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Merge pull request #131 from CartoDB/develop

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Release 0.4.1
This commit is contained in:
Carla
2016-09-21 17:37:32 +02:00
committed by GitHub
parent 2261d11de0 d216b02928
commit ff0363894f
8 changed files with 55 additions and 54 deletions
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9
doc/08_interpolation.md
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@@ -2,7 +2,7 @@
Function to interpolate a numeric attribute of a point in a scatter dataset of points, using one of three methos:
* [Nearest neighbor](https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
* [Nearest neighbor(s)](https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
* [Barycentric](https://en.wikipedia.org/wiki/Barycentric_coordinate_system)
* [IDW](https://en.wikipedia.org/wiki/Inverse_distance_weighting)
@@ -15,7 +15,7 @@ Function to interpolate a numeric attribute of a point in a scatter dataset of p
| query | text | query that returns at least `the_geom` and a numeric value as `attrib` |
| point | geometry | The target point to calc the value |
| method | integer | 0:nearest neighbor, 1: barycentric, 2: IDW|
| p1 | integer | IDW: limit the number of neighbors, 0->no limit|
| p1 | integer | limit the number of neighbors, IDW: 0->no limit, NN: 0-> closest one|
| p2 | integer | IDW: order of distance decay, 0-> order 1|
### CDB_SpatialInterpolation (geom geometry[], values numeric[], point geometry, method integer DEFAULT 1, p1 integer DEFAULT 0, ps integer DEFAULT 0)
@@ -28,7 +28,7 @@ Function to interpolate a numeric attribute of a point in a scatter dataset of p
| values | numeric[] | Array of points' values for the param under study|
| point | geometry | The target point to calc the value |
| method | integer | 0:nearest neighbor, 1: barycentric, 2: IDW|
| p1 | integer | IDW: limit the number of neighbors, 0->no limit|
| p1 | integer | limit the number of neighbors, IDW: 0->no limit, NN: 0-> closest one|
| p2 | integer | IDW: order of distance decay, 0-> order 1|
### Returns
@@ -37,6 +37,9 @@ Function to interpolate a numeric attribute of a point in a scatter dataset of p
|-------------|------|-------------|
| value | numeric | Interpolated value at the given point, `-888.888` if the given point is out of the boundaries of the source points set |
Default values:
* -888.888: when using Barycentric, the target point is out of the realm of the input points
* -777.777: asking for a method not available
#### Example Usage

2
doc/19_contour.md
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@@ -18,7 +18,7 @@ Function to generate a contour map from an scatter dataset of points, using one
| method | integer | 0:nearest neighbor, 1: barycentric, 2: IDW|
| classmethod | integer | 0:equals, 1: heads&tails, 2:jenks, 3:quantiles |
| steps | integer | Number of steps in the classification|
| max_time | integer | Max time in milliseconds for processing time
| max_time | integer | if <= 0: max processing time in seconds (smart resolution) , if >0: resolution in meters
### Returns
Returns a table object

24
src/pg/sql/08_interpolation.sql
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@@ -1,6 +1,8 @@
-- 0: nearest neighbor
-- 0: nearest neighbor(s)
-- 1: barymetric
-- 2: IDW
-- 3: krigin ---> TO DO
CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation(
IN query text,
@@ -50,12 +52,19 @@ DECLARE
vc numeric;
output numeric;
BEGIN
output := -999.999;
-- nearest
-- output := -999.999;
-- nearest neighbors
-- p1: limit the number of neighbors, 0-> closest one
IF method = 0 THEN
WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v)
SELECT a.v INTO output FROM a ORDER BY point<->a.g LIMIT 1;
IF p1 = 0 THEN
p1 := 1;
END IF;
WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v),
b as (SELECT a.v as v FROM a ORDER BY point<->a.g LIMIT p1::integer)
SELECT avg(b.v) INTO output FROM b;
RETURN output;
-- barymetric
@@ -121,6 +130,11 @@ BEGIN
SELECT sum(b.f)/sum(b.k) INTO output FROM b;
RETURN output;
-- krigin
ELSIF method = 3 THEN
-- TO DO
END IF;
RETURN -777.777;

27
src/pg/sql/19_contour.sql
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@@ -17,16 +17,15 @@ RETURNS TABLE(
DECLARE
cell_count integer;
tin geometry[];
resolution integer;
BEGIN
-- calc the cell size in web mercator units
-- WITH center as (
-- SELECT ST_centroid(ST_Collect(geomin)) as c
-- )
-- SELECT
-- round(resolution / cos(ST_y(c) * pi()/180))
-- INTO cell
-- FROM center;
-- raise notice 'Resol: %', cell;
-- nasty trick to override issue #121
IF max_time = 0 THEN
max_time = -90;
END IF;
resolution := max_time;
max_time := -1 * resolution;
-- calc the optimal number of cells for the current dataset
SELECT
@@ -70,9 +69,13 @@ BEGIN
),
resolution as(
SELECT
round(|/ (
ST_area(geom) / cell_count
)) as cell
CASE WHEN resolution <= 0 THEN
round(|/ (
ST_area(geom) / abs(cell_count)
))
ELSE
resolution
END AS cell
FROM envelope3857
),
grid as(

