Allow for target_geoms and target_area override on column-by-column basis
This commit is contained in:
John Krauss
@@ -126,9 +126,23 @@ BEGIN
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geom_filters := (SELECT Array_Agg(val) FILTER (WHERE val IS NOT NULL) FROM (SELECT (JSON_Array_Elements(params))->>'geom_id' val) bar);
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meta_filter_clause := '(m.numer_id = ANY ($6) OR m.geom_id = ANY ($7))';
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scores_clause := 'SELECT *
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FROM cdb_observatory._OBS_GetGeometryScores($1,
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(SELECT Array_Agg(geom_id) FROM meta), $2) scores ';
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scores_clause := ' agg_geoms AS (
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SELECT target_geoms, target_area, ARRAY_AGG(geom_id) geom_ids
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FROM meta
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GROUP BY target_geoms, target_area
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), scores AS (
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SELECT target_geoms, target_area,
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CASE target_area
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-- point-specific, just order by numgeoms instead of score
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WHEN 0 THEN scores.numgeoms
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-- has some area, use proper scoring
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ELSE scores.score
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END AS score,
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scores.numgeoms, scores.table_id, scores.column_id
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FROM agg_geoms,
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LATERAL cdb_observatory._OBS_GetGeometryScores($1,
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geom_ids, COALESCE(target_geoms, $2), target_area) scores
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) ';
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IF JSON_Array_Length(params) = 1 THEN
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IF numer_filters IS NULL AND geom_filters IS NOT NULL THEN
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@@ -142,9 +156,11 @@ BEGIN
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END IF;
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IF geom_filters IS NOT NULL AND numer_filters IS NOT NULL THEN
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scores_clause := 'SELECT 1 score, null, geom_tid table_id, geom_id column_id,
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null, null, null, null, null, null
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FROM meta ';
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scores_clause := 'scores AS (
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SELECT NULL::INTEGER target_geoms, NULL::Numeric target_area,
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1 score, null, geom_tid table_id, geom_id column_id,
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NULL::Integer numgeoms
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FROM meta) ';
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END IF;
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END IF;
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@@ -156,7 +172,11 @@ BEGIN
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(unnest($3))->>'geom_id' geom_id,
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(unnest($3))->>'numer_timespan' numer_timespan,
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(unnest($3))->>'geom_timespan' geom_timespan,
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(unnest($3))->>'normalization' normalization
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(unnest($3))->>'normalization' normalization,
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(unnest($3))->>'max_timespan_rank' max_timespan_rank,
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(unnest($3))->>'max_score_rank' max_score_rank,
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((unnest($3))->>'target_geoms')::INTEGER target_geoms,
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((unnest($3))->>'target_area')::Numeric target_area
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), meta AS (SELECT
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id,
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f.numer_id,
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@@ -166,6 +186,8 @@ BEGIN
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE numer_tablename END numer_tablename,
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE numer_type END numer_type,
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE numer_name END numer_name,
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE numer_description END numer_description,
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE numer_t_description END numer_t_description,
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE m.numer_timespan END numer_timespan,
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE m.denom_id END denom_id,
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE denom_aggregate END denom_aggregate,
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@@ -173,6 +195,8 @@ BEGIN
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE denom_geomref_colname END denom_geomref_colname,
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE denom_tablename END denom_tablename,
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE denom_name END denom_name,
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE denom_description END denom_description,
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE denom_t_description END denom_t_description,
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE denom_type END denom_type,
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CASE WHEN f.numer_id IS NULL THEN NULL ELSE denom_reltype END denom_reltype,
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m.geom_id,
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@@ -182,8 +206,14 @@ BEGIN
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geom_geomref_colname,
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geom_tablename,
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geom_name,
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geom_description,
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geom_t_description,
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geom_type,
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normalization
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normalization,
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max_timespan_rank,
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max_score_rank,
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target_geoms,
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target_area
