This repository combines all the code written for or used in the EMPIR project 17IND12 Metrology for the Factory of the Future to simplify getting started and enable pulling/cloning all the code and all coding related documents at once.

For a general overview on the project visit us on ptb.de/empir2018/met4fof.

Detailed information about the project goals you find in the downloadable publishable summary .

In the software section of the project homepage you will get an overview of all software development activities throughout the duration of the project.

• Getting started
  • Getting into Git and GitHub
  • The content of this repository
  • Tutorials
• Upstream links
• Project's coding conventions and best practices
• Contributing
• Further development
• Data management
• Citation
• Acknowledgement
• Disclaimer
• License

If you are new to Git or GitHub you find extensive guidance on how to start with the repository in our wiki .

Collected in this repository you can find the results of most of the software development activities in the project. The main projects you can find in the subfolders of this repository:

agentMET4FOF is an interactive and flexible open-source implementation of a multi-agent system.

This software contains code for the firmware of the microcontroller board, which we call the "SmartUp Unit".

This repository contains the Python driver to receive the measurement data from the Met4FoF SmartUp Unit.

This software package contains software tools based on agentMET4FOF that can be used to analyze measurement data which contain redundancy. It is fully integrated into agentMET4FOF in form of the Redundancy Agent

This software implements an algorithm to reduce timing and noise effects in the data recorded by sensors in industrial sensor networks. It is fully integrated into agentMET4FOF in form of the Noise-Jitter Removal Agent

This is an implementation of Bayesian machine learning for the ZeMA dataset on condition monitoring of a hydraulic system.

This package provides support for time-series buffering based on the build-in Python collections.deque.

time-series-metadata is a Python implementation of a metadata scheme for time-series with measurement uncertainties.

The goal of this package is to provide a starting point for users in metrology and related areas who deal with time-dependent i.e., dynamic, measurements.

The majority of our code base is accompanied by a series of tutorials in form of Jupyter Notebooks, Python scripts or even parts of our video tutorial series . The videos require a self-registration which takes only a minute and serves to keep track of who is interested in our material.

The collected code originates from GitHub repositories which are of course directly accessible as well. The respective links are the following:

• agentMET4FOF
• Met4FoF-SmartUpUnit
• Met4FoF SmartUp Unit datareceiver
• Met4FoF-redundancy
• Bayesian Noise and Jitter removal algorithm
• ZeMA Testbed Bayesian Machine Learning
• time-series-buffer - a metrological time-series buffer
• A metrologically enabled time-series metadata scheme
• PyDynamic

• agentMET4FOF_anomaly_detection
• agentMET4FOF_bayesian_neural_network_ZeMA
• agentMET4FOF_ml
• agentMET4FOF_sensors
• agentMET4FOF_ZeMA_emc
• connect-agentMET4FOF-smartupunit
• PyDynamic_tutorials
• Strathcylde_AFRC_machine_learning_tutorials
• ZeMA-machine-learning-tutorials

Additional information around code writing and software development in the project you can find in the repository's wiki, in the coding conventions and in our related Blog post on the project homepage.

If you want to contribute back to the project take a look at our open developments in the pull requests and search the 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 your interest is focused on one of the subprojects, please visit their respective repository's pages as well.

More on this topic you can find in the official GitHub documentation on collaboration .

The project itself ended with September 2021. Some subprojects in this repository are still well maintained due to their ongoing use in other projects or personal interest. Upstream changes are usually still mirrored into this repository here on an irregular basis.

All publishable research data sets produced for or used in the project you can find in the related zenodo community side by side with each released version of this source code repository.

If you publish results obtained with the help of any parts of this code base, please cite the linked .

This work was part of the Joint Research Project Metrology for the Factory of the Future (Met4FoF), project number 17IND12 of the European Metrology Programme for Innovation and Research (EMPIR). The EMPIR is jointly funded by the EMPIR participating countries within EURAMET and the European Union.

This software is developed as a joint effort of several project partners under the lead of PTB. 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.

The Met4FoF code base is distributed under the LGPLv3 license.