Release 0.0.3

KMeans clustering and weighted centroid analysis

.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,7 @@
+
+- [ ] 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)
+ 

.gitignore

@@ -1,3 +1,4 @@
 envs/
 *.pyc
 .DS_Store
+.idea/

CONTRIBUTING.md

@@ -45,8 +45,8 @@ source envs/dev/bin/activate
 
 Update extension in a working database with:
 
-* `ALTER EXTENSION crankshaft VERSION TO 'current';`
-  `ALTER EXTENSION crankshaft VERSION TO 'dev';`
+* `ALTER EXTENSION crankshaft UPDATE TO 'current';`
+  `ALTER EXTENSION crankshaft UPDATE TO 'dev';`
 
 Note: we keep the current development version install as 'dev' always;
 we update through the 'current' alias to allow changing the extension
@@ -58,7 +58,10 @@ should be dropped manually before the update.
 If the extension has not previously been installed in a database,
 it can be installed directly with:
 
-* `CREATE EXTENSION crankshaft WITH VERSION 'dev';`
+* `CREATE EXTENSION IF NOT EXISTS plpythonu;`
+  `CREATE EXTENSION IF NOT EXISTS postgis;`
+  `CREATE EXTENSION IF NOT EXISTS cartodb;`
+  `CREATE EXTENSION crankshaft WITH VERSION 'dev';`
 
 Note: the development extension uses the development python virtual
 environment automatically.

Makefile

@@ -11,7 +11,6 @@ PYP_DIR = src/py
 # Generate and install developmet versions of the extension
 # and python package.
 # The extension is named 'dev' with a 'current' alias for easily upgrading.
-# The Python package is installed in a virtual environment envs/dev/
 # Requires sudo.
 install: ## Generate and install development version of the extension; requires sudo.
 	$(MAKE) -C $(PYP_DIR) install
@@ -29,7 +28,6 @@ release: ## Generate a new release of the extension. Only for telease manager
 	$(MAKE) -C $(PYP_DIR) release
 
 # Install the current release.
-# The Python package is installed in a virtual environment envs/X.Y.Z/
 # Requires sudo.
 # Use the RELEASE_VERSION environment variable to deploy a specific version:
 #     sudo make deploy RELEASE_VERSION=1.0.0
@@ -52,11 +50,7 @@ clean-release: ## clean up current release
 	rm -rf release/python/$(RELEASE_VERSION)
 	rm -f release/$(RELEASE_VERSION)--*.sql
 
-# Cleanup all virtual environments
-clean-environments: ## clean up all virtual environments
-	rm -rf envs/*
-
-clean-all: clean-dev clean-release clean-environments
+clean-all: clean-dev clean-release
 
 help:
 	@IFS=$$'\n' ; \

NEWS.md

@@ -1,3 +1,8 @@
+0.0.3 (2016-06-16)
+------------------
+* Adds new functions: kmeans, weighted centroids.
+* Replaces moran functions with new areas of interest naming.
+
 0.0.2 (2016-03-16)
 ------------------
 * New versioning approach using per-version Python virtual environments

README.md

@@ -9,11 +9,10 @@ CartoDB Spatial Analysis extension for PostgreSQL.
 * - *src/pg* contains the PostgreSQL extension source code
 * - *src/py* Python module source code
 * *release* reseleased versions
-* *env* base directory for Python virtual environments
 
 ## Requirements
 
-* pip, virtualenv, PostgreSQL
+* pip, PostgreSQL
 * python-scipy system package (see [src/py/README.md](https://github.com/CartoDB/crankshaft/blob/master/src/py/README.md))
 
 # Working Process -- Quickstart Guide

doc/02_moran.md

@@ -1,71 +1,185 @@
-### Moran's I
+## Areas of Interest Functions
 
-#### What is Moran's I and why is it significant for CartoDB?
+### CDB_AreasOfInterestLocal(subquery text, column_name text)
 
-Moran's I is a geostatistical calculation which gives a measure of the global
-clustering and presence of outliers within the geographies in a map. Here global
-means over all of the geographies in a dataset. Imagine mapping the incidence
-rates of cancer in neighborhoods of a city. If there were areas covering several
-neighborhoods with abnormally low rates of cancer, those areas are positively
-spatially correlated with one another and would be considered a cluster. If
-there was a single neighborhood with a high rate but with all neighbors on
-average having a low rate, it would be considered a spatial outlier.
+This function classifies your data as being part of a cluster, as an outlier, or not part of a pattern based the significance of a classification. The classification happens through an autocorrelation statistic called Local Moran's I.
 
-While Moran's I gives a global snapshot, there are local indicators for
-clustering called Local Indicators of Spatial Autocorrelation. Clustering is a
-process related to autocorrelation -- i.e., a process that compares a
-geography's attribute to the attribute in neighbor geographies.
+#### Arguments
 
-For the example of cancer rates in neighborhoods, since these neighborhoods have
-a high value for rate of cancer, and all of their neighbors do as well, they are
-designated as "High High" or simply **HH**. For areas with multiple neighborhoods
-with low rates of cancer, they are designated as "Low Low" or **LL**. HH and LL
-naturally fit into the concept of clustering and are in the correlated
-variables.
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
+| column_name | TEXT | Name of column (e.g., should be `'interesting_value'` instead of `interesting_value` without single quotes) used for the analysis. |
+| weight 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) | INT | Number of neighbors if using k-nearest neighbors weight type. Defaults to 5. |
+| permutations (optional) | INT | Number of permutations to check against a random arrangement of the values in `column_name`. This influences the accuracy of the output field `significance`. Defaults to 99. |
+| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` |
+| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. |
 
-"Anticorrelated" geogs are in **LH** and **HL** regions -- that is, regions
-where a geog has a high value and it's neighbors, on average, have a low value
-(or vice versa). An example of this is a "gated community" or placement of a
-city housing project in a rich region. These deliberate developments have
-opposite median income as compared to the neighbors around them. They have a
-high (or low) value while their neighbors have a low (or high) value. They exist
-typically as islands, and in rare circumstances can extend as chains dividing
-**LL** or **HH**.
+#### Returns
 
-Strong policies such as rent stabilization (probably) tend to prevent the
-clustering of high rent areas as they integrate middle class incomes. Luxury
-apartment buildings, which are a kind of gated community, probably tend to skew
-an area's median income upwards while housing projects have the opposite effect.
-What are the nuggets in the analysis?
+A table with the following columns.
 
-Two functions are available to compute Moran I statistics:
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| moran | NUMERIC | Value of Moran's I (spatial autocorrelation measure) for the geometry with id of `rowid` |
+| quads | TEXT | Classification of geometry. Result is one of 'HH' (a high value with neighbors high on average), 'LL' (opposite of 'HH'), 'HL' (a high value surrounded by lows on average), and 'LH' (opposite of 'HL'). Null values are returned when nulls exist in the original data. |
+| significance | NUMERIC | The statistical significance (from 0 to 1) of a cluster or outlier classification. Lower numbers are more significant. |
+| rowid | INT | Row id of the values which correspond to the input rows. |
+| vals | NUMERIC | Values from `'column_name'`. |
 
-* `cdb_moran_local` computes Moran I measures, quad classification and
-  significance values from numerial values associated to geometry entities
-  in an input table. The geometries should be contiguous polygons When
-  then `queen` `w_type` is used.
-* `cdb_moran_local_rate` computes the same statistics using a ratio between
-  numerator and denominator columns of a table.
 
-The parameters for `cdb_moran_local` are:
+#### Example Usage
 
-* `table` name of the table that contains the data values
-* `attr` name of the column
-* `signficance` significance threshold for the quads values
-* `num_ngbrs` number of neighbors to consider (default: 5)
-* `permutations` number of random permutations for calculation of
-  pseudo-p values (default: 99)
-* `geom_column` number of the geometry column (default: "the_geom")
-* `id_col` PK column of the table (default: "cartodb_id")
-* `w_type` Weight types: can be "knn" for k-nearest neighbor weights
-  or "queen" for contiguity based weights.
+```sql
+SELECT
+  c.the_geom,
+  aoi.quads,
+  aoi.significance,
+  c.num_cyclists_per_total_population
+FROM CDB_GetAreasOfInterestLocal('SELECT * FROM commute_data'
+                                 'num_cyclists_per_total_population') As aoi
+JOIN commute_data As c
+ON c.cartodb_id = aoi.rowid;
+```
 
-The function returns a table with the following columns:
+### CDB_AreasOfInterestGlobal(subquery text, column_name text)
 
-* `moran` Moran's value
-* `quads` quad classification ('HH', 'LL', 'HL', 'LH' or 'Not significant')
-* `significance` significance value
-* `ids` id of the corresponding record in the input table
+This function identifies the extent to which geometries cluster (the groupings of geometries with similarly high or low values relative to the mean) or form outliers (areas where geometries have values opposite of their neighbors). The output of this function gives values between -1 and 1 as well as a significance of that classification. Values close to 0 mean that there is little to no distribution of values as compared to what one would see in a randomly distributed collection of geometries and values.
 
-Function `cdb_moran_local_rate` only differs in that the `attr` input
-parameter is substituted by `numerator` and `denominator`.
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
+| column_name | TEXT | Name of column (e.g., should be `'interesting_value'` instead of `interesting_value` without single quotes) used for the analysis. |
+| weight 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) | INT | Number of neighbors if using k-nearest neighbors weight type. Defaults to 5. |
+| permutations (optional) | INT | Number of permutations to check against a random arrangement of the values in `column_name`. This influences the accuracy of the output field `significance`. Defaults to 99. |
+| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` |
+| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. |
+
+#### Returns
+
+A table with the following columns.
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| moran | NUMERIC | Value of Moran's I (spatial autocorrelation measure) for the entire dataset. Values closer to one indicate cluster, closer to -1 mean more outliers, and near zero indicates a random distribution of data. |
+| significance | NUMERIC | The statistical significance of the `moran` measure. |
+
+#### Examples
+
+```sql
+SELECT *
+FROM CDB_AreasOfInterestGlobal('SELECT * FROM commute_data', 'num_cyclists_per_total_population')
+```
+
+### CDB_AreasOfInterestLocalRate(subquery text, numerator_column text, denominator_column text)
+
+Just like `CDB_AreasOfInterestLocal`, this function classifies your data as being part of a cluster, as an outlier, or not part of a pattern based the significance of a classification. This function differs in that it calculates the classifications based on input `numerator` and `denominator` columns for finding the areas where there are clusters and outliers for the resulting rate of those two values.
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
+| numerator | TEXT | Name of the numerator for forming a rate to be used in analysis. |
+| denominator | TEXT | Name of the denominator for forming a rate to be used in analysis. |
+| weight 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) | INT | Number of neighbors if using k-nearest neighbors weight type. Defaults to 5. |
+| permutations (optional) | INT | Number of permutations to check against a random arrangement of the values in `column_name`. This influences the accuracy of the output field `significance`. Defaults to 99. |
+| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` |
+| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. |
+
+#### Returns
+
+A table with the following columns.
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| moran | NUMERIC | Value of Moran's I (spatial autocorrelation measure) for the geometry with id of `rowid` |
+| quads | TEXT | Classification of geometry. Result is one of 'HH' (a high value with neighbors high on average), 'LL' (opposite of 'HH'), 'HL' (a high value surrounded by lows on average), and 'LH' (opposite of 'HL'). Null values are returned when nulls exist in the original data. |
+| significance | NUMERIC | The statistical significance (from 0 to 1) of a cluster or outlier classification. Lower numbers are more significant. |
+| rowid | INT | Row id of the values which correspond to the input rows. |
+| vals | NUMERIC | Values from `'column_name'`. |
+
+
+#### Example Usage
+
+```sql
+SELECT
+  c.the_geom,
+  aoi.quads,
+  aoi.significance,
+  c.cyclists_per_total_population
+FROM CDB_GetAreasOfInterestLocalRate('SELECT * FROM commute_data'
+                                     'num_cyclists',
+                                     'total_population') As aoi
+JOIN commute_data As c
+ON c.cartodb_id = aoi.rowid;
+```
+
+### CDB_AreasOfInterestGlobalRate(subquery text, column_name text)
+
+This function identifies the extent to which geometries cluster (the groupings of geometries with similarly high or low values relative to the mean) or form outliers (areas where geometries have values opposite of their neighbors). The output of this function gives values between -1 and 1 as well as a significance of that classification. Values close to 0 mean that there is little to no distribution of values as compared to what one would see in a randomly distributed collection of geometries and values.
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
+| numerator | TEXT | Name of the numerator for forming a rate to be used in analysis. |
+| denominator | TEXT | Name of the denominator for forming a rate to be used in analysis. |
+| weight 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) | INT | Number of neighbors if using k-nearest neighbors weight type. Defaults to 5. |
+| permutations (optional) | INT | Number of permutations to check against a random arrangement of the values in `column_name`. This influences the accuracy of the output field `significance`. Defaults to 99. |
+| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` |
+| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. |
+
+#### Returns
+
+A table with the following columns.
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| moran | NUMERIC | Value of Moran's I (spatial autocorrelation measure) for the entire dataset. Values closer to one indicate cluster, closer to -1 mean more outliers, and near zero indicates a random distribution of data. |
+| significance | NUMERIC | The statistical significance of the `moran` measure. |
+
+#### Examples
+
+```sql
+SELECT *
+FROM CDB_AreasOfInterestGlobalRate('SELECT * FROM commute_data',          
+                                   'num_cyclists',
+                                   'total_population')
+```
+
+## Hotspot, Coldspot, and Outlier Functions
+
+These functions are convenience functions for extracting only information that you are interested in exposing based on the outputs of the `CDB_AreasOfInterest` functions. For instance, you can use `CDB_GetSpatialHotspots` to output only the classifications of `HH` and `HL`.
+
+### Non-rate functions
+
+#### CDB_GetSpatialHotspots
+This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocal` except that the outputs are filtered to be only 'HH' and 'HL' (areas of high values). For more information about this function's use, see `CDB_AreasOfInterestLocal`.
+
+#### CDB_GetSpatialColdspots
+This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocal` except that the outputs are filtered to be only 'LL' and 'LH' (areas of low values). For more information about this function's use, see `CDB_AreasOfInterestLocal`.
+
+#### CDB_GetSpatialOutliers
+This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocal` except that the outputs are filtered to be only 'HL' and 'LH' (areas where highs or lows are surrounded by opposite values on average). For more information about this function's use, see `CDB_AreasOfInterestLocal`.
+
+### Rate functions
+
+#### CDB_GetSpatialHotspotsRate
+
+This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocalRate` except that the outputs are filtered to be only 'HH' and 'HL' (areas of high values). For more information about this function's use, see `CDB_AreasOfInterestLocalRate`.
+
+#### CDB_GetSpatialColdspotsRate
+
+This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocalRate` except that the outputs are filtered to be only 'LL' and 'LH' (areas of low values). For more information about this function's use, see `CDB_AreasOfInterestLocalRate`.
+
+#### CDB_GetSpatialOutliersRate
+
+This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocalRate` except that the outputs are filtered to be only 'HL' and 'LH' (areas where highs or lows are surrounded by opposite values on average). For more information about this function's use, see `CDB_AreasOfInterestLocalRate`.

