georaster

KO | EN

gitlite — search

#javascript #python #ai #golang #go #rust #linux #docker #macos #windows #awesome #security #openai #mcp

(★ 28)

Geographic Raster functionality for Python using osgeo and GDAL.

georaster 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 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).

georaster

georaster 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

georaster

georaster 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README