added function overload with subqueries input

adding template for code reviews

.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,7 @@
+
+- [ ] All declared geometries are `geometry(Geometry, 4326)` for general geoms, or `geometry(Point, 4326)`
+- [ ] Include python is activated for new functions. Include this before importing modules: `plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')`
+- [ ] Docs for public-facing functions are written
+- [ ] New functions follow the naming conventions: `CDB_NameOfFunction`. Where internal functions begin with an underscore `_`.
+- [ ] If appropriate, new functions accepts an arbitrary query as an input (see [Crankshaft Issue #6](https://github.com/CartoDB/crankshaft/issues/6) for more information)
+ 

.gitignore

@@ -0,0 +1,3 @@
+envs/
+*.pyc
+.DS_Store

CONTRIBUTING.md

@@ -0,0 +1,94 @@
+# Development process
+
+Please read the Working Process/Quickstart Guide in [README.md](https://github.com/CartoDB/crankshaft/blob/master/README.md) first.
+
+For any modification of crankshaft, such as adding new features,
+refactoring or bug-fixing, topic branch must be created out of the `develop`
+branch and be used for the development process.
+
+Modifications are done inside `src/pg/sql` and `src/py/crankshaft`.
+
+Take into account:
+
+*  Tests must be added for any new functionality
+   (inside `src/pg/test`, `src/py/crankshaft/test`) as well as to
+   detect any bugs that are being fixed.
+*  Add or modify the corresponding documentation files in the `doc` folder.
+   Since we expect to have highly technical functions here, an extense
+   background explanation would be of great help to users of this extension.
+*  Convention: snake case(i.e. `snake_case` and not `CamelCase`)
+   shall be used for all function names.
+   Prefix function names intended for public use with `cdb_`
+   and private functions (to be used only internally inside
+   the extension)  with `_cdb_`.
+
+Once the code is ready to be tested, update the local development installation
+with `sudo make install`.
+This will update the 'dev' version of the extension in `src/pg/` and
+make it available to PostgreSQL.
+It will also install the python package (crankshaft) in a virtual
+environment `env/dev`.
+
+The version number of the Python package, defined in
+`src/pg/crankshaft/setup.py` will be overridden when
+the package is released and always match the extension version number,
+but for development it shall be kept as '0.0.0'.
+
+Run the tests with `make test`.
+
+To use the python extension for custom tests, activate the virtual
+environment with:
+
+```
+source envs/dev/bin/activate
+```
+
+Update extension in a working database with:
+
+* `ALTER EXTENSION crankshaft UPDATE TO 'current';`
+  `ALTER EXTENSION crankshaft UPDATE TO 'dev';`
+
+Note: we keep the current development version install as 'dev' always;
+we update through the 'current' alias to allow changing the extension
+contents but not the version identifier. This will fail if the
+changes involve incompatible function changes such as a different
+return type; in that case the offending function (or the whole extension)
+should be dropped manually before the update.
+
+If the extension has not previously been installed in a database,
+it can be installed directly with:
+
+* `CREATE EXTENSION IF NOT EXISTS plpythonu;`
+  `CREATE EXTENSION IF NOT EXISTS postgis;`
+  `CREATE EXTENSION IF NOT EXISTS cartodb;`
+  `CREATE EXTENSION crankshaft WITH VERSION 'dev';`
+
+Note: the development extension uses the development python virtual
+environment automatically.
+
+Before proceeding to the release process peer code reviewing of the code is
+a must.
+
+Once the feature or bugfix is completed and all the tests are passing
+a Pull-Request shall be created on the topic branch, reviewed by a peer
+and then merged back into the `develop` branch when all CI tests pass.
+
+When the changes in the `develop` branch are to be released in a new
+version of the extension, a PR must be created on the `develop` branch.
+
+The release manage will take hold of the PR at this moment to proceed
+to the release process for a new revision of the extension.
+
+## Relevant development tasks available in the Makefile
+
+```
+* `make help` show a short description of the available targets
+
+* `sudo make install` will generate the extension scripts for the development
+  version ('dev'/'current') and install the python package into the
+  development virtual environment `envs/dev`.
+  Intended for use by developers.
+
+* `make test` will run the tests for the installed development extension.
+  Intended for use by developers.
+```

DEPLOYING.md

@@ -1,43 +0,0 @@
-# Workflow
-
-... (branching/merging flow)
-
-# Deployment
-
-...
-
-Deployment to db servers: the next command will install both the Python
-package and the extension.
-
-```
-sudo make install
-```
-
-Installing only the Python package:
-
-```
-sudo pip install python/crankshaft --upgrade
-```
-
-Caveat: note that `pip install ./crankshaft` will install
-from local files, but `pip install crankshaft` will not.
-
-CI: Install and run the tests on the installed extension and package:
-
-```
-(sudo make install && PGUSER=postgres make testinstalled)
-```
-
-Installing the extension in user databases:
-Once installed in a server, the extension can be added
-to a database with the next SQL command:
-
-```
-CREATE EXTENSION crankshaft;
-```
-
-To upgrade the extension to an specific version X.Y.Z:
-
-```
-ALTER EXTENSION crankshaft UPGRADE TO 'X.Y.Z';
-```

Makefile

@@ -1,13 +1,70 @@
+include ./Makefile.global
+
 EXT_DIR = src/pg
 PYP_DIR = src/py
 
 .PHONY: install
 .PHONY: run_tests
+.PHONY: release
+.PHONY: deploy
 
-install:
+# Generate and install developmet versions of the extension
+# and python package.
+# The extension is named 'dev' with a 'current' alias for easily upgrading.
+# The Python package is installed in a virtual environment envs/dev/
+# Requires sudo.
+install: ## Generate and install development version of the extension; requires sudo.
 	$(MAKE) -C $(PYP_DIR) install
 	$(MAKE) -C $(EXT_DIR) install
 
-testinstalled:
-	$(MAKE) -C $(PYP_DIR) testinstalled
-	$(MAKE) -C $(EXT_DIR) installcheck
+# Run the tests for the installed development extension and
+# python package
+test:   ## Run the tests for the development version of the extension
+	$(MAKE) -C $(PYP_DIR) test
+	$(MAKE) -C $(EXT_DIR) test
+
+# Generate a new release into release
+release: ## Generate a new release of the extension. Only for telease manager
+	$(MAKE) -C $(EXT_DIR) release
+	$(MAKE) -C $(PYP_DIR) release
+
+# Install the current release.
+# The Python package is installed in a virtual environment envs/X.Y.Z/
+# Requires sudo.
+# Use the RELEASE_VERSION environment variable to deploy a specific version:
+#     sudo make deploy RELEASE_VERSION=1.0.0
+deploy: ## Deploy a released extension. Only for release manager. Requires sudo.
+	$(MAKE) -C $(EXT_DIR) deploy
+	$(MAKE) -C $(PYP_DIR) deploy
+
+# Cleanup development extension script files
+clean-dev: ## clean up development extension script files
+	rm -f src/pg/$(EXTENSION)--*.sql
+
+# Cleanup all releases
+clean-releases: ## clean up all releases
+	rm -rf release/python/*
+	rm -f release/$(EXTENSION)--*.sql
+	rm -f release/$(EXTENSION).control
+
+# Cleanup current/specific version
+clean-release: ## clean up current release
+	rm -rf release/python/$(RELEASE_VERSION)
+	rm -f release/$(RELEASE_VERSION)--*.sql
+
+# Cleanup all virtual environments
+clean-environments: ## clean up all virtual environments
+	rm -rf envs/*
+
+clean-all: clean-dev clean-release clean-environments
+
+help:
+	@IFS=$$'\n' ; \
+	help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//'`); \
+	for help_line in $${help_lines[@]}; do \
+		IFS=$$'#' ; \
+		help_split=($$help_line) ; \
+		help_command=`echo $${help_split[0]} | sed -e 's/^ *//' -e 's/ *$$//'` ; \
+		help_info=`echo $${help_split[2]} | sed -e 's/^ *//' -e 's/ *$$//'` ; \
+		printf "%-30s %s\n" $$help_command $$help_info ; \
+	done

Makefile.global

@@ -0,0 +1,6 @@
+SELF_DIR         := $(dir $(lastword $(MAKEFILE_LIST)))
+EXTENSION        = crankshaft
+PACKAGE          = crankshaft
+EXTVERSION       = $(shell grep default_version $(SELF_DIR)/src/pg/$(EXTENSION).control | sed -e "s/default_version[[:space:]]*=[[:space:]]*'\([^']*\)'/\1/")
+RELEASE_VERSION ?= $(EXTVERSION)
+SED              = sed

NEWS.md

@@ -0,0 +1,7 @@
+0.0.2 (2016-03-16)
+------------------
+* New versioning approach using per-version Python virtual environments
+
+0.0.1 (2016-02-22)
+------------------
+* Preliminar release

README.md

@@ -9,94 +9,63 @@ CartoDB Spatial Analysis extension for PostgreSQL.
 * - *src/pg* contains the PostgreSQL extension source code
 * - *src/py* Python module source code
 * *release* reseleased versions
+* *env* base directory for Python virtual environments
 
 ## Requirements
 
 * pip, virtualenv, PostgreSQL
-* python-scipy system package (see src/py/README.md)
+* python-scipy system package (see [src/py/README.md](https://github.com/CartoDB/crankshaft/blob/master/src/py/README.md))
 
-# Working Process
+# Working Process -- Quickstart Guide
 
-## Development
+We distinguish two roles regarding the development cycle of crankshaft:
 
-Work in `src/pg/sql`, `src/py/crankshaft`;
-use a topic branch. See src/py/README.md
-for the procedure to work with the Python local environment.
+* *developers* will implement new functionality and bugfixes into
+  the codebase and will request for new releases of the extension.
+* A *release manager* will attend these requests and will handle
+  the release process. The release process is sequential:
+  no concurrent releases will ever be in the works.
 
-Take into account:
+We use the default `develop` branch as the basis for development.
+The `master` branch is used to merge and tag releases to be
+deployed in production.
 
-*  Always remember to add tests for any new functionality
-   documentation.
-*  Add or modify the corresponding documentation files in the `doc` folder.
-   Since we expect to have highly technical functions here, an extense
-   background explanation would be of great help to users of this extension.
-*  Convention: Use snake case (i.e. `snake_case` and not `CamelCase`) for all
-   functions. Prefix functions intended for public use with `cdb_`
-   and private functions (to be used only internally inside
-   the extension)  with `_cdb_`.
+Developers shall create a new topic branch from `develop` for any new feature
+or bugfix and commit their changes to it and eventually merge back into
+the `develop` branch. When a new release is required a Pull Request
+will be open against the `develop` branch.
 
-Update local installation with `sudo make install`
-(this will update the 'dev' version of the extension in 'src/pg/')
+The `develop` pull requests will be handled by the release manage,
+who will merge into master where new releases are prepared and tagged.
+The `master` branch is the sole responsibility of the release masters
+and developers must not commit or merge into it.
 
-Run the tests with `PGUSER=postgres make test`
+## Development Guidelines
 
-Update extension in working database with
+For a detailed description of the development process please see
+the [CONTRIBUTING.md](https://github.com/CartoDB/crankshaft/blob/master/CONTRIBUTING.md) guide.
 
-* `ALTER EXTENSION crankshaft VERSION TO 'current';`
-  `ALTER EXTENSION crankshaft VERSION TO 'dev';`
+Any modification to the source code (`src/pg/sql` for the SQL extension,
+`src/py/crankshaft` for the Python package) shall always be done
+in a topic branch created from the `develop` branch.
 
-Note: we keep the current development version install as 'dev' always;
-we update through the 'current' alias to allow changing the extension
-contents but not the version identifier. This will fail if the
-changes involve incompatible function changes such as a different
-return type; in that case the offending function (or the whole extension)
-should be dropped manually before the update.
+Tests, documentation and peer code reviewing are required for all
+modifications.
 
-If the extension has not previously been installed in a database
-we can:
+The tests (both for SQL and Python) are executed by running,
+from the top directory:
 
-* `CREATE EXTENSION crankshaft WITH VERSION 'dev';`
-
-Once the tests are succeeding a new Pull-Request can be created.
-CI-tests must be checked to be successfull.
-
-Before merging a topic branch peer code reviewing of the code is a must.
+```
+sudo make install
+make test
+```
 
+To request a new release, which will be handled by them
+release manager, a Pull Request must be created in the `develop`
+branch.
 
 ## Release
 
-The release process of a new version of the extension
-shall by performed by the designated *Release Manager*.
-
-Note that we expect to gradually automate this process.
-
-Having checkout the topic branch of the PR to be released:
-
-The version number in `pg/cranckshaft.control` must first be updated.
-To do so [Semantic Versioning 2.0](http://semver.org/) is in order.
-
-We now will explain the process for the case of backwards-compatible
-releases (updating the minor or patch version numbers).
-
-TODO: document the complex case of major releases.
-
-The next command must be executed to produce the main installation
-script for the new release, `release/cranckshaft--X.Y.Z.sql`.
-
-```
-make release
-```
-
-Then, the release manager shall produce upgrade and downgrade scripts
-to migrate to/from the previous release. In the case of minor/patch
-releases this simply consist in extracting the functions that have changed
-and placing them in the proper `release/cranckshaft--X.Y.Z--A.B.C.sql`
-file.
-
-TODO: configure the local enviroment to be used by the release;
-currently should be directory `src/py/X.Y.Z`, but this must be fixed;
-a possibility to explore is to use the `cdb_conf` table.
-
-TODO: testing procedure for the new release
-
-TODO: push, merge, tag, deploy procedures.
+The release and deployment process is described in the
+[RELEASE.md](https://github.com/CartoDB/crankshaft/blob/master/RELEASE.md) guide and it is the responsibility of the designated
+release manager.

RELEASE.md

@@ -0,0 +1,93 @@
+# Release & Deployment Process
+
+Please read the Working Process/Quickstart Guide in README.md
+and the Development guidelines in CONTRIBUTING.md.
+
+The release process of a new version of the extension
+shall be performed by the designated *Release Manager*.
+
+Note that we expect to gradually automate more of this process.
+
+Having checked PR to be released it shall be
+merged back into the `master` branch to prepare the new release.
+
+The version number in `pg/cranckshaft.control` must first be updated.
+To do so [Semantic Versioning 2.0](http://semver.org/) is in order.
+
+Thew `NEWS.md` will be updated.
+
+We now will explain the process for the case of backwards-compatible
+releases (updating the minor or patch version numbers).
+
+TODO: document the complex case of major releases.
+
+The next command must be executed to produce the main installation
+script for the new release, `release/cranckshaft--X.Y.Z.sql` and
+also to copy the python package to `release/python/X.Y.Z/crankshaft`.
+
+```
+make release
+```
+
+Then, the release manager shall produce upgrade and downgrade scripts
+to migrate to/from the previous release. In the case of minor/patch
+releases this simply consist in extracting the functions that have changed
+and placing them in the proper `release/cranckshaft--X.Y.Z--A.B.C.sql`
+file.
+
+The new release can be deployed for staging/smoke tests with this command:
+
+```
+sudo make deploy
+```
+
+This will copy the current 'X.Y.Z' released version of the extension to
+PostgreSQL. The corresponding Python extension will be installed in a
+virtual environment in `envs/X.Y.Z`.
+
+It can be activated with:
+
+```
+source envs/X.Y.Z/bin/activate
+```
+
+But note that this is needed only for using the package directly;
+the 'X.Y.Z' version of the extension will automatically use the
+python package from this virtual environment.
+
+The `sudo make deploy` operation can be also used for installing
+the new version after it has been released.
+
+To install a specific version 'X.Y.Z' different from the current one
+(which must be present in `releases/`) you can:
+
+```
+sudo make deploy RELEASE_VERSION=X.Y.Z
+```
+
+TODO: testing procedure for the new release.
+
+TODO: procedure for staging deployment.
+
+TODO: procedure for merging to master, tagging and deploying
+in production.
+
+## Relevant release & deployment tasks available in the Makefile
+
+```
+* `make help` show a short description of the available targets
+
+* `make release` will generate a new release (version number defined in
+  `src/pg/crankshaft.control`) into `release/`.
+  Intended for use by the release manager.
+
+* `sudo make deploy` will install the current release X.Y.Z from the
+  `release/` files into PostgreSQL and a Python virtual environment
+  `envs/X.Y.Z`.
+  Intended for use by the release manager and deployment jobs.
+
+* `sudo make deploy RELEASE_VERSION=X.Y.Z` will install specified version
+  previously generated in `release/`
+  into PostgreSQL and a Python virtual environment `envs/X.Y.Z`.
+  Intended for use by the release manager and deployment jobs.
+```

TODO.md

@@ -1,9 +0,0 @@
-* [x] Support versioning
-* [x] Test use of `plpy` from python Package
-* [x] Add `pysal` etc. dependencies
-* [x] Define documentation practices (general, per extension/package?)
-* [x] Add initial function set (WIP)
-* Unify style of function comments
-* [x] Add integration tests
-* Make target to open a new version development (create symlinks, etc.)
-* [x] Should add cartodb ext. as a dependency?

doc/02_moran.md

@@ -1,71 +1,185 @@
-### Moran's I
+## Areas of Interest Functions
 
-#### What is Moran's I and why is it significant for CartoDB?
+### CDB_AreasOfInterestLocal(subquery text, column_name text)
 
-Moran's I is a geostatistical calculation which gives a measure of the global
-clustering and presence of outliers within the geographies in a map. Here global
-means over all of the geographies in a dataset. Imagine mapping the incidence
-rates of cancer in neighborhoods of a city. If there were areas covering several
-neighborhoods with abnormally low rates of cancer, those areas are positively
-spatially correlated with one another and would be considered a cluster. If
-there was a single neighborhood with a high rate but with all neighbors on
-average having a low rate, it would be considered a spatial outlier.
+This function classifies your data as being part of a cluster, as an outlier, or not part of a pattern based the significance of a classification. The classification happens through an autocorrelation statistic called Local Moran's I.
 
-While Moran's I gives a global snapshot, there are local indicators for
-clustering called Local Indicators of Spatial Autocorrelation. Clustering is a
-process related to autocorrelation -- i.e., a process that compares a
-geography's attribute to the attribute in neighbor geographies.
+#### Arguments
 
-For the example of cancer rates in neighborhoods, since these neighborhoods have
-a high value for rate of cancer, and all of their neighbors do as well, they are
-designated as "High High" or simply **HH**. For areas with multiple neighborhoods
-with low rates of cancer, they are designated as "Low Low" or **LL**. HH and LL
-naturally fit into the concept of clustering and are in the correlated
-variables.
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
+| column_name | TEXT | Name of column (e.g., should be `'interesting_value'` instead of `interesting_value` without single quotes) used for the analysis. |
+| weight type (optional) | TEXT | Type of weight to use when finding neighbors. Currently available options are 'knn' (default) and 'queen'. Read more about weight types in [PySAL's weights documentation](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html). |
+| num_ngbrs (optional) | INT | Number of neighbors if using k-nearest neighbors weight type. Defaults to 5. |
+| permutations (optional) | INT | Number of permutations to check against a random arrangement of the values in `column_name`. This influences the accuracy of the output field `significance`. Defaults to 99. |
+| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` |
+| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. |
 
-"Anticorrelated" geogs are in **LH** and **HL** regions -- that is, regions
-where a geog has a high value and it's neighbors, on average, have a low value
-(or vice versa). An example of this is a "gated community" or placement of a
-city housing project in a rich region. These deliberate developments have
-opposite median income as compared to the neighbors around them. They have a
-high (or low) value while their neighbors have a low (or high) value. They exist
-typically as islands, and in rare circumstances can extend as chains dividing
-**LL** or **HH**.
+#### Returns
 
-Strong policies such as rent stabilization (probably) tend to prevent the
-clustering of high rent areas as they integrate middle class incomes. Luxury
-apartment buildings, which are a kind of gated community, probably tend to skew
-an area's median income upwards while housing projects have the opposite effect.
-What are the nuggets in the analysis?
+A table with the following columns.
 
