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)
 
 

amari/kpi_test.py

@@ -21,7 +21,7 @@
 from __future__ import print_function, division, absolute_import
 
 from xlte.amari.kpi import LogMeasure, LogError, _trace as trace
-from xlte.kpi import Measurement, isNA
+from xlte.kpi import Measurement, isNA, StatT
 from golang import func, defer, b
 import io, json, re
 
@@ -75,7 +75,8 @@ class tLogMeasure:
     def _mok_init(t):
         t._mok = Measurement()
         # init fields extracted by amari.kpi from stats to 0
-        # this will be default values to verify against
+        # this will be default values to verify against.
+        # all other fields remain NA.
         for field in (
             'RRC.ConnEstabAtt.sum',
             'RRC.ConnEstabSucc.sum',
@@ -143,7 +144,7 @@ def test_LogMeasure():
     _ = t.expect1
 
     # empty stats after first attach
-    t.xlog( jstats(1, {}) )
+    t.xlog( jstats(1, {}, {}) )
     _('X.Tstart',                   0.02)
     _('X.δT',                       1-0.02)
     t.expect_nodata()
@@ -173,12 +174,12 @@ def test_LogMeasure():
     τ_xlog = 1          # timestamp of last emitted xlog entry
     τ_logm = τ_xlog-2+1 # timestamp of next measurement to be read from logm
     counters_prev = {}
-    def tstats(counters):
+    def tstats(stat_profiles, counters):
         nonlocal τ_xlog, τ_logm, counters_prev
         trace('\n>>> tstats τ_xlog: %s  τ_logm: %s' % (τ_xlog, τ_logm))
-        t.xlog( jstats(τ_xlog+1, counters) )  # xlog τ+1
-        t.read()                              # read+assert M for τ-1
-        _('X.Tstart',   τ_logm+1)             # start preparing next expected M at τ
+        t.xlog( jstats(τ_xlog+1, stat_profiles, counters) ) # xlog τ+1
+        t.read()                                            # read+assert M for τ-1
+        _('X.Tstart',   τ_logm+1)                           # start preparing next expected M at τ
         _('X.δT',       1)
         τ_xlog += 1
         τ_logm += 1
@@ -189,7 +190,7 @@ def test_LogMeasure():
         counters = counters_prev.copy()
         for k,δv in δcounters.items():
             counters[k] = counters.get(k,0) + δv
-        tstats(counters)
+        tstats({}, counters)
 
     # tevent is the verb to verify handling of events.
     # its logic is similar to tstats.
@@ -216,10 +217,8 @@ def test_LogMeasure():
         trace('\n>>> tdrb_stats τ: %s  τ_xlog: %s  τ_logm: %s' % (τ, τ_xlog, τ_logm))
         t.xlog( jdrb_stats(τ, qci_trx) )
 
-
-
     # further empty stats
-    tstats({})
+    tstats({}, {})
     _('X.Tstart',                   1)
     _('X.δT',                       1)
     _('RRC.ConnEstabAtt.sum',       0)
@@ -232,6 +231,32 @@ def test_LogMeasure():
     _('ERAB.EstabAddSuccNbr.sum',   0)
 
