def test_already_collocated_in_col_ungridded_to_ungridded_in_2d(self):
ug_data = mock.make_regular_2d_ungridded_data()
# This point already exists on the cube with value 5 - which shouldn't be a problem
sample_points = UngriddedData.from_points_array([HyperPoint(0.0, 0.0)])
col = GeneralUngriddedCollocator(fill_value=-999)
new_data = col.collocate(sample_points, ug_data, DummyConstraint(), nn_horizontal_kdtree())[0]
eq_(new_data.data[0], 8.0)
def test_coordinates_outside_grid_in_col_ungridded_to_ungridded_in_2d(self):
ug_data = mock.make_regular_2d_ungridded_data()
sample_points = UngriddedData.from_points_array(
[HyperPoint(5.5, 5.5), HyperPoint(-5.5, 5.5), HyperPoint(5.5, -5.5), HyperPoint(-5.5, -5.5)]
)
col = GeneralUngriddedCollocator(fill_value=-999)
new_data = col.collocate(sample_points, ug_data, DummyConstraint(), nn_horizontal_kdtree())[0]
eq_(new_data.data[0], 12.0)
eq_(new_data.data[1], 6.0)
eq_(new_data.data[2], 10.0)
eq_(new_data.data[3], 4.0)
def test_basic_col_in_2d(self):
# lat: -10 to 10 step 5; lon -5 to 5 step 5
ug_data = mock.make_regular_2d_ungridded_data()
sample_points = UngriddedData.from_points_array(
[HyperPoint(lat=1.0, lon=1.0), HyperPoint(lat=4.0, lon=4.0), HyperPoint(lat=-4.0, lon=-4.0)]
)
col = GeneralUngriddedCollocator(fill_value=-999)
new_data = col.collocate(sample_points, ug_data, DummyConstraint(), nn_horizontal_kdtree())[0]
eq_(new_data.data[0], 8.0)
eq_(new_data.data[1], 12.0)
eq_(new_data.data[2], 4.0)
def test_coordinates_exactly_between_points_in_col_ungridded_to_ungridded_in_2d(self):
"""
This works out the edge case where the points are exactly in the middle or two or more datapoints.
The nn_horizontal algorithm will start with the first point as the nearest and iterates through the
points finding any points which are closer than the current closest. If two distances were exactly the same
you would expect the first point to be chosen. This doesn't seem to always be the case but is probably
down to floating points errors in the haversine calculation as these test points are pretty close
together. This test is only really for documenting the behaviour for equidistant points.
"""
ug_data = mock.make_regular_2d_ungridded_data()
sample_points = UngriddedData.from_points_array(
[HyperPoint(2.5, 2.5), HyperPoint(-2.5, 2.5), HyperPoint(2.5, -2.5), HyperPoint(-2.5, -2.5)]
)
col = GeneralUngriddedCollocator(fill_value=-999)
new_data = col.collocate(sample_points, ug_data, DummyConstraint(), nn_horizontal_kdtree())[0]
eq_(new_data.data[0], 11.0)
eq_(new_data.data[1], 5.0)
eq_(new_data.data[2], 10.0)
eq_(new_data.data[3], 4.0)