import numpy as np
from openmdao.api import ExecComp, IndepVarComp, Group, NLGaussSeidel, \
Component, ParallelGroup, ScipyGMRES
from openmdao.api import Problem, ScipyOptimizer
from openmdao.test.mpi_util import MPITestCase
from openmdao.test.util import assert_rel_error, ConcurrentTestCaseMixin, \
set_pyoptsparse_opt
try:
from openmdao.solvers.petsc_ksp import PetscKSP
from openmdao.core.petsc_impl import PetscImpl as impl
except ImportError:
impl = None
OPT, OPTIMIZER = set_pyoptsparse_opt('SNOPT')
if OPTIMIZER:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
class SellarDis1(Component):
"""Component containing Discipline 1."""
def __init__(self, problem_id=0):
super(SellarDis1, self).__init__()
self.problem_id = problem_id
# Global Design Variable
self.add_param('z', val=np.zeros(2))
import numpy as np
from openmdao.api import ExecComp, IndepVarComp, Group, NLGaussSeidel, \
Component, ParallelGroup, ScipyGMRES
from openmdao.api import Problem, ScipyOptimizer
from openmdao.test.mpi_util import MPITestCase
from openmdao.test.util import assert_rel_error, ConcurrentTestCaseMixin, \
set_pyoptsparse_opt
try:
from openmdao.solvers.petsc_ksp import PetscKSP
from openmdao.core.petsc_impl import PetscImpl as impl
except ImportError:
impl = None
OPT, OPTIMIZER = set_pyoptsparse_opt('SNOPT')
if OPTIMIZER:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
class SellarDis1(Component):
"""Component containing Discipline 1."""
def __init__(self, problem_id=0):
super(SellarDis1, self).__init__()
self.problem_id = problem_id
# Global Design Variable
self.add_param('z', val=np.zeros(2))
import numpy as np
from numpy.testing import assert_allclose
from openmdao.api import Problem, SqliteRecorder, ScipyOptimizer
from openmdao.core.vec_wrapper import _ByObjWrapper
from openmdao.test.converge_diverge import ConvergeDiverge
from openmdao.test.example_groups import ExampleGroup
from openmdao.test.sellar import SellarDerivativesGrouped
from openmdao.test.util import assert_rel_error, set_pyoptsparse_opt
from openmdao.util.record_util import format_iteration_coordinate
from openmdao.recorders.sqlite_recorder import format_version
# check that pyoptsparse is installed
# if it is, try to use SNOPT but fall back to SLSQP
OPT, OPTIMIZER = set_pyoptsparse_opt("SNOPT")
if OPTIMIZER:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
def run_problem(problem):
t0 = time.time()
problem.run()
t1 = time.time()
return t0, t1
def _assertIterationDataRecorded(test, db, expected, tolerance):
sentinel = object()
def test_discs(self):
OPT, OPTIMIZER = set_pyoptsparse_opt('SNOPT')
if OPTIMIZER is not 'SNOPT':
raise unittest.SkipTest("pyoptsparse is not providing SNOPT or SLSQP")
# So we compare the same starting locations.
np.random.seed(123)
radius = 1.0
pin = 15.0
n_disc = 7
prob = Problem()
prob.root = root = Group()
from openmdao.api import pyOptSparseDriver
driver = prob.driver = pyOptSparseDriver()
driver.options['optimizer'] = 'SNOPT'
driver.options['print_results'] = False
# Note, active tolerance requires relevance reduction to work.
root.ln_solver.options['single_voi_relevance_reduction'] = True
# Also, need to be in adjoint
root.ln_solver.options['mode'] = 'rev'
obj_expr = 'obj = '
sep = ''
for i in range(n_disc):
dist = "dist_%d" % i
x1var = 'x_%d' % i
# First disc is pinned
if i == 0:
root.add('p_%d' % i, IndepVarComp(x1var, pin), promotes=(x1var, ))
# The rest are design variables for the optimizer.
else:
init_val = 5.0*np.random.random() - 5.0 + pin
root.add('p_%d' % i, IndepVarComp(x1var, init_val), promotes=(x1var, ))
driver.add_desvar(x1var)
for j in range(i):
x2var = 'x_%d' % j
yvar = 'y_%d_%d' % (i, j)
name = dist + "_%d" % j
expr = '%s= (%s - %s)**2' % (yvar, x1var, x2var)
root.add(name, ExecComp(expr), promotes = (x1var, x2var, yvar))
# Constraint (you can experiment with turning on/off the active_tol)
#driver.add_constraint(yvar, lower=radius)
driver.add_constraint(yvar, lower=radius, active_tol=radius*3.0)
# This pair's contribution to objective
obj_expr += sep + yvar
sep = ' + '
root.add('sum_dist', ExecComp(obj_expr), promotes=('*', ))
driver.add_objective('obj')
prob.setup(check=False)
prob.run()
def test_discs(self):
OPT, OPTIMIZER = set_pyoptsparse_opt('SNOPT')
if OPTIMIZER is not 'SNOPT':
raise unittest.SkipTest(
"pyoptsparse is not providing SNOPT or SLSQP")
