def main(Ca=None):
par = 1500.0
Ci = Ca * 0.7
Rd = None
Tleaf = 25.
deg2kelvin = 273.15
Tleaf += deg2kelvin
Eaj = 27900.0
Eav = 69900.0
deltaSj = 619.0
deltaSv = 634.0
Hdv = 200000.0
Hdj = 200000.0
r25 = 2.0
Q10 = 2.0
N_c = 0.015
N_e = 0.015
N_p = 0.10
#===== Medlyn paper ====#
alpha = 0.425 # (-)
g_m = 0.4 # mol m-2 s-1 bar-1 ie mol m-2 s-1 at 1 atm
K_cat = 24.0 # mmol CO2 mol-1 Rubisco-1
K_s = 1.25E-04 # mol N s umol-1
#=======================#
P = PhotosynthesisModel(peaked_Jmax=True,
peaked_Vcmax=True,
alpha=alpha,
g_m=g_m,
K_cat=K_cat,
K_s=K_s)
O = OptimiseNitrogenAllocation(model=P)
Jm_store = np.zeros(0)
Vm_store = np.zeros(0)
leaf_temp = np.linspace(5, 30, 10)
Npools_store = np.zeros((len(leaf_temp), 4))
for i, Tleaf in enumerate(leaf_temp):
initial_guess = np.array([N_c, N_e])
# ===== OPTIMIZE parameter set ===== #
result = O.main(fit_params=initial_guess,
par=par,
Rd=Rd,
Ci=Ci,
N_p=N_p,
Tleaf=Tleaf + deg2kelvin,
Tref=25.0,
Eaj=Eaj,
Eav=Eav,
deltaSj=deltaSj,
deltaSv=deltaSv,
r25=r25,
Q10=Q10,
Hdv=Hdv,
Hdj=Hdj)
# retrieve all fitted N pools
(N_c, N_e, N_r, N_s) = P.N_pool_store
Npools_store[i] = N_c, N_e, N_r, N_s
fitted_x0 = np.array([N_c, N_e])
(An, Anc, Anj, Jmax25,
Vcmax25) = P.calc_photosynthesis(N_pools=fitted_x0,
par=par,
Ci=Ci,
Rd=Rd,
N_p=N_p,
Tleaf=Tleaf + deg2kelvin,
Tref=25.0,
Eaj=Eaj,
Eav=Eav,
deltaSj=deltaSj,
deltaSv=deltaSv,
r25=r25,
Q10=Q10,
Hdv=Hdv,
Hdj=Hdj)
Jm_store = np.append(Jm_store, Jmax25)
Vm_store = np.append(Vm_store, Vcmax25)
jv_ratio = Jm_store / Vm_store
return jv_ratio, leaf_temp, Npools_store
Hdv = 200000.0
Hdj = 200000.0
r25 = None
Q10 = None
N_c = 0.015
N_e = 0.015
#===== Medlyn paper ====#
alpha = 0.425 # (-)
g_m = 0.4 # mol m-2 s-1 bar-1 ie mol m-2 s-1 at 1 atm
K_cat = 24.0 # mmol CO2 mol-1 Rubisco-1
K_s = 1.25E-04 # mol N s umol-1
#=======================#
P = PhotosynthesisModel(peaked_Jmax=True,
peaked_Vcmax=True,
alpha=alpha,
g_m=g_m,
K_cat=K_cat,
K_s=K_s)
O = OptimiseNitrogenAllocation(model=P)
N_c_store = []
N_e_store = []
N_r_store = []
N_s_store = []
An_store = []
for N_p in np.linspace(0.05, 0.15, 10):
x0 = np.array([N_c, N_e])
# ===== OPTIMIZE parameter set ===== #
result = O.main(fit_params=x0,
par=par,
