<img src="https://www.ptb.de/empir2018/fileadmin/documents/empir/Met4FoF/images/AM4FoF_Logo.svg" alt="agentMET4FOF logo">
<p align="center">
<!-- CircleCI Tests -->
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status badge" src="https://circleci.com/gh/Met4FoF/agentMET4FOF.svg?style=shield"></a>
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</p>
# Multi-Agent System for IIoT
<p align="justify">
agentMET4FOF is an implementation of a multi-agent system for agent-based
analysis and processing of both static data sets and data streams with IIoT
applications in mind. More on the motivation that drives the project can be found
in the section <!--suppress HtmlUnknownAnchorTarget --><a href="#about">About</a>.
</p>
### Key facts
- [FOSS project](#contributing)
- allows to
- quickly set up and run a [metrologically enabled multi-agent system](#about)
- [handle both static data sets and online data streams](#tutorials)
- [consider measurement uncertainties as well as metadata with the provided message system](#tutorials)
- [installable as a Python package or ready-to-deploy Docker image](#installation)
- comes bundled with [several introductary and advanced tutorials](#tutorials)
- accompanied by [several use cases with close-to-industry IIoT applications in
our GitHub organisation](https://github.com/Met4FoF?q=agentMET4FOF&type=&language=&sort=)
- comprehensive and ever-growing [documentation](#documentation-and-screencasts)
## Table of content
- [💫 Quickstart](#quickstart)
- [💬 About](#about)
- [📈 The agentMET4FOF dashboard](#the-agentmet4fof-dashboard)
- [🤓 Tutorials](#tutorials)
- [📖 Documentation and screencasts](#documentation-and-screencasts)
- [💻 Installation](#installation)
- [🐝 Contributing](#contributing)
- [💨 Coming soon](#coming-soon)
- [🖋 Citation](#citation)
- [💎 Acknowledgement](#acknowledgement)
- [⚠ Disclaimer](#disclaimer)
- [© License](#license)
## 💫Quickstart
agentMET4FOF comes bundled with several [tutorials](#tutorials) to get you started
as quick as possible. In your Python console execute the following to run the first
tutorial.
```python
>>> from agentMET4FOF_tutorials.tutorial_1_generator_agent import demonstrate_generator_agent_use
>>> generator_agent_network = demonstrate_generator_agent_use()
```
```shell
Starting NameServer...
Broadcast server running on 0.0.0.0:9091
NS running on 127.0.0.1:3333 (127.0.0.1)
URI = PYRO:Pyro.NameServer@127.0.0.1:3333
|----------------------------------------------------------|
| |
| Your agent network is starting up. Open your browser and |
| visit the agentMET4FOF dashboard on http://0.0.0.0:8050/ |
| |
|----------------------------------------------------------|
INFO [2021-02-05 18:12:52.277759] (SineGeneratorAgent_1): INITIALIZED
INFO [2021-02-05 18:12:52.302862] (MonitorAgent_1): INITIALIZED
[2021-02-05 18:12:52.324078] (SineGeneratorAgent_1): Connected output module: MonitorAgent_1
SET STATE: Running
[...]
```
```python
>>> generator_agent_network.shutdown()
0
NS shut down.
```
## 💬About
<p align="justify">
Sensor deployments in industrial applications usually form networks in all sorts of
environments. This requires a flexible framework for the implementation of the
corresponding data analysis. An excellent way to represent such networks is a
multi-agent system (MAS), where independent software modules (agents) encapsulate
properties and functionalities. agentMET4FOF is an interactive and flexible open-source
implementation of such a MAS. The software engineering process is driven by several
industry-oriented use cases with the aim of enabling IIoT applications. This leads
to a framework that is specialized in representing heterogeneous sensor networks.
</p>
<p align="justify">
A special emphasis is put on supporting metrological treatment of sensor streaming
data. This includes the consideration of measurement uncertainties during data analysis
and processing as well as propagating metadata alongside the data itself.
