Merge pull request #186 from CartoDB/develop

Release for 0.6.0

.brackets.json

@@ -0,0 +1,3 @@
+{
+    "sbruchmann.staticpreview.basepath": "/home/carto/Projects/crankshaft/"
+}

.github/PULL_REQUEST_TEMPLATE.md

@@ -2,6 +2,9 @@
 - [ ] All declared geometries are `geometry(Geometry, 4326)` for general geoms, or `geometry(Point, 4326)`
 - [ ] Existing functions in crankshaft python library called from the extension are kept at least from version N to version N+1 (to avoid breakage during upgrades).
 - [ ] Docs for public-facing functions are written
-- [ ] New functions follow the naming conventions: `CDB_NameOfFunction`. Where internal functions begin with an underscore `_`.
-- [ ] If appropriate, new functions accepts an arbitrary query as an input (see [Crankshaft Issue #6](https://github.com/CartoDB/crankshaft/issues/6) for more information)
- 
+- [ ] New functions follow the naming conventions: `CDB_NameOfFunction`. Where internal functions begin with an underscore
+- [ ] Video explaining the analysis and showing examples 
+- [ ] Analysis Documentation written [template](https://docs.google.com/a/cartodb.com/document/d/1X2KOtaiEBKWNMp8UjwcLB-kE9aIOw09aOjX3oaCjeME/edit?usp=sharing)
+- [ ] Smoke test written
+- [ ] Hand-off document for camshaft node written
+- [ ] If function is in Python, code conforms to [PEP8 Style Guide](https://www.python.org/dev/peps/pep-0008/)

.travis.yml

@@ -1,4 +1,6 @@
 language: c
+dist: precise
+sudo: required
 
 env:
   global:
@@ -35,14 +37,18 @@ before_install:
   - sudo apt-get -y remove --purge postgresql-9.2
   - sudo apt-get -y remove --purge postgresql-9.3
   - sudo apt-get -y remove --purge postgresql-9.4
-  - sudo apt-get -y remove --purge postgis
+  - sudo apt-get -y remove --purge postgresql-9.5
+  - sudo rm -rf /var/lib/postgresql/
+  - sudo rm -rf /var/log/postgresql/
+  - sudo rm -rf /etc/postgresql/
+  - sudo apt-get -y remove --purge postgis-2.2
   - sudo apt-get -y autoremove
 
-  - sudo apt-get -y install postgresql-9.5=9.5.2-2ubuntu1
-  - sudo apt-get -y install postgresql-server-dev-9.5=9.5.2-2ubuntu1
-  - sudo apt-get -y install postgresql-plpython-9.5=9.5.2-2ubuntu1
-  - sudo apt-get -y install postgresql-9.5-postgis-2.2=2.2.2.0-cdb2
+  - sudo apt-get -y install postgresql-9.5=9.5.2-3cdb3
+  - sudo apt-get -y install postgresql-server-dev-9.5=9.5.2-3cdb3
+  - sudo apt-get -y install postgresql-plpython-9.5=9.5.2-3cdb3
   - sudo apt-get -y install postgresql-9.5-postgis-scripts=2.2.2.0-cdb2
+  - sudo apt-get -y install postgresql-9.5-postgis-2.2=2.2.2.0-cdb2
 
   # configure it to accept local connections from postgres
   - echo -e "# TYPE  DATABASE        USER            ADDRESS                 METHOD \nlocal   all             postgres                                trust\nlocal   all             all                                     trust\nhost    all             all             127.0.0.1/32            trust" \

CONTRIBUTING.md

@@ -55,3 +55,7 @@ sudo make install
 #  Run the tests against the installed extension.
 make test
 ```
+
+## Submitting contributions
+
+Before opening a pull request (or submitting a contribution) you will need to sign a Contributor License Agreement (CLA) before making a submission, [learn more here](https://carto.com/contributions).

Makefile.global

@@ -4,3 +4,5 @@ PACKAGE           = crankshaft
 EXTVERSION        = $(shell grep default_version $(SELF_DIR)/src/pg/$(EXTENSION).control | sed -e "s/default_version[[:space:]]*=[[:space:]]*'\([^']*\)'/\1/")
 RELEASE_VERSION  ?= $(EXTVERSION)
 SED               = sed
+PIP               = pip
+NOSETESTS         = nosetests

NEWS.md

@@ -1,3 +1,46 @@
+0.6.0 (2017-11-08)
+------------------
+* Adds new functions: `CDB_GWR` and `CDB_GWR_Predict`
+
+0.5.2 (2017-05-12)
+------------------
+* Fixes missing comma for dict creation #172
+
+0.5.1 (2016-12-12)
+------------------
+* Fixed problem with the upgrade file from 0.4.2 to 0.5.0 that hasn't changes that should be there (as per ethervoid).
+
+0.5.0 (2016-12-12)
+------------------
+* Updated PULL_REQUEST_TEMPLATE
+* Fixed a bug that flips the order of the numerator in denominator for calculating using Moran Local Rate because previously the code sorted the keys alphabetically.
+* Add new CDB_GetisOrdsG functions. Getis-Ord's G\* is a geo-statistical measurement of the intensity of clustering of high or low values
+* Add new outlier detection functions: CDB_StaticOutlier, CDB_PercentOutlier and CDB_StdDevOutlier
+* Updates in the framework for accessing the Python functions.
+
+0.4.2 (2016-09-22)
+------------------
+* Bugfix for cdb_areasofinterestglobal: import correct modules
+
+0.4.1 (2016-09-21)
+------------------
+* Let the user set the resolution in CDB_Contour function
+* Add Nearest Neighbors method to CDB_SpatialInterpolation
+* Improve error reporting for moran and markov functions
+
+0.4.0 (2016-08-30)
+------------------
+* Add CDB_Contour
+* Add CDB_PIA
+* Add CDB_Densify
+* Add CDB_TINmap
+
+0.3.1 (2016-08-18)
+------------------
+* Fix Voronoi projection issue
+* Fix Voronoi spurious segments issue
+* Add tests for interpolation
+
 0.3.0 (2016-08-17)
 ------------------
 * Adds Voronoi function

README.md

@@ -1,6 +1,6 @@
 # Crankshaft [![Build Status](https://travis-ci.org/CartoDB/crankshaft.svg?branch=develop)](https://travis-ci.org/CartoDB/crankshaft)
 
-CartoDB Spatial Analysis extension for PostgreSQL.
+CARTO Spatial Analysis extension for PostgreSQL.
 
 ## Code organization
 

RELEASE.md

@@ -15,11 +15,12 @@ shall be performed by the designated *Release Manager*.
 1. Generate an upgrade path from the previous to the next release by copying the generated release file. E.g:
 
   ```shell
-  cp release/cranckshaft--X.Y.Z.sql release/cranckshaft--A.B.C--X.Y.Z.sql
+  cp release/crankshaft--X.Y.Z.sql release/crankshaft--A.B.C--X.Y.Z.sql
   ```
   NOTE: you can rely on this thanks to the compatibility checks. 
   
   TODO: automate this step [#94](https://github.com/CartoDB/crankshaft/issues/94)
+2. Update the [NEWS.md](https://github.com/CartoDB/crankshaft/blob/master/NEWS.md) file
 1. Commit and push the generated files.
 1. Tag the release:
 
@@ -28,6 +29,8 @@ shall be performed by the designated *Release Manager*.
   git push origin X.Y.Z
   ```
 1. Deploy and test in staging
+1. Deploy and test in production
+1. Merge back into develop
 
 
 ## Some remarks

doc/02_moran.md

@@ -37,7 +37,7 @@ SELECT
   aoi.quads,
   aoi.significance,
   c.num_cyclists_per_total_population
-FROM CDB_GetAreasOfInterestLocal('SELECT * FROM commute_data'
+FROM CDB_AreasOfInterestLocal('SELECT * FROM commute_data'
                                  'num_cyclists_per_total_population') As aoi
 JOIN commute_data As c
 ON c.cartodb_id = aoi.rowid;
@@ -113,7 +113,7 @@ SELECT
   aoi.quads,
   aoi.significance,
   c.cyclists_per_total_population
-FROM CDB_GetAreasOfInterestLocalRate('SELECT * FROM commute_data'
+FROM CDB_AreasOfInterestLocalRate('SELECT * FROM commute_data'
                                      'num_cyclists',
                                      'total_population') As aoi
 JOIN commute_data As c

doc/08_interpolation.md

@@ -2,7 +2,7 @@
 
 Function to interpolate a numeric attribute of a point in a scatter dataset of points, using one of three methos:
 
-* [Nearest neighbor](https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
+* [Nearest neighbor(s)](https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
 * [Barycentric](https://en.wikipedia.org/wiki/Barycentric_coordinate_system)
 * [IDW](https://en.wikipedia.org/wiki/Inverse_distance_weighting)
 
@@ -15,7 +15,7 @@ Function to interpolate a numeric attribute of a point in a scatter dataset of p
 | query   | text | query that returns at least `the_geom` and a numeric value as `attrib` |
 | point   | geometry | The target point to calc the value |
 | method   | integer     | 0:nearest neighbor, 1: barycentric, 2: IDW|
-| p1   | integer     | IDW: limit the number of neighbors, 0->no limit|
+| p1   | integer     | limit the number of neighbors, IDW: 0->no limit, NN: 0-> closest one|
 | p2   | integer     | IDW: order of distance decay, 0-> order 1|
 
 ### CDB_SpatialInterpolation (geom geometry[], values numeric[], point geometry, method integer DEFAULT 1, p1 integer DEFAULT 0, ps integer DEFAULT 0)
@@ -28,7 +28,7 @@ Function to interpolate a numeric attribute of a point in a scatter dataset of p
 | values | numeric[]   | Array of points' values for the param under study|
 | point   | geometry | The target point to calc the value |
 | method   | integer     | 0:nearest neighbor, 1: barycentric, 2: IDW|
-| p1   | integer     | IDW: limit the number of neighbors, 0->no limit|
+| p1   | integer     | limit the number of neighbors, IDW: 0->no limit, NN: 0-> closest one|
 | p2   | integer     | IDW: order of distance decay, 0-> order 1|
 
 ### Returns
@@ -37,6 +37,9 @@ Function to interpolate a numeric attribute of a point in a scatter dataset of p
 |-------------|------|-------------|
 | value  | numeric | Interpolated value at the given point, `-888.888` if the given point is out of the boundaries of the source points set |
 
+Default values:
+* -888.888: when using Barycentric, the target point is out of the realm of the input points
+* -777.777: asking for a method not available
 
 #### Example Usage
 

doc/09_voronoi.md

@@ -27,9 +27,12 @@ PostGIS wil include this in future versions ([doc for dev branch](http://postgis
 ```sql
 WITH a AS (
     SELECT
-    ARRAY[ST_GeomFromText('POINT(2.1744 41.403)'),ST_GeomFromText('POINT(2.1228 41.380)'),ST_GeomFromText('POINT(2.1511 41.374)'),ST_GeomFromText('POINT(2.1528 41.413)'),ST_GeomFromText('POINT(2.165 41.391)'),ST_GeomFromText('POINT(2.1498 41.371)'),ST_GeomFromText('POINT(2.1533 41.368)'),ST_GeomFromText('POINT(2.131386 41.41399)')] AS geomin
+    ARRAY[ST_GeomFromText('POINT(2.1744 41.403)', 4326),ST_GeomFromText('POINT(2.1228 41.380)', 4326),ST_GeomFromText('POINT(2.1511 41.374)', 4326),ST_GeomFromText('POINT(2.1528 41.413)', 4326),ST_GeomFromText('POINT(2.165 41.391)', 4326),ST_GeomFromText('POINT(2.1498 41.371)', 4326),ST_GeomFromText('POINT(2.1533 41.368)', 4326),ST_GeomFromText('POINT(2.131386 41.41399)', 4326)] AS geomin
 )
 SELECT
-    CDB_voronoi(geomin, 0.2, 1e-9) as result
+    st_transform(
+    (st_dump(CDB_voronoi(geomin, 0.2, 1e-9)
+    )).geom
+        , 3857) as the_geom_webmercator
 FROM a;
 ```

doc/13_PIA.md

@@ -0,0 +1,33 @@
+## Pole of inaccessibility (PIA)
+
+Function to find the [PIA](https://en.wikipedia.org/wiki/Pole_of_inaccessibility) from a given polygon and tolerance, following the quadtree approach by [Vladimir Agafonkin](https://github.com/mourner) described [here](https://github.com/mapbox/polylabel)
+
+
+
+### CDB_PIA (polygon geometry, tolerance numeric DEFAULT 1.0)
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| polygon   | geometry  | Target polygon |
+| tolerance   | numeric | Threshold to decide to take a cell into account |
+
+### Returns
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| point  | geometry| Pole of inaccessibility |
+
+
+#### Example Usage
+
+```sql
+with a as(
+ select st_geomfromtext('POLYGON((-432540.453078056 4949775.20452642,-432329.947920966 4951361.232584,-431245.028163694 4952223.31516671,-429131.071033529 4951768.00415574,-424622.07505895 4952843.13503987,-423688.327170174 4953499.20752423,-424086.294349759 4954968.38274191,-423068.388925945 4954378.63345336,-423387.653225542 4953355.67417084,-420594.869840519 4953781.00230592,-416026.095299382 4951484.06849063,-412483.018546414 4951024.5410983,-410490.399661215 4954502.24032205,-408186.197521284 4956398.91417441,-407627.262358013 4959300.94633864,-406948.770061627 4959874.85407739,-404949.583326472 4959047.74518163,-402570.908447199 4953743.46829807,-400971.358683991 4952193.11680804,-403533.488084088 4949649.89857885,-406335.177028373 4950193.19571096,-407790.456731515 4952391.46015616,-412060.672398345 4950381.2389307,-410716.93482498 4949156.7509561,-408464.162289794 4943912.8940387,-409350.599394983 4942819.84896006,-408087.791091424 4942451.6711778,-407274.045613725 4940572.4807777,-404446.196589102 4939976.71501489,-402422.964843936 4940450.3670813,-401010.654464241 4939054.8061663,-397647.247369412 4940679.80737878,-395658.413346901 4940528.84765185,-395536.852462953 4938829.79565997,-394268.923462818 4938003.7277717,-393388.720249116 4934757.80596815,-392393.301362444 4934326.71675815,-392573.527618037 4932323.40974412,-393464.640141837 4931903.10653605,-393085.597275686 4931094.7353605,-398426.261165985 4929156.87541607,-398261.174361137 4926238.00816416,-394045.059966834 4925765.18668498,-392982.960705174 4926391.81893628,-393090.272694301 4927176.84692181,-391648.240010564 4924626.06386961,-391889.914625075 4923086.14787613,-394345.177314013 4923235.086036,-395550.878718795 4917812.79243978,-399009.463978251 4912927.7157945,-398948.794855767 4911941.91010796,-398092.636652078 4911806.57392519,-401991.601817112 4911722.9204501,-406225.972607907 4914505.47286319,-411104.994569885 4912569.26941163,-412925.513522316 4913030.3608866,-414630.148884835 4914436.69169949,-414207.691417276 4919205.78028405,-418306.141109809 4917994.9580478,-424184.700779621 4918938.12432889,-426816.961458921 4923664.37379373,-420956.324227126 4923381.98014807,-420186.661267781 4924286.48693378,-420943.411166194 4926812.76394433,-419779.45457046 4928527.43466337,-419768.767899344 4930681.94459216,-421911.668097113 4930432.40620397,-423482.386112205 4933451.28047252,-427272.814773717 4934151.56473242,-427144.908678797 4939731.77191996,-428982.125554848 4940522.84445172,-428986.133056516 4942437.17281266,-431237.792396792 4947309.68284815,-432476.889648814 4947791.74800037,-432540.453078056 4949775.20452642))', 3857) as g
+),
+b as (
+    select ST_Transform(g, 4326) as g from a
+)
+SELECT st_astext(CDB_PIA(g)) from b;
+```

doc/14_densify.md

@@ -0,0 +1,35 @@
+## Densify function
+
+Iterative densification of a set of scattered points using Delaunay triangulation. The new points are located at the centroids of the grid cells and  have as assigned value the barycentric average value of the cell's vertex.
+
+### CDB_Densify(geomin geometry[], colin numeric[], iterations integer)
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| geomin   | geometry[]  | Array of points geometries |
+| colin | numeric[]   | Array of points' values |
+| iterations   | integer     | Number of iterations |
+
+### Returns
+
+Returns a table object
+
+| Name | Type | Description |
+|------|------|-------------|
+| geomout   | geometry  | Geometries of new dataset of points|
+| colout | numeric   | Values of points|
+
+#### Example Usage
+
+```sql
+with data as (
+    select
+    ARRAY[7.0,8.0,1.0,2.0,3.0,5.0,6.0,4.0] as colin,
+    ARRAY[ST_GeomFromText('POINT(2.1744 41.4036)'),ST_GeomFromText('POINT(2.1228 41.3809)'),ST_GeomFromText('POINT(2.1511 41.3742)'),ST_GeomFromText('POINT(2.1528 41.4136)'),ST_GeomFromText('POINT(2.165 41.3917)'),ST_GeomFromText('POINT(2.1498 41.3713)'),ST_GeomFromText('POINT(2.1533 41.3683)'),ST_GeomFromText('POINT(2.131386 41.413998)')] as geomin
+)
+select CDB_Densify(geomin, colin, 2) from data;
+```
+
+

