Index: /issm/trunk-jpl/src/m/classes/stochasticforcing.py
===================================================================
--- /issm/trunk-jpl/src/m/classes/stochasticforcing.py	(revision 26538)
+++ /issm/trunk-jpl/src/m/classes/stochasticforcing.py	(revision 26539)
@@ -76,16 +76,15 @@
     def marshall(self, prefix, md, fid):  # {{{
         yts = md.constants.yts
-        num_fields = range(self.fields)
-        # Scaling covariance matrix (scale column-by-column and row-by-row)
-        scaledfields = ['SMBautoregression'] # list of fields that need scaling * 1/yts
-        for i in range(num_fields):
-            print(i)
-            if self.fields[i] in scaledfields:
-                print(self.fields[i])
-                inds = range(1 + np.sum(self.dimensions[0:i]), np.sum(self.dimensions[0:i]))
-                for row in inds: # scale rows corresponding to scaled field
-                    self.covariance[row, :] = 1 / yts * self.covariance[row, :]
-                for col in inds: # scale columns corresponding to scaled field
-                    self.covariance[:, col] = 1 / yts * self.covariance[:, col]
+        if (type(self.fields) is list):
+            num_fields = len(self.fields)
+            # Scaling covariance matrix (scale column-by-column and row-by-row)
+            scaledfields = ['SMBautoregression'] # list of fields that need scaling * 1/yts
+            for i in range(num_fields):
+                if self.fields[i] in scaledfields:
+                    inds = range(1 + np.sum(self.dimensions[0:i]), np.sum(self.dimensions[0:i]))
+                    for row in inds: # scale rows corresponding to scaled field
+                        self.covariance[row, :] = 1 / yts * self.covariance[row, :]
+                    for col in inds: # scale columns corresponding to scaled field
+                        self.covariance[:, col] = 1 / yts * self.covariance[:, col]
 
         WriteData(fid, prefix, 'object', self, 'fieldname', 'isstochasticforcing', 'format', 'Boolean')