Test: try to avoid travis virtualenv

.travis.yml

@@ -0,0 +1,35 @@
+sudo: true
+
+language: c
+
+addons:
+  postgresql: "9.4"
+  apt:
+    packages:
+      - python2.7
+      - python2.7-dev 
+      - postgresql-plpython-9.4
+      - postgresql-server-dev-9.4
+      - liblapack-dev
+      - libatlas-dev
+      - gfortran
+
+env:
+  - PGUSER=postgres
+
+# From https://gist.github.com/dan-blanchard/7045057
+before_install:
+  - wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
+  - chmod +x miniconda.sh
+  - ./miniconda.sh -b -p `pwd`/miniconda
+  - export PATH=`pwd`/miniconda/bin:$PATH
+  - conda update --yes conda
+  - conda create --yes -n condaenv python=2.7
+  - source activate condaenv
+  - conda install --yes -n condaenv pip nose
+  - conda install --yes -n condaenv numpy=1.11.0 scipy=0.17.1 pysal=1.11.1 scikit-learn=0.17.1
+
+install:
+  - travis_wait 30 sudo make install
+
+script: make test

Makefile.global

@@ -1,6 +1,6 @@
 SELF_DIR         := $(dir $(lastword $(MAKEFILE_LIST)))
-EXTENSION         = crankshaft
-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
+EXTENSION        = crankshaft
+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

NEWS.md

@@ -1,15 +1,3 @@
-0.1.0 (2016-06-29)
-------------------
-* Adds Spatial Markov function
-* Adds Spacial interpolation function
-* Adds `CDB_pyAgg (columns Numeric[])` helper function
-* Adds Segmentation Functions
-
-0.0.4 (2016-06-20)
-------------------
-* Remove cartodb extension dependency from tests
-* Declare all correct dependencies with correct versions in setup.py
-
 0.0.3 (2016-06-16)
 ------------------
 * Adds new functions: kmeans, weighted centroids.

doc/04_markov.md

@@ -1,47 +0,0 @@
-## Spatial Markov
-
-### CDB_SpatialMarkovTrend(subquery text, column_names text array)
-
-This function takes time series data associated with geometries and outputs likelihoods that the next value of a geometry will move up, down, or stay static as compared to the most recent measurement. For more information, read about [Spatial Dynamics in PySAL](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/dynamics.html).
-
-#### Arguments
-
-| Name | Type | Description |
-|------|------|-------------|
-| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM real_estate_history`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
-| column_names | TEXT Array | Names of column that form the history of measurements for the geometries (e.g., `Array['y2011', 'y2012', 'y2013', 'y2014', 'y2015', 'y2016']`). |
-| num_classes (optional) | INT | Number of quantile classes to separate data into. |
-| weight type (optional) | TEXT | Type of weight to use when finding neighbors. Currently available options are 'knn' (default) and 'queen'. Read more about weight types in [PySAL's weights documentation](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html). |
-| num_ngbrs (optional) | INT | Number of neighbors if using k-nearest neighbors weight type. Defaults to 5. |
-| permutations (optional) | INT | Number of permutations to check against a random arrangement of the values in `column_name`. This influences the accuracy of the output field `significance`. Defaults to 99. |
-| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` |
-| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. |
-
-#### Returns
-
-A table with the following columns.
-
-| Column Name | Type | Description |
-|-------------|------|-------------|
-| trend | NUMERIC | The probability that the measure at this location will move up (a positive number) or down (a negative number) |
-| trend_up | NUMERIC | The probability that a measure will move up in subsequent steps of time |
-| trend_down | NUMERIC | The probability that a measure will move down in subsequent steps of time |
-| volatility | NUMERIC | A measure of the variance of the probabilities returned from the Spatial Markov predictions |
-| rowid | NUMERIC | id of the row that corresponds to the `id_col` (by default `cartodb_id` of the input rows)  |
-
-
-#### Example Usage
-
-```sql
-SELECT
-  c.cartodb_id,
-  c.the_geom,
-  m.trend,
-  m.trend_up,
-  m.trend_down,
-  m.volatility
-FROM CDB_SpatialMarkovTrend('SELECT * FROM nyc_real_estate'
-                            Array['m03y2009','m03y2010','m03y2011','m03y2012','m03y2013','m03y2014','m03y2015','m03y2016']) As m
-JOIN nyc_real_estate As c
-ON c.cartodb_id = m.rowid;
-```

doc/04_pyAgg.md

@@ -1,23 +0,0 @@
-## PyAgg Helper Function 
-
-### CDB_pyAgg (columns Numeric[])
-
-Currently it's not possible to pass a multidiemensional array between plpsql and plpythonu. This function aims to
-help fix that by aggergating the columns provided in the argument across rows in to a rows * columns + 1 length 1D array. The first element of the array is the array\_length of the columns argument so that python can reconstruct 
-the 2D array. 
-
-#### Arguments
-
-| Name | Type | Description |
-|------|------|-------------|
-| columns | NUMERIC[] | The columns to aggregate across rows|
-
-#### Returns
-
-A table with the following columns.
-
-| Column Name | Type | Description |
-|-------------|------|-------------|
-| result | NUMERIC[] | An columns * rows + 1 array where the first entry is the no of columns|
-
-

doc/08_interpolation.md

@@ -1,51 +0,0 @@
-## Spacial interpolation
-
-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)
-* [Barycentric](https://en.wikipedia.org/wiki/Barycentric_coordinate_system)
-* [IDW](https://en.wikipedia.org/wiki/Inverse_distance_weighting)
-
-### CDB_SpatialInterpolation (query text, point geometry, method integer DEFAULT 1, p1 integer DEFAULT 0, ps integer DEFAULT 0)
-
-#### Arguments
-
-| Name | Type | Description |
-|------|------|-------------|
-| 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|
-| 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)
-
-#### Arguments
-
-| Name | Type | Description |
-|------|------|-------------|
-| geom   | geometry[]  | Array of points's geometries |
-| 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|
-| p2   | integer     | IDW: order of distance decay, 0-> order 1|
-
-### Returns
-
-| Column Name | Type | Description |
-|-------------|------|-------------|
-| value  | numeric | Interpolated value at the given point, `-888.888` if the given point is out of the boundaries of the source points set |
-
-
-#### Example Usage
-
-```sql
-with a as (
-    select
-        array_agg(the_geom) as geomin,
-        array_agg(temp::numeric) as colin
-    from table_4804232032
-)
-SELECT CDB_SpatialInterpolation(geomin, colin, CDB_latlng(41.38, 2.15),1) FROM a;
-```

doc/12_segmentation.md

@@ -1,83 +0,0 @@
-
-## Segmentation Functions
-
-### CDB_CreateAndPredictSegment(query TEXT, variable_name TEXT, target_query TEXT)
-
-This function trains a [Gradient Boosting](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) model to attempt to predict the target data and then generates predictions for new data.  
-
-#### Arguments
-
-| Name | Type | Description |
-|------|------|-------------|
-| query | TEXT | The input query to train the algorithm, which should have both the variable of interest and the features that will be used to predict it |
-| variable\_name| TEXT | Specify the variable in the query to predict, all other columns are assumed to be features |
-| target\_table | TEXT | The query which returns the `cartodb_id` and features for the rows your would like to predict the target variable for |
-| n\_estimators (optional) | INTEGER DEFAULT 1200 | Number of estimators to be used. Values should be between 1 and x. |
-| max\_depth (optional) | INTEGER DEFAULT 3 | Max tree depth. Values should be between 1 and n. |
-| subsample (optional)  | DOUBLE PRECISION DEFAULT 0.5 | Subsample parameter for GradientBooster. Values should be within the range 0 to 1. |
-| learning\_rate (optional) | DOUBLE PRECISION DEFAULT 0.01 | Learning rate for the GradientBooster. Values should be between 0 and 1 (??) |
-| min\_samples\_leaf (optional) | INTEGER DEFAULT 1 | Minimum samples to use per leaf. Values should range from x to y |
-
-#### Returns
-
-A table with the following columns.
-
-| Column Name | Type | Description |
-|-------------|------|-------------|
-| cartodb\_id | INTEGER | The CartoDB id of the row in the target\_query |
-| prediction | NUMERIC | The predicted value of the variable of interest |
-| accuracy | NUMERIC | The mean squared accuracy of the model. |
-
-#### Example Usage
-
-```sql
-SELECT * from cdb_crankshaft.CDB_CreateAndPredictSegment(
-'SELECT agg, median_rent::numeric, male_pop::numeric, female_pop::numeric FROM late_night_agg',
-'agg',
-'SELECT row_number() OVER () As cartodb_id, median_rent, male_pop, female_pop FROM ml_learning_ny');                               
-```
-
-### CDB_CreateAndPredictSegment(target numeric[], train_features numeric[], prediction_features numeric[], prediction_ids numeric[])
-
-This function trains a [Gradient Boosting](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) model to attempt to predict the target data and then generates predictions for new data.  
-
-
-#### Arguments
-
-| Name | Type | Description |
-|------|------|-------------|
-| target | numeric[] | An array of target values of the variable you want to predict|
-| train\_features| numeric[] | 1D array of length n features \* n\_rows + 1 with the first entry in the array being the number of features in each row. These are the features the model will be trained on. CDB\_Crankshaft.CDB_pyAgg(Array[feature1, feature2, feature3]::numeric[]) can be used to construct this. |
-| prediction\_features | numeric[] | 1D array of length nfeatures\* n\_rows\_ + 1 with the first entry in the array being the number of features in each row. These are the features that will be used to predict the target variable  CDB\_Crankshaft.CDB\_pyAgg(Array[feature1, feature2, feature3]::numeric[]) can be used to construct this.  |
-| prediction\_ids | numeric[] | 1D array of length n\_rows with the ids that can use used to re-join the data with inputs |
-| n\_estimators (optional) | INTEGER DEFAULT 1200 | Number of estimators to be used |
-| max\_depth (optional) | INTEGER DEFAULT 3 | Max tree depth |
-| subsample (optional)  | DOUBLE PRECISION DEFAULT 0.5 | Subsample parameter for GradientBooster|
-| learning\_rate (optional) | DOUBLE PRECISION DEFAULT 0.01 | Learning rate for the GradientBooster |
-| min\_samples\_leaf (optional) | INTEGER DEFAULT 1 | Minimum samples to use per leaf |
-
-
-#### Returns
-
-A table with the following columns.
-
-| Column Name | Type | Description |
-|-------------|------|-------------|
-| cartodb\_id | INTEGER | The CartoDB id of the row in the target\_query |
-| prediction | NUMERIC | The predicted value of the variable of interest |
-| accuracy | NUMERIC | The mean squared accuracy of the model. |
-
-#### Example Usage
-
-```sql
-WITH training As (
-    SELECT array_agg(agg) As target,
-           cdb_crankshaft.CDB_PyAgg(Array[median_rent, male_pop, female_pop]::Numeric[]) As features
-    FROM late_night_agg),
-target AS (
-    SELECT cdb_crankshaft.CDB_PyAgg(Array[median_rent, male_pop, female_pop]::Numeric[]) As features,
-     array_agg(cartodb_id) As cartodb_ids FROM late_night_agg)  
-
-SELECT cdb_crankshaft.CDB_CreateAndPredictSegment(training.target, training.features, target.features, target.cartodb_ids)
-FROM training, target;
-```

release/crankshaft--0.0.3--0.0.4.sql

@@ -1,8 +0,0 @@
---DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES
--- Complain if script is sourced in psql, rather than via CREATE EXTENSION
-\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit
--- Version number of the extension release
-CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
-RETURNS text AS $$
-  SELECT '0.0.4'::text;
-$$ language 'sql' STABLE STRICT;

release/crankshaft--0.0.4--0.0.3.sql

@@ -1,8 +0,0 @@
---DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES
--- Complain if script is sourced in psql, rather than via CREATE EXTENSION
-\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit
--- Version number of the extension release
-CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
-RETURNS text AS $$
-  SELECT '0.0.3'::text;
-$$ language 'sql' STABLE STRICT;

