Metadata-Version: 1.1
Name: smoomapy
Version: 0.1.7
Summary: Brings smoothed maps through python
Home-page: http://github.com/mthh/smoomapy
Author: Matthieu Viry
Author-email: matthieu.viry@ums-riate.fr
License: MIT
Description: smoomapy
        ========
        
        Make smoothed maps in your python environnement
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        |Build Status Travis| |Build Status Appveyor| |Version| |Coveralls|
        
        More or less a python port of *Stewart method* from R SpatialPositon
        package (https://github.com/Groupe-ElementR/SpatialPosition/).
        
        Allow to set a desired number of class and choose discretization method or
        directly set some custom breaks values.
        
        Input/output can be a path to a geographic layer (GeoJSON, shp, etc.) or a GeoDataFrame.
        
        Requires:
        ^^^^^^^^^
        
        -  Numpy
        -  GeoPandas
        -  SciPy
        -  Matplotlib
        
        Documentation on the method :
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Please refer to https://github.com/Groupe-ElementR/SpatialPosition/
        documentation.
        
        Usage example:
        ~~~~~~~~~~~~~~
        
        One-shot functionnality
        ^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code:: python
        
            >>> result = quick_stewart('nuts3_data.geojson',
                                       "pop1999",
                                       span=65000,
                                       beta=3,
                                       resolution=48000,
                                       mask='nuts3_data.geojson',
                                       nb_class=10,
                                       user_defined_breaks=None,
                                       output="geojson")
        
        Object-oriented API, allowing to easily redraw contours with new breaks values or new interpolation functionnality
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code:: python
        
            >>> StePot = SmoothStewart('nuts3_data.geojson', "pop1999",
                                       span=65000, beta=3,
                                       resolution=60000,
                                       mask='nuts3_data.geojson')
            >>> res = StePot.render(nb_class=8, func_grid="matplotlib",
                                    disc_func="jenks", output="GeoDataFrame")
            >>> res.plot(cmap="YlOrRd", linewidth=0.1)
        
        .. figure:: https://raw.githubusercontent.com/mthh/smoomapy/master/misc/export_plot.png
           :alt: png_example
        
        
        The long part of the computation is done during the initialization of
        ``SmoothStewart`` instance (i.e. actually computing potentials). Some
        convenience methods allows to tweak and re-export the few last steps :
        
        **Allow to quickly redraw polygons with a new classification method (or
        with new interpolation functionnality)**
        Availables classification
        methods are: "equal\_interval", "prog\_geom", "jenks", "percentiles" and
        "head-tail-breaks"
        
        .. code:: python
        
            >>> StePot.change_interp_grid_shape((164, 112))
        
            >>> res = StePot.render(nb_class=6, func_grid="scipy",
                                    disc_func="percentiles", output="GeoDataFrame")
        
        **Allow to set custom break values (highly recommended after a first
        rendering or having take a look at the distibution):**
        
        .. code:: python
        
            >>> my_breaks = [0, 1697631, 3395263, 5092894, 6790526,
                             8488157, 10185789, 11883420, 13581052]
        
            >>> res = StePot.render(nb_class=6, user_defined_breaks=my_breaks,
                                    output="GeoDataFrame")
        
        Installation:
        ~~~~~~~~~~~~~
        
        From PyPI :
        ^^^^^^^^^^^
        
        .. code:: shell
        
            $ pip install smoomapy
        
        
        From github :
        ^^^^^^^^^^^^^
        
        .. code:: shell
        
            $ git clone http://github.com/mthh/smoomapy.git
            $ cd smoomapy/
            $ python setup.py install
        
        .. |Build Status Travis| image:: https://travis-ci.org/mthh/smoomapy.svg?branch=master
           :target: https://travis-ci.org/mthh/smoomapy
        
        .. |Build Status Appveyor| image:: https://ci.appveyor.com/api/projects/status/tc7ynr2ejpp8a4c9/branch/master?svg=true
           :target: https://ci.appveyor.com/project/mthh/smoomapy/branch/master
        
        .. |Version| image:: https://img.shields.io/pypi/v/smoomapy.svg
           :target: https://pypi.python.org/pypi/smoomapy
        
        .. |Coveralls| image:: https://coveralls.io/repos/github/mthh/smoomapy/badge.svg?branch=master
           :target: https://coveralls.io/github/mthh/smoomapy?branch=master
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
