Files
observatory-extension/src/pg/sql/41_observatory_augmentation.sql
Stuart Lynn 21d306898b typo
2016-04-25 13:24:39 -04:00

846 lines
25 KiB
PL/PgSQL

--For Longer term Dev
--Break out table definitions to types
--Automate type creation from a script, something like
----CREATE OR REPLACE FUNCTION OBS_Get<%=tag_name%>(geom GEOMETRY)
----RETURNS TABLE(
----<%=get_dimensions_for_tag(tag_name)%>
----AS $$
----DECLARE
----target_cols text[];
----names text[];
----vals NUMERIC[];-
----q text;
----BEGIN
----target_cols := Array[<%=get_dimensions_for_tag(tag_name)%>],
--Functions for augmenting specific tables
--------------------------------------------------------------------------------
-- Creates a table of demographic snapshot
CREATE OR REPLACE FUNCTION cdb_observatory.OBS_GetDemographicSnapshotJ(geom geometry, time_span text default '2009 - 2013', geometry_level text default '"us.census.tiger".block_group')
RETURNS SETOF JSON
AS $$
DECLARE
target_cols text[];
BEGIN
target_cols := Array['total_pop',
'male_pop',
'female_pop',
'median_age',
'white_pop',
'black_pop',
'asian_pop',
'hispanic_pop',
'amerindian_pop',
'other_race_pop',
'two_or_more_races_pop',
'not_hispanic_pop',
--'not_us_citizen_pop',
--'workers_16_and_over',
--'commuters_by_car_truck_van',
--'commuters_drove_alone',
--'commuters_by_carpool',
--'commuters_by_public_transportation',
--'commuters_by_bus',
--'commuters_by_subway_or_elevated',
--'walked_to_work',
--'worked_at_home',
--'children',
'households',
--'population_3_years_over',
--'in_school',
--'in_grades_1_to_4',
--'in_grades_5_to_8',
--'in_grades_9_to_12',
--'in_undergrad_college',
'pop_25_years_over',
'high_school_diploma',
'less_one_year_college',
'one_year_more_college',
'associates_degree',
'bachelors_degree',
'masters_degree',
--'pop_5_years_over',
--'speak_only_english_at_home',
--'speak_spanish_at_home',
--'pop_determined_poverty_status',
--'poverty',
'median_income',
'gini_index',
'income_per_capita',
'housing_units',
'vacant_housing_units',
'vacant_housing_units_for_rent',
'vacant_housing_units_for_sale',
'median_rent',
'percent_income_spent_on_rent',
'owner_occupied_housing_units',
'million_dollar_housing_units',
'mortgaged_housing_units',
--'pop_15_and_over',
--'pop_never_married',
--'pop_now_married',
--'pop_separated',
--'pop_widowed',
--'pop_divorced',
'commuters_16_over',
'commute_less_10_mins',
'commute_10_14_mins',
'commute_15_19_mins',
'commute_20_24_mins',
'commute_25_29_mins',
'commute_30_34_mins',
'commute_35_44_mins',
'commute_45_59_mins',
'commute_60_more_mins',
'aggregate_travel_time_to_work',
'income_less_10000',
'income_10000_14999',
'income_15000_19999',
'income_20000_24999',
'income_25000_29999',
'income_30000_34999',
'income_35000_39999',
'income_40000_44999',
'income_45000_49999',
'income_50000_59999',
'income_60000_74999',
'income_75000_99999',
'income_100000_124999',
'income_125000_149999',
'income_150000_199999',
'income_200000_or_more',
'land_area'];
RETURN QUERY
EXECUTE
'select * from cdb_observatory._OBS_GetCensus($1, $2 )'
USING geom, target_cols
RETURN;
END;
$$ LANGUAGE plpgsql;
-- CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetDemographicSnapshot(geom geometry, time_span text default '2009 - 2013', geometry_level text default '"us.census.tiger".block_group' )
-- RETURNS TABLE(
-- total_pop NUMERIC,
-- male_pop NUMERIC,
-- female_pop NUMERIC,
-- median_age NUMERIC,
