agentMET4FOF - Metrological Agent-based system¶
agentMET4FOF is a Python library developed at the Institute for Manufacturing of the University of Cambridge (UK) as part of the European joint Research Project EMPIR 17IND12 Met4FoF.
For the agentMET4FOF homepage go to GitHub.
agentMET4FOF is written in Python 3.
CircleCI
Documentation Status
Codecov Badge
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 based on Python 3.8, activate it, then install agentMET4FOF from PyPI.org and then work through the tutorials or examples.
Create a virtual environment on Windows¶
In your Windows PowerShell execute the following to set up a virtual environment in a folder of your choice and execute the first tutorial.
PS C:> cd C:\LOCAL\PATH\TO\ENVS
PS C:\LOCAL\PATH\TO\ENVS> py -3 -m venv agentMET4FOF_venv
PS C:\LOCAL\PATH\TO\ENVS> agentMET4FOF_venv\Scripts\activate
(agentMET4FOF_venv) PS C:\LOCAL\PATH\TO\ENVS> python -m pip install --upgrade pip agentMET4FOF
Collecting agentMET4FOF
...
Successfully installed agentMET4FOF-... ...
(agentMET4FOF_venv) PS C:\LOCAL\PATH\TO\ENVS> 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.demonstrate_generator_agent_use()
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)
...
Create a virtual environment on Mac and Linux¶
In your terminal execute the following to set up a virtual environment in a folder of your choice and execute the first tutorial.
$ cd /LOCAL/PATH/TO/ENVS
$ python3 -m venv agentMET4FOF_venv
$ source agentMET4FOF_venv/bin/activate
(agentMET4FOF_venv) $ pip install --upgrade pip 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.demonstrate_generator_agent_use()
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)
...
Inspect dashboard¶
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 .
Get started developing¶
First clone the repository to your local machine as described here. To get started with your present Anaconda installation just go to Anaconda prompt, navigate to your local clone
cd /LOCAL/PATH/TO/agentMET4FOF
and execute
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 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 and examples or start hacking if you already are familiar with agentMET4FOF and want to customize your agents’ network.
Alternatively, watch the tutorial webinar here
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 - Implemented interactive web application with user interface
Screenshot of web visualization¶
Web Screenshot
Orphaned processes¶
In the event of agents not terminating cleanly, you can end all Python processes running on your system (caution: the following commands affect all running Python processes, not just those that emerged from the agents).
Killing all Python processes in Windows¶
In your Windows command prompt execute the following to terminate all python processes.
> taskkill /f /im python.exe /t
>
Killing all Python processes on Mac and Linux¶
In your terminal execute the following to terminate all python processes.
$ pkill python
$
Tutorial 1 - A simple pipeline to plot a signal¶
First we define a simple pipeline of two agents, of which one will generate a signal (in our case a SineGeneratorAgent) and the other one plots the signal on the dashboard (this is always a MonitorAgent).
We define a SineGeneratorAgent for which we have to override the functions init_parameters() & agent_loop() to define the new agent’s behaviour.
init_parameters()is used to setup the input data stream and potentially other necessary parameters.agent_loop()will be endlessly repeated until further notice. It will sample by sample extract the input data stream’s content and push it to all agents connected to SineGeneratorAgent’s output channel by invokingsend_output().
The MonitorAgent is connected to the SineGeneratorAgent’s output channel and per default automatically plots the output.
Each agent has an internal current_state which can be used as a switch to change the behaviour of the agent. The available states are listed here.
As soon as all agents are initialized and the connections are set up, the agent network is started by accordingly changing all agents’ state simultaneously.
[1]:
# %load tutorial_1_generator_agent.py
from agentMET4FOF.agents import AgentMET4FOF, AgentNetwork, MonitorAgent
from agentMET4FOF.streams import SineGenerator
class SineGeneratorAgent(AgentMET4FOF):
"""An agent streaming a sine signal
Takes samples from the :py:mod:`SineGenerator` and pushes them sample by sample
to connected agents via its output channel.
"""
_sine_stream: SineGenerator
def init_parameters(self):
"""Initialize the input data
Initialize the input data stream as an instance of the
:py:mod:`SineGenerator` class
"""
self._sine_stream = SineGenerator()
def agent_loop(self):
"""Model the agent's behaviour
On state *Running* the agent will extract sample by sample the input data
streams content and push it via invoking :py:method:`AgentMET4FOF.send_output`.
"""
if self.current_state == "Running":
sine_data = self._sine_stream.next_sample() # dictionary
self.send_output(sine_data["x"])
def demonstrate_generator_agent_use():
# Start agent network server.
agent_network = AgentNetwork()
# Initialize agents by adding them to the agent network.
gen_agent = agent_network.add_agent(agentType=SineGeneratorAgent)
monitor_agent = agent_network.add_agent(agentType=MonitorAgent)
# Interconnect agents by either way:
# 1) by agent network.bind_agents(source, target).
agent_network.bind_agents(gen_agent, monitor_agent)
# 2) by the agent.bind_output().
gen_agent.bind_output(monitor_agent)
# Set all agents' states to "Running".
agent_network.set_running_state()
# Allow for shutting down the network after execution
return agent_network
if __name__ == "__main__":
demonstrate_generator_agent_use()
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 [2020-04-24 09:21:23.002156] (AgentController): INITIALIZED
INFO [2020-04-24 09:21:23.200110] (SineGeneratorAgent_1): 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
INFO [2020-04-24 09:21:23.294149] (MonitorAgent_1): INITIALIZED
[2020-04-24 09:21:23.360214] (SineGeneratorAgent_1): Connected output module: MonitorAgent_1
SET STATE: Running
[2020-04-24 09:21:24.218709] (SineGeneratorAgent_1): Pack time: 0.000897
[2020-04-24 09:21:24.223207] (SineGeneratorAgent_1): Sending: [0.]
[2020-04-24 09:21:24.225895] (MonitorAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-04-24 09:21:24.226708] (MonitorAgent_1): Tproc: 0.00017
[2020-04-24 09:21:25.217698] (SineGeneratorAgent_1): Pack time: 0.000419
[2020-04-24 09:21:25.220045] (SineGeneratorAgent_1): Sending: [0.47942554]
[2020-04-24 09:21:25.221406] (MonitorAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.47942554]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-04-24 09:21:25.223795] (MonitorAgent_1): Memory: {'SineGeneratorAgent_1': array([0. , 0.47942554])}
[2020-04-24 09:21:25.224696] (MonitorAgent_1): Tproc: 0.00264
[2020-04-24 09:21:26.217785] (SineGeneratorAgent_1): Pack time: 0.000415
[2020-04-24 09:21:26.218995] (SineGeneratorAgent_1): Sending: [0.84147098]
[2020-04-24 09:21:26.220107] (MonitorAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.84147098]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-04-24 09:21:26.222038] (MonitorAgent_1): Memory: {'SineGeneratorAgent_1': array([0. , 0.47942554, 0.84147098])}
[2020-04-24 09:21:26.223223] (MonitorAgent_1): Tproc: 0.002481
[2020-04-24 09:21:27.216058] (SineGeneratorAgent_1): Pack time: 0.000131
[2020-04-24 09:21:27.216323] (SineGeneratorAgent_1): Sending: [0.99749499]
[2020-04-24 09:21:27.216876] (MonitorAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.99749499]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-04-24 09:21:27.217220] (MonitorAgent_1): Memory: {'SineGeneratorAgent_1': array([0. , 0.47942554, 0.84147098, 0.99749499])}
[2020-04-24 09:21:27.217288] (MonitorAgent_1): Tproc: 0.000314
[2020-04-24 09:21:28.215905] (SineGeneratorAgent_1): Pack time: 0.000102
[2020-04-24 09:21:28.216367] (MonitorAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.90929743]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-04-24 09:21:28.216186] (SineGeneratorAgent_1): Sending: [0.90929743]
[2020-04-24 09:21:28.216623] (MonitorAgent_1): Memory: {'SineGeneratorAgent_1': array([0. , 0.47942554, 0.84147098, 0.99749499, 0.90929743])}
[2020-04-24 09:21:28.216678] (MonitorAgent_1): Tproc: 0.000229
* Running on http://127.0.0.1:8050/ (Press CTRL+C to quit)
<Figure size 432x288 with 0 Axes>
Tutorial 2 - A simple pipeline with signal postprocessing.¶
Here we demonstrate how to build a MathAgent as an intermediate to process the SineGeneratorAgent’s output before plotting. Subsequently, a MultiMathAgent is built to show the ability to send a dictionary of multiple fields to the recipient. We provide a custom on_received_message() function, which is called every time a message is received from input agents.
The received message is a dictionary of the form:
{
'from':agent_name,
'data': data,
'senderType': agent_class_name,
'channel':'channel_name'
}
By default, 'channel' is set to "default", however a custom channel can be set when needed, which is demonstrated in the next tutorial.
