def test_creation_from_matrix(self):
A = ImmutableMatrix(1, 1, [1])
B = ImmutableMatrix(1, 1, [1])
D = A*B
C = CompartmentalMatrix(D)
print(C, type(C))
self.assertEqual(type(C), CompartmentalMatrix)
'r':
'"fraction of decomposer biomass released as CO$_2$"' # "\\text{time}^{-1}"
,
's':
'"decomposers production rate of soil organic matter"' # "\\text{time}^{-1}"
,
'Phi_l':
'fresh organic matter carbon flux' # "(\\text{quantity of carbon})(\\text{time}))^{-1}"
}
for name in sym_dict.keys():
var(name)
t = TimeSymbol("t") # unit: ""
x = StateVariableTuple((C_s, C_ds))
u = InputTuple((0, Phi_l))
B = CompartmentalMatrix([[0, s - A], [0, A - r - s]])
mvs = CMTVS(
{
BibInfo( # Bibliographical Information
name="FB2005 - The model of SOM dynamics (model 2)",
longName="The C-N model of SOM dynamics, two decomposer types",
version="1",
entryAuthor="Holger Metzler",
entryAuthorOrcid="0000-0002-8239-1601",
entryCreationDate="22/03/2016",
doi="10.1111/j.1461-0248.2005.00813.x",
sym_dict=sym_dict),
B, # the overall compartmental matrix
u, # the overall input
t, # time for the complete system
for name in sym_dict.keys():
var(name)
x_nup = Min(1,(N_min/(F_nupmin*Delta_t)))
x_pup = Min(1,(P_lab/(F_pupmin*Delta_t)))
x_npup = Min(x_nup,x_pup)
x_nleaf = (n_leaf/(n_leaf+k_n))
x_pleaf = (p_leaf/(p_leaf+k_p))
x_npleaf = Min(x_nleaf,x_pleaf)
F_c = x_npleaf*x_npup*F_cmax #unit: "gC*m^{-2}*d^{-1}"
u = F_c
x = StateVariableTuple((C_leaf, C_root, C_wood))
b = (a_leaf, a_root, a_wood)
Input = InputTuple(u * ImmutableMatrix(b))
A = CompartmentalMatrix(
diag(-mu_leaf, -mu_root, -mu_wood)
)
t = TimeSymbol("t") #'day'
# timeResolution = daily # or yearly? incongruent turnover units
# Commented out the following lines because original publication only has 3 parameter values
# In original publication:
## See table 1 for parameter values; a_(leaf,wood,root) and 1/mu_(leaf,wood,root) are based on CASA
## 1/mu_(leaf,wood,root) = Mean residence time of plant tissue
## 1/n_max,leaf and 1/p_max,leaf : maximal leaf N:C and P:C ratios = 1.2*mean (min is obtained by 0.8*mean)
# # mean estimates were obtained from Glopnet datasets for each biome (Wright et al., 2004)
#np1 = NumericParameterization(
# par_dict={#"Evergreen needle leaf forest":
# Delta_t: 1,
# k_n: 0.01, #"gN*(gC)^{-1}"
# k_p: 0.0006, #"gP*(gC)^{-1}"
,
'i': 'mean annual carbon input' # "kgC m^{-2}yr^{-1}"
,
'h': 'humification coefficient',
'r': 'climatic and edaphic factors'
}
for name in sym_dict.keys():
var(name)
xi = r #environmental effects multiplier
T = ImmutableMatrix([[-1, 0], [h, -1]]) #transition operator
N = diag(k_1, k_2) #decomposition operator
t = TimeSymbol("t") # unit: "year"
x = StateVariableTuple((Y, O))
u = InputTuple((i, 0))
B = CompartmentalMatrix(xi * T * N)
# - "Bare fallow":
np1 = NumericParameterization(par_dict={
k_1: 0.8,
k_2: 0.00605,
i: 0,
h: 0.13,
r: 1.32
},
func_dict=frozendict({}))
nsv1 = NumericStartValueDict({Y: 0.3, O: 3.96})
# - "+N +straw":
np2 = NumericParameterization(par_dict={
k_1: 0.8,
k_2: 0.00605,
var(name)
epsilon_L = 1 - epsilon_R - epsilon_S
a_S = (epsilon_S + (Omega * (1.5 - L -
(0.5 * N_ava)))) / (1 + (Omega *
