Simplex tree reference manual¶

class gudhi.SimplexTree¶

Bases: object

The simplex tree is an efficient and flexible data structure for representing general (filtered) simplicial complexes. The data structure is described in Jean-Daniel Boissonnat and Clément Maria. The Simplex Tree: An Efficient Data Structure for General Simplicial Complexes. Algorithmica, pages 1–22, 2014.

This class is a filtered, with keys, and non contiguous vertices version of the simplex tree.

__init__()¶: SimplexTree constructor.

assign_filtration()¶

This function assigns a new filtration value to a given N-simplex.

Parameters

simplex¶ (list of int) – The N-simplex, represented by a list of vertex.
filtration¶ (float) – The new filtration value.

Note

Beware that after this operation, the structure may not be a valid filtration anymore, a simplex could have a lower filtration value than one of its faces. Callers are responsible for fixing this (with more assign_filtration() or make_filtration_non_decreasing() for instance) before calling any function that relies on the filtration property, like persistence().

betti_numbers()¶

This function returns the Betti numbers of the simplicial complex.

Returns: The Betti numbers ([B0, B1, …, Bn]).
Return type: list of int
Note: betti_numbers function requires compute_persistence() function to be launched first.

collapse_edges()¶

Assuming the simplex tree is a 1-skeleton graph, this method collapse edges (simplices of higher dimension are ignored) and resets the simplex tree from the remaining edges. A good candidate is to build a simplex tree on top of a RipsComplex of dimension 1 before collapsing edges (cf. rips_complex_edge_collapse_example.py). For implementation details, please refer to [6].

Parameters: nb_iterations¶ (int) – The number of edge collapse iterations to perform. Default is 1.
Note: collapse_edges method requires Eigen >= 3.1.0 and an exception is thrown if this method is not available.

compute_persistence()¶

This function computes the persistence of the simplicial complex, so it can be accessed through persistent_betti_numbers(), persistence_pairs(), etc. This function is equivalent to persistence() when you do not want the list persistence() returns.

Parameters

homology_coeff_field¶ (int) – The homology coefficient field. Must be a prime number. Default value is 11.
min_persistence¶ (float) – The minimum persistence value to take into account (strictly greater than min_persistence). Default value is 0.0. Sets min_persistence to -1.0 to see all values.
persistence_dim_max¶ (bool) – If true, the persistent homology for the maximal dimension in the complex is computed. If false, it is ignored. Default is false.

Returns

Nothing.

dimension()¶

This function returns the dimension of the simplicial complex.

Returns: the simplicial complex dimension.
Return type: int

Note

This function is not constant time because it can recompute dimension if required (can be triggered by remove_maximal_simplex() or prune_above_filtration() methods).

expansion()¶

Expands the simplex tree containing only its one skeleton until dimension max_dim.

The expanded simplicial complex until dimension \(d\) attached to a graph \(G\) is the maximal simplicial complex of dimension at most \(d\) admitting the graph \(G\) as \(1\)-skeleton. The filtration value assigned to a simplex is the maximal filtration value of one of its edges.

The simplex tree must contain no simplex of dimension bigger than 1 when calling the method.

Parameters: max_dim¶ (int) – The maximal dimension.

extend_filtration()¶

Extend filtration for computing extended persistence. This function only uses the filtration values at the 0-dimensional simplices, and computes the extended persistence diagram induced by the lower-star filtration computed with these values.

Note

Note that after calling this function, the filtration values are actually modified within the simplex tree. The function extended_persistence() retrieves the original values.

Note

Note that this code creates an extra vertex internally, so you should make sure that the simplex tree does not contain a vertex with the largest possible value (i.e., 4294967295).

This notebook explains how to compute an extension of persistence called extended persistence.

extended_persistence()¶

This function retrieves good values for extended persistence, and separate the diagrams into the Ordinary, Relative, Extended+ and Extended- subdiagrams.

Parameters

homology_coeff_field¶ (int) – The homology coefficient field. Must be a prime number. Default value is 11.
min_persistence¶ (float) – The minimum persistence value (i.e., the absolute value of the difference between the persistence diagram point coordinates) to take into account (strictly greater than min_persistence). Default value is 0.0. Sets min_persistence to -1.0 to see all values.

