class DummyComp(Component):
r = Float(iotype='in')
r2 = Float(iotype='in')
r3 = Float(iotype='in', desc="some random variable", low=-1.0, high=1.0, other_meta_data="test")
s = Str(iotype='in')
rout = Float(iotype='out', units='ft')
r2out = Float(iotype='out')
sout = Str(iotype='out')
slistout = List(Str, iotype='out')
dummy_in = Instance(Component, iotype='in')
dummy_out = Instance(Component, iotype='out')
dummy_out_no_copy = Instance(Component, iotype='out', copy=None)
def __init__(self):
super(DummyComp, self).__init__()
self.r = 1.0
self.r2 = -1.0
self.rout = 0.0
self.r2out = 0.0
self.s = 'a string'
self.sout = ''
# make a nested container with input and output ContainerVars
self.add('dummy', Multiplier())
self.dummy_in = self.dummy
self.dummy_out = self.dummy
def execute(self):
self.rout = self.r * 1.5
self.r2out = self.r2 + 10.0
self.sout = self.s[::-1]
# pylint: disable-msg=E1101
self.dummy.execute()
class TwrGeoOutputs(VariableTree):
"""Basic Geometric Outputs needed to build Tower of Jacket."""
TwrObj = Instance(Klass=Tube,
desc='Object of Class Tube for Tower portion of Jacket')
Twr2RNAObj = Instance(
Klass=RigidMember,
desc='Object of Class RigidMember for Rigid Tower portion')
joints = Array(np.array([]),
units='m',
dtype=np.float,
desc='Pile Joint (with legs) Coordinates (3,nlegs=nfaces)')
nodes = Array(np.empty((3, 10)),
units='m',
dtype=np.float,
desc='Tower ALL Nodes'
' Coordinates (3,nNodes)')
nNodes = Int(0,
units=None,
desc='Number of Tower Nodes INCLUDING joint at TP')
nElems = Int(0,
units=None,
desc='Number of of elements in the flexible portion of tower')
mass = Float(units='kg', desc='Tower Mass')
TopMass = Array(
np.zeros([10]),
dtype=np.float,
desc=
'Tower Top mass, Ixx, Iyy, Izz,Ixy,Ixz,Iyz,CMxoff,CMyoff,CMzoff from RNA properties in input'
)
TopMass_yaw = Array(
np.zeros([10]),
dtype=np.float,
desc=
'Tower Top mass, Ixx, Iyy, Izz,Ixy,Ixz,Iyz,CMxoff,CMyoff,CMzoff from RNA properties in input including yaw angle w.r.t. Global XYZ'
)
TwrlumpedMass = Array(
np.zeros([1, 11]),
dtype=np.float,
desc='Concentrated masses along tower: first column z'
's from base of tower; 2nd through 11th column: mass and Ixx,Iyy,Izz,Ixy,Ixz,Iyz,CMxoff,CMyoff,CMzoff values'
)
HH = Float(units='m', desc='Hub-Height')
Htwr = Float(units='m', desc='Tower Length')
Htwr2 = Float(units='m', desc='Tower at constant cross section Length')
Thoff_yaw = Array(
np.zeros([3]),
units='m',
dtype=np.float,
desc=
'Tower-top to Hub-center vector in yawed coordinate system (TO BE MOVED SOMEWHERE ELSE at one point)'
)
rna_yawedcm = Array(
np.zeros([3]),
dtype=np.float,
desc=
'Tower Top mass CMxoff,CMyoff,CMzoff in yawed coordinate system(TO BE MOVED SOMEWHERE ELSE at one point)'
)
class ConnectableDOEdriver(DOEdriverBase):
DOEgenerator = Instance(IDOEgenerator,
required=True,
iotype="in",
desc='Iterator supplying normalized DOE values.')
case_filter = Instance(ICaseFilter,
iotype="in",
desc='Selects cases to be run.')
class ConnectableExpectedImprovement(ExpectedImprovementBase):
best_case = Instance(CaseSet,
iotype="in",
desc="CaseSet which contains a single case "
"representing the criteria value.")
predicted_value = Instance(
NormalDistribution,
iotype="in",
desc="The Normal Distribution of the predicted value "
"for some function at some point where you wish to"
" calculate the EI.")
class CompWithNestedPythonObjects(Component):
cx_nested_in = Instance(ClassWithNestedObjects(ComplexClass(1.0, 2.0),
ComplexClass(3.0, 4.0)),
iotype="in",
desc="nested complex number")
cx_nested_out = Instance(ClassWithNestedObjects(ComplexClass(5.0, 6.0),
ComplexClass(7.0, 8.0)),
iotype="out",
desc="nested complex number")
def execute(self):
self.cx_nested_out = copy.deepcopy(self.cx_nested_in)
class ConnectableMultiObjExpectedImprovement(MultiObjExpectedImprovementBase):
best_cases = Instance(
CaseSet,
iotype="in",
desc="CaseIterator which contains only Pareto optimal cases \
according to criteria.")
pass
class MyContainer(Container):
uncertain = Instance(NormalDistribution, iotype="out")
def __init__(self):
super(MyContainer, self).__init__()
self.uncertain = NormalDistribution()
self.add('dyntrait', Float(9., desc='some desc'))
class ConnectableMetaModel(MetaModelBase):
"""
Same functionality as MetaModelBase but warm_start_data is connectable.
