Metadata-Version: 1.1
Name: enlopy
Version: 0.1.dev12
Summary: Python library with methods to generate, process, analyze, and plot energy related timeseries.
Home-page: https://github.com/kavvkon/enlopy
Author: Konstantinos Kavvadias
Author-email: kavvkon@gmail.com
License: BSD-3-Clause
Description: Python toolkit for energy load time series
        ==========================================
        
        |pyversion| |anaconda| |license| |version_status| |build_status| |docs| |cover| |binder|
        
        ``enlopy`` is an open source python library with methods to generate,
        process, analyze, and plot timeseries.
        
        While it can be used for any kind of data it has a strong focus on those
        that are related with energy i.e. electricity/heat demand or generation,
        prices etc. The methods included here are carefully selected to
        fit in that context and they had been, gathered, generalized and
        encapsulated during the last years while working on different research
        studies.
        
        The aim is to provide a higher level API than the one that is already
        available in commonly used scientific packages (pandas, numpy, scipy).
        This facilitates the analysis and processing of energy load timeseries
        that can be used for modelling and statistical analysis. In some cases it
        is just a convenience wrapper of common packages just as pandas and in
        other cases, it implements methods or statistical models found in
        literature.
        
        It consists of four modules that include among others the following:
        
        * ``Analysis``: Overview of descriptive statistics, reshape, load duration curve, extract daily archetypes (clustering), detect outliers
        * ``Plot``: 2d heatmap, 3d plot, boxplot, rugplot, percentiles
        * ``Generate``: generate from daily and monthly profiles, generate from sinusoidal function, sample from given load duration curve, or from given spectral distribution, add noise gaussian and autoregressive noise, generate correlated load profiles, fit to analytical load duration curve
        * ``Statistics``: Feature extraction from timeseries for a quick overview of the characteristics of any load curve. Useful when coupled with machine learning packages.
        
        The library is designed to be robust enough to accept a wide range of inputs (pd.Dataframe, pd.Series, np.ndarray, list)
        This library is not focusing on regression and forecasting (e.g. ARIMA, state-space etc.), since there are numerous relevant libraries around.
        
        Example
        -------
        Enlopy has a simple API and is easy to use see some example below:
        
        >>>  # df is a pandas dataframe with an hourly DateTimeindex. Each column represents a different generation technology
        >>> import enlopy as el
        >>> el.plot_rug(df) # Plots a nice rugplot. Useful for dispatch results
        
        .. image:: ./docs/_static/FR_dispatch_2017.png
           :scale: 60 %
           :alt: Dispatch plot for different generator types sorted by their intermittency.
        
        >>> el.plot_LDC(df, zoom_peak=True) # Plots a cumulative Load Duration Curve with inset zoom plot 
        
        .. image:: ./docs/_static/LDC_zoomed.png
           :scale: 60 %
           :alt: Load duration curve
        
        Run the following code for some more examples:
        
        .. code:: python
        
            >>> import numpy as np
            >>> import enlopy as el
            >>> Load = np.random.rand(8760)  # Create random vector of values
            >>> eload = el.make_timeseries(Load)  # Convenience wrapper around pandas timeseries
        
            >>> el.plot_heatmap(eload, x='day', y='month', aggfunc='mean')  # Plots 2d heatmap
            >>> el.plot_percentiles(eload)  # Plots mean and quantiles
            >>> el.get_load_archetypes(eload, plot_diagnostics=True)  # Splits daily loads in clusters (archetypes)
            >>> el.get_load_stats(eload, per=’m’)  # Get monthly load statistics
            >>> el.remove_outliers(eload, threshold=None, window=5, plot_diagnostics=True)  # Remove outliers and plot diagnostic
        
        More examples can be found in `this jupyter notebook <https://github.com/kavvkon/enlopy/blob/master/notebooks/Basic%20examples.ipynb>`__. You can directly run an online interactive version of the notebook where you can explore all available features by clicking here |binder|.
        
        Documentation
        -------------
        Detailed documentation is still under construction, but you can find an overview of the available methods here: http://enlopy.readthedocs.io/
        
        Install
        -------
        
        The latest stable version exists in conda-forge and pypi. You can install it using `conda package manager <https://conda.io/en/latest/>`__ (recommended) by typing:
        
        ::
        
            conda install -c conda-forge enlopy
        
        or if you prefer pypi
        
        ::
        
            pip install enlopy
        
        Be aware that this library is still in conceptual mode, so the API may change in the upcoming versions.
        
        It should be ready to run out of the box for anyone that has the
        `anaconda distribution <https://www.anaconda.com/distribution/>`__
        installed. The only dependencies required to use ``enlopy`` are the
        following:
        
        -  `numpy <http://numpy.org>`__
        -  `scipy <http://scipy.org>`__
        -  `pandas <http://pandas.pydata.org/>`__
        -  `matpotlib <http://matplotlib.org/>`__
        
        If you want to download the latest version from git for use or development purposes (assuming that you have anaconda installed):
        
        .. code:: bash
        
            git clone https://github.com/kavvkon/enlopy.git
            cd enlopy
            conda env create  # Automatically creates environment based on environment.yml
            source activate enlopy
            pip install -e . # Install editable local version
        
        
        Contribute
        ----------
        
        My vision is to make this library a energy domain-specific wrapper that can be used for any kind of energy analysis or modelling.
        If you think you can contribute with new relevant methods that you are
        currently using or improve the code or documentation in any way, feel free to contact me,
        fork the repository and send your pull requests.
        
        Citing
        ------
        
        If you use this library in an academic work, please consider citing it.
        
        [1] K. Kavvadias, “enlopy: Python toolkit for energy load time series”, http://github.com/kavvkon/enlopy
        
        ``enlopy`` has been already used for processing demand timeseries in this scientific paper:
        http://dx.doi.org/10.1016/j.apenergy.2016.08.077
        
        .. |pyversion| image:: https://img.shields.io/pypi/pyversions/enlopy.svg
            :alt: Supported Python versions.
            :target: https://pypi.python.org/pypi/enlopy
        .. |license| image:: https://img.shields.io/pypi/l/enlopy.svg
            :alt: BSD License
            :target: https://opensource.org/licenses/BSD-3-Clause
        .. |version_status| image:: https://img.shields.io/pypi/v/enlopy.svg?style=flat
           :target: https://pypi.python.org/pypi/enlopy
        .. |build_status| image:: https://img.shields.io/travis/kavvkon/enlopy/master.svg?style=flat
           :target: https://travis-ci.org/kavvkon/enlopy
        .. |docs| image:: https://readthedocs.org/projects/enlopy/badge/
            :alt: Documentation
            :target: https://enlopy.readthedocs.io/en/latest/
        .. |cover| image:: https://coveralls.io/repos/github/kavvkon/enlopy/badge.svg?branch=master
            :target: https://coveralls.io/github/kavvkon/enlopy?branch=master
        .. |anaconda| image:: https://anaconda.org/conda-forge/enlopy/badges/installer/conda.svg
            :target: https://anaconda.org/conda-forge/enlopy
        .. |binder| image:: https://mybinder.org/badge_logo.svg
            :target: https://mybinder.org/v2/gh/kavvkon/enlopy/master?filepath=notebooks%2FBasic%20examples.ipynb
        
        
Keywords: energy,timeseries,statistics,profile,demand
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Utilities
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
