class CycleComponentTestCase(unittest.TestCase):
def setUp(self):
'''Initialization function called before every test function'''
self.g = Group()
self.comp1 = DummyComp()
self.comp2 = DummyComp()
self.g.add('comp1', self.comp1)
self.g.add('comp2', self.comp2)
self.p = Problem(root=self.g)
self.p.setup()
def tearDown(self):
'''Function called after every test function'''
self.g = None
self.p = None
def test_copy_flow(self):
self.comp1.params['flow:in:W'] = 100.0
self.comp1.params['flow:in:Tt'] = 518.0
self.comp1.params['flow:in:Pt'] = 15.0
CycleComponent.copy_from(self.comp1, 'flow', self.comp2, 'flow')
self.p.run()
TOL = 0.0001
assert_rel_error(self, self.comp2.unknowns['flow:out:Tt'], 518.0, TOL)
assert_rel_error(self, self.comp2.unknowns['flow:out:Pt'], 15.0, TOL)
def test_conflicting_promotions(self):
# verify we get an error if we have conflicting promotions
root = Group()
# promoting G1.x will create an implicit connection to G3.x
# this is a conflict because G3.x (aka G3.C4.x) is already connected
# to G3.C3.x
G2 = root.add('G2', Group())
G2.add('C1', ParamComp('x', 5.), promotes=['x'])
G1 = G2.add('G1', Group(), promotes=['x'])
G1.add('C2', ExecComp('y=x*2.0'), promotes=['x'])
G3 = root.add('G3', Group(), promotes=['x'])
G3.add('C3', ExecComp('y=x*2.0'), promotes=['y']) # promoting y
G3.add('C4', ExecComp('y=x*2.0'), promotes=['x', 'y']) # promoting y again.. BAD
prob = Problem(root)
try:
prob.setup(check=False)
except Exception as error:
msg = "Promoted name 'G3.y' matches multiple unknowns: ['G3.C3.y', 'G3.C4.y']"
self.assertEqual(text_type(error), msg)
else:
self.fail("Error expected")
def test_simple_matvec(self):
class VerificationComp(SimpleCompDerivMatVec):
def jacobian(self, params, unknowns, resids):
raise RuntimeError(
"Derivative functions on this comp should not run.")
def apply_linear(self, params, unknowns, dparams, dunknowns,
dresids, mode):
raise RuntimeError(
"Derivative functions on this comp should not run.")
sub = Group()
sub.add('mycomp', VerificationComp())
prob = Problem()
prob.root = Group()
prob.root.add('sub', sub)
prob.root.add('x_param', ParamComp('x', 1.0))
prob.root.connect('x_param.x', "sub.mycomp.x")
sub.fd_options['force_fd'] = True
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x_param.x'], ['sub.mycomp.y'],
mode='fwd',
return_format='dict')
assert_rel_error(self, J['sub.mycomp.y']['x_param.x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x_param.x'], ['sub.mycomp.y'],
mode='rev',
return_format='dict')
assert_rel_error(self, J['sub.mycomp.y']['x_param.x'][0][0], 2.0, 1e-6)
def test_double_arraycomp(self):
# Mainly testing a bug in the array return for multiple arrays
group = Group()
group.add('x_param1', ParamComp('x1', np.ones((2))), promotes=['*'])
group.add('x_param2', ParamComp('x2', np.ones((2))), promotes=['*'])
group.add('mycomp', DoubleArrayComp(), promotes=['*'])
prob = Problem()
prob.root = group
prob.root.ln_solver = ScipyGMRES()
prob.setup(check=False)
prob.run()
Jbase = group.mycomp.JJ
J = prob.calc_gradient(['x1', 'x2'], ['y1', 'y2'],
mode='fwd',
return_format='array')
diff = np.linalg.norm(J - Jbase)
assert_rel_error(self, diff, 0.0, 1e-8)
J = prob.calc_gradient(['x1', 'x2'], ['y1', 'y2'],
mode='fd',
return_format='array')
diff = np.linalg.norm(J - Jbase)
assert_rel_error(self, diff, 0.0, 1e-8)
J = prob.calc_gradient(['x1', 'x2'], ['y1', 'y2'],
mode='rev',
return_format='array')
diff = np.linalg.norm(J - Jbase)
assert_rel_error(self, diff, 0.0, 1e-8)
def test_double_arraycomp(self):
# Mainly testing a bug in the array return for multiple arrays
group = Group()
group.add('x_param1', ParamComp('x1', np.ones((2))), promotes=['*'])
group.add('x_param2', ParamComp('x2', np.ones((2))), promotes=['*'])
group.add('mycomp', DoubleArrayComp(), promotes=['*'])
prob = Problem()
prob.root = group
prob.root.ln_solver = ScipyGMRES()
prob.setup(check=False)
prob.run()
Jbase = group.mycomp.JJ
J = prob.calc_gradient(['x1', 'x2'], ['y1', 'y2'], mode='fwd',
return_format='array')
diff = np.linalg.norm(J - Jbase)
assert_rel_error(self, diff, 0.0, 1e-8)
J = prob.calc_gradient(['x1', 'x2'], ['y1', 'y2'], mode='fd',
return_format='array')
diff = np.linalg.norm(J - Jbase)
assert_rel_error(self, diff, 0.0, 1e-8)
def test_array2D_index_connection(self):
group = Group()
group.add('x_param', IndepVarComp('x', np.ones((2, 2))), promotes=['*'])
sub = group.add('sub', Group(), promotes=['*'])
sub.add('mycomp', ArrayComp2D(), promotes=['x', 'y'])
group.add('obj', ExecComp('b = a'))
group.connect('y', 'obj.a', src_indices=[3])
prob = Problem()
prob.root = group
prob.root.ln_solver = LinearGaussSeidel()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['obj.b'], mode='fwd', return_format='dict')
