def test_computable_mvars(self):
# for debugging we draw the sparse_powerset_graph of the actually present computers and mvars
spsg = sparse_powerset_graph(bgc_md2_computers())
f = plt.figure()
ax = f.add_subplot(1, 1, 1)
draw_ComputerSetMultiDiGraph_matplotlib(ax,
spsg,
bgc_md2_mvar_aliases(),
bgc_md2_computer_aliases(),
targetNode=frozenset(
{SmoothModelRun}))
f.savefig("spgs.pdf")
fig = plt.figure()
draw_update_sequence(bgc_md2_computers(), max_it=8, fig=fig)
fig.savefig("c1.pdf")
# https://docs.python.org/3/library/unittest.html#distinguishing-test-iterations-using-subtests
for mn in [
"Potter1993GlobalBiogeochemicalCycles",
"testVectorFree",
"Williams2005GCB", # just uncomment the one model you are working on and comment the others
]:
with self.subTest(mn=mn):
mvs = MVarSet.from_model_name(mn)
mvars = mvs.computable_mvar_types()
list_str = "\n".join(
["<li> " + str(var.__name__) + " </li>" for var in mvars])
print(list_str)
for var in mvars:
print("########################################")
print(str(var.__name__))
print(mvs._get_single_mvar_value(var))
def test_from_model_name(self):
# alternative constructor
mvs= MVarSet.from_model_name("testVectorFree")
res = frozenset(
[
InFluxesBySymbol,
OutFluxesBySymbol,
InternalFluxesBySymbol,
TimeSymbol,
StateVariableTuple,
]
)
self.assertSetEqual(mvs.provided_mvar_types, res)
def setUp(self):
self.mn = "Williams2005GCB"
self.mvs = MVarSet.from_model_name(self.mn)
self.ref_provided_mvars = frozenset([
CompartmentalMatrix,
TimeSymbol,
StateVariableTuple,
VegetationCarbonInputPartitioningTuple,
VegetationCarbonInputScalar,
InputTuple,
NumericStartValueDict,
NumericSimulationTimes,
NumericParameterization,
QuantityStartValueDict,
QuantitySimulationTimes,
QuantityParameterization,
BibInfo,
# QuantityModelRun,
# QuantityParameterizedModel
])
def setUp(self):
self.mn = "Castanho2013Biogeosciences"
self.mvs = MVarSet.from_model_name(self.mn)
self.ref_provided_mvars = frozenset([
CompartmentalMatrix,
TimeSymbol,
StateVariableTuple,
VegetationCarbonInputPartitioningTuple,
VegetationCarbonInputScalar,
VegetationCarbonStateVariableTuple,
InputTuple,
# # NumericStartValueDict,
# # NumericSimulationTimes,
# NumericParameterization,
# # QuantityStartValueDict,
# # QuantitySimulationTimes,
# # QuantityParameterization,
# BibInfo,
# # QuantityModelRun,
# # QuantityParameterizedModel
])
def setUp(self):
# self.mn = ""
self.mn = "ElMasri2013AgricForMeteorol"
# self.mn = "Pavlick2013Biogeosciences"
# self.mn = "Hilbert1991AnnBot"
# self.mn = "Foley1996GBC"
# self.mn = "Gu2010EcologicalComplexity"
# self.mn = "Arora2005GCB-1"
# self.mn = "King1993TreePhysiol"
# self.mn = "Murty2000EcolModell"
# self.mn = "Wang2010Biogeosciences"
# self.mn = "DeAngelis2012TheorEcol"
# self.mn = "Comins1993EA"
# self.mn = "Running1988EcolModel"
# self.mn = "Luo2012TE"
self.mvs = MVarSet.from_model_name(self.mn)
self.ref_provided_mvars = frozenset(
[
CompartmentalMatrix,
TimeSymbol,
StateVariableTuple,
VegetationCarbonInputPartitioningTuple,
VegetationCarbonInputScalar,
VegetationCarbonStateVariableTuple,
InputTuple,
# # NumericStartValueDict,
# # NumericSimulationTimes,
# NumericParameterization,
# # QuantityStartValueDict,
# # QuantitySimulationTimes,
# # QuantityParameterization,
# BibInfo,
# # QuantityModelRun,
# # QuantityParameterizedModel
]
)
# "ElMasri2013AgricForMeteorol", #Paper shows results for soil carbon, and fig. 1 has litter and C pools, but the equations on table A2 (although very detailed) don't include them. There are also equations for Phenology, but they were not included in the source.py
