def test_uniform(self):
from openmdao.api import Problem, IndepVarComp
from openmdao.test_suite.components.paraboloid import Paraboloid
from openmdao.api import DOEDriver, UniformGenerator, SqliteRecorder, CaseReader
prob = Problem()
model = prob.model
model.add_subsystem('p1', IndepVarComp('x', 0.), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 0.), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_design_var('x', lower=-10, upper=10)
model.add_design_var('y', lower=-10, upper=10)
model.add_objective('f_xy')
prob.driver = DOEDriver(UniformGenerator(num_samples=5))
prob.driver.add_recorder(SqliteRecorder("CASES.db"))
prob.setup()
prob.run_driver()
prob.cleanup()
cases = CaseReader("CASES.db").driver_cases
self.assertEqual(cases.num_cases, 5)
values = []
for n in range(cases.num_cases):
case = cases.get_case(n)
values.append((case.outputs['x'], case.outputs['y'], case.outputs['f_xy']))
def test_plackett_burman(self):
prob = Problem()
model = prob.model
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.add_design_var('x', lower=0.0, upper=1.0)
model.add_design_var('y', lower=0.0, upper=1.0)
model.add_objective('f_xy')
prob.driver = DOEDriver(PlackettBurmanGenerator())
prob.driver.add_recorder(SqliteRecorder("CASES.db"))
prob.setup(check=False)
prob.run_driver()
prob.cleanup()
expected = {
0: {'x': np.array([0.]), 'y': np.array([0.]), 'f_xy': np.array([22.00])},
1: {'x': np.array([1.]), 'y': np.array([0.]), 'f_xy': np.array([17.00])},
2: {'x': np.array([0.]), 'y': np.array([1.]), 'f_xy': np.array([31.00])},
3: {'x': np.array([1.]), 'y': np.array([1.]), 'f_xy': np.array([27.00])},
}
cases = CaseReader("CASES.db").driver_cases
self.assertEqual(cases.num_cases, 4)
for n in range(cases.num_cases):
self.assertEqual(cases.get_case(n).outputs['x'], expected[n]['x'])
self.assertEqual(cases.get_case(n).outputs['y'], expected[n]['y'])
self.assertEqual(cases.get_case(n).outputs['f_xy'], expected[n]['f_xy'])
def _check_recorder_file(self, pb, cstr, filename):
pb.driver = OneraSegoDriver()
pb.driver.options["optimizer"] = "SEGOMOE"
pb.driver.opt_settings["maxiter"] = 10
# default model
n_var = 2
mod_obj = {
"type": "Krig",
"corr": "squared_exponential",
"regr": "constant",
"theta0": [1.0] * n_var,
"thetaL": [0.1] * n_var,
"thetaU": [10.0] * n_var,
"normalize": True,
}
model_type = {"obj": mod_obj}
if cstr:
model_type["con"] = mod_obj
pb.driver.opt_settings["model_type"] = model_type
recorder = SqliteRecorder(self.case_recorder_filename)
pb.model.add_recorder(recorder)
pb.setup()
self.pb.run_driver()
self.assertTrue(os.path.exists(self.case_recorder_filename))
reader = CaseReader(self.case_recorder_filename)
for case_id in reader.list_cases():
case = reader.get_case(case_id)
print(case.outputs["obj"])
def test_box_behnken(self):
upper = 10.
center = 1
prob = Problem()
model = prob.model
indep = model.add_subsystem('indep', IndepVarComp(), promotes=['*'])
indep.add_output('x', 0.0)
indep.add_output('y', 0.0)
indep.add_output('z', 0.0)
model.add_subsystem('comp', ExecComp('a = x**2 + y - z'), promotes=['*'])
model.add_design_var('x', lower=0., upper=upper)
model.add_design_var('y', lower=0., upper=upper)
model.add_design_var('z', lower=0., upper=upper)
model.add_objective('a')
prob.driver = DOEDriver(BoxBehnkenGenerator(center=center))
prob.driver.add_recorder(SqliteRecorder("CASES.db"))
prob.setup(check=False)
prob.run_driver()
prob.cleanup()
