def test_can_715(self):
# this test is just to show the superiority of bicoloring vs. single coloring in
# either direction. Bicoloring gives only 21 colors in this case vs. 105 for either
# fwd or rev.
matdir = os.path.join(os.path.dirname(openmdao.test_suite.__file__), 'matrices')
# uses matrix can_715 from the sparse matrix collection website
mat = load_npz(os.path.join(matdir, 'can_715.npz')).toarray()
mat = np.asarray(mat, dtype=bool)
coloring = get_simul_meta(None, 'auto', include_sparsity=False, setup=False,
run_model=False, bool_jac=mat,
stream=None)
tot_size, tot_colors, fwd_solves, rev_solves, pct = _solves_info(coloring)
self.assertEqual(tot_colors, 21)
# verify that unidirectional colorings are much worse (105 vs 21 for bidirectional)
coloring = get_simul_meta(None, 'fwd', include_sparsity=False, setup=False,
run_model=False, bool_jac=mat,
stream=None)
tot_size, tot_colors, fwd_solves, rev_solves, pct = _solves_info(coloring)
self.assertEqual(tot_colors, 105)
coloring = get_simul_meta(None, 'rev', include_sparsity=False, setup=False,
run_model=False, bool_jac=mat,
stream=None)
tot_size, tot_colors, fwd_solves, rev_solves, pct = _solves_info(coloring)
self.assertEqual(tot_colors, 105)
def test_can_715(self):
# this test is just to show the superiority of bicoloring vs. single coloring in
# either direction. Bicoloring gives only 21 colors in this case vs. 105 for either
# fwd or rev.
matdir = os.path.join(os.path.dirname(openmdao.test_suite.__file__), 'matrices')
# uses matrix can_715 from the sparse matrix collection website
mat = load_npz(os.path.join(matdir, 'can_715.npz')).toarray()
mat = np.asarray(mat, dtype=bool)
coloring = get_simul_meta(None, 'auto', include_sparsity=False, setup=False,
run_model=False, bool_jac=mat,
stream=None)
tot_size, tot_colors, fwd_solves, rev_solves, pct = _solves_info(coloring)
self.assertEqual(tot_colors, 21)
# verify that unidirectional colorings are much worse (105 vs 21 for bidirectional)
coloring = get_simul_meta(None, 'fwd', include_sparsity=False, setup=False,
run_model=False, bool_jac=mat,
stream=None)
tot_size, tot_colors, fwd_solves, rev_solves, pct = _solves_info(coloring)
self.assertEqual(tot_colors, 105)
coloring = get_simul_meta(None, 'rev', include_sparsity=False, setup=False,
run_model=False, bool_jac=mat,
stream=None)
tot_size, tot_colors, fwd_solves, rev_solves, pct = _solves_info(coloring)
self.assertEqual(tot_colors, 105)
def color(self, mode='auto', stream=sys.stdout):
self.coloring = get_simul_meta(None,
mode,
include_sparsity=False,
setup=False,
run_model=False,
bool_jac=self.J,
stream=stream)
return self.coloring
def test_exclude(self):
p = Problem()
model = p.model
indep = model.add_subsystem('indep', IndepVarComp())
indep.add_output("a", val=1.0)
indep.add_output("b", val=1.0)
indep.add_output("c", val=1.0)
indep.add_output("d", val=1.0)
indep.add_output("e", val=1.0)
obj = model.add_subsystem('obj', ExecComp('obj=a+b+c+d+ee'))
con1 = model.add_subsystem('con1', ExecComp('con=a*2.0 + b'))
con2 = model.add_subsystem('con2', ExecComp('con=c*3.0 + 2.0*d'))
con3 = model.add_subsystem('con3', ExecComp('con=ee*1.5 - 3.0*c + a'))
model.connect("indep.a", ("obj.a", "con1.a", "con3.a"))
model.connect("indep.b", ("obj.b", "con1.b"))
model.connect("indep.c", ("obj.c", "con2.c", "con3.c"))
model.connect("indep.d", ("obj.d", "con2.d"))
model.connect("indep.e", ("obj.ee", "con3.ee"))
model.add_design_var("indep.a")
model.add_design_var("indep.b")
model.add_design_var("indep.c")
model.add_design_var("indep.d")
model.add_design_var("indep.e")
model.add_objective('obj.obj')
model.add_constraint('con1.con')
model.add_constraint('con2.con')
model.add_constraint('con3.con', simul_coloring_excludes=True)
p.setup(mode='fwd')
p.run_model()
coloring = get_simul_meta(p,
include_sparsity=False,
setup=False,
run_model=False,
stream=None)
tot_size1, tot_colors1, colored_solves1, opp_solves1, pct1, dominant_mode1 = _solves_info(
coloring)
# this is not a great coloring. It's just done to test simul_coloring_excludes
self.assertEqual(opp_solves1, 2)
self.assertEqual(tot_colors1, 4)
self.assertEqual(coloring['rev'][0][0], [3, 0])
def color(self, mode='auto', stream=sys.stdout):
self.coloring = get_simul_meta(None, mode, include_sparsity=False, setup=False,
run_model=False, bool_jac=self.J,
stream=stream)
return self.coloring