refactored from pylint
This commit is contained in:
Andy Eschbacher
parent
06f5cf9951
commit
8dd8ab37a5
@ -11,10 +11,12 @@ import plpy
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# High level interface ---------------------------------------
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def moran(subquery, attr_name, permutations, geom_col, id_col, w_type, num_ngbrs):
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def moran(subquery, attr_name,
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permutations, geom_col, id_col, w_type, num_ngbrs):
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"""
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Moran's I (global)
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Implementation building neighors with a PostGIS database and Moran's I core clusters with PySAL.
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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 = {"id_col": id_col,
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@ -23,48 +25,39 @@ def moran(subquery, attr_name, permutations, geom_col, id_col, w_type, num_ngbrs
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"subquery": subquery,
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"num_ngbrs": num_ngbrs}
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q = get_query(w_type, qvals)
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query = construct_neighbor_query(w_type, qvals)
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plpy.notice('** Query: %s' % q)
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plpy.notice('** Query: %s' % query)
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try:
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r = plpy.execute(q)
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if (len(r) == 0) & (w_type != 'knn'):
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plpy.notice('** Query returned with 0 rows, trying kNN weights')
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q = get_query('knn', qvals)
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r = plpy.execute(q)
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plpy.notice('** Query returned with %d rows' % len(r))
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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 zip([None], [None])
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plpy.notice('** Query returned with %d rows' % len(result))
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except plpy.SPIError:
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plpy.error('Error: areas of interest query failed, check input parameters')
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plpy.notice('** Query failed: "%s"' % q)
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plpy.notice('** Query failed: "%s"' % query)
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plpy.notice('** Error: %s' % plpy.SPIError)
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plpy.notice('** Exiting function')
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return zip([None], [None])
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## if there are no neighbors, exit
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if len(r) == 0:
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return zip([None], [None])
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## collect attributes
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attr_vals = get_attributes(r, 1)
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attr_vals = get_attributes(result)
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## calculate weights
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weight = get_weight(r, w_type, num_ngbrs)
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weight = 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, permutations=permutations)
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return zip([moran_global.I],[moran_global.EI])
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return zip([moran_global.I], [moran_global.EI])
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def moran_local(subquery, attr, permutations, geom_col, id_col, w_type, num_ngbrs):
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def moran_local(subquery, attr,
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permutations, geom_col, id_col, w_type, num_ngbrs):
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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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# TODO: ensure that the significance output can be smaller that 1e-3 (0.001)
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# TODO: make a wishlist of output features (zscores, pvalues, raw local lisa, what else?)
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plpy.notice('** Constructing query')
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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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@ -75,30 +68,32 @@ def moran_local(subquery, attr, permutations, geom_col, id_col, w_type, num_ngbr
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"subquery": subquery,
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"num_ngbrs": num_ngbrs}
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q = get_query(w_type, qvals)
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query = construct_neighbor_query(w_type, qvals)
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try:
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r = plpy.execute(q)
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plpy.notice('** Query returned with %d rows' % len(r))
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result = plpy.execute(query)
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if len(result) == 0:
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return zip([None], [None], [None], [None], [None])
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except plpy.SPIError:
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plpy.error('Error: areas of interest query failed, check input parameters')
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plpy.notice('** Query failed: "%s"' % q)
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plpy.notice('** Exiting function')
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plpy.notice('** Query failed: "%s"' % query)
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return zip([None], [None], [None], [None], [None])
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y = get_attributes(r, 1)
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w = get_weight(r, w_type)
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attr_vals = get_attributes(result)
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weight = get_weight(result, w_type)
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# calculate LISA values
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lisa = ps.esda.moran.Moran_Local(y, w)
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lisa = ps.esda.moran.Moran_Local(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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plpy.notice('** Finished calculations')
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return zip(lisa.Is, quads, lisa.p_sim, w.id_order, lisa.y)
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return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
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def moran_rate(subquery, numerator, denominator, permutations, geom_col, id_col, w_type, num_ngbrs):
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def moran_rate(subquery, numerator, denominator,
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permutations, geom_col, id_col, w_type, num_ngbrs):
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"""
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Moran's I Rate (global)
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Andy Eschbacher
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@ -110,88 +105,82 @@ def moran_rate(subquery, numerator, denominator, permutations, geom_col, id_col,
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"subquery": subquery,
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"num_ngbrs": num_ngbrs}
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q = get_query(w_type, qvals)
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query = construct_neighbor_query(w_type, qvals)
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plpy.notice('** Query: %s' % q)
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plpy.notice('** Query: %s' % query)
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try:
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r = plpy.execute(q)
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if len(r) == 0:
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plpy.notice('** Query returned with 0 rows, trying kNN weights')
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q = get_query('knn', qvals)
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r = plpy.execute(q)
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plpy.notice('** Query returned with %d rows' % len(r))
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result = plpy.execute(query)
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if len(result) == 0:
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## if there are no values returned, exit
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return zip([None], [None])
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plpy.notice('** Query returned with %d rows' % len(result))
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except plpy.SPIError:
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plpy.error('Error: areas of interest query failed, check input parameters')
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plpy.notice('** Query failed: "%s"' % q)
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plpy.notice('** Query failed: "%s"' % query)
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plpy.notice('** Error: %s' % plpy.SPIError)
