OpenMDAO如何设置子组属性？(OpenMDAO how to set subgroup properties?)

 通常，当我使用我的优化组时，我将其包含在问题中。 然后，我可以设置它的组件属性： 
 
# import modules, prepare data for Problem setup
...

# Initialize problem with my group 
prob = Problem(impl=impl, root=AEPGroup(nTurbines=10,                 
                                      nDirections=5,
                                      minSpacing=2))

# Configure driver, desvars, and constraints
prob.driver = pyOptSparseDriver()
prob.driver.add_desvar('turbineX', lower=np.ones(nTurbs)*min(turbineX), upper=np.ones(nTurbs)*max(turbineX), scaler=1E-2)
prob.driver.add_objective('obj', scaler=1E-8)

# run setup()
prob.setup(check=True)

# Now I set several specifications
prob['turbineX'] = turbineX
....
 
 请参阅下面的示例（改编自test_brute_force.py ）。 在第204行中，我想将AEPGroup作为另一个组内的组运行。 是否有类似的方法来配置子组内的turbineX规格？ 
 
from __future__ import print_function
from florisse.floris import AEPGroup
import unittest

from florisse.GeneralWindFarmComponents import calculate_boundary

from six.moves import range
from six import iteritems

import numpy as np

from openmdao.api import Problem, Group, ParallelGroup, \
                         Component, IndepVarComp, ExecComp, \
                         Driver, ScipyOptimizer, SqliteRecorder

from openmdao.test.sellar import *
from openmdao.test.util import assert_rel_error

from openmdao.core.mpi_wrap import MPI

if MPI:
    from openmdao.core.petsc_impl import PetscImpl as impl
else:
    from openmdao.api import BasicImpl as impl

# load wind rose data
windRose = np.loadtxt('./input_files/windrose_amalia_directionally_averaged_speeds.txt')
indexes = np.where(windRose[:, 1] > 0.1)
#print ("ypppp indexes are ", indexes) 
indexes = [[8]]
#print ("ypppp indexes are ", indexes) ; quit()
windDirections = windRose[indexes[0], 0]
windSpeeds = windRose[indexes[0], 1]
windFrequencies = windRose[indexes[0], 2]
nDirections = len(windDirections)

# load turbine positions
locations = np.loadtxt('./input_files/layout_amalia.txt')
turbineX = locations[:, 0]
turbineY = locations[:, 1]

# generate boundary constraint
boundaryVertices, boundaryNormals = calculate_boundary(locations)
nVertices = boundaryVertices.shape[0]

# define turbine size
rotor_diameter = 126.4  # (m)

# initialize input variable arrays
nTurbines = turbineX.size
rotorDiameter = np.zeros(nTurbines)
axialInduction = np.zeros(nTurbines)
Ct = np.zeros(nTurbines)
Cp = np.zeros(nTurbines)
generatorEfficiency = np.zeros(nTurbines)
yaw = np.zeros(nTurbines)
minSpacing = 2.                         # number of rotor diameters

# define initial values
for turbI in range(0, nTurbines):
    rotorDiameter[turbI] = rotor_diameter      # m
    axialInduction[turbI] = 1.0/3.0
    Ct[turbI] = 4.0*axialInduction[turbI]*(1.0-axialInduction[turbI])
    Cp[turbI] = 0.7737/0.944 * 4.0 * 1.0/3.0 * np.power((1 - 1.0/3.0), 2)
    generatorEfficiency[turbI] = 0.944
    yaw[turbI] = 0.     # deg.

# Define flow properties
air_density = 1.1716    # kg/m^3
class Randomize(Component):
    """ add random uncertainty to params and distribute

    Args
    ----
    n : number of points to generate for each param

    params : collection of (name, value, std_dev) specifying the params
             that are to be randommized.
    """
    def __init__(self, n=0, params=[]):
        super(Randomize, self).__init__()

        self.dists = {}

        for name, value, std_dev in params:
            # add param
            self.add_param(name, val=value)

            # add an output array var to distribute the modified param values
            if isinstance(value, np.ndarray):
                shape = (n, value.size)
            else:
                shape = (n, 1)

            # generate a standard normal distribution (size n) for this param
            self.dists[name] = np.random.normal(0.0, std_dev, n*shape[1]).reshape(shape)
            #self.dists[name] = std_dev*np.random.normal(0.0, 1.0, n*shape[1]).reshape(shape)

            self.add_output('dist_'+name, val=np.zeros(shape))

    def solve_nonlinear(self, params, unknowns, resids):
        """ add random uncertainty to params
        """
        for name, dist in iteritems(self.dists):
            unknowns['dist_'+name] = params[name] + dist

    def linearize(self, params, unknowns, resids):
        """ derivatives
        """
        J = {}
        for u in unknowns:
            name = u.split('_', 1)[1]
            for p in params:
                shape = (unknowns[u].size, params[p].size)
                if p == name:
                    J[u, p] = np.eye(shape[0], shape[1])
                else:
                    J[u, p] = np.zeros(shape)
        return J


class Collector(Component):
    """ collect the inputs and compute the mean of each