-Two functions are available to compute Moran I statistics:
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| moran | NUMERIC | Value of Moran's I (spatial autocorrelation measure) for the geometry with id of `rowid` |
+| quads | TEXT | Classification of geometry. Result is one of 'HH' (a high value with neighbors high on average), 'LL' (opposite of 'HH'), 'HL' (a high value surrounded by lows on average), and 'LH' (opposite of 'HL'). Null values are returned when nulls exist in the original data. |
+| significance | NUMERIC | The statistical significance (from 0 to 1) of a cluster or outlier classification. Lower numbers are more significant. |
+| rowid | INT | Row id of the values which correspond to the input rows. |
+| vals | NUMERIC | Values from `'column_name'`. |
 
-* `cdb_moran_local` computes Moran I measures, quad classification and
-  significance values from numerial values associated to geometry entities
-  in an input table. The geometries should be contiguous polygons When
-  then `queen` `w_type` is used.
-* `cdb_moran_local_rate` computes the same statistics using a ratio between
-  numerator and denominator columns of a table.
 
-The parameters for `cdb_moran_local` are:
+#### Example Usage
 
-* `table` name of the table that contains the data values
-* `attr` name of the column
-* `signficance` significance threshold for the quads values
-* `num_ngbrs` number of neighbors to consider (default: 5)
-* `permutations` number of random permutations for calculation of
-  pseudo-p values (default: 99)
-* `geom_column` number of the geometry column (default: "the_geom")
-* `id_col` PK column of the table (default: "cartodb_id")
-* `w_type` Weight types: can be "knn" for k-nearest neighbor weights
-  or "queen" for contiguity based weights.
+```sql
+SELECT
+  c.the_geom,
+  aoi.quads,
+  aoi.significance,
+  c.num_cyclists_per_total_population
+FROM CDB_GetAreasOfInterestLocal('SELECT * FROM commute_data'
+                                 'num_cyclists_per_total_population') As aoi
+JOIN commute_data As c
+ON c.cartodb_id = aoi.rowid;
+```
 
-The function returns a table with the following columns:
+### CDB_AreasOfInterestGlobal(subquery text, column_name text)
 
-* `moran` Moran's value
-* `quads` quad classification ('HH', 'LL', 'HL', 'LH' or 'Not significant')
-* `significance` significance value
-* `ids` id of the corresponding record in the input table
+This function identifies the extent to which geometries cluster (the groupings of geometries with similarly high or low values relative to the mean) or form outliers (areas where geometries have values opposite of their neighbors). The output of this function gives values between -1 and 1 as well as a significance of that classification. Values close to 0 mean that there is little to no distribution of values as compared to what one would see in a randomly distributed collection of geometries and values.
 
-Function `cdb_moran_local_rate` only differs in that the `attr` input
-parameter is substituted by `numerator` and `denominator`.
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
+| column_name | TEXT | Name of column (e.g., should be `'interesting_value'` instead of `interesting_value` without single quotes) used for the analysis. |
+| weight type (optional) | TEXT | Type of weight to use when finding neighbors. Currently available options are 'knn' (default) and 'queen'. Read more about weight types in [PySAL's weights documentation](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html). |
+| num_ngbrs (optional) | INT | Number of neighbors if using k-nearest neighbors weight type. Defaults to 5. |
+| permutations (optional) | INT | Number of permutations to check against a random arrangement of the values in `column_name`. This influences the accuracy of the output field `significance`. Defaults to 99. |
+| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` |
+| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. |
+
+#### Returns
+
+A table with the following columns.
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| moran | NUMERIC | Value of Moran's I (spatial autocorrelation measure) for the entire dataset. Values closer to one indicate cluster, closer to -1 mean more outliers, and near zero indicates a random distribution of data. |
+| significance | NUMERIC | The statistical significance of the `moran` measure. |
+
+#### Examples
+
+```sql
+SELECT *
+FROM CDB_AreasOfInterestGlobal('SELECT * FROM commute_data', 'num_cyclists_per_total_population')
+```
+
+### CDB_AreasOfInterestLocalRate(subquery text, numerator_column text, denominator_column text)
+
+Just like `CDB_AreasOfInterestLocal`, this function classifies your data as being part of a cluster, as an outlier, or not part of a pattern based the significance of a classification. This function differs in that it calculates the classifications based on input `numerator` and `denominator` columns for finding the areas where there are clusters and outliers for the resulting rate of those two values.
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
+| numerator | TEXT | Name of the numerator for forming a rate to be used in analysis. |
+| denominator | TEXT | Name of the denominator for forming a rate to be used in analysis. |
+| weight type (optional) | TEXT | Type of weight to use when finding neighbors. Currently available options are 'knn' (default) and 'queen'. Read more about weight types in [PySAL's weights documentation](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html). |
+| num_ngbrs (optional) | INT | Number of neighbors if using k-nearest neighbors weight type. Defaults to 5. |
+| permutations (optional) | INT | Number of permutations to check against a random arrangement of the values in `column_name`. This influences the accuracy of the output field `significance`. Defaults to 99. |
+| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` |
+| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. |
+
+#### Returns
+
+A table with the following columns.
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| moran | NUMERIC | Value of Moran's I (spatial autocorrelation measure) for the geometry with id of `rowid` |
+| quads | TEXT | Classification of geometry. Result is one of 'HH' (a high value with neighbors high on average), 'LL' (opposite of 'HH'), 'HL' (a high value surrounded by lows on average), and 'LH' (opposite of 'HL'). Null values are returned when nulls exist in the original data. |
+| significance | NUMERIC | The statistical significance (from 0 to 1) of a cluster or outlier classification. Lower numbers are more significant. |
+| rowid | INT | Row id of the values which correspond to the input rows. |
+| vals | NUMERIC | Values from `'column_name'`. |
+
+
+#### Example Usage
+
+```sql
+SELECT
+  c.the_geom,
+  aoi.quads,
+  aoi.significance,
+  c.cyclists_per_total_population
+FROM CDB_GetAreasOfInterestLocalRate('SELECT * FROM commute_data'
+                                     'num_cyclists',
+                                     'total_population') As aoi
+JOIN commute_data As c
+ON c.cartodb_id = aoi.rowid;
+```
+
+### CDB_AreasOfInterestGlobalRate(subquery text, column_name text)
+
+This function identifies the extent to which geometries cluster (the groupings of geometries with similarly high or low values relative to the mean) or form outliers (areas where geometries have values opposite of their neighbors). The output of this function gives values between -1 and 1 as well as a significance of that classification. Values close to 0 mean that there is little to no distribution of values as compared to what one would see in a randomly distributed collection of geometries and values.
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments |
+| numerator | TEXT | Name of the numerator for forming a rate to be used in analysis. |
+| denominator | TEXT | Name of the denominator for forming a rate to be used in analysis. |
+| weight type (optional) | TEXT | Type of weight to use when finding neighbors. Currently available options are 'knn' (default) and 'queen'. Read more about weight types in [PySAL's weights documentation](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html). |
+| num_ngbrs (optional) | INT | Number of neighbors if using k-nearest neighbors weight type. Defaults to 5. |
+| permutations (optional) | INT | Number of permutations to check against a random arrangement of the values in `column_name`. This influences the accuracy of the output field `significance`. Defaults to 99. |
+| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` |
+| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. |
+
+#### Returns
+
+A table with the following columns.
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| moran | NUMERIC | Value of Moran's I (spatial autocorrelation measure) for the entire dataset. Values closer to one indicate cluster, closer to -1 mean more outliers, and near zero indicates a random distribution of data. |
+| significance | NUMERIC | The statistical significance of the `moran` measure. |
+
+#### Examples
+
+```sql
+SELECT *
+FROM CDB_AreasOfInterestGlobalRate('SELECT * FROM commute_data',          
+                                   'num_cyclists',
+                                   'total_population')
+```
+
+## Hotspot, Coldspot, and Outlier Functions
+
+These functions are convenience functions for extracting only information that you are interested in exposing based on the outputs of the `CDB_AreasOfInterest` functions. For instance, you can use `CDB_GetSpatialHotspots` to output only the classifications of `HH` and `HL`.
+
+### Non-rate functions
+
+#### CDB_GetSpatialHotspots
+This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocal` except that the outputs are filtered to be only 'HH' and 'HL' (areas of high values). For more information about this function's use, see `CDB_AreasOfInterestLocal`.
+
+#### CDB_GetSpatialColdspots
+This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocal` except that the outputs are filtered to be only 'LL' and 'LH' (areas of low values). For more information about this function's use, see `CDB_AreasOfInterestLocal`.
+
+#### CDB_GetSpatialOutliers
+This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocal` except that the outputs are filtered to be only 'HL' and 'LH' (areas where highs or lows are surrounded by opposite values on average). For more information about this function's use, see `CDB_AreasOfInterestLocal`.
+
+### Rate functions
+
+#### CDB_GetSpatialHotspotsRate
+
+This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocalRate` except that the outputs are filtered to be only 'HH' and 'HL' (areas of high values). For more information about this function's use, see `CDB_AreasOfInterestLocalRate`.
+
+#### CDB_GetSpatialColdspotsRate
+
+This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocalRate` except that the outputs are filtered to be only 'LL' and 'LH' (areas of low values). For more information about this function's use, see `CDB_AreasOfInterestLocalRate`.
+
+#### CDB_GetSpatialOutliersRate
+
+This function's inputs and outputs exactly mirror `CDB_AreasOfInterestLocalRate` except that the outputs are filtered to be only 'HL' and 'LH' (areas where highs or lows are surrounded by opposite values on average). For more information about this function's use, see `CDB_AreasOfInterestLocalRate`.

doc/07_gravity.md

@@ -0,0 +1,78 @@
+## Gravity Model
+
+Gravity Models are derived from Newton's Law of Gravity and are used to predict the interaction between a group of populated areas (sources) and a specific target among a group of potential targets, in terms of an attraction factor (weight)
+
+**CDB_Gravity** is based on the model defined in *Huff's Law of Shopper attraction (1963)*
+
+### CDB_Gravity(t_id bigint[], t_geom geometry[], t_weight numeric[], s_id bigint[], s_geom geometry[], s_pop numeric[], target bigint, radius integer, minval numeric DEFAULT -10e307)
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| t_id     | bigint[]    | Array of targets ID |
+| t_geom   | geometry[]  | Array of targets' geometries |
+| t_weight | numeric[]   | Array of targets's weights |
+| s_id     | bigint[]    | Array of sources ID |
+| s_geom   | geometry[]  | Array of sources' geometries |
+| s_pop    | numeric[]   | Array of sources's population |
+| target   | bigint      | ID of the target under study |
+| radius   | integer     | Radius in meters around the target under study that will be taken into account|
+| minval (optional)   | numeric     | Lowest accepted value of weight, defaults to numeric min_value |
+
+### CDB_Gravity( target_query text, weight_column text, source_query text, pop_column text, target bigint, radius integer, minval numeric DEFAULT -10e307)
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| target_query     | text    | Query that defines targets |
+| weight_column   | text  | Column name of weights |
+| source_query     | text    | Query that defines sources |
+| pop_column   | text  | Column name of population |
+| target   | bigint      | cartodb_id of the target under study |
+| radius   | integer     | Radius in meters around the target under study that will be taken into account|
+| minval (optional)   | numeric     | Lowest accepted value of weight, defaults to numeric min_value |
+
+
+### Returns
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| the_geom  | geometry | Geometries of the sources within the radius |
+| source_id | bigint  | ID of the source |
+| target_id | bigint  | Target ID from input |
+| dist      | numeric | Distance in meters source to target (if not points, distance between centroids) |
+| h         | numeric | Probability of patronage |
+| hpop      | numeric | Patronaging population |
+
+
+#### Example Usage
+
+```sql
+with t as (
+SELECT
+    array_agg(cartodb_id::bigint) as id,
+    array_agg(the_geom) as g,
+    array_agg(coalesce(gla,0)::numeric) as w
+FROM
+    abel.centros_comerciales_de_madrid
+WHERE not no_cc
+),
+s as (
+SELECT
+    array_agg(cartodb_id::bigint) as id,
+    array_agg(center) as g,
+    array_agg(coalesce(t1_1, 0)::numeric) as p
+FROM
+    sscc_madrid
+)
+select
+    g.the_geom,
+    trunc(g.h,2) as h,
+    round(g.hpop) as hpop,
+    trunc(g.dist/1000,2) as dist_km
+FROM t, s, CDB_Gravity1(t.id, t.g, t.w, s.id, s.g, s.p, newmall_ID, 100000, 5000) g
+```
+
+

doc/docs_template.md

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

release/crankshaft--0.0.1--0.0.2.sql

@@ -0,0 +1,74 @@
+CREATE OR REPLACE FUNCTION cdb_crankshaft.cdb_crankshaft_version()
+RETURNS text AS $$
+  SELECT '0.0.2'::text;
+$$ language 'sql' STABLE STRICT;
+
+CREATE OR REPLACE FUNCTION cdb_crankshaft._cdb_crankshaft_internal_version()
+RETURNS text AS $$
+  SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL;
+$$ language 'sql' STABLE STRICT;
+CREATE OR REPLACE FUNCTION cdb_crankshaft._cdb_crankshaft_virtualenvs_path()
+RETURNS text
+AS $$
+  BEGIN
+    RETURN '/home/ubuntu/crankshaft/envs';
+  END;
+$$ language plpgsql IMMUTABLE STRICT;
+
+CREATE OR REPLACE FUNCTION cdb_crankshaft._cdb_crankshaft_activate_py()
+RETURNS VOID
+AS $$
+    import os
+    # plpy.notice('%',str(os.environ))
+    # activate virtualenv
+    crankshaft_version = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_internal_version()')[0]['_cdb_crankshaft_internal_version']
+    base_path = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_virtualenvs_path()')[0]['_cdb_crankshaft_virtualenvs_path']
+    default_venv_path = os.path.join(base_path, crankshaft_version)
+    venv_path =  os.environ.get('CRANKSHAFT_VENV', default_venv_path)
+    activate_path = venv_path + '/bin/activate_this.py'
+    exec(open(activate_path).read(), dict(__file__=activate_path))
+$$ LANGUAGE plpythonu;
+
+CREATE OR REPLACE FUNCTION
+cdb_crankshaft._cdb_random_seeds (seed_value INTEGER) RETURNS VOID
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft import random_seeds
+  random_seeds.set_random_seeds(seed_value)
+$$ LANGUAGE plpythonu;
+-- Moran's I
+CREATE OR REPLACE FUNCTION
+cdb_crankshaft.cdb_moran_local (
+      t TEXT,
+  	  attr TEXT,
+  	  significance float DEFAULT 0.05,
+  	  num_ngbrs INT DEFAULT 5,
+  	  permutations INT DEFAULT 99,
+  	  geom_column TEXT DEFAULT 'the_geom',
+  	  id_col TEXT DEFAULT 'cartodb_id',
+      w_type TEXT DEFAULT 'knn')
+RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+$$ LANGUAGE plpythonu;
+
+CREATE OR REPLACE FUNCTION
+cdb_crankshaft.cdb_moran_local_rate(t TEXT,
+		 numerator TEXT,
+		 denominator TEXT,
+		 significance FLOAT DEFAULT 0.05,
+		 num_ngbrs INT DEFAULT 5,
+		 permutations INT DEFAULT 99,
+		 geom_column TEXT DEFAULT 'the_geom',
+		 id_col TEXT DEFAULT 'cartodb_id',
+		 w_type TEXT DEFAULT 'knn')
+RETURNS TABLE(moran FLOAT, quads TEXT, significance FLOAT, ids INT, y numeric)
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft.clustering import moran_local_rate
+  # TODO: use named parameters or a dictionary
+  return moran_local_rate(t, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+$$ LANGUAGE plpythonu;

release/crankshaft--0.0.1.sql

@@ -0,0 +1,148 @@
+--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES
+-- Complain if script is sourced in psql, rather than via CREATE EXTENSION
+\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit
+-- Internal function.
+-- Set the seeds of the RNGs (Random Number Generators)
+-- used internally.
+CREATE OR REPLACE FUNCTION
+_cdb_random_seeds (seed_value INTEGER) RETURNS VOID
+AS $$
+  from crankshaft import random_seeds
+  random_seeds.set_random_seeds(seed_value)
+$$ LANGUAGE plpythonu;
+-- Moran's I
+CREATE OR REPLACE FUNCTION
+  cdb_moran_local (
+      t TEXT,
+  	  attr TEXT,
+  	  significance float DEFAULT 0.05,
+  	  num_ngbrs INT DEFAULT 5,
+  	  permutations INT DEFAULT 99,
+  	  geom_column TEXT DEFAULT 'the_geom',
+  	  id_col TEXT DEFAULT 'cartodb_id',
+      w_type TEXT DEFAULT 'knn')
+RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
+AS $$
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+$$ LANGUAGE plpythonu;
+
+-- Moran's I Local Rate
+CREATE OR REPLACE FUNCTION
+  cdb_moran_local_rate(t TEXT,
+		 numerator TEXT,
+		 denominator TEXT,
+		 significance FLOAT DEFAULT 0.05,
+		 num_ngbrs INT DEFAULT 5,
+		 permutations INT DEFAULT 99,
+		 geom_column TEXT DEFAULT 'the_geom',
+		 id_col TEXT DEFAULT 'cartodb_id',
+		 w_type TEXT DEFAULT 'knn')
+RETURNS TABLE(moran FLOAT, quads TEXT, significance FLOAT, ids INT, y numeric)
+AS $$
+  from crankshaft.clustering import moran_local_rate
+  # TODO: use named parameters or a dictionary
+  return moran_local_rate(t, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+$$ LANGUAGE plpythonu;
+-- Function by Stuart Lynn for a simple interpolation of a value
+-- from a polygon table over an arbitrary polygon
+-- (weighted by the area proportion overlapped)
+-- Aereal weighting is a very simple form of aereal interpolation.
+--
+-- Parameters:
+--   * geom a Polygon geometry which defines the area where a value will be
+--     estimated as the area-weighted sum of a given table/column
+--   * target_table_name table name of the table that provides the values
+--   * target_column column name of the column that provides the values
+--   * schema_name optional parameter to defina the schema the target table
+--     belongs to, which is necessary if its not in the search_path.
+--     Note that target_table_name should never include the schema in it.
+-- Return value:
+--   Aereal-weighted interpolation of the column values over the geometry
+CREATE OR REPLACE
+FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL)
+  RETURNS numeric AS
+$$
+DECLARE
+	result numeric;
+  qualified_name text;
+BEGIN
+  IF schema_name IS NULL THEN
+    qualified_name := Format('%I', target_table_name);
+  ELSE
+    qualified_name := Format('%I.%s', schema_name, target_table_name);
+  END IF;
+  EXECUTE Format('
+    SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom))
+    FROM %s AS a
+    WHERE $1 && a.the_geom
+  ', target_column, qualified_name)
+  USING geom
+  INTO result;
+  RETURN result;
+END;
+$$ LANGUAGE plpgsql;
+--
+-- Creates N points randomly distributed arround the polygon
+--
+-- @param g - the geometry to be turned in to points
+--
+-- @param no_points - the number of points to generate
+--
+-- @params max_iter_per_point - the function generates points in the polygon's bounding box
+-- and discards points which don't lie in the polygon. max_iter_per_point specifies how many
+-- misses per point the funciton accepts before giving up.
+--
+-- Returns: Multipoint with the requested points
+CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000)
+RETURNS GEOMETRY AS $$
+DECLARE
+  extent GEOMETRY;
+  test_point Geometry;
+  width                NUMERIC;
+  height               NUMERIC;
+  x0                   NUMERIC;
+  y0                   NUMERIC;
+  xp                   NUMERIC;
+  yp                   NUMERIC;
+  no_left              INTEGER;
+  remaining_iterations INTEGER;
+  points               GEOMETRY[];
+  bbox_line            GEOMETRY;
+  intersection_line    GEOMETRY;
+BEGIN
+  extent  := ST_Envelope(geom);
+  width   := ST_XMax(extent) - ST_XMIN(extent);
+  height  := ST_YMax(extent) - ST_YMIN(extent);
+  x0 	  := ST_XMin(extent);
+  y0 	  := ST_YMin(extent);
+  no_left := no_points;
+
+  LOOP
+    if(no_left=0) THEN
+      EXIT;
+    END IF;
+    yp = y0 + height*random();
+    bbox_line  = ST_MakeLine(
+      ST_SetSRID(ST_MakePoint(yp, x0),4326),
+      ST_SetSRID(ST_MakePoint(yp, x0+width),4326)
+    );
+    intersection_line = ST_Intersection(bbox_line,geom);
+  	test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random());
+	  points := points || test_point;
+	  no_left = no_left - 1 ;
+  END LOOP;
+  RETURN ST_Collect(points);
+END;
+$$
+LANGUAGE plpgsql VOLATILE;
+-- Make sure by default there are no permissions for publicuser
+-- NOTE: this happens at extension creation time, as part of an implicit transaction.
+-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE;
+
+-- Grant permissions on the schema to publicuser (but just the schema)
+GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser;
+
+-- Revoke execute permissions on all functions in the schema by default
+-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser;

