Encapsulating CBD Models in PythonPDEVS

This module allows CBD models to be executed inside of a DEVS simulation.

See also

PythonPDEVS

class pyCBD.converters.hybrid.CBDRunner(name, cbd, initials=None, stopped=False, crossings=None, algo=<function CrossingDetection.ITP>, **kwargs)[source]

Bases: pypdevs.DEVS.AtomicDEVS

Atomic DEVS model that can be used to execute a CBD model.

Parameters

  • name (str) – The name of the CBD model.

  • cbd (pyCBD.Core.CBD) – The CBD model to run.

  • initials (dict) – The initial conditions for all the inputs.

  • stopped (bool) – Whether to start from paused mode or not. In this paused mode, the model will not progress. Useful when in combination with multiple ODEs. Defaults to False.

  • crossings (dict) – Dictionary of { name -> value } that defines which variable needs to be detected for a crossing through a certain value. The value can be a string, starting with either <, > or =, which respectively means a crossing from low to high, high to low or either side. Notice that = is the default starting character assumed when the value is a float. When None, no detection will happen. Defaults to None.

  • algo – The root finding algorithm function. See CrossingDetection for more info.

  • **kwargs – Optional parameters for the zero-crossing detection algorithm.

reinit()[source]

Re-initializes the CBD model.

Warning

Use this function with care. It is expected a simulation will commence after this function was called.

apply_initials()[source]

Applies the initial values.

get_signal(pname)[source]

Gets the output for a given name.

Parameters

pname (str) – The name of the port.

crossing_detection(signal, y, y0)[source]

Crossing root finder for a signal.

Parameters

  • signal (str) – The port to detect.

  • y (float) – The value to cross.

  • y0 (float) – Y-value of the lower end of the interval.

crossing_function(h, signal, restore=True)[source]

Computes the function value of the simulation after a specified delay. This will be used in combination with CrossingDetection to find the roots of a function.

This function will first rewind the simulator such that h can be applied, before stepping until h was found. Afterwards, the simulation is possibly restored to the original state.

Parameters

  • h (float) – The delay.

  • signal (str) – The signal that must be read. When None, no signal will be read.

  • restore (bool) – When True, the simulation will be restored after the value was found. Defaults to True.

set_delta(val)[source]

Sets the maximal allowed delta of the clock.

Parameters

val (float) – The maximal allowed delta.

get_delta()[source]

Obtains the last used delta.

timeAdvance()[source]

See also

PythonPDEVS documentation

outputFnc()[source]

See also

PythonPDEVS documentation

intTransition()[source]

See also

PythonPDEVS documentation

extTransition(inputs)[source]

See also

PythonPDEVS documentation

pyCBD.converters.hybrid.prepare_cbd(model, initials)[source]

Obtains a CBD model that can be executed with the CBDRunner. The model will be cloned and all input ports will be altered to become constant blocks. Additionally, a pyCBD.lib.std.ConstantBlock and a pyCBD.lib.std.MinBlock will be added in-between the Clock and the block that connects to the Clock over the h input.

Parameters

  • model (pyCBD.Core.CBD) – The CBD model to prepare.

  • initials (dict) – A dictionary in the form of input port name -> initial value.

Returns

A tuple of (prepared CBD, name) where name identifies the name for the

Note

Usually, this function shouldn’t be called by a user.

class pyCBD.converters.hybrid.CrossingDetection[source]

Bases: object

Helper class that implements the crossing detection algorithms.

The following description concerns the default arguments for all functions. On top of that, there may be some additional arguments defined, as listed in the specific documentations below.

Parameters

  • t1 (float) – The x-value of the lower bound of the interval.

  • t2 (float) – The x-value of the higher bound of the interval.

  • y (float) – The value for which a crossing must be checked.

  • f – A function that should return the y-value for a given x (or t).

static linear(t1, t2, y, f, **kwargs)[source]

Finds the root of the crossing using linear interpolation. No additional function calls or arguments are used.

static regula_falsi(t1, t2, y, f, **kwargs)[source]

Implements the Illinois algorithm for finding the root for a crossing problem.

Parameters

  • eps (float) – Half of the upper bound for the relative error. Defaults to 1e-5.

  • n (int) – The maximal amount of iterations to compute. Defaults to 5 million iterations.

See also

https://en.wikipedia.org/wiki/Regula_falsi

static ITP(t1, t2, y, f, **kwargs)[source]

Implements the Interpolation-Truncation-Projection algorithm for finding the root of a function.

Parameters

  • eps (float) – Minimal interval size. Defaults to 1e-5.

  • k1 (float) – First truncation size hyperparameter. Must be in the range of \((0, \infty)\). Defaults to 0.1.

  • k2 (float) – Second truncation size hyperparameter. Must be in the range of \([1, 1 + \frac{1}{2}(1 + \sqrt{5})]\). Defaults to 1.5.

  • n0 (float) – Slack variable to control the size of the interval for the projection step. Must be in \([0, \infty)\). When 0, the average number of iterations will be less than that of the bisection method. Defaults to 0.

See also

https://en.wikipedia.org/wiki/ITP_method