def configure(self):
super(hyperTop, self).configure()
#--------------------------Component Setup-----------------------------------
self.add('NPSS', DesignCase()) #imported from npssCases.py
#---------------------------DOE Setup----------------------------------------
self.add('driver', DOEdriver())
self.driver.add_parameter('NPSS.tube_Pt', low=99.0 / 0.5282,
high=300.) #low=1760., high=1960.)
self.driver.add_parameter('NPSS.capsule_MN', low=0.6,
high=1.) #low=1760., high=1960.)
self.driver.add_parameter('NPSS.bearing_Pt', low=11000.,
high=12000.) #low=1760., high=1960.)
self.driver.DOEgenerator = FullFactorial(num_levels=1)
#------------------------Case Recorders Setup--------------------------------
#outfile = open('test_jeff.txt','w')
#self.driver.recorders = [CSVCaseRecorder(filename='DOEoutdata.csv'), DumpCaseRecorder(outfile)]
##other options DBCase,ListCaseRecorder
#self.driver.printvars = ['designInputs.*']
#self.driver.recorders[0].num_backups = 0 #save backup copies of previous runs (dated)
#-------------------------- Assembly Setup ----------------------------------
# Assembly Workflow
self.driver.workflow.add(['NPSS']) #
def configure(self):
self.add('d', Dummy())
self.add('driver', NeighborhoodDOEdriver())
self.driver.DOEgenerator = FullFactorial(2)
self.driver.recorders = [DumpCaseRecorder()]
self.driver.add_parameter('d.x', low=0, high=10)
self.driver.case_outputs = ['d.y', 'd.bad', 'd.z']
def __init__(self):
super(Simulation,self).__init__()
#Components
self.add("sin_meta_model",MetaModel())
self.sin_meta_model.surrogate = {"default":KrigingSurrogate()}
self.sin_meta_model.model = Sin()
self.sin_meta_model.recorder = DBCaseRecorder()
#Training the MetaModel
self.add("DOE_Trainer",DOEdriver())
self.DOE_Trainer.DOEgenerator = FullFactorial()
self.DOE_Trainer.DOEgenerator.num_levels = 25
self.DOE_Trainer.add_parameter("sin_meta_model.x",low=0,high=20)
self.DOE_Trainer.case_outputs = ["sin_meta_model.f_x"]
self.DOE_Trainer.add_event("sin_meta_model.train_next")
self.DOE_Trainer.recorders = [DBCaseRecorder()]
self.DOE_Trainer.force_execute = True
#MetaModel Validation
self.add("sin_calc",Sin())
self.add("DOE_Validate",DOEdriver())
self.DOE_Validate.DOEgenerator = Uniform()
self.DOE_Validate.DOEgenerator.num_samples = 100
self.DOE_Validate.add_parameter(("sin_meta_model.x","sin_calc.x"),low=0,high=20)
self.DOE_Validate.case_outputs = ["sin_calc.f_x","sin_meta_model.f_x"]
self.DOE_Validate.recorders = [DBCaseRecorder()]
self.DOE_Validate.force_execute = True
#Iteration Hierarchy
self.driver.workflow = SequentialWorkflow()
self.driver.workflow.add(['DOE_Trainer','DOE_Validate'])
self.DOE_Trainer.workflow.add('sin_meta_model')
self.DOE_Validate.workflow.add('sin_meta_model')
self.DOE_Validate.workflow.add('sin_calc')
def configure(self):
# Components
self.add("sin_meta_model", MetaModel())
self.sin_meta_model.model = Sin()
self.sin_meta_model.default_surrogate = FloatKrigingSurrogate()
# Training the MetaModel
self.add("DOE_Trainer", DOEdriver())
self.DOE_Trainer.DOEgenerator = FullFactorial()
self.DOE_Trainer.DOEgenerator.num_levels = 25
self.DOE_Trainer.add_parameter("sin_meta_model.x", low=0, high=20)
self.DOE_Trainer.case_outputs = ["sin_meta_model.f_x"]
self.DOE_Trainer.add_event("sin_meta_model.train_next")
self.DOE_Trainer.recorders = [DBCaseRecorder()]
# MetaModel Validation
self.add("sin_calc", Sin())
self.add("DOE_Validate", DOEdriver())
self.DOE_Validate.DOEgenerator = Uniform()
self.DOE_Validate.DOEgenerator.num_samples = 200
self.DOE_Validate.add_parameter(("sin_meta_model.x", "sin_calc.x"), low=0, high=20)
self.DOE_Validate.case_outputs = ["sin_calc.f_x", "sin_meta_model.f_x"]
self.DOE_Validate.recorders = [DBCaseRecorder()]
# Iteration Hierarchy
self.driver.workflow = SequentialWorkflow()
self.driver.workflow.add(['DOE_Trainer', 'DOE_Validate'])
self.DOE_Trainer.workflow.add('sin_meta_model')
self.DOE_Validate.workflow.add('sin_meta_model')
self.DOE_Validate.workflow.add('sin_calc')
def configure(self):
comp = self.add('comp', Comp())
doe = self.add('doe', NeighborhoodDOEdriver())
doe.DOEgenerator = FullFactorial()
doe.alpha = .1
doe.add_parameter('comp.x')
doe.add_response('comp.y')
doe.workflow.add('comp')
meta = self.add('meta', MetaModel(params=('x', ), responses=('y', )))
meta.default_surrogate = ResponseSurface()
self.connect('doe.case_inputs.comp.x', 'meta.params.x')
self.connect('doe.case_outputs.comp.y', 'meta.responses.y')
opt = self.add('opt', SLSQPdriver())
opt.add_parameter('meta.x', high=10., low=-10.)
