{
    "info": {
        "author": "Cord Kaldemeyer, Francesco Witte",
        "author_email": "francesco.witte@hs-flensburg.de",
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        "description": ".. image:: https://coveralls.io/repos/github/oemof/cydets/badge.svg?branch=master\n    :target: https://coveralls.io/github/oemof/cydets?branch=master\n.. image:: https://travis-ci.org/oemof/cydets.svg?branch=master\n    :target: https://travis-ci.org/oemof/cydets\n.. image:: https://badge.fury.io/py/cydets.svg\n    :target: https://badge.fury.io/py/cydets\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2625698.svg\n   :target: https://doi.org/10.5281/zenodo.2625698\n\nThis package implements an algorithm to detect cycles in a times series\nalong with their respective depth-of-cycle (DoC) and duration.\nIt is maintained as a standalone package within the\n`Open Energy Modelling Framework <https://oemof.org/>`_.\nThe acronym *CyDeTS* stands for *(Cy)cle (De)tection in (T)ime (S)eries* and\nis chosen to prevent confusions with cycle definitions from graph theory.\n\nAlgorithmic results have been tested against the well known rainflow cycle counting\n(RFC) method from mechanical engineering and the equivalence of both counting methods\nhas been proved.\nThe original algorithm has been developed and proposed within the following publication:\n\n*Dambrowski, Jonny; Pichlmaier, Simon & Jossen, Andreas.\nMathematical methods for classification of state-of-charge time series for cycle lifetime prediction.\nAdvanced Automotive Battery Conference. Mainz, Germany. 2012.*\n\nThanks again to Simon Pichlmaier for sharing his code and allowing us\nto port and publish the algorithm under a free license.\n\nDocumentation\n=============\n\nThe probably most extensive description of the algorithm can be found in the\nabovementioned paper. In addition, we have tried to document the single parts of\nthe algorithm as docstrings within the code.\n\nInstallation\n================\n\nIf you have a working Python3 environment, use can pypi to install the latest\nversion.\n\n.. code:: bash\n\n  pip install cydets\n\nUsage\n=====\n\nThe algorithm is implemented as a function which takes an array-like data\nstructure as argument.\nResults are returned as a `pandas <https://pandas.pydata.org/>`_ dataframe.\n\n.. code:: bash\n\n    import pandas as pd\n    from cydets.algorithm import detect_cycles\n\n    # create sample data\n    series = pd.Series([0, 1, 0, 0.5, 0, 1, 0, 0.5, 0, 1, 0])\n\n    # detect cycles\n    cycles = detect_cycles(series)\n\nCitation\n========\n\nPlease use our `entry on Zenodo <https://doi.org/10.5281/zenodo.2625698>`_ to refer a specific version\n\nLicense\n=======\n\nCopyright (C) 2019 oemof developing group\n\nThis program is free software: you can redistribute it and/or modify it under the\nterms of the GNU General Public License as published by the Free Software Foundation,\neither version 3 of the License, or (at your option) any later version.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;\nwithout even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\nSee the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with this program.\nIf not, see http://www.gnu.org/licenses/.",
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        "name": "cydets",
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        "summary": "Cycle Detection in Time Series (CyDeTS). An algorithm to detect cycles in times series along with their respective depth of cycle and duration.",
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