kpi: Establish data model for DRB.IPLatDl and DRB.UEActive

3GPP says that this values are averages over collected samples and over
observed time period. But if we are to follow 3GPP as-is there is a problem how
to aggregate two Measurements corresponding to two different periods into one
bigger Mesurement that corresponds to combined time period.

-> Solve that by introducing "statistical profile" types and use them for DRB.IPLatDl and DRB.UEActive;
-> Teach Calc.aggregate to aggregate such statistical profiles via corresponding math.

See individual patches for details.

/reviewed-by @paul.graydon
/reviewed-on kirr/xlte!9

demo/kpidemo.ipynb

@@ -355,7 +355,7 @@
    "source": [
     "Let's now look at <u>raw counters</u>.\n",
     "\n",
-    "Each Measurement comes with counters measured during particular interval. To get total values we need to aggregate them throughout all observation time via `Calc.sum`. Let's use already-loaded MeasurementLog data to showcase this:"
+    "Each Measurement comes with counters measured during particular interval. To get total values we need to aggregate them throughout all observation time via `Calc.aggregate`. Let's use already-loaded MeasurementLog data to showcase this:"
    ]
   },
   {
@@ -371,7 +371,7 @@
     "mhead = mlog.data()[0]\n",
     "mtail = mlog.data()[-1]\n",
     "calc_total = kpi.Calc(mlog, mhead['X.Tstart'], mtail['X.Tstart']+mtail['X.δT'])\n",
-    "Σ = calc_total.sum()"
+    "Σ = calc_total.aggregate()"
    ]
   },
   {

demo/kpidemo.py

@@ -136,7 +136,7 @@ def main():
     mhead = mlog.data()[0]
     mtail = mlog.data()[-1]
     calc_total = kpi.Calc(mlog, mhead['X.Tstart'], mtail['X.Tstart']+mtail['X.δT'])
-    Σ = calc_total.sum()
+    Σ = calc_total.aggregate()
     print_ΣMeasurement(Σ)
 
 

kpi.py

@@ -21,7 +21,7 @@
 
 - Calc is KPI calculator. It can be instantiated on MeasurementLog and time
   interval over which to perform computations. Use Calc methods such as
-  .erab_accessibility() and .eutran_ip_throughput() to compute KPIs, and .sum()
+  .erab_accessibility() and .eutran_ip_throughput() to compute KPIs, and .aggregate()
   to compute aggregated measurements.
 
 - MeasurementLog maintains journal with result of measurements. Use .append()
@@ -54,6 +54,8 @@ from __future__ import print_function, division, absolute_import
 import numpy as np
 from golang import func
 
+import warnings
+
 
 # Calc provides way to compute KPIs over given measurement data and time interval.
 #
@@ -71,7 +73,7 @@ from golang import func
 #       ──────|─────|────[────|────)──────|──────|────────>
 #                    ←─ τ_lo      τ_hi ──→          time
 #
-# It is also possible to merely aggregate measured values via .sum() .
+# It is also possible to merely aggregate measured values via .aggregate() .
 #
 # See also: MeasurementLog, Measurement, ΣMeasurement.
 class Calc:
@@ -119,6 +121,41 @@ class MeasurementLog:
     pass
 
 
+# Stat[dtype] represents result of statistical profiling with arbitrary sampling
+# for a value with specified dtype.
+#
+# It is organized as NumPy structured scalar with avg, min, max and n fields.
+#
+# It is used inside Measurement for e.g. DRB.IPLatDl.QCI .
+class Stat(np.void):
+    # _dtype_for returns dtype that Stat[dtype] will use.
+    @classmethod
+    def _dtype_for(cls, dtype):
+        return np.dtype((cls, [
+            ('avg', np.float64),    # NOTE even int becomes float on averaging
+            ('min', dtype),
+            ('max', dtype),
+            ('n',   np.int64)]))
+
+# StatT[dtype] represents result of statistical profiling with time-based sampling
+# for a value with specified dtype.
+#
+# It is organized as NumPy structured scalar with avg, min and max fields.
+#
+# NOTE contrary to Stat there is no n field and containing Measurement.X.δT
+#      should be taken to know during which time period the profile was collected.
+#
+# It is used inside Measurement for e.g. DRB.UEActive .
+class StatT(np.void):
+    # _dtype_for returns dtype that StatT[dtype] will use.
+    @classmethod
+    def _dtype_for(cls, dtype):
+        return np.dtype((cls, [
+            ('avg', np.float64),    # see avg note in Stat
+            ('min', dtype),
+            ('max', dtype)]))
+
+
 # Measurement represents set of measured values and events observed and counted
 # during one particular period of time.
 #
@@ -155,16 +192,22 @@ class MeasurementLog:
 class Measurement(np.void):
     Tcc    = np.int32   # cumulative counter
     Ttime  = np.float64 # time is represented in seconds since epoch
+    S  = Stat ._dtype_for   # statistical profile with arbitrary sampling
+    St = StatT._dtype_for   # statistical profile with time-based sampling
 