(3 - L - W - N_ava)))
a_R = (epsilon_R + (Omega * (1.5 - W -
(0.5 * N_ava)))) / (1 + (Omega *
(3 - L - W - N_ava)))
a_L = epsilon_L / (1 + (Omega * (3 - L - W - N_ava))) #, "a_L = 1-a_S-a_R"
x = StateVariableTuple((C_S, C_R, C_L))
u = NPP
b = (a_S, a_R, a_L)
Input = InputTuple(u * ImmutableMatrix(b))
A = CompartmentalMatrix([[-gamma_S, 0, 0], [0, -gamma_R, 0], [0, 0, -gamma_L]])
t = TimeSymbol("t")
mvs = MVarSet({
BibInfo( # Bibliographical Information
name="CEVSA2 ",
longName="",
version="2",
entryAuthor="Verónika Ceballos-Núñez",
entryAuthorOrcid="0000-0002-0046-1160",
entryCreationDate="",
doi="10.1016/j.ecocom.2010.04.002",
sym_dict=sym_dict),
A, # the overall compartmental matrix
Input, # the overall input
t, # time for the complete system
P = f_C * sigma_r * W_r / (f_N * sigma_c * A)
Q = f_N / (Beta * f_C)
lambda_p = P / (1 + P + Q)
lambda_s = Q / (1 + P + Q)
lambda_r = 1 / (1 + P + Q)
x = StateVariableTuple((W_N, W_C, W_p, W_s, W_r))
u = (sigma_c * A, sigma_r * W_r, 0, 0, 0)
b = 1
Input = InputTuple(b * ImmutableMatrix(u))
A = CompartmentalMatrix(
[[
-((f_cp * lambda_p * W_p) + (f_cs * lambda_s * W_s) +
(f_cr * lambda_r * W_r)) * ((kappa * W_N) / W_g**2), 0, 0, 0, 0
],
[
0, -((f_np * lambda_p * W_p) + (f_ns * lambda_s * W_s) +
(f_nr * lambda_r * W_r)) * ((kappa * W_C) / W_g**2), 0, 0, 0
], [0, (kappa * N * lambda_p * W_p) / W_g, 0, 0, 0],
[0, (kappa * N * lambda_s * W_s) / W_g, 0, 0, 0],
[0, (kappa * N * lambda_r * W_r) / W_g, 0, 0, 0]])
t = TimeSymbol("t") # unit: "day"
np1 = NumericParameterization(par_dict={
sigma_r: 0.01,
I_dens: 1000,
f_C: 0.5,
Beta: 0.05,
f_cp: 0.60,
f_cr: 0.45,
f_cs: 0.45,
'l_dr': 'Litter decomposition/respiration',
's_dr': 'Soil decomposition/respiration',
'u': 'scalar function of photosynthetic inputs'
}
for name in sym_dict.keys():
var(name)
#Model based on Fig. 1 in page 127 (3 in the PDF) of doi= "10.1016/0304-3800(88)90112-3", no ODEs in publication
t = TimeSymbol("t") #"the model has a daily and a yearly component. Allocation occurs yearly"
x = StateVariableTuple((C_f, C_r, C_w,C_frl,C_s))
b = ImmutableMatrix((eta_f, eta_w, eta_r))
Input = InputTuple(tuple(u * b)+(0,0))
A = CompartmentalMatrix(
[[-gamma_f, 0 , 0 , (l_p*eta_f) ,(s_p*eta_f)],
[ 0 ,-gamma_r, 0 , (l_p*eta_r) ,(s_p*eta_r)],
[ 0 , 0 ,-gamma_w, (l_p*eta_w) ,(s_p*eta_w)],
[ gamma_f, gamma_r, gamma_w, -(l_p+l_s+l_dr), 0 ],
[ 0 , 0 , 0 , l_s ,-(s_p+s_dr)]
])
## The following are default values suggested by this entry's creator only to be able to run the model:
np1 = NumericParameterization(
par_dict={u: 1400, eta_f: 0.48, eta_r: 0.44, eta_w: 0.49, gamma_r: 3.03, gamma_f: 23.32, gamma_w: 0.04},
func_dict=frozendict({})
)
nsv1 = NumericStartValueDict({
C_f: 200,
C_w: 5000,
C_r: 300
})
g_l * A / ((C_i - Gamma) * (1 + (D / D_0)))
A_n = G_s * (C_a - C_i)
A_c = A_n * (1 - exp(-k * L)) / k
GPP = A_c * 3600 * 12 / 1000000 # Scaling expression from TECO fortran code, line 667.' # gC*day^{-1}
f_W = Min((0.5 * W), 1)
f_T = Q_10 * ((T - 10) / 10)
xi = f_W * f_T
t = TimeSymbol("t") # unit: "day"
x = StateVariableTuple((x_1, x_2, x_3, x_4, x_5, x_6, x_7, x_8))
u = GPP
