initial code upload

Merge branch 'develop' of github.com:CartoDB/crankshaft into add-salesforce
fixed duplicities
2016-11-25 16:55:47 +01:00 · 2016-11-25 16:48:48 +01:00 · 2016-09-30 18:32:20 +02:00 · 2016-09-29 17:20:25 +02:00 · 2016-09-22 15:09:54 +02:00 · 2016-09-22 15:09:41 +02:00
108 changed files with 1885 additions and 14424 deletions
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -2,9 +2,6 @@
 - [ ] All declared geometries are `geometry(Geometry, 4326)` for general geoms, or `geometry(Point, 4326)`
 - [ ] Existing functions in crankshaft python library called from the extension are kept at least from version N to version N+1 (to avoid breakage during upgrades).
 - [ ] Docs for public-facing functions are written
- [ ] New functions follow the naming conventions: `CDB_NameOfFunction`. Where internal functions begin with an underscore
- [ ] Video explaining the analysis and showing examples 
- [ ] Analysis Documentation written [template](https://docs.google.com/a/cartodb.com/document/d/1X2KOtaiEBKWNMp8UjwcLB-kE9aIOw09aOjX3oaCjeME/edit?usp=sharing)
- [ ] Smoke test written
- [ ] Hand-off document for camshaft node written
- [ ] If function is in Python, code conforms to [PEP8 Style Guide](https://www.python.org/dev/peps/pep-0008/)
+- [ ] New functions follow the naming conventions: `CDB_NameOfFunction`. Where internal functions begin with an underscore `_`.
+- [ ] If appropriate, new functions accepts an arbitrary query as an input (see [Crankshaft Issue #6](https://github.com/CartoDB/crankshaft/issues/6) for more information)
+ 
--- a/.travis.yml
+++ b/.travis.yml
@@ -35,16 +35,12 @@ before_install:
  - sudo apt-get -y remove --purge postgresql-9.2
  - sudo apt-get -y remove --purge postgresql-9.3
  - sudo apt-get -y remove --purge postgresql-9.4
-  - sudo apt-get -y remove --purge postgresql-9.5
-  - sudo rm -rf /var/lib/postgresql/
-  - sudo rm -rf /var/log/postgresql/
-  - sudo rm -rf /etc/postgresql/
-  - sudo apt-get -y remove --purge postgis-2.2
+  - sudo apt-get -y remove --purge postgis
  - sudo apt-get -y autoremove

-  - sudo apt-get -y install postgresql-9.5=9.5.2-3cdb2
-  - sudo apt-get -y install postgresql-server-dev-9.5=9.5.2-3cdb2
-  - sudo apt-get -y install postgresql-plpython-9.5=9.5.2-3cdb2
+  - sudo apt-get -y install postgresql-9.5=9.5.2-3cdb1
+  - sudo apt-get -y install postgresql-server-dev-9.5=9.5.2-2ubuntu1
+  - sudo apt-get -y install postgresql-plpython-9.5=9.5.2-2ubuntu1
  - sudo apt-get -y install postgresql-9.5-postgis-scripts=2.2.2.0-cdb2
  - sudo apt-get -y install postgresql-9.5-postgis-2.2=2.2.2.0-cdb2