[4]:
# %load tutorial_2_math_agent.py
from agentMET4FOF.agents import AgentMET4FOF, AgentNetwork, MonitorAgent
from agentMET4FOF.streams import SineGenerator
class MathAgent(AgentMET4FOF):
def on_received_message(self, message):
data = self.divide_by_two(message["data"])
self.send_output(data)
# Define simple math functions.
@staticmethod
def divide_by_two(numerator: float) -> float:
return numerator / 2
class MultiMathAgent(AgentMET4FOF):
_minus_param: float
_plus_param: float
def init_parameters(self, minus_param=0.5, plus_param=0.5):
self._minus_param = minus_param
self._plus_param = plus_param
def on_received_message(self, message):
minus_data = self.minus(message["data"], self._minus_param)
plus_data = self.plus(message["data"], self._plus_param)
self.send_output({"minus": minus_data, "plus": plus_data})
@staticmethod
def minus(minuend: float, subtrahend: float) -> float:
return minuend - subtrahend
@staticmethod
def plus(summand_1: float, summand_2: float) -> float:
return summand_1 + summand_2
class SineGeneratorAgent(AgentMET4FOF):
_stream: SineGenerator
def init_parameters(self):
self._stream = SineGenerator()
def agent_loop(self):
if self.current_state == "Running":
sine_data = self._stream.next_sample() # dictionary
self.send_output(sine_data["x"])
def main():
# start agent network server
agentNetwork = AgentNetwork()
# init agents
gen_agent = agentNetwork.add_agent(agentType=SineGeneratorAgent)
math_agent = agentNetwork.add_agent(agentType=MathAgent)
multi_math_agent = agentNetwork.add_agent(agentType=MultiMathAgent)
monitor_agent = agentNetwork.add_agent(agentType=MonitorAgent)
# connect agents : We can connect multiple agents to any particular agent
agentNetwork.bind_agents(gen_agent, math_agent)
agentNetwork.bind_agents(gen_agent, multi_math_agent)
# connect
agentNetwork.bind_agents(gen_agent, monitor_agent)
agentNetwork.bind_agents(math_agent, monitor_agent)
agentNetwork.bind_agents(multi_math_agent, monitor_agent)
# set all agents states to "Running"
agentNetwork.set_running_state()
# allow for shutting down the network after execution
return agentNetwork
if __name__ == "__main__":
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 [2020-07-08 20:12:37.967786] (AgentController): INITIALIZED
Dash is running on http://127.0.0.1:8050/
Warning: This is a development server. Do not use app.run_server
INFO [2020-07-08 20:12:38.108067] (SineGeneratorAgent_1): INITIALIZED
in production, use a production WSGI server like gunicorn instead.
* 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
INFO [2020-07-08 20:12:38.185234] (MathAgent_1): INITIALIZED
INFO [2020-07-08 20:12:38.266143] (MultiMathAgent_1): INITIALIZED
INFO [2020-07-08 20:12:38.347055] (MonitorAgent_1): INITIALIZED
[2020-07-08 20:12:38.413035] (SineGeneratorAgent_1): Connected output module: MathAgent_1
[2020-07-08 20:12:38.418991] (SineGeneratorAgent_1): Connected output module: MultiMathAgent_1
[2020-07-08 20:12:38.425940] (SineGeneratorAgent_1): Connected output module: MonitorAgent_1
[2020-07-08 20:12:38.465907] (MathAgent_1): Connected output module: MonitorAgent_1
[2020-07-08 20:12:38.538248] (MultiMathAgent_1): Connected output module: MonitorAgent_1
SET STATE: Running
[2020-07-08 20:12:39.125553] (SineGeneratorAgent_1): Pack time: 0.000893
[2020-07-08 20:12:39.128378] (SineGeneratorAgent_1): Sending: [0.]
[2020-07-08 20:12:39.134086] (MultiMathAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-07-08 20:12:39.134202] (MonitorAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-07-08 20:12:39.135154] (MathAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-07-08 20:12:39.137182] (MathAgent_1): Pack time: 0.001182
[2020-07-08 20:12:39.142027] (MultiMathAgent_1): Pack time: 0.00693
[2020-07-08 20:12:39.135086] (MonitorAgent_1): Tproc: 0.000153
[2020-07-08 20:12:39.138563] (MathAgent_1): Sending: [0.]
[2020-07-08 20:12:39.146332] (MultiMathAgent_1): Sending: {'minus': array([-0.5]), 'plus': array([0.5])}
[2020-07-08 20:12:39.140792] (MonitorAgent_1): Received: {'from': 'MultiMathAgent_1', 'data': {'minus': array([-0.5]), 'plus': array([0.5])}, 'senderType': 'MultiMathAgent', 'channel': 'default'}
[2020-07-08 20:12:39.140299] (MathAgent_1): Tproc: 0.00405
[2020-07-08 20:12:39.147118] (MultiMathAgent_1): Tproc: 0.012217
[2020-07-08 20:12:39.141319] (MonitorAgent_1): Tproc: 8.7e-05
[2020-07-08 20:12:39.143377] (MonitorAgent_1): Received: {'from': 'MathAgent_1', 'data': array([0.]), 'senderType': 'MathAgent', 'channel': 'default'}
[2020-07-08 20:12:39.144143] (MonitorAgent_1): Tproc: 4.8e-05
[2020-07-08 20:12:40.127624] (MultiMathAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.47942554]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-07-08 20:12:40.129917] (MultiMathAgent_1): Pack time: 0.000519
[2020-07-08 20:12:40.128351] (MathAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.47942554]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-07-08 20:12:40.125310] (SineGeneratorAgent_1): Pack time: 0.000539
[2020-07-08 20:12:40.127820] (MonitorAgent_1): Received: {'from': 'SineGeneratorAgent_1', 'data': array([0.47942554]), 'senderType': 'SineGeneratorAgent', 'channel': 'default'}
[2020-07-08 20:12:40.131738] (MultiMathAgent_1): Sending: {'minus': array([-0.02057446]), 'plus': array([0.97942554])}
[2020-07-08 20:12:40.129241] (MathAgent_1): Pack time: 0.000428
[2020-07-08 20:12:40.127622] (SineGeneratorAgent_1): Sending: [0.47942554]
[2020-07-08 20:12:40.132038] (MonitorAgent_1): Memory: {'SineGeneratorAgent_1': array([0. , 0.47942554]), 'MultiMathAgent_1': {'minus': array([-0.5]), 'plus': array([0.5])}, 'MathAgent_1': array([0.])}
[2020-07-08 20:12:40.132147] (MultiMathAgent_1): Tproc: 0.004128
[2020-07-08 20:12:40.130428] (MathAgent_1): Sending: [0.23971277]
[2020-07-08 20:12:40.132367] (MonitorAgent_1): Tproc: 0.004143
[2020-07-08 20:12:40.135466] (MonitorAgent_1): Received: {'from': 'MathAgent_1', 'data': array([0.23971277]), 'senderType': 'MathAgent', 'channel': 'default'}
[2020-07-08 20:12:40.130764] (MathAgent_1): Tproc: 0.002032
[2020-07-08 20:12:40.156668] (MonitorAgent_1): Memory: {'SineGeneratorAgent_1': array([0. , 0.47942554]), 'MultiMathAgent_1': {'minus': array([-0.5]), 'plus': array([0.5])}, 'MathAgent_1': array([0. , 0.23971277])}
[2020-07-08 20:12:40.161541] (MonitorAgent_1): Tproc: 0.024927
[2020-07-08 20:12:40.164795] (MonitorAgent_1): Received: {'from': 'MultiMathAgent_1', 'data': {'minus': array([-0.02057446]), 'plus': array([0.97942554])}, 'senderType': 'MultiMathAgent', 'channel': 'default'}
[2020-07-08 20:12:40.171365] (MonitorAgent_1): Memory: {'SineGeneratorAgent_1': array([0. , 0.47942554]), 'MultiMathAgent_1': {'minus': array([-0.5 , -0.02057446]), 'plus': array([0.5 , 0.97942554])}, 'MathAgent_1': array([0. , 0.23971277])}
[2020-07-08 20:12:40.171872] (MonitorAgent_1): Tproc: 0.006705
* Running on http://127.0.0.1:8050/ (Press CTRL+C to quit)
127.0.0.1 - - [08/Jul/2020 22:12:40] "POST /_dash-update-component HTTP/1.1" 200 -
127.0.0.1 - - [08/Jul/2020 22:12:40] "POST /_dash-update-component HTTP/1.1" 200 -
Tutorial 3 - An advanced pipeline with multichannel signals.¶
We can use different channels for the receiver to handle specifically each channel name. This can be useful for example in splitting train and test channels in machine learning Then, the user will need to implement specific handling of each channel in the receiving agent.
In this example, the MultiGeneratorAgent is used to send two different types of data - Sine and Cosine generator. This is done via specifying send_output (channel="sine") and send_output(channel="cosine").
