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)
### 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(
{
BibInfo( # Bibliographical Information
name="CTEM",
longName="Canadian Terrestrial Ecosystem Model",
version="1",
entryAuthor="Verónika Ceballos-Núñez",
entryAuthorOrcid="0000-0002-0046-1160",
entryCreationDate="21/1/2016",
doi="10.1111/j.1365-2486.2004.00890.x",
sym_dict=sym_dict),
A, # the overall compartmental matrix
Input, # the overall input
t, # time for the complete system
x, # state vector of the complete system
# VegetationCarbonInputScalar(u),
# vegetation carbon partitioning.
VegetationCarbonStateVariableTuple((C_L, C_S, C_R)),
},
bgc_md2_computers())
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
# nitrogen fluxes from the global fluxes have been implemented
# NitrogenInFluxesBySymbol(in_fl_n),
# NitrogenOutFluxesBySymbol(out_fl_n),
# NitrogenInternalFluxesBySymbol(internal_fl_n),
VegetationCarbonStateVariableTuple(
(C_labile, C_bud, C_leaf, C_wood, C_root, C_labileRa)),
# the following can be computed automatically
# VegetationCarbonInputScalar(u),
# vegetation carbon partitioning.
# VegetationCarbonInputPartitioningTuple(b),
},
bgc_md2_computers())
# C_root: , #"g*m^{-2}"
# C_wood: #"g*m^{-2}"
#})
#
#ntimes = NumericSimulationTimes(np.arange(, , ))
mvs=MVarSet({
BibInfo(# Bibliographical Information
name="CABLE",
longName="CSIRO Atmosphere Biosphere Land Exchange",
version="1",
# basedOn = "CASA'" # Fung et al. (2005) -> Need to look it up
entryAuthor="Verónika Ceballos-Núñez",
entryAuthorOrcid="0000-0002-0046-1160",
entryCreationDate="14/3/2016",
doi="10.5194/bg-7-2261-2010",
sym_dict=sym_dict
),
A, # the overall compartmental matrix
Input, # the overall input
t, # time for the complete system
x, # state vector of the complete system
VegetationCarbonInputScalar(u),
# vegetation carbon partitioning.
VegetationCarbonInputPartitioningTuple(b),
VegetationCarbonStateVariableTuple((C_leaf, C_root, C_wood)),
# np1
})
# modApproach: process based,
# partitioningScheme: fixed,
# claimedDynamicPart: "no",
# spaceScale: global,
# # unit: "1°",
# timeResolution: monthly,
# # unit: month^{-1},
# bibtex: "@article{Luo2012TE,
# address = {Berkeley},
# author = {Yiqi Luo and Ensheng Weng and Yuanhe Yang},
# booktitle = {Encyclopedia of Theoretical Ecology},
# editor = {Alan Hastings and Louis Gross},
# pages = {219-229},
# publisher = {University of California Press},
# title = {Ecosystem Ecology},
# year = {2012}
# }",
#
# abstract: "Ecosystem ecology is a subdiscipline of ecology that focuses on exchange of energy and materials between organisms and the environment. The materials that are commonly studied in ecosystem ecology include water, carbon, nitrogen, phosphorus, and other elements that organisms use as nutrients. The source of energy for most ecosystems is solar radiation. In this entry, material cy-cling and energy exchange are generally described before the carbon cycle is used as an example to illustrate our quantitative and theoretical understanding of ecosystem ecology.",