doc/15_tinmap.md

@@ -0,0 +1,36 @@
+## TINMAP function
+
+Generates a fake contour map, in the form of a TIN map, from a set of scattered points.Depends on **CDB_Densify**.
+
+Its iterative nature lets the user smooth the final result as much as desired, but with a exponential time cost increase.
+
+### CDB_TINmap(geomin geometry[], colin numeric[], iterations integer)
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| geomin   | geometry[]  | Array of points geometries |
+| colin | numeric[]   | Array of points' values |
+| iterations   | integer     | Number of iterations |
+
+### Returns
+
+Returns a table object
+
+| Name | Type | Description |
+|------|------|-------------|
+| geomout   | geometry  | Geometries of new dataset of polygons|
+| colout | numeric   | Values of each cell|
+
+#### Example Usage
+
+```sql
+with data as (
+    select
+    ARRAY[7.0,8.0,1.0,2.0,3.0,5.0,6.0,4.0] as colin,
+    ARRAY[ST_GeomFromText('POINT(2.1744 41.4036)'),ST_GeomFromText('POINT(2.1228 41.3809)'),ST_GeomFromText('POINT(2.1511 41.3742)'),ST_GeomFromText('POINT(2.1528 41.4136)'),ST_GeomFromText('POINT(2.165 41.3917)'),ST_GeomFromText('POINT(2.1498 41.3713)'),ST_GeomFromText('POINT(2.1533 41.3683)'),ST_GeomFromText('POINT(2.131386 41.413998)')] as geomin
+)
+select CDB_TINmap(geomin, colin, 2) from data;
+```
+

doc/16_getis_ord_gstar.md

@@ -0,0 +1,40 @@
+## Getis-Ord's G\*
+
+Getis-Ord's G\* is a geo-statistical measurement of the intensity of clustering of high or low values. The clustering of high values can be referred to as "hotspots" because these are areas of high activity or large (relative to the global mean) measurement values. Coldspots are clustered areas with low activity or small measurement values.
+
+### CDB_GetisOrdsG(subquery text, column_name text)
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | text | A query of the data you want to pass to the function. It must include `column_name`, a geometry column (usually `the_geom`) and an id column (usually `cartodb_id`) |
+| column_name | text | This is the column of interest for performing this analysis on. This column should be a numeric type. |
+| w_type (optional) | text | Type of weight to use when finding neighbors. Currently available options are 'knn' (default) and 'queen'. Read more about weight types in [PySAL's weights documentation.](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html) |
+| num_ngbrs (optional) | integer | Default: 5. If `knn` is chosen, this will set the number of neighbors. If `knn` is not chosen, any entered value will be ignored. Use `NULL` if not choosing `knn`. |
+| permutations (optional) | integer | The number of permutations for calculating p-values. Default: 999 |
+| geom_col (optional) | text | The column where the geometry information is stored. The format must be PostGIS Geometry type (SRID 4326). Default: `the_geom`. |
+| id_col (optional) | text | The column that has the unique row identifier. |
+
+### Returns
+
+Returns a table with the following columns.
+
+| Name | Type | Description |
+|------|------|-------------|
+| z_score | numeric | z-score, a measure of the intensity of clustering of high values (hotspots) or low values (coldspots). Positive values represent 'hotspots', while negative values represent 'coldspots'. |
+| p_value | numeric  | p-value, a measure of the significance of the intensity of clustering |
+| p_z_sim | numeric | p-value based on standard normal approximation from permutations |
+| rowid | integer | The original `id_col` that can be used to associate the outputs with the original geometry and inputs |
+
+#### Example Usage
+
+The following query returns the original table augmented with the values calculated from the Getis-Ord's G\* analysis.
+
+```sql
+SELECT i.*, m.z_score, m.p_value
+  FROM cdb_crankshaft.CDB_GetisOrdsG('SELECT * FROM incident_reports_clustered',
+                                     'num_incidents') As m
+  JOIN incident_reports_clustered As i
+    ON i.cartodb_id = m.rowid;
+```

doc/18_outliers.md

@@ -0,0 +1,163 @@
+## Outlier Detection
+
+This set of functions detects the presence of outliers. There are three functions for finding outliers from non-spatial data:
+
+1. Static Outliers
+1. Percentage Outliers
+1. Standard Deviation Outliers
+
+### CDB_StaticOutlier(column_value numeric, threshold numeric)
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| column_value | numeric | The column of values on which to apply the threshold |
+| threshold | numeric | The static threshold which is used to indicate whether a `column_value` is an outlier or not |
+
+### Returns
+
+Returns a boolean (true/false) depending on whether a value is above or below (or equal to) the threshold
+
+| Name | Type | Description |
+|------|------|-------------|
+| outlier | boolean | classification of whether a row is an outlier or not |
+
+#### Example Usage
+
+With a table `website_visits` and a column of the number of website visits in units of 10,000 visits:
+
+```
+| id | visits_10k |
+|----|------------|
+| 1  | 1 |
+| 2  | 3 |
+| 3  | 5 |
+| 4  | 1 |
+| 5  | 32 |
+| 6  | 3 |
+| 7  | 57 |
+| 8  | 2 |
+```
+
+```sql
+SELECT
+  id,
+  CDB_StaticOutlier(visits_10k, 11.0) As outlier,
+  visits_10k
+FROM website_visits
+```
+
+```
+| id | outlier | visits_10k |
+|----|---------|------------|
+| 1  | f | 1 |
+| 2  | f | 3 |
+| 3  | f | 5 |
+| 4  | f | 1 |
+| 5  | t | 32 |
+| 6  | f | 3 |
+| 7  | t | 57 |
+| 8  | f | 2 |
+```
+
+### CDB_PercentOutlier(column_values numeric[], outlier_fraction numeric, ids int[])
+
+`CDB_PercentOutlier` calculates whether or not a value falls above a given threshold based on a percentage above the mean value of the input values.
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| column_values | numeric[] | An array of the values to calculate the outlier classification on |
+| outlier_fraction | numeric | The threshold above which a column value divided by the mean of all values is considered an outlier |
+| ids | int[] | An array of the unique row ids of the input data (usually `cartodb_id`) |
+
+### Returns
+
+Returns a table of the outlier classification with the following columns
+
+| Name | Type | Description |
+|------|------|-------------|
+| is_outlier | boolean  | classification of whether a row is an outlier or not |
+| rowid | int | original row id (e.g., input `cartodb_id`) of the row which has the outlier classification |
+
+#### Example Usage
+
+This example find outliers which are more than 100% larger than the average (that is, more than 2.0 times larger).
+
+```sql
+WITH cte As (
+  SELECT
+    unnest(Array[1,2,3,4,5,6,7,8]) As id,
+    unnest(Array[1,3,5,1,32,3,57,2]) As visits_10k
+  )
+SELECT
+  (CDB_PercentOutlier(array_agg(visits_10k), 2.0, array_agg(id))).*
+FROM cte;
+```
+
+Output
+```
+| outlier | rowid |
+|---------+-------|
+| f | 1 |
+| f | 2 |
+| f | 3 |
+| f | 4 |
+| t | 5 |
+| f | 6 |
+| t | 7 |
+| f | 8 |
+```
+
+### CDB_StdDevOutlier(column_values numeric[], num_deviations numeric, ids int[], is_symmetric boolean DEFAULT true)
+
+`CDB_StdDevOutlier` calculates whether or not a value falls above or below a given threshold based on the number of standard deviations from the mean.
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| column_values | numeric[] | An array of the values to calculate the outlier classification on |
+| num_deviations | numeric | The threshold in units of standard deviation |
+| ids | int[] | An array of the unique row ids of the input data (usually `cartodb_id`) |
+| is_symmetric (optional) | boolean | Consider outliers that are symmetric about the mean (default: true) |
+
+### Returns
+
+Returns a table of the outlier classification with the following columns
+
+| Name | Type | Description |
+|------|------|-------------|
+| is_outlier | boolean  | classification of whether a row is an outlier or not |
+| rowid | int | original row id (e.g., input `cartodb_id`) of the row which has the outlier classification |
+
+#### Example Usage
+
+This example find outliers which are more than 100% larger than the average (that is, more than 2.0 times larger).
+
+```sql
+WITH cte As (
+  SELECT
+    unnest(Array[1,2,3,4,5,6,7,8]) As id,
+    unnest(Array[1,3,5,1,32,3,57,2]) As visits_10k
+  )
+SELECT
+  (CDB_StdDevOutlier(array_agg(visits_10k), 2.0, array_agg(id))).*
+FROM cte;
+```
+
+Output
+```
+| outlier | rowid |
+|---------+-------|
+| f | 1 |
+| f | 2 |
+| f | 3 |
+| f | 4 |
+| f | 5 |
+| f | 6 |
+| t | 7 |
+| f | 8 |
+```

doc/19_contour.md

@@ -0,0 +1,50 @@
+## Contour maps
+
+Function to generate a contour map from an scatter dataset of points, using one of these three methods:
+
+* [Nearest neighbor](https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
+* [Barycentric](https://en.wikipedia.org/wiki/Barycentric_coordinate_system)
+* [IDW](https://en.wikipedia.org/wiki/Inverse_distance_weighting)
+
+### CDB_Contour (geom geometry[], values numeric[], resolution integer, buffer numeric, method, classmethod integer, steps integer)
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| geom   | geometry[]  | Array of points's geometries |
+| values | numeric[]   | Array of points' values for the param under study|
+| buffer   | numeric     | Value between 0 and 1 for spatial buffer of the set of points
+| method   | integer     | 0:nearest neighbor, 1: barycentric, 2: IDW|
+| classmethod   | integer     | 0:equals, 1: heads&tails, 2:jenks, 3:quantiles |
+| steps   | integer     | Number of steps in the classification|
+| max_time   | integer     | if <= 0: max processing time in seconds (smart resolution) , if >0: resolution in meters
+
+### Returns
+Returns a table object
+
+| Name | Type | Description |
+|------|------|-------------|
+| the_geom   | geometry  | Geometries of the classified contour map|
+| avg_value | numeric   | Avg value of the area|
+| min_value | numeric   | Min value of the area|
+| max_value | numeric   | Max value of the areal|
+| bin | integer   | Index of the class of the area|
+
+#### Example Usage
+
+```sql
+WITH a AS (
+    SELECT
+    ARRAY[800, 700, 600, 500, 400, 300, 200, 100]::numeric[] AS vals,
+    ARRAY[ST_GeomFromText('POINT(2.1744 41.403)',4326),ST_GeomFromText('POINT(2.1228 41.380)',4326),ST_GeomFromText('POINT(2.1511 41.374)',4326),ST_GeomFromText('POINT(2.1528 41.413)',4326),ST_GeomFromText('POINT(2.165 41.391)',4326),ST_GeomFromText('POINT(2.1498 41.371)',4326),ST_GeomFromText('POINT(2.1533 41.368)',4326),ST_GeomFromText('POINT(2.131386 41.41399)',4326)] AS g
+),
+b as(
+SELECT
+    foo.*
+FROM
+    a,
+    cdb_crankshaft.CDB_contour(a.g, a.vals,  0.0, 1, 3, 5, 60) foo
+)
+SELECT bin, avg_value from b order by bin;
+```

doc/21_gwr.md

@@ -0,0 +1,128 @@
+## Regression
+
+### Predictive geographically weighted regression (GWR)
+
+Predictive GWR generates estimates of the dependent variable at locations where it has not been observed. It predicts these unknown values by first using the GWR model estimation analysis with known data values of the dependent and independent variables sampled from around the prediction location(s) to build a geographically weighted, spatially-varying regression model. It then uses this model and known values of the independent variables at the prediction locations to predict the value of the dependent variable where it is otherwise unknown.
+
+For predictive GWR to work, a dataset needs known independent variables, some known dependent variables, and some unknown dependent variables. The dataset also needs to have geometry data (e.g., point, lines, or polygons).
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that expose the data to be analyzed (e.g., `SELECT * FROM regression_inputs`). This query must have the geometry column name (see the optional `geom_col` for default), the id column name (see `id_col`), and the dependent (`dep_var`) and independent (`ind_vars`) column names. |
+| dep_var | TEXT | Name of the dependent variable in the regression model |
+| ind_vars | TEXT[] | Text array of independent variable column names used in the model to describe the dependent variable. |
+| bw (optional) | NUMERIC | Value of bandwidth. If `NULL` then select optimal (default). |
+| fixed (optional) | BOOLEAN | True for distance based kernel function and False (default) for adaptive (nearest neighbor) kernel function. Defaults to `False`. |
+| kernel (optional)| TEXT | Type of kernel function used to weight observations. One of `gaussian`, `bisquare` (default), or `exponential`. |
+
+
+#### Returns
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| coeffs | JSON | JSON object with parameter estimates for each of the dependent variables. The keys of the JSON object are the dependent variables, with values corresponding to the parameter estimate. |
+| stand_errs | JSON | Standard errors for each of the dependent variables. The keys of the JSON object are the dependent variables, with values corresponding to the respective standard errors. |
+| t_vals | JSON | T-values for each of the dependent variables. The keys of the JSON object are the dependent variable names, with values corresponding to the respective t-value. |
+| predicted | NUMERIC | predicted value of y |
+| residuals | NUMERIC | residuals of the response |
+| r_squared | NUMERIC | R-squared for the parameter fit |
+| bandwidth | NUMERIC | bandwidth value consisting of either a distance or N nearest neighbors |
+| rowid | INTEGER | row id of the original row |
+
+
+#### Example Usage
+
+```sql
+SELECT
+  g.cartodb_id,
+  g.the_geom,
+  g.the_geom_webmercator,
+  (gwr.coeffs->>'pctblack')::numeric as coeff_pctblack,
+  (gwr.coeffs->>'pctrural')::numeric as coeff_pctrural,
+  (gwr.coeffs->>'pcteld')::numeric as coeff_pcteld,
+  (gwr.coeffs->>'pctpov')::numeric as coeff_pctpov,
+  gwr.residuals
+FROM cdb_crankshaft.CDB_GWR_Predict('select * from g_utm'::text,   
+  'pctbach'::text,
+  Array['pctblack', 'pctrural', 'pcteld', 'pctpov']) As gwr
+JOIN g_utm as g
+on g.cartodb_id = gwr.rowid
+```
+
+Note: See [PostgreSQL syntax for parsing JSON objects](https://www.postgresql.org/docs/9.5/static/functions-json.html).
+
+### Geographically weighted regression model estimation
+
+This analysis generates the model coefficients for a geographically weighted, spatially-varying regression. The model coefficients, along with their respective statistics, allow one to make inferences or describe a dependent variable based on a set of independent variables. Similar to traditional linear regression, GWR takes a linear combination of independent variables and a known dependent variable to estimate an optimal set of coefficients. The model coefficients are spatially varying (controlled by the `bandwidth` and `fixed` parameters), so that the model output is allowed to vary from geometry to geometry. This allows GWR to capture non-stationarity -- that is, how local processes vary over space. In contrast, coefficients obtained from estimating a traditional linear regression model assume that processes are constant over space.
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that expose the data to be analyzed (e.g., `SELECT * FROM regression_inputs`). This query must have the geometry column name (see the optional `geom_col` for default), the id column name (see `id_col`), dependent and independent column names. |
+| dep_var | TEXT | name of the dependent variable in the regression model |
+| ind_vars | TEXT[] | Text array of independent variables used in the model to describe the dependent variable |
+| bw (optional) | NUMERIC | Value of bandwidth. If `NULL` then select optimal (default). |
+| fixed (optional) | BOOLEAN | True for distance based kernel function and False for adaptive (nearest neighbor) kernel function (default). Defaults to false. |
+| kernel | TEXT | Type of kernel function used to weight observations. One of `gaussian`, `bisquare` (default), or `exponential`. |
+
+
+#### Returns
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| coeffs | JSON | JSON object with parameter estimates for each of the dependent variables. The keys of the JSON object are the dependent variables, with values corresponding to the parameter estimate. |
+| stand_errs | JSON | Standard errors for each of the dependent variables. The keys of the JSON object are the dependent variables, with values corresponding to the respective standard errors. |
+| t_vals | JSON | T-values for each of the dependent variables. The keys of the JSON object are the dependent variable names, with values corresponding to the respective t-value. |
+| predicted | NUMERIC | predicted value of y |
+| residuals | NUMERIC | residuals of the response |
+| r_squared | NUMERIC | R-squared for the parameter fit |
+| bandwidth | NUMERIC | bandwidth value consisting of either a distance or N nearest neighbors |
+| rowid | INTEGER | row id of the original row |
+
+
+#### Example Usage
+
+```sql
+SELECT
+  g.cartodb_id,
+  g.the_geom,
+  g.the_geom_webmercator,
+  (gwr.coeffs->>'pctblack')::numeric as coeff_pctblack,
+  (gwr.coeffs->>'pctrural')::numeric as coeff_pctrural,
+  (gwr.coeffs->>'pcteld')::numeric as coeff_pcteld,
+  (gwr.coeffs->>'pctpov')::numeric as coeff_pctpov,
+  gwr.residuals
+FROM cdb_crankshaft.CDB_GWR('select * from g_utm'::text, 'pctbach'::text, Array['pctblack', 'pctrural', 'pcteld', 'pctpov']) As gwr
+JOIN g_utm as g
+on g.cartodb_id = gwr.rowid
+```
+
+Note: See [PostgreSQL syntax for parsing JSON objects](https://www.postgresql.org/docs/9.5/static/functions-json.html).
+
+
+## Advanced reading
+
+*   Fotheringham, A. Stewart, Chris Brunsdon, and Martin Charlton. 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley & Sons. <http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471496162.html>
+
+*   Brunsdon, Chris, A. Stewart Fotheringham, and Martin E. Charlton. 1996. "Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity." Geographical Analysis 28 (4): 281–98. <http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.1996.tb00936.x/abstract>
+
+*   Brunsdon, Chris, Stewart Fotheringham, and Martin Charlton. 1998. "Geographically Weighted Regression." Journal of the Royal Statistical Society: Series D (The Statistician) 47 (3): 431–43. <http://onlinelibrary.wiley.com/doi/10.1111/1467-9884.00145/abstract>
+
+*   Fotheringham, A. S., M. E. Charlton, and C. Brunsdon. 1998. "Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis." Environment and Planning A 30 (11): 1905–27. doi:10.1068/a301905. <https://www.researchgate.net/publication/23538637_Geographically_Weighted_Regression_A_Natural_Evolution_Of_The_Expansion_Method_for_Spatial_Data_Analysis>
+
+### GWR for prediction
+
+*   Harris, P., A. S. Fotheringham, R. Crespo, and M. Charlton. 2010. "The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets." Mathematical Geosciences 42 (6): 657–80. doi:10.1007/s11004-010-9284-7. <https://www.researchgate.net/publication/225757830_The_Use_of_Geographically_Weighted_Regression_for_Spatial_Prediction_An_Evaluation_of_Models_Using_Simulated_Data_Sets>
+
+### GWR in application
+
+*   Cahill, Meagan, and Gordon Mulligan. 2007. "Using Geographically Weighted Regression to Explore Local Crime Patterns." Social Science Computer Review 25 (2): 174–93. doi:10.1177/0894439307298925. <http://isites.harvard.edu/fs/docs/icb.topic923297.files/174.pdf>
+
+*   Gilbert, Angela, and Jayajit Chakraborty. 2011. "Using Geographically Weighted Regression for Environmental Justice Analysis: Cumulative Cancer Risks from Air Toxics in Florida." Social Science Research 40 (1): 273–86. doi:10.1016/j.ssresearch.2010.08.006. <http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=2985&context=etd>
+
+*   Ali, Kamar, Mark D. Partridge, and M. Rose Olfert. 2007. "Can Geographically Weighted Regressions Improve Regional Analysis and Policy Making?" International Regional Science Review 30 (3): 300–329. doi:10.1177/0160017607301609. <https://www.researchgate.net/publication/249682503_Can_Geographically_Weighted_Regressions_Improve_Regional_Analysis_and_Policy_Making>
+
+*   Lu, Binbin, Martin Charlton, and A. Stewart Fotheringhama. 2011. "Geographically Weighted Regression Using a Non-Euclidean Distance Metric with a Study on London House Price Data." Procedia Environmental Sciences, Spatial Statistics 2011: Mapping Global Change, 7: 92–97. doi:10.1016/j.proenv.2011.07.017. <https://www.researchgate.net/publication/261960122_Geographically_weighted_regression_with_a_non-Euclidean_distance_metric_A_case_study_using_hedonic_house_price_data>

release/crankshaft.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.3.0'
+default_version = '0.6.0'
 requires = 'plpythonu, postgis'
 superuser = true
 schema = cdb_crankshaft

release/python/0.3.1/crankshaft/crankshaft/__init__.py

@@ -0,0 +1,5 @@
+"""Import all modules"""
+import crankshaft.random_seeds
+import crankshaft.clustering
+import crankshaft.space_time_dynamics
+import crankshaft.segmentation

release/python/0.3.1/crankshaft/crankshaft/clustering/__init__.py

@@ -0,0 +1,3 @@
+"""Import all functions from for clustering"""
+from moran import *
+from kmeans import *

release/python/0.3.1/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/0.3.1/crankshaft/crankshaft/clustering/moran.py

