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', IndepVarComp('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_generate_numpydocstring(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 = ScipyGMRES()
test_string = prob.root.ln_solver.generate_docstring()
original_string = \
""" \"\"\"
Options
-------
options['atol'] : float(1e-12)
Absolute convergence tolerance.
options['err_on_maxiter'] : bool(False)
If True, raise an AnalysisError if not converged at maxiter.
options['iprint'] : int(0)
Set to 0 to disable printing, set to 1 to print the residual to stdout each iteration, set to 2 to print subiteration residuals as well.
options['maxiter'] : int(1000)
Maximum number of iterations.
options['mode'] : str('auto')
Derivative calculation mode, set to 'fwd' for forward mode, 'rev' for reverse mode, or 'auto' to let OpenMDAO determine the best mode.
options['restart'] : int(20)
Number of iterations between restarts. Larger values increase iteration cost, but may be necessary for convergence
\"\"\"
"""
for sorig, stest in zip(original_string.split('\n'),
test_string.split('\n')):
self.assertEqual(sorig, stest)
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_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 example():
# simple test of module
root = Group()
root.add('bos_opex_test', FUSED_OpenMDAO(opex_csm_fused()), promotes=['*'])
prob = Problem(root)
prob.setup()
prob[
'machine_rating'] = 5000.0 # Need to manipulate input or underlying cprob[onent will not execute
prob['net_aep'] = 1701626526.28
prob['sea_depth'] = 20.0
prob['year'] = 2009
prob['month'] = 12
prob['turbine_number'] = 100
prob.run()
print(
"Average annual operational expenditures for an offshore wind plant with 100 NREL 5 MW turbines"
)
for io in root.unknowns:
print(io + ' ' + str(root.unknowns[io]))
prob['sea_depth'] = 0.0
prob.run()
print(
"Average annual operational expenditures for an land-based wind plant with 100 NREL 5 MW turbines"
)
for io in root.unknowns:
print(io + ' ' + str(root.unknowns[io]))
def test_simple_matvec(self):
class VerificationComp(SimpleCompDerivMatVec):
def linearize(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_simple_matvec(self):
class VerificationComp(SimpleCompDerivMatVec):
def linearize(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 example():
# openmdao example of execution
root = Group()
root.add('bos_csm_test', FUSED_OpenMDAO(bos_csm_fused()), promotes=['*'])
prob = Problem(root)
prob.setup()
prob['machine_rating'] = 5000.0
prob['rotor_diameter'] = 126.0
prob['turbine_cost'] = 5950209.28
prob['hub_height'] = 90.0
prob['RNA_mass'] = 256634.5 # RNA mass is not used in this simple model
prob['turbine_number'] = 100
prob['sea_depth'] = 20.0
prob['year'] = 2009
prob['month'] = 12
prob['multiplier'] = 1.0
prob.run()
print(
"Balance of Station Costs for an offshore wind plant with 100 NREL 5 MW turbines"
)
for io in root.unknowns:
print(io + ' ' + str(root.unknowns[io]))
prob['sea_depth'] = 0.0
prob['turbine_cost'] = 5229222.77
prob.run()
print(
"Balance of Station Costs for an land-based wind plant with 100 NREL 5 MW turbines"
)
for io in root.unknowns:
print(io + ' ' + str(root.unknowns[io]))
def test_case1_vs_npss(self):
root = Group()
root.add('p', breakpoint_levitation.BreakPointDrag())