b = ImmutableMatrix((b_1, b_2, b_3))
Input = InputTuple(tuple(u * b) + (0, 0, 0, 0, 0))
B = CompartmentalMatrix([[-c_1, 0, 0, 0, 0, 0, 0, 0],
[0, -c_2, 0, 0, 0, 0, 0, 0],
[0, 0, -c_3, 0, 0, 0, 0, 0],
[f_41, 0, f_43, -c_4, 0, 0, 0, 0],
[f_51, f_52, f_53, 0, -c_5, 0, 0, 0],
[0, 0, 0, f_64, f_65, -c_6, f_67, f_68],
[0, 0, 0, 0, f_75, f_76, -c_7, 0],
[0, 0, 0, 0, 0, f_86, f_87, -c_8]])
np1 = NumericParameterization(par_dict={
GPP: 3.370,
b_1: 0.14,
b_2: 0.26,
b_3: 0.14,
f_41: 0.9,
f_51: 0.1,
f_52: 1,
f_43: 0.2,
f_53: 0.8,
f_64: 0.45,
f_65: 0.275,
x = StateVariableTuple((P, M, Q, B, D, EP, EM))
u = InputTuple((I_P, 0, 0, 0, I_D, 0, 0))
T = ImmutableMatrix(
[[-1, 0, 0, (1 - g_D) * (1 - p_EP - p_EM) * F_E / (F_E + F_R), 0, 0, 0],
[1 - f_D, -1, 0, 0, 0, 0, 0], [0, 0, -1, 0, F_A / (F_U + F_A), 0, 0],
[0, 0, 0, -1, F_U / (F_U + F_A), 0, 0],
[f_D, 1, 1, g_D * (1 - p_EP - p_EM) * F_E / (F_E + F_R), -1, 1, 1],
[0, 0, 0, p_EP * F_E / (F_E + F_R), 0, -1, 0],
[0, 0, 0, p_EM * F_E / (F_E + F_R), 0, 0, -1]])
N = ImmutableMatrix([[V_P * EP / (K_P + P), 0, 0, 0, 0, 0, 0],
[0, V_M * EM / (K_M + M), 0, 0, 0, 0, 0],
[0, 0, K_des / Q_max, 0, 0, 0, 0],
[0, 0, 0, F_E + F_R, 0, 0, 0],
[0, 0, 0, 0, F_U + F_A, 0, 0], [0, 0, 0, 0, 0, r_EP, 0],
[0, 0, 0, 0, 0, 0, r_EM]])
B = CompartmentalMatrix(T * N)
mvs = CMTVS(
{
BibInfo( # Bibliographical Information
name="MEND",
longName="Microbial-Enzyme-Mediated Decomposition model",
version="1",
entryAuthor="Holger Metzler",
entryAuthorOrcid="0000-0002-8239-1601",
entryCreationDate="21/03/2016",
doi="10.1890/12-0681.1",
further_references=BibInfo(
doi="10.2307/1313568"), #Li2014Biogeochemistry
sym_dict=sym_dict),
B, # the overall compartmental matrix
f_W = Min((0.5 * W), 1)
f_T = Q_10**((T - 10) / 10)
xi = f_W * f_T
x = StateVariableTuple((x_1, x_2, x_3, x_4, x_5, x_6, x_7, x_8))
B = (b_1, b_2, b_3, 0, 0, 0, 0, 0)
Input = InputTuple(U * ImmutableMatrix(B))
A = ImmutableMatrix([[-1, 0, 0, 0, 0, 0, 0, 0], [0, -1, 0, 0, 0, 0, 0, 0],
[0, 0, -1, 0, 0, 0, 0, 0],
[f_41, 0, f_43, -1, 0, 0, 0, 0],
[f_51, f_52, f_53, 0, -1, 0, 0, 0],
[0, 0, 0, f_64, f_65, -1, f_67, f_68],
[0, 0, 0, 0, f_75, f_76, -1, 0],
[0, 0, 0, 0, 0, f_86, f_87, -1]])
C = ImmutableMatrix(diag(c_1, c_2, c_3, c_4, c_5, c_6, c_7, c_8))
CM = CompartmentalMatrix(xi * A * C)
t = TimeSymbol("t")
# "Original parameters of the publication. Parameter value of GPP corresponds to an annual average"
# T, Q_10, and W are variables that should be looked at in a data set. What I have here are invented values
np1 = NumericParameterization(
par_dict={
Q_10: 1,
W: 4.2,
T: 25,
U: 3370 #"gC*day^{-1}" # Average estimated value
,
b_1: 0.14,
b_2: 0.14,
b_3: 0.26,
c_1: 0.00258,
allocated to wood""",
'tau_f': 'Inverse Residence time of carbon in foliage',
'tau_r': 'Inverse Residence time of carbon in roots',
'tau_w': 'Inverse Residence time of carbon in wood'
}
for name in sym_dict.keys():
var(name)
IPAR = SOL * FPAR * 0.5
NPP = IPAR * epsilon
x = StateVariableTuple((C_f, C_r, C_w))
u = NPP
b = (alpha_f, alpha_r, alpha_w)
Input = InputTuple(u * ImmutableMatrix(b))
A = CompartmentalMatrix(diag(-tau_f, -tau_r, -tau_w))
t = TimeSymbol("t")