release/crankshaft--0.0.2--0.0.1.sql

@@ -0,0 +1,44 @@
+CREATE OR REPLACE FUNCTION
+cdb_crankshaft._cdb_random_seeds (seed_value INTEGER) RETURNS VOID
+AS $$
+  from crankshaft import random_seeds
+  random_seeds.set_random_seeds(seed_value)
+$$ LANGUAGE plpythonu;
+
+CREATE OR REPLACE FUNCTION
+cdb_crankshaft.cdb_moran_local (
+      t TEXT,
+  	  attr TEXT,
+  	  significance float DEFAULT 0.05,
+  	  num_ngbrs INT DEFAULT 5,
+  	  permutations INT DEFAULT 99,
+  	  geom_column TEXT DEFAULT 'the_geom',
+  	  id_col TEXT DEFAULT 'cartodb_id',
+      w_type TEXT DEFAULT 'knn')
+RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
+AS $$
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+$$ LANGUAGE plpythonu;
+
+CREATE OR REPLACE FUNCTION
+cdb_crankshaft.cdb_moran_local_rate(t TEXT,
+		 numerator TEXT,
+		 denominator TEXT,
+		 significance FLOAT DEFAULT 0.05,
+		 num_ngbrs INT DEFAULT 5,
+		 permutations INT DEFAULT 99,
+		 geom_column TEXT DEFAULT 'the_geom',
+		 id_col TEXT DEFAULT 'cartodb_id',
+		 w_type TEXT DEFAULT 'knn')
+RETURNS TABLE(moran FLOAT, quads TEXT, significance FLOAT, ids INT, y numeric)
+AS $$
+  from crankshaft.clustering import moran_local_rate
+  # TODO: use named parameters or a dictionary
+  return moran_local_rate(t, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+$$ LANGUAGE plpythonu;
+
+DROP FUNCTION IF EXISTS cdb_crankshaft.cdb_crankshaft_version();
+DROP FUNCTION IF EXISTS cdb_crankshaft._cdb_crankshaft_internal_version();
+DROP FUNCTION IF EXISTS cdb_crankshaft._cdb_crankshaft_activate_py();

release/crankshaft--0.0.2.sql

@@ -0,0 +1,186 @@
+--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES
+-- Complain if script is sourced in psql, rather than via CREATE EXTENSION
+\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit
+-- Version number of the extension release
+CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
+RETURNS text AS $$
+  SELECT '0.0.2'::text;
+$$ language 'sql' STABLE STRICT;
+
+-- Internal identifier of the installed extension instence
+-- e.g. 'dev' for current development version
+CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version()
+RETURNS text AS $$
+  SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL;
+$$ language 'sql' STABLE STRICT;
+CREATE OR REPLACE FUNCTION _cdb_crankshaft_virtualenvs_path()
+RETURNS text
+AS $$
+  BEGIN
+    -- RETURN '/opt/virtualenvs/crankshaft';
+    RETURN '/home/ubuntu/crankshaft/envs';
+  END;
+$$ language plpgsql IMMUTABLE STRICT;
+
+-- Use the crankshaft python module
+CREATE OR REPLACE FUNCTION _cdb_crankshaft_activate_py()
+RETURNS VOID
+AS $$
+    import os
+    # plpy.notice('%',str(os.environ))
+    # activate virtualenv
+    crankshaft_version = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_internal_version()')[0]['_cdb_crankshaft_internal_version']
+    base_path = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_virtualenvs_path()')[0]['_cdb_crankshaft_virtualenvs_path']
+    default_venv_path = os.path.join(base_path, crankshaft_version)
+    venv_path =  os.environ.get('CRANKSHAFT_VENV', default_venv_path)
+    activate_path = venv_path + '/bin/activate_this.py'
+    exec(open(activate_path).read(), dict(__file__=activate_path))
+$$ LANGUAGE plpythonu;
+-- Internal function.
+-- Set the seeds of the RNGs (Random Number Generators)
+-- used internally.
+CREATE OR REPLACE FUNCTION
+_cdb_random_seeds (seed_value INTEGER) RETURNS VOID
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft import random_seeds
+  random_seeds.set_random_seeds(seed_value)
+$$ LANGUAGE plpythonu;
+-- Moran's I
+CREATE OR REPLACE FUNCTION
+  cdb_moran_local (
+      t TEXT,
+  	  attr TEXT,
+  	  significance float DEFAULT 0.05,
+  	  num_ngbrs INT DEFAULT 5,
+  	  permutations INT DEFAULT 99,
+  	  geom_column TEXT DEFAULT 'the_geom',
+  	  id_col TEXT DEFAULT 'cartodb_id',
+      w_type TEXT DEFAULT 'knn')
+RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+$$ LANGUAGE plpythonu;
+
+-- Moran's I Local Rate
+CREATE OR REPLACE FUNCTION
+  cdb_moran_local_rate(t TEXT,
+		 numerator TEXT,
+		 denominator TEXT,
+		 significance FLOAT DEFAULT 0.05,
+		 num_ngbrs INT DEFAULT 5,
+		 permutations INT DEFAULT 99,
+		 geom_column TEXT DEFAULT 'the_geom',
+		 id_col TEXT DEFAULT 'cartodb_id',
+		 w_type TEXT DEFAULT 'knn')
+RETURNS TABLE(moran FLOAT, quads TEXT, significance FLOAT, ids INT, y numeric)
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft.clustering import moran_local_rate
+  # TODO: use named parameters or a dictionary
+  return moran_local_rate(t, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+$$ LANGUAGE plpythonu;
+-- Function by Stuart Lynn for a simple interpolation of a value
+-- from a polygon table over an arbitrary polygon
+-- (weighted by the area proportion overlapped)
+-- Aereal weighting is a very simple form of aereal interpolation.
+--
+-- Parameters:
+--   * geom a Polygon geometry which defines the area where a value will be
+--     estimated as the area-weighted sum of a given table/column
+--   * target_table_name table name of the table that provides the values
+--   * target_column column name of the column that provides the values
+--   * schema_name optional parameter to defina the schema the target table
+--     belongs to, which is necessary if its not in the search_path.
+--     Note that target_table_name should never include the schema in it.
+-- Return value:
+--   Aereal-weighted interpolation of the column values over the geometry
+CREATE OR REPLACE
+FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL)
+  RETURNS numeric AS
+$$
+DECLARE
+	result numeric;
+  qualified_name text;
+BEGIN
+  IF schema_name IS NULL THEN
+    qualified_name := Format('%I', target_table_name);
+  ELSE
+    qualified_name := Format('%I.%s', schema_name, target_table_name);
+  END IF;
+  EXECUTE Format('
+    SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom))
+    FROM %s AS a
+    WHERE $1 && a.the_geom
+  ', target_column, qualified_name)
+  USING geom
+  INTO result;
+  RETURN result;
+END;
+$$ LANGUAGE plpgsql;
+--
+-- Creates N points randomly distributed arround the polygon
+--
+-- @param g - the geometry to be turned in to points
+--
+-- @param no_points - the number of points to generate
+--
+-- @params max_iter_per_point - the function generates points in the polygon's bounding box
+-- and discards points which don't lie in the polygon. max_iter_per_point specifies how many
+-- misses per point the funciton accepts before giving up.
+--
+-- Returns: Multipoint with the requested points
+CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000)
+RETURNS GEOMETRY AS $$
+DECLARE
+  extent GEOMETRY;
+  test_point Geometry;
+  width                NUMERIC;
+  height               NUMERIC;
+  x0                   NUMERIC;
+  y0                   NUMERIC;
+  xp                   NUMERIC;
+  yp                   NUMERIC;
+  no_left              INTEGER;
+  remaining_iterations INTEGER;
+  points               GEOMETRY[];
+  bbox_line            GEOMETRY;
+  intersection_line    GEOMETRY;
+BEGIN
+  extent  := ST_Envelope(geom);
+  width   := ST_XMax(extent) - ST_XMIN(extent);
+  height  := ST_YMax(extent) - ST_YMIN(extent);
+  x0 	  := ST_XMin(extent);
+  y0 	  := ST_YMin(extent);
+  no_left := no_points;
+
+  LOOP
+    if(no_left=0) THEN
+      EXIT;
+    END IF;
+    yp = y0 + height*random();
+    bbox_line  = ST_MakeLine(
+      ST_SetSRID(ST_MakePoint(yp, x0),4326),
+      ST_SetSRID(ST_MakePoint(yp, x0+width),4326)
+    );
+    intersection_line = ST_Intersection(bbox_line,geom);
+  	test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random());
+	  points := points || test_point;
+	  no_left = no_left - 1 ;
+  END LOOP;
+  RETURN ST_Collect(points);
+END;
+$$
+LANGUAGE plpgsql VOLATILE;
+-- Make sure by default there are no permissions for publicuser
+-- NOTE: this happens at extension creation time, as part of an implicit transaction.
+-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE;
+
+-- Grant permissions on the schema to publicuser (but just the schema)
+GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser;
+
+-- Revoke execute permissions on all functions in the schema by default
+-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser;

release/crankshaft.control

@@ -0,0 +1,5 @@
+comment = 'CartoDB Spatial Analysis extension'
+default_version = '0.0.2'
+requires = 'plpythonu, postgis, cartodb'
+superuser = true
+schema = cdb_crankshaft

release/python/0.0.1/crankshaft/crankshaft/__init__.py

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

release/python/0.0.1/crankshaft/crankshaft/clustering/__init__.py

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

release/python/0.0.1/crankshaft/crankshaft/clustering/moran.py

@@ -0,0 +1,321 @@
+"""
+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 numpy as np
+import pysal as ps
+import plpy
+
+# High level interface ---------------------------------------
+
+def moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
+    """
+    Moran's I implementation for PL/Python
+    Andy Eschbacher
+    """
+    # TODO: ensure that the significance output can be smaller that 1e-3 (0.001)
+    # TODO: make a wishlist of output features (zscores, pvalues, raw local lisa, what else?)
+
+    plpy.notice('** Constructing query')
+
+    # geometries with attributes that are null are ignored
+    # resulting in a collection of not as near neighbors
+
+    qvals = {"id_col": id_col,
+            "attr1": attr,
+            "geom_col": geom_column,
+             "table": t,
+             "num_ngbrs": num_ngbrs}
+
+    q = get_query(w_type, qvals)
+
+    try:
+        r = plpy.execute(q)
+        plpy.notice('** Query returned with %d rows' % len(r))
+    except plpy.SPIError:
+        plpy.notice('** Query failed: "%s"' % q)
+        plpy.notice('** Exiting function')
+        return zip([None], [None], [None], [None])
+
+    y = get_attributes(r, 1)
+    w = get_weight(r, w_type)
+
+    # calculate LISA values
+    lisa = ps.Moran_Local(y, w)
+
+    # find units of significance
+    lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
+
+    plpy.notice('** Finished calculations')
+
+    return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order)
+
+
+def moran_local_rate(t, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
+    """
+    Moran's I Local Rate
+    Andy Eschbacher
+    """
+
+    plpy.notice('** Constructing query')
+
+    # geometries with attributes that are null are ignored
+    # resulting in a collection of not as near neighbors
+
+    qvals = {"id_col": id_col,
+             "numerator": numerator,
+             "denominator": denominator,
+             "geom_col": geom_column,
+             "table": t,
+             "num_ngbrs": num_ngbrs}
+
+    q = get_query(w_type, qvals)
+
+    try:
+        r = plpy.execute(q)
+        plpy.notice('** Query returned with %d rows' % len(r))
+    except plpy.SPIError:
+        plpy.notice('** Query failed: "%s"' % q)
+        plpy.notice('** Error: %s' % plpy.SPIError)
+        plpy.notice('** Exiting function')
+        return zip([None], [None], [None], [None])
+
+        plpy.notice('r.nrows() = %d' % r.nrows())
+
+    ## collect attributes
+    numer = get_attributes(r, 1)
+    denom = get_attributes(r, 2)
+
+    w = get_weight(r, w_type, num_ngbrs)
+
+    # calculate LISA values
+    lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, w, permutations=permutations)
+
+    # find units of significance
+    lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
+
+    plpy.notice('** Finished calculations')
+
+    ## TODO: Decide on which return values here
+    return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order, lisa.y)
+
+def moran_local_bv(t, attr1, attr2, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
+    plpy.notice('** Constructing query')
+
+    qvals = {"num_ngbrs": num_ngbrs,
+             "attr1": attr1,
+             "attr2": attr2,
+             "table": t,
+             "geom_col": geom_column,
+             "id_col": id_col}
+
+    q = get_query(w_type, qvals)
+
+    try:
+        r = plpy.execute(q)
+        plpy.notice('** Query returned with %d rows' % len(r))
+    except plpy.SPIError:
+        plpy.notice('** Query failed: "%s"' % q)
+        plpy.notice('** Error: %s' % plpy.SPIError)
+        plpy.notice('** Exiting function')
+        return zip([None], [None], [None], [None])
+
+    ## collect attributes
+    attr1_vals = get_attributes(r, 1)
+    attr2_vals = get_attributes(r, 2)
+
+    # create weights
+    w = get_weight(r, w_type, num_ngbrs)
+
+    # calculate LISA values
+    lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, w)
+
+    plpy.notice("len of Is: %d" % len(lisa.Is))
+
+    # find clustering of significance
+    lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
+
+    plpy.notice('** Finished calculations')
+
+    return zip(lisa.Is, lisa_sig, lisa.p_sim, w.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
+    """
+    if coord == 1:
+        return 'HH'
+    elif coord == 2:
+        return 'LH'
+    elif coord == 3:
+        return 'LL'
+    elif coord == 4:
+        return 'HL'
+    else:
+        return None
+
+def query_attr_select(params):
+    """
+        Create portion of SELECT statement for attributes inolved in query.
+        :param params: dict of information used in query (column names,
+                       table name, etc.)
+    """
+
+    attrs = [k for k in params
+             if k not in ('id_col', 'geom_col', 'table', 'num_ngbrs')]
+
+    template = "i.\"{%(col)s}\"::numeric As attr%(alias_num)s, "
+
+    attr_string = ""
+
+    for idx, val in enumerate(sorted(attrs)):
+        attr_string += template % {"col": val, "alias_num": idx + 1}
+
+    return attr_string
+
+def query_attr_where(params):
+    """
+        Create portion of WHERE clauses for weeding out NULL-valued geometries
+    """
+    attrs = sorted([k for k in params
+                    if k not in ('id_col', 'geom_col', 'table', 'num_ngbrs')])
+
+    attr_string = []
+
+    for attr in attrs:
+        attr_string.append("idx_replace.\"{%s}\" IS NOT NULL" % attr)
+
+    if len(attrs) == 2:
+        attr_string.append("idx_replace.\"{%s}\" <> 0" % attrs[1])
+
+    out = " AND ".join(attr_string)
+
+    return out
+
+def knn(params):
+    """SQL query for k-nearest neighbors.
+        :param vars: dict of values to fill template
+    """
+
+    attr_select = query_attr_select(params)
+    attr_where = query_attr_where(params)
+
+    replacements = {"attr_select": attr_select,
+                    "attr_where_i": attr_where.replace("idx_replace", "i"),
+                    "attr_where_j": attr_where.replace("idx_replace", "j")}
+
+    query = "SELECT " \
+                "i.\"{id_col}\" As id, " \
+                "%(attr_select)s" \
+                "(SELECT ARRAY(SELECT j.\"{id_col}\" " \
+                              "FROM \"{table}\" As j " \
+                              "WHERE %(attr_where_j)s " \
+                              "ORDER BY j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \
+                              "LIMIT {num_ngbrs} OFFSET 1 ) " \
+                ") As neighbors " \
+            "FROM \"{table}\" 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 of 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 \"{table}\" As j " \
+                 "WHERE ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \
+                 "%(attr_where_j)s)" \
+                ") As neighbors " \
+            "FROM \"{table}\" 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_query(w_type, query_vals):
+    """Return requested query.
+        :param w_type: type of neighbors to calculate (knn or queen)
+        :param query_vals: values used to construct the query
+    """
+
+    if w_type == 'knn':
+        return knn(query_vals)
+    else:
+        return queen(query_vals)
+
+def get_attributes(query_res, attr_num):
+    """
+        :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)
+
+## Build weight object
+def get_weight(query_res, w_type='queen', num_ngbrs=5):
+    """
+        Construct PySAL weight from return value of query
+        :param query_res: query results with attributes and neighbors
+    """
+    if w_type == 'knn':
+        row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
+        weights = {x['id']: row_normed_weights for x in query_res}
+    elif w_type == 'queen':
+        weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
+                            if len(x['neighbors']) > 0
+                            else [] for x in query_res}
+
+    neighbors = {x['id']: x['neighbors'] for x in query_res}
+
+    return ps.W(neighbors, weights)
+
+def quad_position(quads):
+    """
+        Produce Moran's I classification based of n
+    """
+
+    lisa_sig = np.array([map_quads(q) for q in quads])
+
+    return lisa_sig
+
+def lisa_sig_vals(pvals, quads, threshold):
+    """
+        Produce Moran's I classification based of n
+    """
+
+    sig = (pvals <= threshold)
+
+    lisa_sig = np.empty(len(sig), np.chararray)
+
+    for idx, val in enumerate(sig):
+        if val:
+            lisa_sig[idx] = map_quads(quads[idx])
+        else:
+            lisa_sig[idx] = 'Not significant'
+
+    return lisa_sig

release/python/0.0.1/crankshaft/crankshaft/random_seeds.py

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

release/python/0.0.1/crankshaft/setup.py

@@ -0,0 +1,48 @@
+
+"""
+CartoDB Spatial Analysis Python Library
+See:
+https://github.com/CartoDB/crankshaft
+"""
+
+from setuptools import setup, find_packages
+
+setup(
+    name='crankshaft',
+
+    version='0.0.01',
+
+    description='CartoDB Spatial Analysis Python Library',
+
+    url='https://github.com/CartoDB/crankshaft',
+
+    author='Data Services Team - CartoDB',
+    author_email='dataservices@cartodb.com',
+
+    license='MIT',
+
+    classifiers=[
+        'Development Status :: 3 - Alpha',
+        'Intended Audience :: Mapping comunity',
+        'Topic :: Maps :: Mapping Tools',
+        'License :: OSI Approved :: MIT License',
+        'Programming Language :: Python :: 2.7',
+    ],
+
+    keywords='maps mapping tools spatial analysis geostatistics',
+
+    packages=find_packages(exclude=['contrib', 'docs', 'tests']),
+
+    extras_require={
+        'dev': ['unittest'],
+        'test': ['unittest', 'nose', 'mock'],
+    },
+
+    # The choice of component versions is dictated by what's
+    # provisioned in the production servers.
+    install_requires=['pysal==1.11.0','numpy==1.6.1','scipy==0.17.0'],
+
+    requires=['pysal', 'numpy'],
+
+    test_suite='test'
+)

