Merge tag 'tags/gudhi-release-3.2.0' into dfsg/latest

1 files changed, 52 insertions, 1 deletions

diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst
index a27573e8..819568be 100644
--- a/src/python/doc/rips_complex_user.rst
+++ b/src/python/doc/rips_complex_user.rst
@@ -8,7 +8,7 @@ Definition
 ----------
 
 ====================================================================  ================================  ======================
-:Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse  :Introduced in: GUDHI 2.0.0       :Copyright: GPL v3
+:Authors: Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse  :Since: GUDHI 2.0.0               :License: GPL v3
 ====================================================================  ================================  ======================
 
 +-------------------------------------------+----------------------------------------------------------------------+
@@ -347,3 +347,54 @@ until dimension 1 - one skeleton graph in other words), the output is:
     points in the persistence diagram will be under the diagonal, and
     bottleneck distance and persistence graphical tool will not work properly,
     this is a known issue.
+
+Weighted Rips Complex
+---------------------
+
+`WeightedRipsComplex <rips_complex_ref.html#weighted-rips-complex-reference-manual>`_ builds a simplicial complex from a distance matrix and weights on vertices.
+
+
+Example from a distance matrix and weights
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The following example computes the weighted Rips filtration associated with a distance matrix and weights on vertices.
+
+.. testcode::
+
+    from gudhi.weighted_rips_complex import WeightedRipsComplex
+    dist = [[], [1]]
+    weights = [1, 100]
+    w_rips = WeightedRipsComplex(distance_matrix=dist, weights=weights)
+    st = w_rips.create_simplex_tree(max_dimension=2)
+    print(list(st.get_filtration()))
+
+The output is:
+
+.. testoutput::
+
+    [([0], 2.0), ([1], 200.0), ([0, 1], 200.0)]
+
+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>`_. 
+
+.. testcode::
+
+    import numpy as np
+    from scipy.spatial.distance import cdist
+    from gudhi.point_cloud.dtm import DistanceToMeasure
+    from gudhi.weighted_rips_complex import WeightedRipsComplex
+    pts = np.array([[2.0, 2.0], [0.0, 1.0], [3.0, 4.0]])
+    dist = cdist(pts,pts)
+    dtm = DistanceToMeasure(2, q=2, metric="precomputed")
+    r = dtm.fit_transform(dist)
+    w_rips = WeightedRipsComplex(distance_matrix=dist, weights=r)
+    st = w_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))]