release/crankshaft--0.0.4--0.1.0.sql

@@ -1,258 +0,0 @@
---DO NOT MODIFY THIS FILE, IT IS GENERATED FROM SOURCES
-
--- Complain if script is sourced in psql, rather than via CREATE EXTENSION
-\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit
-
---------------------------------------------------------------------------------
-
--- Version number of the extension release
-CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
-RETURNS text AS $$
-  SELECT '0.1.0'::text;
-$$ language 'sql' STABLE STRICT;
-
---------------------------------------------------------------------------------
-
--- PyAgg stuff
-CREATE OR REPLACE FUNCTION
-    CDB_PyAggS(current_state Numeric[], current_row Numeric[]) 
-    returns NUMERIC[] as $$
-    BEGIN
-        if array_upper(current_state,1) is null  then
-            RAISE NOTICE 'setting state %',array_upper(current_row,1);
-            current_state[1] = array_upper(current_row,1);
-        end if;
-        return array_cat(current_state,current_row) ;
-    END
-    $$ LANGUAGE plpgsql;
-
-
-CREATE AGGREGATE CDB_PyAgg(NUMERIC[])(
-    SFUNC = CDB_PyAggS,
-    STYPE = Numeric[],
-    INITCOND = "{}" 
-);
-
---------------------------------------------------------------------------------
-
--- Segmentation stuff
-CREATE OR REPLACE FUNCTION
-  CDB_CreateAndPredictSegment(
-    target NUMERIC[],
-    features NUMERIC[],
-    target_features NUMERIC[],
-    target_ids NUMERIC[],
-    n_estimators INTEGER DEFAULT 1200,
-    max_depth INTEGER DEFAULT 3,
-    subsample DOUBLE PRECISION DEFAULT 0.5,
-    learning_rate DOUBLE PRECISION DEFAULT 0.01,
-    min_samples_leaf INTEGER DEFAULT 1)
-RETURNS TABLE(cartodb_id NUMERIC, prediction NUMERIC, accuracy NUMERIC)
-AS $$
-    import numpy as np
-    import plpy
-
-    from crankshaft.segmentation import create_and_predict_segment_agg
-    model_params = {'n_estimators': n_estimators,
-                    'max_depth': max_depth,
-                    'subsample': subsample,
-                    'learning_rate': learning_rate,
-                    'min_samples_leaf': min_samples_leaf}
-
-    def unpack2D(data):
-        dimension = data.pop(0)
-        a = np.array(data, dtype=float)
-        return a.reshape(len(a)/dimension, dimension)
-
-    return create_and_predict_segment_agg(np.array(target, dtype=float),
-            unpack2D(features),
-            unpack2D(target_features),
-            target_ids,
-            model_params)
-
-$$ LANGUAGE plpythonu;
-
-CREATE OR REPLACE FUNCTION
-  CDB_CreateAndPredictSegment (
-      query TEXT,
-      variable_name TEXT,
-      target_table TEXT,
-      n_estimators INTEGER DEFAULT 1200,
-      max_depth INTEGER DEFAULT 3,
-      subsample DOUBLE PRECISION DEFAULT 0.5,
-      learning_rate DOUBLE PRECISION DEFAULT 0.01,
-      min_samples_leaf INTEGER DEFAULT 1)
-RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC)
-AS $$
-  from crankshaft.segmentation import create_and_predict_segment
-  model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf}
-  return create_and_predict_segment(query,variable_name,target_table, model_params)
-$$ LANGUAGE plpythonu;
-
---------------------------------------------------------------------------------
-
--- Spatial interpolation
-
--- 0: nearest neighbor
--- 1: barymetric
--- 2: IDW
-
-CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation(
-    IN query text,
-    IN point geometry,
-    IN method integer DEFAULT 1,
-    IN p1 numeric DEFAULT 0,
-    IN p2 numeric DEFAULT 0
-    )
-RETURNS numeric AS
-$$
-DECLARE
-    gs geometry[];
-    vs numeric[];
-    output numeric;
-BEGIN
-    EXECUTE 'WITH a AS('||query||') SELECT array_agg(the_geom), array_agg(attrib) FROM a' INTO gs, vs;
-    SELECT CDB_SpatialInterpolation(gs, vs, point, method, p1,p2) INTO output FROM a;
-
-    RETURN output;
-END;
-$$
-language plpgsql IMMUTABLE;
-
-CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation(
-    IN geomin geometry[],
-    IN colin numeric[],
-    IN point geometry,
-    IN method integer DEFAULT 1,
-    IN p1 numeric DEFAULT 0,
-    IN p2 numeric DEFAULT 0
-    )
-RETURNS numeric AS
-$$
-DECLARE
-    gs geometry[];
-    vs numeric[];
-    gs2 geometry[];
-    vs2 numeric[];
-    g geometry;
-    vertex geometry[];
-    sg numeric;
-    sa numeric;
-    sb numeric;
-    sc numeric;
-    va numeric;
-    vb numeric;
-    vc numeric;
-    output numeric;
-BEGIN
-    output :=  -999.999;
-    -- nearest
-    IF method = 0 THEN
-
-        WITH    a as (SELECT unnest(geomin) as g, unnest(colin) as v)
-        SELECT a.v INTO output FROM a ORDER BY point<->a.g LIMIT 1;
-        RETURN output;
-
-    -- barymetric
-    ELSIF method = 1 THEN
-        WITH    a as (SELECT unnest(geomin) AS e),
-                b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a),
-                c as (SELECT (ST_Dump(t)).geom as v FROM b),
-                d as (SELECT v FROM c WHERE ST_Within(point, v))
-            SELECT v INTO g FROM d;
-        IF g is null THEN
-            -- out of the realm of the input data
-            RETURN -888.888;
-        END IF;
-        -- vertex of the selected cell
-        WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v)
-                SELECT array_agg(v) INTO vertex FROM a;
-
-            -- retrieve the value of each vertex
-        WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
-            SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]);
-        WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
-            SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]);
-        WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
-                SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]);
-
-        SELECT ST_area(g), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[2], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[1], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point,vertex[1],vertex[2], point]))) INTO sg, sa, sb, sc;
-
-        output := (coalesce(sa,0) * coalesce(va,0) + coalesce(sb,0) * coalesce(vb,0) + coalesce(sc,0) * coalesce(vc,0)) / coalesce(sg);
-        RETURN output;
-
-    -- IDW
-    -- p1: limit the number of neighbors, 0->no limit
-    -- p2: order of distance decay, 0-> order 1
-    ELSIF method = 2 THEN
-
-        IF p2 = 0 THEN
-            p2 := 1;
-        END IF;
-
-        WITH    a as (SELECT unnest(geomin) as g, unnest(colin) as v),
-                b as (SELECT a.g, a.v FROM a ORDER BY point<->a.g)
-        SELECT array_agg(b.g), array_agg(b.v) INTO gs, vs FROM b;
-        IF p1::integer>0 THEN
-            gs2:=gs;
-            vs2:=vs;
-            FOR i IN 1..p1
-            LOOP
-                gs2 := gs2 || gs[i];
-                vs2 := vs2 || vs[i];
-            END LOOP;
-        ELSE
-            gs2:=gs;
-            vs2:=vs;
-        END IF;
-
-        WITH    a as (SELECT unnest(gs2) as g, unnest(vs2) as v),
-                b as (
-                    SELECT
-                    (1/ST_distance(point, a.g)^p2::integer) as k,
-                    (a.v/ST_distance(point, a.g)^p2::integer) as f
-                    FROM a
-                )
-        SELECT sum(b.f)/sum(b.k) INTO output FROM b;
-        RETURN output;
-
-    END IF;
-
-    RETURN -777.777;
-
-END;
-$$
-language plpgsql IMMUTABLE;
-
-
---------------------------------------------------------------------------------
-
--- Spatial Markov
-
--- input table format:
--- id | geom | date_1 | date_2 | date_3
---  1 | Pt1  | 12.3   | 13.1   | 14.2
---  2 | Pt2  | 11.0   | 13.2   | 12.5
---  ...
--- Sample Function call:
--- SELECT CDB_SpatialMarkov('SELECT * FROM real_estate',
---                          Array['date_1', 'date_2', 'date_3'])
-
-CREATE OR REPLACE FUNCTION
-  CDB_SpatialMarkovTrend (
-      subquery TEXT,
-      time_cols TEXT[],
-      num_classes INT DEFAULT 7,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-  	  permutations INT DEFAULT 99,
-  	  geom_col TEXT DEFAULT 'the_geom',
-  	  id_col TEXT DEFAULT 'cartodb_id')
-RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT)
-AS $$
-
-  from crankshaft.space_time_dynamics import spatial_markov_trend
-
-  ## TODO: use named parameters or a dictionary
-  return spatial_markov_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpythonu;