-- white_pop NUMERIC,
-- black_pop NUMERIC,
-- asian_pop NUMERIC,
-- hispanic_pop NUMERIC,
-- amerindian_pop NUMERIC,
-- other_race_pop NUMERIC,
-- two_or_more_races_pop NUMERIC,
-- not_hispanic_pop NUMERIC,
-- --not_us_citizen_pop NUMERIC,
-- --workers_16_and_over NUMERIC,
-- --commuters_by_car_truck_van NUMERIC,
-- --commuters_drove_alone NUMERIC,
-- --commuters_by_carpool NUMERIC,
-- --commuters_by_public_transportation NUMERIC,
-- --commuters_by_bus NUMERIC,
-- --commuters_by_subway_or_elevated NUMERIC,
-- --walked_to_work NUMERIC,
-- --worked_at_home NUMERIC,
-- --children NUMERIC, -- TODO we should be able to get this at BG
-- households NUMERIC,
-- --population_3_years_over NUMERIC,
-- --in_school NUMERIC,
-- --in_grades_1_to_4 NUMERIC,
-- --in_grades_5_to_8 NUMERIC,
-- --in_grades_9_to_12 NUMERIC,
-- --in_undergrad_college NUMERIC,
-- pop_25_years_over NUMERIC,
-- high_school_diploma NUMERIC,
-- less_one_year_college NUMERIC,
-- one_year_more_college NUMERIC,
-- associates_degree NUMERIC,
-- bachelors_degree NUMERIC,
-- masters_degree NUMERIC,
-- --pop_5_years_over NUMERIC,
-- --speak_only_english_at_home NUMERIC,
-- --speak_spanish_at_home NUMERIC,
-- --pop_determined_poverty_status NUMERIC,
-- --poverty NUMERIC,
-- median_income NUMERIC,
-- gini_index NUMERIC,
-- income_per_capita NUMERIC,
-- housing_units NUMERIC,
-- vacant_housing_units NUMERIC,
-- vacant_housing_units_for_rent NUMERIC,
-- vacant_housing_units_for_sale NUMERIC,
-- median_rent NUMERIC,
-- percent_income_spent_on_rent NUMERIC,
-- owner_occupied_housing_units NUMERIC,
-- million_dollar_housing_units NUMERIC,
-- mortgaged_housing_units NUMERIC,
-- --pop_15_and_over NUMERIC,
-- --pop_never_married NUMERIC,
-- --pop_now_married NUMERIC,
-- --pop_separated NUMERIC,
-- --pop_widowed NUMERIC,
-- --pop_divorced NUMERIC,
-- commuters_16_over NUMERIC,
-- commute_less_10_mins NUMERIC,
-- commute_10_14_mins NUMERIC,
-- commute_15_19_mins NUMERIC,
-- commute_20_24_mins NUMERIC,
-- commute_25_29_mins NUMERIC,
-- commute_30_34_mins NUMERIC,
-- commute_35_44_mins NUMERIC,
-- commute_45_59_mins NUMERIC,
-- commute_60_more_mins NUMERIC,
-- aggregate_travel_time_to_work NUMERIC,
-- income_less_10000 NUMERIC,
-- income_10000_14999 NUMERIC,
-- income_15000_19999 NUMERIC,
-- income_20000_24999 NUMERIC,
-- income_25000_29999 NUMERIC,
-- income_30000_34999 NUMERIC,
-- income_35000_39999 NUMERIC,
-- income_40000_44999 NUMERIC,
-- income_45000_49999 NUMERIC,
-- income_50000_59999 NUMERIC,
-- income_60000_74999 NUMERIC,
-- income_75000_99999 NUMERIC,
-- income_100000_124999 NUMERIC,
-- income_125000_149999 NUMERIC,
-- income_150000_199999 NUMERIC,
-- income_200000_or_more NUMERIC,
-- land_area NUMERIC)
-- AS $$
-- DECLARE
-- target_cols text[];
-- names text[];
-- vals NUMERIC[];
-- q text;
-- BEGIN
-- target_cols := Array['total_pop',
-- 'male_pop',
-- 'female_pop',
-- 'median_age',
-- 'white_pop',
-- 'black_pop',
-- 'asian_pop',
-- 'hispanic_pop',
-- 'amerindian_pop',
-- 'other_race_pop',
-- 'two_or_more_races_pop',
-- 'not_hispanic_pop',
-- --'not_us_citizen_pop',
-- --'workers_16_and_over',
-- --'commuters_by_car_truck_van',
-- --'commuters_drove_alone',
-- --'commuters_by_carpool',
-- --'commuters_by_public_transportation',
-- --'commuters_by_bus',
-- --'commuters_by_subway_or_elevated',
-- --'walked_to_work',
-- --'worked_at_home',
-- --'children',
-- 'households',