Then on the receiving end, the on_received_message() function checks for message['channel'] to handle it separately.
Note that by default, MonitorAgent is only subscribed to the "default" channel. Hence it will not respond to the "cosine" and "sine" channel.
[1]:
# %load tutorial_3_multi_channel.py
from agentMET4FOF.agents import AgentMET4FOF, AgentNetwork, MonitorAgent
from agentMET4FOF.streams import SineGenerator, CosineGenerator
class MultiGeneratorAgent(AgentMET4FOF):
_sine_stream: SineGenerator
_cos_stream: CosineGenerator
def init_parameters(self):
self._sine_stream = SineGenerator()
self._cos_stream = CosineGenerator()
def agent_loop(self):
if self.current_state == "Running":
sine_data = self._sine_stream.next_sample() # dictionary
cosine_data = self._sine_stream.next_sample() # dictionary
self.send_output(sine_data["x"], channel="sine")
self.send_output(cosine_data["x"], channel="cosine")
class MultiOutputMathAgent(AgentMET4FOF):
_minus_param: float
_plus_param: float
def init_parameters(self, minus_param=0.5, plus_param=0.5):
self._minus_param = minus_param
self._plus_param = plus_param
def on_received_message(self, message):
"""
Checks for message['channel'] and handles them separately
Acceptable channels are "cosine" and "sine"
"""
if message["channel"] == "cosine":
minus_data = self.minus(message["data"], self._minus_param)
self.send_output({"cosine_minus": minus_data})
elif message["channel"] == "sine":
plus_data = self.plus(message["data"], self._plus_param)
self.send_output({"sine_plus": plus_data})
@staticmethod
def minus(data, minus_val):
return data - minus_val
@staticmethod
def plus(data, plus_val):
return data + plus_val
def main():
# start agent network server
agentNetwork = AgentNetwork()
# init agents
gen_agent = agentNetwork.add_agent(agentType=MultiGeneratorAgent)
multi_math_agent = agentNetwork.add_agent(agentType=MultiOutputMathAgent)
monitor_agent = agentNetwork.add_agent(agentType=MonitorAgent)
# connect agents : We can connect multiple agents to any particular agent
# However the agent needs to implement handling multiple inputs
agentNetwork.bind_agents(gen_agent, multi_math_agent)
agentNetwork.bind_agents(gen_agent, monitor_agent)
agentNetwork.bind_agents(multi_math_agent, monitor_agent)
# set all agents states to "Running"
agentNetwork.set_running_state()
# allow for shutting down the network after execution
return agentNetwork
if __name__ == "__main__":
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 [2020-07-08 20:15:03.999193] (AgentController): INITIALIZED
INFO [2020-07-08 20:15:04.160762] (MultiGeneratorAgent_1): INITIALIZED
Dash is running on http://127.0.0.1:8050/
Warning: This is a development server. Do not use app.run_server
in production, use a production WSGI server like gunicorn instead.
* 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
INFO [2020-07-08 20:15:04.238194] (MultiOutputMathAgent_1): INITIALIZED
INFO [2020-07-08 20:15:04.307723] (MonitorAgent_1): INITIALIZED
[2020-07-08 20:15:04.364692] (MultiGeneratorAgent_1): Connected output module: MultiOutputMathAgent_1
[2020-07-08 20:15:04.370956] (MultiGeneratorAgent_1): Connected output module: MonitorAgent_1
[2020-07-08 20:15:04.379114] (MultiOutputMathAgent_1): Connected output module: MonitorAgent_1
SET STATE: Running
[2020-07-08 20:15:05.171751] (MultiGeneratorAgent_1): Pack time: 0.000315
[2020-07-08 20:15:05.172512] (MultiGeneratorAgent_1): Sending: [0.]
[2020-07-08 20:15:05.173200] (MonitorAgent_1): Received: {'from': 'MultiGeneratorAgent_1', 'data': array([0.]), 'senderType': 'MultiGeneratorAgent', 'channel': 'sine'}
[2020-07-08 20:15:05.175533] (MultiOutputMathAgent_1): Received: {'from': 'MultiGeneratorAgent_1', 'data': array([0.]), 'senderType': 'MultiGeneratorAgent', 'channel': 'sine'}
[2020-07-08 20:15:05.172748] (MultiGeneratorAgent_1): Pack time: 0.000102
[2020-07-08 20:15:05.173377] (MonitorAgent_1): Tproc: 5e-06
[2020-07-08 20:15:05.175921] (MultiOutputMathAgent_1): Pack time: 0.00023
[2020-07-08 20:15:05.173245] (MultiGeneratorAgent_1): Sending: [0.47942554]
[2020-07-08 20:15:05.174524] (MonitorAgent_1): Received: {'from': 'MultiGeneratorAgent_1', 'data': array([0.47942554]), 'senderType': 'MultiGeneratorAgent', 'channel': 'cosine'}
[2020-07-08 20:15:05.176514] (MultiOutputMathAgent_1): Sending: {'sine_plus': array([0.5])}
[2020-07-08 20:15:05.176643] (MultiOutputMathAgent_1): Tproc: 0.000988
[2020-07-08 20:15:05.174650] (MonitorAgent_1): Tproc: 5e-06
[2020-07-08 20:15:05.177175] (MultiOutputMathAgent_1): Received: {'from': 'MultiGeneratorAgent_1', 'data': array([0.47942554]), 'senderType': 'MultiGeneratorAgent', 'channel': 'cosine'}
[2020-07-08 20:15:05.178346] (MonitorAgent_1): Received: {'from': 'MultiOutputMathAgent_1', 'data': {'sine_plus': array([0.5])}, 'senderType': 'MultiOutputMathAgent', 'channel': 'default'}
[2020-07-08 20:15:05.177369] (MultiOutputMathAgent_1): Pack time: 9.9e-05
[2020-07-08 20:15:05.178461] (MonitorAgent_1): Tproc: 1.9e-05
[2020-07-08 20:15:05.177650] (MultiOutputMathAgent_1): Sending: {'cosine_minus': array([-0.02057446])}
[2020-07-08 20:15:05.178849] (MonitorAgent_1): Received: {'from': 'MultiOutputMathAgent_1', 'data': {'cosine_minus': array([-0.02057446])}, 'senderType': 'MultiOutputMathAgent', 'channel': 'default'}
[2020-07-08 20:15:05.177710] (MultiOutputMathAgent_1): Tproc: 0.00046
[2020-07-08 20:15:05.179303] (MonitorAgent_1): Memory: {'MultiOutputMathAgent_1': {'sine_plus': array([0.5]), 'cosine_minus': array([-0.02057446])}}
[2020-07-08 20:15:05.179361] (MonitorAgent_1): Tproc: 0.000437
* Running on http://127.0.0.1:8050/ (Press CTRL+C to quit)
127.0.0.1 - - [08/Jul/2020 22:15:05] "POST /_dash-update-component HTTP/1.1" 204 -
Tutorial 4 - A metrological datastream.¶
In this tutorial we introduce the new metrologically enabled agents. We initialize an agent, which generates an infinite sine signal. The signal is generated from the built-in class MetrologicalSineGenerator which delivers on each call one timestamp and one value each with associated uncertainties.
The MetrologicalSineGeneratorAgent is based on the new class agentMET4FOF.metrological_agents.MetrologicalAgent. We only adapt the methods init_parameters() and agent_loop(). This we need to hand over an instance of the signal generating class and to generate the actual samples. The rest of the buffering and plotting logic is encapsulated inside of the new base classes.
[1]:
# %load tutorial_4_metrological_streams.py
from agentMET4FOF.agents import AgentNetwork
from agentMET4FOF.metrological_agents import MetrologicalAgent, MetrologicalMonitorAgent
from agentMET4FOF.metrological_streams import (
MetrologicalDataStreamMET4FOF,
MetrologicalSineGenerator,
)
class MetrologicalSineGeneratorAgent(MetrologicalAgent):
"""An agent streaming a sine signal
Takes samples from an instance of :py:class:`MetrologicalSineGenerator` pushes
them sample by sample to connected agents via its output channel.
"""
# The datatype of the stream will be MetrologicalSineGenerator.
_stream: MetrologicalDataStreamMET4FOF
def init_parameters(
self,
signal: MetrologicalDataStreamMET4FOF = MetrologicalSineGenerator(),
**kwargs
):
"""Initialize the input data stream
Parameters
----------
signal : MetrologicalDataStreamMET4FOF
the underlying signal for the generator
"""
self._stream = signal
super().init_parameters()
self.set_output_data(channel="default", metadata=self._stream.metadata)
def agent_loop(self):
"""Model the agent's behaviour
On state *Running* the agent will extract sample by sample the input
datastream's content and push it via invoking
:py:method:`AgentMET4FOF.send_output`.