cases = CaseReader("CASES.db").driver_cases
# The Box-Behnken design for 3 factors involves three blocks, in each of
# which 2 factors are varied thru the 4 possible combinations of high & low.
# It also includes centre points (all factors at their central values).
# ref: https://en.wikipedia.org/wiki/Box-Behnken_design
self.assertEqual(cases.num_cases, (3*4)+center)
expected = {
0: {'x': np.array([ 0.]), 'y': np.array([ 0.]), 'z': np.array([ 5.])},
1: {'x': np.array([10.]), 'y': np.array([ 0.]), 'z': np.array([ 5.])},
2: {'x': np.array([ 0.]), 'y': np.array([10.]), 'z': np.array([ 5.])},
3: {'x': np.array([10.]), 'y': np.array([10.]), 'z': np.array([ 5.])},
4: {'x': np.array([ 0.]), 'y': np.array([ 5.]), 'z': np.array([ 0.])},
5: {'x': np.array([10.]), 'y': np.array([ 5.]), 'z': np.array([ 0.])},
6: {'x': np.array([ 0.]), 'y': np.array([ 5.]), 'z': np.array([10.])},
7: {'x': np.array([10.]), 'y': np.array([ 5.]), 'z': np.array([10.])},
8: {'x': np.array([ 5.]), 'y': np.array([ 0.]), 'z': np.array([ 0.])},
9: {'x': np.array([ 5.]), 'y': np.array([10.]), 'z': np.array([ 0.])},
10: {'x': np.array([ 5.]), 'y': np.array([ 0.]), 'z': np.array([10.])},
11: {'x': np.array([ 5.]), 'y': np.array([10.]), 'z': np.array([10.])},
12: {'x': np.array([ 5.]), 'y': np.array([ 5.]), 'z': np.array([ 5.])},
}
for n in range(cases.num_cases):
self.assertEqual(cases.get_case(n).outputs['x'], expected[n]['x'])
self.assertEqual(cases.get_case(n).outputs['y'], expected[n]['y'])
self.assertEqual(cases.get_case(n).outputs['z'], expected[n]['z'])
def test_full_factorial(self):
prob = Problem()
model = prob.model
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.add_design_var('x', lower=0.0, upper=1.0)
model.add_design_var('y', lower=0.0, upper=1.0)
model.add_objective('f_xy')
prob.driver = DOEDriver(FullFactorialGenerator(levels=3), parallel=True)
prob.driver.add_recorder(SqliteRecorder("CASES.db"))
prob.setup()
failed, output = run_driver(prob)
self.assertFalse(failed)
prob.cleanup()
expected = {
0: {'x': np.array([0.]), 'y': np.array([0.]), 'f_xy': np.array([22.00])},
1: {'x': np.array([.5]), 'y': np.array([0.]), 'f_xy': np.array([19.25])},
2: {'x': np.array([1.]), 'y': np.array([0.]), 'f_xy': np.array([17.00])},
3: {'x': np.array([0.]), 'y': np.array([.5]), 'f_xy': np.array([26.25])},
4: {'x': np.array([.5]), 'y': np.array([.5]), 'f_xy': np.array([23.75])},
5: {'x': np.array([1.]), 'y': np.array([.5]), 'f_xy': np.array([21.75])},
6: {'x': np.array([0.]), 'y': np.array([1.]), 'f_xy': np.array([31.00])},
7: {'x': np.array([.5]), 'y': np.array([1.]), 'f_xy': np.array([28.75])},
8: {'x': np.array([1.]), 'y': np.array([1.]), 'f_xy': np.array([27.00])},
}
size = prob.comm.size
rank = prob.comm.rank
# cases will be split across files for each proc
filename = "CASES.db_%d" % rank
expect_msg = "Cases from rank %d are being written to %s." % (rank, filename)
self.assertTrue(expect_msg in output)
cases = CaseReader(filename).driver_cases
# cases recorded on this proc
num_cases = cases.num_cases
self.assertEqual(num_cases, len(expected)//size+(rank<len(expected)%size))
for n in range(num_cases):
case = cases.get_case(n)
idx = n * size + rank # index of expected case
self.assertEqual(cases.get_case(n).outputs['x'], expected[idx]['x'])
self.assertEqual(cases.get_case(n).outputs['y'], expected[idx]['y'])
self.assertEqual(cases.get_case(n).outputs['f_xy'], expected[idx]['f_xy'])
def test_uniform(self):
prob = Problem()
model = prob.model
model.add_subsystem('p1', IndepVarComp('x', 0.), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 0.), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_design_var('x', lower=-10, upper=10)
model.add_design_var('y', lower=-10, upper=10)
model.add_objective('f_xy')
prob.driver = DOEDriver(UniformGenerator(num_samples=5, seed=0))
prob.driver.add_recorder(SqliteRecorder("CASES.db"))
prob.setup(check=False)
prob.run_driver()
prob.cleanup()
# all values should be between -10 and 10, check expected values for seed = 0
expected = {
0: {'x': np.array([ 0.97627008]), 'y': np.array([ 4.30378733])},
1: {'x': np.array([ 2.05526752]), 'y': np.array([ 0.89766366])},
2: {'x': np.array([-1.52690401]), 'y': np.array([ 2.91788226])},
3: {'x': np.array([-1.24825577]), 'y': np.array([ 7.83546002])},
4: {'x': np.array([ 9.27325521]), 'y': np.array([-2.33116962])},
}
cases = CaseReader("CASES.db").driver_cases
self.assertEqual(cases.num_cases, 5)
for n in range(cases.num_cases):
assert_rel_error(self, cases.get_case(n).outputs['x'], expected[n]['x'], 1e-4)
assert_rel_error(self, cases.get_case(n).outputs['y'], expected[n]['y'], 1e-4)
def compute(self, inputs, outputs):
folder_output = self.options['folder_output']
optimization_log = self.options['folder_output'] + self.options['optimization_log']
if os.path.exists(optimization_log):
cr = CaseReader(optimization_log)
cases = cr.list_cases()
rec_data = {}
iterations = []
for i, casei in enumerate(cases):
iterations.append(i)
it_data = cr.get_case(casei)
# parameters = it_data.get_responses()
for parameters in [it_data.get_responses(), it_data.get_design_vars()]:
for j, param in enumerate(parameters.keys()):
if i == 0:
rec_data[param] = []
rec_data[param].append(parameters[param])
for param in rec_data.keys():
fig, ax = plt.subplots(1,1,figsize=(5.3, 4))
ax.plot(iterations, rec_data[param])
ax.set(xlabel='Number of Iterations' , ylabel=param)
fig_name = 'Convergence_trend_' + param + '.pdf'
fig.savefig(folder_output + fig_name)
plt.close(fig)
def test_full_factorial_array(self):
prob = Problem()
model = prob.model
model.add_subsystem('p1',
IndepVarComp('xy', np.array([0., 0.])),
promotes=['*'])
model.add_subsystem('comp', ParaboloidArray(), promotes=['*'])
model.add_design_var('xy',
lower=np.array([-50., -50.]),
upper=np.array([50., 50.]))
model.add_objective('f_xy')
prob.driver = DOEDriver(FullFactorialGenerator(levels=3))
prob.driver.add_recorder(SqliteRecorder("cases.sql"))
prob.setup(check=False)
prob.run_driver()
prob.cleanup()
expected = {
0: {
'xy': np.array([-50., -50.])
},
1: {
'xy': np.array([0., -50.])
},
2: {
'xy': np.array([50., -50.])
},
3: {
'xy': np.array([-50., 0.])
},
4: {
'xy': np.array([0., 0.])
},
5: {
'xy': np.array([50., 0.])
},
6: {
'xy': np.array([-50., 50.])
},
7: {
'xy': np.array([0., 50.])
},
8: {
'xy': np.array([50., 50.])