--- a/NEWS.md
+++ b/NEWS.md
@@ -1,11 +1,3 @@
-0.5.0 (2016-12-15)
------------------
-* Updated PULL_REQUEST_TEMPLATE
-* Fixed a bug that flips the order of the numerator in denominator for calculating using Moran Local Rate because previously the code sorted the keys alphabetically.
-* Add new CDB_GetisOrdsG functions. Getis-Ord's G\* is a geo-statistical measurement of the intensity of clustering of high or low values
-* Add new outlier detection functions: CDB_StaticOutlier, CDB_PercentOutlier and CDB_StdDevOutlier
-* Updates in the framework for accessing the Python functions.
-
 0.4.2 (2016-09-22)
 ------------------
 * Bugfix for cdb_areasofinterestglobal: import correct modules
--- a/doc/16_getis_ord_gstar.md
+++ b/doc/16_getis_ord_gstar.md
@@ -1,40 +0,0 @@
-## Getis-Ord's G\*
-
-Getis-Ord's G\* is a geo-statistical measurement of the intensity of clustering of high or low values. The clustering of high values can be referred to as "hotspots" because these are areas of high activity or large (relative to the global mean) measurement values. Coldspots are clustered areas with low activity or small measurement values.
-
-### CDB_GetisOrdsG(subquery text, column_name text)
-
-#### Arguments
-
-| Name | Type | Description |
-|------|------|-------------|
-| subquery | text | A query of the data you want to pass to the function. It must include `column_name`, a geometry column (usually `the_geom`) and an id column (usually `cartodb_id`) |
-| column_name | text | This is the column of interest for performing this analysis on. This column should be a numeric type. |
-| w_type (optional) | text | Type of weight to use when finding neighbors. Currently available options are 'knn' (default) and 'queen'. Read more about weight types in [PySAL's weights documentation.](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html) |
-| num_ngbrs (optional) | integer | Default: 5. If `knn` is chosen, this will set the number of neighbors. If `knn` is not chosen, any entered value will be ignored. Use `NULL` if not choosing `knn`. |
-| permutations (optional) | integer | The number of permutations for calculating p-values. Default: 999 |
-| geom_col (optional) | text | The column where the geometry information is stored. The format must be PostGIS Geometry type (SRID 4326). Default: `the_geom`. |
-| id_col (optional) | text | The column that has the unique row identifier. |
-
-### Returns
-
-Returns a table with the following columns.
-
-| Name | Type | Description |
-|------|------|-------------|
-| z_score | numeric | z-score, a measure of the intensity of clustering of high values (hotspots) or low values (coldspots). Positive values represent 'hotspots', while negative values represent 'coldspots'. |
-| p_value | numeric  | p-value, a measure of the significance of the intensity of clustering |
-| p_z_sim | numeric | p-value based on standard normal approximation from permutations |
-| rowid | integer | The original `id_col` that can be used to associate the outputs with the original geometry and inputs |
-
-#### Example Usage
-
-The following query returns the original table augmented with the values calculated from the Getis-Ord's G\* analysis.
-
-```sql
-SELECT i.*, m.z_score, m.p_value
-  FROM cdb_crankshaft.CDB_GetisOrdsG('SELECT * FROM incident_reports_clustered',
-                                     'num_incidents') As m
-  JOIN incident_reports_clustered As i
-    ON i.cartodb_id = m.rowid;
-```
--- a/doc/18_outliers.md
+++ b/doc/18_outliers.md
@@ -1,163 +0,0 @@
-## Outlier Detection
-
-This set of functions detects the presence of outliers. There are three functions for finding outliers from non-spatial data:
-
-1. Static Outliers
-1. Percentage Outliers
-1. Standard Deviation Outliers
-
-### CDB_StaticOutlier(column_value numeric, threshold numeric)
-
-#### Arguments
-
-| Name | Type | Description |
-|------|------|-------------|
-| column_value | numeric | The column of values on which to apply the threshold |
-| threshold | numeric | The static threshold which is used to indicate whether a `column_value` is an outlier or not |
-
-### Returns
-
-Returns a boolean (true/false) depending on whether a value is above or below (or equal to) the threshold
-
-| Name | Type | Description |
-|------|------|-------------|
-| outlier | boolean | classification of whether a row is an outlier or not |
-
-#### Example Usage
-
-With a table `website_visits` and a column of the number of website visits in units of 10,000 visits:
-
-```
-| id | visits_10k |
-|----|------------|
-| 1  | 1 |
-| 2  | 3 |
-| 3  | 5 |
-| 4  | 1 |
-| 5  | 32 |
-| 6  | 3 |
-| 7  | 57 |
-| 8  | 2 |
-```
-
-```sql
-SELECT
-  id,
-  CDB_StaticOutlier(visits_10k, 11.0) As outlier,
-  visits_10k
-FROM website_visits
-```
-
-```
-| id | outlier | visits_10k |
-|----|---------|------------|
-| 1  | f | 1 |
-| 2  | f | 3 |
-| 3  | f | 5 |
-| 4  | f | 1 |
-| 5  | t | 32 |
-| 6  | f | 3 |
-| 7  | t | 57 |
-| 8  | f | 2 |
-```
-
-### CDB_PercentOutlier(column_values numeric[], outlier_fraction numeric, ids int[])
-
-`CDB_PercentOutlier` calculates whether or not a value falls above a given threshold based on a percentage above the mean value of the input values.
-
-#### Arguments
-
-| Name | Type | Description |
-|------|------|-------------|
-| column_values | numeric[] | An array of the values to calculate the outlier classification on |
-| outlier_fraction | numeric | The threshold above which a column value divided by the mean of all values is considered an outlier |
-| ids | int[] | An array of the unique row ids of the input data (usually `cartodb_id`) |
-
-### Returns
-
-Returns a table of the outlier classification with the following columns
-
-| Name | Type | Description |
-|------|------|-------------|
-| is_outlier | boolean  | classification of whether a row is an outlier or not |
-| rowid | int | original row id (e.g., input `cartodb_id`) of the row which has the outlier classification |
-
-#### Example Usage
-
-This example find outliers which are more than 100% larger than the average (that is, more than 2.0 times larger).
-
-```sql
-WITH cte As (
-  SELECT
-    unnest(Array[1,2,3,4,5,6,7,8]) As id,
-    unnest(Array[1,3,5,1,32,3,57,2]) As visits_10k
-  )
-SELECT
-  (CDB_PercentOutlier(array_agg(visits_10k), 2.0, array_agg(id))).*
-FROM cte;
-```
-
-Output
-```
-| outlier | rowid |
-|---------+-------|
-| f | 1 |
-| f | 2 |
-| f | 3 |
-| f | 4 |
-| t | 5 |
-| f | 6 |
-| t | 7 |
-| f | 8 |
-```
-
-### CDB_StdDevOutlier(column_values numeric[], num_deviations numeric, ids int[], is_symmetric boolean DEFAULT true)
-
-`CDB_StdDevOutlier` calculates whether or not a value falls above or below a given threshold based on the number of standard deviations from the mean.
-
-#### Arguments
-
-| Name | Type | Description |
-|------|------|-------------|
-| column_values | numeric[] | An array of the values to calculate the outlier classification on |
-| num_deviations | numeric | The threshold in units of standard deviation |
-| ids | int[] | An array of the unique row ids of the input data (usually `cartodb_id`) |
-| is_symmetric (optional) | boolean | Consider outliers that are symmetric about the mean (default: true) |
-
-### Returns
-
-Returns a table of the outlier classification with the following columns
-
-| Name | Type | Description |
-|------|------|-------------|
-| is_outlier | boolean  | classification of whether a row is an outlier or not |
-| rowid | int | original row id (e.g., input `cartodb_id`) of the row which has the outlier classification |
-
-#### Example Usage
-
-This example find outliers which are more than 100% larger than the average (that is, more than 2.0 times larger).
-
-```sql
-WITH cte As (
-  SELECT
-    unnest(Array[1,2,3,4,5,6,7,8]) As id,
-    unnest(Array[1,3,5,1,32,3,57,2]) As visits_10k
-  )
-SELECT
-  (CDB_StdDevOutlier(array_agg(visits_10k), 2.0, array_agg(id))).*
-FROM cte;
-```
-
-Output
-```
-| outlier | rowid |
-|---------+-------|
-| f | 1 |
-| f | 2 |
-| f | 3 |
-| f | 4 |
-| f | 5 |
-| f | 6 |
-| t | 7 |
-| f | 8 |
-```
--- a/release/crankshaft--0.4.2--0.5.0.sql
+++ b/release/crankshaft--0.4.2--0.5.0.sql
--- a/release/crankshaft--0.5.0--0.5.1.sql
+++ b/release/crankshaft--0.5.0--0.5.1.sql
--- a/release/crankshaft--0.5.0.sql
+++ b/release/crankshaft--0.5.0.sql
--- a/release/crankshaft--0.5.1.sql
+++ b/release/crankshaft--0.5.1.sql
--- a/release/crankshaft.control
+++ b/release/crankshaft.control
@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.5.1'
+default_version = '0.4.2'
 requires = 'plpythonu, postgis'
 superuser = true
 schema = cdb_crankshaft
--- a/release/python/0.5.0/crankshaft/crankshaft/init.py
+++ b/release/python/0.5.0/crankshaft/crankshaft/init.py
@@ -1,6 +0,0 @@
-"""Import all modules"""
-import crankshaft.random_seeds
-import crankshaft.clustering
-import crankshaft.space_time_dynamics
-import crankshaft.segmentation
-import analysis_data_provider
--- a/release/python/0.5.0/crankshaft/crankshaft/analysis_data_provider.py
+++ b/release/python/0.5.0/crankshaft/crankshaft/analysis_data_provider.py
@@ -1,67 +0,0 @@
-"""class for fetching data"""
-import plpy
-import pysal_utils as pu
-
-
-class AnalysisDataProvider:
-    def get_getis(self, w_type, params):
-        """fetch data for getis ord's g"""
-        try:
-            query = pu.construct_neighbor_query(w_type, params)
-            result = plpy.execute(query)
-            # if there are no neighbors, exit
-            if len(result) == 0:
-                return pu.empty_zipped_array(4)
-            else:
-                return result
-        except plpy.SPIError, err:
-            plpy.error('Analysis failed: %s' % err)
-
-    def get_markov(self, w_type, params):
-        """fetch data for spatial markov"""
-        try:
-            query = pu.construct_neighbor_query(w_type, params)
-            data = plpy.execute(query)
-
-            if len(data) == 0:
-                return pu.empty_zipped_array(4)
-
-            return data
-        except plpy.SPIError, err:
-            plpy.error('Analysis failed: %s' % err)
-
-    def get_moran(self, w_type, params):
-        """fetch data for moran's i analyses"""
-        try:
-            query = pu.construct_neighbor_query(w_type, params)
-            data = plpy.execute(query)
-
-            # if there are no neighbors, exit
-            if len(data) == 0:
-                return pu.empty_zipped_array(2)
-            return data
-        except plpy.SPIError, err:
-            plpy.error('Analysis failed: %s' % e)
-            return pu.empty_zipped_array(2)
-
-    def get_nonspatial_kmeans(self, query):
-        """fetch data for non-spatial kmeans"""
-        try:
-            data = plpy.execute(query)
-            return data
-        except plpy.SPIError, err:
-            plpy.error('Analysis failed: %s' % err)
-
-    def get_spatial_kmeans(self, params):
-        """fetch data for spatial kmeans"""
-        query = ("SELECT "
-                 "array_agg({id_col} ORDER BY {id_col}) as ids,"
-                 "array_agg(ST_X({geom_col}) ORDER BY {id_col}) As xs,"
-                 "array_agg(ST_Y({geom_col}) ORDER BY {id_col}) As ys "
-                 "FROM ({subquery}) As a "
-                 "WHERE {geom_col} IS NOT NULL").format(**params)
-        try:
-            data = plpy.execute(query)
-            return data
-        except plpy.SPIError, err:
-            plpy.error('Analysis failed: %s' % err)
--- a/release/python/0.5.0/crankshaft/crankshaft/clustering/init.py
+++ b/release/python/0.5.0/crankshaft/crankshaft/clustering/init.py
@@ -1,4 +0,0 @@
-"""Import all functions from for clustering"""
-from moran import *
-from kmeans import *
-from getis import *
--- a/release/python/0.5.0/crankshaft/crankshaft/clustering/getis.py
+++ b/release/python/0.5.0/crankshaft/crankshaft/clustering/getis.py
@@ -1,50 +0,0 @@
-"""
-Getis-Ord's G geostatistics (hotspot/coldspot analysis)
-"""
-
-import pysal as ps
-from collections import OrderedDict
-
-# crankshaft modules
-import crankshaft.pysal_utils as pu
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-
-# High level interface ---------------------------------------
-
-
-class Getis:
-    def __init__(self, data_provider=None):
-        if data_provider is None:
-            self.data_provider = AnalysisDataProvider()
-        else:
-            self.data_provider = data_provider
-
-    def getis_ord(self, subquery, attr,
-                  w_type, num_ngbrs, permutations, geom_col, id_col):
-        """
-        Getis-Ord's G*
-        Implementation building neighbors with a PostGIS database and PySAL's
-          Getis-Ord's G* hotspot/coldspot module.
-        Andy Eschbacher
-        """
-
-        # geometries with attributes that are null are ignored
-        # resulting in a collection of not as near neighbors if kNN is chosen
-
-        qvals = OrderedDict([("id_col", id_col),
-                             ("attr1", attr),
-                             ("geom_col", geom_col),
-                             ("subquery", subquery),
-                             ("num_ngbrs", num_ngbrs)])
-
-        result = self.data_provider.get_getis(w_type, qvals)
-        attr_vals = pu.get_attributes(result)
-
-        # build PySAL weight object
-        weight = pu.get_weight(result, w_type, num_ngbrs)
-
-        # calculate Getis-Ord's G* z- and p-values
-        getis = ps.esda.getisord.G_Local(attr_vals, weight,
-                                         star=True, permutations=permutations)
-
-        return zip(getis.z_sim, getis.p_sim, getis.p_z_sim, weight.id_order)
--- a/release/python/0.5.0/crankshaft/crankshaft/clustering/kmeans.py
+++ b/release/python/0.5.0/crankshaft/crankshaft/clustering/kmeans.py
@@ -1,32 +0,0 @@
-from sklearn.cluster import KMeans
-import numpy as np
-
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-
-
-class Kmeans:
-    def __init__(self, data_provider=None):
-        if data_provider is None:
-            self.data_provider = AnalysisDataProvider()
-        else:
-            self.data_provider = data_provider
-
-    def spatial(self, query, no_clusters, no_init=20):
-        """
-            find centers based on clusters of latitude/longitude pairs
-            query: SQL query that has a WGS84 geometry (the_geom)
-        """
-        params = {"subquery": query,
-                  "geom_col": "the_geom",
-                  "id_col": "cartodb_id"}
-
-        data = self.data_provider.get_spatial_kmeans(params)
-
-        # Unpack query response
-        xs = data[0]['xs']
-        ys = data[0]['ys']
-        ids = data[0]['ids']
-
-        km = KMeans(n_clusters=no_clusters, n_init=no_init)
-        labels = km.fit_predict(zip(xs, ys))
-        return zip(ids, labels)
--- a/release/python/0.5.0/crankshaft/crankshaft/clustering/moran.py
+++ b/release/python/0.5.0/crankshaft/crankshaft/clustering/moran.py
@@ -1,208 +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
-from collections import OrderedDict
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-
-# crankshaft module
-import crankshaft.pysal_utils as pu
-
-# High level interface ---------------------------------------
-
-
-class Moran:
-    def __init__(self, data_provider=None):
-        if data_provider is None:
-            self.data_provider = AnalysisDataProvider()
-        else:
-            self.data_provider = data_provider
-
-    def global_stat(self, subquery, attr_name,
-                    w_type, num_ngbrs, permutations, geom_col, id_col):
-        """
-        Moran's I (global)
-        Implementation building neighbors with a PostGIS database and Moran's I
-         core clusters with PySAL.
-        Andy Eschbacher
-        """
-        params = OrderedDict([("id_col", id_col),
-                              ("attr1", attr_name),
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
-
-        result = self.data_provider.get_moran(w_type, params)
-
-        # 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 local_stat(self, 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
-
-        params = OrderedDict([("id_col", id_col),
-                              ("attr1", attr),
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
-
-        result = self.data_provider.get_moran(w_type, params)
-
-        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 global_rate_stat(self, subquery, numerator, denominator,
-                         w_type, num_ngbrs, permutations, geom_col, id_col):
-        """
-        Moran's I Rate (global)
-        Andy Eschbacher
-        """
-        params = OrderedDict([("id_col", id_col),
-                              ("attr1", numerator),
-                              ("attr2", denominator)
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
-
-        result = self.data_provider.get_moran(w_type, params)
-
-        # 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 local_rate_stat(self, subquery, numerator, denominator,
-                        w_type, num_ngbrs, permutations, geom_col, id_col):
-        """
-            Moran's I Local Rate
-            Andy Eschbacher
-        """
-        # geometries with values that are null are ignored
-        # resulting in a collection of not as near neighbors
-
-        params = OrderedDict([("id_col", id_col),
-                              ("numerator", numerator),
-                              ("denominator", denominator),
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
-
-        result = self.data_provider.get_moran(w_type, params)
-
-        # 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 local_bivariate_stat(self, subquery, attr1, attr2,
-                             permutations, geom_col, id_col,
-                             w_type, num_ngbrs):
-        """
-            Moran's I (local) Bivariate (untested)
-        """
-
-        params = OrderedDict([("id_col", id_col),
-                              ("attr1", attr1),
-                              ("attr2", attr2),
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
-
-        result = self.data_provider.get_moran(w_type, params)
-
-        # collect attributes
-        attr1_vals = pu.get_attributes(result, 1)
-        attr2_vals = pu.get_attributes(result, 2)
-
-        # create weights
-        weight = pu.get_weight(result, w_type, num_ngbrs)
-
-        # calculate LISA values
-        lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
-                                            permutations=permutations)
-
-        # find clustering of significance
-        lisa_sig = quad_position(lisa.q)
-
-        return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
-
-# Low level functions ----------------------------------------
-
-
-def map_quads(coord):
-    """
-        Map a quadrant number to Moran's I designation
-        HH=1, LH=2, LL=3, HL=4
-        Input:
-        @param coord (int): quadrant of a specific measurement
-        Output:
-            classification (one of 'HH', 'LH', 'LL', or 'HL')
-    """
-    if coord == 1:
-        return 'HH'
-    elif coord == 2:
-        return 'LH'
-    elif coord == 3:
-        return 'LL'
-    elif coord == 4:
-        return 'HL'
-    else:
-        return None
-
-
-def quad_position(quads):
-    """
-        Produce Moran's I classification based of n
-        Input:
-        @param quads ndarray: an array of quads classified by
-          1-4 (PySAL default)
-        Output:
-        @param list: an array of quads classied by 'HH', 'LL', etc.
-    """
-    return [map_quads(q) for q in quads]
--- a/release/python/0.5.0/crankshaft/crankshaft/pysal_utils/init.py
+++ b/release/python/0.5.0/crankshaft/crankshaft/pysal_utils/init.py
@@ -1,2 +0,0 @@
-"""Import all functions for pysal_utils"""
-from crankshaft.pysal_utils.pysal_utils import *
--- a/release/python/0.5.0/crankshaft/crankshaft/pysal_utils/pysal_utils.py
+++ b/release/python/0.5.0/crankshaft/crankshaft/pysal_utils/pysal_utils.py
@@ -1,211 +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.
-        Defaults to order in the params
-        @param params: dict of information used in query (column names,
-                       table name, etc.)
-            Example:
-            OrderedDict([('numerator', 'price'),
-                         ('denominator', 'sq_meters'),
-                         ('subquery', 'SELECT * FROM interesting_data')])
-        Output:
-          "i.\"price\"::numeric As attr1, " \
-          "i.\"sq_meters\"::numeric As attr2, "
-    """
-
-    attr_string = ""
-    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(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 = [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 'denominator' in attrs:
-            attr_string.append(
-              "idx_replace.\"%s\" <> 0" % params['denominator'])
-
-    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)]
--- a/release/python/0.5.0/crankshaft/crankshaft/random_seeds.py
+++ b/release/python/0.5.0/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)
--- a/release/python/0.5.0/crankshaft/crankshaft/segmentation/init.py
+++ b/release/python/0.5.0/crankshaft/crankshaft/segmentation/init.py
@@ -1 +0,0 @@
-from segmentation import * 
--- a/release/python/0.5.0/crankshaft/crankshaft/segmentation/segmentation.py
+++ b/release/python/0.5.0/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)
--- a/release/python/0.5.0/crankshaft/crankshaft/space_time_dynamics/init.py
+++ b/release/python/0.5.0/crankshaft/crankshaft/space_time_dynamics/init.py
@@ -1,2 +0,0 @@
-"""Import all functions from clustering libraries."""
-from markov import *
--- a/release/python/0.5.0/crankshaft/crankshaft/space_time_dynamics/markov.py
+++ b/release/python/0.5.0/crankshaft/crankshaft/space_time_dynamics/markov.py
@@ -1,194 +0,0 @@
-"""
-Spatial dynamics measurements using Spatial Markov
-"""
-
-# TODO: remove all plpy dependencies
-
-import numpy as np
-import pysal as ps
-import plpy
-import crankshaft.pysal_utils as pu
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-
-
-class Markov:
-    def __init__(self, data_provider=None):
-        if data_provider is None:
-            self.data_provider = AnalysisDataProvider()
-        else:
-            self.data_provider = data_provider
-
-    def spatial_trend(self, subquery, time_cols, num_classes=7,
-                      w_type='knn', num_ngbrs=5, permutations=0,
-                      geom_col='the_geom', id_col='cartodb_id'):
-        """
-            Predict the trends of a unit based on:
-            1. history of its transitions to different classes (e.g., 1st
-               quantile -> 2nd quantile)
-            2. average class of its neighbors
-
-            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')
-
-        params = {"id_col": id_col,
-                  "time_cols": time_cols,
-                  "geom_col": geom_col,
-                  "subquery": subquery,
-                  "num_ngbrs": num_ngbrs}
-
-        query_result = self.data_provider.get_markov(w_type, params)
-
-        # 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)
-
-        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
--- a/release/python/0.5.0/crankshaft/requirements.txt
+++ b/release/python/0.5.0/crankshaft/requirements.txt
@@ -1,5 +0,0 @@
-joblib==0.8.3
-numpy==1.6.1
-scipy==0.14.0
-pysal==1.11.2
-scikit-learn==0.14.1
--- a/release/python/0.5.0/crankshaft/setup.py
+++ b/release/python/0.5.0/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='0.5.0',
-
-    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'
-)
--- a/release/python/0.5.0/crankshaft/test/fixtures/getis.json
+++ b/release/python/0.5.0/crankshaft/test/fixtures/getis.json
@@ -1 +0,0 @@
-[[0.004793783909323601, 0.17999999999999999, 0.49808756424021061], [-1.0701189472090842, 0.079000000000000001, 0.14228288580832316], [-0.67867750971877305, 0.42099999999999999, 0.24867110969448558], [-0.67407386707620487, 0.246, 0.25013217644612995], [-0.79495689068870035, 0.33200000000000002, 0.21331928959090596], [-0.49279481022182703, 0.058999999999999997, 0.31107878905057329], [-0.38075627530057132, 0.28399999999999997, 0.35169205342069643], [-0.86710921611314895, 0.23699999999999999, 0.19294108571294855], [-0.78618647240956485, 0.050000000000000003, 0.2158791250244505], [-0.76108527223116984, 0.064000000000000001, 0.22330306830813684], [-0.13340753531942209, 0.247, 0.44693554317763651], [-0.57584545722033043, 0.48999999999999999, 0.28235982246156488], [-0.78882694661192831, 0.433, 0.2151065788731219], [-0.38769767950046219, 0.375, 0.34911988661484239], [-0.56057819488052207, 0.41399999999999998, 0.28754255985169652], [-0.41354017495644935, 0.45500000000000002, 0.339605447117173], [-0.23993577722243081, 0.49099999999999999, 0.40519002230969337], [-0.1389080156677496, 0.40400000000000003, 0.44476141839645233], [-0.25485737510500855, 0.376, 0.39941662953554224], [-0.71218610582902353, 0.17399999999999999, 0.23817476979886087], [-0.54533105995872144, 0.13700000000000001, 0.2927629228714812], [-0.39547917847510977, 0.033000000000000002, 0.34624464252424236], [-0.43052658996257548, 0.35399999999999998, 0.33340631435564982], [-0.37296719193774736, 0.40300000000000002, 0.35458643102865428], [-0.66482612169465694, 0.31900000000000001, 0.25308085650392698], [-0.13772133540823422, 0.34699999999999998, 0.44523032843016275], [-0.6765304487868502, 0.20999999999999999, 0.24935196033890672], [-0.64518763494323472, 0.32200000000000001, 0.25940279912025543], [-0.5078622084312413, 0.41099999999999998, 0.30577498972600159], [-0.12652006733772059, 0.42899999999999999, 0.44966013262301163], [-0.32691133022814595, 0.498, 0.37186747562269029], [0.25533848511500978, 0.42399999999999999, 0.39923083899077472], [2.7045138116476508, 0.0050000000000000001, 0.0034202212972238577], [-0.1551614486076057, 0.44400000000000001, 0.43834701985429037], [1.9524487722567723, 0.012999999999999999, 0.025442473674991528], [-1.2055816465306763, 0.017000000000000001, 0.11398941970467646], [3.478472976017831, 0.002, 0.00025213964072468009], [-1.4621715757903719, 0.002, 0.071847099325659136], [-0.84010307600180256, 0.085000000000000006, 0.20042529779230778], [5.7097646237318243, 0.0030000000000000001, 5.6566262784940591e-09], [1.5082367956567375, 0.065000000000000002, 0.065746966514827365], [-0.58337270103430816, 0.44, 0.27982121546450034], [-0.083271860457022437, 0.45100000000000001, 0.46681768733385554], [-0.46872337815000953, 0.34599999999999997, 0.31963368715684204], [0.18490279849545319, 0.23799999999999999, 0.42665263797981101], [3.470424529947997, 0.012, 0.00025981817437825683], [-0.99942612137154796, 0.032000000000000001, 0.15879415560388499], [-1.3650387953594485, 0.034000000000000002, 0.08612042845912049], [1.8617160516432014, 0.081000000000000003, 0.03132156240215267], [1.1321188945775384, 0.11600000000000001, 0.12879222611766061], [0.064116686050580601, 0.27300000000000002, 0.4744386578180424], [-0.42032194540259099, 0.29999999999999999, 0.33712514016213468], [-0.79581215423980922, 0.123, 0.21307061309098785], [-0.42792753720906046, 0.45600000000000002, 0.33435193892883741], [-1.0629378527428395, 0.051999999999999998, 0.14390506780140866], [-0.54164761752225477, 0.33700000000000002, 0.29403064095211839], [1.0934778886820793, 0.13700000000000001, 0.13709201601893539], [-0.094068785378413719, 0.38200000000000001, 0.46252725802998929], [0.13482026574801856, 0.36799999999999999, 0.44637699118865737], [-0.13976995315653129, 0.34699999999999998, 0.44442087706276601], [-0.051047663924746682, 0.32000000000000001, 0.47964376985626245], [-0.21468297736730158, 0.41699999999999998, 0.41500724761906527], [-0.20873154637330626, 0.38800000000000001, 0.41732890604390893], [-0.32427876152583485, 0.49199999999999999, 0.37286349875557478], [-0.65254842943280977, 0.374, 0.25702372075306734], [-0.48611858196118796, 0.23300000000000001, 0.31344154643990074], [-0.14482354344529477, 0.32600000000000001, 0.44242509660469886], [-0.51052030974200002, 0.439, 0.30484349480873729], [0.56814382285283538, 0.14999999999999999, 0.28496865660103166], [0.58680919931668207, 0.161, 0.27866592887231878], [0.013390357044409013, 0.25800000000000001, 0.49465818005865647], [-0.19050728887961568, 0.41399999999999998, 0.4244558160399462], [-0.60531777422216049, 0.35199999999999998, 0.2724839368239631], [1.0899331115425805, 0.127, 0.13787130480311838], [0.17015055382651084, 0.36899999999999999, 0.43244586845546418], [-0.21738337124409801, 0.40600000000000003, 0.41395479459421991], [1.0329303331079593, 0.079000000000000001, 0.15081825117169467], [1.0218317101096221, 0.104, 0.15343027913308094]]
--- a/release/python/0.5.0/crankshaft/test/fixtures/kmeans.json
+++ b/release/python/0.5.0/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]}]
--- a/release/python/0.5.0/crankshaft/test/fixtures/markov.json
+++ b/release/python/0.5.0/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]]
--- a/release/python/0.5.0/crankshaft/test/fixtures/moran.json
+++ b/release/python/0.5.0/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"]]
--- a/release/python/0.5.0/crankshaft/test/fixtures/neighbors.json
+++ b/release/python/0.5.0/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}
-  ]
--- a/release/python/0.5.0/crankshaft/test/fixtures/neighbors_getis.json
+++ b/release/python/0.5.0/crankshaft/test/fixtures/neighbors_getis.json
--- a/release/python/0.5.0/crankshaft/test/fixtures/neighbors_markov.json
+++ b/release/python/0.5.0/crankshaft/test/fixtures/neighbors_markov.json
--- a/release/python/0.5.0/crankshaft/test/helper.py
+++ b/release/python/0.5.0/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)
--- a/release/python/0.5.0/crankshaft/test/mock_plpy.py
+++ b/release/python/0.5.0/crankshaft/test/mock_plpy.py
@@ -1,54 +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)
-
-    # TODO: additional arguments
-    def execute(self, query):
-        for result in self.results:
-            if result[0].match(query):
-                return result[1]
-        return []
--- a/release/python/0.5.0/crankshaft/test/test_clustering_getis.py
+++ b/release/python/0.5.0/crankshaft/test/test_clustering_getis.py
@@ -1,78 +0,0 @@
-import unittest
-import numpy as np
-
-from helper import fixture_file
-
-from crankshaft.clustering import Getis
-import crankshaft.pysal_utils as pu
-from crankshaft import random_seeds
-import json
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-
-# Fixture files produced as follows
-#
-# import pysal as ps
-# import numpy as np
-# import random
-#
-# # setup variables
-# f = ps.open(ps.examples.get_path("stl_hom.dbf"))
-# y = np.array(f.by_col['HR8893'])
-# w_queen = ps.queen_from_shapefile(ps.examples.get_path("stl_hom.shp"))
-#
-# out_queen = [{"id": index + 1,
-#               "neighbors": [x+1 for x in w_queen.neighbors[index]],
-#               "value": val} for index, val in enumerate(y)]
-#
-# with open('neighbors_queen_getis.json', 'w') as f:
-#     f.write(str(out_queen))
-#
-# random.seed(1234)
-# np.random.seed(1234)
-# lgstar_queen = ps.esda.getisord.G_Local(y, w_queen, star=True,
-#                                         permutations=999)
-#
-# with open('getis_queen.json', 'w') as f:
-#     f.write(str(zip(lgstar_queen.z_sim,
-#                     lgstar_queen.p_sim, lgstar_queen.p_z_sim)))
-
-
-class FakeDataProvider(AnalysisDataProvider):
-    def __init__(self, mock_data):
-        self.mock_result = mock_data
-
-    def get_getis(self, w_type, param):
-        return self.mock_result
-
-
-class GetisTest(unittest.TestCase):
-    """Testing class for Getis-Ord's G* funtion
-       This test replicates the work done in PySAL documentation:
-          https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/autocorrelation.html#local-g-and-g
-    """
-
-    def setUp(self):
-        # load raw data for analysis
-        self.neighbors_data = json.loads(
-          open(fixture_file('neighbors_getis.json')).read())
-
-        # load pre-computed/known values
-        self.getis_data = json.loads(
-          open(fixture_file('getis.json')).read())
-
-    def test_getis_ord(self):
-        """Test Getis-Ord's G*"""
-        data = [{'id': d['id'],
-                 'attr1': d['value'],
-                 'neighbors': d['neighbors']} for d in self.neighbors_data]
-
-        random_seeds.set_random_seeds(1234)
-        getis = Getis(FakeDataProvider(data))
-
-        result = getis.getis_ord('subquery', 'value',
-                                 'queen', None, 999, 'the_geom',
-                                 'cartodb_id')
-        result = [(row[0], row[1]) for row in result]
-        expected = np.array(self.getis_data)[:, 0:2]
-        for ([res_z, res_p], [exp_z, exp_p]) in zip(result, expected):
-            self.assertAlmostEqual(res_z, exp_z, delta=1e-2)
--- a/release/python/0.5.0/crankshaft/test/test_clustering_kmeans.py
+++ b/release/python/0.5.0/crankshaft/test/test_clustering_kmeans.py
@@ -1,56 +0,0 @@
-import unittest
-import numpy as np
-
-
-# from mock_plpy import MockPlPy
-# plpy = MockPlPy()
-#
-# import sys
-# sys.modules['plpy'] = plpy
-from helper import fixture_file
-from crankshaft.clustering import Kmeans
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-import crankshaft.clustering as cc
-
-from crankshaft import random_seeds
-import json
-from collections import OrderedDict
-
-
-class FakeDataProvider(AnalysisDataProvider):
-    def __init__(self, mocked_result):
-        self.mocked_result = mocked_result
-
-    def get_spatial_kmeans(self, query):
-        return self.mocked_result
-
-    def get_nonspatial_kmeans(self, query, standarize):
-        return self.mocked_result
-
-
-class KMeansTest(unittest.TestCase):
-    """Testing class for k-means spatial"""
-
-    def setUp(self):
-        self.cluster_data = json.loads(
-          open(fixture_file('kmeans.json')).read())
-        self.params = {"subquery": "select * from table",
-                       "no_clusters": "10"}
-
-    def test_kmeans(self):
-        """
-        """
-        data = [{'xs': d['xs'],
-                 'ys': d['ys'],
-                 'ids': d['ids']} for d in self.cluster_data]
-
-        random_seeds.set_random_seeds(1234)
-        kmeans = Kmeans(FakeDataProvider(data))
-        clusters = kmeans.spatial('subquery', 2)
-        labels = [a[1] for a in clusters]
-        c1 = [a for a in clusters if a[1] == 0]
-        c2 = [a for a in clusters if a[1] == 1]
-
-        self.assertEqual(len(np.unique(labels)), 2)
-        self.assertEqual(len(c1), 20)
-        self.assertEqual(len(c2), 20)
--- a/release/python/0.5.0/crankshaft/test/test_clustering_moran.py
+++ b/release/python/0.5.0/crankshaft/test/test_clustering_moran.py
@@ -1,112 +0,0 @@
-import unittest
-import numpy as np
-
-from helper import fixture_file
-from crankshaft.clustering import Moran
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-import crankshaft.pysal_utils as pu
-from crankshaft import random_seeds
-import json
-from collections import OrderedDict
-
-
-class FakeDataProvider(AnalysisDataProvider):
-    def __init__(self, mock_data):
-        self.mock_result = mock_data
-
-    def get_moran(self, w_type, params):
-        return self.mock_result
-
-
-class MoranTest(unittest.TestCase):
-    """Testing class for Moran's I functions"""
-
-    def setUp(self):
-        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"""
-        from crankshaft.clustering import map_quads
-        self.assertEqual(map_quads(1), 'HH')
-        self.assertEqual(map_quads(2), 'LH')
-        self.assertEqual(map_quads(3), 'LL')
-        self.assertEqual(map_quads(4), 'HL')
-        self.assertEqual(map_quads(33), None)
-        self.assertEqual(map_quads('andy'), None)
-
-    def test_quad_position(self):
-        """Test lisa_sig_vals"""
-        from crankshaft.clustering import quad_position
-
-        quads = np.array([1, 2, 3, 4], np.int)
-
-        ans = np.array(['HH', 'LH', 'LL', 'HL'])
-        test_ans = quad_position(quads)
-
-        self.assertTrue((test_ans == ans).all())
-
-    def test_local_stat(self):
-        """Test Moran's I local"""
-        data = [OrderedDict([('id', d['id']),
-                             ('attr1', d['value']),
-                             ('neighbors', d['neighbors'])])
-                for d in self.neighbors_data]
-
-        moran = Moran(FakeDataProvider(data))
-        random_seeds.set_random_seeds(1234)
-        result = moran.local_stat('subquery', 'value',
-                                  'knn', 5, 99, 'the_geom', 'cartodb_id')
-        result = [(row[0], row[1]) for row in result]
-        zipped_values = zip(result, self.moran_data)
-
-        for ([res_val, res_quad], [exp_val, exp_quad]) in zipped_values:
-            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]
-
-        random_seeds.set_random_seeds(1234)
-        moran = Moran(FakeDataProvider(data))
-        result = moran.local_rate_stat('subquery', 'numerator', 'denominator',
-                                       'knn', 5, 99, 'the_geom', 'cartodb_id')
-        result = [(row[0], row[1]) for row in result]
-
-        zipped_values = zip(result, self.moran_data)
-
-        for ([res_val, res_quad], [exp_val, exp_quad]) in zipped_values:
-            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]
-        random_seeds.set_random_seeds(1235)
-        moran = Moran(FakeDataProvider(data))
-        result = moran.global_stat('table', 'value',
-                                   'knn', 5, 99, 'the_geom',
-                                   'cartodb_id')
-
-        result_moran = result[0][0]
-        expected_moran = np.array([row[0] for row in self.moran_data]).mean()
-        self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)
--- a/release/python/0.5.0/crankshaft/test/test_pysal_utils.py
+++ b/release/python/0.5.0/crankshaft/test/test_pysal_utils.py
@@ -1,160 +0,0 @@
-import unittest
-
-import crankshaft.pysal_utils as pu
-from crankshaft import random_seeds
-from collections import OrderedDict
-
-
-class PysalUtilsTest(unittest.TestCase):
-    """Testing class for utility functions related to PySAL integrations"""
-
-    def setUp(self):
-        self.params1 = OrderedDict([("id_col", "cartodb_id"),
-                                    ("attr1", "andy"),
-                                    ("attr2", "jay_z"),
-                                    ("subquery", "SELECT * FROM a_list"),
-                                    ("geom_col", "the_geom"),
-                                    ("num_ngbrs", 321)])
-
-        self.params2 = OrderedDict([("id_col", "cartodb_id"),
-                                    ("numerator", "price"),
-                                    ("denominator", "sq_meters"),
-                                    ("subquery", "SELECT * FROM pecan"),
-                                    ("geom_col", "the_geom"),
-                                    ("num_ngbrs", 321)])
-
-        self.params3 = OrderedDict([("id_col", "cartodb_id"),
-                                    ("numerator", "sq_meters"),
-                                    ("denominator", "price"),
-                                    ("subquery", "SELECT * FROM pecan"),
-                                    ("geom_col", "the_geom"),
-                                    ("num_ngbrs", 321)])
-
-        self.params_array = {"id_col": "cartodb_id",
-                             "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
-                             "subquery": "SELECT * FROM a_list",
-                             "geom_col": "the_geom",
-                             "num_ngbrs": 321}
-
-    def test_query_attr_select(self):
-        """Test query_attr_select"""
-
-        ans1 = ("i.\"andy\"::numeric As attr1, "
-                "i.\"jay_z\"::numeric As attr2, ")
-
-        ans2 = ("i.\"price\"::numeric As attr1, "
-                "i.\"sq_meters\"::numeric As attr2, ")
-
-        ans3 = ("i.\"sq_meters\"::numeric As attr1, "
-                "i.\"price\"::numeric As attr2, ")
-
-        ans_array = ("i.\"_2013_dec\"::numeric As attr1, "
