ms_srs: add max rolling average as metric operation
this allows to calculate the rolling average over a specific
window in time and take the maximum of that
this is useful to get average value for 30s UDP traffic for
example from the UE metrics
Change-Id: I34bbfe08dbc1f27b86c9805f54649d44d697fa18
diff --git a/src/osmo_gsm_tester/obj/ms_srs.py b/src/osmo_gsm_tester/obj/ms_srs.py
index 3fa282e..a147ce6 100644
--- a/src/osmo_gsm_tester/obj/ms_srs.py
+++ b/src/osmo_gsm_tester/obj/ms_srs.py
@@ -337,7 +337,7 @@
return self._get_counter_handover_success()
raise log.Error('counter %s not implemented!' % counter_name)
- def verify_metric(self, value, operation='avg', metric='dl_brate', criterion='gt'):
+ def verify_metric(self, value, operation='avg', metric='dl_brate', criterion='gt', window=1):
# file is not properly flushed until the process has stopped.
if self.running():
self.stop()
@@ -351,13 +351,13 @@
self.err('Failed copying back metrics file from remote host')
raise e
metrics = srsUEMetrics(self.metrics_file)
- return metrics.verify(value, operation, metric, criterion)
+ return metrics.verify(value, operation, metric, criterion, window)
numpy = None
class srsUEMetrics(log.Origin):
- VALID_OPERATIONS = ['avg', 'sum']
+ VALID_OPERATIONS = ['avg', 'sum', 'max_rolling_avg']
VALID_CRITERION = ['eq','gt','lt']
CRITERION_TO_SYM = { 'eq' : '==', 'gt' : '>', 'lt' : '<' }
CRYTERION_TO_SYM_OPPOSITE = { 'eq' : '!=', 'gt' : '<=', 'lt' : '>=' }
@@ -378,7 +378,7 @@
self.err("Error parsing metrics CSV file %s" % self.metrics_file)
raise error
- def verify(self, value, operation='avg', metric='dl_brate', criterion='gt'):
+ def verify(self, value, operation='avg', metric='dl_brate', criterion='gt', window=1):
if operation not in self.VALID_OPERATIONS:
raise log.Error('Unknown operation %s not in %r' % (operation, self.VALID_OPERATIONS))
if criterion not in self.VALID_CRITERION:
@@ -394,6 +394,10 @@
result = numpy.average(sel_data)
elif operation == 'sum':
result = numpy.sum(sel_data)
+ elif operation == 'max_rolling_avg':
+ # calculate rolling average over window and take maximum value
+ result = numpy.amax(numpy.convolve(sel_data, numpy.ones((window,))/window, mode='valid'))
+
self.dbg(result=result, value=value)
success = False