     # _dtype defines measured values and events.
     _dtype = np.dtype([
         ('X.Tstart',                        Ttime),     # when the measurement started
         ('X.δT',                            Ttime),     # time interval during which the measurement was made
 
-        # below come values/events as specified by TS 32.425 and TS 32.450
-        # NOTE all .QCI and .CAUSE are expanded from outside.
+        # below comes definition of values/events as specified by TS 32.425 and TS 32.450
+        #
+        # - .QCI   suffix means a value comes as array of per-QCI values.
+        # - .CAUSE suffix means a value comes as array of per-CAUSE values.
+        #
+        # NOTE both .QCI and .CAUSE are expanded from outside.
         #
-        # NAME                              TYPE          UNIT      TS 32.425 reference + ...
+        # NAME                            TYPE/DTYPE      UNIT      TS 32.425 reference + ...
         ('RRC.ConnEstabAtt.CAUSE',          Tcc),       # 1         4.1.1.1
         ('RRC.ConnEstabSucc.CAUSE',         Tcc),       # 1         4.1.1.2
 
@@ -179,9 +222,10 @@ class Measurement(np.void):
 
         ('DRB.PdcpSduBitrateUl.QCI',        np.float64),# bit/s     4.4.1.1                 NOTE not kbit/s
         ('DRB.PdcpSduBitrateDl.QCI',        np.float64),# bit/s     4.4.1.2                 NOTE not kbit/s
-        # XXX mean is not good for our model
-        # TODO mean -> total + npkt?
-        #('DRB.IPLatDl.QCI',                Ttime),     # s         4.4.5.1  32.450:6.3.2   NOTE not ms
+
+        ('DRB.UEActive',                 St(np.int32)), # 1         4.4.2.4  36.314:4.1.3.3
+
+        ('DRB.IPLatDl.QCI',               S(Ttime)),    # s         4.4.5.1  32.450:6.3.2   NOTE not ms
 
         # DRB.IPThpX.QCI = DRB.IPVolX.QCI / DRB.IPTimeX.QCI         4.4.6.1-2 32.450:6.3.1
         ('DRB.IPVolDl.QCI',                 np.int64),  # bit       4.4.6.3  32.450:6.3.1   NOTE not kbit
@@ -206,6 +250,8 @@ class Measurement(np.void):
         ('PEE.Energy',                      np.float64),# J         4.12.2                  NOTE not kWh
     ])
 
+    del S, St
+
 
 # Interval is NumPy structured scalar that represents [lo,hi) interval.
 #
@@ -224,7 +270,7 @@ class Interval(np.void):
 # It is similar to Measurement, but each value comes accompanied with
 # information about how much time there was no data for that field:
 #
-#       Σ[f].value = Σ Mi[f]     if Mi[f] ≠ NA
+#       Σ[f].value = Aggregate Mi[f]        if Mi[f] ≠ NA
 #                           i
 #
 #       Σ[f].τ_na  =        Σ  Mi[X.δT]     if Mi[f] = NA
@@ -232,10 +278,10 @@ class Interval(np.void):
 class ΣMeasurement(np.void):
     _ = []
     for name in Measurement._dtype.names:
-        typ = Measurement._dtype.fields[name][0].type
+        dtyp = Measurement._dtype.fields[name][0]
         if not name.startswith('X.'):   # X.Tstart, X.δT
-            typ = np.dtype([('value', typ), ('τ_na', Measurement.Ttime)])
-        _.append((name, typ))
+            dtyp = np.dtype([('value', dtyp), ('τ_na', Measurement.Ttime)])
+        _.append((name, dtyp))
     _dtype = np.dtype(_)
     del _
 