-                     "i.\"_2014_jan\"::numeric As attr2, "
-                     "i.\"_2014_feb\"::numeric As attr3, ")
-
-        self.assertEqual(pu.query_attr_select(self.params1), ans1)
-        self.assertEqual(pu.query_attr_select(self.params2), ans2)
-        self.assertEqual(pu.query_attr_select(self.params3), ans3)
-        self.assertEqual(pu.query_attr_select(self.params_array), ans_array)
-
-    def test_query_attr_where(self):
-        """Test pu.query_attr_where"""
-
-        ans1 = ("idx_replace.\"andy\" IS NOT NULL AND "
-                "idx_replace.\"jay_z\" IS NOT NULL")
-
-        ans_array = ("idx_replace.\"_2013_dec\" IS NOT NULL AND "
-                     "idx_replace.\"_2014_jan\" IS NOT NULL AND "
-                     "idx_replace.\"_2014_feb\" IS NOT NULL")
-
-        self.assertEqual(pu.query_attr_where(self.params1), ans1)
-        self.assertEqual(pu.query_attr_where(self.params_array), ans_array)
-
-    def test_knn(self):
-        """Test knn neighbors constructor"""
-
-        ans1 = "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 " \
-                                   "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 " \
-              "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.params1), ans1)
-        self.assertEqual(pu.knn(self.params_array), ans_array)
-
-    def test_queen(self):
-        """Test queen neighbors constructor"""
-
-        ans1 = "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)" \
-                                  ") As neighbors " \
-              "FROM (SELECT * FROM a_list) As i " \
-              "WHERE i.\"andy\" IS NOT NULL AND " \
-                    "i.\"jay_z\" IS NOT NULL " \
-              "ORDER BY i.\"cartodb_id\" ASC;"
-
-        self.assertEqual(pu.queen(self.params1), ans1)
-
-    def test_construct_neighbor_query(self):
-        """Test construct_neighbor_query"""
-
-        # Compare to raw knn query
-        self.assertEqual(pu.construct_neighbor_query('knn', self.params1),
-                         pu.knn(self.params1))
-
-    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)
--- a/release/python/0.5.0/crankshaft/test/test_segmentation.py
+++ b/release/python/0.5.0/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)  )
--- a/release/python/0.5.0/crankshaft/test/test_space_time_dynamics.py
+++ b/release/python/0.5.0/crankshaft/test/test_space_time_dynamics.py
@@ -1,349 +0,0 @@
-import unittest
-import numpy as np
-
-import unittest
-
-
-from helper import fixture_file
-
-from crankshaft.space_time_dynamics import Markov
-import crankshaft.space_time_dynamics as std
-from crankshaft import random_seeds
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-import json
-
-
-class FakeDataProvider(AnalysisDataProvider):
-    def __init__(self, data):
-        self.mock_result = data
-
-    def get_markov(self, w_type, params):
-        return self.mock_result
-
-
-class SpaceTimeTests(unittest.TestCase):
-    """Testing class for Markov Functions."""
-
-    def setUp(self):
-        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]))
-        markov = Markov(FakeDataProvider(data))
-        random_seeds.set_random_seeds(1234)
-
-        result = markov.spatial_trend('subquery',
-                                      ['y1995', 'y1996', 'y1997', 'y1998',
-                                       'y1999', 'y2000', 'y2001', 'y2002',
-                                       'y2003', 'y2004', 'y2005', 'y2006',
-                                       'y2007', 'y2008', 'y2009'],
-                                      5, 'knn', 5, 0, 'the_geom',
-                                      'cartodb_id')
-
-        self.assertTrue(result is not None)
-        result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
-        print result[0]
-        expected = self.markov_data
-        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))
--- a/release/python/0.5.1/crankshaft/crankshaft/init.py
+++ b/release/python/0.5.1/crankshaft/crankshaft/init.py
@@ -1,6 +0,0 @@
-"""Import all modules"""
-import crankshaft.random_seeds
-import crankshaft.clustering
-import crankshaft.space_time_dynamics
-import crankshaft.segmentation
-import analysis_data_provider
--- a/release/python/0.5.1/crankshaft/crankshaft/analysis_data_provider.py
+++ b/release/python/0.5.1/crankshaft/crankshaft/analysis_data_provider.py
@@ -1,67 +0,0 @@
-"""class for fetching data"""
-import plpy
-import pysal_utils as pu
-
-
-class AnalysisDataProvider:
-    def get_getis(self, w_type, params):
-        """fetch data for getis ord's g"""
-        try:
-            query = pu.construct_neighbor_query(w_type, params)
-            result = plpy.execute(query)
-            # if there are no neighbors, exit
-            if len(result) == 0:
-                return pu.empty_zipped_array(4)
-            else:
-                return result
-        except plpy.SPIError, err:
-            plpy.error('Analysis failed: %s' % err)
-
-    def get_markov(self, w_type, params):
-        """fetch data for spatial markov"""
-        try:
-            query = pu.construct_neighbor_query(w_type, params)
-            data = plpy.execute(query)
-
-            if len(data) == 0:
-                return pu.empty_zipped_array(4)
-
-            return data
-        except plpy.SPIError, err:
-            plpy.error('Analysis failed: %s' % err)
-
-    def get_moran(self, w_type, params):
-        """fetch data for moran's i analyses"""
-        try:
-            query = pu.construct_neighbor_query(w_type, params)
-            data = plpy.execute(query)
-
-            # if there are no neighbors, exit
-            if len(data) == 0:
-                return pu.empty_zipped_array(2)
-            return data
-        except plpy.SPIError, err:
-            plpy.error('Analysis failed: %s' % e)
-            return pu.empty_zipped_array(2)
-
-    def get_nonspatial_kmeans(self, query):
-        """fetch data for non-spatial kmeans"""
-        try:
-            data = plpy.execute(query)
-            return data
-        except plpy.SPIError, err:
-            plpy.error('Analysis failed: %s' % err)
-
-    def get_spatial_kmeans(self, params):
-        """fetch data for spatial kmeans"""
-        query = ("SELECT "
-                 "array_agg({id_col} ORDER BY {id_col}) as ids,"
-                 "array_agg(ST_X({geom_col}) ORDER BY {id_col}) As xs,"
-                 "array_agg(ST_Y({geom_col}) ORDER BY {id_col}) As ys "
-                 "FROM ({subquery}) As a "
-                 "WHERE {geom_col} IS NOT NULL").format(**params)
-        try:
-            data = plpy.execute(query)
-            return data
-        except plpy.SPIError, err:
-            plpy.error('Analysis failed: %s' % err)
--- a/release/python/0.5.1/crankshaft/crankshaft/clustering/init.py
+++ b/release/python/0.5.1/crankshaft/crankshaft/clustering/init.py
@@ -1,4 +0,0 @@
-"""Import all functions from for clustering"""
-from moran import *
-from kmeans import *
-from getis import *
--- a/release/python/0.5.1/crankshaft/crankshaft/clustering/getis.py
+++ b/release/python/0.5.1/crankshaft/crankshaft/clustering/getis.py
@@ -1,50 +0,0 @@
-"""
-Getis-Ord's G geostatistics (hotspot/coldspot analysis)
-"""
-
-import pysal as ps
-from collections import OrderedDict
-
-# crankshaft modules
-import crankshaft.pysal_utils as pu
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-
-# High level interface ---------------------------------------
-
-
-class Getis:
-    def __init__(self, data_provider=None):
-        if data_provider is None:
-            self.data_provider = AnalysisDataProvider()
-        else:
-            self.data_provider = data_provider
-
-    def getis_ord(self, subquery, attr,
-                  w_type, num_ngbrs, permutations, geom_col, id_col):
-        """
-        Getis-Ord's G*
-        Implementation building neighbors with a PostGIS database and PySAL's
-          Getis-Ord's G* hotspot/coldspot module.
-        Andy Eschbacher
-        """
-
-        # geometries with attributes that are null are ignored
-        # resulting in a collection of not as near neighbors if kNN is chosen
-
-        qvals = OrderedDict([("id_col", id_col),
-                             ("attr1", attr),
-                             ("geom_col", geom_col),
-                             ("subquery", subquery),
-                             ("num_ngbrs", num_ngbrs)])
-
-        result = self.data_provider.get_getis(w_type, qvals)
-        attr_vals = pu.get_attributes(result)
-
-        # build PySAL weight object
-        weight = pu.get_weight(result, w_type, num_ngbrs)
-
-        # calculate Getis-Ord's G* z- and p-values
-        getis = ps.esda.getisord.G_Local(attr_vals, weight,
-                                         star=True, permutations=permutations)
-
-        return zip(getis.z_sim, getis.p_sim, getis.p_z_sim, weight.id_order)
--- a/release/python/0.5.1/crankshaft/crankshaft/clustering/kmeans.py
+++ b/release/python/0.5.1/crankshaft/crankshaft/clustering/kmeans.py
@@ -1,32 +0,0 @@
-from sklearn.cluster import KMeans
-import numpy as np
-
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-
-
-class Kmeans:
-    def __init__(self, data_provider=None):
-        if data_provider is None:
-            self.data_provider = AnalysisDataProvider()
-        else:
-            self.data_provider = data_provider
-
-    def spatial(self, query, no_clusters, no_init=20):
-        """
-            find centers based on clusters of latitude/longitude pairs
-            query: SQL query that has a WGS84 geometry (the_geom)
-        """
-        params = {"subquery": query,
-                  "geom_col": "the_geom",
-                  "id_col": "cartodb_id"}
-
-        data = self.data_provider.get_spatial_kmeans(params)
-
-        # Unpack query response
-        xs = data[0]['xs']
-        ys = data[0]['ys']
-        ids = data[0]['ids']
-
-        km = KMeans(n_clusters=no_clusters, n_init=no_init)
-        labels = km.fit_predict(zip(xs, ys))
-        return zip(ids, labels)
--- a/release/python/0.5.1/crankshaft/crankshaft/clustering/moran.py
+++ b/release/python/0.5.1/crankshaft/crankshaft/clustering/moran.py
@@ -1,208 +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
-from collections import OrderedDict
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-
-# crankshaft module
-import crankshaft.pysal_utils as pu
-
-# High level interface ---------------------------------------
-
-
-class Moran:
-    def __init__(self, data_provider=None):
-        if data_provider is None:
-            self.data_provider = AnalysisDataProvider()
-        else:
-            self.data_provider = data_provider
-
-    def global_stat(self, subquery, attr_name,
-                    w_type, num_ngbrs, permutations, geom_col, id_col):
-        """
-        Moran's I (global)
-        Implementation building neighbors with a PostGIS database and Moran's I
-         core clusters with PySAL.
-        Andy Eschbacher
-        """
-        params = OrderedDict([("id_col", id_col),
-                              ("attr1", attr_name),
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
-
-        result = self.data_provider.get_moran(w_type, params)
-
-        # 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 local_stat(self, 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
-
-        params = OrderedDict([("id_col", id_col),
-                              ("attr1", attr),
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
-
-        result = self.data_provider.get_moran(w_type, params)
-
-        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 global_rate_stat(self, subquery, numerator, denominator,
-                         w_type, num_ngbrs, permutations, geom_col, id_col):
-        """
-        Moran's I Rate (global)
-        Andy Eschbacher
-        """
-        params = OrderedDict([("id_col", id_col),
-                              ("attr1", numerator),
-                              ("attr2", denominator)
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
-
-        result = self.data_provider.get_moran(w_type, params)
-
-        # 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 local_rate_stat(self, subquery, numerator, denominator,
-                        w_type, num_ngbrs, permutations, geom_col, id_col):
-        """
-            Moran's I Local Rate
-            Andy Eschbacher
-        """
-        # geometries with values that are null are ignored
-        # resulting in a collection of not as near neighbors
-
-        params = OrderedDict([("id_col", id_col),
-                              ("numerator", numerator),
-                              ("denominator", denominator),
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
-
-        result = self.data_provider.get_moran(w_type, params)
-
-        # 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 local_bivariate_stat(self, subquery, attr1, attr2,
-                             permutations, geom_col, id_col,
-                             w_type, num_ngbrs):
-        """
-            Moran's I (local) Bivariate (untested)
-        """
-
-        params = OrderedDict([("id_col", id_col),
-                              ("attr1", attr1),
-                              ("attr2", attr2),
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
-
-        result = self.data_provider.get_moran(w_type, params)
-
-        # collect attributes
-        attr1_vals = pu.get_attributes(result, 1)
-        attr2_vals = pu.get_attributes(result, 2)
-
-        # create weights
-        weight = pu.get_weight(result, w_type, num_ngbrs)
-
-        # calculate LISA values
-        lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
-                                            permutations=permutations)
-
-        # find clustering of significance
-        lisa_sig = quad_position(lisa.q)
-
-        return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
-
-# Low level functions ----------------------------------------
-
-
-def map_quads(coord):
-    """
-        Map a quadrant number to Moran's I designation
-        HH=1, LH=2, LL=3, HL=4
-        Input:
-        @param coord (int): quadrant of a specific measurement
-        Output:
-            classification (one of 'HH', 'LH', 'LL', or 'HL')
-    """
-    if coord == 1:
-        return 'HH'
-    elif coord == 2:
-        return 'LH'
-    elif coord == 3:
-        return 'LL'
-    elif coord == 4:
-        return 'HL'
-    else:
-        return None
-
-
-def quad_position(quads):
-    """
-        Produce Moran's I classification based of n
-        Input:
-        @param quads ndarray: an array of quads classified by
-          1-4 (PySAL default)
-        Output:
-        @param list: an array of quads classied by 'HH', 'LL', etc.
-    """
-    return [map_quads(q) for q in quads]
--- a/release/python/0.5.1/crankshaft/crankshaft/pysal_utils/init.py
+++ b/release/python/0.5.1/crankshaft/crankshaft/pysal_utils/init.py
@@ -1,2 +0,0 @@
-"""Import all functions for pysal_utils"""
-from crankshaft.pysal_utils.pysal_utils import *
--- a/release/python/0.5.1/crankshaft/crankshaft/pysal_utils/pysal_utils.py
+++ b/release/python/0.5.1/crankshaft/crankshaft/pysal_utils/pysal_utils.py
@@ -1,211 +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.
-        Defaults to order in the params
-        @param params: dict of information used in query (column names,
-                       table name, etc.)
-            Example:
-            OrderedDict([('numerator', 'price'),
-                         ('denominator', 'sq_meters'),
-                         ('subquery', 'SELECT * FROM interesting_data')])
-        Output:
-          "i.\"price\"::numeric As attr1, " \
-          "i.\"sq_meters\"::numeric As attr2, "
-    """
-
-    attr_string = ""
-    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(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 = [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 'denominator' in attrs:
-            attr_string.append(
-              "idx_replace.\"%s\" <> 0" % params['denominator'])
-
-    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)]
--- a/release/python/0.5.1/crankshaft/crankshaft/random_seeds.py
+++ b/release/python/0.5.1/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)
--- a/release/python/0.5.1/crankshaft/crankshaft/segmentation/init.py
+++ b/release/python/0.5.1/crankshaft/crankshaft/segmentation/init.py
@@ -1 +0,0 @@
-from segmentation import * 
--- a/release/python/0.5.1/crankshaft/crankshaft/segmentation/segmentation.py
+++ b/release/python/0.5.1/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)
--- a/release/python/0.5.1/crankshaft/crankshaft/space_time_dynamics/init.py
+++ b/release/python/0.5.1/crankshaft/crankshaft/space_time_dynamics/init.py
@@ -1,2 +0,0 @@
-"""Import all functions from clustering libraries."""
-from markov import *
--- a/release/python/0.5.1/crankshaft/crankshaft/space_time_dynamics/markov.py
+++ b/release/python/0.5.1/crankshaft/crankshaft/space_time_dynamics/markov.py
@@ -1,194 +0,0 @@
-"""
-Spatial dynamics measurements using Spatial Markov
-"""
-
-# TODO: remove all plpy dependencies
-
-import numpy as np
-import pysal as ps
-import plpy
-import crankshaft.pysal_utils as pu
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-
-
-class Markov:
-    def __init__(self, data_provider=None):
-        if data_provider is None:
-            self.data_provider = AnalysisDataProvider()
-        else:
-            self.data_provider = data_provider
-
-    def spatial_trend(self, subquery, time_cols, num_classes=7,
-                      w_type='knn', num_ngbrs=5, permutations=0,
-                      geom_col='the_geom', id_col='cartodb_id'):
-        """
-            Predict the trends of a unit based on:
-            1. history of its transitions to different classes (e.g., 1st
-               quantile -> 2nd quantile)
-            2. average class of its neighbors
-
-            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')
-
-        params = {"id_col": id_col,
-                  "time_cols": time_cols,
-                  "geom_col": geom_col,
-                  "subquery": subquery,
-                  "num_ngbrs": num_ngbrs}
-
-        query_result = self.data_provider.get_markov(w_type, params)
-
-        # 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)
-
-        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
--- a/release/python/0.5.1/crankshaft/requirements.txt
+++ b/release/python/0.5.1/crankshaft/requirements.txt
@@ -1,5 +0,0 @@
-joblib==0.8.3
-numpy==1.6.1
-scipy==0.14.0
-pysal==1.11.2
-scikit-learn==0.14.1
--- a/release/python/0.5.1/crankshaft/setup.py
+++ b/release/python/0.5.1/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='0.5.1',
-
-    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'
-)
--- a/release/python/0.5.1/crankshaft/test/fixtures/getis.json
+++ b/release/python/0.5.1/crankshaft/test/fixtures/getis.json
@@ -1 +0,0 @@
-[[0.004793783909323601, 0.17999999999999999, 0.49808756424021061], [-1.0701189472090842, 0.079000000000000001, 0.14228288580832316], [-0.67867750971877305, 0.42099999999999999, 0.24867110969448558], [-0.67407386707620487, 0.246, 0.25013217644612995], [-0.79495689068870035, 0.33200000000000002, 0.21331928959090596], [-0.49279481022182703, 0.058999999999999997, 0.31107878905057329], [-0.38075627530057132, 0.28399999999999997, 0.35169205342069643], [-0.86710921611314895, 0.23699999999999999, 0.19294108571294855], [-0.78618647240956485, 0.050000000000000003, 0.2158791250244505], [-0.76108527223116984, 0.064000000000000001, 0.22330306830813684], [-0.13340753531942209, 0.247, 0.44693554317763651], [-0.57584545722033043, 0.48999999999999999, 0.28235982246156488], [-0.78882694661192831, 0.433, 0.2151065788731219], [-0.38769767950046219, 0.375, 0.34911988661484239], [-0.56057819488052207, 0.41399999999999998, 0.28754255985169652], [-0.41354017495644935, 0.45500000000000002, 0.339605447117173], [-0.23993577722243081, 0.49099999999999999, 0.40519002230969337], [-0.1389080156677496, 0.40400000000000003, 0.44476141839645233], [-0.25485737510500855, 0.376, 0.39941662953554224], [-0.71218610582902353, 0.17399999999999999, 0.23817476979886087], [-0.54533105995872144, 0.13700000000000001, 0.2927629228714812], [-0.39547917847510977, 0.033000000000000002, 0.34624464252424236], [-0.43052658996257548, 0.35399999999999998, 0.33340631435564982], [-0.37296719193774736, 0.40300000000000002, 0.35458643102865428], [-0.66482612169465694, 0.31900000000000001, 0.25308085650392698], [-0.13772133540823422, 0.34699999999999998, 0.44523032843016275], [-0.6765304487868502, 0.20999999999999999, 0.24935196033890672], [-0.64518763494323472, 0.32200000000000001, 0.25940279912025543], [-0.5078622084312413, 0.41099999999999998, 0.30577498972600159], [-0.12652006733772059, 0.42899999999999999, 0.44966013262301163], [-0.32691133022814595, 0.498, 0.37186747562269029], [0.25533848511500978, 0.42399999999999999, 0.39923083899077472], [2.7045138116476508, 0.0050000000000000001, 0.0034202212972238577], [-0.1551614486076057, 0.44400000000000001, 0.43834701985429037], [1.9524487722567723, 0.012999999999999999, 0.025442473674991528], [-1.2055816465306763, 0.017000000000000001, 0.11398941970467646], [3.478472976017831, 0.002, 0.00025213964072468009], [-1.4621715757903719, 0.002, 0.071847099325659136], [-0.84010307600180256, 0.085000000000000006, 0.20042529779230778], [5.7097646237318243, 0.0030000000000000001, 5.6566262784940591e-09], [1.5082367956567375, 0.065000000000000002, 0.065746966514827365], [-0.58337270103430816, 0.44, 0.27982121546450034], [-0.083271860457022437, 0.45100000000000001, 0.46681768733385554], [-0.46872337815000953, 0.34599999999999997, 0.31963368715684204], [0.18490279849545319, 0.23799999999999999, 0.42665263797981101], [3.470424529947997, 0.012, 0.00025981817437825683], [-0.99942612137154796, 0.032000000000000001, 0.15879415560388499], [-1.3650387953594485, 0.034000000000000002, 0.08612042845912049], [1.8617160516432014, 0.081000000000000003, 0.03132156240215267], [1.1321188945775384, 0.11600000000000001, 0.12879222611766061], [0.064116686050580601, 0.27300000000000002, 0.4744386578180424], [-0.42032194540259099, 0.29999999999999999, 0.33712514016213468], [-0.79581215423980922, 0.123, 0.21307061309098785], [-0.42792753720906046, 0.45600000000000002, 0.33435193892883741], [-1.0629378527428395, 0.051999999999999998, 0.14390506780140866], [-0.54164761752225477, 0.33700000000000002, 0.29403064095211839], [1.0934778886820793, 0.13700000000000001, 0.13709201601893539], [-0.094068785378413719, 0.38200000000000001, 0.46252725802998929], [0.13482026574801856, 0.36799999999999999, 0.44637699118865737], [-0.13976995315653129, 0.34699999999999998, 0.44442087706276601], [-0.051047663924746682, 0.32000000000000001, 0.47964376985626245], [-0.21468297736730158, 0.41699999999999998, 0.41500724761906527], [-0.20873154637330626, 0.38800000000000001, 0.41732890604390893], [-0.32427876152583485, 0.49199999999999999, 0.37286349875557478], [-0.65254842943280977, 0.374, 0.25702372075306734], [-0.48611858196118796, 0.23300000000000001, 0.31344154643990074], [-0.14482354344529477, 0.32600000000000001, 0.44242509660469886], [-0.51052030974200002, 0.439, 0.30484349480873729], [0.56814382285283538, 0.14999999999999999, 0.28496865660103166], [0.58680919931668207, 0.161, 0.27866592887231878], [0.013390357044409013, 0.25800000000000001, 0.49465818005865647], [-0.19050728887961568, 0.41399999999999998, 0.4244558160399462], [-0.60531777422216049, 0.35199999999999998, 0.2724839368239631], [1.0899331115425805, 0.127, 0.13787130480311838], [0.17015055382651084, 0.36899999999999999, 0.43244586845546418], [-0.21738337124409801, 0.40600000000000003, 0.41395479459421991], [1.0329303331079593, 0.079000000000000001, 0.15081825117169467], [1.0218317101096221, 0.104, 0.15343027913308094]]
--- a/release/python/0.5.1/crankshaft/test/fixtures/kmeans.json
+++ b/release/python/0.5.1/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]}]
--- a/release/python/0.5.1/crankshaft/test/fixtures/markov.json
+++ b/release/python/0.5.1/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]]
--- a/release/python/0.5.1/crankshaft/test/fixtures/moran.json
+++ b/release/python/0.5.1/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"]]
--- a/release/python/0.5.1/crankshaft/test/fixtures/neighbors.json
+++ b/release/python/0.5.1/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}
-  ]
--- a/release/python/0.5.1/crankshaft/test/fixtures/neighbors_getis.json
+++ b/release/python/0.5.1/crankshaft/test/fixtures/neighbors_getis.json
--- a/release/python/0.5.1/crankshaft/test/fixtures/neighbors_markov.json
+++ b/release/python/0.5.1/crankshaft/test/fixtures/neighbors_markov.json
--- a/release/python/0.5.1/crankshaft/test/helper.py
+++ b/release/python/0.5.1/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)
--- a/release/python/0.5.1/crankshaft/test/mock_plpy.py
+++ b/release/python/0.5.1/crankshaft/test/mock_plpy.py
@@ -1,54 +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)
-
-    # TODO: additional arguments
-    def execute(self, query):
-        for result in self.results:
-            if result[0].match(query):
-                return result[1]
-        return []
--- a/release/python/0.5.1/crankshaft/test/test_clustering_getis.py
+++ b/release/python/0.5.1/crankshaft/test/test_clustering_getis.py
@@ -1,78 +0,0 @@
-import unittest
-import numpy as np
-
-from helper import fixture_file
-
-from crankshaft.clustering import Getis
-import crankshaft.pysal_utils as pu
-from crankshaft import random_seeds
-import json
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-
-# Fixture files produced as follows
-#
-# import pysal as ps
-# import numpy as np
-# import random
-#
-# # setup variables
-# f = ps.open(ps.examples.get_path("stl_hom.dbf"))
-# y = np.array(f.by_col['HR8893'])
-# w_queen = ps.queen_from_shapefile(ps.examples.get_path("stl_hom.shp"))
-#
-# out_queen = [{"id": index + 1,
-#               "neighbors": [x+1 for x in w_queen.neighbors[index]],
-#               "value": val} for index, val in enumerate(y)]
-#
-# with open('neighbors_queen_getis.json', 'w') as f:
-#     f.write(str(out_queen))
-#
-# random.seed(1234)
-# np.random.seed(1234)
-# lgstar_queen = ps.esda.getisord.G_Local(y, w_queen, star=True,
-#                                         permutations=999)
-#
-# with open('getis_queen.json', 'w') as f:
-#     f.write(str(zip(lgstar_queen.z_sim,
-#                     lgstar_queen.p_sim, lgstar_queen.p_z_sim)))
-
-
-class FakeDataProvider(AnalysisDataProvider):
-    def __init__(self, mock_data):
-        self.mock_result = mock_data
-
-    def get_getis(self, w_type, param):
-        return self.mock_result
-
-
-class GetisTest(unittest.TestCase):
-    """Testing class for Getis-Ord's G* funtion
-       This test replicates the work done in PySAL documentation:
-          https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/autocorrelation.html#local-g-and-g
-    """
-
-    def setUp(self):
-        # load raw data for analysis
-        self.neighbors_data = json.loads(
-          open(fixture_file('neighbors_getis.json')).read())
-
-        # load pre-computed/known values
-        self.getis_data = json.loads(
-          open(fixture_file('getis.json')).read())
-
-    def test_getis_ord(self):
-        """Test Getis-Ord's G*"""
-        data = [{'id': d['id'],
-                 'attr1': d['value'],
-                 'neighbors': d['neighbors']} for d in self.neighbors_data]
-
-        random_seeds.set_random_seeds(1234)
-        getis = Getis(FakeDataProvider(data))
-
-        result = getis.getis_ord('subquery', 'value',
-                                 'queen', None, 999, 'the_geom',
-                                 'cartodb_id')
-        result = [(row[0], row[1]) for row in result]
-        expected = np.array(self.getis_data)[:, 0:2]
-        for ([res_z, res_p], [exp_z, exp_p]) in zip(result, expected):
-            self.assertAlmostEqual(res_z, exp_z, delta=1e-2)
--- a/release/python/0.5.1/crankshaft/test/test_clustering_kmeans.py
+++ b/release/python/0.5.1/crankshaft/test/test_clustering_kmeans.py
@@ -1,56 +0,0 @@
-import unittest
-import numpy as np
-
-
-# from mock_plpy import MockPlPy
-# plpy = MockPlPy()
-#
-# import sys
-# sys.modules['plpy'] = plpy
-from helper import fixture_file
-from crankshaft.clustering import Kmeans
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-import crankshaft.clustering as cc
-
-from crankshaft import random_seeds
-import json
-from collections import OrderedDict
-
-
-class FakeDataProvider(AnalysisDataProvider):
-    def __init__(self, mocked_result):
-        self.mocked_result = mocked_result
-
-    def get_spatial_kmeans(self, query):
-        return self.mocked_result
-
-    def get_nonspatial_kmeans(self, query, standarize):
-        return self.mocked_result
-
-
-class KMeansTest(unittest.TestCase):
-    """Testing class for k-means spatial"""
-
-    def setUp(self):
-        self.cluster_data = json.loads(
-          open(fixture_file('kmeans.json')).read())
-        self.params = {"subquery": "select * from table",
-                       "no_clusters": "10"}
-
-    def test_kmeans(self):
-        """
-        """
-        data = [{'xs': d['xs'],
-                 'ys': d['ys'],
-                 'ids': d['ids']} for d in self.cluster_data]
-
-        random_seeds.set_random_seeds(1234)
-        kmeans = Kmeans(FakeDataProvider(data))
-        clusters = kmeans.spatial('subquery', 2)
-        labels = [a[1] for a in clusters]
-        c1 = [a for a in clusters if a[1] == 0]
-        c2 = [a for a in clusters if a[1] == 1]
-
-        self.assertEqual(len(np.unique(labels)), 2)
-        self.assertEqual(len(c1), 20)
-        self.assertEqual(len(c2), 20)
--- a/release/python/0.5.1/crankshaft/test/test_clustering_moran.py
+++ b/release/python/0.5.1/crankshaft/test/test_clustering_moran.py
@@ -1,112 +0,0 @@
-import unittest
-import numpy as np
-
-from helper import fixture_file
-from crankshaft.clustering import Moran
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-import crankshaft.pysal_utils as pu
-from crankshaft import random_seeds
-import json
-from collections import OrderedDict
-
-
-class FakeDataProvider(AnalysisDataProvider):
-    def __init__(self, mock_data):
-        self.mock_result = mock_data
-
-    def get_moran(self, w_type, params):
-        return self.mock_result
-
-
-class MoranTest(unittest.TestCase):
-    """Testing class for Moran's I functions"""
-
-    def setUp(self):
-        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"""
-        from crankshaft.clustering import map_quads
-        self.assertEqual(map_quads(1), 'HH')
-        self.assertEqual(map_quads(2), 'LH')
-        self.assertEqual(map_quads(3), 'LL')
-        self.assertEqual(map_quads(4), 'HL')
-        self.assertEqual(map_quads(33), None)
-        self.assertEqual(map_quads('andy'), None)
-
-    def test_quad_position(self):
-        """Test lisa_sig_vals"""
-        from crankshaft.clustering import quad_position
-
-        quads = np.array([1, 2, 3, 4], np.int)
-
-        ans = np.array(['HH', 'LH', 'LL', 'HL'])
-        test_ans = quad_position(quads)
-
-        self.assertTrue((test_ans == ans).all())
-
-    def test_local_stat(self):
-        """Test Moran's I local"""
-        data = [OrderedDict([('id', d['id']),
-                             ('attr1', d['value']),
-                             ('neighbors', d['neighbors'])])
-                for d in self.neighbors_data]
-
-        moran = Moran(FakeDataProvider(data))
-        random_seeds.set_random_seeds(1234)
-        result = moran.local_stat('subquery', 'value',
-                                  'knn', 5, 99, 'the_geom', 'cartodb_id')
-        result = [(row[0], row[1]) for row in result]
-        zipped_values = zip(result, self.moran_data)
-
-        for ([res_val, res_quad], [exp_val, exp_quad]) in zipped_values:
-            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]
-
-        random_seeds.set_random_seeds(1234)
-        moran = Moran(FakeDataProvider(data))
-        result = moran.local_rate_stat('subquery', 'numerator', 'denominator',
-                                       'knn', 5, 99, 'the_geom', 'cartodb_id')
-        result = [(row[0], row[1]) for row in result]
-
-        zipped_values = zip(result, self.moran_data)
-
-        for ([res_val, res_quad], [exp_val, exp_quad]) in zipped_values:
-            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]
-        random_seeds.set_random_seeds(1235)
-        moran = Moran(FakeDataProvider(data))
-        result = moran.global_stat('table', 'value',
-                                   'knn', 5, 99, 'the_geom',
-                                   'cartodb_id')
-
-        result_moran = result[0][0]
-        expected_moran = np.array([row[0] for row in self.moran_data]).mean()
-        self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)
--- a/release/python/0.5.1/crankshaft/test/test_pysal_utils.py
+++ b/release/python/0.5.1/crankshaft/test/test_pysal_utils.py
@@ -1,160 +0,0 @@
-import unittest
-
-import crankshaft.pysal_utils as pu
-from crankshaft import random_seeds
-from collections import OrderedDict
-
-
-class PysalUtilsTest(unittest.TestCase):
-    """Testing class for utility functions related to PySAL integrations"""
-
-    def setUp(self):
-        self.params1 = OrderedDict([("id_col", "cartodb_id"),
-                                    ("attr1", "andy"),
-                                    ("attr2", "jay_z"),
-                                    ("subquery", "SELECT * FROM a_list"),
-                                    ("geom_col", "the_geom"),
-                                    ("num_ngbrs", 321)])
-
-        self.params2 = OrderedDict([("id_col", "cartodb_id"),
-                                    ("numerator", "price"),
-                                    ("denominator", "sq_meters"),
-                                    ("subquery", "SELECT * FROM pecan"),
-                                    ("geom_col", "the_geom"),
-                                    ("num_ngbrs", 321)])
-
-        self.params3 = OrderedDict([("id_col", "cartodb_id"),
-                                    ("numerator", "sq_meters"),
-                                    ("denominator", "price"),
-                                    ("subquery", "SELECT * FROM pecan"),
-                                    ("geom_col", "the_geom"),
-                                    ("num_ngbrs", 321)])
-
-        self.params_array = {"id_col": "cartodb_id",
-                             "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
-                             "subquery": "SELECT * FROM a_list",
-                             "geom_col": "the_geom",
-                             "num_ngbrs": 321}
-
-    def test_query_attr_select(self):
-        """Test query_attr_select"""
-
-        ans1 = ("i.\"andy\"::numeric As attr1, "
-                "i.\"jay_z\"::numeric As attr2, ")
-
-        ans2 = ("i.\"price\"::numeric As attr1, "
-                "i.\"sq_meters\"::numeric As attr2, ")
-
-        ans3 = ("i.\"sq_meters\"::numeric As attr1, "
-                "i.\"price\"::numeric As attr2, ")
-
-        ans_array = ("i.\"_2013_dec\"::numeric As attr1, "
-                     "i.\"_2014_jan\"::numeric As attr2, "
-                     "i.\"_2014_feb\"::numeric As attr3, ")
-
-        self.assertEqual(pu.query_attr_select(self.params1), ans1)
-        self.assertEqual(pu.query_attr_select(self.params2), ans2)
-        self.assertEqual(pu.query_attr_select(self.params3), ans3)
-        self.assertEqual(pu.query_attr_select(self.params_array), ans_array)
-
-    def test_query_attr_where(self):
-        """Test pu.query_attr_where"""
-
-        ans1 = ("idx_replace.\"andy\" IS NOT NULL AND "
-                "idx_replace.\"jay_z\" IS NOT NULL")
-
-        ans_array = ("idx_replace.\"_2013_dec\" IS NOT NULL AND "
-                     "idx_replace.\"_2014_jan\" IS NOT NULL AND "
-                     "idx_replace.\"_2014_feb\" IS NOT NULL")
-
-        self.assertEqual(pu.query_attr_where(self.params1), ans1)
-        self.assertEqual(pu.query_attr_where(self.params_array), ans_array)
-
-    def test_knn(self):
-        """Test knn neighbors constructor"""
-
-        ans1 = "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 " \
-                                   "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 " \
-              "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.params1), ans1)
-        self.assertEqual(pu.knn(self.params_array), ans_array)
-
-    def test_queen(self):
-        """Test queen neighbors constructor"""
-
-        ans1 = "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)" \
-                                  ") As neighbors " \
-              "FROM (SELECT * FROM a_list) As i " \
-              "WHERE i.\"andy\" IS NOT NULL AND " \
-                    "i.\"jay_z\" IS NOT NULL " \
-              "ORDER BY i.\"cartodb_id\" ASC;"
-
-        self.assertEqual(pu.queen(self.params1), ans1)
-
-    def test_construct_neighbor_query(self):
-        """Test construct_neighbor_query"""
-
-        # Compare to raw knn query
-        self.assertEqual(pu.construct_neighbor_query('knn', self.params1),
-                         pu.knn(self.params1))
-
-    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)
--- a/release/python/0.5.1/crankshaft/test/test_segmentation.py
+++ b/release/python/0.5.1/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)  )
--- a/release/python/0.5.1/crankshaft/test/test_space_time_dynamics.py
+++ b/release/python/0.5.1/crankshaft/test/test_space_time_dynamics.py
@@ -1,349 +0,0 @@
-import unittest
-import numpy as np
-
-import unittest
-
-
-from helper import fixture_file
-
-from crankshaft.space_time_dynamics import Markov
-import crankshaft.space_time_dynamics as std
-from crankshaft import random_seeds
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-import json
-
-
-class FakeDataProvider(AnalysisDataProvider):
-    def __init__(self, data):
-        self.mock_result = data
-
-    def get_markov(self, w_type, params):
-        return self.mock_result
-
-
-class SpaceTimeTests(unittest.TestCase):
-    """Testing class for Markov Functions."""
-
-    def setUp(self):
-        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]))
-        markov = Markov(FakeDataProvider(data))
-        random_seeds.set_random_seeds(1234)
-
-        result = markov.spatial_trend('subquery',
-                                      ['y1995', 'y1996', 'y1997', 'y1998',
-                                       'y1999', 'y2000', 'y2001', 'y2002',
-                                       'y2003', 'y2004', 'y2005', 'y2006',
-                                       'y2007', 'y2008', 'y2009'],
-                                      5, 'knn', 5, 0, 'the_geom',
-                                      'cartodb_id')
-
-        self.assertTrue(result is not None)
-        result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
-        print result[0]
-        expected = self.markov_data
-        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))
--- a/src/pg/crankshaft.control
+++ b/src/pg/crankshaft.control
@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.5.1'
+default_version = '0.4.2'
 requires = 'plpythonu, postgis'
 superuser = true
 schema = cdb_crankshaft
--- a/src/pg/sql/10_moran.sql
+++ b/src/pg/sql/10_moran.sql
@@ -10,11 +10,9 @@ CREATE OR REPLACE FUNCTION
      id_col TEXT DEFAULT 'cartodb_id')
 RETURNS TABLE (moran NUMERIC, significance NUMERIC)
 AS $$
-  from crankshaft.clustering import Moran
+  from crankshaft.clustering import moran
  # TODO: use named parameters or a dictionary
-  moran = Moran()
-  return moran.global_stat(subquery, column_name, w_type,
-                           num_ngbrs, permutations, geom_col, id_col)
+  return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
 $$ LANGUAGE plpythonu;

