feat(MET4FoF redundancy): tutorial 7 added

agentMET4FOF_redundancy/redundancyAgents1.py

@@ -52,7 +52,7 @@ def agent_loop(self):
         streams content and push it via invoking :py:func:`AgentMET4FOF.send_output`.
         """
         if self.current_state == "Running":
-            self.set_output_data(channel="default", data=self._data_stream._next_sample_generator())
+            self.set_output_data(channel="default", data=self._data_stream._next_sample_generator(batch_size=10))
             super().agent_loop()
 
     @property
@@ -185,7 +185,7 @@ def agent_loop(self):
                     print('Not enough data for redundancy agent evaluation.')
                     return 0
 
-            buff = self.buffer.popleft(self.n_pr) # take n_pr entries out from the buffer
+            buff = self.buffer.popleft(self.n_pr)  # take n_pr entries out from the buffer
 
             t_data_arr2d = np.full(shape=(self.n_pr, n_sensors), fill_value=np.nan)
             ut_data_arr2d = np.full(shape=(self.n_pr, n_sensors), fill_value=np.nan)
@@ -246,7 +246,6 @@ def agent_loop(self):
                     data = np.array([t_data_arr2d[-1, 0], ut_data_arr2d[-1, 0], ybest[0], uybest[0]])
 
             # Send the data
-            # data = np.array([1,2,3,4])
             if len(data.shape) == 1:
                 data = data.reshape((1, len(data)))
 

agentMET4FOF_tutorials/tutorial_7_redundancyAgents.py

@@ -0,0 +1,72 @@
+"""
+Example 1 of using a Redundancy Agent.
+Four signals are generated and data is supplied to the Redundancy Agent.
+The Redundancy Agent calculates the best consistent estimate taking into account the supplied uncertainties.
+"""
+
+import numpy as np
+from agentMET4FOF.agents import AgentNetwork
+from agentMET4FOF.metrological_agents import MetrologicalMonitorAgent
+
+from agentMET4FOF.metrological_streams import MetrologicalMultiWaveGenerator
+from agentMET4FOF_redundancy.redundancyAgents1 import MetrologicalMultiWaveGeneratorAgent, RedundancyAgent
+
+
+def demonstrate_redundancy_agent_four_signals():
+    """
+    At the start of the main module all important parameters are defined. Then the agents are defined and the network
+    is started. The network and the calculated results can be monitored in a browser at the address http://127.0.0.1:8050/.
+    """
+    # parameters
+    batch_size = 10
+    n_pr = batch_size
+    fsam = 100
+    intercept = 10
+    freqs = [6, 10, 8, 12]
+    phases = [1, 2, 3, 4]
+    ampls = [0.3, 0.2, 0.5, 0.4]
+    exp_unc_abs = 0.2
+    problim = 0.95
+
+    # start agent network server
+    agent_network = AgentNetwork(dashboard_modules=True)
+
+    # Initialize signal generating class outside of agent framework.
+    signal_arr = [MetrologicalMultiWaveGenerator(sfreq=fsam, freq_arr=np.array([freq]), intercept=intercept,
+                                             ampl_arr=np.array([ampl]), phase_ini_arr=np.array([phi]),
+                                             value_unc=exp_unc_abs) for freq, phi, ampl in zip(freqs, phases, ampls)]
+
+    # Data source agents.
+    source_agents = []
+    sensor_key_list = []
+    for count, signal in enumerate(signal_arr):
+        sensor_key_list += ["Sensor" + str(count+1)]
+        source_agents += [agent_network.add_agent(name=sensor_key_list[count], agentType=MetrologicalMultiWaveGeneratorAgent)]
+        source_agents[count].init_parameters(signal=signal, batch_size=batch_size)
+
+
+    # Redundant data processing agent
+    redundancy_name1 = "RedundancyAgent1"  # Name cannot contain spaces!!
+    redundancy_agent1 = agent_network.add_agent(name=redundancy_name1, agentType=RedundancyAgent)
+    redundancy_agent1.init_parameters1(sensor_key_list=sensor_key_list, calc_type="lcs", n_pr=n_pr, problim=problim)
+
+    # Initialize metrologically enabled plotting agent.
+    monitor_agent1 = agent_network.add_agent(name="MonitorAgent_SensorValues", agentType=MetrologicalMonitorAgent) # Name cannot contain spaces!!
+    monitor_agent2 = agent_network.add_agent(name="MonitorAgent_RedundantEstimate", agentType=MetrologicalMonitorAgent)
+
+    # Bind agents.
+    for source_agent in source_agents:
+        source_agent.bind_output(monitor_agent1)
+        source_agent.bind_output(redundancy_agent1)
+
+    redundancy_agent1.bind_output(monitor_agent2)
+
+    # 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_redundancy_agent_four_signals()