release/crankshaft--0.0.4.sql

@@ -1,403 +0,0 @@
---DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES
--- Complain if script is sourced in psql, rather than via CREATE EXTENSION
-\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit
--- Version number of the extension release
-CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
-RETURNS text AS $$
-  SELECT '0.0.4'::text;
-$$ language 'sql' STABLE STRICT;
-
--- Internal identifier of the installed extension instence
--- e.g. 'dev' for current development version
-CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version()
-RETURNS text AS $$
-  SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL;
-$$ language 'sql' STABLE STRICT;
--- Internal function.
--- Set the seeds of the RNGs (Random Number Generators)
--- used internally.
-CREATE OR REPLACE FUNCTION
-_cdb_random_seeds (seed_value INTEGER) RETURNS VOID
-AS $$
-  from crankshaft import random_seeds
-  random_seeds.set_random_seeds(seed_value)
-$$ LANGUAGE plpythonu;
--- Moran's I Global Measure (public-facing)
-CREATE OR REPLACE FUNCTION
-  CDB_AreasOfInterestGlobal(
-      subquery TEXT,
-      column_name TEXT,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id')
-RETURNS TABLE (moran NUMERIC, significance NUMERIC)
-AS $$
-  from crankshaft.clustering import moran_local
-  # TODO: use named parameters or a dictionary
-  return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpythonu;
-
--- Moran's I Local (internal function)
-CREATE OR REPLACE FUNCTION
-  _CDB_AreasOfInterestLocal(
-      subquery TEXT,
-      column_name TEXT,
-      w_type TEXT,
-      num_ngbrs INT,
-      permutations INT,
-      geom_col TEXT,
-      id_col TEXT)
-RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-  from crankshaft.clustering import moran_local
-  # TODO: use named parameters or a dictionary
-  return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpythonu;
-
--- Moran's I Local (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_AreasOfInterestLocal(
-    subquery TEXT,
-    column_name TEXT,
-    w_type TEXT DEFAULT 'knn',
-    num_ngbrs INT DEFAULT 5,
-    permutations INT DEFAULT 99,
-    geom_col TEXT DEFAULT 'the_geom',
-    id_col TEXT DEFAULT 'cartodb_id')
-RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col);
-
-$$ LANGUAGE SQL;
-
--- Moran's I only for HH and HL (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_GetSpatialHotspots(
-    subquery TEXT,
-    column_name TEXT,
-    w_type TEXT DEFAULT 'knn',
-    num_ngbrs INT DEFAULT 5,
-    permutations INT DEFAULT 99,
-    geom_col TEXT DEFAULT 'the_geom',
-    id_col TEXT DEFAULT 'cartodb_id')
-    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
-  WHERE quads IN ('HH', 'HL');
-
-$$ LANGUAGE SQL;
-
--- Moran's I only for LL and LH (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_GetSpatialColdspots(
-    subquery TEXT,
-    attr TEXT,
-    w_type TEXT DEFAULT 'knn',
-    num_ngbrs INT DEFAULT 5,
-    permutations INT DEFAULT 99,
-    geom_col TEXT DEFAULT 'the_geom',
-    id_col TEXT DEFAULT 'cartodb_id')
-    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
-  WHERE quads IN ('LL', 'LH');
-
-$$ LANGUAGE SQL;
-
--- Moran's I only for LH and HL (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_GetSpatialOutliers(
-    subquery TEXT,
-    attr TEXT,
-    w_type TEXT DEFAULT 'knn',
-    num_ngbrs INT DEFAULT 5,
-    permutations INT DEFAULT 99,
-    geom_col TEXT DEFAULT 'the_geom',
-    id_col TEXT DEFAULT 'cartodb_id')
-    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
-  WHERE quads IN ('HL', 'LH');
-
-$$ LANGUAGE SQL;
-
--- Moran's I Global Rate (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_AreasOfInterestGlobalRate(
-      subquery TEXT,
-      numerator TEXT,
-      denominator TEXT,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id')
-RETURNS TABLE (moran FLOAT, significance FLOAT)
-AS $$
-  from crankshaft.clustering import moran_local
-  # TODO: use named parameters or a dictionary
-  return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpythonu;
-
-
--- Moran's I Local Rate (internal function)
-CREATE OR REPLACE FUNCTION
-  _CDB_AreasOfInterestLocalRate(
-      subquery TEXT,
-      numerator TEXT,
-      denominator TEXT,
-      w_type TEXT,
-      num_ngbrs INT,
-      permutations INT,
-      geom_col TEXT,
-      id_col TEXT)
-RETURNS
-TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-  from crankshaft.clustering import moran_local_rate
-  # TODO: use named parameters or a dictionary
-  return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpythonu;
-
--- Moran's I Local Rate (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_AreasOfInterestLocalRate(
-      subquery TEXT,
-      numerator TEXT,
-      denominator TEXT,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id')
-RETURNS
-TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col);
-
-$$ LANGUAGE SQL;
-
--- Moran's I Local Rate only for HH and HL (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_GetSpatialHotspotsRate(
-      subquery TEXT,
-      numerator TEXT,
-      denominator TEXT,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id')
-RETURNS
-TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
-  WHERE quads IN ('HH', 'HL');
-
-$$ LANGUAGE SQL;
-
--- Moran's I Local Rate only for LL and LH (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_GetSpatialColdspotsRate(
-      subquery TEXT,
-      numerator TEXT,
-      denominator TEXT,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id')
-RETURNS
-TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
-  WHERE quads IN ('LL', 'LH');
-
-$$ LANGUAGE SQL;
-
--- Moran's I Local Rate only for LH and HL (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_GetSpatialOutliersRate(
-      subquery TEXT,
-      numerator TEXT,
-      denominator TEXT,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id')
-RETURNS
-TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
-  WHERE quads IN ('HL', 'LH');
-
-$$ LANGUAGE SQL;
-CREATE OR REPLACE FUNCTION  CDB_KMeans(query text, no_clusters integer,no_init integer default 20)
-RETURNS table (cartodb_id integer, cluster_no integer) as $$
-    
-    from crankshaft.clustering import kmeans
-    return kmeans(query,no_clusters,no_init)
-
-$$ language plpythonu;
-
-
-CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC)
-RETURNS Numeric[] AS 
-$$
-DECLARE 
-    newX NUMERIC;
-    newY NUMERIC;
-    newW NUMERIC;
-BEGIN
-    IF weight IS NULL OR the_geom IS NULL THEN 
-        newX = state[1];
-        newY = state[2];
-        newW = state[3];
-    ELSE
-        newX = state[1] + ST_X(the_geom)*weight;
-        newY = state[2] + ST_Y(the_geom)*weight;
-        newW = state[3] + weight;
-    END IF;
-    RETURN Array[newX,newY,newW];
-
-END
-$$ LANGUAGE plpgsql;
-
-CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[])
-RETURNS GEOMETRY AS 
-$$
-BEGIN
-    IF state[3] = 0 THEN 
-        RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326);
-    ELSE 
-        RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326);
-    END IF;
-END
-$$ LANGUAGE plpgsql;
-
-CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC)(
-    SFUNC = CDB_WeightedMeanS,
-    FINALFUNC = CDB_WeightedMeanF,
-    STYPE = Numeric[],
-    INITCOND = "{0.0,0.0,0.0}" 
-);
--- Function by Stuart Lynn for a simple interpolation of a value
--- from a polygon table over an arbitrary polygon
--- (weighted by the area proportion overlapped)
--- Aereal weighting is a very simple form of aereal interpolation.
---
--- Parameters:
---   * geom a Polygon geometry which defines the area where a value will be
---     estimated as the area-weighted sum of a given table/column
---   * target_table_name table name of the table that provides the values
---   * target_column column name of the column that provides the values
---   * schema_name optional parameter to defina the schema the target table
---     belongs to, which is necessary if its not in the search_path.
---     Note that target_table_name should never include the schema in it.
--- Return value:
---   Aereal-weighted interpolation of the column values over the geometry
-CREATE OR REPLACE
-FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL)
-  RETURNS numeric AS
-$$
-DECLARE
-	result numeric;
-  qualified_name text;
-BEGIN
-  IF schema_name IS NULL THEN
-    qualified_name := Format('%I', target_table_name);
-  ELSE
-    qualified_name := Format('%I.%s', schema_name, target_table_name);
-  END IF;
-  EXECUTE Format('
-    SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom))
-    FROM %s AS a
-    WHERE $1 && a.the_geom
-  ', target_column, qualified_name)
-  USING geom
-  INTO result;
-  RETURN result;
-END;
-$$ LANGUAGE plpgsql;
---
--- Creates N points randomly distributed arround the polygon
---
--- @param g - the geometry to be turned in to points
---
--- @param no_points - the number of points to generate
---
--- @params max_iter_per_point - the function generates points in the polygon's bounding box
--- and discards points which don't lie in the polygon. max_iter_per_point specifies how many
--- misses per point the funciton accepts before giving up.
---
--- Returns: Multipoint with the requested points
-CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000)
-RETURNS GEOMETRY AS $$
-DECLARE
-  extent GEOMETRY;
-  test_point Geometry;
-  width                NUMERIC;
-  height               NUMERIC;
-  x0                   NUMERIC;
-  y0                   NUMERIC;
-  xp                   NUMERIC;
-  yp                   NUMERIC;
-  no_left              INTEGER;
-  remaining_iterations INTEGER;
-  points               GEOMETRY[];
-  bbox_line            GEOMETRY;
-  intersection_line    GEOMETRY;
-BEGIN
-  extent  := ST_Envelope(geom);
-  width   := ST_XMax(extent) - ST_XMIN(extent);
-  height  := ST_YMax(extent) - ST_YMIN(extent);
-  x0 	  := ST_XMin(extent);
-  y0 	  := ST_YMin(extent);
-  no_left := no_points;
-
-  LOOP
-    if(no_left=0) THEN
-      EXIT;
-    END IF;
-    yp = y0 + height*random();
-    bbox_line  = ST_MakeLine(
-      ST_SetSRID(ST_MakePoint(yp, x0),4326),
-      ST_SetSRID(ST_MakePoint(yp, x0+width),4326)
-    );
-    intersection_line = ST_Intersection(bbox_line,geom);
-  	test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random());
-	  points := points || test_point;
-	  no_left = no_left - 1 ;
-  END LOOP;
-  RETURN ST_Collect(points);
-END;
-$$
-LANGUAGE plpgsql VOLATILE;
--- Make sure by default there are no permissions for publicuser
--- NOTE: this happens at extension creation time, as part of an implicit transaction.
--- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE;
-
--- Grant permissions on the schema to publicuser (but just the schema)
-GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser;
-
--- Revoke execute permissions on all functions in the schema by default
--- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser;

release/crankshaft--0.1.0--0.0.4.sql

@@ -1,81 +0,0 @@
---DO NOT MODIFY THIS FILE, IT IS GENERATED FROM SOURCES
-
--- Complain if script is sourced in psql, rather than via CREATE EXTENSION
-\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit
-
--- Version number of the extension release
-CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
-RETURNS text AS $$
-  SELECT '0.0.4'::text;
-$$ language 'sql' STABLE STRICT;
-
---------------------------------------------------------------------------------
-
--- Spatial Markov
-
-DROP FUNCTION
-  CDB_SpatialMarkovTrend (
-      subquery TEXT,
-      time_cols TEXT[],
-      num_classes INT,
-      w_type TEXT,
-      num_ngbrs INT,
-  	  permutations INT,
-  	  geom_col TEXT,
-  	  id_col TEXT);
-
-
---------------------------------------------------------------------------------
-
--- Spatial interpolation
-
-DROP FUNCTION CDB_SpatialInterpolation(
-    IN geomin geometry[],
-    IN colin numeric[],
-    IN point geometry,
-    IN method integer,
-    IN p1 numeric,
-    IN p2 numeric
-    );
-
-DROP FUNCTION CDB_SpatialInterpolation(
-    IN query text,
-    IN point geometry,
-    IN method integer,
-    IN p1 numeric,
-    IN p2 numeric
-    );
-
---------------------------------------------------------------------------------
-
--- Segmentation stuff
-
-DROP FUNCTION
-  CDB_CreateAndPredictSegment (
-      query TEXT,
-      variable_name TEXT,
-      target_table TEXT,
-      n_estimators INTEGER,
-      max_depth INTEGER,
-      subsample DOUBLE PRECISION,
-      learning_rate DOUBLE PRECISION,
-      min_samples_leaf INTEGER);
-
-DROP FUNCTION
-  CDB_CreateAndPredictSegment(
-    target NUMERIC[],
-    features NUMERIC[],
-    target_features NUMERIC[],
-    target_ids NUMERIC[],
-    n_estimators INTEGER,
-    max_depth INTEGER,
-    subsample DOUBLE PRECISION,
-    learning_rate DOUBLE PRECISION,
-    min_samples_leaf INTEGER);
-
---------------------------------------------------------------------------------
-
--- PyAgg stuff
-
-DROP AGGREGATE CDB_PyAgg(NUMERIC[]);
-DROP FUNCTION CDB_PyAggS(Numeric[], Numeric[]);