"""
warm_start_data = Instance(ICaseIterator, iotype="in",
desc="CaseIterator containing cases to use as "
"initial training data. When this is set, all "
"previous training data is cleared and replaced "
"with data from this CaseIterator.")
class PGrafComponent(Component):
num = Int(iotype='in')
# BAN - changed obj to an Instance with iotype defined in order to make obj
# visible in the mpi-enabled framework.
#obj = Slot(PGrafObject)
obj = Instance(PGrafObject, iotype='in')
result = Int(iotype='out')
def execute(self):
self.result = self.num + self.obj.num
class ConnectableParetoFilter(ParetoFilterBase):
"""
Same functionality as ParetoFilter but without slots.
Allows for issuing connections to pareto_set and dominated_set
"""
criteria = List(Str, iotype="in",
desc="List of outputs from the case to consider for "
"filtering. Note that only case outputs are allowed as "
"criteria.")
#case_set = Slot(ICaseIterator,
# desc="CaseIterator with the cases to be filtered to "
# "Find the pareto optimal subset.")
case_sets = List(Instance(ICaseIterator), value=[], iotype="in",
desc="CaseSet with the cases to be filtered to "
"find the pareto optimal subset.")
pareto_set = Instance(CaseSet, iotype="out",
desc="Resulting collection of pareto optimal cases.", copy="shallow")
dominated_set = Instance(CaseSet, iotype="out",
desc="Resulting collection of dominated cases.", copy="shallow")
class Verifier(Component):
""" Verifies evaluated cases. """
cases = Instance(ICaseIterator, iotype='in')
def execute(self):
""" Verify evaluated cases. """
for case in self.cases:
i = int(case.label) # Correlation key.
self._logger.critical('verifying case %d', i)
assert case.msg is None
assert case['driven.rosen_suzuki'] == rosen_suzuki(
case['driven.x'])
assert case['driven.sum_y'] == sum(case['driven.y'])
assert case['driven.extra'] == 2.5
class Generator(Component):
""" Generates cases to be evaluated. """
cases = Instance(ICaseIterator, iotype='out')
def execute(self):
""" Generate some cases to be evaluated. """
cases = []
for i in range(10):
inputs = [('driven.x', numpy_random.normal(size=4)),
('driven.y', numpy_random.normal(size=10)),
('driven.raise_error', False),
('driven.stop_exec', False)]
outputs = ['driven.rosen_suzuki', 'driven.sum_y']
cases.append(Case(inputs, outputs, label=str(i)))
self.cases = ListCaseIterator(cases)
class JcktGeoOutputs(VariableTree):
"""Node Coordinates and Member Connectivity."""
nNodesJckt = Int(units=None,
desc='Total Number of nodes in the Jacket, no Tower')
nodes = Array(units='m',
dtype=np.float,
desc='Node'
's coordinates in the Substructure')
radii = Array(units='m', dtype=np.float, desc='Node' 's Radii')
Reacts = Array(
units=None,
dtype=int,
desc='Node IDs for reactions + Fixity values (1/0)'
) #Fixed for the time being with all fixity (6 dofs per node fixed)
nmems = Int(units=None,
desc='Total Number of Members in the Jacket, no Tower')
mems = Array(
units=None,
dtype=int,
desc='Member Connectivity Node i Node j for every member (i.e.,element)'
)
props = Array(dtype=np.float,
desc='Jacket Member'
's xsectional and material properties')
XnsfM = Array(dtype=np.float,
desc='Jacket Member'
's Global to Local DIrection Cosine Matrices [3,3,nelems]')
TubeObjs = Instance(
Klass=Tube,
desc=
'Object of Class Tube for Jacket Elements. Tube Objects, one per element'
)
jnt_masses = Array(
dtype=np.float,
desc=
'Jacket Concentrated Masses at Joints: (joint no, mass, IXX, IYY, IZZ)'
)
jnt_masses_yaw = Array(
dtype=np.float,
desc=
'Jacket Concentrated Masses at Joints: (joint no, mass, IXX, IYY, IZZ) accounting for RNA yaw'
)
class ExpectedImprovement(Component):
"""Expected Improvement calculation for single objective."""
target = Float(0, iotype="in", desc="Current objective minimum.")
current = Instance(NormalDistribution,
iotype="in",
desc="The Normal Distribution of the predicted value "
"for some function at some point where you wish to"
" calculate the EI.")
EI = Float(0.0, iotype="out",
desc="The expected improvement of the predicted_value " + \
"in current.")
PI = Float(0.0, iotype="out",
desc="The probability of improvement of the predicted_value" + \
" in current.")
def execute(self):
""" Calculates the expected improvement of the model at a given point.
"""
mu = self.current.mu
sigma = self.current.sigma
target = self.target
try:
seterr(divide='raise')
self.PI = 0.5 * erfc(-(1. / 2.**.5) * ((target - mu) / sigma))
T1 = (target - mu) * .5 * (erfc(-(target - mu) / (sigma * 2.**.5)))
T2 = sigma * ((1. /
((2. * pi)**.5)) * exp(-0.5 *
((target - mu) / sigma)**2.))
self.EI = abs(T1 + T2)
except (ValueError, ZeroDivisionError, FloatingPointError):
self.EI = 0
self.PI = 0
class ConnectableNeighborhoodDOEdriver(NeighborhoodDOEdriverBase):
DOEgenerator = Instance(IDOEgenerator,
iotype="in",
required=True,
desc='Iterator supplying normalized DOE values.')
class TExecComp(ExecComp):
data = Instance(iotype='in', desc='Used to check bad JSON data')
def add_region(self, name):
self.add(name + '_i', Instance(Region()))
self.add(name + '_v', VarTree(Region()))
self.add(name + '_f', Float())
