(self, X, distance_matrix=False, metric="euclidean")
return self.dgms_
Compute persistence diagrams for X data array and return the diagrams.

        Parameters
        ----------
        X: ndarray (n_samples, n_features)
            A numpy array of either data or distance matrix.

        distance_matrix: bool
            Indicator that X is a distance matrix, if not we compute a 
            distance matrix from X using the chosen metric.

        metric: string or callable
            The metric to use when calculating distance between instances in a 
            feature array. If metric is a string, it must be one of the options 
            specified in pairwise_distances, including "euclidean", "manhattan", 
            or "cosine". Alternatively, if metric is a callable function, it is 
            called on each pair of instances (rows) and the resulting value 
            recorded. The callable should take two arrays from X as input and 
            return a value indicating the distance between them.

        Returns
        -------
        dgms: list (size maxdim) of ndarray (n_pairs, 2)
            A list of persistence diagrams, one for each dimension less 
            than maxdim. Each diagram is an ndarray of size (n_pairs, 2) with 
            the first column representing the birth time and the second column 
            representing the death time of each pair.
Compute persistence diagrams for X data array and return the diagrams.
Compute persistence diagrams for X data array and return the diagrams .
def fit_transform(self, X, distance_matrix=False, metric="euclidean"):
        """
        Compute persistence diagrams for X data array and return the diagrams.

        Parameters
        ----------
        X: ndarray (n_samples, n_features)
            A numpy array of either data or distance matrix.

        distance_matrix: bool
            Indicator that X is a distance matrix, if not we compute a 
            distance matrix from X using the chosen metric.

        metric: string or callable
            The metric to use when calculating distance between instances in a 
            feature array. If metric is a string, it must be one of the options 
            specified in pairwise_distances, including "euclidean", "manhattan", 
            or "cosine". Alternatively, if metric is a callable function, it is 
            called on each pair of instances (rows) and the resulting value 
            recorded. The callable should take two arrays from X as input and 
            return a value indicating the distance between them.

        Returns
        -------
        dgms: list (size maxdim) of ndarray (n_pairs, 2)
            A list of persistence diagrams, one for each dimension less 
            than maxdim. Each diagram is an ndarray of size (n_pairs, 2) with 
            the first column representing the birth time and the second column 
            representing the death time of each pair.
        """
        self.transform(X, distance_matrix, metric)
        return self.dgms_
def fit_transform ( self , X , distance_matrix = False , metric = "euclidean" ) : self . transform ( X , distance_matrix , metric ) return self . dgms_