release/crankshaft--0.1.0.sql

@@ -1,686 +0,0 @@
---DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES
--- Complain if script is sourced in psql, rather than via CREATE EXTENSION
-\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit
--- Version number of the extension release
-CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
-RETURNS text AS $$
-  SELECT '0.1.0'::text;
-$$ language 'sql' STABLE STRICT;
-
--- Internal identifier of the installed extension instence
--- e.g. 'dev' for current development version
-CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version()
-RETURNS text AS $$
-  SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL;
-$$ language 'sql' STABLE STRICT;
--- Internal function.
--- Set the seeds of the RNGs (Random Number Generators)
--- used internally.
-CREATE OR REPLACE FUNCTION
-_cdb_random_seeds (seed_value INTEGER) RETURNS VOID
-AS $$
-  from crankshaft import random_seeds
-  random_seeds.set_random_seeds(seed_value)
-$$ LANGUAGE plpythonu;
-CREATE OR REPLACE FUNCTION
-    CDB_PyAggS(current_state Numeric[], current_row Numeric[]) 
-    returns NUMERIC[] as $$
-    BEGIN
-        if array_upper(current_state,1) is null  then
-            RAISE NOTICE 'setting state %',array_upper(current_row,1);
-            current_state[1] = array_upper(current_row,1);
-        end if;
-        return array_cat(current_state,current_row) ;
-    END
-    $$ LANGUAGE plpgsql;
-
-
-CREATE AGGREGATE CDB_PyAgg(NUMERIC[])(
-    SFUNC = CDB_PyAggS,
-    STYPE = Numeric[],
-    INITCOND = "{}" 
-);
-
-
-CREATE OR REPLACE FUNCTION
-  CDB_CreateAndPredictSegment(
-    target NUMERIC[],
-    features NUMERIC[],
-    target_features NUMERIC[],
-    target_ids NUMERIC[],
-    n_estimators INTEGER DEFAULT 1200,
-    max_depth INTEGER DEFAULT 3,
-    subsample DOUBLE PRECISION DEFAULT 0.5,
-    learning_rate DOUBLE PRECISION DEFAULT 0.01,
-    min_samples_leaf INTEGER DEFAULT 1)
-RETURNS TABLE(cartodb_id NUMERIC, prediction NUMERIC, accuracy NUMERIC)
-AS $$
-    import numpy as np
-    import plpy
-
-    from crankshaft.segmentation import create_and_predict_segment_agg
-    model_params = {'n_estimators': n_estimators,
-                    'max_depth': max_depth,
-                    'subsample': subsample,
-                    'learning_rate': learning_rate,
-                    'min_samples_leaf': min_samples_leaf}
-
-    def unpack2D(data):
-        dimension = data.pop(0)
-        a = np.array(data, dtype=float)
-        return a.reshape(len(a)/dimension, dimension)
-
-    return create_and_predict_segment_agg(np.array(target, dtype=float),
-            unpack2D(features),
-            unpack2D(target_features),
-            target_ids,
-            model_params)
-
-$$ LANGUAGE plpythonu;
-
-CREATE OR REPLACE FUNCTION
-  CDB_CreateAndPredictSegment (
-      query TEXT,
-      variable_name TEXT,
-      target_table TEXT,
-      n_estimators INTEGER DEFAULT 1200,
-      max_depth INTEGER DEFAULT 3,
-      subsample DOUBLE PRECISION DEFAULT 0.5,
-      learning_rate DOUBLE PRECISION DEFAULT 0.01,
-      min_samples_leaf INTEGER DEFAULT 1)
-RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC)
-AS $$
-  from crankshaft.segmentation import create_and_predict_segment
-  model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf}
-  return create_and_predict_segment(query,variable_name,target_table, model_params)
-$$ LANGUAGE plpythonu;
--- 0: nearest neighbor
--- 1: barymetric
--- 2: IDW
-
-CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation(
-    IN query text,
-    IN point geometry,
-    IN method integer DEFAULT 1,
-    IN p1 numeric DEFAULT 0,
-    IN p2 numeric DEFAULT 0
-    )
-RETURNS numeric AS
-$$
-DECLARE
-    gs geometry[];
-    vs numeric[];
-    output numeric;
-BEGIN
-    EXECUTE 'WITH a AS('||query||') SELECT array_agg(the_geom), array_agg(attrib) FROM a' INTO gs, vs;
-    SELECT CDB_SpatialInterpolation(gs, vs, point, method, p1,p2) INTO output FROM a;
-
-    RETURN output;
-END;
-$$
-language plpgsql IMMUTABLE;
-
-CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation(
-    IN geomin geometry[],
-    IN colin numeric[],
-    IN point geometry,
-    IN method integer DEFAULT 1,
-    IN p1 numeric DEFAULT 0,
-    IN p2 numeric DEFAULT 0
-    )
-RETURNS numeric AS
-$$
-DECLARE
-    gs geometry[];
-    vs numeric[];
-    gs2 geometry[];
-    vs2 numeric[];
-    g geometry;
-    vertex geometry[];
-    sg numeric;
-    sa numeric;
-    sb numeric;
-    sc numeric;
-    va numeric;
-    vb numeric;
-    vc numeric;
-    output numeric;
-BEGIN
-    output :=  -999.999;
-    -- nearest
-    IF method = 0 THEN
-
-        WITH    a as (SELECT unnest(geomin) as g, unnest(colin) as v)
-        SELECT a.v INTO output FROM a ORDER BY point<->a.g LIMIT 1;
-        RETURN output;
-
-    -- barymetric
-    ELSIF method = 1 THEN
-        WITH    a as (SELECT unnest(geomin) AS e),
-                b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a),
-                c as (SELECT (ST_Dump(t)).geom as v FROM b),
-                d as (SELECT v FROM c WHERE ST_Within(point, v))
-            SELECT v INTO g FROM d;
-        IF g is null THEN
-            -- out of the realm of the input data
-            RETURN -888.888;
-        END IF;
-        -- vertex of the selected cell
-        WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v)
-                SELECT array_agg(v) INTO vertex FROM a;
-
-            -- retrieve the value of each vertex
-        WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
-            SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]);
-        WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
-            SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]);
-        WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
-                SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]);
-
-        SELECT ST_area(g), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[2], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[1], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point,vertex[1],vertex[2], point]))) INTO sg, sa, sb, sc;
-
-        output := (coalesce(sa,0) * coalesce(va,0) + coalesce(sb,0) * coalesce(vb,0) + coalesce(sc,0) * coalesce(vc,0)) / coalesce(sg);
-        RETURN output;
-
-    -- IDW
-    -- p1: limit the number of neighbors, 0->no limit
-    -- p2: order of distance decay, 0-> order 1
-    ELSIF method = 2 THEN
-
-        IF p2 = 0 THEN
-            p2 := 1;
-        END IF;
-
-        WITH    a as (SELECT unnest(geomin) as g, unnest(colin) as v),
-                b as (SELECT a.g, a.v FROM a ORDER BY point<->a.g)
-        SELECT array_agg(b.g), array_agg(b.v) INTO gs, vs FROM b;
-        IF p1::integer>0 THEN
-            gs2:=gs;
-            vs2:=vs;
-            FOR i IN 1..p1
-            LOOP
-                gs2 := gs2 || gs[i];
-                vs2 := vs2 || vs[i];
-            END LOOP;
-        ELSE
-            gs2:=gs;
-            vs2:=vs;
-        END IF;
-
-        WITH    a as (SELECT unnest(gs2) as g, unnest(vs2) as v),
-                b as (
-                    SELECT
-                    (1/ST_distance(point, a.g)^p2::integer) as k,
-                    (a.v/ST_distance(point, a.g)^p2::integer) as f
-                    FROM a
-                )
-        SELECT sum(b.f)/sum(b.k) INTO output FROM b;
-        RETURN output;
-
-    END IF;
-
-    RETURN -777.777;
-
-END;
-$$
-language plpgsql IMMUTABLE;
--- Moran's I Global Measure (public-facing)
-CREATE OR REPLACE FUNCTION
-  CDB_AreasOfInterestGlobal(
-      subquery TEXT,
-      column_name TEXT,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id')
-RETURNS TABLE (moran NUMERIC, significance NUMERIC)
-AS $$
-  from crankshaft.clustering import moran_local
-  # TODO: use named parameters or a dictionary
-  return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpythonu;
-
--- Moran's I Local (internal function)
-CREATE OR REPLACE FUNCTION
-  _CDB_AreasOfInterestLocal(
-      subquery TEXT,
-      column_name TEXT,
-      w_type TEXT,
-      num_ngbrs INT,
-      permutations INT,
-      geom_col TEXT,
-      id_col TEXT)
-RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-  from crankshaft.clustering import moran_local
-  # TODO: use named parameters or a dictionary
-  return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpythonu;
-
--- Moran's I Local (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_AreasOfInterestLocal(
-    subquery TEXT,
-    column_name TEXT,
-    w_type TEXT DEFAULT 'knn',
-    num_ngbrs INT DEFAULT 5,
-    permutations INT DEFAULT 99,
-    geom_col TEXT DEFAULT 'the_geom',
-    id_col TEXT DEFAULT 'cartodb_id')
-RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col);
-
-$$ LANGUAGE SQL;
-
--- Moran's I only for HH and HL (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_GetSpatialHotspots(
-    subquery TEXT,
-    column_name TEXT,
-    w_type TEXT DEFAULT 'knn',
-    num_ngbrs INT DEFAULT 5,
-    permutations INT DEFAULT 99,
-    geom_col TEXT DEFAULT 'the_geom',
-    id_col TEXT DEFAULT 'cartodb_id')
-    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
-  WHERE quads IN ('HH', 'HL');
-
-$$ LANGUAGE SQL;
-
--- Moran's I only for LL and LH (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_GetSpatialColdspots(
-    subquery TEXT,
-    attr TEXT,
-    w_type TEXT DEFAULT 'knn',
-    num_ngbrs INT DEFAULT 5,
-    permutations INT DEFAULT 99,
-    geom_col TEXT DEFAULT 'the_geom',
-    id_col TEXT DEFAULT 'cartodb_id')
-    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
-  WHERE quads IN ('LL', 'LH');
-
-$$ LANGUAGE SQL;
-
--- Moran's I only for LH and HL (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_GetSpatialOutliers(
-    subquery TEXT,
-    attr TEXT,
-    w_type TEXT DEFAULT 'knn',
-    num_ngbrs INT DEFAULT 5,
-    permutations INT DEFAULT 99,
-    geom_col TEXT DEFAULT 'the_geom',
-    id_col TEXT DEFAULT 'cartodb_id')
-    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
-  WHERE quads IN ('HL', 'LH');
-
-$$ LANGUAGE SQL;
-
--- Moran's I Global Rate (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_AreasOfInterestGlobalRate(
-      subquery TEXT,
-      numerator TEXT,
-      denominator TEXT,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id')
-RETURNS TABLE (moran FLOAT, significance FLOAT)
-AS $$
-  from crankshaft.clustering import moran_local
-  # TODO: use named parameters or a dictionary
-  return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpythonu;
-
-
--- Moran's I Local Rate (internal function)
-CREATE OR REPLACE FUNCTION
-  _CDB_AreasOfInterestLocalRate(
-      subquery TEXT,
-      numerator TEXT,
-      denominator TEXT,
-      w_type TEXT,
-      num_ngbrs INT,
-      permutations INT,
-      geom_col TEXT,
-      id_col TEXT)
-RETURNS
-TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-  from crankshaft.clustering import moran_local_rate
-  # TODO: use named parameters or a dictionary
-  return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpythonu;
-
--- Moran's I Local Rate (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_AreasOfInterestLocalRate(
-      subquery TEXT,
-      numerator TEXT,
-      denominator TEXT,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id')
-RETURNS
-TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col);
-
-$$ LANGUAGE SQL;
-
--- Moran's I Local Rate only for HH and HL (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_GetSpatialHotspotsRate(
-      subquery TEXT,
-      numerator TEXT,
-      denominator TEXT,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id')
-RETURNS
-TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
-  WHERE quads IN ('HH', 'HL');
-
-$$ LANGUAGE SQL;
-
--- Moran's I Local Rate only for LL and LH (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_GetSpatialColdspotsRate(
-      subquery TEXT,
-      numerator TEXT,
-      denominator TEXT,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id')
-RETURNS
-TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
-  WHERE quads IN ('LL', 'LH');
-
-$$ LANGUAGE SQL;
-
--- Moran's I Local Rate only for LH and HL (public-facing function)
-CREATE OR REPLACE FUNCTION
-  CDB_GetSpatialOutliersRate(
-      subquery TEXT,
-      numerator TEXT,
-      denominator TEXT,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-      permutations INT DEFAULT 99,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id')
-RETURNS
-TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
-AS $$
-
-  SELECT moran, quads, significance, rowid, vals
-  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
-  WHERE quads IN ('HL', 'LH');
-
-$$ LANGUAGE SQL;
-CREATE OR REPLACE FUNCTION  CDB_KMeans(query text, no_clusters integer,no_init integer default 20)
-RETURNS table (cartodb_id integer, cluster_no integer) as $$
-    
-    from crankshaft.clustering import kmeans
-    return kmeans(query,no_clusters,no_init)
-
-$$ language plpythonu;
-
-
-CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC)
-RETURNS Numeric[] AS 
-$$
-DECLARE 
-    newX NUMERIC;
-    newY NUMERIC;
-    newW NUMERIC;
-BEGIN
-    IF weight IS NULL OR the_geom IS NULL THEN 
-        newX = state[1];
-        newY = state[2];
-        newW = state[3];
-    ELSE
-        newX = state[1] + ST_X(the_geom)*weight;
-        newY = state[2] + ST_Y(the_geom)*weight;
-        newW = state[3] + weight;
-    END IF;
-    RETURN Array[newX,newY,newW];
-
-END
-$$ LANGUAGE plpgsql;
-
-CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[])
-RETURNS GEOMETRY AS 
-$$
-BEGIN
-    IF state[3] = 0 THEN 
-        RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326);
-    ELSE 
-        RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326);
-    END IF;
-END
-$$ LANGUAGE plpgsql;
-
-CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC)(
-    SFUNC = CDB_WeightedMeanS,
-    FINALFUNC = CDB_WeightedMeanF,
-    STYPE = Numeric[],
-    INITCOND = "{0.0,0.0,0.0}" 
-);
--- Spatial Markov
-
--- input table format:
--- id | geom | date_1 | date_2 | date_3
---  1 | Pt1  | 12.3   | 13.1   | 14.2
---  2 | Pt2  | 11.0   | 13.2   | 12.5
---  ...
--- Sample Function call:
--- SELECT CDB_SpatialMarkov('SELECT * FROM real_estate',
---                          Array['date_1', 'date_2', 'date_3'])
-
-CREATE OR REPLACE FUNCTION
-  CDB_SpatialMarkovTrend (
-      subquery TEXT,
-      time_cols TEXT[],
-      num_classes INT DEFAULT 7,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-  	  permutations INT DEFAULT 99,
-  	  geom_col TEXT DEFAULT 'the_geom',
-  	  id_col TEXT DEFAULT 'cartodb_id')
-RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT)
-AS $$
-
-  from crankshaft.space_time_dynamics import spatial_markov_trend
-
-  ## TODO: use named parameters or a dictionary
-  return spatial_markov_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpythonu;
-
--- input table format: identical to above but in a predictable format
--- Sample function call:
--- SELECT cdb_spatial_markov('SELECT * FROM real_estate',
---                           'date_1')
-
-
--- CREATE OR REPLACE FUNCTION
---   cdb_spatial_markov (
---       subquery TEXT,
---       time_col_min text,
---       time_col_max text,
---       date_format text, -- '_YYYY_MM_DD'
---       num_time_per_bin INT DEFAULT 1,
---   	  permutations INT DEFAULT 99,
---   	  geom_column TEXT DEFAULT 'the_geom',
---   	  id_col TEXT DEFAULT 'cartodb_id',
---       w_type TEXT DEFAULT 'knn',
---       num_ngbrs int DEFAULT 5)
--- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
--- AS $$
---   plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
---   from crankshaft.clustering import moran_local
---   # TODO: use named parameters or a dictionary
---   return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs)
--- $$ LANGUAGE plpythonu;
---
--- -- input table format:
--- -- id | geom | date  | measurement
--- --  1 | Pt1  | 12/3  | 13.2
--- --  2 | Pt2  | 11/5  | 11.3
--- --  3 | Pt1  | 11/13 | 12.9
--- --  4 | Pt3  | 12/19 | 10.1
--- -- ...
---
--- CREATE OR REPLACE FUNCTION
---   cdb_spatial_markov (
---       subquery TEXT,
---       time_col text,
---       num_time_per_bin INT DEFAULT 1,
---   	  permutations INT DEFAULT 99,
---   	  geom_column TEXT DEFAULT 'the_geom',
---   	  id_col TEXT DEFAULT 'cartodb_id',
---       w_type TEXT DEFAULT 'knn',
---       num_ngbrs int DEFAULT 5)
--- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
--- AS $$
---   plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
---   from crankshaft.clustering import moran_local
---   # TODO: use named parameters or a dictionary
---   return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs)
--- $$ LANGUAGE plpythonu;
--- Function by Stuart Lynn for a simple interpolation of a value
--- from a polygon table over an arbitrary polygon
--- (weighted by the area proportion overlapped)
--- Aereal weighting is a very simple form of aereal interpolation.
---
--- Parameters:
---   * geom a Polygon geometry which defines the area where a value will be
---     estimated as the area-weighted sum of a given table/column
---   * target_table_name table name of the table that provides the values
---   * target_column column name of the column that provides the values
---   * schema_name optional parameter to defina the schema the target table
---     belongs to, which is necessary if its not in the search_path.
---     Note that target_table_name should never include the schema in it.
--- Return value:
---   Aereal-weighted interpolation of the column values over the geometry
-CREATE OR REPLACE
-FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL)
-  RETURNS numeric AS
-$$
-DECLARE
-	result numeric;
-  qualified_name text;
-BEGIN
-  IF schema_name IS NULL THEN
-    qualified_name := Format('%I', target_table_name);
-  ELSE
-    qualified_name := Format('%I.%s', schema_name, target_table_name);
-  END IF;
-  EXECUTE Format('
-    SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom))
-    FROM %s AS a
-    WHERE $1 && a.the_geom
-  ', target_column, qualified_name)
-  USING geom
-  INTO result;
-  RETURN result;
-END;
-$$ LANGUAGE plpgsql;
---
--- Creates N points randomly distributed arround the polygon
---
--- @param g - the geometry to be turned in to points
---
--- @param no_points - the number of points to generate
---
--- @params max_iter_per_point - the function generates points in the polygon's bounding box
--- and discards points which don't lie in the polygon. max_iter_per_point specifies how many
--- misses per point the funciton accepts before giving up.
---
--- Returns: Multipoint with the requested points
-CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000)
-RETURNS GEOMETRY AS $$
-DECLARE
-  extent GEOMETRY;
-  test_point Geometry;
-  width                NUMERIC;
-  height               NUMERIC;
-  x0                   NUMERIC;
-  y0                   NUMERIC;
-  xp                   NUMERIC;
-  yp                   NUMERIC;
-  no_left              INTEGER;
-  remaining_iterations INTEGER;
-  points               GEOMETRY[];
-  bbox_line            GEOMETRY;
-  intersection_line    GEOMETRY;
-BEGIN
-  extent  := ST_Envelope(geom);
-  width   := ST_XMax(extent) - ST_XMIN(extent);
-  height  := ST_YMax(extent) - ST_YMIN(extent);
-  x0 	  := ST_XMin(extent);
-  y0 	  := ST_YMin(extent);
-  no_left := no_points;
-
-  LOOP
-    if(no_left=0) THEN
-      EXIT;
-    END IF;
-    yp = y0 + height*random();
-    bbox_line  = ST_MakeLine(
-      ST_SetSRID(ST_MakePoint(yp, x0),4326),
-      ST_SetSRID(ST_MakePoint(yp, x0+width),4326)
-    );
-    intersection_line = ST_Intersection(bbox_line,geom);
-  	test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random());
-	  points := points || test_point;
-	  no_left = no_left - 1 ;
-  END LOOP;
-  RETURN ST_Collect(points);
-END;
-$$
-LANGUAGE plpgsql VOLATILE;
--- Make sure by default there are no permissions for publicuser
--- NOTE: this happens at extension creation time, as part of an implicit transaction.
--- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE;
-
--- Grant permissions on the schema to publicuser (but just the schema)
-GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser;
-
--- Revoke execute permissions on all functions in the schema by default
--- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser;

