move to class-based module
This commit is contained in:
Andy Eschbacher
parent
a8bd122762
commit
2738c1f29c
@ -10,9 +10,11 @@ CREATE OR REPLACE FUNCTION
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id_col TEXT DEFAULT 'cartodb_id')
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RETURNS TABLE (moran NUMERIC, significance NUMERIC)
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AS $$
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from crankshaft.clustering import moran
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from crankshaft.clustering import Moran
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# TODO: use named parameters or a dictionary
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return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
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moran = Moran()
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return moran.global_stat(subquery, column_name, w_type,
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num_ngbrs, permutations, geom_col, id_col)
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$$ LANGUAGE plpythonu;
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-- Moran's I Local (internal function)
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@ -27,9 +29,11 @@ CREATE OR REPLACE FUNCTION
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id_col TEXT)
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RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
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AS $$
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from crankshaft.clustering import moran_local
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from crankshaft.clustering import Moran
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moran = Moran()
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# TODO: use named parameters or a dictionary
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return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
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return moran.local_stat(subquery, column_name, w_type,
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num_ngbrs, permutations, geom_col, id_col)
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$$ LANGUAGE plpythonu;
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-- Moran's I Local (public-facing function)
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@ -120,9 +124,11 @@ CREATE OR REPLACE FUNCTION
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id_col TEXT DEFAULT 'cartodb_id')
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RETURNS TABLE (moran FLOAT, significance FLOAT)
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AS $$
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from crankshaft.clustering import moran_local
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from crankshaft.clustering import Moran
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moran = Moran()
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# TODO: use named parameters or a dictionary
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return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
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return moran.global_rate_stat(subquery, numerator, denominator, w_type,
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num_ngbrs, permutations, geom_col, id_col)
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$$ LANGUAGE plpythonu;
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@ -140,9 +146,10 @@ CREATE OR REPLACE FUNCTION
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RETURNS
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TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
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AS $$
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from crankshaft.clustering import moran_local_rate
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from crankshaft.clustering import Moran
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moran = Moran()
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# TODO: use named parameters or a dictionary
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return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
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return moran.local_rate_stat(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
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$$ LANGUAGE plpythonu;
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-- Moran's I Local Rate (public-facing function)
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@ -15,204 +15,181 @@ import crankshaft.pysal_utils as pu
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# High level interface ---------------------------------------
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def moran(subquery, attr_name,
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w_type, num_ngbrs, permutations, geom_col, id_col):
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"""
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Moran's I (global)
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Implementation building neighbors with a PostGIS database and Moran's I
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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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("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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try:
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result = plpy.execute(query)
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# if there are no neighbors, exit
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if len(result) == 0:
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return pu.empty_zipped_array(2)
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except plpy.SPIError, e:
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plpy.error('Analysis failed: %s' % e)
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return pu.empty_zipped_array(2)
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# collect attributes
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attr_vals = pu.get_attributes(result)
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# calculate weights
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weight = pu.get_weight(result, w_type, num_ngbrs)
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# calculate moran global
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moran_global = ps.esda.moran.Moran(attr_vals, weight,
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permutations=permutations)
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return zip([moran_global.I], [moran_global.EI])
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class QueryRunner:
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def get_result(self, query):
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try:
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data = plpy.execute(query)
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except plpy.SPIError, err:
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plpy.error("k-means (spatial) cluster analysis failed: %s" % err)
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return data
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def moran_local(subquery, attr,
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w_type, num_ngbrs, permutations, geom_col, id_col):
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"""
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Moran's I implementation for PL/Python
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Andy Eschbacher
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"""
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class Moran:
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def __init__(self, query_runner=None):
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if query_runner is None:
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self.query_runner = QueryRunner()
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else:
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self.query_runner = query_runner
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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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def global_stat(self, subquery, attr_name,
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w_type, num_ngbrs, permutations, geom_col, id_col):
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"""
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Moran's I (global)
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Implementation building neighbors with a PostGIS database and Moran's I
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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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("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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qvals = 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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query = pu.construct_neighbor_query(w_type, qvals)
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result = self.query_runner.get_result(query)
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try:
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result = plpy.execute(query)
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# if there are no neighbors, exit
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if len(result) == 0:
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return pu.empty_zipped_array(5)
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except plpy.SPIError, e:
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plpy.error('Analysis failed: %s' % e)
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return pu.empty_zipped_array(5)
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# collect attributes
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attr_vals = pu.get_attributes(result)
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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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# calculate weights
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weight = pu.get_weight(result, w_type, num_ngbrs)
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# calculate LISA values
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lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
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permutations=permutations)
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# calculate moran global
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moran_global = ps.esda.moran.Moran(attr_vals, weight,
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permutations=permutations)
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# find quadrants for each geometry
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quads = quad_position(lisa.q)
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return zip([moran_global.I], [moran_global.EI])
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return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
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def local_stat(self, subquery, attr,
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w_type, num_ngbrs, permutations, geom_col, id_col):
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"""
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Moran's I implementation for PL/Python
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Andy Eschbacher
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"""
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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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def moran_rate(subquery, numerator, denominator,
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w_type, num_ngbrs, permutations, geom_col, id_col):