 
+    # DRB.UEActive
+    tstats(
+        {'C1.ue_active_count_min': 0,
+         'C1.ue_active_count_avg': 2.73,
+         'C1.ue_active_count_max': 3},
+        {}
+    )
+    _('DRB.UEActive',   StatT(0, 2.73, 3))
+    # p2
+    tstats(
+        {'C1.ue_active_count_min': 1,
+         'C1.ue_active_count_avg': 6.0,
+         'C1.ue_active_count_max': 8},
+        {}
+    )
+    _('DRB.UEActive',   StatT(1, 6.0, 8))
+    # p3
+    tstats(
+        {'C1.ue_active_count_min': 3,
+         'C1.ue_active_count_avg': 4.49,
+         'C1.ue_active_count_max': 7},
+        {}
+    )
+    _('DRB.UEActive',   StatT(3, 4.49, 7))
+
+
     # RRC.ConnEstab
     #
     # For init/fini correction LogMeasure accounts termination events in the
@@ -245,42 +270,59 @@ def test_LogMeasure():
     # init           0     3     2     5     0
     # fini    ø ←─── 2     1←─── 2←─── 4←─── 1
     # fini'          0     3 ²   2 ²   3 ¹   0
-    tstats({'C1.rrc_connection_request':        0,
-            'C1.rrc_connection_setup_complete': 2}) # completions for previous uncovered period
+    tstats(
+        {},
+        {'C1.rrc_connection_request':        0,
+         'C1.rrc_connection_setup_complete': 2} # completions for previous uncovered period
+    )
     _('RRC.ConnEstabAtt.sum',       0)
     _('RRC.ConnEstabSucc.sum',      0)  # not 2
     # p2
-    tstats({'C1.rrc_connection_request':        0 +3,  # 3 new initiations
-            'C1.rrc_connection_setup_complete': 2 +1}) # 1 new completion
+    tstats(
+        {},
+        {'C1.rrc_connection_request':        0 +3,      # 3 new initiations
+         'C1.rrc_connection_setup_complete': 2 +1}      # 1 new completion
+    )
     _('RRC.ConnEstabAtt.sum',       3)
     _('RRC.ConnEstabSucc.sum',      3)  # not 1
     # p3
-    tstats({'C1.rrc_connection_request':        0+3 +2,  # 2 new initiations
-            'C1.rrc_connection_setup_complete': 2+1 +2}) # 2 completions for p2
+    tstats(
+        {},
+        {'C1.rrc_connection_request':        0+3 +2,    # 2 new initiations
+         'C1.rrc_connection_setup_complete': 2+1 +2}    # 2 completions for p2
+    )
     _('RRC.ConnEstabAtt.sum',       2)
     _('RRC.ConnEstabSucc.sum',      2)  # 2, but it is 2 - 2(for_p2) + 2(from_p4)
     # p4
-    tstats({'C1.rrc_connection_request':        0+3+2 +5,  # 5 new initiations
-            'C1.rrc_connection_setup_complete': 2+1+2 +4}) # 2 completions for p3 + 2 new
+    tstats(
+        {},
+        {'C1.rrc_connection_request':        0+3+2 +5,  # 5 new initiations
+         'C1.rrc_connection_setup_complete': 2+1+2 +4}  # 2 completions for p3 + 2 new
+    )
     _('RRC.ConnEstabAtt.sum',       5)
     _('RRC.ConnEstabSucc.sum',      3)
     # p5
-    tstats({'C1.rrc_connection_request':        0+3+2+5 +0,  # no new initiations
-            'C1.rrc_connection_setup_complete': 2+1+2+4 +1}) # 1 completion for p4
+    tstats(
+        {},
+        {'C1.rrc_connection_request':        0+3+2+5 +0,  # no new initiations
+         'C1.rrc_connection_setup_complete': 2+1+2+4 +1}  # 1 completion for p4
+    )
     _('RRC.ConnEstabAtt.sum',       0)
     _('RRC.ConnEstabSucc.sum',      0)
 
 
     # S1SIG.ConnEstab,  ERAB.InitEstab
     tδstats({'s1_initial_context_setup_request':    +3,
-             's1_initial_context_setup_response':   +2})
+             's1_initial_context_setup_response':   +2}
+    )
     _('S1SIG.ConnEstabAtt',         3)
     _('S1SIG.ConnEstabSucc',        3) # 2 + 1(from_next)
     _('ERAB.EstabInitAttNbr.sum',   3) # currently same as S1SIG.ConnEstab
     _('ERAB.EstabInitSuccNbr.sum',  3) # ----//----
 
     tδstats({'s1_initial_context_setup_request':    +4,
-             's1_initial_context_setup_response':   +3})
+             's1_initial_context_setup_response':   +3}
+    )
     _('S1SIG.ConnEstabAtt',         4)
     _('S1SIG.ConnEstabSucc',        2) # 3 - 1(to_prev)
     _('ERAB.EstabInitAttNbr.sum',   4) # currently same as S1SIG.ConnEstab
@@ -407,10 +449,10 @@ def test_LogMeasure():
     tevent("service attach")
     t.expect_nodata()
 
-    t.xlog( jstats(τ_xlog+1, {i:1000, f:1000}) ) # LogMeasure restarts the queue after data starts to
+    t.xlog( jstats(τ_xlog+1, {}, {i:1000, f:1000}) ) # LogMeasure restarts the queue after data starts to
     τ_xlog += 1                                  # come in again. Do one t.xlog step manually to
                                                  # increase t.read - t.xlog distance back to 2.
-    tstats({i:1000+2, f:1000+2})
+    tstats({}, {i:1000+2, f:1000+2})
     _(I, 2) # no "extra" events even if counters start with jumped values after reattach
     _(F, 2) # and no fini correction going back through detach
 