release/crankshaft.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.1.0'
-requires = 'plpythonu, postgis'
+default_version = '0.0.3'
+requires = 'plpythonu, postgis, cartodb'
 superuser = true
 schema = cdb_crankshaft

release/python/0.0.4/crankshaft/crankshaft/__init__.py

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

release/python/0.0.4/crankshaft/crankshaft/clustering/__init__.py

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

release/python/0.0.4/crankshaft/crankshaft/clustering/kmeans.py

@@ -1,18 +0,0 @@
-from sklearn.cluster import KMeans
-import plpy
-
-def kmeans(query, no_clusters, no_init=20):
-    data = plpy.execute('''select array_agg(cartodb_id order by cartodb_id) as ids,
-        array_agg(ST_X(the_geom) order by cartodb_id) xs,
-        array_agg(ST_Y(the_geom) order by cartodb_id) ys from ({query}) a
-        where the_geom is not null
-    '''.format(query=query))
-
-    xs  = data[0]['xs']
-    ys  = data[0]['ys']
-    ids = data[0]['ids']
-
-    km = KMeans(n_clusters= no_clusters, n_init=no_init)
-    labels = km.fit_predict(zip(xs,ys))
-    return zip(ids,labels)
-

release/python/0.0.4/crankshaft/crankshaft/clustering/moran.py

@@ -1,260 +0,0 @@
-"""
-Moran's I geostatistics (global clustering & outliers presence)
-"""
-
-# TODO: Fill in local neighbors which have null/NoneType values with the
-#       average of the their neighborhood
-
-import pysal as ps
-import plpy
-
-# crankshaft module
-import crankshaft.pysal_utils as pu
-
-# High level interface ---------------------------------------
-
-def moran(subquery, attr_name,
-          w_type, num_ngbrs, permutations, geom_col, id_col):
-    """
-    Moran's I (global)
-    Implementation building neighbors with a PostGIS database and Moran's I
-     core clusters with PySAL.
-    Andy Eschbacher
-    """
-    qvals = {"id_col": id_col,
-             "attr1": attr_name,
-             "geom_col": geom_col,
-             "subquery": subquery,
-             "num_ngbrs": num_ngbrs}
-
-    query = pu.construct_neighbor_query(w_type, qvals)
-
-    plpy.notice('** Query: %s' % query)
-
-    try:
-        result = plpy.execute(query)
-        # if there are no neighbors, exit
-        if len(result) == 0:
-            return pu.empty_zipped_array(2)
-        plpy.notice('** Query returned with %d rows' % len(result))
-    except plpy.SPIError:
-        plpy.error('Error: areas of interest query failed, check input parameters')
-        plpy.notice('** Query failed: "%s"' % query)
-        plpy.notice('** Error: %s' % plpy.SPIError)
-        return pu.empty_zipped_array(2)
-
-    ## collect attributes
-    attr_vals = pu.get_attributes(result)
-
-    ## calculate weights
-    weight = pu.get_weight(result, w_type, num_ngbrs)
-
-    ## calculate moran global
-    moran_global = ps.esda.moran.Moran(attr_vals, weight,
-                                       permutations=permutations)
-
-    return zip([moran_global.I], [moran_global.EI])
-
-def moran_local(subquery, attr,
-                w_type, num_ngbrs, permutations, geom_col, id_col):
-    """
-    Moran's I implementation for PL/Python
-    Andy Eschbacher
-    """
-
-    # geometries with attributes that are null are ignored
-    # resulting in a collection of not as near neighbors
-
-    qvals = {"id_col": id_col,
-             "attr1": attr,
-             "geom_col": geom_col,
-             "subquery": subquery,
-             "num_ngbrs": num_ngbrs}
-
-    query = pu.construct_neighbor_query(w_type, qvals)
-
-    try:
-        result = plpy.execute(query)
-        # if there are no neighbors, exit
-        if len(result) == 0:
-            return pu.empty_zipped_array(5)
-    except plpy.SPIError:
-        plpy.error('Error: areas of interest query failed, check input parameters')
-        plpy.notice('** Query failed: "%s"' % query)
-        return pu.empty_zipped_array(5)
-
-    attr_vals = pu.get_attributes(result)
-    weight = pu.get_weight(result, w_type, num_ngbrs)
-
-    # calculate LISA values
-    lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
-                                     permutations=permutations)
-
-    # find quadrants for each geometry
-    quads = quad_position(lisa.q)
-
-    return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
-
-def moran_rate(subquery, numerator, denominator,
-               w_type, num_ngbrs, permutations, geom_col, id_col):
-    """
-    Moran's I Rate (global)
-    Andy Eschbacher
-    """
-    qvals = {"id_col": id_col,
-             "attr1": numerator,
-             "attr2": denominator,
-             "geom_col": geom_col,
-             "subquery": subquery,
-             "num_ngbrs": num_ngbrs}
-
-    query = pu.construct_neighbor_query(w_type, qvals)
-
-    plpy.notice('** Query: %s' % query)
-
-    try:
-        result = plpy.execute(query)
-        # if there are no neighbors, exit
-        if len(result) == 0:
-            return pu.empty_zipped_array(2)
-        plpy.notice('** Query returned with %d rows' % len(result))
-    except plpy.SPIError:
-        plpy.error('Error: areas of interest query failed, check input parameters')
-        plpy.notice('** Query failed: "%s"' % query)
-        plpy.notice('** Error: %s' % plpy.SPIError)
-        return pu.empty_zipped_array(2)
-
-    ## collect attributes
-    numer = pu.get_attributes(result, 1)
-    denom = pu.get_attributes(result, 2)
-
-    weight = pu.get_weight(result, w_type, num_ngbrs)
-
-    ## calculate moran global rate
-    lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
-                                         permutations=permutations)
-
-    return zip([lisa_rate.I], [lisa_rate.EI])
-
-def moran_local_rate(subquery, numerator, denominator,
-                     w_type, num_ngbrs, permutations, geom_col, id_col):
-    """
-        Moran's I Local Rate
-        Andy Eschbacher
-    """
-    # geometries with values that are null are ignored
-    # resulting in a collection of not as near neighbors
-
-    query = pu.construct_neighbor_query(w_type,
-                                     {"id_col": id_col,
-                                      "numerator": numerator,
-                                      "denominator": denominator,
-                                      "geom_col": geom_col,
-                                      "subquery": subquery,
-                                      "num_ngbrs": num_ngbrs})
-
-    try:
-        result = plpy.execute(query)
-        # if there are no neighbors, exit
-        if len(result) == 0:
-            return pu.empty_zipped_array(5)
-    except plpy.SPIError:
-        plpy.error('Error: areas of interest query failed, check input parameters')
-        plpy.notice('** Query failed: "%s"' % query)
-        plpy.notice('** Error: %s' % plpy.SPIError)
-        return pu.empty_zipped_array(5)
-
-    ## collect attributes
-    numer = pu.get_attributes(result, 1)
-    denom = pu.get_attributes(result, 2)
-
-    weight = pu.get_weight(result, w_type, num_ngbrs)
-
-    # calculate LISA values
-    lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
-                                          permutations=permutations)
-
-    # find units of significance
-    quads = quad_position(lisa.q)
-
-    return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
-
-def moran_local_bv(subquery, attr1, attr2,
-                   permutations, geom_col, id_col, w_type, num_ngbrs):
-    """
-        Moran's I (local) Bivariate (untested)
-    """
-    plpy.notice('** Constructing query')
-
-    qvals = {"num_ngbrs": num_ngbrs,
-             "attr1": attr1,
-             "attr2": attr2,
-             "subquery": subquery,
-             "geom_col": geom_col,
-             "id_col": id_col}
-
-    query = pu.construct_neighbor_query(w_type, qvals)
-
-    try:
-        result = plpy.execute(query)
-        # if there are no neighbors, exit
-        if len(result) == 0:
-            return pu.empty_zipped_array(4)
-    except plpy.SPIError:
-        plpy.error("Error: areas of interest query failed, " \
-                   "check input parameters")
-        plpy.notice('** Query failed: "%s"' % query)
-        return pu.empty_zipped_array(4)
-
-    ## collect attributes
-    attr1_vals = pu.get_attributes(result, 1)
-    attr2_vals = pu.get_attributes(result, 2)
-
-    # create weights
-    weight = pu.get_weight(result, w_type, num_ngbrs)
-
-    # calculate LISA values
-    lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
-                                        permutations=permutations)
-
-    plpy.notice("len of Is: %d" % len(lisa.Is))
-
-    # find clustering of significance
-    lisa_sig = quad_position(lisa.q)
-
-    plpy.notice('** Finished calculations')
-
-    return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
-
-# Low level functions ----------------------------------------
-
-def map_quads(coord):
-    """
-        Map a quadrant number to Moran's I designation
-        HH=1, LH=2, LL=3, HL=4
-        Input:
-        @param coord (int): quadrant of a specific measurement
-        Output:
-            classification (one of 'HH', 'LH', 'LL', or 'HL')
-    """
-    if coord == 1:
-        return 'HH'
-    elif coord == 2:
-        return 'LH'
-    elif coord == 3:
-        return 'LL'
-    elif coord == 4:
-        return 'HL'
-    else:
-        return None
-
-def quad_position(quads):
-    """
-        Produce Moran's I classification based of n
-        Input:
-        @param quads ndarray: an array of quads classified by
-          1-4 (PySAL default)
-        Output:
-        @param list: an array of quads classied by 'HH', 'LL', etc.
-    """
-    return [map_quads(q) for q in quads]

