Trailing whitespace

3 files changed, 38 insertions, 38 deletions

diff --git a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h
index 5927bbec..58d9208d 100644
--- a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h
+++ b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex_base.h
@@ -112,8 +112,8 @@ class Bitmap_cubical_complex_base {
   virtual inline std::vector<std::size_t> get_coboundary_of_a_cell(std::size_t cell) const;
 
   /**
-   * This function finds a top-dimensional cell that is incident to the input cell and has 
-   * the same filtration value. In case several cells are suitable, an arbitrary one is 
+   * This function finds a top-dimensional cell that is incident to the input cell and has
+   * the same filtration value. In case several cells are suitable, an arbitrary one is
    * returned. Note that the input parameter can be a cell of any dimension (vertex, edge, etc).
    * On the other hand, the output is always indicating the position of
    * a top-dimensional cube in the data structure.
@@ -617,12 +617,12 @@ void Bitmap_cubical_complex_base<T>::setup_bitmap_based_on_top_dimensional_cells
 template <typename T>
 size_t Bitmap_cubical_complex_base<T>::get_top_dimensional_coface_of_a_cell(size_t splx) {
   if (this->get_dimension_of_a_cell(splx) == this->dimension()){return splx;}
-  else{  
+  else{
     for (auto v : this->get_coboundary_of_a_cell(splx)){
       if(this->get_cell_data(v) == this->get_cell_data(splx)){
         return this->get_top_dimensional_coface_of_a_cell(v);
       }
-    }  
+    }
   }
   BOOST_UNREACHABLE_RETURN(-2);
 }
diff --git a/src/python/gudhi/cubical_complex.pyx b/src/python/gudhi/cubical_complex.pyx
index 9ebd0b30..ca979eda 100644
--- a/src/python/gudhi/cubical_complex.pyx
+++ b/src/python/gudhi/cubical_complex.pyx
@@ -172,31 +172,31 @@ cdef class CubicalComplex:
         return self.pcohptr.get_persistence()
 
     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 
+        """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 
+        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, 
+        :returns: The top-dimensional cells/cofaces of the positive and negative cells,
             together with the corresponding homological dimension, in two lists of numpy arrays of integers.
-            The first list contains the regular persistence pairs, grouped by dimension. 
+            The first list contains the regular persistence pairs, grouped by dimension.
             It contains numpy arrays of shape [number_of_persistence_points, 2].
-            The indices of the arrays in the list correspond to the homological dimensions, and the 
-            integers of each row in each array correspond to: (index of positive top-dimensional cell, 
-            index of negative top-dimensional cell). 
-            The second list contains the essential features, grouped by dimension.             
+            The indices of the arrays in the list correspond to the homological dimensions, and the
+            integers of each row in each array correspond to: (index of positive top-dimensional cell,
+            index of negative top-dimensional cell).
+            The second list contains the essential features, grouped by dimension.
             It contains numpy arrays of shape [number_of_persistence_points, 1].
-            The indices of the arrays in the list correspond to the homological dimensions, and the 
+            The indices of the arrays in the list correspond to the homological dimensions, and the
             integers of each row in each array correspond to: (index of positive top-dimensional cell).
         """
 
diff --git a/src/python/gudhi/periodic_cubical_complex.pyx b/src/python/gudhi/periodic_cubical_complex.pyx
index 3cf2ff01..06309772 100644
--- a/src/python/gudhi/periodic_cubical_complex.pyx
+++ b/src/python/gudhi/periodic_cubical_complex.pyx
@@ -177,31 +177,31 @@ cdef class PeriodicCubicalComplex:
         return self.pcohptr.get_persistence()
 
     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 
+        """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 
+        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, 
+        :returns: The top-dimensional cells/cofaces of the positive and negative cells,
             together with the corresponding homological dimension, in two lists of numpy arrays of integers.
-            The first list contains the regular persistence pairs, grouped by dimension. 
+            The first list contains the regular persistence pairs, grouped by dimension.
             It contains numpy arrays of shape [number_of_persistence_points, 2].
-            The indices of the arrays in the list correspond to the homological dimensions, and the 
-            integers of each row in each array correspond to: (index of positive top-dimensional cell, 
-            index of negative top-dimensional cell). 
-            The second list contains the essential features, grouped by dimension.             
+            The indices of the arrays in the list correspond to the homological dimensions, and the
+            integers of each row in each array correspond to: (index of positive top-dimensional cell,
+            index of negative top-dimensional cell).
+            The second list contains the essential features, grouped by dimension.
             It contains numpy arrays of shape [number_of_persistence_points, 1].
-            The indices of the arrays in the list correspond to the homological dimensions, and the 
+            The indices of the arrays in the list correspond to the homological dimensions, and the
             integers of each row in each array correspond to: (index of positive top-dimensional cell).
         """
         cdef vector[vector[int]] persistence_result