Metadata-Version: 2.1
Name: agentMET4FOF
Version: 0.1.3
Summary: A software package for the integration of metrological input into an agent-based system for the consideration of measurement uncertainty in current industrial manufacturing processes.
Home-page: https://github.com/bangxiangyong/agentMET4FOF
Author: Bang Xiang Yong, Björn Ludwig, Haris Lulic
Author-email: bxy20@cam.ac.uk
License: UNKNOWN
Description: [![CircleCI](https://circleci.com/gh/bangxiangyong/agentMET4FOF.svg?style=shield)](https://circleci.com/gh/bangxiangyong/agentMET4FOF)
        [![Documentation Status](https://readthedocs.org/projects/agentmet4fof/badge/?version=latest)](https://agentmet4fof.readthedocs.io/en/latest/?badge=latest)
        [![Codecov Badge](https://codecov.io/gh/bangxiangyong/agentMet4FoF/branch/master/graph/badge.svg)](https://codecov.io/gh/bangxiangyong/agentMet4FoF)
        
        # Multi-Agent System for Metrology for Factory of the Future (Met4FoF) Code
        This is supported by European Metrology Programme for Innovation and Research (EMPIR) under the project Metrology for the Factory of the Future (Met4FoF), project number 17IND12. (https://www.ptb.de/empir2018/met4fof/home/)
        
        About
        ---
         - How can metrological input be incorporated into an agent-based system for addressing uncertainty of machine learning in future manufacturing?
         - Includes agent-based simulation and implementation
         - Readthedocs documentation is available at (https://agentmet4fof.readthedocs.io)
        
        Use agentMET4FOF
        ---
        
        The easiest way to get started with *agentMET4FOF* is navigating to the folder
        in which you want to create a virtual Python environment (*venv*), create one,
        activate it, first install numpy, then install *agentMET4FOF* from PyPI.org and then
        work through the [tutorials](agentMET4FOF_tutorials) or [examples](examples). To do this, issue the
        following commands on your Shell:
        
        ```shell
        $ cd /LOCAL/PATH/TO/ENVS
        $ python3 -m venv agentMET4FOF_venv
        $ source agentMET4FOF_venv/bin/activate
        (agentMET4FOF_venv) $ pip install numpy
        Collecting numpy
        ...
        Successfully installed numpy-...
        (agentMET4FOF_venv) $ pip install agentMET4FOF
        Collecting agentMET4FOF
        ...
        Successfully installed agentMET4FOF-... ...
        (agentMET4FOF_venv) $ python
        Python ... (default, ..., ...) 
        [GCC ...] on ...
        Type "help", "copyright", "credits" or "license" for more information.
        >>> from agentMET4FOF_tutorials import tutorial_1_generator_agent
        >>> tutorial_1_generator_agent.main()
        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
        INFO [2020-02-21 19:04:26.961014] (AgentController): INITIALIZED
        INFO [2020-02-21 19:04:27.032258] (Logger): INITIALIZED
         * Serving Flask app "agentMET4FOF.dashboard.Dashboard" (lazy loading)
         * Environment: production
           WARNING: This is a development server. Do not use it in a production deployment.
           Use a production WSGI server instead.
         * Debug mode: off
         * Running on http://127.0.0.1:8050/ (Press CTRL+C to quit)
        ...
        ```
        
        Now you can visit `http://127.0.0.1:8050/` with any Browser and watch the
         SineGenerator agent you just spawned.
         
        To get some insights and really get going please visit [agentMET4FOF.readthedocs.io
        ](https://agentmet4fof.readthedocs.io/).
        
        Get started developing
        ---
        First clone the repository to your local machine as described
        [here](https://help.github.com/en/articles/cloning-a-repository). To get started
        with your present *Anaconda* installation just go to *Anaconda
        prompt*, navigate to your local clone
        
        ```shell
        cd /LOCAL/PATH/TO/agentMET4FOF
        ```
        
        and execute
        
        ```shell
        conda env create --file environment.yml 
        ```
        
        This will create an *Anaconda* virtual environment with all dependencies
        satisfied. If you don't have *Anaconda* installed already follow [this guide
        ](https://docs.conda.io/projects/continuumio-conda/en/latest/user-guide/install/download.html)
        first, then create the virtual environment as stated above and then proceed.
        
        Alternatively, for non-conda environments, you can install the dependencies using pip
        ```
        pip install -r requirements.txt
        ```
        
        First take a look at the [tutorials](agentMET4FOF_tutorials/tutorial_1_generator_agent.py) and
        [examples](./examples) or start hacking if you already are familiar with agentMET4FOF
        and want to customize your agents' network.
        
        Alternatively, watch the tutorial webinar [here](https://github.com/bangxiangyong/agentMET4FOF/releases/download/0.1.0/Met4FoF.MAS.webinar.mp4)
        
        Updates
        ---
         - Implemented base class AgentMET4FOF with built-in agent classes DataStreamAgent, MonitorAgent
         - Implemented class AgentNetwork to start or connect to a agent server
         - Implemented with ZEMA prognosis of Electromechanical cylinder data set as use case 
           [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1326278.svg)](https://doi.org/10.5281/zenodo.1326278)
         - Implemented interactive web application with user interface
        
        ## Screenshot of web visualization
        ![Web Screenshot](docs/screenshot_met4fof.png)
        
        Note
        ---
        - In the event of agents not terminating cleanly, run
         
          ```python
          taskkill /f /im python.exe /t
          ```
        
          in Windows Command Prompt to terminate all background python processes.
        
Keywords: uncertainty metrology MAS agent-based agents
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3
Description-Content-Type: text/markdown
