Merge branch 'develop' into pysal_gwr
This commit is contained in:
Andy Eschbacher
@@ -1,5 +1,5 @@
|
||||
comment = 'CartoDB Spatial Analysis extension'
|
||||
default_version = '0.4.2'
|
||||
default_version = '0.5.1'
|
||||
requires = 'plpythonu, postgis'
|
||||
superuser = true
|
||||
schema = cdb_crankshaft
|
||||
|
||||
@@ -10,9 +10,11 @@ CREATE OR REPLACE FUNCTION
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS TABLE (moran NUMERIC, significance NUMERIC)
|
||||
AS $$
|
||||
from crankshaft.clustering import moran
|
||||
from crankshaft.clustering import Moran
|
||||
# TODO: use named parameters or a dictionary
|
||||
return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
moran = Moran()
|
||||
return moran.global_stat(subquery, column_name, w_type,
|
||||
num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
|
||||
-- Moran's I Local (internal function)
|
||||
@@ -27,9 +29,11 @@ CREATE OR REPLACE FUNCTION
|
||||
id_col TEXT)
|
||||
RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
|
||||
AS $$
|
||||
from crankshaft.clustering import moran_local
|
||||
from crankshaft.clustering import Moran
|
||||
moran = Moran()
|
||||
# TODO: use named parameters or a dictionary
|
||||
return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
return moran.local_stat(subquery, column_name, w_type,
|
||||
num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
|
||||
-- Moran's I Local (public-facing function)
|
||||
@@ -120,9 +124,11 @@ CREATE OR REPLACE FUNCTION
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS TABLE (moran FLOAT, significance FLOAT)
|
||||
AS $$
|
||||
from crankshaft.clustering import moran_local
|
||||
from crankshaft.clustering import Moran
|
||||
moran = Moran()
|
||||
# TODO: use named parameters or a dictionary
|
||||
return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
return moran.global_rate_stat(subquery, numerator, denominator, w_type,
|
||||
num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
|
||||
|
||||
@@ -140,9 +146,10 @@ CREATE OR REPLACE FUNCTION
|
||||
RETURNS
|
||||
TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
|
||||
AS $$
|
||||
from crankshaft.clustering import moran_local_rate
|
||||
from crankshaft.clustering import Moran
|
||||
moran = Moran()
|
||||
# TODO: use named parameters or a dictionary
|
||||
return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
return moran.local_rate_stat(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
|
||||
-- Moran's I Local Rate (public-facing function)
|
||||
|
||||
@@ -1,21 +1,24 @@
|
||||
CREATE OR REPLACE FUNCTION CDB_KMeans(query text, no_clusters integer,no_init integer default 20)
|
||||
RETURNS table (cartodb_id integer, cluster_no integer) as $$
|
||||
|
||||
from crankshaft.clustering import kmeans
|
||||
return kmeans(query,no_clusters,no_init)
|
||||
-- Spatial k-means clustering
|
||||
|
||||
$$ language plpythonu;
|
||||
CREATE OR REPLACE FUNCTION CDB_KMeans(query text, no_clusters integer, no_init integer default 20)
|
||||
RETURNS table (cartodb_id integer, cluster_no integer) as $$
|
||||
|
||||
from crankshaft.clustering import Kmeans
|
||||
kmeans = Kmeans()
|
||||
return kmeans.spatial(query, no_clusters, no_init)
|
||||
|
||||
$$ LANGUAGE plpythonu;
|
||||
|
||||
|
||||
CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC)
|
||||
RETURNS Numeric[] AS
|
||||
RETURNS Numeric[] AS
|
||||
$$
|
||||
DECLARE
|
||||
DECLARE
|
||||
newX NUMERIC;
|
||||
newY NUMERIC;
|
||||
newW NUMERIC;
|
||||
BEGIN
|
||||
IF weight IS NULL OR the_geom IS NULL THEN
|
||||
IF weight IS NULL OR the_geom IS NULL THEN
|
||||
newX = state[1];
|
||||
newY = state[2];
|
||||
newW = state[3];
|
||||
@@ -30,12 +33,12 @@ END
|
||||
$$ LANGUAGE plpgsql;
|
||||
|
||||
CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[])
|
||||
RETURNS GEOMETRY AS
|
||||
RETURNS GEOMETRY AS
|
||||
$$
|
||||
BEGIN
|
||||
IF state[3] = 0 THEN
|
||||
IF state[3] = 0 THEN
|
||||
RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326);
|
||||
ELSE
|
||||
ELSE
|
||||
RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326);
|
||||
END IF;
|
||||
END
|
||||
@@ -56,7 +59,7 @@ BEGIN
|
||||
SFUNC = CDB_WeightedMeanS,
|
||||
FINALFUNC = CDB_WeightedMeanF,
|
||||
STYPE = Numeric[],
|
||||
INITCOND = "{0.0,0.0,0.0}"
|
||||
INITCOND = "{0.0,0.0,0.0}"
|
||||
);
|
||||
END IF;
|
||||
END
|
||||
|
||||
@@ -22,10 +22,11 @@ CREATE OR REPLACE FUNCTION
|
||||
RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT)
|
||||
AS $$
|
||||
|
||||
from crankshaft.space_time_dynamics import spatial_markov_trend
|
||||
from crankshaft.space_time_dynamics import Markov
|
||||
markov = Markov()
|
||||
|
||||
## TODO: use named parameters or a dictionary
|
||||
return spatial_markov_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
return markov.spatial_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
|
||||
-- input table format: identical to above but in a predictable format
|
||||
|
||||
19
src/pg/sql/16_getis.sql
Normal file
19
src/pg/sql/16_getis.sql
Normal file
@@ -0,0 +1,19 @@
|
||||
-- Getis-Ord's G
|
||||
-- Hotspot/Coldspot Analysis tool
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_GetisOrdsG(
|
||||
subquery TEXT,
|
||||
column_name TEXT,
|
||||
w_type TEXT DEFAULT 'knn',
|
||||
num_ngbrs INT DEFAULT 5,
|
||||
permutations INT DEFAULT 999,
|
||||
geom_col TEXT DEFAULT 'the_geom',
|
||||
id_col TEXT DEFAULT 'cartodb_id')
|
||||
RETURNS TABLE (z_score NUMERIC, p_value NUMERIC, p_z_sim NUMERIC, rowid BIGINT)
|
||||
AS $$
|
||||
from crankshaft.clustering import Getis
|
||||
getis = Getis()
|
||||
return getis.getis_ord(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
|
||||
-- TODO: make a version that accepts the values as arrays
|
||||
75
src/pg/sql/18_outliers.sql
Normal file
75
src/pg/sql/18_outliers.sql
Normal file
@@ -0,0 +1,75 @@
|
||||
|
||||
-- Find outliers using a static threshold
|
||||
--
|
||||
CREATE OR REPLACE FUNCTION CDB_StaticOutlier(column_value numeric, threshold numeric)
|
||||
RETURNS boolean
|
||||
AS $$
|
||||
BEGIN
|
||||
|
||||
RETURN column_value > threshold;
|
||||
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
|
||||
-- Find outliers by a percentage above the threshold
|
||||
-- TODO: add symmetric option? `is_symmetric boolean DEFAULT false`
|
||||
|
||||
CREATE OR REPLACE FUNCTION CDB_PercentOutlier(column_values numeric[], outlier_fraction numeric, ids int[])
|
||||
RETURNS TABLE(is_outlier boolean, rowid int)
|
||||
AS $$
|
||||
DECLARE
|
||||
avg_val numeric;
|
||||
out_vals boolean[];
|
||||
BEGIN
|
||||
|
||||
SELECT avg(i) INTO avg_val
|
||||
FROM unnest(column_values) As x(i);
|
||||
|
||||
IF avg_val = 0 THEN
|
||||
RAISE EXCEPTION 'Mean value is zero. Try another outlier method.';
|
||||
END IF;
|
||||
|
||||
SELECT array_agg(
|
||||
outlier_fraction < i / avg_val) INTO out_vals
|
||||
FROM unnest(column_values) As x(i);
|
||||
|
||||
RETURN QUERY
|
||||
SELECT unnest(out_vals) As is_outlier,
|
||||
unnest(ids) As rowid;
|
||||
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
|
||||
-- Find outliers above a given number of standard deviations from the mean
|
||||
|
||||
CREATE OR REPLACE FUNCTION CDB_StdDevOutlier(column_values numeric[], num_deviations numeric, ids int[], is_symmetric boolean DEFAULT true)
|
||||
RETURNS TABLE(is_outlier boolean, rowid int)
|
||||
AS $$
|
||||
DECLARE
|
||||
stddev_val numeric;
|
||||
avg_val numeric;
|
||||
out_vals boolean[];
|
||||
BEGIN
|
||||
|
||||
SELECT stddev(i), avg(i) INTO stddev_val, avg_val
|
||||
FROM unnest(column_values) As x(i);
|
||||
|
||||
IF stddev_val = 0 THEN
|
||||
RAISE EXCEPTION 'Standard deviation of input data is zero';
|
||||
END IF;
|
||||
|
||||
IF is_symmetric THEN
|
||||
SELECT array_agg(
|
||||
abs(i - avg_val) / stddev_val > num_deviations) INTO out_vals
|
||||
FROM unnest(column_values) As x(i);
|
||||
ELSE
|
||||
SELECT array_agg(
|
||||
(i - avg_val) / stddev_val > num_deviations) INTO out_vals
|
||||
FROM unnest(column_values) As x(i);
|
||||
END IF;
|
||||
|
||||
RETURN QUERY
|
||||
SELECT unnest(out_vals) As is_outlier,
|
||||
unnest(ids) As rowid;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
@@ -149,135 +149,135 @@ _cdb_random_seeds
|
||||
|
||||
(1 row)
|
||||
code|quads
|
||||
01|LL
|
||||
02|LH
|
||||
03|HH
|
||||
04|HH
|
||||
05|LL
|
||||
06|HH
|
||||
07|LL
|
||||
08|LL
|
||||
09|LL
|
||||
10|HH
|
||||
11|HH
|
||||
12|HL
|
||||
13|LL
|
||||
14|HH
|
||||
01|HH
|
||||
02|HL
|
||||
03|LL
|
||||
04|LL
|
||||
05|LH
|
||||
06|LL
|
||||
07|HH
|
||||
08|HH
|
||||
09|HH
|
||||
10|LL
|
||||
11|LL
|
||||
12|LL
|
||||
13|HL
|
||||
14|LL
|
||||
15|LL
|
||||
16|LL
|
||||
17|LL
|
||||
18|LH
|
||||
19|LL
|
||||
20|LL
|
||||
21|HH
|
||||
22|LL
|
||||
23|HL
|
||||
16|HH
|
||||
17|HH
|
||||
18|LL
|
||||
19|HH
|
||||
20|HH
|
||||
21|LL
|
||||
22|HH
|
||||
23|LL
|
||||
24|LL
|
||||
25|LL
|
||||
26|LL
|
||||
25|HH
|
||||
26|HH
|
||||
27|LL
|
||||
28|LL
|
||||
29|LH
|
||||
30|HH
|
||||
31|LL
|
||||
28|HH
|
||||
29|LL
|
||||
30|LL
|
||||
31|HH
|
||||
32|LL
|
||||
33|LL
|
||||
34|LL
|
||||
35|LH
|
||||
36|HL
|
||||
37|LH
|
||||
38|LH
|
||||
39|LL
|
||||
40|LL
|
||||
41|LH
|
||||
42|HL
|
||||
43|LL
|
||||
44|HL
|
||||
45|LL
|
||||
46|HL
|
||||
33|HL
|
||||
34|LH
|
||||
35|LL
|
||||
36|LL
|
||||
37|HL
|
||||
38|HL
|
||||
39|HH
|
||||
40|HH
|
||||
41|HL
|
||||
42|LH
|
||||
43|LH
|
||||
44|LL
|
||||
45|LH
|
||||
46|LL
|
||||
47|LL
|
||||
48|LL
|
||||
49|HL
|
||||
50|LL
|
||||
51|HH
|
||||
(51 rows)
|
||||
48|HH
|
||||
49|LH
|
||||
50|HH
|
||||
51|LL
|
||||
52|LL
|
||||
(52 rows)
|
||||
_cdb_random_seeds
|
||||
|
||||
(1 row)
|
||||
code|quads
|
||||
03|HH
|
||||
04|HH
|
||||
06|HH
|
||||
10|HH
|
||||
11|HH
|
||||
12|HL
|
||||
14|HH
|
||||
21|HH
|
||||
23|HL
|
||||
30|HH
|
||||
36|HL
|
||||
42|HL
|
||||
44|HL
|
||||
46|HL
|
||||
49|HL
|
||||
51|HH
|
||||
(16 rows)
|
||||
01|HH
|
||||
02|HL
|
||||
07|HH
|
||||
08|HH
|
||||
09|HH
|
||||
13|HL
|
||||
16|HH
|
||||
17|HH
|
||||
19|HH
|
||||
20|HH
|
||||
22|HH
|
||||
25|HH
|
||||
26|HH
|
||||
28|HH
|
||||
31|HH
|
||||
33|HL
|
||||
37|HL
|
||||
38|HL
|
||||
39|HH
|
||||
40|HH
|
||||
41|HL
|
||||
48|HH
|
||||
50|HH
|
||||
(23 rows)
|
||||
_cdb_random_seeds
|
||||
|
||||
(1 row)
|
||||
code|quads
|
||||
01|LL
|
||||
02|LH
|
||||
05|LL
|
||||
07|LL
|
||||
08|LL
|
||||
09|LL
|
||||
13|LL
|
||||
03|LL
|
||||
04|LL
|
||||
05|LH
|
||||
06|LL
|
||||
10|LL
|
||||
11|LL
|
||||
12|LL
|
||||
14|LL
|
||||
15|LL
|
||||
16|LL
|
||||
17|LL
|
||||
18|LH
|
||||
19|LL
|
||||
20|LL
|
||||
22|LL
|
||||
18|LL
|
||||
21|LL
|
||||
23|LL
|
||||
24|LL
|
||||
25|LL
|
||||
26|LL
|
||||
27|LL
|
||||
28|LL
|
||||
29|LH
|
||||
31|LL
|
||||
29|LL
|
||||
30|LL
|
||||
32|LL
|
||||
33|LL
|
||||
34|LL
|
||||
35|LH
|
||||
37|LH
|
||||
38|LH
|
||||
39|LL
|
||||
40|LL
|
||||
41|LH
|
||||
43|LL
|
||||
45|LL
|
||||
34|LH
|
||||
35|LL
|
||||
36|LL
|
||||
42|LH
|
||||
43|LH
|
||||
44|LL
|
||||
45|LH
|
||||
46|LL
|
||||
47|LL
|
||||
48|LL
|
||||
50|LL
|
||||
(35 rows)
|
||||
49|LH
|
||||
51|LL
|
||||
52|LL
|
||||
(29 rows)
|
||||
_cdb_random_seeds
|
||||
|
||||
(1 row)
|
||||
code|quads
|
||||
02|LH
|
||||
12|HL
|
||||
18|LH
|
||||
23|HL
|
||||
29|LH
|
||||
35|LH
|
||||
36|HL
|
||||
37|LH
|
||||
38|LH
|
||||
41|LH
|
||||
42|HL
|
||||
44|HL
|
||||
46|HL
|
||||
49|HL
|
||||
(14 rows)
|
||||
02|HL
|
||||
05|LH
|
||||
13|HL
|
||||
33|HL
|
||||
34|LH
|
||||
37|HL
|
||||
38|HL
|
||||
41|HL
|
||||
42|LH
|
||||
43|LH
|
||||
45|LH
|
||||
49|LH
|
||||
(12 rows)
|
||||
|
||||
21
src/pg/test/expected/16_getis_test.out
Normal file
21
src/pg/test/expected/16_getis_test.out
Normal file
@@ -0,0 +1,21 @@
|
||||
\pset format unaligned
|
||||
\set ECHO all
|
||||
\i test/fixtures/getis_data.sql
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
_cdb_random_seeds
|
||||
|
||||
(1 row)
|
||||
rowid|z_score|p_value
|
||||
9|-0.7862|0.0500
|
||||
22|-0.3955|0.0330
|
||||
33|2.7045|0.0050
|
||||
35|1.9524|0.0130
|
||||
36|-1.2056|0.0170
|
||||
37|3.4785|0.0020
|
||||
38|-1.4622|0.0020
|
||||
40|5.7098|0.0030
|
||||
46|3.4704|0.0120
|
||||
47|-0.9994|0.0320
|
||||
48|-1.3650|0.0340
|
||||
(11 rows)
|
||||
23
src/pg/test/expected/18_outliers_test.out
Normal file
23
src/pg/test/expected/18_outliers_test.out
Normal file
@@ -0,0 +1,23 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
is_outlier|rowid
|
||||
t|11
|
||||
t|16
|
||||
t|17
|
||||
(3 rows)
|
||||
is_outlier|rowid
|
||||
t|16
|
||||
t|17
|
||||
(2 rows)
|
||||
ERROR: Standard deviation of input data is zero
|
||||
is_outlier|rowid
|
||||
t|8
|
||||
t|11
|
||||
t|16
|
||||
(3 rows)
|
||||
is_outlier|rowid
|
||||
t|8
|
||||
t|9
|
||||
t|11
|
||||
t|16
|
||||
(4 rows)
|
||||
98
src/pg/test/fixtures/getis_data.sql
vendored
Normal file
98
src/pg/test/fixtures/getis_data.sql
vendored
Normal file
@@ -0,0 +1,98 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
|
||||
--
|
||||
-- Getis-Ord's G* test dataset, subsetted from PySAL examples:
|
||||
-- https://github.com/pysal/pysal/tree/952ea04029165048a774d9a1846cf86ad000c096/pysal/examples/stl
|
||||
--
|
||||
|
||||
|
||||
CREATE TABLE getis_data (
|
||||
cartodb_id integer,
|
||||
the_geom geometry(Geometry,4326),
|
||||
hr8893 numeric
|
||||
);
|
||||
|
||||
COPY getis_data (cartodb_id, the_geom, hr8893) FROM stdin;
|
||||
22 0106000020E61000000100000001030000000100000007000000000000E0B10056C0000000C0B8964340FFFFFFFF4C1756C00000002054964340000000A00F1E56C00000004072964340000000C02D1E56C0000000A0439B434000000060381E56C00000000036B04340000000E0E20056C0000000608CB04340000000E0B10056C0000000C0B8964340 10.8557430000000004
|
||||