"""
if self.current_state == "Running":
self.set_output_data(channel="default", data=[self._stream.next_sample()])
super().agent_loop()
def demonstrate_metrological_stream():
# start agent network server
agent_network = AgentNetwork(dashboard_modules=True)
# Initialize signal generating class outside of agent framework.
signal = MetrologicalSineGenerator()
# Initialize metrologically enabled agent taking name from signal source metadata.
source_name = signal.metadata.metadata["device_id"]
source_agent = agent_network.add_agent(
name=source_name, agentType=MetrologicalSineGeneratorAgent
)
source_agent.init_parameters(signal)
# Initialize metrologically enabled plotting agent.
monitor_agent = agent_network.add_agent(
"MonitorAgent", agentType=MetrologicalMonitorAgent
)
# agent_network.bind_agents(source_agent, monitor_agent)
# Bind agents.
source_agent.bind_output(monitor_agent)
# Set all agents states to "Running".
agent_network.set_running_state()
# Allow for shutting down the network after execution.
return agent_network
if __name__ == "__main__":
demonstrate_metrological_stream()
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
Dash is running on http://127.0.0.1:8050/
* 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)
INFO [2020-12-08 16:35:51.844625] (SineGenerator): INITIALIZED
INFO [2020-12-08 16:35:51.924866] (MonitorAgent): INITIALIZED
[2020-12-08 16:35:51.950078] (SineGenerator): Connected output module: MonitorAgent
SET STATE: Running
[2020-12-08 16:35:52.851161] (SineGenerator): Pack time: 0.000906
[2020-12-08 16:35:52.856124] (SineGenerator): Sending: [array([[0. , 0. , 0.38113488, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:35:52.858618] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[0. , 0. , 0.38113488, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:35:52.862388] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[0. , 0. , 0.38113488, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>]}}
[2020-12-08 16:35:52.863224] (MonitorAgent): Tproc: 0.003321
[2020-12-08 16:35:53.849489] (SineGenerator): Pack time: 0.000478
[2020-12-08 16:35:53.851348] (SineGenerator): Sending: [array([[0.002 , 0. , 0.38406741, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:35:53.852346] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[0.002 , 0. , 0.38406741, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:35:53.855781] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[0. , 0. , 0.38113488, 0.25 ],
[0.002 , 0. , 0.38406741, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>]}}
[2020-12-08 16:35:53.856668] (MonitorAgent): Tproc: 0.003573
127.0.0.1 - - [08/Dec/2020 17:35:54] "POST /_dash-update-component HTTP/1.1" 204 -
[2020-12-08 16:35:54.849635] (SineGenerator): Pack time: 0.000496
[2020-12-08 16:35:54.851330] (SineGenerator): Sending: [array([[0.004 , 0. , 1.08806646, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:35:54.852559] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[0.004 , 0. , 1.08806646, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:35:54.856011] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[0. , 0. , 0.38113488, 0.25 ],
[0.002 , 0. , 0.38406741, 0.25 ],
[0.004 , 0. , 1.08806646, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>]}}
[2020-12-08 16:35:54.856435] (MonitorAgent): Tproc: 0.003406
NS shut down.
[2020-12-08 16:35:55.849988] (SineGenerator): Pack time: 0.000486
[2020-12-08 16:35:55.851918] (SineGenerator): Sending: [array([[0.006 , 0. , 1.64464102, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:35:55.852713] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[0.006 , 0. , 1.64464102, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:35:55.855808] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[0. , 0. , 0.38113488, 0.25 ],
[0.002 , 0. , 0.38406741, 0.25 ],
[0.004 , 0. , 1.08806646, 0.25 ],
[0.006 , 0. , 1.64464102, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>]}}
[2020-12-08 16:35:55.856235] (MonitorAgent): Tproc: 0.003005
[2020-12-08 16:35:56.850081] (SineGenerator): Pack time: 0.000609
[2020-12-08 16:35:56.852009] (SineGenerator): Sending: [array([[0.008 , 0. , 0.62548625, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:35:56.852667] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[0.008 , 0. , 0.62548625, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:35:56.858056] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[0. , 0. , 0.38113488, 0.25 ],
[0.002 , 0. , 0.38406741, 0.25 ],
[0.004 , 0. , 1.08806646, 0.25 ],
[0.006 , 0. , 1.64464102, 0.25 ],
[0.008 , 0. , 0.62548625, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>]}}
[2020-12-08 16:35:56.858860] (MonitorAgent): Tproc: 0.005392
[2020-12-08 16:35:57.849846] (SineGenerator): Pack time: 0.000699
[2020-12-08 16:35:57.851822] (SineGenerator): Sending: [array([[0.01 , 0. , 0.2340912, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:35:57.853285] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[0.01 , 0. , 0.2340912, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:35:57.858147] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[0. , 0. , 0.38113488, 0.25 ],
[0.002 , 0. , 0.38406741, 0.25 ],
[0.004 , 0. , 1.08806646, 0.25 ],
[0.006 , 0. , 1.64464102, 0.25 ],
[0.008 , 0. , 0.62548625, 0.25 ],
[0.01 , 0. , 0.2340912 , 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>]}}
[2020-12-08 16:35:57.858777] (MonitorAgent): Tproc: 0.00489
[2020-12-08 16:35:58.849640] (SineGenerator): Pack time: 0.000475
[2020-12-08 16:35:58.851474] (SineGenerator): Sending: [array([[ 0.012 , 0. , -0.75106323, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:35:58.852211] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[ 0.012 , 0. , -0.75106323, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b50>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:35:58.855954] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[ 0. , 0. , 0.38113488, 0.25 ],
[ 0.002 , 0. , 0.38406741, 0.25 ],
[ 0.004 , 0. , 1.08806646, 0.25 ],
[ 0.006 , 0. , 1.64464102, 0.25 ],
[ 0.008 , 0. , 0.62548625, 0.25 ],
[ 0.01 , 0. , 0.2340912 , 0.25 ],
[ 0.012 , 0. , -0.75106323, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b50>]}}
[2020-12-08 16:35:58.856424] (MonitorAgent): Tproc: 0.003445
[2020-12-08 16:35:59.849609] (SineGenerator): Pack time: 0.000575
[2020-12-08 16:35:59.851136] (SineGenerator): Sending: [array([[ 0.014 , 0. , -0.78487603, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:35:59.852431] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[ 0.014 , 0. , -0.78487603, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be30dc0>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:35:59.856202] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[ 0. , 0. , 0.38113488, 0.25 ],
[ 0.002 , 0. , 0.38406741, 0.25 ],
[ 0.004 , 0. , 1.08806646, 0.25 ],
[ 0.006 , 0. , 1.64464102, 0.25 ],
[ 0.008 , 0. , 0.62548625, 0.25 ],
[ 0.01 , 0. , 0.2340912 , 0.25 ],
[ 0.012 , 0. , -0.75106323, 0.25 ],
[ 0.014 , 0. , -0.78487603, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30dc0>]}}
[2020-12-08 16:35:59.856536] (MonitorAgent): Tproc: 0.003695
[2020-12-08 16:36:00.849874] (SineGenerator): Pack time: 0.000584
[2020-12-08 16:36:00.851708] (SineGenerator): Sending: [array([[ 0.016 , 0. , -0.32752173, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:36:00.852620] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[ 0.016 , 0. , -0.32752173, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be302e0>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:36:00.857987] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[ 0. , 0. , 0.38113488, 0.25 ],
[ 0.002 , 0. , 0.38406741, 0.25 ],
[ 0.004 , 0. , 1.08806646, 0.25 ],
[ 0.006 , 0. , 1.64464102, 0.25 ],
[ 0.008 , 0. , 0.62548625, 0.25 ],
[ 0.01 , 0. , 0.2340912 , 0.25 ],
[ 0.012 , 0. , -0.75106323, 0.25 ],
[ 0.014 , 0. , -0.78487603, 0.25 ],
[ 0.016 , 0. , -0.32752173, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30dc0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be302e0>]}}
[2020-12-08 16:36:00.858987] (MonitorAgent): Tproc: 0.005973
[2020-12-08 16:36:01.849498] (SineGenerator): Pack time: 0.000485
[2020-12-08 16:36:01.851298] (SineGenerator): Sending: [array([[ 0.018 , 0. , -0.4308233, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:36:01.852216] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[ 0.018 , 0. , -0.4308233, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be30a60>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:36:01.859510] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[ 0. , 0. , 0.38113488, 0.25 ],
[ 0.002 , 0. , 0.38406741, 0.25 ],
[ 0.004 , 0. , 1.08806646, 0.25 ],
[ 0.006 , 0. , 1.64464102, 0.25 ],
[ 0.008 , 0. , 0.62548625, 0.25 ],
[ 0.01 , 0. , 0.2340912 , 0.25 ],
[ 0.012 , 0. , -0.75106323, 0.25 ],
[ 0.014 , 0. , -0.78487603, 0.25 ],
[ 0.016 , 0. , -0.32752173, 0.25 ],
[ 0.018 , 0. , -0.4308233 , 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30dc0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be302e0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30a60>]}}
