SpectralCloudstering

class scimes.SpectralCloudstering(dendrogram, catalog, header, criteria=['volume', 'luminosity'], user_k=None, user_ams=None, user_scalpars=None, user_iter=None, save_isol_leaves=False, save_clust_leaves=False, save_branches=False, save_all_leaves=False, save_all=False, blind=False, rms=<Mock name='mock.nan' id='140089526284816'>, s2nlim=3, locscaling=False)

Bases: object

Apply the spectral clustering to find the best cloud segmentation out from a dendrogram.

Parameters:

dendrogram: ‘astrodendro.dendrogram.Dendrogram’ instance

The dendrogram to clusterize.

catalog: ‘astropy.table.table.Table’ instance

A catalog containing all properties of the dendrogram structures. Generally generated with ppv_catalog module.

header: ‘astropy.io.fits.header.Header’ instance

The header of the fits data the dendrogram was generated from. Necessary to obtain the assignment cubes.

criteria: list of strings

Clustering criteria referred to the structure properties in the catalog (default [‘volume’, ‘luminosity’]).

user_k: int

The expected number of clusters, if not provided it will be guessed automatically through the eigenvalues of the unsmoothed affinity matrix.

user_ams: numpy array

User provided affinity matrix. Whether this is not furnish it is automatically generated through the volume and/or luminosity criteria.

user_scalpars: list of floats

User-provided scaling parameters to smooth the affinity matrices.

user_iter: int

User-provided number of k-means iterations.

save_isol_leaves: bool

Consider the isolated leaves (without parent) as individual ‘clusters’. Useful for low resolution data where the beam size corresponds to the size of a Giant Molecular Cloud.

save_clust_leaves: bool

Consider unclustered leaves as individual ‘clusters’. This keyword will not include the isolated leaves without parents.

save_all_leaves: bool

Trigger both save_isol_leaves and save_clust_leaves.

save_branches: bool

Retain all isolated branches usually discarded by the cluster analysis.

save_all: bool

Trigger all save_isol_leaves, save_clust_leaves, and save_branches.

rms: int or float

Noise level of the observation. Necessary to calculate the scaling parameter above a certain signal-to-noise ratio.

s2nlim: int or float

Signal-to-noise limit above which the scaling parameter is calculated. Needed only if rms is not np.nan.

blind: bool

Show the affinity matrices. Matplotlib required.

locscaling: bool

Smooth the affinity matrices using a local scaling technique. This does not work well …

Methods Summary

plot_connected_clusters(**kwargs)
showdendro([cores_idx, savefile])	Show the clustered dendrogram.

Methods Documentation

plot_connected_clusters(**kwargs)

showdendro(cores_idx=[], savefile=None)

Show the clustered dendrogram. Every color correspond to a different cluster.