32 0106000020E6100000010000000103000000010000000B000000FFFFFF1FC26656C0FFFFFFBFE25E4340000000A0D86656C0000000E0976F4340000000A03A6956C0000000C0966F434000000020526956C0000000E08A7F4340000000E0F26556C000000000C87F4340000000E0066656C0000000209C834340000000407F5056C0000000803C83434000000020635056C0000000E016814340000000A0F45056C0000000A0F980434000000060D25056C000000060FA5E4340FFFFFF1FC26656C0FFFFFFBFE25E4340 9.92424500000000087
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
29 0106000020E610000001000000010300000001000000080000000000002025FD55C0FFFFFF1F7F6D434000000080C61056C0000000A04C6D4340000000A0631756C000000000D56D4340000000C05E1756C0FFFFFF5F24754340FFFFFFFF4C1756C00000002054964340000000E0B10056C0000000C0B89643400000006029FD55C000000080C09643400000002025FD55C0FFFFFF1F7F6D4340 3.12759000000000009
|
||||
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
|
||||
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
|
||||
28 0106000020E61000000100000001030000000100000008000000000000C05E1756C0FFFFFF5F24754340000000C0DD2C56C0000000407A75434000000080DE3356C0000000406375434000000020D73356C000000060729B4340000000C02D1E56C0000000A0439B4340000000A00F1E56C00000004072964340FFFFFFFF4C1756C00000002054964340000000C05E1756C0FFFFFF5F24754340 1.57115800000000005
|
||||
36 0106000020E6100000010000000103000000010000000D00000000000000EE2C56C000000060424E434000000040F72C56C000000000486A4340000000C0DD2C56C0000000407A754340000000C05E1756C0FFFFFF5F24754340000000A0631756C000000000D56D434000000080C61056C0000000A04C6D4340000000C0BE1056C0000000A0065F4340000000407C1256C0FFFFFF7FA75E434000000000BD1156C000000020A954434000000040D01256C0000000605C524340000000404F1256C000000040734F434000000040011156C000000000AF4D434000000000EE2C56C000000060424E4340 0
|
||||
68 0106000020E61000000100000001030000000100000006000000000000809F2D56C00000002078CD4240000000E0F64256C0FFFFFFDF38CD424000000000CE4956C00000004053CD4240000000C0E94956C000000080CDEE424000000020682D56C0FFFFFF7F00EF4240000000809F2D56C00000002078CD4240 3.1461920000000001
|
||||
27 0106000020E6100000010000000103000000010000000D000000000000407F5056C0000000803C83434000000060615056C000000040A99B4340000000201F4956C000000000BE9B434000000020D73356C000000060729B434000000080DE3356C00000004063754340000000C0DD2C56C0000000407A75434000000040F72C56C000000000486A4340000000202B4956C000000080AC69434000000040454956C000000040E25E434000000060D25056C000000060FA5E4340000000A0F45056C0000000A0F980434000000020635056C0000000E016814340000000407F5056C0000000803C834340 1.5920399999999999
|
||||
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
|
||||
52 0106000020E61000000100000001030000000100000008000000000000E0562D56C0000000208910434000000020864856C00000000054104340FFFFFF9FB84956C0000000409110434000000080A84956C0000000A05B1B4340000000803D4956C0FFFFFF9FB63C434000000080062D56C0FFFFFF7FAE3C434000000060492D56C0000000603E214340000000E0562D56C00000002089104340 4.94153299999999973
|
||||
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
|
||||
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
|
||||
2 0106000020E6100000010000000103000000010000001000000000000080FABA56C0000000A079EC43400000004007BB56C00000000007E2434000000020E6D756C00000006071E14340000000006ED856C000000000E2E64340FFFFFF7FBFDC56C00000006078EE434000000000DDDC56C0000000A04FF1434000000080C7DB56C0000000206FF34340000000808BDB56C0000000C0FEF54340000000A09FDC56C0000000A018F94340000000C02FDF56C000000080BC004440000000A041E056C0000000008A084440FFFFFF3F08E156C0000000C038114440000000406BE056C0000000A0A8194440FFFFFFBFF5BA56C0000000E02A1944400000004014BB56C000000060E70D444000000080FABA56C0000000A079EC4340 2.25549199999999983
|
||||
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
|
||||
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
|
||||
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
|
||||
15 0106000020E6100000010000000103000000010000000B000000000000806CDC56C00000006020A9434000000080A4ED56C000000020E3A94340000000A050EE56C0FFFFFFDF8BAB434000000000C4ED56C00000000058CD4340000000A039EE56C0000000C062CD4340000000C041EE56C00000006068D44340000000E031EE56C000000080D5D74340000000A054D456C000000020CCD743400000000002CD56C000000080CDCC434000000040CDDD56C0000000407EBA4340000000806CDC56C00000006020A94340 1.9498120000000001
|
||||
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
|
||||
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
|
||||
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
|
||||
30 0106000020E6100000010000000103000000010000000D0000000000000033DB56C0000000804A5B43400000008099DF56C0000000601B5A4340000000E0EEE356C0000000A0E35643400000000004E656C00000004066574340000000A0D3E956C0000000202C5A4340000000A02EE956C0FFFFFF1F09884340000000A0D9E856C0FFFFFFDF03934340000000A0B8DA56C000000060E592434000000080F4D056C0000000605F9243400000000038D156C0000000A0817F434000000000A0D156C0000000A0FF6B4340000000E0C8DA56C000000080986C43400000000033DB56C0000000804A5B4340 4.41689600000000038
|
||||
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
|
||||
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
|
||||
14 0106000020E6100000010000000103000000010000000A000000000000E0E20056C0000000608CB0434000000060381E56C00000000036B0434000000040441E56C000000020C9B94340000000605A1E56C0000000E042D3434000000040BB0356C00000000030D4434000000060CF0356C000000080C9D74340FFFFFFBFFDFD55C0000000E02BD84340000000A0C5FD55C0000000400DBE434000000040E60056C0FFFFFF9F20BE4340000000E0E20056C0000000608CB04340 1.60801700000000003
|
||||
17 0106000020E61000000100000001030000000100000011000000000000A0B8DA56C000000060E5924340000000806CDC56C00000006020A9434000000040CDDD56C0000000407EBA43400000000002CD56C000000080CDCC434000000000FFC956C000000060BBC64340000000C0FDC556C0000000E0B3C34340000000E01EC456C000000080ABBC43400000006053C256C0FFFFFF7FE2B8434000000040AABC56C00000006046B34340000000806EB656C0000000A0DBAC4340000000C0E0B156C0000000A0FDA54340000000C03CAF56C000000040B89F4340000000A0DEAE56C0FFFFFF7FC49C4340000000E0F3CB56C0FFFFFF9F039D4340000000205CCC56C0000000805392434000000080F4D056C0000000605F924340000000A0B8DA56C000000060E5924340 4.17331800000000008
|
||||
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
|
||||
19 0106000020E61000000100000001030000000100000009000000000000A0B78956C0000000A0BD7F4340000000C0C08956C0000000000BA1434000000040E88956C00000004046C34340000000E0847B56C00000000075C34340000000C00D6D56C0000000A096C34340000000800F6D56C0000000A06CAD4340000000A07A6D56C0000000605DAD4340FFFFFF3FF56C56C000000020977F4340000000A0B78956C0000000A0BD7F4340 2.08513599999999988
|
||||
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
|
||||
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
|
||||
23 0106000020E6100000010000000103000000010000000B000000FFFFFF1F491457C000000060E99F4340FFFFFF5F1C1457C00000000075AC4340000000A050EE56C0FFFFFFDF8BAB434000000080A4ED56C000000020E3A94340000000806CDC56C00000006020A94340000000A0B8DA56C000000060E5924340000000A0D9E856C0FFFFFFDF03934340000000A02EE956C0FFFFFF1F0988434000000020290757C0FFFFFF3F92884340000000A0B00657C000000080FE9E4340FFFFFF1F491457C000000060E99F4340 4.21135400000000004
|
||||
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
|
||||
47 0106000020E61000000100000001030000000100000009000000FFFFFF9FD60956C0000000003521434000000020F21756C000000040DA20434000000060492D56C0000000603E21434000000080062D56C0FFFFFF7FAE3C434000000000EE2C56C000000060424E434000000040011156C000000000AF4D434000000040780956C0000000005A4D4340000000808A0956C000000080C1494340FFFFFF9FD60956C00000000035214340 0.969790999999999959
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
39 0106000020E61000000100000001030000000100000008000000000000A04E4956C0000000E03140434000000040454956C000000040E25E4340000000202B4956C000000080AC69434000000040F72C56C000000000486A434000000000EE2C56C000000060424E434000000080062D56C0FFFFFF7FAE3C4340000000803D4956C0FFFFFF9FB63C4340000000A04E4956C0000000E031404340 3.19004699999999985
|
||||
60 0106000020E6100000010000000103000000010000000A0000000000006098A956C0000000C019DE42400000006010C756C000000020A7DE4240000000A08DC656C000000000241A43400000002017B256C0000000209F1A434000000080FEAB56C0000000C0710E434000000080D3AB56C0FFFFFF5F2F0C434000000080FFA956C000000080EA0C43400000006018AA56C0000000E0FF0A434000000060E4A856C000000080440A43400000006098A956C0000000C019DE4240 3.26394699999999993
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
48 0106000020E6100000010000000103000000010000000E000000FFFFFF9FD60956C00000000035214340000000808A0956C000000080C1494340000000605FFD55C000000020A549434000000060C8FC55C0FFFFFF5FDF444340000000C0AAFD55C0000000808E3F434000000080F9FC55C000000060DA394340000000E068FD55C000000060D537434000000000E6FC55C0000000C05E364340000000E0A8FE55C0000000C056334340FFFFFFFFA6FE55C00000004061304340000000E0ACFD55C0000000E0D02C4340000000407CFD55C000000040D92543400000006042FF55C00000004040214340FFFFFF9FD60956C00000000035214340 0
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
56 0106000020E6100000010000000103000000010000000600000000000000181856C000000020FEF4424000000020572D56C0FFFFFF7F74F44240000000E0562D56C0000000208910434000000060492D56C0000000603E21434000000020F21756C000000040DA20434000000000181856C000000020FEF44240 3.90541099999999997
|
||||
57 0106000020E6100000010000000103000000010000001700000000000020488D56C000000040610B4340000000207D8256C0000000008611434000000020918256C0000000809A1C434000000060DE7956C0000000E08D1C434000000080B86D56C0FFFFFFFF081C434000000020396656C0000000A0C31B434000000000936656C0000000601DFA424000000040B46A56C0000000607CEB424000000060E56B56C0FFFFFFFF16EA4240000000403A6C56C00000008003E74240000000E09E6E56C0000000A0A5EB424000000080827656C000000020D9F34240FFFFFF5F1B7756C000000000E7F34240FFFFFFDF797756C00000000029F24240FFFFFF9FA27956C0000000A01DF0424000000020067C56C0000000C063F0424000000080A67E56C0000000A0B8F44240000000A0537D56C00000006058FB424000000020B18056C0000000A012FC4240000000E0AE8256C0FFFFFF5F21FF424000000040A38756C00000008021044340000000209F8856C0000000E0E706434000000020488D56C000000040610B4340 4.33283899999999988
|
||||
58 0106000020E6100000010000000103000000010000000B00000000000020864856C00000000054104340000000C0FA4856C0FFFFFF3FC70D434000000000E64756C000000060EA0B4340FFFFFFFFF54856C0000000E00F06434000000080504956C0000000C0DEFE4240000000E0744B56C0000000A07CF9424000000000936656C0000000601DFA424000000020396656C0000000A0C31B434000000080A84956C0000000A05B1B4340FFFFFF9FB84956C0000000409110434000000020864856C00000000054104340 3.8941110000000001
|
||||
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
|
||||
62 0106000020E6100000010000000103000000010000000C00000000000020572D56C0FFFFFF7F74F4424000000020682D56C0FFFFFF7F00EF4240000000C0E94956C000000080CDEE4240000000E0EC4956C0000000607AF94240000000E0744B56C0000000A07CF9424000000080504956C0000000C0DEFE4240FFFFFFFFF54856C0000000E00F06434000000000E64756C000000060EA0B4340000000C0FA4856C0FFFFFF3FC70D434000000020864856C00000000054104340000000E0562D56C0000000208910434000000020572D56C0FFFFFF7F74F44240 3.29576199999999986
|
||||
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
|
||||
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
|
||||
65 0106000020E6100000010000000103000000010000000B000000000000E0F40F57C0000000C0B5CD4240FFFFFF9FF10F57C0000000000DD34240000000403A1A57C0000000E038DB4240000000001E1A57C00000002044EE424000000040E21957C0000000A0A6024340FFFFFF1F090C57C0000000202E024340000000A0F60157C0000000C08101434000000080EA0157C0000000409BE4424000000080730157C0000000C094E34240000000A0000257C00000002054CD4240000000E0F40F57C0000000C0B5CD4240 1.61801799999999996
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
71 0106000020E6100000010000000103000000010000000900000000000060378E56C0000000A0EBA74240000000408CA356C0000000C0F4A74240000000E078A356C00000002057CC4240FFFFFF1FC4A256C00000000069CC4240FFFFFF7F82A256C00000008046D2424000000060BC8956C00000004054D2424000000000908956C0000000E0B3CC424000000040968956C000000040EAA7424000000060378E56C0000000A0EBA74240 2.98027100000000011
|
||||
72 0106000020E6100000010000000103000000010000000D00000000000080CD0557C0000000407487424000000000A11057C000000060E7874240000000A0F00F57C000000080AAA04240FFFFFF7F761057C0000000A0EEA0424000000000221057C0000000C028BD4240000000E0F40F57C0000000C0B5CD4240000000A0000257C00000002054CD4240000000007EF456C000000000A6CC424000000060EAF056C0000000E033CC4240000000C0B1F056C00000006063B6424000000060E6E956C000000040FFB5424000000000B0EA56C0000000004286424000000080CD0557C00000004074874240 3.86676699999999984
|
||||
73 0106000020E6100000010000000103000000010000000E000000000000E0FF5D56C0000000C071AB424000000020E35B56C00000002088AD4240000000605D5B56C0000000409CB4424000000020085D56C00000000002BA424000000080AA5F56C0000000E0F0BE424000000020996156C0000000E035C94240FFFFFF7F835E56C000000080FAC84240FFFFFF7F825D56C0FFFFFF1FA9CA4240000000C0655D56C0000000A09ECD424000000000CE4956C00000004053CD4240000000E0F64256C0FFFFFFDF38CD4240000000002E4356C0FFFFFFFF29AB4240000000C0BC4F56C0000000C03CAB4240000000E0FF5D56C0000000C071AB4240 1.86840800000000007
|
||||
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
|
||||
75 0106000020E6100000010000000103000000010000000D000000000000A0A47756C0000000C0DA904240000000E07A7D56C000000060D090424000000060B67D56C00000000057884240000000E0BD7F56C00000008017884240000000C0D87F56C0000000805986424000000040568756C00000000039864240000000605B8756C0000000E0188B4240000000E02F8E56C0000000A0058B424000000060378E56C0000000A0EBA7424000000040968956C000000040EAA7424000000000908956C0000000E0B3CC424000000060627756C000000060F8CC4240000000A0A47756C0000000C0DA904240 7.80359900000000017
|
||||
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
|
||||
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
|
||||
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
|
||||
\.