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"""
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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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("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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qvals = 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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query = pu.construct_neighbor_query(w_type, qvals)
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try:
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result = plpy.execute(query)
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# if there are no neighbors, exit
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if len(result) == 0:
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return pu.empty_zipped_array(2)
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except plpy.SPIError, e:
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plpy.error('Analysis failed: %s' % e)
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return pu.empty_zipped_array(2)
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result = self.query_runner.get_result(query)
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# collect attributes
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numer = pu.get_attributes(result, 1)
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denom = pu.get_attributes(result, 2)
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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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weight = pu.get_weight(result, w_type, num_ngbrs)
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# calculate moran global rate
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lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
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# calculate LISA values
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lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
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permutations=permutations)
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return zip([lisa_rate.I], [lisa_rate.EI])
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# find quadrants for each geometry
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quads = quad_position(lisa.q)
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return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
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def moran_local_rate(subquery, numerator, denominator,
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w_type, num_ngbrs, permutations, geom_col, id_col):
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"""
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Moran's I Local Rate
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def global_rate_stat(self, subquery, numerator, denominator,
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w_type, num_ngbrs, permutations, geom_col, id_col):
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"""
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Moran's I Rate (global)
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Andy Eschbacher
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"""
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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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"""
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qvals = 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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qvals = 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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query = pu.construct_neighbor_query(w_type, qvals)
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result = self.query_runner.get_result(query)
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try:
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result = plpy.execute(query)
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# if there are no neighbors, exit
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if len(result) == 0:
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return pu.empty_zipped_array(5)
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except plpy.SPIError, e:
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plpy.error('Analysis failed: %s' % e)
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return pu.empty_zipped_array(5)
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# collect attributes
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numer = pu.get_attributes(result, 1)
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denom = pu.get_attributes(result, 2)
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# collect attributes
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numer = pu.get_attributes(result, 1)
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denom = pu.get_attributes(result, 2)
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weight = pu.get_weight(result, w_type, num_ngbrs)
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weight = pu.get_weight(result, w_type, num_ngbrs)
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# calculate moran global rate
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lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
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permutations=permutations)
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# calculate LISA values
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lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
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permutations=permutations)
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return zip([lisa_rate.I], [lisa_rate.EI])
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# find quadrants for each geometry
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quads = quad_position(lisa.q)
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def local_rate_stat(self, subquery, numerator, denominator,
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w_type, num_ngbrs, permutations, geom_col, id_col):
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"""
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Moran's I Local Rate
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Andy Eschbacher
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"""
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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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return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
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qvals = 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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def moran_local_bv(subquery, attr1, attr2,
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permutations, geom_col, id_col, w_type, num_ngbrs):
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"""
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Moran's I (local) Bivariate (untested)
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"""
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result = self.query_runner.get_result(query)
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qvals = 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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# collect attributes
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numer = pu.get_attributes(result, 1)
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denom = pu.get_attributes(result, 2)
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query = pu.construct_neighbor_query(w_type, qvals)
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weight = pu.get_weight(result, w_type, num_ngbrs)
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try:
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result = plpy.execute(query)
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# if there are no neighbors, exit
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if len(result) == 0:
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return pu.empty_zipped_array(4)
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except plpy.SPIError:
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plpy.error("Error: areas of interest query failed, "
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"check input parameters")
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return pu.empty_zipped_array(4)
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# calculate LISA values
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lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
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permutations=permutations)
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# collect attributes
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attr1_vals = pu.get_attributes(result, 1)
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attr2_vals = pu.get_attributes(result, 2)
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# find quadrants for each geometry
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quads = quad_position(lisa.q)
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# create weights
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weight = pu.get_weight(result, w_type, num_ngbrs)
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return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
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# calculate LISA values
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lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
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permutations=permutations)
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def local_bivariate_stat(self, subquery, attr1, attr2,
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permutations, geom_col, id_col,
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w_type, num_ngbrs):
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"""
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Moran's I (local) Bivariate (untested)
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"""
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# find clustering of significance
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lisa_sig = quad_position(lisa.q)
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qvals = 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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return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
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query = pu.construct_neighbor_query(w_type, qvals)
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result = self.query_runner.get_result(query)
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# collect attributes
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attr1_vals = pu.get_attributes(result, 1)
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attr2_vals = pu.get_attributes(result, 2)
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# create weights
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weight = pu.get_weight(result, w_type, num_ngbrs)
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# calculate LISA values
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lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
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permutations=permutations)
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# find clustering of significance
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lisa_sig = quad_position(lisa.q)
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return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
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# Low level functions ----------------------------------------
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