tup=(sol_opt, obs),
#tup=(sol_opt,),
#names=('opt')
names=('opt', 'obs'))
fig.savefig('solutions.pdf')
from ComputabilityGraphs.helpers import all_mvars
from ComputabilityGraphs.TypeSet import TypeSet
for t in all_mvars(h.bgc_md2_computers()):
print(t.__name__)
# +
#mvs.get_StateVariableTupleTimeDerivative()
# +
epa_opt = EstimatedParameters._make(C_opt)
from model_specific_helpers import ModelParameters, Parameters
apa = Parameters.from_EstimatedParametersAndUnEstimatedParameters(epa_opt, cpa)
mpa = ModelParameters(
**{k: v
for k, v in apa._asdict().items() if k in ModelParameters._fields})
# -
mpa
Bib = mvs.get_BibInfo()
Bib.sym_dict
# k_root=1/(365*5),
# k_wood=1/(365*40),
# k_metlit=0.5/(365*0.1),
# k_mic=0.3/(365*0.137),
# k_slowsom=0.3/(365*5),
# k_passsom=0.3/(222.22*365),
# C_metlit_0=0.05,
# C_strlit_0=0.1,
# C_mic_0=1,
# C_passom_0=10,
#)
epa_0 = EstimatedParameters._make(
np.concatenate([
# we don't want to use the average for
# the betas since their sum will immediately
# violate our filter condition 3
np.array((0.3, 0.4)),
# but for the rest it is
(c_min + c_max)[2:] / 2.0
]))
# save initial parameters
pd.DataFrame(epa_0).to_csv(dataPath.joinpath('epa_0.csv'), sep=',')
isQualified = make_param_filter_func(c_max, c_min)
# check if the current value passes the filter
# to avoid to get stuck in an inescapable series of rejections
if not (isQualified(np.array(epa_0))):
raise ValueError(
"""the current value does not pass filter_func. This is probably due to an initial value chosen outside the permitted range"""
)