def test_localCorr_1Indexing(self):
dataLocal = [((1, 1, 1), array([1.0, 2.0, 3.0])),
((1, 2, 1), array([2.0, 2.0, 4.0])),
((1, 3, 1), array([9.0, 2.0, 1.0])),
((2, 1, 1), array([5.0, 2.0, 5.0])),
((3, 1, 1), array([4.0, 2.0, 6.0])),
((2, 2, 1), array([4.0, 2.0, 8.0])),
((2, 3, 1), array([5.0, 4.0, 1.0])),
((3, 2, 1), array([6.0, 3.0, 2.0])),
((3, 3, 1), array([0.0, 2.0, 1.0]))]
# get ground truth by correlating mean with the center
ts = map(lambda x: x[1], dataLocal)
mn = mean(ts, axis=0)
truth = corrcoef(mn, array([4.0, 2.0, 8.0]))[0, 1]
data = SpatialSeries(self.sc.parallelize(dataLocal))
corr = data.localCorr(1)
assert (allclose(corr.collect()[4][1], truth))
def test_localcorr_1_indexing(self):
data_local = [
((1, 1, 1), array([1.0, 2.0, 3.0])),
((1, 2, 1), array([2.0, 2.0, 4.0])),
((1, 3, 1), array([9.0, 2.0, 1.0])),
((2, 1, 1), array([5.0, 2.0, 5.0])),
((3, 1, 1), array([4.0, 2.0, 6.0])),
((2, 2, 1), array([4.0, 2.0, 8.0])),
((2, 3, 1), array([5.0, 4.0, 1.0])),
((3, 2, 1), array([6.0, 3.0, 2.0])),
((3, 3, 1), array([0.0, 2.0, 1.0]))
]
# get ground truth by correlating mean with the center
ts = map(lambda x: x[1], data_local)
mn = mean(ts, axis=0)
truth = corrcoef(mn, array([4.0, 2.0, 8.0]))[0, 1]
data = SpatialSeries(self.sc.parallelize(data_local))
corr = data.localCorr(1)
assert(allclose(corr.collect()[4][1], truth))
def toSpatialSeries(self):
"""
Convert Series to SpatialSeries
"""
from thunder.rdds.spatialseries import SpatialSeries
return SpatialSeries(self.rdd).__finalize__(self)