Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (pypi.python.org).
Help us improve Python packaging - Donate today!

Brings smoothed maps through python

Project Description

Make smoothed maps in your python environnement

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)

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

Release History

This version
History Node

0.1.9

History Node

0.1.8

History Node

0.1.7

History Node

0.1.6

History Node

0.1.5

History Node

0.1.4

History Node

0.1.3

History Node

0.1.2

History Node

0.1.1

History Node

0.1.0

Download Files

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

Filename, Size & Hash SHA256 Hash Help File Type Python Version Upload Date
smoomapy-0.1.9.tar.gz
(10.3 kB) Copy SHA256 Hash SHA256
Source None May 2, 2017

Supported By

Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Google Google Cloud Servers DreamHost DreamHost Log Hosting