release/python/0.0.4/crankshaft/crankshaft/pysal_utils/__init__.py

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

release/python/0.0.4/crankshaft/crankshaft/pysal_utils/pysal_utils.py

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

release/python/0.0.4/crankshaft/crankshaft/random_seeds.py

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

release/python/0.0.4/crankshaft/setup.py

@@ -1,48 +0,0 @@
-
-"""
-CartoDB Spatial Analysis Python Library
-See:
-https://github.com/CartoDB/crankshaft
-"""
-
-from setuptools import setup, find_packages
-
-setup(
-    name='crankshaft',
-
-    version='0.0.4',
-
-    description='CartoDB Spatial Analysis Python Library',
-
-    url='https://github.com/CartoDB/crankshaft',
-
-    author='Data Services Team - CartoDB',
-    author_email='dataservices@cartodb.com',
-
-    license='MIT',
-
-    classifiers=[
-        'Development Status :: 3 - Alpha',
-        'Intended Audience :: Mapping comunity',
-        'Topic :: Maps :: Mapping Tools',
-        'License :: OSI Approved :: MIT License',
-        'Programming Language :: Python :: 2.7',
-    ],
-
-    keywords='maps mapping tools spatial analysis geostatistics',
-
-    packages=find_packages(exclude=['contrib', 'docs', 'tests']),
-
-    extras_require={
-        'dev': ['unittest'],
-        'test': ['unittest', 'nose', 'mock'],
-    },
-
-    # The choice of component versions is dictated by what's
-    # provisioned in the production servers.
-    install_requires=['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/0.0.4/crankshaft/test/fixtures/kmeans.json

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

release/python/0.0.4/crankshaft/test/fixtures/moran.json

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

release/python/0.0.4/crankshaft/test/fixtures/neighbors.json

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

release/python/0.0.4/crankshaft/test/helper.py

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

release/python/0.0.4/crankshaft/test/mock_plpy.py

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

release/python/0.0.4/crankshaft/test/test_cluster_kmeans.py

@@ -1,38 +0,0 @@
-import unittest
-import numpy as np
-
-
-# from mock_plpy import MockPlPy
-# plpy = MockPlPy()
-#
-# import sys
-# sys.modules['plpy'] = plpy
-from helper import plpy, fixture_file
-import numpy as np
-import crankshaft.clustering as cc
-import crankshaft.pysal_utils as pu
-from crankshaft import random_seeds
-import json
-
-class KMeansTest(unittest.TestCase):
-    """Testing class for Moran's I functions"""
-
-    def setUp(self):
-        plpy._reset()
-        self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read())
-        self.params = {"subquery": "select * from table",
-                       "no_clusters": "10"
-                       }
-
-    def test_kmeans(self):
-        data = self.cluster_data
-        plpy._define_result('select' ,data)
-        clusters = cc.kmeans('subquery', 2)
-        labels  = [a[1] for a in clusters]
-        c1 = [a for a in clusters if a[1]==0]
-        c2 = [a for a in clusters if a[1]==1]
-
-        self.assertEqual(len(np.unique(labels)),2)
-        self.assertEqual(len(c1),20)
-        self.assertEqual(len(c2),20)
-

release/python/0.0.4/crankshaft/test/test_clustering_moran.py

@@ -1,83 +0,0 @@
-import unittest
-import numpy as np
-
-
-# from mock_plpy import MockPlPy
-# plpy = MockPlPy()
-#
-# import sys
-# sys.modules['plpy'] = plpy
-from helper import plpy, fixture_file
-
-import crankshaft.clustering as cc
-import crankshaft.pysal_utils as pu
-from crankshaft import random_seeds
-import json
-
-class MoranTest(unittest.TestCase):
-    """Testing class for Moran's I functions"""
-
-    def setUp(self):
-        plpy._reset()
-        self.params = {"id_col": "cartodb_id",
-                       "attr1": "andy",
-                       "attr2": "jay_z",
-                       "subquery": "SELECT * FROM a_list",
-                       "geom_col": "the_geom",
-                       "num_ngbrs": 321}
-        self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read())
-        self.moran_data = json.loads(open(fixture_file('moran.json')).read())
-
-    def test_map_quads(self):
-        """Test map_quads"""
-        self.assertEqual(cc.map_quads(1), 'HH')
-        self.assertEqual(cc.map_quads(2), 'LH')
-        self.assertEqual(cc.map_quads(3), 'LL')
-        self.assertEqual(cc.map_quads(4), 'HL')
-        self.assertEqual(cc.map_quads(33), None)
-        self.assertEqual(cc.map_quads('andy'), None)
-
-    def test_quad_position(self):
-        """Test lisa_sig_vals"""
-
-        quads = np.array([1, 2, 3, 4], np.int)
-
-        ans = np.array(['HH', 'LH', 'LL', 'HL'])
-        test_ans = cc.quad_position(quads)
-
-        self.assertTrue((test_ans == ans).all())
-
-    def test_moran_local(self):
-        """Test Moran's I local"""
-        data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
-        plpy._define_result('select', data)
-        random_seeds.set_random_seeds(1234)
-        result = cc.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
-        result = [(row[0], row[1]) for row in result]
-        expected = self.moran_data
-        for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
-            self.assertAlmostEqual(res_val, exp_val)
-            self.assertEqual(res_quad, exp_quad)
-
-    def test_moran_local_rate(self):
-        """Test Moran's I rate"""
-        data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data]
-        plpy._define_result('select', data)
-        random_seeds.set_random_seeds(1234)
-        result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 'knn', 5, 99, 'the_geom', 'cartodb_id')
-        print 'result == None? ', result == None
-        result = [(row[0], row[1]) for row in result]
-        expected = self.moran_data
-        for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
-            self.assertAlmostEqual(res_val, exp_val)
-
-    def test_moran(self):
-        """Test Moran's I global"""
-        data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
-        plpy._define_result('select', data)
-        random_seeds.set_random_seeds(1235)
-        result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
-        print 'result == None?', result == None
-        result_moran = result[0][0]
-        expected_moran = np.array([row[0] for row in self.moran_data]).mean()
-        self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)

release/python/0.0.4/crankshaft/test/test_pysal_utils.py

@@ -1,107 +0,0 @@
-import unittest
-
-import crankshaft.pysal_utils as pu
-from crankshaft import random_seeds
-
-
-class PysalUtilsTest(unittest.TestCase):
-    """Testing class for utility functions related to PySAL integrations"""
-
-    def setUp(self):
-        self.params = {"id_col": "cartodb_id",
-                       "attr1": "andy",
-                       "attr2": "jay_z",
-                       "subquery": "SELECT * FROM a_list",
-                       "geom_col": "the_geom",
-                       "num_ngbrs": 321}
-
-    def test_query_attr_select(self):
-        """Test query_attr_select"""
-
-        ans = "i.\"{attr1}\"::numeric As attr1, " \
-              "i.\"{attr2}\"::numeric As attr2, "
-
-        self.assertEqual(pu.query_attr_select(self.params), ans)
-
-    def test_query_attr_where(self):
-        """Test pu.query_attr_where"""
-
-        ans = "idx_replace.\"{attr1}\" IS NOT NULL AND " \
-              "idx_replace.\"{attr2}\" IS NOT NULL AND " \
-              "idx_replace.\"{attr2}\" <> 0"
-
-        self.assertEqual(pu.query_attr_where(self.params), ans)
-
-    def test_knn(self):
-        """Test knn neighbors constructor"""
-
-        ans = "SELECT i.\"cartodb_id\" As id, " \
-                     "i.\"andy\"::numeric As attr1, " \
-                     "i.\"jay_z\"::numeric As attr2, " \
-                     "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
-                                   "FROM (SELECT * FROM a_list) As j " \
-                                   "WHERE " \
-                                    "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
-                                    "j.\"andy\" IS NOT NULL AND " \
-                                    "j.\"jay_z\" IS NOT NULL AND " \
-                                    "j.\"jay_z\" <> 0 " \
-                                   "ORDER BY " \
-                                    "j.\"the_geom\" <-> i.\"the_geom\" ASC " \
-                      "LIMIT 321)) As neighbors " \
-              "FROM (SELECT * FROM a_list) As i " \
-              "WHERE i.\"andy\" IS NOT NULL AND " \
-                    "i.\"jay_z\" IS NOT NULL AND " \
-                    "i.\"jay_z\" <> 0 " \
-              "ORDER BY i.\"cartodb_id\" ASC;"
-
-        self.assertEqual(pu.knn(self.params), ans)
-
-    def test_queen(self):
-        """Test queen neighbors constructor"""
-
-        ans = "SELECT i.\"cartodb_id\" As id, " \
-                     "i.\"andy\"::numeric As attr1, " \
-                     "i.\"jay_z\"::numeric As attr2, " \
-                     "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
-                                   "FROM (SELECT * FROM a_list) As j " \
-                                   "WHERE " \
-                                   "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
-                                   "ST_Touches(i.\"the_geom\", " \
-                                              "j.\"the_geom\") AND " \
-                                   "j.\"andy\" IS NOT NULL AND " \
-                                   "j.\"jay_z\" IS NOT NULL AND " \
-                                   "j.\"jay_z\" <> 0)" \
-                                  ") As neighbors " \
-              "FROM (SELECT * FROM a_list) As i " \
-              "WHERE i.\"andy\" IS NOT NULL AND " \
-                    "i.\"jay_z\" IS NOT NULL AND " \
-                    "i.\"jay_z\" <> 0 " \
-              "ORDER BY i.\"cartodb_id\" ASC;"
-
-        self.assertEqual(pu.queen(self.params), ans)
-
-    def test_construct_neighbor_query(self):
-        """Test construct_neighbor_query"""
-
-        # Compare to raw knn query
-        self.assertEqual(pu.construct_neighbor_query('knn', self.params),
-                         pu.knn(self.params))
-
-    def test_get_attributes(self):
-        """Test get_attributes"""
-
-        ## need to add tests
-
-        self.assertEqual(True, True)
-
-    def test_get_weight(self):
-        """Test get_weight"""
-
-        self.assertEqual(True, True)
-
-    def test_empty_zipped_array(self):
-        """Test empty_zipped_array"""
-        ans2 = [(None, None)]
-        ans4 = [(None, None, None, None)]
-        self.assertEqual(pu.empty_zipped_array(2), ans2)
-        self.assertEqual(pu.empty_zipped_array(4), ans4)

release/python/0.1.0/crankshaft/crankshaft/__init__.py

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

release/python/0.1.0/crankshaft/crankshaft/clustering/__init__.py

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

release/python/0.1.0/crankshaft/crankshaft/clustering/kmeans.py

@@ -1,18 +0,0 @@
-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/0.1.0/crankshaft/crankshaft/clustering/moran.py

@@ -1,262 +0,0 @@
-"""
-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/0.1.0/crankshaft/crankshaft/pysal_utils/__init__.py

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

release/python/0.1.0/crankshaft/crankshaft/pysal_utils/pysal_utils.py

@@ -1,188 +0,0 @@
-"""
-    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/0.1.0/crankshaft/crankshaft/random_seeds.py

@@ -1,11 +0,0 @@
-"""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/0.1.0/crankshaft/crankshaft/segmentation/__init__.py

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

release/python/0.1.0/crankshaft/crankshaft/segmentation/segmentation.py

@@ -1,176 +0,0 @@
-"""
-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/0.1.0/crankshaft/crankshaft/space_time_dynamics/__init__.py

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

release/python/0.1.0/crankshaft/crankshaft/space_time_dynamics/markov.py

@@ -1,189 +0,0 @@
-"""
-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/0.1.0/crankshaft/setup.py

@@ -1,49 +0,0 @@
-
-"""
-CartoDB Spatial Analysis Python Library
-See:
-https://github.com/CartoDB/crankshaft
-"""
-
-from setuptools import setup, find_packages
-
-setup(
-    name='crankshaft',
-
-    version='0.1.0',
-
-    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/0.1.0/crankshaft/test/fixtures/kmeans.json

@@ -1 +0,0 @@
-[{"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/0.1.0/crankshaft/test/fixtures/markov.json

@@ -1 +0,0 @@
-[[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/0.1.0/crankshaft/test/fixtures/moran.json

@@ -1,52 +0,0 @@
-[[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/0.1.0/crankshaft/test/fixtures/neighbors.json

@@ -1,54 +0,0 @@
-[
-    {"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/0.1.0/crankshaft/test/helper.py