},
}
cases = CaseReader("cases.sql").driver_cases
self.assertEqual(cases.num_cases, 9)
for n in range(cases.num_cases):
outputs = cases.get_case(n).outputs
self.assertEqual(outputs['xy'][0], expected[n]['xy'][0])
self.assertEqual(outputs['xy'][1], expected[n]['xy'][1])
class Reader:
"""`Reader` class loads and displays stored simulations using an OpenMDAO `CaseReader` class instance.
Parameters
----------
db : str
Full path of the database where the solution is stored
case_id : str, optional
Case identifier, ``initial`` to load the first iteration, ``final`` to load the final solution.
Default is ``final``
db_exp : str or ``None``, optional
Full path of the database where the explicit simulation is stored or ``None``. Default is ``None``
Attributes
----------
case_reader : CaseReader
OpenMDAO `CaseReader` class instance for the implicit solution
case_id : str
Case identifier, ``initial`` to load the first iteration, ``final`` to load the final solution.
case : Case
OpenMDAO `Case` class instance identified by `case_id` within the `case_reader` object
case_reader_exp : CaseReader or ``None``
OpenMDAO `CaseReader` class instance for the explicit simulation or ``None``
case : Case or ``None``
OpenMDAO `Case` class instance identified by `case_id` within the `case_reader_exp` object or ``None``
"""
def __init__(self, db, case_id='final', db_exp=None):
"""Init Reader class. """
self.case_reader = CaseReader(db)
if case_id in ['initial', 'final']:
self.case_id = case_id
else:
raise ValueError("Case must be either 'initial' or 'final'")
self.case = self.case_reader.get_case(self.case_id)
# explicit simulation
if db_exp is not None:
self.case_reader_exp = CaseReader(db_exp)
self.case_exp = self.case_reader_exp.get_case(-1)
else:
self.case_reader_exp = self.case_exp = None
def test_sellar(self):
self.pb = pb = Problem(SellarMDA())
pb.model.add_design_var("x", lower=0, upper=10)
pb.model.add_design_var("z", lower=0, upper=10)
pb.model.add_objective("obj")
pb.model.add_constraint("con1", upper=0)
pb.model.add_constraint("con2", upper=0)
pb.driver = OneraSegoDriver(optimizer="SEGOMOE")
pb.driver.opt_settings["maxiter"] = 10
self.case_recorder_filename = "test_segomoe_driver_sellar.sqlite"
recorder = SqliteRecorder(self.case_recorder_filename)
pb.model.add_recorder(recorder)
pb.setup()
self.pb.run_driver()
self.assertTrue(os.path.exists(self.case_recorder_filename))
reader = CaseReader(self.case_recorder_filename)
for case_id in reader.list_cases():
case = reader.get_case(case_id)
print(case.outputs["obj"])
def test_full_factorial(self):
from openmdao.api import Problem, IndepVarComp, PETScVector
from openmdao.test_suite.components.paraboloid import Paraboloid
from openmdao.api import DOEDriver, FullFactorialGenerator
from openmdao.api import SqliteRecorder, CaseReader
from mpi4py import MPI
prob = Problem()
model = prob.model
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.add_design_var('x', lower=0.0, upper=1.0)
model.add_design_var('y', lower=0.0, upper=1.0)
model.add_objective('f_xy')
prob.driver = DOEDriver(FullFactorialGenerator(levels=3))
prob.driver.options['parallel'] = True
prob.driver.add_recorder(SqliteRecorder("CASES.db"))
prob.setup()
prob.run_driver()
prob.cleanup()
self.assertEqual(MPI.COMM_WORLD.size, 2)
# check recorded cases from each case file
rank = MPI.COMM_WORLD.rank
filename = "CASES.db_%d" % rank
self.assertEqual(filename, "CASES.db_%d" % rank)
cases = CaseReader(filename).driver_cases
self.assertEqual(cases.num_cases, 5 if rank == 0 else 4)
values = []
for n in range(cases.num_cases):
case = cases.get_case(n)
values.append((case.outputs['x'], case.outputs['y'], case.outputs['f_xy']))
def test_latin_hypercube_center(self):
samples = 4
upper = 10.
prob = Problem()
model = prob.model
indep = model.add_subsystem('indep', IndepVarComp())
indep.add_output('x', 0.0)
indep.add_output('y', 0.0)
model.add_subsystem('comp', Paraboloid())
model.connect('indep.x', 'comp.x')
model.connect('indep.y', 'comp.y')
model.add_design_var('indep.x', lower=0., upper=upper)
model.add_design_var('indep.y', lower=0., upper=upper)
model.add_objective('comp.f_xy')
prob.driver = DOEDriver(LatinHypercubeGenerator(samples=samples, criterion='c'))
prob.driver.add_recorder(SqliteRecorder("CASES.db"))
prob.setup(check=False)
prob.run_driver()
prob.cleanup()
cases = CaseReader("CASES.db").driver_cases
self.assertEqual(cases.num_cases, samples)
# the sample space for each variable (0 to upper) should be divided into
# equal size buckets and each variable should have a value in each bucket
bucket_size = upper/samples
all_buckets = set(range(samples))
x_buckets_filled = set()
y_buckets_filled = set()
# with criterion of 'center', each value should be in the center of it's bucket
valid_values = [round(bucket_size*(bucket + 1/2), 3) for bucket in all_buckets]
for n in range(cases.num_cases):
x = float(cases.get_case(n).outputs['indep.x'])
y = float(cases.get_case(n).outputs['indep.y'])
x_buckets_filled.add(int(x/bucket_size))
y_buckets_filled.add(int(y/bucket_size))
self.assertTrue(round(x, 3) in valid_values, '%f not in %s' % (x, valid_values))
