def setUp(self):
I_vl, I_vw = symbols("I_vl I_vw")
vl, vw = symbols("vl vw")
k_vl, k_vw = symbols("k_vl k_vw")
self.provided_values=frozenset(
[
InFluxesBySymbol({vl: I_vl, vw: I_vw}),
OutFluxesBySymbol({vl: k_vl * vl, vw: k_vw * vw}),
InternalFluxesBySymbol({(vl, vw): k_vl * vl, (vw, vl): k_vw * vw}),
TimeSymbol("t"),
StateVariableTuple((vl, vw))
]
)
self.mvs=MVarSet(self.provided_values)
def test_vegetation_carbon_input_tuple_2(self):
Labile = Symbol("Labile")
Leaf = Symbol("Leaf")
Root = Symbol("Root")
Wood = Symbol("Wood")
gpp_to_labile = Symbol("gpp_to_labile")
gpp_to_leaf = Symbol("gpp_to_leaf")
gpp_to_root = Symbol("gpp_to_root")
gpp_to_wood = Symbol("gpp_to_wood")
d = {
𝙻𝚊𝚋𝚒𝚕𝚎: gpp_to_labile,
Leaf: gpp_to_leaf,
𝚁𝚘𝚘𝚝: gpp_to_root,
𝚆𝚘𝚘𝚍: gpp_to_wood
}
u = InFluxesBySymbol(d)
vcsv = VegetationCarbonStateVariableTuple((Labile, Leaf, Root, Wood))
res = vegetation_carbon_input_tuple_2(u, vcsv)
self.assertEqual(type(res), VegetationCarbonInputTuple)
(N_soil, N_NH4): t_soilN,
(N_NH4, N_NO3): nitr
}
mvs = CMTVS(
{
BibInfo( # Bibliographical Information
name="ACONITE",
longName=
"A new, simple model of ecosystem C–N cycling and interactions",
version="1",
entryAuthor="Verónika Ceballos-Núñez",
entryAuthorOrcid="0000-0002-0046-1160",
entryCreationDate="29/3/2016",
doi="10.5194/gmd-7-2015-2014",
sym_dict=sym_dict),
InFluxesBySymbol(h.combine(in_fl_c, in_fl_n)),
OutFluxesBySymbol(h.combine(out_fl_c, out_fl_n)),
InternalFluxesBySymbol(h.combine(internal_fl_c, internal_fl_n)),
t, # time for the complete system
x, # state vector of the complete system
xc, # state vector of the carbon sub system
# from which the carbon fluxes and hence
# the CarbonCompartMentalMatrix can be derived
# (even though in this case it is given)
# A_c, # the carbon compartmental matrix
# in_fl_c, #alternatively to the CarbonCompartmentalMatrix
# out_fl_c,#
# internal_fl_c,
xn, # state vector of the nitrogen sub system
# from which the nitrogenfluxes and hence the nitrogen compartmental
# matrix could be derived whence the computers for extraction the
])
roots_in_fluxes = {E: GPP}
roots_out_fluxes = {
E: MR + GR,
B_R: S_R * B_R,
}
roots_internal_fluxes = {
(E, B_R): f_R * C_gR / (C_gR + delta_R) * eta_R * E,
(E, C_R): f_R * delta_R / (C_gR + delta_R) * E,
(C_R, E): S_R * C_R,
}
roots = (roots_sv_set, roots_in_fluxes, roots_out_fluxes,
roots_internal_fluxes)
srm = SmoothReservoirModel.from_state_variable_indexed_fluxes(
Matrix([E, B_R, C_R]), t, roots_in_fluxes, roots_out_fluxes,
roots_internal_fluxes)
mvs = CMTVS(
{
InFluxesBySymbol(roots[1]),
OutFluxesBySymbol(roots[2]),
InternalFluxesBySymbol(roots[3]),
TimeSymbol("t"),
StateVariableTuple((E, B_R, C_R))
}, bgc_md2_computers())
#vw,
C_leaf,
C_wood,
C_root,
C_aom1,
C_aom2,
C_smb1,
C_smb2,
C_smr,
C_nom,
