Skip to main content
Help the Python Software Foundation raise $60,000 USD by December 31st!  Building the PSF Q4 Fundraiser

Brings smoothed maps through python

Project description

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
  • Matplotlib

Documentation on the method :

Please refer to https://github.com/Groupe-ElementR/SpatialPosition/ documentation.

Usage example:

One-shot functionnality

>>> 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

>>> 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)
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

Availables classification methods are: “equal_interval”, “prog_geom”, “jenks”, “percentiles” and “head-tail-breaks”

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

>>> 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 :

$ pip install smoomapy

From github :

$ git clone http://github.com/mthh/smoomapy.git
$ cd smoomapy/
$ python setup.py install

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for smoomapy, version 0.1.9
Filename, size File type Python version Upload date Hashes
Filename, size smoomapy-0.1.9.tar.gz (10.3 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page