def make_daily_iterator_sym(
V_init,
mpa,
func_dict
):
# b (b vector for partial allocation)
beta_wood = 1- mpa.beta_leaf- mpa.beta_root
b = np.array(
[
mpa.beta_leaf,
mpa.beta_root,
beta_wood,
0,
0,
0,
0,
0,
0
],
).reshape(9,1)
# Now construct B matrix B=A*K
B_func = construct_matrix_func_sym(
pa=mpa,
)
# Build the iterator which is the representation of a dynamical system
# for looping forward over the results of a difference equation
# X_{i+1}=f(X_{i},i)
# This is the discrete counterpart to the initial value problem
# of the continuous ODE
# dX/dt = f(X,t) and initial values X(t=0)=X_0
def f(it,V):
X = V[0:9]
co2 = V[9]
npp = func_dict[Symbol('npp')](it,X)
B = B_func(it,X)
X_new = X + npp * b + B@X
# we also compute the respired co2 in every (daily) timestep
# and use this part of the solution later to sum up the monthly amount
co2_new = -np.sum(B @ X) # fixme add computer for respirattion
V_new = np.concatenate((X_new,np.array([co2_new]).reshape(1,1)), axis=0)
return V_new
#X_0 = np.array(x_init).reshape(9,1) #transform the tuple to a matrix
#co2_0 = np.array([0]).reshape(1,1)
#V_0 = np.concatenate((X_0, co2_0), axis=0)
return TimeStepIterator2(
initial_values=V_init,
f=f#,
#max_it=max(day_indices)+1
)
def make_daily_iterator(x_init, npp, clay, silt, lig_wood, lig_leaf, beta_leaf,
beta_root, f_leaf2metlit, f_root2metlit, f_wood2CWD,
f_metlit2mic, k_leaf, k_root, k_wood, k_metlit, k_mic,
k_slowsom, k_passsom):
# Construct npp(day)
# in general this function can depend on the day i and the state_vector X
# e.g. typically the size fo X.leaf...
# In this case it only depends on the day i
def npp_func(day, X):
return npp[day_2_month_index(day)] * b
# b (b vector for partial allocation)
beta_wood = 1 - beta_leaf - beta_root
b = np.array(
[beta_leaf, beta_root, beta_wood, 0, 0, 0, 0, 0, 0], ).reshape(9, 1)
# Now construct B matrix B=A*K
B_func = make_compartmental_matrix_func(clay, silt, lig_wood, lig_leaf,
f_leaf2metlit, f_root2metlit,
f_wood2CWD, f_metlit2mic, k_leaf,
k_root, k_wood, k_metlit, k_mic,
k_slowsom, k_passsom)
# Build the iterator which is the representation of a dynamical system
# for looping forward over the results of a difference equation
# X_{i+1}=f(X_{i},i)
# This is the discrete counterpart to the initial value problem
# of the continuous ODE
# dX/dt = f(X,t) and initial values X(t=0)=X_0
def f(it, V):
X = V[0:9]
co2 = V[9]
npp = npp_func(it, X)
B = B_func(it, X)
X_new = X + npp * b + B @ X
# we also compute the respired co2 in every (daily) timestep
# and use this part of the solution later to sum up the monthly amount
co2_new = respiration_from_compartmental_matrix(B, X)
V_new = np.concatenate((X_new, np.array([co2_new]).reshape(1, 1)),
axis=0)
return V_new
X_0 = np.array(x_init).reshape(9, 1) #transform the tuple to a matrix
co2_0 = np.array([0]).reshape(1, 1)
V_0 = np.concatenate((X_0, co2_0), axis=0)
return TimeStepIterator2(
initial_values=V_0,
f=f,
#max_it=max(day_indices)+1
)