syntax

added the deprecated function signature as a wrapper to the new function
correct srid in final argument
2017-02-10 16:32:05 -05:00 · 2017-02-10 15:27:56 -05:00 · 2017-01-06 14:12:42 -05:00 · 2016-12-02 13:08:45 +01:00 · 2016-12-02 12:48:31 +01:00 · 2016-12-02 12:36:11 +01:00
25 changed files with 1086 additions and 246 deletions
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -2,6 +2,9 @@
 - [ ] All declared geometries are `geometry(Geometry, 4326)` for general geoms, or `geometry(Point, 4326)`
 - [ ] Existing functions in crankshaft python library called from the extension are kept at least from version N to version N+1 (to avoid breakage during upgrades).
 - [ ] Docs for public-facing functions are written
- [ ] New functions follow the naming conventions: `CDB_NameOfFunction`. Where internal functions begin with an underscore `_`.
- [ ] If appropriate, new functions accepts an arbitrary query as an input (see [Crankshaft Issue #6](https://github.com/CartoDB/crankshaft/issues/6) for more information)
- 
+- [ ] New functions follow the naming conventions: `CDB_NameOfFunction`. Where internal functions begin with an underscore
+- [ ] Video explaining the analysis and showing examples 
+- [ ] Analysis Documentation written [template](https://docs.google.com/a/cartodb.com/document/d/1X2KOtaiEBKWNMp8UjwcLB-kE9aIOw09aOjX3oaCjeME/edit?usp=sharing)
+- [ ] Smoke test written
+- [ ] Hand-off document for camshaft node written
+- [ ] If function is in Python, code conforms to [PEP8 Style Guide](https://www.python.org/dev/peps/pep-0008/)
--- a/.travis.yml
+++ b/.travis.yml
@@ -35,14 +35,18 @@ before_install:
  - sudo apt-get -y remove --purge postgresql-9.2
  - sudo apt-get -y remove --purge postgresql-9.3
  - sudo apt-get -y remove --purge postgresql-9.4
-  - sudo apt-get -y remove --purge postgis
+  - sudo apt-get -y remove --purge postgresql-9.5
+  - sudo rm -rf /var/lib/postgresql/
+  - sudo rm -rf /var/log/postgresql/
+  - sudo rm -rf /etc/postgresql/
+  - sudo apt-get -y remove --purge postgis-2.2
  - sudo apt-get -y autoremove

-  - sudo apt-get -y install postgresql-9.5=9.5.2-2ubuntu1
-  - sudo apt-get -y install postgresql-server-dev-9.5=9.5.2-2ubuntu1
-  - sudo apt-get -y install postgresql-plpython-9.5=9.5.2-2ubuntu1
-  - sudo apt-get -y install postgresql-9.5-postgis-2.2=2.2.2.0-cdb2
+  - sudo apt-get -y install postgresql-9.5=9.5.2-3cdb2
+  - sudo apt-get -y install postgresql-server-dev-9.5=9.5.2-3cdb2
+  - sudo apt-get -y install postgresql-plpython-9.5=9.5.2-3cdb2
  - sudo apt-get -y install postgresql-9.5-postgis-scripts=2.2.2.0-cdb2
+  - sudo apt-get -y install postgresql-9.5-postgis-2.2=2.2.2.0-cdb2