doc/11_kmeans.md

@@ -0,0 +1,62 @@
+## K-Means Functions
+
+### CDB_KMeans(subquery text, no_clusters INTEGER)
+
+This function attempts to find n clusters within the input data. It will return a table to CartoDB ids and 
+the number of the cluster each point in the input was assigend to.
+
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
+| no\_clusters | INTEGER | The number of clusters to try and find |
+
+#### Returns
+
+A table with the following columns.
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| cartodb\_id | INTEGER | The CartoDB id of the row in the input table.|
+| cluster\_no | INTEGER | The cluster that this point belongs to. |
+
+
+#### Example Usage
+
+```sql
+SELECT 
+    customers.*, 
+    km.cluster_no 
+    FROM cdb_crankshaft.CDB_Kmeans('SELECT * from customers' , 6) km, customers_3
+    WHERE customers.cartodb_id = km.cartodb_id
+```
+
+### CDB_WeightedMean(subquery text, weight_column text, category_column text)
+
+Function that computes the weighted centroid of a number of clusters by some weight column.
+
+### Arguments 
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column and the columns specified as the weight and category columns|
+| weight\_column | TEXT | The name of the column to use as a weight |
+| category\_column | TEXT | The name of the column to use as a category |
+
+### Returns 
+
+A table with the following columns.
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| the\_geom | GEOMETRY | A point for the weighted cluster center |
+| class | INTEGER | The cluster class | 
+
+### Example Usage 
+
+```sql 
+SELECT ST_TRANSFORM(the_geom, 3857) as the_geom_webmercator, class 
+FROM cdb_weighted_mean('SELECT *, customer_value FROM customers','customer_value','cluster_no')
+```

doc/docs_template.md

@@ -0,0 +1,24 @@
+
+## Name
+
+## Synopsis
+
+## Description
+
+Availability: v...
+
+## Examples
+
+```SQL
+-- example of the function in use
+SELECT cdb_awesome_function(the_geom, 'total_pop')
+FROM table_name
+```
+
+## API Usage
+
+_asdf_
+
+## See Also
+
+_Other function pages_

release/crankshaft--0.0.2--0.0.3.sql

@@ -0,0 +1,413 @@
+--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES
+-- Complain if script is sourced in psql, rather than via CREATE EXTENSION
+\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit
+
+-- [MANUALLY] DROP FUNCTIONS REMOVED SINCE 0.0.2 version
+
+DROP FUNCTION IF EXISTS cdb_moran_local(TEXT, TEXT, float, INT, INT, TEXT, TEXT, TEXT);
+DROP FUNCTION IF EXISTS cdb_moran_local_rate(TEXT, TEXT, TEXT, FLOAT, INT, INT, TEXT, TEXT, TEXT);
+DROP FUNCTION IF EXISTS _cdb_crankshaft_virtualenvs_path();
+DROP FUNCTION IF EXISTS _cdb_crankshaft_activate_py();
+
+-- [END MANUALLY] DROP FUNCTIONS REMOVED SINCE 0.0.2 version
+
+-- Version number of the extension release
+CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
+    RETURNS text AS $$
+  SELECT '0.0.3'::text;
+$$ language 'sql' STABLE STRICT;
+
+-- Internal identifier of the installed extension instence
+-- e.g. 'dev' for current development version
+CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version()
+    RETURNS text AS $$
+  SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL;
+$$ language 'sql' STABLE STRICT;
+-- Internal function.
+-- Set the seeds of the RNGs (Random Number Generators)
+-- used internally.
+CREATE OR REPLACE FUNCTION
+    _cdb_random_seeds (seed_value INTEGER) RETURNS VOID
+AS $$
+  from crankshaft import random_seeds
+  random_seeds.set_random_seeds(seed_value)
+$$ LANGUAGE plpythonu;
+-- Moran's I Global Measure (public-facing)
+CREATE OR REPLACE FUNCTION
+    CDB_AreasOfInterestGlobal(
+    subquery TEXT,
+    column_name TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, significance NUMERIC)
+AS $$
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
+$$ LANGUAGE plpythonu;
+
+-- Moran's I Local (internal function)
+CREATE OR REPLACE FUNCTION
+    _CDB_AreasOfInterestLocal(
+    subquery TEXT,
+    column_name TEXT,
+    w_type TEXT,
+    num_ngbrs INT,
+    permutations INT,
+    geom_col TEXT,
+    id_col TEXT)
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
+$$ LANGUAGE plpythonu;
+
+-- Moran's I Local (public-facing function)
+CREATE OR REPLACE FUNCTION
+    CDB_AreasOfInterestLocal(
+    subquery TEXT,
+    column_name TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col);
+
+$$ LANGUAGE SQL;
+
+-- Moran's I only for HH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+    CDB_GetSpatialHotspots(
+    subquery TEXT,
+    column_name TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HH', 'HL');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I only for LL and LH (public-facing function)
+CREATE OR REPLACE FUNCTION
+    CDB_GetSpatialColdspots(
+    subquery TEXT,
+    attr TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('LL', 'LH');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I only for LH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+    CDB_GetSpatialOutliers(
+    subquery TEXT,
+    attr TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HL', 'LH');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Global Rate (public-facing function)
+CREATE OR REPLACE FUNCTION
+    CDB_AreasOfInterestGlobalRate(
+    subquery TEXT,
+    numerator TEXT,
+    denominator TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran FLOAT, significance FLOAT)
+AS $$
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+$$ LANGUAGE plpythonu;
+
+
+-- Moran's I Local Rate (internal function)
+CREATE OR REPLACE FUNCTION
+    _CDB_AreasOfInterestLocalRate(
+    subquery TEXT,
+    numerator TEXT,
+    denominator TEXT,
+    w_type TEXT,
+    num_ngbrs INT,
+    permutations INT,
+    geom_col TEXT,
+    id_col TEXT)
+    RETURNS
+        TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+  from crankshaft.clustering import moran_local_rate
+  # TODO: use named parameters or a dictionary
+  return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+$$ LANGUAGE plpythonu;
+
+-- Moran's I Local Rate (public-facing function)
+CREATE OR REPLACE FUNCTION
+    CDB_AreasOfInterestLocalRate(
+    subquery TEXT,
+    numerator TEXT,
+    denominator TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS
+        TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col);
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Local Rate only for HH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+    CDB_GetSpatialHotspotsRate(
+    subquery TEXT,
+    numerator TEXT,
+    denominator TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS
+        TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HH', 'HL');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Local Rate only for LL and LH (public-facing function)
+CREATE OR REPLACE FUNCTION
+    CDB_GetSpatialColdspotsRate(
+    subquery TEXT,
+    numerator TEXT,
+    denominator TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS
+        TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('LL', 'LH');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Local Rate only for LH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+    CDB_GetSpatialOutliersRate(
+    subquery TEXT,
+    numerator TEXT,
+    denominator TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS
+        TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HL', 'LH');
+
+$$ LANGUAGE SQL;
+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 $$
+    
+    from crankshaft.clustering import kmeans
+    return kmeans(query,no_clusters,no_init)
+
+$$ language plpythonu;
+
+
+CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC)
+    RETURNS Numeric[] AS
+    $$
+DECLARE 
+    newX NUMERIC;
+    newY NUMERIC;
+    newW NUMERIC;
+BEGIN
+    IF weight IS NULL OR the_geom IS NULL THEN 
+        newX = state[1];
+        newY = state[2];
+        newW = state[3];
+    ELSE
+        newX = state[1] + ST_X(the_geom)*weight;
+        newY = state[2] + ST_Y(the_geom)*weight;
+        newW = state[3] + weight;
+    END IF;
+    RETURN Array[newX,newY,newW];
+
+END
+$$ LANGUAGE plpgsql;
+
+CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[])
+    RETURNS GEOMETRY AS
+    $$
+BEGIN
+    IF state[3] = 0 THEN 
+        RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326);
+    ELSE 
+        RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326);
+    END IF;
+END
+$$ LANGUAGE plpgsql;
+
+CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC)(
+SFUNC = CDB_WeightedMeanS,
+FINALFUNC = CDB_WeightedMeanF,
+STYPE = Numeric[],
+INITCOND = "{0.0,0.0,0.0}"
+);
+-- Function by Stuart Lynn for a simple interpolation of a value
+-- from a polygon table over an arbitrary polygon
+-- (weighted by the area proportion overlapped)
+-- Aereal weighting is a very simple form of aereal interpolation.
+--
+-- Parameters:
+--   * geom a Polygon geometry which defines the area where a value will be
+--     estimated as the area-weighted sum of a given table/column
+--   * target_table_name table name of the table that provides the values
+--   * target_column column name of the column that provides the values
+--   * schema_name optional parameter to defina the schema the target table
+--     belongs to, which is necessary if its not in the search_path.
+--     Note that target_table_name should never include the schema in it.
+-- Return value:
+--   Aereal-weighted interpolation of the column values over the geometry
+CREATE OR REPLACE
+FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL)
+    RETURNS numeric AS
+    $$
+    DECLARE
+        result numeric;
+        qualified_name text;
+    BEGIN
+        IF schema_name IS NULL THEN
+            qualified_name := Format('%I', target_table_name);
+        ELSE
+            qualified_name := Format('%I.%s', schema_name, target_table_name);
+        END IF;
+        EXECUTE Format('
+    SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom))
+    FROM %s AS a
+    WHERE $1 && a.the_geom
+  ', target_column, qualified_name)
+        USING geom
+        INTO result;
+        RETURN result;
+    END;
+    $$ LANGUAGE plpgsql;
+--
+-- Creates N points randomly distributed arround the polygon
+--
+-- @param g - the geometry 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
+CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000)
+    RETURNS GEOMETRY AS $$
+DECLARE
+    extent GEOMETRY;
+    test_point Geometry;
+    width                NUMERIC;
+    height               NUMERIC;
+    x0                   NUMERIC;
+    y0                   NUMERIC;
+    xp                   NUMERIC;
+    yp                   NUMERIC;
+    no_left              INTEGER;
+    remaining_iterations INTEGER;
+    points               GEOMETRY[];
+    bbox_line            GEOMETRY;
+    intersection_line    GEOMETRY;
+BEGIN
+    extent  := ST_Envelope(geom);
+    width   := ST_XMax(extent) - ST_XMIN(extent);
+    height  := ST_YMax(extent) - ST_YMIN(extent);
+    x0 	  := ST_XMin(extent);
+    y0 	  := ST_YMin(extent);
+    no_left := no_points;
+
+    LOOP
+        if(no_left=0) THEN
+            EXIT;
+        END IF;
+        yp = y0 + height*random();
+        bbox_line  = ST_MakeLine(
+            ST_SetSRID(ST_MakePoint(yp, x0),4326),
+            ST_SetSRID(ST_MakePoint(yp, x0+width),4326)
+        );
+        intersection_line = ST_Intersection(bbox_line,geom);
+        test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random());
+        points := points || test_point;
+        no_left = no_left - 1 ;
+    END LOOP;
+    RETURN ST_Collect(points);
+END;
+$$
+LANGUAGE plpgsql VOLATILE;
+-- Make sure by default there are no permissions for publicuser
+-- NOTE: this happens at extension creation time, as part of an implicit transaction.
+-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE;
+
+-- Grant permissions on the schema to publicuser (but just the schema)
+GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser;
+
+-- Revoke execute permissions on all functions in the schema by default
+-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser;