|
||||
|
||||
|
||||
CREATE INDEX getis_data_gix ON getis_data USING GIST(the_geom);
|
||||
15
src/pg/test/sql/16_getis_test.sql
Normal file
15
src/pg/test/sql/16_getis_test.sql
Normal file
@@ -0,0 +1,15 @@
|
||||
\pset format unaligned
|
||||
\set ECHO all
|
||||
\i test/fixtures/getis_data.sql
|
||||
|
||||
-- set random seed
|
||||
SELECT cdb_crankshaft._cdb_random_seeds(1234);
|
||||
|
||||
-- test against PySAL example dataset 'stl_hom'
|
||||
SELECT rowid, round(z_score, 4) As z_score, round(p_value, 4) As p_value
|
||||
FROM cdb_crankshaft.CDB_GetisOrdsG(
|
||||
'select * from getis_data',
|
||||
'hr8893', 'queen', NULL, 999,
|
||||
'the_geom', 'cartodb_id') As t(z_score, p_value, p_z_sim, rowid)
|
||||
WHERE round(p_value, 4) <= 0.05
|
||||
ORDER BY rowid ASC;
|
||||
85
src/pg/test/sql/18_outliers_test.sql
Normal file
85
src/pg/test/sql/18_outliers_test.sql
Normal file
@@ -0,0 +1,85 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
\pset format unaligned
|
||||
|
||||
--
|
||||
-- postgres=# select round(avg(i), 3) as avg,
|
||||
-- round(stddev(i), 3) as stddev,
|
||||
-- round(avg(i) + stddev(i), 3) as one_stddev,
|
||||
-- round(avg(i) + 2 * stddev(i), 3) As two_stddev
|
||||
-- from unnest(ARRAY[1,3,2,3,5,1,2,32,12,3,57,2,1,4,2,100]) As x(i);
|
||||
-- avg | stddev | one_stddev | two_stddev
|
||||
-- --------+--------+------------+------------
|
||||
-- 14.375 | 27.322 | 41.697 | 69.020
|
||||
|
||||
|
||||
-- With an threshold of 1.0 standard deviation, ids 11, 16, and 17 are outliers
|
||||
WITH a AS (
|
||||
SELECT
|
||||
ARRAY[1,3,2,3,5,1,2,32,12, 3,57, 2, 1, 4, 2,100,-100]::numeric[] As vals, ARRAY[1,2,3,4,5,6,7, 8, 9,10,11,12,13,14,15, 16, 17]::int[] As ids
|
||||
), b As (
|
||||
SELECT
|
||||
(cdb_crankshaft.cdb_StdDevOutlier(vals, 1.0, ids)).*
|
||||
FROM a
|
||||
ORDER BY ids)
|
||||
SELECT *
|
||||
FROM b
|
||||
WHERE is_outlier IS TRUE;
|
||||
|
||||
-- With a threshold of 2.0 standard deviations, id 16 is the only outlier
|
||||
WITH a AS (
|
||||
SELECT
|
||||
ARRAY[1,3,2,3,5,1,2,32,12, 3,57, 2, 1, 4, 2,100,-100]::numeric[] As vals,
|
||||
ARRAY[1,2,3,4,5,6,7, 8, 9,10,11,12,13,14,15, 16, 17]::int[] As ids
|
||||
), b As (
|
||||
SELECT
|
||||
(cdb_crankshaft.CDB_StdDevOutlier(vals, 2.0, ids)).*
|
||||
FROM a
|
||||
ORDER BY ids)
|
||||
SELECT *
|
||||
FROM b
|
||||
WHERE is_outlier IS TRUE;
|
||||
|
||||
-- With a Stddev of zero, should throw back error
|
||||
-- With a threshold of 2.0 standard deviations, id 16 is the only outlier
|
||||
WITH a AS (
|
||||
SELECT
|
||||
ARRAY[5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5]::numeric[] As vals,
|
||||
ARRAY[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]::int[] As ids
|
||||
), b As (
|
||||
SELECT
|
||||
(cdb_crankshaft.CDB_StdDevOutlier(vals, 1.0, ids)).*
|
||||
FROM a
|
||||
ORDER BY ids)
|
||||
SELECT *
|
||||
FROM b
|
||||
WHERE is_outlier IS TRUE;
|
||||
|
||||
-- With a ratio threshold of 2.0 threshold (100% above or below the mean)
|
||||
-- which is greater than ~21, which are values
|
||||
WITH a AS (
|
||||
SELECT
|
||||
ARRAY[1,3,2,3,5,1,2,32,12, 3,57, 2, 1, 4, 2,100,-100]::numeric[] As vals,
|
||||
ARRAY[1,2,3,4,5,6,7, 8, 9,10,11,12,13,14,15, 16, 17]::int[] As ids
|
||||
), b As (
|
||||
SELECT
|
||||
(cdb_crankshaft.CDB_PercentOutlier(vals, 2.0, ids)).*
|
||||
FROM a
|
||||
ORDER BY ids)
|
||||
SELECT *
|
||||
FROM b
|
||||
WHERE is_outlier IS TRUE;
|
||||
|
||||
-- With a static threshold of 11, what are the outliers
|
||||
WITH a AS (
|
||||
SELECT
|
||||
ARRAY[1,3,2,3,5,1,2,32,12, 3,57, 2, 1, 4, 2,100,-100]::numeric[] As vals,
|
||||
ARRAY[1,2,3,4,5,6,7, 8, 9,10,11,12,13,14,15, 16, 17]::int[] As ids
|
||||
), b As (
|
||||
SELECT unnest(vals) As v, unnest(ids) as i
|
||||
FROM a
|
||||
)
|
||||
SELECT cdb_crankshaft.CDB_StaticOutlier(v, 11.0) As is_outlier, i As rowid
|
||||
FROM b
|
||||
WHERE cdb_crankshaft.CDB_StaticOutlier(v, 11.0) is True
|
||||
ORDER BY i;
|
||||
@@ -4,3 +4,4 @@ import crankshaft.clustering
|
||||
import crankshaft.space_time_dynamics
|
||||
import crankshaft.segmentation
|
||||
import crankshaft.regression
|
||||
import analysis_data_provider
|
||||
|
||||
67
src/py/crankshaft/crankshaft/analysis_data_provider.py
Normal file
67
src/py/crankshaft/crankshaft/analysis_data_provider.py
Normal file
@@ -0,0 +1,67 @@
|
||||
"""class for fetching data"""
|
||||
import plpy
|
||||
import pysal_utils as pu
|
||||
|
||||
|
||||
class AnalysisDataProvider:
|
||||
def get_getis(self, w_type, params):
|
||||
"""fetch data for getis ord's g"""
|
||||
try:
|
||||
query = pu.construct_neighbor_query(w_type, params)
|
||||
result = plpy.execute(query)
|
||||
# if there are no neighbors, exit
|
||||
if len(result) == 0:
|
||||
return pu.empty_zipped_array(4)
|
||||
else:
|
||||
return result
|
||||
except plpy.SPIError, err:
|
||||
plpy.error('Analysis failed: %s' % err)
|
||||
|
||||
def get_markov(self, w_type, params):
|
||||
"""fetch data for spatial markov"""
|
||||
try:
|
||||
query = pu.construct_neighbor_query(w_type, params)
|
||||
data = plpy.execute(query)
|
||||
|
||||
if len(data) == 0:
|
||||
return pu.empty_zipped_array(4)
|
||||
|
||||
return data
|
||||
except plpy.SPIError, err:
|
||||
plpy.error('Analysis failed: %s' % err)
|
||||
|
||||
def get_moran(self, w_type, params):
|
||||
"""fetch data for moran's i analyses"""
|
||||
try:
|
||||
query = pu.construct_neighbor_query(w_type, params)
|
||||
data = plpy.execute(query)
|
||||
|
||||
# if there are no neighbors, exit
|
||||
if len(data) == 0:
|
||||
return pu.empty_zipped_array(2)
|
||||
return data
|
||||
except plpy.SPIError, err:
|
||||
plpy.error('Analysis failed: %s' % e)
|
||||
return pu.empty_zipped_array(2)
|
||||
|
||||
def get_nonspatial_kmeans(self, query):
|
||||
"""fetch data for non-spatial kmeans"""
|
||||
try:
|
||||
data = plpy.execute(query)
|
||||
return data
|
||||
except plpy.SPIError, err:
|
||||
plpy.error('Analysis failed: %s' % err)
|
||||
|
||||
def get_spatial_kmeans(self, params):
|
||||
"""fetch data for spatial kmeans"""
|
||||
query = ("SELECT "
|
||||
"array_agg({id_col} ORDER BY {id_col}) as ids,"
|
||||
"array_agg(ST_X({geom_col}) ORDER BY {id_col}) As xs,"
|
||||
"array_agg(ST_Y({geom_col}) ORDER BY {id_col}) As ys "
|
||||
"FROM ({subquery}) As a "
|
||||
"WHERE {geom_col} IS NOT NULL").format(**params)
|
||||
try:
|
||||
data = plpy.execute(query)
|
||||
return data
|
||||
except plpy.SPIError, err:
|
||||
plpy.error('Analysis failed: %s' % err)
|
||||
@@ -1,3 +1,4 @@
|
||||
"""Import all functions from for clustering"""
|
||||
from moran import *
|
||||
from kmeans import *
|
||||
from getis import *
|
||||
|
||||
50
src/py/crankshaft/crankshaft/clustering/getis.py
Normal file
50
src/py/crankshaft/crankshaft/clustering/getis.py
Normal file
@@ -0,0 +1,50 @@
|
||||
"""
|
||||
Getis-Ord's G geostatistics (hotspot/coldspot analysis)
|
||||
"""
|
||||
|
||||
import pysal as ps
|
||||
from collections import OrderedDict
|
||||
|
||||
# crankshaft modules
|
||||
import crankshaft.pysal_utils as pu
|
||||
from crankshaft.analysis_data_provider import AnalysisDataProvider
|
||||
|
||||
# High level interface ---------------------------------------
|
||||
|
||||
|
||||
class Getis:
|
||||
def __init__(self, data_provider=None):
|
||||
if data_provider is None:
|
||||
self.data_provider = AnalysisDataProvider()
|
||||
else:
|
||||
self.data_provider = data_provider
|
||||
|
||||
def getis_ord(self, subquery, attr,
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Getis-Ord's G*
|
||||
Implementation building neighbors with a PostGIS database and PySAL's
|
||||
Getis-Ord's G* hotspot/coldspot module.
|
||||
Andy Eschbacher
|
||||
"""
|
||||
|
||||
# geometries with attributes that are null are ignored
|
||||
# resulting in a collection of not as near neighbors if kNN is chosen
|
||||
|
||||
qvals = OrderedDict([("id_col", id_col),
|
||||
("attr1", attr),
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
|
||||
result = self.data_provider.get_getis(w_type, qvals)
|
||||
attr_vals = pu.get_attributes(result)
|
||||
|
||||
# build PySAL weight object
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
# calculate Getis-Ord's G* z- and p-values
|
||||
getis = ps.esda.getisord.G_Local(attr_vals, weight,
|
||||
star=True, permutations=permutations)
|
||||
|
||||
return zip(getis.z_sim, getis.p_sim, getis.p_z_sim, weight.id_order)
|
||||
@@ -1,18 +1,32 @@
|
||||
from sklearn.cluster import KMeans
|
||||
import plpy
|
||||
import numpy as np
|
||||
|
||||
def kmeans(query, no_clusters, no_init=20):
|
||||
data = plpy.execute('''select array_agg(cartodb_id order by cartodb_id) as ids,
|
||||
array_agg(ST_X(the_geom) order by cartodb_id) xs,
|
||||
array_agg(ST_Y(the_geom) order by cartodb_id) ys from ({query}) a
|
||||
where the_geom is not null
|
||||
'''.format(query=query))
|
||||
from crankshaft.analysis_data_provider import AnalysisDataProvider
|
||||
|
||||
xs = data[0]['xs']
|
||||
ys = data[0]['ys']
|
||||
ids = data[0]['ids']
|
||||
|
||||
km = KMeans(n_clusters= no_clusters, n_init=no_init)
|
||||
labels = km.fit_predict(zip(xs,ys))
|
||||
return zip(ids,labels)
|
||||
class Kmeans:
|
||||
def __init__(self, data_provider=None):
|
||||
if data_provider is None:
|
||||
self.data_provider = AnalysisDataProvider()
|
||||
else:
|
||||
self.data_provider = data_provider
|
||||
|
||||
def spatial(self, query, no_clusters, no_init=20):
|
||||
"""
|
||||
find centers based on clusters of latitude/longitude pairs
|
||||
query: SQL query that has a WGS84 geometry (the_geom)
|
||||
"""
|
||||
params = {"subquery": query,
|
||||
"geom_col": "the_geom",
|
||||
"id_col": "cartodb_id"}
|
||||
|
||||
data = self.data_provider.get_spatial_kmeans(params)
|
||||
|
||||
# Unpack query response
|
||||
xs = data[0]['xs']
|
||||
ys = data[0]['ys']
|
||||
ids = data[0]['ids']
|
||||
|
||||
km = KMeans(n_clusters=no_clusters, n_init=no_init)
|
||||
labels = km.fit_predict(zip(xs, ys))
|
||||
return zip(ids, labels)
|
||||
|
||||
@@ -6,8 +6,8 @@ Moran's I geostatistics (global clustering & outliers presence)
|
||||
# average of the their neighborhood
|
||||
|
||||
import pysal as ps
|
||||
import plpy
|
||||
from collections import OrderedDict
|
||||
from crankshaft.analysis_data_provider import AnalysisDataProvider
|
||||
|
||||
# crankshaft module
|
||||
import crankshaft.pysal_utils as pu
|
||||
@@ -15,204 +15,162 @@ import crankshaft.pysal_utils as pu
|
||||
# High level interface ---------------------------------------
|
||||
|
||||
|
||||
def moran(subquery, attr_name,
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I (global)
|
||||
Implementation building neighbors with a PostGIS database and Moran's I
|
||||
core clusters with PySAL.
|
||||
Andy Eschbacher
|
||||
"""
|
||||
qvals = OrderedDict([("id_col", id_col),
|
||||
("attr1", attr_name),
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
class Moran:
|
||||
def __init__(self, data_provider=None):
|
||||
if data_provider is None:
|
||||
self.data_provider = AnalysisDataProvider()
|
||||
else:
|
||||
self.data_provider = data_provider
|
||||
|
||||
query = pu.construct_neighbor_query(w_type, qvals)
|
||||
def global_stat(self, subquery, attr_name,
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I (global)
|
||||
Implementation building neighbors with a PostGIS database and Moran's I
|
||||
core clusters with PySAL.