  # configure it to accept local connections from postgres
  - echo -e "# TYPE  DATABASE        USER            ADDRESS                 METHOD \nlocal   all             postgres                                trust\nlocal   all             all                                     trust\nhost    all             all             127.0.0.1/32            trust" \
--- a/doc/16_getis_ord_gstar.md
+++ b/doc/16_getis_ord_gstar.md
@@ -0,0 +1,40 @@
+## Getis-Ord's G\*
+
+Getis-Ord's G\* is a geo-statistical measurement of the intensity of clustering of high or low values. The clustering of high values can be referred to as "hotspots" because these are areas of high activity or large (relative to the global mean) measurement values. Coldspots are clustered areas with low activity or small measurement values.
+
+### CDB_GetisOrdsG(subquery text, column_name text)
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | text | A query of the data you want to pass to the function. It must include `column_name`, a geometry column (usually `the_geom`) and an id column (usually `cartodb_id`) |
+| column_name | text | This is the column of interest for performing this analysis on. This column should be a numeric type. |
+| w_type (optional) | text | Type of weight to use when finding neighbors. Currently available options are 'knn' (default) and 'queen'. Read more about weight types in [PySAL's weights documentation.](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html) |
+| num_ngbrs (optional) | integer | Default: 5. If `knn` is chosen, this will set the number of neighbors. If `knn` is not chosen, any entered value will be ignored. Use `NULL` if not choosing `knn`. |
+| permutations (optional) | integer | The number of permutations for calculating p-values. Default: 999 |
+| geom_col (optional) | text | The column where the geometry information is stored. The format must be PostGIS Geometry type (SRID 4326). Default: `the_geom`. |
+| id_col (optional) | text | The column that has the unique row identifier. |
+
+### Returns
+
+Returns a table with the following columns.
+
+| Name | Type | Description |
+|------|------|-------------|
+| z_score | numeric | z-score, a measure of the intensity of clustering of high values (hotspots) or low values (coldspots). Positive values represent 'hotspots', while negative values represent 'coldspots'. |
+| p_value | numeric  | p-value, a measure of the significance of the intensity of clustering |
+| p_z_sim | numeric | p-value based on standard normal approximation from permutations |
+| rowid | integer | The original `id_col` that can be used to associate the outputs with the original geometry and inputs |
+
+#### Example Usage
+
+The following query returns the original table augmented with the values calculated from the Getis-Ord's G\* analysis.
+
+```sql
+SELECT i.*, m.z_score, m.p_value
+  FROM cdb_crankshaft.CDB_GetisOrdsG('SELECT * FROM incident_reports_clustered',
+                                     'num_incidents') As m
+  JOIN incident_reports_clustered As i
+    ON i.cartodb_id = m.rowid;
+```
--- a/doc/18_outliers.md
+++ b/doc/18_outliers.md
@@ -0,0 +1,163 @@
+## Outlier Detection
+
+This set of functions detects the presence of outliers. There are three functions for finding outliers from non-spatial data:
+
+1. Static Outliers
+1. Percentage Outliers
+1. Standard Deviation Outliers
+
+### CDB_StaticOutlier(column_value numeric, threshold numeric)
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| column_value | numeric | The column of values on which to apply the threshold |
+| threshold | numeric | The static threshold which is used to indicate whether a `column_value` is an outlier or not |
+
+### Returns
+
+Returns a boolean (true/false) depending on whether a value is above or below (or equal to) the threshold
+
+| Name | Type | Description |
+|------|------|-------------|
+| outlier | boolean | classification of whether a row is an outlier or not |
+
+#### Example Usage
+
+With a table `website_visits` and a column of the number of website visits in units of 10,000 visits:
+
+```
+| id | visits_10k |
+|----|------------|
+| 1  | 1 |
+| 2  | 3 |
+| 3  | 5 |
+| 4  | 1 |
+| 5  | 32 |
+| 6  | 3 |
+| 7  | 57 |
+| 8  | 2 |
+```
+
+```sql
+SELECT
+  id,
+  CDB_StaticOutlier(visits_10k, 11.0) As outlier,
+  visits_10k
+FROM website_visits
+```
+
+```
+| id | outlier | visits_10k |
+|----|---------|------------|
+| 1  | f | 1 |
+| 2  | f | 3 |
+| 3  | f | 5 |
+| 4  | f | 1 |
+| 5  | t | 32 |
+| 6  | f | 3 |
+| 7  | t | 57 |
+| 8  | f | 2 |
+```
+
+### CDB_PercentOutlier(column_values numeric[], outlier_fraction numeric, ids int[])
+
+`CDB_PercentOutlier` calculates whether or not a value falls above a given threshold based on a percentage above the mean value of the input values.
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| column_values | numeric[] | An array of the values to calculate the outlier classification on |
+| outlier_fraction | numeric | The threshold above which a column value divided by the mean of all values is considered an outlier |
+| ids | int[] | An array of the unique row ids of the input data (usually `cartodb_id`) |
+
+### Returns
+
+Returns a table of the outlier classification with the following columns
+
+| Name | Type | Description |
+|------|------|-------------|
+| is_outlier | boolean  | classification of whether a row is an outlier or not |
+| rowid | int | original row id (e.g., input `cartodb_id`) of the row which has the outlier classification |
+
+#### Example Usage
+
+This example find outliers which are more than 100% larger than the average (that is, more than 2.0 times larger).
+
+```sql
+WITH cte As (
+  SELECT
+    unnest(Array[1,2,3,4,5,6,7,8]) As id,
+    unnest(Array[1,3,5,1,32,3,57,2]) As visits_10k
+  )
+SELECT
+  (CDB_PercentOutlier(array_agg(visits_10k), 2.0, array_agg(id))).*
+FROM cte;
+```
+
+Output
+```
+| outlier | rowid |
+|---------+-------|
+| f | 1 |
+| f | 2 |
+| f | 3 |
+| f | 4 |
+| t | 5 |
+| f | 6 |
+| t | 7 |
+| f | 8 |
+```
+
+### CDB_StdDevOutlier(column_values numeric[], num_deviations numeric, ids int[], is_symmetric boolean DEFAULT true)
+
+`CDB_StdDevOutlier` calculates whether or not a value falls above or below a given threshold based on the number of standard deviations from the mean.
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| column_values | numeric[] | An array of the values to calculate the outlier classification on |
+| num_deviations | numeric | The threshold in units of standard deviation |
+| ids | int[] | An array of the unique row ids of the input data (usually `cartodb_id`) |
+| is_symmetric (optional) | boolean | Consider outliers that are symmetric about the mean (default: true) |
+
+### Returns
+
+Returns a table of the outlier classification with the following columns
+
+| Name | Type | Description |
+|------|------|-------------|
+| is_outlier | boolean  | classification of whether a row is an outlier or not |
+| rowid | int | original row id (e.g., input `cartodb_id`) of the row which has the outlier classification |
+
+#### Example Usage
+
+This example find outliers which are more than 100% larger than the average (that is, more than 2.0 times larger).
+
+```sql
+WITH cte As (
+  SELECT
+    unnest(Array[1,2,3,4,5,6,7,8]) As id,
+    unnest(Array[1,3,5,1,32,3,57,2]) As visits_10k
+  )
+SELECT
+  (CDB_StdDevOutlier(array_agg(visits_10k), 2.0, array_agg(id))).*
+FROM cte;
+```
+
+Output
+```
+| outlier | rowid |
+|---------+-------|
+| f | 1 |
+| f | 2 |
+| f | 3 |
+| f | 4 |
+| f | 5 |
+| f | 6 |
+| t | 7 |
+| f | 8 |
+```
--- a/release/python/0.4.2/crankshaft/requirements.txt
+++ b/release/python/0.4.2/crankshaft/requirements.txt
@@ -0,0 +1,5 @@
+joblib==0.8.3
+numpy==1.6.1
+scipy==0.14.0
+pysal==1.11.2
+scikit-learn==0.14.1
--- a/src/pg/sql/16_getis.sql
+++ b/src/pg/sql/16_getis.sql
@@ -0,0 +1,18 @@
+-- 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 $$
+  from crankshaft.clustering import getis_ord
+  return getis_ord(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
+$$ LANGUAGE plpythonu;
+
+-- TODO: make a version that accepts the values as arrays
--- a/src/pg/sql/18_outliers.sql
+++ b/src/pg/sql/18_outliers.sql
@@ -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;
--- a/src/pg/sql/30_dot_density.sql
+++ b/src/pg/sql/30_dot_density.sql
@@ -10,45 +10,192 @@
 -- misses per point the funciton accepts before giving up.
 --
 -- Returns: Multipoint with the requested points
-CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000)
-RETURNS GEOMETRY AS $$
-DECLARE
-  extent GEOMETRY;
-  test_point Geometry;
-  width                NUMERIC;
-  height               NUMERIC;
-  x0                   NUMERIC;
-  y0                   NUMERIC;
-  xp                   NUMERIC;
-  yp                   NUMERIC;
-  no_left              INTEGER;
-  remaining_iterations INTEGER;
-  points               GEOMETRY[];
-  bbox_line            GEOMETRY;
-  intersection_line    GEOMETRY;
-BEGIN
-  extent  := ST_Envelope(geom);
-  width   := ST_XMax(extent) - ST_XMIN(extent);
-  height  := ST_YMax(extent) - ST_YMIN(extent);
-  x0 	  := ST_XMin(extent);
-  y0 	  := ST_YMin(extent);
-  no_left := no_points;