@@ -0,0 +1,262 @@
+"""
+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
+from collections import OrderedDict
+
+# 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 = OrderedDict([("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 = OrderedDict([("id_col", id_col),
+                         ("attr1", attr),
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(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 = OrderedDict([("id_col", id_col),
+                         ("attr1", numerator),
+                         ("attr2", denominator)
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    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
+
+    qvals = OrderedDict([("id_col", id_col),
+                         ("numerator", numerator),
+                         ("denominator", denominator),
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(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 quadrants for each geometry
+    quads = quad_position(lisa.q)
+
+    return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
+
+def moran_local_bv(subquery, attr1, attr2,
+                   permutations, geom_col, id_col, w_type, num_ngbrs):
+    """
+        Moran's I (local) Bivariate (untested)
+    """
+    plpy.notice('** Constructing query')
+
+    qvals = OrderedDict([("id_col", id_col),
+                         ("attr1", attr1),
+                         ("attr2", attr2),
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(4)
+    except plpy.SPIError:
+        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/0.3.1/crankshaft/crankshaft/pysal_utils/__init__.py

@@ -0,0 +1,2 @@
+"""Import all functions for pysal_utils"""
+from crankshaft.pysal_utils.pysal_utils import *

release/python/0.3.1/crankshaft/crankshaft/pysal_utils/pysal_utils.py

@@ -0,0 +1,188 @@
+"""
+    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 dict-like: 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}
+    print 'len of neighbors: %d' % len(neighbors)
+
+    built_weight = ps.W(neighbors)
+    built_weight.transform = 'r'
+
+    return built_weight
+
+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.)
+    """
+
+    attr_string = ""
+    template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, "
+
+    if 'time_cols' in params:
+        ## if markov analysis
+        attrs = params['time_cols']
+
+        for idx, val in enumerate(attrs):
+            attr_string += template % {"col": val, "alias_num": idx + 1}
+    else:
+        ## if moran's analysis
+        attrs = [k for k in params
+                 if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')]
+
+        for idx, val in enumerate(sorted(attrs)):
+            attr_string += template % {"col": params[val], "alias_num": idx + 1}
+
+    return attr_string
+
+def query_attr_where(params):
+    """
+      Construct where conditions when building neighbors query
+        Create portion of WHERE clauses for weeding out NULL-valued geometries
+        Input: dict of params:
+            {'subquery': ...,
+             'numerator': 'data1',
+             'denominator': 'data2',
+             '': ...}
+        Output: 'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL'
+        Input:
+        {'subquery': ...,
+         'time_cols': ['time1', 'time2', 'time3'],
+         'etc': ...}
+        Output: 'idx_replace."time1" IS NOT NULL AND idx_replace."time2" IS NOT
+          NULL AND idx_replace."time3" IS NOT NULL'
+    """
+    attr_string = []
+    template = "idx_replace.\"%s\" IS NOT NULL"
+
+    if 'time_cols' in params:
+        ## markov where clauses
+        attrs = params['time_cols']
+        # add values to template
+        for attr in attrs:
+            attr_string.append(template % attr)
+    else:
+        ## moran where clauses
+
+        # get keys
+        attrs = sorted([k for k in params
+                        if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')])
+        # add values to template
+        for attr in attrs:
+            attr_string.append(template % params[attr])
+
+        if len(attrs) == 2:
+            attr_string.append("idx_replace.\"%s\" <> 0" % params[attrs[1]])
+
+    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/0.3.1/crankshaft/crankshaft/random_seeds.py

@@ -0,0 +1,11 @@
+"""Random seed generator used for non-deterministic functions in crankshaft"""
+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/0.3.1/crankshaft/crankshaft/segmentation/__init__.py

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

release/python/0.3.1/crankshaft/crankshaft/segmentation/segmentation.py

@@ -0,0 +1,176 @@
+"""
+Segmentation creation and prediction
+"""
+
+import sklearn
+import numpy as np
+import plpy
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn import metrics
+from sklearn.cross_validation import train_test_split
+
+# Lower level functions
+#----------------------
+
+def replace_nan_with_mean(array):
+    """
+        Input:
+            @param array: an array of floats which may have null-valued entries
+        Output:
+            array with nans filled in with the mean of the dataset
+    """
+    # returns an array of rows and column indices
+    indices = np.where(np.isnan(array))
+
+    # iterate through entries which have nan values
+    for row, col in zip(*indices):
+            array[row, col] = np.mean(array[~np.isnan(array[:, col]), col])
+
+    return array
+
+def get_data(variable, feature_columns, query):
+    """
+        Fetch data from the database, clean, and package into
+          numpy arrays
+        Input:
+            @param variable: name of the target variable
+            @param feature_columns: list of column names
+            @param query: subquery that data is pulled from for the packaging
+        Output:
+            prepared data, packaged into NumPy arrays
+    """
+
+    columns = ','.join(['array_agg("{col}") As "{col}"'.format(col=col) for col in feature_columns])
+
+    try:
+        data = plpy.execute('''SELECT array_agg("{variable}") As target, {columns} FROM ({query}) As a'''.format(
+            variable=variable,
+            columns=columns,
+            query=query))
+    except Exception, e:
+        plpy.error('Failed to access data to build segmentation model: %s' % e)
+
+    # extract target data from plpy object
+    target = np.array(data[0]['target'])
+
+    # put n feature data arrays into an n x m array of arrays
+    features = np.column_stack([np.array(data[0][col], dtype=float) for col in feature_columns])
+
+    return replace_nan_with_mean(target), replace_nan_with_mean(features)
+
+# High level interface
+# --------------------
+
+def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters):
+    """
+    Version of create_and_predict_segment that works on arrays that come stright form the SQL calling
+    the function.
+
+        Input:
+            @param target: The 1D array of lenth NSamples containing the target variable we want the model to predict
+            @param features: Thw 2D array of size NSamples * NFeatures that form the imput to the model
+            @param target_ids: A 1D array of target_ids that will be used to associate the results of the prediction with the rows which they come from
+            @param model_parameters: A dictionary containing parameters for the model.
+    """
+
+    clean_target = replace_nan_with_mean(target)
+    clean_features = replace_nan_with_mean(features)
+    target_features = replace_nan_with_mean(target_features)
+
+    model, accuracy = train_model(clean_target, clean_features, model_parameters, 0.2)
+    prediction = model.predict(target_features)
+    accuracy_array = [accuracy]*prediction.shape[0]
+    return zip(target_ids, prediction, np.full(prediction.shape, accuracy_array))
+
+
+
+def create_and_predict_segment(query, variable, target_query, model_params):
+    """
+    generate a segment with machine learning
+    Stuart Lynn
+    """
+
+    ## fetch column names
+    try:
+        columns = plpy.execute('SELECT * FROM ({query}) As a LIMIT 1  '.format(query=query))[0].keys()
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    ## extract column names to be used in building the segmentation model
+    feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator'])
+    ## get data from database
+    target, features = get_data(variable, feature_columns, query)
+
+    model, accuracy = train_model(target, features, model_params, 0.2)
+    cartodb_ids, result = predict_segment(model, feature_columns, target_query)
+    accuracy_array = [accuracy]*result.shape[0]
+    return zip(cartodb_ids, result, accuracy_array)
+
+
+def train_model(target, features, model_params, test_split):
+    """
+        Train the Gradient Boosting model on the provided data and calculate the accuracy of the model
+        Input:
+            @param target: 1D Array of the variable that the model is to be trianed to predict
+            @param features: 2D Array NSamples * NFeatures to use in trining the model
+            @param model_params: A dictionary of model parameters, the full specification can be found on the
+                scikit learn page for [GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)
+            @parma test_split: The fraction of the data to be withheld for testing the model / calculating the accuray
+    """
+    features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
+    model = GradientBoostingRegressor(**model_params)
+    model.fit(features_train, target_train)
+    accuracy = calculate_model_accuracy(model, features, target)
+    return model, accuracy
+
+def calculate_model_accuracy(model, features, target):
+    """
+        Calculate the mean squared error of the model prediction
+        Input:
+            @param model: model trained from input features
+            @param features: features to make a prediction from
+            @param target: target to compare prediction to
+        Output:
+            mean squared error of the model prection compared to the target
+    """
+    prediction = model.predict(features)
+    return metrics.mean_squared_error(prediction, target)
+
+def predict_segment(model, features, target_query):
+    """
+    Use the provided model to predict the values for the new feature set
+        Input:
+            @param model: The pretrained model
+            @features: A list of features to use in the model prediction (list of column names)
+            @target_query: The query to run to obtain the data to predict on and the cartdb_ids associated with it.
+    """
+
+    batch_size = 1000
+    joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features])
+
+    try:
+        cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format(
+            joined_features=joined_features,
+            target_query=target_query))
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    results = []
+
+    while True:
+        rows = cursor.fetch(batch_size)
+        if not rows:
+            break
+        batch = np.row_stack([np.array(row['features'], dtype=float) for row in rows])
+
+        #Need to fix this. Should be global mean. This will cause weird effects
+        batch = replace_nan_with_mean(batch)
+        prediction = model.predict(batch)
+        results.append(prediction)
+
+    try:
+        cartodb_ids = plpy.execute('''SELECT array_agg(cartodb_id ORDER BY cartodb_id) As cartodb_ids FROM ({0}) As a'''.format(target_query))[0]['cartodb_ids']
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    return cartodb_ids, np.concatenate(results)

release/python/0.3.1/crankshaft/crankshaft/space_time_dynamics/__init__.py

@@ -0,0 +1,2 @@
+"""Import all functions from clustering libraries."""
+from markov import *

release/python/0.3.1/crankshaft/crankshaft/space_time_dynamics/markov.py

@@ -0,0 +1,189 @@
+"""
+Spatial dynamics measurements using Spatial Markov
+"""
+
+
+import numpy as np
+import pysal as ps
+import plpy
+import crankshaft.pysal_utils as pu
+
+def spatial_markov_trend(subquery, time_cols, num_classes=7,
+                         w_type='knn', num_ngbrs=5, permutations=0,
+                         geom_col='the_geom', id_col='cartodb_id'):
+    """
+        Predict the trends of a unit based on:
+        1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile)
+        2. average class of its neighbors
+
+        Inputs:
+        @param subquery string: e.g., SELECT the_geom, cartodb_id,
+          interesting_time_column FROM table_name
+        @param time_cols list of strings: list of strings of column names
+        @param num_classes (optional): number of classes to break distribution
+          of values into. Currently uses quantile bins.
+        @param w_type string (optional): weight type ('knn' or 'queen')
+        @param num_ngbrs int (optional): number of neighbors (if knn type)
+        @param permutations int (optional): number of permutations for test
+          stats
+        @param geom_col string (optional): name of column which contains the
+          geometries
+        @param id_col string (optional): name of column which has the ids of
+          the table
+
+        Outputs:
+        @param trend_up float: probablity that a geom will move to a higher
+          class
+        @param trend_down float: probablity that a geom will move to a lower
+          class
+        @param trend float: (trend_up - trend_down) / trend_static
+        @param volatility float: a measure of the volatility based on
+          probability stddev(prob array)
+    """
+
+    if len(time_cols) < 2:
+        plpy.error('More than one time column needs to be passed')
+
+    qvals = {"id_col": id_col,
+             "time_cols": time_cols,
+             "geom_col": geom_col,
+             "subquery": subquery,
+             "num_ngbrs": num_ngbrs}
+
+    try:
+        query_result = plpy.execute(
+            pu.construct_neighbor_query(w_type, qvals)
+        )
+        if len(query_result) == 0:
+            return zip([None], [None], [None], [None], [None])
+    except plpy.SPIError, err:
+        plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals)))
+        plpy.error('Query failed, check the input parameters')
+        return zip([None], [None], [None], [None], [None])
+
+    ## build weight
+    weights = pu.get_weight(query_result, w_type)
+    weights.transform = 'r'
+
+    ## prep time data
+    t_data = get_time_data(query_result, time_cols)
+
+    plpy.debug('shape of t_data %d, %d' % t_data.shape)
+    plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape)
+    plpy.debug('first num elements: %f' % t_data[0, 0])
+
+    sp_markov_result = ps.Spatial_Markov(t_data,
+                                         weights,
+                                         k=num_classes,
+                                         fixed=False,
+                                         permutations=permutations)
+
+    ## get lag classes
+    lag_classes = ps.Quantiles(
+        ps.lag_spatial(weights, t_data[:, -1]),
+        k=num_classes).yb
+
+    ## look up probablity distribution for each unit according to class and lag class
+    prob_dist = get_prob_dist(sp_markov_result.P,
+                              lag_classes,
+                              sp_markov_result.classes[:, -1])
+
+    ## find the ups and down and overall distribution of each cell
+    trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist,
+                                                             sp_markov_result.classes[:, -1])
+
+    ## output the results
+    return zip(trend, trend_up, trend_down, volatility, weights.id_order)
+
+def get_time_data(markov_data, time_cols):
+    """
+        Extract the time columns and bin appropriately
+    """
+    num_attrs = len(time_cols)
+    return np.array([[x['attr' + str(i)] for x in markov_data]
+                     for i in range(1, num_attrs+1)], dtype=float).transpose()
+
+## not currently used
+def rebin_data(time_data, num_time_per_bin):
+    """
+        Convert an n x l matrix into an (n/m) x l matrix where the values are
+         reduced (averaged) for the intervening states:
+          1 2 3 4    1.5 3.5
+          5 6 7 8 -> 5.5 7.5
+          9 8 7 6    8.5 6.5
+          5 4 3 2    4.5 2.5
+
+          if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix.
+
+        This process effectively resamples the data at a longer time span n
+         units longer than the input data.
+        For cases when there is a remainder (remainder(5/3) = 2), the remaining
+         two columns are binned together as the last time period, while the
+         first three are binned together for the first period.
+
+        Input:
+          @param time_data n x l  ndarray: measurements of an attribute at
+           different time intervals
+          @param num_time_per_bin int: number of columns to average into a new
+           column
+        Output:
+          ceil(n / m) x l ndarray of resampled time series
+    """
+
+    if time_data.shape[1] % num_time_per_bin == 0:
+        ## if fit is perfect, then use it
+        n_max = time_data.shape[1] / num_time_per_bin
+    else:
+        ## fit remainders into an additional column
+        n_max = time_data.shape[1] / num_time_per_bin + 1
+
+    return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
+                     for i in range(n_max)]).T
+
+def get_prob_dist(transition_matrix, lag_indices, unit_indices):
+    """
+        Given an array of transition matrices, look up the probability
+        associated with the arrangements passed
+
+        Input:
+        @param transition_matrix ndarray[k,k,k]:
+        @param lag_indices ndarray:
+        @param unit_indices ndarray:
+
+        Output:
+        Array of probability distributions
+    """
+
+    return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
+                     for i in range(len(lag_indices))])
+
+def get_prob_stats(prob_dist, unit_indices):
+    """
+        get the statistics of the probability distributions
+
+        Outputs:
+            @param trend_up ndarray(float): sum of probabilities for upward
+               movement (relative to the unit index of that prob)
+            @param trend_down ndarray(float): sum of probabilities for downward
+               movement (relative to the unit index of that prob)
+            @param trend ndarray(float): difference of upward and downward
+               movements
+    """
+
+    num_elements = len(unit_indices)
+    trend_up = np.empty(num_elements, dtype=float)
+    trend_down = np.empty(num_elements, dtype=float)
+    trend = np.empty(num_elements, dtype=float)
+
+    for i in range(num_elements):
+        trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
+        trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
+        if prob_dist[i, unit_indices[i]] > 0.0:
+            trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
+        else:
+            trend[i] = None
+
+    ## calculate volatility of distribution
+    volatility = prob_dist.std(axis=1)
+
+    return trend_up, trend_down, trend, volatility

release/python/0.3.1/crankshaft/setup.py

@@ -0,0 +1,49 @@
+
+"""
+CartoDB Spatial Analysis Python Library
+See:
+https://github.com/CartoDB/crankshaft
+"""
+
+from setuptools import setup, find_packages
+
+setup(
+    name='crankshaft',
+
+    version='0.0.0',
+
+    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.
+    # IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
+    install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
+
+    requires=['pysal', 'numpy', 'sklearn'],
+
+    test_suite='test'
+)

release/python/0.3.1/crankshaft/setup.py-r

@@ -0,0 +1,49 @@
+
+"""
+CartoDB Spatial Analysis Python Library
+See:
+https://github.com/CartoDB/crankshaft
+"""
+
+from setuptools import setup, find_packages
+
+setup(
+    name='crankshaft',
+
+    version='0.0.0',
+
+    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.
+    # IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
+    install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
+
+    requires=['pysal', 'numpy', 'sklearn'],
+
+    test_suite='test'
+)

release/python/0.3.1/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/0.3.1/crankshaft/test/fixtures/markov.json