root.add('q', breakpoint_levitation.MagMass())
prob = Problem(root)
prob.setup()
prob['p.m_pod'] = 3000.0
prob['p.b_res'] = 1.48
prob['p.num_mag_hal'] = 4.0
prob['p.mag_thk'] = .15
prob['p.l_pod'] = 22.0
prob['p.gamma'] = 1.0
prob['p.w_mag'] = 3.0
prob['p.spacing'] = 0.0
prob['p.d_pod'] = 1.0
prob['p.w_strip'] = .005
prob['p.num_sheets'] = 1.0
prob['p.delta_c'] = .0005334
prob['p.strip_c'] = .0105
prob['p.rc'] = 1.713e-8
prob['p.MU0'] = 4.0 * np.pi * (1.0e-7)
prob['p.track_factor'] = .75
prob['p.vel_b'] = 23.0
prob['p.h_lev'] = .01
prob['p.g'] = 9.81
prob['q.m_pod'] = 3000.0
prob['q.mag_thk'] = .15
prob['q.rho_mag'] = 7500.0
prob['q.l_pod'] = 22.0
prob['q.gamma'] = 1.0
prob['q.cost_per_kg'] = 44.0
prob['q.w_mag'] = 3.0
prob['q.d_pod'] = 1.0
prob['q.track_factor'] = .75
prob.run()
# Print Statement for debugging
# print('track_ind is %12.12f' % prob['comp.Drag.track_ind'])
# Test Values
assert np.isclose(prob['q.mag_area'], 16.500000, rtol=.01)
assert np.isclose(prob['q.m_mag'], 18562.500000, rtol=.01)
assert np.isclose(prob['q.cost'], 816750.000000, rtol=.01)
assert np.isclose(prob['q.total_pod_mass'], 21562.500000, rtol=.01)
assert np.isclose(prob['p.lam'], 0.600000, rtol=.01)
assert np.isclose(prob['p.track_ind'], 4.28571e-6, rtol=.01)
assert np.isclose(prob['p.b0'], 1.055475, rtol=.01)
assert np.isclose(prob['p.mag_area'], 16.500000, rtol=.01)
assert np.isclose(prob['p.omegab'], 240.855437, rtol=.01)
assert np.isclose(prob['p.w_track'], .75, rtol=.01)
assert np.isclose(prob['p.fyu'], 520814.278077, rtol=.01)
assert np.isclose(prob['p.fxu'], 2430517.899848, rtol=.01)
assert np.isclose(prob['p.ld_ratio'], 0.214281, rtol=.01)
assert np.isclose(prob['p.pod_weight'], 29430.000000, rtol=.01)
assert np.isclose(prob['p.track_res'], 0.004817, rtol=.01)
def example_turbine():
# openmdao example of execution
root = Group()
root.add('tcc_csm_test', FUSED_OpenMDAO(tcc_csm_fused()), promotes=['*'])
prob = Problem(root)
prob.setup()
# simple test of module
prob['rotor_diameter'] = 126.0
prob['blade_number'] = 3
prob['hub_height'] = 90.0
prob['machine_rating'] = 5000.0
# Rotor force calculations for nacelle inputs
maxTipSpd = 80.0
maxEfficiency = 0.90201
ratedWindSpd = 11.5064
thrustCoeff = 0.50
airDensity = 1.225
ratedHubPower = prob['machine_rating'] / maxEfficiency
rotorSpeed = (maxTipSpd/(0.5*prob['rotor_diameter'])) * (60.0 / (2*np.pi))
prob['rotor_thrust'] = airDensity * thrustCoeff * np.pi * prob['rotor_diameter']**2 * (ratedWindSpd**2) / 8
prob['rotor_torque'] = ratedHubPower/(rotorSpeed*(np.pi/30))*1000
prob['year'] = 2009
prob['month'] = 12
prob.run()
print("The results for the NREL 5 MW Reference Turbine in an offshore 20 m water depth location are:")
for io in root.unknowns:
print(io + ' ' + str(root.unknowns[io]))
def example_finance():
# simple test of module
# openmdao example of execution
root = Group()
root.add('fin_csm_test', FUSED_OpenMDAO(fin_csm_fused(fixed_charge_rate = 0.12, construction_finance_rate=0.0, tax_rate = 0.4, discount_rate = 0.07, \
construction_time = 1.0, project_lifetime = 20.0, sea_depth = 20.0)), promotes=['*'])
prob = Problem(root)
prob.setup()
prob['turbine_cost'] = 6087803.555 / 50
prob['turbine_number'] = 50
preventative_opex = 401819.023