</p>
<p align="justify">
One of the many questions that drive us in the project is:
</p>
<p align="justify">
<blockquote>
How can metrological input be incorporated into an agent-based system for addressing
uncertainty of machine learning in future manufacturing?
</blockquote>
### Features
Some notable features of agentMET4FOF include :
- Modular agent classes for metrological data streams and analytics
- A built-in buffering mechanism to decouple transmission, processing and visualization
of data
- Easy connection among software agents to send and receive data
- Choose backends between:
- [_Osbrain_](https://osbrain.readthedocs.io/en/stable/) for simulating as well as
handling real distributed systems running Python connected via a TCP network, and
- [_Mesa_](https://mesa.readthedocs.io/en/stable/) for local simulations of
distributed systems, debugging and more high-performance execution
- Interactive and customisable dashboard from the get-go to:
- Visualize and change agent-network topologies
- Visualize groups of cooperative agents as _Coalitions_
- View and change the agents' parameters
- View the agents' outputs as plotly or matplotlib plots or generate and embed your
own images
- Generic streams and agents that can be used as starting points in simulations
- A sine generator with an associated agent
- A generator for a sine signal with jitter dynamically or with fixed length
- A white noise agent
- A metrologically enabled sine generator agent which also handles measurement uncertainties
## 📈The agentMET4FOF dashboard
agentMET4FOF comes bundled with our so called _dashboard_. It is an optional component
of every agent network and provides a web browser based view. You can
observe the state of your agents, modify the connections between them and even add
more pre-made agents to your network all during run-time. The address to your
dashboard is printed to the console on every launch of an agent network.
The following image is close to what you will find in your browser on execution of
tutorial 2. For details on the tutorials visit our
[video tutorial series](#screencast-series).
## 🤓Tutorials
As mentioned above, agentMET4FOF comes bundled with several [tutorials
](https://agentmet4fof.readthedocs.io/en/latest/tutorials.html) to
get you started as quick as possible. You will find tutorials on how to set up:
- [a simple pipeline to plot a signal](https://agentmet4fof.readthedocs.io/en/latest/agentMET4FOF_tutorials/tutorial_1_generator_agent.html)
- [a simple pipeline with signal postprocessing](https://agentmet4fof.readthedocs.io/en/latest/agentMET4FOF_tutorials/tutorial_2_math_agent.html)
- [an advanced pipeline with multichannel signals](https://agentmet4fof.readthedocs.io/en/latest/agentMET4FOF_tutorials/tutorial_3_multi_channel.html)
- [a simple metrological datastream](https://agentmet4fof.readthedocs.io/en/latest/agentMET4FOF_tutorials/tutorial_4_metrological_streams.html)
- [pipelines to determine redundancy in sensor networks](https://agentmet4fof.readthedocs.io/en/latest/tutorials.html#working-with-signals-carrying-redundant-information)
- [a pipeline to reduce noise and jitter in sensor readings](https://agentmet4fof.readthedocs.io/en/latest/tutorials.html#reducing-noise-and-jitter-in-signals)
… and [more](https://agentmet4fof.readthedocs.io/en/latest/tutorials.html)!
## 📖Documentation and screencasts
Extended
[documentation can be found on ReadTheDocs](https://agentmet4fof.readthedocs.io).
### Screencast series
Additionally, we provide some
[screencasts based on agentMET4FOF 0.4.1 on the project homepage
](https://www.ptb.de/empir2018/met4fof/information-communication/video-portal/)
in the section _Tutorials for the multi-agent system agentMET4FOF_.
You can self-register on the linked page and get started immediately. The video series
begins with our motivation for creating agentMET4FOF, guide you through the
installation of Python and other recommended software until you execute the tutorials
on your machine.