@@ -0,0 +1 @@
+[[0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 0], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 1], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 2], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 3], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 4], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 5], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 6], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 7], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 8], [0.19047619047619049, 0.16, 0.0, 0.32594478059941379, 9], [-0.23529411764705882, 0.0, 0.19047619047619047, 0.31356338348865387, 10], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 11], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 12], [0.027777777777777783, 0.11111111111111112, 0.088888888888888892, 0.30339641183779581, 13], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 14], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 15], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 16], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 17], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 18], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 19], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 20], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 21], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 22], [-0.16666666666666663, 0.18181818181818182, 0.27272727272727271, 0.20246415864836445, 23], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 24], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 25], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 26], [-0.043478260869565216, 0.0, 0.041666666666666664, 0.37950991789118999, 27], [0.22222222222222221, 0.18181818181818182, 0.0, 0.31701083225750354, 28], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 29], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 30], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 31], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 32], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 33], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 34], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 35], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 36], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 37], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 38], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 39], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 40], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 41], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 42], [0.0, 0.0, 0.0, 0.40000000000000002, 43], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 44], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 45], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 46], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 47]]

release/python/0.3.1/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/0.3.1/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/0.3.1/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/0.3.1/crankshaft/test/mock_plpy.py

@@ -0,0 +1,52 @@
+import re
+
+class MockCursor:
+    def __init__(self, data):
+        self.cursor_pos = 0
+        self.data = data
+
+    def fetch(self, batch_size):
+        batch = self.data[self.cursor_pos : self.cursor_pos + batch_size]
+        self.cursor_pos += batch_size
+        return batch
+
+
+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 debug(self, msg):
+        self.notices.append(msg)
+
+    def info(self, msg):
+        self.infos.append(msg)
+
+    def cursor(self, query):
+        data = self.execute(query)
+        return MockCursor(data)
+
+    def execute(self, query): # TODO: additional arguments
+       for result in self.results:
+          if result[0].match(query):
+            return result[1]
+       return []

release/python/0.3.1/crankshaft/test/test_clustering_moran.py

@@ -0,0 +1,88 @@
+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.params_markov = {"id_col": "cartodb_id",
+                              "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
+                              "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/0.3.1/crankshaft/test/test_pysal_utils.py

@@ -0,0 +1,142 @@
+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}
+
+        self.params_array = {"id_col": "cartodb_id",
+                             "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
+                             "subquery": "SELECT * FROM a_list",
+                             "geom_col": "the_geom",
+                             "num_ngbrs": 321}
+
+    def test_query_attr_select(self):
+        """Test query_attr_select"""
+
+        ans = "i.\"andy\"::numeric As attr1, " \
+              "i.\"jay_z\"::numeric As attr2, "
+
+        ans_array = "i.\"_2013_dec\"::numeric As attr1, " \
+                    "i.\"_2014_jan\"::numeric As attr2, " \
+                    "i.\"_2014_feb\"::numeric As attr3, "
+
+        self.assertEqual(pu.query_attr_select(self.params), ans)
+        self.assertEqual(pu.query_attr_select(self.params_array), ans_array)
+
+    def test_query_attr_where(self):
+        """Test pu.query_attr_where"""
+
+        ans = "idx_replace.\"andy\" IS NOT NULL AND " \
+              "idx_replace.\"jay_z\" IS NOT NULL AND " \
+              "idx_replace.\"jay_z\" <> 0"
+
+        ans_array = "idx_replace.\"_2013_dec\" IS NOT NULL AND " \
+                    "idx_replace.\"_2014_jan\" IS NOT NULL AND " \
+                    "idx_replace.\"_2014_feb\" IS NOT NULL"
+
+        self.assertEqual(pu.query_attr_where(self.params), ans)
+        self.assertEqual(pu.query_attr_where(self.params_array), ans_array)
+
+    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;"
+        
+        ans_array = "SELECT i.\"cartodb_id\" As id, " \
+              "i.\"_2013_dec\"::numeric As attr1, " \
+              "i.\"_2014_jan\"::numeric As attr2, " \
+              "i.\"_2014_feb\"::numeric As attr3, " \
+              "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+                            "FROM (SELECT * FROM a_list) As j " \
+                            "WHERE i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
+                                  "j.\"_2013_dec\" IS NOT NULL AND " \
+                                  "j.\"_2014_jan\" IS NOT NULL AND " \
+                                  "j.\"_2014_feb\" IS NOT NULL " \
+                            "ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC " \
+                            "LIMIT 321)) As neighbors " \
+              "FROM (SELECT * FROM a_list) As i " \
+              "WHERE i.\"_2013_dec\" IS NOT NULL AND " \
+                    "i.\"_2014_jan\" IS NOT NULL AND " \
+                    "i.\"_2014_feb\" IS NOT NULL "\
+              "ORDER BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(pu.knn(self.params), ans)
+        self.assertEqual(pu.knn(self.params_array), ans_array)
+
+    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)

release/python/0.3.1/crankshaft/test/test_segmentation.py

@@ -0,0 +1,64 @@
+import unittest
+import numpy as np
+from helper import plpy, fixture_file
+import crankshaft.segmentation as segmentation
+import json
+
+class SegmentationTest(unittest.TestCase):
+    """Testing class for Moran's I functions"""
+
+    def setUp(self):
+        plpy._reset()
+
+    def generate_random_data(self,n_samples,random_state,  row_type=False):
+        x1 = random_state.uniform(size=n_samples)
+        x2 = random_state.uniform(size=n_samples)
+        x3 = random_state.randint(0, 4, size=n_samples)
+
+        y = x1+x2*x2+x3
+        cartodb_id  = range(len(x1))
+
+        if row_type:
+            return [ {'features': vals} for vals in zip(x1,x2,x3)], y
+        else:
+            return  [dict( zip(['x1','x2','x3','target', 'cartodb_id'],[x1,x2,x3,y,cartodb_id]))]
+
+    def test_replace_nan_with_mean(self):
+        test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
+
+    def test_create_and_predict_segment(self):
+        n_samples = 1000
+
+        random_state_train = np.random.RandomState(13)
+        random_state_test = np.random.RandomState(134)
+        training_data = self.generate_random_data(n_samples, random_state_train)
+        test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True)
+
+
+        ids =  [{'cartodb_ids': range(len(test_data))}]
+        rows =  [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}]
+
+        plpy._define_result('select \* from  \(select \* from training\) a  limit 1',rows)
+        plpy._define_result('.*from \(select \* from training\) as a' ,training_data)
+        plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids)
+        plpy._define_result('.*select \* from test.*' ,test_data)
+
+        model_parameters =  {'n_estimators': 1200,
+                             'max_depth': 3,
+                             'subsample' : 0.5,
+                             'learning_rate': 0.01,
+                             'min_samples_leaf': 1}
+
+        result = segmentation.create_and_predict_segment(
+                'select * from training',
+                'target',
+                'select * from test',
+                model_parameters)
+
+        prediction = [r[1] for r in result]
+
+        accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y))))
+
+        self.assertEqual(len(result),len(test_data))
+        self.assertTrue( result[0][2] < 0.01)
+        self.assertTrue( accuracy < 0.5*np.mean(test_y)  )

release/python/0.3.1/crankshaft/test/test_space_time_dynamics.py

@@ -0,0 +1,324 @@
+import unittest
+import numpy as np
+
+import unittest
+
+
+# from mock_plpy import MockPlPy
+# plpy = MockPlPy()
+#
+# import sys
+# sys.modules['plpy'] = plpy
+from helper import plpy, fixture_file
+
+import crankshaft.space_time_dynamics as std
+from crankshaft import random_seeds
+import json
+
+class SpaceTimeTests(unittest.TestCase):
+    """Testing class for Markov Functions."""
+
+    def setUp(self):
+        plpy._reset()
+        self.params = {"id_col": "cartodb_id",
+                       "time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
+                       "subquery": "SELECT * FROM a_list",
+                       "geom_col": "the_geom",
+                       "num_ngbrs": 321}
+        self.neighbors_data = json.loads(open(fixture_file('neighbors_markov.json')).read())
+        self.markov_data = json.loads(open(fixture_file('markov.json')).read())
+
+        self.time_data = np.array([i * np.ones(10, dtype=float) for i in range(10)]).T
+
+        self.transition_matrix = np.array([
+                [[ 0.96341463, 0.0304878 , 0.00609756, 0.        , 0.        ],
+                 [ 0.06040268, 0.83221477, 0.10738255, 0.        , 0.        ],
+                 [ 0.        , 0.14      , 0.74      , 0.12      , 0.        ],
+                 [ 0.        , 0.03571429, 0.32142857, 0.57142857, 0.07142857],
+                 [ 0.        , 0.        , 0.        , 0.16666667, 0.83333333]],
+                [[ 0.79831933, 0.16806723, 0.03361345, 0.        , 0.        ],
+                 [ 0.0754717 , 0.88207547, 0.04245283, 0.        , 0.        ],
+                 [ 0.00537634, 0.06989247, 0.8655914 , 0.05913978, 0.        ],
+                 [ 0.        , 0.        , 0.06372549, 0.90196078, 0.03431373],
+                 [ 0.        , 0.        , 0.        , 0.19444444, 0.80555556]],
+                [[ 0.84693878, 0.15306122, 0.        , 0.        , 0.        ],
+                 [ 0.08133971, 0.78947368, 0.1291866 , 0.        , 0.        ],
+                 [ 0.00518135, 0.0984456 , 0.79274611, 0.0984456 , 0.00518135],
+                 [ 0.        , 0.        , 0.09411765, 0.87058824, 0.03529412],
+                 [ 0.        , 0.        , 0.        , 0.10204082, 0.89795918]],
+                [[ 0.8852459 , 0.09836066, 0.        , 0.01639344, 0.        ],
+                 [ 0.03875969, 0.81395349, 0.13953488, 0.        , 0.00775194],
+                 [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
+                 [ 0.        , 0.02339181, 0.12865497, 0.75438596, 0.09356725],
+                 [ 0.        , 0.        , 0.        , 0.09661836, 0.90338164]],
+                [[ 0.33333333, 0.66666667, 0.        , 0.        , 0.        ],
+                 [ 0.0483871 , 0.77419355, 0.16129032, 0.01612903, 0.        ],
+                 [ 0.01149425, 0.16091954, 0.74712644, 0.08045977, 0.        ],
+                 [ 0.        , 0.01036269, 0.06217617, 0.89637306, 0.03108808],
+                 [ 0.        , 0.        , 0.        , 0.02352941, 0.97647059]]]
+                 )
+
+    def test_spatial_markov(self):
+        """Test Spatial Markov."""
+        data = [ { 'id': d['id'],
+                   'attr1': d['y1995'],
+                   'attr2': d['y1996'],
+                   'attr3': d['y1997'],
+                   'attr4': d['y1998'],
+                   'attr5': d['y1999'],
+                   'attr6': d['y2000'],
+                   'attr7': d['y2001'],
+                   'attr8': d['y2002'],
+                   'attr9': d['y2003'],
+                   'attr10': d['y2004'],
+                   'attr11': d['y2005'],
+                   'attr12': d['y2006'],
+                   'attr13': d['y2007'],
+                   'attr14': d['y2008'],
+                   'attr15': d['y2009'],
+                   'neighbors': d['neighbors'] } for d in self.neighbors_data]
+        print(str(data[0]))
+        plpy._define_result('select', data)
+        random_seeds.set_random_seeds(1234)
+
+        result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id')
+
+        self.assertTrue(result != None)
+        result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
+        print result[0]
+        expected = self.markov_data
+        for ([res_trend, res_up, res_down, res_vol, res_id],
+             [exp_trend, exp_up, exp_down, exp_vol, exp_id]
+             ) in zip(result, expected):
+            self.assertAlmostEqual(res_trend, exp_trend)
+
+    def test_get_time_data(self):
+        """Test get_time_data"""
+        data = [ { 'attr1': d['y1995'],
+                   'attr2': d['y1996'],
+                   'attr3': d['y1997'],
+                   'attr4': d['y1998'],
+                   'attr5': d['y1999'],
+                   'attr6': d['y2000'],
+                   'attr7': d['y2001'],
+                   'attr8': d['y2002'],
+                   'attr9': d['y2003'],
+                   'attr10': d['y2004'],
+                   'attr11': d['y2005'],
+                   'attr12': d['y2006'],
+                   'attr13': d['y2007'],
+                   'attr14': d['y2008'],
+                   'attr15': d['y2009'] } for d in self.neighbors_data]
+
+        result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'])
+
+        ## expected was prepared from PySAL example:
+        ### f = ps.open(ps.examples.get_path("usjoin.csv"))
+        ### pci = np.array([f.by_col[str(y)] for y in range(1995, 2010)]).transpose()
+        ### rpci = pci / (pci.mean(axis = 0))
+
+        expected = np.array([[ 0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154,  0.83271652
+        ,  0.83786314, 0.85012593, 0.85509656, 0.86416612, 0.87119375, 0.86302631
+        ,  0.86148267, 0.86252252, 0.86746356],
+         [ 0.9188951,  0.91757931, 0.92333258, 0.92517289, 0.92552388, 0.90746978
+        ,  0.89830489, 0.89431991, 0.88924794, 0.89815176, 0.91832091, 0.91706054
+        ,  0.90139505, 0.87897455, 0.86216858],
+         [ 0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522, 0.78964559
+        ,  0.80584442, 0.8084998,  0.82258551, 0.82668196, 0.82373724, 0.81814804
+        ,  0.83675961, 0.83574199, 0.84647177],
+         [ 1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841, 1.14506948
+        ,  1.12151133, 1.11160697, 1.10888621, 1.11399806, 1.12168029, 1.13164797
+        ,  1.12958508, 1.11371818, 1.09936775],
+         [ 1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025, 1.16898201
+        ,  1.17212488, 1.14752303, 1.11843284, 1.11024964, 1.11943471, 1.11736468
+        ,  1.10863242, 1.09642516, 1.07762337],
+         [ 1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684, 1.44184737
+        ,  1.44782832, 1.41978227, 1.39092208, 1.4059372,  1.40788646, 1.44052766
+        ,  1.45241216, 1.43306098, 1.4174431 ],
+         [ 1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149, 1.10888138
+        ,  1.11856629, 1.13062931, 1.11944984, 1.12446239, 1.11671008, 1.10880034
+        ,  1.08401709, 1.06959206, 1.07875225],
+         [ 1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545, 0.99854316
+        ,  0.9880258,  0.99669587, 0.99327676, 1.01400905, 1.03176742, 1.040511
+        ,  1.01749645, 0.9936394,  0.98279746],
+         [ 0.98996986, 1.00143564, 0.99491,  1.00188408, 1.00455845, 0.99127006
+        ,  0.97925917, 0.9683482,  0.95335147, 0.93694787, 0.94308213, 0.92232874
+        ,  0.91284091, 0.89689833, 0.88928858],
+         [ 0.87418391, 0.86416601, 0.84425695, 0.8404494,  0.83903044, 0.8578708
+        ,  0.86036185, 0.86107306, 0.8500772,  0.86981998, 0.86837929, 0.87204141
+        ,  0.86633032, 0.84946077, 0.83287146],
+         [ 1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624, 1.14450183
+        ,  1.12349752, 1.12596664, 1.12213996, 1.1119989,  1.10257792, 1.10491258
+        ,  1.11059842, 1.10509795, 1.10020097],
+         [ 0.97282463, 0.96700147, 0.96252588, 0.9653878,  0.96057687, 0.95831051
+        ,  0.94480909, 0.94804195, 0.95430286, 0.94103989, 0.92122519, 0.91010201
+        ,  0.89280392, 0.89298243, 0.89165385],
+         [ 0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647, 0.9480927
+        ,  0.93539182, 0.95388718, 0.94597005, 0.96918424, 0.94781281, 0.93466815
+        ,  0.94281559, 0.96520315, 0.96715441],
+         [ 0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897, 0.98687073
+        ,  0.99237486, 0.98209969, 0.9877653,  0.97399471, 0.96910087, 0.98416665
+        ,  0.98423613, 0.99823861, 0.99545704],
+         [ 0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012,  0.86191535
+        ,  0.84981451, 0.85472102, 0.84564835, 0.83998883, 0.83478547, 0.82803648
+        ,  0.8198736,  0.82265395, 0.8399404 ],
+         [ 0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136, 0.82785597
+        ,  0.86008789, 0.86776298, 0.86720209, 0.8676334,  0.89179317, 0.94202108
+        ,  0.9422231,  0.93902708, 0.94479184],
+         [ 0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238, 0.90906632
+        ,  0.92693339, 0.93695966, 0.94242697, 0.94338265, 0.91981796, 0.91108804
+        ,  0.90543476, 0.91737138, 0.94793657],
+         [ 1.1977611,  1.18222564, 1.18439158, 1.18267865, 1.19286723, 1.20172869
+        ,  1.21328691, 1.22624778, 1.22397075, 1.23857042, 1.24419893, 1.23929384
+        ,  1.23418676, 1.23626739, 1.26754398],
+         [ 1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667, 1.34790023
+        ,  1.34399863, 1.32575181, 1.30795492, 1.30544841, 1.30303302, 1.32107766
+        ,  1.32936244, 1.33001241, 1.33288462],
+         [ 1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093, 1.05059016
+        ,  1.03405057, 1.02747623, 1.03162734, 0.9961416,  0.97356208, 0.94241549
+        ,  0.92754547, 0.92549227, 0.92138102],
+         [ 1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264, 1.13889622
+        ,  1.12442212, 1.13367018, 1.13982256, 1.14029944, 1.11979401, 1.10905389
+        ,  1.10577769, 1.11166825, 1.09985155],
+         [ 0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284, 0.74480073
+        ,  0.76098396, 0.76156903, 0.76651952, 0.76533288, 0.78205934, 0.76842416
+        ,  0.77487118, 0.77768683, 0.78801192],
+         [ 0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803, 0.97370819
+        ,  0.96419154, 0.97209861, 0.97441313, 0.96356162, 0.94745352, 0.93965462
+        ,  0.93069645, 0.94020973, 0.94358232],
+         [ 0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801, 0.80071489
+        ,  0.83358256, 0.83451613, 0.85175032, 0.85954307, 0.86790024, 0.87170334
+        ,  0.87863799, 0.87497981, 0.87888675],
+         [ 0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619, 0.98733195
+        ,  0.99644997, 0.99669587, 1.02559097, 1.01116651, 0.99988024, 0.97906749
+        ,  0.99323123, 1.00204939, 0.99602148],
+         [ 1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683, 1.08312397
+        ,  1.05192626, 1.04230892, 1.05577278, 1.08569751, 1.12443486, 1.08891079
+        ,  1.08603695, 1.05997314, 1.02160943],
+         [ 1.11368269, 1.1057147,  1.11893431, 1.13778669, 1.1432272,  1.18257029
+        ,  1.16226243, 1.16009196, 1.14467789, 1.14820235, 1.12386598, 1.12680236
+        ,  1.12357937, 1.1159258,  1.12570828],
+         [ 1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667, 1.31210239
+        ,  1.29989156, 1.29203193, 1.27183516, 1.26830786, 1.2617743,  1.28656675
+        ,  1.29734097, 1.29390205, 1.29345446],
+         [ 0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864, 0.78772975
+        ,  0.82848011, 0.8259679,  0.82435705, 0.83108634, 0.84373784, 0.83891093
+        ,  0.84349247, 0.85637272, 0.86539395],
+         [ 1.23450087, 1.2426022,  1.23537935, 1.23581293, 1.24522626, 1.2256767
+        ,  1.21126648, 1.19377804, 1.18355337, 1.19674434, 1.21536573, 1.23653297
+        ,  1.27962009, 1.27968392, 1.25907738],
+         [ 0.9769662,  0.97400719, 0.98035944, 0.97581531, 0.95543282, 0.96480308
+        ,  0.94686376, 0.93679073, 0.92540049, 0.92988835, 0.93442917, 0.92100464
+        ,  0.91475304, 0.90249622, 0.9021363 ],
+         [ 0.84986886, 0.8986851,  0.84295997, 0.87280534, 0.85659368, 0.88937573
+        ,  0.894401, 0.90448993, 0.95495898, 0.92698333, 0.94745352, 0.92562488
+        ,  0.96635366, 1.02520312, 1.0394296 ],
+         [ 1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073, 1.00759019
+        ,  0.99192968, 0.99747298, 0.99550759, 0.97583768, 0.9610168,  0.94779638
+        ,  0.93759089, 0.93353431, 0.94121705],
+         [ 0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613, 0.83434854
+        ,  0.85813595, 0.84667961, 0.84374558, 0.85951183, 0.87194227, 0.89455097
+        ,  0.88283929, 0.90349491, 0.90600675],
+         [ 1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086, 1.00581626
+        ,  0.98850522, 0.99291168, 0.98983209, 0.97511924, 0.96134615, 0.96382634
+        ,  0.95011401, 0.9434686,  0.94637765],
+         [ 1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857, 1.04800023
+        ,  1.03024941, 1.04200483, 1.0402554,  1.03296979, 1.02191682, 1.02476275
+        ,  1.02347523, 1.02517684, 1.04359571],
+         [ 1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043,  1.0531801
+        ,  1.07452771, 1.09383478, 1.1052447,  1.10322136, 1.09167939, 1.08772756
+        ,  1.08859544, 1.09177338, 1.1096083 ],
+         [ 0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809, 0.86287327
+        ,  0.85169796, 0.85411285, 0.84886336, 0.84517414, 0.84843858, 0.84488343
+        ,  0.83374329, 0.82812044, 0.82878599],
+         [ 0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286, 0.92652175
+        ,  0.94278865, 0.93682452, 0.98655146, 0.992237, 0.9798497,  0.93869677
+        ,  0.96947771, 1.00362626, 0.98102351],
+         [ 0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967,  0.93092109
+        ,  0.92662519, 0.93412152, 0.93501274, 0.92879506, 0.92110542, 0.91035556
+        ,  0.90430364, 0.89994694, 0.90073864],
+         [ 0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824, 0.98882205
+        ,  0.97662234, 0.95601578, 0.94905385, 0.94934888, 0.97152609, 0.97163004
+        ,  0.9700702,  0.97158948, 0.95884908],
+         [ 0.83980439, 0.84726737, 0.85747,  0.85467221, 0.8556751,  0.84818516
+        ,  0.85265681, 0.84502402, 0.82645665, 0.81743586, 0.83550406, 0.83338919
+        ,  0.83511679, 0.82136617, 0.80921874],
+         [ 0.95118156, 0.9466212,  0.94688098, 0.9508583,  0.9512441,  0.95440787
+        ,  0.96364363, 0.96804412, 0.97136214, 0.97583768, 0.95571724, 0.96895368
+        ,  0.97001634, 0.97082733, 0.98782366],
+         [ 1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249, 1.10558188
+        ,  1.1214086,  1.12292577, 1.13021031, 1.13342735, 1.14686068, 1.14502975
+        ,  1.14474747, 1.14084037, 1.16142926],
+         [ 1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863, 1.11856702
+        ,  1.09764283, 1.08815849, 1.08044313, 1.09278827, 1.07003204, 1.08398066
+        ,  1.09831768, 1.09298232, 1.09176125],
+         [ 0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744, 0.77751194
+        ,  0.79902974, 0.81437881, 0.80788828, 0.79603865, 0.78966436, 0.79949807
+        ,  0.80172182, 0.82168155, 0.85587911],
+         [ 1.0052447,  1.00007696, 1.00475899, 1.00613942, 1.00639561, 1.00162979
+        ,  0.99860739, 1.00814981, 1.00574316, 0.99030032, 0.97682565, 0.97292596
+        ,  0.96519561, 0.96173403, 0.95890284],
+         [ 0.95808419, 0.9382568,  0.9654441,  0.95561201, 0.96987289, 0.96608031
+        ,  0.99727185, 1.00781194, 1.03484236, 1.05333619, 1.0983263,  1.1704974
+        ,  1.17025154, 1.18730553, 1.14242645]])
+
+        self.assertTrue(np.allclose(result, expected))
+        self.assertTrue(type(result) == type(expected))
+        self.assertTrue(result.shape == expected.shape)
+
+    def test_rebin_data(self):
+        """Test rebin_data"""
+        ## sample in double the time (even case since 10 % 2 = 0):
+        ##   (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
+        ## = 0.5,     2.5,     4.5,     6.5,     8.5
+        ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float)
+                             for i in range(0, 10, 2)]).T
+
+        self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
+
+        ## sample in triple the time (uneven since 10 % 3 = 1):
+        ##   (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
+        ## = 1,         4,         7,         9
+        ans_odd  = np.array([i * np.ones(10, dtype=float)
+                             for i in (1, 4, 7, 9)]).T
+        self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
+
+    def test_get_prob_dist(self):
+        """Test get_prob_dist"""
+        lag_indices = np.array([1, 2, 3, 4])
+        unit_indices = np.array([1, 3, 2, 4])
+        answer = np.array([
+            [ 0.0754717 , 0.88207547, 0.04245283, 0.        , 0.        ],
+            [ 0.        , 0.        , 0.09411765, 0.87058824, 0.03529412],
+            [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
+            [ 0.        , 0.        , 0.        , 0.02352941, 0.97647059]
+        ])
+        result = std.get_prob_dist(self.transition_matrix, lag_indices, unit_indices)
+
+        self.assertTrue(np.array_equal(result, answer))
+
+    def test_get_prob_stats(self):
+        """Test get_prob_stats"""
+
+        probs = np.array([
+            [ 0.0754717 , 0.88207547, 0.04245283, 0.        , 0.        ],
+            [ 0.        , 0.        , 0.09411765, 0.87058824, 0.03529412],
+            [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
+            [ 0.        , 0.        , 0.        , 0.02352941, 0.97647059]
+        ])
+        unit_indices = np.array([1, 3, 2, 4])
+        answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
+        answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
+        answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824,  0.02475248 / 0.77722772, -0.02352941 / 0.97647059])
+        answer_volatility = np.array([ 0.34221495,  0.33705421,  0.29226542,  0.38834223])
+
+        result = std.get_prob_stats(probs, unit_indices)
+        result_up = result[0]
+        result_down = result[1]
+        result_trend = result[2]
+        result_volatility = result[3]
+
+        self.assertTrue(np.allclose(result_up, answer_up))
+        self.assertTrue(np.allclose(result_down, answer_down))
+        self.assertTrue(np.allclose(result_trend, answer_trend))
+        self.assertTrue(np.allclose(result_volatility, answer_volatility))

