53 lines
2.2 KiB
Markdown
53 lines
2.2 KiB
Markdown
[](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub)
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# Controls.kt
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Controls.kt (former DataForge-control) is a data acquisition framework (work in progress). It is based on DataForge, a software framework for automated data processing.
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This repository contains a prototype of API and simple implementation
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of a slow control system, including a demo.
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Controls.kt uses some concepts and modules of DataForge,
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such as `Meta` (tree-like value structure).
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To learn more about DataForge, please consult the following URLs:
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* [Kotlin multiplatform implementation of DataForge](https://github.com/mipt-npm/dataforge-core)
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* [DataForge documentation](http://npm.mipt.ru/dataforge/)
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* [Original implementation of DataForge](https://bitbucket.org/Altavir/dataforge/src/default/)
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DataForge-control is a [Kotlin-multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html)
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application. Asynchronous operations are implemented with
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[kotlinx.coroutines](https://github.com/Kotlin/kotlinx.coroutines) library.
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## Materials and publications
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* Video - [A general overview seminar](https://youtu.be/LO-qjWgXMWc)
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* Video - [A seminar about the system mechanics](https://youtu.be/wES0RV5GpoQ)
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* Article - [A Novel Solution for Controlling Hardware Components of Accelerators and Beamlines](https://www.preprints.org/manuscript/202108.0336/v1)
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### Features
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Among other things, you can:
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- Describe devices and their properties.
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- Collect data from devices and execute arbitrary actions supported by a device.
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- Property values can be cached in the system and requested from devices as needed, asynchronously.
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- Connect devices to event bus via bidirectional message flows.
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Example view of a demo:
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## Documentation
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* [Creating a device](docs/Device%20and%20DeviceSpec.md)
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## Modules
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${modules}
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### `demo` module
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The demo includes a simple mock device with a few properties changing as `sin` and `cos` of
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the current time. The device is configurable via a simple TornadoFX-based control panel.
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You can run a demo by executing `application/run` Gradle task.
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The graphs are displayed using [plotly.kt](https://github.com/mipt-npm/plotly.kt) library.
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