def make_daily_iterator_sym(
V_init,
mpa,
func_dict
):
from bgc_md2.models.cable_yuanyuan.source import mvs
B_func, u_func = make_B_u_funcs(mvs,mpa,func_dict)
def f(it,V):
X = V[0:9]
co2 = V[9]
b = u_func(it,X)
B = B_func(it,X)
X_new = X + 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
return TimeStepIterator2(
initial_values=V_init,
f=f#,
#max_it=max(day_indices)+1
)
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 monthly_matrix_simu(mpa, Xnt, npp, ns):
# here we want to store only monthly values
# although the computation takes place on a dayly basis
# Construct b vector for allocation
b_func = construct_allocation_vector_func(mpa)
# Now construct B matrix B=A*K
B_func = construct_matrix_func(mpa)
def f(it, V):
X = V[0:9]
co2 = V[9]
b = b_func(it, X)
B = B_func(it, X)
X_new = X + b * npp[it] + B @ X
# here we add the co2 up in every step which allows us not to save it
#co2_new = co2 + respiration_from_compartmental_matrix(B,X) #accumulate respiration
co2_new = respiration_from_compartmental_matrix(
B, X) #accumulate respiration
V_new = np.concatenate((X_new, np.array([co2_new]).reshape(1, 1)),
axis=0)
return V_new
X_0 = np.array(Xnt).reshape(9, 1) #transform the tuple to a matrix
co2_0 = np.array([0]).reshape(1, 1)
day_indices = month_2_day_index(ns)
tsi = TimeStepIterator2(initial_values=np.concatenate((X_0, co2_0),
axis=0),
f=f,
max_it=max(day_indices) + 1)
#values = [ np.transpose(val) for val in tsi if tsi.i in day_indices]
#values = [v for i, v in enumerate(tsi) if i in day_indices]
def g(acc, el):
xs, co2s, acc_co2 = acc
i, v = el
d_pools = v[0:-1, :]
d_co2 = v[-1:, :]
acc_co2 += d_co2
if i in day_indices:
xs += [d_pools]
co2s += [acc_co2]
acc_co2 = np.array([0.0]).reshape(1, 1)
acc = (xs, co2s, acc_co2)
return acc
xs, co2s, _ = reduce(g, enumerate(tsi), ([], [], 0))
def h(tup):
x, co2 = tup
return np.transpose(np.concatenate([x, co2]))
values_with_monthly_co2 = [v for v in map(h, zip(xs, co2s))]
RES = np.concatenate(values_with_monthly_co2, axis=0)
return RES
def make_daily_iterator(V_init, mpa):
# 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 mpa.npp[day_2_month_index(day)]
# 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 = make_compartmental_matrix_func(mpa=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 = 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_init,
f=f,
#max_it=max(day_indices)+1
)
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,
)