esda.isolation¶

esda.isolation(X, coordinates, metric='euclidean', middle='mean', return_all=False, progressbar=False)[source]¶

Compute the isolation of each value of X by constructing the distance to the nearest higher value in the data.

Parameters:¶

X : numpy.ndarray¶

(N, p) array of data to use as input. If p > 1, the “elevation” is computed using the topo.to_elevation function.

coordinates : numpy.ndarray¶

(N,k) array of locations for X to compute distances. If metric=’precomputed’, this should contain the distances from each point to every other point, and k == N.

metric : string or callable (default: 'euclidean')¶

name of distance metric in scipy.spatial.distance, or function, that can be used to compute distances between locations. If ‘precomputed’, ad-hoc function will be defined to look up distances between points instead.

middle : string or callable (default: 'mean')¶

method to define the elevation of points. See to_elevation for more details.

return_all : bool (default: False)¶

if False, only return the isolation (distance to nearest higher value).

progressbar : bool (default: False)¶

if True, show a progressbar for the computation.

Returns:¶

• either (N,) array of isolation values, or a pandas dataframe containing the full

• tree of precedence for the isolation tree.