def configure(self):
self.add('d1', Discipline1_WithDerivatives())
self.d1.x1 = 1.0
self.d1.y1 = 1.0
self.d1.y2 = 1.0
self.d1.z1 = 5.0
self.d1.z2 = 2.0
self.add('d2', Discipline2_WithDerivatives())
self.d2.y1 = 1.0
self.d2.y2 = 1.0
self.d2.z1 = 5.0
self.d2.z2 = 2.0
self.add('P1', Paraboloid())
self.add('P2', Paraboloid())
self.connect('d1.y1', 'd2.y1')
self.connect('P1.f_xy', 'd1.x1')
self.connect('d1.y1', 'P2.x')
self.connect('d2.y2', 'P2.y')
self.add('driver', SimpleDriver())
self.add('driver2', Driver())
self.add('subdriver', NewtonSolver())
self.driver.workflow.add(['P1', 'subdriver', 'P2'])
self.subdriver.workflow.add(['d1', 'd2'])
self.subdriver.add_parameter('d1.y2', low=-1e99, high=1e99)
self.subdriver.add_constraint('d1.y2 = d2.y2')
self.driver.add_parameter('P1.x', low=-1e99, high=1e99)
self.driver.add_constraint('P2.f_xy < 0')
def configure(self):
self.add('paraboloid', Paraboloid())
self.add('driver', DOEdriver())
self.driver.log_level = logging.INFO
# Configure the generator.
self.driver.DOEgenerator = MonteCarlo()
self.driver.DOEgenerator.num_samples = 5000
self.driver.DOEgenerator.dist_types = {
'Default':random.uniform, 'y':random.standard_normal}
self.driver.DOEgenerator.dist_args = {'Default':[0, 1], 'y':[]}
self.driver.DOEgenerator.parameters = ['x', 'y']
# Configure driver parameters.
self.driver.add_parameter('paraboloid.x', low=0, high=1)
self.driver.add_parameter('paraboloid.y', low=0, high=1)
#tell the DOEdriver to also record f_xy.
self.driver.add_response('paraboloid.f_xy')
#Simple recorder which stores the cases in memory.
self.recorders = [ListCaseRecorder(),]
self.driver.workflow.add('paraboloid')
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 = MonteCarlo()
self.driver.DOEgenerator.num_samples = 5000
self.driver.DOEgenerator.dist_types = {
'Default': random.uniform,
'y': random.standard_normal
}
self.driver.DOEgenerator.dist_args = {'Default': [0, 1], 'y': []}
self.driver.DOEgenerator.parameters = ['x', 'y']
#DOEdriver will automatically record the values of any parameters for each case
self.driver.add_parameter('paraboloid.x', low=0, high=1)
self.driver.add_parameter('paraboloid.y', low=0, high=1)
#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 __init__(self):
""" Creates a new Assembly containing a Paraboloid and an optimizer"""
# pylint: disable-msg=E1101
super(OptimizationConstrained, self).__init__()
# Create Paraboloid component instances
self.add('paraboloid', Paraboloid())
# Create CONMIN Optimizer instance
self.add('driver', CONMINdriver())
# Driver process definition
self.driver.workflow.add('paraboloid')
# CONMIN Flags
self.driver.iprint = 0
self.driver.itmax = 30
self.driver.fdch = .000001
self.driver.fdchm = .000001
# CONMIN Objective
self.driver.add_objective('paraboloid.f_xy')
# CONMIN Design Variables
self.driver.add_parameter('paraboloid.x', low=-50., high=50.)
self.driver.add_parameter('paraboloid.y', low=-50., high=50.)
# CONMIN Constraints
self.driver.add_constraint('paraboloid.x-paraboloid.y >= 15.0')
def test_linearGS_single_comp(self):
top = set_as_top(Assembly())
top.add('comp', Paraboloid())
top.add('driver', SimpleDriver())
top.driver.workflow.add(['comp'])
top.driver.add_parameter('comp.x', low=-1000, high=1000)
top.driver.add_parameter('comp.y', low=-1000, high=1000)
top.driver.add_objective('comp.f_xy')
top.driver.gradient_options.lin_solver = 'linear_gs'
top.driver.gradient_options.maxiter = 1
top.comp.x = 3
top.comp.y = 5
top.run()
self.assertEqual(top.comp.f_xy, 93.)
self.assertEqual(top._pseudo_0.out0, 93.)
