updated function flow without significance
This commit is contained in:
Andy Eschbacher
parent
3eda8ecd16
commit
1578b17eb8
@ -11,7 +11,51 @@ import plpy
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# High level interface ---------------------------------------
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def moran_local(subquery, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
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def moran(subquery, attr_name, 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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Andy Eschbacher
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"""
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qvals = {"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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q = get_query(w_type, qvals)
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plpy.notice('** Query: %s' % q)
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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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except plpy.SPIError:
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plpy.error('** Moran rate failed executing query to build weight object')
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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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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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## calculate weights
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weight = get_weight(r, 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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def moran_local(subquery, attr, 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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@ -25,8 +69,8 @@ def moran_local(subquery, attr, significance, num_ngbrs, permutations, geom_colu
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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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"attr1": attr,
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"geom_col": geom_column,
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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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@ -38,23 +82,68 @@ def moran_local(subquery, attr, significance, num_ngbrs, permutations, geom_colu
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except plpy.SPIError:
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plpy.notice('** Query failed: "%s"' % q)
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plpy.notice('** Exiting function')
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return zip([None], [None], [None], [None])
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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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# calculate LISA values
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lisa = ps.Moran_Local(y, w)
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lisa = ps.esda.moran.Moran_Local(y, w)
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# find units of significance
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lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
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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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def moran_rate(subquery, numerator, denominator, 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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"""
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qvals = {"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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q = get_query(w_type, qvals)
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plpy.notice('** Query: %s' % q)
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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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except plpy.SPIError:
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plpy.error('Moran rate failed executing query to build weight object')
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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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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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w = get_weight(r, 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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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([mr.I],[mr.EI])
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def moran_local_rate(subquery, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
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def moran_local_rate(subquery, numerator, denominator, 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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@ -68,7 +157,7 @@ def moran_local_rate(subquery, numerator, denominator, significance, num_ngbrs,
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qvals = {"id_col": id_col,
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"numerator": numerator,
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"denominator": denominator,
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"geom_col": geom_column,
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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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@ -81,7 +170,7 @@ def moran_local_rate(subquery, numerator, denominator, significance, num_ngbrs,
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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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return zip([None], [None], [None], [None])
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return zip([None], [None], [None], [None], [None])
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plpy.notice('r.nrows() = %d' % r.nrows())
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@ -95,21 +184,20 @@ def moran_local_rate(subquery, numerator, denominator, significance, num_ngbrs,
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lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, w, permutations=permutations)
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# find units of significance
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lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
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quads = quad_position(lisa.q)
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plpy.notice('** Finished calculations')
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## TODO: Decide on which return values here
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return zip(lisa.Is, lisa_sig, lisa.p_sim, w.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(t, attr1, attr2, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
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def moran_local_bv(subquery, attr1, attr2, permutations, geom_col, id_col, w_type, num_ngbrs):
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plpy.notice('** Constructing query')
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qvals = {"num_ngbrs": num_ngbrs,
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"attr1": attr1,
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"attr2": attr2,
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"table": t,
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"geom_col": geom_column,
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"subquery": subquery,
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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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@ -136,7 +224,7 @@ def moran_local_bv(t, attr1, attr2, significance, num_ngbrs, permutations, geom_
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plpy.notice("len of Is: %d" % len(lisa.Is))
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# find clustering of significance
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lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
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lisa_sig = quad_position(lisa.q)
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plpy.notice('** Finished calculations')
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@ -171,7 +259,7 @@ def query_attr_select(params):
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"""
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attrs = [k for k in params
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if k not in ('id_col', 'geom_col', 'table', 'num_ngbrs', 'subquery')]
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if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')]
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template = "i.\"{%(col)s}\"::numeric As attr%(alias_num)s, "
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@ -187,7 +275,7 @@ def query_attr_where(params):
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Create portion of WHERE clauses for weeding out NULL-valued geometries
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"""
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attrs = sorted([k for k in params
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if k not in ('id_col', 'geom_col', 'table', 'num_ngbrs', 'subquery')])
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if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')])
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attr_string = []
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@ -217,12 +305,12 @@ def knn(params):
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"i.\"{id_col}\" As id, " \
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"%(attr_select)s" \
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"(SELECT ARRAY(SELECT j.\"{id_col}\" " \
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"FROM \"({subquery})\" As j " \
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"FROM ({subquery}) As j " \
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"WHERE %(attr_where_j)s " \
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"ORDER BY j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \
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"LIMIT {num_ngbrs} OFFSET 1 ) " \
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") As neighbors " \
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"FROM \"({subquery})\" As i " \
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"FROM ({subquery}) As i " \
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"WHERE " \
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"%(attr_where_i)s " \
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"ORDER BY i.\"{id_col}\" ASC;" % replacements
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@ -245,11 +333,11 @@ def queen(params):
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"i.\"{id_col}\" As id, " \
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"%(attr_select)s" \
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"(SELECT ARRAY(SELECT j.\"{id_col}\" " \
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"FROM \"({subquery})\" As j " \
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"FROM ({subquery}) As j " \
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"WHERE ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \
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"%(attr_where_j)s)" \
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") As neighbors " \
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"FROM \"({subquery})\" As i " \
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"FROM ({subquery}) As i " \
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"WHERE " \
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"%(attr_where_i)s " \
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"ORDER BY i.\"{id_col}\" ASC;" % replacements
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@ -285,10 +373,10 @@ def get_weight(query_res, w_type='queen', num_ngbrs=5):
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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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weights = {x['id']: row_normed_weights for x in query_res}
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elif w_type == 'queen':
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else:
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weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
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if len(x['neighbors']) > 0
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else [] for x in query_res}
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if len(x['neighbors']) > 0
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else [] for x in query_res}
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neighbors = {x['id']: x['neighbors'] for x in query_res}
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@ -302,20 +390,3 @@ def quad_position(quads):
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lisa_sig = np.array([map_quads(q) for q in quads])
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return lisa_sig
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def lisa_sig_vals(pvals, quads, threshold):
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"""
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Produce Moran's I classification based of n
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"""
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sig = (pvals <= threshold)
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lisa_sig = np.empty(len(sig), np.chararray)
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for idx, val in enumerate(sig):
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if val:
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lisa_sig[idx] = map_quads(quads[idx])
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else:
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lisa_sig[idx] = 'Not significant'
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return lisa_sig
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