release/python/0.4.0/crankshaft/crankshaft/__init__.py

@@ -0,0 +1,5 @@
+"""Import all modules"""
+import crankshaft.random_seeds
+import crankshaft.clustering
+import crankshaft.space_time_dynamics
+import crankshaft.segmentation

release/python/0.4.0/crankshaft/crankshaft/clustering/__init__.py

@@ -0,0 +1,3 @@
+"""Import all functions from for clustering"""
+from moran import *
+from kmeans import *

release/python/0.4.0/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/0.4.0/crankshaft/crankshaft/clustering/moran.py

@@ -0,0 +1,262 @@
+"""
+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
+from collections import OrderedDict
+
+# 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 = OrderedDict([("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 = OrderedDict([("id_col", id_col),
+                         ("attr1", attr),
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(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 = OrderedDict([("id_col", id_col),
+                         ("attr1", numerator),
+                         ("attr2", denominator)
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    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
+
+    qvals = OrderedDict([("id_col", id_col),
+                         ("numerator", numerator),
+                         ("denominator", denominator),
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(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 quadrants for each geometry
+    quads = quad_position(lisa.q)
+
+    return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
+
+def moran_local_bv(subquery, attr1, attr2,
+                   permutations, geom_col, id_col, w_type, num_ngbrs):
+    """
+        Moran's I (local) Bivariate (untested)
+    """
+    plpy.notice('** Constructing query')
+
+    qvals = OrderedDict([("id_col", id_col),
+                         ("attr1", attr1),
+                         ("attr2", attr2),
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(4)
+    except plpy.SPIError:
+        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/0.4.0/crankshaft/crankshaft/pysal_utils/__init__.py

@@ -0,0 +1,2 @@
+"""Import all functions for pysal_utils"""
+from crankshaft.pysal_utils.pysal_utils import *

release/python/0.4.0/crankshaft/crankshaft/pysal_utils/pysal_utils.py

@@ -0,0 +1,188 @@
+"""
+    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 dict-like: 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}
+    print 'len of neighbors: %d' % len(neighbors)
+
+    built_weight = ps.W(neighbors)
+    built_weight.transform = 'r'
+
+    return built_weight
+
+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.)
+    """
+
+    attr_string = ""
+    template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, "
+
+    if 'time_cols' in params:
+        ## if markov analysis
+        attrs = params['time_cols']
+
+        for idx, val in enumerate(attrs):
+            attr_string += template % {"col": val, "alias_num": idx + 1}
+    else:
+        ## if moran's analysis
+        attrs = [k for k in params
+                 if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')]
+
+        for idx, val in enumerate(sorted(attrs)):
+            attr_string += template % {"col": params[val], "alias_num": idx + 1}
+
+    return attr_string
+
+def query_attr_where(params):
+    """
+      Construct where conditions when building neighbors query
+        Create portion of WHERE clauses for weeding out NULL-valued geometries
+        Input: dict of params:
+            {'subquery': ...,
+             'numerator': 'data1',
+             'denominator': 'data2',
+             '': ...}
+        Output: 'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL'
+        Input:
+        {'subquery': ...,
+         'time_cols': ['time1', 'time2', 'time3'],
+         'etc': ...}
+        Output: 'idx_replace."time1" IS NOT NULL AND idx_replace."time2" IS NOT
+          NULL AND idx_replace."time3" IS NOT NULL'
+    """
+    attr_string = []
+    template = "idx_replace.\"%s\" IS NOT NULL"
+
+    if 'time_cols' in params:
+        ## markov where clauses
+        attrs = params['time_cols']
+        # add values to template
+        for attr in attrs:
+            attr_string.append(template % attr)
+    else:
+        ## moran where clauses
+
+        # get keys
+        attrs = sorted([k for k in params
+                        if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')])
+        # add values to template
+        for attr in attrs:
+            attr_string.append(template % params[attr])
+
+        if len(attrs) == 2:
+            attr_string.append("idx_replace.\"%s\" <> 0" % params[attrs[1]])
+
+    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/0.4.0/crankshaft/crankshaft/random_seeds.py

@@ -0,0 +1,11 @@
+"""Random seed generator used for non-deterministic functions in crankshaft"""
+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/0.4.0/crankshaft/crankshaft/segmentation/__init__.py

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

release/python/0.4.0/crankshaft/crankshaft/segmentation/segmentation.py

@@ -0,0 +1,176 @@
+"""
+Segmentation creation and prediction
+"""
+
+import sklearn
+import numpy as np
+import plpy
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn import metrics
+from sklearn.cross_validation import train_test_split
+
+# Lower level functions
+#----------------------
+
+def replace_nan_with_mean(array):
+    """
+        Input:
+            @param array: an array of floats which may have null-valued entries
+        Output:
+            array with nans filled in with the mean of the dataset
+    """
+    # returns an array of rows and column indices
+    indices = np.where(np.isnan(array))
+
+    # iterate through entries which have nan values
+    for row, col in zip(*indices):
+            array[row, col] = np.mean(array[~np.isnan(array[:, col]), col])
+
+    return array
+
+def get_data(variable, feature_columns, query):
+    """
+        Fetch data from the database, clean, and package into
+          numpy arrays
+        Input:
+            @param variable: name of the target variable
+            @param feature_columns: list of column names
+            @param query: subquery that data is pulled from for the packaging
+        Output:
+            prepared data, packaged into NumPy arrays
+    """
+
+    columns = ','.join(['array_agg("{col}") As "{col}"'.format(col=col) for col in feature_columns])
+
+    try:
+        data = plpy.execute('''SELECT array_agg("{variable}") As target, {columns} FROM ({query}) As a'''.format(
+            variable=variable,
+            columns=columns,
+            query=query))
+    except Exception, e:
+        plpy.error('Failed to access data to build segmentation model: %s' % e)
+
+    # extract target data from plpy object
+    target = np.array(data[0]['target'])
+
+    # put n feature data arrays into an n x m array of arrays
+    features = np.column_stack([np.array(data[0][col], dtype=float) for col in feature_columns])
+
+    return replace_nan_with_mean(target), replace_nan_with_mean(features)
+
+# High level interface
+# --------------------
+
+def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters):
+    """
+    Version of create_and_predict_segment that works on arrays that come stright form the SQL calling
+    the function.
+
+        Input:
+            @param target: The 1D array of lenth NSamples containing the target variable we want the model to predict
+            @param features: Thw 2D array of size NSamples * NFeatures that form the imput to the model
+            @param target_ids: A 1D array of target_ids that will be used to associate the results of the prediction with the rows which they come from
+            @param model_parameters: A dictionary containing parameters for the model.
+    """
+
+    clean_target = replace_nan_with_mean(target)
+    clean_features = replace_nan_with_mean(features)
+    target_features = replace_nan_with_mean(target_features)
+
+    model, accuracy = train_model(clean_target, clean_features, model_parameters, 0.2)
+    prediction = model.predict(target_features)
+    accuracy_array = [accuracy]*prediction.shape[0]
+    return zip(target_ids, prediction, np.full(prediction.shape, accuracy_array))
+
+
+
+def create_and_predict_segment(query, variable, target_query, model_params):
+    """
+    generate a segment with machine learning
+    Stuart Lynn
+    """
+
+    ## fetch column names
+    try:
+        columns = plpy.execute('SELECT * FROM ({query}) As a LIMIT 1  '.format(query=query))[0].keys()
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    ## extract column names to be used in building the segmentation model
+    feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator'])
+    ## get data from database
+    target, features = get_data(variable, feature_columns, query)
+
+    model, accuracy = train_model(target, features, model_params, 0.2)
+    cartodb_ids, result = predict_segment(model, feature_columns, target_query)
+    accuracy_array = [accuracy]*result.shape[0]
+    return zip(cartodb_ids, result, accuracy_array)
+
+
+def train_model(target, features, model_params, test_split):
+    """
+        Train the Gradient Boosting model on the provided data and calculate the accuracy of the model
+        Input:
+            @param target: 1D Array of the variable that the model is to be trianed to predict
+            @param features: 2D Array NSamples * NFeatures to use in trining the model
+            @param model_params: A dictionary of model parameters, the full specification can be found on the
+                scikit learn page for [GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)
+            @parma test_split: The fraction of the data to be withheld for testing the model / calculating the accuray
+    """
+    features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
+    model = GradientBoostingRegressor(**model_params)
+    model.fit(features_train, target_train)
+    accuracy = calculate_model_accuracy(model, features, target)
+    return model, accuracy
+
+def calculate_model_accuracy(model, features, target):
+    """
+        Calculate the mean squared error of the model prediction
+        Input:
+            @param model: model trained from input features
+            @param features: features to make a prediction from
+            @param target: target to compare prediction to
+        Output:
+            mean squared error of the model prection compared to the target
+    """
+    prediction = model.predict(features)
+    return metrics.mean_squared_error(prediction, target)
+
+def predict_segment(model, features, target_query):
+    """
+    Use the provided model to predict the values for the new feature set
+        Input:
+            @param model: The pretrained model
+            @features: A list of features to use in the model prediction (list of column names)
+            @target_query: The query to run to obtain the data to predict on and the cartdb_ids associated with it.
+    """
+
+    batch_size = 1000
+    joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features])
+
+    try:
+        cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format(
+            joined_features=joined_features,
+            target_query=target_query))
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    results = []
+
+    while True:
+        rows = cursor.fetch(batch_size)
+        if not rows:
+            break
+        batch = np.row_stack([np.array(row['features'], dtype=float) for row in rows])
+
+        #Need to fix this. Should be global mean. This will cause weird effects
+        batch = replace_nan_with_mean(batch)
+        prediction = model.predict(batch)
+        results.append(prediction)
+
+    try:
+        cartodb_ids = plpy.execute('''SELECT array_agg(cartodb_id ORDER BY cartodb_id) As cartodb_ids FROM ({0}) As a'''.format(target_query))[0]['cartodb_ids']
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    return cartodb_ids, np.concatenate(results)

