def _assert_expected_call(self, sample_points, sample_points_call,
cinterp_patch, linear_patch):
linear_patch.return_value = self.scheme
linear(self.cube, sample_points, self.extrapolation)
linear_patch.assert_called_once_with(self.extrapolation)
cinterp_patch.assert_called_once_with(sample_points_call, self.scheme)
def test_multi(self):
# Testing interpolation on specified points on cube with
# multidimensional coordinates.
interp_cube = linear(self.cube, {'latitude': 1.5, 'longitude': 1.5})
self.assertCMLApproxData(interp_cube, ('experimental', 'analysis',
'interpolate',
'linear_nd_2_coords.cml'))
def test_single(self):
# Interpolation on the 1d coordinate.
interp_cube = linear(self.cube, {'wibble': 20})
self.assertArrayEqual(np.mean(self.cube.data, axis=0),
interp_cube.data)
self.assertCMLApproxData(interp_cube, ('experimental', 'analysis',
'interpolate', 'linear_nd.cml'))
def test_mask_retention(self):
cube = stock.realistic_4d_w_missing_data()
interp_cube = linear(cube, [('pressure', [850, 950])])
self.assertIsInstance(interp_cube.data, ma.MaskedArray)
# this value is masked in the input
self.assertTrue(cube.data.mask[0, 2, 2, 0])
# and is still masked in the output
self.assertTrue(interp_cube.data.mask[0, 1, 2, 0])
def test_mask_retention(self):
cube = stock.realistic_4d_w_missing_data()
interp_cube = linear(cube, [('pressure', [850, 950])])
self.assertIsInstance(interp_cube.data, ma.MaskedArray)
# this value is masked in the input
self.assertTrue(cube.data.mask[0, 2, 2, 0])
# and is still masked in the output
self.assertTrue(interp_cube.data.mask[0, 1, 2, 0])
def test_positive(self):
# Check we can interpolate from a Cube defined over [0, 360).
cube = self._create_cube([0, 90, 180, 270])
samples = [('longitude', range(-360, 720, 45))]
result = interpolate.linear(cube, samples)
self.assertCMLApproxData(result,
('analysis', 'interpolation', 'linear',
'circular_wrapping', 'positive'))
def test_positive(self):
# Check we can interpolate from a Cube defined over [0, 360).
cube = self._create_cube([0, 90, 180, 270])
samples = [('longitude', range(-360, 720, 45))]
result = interpolate.linear(cube, samples)
self.assertCMLApproxData(result, ('analysis', 'interpolation',
'linear', 'circular_wrapping',
'positive'))
def test_symmetric(self):
# Check we can interpolate from a Cube defined over [-180, 180).
cube = self._create_cube([-180, -90, 0, 90])
samples = [('longitude', range(-360, 720, 45))]
result = interpolate.linear(cube, samples)
normalise_order(result)
self.assertCMLApproxData(result,
('analysis', 'interpolation', 'linear',
'circular_wrapping', 'symmetric'))
def test_symmetric(self):
# Check we can interpolate from a Cube defined over [-180, 180).
cube = self._create_cube([-180, -90, 0, 90])
samples = [('longitude', range(-360, 720, 45))]
result = interpolate.linear(cube, samples)
normalise_order(result)
self.assertCMLApproxData(result, ('analysis', 'interpolation',
'linear', 'circular_wrapping',
'symmetric'))
def test_single_extrapolation(self):
# Interpolation on the 1d coordinate with extrapolation.
interp_cube = linear(self.cube, {'wibble': np.float32(1.5)})
expected = ('experimental', 'analysis', 'interpolate',
'linear_nd_with_extrapolation.cml')
self.assertCMLApproxData(interp_cube, expected)