Jbase = prob.root.sub.mycomp._jacobian_cache
assert_rel_error(self, Jbase[('y', 'x')][3][0], J['obj.b']['x'][0][0], 1e-8)
assert_rel_error(self, Jbase[('y', 'x')][3][1], J['obj.b']['x'][0][1], 1e-8)
assert_rel_error(self, Jbase[('y', 'x')][3][2], J['obj.b']['x'][0][2], 1e-8)
assert_rel_error(self, Jbase[('y', 'x')][3][3], J['obj.b']['x'][0][3], 1e-8)
J = prob.calc_gradient(['x'], ['obj.b'], mode='rev', return_format='dict')
Jbase = prob.root.sub.mycomp._jacobian_cache
assert_rel_error(self, Jbase[('y', 'x')][3][0], J['obj.b']['x'][0][0], 1e-8)
assert_rel_error(self, Jbase[('y', 'x')][3][1], J['obj.b']['x'][0][1], 1e-8)
assert_rel_error(self, Jbase[('y', 'x')][3][2], J['obj.b']['x'][0][2], 1e-8)
assert_rel_error(self, Jbase[('y', 'x')][3][3], J['obj.b']['x'][0][3], 1e-8)
def test_conflicting_connections(self):
# verify we get an error if we have conflicting implicit and explicit connections
root = Group()
# promoting G1.x will create an implicit connection to G3.x
# this is a conflict because G3.x (aka G3.C4.x) is already connected
# to G3.C3.x
G2 = root.add('G2', Group(), promotes=['x']) # BAD PROMOTE
G2.add('C1', ParamComp('x', 5.), promotes=['x'])
G1 = G2.add('G1', Group(), promotes=['x'])
G1.add('C2', ExecComp('y=x*2.0'), promotes=['x'])
G3 = root.add('G3', Group(), promotes=['x'])
G3.add('C3', ExecComp('y=x*2.0'))
G3.add('C4', ExecComp('y=x*2.0'), promotes=['x'])
root.connect('G2.G1.C2.y', 'G3.C3.x')
G3.connect('C3.y', 'x')
prob = Problem(root)
try:
prob.setup(check=False)
except Exception as error:
msg = "Target 'G3.C4.x' is connected to multiple unknowns: ['G2.C1.x', 'G3.C3.y']"
self.assertEqual(text_type(error), msg)
else:
self.fail("Error expected")
def test_conflicting_promotions(self):
# verify we get an error if we have conflicting promotions
root = Group()
# promoting G1.x will create an implicit connection to G3.x
# this is a conflict because G3.x (aka G3.C4.x) is already connected
# to G3.C3.x
G2 = root.add('G2', Group())
G2.add('C1', ParamComp('x', 5.), promotes=['x'])
G1 = G2.add('G1', Group(), promotes=['x'])
G1.add('C2', ExecComp('y=x*2.0'), promotes=['x'])
G3 = root.add('G3', Group(), promotes=['x'])
G3.add('C3', ExecComp('y=x*2.0'), promotes=['y']) # promoting y
G3.add('C4', ExecComp('y=x*2.0'),
promotes=['x', 'y']) # promoting y again.. BAD
prob = Problem(root)
try:
prob.setup(check=False)
except Exception as error:
msg = "Promoted name 'G3.y' matches multiple unknowns: ['G3.C3.y', 'G3.C4.y']"
self.assertEqual(text_type(error), msg)
else:
self.fail("Error expected")
def test_heat_exchanger(self):
comp = HeatExchanger()
g = Group()
g.add('comp', comp)
p = Problem(root=g)
p.setup()
comp.params['flow_in:in:Tt'] = 1423.8
comp.params['flow_in:in:Pt'] = 0.302712118187
comp.params['flow_in:in:W'] = 1.0
comp.params['dPqP'] = 0.0
comp.params['design'] = True
p.run()
TOL = 0.005
assert_rel_error(self, comp.unknowns['flow_out:out:Tt'], 539.94, TOL)
assert_rel_error(self, comp.unknowns['Qreleased'], 327.22, TOL)
assert_rel_error(self, comp.unknowns['Qabsorbed'], 327.22, TOL)
assert_rel_error(self, comp.unknowns['Qmax'], 335.1, TOL)
assert_rel_error(self, comp.unknowns['T_cold_out'], 749.96, TOL)
#check off design
comp.params['design'] = False
p.run()
assert_rel_error(self, comp.unknowns['flow_out:out:Tt'], 539.94, TOL)
assert_rel_error(self, comp.unknowns['Qreleased'], 327.22, TOL)
assert_rel_error(self, comp.unknowns['Qabsorbed'], 327.22, TOL)
assert_rel_error(self, comp.unknowns['Qmax'], 335.1, TOL)
assert_rel_error(self, comp.unknowns['T_cold_out'], 749.96, TOL)
def test_conflicting_connections(self):
# verify we get an error if we have conflicting implicit and explicit connections
root = Group()
# promoting G1.x will create an implicit connection to G3.x
# this is a conflict because G3.x (aka G3.C4.x) is already connected
# to G3.C3.x
G2 = root.add('G2', Group(), promotes=['x']) # BAD PROMOTE
G2.add('C1', ParamComp('x', 5.), promotes=['x'])
G1 = G2.add('G1', Group(), promotes=['x'])
G1.add('C2', ExecComp('y=x*2.0'), promotes=['x'])
G3 = root.add('G3', Group(), promotes=['x'])
G3.add('C3', ExecComp('y=x*2.0'))
G3.add('C4', ExecComp('y=x*2.0'), promotes=['x'])
root.connect('G2.G1.C2.y', 'G3.C3.x')
G3.connect('C3.y', 'x')
prob = Problem(root)
try:
prob.setup(check=False)
except Exception as error:
msg = "Target 'G3.C4.x' is connected to multiple unknowns: ['G2.C1.x', 'G3.C3.y']"
self.assertEqual(text_type(error), msg)
else:
self.fail("Error expected")
def test_simple_matvec(self):
class VerificationComp(SimpleCompDerivMatVec):
def jacobian(self, params, unknowns, resids):
raise RuntimeError("Derivative functions on this comp should not run.")