### Live online tutorial during early development
In an early development stage we held a live online tutorial based on
[agentMET4FOF 0.1.0](https://github.com/Met4FoF/agentMET4FOF/releases/0.1.0/)
which you can [download](https://github.com/Met4FoF/agentMET4FOF/releases/download/0.1.0/Met4FoF.MAS.webinar.mp4).
If questions arise, or you feel something is missing, reach out to
[us](https://github.com/Met4FoF/agentMET4FOF/graphs/contributors).
## 💻Installation
There are different ways to run agentMET4FOF. Either:
1. you [install Python](https://www.python.org/downloads/) and our package
[agentMET4FOF](https://pypi.org/project/agentMET4FOF/) in a virtual Python
environment on your computer, or
2. you [install Docker](https://docs.docker.com/get-docker/), [start agentMET4FOF in
a container](https://agentmet4fof.readthedocs.io/en/latest/INSTALL.html#start-a-container-from-the-image)
and [visit the Jupyter Notebook server and the agentMET4FOF dashboard directly in
your browser](https://agentmet4fof.readthedocs.io/en/latest/INSTALL.html#start-a-container-from-the-image-for-local-use)
or even [deploy it over a proper webserver](https://agentmet4fof.readthedocs.io/en/latest/INSTALL.html#deploy-the-containerized-agents-via-a-webserver).
In the [video tutorials series](#screencast-series)
we guide you through every step of option 1. More detailed instructions on both
options you can find in the [installation
section of the docs](https://agentmet4fof.readthedocs.io/en/latest/INSTALL.html).
## 🐝Contributing
Whenever you are involved with agentMET4FOF, please respect our [Code of Conduct
](https://github.com/Met4FoF/agentMET4FOF/blob/develop/CODE_OF_CONDUCT.md).
If you want to contribute back to the project, after reading our Code of Conduct,
take a look at our open developments in the [project board
](https://github.com/Met4FoF/agentMET4FOF/projects/1), [pull requests
](https://github.com/Met4FoF/agentMET4FOF/pulls) and search [the issues
](https://github.com/Met4FoF/agentMET4FOF/issues). If you find something similar to
your ideas or troubles, let us know by leaving a comment or remark. If you have
something new to tell us, feel free to open a feature request or bug report in the
issues. If you want to contribute code or improve our documentation, please check our
[contributing guide](https://agentmet4fof.readthedocs.io/en/latest/CONTRIBUTING.html).
## 💨Coming soon
- Improved handling of metadata
- More advanced signal processing
For a comprehensive overview of current development activities and upcoming tasks,
take a look at the [project board](https://github.com/Met4FoF/agentMET4FOF/projects/1),
[issues](https://github.com/Met4FoF/agentMET4FOF/issues) and
[pull requests](https://github.com/Met4FoF/agentMET4FOF/pulls).
## 🖋Citation
If you publish results obtained with the help of agentMET4FOF, please cite the linked
[
](https://doi.org/10.5281/zenodo.4560343).
## 💎Acknowledgement
This work was part of the Joint Research Project [Metrology for the Factory of the
Future (Met4FoF), project number 17IND12](https://www.ptb.de/empir2018/met4fof/home/)
of the European Metrology Programme for Innovation and Research (EMPIR). The
[EMPIR](http://msu.euramet.org) is jointly funded by the EMPIR participating
countries within EURAMET and the European Union.
## ⚠Disclaimer
This software is developed as a joint effort of several project partners namely:
- [Institute for Manufacturing of the University of Cambridge (IfM)
](https://www.ifm.eng.cam.ac.uk/)
- [Physikalisch-Technische Bundesanstalt (PTB)](https://www.ptb.de/)
- [Van Swinden Laboratory (VSL)](https://www.vsl.nl/en/)
- [National Physics Laboratory (NPL)](https://www.npl.co.uk/)
under the lead of IfM. The software is made available "as is" free of cost. The
authors and their institutions assume no responsibility whatsoever for its use by
other parties, and makes no guarantees, expressed or implied, about its quality,
reliability, safety, suitability or any other characteristic. In no event will the
authors be liable for any direct, indirect or consequential damage arising in
connection with the use of this software.