-  LOOP
-    if(no_left=0) THEN
-      EXIT;
-    END IF;
-    yp = y0 + height*random();
-    bbox_line  = ST_MakeLine(
-      ST_SetSRID(ST_MakePoint(yp, x0),4326),
-      ST_SetSRID(ST_MakePoint(yp, x0+width),4326)
-    );
-    intersection_line = ST_Intersection(bbox_line,geom);
-  	test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random());
-	  points := points || test_point;
-	  no_left = no_left - 1 ;
-  END LOOP;
-  RETURN ST_Collect(points);
+CREATE OR REPLACE FUNCTION CDB_DotDensity(g geometry(Polygon, 4326), no_points integer, max_iter integer DEFAULT 1000)
+ RETURNS SETOF geometry(Point, 4326)
+AS $$
+ DECLARE
+     extent GEOMETRY;
+     eq_area_geom GEOMETRY;
+     test_point Geometry;
+     iter     NUMERIC;
+     width    NUMERIC;
+     height   NUMERIC;
+     x0       NUMERIC;
+     y0       NUMERIC;
+     no_left  INTEGER;
+     sample_points GEOMETRY[];
+     points   GEOMETRY[];
+ BEGIN
+   eq_area_geom := ST_TRANSFORM(g, 2163);
+   extent   := ST_Envelope(eq_area_geom);
+   iter := 0;
+   width    := ST_XMax(extent) - ST_XMIN(extent);
+   height   := ST_YMax(extent) - ST_YMIN(extent);
+   x0       := ST_XMin(extent);
+   y0       := ST_YMin(extent);
+   no_left  := no_points;
+
+   LOOP
+     IF(no_left <= 0 or iter >= max_iter) THEN
+       RETURN;
+     END IF;
+
+
+    with random_points as(
+        SELECT ST_SetSRID(ST_MAKEPOINT( x0 + width*random(), y0 + height*random()), 2163) as p
+        FROM generate_series(1,no_left)
+     )
+     SELECT array_agg(p) from random_points
+     WHERE ST_WITHIN(p, eq_area_geom)
+     into sample_points;
+
+     RETURN QUERY select ST_TRANSFORM(a, 4326) from unnest(sample_points) as a;
+
+     IF sample_points IS NOT null THEN
+        no_left := no_left - array_length(sample_points, 1);
+     END IF;
+     iter = iter + 1;
+   END LOOP;
+
+   RETURN;
+ END;
+$$ LANGUAGE plpgsql;
+
+-- DEPRECATED
+
+CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry, no_points Integer, max_iter_per_point Integer DEFAULT 1000)
+  RETURNS GEOMETRY
+AS $$
+DECLARE
+final_points GEOMETRY;
+
+BEGIN
+
+  with new_points as(
+    SELECT * FROM CDB_DotDensity(geom, no_points, max_iter_per_point) as a
+  )
+  SELECT ST_Collect(a) FROM new_points
+  into final_points;
+  RETURN final_points;
+
 END;
+$$ LANGUAGE plpgsql;
+
+--
+-- Creates N points randomly distributed in the specified secondary polygons
+--
+-- @param g - array of the geometries to be turned in to points
+--
+-- @param no_points - the number of points to generate
+--
+-- @params max_iter_per_point - the function generates points in the polygon's bounding box
+-- and discards points which don't lie in the polygon. max_iter_per_point specifies how many
+-- misses per point the funciton accepts before giving up.
+--
+-- Returns: Multipoint with the requested points
+
+
+
+--
+-- Generate a random response based on the weights given
+--
+-- @param array_ids an array of ids representing the category to return
+--
+-- @param weights an array of weights for each category
+--
+-- Returns : The randomly selected ID.
+
+CREATE OR REPLACE function _cdb_SelectRandomWeights(array_ids numeric[], weights numeric[]) returns NUMERIC
+ as $$
+ DECLARE
+     result NUMERIC;
+ BEGIN
+
+     WITH idw as (
+         select unnest(array_ids) as id, unnest(weights) as percent
+     ),
+     CTE AS (
+         SELECT random() * (SELECT SUM(percent) FROM idw) R
+     )
+     SELECT *
+     FROM (
+         SELECT id, SUM(percent) OVER (ORDER BY id) S, R
+         FROM idw as percent CROSS JOIN CTE
+     ) Q
+     WHERE S >= R
+     ORDER BY id
+     LIMIT 1
+     into result;
+     return result;
+ END
+ $$ LANGUAGE plpgsql;
+
+--
+-- Weighted Dot Density
+--
+-- @param no_points the number of points to generate
+--
+-- @param geoms the target geometries to place the points in
+--
+-- @param weights the weight for each of the target polygons
+--
+-- RETURNS set of points
+
+CREATE OR REPLACE FUNCTION _cdb_WeightedDD(no_points numeric, geoms geometry[], weights numeric[])
+ RETURNS SETOF geometry
+AS $$
+DECLARE
+ i NUMERIC;
+ ids NUMERIC[];
+ perGeom NUMERIC[];
+ selected_poly NUMERIC;
+BEGIN
+ with idseries as (
+     select generate_series(1,array_upper(geoms,1)) as id
+ )
+ select array_agg(id) from idseries into ids;
+
+ FOR i in 1..no_points
+ LOOP
+    select cdb_crankshaft._cdb_SelectRandomWeights(ids, weights) INTO selected_poly;
+    perGeom[selected_poly] = coalesce(perGeom[selected_poly] + 1, 0 );
+ END LOOP;
+
+ raise notice 'pergeom %', perGeom;
+
+ FOR i in 1..array_length(ids,1)
+ LOOP
+     return QUERY
+     select cdb_crankshaft.CDB_DotDensity(geoms[i], coalesce(perGeom[i],0)::INTEGER);
+ END LOOP;
+END
 $$
-LANGUAGE plpgsql VOLATILE;
+LANGUAGE plpgsql;
+