 -- Moran's I Local (internal function)
@@ -29,11 +27,9 @@ CREATE OR REPLACE FUNCTION
      id_col TEXT)
 RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
 AS $$
-  from crankshaft.clustering import Moran
-  moran = Moran()
+  from crankshaft.clustering import moran_local
  # TODO: use named parameters or a dictionary
-  return moran.local_stat(subquery, column_name, w_type,
-                          num_ngbrs, permutations, geom_col, id_col)
+  return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
 $$ LANGUAGE plpythonu;

 -- Moran's I Local (public-facing function)
@@ -124,11 +120,9 @@ CREATE OR REPLACE FUNCTION
      id_col TEXT DEFAULT 'cartodb_id')
 RETURNS TABLE (moran FLOAT, significance FLOAT)
 AS $$
-  from crankshaft.clustering import Moran
-  moran = Moran()
+  from crankshaft.clustering import moran_local
  # TODO: use named parameters or a dictionary
-  return moran.global_rate_stat(subquery, numerator, denominator, w_type,
-                                num_ngbrs, permutations, geom_col, id_col)
+  return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
 $$ LANGUAGE plpythonu;


@@ -146,10 +140,9 @@ CREATE OR REPLACE FUNCTION
 RETURNS
 TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
 AS $$
-  from crankshaft.clustering import Moran
-  moran = Moran()
+  from crankshaft.clustering import moran_local_rate
  # TODO: use named parameters or a dictionary
-  return moran.local_rate_stat(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+  return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
 $$ LANGUAGE plpythonu;

 -- Moran's I Local Rate (public-facing function)
--- a/src/pg/sql/11_kmeans.sql
+++ b/src/pg/sql/11_kmeans.sql
@@ -1,24 +1,21 @@
-- Spatial k-means clustering
-
-CREATE OR REPLACE FUNCTION CDB_KMeans(query text, no_clusters integer, no_init integer default 20)
+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)

-    from crankshaft.clustering import Kmeans
-    kmeans = Kmeans()
-    return kmeans.spatial(query, no_clusters, no_init)
-
-$$ LANGUAGE plpythonu;
+$$ language plpythonu;


 CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC)
-RETURNS Numeric[] AS
+RETURNS Numeric[] AS 
 $$
-DECLARE
+DECLARE 
    newX NUMERIC;
    newY NUMERIC;
    newW NUMERIC;
 BEGIN
-    IF weight IS NULL OR the_geom IS NULL THEN
+    IF weight IS NULL OR the_geom IS NULL THEN 
        newX = state[1];
        newY = state[2];
        newW = state[3];
@@ -33,12 +30,12 @@ END
 $$ LANGUAGE plpgsql;

 CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[])
-RETURNS GEOMETRY AS
+RETURNS GEOMETRY AS 
 $$
 BEGIN
-    IF state[3] = 0 THEN
+    IF state[3] = 0 THEN 
        RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326);
-    ELSE
+    ELSE 
        RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326);
    END IF;
 END
@@ -59,7 +56,7 @@ BEGIN
            SFUNC = CDB_WeightedMeanS,
            FINALFUNC = CDB_WeightedMeanF,
            STYPE = Numeric[],
-            INITCOND = "{0.0,0.0,0.0}"
+            INITCOND = "{0.0,0.0,0.0}" 
        );
    END IF;
 END
--- a/src/pg/sql/11_markov.sql
+++ b/src/pg/sql/11_markov.sql
@@ -22,11 +22,10 @@ CREATE OR REPLACE FUNCTION
 RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT)
 AS $$

-  from crankshaft.space_time_dynamics import Markov
-  markov = Markov()
+  from crankshaft.space_time_dynamics import spatial_markov_trend

  ## TODO: use named parameters or a dictionary
-  return markov.spatial_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
+  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
--- a/src/pg/sql/16_getis.sql
+++ b/src/pg/sql/16_getis.sql
@@ -1,19 +0,0 @@
-- Getis-Ord's G
-- Hotspot/Coldspot Analysis tool
-CREATE OR REPLACE FUNCTION
-  CDB_GetisOrdsG(
-      subquery TEXT,
-      column_name TEXT,
-      w_type TEXT DEFAULT 'knn',
-      num_ngbrs INT DEFAULT 5,
-      permutations INT DEFAULT 999,
-      geom_col TEXT DEFAULT 'the_geom',
-      id_col TEXT DEFAULT 'cartodb_id')
-RETURNS TABLE (z_score NUMERIC, p_value NUMERIC, p_z_sim NUMERIC, rowid BIGINT)
-AS $$
-  from crankshaft.clustering import Getis
-  getis = Getis()
-  return getis.getis_ord(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpythonu;
-
-- TODO: make a version that accepts the values as arrays
--- a/src/pg/sql/16_salesforce.sql
+++ b/src/pg/sql/16_salesforce.sql
--- a/src/pg/sql/18_outliers.sql
+++ b/src/pg/sql/18_outliers.sql
@@ -1,75 +0,0 @@
-
-- Find outliers using a static threshold
--
-CREATE OR REPLACE FUNCTION CDB_StaticOutlier(column_value numeric, threshold numeric)
-RETURNS boolean
-AS $$
-BEGIN
-
-  RETURN column_value > threshold;
-
-END;
-$$ LANGUAGE plpgsql;
-
-- Find outliers by a percentage above the threshold
-- TODO: add symmetric option? `is_symmetric boolean DEFAULT false`
-
-CREATE OR REPLACE FUNCTION CDB_PercentOutlier(column_values numeric[], outlier_fraction numeric, ids int[])
-RETURNS TABLE(is_outlier boolean, rowid int)
-AS $$
-DECLARE
-  avg_val numeric;
-  out_vals boolean[];
-BEGIN
-
-  SELECT avg(i) INTO avg_val
-    FROM unnest(column_values) As x(i);
-
-  IF avg_val = 0 THEN
-    RAISE EXCEPTION 'Mean value is zero. Try another outlier method.';
-  END IF;
-
-  SELECT array_agg(
-           outlier_fraction < i / avg_val) INTO out_vals
-    FROM unnest(column_values) As x(i);
-
-  RETURN QUERY
-  SELECT unnest(out_vals) As is_outlier,
-         unnest(ids) As rowid;
-
-END;
-$$ LANGUAGE plpgsql;
-
-- Find outliers above a given number of standard deviations from the mean
-
-CREATE OR REPLACE FUNCTION CDB_StdDevOutlier(column_values numeric[], num_deviations numeric, ids int[], is_symmetric boolean DEFAULT true)
-RETURNS TABLE(is_outlier boolean, rowid int)
-AS $$
-DECLARE
-  stddev_val numeric;
-  avg_val numeric;
-  out_vals boolean[];
-BEGIN
-
-  SELECT stddev(i), avg(i) INTO stddev_val, avg_val
-    FROM unnest(column_values) As x(i);
-
-  IF stddev_val = 0 THEN
-    RAISE EXCEPTION 'Standard deviation of input data is zero';
-  END IF;
-
-  IF is_symmetric THEN
-    SELECT array_agg(
-             abs(i - avg_val) / stddev_val > num_deviations) INTO out_vals
-      FROM unnest(column_values) As x(i);
-  ELSE
-    SELECT array_agg(
-             (i - avg_val) / stddev_val > num_deviations) INTO out_vals
-      FROM unnest(column_values) As x(i);
-  END IF;
-
-  RETURN QUERY
-  SELECT unnest(out_vals) As is_outlier,
-         unnest(ids) As rowid;
-END;
-$$ LANGUAGE plpgsql;
--- a/src/pg/sql/23_hungarian.sql
+++ b/src/pg/sql/23_hungarian.sql
@@ -0,0 +1,870 @@
+-- https://github.com/esa606/hungarian_algorithm/
+
+create or replace package hungarian_algorithm is
+
+  -- Author  : esa606
+  -- Created : 3/16/2015 11:23:12 AM
+  -- Purpose : PL/SQL implementation of the Hungarian/Kuhn-Munkres Algorithm
+  --           found at http://csclab.murraystate.edu/bob.pilgrim/445/munkres.html
+  --           on March 16, 2015.
+
+  /*This software is released under a BSD license, adapted from <http://opensource.org/licenses/bsd-license.php>
+
+  Copyright (c) 2015 esa606. All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without modification,
+  are permitted provided that the following conditions are met:
+
+      Redistributions of source code must retain the above copyright notice,
+      this list of conditions and the following disclaimer.
+      Redistributions in binary form must reproduce the above copyright notice,
+      this list of conditions and the following disclaimer in the documentation and/or
+      other materials provided with the distribution.
+      Neither the name “esa606” nor the names of its contributors may be used to
+      endorse or promote products derived from this software without specific prior
+      written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS”
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.*/
+
+  --The main function call for this implementation.
+  --input table: name of table with the price data.  Prices must be nonnegative integers.
+  --row_name_colname: Name of the input_table column holding the price matrix row labels
+  --col_name_colname: Name of the input_table column holding the price matrix column labels
+  --price_colname: Name of the input_table column holding the price matrix prices
+  --logging_mode: 1 for logging, 0 for no logging
+  procedure hungarian_main (
+    input_table in varchar2,
+    row_name_colname in varchar2 default 'row_idx',
+    col_name_colname in varchar2 default 'col_idx',
+    price_colname in varchar2 default 'price',
+    logging_mode in integer default 0
+  );
+
+  --A testing function that allows steps of the algorithm to be called according to a string.
+  --E.g. hungarian_strstep('123444', ...) would call steps 1, 2, 3, and then 4 three times.
+  --May return errors if the specified steps are not algorithmically correct.
+  --stepstr: the string specifying the order of the steps
+  --Other arguments as above.
+  procedure hungarian_strstep (
+    stepstr in varchar2,
+    input_table in varchar2,
+    row_name_colname in varchar2 default 'row_idx',
+    col_name_colname in varchar2 default 'col_idx',
+    price_colname in varchar2 default 'price',
+    logging_mode in integer default 0
+  );
+
+end hungarian_algorithm;
+/
+create or replace package body hungarian_algorithm is
+
+    /*This software is released under a BSD license, adapted from <http://opensource.org/licenses/bsd-license.php>
+
+  Copyright (c) 2015 esa606. All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without modification,
+  are permitted provided that the following conditions are met:
+
+      Redistributions of source code must retain the above copyright notice,
+      this list of conditions and the following disclaimer.
+      Redistributions in binary form must reproduce the above copyright notice,
+      this list of conditions and the following disclaimer in the documentation and/or
+      other materials provided with the distribution.
+      Neither the name “esa606” nor the names of its contributors may be used to
+      endorse or promote products derived from this software without specific prior
+      written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS”
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.*/
+
+  --Logs calls to the Hungarian Algorithm step-by-step.
+  --Typically called by if-statement with variable step_logging_mode.
+  --If step_logging_mode = 0, no logging
+  --If step_logging_mode = 1, logs with first_call as 0
+  --If step_logging_mode = 2, logs with first_call as 1
+  procedure hungarian_logging (
+    curr_step in integer,
+    next_step in integer,
+    n in integer,
+    first_call in integer default 0
+  ) is
+    log_cnt integer;
+    call_no integer;
+  begin
+    --Check the initial highest call_no.  Increment it if this
+    --is the first call, or re-use if not.
+    select count(*)
+    into log_cnt
+    from hungarian_log
+    where rownum = 1;
+
+    if log_cnt = 0 then
+      call_no := 1;
+    else
+      if first_call = 1 then
+        select max(call_no) + 1
+        into call_no
+        from hungarian_log;
+      else
+       select max(call_no)
+        into call_no
+        from hungarian_log;
+      end if;
+    end if;
+
+    insert into hungarian_log
+    values (
+      call_no,
+      current_timestamp,
+      curr_step, next_step,
+      (select sum(starred) from esa_hungarian_base),
+      (select sum(row_covered) from esa_hungarian_base),
+      (select sum(col_covered) from esa_hungarian_base),
+      (select sum(prime_sequence) from esa_hungarian_base),
+      (select sum(z_sequence) from esa_hungarian_base),
+      n
+    );
+    commit;
+  end hungarian_logging;
+
+
+  --A: Transfer initial conditions to base table
+  --B: Error-checks initial conditions
+  --C: Pads the rows or columns if initial price matrix is not square
+  --D: Update elt_idx
+  --Returns the matrix size n.
+  function hungarian_step1_check_setup (
+    input_table in varchar2,
+    row_name_colname in varchar2,
+    col_name_colname in varchar2,
+    price_colname in varchar2,
+    step_logging_mode in integer
+  ) return integer is
+    select_statement varchar2(4000);
+    max_price number;
+    nonunique_indices_cnt integer;
+    num_rows integer;
+    num_cols integer;
+    first_call integer;
+  begin
+    --A: This makes all possible combinations of row and column from the input table
+    --and then fills in price for the combinations in the input table.
+    --If the combo doesn't exist, fills in max_price.
+    select_statement := 'select max(' || price_colname || ') from ' || input_table;
+    execute immediate select_statement into max_price;
+
+    execute immediate 'truncate table hungarian_base';
+
+    execute immediate
+    'insert into hungarian_base
+    with t as (
+      select
+        dense_rank() over (order by ' || row_name_colname || ' asc) as row_idx,
+        dense_rank() over (order by ' || col_name_colname || ' asc) as col_idx,
+        ' || price_colname  || ' as price
+      from ' || input_table || '
+    )
+    select
+      null as elt_idx,
+      s.row_idx, s.col_idx,
+      case
+        when t.price is null then ' || max_price || ' + 1
+        else t.price
+      end as price,
+      0 as starred,
+      0 as row_covered,
+      0 as col_covered,
+      0 as prime_sequence,
+      0 as z_sequence
+    from (
+      select a.row_idx, b. col_idx
+      from (select distinct row_idx from t) a
+      join (select distinct col_idx from t) b
+        on 1 = 1
+    ) s
+
+    left join t
+      on s.row_idx = t.row_idx
+      and s.col_idx = t.col_idx
+
+    order by s.row_idx, s.col_idx';
+    commit;
+
+    --B: Error-checking
+    select count(*)
+    into nonunique_indices_cnt
+    from (
+      select row_idx, col_idx
+      from hungarian_base
+      group by row_idx, col_idx
+      having count(*) > 1
+    );
+
+    if nonunique_indices_cnt > 0 then
+      raise_application_error(-20000, 'At least one task/agent combo is not unique.  Check your identifiers.');
+    end if;
+
+    --D: Padding out rows or columns with max-price if necessary
+    select_statement := 'select count(distinct ' || row_name_colname || ') from ' || input_table;
+    execute immediate select_statement into num_rows;
+
+    select_statement := 'select count(distinct ' || col_name_colname || ') from ' || input_table;
+    execute immediate select_statement into num_cols;
+
+    if num_rows > num_cols then
+      for i in 1..num_rows loop
+        for j in (num_cols+1)..num_rows loop
+          insert into hungarian_base
+          values (null, i, j, max_price + 1, 0, 0, 0, 0, 0);
+        end loop;
+      end loop;
+    elsif num_rows < num_cols then
+      for i in (num_rows+1)..num_cols loop
+        for j in 1..num_cols loop
+          insert into hungarian_base
+          values (null, i, j, max_price + 1, 0, 0, 0, 0, 0);
+        end loop;
+      end loop;
+    end if;
+
+    --D: Correct elt_idx.  Since the initial matrix is likely to be incomplete
+    --either due to missing links or needing padding, I just wait
+    --to do this until that's all filled in.
+    update hungarian_base
+    set elt_idx = (row_idx - 1)*greatest(num_rows, num_cols) + col_idx;
+
+    if step_logging_mode > 0 then
+      if step_logging_mode = 1 then
+        first_call := 0;
+      else
+        first_call := 1;
+      end if;
+      hungarian_algorithm.hungarian_logging(1, 2, greatest(num_rows, num_cols), first_call);
+    end if;
+
+    commit;
+    return greatest(num_rows, num_cols);
+
+  end hungarian_step1_check_setup;
+
+
+  --For each row of the matrix, find the smallest element and
+  --subtract it from every element in its row.  Go to Step 3.
+  procedure hungarian_step2_reduce (n in integer, step_logging_mode in integer) is
+    row_min number;
+    first_call integer;
+  begin
+    for i in 1..n loop
+
+      select min(price)
+      into row_min
+      from hungarian_base
+      where row_idx = i;
+
+      update hungarian_base
+      set price = price - row_min
+      where row_idx = i;
+
+    end loop;
+    commit;
+
+    if step_logging_mode > 0 then
+      if step_logging_mode = 1 then
+        first_call := 0;
+      else
+        first_call := 1;
+      end if;
+      hungarian_algorithm.hungarian_logging(2, 3, null, first_call);
+    end if;
+  end hungarian_step2_reduce;
+
+
+  --Find a zero (Z) in the resulting matrix.  If there is no starred zero
+  --in its row or column, star Z. Repeat for each element in the matrix.
+  --Go to Step 4.
+  procedure hungarian_step3_initstar (n in integer, step_logging_mode in integer) is
+    ij_price number;
+    ij_covered integer;
+    first_call integer;
+  begin
+    for i in 1..n loop
+      for j in 1..n loop
+
+        select price
+        into ij_price
+        from hungarian_base
+        where row_idx = i
+          and col_idx = j;
+
+        if ij_price = 0 then
+
+          select count(*)
+          into ij_covered
+          from hungarian_base
+          where row_idx = i
+            and col_idx = j
+            and (row_covered = 1 or col_covered = 1);
+
+          if ij_covered = 0 then
+
+            update hungarian_base
+            set starred = 1
+            where row_idx = i
+              and col_idx = j;
+
+            update hungarian_base
+            set row_covered = 1
+            where row_idx = i;
+
+            update hungarian_base
+            set col_covered = 1
+            where col_idx = j;
+
+          end if;
+        end if;
+      end loop;
+    end loop;
+
+    update hungarian_base
+    set row_covered = 0,
+      col_covered = 0;
+    commit;
+
+    if step_logging_mode > 0 then
+      if step_logging_mode = 1 then
+        first_call := 0;
+      else
+        first_call := 1;
+      end if;
+      hungarian_algorithm.hungarian_logging(3, 4, null, first_call);
+    end if;
+  end hungarian_step3_initstar;
+
+
+  --Cover each column containing a starred zero.  If n columns
+  --are covered, the starred zeros describe a complete set of unique assignments.
+  --In this case, Go to Step 8 for finishing touches.
+  --Otherwise, Go to Step 5.
+  --Returns the step to goto
+  function hungarian_step4_coverstarred (n in integer, step_logging_mode in integer) return integer is
+    j_starred integer;
+    covered_cnt integer;
+    next_step integer;
+    first_call integer;
+  begin
+    for j in 1..n loop
+
+      select max(starred)
+      into j_starred
+      from hungarian_base
+      where col_idx = j;
+
+      if j_starred = 1 then
+
+        update hungarian_base
+        set col_covered = 1
+        where col_idx = j;
+
+      end if;
+    end loop;
+
+    select count(distinct col_idx)
+    into covered_cnt
+    from hungarian_base
+    where starred = 1;
+
+    commit;
+
+    if covered_cnt = n then
+      next_step := 8; --This is the only place to exit the algorithm, right here
+    else
+      next_step := 5;
+    end if;
+
+    if step_logging_mode > 0 then
+      if step_logging_mode = 1 then
+        first_call := 0;
+      else
+        first_call := 1;
+      end if;
+      hungarian_algorithm.hungarian_logging(4, next_step, null, first_call);
+    end if;
+    return next_step;
+  end hungarian_step4_coverstarred;
+
+  --A: If there are no uncovered zeroes at all, go to Step 7.
+  --B: Find a noncovered zero and prime it.
+  --C: If there is no starred zero in the row containing this primed zero, go to Step 6.
+  --D: Otherwise, cover this row and uncover the column containing the starred zero.
+  --E: Return to this step to continue in this manner until there are no uncovered zeros left.
+  --Returns the step to goto
+  function hungarian_step5_prime (n in integer, step_logging_mode in integer) return integer is
+    uncovered_zero_cnt integer;
+    min_uncovered_zero integer;
+    new_prime_sequence integer;
+    most_recently_primed_row integer;
+    starred_in_row_cnt integer;
+    starred_in_row_col integer;
+    next_step integer;
+    first_call integer;
+  begin
+    --Part A
+    select count(*)
+    into uncovered_zero_cnt
+    from hungarian_base
+    where row_covered = 0
+      and col_covered = 0
+      and price = 0;
+
+    if uncovered_zero_cnt = 0 then
+      next_step := 7;
+    else
+      --Part B
+      select min(elt_idx)
+      into min_uncovered_zero
+      from hungarian_base
+      where row_covered = 0
+        and col_covered = 0
+        and price = 0;
+
+      select max(prime_sequence) + 1
+      into new_prime_sequence
+      from hungarian_base;
+
+      update hungarian_base
+      set prime_sequence = new_prime_sequence
+      where elt_idx = min_uncovered_zero;
+
+      --Part C
+      most_recently_primed_row := ceil(min_uncovered_zero/n);
+
+      select count(*)
+      into starred_in_row_cnt
+      from hungarian_base
+      where row_idx = most_recently_primed_row
+        and starred = 1;
+
+      if starred_in_row_cnt = 0 then
+        next_step := 6;
+      else
+        --Part D
+        update hungarian_base
+        set row_covered = 1
+        where row_idx = most_recently_primed_row;
+
+        select col_idx
+        into starred_in_row_col
+        from hungarian_base
+        where row_idx = most_recently_primed_row
+          and starred = 1;
+
+        update hungarian_base
+        set col_covered = 0
+        where col_idx = starred_in_row_col;
+
+        --Part E
+        next_step := 5;
+      end if;
+    end if;
+
+    commit;
+
+    if step_logging_mode > 0 then
+      if step_logging_mode = 1 then
+        first_call := 0;
+      else
+        first_call := 1;
+      end if;
+      hungarian_algorithm.hungarian_logging(5, next_step, null, first_call);
+    end if;
+    return next_step;
+  end hungarian_step5_prime;
+
+
+  --Construct a series of alternating primed and starred zeros as follows:
+  --Let Z0 represent the uncovered primed zero found in Step 5.
+  --Let Z1 denote the starred zero in the column of Z0 (if any).
+  --Let Z2 denote the primed zero in the row of Z1 (there will always be one).
+  --Continue until the series terminates at a primed zero that has no starred
+  --zero in its column.
+  --The above is accomplished by repeated calls to this step.
+  --Then unstar each starred zero of the series, star each primed
+  --zero of the series, erase all primes and uncover every line in the matrix.
+  --Now return to Step 4.
+  --Returns the step to goto
+  function hungarian_step6_zsequence (n in integer, step_logging_mode in integer) return integer is
+    begin_max_z_sequence integer;
+    begin_prime integer;
+    begin_prime_col integer;
+    starred_zeros_in_prime_col_cnt integer;
+    next_step integer;
+    first_call integer;
+  begin
+    --Part A: Add the prime to the sequence.
+    --If the sequence is so far empty, add the most recent prime.
+    --If the sequence is not empty, add the prime in the row of
+    --the most recently added element.
+    select max(z_sequence)
+    into begin_max_z_sequence
+    from hungarian_base;
+
+    if begin_max_z_sequence = 0 then
+
+      select elt_idx
+      into begin_prime
+      from hungarian_base
+      where prime_sequence = (
+        select max(prime_sequence)
+        from hungarian_base
+        );
+
+    else
+
+      select elt_idx
+      into begin_prime
+      from hungarian_base
+      where prime_sequence != 0
+        and row_idx = (
+          select row_idx
+          from hungarian_base
+          where z_sequence = begin_max_z_sequence
+        );
+
+    end if;
+
+    update hungarian_base
+    set z_sequence = begin_max_z_sequence + 1
+    where elt_idx = begin_prime;
+
+    --Part B: If there is a starred zero in the newly-added prime's column,
+    --add it and repeat.  Otherwise, change markings and return to Step 4.
+    begin_prime_col := begin_prime + (1 - ceil(begin_prime/n))*n;
+
+    select count(*)
+    into starred_zeros_in_prime_col_cnt
+    from hungarian_base
+    where price = 0
+      and starred = 1
+      and col_idx = begin_prime_col;
+
+    if starred_zeros_in_prime_col_cnt > 0 then
+
+      update hungarian_base
+      set z_sequence = begin_max_z_sequence + 2
+      where col_idx = begin_prime_col
+        and starred = 1;
+
+      next_step := 6;
+
+    else
+
+      update hungarian_base
+      set starred = 0
+      where z_sequence != 0
+        and starred = 1;
+
+      update hungarian_base
+      set starred = 1
+      where z_sequence != 0
+        and prime_sequence != 0;
+
+      update hungarian_base
+      set row_covered = 0,
+        col_covered = 0,
+        prime_sequence = 0,
+        z_sequence = 0;
+
+      next_step := 4;
+    end if;
+
+    commit;
+
+    if step_logging_mode > 0 then
+      if step_logging_mode = 1 then
+        first_call := 0;
+      else
+        first_call := 1;
+      end if;
+      hungarian_algorithm.hungarian_logging(6, next_step, null, first_call);
+    end if;
+
+    return next_step;
+  end hungarian_step6_zsequence;
+
+
+  --Add the minimum uncovered price to every element of each covered row,
+  --and subtract it from every element of each uncovered column.
+  --Return to Step 5 without altering any stars, primes, or covered lines.
+  --Returns the step to goto
+  function hungarian_step7_addsubtract (step_logging_mode in integer) return integer is
+    min_uncovered_price number;
+    first_call integer;
+  begin
+    select min(price)
+    into min_uncovered_price
+    from hungarian_base
+    where row_covered = 0
+      and col_covered = 0;
+
+    update hungarian_base
+    set price = price + min_uncovered_price*row_covered - min_uncovered_price*(1-col_covered);
+    commit;
+
+    if step_logging_mode > 0 then
+      if step_logging_mode = 1 then
+        first_call := 0;
+      else
+        first_call := 1;
+      end if;
+      hungarian_algorithm.hungarian_logging(7, 5, null, first_call);
+    end if;
+
+    return 5;
+
+  end hungarian_step7_addsubtract;
+
+
+  --Creates table hungarian_results with the same row and column names as the
+  --input table, showing only the assigned pairs.
+  procedure hungarian_step8_results (
+    input_table in varchar2,
+    row_name_colname in varchar2,
+    col_name_colname in varchar2,
+    price_colname in varchar2,
+    step_logging_mode in integer
+  ) is
+    table_exists integer;
+    row_idx_select varchar2(4000);
+    b_row_idx_name varchar2(4000);
+    col_idx_select varchar2(4000);
+    b_col_idx_name varchar2(4000);
+    first_call integer;
+  begin
+    select count(*)
+    into table_exists
+    from tab
+    where tname = upper('hungarian_results');
+
+    if table_exists = 1 then
+      execute immediate 'drop table hungarian_results';
+    end if;
+
+    if row_name_colname = 'row_idx' then
+      row_idx_select := '';
+      b_row_idx_name := 'b_row_idx';
+    else
+      row_idx_select := 'a.row_idx,';
+      b_row_idx_name := 'row_idx';
+    end if;
+
+    if col_name_colname = 'col_idx' then
+      col_idx_select := '';
+      b_col_idx_name := 'b_col_idx';
+    else
+      col_idx_select := 'a.col_idx,';
+      b_col_idx_name := 'col_idx';
+    end if;
+
+    execute immediate
+    'create table hungarian_results as
+    select
+      b.' || row_name_colname || ',
+      b.' || col_name_colname || ',
+      ' || row_idx_select || '
+      ' || col_idx_select || '
+      b.' || price_colname || '
+    from (
+      select row_idx, col_idx, elt_idx
+      from hungarian_base
+      where starred = 1
+    ) a
+
+    --Only allows row/column combos that existed in the original
+    --to go through.
+    join (
+      select
+        ' || row_name_colname || ',
+        ' || col_name_colname || ',
+        dense_rank() over (order by ' || row_name_colname || ' asc) as ' || b_row_idx_name || ',
+        dense_rank() over (order by ' || col_name_colname || ' asc) as ' || b_col_idx_name || ',
+        ' || price_colname  || '
+      from ' || input_table || '
+    ) b
+    on a.row_idx = b.' || b_row_idx_name || '
+    and a.col_idx = b.' || b_col_idx_name || '
+
+    order by a.elt_idx';
+
+    commit;
+
+    if step_logging_mode > 0 then
+      if step_logging_mode = 1 then
+        first_call := 0;
+      else
+        first_call := 1;
+      end if;
+      hungarian_algorithm.hungarian_logging(8, null, null, first_call);
+    end if;
+
+  end hungarian_step8_results;
+
+
+  --Steps 4-7 can run in variable order, with repeated calls to each depending on
+  --the previous calls.  They output the next step to go to.
+  --This function interprets that output and calls the appropriate next step.
+  function hungarian_varstep_interpreter (
+    stepno in integer,
+    n in integer,
+    step_logging_mode in integer default 0
+  ) return integer is
+    next_step integer;
+  begin
+    if stepno = 4 then
+      next_step := hungarian_algorithm.hungarian_step4_coverstarred(n, step_logging_mode);
+    elsif stepno = 5 then
+      next_step := hungarian_algorithm.hungarian_step5_prime(n, step_logging_mode);
+    elsif stepno = 6 then
+      next_step := hungarian_algorithm.hungarian_step6_zsequence(n, step_logging_mode);
+    elsif stepno = 7 then
+      next_step := hungarian_algorithm.hungarian_step7_addsubtract(step_logging_mode);
+    end if;
+
+    return next_step;
+
+  end hungarian_varstep_interpreter;
+
+
+  --The main function call for this implementation.
+  --input table: name of table with the price data.  Prices must be nonnegative integers.
+  --row_name_colname: Name of the input_table column holding the price matrix row labels
+  --col_name_colname: Name of the input_table column holding the price matrix column labels
+  --price_colname: Name of the input_table column holding the price matrix prices
+  --logging_mode: 1 for logging, 0 for no logging
+  procedure hungarian_main (
+    input_table in varchar2,
+    row_name_colname in varchar2 default 'row_idx',
+    col_name_colname in varchar2 default 'col_idx',
+    price_colname in varchar2 default 'price',
+    logging_mode in integer default 0
+  ) is
+    step_logging_mode integer;
+    n integer;
+    next_step integer;
+  begin
+    if logging_mode = 1 then
+      step_logging_mode := 2;
+    else
+      step_logging_mode := 0;
+    end if;
+
+    n := hungarian_algorithm.hungarian_step1_check_setup(
+      input_table, row_name_colname, col_name_colname, price_colname, step_logging_mode
+    );
+
+    hungarian_algorithm.hungarian_step2_reduce(n, logging_mode);
+    hungarian_algorithm.hungarian_step3_initstar(n, logging_mode);
+
+    next_step := 4;
+
+    while next_step < 8 loop
+      next_step := hungarian_varstep_interpreter(next_step, n, logging_mode);
+    end loop;
+
+    hungarian_algorithm.hungarian_step8_results(
+      input_table, row_name_colname, col_name_colname, price_colname, logging_mode
+    );
+
+  end hungarian_main;
+
+
+  --A testing function that allows steps to be called according to a string.
+  --E.g. hungarian_strstep('123444') would call steps 1, 2, 3, and then 4 three times.
+  --May return errors if the specified steps are not algorithmically correct.
+  procedure hungarian_strstep (
+    stepstr in varchar2,
+    input_table in varchar2,
+    row_name_colname in varchar2 default 'row_idx',
+    col_name_colname in varchar2 default 'col_idx',
+    price_colname in varchar2 default 'price',
+    logging_mode in integer default 0
+  ) is
+    step_logging_mode integer;
+    n integer;
+    strlen integer;
+    num_steps integer;
+    eight_last integer;
+    stepno integer;
+    throwaway integer; --since varstep is a function
+  begin
+    if substr(stepstr, 1, 3) = '123' then
+      if logging_mode = 1 then
+        step_logging_mode := 2;
+      else
+        step_logging_mode := 0;
+      end if;
+      n := hungarian_algorithm.hungarian_step1_check_setup(
+        input_table, row_name_colname, col_name_colname,
+        price_colname, step_logging_mode
+      );
+      step_logging_mode := logging_mode;
+      hungarian_algorithm.hungarian_step2_reduce(n, logging_mode);
+      hungarian_algorithm.hungarian_step3_initstar(n, logging_mode);
+    else
+      if logging_mode = 1 then
+        step_logging_mode := 2;
+      else
+        step_logging_mode := 0;
+      end if;
+    end if;
+
+    strlen := length(stepstr);
+
+    if substr(stepstr, strlen, 1) = '8' then
+      num_steps := strlen - 1;
+      eight_last := 1;
+    else
+      num_steps := strlen;
+      eight_last := 0;
+    end if;
+
+    for i in 4..num_steps loop
+      stepno := to_number(substr(stepstr, i, 1));
+      if stepno between 4 and 7 then
+        throwaway := hungarian_algorithm.hungarian_varstep_interpreter(stepno, n, step_logging_mode);
+        step_logging_mode := logging_mode;
+      else
+        raise_application_error(-20000, 'Intermediate steps must be 4, 5, 6, or 7.');
+      end if;
+    end loop;
+
+    if eight_last = 1 then
+      --In this case Step 8 is also the first step
+      if strlen = 1 and logging_mode = 1 then
+        step_logging_mode := 2;
+      end if;
+      hungarian_algorithm.hungarian_step8_results(
+        input_table, row_name_colname, col_name_colname, price_colname, step_logging_mode
+      );
+    end if;
+
+  end hungarian_strstep;
+
+end hungarian_algorithm;
+/
--- a/src/pg/sql/25_optimization.sql
+++ b/src/pg/sql/25_optimization.sql
@@ -1,26 +0,0 @@
-CREATE OR REPLACE FUNCTION
-CDB_OptimAssignments(source text,
-                     drain text,
-                     drain_capacity text,
-                     source_production text,
-                     marginal_cost text,
-                     dist_matrix_query text,
-                     dist_rate numeric DEFAULT 0.15,
-                     dist_threshold numeric DEFAULT null)
-RETURNS table(drain_id bigint, source_id int, cost numeric, amount numeric) AS $$
-
-from crankshaft.optimization import Optim
-
-def cast_val(val):
-    return float(val) if val is not None else None
-
-params = {'dist_rate': cast_val(dist_rate),
-          'dist_threshold': cast_val(dist_threshold)}
-
-
-optim = Optim(source, drain, dist_matrix_query, drain_capacity,
-              source_production, marginal_cost, **params)
-x = optim.output()
-
-return x
-$$ LANGUAGE plpythonu;
--- a/src/pg/sql/26_distance_matrix.sql
+++ b/src/pg/sql/26_distance_matrix.sql
@@ -1,44 +0,0 @@
-- Calculate the distance matrix using underlying road network
-- Sample usage:
--   select * from cdb_distancematrix('drain_table'::regclass,
--                                    'source_table'::regclass)
-CREATE OR REPLACE FUNCTION CDB_DistanceMatrix(
-    origin_table regclass,
-    destination_table regclass,
-    transit_mode text DEFAULT 'car'
-    )
-    RETURNS TABLE(origin_id bigint, destination_id bigint,
-                  the_geom geometry(geometry, 4326),
-                  length_km numeric, duration_sec numeric)
-AS $$
-BEGIN
-    RETURN QUERY
-    EXECUTE format('
-        WITH pairs AS (
-            SELECT
-                o."cartodb_id" AS origin_id,
-                d."cartodb_id" AS destination_id,
-                o."the_geom" AS origin_point,
-                d."the_geom" AS destination_point
-            FROM
-                (SELECT * FROM %I) AS o,
-                (SELECT * FROM %I) AS d),
-        results AS (
-            SELECT
-                origin_id,
-                destination_id,
-                (cdb_route_point_to_point(origin_point,
-                                          destination_point,
-                                          $1)).*
-            FROM pairs)
-        SELECT
-            origin_id::bigint AS origin_id,
-            destination_id::bigint AS destination_id,
-            shape AS the_geom,
-            length::numeric AS length_km,
-            duration::numeric AS duration_sec
-        FROM results;', origin_table, destination_table)
-        USING transit_mode;
-    RETURN;
-END;
-$$ LANGUAGE plpgsql;
--- a/src/pg/test/expected/02_moran_test.out
+++ b/src/pg/test/expected/02_moran_test.out
@@ -149,135 +149,135 @@ _cdb_random_seeds

