try blacklisting _read_from_self from results

Still determining the reason _read_from_self has an abnormally large average callback and biases the results.

1 files changed, 18 insertions, 5 deletions

diff --git a/nemesis/html_gen.py b/nemesis/html_gen.py
index 841fbbc..60d0c06 100644
--- a/nemesis/html_gen.py
+++ b/nemesis/html_gen.py
@@ -7,7 +7,7 @@ def get_color(name):
     color_index = int(hash_object.hexdigest(), 16) % 360
     return f'hsl({color_index}, 100%, 50%)'
 
-def plot_results(results, filename):
+def plot_results(results, output_file, input_file):
     fig = make_subplots(rows=4, cols=1)
 
     for i, (coro_name, x_values) in enumerate(results.items(), start=1):
@@ -16,6 +16,8 @@ def plot_results(results, filename):
         y_throughput_list = []
         y_max_latency_list = []
         y_num_callbacks_list = []
+        latency_hover_text = []
+        max_latency_hover_text = []
 
         for speedup, experiments in x_values.items():
             for experiment in experiments:
@@ -29,16 +31,24 @@ def plot_results(results, filename):
                 y_num_callbacks_list.append(num_callbacks)
 
                 # handle average latency graph
+
                 if num_callbacks > 0:
 
+                    breakdown = "<br>".join([f"  {cb[0]}: {round(cb[1], 4)}" for cb in completed_callbacks])
                     total_wait = sum([cb[1] for cb in completed_callbacks])
-                    max_wait = max([cb[1] for cb in completed_callbacks])
+                    max_cb = max(completed_callbacks, key=lambda cb: cb[1])
+
                     latency = total_wait / num_callbacks
 
-                    y_max_latency_list.append(max_wait)
+                    y_max_latency_list.append(max_cb[1])
                     y_latency_list.append(latency)
+
                 else:
-                    y_latency_list.append(0)
+                    latency = 0
+                    y_latency_list.append(latency)
+
+                latency_hover_text.append(f"{coro_name}<br>Speedup: {speedup}<br>Average Wait: {round(latency, 4)}<br>Breakdown:<br>{breakdown}")
+                max_latency_hover_text.append(f"{coro_name}<br>Speedup: {speedup}<br>Max Wait: {round(max_cb[1], 4)}<br>Handle: {max_cb[0]}")
 
                 # handle throughput graph
                 throughput = num_callbacks / virtual_run_time
@@ -50,6 +60,7 @@ def plot_results(results, filename):
             mode='markers',
             name=coro_name,
             marker=dict(color=get_color(coro_name)),
+            hovertext=latency_hover_text,
             showlegend=True,
         ), row=1, col=1)
 
@@ -68,6 +79,7 @@ def plot_results(results, filename):
             mode='markers',
             name=coro_name,
             marker=dict(color=get_color(coro_name)),
+            hovertext=max_latency_hover_text,
             showlegend=False,
         ), row=3, col=1)
 
@@ -80,10 +92,11 @@ def plot_results(results, filename):
             showlegend=False,
         ), row=4, col=1)
 
+    fig.update_layout(title=input_file)
     fig.update_xaxes(title_text="speedup (% optimized away)", row=4, col=1)
     fig.update_yaxes(title_text="average latency (seconds)", row=1, col=1)
     fig.update_yaxes(title_text="throughput (handles per second)", row=2, col=1)
     fig.update_yaxes(title_text="maximum latency (seconds)", row=3, col=1)
     fig.update_yaxes(title_text="# of callbacks", row=4, col=1)
 
-    fig.write_html(filename)
+    fig.write_html(output_file)