# "Scheiter2009GlobalChangeBiol", #No soil compartments
# "Turgman2018EcologyLetters", #No soil compartments
# "Haverd2016Biogeosciences", #No soil compartments
# "Foley1996GBC", #No equations for litter and soil, but the figure has those compartments. See Markus’ Ibis.yaml?
# "Gu2010EcologicalComplexity", #No equations for litter and soil, but the model description (CEVSA) mentions them
# "King1993TreePhysiol", #No soil compartments
# "DeAngelis2012TheorEcol", #No soil compartments (model based on G’Day, but removed litter and soil compartments)
# "Potter1993GlobalBiogeochemicalCycles", #No equations for litter and soil, but the model description mentions them
# "testVectorFree",
# "Williams2005GCB",
"CARDAMOM",
]
######################################################################
####### MODELS NOT TRANSLATED FROM .YAML TO SOURCE.PY
# Sitch2003GlobChangBiol: Not included because allocation (see pg 8) has no ODEs; biomass increment is allocated to the tissue pools while satisfying the functional balance difference equations...
# VanDerWerf1993PlantSoil: this model has no compartment for wood. It is used to simulate effect of nitrogen on growth of a grass (Dactylis glomerata L.).
# ICBM: same as Andren1997EA but less parameter sets
# Schimel2003SoilBiologyandBiochemistry.yaml, Schimel2003SoilBiologyandBiochemistry_rMM.yaml, Schimel2003SoilBiologyandBiochemistry_rMM_improved.yaml: Original model has no outputs, corrected by Holger
# Ibis: includes soil, no metadata -added by Markus. Vero’s version: Foley
######################################################################
for mn in model_names:
mvs = MVarSet.from_model_name(mn)
mvars = mvs.computable_mvar_types()
list_str = "\n".join(["<li> " + str(var.__name__) + " </li>" for var in mvars])
print(list_str)
for var in mvars:
print("########################################")
print(str(var.__name__))
print(mvs._get_single_mvar_value(var))
def setUp(self):
self.mn = "cable_all"
self.mvs = MVarSet.from_model_name(self.mn)
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.10.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
from IPython.display import HTML
from bgc_md2.resolve.mvars import CompartmentalMatrix, StateVariableTuple, VegetationCarbonInputPartitioningTuple,VegetationCarbonInputTuple
from bgc_md2.resolve.MVarSet import MVarSet
display(HTML("<style>.container { width:100% !important; }</style>"))
mvs_mm = MVarSet.from_model_name('TECOmm')
import igraph
g=igraph.Graph(directed=True)
in_fluxes, internal_fluxes, out_fluxes = mvs_mm.get_InFluxesBySymbol(),mvs_mm.get_InternalFluxesBySymbol(),mvs_mm.get_OutFluxesBySymbol()
in_flux_targets, out_flux_sources = [[str(k) for k in d.keys()] for d in (in_fluxes, out_fluxes)]
internal_connections = [(str(s),str(t)) for s,t in internal_fluxes.keys()]
# +
G=igraph.Graph(directed=True)
for i,s in enumerate(mvs_mm.get_StateVariableTuple()):
G.add_vertex(str(s))# the name attribute is set automatically
{
VegetationCarbonInputPartitioningTuple,
NumericSolutionArray
}
),
# explicit_exclude_models=frozenset({'CARDAMOM'})
)
li
# From these we chose two models to investigate more thoroughly.
# We load the first one with a helper function and the second by using standard python tools.
#
#
mvs_Luo = MVarSet.from_model_name('Luo2012TE')
# or using a moduel import
from bgc_md2.models.TECO.source import mvs as mvs_TECO
# # ComputabilityGraphs
# Now that we actually have two records (MVarSets) we can exlore what we can comptute from them.
# Just add a dot "." behind the variable in the next cell and press the tab key!
#
mvs_TECO
# The options provided by the python interpreter are actually the result of a graph computation.
# To see all computable mvars of the TECO MVarSet execute the next cell!
#
mvs_TECO.computable_mvar_names