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Polygon features (via their centroids)\nand compute interpolated values on a grid using one of the following method:\n**Stewart Potentials** or **Inverse Distance Weighting**.\n*Grid resolution*, *distance function* (euclidian or haversine) and *projection* are configurable.\n\nThe resulting values are used to render a layer of contours, according to breaks values defined manually or using\na classification method such as *quantiles* or *Jenks natural breaks*.\n\nThis package had been partially developed for computing smoothed maps in **Magrit_**.\nThe \"Stewart Potential\" part is more or less a python port of *Stewart method*\nfrom R **SpatialPositon_** package.\n\nInput/output can be a path to a geographic layer (GeoJSON, shp, etc.) or a GeoDataFrame.\n\nRequires:\n^^^^^^^^^\n\n-  Numpy\n-  GeoPandas\n-  Matplotlib\n\nDocumentation on the method :\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nPlease refer to https://github.com/Groupe-ElementR/SpatialPosition/ for documentation about **Stewarts potentials**.\n\nUsage example:\n~~~~~~~~~~~~~~\n\nOne-shot functionnality for Stewart potentials\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code:: python\n\n    \u003e\u003e\u003e result = quick_stewart('nuts3_data.geojson',\n                               \"pop1999\",\n                               span=65000,\n                               beta=3,\n                               resolution=48000,\n                               mask='nuts3_data.geojson',\n                               nb_class=10,\n                               user_defined_breaks=None,\n                               output=\"geojson\")\n\nOne-shot functionnality for IDW\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code:: python\n\n    \u003e\u003e\u003e result = quick_idw('nuts3_data.geojson',\n                           'pop1999',\n                           power=1,\n                           mask='nuts3_data.geojson',\n                           nb_class=10,\n                           user_defined_breaks=None,\n                           output=\"geojson\")\n\nObject-oriented API for Stewart potentials\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code:: python\n\n    \u003e\u003e\u003e StePot = SmoothStewart('nuts3_data.geojson', \"pop1999\",\n                               span=65000, beta=3,\n                               resolution=60000,\n                               mask='nuts3_data.geojson')\n    \u003e\u003e\u003e res = StePot.render(nb_class=8, disc_func=\"jenks\",\n                            output=\"GeoDataFrame\")\n    \u003e\u003e\u003e res.plot(cmap=\"YlOrRd\", linewidth=0.1)\n\n.. figure:: https://raw.githubusercontent.com/mthh/smoomapy/master/misc/export_plot.png\n   :alt: png_example\n\n\nObject-oriented API for IDW\n^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code:: python\n\n    \u003e\u003e\u003e idw = SmoothIdw('nuts3_data.geojson', 'pop2008',\n                        power=1, nb_pts=12000,\n                        mask='nuts3_data.geojson')\n    \u003e\u003e\u003e res = idw.render(nb_class=8, disc_func=\"jenks\",\n                            output=\"GeoDataFrame\")\n    \u003e\u003e\u003e res.plot(cmap=\"YlOrRd\", linewidth=0.1)\n\n\nUsage hints:\n~~~~~~~~~~~~~~\n\nThe long part of the computation is done during actually interpolating values.\nUsing the Object-oriented API, you can compute new contours with the same interpolated values using the `render` method\n(and using an other classification method within available ones: \"equal\\_interval\", \"prog\\_geom\", \"jenks\", \"percentiles\" and\n\"head-tail-breaks\").\n\n**Redraw polygons with a new classification method**\n\n.. code:: python\n\n    \u003e\u003e\u003e res = StePot.render(nb_class=6,\n                            disc_func=\"percentiles\",\n                            output=\"GeoDataFrame\")\n\n**Redraw contours using custom break values\n(highly recommended after a first rendering or having take a look at the distibution):**\n\n.. code:: python\n\n    \u003e\u003e\u003e my_breaks = [0, 1697631, 3395263, 5092894, 6790526,\n                     8488157, 10185789, 11883420, 13581052]\n\n    \u003e\u003e\u003e res = StePot.render(nb_class=6, user_defined_breaks=my_breaks,\n                            output=\"GeoDataFrame\")\n\nInstallation:\n~~~~~~~~~~~~~\n\nFrom PyPI :\n^^^^^^^^^^^\n\n.. code:: shell\n\n    $ pip install smoomapy\n\n\nFrom github :\n^^^^^^^^^^^^^\n\n.. code:: shell\n\n    $ git clone http://github.com/mthh/smoomapy.git\n    $ cd smoomapy/\n    $ python setup.py install\n\n.. |Build Status Travis| image:: https://travis-ci.org/mthh/smoomapy.svg?branch=master\n   :target: https://travis-ci.org/mthh/smoomapy\n\n.. |Build Status Appveyor| image:: https://ci.appveyor.com/api/projects/status/tc7ynr2ejpp8a4c9/branch/master?svg=true\n   :target: https://ci.appveyor.com/project/mthh/smoomapy/branch/master\n\n.. |Version| image:: https://img.shields.io/pypi/v/smoomapy.svg\n   :target: https://pypi.python.org/pypi/smoomapy\n\n.. |Coveralls| image:: https://coveralls.io/repos/github/mthh/smoomapy/badge.svg?branch=master\n   :target: https://coveralls.io/github/mthh/smoomapy?branch=master\n\n.. _Magrit: http://magrit.cnrs.fr/\n\n.. _SpatialPositon: https://github.com/Groupe-ElementR/SpatialPosition/\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmthh%2Fsmoomapy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmthh%2Fsmoomapy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmthh%2Fsmoomapy/lists"}