def apply_linear(self, params, unknowns, dparams, dunknowns, dresids, mode):
raise RuntimeError("Derivative functions on this comp should not run.")
sub = Group()
sub.add("mycomp", VerificationComp())
prob = Problem()
prob.root = Group()
prob.root.add("sub", sub)
prob.root.add("x_param", ParamComp("x", 1.0))
prob.root.connect("x_param.x", "sub.mycomp.x")
sub.fd_options["force_fd"] = True
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(["x_param.x"], ["sub.mycomp.y"], mode="fwd", return_format="dict")
assert_rel_error(self, J["sub.mycomp.y"]["x_param.x"][0][0], 2.0, 1e-6)
J = prob.calc_gradient(["x_param.x"], ["sub.mycomp.y"], mode="rev", return_format="dict")
assert_rel_error(self, J["sub.mycomp.y"]["x_param.x"][0][0], 2.0, 1e-6)
def test_simple_matvec(self):
class VerificationComp(SimpleCompDerivMatVec):
def jacobian(self, params, unknowns, resids):
raise RuntimeError("Derivative functions on this comp should not run.")
def apply_linear(self, params, unknowns, dparams, dunknowns,
dresids, mode):
raise RuntimeError("Derivative functions on this comp should not run.")
sub = Group()
sub.add('mycomp', VerificationComp())
prob = Problem()
prob.root = Group()
prob.root.add('sub', sub)
prob.root.add('x_param', IndepVarComp('x', 1.0))
prob.root.connect('x_param.x', "sub.mycomp.x")
sub.fd_options['force_fd'] = True
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x_param.x'], ['sub.mycomp.y'], mode='fwd',
return_format='dict')
assert_rel_error(self, J['sub.mycomp.y']['x_param.x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x_param.x'], ['sub.mycomp.y'], mode='rev',
return_format='dict')
assert_rel_error(self, J['sub.mycomp.y']['x_param.x'][0][0], 2.0, 1e-6)
def test_calc_gradient(self):
root = Group()
parm = root.add("parm", ParamComp("x", np.array([1.0, 1.0, 1.0, 1.0])))
comp = root.add("comp", RosenSuzuki())
root.connect("parm.x", "comp.x")
prob = Problem(root)
prob.setup(check=False)
prob.run()
param_list = ["parm.x"]
unknown_list = ["comp.f", "comp.g"]
# check that calc_gradient returns proper dict value when mode is 'fwd'
J = prob.calc_gradient(param_list, unknown_list, mode="fwd", return_format="dict")
np.testing.assert_almost_equal(J["comp.f"]["parm.x"], np.array([[-3.0, -3.0, -17.0, 9.0]]))
np.testing.assert_almost_equal(
J["comp.g"]["parm.x"], np.array([[3.0, 1.0, 3.0, 1.0], [1.0, 4.0, 2.0, 3.0], [6.0, 1.0, 2.0, -1.0]])
)
# check that calc_gradient returns proper array value when mode is 'fwd'
J = prob.calc_gradient(param_list, unknown_list, mode="fwd", return_format="array")
np.testing.assert_almost_equal(
J, np.array([[-3.0, -3.0, -17.0, 9.0], [3.0, 1.0, 3.0, 1.0], [1.0, 4.0, 2.0, 3.0], [6.0, 1.0, 2.0, -1.0]])
)
# check that calc_gradient returns proper dict value when mode is 'rev'
J = prob.calc_gradient(param_list, unknown_list, mode="rev", return_format="dict")
np.testing.assert_almost_equal(J["comp.f"]["parm.x"], np.array([[-3.0, -3.0, -17.0, 9.0]]))
np.testing.assert_almost_equal(
J["comp.g"]["parm.x"], np.array([[3.0, 1.0, 3.0, 1.0], [1.0, 4.0, 2.0, 3.0], [6.0, 1.0, 2.0, -1.0]])
)
# check that calc_gradient returns proper array value when mode is 'rev'
J = prob.calc_gradient(param_list, unknown_list, mode="rev", return_format="array")
np.testing.assert_almost_equal(
J, np.array([[-3.0, -3.0, -17.0, 9.0], [3.0, 1.0, 3.0, 1.0], [1.0, 4.0, 2.0, 3.0], [6.0, 1.0, 2.0, -1.0]])
)
# check that calc_gradient returns proper dict value when mode is 'fd'
J = prob.calc_gradient(param_list, unknown_list, mode="fd", return_format="dict")
np.testing.assert_almost_equal(J["comp.f"]["parm.x"], np.array([[-3.0, -3.0, -17.0, 9.0]]), decimal=5)
np.testing.assert_almost_equal(
J["comp.g"]["parm.x"],
np.array([[3.0, 1.0, 3.0, 1.0], [1.0, 4.0, 2.0, 3.0], [6.0, 1.0, 2.0, -1.0]]),
decimal=5,
)
# check that calc_gradient returns proper array value when mode is 'fd'
J = prob.calc_gradient(param_list, unknown_list, mode="fd", return_format="array")
np.testing.assert_almost_equal(
J,
np.array([[-3.0, -3.0, -17.0, 9.0], [3.0, 1.0, 3.0, 1.0], [1.0, 4.0, 2.0, 3.0], [6.0, 1.0, 2.0, -1.0]]),
decimal=5,
)
def test_variables(self):
group = Group()
group.add('C1', ExecComp('y=x*2.0'), promotes=['x'])
group.add("C2", ExecComp('y=x*2.0'), promotes=['y'])
# paths must be initialized prior to calling _setup_variables
group._setup_paths('')
params_dict, unknowns_dict = group._setup_variables()
self.assertEqual(list(params_dict.keys()), ['C1.x', 'C2.x'])
self.assertEqual(list(unknowns_dict.keys()), ['C1.y', 'C2.y'])
self.assertEqual([m['promoted_name'] for n,m in params_dict.items()], ['x', 'C2.x'])
self.assertEqual([m['promoted_name'] for n,m in unknowns_dict.items()], ['C1.y', 'y'])
def test_variables(self):
group = Group()
group.add("C1", ExecComp("y=x*2.0"), promotes=["x"])
group.add("C2", ExecComp("y=x*2.0"), promotes=["y"])
# paths must be initialized prior to calling _setup_variables
group._setup_paths("")
params_dict, unknowns_dict = group._setup_variables()
self.assertEqual(list(params_dict.keys()), ["C1.x", "C2.x"])
self.assertEqual(list(unknowns_dict.keys()), ["C1.y", "C2.y"])
self.assertEqual([m["promoted_name"] for n, m in params_dict.items()], ["x", "C2.x"])
self.assertEqual([m["promoted_name"] for n, m in unknowns_dict.items()], ["C1.y", "y"])