|
||||
Andy Eschbacher
|
||||
"""
|
||||
params = OrderedDict([("id_col", id_col),
|
||||
("attr1", attr_name),
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
|
||||
try:
|
||||
result = plpy.execute(query)
|
||||
# if there are no neighbors, exit
|
||||
if len(result) == 0:
|
||||
return pu.empty_zipped_array(2)
|
||||
except plpy.SPIError, e:
|
||||
plpy.error('Analysis failed: %s' % e)
|
||||
return pu.empty_zipped_array(2)
|
||||
result = self.data_provider.get_moran(w_type, params)
|
||||
|
||||
# collect attributes
|
||||
attr_vals = pu.get_attributes(result)
|
||||
# collect attributes
|
||||
attr_vals = pu.get_attributes(result)
|
||||
|
||||
# calculate weights
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
# calculate weights
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
# calculate moran global
|
||||
moran_global = ps.esda.moran.Moran(attr_vals, weight,
|
||||
permutations=permutations)
|
||||
# calculate moran global
|
||||
moran_global = ps.esda.moran.Moran(attr_vals, weight,
|
||||
permutations=permutations)
|
||||
|
||||
return zip([moran_global.I], [moran_global.EI])
|
||||
return zip([moran_global.I], [moran_global.EI])
|
||||
|
||||
def local_stat(self, subquery, attr,
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I implementation for PL/Python
|
||||
Andy Eschbacher
|
||||
"""
|
||||
|
||||
def moran_local(subquery, attr,
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I implementation for PL/Python
|
||||
Andy Eschbacher
|
||||
"""
|
||||
# geometries with attributes that are null are ignored
|
||||
# resulting in a collection of not as near neighbors
|
||||
|
||||
# geometries with attributes that are null are ignored
|
||||
# resulting in a collection of not as near neighbors
|
||||
params = OrderedDict([("id_col", id_col),
|
||||
("attr1", attr),
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
|
||||
qvals = OrderedDict([("id_col", id_col),
|
||||
("attr1", attr),
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
result = self.data_provider.get_moran(w_type, params)
|
||||
|
||||
query = pu.construct_neighbor_query(w_type, qvals)
|
||||
attr_vals = pu.get_attributes(result)
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
try:
|
||||
result = plpy.execute(query)
|
||||
# if there are no neighbors, exit
|
||||
if len(result) == 0:
|
||||
return pu.empty_zipped_array(5)
|
||||
except plpy.SPIError, e:
|
||||
plpy.error('Analysis failed: %s' % e)
|
||||
return pu.empty_zipped_array(5)
|
||||
|
||||
attr_vals = pu.get_attributes(result)
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
# calculate LISA values
|
||||
lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
|
||||
permutations=permutations)
|
||||
|
||||
# find quadrants for each geometry
|
||||
quads = quad_position(lisa.q)
|
||||
|
||||
return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
|
||||
|
||||
|
||||
def moran_rate(subquery, numerator, denominator,
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I Rate (global)
|
||||
Andy Eschbacher
|
||||
"""
|
||||
qvals = OrderedDict([("id_col", id_col),
|
||||
("attr1", numerator),
|
||||
("attr2", denominator)
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
|
||||
query = pu.construct_neighbor_query(w_type, qvals)
|
||||
|
||||
try:
|
||||
result = plpy.execute(query)
|
||||
# if there are no neighbors, exit
|
||||
if len(result) == 0:
|
||||
return pu.empty_zipped_array(2)
|
||||
except plpy.SPIError, e:
|
||||
plpy.error('Analysis failed: %s' % e)
|
||||
return pu.empty_zipped_array(2)
|
||||
|
||||
# collect attributes
|
||||
numer = pu.get_attributes(result, 1)
|
||||
denom = pu.get_attributes(result, 2)
|
||||
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
# calculate moran global rate
|
||||
lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
|
||||
# calculate LISA values
|
||||
lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
|
||||
permutations=permutations)
|
||||
|
||||
return zip([lisa_rate.I], [lisa_rate.EI])
|
||||
# find quadrants for each geometry
|
||||
quads = quad_position(lisa.q)
|
||||
|
||||
return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
|
||||
|
||||
def moran_local_rate(subquery, numerator, denominator,
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I Local Rate
|
||||
def global_rate_stat(self, subquery, numerator, denominator,
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I Rate (global)
|
||||
Andy Eschbacher
|
||||
"""
|
||||
# geometries with values that are null are ignored
|
||||
# resulting in a collection of not as near neighbors
|
||||
"""
|
||||
params = OrderedDict([("id_col", id_col),
|
||||
("attr1", numerator),
|
||||
("attr2", denominator)
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
|
||||
qvals = OrderedDict([("id_col", id_col),
|
||||
("numerator", numerator),
|
||||
("denominator", denominator),
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
result = self.data_provider.get_moran(w_type, params)
|
||||
|
||||
query = pu.construct_neighbor_query(w_type, qvals)
|
||||
# collect attributes
|
||||
numer = pu.get_attributes(result, 1)
|
||||
denom = pu.get_attributes(result, 2)
|
||||
|
||||
try:
|
||||
result = plpy.execute(query)
|
||||
# if there are no neighbors, exit
|
||||
if len(result) == 0:
|
||||
return pu.empty_zipped_array(5)
|
||||
except plpy.SPIError, e:
|
||||
plpy.error('Analysis failed: %s' % e)
|
||||
return pu.empty_zipped_array(5)
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
# collect attributes
|
||||
numer = pu.get_attributes(result, 1)
|
||||
denom = pu.get_attributes(result, 2)
|
||||
# calculate moran global rate
|
||||
lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
|
||||
permutations=permutations)
|
||||
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
return zip([lisa_rate.I], [lisa_rate.EI])
|
||||
|
||||
# calculate LISA values
|
||||
lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
|
||||
permutations=permutations)
|
||||
def local_rate_stat(self, subquery, numerator, denominator,
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I Local Rate
|
||||
Andy Eschbacher
|
||||
"""
|
||||
# geometries with values that are null are ignored
|
||||
# resulting in a collection of not as near neighbors
|
||||
|
||||
# find quadrants for each geometry
|
||||
quads = quad_position(lisa.q)
|
||||
params = OrderedDict([("id_col", id_col),
|
||||
("numerator", numerator),
|
||||
("denominator", denominator),
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
|
||||
return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
|
||||
result = self.data_provider.get_moran(w_type, params)
|
||||
|
||||
# collect attributes
|
||||
numer = pu.get_attributes(result, 1)
|
||||
denom = pu.get_attributes(result, 2)
|
||||
|
||||
def moran_local_bv(subquery, attr1, attr2,
|
||||
permutations, geom_col, id_col, w_type, num_ngbrs):
|
||||
"""
|
||||
Moran's I (local) Bivariate (untested)
|
||||
"""
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
qvals = OrderedDict([("id_col", id_col),
|
||||
("attr1", attr1),
|
||||
("attr2", attr2),
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
# calculate LISA values
|
||||
lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
|
||||
permutations=permutations)
|
||||
|
||||
query = pu.construct_neighbor_query(w_type, qvals)
|
||||
# find quadrants for each geometry
|
||||
quads = quad_position(lisa.q)
|
||||
|
||||
try:
|
||||
result = plpy.execute(query)
|
||||
# if there are no neighbors, exit
|
||||
if len(result) == 0:
|
||||
return pu.empty_zipped_array(4)
|
||||
except plpy.SPIError:
|
||||
plpy.error("Error: areas of interest query failed, "
|
||||
"check input parameters")
|
||||
return pu.empty_zipped_array(4)
|
||||
return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
|
||||
|
||||
# collect attributes
|
||||
attr1_vals = pu.get_attributes(result, 1)
|
||||
attr2_vals = pu.get_attributes(result, 2)
|
||||
def local_bivariate_stat(self, subquery, attr1, attr2,
|
||||
permutations, geom_col, id_col,
|
||||
w_type, num_ngbrs):
|
||||
"""
|
||||
Moran's I (local) Bivariate (untested)
|
||||
"""
|
||||
|
||||
# create weights
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
params = OrderedDict([("id_col", id_col),
|
||||
("attr1", attr1),
|
||||
("attr2", attr2),
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
|
||||
# calculate LISA values
|
||||
lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
|
||||
permutations=permutations)
|
||||
result = self.data_provider.get_moran(w_type, params)
|
||||
|
||||
# find clustering of significance
|
||||
lisa_sig = quad_position(lisa.q)
|
||||
# collect attributes
|
||||
attr1_vals = pu.get_attributes(result, 1)
|
||||
attr2_vals = pu.get_attributes(result, 2)
|
||||
|
||||
return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
|
||||
# create weights
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
# calculate LISA values
|
||||
lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
|
||||
permutations=permutations)
|
||||
|
||||
# find clustering of significance
|
||||
lisa_sig = quad_position(lisa.q)
|
||||
|
||||
return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
|
||||
|
||||
# Low level functions ----------------------------------------
|
||||
|
||||
|
||||
@@ -45,8 +45,16 @@ def get_weight(query_res, w_type='knn', num_ngbrs=5):
|
||||
def query_attr_select(params, table_ref=True):
|
||||
"""
|
||||
Create portion of SELECT statement for attributes inolved in query.
|
||||
Defaults to order in the params
|
||||
@param params: dict of information used in query (column names,
|
||||
table name, etc.)
|
||||
Example:
|
||||
OrderedDict([('numerator', 'price'),
|
||||
('denominator', 'sq_meters'),
|
||||
('subquery', 'SELECT * FROM interesting_data')])
|
||||
Output:
|
||||
"i.\"price\"::numeric As attr1, " \
|
||||
"i.\"sq_meters\"::numeric As attr2, "
|
||||
"""
|
||||
|
||||
attr_string = ""
|
||||
@@ -70,7 +78,7 @@ def query_attr_select(params, table_ref=True):
|
||||
if k not in ('id_col', 'geom_col', 'subquery',
|
||||
'num_ngbrs', 'subquery')]
|
||||
|
||||
for idx, val in enumerate(sorted(attrs)):
|
||||
for idx, val in enumerate(attrs):
|
||||
attr_string += template % {"col": params[val],
|
||||
"alias_num": idx + 1}
|
||||
|
||||
@@ -86,8 +94,8 @@ def query_attr_where(params, table_ref=True):
|
||||
'numerator': 'data1',
|
||||
'denominator': 'data2',
|
||||
'': ...}
|
||||
Output: 'idx_replace."data1" IS NOT NULL AND idx_replace."data2"
|
||||
IS NOT NULL'
|
||||
Output:
|
||||
'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL'
|
||||
Input:
|
||||
{'subquery': ...,
|
||||
'time_cols': ['time1', 'time2', 'time3'],
|
||||
@@ -111,18 +119,17 @@ def query_attr_where(params, table_ref=True):
|
||||
# moran where clauses
|
||||
|
||||
# get keys
|
||||
attrs = sorted([k for k in params
|
||||
if k not in ('id_col', 'geom_col', 'subquery',
|
||||
'num_ngbrs', 'ind_vars')])
|
||||
attrs = [k for k in params
|
||||
if k not in ('id_col', 'geom_col', 'subquery',
|
||||
'num_ngbrs', 'subquery')]
|
||||
|
||||
# add values to template
|
||||
for attr in attrs:
|
||||
attr_string.append(template % params[attr])
|
||||
|
||||
if len(attrs) == 2:
|
||||
check_zero = "\"%s\" <> 0" % params[attrs[1]]
|
||||
if table_ref is not None:
|
||||
check_zero = "idx_replace." + check_zero
|
||||
attr_string.append(check_zero)
|
||||
if 'denominator' in attrs:
|
||||
attr_string.append(
|
||||
"idx_replace.\"%s\" <> 0" % params['denominator'])
|
||||
|
||||
out = " AND ".join(attr_string)
|
||||
|
||||
|
||||
@@ -2,98 +2,98 @@
|
||||
Spatial dynamics measurements using Spatial Markov
|
||||
"""
|
||||
|
||||
# TODO: remove all plpy dependencies
|
||||
|
||||
import numpy as np
|
||||
import pysal as ps
|
||||
import plpy
|
||||
import crankshaft.pysal_utils as pu
|
||||
from crankshaft.analysis_data_provider import AnalysisDataProvider
|
||||
|
||||
def spatial_markov_trend(subquery, time_cols, num_classes=7,
|
||||
w_type='knn', num_ngbrs=5, permutations=0,
|
||||
geom_col='the_geom', id_col='cartodb_id'):
|
||||
"""
|
||||
Predict the trends of a unit based on:
|
||||
1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile)
|
||||
2. average class of its neighbors
|
||||
|
||||
Inputs:
|
||||
@param subquery string: e.g., SELECT the_geom, cartodb_id,
|
||||
interesting_time_column FROM table_name
|
||||
@param time_cols list of strings: list of strings of column names
|
||||
@param num_classes (optional): number of classes to break distribution
|
||||
of values into. Currently uses quantile bins.
|
||||
@param w_type string (optional): weight type ('knn' or 'queen')
|
||||
@param num_ngbrs int (optional): number of neighbors (if knn type)
|
||||
@param permutations int (optional): number of permutations for test
|
||||
stats
|
||||
@param geom_col string (optional): name of column which contains the
|
||||
geometries
|
||||
@param id_col string (optional): name of column which has the ids of
|
||||
the table
|
||||
class Markov:
|
||||
def __init__(self, data_provider=None):
|
||||
if data_provider is None:
|
||||
self.data_provider = AnalysisDataProvider()
|
||||
else:
|
||||
self.data_provider = data_provider
|
||||
|
||||
Outputs:
|
||||
@param trend_up float: probablity that a geom will move to a higher
|
||||
class
|
||||
@param trend_down float: probablity that a geom will move to a lower
|
||||
class
|
||||
@param trend float: (trend_up - trend_down) / trend_static
|
||||
@param volatility float: a measure of the volatility based on
|
||||
probability stddev(prob array)
|
||||
"""
|
||||
def spatial_trend(self, subquery, time_cols, num_classes=7,
|
||||
w_type='knn', num_ngbrs=5, permutations=0,
|
||||
geom_col='the_geom', id_col='cartodb_id'):
|
||||
"""
|
||||
Predict the trends of a unit based on:
|
||||
1. history of its transitions to different classes (e.g., 1st
|
||||
quantile -> 2nd quantile)
|
||||
2. average class of its neighbors
|
||||
|
||||
if len(time_cols) < 2:
|
||||
plpy.error('More than one time column needs to be passed')
|
||||
Inputs:
|
||||
@param subquery string: e.g., SELECT the_geom, cartodb_id,
|
||||
interesting_time_column FROM table_name
|
||||
@param time_cols list of strings: list of strings of column names
|
||||
@param num_classes (optional): number of classes to break
|
||||
distribution of values into. Currently uses quantile bins.
