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Fast Python interface for geodata - either on disk or in memory.

Project description

geoarray

Fast Python interface for geodata - either on disk or in memory.

The geoarray package facilitates reading and writing of all GDAL compatible image file formats and provides functions for geospatial processing.

Status

https://gitext.gfz-potsdam.de/danschef/geoarray/badges/master/build.svg https://gitext.gfz-potsdam.de/danschef/geoarray/badges/master/coverage.svg https://img.shields.io/pypi/v/geoarray.svg https://img.shields.io/pypi/l/geoarray.svg https://img.shields.io/pypi/pyversions/geoarray.svg

See also the latest coverage report and the nosetests HTML report.

Features and usage

  • There is an example notebook that shows how to use geoarray: here.

Installation

geoarray depends on some open source packages which are usually installed without problems by the automatic install routine. However, for some projects, we strongly recommend resolving the dependency before the automatic installer is run. This approach avoids problems with conflicting versions of the same software. Using conda, the recommended approach is:

# create virtual environment for geoarray, this is optional
conda create -y -q --name geoarray python=3
source activate geoarray
conda install -y -q -c conda-forge numpy gdal scikit-image matplotlib pandas rasterio pyproj basemap shapely geopandas
conda install --yes -c ioam bokeh holoviews  # optional

To install geoarray, use the pip installer:

pip install geoarray
  • Or clone the repository via GIT and update the PATH environment variable:

cd /your/installation/folder
git clone https://gitext.gfz-potsdam.de/danschef/geoarray.git
PATH=$PATH:~/path/to/your/installation/folder/geoarray

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.0 (2017-03-31)

  • Package creation.

0.4.5 (2017-07-03)

  • First release on PyPI.

Project details


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