@@ -273,6 +319,25 @@ def __new__(cls):
             Σ[field]['τ_na']  = 0
     return Σ
 
+# Stat() creates new Stat instance with specified values and dtype.
+@func(Stat)
+def __new__(cls, min, avg, max, n, dtype=np.float64):
+    s = _newscalar(cls, cls._dtype_for(dtype))
+    s['min'] = min
+    s['avg'] = avg
+    s['max'] = max
+    s['n']   = n
+    return s
+
+# StatT() creates new StatT instance with specified values and dtype.
+@func(StatT)
+def __new__(cls, min, avg, max, dtype=np.float64):
+    s = _newscalar(cls, cls._dtype_for(dtype))
+    s['min'] = min
+    s['avg'] = avg
+    s['max'] = max
+    return s
+
 
 # _all_qci expands <name>.QCI into <name>.sum and [] of <name>.<qci> for all possible qci values.
 # TODO remove and use direct array access (after causes are expanded into array too)
@@ -366,6 +431,34 @@ def __str__(m):
         vv.append(_vstr(m[field]))
     return "(%s)" % ', '.join(vv)
 
+
+# __repr__ returns Stat(min, avg, max, n, dtype=...)
+# NA values are represented as "ø".
+@func(Stat)
+def __repr__(s):
+    return "Stat(%s, %s, %s, %s, dtype=%s)" % (_vstr(s['min']), _vstr(s['avg']),
+                _vstr(s['max']), _vstr(s['n']), s['min'].dtype)
+
+# __repr__ returns StatT(min, avg, max, dtype=...)
+# NA values are represented as "ø".
+@func(StatT)
+def __repr__(s):
+    return "StatT(%s, %s, %s, dtype=%s)" % (_vstr(s['min']), _vstr(s['avg']),
+                _vstr(s['max']), s['min'].dtype)
+
+# __str__ returns "<min avg max>·n"
+# NA values are represented as "ø".
+@func(Stat)
+def __str__(s):
+    return "<%s %s %s>·%s" % (_vstr(s['min']), _vstr(s['avg']), _vstr(s['max']), _vstr(s['n']))
+
+# __str__ returns "<min avg max>"
+# NA values are represented as "ø".
+@func(StatT)
+def __str__(s):
+    return "<%s %s %s>" % (_vstr(s['min']), _vstr(s['avg']), _vstr(s['max']))
+
+
 # _vstr returns string representation of scalar or subarray v.
 def _vstr(v):  # -> str
     if v.shape == ():                       # scalar
@@ -377,9 +470,17 @@ def _vstr(v):  # -> str
 
     va = []                                 # subarray with some non-ø data
     for k in range(v.shape[0]):
-        if v[k] == 0:
+        vk = v[k]
+        if isinstance(vk, np.void):
+            for name in vk.dtype.names:
+                if vk[name] != 0:
+                    break
+            else:
                 continue
-        va.append('%d:%s' % (k, 'ø' if isNA(v[k]) else str(v[k])))
+        else:
+            if vk == 0:
+                continue
+        va.append('%d:%s' % (k, 'ø' if isNA(vk) else str(vk)))
     return "{%s}" % ' '.join(va)
 
 
@@ -422,8 +523,14 @@ def _check_valid(m):
             continue
 
         # * ≥ 0
+        if not isinstance(v, np.void):
             if v < 0:
                 bad(".%s < 0  (%s)" % (field, v))
+        else:
+            for vfield in v.dtype.names:
+                vf = v[vfield]
+                if not isNA(vf) and vf < 0:
+                    bad(".%s.%s < 0  (%s)" % (field, vfield, vf))
 
         # fini ≤ init
         if "Succ" in field:
@@ -696,14 +803,32 @@ def eutran_ip_throughput(calc): # -> IPThp[QCI][dl,ul]
     return thp
 
 
-# sum aggregates values of all Measurements in covered time interval.
-# TODO tests
+# aggregate aggregates values of all Measurements in covered time interval.
 @func(Calc)
-def sum(calc): # -> ΣMeasurement
+def aggregate(calc): # -> ΣMeasurement
     Σ = ΣMeasurement()
     Σ['X.Tstart'] = calc.τ_lo
     Σ['X.δT']     = calc.τ_hi - calc.τ_lo
 
