Merge pull request #19 from CartoDB/restructure-moran-redux

Restructure moran redux

src/pg/sql/10_moran.sql

@@ -1,37 +1,89 @@
--- Moran's I
+-- Moran's I (global)
 CREATE OR REPLACE FUNCTION
-  cdb_moran_local (
+  CDB_AreasOfInterest_Global (
-      t TEXT,
+      subquery TEXT,
-  	  attr TEXT,
+      attr_name TEXT,
-  	  significance float DEFAULT 0.05,
+      permutations INT DEFAULT 99,
-  	  num_ngbrs INT DEFAULT 5,
+      geom_col TEXT DEFAULT 'the_geom',
-  	  permutations INT DEFAULT 99,
+      id_col TEXT DEFAULT 'cartodb_id',
-  	  geom_column TEXT DEFAULT 'the_geom',
+      w_type TEXT DEFAULT 'knn',
-  	  id_col TEXT DEFAULT 'cartodb_id',
+      num_ngbrs INT DEFAULT 5)
-      w_type TEXT DEFAULT 'knn')
+RETURNS TABLE (moran NUMERIC, significance NUMERIC)
-RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
 AS $$
-  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
   from crankshaft.clustering import moran_local
-  # TODO: use named parameters or a dictionary
+  # TODO: use named parameters or a dictionary
-  return moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+  return moran(subquery, attr, num_ngbrs, permutations, geom_col, id_col, w_type)
 $$ LANGUAGE plpythonu;
 
+-- Moran's I Local
+CREATE OR REPLACE FUNCTION
+  CDB_AreasOfInterest_Local(
+      subquery TEXT,
+      attr TEXT,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id',
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5)
+RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, ids INT, y NUMERIC)
+AS $$
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran_local(subquery, attr, permutations, geom_col, id_col, w_type, num_ngbrs)
+$$ LANGUAGE plpythonu;
+
+-- Moran's I Rate (global)
+CREATE OR REPLACE FUNCTION
+  CDB_AreasOfInterest_Global_Rate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id',
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5)
+RETURNS TABLE (moran FLOAT, significance FLOAT)
+AS $$
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran_rate(subquery, numerator, denominator, permutations, geom_col, id_col, w_type, num_ngbrs)
+$$ LANGUAGE plpythonu;
+
+
 -- Moran's I Local Rate
 CREATE OR REPLACE FUNCTION
-  cdb_moran_local_rate(t TEXT,
+  CDB_AreasOfInterest_Local_Rate(
-		 numerator TEXT,
+      subquery TEXT,
-		 denominator TEXT,
+      numerator TEXT,
-		 significance FLOAT DEFAULT 0.05,
+      denominator TEXT,
-		 num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
-		 permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
-		 geom_column TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id',
-		 id_col TEXT DEFAULT 'cartodb_id',
+      w_type TEXT DEFAULT 'knn',
-		 w_type TEXT DEFAULT 'knn')
+      num_ngbrs INT DEFAULT 5)
-RETURNS TABLE(moran FLOAT, quads TEXT, significance FLOAT, ids INT, y numeric)
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, ids INT, y NUMERIC)
 AS $$
-  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
   from crankshaft.clustering import moran_local_rate
-  # TODO: use named parameters or a dictionary
+  # TODO: use named parameters or a dictionary
-  return moran_local_rate(t, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+  return moran_local_rate(subquery, numerator, denominator, permutations, geom_col, id_col, w_type, num_ngbrs)
 $$ LANGUAGE plpythonu;
+
+-- -- Moran's I Local Bivariate
+-- CREATE OR REPLACE FUNCTION
+--   cdb_moran_local_bv(
+--       subquery TEXT,
+--       attr1 TEXT,
+--       attr2 TEXT,
+--       permutations INT DEFAULT 99,
+--       geom_col TEXT DEFAULT 'the_geom',
+--       id_col TEXT DEFAULT 'cartodb_id',
+--       w_type TEXT DEFAULT 'knn',
+--       num_ngbrs INT DEFAULT 5)
+-- RETURNS TABLE(moran FLOAT, quads TEXT, significance FLOAT, ids INT, y numeric)
+-- AS $$
+--   from crankshaft.clustering import moran_local_bv
+--   # TODO: use named parameters or a dictionary
+--   return moran_local_bv(t, attr1, attr2, permutations, geom_col, id_col, w_type, num_ngbrs)
+-- $$ LANGUAGE plpythonu;

src/pg/test/expected/02_moran_test.out

@@ -110,7 +110,7 @@ INSERT INTO ppoints2 VALUES
 (24,'0101000020E61000009C5F91C5095C17C0C78784B15A4F4540'::geometry,'24','07',0.3, 1.0),
 (29,'0101000020E6100000C34D4A5B48E712C092E680892C684240'::geometry,'29','01',0.3, 1.0),
 (52,'0101000020E6100000406A545EB29A07C04E5F0BDA39A54140'::geometry,'52','19',0.0, 1.01)
--- Moral functions perform some nondeterministic computations
+-- Areas of Interest functions perform some nondeterministic computations
 -- (to estimate the significance); we will set the seeds for the RNGs
 -- that affect those results to have repeateble results
 SELECT cdb_crankshaft._cdb_random_seeds(1234);
@@ -121,18 +121,64 @@ SELECT cdb_crankshaft._cdb_random_seeds(1234);
 
