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Geographic Raster functionality for Python using osgeo and GDAL.

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# 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!

---

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](https://rasterio.readthedocs.io/) is a good alternative for many tasks.

# Full Documentation #

http://georaster.readthedocs.io

# Current release info #
====================

| Name | Downloads | Version | Platforms |
| --- | --- | --- | --- |
| [![Conda Recipe](https://img.shields.io/badge/recipe-georaster-green.svg)](https://anaconda.org/conda-forge/georaster) | [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/georaster.svg)](https://anaconda.org/conda-forge/georaster) | [![Conda Version](https://img.shields.io/conda/vn/conda-forge/georaster.svg)](https://anaconda.org/conda-forge/georaster) | [![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/georaster.svg)](https://anaconda.org/conda-forge/georaster) |

# 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).

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