lease_opex = 22225.395
corrective_opex = 91048.387
prob['avg_annual_opex'] = preventative_opex + corrective_opex + lease_opex
prob['bos_costs'] = 7668775.3
prob['net_aep'] = 15756299.843
prob['sea_depth'] = 20.0
prob.run()
print(
"Overall cost of energy for an offshore wind plant with 100 NREL 5 MW turbines"
)
for io in root.unknowns:
print(io + ' ' + str(root.unknowns[io]))
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_case1_vs_npss(self):
root = Group()
root.add('p', breakpoint_levitation.BreakPointDrag())
root.add('q', breakpoint_levitation.MagMass())
prob = Problem(root)
prob.setup()
prob['p.m_pod'] = 3000.0
prob['p.b_res'] = 1.48
prob['p.num_mag_hal'] = 4.0
prob['p.mag_thk'] = .15
prob['p.l_pod'] = 22.0
prob['p.gamma'] = 1.0
prob['p.w_mag'] = 3.0
prob['p.spacing'] = 0.0
prob['p.d_pod'] = 1.0
prob['p.w_strip'] = .005
prob['p.num_sheets'] = 1.0
prob['p.delta_c'] = .0005334
prob['p.strip_c'] = .0105
prob['p.rc'] = 1.713e-8
prob['p.MU0'] = 4.0*np.pi*(1.0e-7)
prob['p.track_factor'] = .75
prob['p.vel_b'] = 23.0
prob['p.h_lev'] = .01
prob['p.g'] = 9.81
prob['q.m_pod'] = 3000.0
prob['q.mag_thk'] = .15
prob['q.rho_mag'] = 7500.0
prob['q.l_pod'] = 22.0
prob['q.gamma'] = 1.0
prob['q.cost_per_kg'] = 44.0
prob['q.w_mag'] = 3.0
prob['q.d_pod'] = 1.0
prob['q.track_factor'] = .75
prob.run()
# Print Statement for debugging
# print('track_ind is %12.12f' % prob['comp.Drag.track_ind'])
# Test Values
assert np.isclose(prob['q.mag_area'], 16.500000, rtol = .01)
assert np.isclose(prob['q.m_mag'], 18562.500000, rtol = .01)
assert np.isclose(prob['q.cost'], 816750.000000, rtol = .01)
assert np.isclose(prob['q.total_pod_mass'], 21562.500000, rtol = .01)
assert np.isclose(prob['p.lam'], 0.600000, rtol = .01)
assert np.isclose(prob['p.track_ind'], 4.28571e-6, rtol = .01)
assert np.isclose(prob['p.b0'], 1.055475, rtol = .01)
assert np.isclose(prob['p.mag_area'], 16.500000, rtol = .01)
assert np.isclose(prob['p.omegab'], 240.855437, rtol = .01)
assert np.isclose(prob['p.w_track'], .75, rtol = .01)
assert np.isclose(prob['p.fyu'], 520814.278077, rtol = .01)
assert np.isclose(prob['p.fxu'], 2430517.899848, rtol = .01)
assert np.isclose(prob['p.ld_ratio'], 0.214281, rtol = .01)
assert np.isclose(prob['p.pod_weight'], 29430.000000, rtol = .01)
assert np.isclose(prob['p.track_res'], 0.004817, rtol = .01)
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', IndepVarComp('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.assertTrue(msg in str(error))
else:
self.fail("Error expected")
def test_generate_numpydocstring(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 = ScipyGMRES()
test_string = prob.root.ln_solver.generate_docstring()
original_string = """ \"\"\"
Options
-------
options['atol'] : float(1e-12)
Absolute convergence tolerance.
options['err_on_maxiter'] : bool(False)
If True, raise an AnalysisError if not converged at maxiter.
options['iprint'] : int(0)
Set to 0 to disable printing, set to 1 to print iteration totals to stdout, set to 2 to print the residual each iteration to stdout.
options['maxiter'] : int(1000)
Maximum number of iterations.
options['mode'] : str('auto')
Derivative calculation mode, set to 'fwd' for forward mode, 'rev' for reverse mode, or 'auto' to let OpenMDAO determine the best mode.
