Junctions Module

Copyright (C) 2013-2017 Johannes Schlatow

TU Braunschweig, Germany

All rights reserved.

See LICENSE file for copyright and license details.

Authors:

• Johannes Schlatow

Description

Local model propagation functions (junctions)

class pycpa.junctions.ANDJoin

Compute output event models for an AND junction. This corresponds to Lemma 4.2 in [Jersak2005].

class pycpa.junctions.OREventModel(in_event_models)

Compute output event model for an OR junction. This corresponds to Section 4.2, Equations 4.11 and 4.12 in [Jersak2005].

eta_min(w)

Eta-minus Function Return the minimum number of events in a time window w. Derived from Equation 3.6 from [Schliecker2011], but different, as Eq. 3.6 is wrong.

eta_min_closed(w)

Eta-minus Function Return the minimum number of events in a time window w. Using CLOSED intevals

eta_plus(w)

Eta-plus Function Return the maximum number of events in a time window w. Derived from Equation 3.5 from [Schliecker2011], but assuming half-open intervals for w as defined in [Richter2005].

eta_plus_closed(w)

Eta-plus Function Return the maximum number of events in a time window w. Derived from Equation 3.5 from [Schliecker2011], but assuming CLOSED intervals for w as defined in [Richter2005].

This is technically identical to eta_plus(w + EPSILON), but the use of epsilon has issues with float precision, as w+EPSILON == w for large w and small Epsilon (e.g. 40000000+1e-9)

class pycpa.junctions.ORJoin

Compute output event models for an OR junction (see [Jersak2005]).

class pycpa.junctions.SampledInput

Uses a fixed event model (trigger) as output event model. Serves as a workaround for defining a Path over time-triggered tasks. The sampling delay is conservatively computed and automatically added to the path latency.