    Args
    ----
    n : number of points to collect for each input

    names : collection of `Str` specifying the names of the inputs to
            collect and the resulting outputs.
    """
    def __init__(self, n=10, names=[]):
        super(Collector, self).__init__()

        self.names = names

        # create n params for each input
        for i in range(n):
            for name in names:
                self.add_param('%s_%i' % (name, i),  val=0.)

        # create an output for the mean of each input
        for name in names:
            self.add_output(name,  val=0.)

    def solve_nonlinear(self, params, unknowns, resids):
        """ compute the mean of each input
        """
        inputs = {}

        for p in params:
            name = p.split('_', 1)[0]
            if name not in inputs:
                inputs[name] = data = [0.0, 0.0]
            else:
                data = inputs[name]
            data[0] += 1
            data[1] += params[p]

        for name in self.names:
            unknowns[name]  = inputs[name][1]/inputs[name][0]

    def linearize(self, params, unknowns, resids):
        """ derivatives
        """
        J = {}
        for p in params:
            name, idx = p.split('_', 1)
            for u in unknowns:
                if u == name:
                    J[u, p] = 1
                else:
                    J[u, p] = 0
        return J


class BruteForceSellarProblem(Problem):
    """ Performs optimization on the AEP problem.

        Applies a normal distribution to the design vars and runs all of the
        samples, then collects the values of all of the outputs, calculates
        the mean of those and stuffs that back into the unknowns vector.

        This is the brute force version that just stamps out N separate
        AEP models in a parallel group and sets the input of each
        one to be one of these random design vars.

    Args
    ----
    n : number of randomized points to generate for each input value

    derivs : if True, use user-defined derivatives, else use Finite Difference
    """
    def __init__(self, n=10, derivs=False):
        super(BruteForceSellarProblem, self).__init__(impl=impl)

        root = self.root = Group()
        if not derivs:
            root.deriv_options['type'] = 'fd'

        sellars = root.add('sellars', ParallelGroup())
        for i in range(n):
            name = 'sellar%i' % i
            sellars.add(name, AEPGroup(nTurbines=nTurbines, nDirections=nDirections,
                                          differentiable=True,
                                          use_rotor_components=False))
            #sellars.add(name, SellarDerivatives())

            root.connect('dist_air_density', 'sellars.'+name+'.air_density', src_indices=[i])
            #root.connect('yaw0', 'sellars.'+name+'.yaw0')#, src_indices=[i])
            #root.connect('dist_z', 'sellars.'+name+'.z', src_indices=[i*2, i*2+1])

            root.connect('sellars.'+name+'.AEP',  'collect.obj_%i'  % i)
            #root.connect('sellars.'+name+'.con1', 'collect.con1_%i' % i)
            #root.connect('sellars.'+name+'.con2', 'collect.con2_%i' % i)

        root.add('indep', IndepVarComp([
                    ('air_density', 1.0),
                    ('z', np.array([5.0, 2.0]))
                ]),
                promotes=['air_density', 'z'])

        root.add('random', Randomize(n=n, params=[
                    # name, value, std dev
                    ('air_density', 1.0, 1e-2),
                    ('z', np.array([5.0, 2.0]), 1e-2)
                ]),
                promotes=['z', 'dist_air_density', 'dist_z'])
                #promotes=['x', 'z', 'dist_x', 'dist_z'])

        root.add('collect', Collector(n=n, names=['obj', 'con1', 'con2']),
                promotes=['obj', 'con1', 'con2'])

        # top level driver setup
        self.driver = ScipyOptimizer()
        self.driver.options['optimizer'] = 'SLSQP'
        self.driver.options['tol'] = 1.0e-8
        self.driver.options['maxiter'] = 50
        self.driver.options['disp'] = False

        self.driver.add_desvar('z', lower=np.array([-10.0,  0.0]),
                                    upper=np.array([ 10.0, 10.0]))
        #self.driver.add_desvar('x', lower=0.0, upper=10.0)

        self.driver.add_objective('obj')
        self.driver.add_constraint('con1', upper=0.0)
        self.driver.add_constraint('con2', upper=0.0)

prob = BruteForceSellarProblem(100, derivs=False)
prob.setup(check=False)
prob.run()
print (prob["obj"])

