def test_nearneighbor_wrong_kind_of_input(ship_data):
"""
Run nearneighbor using grid input that is not file/matrix/vectors.
"""
data = ship_data.bathymetry.to_xarray() # convert pandas.Series to xarray.DataArray
assert data_kind(data) == "grid"
with pytest.raises(GMTInvalidInput):
nearneighbor(
data=data, spacing="5m", region=[245, 255, 20, 30], search_radius="10m"
)
def test_nearneighbor_input_data(array_func, ship_data):
"""
Run nearneighbor by passing in a numpy.array or xarray.Dataset.
"""
data = array_func(ship_data)
output = nearneighbor(
data=data, spacing="5m", region=[245, 255, 20, 30], search_radius="10m"
)
assert isinstance(output, xr.DataArray)
assert output.gmt.registration == 0 # Gridline registration
assert output.gmt.gtype == 1 # Geographic type
assert output.shape == (121, 121)
npt.assert_allclose(output.mean(), -2378.2385)
def test_nearneighbor_input_xyz(ship_data):
"""
Run nearneighbor by passing in x, y, z numpy.ndarrays individually.
"""
output = nearneighbor(
x=ship_data.longitude,
y=ship_data.latitude,
z=ship_data.bathymetry,
spacing="5m",
region=[245, 255, 20, 30],
search_radius="10m",
)
assert isinstance(output, xr.DataArray)
assert output.shape == (121, 121)
npt.assert_allclose(output.mean(), -2378.2385)
def test_nearneighbor_with_outgrid_param(ship_data):
"""
Run nearneighbor with the 'outgrid' parameter.
"""
with GMTTempFile() as tmpfile:
output = nearneighbor(
data=ship_data,
spacing="5m",
region=[245, 255, 20, 30],
outgrid=tmpfile.name,
search_radius="10m",
)
assert output is None # check that output is None since outgrid is set
assert os.path.exists(path=tmpfile.name) # check that outgrid exists at path
with xr.open_dataarray(tmpfile.name) as grid:
assert isinstance(grid, xr.DataArray) # ensure netcdf grid loads ok
assert grid.shape == (121, 121)
npt.assert_allclose(grid.mean(), -2378.2385)