akuire 0.1.0

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akuire

Akuire is a prototype of asynchronous acquisiton engine for smart-microscopy workflows. It was designed during the "Smart Microscopy" hackathon at the Lund Bioimaging Center.

The main goal of Akuire is to provide a simple and flexible way to acquire images from a microscope in an asynchronous way, interfacing with different microscopy software backbones.

Installation

To install Akuire, you can use pip:

pip install akuire

Usage

To use Akuire, you need to create an instance of the AcquistionEinge class, passing the desired device managers and the system settings. You can use the acquisition engine to acquire images from the microscope through predefined plans in an Acquisition object.

from akuire import AcquisitionEngine, Acquisition, MoveEvent, AcquireZStackEvent, SystemConfig
from arkuire.managers.testing import NonSweepableCamera, ZStageManager, VirtualStageManager
import asyncio

engine = AcquisitionEngine(
        system_config=SystemConfig(
            managers={
                "virtual_camera": NonSweepableCamera(),
                "virtual_z_stage": ZStageManager(),
                "virtual_stage": VirtualStageManager(),
            }
        ),
        compiler=compile_events,
    )


x = Acquisition(
    events=[
        MoveEvent(x=1, y=2),
        AcquireZStackEvent(z_steps=30, item_exposure_time=0.1),
    ]
)
# The acquisition plan is compiled into a list of events



async def main():
    async with engine as e:

        # A blocking operation that acquires the ZStack
        # The event plan will be "compiled" into a list of events
        # that are compatible with the underlying hardware
        # trying to optimize the acquisition process
        result = await e.acquire(x)
        # result is a ZStack object
        
        # A hyperstack can be created from the ZStack object
        stack = result.to_z_stack()


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

Documentation

Right now this is the only documentation available. More documentation will be added in the future. If you are interested in potential use cases, please check the examples folder.

Contributing

Pull requests are welcome. Happy for any feedback or suggestions.

License

This project is licensed under the MIT License - see the LICENSE file for details.