def test_int_mean_options(od, varNameList, axesList, storeWeights):
if axesList == "wrong":
with pytest.raises(ValueError):
weighted_mean(
od,
varNameList=varNameList,
axesList=axesList,
storeWeights=storeWeights,
)
else:
weighted_mean(od,
varNameList=varNameList,
axesList=axesList,
storeWeights=storeWeights)
def test_integral(od, varNameList):
int_ds = integral(od, varNameList)
w_mean_ds = weighted_mean(od, varNameList)
int_name = [var for var in int_ds.data_vars][0]
var = int_ds[int_name]
check = (w_mean_ds["w_mean_" + varNameList] *
w_mean_ds["weight_" + varNameList].sum())
assert var.values == check.values
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_weighted_mean(od, varNameList):
w_mean_ds = weighted_mean(od, varNameList)
var = w_mean_ds["w_mean_" + varNameList].values
check = od.dataset[varNameList].mean().values
assert var == check
