def test_esmf_grid_with_corner():
# only center coordinate, no corners
# remember to pass transpose (F-ordered) to backend
grid = esmf_grid(lon.T, lat.T)
# make sure coordinate values agree
assert_equal(grid.coords[0][0], lon.T)
assert_equal(grid.coords[0][1], lat.T)
# make sure meta data agree
assert not grid.has_corners # no corner yet!
assert grid.staggerloc == [True, False, False, False]
assert grid.coord_sys == 1
assert grid.rank == 2
assert_equal(grid.size[0], lon.T.shape)
assert_equal(grid.upper_bounds[0], lon.T.shape)
assert_equal(grid.lower_bounds[0], np.array([0, 0]))
# now add corner information
add_corner(grid, lon_b.T, lat_b.T)
# coordinate values
assert_equal(grid.coords[3][0], lon_b.T)
assert_equal(grid.coords[3][1], lat_b.T)
# metadata
assert grid.has_corners # should have corner now
assert grid.staggerloc == [True, False, False, True]
assert_equal(grid.size[3], lon_b.T.shape)
assert_equal(grid.upper_bounds[3], lon_b.T.shape)
assert_equal(grid.lower_bounds[3], np.array([0, 0]))
def test_regrid():
# use conservative regridding as an example,
# since it is the most well-tested studied one in papers
# TODO: possible to break this long test into smaller tests?
# not easy due to strong dependencies.
grid_in = esmf_grid(lon_in.T, lat_in.T)
grid_out = esmf_grid(lon_out.T, lat_out.T)
# no corner info yet, should not be able to use conservative
with pytest.raises(ValueError):
esmf_regrid_build(grid_in, grid_out, 'conservative')
# now add corners
add_corner(grid_in, lon_b_in.T, lat_b_in.T)
add_corner(grid_out, lon_b_out.T, lat_b_out.T)
# also write to file for scipy regridding
filename = 'test_weights.nc'
if os.path.exists(filename):
os.remove(filename)
regrid = esmf_regrid_build(grid_in,
grid_out,
'conservative',
filename=filename)
assert regrid.regrid_method is ESMF.RegridMethod.CONSERVE
# apply regridding using ESMPy's native method
data_out_esmpy = esmf_regrid_apply(regrid, data_in.T).T
rel_err = (data_out_esmpy - data_ref) / data_ref # relative error
assert np.max(np.abs(rel_err)) < 0.05
# apply regridding using scipy
weights = read_weights(filename, lon_in.size, lon_out.size)
shape_in = lon_in.shape
shape_out = lon_out.shape
data_out_scipy = apply_weights(weights, data_in, shape_in, shape_out)
# must be exactly the same as esmpy's result!
# TODO: this fails once but I cannot replicate it.
# Maybe assert_equal is too strict for scipy vs esmpy comparision
assert_equal(data_out_scipy, data_out_esmpy)
# finally, test broadcasting with scipy
# TODO: need to test broadcasting with ESMPy backend?
# We only use Scipy in frontend, and ESMPy is just for backend benchmark
# However, it is useful to compare performance and show scipy is 3x faster
data4D_out = apply_weights(weights, data4D_in, shape_in, shape_out)
# data over broadcasting dimensions should agree
assert_almost_equal(data4D_in.mean(axis=(2, 3)),
data4D_out.mean(axis=(2, 3)),
decimal=10)
# clean-up
esmf_regrid_finalize(regrid)
os.remove(filename)