sym_dict=sym_dict),
A, # the overall compartmental matrix
Input, # the overall input
t, # time for the complete system
x, # state vector of the complete system
VegetationCarbonInputScalar(u),
# vegetation carbon partitioning.
VegetationCarbonInputPartitioningTuple(b),
VegetationCarbonStateVariableTuple((C_f, C_w, C_r)),
np1
})
entryAuthorOrcid="0000-0003-0009-4169",
entryCreationDate="12/4/2018",
doi="",
sym_dict=sym_dict
# bibtex= "@incollection{Luo2012TE,
# Address = {Berkeley},
# Author = {Yiqi Luo and Ensheng Weng and Yuanhe Yang},
# Booktitle = {Encyclopedia of Theoretical Ecology},
# Date-Added = {2015-05-05 15:20:40 +0000},
# Date-Modified = {2015-05-05 15:20:40 +0000},
# Editor = {Alan Hastings and Louis Gross},
# Pages = {219-229},
# Publisher = {University of California Press},
# Title = {Ecosystem Ecology},
# Year = {2012}}
# abstract = {"Ecosystem ecology is a subdiscipline of ecology that focuses on exchange of energy and materials between organisms and the environment. The materials that are commonly studied in ecosystem ecology include water, carbon, nitrogen, phosphorus, and other elements that organisms use as nutrients. The source of energy for most ecosystems is solar radiation. In this entry, material cy-cling and energy exchange are generally described before the carbon cycle is used as an example to illustrate our quantitative and theoretical understanding of ecosystem ecology."}",
),
B, # the overall compartmental matrix
Input, # the overall input
#InFluxesBySymbol()
t, # time for the complete system
x, # state vector of the complete system
VegetationCarbonInputScalar(u),
VegetationCarbonInputPartitioningTuple(b),
VegetationCarbonStateVariableTuple((C_foliage, C_roots, C_wood)),
np1,
nsv1,
ntimes
},
bgc_md2_computers())
W_s: 100,
W_r: 100,
W_C: 45,
W_N: 30
})
ntimes = NumericSimulationTimes(np.arange(0, 150, 25))
#ntimes = NumericSimulationTimes(np.arange(0, 1, 0.001)) #Fixme: There were 2 in the yaml file, not sure which one works best
mvs = MVarSet({
BibInfo( # Bibliographical Information
name="",
longName="",
version="1",
entryAuthor="Verónika Ceballos-Núñez",
entryAuthorOrcid="0000-0002-0046-1160",
entryCreationDate="29/7/2015",
doi="10.1093/oxfordjournals.aob.a088273",
sym_dict=sym_dict),
A, # the overall compartmental matrix
Input, # the overall input
t, # time for the complete system
x, # state vector of the complete system
VegetationCarbonInputScalar(b),
# vegetation carbon partitioning.
VegetationCarbonInputPartitioningTuple(u),
VegetationCarbonStateVariableTuple((W_N, W_C, W_p, W_s, W_r)),
np1,
nsv1,
ntimes
})
# # time_unit=day
#)
#nsv1 = NumericStartValueDict({
# F: , #"Mg/ha"
# W: , #"Mg/ha"
# R: #"Mg/ha"
#})
#
#ntimes = NumericSimulationTimes(np.arange(, , ))
mvs = MVarSet({
BibInfo( # Bibliographical Information
name="G'DAY",
longName="Generic Decomposition and Yield",
version="1",
entryAuthor="Verónika Ceballos-Núñez",
entryAuthorOrcid="0000-0002-0046-1160",
entryCreationDate="27/1/2016",
doi="10.2307/1942099",
sym_dict=sym_dict),
A, # the overall compartmental matrix
Input, # the overall input
t, # time for the complete system
x, # state vector of the complete system
VegetationCarbonInputScalar(u),
# vegetation carbon partitioning.
VegetationCarbonInputPartitioningTuple(b),
VegetationCarbonStateVariableTuple((F, W, R)),
# np1
})
entryAuthor="Carlos A. Sierra",
entryAuthorOrcid="0000-0003-0009-4169",
entryCreationDate="12/4/2018",
doi="",
sym_dict=sym_dict
# bibtex= "@incollection{Luo2012TE,
# Address = {Berkeley},
# Author = {Yiqi Luo and Ensheng Weng and Yuanhe Yang},
# Booktitle = {Encyclopedia of Theoretical Ecology},
# Date-Added = {2015-05-05 15:20:40 +0000},
# Date-Modified = {2015-05-05 15:20:40 +0000},
# Editor = {Alan Hastings and Louis Gross},
# Pages = {219-229},
# Publisher = {University of California Press},
# Title = {Ecosystem Ecology},
# Year = {2012}}
# abstract = {"Ecosystem ecology is a subdiscipline of ecology that focuses on exchange of energy and materials between organisms and the environment. The materials that are commonly studied in ecosystem ecology include water, carbon, nitrogen, phosphorus, and other elements that organisms use as nutrients. The source of energy for most ecosystems is solar radiation. In this entry, material cy-cling and energy exchange are generally described before the carbon cycle is used as an example to illustrate our quantitative and theoretical understanding of ecosystem ecology."}",
),
B, # the overall compartmental matrix
Input, # the overall input
t, # time for the complete system
x, # state vector of the complete system
VegetationCarbonInputScalar(u),
VegetationCarbonInputPartitioningTuple(b),
VegetationCarbonStateVariableTuple((x_1, x_2, x_3)),
np1,
nsv1,
ntimes
},
bgc_md2_computers())
