ms_srs: trim leading zeros in UE metrics when calculating min_rolling_avg
this avoids a false negative detection when the UE attach takes a bit
longer and the first seconds all zeros are reported in the CSV
the HO test, for example, would fail in such a case as it expects
no zero TP over the course of the experiment.
Change-Id: I96dab17bb19249504dedda6659aed5eac0a65a26
diff --git a/src/osmo_gsm_tester/obj/ms_srs.py b/src/osmo_gsm_tester/obj/ms_srs.py
index 124b113..1c0f728 100644
--- a/src/osmo_gsm_tester/obj/ms_srs.py
+++ b/src/osmo_gsm_tester/obj/ms_srs.py
@@ -460,6 +460,8 @@
# calculate rolling average over window and take maximum value
result = numpy.amax(numpy.convolve(sel_data, numpy.ones((window,))/window, mode='valid'))
elif operation == 'min_rolling_avg':
+ # trim leading zeros to avoid false negative when UE attach takes longer
+ sel_data = numpy.trim_zeros(sel_data, 'f')
# calculate rolling average over window and take minimum value
result = numpy.amin(numpy.convolve(sel_data, numpy.ones((window,))/window, mode='valid'))