bar plot 2+ plots added

examples/example2.py

@@ -60,34 +60,51 @@ def run_simulation(simulation):
 def bar_plot(graphing_options, results):
     """
     bar plot sim results
-    todo: 2+ plots, error bars?
+    todo: error bars?
     """
     choice = graphing_options["choice"]
     dependant = graphing_options["dependant"]
     data = [[inp] for inp in graphing_options["input"]]
     columns = [choice]
+    columns.extend(results.keys())
 
-    for node, stats in results.items():
-        columns.append(node)
-        sorted_results = sorted(stats, key=lambda x: x["tag"])
+    if len(dependant) > 1:
+        fig, axes = plt.subplots(len(dependant))
 
-        batch_y = [[] for _ in range(len(data))]
-        # split the stats into batches
-        for stat in sorted_results:
-            batch_y[stat["tag"]].append([stat[dependant[0]]])
+        for ax, dep in zip(axes, dependant):
+            this_data = copy.deepcopy(data)
+            for node, stats in results.items():
+                sorted_results = sorted(stats, key=lambda x: x["tag"])
 
-        for dat, batch in zip(data, batch_y):
-            dat.append(mean(batch))
+                batch_y = [[] for _ in range(len(data))]
+                # split the stats into batches
+                for stat in sorted_results:
+                    batch_y[stat["tag"]].append([stat[dep]])
 
-    pd.DataFrame(data, columns=columns).plot(
-        x=choice, kind="bar", stacked=False, rot=0, ylabel=dependant[0]
-    )
+                for dat, batch in zip(this_data, batch_y):
+                    dat.append(mean(batch))
 
-    
-    plt.subplots_adjust(right=0.75)
+            pd.DataFrame(this_data, columns=columns).plot(
+                x=choice, kind="bar", stacked=False, rot=0, ylabel=dep, ax=ax
+            ).legend(bbox_to_anchor=(1.05, 1), loc="upper left", borderaxespad=0)
+    else:
+        for node, stats in results.items():
+            sorted_results = sorted(stats, key=lambda x: x["tag"])
 
+            batch_y = [[] for _ in range(len(data))]
+            # split the stats into batches
+            for stat in sorted_results:
+                batch_y[stat["tag"]].append([stat[dependant[0]]])
+
+            for dat, batch in zip(data, batch_y):
+                dat.append(mean(batch))
+
+        pd.DataFrame(data, columns=columns).plot(
+            x=choice, kind="bar", stacked=False, rot=0, ylabel=dependant[0]
+        ).legend(bbox_to_anchor=(1.05, 1), loc="upper left", borderaxespad=0)
+
+    plt.subplots_adjust(right=0.75)
     plt.xlabel(choice)
-    plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left", borderaxespad=0)
     plt.show()