Merge branch 'develop' into add-spatial-markov

src/py/crankshaft/crankshaft/clustering/moran.py

@@ -14,7 +14,7 @@ import crankshaft.pysal_utils as pu
 # High level interface ---------------------------------------
 
 def moran(subquery, attr_name,
-          permutations, geom_col, id_col, w_type, num_ngbrs):
+          w_type, num_ngbrs, permutations, geom_col, id_col):
     """
     Moran's I (global)
     Implementation building neighbors with a PostGIS database and Moran's I
@@ -56,7 +56,7 @@ def moran(subquery, attr_name,
     return zip([moran_global.I], [moran_global.EI])
 
 def moran_local(subquery, attr,
-                permutations, geom_col, id_col, w_type, num_ngbrs):
+                w_type, num_ngbrs, permutations, geom_col, id_col):
     """
     Moran's I implementation for PL/Python
     Andy Eschbacher
@@ -96,7 +96,7 @@ def moran_local(subquery, attr,
     return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
 
 def moran_rate(subquery, numerator, denominator,
-               permutations, geom_col, id_col, w_type, num_ngbrs):
+               w_type, num_ngbrs, permutations, geom_col, id_col):
     """
     Moran's I Rate (global)
     Andy Eschbacher
@@ -137,7 +137,7 @@ def moran_rate(subquery, numerator, denominator,
     return zip([lisa_rate.I], [lisa_rate.EI])
 
 def moran_local_rate(subquery, numerator, denominator,
-                     permutations, geom_col, id_col, w_type, num_ngbrs):
+                     w_type, num_ngbrs, permutations, geom_col, id_col):
     """
         Moran's I Local Rate
         Andy Eschbacher

src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py

@@ -11,7 +11,7 @@ def construct_neighbor_query(w_type, query_vals):
         @param query_vals dict: values used to construct the query
     """
 
-    if w_type == 'knn':
+    if w_type.lower() == 'knn':
         return knn(query_vals)
     else:
         return queen(query_vals)
@@ -22,13 +22,13 @@ def get_weight(query_res, w_type='knn', num_ngbrs=5):
         Construct PySAL weight from return value of query
         @param query_res dict-like: query results with attributes and neighbors
     """
-    # if w_type == 'knn':
-    #     row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
-    #     weights = {x['id']: row_normed_weights for x in query_res}
-    # else:
-    #     weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
-    #                         if len(x['neighbors']) > 0
-    #                         else [] for x in query_res}
+    if w_type.lower() == 'knn':
+        row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
+        weights = {x['id']: row_normed_weights for x in query_res}
+    else:
+        weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
+                            if len(x['neighbors']) > 0
+                            else [] for x in query_res}
 
     neighbors = {x['id']: x['neighbors'] for x in query_res}
     print 'len of neighbors: %d' % len(neighbors)

src/py/crankshaft/test/test_clustering_moran.py

@@ -57,7 +57,7 @@ class MoranTest(unittest.TestCase):
         data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
         plpy._define_result('select', data)
         random_seeds.set_random_seeds(1234)
-        result = cc.moran_local('subquery', 'value', 99, 'the_geom', 'cartodb_id', 'knn', 5)
+        result = cc.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
         result = [(row[0], row[1]) for row in result]
         expected = self.moran_data
         for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
@@ -69,7 +69,7 @@ class MoranTest(unittest.TestCase):
         data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data]
         plpy._define_result('select', data)
         random_seeds.set_random_seeds(1234)
-        result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 99, 'the_geom', 'cartodb_id', 'knn', 5)
+        result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 'knn', 5, 99, 'the_geom', 'cartodb_id')
         print 'result == None? ', result == None
         result = [(row[0], row[1]) for row in result]
         expected = self.moran_data
@@ -81,7 +81,7 @@ class MoranTest(unittest.TestCase):
         data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
         plpy._define_result('select', data)
         random_seeds.set_random_seeds(1235)
-        result = cc.moran('subquery', 'value', 99, 'the_geom', 'cartodb_id', 'knn', 5)
+        result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
         print 'result == None?', result == None
         result_moran = result[0][0]
         expected_moran = np.array([row[0] for row in self.moran_data]).mean()