コード例 #1
0
    def test_bgc_dep_graph_computation(self):
        cs = h.bgc_md2_computers()
        possible_types = all_mvars(cs).difference(
            {VegetationCarbonInputPartitioningTuple})
        #print(TypeSet(possible_types))
        for t in possible_types:
            print(t.__name__)

        for root in possible_types:
            before_dg = now()
            with self.subTest(root=root):
                gs = list(
                    all_dep_graphs(root_type=root, cs=cs, given=frozenset()))
                fig = plt.figure()
                axs = fig.subplots(len(gs), 1)
                for i, ax in enumerate(axs):
                    g = gs[i]
                    g.draw_matplotlib(ax)
                fig.savefig("depgraphs_" + root.__name__ + '.pdf')

            after_dg = now()
            time = (after_dg - before_dg)
            #if time >1:
            if True:
                print("depgraph time", root, time, len(gs))
コード例 #2
0
    def test_bgc_fast_graph_computation(self):
        cs = h.bgc_md2_computers()
        possible_types = all_mvars(cs).difference(
            {VegetationCarbonInputPartitioningTuple})
        #print(TypeSet(possible_types))
        for t in possible_types:
            print(t.__name__)

        for root in possible_types:
            before_dg = now()
            with self.subTest(root=root):
                before_fg = now()
                fg = fast_graph(cs=cs, root_type=root, given=frozenset())
                after_fg = now()
                time = (after_fg - before_fg)
                if time > 1:
                    print("fast_graph time", root, time)

                fig = plt.figure(figsize=(20, 20))
                ax1 = fig.add_subplot(1, 1, 1)
                fg.draw_matplotlib(ax1)
                fig.savefig(root.__name__ + '.pdf')
            after_dg = now()
            time = (after_dg - before_dg)
            #if time >1:
            if True:
                print("fast_graph time", root, time)
コード例 #3
0
ファイル: source.py プロジェクト: MPIBGC-TEE/bgc_md2
### 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())
コード例 #4
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# -

fig = plt.figure()
plot_solutions(
    fig,
    times=range(sol_opt.shape[0]),
    var_names=Observables._fields,
    tup=(sol_opt, obs),
    #tup=(sol_opt,),
    #names=('opt')
    names=('opt', 'obs'))
fig.savefig('solutions.pdf')

from ComputabilityGraphs.helpers import all_mvars
from ComputabilityGraphs.TypeSet import TypeSet
for t in all_mvars(h.bgc_md2_computers()):
    print(t.__name__)

# +
#mvs.get_StateVariableTupleTimeDerivative()

# +
epa_opt = EstimatedParameters._make(C_opt)

from model_specific_helpers import ModelParameters, Parameters
apa = Parameters.from_EstimatedParametersAndUnEstimatedParameters(epa_opt, cpa)
mpa = ModelParameters(
    **{k: v
       for k, v in apa._asdict().items() if k in ModelParameters._fields})

# -
コード例 #5
0
 def test_duplicated_computers(self):
     dcd = duplicated_computer_dict(h.bgc_md2_computers())
     for key, val in dcd.items():
         print(key, len(val))
         for f in val:
             print(f.__name__)