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))
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)
### 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())
# -
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})
# -
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__)
