odp-amsterdam 6.0.2

Asynchronous Python client providing Open Data information of Amsterdam

Asynchronous Python client for the open datasets of Amsterdam (The Netherlands).

About

A python package with which you can retrieve data from the Open Data Platform of Amsterdam via their API. This package was initially created to only retrieve parking data from the API, but the code base is made in such a way that it is easy to extend for other datasets from the same platform.

Installation

pip install odp-amsterdam

Datasets

You can read the following datasets with this package:

• Parking garages occupancy / Garages parkeerbezetting (53 garages)
• Parking locations / Parkeervakken

Click here to get more details

Parking garages

Read the occupancy of a garage in Amsterdam (The Netherlands), both for day visitors (short-term parking) and season ticket holders (long-term parking). The dataset gives garages for 🚲 bicycles (we ❤️ bikes) and for 🚗 cars

NOTE: Not all garages have data for long-term parking.

You can use the following parameters in your request:

• vehicle - Filter based on the type of vehicle that can park in the garage (car, bicycle or touringcar).
• category - Filter based on the category of the garage (garage or park_and_ride).

Variable	Type	Description
garage_id	string	The id of the garage
garage_name	string	The name of the garage
vehicle	string	The type of vehicle that can park in the garage
category	string	The category of the garage (garage or park_and_ride)
state	string	The state of the garage (ok or problem)
free_space_short	integer	The number of free spaces for day visitors
free_space_long	integer (or None)	The number of free spaces for season ticket holders
short_capacity	integer	The total capacity of the garage for day visitors
long_capacity	integer (or None)	The total capacity of the garage for season ticket holders
availability_pct	float	The percentage of free parking spaces
longitude	float	The longitude of the garage
latitude	float	The latitude of the garage
updated_at	datetime	The last time the data was updated

Parking locations

You can use the following parameters in your request:

• limit (default: 10) - How many results you want to retrieve.
• parking_type (default: "") - Filter based on the eType from the geojson data.

Variable	Type	Description
spot_id	string	The id of the location
spot_type	string (or None)	The type of the location (e.g. E6a)
spot_description	string (or None)	The description of the location type
street	string (or None)	The street name of the location
number	integer (or None)	How many parking spots there are on this location
orientation	string (or None)	The parking orientation of the location (visgraag, langs or file)
coordinates	list[float]	The coordinates of the location

Usage

import asyncio

from odp_amsterdam import ODPAmsterdam


async def main():
    """Show example on using the ODP Amsterdam API client."""
    async with ODPAmsterdam() as client:
        # Parking locations
        locations: list[ParkingSpot] = await client.location(
            limit=5, parking_type="E6a"
        )

        # Garages
        all_garages: list[Garage] = await client.all_garages()
        garage: Garage = await client.garage(garage_id="ID_OF_GARAGE")

        print(locations)
        print(all_garages)
        print(garage)


if __name__ == "__main__":
    asyncio.run(main())

Use cases

NIPKaart.nl

A website that provides insight into where disabled parking spaces are, based on data from users and municipalities. Operates mainly in the Netherlands, but also has plans to process data from abroad.

Contributing

This is an active open-source project. We are always open to people who want to use the code or contribute to it.

We've set up a separate document for our contribution guidelines.

Thank you for being involved! :heart_eyes:

Setting up development environment

The simplest way to begin is by utilizing the Dev Container feature of Visual Studio Code or by opening a CodeSpace directly on GitHub. By clicking the button below you immediately start a Dev Container in Visual Studio Code.

This Python project relies on Poetry as its dependency manager, providing comprehensive management and control over project dependencies.

You need at least:

• Python 3.11+
• Poetry

Install all packages, including all development requirements:

poetry install

Poetry creates by default an virtual environment where it installs all necessary pip packages, to enter or exit the venv run the following commands:

poetry shell
exit

Setup the pre-commit check, you must run this inside the virtual environment:

pre-commit install

Now you're all set to get started!

As this repository uses the pre-commit framework, all changes are linted and tested with each commit. You can run all checks and tests manually, using the following command:

poetry run pre-commit run --all-files

To run just the Python tests:

poetry run pytest

To update the syrupy snapshot tests:

poetry run pytest --snapshot-update

License

MIT License

Copyright (c) 2020-2024 Klaas Schoute

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.