self.assertTrue(round(y, 3) in valid_values, '%f not in %s' % (y, valid_values))
self.assertEqual(x_buckets_filled, all_buckets)
self.assertEqual(y_buckets_filled, all_buckets)
def test_full_factorial(self):
prob = Problem()
model = prob.model
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.add_design_var('x', lower=0.0, upper=1.0)
model.add_design_var('y', lower=0.0, upper=1.0)
model.add_objective('f_xy')
prob.driver = DOEDriver(generator=FullFactorialGenerator(levels=3))
prob.driver.add_recorder(SqliteRecorder("CASES.db"))
prob.setup(check=False)
prob.run_driver()
prob.cleanup()
expected = {
0: {'x': np.array([0.]), 'y': np.array([0.]), 'f_xy': np.array([22.00])},
1: {'x': np.array([.5]), 'y': np.array([0.]), 'f_xy': np.array([19.25])},
2: {'x': np.array([1.]), 'y': np.array([0.]), 'f_xy': np.array([17.00])},
3: {'x': np.array([0.]), 'y': np.array([.5]), 'f_xy': np.array([26.25])},
4: {'x': np.array([.5]), 'y': np.array([.5]), 'f_xy': np.array([23.75])},
5: {'x': np.array([1.]), 'y': np.array([.5]), 'f_xy': np.array([21.75])},
6: {'x': np.array([0.]), 'y': np.array([1.]), 'f_xy': np.array([31.00])},
7: {'x': np.array([.5]), 'y': np.array([1.]), 'f_xy': np.array([28.75])},
8: {'x': np.array([1.]), 'y': np.array([1.]), 'f_xy': np.array([27.00])},
}
cases = CaseReader("CASES.db").driver_cases
self.assertEqual(cases.num_cases, 9)
for n in range(cases.num_cases):
self.assertEqual(cases.get_case(n).outputs['x'], expected[n]['x'])
self.assertEqual(cases.get_case(n).outputs['y'], expected[n]['y'])
self.assertEqual(cases.get_case(n).outputs['f_xy'], expected[n]['f_xy'])
def test_fan_in_grouped(self):
from openmdao.api import Problem, PETScVector
from openmdao.test_suite.groups.parallel_groups import FanInGrouped
from openmdao.api import DOEDriver, FullFactorialGenerator
from openmdao.api import SqliteRecorder, CaseReader
from mpi4py import MPI
prob = Problem(FanInGrouped())
model = prob.model
model.add_design_var('iv.x1', lower=0.0, upper=1.0)
model.add_design_var('iv.x2', lower=0.0, upper=1.0)
model.add_objective('c3.y')
prob.driver = DOEDriver(FullFactorialGenerator(levels=3))
prob.driver.add_recorder(SqliteRecorder("CASES.db"))
doe_parallel = prob.driver.options['parallel'] = 2
prob.setup()
prob.run_driver()
prob.cleanup()
rank = MPI.COMM_WORLD.rank
# check recorded cases from each case file
if rank < doe_parallel:
filename = "CASES.db_%d" % rank
cases = CaseReader(filename).driver_cases
values = []
for n in range(cases.num_cases):
case = cases.get_case(n)
values.append((case.outputs['iv.x1'], case.outputs['iv.x2'], case.outputs['c3.y']))
self.assertEqual("\n"+"\n".join(["iv.x1: %5.2f, iv.x2: %5.2f, c3.y: %6.2f" % (x1, x2, y) for x1, x2, y in values]),
self.expect_text)
def test_unknowns(self):
""" Tests that the reader returns unknowns correctly. """
cr = CaseReader(self.filename)
last_case = cr.get_case(-1)
last_case_id = cr.list_cases()[-1]
with h5py.File(self.filename, 'r') as f:
for key in f[last_case_id]['Unknowns'].keys():
val = f[last_case_id]['Unknowns'][key][()]
np.testing.assert_almost_equal(last_case[key], val,
err_msg='Case reader gives '
'incorrect Unknown value'
' for {0}'.format(key))
def test_resids(self):
""" Tests that the reader returns resids correctly. """
cr = CaseReader(self.filename)
last_case = cr.get_case(-1)
last_case_id = cr.list_cases()[-1]
with SqliteDict(self.filename, 'iterations', flag='r') as db:
for key in db[last_case_id]['Residuals'].keys():
val = db[last_case_id]['Residuals'][key][()]
np.testing.assert_almost_equal(last_case.resids[key], val,
err_msg='Case reader gives '
'incorrect Unknown value'
' for {0}'.format(key))
def test_unknowns(self):
""" Tests that the reader returns unknowns correctly. """
cr = CaseReader(self.filename)
last_case = cr.get_case(-1)
last_case_id = cr.list_cases()[-1]
with h5py.File(self.filename, 'r') as f:
for key in f[last_case_id]['Unknowns'].keys():
val = f[last_case_id]['Unknowns'][key][()]
np.testing.assert_almost_equal(last_case[key],
val,
err_msg='Case reader gives '
'incorrect Unknown value'
' for {0}'.format(key))
def test_params(self):
""" Tests that the reader returns params correctly. """
cr = CaseReader(self.filename)
last_case = cr.get_case(-1)
last_case_id = cr.list_cases()[-1]
n = cr.num_cases
with SqliteDict(self.filename, 'iterations', flag='r') as db:
for key in db[last_case_id]['Parameters'].keys():
val = db[last_case_id]['Parameters'][key]
np.testing.assert_almost_equal(last_case.parameters[key], val,
err_msg='Case reader gives '
'incorrect Parameter value'
' for {0}'.format(key))
def test_resids(self):
""" Tests that the reader returns resids correctly. """
cr = CaseReader(self.filename)
last_case = cr.get_case(-1)
last_case_id = cr.list_cases()[-1]
n = cr.num_cases
with h5py.File(self.filename, 'r') as f:
for key in f[last_case_id]['Residuals'].keys():
val = f[last_case_id]['Residuals'][key][()]
np.testing.assert_almost_equal(last_case.resids[key], val,
err_msg='Case reader gives'
' incorrect Unknown'
' value for'
' {0}'.format(key))
def test_resids(self):
""" Tests that the reader returns resids correctly. """
cr = CaseReader(self.filename)
last_case = cr.get_case(-1)
last_case_id = cr.list_cases()[-1]
