def test_divergence(od, varNameList):
# Add units
if None not in varNameList:
for varName in varNameList:
od._ds[varName].attrs["units"] = "m/s"
# Compute divergence
dive_ds = divergence(od,
iName=varNameList[0],
jName=varNameList[1],
kName=varNameList[2])
# sin' = cos
for varName in varNameList:
if varName is not None:
axis = varName[-1]
diveName = "d" + varName + "_" + "d" + axis
var = dive_ds[diveName]
coords = {coord[0]: var[coord] for coord in var.coords}
coords["Z"], coords["Y"], coords["X"] = xr.broadcast(
coords["Z"], coords["Y"], coords["X"])
check = np.cos(coords[axis])
mask = xr.where(np.logical_or(check.isnull(), var.isnull()), 0, 1)
# Assert using numpy
var = var.where(mask, drop=True).values
check = check.where(mask, drop=True).values
assert_array_almost_equal(var, check, 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_normal_strain(od_in):
# Compute n_strain
ds_out = normal_strain(od_in)
assert ds_out["n_strain"].attrs["units"] == "s^-1"
long_name = "normal component of strain"
assert ds_out["n_strain"].attrs["long_name"] == long_name
# Check values
divs = divergence(od_in, iName="U", jName="V")
assert_allclose((divs["dU_dX"] - divs["dV_dY"]).values,
ds_out["n_strain"].values)
# Test shortcut
od_out = od_in.compute.normal_strain()
ds_out_IN_od_out(ds_out, od_out)
def test_horizontal_divergence_velocity(od_in):
# Compute hor_div_vel
ds_out = horizontal_divergence_velocity(od_in)
assert ds_out["hor_div_vel"].attrs["units"] == "m s^-2"
long_name = "horizontal divergence of the velocity field"
assert ds_out["hor_div_vel"].attrs["long_name"] == long_name
# Check values
hor_div = divergence(od_in, iName="U", jName="V")
hor_div = hor_div["dU_dX"] + hor_div["dV_dY"]
assert_allclose(hor_div.values, ds_out["hor_div_vel"].values)
# Test shortcut
od_out = od_in.compute.horizontal_divergence_velocity()
ds_out_IN_od_out(ds_out, od_out)
def test_normal_strain(od_in):
# Compute n_strain
ds_out = normal_strain(od_in)
assert ds_out['n_strain'].attrs['units'] == 's^-1'
long_name = 'normal component of strain'
assert ds_out['n_strain'].attrs['long_name'] == long_name
# Check values
divs = divergence(od_in, iName='U', jName='V')
assert_allclose((divs['dU_dX'] - divs['dV_dY']).values,
ds_out['n_strain'].values)
# Test shortcut
od_out = od_in.compute.normal_strain()
ds_out_IN_od_out(ds_out, od_out)
def test_horizontal_divergence_velocity(od_in):
# Compute hor_div_vel
ds_out = horizontal_divergence_velocity(od_in)
assert ds_out['hor_div_vel'].attrs['units'] == 'm s^-2'
long_name = 'horizontal divergence of the velocity field'
assert ds_out['hor_div_vel'].attrs['long_name'] == long_name
# Check values
hor_div = divergence(od_in, iName='U', jName='V')
hor_div = hor_div['dU_dX'] + hor_div['dV_dY']
assert_allclose(hor_div.values, ds_out['hor_div_vel'].values)
# Test shortcut
od_out = od_in.compute.horizontal_divergence_velocity()
ds_out_IN_od_out(ds_out, od_out)
def test_div_errors(od, iName, jName, kName):
with pytest.raises(ValueError):
divergence(od, iName=iName, jName=jName, kName=kName)