+
+--
+-- Daysymetric Dot Density
+--
+-- @param geom: the geometry that has the
+--
+-- @param no_points: the total number of points to create
+--
+-- @param targetGeoms: the geometry that has the
+--
+-- @param weights: targetGeom weights
+--
+-- RETURNS setof points
+
+CREATE OR REPLACE FUNCTION CDB_DasymetricDotDensity(geom GEOMETRY, no_points NUMERIC, targetGeoms GEOMETRY[], weights numeric [])
+RETURNS setof GEOMETRY
+AS $$
+  BEGIN
+    RAISE NOTICE 'running Dasymetric';
+    RETURN QUERY
+        SELECT cdb_crankshaft._CDB_WeightedDD(no_points, array_agg( ST_INTERSECTION(geom,g)), array_agg(ST_AREA(ST_INTERSECTION(geom,g))*w)::NUMERIC[])
+        FROM unnest(targetGeoms) as g , unnest(weights) as w
+        WHERE geom && g;
+  END
+$$
+LANGUAGE plpgsql;
--- a/src/pg/test/expected/02_moran_test.out
+++ b/src/pg/test/expected/02_moran_test.out
@@ -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
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+12|LL
+13|HL
+14|LL
 15|LL
-16|LL
-17|LL
-18|LH
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-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)
--- a/src/pg/test/expected/04_dot_density_test.out
+++ b/src/pg/test/expected/04_dot_density_test.out
@@ -1,16 +0,0 @@
-WITH g AS (
-  SELECT ST_Buffer(ST_SetSRID(ST_MakePoint(0,0),4326)::geometry, 1000)::geometry AS g
-),
-points AS(
-  SELECT (
-    ST_Dump(
-      cdb_crankshaft.cdb_dot_density(g.g, 100)
-    )
-  ).geom AS p FROM g
-)
-SELECT count(*), sum(CASE WHEN ST_Contains(g,p) THEN 1 ELSE 0 END) FROM points, g
- count | sum 
-------+-----
-   100 | 100
-(1 row)
-
--- a/src/pg/test/expected/16_getis_test.out
+++ b/src/pg/test/expected/16_getis_test.out
@@ -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)
--- a/src/pg/test/expected/18_outliers_test.out
+++ b/src/pg/test/expected/18_outliers_test.out
@@ -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)
--- a/src/pg/test/fixtures/getis_data.sql
+++ b/src/pg/test/fixtures/getis_data.sql
@@ -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
+10	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	3.79060700000000006
+43	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	5.93309800000000021
+6	0106000020E6100000010000000103000000010000000F000000000000A00F4256C0000000E008D4434000000000674956C00000004015D44340000000608C4956C00000004098E64340FFFFFFBF434C56C0FFFFFF3F77E84340000000004C4E56C000000020E5E74340000000C0624E56C0FFFFFF3F97F5434000000020B44956C000000000AFF54340000000E0C64956C00000004009074440FFFFFF1F523056C0FFFFFFDF91074440000000C0EB2F56C000000040BBE54340000000E0B93056C0000000E09FE54340000000E0D63056C0000000007DDE4340000000E0213456C0000000005ADE4340000000802E3456C000000020F7D34340000000A00F4256C0000000E008D44340	9.04867300000000085
+16	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	0.74509000000000003
+29	0106000020E610000001000000010300000001000000080000000000002025FD55C0FFFFFF1F7F6D434000000080C61056C0000000A04C6D4340000000A0631756C000000000D56D4340000000C05E1756C0FFFFFF5F24754340FFFFFFFF4C1756C00000002054964340000000E0B10056C0000000C0B89643400000006029FD55C000000080C09643400000002025FD55C0FFFFFF1F7F6D4340	3.12759000000000009
+54	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	2.06432400000000005
+13	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	0
+28	0106000020E61000000100000001030000000100000008000000000000C05E1756C0FFFFFF5F24754340000000C0DD2C56C0000000407A75434000000080DE3356C0000000406375434000000020D73356C000000060729B4340000000C02D1E56C0000000A0439B4340000000A00F1E56C00000004072964340FFFFFFFF4C1756C00000002054964340000000C05E1756C0FFFFFF5F24754340	1.57115800000000005
+36	0106000020E6100000010000000103000000010000000D00000000000000EE2C56C000000060424E434000000040F72C56C000000000486A4340000000C0DD2C56C0000000407A754340000000C05E1756C0FFFFFF5F24754340000000A0631756C000000000D56D434000000080C61056C0000000A04C6D4340000000C0BE1056C0000000A0065F4340000000407C1256C0FFFFFF7FA75E434000000000BD1156C000000020A954434000000040D01256C0000000605C524340000000404F1256C000000040734F434000000040011156C000000000AF4D434000000000EE2C56C000000060424E4340	0
+68	0106000020E61000000100000001030000000100000006000000000000809F2D56C00000002078CD4240000000E0F64256C0FFFFFFDF38CD424000000000CE4956C00000004053CD4240000000C0E94956C000000080CDEE424000000020682D56C0FFFFFF7F00EF4240000000809F2D56C00000002078CD4240	3.1461920000000001
+27	0106000020E6100000010000000103000000010000000D000000000000407F5056C0000000803C83434000000060615056C000000040A99B4340000000201F4956C000000000BE9B434000000020D73356C000000060729B434000000080DE3356C00000004063754340000000C0DD2C56C0000000407A75434000000040F72C56C000000000486A4340000000202B4956C000000080AC69434000000040454956C000000040E25E434000000060D25056C000000060FA5E4340000000A0F45056C0000000A0F980434000000020635056C0000000E016814340000000407F5056C0000000803C834340	1.5920399999999999