 SELECT ppoints.code, m.quads
   FROM ppoints
-  JOIN cdb_crankshaft.cdb_moran_local('SELECT * FROM ppoints', 'value') m
+  JOIN cdb_crankshaft.CDB_AreasOfInterest_Local('SELECT * FROM ppoints', 'value') m
     ON ppoints.cartodb_id = m.ids
   ORDER BY ppoints.code;
-NOTICE:  ** Constructing query
-CONTEXT:  PL/Python function "cdb_moran_local"
-NOTICE:  ** Query failed: "SELECT i."cartodb_id" As id, i."value"::numeric As attr1, (SELECT ARRAY(SELECT j."cartodb_id" FROM "(SELECT * FROM ppoints)" As j WHERE j."value" IS NOT NULL ORDER BY j."the_geom" <-> i."the_geom" ASC LIMIT 5 OFFSET 1 ) ) As neighbors FROM "(SELECT * FROM ppoints)" As i WHERE i."value" IS NOT NULL ORDER BY i."cartodb_id" ASC;"
-CONTEXT:  PL/Python function "cdb_moran_local"
-NOTICE:  ** Exiting function
-CONTEXT:  PL/Python function "cdb_moran_local"
  code | quads 
 ------+-------
-(0 rows)
+ 01   | HH
+ 02   | HL
+ 03   | LL
+ 04   | LL
+ 05   | LH
+ 06   | LL
+ 07   | HH
+ 08   | HH
+ 09   | HH
+ 10   | LL
+ 11   | LL
+ 12   | LL
+ 13   | HL
+ 14   | LL
+ 15   | LL
+ 16   | HH
+ 17   | HH
+ 18   | LL
+ 19   | HH
+ 20   | HH
+ 21   | LL
+ 22   | HH
+ 23   | LL
+ 24   | LL
+ 25   | HH
+ 26   | HH
+ 27   | LL
+ 28   | HH
+ 29   | LL
+ 30   | LL
+ 31   | HH
+ 32   | LL
+ 33   | HL
+ 34   | LH
+ 35   | LL
+ 36   | LL
+ 37   | HL
+ 38   | HL
+ 39   | HH
+ 40   | HH
+ 41   | HL
+ 42   | LH
+ 43   | LH
+ 44   | LL
+ 45   | LH
+ 46   | LL
+ 47   | LL
+ 48   | HH
+ 49   | LH
+ 50   | HH
+ 51   | LL
+ 52   | LL
+(52 rows)
 
 SELECT cdb_crankshaft._cdb_random_seeds(1234);
  _cdb_random_seeds 
@@ -142,17 +188,61 @@ SELECT cdb_crankshaft._cdb_random_seeds(1234);
 
 SELECT ppoints2.code, m.quads
   FROM ppoints2
-  JOIN cdb_crankshaft.cdb_moran_local_rate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
+  JOIN cdb_crankshaft.CDB_AreasOfInterest_Local_Rate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
     ON ppoints2.cartodb_id = m.ids
   ORDER BY ppoints2.code;
-NOTICE:  ** Constructing query
+ code | quads 
-CONTEXT:  PL/Python function "cdb_moran_local_rate"
+------+-------
-NOTICE:  ** Query failed: "SELECT i."cartodb_id" As id, i."denominator"::numeric As attr1, i."numerator"::numeric As attr2, (SELECT ARRAY(SELECT j."cartodb_id" FROM "(SELECT * FROM ppoints2)" As j WHERE j."denominator" IS NOT NULL AND j."numerator" IS NOT NULL AND j."numerator" <> 0 ORDER BY j."the_geom" <-> i."the_geom" ASC LIMIT 5 OFFSET 1 ) ) As neighbors FROM "(SELECT * FROM ppoints2)" As i WHERE i."denominator" IS NOT NULL AND i."numerator" IS NOT NULL AND i."numerator" <> 0 ORDER BY i."cartodb_id" ASC;"
+ 01   | LL
-CONTEXT:  PL/Python function "cdb_moran_local_rate"
+ 02   | LH
-NOTICE:  ** Error: <class 'plpy.SPIError'>
+ 03   | HH
-CONTEXT:  PL/Python function "cdb_moran_local_rate"
+ 04   | HH
-NOTICE:  ** Exiting function
+ 05   | LL
-CONTEXT:  PL/Python function "cdb_moran_local_rate"
+ 06   | HH
-ERROR:  length of returned sequence did not match number of columns in row
+ 07   | LL
-CONTEXT:  while creating return value
+ 08   | LL
-PL/Python function "cdb_moran_local_rate"
+ 09   | LL
+ 10   | HH
+ 11   | HH
+ 12   | HL
+ 13   | LL
+ 14   | HH
+ 15   | LL
+ 16   | LL
+ 17   | LL
+ 18   | LH
+ 19   | LL
+ 20   | LL
+ 21   | HH
+ 22   | LL
+ 23   | HL
+ 24   | LL
+ 25   | LL
+ 26   | LL
+ 27   | LL
+ 28   | LL
+ 29   | LH
+ 30   | HH
+ 31   | LL
+ 32   | LL
+ 33   | LL
+ 34   | LL
+ 35   | LH
+ 36   | HL
+ 37   | LH
+ 38   | LH
+ 39   | LL
+ 40   | LL
+ 41   | LH
+ 42   | HL
+ 43   | LL
+ 44   | HL
+ 45   | LL
+ 46   | HL
+ 47   | LL
+ 48   | LL
+ 49   | HL
+ 50   | LL
+ 51   | HH
+(51 rows)
+

src/pg/test/sql/02_moran_test.sql

@@ -1,14 +1,14 @@
 \i test/fixtures/ppoints.sql
 \i test/fixtures/ppoints2.sql
 
--- Moral functions perform some nondeterministic computations
+-- Areas of Interest functions perform some nondeterministic computations
 -- (to estimate the significance); we will set the seeds for the RNGs
 -- that affect those results to have repeateble results
 SELECT cdb_crankshaft._cdb_random_seeds(1234);
 
 SELECT ppoints.code, m.quads
   FROM ppoints
-  JOIN cdb_crankshaft.cdb_moran_local('SELECT * FROM ppoints', 'value') m
+  JOIN cdb_crankshaft.CDB_AreasOfInterest_Local('SELECT * FROM ppoints', 'value') m
     ON ppoints.cartodb_id = m.ids
   ORDER BY ppoints.code;
 
