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osmo-gsm-tester
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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
This commit is contained in:
Andre Puschmann 2020-05-22 16:15:25 +02:00 committed by pespin
parent 9dbdb623a0
commit 2cfa4a3d3c
1 changed files with 8 additions and 4 deletions
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12
src/osmo_gsm_tester/obj/ms_srs.py
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@ -337,7 +337,7 @@ class srsUE(MS):
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 @@ class srsUE(MS):
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 @@ class srsUEMetrics(log.Origin):
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 @@ class srsUEMetrics(log.Origin):
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