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FROM observatory.obs_meta m JOIN _filters f
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ON CASE WHEN f.numer_id IS NULL THEN m.geom_id ELSE m.numer_id END =
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CASE WHEN f.numer_id IS NULL THEN f.geom_id ELSE f.numer_id END
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@@ -194,9 +224,8 @@ BEGIN
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AND (m.geom_id = f.geom_id OR COALESCE(f.geom_id, '') = '')
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AND (m.geom_timespan = f.geom_timespan OR COALESCE(f.geom_timespan, '') = '')
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AND (m.numer_timespan = f.numer_timespan OR COALESCE(f.numer_timespan, '') = '')
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), scores AS (
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%s
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), groups AS (SELECT
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), %s
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, groups AS (SELECT
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id,
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scores.score,
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numer_timespan,
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@@ -213,39 +242,46 @@ BEGIN
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'numer_geomref_colname', cdb_observatory.FIRST(meta.numer_geomref_colname),
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'numer_tablename', cdb_observatory.FIRST(meta.numer_tablename),
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'numer_type', cdb_observatory.FIRST(meta.numer_type),
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--'numer_description', cdb_observatory.FIRST(meta.numer_description),
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--'numer_t_description', cdb_observatory.FIRST(meta.numer_t_description),
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'numer_description', cdb_observatory.FIRST(meta.numer_description),
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'numer_t_description', cdb_observatory.FIRST(meta.numer_t_description),
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'denom_aggregate', cdb_observatory.FIRST(meta.denom_aggregate),
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'denom_colname', cdb_observatory.FIRST(denom_colname),
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'denom_geomref_colname', cdb_observatory.FIRST(denom_geomref_colname),
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'denom_tablename', cdb_observatory.FIRST(denom_tablename),
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'denom_type', cdb_observatory.FIRST(meta.denom_type),
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'denom_reltype', cdb_observatory.FIRST(meta.denom_reltype),
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--'denom_description', cdb_observatory.FIRST(meta.denom_description),
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--'denom_t_description', cdb_observatory.FIRST(meta.denom_t_description),
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'denom_description', cdb_observatory.FIRST(meta.denom_description),
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'denom_t_description', cdb_observatory.FIRST(meta.denom_t_description),
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'geom_colname', cdb_observatory.FIRST(geom_colname),
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'geom_geomref_colname', cdb_observatory.FIRST(geom_geomref_colname),
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'geom_tablename', cdb_observatory.FIRST(geom_tablename),
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'geom_type', cdb_observatory.FIRST(meta.geom_type),
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'geom_timespan', cdb_observatory.FIRST(meta.geom_timespan),
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--'geom_description', cdb_observatory.FIRST(meta.geom_description),
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--'geom_t_description', cdb_observatory.FIRST(meta.geom_t_description),
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'geom_description', cdb_observatory.FIRST(meta.geom_description),
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'geom_t_description', cdb_observatory.FIRST(meta.geom_t_description),
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'numer_timespan', cdb_observatory.FIRST(numer_timespan),
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'numer_name', cdb_observatory.FIRST(numer_name),
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'denom_name', cdb_observatory.FIRST(denom_name),
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'geom_name', cdb_observatory.FIRST(geom_name),
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'normalization', cdb_observatory.FIRST(normalization),
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'max_timespan_rank', cdb_observatory.FIRST(max_timespan_rank),
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'max_score_rank', cdb_observatory.FIRST(max_score_rank),
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'target_geoms', cdb_observatory.FIRST(scores.target_geoms),
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'target_area', cdb_observatory.FIRST(scores.target_area),
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'num_geoms', cdb_observatory.FIRST(scores.numgeoms),
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'denom_id', denom_id,
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'geom_id', meta.geom_id
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) metadata
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FROM meta, scores
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WHERE meta.geom_id = scores.column_id
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AND meta.geom_tid = scores.table_id
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AND COALESCE(meta.target_geoms, 0) = COALESCE(scores.target_geoms, 0)
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AND COALESCE(meta.target_area, 0) = COALESCE(scores.target_area, 0)
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GROUP BY id, score, numer_id, denom_id, geom_id, numer_timespan
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) SELECT JSON_AGG(metadata ORDER BY id)
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FROM groups
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WHERE timespan_rank <= $4
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AND score_rank <= $5
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WHERE timespan_rank <= Coalesce((metadata->>'max_timespan_rank')::INTEGER, $4)
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AND score_rank <= Coalesce((metadata->>'max_score_rank')::INTEGER, $5)
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$string$, meta_filter_clause, scores_clause)
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INTO result
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USING