release/python/0.0.1/crankshaft/test/fixtures/moran.json

@@ -0,0 +1,52 @@
+[[0.9319096128346788, "HH"],
+[-1.135787401862846, "HL"],
+[0.11732030672508517, "Not significant"],
+[0.6152779669180425, "Not significant"],
+[-0.14657336660125297, "Not significant"],
+[0.6967858120189607, "Not significant"],
+[0.07949310115714454, "Not significant"],
+[0.4703198759258987, "Not significant"],
+[0.4421125200498064, "Not significant"],
+[0.5724288737143592, "Not significant"],
+[0.8970743435692062, "LL"],
+[0.18327334401918674, "Not significant"],
+[-0.01466729201304962, "Not significant"],
+[0.3481559372544409, "Not significant"],
+[0.06547094736902978, "Not significant"],
+[0.15482141569329988, "HH"],
+[0.4373841193538136, "Not significant"],
+[0.15971286468915544, "Not significant"],
+[1.0543588860308968, "Not significant"],
+[1.7372866900020818, "HH"],
+[1.091998586053999, "LL"],
+[0.1171572584252222, "Not significant"],
+[0.08438455015300014, "Not significant"],
+[0.06547094736902978, "Not significant"],
+[0.15482141569329985, "HH"],
+[1.1627044812890683, "HH"],
+[0.06547094736902978, "Not significant"],
+[0.795275137550483, "Not significant"],
+[0.18562939195219, "LL"],
+[0.3010757406693439, "Not significant"],
+[2.8205795942839376, "HH"],
+[0.11259190602909264, "Not significant"],
+[-0.07116352791516614, "Not significant"],
+[-0.09945240794119009, "Not significant"],
+[0.18562939195219, "LL"],
+[0.1832733440191868, "Not significant"],
+[-0.39054253768447705, "Not significant"],
+[-0.1672071289487642, "HL"],
+[0.3337669247916343, "Not significant"],
+[0.2584386102554792, "Not significant"],
+[-0.19733845476322634, "HL"],
+[-0.9379282899805409, "LH"],
+[-0.028770969951095866, "Not significant"],
+[0.051367269430983485, "Not significant"],
+[-0.2172548045913472, "LH"],
+[0.05136726943098351, "Not significant"],
+[0.04191046803899837, "Not significant"],
+[0.7482357030403517, "HH"],
+[-0.014585767863118111, "Not significant"],
+[0.5410013139159929, "Not significant"],
+[1.0223932668429925, "LL"],
+[1.4179402898927476, "LL"]]

release/python/0.0.1/crankshaft/test/fixtures/neighbors.json

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

release/python/0.0.1/crankshaft/test/helper.py

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

release/python/0.0.1/crankshaft/test/mock_plpy.py

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

release/python/0.0.1/crankshaft/test/test_clustering_moran.py

@@ -0,0 +1,144 @@
+import unittest
+import numpy as np
+
+import unittest
+
+
+# from mock_plpy import MockPlPy
+# plpy = MockPlPy()
+#
+# import sys
+# sys.modules['plpy'] = plpy
+from helper import plpy, fixture_file
+
+import crankshaft.clustering as cc
+from crankshaft import random_seeds
+import json
+
+class MoranTest(unittest.TestCase):
+    """Testing class for Moran's I functions."""
+
+    def setUp(self):
+        plpy._reset()
+        self.params = {"id_col": "cartodb_id",
+                       "attr1": "andy",
+                       "attr2": "jay_z",
+                       "table": "a_list",
+                       "geom_col": "the_geom",
+                       "num_ngbrs": 321}
+        self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read())
+        self.moran_data = json.loads(open(fixture_file('moran.json')).read())
+
+    def test_map_quads(self):
+        """Test map_quads."""
+        self.assertEqual(cc.map_quads(1), 'HH')
+        self.assertEqual(cc.map_quads(2), 'LH')
+        self.assertEqual(cc.map_quads(3), 'LL')
+        self.assertEqual(cc.map_quads(4), 'HL')
+        self.assertEqual(cc.map_quads(33), None)
+        self.assertEqual(cc.map_quads('andy'), None)
+
+    def test_query_attr_select(self):
+        """Test query_attr_select."""
+
+        ans = "i.\"{attr1}\"::numeric As attr1, " \
+              "i.\"{attr2}\"::numeric As attr2, "
+
+        self.assertEqual(cc.query_attr_select(self.params), ans)
+
+    def test_query_attr_where(self):
+        """Test query_attr_where."""
+
+        ans = "idx_replace.\"{attr1}\" IS NOT NULL AND "\
+              "idx_replace.\"{attr2}\" IS NOT NULL AND "\
+              "idx_replace.\"{attr2}\" <> 0"
+
+        self.assertEqual(cc.query_attr_where(self.params), ans)
+
+    def test_knn(self):
+        """Test knn function."""
+
+        ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
+              "i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+              "FROM \"a_list\" As j WHERE j.\"andy\" IS NOT NULL AND " \
+              "j.\"jay_z\" IS NOT NULL AND j.\"jay_z\" <> 0 ORDER BY " \
+              "j.\"the_geom\" <-> i.\"the_geom\" ASC LIMIT 321 OFFSET 1 ) ) " \
+              "As neighbors FROM \"a_list\" As i WHERE i.\"andy\" IS NOT " \
+              "NULL AND i.\"jay_z\" IS NOT NULL AND i.\"jay_z\" <> 0 ORDER " \
+              "BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(cc.knn(self.params), ans)
+
+    def test_queen(self):
+        """Test queen neighbors function."""
+
+        ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
+              "i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT " \
+              "j.\"cartodb_id\" FROM \"a_list\" As j WHERE ST_Touches(" \
+              "i.\"the_geom\", j.\"the_geom\") AND j.\"andy\" IS NOT NULL " \
+              "AND j.\"jay_z\" IS NOT NULL AND j.\"jay_z\" <> 0)) As " \
+              "neighbors FROM \"a_list\" As i WHERE i.\"andy\" IS NOT NULL " \
+              "AND i.\"jay_z\" IS NOT NULL AND i.\"jay_z\" <> 0 ORDER BY " \
+              "i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(cc.queen(self.params), ans)
+
+    def test_get_query(self):
+        """Test get_query."""
+
+        ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
+              "i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT " \
+              "j.\"cartodb_id\" FROM \"a_list\" As j WHERE j.\"andy\" IS " \
+              "NOT NULL AND j.\"jay_z\" IS NOT NULL AND j.\"jay_z\" <> 0 " \
+              "ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC LIMIT 321 " \
+              "OFFSET 1 ) ) As neighbors FROM \"a_list\" As i WHERE " \
+              "i.\"andy\" IS NOT NULL AND i.\"jay_z\" IS NOT NULL AND " \
+              "i.\"jay_z\" <> 0 ORDER BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(cc.get_query('knn', self.params), ans)
+
+    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_quad_position(self):
+        """Test lisa_sig_vals."""
+
+        quads = np.array([1, 2, 3, 4], np.int)
+
+        ans = np.array(['HH', 'LH', 'LL', 'HL'])
+        test_ans = cc.quad_position(quads)
+
+        self.assertTrue((test_ans == ans).all())
+
+    def test_moran_local(self):
+        """Test Moran's I local"""
+        data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
+        plpy._define_result('select', data)
+        random_seeds.set_random_seeds(1234)
+        result = cc.moran_local('table', 'value', 0.05, 5, 99, 'the_geom', 'cartodb_id', 'knn')
+        result = [(row[0], row[1]) for row in result]
+        expected = self.moran_data
+        for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
+            self.assertAlmostEqual(res_val, exp_val)
+            self.assertEqual(res_quad, exp_quad)
+
+    def test_moran_local_rate(self):
+        """Test Moran's I rate"""
+        data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data]
+        plpy._define_result('select', data)
+        random_seeds.set_random_seeds(1234)
+        result = cc.moran_local_rate('table', 'numerator', 'denominator', 0.05, 5, 99, 'the_geom', 'cartodb_id', 'knn')
+        result = [(row[0], row[1]) for row in result]
+        expected = self.moran_data
+        for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
+            self.assertAlmostEqual(res_val, exp_val)

release/python/0.0.2/crankshaft/crankshaft/__init__.py

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

release/python/0.0.2/crankshaft/crankshaft/clustering/__init__.py

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

release/python/0.0.2/crankshaft/crankshaft/clustering/moran.py

@@ -0,0 +1,321 @@
+"""
+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 numpy as np
+import pysal as ps
+import plpy
+
+# High level interface ---------------------------------------
+
+def moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
+    """
+    Moran's I implementation for PL/Python
+    Andy Eschbacher
+    """
+    # TODO: ensure that the significance output can be smaller that 1e-3 (0.001)
+    # TODO: make a wishlist of output features (zscores, pvalues, raw local lisa, what else?)
+
+    plpy.notice('** Constructing query')
+
+    # geometries with attributes that are null are ignored
+    # resulting in a collection of not as near neighbors
+
+    qvals = {"id_col": id_col,
+            "attr1": attr,
+            "geom_col": geom_column,
+             "table": t,
+             "num_ngbrs": num_ngbrs}
+
+    q = get_query(w_type, qvals)
+
+    try:
+        r = plpy.execute(q)
+        plpy.notice('** Query returned with %d rows' % len(r))
+    except plpy.SPIError:
+        plpy.notice('** Query failed: "%s"' % q)
+        plpy.notice('** Exiting function')
+        return zip([None], [None], [None], [None])
+
+    y = get_attributes(r, 1)
+    w = get_weight(r, w_type)
+
+    # calculate LISA values
+    lisa = ps.Moran_Local(y, w)
+
+    # find units of significance
+    lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
+
+    plpy.notice('** Finished calculations')
+
+    return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order)
+
+
+def moran_local_rate(t, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
+    """
+    Moran's I Local Rate
+    Andy Eschbacher
+    """
+
+    plpy.notice('** Constructing query')
+
+    # geometries with attributes that are null are ignored
+    # resulting in a collection of not as near neighbors
+
+    qvals = {"id_col": id_col,
+             "numerator": numerator,
+             "denominator": denominator,
+             "geom_col": geom_column,
+             "table": t,
+             "num_ngbrs": num_ngbrs}
+
+    q = get_query(w_type, qvals)
+
+    try:
+        r = plpy.execute(q)
+        plpy.notice('** Query returned with %d rows' % len(r))
+    except plpy.SPIError:
+        plpy.notice('** Query failed: "%s"' % q)
+        plpy.notice('** Error: %s' % plpy.SPIError)
+        plpy.notice('** Exiting function')
+        return zip([None], [None], [None], [None])
+
+        plpy.notice('r.nrows() = %d' % r.nrows())
+
+    ## collect attributes
+    numer = get_attributes(r, 1)
+    denom = get_attributes(r, 2)
+
+    w = get_weight(r, w_type, num_ngbrs)
+
+    # calculate LISA values
+    lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, w, permutations=permutations)
+
+    # find units of significance
+    lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
+
+    plpy.notice('** Finished calculations')
+
+    ## TODO: Decide on which return values here
+    return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order, lisa.y)
+
+def moran_local_bv(t, attr1, attr2, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
+    plpy.notice('** Constructing query')
+
+    qvals = {"num_ngbrs": num_ngbrs,
+             "attr1": attr1,
+             "attr2": attr2,
+             "table": t,
+             "geom_col": geom_column,
+             "id_col": id_col}
+
+    q = get_query(w_type, qvals)
+
+    try:
+        r = plpy.execute(q)
+        plpy.notice('** Query returned with %d rows' % len(r))
+    except plpy.SPIError:
+        plpy.notice('** Query failed: "%s"' % q)
+        plpy.notice('** Error: %s' % plpy.SPIError)
+        plpy.notice('** Exiting function')
+        return zip([None], [None], [None], [None])
+
+    ## collect attributes
+    attr1_vals = get_attributes(r, 1)
+    attr2_vals = get_attributes(r, 2)
+
+    # create weights
+    w = get_weight(r, w_type, num_ngbrs)
+
+    # calculate LISA values
+    lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, w)
+
+    plpy.notice("len of Is: %d" % len(lisa.Is))
+
+    # find clustering of significance
+    lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
+
+    plpy.notice('** Finished calculations')
+
+    return zip(lisa.Is, lisa_sig, lisa.p_sim, w.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
+    """
+    if coord == 1:
+        return 'HH'
+    elif coord == 2:
+        return 'LH'
+    elif coord == 3:
+        return 'LL'
+    elif coord == 4:
+        return 'HL'
+    else:
+        return None
+
+def query_attr_select(params):
+    """
+        Create portion of SELECT statement for attributes inolved in query.
+        :param params: dict of information used in query (column names,
+                       table name, etc.)
+    """
+
+    attrs = [k for k in params
+             if k not in ('id_col', 'geom_col', 'table', 'num_ngbrs')]
+
+    template = "i.\"{%(col)s}\"::numeric As attr%(alias_num)s, "
+
+    attr_string = ""
+
+    for idx, val in enumerate(sorted(attrs)):
+        attr_string += template % {"col": val, "alias_num": idx + 1}
+
+    return attr_string
+
+def query_attr_where(params):
+    """
+        Create portion of WHERE clauses for weeding out NULL-valued geometries
+    """
+    attrs = sorted([k for k in params
+                    if k not in ('id_col', 'geom_col', 'table', 'num_ngbrs')])
+
+    attr_string = []
+
+    for attr in attrs:
+        attr_string.append("idx_replace.\"{%s}\" IS NOT NULL" % attr)
+
+    if len(attrs) == 2:
+        attr_string.append("idx_replace.\"{%s}\" <> 0" % attrs[1])
+
+    out = " AND ".join(attr_string)
+
+    return out
+
+def knn(params):
+    """SQL query for k-nearest neighbors.
+        :param vars: dict of values to fill template
+    """
+
+    attr_select = query_attr_select(params)
+    attr_where = query_attr_where(params)
+
+    replacements = {"attr_select": attr_select,
+                    "attr_where_i": attr_where.replace("idx_replace", "i"),
+                    "attr_where_j": attr_where.replace("idx_replace", "j")}
+
+    query = "SELECT " \
+                "i.\"{id_col}\" As id, " \
+                "%(attr_select)s" \
+                "(SELECT ARRAY(SELECT j.\"{id_col}\" " \
+                              "FROM \"{table}\" As j " \
+                              "WHERE %(attr_where_j)s " \
+                              "ORDER BY j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \
+                              "LIMIT {num_ngbrs} OFFSET 1 ) " \
+                ") As neighbors " \
+            "FROM \"{table}\" 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 of 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 \"{table}\" As j " \
+                 "WHERE ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \
+                 "%(attr_where_j)s)" \
+                ") As neighbors " \
+            "FROM \"{table}\" 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_query(w_type, query_vals):
+    """Return requested query.
+        :param w_type: type of neighbors to calculate (knn or queen)
+        :param query_vals: values used to construct the query
+    """
+
+    if w_type == 'knn':
+        return knn(query_vals)
+    else:
+        return queen(query_vals)
+
+def get_attributes(query_res, attr_num):
+    """
+        :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)
+
+## Build weight object
+def get_weight(query_res, w_type='queen', num_ngbrs=5):
+    """
+        Construct PySAL weight from return value of query
+        :param query_res: query results with attributes and neighbors
+    """
+    if w_type == 'knn':
+        row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
+        weights = {x['id']: row_normed_weights for x in query_res}
+    elif w_type == 'queen':
+        weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
+                            if len(x['neighbors']) > 0
+                            else [] for x in query_res}
+
+    neighbors = {x['id']: x['neighbors'] for x in query_res}
+
+    return ps.W(neighbors, weights)
+
+def quad_position(quads):
+    """
+        Produce Moran's I classification based of n
+    """
+
+    lisa_sig = np.array([map_quads(q) for q in quads])
+
+    return lisa_sig
+
+def lisa_sig_vals(pvals, quads, threshold):
+    """
+        Produce Moran's I classification based of n
+    """
+
+    sig = (pvals <= threshold)
+
+    lisa_sig = np.empty(len(sig), np.chararray)
+
+    for idx, val in enumerate(sig):
+        if val:
+            lisa_sig[idx] = map_quads(quads[idx])
+        else:
+            lisa_sig[idx] = 'Not significant'
+
+    return lisa_sig

release/python/0.0.2/crankshaft/crankshaft/random_seeds.py

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

release/python/0.0.2/crankshaft/setup.py

@@ -0,0 +1,48 @@
+
+"""
+CartoDB Spatial Analysis Python Library
+See:
+https://github.com/CartoDB/crankshaft
+"""
+
+from setuptools import setup, find_packages
+
+setup(
+    name='crankshaft',
+
+    version='0.0.2',
+
+    description='CartoDB Spatial Analysis Python Library',
+
+    url='https://github.com/CartoDB/crankshaft',
+
+    author='Data Services Team - CartoDB',
+    author_email='dataservices@cartodb.com',
+
+    license='MIT',
+
+    classifiers=[
+        'Development Status :: 3 - Alpha',
+        'Intended Audience :: Mapping comunity',
+        'Topic :: Maps :: Mapping Tools',
+        'License :: OSI Approved :: MIT License',
+        'Programming Language :: Python :: 2.7',
+    ],
+
+    keywords='maps mapping tools spatial analysis geostatistics',
+
+    packages=find_packages(exclude=['contrib', 'docs', 'tests']),
+
+    extras_require={
+        'dev': ['unittest'],
+        'test': ['unittest', 'nose', 'mock'],
+    },
+
+    # The choice of component versions is dictated by what's
+    # provisioned in the production servers.
+    install_requires=['pysal==1.9.1'],
+
+    requires=['pysal', 'numpy' ],
+
+    test_suite='test'
+)

release/python/0.0.2/crankshaft/test/fixtures/moran.json

@@ -0,0 +1,52 @@
+[[0.9319096128346788, "HH"],
+[-1.135787401862846, "HL"],
+[0.11732030672508517, "Not significant"],
+[0.6152779669180425, "Not significant"],
+[-0.14657336660125297, "Not significant"],
+[0.6967858120189607, "Not significant"],
+[0.07949310115714454, "Not significant"],
+[0.4703198759258987, "Not significant"],
+[0.4421125200498064, "Not significant"],
+[0.5724288737143592, "Not significant"],
+[0.8970743435692062, "LL"],
+[0.18327334401918674, "Not significant"],
+[-0.01466729201304962, "Not significant"],
+[0.3481559372544409, "Not significant"],
+[0.06547094736902978, "Not significant"],
+[0.15482141569329988, "HH"],
+[0.4373841193538136, "Not significant"],
+[0.15971286468915544, "Not significant"],
+[1.0543588860308968, "Not significant"],
+[1.7372866900020818, "HH"],
+[1.091998586053999, "LL"],
+[0.1171572584252222, "Not significant"],
+[0.08438455015300014, "Not significant"],
+[0.06547094736902978, "Not significant"],
+[0.15482141569329985, "HH"],
+[1.1627044812890683, "HH"],
+[0.06547094736902978, "Not significant"],
+[0.795275137550483, "Not significant"],
+[0.18562939195219, "LL"],
+[0.3010757406693439, "Not significant"],
+[2.8205795942839376, "HH"],
+[0.11259190602909264, "Not significant"],
+[-0.07116352791516614, "Not significant"],
+[-0.09945240794119009, "Not significant"],
+[0.18562939195219, "LL"],
+[0.1832733440191868, "Not significant"],
+[-0.39054253768447705, "Not significant"],
+[-0.1672071289487642, "HL"],
+[0.3337669247916343, "Not significant"],
+[0.2584386102554792, "Not significant"],
+[-0.19733845476322634, "HL"],
+[-0.9379282899805409, "LH"],
+[-0.028770969951095866, "Not significant"],
+[0.051367269430983485, "Not significant"],
+[-0.2172548045913472, "LH"],
+[0.05136726943098351, "Not significant"],
+[0.04191046803899837, "Not significant"],
+[0.7482357030403517, "HH"],
+[-0.014585767863118111, "Not significant"],
+[0.5410013139159929, "Not significant"],
+[1.0223932668429925, "LL"],
+[1.4179402898927476, "LL"]]

