diff --git a/tslearn/docs/dtw.rst b/tslearn/docs/dtw.rst
index 444e55782..471a56758 100644
--- a/tslearn/docs/dtw.rst
+++ b/tslearn/docs/dtw.rst
@@ -114,6 +114,14 @@ as illustrated below:
     :math:`n = m = 10, r = 3`. Diagonal is marked in grey for better
     readability.
 
+The corresponding code would be:
+
+.. code-block:: python
+
+    from tslearn.metrics import dtw
+    cost = dtw(x, y, global_constraint="sakoe_chiba", sakoe_chiba_radius=3)
+
+
 Second, the Itakura parallelogram sets a maximum slope :math:`s` for alignment
 paths, which leads to a parallelogram-shaped constraint:
 
@@ -124,6 +132,14 @@ paths, which leads to a parallelogram-shaped constraint:
     :math:`n = m = 10, s = 2`. Diagonal is marked in grey for better
     readability.
 
+The corresponding code would be:
+
+.. code-block:: python
+
+    from tslearn.metrics import dtw
+    cost = dtw(x, y, global_constraint="itakura", itakura_max_slope=2.)
+
+
 Alternatively, one can put an upper bound on the warping path length so as to
 discard complex paths, as described in [2]_:
 
diff --git a/tslearn/docs/variablelength.rst b/tslearn/docs/variablelength.rst
index 52eb61144..c604e98f3 100644
--- a/tslearn/docs/variablelength.rst
+++ b/tslearn/docs/variablelength.rst
@@ -1,5 +1,5 @@
-Methods for variable-length time series datasets
-================================================
+Methods for variable-length time series
+=======================================
 
 This page lists machine learning methods in `tslearn` that are able to deal
 with datasets containing time series of different lengths.