+40	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	45.9054059999999993
+52	0106000020E61000000100000001030000000100000008000000000000E0562D56C0000000208910434000000020864856C00000000054104340FFFFFF9FB84956C0000000409110434000000080A84956C0000000A05B1B4340000000803D4956C0FFFFFF9FB63C434000000080062D56C0FFFFFF7FAE3C434000000060492D56C0000000603E214340000000E0562D56C00000002089104340	4.94153299999999973
+59	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	6.82879400000000025
+1	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	1.62445799999999996
+2	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	2.25549199999999983
+3	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	1.46788999999999992
+4	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	2.4842559999999998
+5	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	0
+15	0106000020E6100000010000000103000000010000000B000000000000806CDC56C00000006020A9434000000080A4ED56C000000020E3A94340000000A050EE56C0FFFFFFDF8BAB434000000000C4ED56C00000000058CD4340000000A039EE56C0000000C062CD4340000000C041EE56C00000006068D44340000000E031EE56C000000080D5D74340000000A054D456C000000020CCD743400000000002CD56C000000080CDCC434000000040CDDD56C0000000407EBA4340000000806CDC56C00000006020A94340	1.9498120000000001
+7	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	6.02948899999999988
+8	0106000020E6100000010000000103000000010000000E000000FFFFFF1F72F656C000000080A1D443400000004048F656C0000000008EF94340000000A09FDC56C0000000A018F94340000000808BDB56C0000000C0FEF5434000000080C7DB56C0000000206FF3434000000000DDDC56C0000000A04FF14340FFFFFF7FBFDC56C00000006078EE4340000000006ED856C000000000E2E6434000000020E6D756C00000006071E14340000000607ED756C000000000C1DC4340000000A054D456C000000020CCD74340000000E031EE56C000000080D5D74340000000C041EE56C00000006068D44340FFFFFF1F72F656C000000080A1D44340	1.8003849999999999
+9	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	4.58125099999999996
+30	0106000020E6100000010000000103000000010000000D0000000000000033DB56C0000000804A5B43400000008099DF56C0000000601B5A4340000000E0EEE356C0000000A0E35643400000000004E656C00000004066574340000000A0D3E956C0000000202C5A4340000000A02EE956C0FFFFFF1F09884340000000A0D9E856C0FFFFFFDF03934340000000A0B8DA56C000000060E592434000000080F4D056C0000000605F9243400000000038D156C0000000A0817F434000000000A0D156C0000000A0FF6B4340000000E0C8DA56C000000080986C43400000000033DB56C0000000804A5B4340	4.41689600000000038
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+\.
+
+
+CREATE INDEX getis_data_gix ON getis_data USING GIST(the_geom);
--- a/src/pg/test/sql/04_dot_density_test.sql
+++ b/src/pg/test/sql/04_dot_density_test.sql
@@ -1,12 +0,0 @@
-WITH g AS (
-  SELECT ST_Buffer(ST_SetSRID(ST_MakePoint(0,0),4326)::geometry, 1000)::geometry AS g
-),
-points AS(
-  SELECT (
-    ST_Dump(
-      cdb_crankshaft.cdb_dot_density(g.g, 100)
-    )
-  ).geom AS p FROM g
-)
-
-SELECT count(*), sum(CASE WHEN ST_Contains(g,p) THEN 1 ELSE 0 END) FROM points, g
--- a/src/pg/test/sql/16_getis_test.sql
+++ b/src/pg/test/sql/16_getis_test.sql
@@ -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;
--- a/src/pg/test/sql/18_outliers_test.sql
+++ b/src/pg/test/sql/18_outliers_test.sql
@@ -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;
--- a/src/py/crankshaft/crankshaft/clustering/init.py
+++ b/src/py/crankshaft/crankshaft/clustering/init.py
@@ -1,3 +1,4 @@
 """Import all functions from for clustering"""
 from moran import *
 from kmeans import *
+from getis import *
--- a/src/py/crankshaft/crankshaft/clustering/getis.py
+++ b/src/py/crankshaft/crankshaft/clustering/getis.py
@@ -0,0 +1,52 @@
+"""
+Getis-Ord's G geostatistics (hotspot/coldspot analysis)
+"""
+
+import pysal as ps
+import plpy
+from collections import OrderedDict
+
+# crankshaft module
+import crankshaft.pysal_utils as pu
+
+# High level interface ---------------------------------------
+
+
+def getis_ord(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)])
+
+    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(4)
+    except plpy.SPIError, err:
+        plpy.error('Query failed: %s' % err)
+
+    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)
--- a/src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py
+++ b/src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py
@@ -45,8 +45,16 @@ def get_weight(query_res, w_type='knn', num_ngbrs=5):
 def query_attr_select(params):
    """
        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 = ""
@@ -64,7 +72,7 @@ def query_attr_select(params):
                 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}

