Aggegrated agent metric in DataCollection, graph in ChartModule

mesa/datacollection.py

@@ -40,6 +40,7 @@
 from operator import attrgetter
 import pandas as pd
 import types
+import statistics
 
 
 class DataCollector:
@@ -99,6 +100,7 @@
         self.agent_reporters = {}
 
         self.model_vars = {}
+        self.agent_vars = {}
         self._agent_records = {}
         self.tables = {}
 
@@ -159,6 +161,7 @@
         if all([hasattr(rep, "attribute_name") for rep in rep_funcs]):
             prefix = ["model.schedule.steps", "unique_id"]
             attributes = [func.attribute_name for func in rep_funcs]
+            self.agent_vars = {k: v for v, k in enumerate(prefix + attributes)}
             get_reports = attrgetter(*prefix + attributes)
         else:
 
@@ -256,3 +259,36 @@
         if table_name not in self.tables:
             raise Exception("No such table.")
         return pd.DataFrame(self.tables[table_name])
+
+    def get_agent_metric(self, var_name, metric="mean"):
+        """Get a single aggegrated value from an agent variable.
+
+        Args:
+            var_name: The name of the variable to aggegrate.
+            metric: Statistics metric to be used (default: mean)
+                    all functions from built-in statistics module are supported
+                    as well as "min", "max", "sum" and "len"
+
+        """
+        # Get the reporter from the name
+        reporter = self.agent_reporters[var_name]
+
+        # Get the index of the reporter
+        attr_index = self.agent_vars[reporter]
+
+        # Create a dictionary with all attributes from all agents
+        attr_dict = self._agent_records
+
+        # Get the values from all agents in a list
+        values_tuples = list(attr_dict.values())[-1]
+
+        # Get the correct value using the attribute index
+        values = [value_tuple[attr_index] for value_tuple in values_tuples]
+
+        # Calculate the metric among all agents (mean by default)
+        if metric in ["min", "max", "sum", "len"]:
+            value = eval(f"{metric}(values)")
+        else:
+            stat_function = getattr(statistics, metric)
+            value = stat_function(values)
+        return value

mesa/visualization/modules/ChartVisualization.py

@@ -34,7 +34,8 @@
                                       data_collector_name="datacollector")
 
     TODO:
-        Have it be able to handle agent-level variables as well.
+        Aggregate agent level variables other than mean (requires ChartModule
+        API change)
 
         More Pythonic customization; in particular, have both series-level and
         chart-level options settable in Python, and passed to the front-end
@@ -78,9 +79,18 @@
 
         for s in self.series:
             name = s["Label"]
-            try:
-                val = data_collector.model_vars[name][-1]  # Latest value
-            except (IndexError, KeyError):
+            if name in data_collector.model_vars.keys():
+                try:
+                    val = data_collector.model_vars[name][-1]  # Latest value
+                except (IndexError, KeyError):
+                    val = 0
+            elif name in data_collector.agent_reporters.keys():
+                try:
+                    # Returns mean of latest value of all agents
+                    val = data_collector.get_agent_metric(name)
+                except (IndexError, KeyError):
+                    val = 0
+            else:
                 val = 0
             current_values.append(val)
         return current_values