Utilities and Constants

Helper functions, constants, and enumerations.

These modules provide utility functions, constants, and example models.

Utility Functions (line_solver.utils)

The utils module contains helper functions for working with LINE models.

Mock module that can handle any attribute access

Constants and Enumerations (line_solver.constants)

The constants module defines enumerations and global constants used throughout the LINE Solver package.

Scheduling Strategies

class SchedStrategy(*values)[source]

Bases: Enum

Scheduling strategies for service stations.

Values match lang/base.py SchedStrategy for consistency.

LCFSPI = 3

HOL = 9

LPS = 17

SETF = 18

FCFSPR = 22

EDF = 23

JOIN = 25

EDD = 27

SRPT = 28

SRPTPRIO = 29

LCFSPRIO = 30

LCFSPRPRIO = 31

LCFSPIPRIO = 32

FCFSPRPRIO = 33

FCFSPIPRIO = 34

PSJF = 36

FB = 37

LAS = 38

LRPT = 39

FSP = 40

PAS = 41

OI = 42

static fromString(obj)[source]

static fromLINEString(sched)[source]

static toID(sched)[source]

class SchedStrategyType(*values)[source]

Bases: Enum

Categories of scheduling strategies by preemption behavior.

Distribution and Process Types

class ProcessType(*values)[source]

Bases: Enum

Types of stochastic processes for arrivals and service times.

BMAP = 22

MMAP = 23

DUNIFORM = 24

BERNOULLI = 25

PRIOR = 26

GEOMETRIC = 27

ME = 28

RAP = 29

DISCRETESAMPLER = 30

ZIPF = 31

DMAP = 32

EMPIRICALCDF = 33

NHPP = 34

static isMarkovian(t)[source]

True when sn.proc carries an exact matrix representation of a process of type t: a genuine (D0, D1) pair, or its matrix-exponential analogue for ME/RAP.

The distinction is about sn.proc, NOT about what getProcess returns. getProcess hands back raw distribution PARAMETERS for several non-Markovian families – Gamma, Weibull, Lognormal, Pareto and Uniform return two scalars (Pareto {alpha, k}, Uniform {min, max}) – and the network refresh replaces those with map_erlang(mean, n) before storing them in sn.proc, where n = ceil(1/SCV) capped at 100 (n = 20 when SCV < CoarseTol). That fit matches the mean, and matches the SCV only when SCV <= 1: Pareto with SCV 64 gives n = 1, a single exponential of SCV 1. So for these types sn.proc is an approximation, not the law that was requested, and nothing on the cell says so – the only signal is sn.procid.

Solvers that read sn.proc as if it were the exact law must gate on this predicate. It is the procid-level counterpart of the JAR’s Distribution.isMarkovian(), i.e. of the Markovian class hierarchy, so the two lists must stay in step.

static fromString(obj)[source]

Node Types

class NodeType(*values)[source]

Bases: Enum

Types of nodes in queueing network models.

static from_line(obj)[source]

Job Class Types

class JobClassType(*values)[source]

Bases: Enum

Types of job classes in queueing networks.

OPEN: Open class (jobs arrive from outside the system)
CLOSED: Closed class (fixed population circulating in the system)
DISABLED: Disabled class (not currently active)

Routing Strategies

class RoutingStrategy(*values)[source]

Bases: Enum

Strategies for routing jobs between network nodes. Values must match MATLAB’s RoutingStrategy constants for JMT compatibility.

SQ = 6

RL = 7

Service Strategies

class ServiceStrategy(*values)[source]

Bases: Enum

Service strategies defining service time dependence.

Solver Types

class SolverType(*values)[source]

Bases: Enum

Types of solvers available in LINE.

BA = 2

LDES = 4

Metric Types

class MetricType(*values)[source]

Bases: Enum

Types of performance metrics that can be computed.