# model_run_data is incomplete :
# Even parameter_sets cannot fully parameterize the symbolic model: mm made up
# a purely fictional parameters to show how a purely numeric could be build.
# (The yaml file lacks consistent unit information).
np1 = NumericParameterization(
par_dict={
alpha_f: Rational(1, 3),
alpha_r: Rational(1, 3),
alpha_w: Rational(1, 3),
tau_f: 1.5,
tau_r: 3,
tau_w: 50,
SOL: 1, # completely faked by mm
x_pup = Min(1,(P_lab/(F_pupmin*Delta_t)))
x_npup = Min(x_nup,x_pup)
x_nleaf = (n_leaf/(n_leaf+k_n))
x_pleaf = (p_leaf/(p_leaf+k_p))
x_npleaf = Min(x_nleaf,x_pleaf)
F_c = x_npleaf*x_npup*F_cmax #unit: "gC*m^{-2}*d^{-1}"
u = F_c
x = StateVariableTuple((C_leaf, C_root, C_wood, C_j1, C_j2, C_j3, C_k1, C_k2, C_k3))
b = ImmutableMatrix((a_leaf, a_root, a_wood))
Input = InputTuple(tuple(u*b)+(0,0,0,0,0,0))
A = CompartmentalMatrix([
[-mu_leaf*(b1l+b2l), 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ]
,[ 0 ,-mu_root*(b1r+b2r), 0 , 0 , 0 , 0 , 0 , 0 , 0 ]
,[ 0 , 0 ,-mu_wood*b3w, 0 , 0 , 0 , 0 , 0 , 0 ]
,[ mu_leaf*b1l , mu_root*b1r , 0 ,-mu_j1*m_n*(c11+c21), 0 , 0 , 0 , 0 , 0 ]
,[ mu_leaf*b2l , mu_root*b2r , 0 , 0 ,-mu_j2*m_n*(c12+c22), 0 , 0 , 0 , 0 ]
,[ 0 , 0 , mu_wood*b3w, 0 , 0 ,-mu_j3*m_n*(c23+c33), 0 , 0 , 0 ]
,[ 0 , 0 , 0 , mu_j1*m_n*c11 , mu_j2*m_n*c12 , 0 ,-mu_k1*(d21+d31), mu_k2*d12 , mu_k3*d13 ]
,[ 0 , 0 , 0 , mu_j1*m_n*c21 , mu_j2*m_n*c22 , mu_j3*m_n*c23 , mu_k1*d21 ,-mu_k2*(d12+d32), mu_k3*d23 ]
,[ 0 , 0 , 0 , 0 , 0 , mu_j3*m_n*c33 , mu_k1*d31 , mu_k2*d32 ,-mu_k3*(d13+d23)]
])
### When not explicit in equations B1, B2, B3 (Appendix B), followed the fluxes from Fig 1. However, the soil fluxes are not clear.
t = TimeSymbol("t") #'day' # or 'year'? incongruent turnover units
# Commented out the following lines because original publication only has 3 parameter values
# In original publication:
## See table 1 for parameter values; a_(leaf,wood,root) and 1/mu_(leaf,wood,root) are based on CASA
## 1/mu_(leaf,wood,root) = Mean residence time of plant tissue
## 1/n_max,leaf and 1/p_max,leaf : maximal leaf N:C and P:C ratios = 1.2*mean (min is obtained by 0.8*mean)
# # mean estimates were obtained from Glopnet datasets for each biome (Wright et al., 2004)
#np1 = NumericParameterization(
,'k': '"rate of fresh organic matter decomposition under substrate limitation ($N$ excess)"' # "\\text{time}^{-1}"
,'alpha': '"$N:C$ ratio in soil organic matter and in decomposers"'
,'beta': '"$N:C$ ratio in fresh organic matter"'
,'Phi_l': 'fresh organic matter carbon flux' # "(\\text{quantity of carbon})(\\text{time}))^{-1}"
,'Phi_i': 'nitrogen that flows into the ecosystem' # "(\\text{quantity of nitrogen})(\\text{time}))^{-1}"
,'Phi_o': 'nitrogen that flows out of the ecosystem' # "(\\text{quantity of nitrogen})(\\text{time}))^{-1}"
,'Phi_up': 'nitrogen flux associated with the nitrogen uptake by the plant cover' # "(\\text{quantity of nitrogen})(\\text{time}))^{-1}"
}
for name in sym_dict.keys():
var(name)
t = TimeSymbol("t") # unit: ""
x = StateVariableTuple((C_s, C_f, C_ds, N))
u = InputTuple((0, Phi_l, 0, Phi_i-Phi_o-Phi_up))
B = CompartmentalMatrix([[0, 0, s-A, 0],
[0, -k, 0, 0],
[0, k, A-s-r, 0],
[0, k*(beta-alpha), alpha*r, 0]])
mvs = CMTVS(
{
BibInfo(# Bibliographical Information
name="FB2005 - The C-N model of SOM dynamics with a single type of decomposer (model 3)",