n = cr.num_cases
with h5py.File(self.filename, 'r') as f:
for key in f[last_case_id]['Residuals'].keys():
val = f[last_case_id]['Residuals'][key][()]
np.testing.assert_almost_equal(last_case.resids[key],
val,
err_msg='Case reader gives'
' incorrect Unknown'
' value for'
' {0}'.format(key))
def test_brachistochrone_recording(self):
import matplotlib
matplotlib.use('Agg')
from openmdao.api import Problem, Group, ScipyOptimizeDriver, DirectSolver, \
SqliteRecorder, CaseReader
from openmdao.utils.assert_utils import assert_rel_error
from dymos import Phase, GaussLobatto
from dymos.examples.brachistochrone.brachistochrone_ode import BrachistochroneODE
p = Problem(model=Group())
p.driver = ScipyOptimizeDriver()
phase = Phase(ode_class=BrachistochroneODE,
transcription=GaussLobatto(num_segments=10))
p.model.add_subsystem('phase0', phase)
phase.set_time_options(initial_bounds=(0, 0), duration_bounds=(.5, 10))
phase.set_state_options('x', fix_initial=True, fix_final=True)
phase.set_state_options('y', fix_initial=True, fix_final=True)
phase.set_state_options('v', fix_initial=True)
phase.add_control('theta',
units='deg',
rate_continuity=False,
lower=0.01,
upper=179.9)
phase.add_design_parameter('g', units='m/s**2', opt=False, val=9.80665)
# Minimize time at the end of the phase
phase.add_objective('time', loc='final', scaler=10)
p.model.linear_solver = DirectSolver()
# Recording
rec = SqliteRecorder('brachistochrone_solution.db')
p.driver.recording_options['record_desvars'] = True
p.driver.recording_options['record_responses'] = True
p.driver.recording_options['record_objectives'] = True
p.driver.recording_options['record_constraints'] = True
p.model.recording_options['record_metadata'] = True
p.driver.add_recorder(rec)
p.model.add_recorder(rec)
phase.add_recorder(rec)
p.setup()
p['phase0.t_initial'] = 0.0
p['phase0.t_duration'] = 2.0
p['phase0.states:x'] = phase.interpolate(ys=[0, 10],
nodes='state_input')
p['phase0.states:y'] = phase.interpolate(ys=[10, 5],
nodes='state_input')
p['phase0.states:v'] = phase.interpolate(ys=[0, 9.9],
nodes='state_input')
p['phase0.controls:theta'] = phase.interpolate(ys=[5, 100.5],
nodes='control_input')
# Solve for the optimal trajectory
p.run_driver()
# Test the results
assert_rel_error(self,
p.get_val('phase0.timeseries.time')[-1],
1.8016,
tolerance=1.0E-3)
cr = CaseReader('brachistochrone_solution.db')
system_cases = cr.list_cases('root')
case = cr.get_case(system_cases[-1])
outputs = dict([
(o[0], o[1])
for o in case.list_outputs(units=True, shape=True, out_stream=None)
])
assert_rel_error(
self, p['phase0.controls:theta'],
outputs['phase0.control_group.indep_controls.controls:theta']
['value'])
prob.set_val("traj.phase0.controls:beta", phase0.interpolate(ys=[-75*np.pi/180, 0*np.pi/180], nodes="control_input"), units="rad")
recorder = SqliteRecorder("reentry.sql")
prob.driver.add_recorder(recorder)
prob.run_driver()
# prob.run_model()
sim_out = traj.simulate()
prob.cleanup()
case_reader = CaseReader("reentry.sql")
driver_cases = case_reader.list_cases("driver")
last_case = case_reader.get_case(driver_cases[-1])
final_constraints = last_case.get_constraints()
final_q = final_constraints["traj.phase0.path_constraints.path:q"]
plt.figure(0)
plt.plot(prob.get_val("traj.phase0.timeseries.time", units="s"), prob.get_val("traj.phase0.timeseries.controls:alpha", units="deg"), "ro", label="Solution")
plt.plot(sim_out.get_val("traj.phase0.timeseries.time", units="s"), sim_out.get_val("traj.phase0.timeseries.controls:alpha", units="deg"), "b-", label="Simulation")
plt.title("Angle of Attack over Time")
plt.xlabel("Time (s)")
plt.ylabel("Angle of Attack (degrees)")
plt.legend()
plt.figure(1)
plt.plot(prob.get_val("traj.phase0.timeseries.time", units="s"), prob.get_val("traj.phase0.timeseries.controls:beta", units="deg"), "ro", label="Solution")
plt.plot(sim_out.get_val("traj.phase0.timeseries.time", units="s"), sim_out.get_val("traj.phase0.timeseries.controls:beta", units="deg"), "b-", label="Simulation")
plt.title("Bank Angle over Time")
def test_fan_in_grouped_serial(self):
# run cases on all procs (parallel model will run on single proc)
doe_parallel = True
prob = Problem(FanInGrouped())
model = prob.model
model.add_design_var('iv.x1', lower=0.0, upper=1.0)
model.add_design_var('iv.x2', lower=0.0, upper=1.0)
model.add_objective('c3.y')
prob.driver = DOEDriver(FullFactorialGenerator(levels=3))
prob.driver.add_recorder(SqliteRecorder("cases.sql"))
prob.driver.options['parallel'] = doe_parallel
prob.setup(check=False)
failed, output = run_driver(prob)
self.assertFalse(failed)
prob.cleanup()
expected = {
0: {
'iv.x1': np.array([0.]),
'iv.x2': np.array([0.]),
'c3.y': np.array([0.0])
},
1: {
'iv.x1': np.array([.5]),
'iv.x2': np.array([0.]),
'c3.y': np.array([-3.0])
},
2: {
'iv.x1': np.array([1.]),
'iv.x2': np.array([0.]),
'c3.y': np.array([-6.0])
},
3: {
'iv.x1': np.array([0.]),
'iv.x2': np.array([.5]),
'c3.y': np.array([17.5])
},
4: {
'iv.x1': np.array([.5]),
'iv.x2': np.array([.5]),
'c3.y': np.array([14.5])
},
5: {
'iv.x1': np.array([1.]),
'iv.x2': np.array([.5]),
'c3.y': np.array([11.5])
},
6: {
'iv.x1': np.array([0.]),
