def setUp(self):
if OPT is None:
raise unittest.SkipTest("pyoptsparse is not installed")
if OPTIMIZER is None:
raise unittest.SkipTest(
"pyoptsparse is not providing SNOPT or SLSQP")
prob = Problem(impl=impl)
root = prob.root = Group()
#root.ln_solver = lin_solver()
root.ln_solver = LinearGaussSeidel()
par = root.add('par', ParallelGroup())
par.ln_solver = LinearGaussSeidel()
ser1 = par.add('ser1', Group())
ser1.ln_solver = LinearGaussSeidel()
ser1.add('p1', IndepVarComp('x', np.zeros([2])), promotes=['x'])
ser1.add('comp', SimpleArrayComp(), promotes=['x', 'y'])
ser1.add('con',
ExecComp('c = y - 20.0',
c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])),
promotes=['c', 'y'])
ser1.add('obj',
ExecComp('o = y[0]', y=np.array([0.0, 0.0])),
promotes=['y', 'o'])
ser2 = par.add('ser2', Group())
ser2.ln_solver = LinearGaussSeidel()
ser2.add('p1', IndepVarComp('x', np.zeros([2])), promotes=['x'])
ser2.add('comp', SimpleArrayComp(), promotes=['x', 'y'])
ser2.add('con',
ExecComp('c = y - 30.0',
c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])),
promotes=['c', 'y'])
ser2.add('obj',
ExecComp('o = y[0]', y=np.array([0.0, 0.0])),
promotes=['o', 'y'])
root.add('total', ExecComp('obj = x1 + x2'))
root.connect('par.ser1.o', 'total.x1')
root.connect('par.ser2.o', 'total.x2')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('par.ser1.x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('par.ser2.x', lower=-50.0, upper=50.0)
prob.driver.add_objective('total.obj')
prob.driver.add_constraint('par.ser1.c', equals=0.0)
prob.driver.add_constraint('par.ser2.c', equals=0.0)
self.prob = prob
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_simple_array_Jacobian(self):
# Tests that we can correctly handle user-defined Jacobians.
# Now with arrays
empty = {}
mycomp = SimpleArrayComp()
mycomp._jacobian_cache = mycomp.linearize(empty, empty, empty)
# Forward
dparams = {}
dparams['x'] = np.array([1.5, 3.1])
dresids = {}
dresids['y'] = np.array([0.0, 0.0])
mycomp.apply_linear(empty, empty, dparams, empty,
dresids, 'fwd')
target = mycomp._jacobian_cache[('y', 'x')].dot(dparams['x'])
diff = abs(dresids['y'] - target).max()
self.assertAlmostEqual(diff, 0.0, places=3)
# Reverse
dparams = {}
dparams['x'] = np.array([0.0, 0.0])
dresids = {}
dresids['y'] = np.array([1.5, 3.1])
mycomp.apply_linear(empty, empty, dparams, empty,
dresids, 'rev')
target = mycomp._jacobian_cache[('y', 'x')].T.dot(dresids['y'])
diff = abs(dparams['x'] - target).max()
self.assertAlmostEqual(diff, 0.0, places=3)
def test_simple_array_comp_SLSQP_scaler_unity_eq(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', np.zeros([2])), promotes=['*'])
root.add('comp', SimpleArrayComp(), promotes=['*'])
root.add('con',
ExecComp('c = y - 20.0',
c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])),
promotes=['*'])
root.add('obj',
ExecComp('o = y[0]', y=np.array([0.0, 0.0])),
promotes=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.add_desvar('x',
lower=-50.0,
upper=50.0,
scaler=np.array([1.0, 1.0]))
prob.driver.add_objective('o', scaler=np.array([1.0, 1.0]))
prob.driver.add_constraint('c',
equals=0.0,
scaler=np.array([1.0, 1.0]))
prob.driver.options['disp'] = False
prob.setup(check=False)
prob.run()
obj = prob['o']
assert_rel_error(self, obj, 20.0, 1e-6)
