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)
normalise_order(result)
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)
normalise_order(result)
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', np.arange(-360, 720, 45))]
result = interpolate.linear(cube, samples, extrapolation_mode='nan')
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', np.arange(-360, 720, 45))]
result = interpolate.linear(cube, samples, extrapolation_mode='nan')
normalise_order(result)
self.assertCMLApproxData(result,
('analysis', 'interpolation', 'linear',
'circular_wrapping', 'symmetric'))
def test_contrived_non_spherical_curl2(self):
# testing :
# F(x, y, z) = (z^3, x+2, y^2)
# curl( F(x, y, z) ) = (2y, 3z^2, 1)
cube = build_cube(np.empty((10, 25, 50)), spherical=False)
x_pts, x_ones, y_pts, y_ones, z_pts, z_ones = self.get_coord_pts(cube)
u = cube.copy(data=pow(z_pts, 3) * x_ones * y_ones)
v = cube.copy(data=z_ones * (x_pts + 2.) * y_ones)
w = cube.copy(data=z_ones * x_ones * pow(y_pts, 2.))
u.rename('u_wind')
v.rename('v_wind')
w.rename('w_wind')
r = iris.analysis.calculus.curl(u, v, w)
# TODO #235 When regridding is not nearest neighbour: the commented out code could be made to work
# r[0].data should now be tending towards result.data as the resolution of the grid gets higher.
# result = r[0].copy(data=True)
# x_pts, x_ones, y_pts, y_ones, z_pts, z_ones = self.get_coord_pts(result)
# result.data = y_pts * 2. * x_ones * z_ones
# print(repr(r[0].data[0:1, 0:5, 0:25:5]))
# print(repr(result.data[0:1, 0:5, 0:25:5]))
# np.testing.assert_array_almost_equal(result.data, r[0].data, decimal=2)
#
# result = r[1].copy(data=True)
# x_pts, x_ones, y_pts, y_ones, z_pts, z_ones = self.get_coord_pts(result)
# result.data = pow(z_pts, 2) * x_ones * y_ones
# np.testing.assert_array_almost_equal(result.data, r[1].data, decimal=6)
result = r[2].copy()
result.data = result.data * 0 + 1
np.testing.assert_array_almost_equal(result.data, r[2].data, decimal=4)
normalise_order(r[1])
self.assertCML(
r, ('analysis', 'calculus', 'curl_contrived_cartesian2.cml'),
checksum=False)
def test_contrived_non_spherical_curl2(self):
# testing :
# F(x, y, z) = (z^3, x+2, y^2)
# curl( F(x, y, z) ) = (2y, 3z^2, 1)
cube = build_cube(np.empty((10, 25, 50)), spherical=False)
x_pts, x_ones, y_pts, y_ones, z_pts, z_ones = self.get_coord_pts(cube)
u = cube.copy(data=pow(z_pts, 3) * x_ones * y_ones)
v = cube.copy(data=z_ones * (x_pts + 2.) * y_ones)
w = cube.copy(data=z_ones * x_ones * pow(y_pts, 2.))
u.rename('u_wind')
v.rename('v_wind')
w.rename('w_wind')
r = iris.analysis.calculus.curl(u, v, w)
# TODO #235 When regridding is not nearest neighbour: the commented out code could be made to work
# r[0].data should now be tending towards result.data as the resolution of the grid gets higher.
# result = r[0].copy(data=True)
# x_pts, x_ones, y_pts, y_ones, z_pts, z_ones = self.get_coord_pts(result)
# result.data = y_pts * 2. * x_ones * z_ones
# print repr(r[0].data[0:1, 0:5, 0:25:5])
# print repr(result.data[0:1, 0:5, 0:25:5])
# np.testing.assert_array_almost_equal(result.data, r[0].data, decimal=2)
#
# result = r[1].copy(data=True)
# x_pts, x_ones, y_pts, y_ones, z_pts, z_ones = self.get_coord_pts(result)
# result.data = pow(z_pts, 2) * x_ones * y_ones
# np.testing.assert_array_almost_equal(result.data, r[1].data, decimal=6)
result = r[2].copy()
result.data = result.data * 0 + 1
np.testing.assert_array_almost_equal(result.data, r[2].data, decimal=4)
normalise_order(r[1])
self.assertCML(r, ('analysis', 'calculus', 'curl_contrived_cartesian2.cml'), checksum=False)
