rid.select package

Submodules

rid.select.cluster module

class rid.select.cluster.Cluster(cvs: Union[ndarray, List], threshold: float, angular_mask: Optional[Union[ndarray, List]] = None, weights: Optional[Union[ndarray, List]] = None, max_search_step: int = 500, max_selection: int = 1000)[source]

Bases: object

get_cluster_selection()[source]

make_threshold(numb_cluster_lower, numb_cluster_upper)[source]

rid.select.cluster.cv_dist(cv1, cv2, angular_mask, weights)[source]

rid.select.cluster.mk_cluster(dist, distance_threshold)[source]

rid.select.cluster.mk_dist(cv, angular_mask, weights)[source]

rid.select.cluster.sel_from_cluster(cvs, threshold, angular_mask=None, weights=None, max_selection=1000)[source]

rid.select.conf_select module

class rid.select.conf_select.ConfSelector(threshold: float, model_list: List)[source]

Bases: object

select(data: Union[ndarray, List])[source]

rid.select.conf_select.select_from_devi(model_devi, threshold)[source]

rid.select.model_devi module

rid.select.model_devi.compute_std(forces)[source]

rid.select.model_devi.make_std(data: Union[List, ndarray], models: List = ['graph.000.pb'])[source]

rid.select.model_devi.test_ef(sess, data_in)[source]