release/crankshaft--0.0.3--0.0.2.sql

@@ -0,0 +1,209 @@
+--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES
+-- Complain if script is sourced in psql, rather than via CREATE EXTENSION
+\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit
+
+-- [MANUALLY] DROP FUNCTIONS INTRODUCED IN 0.0.3 version
+
+DROP FUNCTION IF EXISTS CDB_AreasOfInterestGlobal(TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT);
+DROP FUNCTION IF EXISTS _CDB_AreasOfInterestLocal(TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT);
+DROP FUNCTION IF EXISTS CDB_AreasOfInterestLocal(TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT);
+DROP FUNCTION IF EXISTS CDB_GetSpatialHotspots(TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT);
+DROP FUNCTION IF EXISTS CDB_GetSpatialColdspots(TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT);
+DROP FUNCTION IF EXISTS CDB_GetSpatialOutliers(TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT);
+DROP FUNCTION IF EXISTS CDB_AreasOfInterestGlobalRate(TEXT,TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT);
+DROP FUNCTION IF EXISTS CDB_AreasOfInterestLocalRate(TEXT,TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT);
+DROP FUNCTION IF EXISTS _CDB_AreasOfInterestLocalRate(TEXT,TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT);
+DROP FUNCTION IF EXISTS CDB_GetSpatialHotspotsRate(TEXT,TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT);
+DROP FUNCTION IF EXISTS CDB_GetSpatialColdspotsRate(TEXT,TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT);
+DROP FUNCTION IF EXISTS CDB_GetSpatialOutliersRate(TEXT,TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT);
+DROP FUNCTION IF EXISTS CDB_KMeans(text,integer,integer);
+DROP AGGREGATE IF EXISTS CDB_WeightedMean(geometry(Point, 4326), NUMERIC);
+DROP FUNCTION IF EXISTS CDB_WeightedMeanS(Numeric[], GEOMETRY(Point, 4326), NUMERIC);
+DROP FUNCTION IF EXISTS CDB_WeightedMeanF(Numeric[]);
+
+
+-- [END MANUALLY] DROP FUNCTIONS INTRODUCED IN 0.0.3 version
+
+-- Version number of the extension release
+CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
+RETURNS text AS $$
+  SELECT '0.0.2'::text;
+$$ language 'sql' STABLE STRICT;
+
+-- Internal identifier of the installed extension instence
+-- e.g. 'dev' for current development version
+CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version()
+RETURNS text AS $$
+  SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL;
+$$ language 'sql' STABLE STRICT;
+CREATE OR REPLACE FUNCTION _cdb_crankshaft_virtualenvs_path()
+RETURNS text
+AS $$
+  BEGIN
+    -- RETURN '/opt/virtualenvs/crankshaft';
+    RETURN '/home/ubuntu/crankshaft/envs';
+  END;
+$$ language plpgsql IMMUTABLE STRICT;
+
+-- Use the crankshaft python module
+CREATE OR REPLACE FUNCTION _cdb_crankshaft_activate_py()
+RETURNS VOID
+AS $$
+    import os
+    # plpy.notice('%',str(os.environ))
+    # activate virtualenv
+    crankshaft_version = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_internal_version()')[0]['_cdb_crankshaft_internal_version']
+    base_path = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_virtualenvs_path()')[0]['_cdb_crankshaft_virtualenvs_path']
+    default_venv_path = os.path.join(base_path, crankshaft_version)
+    venv_path =  os.environ.get('CRANKSHAFT_VENV', default_venv_path)
+    activate_path = venv_path + '/bin/activate_this.py'
+    exec(open(activate_path).read(), dict(__file__=activate_path))
+$$ LANGUAGE plpythonu;
+-- Internal function.
+-- Set the seeds of the RNGs (Random Number Generators)
+-- used internally.
+CREATE OR REPLACE FUNCTION
+_cdb_random_seeds (seed_value INTEGER) RETURNS VOID
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft import random_seeds
+  random_seeds.set_random_seeds(seed_value)
+$$ LANGUAGE plpythonu;
+-- Moran's I
+CREATE OR REPLACE FUNCTION
+  cdb_moran_local (
+      t TEXT,
+  	  attr TEXT,
+  	  significance float DEFAULT 0.05,
+  	  num_ngbrs INT DEFAULT 5,
+  	  permutations INT DEFAULT 99,
+  	  geom_column TEXT DEFAULT 'the_geom',
+  	  id_col TEXT DEFAULT 'cartodb_id',
+      w_type TEXT DEFAULT 'knn')
+RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+$$ LANGUAGE plpythonu;
+
+-- Moran's I Local Rate
+CREATE OR REPLACE FUNCTION
+  cdb_moran_local_rate(t TEXT,
+		 numerator TEXT,
+		 denominator TEXT,
+		 significance FLOAT DEFAULT 0.05,
+		 num_ngbrs INT DEFAULT 5,
+		 permutations INT DEFAULT 99,
+		 geom_column TEXT DEFAULT 'the_geom',
+		 id_col TEXT DEFAULT 'cartodb_id',
+		 w_type TEXT DEFAULT 'knn')
+RETURNS TABLE(moran FLOAT, quads TEXT, significance FLOAT, ids INT, y numeric)
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft.clustering import moran_local_rate
+  # TODO: use named parameters or a dictionary
+  return moran_local_rate(t, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+$$ LANGUAGE plpythonu;
+-- Function by Stuart Lynn for a simple interpolation of a value
+-- from a polygon table over an arbitrary polygon
+-- (weighted by the area proportion overlapped)
+-- Aereal weighting is a very simple form of aereal interpolation.
+--
+-- Parameters:
+--   * geom a Polygon geometry which defines the area where a value will be
+--     estimated as the area-weighted sum of a given table/column
+--   * target_table_name table name of the table that provides the values
+--   * target_column column name of the column that provides the values
+--   * schema_name optional parameter to defina the schema the target table
+--     belongs to, which is necessary if its not in the search_path.
+--     Note that target_table_name should never include the schema in it.
+-- Return value:
+--   Aereal-weighted interpolation of the column values over the geometry
+CREATE OR REPLACE
+FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL)
+  RETURNS numeric AS
+$$
+DECLARE
+	result numeric;
+  qualified_name text;
+BEGIN
+  IF schema_name IS NULL THEN
+    qualified_name := Format('%I', target_table_name);
+  ELSE
+    qualified_name := Format('%I.%s', schema_name, target_table_name);
+  END IF;
+  EXECUTE Format('
+    SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom))
+    FROM %s AS a
+    WHERE $1 && a.the_geom
+  ', target_column, qualified_name)
+  USING geom
+  INTO result;
+  RETURN result;
+END;
+$$ LANGUAGE plpgsql;
+--
+-- Creates N points randomly distributed arround the polygon
+--
+-- @param g - the geometry 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
+CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000)
+RETURNS GEOMETRY AS $$
+DECLARE
+  extent GEOMETRY;
+  test_point Geometry;
+  width                NUMERIC;
+  height               NUMERIC;
+  x0                   NUMERIC;
+  y0                   NUMERIC;
+  xp                   NUMERIC;
+  yp                   NUMERIC;
+  no_left              INTEGER;
+  remaining_iterations INTEGER;
+  points               GEOMETRY[];
+  bbox_line            GEOMETRY;
+  intersection_line    GEOMETRY;
+BEGIN
+  extent  := ST_Envelope(geom);
+  width   := ST_XMax(extent) - ST_XMIN(extent);
+  height  := ST_YMax(extent) - ST_YMIN(extent);
+  x0 	  := ST_XMin(extent);
+  y0 	  := ST_YMin(extent);
+  no_left := no_points;
+
+  LOOP
+    if(no_left=0) THEN
+      EXIT;
+    END IF;
+    yp = y0 + height*random();
+    bbox_line  = ST_MakeLine(
+      ST_SetSRID(ST_MakePoint(yp, x0),4326),
+      ST_SetSRID(ST_MakePoint(yp, x0+width),4326)
+    );
+    intersection_line = ST_Intersection(bbox_line,geom);
+  	test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random());
+	  points := points || test_point;
+	  no_left = no_left - 1 ;
+  END LOOP;
+  RETURN ST_Collect(points);
+END;
+$$
+LANGUAGE plpgsql VOLATILE;
+-- Make sure by default there are no permissions for publicuser
+-- NOTE: this happens at extension creation time, as part of an implicit transaction.
+-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE;
+
+-- Grant permissions on the schema to publicuser (but just the schema)
+GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser;
+
+-- Revoke execute permissions on all functions in the schema by default
+-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser;

release/crankshaft--0.0.3.sql

@@ -0,0 +1,403 @@
+--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES
+-- Complain if script is sourced in psql, rather than via CREATE EXTENSION
+\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit
+-- Version number of the extension release
+CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
+RETURNS text AS $$
+  SELECT '0.0.3'::text;
+$$ language 'sql' STABLE STRICT;
+
+-- Internal identifier of the installed extension instence
+-- e.g. 'dev' for current development version
+CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version()
+RETURNS text AS $$
+  SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL;
+$$ language 'sql' STABLE STRICT;
+-- Internal function.
+-- Set the seeds of the RNGs (Random Number Generators)
+-- used internally.
+CREATE OR REPLACE FUNCTION
+_cdb_random_seeds (seed_value INTEGER) RETURNS VOID
+AS $$
+  from crankshaft import random_seeds
+  random_seeds.set_random_seeds(seed_value)
+$$ LANGUAGE plpythonu;
+-- Moran's I Global Measure (public-facing)
+CREATE OR REPLACE FUNCTION
+  CDB_AreasOfInterestGlobal(
+      subquery TEXT,
+      column_name TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS TABLE (moran NUMERIC, significance NUMERIC)
+AS $$
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
+$$ LANGUAGE plpythonu;
+
+-- Moran's I Local (internal function)
+CREATE OR REPLACE FUNCTION
+  _CDB_AreasOfInterestLocal(
+      subquery TEXT,
+      column_name TEXT,
+      w_type TEXT,
+      num_ngbrs INT,
+      permutations INT,
+      geom_col TEXT,
+      id_col TEXT)
+RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
+$$ LANGUAGE plpythonu;
+
+-- Moran's I Local (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_AreasOfInterestLocal(
+    subquery TEXT,
+    column_name TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col);
+
+$$ LANGUAGE SQL;
+
+-- Moran's I only for HH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialHotspots(
+    subquery TEXT,
+    column_name TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HH', 'HL');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I only for LL and LH (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialColdspots(
+    subquery TEXT,
+    attr TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('LL', 'LH');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I only for LH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialOutliers(
+    subquery TEXT,
+    attr TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HL', 'LH');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Global Rate (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_AreasOfInterestGlobalRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS TABLE (moran FLOAT, significance FLOAT)
+AS $$
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+$$ LANGUAGE plpythonu;
+
+
+-- Moran's I Local Rate (internal function)
+CREATE OR REPLACE FUNCTION
+  _CDB_AreasOfInterestLocalRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT,
+      num_ngbrs INT,
+      permutations INT,
+      geom_col TEXT,
+      id_col TEXT)
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+  from crankshaft.clustering import moran_local_rate
+  # TODO: use named parameters or a dictionary
+  return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+$$ LANGUAGE plpythonu;
+
+-- Moran's I Local Rate (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_AreasOfInterestLocalRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col);
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Local Rate only for HH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialHotspotsRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HH', 'HL');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Local Rate only for LL and LH (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialColdspotsRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('LL', 'LH');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Local Rate only for LH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialOutliersRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HL', 'LH');
+
+$$ LANGUAGE SQL;
+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 $$
+    
+    from crankshaft.clustering import kmeans
+    return kmeans(query,no_clusters,no_init)
+
+$$ language plpythonu;
+
+
+CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC)
+RETURNS Numeric[] AS 
+$$
+DECLARE 
+    newX NUMERIC;
+    newY NUMERIC;
+    newW NUMERIC;
+BEGIN
+    IF weight IS NULL OR the_geom IS NULL THEN 
+        newX = state[1];
+        newY = state[2];
+        newW = state[3];
+    ELSE
+        newX = state[1] + ST_X(the_geom)*weight;
+        newY = state[2] + ST_Y(the_geom)*weight;
+        newW = state[3] + weight;
+    END IF;
+    RETURN Array[newX,newY,newW];
+
+END
+$$ LANGUAGE plpgsql;
+
+CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[])
+RETURNS GEOMETRY AS 
+$$
+BEGIN
+    IF state[3] = 0 THEN 
+        RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326);
+    ELSE 
+        RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326);
+    END IF;
+END
+$$ LANGUAGE plpgsql;
+
+CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC)(
+    SFUNC = CDB_WeightedMeanS,
+    FINALFUNC = CDB_WeightedMeanF,
+    STYPE = Numeric[],
+    INITCOND = "{0.0,0.0,0.0}" 
+);
+-- Function by Stuart Lynn for a simple interpolation of a value
+-- from a polygon table over an arbitrary polygon
+-- (weighted by the area proportion overlapped)
+-- Aereal weighting is a very simple form of aereal interpolation.
+--
+-- Parameters:
+--   * geom a Polygon geometry which defines the area where a value will be
+--     estimated as the area-weighted sum of a given table/column
+--   * target_table_name table name of the table that provides the values
+--   * target_column column name of the column that provides the values
+--   * schema_name optional parameter to defina the schema the target table
+--     belongs to, which is necessary if its not in the search_path.
+--     Note that target_table_name should never include the schema in it.
+-- Return value:
+--   Aereal-weighted interpolation of the column values over the geometry
+CREATE OR REPLACE
+FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL)
+  RETURNS numeric AS
+$$
+DECLARE
+	result numeric;
+  qualified_name text;
+BEGIN
+  IF schema_name IS NULL THEN
+    qualified_name := Format('%I', target_table_name);
+  ELSE
+    qualified_name := Format('%I.%s', schema_name, target_table_name);
+  END IF;
+  EXECUTE Format('
+    SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom))
+    FROM %s AS a
+    WHERE $1 && a.the_geom
+  ', target_column, qualified_name)
+  USING geom
+  INTO result;
+  RETURN result;
+END;
+$$ LANGUAGE plpgsql;
+--
+-- Creates N points randomly distributed arround the polygon
+--
+-- @param g - the geometry 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
+CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000)
+RETURNS GEOMETRY AS $$
+DECLARE
+  extent GEOMETRY;
+  test_point Geometry;
+  width                NUMERIC;
+  height               NUMERIC;
+  x0                   NUMERIC;
+  y0                   NUMERIC;
+  xp                   NUMERIC;
+  yp                   NUMERIC;
+  no_left              INTEGER;
+  remaining_iterations INTEGER;
+  points               GEOMETRY[];
+  bbox_line            GEOMETRY;
+  intersection_line    GEOMETRY;
+BEGIN
+  extent  := ST_Envelope(geom);
+  width   := ST_XMax(extent) - ST_XMIN(extent);
+  height  := ST_YMax(extent) - ST_YMIN(extent);
+  x0 	  := ST_XMin(extent);
+  y0 	  := ST_YMin(extent);
+  no_left := no_points;
+
+  LOOP
+    if(no_left=0) THEN
+      EXIT;
+    END IF;
+    yp = y0 + height*random();
+    bbox_line  = ST_MakeLine(
+      ST_SetSRID(ST_MakePoint(yp, x0),4326),
+      ST_SetSRID(ST_MakePoint(yp, x0+width),4326)
+    );
+    intersection_line = ST_Intersection(bbox_line,geom);
+  	test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random());
+	  points := points || test_point;
+	  no_left = no_left - 1 ;
+  END LOOP;
+  RETURN ST_Collect(points);
+END;
+$$
+LANGUAGE plpgsql VOLATILE;
+-- Make sure by default there are no permissions for publicuser
+-- NOTE: this happens at extension creation time, as part of an implicit transaction.
+-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE;
+
+-- Grant permissions on the schema to publicuser (but just the schema)
+GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser;
+
+-- Revoke execute permissions on all functions in the schema by default
+-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser;

release/crankshaft.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.0.2'
+default_version = '0.0.3'
 requires = 'plpythonu, postgis, cartodb'
 superuser = true
 schema = cdb_crankshaft

release/python/0.0.3/crankshaft/crankshaft/__init__.py

@@ -0,0 +1,2 @@
+import random_seeds
+import clustering

release/python/0.0.3/crankshaft/crankshaft/clustering/__init__.py

@@ -0,0 +1,2 @@
+from moran import *
+from kmeans import *

release/python/0.0.3/crankshaft/crankshaft/clustering/kmeans.py

@@ -0,0 +1,18 @@
+from sklearn.cluster import KMeans
+import plpy
+
+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))
+
+    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)
+

