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Diffstat (limited to 'src/python/doc/witness_complex_user.rst')
| -rw-r--r-- | src/python/doc/witness_complex_user.rst | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/src/python/doc/witness_complex_user.rst b/src/python/doc/witness_complex_user.rst index 40e94134..7087fa98 100644 --- a/src/python/doc/witness_complex_user.rst +++ b/src/python/doc/witness_complex_user.rst @@ -47,7 +47,7 @@ which leads to definitions of **weak relaxed witness complex** (or just relaxed In particular case of 0-relaxation, weak complex corresponds to **witness complex** introduced in :cite:`de2004topological`, whereas 0-relaxed strong witness complex consists of just vertices and is not very interesting. Hence for small relaxation weak version is preferable. -However, to capture the homotopy type (for example using Gudhi::persistent_cohomology::Persistent_cohomology) it is +However, to capture the homotopy type (for example using :func:`gudhi.SimplexTree.persistence`) it is often necessary to work with higher filtration values. In this case strong relaxed witness complex is faster to compute and offers similar results. @@ -69,7 +69,7 @@ The construction of the Euclidean versions of complexes follow the same scheme: In the non-Euclidean classes, the lists of nearest landmarks are supposed to be given as input. -The constructors take on the steps 1 and 2, while the function 'create_complex' executes the step 3. +The constructors take on the steps 1 and 2, while the function :func:`!create_complex` executes the step 3. Constructing weak relaxed witness complex from an off file ---------------------------------------------------------- @@ -101,7 +101,7 @@ Let's start with a simple example, which reads an off point file and computes a print("#####################################################################") print("EuclideanWitnessComplex creation from points read in a OFF file") - witnesses = gudhi.read_off(off_file=args.file) + witnesses = gudhi.read_points_from_off_file(off_file=args.file) landmarks = gudhi.pick_n_random_points(points=witnesses, nb_points=args.number_of_landmarks) message = "EuclideanWitnessComplex with max_edge_length=" + repr(args.max_alpha_square) + \ |