'iv.x2': np.array([1.]),
'c3.y': np.array([35.0])
},
7: {
'iv.x1': np.array([.5]),
'iv.x2': np.array([1.]),
'c3.y': np.array([32.0])
},
8: {
'iv.x1': np.array([1.]),
'iv.x2': np.array([1.]),
'c3.y': np.array([29.0])
},
}
rank = prob.comm.rank
size = prob.comm.size // doe_parallel
num_cases = 0
# cases will be split across files for each proc up to the number requested
filename = "cases.sql_%d" % rank
expect_msg = "Cases from rank %d are being written to %s." % (rank,
filename)
self.assertTrue(expect_msg in output)
cases = CaseReader(filename).driver_cases
# cases recorded on this proc
num_cases = cases.num_cases
self.assertEqual(num_cases,
len(expected) // size + (rank < len(expected) % size))
for n in range(num_cases):
idx = n * size + rank # index of expected case
outputs = cases.get_case(n).outputs
self.assertEqual(outputs['iv.x1'], expected[idx]['iv.x1'])
self.assertEqual(outputs['iv.x2'], expected[idx]['iv.x2'])
self.assertEqual(outputs['c3.y'], expected[idx]['c3.y'])
# total number of cases recorded across all requested procs
num_cases = prob.comm.allgather(num_cases)
self.assertEqual(sum(num_cases), len(expected))
twistcp = []
mesh = []
chordcp = []
forces = []
loads = []
vm = []
sparThickness = []
skinThickness = []
tbs = []
widths = []
sparThincknesscp = []
skinThincknesscp = []
for i in range(iterations):
#for i in range(350,380):
case = cr.get_case(driver_cases[i])
design_vars = case.get_design_vars()
objective = case.get_objectives()
constraints = case.get_constraints()
mrho.append(design_vars['mrho'])
masse.append(case.outputs['wing.structural_mass'][0])
co2.append(objective['emitted_co2'][0])
taper.append(case.inputs['wing.geometry.mesh.taper.taper'][0])
span.append(case.inputs['wing.geometry.mesh.stretch.span'][0])
chord.append(case.inputs['wing.geometry.mesh.scale_x.chord'][0])
chordTip.append(case.inputs['wing.geometry.mesh.scale_x.chord'][-1])
surface0.append(case.outputs['AS_point_0.coupled.wing.S_ref'][0])
# surface1.append(case.outputs['AS_point_1.coupled.wing.S_ref'][0])
sparThicknessRoot.append(design_vars['wing.spar_thickness_cp'][-1])
sparThicknessTip.append(design_vars['wing.spar_thickness_cp'][0])
skinThicknessRoot.append(design_vars['wing.skin_thickness_cp'][-1])
if options.batch or options.parallel:
exit(0)
reader = CaseReader(case_recorder_filename)
cases = reader.list_cases('driver')
n = len(cases)
data = {'inputs': {}, 'outputs': {}}
data['inputs']['LpA'] = np.zeros((n, ) + (1, ))
data['inputs']['Ns'] = np.zeros((n, ) + (1, ))
data['inputs']['p'] = np.zeros((n, ) + (1, ))
data['inputs']['PayCap'] = np.zeros((n, ) + (1, ))
data['outputs']['C_TOTAL'] = np.zeros((n, ) + (1, ))
for i in range(len(cases)):
case = reader.get_case(cases[i])
data['inputs']['LpA'][i, :] = case.outputs['LpA']
data['inputs']['Ns'][i, :] = case.outputs['Ns']
data['inputs']['p'][i, :] = case.outputs['p']
data['inputs']['PayCap'][i, :] = case.outputs['PayCap']
data['outputs']['C_TOTAL'][i, :] = case.outputs['C_TOTAL']
output = data['outputs']['C_TOTAL'].reshape(-1)
input = data['inputs']['LpA'].reshape(-1)
plt.subplot(1, 4, 1)
plt.plot(input[0::1], output[0::1], '.')
plt.ylabel('C_TOTAL')
plt.xlabel('LpA')
input = data['inputs']['Ns'].reshape(-1)
def setup_prob(self):
"""
Short method to select the optimizer. Uses pyOptSparse if available,
or Scipy's SLSQP otherwise.
"""
try: # Use pyOptSparse optimizer if installed
from openmdao.api import pyOptSparseDriver
self.prob.driver = pyOptSparseDriver()
if self.prob_dict['optimizer'] == 'SNOPT':
self.prob.driver.options['optimizer'] = "SNOPT"
self.prob.driver.opt_settings = {'Major optimality tolerance': 1.0e-8,
'Major feasibility tolerance': 1.0e-8,
'Major iterations limit':400,
'Minor iterations limit':2000,
'Iterations limit':1000
}
elif self.prob_dict['optimizer'] == 'ALPSO':
self.prob.driver.options['optimizer'] = 'ALPSO'
self.prob.driver.opt_settings = {'SwarmSize': 40,
'maxOuterIter': 200,
'maxInnerIter': 6,
'rtol': 1e-5,
'atol': 1e-5,
'dtol': 1e-5,
'printOuterIters': 1
}
elif self.prob_dict['optimizer'] == 'NOMAD':
self.prob.driver.options['optimizer'] = 'NOMAD'
self.prob.driver.opt_settings = {'maxiter':1000,
'minmeshsize':1e-12,
'minpollsize':1e-12,
'displaydegree':0,
'printfile':1
}
elif self.prob_dict['optimizer'] == 'SLSQP':
self.prob.driver.options['optimizer'] = 'SLSQP'
self.prob.driver.opt_settings = {'ACC' : 1e-10
}
except: # Use Scipy SLSQP optimizer if pyOptSparse not installed
self.prob.driver = ScipyOptimizer()
self.prob.driver.options['optimizer'] = 'SLSQP'
self.prob.driver.options['disp'] = True
self.prob.driver.options['tol'] = 1.0e-10
# Actually call the OpenMDAO functions to add the design variables,
# constraints, and objective.
for desvar_name, desvar_data in iteritems(self.desvars):
self.prob.driver.add_desvar(desvar_name, **desvar_data)
for con_name, con_data in iteritems(self.constraints):
self.prob.driver.add_constraint(con_name, **con_data)
for obj_name, obj_data in iteritems(self.objective):
self.prob.driver.add_objective(obj_name, **obj_data)
# Use finite differences over the entire model if user selected it
if self.prob_dict['force_fd']:
self.prob.root.deriv_options['type'] = 'fd'
