def test_convert(self):
masked_data = np.ma.masked_array(data=np.arange(2), mask=[1, 0], fill_value=-1)
unit = "d"
var = Variable(data=masked_data, unit=unit)
tar_unit = "s"
var.convert(tar_unit)
self.assertTrue(np.all(var.data.filled() == [-1, 86400]))
self.assertEqual(var.unit, "s")
tar_unit = "m"
with self.assertRaises(ValueError):
var.convert(tar_unit)
def create_Daelta_14C_dataset(mdo, ds, mr, mr_14C):
alpha = 1.18e-12
t_conv = lambda t: 2001 + (15 + t) / 365.25
F_Delta_14C = lambda C12, C14: (C14 / C12 / alpha - 1) * 1000
ms = mdo.model_structure
## create 14C dataset
data_vars = {}
## save stocks
for pool_nr, pool_name in enumerate(ms.pool_names):
var_name_ds = pool_name
soln = mr.solve()
soln_14C = mr_14C.solve()
new_var = Variable(
data=F_Delta_14C(soln[:, pool_nr], soln_14C[:, pool_nr]),
unit=mdo.stock_unit,
)
add_variable(ds, data_vars, var_name_ds, new_var)
## save external input fluxes
# insert np.nan at time t0
# raise(Exception('check units and values, use acc variants'))
Us_monthly = Variable(
data=np.concatenate(
[
np.nan * np.ones((1, len(ms.pool_names))),
mr.acc_gross_external_input_vector(),
],
axis=0,
),
unit="g/(365.25/12 d)",
)
print(mr, Us_monthly)
Us_monthly_14C = Variable(
data=np.concatenate(
[
np.nan * np.ones((1, len(ms.pool_names))),
mr_14C.acc_gross_external_input_vector(),
],
axis=0,
),
unit="g/(365.25/12 d)",
)
# convert to daily flux rates
us = Us_monthly.convert("g/d")
us_14C = Us_monthly_14C.convert("g/d")
for pool_nr, pool_name in enumerate(ms.pool_names):
var_name_ds = "NPP_to_" + pool_name
new_var = Variable(
data=F_Delta_14C(us.data[:, pool_nr], us_14C.data[:, pool_nr]), unit=us.unit
)
add_variable(ds, data_vars, var_name_ds, new_var)
## save internal fluxes
# insert np.nan at time t0
Fs_monthly = Variable(
data=np.concatenate(
[
np.nan * np.ones((1, len(ms.pool_names), len(ms.pool_names))),
mr.acc_gross_internal_flux_matrix(),
],
axis=0,
),
unit="g/(365.25/12 d)",
)
Fs_monthly_14C = Variable(
data=np.concatenate(
[
np.nan * np.ones((1, len(ms.pool_names), len(ms.pool_names))),
mr_14C.acc_gross_internal_flux_matrix(),
],
axis=0,
),
unit="g/(365.25/12 d)",
)
# convert to daily flux rates
fs = Fs_monthly.convert("g/d")
fs_14C = Fs_monthly_14C.convert("g/d")
for pnr_from, pn_from in enumerate(ms.pool_names):
for pnr_to, pn_to in enumerate(ms.pool_names):
var_name_ds = pn_from + "_to_" + pn_to
new_var = Variable(
data=F_Delta_14C(
fs.data[:, pnr_to, pnr_from], fs_14C.data[:, pnr_to, pnr_from]
),
unit=fs.unit,
)
add_variable(ds, data_vars, var_name_ds, new_var)
## save external output fluxes
# insert np.nan at time t0
Rs_monthly = Variable(
data=np.concatenate(
[
np.nan * np.ones((1, len(ms.pool_names))),
mr.acc_gross_external_output_vector(),
],
axis=0,
),
unit="g/(365.25/12 d)",
)
Rs_monthly_14C = Variable(
data=np.concatenate(
[
np.nan * np.ones((1, len(ms.pool_names))),
mr_14C.acc_gross_external_output_vector(),
],
axis=0,
),
unit="g/(365.25/12 d)",
)
# convert to daily flux rates
rs = Rs_monthly.convert("g/d")
rs_14C = Rs_monthly_14C.convert("g/d")
for pool_nr, pool_name in enumerate(ms.pool_names):
var_name_ds = pool_name + "_to_RH"
new_var = Variable(
data=F_Delta_14C(rs.data[:, pool_nr], rs_14C.data[:, pool_nr]), unit=rs.unit
)
add_variable(ds, data_vars, var_name_ds, new_var)
ds_Delta_14C = xr.Dataset(data_vars=data_vars, coords=ds.coords, attrs=ds.attrs)
return ds_Delta_14C