@@ -424,31 +466,69 @@ def test_LogMeasure():
 
     # multiple cells
     # TODO emit per-cell measurements instead of accumulating all cells
-    tstats({})
+    tstats({}, {})
     t.expect_nodata()
-    tstats({})
+    tstats({}, {})
     _('RRC.ConnEstabAtt.sum',       0)
     _('RRC.ConnEstabSucc.sum',      0)
     #  C1 appears
-    tstats({'C1.rrc_connection_request':    12,     'C1.rrc_connection_setup_complete': 11})
+    tstats(
+        {
+            'C1.ue_active_count_min': 2,
+            'C1.ue_active_count_avg': 3.59,
+            'C1.ue_active_count_max': 4
+        },
+        {'C1.rrc_connection_request':    12,     'C1.rrc_connection_setup_complete': 11}
+    )
     _('RRC.ConnEstabAtt.sum',       12)
     _('RRC.ConnEstabSucc.sum',      11+1)
+    _('DRB.UEActive',               StatT(2, 3.59, 4))
     #  C2 appears
-    tstats({'C1.rrc_connection_request':    12+3,   'C1.rrc_connection_setup_complete': 11+3,
-            'C2.rrc_connection_request':    22,     'C2.rrc_connection_setup_complete': 21})
+    tstats(
+        {
+            'C1.ue_active_count_min': 1,
+            'C1.ue_active_count_avg': 2.87,
+            'C1.ue_active_count_max': 5,
+            'C2.ue_active_count_min': 1,
+            'C2.ue_active_count_avg': 1.43,
+            'C2.ue_active_count_max': 3
+        },
+        {'C1.rrc_connection_request':    12+3,   'C1.rrc_connection_setup_complete': 11+3,
+         'C2.rrc_connection_request':    22,     'C2.rrc_connection_setup_complete': 21}
+    )
     _('RRC.ConnEstabAtt.sum',       3+22)
     _('RRC.ConnEstabSucc.sum',      -1+3+21+2)
+    _('DRB.UEActive',               StatT(1+1, 2.87+1.43, 5+3))
     #  C1 and C2 stays
-    tstats({'C1.rrc_connection_request':    12+3+3, 'C1.rrc_connection_setup_complete': 11+3+3,
-            'C2.rrc_connection_request':    22+4,   'C2.rrc_connection_setup_complete': 21+4})
+    tstats(
+        {
+            'C1.ue_active_count_min': 3,
+            'C1.ue_active_count_avg': 3.10,
+            'C1.ue_active_count_max': 5,
+            'C2.ue_active_count_min': 0,
+            'C2.ue_active_count_avg': 0.62,
+            'C2.ue_active_count_max': 1
+        },
+        {'C1.rrc_connection_request':    12+3+3, 'C1.rrc_connection_setup_complete': 11+3+3,
+         'C2.rrc_connection_request':    22+4,   'C2.rrc_connection_setup_complete': 21+4}
+    )
     _('RRC.ConnEstabAtt.sum',       3+4)
     _('RRC.ConnEstabSucc.sum',      -2+3+4+2)
+    _('DRB.UEActive',               StatT(3+0, 3.10+0.62, 5+1))
     #  C1 disappears
-    tstats({'C2.rrc_connection_request':    22+4+4, 'C2.rrc_connection_setup_complete': 21+4+4})
+    tstats(
+        {
+            'C2.ue_active_count_min': 0,
+            'C2.ue_active_count_avg': 1.19,
+            'C2.ue_active_count_max': 3
+        },
+        {'C2.rrc_connection_request':    22+4+4, 'C2.rrc_connection_setup_complete': 21+4+4}
+    )
     _('RRC.ConnEstabAtt.sum',       4)
     _('RRC.ConnEstabSucc.sum',      4-2)
+    _('DRB.UEActive',               StatT(0, 1.19, 3))
     #  C2 disappears
-    tstats({})
+    tstats({}, {})
     _('RRC.ConnEstabAtt.sum',       0)
     _('RRC.ConnEstabSucc.sum',      0)
 
@@ -467,28 +547,28 @@ def test_LogMeasure_badinput():
     CC = 'RRC.ConnEstabAtt.sum'
 
