Merge branch 'develop' of git.nt.uni-saarland.de:as/X-Lap into HEAD

    cycleevent = [x for x in list(df) if str(x) == cname][0]

    return list(df[cycleevent])[idx]

def _happens_before(df, a, b):

def _get_thread_for_event(config, e):

    for name, data in config['stamps'].items():

        if name == str(e)[:-2]:

            return data['Thread']

    return None

def _happens_before(df, a, b, config):

    """

    check if a happens-before b in the trace

    """

    l = len(df[a])

    # first, check if the timestamps are often equal. In this case, we have a

    # very short region and we should therefore check cyclestamps as well.

    #

    # EPSILON represents a grace period, in microseconds, for detecting similar

    # timestamps.

    #

    # TODO: we should only check the cycle stamps if a and b occur in the same

    # thread.

    EPSILON=1

    numeq = len([i for i in range(l) if _get_timestamp(df, a, i) + EPSILON >= _get_timestamp(df, b, i) and _get_timestamp(df, a, i) <= _get_timestamp(df, b, i) + EPSILON])

    if (numeq >= 0.95 * l):

        # The region is so short that we have to take cyclestamps into consideration

    # check whether a and b occur in the same thread. If so, a and b cannot be

    # concurrent.

    ta = _get_thread_for_event(config, a)

    tb = _get_thread_for_event(config, b)

    if (ta == tb and ta != None and tb != None):

        for i in range(l):

            tsa = _get_timestamp(df, a, i)

            tsb = _get_timestamp(df, b, i)

            if tsa > tsb:

                return False

            if tsa == tsb and _get_cyclestamp(df, a, i) > _get_cyclestamp(df, b, i):

            csa = _get_cyclestamp(df, a, i)

            csb = _get_cyclestamp(df, b, i)

            if tsa == tsb and csa > csb and csb != 0:

                return False

    else:

        # The region i long enough to use timestamps only

        return True

    # since a and b occur in different threads, we cannot compare cyclestamps.

    # If in doubt, a and b are concurrent.

    for i in range(l):

        if _get_timestamp(df, a, i) >= _get_timestamp(df, b, i):

            return False

            return False

    return True

def _locally_happens_directly_before(df, a, b, hb, config):

    """

    check if a happens-directly-before b in the trace but, be a little bit more

    tolerant regarding intra-thread durations. Consider the following scenario

    Thread A: A1 -> A2 -> A3

    Thread B: B1 -> B2

    This setup can result in the following directly-happens-before graph:

    A1   B1

     \  /

      A2

     /  \

    A3   B2

    In this case, B1 does not happens-directly-before B2 because, by chance, A2

    happens inbetween. If we ignore this anomaly, we only analyse <B1,A2> and

    <A2,B2>. Both ranges probably have low latency criticality, since their

    durations are random (because they depend on thread interleavings).

    However, we really want to analyse <B1,B2> because they actually represent

    a meaningful range in thread B.

    This function therefore computes a modified happens-directly-before graph

    with relaxed restrictions for intra-thread ranges:

    A1    B1

      \  /|

       A2 |

      /  \|

    A3    B2

    """

    if not _fast_happens_before(df, a, b, hb):

        return False

    ta = _get_thread_for_event(config, a)

    tb = _get_thread_for_event(config, b)

    if ta == tb and ta != None and tb != None:

        for c in df:

            if _get_thread_for_event(config, c) != ta:

                continue

            if _fast_happens_before(df, a, c, hb) and _fast_happens_before(df, c, b, hb):

                return False

        return True

    else:

        return _happens_directly_before(df, a, b, hb)

def _plot_controlflow_graph(df, hdb):

    """

    print the control flow graph in a format that dot understands

        print("%-10f %10s -> %10s"%(region['Correlation'], region['Start'], region['End']), file=sys.stderr)

def analyse(df):

    #print(str(_happens_before(df, 'PrrtSendStart_T', 'PrrtSendEnd_T')), file=sys.stderr)

    #print(str(len([i for i in range(len(df['PrrtSendStart_T'])) if _get_timestamp(df, 'PrrtSendStart_T', i) == _get_timestamp(df, 'PrrtSendEnd_T', i) and _get_cyclestamp(df, 'PrrtSendStart_T', i) < _get_cyclestamp(df, 'PrrtSendEnd_T', i)])), file=sys.stderr)

def analyse(df, config):

    hb = []

    for event1 in df:

        if not str(event1).endswith("_T"):

                continue

            if str(event1) == str(event2):

                continue

            if _happens_before(df, event1, event2):

            if _happens_before(df, event1, event2, config):

                hb += [{'Start':str(event1), 'End':str(event2)}]

    #print (",".join(map(str,hb)), file=sys.stderr)

    hdb = []

    e2e = list(df['EndToEnd_D'])

    for event1 in df:

                continue

            if str(event1) == str(event2):

                continue

            #if _locally_happens_directly_before(df, event1, event2, hb, config):

            if _happens_directly_before(df, event1, event2, hb):

                # compute the correlation between e2e latency and event1-event2 latency

                l1 = list(df[event1])

                l3 = []

                for i in range(len(l1)):

                    l3 += [_get_timestamp(df,event2,i) - _get_timestamp(df,event1,i)]

                if any(map(lambda x: x != 0, l3)):

                    correlation = pearsonr(l3, e2e)[0]

                else:

                    correlation = 0

                hdb += [{'Start':str(event1), 'End':str(event2), 'Correlation':correlation}]

                print(output)

        elif command == "latency":

            df_data = evaluate(data_files["sender"], data_files["receiver"], config=config, kind=0)

            latency.analyse(df_data)

            latency.analyse(df_data, config)

        else:

            df_data = evaluate(data_files["sender"], data_files["receiver"], config=config, kind=0)