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plpy.notice('** Exiting function')
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return zip([None], [None])
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## if there are no values returned, exit
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if len(r) == 0:
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return zip([None], [None])
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## collect attributes
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numer = get_attributes(r, 1)
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denom = get_attributes(r, 2)
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numer = get_attributes(result, 1)
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denom = get_attributes(result, 2)
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w = get_weight(r, w_type, num_ngbrs)
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weight = get_weight(result, w_type, num_ngbrs)
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## calculate moran global rate
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mr = ps.esda.moran.Moran_Rate(numer, denom, w, permutations=permutations)
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lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
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permutations=permutations)
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plpy.notice('** Finished calculations')
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return zip([lisa_rate.I], [lisa_rate.EI])
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return zip([mr.I],[mr.EI])
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def moran_local_rate(subquery, numerator, denominator, permutations, geom_col, id_col, w_type, num_ngbrs):
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def moran_local_rate(subquery, numerator, denominator,
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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 Rate
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Andy Eschbacher
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"""
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plpy.notice('** Constructing query')
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# geometries with attributes that are null are ignored
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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 = {"id_col": id_col,
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query = construct_neighbor_query(w_type,
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{"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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q = get_query(w_type, qvals)
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"num_ngbrs": num_ngbrs})
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try:
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r = plpy.execute(q)
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plpy.notice('** Query returned with %d rows' % len(r))
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result = plpy.execute(query)
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plpy.notice('** Query returned with %d rows' % len(result))
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if len(result) == 0:
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return zip([None], [None], [None], [None], [None])
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except plpy.SPIError:
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plpy.error('Error: areas of interest query failed, check input parameters')
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plpy.notice('** Query failed: "%s"' % q)
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plpy.notice('** Query failed: "%s"' % query)
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plpy.notice('** Error: %s' % plpy.SPIError)
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plpy.notice('** Exiting function')
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return zip([None], [None], [None], [None], [None])
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## collect attributes
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numer = get_attributes(r, 1)
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denom = get_attributes(r, 2)
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numer = get_attributes(result, 1)
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denom = get_attributes(result, 2)
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w = get_weight(r, w_type, num_ngbrs)
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weight = get_weight(result, w_type, num_ngbrs)
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# calculate LISA values
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lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, w, permutations=permutations)
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lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
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permutations=permutations)
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# find units of significance
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quads = quad_position(lisa.q)
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plpy.notice('** Finished calculations')
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return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
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return zip(lisa.Is, quads, lisa.p_sim, w.id_order, lisa.y)
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def moran_local_bv(subquery, attr1, attr2, permutations, geom_col, id_col, w_type, num_ngbrs):
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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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plpy.notice('** Constructing query')
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qvals = {"num_ngbrs": num_ngbrs,
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@ -201,27 +190,28 @@ def moran_local_bv(subquery, attr1, attr2, permutations, geom_col, id_col, w_typ
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"geom_col": geom_col,
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"id_col": id_col}
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q = get_query(w_type, qvals)
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query = construct_neighbor_query(w_type, qvals)
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try:
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r = plpy.execute(q)
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plpy.notice('** Query returned with %d rows' % len(r))
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result = plpy.execute(query)
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plpy.notice('** Query returned with %d rows' % len(result))
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if len(result) == 0:
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return zip([None], [None], [None], [None])
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except plpy.SPIError:
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plpy.error('Error: areas of interest query failed, check input parameters')
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plpy.notice('** Query failed: "%s"' % q)
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plpy.notice('** Error: %s' % plpy.SPIError)
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plpy.notice('** Exiting function')
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plpy.notice('** Query failed: "%s"' % query)
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return zip([None], [None], [None], [None])
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## collect attributes
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attr1_vals = get_attributes(r, 1)
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attr2_vals = get_attributes(r, 2)
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attr1_vals = get_attributes(result, 1)
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attr2_vals = get_attributes(result, 2)
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# create weights
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w = get_weight(r, w_type, num_ngbrs)
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weight = 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, w)
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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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plpy.notice("len of Is: %d" % len(lisa.Is))
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@ -230,7 +220,7 @@ def moran_local_bv(subquery, attr1, attr2, permutations, geom_col, id_col, w_typ
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plpy.notice('** Finished calculations')
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return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order)
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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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@ -240,7 +230,7 @@ def map_quads(coord):
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Map a quadrant number to Moran's I designation
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HH=1, LH=2, LL=3, HL=4
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Input:
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:param coord (int): quadrant of a specific measurement
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@param coord (int): quadrant of a specific measurement
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"""
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if coord == 1:
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return 'HH'
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@ -256,7 +246,7 @@ def map_quads(coord):
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def query_attr_select(params):
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"""
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Create portion of SELECT statement for attributes inolved in query.