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",
entryCreationDate="26/1/2016",
doi="10.1029/96GB02692 ",
#further_references=BibInfo(doi=""),
sym_dict=sym_dict),
A, # the overall compartmental matrix
Input, # the overall input
t, # time for the complete system
x, # state vector of the complete system
VegetationCarbonInputScalar(u),
# vegetation carbon partitioning.
VegetationCarbonInputPartitioningTuple(b),
VegetationCarbonStateVariableTuple((C_il, C_is, C_ir)),
},
bgc_md2_computers())
# 𝙻𝚒𝚝𝚝𝚎𝚛: 𝚏𝚒𝚛𝚎⎯𝚎𝚖⎯𝚕𝚒𝚝𝚝𝚎𝚛 + 𝚑𝚎𝚝𝚛𝚎𝚜𝚙⎯𝚕𝚒𝚝𝚝𝚎𝚛,
# 𝚂𝚘𝚒𝚕: 𝚏𝚒𝚛𝚎⎯𝚎𝚖⎯𝚜𝚘𝚖 + 𝚑𝚎𝚝𝚛𝚎𝚜𝚙⎯𝚜𝚘𝚖
# }
# ),
InternalFluxesBySymbol(
{
(Symbol(name_tup[0]),Symbol(name_tup[1])): sum([ Symbol(flux) for flux in val])
for name_tup, val in ms.horizontal_structure.items()
}
),
# direct description would be
# InternalFluxesBySymbol(
# {
# (𝙻𝚊𝚋𝚒𝚕𝚎, 𝙻𝚎𝚊𝚏): 𝚕𝚊𝚋𝚒𝚕𝚎⎯𝚝𝚘⎯𝚏𝚘𝚕𝚒𝚊𝚛,
# (𝙻𝚊𝚋𝚒𝚕𝚎, 𝙻𝚒𝚝𝚝𝚎𝚛): 𝚏𝚒𝚛𝚎⎯𝚕𝚊𝚋𝚒𝚕𝚎⎯𝚝𝚘⎯𝚕𝚒𝚝𝚝𝚎𝚛,
# (𝙻𝚎𝚊𝚏, 𝙻𝚒𝚝𝚝𝚎𝚛): 𝚏𝚒𝚛𝚎⎯𝚏𝚘𝚕𝚒𝚊𝚛⎯𝚝𝚘⎯𝚕𝚒𝚝𝚝𝚎𝚛 + 𝚕𝚎𝚊𝚏⎯𝚝𝚘⎯𝚕𝚒𝚝𝚝𝚎𝚛,
# (𝚆𝚘𝚘𝚍, 𝚂𝚘𝚒𝚕): 𝚏𝚒𝚛𝚎⎯𝚠𝚘𝚘𝚍⎯𝚝𝚘⎯𝚜𝚘𝚖 + 𝚠𝚘𝚘𝚍⎯𝚝𝚘⎯𝚜𝚘𝚒𝚕𝚌,
# (𝚁𝚘𝚘𝚝, 𝙻𝚒𝚝𝚝𝚎𝚛): 𝚏𝚒𝚛𝚎⎯𝚛𝚘𝚘𝚝⎯𝚝𝚘⎯𝚕𝚒𝚝𝚝𝚎𝚛 + 𝚛𝚘𝚘𝚝⎯𝚝𝚘⎯𝚕𝚒𝚝𝚝𝚎𝚛,
# (𝙻𝚒𝚝𝚝𝚎𝚛, 𝚂𝚘𝚒𝚕): 𝚏𝚒𝚛𝚎⎯𝚕𝚒𝚝𝚝𝚎𝚛⎯𝚝𝚘⎯𝚜𝚘𝚖 + 𝚕𝚒𝚝𝚝𝚎𝚛⎯𝚝𝚘⎯𝚜𝚘𝚖
# }
# ),
t, # time symbol
x, # state vector of the complete system
# VegetationCarbonInputScalar(gpp), # ? not sure see ticket
# vegetation carbon partitioning.
# VegetationCarbonInputPartitioningTuple(b),
VegetationCarbonStateVariableTuple((Labile, Leaf, Root, Wood)),
},
bgc_md2_computers()
)
Phi_0: 2500, #"MJ*m*^{-2}*year^{-1}"
omega: 5, #"m*^2*kg^{-1}"
gamma_r: 2, #"kg^{-1}"
gamma_f: 0.5
}, #"kg^{-1}"
func_dict=frozendict({}))
