Merge tag 'tags/gudhi-release-3.3.0' into dfsg/latest

1 files changed, 22 insertions, 0 deletions

diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst
index 819568be..6048cc4e 100644
--- a/src/python/doc/rips_complex_user.rst
+++ b/src/python/doc/rips_complex_user.rst
@@ -378,6 +378,7 @@ Example from a point cloud combined with DistanceToMeasure
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Combining with DistanceToMeasure, one can compute the DTM-filtration of a point set, as in `this notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-DTM-filtrations.ipynb>`_. 
+Remark that `DTMRipsComplex <rips_complex_user.html#dtm-rips-complex>`_ class provides exactly this function.
 
 .. testcode::
 
@@ -398,3 +399,24 @@ The output is:
 .. testoutput::
 
     [(0, (3.1622776601683795, inf)), (0, (3.1622776601683795, 5.39834563766817)), (0, (3.1622776601683795, 5.39834563766817))]
+
+DTM Rips Complex
+----------------
+
+:class:`~gudhi.dtm_rips_complex.DTMRipsComplex` builds a simplicial complex from a point set or a full distance matrix (in the form of ndarray), as described in the above example.
+This class constructs a weighted Rips complex giving larger weights to outliers, which reduces their impact on the persistence diagram. See `this notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-DTM-filtrations.ipynb>`_ for some experiments.
+
+.. testcode::
+
+    import numpy as np
+    from gudhi.dtm_rips_complex import DTMRipsComplex
+    pts = np.array([[2.0, 2.0], [0.0, 1.0], [3.0, 4.0]])
+    dtm_rips = DTMRipsComplex(points=pts, k=2)
+    st = dtm_rips.create_simplex_tree(max_dimension=2)
+    print(st.persistence())
+
+The output is:
+
+.. testoutput::
+
+    [(0, (3.1622776601683795, inf)), (0, (3.1622776601683795, 5.39834563766817)), (0, (3.1622776601683795, 5.39834563766817))]