release/python/0.0.3/crankshaft/crankshaft/clustering/moran.py

@@ -0,0 +1,260 @@
+"""
+Moran's I geostatistics (global clustering & outliers presence)
+"""
+
+# TODO: Fill in local neighbors which have null/NoneType values with the
+#       average of the their neighborhood
+
+import pysal as ps
+import plpy
+
+# crankshaft module
+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 = {"id_col": id_col,
+             "attr1": attr_name,
+             "geom_col": geom_col,
+             "subquery": subquery,
+             "num_ngbrs": num_ngbrs}
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    plpy.notice('** Query: %s' % query)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(2)
+        plpy.notice('** Query returned with %d rows' % len(result))
+    except plpy.SPIError:
+        plpy.error('Error: areas of interest query failed, check input parameters')
+        plpy.notice('** Query failed: "%s"' % query)
+        plpy.notice('** Error: %s' % plpy.SPIError)
+        return pu.empty_zipped_array(2)
+
+    ## collect attributes
+    attr_vals = pu.get_attributes(result)
+
+    ## 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)
+
+    return zip([moran_global.I], [moran_global.EI])
+
+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
+
+    qvals = {"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(5)
+    except plpy.SPIError:
+        plpy.error('Error: areas of interest query failed, check input parameters')
+        plpy.notice('** Query failed: "%s"' % query)
+        return pu.empty_zipped_array(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 = {"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)
+
+    plpy.notice('** Query: %s' % query)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(2)
+        plpy.notice('** Query returned with %d rows' % len(result))
+    except plpy.SPIError:
+        plpy.error('Error: areas of interest query failed, check input parameters')
+        plpy.notice('** Query failed: "%s"' % query)
+        plpy.notice('** Error: %s' % plpy.SPIError)
+        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,
+                                         permutations=permutations)
+
+    return zip([lisa_rate.I], [lisa_rate.EI])
+
+def moran_local_rate(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
+
+    query = pu.construct_neighbor_query(w_type,
+                                     {"id_col": id_col,
+                                      "numerator": numerator,
+                                      "denominator": denominator,
+                                      "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(5)
+    except plpy.SPIError:
+        plpy.error('Error: areas of interest query failed, check input parameters')
+        plpy.notice('** Query failed: "%s"' % query)
+        plpy.notice('** Error: %s' % plpy.SPIError)
+        return pu.empty_zipped_array(5)
+
+    ## collect attributes
+    numer = pu.get_attributes(result, 1)
+    denom = pu.get_attributes(result, 2)
+
+    weight = pu.get_weight(result, w_type, num_ngbrs)
+
+    # calculate LISA values
+    lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
+                                          permutations=permutations)
+
+    # find units of significance
+    quads = quad_position(lisa.q)
+
+    return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
+
+def moran_local_bv(subquery, attr1, attr2,
+                   permutations, geom_col, id_col, w_type, num_ngbrs):
+    """
+        Moran's I (local) Bivariate (untested)
+    """
+    plpy.notice('** Constructing query')
+
+    qvals = {"num_ngbrs": num_ngbrs,
+             "attr1": attr1,
+             "attr2": attr2,
+             "subquery": subquery,
+             "geom_col": geom_col,
+             "id_col": id_col}
+
+    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:
+        plpy.error("Error: areas of interest query failed, " \
+                   "check input parameters")
+        plpy.notice('** Query failed: "%s"' % query)
+        return pu.empty_zipped_array(4)
+
+    ## collect attributes
+    attr1_vals = pu.get_attributes(result, 1)
+    attr2_vals = pu.get_attributes(result, 2)
+
+    # 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)
+
+    plpy.notice("len of Is: %d" % len(lisa.Is))
+
+    # find clustering of significance
+    lisa_sig = quad_position(lisa.q)
+
+    plpy.notice('** Finished calculations')
+
+    return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
+
+# Low level functions ----------------------------------------
+
+def map_quads(coord):
+    """
+        Map a quadrant number to Moran's I designation
+        HH=1, LH=2, LL=3, HL=4
+        Input:
+        @param coord (int): quadrant of a specific measurement
+        Output:
+            classification (one of 'HH', 'LH', 'LL', or 'HL')
+    """
+    if coord == 1:
+        return 'HH'
+    elif coord == 2:
+        return 'LH'
+    elif coord == 3:
+        return 'LL'
+    elif coord == 4:
+        return 'HL'
+    else:
+        return None
+
+def quad_position(quads):
+    """
+        Produce Moran's I classification based of n
+        Input:
+        @param quads ndarray: an array of quads classified by
+          1-4 (PySAL default)
+        Output:
+        @param list: an array of quads classied by 'HH', 'LL', etc.
+    """
+    return [map_quads(q) for q in quads]

release/python/0.0.3/crankshaft/crankshaft/pysal_utils/__init__.py

@@ -0,0 +1 @@
+from pysal_utils import *

release/python/0.0.3/crankshaft/crankshaft/pysal_utils/pysal_utils.py

@@ -0,0 +1,152 @@
+"""
+    Utilities module for generic PySAL functionality, mainly centered on translating queries into numpy arrays or PySAL weights objects
+"""
+
+import numpy as np
+import pysal as ps
+
+def construct_neighbor_query(w_type, query_vals):
+    """Return query (a string) used for finding neighbors
+        @param w_type text: type of neighbors to calculate ('knn' or 'queen')
+        @param query_vals dict: values used to construct the query
+    """
+
+    if w_type.lower() == 'knn':
+        return knn(query_vals)
+    else:
+        return queen(query_vals)
+
+## Build weight object
+def get_weight(query_res, w_type='knn', num_ngbrs=5):
+    """
+        Construct PySAL weight from return value of query
+        @param query_res: query results with attributes and neighbors
+    """
+    if w_type.lower() == 'knn':
+        row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
+        weights = {x['id']: row_normed_weights for x in query_res}
+    else:
+        weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
+                            if len(x['neighbors']) > 0
+                            else [] for x in query_res}
+
+    neighbors = {x['id']: x['neighbors'] for x in query_res}
+
+    return ps.W(neighbors, weights)
+
+def query_attr_select(params):
+    """
+        Create portion of SELECT statement for attributes inolved in query.
+        @param params: dict of information used in query (column names,
+                       table name, etc.)
+    """
+
+    attrs = [k for k in params
+             if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')]
+
+    template = "i.\"{%(col)s}\"::numeric As attr%(alias_num)s, "
+
+    attr_string = ""
+
+    for idx, val in enumerate(sorted(attrs)):
+        attr_string += template % {"col": val, "alias_num": idx + 1}
+
+    return attr_string
+
+def query_attr_where(params):
+    """
+        Create portion of WHERE clauses for weeding out NULL-valued geometries
+    """
+    attrs = sorted([k for k in params
+                    if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')])
+
+    attr_string = []
+
+    for attr in attrs:
+        attr_string.append("idx_replace.\"{%s}\" IS NOT NULL" % attr)
+
+    if len(attrs) == 2:
+        attr_string.append("idx_replace.\"{%s}\" <> 0" % attrs[1])
+
+    out = " AND ".join(attr_string)
+
+    return out
+
+def knn(params):
+    """SQL query for k-nearest neighbors.
+        @param vars: dict of values to fill template
+    """
+
+    attr_select = query_attr_select(params)
+    attr_where = query_attr_where(params)
+
+    replacements = {"attr_select": attr_select,
+                    "attr_where_i": attr_where.replace("idx_replace", "i"),
+                    "attr_where_j": attr_where.replace("idx_replace", "j")}
+
+    query = "SELECT " \
+                "i.\"{id_col}\" As id, " \
+                "%(attr_select)s" \
+                "(SELECT ARRAY(SELECT j.\"{id_col}\" " \
+                              "FROM ({subquery}) As j " \
+                              "WHERE " \
+                                "i.\"{id_col}\" <> j.\"{id_col}\" AND " \
+                                "%(attr_where_j)s " \
+                              "ORDER BY " \
+                                "j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \
+                              "LIMIT {num_ngbrs})" \
+                ") As neighbors " \
+            "FROM ({subquery}) As i " \
+            "WHERE " \
+                "%(attr_where_i)s " \
+            "ORDER BY i.\"{id_col}\" ASC;" % replacements
+
+    return query.format(**params)
+
+## SQL query for finding queens neighbors (all contiguous polygons)
+def queen(params):
+    """SQL query for queen neighbors.
+        @param params dict: information to fill query
+    """
+    attr_select = query_attr_select(params)
+    attr_where = query_attr_where(params)
+
+    replacements = {"attr_select": attr_select,
+                    "attr_where_i": attr_where.replace("idx_replace", "i"),
+                    "attr_where_j": attr_where.replace("idx_replace", "j")}
+
+    query = "SELECT " \
+                "i.\"{id_col}\" As id, " \
+                "%(attr_select)s" \
+                "(SELECT ARRAY(SELECT j.\"{id_col}\" " \
+                 "FROM ({subquery}) As j " \
+                 "WHERE i.\"{id_col}\" <> j.\"{id_col}\" AND " \
+                       "ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \
+                       "%(attr_where_j)s)" \
+                ") As neighbors " \
+            "FROM ({subquery}) As i " \
+            "WHERE " \
+                "%(attr_where_i)s " \
+            "ORDER BY i.\"{id_col}\" ASC;" % replacements
+
+    return query.format(**params)
+
+## to add more weight methods open a ticket or pull request
+
+def get_attributes(query_res, attr_num=1):
+    """
+        @param query_res: query results with attributes and neighbors
+        @param attr_num: attribute number (1, 2, ...)
+    """
+    return np.array([x['attr' + str(attr_num)] for x in query_res], dtype=np.float)
+
+def empty_zipped_array(num_nones):
+    """
+        prepare return values for cases of empty weights objects (no neighbors)
+        Input:
+        @param num_nones int: number of columns (e.g., 4)
+        Output:
+        [(None, None, None, None)]
+    """
+
+    return [tuple([None] * num_nones)]

release/python/0.0.3/crankshaft/crankshaft/random_seeds.py

@@ -0,0 +1,10 @@
+import random
+import numpy
+
+def set_random_seeds(value):
+    """
+    Set the seeds of the RNGs (Random Number Generators)
+    used internally.
+    """
+    random.seed(value)
+    numpy.random.seed(value)

release/python/0.0.3/crankshaft/setup.py

@@ -0,0 +1,48 @@
+
+"""
+CartoDB Spatial Analysis Python Library
+See:
+https://github.com/CartoDB/crankshaft
+"""
+
+from setuptools import setup, find_packages
+
+setup(
+    name='crankshaft',
+
+    version='0.0.3',
+
+    description='CartoDB Spatial Analysis Python Library',
+
+    url='https://github.com/CartoDB/crankshaft',
+
+    author='Data Services Team - CartoDB',
+    author_email='dataservices@cartodb.com',
+
+    license='MIT',
+
+    classifiers=[
+        'Development Status :: 3 - Alpha',
+        'Intended Audience :: Mapping comunity',
+        'Topic :: Maps :: Mapping Tools',
+        'License :: OSI Approved :: MIT License',
+        'Programming Language :: Python :: 2.7',
+    ],
+
+    keywords='maps mapping tools spatial analysis geostatistics',
+
+    packages=find_packages(exclude=['contrib', 'docs', 'tests']),
+
+    extras_require={
+        'dev': ['unittest'],
+        'test': ['unittest', 'nose', 'mock'],
+    },
+
+    # The choice of component versions is dictated by what's
+    # provisioned in the production servers.
+    install_requires=['pysal==1.9.1', 'scikit-learn==0.17.1'],
+
+    requires=['pysal', 'numpy', 'sklearn'],
+
+    test_suite='test'
+)

release/python/0.0.3/crankshaft/test/fixtures/kmeans.json

@@ -0,0 +1 @@
+[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}]

release/python/0.0.3/crankshaft/test/fixtures/moran.json

@@ -0,0 +1,52 @@
+[[0.9319096128346788, "HH"],
+[-1.135787401862846, "HL"],
+[0.11732030672508517, "LL"],
+[0.6152779669180425, "LL"],
+[-0.14657336660125297, "LH"],
+[0.6967858120189607, "LL"],
+[0.07949310115714454, "HH"],
+[0.4703198759258987, "HH"],
+[0.4421125200498064, "HH"],
+[0.5724288737143592, "LL"],
+[0.8970743435692062, "LL"],
+[0.18327334401918674, "LL"],
+[-0.01466729201304962, "HL"],
+[0.3481559372544409, "LL"],
+[0.06547094736902978, "LL"],
+[0.15482141569329988, "HH"],
+[0.4373841193538136, "HH"],
+[0.15971286468915544, "LL"],
+[1.0543588860308968, "HH"],
+[1.7372866900020818, "HH"],
+[1.091998586053999, "LL"],
+[0.1171572584252222, "HH"],
+[0.08438455015300014, "LL"],
+[0.06547094736902978, "LL"],
+[0.15482141569329985, "HH"],
+[1.1627044812890683, "HH"],
+[0.06547094736902978, "LL"],
+[0.795275137550483, "HH"],
+[0.18562939195219, "LL"],
+[0.3010757406693439, "LL"],
+[2.8205795942839376, "HH"],
+[0.11259190602909264, "LL"],
+[-0.07116352791516614, "HL"],
+[-0.09945240794119009, "LH"],
+[0.18562939195219, "LL"],
+[0.1832733440191868, "LL"],
+[-0.39054253768447705, "HL"],
+[-0.1672071289487642, "HL"],
+[0.3337669247916343, "HH"],
+[0.2584386102554792, "HH"],
+[-0.19733845476322634, "HL"],
+[-0.9379282899805409, "LH"],
+[-0.028770969951095866, "LH"],
+[0.051367269430983485, "LL"],
+[-0.2172548045913472, "LH"],
+[0.05136726943098351, "LL"],
+[0.04191046803899837, "LL"],
+[0.7482357030403517, "HH"],
+[-0.014585767863118111, "LH"],
+[0.5410013139159929, "HH"],
+[1.0223932668429925, "LL"],
+[1.4179402898927476, "LL"]]

