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.pyx | 41 | ||||
| -rw-r--r-- | src/python/gudhi/subsampling.pyx | 2 |
3 files changed, 29 insertions, 31 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.pyx b/src/python/gudhi/simplex_tree.pyx index 813dc5c2..cdd2e87b 100644 --- a/src/python/gudhi/simplex_tree.pyx +++ b/src/python/gudhi/simplex_tree.pyx @@ -389,37 +389,39 @@ cdef class SimplexTree: 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 computed with these values. + """ 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 + computed with these values. .. note:: - 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 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 not contain a vertex with the largest possible value (i.e., 4294967295). + 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>`_ + explains how to compute an extension of persistence called extended persistence. """ self.get_ptr().compute_extended_filtration() def extended_persistence(self, homology_coeff_field=11, min_persistence=0): - """This function retrieves good values for extended persistence, and separate the diagrams - into the Ordinary, Relative, Extended+ and Extended- subdiagrams. + """This function retrieves good values for extended persistence, and separate the diagrams into the Ordinary, + Relative, Extended+ and Extended- subdiagrams. - :param homology_coeff_field: The homology coefficient field. Must be a - prime number. Default value is 11. + :param homology_coeff_field: The homology coefficient field. Must be a prime number. Default value is 11. :type homology_coeff_field: int - :param min_persistence: The minimum persistence value (i.e., the absolute value of the difference between the persistence diagram point coordinates) to take into - account (strictly greater than min_persistence). Default value is - 0.0. - Sets min_persistence to -1.0 to see all values. + :param min_persistence: The minimum persistence value (i.e., the absolute value of the difference between the + persistence diagram point coordinates) to take into account (strictly greater than min_persistence). + Default value is 0.0. Sets min_persistence to -1.0 to see all values. :type min_persistence: float - :returns: A list of four persistence diagrams in the format described in :func:`persistence`. The first one is Ordinary, the second one is Relative, the third one is Extended+ and the fourth one is Extended-. See https://link.springer.com/article/10.1007/s10208-008-9027-z and/or section 2.2 in https://link.springer.com/article/10.1007/s10208-017-9370-z for a description of these subtypes. + :returns: A list of four persistence diagrams in the format described in :func:`persistence`. The first one is + Ordinary, the second one is Relative, the third one is Extended+ and the fourth one is Extended-. + See https://link.springer.com/article/10.1007/s10208-008-9027-z and/or section 2.2 in + https://link.springer.com/article/10.1007/s10208-017-9370-z for a description of these subtypes. .. note:: @@ -430,6 +432,9 @@ cdef class SimplexTree: The coordinates of the persistence diagram points might be a little different than the original filtration values due to the internal transformation (scaling to [-2,-1]) that is performed on these values during the computation of extended persistence. + + This `notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-extended-persistence.ipynb>`_ + explains how to compute an extension of persistence called extended persistence. """ cdef vector[pair[int, pair[double, double]]] persistence_result if self.pcohptr != NULL: diff --git a/src/python/gudhi/subsampling.pyx b/src/python/gudhi/subsampling.pyx index b11d07e5..46f32335 100644 --- a/src/python/gudhi/subsampling.pyx +++ b/src/python/gudhi/subsampling.pyx @@ -105,7 +105,7 @@ def pick_n_random_points(points=None, off_file='', nb_points=0): def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0): """Outputs a subset of the input points so that the squared distance - between any two points is greater than or equal to min_squared_dist. + between any two points is greater than min_squared_dist. :param points: The input point set. :type points: Iterable[Iterable[float]] |