merge master and resolve conflicts

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]]
     """