renaming to fix typo

Adapt Makefile of the extension for some PGXS versions

CONTRIBUTING.md

@@ -12,7 +12,7 @@ name must be created.
 ### Version numbers
 
 The version of both the SQL extension and the Python package shall
-follow the[Semantic Versioning 2.0](http://semver.org/) guidelines:
+follow the [Semantic Versioning 2.0](http://semver.org/) guidelines:
 
 * When backwards incompatibility is introduced the major number is incremented
 * When functionally is added (in a backwards-compatible manner) the minor number

README.md

@@ -7,8 +7,6 @@ CartoDB Spatial Analysis extension for PostgreSQL.
 * *pg* contains the PostgreSQL extension source code
 * *python* Python module
 
-FIXME: should it be `./extension` and `./lib/python' ?
-
 ## Requirements
 
 * pip

pg/Makefile

@@ -28,3 +28,6 @@ REGRESS_OPTS = --inputdir='$(TEST_DIR)' --outputdir='$(TEST_DIR)'
 PG_CONFIG = pg_config
 PGXS := $(shell $(PG_CONFIG) --pgxs)
 include $(PGXS)
+
+# This seems to be needed at least for PG 9.3.11
+all: $(DATA)

pg/crankshaft--0.0.1.sql

@@ -1,3 +1,6 @@
+--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.
@@ -133,4 +136,60 @@ BEGIN
   RETURN ST_Collect(points);
 END;
 $$
-LANGUAGE plpgsql VOLATILE
+LANGUAGE plpgsql VOLATILE;
+CREATE OR REPLACE FUNCTION
+  cdb_create_segment (
+      segment_name TEXT,
+      table_name TEXT,
+  	  column_name TEXT,
+      geoid_column TEXT DEFAULT 'geoid',
+      census_table TEXT DEFAULT 'block_groups'
+  )
+RETURNS NUMERIC
+AS $$
+  from crankshaft.segmentation import create_segemnt
+  # TODO: use named parameters or a dictionary
+  return create_segment('table')
+$$ LANGUAGE plpythonu;
+
+CREATE OR REPLACE FUNCTION
+  cdb_predict_segment (
+      segment_name TEXT,
+      geoid_column TEXT DEFAULT 'geoid',
+      census_table TEXT DEFAULT 'block_groups'
+  )
+RETURNS TABLE(geoid TEXT, prediction NUMERIC)
+AS $$
+  from crankshaft.segmentation import create_segemnt
+  # TODO: use named parameters or a dictionary
+  return create_segment('table')
+$$ LANGUAGE plpythonu;
+CREATE OR REPLACE FUNCTION
+  cdb_adaptive_histogram (
+      table_name  TEXT,
+      column_name TEXT
+  )
+RETURNS TABLE (bin_start numeric,bin_end numeric,value numeric)
+
+AS $$
+  from crankshaft.bayesian_blocks import adaptive_histogram
+  return adaptive_histogram(table_name,column_name)
+$$ LANGUAGE plpythonu;
+
+CREATE OR REPLACE FUNCTION
+  cdb_simple_test (
+  )
+RETURNS NUMERIC
+
+AS $$
+  return 5
+$$ LANGUAGE plpythonu;
+-- 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;

pg/sql/0.0.1/00_header.sql

@@ -0,0 +1,3 @@
+--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

pg/sql/0.0.1/04_dot_density.sql

@@ -51,4 +51,4 @@ BEGIN
   RETURN ST_Collect(points);
 END;
 $$
-LANGUAGE plpgsql VOLATILE
+LANGUAGE plpgsql VOLATILE;

pg/sql/0.0.1/06_bayesian_blocks.sql

@@ -0,0 +1,11 @@
+CREATE OR REPLACE FUNCTION
+  cdb_adaptive_histogram (
+      table_name  TEXT,
+      column_name TEXT
+  )
+RETURNS TABLE (bin_start numeric,bin_end numeric,value numeric)
+
+AS $$
+  from crankshaft.bayesian_blocks import adaptive_histogram
+  return adaptive_histogram(table_name,column_name)
+$$ LANGUAGE plpythonu;

pg/sql/0.0.1/90_permissions.sql

@@ -0,0 +1,9 @@
+-- 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;

