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        "author": "Konstantinos Kavvadias",
        "author_email": "kavvkon@gmail.com",
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            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Scientific/Engineering :: Visualization",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Utilities"
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        "description": "Python toolkit for energy load time series\n==========================================\n\n|pyversion| |license| |version_status| |build_status| |docs|\n\n``enlopy`` is an open source python library with methods to generate,\nprocess, analyze, and plot energy related timeseries.\n\nWhile it can be used for any kind of data it has a strong focus on those\nthat are related with energy i.e. electricity/heat demand or generation,\nprices etc. The methods included here are carefully selected to\nfit in that context and they had been, gathered, generalized and\nencapsulated during the last years while working on different research\nstudies.\n\nThe aim is to provide a higher level API than the one that is already\navailable in commonly used scientific packages (pandas, numpy, scipy).\nThis facilitates the analysis and processing of energy load timeseries\nthat can be used for modelling and statistical analysis. In some cases it\nis just a convenience wrapper of common packages just as pandas and in\nother cases it implements methods or statistical models found in\nliterature.\n\nIt consists of four modules that include among others the following:\n\n* ``Analysis``: Overview of descriptive statistics, reshape, load duration curve, extract daily archetypes (clustering)\n* ``Plot``: 2d heatmap, 3d plot, boxplot, rugplot\n* ``Generate``: generate from daily and monthly profiles, generate from sinusoidal function, sample from given load duration curve, or from given PSD, add noise gaussian and autoregressive noise, genrate correlated load profiles , fit to analytical load duration curve\n* ``Statistics``: Feature extraction from timeseries for a quick overview of the characteristics of any load curve. Useful when coupled with machine learning packages.\n\nThis library is not focusing on regression and prediction (e.g. ARIMA, state-space etc.), since there are numerous relevant libraries around.\n\nExample\n-------\nTry to run one of the following commands to explore some of this library's features:\n\n.. code:: python\n\n    >>> import numpy as np\n    >>> import enlopy as el\n    >>> Load = np.random.rand(8760) # Create random vector of values\n    >>> eload = el.make_timeseries(Load) # Convenience wrapper around pandas timeseries\n\n    >>> el.plot_heatmap(eload, x='day', y='month', aggfunc='mean') # Plots 2d heatmap\n    >>> el.plot_percentiles(eload) # Plots mean and quantiles\n    >>> el.plot_LDC(eload) # Plots a Load Duration Curve\n    >>> el.plot_rug(eload) # Plots a nice rugplot. Useful for dispatch results\n    >>> el.get_load_archetypes(eload, plot_diagnostics=True) # Splits daily loads in clusters (archetypes)\n\nMore examples can be found in `this jupyter notebook <https://github.com/kavvkon/enlopy/blob/master/notebooks/Basic%20examples.ipynb>`__.\n\nDocumentation\n-------------\nDetailed documentation is still under construction, but you can find an overview of the available methods here: http://enlopy.readthedocs.io/\n\nInstall\n-------\n\nCurrently you can find the latest stable version in pypi. You can install it with:\n\n::\n\n    pip install enlopy\n\nBe aware that this library is still in conceptual mode, so the API is most probably going to change in the following versions.\nIf you already have it installed and you want to upgrade to the latest stable version please use the following:\n\n::\n\n    pip install -U --upgrade-strategy only-if-needed enlopy\n\nThis will ensure that the dependencies will not be updated if the minimum requirements are already satisfied with the current version.\n\nIf you want to download the latest version from git for use or development purposes:\n\n.. code:: bash\n\n    git clone https://github.com/kavvkon/enlopy.git\n    cd enlopy\n    conda env create  # Automatically creates environment based on environment.yml\n    source activate enlopy\n    pip install -e . # Install editable local version\n\nIt should be ready to run out of the box for anyone that has the\n`anaconda distribution <https://www.continuum.io/downloads>`__\ninstalled. The only dependencies required to use ``enlopy`` are the\nfollowing:\n\n-  `numpy <http://numpy.org>`__\n-  `scipy <http://scipy.org>`__\n-  `pandas <http://pandas.pydata.org/>`__\n-  `matpotlib <http://matplotlib.org/>`__\n\nContribute\n----------\n\nIf you think you can contribute with new relevant methods that you are\ncurrently using or improve the code or documentation in any way, feel free to contact me,\nfork the repository and send your pull requests.\n\nCiting\n------\n\nIf you use this library in an academic work, please consider citing it.\n\n[1] K. Kavvadias, \u201cenlopy: Python toolkit for energy load time series\u201d\n\n``enlopy`` has been already used for processing demand timeseries in this scientific paper:\nhttp://dx.doi.org/10.1016/j.apenergy.2016.08.077\n\n.. |pyversion| image:: https://img.shields.io/pypi/pyversions/enlopy.svg\n    :alt: Supported Python versions.\n    :target: http://pypi.python.org/pypi/enlopy\n.. |license| image:: https://img.shields.io/pypi/l/enlopy.svg\n    :alt: BSD License\n    :target: https://opensource.org/licenses/BSD-3-Clause\n.. |version_status| image:: http://img.shields.io/pypi/v/enlopy.svg?style=flat\n   :target: https://pypi.python.org/pypi/enlopy\n.. |build_status| image:: http://img.shields.io/travis/kavvkon/enlopy/master.svg?style=flat\n   :target: https://travis-ci.org/kavvkon/enlopy\n.. |docs| image:: https://readthedocs.org/projects/enlopy/badge/\n    :alt: Documentation\n    :target: http://enlopy.readthedocs.io/en/latest/\n\n\n\n",
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