def setUp(self):
self.comp = comp = Nozzle()
g = Group()
g.add('comp', comp)
self.p = p = Problem(root=g)
p.setup(check=False)
comp.params['flow_in:in:W'] = 100.0
comp.params['flow_in:in:Tt'] = 700.0
comp.params['flow_in:in:Pt'] = 50.0
comp.params['flow_in:in:Mach'] = 0.40
comp.params['back_Ps'] = 15.0
comp.params['design'] = True
p.run()
def build_sequence(child_factory, num_children, conns=(), parent=None):
if parent is None:
parent = Group()
cnames = []
for i in range(num_children):
child = child_factory()
cname = _child_name(child, i)
parent.add(cname, child)
if i:
for u, v in conns:
parent.connect('.'.join((cnames[-1], u)), '.'.join((cname, v)))
cnames.append(cname)
return parent
def test_multiple_connect(self):
root = Group()
C1 = root.add("C1", ExecComp("y=x*2.0"))
C2 = root.add("C2", ExecComp("y=x*2.0"))
C3 = root.add("C3", ExecComp("y=x*2.0"))
root.connect("C1.y", ["C2.x", "C3.x"])
root._setup_paths("")
params_dict, unknowns_dict = root._setup_variables()
# verify we get correct connection information
connections = root._get_explicit_connections()
expected_connections = {"C2.x": ["C1.y"], "C3.x": ["C1.y"]}
self.assertEqual(connections, expected_connections)
def test_multiple_connect(self):
root = Group()
C1 = root.add('C1', ExecComp('y=x*2.0'))
C2 = root.add('C2', ExecComp('y=x*2.0'))
C3 = root.add('C3', ExecComp('y=x*2.0'))
root.connect('C1.y', ['C2.x', 'C3.x'])
root._setup_paths('')
params_dict, unknowns_dict = root._setup_variables()
# verify we get correct connection information
connections = root._get_explicit_connections()
expected_connections = {'C2.x': ['C1.y'], 'C3.x': ['C1.y']}
self.assertEqual(connections, expected_connections)
def test_splitterW(self):
g = Group()
p = Problem(root=g)
comp = g.add('comp', SplitterW())
p.setup(check=False)
comp.params['W1_des'] = 1.08
comp.params['MNexit1_des'] = 1.0
comp.params['MNexit2_des'] = 1.0
comp.params['design'] = True
comp.params['flow_in:in:W'] = 3.48771299
comp.params['flow_in:in:Tt'] = 630.74523
comp.params['flow_in:in:Pt'] = 0.0271945
comp.params['flow_in:in:Mach'] = 1.0
p.run()
self.check(comp)
comp.params['design'] = False
comp.params['flow_out_1:in:is_super'] = True
comp.params['flow_out_2:in:is_super'] = True
p.run()
self.check(comp)
comp.params['flow_in:in:W'] *= 0.95
comp.params['flow_out_1:in:is_super'] = False
comp.params['flow_out_2:in:is_super'] = False
p.run()
TOL = 0.001
assert_rel_error(self, comp.unknowns['flow_out_1:out:Mach'], 0.76922, TOL)
assert_rel_error(self, comp.unknowns['flow_out_2:out:Mach'], 0.76922, TOL)
def test_simple_jac(self):
group = Group()
group.add('x_param', ParamComp('x', 1.0), promotes=['*'])
group.add('mycomp', ExecComp(['y=2.0*x']), promotes=['x', 'y'])
prob = Problem()
prob.root = group
prob.root.ln_solver = ScipyGMRES()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
def build_sequence(child_factory, num_children, conns=(), parent=None):
if parent is None:
parent = Group()
cnames = []
for i in range(num_children):
child = child_factory()
cname = _child_name(child, i)
parent.add(cname, child)
if i:
for u,v in conns:
parent.connect('.'.join((cnames[-1],u)),
'.'.join((cname,v)))
cnames.append(cname)
return parent
def test_simple_matvec(self):
group = Group()
group.add('x_param', ParamComp('x', 1.0), promotes=['*'])
group.add('mycomp', SimpleCompDerivMatVec(), promotes=['x', 'y'])
prob = Problem()
prob.root = group
prob.root.ln_solver = LinearGaussSeidel()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
def test_simple_jac(self):
group = Group()
group.add('x_param', ParamComp('x', 1.0), promotes=['*'])
group.add('mycomp', ExecComp(['y=2.0*x']), promotes=['x', 'y'])
prob = Problem()
prob.root = group
prob.root.ln_solver = ScipyGMRES()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
def test_simple_matvec(self):
group = Group()
group.add('x_param', IndepVarComp('x', 1.0), promotes=['*'])
group.add('mycomp', SimpleCompDerivMatVec(), promotes=['x', 'y'])
prob = Problem()
prob.root = group
prob.root.ln_solver = LinearGaussSeidel()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
def test_nozzle_very_low_temperatures(self):
comp = Nozzle()
g = Group()
g.add('comp', comp)
p = Problem(root=g)
p.setup(check=False)
comp.params['flow_in:in:W'] = 0.639
comp.params['flow_in:in:Tt'] = 540.0
comp.params['flow_in:in:Pt'] = 0.34
comp.params['flow_in:in:Mach'] = 0.4
comp.params['back_Ps'] = 0.0272
comp.params['design'] = True
p.run()
TOL = 0.01 #this test needs larger tollerance due to exteremely low temperatures
assert_rel_error(self, comp.unknowns['flow_out:out:W'], 0.639, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Pt'], 0.34, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Tt'], 540.0, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Mach'], 2.7092, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:area'], 264.204, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:rhos'], .000177443, TOL)
assert_rel_error(self, comp.unknowns['Fg'], 38.98, TOL)
#off design calcs
comp.params['design'] = False
p.run()
self.assertEqual(comp.unknowns['switchRegime'], 'PERFECTLY_EXPANDED')