pg/sql/0.0.1/population.sql

@@ -1,138 +0,0 @@
--- Function to obtain an estimate of the population living inside
--- an area (polygon) from the CartoDB Data Observatory
-CREATE OR REPLACE FUNCTION cdb_population(area geometry)
-RETURNS NUMERIC AS $$
-DECLARE
-  georef_column TEXT;
-  table_id TEXT;
-  tag_value TEXT;
-  table_name TEXT;
-  column_name TEXT;
-  population NUMERIC;
-BEGIN
-
-  -- Note: comments contain pseudo-code that should be implemented
-
-  -- Register metadata tables:
-  -- This would require super-user privileges
-  /*
-  SELECT cdb_add_remote_table('observatory', 'bmd_column_table');
-  SELECT cdb_add_remote_table('observatory', 'bmd_column_2_column');
-  SELECT cdb_add_remote_table('observatory', 'bmd_table');
-  SELECT cdb_add_remote_table('observatory', 'bmd_column_table');
-  SELECT cdb_add_remote_table('observatory', 'bmd_column_tag');
-  SELECT cdb_add_remote_table('observatory', 'bmd_tag');
-  */
-
-  tag_value := 'population';
-
-
-  -- Determine the georef column id to be used: it must have type 'geometry',
-  -- the maximum weight.
-  -- TODO: in general, multiple columns with maximal weight could be found;
-  -- we should use the timespan of the table to disambiguate (choose the
-  -- most recent). Also a rank of geometry columns should be introduced to
-  -- find select the greatest resolution available.
-  /*
-  WITH selected_tables AS (
-    -- Find tables that have population columns and cover the input area
-    SELECT tab.id AS id
-    FROM observatory.bmd_column col,
-         observatory.bmd_column_table coltab,
-         observatory.bmd_table tab,
-         observatory.bmd_tag tag,
-         observatory.bmd_column_tag coltag
-    WHERE coltab.column_id = col.id
-      AND coltab.table_id = tab.id
-      AND coltag.tag_id = tag.id
-      AND coltag.column_id = col.id
-      AND tag.name ILIKE tag_value
-      AND tab.id = table_id
-      AND tab.bounds && area;
-  )
-  SELECT
-    FROM bmd_column col
-    JOIN bmd_table tab ON col.table_id = tab.id
-    WHERE type = 'geometry'
-      AND tab.id IN (selected_tables)
-    ORDER BY weight DESC LIMIT 1;
-  */
-  georef_column := '"us.census.tiger".block_group_2013';
-
-  -- Now we will query the metadata to find which actual tables correspond
-  -- to this datasource and resolution/timespan
-  -- and choose the 'parent' or more general of them.
-  /*
-  SELECT from_table_geoid.id data_table_id
-  FROM observatory.bmd_column_table from_column_table_geoid,
-       observatory.bmd_column_table to_column_table_geoid,
-       observatory.bmd_column_2_column rel,
-       observatory.bmd_column_table to_column_table_geom,
-       observatory.bmd_table from_table_geoid,
-       observatory.bmd_table to_table_geoid,
-       observatory.bmd_table to_table_geom
-  WHERE from_column_table_geoid.column_id = to_column_table_geoid.column_id
-    AND to_column_table_geoid.column_id = rel.from_id
-    AND rel.reltype = 'geom_ref'
-    AND rel.to_id = to_column_table_geom.column_id
-    AND to_column_table_geom.column_id = georef_column
-    AND from_table_geoid.id = from_column_table_geoid.table_id
-    AND to_table_geoid.id = to_column_table_geoid.table_id
-    AND to_table_geom.id = to_column_table_geom.table_id
-    AND from_table_geoid.bounds && area
-  ORDER by from_table_geoid.timespan desc
-  INTO table_id;
-  */
-  table_id := '"us.census.acs".extract_2013_5yr_block_group';
-
-  -- Next will fetch the columns of that table that are tagged as population:
-  -- and get the more general one (not having a parent or denominator)
-  /*
-  WITH column_ids AS (
-    SELECT col.id AS id
-    FROM observatory.bmd_column col,
-         observatory.bmd_column_table coltab,
-         observatory.bmd_table tab,
-         observatory.bmd_tag tag,
-         observatory.bmd_column_tag coltag
-    WHERE coltab.column_id = col.id
-      AND coltab.table_id = tab.id
-      AND coltag.tag_id = tag.id
-      AND coltag.column_id = col.id
-      AND tag.name ILIKE tag_value
-      AND tab.id = table_id;
-  ),
-  excluded_column_ids AS (
-    SELECT from_id AS id
-    FROM observatory.bmd_column_2_column
-    WHERE from_id in (column_ids)
-      AND reltype in ('parent', 'denominator')
-      AND to_id in (column_ids)
-  ),
-  SELECT bmd_table.tablename, bmd_column_table.colname
-  FROM observatory.bmd_column_table,
-       observatory.bmd_table
-  WHERE bmd_column_table.table_id = bmd_table.id
-    AND bmd_column_table.column_id IN (column_ids)
-    AND NOT bmd_column_table.column_id IN (exclude_column_ids)
-  INTO (table_name, column_name);
-  */
-  table_name := 'us_census_acs2013_5yr_block_group';
-  column_name := 'total_pop';
-
-  -- Register the foreign table
-  -- This would require super-user privileges
-  -- SELECT cdb_add_remote_table('observatory', table_name);
-
-  -- Perform the query
-  SELECT cdb_crankshaft.cdb_overlap_sum(
-    area,
-    table_name,
-    column_name,
-    schema_name := 'observatory')
-  INTO population;
-
-  RETURN population;
-END;
-$$
-LANGUAGE plpgsql VOLATILE

