cyclictest: Only log and show statistic data when samples are collected

Avoids saving misinformation in the report, by just reporting the latency
statistics when samples have been collected.  In addition also avoid
displaying these empty fields in the summary report.

Signed-off-by: David Sommerseth <davids@redhat.com>

1 files changed, 27 insertions, 26 deletions

diff --git a/rteval/modules/measurement/cyclictest.py b/rteval/modules/measurement/cyclictest.py
index efdf221..cdac13a 100644
--- a/rteval/modules/measurement/cyclictest.py
+++ b/rteval/modules/measurement/cyclictest.py
@@ -134,41 +134,42 @@ class RunData(object):
 
         stat_n.newTextChild(None, 'samples', str(self.numsamples))
 
-        n = stat_n.newTextChild(None, 'minimum', str(self.min))
-        n.newProp('unit', 'us')
+        if self.numsamples > 0:
+            n = stat_n.newTextChild(None, 'minimum', str(self.min))
+            n.newProp('unit', 'us')
 
-        n = stat_n.newTextChild(None, 'maximum', str(self.max))
-        n.newProp('unit', 'us')
+            n = stat_n.newTextChild(None, 'maximum', str(self.max))
+            n.newProp('unit', 'us')
 
-        n = stat_n.newTextChild(None, 'median', str(self.median))
-        n.newProp('unit', 'us')
+            n = stat_n.newTextChild(None, 'median', str(self.median))
+            n.newProp('unit', 'us')
 
-        n = stat_n.newTextChild(None, 'mode', str(self.mode))
-        n.newProp('unit', 'us')
+            n = stat_n.newTextChild(None, 'mode', str(self.mode))
+            n.newProp('unit', 'us')
 
-        n = stat_n.newTextChild(None, 'range', str(self.range))
-        n.newProp('unit', 'us')
+            n = stat_n.newTextChild(None, 'range', str(self.range))
+            n.newProp('unit', 'us')
 
-        n = stat_n.newTextChild(None, 'mean', str(self.mean))
-        n.newProp('unit', 'us')
+            n = stat_n.newTextChild(None, 'mean', str(self.mean))
+            n.newProp('unit', 'us')
 
-        n = stat_n.newTextChild(None, 'mean_absolute_deviation', str(self.mad))
-        n.newProp('unit', 'us')
+            n = stat_n.newTextChild(None, 'mean_absolute_deviation', str(self.mad))
+            n.newProp('unit', 'us')
 
-        n = stat_n.newTextChild(None, 'variance', str(self.variance))
-        n.newProp('unit', 'us')
+            n = stat_n.newTextChild(None, 'variance', str(self.variance))
+            n.newProp('unit', 'us')
 
-        n = stat_n.newTextChild(None, 'standard_deviation', str(self.stddev))
-        n.newProp('unit', 'us')
+            n = stat_n.newTextChild(None, 'standard_deviation', str(self.stddev))
+            n.newProp('unit', 'us')
 
-        hist_n = rep_n.newChild(None, 'histogram', None)
-        hist_n.newProp('nbuckets', str(len(self.samples)))
-        keys = self.samples.keys()
-        keys.sort()
-        for k in keys:
-            b_n = hist_n.newChild(None, 'bucket', None)
-            b_n.newProp('index', str(k))
-            b_n.newProp('value', str(self.samples[k]))
+            hist_n = rep_n.newChild(None, 'histogram', None)
+            hist_n.newProp('nbuckets', str(len(self.samples)))
+            keys = self.samples.keys()
+            keys.sort()
+            for k in keys:
+                b_n = hist_n.newChild(None, 'bucket', None)
+                b_n.newProp('index', str(k))
+                b_n.newProp('value', str(self.samples[k]))
 
         return rep_n