def test_partials(self):
self.p.setup(force_alloc_complex=True)
v0 = 100.0
gam0 = np.radians(30.0)
t_duration = 10.0
phase = self.p.model.phase0
self.p['phase0.t_initial'] = 0.0
self.p['phase0.t_duration'] = t_duration
self.p['phase0.states:r'] = phase.interpolate(
ys=[0, v0 * np.cos(gam0) * t_duration], nodes='state_disc')
self.p['phase0.states:h'] = phase.interpolate(ys=[0, 0],
nodes='state_input')
self.p['phase0.states:v'] = phase.interpolate(ys=[v0, v0],
nodes='state_input')
self.p['phase0.states:gam'] = phase.interpolate(ys=[gam0, -gam0],
nodes='state_input')
self.p.run_model()
cpd = self.p.check_partials(compact_print=True, method='cs')
assert_check_partials(cpd)
def test_rk_stepsize_comp_nonuniform(self):
p = om.Problem(model=om.Group())
ivc = p.model.add_subsystem('ivc',
om.IndepVarComp(),
promotes_outputs=['*'])
ivc.add_output('t_duration', shape=(1, ), units='s')
p.model.add_subsystem(
'c',
RungeKuttaStepsizeComp(num_segments=4,
seg_rel_lengths=[1, 1, 0.5, 0.25],
time_units='s'))
p.model.connect('t_duration', 'c.t_duration')
p.setup(check=True, force_alloc_complex=True)
p['t_duration'] = 5.5
np.set_printoptions(linewidth=1024)
p.run_model()
expected = np.array([2.0, 2.0, 1.0, 0.5])
assert_near_equal(p.get_val('c.h'), expected, tolerance=1.0E-9)
cpd = p.check_partials(method='cs', out_stream=None)
assert_check_partials(cpd)
def test_brachistochrone_partials(self):
import numpy as np
import openmdao.api as om
from dymos.utils.testing_utils import assert_check_partials
from dymos.examples.brachistochrone.brachistochrone_ode import BrachistochroneODE
num_nodes = 5
p = om.Problem(model=om.Group())
ivc = p.model.add_subsystem('vars', om.IndepVarComp())
ivc.add_output('v', shape=(num_nodes,), units='m/s')
ivc.add_output('theta', shape=(num_nodes,), units='deg')
p.model.add_subsystem('ode', BrachistochroneODE(num_nodes=num_nodes, static_gravity=True))
p.model.connect('vars.v', 'ode.v')
p.model.connect('vars.theta', 'ode.theta')
p.setup(force_alloc_complex=True)
p.set_val('vars.v', 10*np.random.random(num_nodes))
p.set_val('vars.theta', 10*np.random.uniform(1, 179, num_nodes))
p.run_model()
cpd = p.check_partials(method='cs', compact_print=True)
assert_check_partials(cpd)
def test_state_interp_comp_lobatto_vectorized_different_orders(self):
segends = np.array([0.0, 3.0, 10.0])
gd = GridData(num_segments=2,
transcription_order=[3, 5],
segment_ends=segends,
transcription='gauss-lobatto')
p = om.Problem(model=om.Group())
states = {'pos': {'units': 'm', 'shape': (2,)}}
X_ivc = om.IndepVarComp()
p.model.add_subsystem('X_ivc', X_ivc, promotes=['state_disc:pos'])
X_ivc.add_output('state_disc:pos',
val=np.zeros((gd.subset_num_nodes['state_disc'], 2)), units='m')
F_ivc = om.IndepVarComp()
p.model.add_subsystem('F_ivc', F_ivc, promotes=['staterate_disc:pos'])
F_ivc.add_output('staterate_disc:pos',
val=np.zeros((gd.subset_num_nodes['state_disc'], 2)),
units='m/s')
dt_dtau_ivc = om.IndepVarComp()
p.model.add_subsystem('dt_dstau_ivc', dt_dtau_ivc, promotes=['dt_dstau'])
dt_dtau_ivc.add_output('dt_dstau', val=0.0*np.zeros(gd.subset_num_nodes['col']), units='s')
p.model.add_subsystem('state_interp_comp',
subsys=StateInterpComp(transcription='gauss-lobatto',
grid_data=gd,
state_options=states,
time_units='s'))
p.model.connect('state_disc:pos', 'state_interp_comp.state_disc:pos')
p.model.connect('staterate_disc:pos', 'state_interp_comp.staterate_disc:pos')
p.model.connect('dt_dstau', 'state_interp_comp.dt_dstau')
p.setup(force_alloc_complex=True)
segends_disc = np.array((0, 3, 3, 6.5, 10))
p['state_disc:pos'][:, 0] = [x(t) for t in segends_disc] # [0.0, 25.0, 25.0, 100.0]
p['staterate_disc:pos'][:, 0] = [f_x(t) for t in segends_disc]
p['state_disc:pos'][:, 1] = [v(t) for t in segends_disc]
p['staterate_disc:pos'][:, 1] = [f_v(t) for t in segends_disc]
p['dt_dstau'] = [3.0/2., 7.0/2, 7.0/2]
p.run_model()
cpd = p.check_partials(compact_print=True, method='cs')
assert_check_partials(cpd, atol=5.0E-5)
def test_partials(self):
n = 10
p = om.Problem(model=om.Group())
p.model.add_subsystem(name='kappa_comp', subsys=KappaComp(num_nodes=n))
p.setup()
p.set_val('kappa_comp.mach', np.linspace(0, 1.8, n))
p.run_model()
cpd = p.check_partials(compact_print=False, out_stream=None)
assert_check_partials(cpd, atol=1.0E-5, rtol=1.0E-4)