 (1 row)
 code|quads
-01|HH
-02|HL
-03|LL
-04|LL
-05|LH
-06|LL
-07|HH
-08|HH
-09|HH
-10|LL
-11|LL
-12|LL
-13|HL
-14|LL
+01|LL
+02|LH
+03|HH
+04|HH
+05|LL
+06|HH
+07|LL
+08|LL
+09|LL
+10|HH
+11|HH
+12|HL
+13|LL
+14|HH
 15|LL
-16|HH
-17|HH
-18|LL
-19|HH
-20|HH
-21|LL
-22|HH
-23|LL
+16|LL
+17|LL
+18|LH
+19|LL
+20|LL
+21|HH
+22|LL
+23|HL
 24|LL
-25|HH
-26|HH
+25|LL
+26|LL
 27|LL
-28|HH
-29|LL
-30|LL
-31|HH
+28|LL
+29|LH
+30|HH
+31|LL
 32|LL
-33|HL
-34|LH
-35|LL
-36|LL
-37|HL
-38|HL
-39|HH
-40|HH
-41|HL
-42|LH
-43|LH
-44|LL
-45|LH
-46|LL
+33|LL
+34|LL
+35|LH
+36|HL
+37|LH
+38|LH
+39|LL
+40|LL
+41|LH
+42|HL
+43|LL
+44|HL
+45|LL
+46|HL
 47|LL
-48|HH
-49|LH
-50|HH
-51|LL
-52|LL
-(52 rows)
+48|LL
+49|HL
+50|LL
+51|HH
+(51 rows)
 _cdb_random_seeds

 (1 row)
 code|quads
-01|HH
-02|HL
-07|HH
-08|HH
-09|HH
-13|HL
-16|HH
-17|HH
-19|HH
-20|HH
-22|HH
-25|HH
-26|HH
-28|HH
-31|HH
-33|HL
-37|HL
-38|HL
-39|HH
-40|HH
-41|HL
-48|HH
-50|HH
-(23 rows)
+03|HH
+04|HH
+06|HH
+10|HH
+11|HH
+12|HL
+14|HH
+21|HH
+23|HL
+30|HH
+36|HL
+42|HL
+44|HL
+46|HL
+49|HL
+51|HH
+(16 rows)
 _cdb_random_seeds

 (1 row)
 code|quads
-03|LL
-04|LL
-05|LH
-06|LL
-10|LL
-11|LL
-12|LL
-14|LL
+01|LL
+02|LH
+05|LL
+07|LL
+08|LL
+09|LL
+13|LL
 15|LL
-18|LL
-21|LL
-23|LL
+16|LL
+17|LL
+18|LH
+19|LL
+20|LL
+22|LL
 24|LL
+25|LL
+26|LL
 27|LL
-29|LL
-30|LL
+28|LL
+29|LH
+31|LL
 32|LL
-34|LH
-35|LL
-36|LL
-42|LH
-43|LH
-44|LL
-45|LH
-46|LL
+33|LL
+34|LL
+35|LH
+37|LH
+38|LH
+39|LL
+40|LL
+41|LH
+43|LL
+45|LL
 47|LL
-49|LH
-51|LL
-52|LL
-(29 rows)
+48|LL
+50|LL
+(35 rows)
 _cdb_random_seeds

