IMLCV package

IMLCV package#

Subpackages#

Submodules#

IMLCV.scheme module#

class IMLCV.scheme.Scheme(rounds)#

Bases: object

base class that implements iterative scheme.

Parameters:

  • format (String) – intermediate file type between rounds

  • CVs – list of CV instances.

FESBias(rnd=None, cv_round=None, chunk_size=None, **plotkwargs)#

replace the current md bias with the computed FES from current round.

Return type:

Bias

property bias#
static from_refs(mde, folder, refs, steps=2000.0)#
Return type:

Scheme

grid_umbrella(steps=10000.0, k=None, n=8, max_grad=None, plot=True, scale_n=None, cv_round=None, ignore_invalid=False, eps=0.1, min_traj_length=None, recalc_cv=False, only_finished=True, chunk_size=None, use_common_bias=True)#
inner_loop(rnds=10, convergence_kl=0.1, steps=50000.0, K=None, update_metric=False, n=4, samples_per_bin=5, min_samples_per_bin=1, init_max_grad=None, plot=True, choice='rbf', fes_bias_rnds=4, scale_n=None, cv_round=None, chunk_size=None, eps_umbrella=0.1, plot_margin=0.1, enforce_min_traj_length=False, recalc_cv=False, only_finished=True, plot_umbrella=False, max_bias=0.03808799176039228, n_max_fes=100000.0, thermolib=False, macro_chunk=10000, koopman=True, lag_n=30, koopman_wham=None, out=-1, direct_bias=False, init=False, first_round_without_bias=False, executors=Executors.training, use_common_bias=True, first_round_without_ground_bias=False, first_round_no_fes_bias=False)#
property md#
rounds: Rounds#
property sti#
transform_CV(cv_trans, dlo_kwargs=None, dlo=None, chunk_size=None, new_r_cut=None, plot=True)#
update_CV(transformer, dlo_kwargs={}, dlo=None, chunk_size=None, plot=True, new_r_cut=None, save_samples=True, save_multiple_cvs=False, jac=<function jacrev>, cv_round_from=None, test=False, max_bias=None, transform_bias=True, samples_per_bin=5, min_samples_per_bin=1, percentile=0.1, use_executor=True, n_max=100000.0, vmax=0.03808799176039228, macro_chunk=2000, macro_chunk_nl=5000, verbose=False)#

Module contents#

summary IMLCV is still underdevelopement.

class IMLCV.JaxHandler(context)#

Bases: BaseHandler

flattens the jax array to numpy array, which is already handled by jsonpickle

flatten(obj, data)#

Flatten obj into a json-friendly form and write result to data.

Parameters:

  • obj (object) – The object to be serialized.

  • data (dict) – A partially filled dictionary which will contain the json-friendly representation of obj once this method has finished.

restore(data)#

Restore an object of the registered type from the json-friendly representation obj and return it.

class IMLCV.Unpickler(backend=None, keys=False, safe=True, v1_decode=False, on_missing='ignore', handle_readonly=False)#

Bases: Unpickler

IMLCV.pytreenode_equal(self, other)#
Contents