def test_horizontal_constraint_for_same_3d_grids_returns_original_data(self):
# Create sample and data cubes that include a time coordinate with the dimensions in reverse of normal order.
sample_cube = gridded_data.make_from_cube(
mock.make_mock_cube(lat_dim_length=5, lon_dim_length=3, time_dim_length=2, dim_order=["time", "lon", "lat"])
)
data_cube = gridded_data.make_from_cube(
mock.make_mock_cube(lat_dim_length=5, lon_dim_length=3, time_dim_length=2, dim_order=["time", "lon", "lat"])
)
data_points = data_cube.get_non_masked_points()
sample_points = sample_cube.get_all_points()
coord_map = make_coord_map(sample_cube, data_cube)
# Make separation constraint small enough to include only the corresponding point in the data cube.
constraint = SepConstraintKdtree(h_sep=400)
index = HaversineDistanceKDTreeIndex()
index.index_data(sample_points, data_points, coord_map, leafsize=2)
constraint.haversine_distance_kd_tree_index = index
for idx, sample_point in enumerate(sample_points):
out_points = constraint.constrain_points(sample_point, data_points)
# Two times for each spatial position.
assert len(out_points) == 2
assert data_points[idx].val[0] in [p.val[0] for p in out_points]
def test_GIVEN_single_masked_point_in_cube_WHEN_iterate_THEN_return_no_points(
self):
sample_cube = make_square_5x3_2d_cube_with_time(offset=0,
time_offset=0)
data_point = make_dummy_ungridded_data_single_point(
0.5,
0.5,
1.2,
time=datetime.datetime(1984, 8, 28, 0, 0),
mask=True)
coord_map = make_coord_map(sample_cube, data_point)
coords = sample_cube.coords()
for (hpi, ci, shi) in coord_map:
coord = coords[ci]
if coord.ndim > 1:
raise NotImplementedError(
"Co-location of data onto a cube with a coordinate of dimension greater"
" than one is not supported (coordinate %s)", coord.name())
# Ensure that bounds exist.
if not coord.has_bounds():
coord.guess_bounds()
constraint = BinnedCubeCellOnlyConstraint()
data_index.create_indexes(constraint, coords,
data_point.get_non_masked_points(),
coord_map)
iterator = constraint.get_iterator(False, coord_map, coords,
data_point.get_non_masked_points(),
None, sample_cube, None)
final_points_index = [(out_index, hp, points)
for out_index, hp, points in iterator]
assert_that(len(final_points_index), is_(0),
"Masked points should not be iterated over")
def test_GIVEN_single_point_in_cube_WHEN_iterate_THEN_return_point_in_middle(self):
sample_cube = make_square_5x3_2d_cube_with_time(offset=0, time_offset=0)
data_point = make_dummy_ungridded_data_single_point(0.5, 0.5, 1.2, time=datetime.datetime(1984, 8, 28, 0, 0))
coord_map = make_coord_map(sample_cube, data_point)
coords = sample_cube.coords()
for (hpi, ci, shi) in coord_map:
coord = coords[ci]
if coord.ndim > 1:
raise NotImplementedError("Co-location of data onto a cube with a coordinate of dimension greater"
" than one is not supported (coordinate %s)", coord.name())
# Ensure that bounds exist.
if not coord.has_bounds():
coord.guess_bounds()
constraint = BinnedCubeCellOnlyConstraint()
data_index.create_indexes(constraint, coords, data_point.get_non_masked_points(), coord_map)
iterator = constraint.get_iterator(False, coord_map, coords, data_point.get_non_masked_points(), None,
sample_cube, None)
final_points_index = [(out_index, hp, points) for out_index, hp, points in iterator]
assert_that(len(final_points_index), is_(1), "There is one mapping from sample_cube to the final grid")
assert_that(final_points_index[0][0], is_((2, 1, 1)), "The points should map to index")
assert_that(final_points_index[0][1], is_(HyperPoint(lat=0, lon=0, t=datetime.datetime(1984, 8, 28))),
"The points should map to index")
assert_that(final_points_index[0][2].latitudes, is_([0.5]), "The points should map to index")
assert_that(final_points_index[0][2].longitudes, is_([0.5]), "The points should map to index")
assert_that(final_points_index[0][2].times,
is_([convert_datetime_to_std_time(datetime.datetime(1984, 8, 28, 0, 0))]),
"The points should map to index")
assert_that(final_points_index[0][2].vals, is_([1.2]), "The points should map to index")
def test_GIVEN_single_point_in_cube_WHEN_iterate_THEN_return_point_in_middle(
self):
sample_cube = make_square_5x3_2d_cube_with_time(offset=0,
time_offset=0)
data_point = make_dummy_ungridded_data_single_point(
0.5, 0.5, 1.2, time=datetime.datetime(1984, 8, 28, 0, 0))
coord_map = make_coord_map(sample_cube, data_point)
coords = sample_cube.coords()
for (hpi, ci, shi) in coord_map:
coord = coords[ci]
if coord.ndim > 1:
raise NotImplementedError(
"Co-location of data onto a cube with a coordinate of dimension greater"
" than one is not supported (coordinate %s)", coord.name())