Returns

A list of four persistence diagrams in the format described in persistence(). The first one is Ordinary, the second one is Relative, the third one is Extended+ and the fourth one is Extended-. See https://link.springer.com/article/10.1007/s10208-008-9027-z and/or section 2.2 in https://link.springer.com/article/10.1007/s10208-017-9370-z for a description of these subtypes.

Note

This function should be called only if extend_filtration() has been called first!

Note

The coordinates of the persistence diagram points might be a little different than the original filtration values due to the internal transformation (scaling to [-2,-1]) that is performed on these values during the computation of extended persistence.

This notebook explains how to compute an extension of persistence called extended persistence.

filtration()¶

This function returns the filtration value for a given N-simplex in this simplicial complex, or +infinity if it is not in the complex.

Parameters: simplex¶ (list of int) – The N-simplex, represented by a list of vertex.
Returns: The simplicial complex filtration value.
Return type: float

find()¶

This function returns if the N-simplex was found in the simplicial complex or not.

Parameters: simplex¶ (list of int) – The N-simplex to find, represented by a list of vertex.
Returns: true if the simplex was found, false otherwise.
Return type: bool

flag_persistence_generators()¶

Assuming this is a flag complex, this function returns the persistence pairs, where each simplex is replaced with the vertices of the edges that gave it its filtration value.

Returns: First the regular persistence pairs of dimension 0, with one vertex for birth and two for death; then the other regular persistence pairs, grouped by dimension, with 2 vertices per extremity; then the connected components, with one vertex each; finally the other essential features, grouped by dimension, with 2 vertices for birth.
Return type: Tuple[numpy.array[int] of shape (n,3), List[numpy.array[int] of shape (m,4)], numpy.array[int] of shape (l,), List[numpy.array[int] of shape (k,2)]]
Note: flag_persistence_generators requires that persistence() be called first.

get_boundaries()¶

This function returns a generator with the boundaries of a given N-simplex. If you do not need the filtration values, the boundary can also be obtained as itertools.combinations(simplex,len(simplex)-1).

Parameters: simplex¶ (list of int.) – The N-simplex, represented by a list of vertex.
Returns: The (simplices of the) boundary of a simplex
Return type: generator with tuples(simplex, filtration)

get_cofaces()¶

This function returns the cofaces of a given N-simplex with a given codimension.

Parameters

simplex¶ (list of int) – The N-simplex, represented by a list of vertex.
codimension¶ (int) – The codimension. If codimension = 0, all cofaces are returned (equivalent of get_star function)

Returns

The (simplices of the) cofaces of a simplex

Return type

list of tuples(simplex, filtration)

get_filtration()¶

This function returns a generator with simplices and their given filtration values sorted by increasing filtration values.

Returns: The simplices sorted by increasing filtration values.
Return type: generator with tuples(simplex, filtration)

get_simplices()¶

This function returns a generator with simplices and their given filtration values.

Returns: The simplices.
Return type: generator with tuples(simplex, filtration)

get_skeleton()¶

This function returns a generator with the (simplices of the) skeleton of a maximum given dimension.

Parameters: dimension¶ (int) – The skeleton dimension value.
Returns: The (simplices of the) skeleton of a maximum dimension.
Return type: generator with tuples(simplex, filtration)

get_star()¶

This function returns the star of a given N-simplex.

Parameters: simplex¶ (list of int) – The N-simplex, represented by a list of vertex.
Returns: The (simplices of the) star of a simplex.
Return type: list of tuples(simplex, filtration)

initialize_filtration()¶: This function initializes and sorts the simplicial complex filtration vector.

Deprecated since version 3.2.0.

insert()¶

This function inserts the given N-simplex and its subfaces with the given filtration value (default value is ‘0.0’). If some of those simplices are already present with a higher filtration value, their filtration value is lowered.

Parameters

simplex¶ (list of int) – The N-simplex to insert, represented by a list of vertex.
filtration¶ (float) – The filtration value of the simplex.

Returns

true if the simplex was not yet in the complex, false otherwise (whatever its original filtration value).

Return type

bool

lower_star_persistence_generators()¶

Assuming this is a lower-star filtration, this function returns the persistence pairs, where each simplex is replaced with the vertex that gave it its filtration value.

