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Merge branch 'adds-getis-analysis' of https://github.com/cartodb/crankshaft into adds-getis-analysis

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This commit is contained in:
Andy Eschbacher 2016-09-19 19:24:43 +00:00
commit 5127845100
2 changed files with 14 additions and 11 deletions
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7
src/py/crankshaft/crankshaft/clustering/getis.py
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@ -11,12 +11,13 @@ import crankshaft.pysal_utils as pu
# High level interface ---------------------------------------
def getis_ord(subquery, attr,
w_type, num_ngbrs, permutations, geom_col, id_col):
"""
Getis-Ord's G*
Implementation building neighbors with a PostGIS database and PySAL's Getis-Ord's G*
hotspot/coldspot module.
Implementation building neighbors with a PostGIS database and PySAL's
Getis-Ord's G* hotspot/coldspot module.
Andy Eschbacher
"""
@ -41,7 +42,7 @@ def getis_ord(subquery, attr,
attr_vals = pu.get_attributes(result)
## build PySAL weight object
# build PySAL weight object
weight = pu.get_weight(result, w_type, num_ngbrs)
# calculate Getis-Ord's G* z- and p-values

8
src/py/crankshaft/test/test_clustering_getis.py
View File

@ -14,6 +14,7 @@ import crankshaft.pysal_utils as pu
from crankshaft import random_seeds
import json
class GetisTest(unittest.TestCase):
"""Testing class for Getis-Ord's G funtion
This test replicates the work done in PySAL documentation:
@ -22,7 +23,8 @@ class GetisTest(unittest.TestCase):
def setUp(self):
plpy._reset()
self.neighbors_data = json.loads(open(fixture_file('neighbors_getis.json')).read())
self.neighbors_data = json.loads(
open(fixture_file('neighbors_getis.json')).read())
self.getis_data = json.loads(open(fixture_file('getis.json')).read())
def test_getis_ord(self):
@ -32,11 +34,11 @@ class GetisTest(unittest.TestCase):
'neighbors': d['neighbors']} for d in self.neighbors_data]
plpy._define_result('select', data)
random_seeds.set_random_seeds(1234)
result = cc.getis_ord('subquery', 'value', 'knn', 5, 999, 'the_geom', 'cartodb_id')
result = cc.getis_ord('subquery', 'value',
'knn', 5, 999, 'the_geom', 'cartodb_id')
result = [(row[0], row[1]) for row in result]
expected = np.array(self.getis_data)[:, 0:2]
for ([res_z, res_p], [exp_z, exp_p]) in zip(result, expected):
self.assertAlmostEqual(res_z, exp_z, delta=1e-2)
if exp_p <= 0.05:
self.assertTrue(res_p < 0.05)