expand outputs for python moran, adjust existing functions to return
previous values
This commit is contained in:
Andy Eschbacher
parent
0d050a2206
commit
7b1ce57abc
@ -39,7 +39,7 @@ AS $$
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result = moran.local_stat(subquery, column_name, w_type,
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num_ngbrs, permutations, geom_col, id_col)
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# remove spatial lag
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return [r[:-1] for r in result]
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return [(r[6], r[0], r[1], r[7], r[5]) for r in result]
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$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
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-- Moran's I Local (internal function)
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@ -53,17 +53,21 @@ CREATE OR REPLACE FUNCTION
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geom_col TEXT,
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id_col TEXT)
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RETURNS TABLE (
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moran NUMERIC,
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quads TEXT,
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significance NUMERIC,
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rowid INT,
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vals NUMERIC,
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spatial_lag NUMERIC)
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spatial_lag NUMERIC,
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spatial_lag_std NUMERIC,
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orig_val NUMERIC,
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orig_val_std NUMERIC,
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moran_stat NUMERIC,
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rowid INT)
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AS $$
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from crankshaft.clustering import Moran
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moran = Moran()
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return moran.local_stat(subquery, column_name, w_type,
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num_ngbrs, permutations, geom_col, id_col)
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from crankshaft.clustering import Moran
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moran = Moran()
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return moran.local_stat(subquery, column_name, w_type,
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num_ngbrs, permutations, geom_col, id_col)
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$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
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@ -79,15 +83,19 @@ CREATE OR REPLACE FUNCTION
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geom_col TEXT DEFAULT 'the_geom',
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id_col TEXT DEFAULT 'cartodb_id')
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RETURNS TABLE (
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moran NUMERIC,
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quads TEXT,
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significance NUMERIC,
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rowid INT,
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vals NUMERIC,
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spatial_lag NUMERIC)
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spatial_lag NUMERIC,
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spatial_lag_std NUMERIC,
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orig_val NUMERIC,
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orig_val_std NUMERIC,
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moran_stat NUMERIC,
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rowid INT)
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AS $$
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SELECT moran, quads, significance, rowid, vals, spatial_lag
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SELECT
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quads, significance, spatial_lag, spatial_lag_std,
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orig_val, orig_val_std, moran_stat, rowid
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FROM cdb_crankshaft._CDB_MoransILocal(
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subquery, column_name, w_type,
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num_ngbrs, permutations, geom_col, id_col);
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@ -214,7 +222,7 @@ AS $$
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# TODO: use named parameters or a dictionary
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result = moran.local_rate_stat(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
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# remove spatial lag
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return [r[:-1] for r in result]
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return [(r[6], r[0], r[1], r[7], r[4]) for r in result]
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$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
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-- Moran's I Local Rate (public-facing function) - DEPRECATED
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@ -250,17 +258,27 @@ CREATE OR REPLACE FUNCTION
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id_col TEXT)
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RETURNS
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TABLE(
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moran NUMERIC,
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quads TEXT,
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significance NUMERIC,
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rowid INT,
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vals NUMERIC,
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spatial_lag NUMERIC)
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spatial_lag NUMERIC,
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spatial_lag_std NUMERIC,
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orig_val NUMERIC,
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orig_val_std NUMERIC,
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moran_stat NUMERIC,
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rowid INT)
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AS $$
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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_stat(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
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from crankshaft.clustering import Moran
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moran = Moran()
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return moran.local_rate_stat(
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subquery,
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numerator,
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denominator,
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w_type,
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num_ngbrs,
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permutations,
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geom_col,
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id_col
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)
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$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
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-- Moran's I Rate
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@ -277,18 +295,22 @@ CREATE OR REPLACE FUNCTION
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id_col TEXT DEFAULT 'cartodb_id')
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RETURNS
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TABLE(
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moran NUMERIC,
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quads TEXT,
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significance NUMERIC,
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rowid INT,
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vals NUMERIC,
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spatial_lag NUMERIC)
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spatial_lag NUMERIC,
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spatial_lag_std NUMERIC,
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orig_val NUMERIC,
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orig_val_std NUMERIC,
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moran_stat NUMERIC,
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rowid INT)
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AS $$
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SELECT moran, quads, significance, rowid, vals, spatial_lag
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FROM cdb_crankshaft._CDB_MoransILocalRate(
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subquery, numerator, denominator, w_type,
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num_ngbrs, permutations, geom_col, id_col);
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SELECT
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quads, significance, spatial_lag, spatial_lag_std,
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orig_val, orig_val_std, moran_stat, rowid
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FROM cdb_crankshaft._CDB_MoransILocalRate(
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subquery, numerator, denominator, w_type,
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num_ngbrs, permutations, geom_col, id_col);
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$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE;
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@ -1,6 +1,9 @@
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"""
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Moran's I geostatistics (global clustering & outliers presence)
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Functionality relies PySAL: http://pysal.readthedocs.io/en/latest/
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Functionality relies on a combination of `PySAL
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<http://pysal.readthedocs.io/en/latest/>`__ and the data providered provided in
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the class instantiation (which defaults to PostgreSQL's plpy module's `database
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access functions <https://www.postgresql.org/docs/10/static/plpython.html>`__).
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"""
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from collections import OrderedDict
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@ -97,6 +100,18 @@ class Moran(object):
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geom_col (str): Name of the geometry column in the dataset for
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finding the spatial neighborhoods.
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id_col (str): Row index for each value. Usually the database index.
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Returns:
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list of tuples: Where each tuple consists of the following values:
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- quadrants classification (one of `HH`, `HL`, `LL`, or `LH`)
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- p-value
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- spatial lag
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- standardized spatial lag (centered on the mean, normalized by the
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standard deviation)
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- original value
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- standardized value
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- Moran's I statistic
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- original row index
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"""
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# geometries with attributes that are null are ignored
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@ -122,9 +137,18 @@ class Moran(object):
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# calculate spatial lag
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lag = ps.weights.spatial_lag.lag_spatial(weight, lisa.y)
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lag_std = ps.weights.spatial_lag.lag_spatial(weight, lisa.z)
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return zip(lisa.Is, quads, lisa.p_sim, weight.id_order,
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lisa.y, lag)
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return zip(
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quads,
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lisa.p_sim,
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lag,
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lag_std,
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lisa.y,
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lisa.z,
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lisa.Is,
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weight.id_order
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)
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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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@ -196,6 +220,18 @@ class Moran(object):
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geom_col (str): Name of the geometry column in the dataset for
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finding the spatial neighborhoods.
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id_col (str): Row index for each value. Usually the database index.
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Returns:
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list of tuples: Where each tuple consists of the following values:
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- quadrants classification (one of `HH`, `HL`, `LL`, or `LH`)
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- p-value
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- spatial lag
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- standardized spatial lag (centered on the mean, normalized by the
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standard deviation)
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- original value (roughly numerator divided by denominator)
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- standardized value
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- Moran's I statistic
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- original row index
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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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@ -224,8 +260,18 @@ class Moran(object):
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# spatial lag
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lag = ps.weights.spatial_lag.lag_spatial(weight, lisa.y)
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lag_std = ps.weights.spatial_lag.lag_spatial(weight, lisa.z)
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return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y, lag)
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return zip(
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quads,
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lisa.p_sim,
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lag,
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lag_std,
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lisa.y,
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lisa.z,
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lisa.Is,
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weight.id_order
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)
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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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