assert_rel_error(self, comp.unknowns['flow_out:out:W'], 0.639, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Pt'], 0.34, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Tt'], 540.0, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Mach'], 2.7092, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:area'], 264.204, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:rhos'], 0.000177443, TOL)
comp.params['back_Ps'] = 0.03
p.run()
self.assertEqual(comp.unknowns['switchRegime'], 'OVEREXPANDED')
comp.params['back_Ps'] = 0.026
p.run()
self.assertEqual(comp.unknowns['switchRegime'], 'UNDEREXPANDED')
def test_simple_in_group_matvec(self):
group = Group()
sub = group.add('sub', Group(), promotes=['x', 'y'])
group.add('x_param', ParamComp('x', 1.0), promotes=['*'])
sub.add('mycomp', SimpleCompDerivMatVec(), promotes=['x', 'y'])
prob = Problem()
prob.root = group
prob.root.ln_solver = ExplicitSolver()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
def test_compressor(self):
comp = Compressor()
g = Group()
g.add('comp', comp)
p = Problem(root=g)
p.setup()
comp.params['PR_des'] = 12.47
comp.params['MNexit_des'] = 0.4
comp.params['eff_des'] = 0.8
comp.params['flow_in:in:W'] = 1.08
comp.params['flow_in:in:Tt'] = 630.74523
comp.params['flow_in:in:Pt'] = 0.0271945
comp.params['flow_in:in:Mach'] = 0.6
comp.params['design'] = True
p.run()
TOL = 0.001
assert_rel_error(self, comp.unknowns['flow_out:out:W'], 1.08, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Pt'], 0.33899, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Tt'], 1424.01, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:rhos'], 0.000594, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Mach'], 0.4 ,TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:area'], 364.7, TOL)
assert_rel_error(self, comp.unknowns['pwr'], 303.2, TOL)
assert_rel_error(self, comp.unknowns['eff_poly'], 0.8545, TOL)
# run off design
comp.params['design'] = False
p.run()
# values should remain unchanged in off-design at design condition
assert_rel_error(self, comp.unknowns['flow_out:out:W'], 1.08, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Pt'], 0.33899, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Tt'], 1424.01, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:rhos'], 0.000594, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Mach'], 0.4 ,TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:area'], 364.7, TOL)
assert_rel_error(self, comp.unknowns['pwr'], 303.2, TOL)
assert_rel_error(self, comp.unknowns['eff_poly'], 0.8545, TOL)
# try changing something
comp.params['flow_in:in:W'] *= 1.1
p.run()
assert_rel_error(self, comp.unknowns['PR'], 13.52995, TOL)
def test_variables(self):
group = Group()
group.add('C1', ExecComp('y=x*2.0'), promotes=['x'])
group.add("C2", ExecComp('y=x*2.0'), promotes=['y'])
# paths must be initialized prior to calling _setup_variables
group._setup_paths('')
params_dict, unknowns_dict = group._setup_variables()
self.assertEqual(list(params_dict.keys()), ['C1.x', 'C2.x'])
self.assertEqual(list(unknowns_dict.keys()), ['C1.y', 'C2.y'])
self.assertEqual([m['promoted_name'] for n, m in params_dict.items()],
['x', 'C2.x'])
self.assertEqual(
[m['promoted_name'] for n, m in unknowns_dict.items()],
['C1.y', 'y'])
def test_multiple_connect(self):
root = Group()
C1 = root.add('C1', ExecComp('y=x*2.0'))
C2 = root.add('C2', ExecComp('y=x*2.0'))
C3 = root.add('C3', ExecComp('y=x*2.0'))
root.connect('C1.y',['C2.x', 'C3.x'])
root._setup_paths('')
params_dict, unknowns_dict = root._setup_variables()
# verify we get correct connection information
connections = root._get_explicit_connections()
expected_connections = {
'C2.x': ['C1.y'],
'C3.x': ['C1.y']
}
self.assertEqual(connections, expected_connections)
def test_conflicting_promoted_state_vars(self):
# verify we get an error if we have conflicting promoted state variables
root = Group()
comp1 = SimpleImplicitComp()
comp2 = SimpleImplicitComp()
root.add('c1', comp1, promotes=['z']) # promote the state, z
root.add('c2', comp2, promotes=['z']) # promote the state, z, again.. BAD
prob = Problem(root)
with self.assertRaises(RuntimeError) as err:
prob.setup(check=False)
expected_msg = "Promoted name 'z' matches multiple unknowns: ['c1.z', 'c2.z']"
self.assertEqual(str(err.exception), expected_msg)
def test_inlet(self):
comp = Inlet()
g = Group()
g.add('comp', comp)
p = Problem(root=g)
p.setup()
comp.params['ram_recovery'] = 1.0
comp.params['MNexit_des'] = 0.6
comp.params['flow_in:in:W'] = 1.08
comp.params['flow_in:in:Tt'] = 630.75
comp.params['flow_in:in:Pt'] = 0.0272
comp.params['flow_in:in:Mach'] = 1.0
comp.params['design'] = True
p.run()
TOL = 0.005
assert_rel_error(self, comp.unknowns['flow_out:out:W'], 1.080, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Pt'], 0.0272, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Tt'], 630.75, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:rhos'], 0.000098, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Mach'], 0.6, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:area'], 2230.8, TOL)
#check off design
comp.params['design'] = False
p.run()
assert_rel_error(self, comp.unknowns['flow_out:out:W'], 1.080, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Pt'], 0.0272, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Tt'], 630.75, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:rhos'], 0.000098, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Mach'], 0.6, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:area'], 2230.8, TOL)
