further encourage experienced users to propose patches and pull requests :)\n\nWe also welcome any suggestion for improvement (though an Issue,\nor an email--see setup). Thanks for your help!\n\n\n\n\n",
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        "keywords": "microscopy time-lapse bacteria cell-division analysis statistics dynamics cross-correlations",
        "license": "MIT",
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        "name": "tunacell",
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        "summary": "Analysis of Timeseries from dividing UNicellular microorganisms",
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