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(impl=impl)
prob.root = group
prob.root.ln_solver = PetscKSP()
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', IndepVarComp('x1', np.ones((2))), promotes=['*'])
group.add('x_param2', IndepVarComp('x2', np.ones((2))), promotes=['*'])
group.add('mycomp', DoubleArrayComp(), promotes=['*'])
prob = Problem(impl=impl)
prob.root = group
prob.root.ln_solver = PetscKSP()
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_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 = 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_simple(self):
group = Group()
group.add('x_param', ParamComp('x', 1.0), promotes=['*'])
group.add('mycomp', SimpleCompDerivMatVec(), promotes=['x', 'y'])
prob = Problem(impl=impl)
prob.root = group
prob.root.ln_solver = PetscKSP()
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(self):
group = Group()
group.add('x_param', IndepVarComp('x', 1.0), promotes=['*'])
group.add('mycomp', SimpleCompDerivMatVec(), promotes=['x', 'y'])
prob = Problem(impl=impl)
prob.root = group
prob.root.ln_solver = PetscKSP()
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', IndepVarComp('x', 1.0), promotes=['*'])
group.add('mycomp', ExecComp(['y=2.0*x']), promotes=['x', 'y'])
prob = Problem()
prob.root = group
prob.root.ln_solver = DirectSolver()
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_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_linear_system(self):
root = Group()
root.add('lin', LinearSystem(3))
x = np.array([1, 2, -3])
A = np.array([[5.0, -3.0, 2.0], [1.0, 7.0, -4.0], [1.0, 0.0, 8.0]])
b = A.dot(x)
root.add('p1', ParamComp('A', A))
root.add('p2', ParamComp('b', b))
root.connect('p1.A', 'lin.A')
root.connect('p2.b', 'lin.b')
prob = Problem(root)
prob.setup(check=False)
prob.run()
# Make sure it gets the right answer
assert_rel_error(self, prob['lin.x'], x, .0001)
assert_rel_error(self, np.linalg.norm(prob.root.resids.vec), 0.0,
1e-10)
# Compare against calculated derivs
Ainv = np.linalg.inv(A)
dx_dA = np.outer(Ainv, -x).reshape(3, 9)
dx_db = Ainv
J = prob.calc_gradient(['p1.A', 'p2.b'], ['lin.x'],
mode='fwd',
return_format='dict')
assert_rel_error(self, J['lin.x']['p1.A'], dx_dA, .0001)
assert_rel_error(self, J['lin.x']['p2.b'], dx_db, .0001)
J = prob.calc_gradient(['p1.A', 'p2.b'], ['lin.x'],
mode='rev',
return_format='dict')
assert_rel_error(self, J['lin.x']['p1.A'], dx_dA, .0001)
assert_rel_error(self, J['lin.x']['p2.b'], dx_db, .0001)
J = prob.calc_gradient(['p1.A', 'p2.b'], ['lin.x'],
mode='fd',
return_format='dict')
assert_rel_error(self, J['lin.x']['p1.A'], dx_dA, .0001)
assert_rel_error(self, J['lin.x']['p2.b'], dx_db, .0001)
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(impl=impl)
prob.root = group
prob.root.ln_solver = PetscKSP()
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_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 = ExplicitSolver()
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_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_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_linear_system(self):
root = Group()
root.add('lin', LinearSystem(3))
x = np.array([1, 2, -3])
A = np.array([[ 5.0, -3.0, 2.0], [1.0, 7.0, -4.0], [1.0, 0.0, 8.0]])
b = A.dot(x)
root.add('p1', IndepVarComp('A', A))
root.add('p2', IndepVarComp('b', b))
root.connect('p1.A', 'lin.A')
root.connect('p2.b', 'lin.b')
prob = Problem(root)
prob.setup(check=False)
prob.run()
# Make sure it gets the right answer
assert_rel_error(self, prob['lin.x'], x, .0001)
assert_rel_error(self, np.linalg.norm(prob.root.resids.vec), 0.0, 1e-10)
# Compare against calculated derivs
Ainv = np.linalg.inv(A)
dx_dA = np.outer(Ainv, -x).reshape(3, 9)
dx_db = Ainv
J = prob.calc_gradient(['p1.A', 'p2.b'], ['lin.x'], mode='fwd', return_format='dict')
assert_rel_error(self, J['lin.x']['p1.A'], dx_dA, .0001)
assert_rel_error(self, J['lin.x']['p2.b'], dx_db, .0001)
J = prob.calc_gradient(['p1.A', 'p2.b'], ['lin.x'], mode='rev', return_format='dict')
assert_rel_error(self, J['lin.x']['p1.A'], dx_dA, .0001)
assert_rel_error(self, J['lin.x']['p2.b'], dx_db, .0001)
J = prob.calc_gradient(['p1.A', 'p2.b'], ['lin.x'], mode='fd', return_format='dict')
assert_rel_error(self, J['lin.x']['p1.A'], dx_dA, .0001)
assert_rel_error(self, J['lin.x']['p2.b'], dx_db, .0001)
def test_array_to_scalar(self):
root = Group()
root.add('P1', ParamComp('x', np.array([2., 3.])))