# Ensure that bounds exist.
if not coord.has_bounds():
coord.guess_bounds()
constraint = BinnedCubeCellOnlyConstraint()
data_index.create_indexes(constraint, coords,
data_point.get_non_masked_points(),
coord_map)
iterator = constraint.get_iterator(False, coord_map, coords,
data_point.get_non_masked_points(),
None, sample_cube, None)
final_points_index = [(out_index, hp, points)
for out_index, hp, points in iterator]
assert_that(len(final_points_index), is_(1),
"There is one mapping from sample_cube to the final grid")
assert_that(final_points_index[0][0], is_((2, 1, 1)),
"The points should map to index")
assert_that(
final_points_index[0][1],
is_(HyperPoint(lat=0, lon=0, t=datetime.datetime(1984, 8, 28))),
"The points should map to index")
assert_that(final_points_index[0][2].latitudes, is_([0.5]),
"The points should map to index")
assert_that(final_points_index[0][2].longitudes, is_([0.5]),
"The points should map to index")
assert_that(
final_points_index[0][2].times,
is_([
convert_datetime_to_std_time(
datetime.datetime(1984, 8, 28, 0, 0))
]), "The points should map to index")
assert_that(final_points_index[0][2].vals, is_([1.2]),
"The points should map to index")
def test_horizontal_constraint_for_same_2d_grids_returns_original_data(self):
# Simple case of lat/lon grid with dimensions in that order.
sample_cube = gridded_data.make_from_cube(mock.make_mock_cube())
data_cube = gridded_data.make_from_cube(mock.make_mock_cube())
data_points = data_cube.get_non_masked_points()
sample_points = sample_cube.get_all_points()
coord_map = make_coord_map(sample_cube, data_cube)
# Make separation constraint small enough to include only the corresponding point in the data cube.
constraint = SepConstraintKdtree(h_sep=400)
index = HaversineDistanceKDTreeIndex()
index.index_data(sample_points, data_points, coord_map, leafsize=2)
constraint.haversine_distance_kd_tree_index = index
for idx, sample_point in enumerate(sample_points):
out_points = constraint.constrain_points(sample_point, data_points)
assert len(out_points) == 1
assert out_points[0].val[0] == data_points[idx].val[0]
def test_GIVEN_single_masked_point_in_cube_WHEN_iterate_THEN_return_no_points(self):
sample_cube = make_square_5x3_2d_cube_with_time(offset=0, time_offset=0)
data_point = make_dummy_ungridded_data_single_point(0.5, 0.5, 1.2, time=datetime.datetime(1984, 8, 28, 0, 0),
mask=True)
coord_map = make_coord_map(sample_cube, data_point)
coords = sample_cube.coords()
for (hpi, ci, shi) in coord_map:
coord = coords[ci]
if coord.ndim > 1:
raise NotImplementedError("Co-location of data onto a cube with a coordinate of dimension greater"
" than one is not supported (coordinate %s)", coord.name())
# Ensure that bounds exist.
if not coord.has_bounds():
coord.guess_bounds()
constraint = BinnedCubeCellOnlyConstraint()
data_index.create_indexes(constraint, coords, data_point.get_non_masked_points(), coord_map)
iterator = constraint.get_iterator(False, coord_map, coords, data_point.get_non_masked_points(), None,
sample_cube, None)
final_points_index = [(out_index, hp, points) for out_index, hp, points in iterator]
assert_that(len(final_points_index), is_(0), "Masked points should not be iterated over")
def test_horizontal_constraint_in_2d_with_missing_values(self):
# Test with standard 2d grids but with missing data.
sample_cube = gridded_data.make_from_cube(mock.make_mock_cube())
data_cube = gridded_data.make_from_cube(mock.make_square_5x3_2d_cube_with_missing_data())
data_points = data_cube.get_non_masked_points()
sample_points = sample_cube.get_all_points()
coord_map = make_coord_map(sample_cube, data_cube)
# Make separation constraint small enough to include only the corresponding point in the data cube.
constraint = SepConstraintKdtree(h_sep=400)
index = HaversineDistanceKDTreeIndex()
index.index_data(sample_points, data_points, coord_map, leafsize=2)
constraint.haversine_distance_kd_tree_index = index
for idx, sample_point in enumerate(sample_points):
out_points = constraint.constrain_points(sample_point, data_points)
if data_points[idx].val[0] is np.ma.masked:
assert len(out_points) == 0
else:
assert len(out_points) == 1
assert out_points[0].val[0] == data_points[idx].val[0]