     # initial ok entries
-    t.xlog( jstats(1, {}) )
-    t.xlog( jstats(2, {cc: 2}) )
-    t.xlog( jstats(3, {cc: 2+3}) )
+    t.xlog( jstats(1, {}, {}) )
+    t.xlog( jstats(2, {}, {cc: 2}) )
+    t.xlog( jstats(3, {}, {cc: 2+3}) )
     # bad: no counters
     t.xlog('{"message":"stats", "utc":21, "counters": {"messages": {}}, "cells": {"1": {}}}')
     t.xlog('{"message":"stats", "utc":22, "counters": {"messages": {}}, "cells": {"1": {"counters": {}}}}')
     t.xlog('{"message":"stats", "utc":23, "cells": {"1": {"counters": {"messages": {}}}}}')
     t.xlog('{"message":"stats", "utc":24, "counters": {}, "cells": {"1": {"counters": {"messages": {}}}}}')
     # follow-up ok entries
-    t.xlog( jstats(31, {cc: 30+4}) )
-    t.xlog( jstats(32, {cc: 30+4+5}) )
+    t.xlog( jstats(31, {}, {cc: 30+4}) )
+    t.xlog( jstats(32, {}, {cc: 30+4+5}) )
     # badline 1
     t.xlog( "zzzqqqrrr" )
     # more ok entries
-    t.xlog( jstats(41, {cc: 40+6}) )
-    t.xlog( jstats(42, {cc: 40+6+7}) )
+    t.xlog( jstats(41, {}, {cc: 40+6}) )
+    t.xlog( jstats(42, {}, {cc: 40+6+7}) )
     # badline 2 + followup event
     t.xlog( "hello world" )
     t.xlog( '{"meta": {"event": "service attach", "time": 50}}' )
     # more ok entries
-    t.xlog( jstats(51, {cc: 50+8}) )
-    t.xlog( jstats(52, {cc: 50+8+9}) )
+    t.xlog( jstats(51, {}, {cc: 50+8}) )
+    t.xlog( jstats(52, {}, {cc: 50+8+9}) )
 
     def readok(τ, CC_value):
         _('X.Tstart',   τ)
@@ -540,11 +620,11 @@ def test_LogMeasure_cc_wraparound():
     cc = 'C1.rrc_connection_request'
     CC = 'RRC.ConnEstabAtt.sum'
 
-    t.xlog( jstats(1, {}) )
-    t.xlog( jstats(2, {cc: 13}) )
-    t.xlog( jstats(3, {cc: 12}) )   # cc↓   - should be reported
-    t.xlog( jstats(4, {cc: 140}) )  # cc↑↑  - should start afresh
-    t.xlog( jstats(5, {cc: 150}) )
+    t.xlog( jstats(1, {}, {}) )
+    t.xlog( jstats(2, {}, {cc: 13}) )
+    t.xlog( jstats(3, {}, {cc: 12}) )   # cc↓   - should be reported
+    t.xlog( jstats(4, {}, {cc: 140}) )  # cc↑↑  - should start afresh
+    t.xlog( jstats(5, {}, {cc: 150}) )
 
     def readok(τ, CC_value):
         _('X.Tstart',   τ)
@@ -574,10 +654,10 @@ def test_LogMeasure_sync():
     cc = 'C1.rrc_connection_request'
     CC = 'RRC.ConnEstabAtt.sum'
 
-    t.xlog( jstats(1, {}) )
-    t.xlog( jstats(2, {cc: 4}) )
+    t.xlog( jstats(1, {}, {}) )
+    t.xlog( jstats(2, {}, {cc: 4}) )
     t.xlog( '{"meta": {"event": "sync", "time": 2.5, "state": "attached", "reason": "periodic", "generator": "xlog ws://localhost:9001 stats[]/30.0s"}}' )
-    t.xlog( jstats(3, {cc: 7}) )
+    t.xlog( jstats(3, {}, {cc: 7}) )
 
     def readok(τ, CC_value):
         _('X.Tstart',   τ)
@@ -593,14 +673,29 @@ def test_LogMeasure_sync():
     readok(2, 3)            # 2-3  jumping over sync
 
 
-# jstats returns json-encoded stats message corresponding to counters dict.
+# jstats returns json-encoded stats message corresponding to
+# the given statistical profile and counter dicts.
 #
-# if a counter goes as "Cxxx.yyy" it is emitted as counter yyy of cell xxx in the output.
+# a key formatted as "Cxxx.yyy" is emitted as measurement yyy of cell xxx in the output.
 # τ goes directly to stats['utc'] as is.
-def jstats(τ, counters):  # -> str
+def jstats(τ, stat_profiles, counters):  # -> str
+    g_sp    = {}  # global statistical profiles
     g_cc    = {}  # global cumulative counters
     cells   = {}  # .cells
 