release/python/0.4.0/crankshaft/crankshaft/space_time_dynamics/__init__.py

@@ -0,0 +1,2 @@
+"""Import all functions from clustering libraries."""
+from markov import *

release/python/0.4.0/crankshaft/crankshaft/space_time_dynamics/markov.py

@@ -0,0 +1,189 @@
+"""
+Spatial dynamics measurements using Spatial Markov
+"""
+
+
+import numpy as np
+import pysal as ps
+import plpy
+import crankshaft.pysal_utils as pu
+
+def spatial_markov_trend(subquery, time_cols, num_classes=7,
+                         w_type='knn', num_ngbrs=5, permutations=0,
+                         geom_col='the_geom', id_col='cartodb_id'):
+    """
+        Predict the trends of a unit based on:
+        1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile)
+        2. average class of its neighbors
+
+        Inputs:
+        @param subquery string: e.g., SELECT the_geom, cartodb_id,
+          interesting_time_column FROM table_name
+        @param time_cols list of strings: list of strings of column names
+        @param num_classes (optional): number of classes to break distribution
+          of values into. Currently uses quantile bins.
+        @param w_type string (optional): weight type ('knn' or 'queen')
+        @param num_ngbrs int (optional): number of neighbors (if knn type)
+        @param permutations int (optional): number of permutations for test
+          stats
+        @param geom_col string (optional): name of column which contains the
+          geometries
+        @param id_col string (optional): name of column which has the ids of
+          the table
+
+        Outputs:
+        @param trend_up float: probablity that a geom will move to a higher
+          class
+        @param trend_down float: probablity that a geom will move to a lower
+          class
+        @param trend float: (trend_up - trend_down) / trend_static
+        @param volatility float: a measure of the volatility based on
+          probability stddev(prob array)
+    """
+
+    if len(time_cols) < 2:
+        plpy.error('More than one time column needs to be passed')
+
+    qvals = {"id_col": id_col,
+             "time_cols": time_cols,
+             "geom_col": geom_col,
+             "subquery": subquery,
+             "num_ngbrs": num_ngbrs}
+
+    try:
+        query_result = plpy.execute(
+            pu.construct_neighbor_query(w_type, qvals)
+        )
+        if len(query_result) == 0:
+            return zip([None], [None], [None], [None], [None])
+    except plpy.SPIError, err:
+        plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals)))
+        plpy.error('Query failed, check the input parameters')
+        return zip([None], [None], [None], [None], [None])
+
+    ## build weight
+    weights = pu.get_weight(query_result, w_type)
+    weights.transform = 'r'
+
+    ## prep time data
+    t_data = get_time_data(query_result, time_cols)
+
+    plpy.debug('shape of t_data %d, %d' % t_data.shape)
+    plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape)
+    plpy.debug('first num elements: %f' % t_data[0, 0])
+
+    sp_markov_result = ps.Spatial_Markov(t_data,
+                                         weights,
+                                         k=num_classes,
+                                         fixed=False,
+                                         permutations=permutations)
+
+    ## get lag classes
+    lag_classes = ps.Quantiles(
+        ps.lag_spatial(weights, t_data[:, -1]),
+        k=num_classes).yb
+
+    ## look up probablity distribution for each unit according to class and lag class
+    prob_dist = get_prob_dist(sp_markov_result.P,
+                              lag_classes,
+                              sp_markov_result.classes[:, -1])
+
+    ## find the ups and down and overall distribution of each cell
+    trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist,
+                                                             sp_markov_result.classes[:, -1])
+
+    ## output the results
+    return zip(trend, trend_up, trend_down, volatility, weights.id_order)
+
+def get_time_data(markov_data, time_cols):
+    """
+        Extract the time columns and bin appropriately
+    """
+    num_attrs = len(time_cols)
+    return np.array([[x['attr' + str(i)] for x in markov_data]
+                     for i in range(1, num_attrs+1)], dtype=float).transpose()
+
+## not currently used
+def rebin_data(time_data, num_time_per_bin):
+    """
+        Convert an n x l matrix into an (n/m) x l matrix where the values are
+         reduced (averaged) for the intervening states:
+          1 2 3 4    1.5 3.5
+          5 6 7 8 -> 5.5 7.5
+          9 8 7 6    8.5 6.5
+          5 4 3 2    4.5 2.5
+
+          if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix.
+
+        This process effectively resamples the data at a longer time span n
+         units longer than the input data.
+        For cases when there is a remainder (remainder(5/3) = 2), the remaining
+         two columns are binned together as the last time period, while the
+         first three are binned together for the first period.
+
+        Input:
+          @param time_data n x l  ndarray: measurements of an attribute at
+           different time intervals
+          @param num_time_per_bin int: number of columns to average into a new
+           column
+        Output:
+          ceil(n / m) x l ndarray of resampled time series
+    """
+
+    if time_data.shape[1] % num_time_per_bin == 0:
+        ## if fit is perfect, then use it
+        n_max = time_data.shape[1] / num_time_per_bin
+    else:
+        ## fit remainders into an additional column
+        n_max = time_data.shape[1] / num_time_per_bin + 1
+
+    return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
+                     for i in range(n_max)]).T
+
+def get_prob_dist(transition_matrix, lag_indices, unit_indices):
+    """
+        Given an array of transition matrices, look up the probability
+        associated with the arrangements passed
+
+        Input:
+        @param transition_matrix ndarray[k,k,k]:
+        @param lag_indices ndarray:
+        @param unit_indices ndarray:
+
+        Output:
+        Array of probability distributions
+    """
+
+    return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
+                     for i in range(len(lag_indices))])
+
+def get_prob_stats(prob_dist, unit_indices):
+    """
+        get the statistics of the probability distributions
+
+        Outputs:
+            @param trend_up ndarray(float): sum of probabilities for upward
+               movement (relative to the unit index of that prob)
+            @param trend_down ndarray(float): sum of probabilities for downward
+               movement (relative to the unit index of that prob)
+            @param trend ndarray(float): difference of upward and downward
+               movements
+    """
+
+    num_elements = len(unit_indices)
+    trend_up = np.empty(num_elements, dtype=float)
+    trend_down = np.empty(num_elements, dtype=float)
+    trend = np.empty(num_elements, dtype=float)
+
+    for i in range(num_elements):
+        trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
+        trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
+        if prob_dist[i, unit_indices[i]] > 0.0:
+            trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
+        else:
+            trend[i] = None
+
+    ## calculate volatility of distribution
+    volatility = prob_dist.std(axis=1)
+
+    return trend_up, trend_down, trend, volatility

release/python/0.4.0/crankshaft/setup.py

@@ -0,0 +1,49 @@
+
+"""
+CartoDB Spatial Analysis Python Library
+See:
+https://github.com/CartoDB/crankshaft
+"""
+
+from setuptools import setup, find_packages
+
+setup(
+    name='crankshaft',
+
+    version='0.0.0',
+
+    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.
+    # IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
+    install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
+
+    requires=['pysal', 'numpy', 'sklearn'],
+
+    test_suite='test'
+)

release/python/0.4.0/crankshaft/setup.py-r

@@ -0,0 +1,49 @@
+
+"""
+CartoDB Spatial Analysis Python Library
+See:
+https://github.com/CartoDB/crankshaft
+"""
+
+from setuptools import setup, find_packages
+
+setup(
+    name='crankshaft',
+
+    version='0.0.0',
+
+    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.
+    # IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
+    install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
+
+    requires=['pysal', 'numpy', 'sklearn'],
+
+    test_suite='test'
+)

release/python/0.4.0/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/0.4.0/crankshaft/test/fixtures/markov.json

@@ -0,0 +1 @@
+[[0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 0], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 1], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 2], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 3], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 4], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 5], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 6], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 7], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 8], [0.19047619047619049, 0.16, 0.0, 0.32594478059941379, 9], [-0.23529411764705882, 0.0, 0.19047619047619047, 0.31356338348865387, 10], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 11], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 12], [0.027777777777777783, 0.11111111111111112, 0.088888888888888892, 0.30339641183779581, 13], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 14], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 15], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 16], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 17], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 18], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 19], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 20], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 21], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 22], [-0.16666666666666663, 0.18181818181818182, 0.27272727272727271, 0.20246415864836445, 23], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 24], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 25], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 26], [-0.043478260869565216, 0.0, 0.041666666666666664, 0.37950991789118999, 27], [0.22222222222222221, 0.18181818181818182, 0.0, 0.31701083225750354, 28], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 29], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 30], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 31], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 32], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 33], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 34], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 35], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 36], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 37], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 38], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 39], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 40], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 41], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 42], [0.0, 0.0, 0.0, 0.40000000000000002, 43], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 44], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 45], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 46], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 47]]

release/python/0.4.0/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/0.4.0/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/0.4.0/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/0.4.0/crankshaft/test/mock_plpy.py

@@ -0,0 +1,52 @@
+import re
+
+class MockCursor:
+    def __init__(self, data):
+        self.cursor_pos = 0
+        self.data = data
+
+    def fetch(self, batch_size):
+        batch = self.data[self.cursor_pos : self.cursor_pos + batch_size]
+        self.cursor_pos += batch_size
+        return batch
+
+
+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 debug(self, msg):
+        self.notices.append(msg)
+
+    def info(self, msg):
+        self.infos.append(msg)
+
+    def cursor(self, query):
+        data = self.execute(query)
+        return MockCursor(data)
+
+    def execute(self, query): # TODO: additional arguments
+       for result in self.results:
+          if result[0].match(query):
+            return result[1]
+       return []

release/python/0.4.0/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/0.4.0/crankshaft/test/test_clustering_moran.py

@@ -0,0 +1,88 @@
+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.params_markov = {"id_col": "cartodb_id",
+                              "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
+                              "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/0.4.0/crankshaft/test/test_pysal_utils.py

@@ -0,0 +1,142 @@
+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}
+
+        self.params_array = {"id_col": "cartodb_id",
+                             "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
+                             "subquery": "SELECT * FROM a_list",
+                             "geom_col": "the_geom",
+                             "num_ngbrs": 321}
+
+    def test_query_attr_select(self):
+        """Test query_attr_select"""
+
+        ans = "i.\"andy\"::numeric As attr1, " \
+              "i.\"jay_z\"::numeric As attr2, "
+
+        ans_array = "i.\"_2013_dec\"::numeric As attr1, " \
+                    "i.\"_2014_jan\"::numeric As attr2, " \
+                    "i.\"_2014_feb\"::numeric As attr3, "
+
+        self.assertEqual(pu.query_attr_select(self.params), ans)
+        self.assertEqual(pu.query_attr_select(self.params_array), ans_array)
+
+    def test_query_attr_where(self):
+        """Test pu.query_attr_where"""
+
+        ans = "idx_replace.\"andy\" IS NOT NULL AND " \
+              "idx_replace.\"jay_z\" IS NOT NULL AND " \
+              "idx_replace.\"jay_z\" <> 0"
+
+        ans_array = "idx_replace.\"_2013_dec\" IS NOT NULL AND " \
+                    "idx_replace.\"_2014_jan\" IS NOT NULL AND " \
+                    "idx_replace.\"_2014_feb\" IS NOT NULL"
+
+        self.assertEqual(pu.query_attr_where(self.params), ans)
+        self.assertEqual(pu.query_attr_where(self.params_array), ans_array)
+
+    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;"
+        
+        ans_array = "SELECT i.\"cartodb_id\" As id, " \
+              "i.\"_2013_dec\"::numeric As attr1, " \
+              "i.\"_2014_jan\"::numeric As attr2, " \
+              "i.\"_2014_feb\"::numeric As attr3, " \
+              "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+                            "FROM (SELECT * FROM a_list) As j " \
+                            "WHERE i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
+                                  "j.\"_2013_dec\" IS NOT NULL AND " \
+                                  "j.\"_2014_jan\" IS NOT NULL AND " \
+                                  "j.\"_2014_feb\" IS NOT NULL " \
+                            "ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC " \
+                            "LIMIT 321)) As neighbors " \
+              "FROM (SELECT * FROM a_list) As i " \
+              "WHERE i.\"_2013_dec\" IS NOT NULL AND " \
+                    "i.\"_2014_jan\" IS NOT NULL AND " \
+                    "i.\"_2014_feb\" IS NOT NULL "\
+              "ORDER BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(pu.knn(self.params), ans)
+        self.assertEqual(pu.knn(self.params_array), ans_array)
+
+    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)

release/python/0.4.0/crankshaft/test/test_segmentation.py

@@ -0,0 +1,64 @@
+import unittest
+import numpy as np
+from helper import plpy, fixture_file
+import crankshaft.segmentation as segmentation
+import json
+
+class SegmentationTest(unittest.TestCase):
+    """Testing class for Moran's I functions"""
+
+    def setUp(self):
+        plpy._reset()
+
+    def generate_random_data(self,n_samples,random_state,  row_type=False):
+        x1 = random_state.uniform(size=n_samples)
+        x2 = random_state.uniform(size=n_samples)
+        x3 = random_state.randint(0, 4, size=n_samples)
+
+        y = x1+x2*x2+x3
+        cartodb_id  = range(len(x1))
+
+        if row_type:
+            return [ {'features': vals} for vals in zip(x1,x2,x3)], y
+        else:
+            return  [dict( zip(['x1','x2','x3','target', 'cartodb_id'],[x1,x2,x3,y,cartodb_id]))]
+
+    def test_replace_nan_with_mean(self):
+        test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
+
+    def test_create_and_predict_segment(self):
+        n_samples = 1000
+
+        random_state_train = np.random.RandomState(13)
+        random_state_test = np.random.RandomState(134)
+        training_data = self.generate_random_data(n_samples, random_state_train)
+        test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True)
+
+
+        ids =  [{'cartodb_ids': range(len(test_data))}]
+        rows =  [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}]
+
+        plpy._define_result('select \* from  \(select \* from training\) a  limit 1',rows)
+        plpy._define_result('.*from \(select \* from training\) as a' ,training_data)
+        plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids)
+        plpy._define_result('.*select \* from test.*' ,test_data)
+
+        model_parameters =  {'n_estimators': 1200,
+                             'max_depth': 3,
+                             'subsample' : 0.5,
+                             'learning_rate': 0.01,
+                             'min_samples_leaf': 1}
+
+        result = segmentation.create_and_predict_segment(
+                'select * from training',
+                'target',
+                'select * from test',
+                model_parameters)
+
+        prediction = [r[1] for r in result]
+
+        accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y))))
+
+        self.assertEqual(len(result),len(test_data))
+        self.assertTrue( result[0][2] < 0.01)
+        self.assertTrue( accuracy < 0.5*np.mean(test_y)  )

