pyemma.thermo.estimate_umbrella_sampling

pyemma.thermo.estimate_umbrella_sampling(us_trajs, us_dtrajs, us_centers, us_force_constants, md_trajs=None, md_dtrajs=None, kT=None, maxiter=10000, maxerr=1e-15, save_convergence_info=0, estimator='wham', lag=1, dt_traj='1 step', init=None)

Wraps umbrella sampling data or a mix of umbrella sampling and and direct molecular dynamics.

Parameters:
  • us_trajs (list of N arrays, each of shape (T_i, d)) – List of arrays, each having T_i rows, one for each time step, and d columns where d is the dimension in which umbrella sampling was applied. Often d=1, and thus us_trajs will be a list of 1d-arrays.
  • us_dtrajs (list of N int arrays, each of shape (T_i,)) – The integers are indexes in 0,...,n-1 enumerating the n discrete states or the bins the trajectory is in at any time.
  • us_centers (array-like of size N) – List or array of N center positions. Each position must be a d-dimensional vector. For 1d umbrella sampling, one can simply pass a list of centers, e.g. [-5.0, -4.0, -3.0, ... ].
  • us_force_constants (float or array-like of float) – The force constants used in the umbrellas, unit-less (e.g. kT per length unit). If different force constants were used for different umbrellas, a list or array of N force constants can be given. For multidimensional umbrella sampling, the force matrix must be used.
  • md_trajs (list of M arrays, each of shape (T_i, d), optional, default=None) – Unbiased molecular dynamics simulations. Format like umbrella_trajs.
  • md_dtrajs (list of M int arrays, each of shape (T_i,)) – The integers are indexes in 0,...,n-1 enumerating the n discrete states or the bins the trajectory is in at any time.
  • kT (float (optinal)) – Use this attribute if the supplied force constants are NOT unit-less.
  • maxiter (int, optional, default=10000) – The maximum number of self-consistent iterations before the estimator exits unsuccessfully.
  • maxerr (float, optional, default=1E-15) – Convergence criterion based on the maximal free energy change in a self-consistent iteration step.
  • save_convergence_info (int, optional, default=0) – Every save_convergence_info iteration steps, store the actual increment and the actual loglikelihood; 0 means no storage.
  • estimator (str, optional, default='wham') –

    Specify one of the available estimators

    ‘wham’: use WHAM
    ‘dtram’: use the discrete version of TRAM
  • lag (int, optional, default=1) – Integer lag time at which transitions are counted.
  • dt_traj (str, optional, default='1 step') –

    Description of the physical time corresponding to the lag. May be used by analysis algorithms such as plotting tools to pretty-print the axes. By default ‘1 step’, i.e. there is no physical time unit. Specify by a number, whitespace and unit. Permitted units are (* is an arbitrary string):

    ‘fs’, ‘femtosecond*’
    ‘ps’, ‘picosecond*’
    ‘ns’, ‘nanosecond*’
    ‘us’, ‘microsecond*’
    ‘ms’, ‘millisecond*’
    ‘s’, ‘second*’
  • init (str, optional, default=None) –

    Use a specific initialization for self-consistent iteration:

    None: use a hard-coded guess for free energies and Lagrangian multipliers
    ‘wham’: perform a short WHAM estimate to initialize the free energies
Returns:

_estimator – The requested estimator/model object, i.e., WHAM or DTRAM.
Return type:

MEMM or StationaryModel