+    for sp, value in stat_profiles.items():
+        _ = re.match(r"^C([^.]+)\.(.+)$", sp)
+        if _ is not None:
+            cell = _.group(1)
+            sp   = _.group(2)
+            # enforce correct cell structure
+            cells.setdefault(cell, {})          \
+                 .setdefault("counters", {})    \
+                 .setdefault("messages", {})
+            cells[cell][sp] = value
+        else:
+            g_sp[sp] = value
+
     for cc, value in counters.items():
         _ = re.match(r"^C([^.]+)\.(.+)$", cc)
         if _ is not None:
@@ -616,6 +711,7 @@ def jstats(τ, counters):  # -> str
     s = {
         "message":  "stats",
         "utc":      τ,
+        **g_sp,
         "cells":    cells,
         "counters": {"messages": g_cc},
     }
@@ -623,17 +719,29 @@ def jstats(τ, counters):  # -> str
     return json.dumps(s)
 
 def test_jstats():
-    assert jstats(0, {}) == '{"message": "stats", "utc": 0, "cells": {}, "counters": {"messages": {}}}'
-    assert jstats(123.4, {"C1.rrc_x": 1, "s1_y": 2, "C1.rrc_z": 3, "x2_zz": 4}) == \
-            '{"message": "stats", "utc": 123.4, "cells": {"1": {"counters": {"messages": {"rrc_x": 1, "rrc_z": 3}}}}, "counters": {"messages": {"s1_y": 2, "x2_zz": 4}}}'
-
-    # multiple cells
-    assert jstats(432.1, {"C1.rrc_x": 11, "C2.rrc_y": 22, "C3.xyz": 33, "C1.abc": 111, "xyz": 44}) == \
-            '{"message": "stats", "utc": 432.1, "cells": {'                 + \
-            '"1": {"counters": {"messages": {"rrc_x": 11, "abc": 111}}}, '  + \
-            '"2": {"counters": {"messages": {"rrc_y": 22}}}, '              + \
-            '"3": {"counters": {"messages": {"xyz": 33}}}}, '               + \
-            '"counters": {"messages": {"xyz": 44}}}'
+    assert jstats(0, {}, {}) == '{"message": "stats", "utc": 0, "cells": {}, "counters": {"messages": {}}}'
+
+    # only statistical profiles
+    assert jstats(1.2, {"C1.ue_x_min": 1, "r1_y": 2, "C1.ue_x_avg": 3, "s2_z": 4, "C1.ue_x_max": 5}, {}) == \
+            '{"message": "stats", "utc": 1.2, "r1_y": 2, "s2_z": 4, '                                        + \
+            '"cells": {"1": {"counters": {"messages": {}}, "ue_x_min": 1, "ue_x_avg": 3, "ue_x_max": 5}}, '  + \
+            '"counters": {"messages": {}}}'
+
+    # only counters
+    assert jstats(12.34, {}, {"C1.rrc_x": 1, "s1_y": 2, "C1.rrc_z": 3, "x2_zz": 4}) == \
+            '{"message": "stats", "utc": 12.34, "cells": {"1": {"counters": {"messages": {"rrc_x": 1, "rrc_z": 3}}}}, ' + \
+            '"counters": {"messages": {"s1_y": 2, "x2_zz": 4}}}'
+
+    # multiple cells with both statistical profiles and counters
+    assert jstats(
+        432.1,
+        {"C1.ue_w": 11, "C2.ue_ww": 22, "C3.ue_x": 33, "C1.ue_xx": 44, "rst": 55},
+        {"C1.rrc_x": 11, "C2.rrc_y": 22, "C3.xyz": 33, "C1.abc": 44, "xyz": 55}
+    ) == '{"message": "stats", "utc": 432.1, "rst": 55, "cells": {'                             + \
+        '"1": {"counters": {"messages": {"rrc_x": 11, "abc": 44}}, "ue_w": 11, "ue_xx": 44}, '  + \
+        '"2": {"counters": {"messages": {"rrc_y": 22}}, "ue_ww": 22}, '                         + \
+        '"3": {"counters": {"messages": {"xyz": 33}}, "ue_x": 33}}, '                           + \
+        '"counters": {"messages": {"xyz": 55}}}'
 
 
 # jdrb_stats, similarly to jstats, returns json-encoded x.drb_stats message