+    def xmin(a, b):
+        if isNA(a): return b
+        if isNA(b): return a
+        return min(a, b)
+
+    def xmax(a, b):
+        if isNA(a): return b
+        if isNA(b): return a
+        return max(a, b)
+
+    def xavg(a, na, b, nb): # -> <ab>, na+nb
+        if isNA(a) or isNA(na):
+            return b, nb
+        if isNA(b) or isNA(nb):
+            return a, na
+        nab = na+nb
+        ab = (a*na + b*nb)/nab
+        return ab, nab
+
     for m in calc._miter():
         for field in m.dtype.names:
             if field.startswith('X.'):  # X.Tstart, X.δT
@@ -713,15 +838,45 @@ def sum(calc): # -> ΣMeasurement
             if v.shape != ():           # skip subarrays - rely on aliases
                 continue
 
+            Σf = Σ[field]       # view to Σ[field]
+            Σv = Σf['value']    # view to Σ[field]['value']
+
             if isNA(v):
-                Σ[field]['τ_na'] += m['X.δT']
+                Σf['τ_na'] += m['X.δT']
+                continue
+
+            if isNA(Σv):
+                Σf['value'] = v
+                continue
+
+            if isinstance(v, np.number):
+                Σf['value'] += v
+
+            elif isinstance(v, StatT):
+                Σv['min'] = xmin(Σv['min'], v['min'])
+                Σv['max'] = xmax(Σv['max'], v['max'])
+                # TODO better sum everything and then divide as a whole to avoid loss of precision
+                Σv['avg'], _ = xavg(Σv['avg'], m['X.Tstart'] - Σ['X.Tstart'] - Σf['τ_na'],
+                                     v['avg'], m['X.δT'])
+
+            elif isinstance(v, Stat):
+                Σv['min'] = xmin(Σv['min'], v['min'])
+                Σv['max'] = xmax(Σv['max'], v['max'])
+                # TODO better sum everything and then divide as a whole to avoid loss of precision
+                Σv['avg'], Σv['n'] = xavg(Σv['avg'], Σv['n'],
+                                           v['avg'],  v['n'])
+
             else:
-                if isNA(Σ[field]['value']):
-                    Σ[field]['value'] = 0
-                Σ[field]['value'] += v
+                raise AssertionError("Calc.aggregate: unexpected type %r" % type(v))
 
     return Σ
 
+# sum is deprecated alias to aggregate.
+@func(Calc)
+def sum(calc):
+    warnings.warn("Calc.sum is deprecated -> use Calc.aggregate instead", DeprecationWarning, stacklevel=4)
+    return calc.aggregate()
+
 
 # _miter iterates through [.τ_lo, .τ_hi) yielding Measurements.
 #
@@ -819,10 +974,13 @@ def _i2pc(x: Interval): # -> Interval
 
 # _newscalar creates new NumPy scalar instance with specified type and dtype.
 def _newscalar(typ, dtype):
-    _ = np.zeros(shape=(), dtype=(typ, dtype))
+    dtyp = np.dtype((typ, dtype))   # dtype with .type adjusted to be typ
+    assert dtyp == dtype
+    assert dtyp.type is typ
+    _ = np.zeros(shape=(), dtype=dtyp)
     s = _[()]
     assert type(s) is typ
-    assert s.dtype == dtype
+    assert s.dtype is dtyp
     return s
 
 
@@ -833,15 +991,20 @@ def NA(dtype):
     typ = dtype.type
     # float
     if issubclass(typ, np.floating):
-        na = np.nan
+        na = typ(np.nan)  # return the same type as dtype has, e.g. np.int32, not int
     # int: NA is min value
     elif issubclass(typ, np.signedinteger):
-        na = np.iinfo(typ).min
-
+        na = typ(np.iinfo(typ).min)
+    # structure: NA is combination of NAs for fields
+    elif issubclass(typ, np.void):
+        na = _newscalar(typ, dtype)
+        for field in dtype.names:
+            na[field] = NA(dtype.fields[field][0])
     else:
         raise AssertionError("NA not defined for dtype %s" % (dtype,))
 
-    return typ(na)  # return the same type as dtype has, e.g. np.int32, not int
+    assert type(na) is typ
+    return na
 