def test_rk_k_comp_rk4_scalar(self):
state_options = {'y': {'shape': (1, ), 'units': 'm'}}
p = om.Problem(model=om.Group())
ivc = p.model.add_subsystem('ivc',
om.IndepVarComp(),
promotes_outputs=['*'])
ivc.add_output('h', shape=(4, ), units='s')
ivc.add_output('f:y', shape=(4, 4, 1), units='m/s')
p.model.add_subsystem(
'c',
RungeKuttaKComp(num_segments=4,
method='RK4',
state_options=state_options,
time_units='s'))
p.model.connect('f:y', 'c.f:y')
p.model.connect('h', 'c.h')
p.setup(check=True, force_alloc_complex=True)
p['h'] = [0.5, 0.5, 0.5, 0.5]
p['f:y'] = np.array([[[1.50000000], [1.81250000], [1.89062500],
[2.19531250]],
[[2.17513021], [2.40641276], [2.46423340],
[2.65724691]],
[[2.63960266], [2.73700333], [2.76135349],
[2.77027941]],
[[2.75681897], [2.63352371], [2.60269990],
[2.30816892]]])
np.set_printoptions(linewidth=1024)
p.run_model()
expected = np.array([[[0.75000000], [0.90625000], [0.94531250],
[1.09765625]],
[[1.087565104166667], [1.203206380208333],
[1.232116699218750], [1.328623453776042]],
[[1.319801330566406], [1.368501663208008],
[1.380676746368408], [1.385139703750610]],
[[1.378409485022227], [1.316761856277783],
[1.301349949091673], [1.154084459568063]]])
assert_near_equal(p.get_val('c.k:y'), expected, tolerance=1.0E-9)
p.model.list_outputs(print_arrays=True)
cpd = p.check_partials(method='cs', out_stream=None)
assert_check_partials(cpd)
def test_rk_state_advance_comp_rk4_matrix(self):
state_options = {'y': {'shape': (2, 2), 'units': 'm'}}
p = om.Problem(model=om.Group())
ivc = p.model.add_subsystem('ivc',
om.IndepVarComp(),
promotes_outputs=['*'])
ivc.add_output('k:y', shape=(1, 4, 2, 2), units='m')
ivc.add_output('y0', shape=(1, 2, 2), units='m')
p.model.add_subsystem(
'c',
RungeKuttaStatePredictComp(num_segments=1,
method='RK4',
state_options=state_options))
p.model.connect('k:y', 'c.k:y')
p.model.connect('y0', 'c.initial_states_per_seg:y')
p.setup(check=True, force_alloc_complex=True)
p['y0'] = [[[0.5, 1.425130208333333],
[2.639602661132812, 4.006818970044454]]]
p['k:y'] = np.array([[[[0.75, 1.087565104166667],
[1.319801330566406, 1.378409485022227]],
[[0.90625, 1.203206380208333],
[1.368501663208008, 1.316761856277783]],
[[0.9453125, 1.23211669921875],
[1.380676746368408, 1.301349949091673]],
[[1.09765625, 1.328623453776042],
[1.385139703750610, 1.154084459568063]]]])
p.run_model()
p.model.list_outputs(print_arrays=True)
assert_near_equal(p.get_val('c.predicted_states:y'),
[[[0.5, 1.425130208333333],
[2.639602661132812, 4.006818970044454]],
[[0.875, 1.968912760416667],
[3.299503326416016, 4.696023712555567]],
[[0.953125, 2.0267333984375],
[3.323853492736816, 4.665199898183346]],
[[1.4453125, 2.657246907552083],
[4.020279407501221, 5.308168919136127]]])
np.set_printoptions(linewidth=1024)
cpd = p.check_partials(method='cs', out_stream=None)
assert_check_partials(cpd)
def test_partials(self):
np.set_printoptions(linewidth=1024, edgeitems=1e1000)
p = self.make_prob('radau-ps', n_segs=2, order=5, compressed=False)
cpd = p.check_partials(compact_print=True, method='fd')
del cpd['state_indep']
assert_check_partials(cpd)
p = self.make_prob('radau-ps', n_segs=2, order=5, compressed=True)
cpd = p.check_partials(compact_print=True, method='fd')
del cpd['state_indep']
assert_check_partials(cpd)
p = self.make_prob('gauss-lobatto',
n_segs=3,
order=5,
compressed=False)
cpd = p.check_partials(compact_print=True, method='fd')
del cpd['state_indep']
assert_check_partials(cpd)
p = self.make_prob('gauss-lobatto', n_segs=4, order=3, compressed=True)
cpd = p.check_partials(compact_print=True, method='fd')
del cpd['state_indep']
assert_check_partials(cpd)
def test_robertson_problem_for_docs(self):
import openmdao.api as om
from dymos.utils.testing_utils import assert_check_partials
from openmdao.utils.assert_utils import assert_near_equal
import matplotlib.pyplot as plt
num_nodes = 3
p = om.Problem(model=om.Group())
p.model.add_subsystem('ode', RobertsonODE(num_nodes=num_nodes), promotes=['*'])
p.setup(force_alloc_complex=True)
p.set_val('x', [10., 100., 1000.])
p.set_val('y', [1, 0.1, 0.01])
p.set_val('z', [1., 2., 3.])
p.run_model()
cpd = p.check_partials(method='cs', compact_print=True)
assert_check_partials(cpd)
assert_near_equal(p.get_val('xdot'), [9999.6, 1996., 260.])
