rename queryrunner to analysisdataprovider

src/py/crankshaft/crankshaft/__init__.py

@@ -3,4 +3,4 @@ import crankshaft.random_seeds
 import crankshaft.clustering
 import crankshaft.space_time_dynamics
 import crankshaft.segmentation
-import query_runner
+import analysis_data_provider

src/py/crankshaft/crankshaft/analysis_data_provider.py

@@ -2,7 +2,7 @@
 import plpy
 
 
-class QueryRunner:
+class AnalysisDataProvider:
     def get_markov(self, query):
         try:
             data = plpy.execute(query)

src/py/crankshaft/crankshaft/clustering/kmeans.py

@@ -1,15 +1,15 @@
 from sklearn.cluster import KMeans
 import numpy as np
 
-from crankshaft.query_runner import QueryRunner
+from crankshaft.analysis_data_provider import AnalysisDataProvider
 
 
 class Kmeans:
-    def __init__(self, query_runner=None):
+    def __init__(self, data_provider=None):
-        if query_runner is None:
+        if data_provider is None:
-            self.query_runner = QueryRunner()
+            self.data_provider = AnalysisDataProvider()
         else:
-            self.query_runner = query_runner
+            self.data_provider = data_provider
 
     def spatial(self, query, no_clusters, no_init=20):
         """
@@ -23,7 +23,7 @@ class Kmeans:
                       "FROM ({query}) As a "
                       "WHERE the_geom IS NOT NULL").format(query=query)
 
-        data = self.query_runner.get_spatial_kmeans(full_query)
+        data = self.data_provider.get_spatial_kmeans(full_query)
 
         # Unpack query response
         xs = data[0]['xs']

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

@@ -8,7 +8,7 @@ Moran's I geostatistics (global clustering & outliers presence)
 import pysal as ps
 import plpy
 from collections import OrderedDict
-from crankshaft.query_runner import QueryRunner
+from crankshaft.analysis_data_provider import AnalysisDataProvider
 
 # crankshaft module
 import crankshaft.pysal_utils as pu
@@ -17,11 +17,11 @@ import crankshaft.pysal_utils as pu
 
 
 class Moran:
-    def __init__(self, query_runner=None):
+    def __init__(self, data_provider=None):
-        if query_runner is None:
+        if data_provider is None:
-            self.query_runner = QueryRunner()
+            self.data_provider = AnalysisDataProvider()
         else:
-            self.query_runner = query_runner
+            self.data_provider = data_provider
 
     def global_stat(self, subquery, attr_name,
                     w_type, num_ngbrs, permutations, geom_col, id_col):
@@ -39,7 +39,7 @@ class Moran:
 
         query = pu.construct_neighbor_query(w_type, qvals)
 
-        result = self.query_runner.get_moran(query)
+        result = self.data_provider.get_moran(query)
 
         # collect attributes
         attr_vals = pu.get_attributes(result)
@@ -71,7 +71,7 @@ class Moran:
 
         query = pu.construct_neighbor_query(w_type, qvals)
 
-        result = self.query_runner.get_moran(query)
+        result = self.data_provider.get_moran(query)
 
         attr_vals = pu.get_attributes(result)
         weight = pu.get_weight(result, w_type, num_ngbrs)
@@ -100,7 +100,7 @@ class Moran:
 
         query = pu.construct_neighbor_query(w_type, qvals)
 
-        result = self.query_runner.get_moran(query)
+        result = self.data_provider.get_moran(query)
 
         # collect attributes
         numer = pu.get_attributes(result, 1)
@@ -132,7 +132,7 @@ class Moran:
 
         query = pu.construct_neighbor_query(w_type, qvals)
 
-        result = self.query_runner.get_moran(query)
+        result = self.data_provider.get_moran(query)
 
         # collect attributes
         numer = pu.get_attributes(result, 1)
@@ -165,7 +165,7 @@ class Moran:
 
         query = pu.construct_neighbor_query(w_type, qvals)
 
-        result = self.query_runner.get_moran(query)
+        result = self.data_provider.get_moran(query)
 
         # collect attributes
         attr1_vals = pu.get_attributes(result, 1)

src/py/crankshaft/crankshaft/space_time_dynamics/markov.py

@@ -7,15 +7,15 @@ import numpy as np
 import pysal as ps
 import plpy
 import crankshaft.pysal_utils as pu
-from crankshaft.query_runner import QueryRunner
+from crankshaft.analysis_data_provider import AnalysisDataProvider
 
 
 class Markov:
-    def __init__(self, query_runner=None):
+    def __init__(self, data_provider=None):
-        if query_runner is None:
+        if data_provider is None:
-            self.query_runner = QueryRunner()
+            self.data_provider = AnalysisDataProvider()
         else:
-            self.query_runner = query_runner
+            self.data_provider = data_provider
 
     def spatial_trend(self, subquery, time_cols, num_classes=7,
                       w_type='knn', num_ngbrs=5, permutations=0,
@@ -62,7 +62,7 @@ class Markov:
 
         query = pu.construct_neighbor_query(w_type, qvals)
 
-        query_result = self.query_runner.get_markov(query)
+        query_result = self.data_provider.get_markov(query)
 
         # build weight
         weights = pu.get_weight(query_result, w_type)