options['restart'] : int(20)
Number of iterations between restarts. Larger values increase iteration cost, but may be necessary for convergence
\"\"\"
"""
for sorig, stest in zip(original_string.split("\n"), test_string.split("\n")):
self.assertEqual(sorig, stest)
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()
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_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', IndepVarComp('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 = "'G3': promoted name 'y' matches multiple unknowns: ('G3.C3.y', 'G3.C4.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', IndepVarComp('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 = "'G3': promoted name 'y' matches multiple unknowns: ('G3.C3.y', 'G3.C4.y')"
self.assertEqual(text_type(error), msg)
else:
self.fail("Error expected")
def test_case1_vs_inductrack(self):
root = Group()
root.add('lev', levitation_group.LevGroup())
prob = Problem(root)
params = (('m_pod', 3000.0, {'units': 'kg'}),
('l_pod', 22.0, {'units': 'm'}),
('d_pod', 1.0, {'units': 'm'}),
('vel_b', 23.0, {'units': 'm/s'}),
('h_lev', 0.01, {'unit': 'm'}),
('vel', 350.0, {'units': 'm/s'}))
prob.root.add('input_vars', IndepVarComp(params))
prob.root.connect('input_vars.m_pod', 'lev.m_pod')
prob.root.connect('input_vars.l_pod', 'lev.l_pod')
prob.root.connect('input_vars.d_pod', 'lev.d_pod')
prob.root.connect('input_vars.vel_b', 'lev.vel_b')
prob.root.connect('input_vars.h_lev', 'lev.h_lev')
prob.root.connect('input_vars.vel', 'lev.vel')
prob.setup()
prob['lev.Drag.b_res'] = 1.48
prob['lev.Drag.num_mag_hal'] = 4.0
prob['lev.Drag.gamma'] = 1.0
prob['lev.Drag.w_mag'] = 3.0
prob['lev.Drag.spacing'] = 0.0
prob['lev.Drag.w_strip'] = .005
prob['lev.Drag.num_sheets'] = 1.0
prob['lev.Drag.delta_c'] = .0005334
prob['lev.Drag.strip_c'] = .0105
prob['lev.Drag.rc'] = 1.713e-8
prob['lev.Drag.MU0'] = 4.0*np.pi*(1.0e-7)
prob['lev.Drag.track_factor'] = .75
prob['lev.Drag.g'] = 9.81
prob['lev.Drag.mag_thk'] = .15
prob['lev.Mass.mag_thk'] = .15
prob['lev.Mass.rho_mag'] = 7500.0
prob['lev.Mass.gamma'] = 1.0
prob['lev.Mass.cost_per_kg'] = 44.0
prob['lev.Mass.w_mag'] = 3.0
prob['lev.Mass.track_factor'] = .75
prob.run()
# Print Statements for debugging
# print('Mag Mass %f' % prob['lev.m_mag'])
# print('Mag Drag is %f' % prob['lev.mag_drag'])
# Test Values
assert np.isclose(prob['lev.mag_drag'], 9025.39, rtol=.01)
assert np.isclose(prob['lev.total_pod_mass'], 21562.50, rtol=.01)
def test_bad_sysname(self):
group = Group()
try:
group.add('0', ExecComp('y=x*2.0'), promotes=['x'])
except NameError as err:
self.assertEqual(str(err), ": '0' is not a valid system name.")
try:
group.add('foo:bar', ExecComp('y=x*2.0'), promotes=['x'])
except NameError as err:
self.assertEqual(str(err), ": 'foo:bar' is not a valid system name.")
def test_bad_sysname(self):
group = Group()
try:
group.add("0", ExecComp("y=x*2.0"), promotes=["x"])
except NameError as err:
self.assertEqual(str(err), ": '0' is not a valid system name.")
try:
group.add("foo:bar", ExecComp("y=x*2.0"), promotes=["x"])
except NameError as err:
self.assertEqual(str(err), ": 'foo:bar' is not a valid system name.")
def test_bad_sysname(self):
group = Group()
try:
group.add('0', ExecComp('y=x*2.0'), promotes=['x'])
except NameError as err:
self.assertEqual(str(err), ": '0' is not a valid system name.")
try:
group.add('foo:bar', ExecComp('y=x*2.0'), promotes=['x'])
except NameError as err:
self.assertEqual(str(err),
": 'foo:bar' is not a valid system name.")
def test_multiple_connect_alt(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'))
with self.assertRaises(TypeError) as err:
root.connect('C1.y', 'C2.x', 'C3.x')
msg = "src_indices must be an index array, did you mean connect('C1.y', ['C2.x', 'C3.x'])?"
self.assertEqual(msg, str(err.exception))
def test_multiple_connect_alt(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"))
with self.assertRaises(TypeError) as err:
root.connect("C1.y", "C2.x", "C3.x")
msg = "src_indices must be an index array, did you mean connect('C1.y', ['C2.x', 'C3.x'])?"
self.assertEqual(msg, str(err.exception))
def test_calc_gradient(self):
root = Group()
root.add('indep', IndepVarComp('x', np.array([1., 1., 1., 1.])))
root.add('comp', RosenSuzuki())
root.connect('indep.x', 'comp.x')
subprob = Problem(root)
subprob.driver.add_desvar('indep.x', lower=-10, upper=99)
subprob.driver.add_objective('comp.f')
subprob.driver.add_constraint('comp.g', upper=0.)