-- --'population_3_years_over',
-- --'in_school',
-- --'in_grades_1_to_4',
-- --'in_grades_5_to_8',
-- --'in_grades_9_to_12',
-- --'in_undergrad_college',
-- 'pop_25_years_over',
-- 'high_school_diploma',
-- 'less_one_year_college',
-- 'one_year_more_college',
-- 'associates_degree',
-- 'bachelors_degree',
-- 'masters_degree',
-- --'pop_5_years_over',
-- --'speak_only_english_at_home',
-- --'speak_spanish_at_home',
-- --'pop_determined_poverty_status',
-- --'poverty',
-- 'median_income',
-- 'gini_index',
-- 'income_per_capita',
-- 'housing_units',
-- 'vacant_housing_units',
-- 'vacant_housing_units_for_rent',
-- 'vacant_housing_units_for_sale',
-- 'median_rent',
-- 'percent_income_spent_on_rent',
-- 'owner_occupied_housing_units',
-- 'million_dollar_housing_units',
-- 'mortgaged_housing_units',
-- --'pop_15_and_over',
-- --'pop_never_married',
-- --'pop_now_married',
-- --'pop_separated',
-- --'pop_widowed',
-- --'pop_divorced',
-- 'commuters_16_over',
-- 'commute_less_10_mins',
-- 'commute_10_14_mins',
-- 'commute_15_19_mins',
-- 'commute_20_24_mins',
-- 'commute_25_29_mins',
-- 'commute_30_34_mins',
-- 'commute_35_44_mins',
-- 'commute_45_59_mins',
-- 'commute_60_more_mins',
-- 'aggregate_travel_time_to_work',
-- 'income_less_10000',
-- 'income_10000_14999',
-- 'income_15000_19999',
-- 'income_20000_24999',
-- 'income_25000_29999',
-- 'income_30000_34999',
-- 'income_35000_39999',
-- 'income_40000_44999',
-- 'income_45000_49999',
-- 'income_50000_59999',
-- 'income_60000_74999',
-- 'income_75000_99999',
-- 'income_100000_124999',
-- 'income_125000_149999',
-- 'income_150000_199999',
-- 'income_200000_or_more',
-- 'land_area'];
--
-- q :=
-- $query$
-- WITH a As (
-- SELECT
-- array_agg(_OBS_GetCensusJ->>'name') As names,
-- array_agg(_OBS_GetCensusJ->>'value') As vals
-- FROM cdb_observatory._OBS_GetCensusJ($1,$2,$3,$4)
-- )$query$ ||
-- cdb_observatory._OBS_BuildSnapshotQuery(target_cols) ||
-- ' FROM a'
-- ;
--
-- RETURN QUERY
-- EXECUTE
-- q
-- USING geom, target_cols, time_span, geometry_level;
--
-- RETURN;
-- END;
-- $$ LANGUAGE plpgsql;
--Base functions for performing augmentation
----------------------------------------------------------------------------------------
--Returns arrays of values for the given census dimension names for a given
--point or polygon
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetCensus(
geom geometry,
dimension_names text[],
time_span text DEFAULT '2009 - 2013',
geometry_level text DEFAULT '"us.census.tiger".block_group'
)
RETURNS SETOF JSON
AS $$
DECLARE
ids text[];
BEGIN
ids := cdb_observatory._OBS_LookupCensusHuman(dimension_names);
RETURN QUERY
SELECT * FROM cdb_observatory._OBS_Get(geom, ids, time_span, geometry_level);
END;
$$ LANGUAGE plpgsql;
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetCensus(
geom geometry,
dimension_name text,
time_span text DEFAULT '2009 - 2013',
geometry_level text DEFAULT '"us.census.tiger".block_group'
)
RETURNS NUMERIC
AS $$
DECLARE
ids Text[];
result_json json;
result Numeric;
BEGIN
ids := cdb_observatory._OBS_LookupCensusHuman(Array[dimension_name]);
result_json := (SELECT a FROM cdb_observatory._OBS_Get(geom, ids, time_span, geometry_level) as a limit 1);
EXECUTE
format('select $1::numeric as "%s"', result_json->>'name')
INTO result
USING
result_json->>'value';
return result;
END;
$$ LANGUAGE plpgsql;
-- Base augmentation fucntion.
