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_update_generator_bgc_md2(self):
fig = plt.figure()
draw_update_sequence(
bgc_md2_computers(),
8,
fig,
bgc_md2_mvar_aliases(),
bgc_md2_computer_aliases(),
)
fig.savefig("c1.pdf")
def test_ComputerSetMultiGraph_matplotlib_bgc_md2(self):
# The direct visualization of networkx (using matplotlib)
# is very rudimentary.
spsg = sparse_powerset_graph(bgc_md2_computers())
fig = plt.figure(figsize=(20, 20))
ax = fig.add_subplot(1, 1, 1)
draw_ComputerSetMultiDiGraph_matplotlib(
ax,
spsg,
bgc_md2_mvar_aliases(),
bgc_md2_computer_aliases(),
)
fig.savefig("SetMultiGraph.pdf")
def test_computable_mvars(self):
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")
mvs=self.mvs
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))