# Record optimization history to a database.
# Data saved here can be examined using `plot_all.py` or `OptView.py`
if self.prob_dict['record_db']:
recorder = SqliteRecorder(self.prob_dict['prob_name']+".db")
recorder.options['record_params'] = True
recorder.options['record_derivs'] = True
self.prob.driver.add_recorder(recorder)
# Profile (time) the problem
if self.prob_dict['profile']:
profile.setup(self.prob)
profile.start()
# Set up the problem
self.prob.setup()
# Use warm start from previous db file if desired.
# Note that we only have access to the unknowns, not the gradient history.
if self.prob_dict['previous_case_db'] is not None:
# Open the previous case and start from the last iteration.
# Change the -1 value in get_case() if you want to select a different iteration.
cr = CaseReader(self.prob_dict['previous_case_db'])
case = cr.get_case(-1)
# Loop through the unknowns and set them for this problem.
for param_name, param_data in iteritems(case.unknowns):
self.prob[param_name] = param_data
def test_derivs(self):
""" Test that derivs is None if not provided in the recording. """
cr = CaseReader(self.filename)
last_case = cr.get_case(-1)
self.assertIsNone(last_case.derivs,
"Case erroneously contains derivs.")
def test_resids(self):
""" Test that params is None if not provided in the recording. """
cr = CaseReader(self.filename)
last_case = cr.get_case(-1)
self.assertIsNone(last_case.resids,
"Case erroneously contains resids.")
def compute(self, inputs, outputs):
# type: (Vector, Vector) -> None
"""Computation performed by the component.
Parameters
----------
inputs : all inputs coming from outside the component in the group
outputs : all outputs provided outside the component in the group"""
# Define problem of subdriver
p = self.prob
m = p.model
# Push global inputs down
for input_name in m.model_constants:
p[input_name] = inputs[input_name]
failed_experiments = {}
# sort to have outputs first
sorted_model_super_inputs = sorted(m.model_super_inputs.keys(), reverse=True)
for input_name in sorted_model_super_inputs:
if input_name in m.sm_of_training_params.keys(): # Add these inputs as training data
sm_uid = m.sm_of_training_params[input_name]
pred_param = m.find_mapped_parameter(input_name,
m.sm_prediction_inputs[sm_uid] |
m.sm_prediction_outputs[sm_uid])
sm_comp = getattr(m, str_to_valid_sys_name(sm_uid))
if sm_uid not in failed_experiments.keys():
failed_experiments[sm_uid] = (None, None)
sm_comp.options['train:'+pred_param], failed_experiments[sm_uid]\
= p.postprocess_experiments(inputs[input_name], input_name,
failed_experiments[sm_uid])
else:
p[input_name] = inputs[input_name]
# Provide message on failed experiments
warn_about_failed_experiments(failed_experiments)
# Set initial values of design variables back to original ones (to avoid using values of
# last run)
for des_var, attrbs in m.design_vars.items():
p[des_var] = attrbs['initial']
# Run the driver
print('Running subdriver {}'.format(self.options['driver_uid']))
if 'Sub-Optimizer' not in p.case_reader_path:
p.driver.cleanup()
basename, extension = os.path.splitext(p.case_reader_path)
case_reader_filename = basename + '_loop' + str(self._run_count) + extension
p.driver.add_recorder(SqliteRecorder(case_reader_filename))
p.driver.recording_options['includes'] = ['*']
p.driver.recording_options['record_model_metadata'] = True
p.driver._setup_recording()
p.run_driver()
self._add_run_count()
# Pull the value back up to the output array
doe_out_vecs = {}
for output_name in m.model_super_outputs:
# Add these outputs as vectors based on DOE driver
if output_name in m.doe_parameters.keys():
doe_out_vecs[output_name] = []
else:
if not p.driver.fail:
outputs[output_name] = p[output_name]
else:
outputs[output_name] = float('nan')
# If the driver failed (hence, optimization failed), then send message and clean
if p.driver.fail:
print('Driver run failed!')
p.clean_driver_after_failure()
# Provide DOE output vectors as output of the component, if this is expected
if doe_out_vecs:
# First read out the case reader
cr = CaseReader(case_reader_filename)
cases = cr.list_cases('driver')
for n in range(len(cases)):
cr_outputs = cr.get_case(n).outputs
doe_param_matches = {}
for output_name in doe_out_vecs.keys():
doe_param_matches[output_name] = doe_param_match \
= m.find_mapped_parameter(output_name, cr_outputs.keys())
doe_out_vecs[output_name].append(cr_outputs[doe_param_match][0])
# Then write the final vectors to the global output array
for output_name in doe_out_vecs.keys():
if output_name in p.doe_samples[p.driver_uid]['inputs']:
des_var_match = m.find_mapped_parameter(output_name, m._design_vars.keys())
doe_out_vecs[output_name] = unscale_value(doe_out_vecs[output_name],
m._design_vars[des_var_match]['ref0'],
m._design_vars[des_var_match]['ref'])
outputs[output_name] = np.array(doe_out_vecs[output_name])
def test_resids(self):
""" Test that params is None if not provided in the recording. """
cr = CaseReader(self.filename)
last_case = cr.get_case(-1)
self.assertIsNone(last_case.resids,
"Case erroneously contains resids.")