release/python/0.0.2/crankshaft/test/fixtures/neighbors.json

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

release/python/0.0.2/crankshaft/test/helper.py

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

release/python/0.0.2/crankshaft/test/mock_plpy.py

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

release/python/0.0.2/crankshaft/test/test_clustering_moran.py

@@ -0,0 +1,144 @@
+import unittest
+import numpy as np
+
+import unittest
+
+
+# from mock_plpy import MockPlPy
+# plpy = MockPlPy()
+#
+# import sys
+# sys.modules['plpy'] = plpy
+from helper import plpy, fixture_file
+
+import crankshaft.clustering as cc
+from crankshaft import random_seeds
+import json
+
+class MoranTest(unittest.TestCase):
+    """Testing class for Moran's I functions."""
+
+    def setUp(self):
+        plpy._reset()
+        self.params = {"id_col": "cartodb_id",
+                       "attr1": "andy",
+                       "attr2": "jay_z",
+                       "table": "a_list",
+                       "geom_col": "the_geom",
+                       "num_ngbrs": 321}
+        self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read())
+        self.moran_data = json.loads(open(fixture_file('moran.json')).read())
+
+    def test_map_quads(self):
+        """Test map_quads."""
+        self.assertEqual(cc.map_quads(1), 'HH')
+        self.assertEqual(cc.map_quads(2), 'LH')
+        self.assertEqual(cc.map_quads(3), 'LL')
+        self.assertEqual(cc.map_quads(4), 'HL')
+        self.assertEqual(cc.map_quads(33), None)
+        self.assertEqual(cc.map_quads('andy'), None)
+
+    def test_query_attr_select(self):
+        """Test query_attr_select."""
+
+        ans = "i.\"{attr1}\"::numeric As attr1, " \
+              "i.\"{attr2}\"::numeric As attr2, "
+
+        self.assertEqual(cc.query_attr_select(self.params), ans)
+
+    def test_query_attr_where(self):
+        """Test query_attr_where."""
+
+        ans = "idx_replace.\"{attr1}\" IS NOT NULL AND "\
+              "idx_replace.\"{attr2}\" IS NOT NULL AND "\
+              "idx_replace.\"{attr2}\" <> 0"
+
+        self.assertEqual(cc.query_attr_where(self.params), ans)
+
+    def test_knn(self):
+        """Test knn function."""
+
+        ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
+              "i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+              "FROM \"a_list\" As j WHERE j.\"andy\" IS NOT NULL AND " \
+              "j.\"jay_z\" IS NOT NULL AND j.\"jay_z\" <> 0 ORDER BY " \
+              "j.\"the_geom\" <-> i.\"the_geom\" ASC LIMIT 321 OFFSET 1 ) ) " \
+              "As neighbors FROM \"a_list\" As i WHERE i.\"andy\" IS NOT " \
+              "NULL AND i.\"jay_z\" IS NOT NULL AND i.\"jay_z\" <> 0 ORDER " \
+              "BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(cc.knn(self.params), ans)
+
+    def test_queen(self):
+        """Test queen neighbors function."""
+
+        ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
+              "i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT " \
+              "j.\"cartodb_id\" FROM \"a_list\" As j WHERE ST_Touches(" \
+              "i.\"the_geom\", j.\"the_geom\") AND j.\"andy\" IS NOT NULL " \
+              "AND j.\"jay_z\" IS NOT NULL AND j.\"jay_z\" <> 0)) As " \
+              "neighbors FROM \"a_list\" As i WHERE i.\"andy\" IS NOT NULL " \
+              "AND i.\"jay_z\" IS NOT NULL AND i.\"jay_z\" <> 0 ORDER BY " \
+              "i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(cc.queen(self.params), ans)
+
+    def test_get_query(self):
+        """Test get_query."""
+
+        ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
+              "i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT " \
+              "j.\"cartodb_id\" FROM \"a_list\" As j WHERE j.\"andy\" IS " \
+              "NOT NULL AND j.\"jay_z\" IS NOT NULL AND j.\"jay_z\" <> 0 " \
+              "ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC LIMIT 321 " \
+              "OFFSET 1 ) ) As neighbors FROM \"a_list\" As i WHERE " \
+              "i.\"andy\" IS NOT NULL AND i.\"jay_z\" IS NOT NULL AND " \
+              "i.\"jay_z\" <> 0 ORDER BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(cc.get_query('knn', self.params), ans)
+
+    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_quad_position(self):
+        """Test lisa_sig_vals."""
+
+        quads = np.array([1, 2, 3, 4], np.int)
+
+        ans = np.array(['HH', 'LH', 'LL', 'HL'])
+        test_ans = cc.quad_position(quads)
+
+        self.assertTrue((test_ans == ans).all())
+
+    def test_moran_local(self):
+        """Test Moran's I local"""
+        data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
+        plpy._define_result('select', data)
+        random_seeds.set_random_seeds(1234)
+        result = cc.moran_local('table', 'value', 0.05, 5, 99, 'the_geom', 'cartodb_id', 'knn')
+        result = [(row[0], row[1]) for row in result]
+        expected = self.moran_data
+        for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
+            self.assertAlmostEqual(res_val, exp_val)
+            self.assertEqual(res_quad, exp_quad)
+
+    def test_moran_local_rate(self):
+        """Test Moran's I rate"""
+        data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data]
+        plpy._define_result('select', data)
+        random_seeds.set_random_seeds(1234)
+        result = cc.moran_local_rate('table', 'numerator', 'denominator', 0.05, 5, 99, 'the_geom', 'cartodb_id', 'knn')
+        result = [(row[0], row[1]) for row in result]
+        expected = self.moran_data
+        for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
+            self.assertAlmostEqual(res_val, exp_val)

src/pg/Makefile

@@ -1,13 +1,15 @@
-# Generation of a new development version 'dev' (with an alias 'current' for
-# updating easily by upgrading to 'current', then 'dev')
+include ../../Makefile.global
 
-# sudo make install -- generate the 'dev' version from current source
-#                      and make it available to PostgreSQL
-# PGUSER=postgres make installcheck -- test the 'dev' extension
-
-SED = sed
-
-EXTENSION    = crankshaft
+# Development tasks:
+#
+# * install generates the control & script files into src/pg/
+#   and installs then into the PostgreSQL extensions directory;
+#   requires sudo. In additionof the current development version
+#   named 'dev', an alias 'current' is generating for ease of
+#   update (upgrade to 'current', then to 'dev').
+#   the python module is installed in a virtualenv in envs/dev/
+# * test runs the tests for the currently generated Development
+#   extension.
 
 DATA         = $(EXTENSION)--dev.sql \
 	             $(EXTENSION)--current--dev.sql \
@@ -16,7 +18,7 @@ DATA         = $(EXTENSION)--dev.sql \
 SOURCES_DATA_DIR = sql
 SOURCES_DATA = $(wildcard $(SOURCES_DATA_DIR)/*.sql)
 
-VIRTUALENV_PATH = $(realpath ../py/)
+VIRTUALENV_PATH = $(realpath ../../envs)
 ESC_VIRVIRTUALENV_PATH = $(subst /,\/,$(VIRTUALENV_PATH))
 
 REPLACEMENTS = -e 's/@@VERSION@@/$(EXTVERSION)/g' \
@@ -36,13 +38,23 @@ include $(PGXS)
 # This seems to be needed at least for PG 9.3.11
 all: $(DATA)
 
+test: export PGUSER=postgres
+test: installcheck
 
-# WIP: goals for releasing the extension...
+# Release tasks
 
-EXTVERSION   = $(shell grep default_version $(EXTENSION).control | sed -e "s/default_version[[:space:]]*=[[:space:]]*'\([^']*\)'/\1/")
-
-../release/$(EXTENSION).control: $(EXTENSION).control
+../../release/$(EXTENSION).control: $(EXTENSION).control
 	cp $< $@
 
-release: ../release/$(EXTENSION).control
-	cp $(EXTENSION)--dev.sql $(EXTENSION)--$(EXTVERSION).sql
+# Prepare new release from the currently installed development version,
+# for the current version X.Y.Z (defined in the control file)
+# producing the extension script and control files in releases/
+# and the python package in releases/python/X.Y.Z/crankshaft/
+release: ../../release/$(EXTENSION).control $(SOURCES_DATA)
+	$(SED) $(REPLACEMENTS) $(SOURCES_DATA_DIR)/*.sql > ../../release/$(EXTENSION)--$(EXTVERSION).sql
+
+# Install the current relese into the PostgreSQL extensions directory
+# and the Python package in a virtual environment envs/X.Y.Z
+deploy:
+	$(INSTALL_DATA) ../../release/$(EXTENSION).control '$(DESTDIR)$(datadir)/extension/'
+	$(INSTALL_DATA) ../../release/*.sql '$(DESTDIR)$(datadir)/extension/'

src/pg/README.md

@@ -1,7 +0,0 @@
-
-# Running the tests:
-
-```
-sudo make install
-PGUSER=postgres make installcheck
-```

src/pg/crankshaft.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.0.1'
+default_version = '0.0.2'
 requires = 'plpythonu, postgis, cartodb'
 superuser = true
 schema = cdb_crankshaft

src/pg/sql/01_version.sql

@@ -2,11 +2,11 @@
 CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
 RETURNS text AS $$
   SELECT '@@VERSION@@'::text;
-$$ language 'sql' IMMUTABLE STRICT;
+$$ language 'sql' STABLE STRICT;
 
 -- Internal identifier of the installed extension instence
 -- e.g. 'dev' for current development version
-CREATE OR REPLACE FUNCTION cdb_crankshaft_internal_version()
+CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version()
 RETURNS text AS $$
   SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL;
-$$ language 'sql' IMMUTABLE STRICT;
+$$ language 'sql' STABLE STRICT;

src/pg/sql/02_py.sql

@@ -14,7 +14,7 @@ AS $$
     import os
     # plpy.notice('%',str(os.environ))
     # activate virtualenv
-    crankshaft_version = plpy.execute('SELECT cdb_crankshaft.cdb_crankshaft_internal_version()')[0]['cdb_crankshaft_internal_version']
+    crankshaft_version = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_internal_version()')[0]['_cdb_crankshaft_internal_version']
     base_path = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_virtualenvs_path()')[0]['_cdb_crankshaft_virtualenvs_path']
     default_venv_path = os.path.join(base_path, crankshaft_version)
     venv_path =  os.environ.get('CRANKSHAFT_VENV', default_venv_path)

src/pg/sql/07_gravity.sql

@@ -0,0 +1,115 @@
+CREATE OR REPLACE FUNCTION CDB_Gravity(
+    IN target_query text,
+    IN weight_column text,
+    IN source_query text,
+    IN pop_column text,
+    IN target bigint,
+    IN radius integer,
+    IN minval numeric DEFAULT -10e307
+    )
+RETURNS TABLE(
+    the_geom geometry,
+    source_id bigint,
+    target_id bigint,
+    dist numeric,
+    h numeric,
+    hpop numeric)  AS $$
+DECLARE
+    t_id bigint[];
+    t_geom geometry[];
+    t_weight numeric[];
+    s_id bigint[];
+    s_geom geometry[];
+    s_pop numeric[];
+BEGIN
+    EXECUTE 'WITH foo as('+target_query+') SELECT array_agg(cartodb_id), array_agg(the_geom), array_agg(' || weight_column || ') FROM foo' INTO t_id, t_geom, t_weight;
+    EXECUTE 'WITH foo as('+source_query+') SELECT array_agg(cartodb_id), array_agg(the_geom), array_agg(' || pop_column || ') FROM foo' INTO s_id, s_geom, s_pop;
+    RETURN QUERY
+    SELECT g.* FROM t, s, CDB_Gravity(t_id, t_geom, t_weight, s_id, s_geom, s_pop, target, radius, minval) g;
+END;
+$$ language plpgsql;
+
+CREATE OR REPLACE FUNCTION CDB_Gravity(
+    IN t_id bigint[],
+    IN t_geom geometry[],
+    IN t_weight numeric[],
+    IN s_id bigint[],
+    IN s_geom geometry[],
+    IN s_pop numeric[],
+    IN target bigint,
+    IN radius integer,
+    IN minval numeric DEFAULT -10e307
+    )
+RETURNS TABLE(
+    the_geom geometry,
+    source_id bigint,
+    target_id bigint,
+    dist numeric,
+    h numeric,
+    hpop numeric)  AS $$
+DECLARE
+    t_type text;
+    s_type text;
+    t_center geometry[];
+    s_center geometry[];
+BEGIN
+    t_type := GeometryType(t_geom[1]);
+    s_type := GeometryType(s_geom[1]);
+    IF t_type = 'POINT' THEN
+        t_center := t_geom;
+    ELSE
+        WITH tmp as (SELECT unnest(t_geom) as g) SELECT array_agg(ST_Centroid(g)) INTO t_center FROM tmp;
+    END IF;
+    IF s_type = 'POINT' THEN
+        s_center := s_geom;
+    ELSE
+        WITH tmp as (SELECT unnest(s_geom) as g) SELECT array_agg(ST_Centroid(g)) INTO s_center FROM tmp;
+    END IF;
+    RETURN QUERY
+        with target0 as(
+            SELECT unnest(t_center) as tc, unnest(t_weight) as tw, unnest(t_id) as td
+        ),
+        source0 as(
+            SELECT unnest(s_center) as sc, unnest(s_id) as sd, unnest (s_geom) as sg, unnest(s_pop) as sp
+        ),
+        prev0 as(
+            SELECT
+                source0.sg,
+                source0.sd as sourc_id,
+                coalesce(source0.sp,0) as sp,
+                target.td as targ_id,
+                coalesce(target.tw,0) as tw,
+                GREATEST(1.0,ST_Distance(geography(target.tc), geography(source0.sc)))::numeric as distance
+            FROM source0
+            CROSS JOIN LATERAL
+                (
+                SELECT
+                    *
+                FROM target0
+                    WHERE tw > minval
+                    AND ST_DWithin(geography(source0.sc), geography(tc), radius)
+                ) AS target
+        ),
+        deno as(
+            SELECT
+                sourc_id,
+                sum(tw/distance) as h_deno
+            FROM
+                prev0
+            GROUP BY sourc_id
+        )
+        SELECT
+            p.sg as the_geom,
+            p.sourc_id as source_id,
+            p.targ_id as target_id,
+            case when p.distance > 1 then p.distance else 0.0 end as dist,
+            100*(p.tw/p.distance)/d.h_deno as h,
+            p.sp*(p.tw/p.distance)/d.h_deno as hpop
+        FROM
+            prev0 p,
+            deno d
+        WHERE
+            p.targ_id = target AND
+            p.sourc_id = d.sourc_id;
+END;
+$$ language plpgsql;

src/pg/sql/10_moran.sql

@@ -1,37 +1,233 @@
--- Moran's I
+-- Moran's I Global Measure (public-facing)
 CREATE OR REPLACE FUNCTION
-  cdb_moran_local (
-      t TEXT,
-  	  attr TEXT,
-  	  significance float DEFAULT 0.05,
-  	  num_ngbrs INT DEFAULT 5,
-  	  permutations INT DEFAULT 99,
-  	  geom_column TEXT DEFAULT 'the_geom',
-  	  id_col TEXT DEFAULT 'cartodb_id',
-      w_type TEXT DEFAULT 'knn')
-RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
+  CDB_AreasOfInterestGlobal(
+      subquery TEXT,
+      column_name TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS TABLE (moran NUMERIC, significance NUMERIC)
 AS $$
   plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
   from crankshaft.clustering import moran_local
-  # TODO: use named parameters or a dictionary
-  return moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+  # TODO: use named parameters or a dictionary
+  return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
 $$ LANGUAGE plpythonu;
 
--- Moran's I Local Rate
+-- Moran's I Local (internal function)
 CREATE OR REPLACE FUNCTION
-  cdb_moran_local_rate(t TEXT,
-		 numerator TEXT,
-		 denominator TEXT,
-		 significance FLOAT DEFAULT 0.05,
-		 num_ngbrs INT DEFAULT 5,
-		 permutations INT DEFAULT 99,
-		 geom_column TEXT DEFAULT 'the_geom',
-		 id_col TEXT DEFAULT 'cartodb_id',
-		 w_type TEXT DEFAULT 'knn')
-RETURNS TABLE(moran FLOAT, quads TEXT, significance FLOAT, ids INT, y numeric)
+  _CDB_AreasOfInterestLocal(
+      subquery TEXT,
+      column_name TEXT,
+      w_type TEXT,
+      num_ngbrs INT,
+      permutations INT,
+      geom_col TEXT,
+      id_col TEXT)
+RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
+$$ LANGUAGE plpythonu;
+
+-- Moran's I Local (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_AreasOfInterestLocal(
+    subquery TEXT,
+    column_name TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col);
+
+$$ LANGUAGE SQL;
+
+-- Moran's I only for HH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialHotspots(
+    subquery TEXT,
+    column_name TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HH', 'HL');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I only for LL and LH (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialColdspots(
+    subquery TEXT,
+    attr TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('LL', 'LH');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I only for LH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialOutliers(
+    subquery TEXT,
+    attr TEXT,
+    w_type TEXT DEFAULT 'knn',
+    num_ngbrs INT DEFAULT 5,
+    permutations INT DEFAULT 99,
+    geom_col TEXT DEFAULT 'the_geom',
+    id_col TEXT DEFAULT 'cartodb_id')
+    RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HL', 'LH');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Global Rate (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_AreasOfInterestGlobalRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS TABLE (moran FLOAT, significance FLOAT)
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+$$ LANGUAGE plpythonu;
+
+
+-- Moran's I Local Rate (internal function)
+CREATE OR REPLACE FUNCTION
+  _CDB_AreasOfInterestLocalRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT,
+      num_ngbrs INT,
+      permutations INT,
+      geom_col TEXT,
+      id_col TEXT)
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
 AS $$
   plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
   from crankshaft.clustering import moran_local_rate
-  # TODO: use named parameters or a dictionary
-  return moran_local_rate(t, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
+  # TODO: use named parameters or a dictionary
+  return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
 $$ LANGUAGE plpythonu;
+
+-- Moran's I Local Rate (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_AreasOfInterestLocalRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col);
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Local Rate only for HH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialHotspotsRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HH', 'HL');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Local Rate only for LL and LH (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialColdspotsRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('LL', 'LH');
+
+$$ LANGUAGE SQL;
+
+-- Moran's I Local Rate only for LH and HL (public-facing function)
+CREATE OR REPLACE FUNCTION
+  CDB_GetSpatialOutliersRate(
+      subquery TEXT,
+      numerator TEXT,
+      denominator TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      permutations INT DEFAULT 99,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS
+TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
+AS $$
+
+  SELECT moran, quads, significance, rowid, vals
+  FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
+  WHERE quads IN ('HL', 'LH');
+
+$$ LANGUAGE SQL;