longName="The C-N model of SOM dynamics, two decomposer types",
version="1 - C limitation",
entryAuthor="Holger Metzler",
entryAuthorOrcid="0000-0002-8239-1601",
entryCreationDate="22/03/2016",
doi="10.1111/j.1461-0248.2005.00813.x",
sym_dict=sym_dict
),
B, # the overall compartmental matrix
,
'alpha': 'annual decomposition rate of labile pool' # "yr^{-1}"
,
'beta': 'annual decomposition rate of stable pool' # "yr^{-1}"
,
'm': 'annual organic matter input' # "MgC yr^{-1}"
,
'K': 'isohumic coefficient'
}
for name in sym_dict.keys():
var(name)
t = TimeSymbol("t") # unit: "year"
C = StateVariableTuple((A, B))
I = InputTuple((m, 0))
A_GeM = CompartmentalMatrix([[-alpha, 0], [alpha * K, -beta]])
mvs = CMTVS(
{
BibInfo( # Bibliographical Information
name="",
longName="Henin and Dupois",
version="1",
entryAuthor="Holger Metzler",
entryAuthorOrcid="0000-0002-8239-1601",
entryCreationDate="09/03/2016",
doi="",
# bibtex="@inproceedings{Henin1945Annalesagronomiques,
# author = {H\\'{e}nin, S and Dupuis, M},
# booktitle = {Annales agronomiques},
# pages = {17--29},
alpha_53 = 0.45 * (1 - A_l)
alpha_61 = 0.7 * A_l
alpha_63 = alpha_61
alpha_65 = 1 - E_s - 0.004
t = TimeSymbol("t") # unit: "year" #??? monthly
x = StateVariableTuple((C_1, C_2, C_3, C_4, C_5, C_6, C_7))
u = InputTuple((J_1 * F_s, J_1 * F_m, J_2 * F_s, J_2 * F_m, 0, 0, 0))
xi = f_T * f_W #environmental effects multiplier (DEFAG)
A = ([-k_1, 0, 0, 0, 0, 0,
0], [0, -K_2, 0, 0, 0, 0,
0], [0, 0, -k_3, 0, 0, 0, 0], [0, 0, 0, -K_4, 0, 0, 0], [
alpha_51 * k_1, 0.45 * K_2, alpha_53 * k_3, 0.45 * K_4, -k_5,
0.42 * K_6, 0.45 * K_7
], [alpha_61 * k_1, 0, alpha_63 * k_3, 0, alpha_65 * k_5, -K_6,
0], [0, 0, 0, 0, 0.004 * k_5, 0.03 * K_6, -K_7])
B = CompartmentalMatrix(xi * ImmutableMatrix(A))
#Original values without effects of temperature and soil moisture:
np1 = NumericParameterization(par_dict={
K_1: 0.076,
K_2: 0.28,
K_3: 0.094,
K_4: 0.35,
K_5: 0.14,
K_6: 0.0038,
K_7: 0.00013,
xi: 1
},
func_dict=frozendict({}))
nsv1 = NumericStartValueDict({
C_1: 100,
C_2: 200,
def test_immutability(self):
A = CompartmentalMatrix(2, 2, [1, 0, 1, 0])
with self.assertRaises(TypeError):
A[1, 1] = 1
A_g = Min(J_e, J_c, J_s)
# A_g = Min(J_i,J_e,J_c) #C4
fT_stem = exp(E_0 * ((1 / (15 - T_0)) - (1 / (T_stem - T_0))))
R_leaf = gamma * V_m
fT_soil = exp(E_0 * ((1 / (15 - T_0)) - (1 / (T_soil - T_0))))
R_stem = Beta_stem * lambda_sapwood * C_is * fT_stem
R_root = Beta_root * C_ir * fT_soil
A_n = A_g - R_leaf
GPP_i = integrate(A_g, t)
NPP_i = ((1 - eta) * (integrate(A_g - R_leaf - R_stem - R_root, t)))
x = StateVariableTuple((C_il, C_is, C_ir))
u = NPP_i
b = (a_il, a_is, a_ir)
Input = InputTuple(u * ImmutableMatrix(b))
A = CompartmentalMatrix(diag(-1 / tau_il, -1 / tau_is, -1 / tau_ir))
t = TimeSymbol("t")
#model_run_data:
# parameter_sets:
# - "Tropical evergreen trees":
# values: {a_il: 0.25,a_is: 0.5,a_ir: 0.25}
mvs = CMTVS(
{
BibInfo( # Bibliographical Information
name="IBIS",
longName="Integrated Biosphere Simulator",
version="1",
entryAuthor="Verónika Ceballos-Núñez",
entryAuthorOrcid="0000-0002-0046-1160",
R = 0.008314 # "kJ mol^{-1} K^{-1}"
V_U = V_Umax * exp(-E_aU / (R * (T + 273))) #
V = V_max * exp(-E_a / (R * (T + 273))) #
E_C = epsilon_0 + epsilon_s * T #
K_U = K_U0 + K_Us * T # "mg C cm^{-3}"
K = K_0 + K_s * T # "mg C cm^{-3}"
t = TimeSymbol("t") # unit: "hour"
