Biomass Dashboard Data

This repository contains the configuration for the Biomass Dashboard API datasets, products, and country pilots.

Configuration

For each of these entity types, the pattern is to create a config file or files for that entity, and then put the id value in the config.yml file. Note that in other versions of the dashboard, the values in config. yaml might be the prefix or entire name of the json files, but in this project, it always the id used within the json file.

Datasets

The datasets directory contains a directory for the available datasets. Each dataset is a json file (see example: datasets/NCEO_Africa.json)

Each dataset ID (not the name of the file) must be included in the DATASETS array in config.yml.

Products

The products directory contains a directory for each product. Within each directory, there are two files:

1. a json file containing the definition of the product
2. summary.html - an HTML fragment that's used as the summary description in the dashboard

Each product ID (not the name of the file) must be included in the PRODUCTS array in config.yml.

The global product is used for the All or Global product in the dashboard.

Country Pilots

The country_pilots directory contains a directory for each country pilot. Within each directory, there are two files:

1. a json file containing the definition of the country pilot
2. summary.html - an HTML fragment that's used as the summary description in the dashboard

Each country pilot ID (not the name of the file) must be included in the COUNTRY_PILOTS array in config.yml.

Usage

Execution via GitHub Actions

By default, these dashboard api configuration files will be deployed to S3 via a GitHub Action. These will be deployed for the branches main, staging, and production.

To configure this:

1. In the GitHub repository, add secrets (Settings -> Secrets) for accessing AWS (AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY)
2. Update config.yml with the appropriate BUCKET configuration.
3. Push config.yml to GitHub and verify it runs correctly. Note only branches configured in .github/workflows/update-datasets-and-sites.yml will run the workflow (generate the datasets/sites metadata files).

Manual Execution

If you wish to deploy manually, e.g., to test using a custom stage named for you.

This will create and copy the configuration files to the S3 location indicated in BUCKET and print the final JSON description.

1. Update config.yml with the appropriate BUCKET configuration
2. Export a shell variable for STAGE, e.g., export STAGE=pvarner
3. Run the generators.

export STAGE=local
python dataset_metadata_generator/src/main.py | jq .
python products_metadata_generator/src/main.py | jq .
python country_pilots_metadata_generator/src/main.py | jq .