assert_near_equal(p.get_val('ydot'), [-3.00099996E7, -3.01996E5, -3.26E3])
assert_near_equal(p.get_val('zdot'), [3.0E7, 3.0E5, 3.0E3])
# just set up the problem, test it elsewhere
p = self.robertson_problem(t_final=40)
p.run_model()
p_sim = p.model.traj.simulate(method='LSODA')
assert_near_equal(p_sim.get_val('traj.phase0.timeseries.states:x0')[-1], 0.71583161, tolerance=1E-5)
assert_near_equal(p_sim.get_val('traj.phase0.timeseries.states:y0')[-1], 9.18571144e-06, tolerance=1E-1)
assert_near_equal(p_sim.get_val('traj.phase0.timeseries.states:z0')[-1], 0.2841592, tolerance=1E-5)
t_sim = p_sim.get_val('traj.phase0.timeseries.time')
states = ['x0', 'y0', 'z0']
fig, axes = plt.subplots(len(states), 1)
for i, state in enumerate(states):
axes[i].plot(t_sim, p_sim.get_val(f'traj.phase0.timeseries.states:{state}'), '-')
axes[i].set_ylabel(state[0])
axes[-1].set_xlabel('time (s)')
plt.tight_layout()
plt.show()
def test_rk_state_predict_comp_rk4_3seg(self):
num_seg = 3
state_options = {'y': {'shape': (1, ), 'units': 'm'}}
p = om.Problem(model=om.Group())
ivc = p.model.add_subsystem('ivc',
om.IndepVarComp(),
promotes_outputs=['*'])
ivc.add_output('k:y', shape=(num_seg, 4, 1), units='m')
ivc.add_output('y0', shape=(num_seg, 1), units='m')
p.model.add_subsystem(
'c',
RungeKuttaStatePredictComp(num_segments=num_seg,
method='RK4',
state_options=state_options))
p.model.connect('k:y', 'c.k:y')
p.model.connect('y0', 'c.initial_states_per_seg:y')
p.setup(check=True, force_alloc_complex=True)
p['y0'] = np.array([[0.5, 1.425130208333333, 2.639602661132812]]).T
p['k:y'] = np.array([[[0.75000000], [0.90625000], [0.94531250],
[1.09765625]],
[[1.087565104166667], [1.203206380208333],
[1.232116699218750], [1.328623453776042]],
[[1.319801330566406], [1.368501663208008],
[1.380676746368408], [1.385139703750610]]])
p.run_model()
p.model.list_outputs(print_arrays=True)
expected = np.array([[0.50000000], [0.87500000], [0.953125],
[1.4453125], [1.425130208333333],
[1.968912760416667], [2.026733398437500],
[2.657246907552083], [2.639602661132812],
[3.299503326416016], [3.323853492736816],
[4.020279407501221]])
assert_near_equal(p.get_val('c.predicted_states:y'), expected)
cpd = p.check_partials(method='cs', out_stream=None)
assert_check_partials(cpd)
def test_rk_state_advance_comp_rk4_scalar(self):
state_options = {'y': {'shape': (1, ), 'units': 'm'}}
p = om.Problem(model=om.Group())
ivc = p.model.add_subsystem('ivc',
om.IndepVarComp(),
promotes_outputs=['*'])
ivc.add_output('k:y', shape=(4, 4, 1), units='m')
ivc.add_output('y0', shape=(4, 1), units='m')
p.model.add_subsystem(
'c',
RungeKuttaStateAdvanceComp(num_segments=4,
method='RK4',
state_options=state_options))
p.model.connect('k:y', 'c.k:y')
p.model.connect('y0', 'c.initial_states_per_seg:y')
p.setup(check=True, force_alloc_complex=True)
p['y0'] = np.array(
[[0.5, 1.425130208333333, 2.639602661132812, 4.006818970044454]]).T
p['k:y'] = np.array([[[0.75000000], [0.90625000], [0.94531250],
[1.09765625]],
[[1.08756510], [1.20320638], [1.23211670],
[1.32862345]],
[[1.31980133], [1.36850166], [1.38067675],
[1.38513970]],
[[1.37840949], [1.31676186], [1.30134995],
[1.15408446]]])
p.run_model()
assert_near_equal(p.get_val('c.final_states:y'),
np.array([[1.425130208333333], [2.639602661132812],
[4.006818970044454], [5.301605229265987]]),
tolerance=1.0E-9)
np.set_printoptions(linewidth=1024)
cpd = p.check_partials(method='cs', out_stream=None)
assert_check_partials(cpd)
def test_partials(self):
n = 5
p = om.Problem()
tas = np.linspace(0, 100, n)
ivc = p.model.add_subsystem('ivc',
subsys=om.IndepVarComp(),
promotes_outputs=['*'])
ivc.add_output(name='tas', val=tas, units='m/s')
ivc.add_output(name='h', val=0.0, units='m')
p.model.add_subsystem('atmos', subsys=USatm1976Comp(num_nodes=1))
p.model.add_subsystem(name='dyn_press',
subsys=DynamicPressureComp(num_nodes=n))
p.model.connect('tas', 'dyn_press.tas')
p.model.connect('h', 'atmos.h')
p.model.connect('atmos.rho', 'dyn_press.rho')
p.setup()
p.run_model()
cpd = p.check_partials(compact_print=False, out_stream=None)
assert_check_partials(cpd, atol=1.0E-5, rtol=1.0E-4)
def test_partials(self):
n = 10
p = om.Problem(model=om.Group())
ivc = om.IndepVarComp()
ivc.add_output(name='mach', units=None, val=np.zeros(n))
p.model.add_subsystem(name='ivc', subsys=ivc, promotes_outputs=['*'])
p.model.add_subsystem(name='cla_comp', subsys=CLaComp(num_nodes=n))
p.model.connect('mach', 'cla_comp.mach')
p.setup()
p['mach'] = np.linspace(0, 1.8, n)
p.run_model()
cpd = p.check_partials(compact_print=False, out_stream=None)
