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 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 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 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 _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_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])
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 assert_morris_case_generation(self, nt, driver):
pb, case_recorder_filename = TestSalibDoeDriver.run_driver(
"morris" + str(nt), driver)
self.assertTrue(os.path.exists(case_recorder_filename))
reader = CaseReader(case_recorder_filename)
cases = reader.list_cases("driver")
os.remove(case_recorder_filename)
n = sum(data["size"] for data in pb.model.get_design_vars().values())
self.assertEqual(len(cases), (n + 1) * nt)
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_recording(self):
# coloring involves an underlying call to run_model (and final_setup),
# this verifies that it is handled properly by the recording setup logic
recorder = SqliteRecorder('cases.sql')
p = run_opt(pyOptSparseDriver, 'auto', assemble_type='csc', optimizer='SNOPT',
dynamic_simul_derivs=True, print_results=False, recorder=recorder)
cr = CaseReader('cases.sql')
self.assertEqual(cr.list_cases(), ['rank0:SNOPT|%d' % i for i in range(p.driver.iter_count)])
def test_recording(self):
# coloring involves an underlying call to run_model (and final_setup),
# this verifies that it is handled properly by the recording setup logic
recorder = SqliteRecorder('cases.sql')
p = run_opt(pyOptSparseDriver, 'auto', assemble_type='csc', optimizer='SNOPT',
dynamic_simul_derivs=True, print_results=False, recorder=recorder)
cr = CaseReader('cases.sql')
self.assertEqual(cr.list_cases(), ['rank0:SNOPT|%d' % i for i in range(p.driver.iter_count)])
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_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 assert_case_generation(self, n, driver):
pb = SellarProblem()
pb.driver = driver
case_recorder_filename = "test_smt_doe_driver_{}.sqlite".format(n)
recorder = SqliteRecorder(case_recorder_filename)
pb.driver.add_recorder(recorder)
pb.setup()
pb.run_driver()
pb.cleanup()
reader = CaseReader(case_recorder_filename)
cases = reader.list_cases("driver")
os.remove(case_recorder_filename)
self.assertEqual(len(cases), n)
def test_full_factorial(self):
from openmdao.api import Problem, IndepVarComp
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.sql"))
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.sql_%d" % rank
self.assertEqual(filename, "cases.sql_%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):
outputs = cases.get_case(n).outputs
values.append((outputs['x'], outputs['y'], outputs['f_xy']))
self.assertEqual(
"\n" + "\n".join([
"x: %5.2f, y: %5.2f, f_xy: %6.2f" % (x, y, f_xy)
for x, y, f_xy in values
]), self.expect_text)
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 load_from_sqlite(filename):
reader = CaseReader(filename)
cases = reader.list_cases("driver")
if len(cases) == 0:
raise Exception("No case found in {}".format(filename))
# find driver name
driver_first_coord = cases[0]
m = re.match(r"\w+:(\w+)|.*", driver_first_coord)
name = os.path.splitext(os.path.basename(filename))[0]
if m:
name = m.group(1)
# format cases and statuses
cases, statuses = format_upload_cases(reader)
return name, cases, statuses
def upload(self, sqlite_filename, analysis_id=None, operation_id=None, cleanup=False, dry_run=False):
from socket import gethostname
mda_id = self.get_analysis_id() if not analysis_id else analysis_id
reader = CaseReader(sqlite_filename)
cases = reader.list_cases('driver')
if len(cases)==0:
raise Exception("No case found in {}".format(sqlite_filename))
driver_first_coord = cases[0]
name = os.path.splitext(sqlite_filename)[0]
m = re.match(r"\w+:(\w+)|.*", driver_first_coord)
if m:
name = m.group(1)
cases, statuses = self._format_upload_cases(reader)
if dry_run:
WhatsOpt._print_cases(cases, statuses)
exit()
resp = None
if operation_id:
url = self._endpoint(('/api/v1/operations/%s') % operation_id)
operation_params = {'cases': cases}
resp = self.session.patch(url, headers=self.headers,
json={'operation': operation_params})
else:
url = self._endpoint(('/api/v1/analyses/%s/operations') % mda_id)
if name=='LHS':
driver='smt_doe_lhs'
elif name=='Morris':
driver='salib_doe_morris'
elif name=='SLSQP':
