Metadata-Version: 1.1
Name: ModelicaRes
Version: 0.12.1
Summary: Utilities to set up and analyze Modelica simulation experiments
Home-page: http://kdavies4.github.io/ModelicaRes/
Author: Kevin Davies
Author-email: kdavies4@gmail.com
License: BSD-compatible (see LICENSE.txt)
Download-URL: https://github.com/kdavies4/ModelicaRes/archive/v0.12.1.zip
Description: #############
         ModelicaRes
        #############
        
        **Set up and analyze Modelica simulations**
        
        ModelicaRes is a free, open-source tool that can be used to
        
        - generate simulation scripts,
        - load and browse data,
        - perform custom calculations,
        - filter and sort groups of results,
        - produce various plots and diagrams, and
        - export data to various formats via pandas_.
        
        The figures are generated via matplotlib_, which offers a rich set of
        publication-quality plotting routines.  ModelicaRes has methods to create and
        automatically label xy plots, Bode and Nyquist plots, and Sankey diagrams.
        ModelicaRes can be scripted or used in an interactive Python_ session with math
        and matrix functions from NumPy_.
        
        The figures are generated via matplotlib_, which offers a rich set of plotting
        routines.  ModelicaRes has methods to create and automatically label
        `xy plots`_, Bode_ and Nyquist_ plots, and `Sankey diagrams`_.  ModelicaRes can
        be scripted or used in an interactive Python_ session with math and matrix
        functions from NumPy_.
        
        Currently, ModelicaRes only loads Dymola/OpenModelica_-formatted results
        (\*.mat), but the loading functions are modular so that other formats can be
        added easily.
        
        Please see the tutorial, which is available as an `IPython notebook
        <https://github.com/kdavies4/ModelicaRes/blob/master/examples/tutorial.ipynb>`_
        or online as a `static page
        <http://nbviewer.ipython.org/github/kdavies4/ModelicaRes/blob/master/examples/tutorial.ipynb>`_.
        For the full documentation and many more examples, see the `main website`_.
        
        For a list of changes, please see the `change log
        <http://kdavies4.github.io/ModelicaRes/changelog.html>`_.
        
        Installation
        ~~~~~~~~~~~~
        
        The easiest way to install this package is to use pip_::
        
            pip install modelicares
        
        On Linux, it may be necessary to have root privileges::
        
            sudo pip install modelicares
        
        Another way is to download and extract a copy of the package.  The `main
        website`_, the `GitHub repository`_, and the `PyPI page`_ have copies which
        include the source code as well as examples and supporting files to build the
        documentation and run tests.  Once you have a copy, run the following command
        from the base folder::
        
            python setup.py install
        
        Or, on Linux::
        
            sudo python setup.py install
        
        Some of the required packages may not install automatically.
        
        - SciPy_ can be installed according to the instructions at
          http://www.scipy.org/install.html.
        - The GUIs require Qt_, which can be installed via PyQt4_, guidata_, or PySide_.
        
        The `matplotlibrc file
        <https://github.com/kdavies4/ModelicaRes/blob/master/examples/matplotlibrc>`_
        file has some recommended revisions to matplotlib_'s defaults.  To use it, copy
        it to the working directory or matplotlib_'s configuration directory.  See
        http://matplotlib.org/users/customizing.html for details.
        
        Credits
        ~~~~~~~
        
        The main author is Kevin Davies.  Code has been included from:
        
        - Richard Murray (**control.freqplot**---part of python-control_),
        - Joerg Raedler (method to expand a Modelica_ variable tree---from DyMat_),
        - Jason Grout (`ArrowLine class`_), and
        - Jason Heeris (`efficient base-10 logarithm`_).
        
        Suggestions and bug fixes have been provided by Arnout Aertgeerts, Kevin Bandy,
        Thomas Beutlich, Moritz Lauster, Martin Sjölund, Mike Tiller, and Michael
        Wetter.
        