[2020-12-08 16:36:01.859950] (MonitorAgent): Tproc: 0.007002
[2020-12-08 16:36:02.850083] (SineGenerator): Pack time: 0.000636
[2020-12-08 16:36:02.852163] (SineGenerator): Sending: [array([[0.02 , 0. , 0.17061022, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:36:02.853465] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[0.02 , 0. , 0.17061022, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b80>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:36:02.860400] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[ 0. , 0. , 0.38113488, 0.25 ],
[ 0.002 , 0. , 0.38406741, 0.25 ],
[ 0.004 , 0. , 1.08806646, 0.25 ],
[ 0.006 , 0. , 1.64464102, 0.25 ],
[ 0.008 , 0. , 0.62548625, 0.25 ],
[ 0.01 , 0. , 0.2340912 , 0.25 ],
[ 0.012 , 0. , -0.75106323, 0.25 ],
[ 0.014 , 0. , -0.78487603, 0.25 ],
[ 0.016 , 0. , -0.32752173, 0.25 ],
[ 0.018 , 0. , -0.4308233 , 0.25 ],
[ 0.02 , 0. , 0.17061022, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30dc0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be302e0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30a60>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b80>]}}
[2020-12-08 16:36:02.861199] (MonitorAgent): Tproc: 0.007118
[2020-12-08 16:36:03.850169] (SineGenerator): Pack time: 0.000671
[2020-12-08 16:36:03.852236] (SineGenerator): Sending: [array([[0.022 , 0. , 0.61248461, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:36:03.853339] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[0.022 , 0. , 0.61248461, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be42970>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:36:03.859922] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[ 0. , 0. , 0.38113488, 0.25 ],
[ 0.002 , 0. , 0.38406741, 0.25 ],
[ 0.004 , 0. , 1.08806646, 0.25 ],
[ 0.006 , 0. , 1.64464102, 0.25 ],
[ 0.008 , 0. , 0.62548625, 0.25 ],
[ 0.01 , 0. , 0.2340912 , 0.25 ],
[ 0.012 , 0. , -0.75106323, 0.25 ],
[ 0.014 , 0. , -0.78487603, 0.25 ],
[ 0.016 , 0. , -0.32752173, 0.25 ],
[ 0.018 , 0. , -0.4308233 , 0.25 ],
[ 0.02 , 0. , 0.17061022, 0.25 ],
[ 0.022 , 0. , 0.61248461, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30dc0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be302e0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30a60>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b80>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42970>]}}
[2020-12-08 16:36:03.860947] (MonitorAgent): Tproc: 0.007144
[2020-12-08 16:36:04.850068] (SineGenerator): Pack time: 0.000676
[2020-12-08 16:36:04.852543] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[0.024 , 0. , 0.92631304, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be42490>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:36:04.853209] (SineGenerator): Sending: [array([[0.024 , 0. , 0.92631304, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:36:04.860899] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[ 0. , 0. , 0.38113488, 0.25 ],
[ 0.002 , 0. , 0.38406741, 0.25 ],
[ 0.004 , 0. , 1.08806646, 0.25 ],
[ 0.006 , 0. , 1.64464102, 0.25 ],
[ 0.008 , 0. , 0.62548625, 0.25 ],
[ 0.01 , 0. , 0.2340912 , 0.25 ],
[ 0.012 , 0. , -0.75106323, 0.25 ],
[ 0.014 , 0. , -0.78487603, 0.25 ],
[ 0.016 , 0. , -0.32752173, 0.25 ],
[ 0.018 , 0. , -0.4308233 , 0.25 ],
[ 0.02 , 0. , 0.17061022, 0.25 ],
[ 0.022 , 0. , 0.61248461, 0.25 ],
[ 0.024 , 0. , 0.92631304, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30dc0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be302e0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30a60>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b80>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42970>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42490>]}}
[2020-12-08 16:36:04.861657] (MonitorAgent): Tproc: 0.008434
[2020-12-08 16:36:05.849677] (SineGenerator): Pack time: 0.00048
[2020-12-08 16:36:05.851593] (SineGenerator): Sending: [array([[0.026 , 0. , 1.1406959, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:36:05.852250] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[0.026 , 0. , 1.1406959, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be42b50>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:36:05.860686] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[ 0. , 0. , 0.38113488, 0.25 ],
[ 0.002 , 0. , 0.38406741, 0.25 ],
[ 0.004 , 0. , 1.08806646, 0.25 ],
[ 0.006 , 0. , 1.64464102, 0.25 ],
[ 0.008 , 0. , 0.62548625, 0.25 ],
[ 0.01 , 0. , 0.2340912 , 0.25 ],
[ 0.012 , 0. , -0.75106323, 0.25 ],
[ 0.014 , 0. , -0.78487603, 0.25 ],
[ 0.016 , 0. , -0.32752173, 0.25 ],
[ 0.018 , 0. , -0.4308233 , 0.25 ],
[ 0.02 , 0. , 0.17061022, 0.25 ],
[ 0.022 , 0. , 0.61248461, 0.25 ],
[ 0.024 , 0. , 0.92631304, 0.25 ],
[ 0.026 , 0. , 1.1406959 , 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30dc0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be302e0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30a60>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b80>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42970>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42490>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42b50>]}}
[2020-12-08 16:36:05.861250] (MonitorAgent): Tproc: 0.008546
[2020-12-08 16:36:06.847952] (SineGenerator): Pack time: 8.6e-05
[2020-12-08 16:36:06.848184] (SineGenerator): Sending: [array([[0.028 , 0. , 0.50636166, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:36:06.848427] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[0.028 , 0. , 0.50636166, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be429a0>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:36:06.849281] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[ 0. , 0. , 0.38113488, 0.25 ],
[ 0.002 , 0. , 0.38406741, 0.25 ],
[ 0.004 , 0. , 1.08806646, 0.25 ],
[ 0.006 , 0. , 1.64464102, 0.25 ],
[ 0.008 , 0. , 0.62548625, 0.25 ],
[ 0.01 , 0. , 0.2340912 , 0.25 ],
[ 0.012 , 0. , -0.75106323, 0.25 ],
[ 0.014 , 0. , -0.78487603, 0.25 ],
[ 0.016 , 0. , -0.32752173, 0.25 ],
[ 0.018 , 0. , -0.4308233 , 0.25 ],
[ 0.02 , 0. , 0.17061022, 0.25 ],
[ 0.022 , 0. , 0.61248461, 0.25 ],
[ 0.024 , 0. , 0.92631304, 0.25 ],
[ 0.026 , 0. , 1.1406959 , 0.25 ],
[ 0.028 , 0. , 0.50636166, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30dc0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be302e0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30a60>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b80>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42970>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42490>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be429a0>]}}
[2020-12-08 16:36:06.849380] (MonitorAgent): Tproc: 0.000878
[2020-12-08 16:36:07.849551] (SineGenerator): Pack time: 0.000487
[2020-12-08 16:36:07.851518] (SineGenerator): Sending: [array([[0.03 , 0. , 0.16248346, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:36:07.852346] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[0.03 , 0. , 0.16248346, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c5be427f0>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:36:07.866588] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[ 0. , 0. , 0.38113488, 0.25 ],
[ 0.002 , 0. , 0.38406741, 0.25 ],
[ 0.004 , 0. , 1.08806646, 0.25 ],
[ 0.006 , 0. , 1.64464102, 0.25 ],
[ 0.008 , 0. , 0.62548625, 0.25 ],
[ 0.01 , 0. , 0.2340912 , 0.25 ],
[ 0.012 , 0. , -0.75106323, 0.25 ],
[ 0.014 , 0. , -0.78487603, 0.25 ],
[ 0.016 , 0. , -0.32752173, 0.25 ],
[ 0.018 , 0. , -0.4308233 , 0.25 ],
[ 0.02 , 0. , 0.17061022, 0.25 ],
[ 0.022 , 0. , 0.61248461, 0.25 ],
[ 0.024 , 0. , 0.92631304, 0.25 ],
[ 0.026 , 0. , 1.1406959 , 0.25 ],
[ 0.028 , 0. , 0.50636166, 0.25 ],
[ 0.03 , 0. , 0.16248346, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30dc0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be302e0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30a60>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b80>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42970>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42490>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be429a0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be427f0>]}}
[2020-12-08 16:36:07.867344] (MonitorAgent): Tproc: 0.014357
[2020-12-08 16:36:08.849701] (SineGenerator): Pack time: 0.000475
[2020-12-08 16:36:08.851602] (SineGenerator): Sending: [array([[ 0.032 , 0. , -0.66261441, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:36:08.852274] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[ 0.032 , 0. , -0.66261441, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c425a8910>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:36:08.860981] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[ 0. , 0. , 0.38113488, 0.25 ],
[ 0.002 , 0. , 0.38406741, 0.25 ],
[ 0.004 , 0. , 1.08806646, 0.25 ],
[ 0.006 , 0. , 1.64464102, 0.25 ],
[ 0.008 , 0. , 0.62548625, 0.25 ],
[ 0.01 , 0. , 0.2340912 , 0.25 ],
[ 0.012 , 0. , -0.75106323, 0.25 ],