prob = Problem(root=Group())
prob.root.add('desvars', IndepVarComp('x', np.ones(4)))
prob.root.add('subprob', SubProblem(subprob,
params=['indep.x'],
unknowns=['comp.f', 'comp.g']))
prob.root.connect('desvars.x', 'subprob.indep.x')
prob.setup(check=False)
prob.run()
indep_list = ['desvars.x']
unknown_list = ['subprob.comp.f', 'subprob.comp.g']
# check that calc_gradient returns proper dict value when mode is 'fwd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fwd', return_format='dict')
assert_almost_equal(J['subprob.comp.f']['desvars.x'],
expectedJ['subprob.comp.f']['desvars.x'])
assert_almost_equal(J['subprob.comp.g']['desvars.x'],
expectedJ['subprob.comp.g']['desvars.x'])
# check that calc_gradient returns proper array value when mode is 'fwd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fwd', return_format='array')
assert_almost_equal(J, expectedJ_array)
# check that calc_gradient returns proper dict value when mode is 'rev'
J = prob.calc_gradient(indep_list, unknown_list, mode='rev', return_format='dict')
assert_almost_equal(J['subprob.comp.f']['desvars.x'], expectedJ['subprob.comp.f']['desvars.x'])
assert_almost_equal(J['subprob.comp.g']['desvars.x'], expectedJ['subprob.comp.g']['desvars.x'])
# check that calc_gradient returns proper array value when mode is 'rev'
J = prob.calc_gradient(indep_list, unknown_list, mode='rev', return_format='array')
assert_almost_equal(J, expectedJ_array)
# check that calc_gradient returns proper dict value when mode is 'fd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='dict')
assert_almost_equal(J['subprob.comp.f']['desvars.x'], expectedJ['subprob.comp.f']['desvars.x'], decimal=5)
assert_almost_equal(J['subprob.comp.g']['desvars.x'], expectedJ['subprob.comp.g']['desvars.x'], decimal=5)
# check that calc_gradient returns proper array value when mode is 'fd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='array')
assert_almost_equal(J, expectedJ_array, decimal=5)
def test_generate_numpydocstring(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 = ScipyGMRES()
test_string = prob.root.ln_solver.generate_docstring()
original_string = ' """\n\n Options\n -------\n options[\'atol\'] : float(1e-12)\n Absolute convergence tolerance.\n options[\'iprint\'] : int(0)\n Set to 0 to disable printing, set to 1 to print the residual to stdout each iteration, set to 2 to print subiteration residuals as well.\n options[\'maxiter\'] : int(1000)\n Maximum number of iterations.\n options[\'mode\'] : str(\'auto\')\n Derivative calculation mode, set to \'fwd\' for forward mode, \'rev\' for reverse mode, or \'auto\' to let OpenMDAO determine the best mode.\n options[\'precondition\'] : bool(False)\n Set to True to turn on preconditioning.\n options[\'restart\'] : int(20)\n Number of iterations between restarts. Larger values increase iteration cost, but may be necessary for convergence\n\n """\n'
self.assertEqual(original_string, test_string)
def test_calc_gradient_with_qoi_indices(self):
q_idxs = [0, 2]
root = Group()
root.add('parm', IndepVarComp('x', np.array([1., 1., 1., 1.])))