assert_check_partials(cpd, atol=1.0E-3, rtol=1.0E-4)
def test_control_interp_matrix_2x2(self, transcription='gauss-lobatto', compressed=True):
segends = np.array([0.0, 3.0, 10.0])
gd = GridData(num_segments=2,
transcription_order=5,
segment_ends=segends,
transcription=transcription,
compressed=compressed)
p = om.Problem(model=om.Group())
controls = {'a': {'units': 'm', 'shape': (2, 2), 'dynamic': True}}
ivc = om.IndepVarComp()
p.model.add_subsystem('ivc', ivc, promotes_outputs=['*'])
ivc.add_output('controls:a', val=np.zeros((gd.subset_num_nodes['control_input'], 2, 2)),
units='m')
ivc.add_output('t_initial', val=0.0, units='s')
ivc.add_output('t_duration', val=10.0, units='s')
p.model.add_subsystem('time_comp',
subsys=TimeComp(num_nodes=gd.num_nodes, node_ptau=gd.node_ptau,
node_dptau_dstau=gd.node_dptau_dstau, units='s'),
promotes_inputs=['t_initial', 't_duration'],
promotes_outputs=['time', 'dt_dstau'])
p.model.add_subsystem('control_interp_comp',
subsys=ControlInterpComp(grid_data=gd,
control_options=controls,
time_units='s'),
promotes_inputs=['controls:*'])
p.model.connect('dt_dstau', 'control_interp_comp.dt_dstau')
p.setup(force_alloc_complex=True)
p['t_initial'] = 0.0
p['t_duration'] = 3.0
p.run_model()
t = p['time']
control_input_idxs = gd.subset_node_indices['control_input']
p['controls:a'][:, 0, 0] = f_a(t[control_input_idxs])
p['controls:a'][:, 0, 1] = f_b(t[control_input_idxs])
p['controls:a'][:, 1, 0] = f_c(t[control_input_idxs])
p['controls:a'][:, 1, 1] = f_d(t[control_input_idxs])
p.run_model()
a0_value_expected = f_a(t)
a1_value_expected = f_b(t)
a2_value_expected = f_c(t)
a3_value_expected = f_d(t)
a0_rate_expected = f1_a(t)
a1_rate_expected = f1_b(t)
a2_rate_expected = f1_c(t)
a3_rate_expected = f1_d(t)
a0_rate2_expected = f2_a(t)
a1_rate2_expected = f2_b(t)
a2_rate2_expected = f2_c(t)
a3_rate2_expected = f2_d(t)
assert_almost_equal(p['control_interp_comp.control_values:a'][:, 0, 0],
a0_value_expected)
assert_almost_equal(p['control_interp_comp.control_values:a'][:, 0, 1],
a1_value_expected)
assert_almost_equal(p['control_interp_comp.control_values:a'][:, 1, 0],
a2_value_expected)
assert_almost_equal(p['control_interp_comp.control_values:a'][:, 1, 1],
a3_value_expected)
assert_almost_equal(p['control_interp_comp.control_rates:a_rate'][:, 0, 0],
a0_rate_expected)
assert_almost_equal(p['control_interp_comp.control_rates:a_rate'][:, 0, 1],
a1_rate_expected)
assert_almost_equal(p['control_interp_comp.control_rates:a_rate'][:, 1, 0],
a2_rate_expected)
assert_almost_equal(p['control_interp_comp.control_rates:a_rate'][:, 1, 1],
a3_rate_expected)
assert_almost_equal(p['control_interp_comp.control_rates:a_rate2'][:, 0, 0],
a0_rate2_expected)
assert_almost_equal(p['control_interp_comp.control_rates:a_rate2'][:, 0, 1],
a1_rate2_expected)
assert_almost_equal(p['control_interp_comp.control_rates:a_rate2'][:, 1, 0],
a2_rate2_expected)
assert_almost_equal(p['control_interp_comp.control_rates:a_rate2'][:, 1, 1],
a3_rate2_expected)
with np.printoptions(linewidth=100000, edgeitems=100000):
cpd = p.check_partials(compact_print=True, method='cs')
assert_check_partials(cpd)
def test_partials(self):
np.set_printoptions(linewidth=1024, edgeitems=1000)
cpd = self.p.check_partials(out_stream=None)
assert_check_partials(cpd)
def test_partials(self):
np.set_printoptions(linewidth=1024, edgeitems=1000)
cpd = self.p.check_partials(method='cs')
assert_check_partials(cpd)
def test_continuity_comp_connected_scalar_no_iteration_fwd(self):
num_seg = 4
state_options = {
'y': {
'shape': (1, ),
'units': 'm',
'targets': ['y'],
'defect_scaler': None,
'defect_ref': None,
'lower': None,
'upper': None,
'connected_initial': True
}
}
p = om.Problem(model=om.Group())
ivc = p.model.add_subsystem('ivc',
om.IndepVarComp(),
promotes_outputs=['*'])
ivc.add_output('initial_states:y', units='m', shape=(1, 1))
p.model.add_subsystem('continuity_comp',
RungeKuttaStateContinuityComp(
num_segments=num_seg,
state_options=state_options),
promotes_inputs=['*'],
promotes_outputs=['*'])
p.model.nonlinear_solver = om.NonlinearRunOnce()
p.model.linear_solver = om.DirectSolver()
p.setup(check=True, force_alloc_complex=True)
p['initial_states:y'] = 0.5
p['states:y'] = np.array([[0.50000000], [1.425130208333333],
[2.639602661132812], [4.006818970044454],
[5.301605229265987]])
p['state_integrals:y'] = np.array([[1.0], [1.0], [1.0], [1.0]])
p.run_model()
p.model.run_apply_nonlinear()
# Test that the residuals of the states are the expected values
outputs = p.model.list_outputs(print_arrays=True,
residuals=True,
out_stream=None)
y_f = p['states:y'][1:, ...]