## ©License
agentMET4FOF is distributed under the
[LGPLv3 license](https://github.com/Met4FoF/agentMET4FOF/blob/develop/license.md).
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"description": "<img src=\"https://www.ptb.de/empir2018/fileadmin/documents/empir/Met4FoF/images/AM4FoF_Logo.svg\" alt=\"agentMET4FOF logo\">\n<p align=\"center\">\n <!-- CircleCI Tests -->\n <a href=\"https://circleci.com/gh/Met4FoF/agentMET4FOF\"><img alt=\"CircleCI pipeline\n status badge\" src=\"https://circleci.com/gh/Met4FoF/agentMET4FOF.svg?style=shield\"></a>\n <!-- ReadTheDocs Documentation -->\n <a href=\"https://agentmet4fof.readthedocs.io/\">\n <img src=\"https://readthedocs.org/projects/agentmet4fof/badge/?version=latest\" alt=\"ReadTheDocs badge\">\n </a>\n <!-- CodeCov(erage) -->\n <a href=\"https://codecov.io/gh/Met4FoF/agentMET4FOF\">\n <img src=\"https://codecov.io/gh/Met4FoF/agentMET4FOF/branch/develop/graph/badge.svg?token=ofAPdSudLy\" alt=\"CodeCov badge\"/>\n </a>\n <!-- PyPI Version -->\n <a href=\"https://pypi.org/project/agentmet4fof\">\n <img src=\"https://img.shields.io/pypi/v/agentmet4fof.svg?label=release&color=blue&style=flat-square\" alt=\"pypi\">\n </a>\n <!-- PyPI License -->\n <a href=\"https://www.gnu.org/licenses/lgpl-3.0.en.html\">\n <img alt=\"PyPI - license badge\" \n src=\"https://img.shields.io/pypi/l/agentMET4FOF?color=bright\">\n </a>\n <!-- Zenodo DOI -->\n <a href=\"https://doi.org/10.5281/zenodo.4560343\">\n <img src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4560343.svg\" alt=\"DOI\"></a>\n <!-- Contributor Covenant -->\n <a href=\"https://github.com/Met4FoF/agentMET4FOF/blob/develop/CODE_OF_CONDUCT.md\">\n <img src=\"https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg\" alt=\"Contributor Covenant\"></a>\n <!-- Docker Hub -->\n <a href=\"https://hub.docker.com/r/met4fof/agentmet4fof\">\n <img src=\"https://img.shields.io/docker/pulls/met4fof/agentmet4fof.svg\" alt=\"Docker Hub badge\"></a>\n</p>\n\n# Multi-Agent System for IIoT\n\n<p align=\"justify\">\nagentMET4FOF is an implementation of a multi-agent system for agent-based \nanalysis and processing of both static data sets and data streams with IIoT \napplications in mind. More on the motivation that drives the project can be found\nin the section <!--suppress HtmlUnknownAnchorTarget --><a href=\"#about\">About</a>.\n</p>\n\n### Key facts\n\n- [FOSS project](#contributing)\n- allows to\n - quickly set up and run a [metrologically enabled multi-agent system](#about)\n - [handle both static data sets and online data streams](#tutorials)\n - [consider measurement uncertainties as well as metadata with the provided message system](#tutorials)\n- [installable as a Python package or ready-to-deploy Docker image](#installation)\n- comes bundled with [several introductary and advanced tutorials](#tutorials)\n- accompanied by [several use cases with close-to-industry IIoT applications in \n our GitHub organisation](https://github.com/Met4FoF?q=agentMET4FOF&type=&language=&sort=)\n- comprehensive and ever-growing [documentation](#documentation-and-screencasts)\n\n## Table of content\n\n- [\ud83d\udcab Quickstart](#quickstart)\n- [\ud83d\udcac About](#about)\n- [\ud83d\udcc8 The agentMET4FOF dashboard](#the-agentmet4fof-dashboard)\n- [\ud83e\udd13 Tutorials](#tutorials)\n- [\ud83d\udcd6 Documentation and screencasts](#documentation-and-screencasts)\n- [\ud83d\udcbb Installation](#installation)\n- [\ud83d\udc1d Contributing](#contributing)\n- [\ud83d\udca8 Coming soon](#coming-soon)\n- [\ud83d\udd8b Citation](#citation)\n- [\ud83d\udc8e Acknowledgement](#acknowledgement)\n- [\u26a0 Disclaimer](#disclaimer)\n- [\u00a9 License](#license)\n\n## \ud83d\udcabQuickstart\n\nagentMET4FOF comes bundled with several [tutorials](#tutorials) to get you started \nas quick as possible. In your Python console execute the following to run the first \ntutorial.