@@ -80,8 +88,8 @@ def query_attr_where(params):
             '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'],
@@ -102,15 +110,17 @@ def query_attr_where(params):
        # moran where clauses

        # get keys
-        attrs = sorted([k for k in params
-                        if k not in ('id_col', 'geom_col', 'subquery',
-                                     'num_ngbrs', 'subquery')])
+        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:
-            attr_string.append("idx_replace.\"%s\" <> 0" % params[attrs[1]])
+        if 'denominator' in attrs:
+            attr_string.append(
+              "idx_replace.\"%s\" <> 0" % params['denominator'])

    out = " AND ".join(attr_string)

--- a/src/py/crankshaft/crankshaft/space_time_dynamics/markov.py
+++ b/src/py/crankshaft/crankshaft/space_time_dynamics/markov.py
@@ -8,12 +8,14 @@ import pysal as ps
 import plpy
 import crankshaft.pysal_utils as pu

+
 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)
+        1. history of its transitions to different classes (e.g., 1st quantile
+             -> 2nd quantile)
        2. average class of its neighbors

        Inputs:
@@ -56,16 +58,15 @@ def spatial_markov_trend(subquery, time_cols, num_classes=7,
        )
        if len(query_result) == 0:
            return zip([None], [None], [None], [None], [None])
-    except plpy.SPIError, e:
-        plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals)))
-        plpy.error('Analysis failed: %s' % e)
+    except plpy.SPIError, err:
+        plpy.error('Analysis failed: %s' % err)
        return zip([None], [None], [None], [None], [None])

-    ## build weight
+    # build weight
    weights = pu.get_weight(query_result, w_type)
    weights.transform = 'r'

-    ## prep time data
+    # prep time data
    t_data = get_time_data(query_result, time_cols)

    plpy.debug('shape of t_data %d, %d' % t_data.shape)
@@ -78,23 +79,26 @@ def spatial_markov_trend(subquery, time_cols, num_classes=7,
                                         fixed=False,
                                         permutations=permutations)

-    ## get lag classes
+    # 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
+    # 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])
+    # 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
+    # output the results
    return zip(trend, trend_up, trend_down, volatility, weights.id_order)