y_i = p['states:y'][:-1, ...]
dy_given = y_f - y_i
dy_computed = p['state_integrals:y']
expected_resids = np.zeros((num_seg + 1, 1))
expected_resids[1:, ...] = dy_given - dy_computed
op_dict = dict([op for op in outputs])
assert_near_equal(op_dict['continuity_comp.states:y']['resids'],
expected_resids)
# Test the partials
cpd = p.check_partials(method='cs')
assert_check_partials(cpd)
def test_rk4_scalar_nonlinearblockgs(self):
num_seg = 4
num_stages = 4
state_options = {'y': {'shape': (1, ), 'units': 'm', 'targets': ['y']}}
p = om.Problem(model=om.Group())
ivc = p.model.add_subsystem('ivc',
om.IndepVarComp(),
promotes_outputs=['*'])
ivc.add_output('initial_states_per_seg:y',
shape=(num_seg, 1),
units='m')
ivc.add_output('h', shape=(num_seg, 1), units='s')
ivc.add_output('t', shape=(num_seg * num_stages, 1), units='s')
p.model.add_subsystem(
'k_iter_group',
RungeKuttaKIterGroup(num_segments=num_seg,
method='RK4',
state_options=state_options,
time_units='s',
ode_class=TestODE,
ode_init_kwargs={},
solver_class=om.NonlinearBlockGS,
solver_options={'iprint': 2}))
p.model.connect('t', 'k_iter_group.ode.t')
p.model.connect('h', 'k_iter_group.h')
p.model.connect('initial_states_per_seg:y',
'k_iter_group.initial_states_per_seg:y')
src_idxs = np.arange(16, dtype=int).reshape((num_seg, num_stages, 1))
p.model.connect('k_iter_group.ode.ydot',
'k_iter_group.k_comp.f:y',
src_indices=src_idxs,
flat_src_indices=True)
p.setup(check=True, force_alloc_complex=True)
p['t'] = np.array([[
0.00, 0.25, 0.25, 0.50, 0.50, 0.75, 0.75, 1.00, 1.00, 1.25, 1.25,
1.50, 1.50, 1.75, 1.75, 2.00
]]).T
p['h'] = np.array([[0.5, 0.5, 0.5, 0.5]]).T
p['initial_states_per_seg:y'] = np.array([[0.50000000],
[1.425130208333333],
[2.639602661132812],
[4.006818970044454]])
# Start k with a terrible guess
p['k_iter_group.k_comp.k:y'][...] = 0
p.run_model()
# Test that the residuals of k are zero (we started k at the expected converged value)
outputs = p.model.list_outputs(print_arrays=True,
residuals=True,
out_stream=False)
op_dict = dict([op for op in outputs])
assert_almost_equal(op_dict['k_iter_group.k_comp.k:y']['resids'], 0.0)
# Test the partials
cpd = p.check_partials(method='cs', out_stream=None)
assert_check_partials(cpd)
def test_polynomial_control_group_scalar_gl(self):
transcription = 'gauss-lobatto'
compressed = True
segends = np.array([0.0, 3.0, 10.0])
gd = GridData(num_segments=2,
transcription_order=5,
segment_ends=segends,
transcription=transcription,
compressed=compressed)
p = om.Problem(model=om.Group())
controls = {
'a': PolynomialControlOptionsDictionary(),
'b': PolynomialControlOptionsDictionary()
}
controls['a']['units'] = 'm'
controls['a']['order'] = 3
controls['a']['shape'] = (1, )
controls['a']['opt'] = True
controls['b']['units'] = 'm'
controls['b']['order'] = 3
controls['b']['shape'] = (1, )
controls['b']['opt'] = True
ivc = om.IndepVarComp()
p.model.add_subsystem('ivc', ivc, promotes_outputs=['*'])
ivc.add_output('t_initial', val=0.0, units='s')
ivc.add_output('t_duration', val=10.0, units='s')
p.model.add_subsystem('time_comp',
subsys=TimeComp(
num_nodes=gd.num_nodes,
node_ptau=gd.node_ptau,
node_dptau_dstau=gd.node_dptau_dstau,
units='s'),
promotes_inputs=['t_initial', 't_duration'],
promotes_outputs=['time', 'dt_dstau'])
polynomial_control_group = PolynomialControlGroup(
grid_data=gd, polynomial_control_options=controls, time_units='s')
p.model.add_subsystem('polynomial_control_group',
subsys=polynomial_control_group,
promotes_inputs=['*'],
promotes_outputs=['*'])
p.setup(force_alloc_complex=True)
p['t_initial'] = 0.0
p['t_duration'] = 3.0
p.run_model()
control_nodes_ptau, _ = lgl(controls['a']['order'] + 1)
t_control_input = p['t_initial'] + 0.5 * (control_nodes_ptau +
1) * p['t_duration']
t_all = p['time']
p['polynomial_controls:a'][:, 0] = f_a(t_control_input)
p['polynomial_controls:b'][:, 0] = f_b(t_control_input)
p.run_model()
a_value_expected = f_a(t_all)
b_value_expected = f_b(t_all)
a_rate_expected = f1_a(t_all)
b_rate_expected = f1_b(t_all)
a_rate2_expected = f2_a(t_all)
b_rate2_expected = f2_b(t_all)
