def test_MissingErrors(self):
with pytest.raises(ipol.MissingSourcesError):
ipol.Nearest(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.Nearest(self.src, np.array([]))
with pytest.raises(ipol.MissingSourcesError):
ipol.Idw(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.Idw(self.src, np.array([]))
with pytest.raises(ipol.MissingSourcesError):
ipol.Linear(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.Linear(self.src, np.array([]))
with pytest.raises(ipol.MissingSourcesError):
ipol.OrdinaryKriging(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.OrdinaryKriging(self.src, np.array([]))
with pytest.raises(ipol.MissingSourcesError):
ipol.ExternalDriftKriging(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.ExternalDriftKriging(self.src, np.array([]))
def test_Linear_1(self):
"""testing the basic behaviour of the Linear class"""
ip = ipol.Linear(self.src_lin, self.trg_lin)
# input more than one dataset
res = ip(self.vals_lin)
self.assertTrue(
np.allclose(
res,
np.array([[1., 2., 3.], [2., 2., 2.], [1.5, 2., 2.5],
[3., 2., 1.]])))
# input only one flat array
res = ip(self.vals_lin[:, 2])
self.assertTrue(np.allclose(res, np.array([3., 2., 2.5, 1.])))