+/- 5 standard deviations lines

conbench/app/_plots.py

@@ -125,6 +125,14 @@ def time_series_plot(history, distribution, benchmark_id, height=250, width=1000
     commits_mean = [w["message"] for w in with_dist]
     dates_mean = [dateutil.parser.isoparse(w["timestamp"]) for w in with_dist]
 
+    alert_min, alert_max, commits_alert, dates_alert = [], [], [], []
+    for w in with_dist:
+        alert = 5 * float(w["mean_sd"])
+        alert_min.append(float(w["mean_mean"]) - alert)
+        alert_max.append(float(w["mean_mean"]) + alert)
+        commits_alert.append(w["message"])
+        dates_alert.append(dateutil.parser.isoparse(w["timestamp"]))
+
     source_data = dict(x=dates, y=times, commit=commits)
     source = bokeh.models.ColumnDataSource(data=source_data)
 
@@ -134,6 +142,12 @@ def time_series_plot(history, distribution, benchmark_id, height=250, width=1000
     source_data_mean = dict(x=dates_mean, y=times_mean, commit=commits_mean)
     source_mean = bokeh.models.ColumnDataSource(data=source_data_mean)
 
+    source_data_alert_min = dict(x=dates_alert, y=alert_min, commit=commits_alert)
+    source_alert_min = bokeh.models.ColumnDataSource(data=source_data_alert_min)
+
+    source_data_alert_max = dict(x=dates_alert, y=alert_max, commit=commits_alert)
+    source_alert_max = bokeh.models.ColumnDataSource(data=source_data_alert_max)
+
     tooltips = [
         ("date", "$x{%F}"),
         ("mean", "$y{0.000}"),
@@ -158,6 +172,8 @@ def time_series_plot(history, distribution, benchmark_id, height=250, width=1000
 
     p.line(source=source, legend_label="History")
     p.line(source=source_mean, color="#ffa600", legend_label="Mean")
+    p.line(source=source_alert_min, color="Silver", legend_label="+/- 5 σ")
+    p.line(source=source_alert_max, color="Silver")
     p.circle(source=source_x, size=8, color="#ff6361", legend_label="Benchmark")
 
     p.legend.location = "bottom_left"