        License terms and development
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        ModelicaRes is published under a `BSD-compatible license
        <https://github.com/kdavies4/ModelicaRes/blob/release/LICENSE.txt>`_.  Please
        share any modifications you make (preferably as a pull request to the ``master``
        branch of the `GitHub repository`_) in order to help others.  There are useful
        development scripts in the `hooks folder
        <https://github.com/kdavies4/ModelicaRes/blob/master/hooks/>`_.  If you find a
        bug, please `report it
        <https://github.com/kdavies4/ModelicaRes/issues/new>`_.  If you have
        suggestions, please `share them
        <https://github.com/kdavies4/ModelicaRes/wiki/Suggestions>`_.
        
        See also
        ~~~~~~~~
        
        - awesim_: helps run simulation experiments and organize results
        - BuildingsPy_: supports unit testing
        - DyMat_: exports Modelica_ simulation data to comma-separated values
          (CSV_), Gnuplot_, MATLAB®, and Network Common Data Form (netCDF_)
        - PyFMI_: tools to work with models through the Functional Mock-Up Interface
          (FMI_) standard
        - PySimulator_: elaborate GUI; supports FMI_
        
        
        .. _main website: http://kdavies4.github.io/ModelicaRes/
        .. _PyPI page: http://pypi.python.org/pypi/ModelicaRes
        .. _GitHub repository: https://github.com/kdavies4/ModelicaRes
        
        .. _xy plots: http://kdavies4.github.io/ModelicaRes/simres.html#modelicares.simres.SimRes.plot
        .. _Bode: http://kdavies4.github.io/ModelicaRes/linres.html#modelicares.linres.LinRes.bode
        .. _Nyquist: http://kdavies4.github.io/ModelicaRes/linres.html#modelicares.linres.LinRes.nyquist
        .. _Sankey diagrams: http://kdavies4.github.io/ModelicaRes/simres.html#modelicares.simres.SimRes.sankey
        
        .. _Modelica: http://www.modelica.org/
        .. _Python: http://www.python.org/
        .. _pandas: http://pandas.pydata.org/
        .. _matplotlib: http://www.matplotlib.org/
        .. _NumPy: http://numpy.scipy.org/
        .. _SciPy: http://www.scipy.org/index.html
        .. _OpenModelica: https://www.openmodelica.org/
        .. _Qt: http://qt-project.org/
        .. _PyQt4: http://www.riverbankcomputing.co.uk/software/pyqt/
        .. _guidata: https://code.google.com/p/guidata/
        .. _PySide: http://qt-project.org/wiki/pyside
        .. _pip: https://pypi.python.org/pypi/pip
        .. _awesim: https://github.com/saroele/awesim
        .. _BuildingsPy: http://simulationresearch.lbl.gov/modelica/buildingspy/
        .. _DyMat: http://www.j-raedler.de/projects/dymat/
        .. _PyFMI: https://pypi.python.org/pypi/PyFMI
        .. _PySimulator: https://github.com/PySimulator/PySimulator
        .. _Gnuplot: http://www.gnuplot.info
        .. _CSV: http://en.wikipedia.org/wiki/Comma-separated_values
        .. _netCDF: http://www.unidata.ucar.edu/software/netcdf/
        .. _FMI: https://www.fmi-standard.org
        .. _python-control: http://sourceforge.net/apps/mediawiki/python-control
        .. _ArrowLine class: http://old.nabble.com/Arrows-using-Line2D-and-shortening-lines-td19104579.html
        .. _efficient base-10 logarithm: http://www.mail-archive.com/matplotlib-users@lists.sourceforge.net/msg14433.html
        
Keywords: Modelica,plot,results,simulation,experiment,Dymola,matplotlib,pandas
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Environment :: Console
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Utilities
Requires: numpy
Requires: scipy (>=0.10.0)
Requires: matplotlib (>=1.3.1)
Requires: pandas
Requires: control
Requires: six
Provides: modelicares