 
 # isNA returns whether value represent NA.
@@ -850,6 +1013,26 @@ def NA(dtype):
 # returns array(True/False) if value is array.
 def isNA(value):
     na = NA(value.dtype)
+
+    # `nan == nan` gives False
+    # work it around by checking for nan explicitly
+    if isinstance(na, np.void): # items are structured scalars
+        vna = None
+        for field in value.dtype.names:
+            nf = na[field]
+            vf = value[field]
+            if np.isnan(nf):
+                x = np.isnan(vf)
+            else:
+                x = (vf == nf)
+
+            if vna is None:
+                vna = x
+            else:
+                vna &= x
+        return vna
+    else:
         if np.isnan(na):
-        return np.isnan(value)  # `nan == nan` gives False
+            return np.isnan(value)
+
     return value == na

kpi_test.py

 # -*- coding: utf-8 -*-
-# Copyright (C) 2022-2023  Nexedi SA and Contributors.
+# Copyright (C) 2022-2024  Nexedi SA and Contributors.
 #                          Kirill Smelkov <kirr@nexedi.com>
 #
 # This program is free software: you can Use, Study, Modify and Redistribute
@@ -20,10 +20,13 @@
 
 from __future__ import print_function, division, absolute_import
 
-from xlte.kpi import Calc, MeasurementLog, Measurement, Interval, NA, isNA, Σqci, Σcause, nqci
+from xlte.kpi import Calc, MeasurementLog, Measurement, ΣMeasurement, Interval, \
+                     Stat, StatT, NA, isNA, Σqci, Σcause, nqci
 import numpy as np
 from pytest import raises
 
+ms = 1e-3
+
 
 def test_Measurement():
     m = Measurement()
@@ -43,6 +46,8 @@ def test_Measurement():
     _('DRB.IPVolDl.sum')                # int64
     _('DRB.IPTimeDl.7')                 # .QCI alias
     _('DRB.IPTimeDl.QCI')               # .QCI array
+    _('DRB.IPLatDl.7')                  # .QCI alias to Stat
+    _('DRB.IPLatDl.QCI')                # .QCI array of Stat
     # everything automatically
     for name in m.dtype.names:
         _(name)
@@ -53,6 +58,12 @@ def test_Measurement():
     assert m['S1SIG.ConnEstabAtt'] == 123
     m['RRC.ConnEstabAtt.sum'] = 17
     assert m['RRC.ConnEstabAtt.sum'] == 17
+    m['DRB.UEActive']['min'] = 1
+    m['DRB.UEActive']['avg'] = 6
+    m['DRB.UEActive']['max'] = 8
+    assert m['DRB.UEActive']['min'] == 1
+    assert m['DRB.UEActive']['avg'] == 6
+    assert m['DRB.UEActive']['max'] == 8
     m['DRB.IPVolDl.QCI'][:] = 0
     m['DRB.IPVolDl.5'] = 55
     m['DRB.IPVolDl.7'] = NA(m['DRB.IPVolDl.7'].dtype)
@@ -65,17 +76,32 @@ def test_Measurement():
             continue
         assert m['DRB.IPVolDl.%d' % k] == 0
         assert m['DRB.IPVolDl.QCI'][k] == 0
+    m['DRB.IPLatDl.QCI'][:]['avg'] = 0
+    m['DRB.IPLatDl.QCI'][:]['min'] = 0
+    m['DRB.IPLatDl.QCI'][:]['max'] = 0
+    m['DRB.IPLatDl.QCI'][:]['n']   = 0
+    m['DRB.IPLatDl.QCI'][3]['avg'] = 33
+    m['DRB.IPLatDl.QCI'][3]['n']   = 123
+    m['DRB.IPLatDl.4']['avg'] = 44
+    m['DRB.IPLatDl.4']['n']   = 432
+    m['DRB.IPLatDl.8']['avg'] = NA(m['DRB.IPLatDl.8']['avg'].dtype)
+    m['DRB.IPLatDl.8']['n']   = NA(m['DRB.IPLatDl.8']['n']  .dtype)
+
 