+
 def get_time_data(markov_data, time_cols):
    """
        Extract the time columns and bin appropriately
@@ -103,7 +107,8 @@ 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 +136,16 @@ 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 +164,7 @@ 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 +187,12 @@ 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
--- a/src/py/crankshaft/requirements.txt
+++ b/src/py/crankshaft/requirements.txt
@@ -0,0 +1,5 @@
+joblib==0.8.3
+numpy==1.6.1
+scipy==0.14.0
+pysal==1.11.2
+scikit-learn==0.14.1
--- a/src/py/crankshaft/test/fixtures/getis.json
+++ b/src/py/crankshaft/test/fixtures/getis.json
@@ -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]]
--- a/src/py/crankshaft/test/fixtures/neighbors_getis.json
+++ b/src/py/crankshaft/test/fixtures/neighbors_getis.json
--- a/src/py/crankshaft/test/test_clustering_getis.py
+++ b/src/py/crankshaft/test/test_clustering_getis.py
@@ -0,0 +1,74 @@
+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
+import crankshaft.pysal_utils as pu
+from crankshaft import random_seeds
+import json
+
+# 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 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):
+        plpy._reset()
+
+        # 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]
+        plpy._define_result('select', data)
+        random_seeds.set_random_seeds(1234)
+        result = cc.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)
--- a/src/py/crankshaft/test/test_pysal_utils.py
+++ b/src/py/crankshaft/test/test_pysal_utils.py
@@ -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"""
Author	SHA1	Message	Date
mehak-sachdeva	e479f84cf9	syntax	2017-02-10 16:32:05 -05:00
mehak-sachdeva	f9c1b57dfd	added the deprecated function signature as a wrapper to the new function	2017-02-10 15:27:56 -05:00
Andy Eschbacher	4c175c9565	correct srid in final argument	2017-01-06 14:12:42 -05:00
Mario de Frutos	de6f228e72	Remove old configuration for postgresql 9.5 in travis	2016-12-02 13:08:45 +01:00
Mario de Frutos	87f9b2c787	Merge branch 'develop' into better_dot_density	2016-12-02 12:48:31 +01:00
Mario de Frutos	3c8ac7d45d	Remove default postgres-9.5 from travis	2016-12-02 12:36:11 +01:00
Stuart Lynn	fda5d93cf4	Adding geometry types to function signature	2016-12-01 11:09:55 -05:00
Mario de Frutos	0748212610	Fixing travis config	2016-12-01 14:58:22 +01:00
Mario de Frutos	27b18f5e1c	Merge branch 'develop' into better_dot_density	2016-12-01 13:02:11 +01:00
Mario de Frutos	3649f958c8	Update travis yml to the new postgres-9.5 package	2016-12-01 13:00:28 +01:00
Mario de Frutos	15b460eeb9	Merge pull request #142 from CartoDB/markov-error-fix fix error variable name bug, pep8 updates	2016-12-01 10:44:20 +01:00
Mario de Frutos	b0dcd7f572	Merge pull request #137 from CartoDB/adds-outlier-functions Adds (nonspatial) outlier functions	2016-12-01 10:43:51 +01:00
Mario de Frutos	2547318f59	Merge pull request #127 from CartoDB/adds-getis-analysis Adds getis analysis	2016-12-01 10:42:13 +01:00
Mario de Frutos	25e453a882	Merge pull request #122 from CartoDB/moran-query-ordering-fix Moran query ordering fix	2016-12-01 10:41:19 +01:00
Mario de Frutos	62076bb48c	Merge pull request #126 from CartoDB/stuartlynn-patch-1 Update PULL_REQUEST_TEMPLATE.md	2016-12-01 10:39:29 +01:00
Stuart Lynn	faaf5e419a	Merge branch 'master' into better_dot_density	2016-11-30 22:35:00 +00:00
Stuart Lynn	1f3b74e54f	dot density fixes for geom's with holes and equal area	2016-11-30 22:34:46 +00:00
Mario de Frutos	7c63b66fdd	Update travis yml to the new postgres-9.5 package	2016-11-29 15:52:29 +01:00
Mario de Frutos	538ab9a071	Changed to the last postgresql-9.5 package	2016-11-21 16:14:48 +01:00
Javier Villar	2bc6b0782a	Adding requirements.txt to master branch	2016-10-21 16:28:17 +02:00
Rafa de la Torre	ffd651b91a	Merge remote-tracking branch 'origin/develop' into stuartlynn-patch-1	2016-10-11 15:26:03 +02:00
jvillarf	a271593fe9	Merge pull request #144 from CartoDB/2547_python_requirements_txt Creating requirements.txt file for python	2016-10-07 16:52:59 +02:00
Javier Villar	83219270ae	Copying requirements.txt to python 0.4.2 folder	2016-10-07 16:47:28 +02:00
Javier Villar	215e61396a	Creating requirements.txt file for python	2016-10-07 13:45:09 +02:00
Andy Eschbacher	c7e690980f	update column names in tests	2016-10-06 10:29:52 -04:00
Andy Eschbacher	da1449331c	update signature variable names	2016-10-06 09:53:38 -04:00
Andy Eschbacher	c7f5c24510	update signature names	2016-10-06 09:53:22 -04:00
Andy Eschbacher	11c33ce3fa	adds pep8 check item	2016-10-06 08:56:14 -04:00
Andy Eschbacher	0a53a6e71d	fix error variable name bug, pep8 updates	2016-10-06 08:19:57 -04:00
Stuart Lynn	b8fe05b388	CDB_DasymetricDotDensity and fixed for basic DD	2016-10-03 21:22:40 +00:00