release/python/0.0.3/crankshaft/test/fixtures/neighbors.json

@@ -0,0 +1,54 @@
+[
+    {"neighbors": [48, 26, 20, 9, 31], "id": 1, "value": 0.5},
+    {"neighbors": [30, 16, 46, 3, 4], "id": 2, "value": 0.7},
+    {"neighbors": [46, 30, 2, 12, 16], "id": 3, "value": 0.2},
+    {"neighbors": [18, 30, 23, 2, 52], "id": 4, "value": 0.1},
+    {"neighbors": [47, 40, 45, 37, 28], "id": 5, "value": 0.3},
+    {"neighbors": [10, 21, 41, 14, 37], "id": 6, "value": 0.05},
+    {"neighbors": [8, 17, 43, 25, 12], "id": 7, "value": 0.4},
+    {"neighbors": [17, 25, 43, 22, 7], "id": 8, "value": 0.7},
+    {"neighbors": [39, 34, 1, 26, 48], "id": 9, "value": 0.5},
+    {"neighbors": [6, 37, 5, 45, 49], "id": 10, "value": 0.04},
+    {"neighbors": [51, 41, 29, 21, 14], "id": 11, "value": 0.08},
+    {"neighbors": [44, 46, 43, 50, 3], "id": 12, "value": 0.2},
+    {"neighbors": [45, 23, 14, 28, 18], "id": 13, "value": 0.4},
+    {"neighbors": [41, 29, 13, 23, 6], "id": 14, "value": 0.2},
+    {"neighbors": [36, 27, 32, 33, 24], "id": 15, "value": 0.3},
+    {"neighbors": [19, 2, 46, 44, 28], "id": 16, "value": 0.4},
+    {"neighbors": [8, 25, 43, 7, 22], "id": 17, "value": 0.6},
+    {"neighbors": [23, 4, 29, 14, 13], "id": 18, "value": 0.3},
+    {"neighbors": [42, 16, 28, 26, 40], "id": 19, "value": 0.7},
+    {"neighbors": [1, 48, 31, 26, 42], "id": 20, "value": 0.8},
+    {"neighbors": [41, 6, 11, 14, 10], "id": 21, "value": 0.1},
+    {"neighbors": [25, 50, 43, 31, 44], "id": 22, "value": 0.4},
+    {"neighbors": [18, 13, 14, 4, 2], "id": 23, "value": 0.1},
+    {"neighbors": [33, 49, 34, 47, 27], "id": 24, "value": 0.3},
+    {"neighbors": [43, 8, 22, 17, 50], "id": 25, "value": 0.4},
+    {"neighbors": [1, 42, 20, 31, 48], "id": 26, "value": 0.6},
+    {"neighbors": [32, 15, 36, 33, 24], "id": 27, "value": 0.3},
+    {"neighbors": [40, 45, 19, 5, 13], "id": 28, "value": 0.8},
+    {"neighbors": [11, 51, 41, 14, 18], "id": 29, "value": 0.3},
+    {"neighbors": [2, 3, 4, 46, 18], "id": 30, "value": 0.1},
+    {"neighbors": [20, 26, 1, 50, 48], "id": 31, "value": 0.9},
+    {"neighbors": [27, 36, 15, 49, 24], "id": 32, "value": 0.3},
+    {"neighbors": [24, 27, 49, 34, 32], "id": 33, "value": 0.4},
+    {"neighbors": [47, 9, 39, 40, 24], "id": 34, "value": 0.3},
+    {"neighbors": [38, 51, 11, 21, 41], "id": 35, "value": 0.3},
+    {"neighbors": [15, 32, 27, 49, 33], "id": 36, "value": 0.2},
+    {"neighbors": [49, 10, 5, 47, 24], "id": 37, "value": 0.5},
+    {"neighbors": [35, 21, 51, 11, 41], "id": 38, "value": 0.4},
+    {"neighbors": [9, 34, 48, 1, 47], "id": 39, "value": 0.6},
+    {"neighbors": [28, 47, 5, 9, 34], "id": 40, "value": 0.5},
+    {"neighbors": [11, 14, 29, 21, 6], "id": 41, "value": 0.4},
+    {"neighbors": [26, 19, 1, 9, 31], "id": 42, "value": 0.2},
+    {"neighbors": [25, 12, 8, 22, 44], "id": 43, "value": 0.3},
+    {"neighbors": [12, 50, 46, 16, 43], "id": 44, "value": 0.2},
+    {"neighbors": [28, 13, 5, 40, 19], "id": 45, "value": 0.3},
+    {"neighbors": [3, 12, 44, 2, 16], "id": 46, "value": 0.2},
+    {"neighbors": [34, 40, 5, 49, 24], "id": 47, "value": 0.3},
+    {"neighbors": [1, 20, 26, 9, 39], "id": 48, "value": 0.5},
+    {"neighbors": [24, 37, 47, 5, 33], "id": 49, "value": 0.2},
+    {"neighbors": [44, 22, 31, 42, 26], "id": 50, "value": 0.6},
+    {"neighbors": [11, 29, 41, 14, 21], "id": 51, "value": 0.01},
+    {"neighbors": [4, 18, 29, 51, 23], "id": 52, "value": 0.01}
+  ]

release/python/0.0.3/crankshaft/test/helper.py

@@ -0,0 +1,13 @@
+import unittest
+
+from mock_plpy import MockPlPy
+plpy = MockPlPy()
+
+import sys
+sys.modules['plpy'] = plpy
+
+import os
+
+def fixture_file(name):
+    dir = os.path.dirname(os.path.realpath(__file__))
+    return os.path.join(dir, 'fixtures', name)

release/python/0.0.3/crankshaft/test/mock_plpy.py

@@ -0,0 +1,34 @@
+import re
+
+class MockPlPy:
+    def __init__(self):
+        self._reset()
+
+    def _reset(self):
+        self.infos = []
+        self.notices = []
+        self.debugs = []
+        self.logs = []
+        self.warnings = []
+        self.errors = []
+        self.fatals = []
+        self.executes = []
+        self.results = []
+        self.prepares = []
+        self.results = []
+
+    def _define_result(self, query, result):
+        pattern = re.compile(query, re.IGNORECASE | re.MULTILINE)
+        self.results.append([pattern, result])
+
+    def notice(self, msg):
+        self.notices.append(msg)
+
+    def info(self, msg):
+        self.infos.append(msg)
+
+    def execute(self, query): # TODO: additional arguments
+       for result in self.results:
+          if result[0].match(query):
+            return result[1]
+       return []

release/python/0.0.3/crankshaft/test/test_cluster_kmeans.py

@@ -0,0 +1,38 @@
+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)
+

release/python/0.0.3/crankshaft/test/test_clustering_moran.py

@@ -0,0 +1,83 @@
+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
+
+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",
+                       "subquery": "SELECT * FROM a_list",
+                       "geom_col": "the_geom",
+                       "num_ngbrs": 321}
+        self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read())
+        self.moran_data = json.loads(open(fixture_file('moran.json')).read())
+
+    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)
+
+    def test_quad_position(self):
+        """Test lisa_sig_vals"""
+
+        quads = np.array([1, 2, 3, 4], np.int)
+
+        ans = np.array(['HH', 'LH', 'LL', 'HL'])
+        test_ans = cc.quad_position(quads)
+
+        self.assertTrue((test_ans == ans).all())
+
+    def test_moran_local(self):
+        """Test Moran's I local"""
+        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.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
+        result = [(row[0], row[1]) for row in result]
+        expected = self.moran_data
+        for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
+            self.assertAlmostEqual(res_val, exp_val)
+            self.assertEqual(res_quad, exp_quad)
+
+    def test_moran_local_rate(self):
+        """Test Moran's I rate"""
+        data = [ { 'id': d['id'], 'attr1': d['value'], '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')
+        print 'result == None? ', result == None
+        result = [(row[0], row[1]) for row in result]
+        expected = self.moran_data
+        for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
+            self.assertAlmostEqual(res_val, exp_val)
+
+    def test_moran(self):
+        """Test Moran's I global"""
+        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')
+        print 'result == None?', result == None
+        result_moran = result[0][0]
+        expected_moran = np.array([row[0] for row in self.moran_data]).mean()
+        self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)

release/python/0.0.3/crankshaft/test/test_pysal_utils.py

@@ -0,0 +1,107 @@
+import unittest
+
+import crankshaft.pysal_utils as pu
+from crankshaft import random_seeds
+
+
+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}
+
+    def test_query_attr_select(self):
+        """Test query_attr_select"""
+
+        ans = "i.\"{attr1}\"::numeric As attr1, " \
+              "i.\"{attr2}\"::numeric As attr2, "
+
+        self.assertEqual(pu.query_attr_select(self.params), ans)
+
+    def test_query_attr_where(self):
+        """Test pu.query_attr_where"""
+
+        ans = "idx_replace.\"{attr1}\" IS NOT NULL AND " \
+              "idx_replace.\"{attr2}\" IS NOT NULL AND " \
+              "idx_replace.\"{attr2}\" <> 0"
+
+        self.assertEqual(pu.query_attr_where(self.params), ans)
+
+    def test_knn(self):
+        """Test knn neighbors constructor"""
+
+        ans = "SELECT i.\"cartodb_id\" As id, " \
+                     "i.\"andy\"::numeric As attr1, " \
+                     "i.\"jay_z\"::numeric As attr2, " \
+                     "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+                                   "FROM (SELECT * FROM a_list) As j " \
+                                   "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 " \
+                                   "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 " \
+              "ORDER BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(pu.knn(self.params), ans)
+
+    def test_queen(self):
+        """Test queen neighbors constructor"""
+
+        ans = "SELECT i.\"cartodb_id\" As id, " \
+                     "i.\"andy\"::numeric As attr1, " \
+                     "i.\"jay_z\"::numeric As attr2, " \
+                     "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+                                   "FROM (SELECT * FROM a_list) As j " \
+                                   "WHERE " \
+                                   "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
+                                   "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)" \
+                                  ") 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 " \
+              "ORDER BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(pu.queen(self.params), ans)
+
+    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))
+
+    def test_get_attributes(self):
+        """Test get_attributes"""
+
+        ## need to add tests
+
+        self.assertEqual(True, True)
+
+    def test_get_weight(self):
+        """Test get_weight"""
+
+        self.assertEqual(True, True)
+
+    def test_empty_zipped_array(self):
+        """Test empty_zipped_array"""
+        ans2 = [(None, None)]
+        ans4 = [(None, None, None, None)]
+        self.assertEqual(pu.empty_zipped_array(2), ans2)
+        self.assertEqual(pu.empty_zipped_array(4), ans4)

src/pg/Makefile

@@ -7,7 +7,6 @@ include ../../Makefile.global
 #   requires sudo. In additionof the current development version
 #   named 'dev', an alias 'current' is generating for ease of
 #   update (upgrade to 'current', then to 'dev').
-#   the python module is installed in a virtualenv in envs/dev/
 # * test runs the tests for the currently generated Development
 #   extension.
 
@@ -18,11 +17,8 @@ DATA         = $(EXTENSION)--dev.sql \
 SOURCES_DATA_DIR = sql
 SOURCES_DATA = $(wildcard $(SOURCES_DATA_DIR)/*.sql)
 
-VIRTUALENV_PATH = $(realpath ../../envs)
-ESC_VIRVIRTUALENV_PATH = $(subst /,\/,$(VIRTUALENV_PATH))
 
-REPLACEMENTS = -e 's/@@VERSION@@/$(EXTVERSION)/g' \
-               -e 's/@@VIRTUALENV_PATH@@/$(ESC_VIRVIRTUALENV_PATH)/g'
+REPLACEMENTS = -e 's/@@VERSION@@/$(EXTVERSION)/g'
 
 $(DATA): $(SOURCES_DATA)
 	$(SED) $(REPLACEMENTS) $(SOURCES_DATA_DIR)/*.sql > $@
@@ -54,7 +50,6 @@ release: ../../release/$(EXTENSION).control $(SOURCES_DATA)
 	$(SED) $(REPLACEMENTS) $(SOURCES_DATA_DIR)/*.sql > ../../release/$(EXTENSION)--$(EXTVERSION).sql
 
 # Install the current relese into the PostgreSQL extensions directory
-# and the Python package in a virtual environment envs/X.Y.Z
 deploy:
 	$(INSTALL_DATA) ../../release/$(EXTENSION).control '$(DESTDIR)$(datadir)/extension/'
 	$(INSTALL_DATA) ../../release/*.sql '$(DESTDIR)$(datadir)/extension/'

src/pg/crankshaft.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.0.2'
+default_version = '0.0.3'
 requires = 'plpythonu, postgis, cartodb'
 superuser = true
 schema = cdb_crankshaft

src/pg/sql/02_py.sql

@@ -1,23 +0,0 @@
-CREATE OR REPLACE FUNCTION _cdb_crankshaft_virtualenvs_path()
-RETURNS text
-AS $$
-  BEGIN
-    -- RETURN '/opt/virtualenvs/crankshaft';
-    RETURN '@@VIRTUALENV_PATH@@';
-  END;
-$$ language plpgsql IMMUTABLE STRICT;
-
--- Use the crankshaft python module
-CREATE OR REPLACE FUNCTION _cdb_crankshaft_activate_py()
-RETURNS VOID
-AS $$
-    import os
-    # plpy.notice('%',str(os.environ))
-    # activate virtualenv
-    crankshaft_version = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_internal_version()')[0]['_cdb_crankshaft_internal_version']
-    base_path = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_virtualenvs_path()')[0]['_cdb_crankshaft_virtualenvs_path']
-    default_venv_path = os.path.join(base_path, crankshaft_version)
-    venv_path =  os.environ.get('CRANKSHAFT_VENV', default_venv_path)
-    activate_path = venv_path + '/bin/activate_this.py'
-    exec(open(activate_path).read(), dict(__file__=activate_path))
-$$ LANGUAGE plpythonu;

src/pg/sql/03_random_seeds.sql

@@ -4,7 +4,6 @@
 CREATE OR REPLACE FUNCTION
 _cdb_random_seeds (seed_value INTEGER) RETURNS VOID
 AS $$
-  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
   from crankshaft import random_seeds
   random_seeds.set_random_seeds(seed_value)
 $$ LANGUAGE plpythonu;

src/pg/sql/10_moran.sql

@@ -1,89 +1,229 @@
--- Moran's I (global)
+-- Moran's I Global Measure (public-facing)
 CREATE OR REPLACE FUNCTION
-  CDB_AreasOfInterest_Global (
+  CDB_AreasOfInterestGlobal(
       subquery TEXT,
-      attr_name TEXT,
+      column_name TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
       permutations INT DEFAULT 99,
       geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id',
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5)
+      id_col TEXT DEFAULT 'cartodb_id')
 RETURNS TABLE (moran NUMERIC, significance NUMERIC)
 AS $$
   from crankshaft.clustering import moran_local
   # TODO: use named parameters or a dictionary
-  return moran(subquery, attr, num_ngbrs, permutations, geom_col, id_col, w_type)
+  return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
 $$ LANGUAGE plpythonu;
 
