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diff --git a/src/python/gudhi/weighted_rips_complex.py b/src/python/gudhi/weighted_rips_complex.py
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+# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
+# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
+# Author(s):       Raphaël Tinarrage, Yuichi Ike, Masatoshi Takenouchi
+#
+# Copyright (C) 2020 Inria, Copyright (C) 2020 FUjitsu Laboratories Ltd.
+#
+# Modification(s):
+#   - YYYY/MM Author: Description of the modification
+
+from gudhi import SimplexTree
+
+class WeightedRipsComplex:
+    """
+    Class to generate a weighted Rips complex from a distance matrix and weights on vertices, 
+    in the way described in :cite:`dtmfiltrations`.
+    Remark that all the filtration values are doubled compared to the definition in the paper 
+    for the consistency with RipsComplex.
+    """
+    def __init__(self, 
+                distance_matrix, 
+                weights=None,
+                max_filtration=float('inf')):
+        """
+        Args:
+            distance_matrix (Sequence[Sequence[float]]): distance matrix (full square or lower triangular).
+            weights (Sequence[float]): (one half of) weight for each vertex.
+            max_filtration (float): specifies the maximal filtration value to be considered.      
+        """
+        self.distance_matrix = distance_matrix
+        if weights is not None:
+            self.weights = weights
+        else:
+            self.weights = [0] * len(distance_matrix)
+        self.max_filtration = max_filtration
+            
+    def create_simplex_tree(self, max_dimension):
+        """
+        Args:
+            max_dimension (int): graph expansion until this given dimension.
+        """
+        dist = self.distance_matrix
+        F = self.weights
+        num_pts = len(dist)
+        
+        st = SimplexTree()
+        
+        for i in range(num_pts):
+            if 2*F[i] <= self.max_filtration:
+                st.insert([i], 2*F[i])
+        for i in range(num_pts):
+            for j in range(i):
+                value = max(2*F[i], 2*F[j], dist[i][j] + F[i] + F[j])
+                # max is needed when F is not 1-Lipschitz
+                if value <= self.max_filtration:
+                    st.insert([i,j], filtration=value)
+                    
+        st.expansion(max_dimension) 
+        return st
+