Module: dbscan_unbinned¶

class DBSCANUnbinned(eps, K, buffer_size=None, metric='euclidean', check_core_is_already_cluster_min_size=100, K_pix=False)[source]¶

Bases: asterism.core.clustering.density_based.dbscan.BaseDBSCAN

Methods

`build_cluster`(cluster_ID, cluster_members_list)
`check_core_is_already_cluster`(core_list)
`dist_eval`(index[, selected])
`expand_list`(list_to_expand, candidate_list)
`get_buffer_mask`(ID)
`get_neigh`(ID)
`grow_core`(core_seed)
`run`(position_array[, weight_array, ...])
`scan`(ID)
`set_point_type`(ID[, density_reach])	set point type (core,border,noise), according to local density and K

build_cluster(cluster_ID, cluster_members_list, IDs=None)[source]¶

check_core_is_already_cluster(core_list)[source]¶

dist_eval(index, selected=None)[source]¶

expand_list(list_to_expand, candidate_list)[source]¶

get_buffer_mask(ID)[source]¶

get_neigh(ID)[source]¶

grow_core(core_seed)[source]¶

run(position_array, weight_array=None, store_position_array_IDs=False, verbose=False)[source]¶

scan(ID)[source]¶

set_point_type(ID, density_reach=False)[source]¶

set point type (core,border,noise), according to local density and K Args:

ID: neigh_id_array: density_reach:

Returns: