Transcript ESIP_Geospatial_Cluster_Python_Discussion_3.26.2012

GIS and Python
CAMERON GOODALE
PAUL RAMIREZ
PyCon 2012
 https://us.pycon.org/2012/
 March 7th-11th in Santa Clara, CA
 Largest gathering of Python Developers (well over
2000)
 Lots of big name sponsors and plenty of job offerings
 Supported ESDSWG Software Reuse WG
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To learn about and proliferate open source and GIS
Python
 http://www.python.org/
 Open source programming language
 Freely usable and distributable, even for commercial use
 Runs on Window, Linux/Unix, Mac OS X, and has
been ported to Java and .NET virtual machines
 Use version 2.7 over 3.x for now as there are still
some libraries that have yet to be ported
GDAL/OGR
 Geospatial Data Abstraction Library
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X/MIT style Open Source License
http://www.gdal.org
 Manipulation of Raster Data
 Supported Formats
http://www.gdal.org/formats_list.html
 OGR ships in GDAL source tree
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Manipulation of Vector data
 Python bindings available
 Plays well with NumPy
 GIS Swiss Army Knife
Shapely
 http://toblerity.github.com/shapely/
 BSD Licensed
 Geometric objects, predicates, and operations outside the
context of a database
 Manipulation and analysis of planar geometric objects
 Based on GEOS (i.e. the PostGIS engine)
 Interoperation with
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Well Known Text (WKT)
Well Known Binary (WKB)
Numpy and Python Arrays
Any object that provides GeoJSON-like Python geo interface
NumPy
 http://numpy.scipy.org/
 C/C++/Fortran
 Linear Algebra, N-dimensional arrays
 BSD License
 PyNGL and PyNIO
 From NCAR
 License on Earth System Grid site.
Pandas
 http://pandas.pydata.org/
 Data Analysis
 New BSD License
 Great Video from a PyCon Tutorial
 http://blog.lambdafoundry.com/tutorial-data-analysis-inpython-with-pandas-at-pycon-2012/
pyKML
 Python library for creating, parsing, manipulating,
and validating KML
 BSD License
 http://packages.python.org/pykml/
ArcPy
 http://help.arcgis.com/en/arcgisdesktop/10.0/help/
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index.html#/What_is_ArcPy/000v000000v70000
00/
ESRI Python Library
License ?
ArcGIS scripting to do geographic data analysis, data
conversion, data management, and map automation
Access to geoprocessing tools
Integrated with ESRI ArcGIS Suite
Mapnik
 http://mapnik.org/
 Toolkit for Developing Mapping Apps
 Written in C++
 Can be used with XML, Python, and node.js
 Runs on Linux, OS X, Windows
 License LGPL
TileStache
 http://tilestache.org/
 Python-based Tile Server
 Simpler version of Tilecache (http://tilecache.org/)
 Runs on Linux, OS X, Windows
 License BSD
GeoDjango
 http://geodjango.org/
 GIS for Django
 Supports Multiple Databases
 PostgreSQL + PostGIS
 Oracle Spatial
 Spatialite
 MySQL (least functional)
 License BSD
Leaflet
 http://leaflet.cloudmade.com/
 Mapping component to support vector and image
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data
Still early in development but very usable
Replacement for Google Maps
Many people moving away due to Google’s recent
changes
Similar to OpenLayers but more compact and
modern
QGIS
 http://www.qgis.org/
 GPL License
 Desktop Software akin ArcGIS for Desktop
 Can be extended through plugins
 Several ways to interact with Python
http://qgis.org/pyqgis-cookbook/intro.html
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Integrated Python Console
Write a Python Plugin
Write a python application to automate some process or create
a GUI to measure data, export a map to PDF, etc.
Contact Us
 Cameron Goodale
 [email protected]
 @sigep311
 Paul Ramirez
 [email protected]
 @pramirez624
Backups
GDAL Example
>>> import gdal
>>> from gdalconst import *
>>> dataset = gdal.Open(‘MOD09GA.A2012081.h23v08.005.2012083065418.hdf’)
>>> dataset.GetMetadata('SUBDATASETS')
>>> state_1km = gdal.Open(dataset.GetMetadata('SUBDATASETS')['SUBDATASET_2_NAME'])
>>> state_1km.GetProjection()
'PROJCS["unnamed",GEOGCS["Unknown datum based upon the custom spheroid",DATUM["Not
specified (based on custom spheroid)",SPHEROID["Custom
spheroid",6371007.181,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],P
ROJECTION["Sinusoidal"],PARAMETER["longitude_of_center",0],PARAMETER["false_easti
ng",0],PARAMETER["false_northing",0],UNIT["Meter",1]]’
>>> layer = state_1km.ReadAsArray()
>>> type(layer)
<type 'numpy.ndarray'>
>>> layer.shape
(1200, 1200)
>>> state_1km = None
>>> dataset = None
OGR Example
>>> import ogr
>>> datasource = driver.Open(‘sites.shp')
>>> layer = datasource.GetLayer()
>>> feature = layer.GetNextFeature()
>>> while feature:
... cover = feature.GetFieldAsString('cover')
... geom = feature.GetGeometryRef()
... print "x: %s y: %s" % (geom.GetX(), geom.GetY())
... feature.Destroy()
... feature = layer.GetNextFeature()
...
x: 455552.418361 y: 4641822.05368
x: 449959.840851 y: 4633802.50858
>>> datasource.Destroy()
Shapely Example
>>> from shapely.geometry import Point, LineString, LinearRing, Polygon
>>> Point(0,0).distance(Point(1,1))
1.4142135623730951
>>> donut = Point(0, 0).buffer(2.0).difference(Point(0, 0).buffer(1.0))
>>> donut.area
9.409645471637816
>>> line = LineString([(0, 0), (1, 1)])
>>> line.length
1.4142135623730951
>>> line.coords[1:]
[(1.0, 1.0)]
>>> ring = LinearRing([(0, 0), (1, 1), (1, 0)])
>>> ring.length
3.414213562373095
>>> polygon = Polygon([(0, 0), (1, 1), (1, 0)])
>>> polygon.area
0.5
>>> LineString([(0, 0), (1, 1)]).contains(Point(0.5, 0.5))
True
>>> Point(0.5, 0.5).within(LineString(coords))
True