|
||||
@param w_type string (optional): weight type ('knn' or 'queen')
|
||||
@param num_ngbrs int (optional): number of neighbors (if knn type)
|
||||
@param permutations int (optional): number of permutations for test
|
||||
stats
|
||||
@param geom_col string (optional): name of column which contains
|
||||
the geometries
|
||||
@param id_col string (optional): name of column which has the ids
|
||||
of the table
|
||||
|
||||
qvals = {"id_col": id_col,
|
||||
"time_cols": time_cols,
|
||||
"geom_col": geom_col,
|
||||
"subquery": subquery,
|
||||
"num_ngbrs": num_ngbrs}
|
||||
Outputs:
|
||||
@param trend_up float: probablity that a geom will move to a higher
|
||||
class
|
||||
@param trend_down float: probablity that a geom will move to a
|
||||
lower class
|
||||
@param trend float: (trend_up - trend_down) / trend_static
|
||||
@param volatility float: a measure of the volatility based on
|
||||
probability stddev(prob array)
|
||||
"""
|
||||
|
||||
try:
|
||||
query_result = plpy.execute(
|
||||
pu.construct_neighbor_query(w_type, qvals)
|
||||
)
|
||||
if len(query_result) == 0:
|
||||
return zip([None], [None], [None], [None], [None])
|
||||
except plpy.SPIError, e:
|
||||
plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals)))
|
||||
plpy.error('Analysis failed: %s' % e)
|
||||
return zip([None], [None], [None], [None], [None])
|
||||
if len(time_cols) < 2:
|
||||
plpy.error('More than one time column needs to be passed')
|
||||
|
||||
## build weight
|
||||
weights = pu.get_weight(query_result, w_type)
|
||||
weights.transform = 'r'
|
||||
params = {"id_col": id_col,
|
||||
"time_cols": time_cols,
|
||||
"geom_col": geom_col,
|
||||
"subquery": subquery,
|
||||
"num_ngbrs": num_ngbrs}
|
||||
|
||||
## prep time data
|
||||
t_data = get_time_data(query_result, time_cols)
|
||||
query_result = self.data_provider.get_markov(w_type, params)
|
||||
|
||||
plpy.debug('shape of t_data %d, %d' % t_data.shape)
|
||||
plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape)
|
||||
plpy.debug('first num elements: %f' % t_data[0, 0])
|
||||
# build weight
|
||||
weights = pu.get_weight(query_result, w_type)
|
||||
weights.transform = 'r'
|
||||
|
||||
sp_markov_result = ps.Spatial_Markov(t_data,
|
||||
weights,
|
||||
k=num_classes,
|
||||
fixed=False,
|
||||
permutations=permutations)
|
||||
# prep time data
|
||||
t_data = get_time_data(query_result, time_cols)
|
||||
|
||||
## get lag classes
|
||||
lag_classes = ps.Quantiles(
|
||||
ps.lag_spatial(weights, t_data[:, -1]),
|
||||
k=num_classes).yb
|
||||
sp_markov_result = ps.Spatial_Markov(t_data,
|
||||
weights,
|
||||
k=num_classes,
|
||||
fixed=False,
|
||||
permutations=permutations)
|
||||
|
||||
## look up probablity distribution for each unit according to class and lag class
|
||||
prob_dist = get_prob_dist(sp_markov_result.P,
|
||||
lag_classes,
|
||||
sp_markov_result.classes[:, -1])
|
||||
# get lag classes
|
||||
lag_classes = ps.Quantiles(
|
||||
ps.lag_spatial(weights, t_data[:, -1]),
|
||||
k=num_classes).yb
|
||||
|
||||
# look up probablity distribution for each unit according to class and
|
||||
# lag class
|
||||
prob_dist = get_prob_dist(sp_markov_result.P,
|
||||
lag_classes,
|
||||
sp_markov_result.classes[:, -1])
|
||||
|
||||
# find the ups and down and overall distribution of each cell
|
||||
trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist, sp_markov_result.classes[:, -1])
|
||||
|
||||
# output the results
|
||||
return zip(trend, trend_up, trend_down, volatility, weights.id_order)
|
||||
|
||||
## find the ups and down and overall distribution of each cell
|
||||
trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist,
|
||||
sp_markov_result.classes[:, -1])
|
||||
|
||||
## output the results
|
||||
return zip(trend, trend_up, trend_down, volatility, weights.id_order)
|
||||
|
||||
def get_time_data(markov_data, time_cols):
|
||||
"""
|
||||
@@ -103,7 +103,8 @@ def get_time_data(markov_data, time_cols):
|
||||
return np.array([[x['attr' + str(i)] for x in markov_data]
|
||||
for i in range(1, num_attrs+1)], dtype=float).transpose()
|
||||
|
||||
## not currently used
|
||||
|
||||
# not currently used
|
||||
def rebin_data(time_data, num_time_per_bin):
|
||||
"""
|
||||
Convert an n x l matrix into an (n/m) x l matrix where the values are
|
||||
@@ -131,14 +132,16 @@ def rebin_data(time_data, num_time_per_bin):
|
||||
"""
|
||||
|
||||
if time_data.shape[1] % num_time_per_bin == 0:
|
||||
## if fit is perfect, then use it
|
||||
# if fit is perfect, then use it
|
||||
n_max = time_data.shape[1] / num_time_per_bin
|
||||
else:
|
||||
## fit remainders into an additional column
|
||||
# fit remainders into an additional column
|
||||
n_max = time_data.shape[1] / num_time_per_bin + 1
|
||||
|
||||
return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
|
||||
for i in range(n_max)]).T
|
||||
return np.array(
|
||||
[time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
|
||||
for i in range(n_max)]).T
|
||||
|
||||
|
||||
def get_prob_dist(transition_matrix, lag_indices, unit_indices):
|
||||
"""
|
||||
@@ -157,6 +160,7 @@ def get_prob_dist(transition_matrix, lag_indices, unit_indices):
|
||||
return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
|
||||
for i in range(len(lag_indices))])
|
||||
|
||||
|
||||
def get_prob_stats(prob_dist, unit_indices):
|
||||
"""
|
||||
get the statistics of the probability distributions
|
||||
@@ -179,11 +183,12 @@ def get_prob_stats(prob_dist, unit_indices):
|
||||
trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
|
||||
trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
|
||||
if prob_dist[i, unit_indices[i]] > 0.0:
|
||||
trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
|
||||
trend[i] = (trend_up[i] - trend_down[i]) / (
|
||||
prob_dist[i, unit_indices[i]])
|
||||
else:
|
||||
trend[i] = None
|
||||
|
||||
## calculate volatility of distribution
|
||||
# calculate volatility of distribution
|
||||
volatility = prob_dist.std(axis=1)
|
||||
|
||||
return trend_up, trend_down, trend, volatility
|
||||
|
||||
1
src/py/crankshaft/test/fixtures/getis.json
vendored
Normal file
1
src/py/crankshaft/test/fixtures/getis.json
vendored
Normal file
@@ -0,0 +1 @@
|
||||
[[0.004793783909323601, 0.17999999999999999, 0.49808756424021061], [-1.0701189472090842, 0.079000000000000001, 0.14228288580832316], [-0.67867750971877305, 0.42099999999999999, 0.24867110969448558], [-0.67407386707620487, 0.246, 0.25013217644612995], [-0.79495689068870035, 0.33200000000000002, 0.21331928959090596], [-0.49279481022182703, 0.058999999999999997, 0.31107878905057329], [-0.38075627530057132, 0.28399999999999997, 0.35169205342069643], [-0.86710921611314895, 0.23699999999999999, 0.19294108571294855], [-0.78618647240956485, 0.050000000000000003, 0.2158791250244505], [-0.76108527223116984, 0.064000000000000001, 0.22330306830813684], [-0.13340753531942209, 0.247, 0.44693554317763651], [-0.57584545722033043, 0.48999999999999999, 0.28235982246156488], [-0.78882694661192831, 0.433, 0.2151065788731219], [-0.38769767950046219, 0.375, 0.34911988661484239], [-0.56057819488052207, 0.41399999999999998, 0.28754255985169652], [-0.41354017495644935, 0.45500000000000002, 0.339605447117173], [-0.23993577722243081, 0.49099999999999999, 0.40519002230969337], [-0.1389080156677496, 0.40400000000000003, 0.44476141839645233], [-0.25485737510500855, 0.376, 0.39941662953554224], [-0.71218610582902353, 0.17399999999999999, 0.23817476979886087], [-0.54533105995872144, 0.13700000000000001, 0.2927629228714812], [-0.39547917847510977, 0.033000000000000002, 0.34624464252424236], [-0.43052658996257548, 0.35399999999999998, 0.33340631435564982], [-0.37296719193774736, 0.40300000000000002, 0.35458643102865428], [-0.66482612169465694, 0.31900000000000001, 0.25308085650392698], [-0.13772133540823422, 0.34699999999999998, 0.44523032843016275], [-0.6765304487868502, 0.20999999999999999, 0.24935196033890672], [-0.64518763494323472, 0.32200000000000001, 0.25940279912025543], [-0.5078622084312413, 0.41099999999999998, 0.30577498972600159], [-0.12652006733772059, 0.42899999999999999, 0.44966013262301163], [-0.32691133022814595, 0.498, 0.37186747562269029], [0.25533848511500978, 0.42399999999999999, 0.39923083899077472], [2.7045138116476508, 0.0050000000000000001, 0.0034202212972238577], [-0.1551614486076057, 0.44400000000000001, 0.43834701985429037], [1.9524487722567723, 0.012999999999999999, 0.025442473674991528], [-1.2055816465306763, 0.017000000000000001, 0.11398941970467646], [3.478472976017831, 0.002, 0.00025213964072468009], [-1.4621715757903719, 0.002, 0.071847099325659136], [-0.84010307600180256, 0.085000000000000006, 0.20042529779230778], [5.7097646237318243, 0.0030000000000000001, 5.6566262784940591e-09], [1.5082367956567375, 0.065000000000000002, 0.065746966514827365], [-0.58337270103430816, 0.44, 0.27982121546450034], [-0.083271860457022437, 0.45100000000000001, 0.46681768733385554], [-0.46872337815000953, 0.34599999999999997, 0.31963368715684204], [0.18490279849545319, 0.23799999999999999, 0.42665263797981101], [3.470424529947997, 0.012, 0.00025981817437825683], [-0.99942612137154796, 0.032000000000000001, 0.15879415560388499], [-1.3650387953594485, 0.034000000000000002, 0.08612042845912049], [1.8617160516432014, 0.081000000000000003, 0.03132156240215267], [1.1321188945775384, 0.11600000000000001, 0.12879222611766061], [0.064116686050580601, 0.27300000000000002, 0.4744386578180424], [-0.42032194540259099, 0.29999999999999999, 0.33712514016213468], [-0.79581215423980922, 0.123, 0.21307061309098785], [-0.42792753720906046, 0.45600000000000002, 0.33435193892883741], [-1.0629378527428395, 0.051999999999999998, 0.14390506780140866], [-0.54164761752225477, 0.33700000000000002, 0.29403064095211839], [1.0934778886820793, 0.13700000000000001, 0.13709201601893539], [-0.094068785378413719, 0.38200000000000001, 0.46252725802998929], [0.13482026574801856, 0.36799999999999999, 0.44637699118865737], [-0.13976995315653129, 0.34699999999999998, 0.44442087706276601], [-0.051047663924746682, 0.32000000000000001, 0.47964376985626245], [-0.21468297736730158, 0.41699999999999998, 0.41500724761906527], [-0.20873154637330626, 0.38800000000000001, 0.41732890604390893], [-0.32427876152583485, 0.49199999999999999, 0.37286349875557478], [-0.65254842943280977, 0.374, 0.25702372075306734], [-0.48611858196118796, 0.23300000000000001, 0.31344154643990074], [-0.14482354344529477, 0.32600000000000001, 0.44242509660469886], [-0.51052030974200002, 0.439, 0.30484349480873729], [0.56814382285283538, 0.14999999999999999, 0.28496865660103166], [0.58680919931668207, 0.161, 0.27866592887231878], [0.013390357044409013, 0.25800000000000001, 0.49465818005865647], [-0.19050728887961568, 0.41399999999999998, 0.4244558160399462], [-0.60531777422216049, 0.35199999999999998, 0.2724839368239631], [1.0899331115425805, 0.127, 0.13787130480311838], [0.17015055382651084, 0.36899999999999999, 0.43244586845546418], [-0.21738337124409801, 0.40600000000000003, 0.41395479459421991], [1.0329303331079593, 0.079000000000000001, 0.15081825117169467], [1.0218317101096221, 0.104, 0.15343027913308094]]
|
||||
1
src/py/crankshaft/test/fixtures/neighbors_getis.json
vendored
Normal file
1
src/py/crankshaft/test/fixtures/neighbors_getis.json
vendored
Normal file
File diff suppressed because one or more lines are too long
@@ -1,12 +1,13 @@
|
||||
import re
|
||||
|
||||
|
||||
class MockCursor:
|
||||
def __init__(self, data):
|
||||
self.cursor_pos = 0
|
||||
self.data = data
|
||||
|
||||
def fetch(self, batch_size):
|
||||
batch = self.data[self.cursor_pos : self.cursor_pos + batch_size]
|
||||
batch = self.data[self.cursor_pos:self.cursor_pos + batch_size]
|
||||
self.cursor_pos += batch_size
|
||||
return batch
|
||||
|
||||
@@ -45,8 +46,9 @@ class MockPlPy:
|
||||
data = self.execute(query)
|
||||
return MockCursor(data)
|
||||
|
||||
def execute(self, query): # TODO: additional arguments
|
||||
for result in self.results:
|
||||
if result[0].match(query):
|
||||
return result[1]
|
||||
return []
|
||||
# TODO: additional arguments
|
||||
def execute(self, query):
|
||||
for result in self.results:
|
||||
if result[0].match(query):
|
||||
return result[1]
|
||||
return []
|
||||
|
||||
@@ -1,38 +0,0 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
|
||||
|
||||
# from mock_plpy import MockPlPy
|
||||
# plpy = MockPlPy()
|
||||
#
|
||||
# import sys
|
||||
# sys.modules['plpy'] = plpy
|
||||
from helper import plpy, fixture_file
|
||||
import numpy as np
|
||||
import crankshaft.clustering as cc
|
||||
import crankshaft.pysal_utils as pu
|
||||
from crankshaft import random_seeds
|
||||
import json
|
||||
|
||||
class KMeansTest(unittest.TestCase):
|
||||
"""Testing class for Moran's I functions"""
|
||||
|
||||
def setUp(self):
|
||||
plpy._reset()
|
||||
self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read())
|
||||
self.params = {"subquery": "select * from table",
|
||||
"no_clusters": "10"
|
||||
}
|
||||
|
||||
def test_kmeans(self):
|
||||
data = self.cluster_data
|
||||
plpy._define_result('select' ,data)
|
||||
clusters = cc.kmeans('subquery', 2)
|
||||
labels = [a[1] for a in clusters]
|
||||
c1 = [a for a in clusters if a[1]==0]
|
||||
c2 = [a for a in clusters if a[1]==1]
|
||||
|
||||
self.assertEqual(len(np.unique(labels)),2)
|
||||
self.assertEqual(len(c1),20)
|
||||
self.assertEqual(len(c2),20)
|
||||
|
||||
78
src/py/crankshaft/test/test_clustering_getis.py
Normal file
78
src/py/crankshaft/test/test_clustering_getis.py
Normal file
@@ -0,0 +1,78 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
|
||||
from helper import fixture_file
|
||||
|
||||
from crankshaft.clustering import Getis
|
||||
import crankshaft.pysal_utils as pu
|
||||
from crankshaft import random_seeds
|
||||
import json
|
||||
from crankshaft.analysis_data_provider import AnalysisDataProvider
|
||||
|
||||
# Fixture files produced as follows
|
||||
#
|
||||
# import pysal as ps
|
||||
# import numpy as np
|
||||
# import random
|
||||
#
|
||||
# # setup variables
|
||||
# f = ps.open(ps.examples.get_path("stl_hom.dbf"))
|
||||
# y = np.array(f.by_col['HR8893'])
|
||||
# w_queen = ps.queen_from_shapefile(ps.examples.get_path("stl_hom.shp"))
|
||||
#
|
||||
# out_queen = [{"id": index + 1,
|
||||
# "neighbors": [x+1 for x in w_queen.neighbors[index]],
|
||||
# "value": val} for index, val in enumerate(y)]
|
||||
#
|
||||
# with open('neighbors_queen_getis.json', 'w') as f:
|
||||
# f.write(str(out_queen))
|
||||
#
|
||||
# random.seed(1234)
|
||||
# np.random.seed(1234)
|
||||
# lgstar_queen = ps.esda.getisord.G_Local(y, w_queen, star=True,
|
||||
# permutations=999)
|
||||
#
|
||||
# with open('getis_queen.json', 'w') as f:
|
||||
# f.write(str(zip(lgstar_queen.z_sim,
|
||||
# lgstar_queen.p_sim, lgstar_queen.p_z_sim)))
|
||||
|