Normally when I use my optimization group I include it in a problem. Then, I can set it's component properties:
 
# import modules, prepare data for Problem setup
...

# Initialize problem with my group 
prob = Problem(impl=impl, root=AEPGroup(nTurbines=10,                 
                                      nDirections=5,
                                      minSpacing=2))

# Configure driver, desvars, and constraints
prob.driver = pyOptSparseDriver()
prob.driver.add_desvar('turbineX', lower=np.ones(nTurbs)*min(turbineX), upper=np.ones(nTurbs)*max(turbineX), scaler=1E-2)
prob.driver.add_objective('obj', scaler=1E-8)

# run setup()
prob.setup(check=True)

# Now I set several specifications
prob['turbineX'] = turbineX
....
 
Please see my example below (adapted from test_brute_force.py). In line 204 I want to run AEPGroup as a group inside of another group. Is there a analogous way to configure specifications like turbineX within the subgroup? 
 
from __future__ import print_function
from florisse.floris import AEPGroup
import unittest

from florisse.GeneralWindFarmComponents import calculate_boundary

from six.moves import range
from six import iteritems

import numpy as np

from openmdao.api import Problem, Group, ParallelGroup, \
                         Component, IndepVarComp, ExecComp, \
                         Driver, ScipyOptimizer, SqliteRecorder

from openmdao.test.sellar import *
from openmdao.test.util import assert_rel_error

from openmdao.core.mpi_wrap import MPI

if MPI:
    from openmdao.core.petsc_impl import PetscImpl as impl
else:
    from openmdao.api import BasicImpl as impl

# load wind rose data
windRose = np.loadtxt('./input_files/windrose_amalia_directionally_averaged_speeds.txt')
indexes = np.where(windRose[:, 1] > 0.1)
#print ("ypppp indexes are ", indexes) 
indexes = [[8]]
#print ("ypppp indexes are ", indexes) ; quit()
windDirections = windRose[indexes[0], 0]
windSpeeds = windRose[indexes[0], 1]
windFrequencies = windRose[indexes[0], 2]
nDirections = len(windDirections)

# load turbine positions
locations = np.loadtxt('./input_files/layout_amalia.txt')
turbineX = locations[:, 0]
turbineY = locations[:, 1]

# generate boundary constraint
boundaryVertices, boundaryNormals = calculate_boundary(locations)
nVertices = boundaryVertices.shape[0]

# define turbine size
rotor_diameter = 126.4  # (m)

# initialize input variable arrays
nTurbines = turbineX.size
rotorDiameter = np.zeros(nTurbines)
axialInduction = np.zeros(nTurbines)
Ct = np.zeros(nTurbines)
Cp = np.zeros(nTurbines)
generatorEfficiency = np.zeros(nTurbines)
yaw = np.zeros(nTurbines)
minSpacing = 2.                         # number of rotor diameters

# define initial values
for turbI in range(0, nTurbines):
    rotorDiameter[turbI] = rotor_diameter      # m
    axialInduction[turbI] = 1.0/3.0
    Ct[turbI] = 4.0*axialInduction[turbI]*(1.0-axialInduction[turbI])
    Cp[turbI] = 0.7737/0.944 * 4.0 * 1.0/3.0 * np.power((1 - 1.0/3.0), 2)
    generatorEfficiency[turbI] = 0.944
    yaw[turbI] = 0.     # deg.

# Define flow properties
air_density = 1.1716    # kg/m^3
class Randomize(Component):
    """ add random uncertainty to params and distribute

    Args
    ----
    n : number of points to generate for each param

    params : collection of (name, value, std_dev) specifying the params
             that are to be randommized.
    """
    def __init__(self, n=0, params=[]):
        super(Randomize, self).__init__()

        self.dists = {}

        for name, value, std_dev in params:
            # add param
            self.add_param(name, val=value)

            # add an output array var to distribute the modified param values
            if isinstance(value, np.ndarray):
                shape = (n, value.size)
            else:
                shape = (n, 1)

            # generate a standard normal distribution (size n) for this param
            self.dists[name] = np.random.normal(0.0, std_dev, n*shape[1]).reshape(shape)
            #self.dists[name] = std_dev*np.random.normal(0.0, 1.0, n*shape[1]).reshape(shape)

            self.add_output('dist_'+name, val=np.zeros(shape))

    def solve_nonlinear(self, params, unknowns, resids):
        """ add random uncertainty to params
        """
        for name, dist in iteritems(self.dists):
            unknowns['dist_'+name] = params[name] + dist

    def linearize(self, params, unknowns, resids):
        """ derivatives
        """
        J = {}
        for u in unknowns:
            name = u.split('_', 1)[1]
            for p in params:
                shape = (unknowns[u].size, params[p].size)
                if p == name:
                    J[u, p] = np.eye(shape[0], shape[1])
                else:
                    J[u, p] = np.zeros(shape)
        return J


class Collector(Component):
    """ collect the inputs and compute the mean of each

