new fixes

6 files changed, 52 insertions, 19 deletions

diff --git a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h
index bf09532e..37514dee 100644
--- a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h
+++ b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h
@@ -340,7 +340,6 @@ class Bitmap_cubical_complex : public T {
    * that provides ranges for the Boundary_simplex_iterator.
    **/
   Boundary_simplex_range boundary_simplex_range(Simplex_handle sh) { return this->get_boundary_of_a_cell(sh); }
-  Boundary_simplex_range coboundary_simplex_range(Simplex_handle sh) { return this->get_coboundary_of_a_cell(sh); }
 
   /**
    * filtration_simplex_range creates an object of a Filtration_simplex_range class
diff --git a/src/python/gudhi/cubical_complex.pyx b/src/python/gudhi/cubical_complex.pyx
index 8cf43539..9e701fe6 100644
--- a/src/python/gudhi/cubical_complex.pyx
+++ b/src/python/gudhi/cubical_complex.pyx
@@ -148,22 +148,22 @@ cdef class CubicalComplex:
 
     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.
-        :rtype:  list of pairs(index of positive top-dimensional cell, index of negative top-dimensional cell)
+        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:
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.
 
diff --git a/src/python/gudhi/simplex_tree.pyx b/src/python/gudhi/simplex_tree.pyx
index 85d25492..b18627c4 100644
--- a/src/python/gudhi/simplex_tree.pyx
+++ b/src/python/gudhi/simplex_tree.pyx
@@ -508,7 +508,7 @@ cdef class SimplexTree:
         """
         if self.pcohptr != NULL:
             if persistence_file != '':
-                self.pcohptr.write_output_diagram(str.encode(persistence_file))
+                self.pcohptr.write_output_diagram(persistence_file.encode('utf-8'))
             else:
                 print("persistence_file must be specified")
         else:
diff --git a/src/python/include/Persistent_cohomology_interface.h b/src/python/include/Persistent_cohomology_interface.h
index e5accf50..defac88c 100644
--- a/src/python/include/Persistent_cohomology_interface.h
+++ b/src/python/include/Persistent_cohomology_interface.h
@@ -75,12 +75,13 @@ persistent_cohomology::Persistent_cohomology<FilteredComplex, persistent_cohomol
   int top_dimensional_coface(int splx){
     if (stptr_->dimension(splx) == stptr_->dimension()){return splx;}
     else{  
-      for (auto v : stptr_->coboundary_simplex_range(splx)){
+      for (auto v : stptr_->get_coboundary_of_a_cell(splx)){
         if(stptr_->filtration(v) == stptr_->filtration(splx)){
           return top_dimensional_coface(v);
         }
       }  
     }
+    return splx;
   }
 
   std::vector<std::vector<int>> cofaces_of_cubical_persistence_pairs() {
diff --git a/src/python/test/test_cubical_complex.py b/src/python/test/test_cubical_complex.py
index 8c1b2600..8af63355 100755
--- a/src/python/test/test_cubical_complex.py
+++ b/src/python/test/test_cubical_complex.py
@@ -147,3 +147,8 @@ def test_connected_sublevel_sets():
                                  periodic_dimensions = periodic_dimensions)
     assert cub.persistence() == [(0, (2.0, float("inf")))]
     assert cub.betti_numbers() == [1, 0, 0]
+
+def test_connected_sublevel_sets():
+    cub = CubicalComplex(top_dimensional_cells = [[0, 0, 0], [0, 1, 0], [0, 0, 0]])
+    cub.persistence()
+    assert cub.cofaces_of_persistence_pairs() == np.array([[1, 7, 4], [0, 8, -1]])