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 init_parameters(self, amplitude=1.0, frequency=0.5):
"""Initialize the input data
Initialize the input data stream as an instance of the
:py:mod:`SineGenerator` class
"""
self.frequency = frequency
self.amplitude = amplitude
self._sine_stream = SineGenerator()
def init_parameters(self, nested_output=False, scaler=1.0):
"""Initialize the input data
Initialize the input data stream as an instance of the
:py:mod:`SineGenerator` class
"""
self._sine_stream = SineGenerator()
self.nested_output = nested_output
self.scaler = scaler
class NoisySineGeneratorAgent(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.
"""
# The datatype of the stream will be SineGenerator.
_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
sine_data["quantities"] = sine_data["quantities"] + np.random.rand(
*sine_data["quantities"].shape)
self.send_output(sine_data)
class ParameterisedSineGeneratorAgent(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.
"""
# The datatype of the stream will be SineGenerator.
_sine_stream: SineGenerator
# dictionary of possible parameter options for init
# every {key:iterable} will be displayed on the dashboard as a dropdown
# NOTE: Currently supports keyword arguments only.
parameter_choices = {
"amplitude": {0, 1, 2, 3, 4, 5, 6},
"frequency": {1, 2, 3}
}
stylesheet = "ellipse"
def init_parameters(self, amplitude=1.0, frequency=0.5):
"""Initialize the input data
Initialize the input data stream as an instance of the
:py:mod:`SineGenerator` class
"""
self.frequency = frequency
self.amplitude = amplitude
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["quantities"])
class BufferSineGeneratorAgent(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.
"""
# The datatype of the stream will be SineGenerator.
_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
# store buffer with received data from input agent
# By default, the AgentBuffer is a FIFO buffer and when new n entries are added to a filled buffer,
# n entries from the left of buffer will be automatically removed.
# Note: self.buffer.store and self.buffer_store works the same way, but self.buffer_store has an additional `log_info` to it,
# so it will print out its content if logging is enabled.
self.buffer.store(
agent_from=self.name, data=sine_data
) # equivalent to self.buffer_store but without logging.
# check if buffer is filled up, then send out computed mean on the buffer
if self.buffer.buffer_filled(self.name):
# access buffer content by accessing its key, it works like a dictionary
# the actual dictionary is stored in self.buffer.buffer
buffer_content = self.buffer[self.name]
buffer_quantities = buffer_content["quantities"]
buffer_time = buffer_content["time"]
# print out the buffer content
self.log_info("buffer_content:" + str(buffer_content))
# send out processed data
# note that 'time' is a special keyword for MonitorAgent's default plotting behaviour
# which renders it as the x-axis in a time-series graph
# for more customisation, see the tutorials on custom plotting.
self.send_output({"Sine": buffer_quantities, "time": buffer_time})
# clear buffer
self.buffer.clear(self.name)
class TimeSineGeneratorAgent(AgentMET4FOF):
def init_parameters(self):
self.stream = SineGenerator()
def agent_loop(self):
if self.current_state == "Running":
sine_data = self.stream.next_sample() # dictionary
# read current time stamp
current_time = datetime.today().strftime("%H:%M:%S")
# send out data in form of dictionary {"Time","Y"}
self.send_output({"Time": current_time, "Y": sine_data["quantities"]})
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.
"""
# The datatype of the stream will be SineGenerator.
_sine_stream: SineGenerator
def init_parameters(self, nested_output=False, scaler=1.0):
"""Initialize the input data
Initialize the input data stream as an instance of the
:py:mod:`SineGenerator` class
"""
self._sine_stream = SineGenerator()
self.nested_output = nested_output
self.scaler = scaler
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
if self.nested_output:
self.send_output({
"Sensor1":
sine_data["quantities"] * self.scaler,
"Sensor2":
sine_data["quantities"] * self.scaler + 1.1,
})
else:
self.send_output(sine_data["quantities"] * self.scaler)