@@ -16,6 +16,6 @@ SELECT cdb_crankshaft._cdb_random_seeds(1234);
 
 SELECT ppoints2.code, m.quads
   FROM ppoints2
-  JOIN cdb_crankshaft.cdb_moran_local_rate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
+  JOIN cdb_crankshaft.CDB_AreasOfInterest_Local_Rate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
     ON ppoints2.cartodb_id = m.ids
   ORDER BY ppoints2.code;

src/pg/test/sql/90_permissions.sql

@@ -9,7 +9,7 @@ SET search_path TO public,cartodb,cdb_crankshaft;
 -- Exercise public functions
 SELECT ppoints.code, m.quads
   FROM ppoints
-  JOIN cdb_moran_local('ppoints', 'value') m
+  JOIN CDB_AreasOfInterest_Local('ppoints', 'value') m
     ON ppoints.cartodb_id = m.ids
   ORDER BY ppoints.code;
 SELECT round(cdb_overlap_sum(

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

@@ -5,143 +5,226 @@ Moran's I geostatistics (global clustering & outliers presence)
 # TODO: Fill in local neighbors which have null/NoneType values with the
 #       average of the their neighborhood
 
-import numpy as np
 import pysal as ps
 import plpy
 
+# crankshaft module
+import crankshaft.pysal_utils as pu
+
 # High level interface ---------------------------------------
 
-def moran_local(subquery, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
+def moran(subquery, attr_name,
+          permutations, geom_col, id_col, w_type, num_ngbrs):
+    """
+    Moran's I (global)
+    Implementation building neighbors with a PostGIS database and Moran's I
+     core clusters with PySAL.
+    Andy Eschbacher
+    """
+    qvals = {"id_col": id_col,
+             "attr1": attr_name,
+             "geom_col": geom_col,
+             "subquery": subquery,
+             "num_ngbrs": num_ngbrs}
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    plpy.notice('** Query: %s' % query)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(2)
+        plpy.notice('** Query returned with %d rows' % len(result))
+    except plpy.SPIError:
+        plpy.error('Error: areas of interest query failed, check input parameters')
+        plpy.notice('** Query failed: "%s"' % query)
+        plpy.notice('** Error: %s' % plpy.SPIError)
+        return pu.empty_zipped_array(2)
+
+    ## collect attributes
+    attr_vals = pu.get_attributes(result)
+
+    ## calculate weights
+    weight = pu.get_weight(result, w_type, num_ngbrs)
+
+    ## calculate moran global
+    moran_global = ps.esda.moran.Moran(attr_vals, weight,
+                                       permutations=permutations)
+
+    return zip([moran_global.I], [moran_global.EI])
+
+def moran_local(subquery, attr,
+                permutations, geom_col, id_col, w_type, num_ngbrs):
     """
     Moran's I implementation for PL/Python
     Andy Eschbacher
     """
-    # TODO: ensure that the significance output can be smaller that 1e-3 (0.001)
-    # TODO: make a wishlist of output features (zscores, pvalues, raw local lisa, what else?)
-
-    plpy.notice('** Constructing query')
 
     # geometries with attributes that are null are ignored
     # resulting in a collection of not as near neighbors
 
     qvals = {"id_col": id_col,
-            "attr1": attr,
+             "attr1": attr,
-            "geom_col": geom_column,
+             "geom_col": geom_col,
              "subquery": subquery,
              "num_ngbrs": num_ngbrs}
 
-    q = get_query(w_type, qvals)
+    query = pu.construct_neighbor_query(w_type, qvals)
 
     try:
-        r = plpy.execute(q)
+        result = plpy.execute(query)
-        plpy.notice('** Query returned with %d rows' % len(r))
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(5)
     except plpy.SPIError:
-        plpy.notice('** Query failed: "%s"' % q)
+        plpy.error('Error: areas of interest query failed, check input parameters')
-        plpy.notice('** Exiting function')
+        plpy.notice('** Query failed: "%s"' % query)
-        return zip([None], [None], [None], [None])
+        return pu.empty_zipped_array(5)
 
-    y = get_attributes(r, 1)
+    attr_vals = pu.get_attributes(result)
-    w = get_weight(r, w_type)
+    weight = pu.get_weight(result, w_type, num_ngbrs)
 
     # calculate LISA values
-    lisa = ps.Moran_Local(y, w)
+    lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
+                                     permutations=permutations)
 
-    # find units of significance
+    # find quadrants for each geometry
-    lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
+    quads = quad_position(lisa.q)
 
-    plpy.notice('** Finished calculations')
+    return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
 
-    return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order)
+def moran_rate(subquery, numerator, denominator,
-
+               permutations, geom_col, id_col, w_type, num_ngbrs):
-
-def moran_local_rate(subquery, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
     """
-    Moran's I Local Rate
+    Moran's I Rate (global)
     Andy Eschbacher
     """
-
-    plpy.notice('** Constructing query')
-
-    # geometries with attributes that are null are ignored
-    # resulting in a collection of not as near neighbors
-
     qvals = {"id_col": id_col,
-             "numerator": numerator,
+             "attr1": numerator,
-             "denominator": denominator,
+             "attr2": denominator,
-             "geom_col": geom_column,
+             "geom_col": geom_col,
              "subquery": subquery,
              "num_ngbrs": num_ngbrs}
 
