diff options
Diffstat (limited to 'src/python/gudhi')
-rw-r--r-- | src/python/gudhi/representations/vector_methods.py | 17 | ||||
-rw-r--r-- | src/python/gudhi/simplex_tree.pxd | 8 | ||||
-rw-r--r-- | src/python/gudhi/simplex_tree.pyx | 38 | ||||
-rw-r--r-- | src/python/gudhi/subsampling.pyx | 21 |
4 files changed, 58 insertions, 26 deletions
diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py index 5ca127f6..cdcb1fde 100644 --- a/src/python/gudhi/representations/vector_methods.py +++ b/src/python/gudhi/representations/vector_methods.py @@ -323,22 +323,15 @@ class BettiCurve(BaseEstimator, TransformerMixin): Returns: numpy array with shape (number of diagrams) x (**resolution**): output Betti curves. """ - num_diag, Xfit = len(X), [] + Xfit = [] x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution) step_x = x_values[1] - x_values[0] - for i in range(num_diag): - - diagram, num_pts_in_diag = X[i], X[i].shape[0] - + for diagram in X: + diagram_int = np.clip(np.ceil((diagram[:,:2] - self.sample_range[0]) / step_x), 0, self.resolution).astype(int) bc = np.zeros(self.resolution) - for j in range(num_pts_in_diag): - [px,py] = diagram[j,:2] - min_idx = np.clip(np.ceil((px - self.sample_range[0]) / step_x).astype(int), 0, self.resolution) - max_idx = np.clip(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0, self.resolution) - for k in range(min_idx, max_idx): - bc[k] += 1 - + for interval in diagram_int: + bc[interval[0]:interval[1]] += 1 Xfit.append(np.reshape(bc,[1,-1])) Xfit = np.concatenate(Xfit, 0) diff --git a/src/python/gudhi/simplex_tree.pxd b/src/python/gudhi/simplex_tree.pxd index 75e94e0b..3c4cbed3 100644 --- a/src/python/gudhi/simplex_tree.pxd +++ b/src/python/gudhi/simplex_tree.pxd @@ -36,6 +36,12 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": Simplex_tree_skeleton_iterator operator++() nogil bint operator!=(Simplex_tree_skeleton_iterator) nogil + cdef cppclass Simplex_tree_boundary_iterator "Gudhi::Simplex_tree_interface<Gudhi::Simplex_tree_options_full_featured>::Boundary_simplex_iterator": + Simplex_tree_boundary_iterator() nogil + Simplex_tree_simplex_handle& operator*() nogil + Simplex_tree_boundary_iterator operator++() nogil + bint operator!=(Simplex_tree_boundary_iterator) nogil + cdef cppclass Simplex_tree_interface_full_featured "Gudhi::Simplex_tree_interface<Gudhi::Simplex_tree_options_full_featured>": Simplex_tree() nogil @@ -58,6 +64,7 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": void compute_extended_filtration() nogil vector[vector[pair[int, pair[double, double]]]] compute_extended_persistence_subdiagrams(vector[pair[int, pair[double, double]]] dgm, double min_persistence) nogil Simplex_tree_interface_full_featured* collapse_edges(int nb_collapse_iteration) nogil + void reset_filtration(double filtration, int dimension) nogil # Iterators over Simplex tree pair[vector[int], double] get_simplex_and_filtration(Simplex_tree_simplex_handle f_simplex) nogil Simplex_tree_simplices_iterator get_simplices_iterator_begin() nogil @@ -66,6 +73,7 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi": vector[Simplex_tree_simplex_handle].const_iterator get_filtration_iterator_end() nogil Simplex_tree_skeleton_iterator get_skeleton_iterator_begin(int dimension) nogil Simplex_tree_skeleton_iterator get_skeleton_iterator_end(int dimension) nogil + pair[Simplex_tree_boundary_iterator, Simplex_tree_boundary_iterator] get_boundary_iterators(vector[int] simplex) nogil except + cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi": cdef cppclass Simplex_tree_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_full_featured>>": diff --git a/src/python/gudhi/simplex_tree.pyx b/src/python/gudhi/simplex_tree.pyx index 92645ffc..cdd2e87b 100644 --- a/src/python/gudhi/simplex_tree.pyx +++ b/src/python/gudhi/simplex_tree.pyx @@ -285,6 +285,22 @@ cdef class SimplexTree: ct.append((v, filtered_simplex.second)) return ct + def get_boundaries(self, simplex): + """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 + :code:`itertools.combinations(simplex,len(simplex)-1)`. + + :param simplex: The N-simplex, represented by a list of vertex. + :type simplex: list of int. + :returns: The (simplices of the) boundary of a simplex + :rtype: generator with tuples(simplex, filtration) + """ + cdef