root.add('comp', RosenSuzuki())
root.connect('parm.x', 'comp.x')
prob = Problem(root)
prob.driver.add_desvar('parm.x', lower=-10, upper=99)
prob.driver.add_objective('comp.f')
prob.driver.add_constraint('comp.g', upper=0., indices=q_idxs)
prob.setup(check=False)
prob.run()
indep_list = ['parm.x']
unknown_list = ['comp.f', 'comp.g']
# override expected array value to reflect qoi indices
expectedJ_array = np.concatenate((
expectedJ['comp.f']['parm.x'],
expectedJ['comp.g']['parm.x'][q_idxs, :]
))
# check that calc_gradient returns proper dict value when mode is 'fwd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fwd', return_format='dict')
assert_almost_equal(J['comp.f']['parm.x'], expectedJ['comp.f']['parm.x'])
assert_almost_equal(J['comp.g']['parm.x'], expectedJ['comp.g']['parm.x'][q_idxs, :])
# check that calc_gradient returns proper array value when mode is 'fwd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fwd', return_format='array')
assert_almost_equal(J, expectedJ_array)
# check that calc_gradient returns proper dict value when mode is 'rev'
J = prob.calc_gradient(indep_list, unknown_list, mode='rev', return_format='dict')
assert_almost_equal(J['comp.f']['parm.x'], expectedJ['comp.f']['parm.x'])
assert_almost_equal(J['comp.g']['parm.x'], expectedJ['comp.g']['parm.x'][q_idxs, :])
# check that calc_gradient returns proper array value when mode is 'rev'
J = prob.calc_gradient(indep_list, unknown_list, mode='rev', return_format='array')
assert_almost_equal(J, expectedJ_array)
# check that calc_gradient returns proper dict value when mode is 'fd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='dict')
assert_almost_equal(J['comp.f']['parm.x'], expectedJ['comp.f']['parm.x'], decimal=5)
assert_almost_equal(J['comp.g']['parm.x'], expectedJ['comp.g']['parm.x'][q_idxs, :], decimal=5)
# check that calc_gradient returns proper array value when mode is 'fd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='array')
assert_almost_equal(J, expectedJ_array, 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'])
prob = Problem()
# paths must be initialized prior to calling _setup_variables
group._init_sys_data('', prob._probdata)
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_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(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_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_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 = 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_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"])
prob = Problem()
root._init_sys_data("", prob._probdata)
params_dict, unknowns_dict = root._setup_variables()
# verify we get correct connection information
connections = root._get_explicit_connections()
expected_connections = {"C2.x": [("C1.y", None)], "C3.x": [("C1.y", None)]}
self.assertEqual(connections, expected_connections)
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"])
prob = Problem(root=group)
prob.setup(check=False)
params_dict = prob.root._params_dict
unknowns_dict = prob.root._unknowns_dict
self.assertEqual(list(params_dict.keys()), ["C1.x", "C2.x"])
self.assertEqual(list(unknowns_dict.keys()), ["C1.y", "C2.y"])
to_prom_name = prob.root._sysdata.to_prom_name
self.assertEqual([to_prom_name[n] for n in params_dict], ["x", "C2.x"])
self.assertEqual([to_prom_name[n] for n in unknowns_dict], ["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'])
prob = Problem(root=group)
prob.setup(check=False)
params_dict = prob.root._params_dict
unknowns_dict = prob.root._unknowns_dict
self.assertEqual(list(params_dict.keys()), ['C1.x', 'C2.x'])
self.assertEqual(list(unknowns_dict.keys()), ['C1.y', 'C2.y'])
to_prom_name = prob.root._sysdata.to_prom_name
self.assertEqual([to_prom_name[n] for n in params_dict], ['x', 'C2.x'])
self.assertEqual([to_prom_name[n] for n in unknowns_dict], ['C1.y', 'y'])
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 = 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_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(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_linear_system(self):
root = Group()
lingrp = root.add('lingrp', Group(), promotes=['*'])
lingrp.add('lin', LinearSystem(3))
lingrp.ln_solver = ScipyGMRES()
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_simple_choose_different_alg(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.root.ln_solver.options['ksp_type'] = 'gmres'
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_choose_different_alg(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.root.ln_solver.options["ksp_type"] = "gmres"
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_variables(self):
group = Group()
group.add('C1', ExecComp('y=x*2.0'), promotes=['x'])
group.add("C2", ExecComp('y=x*2.0'), promotes=['y'])
prob = Problem(root=group)
prob.setup(check=False)
params_dict = prob.root._params_dict
unknowns_dict = prob.root._unknowns_dict