J = top.driver.workflow.calc_gradient(inputs=['comp.x', 'comp.y'],
outputs=['comp.f_xy'],
mode='forward')
assert_rel_error(self, J[0, 0], 5.0, 0.0001)
assert_rel_error(self, J[0, 1], 21.0, 0.0001)
J = top.driver.workflow.calc_gradient(inputs=['comp.x', 'comp.y'],
mode='adjoint')
assert_rel_error(self, J[0, 0], 5.0, 0.0001)
assert_rel_error(self, J[0, 1], 21.0, 0.0001)
def configure(self):
""" Creates a new Assembly containing a Paraboloid and an optimizer"""
# pylint: disable=E1101
# Create Paraboloid component instances
self.add('paraboloid', Paraboloid())
# Create CONMIN Optimizer instance
self.add('driver', pyOptDriver())
# Driver process definition
self.driver.workflow.add('paraboloid')
# CONMIN Objective
self.driver.add_objective('paraboloid.f_xy')
# CONMIN Design Variables
self.driver.add_parameter('paraboloid.x', low=-50., high=50.)
self.driver.add_parameter('paraboloid.y', low=-50., high=50.)
# CONMIN Constraints
self.driver.add_constraint('paraboloid.x-paraboloid.y >= 15.0')
self.driver.print_results = False
def configure(self):
self.add('paraboloid', Paraboloid())
doe = self.add('driver', DOEdriver())
doe.DOEgenerator = Uniform(num_samples=1000)
doe.add_parameter('paraboloid.x', low=-50, high=50)
doe.add_parameter('paraboloid.y', low=-50, high=50)
doe.case_outputs = ['paraboloid.f_xy']
doe.workflow.add('paraboloid')
def configure(self):
self.add('paraboloid', Paraboloid())
self.add('driver', COBYLAdriver())
self.driver.add_parameter('paraboloid.x', low=-50, high=50)
self.driver.add_parameter('paraboloid.y', low=-50, high=50)
self.driver.add_objective('paraboloid.f_xy')
self.recorders = [ListCaseRecorder()]
def configure(self):
self.add('paraboloid', Paraboloid())
self.add('driver', DOEdriver())
self.driver.DOEgenerator = Uniform(1000)
self.driver.add_parameter('paraboloid.x', low=-50, high=50)
self.driver.add_parameter('paraboloid.y', low=-50, high=50)
self.driver.add_response('paraboloid.f_xy')
self.recorders = [JSONCaseRecorder(out='doe.json')]
def configure(self):
""" Creates a new Assembly containing a chain of Paraboloid components"""
self.add("paraboloid", Paraboloid())
self.add("optimizer", SLSQPdriver())
self.optimizer.add_parameter("paraboloid.x", low='-50', high='50')
self.optimizer.add_parameter("paraboloid.y", low='-50', high='50')
self.optimizer.add_objective("paraboloid.f_xy")
self.optimizer.workflow.add('paraboloid')
def test_petsc_ksp_single_comp_dict(self):
top = set_as_top(Assembly())
top.add('comp', Paraboloid())
top.add('driver', SimpleDriver())
top.driver.workflow.add(['comp'])
top.driver.add_parameter('comp.x', low=-1000, high=1000)
top.driver.add_parameter('comp.y', low=-1000, high=1000)
top.driver.add_objective('comp.f_xy')
top.driver.gradient_options.lin_solver = 'petsc_ksp'
top.comp.x = 3
top.comp.y = 5
top.run()
self.assertEqual(top.comp.f_xy, 93.)
self.assertEqual(top._pseudo_0.out0, 93.)