#vary something
comp.params['flow_in:in:W'] = 0.9
p.run()
assert_rel_error(self, comp.unknowns['flow_out:out:W'], 0.9, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Pt'], 0.0272, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Tt'], 630.75, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:Mach'], 0.45955, TOL)
assert_rel_error(self, comp.unknowns['flow_out:out:area'], 2230.8, TOL)
def test_array2D(self):
group = Group()
group.add('x_param', ParamComp('x', np.ones((2, 2))), promotes=['*'])
group.add('mycomp', ArrayComp2D(), promotes=['x', 'y'])
prob = Problem()
prob.root = group
prob.root.ln_solver = LinearGaussSeidel()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
Jbase = prob.root.mycomp._jacobian_cache
diff = np.linalg.norm(J['y']['x'] - Jbase['y', 'x'])
assert_rel_error(self, diff, 0.0, 1e-8)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
diff = np.linalg.norm(J['y']['x'] - Jbase['y', 'x'])
assert_rel_error(self, diff, 0.0, 1e-8)
def test_conflicting_promoted_state_vars(self):
# verify we get an error if we have conflicting promoted state variables
root = Group()
comp1 = SimpleImplicitComp()
comp2 = SimpleImplicitComp()
root.add('c1', comp1, promotes=['z']) # promote the state, z
root.add('c2', comp2,
promotes=['z']) # promote the state, z, again.. BAD
prob = Problem(root)
with self.assertRaises(RuntimeError) as err:
prob.setup(check=False)
expected_msg = "Promoted name 'z' matches multiple unknowns: ['c1.z', 'c2.z']"
self.assertEqual(str(err.exception), expected_msg)
def test_array2D(self):
group = Group()
group.add('x_param', IndepVarComp('x', np.ones((2, 2))), promotes=['*'])
group.add('mycomp', ArrayComp2D(), promotes=['x', 'y'])
prob = Problem()
prob.root = group
prob.root.ln_solver = LinearGaussSeidel()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
Jbase = prob.root.mycomp._jacobian_cache
diff = np.linalg.norm(J['y']['x'] - Jbase['y', 'x'])
assert_rel_error(self, diff, 0.0, 1e-8)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
diff = np.linalg.norm(J['y']['x'] - Jbase['y', 'x'])
assert_rel_error(self, diff, 0.0, 1e-8)
def test_two_simple(self):
group = Group()
group.add('x_param', IndepVarComp('x', 1.0))
group.add('comp1', ExecComp(['y=2.0*x']))
group.add('comp2', ExecComp(['z=3.0*y']))
prob = Problem()
prob.root = group
prob.root.ln_solver = LinearGaussSeidel()
prob.root.connect('x_param.x', 'comp1.x')
prob.root.connect('comp1.y', 'comp2.y')
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x_param.x'], ['comp2.z'], mode='fwd', return_format='dict')
assert_rel_error(self, J['comp2.z']['x_param.x'][0][0], 6.0, 1e-6)
J = prob.calc_gradient(['x_param.x'], ['comp2.z'], mode='rev', return_format='dict')
assert_rel_error(self, J['comp2.z']['x_param.x'][0][0], 6.0, 1e-6)
def test_two_simple(self):
group = Group()
group.add('x_param', ParamComp('x', 1.0))
group.add('comp1', ExecComp(['y=2.0*x']))
group.add('comp2', ExecComp(['z=3.0*y']))
prob = Problem()
prob.root = group
prob.root.ln_solver = LinearGaussSeidel()
prob.root.connect('x_param.x', 'comp1.x')
prob.root.connect('comp1.y', 'comp2.y')
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x_param.x'], ['comp2.z'], mode='fwd', return_format='dict')
assert_rel_error(self, J['comp2.z']['x_param.x'][0][0], 6.0, 1e-6)
J = prob.calc_gradient(['x_param.x'], ['comp2.z'], mode='rev', return_format='dict')
assert_rel_error(self, J['comp2.z']['x_param.x'][0][0], 6.0, 1e-6)
def test_calc_gradient_interface_errors(self):
root = Group()
prob = Problem(root=root)
root.add('comp', ExecComp('y=x*2.0'))
try:
prob.calc_gradient(['comp.x'], ['comp.y'], mode='junk')
except Exception as error:
msg = "mode must be 'auto', 'fwd', 'rev', or 'fd'"
self.assertEqual(text_type(error), msg)
else:
self.fail("Error expected")
try:
prob.calc_gradient(['comp.x'], ['comp.y'], return_format='junk')
except Exception as error:
msg = "return_format must be 'array' or 'dict'"
self.assertEqual(text_type(error), msg)
else:
self.fail("Error expected")
def test_calc_gradient_interface_errors(self):
root = Group()
prob = Problem(root=root)
root.add('comp', ExecComp('y=x*2.0'))
try:
prob.calc_gradient(['comp.x'], ['comp.y'], mode='junk')
except Exception as error:
msg = "mode must be 'auto', 'fwd', 'rev', or 'fd'"
self.assertEqual(text_type(error), msg)
else:
self.fail("Error expected")
try:
prob.calc_gradient(['comp.x'], ['comp.y'], return_format='junk')
except Exception as error:
msg = "return_format must be 'array' or 'dict'"
self.assertEqual(text_type(error), msg)
else:
self.fail("Error expected")
def test_add(self):
group = Group()
comp = ExecComp('y=x*2.0')
group.add('mycomp', comp)
subs = list(group.subsystems())
self.assertEqual(len(subs), 1)
self.assertEqual(subs[0], comp)
self.assertEqual(subs[0].name, 'mycomp')
comp2 = ExecComp('y=x*2.0')
group.add("nextcomp", comp2)
subs = list(group.subsystems())
self.assertEqual(len(subs), 2)
self.assertEqual(subs[0], comp)
self.assertEqual(subs[1], comp2)
with self.assertRaises(RuntimeError) as cm:
group.add('mycomp', comp)
expected_msg = "Group '' already contains a subsystem with name 'mycomp'."