-    q = get_query(w_type, qvals)
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    plpy.notice('** Query: %s' % query)
 
     try:
-        r = plpy.execute(q)
+        result = plpy.execute(query)
-        plpy.notice('** Query returned with %d rows' % len(r))
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(2)
+        plpy.notice('** Query returned with %d rows' % len(result))
     except plpy.SPIError:
-        plpy.notice('** Query failed: "%s"' % q)
+        plpy.error('Error: areas of interest query failed, check input parameters')
+        plpy.notice('** Query failed: "%s"' % query)
         plpy.notice('** Error: %s' % plpy.SPIError)
-        plpy.notice('** Exiting function')
+        return pu.empty_zipped_array(2)
-        return zip([None], [None], [None], [None])
-
-        plpy.notice('r.nrows() = %d' % r.nrows())
 
     ## collect attributes
-    numer = get_attributes(r, 1)
+    numer = pu.get_attributes(result, 1)
-    denom = get_attributes(r, 2)
+    denom = pu.get_attributes(result, 2)
 
-    w = get_weight(r, w_type, num_ngbrs)
+    weight = pu.get_weight(result, w_type, num_ngbrs)
+
+    ## calculate moran global rate
+    lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
+                                         permutations=permutations)
+
+    return zip([lisa_rate.I], [lisa_rate.EI])
+
+def moran_local_rate(subquery, numerator, denominator,
+                     permutations, geom_col, id_col, w_type, num_ngbrs):
+    """
+        Moran's I Local Rate
+        Andy Eschbacher
+    """
+    # geometries with values that are null are ignored
+    # resulting in a collection of not as near neighbors
+
+    query = pu.construct_neighbor_query(w_type,
+                                     {"id_col": id_col,
+                                      "numerator": numerator,
+                                      "denominator": denominator,
+                                      "geom_col": geom_col,
+                                      "subquery": subquery,
+                                      "num_ngbrs": num_ngbrs})
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(5)
+    except plpy.SPIError:
+        plpy.error('Error: areas of interest query failed, check input parameters')
+        plpy.notice('** Query failed: "%s"' % query)
+        plpy.notice('** Error: %s' % plpy.SPIError)
+        return pu.empty_zipped_array(5)
+
+    ## collect attributes
+    numer = pu.get_attributes(result, 1)
+    denom = pu.get_attributes(result, 2)
+
+    weight = pu.get_weight(result, w_type, num_ngbrs)
 
     # calculate LISA values
-    lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, w, permutations=permutations)
+    lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
+                                          permutations=permutations)
 
     # find units of significance
-    lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
+    quads = quad_position(lisa.q)
 
-    plpy.notice('** Finished calculations')
+    return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
 
-    ## TODO: Decide on which return values here
+def moran_local_bv(subquery, attr1, attr2,
-    return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order, lisa.y)
+                   permutations, geom_col, id_col, w_type, num_ngbrs):
-
+    """
-def moran_local_bv(t, attr1, attr2, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
+        Moran's I (local) Bivariate (untested)
+    """
     plpy.notice('** Constructing query')
 
     qvals = {"num_ngbrs": num_ngbrs,
              "attr1": attr1,
              "attr2": attr2,
-             "table": t,
+             "subquery": subquery,
-             "geom_col": geom_column,
+             "geom_col": geom_col,
              "id_col": id_col}
 
-    q = get_query(w_type, qvals)
+    query = pu.construct_neighbor_query(w_type, qvals)
 
     try:
-        r = plpy.execute(q)
+        result = plpy.execute(query)
-        plpy.notice('** Query returned with %d rows' % len(r))
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(4)
     except plpy.SPIError:
-        plpy.notice('** Query failed: "%s"' % q)
+        plpy.error("Error: areas of interest query failed, " \
-        plpy.notice('** Error: %s' % plpy.SPIError)
+                   "check input parameters")
-        plpy.notice('** Exiting function')
+        plpy.notice('** Query failed: "%s"' % query)
-        return zip([None], [None], [None], [None])
+        return pu.empty_zipped_array(4)
 
     ## collect attributes
-    attr1_vals = get_attributes(r, 1)
+    attr1_vals = pu.get_attributes(result, 1)
-    attr2_vals = get_attributes(r, 2)
+    attr2_vals = pu.get_attributes(result, 2)
 
     # create weights
-    w = get_weight(r, w_type, num_ngbrs)
+    weight = pu.get_weight(result, w_type, num_ngbrs)
 
     # calculate LISA values
-    lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, w)
+    lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
+                                        permutations=permutations)
 
     plpy.notice("len of Is: %d" % len(lisa.Is))
 
     # find clustering of significance
-    lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
+    lisa_sig = quad_position(lisa.q)
 
     plpy.notice('** Finished calculations')
 
-    return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order)
+    return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
-
 
 # Low level functions ----------------------------------------
 
@@ -150,7 +233,9 @@ def map_quads(coord):
         Map a quadrant number to Moran's I designation
         HH=1, LH=2, LL=3, HL=4
         Input:
-        :param coord (int): quadrant of a specific measurement
+        @param coord (int): quadrant of a specific measurement
+        Output:
+            classification (one of 'HH', 'LH', 'LL', or 'HL')
     """
     if coord == 1:
         return 'HH'
@@ -163,159 +248,13 @@ def map_quads(coord):
     else:
         return None
 
