def make_daily_iterator_sym(mvs, V_init: StartVector, par_dict, func_dict):
B_func, u_func = make_B_u_funcs_2(mvs, par_dict, func_dict)
sv = mvs.get_StateVariableTuple()
n = len(sv)
# build an array in the correct order of the StateVariables which in our case is already correct
# since V_init was built automatically but in case somebody handcrafted it and changed
# the order later in the symbolic formulation....
V_arr = np.array([V_init.__getattribute__(str(v))
for v in sv] + [V_init.rh]).reshape(
n + 1, 1) #reshaping is neccessary for matmux
def f(it, V):
X = V[0:n]
b = u_func(it, X)
B = B_func(it, X)
outfluxes = B @ X
X_new = X + b + outfluxes
# we also compute the autotrophic and heterotrophic respiration in every (daily) timestep
#ra= -np.sum(outfluxes[0:3]) # check with mvs.get_StateVariableTuple()[0:3]
rh = -(outfluxes[3:n]).reshape(
(n - 3, )).sum() # check with mvs.get_StateVariableTuple()[3:9]
V_new = np.concatenate((X_new.reshape(n, 1), rh.reshape(1, 1)), axis=0)
return V_new
return TimeStepIterator2(
initial_values=V_arr,
f=f,
)
def make_daily_iterator_sym(
mvs,
V_init: StartVector,
par_dict,
func_dict
):
B_func, u_func = make_B_u_funcs_2(mvs,par_dict,func_dict)
sv=mvs.get_StateVariableTuple()
n=len(sv)
# build an array in the correct order of the StateVariables which in our case is already correct
# since V_init was built automatically but in case somebody handcrafted it and changed
# the order later in the symbolic formulation....
V_arr=np.array(
[V_init.__getattribute__(str(v)) for v in sv]+
[V_init.ra,V_init.rh]
).reshape(n+2,1) #reshaping is neccessary for matmul
def f(it,V):
X = V[0:n]
b = u_func(it,X)
B = B_func(it,X)
X_new = X + b + B @ X
# we also compute the autotrophic and heterotrophic respiration in every (daily) timestep
ra = np.sum(
[
numOutFluxesBySymbol[k](0,*X_0)
for k in [C_leaf,C_wood,C_root]
if k in numOutFluxesBySymbol.keys()
]
)
rh = np.sum(
[
numOutFluxesBySymbol[k](0,*X_0)
for k in [C_leaf_litter,C_wood_litter,C_root_litter,C_soil_fast,C_soil_slow,C_soil_passive]
if k in numOutFluxesBySymbol.keys()
]
)
V_new = np.concatenate((X_new.reshape(n,1),np.array([ra,rh]).reshape(2,1)), axis=0)
return V_new
return TimeStepIterator2(
initial_values=V_arr,
f=f,
)
def npp_func(day):
month = day_2_month_index(day)
return dvs.npp[month] * 86400 # kg/m2/s kg/m2/day
def xi_func(day):
return 1.0 # preliminary fake for lack of better data...
func_dict = {'NPP': npp_func, 'xi': xi_func}
# for the next line to work the
# two dictionaries par_dict and func_dict have to be complete.
# In sympy terms this means that the expressions for
B_func, u_func = make_B_u_funcs_2(mvs, par_dict, func_dict)
# we create a numeric startvector for the compartmental system
#
svs_0 = Observables(*map(lambda v: v[0], svs))
X_0 = np.array((
svs_0.cVeg / 3,
svs_0.cVeg / 3,
svs_0.cVeg / 3,
svs_0.cLitter / 2,
svs_0.cLitter / 2,
svs_0.cSoil / 6,
svs_0.cSoil / 6,
svs_0.cSoil / 6,
svs_0.cSoil / 6,
svs_0.cSoil / 6,