C_dom,
C_psom,
)),
InFluxesBySymbol({
#vl: I_vl, vw: I_vw
C_leaf: NPP(t) * beta_leaf,
C_root: NPP(t) * beta_root,
C_wood: NPP(t) * beta_wood
}),
OutFluxesBySymbol({
#vl: k_vl_o * vl, vw: k_vw_o * vw
C_aom1: r_C_aom1_rh * C_aom1 * xi(t),
C_aom2: r_C_aom2_rh * C_aom2 * xi(t),
C_smb1: r_C_smb1_rh * C_smb1 * xi(t),
C_smb2: r_C_smb2_rh * C_smb2 * xi(t),
C_smr: r_C_smr_rh * C_smr * xi(t),
C_nom: r_C_nom_rh * C_nom * xi(t),
C_dom: r_C_dom_rh * C_dom * xi(t),
C_psom: r_C_psom_rh * C_psom * xi(t),
}),
InternalFluxesBySymbol({
#(vl, vw): k_vl_2_vw * vl, (vw, vl): k_vw_2_vl * vw
vl, vw = symbols("vl vw")
k_vl, k_vw = symbols("k_vl k_vw")
# the keys of the internal flux dictionary are tuples (source_pool,target_pool)
# srm:SmoothReservoirModel
# srm=SmoothReservoirModel.from_state_variable_indexed_fluxes(
# in_fluxes
# ,out_fluxes
# ,internal_fluxes
# )
#specialVars = {
mvs = MVarSet({
InFluxesBySymbol({
vl: I_vl,
vw: I_vw
}),
OutFluxesBySymbol({
vl: k_vl * vl,
vw: k_vw * vw
}),
InternalFluxesBySymbol({
(vl, vw): k_vl * vl,
(vw, vl): k_vw * vw
}),
TimeSymbol("t"),
StateVariableTuple((vl, vw))
# ,'SmoothReservoirModel':srm
})
{
t,
StateVariableTuple((
C_leaf,
C_wood,
C_root,
C_leaf_litter,
C_wood_litter,
C_root_litter,
C_soil_fast,
C_soil_slow,
C_soil_passive,
)),
InFluxesBySymbol({
C_leaf: NPP(t) * beta_leaf,
C_root: NPP(t) * beta_root,
C_wood: NPP(t) * beta_wood
}),
OutFluxesBySymbol(
{
C_leaf_litter: r_C_leaf_litter_rh * C_leaf_litter * xi(t),
C_wood_litter: r_C_wood_litter_rh * C_wood_litter * xi(t),
C_root_litter: r_C_root_litter_rh * C_root_litter * xi(t),
C_soil_fast: r_C_soil_fast_rh * C_soil_fast * xi(t),
C_soil_slow: r_C_soil_slow_rh * C_soil_slow * xi(t),
C_soil_passive: r_C_soil_passive_rh * C_soil_passive * xi(t),
}),
InternalFluxesBySymbol({
(C_leaf, C_leaf_litter):
r_C_leaf_2_C_leaf_litter * C_leaf,
(C_wood, C_wood_litter):
# some we will also use some symbols for functions (which appear with an argument)
func_dict={
"I_vl": "Influx into vegetation leaf pool",
"r_vl_o": "out flux rate of leaf pool",
}
for k in func_dict.keys():
code=k+" = Function('{0}')".format(k)
exec(code)
t=TimeSymbol("t")
mvs = CMTVS(
{
StateVariableTuple((vl, vw)),
t,
InFluxesBySymbol({vl: I_vl(t), vw: I_vw}),
OutFluxesBySymbol({vl: r_vl_o(t) * vl, vw: r_vw_o * vw}),
InternalFluxesBySymbol({(vl, vw): r_vl_2_vw * vl, (vw, vl): r_vw_2_vl * vw}),
},
computers=module_computers(bgc_c)
)
# -
# The last statement in the code defines a variable `mvs` which is
# an instance of CMTVS which stands for `C`onnected`M`ulti`T`ype`V`ariable`S`et".
# It contains information in two forms.
# 1. Variables of certain type (like InFluxesBySymbol)
# 2. a set of functions that "connect" these Variables and to other results we did not specify but which can be computed.