-def query_attr_select(params):
-    """
-        Create portion of SELECT statement for attributes inolved in query.
-        :param params: dict of information used in query (column names,
-                       table name, etc.)
-    """
-
-    attrs = [k for k in params
-             if k not in ('id_col', 'geom_col', 'table', 'num_ngbrs', 'subquery')]
-
-    template = "i.\"{%(col)s}\"::numeric As attr%(alias_num)s, "
-
-    attr_string = ""
-
-    for idx, val in enumerate(sorted(attrs)):
-        attr_string += template % {"col": val, "alias_num": idx + 1}
-
-    return attr_string
-
-def query_attr_where(params):
-    """
-        Create portion of WHERE clauses for weeding out NULL-valued geometries
-    """
-    attrs = sorted([k for k in params
-                    if k not in ('id_col', 'geom_col', 'table', 'num_ngbrs', 'subquery')])
-
-    attr_string = []
-
-    for attr in attrs:
-        attr_string.append("idx_replace.\"{%s}\" IS NOT NULL" % attr)
-
-    if len(attrs) == 2:
-        attr_string.append("idx_replace.\"{%s}\" <> 0" % attrs[1])
-
-    out = " AND ".join(attr_string)
-
-    return out
-
-def knn(params):
-    """SQL query for k-nearest neighbors.
-        :param vars: dict of values to fill template
-    """
-
-    attr_select = query_attr_select(params)
-    attr_where = query_attr_where(params)
-
-    replacements = {"attr_select": attr_select,
-                    "attr_where_i": attr_where.replace("idx_replace", "i"),
-                    "attr_where_j": attr_where.replace("idx_replace", "j")}
-
-    query = "SELECT " \
-                "i.\"{id_col}\" As id, " \
-                "%(attr_select)s" \
-                "(SELECT ARRAY(SELECT j.\"{id_col}\" " \
-                              "FROM \"({subquery})\" As j " \
-                              "WHERE %(attr_where_j)s " \
-                              "ORDER BY j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \
-                              "LIMIT {num_ngbrs} OFFSET 1 ) " \
-                ") As neighbors " \
-            "FROM \"({subquery})\" As i " \
-            "WHERE " \
-                "%(attr_where_i)s " \
-            "ORDER BY i.\"{id_col}\" ASC;" % replacements
-
-    return query.format(**params)
-
-## SQL query for finding queens neighbors (all contiguous polygons)
-def queen(params):
-    """SQL query for queen neighbors.
-        :param params: dict of information to fill query
-    """
-    attr_select = query_attr_select(params)
-    attr_where = query_attr_where(params)
-
-    replacements = {"attr_select": attr_select,
-                    "attr_where_i": attr_where.replace("idx_replace", "i"),
-                    "attr_where_j": attr_where.replace("idx_replace", "j")}
-
-    query = "SELECT " \
-                "i.\"{id_col}\" As id, " \
-                "%(attr_select)s" \
-                "(SELECT ARRAY(SELECT j.\"{id_col}\" " \
-                 "FROM \"({subquery})\" As j " \
-                 "WHERE ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \
-                 "%(attr_where_j)s)" \
-                ") As neighbors " \
-            "FROM \"({subquery})\" As i " \
-            "WHERE " \
-                "%(attr_where_i)s " \
-            "ORDER BY i.\"{id_col}\" ASC;" % replacements
-
-    return query.format(**params)
-
-## to add more weight methods open a ticket or pull request
-
-def get_query(w_type, query_vals):
-    """Return requested query.
-        :param w_type: type of neighbors to calculate (knn or queen)
-        :param query_vals: values used to construct the query
-    """
-
-    if w_type == 'knn':
-        return knn(query_vals)
-    else:
-        return queen(query_vals)
-
-def get_attributes(query_res, attr_num):
-    """
-        :param query_res: query results with attributes and neighbors
-        :param attr_num: attribute number (1, 2, ...)
-    """
-    return np.array([x['attr' + str(attr_num)] for x in query_res], dtype=np.float)
-
-## Build weight object
-def get_weight(query_res, w_type='queen', num_ngbrs=5):
-    """
-        Construct PySAL weight from return value of query
-        :param query_res: 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}
-    elif w_type == 'queen':
-        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}
-
-    return ps.W(neighbors, weights)
-
 def quad_position(quads):
     """
         Produce Moran's I classification based of n
+        Input:
+        @param quads ndarray: an array of quads classified by
+          1-4 (PySAL default)
+        Output:
+        @param list: an array of quads classied by 'HH', 'LL', etc.
     """
-
+    return [map_quads(q) for q in quads]
-    lisa_sig = np.array([map_quads(q) for q in quads])
-
-    return lisa_sig
-
-def lisa_sig_vals(pvals, quads, threshold):
-    """
-        Produce Moran's I classification based of n
-    """
-
-    sig = (pvals <= threshold)
-
-    lisa_sig = np.empty(len(sig), np.chararray)
-
-    for idx, val in enumerate(sig):
-        if val:
-            lisa_sig[idx] = map_quads(quads[idx])
-        else:
-            lisa_sig[idx] = 'Not significant'
-
-    return lisa_sig

