Power Systems Data API Demonstrator

The demonstrator is available here.

The old demonstrator is available here.

Poetry

This project uses poetry. It's a modern dependency management tool.

To run the project use this set of commands:

poetry install
poetry run python -m power_systems_data_api_demonstrator

This will start the server on the configured host.

You can find swagger documentation at /api/docs.

You can read more about poetry here: https://python-poetry.org/

Docker

You can start the project with docker using this command:

docker-compose  up --build

This command exposes the web application on port 8000, mounts current directory and enables autoreload.

But you have to rebuild image every time you modify poetry.lock or pyproject.toml.

Project structure

$ tree "power_systems_data_api_demonstrator"
power_systems_data_api_demonstrator
├── __main__.py
├── settings.py
├── src
│   ├── api
│   │   ├── docs
│   │   ├── grid_node
│   │   └── router.py
│   ├── application.py
│   ├── lib
│   │   ├── db
│   └── lifetime.py
└── tests
    ├── conftest.py
    └── test_****.py

Configuration

This application can be configured with environment variables.

You can create .env file in the root directory and place all environment variables here.

All environment variables should start with "POWERSYSTEMS_DATA_API_DEMONSTRATOR" prefix.

For example if you see in your "power_systems_data_api_demonstrator/settings.py" a variable named like random_parameter, you should provide the "POWER_SYSTEMS_DATA_API_DEMONSTRATOR_RANDOM_PARAMETER" variable to configure the value. This behaviour can be changed by overriding env_prefix property in power_systems_data_api_demonstrator.settings.Settings.Config.

You can read more about BaseSettings class here: https://pydantic-docs.helpmanual.io/usage/settings/

An example of .env file:

POWER_SYSTEMS_DATA_API_DEMONSTRATOR_PORT="8000"

If you want to use this env file, you will need to run docker in the following way:

docker-compose --env-file .env up

Pre-commit

To install pre-commit simply run inside the shell:

pre-commit install

pre-commit is very useful to check your code before publishing it. It's configured using .pre-commit-config.yaml file.

By default it runs:

• black (formats your code);
• mypy (validates types);
• isort (sorts imports in all files);
• flake8 (spots possibe bugs);

You can read more about pre-commit here: https://pre-commit.com/

Running tests

If you want to run it in docker, simply run:

docker-compose -f docker-compose.yml --project-directory . run --rm api pytest -vv .
docker-compose -f docker-compose.yml --project-directory . down

For running tests on your local machine.

2. Run the pytest.

pytest -vv .

Seeded Data

The data has been seeded by pulling from a variety of public sources. The data was extracted using python in notebooks that can be accessed using the following command. :

poetry run jupyter notebook data/

However these scripts require API keys so you will need to set the following variables:

# for ELEXON
export BMRS_API_KEY=ABC

# for EIA
export EIA_API_KEY=DEF

To seed this data you need to run the following command:

docker-compose run --rm api python power_systems_data_api_demonstrator/seed/seed.py