\n\n```python\n>>> from agentMET4FOF_tutorials.tutorial_1_generator_agent import demonstrate_generator_agent_use\n>>> generator_agent_network = demonstrate_generator_agent_use()\n```\n\n```shell\nStarting NameServer...\nBroadcast server running on 0.0.0.0:9091\nNS running on 127.0.0.1:3333 (127.0.0.1)\nURI = PYRO:Pyro.NameServer@127.0.0.1:3333\n\n|----------------------------------------------------------|\n| |\n| Your agent network is starting up. Open your browser and |\n| visit the agentMET4FOF dashboard on http://0.0.0.0:8050/ |\n| |\n|----------------------------------------------------------|\n\nINFO [2021-02-05 18:12:52.277759] (SineGeneratorAgent_1): INITIALIZED\nINFO [2021-02-05 18:12:52.302862] (MonitorAgent_1): INITIALIZED\n[2021-02-05 18:12:52.324078] (SineGeneratorAgent_1): Connected output module: MonitorAgent_1\nSET STATE: Running\n[...]\n```\n```python\n>>> generator_agent_network.shutdown()\n0\nNS shut down.\n```\n\n\n## \ud83d\udcacAbout\n\n<p align=\"justify\">\nSensor deployments in industrial applications usually form networks in all sorts of\nenvironments. This requires a flexible framework for the implementation of the\ncorresponding data analysis. An excellent way to represent such networks is a\nmulti-agent system (MAS), where independent software modules (agents) encapsulate\nproperties and functionalities. agentMET4FOF is an interactive and flexible open-source\nimplementation of such a MAS. The software engineering process is driven by several\nindustry-oriented use cases with the aim of enabling IIoT applications. This leads\nto a framework that is specialized in representing heterogeneous sensor networks.\n</p>\n<p align=\"justify\">\nA special emphasis is put on supporting metrological treatment of sensor streaming\ndata. This includes the consideration of measurement uncertainties during data analysis\nand processing as well as propagating metadata alongside the data itself.\n</p>\n<p align=\"justify\">\nOne of the many questions that drive us in the project is:\n</p>\n<p align=\"justify\">\n <blockquote>\n How can metrological input be incorporated into an agent-based system for addressing\n uncertainty of machine learning in future manufacturing?\n </blockquote>\n\n### Features\n\nSome notable features of agentMET4FOF include : \n\n- Modular agent classes for metrological data streams and analytics \n- A built-in buffering mechanism to decouple transmission, processing and visualization\n of data\n- Easy connection among software agents to send and receive data\n- Choose backends between:\n - [_Osbrain_](https://osbrain.readthedocs.io/en/stable/) for simulating as well as \n handling real distributed systems running Python connected via a TCP network, and \n - [_Mesa_](https://mesa.readthedocs.io/en/stable/) for local simulations of\n distributed systems, debugging and more high-performance execution\n- Interactive and customisable dashboard from the get-go to:\n - Visualize and change agent-network topologies\n - Visualize groups of cooperative agents as _Coalitions_\n - View and change the agents' parameters\n - View the agents' outputs as plotly or matplotlib plots or generate and embed your \n own images \n- Generic streams and agents that can be used as starting points in simulations\n - A sine generator with an associated agent\n - A generator for a sine