root.add('C1', SimpleComp())
root.add('C2', ExecComp('y = x * 3.', y=0., x=0.))
root.connect('P1.x', 'C1.x', src_indices=[0,])
root.connect('P1.x', 'C2.x', src_indices=[1,])
prob = Problem(root)
prob.setup(check=False)
prob.run()
self.assertAlmostEqual(root.C1.params['x'], 2.)
self.assertAlmostEqual(root.C2.params['x'], 3.)
def test_subarray_to_promoted_var(self):
root = Group()
P = root.add('P', IndepVarComp('x', np.array([1., 2., 3., 4., 5.])))
G = root.add('G', Group())
C = root.add('C', SimpleComp())
A = G.add('A', SimpleArrayComp())
G2 = G.add('G2', Group())
A2 = G2.add('A2', SimpleArrayComp())
root.connect('P.x', 'G.A.x', src_indices=[0,1])
root.connect('P.x', 'C.x', src_indices=[2,])
root.connect('P.x', 'G.G2.A2.x', src_indices=[3, 4])
prob = Problem(root)
prob.setup(check=False)
prob.run()
assert_rel_error(self, root.G.A.params['x'], np.array([1., 2.]), 0.0001)
self.assertAlmostEqual(root.C.params['x'], 3.)
assert_rel_error(self, root.G.G2.A2.params['x'], np.array([4., 5.]), 0.0001)
# now try the same thing with promoted var
root = Group()
P = root.add('P', IndepVarComp('x', np.array([1., 2., 3., 4., 5.])))
G = root.add('G', Group())
C = root.add('C', SimpleComp())
A = G.add('A', SimpleArrayComp(), promotes=['x', 'y'])
G2 = G.add('G2', Group())
A2 = G2.add('A2', SimpleArrayComp(), promotes=['x', 'y'])
root.connect('P.x', 'G.x', src_indices=[0,1])
root.connect('P.x', 'C.x', src_indices=[2,])
root.connect('P.x', 'G.G2.x', src_indices=[3, 4])
prob = Problem(root)
prob.setup(check=False)
prob.run()
assert_rel_error(self, root.G.A.params['x'], np.array([1., 2.]), 0.0001)
self.assertAlmostEqual(root.C.params['x'], 3.)
assert_rel_error(self, root.G.G2.A2.params['x'], np.array([4., 5.]), 0.0001)
def test_subarray_to_promoted_var(self):
root = Group()
P = root.add('P', ParamComp('x', np.array([1., 2., 3.])))
G = root.add('G', Group())
C = root.add('C', SimpleComp())
A = G.add('A', SimpleArrayComp()) # , promotes=['x', 'y'])
root.connect('P.x', 'G.A.x', src_indices=[0, 1])
root.connect('P.x', 'C.x', src_indices=[
2,
])
prob = Problem(root)
prob.setup(check=False)
prob.run()
assert_rel_error(self, root.G.A.params['x'], np.array([1., 2.]),
0.0001)
self.assertAlmostEqual(root.C.params['x'], 3.)
