new fixes

1 files changed, 28 insertions, 0 deletions

diff --git a/src/python/gudhi/periodic_cubical_complex.pyx b/src/python/gudhi/periodic_cubical_complex.pyx
index 37f76201..ba039e80 100644
--- a/src/python/gudhi/periodic_cubical_complex.pyx
+++ b/src/python/gudhi/periodic_cubical_complex.pyx
@@ -31,6 +31,7 @@ cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi":
     cdef cppclass Periodic_cubical_complex_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Cubical_complex::Cubical_complex_interface<Gudhi::cubical_complex::Bitmap_cubical_complex_periodic_boundary_conditions_base<double>>>":
         Periodic_cubical_complex_persistence_interface(Periodic_cubical_complex_base_interface * st, bool persistence_dim_max)
         vector[pair[int, pair[double, double]]] get_persistence(int homology_coeff_field, double min_persistence)
+        vector[vector[int]] cofaces_of_cubical_persistence_pairs()
         vector[int] betti_numbers()
         vector[int] persistent_betti_numbers(double from_value, double to_value)
         vector[pair[double,double]] intervals_in_dimension(int dimension)
@@ -155,6 +156,33 @@ cdef class PeriodicCubicalComplex:
             persistence_result = self.pcohptr.get_persistence(homology_coeff_field, min_persistence)
         return persistence_result
 
+    def cofaces_of_persistence_pairs(self):
+        """A persistence interval is described by a pair of cells, one that creates the 
+        feature and one that kills it. The filtration values of those 2 cells give coordinates 
+        for a point in a persistence diagram, or a bar in a barcode. Structurally, in the 
+        cubical complexes provided here, the filtration value of any cell is the minimum of the 
+        filtration values of the maximal cells that contain it. Connecting persistence diagram 
+        coordinates to the corresponding value in the input (i.e. the filtration values of 
+        the top-dimensional cells) is useful for differentiation purposes.
+
+        This function returns a list of pairs of top-dimensional cells corresponding to 
+        the persistence birth and death cells of the filtration. The cells are represented by 
+        their indices in the input list of top-dimensional cells (and not their indices in the 
+        internal datastructure that includes non-maximal cells). Note that when two adjacent 
+        top-dimensional cells have the same filtration value, we arbitrarily return one of the two
+        when calling the function on one of their common faces.
+
+        :returns: The top-dimensional cells/cofaces of the positive and negative cells, together with the corresponding homological dimension.
+        :rtype:  numpy array of integers of shape [number_of_persistence_points, 3], the integers of eah row being: (homological dimension, index of positive top-dimensional cell, index of negative top-dimensional cell). If the homological feature is essential, i.e., if the death time is +infinity, then the index of the corresponding negative top-dimensional cell is -1.
+        """
+        cdef vector[vector[int]] persistence_result
+        if self.pcohptr != NULL:
+            persistence_result = self.pcohptr.cofaces_of_cubical_persistence_pairs()
+        else:
+            print("cofaces_of_persistence_pairs function requires persistence function"
+                  " to be launched first.")
+        return np.array(persistence_result)
+
     def betti_numbers(self):
         """This function returns the Betti numbers of the complex.