opt.add_objective('meta.y')
opt.workflow.add('meta')
drv = self.add('driver', FixedPointIterator())
drv.max_iteration = 2
drv.add_parameter('y')
drv.add_constraint('y=meta.y')
drv.workflow.add(['doe', 'opt'])
def configure(self):
self.add('driver', DOEdriver())
self.add('driven', ComponentWhichRaisesException())
self.driver.workflow.add('driven')
self.driver.error_policy = 'RETRY'
self.driver.DOEgenerator = FullFactorial(2)
self.driver.add_parameter('driven.x', low=-50, high=50)
self.driver.add_response('driven.f_x')
def configure(self):
self.add('driver', IterateUntil())
self.add('adaptive', AdaptiveSampleDriver())
self.add('driven', DrivenComponent())
self.driver.workflow.add('adaptive')
self.adaptive.workflow.add('driven')
self.adaptive.DOEgenerator = FullFactorial()
self.adaptive.DOEgenerator.num_levels = 2
self.adaptive.add_parameter('driven.x', low=-10., high=10.)
self.adaptive.add_response('driven.y')
self.adaptive.record_doe = False
def test_AutoBEM_DOE(self):
# perform a DOE
self.top.replace('driver', DOEdriver())
self.top.driver.DOEgenerator = FullFactorial(3)
self.top.driver.add_parameter('b.chord_hub', low=.1, high=2)
self.top.driver.add_parameter('b.chord_tip', low=.1, high=2)
self.top.driver.add_parameter('b.rpm', low=20, high=300)
self.top.driver.add_parameter('b.twist_hub', low=-5, high=50)
self.top.driver.add_parameter('b.twist_tip', low=-5, high=50)
self.top.run()
self.assertEqual(self.top.b.exec_count, 243)
def configure(self):
# Our component to be meta-modeled
self.add("trig_calc", Trig())
# Create meta_model for two responsese
self.add("trig_meta_model",
MetaModel(params=('x', ), responses=('f_x_sin', 'f_x_cos')))
# Use Kriging for the f_x output
self.trig_meta_model.surrogates['f_x_sin'] = LogisticRegression()
self.trig_meta_model.surrogates['f_x_cos'] = FloatKrigingSurrogate()
# Training the MetaModel
self.add("DOE_Trainer", DOEdriver())
self.DOE_Trainer.DOEgenerator = FullFactorial()
self.DOE_Trainer.DOEgenerator.num_levels = 20
self.DOE_Trainer.add_parameter("trig_calc.x", low=0, high=20)
self.DOE_Trainer.add_response('trig_calc.f_x_sin')
self.DOE_Trainer.add_response('trig_calc.f_x_cos')
