Index: /issm/trunk-jpl/src/m/interp/averaging.py
===================================================================
--- /issm/trunk-jpl/src/m/interp/averaging.py	(revision 17550)
+++ /issm/trunk-jpl/src/m/interp/averaging.py	(revision 17551)
@@ -25,5 +25,5 @@
 	'''
 
-	if (len(data)!=md.mesh.numberofelements & len(data)!=md.mesh.numberofvertices):
+	if len(data)!=md.mesh.numberofelements and len(data)!=md.mesh.numberofvertices:
 		raise StandardError('averaging error message: data not supported yet')
 	if md.mesh.dimension==3 and layer!=0:
Index: /issm/trunk-jpl/src/m/interp/holefiller.py
===================================================================
--- /issm/trunk-jpl/src/m/interp/holefiller.py	(revision 17551)
+++ /issm/trunk-jpl/src/m/interp/holefiller.py	(revision 17551)
@@ -0,0 +1,45 @@
+import numpy as npy
+from scipy.spatial import cKDTree
+
+def nearestneighbors(md,data,goodids,badids,knn):
+	'''
+	fill holes using nearest neigbors.  Arguments include:
+
+	md:		the model
+	data:		the data field to be filled (full field, including holes)
+	goodids:	id's into the vertices that have good data
+	badids:	id's into the vertices with missing/bad data
+	knn:		integer representing the k nearest neighbors to use for filling
+				holes.  The average data value over the k nearest neighbors is 
+				then used to fill the hole.
+
+	Usage:
+		filleddata=nearestneighbors(md,goodids,badids,knn)
+
+	Example:
+		filledthickness=nearestneighbors(md,goodids,badids,5)
+	'''
+
+	if type(knn) != int or knn<1:
+		raise TypeError('nearestneighbors error: knn should be an integer>1')
+
+	if len(data) != md.mesh.numberofvertices:
+		raise StandardError('nearestneighbors error: "data" should have length md.mesh.numberofvertices')
+
+	filled=data
+	
+	XYGood=npy.dstack([md.mesh.x[goodids],md.mesh.y[goodids]])[0]
+	XYBad=npy.dstack([md.mesh.x[badids],md.mesh.y[badids]])[0]
+	tree=cKDTree(XYGood)
+	nearest=tree.query(XYBad,k=knn)[1]
+	
+	if knn==1:
+		filled[badids]=filled[goodids][nearest] # can add k=N to return the N nearest neighbors
+	else:
+		for i in range(len(badids)):
+			neardat=[]
+			for j in range(knn):
+				neardat.append(filled[goodids][nearest[i][j]])
+				filled[badids[i]]=npy.mean(neardat)
+				
+	return filled