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A simple API for AirVisual air quality data

Project description

☀️ pyairvisual: a thin Python wrapper for the AirVisual© API

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pyairvisual is a simple, clean, well-tested library for interacting with AirVisual to retrieve air quality information.

Python Versions

pyairvisual is currently supported on:

  • Python 3.8
  • Python 3.9
  • Python 3.10

Installation

pip install pyairvisual

API Key

You can get an AirVisual API key from the AirVisual API site. Depending on the plan you choose, more functionality will be available from the API:

Community

The Community Plan gives access to:

  • List supported countries
  • List supported states
  • List supported cities
  • Get data from the nearest city based on IP address
  • Get data from the nearest city based on latitude/longitude
  • Get data from a specific city

Startup

The Startup Plan gives access to:

  • List supported stations in a city
  • Get data from the nearest station based on IP address
  • Get data from the nearest station based on latitude/longitude
  • Get data from a specific station

Enterprise

The Enterprise Plan gives access to:

  • Get a global city ranking of air quality

Usage

Using the Cloud API

import asyncio

from pyairvisual import CloudAPI


async def main() -> None:
    """Run!"""
    cloud_api = CloudAPI("<YOUR_AIRVISUAL_API_KEY>")

    # Get data based on the city nearest to your IP address:
    data = await cloud_api.air_quality.nearest_city()

    # ...or get data based on the city nearest to a latitude/longitude:
    data = await cloud_api.air_quality.nearest_city(
        latitude=39.742599, longitude=-104.9942557
    )

    # ...or get it explicitly:
    data = await cloud_api.air_quality.city(
        city="Los Angeles", state="California", country="USA"
    )

    # If you have the appropriate API key, you can also get data based on
    # station (nearest or explicit):
    data = await cloud_api.air_quality.nearest_station()
    data = await cloud_api.air_quality.nearest_station(
        latitude=39.742599, longitude=-104.9942557
    )
    data = await cloud_api.air_quality.station(
        station="US Embassy in Beijing",
        city="Beijing",
        state="Beijing",
        country="China",
    )

    # With the appropriate API key, you can get an air quality ranking:
    data = await cloud_api.air_quality.ranking()

    # pyairvisual gives you several methods to look locations up:
    countries = await cloud_api.supported.countries()
    states = await cloud_api.supported.states("USA")
    cities = await cloud_api.supported.cities("USA", "Colorado")
    stations = await cloud_api.supported.stations("USA", "Colorado", "Denver")


asyncio.run(main())

By default, the library creates a new connection to AirVisual with each coroutine. If you are calling a large number of coroutines (or merely want to squeeze out every second of runtime savings possible), an aiohttp ClientSession can be used for connection pooling:

import asyncio

from aiohttp import ClientSession

from pyairvisual import CloudAPI


async def main() -> None:
    """Run!"""
    async with ClientSession() as session:
        cloud_api = CloudAPI("<YOUR_AIRVISUAL_API_KEY>", session=session)

        # ...


asyncio.run(main())

Working with Node/Pro Units

pyairvisual also allows users to interact with Node/Pro units, both via the cloud API:

import asyncio

from aiohttp import ClientSession

from pyairvisual import CloudAPI


async def main() -> None:
    """Run!"""
    cloud_api = CloudAPI("<YOUR_AIRVISUAL_API_KEY>")

    # The Node/Pro unit ID can be retrieved from the "API" section of the cloud
    # dashboard:
    data = await cloud_api.node.get_by_node_id("<NODE_ID>")


asyncio.run(main())

...or over the local network via Samba (the unit password can be found on the device itself):

import asyncio

from aiohttp import ClientSession

from pyairvisual.node import NodeSamba


async def main() -> None:
    """Run!"""
    async with NodeSamba("<IP_ADDRESS_OR_HOST>", "<PASSWORD>") as node:
        measurements = node.async_get_latest_measurements()

        # Can take some optional parameters:
        #   1. include_trends: include trends (defaults to True)
        #   2. measurements_to_use: the number of measurements to use when calculating
        #      trends (defaults to -1, which means "use all measurements")
        history = node.async_get_history()


asyncio.run(main())

Check out the examples, the tests, and the source files themselves for method signatures and more examples.

Contributing

  1. Check for open features/bugs or initiate a discussion on one.
  2. Fork the repository.
  3. (optional, but highly recommended) Create a virtual environment: python3 -m venv .venv
  4. (optional, but highly recommended) Enter the virtual environment: source ./.venv/bin/activate
  5. Install the dev environment: script/setup
  6. Code your new feature or bug fix.
  7. Write tests that cover your new functionality.
  8. Run tests and ensure 100% code coverage: script/test
  9. Update README.md with any new documentation.
  10. Add yourself to AUTHORS.md.
  11. Submit a pull request!

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