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

@@ -1,260 +1,277 @@
+\pset format unaligned
+\set ECHO all
 \i test/fixtures/ppoints.sql
--- test table (spanish province centroids with some invented values)
-CREATE TABLE ppoints (cartodb_id integer, the_geom geometry, the_geom_webmercator geometry, code text, region_code text, value float);
-INSERT INTO ppoints VALUES
-( 1,'0101000020E6100000A8306DC0CBC305C051D14B6CE56A4540'::geometry,ST_Transform('0101000020E6100000A8306DC0CBC305C051D14B6CE56A4540'::geometry, 3857),'01','16',0.5),
-( 4,'0101000020E6100000E220A4362DC202C0FD8AFA5119994240'::geometry,ST_Transform('0101000020E6100000E220A4362DC202C0FD8AFA5119994240'::geometry, 3857),'04','01',0.1),
-( 5,'0101000020E610000004377E573AC813C0CB5871BB17494440'::geometry,ST_Transform('0101000020E610000004377E573AC813C0CB5871BB17494440'::geometry, 3857),'05','07',0.3),
-( 2,'0101000020E610000000F49BE19BAFFFBF639958FDA6694340'::geometry,ST_Transform('0101000020E610000000F49BE19BAFFFBF639958FDA6694340'::geometry, 3857),'02','08',0.7),
-( 3,'0101000020E61000005D0B7E63C832E2BFDB63EB00443D4340'::geometry,ST_Transform('0101000020E61000005D0B7E63C832E2BFDB63EB00443D4340'::geometry, 3857),'03','10',0.2),
-( 6,'0101000020E61000006F3742B7FB9018C0DD967DC4D95A4340'::geometry,ST_Transform('0101000020E61000006F3742B7FB9018C0DD967DC4D95A4340'::geometry, 3857),'06','11',0.05),
-( 7,'0101000020E6100000E4BB36995F4C0740EAC0E5CA9FC94340'::geometry,ST_Transform('0101000020E6100000E4BB36995F4C0740EAC0E5CA9FC94340'::geometry, 3857),'07','04',0.4),
-( 8,'0101000020E61000003D43CC6CAFBEFF3F6B52E66F91DD4440'::geometry,ST_Transform('0101000020E61000003D43CC6CAFBEFF3F6B52E66F91DD4440'::geometry, 3857),'08','09',0.7),
-( 9,'0101000020E61000003CC797BD99AF0CC0495A87FA312F4540'::geometry,ST_Transform('0101000020E61000003CC797BD99AF0CC0495A87FA312F4540'::geometry, 3857),'09','07',0.5),
-(13,'0101000020E61000001CAA00A9F19F0EC05DF9267B7A764340'::geometry,ST_Transform('0101000020E61000001CAA00A9F19F0EC05DF9267B7A764340'::geometry, 3857),'13','08',0.4),
-(16,'0101000020E6100000D8208F3CBC9001C065638DC1B1F24340'::geometry,ST_Transform('0101000020E6100000D8208F3CBC9001C065638DC1B1F24340'::geometry, 3857),'16','08',0.4),
-(17,'0101000020E6100000E9E6A94A71630540AD7A0CB062104540'::geometry,ST_Transform('0101000020E6100000E9E6A94A71630540AD7A0CB062104540'::geometry, 3857),'17','09',0.6),
-(18,'0101000020E6100000719792D59E240AC098AC548E00A84240'::geometry,ST_Transform('0101000020E6100000719792D59E240AC098AC548E00A84240'::geometry, 3857),'18','01',0.3),
-(19,'0101000020E6100000972C878B50FD04C0123C881D1F684440'::geometry,ST_Transform('0101000020E6100000972C878B50FD04C0123C881D1F684440'::geometry, 3857),'19','08',0.7),
-(21,'0101000020E6100000F7893E9934511BC0EAA4BF03E1C94240'::geometry,ST_Transform('0101000020E6100000F7893E9934511BC0EAA4BF03E1C94240'::geometry, 3857),'21','01',0.1),
-(22,'0101000020E6100000572C2123B2A8B2BF7ED7FABAFD194540'::geometry,ST_Transform('0101000020E6100000572C2123B2A8B2BF7ED7FABAFD194540'::geometry, 3857),'22','02',0.4),
-(25,'0101000020E6100000461B67D688C4F03FD990EEC3A0054540'::geometry,ST_Transform('0101000020E6100000461B67D688C4F03FD990EEC3A0054540'::geometry, 3857),'25','09',0.4),
-(26,'0101000020E6100000A139FB06E82204C0539D84F62E234540'::geometry,ST_Transform('0101000020E6100000A139FB06E82204C0539D84F62E234540'::geometry, 3857),'26','17',0.6),
-(27,'0101000020E6100000A92E54E618C91DC00D3A947B81814540'::geometry,ST_Transform('0101000020E6100000A92E54E618C91DC00D3A947B81814540'::geometry, 3857),'27','12',0.3),
-(28,'0101000020E6100000971DC8B682BC0DC016D0E8055F3F4440'::geometry,ST_Transform('0101000020E6100000971DC8B682BC0DC016D0E8055F3F4440'::geometry, 3857),'28','13',0.8),
-(30,'0101000020E6100000A2DC1964A8C5F7BF19299C994D004340'::geometry,ST_Transform('0101000020E6100000A2DC1964A8C5F7BF19299C994D004340'::geometry, 3857),'30','14',0.1),
-(31,'0101000020E6100000DCA1FCC87B56FABF9B88E9D866554540'::geometry,ST_Transform('0101000020E6100000DCA1FCC87B56FABF9B88E9D866554540'::geometry, 3857),'31','15',0.9),
-(32,'0101000020E6100000E1517AFCD15E1EC0A18D8D4825194540'::geometry,ST_Transform('0101000020E6100000E1517AFCD15E1EC0A18D8D4825194540'::geometry, 3857),'32','12',0.3),
-(33,'0101000020E6100000A7FF33825AF917C0FABE7DFB6BA54540'::geometry,ST_Transform('0101000020E6100000A7FF33825AF917C0FABE7DFB6BA54540'::geometry, 3857),'33','03',0.4),
-(34,'0101000020E6100000FB4E4EBEB72412C0898E7240982F4540'::geometry,ST_Transform('0101000020E6100000FB4E4EBEB72412C0898E7240982F4540'::geometry, 3857),'34','07',0.3),
-(35,'0101000020E6100000224682B01B1A2DC011091656CC5C3C40'::geometry,ST_Transform('0101000020E6100000224682B01B1A2DC011091656CC5C3C40'::geometry, 3857),'35','05',0.3),
-(36,'0101000020E6100000F7C9447110EC20C04C5D4823C7374540'::geometry,ST_Transform('0101000020E6100000F7C9447110EC20C04C5D4823C7374540'::geometry, 3857),'36','12',0.2),
-(37,'0101000020E610000053D6A26DFB4218C09D58FAE209674440'::geometry,ST_Transform('0101000020E610000053D6A26DFB4218C09D58FAE209674440'::geometry, 3857),'37','07',0.5),
-(38,'0101000020E6100000B1D1B5FC910431C03C0C89BA03503C40'::geometry,ST_Transform('0101000020E6100000B1D1B5FC910431C03C0C89BA03503C40'::geometry, 3857),'38','05',0.4),
-(39,'0101000020E610000086E6FEE1BD1E10C00417096748994540'::geometry,ST_Transform('0101000020E610000086E6FEE1BD1E10C00417096748994540'::geometry, 3857),'39','06',0.6),
-(40,'0101000020E6100000FB51C33F733710C038D01729E4954440'::geometry,ST_Transform('0101000020E6100000FB51C33F733710C038D01729E4954440'::geometry, 3857),'40','07',0.5),
-(41,'0101000020E6100000912D6FDA28BB16C031321F08C4B74240'::geometry,ST_Transform('0101000020E6100000912D6FDA28BB16C031321F08C4B74240'::geometry, 3857),'41','01',0.4),
-(42,'0101000020E6100000554432EABEB504C069ECD78775CF4440'::geometry,ST_Transform('0101000020E6100000554432EABEB504C069ECD78775CF4440'::geometry, 3857),'42','07',0.2),
-(43,'0101000020E6100000157F117C1A2EEA3F027CD1F2368B4440'::geometry,ST_Transform('0101000020E6100000157F117C1A2EEA3F027CD1F2368B4440'::geometry, 3857),'43','09',0.3),
-(44,'0101000020E610000051AA5B1BD718EABFEE67613BA4544440'::geometry,ST_Transform('0101000020E610000051AA5B1BD718EABFEE67613BA4544440'::geometry, 3857),'44','02',0.2),
-(45,'0101000020E610000022C5C01BB69710C08563BC1499E54340'::geometry,ST_Transform('0101000020E610000022C5C01BB69710C08563BC1499E54340'::geometry, 3857),'45','08',0.3),
-(46,'0101000020E6100000D5FCF78A11A0E9BFDEA46F8E64AF4340'::geometry,ST_Transform('0101000020E6100000D5FCF78A11A0E9BFDEA46F8E64AF4340'::geometry, 3857),'46','10',0.2),
-(47,'0101000020E61000003AE63525866313C02100050B2BD14440'::geometry,ST_Transform('0101000020E61000003AE63525866313C02100050B2BD14440'::geometry, 3857),'47','07',0.3),
-(48,'0101000020E610000030F187FD1FD206C0C767E1496C9E4540'::geometry,ST_Transform('0101000020E610000030F187FD1FD206C0C767E1496C9E4540'::geometry, 3857),'48','16',0.5),
-(49,'0101000020E61000009C22867B12EC17C006C5F40C14DD4440'::geometry,ST_Transform('0101000020E61000009C22867B12EC17C006C5F40C14DD4440'::geometry, 3857),'49','07',0.2),
-(50,'0101000020E6100000F7D5EFC62D08F1BF69D1231D68CF4440'::geometry,ST_Transform('0101000020E6100000F7D5EFC62D08F1BF69D1231D68CF4440'::geometry, 3857),'50','02',0.6),
-(51,'0101000020E61000005B0E1F8DAA5F15C0530BFE285BF24140'::geometry,ST_Transform('0101000020E61000005B0E1F8DAA5F15C0530BFE285BF24140'::geometry, 3857),'51','18',0.01),
-(10,'0101000020E61000000FD65D82AEA418C06192D1351FDB4340'::geometry,ST_Transform('0101000020E61000000FD65D82AEA418C06192D1351FDB4340'::geometry, 3857),'10','11',0.04),
-(11,'0101000020E6100000B305531DAB0A17C0DEAFCD4EE5464240'::geometry,ST_Transform('0101000020E6100000B305531DAB0A17C0DEAFCD4EE5464240'::geometry, 3857),'11','01',0.08),
-(12,'0101000020E610000059721A7297C9C2BF9EBE383BE51E4440'::geometry,ST_Transform('0101000020E610000059721A7297C9C2BF9EBE383BE51E4440'::geometry, 3857),'12','10',0.2),
-(14,'0101000020E610000000C86313AF3C13C0E530879C10FF4240'::geometry,ST_Transform('0101000020E610000000C86313AF3C13C0E530879C10FF4240'::geometry, 3857),'14','01',0.2),
-(15,'0101000020E61000002A475497B6ED20C06643D4131A904540'::geometry,ST_Transform('0101000020E61000002A475497B6ED20C06643D4131A904540'::geometry, 3857),'15','12',0.3),
-(20,'0101000020E6100000F975566FAD8D01C0E840C33F67924540'::geometry,ST_Transform('0101000020E6100000F975566FAD8D01C0E840C33F67924540'::geometry, 3857),'20','16',0.8),
-(23,'0101000020E610000025FA13E595880BC022BB07131D024340'::geometry,ST_Transform('0101000020E610000025FA13E595880BC022BB07131D024340'::geometry, 3857),'23','01',0.1),
-(24,'0101000020E61000009C5F91C5095C17C0C78784B15A4F4540'::geometry,ST_Transform('0101000020E61000009C5F91C5095C17C0C78784B15A4F4540'::geometry, 3857),'24','07',0.3),
-(29,'0101000020E6100000C34D4A5B48E712C092E680892C684240'::geometry,ST_Transform('0101000020E6100000C34D4A5B48E712C092E680892C684240'::geometry, 3857),'29','01',0.3),
-(52,'0101000020E6100000406A545EB29A07C04E5F0BDA39A54140'::geometry,ST_Transform('0101000020E6100000406A545EB29A07C04E5F0BDA39A54140'::geometry, 3857),'52','19',0.01)
-\i test/fixtures/ppoints2.sql
--- test table (spanish province centroids with some invented values)
-CREATE TABLE ppoints2 (cartodb_id integer, the_geom geometry, code text, region_code text, numerator float, denominator float);
-INSERT INTO ppoints2 VALUES
-( 1,'0101000020E6100000A8306DC0CBC305C051D14B6CE56A4540'::geometry,'01','16',0.5, 1.0),
-( 4,'0101000020E6100000E220A4362DC202C0FD8AFA5119994240'::geometry,'04','01',0.1, 1.0),
-( 5,'0101000020E610000004377E573AC813C0CB5871BB17494440'::geometry,'05','07',0.3, 1.0),
-( 2,'0101000020E610000000F49BE19BAFFFBF639958FDA6694340'::geometry,'02','08',0.7, 1.0),
-( 3,'0101000020E61000005D0B7E63C832E2BFDB63EB00443D4340'::geometry,'03','10',0.2, 1.0),
-( 6,'0101000020E61000006F3742B7FB9018C0DD967DC4D95A4340'::geometry,'06','11',0.05, 1.0),
-( 7,'0101000020E6100000E4BB36995F4C0740EAC0E5CA9FC94340'::geometry,'07','04',0.4, 1.0),
-( 8,'0101000020E61000003D43CC6CAFBEFF3F6B52E66F91DD4440'::geometry,'08','09',0.7, 1.0),
-( 9,'0101000020E61000003CC797BD99AF0CC0495A87FA312F4540'::geometry,'09','07',0.5, 1.0),
-(13,'0101000020E61000001CAA00A9F19F0EC05DF9267B7A764340'::geometry,'13','08',0.4, 1.0),
-(16,'0101000020E6100000D8208F3CBC9001C065638DC1B1F24340'::geometry,'16','08',0.4, 1.0),
-(17,'0101000020E6100000E9E6A94A71630540AD7A0CB062104540'::geometry,'17','09',0.6, 1.0),
-(18,'0101000020E6100000719792D59E240AC098AC548E00A84240'::geometry,'18','01',0.3, 1.0),
-(19,'0101000020E6100000972C878B50FD04C0123C881D1F684440'::geometry,'19','08',0.7, 1.0),
-(21,'0101000020E6100000F7893E9934511BC0EAA4BF03E1C94240'::geometry,'21','01',0.1, 1.0),
-(22,'0101000020E6100000572C2123B2A8B2BF7ED7FABAFD194540'::geometry,'22','02',0.4, 1.0),
-(25,'0101000020E6100000461B67D688C4F03FD990EEC3A0054540'::geometry,'25','09',0.4, 1.0),
-(26,'0101000020E6100000A139FB06E82204C0539D84F62E234540'::geometry,'26','17',0.6, 1.0),
-(27,'0101000020E6100000A92E54E618C91DC00D3A947B81814540'::geometry,'27','12',0.3, 1.0),
-(28,'0101000020E6100000971DC8B682BC0DC016D0E8055F3F4440'::geometry,'28','13',0.8, 1.0),
-(30,'0101000020E6100000A2DC1964A8C5F7BF19299C994D004340'::geometry,'30','14',0.1, 1.0),
-(31,'0101000020E6100000DCA1FCC87B56FABF9B88E9D866554540'::geometry,'31','15',0.9, 1.0),
-(32,'0101000020E6100000E1517AFCD15E1EC0A18D8D4825194540'::geometry,'32','12',0.3, 1.0),
-(33,'0101000020E6100000A7FF33825AF917C0FABE7DFB6BA54540'::geometry,'33','03',0.4, 1.0),
-(34,'0101000020E6100000FB4E4EBEB72412C0898E7240982F4540'::geometry,'34','07',0.3, 1.0),
-(35,'0101000020E6100000224682B01B1A2DC011091656CC5C3C40'::geometry,'35','05',0.3, 1.0),
-(36,'0101000020E6100000F7C9447110EC20C04C5D4823C7374540'::geometry,'36','12',0.2, 1.0),
-(37,'0101000020E610000053D6A26DFB4218C09D58FAE209674440'::geometry,'37','07',0.5, 1.0),
-(38,'0101000020E6100000B1D1B5FC910431C03C0C89BA03503C40'::geometry,'38','05',0.4, 1.0),
-(39,'0101000020E610000086E6FEE1BD1E10C00417096748994540'::geometry,'39','06',0.6, 1.0),
-(40,'0101000020E6100000FB51C33F733710C038D01729E4954440'::geometry,'40','07',0.5, 1.0),
-(41,'0101000020E6100000912D6FDA28BB16C031321F08C4B74240'::geometry,'41','01',0.4, 1.0),
-(42,'0101000020E6100000554432EABEB504C069ECD78775CF4440'::geometry,'42','07',0.2, 1.0),
-(43,'0101000020E6100000157F117C1A2EEA3F027CD1F2368B4440'::geometry,'43','09',0.3, 1.0),
-(44,'0101000020E610000051AA5B1BD718EABFEE67613BA4544440'::geometry,'44','02',0.2, 1.0),
-(45,'0101000020E610000022C5C01BB69710C08563BC1499E54340'::geometry,'45','08',0.3, 1.0),
-(46,'0101000020E6100000D5FCF78A11A0E9BFDEA46F8E64AF4340'::geometry,'46','10',0.2, 1.0),
-(47,'0101000020E61000003AE63525866313C02100050B2BD14440'::geometry,'47','07',0.3, 1.0),
-(48,'0101000020E610000030F187FD1FD206C0C767E1496C9E4540'::geometry,'48','16',0.5, 1.0),
-(49,'0101000020E61000009C22867B12EC17C006C5F40C14DD4440'::geometry,'49','07',0.2, 1.0),
-(50,'0101000020E6100000F7D5EFC62D08F1BF69D1231D68CF4440'::geometry,'50','02',0.6, 1.0),
-(51,'0101000020E61000005B0E1F8DAA5F15C0530BFE285BF24140'::geometry,'51','18',0.01, 1.0),
-(10,'0101000020E61000000FD65D82AEA418C06192D1351FDB4340'::geometry,'10','11',0.04, 1.0),
-(11,'0101000020E6100000B305531DAB0A17C0DEAFCD4EE5464240'::geometry,'11','01',0.08, 1.0),
-(12,'0101000020E610000059721A7297C9C2BF9EBE383BE51E4440'::geometry,'12','10',0.2, 1.0),
-(14,'0101000020E610000000C86313AF3C13C0E530879C10FF4240'::geometry,'14','01',0.2, 1.0),
-(15,'0101000020E61000002A475497B6ED20C06643D4131A904540'::geometry,'15','12',0.3, 1.0),
-(20,'0101000020E6100000F975566FAD8D01C0E840C33F67924540'::geometry,'20','16',0.8, 1.0),
-(23,'0101000020E610000025FA13E595880BC022BB07131D024340'::geometry,'23','01',0.1, 1.0),
-(24,'0101000020E61000009C5F91C5095C17C0C78784B15A4F4540'::geometry,'24','07',0.3, 1.0),
-(29,'0101000020E6100000C34D4A5B48E712C092E680892C684240'::geometry,'29','01',0.3, 1.0),
-(52,'0101000020E6100000406A545EB29A07C04E5F0BDA39A54140'::geometry,'52','19',0.0, 1.01)
--- Moral functions perform some nondeterministic computations
--- (to estimate the significance); we will set the seeds for the RNGs
--- that affect those results to have repeateble results
-SELECT cdb_crankshaft._cdb_random_seeds(1234);
- _cdb_random_seeds 
--------------------
- 
-(1 row)
+SET client_min_messages TO WARNING;
+\set ECHO none
+_cdb_random_seeds
 
-SELECT ppoints.code, m.quads
-  FROM ppoints
-  JOIN cdb_crankshaft.cdb_moran_local('ppoints', 'value') m
-    ON ppoints.cartodb_id = m.ids
-  ORDER BY ppoints.code;
-NOTICE:  ** Constructing query
-CONTEXT:  PL/Python function "cdb_moran_local"
-NOTICE:  ** Query returned with 52 rows
-CONTEXT:  PL/Python function "cdb_moran_local"
-NOTICE:  ** Finished calculations
-CONTEXT:  PL/Python function "cdb_moran_local"
- code |      quads      
-------+-----------------
- 01   | HH
- 02   | HL
- 03   | Not significant
- 04   | Not significant
- 05   | Not significant
- 06   | Not significant
- 07   | Not significant
- 08   | Not significant
- 09   | Not significant
- 10   | Not significant
- 11   | LL
- 12   | Not significant
- 13   | Not significant
- 14   | Not significant
- 15   | Not significant
- 16   | HH
- 17   | Not significant
- 18   | Not significant
- 19   | Not significant
- 20   | HH
- 21   | LL
- 22   | Not significant
- 23   | Not significant
- 24   | Not significant
- 25   | HH
- 26   | HH
- 27   | Not significant
- 28   | Not significant
- 29   | LL
- 30   | Not significant
- 31   | HH
- 32   | Not significant
- 33   | Not significant
- 34   | Not significant
- 35   | LL
- 36   | Not significant
- 37   | Not significant
- 38   | HL
- 39   | Not significant
- 40   | Not significant
- 41   | HL
- 42   | LH
- 43   | Not significant
- 44   | Not significant
- 45   | LH
- 46   | Not significant
- 47   | Not significant
- 48   | HH
- 49   | Not significant
- 50   | Not significant
- 51   | LL
- 52   | LL
+(1 row)
+code|quads
+01|HH
+02|HL
+03|LL
+04|LL
+05|LH
+06|LL
+07|HH
+08|HH
+09|HH
+10|LL
+11|LL
+12|LL
+13|HL
+14|LL
+15|LL
+16|HH
+17|HH
+18|LL
+19|HH
+20|HH
+21|LL
+22|HH
+23|LL
+24|LL
+25|HH
+26|HH
+27|LL
+28|HH
+29|LL
+30|LL
+31|HH
+32|LL
+33|HL
+34|LH
+35|LL
+36|LL
+37|HL
+38|HL
+39|HH
+40|HH
+41|HL
+42|LH
+43|LH
+44|LL
+45|LH
+46|LL
+47|LL
+48|HH
+49|LH
+50|HH
+51|LL
+52|LL
 (52 rows)
+_cdb_random_seeds
 