J = top.driver.workflow.calc_gradient(inputs=['comp.x', 'comp.y'],
outputs=['comp.f_xy'],
mode='forward',
return_format='dict')
assert_rel_error(self, J['comp.f_xy']['comp.x'][0][0], 5.0, 0.0001)
assert_rel_error(self, J['comp.f_xy']['comp.y'][0][0], 21.0, 0.0001)
J = top.driver.workflow.calc_gradient(inputs=['comp.x', 'comp.y'],
mode='adjoint',
return_format='dict')
assert_rel_error(self, J['_pseudo_0.out0']['comp.x'][0][0], 5.0,
0.0001)
assert_rel_error(self, J['_pseudo_0.out0']['comp.y'][0][0], 21.0,
0.0001)
# Make sure we aren't add-scattering out p vector
top.run()
J = top.driver.workflow.calc_gradient(inputs=['comp.x', 'comp.y'],
mode='forward',
return_format='dict')
assert_rel_error(self, J['_pseudo_0.out0']['comp.x'][0][0], 5.0,
0.0001)
assert_rel_error(self, J['_pseudo_0.out0']['comp.y'][0][0], 21.0,
0.0001)
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 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
self.driver.DOEgenerator = Uniform(1000)
# 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.add_response('paraboloid.f_xy')
self.recorders = [
JSONCaseRecorder('doe.json'),
BSONCaseRecorder('doe.bson')
]
def configure(self):
self.add('p1pre', Paraboloid())
#self.add('p1', Paraboloid())
self.add('down', Downstream())
self.add('driver', SimpleDriver())
self.add('sens', SensitivityDriver())
self.driver.add_parameter('p1pre.x', low=-100, high=101)
self.driver.add_objective('down.y')
self.sens.add_parameter('p1pre.x', low=100, high=101)
self.sens.add_objective('p1pre.f_xy')
self.connect('sens.dF', 'down.x')
self.driver.workflow.add(['sens', 'down'])
self.sens.workflow.add(['p1pre'])
def configure(self):
# Create Optimizer instance
self.add('driver', SLSQPdriver())
# Create Paraboloid component instances
self.add('paraboloid', Paraboloid())
# Iteration Hierarchy
self.driver.workflow.add('paraboloid')
# SLSQP Flags
self.driver.iprint = 0
# Objective
self.driver.add_objective('paraboloid.f_xy')
# Design Variables
self.driver.add_parameter('paraboloid.x', low=-50., high=50.)
self.driver.add_parameter('paraboloid.y', low=-50., high=50.)
# SQLSQP Flags
self.driver.iprint = 1
def configure(self):
""" Creates a new Assembly containing a Paraboloid and an optimizer"""
# pylint: disable-msg=E1101
# Create Optimizer instance
self.add('driver', SLSQPdriver())
# Create Paraboloid component instances
self.add('paraboloid', Paraboloid())
# Driver process definition
self.driver.workflow.add('paraboloid')
# SQLSQP Flags
self.driver.iprint = 0
# Objective
self.driver.add_objective('paraboloid.f_xy')
# Design Variables
self.driver.add_parameter('paraboloid.x', low=-50., high=50.)
self.driver.add_parameter('paraboloid.y', low=-50., high=50.)
def configure(self):
""" Creates counter new Assembly containing counter Paraboloid and an optimizer"""
# Create Paraboloid component instances
self.add('paraboloid', Paraboloid())
# Create Optimizer instance
self.add('driver', SLSQPdriver())
# Driver process definition
self.driver.workflow.add('paraboloid')
# Optimzier Flags
self.driver.iprint = 0
# Objective
self.driver.add_objective('paraboloid.f_xy')
# Design Variables
self.driver.add_parameter('paraboloid.x', low=-50., high=50.)
self.driver.add_parameter('paraboloid.y', low=-50., high=50.)
# Constraints
self.driver.add_constraint('paraboloid.x-paraboloid.y >= 15.0')
# In[ ]:
get_ipython().magic(u'matplotlib inline')
import pylab as plt
import numpy as np
from fusedwind.fused_helper import *
# In[ ]:
def contour_plot(func):
rose = func()
XS, YS = plt.meshgrid(np.linspace(-2, 2, 20), np.linspace(-2, 2, 20))
ZS = np.array([
rose(x1=x, x2=y).f_xy for x, y in zip(XS.flatten(), YS.flatten())
]).reshape(XS.shape)
plt.contourf(XS, YS, ZS, 50)
plt.colorbar()
contour_plot(Paraboloid())
df.plot(x='paraboloid.x', y='paraboloid.y', ls='', marker='.')
plt.draw()
# In[ ]:
plt.show()
# In[ ]:
def configure(self):
""" Creates a new Assembly containing a chain of Paraboloid components"""
self.add("paraboloid", Paraboloid())