--- Moran's I Local
+-- Moran's I Local (internal function)
 CREATE OR REPLACE FUNCTION
-  CDB_AreasOfInterest_Local(
+  _CDB_AreasOfInterestLocal(
       subquery TEXT,
-      attr TEXT,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id',
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5)
-RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, ids INT, y NUMERIC)
+      column_name TEXT,
+      w_type TEXT,
+      num_ngbrs INT,
+      permutations INT,
+      geom_col TEXT,
+      id_col TEXT)
+RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
 AS $$
   from crankshaft.clustering import moran_local
   # TODO: use named parameters or a dictionary
-  return moran_local(subquery, attr, permutations, geom_col, id_col, w_type, num_ngbrs)
+  return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
 $$ LANGUAGE plpythonu;
 
--- Moran's I Rate (global)
+-- Moran's I Local (public-facing function)
 CREATE OR REPLACE FUNCTION
-  CDB_AreasOfInterest_Global_Rate(
+  CDB_AreasOfInterestLocal(
+    subquery TEXT,
+    column_name TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col);
+
+$$ LANGUAGE SQL;
+
+-- Moran's I only for HH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialHotspots(
+    subquery TEXT,
+    column_name TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HH', 'HL');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I only for LL and LH (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialColdspots(
+    subquery TEXT,
+    attr TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('LL', 'LH');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I only for LH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialOutliers(
+    subquery TEXT,
+    attr TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HL', 'LH');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Global Rate (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_AreasOfInterestGlobalRate(
       subquery TEXT,
       numerator TEXT,
       denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
       permutations INT DEFAULT 99,
       geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id',
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5)
+      id_col TEXT DEFAULT 'cartodb_id')
 RETURNS TABLE (moran FLOAT, significance FLOAT)
 AS $$
   from crankshaft.clustering import moran_local
   # TODO: use named parameters or a dictionary
-  return moran_rate(subquery, numerator, denominator, permutations, geom_col, id_col, w_type, num_ngbrs)
+  return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
 $$ LANGUAGE plpythonu;
 
 
--- Moran's I Local Rate
+-- Moran's I Local Rate (internal function)
 CREATE OR REPLACE FUNCTION
-  CDB_AreasOfInterest_Local_Rate(
+  _CDB_AreasOfInterestLocalRate(
       subquery TEXT,
       numerator TEXT,
       denominator TEXT,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id',
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5)
+      w_type TEXT,
+      num_ngbrs INT,
+      permutations INT,
+      geom_col TEXT,
+      id_col TEXT)
 RETURNS
-TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, ids INT, y NUMERIC)
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
 AS $$
   from crankshaft.clustering import moran_local_rate
   # TODO: use named parameters or a dictionary
-  return moran_local_rate(subquery, numerator, denominator, permutations, geom_col, id_col, w_type, num_ngbrs)
+  return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
 $$ LANGUAGE plpythonu;
 
--- -- Moran's I Local Bivariate
--- CREATE OR REPLACE FUNCTION
---   cdb_moran_local_bv(
---       subquery TEXT,
---       attr1 TEXT,
---       attr2 TEXT,
---       permutations INT DEFAULT 99,
---       geom_col TEXT DEFAULT 'the_geom',
---       id_col TEXT DEFAULT 'cartodb_id',
---       w_type TEXT DEFAULT 'knn',
---       num_ngbrs INT DEFAULT 5)
--- RETURNS TABLE(moran FLOAT, quads TEXT, significance FLOAT, ids INT, y numeric)
--- AS $$
---   from crankshaft.clustering import moran_local_bv
---   # TODO: use named parameters or a dictionary
---   return moran_local_bv(t, attr1, attr2, permutations, geom_col, id_col, w_type, num_ngbrs)
--- $$ LANGUAGE plpythonu;
+-- Moran's I Local Rate (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_AreasOfInterestLocalRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col);
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Local Rate only for HH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialHotspotsRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HH', 'HL');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Local Rate only for LL and LH (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialColdspotsRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('LL', 'LH');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Local Rate only for LH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialOutliersRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HL', 'LH');
+
+$$ LANGUAGE SQL;

src/pg/sql/11_kmeans.sql

@@ -0,0 +1,49 @@
+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 $$
+    
+    from crankshaft.clustering import kmeans
+    return kmeans(query,no_clusters,no_init)
+
+$$ language plpythonu;
+
+
+CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC)
+RETURNS Numeric[] AS 
+$$
+DECLARE 
+    newX NUMERIC;
+    newY NUMERIC;
+    newW NUMERIC;
+BEGIN
+    IF weight IS NULL OR the_geom IS NULL THEN 
+        newX = state[1];
+        newY = state[2];
+        newW = state[3];
+    ELSE
+        newX = state[1] + ST_X(the_geom)*weight;
+        newY = state[2] + ST_Y(the_geom)*weight;
+        newW = state[3] + weight;
+    END IF;
+    RETURN Array[newX,newY,newW];
+
+END
+$$ LANGUAGE plpgsql;
+
+CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[])
+RETURNS GEOMETRY AS 
+$$
+BEGIN
+    IF state[3] = 0 THEN 
+        RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326);
+    ELSE 
+        RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326);
+    END IF;
+END
+$$ LANGUAGE plpgsql;
+
+CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC)(
+    SFUNC = CDB_WeightedMeanS,
+    FINALFUNC = CDB_WeightedMeanF,
+    STYPE = Numeric[],
+    INITCOND = "{0.0,0.0,0.0}" 
+);