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

@@ -0,0 +1 @@
+from pysal_utils import *

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

@@ -0,0 +1,149 @@
+"""
+    Utilities module for generic PySAL functionality, mainly centered on translating queries into numpy arrays or PySAL weights objects
+"""
+
+import numpy as np
+import pysal as ps
+
+def construct_neighbor_query(w_type, query_vals):
+    """Return query (a string) used for finding neighbors
+        @param w_type text: type of neighbors to calculate ('knn' or 'queen')
+        @param query_vals dict: values used to construct the query
+    """
+
+    if w_type == 'knn':
+        return knn(query_vals)
+    else:
+        return queen(query_vals)
+
+## Build weight object
+def get_weight(query_res, w_type='knn', num_ngbrs=5):
+    """
+        Construct PySAL weight from return value of query
+        @param query_res: 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}
+
+    neighbors = {x['id']: x['neighbors'] for x in query_res}
+
+    return ps.W(neighbors, weights)
+
+def query_attr_select(params):
+    """
+        Create portion of SELECT statement for attributes inolved in query.
+        @param params: dict of information used in query (column names,
+                       table name, etc.)
+    """
+
+    attrs = [k for k in params
+             if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')]
+
+    template = "i.\"{%(col)s}\"::numeric As attr%(alias_num)s, "
+
+    attr_string = ""
+
+    for idx, val in enumerate(sorted(attrs)):
+        attr_string += template % {"col": val, "alias_num": idx + 1}
+
+    return attr_string
+
+def query_attr_where(params):
+    """
+        Create portion of WHERE clauses for weeding out NULL-valued geometries
+    """
+    attrs = sorted([k for k in params
+                    if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')])
+
+    attr_string = []
+
+    for attr in attrs:
+        attr_string.append("idx_replace.\"{%s}\" IS NOT NULL" % attr)
+
+    if len(attrs) == 2:
+        attr_string.append("idx_replace.\"{%s}\" <> 0" % attrs[1])
+
+    out = " AND ".join(attr_string)
+
+    return out
+
+def knn(params):
+    """SQL query for k-nearest neighbors.
+        @param vars: dict of values to fill template
+    """
+
+    attr_select = query_attr_select(params)
+    attr_where = query_attr_where(params)
+
+    replacements = {"attr_select": attr_select,
+                    "attr_where_i": attr_where.replace("idx_replace", "i"),
+                    "attr_where_j": attr_where.replace("idx_replace", "j")}
+
+    query = "SELECT " \
+                "i.\"{id_col}\" As id, " \
+                "%(attr_select)s" \
+                "(SELECT ARRAY(SELECT j.\"{id_col}\" " \
+                              "FROM ({subquery}) As j " \
+                              "WHERE %(attr_where_j)s AND " \
+                                    "i.\"{id_col}\" <> j.\"{id_col}\" " \
+                              "ORDER BY j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \
+                              "LIMIT {num_ngbrs})" \
+                ") As neighbors " \
+            "FROM ({subquery}) As i " \
+            "WHERE " \
+                "%(attr_where_i)s " \
+            "ORDER BY i.\"{id_col}\" ASC;" % replacements
+
+    return query.format(**params)
+
+## SQL query for finding queens neighbors (all contiguous polygons)
+def queen(params):
+    """SQL query for queen neighbors.
+        @param params dict: information to fill query
+    """
+    attr_select = query_attr_select(params)
+    attr_where = query_attr_where(params)
+
+    replacements = {"attr_select": attr_select,
+                    "attr_where_i": attr_where.replace("idx_replace", "i"),
+                    "attr_where_j": attr_where.replace("idx_replace", "j")}
+
+    query = "SELECT " \
+                "i.\"{id_col}\" As id, " \
+                "%(attr_select)s" \
+                "(SELECT ARRAY(SELECT j.\"{id_col}\" " \
+                 "FROM ({subquery}) As j " \
+                 "WHERE ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \
+                 "%(attr_where_j)s)" \
+                ") As neighbors " \
+            "FROM ({subquery}) As i " \
+            "WHERE " \
+                "%(attr_where_i)s " \
+            "ORDER BY i.\"{id_col}\" ASC;" % replacements
+
+    return query.format(**params)
+
+## to add more weight methods open a ticket or pull request
+
+def get_attributes(query_res, attr_num=1):
+    """
+        @param query_res: query results with attributes and neighbors
+        @param attr_num: attribute number (1, 2, ...)
+    """
+    return np.array([x['attr' + str(attr_num)] for x in query_res], dtype=np.float)
+
+def empty_zipped_array(num_nones):
+    """
+        prepare return values for cases of empty weights objects (no neighbors)
+        Input:
+        @param num_nones int: number of columns (e.g., 4)
+        Output:
+        [(None, None, None, None)]
+    """
+
+    return [tuple([None] * num_nones)]

src/py/crankshaft/test/fixtures/moran.json

@@ -1,52 +1,52 @@
 [[0.9319096128346788, "HH"],
 [-1.135787401862846, "HL"],
-[0.11732030672508517, "Not significant"],
+[0.11732030672508517, "LL"],
-[0.6152779669180425, "Not significant"],
+[0.6152779669180425, "LL"],
-[-0.14657336660125297, "Not significant"],
+[-0.14657336660125297, "LH"],
-[0.6967858120189607, "Not significant"],
+[0.6967858120189607, "LL"],
-[0.07949310115714454, "Not significant"],
+[0.07949310115714454, "HH"],
-[0.4703198759258987, "Not significant"],
+[0.4703198759258987, "HH"],
-[0.4421125200498064, "Not significant"],
+[0.4421125200498064, "HH"],
-[0.5724288737143592, "Not significant"],
+[0.5724288737143592, "LL"],
 [0.8970743435692062, "LL"],
-[0.18327334401918674, "Not significant"],
+[0.18327334401918674, "LL"],
-[-0.01466729201304962, "Not significant"],
+[-0.01466729201304962, "HL"],
-[0.3481559372544409, "Not significant"],
+[0.3481559372544409, "LL"],
-[0.06547094736902978, "Not significant"],
+[0.06547094736902978, "LL"],
 [0.15482141569329988, "HH"],
-[0.4373841193538136, "Not significant"],
+[0.4373841193538136, "HH"],
-[0.15971286468915544, "Not significant"],
+[0.15971286468915544, "LL"],
-[1.0543588860308968, "Not significant"],
+[1.0543588860308968, "HH"],
 [1.7372866900020818, "HH"],
 [1.091998586053999, "LL"],
-[0.1171572584252222, "Not significant"],
+[0.1171572584252222, "HH"],
-[0.08438455015300014, "Not significant"],
+[0.08438455015300014, "LL"],
-[0.06547094736902978, "Not significant"],
+[0.06547094736902978, "LL"],
 [0.15482141569329985, "HH"],
 [1.1627044812890683, "HH"],
-[0.06547094736902978, "Not significant"],
+[0.06547094736902978, "LL"],
-[0.795275137550483, "Not significant"],
+[0.795275137550483, "HH"],
 [0.18562939195219, "LL"],
-[0.3010757406693439, "Not significant"],
+[0.3010757406693439, "LL"],
 [2.8205795942839376, "HH"],
-[0.11259190602909264, "Not significant"],
+[0.11259190602909264, "LL"],
-[-0.07116352791516614, "Not significant"],
+[-0.07116352791516614, "HL"],
-[-0.09945240794119009, "Not significant"],
+[-0.09945240794119009, "LH"],
 [0.18562939195219, "LL"],
-[0.1832733440191868, "Not significant"],
+[0.1832733440191868, "LL"],
-[-0.39054253768447705, "Not significant"],
+[-0.39054253768447705, "HL"],
 [-0.1672071289487642, "HL"],
-[0.3337669247916343, "Not significant"],
+[0.3337669247916343, "HH"],
-[0.2584386102554792, "Not significant"],
+[0.2584386102554792, "HH"],
 [-0.19733845476322634, "HL"],
 [-0.9379282899805409, "LH"],
-[-0.028770969951095866, "Not significant"],
+[-0.028770969951095866, "LH"],
-[0.051367269430983485, "Not significant"],
+[0.051367269430983485, "LL"],
 [-0.2172548045913472, "LH"],
-[0.05136726943098351, "Not significant"],
+[0.05136726943098351, "LL"],
-[0.04191046803899837, "Not significant"],
+[0.04191046803899837, "LL"],
 [0.7482357030403517, "HH"],
-[-0.014585767863118111, "Not significant"],
+[-0.014585767863118111, "LH"],
-[0.5410013139159929, "Not significant"],
+[0.5410013139159929, "HH"],
 [1.0223932668429925, "LL"],
-[1.4179402898927476, "LL"]]
+[1.4179402898927476, "LL"]]