mvs = MVarSet({
BibInfo( # Bibliographical Information
name="",
longName="",
version="1",
entryAuthor="Verónika Ceballos-Núñez",
entryAuthorOrcid="0000-0002-0046-1160",
entryCreationDate="29/7/2015",
doi="10.1093/treephys/12.2.119",
sym_dict=sym_dict),
#
# the following variables constitute the compartmental system:
#
A, # the overall compartmental matrix
Input, # the overall input
t, # time for the complete system
x, # state vector of the complete system
VegetationCarbonInputScalar(u),
# vegetation carbon partitioning.
VegetationCarbonInputPartitioningTuple(b),
VegetationCarbonStateVariableTuple((F, R, W)),
np1
})
},
#Q_i: 1 when light is highly available (See sup. material 1, pg 17)
func_dict=frozendict({}))
mvs = CMTVS(
{
BibInfo( # Bibliographical Information
name="aDGVM",
longName="",
version="",
# modApproach= "individuals based"
entryAuthor="Verónika Ceballos-Núñez",
entryAuthorOrcid="0000-0002-0046-1160",
entryCreationDate="17/7/2015",
doi="10.1111/j.1365-2486.2008.01838.x",
sym_dict=sym_dict),
B, # the overall compartmental matrix
Input, # the overall input
t, # time for the complete system
x, # state vector of the complete system
VegetationCarbonInputScalar(u),
# vegetation carbon partitioning.
VegetationCarbonInputPartitioningTuple(b),
VegetationCarbonStateVariableTuple((B_L, B_R, B_S)),
# fixme mm 01-20-2022 the parameterization is incomplete error: The
# following free symbols: {gamma_f, gamma_r, gamma_w} of the
# expression: {gamma_f, gamma_r, gamma_w} are not arguments.
#np1,
},
bgc_md2_computers())
x = StateVariableTuple((C_L, C_R, C_stem))
u = F_Cgrowth
beta = (alpha_L, alpha_R, alpha_stem)
alpha_L = 1 - (alpha_R + alpha_stem)
Input = InputTuple(u * ImmutableMatrix(beta))
B = CompartmentalMatrix([[-(k_L + (m_stem / C_stem)), 0, 0],
[0, -(k_R + (m_stem / C_stem)), 0], [0, 0, -m_stem]])
mvs = CMTVS(
{
BibInfo( # Bibliographical Information
name="HAVANA",
longName=
"Hydrology and Vegetation-dynamics Algorithm for Northern Australia",
version="1",
entryAuthor="Verónika Ceballos-Núñez",
entryAuthorOrcid="0000-0002-0046-1160",
entryCreationDate="",
doi="10.5194/bg-13-761-2016",
sym_dict=sym_dict),
B, # the overall compartmental matrix
Input, # the overall input
t, # time for the complete system
x, # state vector of the complete system
# VegetationCarbonInputScalar(u),
# vegetation carbon partitioning.
# VegetationCarbonInputPartitioningTuple(beta),
VegetationCarbonStateVariableTuple((C_L, C_R, C_stem)),
},
bgc_md2_computers())
# of sum(Iv) where Iv is the input to the vegetation pools (VegetationCarbonInputTuple)
# my take would actually be:
#u = VegetationCarbonInputScalar(
# Piecewise((GPP,NPP<0),(NPP,NPP>=0))
#)
#b = VegetationCarbonInputPartitioningTuple(
# (
# Piecewise((Allo_fact_leaf-R_leaf/GPP,NPP<0),(Allo_fact_leaf,NPP>=0)),
# Piecewise((Allo_fact_stem-R_stem/GPP,NPP<0),(Allo_fact_stem,NPP>=0)),
# Piecewise((Allo_fact_roots-R_roots/GPP,NPP<0),(Allo_fact_roots,NPP>=0))
# )
#)
#Input = InputTuple(b*u)
Input = InputTuple((a_L, a_S, a_R))
vcsvt = VegetationCarbonStateVariableTuple((C_leaf, C_stem, C_roots))
A = CompartmentalMatrix([[-(r_n + r_w + r_t), 0, 0],
[0, -(1 / (Y_stem * 365)), 0],
[0, 0, -(1 / (Y_roots * 365))]])
t = TimeSymbol("t")
mvs = CMTVS(
{
BibInfo( # Bibliographical Information
name="ISAM",
longName="Integrated Science Assessment Model",
version="",
entryAuthor="Verónika Ceballos-Núñez",
entryAuthorOrcid="0000-0002-0046-1160",
entryCreationDate="3/5/2018",
doi="10.1111/j.1365-2486.2004.00890.x",