assert_almost_equal(p['polynomial_control_values:a'],
np.atleast_2d(a_value_expected).T)
assert_almost_equal(p['polynomial_control_values:b'],
np.atleast_2d(b_value_expected).T)
assert_almost_equal(p['polynomial_control_rates:a_rate'],
np.atleast_2d(a_rate_expected).T)
assert_almost_equal(p['polynomial_control_rates:b_rate'],
np.atleast_2d(b_rate_expected).T)
assert_almost_equal(p['polynomial_control_rates:a_rate2'],
np.atleast_2d(a_rate2_expected).T)
assert_almost_equal(p['polynomial_control_rates:b_rate2'],
np.atleast_2d(b_rate2_expected).T)
np.set_printoptions(linewidth=1024)
cpd = p.check_partials(compact_print=False,
out_stream=None,
method='cs')
assert_check_partials(cpd)
def test_partials(self):
cpd = self.p.check_partials(method='cs', out_stream=None)
assert_check_partials(cpd)
def test_state_interp_comp_lobatto(self):
segends = np.array([0.0, 3.0, 10.0])
gd = GridData(num_segments=2,
transcription_order=3,
segment_ends=segends,
transcription='gauss-lobatto')
p = om.Problem(model=om.Group())
states = {'x': {'units': 'm', 'shape': (1,)},
'v': {'units': 'm/s', 'shape': (1,)}}
X_ivc = om.IndepVarComp()
p.model.add_subsystem('X_ivc', X_ivc, promotes=['state_disc:x', 'state_disc:v'])
X_ivc.add_output('state_disc:x', val=np.zeros(gd.subset_num_nodes['state_disc']),
units='m')
X_ivc.add_output('state_disc:v', val=np.zeros(gd.subset_num_nodes['state_disc']),
units='m/s')
F_ivc = om.IndepVarComp()
p.model.add_subsystem('F_ivc', F_ivc, promotes=['staterate_disc:x', 'staterate_disc:v'])
F_ivc.add_output('staterate_disc:x',
val=np.zeros(gd.subset_num_nodes['state_disc']),
units='m/s')
F_ivc.add_output('staterate_disc:v',
val=np.zeros(gd.subset_num_nodes['state_disc']),
units='m/s**2')
dt_dtau_ivc = om.IndepVarComp()
p.model.add_subsystem('dt_dstau_ivc', dt_dtau_ivc, promotes=['dt_dstau'])
dt_dtau_ivc.add_output('dt_dstau', val=0.0*np.zeros(gd.subset_num_nodes['col']), units='s')
p.model.add_subsystem('state_interp_comp',
subsys=StateInterpComp(transcription='gauss-lobatto',
grid_data=gd,
state_options=states,
time_units='s'))
p.model.connect('state_disc:x', 'state_interp_comp.state_disc:x')
p.model.connect('state_disc:v', 'state_interp_comp.state_disc:v')
p.model.connect('staterate_disc:x', 'state_interp_comp.staterate_disc:x')
p.model.connect('staterate_disc:v', 'state_interp_comp.staterate_disc:v')
p.model.connect('dt_dstau', 'state_interp_comp.dt_dstau')
p.setup(force_alloc_complex=True)
segends_disc = segends[np.array((0, 1, 1, 2), dtype=int)]
p['state_disc:x'] = [x(t) for t in segends_disc]
p['staterate_disc:x'] = [f_x(t) for t in segends_disc]
p['state_disc:v'] = [v(t) for t in segends_disc]
p['staterate_disc:v'] = [f_v(t) for t in segends_disc]
p['dt_dstau'] = (segends[1:] - segends[:-1]) / 2.0
p.run_model()
t_disc = segends_disc
t_col = (segends[1:] + segends[:-1]) / 2.0
if SHOW_PLOTS: # pragma: no cover
f, ax = plt.subplots(2, 1)
t = np.linspace(0, 10, 100)
x1 = x(t)
xdot1 = f_x(t)
x2 = v(t)
xdot2 = f_v(t)
ax[0].plot(t, x1, 'b-', label='$x$')
ax[0].plot(t, xdot1, 'b--', label=r'$\dot{x}$')
ax[0].plot(t_disc, p['state_disc:x'], 'bo', label='$X_d:x$')
ax[0].plot(t_col, p['state_interp_comp.state_col:x'], 'bv', label='$X_c:x$')
ax[0].plot(t_col, p['state_interp_comp.staterate_col:x'], marker='v', color='None',
mec='b', label='$Xdot_c:x$')
ax[1].plot(t, x2, 'r-', label='$v$')
ax[1].plot(t, xdot2, 'r--', label=r'$\dot{v}$')
ax[1].plot(t_disc, p['state_disc:v'], 'ro', label='$X_d:v$')
ax[1].plot(t_col, p['state_interp_comp.state_col:v'], 'rv', label='$X_c:v$')
ax[1].plot(t_col, p['state_interp_comp.staterate_col:v'], marker='v', color='None',
mec='r', label='$Xdot_c:v$')
ax[0].legend(loc='upper left', ncol=3)
ax[1].legend(loc='upper left', ncol=3)
plt.show()
# Test 1
assert_almost_equal(
p['state_interp_comp.state_col:x'][:, 0], x(t_col))
assert_almost_equal(
p['state_interp_comp.staterate_col:x'][:, 0], f_x(t_col))
# Test 2
assert_almost_equal(
p['state_interp_comp.state_col:v'][:, 0], v(t_col))
assert_almost_equal(
p['state_interp_comp.staterate_col:v'][:, 0], f_v(t_col))
cpd = p.check_partials(compact_print=True, method='cs')