x = StateVariableTuple((S, D, B, E))
u = InputTuple((I_S, I_D, 0, 0))
T_M = ImmutableMatrix([[-1, 0, a_BS * r_B / (r_B + r_E), 0],
[1, -1, (1 - a_BS) * r_B / (r_B + r_E), 1],
[1, E_C, -1, 0], [0, 0, r_E / (r_B + r_E), -1]])
N = ImmutableMatrix([[V * E / (K + S), 0, 0, 0],
[0, V_U * B / (K_U + D), 0, 0], [0, 0, r_B + r_E, 0],
[0, 0, 0, r_L]])
B = CompartmentalMatrix((T_M * N))
#np1 = NumericParameterization(
# par_dict={
#},
# func_dict=frozendict({})
#)
#
#nsv1 = NumericStartValueDict({
#})
#ntimes = NumericSimulationTimes(np.arange())
# still don't know how to bring parameter sets here
# different model descriptions in Li and in original paper
# different units
# we would need to be able to split the yaml file into two versions of the model
NPP = GPP - (R_c + R_m)
a_w = 1 - a_f - a_r
x = StateVariableTuple(
(C_f, C_r, C_w, C_wl, C_u, C_m, C_v, C_n, C_a, C_s, C_p))
u = NPP
b = ImmutableMatrix((a_f, a_r, a_w))
Input = InputTuple(tuple(u * b) + (0, 0, 0, 0, 0, 0, 0, 0))
A = CompartmentalMatrix([[-s_f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, -s_r, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, -s_w, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, s_w, -d_wl, 0, 0, 0, 0, 0, 0, 0],
[buf * s_f, 0, 0, 0, -d_u, 0, 0, 0, 0, 0, 0],
[bmf * s_f, bmr * s_r, 0, 0, 0, -d_m, 0, 0, 0, 0, 0],
[0, bvr * s_r, 0, 0, 0, 0, -d_v, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, -d_n, 0, 0, 0],
[
0, 0, 0, baw * d_wl, bau * d_u, bam * d_m,
bav * d_v, ban * d_n, -d_a, bas * d_s, bap * d_p
],
[
0, 0, 0, bsw * d_wl, bsu * d_u, 0, bsv * d_v, 0,
bsa * d_a, -d_s, 0
],
[0, 0, 0, 0, 0, 0, 0, 0, bpa * d_a, bps * d_s, -d_p]])
t = TimeSymbol("t")
np1 = NumericParameterization(
par_dict={
a_f: 0.16
# ,buf:
# ,bvr:
K = ImmutableMatrix.diag([
k_leaf, k_root, k_wood, k_metlit, k_stlit, k_CWD, k_mic, k_slowsom,
k_passsom
])
A = ImmutableMatrix(
[[-1, 0, 0, 0, 0, 0, 0, 0, 0], [0, -1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, -1, 0, 0, 0, 0,
0, 0], [f_leaf2metlit, f_root2metlit, 0, -1, 0, 0, 0, 0, 0],
[f_leaf2stlit, f_root2stlit, 0, 0, -1, 0, 0, 0, 0],
[0, 0, f_wood2CWD, 0, 0, -1, 0, 0,
0], [0, 0, 0, f_metlit2mic, f_stlit2mic, 0, -1, 0, 0],
[0, 0, 0, 0, f_stlit2slowsom, f_CWD2slowsom, f_mic2slowsom, -1, 0],
[0, 0, 0, 0, 0, f_CWD2passsom, f_mic2passsom, f_slowsom2passsom,
-1]]) # tranfer
B = CompartmentalMatrix(A * K)
t = TimeSymbol("t") # unit: "day"
u = NPP(t)
b = ImmutableMatrix([beta_leaf, beta_root, beta_wood, 0, 0, 0, 0, 0, 0])
Input = InputTuple(u * b)
mvs = CMTVS(
{
BibInfo( # Bibliographical Information
name="CABLE_yuanyuan",
longName="Terrestrial Ecosystem Model",
version="",
entryAuthor="Markus Müller",
entryAuthorOrcid="0000-0003-0009-4169",
entryCreationDate="08/24/2021",
doi="",
# matrix of cycling rates
A = CompartmentalMatrix(
[
[
(
(-1)
* (
((1 - p_14) * p_5 * (1 - p_16) * multtf * T_rate)
+ ((1 - p_14) * p_5 * p_16 * multtf * T_rate)
+ (p_5 * p_14 * multtf)
)
),
(p_15 * (1 - p_16) * multtl * T_rate),
0,
0,
],
[
((1 - p_14) * p_5 * (1 - p_16) * multtf * T_rate),
(
(-p_15 * (1 - p_16) * multtl * T_rate)
- (p_15 * p_16 * multtl * T_rate)
),
0,
0,
],
[0, 0, -p_6, 0],
[0, 0, 0, -p_7],
]
)
Input = InputTuple(u * ImmutableMatrix(b))
,'beta': '"$N:C$ ratio in fresh organic matter"'
,'i': '"rate of mineral $N$ diffusion in soil"' # "\\text{time}^{-1}"
