def test_gradient(od, axesList):
varNameList = ["sinZ", "sinY", "sinX", "sintime"]
if axesList == "wrong":
with pytest.raises(ValueError):
gradient(od, varNameList=varNameList, axesList=axesList)
else:
grad_ds = gradient(od, varNameList=varNameList, axesList=axesList)
if axesList is None:
axesList = list(od.grid_coords.keys())
# sin' = cos
for varName in varNameList:
for axis in axesList:
gradName = "d" + varName + "_" + "d" + axis
var = grad_ds[gradName]
if axis not in varName:
assert var.min().values == grad_ds[gradName].max(
).values == 0
else:
check = od.dataset["cos" + axis].where(var)
mask = xr.where(
np.logical_or(check.isnull(), var.isnull()), 0, 1)
assert_allclose(
var.where(mask, drop=True).values,
check.where(mask, drop=True).values,
1.0e-3,
)
def test_shortcuts(od_in):
# Only use some variables
list_calc = [
"Temp",
"U",
"V",
"W",
"HFacC",
"HFacW",
"HFacS",
"drC",
"drF",
"dxC",
"dyC",
"dxF",
"dyF",
"dxG",
"dyG",
"dxV",
"dyU",
"rA",
"rAw",
"rAs",
"rAz",
]
od_in = od_in.subsample.cutout(varList=list_calc)
# Gradient
ds_out = gradient(od_in)
od_out = od_in.compute.gradient()
ds_out_IN_od_out(ds_out, od_out)
# Divergence
ds_out = divergence(od_in, iName="U", jName="V", kName="W")
od_out = od_in.compute.divergence(iName="U", jName="V", kName="W")
ds_out_IN_od_out(ds_out, od_out)
# Curl
ds_out = curl(od_in, iName="U", jName="V", kName="W")
od_out = od_in.compute.curl(iName="U", jName="V", kName="W")
ds_out_IN_od_out(ds_out, od_out)
# Laplacian
ds_out = laplacian(od_in, "Temp")
od_out = od_in.compute.laplacian(varNameList="Temp")
ds_out_IN_od_out(ds_out, od_out)
# Weighted mean
ds_out = weighted_mean(od_in)
od_out = od_in.compute.weighted_mean()
ds_out_IN_od_out(ds_out, od_out)
# Integral
ds_out = integral(od_in)
od_out = od_in.compute.integral()
ds_out_IN_od_out(ds_out, od_out)
def test_all_gradients(od):
od_moor = od.subsample.mooring_array(
Xmoor=[od.dataset["X"].min().values, od.dataset["X"].max().values],
Ymoor=[od.dataset["Y"].min().values, od.dataset["Y"].max().values],
)
with pytest.warns(UserWarning):
X = od_moor.dataset["XC"].squeeze().values
Y = od_moor.dataset["YC"].squeeze().values
od_surv = od.subsample.survey_stations(Xsurv=X, Ysurv=Y)
# Test all dimension
DIMS = []
VARS = []
for var in od.dataset.data_vars:
this_dims = list(od.dataset[var].dims)
append = True
for dims in DIMS:
checks = [
set(this_dims).issubset(set(dims)),
set(dims).issubset(set(this_dims)),
]
if all(checks):
append = False
continue
if append:
VARS = VARS + [var]
DIMS = DIMS + [list(this_dims)]
gradient(od, varNameList=VARS)
gradient(od_moor, varNameList=VARS)
gradient(od_surv, varNameList=VARS)
def test_Brunt_Vaisala_frequency(od_in):
# Compute N2
ds_out = Brunt_Vaisala_frequency(od_in)
assert ds_out['N2'].attrs['units'] == 's^-2'
assert ds_out['N2'].attrs['long_name'] == 'Brunt-Väisälä Frequency'
check_params(ds_out, 'N2', ['g', 'rho0'])
# Check values
dSigma0_dZ = gradient(od_in, 'Sigma0', 'Z')
dSigma0_dZ = dSigma0_dZ['dSigma0_dZ']
assert_allclose(-dSigma0_dZ.values
* od_in.parameters['g']
/ od_in.parameters['rho0'], ds_out['N2'].values)
# Test shortcut
od_out = od_in.compute.Brunt_Vaisala_frequency()
ds_out_IN_od_out(ds_out, od_out)
def test_Brunt_Vaisala_frequency(od_in):
# Compute N2
ds_out = Brunt_Vaisala_frequency(od_in)
assert ds_out["N2"].attrs["units"] == "s^-2"
assert ds_out["N2"].attrs["long_name"] == "Brunt-Väisälä Frequency"
check_params(ds_out, "N2", ["g", "rho0"])
# Check values
dSigma0_dZ = gradient(od_in, "Sigma0", "Z")
dSigma0_dZ = dSigma0_dZ["dSigma0_dZ"]
assert_allclose(
-dSigma0_dZ.values * od_in.parameters["g"] / od_in.parameters["rho0"],
ds_out["N2"].values,
)
# Test shortcut
od_out = od_in.compute.Brunt_Vaisala_frequency()
ds_out_IN_od_out(ds_out, od_out)