-SELECT cdb_crankshaft._cdb_random_seeds(1234);
- _cdb_random_seeds 
--------------------
- 
 (1 row)
+code|quads
+01|HH
+02|HL
+07|HH
+08|HH
+09|HH
+13|HL
+16|HH
+17|HH
+19|HH
+20|HH
+22|HH
+25|HH
+26|HH
+28|HH
+31|HH
+33|HL
+37|HL
+38|HL
+39|HH
+40|HH
+41|HL
+48|HH
+50|HH
+(23 rows)
+_cdb_random_seeds
 
-SELECT ppoints2.code, m.quads
-  FROM ppoints2
-  JOIN cdb_crankshaft.cdb_moran_local_rate('ppoints2', 'numerator', 'denominator') m
-    ON ppoints2.cartodb_id = m.ids
-  ORDER BY ppoints2.code;
-NOTICE:  ** Constructing query
-CONTEXT:  PL/Python function "cdb_moran_local_rate"
-NOTICE:  ** Query returned with 51 rows
-CONTEXT:  PL/Python function "cdb_moran_local_rate"
-NOTICE:  ** Finished calculations
-CONTEXT:  PL/Python function "cdb_moran_local_rate"
- code |      quads      
-------+-----------------
- 01   | LL
- 02   | Not significant
- 03   | Not significant
- 04   | Not significant
- 05   | Not significant
- 06   | Not significant
- 07   | Not significant
- 08   | Not significant
- 09   | LL
- 10   | Not significant
- 11   | HH
- 12   | Not significant
- 13   | Not significant
- 14   | Not significant
- 15   | Not significant
- 16   | Not significant
- 17   | LL
- 18   | Not significant
- 19   | Not significant
- 20   | LL
- 21   | Not significant
- 22   | Not significant
- 23   | Not significant
- 24   | Not significant
- 25   | LL
- 26   | LL
- 27   | Not significant
- 28   | Not significant
- 29   | LH
- 30   | Not significant
- 31   | LL
- 32   | Not significant
- 33   | Not significant
- 34   | Not significant
- 35   | LH
- 36   | Not significant
- 37   | Not significant
- 38   | LH
- 39   | Not significant
- 40   | Not significant
- 41   | LH
- 42   | HL
- 43   | Not significant
- 44   | Not significant
- 45   | LL
- 46   | Not significant
- 47   | Not significant
- 48   | LL
- 49   | Not significant
- 50   | Not significant
- 51   | Not significant
+(1 row)
+code|quads
+03|LL
+04|LL
+05|LH
+06|LL
+10|LL
+11|LL
+12|LL
+14|LL
+15|LL
+18|LL
+21|LL
+23|LL
+24|LL
+27|LL
+29|LL
+30|LL
+32|LL
+34|LH
+35|LL
+36|LL
+42|LH
+43|LH
+44|LL
+45|LH
+46|LL
+47|LL
+49|LH
+51|LL
+52|LL
+(29 rows)
+_cdb_random_seeds
+
+(1 row)
+code|quads
+02|HL
+05|LH
+13|HL
+33|HL
+34|LH
+37|HL
+38|HL
+41|HL
+42|LH
+43|LH
+45|LH
+49|LH
+(12 rows)
+_cdb_random_seeds
+
+(1 row)
+code|quads
+01|LL
+02|LH
+03|HH
+04|HH
+05|LL
+06|HH
+07|LL
+08|LL
+09|LL
+10|HH
+11|HH
+12|HL
+13|LL
+14|HH
+15|LL
+16|LL
+17|LL
+18|LH
+19|LL
+20|LL
+21|HH
+22|LL
+23|HL
+24|LL
+25|LL
+26|LL
+27|LL
+28|LL
+29|LH
+30|HH
+31|LL
+32|LL
+33|LL
+34|LL
+35|LH
+36|HL
+37|LH
+38|LH
+39|LL
+40|LL
+41|LH
+42|HL
+43|LL
+44|HL
+45|LL
+46|HL
+47|LL
+48|LL
+49|HL
+50|LL
+51|HH
 (51 rows)
+_cdb_random_seeds
 
+(1 row)
+code|quads
+03|HH
+04|HH
+06|HH
+10|HH
+11|HH
+12|HL
+14|HH
+21|HH
+23|HL
+30|HH
+36|HL
+42|HL
+44|HL
+46|HL
+49|HL
+51|HH
+(16 rows)
+_cdb_random_seeds
+
+(1 row)
+code|quads
+01|LL
+02|LH
+05|LL
+07|LL
+08|LL
+09|LL
+13|LL
+15|LL
+16|LL
+17|LL
+18|LH
+19|LL
+20|LL
+22|LL
+24|LL
+25|LL
+26|LL
+27|LL
+28|LL
+29|LH
+31|LL
+32|LL
+33|LL
+34|LL
+35|LH
+37|LH
+38|LH
+39|LL
+40|LL
+41|LH
+43|LL
+45|LL
+47|LL
+48|LL
+50|LL
+(35 rows)
+_cdb_random_seeds
+
+(1 row)
+code|quads
+02|LH
+12|HL
+18|LH
+23|HL
+29|LH
+35|LH
+36|HL
+37|LH
+38|LH
+41|LH
+42|HL
+44|HL
+46|HL
+49|HL
+(14 rows)

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

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

src/pg/test/expected/07_gravity_test.out

@@ -0,0 +1,11 @@
+                  the_geom                  |            h            |           hpop           |      dist
+--------------------------------------------+-------------------------+--------------------------+----------------
+ 01010000001361C3D32B650140DD24068195B34440 |  1.51078258369747945249 |  12.08626066957983561994 | 4964.714459152
+ 01010000002497FF907EFB0040713D0AD7A3B04440 | 98.29730954183620807430 | 688.08116679285345652007 |   99.955141922
+ 0101000000A167B3EA733501401D5A643BDFAF4440 | 63.70532894711274639196 | 382.23197368267647835174 | 2488.330566505
+ 010100000062A1D634EF380140BE9F1A2FDDB44440 | 35.35415870080995954879 | 176.77079350404979774397 | 4359.370460594
+ 010100000052B81E85EB510140355EBA490CB24440 | 33.12290506987740864904 | 132.49162027950963459615 | 3703.664449828
+ 0101000000C286A757CA320140736891ED7CAF4440 | 65.45251754279248087849 | 196.35755262837744263547 | 2512.092358644
+ 01010000007DD0B359F5390140C976BE9F1AAF4440 | 62.83927792471345639225 | 125.67855584942691278449 |  2926.25725244
+ 0101000000D237691A140D01407E6FD39FFDB44440 | 53.54905726651871279586 |  53.54905726651871279586 | 3744.515577777
+(8 rows)

src/pg/test/fixtures/polyg_values.sql

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

src/pg/test/fixtures/ppoints.sql

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

src/pg/test/fixtures/ppoints2.sql

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

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

@@ -1,21 +1,79 @@
+\pset format unaligned
+\set ECHO all
 \i test/fixtures/ppoints.sql
 \i test/fixtures/ppoints2.sql
 
--- Moral functions perform some nondeterministic computations
+-- Areas of Interest functions perform some nondeterministic computations
 -- (to estimate the significance); we will set the seeds for the RNGs
 -- that affect those results to have repeateble results
 SELECT cdb_crankshaft._cdb_random_seeds(1234);
 
 SELECT ppoints.code, m.quads
   FROM ppoints
-  JOIN cdb_crankshaft.cdb_moran_local('ppoints', 'value') m
-    ON ppoints.cartodb_id = m.ids
+  JOIN cdb_crankshaft.CDB_AreasOfInterestLocal('SELECT * FROM ppoints', 'value') m
+    ON ppoints.cartodb_id = m.rowid
   ORDER BY ppoints.code;
 
 SELECT cdb_crankshaft._cdb_random_seeds(1234);
 
+-- Spatial Hotspots
+SELECT ppoints.code, m.quads
+  FROM ppoints
+  JOIN cdb_crankshaft.CDB_GetSpatialHotspots('SELECT * FROM ppoints', 'value') m
+    ON ppoints.cartodb_id = m.rowid
+  ORDER BY ppoints.code;
+
+SELECT cdb_crankshaft._cdb_random_seeds(1234);
+
+-- Spatial Coldspots
+SELECT ppoints.code, m.quads
+  FROM ppoints
+  JOIN cdb_crankshaft.CDB_GetSpatialColdspots('SELECT * FROM ppoints', 'value') m
+    ON ppoints.cartodb_id = m.rowid
+  ORDER BY ppoints.code;
+
+SELECT cdb_crankshaft._cdb_random_seeds(1234);
+
+  -- Spatial Outliers
+SELECT ppoints.code, m.quads
+  FROM ppoints
+  JOIN cdb_crankshaft.CDB_GetSpatialOutliers('SELECT * FROM ppoints', 'value') m
+    ON ppoints.cartodb_id = m.rowid
+  ORDER BY ppoints.code;
+
+
+SELECT cdb_crankshaft._cdb_random_seeds(1234);
+
+-- Areas of Interest (rate)
 SELECT ppoints2.code, m.quads
   FROM ppoints2
-  JOIN cdb_crankshaft.cdb_moran_local_rate('ppoints2', 'numerator', 'denominator') m
-    ON ppoints2.cartodb_id = m.ids
+  JOIN cdb_crankshaft.CDB_AreasOfInterestLocalRate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
+    ON ppoints2.cartodb_id = m.rowid
+  ORDER BY ppoints2.code;
+
+SELECT cdb_crankshaft._cdb_random_seeds(1234);
+
+-- Spatial Hotspots (rate)
+SELECT ppoints2.code, m.quads
+  FROM ppoints2
+  JOIN cdb_crankshaft.CDB_GetSpatialHotspotsRate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
+    ON ppoints2.cartodb_id = m.rowid
+  ORDER BY ppoints2.code;
+
+SELECT cdb_crankshaft._cdb_random_seeds(1234);
+
+-- Spatial Coldspots (rate)
+SELECT ppoints2.code, m.quads
+  FROM ppoints2
+  JOIN cdb_crankshaft.CDB_GetSpatialColdspotsRate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
+    ON ppoints2.cartodb_id = m.rowid
+  ORDER BY ppoints2.code;
+
+SELECT cdb_crankshaft._cdb_random_seeds(1234);
+
+-- Spatial Outliers (rate)
+SELECT ppoints2.code, m.quads
+  FROM ppoints2
+  JOIN cdb_crankshaft.CDB_GetSpatialOutliersRate('SELECT * FROM ppoints2', 'numerator', 'denominator') m
+    ON ppoints2.cartodb_id = m.rowid
   ORDER BY ppoints2.code;

src/pg/test/sql/07_gravity_test.sql

@@ -0,0 +1,21 @@
+WITH t AS (
+    SELECT
+    ARRAY[1,2,3] AS id,
+    ARRAY[7.0,8.0,3.0] AS w,
+    ARRAY[ST_GeomFromText('POINT(2.1744 41.4036)'),ST_GeomFromText('POINT(2.1228 41.3809)'),ST_GeomFromText('POINT(2.1511 41.3742)')] AS g
+),
+s AS (
+    SELECT
+    ARRAY[10,20,30,40,50,60,70,80] AS id,
+    ARRAY[800, 700, 600, 500, 400, 300, 200, 100] AS p,
+    ARRAY[ST_GeomFromText('POINT(2.1744 41.403)'),ST_GeomFromText('POINT(2.1228 41.380)'),ST_GeomFromText('POINT(2.1511 41.374)'),ST_GeomFromText('POINT(2.1528 41.413)'),ST_GeomFromText('POINT(2.165 41.391)'),ST_GeomFromText('POINT(2.1498 41.371)'),ST_GeomFromText('POINT(2.1533 41.368)'),ST_GeomFromText('POINT(2.131386 41.41399)')] AS g
+)
+SELECT
+    g.the_geom,
+    g.h,
+    g.hpop,
+    g.dist
+FROM
+    t,
+    s,
+    CDB_Gravity(t.id, t.g, t.w, s.id, s.g, s.p, 2, 100000, 3) g;

src/pg/test/sql/90_permissions.sql

@@ -9,7 +9,7 @@ SET search_path TO public,cartodb,cdb_crankshaft;
 -- Exercise public functions
 SELECT ppoints.code, m.quads
   FROM ppoints
-  JOIN cdb_moran_local('ppoints', 'value') m
+  JOIN CDB_AreasOfInterest_Local('ppoints', 'value') m
     ON ppoints.cartodb_id = m.ids
   ORDER BY ppoints.code;
 SELECT round(cdb_overlap_sum(

src/py/.gitignore

@@ -1,2 +0,0 @@
-*.pyc
-dev/

src/py/Makefile

@@ -1,9 +1,22 @@
+include ../../Makefile.global
+
 # Install the package locally for development
 install:
-	virtualenv --system-site-packages dev
-	./dev/bin/pip install -I ./crankshaft
-	./dev/bin/pip install -I nose
+	virtualenv --system-site-packages ../../envs/dev
+	# source ../../envs/dev/bin/activate
+	../../envs/dev/bin/pip install -I ./crankshaft
+	../../envs/dev/bin/pip install -I nose
 
 # Test develpment install
-testinstalled:
-	./dev/bin/nosetests crankshaft/test/
+test:
+	../../envs/dev/bin/nosetests crankshaft/test/
+
+release: ../../release/$(EXTENSION).control $(SOURCES_DATA)
+	mkdir -p ../../release/python/$(EXTVERSION)
+	cp -r ./$(PACKAGE) ../../release/python/$(EXTVERSION)/
+	$(SED) -i -r 's/version='"'"'[0-9]+\.[0-9]+\.[0-9]+'"'"'/version='"'"'$(EXTVERSION)'"'"'/g'  ../../release/python/$(EXTVERSION)/$(PACKAGE)/setup.py
+
+deploy:
+	virtualenv --system-site-packages $(VIRTUALENV_PATH)/$(RELEASE_VERSION)
+	$(VIRTUALENV_PATH)/$(RELEASE_VERSION)/bin/pip install -I -U ../../release/python/$(RELEASE_VERSION)/$(PACKAGE)
+	$(VIRTUALENV_PATH)/$(RELEASE_VERSION)/bin/pip install -I nose