signal with jitter dynamically or with fixed length\n - A white noise agent\n - A metrologically enabled sine generator agent which also handles measurement uncertainties\n\n## \ud83d\udcc8The agentMET4FOF dashboard\n\nagentMET4FOF comes bundled with our so called _dashboard_. It is an optional component\nof every agent network and provides a web browser based view. You can\nobserve the state of your agents, modify the connections between them and even add\nmore pre-made agents to your network all during run-time. The address to your\ndashboard is printed to the console on every launch of an agent network.\n\nThe following image is close to what you will find in your browser on execution of\ntutorial 2. For details on the tutorials visit our\n[video tutorial series](#screencast-series).\n\n\n\n## \ud83e\udd13Tutorials\n\nAs mentioned above, agentMET4FOF comes bundled with several [tutorials\n](https://agentmet4fof.readthedocs.io/en/latest/tutorials.html) to \nget you started as quick as possible. You will find tutorials on how to set up:\n\n- [a simple pipeline to plot a signal](https://agentmet4fof.readthedocs.io/en/latest/agentMET4FOF_tutorials/tutorial_1_generator_agent.html)\n- [a simple pipeline with signal postprocessing](https://agentmet4fof.readthedocs.io/en/latest/agentMET4FOF_tutorials/tutorial_2_math_agent.html)\n- [an advanced pipeline with multichannel signals](https://agentmet4fof.readthedocs.io/en/latest/agentMET4FOF_tutorials/tutorial_3_multi_channel.html)\n- [a simple metrological datastream](https://agentmet4fof.readthedocs.io/en/latest/agentMET4FOF_tutorials/tutorial_4_metrological_streams.html)\n- [pipelines to determine redundancy in sensor networks](https://agentmet4fof.readthedocs.io/en/latest/tutorials.html#working-with-signals-carrying-redundant-information)\n- [a pipeline to reduce noise and jitter in sensor readings](https://agentmet4fof.readthedocs.io/en/latest/tutorials.html#reducing-noise-and-jitter-in-signals)\n\n\u2026 and [more](https://agentmet4fof.readthedocs.io/en/latest/tutorials.html)!\n\n## \ud83d\udcd6Documentation and screencasts\n\nExtended\n[documentation can be found on ReadTheDocs](https://agentmet4fof.readthedocs.io).\n\n### Screencast series\n\nAdditionally, we provide some\n[screencasts based on agentMET4FOF 0.4.1 on the project homepage\n](https://www.ptb.de/empir2018/met4fof/information-communication/video-portal/)\nin the section _Tutorials for the multi-agent system agentMET4FOF_. \nYou can self-register on the linked page and get started immediately. The video series\nbegins with our motivation for creating agentMET4FOF, guide you through the\ninstallation of Python and other recommended software until you execute the tutorials\non your machine.\n\n### Live online tutorial during early development\n\nIn an early development stage we held a live online tutorial based on \n[agentMET4FOF 0.1.0](https://github.com/Met4FoF/agentMET4FOF/releases/0.1.0/) \nwhich you can [download](https://github.com/Met4FoF/agentMET4FOF/releases/download/0.1.0/Met4FoF.MAS.webinar.mp4).\n\nIf questions arise, or you feel something is missing, reach out to\n[us](https://github.com/Met4FoF/agentMET4FOF/graphs/contributors).\n\n## \ud83d\udcbbInstallation\n\nThere are different ways to run agentMET4FOF. Either:\n\n1. you [install Python](https://www.python.org/downloads/) and our package\n [agentMET4FOF](https://pypi.org/project/agentMET4FOF/) in a virtual Python \n environment on your computer, or\n2. you [install Docker](https://docs.docker.com/get-docker/), [start agentMET4FOF in \n a container](https://agentmet4fof.readthedocs.io/en/latest/INSTALL.html#start-a-container-from-the-image)\n and [visit the Jupyter Notebook server and the agentMET4FOF dashboard directly in \n your browser](https://agentmet4fof.readthedocs.io/en/latest/INSTALL.html#start-a-container-from-the-image-for-local-use)\n or even [deploy it over a proper webserver](https://agentmet4fof.readthedocs.io/en/latest/INSTALL.html#deploy-the-containerized-agents-via-a-webserver).