dea-tools 0.3.5.dev13

Functions and algorithms for analysing Digital Earth Australia data.

Python functions and algorithms developed to assist in analysing Digital Earth Australia (DEA) data (e.g. loading data, plotting, spatial analysis, machine learning). This includes the following modules:

Loading data

• dea_tools.datahandling: Loading and handling DEA data (e.g. combining multiple products, handling CRSs, pansharpening)

Plotting and transforming data

• dea_tools.plotting: Plotting DEA data (e.g. RGB plots, animations, interactive maps)

• dea_tools.bandindices.py: Calculating remote sensing band indices (e.g. NDVI, NDWI)

Spatial and temporal analysis

• dea_tools.spatial: Spatial analysis tools (e.g. rasterising, vectorising, contour extraction, image processing)

• dea_tools.temporal: Temporal analysis tools (e.g. phenology, temporal statistics, multi-dimensional regression)

Classification and segmentation

• dea_tools.classification.py: Machine learning classification (e.g. training and applying machine learning models on satellite data)

• dea_tools.segmentation.py: Image segmentation tools (e.g. applying image segmentation with RSGISLIB)

Parallel processing

• dea_tools.dask: Parallel processing with Dask (e.g. creating Dask clusters for scalable analysis)

Domain-specific analysis

• dea_tools.land_cover: Functions for plotting Digital Earth Australia Land Cover data.

• dea_tools.coastal: Coastal and intertidal analysis tools (e.g. tidal tagging, coastal change timeseries)

• dea_tools.bom: Loading Bureau of Meteorology water data service data (e.g. gauge data, discharge data)

• dea_tools.climate: Retrieving and manipulating gridded climate data (e.g. ERA5)

• dea_tools.waterbodies: Loading and processing DEA Waterbodies data (e.g. finding and loading waterbody timeseries data)

Installation

With conda

wget -O conda-environment.yml https://raw.githubusercontent.com/opendatacube/datacube-core/develop/conda-environment.yml

mamba env create -f conda-environment.yml
conda activate cubeenv

Install dea-tools

You can install dea-tools with pip in a Python environment where GDAL and pyproj are already installed.

pip install dea-tools

To work with this module on the DEA Sandbox or National Computational Infrastructure environments without installing it, you can add the Tools directory to the system path from within the dea-notebooks repository:

import sys
sys.path.insert(1, '../Tools/')
import dea_tools.datahandling  # or some other submodule

You can also pip install the module directly from the local Tools directory. To do this on the DEA Sandbox, run pip from the terminal:

pip install -e Tools/

Importing functions in Python

To use functions from dea-tools, import them using:

from dea_tools.datahandling import load_ard
from dea_tools.plotting import rgb

Citing DEA Tools

If you use any of the notebooks, code or tools in this repository in your work, please reference them using the following citation:

Krause, C., Dunn, B., Bishop-Taylor, R., Adams, C., Burton, C., Alger, M., Chua, S., Phillips, C., Newey, V., Kouzoubov, K., Leith, A., Ayers, D., Hicks, A., DEA Notebooks contributors 2021. Digital Earth Australia notebooks and tools repository. Geoscience Australia, Canberra. https://doi.org/10.26186/145234

Building and Releasing

This section is only relevant to you if you are a developer of this package.

Building and releasing dea-tools requires that the package is built in-place. Either build with an editable pip installation or with pip>=21.2 and --use-feature=in-tree-build. Building will generate a file, dea_tools/__version__.py, that is dynamic on release. It should not be committed. setup.py will detect if __version__.py exists and change its behaviour accordingly.

Build instructions:

cd Tools
rm dea_tools/__version__.py  # if necessary
pip install . --use-feature=in-tree-build
python -m build