# Pass training data to the meta model.
self.connect('DOE_Trainer.case_inputs.trig_calc.x',
'trig_meta_model.params.x')
self.connect('DOE_Trainer.case_outputs.trig_calc.f_x_sin',
'trig_meta_model.responses.f_x_sin')
self.connect('DOE_Trainer.case_outputs.trig_calc.f_x_cos',
'trig_meta_model.responses.f_x_cos')
#MetaModel Validation
self.add("DOE_Validate", DOEdriver())
self.DOE_Validate.DOEgenerator = Uniform()
self.DOE_Validate.DOEgenerator.num_samples = 20
self.DOE_Validate.add_parameter(("trig_meta_model.x", "trig_calc.x"),
low=0,
high=20)
self.DOE_Validate.add_response("trig_calc.f_x_sin")
self.DOE_Validate.add_response("trig_calc.f_x_cos")
self.DOE_Validate.add_response("trig_meta_model.f_x_sin")
self.DOE_Validate.add_response("trig_meta_model.f_x_cos")
#Iteration Hierarchy
self.driver.workflow.add(['DOE_Trainer', 'DOE_Validate'])
self.DOE_Trainer.workflow.add('trig_calc')
self.DOE_Validate.workflow.add(['trig_calc', 'trig_meta_model'])
def configure(self):
""" Configure a simple DOE to set start points for CONMIN. """
self.add('gp_fun', GoldsteinPrice())
conmin = self.add('conmin', CONMINdriver())
conmin.workflow.add('gp_fun')
conmin.add_parameter('gp_fun.x1')
conmin.add_parameter('gp_fun.x2')
conmin.add_objective('gp_fun.f')
doe = self.add('driver', DOEdriver())
doe.workflow.add('conmin')
doe.add_parameter('gp_fun.x1', low=-1.5, high=1.5, start=1)
doe.add_parameter('gp_fun.x2', low=-1.5, high=1.5, start=1)
doe.DOEgenerator = FullFactorial(5)
doe.add_responses(
['gp_fun.f', 'gp_fun.x1', 'gp_fun.x2', 'gp_fun.exec_count'])
self.recorders = [CSVCaseRecorder(), DumpCaseRecorder()]
def configure(self):
self.add('paraboloid',Paraboloid())
self.add('driver',DOEdriver())
#There are a number of different kinds of DOE available in openmdao.lib.doegenerators
self.driver.DOEgenerator = FullFactorial(10) #Full Factorial DOE with 10 levels for each variable
#DOEdriver will automatically record the values of any parameters for each case
self.driver.add_parameter('paraboloid.x',low=-50,high=50)
self.driver.add_parameter('paraboloid.y',low=-50,high=50)
#tell the DOEdriver to also record any other variables you want to know for each case
self.driver.case_outputs = ['paraboloid.f_xy',]
#Simple recorder which stores the cases in memory.
self.driver.recorders = [ListCaseRecorder(),]
self.driver.workflow.add('paraboloid')
def test_AutoBEM_DOE(self):
# perform a DOE
self.top.replace('driver', DOEdriver())
self.top.driver.DOEgenerator = FullFactorial(3)
self.top.driver.recorders = [ListCaseRecorder()]
self.top.driver.case_outputs = [
'b.perf.data.tip_speed_ratio', 'b.perf.data.Cp', 'b.perf.data.Ct'
]
self.top.driver.add_parameter('b.chord_hub', low=.1, high=2)
self.top.driver.add_parameter('b.chord_tip', low=.1, high=2)
self.top.driver.add_parameter('b.rpm', low=20, high=300)
self.top.driver.add_parameter('b.twist_hub', low=-5, high=50)
self.top.driver.add_parameter('b.twist_tip', low=-5, high=50)
self.top.run()
self.assertEqual(len(self.top.driver.recorders[0]), 243)
def configure(self):
self.add("driver", DOEdriver())
self.recorders = [ListCaseRecorder()]
self.driver.DOEgenerator = FullFactorial()
# configure the specific DOE options
self.driver.DOEgenerator.num_levels = 3
self.add("dis1", sellar.Discipline1())
self.add("dis2", sellar.Discipline2())
# setting some variables to fixed values
self.dis1.y2 = 3.15
self.dis2.y1 = 3.78
# adding three parameters to the DOEDriver
self.driver.add_parameter(("dis1.z1", "dis2.z1"), low=-10.0, high=10.0)
self.driver.add_parameter(("dis1.z2", "dis2.z2"), low=0.0, high=10.0)
self.driver.add_parameter("dis1.x1", low=0.0, high=10.0)
def configure(self):
# Our component to be meta-modeled
self.add("sin_calc", Sin())
# Another instance of our component for head-to-head comparison with
# the metamodel.
self.add("sin_verify", Sin())
# Create meta_model for f_x as the response
self.add("sin_meta_model",
MetaModel(params=('x', ), responses=('f_x', )))
# Use Kriging for the f_x output
self.sin_meta_model.default_surrogate = FloatKrigingSurrogate()
# Training the MetaModel
self.add("DOE_Trainer", DOEdriver())
self.DOE_Trainer.DOEgenerator = FullFactorial()
self.DOE_Trainer.DOEgenerator.num_levels = 25
self.DOE_Trainer.add_parameter("sin_calc.x", low=0, high=20)
self.DOE_Trainer.add_response('sin_calc.f_x')
