• AWS s3 tools
• Rasterio from S3 investigations
• Utilities for data visualizations in notebooks

This repository provides a number of small libraries and CLI tools.

Full list of libraries, and install instructions:

• odc.ui tools for data visualization in notebook/lab
• odc.index extra utils for working with datacube database
• odc.geom geometry utils and prototypes
• odc.algo algorithms (GeoMedian wrapper is here)
• odc.io common IO utilities, used by apps mainly
• odc.aws AWS/S3 utilities, used by apps mainly
• odc.aio faster concurrent fetching from S3 with async, used by apps
• odc.dscache experimental key-value store where key=UUID, value=Dataset
• odc.dtools tools/experiments in the area of dask.distributed/dask<>datacube integration
• odc.ppt parallel processing helper methods, internal lib

Installation requires using custom package repo https://packages.dea.ga.gov.au.

pip install --extra-index-url="https://packages.dea.ga.gov.au" \
  odc-ui \
  odc-index \
  odc-geom \
  odc-algo \
  odc-io \
  odc-aws \
  odc-aio \
  odc-dscache \
  odc-dtools

NOTE: on Ubuntu 18.04 default pip version is awfully old and does not support --extra-index-url command line option, so make sure to upgrade pip first: pip3 install --upgrade pip.

1. For cloud (AWS only)

   pip install --extra-index-url="https://packages.dea.ga.gov.au" odc-apps-cloud
   

2. For cloud (GCP, THREDDS and AWS)

   pip install --extra-index-url="https://packages.dea.ga.gov.au" 'odc-apps-cloud[GCP,THREDDS]'
   

3. For dc-index-from-tar (indexing to datacube from tar archive)

   pip install --extra-index-url="https://packages.dea.ga.gov.au" odc-apps-dc-tools
   

NOTE: cloud tools depend on aiobotocore which has a dependency on a specific version of botocore, boto3 also depends on a specific version of botocore as a result having both aiobotocore and boto3 in one environment can be a bit tricky. The easiest way to solve this is to install aiobotocore[awscli,boto3] before anything else, which will pull in a compatible version of boto3 and awscli into the environment.

pip install -U 'aiobotocore[awscli,boto3]'

1. s3-find list S3 bucket with wildcard
2. s3-to-tar fetch documents from S3 and dump them to tar archive
3. gs-to-tar search GS for documents and dump them to tar archive
4. dc-index-from-tar read yaml documents from tar archive and add them to datacube

Example:

#!/bin/bash

s3_src='s3://dea-public-data/L2/sentinel-2-nrt/**/*.yaml'

s3-find "${s3_src}" | \
  s3-to-tar | \
    dc-index-from-tar --env s2 --ignore-lineage

Fastest way to list regularly placed files is to use fixed depth listing:

#!/bin/bash

# only works when your metadata is same depth and has fixed file name
s3_src='s3://dea-public-data/L2/sentinel-2-nrt/S2MSIARD/*/*/ARD-METADATA.yaml'

s3-find --skip-check "${s3_src}" | \
  s3-to-tar | \
    dc-index-from-tar --env s2 --ignore-lineage

When using Google Storage:

#!/bin/bash

# Google Storage support
gs-to-tar --bucket data.deadev.com --prefix mangrove_cover
dc-index-from-tar --protocol gs --env mangroves --ignore-lineage metadata.tar.gz