src/pg/test/expected/02_moran_test.out

@@ -1,248 +1,277 @@
+\pset format unaligned
+\set ECHO all
 \i test/fixtures/ppoints.sql
--- test table (spanish province centroids with some invented values)
-CREATE TABLE ppoints (cartodb_id integer, the_geom geometry, the_geom_webmercator geometry, code text, region_code text, value float);
-INSERT INTO ppoints VALUES
-( 1,'0101000020E6100000A8306DC0CBC305C051D14B6CE56A4540'::geometry,ST_Transform('0101000020E6100000A8306DC0CBC305C051D14B6CE56A4540'::geometry, 3857),'01','16',0.5),
-( 4,'0101000020E6100000E220A4362DC202C0FD8AFA5119994240'::geometry,ST_Transform('0101000020E6100000E220A4362DC202C0FD8AFA5119994240'::geometry, 3857),'04','01',0.1),
-( 5,'0101000020E610000004377E573AC813C0CB5871BB17494440'::geometry,ST_Transform('0101000020E610000004377E573AC813C0CB5871BB17494440'::geometry, 3857),'05','07',0.3),
-( 2,'0101000020E610000000F49BE19BAFFFBF639958FDA6694340'::geometry,ST_Transform('0101000020E610000000F49BE19BAFFFBF639958FDA6694340'::geometry, 3857),'02','08',0.7),
-( 3,'0101000020E61000005D0B7E63C832E2BFDB63EB00443D4340'::geometry,ST_Transform('0101000020E61000005D0B7E63C832E2BFDB63EB00443D4340'::geometry, 3857),'03','10',0.2),
-( 6,'0101000020E61000006F3742B7FB9018C0DD967DC4D95A4340'::geometry,ST_Transform('0101000020E61000006F3742B7FB9018C0DD967DC4D95A4340'::geometry, 3857),'06','11',0.05),
-( 7,'0101000020E6100000E4BB36995F4C0740EAC0E5CA9FC94340'::geometry,ST_Transform('0101000020E6100000E4BB36995F4C0740EAC0E5CA9FC94340'::geometry, 3857),'07','04',0.4),
-( 8,'0101000020E61000003D43CC6CAFBEFF3F6B52E66F91DD4440'::geometry,ST_Transform('0101000020E61000003D43CC6CAFBEFF3F6B52E66F91DD4440'::geometry, 3857),'08','09',0.7),
-( 9,'0101000020E61000003CC797BD99AF0CC0495A87FA312F4540'::geometry,ST_Transform('0101000020E61000003CC797BD99AF0CC0495A87FA312F4540'::geometry, 3857),'09','07',0.5),
-(13,'0101000020E61000001CAA00A9F19F0EC05DF9267B7A764340'::geometry,ST_Transform('0101000020E61000001CAA00A9F19F0EC05DF9267B7A764340'::geometry, 3857),'13','08',0.4),
-(16,'0101000020E6100000D8208F3CBC9001C065638DC1B1F24340'::geometry,ST_Transform('0101000020E6100000D8208F3CBC9001C065638DC1B1F24340'::geometry, 3857),'16','08',0.4),
-(17,'0101000020E6100000E9E6A94A71630540AD7A0CB062104540'::geometry,ST_Transform('0101000020E6100000E9E6A94A71630540AD7A0CB062104540'::geometry, 3857),'17','09',0.6),
-(18,'0101000020E6100000719792D59E240AC098AC548E00A84240'::geometry,ST_Transform('0101000020E6100000719792D59E240AC098AC548E00A84240'::geometry, 3857),'18','01',0.3),
-(19,'0101000020E6100000972C878B50FD04C0123C881D1F684440'::geometry,ST_Transform('0101000020E6100000972C878B50FD04C0123C881D1F684440'::geometry, 3857),'19','08',0.7),
-(21,'0101000020E6100000F7893E9934511BC0EAA4BF03E1C94240'::geometry,ST_Transform('0101000020E6100000F7893E9934511BC0EAA4BF03E1C94240'::geometry, 3857),'21','01',0.1),
-(22,'0101000020E6100000572C2123B2A8B2BF7ED7FABAFD194540'::geometry,ST_Transform('0101000020E6100000572C2123B2A8B2BF7ED7FABAFD194540'::geometry, 3857),'22','02',0.4),
-(25,'0101000020E6100000461B67D688C4F03FD990EEC3A0054540'::geometry,ST_Transform('0101000020E6100000461B67D688C4F03FD990EEC3A0054540'::geometry, 3857),'25','09',0.4),
-(26,'0101000020E6100000A139FB06E82204C0539D84F62E234540'::geometry,ST_Transform('0101000020E6100000A139FB06E82204C0539D84F62E234540'::geometry, 3857),'26','17',0.6),
-(27,'0101000020E6100000A92E54E618C91DC00D3A947B81814540'::geometry,ST_Transform('0101000020E6100000A92E54E618C91DC00D3A947B81814540'::geometry, 3857),'27','12',0.3),
-(28,'0101000020E6100000971DC8B682BC0DC016D0E8055F3F4440'::geometry,ST_Transform('0101000020E6100000971DC8B682BC0DC016D0E8055F3F4440'::geometry, 3857),'28','13',0.8),
-(30,'0101000020E6100000A2DC1964A8C5F7BF19299C994D004340'::geometry,ST_Transform('0101000020E6100000A2DC1964A8C5F7BF19299C994D004340'::geometry, 3857),'30','14',0.1),
-(31,'0101000020E6100000DCA1FCC87B56FABF9B88E9D866554540'::geometry,ST_Transform('0101000020E6100000DCA1FCC87B56FABF9B88E9D866554540'::geometry, 3857),'31','15',0.9),
-(32,'0101000020E6100000E1517AFCD15E1EC0A18D8D4825194540'::geometry,ST_Transform('0101000020E6100000E1517AFCD15E1EC0A18D8D4825194540'::geometry, 3857),'32','12',0.3),
-(33,'0101000020E6100000A7FF33825AF917C0FABE7DFB6BA54540'::geometry,ST_Transform('0101000020E6100000A7FF33825AF917C0FABE7DFB6BA54540'::geometry, 3857),'33','03',0.4),
-(34,'0101000020E6100000FB4E4EBEB72412C0898E7240982F4540'::geometry,ST_Transform('0101000020E6100000FB4E4EBEB72412C0898E7240982F4540'::geometry, 3857),'34','07',0.3),
-(35,'0101000020E6100000224682B01B1A2DC011091656CC5C3C40'::geometry,ST_Transform('0101000020E6100000224682B01B1A2DC011091656CC5C3C40'::geometry, 3857),'35','05',0.3),
-(36,'0101000020E6100000F7C9447110EC20C04C5D4823C7374540'::geometry,ST_Transform('0101000020E6100000F7C9447110EC20C04C5D4823C7374540'::geometry, 3857),'36','12',0.2),
-(37,'0101000020E610000053D6A26DFB4218C09D58FAE209674440'::geometry,ST_Transform('0101000020E610000053D6A26DFB4218C09D58FAE209674440'::geometry, 3857),'37','07',0.5),
-(38,'0101000020E6100000B1D1B5FC910431C03C0C89BA03503C40'::geometry,ST_Transform('0101000020E6100000B1D1B5FC910431C03C0C89BA03503C40'::geometry, 3857),'38','05',0.4),
-(39,'0101000020E610000086E6FEE1BD1E10C00417096748994540'::geometry,ST_Transform('0101000020E610000086E6FEE1BD1E10C00417096748994540'::geometry, 3857),'39','06',0.6),
-(40,'0101000020E6100000FB51C33F733710C038D01729E4954440'::geometry,ST_Transform('0101000020E6100000FB51C33F733710C038D01729E4954440'::geometry, 3857),'40','07',0.5),
-(41,'0101000020E6100000912D6FDA28BB16C031321F08C4B74240'::geometry,ST_Transform('0101000020E6100000912D6FDA28BB16C031321F08C4B74240'::geometry, 3857),'41','01',0.4),
-(42,'0101000020E6100000554432EABEB504C069ECD78775CF4440'::geometry,ST_Transform('0101000020E6100000554432EABEB504C069ECD78775CF4440'::geometry, 3857),'42','07',0.2),
-(43,'0101000020E6100000157F117C1A2EEA3F027CD1F2368B4440'::geometry,ST_Transform('0101000020E6100000157F117C1A2EEA3F027CD1F2368B4440'::geometry, 3857),'43','09',0.3),
-(44,'0101000020E610000051AA5B1BD718EABFEE67613BA4544440'::geometry,ST_Transform('0101000020E610000051AA5B1BD718EABFEE67613BA4544440'::geometry, 3857),'44','02',0.2),
-(45,'0101000020E610000022C5C01BB69710C08563BC1499E54340'::geometry,ST_Transform('0101000020E610000022C5C01BB69710C08563BC1499E54340'::geometry, 3857),'45','08',0.3),
-(46,'0101000020E6100000D5FCF78A11A0E9BFDEA46F8E64AF4340'::geometry,ST_Transform('0101000020E6100000D5FCF78A11A0E9BFDEA46F8E64AF4340'::geometry, 3857),'46','10',0.2),
-(47,'0101000020E61000003AE63525866313C02100050B2BD14440'::geometry,ST_Transform('0101000020E61000003AE63525866313C02100050B2BD14440'::geometry, 3857),'47','07',0.3),
-(48,'0101000020E610000030F187FD1FD206C0C767E1496C9E4540'::geometry,ST_Transform('0101000020E610000030F187FD1FD206C0C767E1496C9E4540'::geometry, 3857),'48','16',0.5),
-(49,'0101000020E61000009C22867B12EC17C006C5F40C14DD4440'::geometry,ST_Transform('0101000020E61000009C22867B12EC17C006C5F40C14DD4440'::geometry, 3857),'49','07',0.2),
-(50,'0101000020E6100000F7D5EFC62D08F1BF69D1231D68CF4440'::geometry,ST_Transform('0101000020E6100000F7D5EFC62D08F1BF69D1231D68CF4440'::geometry, 3857),'50','02',0.6),
-(51,'0101000020E61000005B0E1F8DAA5F15C0530BFE285BF24140'::geometry,ST_Transform('0101000020E61000005B0E1F8DAA5F15C0530BFE285BF24140'::geometry, 3857),'51','18',0.01),
-(10,'0101000020E61000000FD65D82AEA418C06192D1351FDB4340'::geometry,ST_Transform('0101000020E61000000FD65D82AEA418C06192D1351FDB4340'::geometry, 3857),'10','11',0.04),
-(11,'0101000020E6100000B305531DAB0A17C0DEAFCD4EE5464240'::geometry,ST_Transform('0101000020E6100000B305531DAB0A17C0DEAFCD4EE5464240'::geometry, 3857),'11','01',0.08),
-(12,'0101000020E610000059721A7297C9C2BF9EBE383BE51E4440'::geometry,ST_Transform('0101000020E610000059721A7297C9C2BF9EBE383BE51E4440'::geometry, 3857),'12','10',0.2),
-(14,'0101000020E610000000C86313AF3C13C0E530879C10FF4240'::geometry,ST_Transform('0101000020E610000000C86313AF3C13C0E530879C10FF4240'::geometry, 3857),'14','01',0.2),
-(15,'0101000020E61000002A475497B6ED20C06643D4131A904540'::geometry,ST_Transform('0101000020E61000002A475497B6ED20C06643D4131A904540'::geometry, 3857),'15','12',0.3),
-(20,'0101000020E6100000F975566FAD8D01C0E840C33F67924540'::geometry,ST_Transform('0101000020E6100000F975566FAD8D01C0E840C33F67924540'::geometry, 3857),'20','16',0.8),
-(23,'0101000020E610000025FA13E595880BC022BB07131D024340'::geometry,ST_Transform('0101000020E610000025FA13E595880BC022BB07131D024340'::geometry, 3857),'23','01',0.1),
-(24,'0101000020E61000009C5F91C5095C17C0C78784B15A4F4540'::geometry,ST_Transform('0101000020E61000009C5F91C5095C17C0C78784B15A4F4540'::geometry, 3857),'24','07',0.3),
-(29,'0101000020E6100000C34D4A5B48E712C092E680892C684240'::geometry,ST_Transform('0101000020E6100000C34D4A5B48E712C092E680892C684240'::geometry, 3857),'29','01',0.3),
-(52,'0101000020E6100000406A545EB29A07C04E5F0BDA39A54140'::geometry,ST_Transform('0101000020E6100000406A545EB29A07C04E5F0BDA39A54140'::geometry, 3857),'52','19',0.01)
-\i test/fixtures/ppoints2.sql
--- test table (spanish province centroids with some invented values)
-CREATE TABLE ppoints2 (cartodb_id integer, the_geom geometry, code text, region_code text, numerator float, denominator float);
-INSERT INTO ppoints2 VALUES
-( 1,'0101000020E6100000A8306DC0CBC305C051D14B6CE56A4540'::geometry,'01','16',0.5, 1.0),
-( 4,'0101000020E6100000E220A4362DC202C0FD8AFA5119994240'::geometry,'04','01',0.1, 1.0),
-( 5,'0101000020E610000004377E573AC813C0CB5871BB17494440'::geometry,'05','07',0.3, 1.0),
-( 2,'0101000020E610000000F49BE19BAFFFBF639958FDA6694340'::geometry,'02','08',0.7, 1.0),
-( 3,'0101000020E61000005D0B7E63C832E2BFDB63EB00443D4340'::geometry,'03','10',0.2, 1.0),
-( 6,'0101000020E61000006F3742B7FB9018C0DD967DC4D95A4340'::geometry,'06','11',0.05, 1.0),
-( 7,'0101000020E6100000E4BB36995F4C0740EAC0E5CA9FC94340'::geometry,'07','04',0.4, 1.0),
-( 8,'0101000020E61000003D43CC6CAFBEFF3F6B52E66F91DD4440'::geometry,'08','09',0.7, 1.0),
-( 9,'0101000020E61000003CC797BD99AF0CC0495A87FA312F4540'::geometry,'09','07',0.5, 1.0),
-(13,'0101000020E61000001CAA00A9F19F0EC05DF9267B7A764340'::geometry,'13','08',0.4, 1.0),
-(16,'0101000020E6100000D8208F3CBC9001C065638DC1B1F24340'::geometry,'16','08',0.4, 1.0),
-(17,'0101000020E6100000E9E6A94A71630540AD7A0CB062104540'::geometry,'17','09',0.6, 1.0),
-(18,'0101000020E6100000719792D59E240AC098AC548E00A84240'::geometry,'18','01',0.3, 1.0),
-(19,'0101000020E6100000972C878B50FD04C0123C881D1F684440'::geometry,'19','08',0.7, 1.0),
-(21,'0101000020E6100000F7893E9934511BC0EAA4BF03E1C94240'::geometry,'21','01',0.1, 1.0),
-(22,'0101000020E6100000572C2123B2A8B2BF7ED7FABAFD194540'::geometry,'22','02',0.4, 1.0),
-(25,'0101000020E6100000461B67D688C4F03FD990EEC3A0054540'::geometry,'25','09',0.4, 1.0),
-(26,'0101000020E6100000A139FB06E82204C0539D84F62E234540'::geometry,'26','17',0.6, 1.0),
-(27,'0101000020E6100000A92E54E618C91DC00D3A947B81814540'::geometry,'27','12',0.3, 1.0),
-(28,'0101000020E6100000971DC8B682BC0DC016D0E8055F3F4440'::geometry,'28','13',0.8, 1.0),
-(30,'0101000020E6100000A2DC1964A8C5F7BF19299C994D004340'::geometry,'30','14',0.1, 1.0),
-(31,'0101000020E6100000DCA1FCC87B56FABF9B88E9D866554540'::geometry,'31','15',0.9, 1.0),
-(32,'0101000020E6100000E1517AFCD15E1EC0A18D8D4825194540'::geometry,'32','12',0.3, 1.0),
-(33,'0101000020E6100000A7FF33825AF917C0FABE7DFB6BA54540'::geometry,'33','03',0.4, 1.0),
-(34,'0101000020E6100000FB4E4EBEB72412C0898E7240982F4540'::geometry,'34','07',0.3, 1.0),
-(35,'0101000020E6100000224682B01B1A2DC011091656CC5C3C40'::geometry,'35','05',0.3, 1.0),
-(36,'0101000020E6100000F7C9447110EC20C04C5D4823C7374540'::geometry,'36','12',0.2, 1.0),
-(37,'0101000020E610000053D6A26DFB4218C09D58FAE209674440'::geometry,'37','07',0.5, 1.0),
-(38,'0101000020E6100000B1D1B5FC910431C03C0C89BA03503C40'::geometry,'38','05',0.4, 1.0),
-(39,'0101000020E610000086E6FEE1BD1E10C00417096748994540'::geometry,'39','06',0.6, 1.0),
-(40,'0101000020E6100000FB51C33F733710C038D01729E4954440'::geometry,'40','07',0.5, 1.0),
-(41,'0101000020E6100000912D6FDA28BB16C031321F08C4B74240'::geometry,'41','01',0.4, 1.0),
-(42,'0101000020E6100000554432EABEB504C069ECD78775CF4440'::geometry,'42','07',0.2, 1.0),
-(43,'0101000020E6100000157F117C1A2EEA3F027CD1F2368B4440'::geometry,'43','09',0.3, 1.0),
-(44,'0101000020E610000051AA5B1BD718EABFEE67613BA4544440'::geometry,'44','02',0.2, 1.0),
-(45,'0101000020E610000022C5C01BB69710C08563BC1499E54340'::geometry,'45','08',0.3, 1.0),
-(46,'0101000020E6100000D5FCF78A11A0E9BFDEA46F8E64AF4340'::geometry,'46','10',0.2, 1.0),
-(47,'0101000020E61000003AE63525866313C02100050B2BD14440'::geometry,'47','07',0.3, 1.0),
-(48,'0101000020E610000030F187FD1FD206C0C767E1496C9E4540'::geometry,'48','16',0.5, 1.0),
-(49,'0101000020E61000009C22867B12EC17C006C5F40C14DD4440'::geometry,'49','07',0.2, 1.0),
-(50,'0101000020E6100000F7D5EFC62D08F1BF69D1231D68CF4440'::geometry,'50','02',0.6, 1.0),
-(51,'0101000020E61000005B0E1F8DAA5F15C0530BFE285BF24140'::geometry,'51','18',0.01, 1.0),
-(10,'0101000020E61000000FD65D82AEA418C06192D1351FDB4340'::geometry,'10','11',0.04, 1.0),
-(11,'0101000020E6100000B305531DAB0A17C0DEAFCD4EE5464240'::geometry,'11','01',0.08, 1.0),
-(12,'0101000020E610000059721A7297C9C2BF9EBE383BE51E4440'::geometry,'12','10',0.2, 1.0),
-(14,'0101000020E610000000C86313AF3C13C0E530879C10FF4240'::geometry,'14','01',0.2, 1.0),
-(15,'0101000020E61000002A475497B6ED20C06643D4131A904540'::geometry,'15','12',0.3, 1.0),
-(20,'0101000020E6100000F975566FAD8D01C0E840C33F67924540'::geometry,'20','16',0.8, 1.0),
-(23,'0101000020E610000025FA13E595880BC022BB07131D024340'::geometry,'23','01',0.1, 1.0),
-(24,'0101000020E61000009C5F91C5095C17C0C78784B15A4F4540'::geometry,'24','07',0.3, 1.0),
-(29,'0101000020E6100000C34D4A5B48E712C092E680892C684240'::geometry,'29','01',0.3, 1.0),
-(52,'0101000020E6100000406A545EB29A07C04E5F0BDA39A54140'::geometry,'52','19',0.0, 1.01)
--- Areas of Interest functions perform some nondeterministic computations
--- (to estimate the significance); we will set the seeds for the RNGs
--- that affect those results to have repeateble results
-SELECT cdb_crankshaft._cdb_random_seeds(1234);
- _cdb_random_seeds 
--------------------
- 
-(1 row)
+SET client_min_messages TO WARNING;
+\set ECHO none
+_cdb_random_seeds
 
-SELECT ppoints.code, m.quads
-  FROM ppoints
-  JOIN cdb_crankshaft.CDB_AreasOfInterest_Local('SELECT * FROM ppoints', 'value') m
-    ON ppoints.cartodb_id = m.ids
-  ORDER BY ppoints.code;
- code | quads 
-------+-------
- 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   | HH
- 17   | HH
- 18   | LL
- 19   | HH
- 20   | HH
- 21   | LL
- 22   | HH
- 23   | LL
- 24   | LL
- 25   | HH
- 26   | HH
- 27   | LL
- 28   | HH
- 29   | LL
- 30   | LL
- 31   | HH
- 32   | LL
- 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   | HH
- 49   | LH
- 50   | HH
- 51   | LL
- 52   | LL
+(1 row)
+code|quads
+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|HH
+17|HH
+18|LL
+19|HH
+20|HH
+21|LL
+22|HH
+23|LL
+24|LL
+25|HH
+26|HH
+27|LL
+28|HH
+29|LL
+30|LL
+31|HH
+32|LL
+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|HH
+49|LH
+50|HH
+51|LL
+52|LL
 (52 rows)
+_cdb_random_seeds
 
-SELECT cdb_crankshaft._cdb_random_seeds(1234);
- _cdb_random_seeds 
--------------------
- 
 (1 row)
+code|quads
+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
 
-SELECT ppoints2.code, m.quads
-  FROM ppoints2
-  JOIN cdb_crankshaft.CDB_AreasOfInterest_Local_Rate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
-    ON ppoints2.cartodb_id = m.ids
-  ORDER BY ppoints2.code;
- 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
- 15   | LL
- 16   | LL
- 17   | LL
- 18   | LH
- 19   | LL
- 20   | LL
- 21   | HH
- 22   | LL
- 23   | HL
- 24   | LL
- 25   | LL
- 26   | LL
- 27   | LL
- 28   | LL
- 29   | LH
- 30   | HH
- 31   | LL
- 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
- 47   | LL
- 48   | LL
- 49   | HL
- 50   | LL
- 51   | HH
+(1 row)
+code|quads
+03|LL
+04|LL
+05|LH
+06|LL
+10|LL
+11|LL
+12|LL
+14|LL
+15|LL
+18|LL
+21|LL
+23|LL
+24|LL
+27|LL
+29|LL
+30|LL
+32|LL
+34|LH
+35|LL
+36|LL
+42|LH
+43|LH
+44|LL
+45|LH
+46|LL
+47|LL
+49|LH
+51|LL
+52|LL
+(29 rows)
+_cdb_random_seeds
+
+(1 row)
+code|quads
+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)
+_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
+15|LL
+16|LL
+17|LL
+18|LH
+19|LL
+20|LL
+21|HH
+22|LL
+23|HL
+24|LL
+25|LL
+26|LL
+27|LL
+28|LL
+29|LH
+30|HH
+31|LL
+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
+47|LL
+48|LL
+49|HL
+50|LL
+51|HH
 (51 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)
+_cdb_random_seeds
+
+(1 row)
+code|quads
+01|LL
+02|LH
+05|LL
+07|LL
+08|LL
+09|LL
+13|LL
+15|LL
+16|LL
+17|LL
+18|LH
+19|LL
+20|LL
+22|LL
+24|LL
+25|LL
+26|LL
+27|LL
+28|LL
+29|LH
+31|LL
+32|LL
+33|LL
+34|LL
+35|LH
+37|LH
+38|LH
+39|LL
+40|LL
+41|LH
+43|LL
+45|LL
+47|LL
+48|LL
+50|LL
+(35 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)

src/pg/test/expected/03_overlap_sum_test.out

@@ -1,21 +1,11 @@
 \i test/fixtures/polyg_values.sql
-CREATE TABLE values (cartodb_id integer, value float, the_geom geometry);
-INSERT INTO values(cartodb_id, value, the_geom) VALUES
-(1,10,'0106000020E61000000100000001030000000100000005000000E5AF3500C03608C08068629111374440C7BC0A00C00F02C0AC0551523B414440C7BC0A00C0A700C0CAF23B6E74FB4340A7267FFFFF5206C0FBB7E41B7EE74340E5AF3500C03608C08068629111374440'::geometry),
-(2,20,'0106000020E610000001000000010300000001000000050000002439EC00804AF7BF07D6CCB5C3064440C7BC0A00C0A700C0CAF23B6E74FB4340C7BC0A00C00F02C0AC0551523B414440E20CD5FFFF30FABFBE4F76AFEA4B44402439EC00804AF7BF07D6CCB5C3064440'::geometry)
-SELECT round(cdb_crankshaft.cdb_overlap_sum(
-  '0106000020E61000000100000001030000000100000004000000FFFFFFFFFF3604C09A0B9ECEC42E444000000000C060FBBF30C7FD70E01D44400000000040AD02C06481F1C8CD034440FFFFFFFFFF3604C09A0B9ECEC42E4440'::geometry,
-  'values', 'value'
-), 2);
+SET client_min_messages TO WARNING;
+\set ECHO none
  round 
 -------
   4.42
 (1 row)
 
