# check_point_clouds¶

gtda.utils.check_point_clouds(X, distance_matrices=False, **kwargs)[source]

Input validation on arrays or lists representing collections of point clouds or of distance/adjacency matrices.

The input is checked to be either a single 3D array using a single call to sklearn.utils.validation.check_array, or a list of 2D arrays by calling sklearn.utils.validation.check_array on each entry.

Parameters
• X (object) – Input object to check / convert.

• distance_matrices (bool, optional, default: False) – Whether the input represents a collection of distance matrices or of concrete point clouds in Euclidean space. In the first case, entries are allowed to be infinite unless otherwise specified in kwargs.

• **kwargs – Keyword arguments accepted by sklearn.utils.validation.check_array, with the following caveats: 1) ensure_2d and allow_nd are ignored; 2) if not passed explicitly, force_all_finite is set to be the boolean negation of distance_matrices; 3) when force_all_finite is set to False, NaN inputs are not allowed; 4) accept_sparse and accept_large_sparse are only meaningful in the case of lists of 2D arrays, in which case they are passed to individual instances of sklearn.utils.validation.check_array validating each entry in the list.

Returns

Xnew – The converted and validated object.

Return type

ndarray or list