# load the corresponding model runs
# convert monthly time steps to daily time steps, each month comprising 31 days
data_times_m = np.arange(len(sub_ds_m["time"])) * 31
data_times_y0 = np.arange(len(sub_ds_y0["time"])) * 31 * 12
data_times_y6 = np.arange(len(sub_ds_y6["time"])) * 31 * 12
# +
# monthly
start_values_m = sub_ds_m["start_values"]
us_m = sub_ds_m["us"]
Bs_m = sub_ds_m["Bs"]
pwc_mr_m = PWCModelRunFD.from_Bs_and_us(symbols("t"),
data_times_m,
start_values_m.values,
Bs_m[:-1],
us_m[:-1],
state_variables=sub_ds_m.pool.values)
# yearly (Jan)
start_values_y0 = sub_ds_y0["start_values"]
us_y0 = sub_ds_y0["us"]
Bs_y0 = sub_ds_y0["Bs"]
pwc_mr_y0 = PWCModelRunFD.from_Bs_and_us(symbols("t"),
data_times_y0,
start_values_y0.values,
Bs_y0[:-1],
us_y0[:-1],
state_variables=sub_ds_y0.pool.values)
sub_ds = ds.isel(lat=lat, lon=lon, prob=prob)
sub_ds
# +
# load the corresponding model run
# convert monthly time steps to daily time steps, each month comprising 31 days
data_times = np.arange(len(sub_ds["time"])) * 31
start_values = sub_ds["start_values"]
us = sub_ds["us"]
Bs = sub_ds["Bs"]
pwc_mr = PWCModelRunFD.from_Bs_and_us(symbols("t"),
data_times,
start_values.values,
Bs[:-1],
us[:-1],
state_variables=sub_ds.pool.values)
# -
fig, ax = plt.subplots(figsize=(8, 8))
pwc_mr.model.plot_pools_and_fluxes(ax, legend=False)
_ = ax.set_title("CARDAMOM")
# compute the solution
#soln = pwc_mr.solve()
soln = sub_ds["solution"].values
# +
fig, axes = plt.subplots(ncols=2, nrows=3, figsize=(18, 12))
def func(ds_single_site):
data_times = ds_single_site.times.data #.compute()
start_values = ds_single_site.start_values.data #.compute()
Bs = ds_single_site.Bs.data[:-1] #.compute()
us = ds_single_site.us.data[:-1] #.compute()
mr = PWCModelRunFD.from_Bs_and_us(symbols('t'), data_times, start_values,
Bs, us)
coords_time = ds_single_site.time
coords_pool = ds_single_site.pool
data_vars = dict()
soln = mr.solve()
data_vars['soln'] = xr.DataArray(
data=soln,
dims=['time', 'pool'],
coords={
'time': coords_time,
'pool': coords_pool
},
attrs={'units': ds_single_site.start_values.attrs['units']})
mav = mr.age_moment_vector(1) / 365.25
data_vars['mean_age_vector'] = xr.DataArray(data=mav,
dims=['time', 'pool'],
coords={
'time': coords_time,
'pool': coords_pool
},
attrs={'units': 'yr'})
msa = mr.system_age_moment(1) / 365.25
data_vars['mean_system_age'] = xr.DataArray(data=msa,
dims=['time'],
coords={'time': coords_time},
attrs={'units': 'yr'})
asdv = np.sqrt(mr.age_moment_vector(2)) / 365.25
data_vars['age_standard_deviation_vector'] = xr.DataArray(
data=asdv,
dims=['time', 'pool'],
coords={
'time': coords_time,
'pool': coords_pool
},
attrs={'units': 'yr'})
sasd = np.sqrt(mr.system_age_moment(2)) / 365.25
data_vars['system_age_standard_deviation'] = xr.DataArray(
data=sasd,
dims=['time'],
coords={'time': coords_time},
attrs={'units': 'yr'})
res = xr.Dataset(coords={
'time': coords_time,
'pool': coords_pool
},
data_vars=data_vars)
return res