move query generation to inside analysis data provider class
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6fe4fc9668
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@ -1,10 +1,12 @@
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"""class for fetching data"""
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import plpy
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import pysal_utils as pu
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class AnalysisDataProvider:
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def get_markov(self, query):
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def get_markov(self, w_type, params):
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try:
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query = pu.construct_neighbor_query(w_type, params)
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data = plpy.execute(query)
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if len(data) == 0:
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@ -14,10 +16,12 @@ class AnalysisDataProvider:
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except plpy.SPIError, err:
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plpy.error('Analysis failed: %s' % err)
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def get_moran(self, query):
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def get_moran(self, w_type, params):
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"""fetch data for moran's i analyses"""
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try:
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query = pu.construct_neighbor_query(w_type, params)
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data = plpy.execute(query)
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# if there are no neighbors, exit
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if len(data) == 0:
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return pu.empty_zipped_array(2)
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@ -31,15 +31,13 @@ class Moran:
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core clusters with PySAL.
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Andy Eschbacher
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"""
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qvals = OrderedDict([("id_col", id_col),
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params = OrderedDict([("id_col", id_col),
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("attr1", attr_name),
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("geom_col", geom_col),
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("subquery", subquery),
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("num_ngbrs", num_ngbrs)])
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query = pu.construct_neighbor_query(w_type, qvals)
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result = self.data_provider.get_moran(query)
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result = self.data_provider.get_moran(w_type, params)
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# collect attributes
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attr_vals = pu.get_attributes(result)
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@ -63,15 +61,13 @@ class Moran:
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# geometries with attributes that are null are ignored
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# resulting in a collection of not as near neighbors
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qvals = OrderedDict([("id_col", id_col),
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params = OrderedDict([("id_col", id_col),
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("attr1", attr),
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("geom_col", geom_col),
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("subquery", subquery),
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("num_ngbrs", num_ngbrs)])
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query = pu.construct_neighbor_query(w_type, qvals)
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result = self.data_provider.get_moran(query)
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result = self.data_provider.get_moran(w_type, params)
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attr_vals = pu.get_attributes(result)
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weight = pu.get_weight(result, w_type, num_ngbrs)
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@ -91,16 +87,14 @@ class Moran:
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Moran's I Rate (global)
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Andy Eschbacher
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"""
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qvals = OrderedDict([("id_col", id_col),
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params = OrderedDict([("id_col", id_col),
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("attr1", numerator),
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("attr2", denominator)
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("geom_col", geom_col),
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("subquery", subquery),
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("num_ngbrs", num_ngbrs)])
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query = pu.construct_neighbor_query(w_type, qvals)
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result = self.data_provider.get_moran(query)
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result = self.data_provider.get_moran(w_type, params)
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# collect attributes
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numer = pu.get_attributes(result, 1)
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@ -123,16 +117,14 @@ class Moran:
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# geometries with values that are null are ignored
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# resulting in a collection of not as near neighbors
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qvals = OrderedDict([("id_col", id_col),
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params = OrderedDict([("id_col", id_col),
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("numerator", numerator),
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("denominator", denominator),
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("geom_col", geom_col),
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("subquery", subquery),
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("num_ngbrs", num_ngbrs)])
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query = pu.construct_neighbor_query(w_type, qvals)
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result = self.data_provider.get_moran(query)
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result = self.data_provider.get_moran(w_type, params)
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# collect attributes
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numer = pu.get_attributes(result, 1)
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@ -156,16 +148,14 @@ class Moran:
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Moran's I (local) Bivariate (untested)
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"""
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qvals = OrderedDict([("id_col", id_col),
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params = OrderedDict([("id_col", id_col),
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("attr1", attr1),
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("attr2", attr2),
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("geom_col", geom_col),
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("subquery", subquery),
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("num_ngbrs", num_ngbrs)])
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query = pu.construct_neighbor_query(w_type, qvals)
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result = self.data_provider.get_moran(query)
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result = self.data_provider.get_moran(w_type, params)
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# collect attributes
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attr1_vals = pu.get_attributes(result, 1)
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@ -54,15 +54,13 @@ class Markov:
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if len(time_cols) < 2:
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plpy.error('More than one time column needs to be passed')
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qvals = {"id_col": id_col,
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params = {"id_col": id_col,
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"time_cols": time_cols,
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"geom_col": geom_col,
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"subquery": subquery,
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"num_ngbrs": num_ngbrs}
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query = pu.construct_neighbor_query(w_type, qvals)
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query_result = self.data_provider.get_markov(query)
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query_result = self.data_provider.get_markov(w_type, params)
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# build weight
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weights = pu.get_weight(query_result, w_type)
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@ -21,7 +21,7 @@ class FakeDataProvider(AnalysisDataProvider):
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def __init__(self, mocked_result):
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self.mocked_result = mocked_result
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def get_spatial_kmeans(self, w_type, params):
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def get_spatial_kmeans(self, query):
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return self.mocked_result
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def get_nonspatial_kmeans(self, query, standarize):
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@ -17,7 +17,7 @@ class FakeDataProvider(AnalysisDataProvider):
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def __init__(self, data):
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self.mock_result = data
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def get_markov(self, query):
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def get_markov(self, w_type, params):
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return self.mock_result
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