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_Get(
geom geometry,
column_ids text[],
time_span text,
geometry_level text
)
RETURNS SETOF JSON
AS $$
DECLARE
results json[];
geom_table_name text;
names text[];
query text;
data_table_info json[];
BEGIN
geom_table_name := cdb_observatory._OBS_GeomTable(geom, geometry_level);
IF geom_table_name IS NULL
THEN
RAISE NOTICE 'Point % is outside of the data region', geom;
RETURN QUERY SELECT '{}'::text[], '{}'::NUMERIC[];
END IF;
execute'
select array_agg( _obs_getcolumndata) from cdb_observatory._OBS_GetColumnData($1,
$2,
$3);'
INTO data_table_info
using geometry_level, column_ids, time_span;
IF ST_GeometryType(geom) = 'ST_Point'
THEN
results := cdb_observatory._OBS_GetPoints(geom,
geom_table_name,
data_table_info);
ELSIF ST_GeometryType(geom) IN ('ST_Polygon', 'ST_MultiPolygon')
THEN
-- RAISE EXCEPTION 'polygons not supported for now';
results := cdb_observatory._OBS_GetPolygons(geom,
geom_table_name,
data_table_info);
END IF;
RETURN QUERY
EXECUTE
$query$
SELECT unnest($1)
$query$
USING results;
END;
$$ LANGUAGE plpgsql;
-- If the variable of interest is just a rate return it as such,
-- otherwise normalize it to the census block area and return that
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetPoints(
geom geometry,
geom_table_name text,
data_table_info json[]
)
RETURNS json[]
AS $$
DECLARE
result NUMERIC[];
json_result json[];
query text;
i int;
geoid text;
area NUMERIC;
BEGIN
-- TODO: does 'geoid' need to be generalized to geom_ref??
EXECUTE
format('SELECT geoid
FROM observatory.%I
WHERE ST_WITHIN($1, the_geom)',
geom_table_name)
USING geom
INTO geoid;
RAISE NOTICE 'geoid is %, geometry table is % ', geoid, geom_table_name;
EXECUTE
format('SELECT ST_Area(the_geom::geography) / (1000 * 1000)
FROM observatory.%I
WHERE geoid = %L',
geom_table_name,
geoid)
INTO area;
IF area IS NULL
THEN
RAISE NOTICE 'No geometry at %', ST_AsText(geom);
END IF;
query := 'SELECT Array[';
FOR i IN 1..array_upper(data_table_info, 1)
LOOP
IF area is NULL OR area = 0
THEN
-- give back null values
query := query || format('NULL::numeric ');
ELSIF ((data_table_info)[i])->>'aggregate' != 'sum'
THEN
-- give back full variable
query := query || format('%I ', ((data_table_info)[i])->>'colname');
ELSE
-- give back variable normalized by area of geography
query := query || format('%I/%s ',
((data_table_info)[i])->>'colname',
area);
END IF;
IF i < array_upper(data_table_info, 1)
THEN
query := query || ',';
END IF;
END LOOP;
query := query || format(' ]::numeric[]
FROM observatory.%I
WHERE %I.geoid = %L
',
((data_table_info)[1])->>'tablename',
((data_table_info)[1])->>'tablename',
geoid
);
EXECUTE
query
INTO result
USING geom;
EXECUTE
$query$
select array_agg(row_to_json(t)) from(
select values as value,
meta->>'name' as name,
meta->>'tablename' as tablename,
meta->>'aggregate' as aggregate,
meta->>'type' as type,
meta->>'description' as description
from (select unnest($1) as values, unnest($2) as meta) b
) t
$query$
INTO json_result
USING result, data_table_info;
RETURN json_result;
END;
$$ LANGUAGE plpgsql;
CREATE OR REPLACE FUNCTION cdb_observatory.OBS_GetMeasure(
geom GEOMETRY,
measure_id TEXT,
normalize TEXT DEFAULT 'area', -- TODO denominator, none
boundary_id TEXT DEFAULT NULL,