\n\nIn the [video tutorials series](#screencast-series)\nwe guide you through every step of option 1. More detailed instructions on both \noptions you can find in the [installation \nsection of the docs](https://agentmet4fof.readthedocs.io/en/latest/INSTALL.html).\n\n## \ud83d\udc1dContributing\n\nWhenever you are involved with agentMET4FOF, please respect our [Code of Conduct\n](https://github.com/Met4FoF/agentMET4FOF/blob/develop/CODE_OF_CONDUCT.md).\nIf you want to contribute back to the project, after reading our Code of Conduct,\ntake a look at our open developments in the [project board\n](https://github.com/Met4FoF/agentMET4FOF/projects/1), [pull requests\n](https://github.com/Met4FoF/agentMET4FOF/pulls) and search [the issues\n](https://github.com/Met4FoF/agentMET4FOF/issues). If you find something similar to\nyour ideas or troubles, let us know by leaving a comment or remark. If you have\nsomething new to tell us, feel free to open a feature request or bug report in the\nissues. If you want to contribute code or improve our documentation, please check our\n[contributing guide](https://agentmet4fof.readthedocs.io/en/latest/CONTRIBUTING.html).\n\n## \ud83d\udca8Coming soon\n\n- Improved handling of metadata\n- More advanced signal processing\n\nFor a comprehensive overview of current development activities and upcoming tasks,\ntake a look at the [project board](https://github.com/Met4FoF/agentMET4FOF/projects/1),\n[issues](https://github.com/Met4FoF/agentMET4FOF/issues) and\n[pull requests](https://github.com/Met4FoF/agentMET4FOF/pulls).\n\n## \ud83d\udd8bCitation\n\nIf you publish results obtained with the help of agentMET4FOF, please cite the linked\n[\n](https://doi.org/10.5281/zenodo.4560343).\n\n## \ud83d\udc8eAcknowledgement\n\nThis work was part of the Joint Research Project [Metrology for the Factory of the \nFuture (Met4FoF), project number 17IND12](https://www.ptb.de/empir2018/met4fof/home/)\nof the European Metrology Programme for Innovation and Research (EMPIR). The \n[EMPIR](http://msu.euramet.org) is jointly funded by the EMPIR participating \ncountries within EURAMET and the European Union.\n\n## \u26a0Disclaimer\n\nThis software is developed as a joint effort of several project partners namely:\n\n- [Institute for Manufacturing of the University of Cambridge (IfM)\n ](https://www.ifm.eng.cam.ac.uk/)\n- [Physikalisch-Technische Bundesanstalt (PTB)](https://www.ptb.de/)\n- [Van Swinden Laboratory (VSL)](https://www.vsl.nl/en/)\n- [National Physics Laboratory (NPL)](https://www.npl.co.uk/)\n\nunder the lead of IfM. The software is made available \"as is\" free of cost. The \nauthors and their institutions assume no responsibility whatsoever for its use by \nother parties, and makes no guarantees, expressed or implied, about its quality, \nreliability, safety, suitability or any other characteristic. In no event will the \nauthors be liable for any direct, indirect or consequential damage arising in \nconnection with the use of this software.\n\n## \u00a9License\n\nagentMET4FOF is distributed under the\n[LGPLv3 license](https://github.com/Met4FoF/agentMET4FOF/blob/develop/license.md).\n",
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