,'Phi_l': 'fresh organic matter carbon flux' # "(\\text{quantity of carbon})(\\text{time}))^{-1}"
,'Phi_i': 'nitrogen that flows into the ecosystem' # "(\\text{quantity of nitrogen})(\\text{time}))^{-1}"
,'Phi_o': 'nitrogen that flows out of the ecosystem' # "(\\text{quantity of nitrogen})(\\text{time}))^{-1}"
,'Phi_up': 'nitrogen flux associated with the nitrogen uptake by the plant cover' # "(\\text{quantity of nitrogen})(\\text{time}))^{-1}"
}
for name in sym_dict.keys():
var(name)
t = TimeSymbol("t") # unit: ""
x = StateVariableTuple((C_s, C_f, C_ds, C_df, N))
u = InputTuple((0, Phi_l, 0, 0, Phi_i-Phi_o-Phi_up))
B = CompartmentalMatrix([[-A/C_s*C_ds, 0, s, s, 0],
[ 0, -y, 0, -alpha*r/(alpha-beta), -i/(alpha-beta)],
[ A/C_s*C_ds, y, -(s+r), 0, 0],
[ 0, 0, 0, -(s+r)+alpha*r/(alpha-beta), i/(alpha-beta)],
[ 0, (beta-alpha)*y, alpha*r, 0, -i]])
#suggested_steady_states:
# - C_f: (s+r-3)/y*C_ds
# C_df: (s+r-A)*(Phi_i-Phi_o-Phi_up+beta*Phi_l-alpha*(A-s)*Phi_l/(s+r-A))
# C_ds: (s+r)*(-(Phi_i-Phi_o-Phi_up+beta*Phi_l) + alpha*s*Phi_l/(s+r))/(A*r*alpha)
# N: alpha*s-beta*(s+r)/i*C_df
mvs = CMTVS(
{
BibInfo(# Bibliographical Information
name="",
longName="The C-N model of SOM dynamics, two decomposer types",
version="SOM decomposers C limited, FOM composers N limited (case 4)",
entryAuthor="Holger Metzler",
GPP = A_c * 3600 * 12 / 1000000 # Scaling expression from TECO fortran code, line 667.' # gC*day^{-1}
f_W = Min((0.5 * W), 1)
f_T = Q_10 * ((T - 10) / 10)
xi = f_W * f_T
t = TimeSymbol("t") # unit: "day"
#x = StateVariableTuple((C_foliage, C_roots, C_wood, C_metlit, C_stlit, C_fastsom, C_slowsom, C_passsom))
x = StateVariableTuple((C_foliage, C_wood, C_roots, C_metlit, C_stlit,
C_fastsom, C_slowsom, C_passsom))
u = GPP
b = (b_foliage, b_roots, b_wood, 0, 0, 0, 0, 0)
Input = InputTuple(u * ImmutableMatrix(b))
B = CompartmentalMatrix(
[[-cr_foliage, 0, 0, 0, 0, 0, 0, 0], [0, -cr_wood, 0, 0, 0, 0, 0, 0],
[0, 0, -cr_root, 0, 0, 0, 0,
0], [f_foliage2metlit, 0, f_roots2metlit, -cr_metlit, 0, 0, 0, 0],
[f_foliage2stlit, f_wood2stlit, f_roots2stlit, 0, -cr_stlit, 0, 0, 0],
[
0, 0, 0, f_metlit2fastsom, f_stlit2fastsom, -cr_fastsom,
f_slowsom2fastsom, f_passsom2fastsom
], [0, 0, 0, 0, f_stlit2slowsom, f_fastsom2slowsom, -cr_slowsom, 0],
[0, 0, 0, 0, 0, f_fastsom2passsom, f_slowsom2passsom, -cr_passsom]])
np1 = NumericParameterization(par_dict={
GPP: 3.370,
b_foliage: 0.14,
b_roots: 0.26,
b_wood: 0.14,
f_foliage2metlit: 0.9,
f_foliage2stlit: 0.1,
f_wood2stlit: 1,
f_roots2metlit: 0.2,
f_roots2stlit: 0.8,
f_metlit2fastsom: 0.45,
def test_creation_from_list(self):
C = CompartmentalMatrix(2, 2, [1, 2, 3, 4])
print(C, type(C))
self.assertEqual(type(C), CompartmentalMatrix)
A_S = (Piecewise((a_S * G, N < 0), (a_S * N + R_gS + R_mS, N > 0)))
A_R = (Piecewise((a_R * G, N < 0), (a_R * N + R_gR + R_mR, N >= 0)))
#beta_T=(Piecewise((1,T_air>= T_cold),(Piecewise((((T_air-T_cold-5)/5),T_air>(T_cold-5)),(0,T_air<=(T_cold-5))),T_cold>T_air))) # Had to comment it out because of problems of piecewise in matrix
gamma_T = gamma_Tmax * (1 - beta_T)**b_T
D_L = (gamma_N + gamma_W + gamma_T) * C_L
D_S = gamma_S * C_S
D_R = gamma_R * C_R
x = StateVariableTuple((C_L, C_S, C_R))
b = 1
u = (C_L, C_S, C_R)
Input = InputTuple(
ImmutableMatrix(u) * 1
) #Fixme: does input always have to be a Tuple? what happens when, in this case, "f_v = u + A*x"?