self.assertEqual(str(cm.exception), expected_msg)
def test_add(self):
group = Group()
comp = ExecComp('y=x*2.0')
group.add('mycomp', comp)
subs = list(group.subsystems())
self.assertEqual(len(subs), 1)
self.assertEqual(subs[0], comp)
self.assertEqual(subs[0].name, 'mycomp')
comp2 = ExecComp('y=x*2.0')
group.add("nextcomp", comp2)
subs = list(group.subsystems())
self.assertEqual(len(subs), 2)
self.assertEqual(subs[0], comp)
self.assertEqual(subs[1], comp2)
with self.assertRaises(RuntimeError) as cm:
group.add('mycomp', comp)
expected_msg = "Group '' already contains a subsystem with name 'mycomp'."
self.assertEqual(str(cm.exception), expected_msg)
def test_too_few_procs(self):
size = 3
group = Group()
group.add('P', IndepVarComp('x', numpy.ones(size)))
group.add('C1', DistribExecComp(['y=2.0*x'], arr_size=size,
x=numpy.zeros(size),
y=numpy.zeros(size)))
group.add('C2', ExecComp(['z=3.0*y'], y=numpy.zeros(size),
z=numpy.zeros(size)))
prob = Problem(impl=impl)
prob.root = group
prob.root.ln_solver = LinearGaussSeidel()
prob.root.connect('P.x', 'C1.x')
prob.root.connect('C1.y', 'C2.y')
try:
prob.setup(check=False)
except Exception as err:
self.assertEqual(str(err),
"This problem was given 1 MPI processes, "
"but it requires between 2 and 2.")
else:
if MPI: # pragma: no cover
self.fail("Exception expected")
def test_src_indices_error(self):
size = 3
group = Group()
group.add('P', IndepVarComp('x', numpy.ones(size)))
group.add('C1', DistribExecComp(['y=2.0*x'], arr_size=size,
x=numpy.zeros(size),
y=numpy.zeros(size)))
group.add('C2', ExecComp(['z=3.0*y'], y=numpy.zeros(size),
z=numpy.zeros(size)))
prob = Problem(impl=impl)
prob.root = group
prob.root.ln_solver = LinearGaussSeidel()
prob.root.connect('P.x', 'C1.x')
prob.root.connect('C1.y', 'C2.y')
prob.driver.add_desvar('P.x')
prob.driver.add_objective('C1.y')
try:
prob.setup(check=False)
except Exception as err:
self.assertEqual(str(err),
"'C1.y' is a distributed variable and may not be used as a "
"design var, objective, or constraint.")
else:
if MPI: # pragma: no cover
self.fail("Exception expected")
def test_indices(self):
size = 10
root = Group()
root.add('P1', ParamComp('x', np.zeros(size)))
root.add('C1', ExecComp('y = x * 2.', y=np.zeros(size//2), x=np.zeros(size//2)))
root.add('C2', ExecComp('y = x * 3.', y=np.zeros(size//2), x=np.zeros(size//2)))
root.connect('P1.x', "C1.x", src_indices=list(range(size//2)))
root.connect('P1.x', "C2.x", src_indices=list(range(size//2, size)))
prob = Problem(root)
prob.setup(check=False)
root.P1.unknowns['x'][0:size//2] += 1.0
root.P1.unknowns['x'][size//2:size] -= 1.0
prob.run()
assert_rel_error(self, root.C1.params['x'], np.ones(size//2), 0.0001)
assert_rel_error(self, root.C2.params['x'], -np.ones(size//2), 0.0001)
def test_simple_matvec_subbed_like_multipoint(self):
group = Group()
group.add('mycomp', SimpleCompDerivMatVec(), promotes=['x', 'y'])
prob = Problem()
prob.root = Group()
prob.root.add('sub', group, promotes=['*'])
prob.root.sub.add('x_param', ParamComp('x', 1.0), promotes=['*'])
prob.root.ln_solver = ScipyGMRES()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='fd', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='fd', return_format='array')
assert_rel_error(self, J[0][0], 2.0, 1e-6)
# plt.title('Optimum Magnet Thickness')
# plt.ylabel('Number of Magnets M')
# plt.xlabel('Magnet Thickness d as %/ lamda')
ay = fig.add_subplot(212)
ay.plot(lamda2, Lh2)
plt.title('Maximum Levitation Height for Wavelength')
plt.xlabel('Wavelength lamda')
plt.ylabel('Levitation Height y')
plt.show()
if __name__ == "__main__":
from openmdao.core.problem import Problem
root = Group()
root.add('lift', Lift())
p = Problem(root)
recorder = SqliteRecorder('maglev')
recorder.options['record_params'] = True
recorder.options['record_metadata'] = True
p.driver.add_recorder(recorder)
p.setup()
p.run()
p.root.dump()
import sqlitedict
from pprint import pprint
if 'petsc' in sys.argv:
from openmdao.core.petsc_impl import PetscImpl
impl = PetscImpl
else:
from openmdao.core.basic_impl import BasicImpl
impl = BasicImpl
g = Group()
p = Problem(impl=impl, root=g)
if 'gmres' in sys.argv:
from openmdao.solvers.scipy_gmres import ScipyGMRES
p.root.ln_solver = ScipyGMRES()
g.add("P", IndepVarComp('x', numpy.ones(vec_size)))
p.driver.add_desvar("P.x")
par = g.add("par", ParallelGroup())
for pt in range(pts):
ptname = "G%d" % pt
ptg = par.add(ptname, Group())
create_dyncomps(ptg,
num_comps,
2,
2,
2,
var_factory=lambda: numpy.zeros(vec_size))
g.connect("P.x", "par.%s.C0.p0" % ptname)
unknowns['Qout_tot'] = unknowns['Qradiated_tot'] + unknowns['Qradiated_nat_convection_per_area']
unknowns['Qin_tot'] = unknowns['Qsolar_tot'] + unknowns['heat_rate_tot']
unknowns['Q_resid'] = abs(unknowns['Qout_tot'] - unknowns['Qin_tot'])
if __name__ == '__main__':
from openmdao.core.group import Group
from openmdao.core.problem import Problem
from openmdao.drivers.scipy_optimizer import ScipyOptimizer
from openmdao.components.param_comp import ParamComp
from openmdao.components.constraint import ConstraintComp
g = Group()
p = Problem(root=g, driver=ScipyOptimizer())
p.driver.options['optimizer'] = 'COBYLA'
g.add('temp_boundary', ParamComp('T', 340.0))
g.add('tube_wall', TubeWallTemp())
g.connect('temp_boundary.T', 'tube_wall.temp_boundary')
g.add('con', ConstraintComp('temp_boundary > 305.7', out='out'))
g.connect('temp_boundary.T', 'con.temp_boundary')
p.driver.add_param('temp_boundary.T', low=0.0, high=10000.0)
p.driver.add_objective('tube_wall.Q_resid')
p.driver.add_constraint('con.out')
p.setup()
g.tube_wall.params['flow_nozzle:in:Tt'] = 1710.0
g.tube_wall.params['flow_nozzle:in:Pt'] = 0.304434211
g.tube_wall.params['flow_nozzle:in:W'] = 1.08
g.tube_wall.params['flow_bearings:in:W'] = 0.0
def test_calc_gradient(self):
root = Group()
parm = root.add('parm', ParamComp('x', np.array([1., 1., 1., 1.])))
comp = root.add('comp', RosenSuzuki())
root.connect('parm.x', 'comp.x')
prob = Problem(root)
prob.setup(check=False)
prob.run()
param_list = ['parm.x']
unknown_list = ['comp.f', 'comp.g']
# check that calc_gradient returns proper dict value when mode is 'fwd'
J = prob.calc_gradient(param_list,
unknown_list,
mode='fwd',
return_format='dict')
np.testing.assert_almost_equal(J['comp.f']['parm.x'],
np.array([
[-3., -3., -17., 9.],
]))
np.testing.assert_almost_equal(
J['comp.g']['parm.x'],
np.array([
[3., 1., 3., 1.],
[1., 4., 2., 3.],
[6., 1., 2., -1.],
]))
# check that calc_gradient returns proper array value when mode is 'fwd'
J = prob.calc_gradient(param_list,
unknown_list,
mode='fwd',
return_format='array')
np.testing.assert_almost_equal(
J,
np.array([
[-3., -3., -17., 9.],
[3., 1., 3., 1.],
[1., 4., 2., 3.],
[6., 1., 2., -1.],
]))
# check that calc_gradient returns proper dict value when mode is 'rev'
J = prob.calc_gradient(param_list,
unknown_list,
mode='rev',
return_format='dict')
np.testing.assert_almost_equal(J['comp.f']['parm.x'],
np.array([
[-3., -3., -17., 9.],
]))
np.testing.assert_almost_equal(
J['comp.g']['parm.x'],
np.array([
[3., 1., 3., 1.],
[1., 4., 2., 3.],
[6., 1., 2., -1.],
]))
# check that calc_gradient returns proper array value when mode is 'rev'
J = prob.calc_gradient(param_list,
unknown_list,
mode='rev',
return_format='array')
np.testing.assert_almost_equal(
J,
np.array([
[-3., -3., -17., 9.],
[3., 1., 3., 1.],
[1., 4., 2., 3.],
[6., 1., 2., -1.],
]))
# check that calc_gradient returns proper dict value when mode is 'fd'
J = prob.calc_gradient(param_list,
unknown_list,
mode='fd',
return_format='dict')
np.testing.assert_almost_equal(J['comp.f']['parm.x'],
np.array([
[-3., -3., -17., 9.],
]),
decimal=5)
np.testing.assert_almost_equal(J['comp.g']['parm.x'],
np.array([
[3., 1., 3., 1.],
[1., 4., 2., 3.],
[6., 1., 2., -1.],
]),
decimal=5)
# check that calc_gradient returns proper array value when mode is 'fd'
J = prob.calc_gradient(param_list,
unknown_list,
mode='fd',
return_format='array')
np.testing.assert_almost_equal(J,
np.array([
[-3., -3., -17., 9.],
[3., 1., 3., 1.],
[1., 4., 2., 3.],
[6., 1., 2., -1.],
]),
decimal=5)
