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Diffstat (limited to 'src/python/doc/alpha_complex_user.rst')
| -rw-r--r-- | src/python/doc/alpha_complex_user.rst | 7 |
1 files changed, 4 insertions, 3 deletions
diff --git a/src/python/doc/alpha_complex_user.rst b/src/python/doc/alpha_complex_user.rst index d1e9c7cd..f9662a6d 100644 --- a/src/python/doc/alpha_complex_user.rst +++ b/src/python/doc/alpha_complex_user.rst @@ -28,7 +28,7 @@ This example builds the Delaunay triangulation from the given points, and initia import gudhi alpha_complex = gudhi.AlphaComplex(points=[[1, 1], [7, 0], [4, 6], [9, 6], [0, 14], [2, 19], [9, 17]]) - simplex_tree = alpha_complex.create_simplex_tree(max_alpha_square=60.0) + simplex_tree = alpha_complex.create_simplex_tree() result_str = 'Alpha complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ repr(simplex_tree.num_simplices()) + ' simplices - ' + \ repr(simplex_tree.num_vertices()) + ' vertices.' @@ -146,8 +146,9 @@ Prune above given filtration value ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The simplex tree is pruned from the given maximum alpha squared value (cf. `Simplex_tree::prune_above_filtration()` -in the `C++ version <http://gudhi.gforge.inria.fr/doc/latest/index.html>`_). -In the following example, the value is given by the user as argument of the program. +in the `C++ version <http://gudhi.gforge.inria.fr/doc/latest/index.html>`_). Note that this does not provide any kind +of speed-up, since we always first build the full filtered complex, so it is recommended not to use `max_alpha_square`. +In the following example, a threshold of 59 is used. Example from OFF file |