# no try the same thing with promoted var
root = Group()
P = root.add('P', ParamComp('x', np.array([1., 2., 3.])))
G = root.add('G', Group())
C = root.add('C', SimpleComp())
A = G.add('A', SimpleArrayComp(), promotes=['x', 'y'])
root.connect('P.x', 'G.x', src_indices=[0, 1])
root.connect('P.x', 'C.x', src_indices=[
2,
])
prob = Problem(root)
prob.setup(check=False)
prob.run()
assert_rel_error(self, root.G.A.params['x'], np.array([1., 2.]),
0.0001)
self.assertAlmostEqual(root.C.params['x'], 3.)
def test_array_to_scalar(self):
root = Group()
root.add('P1', ParamComp('x', np.array([2., 3.])))
root.add('C1', SimpleComp())
root.add('C2', ExecComp('y = x * 3.', y=0., x=0.))
root.connect('P1.x', 'C1.x', src_indices=[
0,
])
root.connect('P1.x', 'C2.x', src_indices=[
1,
])
prob = Problem(root)
prob.setup(check=False)
prob.run()
self.assertAlmostEqual(root.C1.params['x'], 2.)
self.assertAlmostEqual(root.C2.params['x'], 3.)
def test_indices_connect_error(self):
root = Group()
P = root.add('P', IndepVarComp('x', np.array([1., 2., 3., 4., 5.])))
G = root.add('G', Group())
C = root.add('C', SimpleComp())
A = G.add('A', SimpleArrayComp())
root.connect('P.x', 'G.A.x', src_indices=[0])
root.connect('P.x', 'C.x', src_indices=[2,])
expected_error_message = py3fix("Size 1 of the indexed sub-part of "
"source 'P.x' must match the size "
"'2' of the target 'G.A.x'")
prob = Problem(root)
with self.assertRaises(ConnectError) as cm:
prob.setup(check=False)
self.assertEqual(str(cm.exception), expected_error_message)
# now try the same thing with promoted var
root = Group()
P = root.add('P', IndepVarComp('x', np.array([1., 2., 3., 4., 5.])))
G = root.add('G', Group())
C = root.add('C', SimpleComp())
A = G.add('A', SimpleArrayComp(), promotes=['x', 'y'])
root.connect('P.x', 'G.x', src_indices=[0,1,2])
root.connect('P.x', 'C.x', src_indices=[2,])
expected_error_message = py3fix("Size 3 of the indexed sub-part of "
"source 'P.x' must match the size "
"'2' of the target 'G.x'")
prob = Problem(root)
with self.assertRaises(ConnectError) as cm:
prob.setup(check=False)
self.assertEqual(str(cm.exception), expected_error_message)
self.connect("nozzle_air.Fl_O:tot:Cp","tm.nozzle_air_Cp")
self.connect("nozzle_air.Fl_O:stat:W","tm.nozzle_air_W")
self.connect("bearing_air.Fl_O:tot:T","tm.bearing_air_Tt")
self.connect("bearing_air.Fl_O:tot:Cp","tm.bearing_air_Cp")
self.connect("bearing_air.Fl_O:stat:W","tm.bearing_air_W")
self.connect('tm.ss_temp_residual','tmp_balance.ss_temp_residual')
self.connect('tmp_balance.temp_boundary','tm.temp_boundary')
#run stand-alone component
if __name__ == "__main__":
root = Group()
root.add('fs', FlowStuff())
prob = Problem(root)
prob.root.nl_solver = Newton()
prob.root.nl_solver.options['atol'] = 1e-5
prob.root.nl_solver.options['iprint'] = 1
prob.root.nl_solver.options['rtol'] = 1e-5
prob.root.nl_solver.options['maxiter'] = 50
params = (
('P', 0.3, {'units':'psi'}),
('T', 1500.0, {'units':'degR'}),
('W', 1.0, {'units':'lbm/s'})
)
#nozzle