pair[Simplex_tree_boundary_iterator, Simplex_tree_boundary_iterator] it = self.get_ptr().get_boundary_iterators(simplex) + + while it.first != it.second: + yield self.get_ptr().get_simplex_and_filtration(dereference(it.first)) + preincrement(it.first) + def remove_maximal_simplex(self, simplex): """This function removes a given maximal N-simplex from the simplicial complex. @@ -328,7 +344,7 @@ cdef class SimplexTree: return self.get_ptr().prune_above_filtration(filtration) def expansion(self, max_dim): - """Expands the Simplex_tree containing only its one skeleton + """Expands the simplex tree containing only its one skeleton until dimension max_dim. The expanded simplicial complex until dimension :math:`d` @@ -338,7 +354,7 @@ cdef class SimplexTree: 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 + The simplex tree must contain no simplex of dimension bigger than 1 when calling the method. :param max_dim: The maximal dimension. @@ -358,6 +374,20 @@ cdef class SimplexTree: """ return self.get_ptr().make_filtration_non_decreasing() + def reset_filtration(self, filtration, min_dim = 0): + """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). + + :param filtration: New threshold value. + :type filtration: float. + :param min_dim: The minimal dimension. Default value is 0. + :type min_dim: int. + """ + self.get_ptr().reset_filtration(filtration, min_dim) + def extend_filtration(self): """ 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 @@ -365,12 +395,12 @@ cdef class SimplexTree: .. note:: - Note that after calling this function, the filtration values are actually modified within the Simplex_tree. + Note that after calling this function, the filtration values are actually modified within the simplex tree. The function :func:`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 + 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 <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-extended-persistence.ipynb>`_ diff --git a/src/python/gudhi/subsampling.pyx b/src/python/gudhi/subsampling.pyx index f77c6f75..b11d07e5 100644 --- a/src/python/gudhi/subsampling.pyx +++ b/src/python/gudhi/subsampling.pyx @@ -33,7 +33,7 @@ def choose_n_farthest_points(points=None, off_file='', nb_points=0, starting_poi The iteration starts with the landmark `starting point`. :param points: The input point set. - :type points: Iterable[Iterable[float]]. + :type points: Iterable[Iterable[float]] Or @@ -42,14 +42,15 @@ def choose_n_farthest_points(points=None, off_file='', nb_points=0, starting_poi And in both cases - :param nb_points: Number of points of the subsample. - :type nb_points: unsigned. + :param nb_points: Number of points of the subsample (the subsample may be \ + smaller if there are fewer than nb_points distinct input points) + :type nb_points: int :param starting_point: The iteration starts with the landmark `starting \ - point`,which is the index of the point to start with. If not set, this \ + point`, which is the index of the point to start with. If not set, this \ index is chosen randomly. - :type starting_point: unsigned. + :type starting_point: int :returns: The subsample point set. - :rtype: List[List[float]]. + :rtype: List[List[float]] """ if off_file: if os.path.isfile(off_file): @@ -76,7 +77,7 @@ def pick_n_random_points(points=None, off_file='', nb_points=0): """Subsample a point set by picking random vertices. :param points: The input point set. - :type points: Iterable[Iterable[float]]. + :type points: Iterable[Iterable[float]] Or @@ -86,7 +87,7 @@ def pick_n_random_points(points=None, off_file='', nb_points=0): And in both cases :param nb_points: Number of points of the subsample. - :type nb_points: unsigned. + :type nb_points: int :returns: The subsample point set. :rtype: List[List[float]] """ @@ -107,7 +108,7 @@ def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0): between any two points is greater than or equal to min_squared_dist. :param points: The input point set. - :type points: Iterable[Iterable[float]]. + :type points: Iterable[Iterable[float]] Or @@ -118,7 +119,7 @@ def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0): :param min_squared_dist: Minimum squared distance separating the output \ points. - :type min_squared_dist: float. + :type min_squared_dist: float :returns: The subsample point set. :rtype: List[List[float]] """ |