library(phutil)library(phutil)An object of class persistence is a list of 2 elements:
pairs: A list of 2-column matrices containing birth-death pairs. The i-th element of the list corresponds to the (i − 1)-th homology dimension. If there is no pairs for a given dimension but there are pairs in higher dimensions, the corresponding element(s) is/are filled with a numeric matrix with 0 rows.
metadata: A list of length 6 containing information about how the data was computed:
orderered_pairs: A boolean indicating whether the pairs in the pairs list are ordered (i.e. the first column is strictly less than the second column).data: The name of the object containing the original data on which the persistence data was computed.engine: The name of the package and the function of this package that computed the persistence data in the form "package_name::package_function".filtration: The filtration used in the computation in a human-readable format (i.e. full names, capitals where needed, etc.).parameters: A list of parameters used in the computation.call: The exact call that generated the persistence data.The persistence class is designed to support a variety of inputs, including
If the user provides a matrix, it must have at least 2 columns and each row represents a topological feature.
If it has 2 columns, we assume that the first column corresponds to the birth of a feature and the second column corresponds to the death of a feature, irrespective of the order of the columns. In this case, we assume that the homology dimension of the feature is 0.
If it has more than 2 columns, we assume that the first column corresponds to the homology dimension of the feature, the second column corresponds to the birth of a feature, and the third column corresponds to the death of a feature, irrespective of the order of the columns. The remaining columns are ignored.
If the user provides a list of matrices, each list element corresponds to an homology dimension, from 0 to some maximum value. Each matrix must have at least 2 columns and each row represents a topological feature in the corresponding homology dimension (given by the matrix index in the list minus 1). Each matrix is parsed as described above.
If the user provides an object of class data.frame, it must have at least 2 columns and each row represents a topological feature. If it has exactly 2 columns, we add a dimension column with all values set to 0. If it has more than 2 columns, we require that birth and death exist in the column names. The birth and death columns are parsed as described above. The remaining columns are ignored.
If the user provides an object of class ‘PHom’ as typically produced by ripserr::vietoris_rips(), it means that it is a base::data.frame with columns dimension, birth, and death in that specific order. The dimension column is of type integer while the birth and death columns are of type numeric. The dimension column is used to create a list of matrices, where each matrix corresponds to an homology dimension, from 0 to the maximum value in the dimension column.
If the user provides an object of class ‘diagram’ as typically produced by TDA::*Diag() functions in entry diagram, it means that it is a base::matrix with 3 columns with names dimension, Birth and Death in that specific order. The dimension column is of type integer while the Birth and Death columns are of type numeric. Furthermore, the object stores as attributes the parameters used to compute the diagram and the entire call to the function that produced the diagram. We first lowercase Birth and Death. Next, the dimension column is used to create a list of matrices, where each matrix corresponds to an homology dimension, from 0 to the maximum value in the dimension column. The birth and death columns are parsed as described above. The remaining columns are ignored.
If the user provides an object of class ‘hclust’ as typically produced by stats::hclust(), it means that it is a base::list which contains the height element which is a set of n − 1 real values (non-decreasing for ultrametric trees) storing the clustering height, that is, the value of the criterion associated with the clustering method for the particular agglomeration. This is used as homological feature death while a birth of 0 is typically used.