def test_Kernel_Smoother_tabular(self):
point_array = np.array(self.points)
bbox = [[0, 0], [45, 45]]
dfa = self.dfa
sf = sm.Spatial_Filtering(bbox, point_array, dfa.e, dfa.b, 2, 2, r=30)
np.testing.assert_allclose(sf.r, self.sf_exp, rtol=RTOL, atol=ATOL)
dfa['geometry'] = self.points
sf = sm.Spatial_Filtering.by_col(dfa, 'e', 'b', 3, 3, r=30)
r_answer = np.array([
0.07692308, 0.07213115, 0.07213115, 0.07692308, 0.07692308,
0.07692308, 0.07692308, 0.07692308, 0.07692308
])
x_answer = np.array(
[10.0, 10.0, 10.0, 20.0, 20.0, 20.0, 30.0, 30.0, 30.0])
y_answer = np.array([
10.000000, 16.666667, 23.333333, 10.000000, 16.666667, 23.333333,
10.000000, 16.666667, 23.333333
])
columns = [
'e-b_spatial_filtering_{}'.format(name)
for name in ['X', 'Y', 'R']
]
for col, answer in zip(columns, [x_answer, y_answer, r_answer]):
np.testing.assert_allclose(sf[col].values,
answer,
rtol=RTOL,
atol=ATOL)
def test_Spatial_Filtering(self):
points = np.array(self.points)
bbox = [[0, 0], [45, 45]]
sf = sm.Spatial_Filtering(bbox, points, self.e, self.b, 2, 2, r=30)
exp = [0.11111111, 0.11111111, 0.20000000000000001, 0.085106379999999995,
0.076923080000000005, 0.05789474, 0.052173909999999997, 0.066666669999999997, 0.04117647]
self.assertEqual(list(sf.r.round(8)), exp)
def test_Spatial_Filtering(self):
points = np.array(self.points)
bbox = [[0, 0], [45, 45]]
sf = sm.Spatial_Filtering(bbox, points, self.e, self.b, 2, 2, r=30)
np.testing.assert_allclose(sf.r, self.sf_exp, rtol=RTOL, atol=ATOL)