driver='scipy_optimizer_slsqp'
else:
# suppose name well-formed <lib>-<doe|optimizer|screening>-<algoname>
# otherwise it will default to doe
driver=name.lower()
operation_params = {'name': name,
'driver': driver,
'host': gethostname(),
'cases': cases,
'success': statuses}
resp = self.session.post(url, headers=self.headers,
json={'operation': operation_params})
resp.raise_for_status()
print("Results data from {} uploaded with driver {}".format(sqlite_filename, driver))
if cleanup:
os.remove(sqlite_filename)
print("%s removed" % sqlite_filename)
def test_fan_in_grouped(self):
from openmdao.api import Problem
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.sql"))
# run 2 cases at a time, each using 2 of our 4 procs
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.sql_%d" % rank
cases = CaseReader(filename).driver_cases
values = []
for n in range(cases.num_cases):
outputs = cases.get_case(n).outputs
values.append(
(outputs['iv.x1'], outputs['iv.x2'], 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_format_upload_cases(self):
data, statuses = format_upload_cases(
CaseReader(os.path.join(TestUtils.DATA_PATH, "test_doe.sqlite")))
self.assertEqual(19.52945, round(data[0]["values"][4], 5))
self.assertEqual("z", data[1]["varname"])
self.assertEqual(1, data[2]["coord_index"])
for i in statuses:
self.assertEqual(1, i)
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 __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 load_sqlite_file(filename):
log("Load {}...".format(filename))
reader = CaseReader(filename)
cases = reader.list_cases("driver", out_stream=None)
if len(cases) == 0:
raise Exception("No case found in {}".format(filename))
# find driver name
driver_first_coord = cases[0]
m = re.match(r"\w+:(\w+)|.*", driver_first_coord)
name = os.path.splitext(os.path.basename(filename))[0]
if m:
name = m.group(1)
# format cases and statuses
# cases : [{"varname": varname, "coord_index": idx, "values": [...]}*]
cases, statuses = _format_upload_cases(reader)
return name, cases, statuses
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):
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_metadata(self):
""" Test that metadata is correctly read.
format_version should always be present.
"""
cr = CaseReader(self.filename)
self.assertEqual(cr.format_version,
format_version,
msg='incorrect format version')
self.assertIsNone(cr.parameters,
msg='parameter metadata should be None')
self.assertIsNone(cr.unknowns, msg='unknown metadata should be None')
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 load_simulation_results(f):
"""
Load PhaseSimulationResults or TrajectorySimulationResults from the given file
Parameters
----------
f : str
The file path from which to load the simulation results.
Returns
-------
res : PhaseSimulationResults or TrajectorySimulationResults
The PhaseSimulationResults or TrajectorySimulationResults loaded from the given file.
"""
cr = CaseReader(f)
try:
case = cr.system_cases.get_case(-1)
def read_db(filename):
dvs = {
'r': 'climb.states:r',
'h': 'climb.states:h',
'v': 'climb.states:v',
'gam': 'climb.states:gam',
'm': 'climb.states:m',
'alpha': 'climb.controls:alpha',
't_duration': 'climb.t_duration',
}
states = {}
cons = {}
objs = {}
data_all_iters = []
cr = CaseReader(filename)
case_keys = cr.driver_cases.list_cases()
for case_key in case_keys:
case = cr.driver_cases.get_case(case_key)
data = {}
for key, cr_key in iteritems(dvs):
data[key] = case.desvars[cr_key]
for key, cr_key in iteritems(states):
data[key] = case.sysincludes[cr_key]
for key, cr_key in iteritems(objs):
data[key] = case.objectives[cr_key]
for key, cr_key in iteritems(cons):
data[key] = case.constraints[cr_key]
data_all_iters.append(data)
def get_case_reader(self):
# type: () -> CaseReader
"""Get the case reader of the problem
Returns
-------
CaseReader
The configured case reader for the Problem object
Raises
------
AssertionError
If case reader file cannot be found
"""
if not os.path.isfile(self.case_reader_path):
raise AssertionError(
'Could not find the case reader file {}.'.format(
self.case_reader_path))
# Get all cases from the case reader and determine amount of cases
return CaseReader(self.case_reader_path)
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_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_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.")