||||
|
||||
class FakeDataProvider(AnalysisDataProvider):
|
||||
def __init__(self, mock_data):
|
||||
self.mock_result = mock_data
|
||||
|
||||
def get_getis(self, w_type, param):
|
||||
return self.mock_result
|
||||
|
||||
|
||||
class GetisTest(unittest.TestCase):
|
||||
"""Testing class for Getis-Ord's G* funtion
|
||||
This test replicates the work done in PySAL documentation:
|
||||
https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/autocorrelation.html#local-g-and-g
|
||||
"""
|
||||
|
||||
def setUp(self):
|
||||
# load raw data for analysis
|
||||
self.neighbors_data = json.loads(
|
||||
open(fixture_file('neighbors_getis.json')).read())
|
||||
|
||||
# load pre-computed/known values
|
||||
self.getis_data = json.loads(
|
||||
open(fixture_file('getis.json')).read())
|
||||
|
||||
def test_getis_ord(self):
|
||||
"""Test Getis-Ord's G*"""
|
||||
data = [{'id': d['id'],
|
||||
'attr1': d['value'],
|
||||
'neighbors': d['neighbors']} for d in self.neighbors_data]
|
||||
|
||||
random_seeds.set_random_seeds(1234)
|
||||
getis = Getis(FakeDataProvider(data))
|
||||
|
||||
result = getis.getis_ord('subquery', 'value',
|
||||
'queen', None, 999, 'the_geom',
|
||||
'cartodb_id')
|
||||
result = [(row[0], row[1]) for row in result]
|
||||
expected = np.array(self.getis_data)[:, 0:2]
|
||||
for ([res_z, res_p], [exp_z, exp_p]) in zip(result, expected):
|
||||
self.assertAlmostEqual(res_z, exp_z, delta=1e-2)
|
||||
56
src/py/crankshaft/test/test_clustering_kmeans.py
Normal file
56
src/py/crankshaft/test/test_clustering_kmeans.py
Normal file
@@ -0,0 +1,56 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
|
||||
|
||||
# from mock_plpy import MockPlPy
|
||||
# plpy = MockPlPy()
|
||||
#
|
||||
# import sys
|
||||
# sys.modules['plpy'] = plpy
|
||||
from helper import fixture_file
|
||||
from crankshaft.clustering import Kmeans
|
||||
from crankshaft.analysis_data_provider import AnalysisDataProvider
|
||||
import crankshaft.clustering as cc
|
||||
|
||||
from crankshaft import random_seeds
|
||||
import json
|
||||
from collections import OrderedDict
|
||||
|
||||
|
||||
class FakeDataProvider(AnalysisDataProvider):
|
||||
def __init__(self, mocked_result):
|
||||
self.mocked_result = mocked_result
|
||||
|
||||
def get_spatial_kmeans(self, query):
|
||||
return self.mocked_result
|
||||
|
||||
def get_nonspatial_kmeans(self, query, standarize):
|
||||
return self.mocked_result
|
||||
|
||||
|
||||
class KMeansTest(unittest.TestCase):
|
||||
"""Testing class for k-means spatial"""
|
||||
|
||||
def setUp(self):
|
||||
self.cluster_data = json.loads(
|
||||
open(fixture_file('kmeans.json')).read())
|
||||
self.params = {"subquery": "select * from table",
|
||||
"no_clusters": "10"}
|
||||
|
||||
def test_kmeans(self):
|
||||
"""
|
||||
"""
|
||||
data = [{'xs': d['xs'],
|
||||
'ys': d['ys'],
|
||||
'ids': d['ids']} for d in self.cluster_data]
|
||||
|
||||
random_seeds.set_random_seeds(1234)
|
||||
kmeans = Kmeans(FakeDataProvider(data))
|
||||
clusters = kmeans.spatial('subquery', 2)
|
||||
labels = [a[1] for a in clusters]
|
||||
c1 = [a for a in clusters if a[1] == 0]
|
||||
c2 = [a for a in clusters if a[1] == 1]
|
||||
|
||||
self.assertEqual(len(np.unique(labels)), 2)
|
||||
self.assertEqual(len(c1), 20)
|
||||
self.assertEqual(len(c2), 20)
|
||||
@@ -1,25 +1,27 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
|
||||
|
||||
# from mock_plpy import MockPlPy
|
||||
# plpy = MockPlPy()
|
||||
#
|
||||
# import sys
|
||||
# sys.modules['plpy'] = plpy
|
||||
from helper import plpy, fixture_file
|
||||
|
||||
import crankshaft.clustering as cc
|
||||
from helper import fixture_file
|
||||
from crankshaft.clustering import Moran
|
||||
from crankshaft.analysis_data_provider import AnalysisDataProvider
|
||||
import crankshaft.pysal_utils as pu
|
||||
from crankshaft import random_seeds
|
||||
import json
|
||||
from collections import OrderedDict
|
||||
|
||||
|
||||
class FakeDataProvider(AnalysisDataProvider):
|
||||
def __init__(self, mock_data):
|
||||
self.mock_result = mock_data
|
||||
|
||||
def get_moran(self, w_type, params):
|
||||
return self.mock_result
|
||||
|
||||
|
||||
class MoranTest(unittest.TestCase):
|
||||
"""Testing class for Moran's I functions"""
|
||||
|
||||
def setUp(self):
|
||||
plpy._reset()
|
||||
self.params = {"id_col": "cartodb_id",
|
||||
"attr1": "andy",
|
||||
"attr2": "jay_z",
|
||||
@@ -39,33 +41,36 @@ class MoranTest(unittest.TestCase):
|
||||
|
||||
def test_map_quads(self):
|
||||
"""Test map_quads"""
|
||||
self.assertEqual(cc.map_quads(1), 'HH')
|
||||
self.assertEqual(cc.map_quads(2), 'LH')
|
||||
self.assertEqual(cc.map_quads(3), 'LL')
|
||||
self.assertEqual(cc.map_quads(4), 'HL')
|
||||
self.assertEqual(cc.map_quads(33), None)
|
||||
self.assertEqual(cc.map_quads('andy'), None)
|
||||
from crankshaft.clustering import map_quads
|
||||
self.assertEqual(map_quads(1), 'HH')
|
||||
self.assertEqual(map_quads(2), 'LH')
|
||||
self.assertEqual(map_quads(3), 'LL')
|
||||
self.assertEqual(map_quads(4), 'HL')
|
||||
self.assertEqual(map_quads(33), None)
|
||||
self.assertEqual(map_quads('andy'), None)
|
||||
|
||||
def test_quad_position(self):
|
||||
"""Test lisa_sig_vals"""
|
||||
from crankshaft.clustering import quad_position
|
||||
|
||||
quads = np.array([1, 2, 3, 4], np.int)
|
||||
|
||||
ans = np.array(['HH', 'LH', 'LL', 'HL'])
|
||||
test_ans = cc.quad_position(quads)
|
||||
test_ans = quad_position(quads)
|
||||
|
||||
self.assertTrue((test_ans == ans).all())
|
||||
|
||||
def test_moran_local(self):
|
||||
def test_local_stat(self):
|
||||
"""Test Moran's I local"""
|
||||
data = [{'id': d['id'],
|
||||
'attr1': d['value'],
|
||||
'neighbors': d['neighbors']} for d in self.neighbors_data]
|
||||
data = [OrderedDict([('id', d['id']),
|
||||
('attr1', d['value']),
|
||||
('neighbors', d['neighbors'])])
|
||||
for d in self.neighbors_data]
|
||||
|
||||
plpy._define_result('select', data)
|
||||
moran = Moran(FakeDataProvider(data))
|
||||
random_seeds.set_random_seeds(1234)
|
||||
result = cc.moran_local('subquery', 'value',
|
||||
'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
result = moran.local_stat('subquery', 'value',
|
||||
'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
result = [(row[0], row[1]) for row in result]
|
||||
zipped_values = zip(result, self.moran_data)
|
||||
|
||||
@@ -80,10 +85,10 @@ class MoranTest(unittest.TestCase):
|
||||
'attr2': 1,
|
||||
'neighbors': d['neighbors']} for d in self.neighbors_data]
|
||||
|
||||
plpy._define_result('select', data)
|
||||
random_seeds.set_random_seeds(1234)
|
||||
result = cc.moran_local_rate('subquery', 'numerator', 'denominator',
|
||||
'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
moran = Moran(FakeDataProvider(data))
|
||||
result = moran.local_rate_stat('subquery', 'numerator', 'denominator',
|
||||
'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
result = [(row[0], row[1]) for row in result]
|
||||
|
||||
zipped_values = zip(result, self.moran_data)
|
||||
@@ -96,10 +101,11 @@ class MoranTest(unittest.TestCase):
|
||||
data = [{'id': d['id'],
|
||||
'attr1': d['value'],
|
||||
'neighbors': d['neighbors']} for d in self.neighbors_data]
|
||||
plpy._define_result('select', data)
|
||||
random_seeds.set_random_seeds(1235)
|
||||
result = cc.moran('table', 'value',
|
||||
'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
moran = Moran(FakeDataProvider(data))
|
||||
result = moran.global_stat('table', 'value',
|
||||
'knn', 5, 99, 'the_geom',
|
||||
'cartodb_id')
|
||||
|
||||
result_moran = result[0][0]
|
||||
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
|
||||
|
||||
@@ -2,18 +2,33 @@ import unittest
|
||||
|
||||
import crankshaft.pysal_utils as pu
|
||||
from crankshaft import random_seeds
|
||||
from collections import OrderedDict
|
||||
|
||||
|
||||
class PysalUtilsTest(unittest.TestCase):
|
||||
"""Testing class for utility functions related to PySAL integrations"""
|
||||
|
||||
def setUp(self):
|
||||
self.params = {"id_col": "cartodb_id",
|
||||
"attr1": "andy",
|
||||
"attr2": "jay_z",
|
||||
"subquery": "SELECT * FROM a_list",
|
||||
"geom_col": "the_geom",
|
||||
"num_ngbrs": 321}
|
||||
self.params1 = OrderedDict([("id_col", "cartodb_id"),
|
||||
("attr1", "andy"),
|
||||
("attr2", "jay_z"),
|
||||
("subquery", "SELECT * FROM a_list"),
|
||||
("geom_col", "the_geom"),
|
||||
("num_ngbrs", 321)])
|
||||
|
||||
self.params2 = OrderedDict([("id_col", "cartodb_id"),
|
||||
("numerator", "price"),
|
||||
("denominator", "sq_meters"),
|
||||
("subquery", "SELECT * FROM pecan"),
|
||||
("geom_col", "the_geom"),
|
||||
("num_ngbrs", 321)])
|
||||
|
||||
self.params3 = OrderedDict([("id_col", "cartodb_id"),
|
||||
("numerator", "sq_meters"),
|
||||
("denominator", "price"),
|
||||
("subquery", "SELECT * FROM pecan"),
|
||||
("geom_col", "the_geom"),
|
||||
("num_ngbrs", 321)])
|
||||
|
||||
self.params_array = {"id_col": "cartodb_id",
|
||||
"time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
|
||||
@@ -24,34 +39,41 @@ class PysalUtilsTest(unittest.TestCase):
|
||||
def test_query_attr_select(self):
|
||||
"""Test query_attr_select"""
|
||||
|
||||
ans = "i.\"andy\"::numeric As attr1, " \
|
||||
"i.\"jay_z\"::numeric As attr2, "
|
||||
ans1 = ("i.\"andy\"::numeric As attr1, "
|
||||
"i.\"jay_z\"::numeric As attr2, ")
|
||||
|
||||
ans_array = "i.\"_2013_dec\"::numeric As attr1, " \
|
||||
"i.\"_2014_jan\"::numeric As attr2, " \
|
||||
"i.\"_2014_feb\"::numeric As attr3, "
|
||||
ans2 = ("i.\"price\"::numeric As attr1, "
|
||||
"i.\"sq_meters\"::numeric As attr2, ")
|
||||
|
||||
self.assertEqual(pu.query_attr_select(self.params), ans)
|
||||
ans3 = ("i.\"sq_meters\"::numeric As attr1, "
|
||||
"i.\"price\"::numeric As attr2, ")
|
||||
|
||||
ans_array = ("i.\"_2013_dec\"::numeric As attr1, "
|
||||
"i.\"_2014_jan\"::numeric As attr2, "
|
||||
"i.\"_2014_feb\"::numeric As attr3, ")
|
||||
|
||||
self.assertEqual(pu.query_attr_select(self.params1), ans1)
|
||||
self.assertEqual(pu.query_attr_select(self.params2), ans2)
|
||||
self.assertEqual(pu.query_attr_select(self.params3), ans3)
|
||||
self.assertEqual(pu.query_attr_select(self.params_array), ans_array)
|
||||
|
||||
def test_query_attr_where(self):
|
||||
"""Test pu.query_attr_where"""
|
||||
|
||||
ans = "idx_replace.\"andy\" IS NOT NULL AND " \
|
||||
"idx_replace.\"jay_z\" IS NOT NULL AND " \
|
||||
"idx_replace.\"jay_z\" <> 0"
|
||||
ans1 = ("idx_replace.\"andy\" IS NOT NULL AND "
|
||||
"idx_replace.\"jay_z\" IS NOT NULL")
|
||||
|
||||
ans_array = "idx_replace.\"_2013_dec\" IS NOT NULL AND " \
|
||||
"idx_replace.\"_2014_jan\" IS NOT NULL AND " \
|
||||
"idx_replace.\"_2014_feb\" IS NOT NULL"
|
||||
ans_array = ("idx_replace.\"_2013_dec\" IS NOT NULL AND "
|
||||
"idx_replace.\"_2014_jan\" IS NOT NULL AND "
|
||||
"idx_replace.\"_2014_feb\" IS NOT NULL")
|
||||
|
||||
self.assertEqual(pu.query_attr_where(self.params), ans)
|
||||
self.assertEqual(pu.query_attr_where(self.params1), ans1)
|
||||
self.assertEqual(pu.query_attr_where(self.params_array), ans_array)
|
||||
|
||||
def test_knn(self):
|
||||
"""Test knn neighbors constructor"""
|
||||
|
||||
ans = "SELECT i.\"cartodb_id\" As id, " \
|
||||
ans1 = "SELECT i.\"cartodb_id\" As id, " \
|
||||
"i.\"andy\"::numeric As attr1, " \
|
||||
"i.\"jay_z\"::numeric As attr2, " \
|
||||
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
|
||||
@@ -59,17 +81,15 @@ class PysalUtilsTest(unittest.TestCase):
|
||||
"WHERE " \
|
||||
"i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
|
||||
"j.\"andy\" IS NOT NULL AND " \
|
||||
"j.\"jay_z\" IS NOT NULL AND " \
|
||||
"j.\"jay_z\" <> 0 " \
|
||||
"j.\"jay_z\" IS NOT NULL " \
|
||||
"ORDER BY " \
|
||||
"j.\"the_geom\" <-> i.\"the_geom\" ASC " \
|
||||
"LIMIT 321)) As neighbors " \
|
||||
"FROM (SELECT * FROM a_list) As i " \
|
||||
"WHERE i.\"andy\" IS NOT NULL AND " \
|
||||
"i.\"jay_z\" IS NOT NULL AND " \
|
||||
"i.\"jay_z\" <> 0 " \
|
||||
"i.\"jay_z\" IS NOT NULL " \
|
||||
"ORDER BY i.\"cartodb_id\" ASC;"
|
||||
|
||||
|
||||
ans_array = "SELECT i.\"cartodb_id\" As id, " \
|
||||
"i.\"_2013_dec\"::numeric As attr1, " \
|
||||
"i.\"_2014_jan\"::numeric As attr2, " \
|
||||
@@ -88,13 +108,13 @@ class PysalUtilsTest(unittest.TestCase):
|
||||
"i.\"_2014_feb\" IS NOT NULL "\
|
||||
"ORDER BY i.\"cartodb_id\" ASC;"
|
||||
|
||||
self.assertEqual(pu.knn(self.params), ans)
|
||||
self.assertEqual(pu.knn(self.params1), ans1)
|
||||
self.assertEqual(pu.knn(self.params_array), ans_array)
|
||||
|
||||
def test_queen(self):
|
||||
"""Test queen neighbors constructor"""
|
||||
|
||||
ans = "SELECT i.\"cartodb_id\" As id, " \
|
||||
ans1 = "SELECT i.\"cartodb_id\" As id, " \
|
||||
"i.\"andy\"::numeric As attr1, " \
|
||||
"i.\"jay_z\"::numeric As attr2, " \
|
||||
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
|
||||
@@ -104,23 +124,21 @@ class PysalUtilsTest(unittest.TestCase):
|
||||
"ST_Touches(i.\"the_geom\", " \
|
||||
"j.\"the_geom\") AND " \
|
||||
"j.\"andy\" IS NOT NULL AND " \
|
||||
"j.\"jay_z\" IS NOT NULL AND " \
|
||||
"j.\"jay_z\" <> 0)" \
|
||||
"j.\"jay_z\" IS NOT NULL)" \
|
||||
") As neighbors " \
|
||||
"FROM (SELECT * FROM a_list) As i " \
|
||||
"WHERE i.\"andy\" IS NOT NULL AND " \
|
||||
"i.\"jay_z\" IS NOT NULL AND " \
|
||||
"i.\"jay_z\" <> 0 " \
|
||||
"i.\"jay_z\" IS NOT NULL " \
|
||||
"ORDER BY i.\"cartodb_id\" ASC;"
|
||||
|
||||
self.assertEqual(pu.queen(self.params), ans)
|
||||
self.assertEqual(pu.queen(self.params1), ans1)
|
||||
|
||||
def test_construct_neighbor_query(self):
|
||||
"""Test construct_neighbor_query"""
|
||||
|
||||
# Compare to raw knn query
|
||||
self.assertEqual(pu.construct_neighbor_query('knn', self.params),
|
||||
pu.knn(self.params))
|
||||
self.assertEqual(pu.construct_neighbor_query('knn', self.params1),
|
||||
pu.knn(self.params1))
|
||||
|
||||
def test_get_attributes(self):
|
||||
"""Test get_attributes"""
|
||||
|
||||
@@ -4,86 +4,99 @@ import numpy as np
|
||||
import unittest
|
||||
|
||||
|
||||
# from mock_plpy import MockPlPy
|
||||
# plpy = MockPlPy()
|
||||
#
|
||||
# import sys
|
||||
# sys.modules['plpy'] = plpy
|
||||
from helper import plpy, fixture_file
|
||||
from helper import fixture_file
|
||||
|
||||
from crankshaft.space_time_dynamics import Markov
|
||||
import crankshaft.space_time_dynamics as std
|
||||
from crankshaft import random_seeds
|
||||
from crankshaft.analysis_data_provider import AnalysisDataProvider
|
||||
import json
|
||||
|
||||
|
||||
class FakeDataProvider(AnalysisDataProvider):
|
||||
def __init__(self, data):
|
||||
self.mock_result = data
|
||||
|
||||
def get_markov(self, w_type, params):
|
||||
return self.mock_result
|
||||
|
||||
|
||||
class SpaceTimeTests(unittest.TestCase):
|
||||
"""Testing class for Markov Functions."""
