def test_dataarray_to_matrix_inc_fails():
"Check that it fails for variable increments"
data = np.ones((4, 5), dtype="float64")
x = np.linspace(0, 1, 5)
y = np.logspace(2, 3, 4)
grid = xr.DataArray(data, coords=[("y", y), ("x", x)])
with pytest.raises(GMTInvalidInput):
dataarray_to_matrix(grid)
def test_dataarray_to_matrix_dims_fails():
"Check that it fails for > 2 dims"
# Make a 3D regular grid
data = np.ones((10, 12, 11), dtype="float32")
x = np.arange(11)
y = np.arange(12)
z = np.arange(10)
grid = xr.DataArray(data, coords=[("z", z), ("y", y), ("x", x)])
with pytest.raises(GMTInvalidInput):
dataarray_to_matrix(grid)
def test_dataarray_to_matrix_irregular_inc_warning():
"""
Check that it warns for variable increments, see also
https://github.com/GenericMappingTools/pygmt/issues/1468.
"""
data = np.ones((4, 5), dtype="float64")
x = np.linspace(0, 1, 5)
y = np.logspace(2, 3, 4)
grid = xr.DataArray(data, coords=[("y", y), ("x", x)])
with pytest.warns(expected_warning=RuntimeWarning) as record:
dataarray_to_matrix(grid)
assert len(record) == 1
def test_dataarray_to_matrix_zero_inc_fails():
"""
Check that dataarray_to_matrix fails for zero increments grid.
"""
data = np.ones((5, 5), dtype="float32")
x = np.linspace(0, 1, 5)
y = np.zeros_like(x)
grid = xr.DataArray(data, coords=[("y", y), ("x", x)])
with pytest.raises(GMTInvalidInput):
dataarray_to_matrix(grid)
y = np.linspace(0, 1, 5)
x = np.zeros_like(x)
grid = xr.DataArray(data, coords=[("y", y), ("x", x)])
with pytest.raises(GMTInvalidInput):
dataarray_to_matrix(grid)
def test_dataarray_to_matrix_works():
"Check that dataarray_to_matrix returns correct output"
data = np.diag(v=np.arange(3))
x = np.linspace(start=0, stop=4, num=3)
y = np.linspace(start=5, stop=9, num=3)
grid = xr.DataArray(data, coords=[("y", y), ("x", x)])
matrix, region, inc = dataarray_to_matrix(grid)
npt.assert_allclose(actual=matrix, desired=np.flipud(data))
npt.assert_allclose(actual=region,
desired=[x.min(), x.max(),
y.min(), y.max()])
npt.assert_allclose(actual=inc, desired=[x[1] - x[0], y[1] - y[0]])
def test_dataarray_to_matrix_negative_x_and_y_increment():
"Check that dataarray_to_matrix returns correct output with flipped x/y"
data = np.diag(v=np.arange(3))
x = np.linspace(start=4, stop=0, num=3)
y = np.linspace(start=9, stop=5, num=3)
grid = xr.DataArray(data, coords=[("y", y), ("x", x)])
matrix, region, inc = dataarray_to_matrix(grid)
npt.assert_allclose(actual=matrix, desired=np.fliplr(data))
npt.assert_allclose(actual=region,
desired=[x.min(), x.max(),
y.min(), y.max()])
npt.assert_allclose(actual=inc,
desired=[abs(x[1] - x[0]),
abs(y[1] - y[0])])
