IMLCV.base.Observable

IMLCV.base.Observable#

class to convert data and CVs to different thermodynamic/ kinetic

class IMLCV.base.Observable.Observable#

class to convert data and CVs to different thermodynamic/ kinetic observables.

static create(rounds: IMLCV.base.rounds.Rounds, rnd=None, cv_round: int | None = None, collective_variable: IMLCV.base.CV.CollectiveVariable | None = None) → Observable#

static _fes_nd_thermolib(dlo_kwargs: dict, dlo: IMLCV.base.rounds.DataLoaderOutput | None = None, update_bounding_box=True, bounds_percentile=1, samples_per_bin=5, min_samples_per_bin=1, n=None, n_max=100000.0, temp=None, chunk_size=None, shmap=False, rounds: IMLCV.base.rounds.Rounds | None = None)#

fes_nd_thermolib(num_rnds=4, start_r=1, update_bounding_box=True, samples_per_bin=5, min_samples_per_bin=1, chunk_size=None, n_max=100000.0, n=None, min_traj_length=None, dlo=None, directory=None, temp=None, shmap=False, only_finished=True, bounds_percentile=1, max_bias=None, rbf_kernel='gaussian', rbf_degree=None)#

static _fes_nd_weights(rounds: IMLCV.base.rounds.Rounds, num_rnds=4, out=int(30000.0), lag_n=10, start_r=1, min_traj_length=None, only_finished=True, chunk_size=None, macro_chunk=1000, n_max=100000.0, cv_round=None, koopman=True, plot_selected_points=True, verbose=True, max_bias: float = 100 * kjmol, kooopman_wham=None, samples_per_bin=10, min_samples_per_bin=5, resample=False, direct_bias=False)#

fes_nd_weights(num_rnds: int = 4, out: int = int(100000.0), lag_n: int = 10, start_r: int = 1, min_traj_length: int | None = None, only_finished: bool = True, chunk_size: int | None = None, macro_chunk: int = 1000, n_max: int | float = 100000.0, cv_round: int | None = None, directory: str | pathlib.Path | None = None, koopman: bool = True, verbose=True, max_bias: float | None = 100 * kjmol, kooopman_wham=None, samples_per_bin=5, min_samples_per_bin=1, executors=Executors.training, direct_bias=False)#

fes_bias(plot=True, fes=None, max_bias=None, choice='rbf', num_rnds=8, start_r=1, rbf_kernel='gaussian', rbf_degree=None, samples_per_bin=5, min_samples_per_bin=1, chunk_size=None, macro_chunk=10000, update_bounding_box=True, n_max=100000.0, min_traj_length=None, margin=0.1, only_finished=True, shmap=False, thermolib=False, lag_n=30, out=int(100000.0), vmax=100 * kjmol, koopman=True, verbose=True, koopman_wham=None, executors=Executors.training, direct_bias=False)#