SysDropR = 11

SysPower = 13

Tard = 24

SysTard = 25

Drop and Replacement Strategies

class DropStrategy(*values)[source]

Bases: Enum

Strategies for handling queue overflow and capacity limits.

WaitingQueue: Jobs wait in a waiting queue when capacity is exceeded
Queue: Alias for WaitingQueue
Drop: Jobs are dropped (lost) when capacity is exceeded
BlockingAfterService: Jobs are blocked after service completion
BlockingBeforeService: Jobs are blocked before service starts
ReServiceOnRejection: Rejected jobs are re-served at the upstream station

Values match the MATLAB DropStrategy constants and the JAR jline.lang.constant.DropStrategy ids, which are the interchange encoding of sn.droprule and sn.regionrule. Keep the three Python definitions of this enum (here, lang/base.py, api/sn/network_struct.py) numerically identical: they are written and read by different modules over the same sn fields.

BlockingBeforeService = 3

ReServiceOnRejection = 4

Join Strategies

class JoinStrategy(*values)[source]

Bases: Enum

Strategies for join node synchronization in fork-join networks.

STD: Standard join (wait for all parallel branches)
PARTIAL: Partial join (proceed when some branches complete)
Quorum: Quorum-based join (wait for minimum number of branches)
Guard: Guard condition join (custom completion criteria)

Layered Network Types

class CallType(*values)[source]

Bases: Enum

Types of calls between tasks in layered networks.

SYNC: Synchronous call (caller waits for response)
ASYNC: Asynchronous call (caller continues immediately)
FWD: Forward call (caller terminates, response goes to caller’s caller)

class ActivityPrecedenceType(*values)[source]

Bases: Enum

Types of activity precedence relationships in layered networks.

These specify how activities are ordered and synchronized: - PRE_SEQ: Sequential prerequisite (must complete before) - PRE_AND: AND prerequisite (all must complete before) - PRE_OR: OR prerequisite (any must complete before) - POST_SEQ: Sequential post-condition - POST_AND: AND post-condition - POST_OR: OR post-condition - POST_LOOP: Loop post-condition - POST_CACHE: Cache post-condition

Polling Types

class PollingType(*values)[source]

Bases: Enum

Polling strategies for polling systems.

DECREMENTING = 4

static fromString(obj)[source]

Event and Timing Types

class EventType(*values)[source]

Bases: Enum

Types of events in discrete-event simulation.

INIT: Initialization event
LOCAL: Local processing event
ARV: Job arrival event
DEP: Job departure event
PHASE: Phase transition event in multi-phase processes
READ: Cache read event
STAGE: Staging area event

READ = 6

ENABLE = 8

FIRE = 9

PRE = 10

POST = 11

RENEGE = 12

RETRY = 13

SWITCH = 14

FAILURE = 15

REPAIR = 16

class TimingStrategy(*values)[source]

Bases: Enum

Timing strategies for transitions in Petri nets.

Verbose Level

class VerboseLevel(*values)[source]

Bases: Enum

Verbosity levels for LINE solver output.

Global Constants

class GlobalConstants[source]

Bases: object

Global constants and configuration for the LINE solver.

Immediate = 100000000.0

classmethod isLibraryAttributionShown()[source]

True if the library attribution has already been printed.

classmethod setLibraryAttributionShown(value=True)[source]

Record that the library attribution has been printed.

classmethod getInstance()[source]: Get the singleton instance of GlobalConstants.

classmethod get_instance(): Get the singleton instance of GlobalConstants.

classmethod getVerbose()[source]: Get the current verbosity level.

classmethod get_verbose(): Get the current verbosity level.

classmethod setVerbose(verbosity)[source]: Set the verbosity level for solver output.

classmethod set_verbose(verbosity): Set the verbosity level for solver output.

classmethod getConstants()[source]: Get a dictionary of all global constants.

classmethod get_constants(): Get a dictionary of all global constants.

Example Gallery (line_solver.gallery)

The gallery module provides a collection of example queueing models for validation, testing, and learning purposes.