1 files changed, 11 insertions, 7 deletions
diff --git a/src/python/gudhi/weighted_rips_complex.py b/src/python/gudhi/weighted_rips_complex.py
index 83fa82c5..7401c428 100644
--- a/src/python/gudhi/weighted_rips_complex.py
+++ b/src/python/gudhi/weighted_rips_complex.py
@@ -11,23 +11,26 @@ from gudhi import SimplexTree
 
 class WeightedRipsComplex:
     """
-    Class to generate a weighted Rips complex from a distance matrix and weights on vertices.
+    Class to generate a weighted Rips complex from a distance matrix and weights on vertices, 
+    in the way described in the paper 'DTM-based filtrations' https://arxiv.org/abs/1811.04757.
+    Remark that the filtration value of a vertex is twice of its weight for the consistency with 
+    RipsComplex, which is different from the definition in the paper.
     """
     def __init__(self, 
                 distance_matrix, 
-                weights="diagonal",
+                weights=None,
                 max_filtration=float('inf')):
         """
         Args:
-            distance_matrix (list of list of float): distance matrix (full square or lower triangular).
-            weights (list of float): (one half of) weight for each vertex.
+            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 == "diagonal":
-            self.weights = [distance_matrix[i][i] for i in range(len(distance_matrix))]
-        else:
+        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):
@@ -47,6 +50,7 @@ class WeightedRipsComplex:
         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)