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diff --git a/src/python/doc/rips_complex_ref.rst b/src/python/doc/rips_complex_ref.rst
index 22b5616c..8fc7e1b0 100644
--- a/src/python/doc/rips_complex_ref.rst
+++ b/src/python/doc/rips_complex_ref.rst
@@ -12,3 +12,54 @@ Rips complex reference manual
    :show-inheritance:
 
    .. automethod:: gudhi.RipsComplex.__init__
+
+======================================
+Weighted Rips complex reference manual
+======================================
+
+.. autoclass:: gudhi.WeightedRipsComplex
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+   .. automethod:: gudhi.WeightedRipsComplex.__init__
+
+Basic examples
+-------------
+
+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(st.get_filtration())
+
+The output is:
+
+.. testoutput::
+
+    [([0], 2.0), ([1], 200.0), ([0, 1], 200.0)]
+
+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())
+
+.. testoutput::
+
+    [(0, (3.1622776601683795, inf)), (0, (3.1622776601683795, 5.39834563766817)), (0, (3.1622776601683795, 5.39834563766817))]