time_span TEXT DEFAULT NULL
)
RETURNS JSON
AS $$
DECLARE
result json;
BEGIN
IF boundary_id IS NULL THEN
-- TODO we should determine best boundary for this geom
boundary_id := '"us.census.tiger".block_group';
END IF;
IF time_span IS NULL THEN
-- TODO we should determine latest timespan for this measure
time_span := '2009 - 2013';
END IF;
EXECUTE '
SELECT * FROM cdb_observatory._OBS_Get($1, ARRAY[$2], $3, $4) LIMIT 1
'
INTO result
USING geom, measure_id, time_span, boundary_id;
RETURN result;
END;
$$ LANGUAGE plpgsql;
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetPolygons(
geom geometry,
geom_table_name text,
data_table_info json[]
)
RETURNS json[]
AS $$
DECLARE
result numeric[];
json_result json[];
q_select text;
q_sum text;
q text;
i NUMERIC;
BEGIN
q_select := 'SELECT geoid, ';
q_sum := 'SELECT Array[';
FOR i IN 1..array_upper(data_table_info, 1)
LOOP
q_select := q_select || format( '%I ', ((data_table_info)[i])->>'colname');
IF ((data_table_info)[i])->>'aggregate' ='sum'
THEN
q_sum := q_sum || format('sum(overlap_fraction * COALESCE(%I, 0)) ',((data_table_info)[i])->>'colname',((data_table_info)[i])->>'colname');
ELSE
q_sum := q_sum || ' NULL::numeric ';
END IF;
IF i < array_upper(data_table_info,1)
THEN
q_select := q_select || format(',');
q_sum := q_sum || format(',');
END IF;
END LOOP;
q = format('
WITH _overlaps As (
SELECT ST_Area(
ST_Intersection($1, a.the_geom)
) / ST_Area(a.the_geom) As overlap_fraction,
geoid
FROM observatory.%I As a
WHERE $1 && a.the_geom
),
values As (
', geom_table_name);
q := q || q_select || format('FROM observatory.%I ', ((data_table_info)[1]->>'tablename'));
q := q || ' ) ' || q_sum || ' ]::numeric[] FROM _overlaps, values
WHERE values.geoid = _overlaps.geoid';
EXECUTE
q
INTO result
USING geom;
EXECUTE
$query$
select array_agg(row_to_json(t)) from(
select values as value,
meta->>'name' as name,
meta->>'tablename' as tablename,
meta->>'aggregate' as aggregate,
meta->>'type' as type,
meta->>'description' as description
from (select unnest($1) as values, unnest($2) as meta) b
) t
$query$
INTO json_result
USING result, data_table_info;
RETURN json_result;
END;
$$ LANGUAGE plpgsql;
CREATE OR REPLACE FUNCTION cdb_observatory.OBS_GetSegmentSnapshot(
geom geometry,
geometry_level text DEFAULT '"us.census.tiger".census_tract'
)
RETURNS JSON
AS $$
DECLARE
target_cols text[];
result json;
seg_name Text;
geom_id Text;
q Text;
segment_name Text;
BEGIN
target_cols := Array[
'"us.census.acs".B01001001_quantile',
'"us.census.acs".B01001002_quantile',
'"us.census.acs".B01001026_quantile',
'"us.census.acs".B01002001_quantile',
'"us.census.acs".B03002003_quantile',
'"us.census.acs".B03002004_quantile',
'"us.census.acs".B03002006_quantile',
'"us.census.acs".B03002012_quantile',
'"us.census.acs".B05001006_quantile',--
'"us.census.acs".B08006001_quantile',--
'"us.census.acs".B08006002_quantile',--
'"us.census.acs".B08006008_quantile',--
'"us.census.acs".B08006009_quantile',--
'"us.census.acs".B08006011_quantile',--
'"us.census.acs".B08006015_quantile',--
'"us.census.acs".B08006017_quantile',--
'"us.census.acs".B09001001_quantile',--
'"us.census.acs".B11001001_quantile',
'"us.census.acs".B14001001_quantile',--
'"us.census.acs".B14001002_quantile',--
'"us.census.acs".B14001005_quantile',--
'"us.census.acs".B14001006_quantile',--