pg/test/0.0.1/sql/90_permissions.sql

@@ -0,0 +1,18 @@
+SELECT cdb_crankshaft._cdb_random_seeds(1234);
+
+-- Use regular user role
+SET ROLE test_regular_user;
+
+-- Add to the search path the schema
+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
+    ON ppoints.cartodb_id = m.ids
+  ORDER BY ppoints.code;
+SELECT round(cdb_overlap_sum(
+  '0106000020E61000000100000001030000000100000004000000FFFFFFFFFF3604C09A0B9ECEC42E444000000000C060FBBF30C7FD70E01D44400000000040AD02C06481F1C8CD034440FFFFFFFFFF3604C09A0B9ECEC42E4440'::geometry,
+  'values', 'value'
+), 2);

python/crankshaft/crankshaft/__init__.py

@@ -1,2 +1,3 @@
 import random_seeds
 import clustering
+import bayesian_blocks

python/crankshaft/crankshaft/bayesian_blocks/__init__.py

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

python/crankshaft/crankshaft/bayesian_blocks/bayesian_blocks.py

@@ -0,0 +1,84 @@
+import plpy
+import numpy as np
+
+
+def adaptive_histogram(table_name,column_name):
+    data = plpy.execute("select {column_name} from {table_name}".format(**locals()))
+
+    data = [float(d['count']) for d in data]
+    plpy.notice(data)
+    vals, bins = np.histogram( data, bins=_bayesian_blocks(data))
+    return zip(vals,bins, bins[1:])
+
+
+def _bayesian_blocks(t):
+    """Bayesian Blocks Implementation
+
+    By Jake Vanderplas.  License: BSD
+    Based on algorithm outlined in http://adsabs.harvard.edu/abs/2012arXiv1207.5578S
+
+    Parameters
+    ----------
+    t : ndarray, length N
+        data to be histogrammed
+
+    Returns
+    -------
+    bins : ndarray
+        array containing the (N+1) bin edges
+
+    Notes
+    -----
+    This is an incomplete implementation: it may fail for some
+    datasets.  Alternate fitness functions and prior forms can
+    be found in the paper listed above.
+    """
+    # copy and sort the array
+    t = np.sort(t)
+    N = t.size
+
+    # create length-(N + 1) array of cell edges
+    edges = np.concatenate([t[:1],
+                            0.5 * (t[1:] + t[:-1]),
+                            t[-1:]])
+    block_length = t[-1] - edges
+
+    # arrays needed for the iteration
+    nn_vec = np.ones(N)
+    best = np.zeros(N, dtype=float)
+    last = np.zeros(N, dtype=int)
+
+    #-----------------------------------------------------------------
+    # Start with first data cell; add one cell at each iteration
+    #-----------------------------------------------------------------
+    for K in range(N):
+        # Compute the width and count of the final bin for all possible
+        # locations of the K^th changepoint
+        width = block_length[:K + 1] - block_length[K + 1]
+        count_vec = np.cumsum(nn_vec[:K + 1][::-1])[::-1]
+
+        # evaluate fitness function for these possibilities
+        fit_vec = count_vec * (np.log(count_vec) - np.log(width))
+        fit_vec -= 4  # 4 comes from the prior on the number of changepoints
+        fit_vec[1:] += best[:K]
+
+        # find the max of the fitness: this is the K^th changepoint
+        i_max = np.argmax(fit_vec)
+        last[K] = i_max
+        best[K] = fit_vec[i_max]
+
+    #-----------------------------------------------------------------
+    # Recover changepoints by iteratively peeling off the last block
+    #-----------------------------------------------------------------
+    change_points =  np.zeros(N, dtype=int)
+    i_cp = N
+    ind = N
+    while True:
+        i_cp -= 1
+        change_points[i_cp] = ind
+        if ind == 0:
+            break
+        ind = last[ind - 1]
+    change_points = change_points[i_cp:]
+
+    return edges[change_points]

python/crankshaft/setup.py

@@ -10,7 +10,7 @@ from setuptools import setup, find_packages
 setup(
     name='crankshaft',
 
-    version='0.0.01',
+    version='0.0.1',
 
     description='CartoDB Spatial Analysis Python Library',
 
@@ -40,7 +40,7 @@ setup(
 
     # 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'],
+    install_requires=['pysal==1.11.0','numpy==1.10.1','scipy==0.17.0'],
 
     requires=['pysal', 'numpy'],