src/py/crankshaft/test/test_clustering_moran.py

@@ -1,8 +1,6 @@
 import unittest
 import numpy as np
 
-import unittest
-
 
 # from mock_plpy import MockPlPy
 # plpy = MockPlPy()
@@ -12,11 +10,12 @@ import unittest
 from helper import plpy, fixture_file
 
 import crankshaft.clustering as cc
+import crankshaft.pysal_utils as pu
 from crankshaft import random_seeds
 import json
 
 class MoranTest(unittest.TestCase):
-    """Testing class for Moran's I functions."""
+    """Testing class for Moran's I functions"""
 
     def setUp(self):
         plpy._reset()
@@ -30,7 +29,7 @@ class MoranTest(unittest.TestCase):
         self.moran_data = json.loads(open(fixture_file('moran.json')).read())
 
     def test_map_quads(self):
-        """Test map_quads."""
+        """Test map_quads"""
         self.assertEqual(cc.map_quads(1), 'HH')
         self.assertEqual(cc.map_quads(2), 'LH')
         self.assertEqual(cc.map_quads(3), 'LL')
@@ -38,80 +37,8 @@ class MoranTest(unittest.TestCase):
         self.assertEqual(cc.map_quads(33), None)
         self.assertEqual(cc.map_quads('andy'), None)
 
-    def test_query_attr_select(self):
-        """Test query_attr_select."""
-
-        ans = "i.\"{attr1}\"::numeric As attr1, " \
-              "i.\"{attr2}\"::numeric As attr2, "
-
-        self.assertEqual(cc.query_attr_select(self.params), ans)
-
-    def test_query_attr_where(self):
-        """Test query_attr_where."""
-
-        ans = "idx_replace.\"{attr1}\" IS NOT NULL AND "\
-              "idx_replace.\"{attr2}\" IS NOT NULL AND "\
-              "idx_replace.\"{attr2}\" <> 0"
-
-        self.assertEqual(cc.query_attr_where(self.params), ans)
-
-    def test_knn(self):
-        """Test knn function."""
-
-        ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
-              "i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT j.\"cartodb_id\" " \
-              "FROM \"(SELECT * FROM a_list)\" As j WHERE j.\"andy\" IS NOT NULL AND " \
-              "j.\"jay_z\" IS NOT NULL AND j.\"jay_z\" <> 0 ORDER BY " \
-              "j.\"the_geom\" <-> i.\"the_geom\" ASC LIMIT 321 OFFSET 1 ) ) " \
-              "As neighbors FROM \"(SELECT * FROM a_list)\" As i WHERE i.\"andy\" IS NOT " \
-              "NULL AND i.\"jay_z\" IS NOT NULL AND i.\"jay_z\" <> 0 ORDER " \
-              "BY i.\"cartodb_id\" ASC;"
-
-        self.assertEqual(cc.knn(self.params), ans)
-
-    def test_queen(self):
-        """Test queen neighbors function."""
-
-        ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
-              "i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT " \
-              "j.\"cartodb_id\" FROM \"(SELECT * FROM a_list)\" As j WHERE ST_Touches(" \
-              "i.\"the_geom\", j.\"the_geom\") AND j.\"andy\" IS NOT NULL " \
-              "AND j.\"jay_z\" IS NOT NULL AND j.\"jay_z\" <> 0)) As " \
-              "neighbors FROM \"(SELECT * FROM a_list)\" As i WHERE i.\"andy\" IS NOT NULL " \
-              "AND i.\"jay_z\" IS NOT NULL AND i.\"jay_z\" <> 0 ORDER BY " \
-              "i.\"cartodb_id\" ASC;"
-
-        self.assertEqual(cc.queen(self.params), ans)
-
-    def test_get_query(self):
-        """Test get_query."""
-
-        ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
-              "i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT " \
-              "j.\"cartodb_id\" FROM \"(SELECT * FROM a_list)\" As j WHERE j.\"andy\" IS " \
-              "NOT NULL AND j.\"jay_z\" IS NOT NULL AND j.\"jay_z\" <> 0 " \
-              "ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC LIMIT 321 " \
-              "OFFSET 1 ) ) As neighbors FROM \"(SELECT * FROM a_list)\" As i WHERE " \
-              "i.\"andy\" IS NOT NULL AND i.\"jay_z\" IS NOT NULL AND " \
-              "i.\"jay_z\" <> 0 ORDER BY i.\"cartodb_id\" ASC;"
-
-        self.assertEqual(cc.get_query('knn', self.params), ans)
-
-    def test_get_attributes(self):
-        """Test get_attributes."""
-
-        ## need to add tests
-
-        self.assertEqual(True, True)
-
-    def test_get_weight(self):
-        """Test get_weight."""
-
-        self.assertEqual(True, True)
-
-
     def test_quad_position(self):
-        """Test lisa_sig_vals."""
+        """Test lisa_sig_vals"""
 
         quads = np.array([1, 2, 3, 4], np.int)
 