     # str/repr
-    assert repr(m) == "Measurement(RRC.ConnEstabAtt.sum=17, DRB.IPVolDl.QCI={5:55 7:ø 9:99}, S1SIG.ConnEstabAtt=123)"
+    assert repr(m) == "Measurement(RRC.ConnEstabAtt.sum=17, DRB.UEActive=<1 6.0 8>, DRB.IPLatDl.QCI={3:<0.0 33.0 0.0>·123 4:<0.0 44.0 0.0>·432 8:<0.0 ø 0.0>·ø}, DRB.IPVolDl.QCI={5:55 7:ø 9:99}, S1SIG.ConnEstabAtt=123)"
+    assert repr(m['DRB.UEActive'])  == "StatT(1, 6.0, 8, dtype=int32)"
+    assert repr(m['DRB.IPLatDl.3']) == "Stat(0.0, 33.0, 0.0, 123, dtype=float64)"
     s = str(m)
     assert s[0]  == '('
     assert s[-1] == ')'
     v = s[1:-1].split(', ')
     vok = ['ø'] * len(m._dtype0.names)
     vok[m.dtype.names.index("RRC.ConnEstabAtt.sum")]   = "17"
+    vok[m.dtype.names.index("DRB.UEActive")]           = "<1 6.0 8>"
     vok[m.dtype.names.index("S1SIG.ConnEstabAtt")]     = "123"
     vok[m.dtype.names.index("DRB.IPVolDl.QCI")]        = "{5:55 7:ø 9:99}"
+    vok[m.dtype.names.index("DRB.IPLatDl.QCI")]        = "{3:<0.0 33.0 0.0>·123 4:<0.0 44.0 0.0>·432 8:<0.0 ø 0.0>·ø}"
     assert v == vok
 
     # verify that time fields has enough precision
@@ -496,6 +522,65 @@ def test_Calc_eutran_ip_throughput():
         assert thp[qci]['ul'] == I(0)
 
 
+# verify Calc.aggregate .
+def test_Calc_aggregate():
+    mlog = MeasurementLog()
+
+    m1 = Measurement()
+    m1['X.Tstart']  = 1
+    m1['X.δT']      = 2
+    m1['S1SIG.ConnEstabAtt'] = 12                               # Tcc
+    m1['ERAB.SessionTimeUE'] = 1.2                              # Ttime
+    m1['DRB.UEActive']       = StatT(1, 3.7, 5)                 # StatT
+    m1['DRB.IPLatDl.7']      = Stat(4*ms, 7.32*ms, 25*ms, 17)   # Stat
+
+    m2 = Measurement()
+    m2['X.Tstart']  = 5 # NOTE [3,5) is NA hole
+    m2['X.δT']      = 3
+    m2['S1SIG.ConnEstabAtt'] = 11
+    m2['ERAB.SessionTimeUE'] = 0.7
+    m2['DRB.UEActive']       = StatT(2, 3.2, 7)
+    m2['DRB.IPLatDl.7']      = Stat(3*ms, 5.23*ms, 11*ms, 11)
+
+    mlog.append(m1)
+    mlog.append(m2)
+
+    calc = Calc(mlog, 0, 10)
+    assert calc.τ_lo == 0
+    assert calc.τ_hi == 10
+
+    M = calc.aggregate()
+    assert isinstance(M, ΣMeasurement)
+
+    assert M['X.Tstart'] == 0
+    assert M['X.δT']     == 10
+
+    assert M['S1SIG.ConnEstabAtt']['value'] == 12 + 11
+    assert M['S1SIG.ConnEstabAtt']['τ_na']  == 5    # [0,1) [3,5) [8,10)
+
+    assert M['ERAB.SessionTimeUE']['value'] == 1.2 + 0.7
+    assert M['ERAB.SessionTimeUE']['τ_na']  == 5
+
+    assert M['DRB.UEActive']['value']   == StatT(1, (3.7*2 + 3.2*3)/(2+3), 7)
+    assert M['DRB.UEActive']['τ_na']    == 5
+
+    assert M['DRB.IPLatDl.7']['value']  == Stat(3*ms, (7.32*17 + 5.23*11)/(17+11)*ms, 25*ms, 17+11)
+    assert M['DRB.IPLatDl.7']['τ_na']   == 5
+
+
+    # assert that everything else is NA with τ_na == 10
+    def _(name):
+        f = M[name]
+        if f.shape != ():
+            return  # don't check X.QCI - rely on aliases
+        assert isNA(f['value'])
+        assert f['τ_na'] == 10
+    for name in M.dtype.names:
+        if name not in ('X.Tstart', 'X.δT', 'S1SIG.ConnEstabAtt',
+                        'ERAB.SessionTimeUE', 'DRB.UEActive', 'DRB.IPLatDl.7'):
+            _(name)
+
+
 # verify Σqci.
 def test_Σqci():
     m = Measurement()