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https://github.com/mthh/smoomapy

Brings smoothed maps through python
https://github.com/mthh/smoomapy

smoothing spatial-analysis stewart-potentials

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Brings smoothed maps through python

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smoomapy

========



Make smoothed maps in your python environnement

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



|Build Status Travis| |Build Status Appveyor| |Version| |Coveralls|



Takes an input of values located on Point features or on Polygon features (via their centroids)

and compute interpolated values on a grid using one of the following method:

**Stewart Potentials** or **Inverse Distance Weighting**.

*Grid resolution*, *distance function* (euclidian or haversine) and *projection* are configurable.



The resulting values are used to render a layer of contours, according to breaks values defined manually or using

a classification method such as *quantiles* or *Jenks natural breaks*.



This package had been partially developed for computing smoothed maps in **Magrit_**.

The "Stewart Potential" part is more or less a python port of *Stewart method*

from R **SpatialPositon_** package.



Input/output can be a path to a geographic layer (GeoJSON, shp, etc.) or a GeoDataFrame.



Requires:

^^^^^^^^^



-  Numpy

-  GeoPandas

-  Matplotlib



Documentation on the method :

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



Please refer to https://github.com/Groupe-ElementR/SpatialPosition/ for documentation about **Stewarts potentials**.



Usage example:

~~~~~~~~~~~~~~



One-shot functionnality for Stewart potentials

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^



.. 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")



One-shot functionnality for IDW

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^



.. code:: python



    >>> result = quick_idw('nuts3_data.geojson',

                           'pop1999',

                           power=1,

                           mask='nuts3_data.geojson',

                           nb_class=10,

                           user_defined_breaks=None,

                           output="geojson")



Object-oriented API for Stewart potentials

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^



.. code:: python



    >>> StePot = SmoothStewart('nuts3_data.geojson', "pop1999",

                               span=65000, beta=3,

                               resolution=60000,

                               mask='nuts3_data.geojson')

    >>> res = StePot.render(nb_class=8, 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



Object-oriented API for IDW

^^^^^^^^^^^^^^^^^^^^^^^^^^^



.. code:: python



    >>> idw = SmoothIdw('nuts3_data.geojson', 'pop2008',

                        power=1, nb_pts=12000,

                        mask='nuts3_data.geojson')

    >>> res = idw.render(nb_class=8, disc_func="jenks",

                            output="GeoDataFrame")

    >>> res.plot(cmap="YlOrRd", linewidth=0.1)



Usage hints:

~~~~~~~~~~~~~~



The long part of the computation is done during actually interpolating values.

Using the Object-oriented API, you can compute new contours with the same interpolated values using the `render` method

(and using an other classification method within available ones: "equal\_interval", "prog\_geom", "jenks", "percentiles" and

"head-tail-breaks").



**Redraw polygons with a new classification method**



.. code:: python



    >>> res = StePot.render(nb_class=6,

                            disc_func="percentiles",

                            output="GeoDataFrame")



**Redraw contours using 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



.. _Magrit: http://magrit.cnrs.fr/



.. _SpatialPositon: https://github.com/Groupe-ElementR/SpatialPosition/