Returns: First the regular persistence pairs, grouped by dimension, with one vertex per extremity, and second the essential features, grouped by dimension, with one vertex each
Return type: Tuple[List[numpy.array[int] of shape (n,2)], List[numpy.array[int] of shape (m,)]]
Note: lower_star_persistence_generators requires that persistence() be called first.

make_filtration_non_decreasing()¶

This function ensures that each simplex has a higher filtration value than its faces by increasing the filtration values.

Returns: True if any filtration value was modified, False if the filtration was already non-decreasing.
Return type: bool

num_simplices()¶

This function returns the number of simplices of the simplicial complex.

Returns: the simplicial complex number of simplices.
Return type: int

num_vertices()¶

This function returns the number of vertices of the simplicial complex.

Returns: The simplicial complex number of vertices.
Return type: int

persistence()¶

This function computes and returns the persistence of the simplicial complex.

Parameters

homology_coeff_field¶ (int) – The homology coefficient field. Must be a prime number. Default value is 11.
min_persistence¶ (float) – The minimum persistence value to take into account (strictly greater than min_persistence). Default value is 0.0. Set min_persistence to -1.0 to see all values.
persistence_dim_max¶ (bool) – If true, the persistent homology for the maximal dimension in the complex is computed. If false, it is ignored. Default is false.

Returns

The persistence of the simplicial complex.

Return type

list of pairs(dimension, pair(birth, death))

persistence_intervals_in_dimension()¶

This function returns the persistence intervals of the simplicial complex in a specific dimension.

Parameters: dimension¶ (int) – The specific dimension.
Returns: The persistence intervals.
Return type: numpy array of dimension 2
Note: intervals_in_dim function requires compute_persistence() function to be launched first.

persistence_pairs()¶

This function returns a list of persistence birth and death simplices pairs.

Returns: A list of persistence simplices intervals.
Return type: list of pair of list of int
Note: persistence_pairs function requires compute_persistence() function to be launched first.

persistent_betti_numbers()¶

This function returns the persistent Betti numbers of the simplicial complex.

Parameters

from_value¶ (float) – The persistence birth limit to be added in the numbers (persistent birth <= from_value).
to_value¶ (float) – The persistence death limit to be added in the numbers (persistent death > to_value).

Returns

The persistent Betti numbers ([B0, B1, …, Bn]).

Return type

list of int

Note

persistent_betti_numbers function requires compute_persistence() function to be launched first.

prune_above_filtration()¶

Prune above filtration value given as parameter.

Parameters: filtration¶ (float) – Maximum threshold value.
Returns: The filtration modification information.
Return type: bool

Note

Note that the dimension of the simplicial complex may be lower after calling prune_above_filtration() than it was before. However, upper_bound_dimension() will return the old value, which remains a valid upper bound. If you care, you can call dimension() method to recompute the exact dimension.

remove_maximal_simplex()¶

This function removes a given maximal N-simplex from the simplicial complex.

Parameters: simplex¶ (list of int) – The N-simplex, represented by a list of vertex.

Note

The dimension of the simplicial complex may be lower after calling remove_maximal_simplex than it was before. However, upper_bound_dimension() method will return the old value, which remains a valid upper bound. If you care, you can call dimension() to recompute the exact dimension.

reset_filtration()¶

This function resets the filtration value of all the simplices of dimension at least min_dim. Resets all the simplex tree when min_dim = 0. reset_filtration may break the filtration property with min_dim > 0, and it is the user’s responsibility to make it a valid filtration (using a large enough filt_value, or calling make_filtration_non_decreasing afterwards for instance).

Parameters

filtration¶ (float.) – New threshold value.
min_dim¶ (int.) – The minimal dimension. Default value is 0.

set_dimension()¶

This function sets the dimension of the simplicial complex.

Parameters: dimension¶ (int) – The new dimension value.

Note

This function must be used with caution because it disables dimension recomputation when required (this recomputation can be triggered by remove_maximal_simplex() or prune_above_filtration() ).

upper_bound_dimension()¶

This function returns a valid dimension upper bound of the simplicial complex.

Returns: an upper bound on the dimension of the simplicial complex.
Return type: int

write_persistence_diagram()¶

This function writes the persistence intervals of the simplicial complex in a user given file name.

Parameters: persistence_file¶ (string) – Name of the file.
Note: intervals_in_dim function requires compute_persistence() function to be launched first.