# Pass training data to the meta model.
self.connect('DOE_Trainer.case_inputs.sin_calc.x',
'sin_meta_model.params.x')
self.connect('DOE_Trainer.case_outputs.sin_calc.f_x',
'sin_meta_model.responses.f_x')
# Cross-validate the metamodel using random data
self.add("DOE_Validate", DOEdriver())
self.DOE_Validate.DOEgenerator = Uniform()
self.DOE_Validate.DOEgenerator.num_samples = 100
self.DOE_Validate.add_parameter(("sin_meta_model.x", "sin_verify.x"),
low=0,
high=20) # , name="combined_input"
self.DOE_Validate.add_response("sin_verify.f_x")
self.DOE_Validate.add_response("sin_meta_model.f_x")
#Iteration Hierarchy
self.driver.workflow.add(['DOE_Trainer', 'DOE_Validate'])
self.DOE_Trainer.workflow.add('sin_calc')
self.DOE_Validate.workflow.add(['sin_verify', 'sin_meta_model'])
def configure(self):
#Components
self.add("trig_meta_model", MetaModel())
self.trig_meta_model.surrogate = {
"f_x_sin": LogisticRegression(),
"f_x_cos": KrigingSurrogate()
}
self.trig_meta_model.model = Trig()
self.trig_meta_model.recorder = DBCaseRecorder()
#Training the MetaModel
self.add("DOE_Trainer", DOEdriver())
self.DOE_Trainer.DOEgenerator = FullFactorial()
self.DOE_Trainer.DOEgenerator.num_levels = 20
self.DOE_Trainer.add_parameter("trig_meta_model.x", low=0, high=20)
self.DOE_Trainer.case_outputs = [
"trig_meta_model.f_x_sin", "trig_meta_model.f_x_cos"
]
self.DOE_Trainer.add_event("trig_meta_model.train_next")
self.DOE_Trainer.recorders = [DBCaseRecorder()]
#MetaModel Validation
self.add("trig_calc", Trig())
self.add("DOE_Validate", DOEdriver())
self.DOE_Validate.DOEgenerator = Uniform()
self.DOE_Validate.DOEgenerator.num_samples = 20
self.DOE_Validate.add_parameter(("trig_meta_model.x", "trig_calc.x"),
low=0,
high=20)
self.DOE_Validate.case_outputs = [
"trig_calc.f_x_sin", "trig_calc.f_x_cos",
"trig_meta_model.f_x_sin", "trig_meta_model.f_x_cos"
]
self.DOE_Validate.recorders = [DBCaseRecorder()]
#Iteration Hierarchy
self.driver.workflow = SequentialWorkflow()
self.driver.workflow.add(['DOE_Trainer', 'DOE_Validate'])
self.DOE_Trainer.workflow.add('trig_meta_model')
self.DOE_Validate.workflow.add('trig_meta_model')
self.DOE_Validate.workflow.add('trig_calc')
def __init__(self):
super(Simulation, self).__init__()
#Components
self.add("sin_calc", Sin())
self.add("sin_verify", Sin())
self.add("sin_meta_model", MetaModel(params=('x',),
responses=('f_x',)))
self.sin_meta_model.default_surrogate = NeuralNet(n_hidden_nodes=5)
#Training the MetaModel
self.add("DOE_Trainer", DOEdriver())
self.DOE_Trainer.DOEgenerator = FullFactorial()
# Seems to need a lot of training data for decent prediction of sin(x),
# at least with default 'cg' method.
self.DOE_Trainer.DOEgenerator.num_levels = 2500
self.DOE_Trainer.add_parameter("sin_calc.x", low=0, high=20)
self.DOE_Trainer.add_response("sin_calc.f_x")
self.connect('DOE_Trainer.case_inputs.sin_calc.x',
'sin_meta_model.params.x')
self.connect('DOE_Trainer.case_outputs.sin_calc.f_x',
'sin_meta_model.responses.f_x')
#MetaModel Validation
self.add("DOE_Validate", DOEdriver())
self.DOE_Validate.DOEgenerator = Uniform()
self.DOE_Validate.DOEgenerator.num_samples = 100
self.DOE_Validate.add_parameter(("sin_meta_model.x", "sin_verify.x"),
low=0, high=20)
self.DOE_Validate.add_response("sin_verify.f_x")
self.DOE_Validate.add_response("sin_meta_model.f_x")
#Iteration Hierarchy
self.driver.workflow.add(['DOE_Trainer', 'DOE_Validate'])
self.DOE_Trainer.workflow.add('sin_calc')
self.DOE_Validate.workflow.add(('sin_verify', 'sin_meta_model'))
def test_doegen_remove(self):
top = set_as_top(Assembly())
top.add("driver", DOEdriver())
top.driver.remove("DOEgenerator")
top.driver.add("DOEgenerator", FullFactorial())
def test_scaling(self):
self.model.driver.DOEgenerator = ff = FullFactorial(num_levels=3)
ff.num_parameters = 4
for case in self.model.driver._get_cases():
print case