@@ -1,13 +0,0 @@
-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/0.1.0/crankshaft/test/mock_plpy.py

@@ -1,52 +0,0 @@
-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/0.1.0/crankshaft/test/test_cluster_kmeans.py

@@ -1,38 +0,0 @@
-import unittest
-import numpy as np
-
-
-# from mock_plpy import MockPlPy
-# plpy = MockPlPy()
-#
-# import sys
-# sys.modules['plpy'] = plpy
-from helper import plpy, fixture_file
-import numpy as np
-import crankshaft.clustering as cc
-import crankshaft.pysal_utils as pu
-from crankshaft import random_seeds
-import json
-
-class KMeansTest(unittest.TestCase):
-    """Testing class for Moran's I functions"""
-
-    def setUp(self):
-        plpy._reset()
-        self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read())
-        self.params = {"subquery": "select * from table",
-                       "no_clusters": "10"
-                       }
-
-    def test_kmeans(self):
-        data = self.cluster_data
-        plpy._define_result('select' ,data)
-        clusters = cc.kmeans('subquery', 2)
-        labels  = [a[1] for a in clusters]
-        c1 = [a for a in clusters if a[1]==0]
-        c2 = [a for a in clusters if a[1]==1]
-
-        self.assertEqual(len(np.unique(labels)),2)
-        self.assertEqual(len(c1),20)
-        self.assertEqual(len(c2),20)
-

release/python/0.1.0/crankshaft/test/test_clustering_moran.py

@@ -1,88 +0,0 @@
-import unittest
-import numpy as np
-
-
-# from mock_plpy import MockPlPy
-# plpy = MockPlPy()
-#
-# import sys
-# sys.modules['plpy'] = plpy
-from helper import plpy, fixture_file
-
-import 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/0.1.0/crankshaft/test/test_pysal_utils.py

@@ -1,142 +0,0 @@
-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/0.1.0/crankshaft/test/test_segmentation.py

@@ -1,64 +0,0 @@
-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/0.1.0/crankshaft/test/test_space_time_dynamics.py

@@ -1,324 +0,0 @@
-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))

src/pg/crankshaft.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.1.0'
+default_version = '0.0.3'
 requires = 'plpythonu, postgis'
 superuser = true
 schema = cdb_crankshaft

src/pg/sql/04_py_agg.sql

@@ -1,19 +0,0 @@
-CREATE OR REPLACE FUNCTION
-    CDB_PyAggS(current_state Numeric[], current_row Numeric[]) 
-    returns NUMERIC[] as $$
-    BEGIN
-        if array_upper(current_state,1) is null  then
-            RAISE NOTICE 'setting state %',array_upper(current_row,1);
-            current_state[1] = array_upper(current_row,1);
-        end if;
-        return array_cat(current_state,current_row) ;
-    END
-    $$ LANGUAGE plpgsql;
-
-
-CREATE AGGREGATE CDB_PyAgg(NUMERIC[])(
-    SFUNC = CDB_PyAggS,
-    STYPE = Numeric[],
-    INITCOND = "{}" 
-);
-

src/pg/sql/05_segmentation.sql

@@ -1,53 +0,0 @@
-
-CREATE OR REPLACE FUNCTION
-  CDB_CreateAndPredictSegment(
-    target NUMERIC[],
-    features NUMERIC[],
-    target_features NUMERIC[],
-    target_ids NUMERIC[],
-    n_estimators INTEGER DEFAULT 1200,
-    max_depth INTEGER DEFAULT 3,
-    subsample DOUBLE PRECISION DEFAULT 0.5,
-    learning_rate DOUBLE PRECISION DEFAULT 0.01,
-    min_samples_leaf INTEGER DEFAULT 1)
-RETURNS TABLE(cartodb_id NUMERIC, prediction NUMERIC, accuracy NUMERIC)
-AS $$
-    import numpy as np
-    import plpy
-
-    from crankshaft.segmentation import create_and_predict_segment_agg
-    model_params = {'n_estimators': n_estimators,
-                    'max_depth': max_depth,
-                    'subsample': subsample,
-                    'learning_rate': learning_rate,
-                    'min_samples_leaf': min_samples_leaf}
-
-    def unpack2D(data):
-        dimension = data.pop(0)
-        a = np.array(data, dtype=float)
-        return a.reshape(len(a)/dimension, dimension)
-
-    return create_and_predict_segment_agg(np.array(target, dtype=float),
-            unpack2D(features),
-            unpack2D(target_features),
-            target_ids,
-            model_params)
-
-$$ LANGUAGE plpythonu;
-
-CREATE OR REPLACE FUNCTION
-  CDB_CreateAndPredictSegment (
-      query TEXT,
-      variable_name TEXT,
-      target_table TEXT,
-      n_estimators INTEGER DEFAULT 1200,
-      max_depth INTEGER DEFAULT 3,
-      subsample DOUBLE PRECISION DEFAULT 0.5,
-      learning_rate DOUBLE PRECISION DEFAULT 0.01,
-      min_samples_leaf INTEGER DEFAULT 1)
-RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC)
-AS $$
-  from crankshaft.segmentation import create_and_predict_segment
-  model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf}
-  return create_and_predict_segment(query,variable_name,target_table, model_params)
-$$ LANGUAGE plpythonu;

src/pg/sql/08_interpolation.sql

@@ -1,130 +0,0 @@
--- 0: nearest neighbor
--- 1: barymetric
--- 2: IDW
-
-CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation(
-    IN query text,
-    IN point geometry,
-    IN method integer DEFAULT 1,
-    IN p1 numeric DEFAULT 0,
-    IN p2 numeric DEFAULT 0
-    )
-RETURNS numeric AS
-$$
-DECLARE
-    gs geometry[];
-    vs numeric[];
-    output numeric;
-BEGIN
-    EXECUTE 'WITH a AS('||query||') SELECT array_agg(the_geom), array_agg(attrib) FROM a' INTO gs, vs;
-    SELECT CDB_SpatialInterpolation(gs, vs, point, method, p1,p2) INTO output FROM a;
-
-    RETURN output;
-END;
-$$
-language plpgsql IMMUTABLE;
-
-CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation(
-    IN geomin geometry[],
-    IN colin numeric[],
-    IN point geometry,
-    IN method integer DEFAULT 1,
-    IN p1 numeric DEFAULT 0,
-    IN p2 numeric DEFAULT 0
-    )
-RETURNS numeric AS
-$$
-DECLARE
-    gs geometry[];
-    vs numeric[];
-    gs2 geometry[];
-    vs2 numeric[];
-    g geometry;
-    vertex geometry[];
-    sg numeric;
-    sa numeric;
-    sb numeric;
-    sc numeric;
-    va numeric;
-    vb numeric;
-    vc numeric;
-    output numeric;
-BEGIN
-    output :=  -999.999;
-    -- nearest
-    IF method = 0 THEN
-
-        WITH    a as (SELECT unnest(geomin) as g, unnest(colin) as v)
-        SELECT a.v INTO output FROM a ORDER BY point<->a.g LIMIT 1;
-        RETURN output;
-
-    -- barymetric
-    ELSIF method = 1 THEN
-        WITH    a as (SELECT unnest(geomin) AS e),
-                b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a),
-                c as (SELECT (ST_Dump(t)).geom as v FROM b),
-                d as (SELECT v FROM c WHERE ST_Within(point, v))
-            SELECT v INTO g FROM d;
-        IF g is null THEN
-            -- out of the realm of the input data
-            RETURN -888.888;
-        END IF;
-        -- vertex of the selected cell
-        WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v)
-                SELECT array_agg(v) INTO vertex FROM a;
-
-            -- retrieve the value of each vertex
-        WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
-            SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]);
-        WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
-            SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]);
-        WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
-                SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]);
-
-        SELECT ST_area(g), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[2], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[1], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point,vertex[1],vertex[2], point]))) INTO sg, sa, sb, sc;
-
-        output := (coalesce(sa,0) * coalesce(va,0) + coalesce(sb,0) * coalesce(vb,0) + coalesce(sc,0) * coalesce(vc,0)) / coalesce(sg);
-        RETURN output;
-
-    -- IDW
-    -- p1: limit the number of neighbors, 0->no limit
-    -- p2: order of distance decay, 0-> order 1
-    ELSIF method = 2 THEN
-
-        IF p2 = 0 THEN
-            p2 := 1;
-        END IF;
-
-        WITH    a as (SELECT unnest(geomin) as g, unnest(colin) as v),
-                b as (SELECT a.g, a.v FROM a ORDER BY point<->a.g)
-        SELECT array_agg(b.g), array_agg(b.v) INTO gs, vs FROM b;
-        IF p1::integer>0 THEN
-            gs2:=gs;
-            vs2:=vs;
-            FOR i IN 1..p1
-            LOOP
-                gs2 := gs2 || gs[i];
-                vs2 := vs2 || vs[i];
-            END LOOP;
-        ELSE
-            gs2:=gs;
-            vs2:=vs;
-        END IF;
-
-        WITH    a as (SELECT unnest(gs2) as g, unnest(vs2) as v),
-                b as (
-                    SELECT
-                    (1/ST_distance(point, a.g)^p2::integer) as k,
-                    (a.v/ST_distance(point, a.g)^p2::integer) as f
-                    FROM a
-                )
-        SELECT sum(b.f)/sum(b.k) INTO output FROM b;
-        RETURN output;
-
-    END IF;
-
-    RETURN -777.777;
-
-END;
-$$
-language plpgsql IMMUTABLE;

src/pg/sql/11_markov.sql

@@ -1,81 +0,0 @@
--- Spatial Markov
-
--- input table format:
--- id | geom | date_1 | date_2 | date_3
---  1 | Pt1  | 12.3   | 13.1   | 14.2
---  2 | Pt2  | 11.0   | 13.2   | 12.5
---  ...
--- Sample Function call:
--- SELECT CDB_SpatialMarkov('SELECT * FROM real_estate',
---                          Array['date_1', 'date_2', 'date_3'])
-
-CREATE OR REPLACE FUNCTION
-  CDB_SpatialMarkovTrend (
-      subquery TEXT,
-      time_cols TEXT[],
-      num_classes INT DEFAULT 7,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-  	  permutations INT DEFAULT 99,
-  	  geom_col TEXT DEFAULT 'the_geom',
-  	  id_col TEXT DEFAULT 'cartodb_id')
-RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT)
-AS $$
-
-  from crankshaft.space_time_dynamics import spatial_markov_trend
-
-  ## TODO: use named parameters or a dictionary
-  return spatial_markov_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpythonu;
-
--- input table format: identical to above but in a predictable format
--- Sample function call:
--- SELECT cdb_spatial_markov('SELECT * FROM real_estate',
---                           'date_1')
-
-
--- CREATE OR REPLACE FUNCTION
---   cdb_spatial_markov (
---       subquery TEXT,
---       time_col_min text,
---       time_col_max text,
---       date_format text, -- '_YYYY_MM_DD'
---       num_time_per_bin INT DEFAULT 1,
---   	  permutations INT DEFAULT 99,
---   	  geom_column TEXT DEFAULT 'the_geom',
---   	  id_col TEXT DEFAULT 'cartodb_id',
---       w_type TEXT DEFAULT 'knn',
---       num_ngbrs int DEFAULT 5)
--- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
--- AS $$
---   plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
---   from crankshaft.clustering import moran_local
---   # TODO: use named parameters or a dictionary
---   return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs)
--- $$ LANGUAGE plpythonu;
---
--- -- input table format:
--- -- id | geom | date  | measurement
--- --  1 | Pt1  | 12/3  | 13.2
--- --  2 | Pt2  | 11/5  | 11.3
--- --  3 | Pt1  | 11/13 | 12.9
--- --  4 | Pt3  | 12/19 | 10.1
--- -- ...
---
--- CREATE OR REPLACE FUNCTION
---   cdb_spatial_markov (
---       subquery TEXT,
---       time_col text,
---       num_time_per_bin INT DEFAULT 1,
---   	  permutations INT DEFAULT 99,
---   	  geom_column TEXT DEFAULT 'the_geom',
---   	  id_col TEXT DEFAULT 'cartodb_id',
---       w_type TEXT DEFAULT 'knn',
---       num_ngbrs int DEFAULT 5)
--- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
--- AS $$
---   plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
---   from crankshaft.clustering import moran_local
---   # TODO: use named parameters or a dictionary
---   return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs)
--- $$ LANGUAGE plpythonu;