|
||||
|
||||
def setUp(self):
|
||||
plpy._reset()
|
||||
self.params = {"id_col": "cartodb_id",
|
||||
"time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
|
||||
"subquery": "SELECT * FROM a_list",
|
||||
"geom_col": "the_geom",
|
||||
"num_ngbrs": 321}
|
||||
self.neighbors_data = json.loads(open(fixture_file('neighbors_markov.json')).read())
|
||||
self.neighbors_data = json.loads(
|
||||
open(fixture_file('neighbors_markov.json')).read())
|
||||
self.markov_data = json.loads(open(fixture_file('markov.json')).read())
|
||||
|
||||
self.time_data = np.array([i * np.ones(10, dtype=float) for i in range(10)]).T
|
||||
self.time_data = np.array([i * np.ones(10, dtype=float)
|
||||
for i in range(10)]).T
|
||||
|
||||
self.transition_matrix = np.array([
|
||||
[[ 0.96341463, 0.0304878 , 0.00609756, 0. , 0. ],
|
||||
[ 0.06040268, 0.83221477, 0.10738255, 0. , 0. ],
|
||||
[ 0. , 0.14 , 0.74 , 0.12 , 0. ],
|
||||
[ 0. , 0.03571429, 0.32142857, 0.57142857, 0.07142857],
|
||||
[ 0. , 0. , 0. , 0.16666667, 0.83333333]],
|
||||
[[ 0.79831933, 0.16806723, 0.03361345, 0. , 0. ],
|
||||
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
|
||||
[ 0.00537634, 0.06989247, 0.8655914 , 0.05913978, 0. ],
|
||||
[ 0. , 0. , 0.06372549, 0.90196078, 0.03431373],
|
||||
[ 0. , 0. , 0. , 0.19444444, 0.80555556]],
|
||||
[[ 0.84693878, 0.15306122, 0. , 0. , 0. ],
|
||||
[ 0.08133971, 0.78947368, 0.1291866 , 0. , 0. ],
|
||||
[ 0.00518135, 0.0984456 , 0.79274611, 0.0984456 , 0.00518135],
|
||||
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
|
||||
[ 0. , 0. , 0. , 0.10204082, 0.89795918]],
|
||||
[[ 0.8852459 , 0.09836066, 0. , 0.01639344, 0. ],
|
||||
[ 0.03875969, 0.81395349, 0.13953488, 0. , 0.00775194],
|
||||
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
|
||||
[ 0. , 0.02339181, 0.12865497, 0.75438596, 0.09356725],
|
||||
[ 0. , 0. , 0. , 0.09661836, 0.90338164]],
|
||||
[[ 0.33333333, 0.66666667, 0. , 0. , 0. ],
|
||||
[ 0.0483871 , 0.77419355, 0.16129032, 0.01612903, 0. ],
|
||||
[ 0.01149425, 0.16091954, 0.74712644, 0.08045977, 0. ],
|
||||
[ 0. , 0.01036269, 0.06217617, 0.89637306, 0.03108808],
|
||||
[ 0. , 0. , 0. , 0.02352941, 0.97647059]]]
|
||||
[[0.96341463, 0.0304878, 0.00609756, 0., 0.],
|
||||
[0.06040268, 0.83221477, 0.10738255, 0., 0.],
|
||||
[0., 0.14, 0.74, 0.12, 0.],
|
||||
[0., 0.03571429, 0.32142857, 0.57142857, 0.07142857],
|
||||
[0., 0., 0., 0.16666667, 0.83333333]],
|
||||
[[0.79831933, 0.16806723, 0.03361345, 0., 0.],
|
||||
[0.0754717, 0.88207547, 0.04245283, 0., 0.],
|
||||
[0.00537634, 0.06989247, 0.8655914, 0.05913978, 0.],
|
||||
[0., 0., 0.06372549, 0.90196078, 0.03431373],
|
||||
[0., 0., 0., 0.19444444, 0.80555556]],
|
||||
[[0.84693878, 0.15306122, 0., 0., 0.],
|
||||
[0.08133971, 0.78947368, 0.1291866, 0., 0.],
|
||||
[0.00518135, 0.0984456, 0.79274611, 0.0984456, 0.00518135],
|
||||
[0., 0., 0.09411765, 0.87058824, 0.03529412],
|
||||
[0., 0., 0., 0.10204082, 0.89795918]],
|
||||
[[0.8852459, 0.09836066, 0., 0.01639344, 0.],
|
||||
[0.03875969, 0.81395349, 0.13953488, 0., 0.00775194],
|
||||
[0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
|
||||
[0., 0.02339181, 0.12865497, 0.75438596, 0.09356725],
|
||||
[0., 0., 0., 0.09661836, 0.90338164]],
|
||||
[[0.33333333, 0.66666667, 0., 0., 0.],
|
||||
[0.0483871, 0.77419355, 0.16129032, 0.01612903, 0.],
|
||||
[0.01149425, 0.16091954, 0.74712644, 0.08045977, 0.],
|
||||
[0., 0.01036269, 0.06217617, 0.89637306, 0.03108808],
|
||||
[0., 0., 0., 0.02352941, 0.97647059]]]
|
||||
)
|
||||
|
||||
def test_spatial_markov(self):
|
||||
"""Test Spatial Markov."""
|
||||
data = [ { 'id': d['id'],
|
||||
'attr1': d['y1995'],
|
||||
'attr2': d['y1996'],
|
||||
'attr3': d['y1997'],
|
||||
'attr4': d['y1998'],
|
||||
'attr5': d['y1999'],
|
||||
'attr6': d['y2000'],
|
||||
'attr7': d['y2001'],
|
||||
'attr8': d['y2002'],
|
||||
'attr9': d['y2003'],
|
||||
'attr10': d['y2004'],
|
||||
'attr11': d['y2005'],
|
||||
'attr12': d['y2006'],
|
||||
'attr13': d['y2007'],
|
||||
'attr14': d['y2008'],
|
||||
'attr15': d['y2009'],
|
||||
'neighbors': d['neighbors'] } for d in self.neighbors_data]
|
||||
print(str(data[0]))
|
||||
plpy._define_result('select', data)
|
||||
data = [{'id': d['id'],
|
||||
'attr1': d['y1995'],
|
||||
'attr2': d['y1996'],
|
||||
'attr3': d['y1997'],
|
||||
'attr4': d['y1998'],
|
||||
'attr5': d['y1999'],
|
||||
'attr6': d['y2000'],
|
||||
'attr7': d['y2001'],
|
||||
'attr8': d['y2002'],
|
||||
'attr9': d['y2003'],
|
||||
'attr10': d['y2004'],
|
||||
'attr11': d['y2005'],
|
||||
'attr12': d['y2006'],
|
||||
'attr13': d['y2007'],
|
||||
'attr14': d['y2008'],
|
||||
'attr15': d['y2009'],
|
||||
'neighbors': d['neighbors']} for d in self.neighbors_data]
|
||||
# print(str(data[0]))
|
||||
markov = Markov(FakeDataProvider(data))
|
||||
random_seeds.set_random_seeds(1234)
|
||||
|
||||
result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id')
|
||||
result = markov.spatial_trend('subquery',
|
||||
['y1995', 'y1996', 'y1997', 'y1998',
|
||||
'y1999', 'y2000', 'y2001', 'y2002',
|
||||
'y2003', 'y2004', 'y2005', 'y2006',
|
||||
'y2007', 'y2008', 'y2009'],
|
||||
5, 'knn', 5, 0, 'the_geom',
|
||||
'cartodb_id')
|
||||
|
||||
self.assertTrue(result != None)
|
||||
self.assertTrue(result is not None)
|
||||
result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
|
||||
print result[0]
|
||||
expected = self.markov_data
|
||||
@@ -94,173 +107,178 @@ class SpaceTimeTests(unittest.TestCase):
|
||||
|
||||
def test_get_time_data(self):
|
||||
"""Test get_time_data"""
|
||||
data = [ { 'attr1': d['y1995'],
|
||||
'attr2': d['y1996'],
|
||||
'attr3': d['y1997'],
|
||||
'attr4': d['y1998'],
|
||||
'attr5': d['y1999'],
|
||||
'attr6': d['y2000'],
|
||||
'attr7': d['y2001'],
|
||||
'attr8': d['y2002'],
|
||||
'attr9': d['y2003'],
|
||||
'attr10': d['y2004'],
|
||||
'attr11': d['y2005'],
|
||||
'attr12': d['y2006'],
|
||||
'attr13': d['y2007'],
|
||||
'attr14': d['y2008'],
|
||||
'attr15': d['y2009'] } for d in self.neighbors_data]
|
||||
data = [{'attr1': d['y1995'],
|
||||
'attr2': d['y1996'],
|
||||
'attr3': d['y1997'],
|
||||
'attr4': d['y1998'],
|
||||
'attr5': d['y1999'],
|
||||
'attr6': d['y2000'],
|
||||
'attr7': d['y2001'],
|
||||
'attr8': d['y2002'],
|
||||
'attr9': d['y2003'],
|
||||
'attr10': d['y2004'],
|
||||
'attr11': d['y2005'],
|
||||
'attr12': d['y2006'],
|
||||
'attr13': d['y2007'],
|
||||
'attr14': d['y2008'],
|
||||
'attr15': d['y2009']} for d in self.neighbors_data]
|
||||
|
||||
result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'])
|
||||
result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998',
|
||||
'y1999', 'y2000', 'y2001', 'y2002',
|
||||
'y2003', 'y2004', 'y2005', 'y2006',
|
||||
'y2007', 'y2008', 'y2009'])
|
||||
|
||||
## expected was prepared from PySAL example:
|
||||
### f = ps.open(ps.examples.get_path("usjoin.csv"))
|
||||
### pci = np.array([f.by_col[str(y)] for y in range(1995, 2010)]).transpose()
|
||||
### rpci = pci / (pci.mean(axis = 0))
|
||||
# expected was prepared from PySAL example:
|
||||
# f = ps.open(ps.examples.get_path("usjoin.csv"))
|
||||
# pci = np.array([f.by_col[str(y)]
|
||||
# for y in range(1995, 2010)]).transpose()
|
||||
# rpci = pci / (pci.mean(axis = 0))
|
||||
|
||||
expected = np.array([[ 0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154, 0.83271652
|
||||
, 0.83786314, 0.85012593, 0.85509656, 0.86416612, 0.87119375, 0.86302631
|
||||
, 0.86148267, 0.86252252, 0.86746356],
|
||||
[ 0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388, 0.90746978
|
||||
, 0.89830489, 0.89431991, 0.88924794, 0.89815176, 0.91832091, 0.91706054
|
||||
, 0.90139505, 0.87897455, 0.86216858],
|
||||
[ 0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522, 0.78964559
|
||||
, 0.80584442, 0.8084998, 0.82258551, 0.82668196, 0.82373724, 0.81814804
|
||||
, 0.83675961, 0.83574199, 0.84647177],
|
||||
[ 1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841, 1.14506948
|
||||
, 1.12151133, 1.11160697, 1.10888621, 1.11399806, 1.12168029, 1.13164797
|
||||
, 1.12958508, 1.11371818, 1.09936775],
|
||||
[ 1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025, 1.16898201
|
||||
, 1.17212488, 1.14752303, 1.11843284, 1.11024964, 1.11943471, 1.11736468
|
||||
, 1.10863242, 1.09642516, 1.07762337],
|
||||
[ 1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684, 1.44184737
|
||||
, 1.44782832, 1.41978227, 1.39092208, 1.4059372, 1.40788646, 1.44052766
|
||||
, 1.45241216, 1.43306098, 1.4174431 ],
|
||||
[ 1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149, 1.10888138
|
||||
, 1.11856629, 1.13062931, 1.11944984, 1.12446239, 1.11671008, 1.10880034
|
||||
, 1.08401709, 1.06959206, 1.07875225],
|
||||
[ 1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545, 0.99854316
|
||||
, 0.9880258, 0.99669587, 0.99327676, 1.01400905, 1.03176742, 1.040511
|
||||
, 1.01749645, 0.9936394, 0.98279746],
|
||||
[ 0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845, 0.99127006
|
||||
, 0.97925917, 0.9683482, 0.95335147, 0.93694787, 0.94308213, 0.92232874
|
||||
, 0.91284091, 0.89689833, 0.88928858],
|
||||
[ 0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044, 0.8578708
|
||||
, 0.86036185, 0.86107306, 0.8500772, 0.86981998, 0.86837929, 0.87204141
|
||||
, 0.86633032, 0.84946077, 0.83287146],
|
||||
[ 1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624, 1.14450183
|
||||
, 1.12349752, 1.12596664, 1.12213996, 1.1119989, 1.10257792, 1.10491258
|
||||
, 1.11059842, 1.10509795, 1.10020097],
|
||||
[ 0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687, 0.95831051
|
||||
, 0.94480909, 0.94804195, 0.95430286, 0.94103989, 0.92122519, 0.91010201
|
||||
, 0.89280392, 0.89298243, 0.89165385],
|
||||
[ 0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647, 0.9480927
|
||||
, 0.93539182, 0.95388718, 0.94597005, 0.96918424, 0.94781281, 0.93466815
|
||||
, 0.94281559, 0.96520315, 0.96715441],
|
||||
[ 0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897, 0.98687073
|
||||
, 0.99237486, 0.98209969, 0.9877653, 0.97399471, 0.96910087, 0.98416665
|
||||
, 0.98423613, 0.99823861, 0.99545704],
|
||||
[ 0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012, 0.86191535
|
||||
, 0.84981451, 0.85472102, 0.84564835, 0.83998883, 0.83478547, 0.82803648
|
||||
, 0.8198736, 0.82265395, 0.8399404 ],
|
||||
[ 0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136, 0.82785597
|
||||
, 0.86008789, 0.86776298, 0.86720209, 0.8676334, 0.89179317, 0.94202108
|
||||
, 0.9422231, 0.93902708, 0.94479184],
|
||||
[ 0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238, 0.90906632
|
||||
, 0.92693339, 0.93695966, 0.94242697, 0.94338265, 0.91981796, 0.91108804
|
||||
, 0.90543476, 0.91737138, 0.94793657],
|
||||
[ 1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723, 1.20172869
|
||||
, 1.21328691, 1.22624778, 1.22397075, 1.23857042, 1.24419893, 1.23929384
|
||||
, 1.23418676, 1.23626739, 1.26754398],
|
||||
[ 1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667, 1.34790023
|
||||
, 1.34399863, 1.32575181, 1.30795492, 1.30544841, 1.30303302, 1.32107766
|
||||
, 1.32936244, 1.33001241, 1.33288462],
|
||||
[ 1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093, 1.05059016
|
||||
, 1.03405057, 1.02747623, 1.03162734, 0.9961416, 0.97356208, 0.94241549
|
||||
, 0.92754547, 0.92549227, 0.92138102],
|
||||
[ 1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264, 1.13889622
|
||||
, 1.12442212, 1.13367018, 1.13982256, 1.14029944, 1.11979401, 1.10905389
|
||||
, 1.10577769, 1.11166825, 1.09985155],
|
||||
[ 0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284, 0.74480073
|
||||
, 0.76098396, 0.76156903, 0.76651952, 0.76533288, 0.78205934, 0.76842416
|
||||
, 0.77487118, 0.77768683, 0.78801192],
|
||||
[ 0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803, 0.97370819
|
||||
, 0.96419154, 0.97209861, 0.97441313, 0.96356162, 0.94745352, 0.93965462
|
||||
, 0.93069645, 0.94020973, 0.94358232],
|
||||
[ 0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801, 0.80071489
|
||||
, 0.83358256, 0.83451613, 0.85175032, 0.85954307, 0.86790024, 0.87170334
|
||||
, 0.87863799, 0.87497981, 0.87888675],
|
||||
[ 0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619, 0.98733195
|
||||
, 0.99644997, 0.99669587, 1.02559097, 1.01116651, 0.99988024, 0.97906749
|
||||
, 0.99323123, 1.00204939, 0.99602148],
|
||||
[ 1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683, 1.08312397
|
||||
, 1.05192626, 1.04230892, 1.05577278, 1.08569751, 1.12443486, 1.08891079
|
||||
, 1.08603695, 1.05997314, 1.02160943],
|
||||
[ 1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272, 1.18257029
|
||||
, 1.16226243, 1.16009196, 1.14467789, 1.14820235, 1.12386598, 1.12680236
|
||||
, 1.12357937, 1.1159258, 1.12570828],
|
||||
[ 1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667, 1.31210239
|
||||
, 1.29989156, 1.29203193, 1.27183516, 1.26830786, 1.2617743, 1.28656675
|
||||
, 1.29734097, 1.29390205, 1.29345446],
|
||||
[ 0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864, 0.78772975
|
||||
, 0.82848011, 0.8259679, 0.82435705, 0.83108634, 0.84373784, 0.83891093
|
||||
, 0.84349247, 0.85637272, 0.86539395],
|
||||
[ 1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626, 1.2256767
|
||||
, 1.21126648, 1.19377804, 1.18355337, 1.19674434, 1.21536573, 1.23653297
|
||||
, 1.27962009, 1.27968392, 1.25907738],
|
||||
[ 0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282, 0.96480308
|
||||
, 0.94686376, 0.93679073, 0.92540049, 0.92988835, 0.93442917, 0.92100464
|
||||
, 0.91475304, 0.90249622, 0.9021363 ],
|
||||
[ 0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368, 0.88937573
|
||||
, 0.894401, 0.90448993, 0.95495898, 0.92698333, 0.94745352, 0.92562488
|
||||
, 0.96635366, 1.02520312, 1.0394296 ],
|
||||
[ 1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073, 1.00759019
|
||||
, 0.99192968, 0.99747298, 0.99550759, 0.97583768, 0.9610168, 0.94779638
|
||||
, 0.93759089, 0.93353431, 0.94121705],
|
||||
[ 0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613, 0.83434854
|
||||
, 0.85813595, 0.84667961, 0.84374558, 0.85951183, 0.87194227, 0.89455097
|
||||
, 0.88283929, 0.90349491, 0.90600675],
|
||||
[ 1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086, 1.00581626
|
||||
, 0.98850522, 0.99291168, 0.98983209, 0.97511924, 0.96134615, 0.96382634
|
||||
, 0.95011401, 0.9434686, 0.94637765],
|
||||
[ 1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857, 1.04800023
|
||||
, 1.03024941, 1.04200483, 1.0402554, 1.03296979, 1.02191682, 1.02476275
|
||||
, 1.02347523, 1.02517684, 1.04359571],
|
||||
[ 1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043, 1.0531801
|
||||
, 1.07452771, 1.09383478, 1.1052447, 1.10322136, 1.09167939, 1.08772756
|
||||
, 1.08859544, 1.09177338, 1.1096083 ],
|
||||
[ 0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809, 0.86287327
|
||||
, 0.85169796, 0.85411285, 0.84886336, 0.84517414, 0.84843858, 0.84488343
|
||||
, 0.83374329, 0.82812044, 0.82878599],
|
||||
[ 0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286, 0.92652175
|
||||
, 0.94278865, 0.93682452, 0.98655146, 0.992237, 0.9798497, 0.93869677
|
||||
, 0.96947771, 1.00362626, 0.98102351],
|
||||