release/python/0.4.0/crankshaft/test/test_space_time_dynamics.py

@@ -0,0 +1,324 @@
+import unittest
+import numpy as np
+
+import unittest
+
+
+# from mock_plpy import MockPlPy
+# plpy = MockPlPy()
+#
+# import sys
+# sys.modules['plpy'] = plpy
+from helper import plpy, fixture_file
+
+import crankshaft.space_time_dynamics as std
+from crankshaft import random_seeds
+import json
+
+class SpaceTimeTests(unittest.TestCase):
+    """Testing class for Markov Functions."""
+
+    def setUp(self):
+        plpy._reset()
+        self.params = {"id_col": "cartodb_id",
+                       "time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
+                       "subquery": "SELECT * FROM a_list",
+                       "geom_col": "the_geom",
+                       "num_ngbrs": 321}
+        self.neighbors_data = json.loads(open(fixture_file('neighbors_markov.json')).read())
+        self.markov_data = json.loads(open(fixture_file('markov.json')).read())
+
+        self.time_data = np.array([i * np.ones(10, dtype=float) for i in range(10)]).T
+
+        self.transition_matrix = np.array([
+                [[ 0.96341463, 0.0304878 , 0.00609756, 0.        , 0.        ],
+                 [ 0.06040268, 0.83221477, 0.10738255, 0.        , 0.        ],
+                 [ 0.        , 0.14      , 0.74      , 0.12      , 0.        ],
+                 [ 0.        , 0.03571429, 0.32142857, 0.57142857, 0.07142857],
+                 [ 0.        , 0.        , 0.        , 0.16666667, 0.83333333]],
+                [[ 0.79831933, 0.16806723, 0.03361345, 0.        , 0.        ],
+                 [ 0.0754717 , 0.88207547, 0.04245283, 0.        , 0.        ],
+                 [ 0.00537634, 0.06989247, 0.8655914 , 0.05913978, 0.        ],
+                 [ 0.        , 0.        , 0.06372549, 0.90196078, 0.03431373],
+                 [ 0.        , 0.        , 0.        , 0.19444444, 0.80555556]],
+                [[ 0.84693878, 0.15306122, 0.        , 0.        , 0.        ],
+                 [ 0.08133971, 0.78947368, 0.1291866 , 0.        , 0.        ],
+                 [ 0.00518135, 0.0984456 , 0.79274611, 0.0984456 , 0.00518135],
+                 [ 0.        , 0.        , 0.09411765, 0.87058824, 0.03529412],
+                 [ 0.        , 0.        , 0.        , 0.10204082, 0.89795918]],
+                [[ 0.8852459 , 0.09836066, 0.        , 0.01639344, 0.        ],
+                 [ 0.03875969, 0.81395349, 0.13953488, 0.        , 0.00775194],
+                 [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
+                 [ 0.        , 0.02339181, 0.12865497, 0.75438596, 0.09356725],
+                 [ 0.        , 0.        , 0.        , 0.09661836, 0.90338164]],
+                [[ 0.33333333, 0.66666667, 0.        , 0.        , 0.        ],
+                 [ 0.0483871 , 0.77419355, 0.16129032, 0.01612903, 0.        ],
+                 [ 0.01149425, 0.16091954, 0.74712644, 0.08045977, 0.        ],
+                 [ 0.        , 0.01036269, 0.06217617, 0.89637306, 0.03108808],
+                 [ 0.        , 0.        , 0.        , 0.02352941, 0.97647059]]]
+                 )
+
+    def test_spatial_markov(self):
+        """Test Spatial Markov."""
+        data = [ { 'id': d['id'],
+                   'attr1': d['y1995'],
+                   'attr2': d['y1996'],
+                   'attr3': d['y1997'],
+                   'attr4': d['y1998'],
+                   'attr5': d['y1999'],
+                   'attr6': d['y2000'],
+                   'attr7': d['y2001'],
+                   'attr8': d['y2002'],
+                   'attr9': d['y2003'],
+                   'attr10': d['y2004'],
+                   'attr11': d['y2005'],
+                   'attr12': d['y2006'],
+                   'attr13': d['y2007'],
+                   'attr14': d['y2008'],
+                   'attr15': d['y2009'],
+                   'neighbors': d['neighbors'] } for d in self.neighbors_data]
+        print(str(data[0]))
+        plpy._define_result('select', data)
+        random_seeds.set_random_seeds(1234)
+
+        result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id')
+
+        self.assertTrue(result != None)
+        result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
+        print result[0]
+        expected = self.markov_data
+        for ([res_trend, res_up, res_down, res_vol, res_id],
+             [exp_trend, exp_up, exp_down, exp_vol, exp_id]
+             ) in zip(result, expected):
+            self.assertAlmostEqual(res_trend, exp_trend)
+
+    def test_get_time_data(self):
+        """Test get_time_data"""
+        data = [ { 'attr1': d['y1995'],
+                   'attr2': d['y1996'],
+                   'attr3': d['y1997'],
+                   'attr4': d['y1998'],
+                   'attr5': d['y1999'],
+                   'attr6': d['y2000'],
+                   'attr7': d['y2001'],
+                   'attr8': d['y2002'],
+                   'attr9': d['y2003'],
+                   'attr10': d['y2004'],
+                   'attr11': d['y2005'],
+                   'attr12': d['y2006'],
+                   'attr13': d['y2007'],
+                   'attr14': d['y2008'],
+                   'attr15': d['y2009'] } for d in self.neighbors_data]
+
+        result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'])
+
+        ## expected was prepared from PySAL example:
+        ### f = ps.open(ps.examples.get_path("usjoin.csv"))
+        ### pci = np.array([f.by_col[str(y)] for y in range(1995, 2010)]).transpose()
+        ### rpci = pci / (pci.mean(axis = 0))
+
+        expected = np.array([[ 0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154,  0.83271652
+        ,  0.83786314, 0.85012593, 0.85509656, 0.86416612, 0.87119375, 0.86302631
+        ,  0.86148267, 0.86252252, 0.86746356],
+         [ 0.9188951,  0.91757931, 0.92333258, 0.92517289, 0.92552388, 0.90746978
+        ,  0.89830489, 0.89431991, 0.88924794, 0.89815176, 0.91832091, 0.91706054
+        ,  0.90139505, 0.87897455, 0.86216858],
+         [ 0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522, 0.78964559
+        ,  0.80584442, 0.8084998,  0.82258551, 0.82668196, 0.82373724, 0.81814804
+        ,  0.83675961, 0.83574199, 0.84647177],
+         [ 1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841, 1.14506948
+        ,  1.12151133, 1.11160697, 1.10888621, 1.11399806, 1.12168029, 1.13164797
+        ,  1.12958508, 1.11371818, 1.09936775],
+         [ 1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025, 1.16898201
+        ,  1.17212488, 1.14752303, 1.11843284, 1.11024964, 1.11943471, 1.11736468
+        ,  1.10863242, 1.09642516, 1.07762337],
+         [ 1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684, 1.44184737
+        ,  1.44782832, 1.41978227, 1.39092208, 1.4059372,  1.40788646, 1.44052766
+        ,  1.45241216, 1.43306098, 1.4174431 ],
+         [ 1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149, 1.10888138
+        ,  1.11856629, 1.13062931, 1.11944984, 1.12446239, 1.11671008, 1.10880034
+        ,  1.08401709, 1.06959206, 1.07875225],
+         [ 1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545, 0.99854316
+        ,  0.9880258,  0.99669587, 0.99327676, 1.01400905, 1.03176742, 1.040511
+        ,  1.01749645, 0.9936394,  0.98279746],
+         [ 0.98996986, 1.00143564, 0.99491,  1.00188408, 1.00455845, 0.99127006
+        ,  0.97925917, 0.9683482,  0.95335147, 0.93694787, 0.94308213, 0.92232874
+        ,  0.91284091, 0.89689833, 0.88928858],
+         [ 0.87418391, 0.86416601, 0.84425695, 0.8404494,  0.83903044, 0.8578708
+        ,  0.86036185, 0.86107306, 0.8500772,  0.86981998, 0.86837929, 0.87204141
+        ,  0.86633032, 0.84946077, 0.83287146],
+         [ 1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624, 1.14450183
+        ,  1.12349752, 1.12596664, 1.12213996, 1.1119989,  1.10257792, 1.10491258
+        ,  1.11059842, 1.10509795, 1.10020097],
+         [ 0.97282463, 0.96700147, 0.96252588, 0.9653878,  0.96057687, 0.95831051
+        ,  0.94480909, 0.94804195, 0.95430286, 0.94103989, 0.92122519, 0.91010201
+        ,  0.89280392, 0.89298243, 0.89165385],
+         [ 0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647, 0.9480927
+        ,  0.93539182, 0.95388718, 0.94597005, 0.96918424, 0.94781281, 0.93466815
+        ,  0.94281559, 0.96520315, 0.96715441],
+         [ 0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897, 0.98687073
+        ,  0.99237486, 0.98209969, 0.9877653,  0.97399471, 0.96910087, 0.98416665
+        ,  0.98423613, 0.99823861, 0.99545704],
+         [ 0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012,  0.86191535
+        ,  0.84981451, 0.85472102, 0.84564835, 0.83998883, 0.83478547, 0.82803648
+        ,  0.8198736,  0.82265395, 0.8399404 ],
+         [ 0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136, 0.82785597
+        ,  0.86008789, 0.86776298, 0.86720209, 0.8676334,  0.89179317, 0.94202108
+        ,  0.9422231,  0.93902708, 0.94479184],
+         [ 0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238, 0.90906632
+        ,  0.92693339, 0.93695966, 0.94242697, 0.94338265, 0.91981796, 0.91108804
+        ,  0.90543476, 0.91737138, 0.94793657],
+         [ 1.1977611,  1.18222564, 1.18439158, 1.18267865, 1.19286723, 1.20172869
+        ,  1.21328691, 1.22624778, 1.22397075, 1.23857042, 1.24419893, 1.23929384
+        ,  1.23418676, 1.23626739, 1.26754398],
+         [ 1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667, 1.34790023
+        ,  1.34399863, 1.32575181, 1.30795492, 1.30544841, 1.30303302, 1.32107766
+        ,  1.32936244, 1.33001241, 1.33288462],
+         [ 1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093, 1.05059016
+        ,  1.03405057, 1.02747623, 1.03162734, 0.9961416,  0.97356208, 0.94241549
+        ,  0.92754547, 0.92549227, 0.92138102],
+         [ 1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264, 1.13889622
+        ,  1.12442212, 1.13367018, 1.13982256, 1.14029944, 1.11979401, 1.10905389
+        ,  1.10577769, 1.11166825, 1.09985155],
+         [ 0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284, 0.74480073
+        ,  0.76098396, 0.76156903, 0.76651952, 0.76533288, 0.78205934, 0.76842416
+        ,  0.77487118, 0.77768683, 0.78801192],
+         [ 0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803, 0.97370819
+        ,  0.96419154, 0.97209861, 0.97441313, 0.96356162, 0.94745352, 0.93965462
+        ,  0.93069645, 0.94020973, 0.94358232],
+         [ 0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801, 0.80071489
+        ,  0.83358256, 0.83451613, 0.85175032, 0.85954307, 0.86790024, 0.87170334
+        ,  0.87863799, 0.87497981, 0.87888675],
+         [ 0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619, 0.98733195
+        ,  0.99644997, 0.99669587, 1.02559097, 1.01116651, 0.99988024, 0.97906749
+        ,  0.99323123, 1.00204939, 0.99602148],
+         [ 1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683, 1.08312397
+        ,  1.05192626, 1.04230892, 1.05577278, 1.08569751, 1.12443486, 1.08891079
+        ,  1.08603695, 1.05997314, 1.02160943],
+         [ 1.11368269, 1.1057147,  1.11893431, 1.13778669, 1.1432272,  1.18257029
+        ,  1.16226243, 1.16009196, 1.14467789, 1.14820235, 1.12386598, 1.12680236
+        ,  1.12357937, 1.1159258,  1.12570828],
+         [ 1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667, 1.31210239
+        ,  1.29989156, 1.29203193, 1.27183516, 1.26830786, 1.2617743,  1.28656675
+        ,  1.29734097, 1.29390205, 1.29345446],
+         [ 0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864, 0.78772975
+        ,  0.82848011, 0.8259679,  0.82435705, 0.83108634, 0.84373784, 0.83891093
+        ,  0.84349247, 0.85637272, 0.86539395],
+         [ 1.23450087, 1.2426022,  1.23537935, 1.23581293, 1.24522626, 1.2256767
+        ,  1.21126648, 1.19377804, 1.18355337, 1.19674434, 1.21536573, 1.23653297
+        ,  1.27962009, 1.27968392, 1.25907738],
+         [ 0.9769662,  0.97400719, 0.98035944, 0.97581531, 0.95543282, 0.96480308
+        ,  0.94686376, 0.93679073, 0.92540049, 0.92988835, 0.93442917, 0.92100464
+        ,  0.91475304, 0.90249622, 0.9021363 ],
+         [ 0.84986886, 0.8986851,  0.84295997, 0.87280534, 0.85659368, 0.88937573
+        ,  0.894401, 0.90448993, 0.95495898, 0.92698333, 0.94745352, 0.92562488
+        ,  0.96635366, 1.02520312, 1.0394296 ],
+         [ 1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073, 1.00759019
+        ,  0.99192968, 0.99747298, 0.99550759, 0.97583768, 0.9610168,  0.94779638
+        ,  0.93759089, 0.93353431, 0.94121705],
+         [ 0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613, 0.83434854
+        ,  0.85813595, 0.84667961, 0.84374558, 0.85951183, 0.87194227, 0.89455097
+        ,  0.88283929, 0.90349491, 0.90600675],
+         [ 1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086, 1.00581626
+        ,  0.98850522, 0.99291168, 0.98983209, 0.97511924, 0.96134615, 0.96382634
+        ,  0.95011401, 0.9434686,  0.94637765],
+         [ 1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857, 1.04800023
+        ,  1.03024941, 1.04200483, 1.0402554,  1.03296979, 1.02191682, 1.02476275
+        ,  1.02347523, 1.02517684, 1.04359571],
+         [ 1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043,  1.0531801
+        ,  1.07452771, 1.09383478, 1.1052447,  1.10322136, 1.09167939, 1.08772756
+        ,  1.08859544, 1.09177338, 1.1096083 ],
+         [ 0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809, 0.86287327
+        ,  0.85169796, 0.85411285, 0.84886336, 0.84517414, 0.84843858, 0.84488343
+        ,  0.83374329, 0.82812044, 0.82878599],
+         [ 0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286, 0.92652175
+        ,  0.94278865, 0.93682452, 0.98655146, 0.992237, 0.9798497,  0.93869677
+        ,  0.96947771, 1.00362626, 0.98102351],
+         [ 0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967,  0.93092109
+        ,  0.92662519, 0.93412152, 0.93501274, 0.92879506, 0.92110542, 0.91035556
+        ,  0.90430364, 0.89994694, 0.90073864],
+         [ 0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824, 0.98882205
+        ,  0.97662234, 0.95601578, 0.94905385, 0.94934888, 0.97152609, 0.97163004
+        ,  0.9700702,  0.97158948, 0.95884908],
+         [ 0.83980439, 0.84726737, 0.85747,  0.85467221, 0.8556751,  0.84818516
+        ,  0.85265681, 0.84502402, 0.82645665, 0.81743586, 0.83550406, 0.83338919
+        ,  0.83511679, 0.82136617, 0.80921874],
+         [ 0.95118156, 0.9466212,  0.94688098, 0.9508583,  0.9512441,  0.95440787
+        ,  0.96364363, 0.96804412, 0.97136214, 0.97583768, 0.95571724, 0.96895368
+        ,  0.97001634, 0.97082733, 0.98782366],
+         [ 1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249, 1.10558188
+        ,  1.1214086,  1.12292577, 1.13021031, 1.13342735, 1.14686068, 1.14502975
+        ,  1.14474747, 1.14084037, 1.16142926],
+         [ 1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863, 1.11856702
+        ,  1.09764283, 1.08815849, 1.08044313, 1.09278827, 1.07003204, 1.08398066
+        ,  1.09831768, 1.09298232, 1.09176125],
+         [ 0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744, 0.77751194
+        ,  0.79902974, 0.81437881, 0.80788828, 0.79603865, 0.78966436, 0.79949807
+        ,  0.80172182, 0.82168155, 0.85587911],
+         [ 1.0052447,  1.00007696, 1.00475899, 1.00613942, 1.00639561, 1.00162979
+        ,  0.99860739, 1.00814981, 1.00574316, 0.99030032, 0.97682565, 0.97292596
+        ,  0.96519561, 0.96173403, 0.95890284],
+         [ 0.95808419, 0.9382568,  0.9654441,  0.95561201, 0.96987289, 0.96608031
+        ,  0.99727185, 1.00781194, 1.03484236, 1.05333619, 1.0983263,  1.1704974
+        ,  1.17025154, 1.18730553, 1.14242645]])
+
+        self.assertTrue(np.allclose(result, expected))
+        self.assertTrue(type(result) == type(expected))
+        self.assertTrue(result.shape == expected.shape)
+
+    def test_rebin_data(self):
+        """Test rebin_data"""
+        ## sample in double the time (even case since 10 % 2 = 0):
+        ##   (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
+        ## = 0.5,     2.5,     4.5,     6.5,     8.5
+        ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float)
+                             for i in range(0, 10, 2)]).T
+
+        self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
+
+        ## sample in triple the time (uneven since 10 % 3 = 1):
+        ##   (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
+        ## = 1,         4,         7,         9
+        ans_odd  = np.array([i * np.ones(10, dtype=float)
+                             for i in (1, 4, 7, 9)]).T
+        self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
+
+    def test_get_prob_dist(self):
+        """Test get_prob_dist"""
+        lag_indices = np.array([1, 2, 3, 4])
+        unit_indices = np.array([1, 3, 2, 4])
+        answer = np.array([
+            [ 0.0754717 , 0.88207547, 0.04245283, 0.        , 0.        ],
+            [ 0.        , 0.        , 0.09411765, 0.87058824, 0.03529412],
+            [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
+            [ 0.        , 0.        , 0.        , 0.02352941, 0.97647059]
+        ])
+        result = std.get_prob_dist(self.transition_matrix, lag_indices, unit_indices)
+
+        self.assertTrue(np.array_equal(result, answer))
+
+    def test_get_prob_stats(self):
+        """Test get_prob_stats"""
+
+        probs = np.array([
+            [ 0.0754717 , 0.88207547, 0.04245283, 0.        , 0.        ],
+            [ 0.        , 0.        , 0.09411765, 0.87058824, 0.03529412],
+            [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
+            [ 0.        , 0.        , 0.        , 0.02352941, 0.97647059]
+        ])
+        unit_indices = np.array([1, 3, 2, 4])
+        answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
+        answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
+        answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824,  0.02475248 / 0.77722772, -0.02352941 / 0.97647059])
+        answer_volatility = np.array([ 0.34221495,  0.33705421,  0.29226542,  0.38834223])
+
+        result = std.get_prob_stats(probs, unit_indices)
+        result_up = result[0]
+        result_down = result[1]
+        result_trend = result[2]
+        result_volatility = result[3]
+
+        self.assertTrue(np.allclose(result_up, answer_up))
+        self.assertTrue(np.allclose(result_down, answer_down))
+        self.assertTrue(np.allclose(result_trend, answer_trend))
+        self.assertTrue(np.allclose(result_volatility, answer_volatility))

release/python/0.4.1/crankshaft/crankshaft/__init__.py

@@ -0,0 +1,5 @@
+"""Import all modules"""
+import crankshaft.random_seeds
+import crankshaft.clustering
+import crankshaft.space_time_dynamics
+import crankshaft.segmentation

release/python/0.4.1/crankshaft/crankshaft/clustering/__init__.py

@@ -0,0 +1,3 @@
+"""Import all functions from for clustering"""
+from moran import *
+from kmeans import *

release/python/0.4.1/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/0.4.1/crankshaft/crankshaft/clustering/moran.py

