diff --git a/docs/examples.md b/docs/examples.md
index 8c47a0b..599d791 100644
--- a/docs/examples.md
+++ b/docs/examples.md
@@ -75,6 +75,7 @@ for i in range(band1.shape[0]):
          x = t[2] + (j * t[0]) + (t[0] / 2)
          y = t[5] + (i * t[4]) + (t[4] / 2)
          z = band1[i][j]
+         #-- skip no_data + select randomly only 1% of the points
          if (z != d.nodatavals) and (random.randint(0, 100) == 5):
              pts.append([x, y, z])
 dt = startinpy.DT()
@@ -193,6 +194,8 @@ def main():
     centres = np.asarray(centres)
     print("Interpolating at {} locations".format(centres.shape[0]))
     zhat = dt.interpolate({"method": "TIN"}, centres)
+    # zhat = dt.interpolate({"method": "Laplace"}, centres)
+    # zhat = dt.interpolate({"method": "IDW", "radius": 20, "power": 2.0}, centres, strict=True)
 
     #-- save to a GeoTIFF with rasterio
     write_rasterio('grid.tiff', zhat.reshape((deltay, deltax)), (bbox[0], bbox[1]), cellsize)
diff --git a/src/lib.rs b/src/lib.rs
index b36ffd6..9f1da9a 100755
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -919,7 +919,7 @@ impl DT {
         }
     }
 
-    /// Interpolate with 5 different methods:
+    /// Estimate the z-value with 5 different spatial interpolation methods:
     ///
     /// 1. **IDW**: inverse distance weighing
     /// 2. **Laplace**: a faster NNI with almost the same results
@@ -927,6 +927,9 @@ impl DT {
     /// 4. **NNI**: natural neighbour interpolation
     /// 5. **TIN**: linear interpolation in TIN
     ///
+    /// The interpolation does not modify the triangulation, it only returns an estimation for
+    /// the z-values at the xy-location provided as argument.
+    ///
     /// :param interpolant: a JSON/dict Python object with a `"method": "IDW"` (or others). IDW has 2 more params: "power" and "radius"
     /// :param locations: an array of [x, y] locations where the function should interpolate
     /// :param strict: (default=False) if the interpolation cannot find a value (because outside convex hull or search radius too small) then strict==True will stop at the first error and return that error. If strict==False then numpy.nan is returned.