'"us.census.acs".B14001007_quantile',--
'"us.census.acs".B14001008_quantile',--
'"us.census.acs".B15003001_quantile',
'"us.census.acs".B15003017_quantile',
'"us.census.acs".B15003022_quantile',
'"us.census.acs".B15003023_quantile',
'"us.census.acs".B16001001_quantile',--
'"us.census.acs".B16001002_quantile',--
'"us.census.acs".B16001003_quantile',--
'"us.census.acs".B17001001_quantile',--
'"us.census.acs".B17001002_quantile',--
'"us.census.acs".B19013001_quantile',
'"us.census.acs".B19083001_quantile',
'"us.census.acs".B19301001_quantile',
'"us.census.acs".B25001001_quantile',
'"us.census.acs".B25002003_quantile',
'"us.census.acs".B25004002_quantile',
'"us.census.acs".B25004004_quantile',
'"us.census.acs".B25058001_quantile',
'"us.census.acs".B25071001_quantile',
'"us.census.acs".B25075001_quantile',
'"us.census.acs".B25075025_quantile'
];
EXECUTE
$query$
SELECT (_OBS_GetCategories)->>'name'
FROM cdb_observatory._OBS_GetCategories(
$1,
Array['"us.census.spielman_singleton_segments".X10'],
$2)
LIMIT 1
$query$
INTO segment_name
USING geom, geometry_level;
q :=
format($query$
WITH a As (
SELECT
array_agg(_OBS_GET->>'name') As names,
array_agg(_OBS_GET->>'value') As vals
FROM cdb_observatory._OBS_Get($1,
$2,
'2009 - 2013',
$3)
), percentiles As (
%s
FROM a)
SELECT row_to_json(r) FROM
( SELECT $4 as segment_name, percentiles.*
FROM percentiles) r
$query$, cdb_observatory._OBS_BuildSnapshotQuery(target_cols)) results;
EXECUTE
q
into result
USING geom, target_cols, geometry_level, segment_name;
return result;
END;
$$ LANGUAGE plpgsql;
--Get categorical variables from point
CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetCategories(
geom geometry,
dimension_names text[],
geometry_level text DEFAULT '"us.census.tiger".block_group',
time_span text DEFAULT '2009 - 2013'
)
RETURNS SETOF JSON as $$
DECLARE
geom_table_name text;
geoid text;
names text[];
results text[];
query text;
data_table_info json[];
BEGIN
geom_table_name := cdb_observatory._OBS_GeomTable(geom, geometry_level);
IF geom_table_name IS NULL
THEN
RAISE NOTICE 'Point % is outside of the data region', ST_AsText(geom);
RETURN QUERY SELECT '{}'::text[], '{}'::text[];
END IF;
execute'
select array_agg( _obs_getcolumndata) from cdb_observatory._OBS_GetColumnData($1,
$2,
$3);'
INTO data_table_info
using geometry_level, dimension_names, time_span;
EXECUTE
format('SELECT geoid
FROM observatory.%I
WHERE the_geom && $1',
geom_table_name)
USING geom
INTO geoid;
query := 'SELECT ARRAY[';
FOR i IN 1..array_upper(data_table_info, 1)
LOOP
query = query || format('%I ', lower(((data_table_info)[i])->>'colname'));
IF i < array_upper(data_table_info, 1)
THEN
query := query || ',';
END IF;
END LOOP;
query := query || format(' ]::text[]
FROM observatory.%I
WHERE %I.geoid = %L
',
((data_table_info)[1])->>'tablename',
((data_table_info)[1])->>'tablename',
geoid
);
EXECUTE
query
INTO results
USING geom;
RETURN QUERY
EXECUTE
$query$
select row_to_json(t) from(
select categories as category,
meta->>'name' as name,
meta->>'tablename' as tablename,
meta->>'aggregate' as aggregate,
meta->>'type' as type,
meta->>'description' as description
from (select unnest($1) as categories, unnest($2) as meta) b
) t
$query$
USING results, data_table_info;
RETURN;
END;
$$ LANGUAGE plpgsql;