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:param params: dict of information used in query (column names,
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@param params: dict of information used in query (column names,
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table name, etc.)
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"""
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@ -293,7 +283,7 @@ def query_attr_where(params):
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def knn(params):
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"""SQL query for k-nearest neighbors.
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:param vars: dict of values to fill template
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@param vars: dict of values to fill template
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"""
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attr_select = query_attr_select(params)
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@ -322,7 +312,7 @@ def knn(params):
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## SQL query for finding queens neighbors (all contiguous polygons)
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def queen(params):
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"""SQL query for queen neighbors.
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:param params: dict of information to fill query
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@param params dict: information to fill query
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"""
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attr_select = query_attr_select(params)
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attr_where = query_attr_where(params)
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@ -348,10 +338,10 @@ def queen(params):
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## to add more weight methods open a ticket or pull request
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def get_query(w_type, query_vals):
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def construct_neighbor_query(w_type, query_vals):
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"""Return requested query.
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:param w_type: type of neighbors to calculate (knn or queen)
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:param query_vals: values used to construct the query
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@param w_type text: type of neighbors to calculate ('knn' or 'queen')
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@param query_vals dict: values used to construct the query
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"""
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if w_type == 'knn':
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@ -359,10 +349,10 @@ def get_query(w_type, query_vals):
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else:
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return queen(query_vals)
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def get_attributes(query_res, attr_num):
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def get_attributes(query_res, attr_num=1):
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"""
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:param query_res: query results with attributes and neighbors
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:param attr_num: attribute number (1, 2, ...)
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@param query_res: query results with attributes and neighbors
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@param attr_num: attribute number (1, 2, ...)
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"""
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return np.array([x['attr' + str(attr_num)] for x in query_res], dtype=np.float)
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@ -370,7 +360,7 @@ def get_attributes(query_res, attr_num):
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def get_weight(query_res, w_type='queen', num_ngbrs=5):
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"""
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Construct PySAL weight from return value of query
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:param query_res: query results with attributes and neighbors
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@param query_res: query results with attributes and neighbors
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"""
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if w_type == 'knn':
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row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
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@ -387,6 +377,11 @@ def get_weight(query_res, w_type='queen', num_ngbrs=5):
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def quad_position(quads):
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"""
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Produce Moran's I classification based of n
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Input:
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@param quads ndarray: an array of quads classified by
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1-4 (PySAL default)
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Output:
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@param ndarray: an array of quads classied by 'HH', 'LL', etc.
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"""
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lisa_sig = np.array([map_quads(q) for q in quads])
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@ -56,7 +56,7 @@ class MoranTest(unittest.TestCase):
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self.assertEqual(cc.query_attr_where(self.params), ans)
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def test_knn(self):
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"""Test knn function."""
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"""Test knn neighbors constructor"""
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ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
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"i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT j.\"cartodb_id\" " \
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@ -70,7 +70,7 @@ class MoranTest(unittest.TestCase):
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self.assertEqual(cc.knn(self.params), ans)
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def test_queen(self):
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"""Test queen neighbors function."""
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"""Test queen neighbors constructor"""
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ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
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"i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT " \
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@ -83,19 +83,20 @@ class MoranTest(unittest.TestCase):
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self.assertEqual(cc.queen(self.params), ans)
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def test_get_query(self):
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"""Test get_query."""
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def test_construct_neighbor_query(self):
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"""Test construct_neighbor_query"""
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ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
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"i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT " \
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"j.\"cartodb_id\" FROM (SELECT * FROM a_list) As j WHERE j.\"andy\" IS " \
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"NOT NULL AND j.\"jay_z\" IS NOT NULL AND j.\"jay_z\" <> 0 " \
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"ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC LIMIT 321 " \
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"OFFSET 1 ) ) As neighbors FROM (SELECT * FROM a_list) As i WHERE " \
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"i.\"andy\" IS NOT NULL AND i.\"jay_z\" IS NOT NULL AND " \
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"i.\"jay_z\" <> 0 ORDER BY i.\"cartodb_id\" ASC;"
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"OFFSET 1 ) ) As neighbors FROM (SELECT * FROM a_list) As i " \
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"WHERE i.\"andy\" IS NOT NULL AND i.\"jay_z\" IS NOT NULL AND " \
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"i.\"jay_z\" <> 0 " \
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"ORDER BY i.\"cartodb_id\" ASC;"
|
||||
|
||||
self.assertEqual(cc.get_query('knn', self.params), ans)
|
||||
self.assertEqual(cc.construct_neighbor_query('knn', self.params), ans)
|
||||
|
||||
def test_get_attributes(self):
|
||||
"""Test get_attributes."""
|
||||
|
||||
Loading…
Reference in New Issue
Block a user