[ 0.014 , 0. , -0.78487603, 0.25 ],
[ 0.016 , 0. , -0.32752173, 0.25 ],
[ 0.018 , 0. , -0.4308233 , 0.25 ],
[ 0.02 , 0. , 0.17061022, 0.25 ],
[ 0.022 , 0. , 0.61248461, 0.25 ],
[ 0.024 , 0. , 0.92631304, 0.25 ],
[ 0.026 , 0. , 1.1406959 , 0.25 ],
[ 0.028 , 0. , 0.50636166, 0.25 ],
[ 0.03 , 0. , 0.16248346, 0.25 ],
[ 0.032 , 0. , -0.66261441, 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30dc0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be302e0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30a60>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b80>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42970>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42490>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be429a0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be427f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c425a8910>]}}
[2020-12-08 16:36:08.861531] (MonitorAgent): Tproc: 0.008564
[2020-12-08 16:36:09.850808] (SineGenerator): Pack time: 0.000846
[2020-12-08 16:36:09.854304] (SineGenerator): Sending: [array([[ 0.034 , 0. , -1.0231325, 0.25 ]]), <time_series_metadata.scheme.MetaData object at 0x7f9c5be41280>]
[2020-12-08 16:36:09.855767] (MonitorAgent): Received: {'from': 'SineGenerator', 'data': array([[ 0.034 , 0. , -1.0231325, 0.25 ]]), 'metadata': <time_series_metadata.scheme.MetaData object at 0x7f9c88ab1310>, 'senderType': 'MetrologicalSineGeneratorAgent', 'channel': 'default'}
[2020-12-08 16:36:09.880904] (MonitorAgent): Buffer: {'SineGenerator': {'data': array([[ 0. , 0. , 0.38113488, 0.25 ],
[ 0.002 , 0. , 0.38406741, 0.25 ],
[ 0.004 , 0. , 1.08806646, 0.25 ],
[ 0.006 , 0. , 1.64464102, 0.25 ],
[ 0.008 , 0. , 0.62548625, 0.25 ],
[ 0.01 , 0. , 0.2340912 , 0.25 ],
[ 0.012 , 0. , -0.75106323, 0.25 ],
[ 0.014 , 0. , -0.78487603, 0.25 ],
[ 0.016 , 0. , -0.32752173, 0.25 ],
[ 0.018 , 0. , -0.4308233 , 0.25 ],
[ 0.02 , 0. , 0.17061022, 0.25 ],
[ 0.022 , 0. , 0.61248461, 0.25 ],
[ 0.024 , 0. , 0.92631304, 0.25 ],
[ 0.026 , 0. , 1.1406959 , 0.25 ],
[ 0.028 , 0. , 0.50636166, 0.25 ],
[ 0.03 , 0. , 0.16248346, 0.25 ],
[ 0.032 , 0. , -0.66261441, 0.25 ],
[ 0.034 , 0. , -1.0231325 , 0.25 ]]), 'metadata': [<time_series_metadata.scheme.MetaData object at 0x7f9c5be42430>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be424f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c4259fa90>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30ca0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30280>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30c40>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30dc0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be302e0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30a60>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be30b80>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42970>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42490>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be42b50>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be429a0>, <time_series_metadata.scheme.MetaData object at 0x7f9c5be427f0>, <time_series_metadata.scheme.MetaData object at 0x7f9c425a8910>, <time_series_metadata.scheme.MetaData object at 0x7f9c88ab1310>]}}
[2020-12-08 16:36:09.881966] (MonitorAgent): Tproc: 0.023787
agentMET4FOF agents¶
-
class
agentMET4FOF.agents.AgentBuffer(buffer_size=1000)[source]¶ Buffer class which is instantiated in every agent to store data incrementally. This buffer is necessary to handle multiple inputs coming from agents. The buffer can be a dict of iterables, or a dict of dict of iterables for nested named data. The keys are the names of agents.
We can access the buffer like a dict with exposed functions such as .values(), .keys() and .items(), The actual dict object is stored in the variable self.buffer
-
buffer_filled(agent_from=None)[source]¶ Checks whether buffer is filled, by comparing against the buffer_size.
Parameters: agent_from (str) – Name of input agent in the buffer dict to be looked up for. If agent_from is not provided, we check for all iterables in the buffer. For nested dict, this returns true for any iterable which is beyond the buffer_size.
-
check_supported_datatype(value)[source]¶ Checks whether value is one of the supported data types.
Parameters: value (iterable) – Value to be checked. Returns: result Return type: boolean
-
clear(agent_from=None)[source]¶ Clears the data in the buffer. if agent_from is not given, the entire buffer is removed.
- agent_from : str
- Name of agent
-
store(agent_from, data=None, concat_axis=0)[source]¶ Stores data into self.buffer with the received message
Checks if sender agent has sent any message before If it did, then append, otherwise create new entry for it
Parameters: - agent_from (dict | str) – if type is dict, we expect it to be the agentMET4FOF dict message to be compliant with older code otherwise, we expect it to be name of agent sender and data will need to be passed as parameter
- data – optional if agent_from is a dict. Otherwise this parameter is compulsory. Any supported data which can be stored in dict as buffering.
- concat_axis (int) – optional axis to concatenate on with the buffering for numpy arrays. Default is 0.
-
-
class
agentMET4FOF.agents.AgentMET4FOF(name='', host=None, serializer=None, transport=None, attributes=None, backend='osbrain', mesa_model=None)[source]¶ Base class for all agents with specific functions to be overridden/supplied by user.
Behavioral functions for users to provide are init_parameters, agent_loop and on_received_message. Communicative functions are bind_output, unbind_output and send_output.
-
agent_loop()[source]¶ User defined method for the agent to execute for loop_wait seconds specified either in self.loop_wait or explicitly via`init_agent_loop(loop_wait)`
To start a new loop, call init_agent_loop(loop_wait) on the agent Example of usage is to check the current_state of the agent and send data periodically
-
bind_output(output_agent)[source]¶ Forms Output connection with another agent. Any call on send_output will reach this newly binded agent
Adds the agent to its list of Outputs.
Parameters: output_agent (AgentMET4FOF or list) – Agent(s) to be binded to this agent’s output channel
-
buffer_clear(agent_name=None)[source]¶ Empties buffer which is a dict indexed by the agent_name.
Parameters: agent_name (str) – Key of the memory dict, which can be the name of input agent, or self.name. If one is not supplied, we assume to clear the entire memory.
-
buffer_filled(agent_name=None)[source]¶ Checks whether the internal buffer has been filled to the maximum allowed specified by self.buffer_size
Parameters: agent_name (str) – Index of the buffer which is the name of input agent. Returns: status of buffer filled Return type: boolean
-
buffer_store(agent_from: str, data=None, concat_axis=0)[source]¶ Updates data stored in self.buffer with the received message
Checks if sender agent has sent any message before If it did,then append, otherwise create new entry for it
Parameters: - agent_from (str) – Name of agent sender
- data – Any supported data which can be stored in dict as buffer. See AgentBuffer for more information.
-
get_attr(attr)[source]¶ Return the specified attribute of the agent.
Parameters: name – Name of the attribute to be retrieved.
-
handle_process_data(message)[source]¶ Internal method to handle incoming message before calling user-defined on_received_message method.
If current_state is either Stop or Reset, it will terminate early before entering on_received_message
-
init_agent(buffer_size=1000, log_mode=True)[source]¶ Internal initialization to setup the agent: mainly on setting the dictionary of Inputs, Outputs, PubAddr.
Calls user-defined init_parameters() upon finishing.
-
Inputs¶ Dictionary of Agents connected to its input channels. Messages will arrive from agents in this dictionary. Automatically updated when bind_output() function is called
Type: dict
-
Outputs¶ Dictionary of Agents connected to its output channels. Messages will be sent to agents in this dictionary. Automatically updated when bind_output() function is called
Type: dict
-
PubAddr_alias¶ Name of Publish address socket
Type: str
-
PubAddr¶ Publish address socket handle
Type: str
-
AgentType¶ Name of class
Type: str
-
current_state¶ Current state of agent. Can be used to define different states of operation such as “Running”, “Idle, “Stop”, etc.. Users will need to define their own flow of handling each type of self.current_state in the agent_loop
Type: str
-
loop_wait¶ The interval to wait between loop. Call init_agent_loop to restart the timer or set the value of loop_wait in init_parameters when necessary.
Type: int
-
buffer_size¶ The total number of elements to be stored in the agent buffer When total elements exceeds this number, the latest elements will be replaced with the incoming data elements
Type: int
-
-
init_agent_loop(loop_wait: Optional[int] = None)[source]¶ Initiates the agent loop, which iterates every loop_wait seconds
Stops every timers and initiate a new loop.