#
#
mvs = CMTVS(
{
BibInfo(# Bibliographical Information
name="CARDAMOM",
longName="?",
version="?",
entryAuthor="Holger Metzler",
entryAuthorOrcid="",
entryCreationDate="02/02/2021",
doi="",
#further_references=BibInfo(doi="10.5194/bg-10-2255-2013"),
sym_dict=sym_dict
),
InFluxesBySymbol(
{
Symbol(name): sum([Symbol(flux) for flux in val])
for name, val in ms.external_input_structure.items()
}
),
# direct description would be
# InFluxesBySymbol(
# {
# 𝙻𝚊𝚋𝚒𝚕𝚎: 𝚐𝚙𝚙⎯𝚝𝚘⎯𝚕𝚊𝚋𝚒𝚕𝚎,
# 𝙻𝚎𝚊𝚏: 𝚐𝚙𝚙⎯𝚝𝚘⎯𝚕𝚎𝚊𝚏,
# 𝚁𝚘𝚘𝚝: 𝚐𝚙𝚙⎯𝚝𝚘_𝚛𝚘𝚘𝚝,
# 𝚆𝚘𝚘𝚍: 𝚐𝚙𝚙⎯𝚝𝚘⎯𝚠𝚘𝚘𝚍
# }
# ),
OutFluxesBySymbol(
{
Symbol(name): sum([Symbol(flux) for flux in val])
for name, val in ms.external_output_structure.items()
# sapwood production and senesence
(E, C_S):
f_O * delta_W / (C_gW + delta_W) * E,
(C_S, E):
S_O * B_OS / (B_OS + B_TS) * C_S,
(E, B_OS):
f_O * C_gW / (C_gW + delta_W) * eta_W * E,
# coarse roots and branches heartwood production
(B_OS, B_OH):
v_O * B_OS,
(C_S, B_OH):
v_O * 1 / C_gHW * B_OS / (B_OS + B_TS) * zeta_dw / zeta_gluc * C_S,
}
other = (other_sv_set, other_in_fluxes, other_out_fluxes,
other_internal_fluxes)
srm = SmoothReservoirModel.from_state_variable_indexed_fluxes(
Matrix([E, C_S, B_OH, B_OS]), t, other_in_fluxes, other_out_fluxes,
other_internal_fluxes)
mvs = CMTVS(
{
InFluxesBySymbol(other[1]),
OutFluxesBySymbol(other[2]),
InternalFluxesBySymbol(other[3]),
TimeSymbol("t"),
StateVariableTuple((E, C_S, B_OH, B_OS))
}, bgc_md2_computers())
from bgc_md2.resolve.mvars import (
InFluxesBySymbol,
OutFluxesBySymbol,
InternalFluxesBySymbol,
TimeSymbol,
StateVariableTuple,
)
import bgc_md2.resolve.computers as bgc_c
var("I_vl I_vw vl vw k_vl k_vw")
mvs = CMTVS(
{
StateVariableTuple((vl, vw)),
TimeSymbol("t"),
InFluxesBySymbol({vl: I_vl, vw: I_vw}),
OutFluxesBySymbol({vl: k_vl * vl, vw: k_vw * vw}),
InternalFluxesBySymbol({(vl, vw): k_vl * vl, (vw, vl): k_vw * vw}),
},
computers=module_computers(bgc_c)
)