def test_latin_hypercube(self):
samples = 4
bounds = np.array([
[-1, -10], # lower bounds for x and y
[1, 10] # upper bounds for x and y
])
xlb, xub = bounds[0][0], bounds[1][0]
ylb, yub = bounds[0][1], bounds[1][1]
prob = Problem()
model = prob.model
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.add_design_var('x', lower=xlb, upper=xub)
model.add_design_var('y', lower=ylb, upper=yub)
model.add_objective('f_xy')
prob.driver = DOEDriver()
prob.driver.options['generator'] = LatinHypercubeGenerator(samples=4,
seed=0)
prob.driver.add_recorder(SqliteRecorder("cases.sql"))
prob.setup(check=False)
prob.run_driver()
prob.cleanup()
# the sample space for each variable should be divided into equal
# size buckets and each variable should have a value in each bucket
all_buckets = set(range(samples))
xlb, xub = bounds[0][0], bounds[1][0]
x_offset = 0 - xlb
x_bucket_size = xub - xlb
x_buckets_filled = set()
ylb, yub = bounds[0][1], bounds[1][1]
y_offset = 0 - ylb
y_bucket_size = yub - ylb
y_buckets_filled = set()
# expected values for seed = 0
expected = {
0: {
'x': np.array([-0.19861831]),
'y': np.array([-6.42405317])
},
1: {
'x': np.array([0.2118274]),
'y': np.array([9.458865])
},
2: {
'x': np.array([0.71879361]),
'y': np.array([3.22947057])
},
3: {
'x': np.array([-0.72559325]),
'y': np.array([-2.27558409])
},
}
cases = CaseReader("cases.sql").driver_cases
self.assertEqual(cases.num_cases, 4)
for n in range(cases.num_cases):
outputs = cases.get_case(n).outputs
x = outputs['x']
y = outputs['y']
bucket = int((x + x_offset) / (x_bucket_size / samples))
x_buckets_filled.add(bucket)
bucket = int((y + y_offset) / (y_bucket_size / samples))
y_buckets_filled.add(bucket)
assert_rel_error(self, x, expected[n]['x'], 1e-4)
assert_rel_error(self, y, expected[n]['y'], 1e-4)
self.assertEqual(x_buckets_filled, all_buckets)
self.assertEqual(y_buckets_filled, all_buckets)
def test_derivs(self):
""" Test that derivs is None if not provided in the recording. """
cr = CaseReader(self.filename)
last_case = cr.get_case(-1)
self.assertIsNone(last_case.derivs,
"Case erroneously contains derivs.")
def test_latin_hypercube_array(self):
samples = 4
bounds = np.array([
[-10, -50], # lower bounds for x and y
[10, 50] # upper bounds for x and y
])
prob = Problem()
model = prob.model
model.add_subsystem('p1',
IndepVarComp('xy', np.array([50., 50.])),
promotes=['*'])
model.add_subsystem('comp', ParaboloidArray(), promotes=['*'])
model.add_design_var('xy', lower=bounds[0], upper=bounds[1])
model.add_objective('f_xy')
prob.driver = DOEDriver(LatinHypercubeGenerator(samples=4, seed=0))
prob.driver.add_recorder(SqliteRecorder("cases.sql"))
prob.setup(check=False)
prob.run_driver()
prob.cleanup()
# the sample space for each variable should be divided into equal
# size buckets and each variable should have a value in each bucket
all_buckets = set(range(samples))
xlb, xub = bounds[0][0], bounds[1][0]
x_offset = 0 - xlb
x_bucket_size = xub - xlb
x_buckets_filled = set()
ylb, yub = bounds[0][1], bounds[1][1]
y_offset = 0 - ylb
y_bucket_size = yub - ylb
y_buckets_filled = set()
# expected values for seed = 0
expected = {
0: {
'xy': np.array([-1.98618312, -32.12026584])
},
1: {
'xy': np.array([2.118274, 47.29432502])
},
2: {
'xy': np.array([7.18793606, 16.14735283])
},
3: {
'xy': np.array([-7.25593248, -11.37792043])
},
}
cases = CaseReader("cases.sql").driver_cases
self.assertEqual(cases.num_cases, 4)
for n in range(cases.num_cases):
outputs = cases.get_case(n).outputs
x = outputs['xy'][0]
y = outputs['xy'][1]
bucket = int((x + x_offset) / (x_bucket_size / samples))
x_buckets_filled.add(bucket)
bucket = int((y + y_offset) / (y_bucket_size / samples))
y_buckets_filled.add(bucket)
assert_rel_error(self, x, expected[n]['xy'][0], 1e-4)
assert_rel_error(self, y, expected[n]['xy'][1], 1e-4)
self.assertEqual(x_buckets_filled, all_buckets)
self.assertEqual(y_buckets_filled, all_buckets)
Plots objective and constraint histories from the recorded data in 'data.sql'.
"""
from __future__ import print_function
from six.moves import range
import numpy as np
from matplotlib import pylab
from openmdao.api import CaseReader
# load cases from recording database
cr = CaseReader('ru_mdp.sql')
#cases = cr.get_cases('driver')
cases = cr.list_cases('driver')
case = cr.get_case(cases[0])
num_cases = len(cases)
if num_cases == 0:
print('No data yet...')
quit()
else:
print('# cases:', num_cases)
# determine # of constraints
constraints = list(case.get_constraints())
n_con = len(constraints)
# collect data into arrays for plotting
X = np.zeros(num_cases) # obj.val
# model.add_design_var('eta_sa', lower = 0.01, upper=1)
model.add_design_var('M_ps', lower=0.01, upper=100)
model.add_constraint('con1',)
model.add_objective('t_tot')
#Case Recorder Setting
recorder = SqliteRecorder('test.sql')
p.driver.add_recorder(recorder)
p.add_recorder(recorder)
#Set-up and Run
p.setup()
p.set_solver_print(2)
p.run_driver()
# view_model(p)
p.record_iteration('final')
p.cleanup()
cr = CaseReader('test.sql')
drivercases = cr.list_cases('driver')
case = cr.get_case(drivercases[0])
print(sorted(case.outputs.keys()))
print('P_req',['P_req'])
print('A_sa',p['A_sa'])
print('M_u',p['M_u'])
print('M_ps',p['M_ps'])
print(p['t_tot'])
print(p['cycles'])