assert_check_partials(cpd, atol=5.0E-5)
def test_partials(self):
cpd = self.p.check_partials(out_stream=None)
assert_check_partials(cpd)
def test_partials(self):
cpd = self.p.check_partials(out_stream=None, method='fd', step=1.0E-6)
assert_check_partials(cpd, atol=5.0E-3, rtol=2.0)
def test_partials(self):
np.set_printoptions(linewidth=1024)
cpd = self.p.check_partials(compact_print=False,
method='fd',
out_stream=None)
assert_check_partials(cpd)
def test_control_interp_scalar(self):
param_list = itertools.product(
['gauss-lobatto', 'radau-ps'], # transcription
[True, False], # compressed
)
for transcription, compressed in param_list:
with self.subTest():
segends = np.array([0.0, 3.0, 10.0])
gd = GridData(num_segments=2,
transcription_order=5,
segment_ends=segends,
transcription=transcription,
compressed=compressed)
p = om.Problem(model=om.Group())
controls = {
'a': {
'units': 'm',
'shape': (1, ),
'dynamic': True
},
'b': {
'units': 'm',
'shape': (1, ),
'dynamic': True
}
}
ivc = om.IndepVarComp()
p.model.add_subsystem('ivc', ivc, promotes_outputs=['*'])
ivc.add_output('controls:a',
val=np.zeros(
(gd.subset_num_nodes['control_input'], 1)),
units='m')
ivc.add_output('controls:b',
val=np.zeros(
(gd.subset_num_nodes['control_input'], 1)),
units='m')
ivc.add_output('t_initial', val=0.0, units='s')
ivc.add_output('t_duration', val=10.0, units='s')
p.model.add_subsystem(
'time_comp',
subsys=TimeComp(num_nodes=gd.num_nodes,
node_ptau=gd.node_ptau,
node_dptau_dstau=gd.node_dptau_dstau,
units='s'),
promotes_inputs=['t_initial', 't_duration'],
promotes_outputs=['time', 'dt_dstau'])
p.model.add_subsystem('control_interp_comp',
subsys=ControlInterpComp(
grid_data=gd,
control_options=controls,
time_units='s'),
promotes_inputs=['controls:*'])
p.model.connect('dt_dstau', 'control_interp_comp.dt_dstau')
p.setup(force_alloc_complex=True)
p['t_initial'] = 0.0
p['t_duration'] = 3.0
p.run_model()
t = p['time']
p['controls:a'][:, 0] = f_a(
t[gd.subset_node_indices['control_input']])
p['controls:b'][:, 0] = f_b(
t[gd.subset_node_indices['control_input']])
p.run_model()
a_value_expected = f_a(t)
b_value_expected = f_b(t)
a_rate_expected = f1_a(t)
b_rate_expected = f1_b(t)
a_rate2_expected = f2_a(t)
b_rate2_expected = f2_b(t)
assert_almost_equal(p['control_interp_comp.control_values:a'],
np.atleast_2d(a_value_expected).T)
assert_almost_equal(p['control_interp_comp.control_values:b'],
np.atleast_2d(b_value_expected).T)
assert_almost_equal(
p['control_interp_comp.control_rates:a_rate'],
np.atleast_2d(a_rate_expected).T)
assert_almost_equal(
p['control_interp_comp.control_rates:b_rate'],
np.atleast_2d(b_rate_expected).T)
assert_almost_equal(
p['control_interp_comp.control_rates:a_rate2'],
np.atleast_2d(a_rate2_expected).T)
assert_almost_equal(
p['control_interp_comp.control_rates:b_rate2'],
np.atleast_2d(b_rate2_expected).T)
np.set_printoptions(linewidth=1024)
cpd = p.check_partials(compact_print=False, method='cs')
assert_check_partials(cpd)
def test_partials(self):
cpd = self.p.check_partials(compact_print=True, method='cs')
assert_check_partials(cpd)
def test_partials(self):
cpd = self.p.check_partials(compact_print=True,
method='cs',
out_stream=None)
assert_check_partials(cpd)
def test_state_interp_comp_radau(self):
gd = GridData(num_segments=1,
transcription_order=3,
segment_ends=np.array([0, 10]),
transcription='radau-ps')
p = om.Problem(model=om.Group())
states = {'x': {'units': 'm', 'shape': (1,)},
'v': {'units': 'm/s', 'shape': (1,)}}
X_ivc = om.IndepVarComp()
p.model.add_subsystem('X_ivc', X_ivc, promotes=['state_disc:x', 'state_disc:v'])
X_ivc.add_output('state_disc:x', val=np.zeros(gd.subset_num_nodes['state_disc']),
units='m')
X_ivc.add_output('state_disc:v', val=np.zeros(gd.subset_num_nodes['state_disc']),
units='m/s')
dt_dtau_ivc = om.IndepVarComp()
dt_dtau_ivc.add_output('dt_dstau', val=0.0*np.zeros(gd.subset_num_nodes['col']), units='s')
p.model.add_subsystem('dt_dstau_ivc', dt_dtau_ivc, promotes=['dt_dstau'])
p.model.add_subsystem('state_interp_comp',
subsys=StateInterpComp(transcription='radau-ps',