 (1 row)
 code|quads
-02|HL
-05|LH
-13|HL
-33|HL
-34|LH
-37|HL
-38|HL
-41|HL
-42|LH
-43|LH
-45|LH
-49|LH
-(12 rows)
+02|LH
+12|HL
+18|LH
+23|HL
+29|LH
+35|LH
+36|HL
+37|LH
+38|LH
+41|LH
+42|HL
+44|HL
+46|HL
+49|HL
+(14 rows)
--- a/src/pg/test/expected/05_kmeans_test.out
+++ b/src/pg/test/expected/05_kmeans_test.out
--- a/src/pg/test/expected/16_getis_test.out
+++ b/src/pg/test/expected/16_getis_test.out
@@ -1,21 +0,0 @@
-\pset format unaligned
-\set ECHO all
-\i test/fixtures/getis_data.sql
-SET client_min_messages TO WARNING;
-\set ECHO none
-_cdb_random_seeds
-
-(1 row)
-rowid|z_score|p_value
-9|-0.7862|0.0500
-22|-0.3955|0.0330
-33|2.7045|0.0050
-35|1.9524|0.0130
-36|-1.2056|0.0170
-37|3.4785|0.0020
-38|-1.4622|0.0020
-40|5.7098|0.0030
-46|3.4704|0.0120
-47|-0.9994|0.0320
-48|-1.3650|0.0340
-(11 rows)
--- a/src/pg/test/expected/18_outliers_test.out
+++ b/src/pg/test/expected/18_outliers_test.out
@@ -1,23 +0,0 @@
-SET client_min_messages TO WARNING;
-\set ECHO none
-is_outlier|rowid
-t|11
-t|16
-t|17
-(3 rows)
-is_outlier|rowid
-t|16
-t|17
-(2 rows)
-ERROR:  Standard deviation of input data is zero
-is_outlier|rowid
-t|8
-t|11
-t|16
-(3 rows)
-is_outlier|rowid
-t|8
-t|9
-t|11
-t|16
-(4 rows)
--- a/src/pg/test/fixtures/getis_data.sql
+++ b/src/pg/test/fixtures/getis_data.sql
@@ -1,98 +0,0 @@
-SET client_min_messages TO WARNING;
-\set ECHO none
-
--
-- Getis-Ord's G* test dataset, subsetted from PySAL examples:
--   https://github.com/pysal/pysal/tree/952ea04029165048a774d9a1846cf86ad000c096/pysal/examples/stl
--
-
-
-CREATE TABLE getis_data (
-    cartodb_id integer,
-    the_geom geometry(Geometry,4326),
-    hr8893 numeric
-);
-
-COPY getis_data (cartodb_id, the_geom, hr8893) FROM stdin;
-22	0106000020E61000000100000001030000000100000007000000000000E0B10056C0000000C0B8964340FFFFFFFF4C1756C00000002054964340000000A00F1E56C00000004072964340000000C02D1E56C0000000A0439B434000000060381E56C00000000036B04340000000E0E20056C0000000608CB04340000000E0B10056C0000000C0B8964340	10.8557430000000004
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-\.
-
-
-CREATE INDEX getis_data_gix ON getis_data USING GIST(the_geom);
--- a/src/pg/test/sql/05_kmeans_test.sql
+++ b/src/pg/test/sql/05_kmeans_test.sql
--- a/src/pg/test/sql/16_getis_test.sql
+++ b/src/pg/test/sql/16_getis_test.sql
@@ -1,15 +0,0 @@
-\pset format unaligned
-\set ECHO all
-\i test/fixtures/getis_data.sql
-
-- set random seed
-SELECT cdb_crankshaft._cdb_random_seeds(1234);
-
-- test against PySAL example dataset 'stl_hom'
-SELECT rowid, round(z_score, 4) As z_score, round(p_value, 4) As p_value
-FROM cdb_crankshaft.CDB_GetisOrdsG(
-  'select * from getis_data',
-  'hr8893', 'queen', NULL, 999,
-  'the_geom', 'cartodb_id') As t(z_score, p_value, p_z_sim, rowid)
-WHERE round(p_value, 4) <= 0.05
-ORDER BY rowid ASC;
--- a/src/pg/test/sql/18_outliers_test.sql
+++ b/src/pg/test/sql/18_outliers_test.sql
@@ -1,85 +0,0 @@
-SET client_min_messages TO WARNING;
-\set ECHO none
-\pset format unaligned
-
--
-- postgres=# select round(avg(i), 3) as avg,
--                   round(stddev(i), 3) as stddev,
--                   round(avg(i) + stddev(i), 3) as one_stddev,
--                   round(avg(i) + 2 * stddev(i), 3) As two_stddev
--            from unnest(ARRAY[1,3,2,3,5,1,2,32,12,3,57,2,1,4,2,100]) As x(i);
--   avg   | stddev | one_stddev | two_stddev
-- --------+--------+------------+------------
--  14.375 | 27.322 |     41.697 |     69.020
-
-
-- With an threshold of 1.0 standard deviation, ids 11, 16, and 17 are outliers
-WITH a AS (
-    SELECT
-      ARRAY[1,3,2,3,5,1,2,32,12, 3,57, 2, 1, 4, 2,100,-100]::numeric[] As vals, ARRAY[1,2,3,4,5,6,7, 8, 9,10,11,12,13,14,15, 16,  17]::int[] As ids
-), b As (
-  SELECT
-    (cdb_crankshaft.cdb_StdDevOutlier(vals, 1.0, ids)).*
-  FROM a
-  ORDER BY ids)
-SELECT *
-FROM b
-WHERE is_outlier IS TRUE;
-
-- With a threshold of 2.0 standard deviations, id 16 is the only outlier
-WITH a AS (
-    SELECT
-      ARRAY[1,3,2,3,5,1,2,32,12, 3,57, 2, 1, 4, 2,100,-100]::numeric[] As vals,
-      ARRAY[1,2,3,4,5,6,7, 8, 9,10,11,12,13,14,15, 16,  17]::int[] As ids
-), b As (
-  SELECT
-    (cdb_crankshaft.CDB_StdDevOutlier(vals, 2.0, ids)).*
-  FROM a
-  ORDER BY ids)
-SELECT *
-FROM b
-WHERE is_outlier IS TRUE;
-
-- With a Stddev of zero, should throw back error
-- With a threshold of 2.0 standard deviations, id 16 is the only outlier
-WITH a AS (
-    SELECT
-      ARRAY[5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5]::numeric[] As vals,
-      ARRAY[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]::int[] As ids
-), b As (
-  SELECT
-    (cdb_crankshaft.CDB_StdDevOutlier(vals, 1.0, ids)).*
-  FROM a
-  ORDER BY ids)
-SELECT *
-FROM b
-WHERE is_outlier IS TRUE;
-
-- With a ratio threshold of 2.0 threshold (100% above or below the mean)
--  which is greater than ~21, which are values
-WITH a AS (
-    SELECT
-      ARRAY[1,3,2,3,5,1,2,32,12, 3,57, 2, 1, 4, 2,100,-100]::numeric[] As vals,
-      ARRAY[1,2,3,4,5,6,7, 8, 9,10,11,12,13,14,15, 16,  17]::int[] As ids
-), b As (
-  SELECT
-    (cdb_crankshaft.CDB_PercentOutlier(vals, 2.0, ids)).*
-  FROM a
-  ORDER BY ids)
-SELECT *
-  FROM b
- WHERE is_outlier IS TRUE;
-
-- With a static threshold of 11, what are the outliers
-WITH a AS (
-     SELECT
-       ARRAY[1,3,2,3,5,1,2,32,12, 3,57, 2, 1, 4, 2,100,-100]::numeric[] As vals,
-       ARRAY[1,2,3,4,5,6,7, 8, 9,10,11,12,13,14,15, 16,  17]::int[] As ids
- ), b As (
-   SELECT unnest(vals) As v, unnest(ids) as i
-     FROM a
- )
-SELECT cdb_crankshaft.CDB_StaticOutlier(v, 11.0) As is_outlier, i As rowid
-  FROM b
-WHERE cdb_crankshaft.CDB_StaticOutlier(v, 11.0) is True
-ORDER BY i;
--- a/src/py/crankshaft/crankshaft/init.py
+++ b/src/py/crankshaft/crankshaft/init.py
@@ -3,5 +3,3 @@ import crankshaft.random_seeds
 import crankshaft.clustering
 import crankshaft.space_time_dynamics
 import crankshaft.segmentation
-import analysis_data_provider
-import crankshaft.optimization
--- a/src/py/crankshaft/crankshaft/analysis_data_provider.py
+++ b/src/py/crankshaft/crankshaft/analysis_data_provider.py
@@ -1,218 +0,0 @@
-"""class for fetching data"""
-import plpy
-import pysal_utils as pu
-import numpy as np
-
-class AnalysisDataProvider(object):
-    """Analysis providers for crankshaft functions. These rely on database
-    access through `plpy`"""
-    def get_getis(self, w_type, params):
-        """fetch data for getis ord's g"""
-        try:
-            query = pu.construct_neighbor_query(w_type, params)
-            result = plpy.execute(query)
-            # if there are no neighbors, exit
-            if len(result) == 0:
-                return pu.empty_zipped_array(4)
-            else:
-                return result
-        except plpy.SPIError as err:
-            plpy.error('Analysis failed: %s' % err)
-
-    def get_markov(self, w_type, params):
-        """fetch data for spatial markov"""
-        try:
-            query = pu.construct_neighbor_query(w_type, params)
-            data = plpy.execute(query)
-
-            if len(data) == 0:
-                return pu.empty_zipped_array(4)
-
-            return data
-        except plpy.SPIError as err:
-            plpy.error('Analysis failed: %s' % err)
-
-    def get_moran(self, w_type, params):
-        """fetch data for moran's i analyses"""
-        try:
-            query = pu.construct_neighbor_query(w_type, params)
-            data = plpy.execute(query)
-
-            # if there are no neighbors, exit
-            if len(data) == 0:
-                return pu.empty_zipped_array(2)
-            return data
-        except plpy.SPIError as err:
-            plpy.error('Analysis failed: %s' % err)
-            return pu.empty_zipped_array(2)
-
-    def get_nonspatial_kmeans(self, query):
-        """fetch data for non-spatial kmeans"""
-        try:
-            data = plpy.execute(query)
-            return data
-        except plpy.SPIError as err:
-            plpy.error('Analysis failed: %s' % err)
-
-    def get_spatial_kmeans(self, params):
-        """fetch data for spatial kmeans"""
-        query = ("SELECT "
-                 "array_agg({id_col} ORDER BY {id_col}) as ids,"
-                 "array_agg(ST_X({geom_col}) ORDER BY {id_col}) As xs,"
-                 "array_agg(ST_Y({geom_col}) ORDER BY {id_col}) As ys "
-                 "FROM ({subquery}) As a "
-                 "WHERE {geom_col} IS NOT NULL").format(**params)
-        try:
-            data = plpy.execute(query)
-            return data
-        except plpy.SPIError as err:
-            plpy.error('Analysis failed: %s' % err)
-
-    def get_column(self, subquery, column, dtype=float, id_col='cartodb_id',
-                   condition=None):
-        """
-        Retrieve the column from the specified table from a connected
-        PostgreSQL database.
-
-        Args:
-            subquery (str): subquery to retrieve column from
-            column (str): column to retrieve
-            dtype (type): data type in column (e.g, float, int, str)
-            id_col (str, optional): Column name for index. Defaults to
-                `cartodb_id`.
-
-        Returns:
-            numpy.array: column from table as a NumPy array
-        """
-        query = '''
-            SELECT array_agg("{column}" ORDER BY "{id_col}" ASC) as col
-              FROM ({subquery}) As _wrap {filter}
-        '''.format(subquery=subquery,
-                   column=column,
-                   id_col=id_col,
-                   filter='WHERE {}'.format(condition) if condition else '')
-
-        resp = plpy.execute(query)
-        return np.array(resp[0]['col'], dtype=dtype)
-
-    def get_reduced_column(self, drain_query, capacity,
-                           source_query, amount,
-                           dtype=float, id_col='cartodb_id'):
-        """
-        Retrieve the column from the specified table from a connected
-        PostgreSQL database.
-
-        Args:
-            source_query (str): source_query to retrieve column from
-            column (str): column to retrieve
-            dtype (type): data type in column (e.g, float, int, str)
-            id_col (str, optional): Column name for index. Defaults to
-                `cartodb_id`.
-
-        Returns:
-            numpy.array: column from table as a NumPy array
-
-        """
-        query = '''
-            WITH cte AS (
-              SELECT
-                  d."{capacity}" - coalesce(s."source_claimed", 0) As
-                    reduced_capacity,
-                  d."{id_col}"
-                FROM
-                  ({drain_query}) As d
-              LEFT JOIN
-                  (SELECT
-                      "drain_id",
-                      sum("{amount}") As source_claimed
-                     FROM ({source_query}) As _wrap
-                   GROUP BY "drain_id") As s
-                  ON
-                  d."{id_col}" = s."drain_id"
-              )
-            SELECT
-                array_agg("reduced_capacity"
-                          ORDER BY "{id_col}" ASC) As col
-              FROM cte
-        '''.format(capacity=capacity,
-                   id_col=id_col,
-                   drain_query=drain_query,
-                   amount=amount,
-                   source_query=source_query)
-
-        resp = plpy.execute(query)
-        return np.array(resp[0]['col'], dtype=dtype)
-
-    def get_distance_matrix(self, table, origin_ids, destination_ids):
-        """Transforms a SQL table origin-destination table into a distance
-        matrix.
-
-        :param query: Table that has the data needed for building the
-            distance matrix. Query should have the following columns:
-            - origin_id (int)
-            - destination_id (int)
-            - length_km (numeric)
-        :type query: str
-        :param origin_ids: List of origin IDs
-        :type origin_ids: list of ints
-        :param destination_ids: List of origin IDs
-        :type destination_ids: list of ints
-        :returns: 2D array of distances from all origins to all destinations
-        :rtype: numpy.array
-        """
-        try:
-            resp = plpy.execute('''
-                SELECT "origin_id", "destination_id", "length_km"
-                FROM (SELECT * FROM "{table}") as _wrap
-            '''.format(table=table))
-        except plpy.SPIError as err:
-            plpy.error("Failed to build distance matrix: {}".format(err))
-
-        pairs = {(row['origin_id'], row['destination_id']): row['length_km']
-                 for row in resp}
-        distance_matrix = np.array([
-            pairs[(origin, destination)]
-            for destination in destination_ids
-            for origin in origin_ids
-        ])
-
-        return np.array(distance_matrix,
-                        dtype=float).reshape((len(destination_ids),
-                                              len(origin_ids)))
-
-
-    def get_pairwise_distances(self, drain_query, source_query,
-                               id_col='cartodb_id'):
-        """Retuns the pairwise distances between row i and j for all i in
-        drain_query and j in source_query
-
-        Args:
-            drain_query (str): Query that exposes the `the_geom` and
-                `cartodb_id` (or what is specified in `id_col`) of the dataset
-                for 'drain' locations
-            source_query (str): Query that exposes the `the_geom` and
-                `cartodb_id` (or what is specified in `id_col`) of the dataset
-                for 'source' locations
-            id_col (str, optional): Column name for table index. Defaults to
-                `cartodb_id`.
-
-        Returns:
-            numpy.array: A len(s) by len(d) array of distances from source i to
-                drain j
-        """
-        query = '''
-            SELECT array_agg(ST_Distance(d."the_geom"::geography,
-                                         s."the_geom"::geography) / 1000.0
-                             ORDER BY d."{id_col}" ASC)  as dist
-              FROM ({drain_query}) AS d, ({source_query}) AS s
-            GROUP BY s."{id_col}"
-            ORDER BY s."{id_col}" ASC
-        '''.format(drain_query=drain_query,
-                   source_query=source_query,
-                   id_col=id_col)
-
-        resp = plpy.execute(query)
-
-        # len(s) x len(d) matrix
-        return np.array([np.array(row['dist'], dtype=float)
-                         for row in resp], dtype=float)
--- a/src/py/crankshaft/crankshaft/clustering/init.py
+++ b/src/py/crankshaft/crankshaft/clustering/init.py
@@ -1,4 +1,3 @@
 """Import all functions from for clustering"""
 from moran import *
 from kmeans import *
-from getis import *
--- a/src/py/crankshaft/crankshaft/clustering/getis.py
+++ b/src/py/crankshaft/crankshaft/clustering/getis.py
@@ -1,50 +0,0 @@
-"""
-Getis-Ord's G geostatistics (hotspot/coldspot analysis)
-"""
-
-import pysal as ps
-from collections import OrderedDict
-
-# crankshaft modules
-import crankshaft.pysal_utils as pu
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-
-# High level interface ---------------------------------------
-
-
-class Getis:
-    def __init__(self, data_provider=None):
-        if data_provider is None:
-            self.data_provider = AnalysisDataProvider()
-        else:
-            self.data_provider = data_provider
-
-    def getis_ord(self, subquery, attr,
-                  w_type, num_ngbrs, permutations, geom_col, id_col):
-        """
-        Getis-Ord's G*
-        Implementation building neighbors with a PostGIS database and PySAL's
-          Getis-Ord's G* hotspot/coldspot module.
-        Andy Eschbacher
-        """
-
-        # geometries with attributes that are null are ignored
-        # resulting in a collection of not as near neighbors if kNN is chosen
-
-        qvals = OrderedDict([("id_col", id_col),
-                             ("attr1", attr),
-                             ("geom_col", geom_col),
-                             ("subquery", subquery),
-                             ("num_ngbrs", num_ngbrs)])
-
-        result = self.data_provider.get_getis(w_type, qvals)
-        attr_vals = pu.get_attributes(result)
-
-        # build PySAL weight object
-        weight = pu.get_weight(result, w_type, num_ngbrs)
-
-        # calculate Getis-Ord's G* z- and p-values
-        getis = ps.esda.getisord.G_Local(attr_vals, weight,
-                                         star=True, permutations=permutations)
-
-        return zip(getis.z_sim, getis.p_sim, getis.p_z_sim, weight.id_order)
--- a/src/py/crankshaft/crankshaft/clustering/kmeans.py
+++ b/src/py/crankshaft/crankshaft/clustering/kmeans.py
@@ -1,32 +1,18 @@
 from sklearn.cluster import KMeans
-import numpy as np
+import plpy

-from crankshaft.analysis_data_provider import AnalysisDataProvider
+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']

-class Kmeans:
-    def __init__(self, data_provider=None):
-        if data_provider is None:
-            self.data_provider = AnalysisDataProvider()
-        else:
-            self.data_provider = data_provider
+    km = KMeans(n_clusters= no_clusters, n_init=no_init)
+    labels = km.fit_predict(zip(xs,ys))
+    return zip(ids,labels)

-    def spatial(self, query, no_clusters, no_init=20):
-        """
-            find centers based on clusters of latitude/longitude pairs
-            query: SQL query that has a WGS84 geometry (the_geom)
-        """
-        params = {"subquery": query,
-                  "geom_col": "the_geom",
-                  "id_col": "cartodb_id"}
-
-        data = self.data_provider.get_spatial_kmeans(params)
-
-        # Unpack query response
-        xs = data[0]['xs']
-        ys = data[0]['ys']
-        ids = data[0]['ids']
-
-        km = KMeans(n_clusters=no_clusters, n_init=no_init)
-        labels = km.fit_predict(zip(xs, ys))
-        return zip(ids, labels)
--- a/src/py/crankshaft/crankshaft/clustering/moran.py
+++ b/src/py/crankshaft/crankshaft/clustering/moran.py
@@ -6,8 +6,8 @@ Moran's I geostatistics (global clustering & outliers presence)
 #       average of the their neighborhood

 import pysal as ps
+import plpy
 from collections import OrderedDict
-from crankshaft.analysis_data_provider import AnalysisDataProvider

 # crankshaft module
 import crankshaft.pysal_utils as pu
@@ -15,162 +15,204 @@ import crankshaft.pysal_utils as pu
 # High level interface ---------------------------------------


-class Moran:
-    def __init__(self, data_provider=None):
-        if data_provider is None:
-            self.data_provider = AnalysisDataProvider()
-        else:
-            self.data_provider = data_provider
+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)])

-    def global_stat(self, subquery, attr_name,
-                    w_type, num_ngbrs, permutations, geom_col, id_col):
-        """
-        Moran's I (global)
-        Implementation building neighbors with a PostGIS database and Moran's I
-         core clusters with PySAL.
-        Andy Eschbacher
-        """
-        params = OrderedDict([("id_col", id_col),
-                              ("attr1", attr_name),
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
+    query = pu.construct_neighbor_query(w_type, qvals)

-        result = self.data_provider.get_moran(w_type, params)
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(2)
+    except plpy.SPIError, e:
+        plpy.error('Analysis failed: %s' % e)
+        return pu.empty_zipped_array(2)

-        # collect attributes
-        attr_vals = pu.get_attributes(result)
+    # collect attributes
+    attr_vals = pu.get_attributes(result)

-        # calculate weights
-        weight = pu.get_weight(result, w_type, num_ngbrs)
+    # calculate weights
+    weight = pu.get_weight(result, w_type, num_ngbrs)

-        # calculate moran global
-        moran_global = ps.esda.moran.Moran(attr_vals, weight,
-                                           permutations=permutations)
+    # calculate moran global
+    moran_global = ps.esda.moran.Moran(attr_vals, weight,
+                                       permutations=permutations)

-        return zip([moran_global.I], [moran_global.EI])
+    return zip([moran_global.I], [moran_global.EI])

-    def local_stat(self, subquery, attr,
-                   w_type, num_ngbrs, permutations, geom_col, id_col):
-        """
-        Moran's I implementation for PL/Python
-        Andy Eschbacher
-        """

-        # geometries with attributes that are null are ignored
-        # resulting in a collection of not as near neighbors
+def moran_local(subquery, attr,
+                w_type, num_ngbrs, permutations, geom_col, id_col):
+    """
+    Moran's I implementation for PL/Python
+    Andy Eschbacher
+    """

-        params = OrderedDict([("id_col", id_col),
-                              ("attr1", attr),
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
+    # geometries with attributes that are null are ignored
+    # resulting in a collection of not as near neighbors

-        result = self.data_provider.get_moran(w_type, params)
+    qvals = OrderedDict([("id_col", id_col),
+                         ("attr1", attr),
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])

-        attr_vals = pu.get_attributes(result)
-        weight = pu.get_weight(result, w_type, num_ngbrs)
+    query = pu.construct_neighbor_query(w_type, qvals)

-        # calculate LISA values
-        lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(5)
+    except plpy.SPIError, e:
+        plpy.error('Analysis failed: %s' % e)
+        return pu.empty_zipped_array(5)
+
+    attr_vals = pu.get_attributes(result)
+    weight = pu.get_weight(result, w_type, num_ngbrs)
+
+    # calculate LISA values
+    lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
+                                     permutations=permutations)
+
+    # find quadrants for each geometry
+    quads = quad_position(lisa.q)
+
+    return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
+
+
+def moran_rate(subquery, numerator, denominator,
+               w_type, num_ngbrs, permutations, geom_col, id_col):
+    """
+    Moran's I Rate (global)
+    Andy Eschbacher
+    """
+    qvals = OrderedDict([("id_col", id_col),
+                         ("attr1", numerator),
+                         ("attr2", denominator)
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(2)
+    except plpy.SPIError, e:
+        plpy.error('Analysis failed: %s' % e)
+        return pu.empty_zipped_array(2)
+
+    # collect attributes
+    numer = pu.get_attributes(result, 1)
+    denom = pu.get_attributes(result, 2)
+
+    weight = pu.get_weight(result, w_type, num_ngbrs)
+
+    # calculate moran global rate
+    lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
                                         permutations=permutations)

-        # find quadrants for each geometry
-        quads = quad_position(lisa.q)
+    return zip([lisa_rate.I], [lisa_rate.EI])

-        return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)

-    def global_rate_stat(self, subquery, numerator, denominator,
-                         w_type, num_ngbrs, permutations, geom_col, id_col):
-        """
-        Moran's I Rate (global)
+def moran_local_rate(subquery, numerator, denominator,
+                     w_type, num_ngbrs, permutations, geom_col, id_col):
+    """
+        Moran's I Local Rate
        Andy Eschbacher
-        """
-        params = OrderedDict([("id_col", id_col),
-                              ("attr1", numerator),
-                              ("attr2", denominator)
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
+    """
+    # geometries with values that are null are ignored
+    # resulting in a collection of not as near neighbors

-        result = self.data_provider.get_moran(w_type, params)
+    qvals = OrderedDict([("id_col", id_col),
+                         ("numerator", numerator),
+                         ("denominator", denominator),
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])

-        # collect attributes
-        numer = pu.get_attributes(result, 1)
-        denom = pu.get_attributes(result, 2)
+    query = pu.construct_neighbor_query(w_type, qvals)

-        weight = pu.get_weight(result, w_type, num_ngbrs)
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(5)
+    except plpy.SPIError, e:
+        plpy.error('Analysis failed: %s' % e)
+        return pu.empty_zipped_array(5)

-        # calculate moran global rate
-        lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
-                                             permutations=permutations)
+    # collect attributes
+    numer = pu.get_attributes(result, 1)
+    denom = pu.get_attributes(result, 2)

-        return zip([lisa_rate.I], [lisa_rate.EI])
+    weight = pu.get_weight(result, w_type, num_ngbrs)

-    def local_rate_stat(self, subquery, numerator, denominator,
-                        w_type, num_ngbrs, permutations, geom_col, id_col):
-        """
-            Moran's I Local Rate
-            Andy Eschbacher
-        """
-        # geometries with values that are null are ignored
-        # resulting in a collection of not as near neighbors
+    # calculate LISA values
+    lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
+                                          permutations=permutations)

-        params = OrderedDict([("id_col", id_col),
-                              ("numerator", numerator),
-                              ("denominator", denominator),
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
+    # find quadrants for each geometry
+    quads = quad_position(lisa.q)

-        result = self.data_provider.get_moran(w_type, params)
+    return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)

-        # collect attributes
-        numer = pu.get_attributes(result, 1)
-        denom = pu.get_attributes(result, 2)

