diff --git a/src/pg/sql/50_contours.sql b/src/pg/sql/50_contours.sql
index 545f518..5501160 100644
--- a/src/pg/sql/50_contours.sql
+++ b/src/pg/sql/50_contours.sql
@@ -21,10 +21,12 @@ CREATE OR REPLACE FUNCTION
     bandwidth NUMERIC DEFAULT 0.0001,
     levels NUMERIC[] DEFAULT null
     )
-RETURNS table (level Numeric, geom text )
+RETURNS table (level Numeric, geom geometry )
 AS $$
 BEGIN
+
   RETURN QUERY
-    select l as level, ST_GeomFromText(geom_text, 4326) as geom from _CDB_Contours(subquery,grid_size,bandwidth,levels);
+    select cont.level as level, ST_GeomFromText(cont.geom_text, 4326)::geometry as geom from _CDB_Contours(subquery,grid_size,bandwidth,levels) as cont;
 END;
 $$ LANGUAGE plpgsql;
+
diff --git a/src/py/crankshaft/crankshaft/contours/contours.py b/src/py/crankshaft/crankshaft/contours/contours.py
index feb249d..14c55f8 100644
--- a/src/py/crankshaft/crankshaft/contours/contours.py
+++ b/src/py/crankshaft/crankshaft/contours/contours.py
@@ -5,15 +5,20 @@ from sklearn.neighbors import KernelDensity
 from skimage.measure import find_contours
 import plpy
 
-
 def cdb_generate_contours(query, grid_size, bandwidth, levels):
-    data   = plpy.execute( 'select ST_X(the_geom) as x , ST_Y(the_geom) as y from ({query})')
-    xs, ys = [d['x'], d['y'] from data]
-    return generate_contours(xs,xy,grid_size,bandwidth,levels)
-    
+    plpy.notice('one')
+    data   = plpy.execute( 'select ST_X(the_geom) as x , ST_Y(the_geom) as y from ({0}) as a '.format(query))
+    plpy.notice('two')
+
+    xs = [d['x'] for d in data]
+    ys = [d['y'] for d in data]
+    plpy.notice('three')
+    return generate_contours(xs,ys,grid_size,bandwidth,levels)
+  
 def scale_coord(coord, x_range,y_range,grid_size):
-    return [coord[0]*(x_range[1]-x_range[0])/grid_size+x_range[0],
-            coord[1]*(y_range[1]-y_range[0])/grid_size+y_range[0]]
+    plpy.notice('ranges %,  % ', x_range, y_range)
+    return [coord[0]*(x_range[1]-x_range[0])/float(grid_size)+x_range[0],
+            coord[1]*(y_range[1]-y_range[0])/float(grid_size)+y_range[0]]
     
 def make_wkt(data,x_range, y_range, grid_size):
     joined = ','.join([' '.join(map(str,scale_coord(coord_pair, x_range, y_range, grid_size))) for coord_pair in data])
@@ -23,15 +28,17 @@ def make_multi_line(data,x_range,y_range, grid_size):
     joined = ','.join([ make_wkt(ring,x_range,y_range,grid_size)  for ring in data ])
     return 'MULTILINESTRING({0})'.format(joined)
 
-def generate_contours(xs,ys, grid_res=100, bandwidth=0.0001, levels=None):
-    max_y, min_y = ys.max(), ys.min()
-    max_x, min_x = xs.max(), xs.min()
+def generate_contours(xs,ys, grid_res=100, bandwidth=0.001, levels=None):
+    plpy.notice("HERE")
+    max_y, min_y = np.max(ys), np.min(ys)
+    max_x, min_x = np.max(xs), np.min(xs)
     positions = np.vstack([ys,xs]).T
     grid_x,grid_y = np.meshgrid(np.linspace(min_x, max_x , grid_res), np.linspace(min_y, max_y, grid_res))
     xy = np.vstack([grid_y.ravel(), grid_x.ravel()]).T
     xy *= np.pi / 180.
 
-    kde = KernelDensity(bandwidth=0.0001, metric='haversine',
+    plpy.notice(" Generating kernel density")
+    kde = KernelDensity(bandwidth=bandwidth, metric='haversine',
                         kernel='gaussian', algorithm='ball_tree')
     kde.fit(positions*np.pi/180.)
     results = np.exp(kde.score_samples(xy))
@@ -39,7 +46,7 @@ def generate_contours(xs,ys, grid_res=100, bandwidth=0.0001, levels=None):
     
     if not levels:
         levels = np.linspace(results.min(), results.max(),60)
-
+    plpy.notice(' finding contours')
     CS = [find_contours(results, level) for level in levels]
     
     vertices = []
@@ -47,5 +54,5 @@ def generate_contours(xs,ys, grid_res=100, bandwidth=0.0001, levels=None):
         if len(contours)>0:
             multiline = make_multi_line(contours, (min_x,max_x), (min_y, max_y), grid_res)
             vertices.append([level, multiline ])
-    
+    plpy.notice('generated vertices retunring ?')
     return vertices