Parameters: loop_wait (int, optional) – The wait between each iteration of the loop
-
log_info(message)[source]¶ Prints logs to be saved into logfile with Logger Agent
Parameters: message (str) – Message to be logged to the internal Logger Agent
-
on_received_message(message)[source]¶ User-defined method and is triggered to handle the message passed by Input.
Parameters: message (Dictionary) – The message received is in form {‘from’:agent_name, ‘data’: data, ‘senderType’: agent_class, ‘channel’:channel_name} agent_name is the name of the Input agent which sent the message data is the actual content of the message
-
pack_data(data, channel='default')[source]¶ Internal method to pack the data content into a dictionary before sending out.
Special case : if the data is already a message, then the from and senderType will be altered to this agent, without altering the data and channel within the message this is used for more succinct data processing and passing.
Parameters: - data (argument) – Data content to be packed before sending out to agents.
- channel (str) – Key of dictionary which stores data
Returns: Packed message data
Return type: dict of the form {‘from’:agent_name, ‘data’: data, ‘senderType’: agent_class, ‘channel’:channel_name}.
-
reset()[source]¶ This method will be called on all agents when the global reset_agents is called by the AgentNetwork and when the Reset button is clicked on the dashboard.
Method to reset the agent’s states and parameters. User can override this method to reset the specific parameters.
-
send_output(data, channel='default')[source]¶ Sends message data to all connected agents in self.Outputs.
Output connection can first be formed by calling bind_output. By default calls pack_data(data) before sending out. Can specify specific channel as opposed to ‘default’ channel.
Parameters: - data (argument) – Data content to be sent out
- channel (str) – Key of message dictionary which stores data
Returns: message
Return type: dict of the form {‘from’:agent_name, ‘data’: data, ‘senderType’: agent_class, ‘channel’:channel_name}.
-
send_plot(fig: Union[matplotlib.figure.Figure, Dict[str, matplotlib.figure.Figure]], mode: str = 'image')[source]¶ Sends plot to agents connected to this agent’s Output channel.
This method is different from send_output which will be sent to through the ‘plot’ channel to be handled.
Tradeoffs between “image” and “plotly” modes are that “image” are more stable and “plotly” are interactive. Note not all (complicated) matplotlib figures can be converted into a plotly figure.
Parameters: - fig (matplotlib.figure.Figure or dict of matplotlib.figure.Figure) – Alternatively, multiple figures can be nested in a dict (with any preferred keys) e.g {“Temperature”:matplotlib.Figure, “Acceleration”:matplotlib.Figure}
- mode (str) – “image” - converts into image via encoding at base64 string. “plotly” - converts into plotly figure using mpl_to_plotly Default: “image”
Returns: graph
Return type: str or plotly figure or dict of one of those converted figure(s)
-
set_attr(**kwargs)[source]¶ Set object attributes.
Parameters: kwargs ([name, value]) – Keyword arguments will be used to set the object attributes.
-
shutdown()[source]¶ Cleanly stop and shut down the agent assuming the agent is running.
Will let the main thread do the tear down.
-
stop_agent_loop()[source]¶ Stops agent_loop from running. Note that the agent will still be responding to messages
-
unbind_output(output_agent)[source]¶ Remove existing output connection with another agent. This reverses the bind_output method
Parameters: output_agent (AgentMET4FOF) – Agent binded to this agent’s output channel
-
-
class
agentMET4FOF.agents.AgentNetwork(ip_addr='127.0.0.1', port=3333, connect=False, log_filename='log_file.csv', dashboard_modules=True, dashboard_extensions=[], dashboard_update_interval=3, dashboard_max_monitors=10, dashboard_port=8050, backend='osbrain', mesa_update_interval=0.1)[source]¶ Object for starting a new Agent Network or connect to an existing Agent Network specified by ip & port
Provides function to add agents, (un)bind agents, query agent network state, set global agent states Interfaces with an internal _AgentController which is hidden from user
-
add_agent(name=' ', agentType=<class 'agentMET4FOF.agents.AgentMET4FOF'>, log_mode=True, buffer_size=1000, ip_addr=None, loop_wait=None, **kwargs)[source]¶ Instantiates a new agent in the network.
Parameters: - str (name) – with the same name. Defaults to the agent’s class name.
- AgentMET4FOF (agentType) – network. Defaults to
AgentMET4FOF - bool (log_mode) – Logger Agent. Defaults to True.
Returns: AgentMET4FOF
Return type: Newly instantiated agent
-
agents(filter_agent=None)[source]¶ Returns all agent names connected to Agent Network.
Returns: list Return type: names of all agents
-
bind_agents(source, target)[source]¶ Binds two agents communication channel in a unidirectional manner from source Agent to target Agent
Any subsequent calls of source.send_output() will reach target Agent’s message queue.
Parameters: - source (AgentMET4FOF) – Source agent whose Output channel will be binded to target
- target (AgentMET4FOF) – Target agent whose Input channel will be binded to source
-
connect(ip_addr='127.0.0.1', port=3333, verbose=True)[source]¶ Only for osbrain backend. Connects to an existing AgentNetwork.
Parameters: - ip_addr (str) – IP Address of server to connect to
- port (int) – Port of server to connect to
-
get_agent(agent_name)[source]¶ Returns a particular agent connected to Agent Network.
Parameters: agent_name (str) – Name of agent to search for in the network
-
set_agents_state(filter_agent=None, state='Idle')[source]¶ Blanket operation on all agents to set their current_state attribute to given state
Can be used to define different states of operation such as “Running”, “Idle, “Stop”, etc.. Users will need to define their own flow of handling each type of self.current_state in the agent_loop
Parameters: - filter_agent (str) – (Optional) Filter name of agents to set the states
- state (str) – State of agents to set
-
set_running_state(filter_agent=None)[source]¶ Blanket operation on all agents to set their current_state attribute to “Running”
Users will need to define their own flow of handling each type of self.current_state in the agent_loop
Parameters: filter_agent (str) – (Optional) Filter name of agents to set the states
-
set_stop_state(filter_agent=None)[source]¶ Blanket operation on all agents to set their current_state attribute to “Stop”
Users will need to define their own flow of handling each type of self.current_state in the agent_loop
Parameters: filter_agent (str) – (Optional) Filter name of agents to set the states
-
start_server_mesa()[source]¶ Handles the initialisation for backend == “mesa”. Involves spawning two nested objects : MesaModel and AgentController
-
start_server_osbrain(ip_addr='127.0.0.1', port=3333)[source]¶ Only for osbrain backend. Starts a new AgentNetwork.
Parameters: - ip_addr (str) – IP Address of server to start
- port (int) – Port of server to start
-
unbind_agents(source, target)[source]¶ Unbinds two agents communication channel in a unidirectional manner from source Agent to target Agent
This is the reverse of bind_agents()
Parameters: - source (AgentMET4FOF) – Source agent whose Output channel will be unbinded from target
- target (AgentMET4FOF) – Target agent whose Input channel will be unbinded from source
-
-
class
agentMET4FOF.agents.DataStreamAgent(name='', host=None, serializer=None, transport=None, attributes=None, backend='osbrain', mesa_model=None)[source]¶ Able to simulate generation of datastream by loading a given DataStreamMET4FOF object.
Can be used in incremental training or batch training mode. To simulate batch training mode, set pretrain_size=-1 , otherwise, set pretrain_size and batch_size for the respective See DataStreamMET4FOF on loading your own data set as a data stream.
-
agent_loop()[source]¶ User defined method for the agent to execute for loop_wait seconds specified either in self.loop_wait or explicitly via`init_agent_loop(loop_wait)`
To start a new loop, call init_agent_loop(loop_wait) on the agent Example of usage is to check the current_state of the agent and send data periodically
-
init_parameters(stream=<agentMET4FOF.streams.DataStreamMET4FOF object>, pretrain_size=None, batch_size=1, loop_wait=1, randomize=False)[source]¶ Parameters: - stream (DataStreamMET4FOF) – A DataStreamMET4FOF object which provides the sample data
- pretrain_size (int) – The number of sample data to send through in the first loop cycle, and subsequently, the batch_size will be used
- batch_size (int) – The number of sample data to send in every loop cycle
- loop_wait (int) – The duration to wait (seconds) at the end of each loop cycle before going into the next cycle
- randomize (bool) – Determines if the dataset should be shuffled before streaming
-
-
class
agentMET4FOF.agents.MonitorAgent(name='', host=None, serializer=None, transport=None, attributes=None, backend='osbrain', mesa_model=None)[source]¶ Unique Agent for storing plots and data from messages received from input agents.
