From b0ae08e93fdba8a1faec56c2230b6f542653c49e Mon Sep 17 00:00:00 2001 From: Marc Glisse Date: Wed, 13 May 2020 20:17:26 +0200 Subject: Trailing whitespace --- .../include/gudhi/Bitmap_cubical_complex_base.h | 8 ++--- src/python/gudhi/cubical_complex.pyx | 34 +++++++++++----------- src/python/gudhi/periodic_cubical_complex.pyx | 34 +++++++++++----------- 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 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::setup_bitmap_based_on_top_dimensional_cells template size_t Bitmap_cubical_complex_base::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 -- cgit v1.2.3