6
src/pg/test/expected/08_interpolation_test.out
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@@ -1,7 +1,7 @@
SET client_min_messages TO WARNING;
\set ECHO none
nn | nni | idw
-----+--------------------------+-----------------
200 | 238.41059602632179224595 | 341.46260750526
nn | nni | idw
----------------------+--------------------------+-----------------
200.0000000000000000 | 238.41059602632179224595 | 341.46260750526
(1 row)

2
src/pg/test/sql/19_contour_test.sql
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@@ -12,6 +12,6 @@ SELECT
foo.*
FROM
a,
cdb_crankshaft.CDB_contour(a.g, a.vals, 0.0, 1, 3, 5, 60) foo
cdb_crankshaft.CDB_contour(a.g, a.vals, 0.0, 1, 3, 5, -60) foo
)
SELECT bin, avg_value from b order by bin;

35
src/py/crankshaft/crankshaft/clustering/moran.py
View File

@@ -30,18 +30,13 @@ def moran(subquery, attr_name,
query = pu.construct_neighbor_query(w_type, qvals)
plpy.notice('** Query: %s' % query)
try:
result = plpy.execute(query)
# if there are no neighbors, exit
if len(result) == 0:
return pu.empty_zipped_array(2)
plpy.notice('** Query returned with %d rows' % len(result))
except plpy.SPIError:
plpy.error('Error: areas of interest query failed, check input parameters')
plpy.notice('** Query failed: "%s"' % query)
plpy.notice('** Error: %s' % plpy.SPIError)
except plpy.SPIError, e:
plpy.error('Analysis failed: %s' % e)
return pu.empty_zipped_array(2)
## collect attributes
@@ -79,9 +74,8 @@ def moran_local(subquery, attr,
# if there are no neighbors, exit
if len(result) == 0:
return pu.empty_zipped_array(5)
except plpy.SPIError:
plpy.error('Error: areas of interest query failed, check input parameters')
plpy.notice('** Query failed: "%s"' % query)
except plpy.SPIError, e:
plpy.error('Analysis failed: %s' % e)
return pu.empty_zipped_array(5)
attr_vals = pu.get_attributes(result)
@@ -111,18 +105,13 @@ def moran_rate(subquery, numerator, denominator,
query = pu.construct_neighbor_query(w_type, qvals)
plpy.notice('** Query: %s' % query)
try:
result = plpy.execute(query)
# if there are no neighbors, exit
if len(result) == 0:
return pu.empty_zipped_array(2)
plpy.notice('** Query returned with %d rows' % len(result))
except plpy.SPIError:
plpy.error('Error: areas of interest query failed, check input parameters')
plpy.notice('** Query failed: "%s"' % query)
plpy.notice('** Error: %s' % plpy.SPIError)
except plpy.SPIError, e:
plpy.error('Analysis failed: %s' % e)
return pu.empty_zipped_array(2)
## collect attributes
@@ -160,10 +149,8 @@ def moran_local_rate(subquery, numerator, denominator,
# if there are no neighbors, exit
if len(result) == 0:
return pu.empty_zipped_array(5)
except plpy.SPIError:
plpy.error('Error: areas of interest query failed, check input parameters')
plpy.notice('** Query failed: "%s"' % query)
plpy.notice('** Error: %s' % plpy.SPIError)
except plpy.SPIError, e:
plpy.error('Analysis failed: %s' % e)
return pu.empty_zipped_array(5)
## collect attributes
@@ -186,7 +173,6 @@ def moran_local_bv(subquery, attr1, attr2,
"""
Moran's I (local) Bivariate (untested)
"""
plpy.notice('** Constructing query')
qvals = OrderedDict([("id_col", id_col),
("attr1", attr1),
@@ -205,7 +191,6 @@ def moran_local_bv(subquery, attr1, attr2,
except plpy.SPIError:
plpy.error("Error: areas of interest query failed, " \
"check input parameters")
plpy.notice('** Query failed: "%s"' % query)
return pu.empty_zipped_array(4)
## collect attributes
@@ -219,13 +204,9 @@ def moran_local_bv(subquery, attr1, attr2,
lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
permutations=permutations)
plpy.notice("len of Is: %d" % len(lisa.Is))
# find clustering of significance
lisa_sig = quad_position(lisa.q)
plpy.notice('** Finished calculations')
return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
# Low level functions ----------------------------------------

4
src/py/crankshaft/crankshaft/space_time_dynamics/markov.py
View File

@@ -56,9 +56,9 @@ def spatial_markov_trend(subquery, time_cols, num_classes=7,
)
if len(query_result) == 0:
return zip([None], [None], [None], [None], [None])
except plpy.SPIError, err:
except plpy.SPIError, e:
plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals)))
plpy.error('Query failed, check the input parameters')
plpy.error('Analysis failed: %s' % e)
return zip([None], [None], [None], [None], [None])
## build weight