The dashboard searches for Monitor Agents’ memory and plots to draw the graphs “plot” channel is used to receive base64 images from agents to plot on dashboard
-
memory¶ Dictionary of format {agent1_name : agent1_data, agent2_name : agent2_data}
Type: dict
-
plots¶ Dictionary of format {agent1_name : agent1_plot, agent2_name : agent2_plot}
Type: dict
-
plot_filter¶ List of keys to filter the ‘data’ upon receiving message to be saved into memory Used to specifically select only a few keys to be plotted
Type: list of str
-
init_parameters(plot_filter=[], custom_plot_function=-1, *args, **kwargs)[source]¶ User provided function to initialize parameters of choice.
-
on_received_message(message)[source]¶ Handles incoming data from ‘default’ and ‘plot’ channels.
Stores ‘default’ data into self.memory and ‘plot’ data into self.plots
Parameters: message (dict) – Acceptable channel values are ‘default’ or ‘plot’
-
reset()[source]¶ This method will be called on all agents when the global reset_agents is called by the AgentNetwork and when the Reset button is clicked on the dashboard.
Method to reset the agent’s states and parameters. User can override this method to reset the specific parameters.
-
update_plot_memory(message)[source]¶ Updates plot figures stored in self.plots with the received message
Parameters: message (dict) – Standard message format specified by AgentMET4FOF class Message[‘data’] needs to be base64 image string and can be nested in dictionary for multiple plots Only the latest plot will be shown kept and does not keep a history of the plots.
-
agentMET4FOF metrologically enabled agents¶
-
class
agentMET4FOF.metrological_agents.MetrologicalAgent(name='', host=None, serializer=None, transport=None, attributes=None, backend='osbrain', mesa_model=None)[source]¶ -
agent_loop()[source]¶ User defined method for the agent to execute for loop_wait seconds specified either in self.loop_wait or explicitly via`init_agent_loop(loop_wait)`
To start a new loop, call init_agent_loop(loop_wait) on the agent Example of usage is to check the current_state of the agent and send data periodically
-
init_parameters(input_data_maxlen=25, output_data_maxlen=25)[source]¶ User provided function to initialize parameters of choice.
-
on_received_message(message)[source]¶ User-defined method and is triggered to handle the message passed by Input.
Parameters: message (Dictionary) – The message received is in form {‘from’:agent_name, ‘data’: data, ‘senderType’: agent_class, ‘channel’:channel_name} agent_name is the name of the Input agent which sent the message data is the actual content of the message
-
pack_data(data, channel='default')[source]¶ Internal method to pack the data content into a dictionary before sending out.
Special case : if the data is already a message, then the from and senderType will be altered to this agent, without altering the data and channel within the message this is used for more succinct data processing and passing.
Parameters: - data (argument) – Data content to be packed before sending out to agents.
- channel (str) – Key of dictionary which stores data
Returns: Packed message data
Return type: dict of the form {‘from’:agent_name, ‘data’: data, ‘senderType’: agent_class, ‘channel’:channel_name}.
-
-
class
agentMET4FOF.metrological_agents.MetrologicalMonitorAgent(name='', host=None, serializer=None, transport=None, attributes=None, backend='osbrain', mesa_model=None)[source]¶ -
init_parameters(*args, **kwargs)[source]¶ User provided function to initialize parameters of choice.
-
on_received_message(message)[source]¶ Handles incoming data from ‘default’ and ‘plot’ channels.
Stores ‘default’ data into self.memory and ‘plot’ data into self.plots
Parameters: message (dict) – Acceptable channel values are ‘default’ or ‘plot’
-
reset()[source]¶ This method will be called on all agents when the global reset_agents is called by the AgentNetwork and when the Reset button is clicked on the dashboard.
Method to reset the agent’s states and parameters. User can override this method to reset the specific parameters.
-
update_plot_memory(message)[source]¶ Updates plot figures stored in self.plots with the received message
Parameters: message (dict) – Standard message format specified by AgentMET4FOF class Message[‘data’] needs to be base64 image string and can be nested in dictionary for multiple plots Only the latest plot will be shown kept and does not keep a history of the plots.
-
agentMET4FOF streams¶
-
class
agentMET4FOF.streams.CosineGenerator(sfreq=500, F=5)[source]¶ Built-in class of cosine wave generator. sfreq is sampling frequency which determines the time step when next_sample is called F is frequency of wave function cosine_wave_function is a custom defined function which has a required keyword time as argument and any number of optional additional arguments (e.g F).to be supplied to the set_generator_function
-
class
agentMET4FOF.streams.DataStreamMET4FOF[source]¶ Abstract class for creating datastreams.
Data can be fetched sequentially using next_sample() or all at once all_samples(). This increments the internal sample index sample_idx.
For sensors data, we assume: The format shape for 2D data stream (timesteps, n_sensors) The format shape for 3D data stream (num_cycles, timesteps , n_sensors)
To create a new DataStreamMET4FOF class, inherit this class and call set_metadata in the constructor. Choose one of two types of datastreams to be created: from dataset file (set_data_source), or a waveform generator function (set_generator_function). Alternatively, override the next_sample function if neither option suits the application. For generator functions, sfreq is a required variable to be set on init which sets the sampling frequency and the time-step which occurs when next_sample() is called.
For an example implementation of using generator function, see the built-in SineGenerator class. See tutorials for more implementations.
-
all_samples()[source]¶ Returns all the samples in the data stream
Returns: samples Return type: dict of the form {‘x’: current_sample_x, ‘y’: current_sample_y}
-
next_sample(batch_size=1)[source]¶ Fetches the latest batch_size samples from the iterables: quantities, time and target. This advances the internal pointer current_idx by batch_size.
Parameters: batch_size (int) – number of batches to get from data stream Returns: - samples (dict of the form `{‘time’:current_sample_time,’quantities’:)
- current_sample_quantities, ‘target’ (current_sample_target}`)
-
set_data_source(quantities=None, target=None, time=None)[source]¶ This sets the data source by providing three iterables: quantities , time and target which are assumed to be aligned.
For sensors data, we assume: The format shape for 2D data stream (timesteps, n_sensors) The format shape for 3D data stream (num_cycles, timesteps , n_sensors)
Parameters: - quantities (iterable) – Measured quantities such as sensors readings.
- target (iterable) – (Optional) Target label in the context of machine learning. This can be Remaining Useful Life in predictive maintenance application. Note this can be an unobservable variable in real-time and applies only for validation during offline analysis.
- time (iterable) – (Optional) dtype can be either float or datetime64 to indicate the time when the quantities were measured.
-
set_data_source_type(dt_type='function')[source]¶ To explicitly account for the type of data source: either from dataset, or a generator function.
Parameters: dt_type (str) – Either “function” or “dataset”
-
set_generator_function(generator_function=None, sfreq=None, **kwargs)[source]¶ Sets the data source to a generator function. By default, this function resorts to a sine wave generator function. Initialisation of the generator’s parameters should be done here such as setting the sampling frequency and wave frequency. For setting it with a dataset instead, see set_data_source.
Parameters: - generator_function (method) – A generator function which takes in at least one argument time which will be used in next_sample. Parameters of the function can be fixed by providing additional arguments such as the wave frequency.
- sfreq (int) – Sampling frequency.
- **kwargs – Any additional keyword arguments to be supplied to the generator function.
The
**kwargswill be saved as generator_parameters. The generator function call for every sample will be supplied with the**generator_parameters.
-
-
class
agentMET4FOF.streams.SineGenerator(sfreq=500, F=5)[source]¶ Built-in class of sine wave generator. sfreq is sampling frequency which determines the time step when next_sample is called F is frequency of wave function sine_wave_function is a custom defined function which has a required keyword time as argument and any number of optional additional arguments (e.g F). to be supplied to the set_generator_function
agentMET4FOF dashboard¶
-
class
agentMET4FOF.dashboard.Dashboard.AgentDashboard(dashboard_modules=[], dashboard_layouts=[], dashboard_update_interval=3, max_monitors=10, ip_addr='127.0.0.1', port=8050, agentNetwork='127.0.0.1', agent_ip_addr=3333, agent_port=None)[source]¶ Class for the web dashboard which runs with the AgentNetwork object, which by default are on the same IP. Optional to run the dashboard on a separate IP by providing the right parameters. See example for an implementation of a separate run of dashboard to connect to an existing agent network. If there is no existing agent network, error will show up. An internal _Dashboard_Control object is instantiated inside this object, which manages access to the AgentNetwork.
-
init_app_layout(update_interval_seconds=3, max_monitors=10, dashboard_layouts=[])[source]¶ Initialises the overall dash app “layout” which has two sub-pages (Agent network and ML experiment)
Parameters: - update_interval_seconds (float or int) – Auto refresh rate which the app queries the states of Agent Network to update the graphs and display
- max_monitors (int) – Due to complexity in managing and instantiating dynamic figures, a maximum number of monitors is specified first and only the each Monitor Agent will occupy one of these figures. It is not ideal, but will undergo changes for the better.
Returns: app
Return type: Dash app object
-
Indices and tables¶
References¶
| [Bang2019] | Bang X. Yong, A. Brintrup Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System, 9th Workshop on Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future |