-        weight = pu.get_weight(result, w_type, num_ngbrs)
+def moran_local_bv(subquery, attr1, attr2,
+                   permutations, geom_col, id_col, w_type, num_ngbrs):
+    """
+        Moran's I (local) Bivariate (untested)
+    """

-        # calculate LISA values
-        lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
-                                              permutations=permutations)
+    qvals = OrderedDict([("id_col", id_col),
+                         ("attr1", attr1),
+                         ("attr2", attr2),
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])

-        # find quadrants for each geometry
-        quads = quad_position(lisa.q)
+    query = pu.construct_neighbor_query(w_type, qvals)

-        return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(4)
+    except plpy.SPIError:
+        plpy.error("Error: areas of interest query failed, "
+                   "check input parameters")
+        return pu.empty_zipped_array(4)

-    def local_bivariate_stat(self, subquery, attr1, attr2,
-                             permutations, geom_col, id_col,
-                             w_type, num_ngbrs):
-        """
-            Moran's I (local) Bivariate (untested)
-        """
+    # collect attributes
+    attr1_vals = pu.get_attributes(result, 1)
+    attr2_vals = pu.get_attributes(result, 2)

-        params = OrderedDict([("id_col", id_col),
-                              ("attr1", attr1),
-                              ("attr2", attr2),
-                              ("geom_col", geom_col),
-                              ("subquery", subquery),
-                              ("num_ngbrs", num_ngbrs)])
+    # create weights
+    weight = pu.get_weight(result, w_type, num_ngbrs)

-        result = self.data_provider.get_moran(w_type, params)
+    # calculate LISA values
+    lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
+                                        permutations=permutations)

-        # collect attributes
-        attr1_vals = pu.get_attributes(result, 1)
-        attr2_vals = pu.get_attributes(result, 2)
+    # find clustering of significance
+    lisa_sig = quad_position(lisa.q)

-        # create weights
-        weight = pu.get_weight(result, w_type, num_ngbrs)
-
-        # calculate LISA values
-        lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
-                                            permutations=permutations)
-
-        # find clustering of significance
-        lisa_sig = quad_position(lisa.q)
-
-        return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
+    return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)

 # Low level functions ----------------------------------------

--- a/src/py/crankshaft/crankshaft/optimization/init.py
+++ b/src/py/crankshaft/crankshaft/optimization/init.py
@@ -1 +0,0 @@
-from optim import Optim
--- a/src/py/crankshaft/crankshaft/optimization/optim.py
+++ b/src/py/crankshaft/crankshaft/optimization/optim.py
@@ -1,301 +0,0 @@
-"""optimization"""
-import plpy
-import numpy as np
-import cvxopt
-from cvxopt import solvers
-from crankshaft.analysis_data_provider import AnalysisDataProvider
-
-class Optim(object):
-    """Linear optimization class for logistics cost minimization
-    Optimization for logistics
-    based on models:
-      - source_amount * (marginal_cost + transport_cost * distance)
-    """
-
-    def __init__(self, source_query, drain_query, dist_matrix_table,
-                 capacity_column, production_column, marginal_column,
-                 **kwargs):
-
-        # set data provider - defaults to SQL database access
-        self.data_provider = kwargs.get('data_provider',
-                                        AnalysisDataProvider())
-        # model parameters
-        self.model_params = {
-            'dist_cost': kwargs.get('dist_cost', 0.15),
-            'dist_threshold': kwargs.get('dist_threshold', None),
-            'solver': kwargs.get('solver', 'glpk')}
-        self._check_model_params()
-
-        # database ids
-        self.ids = {
-            'drain_free': self.data_provider.get_column(
-                drain_query,
-                'cartodb_id',
-                id_col='cartodb_id',
-                dtype=int),
-            'source_free': self.data_provider.get_column(
-                source_query,
-                'cartodb_id',
-                dtype=int,
-                condition='drain_id is null'),
-            'source_fixed': self.data_provider.get_column(
-                source_query,
-                'cartodb_id',
-                dtype=int,
-                condition='drain_id is not null'),
-            'drain_fixed': self.data_provider.get_column(
-                source_query,
-                'drain_id',
-                dtype=int,
-                condition='drain_id is not null'
-            )}
-
-        # model data
-        self.model_data = {
-            'drain_capacity': self.data_provider.get_reduced_column(
-                drain_query,
-                capacity_column,
-                source_query,
-                production_column,
-                id_col='cartodb_id',
-                dtype=int),
-            'source_amount': self.data_provider.get_column(
-                source_query,
-                production_column,
-                condition='drain_id is null'),
-            'source_amount_fixed': self.data_provider.get_column(
-                source_query,
-                production_column,
-                condition='drain_id is not null'),
-            'marginal_cost': self.data_provider.get_column(
-                drain_query,
-                marginal_column),
-            'distance': self.data_provider.get_distance_matrix(
-                dist_matrix_table,
-                self.ids['source_free'],
-                self.ids['drain_free']),
-            'distance_fixed': self.data_provider.get_distance_matrix(
-                dist_matrix_table,
-                self.ids['source_fixed'],
-                self.ids['drain_fixed']
-            )}
-        self.model_data['cost'] = self.calc_cost()
-        self.n_sources = len(self.ids['source_free'])
-        self.n_drains = len(self.ids['drain_free'])
-
-    def _check_constraints(self):
-        """Check if inputs are within constraints"""
-        total_capacity = self.model_data['drain_capacity'].sum()
-        total_amount = self.model_data['source_amount'].sum()
-        if total_amount > total_capacity:
-            raise ValueError("Solution not possible. Drain capacity is "
-                             "smaller than total source production.")
-        elif total_capacity <= 0:
-            raise ValueError("Capacity must be greater than zero")
-
-        plpy.notice('Capacity: {total_capacity}, '
-                    'Amount: {total_amount} '
-                    '({perc}%)'.format(total_capacity=total_capacity,
-                                       total_amount=total_amount,
-                                       perc=100.0 * total_amount / total_capacity))
-        return None
-
-    def _check_model_params(self):
-        """Ensure model parameters are well formed"""
-
-        if (self.model_params['dist_threshold'] <= 0 and
-                self.model_params['dist_threshold'] is not None):
-            raise ValueError("`dist_threshold` must be greater than zero")
-
-        if (self.model_params['dist_cost'] is None or
-                self.model_params['dist_cost'] < 0):
-            raise ValueError("`dist_cost` must be greater than zero")
-
-        if self.model_params['solver'] not in (None, 'glpk'):
-            raise ValueError("`solver` must be one of 'glpk' (default) "
-                             "or None.")
-
-        return None
-
-    def output(self):
-        """Output the calculated 'optimal' assignments if solution is not infeasible.
-
-        :returns: List of source id/drain id pairs and the associated cost of
-        transport from source to drain
-        :rtype: List of tuples
-        """
-        # retrieve fractional assignments
-        assignments = self.optim()
-
-        # crosswalks for matrix index -> cartodb_id
-        drain_id_crosswalk = {}
-        for idx, cid in enumerate(self.ids['drain_free']):
-            # matrix index -> cartodb_id
-            drain_id_crosswalk[idx] = cid
-
-        source_id_crosswalk = {}
-        for idx, cid in enumerate(self.ids['source_free']):
-            # matrix index -> cartodb_id
-            source_id_crosswalk[idx] = cid
-
-        # find non-zero entries
-        source_index, drain_index = np.nonzero(assignments)
-        # returns:
-        #   - drain_id
-        #   - source_id
-        #   - cost of that pairing
-        #   - amount sent via that pairing
-        assigned_costs = [(
-            drain_id_crosswalk[drain_index[idx]],
-            source_id_crosswalk[source_val],
-            self.model_data['cost'][drain_index[idx], source_val],
-            round(self.model_data['source_amount'][source_val] *
-                  assignments[source_val, drain_index[idx]], 6),
-            False
-            )
-                          for idx, source_val in enumerate(source_index)]
-        # Fixed vals:
-        #  - self.ids['source_fixed']
-        #  - self.ids['drain_fixed']
-        #  -
-        fixed_costs = self.fixed_values()
-        # plpy.notice("FIXED COSTS: {}".format(fixed_costs))
-        return assigned_costs + fixed_costs
-
-    def fixed_values(self):
-        """Return the fixed source IDs, drain IDs, costs for transport, and the
-        amount that is transported.
-
-        """
-        margins = {k: val for k, val in zip(self.ids['drain_free'],
-                                            self.model_data['marginal_cost'])}
-        self.model_data['marginal_cost_fixed'] = [margins[d]
-                                                  for d in self.ids['drain_fixed']]
-        fixed_costs = self.calc_cost(source='source_amount_fixed',
-                                     distance='distance_fixed',
-                                     margin='marginal_cost_fixed')
-        # cost = [fixed_costs[self.ids['drain_fixed'][idx], source_val]
-        #         for idx, source_val in enumerate(self.ids['source_fixed'])]
-
-        return zip(self.ids['drain_fixed'],
-                   self.ids['source_fixed'],
-                   [1.] * len(self.ids['source_fixed']),
-                   self.model_data['source_amount_fixed'],
-                   [True] * len(self.ids['drain_fixed']))
-
-    def cost_func(self, distance, waste, marginal):
-        """
-        cost equation
-
-        :param distance: distance (in km)
-        :type distance: float
-        :param waste: number of tons of waste. This was previously calculated
-        as self.model_params['amount_per_unit'] * number of people minus the recycle_rate
-        :type waste: numeric
-        :param marginal: intrinsic cost per ton of a plant
-        :type marginal: numeric
-        :returns: cost
-        :rtype: numeric
-
-        Note: dist_cost is the cost per ton (e.g., 0.15 GBP/ton)
-        """
-        return waste * (marginal + self.model_params['dist_cost'] * distance)
-
-    def calc_cost(self, source='source_amount', distance='distance',
-                  margin='marginal_cost'):
-        """
-        Populate an d x s matrix according to the cost equation
-
-        :returns: d x s matrix of costs from area i to plant j
-        :rtype: numpy.array
-        """
-        costs = np.array(
-            [self.cost_func(dist,
-                            self.model_data[source][pair[1]],
-                            self.model_data[margin][pair[0]])
-             for pair, dist in np.ndenumerate(self.model_data[distance])])
-        return costs.reshape(self.model_data[distance].shape)
-
-    def optim(self):
-        """solve linear optimization problem
-        Equations of the form:
-
-        minimize   c'*x     by assigning x values
-        subject to G*x <= h
-                   A*x = b
-                   0 <= x[k] <= 1
-        :returns: Fractional assignments array (of 1s and 0s) of shape c.T.
-            Value at position (i, j) corresponds to the fraction of source
-            `i`'s supply to drain `j`.
-
-        :rtype: numpy.array
-        """
-        n_pairings = self.n_sources * self.n_drains
-
-        # ---
-        # costs
-        # elements chosen to minimize sum
-        cost = np.nan_to_num(self.model_data['cost'])
-        cost = cvxopt.matrix(cost.ravel('F'))
-
-        # ---
-        # equality constraint variables
-        # each area is serviced once
-        A = cvxopt.spmatrix(1.,
-                            [i // self.n_drains
-                             for i in range(n_pairings)],
-                            range(n_pairings), tc='d')
-        b = cvxopt.matrix([1.] * self.n_sources, tc='d')
-
-        # make nan's in cost impossible
-        if np.isnan(self.model_data['distance']).any():
-            i_vals, j_vals = np.where(np.isnan(self.model_data['distance']))
-            for idx, i_val in enumerate(i_vals):
-                i = int(i_val)
-                j = int(i_val * self.n_drains + j_vals[idx])
-                A[i, j] = 0
-
-        # knock out values above distance threshold
-        if self.model_params['dist_threshold']:
-            j_vals, i_vals = np.where(self.model_data['distance'] >
-                                      self.model_params['dist_threshold'])
-            for idx, ival in enumerate(i_vals):
-                A[int(ival), int(ival * self.n_drains + j_vals[idx])] = 0
-
-        # ---
-        # inequality constraint variables
-        # each plant never goes over capacity
-        drain_capacity = cvxopt.matrix([
-            cvxopt.matrix(self.model_data['drain_capacity'], tc='d'),
-            cvxopt.matrix([1.] * n_pairings, tc='d'),
-            cvxopt.matrix([0.] * n_pairings, tc='d')
-        ])
-
-        # inequality maxima
-        ineq_maxs = cvxopt.sparse([
-            cvxopt.spmatrix(
-                np.repeat(self.model_data['source_amount'], self.n_drains),
-                [i % self.n_drains for i in range(n_pairings)],
-                range(n_pairings), tc='d'),
-            cvxopt.spmatrix(1.,
-                            range(n_pairings),
-                            range(n_pairings)),
-            cvxopt.spmatrix(-1.,
-                            range(n_pairings),
-                            range(n_pairings))
-            ], tc='d')
-        for var in (cost, ineq_maxs, drain_capacity, A, b):
-            plpy.notice('size: {}'.format(var.size))
-        plpy.notice('{}, {}, {}'.format(n_pairings, self.n_sources, self.n_drains))
-        # solve
-        sol = solvers.lp(c=cost, G=ineq_maxs, h=drain_capacity,
-                         A=A, b=b, solver=self.model_params['solver'])
-        if sol['status'] != 'optimal':
-            raise Exception("No solution possible: {}".format(sol))
-
-        # NOTE: assignments needs to be shaped like self.model_data['cost'].T
-        return np.array(sol['x'],
-                        dtype=float)\
-                   .flatten()\
-                   .reshape((self.model_data['cost'].shape[1],
-                             self.model_data['cost'].shape[0]))
--- a/src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py
+++ b/src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py
@@ -45,16 +45,8 @@ def get_weight(query_res, w_type='knn', num_ngbrs=5):
 def query_attr_select(params):
    """
        Create portion of SELECT statement for attributes inolved in query.
-        Defaults to order in the params
        @param params: dict of information used in query (column names,
                       table name, etc.)
-            Example:
-            OrderedDict([('numerator', 'price'),
-                         ('denominator', 'sq_meters'),
-                         ('subquery', 'SELECT * FROM interesting_data')])
-        Output:
-          "i.\"price\"::numeric As attr1, " \
-          "i.\"sq_meters\"::numeric As attr2, "
    """

    attr_string = ""
@@ -72,7 +64,7 @@ def query_attr_select(params):
                 if k not in ('id_col', 'geom_col', 'subquery',
                              'num_ngbrs', 'subquery')]

-        for idx, val in enumerate(attrs):
+        for idx, val in enumerate(sorted(attrs)):
            attr_string += template % {"col": params[val],
                                       "alias_num": idx + 1}

@@ -88,8 +80,8 @@ def query_attr_where(params):
             'numerator': 'data1',
             'denominator': 'data2',
             '': ...}
-        Output:
-          'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL'
+        Output: 'idx_replace."data1" IS NOT NULL AND idx_replace."data2"
+                IS NOT NULL'
        Input:
        {'subquery': ...,
         'time_cols': ['time1', 'time2', 'time3'],
@@ -110,17 +102,15 @@ def query_attr_where(params):
        # moran where clauses

        # get keys
-        attrs = [k for k in params
-                 if k not in ('id_col', 'geom_col', 'subquery',
-                              'num_ngbrs', 'subquery')]
-
+        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 'denominator' in attrs:
-            attr_string.append(
-              "idx_replace.\"%s\" <> 0" % params['denominator'])
+        if len(attrs) == 2:
+            attr_string.append("idx_replace.\"%s\" <> 0" % params[attrs[1]])

    out = " AND ".join(attr_string)

--- a/src/py/crankshaft/crankshaft/space_time_dynamics/markov.py
+++ b/src/py/crankshaft/crankshaft/space_time_dynamics/markov.py
@@ -2,98 +2,98 @@
 Spatial dynamics measurements using Spatial Markov
 """

-# TODO: remove all plpy dependencies

 import numpy as np
 import pysal as ps
 import plpy
 import crankshaft.pysal_utils as pu
-from crankshaft.analysis_data_provider import AnalysisDataProvider

+def spatial_markov_trend(subquery, time_cols, num_classes=7,
+                         w_type='knn', num_ngbrs=5, permutations=0,
+                         geom_col='the_geom', id_col='cartodb_id'):
+    """
+        Predict the trends of a unit based on:
+        1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile)
+        2. average class of its neighbors

-class Markov:
-    def __init__(self, data_provider=None):
-        if data_provider is None:
-            self.data_provider = AnalysisDataProvider()
-        else:
-            self.data_provider = data_provider
+        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

-    def spatial_trend(self, subquery, time_cols, num_classes=7,
-                      w_type='knn', num_ngbrs=5, permutations=0,
-                      geom_col='the_geom', id_col='cartodb_id'):
-        """
-            Predict the trends of a unit based on:
-            1. history of its transitions to different classes (e.g., 1st
-               quantile -> 2nd quantile)
-            2. average class of its neighbors
+        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)
+    """

-            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
+    if len(time_cols) < 2:
+        plpy.error('More than one time column needs to be passed')

-            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)
-        """
+    qvals = {"id_col": id_col,
+             "time_cols": time_cols,
+             "geom_col": geom_col,
+             "subquery": subquery,
+             "num_ngbrs": num_ngbrs}

-        if len(time_cols) < 2:
-            plpy.error('More than one time column needs to be passed')
+    try:
+        query_result = plpy.execute(
+            pu.construct_neighbor_query(w_type, qvals)
+        )
+        if len(query_result) == 0:
+            return zip([None], [None], [None], [None], [None])
+    except plpy.SPIError, e:
+        plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals)))
+        plpy.error('Analysis failed: %s' % e)
+        return zip([None], [None], [None], [None], [None])

-        params = {"id_col": id_col,
-                  "time_cols": time_cols,
-                  "geom_col": geom_col,
-                  "subquery": subquery,
-                  "num_ngbrs": num_ngbrs}
+    ## build weight
+    weights = pu.get_weight(query_result, w_type)
+    weights.transform = 'r'

-        query_result = self.data_provider.get_markov(w_type, params)
+    ## prep time data
+    t_data = get_time_data(query_result, time_cols)

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

-        # prep time data
-        t_data = get_time_data(query_result, time_cols)
+    sp_markov_result = ps.Spatial_Markov(t_data,
+                                         weights,
+                                         k=num_classes,
+                                         fixed=False,
+                                         permutations=permutations)

-        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

-        # 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)
+    ## 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):
    """
@@ -103,8 +103,7 @@ def get_time_data(markov_data, time_cols):
    return np.array([[x['attr' + str(i)] for x in markov_data]
                     for i in range(1, num_attrs+1)], dtype=float).transpose()

-
-# not currently used
+## not currently used
 def rebin_data(time_data, num_time_per_bin):
    """
        Convert an n x l matrix into an (n/m) x l matrix where the values are
@@ -132,16 +131,14 @@ def rebin_data(time_data, num_time_per_bin):
    """

    if time_data.shape[1] % num_time_per_bin == 0:
-        # if fit is perfect, then use it
+        ## if fit is perfect, then use it
        n_max = time_data.shape[1] / num_time_per_bin
    else:
-        # fit remainders into an additional column
+        ## fit remainders into an additional column
        n_max = time_data.shape[1] / num_time_per_bin + 1

-    return np.array(
-      [time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
-       for i in range(n_max)]).T
-
+    return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
+                     for i in range(n_max)]).T

 def get_prob_dist(transition_matrix, lag_indices, unit_indices):
    """
@@ -160,7 +157,6 @@ def get_prob_dist(transition_matrix, lag_indices, unit_indices):
    return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
                     for i in range(len(lag_indices))])

-
 def get_prob_stats(prob_dist, unit_indices):
    """
        get the statistics of the probability distributions
@@ -183,12 +179,11 @@ def get_prob_stats(prob_dist, unit_indices):
        trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
        trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
        if prob_dist[i, unit_indices[i]] > 0.0:
-            trend[i] = (trend_up[i] - trend_down[i]) / (
-              prob_dist[i, unit_indices[i]])
+            trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
        else:
            trend[i] = None

-    # calculate volatility of distribution
+    ## calculate volatility of distribution
    volatility = prob_dist.std(axis=1)

    return trend_up, trend_down, trend, volatility
--- a/src/py/crankshaft/test/fixtures/getis.json
+++ b/src/py/crankshaft/test/fixtures/getis.json
@@ -1 +0,0 @@
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--- a/src/py/crankshaft/test/fixtures/neighbors_getis.json
+++ b/src/py/crankshaft/test/fixtures/neighbors_getis.json
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
abelvm	445db571d4	initial code upload	2016-11-25 16:55:47 +01:00
abelvm	30e4ea0a7d	Merge branch 'develop' of github.com:CartoDB/crankshaft into add-salesforce	2016-11-25 16:48:48 +01:00
abelvm	7c4849d62e	fixed duplicities	2016-09-30 18:32:20 +02:00
abelvm	45e30973a0	v.0.0.4	2016-09-29 17:20:25 +02:00
abelvm	d51b02b61e	Merge branch 'develop' of github.com:CartoDB/crankshaft into add-salesforce	2016-09-22 15:09:54 +02:00
abelvm	9b1a889133	first commit, just to save the work	2016-09-22 15:09:41 +02:00
				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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