def param2res(pa):
epa = EstimatedParameters(*pa)
# here we want to store only monthly values
# although the computation takes place on a daily basis
V_init = construct_V0(cpa, epa)
# compute the days when we need the results
# to be able to compare to the monthly output
day_indices = month_2_day_index(range(cpa.number_of_months))
print("day_indices=", day_indices)
apa = Parameters.from_EstimatedParametersAndUnEstimatedParameters(
epa, cpa)
mpa = ModelParameters(**{
k: v
for k, v in apa._asdict().items() if k in ModelParameters._fields
})
full_res = run_forward_simulation(V_init=V_init,
day_indices=day_indices,
mpa=mpa)
# project the litter and soil pools
tot_len = cpa.number_of_months
c_litter = np.sum(full_res[:, 3:6], axis=1).reshape(tot_len, 1)
c_soil = np.sum(full_res[:, 6:9], axis=1).reshape(tot_len, 1)
out_simu = np.concatenate(
[
full_res[:, 0:3], # the first 3 pools are used as they are
c_litter,
c_soil,
full_res[:, 9:10]
],
axis=1)
return out_simu
def param2res(pa):
epa = EstimatedParameters(*pa)
# here we want to store only monthly values
# although the computation takes place on a daily basis
#x_init = np.array([cleaf_0,croot_0,cwood_0,epa[12],epa[13],clitter_0-epa[12]-epa[13],epa[14],csoil_0- epa[14] - epa[15], epa[15]]).reshape([9,1])
# Initial carbon pool size
x_init = construct_X0(cpa, epa)
# compute the days when we need the results
# to be able to compare to the monthly output
day_indices = month_2_day_index(range(cpa.number_of_months))
apa = Parameters.from_EstimatedParametersAndUnEstimatedParameters(
epa, cpa)
mpa = ModelParameters(**{
k: v
for k, v in apa._asdict().items() if k in ModelParameters._fields
})
full_res = run_forward_simulation(
x_init=x_init,
day_indices=day_indices,
**mpa._asdict()
#npp=npp,
#clay=clay,
#silt=silt,
#lig_wood=lig_wood,
#lig_leaf=epa.lig_leaf,
#beta_leaf=epa.beta_leaf,
#beta_root=epa.beta_root,
#f_leaf2metlit=epa.f_leaf2metlit,
#f_root2metlit=epa.f_root2metlit,
#f_wood2CWD=f_wood2CWD,
#f_metlit2mic=f_metlit2mic,
#k_leaf=epa.k_leaf,
#k_root=epa.k_root,
#k_wood=epa.k_wood,
#k_metlit=epa.k_metlit,
#k_mic=epa.k_mic,
#k_slowsom=epa.k_slowsom,
#k_passsom=epa.k_passsom
)
# project the litter and soil pools
tot_len = cpa.number_of_months
c_litter = np.sum(full_res[:, 3:6], axis=1).reshape(tot_len, 1)
c_soil = np.sum(full_res[:, 6:9], axis=1).reshape(tot_len, 1)
out_simu = np.concatenate(
[
full_res[:, 0:3], # the first 3 pools are used as they are
c_litter,
c_soil,
full_res[:, 9:10]
],
axis=1)
return out_simu
def test_param2res_sym(self):
const_params = self.cpa
param2res_sym = make_param2res_sym(const_params)
res_sym = param2res_sym(self.epa0)
day_indices = month_2_day_index(range(self.pa.number_of_months)),
fig = plt.figure()
plot_solutions(fig,
times=day_indices,
var_names=Observables._fields,
tup=(res_sym, ))
fig.savefig('solutions.pdf')
sol = np.stack(sols)
return sol
return param2res
# +
# now test it
import matplotlib.pyplot as plt
from general_helpers import plot_solutions
const_params = cpa
param2res_sym = make_param2res_sym(const_params)
xs = param2res_sym(epa_0)
day_indices = month_2_day_index(range(cpa.number_of_months)),
fig = plt.figure()
plot_solutions(
fig,
#times=day_indices,
times=range(cpa.number_of_months),
var_names=Observables._fields,
tup=(xs, ))
fig.savefig('solutions.pdf')
# -
# ### mcmc to optimize parameters
# coming soon
def test_month_2_day_index(self):
self.assertEqual(month_2_day_index([0]), [0])
self.assertEqual(month_2_day_index([1]), [31])
self.assertEqual(month_2_day_index([2]), [59])
self.assertEqual(month_2_day_index([3]), [90])
self.assertEqual(month_2_day_index([1, 3]), [31, 90])