-SELECT round(cdb_crankshaft.cdb_overlap_sum(
-  '0106000020E61000000100000001030000000100000004000000FFFFFFFFFF3604C09A0B9ECEC42E444000000000C060FBBF30C7FD70E01D44400000000040AD02C06481F1C8CD034440FFFFFFFFFF3604C09A0B9ECEC42E4440'::geometry,
-  'values', 'value', schema_name := 'public'
-), 2);
  round 
 -------
   4.42

src/pg/test/expected/05_kmeans_test.out

@@ -0,0 +1,10 @@
+\pset format unaligned
+\set ECHO all
+SELECT count(DISTINCT cluster_no) as clusters from cdb_crankshaft.cdb_kmeans('select * from ppoints', 2);
+clusters
+2
+(1 row)
+SELECT count(*) clusters from (select  cdb_crankshaft.CDB_WeightedMean(the_geom, value::NUMERIC), code from ppoints group by code) p;
+clusters
+52
+(1 row)

src/pg/test/fixtures/polyg_values.sql

@@ -1,3 +1,5 @@
+SET client_min_messages TO WARNING;
+\set ECHO none
 CREATE TABLE values (cartodb_id integer, value float, the_geom geometry);
 INSERT INTO values(cartodb_id, value, the_geom) VALUES
 (1,10,'0106000020E61000000100000001030000000100000005000000E5AF3500C03608C08068629111374440C7BC0A00C00F02C0AC0551523B414440C7BC0A00C0A700C0CAF23B6E74FB4340A7267FFFFF5206C0FBB7E41B7EE74340E5AF3500C03608C08068629111374440'::geometry),

src/pg/test/fixtures/ppoints.sql

@@ -1,3 +1,5 @@
+SET client_min_messages TO WARNING;
+\set ECHO none
 -- test table (spanish province centroids with some invented values)
 CREATE TABLE ppoints (cartodb_id integer, the_geom geometry, the_geom_webmercator geometry, code text, region_code text, value float);
 INSERT INTO ppoints VALUES

src/pg/test/fixtures/ppoints2.sql

@@ -1,3 +1,5 @@
+SET client_min_messages TO WARNING;
+\set ECHO none
 -- test table (spanish province centroids with some invented values)
 CREATE TABLE ppoints2 (cartodb_id integer, the_geom geometry, code text, region_code text, numerator float, denominator float);
 INSERT INTO ppoints2 VALUES

src/pg/test/sql/02_moran_test.sql

@@ -1,3 +1,5 @@
+\pset format unaligned
+\set ECHO all
 \i test/fixtures/ppoints.sql
 \i test/fixtures/ppoints2.sql
 
@@ -8,14 +10,70 @@ SELECT cdb_crankshaft._cdb_random_seeds(1234);
 
 SELECT ppoints.code, m.quads
   FROM ppoints
-  JOIN cdb_crankshaft.CDB_AreasOfInterest_Local('SELECT * FROM ppoints', 'value') m
-    ON ppoints.cartodb_id = m.ids
+  JOIN cdb_crankshaft.CDB_AreasOfInterestLocal('SELECT * FROM ppoints', 'value') m
+    ON ppoints.cartodb_id = m.rowid
   ORDER BY ppoints.code;
 
 SELECT cdb_crankshaft._cdb_random_seeds(1234);
 
+-- Spatial Hotspots
+SELECT ppoints.code, m.quads
+  FROM ppoints
+  JOIN cdb_crankshaft.CDB_GetSpatialHotspots('SELECT * FROM ppoints', 'value') m
+    ON ppoints.cartodb_id = m.rowid
+  ORDER BY ppoints.code;
+
+SELECT cdb_crankshaft._cdb_random_seeds(1234);
+
+-- Spatial Coldspots
+SELECT ppoints.code, m.quads
+  FROM ppoints
+  JOIN cdb_crankshaft.CDB_GetSpatialColdspots('SELECT * FROM ppoints', 'value') m
+    ON ppoints.cartodb_id = m.rowid
+  ORDER BY ppoints.code;
+
+SELECT cdb_crankshaft._cdb_random_seeds(1234);
+
+  -- Spatial Outliers
+SELECT ppoints.code, m.quads
+  FROM ppoints
+  JOIN cdb_crankshaft.CDB_GetSpatialOutliers('SELECT * FROM ppoints', 'value') m
+    ON ppoints.cartodb_id = m.rowid
+  ORDER BY ppoints.code;
+
+
+SELECT cdb_crankshaft._cdb_random_seeds(1234);
+
+-- Areas of Interest (rate)
 SELECT ppoints2.code, m.quads
   FROM ppoints2
-  JOIN cdb_crankshaft.CDB_AreasOfInterest_Local_Rate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
-    ON ppoints2.cartodb_id = m.ids
+  JOIN cdb_crankshaft.CDB_AreasOfInterestLocalRate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
+    ON ppoints2.cartodb_id = m.rowid
+  ORDER BY ppoints2.code;
+
+SELECT cdb_crankshaft._cdb_random_seeds(1234);
+
+-- Spatial Hotspots (rate)
+SELECT ppoints2.code, m.quads
+  FROM ppoints2
+  JOIN cdb_crankshaft.CDB_GetSpatialHotspotsRate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
+    ON ppoints2.cartodb_id = m.rowid
+  ORDER BY ppoints2.code;
+
+SELECT cdb_crankshaft._cdb_random_seeds(1234);
+
+-- Spatial Coldspots (rate)
+SELECT ppoints2.code, m.quads
+  FROM ppoints2
+  JOIN cdb_crankshaft.CDB_GetSpatialColdspotsRate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
+    ON ppoints2.cartodb_id = m.rowid
+  ORDER BY ppoints2.code;
+
+SELECT cdb_crankshaft._cdb_random_seeds(1234);
+
+-- Spatial Outliers (rate)
+SELECT ppoints2.code, m.quads
+  FROM ppoints2
+  JOIN cdb_crankshaft.CDB_GetSpatialOutliersRate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
+    ON ppoints2.cartodb_id = m.rowid
   ORDER BY ppoints2.code;

src/pg/test/sql/05_kmeans_test.sql

@@ -0,0 +1,6 @@
+\pset format unaligned
+\set ECHO all
+
+SELECT count(DISTINCT cluster_no) as clusters from cdb_crankshaft.cdb_kmeans('select * from ppoints', 2);
+
+SELECT count(*) clusters from (select  cdb_crankshaft.CDB_WeightedMean(the_geom, value::NUMERIC), code from ppoints group by code) p;

src/py/Makefile

@@ -2,14 +2,11 @@ include ../../Makefile.global
 
 # Install the package locally for development
 install:
-	virtualenv --system-site-packages ../../envs/dev
-	# source ../../envs/dev/bin/activate
-	../../envs/dev/bin/pip install -I ./crankshaft
-	../../envs/dev/bin/pip install -I nose
+	pip install ./crankshaft
 
 # Test develpment install
 test:
-	../../envs/dev/bin/nosetests crankshaft/test/
+	nosetests crankshaft/test/
 
 release: ../../release/$(EXTENSION).control $(SOURCES_DATA)
 	mkdir -p ../../release/python/$(EXTVERSION)
@@ -17,6 +14,4 @@ release: ../../release/$(EXTENSION).control $(SOURCES_DATA)
 	$(SED) -i -r 's/version='"'"'[0-9]+\.[0-9]+\.[0-9]+'"'"'/version='"'"'$(EXTVERSION)'"'"'/g'  ../../release/python/$(EXTVERSION)/$(PACKAGE)/setup.py
 
 deploy:
-	virtualenv --system-site-packages $(VIRTUALENV_PATH)/$(RELEASE_VERSION)
-	$(VIRTUALENV_PATH)/$(RELEASE_VERSION)/bin/pip install -I -U ../../release/python/$(RELEASE_VERSION)/$(PACKAGE)
-	$(VIRTUALENV_PATH)/$(RELEASE_VERSION)/bin/pip install -I nose
+	pip install --upgrade ../../release/python/$(RELEASE_VERSION)/$(PACKAGE)

src/py/README.md

@@ -10,7 +10,6 @@ nosetests test/
 
 ## Notes about Python dependencies
 * This extension is targeted at production databases. Therefore certain restrictions must be assumed about the production environment vs other experimental environments.
-* We're using `pip` and `virtualenv` to generate a suitable isolated environment for python code that has  all the dependencies
 * Every dependency should be:
   - Added to the `setup.py` file
   - Installed through it
@@ -30,21 +29,7 @@ PySAL 1.10 or later, so we'll stick to 1.9.1.
 apt-get install -y python-scipy
 ```
 
-We'll use virtual environments to install our packages,
-but configued to use also system modules so that the
-mentioned scipy and numpy are used.
-
-    # Create a virtual environment for python
-    $ virtualenv --system-site-packages dev
-
-    # Activate the virtualenv
-    $ source dev/bin/activate
-
-    # Install all the requirements
-    # expect this to take a while, as it will trigger a few compilations
-    (dev) $ pip install -I ./crankshaft
-
-#### Test the libraries with that virtual env
+#### Test the libraries
 
 ##### Test numpy library dependency:
 

src/py/crankshaft/crankshaft/clustering/__init__.py

@@ -1 +1,2 @@
 from moran import *
+from kmeans import *

src/py/crankshaft/crankshaft/clustering/kmeans.py

@@ -0,0 +1,18 @@
+from sklearn.cluster import KMeans
+import plpy
+
+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))
+
+    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)
+

src/py/crankshaft/crankshaft/clustering/moran.py

@@ -14,7 +14,7 @@ import crankshaft.pysal_utils as pu
 # High level interface ---------------------------------------
 
 def moran(subquery, attr_name,
-          permutations, geom_col, id_col, w_type, num_ngbrs):
+          w_type, num_ngbrs, permutations, geom_col, id_col):
     """
     Moran's I (global)
     Implementation building neighbors with a PostGIS database and Moran's I
@@ -56,7 +56,7 @@ def moran(subquery, attr_name,
     return zip([moran_global.I], [moran_global.EI])
 
 def moran_local(subquery, attr,
-                permutations, geom_col, id_col, w_type, num_ngbrs):
+                w_type, num_ngbrs, permutations, geom_col, id_col):
     """
     Moran's I implementation for PL/Python
     Andy Eschbacher
@@ -96,7 +96,7 @@ def moran_local(subquery, attr,
     return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
 
 def moran_rate(subquery, numerator, denominator,
-               permutations, geom_col, id_col, w_type, num_ngbrs):
+               w_type, num_ngbrs, permutations, geom_col, id_col):
     """
     Moran's I Rate (global)
     Andy Eschbacher
@@ -137,7 +137,7 @@ def moran_rate(subquery, numerator, denominator,
     return zip([lisa_rate.I], [lisa_rate.EI])
 
 def moran_local_rate(subquery, numerator, denominator,
-                     permutations, geom_col, id_col, w_type, num_ngbrs):
+                     w_type, num_ngbrs, permutations, geom_col, id_col):
     """
         Moran's I Local Rate
         Andy Eschbacher

src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py

@@ -11,7 +11,7 @@ def construct_neighbor_query(w_type, query_vals):
         @param query_vals dict: values used to construct the query
     """
 
-    if w_type == 'knn':
+    if w_type.lower() == 'knn':
         return knn(query_vals)
     else:
         return queen(query_vals)
@@ -22,7 +22,7 @@ def get_weight(query_res, w_type='knn', num_ngbrs=5):
         Construct PySAL weight from return value of query
         @param query_res: query results with attributes and neighbors
     """
-    if w_type == 'knn':
+    if w_type.lower() == 'knn':
         row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
         weights = {x['id']: row_normed_weights for x in query_res}
     else:

src/py/crankshaft/setup.py

@@ -40,9 +40,9 @@ setup(
 
     # The choice of component versions is dictated by what's
     # provisioned in the production servers.
-    install_requires=['pysal==1.9.1'],
+    install_requires=['pysal==1.9.1', 'scikit-learn==0.17.1'],
 
-    requires=['pysal', 'numpy' ],
+    requires=['pysal', 'numpy', 'sklearn'],
 
     test_suite='test'
 )

src/py/crankshaft/test/fixtures/kmeans.json

@@ -0,0 +1 @@
+[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}]

src/py/crankshaft/test/test_cluster_kmeans.py

@@ -0,0 +1,38 @@
+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)
+

src/py/crankshaft/test/test_clustering_moran.py

@@ -52,7 +52,7 @@ 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(1234)
-        result = cc.moran_local('subquery', 'value', 99, 'the_geom', 'cartodb_id', 'knn', 5)
+        result = cc.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
         result = [(row[0], row[1]) for row in result]
         expected = self.moran_data
         for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
@@ -64,7 +64,7 @@ class MoranTest(unittest.TestCase):
         data = [ { 'id': d['id'], 'attr1': d['value'], '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', 99, 'the_geom', 'cartodb_id', 'knn', 5)
+        result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 'knn', 5, 99, 'the_geom', 'cartodb_id')
         print 'result == None? ', result == None
         result = [(row[0], row[1]) for row in result]
         expected = self.moran_data
@@ -76,7 +76,7 @@ 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', 99, 'the_geom', 'cartodb_id', 'knn', 5)
+        result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
         print 'result == None?', result == None
         result_moran = result[0][0]
         expected_moran = np.array([row[0] for row in self.moran_data]).mean()