    Args
    ----
    n : number of points to collect for each input

    names : collection of `Str` specifying the names of the inputs to
            collect and the resulting outputs.
    """
    def __init__(self, n=10, names=[]):
        super(Collector, self).__init__()

        self.names = names

        # create n params for each input
        for i in range(n):
            for name in names:
                self.add_param('%s_%i' % (name, i),  val=0.)

        # create an output for the mean of each input
        for name in names:
            self.add_output(name,  val=0.)

    def solve_nonlinear(self, params, unknowns, resids):
        """ compute the mean of each input
        """
        inputs = {}

        for p in params:
            name = p.split('_', 1)[0]
            if name not in inputs:
                inputs[name] = data = [0.0, 0.0]
            else:
                data = inputs[name]
            data[0] += 1
            data[1] += params[p]

        for name in self.names:
            unknowns[name]  = inputs[name][1]/inputs[name][0]

    def linearize(self, params, unknowns, resids):
        """ derivatives
        """
        J = {}
        for p in params:
            name, idx = p.split('_', 1)
            for u in unknowns:
                if u == name:
                    J[u, p] = 1
                else:
                    J[u, p] = 0
        return J


class BruteForceSellarProblem(Problem):
    """ Performs optimization on the AEP problem.

        Applies a normal distribution to the design vars and runs all of the
        samples, then collects the values of all of the outputs, calculates
        the mean of those and stuffs that back into the unknowns vector.

        This is the brute force version that just stamps out N separate
        AEP models in a parallel group and sets the input of each
        one to be one of these random design vars.

    Args
    ----
    n : number of randomized points to generate for each input value

    derivs : if True, use user-defined derivatives, else use Finite Difference
    """
    def __init__(self, n=10, derivs=False):
        super(BruteForceSellarProblem, self).__init__(impl=impl)

        root = self.root = Group()
        if not derivs:
            root.deriv_options['type'] = 'fd'

        sellars = root.add('sellars', ParallelGroup())
        for i in range(n):
            name = 'sellar%i' % i
            sellars.add(name, AEPGroup(nTurbines=nTurbines, nDirections=nDirections,
                                          differentiable=True,
                                          use_rotor_components=False))
            #sellars.add(name, SellarDerivatives())

            root.connect('dist_air_density', 'sellars.'+name+'.air_density', src_indices=[i])
            #root.connect('yaw0', 'sellars.'+name+'.yaw0')#, src_indices=[i])
            #root.connect('dist_z', 'sellars.'+name+'.z', src_indices=[i*2, i*2+1])

            root.connect('sellars.'+name+'.AEP',  'collect.obj_%i'  % i)
            #root.connect('sellars.'+name+'.con1', 'collect.con1_%i' % i)
            #root.connect('sellars.'+name+'.con2', 'collect.con2_%i' % i)

        root.add('indep', IndepVarComp([
                    ('air_density', 1.0),
                    ('z', np.array([5.0, 2.0]))
                ]),
                promotes=['air_density', 'z'])

        root.add('random', Randomize(n=n, params=[
                    # name, value, std dev
                    ('air_density', 1.0, 1e-2),
                    ('z', np.array([5.0, 2.0]), 1e-2)
                ]),
                promotes=['z', 'dist_air_density', 'dist_z'])
                #promotes=['x', 'z', 'dist_x', 'dist_z'])

        root.add('collect', Collector(n=n, names=['obj', 'con1', 'con2']),
                promotes=['obj', 'con1', 'con2'])

        # top level driver setup
        self.driver = ScipyOptimizer()
        self.driver.options['optimizer'] = 'SLSQP'
        self.driver.options['tol'] = 1.0e-8
        self.driver.options['maxiter'] = 50
        self.driver.options['disp'] = False

        self.driver.add_desvar('z', lower=np.array([-10.0,  0.0]),
                                    upper=np.array([ 10.0, 10.0]))
        #self.driver.add_desvar('x', lower=0.0, upper=10.0)

        self.driver.add_objective('obj')
        self.driver.add_constraint('con1', upper=0.0)
        self.driver.add_constraint('con2', upper=0.0)

prob = BruteForceSellarProblem(100, derivs=False)
prob.setup(check=False)
prob.run()
print (prob["obj"])

原文：https://stackoverflow.com/questions/41624671