class DoubleWellDiscrete

class deeptime.data.DoubleWellDiscrete

MCMC process in a symmetric double well potential, spatially discretized to 100 bins.

Encapsulates discrete trajectories and markov state model (see MarkovStateModel) with exact transition matrix.

Attributes

analytic_msm	Returns a MarkovStateModel instance with the exact transition matrix.
dtraj	100K frames trajectory at timestep 10, 100 microstates (not all are populated).
dtraj_n2bad	100K frames trajectory at timestep 10, bad 2-state discretization (off transition state).
dtraj_n2good	100K frames trajectory at timestep 10, good 2-state discretization (at transition state).
dtraj_n6good	100K frames trajectory at timestep 10, good 6-state discretization.
transition_matrix	Exact transition matrix used to generate the data

Methods

dtraj_n(divides)	100K frames trajectory at timestep 10, arbitrary n-state discretization.
dtraj_n2(divide)	100K frames trajectory at timestep 10, arbitrary 2-state discretization.
simulate_trajectories(n_trajectories, n_steps)	Simulates n_trajectories discrete trajectories.
simulate_trajectory(n_steps[, start, stop, dt])	Generates a discrete trajectory of length less or equal n_steps.

dtraj_n(divides)

100K frames trajectory at timestep 10, arbitrary n-state discretization.

Parameters:

divides ((n, dtype=int) ndarray) – The state boundaries.

Returns:

dtraj – Discrete trajectory with len(divides) states.

Return type:

(T,) ndarray

dtraj_n2(divide)

100K frames trajectory at timestep 10, arbitrary 2-state discretization.

simulate_trajectories(n_trajectories: int, n_steps: int, start=None, stop=None, dt=1) → List[ndarray]

Simulates n_trajectories discrete trajectories. For a more detailed description of the arguments, see simulate_trajectory().

simulate_trajectory(n_steps, start=None, stop=None, dt=1) → ndarray

Generates a discrete trajectory of length less or equal n_steps.

Parameters:

• n_steps (int) – maximum number of steps to simulate

• start (int, optional, default=None) – Starting state. If None is given, it is sampled from the stationary distribution.

• stop (int, optional, default=None) – Stopping state. If not None and encountered, stops the simulation. This can lead to fewer than n_steps steps.

• dt (int, optional, default=1) – Time step to apply when simulating the trajectory.

Returns:

dtraj – A discrete trajectory.

Return type:

(T, 1) ndarray

property analytic_msm

Returns a MarkovStateModel instance with the exact transition matrix.

property dtraj

100K frames trajectory at timestep 10, 100 microstates (not all are populated).

property dtraj_n2bad

100K frames trajectory at timestep 10, bad 2-state discretization (off transition state).

property dtraj_n2good

100K frames trajectory at timestep 10, good 2-state discretization (at transition state).

property dtraj_n6good

100K frames trajectory at timestep 10, good 6-state discretization.

property transition_matrix

Exact transition matrix used to generate the data