[LNT] Print machine name in LNT graph tooltip.

[marxin]

Summary

When having multiple lines in a graph, I consider it handy to print also machine name in other to navigate fast.

[MatzeB]

LGTM

lnt/server/ui/static/lnt_graph.js

function show_tooltip(x, y, item, pos, graph_data) {
     var data = item.datapoint;
     var meta_data = extract_metadata(item);
     var tip_body = '<div id="tooltip">';
 
     if (meta_data.title) {
         tip_body +=  "<b><a href=\"" + meta_data.link + "\">" + meta_data.title + "</a></b></br>";
     }
 
+    if (meta_data.machine) {
+        tip_body += "<b>Machine:</b> " + meta_data.machine + "<br>";
+    }
+
     if (meta_data.test_name) {
         tip_body += "<b>Test:</b> " + meta_data.test_name + "<br>";
     }
 
     if (meta_data.label) {
         tip_body += "<b>Revision:</b> " + meta_data.label + "<br>";
     }
     tip_body += "<b>Value:</b> " + data[1].toFixed(4) + "<br>";

lnt/server/ui/views.py

for i, (machine, test, field, field_index) in enumerate(graph_parameters):
                              (field.status_field.column.is_(None)))
 
         # Aggregate by revision.
         data = multidict.multidict((rev, (val, date, run_id))
                                    for val, rev, date, run_id in q).items()
 
         data.sort(key=lambda sample: convert_revision(sample[0], cache=revision_cache))
 
-        graph_datum.append((test.name, data, col, field, url))
+        graph_datum.append((test.name, data, col, field, url, machine))
 
         # Get baselines for this line
         num_baselines = len(baseline_parameters)
         for baseline_id, (baseline, baseline_title) in \
                 enumerate(baseline_parameters):
             q_baseline = session.query(field.column,
                                        ts.Order.llvm_project_revision,
                                        ts.Run.start_time, ts.Machine.name) \
if mean_parameter:
                     ((rev, date), val) for val, rev, date in q).items()
         data = [(rev,
                  [(lnt.server.reporting.analysis.calc_geomean(vals), date)])
                 for ((rev, date), vals) in data]
 
         # Sort data points according to revision number.
         data.sort(key=lambda sample: convert_revision(sample[0]))
 
-        graph_datum.append((test_name, data, col, field, None))
+        graph_datum.append((test_name, data, col, field, None, machine))
 
-    for name, data, col, field, url in graph_datum:
+    for name, data, col, field, url, machine in graph_datum:
         # Compute the graph points.
         errorbar_data = []
         points_data = []
         pts = []
         moving_median_data = []
         moving_average_data = []
 
         if normalize_by_median:
for name, data, col, field, url, machine in graph_datum:
                 aggregation_fn = max
 
             agg_value, agg_index = \
                 aggregation_fn((value, index)
                                for (index, value) in enumerate(values))
 
             # Generate metadata.
             metadata = {"label": point_label}
+            metadata["machine"] = machine.name
             metadata["date"] = str(dates[agg_index])
             if runs:
                 metadata["runID"] = str(runs[agg_index])
 
             if len(graph_datum) > 1:
                 # If there are more than one plot in the graph, also label the
                 # test name.
                 metadata["test_name"] = name