@@ -0,0 +1,243 @@
+"""
+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
+from collections import OrderedDict
+
+# 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 = OrderedDict([("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)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(2)
+    except plpy.SPIError, e:
+        plpy.error('Analysis failed: %s' % e)
+        return pu.empty_zipped_array(2)
+
+    ## collect attributes
+    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 = OrderedDict([("id_col", id_col),
+                         ("attr1", attr),
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(5)
+    except plpy.SPIError, e:
+        plpy.error('Analysis failed: %s' % e)
+        return pu.empty_zipped_array(5)
+
+    attr_vals = pu.get_attributes(result)
+    weight = pu.get_weight(result, w_type, num_ngbrs)
+
+    # calculate LISA values
+    lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
+                                     permutations=permutations)
+
+    # find quadrants for each geometry
+    quads = quad_position(lisa.q)
+
+    return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
+
+def moran_rate(subquery, numerator, denominator,
+               w_type, num_ngbrs, permutations, geom_col, id_col):
+    """
+    Moran's I Rate (global)
+    Andy Eschbacher
+    """
+    qvals = OrderedDict([("id_col", id_col),
+                         ("attr1", numerator),
+                         ("attr2", denominator)
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(2)
+    except plpy.SPIError, e:
+        plpy.error('Analysis failed: %s' % e)
+        return pu.empty_zipped_array(2)
+
+    ## collect attributes
+    numer = pu.get_attributes(result, 1)
+    denom = pu.get_attributes(result, 2)
+
+    weight = pu.get_weight(result, w_type, num_ngbrs)
+
+    ## calculate moran global rate
+    lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
+                                         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
+
+    qvals = OrderedDict([("id_col", id_col),
+                         ("numerator", numerator),
+                         ("denominator", denominator),
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(5)
+    except plpy.SPIError, e:
+        plpy.error('Analysis failed: %s' % e)
+        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 quadrants for each geometry
+    quads = quad_position(lisa.q)
+
+    return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
+
+def moran_local_bv(subquery, attr1, attr2,
+                   permutations, geom_col, id_col, w_type, num_ngbrs):
+    """
+        Moran's I (local) Bivariate (untested)
+    """
+
+    qvals = OrderedDict([("id_col", id_col),
+                         ("attr1", attr1),
+                         ("attr2", attr2),
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(4)
+    except plpy.SPIError:
+        plpy.error("Error: areas of interest query failed, " \
+                   "check input parameters")
+        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)
+
+    # find clustering of significance
+    lisa_sig = quad_position(lisa.q)
+
+    return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
+
+# Low level functions ----------------------------------------
+
+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/0.4.1/crankshaft/crankshaft/pysal_utils/__init__.py

@@ -0,0 +1,2 @@
+"""Import all functions for pysal_utils"""
+from crankshaft.pysal_utils.pysal_utils import *

release/python/0.4.1/crankshaft/crankshaft/pysal_utils/pysal_utils.py

@@ -0,0 +1,188 @@
+"""
+    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 dict-like: 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}
+    print 'len of neighbors: %d' % len(neighbors)
+
+    built_weight = ps.W(neighbors)
+    built_weight.transform = 'r'
+
+    return built_weight
+
+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.)
+    """
+
+    attr_string = ""
+    template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, "
+
+    if 'time_cols' in params:
+        ## if markov analysis
+        attrs = params['time_cols']
+
+        for idx, val in enumerate(attrs):
+            attr_string += template % {"col": val, "alias_num": idx + 1}
+    else:
+        ## if moran's analysis
+        attrs = [k for k in params
+                 if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')]
+
+        for idx, val in enumerate(sorted(attrs)):
+            attr_string += template % {"col": params[val], "alias_num": idx + 1}
+
+    return attr_string
+
+def query_attr_where(params):
+    """
+      Construct where conditions when building neighbors query
+        Create portion of WHERE clauses for weeding out NULL-valued geometries
+        Input: dict of params:
+            {'subquery': ...,
+             'numerator': 'data1',
+             'denominator': 'data2',
+             '': ...}
+        Output: 'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL'
+        Input:
+        {'subquery': ...,
+         'time_cols': ['time1', 'time2', 'time3'],
+         'etc': ...}
+        Output: 'idx_replace."time1" IS NOT NULL AND idx_replace."time2" IS NOT
+          NULL AND idx_replace."time3" IS NOT NULL'
+    """
+    attr_string = []
+    template = "idx_replace.\"%s\" IS NOT NULL"
+
+    if 'time_cols' in params:
+        ## markov where clauses
+        attrs = params['time_cols']
+        # add values to template
+        for attr in attrs:
+            attr_string.append(template % attr)
+    else:
+        ## moran where clauses
+
+        # get keys
+        attrs = sorted([k for k in params
+                        if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')])
+        # add values to template
+        for attr in attrs:
+            attr_string.append(template % params[attr])
+
+        if len(attrs) == 2:
+            attr_string.append("idx_replace.\"%s\" <> 0" % params[attrs[1]])
+
+    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/0.4.1/crankshaft/crankshaft/random_seeds.py

@@ -0,0 +1,11 @@
+"""Random seed generator used for non-deterministic functions in crankshaft"""
+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/0.4.1/crankshaft/crankshaft/segmentation/__init__.py

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

release/python/0.4.1/crankshaft/crankshaft/segmentation/segmentation.py

@@ -0,0 +1,176 @@
+"""
+Segmentation creation and prediction
+"""
+
+import sklearn
+import numpy as np
+import plpy
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn import metrics
+from sklearn.cross_validation import train_test_split
+
+# Lower level functions
+#----------------------
+
+def replace_nan_with_mean(array):
+    """
+        Input:
+            @param array: an array of floats which may have null-valued entries
+        Output:
+            array with nans filled in with the mean of the dataset
+    """
+    # returns an array of rows and column indices
+    indices = np.where(np.isnan(array))
+
+    # iterate through entries which have nan values
+    for row, col in zip(*indices):
+            array[row, col] = np.mean(array[~np.isnan(array[:, col]), col])
+
+    return array
+
+def get_data(variable, feature_columns, query):
+    """
+        Fetch data from the database, clean, and package into
+          numpy arrays
+        Input:
+            @param variable: name of the target variable
+            @param feature_columns: list of column names
+            @param query: subquery that data is pulled from for the packaging
+        Output:
+            prepared data, packaged into NumPy arrays
+    """
+
+    columns = ','.join(['array_agg("{col}") As "{col}"'.format(col=col) for col in feature_columns])
+
+    try:
+        data = plpy.execute('''SELECT array_agg("{variable}") As target, {columns} FROM ({query}) As a'''.format(
+            variable=variable,
+            columns=columns,
+            query=query))
+    except Exception, e:
+        plpy.error('Failed to access data to build segmentation model: %s' % e)
+
+    # extract target data from plpy object
+    target = np.array(data[0]['target'])
+
+    # put n feature data arrays into an n x m array of arrays
+    features = np.column_stack([np.array(data[0][col], dtype=float) for col in feature_columns])
+
+    return replace_nan_with_mean(target), replace_nan_with_mean(features)
+
+# High level interface
+# --------------------
+
+def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters):
+    """
+    Version of create_and_predict_segment that works on arrays that come stright form the SQL calling
+    the function.
+
+        Input:
+            @param target: The 1D array of lenth NSamples containing the target variable we want the model to predict
+            @param features: Thw 2D array of size NSamples * NFeatures that form the imput to the model
+            @param target_ids: A 1D array of target_ids that will be used to associate the results of the prediction with the rows which they come from
+            @param model_parameters: A dictionary containing parameters for the model.
+    """
+
+    clean_target = replace_nan_with_mean(target)
+    clean_features = replace_nan_with_mean(features)
+    target_features = replace_nan_with_mean(target_features)
+
+    model, accuracy = train_model(clean_target, clean_features, model_parameters, 0.2)
+    prediction = model.predict(target_features)
+    accuracy_array = [accuracy]*prediction.shape[0]
+    return zip(target_ids, prediction, np.full(prediction.shape, accuracy_array))
+
+
+
+def create_and_predict_segment(query, variable, target_query, model_params):
+    """
+    generate a segment with machine learning
+    Stuart Lynn
+    """
+
+    ## fetch column names
+    try:
+        columns = plpy.execute('SELECT * FROM ({query}) As a LIMIT 1  '.format(query=query))[0].keys()
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    ## extract column names to be used in building the segmentation model
+    feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator'])
+    ## get data from database
+    target, features = get_data(variable, feature_columns, query)
+
+    model, accuracy = train_model(target, features, model_params, 0.2)
+    cartodb_ids, result = predict_segment(model, feature_columns, target_query)
+    accuracy_array = [accuracy]*result.shape[0]
+    return zip(cartodb_ids, result, accuracy_array)
+
+
+def train_model(target, features, model_params, test_split):
+    """
+        Train the Gradient Boosting model on the provided data and calculate the accuracy of the model
+        Input:
+            @param target: 1D Array of the variable that the model is to be trianed to predict
+            @param features: 2D Array NSamples * NFeatures to use in trining the model
+            @param model_params: A dictionary of model parameters, the full specification can be found on the
+                scikit learn page for [GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)
+            @parma test_split: The fraction of the data to be withheld for testing the model / calculating the accuray
+    """
+    features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
+    model = GradientBoostingRegressor(**model_params)
+    model.fit(features_train, target_train)
+    accuracy = calculate_model_accuracy(model, features, target)
+    return model, accuracy
+
+def calculate_model_accuracy(model, features, target):
+    """
+        Calculate the mean squared error of the model prediction
+        Input:
+            @param model: model trained from input features
+            @param features: features to make a prediction from
+            @param target: target to compare prediction to
+        Output:
+            mean squared error of the model prection compared to the target
+    """
+    prediction = model.predict(features)
+    return metrics.mean_squared_error(prediction, target)
+
+def predict_segment(model, features, target_query):
+    """
+    Use the provided model to predict the values for the new feature set
+        Input:
+            @param model: The pretrained model
+            @features: A list of features to use in the model prediction (list of column names)
+            @target_query: The query to run to obtain the data to predict on and the cartdb_ids associated with it.
+    """
+
+    batch_size = 1000
+    joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features])
+
+    try:
+        cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format(
+            joined_features=joined_features,
+            target_query=target_query))
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    results = []
+
+    while True:
+        rows = cursor.fetch(batch_size)
+        if not rows:
+            break
+        batch = np.row_stack([np.array(row['features'], dtype=float) for row in rows])
+
+        #Need to fix this. Should be global mean. This will cause weird effects
+        batch = replace_nan_with_mean(batch)
+        prediction = model.predict(batch)
+        results.append(prediction)
+
+    try:
+        cartodb_ids = plpy.execute('''SELECT array_agg(cartodb_id ORDER BY cartodb_id) As cartodb_ids FROM ({0}) As a'''.format(target_query))[0]['cartodb_ids']
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    return cartodb_ids, np.concatenate(results)

release/python/0.4.1/crankshaft/crankshaft/space_time_dynamics/__init__.py

@@ -0,0 +1,2 @@
+"""Import all functions from clustering libraries."""
+from markov import *

release/python/0.4.1/crankshaft/crankshaft/space_time_dynamics/markov.py

@@ -0,0 +1,189 @@
+"""
+Spatial dynamics measurements using Spatial Markov
+"""
+
+
+import numpy as np
+import pysal as ps
+import plpy
+import crankshaft.pysal_utils as pu
+
+def spatial_markov_trend(subquery, time_cols, num_classes=7,
+                         w_type='knn', num_ngbrs=5, permutations=0,
+                         geom_col='the_geom', id_col='cartodb_id'):
+    """
+        Predict the trends of a unit based on:
+        1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile)
+        2. average class of its neighbors
+
+        Inputs:
+        @param subquery string: e.g., SELECT the_geom, cartodb_id,
+          interesting_time_column FROM table_name
+        @param time_cols list of strings: list of strings of column names
+        @param num_classes (optional): number of classes to break distribution
+          of values into. Currently uses quantile bins.
+        @param w_type string (optional): weight type ('knn' or 'queen')
+        @param num_ngbrs int (optional): number of neighbors (if knn type)
+        @param permutations int (optional): number of permutations for test
+          stats
+        @param geom_col string (optional): name of column which contains the
+          geometries
+        @param id_col string (optional): name of column which has the ids of
+          the table
+
+        Outputs:
+        @param trend_up float: probablity that a geom will move to a higher
+          class
+        @param trend_down float: probablity that a geom will move to a lower
+          class
+        @param trend float: (trend_up - trend_down) / trend_static
+        @param volatility float: a measure of the volatility based on
+          probability stddev(prob array)
+    """
+
+    if len(time_cols) < 2:
+        plpy.error('More than one time column needs to be passed')
+
+    qvals = {"id_col": id_col,
+             "time_cols": time_cols,
+             "geom_col": geom_col,
+             "subquery": subquery,
+             "num_ngbrs": num_ngbrs}
+
+    try:
+        query_result = plpy.execute(
+            pu.construct_neighbor_query(w_type, qvals)
+        )
+        if len(query_result) == 0:
+            return zip([None], [None], [None], [None], [None])
+    except plpy.SPIError, e:
+        plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals)))
+        plpy.error('Analysis failed: %s' % e)
+        return zip([None], [None], [None], [None], [None])
+
+    ## build weight
+    weights = pu.get_weight(query_result, w_type)
+    weights.transform = 'r'
+
+    ## prep time data
+    t_data = get_time_data(query_result, time_cols)
+
+    plpy.debug('shape of t_data %d, %d' % t_data.shape)
+    plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape)
+    plpy.debug('first num elements: %f' % t_data[0, 0])
+
+    sp_markov_result = ps.Spatial_Markov(t_data,
+                                         weights,
+                                         k=num_classes,
+                                         fixed=False,
+                                         permutations=permutations)
+
+    ## get lag classes
+    lag_classes = ps.Quantiles(
+        ps.lag_spatial(weights, t_data[:, -1]),
+        k=num_classes).yb
+
+    ## look up probablity distribution for each unit according to class and lag class
+    prob_dist = get_prob_dist(sp_markov_result.P,
+                              lag_classes,
+                              sp_markov_result.classes[:, -1])
+
+    ## find the ups and down and overall distribution of each cell
+    trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist,
+                                                             sp_markov_result.classes[:, -1])
+
+    ## output the results
+    return zip(trend, trend_up, trend_down, volatility, weights.id_order)
+
+def get_time_data(markov_data, time_cols):
+    """
+        Extract the time columns and bin appropriately
+    """
+    num_attrs = len(time_cols)
+    return np.array([[x['attr' + str(i)] for x in markov_data]
+                     for i in range(1, num_attrs+1)], dtype=float).transpose()
+
+## not currently used
+def rebin_data(time_data, num_time_per_bin):
+    """
+        Convert an n x l matrix into an (n/m) x l matrix where the values are
+         reduced (averaged) for the intervening states:
+          1 2 3 4    1.5 3.5
+          5 6 7 8 -> 5.5 7.5
+          9 8 7 6    8.5 6.5
+          5 4 3 2    4.5 2.5
+
+          if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix.
+
+        This process effectively resamples the data at a longer time span n
+         units longer than the input data.
+        For cases when there is a remainder (remainder(5/3) = 2), the remaining
+         two columns are binned together as the last time period, while the
+         first three are binned together for the first period.
+
+        Input:
+          @param time_data n x l  ndarray: measurements of an attribute at
+           different time intervals
+          @param num_time_per_bin int: number of columns to average into a new
+           column
+        Output:
+          ceil(n / m) x l ndarray of resampled time series
+    """
+
+    if time_data.shape[1] % num_time_per_bin == 0:
+        ## if fit is perfect, then use it
+        n_max = time_data.shape[1] / num_time_per_bin
+    else:
+        ## fit remainders into an additional column
+        n_max = time_data.shape[1] / num_time_per_bin + 1
+
+    return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
+                     for i in range(n_max)]).T
+
+def get_prob_dist(transition_matrix, lag_indices, unit_indices):
+    """
+        Given an array of transition matrices, look up the probability
+        associated with the arrangements passed
+
+        Input:
+        @param transition_matrix ndarray[k,k,k]:
+        @param lag_indices ndarray:
+        @param unit_indices ndarray:
+
+        Output:
+        Array of probability distributions
+    """
+
+    return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
+                     for i in range(len(lag_indices))])
+
+def get_prob_stats(prob_dist, unit_indices):
+    """
+        get the statistics of the probability distributions
+
+        Outputs:
+            @param trend_up ndarray(float): sum of probabilities for upward
+               movement (relative to the unit index of that prob)
+            @param trend_down ndarray(float): sum of probabilities for downward
+               movement (relative to the unit index of that prob)
+            @param trend ndarray(float): difference of upward and downward
+               movements
+    """
+
+    num_elements = len(unit_indices)
+    trend_up = np.empty(num_elements, dtype=float)
+    trend_down = np.empty(num_elements, dtype=float)
+    trend = np.empty(num_elements, dtype=float)
+
+    for i in range(num_elements):
+        trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
+        trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
+        if prob_dist[i, unit_indices[i]] > 0.0:
+            trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
+        else:
+            trend[i] = None
+
+    ## calculate volatility of distribution
+    volatility = prob_dist.std(axis=1)
+
+    return trend_up, trend_down, trend, volatility

release/python/0.4.1/crankshaft/setup.py

@@ -0,0 +1,49 @@
+
+"""
+CartoDB Spatial Analysis Python Library
+See:
+https://github.com/CartoDB/crankshaft
+"""
+
+from setuptools import setup, find_packages
+
+setup(
+    name='crankshaft',
+
+    version='0.0.0',
+
+    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.
+    # IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
+    install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
+
+    requires=['pysal', 'numpy', 'sklearn'],
+
+    test_suite='test'
+)

release/python/0.4.1/crankshaft/setup.py-r

@@ -0,0 +1,49 @@
+
+"""
+CartoDB Spatial Analysis Python Library
+See:
+https://github.com/CartoDB/crankshaft
+"""
+
+from setuptools import setup, find_packages
+
+setup(
+    name='crankshaft',
+
+    version='0.0.0',
+
+    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.
+    # IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
+    install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
+
+    requires=['pysal', 'numpy', 'sklearn'],
+
+    test_suite='test'
+)

release/python/0.4.1/crankshaft/test/fixtures/kmeans.json

@@ -0,0 +1 @@
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release/python/0.4.1/crankshaft/test/fixtures/markov.json

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