From 5be5a48894addee9ea4fe2eae2a1b36e677b258b Mon Sep 17 00:00:00 2001
From: Andy Eschbacher <andy.eschbacher@gmail.com>
Date: Mon, 28 Nov 2016 19:49:21 +0000
Subject: [PATCH] adds psql connector

---
 .../crankshaft/crankshaft/regression/gwr.py   | 49 ++++++++++++++-----
 1 file changed, 37 insertions(+), 12 deletions(-)

diff --git a/src/py/crankshaft/crankshaft/regression/gwr.py b/src/py/crankshaft/crankshaft/regression/gwr.py
index 9cf6fa8..be25e27 100644
--- a/src/py/crankshaft/crankshaft/regression/gwr.py
+++ b/src/py/crankshaft/crankshaft/regression/gwr.py
@@ -1,35 +1,59 @@
 import numpy as np
-from base.gwr import GWR
-from base.sel_bw import Sel_BW
+from gwr.base.gwr import GWR
+from gwr.base.sel_bw import Sel_BW
 
-def gwr(subquery, dep_var, ind_vars, fixed=False, kernel='bisquare'):
+
+def gwr(subquery, dep_var, ind_vars,
+        fixed=False, kernel='bisquare'):
     """
-    subquery: 'select * from interesting_table'
+    subquery: 'select * from demographics'
     dep_var: 'pctbachelor'
     ind_vars: ['intercept', 'pctpov', 'pctrural', 'pctblack']
     fixed: False (kNN) or True ('distance')
     kernel: 'bisquare' (default), or 'exponential', 'gaussian'
     """
 
-    query_result = subquery
-    rowid = np.array(query_result[0]['rowid'])
+    # query_result = subquery
+    # rowid = np.array(query_result[0]['rowid'])
+    params = {'geom_col': 'the_geom',
+              'id_col': 'cartodb_id',
+              'subquery': subquery,
+              'dep_var': dep_var,
+              'ind_vars': ind_vars}
 
+    try:
+        query_result = plpy.execute(pu.gwr_query(params))
+    except plpy.SPIError, err:
+        plpy.error('Analysis failed: %s' % err)
+
+    # TODO: should x, y be centroids? point on surface?
+    #       lat, long coordinates
     x = np.array(query_result[0]['x'])
     y = np.array(query_result[0]['y'])
     coords = zip(x, y)
 
-    Y = query_result[0]['dep'].reshape((-1, 1))
+    # extract dependent variable
+    Y = query_result[0]['dep_var'].reshape((-1, 1))
+
     n = Y.shape[0]
     k = len(ind_vars)
     X = np.zeros((n, k))
 
     for attr in range(0, k):
-        attr_name = 'attr' + str(attr+1)
-        X[:, attr] = np.array(query_result[0][attr_name]).flatten()
+        attr_name = 'attr' + str(attr + 1)
+        X[:, attr] = np.array(
+          query_result[0][attr_name]).flatten()
 
-    bw = Sel_BW(coords, Y, X, fixed=fixed, kernel=kernel).search()
-    model = GWR(coords, Y, X, bw, fixed=fixed, kernel=kernel).fit()
+    # calculate bandwidth
+    bw = Sel_BW(coords, Y, X,
+                fixed=fixed, kernel=kernel).search()
+    model = GWR(coords, Y, X, bw,
+                fixed=fixed, kernel=kernel).fit()
 
+    # TODO: iterate from 0, n-1 and fill objects like this, for a
+    #       column called coeffs:
+    #       {'pctrural': ..., 'pctpov': ..., ...}
+    #       Follow the same structure for other outputs
     coefficients = model.params.reshape((-1,))
     t_vals = model.tvalues.reshape((-1,))
     stand_errs = model.bse.reshape((-1))
@@ -39,4 +63,5 @@ def gwr(subquery, dep_var, ind_vars, fixed=False, kernel='bisquare'):
     rowid = np.tile(rowid, k+1).reshape((-1,))
     var_name = np.tile(ind_vars, k+1).reshape((-1,))
 
-    return zip(coefficients, stand_errs, t_vals, predicted, residuals, r_squared, rowid, var_name)
+    return zip(coefficients, stand_errs, t_vals, predicted,
+               residuals, r_squared, rowid)