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

@@ -1,10 +0,0 @@
-SET client_min_messages TO WARNING;
-\set ECHO none
-_cdb_random_seeds
-
-(1 row)
-cartodb_id|trend_test|trend_up_test|trend_down_test|volatility_test
-1|t|t|t|t
-2|t|t|t|t
-3|t|t|t|t
-(3 rows)

src/pg/test/expected/06_segmentation_test.out

@@ -1,27 +0,0 @@
-\pset format unaligned
-\set ECHO none
-_cdb_random_seeds
-
-(1 row)
-within_tolerance
-t
-t
-t
-t
-t
-t
-t
-t
-t
-t
-t
-t
-t
-t
-t
-t
-t
-t
-t
-t
-(20 rows)

src/pg/test/expected/08_interpolation_test.out

@@ -1,5 +0,0 @@
-SET client_min_messages TO WARNING;
-\set ECHO none
-cdb_spatialinterpolation
-t
-(1 row)

src/pg/test/sql/05_markov_test.sql

@@ -1,33 +0,0 @@
-SET client_min_messages TO WARNING;
-\set ECHO none
-\pset format unaligned
-\i test/fixtures/markov_usjoin_example.sql
-
--- Areas of Interest functions perform some nondeterministic computations
--- (to estimate the significance); we will set the seeds for the RNGs
--- that affect those results to have repeatable results
-SELECT cdb_crankshaft._cdb_random_seeds(1234);
-
-SELECT
-  m1.cartodb_id,
-  CASE WHEN m1.cartodb_id = 1 THEN abs(m2.trend - 0.069767441860465115) / 0.069767441860465115 < 0.05
-       WHEN m1.cartodb_id = 2 THEN abs(m2.trend - 0.15151515151515152) / 0.15151515151515152 < 0.05
-       WHEN m1.cartodb_id = 3 THEN abs(m2.trend - 0.069767441860465115) / 0.069767441860465115 < 0.05
-       ELSE NULL END As trend_test,
-  CASE WHEN m1.cartodb_id = 1 THEN abs(m2.trend_up - 0.065217391304347824) / 0.065217391304347824 < 0.05
-       WHEN m1.cartodb_id = 2 THEN abs(m2.trend_up - 0.13157894736842105) / 0.13157894736842105 < 0.05
-       WHEN m1.cartodb_id = 3 THEN abs(m2.trend_up - 0.065217391304347824) / 0.065217391304347824 < 0.05
-       ELSE NULL END As trend_up_test,
-  CASE WHEN m1.cartodb_id = 1 THEN m2.trend_down = 0.0
-       WHEN m1.cartodb_id = 2 THEN m2.trend_down = 0.0
-       WHEN m1.cartodb_id = 3 THEN m2.trend_down = 0.0
-       ELSE NULL END As trend_down_test,
-  CASE WHEN m1.cartodb_id = 1 THEN abs(m2.volatility - 0.367574633389) / 0.367574633389  < 0.1
-       WHEN m1.cartodb_id = 2 THEN abs(m2.volatility - 0.33807340742635211) / 0.33807340742635211  < 0.1
-       WHEN m1.cartodb_id = 3 THEN abs(m2.volatility - 0.3682585596149513) / 0.3682585596149513  < 0.1
-       ELSE NULL END As volatility_test
-  FROM markov_usjoin_example As m1
-  JOIN cdb_crankshaft.CDB_SpatialMarkovTrend('SELECT * FROM markov_usjoin_example ORDER BY cartodb_id DESC', Array['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009']::text[], 5::int, 'knn'::text, 5::int, 0::int, 'the_geom'::text, 'cartodb_id'::text) As m2
-    ON m1.cartodb_id = m2.rowid
-ORDER BY m1.cartodb_id
-LIMIT 3;

src/pg/test/sql/06_segmentation_test.sql

@@ -1,33 +0,0 @@
-\pset format unaligned
-\set ECHO none
-\i test/fixtures/ml_values.sql
-SELECT cdb_crankshaft._cdb_random_seeds(1234);
-
-WITH expected AS (
-  SELECT generate_series(1000,1020) AS id, unnest(ARRAY[
-    4.5656517130822492,
-    1.7928053473230694,
-    1.0283378773916563,
-    2.6586517814904593,
-    2.9699056242935944,
-    3.9550646059951347,
-    4.1662572444459745,
-    3.8126334839264162,
-    1.8809821053623488,
-    1.6349065129019873,
-    3.0391288591472954,
-    3.3035970359672553,
-    1.5835471589451968,
-    3.7530378537263638,
-    1.0833589653009252,
-    3.8104965452882897,
-    2.665217959294802,
-    1.5850334252802472,
-    3.679401198805563,
-    3.5332033186588636
-    ]) AS expected LIMIT 20
-), prediction AS (
-  SELECT cartodb_id::integer id, prediction
-  FROM cdb_crankshaft.CDB_CreateAndPredictSegment('SELECT target, x1, x2, x3 FROM ml_values WHERE class = $$train$$','target','SELECT cartodb_id, target, x1, x2, x3 FROM ml_values WHERE class = $$test$$')
-  LIMIT 20
-) SELECT abs(e.expected - p.prediction) <= 1e-9 AS within_tolerance FROM expected e, prediction p WHERE e.id = p.id;

src/pg/test/sql/08_interpolation_test.sql

@@ -1,10 +0,0 @@
-SET client_min_messages TO WARNING;
-\set ECHO none
-\pset format unaligned
-
-WITH a AS (
-    SELECT
-    ARRAY[800, 700, 600, 500, 400, 300, 200, 100] AS vals,
-    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 g
-)
-SELECT (cdb_crankshaft.CDB_SpatialInterpolation(g, vals, ST_GeomFromText('POINT(2.154 41.37)'), 1) - 780.79470198683925288365) / 780.79470198683925288365 < 0.001 As cdb_spatialinterpolation FROM a;

src/py/Makefile

@@ -2,7 +2,7 @@ include ../../Makefile.global
 
 # Install the package locally for development
 install:
-	pip install --upgrade ./crankshaft
+	pip install ./crankshaft
 
 # Test develpment install
 test:

src/py/crankshaft/crankshaft/__init__.py

@@ -1,5 +1,2 @@
-"""Import all modules"""
-import crankshaft.random_seeds
-import crankshaft.clustering
-import crankshaft.space_time_dynamics
-import crankshaft.segmentation
+import random_seeds
+import clustering

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

@@ -1,3 +1,2 @@
-"""Import all functions from for clustering"""
 from moran import *
 from kmeans import *

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

@@ -7,7 +7,6 @@ Moran's I geostatistics (global clustering & outliers presence)
 
 import pysal as ps
 import plpy
-from collections import OrderedDict
 
 # crankshaft module
 import crankshaft.pysal_utils as pu
@@ -22,11 +21,11 @@ def moran(subquery, attr_name,
      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)])
+    qvals = {"id_col": id_col,
+             "attr1": attr_name,
+             "geom_col": geom_col,
+             "subquery": subquery,
+             "num_ngbrs": num_ngbrs}
 
     query = pu.construct_neighbor_query(w_type, qvals)
 
@@ -66,11 +65,11 @@ def moran_local(subquery, attr,
     # 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)])
+    qvals = {"id_col": id_col,
+             "attr1": attr,
+             "geom_col": geom_col,
+             "subquery": subquery,
+             "num_ngbrs": num_ngbrs}
 
     query = pu.construct_neighbor_query(w_type, qvals)
 
@@ -102,12 +101,12 @@ def moran_rate(subquery, numerator, denominator,
     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)])
+    qvals = {"id_col": id_col,
+             "attr1": numerator,
+             "attr2": denominator,
+             "geom_col": geom_col,
+             "subquery": subquery,
+             "num_ngbrs": num_ngbrs}
 
     query = pu.construct_neighbor_query(w_type, qvals)
 
@@ -146,14 +145,13 @@ def moran_local_rate(subquery, numerator, denominator,
     # 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)
+    query = pu.construct_neighbor_query(w_type,
+                                     {"id_col": id_col,
+                                      "numerator": numerator,
+                                      "denominator": denominator,
+                                      "geom_col": geom_col,
+                                      "subquery": subquery,
+                                      "num_ngbrs": num_ngbrs})
 
     try:
         result = plpy.execute(query)
@@ -176,7 +174,7 @@ def moran_local_rate(subquery, numerator, denominator,
     lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
                                           permutations=permutations)
 
-    # find quadrants for each geometry
+    # find units of significance
     quads = quad_position(lisa.q)
 
     return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
@@ -188,12 +186,12 @@ def moran_local_bv(subquery, attr1, attr2,
     """
     plpy.notice('** Constructing query')
 
-    qvals = OrderedDict([("id_col", id_col),
-                         ("attr1", attr1),
-                         ("attr2", attr2),
-                         ("geom_col", geom_col),
-                         ("subquery", subquery),
-                         ("num_ngbrs", num_ngbrs)])
+    qvals = {"num_ngbrs": num_ngbrs,
+             "attr1": attr1,
+             "attr2": attr2,
+             "subquery": subquery,
+             "geom_col": geom_col,
+             "id_col": id_col}
 
     query = pu.construct_neighbor_query(w_type, qvals)
 

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

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

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

@@ -1,6 +1,5 @@
 """
-    Utilities module for generic PySAL functionality, mainly centered on
-      translating queries into numpy arrays or PySAL weights objects
+    Utilities module for generic PySAL functionality, mainly centered on translating queries into numpy arrays or PySAL weights objects
 """
 
 import numpy as np
@@ -21,23 +20,19 @@ def construct_neighbor_query(w_type, query_vals):
 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
+        @param query_res: query results with attributes and neighbors
     """
-    # if w_type.lower() == 'knn':
-    #     row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
-    #     weights = {x['id']: row_normed_weights for x in query_res}
-    # else:
-    #     weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
-    #                         if len(x['neighbors']) > 0
-    #                         else [] for x in query_res}
+    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
+    return ps.W(neighbors, weights)
 
 def query_attr_select(params):
     """
@@ -46,63 +41,32 @@ def query_attr_select(params):
                        table name, etc.)
     """
 
+    attrs = [k for k in params
+             if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')]
+
+    template = "i.\"{%(col)s}\"::numeric As attr%(alias_num)s, "
+
     attr_string = ""
-    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}
+    for idx, val in enumerate(sorted(attrs)):
+        attr_string += template % {"col": 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'
     """
+    attrs = sorted([k for k in params
+                    if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')])
+
     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
+    for attr in attrs:
+        attr_string.append("idx_replace.\"{%s}\" IS NOT NULL" % attr)
 
-        # 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]])
+    if len(attrs) == 2:
+        attr_string.append("idx_replace.\"{%s}\" <> 0" % attrs[1])
 
     out = " AND ".join(attr_string)
 

src/py/crankshaft/crankshaft/random_seeds.py

@@ -1,4 +1,3 @@
-"""Random seed generator used for non-deterministic functions in crankshaft"""
 import random
 import numpy
 

src/py/crankshaft/crankshaft/segmentation/__init__.py

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

src/py/crankshaft/crankshaft/segmentation/segmentation.py

@@ -1,176 +0,0 @@
-"""
-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)

src/py/crankshaft/crankshaft/space_time_dynamics/__init__.py

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

src/py/crankshaft/crankshaft/space_time_dynamics/markov.py

@@ -1,189 +0,0 @@
-"""
-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

src/py/crankshaft/setup.py

@@ -40,8 +40,7 @@ setup(
 
     # 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'],
+    install_requires=['numpy==1.11.0', 'scipy==0.17.1', 'pysal==1.11.1', 'scikit-learn==0.17.1'],
 
     requires=['pysal', 'numpy', 'sklearn'],
 

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

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

src/py/crankshaft/test/mock_plpy.py

@@ -1,16 +1,5 @@
 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()
@@ -35,16 +24,9 @@ class MockPlPy:
     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):

src/py/crankshaft/test/test_clustering_moran.py

@@ -25,11 +25,6 @@ class MoranTest(unittest.TestCase):
                        "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())
 

src/py/crankshaft/test/test_pysal_utils.py

@@ -15,38 +15,22 @@ class PysalUtilsTest(unittest.TestCase):
                        "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, "
+        ans = "i.\"{attr1}\"::numeric As attr1, " \
+              "i.\"{attr2}\"::numeric As attr2, "
 
         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"
+        ans = "idx_replace.\"{attr1}\" IS NOT NULL AND " \
+              "idx_replace.\"{attr2}\" IS NOT NULL AND " \
+              "idx_replace.\"{attr2}\" <> 0"
 
         self.assertEqual(pu.query_attr_where(self.params), ans)
-        self.assertEqual(pu.query_attr_where(self.params_array), ans_array)
 
     def test_knn(self):
         """Test knn neighbors constructor"""
@@ -69,27 +53,8 @@ class PysalUtilsTest(unittest.TestCase):
                     "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"""

src/py/crankshaft/test/test_segmentation.py

@@ -1,64 +0,0 @@
-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)  )

src/py/crankshaft/test/test_space_time_dynamics.py

@@ -1,324 +0,0 @@
-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))