pyairvisual 2023.12.0

Description:

pyairvisual 2023.12.0

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







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

Python Versions
Installation
API Key

Community
Startup
Enterprise


Usage

Using the Cloud API
Working with Node/Pro Units


Contributing

Python Versions
pyairvisual is currently supported on:

Python 3.10
Python 3.11
Python 3.12

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.cloud_api 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.cloud_api 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.cloud_api 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 = await 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 = await 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
Thanks to all of our contributors so far!

Check for open features/bugs or initiate a discussion on one.
Fork the repository.
(optional, but highly recommended) Create a virtual environment: python3 -m venv .venv
(optional, but highly recommended) Enter the virtual environment: source ./.venv/bin/activate
Install the dev environment: script/setup
Code your new feature or bug fix on a new branch.
Write tests that cover your new functionality.
Run tests and ensure 100% code coverage: poetry run pytest --cov pyairvisual tests
Update README.md with any new documentation.
Submit a pull request!