def test_simple_array_comp(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', np.zeros([2])), promotes=['*'])
root.add('comp', SimpleArrayComp(), promotes=['*'])
root.add('con',
ExecComp('c = y - 20.0',
c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])),
promotes=['*'])
root.add('obj',
ExecComp('o = y[0]', y=np.array([0.0, 0.0])),
promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_objective('o')
prob.driver.add_constraint('c', equals=0.0)
prob.setup(check=False)
prob.run()
obj = prob['o']
assert_rel_error(self, obj, 20.0, 1e-6)
def test_simple_array_comp(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', np.zeros([2])), promotes=['*'])
root.add('comp', SimpleArrayComp(), promotes=['*'])
root.add('con',
ExecComp('c = y - 20.0',
c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])),
promotes=['*'])
root.add('obj',
ExecComp('o = y[0]', y=np.array([0.0, 0.0])),
promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.add_param('x', low=-50.0, high=50.0)
prob.driver.add_objective('o')
prob.driver.add_constraint('c', ctype='eq')
prob.setup(check=False)
prob.run()
obj = prob['o']
assert_rel_error(self, obj, 20.0, 1e-6)
def test_src_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,
])
prob = Problem(root)
with self.assertRaises(Exception) as cm:
prob.setup(check=False)
expected = py3fix("Size 1 of the indexed sub-part of source 'P.x' "
"must be the same as size 2 of target 'G.A.x'.")
self.assertTrue(expected in str(cm.exception))
# 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,
])
prob = Problem(root)
with self.assertRaises(Exception) as cm:
prob.setup(check=False)
expected = py3fix(
"Size 3 of the indexed sub-part of source 'P.x' "
"must be the same as size 2 of target 'G.A.x' (G.x).")
self.assertTrue(expected in str(cm.exception))
def __init__(self):
super(TestProb, self).__init__()
self.root = root = Group()
root.add('c1', SimpleArrayComp())
root.add('p1', ParamComp('p', 1 * np.ones(2)))
root.connect('p1.p', 'c1.x')
root.add('ci1', SimpleImplicitComp())
root.add('pi1', ParamComp('p', 1.))
root.connect('pi1.p', 'ci1.x')
def __init__(self):
super(TestProb, self).__init__()
self.root = root = Group()
root.ln_solver = ScipyGMRES()
root.add('c1', SimpleArrayComp())
root.add('p1', IndepVarComp('p', 1*np.ones(2)))
root.connect('p1.p','c1.x')
root.add('ci1', SimpleImplicitComp())
root.add('pi1', IndepVarComp('p', 1.))
root.connect('pi1.p','ci1.x')
root.add('pjunk', IndepVarComp('pj', np.ones((2,2))))
root.add('junk', ExecComp('y=x', x=np.zeros((2,2)), y=np.zeros((2,2))))
root.connect('pjunk.pj', 'junk.x')
def test_simple_array_model(self):
prob = Problem()
prob.root = Group()
prob.root.add('comp', SimpleArrayComp())
prob.root.add('p1', IndepVarComp('x', np.ones([2])))
prob.root.connect('p1.x', 'comp.x')
prob.setup(check=False)
prob.run()
data = prob.check_partial_derivatives(out_stream=None)
for key1, val1 in iteritems(data):
for key2, val2 in iteritems(val1):
assert_rel_error(self, val2['abs error'][0], 0.0, 1e-5)
assert_rel_error(self, val2['abs error'][1], 0.0, 1e-5)
assert_rel_error(self, val2['abs error'][2], 0.0, 1e-5)
assert_rel_error(self, val2['rel error'][0], 0.0, 1e-5)
assert_rel_error(self, val2['rel error'][1], 0.0, 1e-5)
assert_rel_error(self, val2['rel error'][2], 0.0, 1e-5)
