GeoRaster - easy use of geographic rasters in Python

DEPRECATION NOTICE: As of 19 February 2021, this repository is marked deprecated and read-only. No further maintenance will be undertaken. Our efforts are now focused on https://www.github.com/GlacioHack/GeoUtils , which incorporates a lot of the functionality of GeoRaster but is built on top of rasterio rather than GDAL. Check it out!

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This package makes it easy to load, query and save geographic raster datasets in the Python programming language. The package uses Geospatial Data Abstraction Library (GDAL, http://www.gdal.org/) bindings, and so in a single command can import any geo-referenced dataset that is understood by GDAL (http://www.gdal.org/formats_list.html), complete with all geo-referencing information and various helper functions.

GeoRaster is compatible with Python 2.4-3.x.

There are two basic types of raster: either a single-band dataset, which you load into a SingleBandRaster object, or a dataset containing multiple bands to be loaded, which you load into a MultiBandRaster object.

There is also an 'advanced' option where you can load a raster dataset, manually specifying your geo-referencing information. See example below.

GeoRaster continues to be maintained but is no longer actively developed with new features. Rasterio is a good alternative for many tasks.

Full Documentation

http://georaster.readthedocs.io

Current release info

====================

Name	Downloads	Version	Platforms

Installation

Please install georaster into a virtual environment (e.g. conda) so that its requirements don't cause problems with your system's Python installation.

Conda

This is the preferred method of installation. It will resolve all the complex dependencies associated with GDAL automatically.

conda install -c conda-forge georaster

pip

Installation via pip requires your system to already have a working GDAL installation. We do not recommend this option - use conda where possible.

pip install georaster

Examples

Before doing anything you must import the package.

import georaster

The examples below also require matplotlib:

import matplotlib.pyplot as plt

Load a GeoTIFF with a single band of data

Load the image with a single command:

my_image = georaster.SingleBandRaster('myfile.tif')

The single data band is loaded into the r attribute of my_image. Use the extent attribute of the my_image object to set the coordinates of the plotted image:

plt.imshow(my_image.r,extent=my_image.extent)

Single band of data, loading a subset area of the image

In lat/lon (WGS84) - note that this will also set the class georeferencing information to match (i.e. self.nx, .yx, .extent, .xoffset, .yoffset):

my_image = georaster.SingleBandRaster('myfile.tif',load_data=(lonll,lonur,latll,latur),latlon=True)

Or in projection system of the image:

my_image = georaster.SingleBandRaster('myfile.tif',load_data=
                                        (xstart,xend,ystart,yend),
                                        latlon=False)

Just get the georeferencing info, without also loading data into memory

Each class works as a wrapper to the GDAL API. A raster dataset can be loaded without needing to load the actual data as well, which is useful for querying geo-referencing information without memory overheads. Simply set the load_data flag to False:

my_image = georaster.SingleBandRaster('myfile.tif',load_data=False)
print(my_image.srs.GetProjParm('central_meridian'))

(See the 'Accessing geo-referencing information' section below for informtation on srs)

Raster dataset with multiple bands, load just one band

For example, load GDAL band 2:

my_image = georaster.MultiBandRaster('myfile.tif',band=2)

Raster dataset with multiple bands, loading all bands

my_image = georaster.MultiBandRaster('myfile.tif')
plt.imshow(my_image.r)

Raster dataset with multiple bands, loading just a couple of them

my_image = georaster.MultiBandRaster('myfile.tif',bands=[1,3])
plt.imshow(my_image.r[:,:,my_image.gdal_band(3)])

Load dataset, providing your own geo-referencing

from osgeo import osr
spatial_ref = osr.SpatialReference()
# This georef example is from http://nsidc.org/data/docs/daac/nsidc0092_greenland_ice_thickness.gd.html
spatial_ref.ImportFromProj4('+proj=stere +lat_0=90 +lat_ts=71 +lon_0=-39 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs')    
my_image = georaster.SingleBandRaster('myfile.tif',
            geo_transform=(-800000,5000,0,-600000,0,-5000),
            spatial_ref=ref)

Accessing and using geo-referencing information

Once an image is loaded, the object provides the following attributes:

• Raster.ds : the GDAL handle to the dataset, which provides access to all GDAL functions - see http://www.gdal.org/classGDALDataset.html

• Raster.srs : an OGR Spatial Reference object representation of the dataset - see http://www.gdal.org/classOGRSpatialReference.html

• Raster.proj : a pyproj coordinate conversion function between the dataset coordinate system and WGS84 (latitude/longitude)

• Raster.extent : a tuple of the corners of the dataset in its native coordinate system as (left, right, bottom, top).

• Raster.nx, ny : x and y sizes of the loaded raster area.

• Raster.xres, yres : x and y pixel resolution of loaded raster area.

• Raster.x0, y0 : the pixel offsets in x and y of the loaded area. These will be zero unless a subset area has been loaded (using load_data=(tuple)).

• Raster.get_extent_latlon() : returns WGS84 (lat/lon) extent of raster as tuple.

• Raster.get_extent_projected(pyproj_obj) : returns raster extent in the coordinate system specified by the pyproj object (e.g. provide a Basemap instance).

• Raster.coord_to_px(x,y,latlon=True/False) : convert x,y coodinates into raster pixel coordinates.

• Raster.coordinates() : return projected or geographic coordinates of the whole image (or, with optional arguments, a subset of the image)

Get in touch

• Report bugs, suggest features or view the code on GitHub.

License

GeoRaster is licenced under GNU Lesser General Public License (LGPLv3).