kpi: Start of the package

Start the package to process measurements and compute KPIs from them.

In this patch we add kpi.Measurement - a central part to represent
measurement results in intermediate generic form. kpi.Measurement will
be used by both KPI calculator, and by drivers for particular LTE stacks
to provide their KPI-related data in this uniform common format.

kpi.Measurement also establishes semantic for such measurement results
to be followed by drivers. The semantic is stated in kpi.Measurement
docstring and in comment for every field. Also in particular, according
to TS 32.401 and common sense, measurement data are required to be
correctly accounted for initiation/termination events to avoid
discrepancies. Quoting kpi.Measurement documentation:

    Important note (init/fini correction):

      Termination events should be counted in the same granularity period, where
      corresponding initiation event occurred, even if termination event happens
      _after_ granularity period covering the initiation event. For example in the
      following illustration "ConnEstab Success" event should be counted in the
      same granularity period 1 as "ConnEstab Initiate" event:

                     -----------------------
                    '                       '
            | p e r ' i o d 1       | p e r ' i o d 2    |
            |       '               |       v            |
        ────'───────x───────────────'───────x────────────'────────────>
                ConnEstab               ConnEstab                time
                Initiate                 Success

      This preserves invariant that N(initiations) is always ≥ N(results) and
      goes in line with what TS 32.401 4.3.2 "Perceived accuracy -> Same period
      for the same two events" requires.

kpi.Measurement comes accompanied by kpi.MeasurementLog which in essence
is array of kpi.Measurements.

We will use kpi.Measurement and kpi.MeasurementLog in later patches to
both provide Amarisoft-specific data in this common format, and to
compute KPIs from it.

README.rst

@@ -4,5 +4,6 @@
 
 XLTE repository provides assorted tools and packages with functionality related to LTE:
 
+- `kpi` - process measurements and compute KPIs from them.
 - `amari.xlog` - extra logging facilities for Amarisoft LTE stack.
 - `xamari` - supplementary tool for managing Amarisoft LTE services.