# -
# The last statement in the code defines a variable `mvs` which is
# an instance of CMTVS which stands for `C`onnected`M`ulti`T`ype`V`ariable`S`et".
# It contains information in two forms.
# 1. Variables of certain type (like InFluxesBySymbol)
# 2. a set of functions that "connect" these Variables and to other results we did not specify
# explicitly.
#
(E, C_L): f_L * delta_L/(C_gL+delta_L) * E,
(C_L, E): S_L * C_L,
}
leaves = (
leaves_sv_set,
leaves_in_fluxes,
leaves_out_fluxes,
leaves_internal_fluxes
)
srm = SmoothReservoirModel.from_state_variable_indexed_fluxes(
Matrix([E, B_L, C_L]),
t,
leaves_in_fluxes,
leaves_out_fluxes,
leaves_internal_fluxes
)
mvs = CMTVS(
{
InFluxesBySymbol(leaves[1]),
OutFluxesBySymbol(leaves[2]),
InternalFluxesBySymbol(leaves[3]),
TimeSymbol("t"),
StateVariableTuple((E, B_L, C_L))
},
bgc_md2_computers()
)
trunk_in_fluxes = {E: GPP}
trunk_out_fluxes = {E: MS + G_TS}
trunk_internal_fluxes = {
# sapwood production
(E, C_S): f_T * delta_W / (C_gW + delta_W) * E,
(E, B_TS): f_T * C_gW / (C_gW + delta_W) * eta_W * E,
# trunk heartwood production
(B_TS, B_TH): v_T * B_TS,
(C_S, B_TH):
v_T * 1 / C_gHW * B_TS / (B_OS + B_TS) * zeta_dw / zeta_gluc * C_S
}
trunk = (trunk_sv_set, trunk_in_fluxes, trunk_out_fluxes,
trunk_internal_fluxes)
srm = SmoothReservoirModel.from_state_variable_indexed_fluxes(
Matrix([E, C_S, B_TH, B_TS]), t, trunk_in_fluxes, trunk_out_fluxes,
trunk_internal_fluxes)
mvs = CMTVS(
{
InFluxesBySymbol(trunk[1]),
OutFluxesBySymbol(trunk[2]),
InternalFluxesBySymbol(trunk[3]),
TimeSymbol("t"),
StateVariableTuple((E, C_S, B_TH, B_TS))
}, bgc_md2_computers())
}
internal_fluxes = dict()
r_S, r_L = symbols("r_S r_L")
output_fluxes = {
WP_S: r_S * WP_S,
WP_L: r_L * WP_L
}
t = symbols("t")
srm = SmoothReservoirModel.from_state_variable_indexed_fluxes(
state_vector,
t,
input_fluxes,
output_fluxes,
internal_fluxes
)
mvs = CMTVS(
{
InFluxesBySymbol(input_fluxes),
OutFluxesBySymbol(output_fluxes),
InternalFluxesBySymbol(internal_fluxes),
TimeSymbol(t.name),
StateVariableTuple(state_vector)
},
bgc_md2_computers()
)
# other + trunk
intersect = ({E: GPP, C_S: 0}, {E: MS + G_OS + G_TS, C_S: 0})
OT = hr.combine(other, trunk, {}, {}, intersect)
# complete model
intersect = ({
E: GPP,
C_S: 0
}, {
E: ML + GL + MR + GR + MS + G_OS + G_TS,
C_S: 0
})
LROT = hr.combine(LR, OT, {}, {}, intersect)
srm = SmoothReservoirModel.from_state_variable_indexed_fluxes(
Matrix([E, B_L, C_L, B_OS, B_OH, C_S, B_TH, B_TS, C_R, B_R]), t, LROT[1],
LROT[2], LROT[3])
mvs = CMTVS(
{
InFluxesBySymbol(LROT[1]),
OutFluxesBySymbol(LROT[2]),
InternalFluxesBySymbol(LROT[3]),
TimeSymbol("t"),
StateVariableTuple(
(E, B_L, C_L, B_OS, B_OH, C_S, B_TH, B_TS, C_R, B_R))
}, bgc_md2_computers())