A = CompartmentalMatrix([[-(gamma_N + gamma_W + gamma_T), 0, 0],
[0, -gamma_S - R_gS - R_mS, 0],
[0, 0, -gamma_R - R_gR - R_mR]])
#
t = TimeSymbol("t")
# parameter_sets:
# - "Original dataset of the publication":
# values: {k_n: 0.5, omega: 0.8, epsilon_L: 0.35, epsilon_S: 0.1, epsilon_R: 0.55}
# desc: Eastern US and Germany, cold broadleaf deciduous
# doi: 10.1111/j.1365-2486.2004.00890.x
mvs = MVarSet({
BibInfo( # Bibliographical Information
name="CTEM",
longName="Canadian Terrestrial Ecosystem Model",
version="1",
def test_creation_from_index_function(self):
C = CompartmentalMatrix(2, 2, lambda i, j: i + j)
print(C, type(C))
self.assertEqual(type(C), CompartmentalMatrix)
'gamma_f': 'Foliage turnover rate',
'gamma_r': 'Roots turnover rate',
'gamma_w': 'Wood turnover rate'
}
for name in sym_dict.keys():
var(name)
f_W = Min((0.5 * W), 1)
f_T = Q_10**((T - 10) / 10)
epsilon_t = f_W * f_T
x = StateVariableTuple((C_f, C_r, C_w))
u = GPP * epsilon_t
b = (eta_f, eta_w, eta_r)
Input = InputTuple(u * ImmutableMatrix(b))
A = CompartmentalMatrix(diag(-gamma_f, -gamma_w, -gamma_r))
t = TimeSymbol("t")
# "Original parameters of the publication. Parameter value of GPP corresponds to an annual average"
# T, Q_10, and W are variables that should be looked at in a data set. What I have here are invented values
np1 = NumericParameterization(
par_dict={
Q_10: 1,
W: 4.2,
T: 25,
GPP: 3370, #"gC*day^{-1}"
eta_f: 0.14,
eta_r: 0.26,
eta_w: 0.14,
gamma_f: 0.00258,
gamma_w: 0.0000586,
def test_creation_from_expression(self):
A = ImmutableMatrix(1, 1, [1])
B = ImmutableMatrix(1, 1, [1])
C = CompartmentalMatrix(A*B)
print(C, type(C))
self.assertEqual(type(C), CompartmentalMatrix)
A_S = (Piecewise((a_S * G, N < 0), (a_S * N + R_gS + R_mS, N > 0)))
A_R = (Piecewise((a_R * G, N < 0), (a_R * N + R_gR + R_mR, N >= 0)))
#beta_T=(Piecewise((1,T_air>= T_cold),(Piecewise((((T_air-T_cold-5)/5),T_air>(T_cold-5)),(0,T_air<=(T_cold-5))),T_cold>T_air))) # Had to comment it out because of problems of piecewise in matrix
gamma_T = gamma_Tmax * (1 - beta_T)**b_T
D_L = (gamma_N + gamma_W + gamma_T) * C_L
D_S = gamma_S * C_S
D_R = gamma_R * C_R
x = StateVariableTuple((C_L, C_S, C_R, C_D, C_H))
u = (G, 0, 0, 0, 0)
Input = InputTuple(u) #f_v = u + A*x
A = CompartmentalMatrix([[
-(gamma_N + gamma_W + gamma_T + (A_R + A_S + ((R_gL + R_mL) / C_L))), 0, 0,
0, 0
], [A_S, -gamma_S - ((R_gS - R_mS) / C_S), 0, 0, 0],
[A_R, 0, -gamma_R - ((R_gR - R_mR) / C_R), 0, 0],
[
gamma_N + gamma_W + gamma_T, gamma_S, gamma_R,
-gamma_DH - (R_hD / C_D), 0
], [0, 0, 0, gamma_DH, -(R_hH / C_H)]])
### Had to divide Respiration fluxes by state variables because the flux was not presented as a rate*state variable tuple.
t = TimeSymbol("t")
# parameter_sets:
# - "Original dataset of the publication":
# values: {k_n: 0.5, omega: 0.8, epsilon_L: 0.35, epsilon_S: 0.1, epsilon_R: 0.55}
# desc: Eastern US and Germany, cold broadleaf deciduous
# doi: 10.1111/j.1365-2486.2004.00890.x
mvs = CMTVS(
'nitrogen that flows into the ecosystem' # "(\\text{quantity of nitrogen})(\\text{time}))^{-1}"
,
'Phi_o':
'nitrogen that flows out of the ecosystem' # "(\\text{quantity of nitrogen})(\\text{time}))^{-1}"
,
'Phi_up':
'nitrogen flux associated with the nitrogen uptake by the plant cover' # "(\\text{quantity of nitrogen})(\\text{time}))^{-1}"
}
for name in sym_dict.keys():
var(name)
t = TimeSymbol("t") # unit: ""
C = StateVariableTuple((C_s, C_f, C_ds, C_df, N))
I = InputTuple((0, Phi_l, 0, 0, Phi_i - Phi_o - Phi_up))
A_GeM = CompartmentalMatrix(
[[0, 0, s - A, s, 0], [0, -(y + u), 0, 0, 0], [0, y, A - s - r, 0, 0],
[0, u, 0, -(s + r), 0],
[0, (beta - alpha) * (y + u), alpha * r, alpha * r, 0]])
mvs = CMTVS(
{
BibInfo( # Bibliographical Information
name=
"FB2005 - The C-N model of SOM dynamics, two decomposer types (model 4)",
longName="The C-N model of SOM dynamics, two decomposer types",
version="1 - C limitation",
entryAuthor="Holger Metzler",
entryAuthorOrcid="0000-0002-8239-1601",
entryCreationDate="22/03/2016",
doi="10.1111/j.1461-0248.2005.00813.x",
sym_dict=sym_dict),
A_GeM, # the overall compartmental matrix