self.assertEqual(list(params_dict.keys()), ['C1.x', 'C2.x'])
self.assertEqual(list(unknowns_dict.keys()), ['C1.y', 'C2.y'])
to_prom_name = prob.root._sysdata.to_prom_name
self.assertEqual([to_prom_name[n] for n in params_dict], ['x', 'C2.x'])
self.assertEqual([to_prom_name[n] for n in unknowns_dict],
['C1.y', 'y'])
def example():
root = Group()
root.add('aep_test', FUSED_OpenMDAO(aep_csm_fused()), promotes=['*'])
prob = Problem(root)
prob.setup()
prob[
'machine_rating'] = 5000.0 #Float(units = 'kW', iotype='in', desc= 'rated machine power in kW')
prob[
'max_tip_speed'] = 80.0 #Float(units = 'm/s', iotype='in', desc= 'maximum allowable tip speed for the rotor')
prob[
'rotor_diameter'] = 126.0 #Float(units = 'm', iotype='in', desc= 'rotor diameter of the machine')
prob[
'max_power_coefficient'] = 0.488 #Float(iotype='in', desc= 'maximum power coefficient of rotor for operation in region 2')
prob[
'opt_tsr'] = 7.525 #Float(iotype='in', desc= 'optimum tip speed ratio for operation in region 2')
prob[
'cut_in_wind_speed'] = 3.0 #Float(units = 'm/s', iotype='in', desc= 'cut in wind speed for the wind turbine')
prob[
'cut_out_wind_speed'] = 25.0 #Float(units = 'm/s', iotype='in', desc= 'cut out wind speed for the wind turbine')
prob[
'hub_height'] = 90.0 #Float(units = 'm', iotype='in', desc= 'hub height of wind turbine above ground / sea level')
prob[
'altitude'] = 0.0 #Float(units = 'm', iotype='in', desc= 'altitude of wind plant')
prob[
'air_density'] = 0 #Float(units = 'kg / (m * m * m)', iotype='in', desc= 'air density at wind plant site') # default air density value is 0.0 - forces aero csm to calculate air density in model
prob[
'max_efficiency'] = 0.902 #Float(iotype='in', desc = 'maximum efficiency of rotor and drivetrain - at rated power')
prob[
'thrust_coefficient'] = 0.5 #Float(iotype='in', desc='thrust coefficient at rated power')
prob['soiling_losses'] = 0.0
prob['array_losses'] = 0.1
prob['availability'] = 0.941
prob['turbine_number'] = 100
prob['shear_exponent'] = 0.1
prob['wind_speed_50m'] = 8.02
prob['weibull_k'] = 2.15
prob.run()
print("AEP output")
for io in root.unknowns:
print(io + ' ' + str(root.unknowns[io]))
def test_simple_matvec_subbed(self):
group = Group()
group.add('mycomp', SimpleCompDerivMatVec(), promotes=['x', 'y'])
prob = Problem()
prob.root = Group()
prob.root.add('x_param', IndepVarComp('x', 1.0), promotes=['*'])
prob.root.add('sub', group, promotes=['*'])
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_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_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'])
prob = Problem()
root._init_sys_data('', prob._probdata)
params_dict, unknowns_dict = root._setup_variables()
# verify we get correct connection information
connections = root._get_explicit_connections()
expected_connections = {
'C2.x': [('C1.y', None)],
'C3.x': [('C1.y', None)]
}
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'])
prob = Problem()
root._init_sys_data('', prob._probdata)
params_dict, unknowns_dict = root._setup_variables()
# verify we get correct connection information
connections = root._get_explicit_connections()
expected_connections = {
'C2.x': [('C1.y', None)],
'C3.x': [('C1.y', None)]
}
self.assertEqual(connections, expected_connections)
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', IndepVarComp('x', np.ones((2, 2))), promotes=['*'])
group.add('mycomp', ArrayComp2D(), 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')
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", IndepVarComp("x", np.ones((2, 2))), promotes=["*"])
group.add("mycomp", ArrayComp2D(), 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")
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_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 = DirectSolver()
prob.root.ln_solver.options['jacobian_method'] = 'assemble'
prob.setup(check=False)
prob.run()
with self.assertRaises(RuntimeError) as cm:
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
expected_msg = "The 'assemble' jacobian_method is not supported when " + \
"'apply_linear' is used on a component (mycomp)."
self.assertEqual(str(cm.exception), expected_msg)
def test_group_add(self):
model = Group()
ecomp = ExecComp('b=2.0*a', a=3.0, b=6.0)
msg = "The 'add' method provides backwards compatibility with OpenMDAO <= 1.x ; " \
"use 'add_subsystem' instead."
with assert_warning(DeprecationWarning, msg):
comp1 = model.add('comp1', ecomp)
self.assertTrue(ecomp is comp1)
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_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_array2D_no_decompose(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 = DirectSolver()
prob.root.ln_solver.options['jacobian_method'] = 'assemble'
prob.root.ln_solver.options['save_LU_decomposition'] = False
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_src_indices(self):
size = 10
root = Group()
root.add('P1', IndepVarComp('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)