src/py/README.md

@@ -8,7 +8,7 @@ cd crankshaft
 nosetests test/
 ```
 
-## Notes about python dependencies
+## Notes about Python dependencies
 * This extension is targeted at production databases. Therefore certain restrictions must be assumed about the production environment vs other experimental environments.
 * We're using `pip` and `virtualenv` to generate a suitable isolated environment for python code that has  all the dependencies
 * Every dependency should be:
@@ -20,37 +20,29 @@ nosetests test/
 
 ---
 
-We have two possible approaches being considered as to how manage
-the Python virtual environment: using a pure virtual enviroment
-or combine it with some system packages that include depencencies
-for the *hard-to-compile* packages (and pin them in somewhat old versions).
+To avoid troublesome compilations/linkings we will use
+the available system package `python-scipy`.
+This package and its dependencies provide numpy 1.6.1
+and scipy 0.9.0. To be able to use these versions we cannot
+PySAL 1.10 or later, so we'll stick to 1.9.1.
 
-### Alternative A: pure virtual environment
+```
+apt-get install -y python-scipy
+```
 
-In this case we will install all the packages needed in the
-virtual environment.
-This will involve, specially for the numerical packages compiling
-and linking code that uses a number of third party libraries,
-and requires having theses depencencies solved for the production
-environments.
+We'll use virtual environments to install our packages,
+but configued to use also system modules so that the
+mentioned scipy and numpy are used.
 
-#### Create and use a virtual env
-
-We'll use a virtual enviroment directory `dev`
-under the `src/pg` directory.
-
-    # Create the virtual environment for python
-    $ virtualenv dev
+    # Create a virtual environment for python
+    $ virtualenv --system-site-packages dev
 
     # Activate the virtualenv
     $ source dev/bin/activate
 
     # Install all the requirements
     # expect this to take a while, as it will trigger a few compilations
-    (dev) $ pip install -r requirements.txt
-
-    # Add a new pip to the party
-    (dev) $ pip install pandas
+    (dev) $ pip install -I ./crankshaft
 
 #### Test the libraries with that virtual env
 
@@ -94,37 +86,3 @@ Then, execute the tests with:
     import pysal
     import nose
     nose.runmodule('pysal')
-
-
-### Alternative B: using some packaged modules
-
-This option avoids troublesome compilations/linkings, at the cost
-of freezing some module versions as available in system packages,
-namely numpy 1.6.1 and scipy 0.9.0. (in turn, this implies
-the most recent version of PySAL we can use is 1.9.1)
-
-
-TODO: to use this alternative the python-scipy package must be
-installed (this will have to be included in server provisioning)
-
-```
-apt-get install -y python-scipy
-```
-
-#### Create and use a virtual env
-
-We'll use a `dev` enviroment as before, but will configure it to
-use also system modules.
-
-
-    # Create the virtual environment for python
-    $ virtualenv --system-site-packages dev
-
-    # Activate the virtualenv
-    $ source dev/bin/activate
-
-    # Install all the requirements
-    # expect this to take a while, as it will trigger a few compilations
-    (dev) $ pip install -I ./crankshaft
-
-Then we can proceed to testing as in Alternative A.

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

@@ -5,143 +5,226 @@ 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 numpy as np
 import pysal as ps
 import plpy
 
+# crankshaft module
+import crankshaft.pysal_utils as pu
+
 # High level interface ---------------------------------------
 
-def moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
+def moran(subquery, attr_name,
+          w_type, num_ngbrs, permutations, geom_col, id_col):
+    """
+    Moran's I (global)
+    Implementation building neighbors with a PostGIS database and Moran's I
+     core clusters with PySAL.
+    Andy Eschbacher
+    """
+    qvals = {"id_col": id_col,
+             "attr1": attr_name,
+             "geom_col": geom_col,
+             "subquery": subquery,
+             "num_ngbrs": num_ngbrs}
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    plpy.notice('** Query: %s' % query)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(2)
+        plpy.notice('** Query returned with %d rows' % len(result))
+    except plpy.SPIError:
+        plpy.error('Error: areas of interest query failed, check input parameters')
+        plpy.notice('** Query failed: "%s"' % query)
+        plpy.notice('** Error: %s' % plpy.SPIError)
+        return pu.empty_zipped_array(2)
+
+    ## collect attributes
+    attr_vals = pu.get_attributes(result)
+
+    ## calculate weights
+    weight = pu.get_weight(result, w_type, num_ngbrs)
+
+    ## calculate moran global
+    moran_global = ps.esda.moran.Moran(attr_vals, weight,
+                                       permutations=permutations)
+
+    return zip([moran_global.I], [moran_global.EI])
+
+def moran_local(subquery, attr,
+                w_type, num_ngbrs, permutations, geom_col, id_col):
     """
     Moran's I implementation for PL/Python
     Andy Eschbacher
     """
-    # TODO: ensure that the significance output can be smaller that 1e-3 (0.001)
-    # TODO: make a wishlist of output features (zscores, pvalues, raw local lisa, what else?)
-
-    plpy.notice('** Constructing query')
 
     # geometries with attributes that are null are ignored
     # resulting in a collection of not as near neighbors
 
     qvals = {"id_col": id_col,
-            "attr1": attr,
-            "geom_col": geom_column,
-             "table": t,
+             "attr1": attr,
+             "geom_col": geom_col,
+             "subquery": subquery,
              "num_ngbrs": num_ngbrs}
 
-    q = get_query(w_type, qvals)
+    query = pu.construct_neighbor_query(w_type, qvals)
 
     try:
-        r = plpy.execute(q)
-        plpy.notice('** Query returned with %d rows' % len(r))
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(5)
     except plpy.SPIError:
-        plpy.notice('** Query failed: "%s"' % q)
-        plpy.notice('** Exiting function')
-        return zip([None], [None], [None], [None])
+        plpy.error('Error: areas of interest query failed, check input parameters')
+        plpy.notice('** Query failed: "%s"' % query)
+        return pu.empty_zipped_array(5)
 
-    y = get_attributes(r, 1)
-    w = get_weight(r, w_type)
+    attr_vals = pu.get_attributes(result)
+    weight = pu.get_weight(result, w_type, num_ngbrs)
 
     # calculate LISA values
-    lisa = ps.Moran_Local(y, w)
+    lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
+                                     permutations=permutations)
 
-    # find units of significance
-    lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
+    # find quadrants for each geometry
+    quads = quad_position(lisa.q)
 
-    plpy.notice('** Finished calculations')
+    return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
 
-    return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order)
-
-
-def moran_local_rate(t, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
+def moran_rate(subquery, numerator, denominator,
+               w_type, num_ngbrs, permutations, geom_col, id_col):
     """
-    Moran's I Local Rate
+    Moran's I Rate (global)
     Andy Eschbacher
     """
-
-    plpy.notice('** Constructing query')
-
-    # geometries with attributes that are null are ignored
-    # resulting in a collection of not as near neighbors
-
     qvals = {"id_col": id_col,
-             "numerator": numerator,
-             "denominator": denominator,
-             "geom_col": geom_column,
-             "table": t,
+             "attr1": numerator,
+             "attr2": denominator,
+             "geom_col": geom_col,
+             "subquery": subquery,
              "num_ngbrs": num_ngbrs}
 
-    q = get_query(w_type, qvals)
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    plpy.notice('** Query: %s' % query)
 
     try:
-        r = plpy.execute(q)
-        plpy.notice('** Query returned with %d rows' % len(r))
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(2)
+        plpy.notice('** Query returned with %d rows' % len(result))
     except plpy.SPIError:
-        plpy.notice('** Query failed: "%s"' % q)
+        plpy.error('Error: areas of interest query failed, check input parameters')
+        plpy.notice('** Query failed: "%s"' % query)
         plpy.notice('** Error: %s' % plpy.SPIError)
-        plpy.notice('** Exiting function')
-        return zip([None], [None], [None], [None])
-
-        plpy.notice('r.nrows() = %d' % r.nrows())
+        return pu.empty_zipped_array(2)
 
     ## collect attributes
-    numer = get_attributes(r, 1)
-    denom = get_attributes(r, 2)
+    numer = pu.get_attributes(result, 1)
+    denom = pu.get_attributes(result, 2)
 
-    w = get_weight(r, w_type, num_ngbrs)
+    weight = pu.get_weight(result, w_type, num_ngbrs)
+
+    ## calculate moran global rate
+    lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
+                                         permutations=permutations)
+
+    return zip([lisa_rate.I], [lisa_rate.EI])
+
+def moran_local_rate(subquery, numerator, denominator,
+                     w_type, num_ngbrs, permutations, geom_col, id_col):
+    """
+        Moran's I Local Rate
+        Andy Eschbacher
+    """
+    # geometries with values that are null are ignored
+    # resulting in a collection of not as near neighbors
+
+    query = pu.construct_neighbor_query(w_type,
+                                     {"id_col": id_col,
+                                      "numerator": numerator,
+                                      "denominator": denominator,
+                                      "geom_col": geom_col,
+                                      "subquery": subquery,
+                                      "num_ngbrs": num_ngbrs})
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(5)
+    except plpy.SPIError:
+        plpy.error('Error: areas of interest query failed, check input parameters')
+        plpy.notice('** Query failed: "%s"' % query)
+        plpy.notice('** Error: %s' % plpy.SPIError)
+        return pu.empty_zipped_array(5)
+
+    ## collect attributes
+    numer = pu.get_attributes(result, 1)
+    denom = pu.get_attributes(result, 2)
+
+    weight = pu.get_weight(result, w_type, num_ngbrs)
 
     # calculate LISA values
-    lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, w, permutations=permutations)
+    lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
+                                          permutations=permutations)
 
     # find units of significance
-    lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
+    quads = quad_position(lisa.q)
 
-    plpy.notice('** Finished calculations')
+    return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
 
-    ## TODO: Decide on which return values here
-    return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order, lisa.y)
-
-def moran_local_bv(t, attr1, attr2, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
+def moran_local_bv(subquery, attr1, attr2,
+                   permutations, geom_col, id_col, w_type, num_ngbrs):
+    """
+        Moran's I (local) Bivariate (untested)
+    """
     plpy.notice('** Constructing query')
 
     qvals = {"num_ngbrs": num_ngbrs,
              "attr1": attr1,
              "attr2": attr2,
-             "table": t,
-             "geom_col": geom_column,
+             "subquery": subquery,
+             "geom_col": geom_col,
              "id_col": id_col}
 
-    q = get_query(w_type, qvals)
+    query = pu.construct_neighbor_query(w_type, qvals)
 
     try:
-        r = plpy.execute(q)
-        plpy.notice('** Query returned with %d rows' % len(r))
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(4)
     except plpy.SPIError:
-        plpy.notice('** Query failed: "%s"' % q)
-        plpy.notice('** Error: %s' % plpy.SPIError)
-        plpy.notice('** Exiting function')
-        return zip([None], [None], [None], [None])
+        plpy.error("Error: areas of interest query failed, " \
+                   "check input parameters")
+        plpy.notice('** Query failed: "%s"' % query)
+        return pu.empty_zipped_array(4)
 
     ## collect attributes
-    attr1_vals = get_attributes(r, 1)
-    attr2_vals = get_attributes(r, 2)
+    attr1_vals = pu.get_attributes(result, 1)
+    attr2_vals = pu.get_attributes(result, 2)
 
     # create weights
-    w = get_weight(r, w_type, num_ngbrs)
+    weight = pu.get_weight(result, w_type, num_ngbrs)
 
     # calculate LISA values
-    lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, w)
+    lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
+                                        permutations=permutations)
 
     plpy.notice("len of Is: %d" % len(lisa.Is))
 
     # find clustering of significance
-    lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
+    lisa_sig = quad_position(lisa.q)
 
     plpy.notice('** Finished calculations')
 
-    return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order)
-
+    return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
 
 # Low level functions ----------------------------------------
 
@@ -150,7 +233,9 @@ 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
+        @param coord (int): quadrant of a specific measurement
+        Output:
+            classification (one of 'HH', 'LH', 'LL', or 'HL')
     """
     if coord == 1:
         return 'HH'
@@ -163,159 +248,13 @@ def map_quads(coord):
     else:
         return None
 
-def query_attr_select(params):
-    """
-        Create portion of SELECT statement for attributes inolved in query.
-        :param params: dict of information used in query (column names,
-                       table name, etc.)
-    """
-
-    attrs = [k for k in params
-             if k not in ('id_col', 'geom_col', 'table', 'num_ngbrs')]
-
-    template = "i.\"{%(col)s}\"::numeric As attr%(alias_num)s, "
-
-    attr_string = ""
-
-    for idx, val in enumerate(sorted(attrs)):
-        attr_string += template % {"col": val, "alias_num": idx + 1}
-
-    return attr_string
-
-def query_attr_where(params):
-    """
-        Create portion of WHERE clauses for weeding out NULL-valued geometries
-    """
-    attrs = sorted([k for k in params
-                    if k not in ('id_col', 'geom_col', 'table', 'num_ngbrs')])
-
-    attr_string = []
-
-    for attr in attrs:
-        attr_string.append("idx_replace.\"{%s}\" IS NOT NULL" % attr)
-
-    if len(attrs) == 2:
-        attr_string.append("idx_replace.\"{%s}\" <> 0" % attrs[1])
-
-    out = " AND ".join(attr_string)
-
-    return out
-
-def knn(params):
-    """SQL query for k-nearest neighbors.
-        :param vars: dict of values to fill template
-    """
-
-    attr_select = query_attr_select(params)
-    attr_where = query_attr_where(params)
-
-    replacements = {"attr_select": attr_select,
-                    "attr_where_i": attr_where.replace("idx_replace", "i"),
-                    "attr_where_j": attr_where.replace("idx_replace", "j")}
-
-    query = "SELECT " \
-                "i.\"{id_col}\" As id, " \
-                "%(attr_select)s" \
-                "(SELECT ARRAY(SELECT j.\"{id_col}\" " \
-                              "FROM \"{table}\" As j " \
-                              "WHERE %(attr_where_j)s " \
-                              "ORDER BY j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \
-                              "LIMIT {num_ngbrs} OFFSET 1 ) " \
-                ") As neighbors " \
-            "FROM \"{table}\" 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 of 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 \"{table}\" As j " \
-                 "WHERE ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \
-                 "%(attr_where_j)s)" \
-                ") As neighbors " \
-            "FROM \"{table}\" 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_query(w_type, query_vals):
-    """Return requested query.
-        :param w_type: type of neighbors to calculate (knn or queen)
-        :param query_vals: values used to construct the query
-    """
-
-    if w_type == 'knn':
-        return knn(query_vals)
-    else:
-        return queen(query_vals)
-
-def get_attributes(query_res, attr_num):
-    """
-        :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)
-
-## Build weight object
-def get_weight(query_res, w_type='queen', num_ngbrs=5):
-    """
-        Construct PySAL weight from return value of query
-        :param query_res: query results with attributes and neighbors
-    """
-    if w_type == 'knn':
-        row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
-        weights = {x['id']: row_normed_weights for x in query_res}
-    elif w_type == 'queen':
-        weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
-                            if len(x['neighbors']) > 0
-                            else [] for x in query_res}
-
-    neighbors = {x['id']: x['neighbors'] for x in query_res}
-
-    return ps.W(neighbors, weights)
-
 def 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.
     """
-
-    lisa_sig = np.array([map_quads(q) for q in quads])
-
-    return lisa_sig
-
-def lisa_sig_vals(pvals, quads, threshold):
-    """
-        Produce Moran's I classification based of n
-    """
-
-    sig = (pvals <= threshold)
-
-    lisa_sig = np.empty(len(sig), np.chararray)
-
-    for idx, val in enumerate(sig):
-        if val:
-            lisa_sig[idx] = map_quads(quads[idx])
-        else:
-            lisa_sig[idx] = 'Not significant'
-
-    return lisa_sig
+    return [map_quads(q) for q in quads]

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

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

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

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

src/py/crankshaft/setup.py

@@ -10,7 +10,7 @@ from setuptools import setup, find_packages
 setup(
     name='crankshaft',
 
-    version='0.0.1',
+    version='0.0.0',
 
     description='CartoDB Spatial Analysis Python Library',
 

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

@@ -1,52 +1,52 @@
 [[0.9319096128346788, "HH"],
 [-1.135787401862846, "HL"],
-[0.11732030672508517, "Not significant"],
-[0.6152779669180425, "Not significant"],
-[-0.14657336660125297, "Not significant"],
-[0.6967858120189607, "Not significant"],
-[0.07949310115714454, "Not significant"],
-[0.4703198759258987, "Not significant"],
-[0.4421125200498064, "Not significant"],
-[0.5724288737143592, "Not significant"],
+[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, "Not significant"],
-[-0.01466729201304962, "Not significant"],
-[0.3481559372544409, "Not significant"],
-[0.06547094736902978, "Not significant"],
+[0.18327334401918674, "LL"],
+[-0.01466729201304962, "HL"],
+[0.3481559372544409, "LL"],
+[0.06547094736902978, "LL"],
 [0.15482141569329988, "HH"],
-[0.4373841193538136, "Not significant"],
-[0.15971286468915544, "Not significant"],
-[1.0543588860308968, "Not significant"],
+[0.4373841193538136, "HH"],
+[0.15971286468915544, "LL"],
+[1.0543588860308968, "HH"],
 [1.7372866900020818, "HH"],
 [1.091998586053999, "LL"],
-[0.1171572584252222, "Not significant"],
-[0.08438455015300014, "Not significant"],
-[0.06547094736902978, "Not significant"],
+[0.1171572584252222, "HH"],
+[0.08438455015300014, "LL"],
+[0.06547094736902978, "LL"],
 [0.15482141569329985, "HH"],
 [1.1627044812890683, "HH"],
-[0.06547094736902978, "Not significant"],
-[0.795275137550483, "Not significant"],
+[0.06547094736902978, "LL"],
+[0.795275137550483, "HH"],
 [0.18562939195219, "LL"],
-[0.3010757406693439, "Not significant"],
+[0.3010757406693439, "LL"],
 [2.8205795942839376, "HH"],
-[0.11259190602909264, "Not significant"],
-[-0.07116352791516614, "Not significant"],
-[-0.09945240794119009, "Not significant"],
+[0.11259190602909264, "LL"],
+[-0.07116352791516614, "HL"],
+[-0.09945240794119009, "LH"],
 [0.18562939195219, "LL"],
-[0.1832733440191868, "Not significant"],
-[-0.39054253768447705, "Not significant"],
+[0.1832733440191868, "LL"],
+[-0.39054253768447705, "HL"],
 [-0.1672071289487642, "HL"],
-[0.3337669247916343, "Not significant"],
-[0.2584386102554792, "Not significant"],
+[0.3337669247916343, "HH"],
+[0.2584386102554792, "HH"],
 [-0.19733845476322634, "HL"],
 [-0.9379282899805409, "LH"],
-[-0.028770969951095866, "Not significant"],
-[0.051367269430983485, "Not significant"],
+[-0.028770969951095866, "LH"],
+[0.051367269430983485, "LL"],
 [-0.2172548045913472, "LH"],
-[0.05136726943098351, "Not significant"],
-[0.04191046803899837, "Not significant"],
+[0.05136726943098351, "LL"],
+[0.04191046803899837, "LL"],
 [0.7482357030403517, "HH"],
-[-0.014585767863118111, "Not significant"],
-[0.5410013139159929, "Not significant"],
+[-0.014585767863118111, "LH"],
+[0.5410013139159929, "HH"],
 [1.0223932668429925, "LL"],
-[1.4179402898927476, "LL"]]
+[1.4179402898927476, "LL"]]

src/py/crankshaft/test/test_clustering_moran.py

@@ -1,8 +1,6 @@
 import unittest
 import numpy as np
 
-import unittest
-
 
 # from mock_plpy import MockPlPy
 # plpy = MockPlPy()
@@ -12,25 +10,26 @@ import unittest
 from helper import plpy, fixture_file
 
 import crankshaft.clustering as cc
+import crankshaft.pysal_utils as pu
 from crankshaft import random_seeds
 import json
 
 class MoranTest(unittest.TestCase):
-    """Testing class for Moran's I functions."""
+    """Testing class for Moran's I functions"""
 
     def setUp(self):
         plpy._reset()
         self.params = {"id_col": "cartodb_id",
                        "attr1": "andy",
                        "attr2": "jay_z",
-                       "table": "a_list",
+                       "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."""
+        """Test map_quads"""
         self.assertEqual(cc.map_quads(1), 'HH')
         self.assertEqual(cc.map_quads(2), 'LH')
         self.assertEqual(cc.map_quads(3), 'LL')
@@ -38,80 +37,8 @@ class MoranTest(unittest.TestCase):
         self.assertEqual(cc.map_quads(33), None)
         self.assertEqual(cc.map_quads('andy'), None)
 
-    def test_query_attr_select(self):
-        """Test query_attr_select."""
-
-        ans = "i.\"{attr1}\"::numeric As attr1, " \
-              "i.\"{attr2}\"::numeric As attr2, "
-
-        self.assertEqual(cc.query_attr_select(self.params), ans)
-
-    def test_query_attr_where(self):
-        """Test query_attr_where."""
-
-        ans = "idx_replace.\"{attr1}\" IS NOT NULL AND "\
-              "idx_replace.\"{attr2}\" IS NOT NULL AND "\
-              "idx_replace.\"{attr2}\" <> 0"
-
-        self.assertEqual(cc.query_attr_where(self.params), ans)
-
-    def test_knn(self):
-        """Test knn function."""
-
-        ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
-              "i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT j.\"cartodb_id\" " \
-              "FROM \"a_list\" As j WHERE j.\"andy\" IS NOT NULL AND " \
-              "j.\"jay_z\" IS NOT NULL AND j.\"jay_z\" <> 0 ORDER BY " \
-              "j.\"the_geom\" <-> i.\"the_geom\" ASC LIMIT 321 OFFSET 1 ) ) " \
-              "As neighbors FROM \"a_list\" As i WHERE i.\"andy\" IS NOT " \
-              "NULL AND i.\"jay_z\" IS NOT NULL AND i.\"jay_z\" <> 0 ORDER " \
-              "BY i.\"cartodb_id\" ASC;"
-
-        self.assertEqual(cc.knn(self.params), ans)
-
-    def test_queen(self):
-        """Test queen neighbors function."""
-
-        ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
-              "i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT " \
-              "j.\"cartodb_id\" FROM \"a_list\" As j WHERE ST_Touches(" \
-              "i.\"the_geom\", j.\"the_geom\") AND j.\"andy\" IS NOT NULL " \
-              "AND j.\"jay_z\" IS NOT NULL AND j.\"jay_z\" <> 0)) As " \
-              "neighbors FROM \"a_list\" As i WHERE i.\"andy\" IS NOT NULL " \
-              "AND i.\"jay_z\" IS NOT NULL AND i.\"jay_z\" <> 0 ORDER BY " \
-              "i.\"cartodb_id\" ASC;"
-
-        self.assertEqual(cc.queen(self.params), ans)
-
-    def test_get_query(self):
-        """Test get_query."""
-
-        ans = "SELECT i.\"cartodb_id\" As id, i.\"andy\"::numeric As attr1, " \
-              "i.\"jay_z\"::numeric As attr2, (SELECT ARRAY(SELECT " \
-              "j.\"cartodb_id\" FROM \"a_list\" As j WHERE j.\"andy\" IS " \
-              "NOT NULL AND j.\"jay_z\" IS NOT NULL AND j.\"jay_z\" <> 0 " \
-              "ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC LIMIT 321 " \
-              "OFFSET 1 ) ) As neighbors FROM \"a_list\" As i WHERE " \
-              "i.\"andy\" IS NOT NULL AND i.\"jay_z\" IS NOT NULL AND " \
-              "i.\"jay_z\" <> 0 ORDER BY i.\"cartodb_id\" ASC;"
-
-        self.assertEqual(cc.get_query('knn', self.params), ans)
-
-    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_quad_position(self):
-        """Test lisa_sig_vals."""
+        """Test lisa_sig_vals"""
 
         quads = np.array([1, 2, 3, 4], np.int)
 
@@ -125,7 +52,7 @@ class MoranTest(unittest.TestCase):
         data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
         plpy._define_result('select', data)
         random_seeds.set_random_seeds(1234)
-        result = cc.moran_local('table', 'value', 0.05, 5, 99, 'the_geom', 'cartodb_id', 'knn')
+        result = cc.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
         result = [(row[0], row[1]) for row in result]
         expected = self.moran_data
         for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
@@ -137,8 +64,20 @@ class MoranTest(unittest.TestCase):
         data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data]
         plpy._define_result('select', data)
         random_seeds.set_random_seeds(1234)
-        result = cc.moran_local_rate('table', 'numerator', 'denominator', 0.05, 5, 99, 'the_geom', 'cartodb_id', 'knn')
+        result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 'knn', 5, 99, 'the_geom', 'cartodb_id')
+        print 'result == None? ', result == None
         result = [(row[0], row[1]) for row in result]
         expected = self.moran_data
         for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
             self.assertAlmostEqual(res_val, exp_val)
+
+    def test_moran(self):
+        """Test Moran's I global"""
+        data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
+        plpy._define_result('select', data)
+        random_seeds.set_random_seeds(1235)
+        result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
+        print 'result == None?', result == None
+        result_moran = result[0][0]
+        expected_moran = np.array([row[0] for row in self.moran_data]).mean()
+        self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)

src/py/crankshaft/test/test_pysal_utils.py

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