kpi_test.py

+# -*- coding: utf-8 -*-
+# Copyright (C) 2022  Nexedi SA and Contributors.
+#                     Kirill Smelkov <kirr@nexedi.com>
+#
+# This program is free software: you can Use, Study, Modify and Redistribute
+# it under the terms of the GNU General Public License version 3, or (at your
+# option) any later version, as published by the Free Software Foundation.
+#
+# You can also Link and Combine this program with other software covered by
+# the terms of any of the Free Software licenses or any of the Open Source
+# Initiative approved licenses and Convey the resulting work. Corresponding
+# source of such a combination shall include the source code for all other
+# software used.
+#
+# This program is distributed WITHOUT ANY WARRANTY; without even the implied
+# warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+#
+# See COPYING file for full licensing terms.
+# See https://www.nexedi.com/licensing for rationale and options.
+
+from xlte.kpi import MeasurementLog, Measurement, NA, isNA
+import numpy as np
+from pytest import raises
+
+
+def test_Measurement():
+    m = Measurement()
+    assert type(m) is Measurement
+
+    # verify that all fields are initialized to NA
+    def _(name):
+        assert isNA(m[name])
+    # several fields explicitly
+    _('X.Tstart')                       # time
+    _('RRC.ConnEstabAtt.sum')           # Tcc
+    _('DRB.PdcpSduBitrateDl.sum')       # float32
+    _('DRB.IPThpVolDl.sum')             # int64
+    # everything automatically
+    for name in m.dtype.names:
+        _(name)
+
+    # setting values
+    with raises(ValueError): m['XXXunknownfield']
+    m['S1SIG.ConnEstabAtt'] = 123
+    assert m['S1SIG.ConnEstabAtt'] == 123
+    m['RRC.ConnEstabAtt.sum'] = 17
+    assert m['RRC.ConnEstabAtt.sum'] == 17
+
+    # str/repr
+    assert repr(m) == "Measurement(RRC.ConnEstabAtt.sum=17, S1SIG.ConnEstabAtt=123)"
+    s = str(m)
+    assert s[0]  == '('
+    assert s[-1] == ')'
+    v = s[1:-1].split(', ')
+    vok = ['ø'] * len(m.dtype.names)
+    vok[m.dtype.names.index("RRC.ConnEstabAtt.sum")]   = "17"
+    vok[m.dtype.names.index("S1SIG.ConnEstabAtt")]     = "123"
+    assert v == vok
+
+    # verify that time fields has enough precision
+    t2022 = 1670691601.8999548  # in 2022.Dec
+    t2118 = 4670691601.1234567  # in 2118.Jan
+    def _(τ):
+        m['X.Tstart'] = τ
+        τ_ = m['X.Tstart']
+        assert τ_ == τ
+    _(t2022)
+    _(t2118)
+
+
+def test_MeasurementLog():
+    # empty
+    mlog = MeasurementLog()
+    _ = mlog.data()
+    assert isinstance(_, np.ndarray)
+    assert _.dtype == (Measurement, Measurement._dtype)
+    assert _.shape == (0,)
+
+    # append₁
+    m1 = Measurement()
+    m1['X.Tstart'] = 1
+    m1['X.δT']     = 1
+    m1['S1SIG.ConnEstabAtt'] = 11
+    mlog.append(m1)
+    _ = mlog.data()
+    assert isinstance(_, np.ndarray)
+    assert _.dtype == (Measurement, Measurement._dtype)
+    assert _.shape == (1,)
+    m1_ = _[0]
+    assert isinstance(m1_, Measurement)
+    assert m1_['X.Tstart'] == 1
+    assert m1_ == m1
+
+    # append₂
+    m2 = Measurement()
+    m2['X.Tstart'] = 2
+    m2['X.δT']     = 1
+    m2['S1SIG.ConnEstabSucc'] = 22
+    mlog.append(m2)
+    _ = mlog.data()
+    assert isinstance(_, np.ndarray)
+    assert _.dtype == (Measurement, Measurement._dtype)
+    assert _.shape == (2,)
+    assert _[0] == m1
+    assert _[1] == m2
+
+    # append₃
+    m3 = Measurement()
+    m3['X.Tstart'] = 3
+    m3['X.δT']     = 1
+    m3['RRC.ConnEstabAtt.sum'] = 333
+    mlog.append(m3)
+    _ = mlog.data()
+    assert isinstance(_, np.ndarray)
+    assert _.dtype == (Measurement, Measurement._dtype)
+    assert _.shape == (3,)
+    assert _[0] == m1
+    assert _[1] == m2
+    assert _[2] == m3
+
+    # forget₀
+    mlog.forget_past(0)
+    _ = mlog.data()
+    assert isinstance(_, np.ndarray)
+    assert _.dtype == (Measurement, Measurement._dtype)
+    assert _.shape == (3,)
+    assert _[0] == m1
+    assert _[1] == m2
+    assert _[2] == m3
+
+    # forget₁
+    mlog.forget_past(1)
+    _ = mlog.data()
+    assert isinstance(_, np.ndarray)
+    assert _.dtype == (Measurement, Measurement._dtype)
+    assert _.shape == (2,)
+    assert _[0] == m2
+    assert _[1] == m3
+
+    # forget₃
+    mlog.forget_past(3)
+    _ = mlog.data()
+    assert isinstance(_, np.ndarray)
+    assert _.dtype == (Measurement, Measurement._dtype)
+    assert _.shape == (0,)
+
+
+def test_NA():
+    def _(typ):
+        return NA(typ(0).dtype)
+
+    assert np.isnan( _(np.float16) )
+    assert np.isnan( _(np.float32) )
+    assert np.isnan( _(np.float64) )
+
+    assert _(np.int8)   == -0x80
+    assert _(np.int16)  == -0x8000
+    assert _(np.int32)  == -0x80000000
+    assert _(np.int64)  == -0x8000000000000000

setup.py

@@ -87,6 +87,7 @@ setup(
     install_requires = [
                    'websocket-client',
                    'pygolang',
+                   'numpy',
                   ],
 
     extras_require = {