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@@ -772,8 +808,8 @@ BEGIN
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RETURN QUERY EXECUTE format($query$
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WITH _raw_geoms AS (%s),
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_geoms AS (SELECT id,
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CASE WHEN (ST_NPoints(geom) > 500)
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THEN ST_CollectionExtract(ST_MakeValid(ST_SimplifyVW(geom, 0.0001)), 3)
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CASE WHEN (ST_NPoints(geom) > 1000)
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THEN ST_CollectionExtract(ST_MakeValid(ST_SimplifyVW(geom, 0.00001)), 3)
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ELSE geom END geom
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FROM _raw_geoms),
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_procgeoms AS (SELECT _geoms.id, _geoms.geom %s %s
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@@ -418,7 +418,8 @@ $$ LANGUAGE plpgsql;
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CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetGeometryScores(
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bounds Geometry(Geometry, 4326) DEFAULT NULL,
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filter_geom_ids TEXT[] DEFAULT NULL,
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desired_num_geoms INTEGER DEFAULT NULL
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desired_num_geoms INTEGER DEFAULT NULL,
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desired_area NUMERIC DEFAULT NULL
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) RETURNS TABLE (
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score NUMERIC,
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numtiles BIGINT,
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@@ -430,6 +431,8 @@ CREATE OR REPLACE FUNCTION cdb_observatory._OBS_GetGeometryScores(
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estnumgeoms NUMERIC,
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meanmediansize NUMERIC
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) AS $$
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DECLARE
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num_geoms_multiplier Numeric;
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BEGIN
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IF desired_num_geoms IS NULL THEN
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desired_num_geoms := 3000;
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@@ -440,6 +443,18 @@ BEGIN
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IF ST_Npoints(bounds) > 10000 THEN
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bounds := ST_Envelope(bounds);
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END IF;
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IF desired_area IS NULL THEN
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desired_area := ST_Area(bounds);
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END IF;
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-- In case of points, desired_area will be 0. We still want an accurate
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-- estimate of numgeoms in that case.
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IF desired_area = 0 THEN
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num_geoms_multiplier := 1;
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ELSE
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num_geoms_multiplier := Coalesce(desired_area / Nullif(ST_Area(bounds), 0), 1);
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END IF;
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RETURN QUERY
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EXECUTE $string$
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WITH clipped_geom AS (
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@@ -453,13 +468,11 @@ BEGIN
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), clipped_geom_countagg AS (
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SELECT column_id, table_id
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, BOOL_AND(ST_BandIsNoData(clipped_tile, 1)) nodata
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, ST_CountAgg(clipped_tile, 1, False)::Numeric pixels -- -10
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FROM clipped_geom
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GROUP BY column_id, table_id
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), clipped_geom_reagg AS (
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SELECT COUNT(*)::BIGINT cnt, a.column_id, a.table_id,
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cdb_observatory.FIRST(nodata) first_nodata,
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cdb_observatory.FIRST(pixels) first_pixel,
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cdb_observatory.FIRST(tile) first_tile,
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(ST_SummaryStatsAgg(clipped_tile, 1, False)).sum::Numeric sum_geoms, -- ND
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(ST_SummaryStatsAgg(clipped_tile, 2, False)).mean::Numeric / 255 mean_fill --ND
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@@ -474,9 +487,8 @@ BEGIN
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, (CASE WHEN first_nodata IS FALSE
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THEN sum_geoms
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ELSE COALESCE(ST_Value(first_tile, 1, ST_PointOnSurface($1)), 0)
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* (ST_Area($1) / ST_Area(ST_PixelAsPolygon(first_tile, 0, 0))
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* first_pixel) -- -20
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END)::Numeric
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* (ST_Area($1) / ST_Area(ST_PixelAsPolygon(first_tile, 0, 0)))
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END)::Numeric * $4
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AS numgeoms
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, (CASE WHEN first_nodata IS FALSE
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THEN mean_fill
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@@ -490,7 +502,7 @@ BEGIN
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((100.0 / (1+abs(log(0.0001 + $3) - log(0.0001 + numgeoms::Numeric)))) * percentfill)::Numeric
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AS score, *
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FROM final
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$string$ USING bounds, filter_geom_ids, desired_num_geoms;
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$string$ USING bounds, filter_geom_ids, desired_num_geoms, num_geoms_multiplier;
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RETURN;
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END
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$$ LANGUAGE plpgsql IMMUTABLE;
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