def calcGNMDomains(modes, method=Hierarchy, **kwargs):
"""Uses spectral clustering to separate structural domains in the chromosome.
:arg modes: GNM modes used for segmentation
:type modes: :class:`ModeSet`
:arg method: Label assignment algorithm used after Laplacian embedding of loci.
:type method: func
"""
V, mask = _getEigvecs(modes, row_norm=True, remove_zero_rows=True)
labels_ = method(V, **kwargs)
labels = np.empty(len(mask))
labels.fill(np.nan)
labels[mask] = labels_
currlbl = labels_[np.argmax(~np.isnan(labels_))]
for i in range(len(labels)):
l = labels[i]
if np.isnan(l):
labels[i] = currlbl
elif currlbl != l:
currlbl = l
return labels
def showLinkage(V, **kwargs):
"""Shows the dendrogram of hierarchical clustering on *V*. See :func:`scipy.cluster.hierarchy.dendrogram` for details.
:arg V: row-normalized eigenvectors for the purpose of clustering.
:type V: :class:`numpy.ndarray`
"""
V, _ = _getEigvecs(V, row_norm=True, remove_zero_rows=True)
try:
from scipy.cluster.hierarchy import linkage, dendrogram
except ImportError:
raise ImportError('Use of this function (showLinkage) requires the '
'installation of scipy.')
method = kwargs.pop('method', 'single')
metric = kwargs.pop('metric', 'euclidean')
Z = linkage(V, method=method, metric=metric)
no_labels = kwargs.pop('no_labels', True)
dendrogram(Z, no_labels=no_labels, **kwargs)
if SETTINGS['auto_show']:
showFigure()
return Z