pydase/docs/user-guide/Tasks.md

Understanding Tasks

In pydase, a task is defined as an asynchronous function without arguments that is decorated with the @task decorator and contained in a class that inherits from pydase.DataService. These tasks usually contain a while loop and are designed to carry out periodic functions. For example, a task might be used to periodically read sensor data, update a database, or perform any other recurring job.

pydase allows you to control task execution via both the frontend and Python clients and can automatically start tasks upon initialization of the service. By using the @task decorator with the autostart=True argument in your service class, pydase will automatically start these tasks when the server is started. Here's an example:

import pydase
from pydase.task.decorator import task


class SensorService(pydase.DataService):
    def __init__(self):
        super().__init__()
        self.readout_frequency = 1.0

    def _process_data(self, data: ...) -> None:
        ...

    def _read_from_sensor(self) -> Any:
        ...

    @task(autostart=True)
    async def read_sensor_data(self):
        while True:
            data = self._read_from_sensor()
            self._process_data(data)  # Process the data as needed
            await asyncio.sleep(self.readout_frequency)


if __name__ == "__main__":
    service = SensorService()
    pydase.Server(service=service).run()

In this example, read_sensor_data is a task that continuously reads data from a sensor. By decorating it with @task(autostart=True), it will automatically start running when pydase.Server(service).run() is executed.

The @task decorator replaces the function with a task object that has start() and stop() methods. This means you can control the task execution directly using these methods. For instance, you can manually start or stop the task by calling service.read_sensor_data.start() and service.read_sensor_data.stop(), respectively.