class TimeIndependentSystem¶

class deeptime.data.TimeIndependentSystem(system, h: float, n_steps: int, props=None)¶

Wraps systems with a time-independent right-hand side defined in extension code.

Parameters:

system – The implementation.
h (float) – Integration step size.
n_steps (int) – Number of steps between each evaluation of the state.
props (dict or None, optional, default=None) – Additional properties of the system.

Attributes

`dimension`	The dimension of the system's state.
`f`	The right-hand side of the system as callable function.
`h`	The integration step size.
`has_potential_function`	Whether the system defines a potential.
`integrator`	The type of integrator.
`n_steps`	The number of integration steps between each evaluation of the state.
`time_dependent`	Whether the potential (if available) and the right-hand side depend on time.
`vectorized_f`	Yields whether the right-hand side function is vectorized.

Methods

`potential`(points)	Evaluates the system's potential function at given points in state space.
`trajectory`(x0, length[, seed, n_jobs])	Simulates one or multiple trajectories.

__call__(test_points, seed=-1, n_jobs=None)¶

Evolves the provided tests points under the dynamic for n_steps and returns.

Parameters:

test_points (array_like) – The test points.
seed (int, optional, default=-1) – The seed for reproducibility. In case it is set to seed >= 0 the number of jobs needs to be fixed to n_jobs=1.
n_jobs (int, optional, default=None) – Specify number of jobs according to deeptime.util.parallel.handle_n_jobs().

Returns:

points – The evolved test points.

Return type:

np.ndarray

potential(points)¶

Evaluates the system’s potential function at given points in state space.

Parameters:: points (array_like) – The points.
Returns:: energy – The energy for each of these points.
Return type:: np.ndarray
Raises:: AssertionError – If the system does not have a potential function defined.

trajectory(x0, length, seed=-1, n_jobs=None)¶

Simulates one or multiple trajectories.

Parameters:

x0 (array_like) – The initial condition. Must be compatible in shape to a (n_test_points, dimension)-array.
length (int) – The length of the trajectory that is to be generated.
seed (int, optional, default=-1) – The random seed. In case it is specified to be something else than -1, n_jobs must be set to n_jobs=1.
n_jobs (int, optional, default=None) – Specify number of jobs according to deeptime.util.parallel.handle_n_jobs().

Returns:

trajectory – The trajectory of shape (n_initial_conditions, n_evaluations, dimension). In case of just one initial condition the shape is squeezed to (n_evaluations, dimension).

Return type:

np.ndarray

property dimension: int¶

The dimension of the system’s state.

Type:: int

property f: Callable[[float, ndarray], ndarray]¶

The right-hand side of the system as callable function. In case of SDEs, this only evaluates the deterministic part of the function definition.

Some of these definitions are vectorized.

Returns:: right_hand_side – The right-hand side.
Return type:: Callable[[float, ndarray], ndarray]

property h: float¶: The integration step size.

property has_potential_function: bool¶: Whether the system defines a potential. This means that the deterministic part of the right-hand side is the negative gradient of that potential.

property integrator: str¶

The type of integrator.

Type:: str

property n_steps: int¶: The number of integration steps between each evaluation of the state.

property time_dependent: bool¶

Whether the potential (if available) and the right-hand side depend on time.

Type:: bool

property vectorized_f: bool¶

Yields whether the right-hand side function is vectorized.

Type:: bool