@@ -125,7 +52,7 @@ 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('table', 'value', 0.05, 5, 99, 'the_geom', 'cartodb_id', 'knn')
+        result = cc.moran_local('subquery', 'value', 99, 'the_geom', 'cartodb_id', 'knn', 5)
         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):
@@ -137,8 +64,20 @@ 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('table', 'numerator', 'denominator', 0.05, 5, 99, 'the_geom', 'cartodb_id', 'knn')
+        result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 99, 'the_geom', 'cartodb_id', 'knn', 5)
+        print 'result == None? ', result == None
         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):
             self.assertAlmostEqual(res_val, exp_val)
+
+    def test_moran(self):
+        """Test Moran's I global"""
+        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('table', 'value', 99, 'the_geom', 'cartodb_id', 'knn', 5)
+        print 'result == None?', result == None
+        result_moran = result[0][0]
+        expected_moran = np.array([row[0] for row in self.moran_data]).mean()
+        self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)

src/py/crankshaft/test/test_pysal_utils.py

@@ -0,0 +1,104 @@
+import unittest
+
+import crankshaft.pysal_utils as pu
+from crankshaft import random_seeds
+
+
+class PysalUtilsTest(unittest.TestCase):
+    """Testing class for utility functions related to PySAL integrations"""
+
+    def setUp(self):
+        self.params = {"id_col": "cartodb_id",
+                       "attr1": "andy",
+                       "attr2": "jay_z",
+                       "subquery": "SELECT * FROM a_list",
+                       "geom_col": "the_geom",
+                       "num_ngbrs": 321}
+
+    def test_query_attr_select(self):
+        """Test query_attr_select"""
+
+        ans = "i.\"{attr1}\"::numeric As attr1, " \
+              "i.\"{attr2}\"::numeric As attr2, "
+
+        self.assertEqual(pu.query_attr_select(self.params), ans)
+
+    def test_query_attr_where(self):
+        """Test pu.query_attr_where"""
+
+        ans = "idx_replace.\"{attr1}\" IS NOT NULL AND " \
+              "idx_replace.\"{attr2}\" IS NOT NULL AND " \
+              "idx_replace.\"{attr2}\" <> 0"
+
+        self.assertEqual(pu.query_attr_where(self.params), ans)
+
+    def test_knn(self):
+        """Test knn neighbors constructor"""
+
+        ans = "SELECT i.\"cartodb_id\" As id, " \
+                     "i.\"andy\"::numeric As attr1, " \
+                     "i.\"jay_z\"::numeric As attr2, " \
+                     "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+                                   "FROM (SELECT * FROM a_list) As j " \
+                                   "WHERE j.\"andy\" IS NOT NULL AND " \
+                                         "j.\"jay_z\" IS NOT NULL AND " \
+                                         "j.\"jay_z\" <> 0 AND " \
+                                         "i.\"cartodb_id\" <> j.\"cartodb_id\" " \
+                                   "ORDER BY " \
+                                     "j.\"the_geom\" <-> i.\"the_geom\" ASC " \
+                      "LIMIT 321)) As neighbors " \
+              "FROM (SELECT * FROM a_list) As i " \
+              "WHERE i.\"andy\" IS NOT NULL AND " \
+                    "i.\"jay_z\" IS NOT NULL AND " \
+                    "i.\"jay_z\" <> 0 " \
+              "ORDER BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(pu.knn(self.params), ans)
+
+    def test_queen(self):
+        """Test queen neighbors constructor"""
+
+        ans = "SELECT i.\"cartodb_id\" As id, " \
+                     "i.\"andy\"::numeric As attr1, " \
+                     "i.\"jay_z\"::numeric As attr2, " \
+                     "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+                                   "FROM (SELECT * FROM a_list) As j " \
+                                   "WHERE ST_Touches(i.\"the_geom\", " \
+                                                    "j.\"the_geom\") AND " \
+                                         "j.\"andy\" IS NOT NULL AND " \
+                                         "j.\"jay_z\" IS NOT NULL AND " \
+                                         "j.\"jay_z\" <> 0)" \
+                                  ") As neighbors " \
+              "FROM (SELECT * FROM a_list) As i " \
+              "WHERE i.\"andy\" IS NOT NULL AND " \
+                    "i.\"jay_z\" IS NOT NULL AND " \
+                    "i.\"jay_z\" <> 0 " \
+              "ORDER BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(pu.queen(self.params), ans)
+
+    def test_construct_neighbor_query(self):
+        """Test construct_neighbor_query"""
+
+        # Compare to raw knn query
+        self.assertEqual(pu.construct_neighbor_query('knn', self.params),
+                         pu.knn(self.params))
+
+    def test_get_attributes(self):
+        """Test get_attributes"""
+
+        ## need to add tests
+
+        self.assertEqual(True, True)
+
+    def test_get_weight(self):
+        """Test get_weight"""
+
+        self.assertEqual(True, True)
+
+    def test_empty_zipped_array(self):
+        """Test empty_zipped_array"""
+        ans2 = [(None, None)]
+        ans4 = [(None, None, None, None)]
+        self.assertEqual(pu.empty_zipped_array(2), ans2)
+        self.assertEqual(pu.empty_zipped_array(4), ans4)