grid_data=gd,
state_options=states,
time_units='s'))
p.model.connect('state_disc:x', 'state_interp_comp.state_disc:x')
p.model.connect('state_disc:v', 'state_interp_comp.state_disc:v')
p.model.connect('dt_dstau', 'state_interp_comp.dt_dstau')
p.setup(force_alloc_complex=True)
lgr_nodes, lgr_weights = lgr(3, include_endpoint=True)
t_disc = (lgr_nodes + 1.0) * 5.0
t_col = t_disc[:-1]
# Test 1: Let x = t**2, f = 2*t
p['state_disc:x'] = t_disc**2
# Test 1: Let v = t**3-10*t**2, f = 3*t**2 - 20*t
p['state_disc:v'] = t_disc**3-10*t_disc**2
p['dt_dstau'] = 10/2.0
p.run_model()
if SHOW_PLOTS: # pragma: no cover
f, ax = plt.subplots(2, 1)
t_disc = np.array([0, 5, 10])
t_col = np.array([2.5, 7.5])
t = np.linspace(0, 10, 100)
x1 = t**2
xdot1 = 2*t
x2 = t**3 - 10*t**2
xdot2 = 3*t**2 - 20*t
ax[0].plot(t, x1, 'b-', label='$x$')
ax[0].plot(t, xdot1, 'b--', label='$\dot{x}$')
ax[0].plot(t_disc, p['state_disc:x'], 'bo', label='$X_d:x$')
ax[0].plot(t_col, p['state_interp_comp.state_col:x'], 'bv', label='$X_c:x$')
ax[0].plot(t_col, p['state_interp_comp.staterate_col:x'], marker='v', color='None',
mec='b', label='$Xdot_c:x$')
ax[1].plot(t, x2, 'r-', label='$v$')
ax[1].plot(t, xdot2, 'r--', label='$\dot{v}$')
ax[1].plot(t_disc, p['state_disc:v'], 'ro', label='$X_d:v$')
ax[1].plot(t_col, p['state_interp_comp.state_col:v'], 'rv', label='$X_c:v$')
ax[1].plot(t_col, p['state_interp_comp.staterate_col:v'], marker='v', color='None',
mec='r', label='$Xdot_c:v$')
ax[0].legend(loc='upper left', ncol=3)
ax[1].legend(loc='upper left', ncol=3)
plt.show()
# Test 1
assert_almost_equal(
p['state_interp_comp.staterate_col:x'][:, 0], 2*t_col)
# Test 2
assert_almost_equal(
p['state_interp_comp.staterate_col:v'][:, 0], 3*t_col**2 - 20*t_col)
cpd = p.check_partials(compact_print=True, method='cs')
assert_check_partials(cpd, atol=1.0E-5)
def assert_partials(self, p):
with printoptions(linewidth=1024, edgeitems=100):
cpd = p.check_partials(method='cs', compact_print=True)
assert_check_partials(cpd)
def test_rk4_scalar_no_iteration(self):
num_seg = 4
num_stages = 4
state_options = {'y': {'shape': (1, ), 'units': 'm', 'targets': ['y']}}
p = om.Problem(model=om.Group())
ivc = p.model.add_subsystem('ivc',
om.IndepVarComp(),
promotes_outputs=['*'])
ivc.add_output('initial_states_per_seg:y',
shape=(num_seg, 1),
units='m')
ivc.add_output('h', shape=(num_seg, 1), units='s')
ivc.add_output('t', shape=(num_seg * num_stages, 1), units='s')
p.model.add_subsystem(
'k_iter_group',
RungeKuttaKIterGroup(num_segments=num_seg,
method='RK4',
state_options=state_options,
time_units='s',
ode_class=TestODE,
ode_init_kwargs={},
solver_class=om.NonlinearRunOnce))
p.model.connect('t', 'k_iter_group.ode.t')
p.model.connect('h', 'k_iter_group.h')
p.model.connect('initial_states_per_seg:y',
'k_iter_group.initial_states_per_seg:y')
src_idxs = np.arange(16, dtype=int).reshape((num_seg, num_stages, 1))
p.model.connect('k_iter_group.ode.ydot',
'k_iter_group.k_comp.f:y',
src_indices=src_idxs,
flat_src_indices=True)
p.setup(check=True, force_alloc_complex=True)
p['t'] = np.array([[
0.00, 0.25, 0.25, 0.50, 0.50, 0.75, 0.75, 1.00, 1.00, 1.25, 1.25,
1.50, 1.50, 1.75, 1.75, 2.00
]]).T
p['h'] = np.array([[0.5, 0.5, 0.5, 0.5]]).T
p['initial_states_per_seg:y'] = np.array([[0.50000000],
[1.425130208333333],
[2.639602661132812],
[4.006818970044454]])
p['k_iter_group.k_comp.k:y'] = np.array([[[0.75000000], [0.90625000],
[0.94531250], [1.09765625]],
[[1.087565104166667],
[1.203206380208333],
[1.232116699218750],
[1.328623453776042]],
[[1.319801330566406],
[1.368501663208008],
[1.380676746368408],
[1.385139703750610]],
[[1.378409485022227],
[1.316761856277783],
[1.301349949091673],
[1.154084459568063]]])
p.run_model()
# Test that the residuals of k are zero (we started k at the expected converged value)
outputs = p.model.list_outputs(print_arrays=True,
residuals=True,
out_stream=False)
op_dict = dict([op for op in outputs])
assert_almost_equal(op_dict['k_iter_group.k_comp.k:y']['resids'], 0.0)
# Test the partials
cpd = p.check_partials(method='cs', out_stream=None)
assert_check_partials(cpd)
