pytams.fmodel

A base class for the stochastic forward model.

Classes

ForwardModelBaseClass

A base class for the stochastic forward model.

Module Contents

class ForwardModelBaseClass(params: dict[Any, Any], ioprefix: str | None = None, workdir: pathlib.Path | None = None)

A base class for the stochastic forward model.

pyTAMS relies on a separation of the stochastic model, encapsulating the physics of interest, and the TAMS algorithm itself. The ForwardModelBaseClass defines the API the TAMS algorithm requires from the stochastic model.

Concrete model classes must implement all the abstract functions defined in this base class.

The base class handles some components needed by TAMS, so that the user does not have to ensure compatibility with TAMS requirements.

Variables:

• _noise – the noise to be used in the next model step

• _step – the current stochastic step counter

• _time – the current stochastic time

• _workdir – the working directory

advance(dt: float, need_end_state: bool) → float

Base class advance function of the model.

This is the advance function called by TAMS internals. It handles updating the model time and step counter, as well as reusing or generating noise only when needed. It also handles exceptions.

Parameters:

• dt – the time step size over which to advance

• need_end_state – whether the step end state is needed

Returns:

Some model will not do exactly dt (e.g. sub-stepping) return the actual dt

get_noise() → Any

Return the model’s latest noise increment.

set_noise(a_noise: Any) → None

Set the model’s next noise increment.

clear() → None

Destroy internal data.

set_workdir(workdir: pathlib.Path) → None

Setter of the model working directory.

Parameters:

workdir – the new working directory

abstract get_current_state() → Any

Return the current state of the model.

abstract set_current_state(state: Any) → Any

Set the current state of the model.

abstract score() → float

Return the model’s current state score.

abstract make_noise() → Any

Return the model’s latest noise increment.

post_trajectory_branching_hook(step: int, time: float) → None

Model post trajectory branching hook.

Parameters:

• step – the current step counter

• time – the time of the simulation

post_trajectory_restore_hook(step: int, time: float) → None

Model post trajectory restore hook.

Parameters:

• step – the current step counter

• time – the time of the simulation

check_convergence(step: int, time: float, current_score: float, target_score: float) → bool

Check if the model has converged.

This default implementation checks if the current score is greater than or equal to the target score. The user can override this method to implement a different convergence criterion.

Parameters:

• step – the current step counter

• time – the time of the simulation

• current_score – the current score

• target_score – the target score

classmethod name() → str

Return a the model name.