amari.kpi: Add support for DRB.UEActive measurement

amari/kpi.py

@@ -205,9 +205,9 @@ def _read(logm):
 # _handle_stats handles next stats xlog entry upon _read request.
 @func(LogMeasure)
 def _handle_stats(logm, stats: xlog.Message, m_prev: kpi.Measurement):
-    # build Measurement from stats' counters.
+    # build Measurement from stats' statistical profiles and counters.
     #
-    # we take δ(stats_prev, stat) and process it mapping Amarisoft counters to
+    # we take δ(stats_prev, stat) and process it, mapping Amarisoft counters to
     # 3GPP ones specified by kpi.Measurement. This approach has following limitations:
     #
     # - for most of the counters there is no direct mapping in between
@@ -260,6 +260,13 @@ def _handle_stats(logm, stats: xlog.Message, m_prev: kpi.Measurement):
     # do init/fini correction if there was also third preceding stats message.
     m = logm._m.copy() # [stats_prev, stats)
 
+    # cell_statt_profile returns the StatT statistical profile value triplet in stats
+    def cell_statt_profile(cell, statt_profile):
+        return tuple(
+            _stats_cell_sp_el(stats, cell, statt_profile + '_' + el)
+            for el in ['min', 'avg', 'max']
+        )
+
     # δcc(counter) tells how specified global cumulative counter changed since last stats result.
     def δcc(counter):
         old = _stats_cc(stats_prev, counter)
@@ -305,6 +312,30 @@ def _handle_stats(logm, stats: xlog.Message, m_prev: kpi.Measurement):
     # compute δ for counters.
     # any logic error in data will be reported via LogError.
     try:
+        cells = set(stats['cells'].keys())  # NOTE cells are taken only from stats, not from stat_prev
+
+        # DRB: number of active UEs
+        #
+        # Aggregate statistical profile for all cells.
+        # While summing the averages is correct, it is impossible to compute
+        # an accurate value of the aggregate minimum and maximum across all cells.
+        # Summing them is the best approximation that will produce
+        # a wider interval containing the correct values, i.e.:
+        # Σ(minimums) <= min(Σ) and max(Σ) <= Σ(maximums)
+        #                  [ min,  avg,  max]
+        Σue_active_count = [None, None, None]
+
+        for cell in cells:
+            cell_ue_active_count = cell_statt_profile(cell, 'ue_active_count')
+
+            for i, el in enumerate(cell_ue_active_count):
+                if el is not None:
+                    Σue_active_count[i] = Σue_active_count[i] or 0
+                    Σue_active_count[i] += el
+
+        # any None here will map to kpi.NA
+        m['DRB.UEActive'] = kpi.StatT(*Σue_active_count)
+
         # RRC: connection establishment
         #
         # Aggregate statistics for all cells because in E-RAB Accessibility we need
@@ -315,7 +346,6 @@ def _handle_stats(logm, stats: xlog.Message, m_prev: kpi.Measurement):
         # same whether we do aggregation here or in kpi.Calc.erab_accessibility().
         #
         # TODO rework to emit per-cell measurements when/if we need per-cell KPIs
-        cells = set(stats['cells'].keys())  # NOTE cells are taken only from stats, not from stat_prev
         δΣcell_rrc_connection_request = 0   # (if a cell disappears its counters stop to be accounted)
         δΣcell_rrc_connection_setup_complete = 0
         for cell in cells:
@@ -368,6 +398,15 @@ def _stats_check(stats: xlog.Message):
         raise LogError(stats.timestamp, "stats: %s" % e)  from None
     return
 
+# _stats_cell_sp_el returns specified per-cell element of a statistical profile.
+#
+# stats is assumed to be already verified by _stats_check.
+def _stats_cell_sp_el(stats: xlog.Message, cell: str, stat_profile_el: str):
+    _ = stats['cells'].get(cell)
+    if _ is None:
+        return None     # cell is absent in this stats
+    return _.get(stat_profile_el, None)
+
 # _stats_cc returns specified global cumulative counter from stats result.
 #
 # stats is assumed to be already verified by _stats_check.
@@ -378,7 +417,7 @@ def _stats_cc(stats: xlog.Message, counter: str):
 def _stats_cell_cc(stats: xlog.Message, cell: str, counter: str):
     _ = stats['cells'].get(cell)
     if _ is None:
-        return 0    # cell is absent in this stats
+        return 0        # cell is absent in this stats
     return _['counters']['messages'].get(counter, 0)