[ 0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967, 0.93092109
|
||||
, 0.92662519, 0.93412152, 0.93501274, 0.92879506, 0.92110542, 0.91035556
|
||||
, 0.90430364, 0.89994694, 0.90073864],
|
||||
[ 0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824, 0.98882205
|
||||
, 0.97662234, 0.95601578, 0.94905385, 0.94934888, 0.97152609, 0.97163004
|
||||
, 0.9700702, 0.97158948, 0.95884908],
|
||||
[ 0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751, 0.84818516
|
||||
, 0.85265681, 0.84502402, 0.82645665, 0.81743586, 0.83550406, 0.83338919
|
||||
, 0.83511679, 0.82136617, 0.80921874],
|
||||
[ 0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441, 0.95440787
|
||||
, 0.96364363, 0.96804412, 0.97136214, 0.97583768, 0.95571724, 0.96895368
|
||||
, 0.97001634, 0.97082733, 0.98782366],
|
||||
[ 1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249, 1.10558188
|
||||
, 1.1214086, 1.12292577, 1.13021031, 1.13342735, 1.14686068, 1.14502975
|
||||
, 1.14474747, 1.14084037, 1.16142926],
|
||||
[ 1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863, 1.11856702
|
||||
, 1.09764283, 1.08815849, 1.08044313, 1.09278827, 1.07003204, 1.08398066
|
||||
, 1.09831768, 1.09298232, 1.09176125],
|
||||
[ 0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744, 0.77751194
|
||||
, 0.79902974, 0.81437881, 0.80788828, 0.79603865, 0.78966436, 0.79949807
|
||||
, 0.80172182, 0.82168155, 0.85587911],
|
||||
[ 1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561, 1.00162979
|
||||
, 0.99860739, 1.00814981, 1.00574316, 0.99030032, 0.97682565, 0.97292596
|
||||
, 0.96519561, 0.96173403, 0.95890284],
|
||||
[ 0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289, 0.96608031
|
||||
, 0.99727185, 1.00781194, 1.03484236, 1.05333619, 1.0983263, 1.1704974
|
||||
, 1.17025154, 1.18730553, 1.14242645]])
|
||||
expected = np.array(
|
||||
[[0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154,
|
||||
0.83271652, 0.83786314, 0.85012593, 0.85509656, 0.86416612,
|
||||
0.87119375, 0.86302631, 0.86148267, 0.86252252, 0.86746356],
|
||||
[0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388,
|
||||
0.90746978, 0.89830489, 0.89431991, 0.88924794, 0.89815176,
|
||||
0.91832091, 0.91706054, 0.90139505, 0.87897455, 0.86216858],
|
||||
[0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522,
|
||||
0.78964559, 0.80584442, 0.8084998, 0.82258551, 0.82668196,
|
||||
0.82373724, 0.81814804, 0.83675961, 0.83574199, 0.84647177],
|
||||
[1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841,
|
||||
1.14506948, 1.12151133, 1.11160697, 1.10888621, 1.11399806,
|
||||
1.12168029, 1.13164797, 1.12958508, 1.11371818, 1.09936775],
|
||||
[1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025,
|
||||
1.16898201, 1.17212488, 1.14752303, 1.11843284, 1.11024964,
|
||||
1.11943471, 1.11736468, 1.10863242, 1.09642516, 1.07762337],
|
||||
[1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684,
|
||||
1.44184737, 1.44782832, 1.41978227, 1.39092208, 1.4059372,
|
||||
1.40788646, 1.44052766, 1.45241216, 1.43306098, 1.4174431],
|
||||
[1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149,
|
||||
1.10888138, 1.11856629, 1.13062931, 1.11944984, 1.12446239,
|
||||
1.11671008, 1.10880034, 1.08401709, 1.06959206, 1.07875225],
|
||||
[1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545,
|
||||
0.99854316, 0.9880258, 0.99669587, 0.99327676, 1.01400905,
|
||||
1.03176742, 1.040511, 1.01749645, 0.9936394, 0.98279746],
|
||||
[0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845,
|
||||
0.99127006, 0.97925917, 0.9683482, 0.95335147, 0.93694787,
|
||||
0.94308213, 0.92232874, 0.91284091, 0.89689833, 0.88928858],
|
||||
[0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044,
|
||||
0.8578708, 0.86036185, 0.86107306, 0.8500772, 0.86981998,
|
||||
0.86837929, 0.87204141, 0.86633032, 0.84946077, 0.83287146],
|
||||
[1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624,
|
||||
1.14450183, 1.12349752, 1.12596664, 1.12213996, 1.1119989,
|
||||
1.10257792, 1.10491258, 1.11059842, 1.10509795, 1.10020097],
|
||||
[0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687,
|
||||
0.95831051, 0.94480909, 0.94804195, 0.95430286, 0.94103989,
|
||||
0.92122519, 0.91010201, 0.89280392, 0.89298243, 0.89165385],
|
||||
[0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647,
|
||||
0.9480927, 0.93539182, 0.95388718, 0.94597005, 0.96918424,
|
||||
0.94781281, 0.93466815, 0.94281559, 0.96520315, 0.96715441],
|
||||
[0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897,
|
||||
0.98687073, 0.99237486, 0.98209969, 0.9877653, 0.97399471,
|
||||
0.96910087, 0.98416665, 0.98423613, 0.99823861, 0.99545704],
|
||||
[0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012,
|
||||
0.86191535, 0.84981451, 0.85472102, 0.84564835, 0.83998883,
|
||||
0.83478547, 0.82803648, 0.8198736, 0.82265395, 0.8399404],
|
||||
[0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136,
|
||||
0.82785597, 0.86008789, 0.86776298, 0.86720209, 0.8676334,
|
||||
0.89179317, 0.94202108, 0.9422231, 0.93902708, 0.94479184],
|
||||
[0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238,
|
||||
0.90906632, 0.92693339, 0.93695966, 0.94242697, 0.94338265,
|
||||
0.91981796, 0.91108804, 0.90543476, 0.91737138, 0.94793657],
|
||||
[1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723,
|
||||
1.20172869, 1.21328691, 1.22624778, 1.22397075, 1.23857042,
|
||||
1.24419893, 1.23929384, 1.23418676, 1.23626739, 1.26754398],
|
||||
[1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667,
|
||||
1.34790023, 1.34399863, 1.32575181, 1.30795492, 1.30544841,
|
||||
1.30303302, 1.32107766, 1.32936244, 1.33001241, 1.33288462],
|
||||
[1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093,
|
||||
1.05059016, 1.03405057, 1.02747623, 1.03162734, 0.9961416,
|
||||
0.97356208, 0.94241549, 0.92754547, 0.92549227, 0.92138102],
|
||||
[1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264,
|
||||
1.13889622, 1.12442212, 1.13367018, 1.13982256, 1.14029944,
|
||||
1.11979401, 1.10905389, 1.10577769, 1.11166825, 1.09985155],
|
||||
[0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284,
|
||||
0.74480073, 0.76098396, 0.76156903, 0.76651952, 0.76533288,
|
||||
0.78205934, 0.76842416, 0.77487118, 0.77768683, 0.78801192],
|
||||
[0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803,
|
||||
0.97370819, 0.96419154, 0.97209861, 0.97441313, 0.96356162,
|
||||
0.94745352, 0.93965462, 0.93069645, 0.94020973, 0.94358232],
|
||||
[0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801,
|
||||
0.80071489, 0.83358256, 0.83451613, 0.85175032, 0.85954307,
|
||||
0.86790024, 0.87170334, 0.87863799, 0.87497981, 0.87888675],
|
||||
[0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619,
|
||||
0.98733195, 0.99644997, 0.99669587, 1.02559097, 1.01116651,
|
||||
0.99988024, 0.97906749, 0.99323123, 1.00204939, 0.99602148],
|
||||
[1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683,
|
||||
1.08312397, 1.05192626, 1.04230892, 1.05577278, 1.08569751,
|
||||
1.12443486, 1.08891079, 1.08603695, 1.05997314, 1.02160943],
|
||||
[1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272,
|
||||
1.18257029, 1.16226243, 1.16009196, 1.14467789, 1.14820235,
|
||||
1.12386598, 1.12680236, 1.12357937, 1.1159258, 1.12570828],
|
||||
[1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667,
|
||||
1.31210239, 1.29989156, 1.29203193, 1.27183516, 1.26830786,
|
||||
1.2617743, 1.28656675, 1.29734097, 1.29390205, 1.29345446],
|
||||
[0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864,
|
||||
0.78772975, 0.82848011, 0.8259679, 0.82435705, 0.83108634,
|
||||
0.84373784, 0.83891093, 0.84349247, 0.85637272, 0.86539395],
|
||||
[1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626,
|
||||
1.2256767, 1.21126648, 1.19377804, 1.18355337, 1.19674434,
|
||||
1.21536573, 1.23653297, 1.27962009, 1.27968392, 1.25907738],
|
||||
[0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282,
|
||||
0.96480308, 0.94686376, 0.93679073, 0.92540049, 0.92988835,
|
||||
0.93442917, 0.92100464, 0.91475304, 0.90249622, 0.9021363],
|
||||
[0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368,
|
||||
0.88937573, 0.894401, 0.90448993, 0.95495898, 0.92698333,
|
||||
0.94745352, 0.92562488, 0.96635366, 1.02520312, 1.0394296],
|
||||
[1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073,
|
||||
1.00759019, 0.99192968, 0.99747298, 0.99550759, 0.97583768,
|
||||
0.9610168, 0.94779638, 0.93759089, 0.93353431, 0.94121705],
|
||||
[0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613,
|
||||
0.83434854, 0.85813595, 0.84667961, 0.84374558, 0.85951183,
|
||||
0.87194227, 0.89455097, 0.88283929, 0.90349491, 0.90600675],
|
||||
[1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086,
|
||||
1.00581626, 0.98850522, 0.99291168, 0.98983209, 0.97511924,
|
||||
0.96134615, 0.96382634, 0.95011401, 0.9434686, 0.94637765],
|
||||
[1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857,
|
||||
1.04800023, 1.03024941, 1.04200483, 1.0402554, 1.03296979,
|
||||
1.02191682, 1.02476275, 1.02347523, 1.02517684, 1.04359571],
|
||||
[1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043,
|
||||
1.0531801, 1.07452771, 1.09383478, 1.1052447, 1.10322136,
|
||||
1.09167939, 1.08772756, 1.08859544, 1.09177338, 1.1096083],
|
||||
[0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809,
|
||||
0.86287327, 0.85169796, 0.85411285, 0.84886336, 0.84517414,
|
||||
0.84843858, 0.84488343, 0.83374329, 0.82812044, 0.82878599],
|
||||
[0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286,
|
||||
0.92652175, 0.94278865, 0.93682452, 0.98655146, 0.992237,
|
||||
0.9798497, 0.93869677, 0.96947771, 1.00362626, 0.98102351],
|
||||
[0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967,
|
||||
0.93092109, 0.92662519, 0.93412152, 0.93501274, 0.92879506,
|
||||
0.92110542, 0.91035556, 0.90430364, 0.89994694, 0.90073864],
|
||||
[0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824,
|
||||
0.98882205, 0.97662234, 0.95601578, 0.94905385, 0.94934888,
|
||||
0.97152609, 0.97163004, 0.9700702, 0.97158948, 0.95884908],
|
||||
[0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751,
|
||||
0.84818516, 0.85265681, 0.84502402, 0.82645665, 0.81743586,
|
||||
0.83550406, 0.83338919, 0.83511679, 0.82136617, 0.80921874],
|
||||
[0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441,
|
||||
0.95440787, 0.96364363, 0.96804412, 0.97136214, 0.97583768,
|
||||
0.95571724, 0.96895368, 0.97001634, 0.97082733, 0.98782366],
|
||||
[1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249,
|
||||
1.10558188, 1.1214086, 1.12292577, 1.13021031, 1.13342735,
|
||||
1.14686068, 1.14502975, 1.14474747, 1.14084037, 1.16142926],
|
||||
[1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863,
|
||||
1.11856702, 1.09764283, 1.08815849, 1.08044313, 1.09278827,
|
||||
1.07003204, 1.08398066, 1.09831768, 1.09298232, 1.09176125],
|
||||
[0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744,
|
||||
0.77751194, 0.79902974, 0.81437881, 0.80788828, 0.79603865,
|
||||
0.78966436, 0.79949807, 0.80172182, 0.82168155, 0.85587911],
|
||||
[1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561,
|
||||
1.00162979, 0.99860739, 1.00814981, 1.00574316, 0.99030032,
|
||||
0.97682565, 0.97292596, 0.96519561, 0.96173403, 0.95890284],
|
||||
[0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289,
|
||||
0.96608031, 0.99727185, 1.00781194, 1.03484236, 1.05333619,
|
||||
1.0983263, 1.1704974, 1.17025154, 1.18730553, 1.14242645]])
|
||||
|
||||
self.assertTrue(np.allclose(result, expected))
|
||||
self.assertTrue(type(result) == type(expected))
|
||||
@@ -268,32 +286,35 @@ class SpaceTimeTests(unittest.TestCase):
|
||||
|
||||
def test_rebin_data(self):
|
||||
"""Test rebin_data"""
|
||||
## sample in double the time (even case since 10 % 2 = 0):
|
||||
## (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
|
||||
## = 0.5, 2.5, 4.5, 6.5, 8.5
|
||||
# sample in double the time (even case since 10 % 2 = 0):
|
||||
# (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
|
||||
# = 0.5, 2.5, 4.5, 6.5, 8.5
|
||||
ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float)
|
||||
for i in range(0, 10, 2)]).T
|
||||
|
||||
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
|
||||
self.assertTrue(
|
||||
np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
|
||||
|
||||
## sample in triple the time (uneven since 10 % 3 = 1):
|
||||
## (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
|
||||
## = 1, 4, 7, 9
|
||||
ans_odd = np.array([i * np.ones(10, dtype=float)
|
||||
for i in (1, 4, 7, 9)]).T
|
||||
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
|
||||
# sample in triple the time (uneven since 10 % 3 = 1):
|
||||
# (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
|
||||
# = 1, 4, 7, 9
|
||||
ans_odd = np.array([i * np.ones(10, dtype=float)
|
||||
for i in (1, 4, 7, 9)]).T
|
||||
self.assertTrue(
|
||||
np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
|
||||
|
||||
def test_get_prob_dist(self):
|
||||
"""Test get_prob_dist"""
|
||||
lag_indices = np.array([1, 2, 3, 4])
|
||||
unit_indices = np.array([1, 3, 2, 4])
|
||||
answer = np.array([
|
||||
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
|
||||
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
|
||||
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
|
||||
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
|
||||
[0.0754717, 0.88207547, 0.04245283, 0., 0.],
|
||||
[0., 0., 0.09411765, 0.87058824, 0.03529412],
|
||||
[0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
|
||||
[0., 0., 0., 0.02352941, 0.97647059]
|
||||
])
|
||||
result = std.get_prob_dist(self.transition_matrix, lag_indices, unit_indices)
|
||||
result = std.get_prob_dist(self.transition_matrix,
|
||||
lag_indices, unit_indices)
|
||||
|
||||
self.assertTrue(np.array_equal(result, answer))
|
||||
|
||||
@@ -301,16 +322,20 @@ class SpaceTimeTests(unittest.TestCase):
|
||||
"""Test get_prob_stats"""
|
||||
|
||||
probs = np.array([
|
||||
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
|
||||
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
|
||||
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
|
||||
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
|
||||
[0.0754717, 0.88207547, 0.04245283, 0., 0.],
|
||||
[0., 0., 0.09411765, 0.87058824, 0.03529412],
|
||||
[0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
|
||||
[0., 0., 0., 0.02352941, 0.97647059]
|
||||
])
|
||||
unit_indices = np.array([1, 3, 2, 4])
|
||||
answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
|
||||
answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
|
||||
answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824, 0.02475248 / 0.77722772, -0.02352941 / 0.97647059])
|
||||
answer_volatility = np.array([ 0.34221495, 0.33705421, 0.29226542, 0.38834223])
|
||||
answer_trend = np.array([-0.03301887 / 0.88207547,
|
||||
-0.05882353 / 0.87058824,
|
||||
0.02475248 / 0.77722772,
|
||||
-0.02352941 / 0.97647059])
|
||||
answer_volatility = np.array([0.34221495, 0.33705421,
|
||||
0.29226542, 0.38834223])
|
||||
|
||||
result = std.get_prob_stats(probs, unit_indices)
|
||||
result_up = result[0]
|
||||
|
||||
Reference in New Issue
Block a user