Andy Eschbacher	fa4e5ae686	Merge branch 'develop' into adds-outlier-functions	2016-09-30 09:48:02 -04:00
Javier Goizueta	ecb4bd9606	Merge pull request #140 from CartoDB/138-fix-travis-tests Reorder package installation	2016-09-30 11:40:44 +02:00
Javier Goizueta	ecc9814a88	Reorder package installation Fixes #138 It seems that package postgresql-9.5-postgis-2.2 is now indirectly depending on postgresql-9.5-postgis-2.3-scripts which is not compatible with the packages in cartodb launchpad repos	2016-09-30 11:31:57 +02:00
Andy Eschbacher	6846014a4f	adding docs	2016-09-29 11:42:11 -04:00
Andy Eschbacher	23b2ad57c5	test updates	2016-09-29 11:37:42 -04:00
Andy Eschbacher	99856ce956	flip inequality	2016-09-29 11:37:22 -04:00
Andy Eschbacher	f11982f531	Merge branch 'adds-outlier-functions' of https://github.com/CartoDB/crankshaft into adds-outlier-functions	2016-09-29 11:11:36 -04:00
Andy Eschbacher	bd05e7739d	add test to produce error	2016-09-29 11:10:54 -04:00
Andy Eschbacher	5754087140	adds symmetric option for stddev outlier	2016-09-29 11:09:10 -04:00
Andy Eschbacher	8bc6f69a1b	adding exceptions to improve robustness	2016-09-29 10:12:32 -04:00
Andy Eschbacher	b54c62890f	adds hand-off doc line	2016-09-29 08:48:22 -04:00
Andy Eschbacher	acde384157	update tests	2016-09-28 16:27:41 -04:00
Andy Eschbacher	b8accb48fc	adds tests	2016-09-28 15:55:56 -04:00
Andy Eschbacher	f2bb0b496b	small fixes	2016-09-26 16:51:22 -04:00
Andy Eschbacher	aaa36569de	first add	2016-09-26 16:26:34 -04:00
Andy Eschbacher	803816f5c9	Merge branch 'moran-query-ordering-fix' of https://github.com/CartoDB/crankshaft into moran-query-ordering-fix	2016-09-26 10:15:49 -04:00
Andy Eschbacher	1ef3f86474	small updates after ordering fix	2016-09-26 10:13:27 -04:00
Andy Eschbacher	f1d420a6f7	ordering fixes	2016-09-26 10:11:16 -04:00
Andy Eschbacher	06452562b9	fix ordering problems in input columns	2016-09-26 10:10:52 -04:00
Andy Eschbacher	07e4062237	Merge branch 'develop' into moran-query-ordering-fix	2016-09-23 13:25:36 -04:00
Andy Eschbacher	5443b67470	adding docs for getis ord's g	2016-09-22 08:58:22 -04:00
Andy Eschbacher	795413e46d	cleaning test files	2016-09-21 12:01:42 -04:00
Andy Eschbacher	e5ea836493	fix json format	2016-09-21 11:53:17 -04:00
Andy Eschbacher	258322fcca	update tests to queen weights from knn	2016-09-21 11:46:07 -04:00
Andy Eschbacher	166e9e223f	minor formatting changes	2016-09-20 09:55:33 -04:00
Andy Eschbacher	29de72de33	output column renaming	2016-09-20 09:55:13 -04:00
Andy Eschbacher	eff548dec9	aligning parameters for fistures and tests	2016-09-19 17:17:51 -04:00
Andy Eschbacher	dcb364c3ee	up default number of permutations	2016-09-19 17:16:53 -04:00
Andy Eschbacher	1d09eac3e7	adding pgsql tests	2016-09-19 16:10:29 -04:00
Andy Eschbacher	5127845100	Merge branch 'adds-getis-analysis' of https://github.com/cartodb/crankshaft into adds-getis-analysis	2016-09-19 19:24:43 +00:00
Andy Eschbacher	ee4eb795b7	adding getis fixture file	2016-09-19 19:24:23 +00:00
Andy Eschbacher	2ede55d165	pep8 updates	2016-09-19 12:17:01 -04:00
Andy Eschbacher	df5faa6745	Merge branch 'adds-getis-analysis' of https://github.com/cartodb/crankshaft into adds-getis-analysis Conflicts: src/py/crankshaft/test/test_clustering_getis.py	2016-09-19 15:54:46 +00:00
Andy Eschbacher	06f0cb0dc4	updating how p values are tested	2016-09-19 15:45:10 +00:00
Stuart Lynn	11176b71b3	Update PULL_REQUEST_TEMPLATE.md	2016-09-19 10:47:59 -04:00
Andy Eschbacher	b5445da303	remove kinks in test	2016-09-14 12:45:43 +00:00
Andy Eschbacher	5d109acd8d	remove debug messages	2016-09-13 17:59:15 -04:00
Andy Eschbacher	2937c97fea	including correct fixtures	2016-09-13 17:47:30 -04:00
Andy Eschbacher	c392aec98a	re-ordered columns	2016-09-13 15:32:32 -04:00
Andy Eschbacher	4e42625d79	fix indexing of fixture	2016-09-13 14:29:06 -04:00
Andy Eschbacher	b71152a884	adds fixtures and tests	2016-09-13 09:06:09 -04:00
Andy Eschbacher	ce4cc637ae	adding permutations to interface	2016-09-13 09:05:24 -04:00
Andy Eschbacher	ccccf68066	fix module call	2016-09-12 11:39:21 -04:00
Andy Eschbacher	60f52633fa	adds hotspot/coldspot function	2016-09-09 11:11:32 -04:00
Andy Eschbacher	1148aa417a	additional test on alphabetical ordering	2016-09-06 09:23:59 -04:00
Andy Eschbacher	e29f6f2861	add more comments	2016-09-06 09:23:39 -04:00
Andy Eschbacher	44dc5811b5	updating tests for query ordering error	2016-09-01 16:47:57 -04:00
Andy Eschbacher	40481f1286	adding more tests	2016-08-29 17:10:58 -04:00
Andy Eschbacher	622235d787	:P adding commas	2016-08-29 16:52:40 -04:00
Andy Eschbacher	623613aa5c	adding ordered dict to tests	2016-08-29 16:46:49 -04:00
Andy Eschbacher	a451fb5b6a	minor ordering changes	2016-08-29 15:50:19 -04:00
				`@@ -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]]