Mose Müller ece68b4b99 docs: updates Task documentation
- updates Tasks.md
- updates docstrings
- adds api section
2024-09-16 15:30:47 +02:00

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# 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:
```python
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.