# So we compare the same starting locations.
np.random.seed(123)
radius = 1.0
pin = 15.0
n_disc = 7
prob = Problem()
prob.root = root = Group()
from openmdao.api import pyOptSparseDriver
driver = prob.driver = pyOptSparseDriver()
driver.options['optimizer'] = 'SNOPT'
driver.options['print_results'] = False
# Note, active tolerance requires relevance reduction to work.
root.ln_solver.options['single_voi_relevance_reduction'] = True
# Also, need to be in adjoint
root.ln_solver.options['mode'] = 'rev'
obj_expr = 'obj = '
sep = ''
for i in range(n_disc):
dist = "dist_%d" % i
x1var = 'x_%d' % i
# First disc is pinned
if i == 0:
root.add('p_%d' % i,
IndepVarComp(x1var, pin),
promotes=(x1var, ))
# The rest are design variables for the optimizer.
else:
init_val = 5.0 * np.random.random() - 5.0 + pin
root.add('p_%d' % i,
IndepVarComp(x1var, init_val),
promotes=(x1var, ))
driver.add_desvar(x1var)
for j in range(i):
x2var = 'x_%d' % j
yvar = 'y_%d_%d' % (i, j)
name = dist + "_%d" % j
expr = '%s= (%s - %s)**2' % (yvar, x1var, x2var)
root.add(name, ExecComp(expr), promotes=(x1var, x2var, yvar))
# Constraint (you can experiment with turning on/off the active_tol)
#driver.add_constraint(yvar, lower=radius)
driver.add_constraint(yvar,
lower=radius,
active_tol=radius * 3.0)
# This pair's contribution to objective
obj_expr += sep + yvar
sep = ' + '
root.add('sum_dist', ExecComp(obj_expr), promotes=('*', ))
driver.add_objective('obj')
prob.setup(check=False)
prob.run()
def __init__(self, n=1500, m=300, npts=6):
super(CADRE_Optimization, self).__init__()
# add SNOPT driver
OPT, OPTIMIZER = set_pyoptsparse_opt('SNOPT')
self.driver = pyOptSparseDriver()
self.driver.options['optimizer'] = OPTIMIZER
#self.add("driver", pyopt_driver.pyOptDriver())
#self.driver.optimizer = "SNOPT"
#self.driver.options = {'Major optimality tolerance': 1e-3,
# 'Iterations limit': 500000000,
# "New basis file": 10}
#if os.path.exists("fort.10"):
# self.driver.options["Old basis file"] = 10
#driver = self.add("driver", CONMINdriver())
self.driver = pyOptSparseDriver()
self.driver.options['optimizer'] = 'SNOPT'
# Raw data to load
fpath = os.path.dirname(os.path.realpath(__file__))
fpath = os.path.join(fpath, 'CADRE/data')
solar_raw1 = np.genfromtxt(fpath + '/Solar/Area10.txt')
solar_raw2 = np.loadtxt(fpath + '/Solar/Area_all.txt')
comm_rawGdata = np.genfromtxt(fpath + '/Comm/Gain.txt')
comm_raw = (10 ** (comm_rawGdata / 10.0)
).reshape((361, 361), order='F')
power_raw = np.genfromtxt(fpath + '/Power/curve.dat')
# Load launch data
launch_data = np.loadtxt(fpath + '/Launch/launch1.dat')
# orbit position and velocity data for each design point
r_e2b_I0s = launch_data[1::2, 1:]
# number of days since launch for each design point
LDs = launch_data[1::2, 0] - 2451545
# build design points
names = ['pt%s' % i for i in range(npts)]
for i, name in enumerate(names):
#comp = self.add(name, CADRE(n, m, solar_raw1, solar_raw2,
# comm_raw, power_raw))
#self.add(name, CADRE(n, m, solar_raw1, solar_raw2,comm_raw, power_raw))
name = CADRE(n, m, solar_raw1, solar_raw2, comm_raw, power_raw)
self.driver.workflow.add(name)
#comp.set("LD", LDs[i])
#comp.set("r_e2b_I0", r_e2b_I0s[i])
# add parameters to driver
self.driver.add_parameter("%s.CP_Isetpt" % name, low=0., high=0.4)
self.driver.add_parameter("%s.CP_gamma" %
name, low=0, high=np.pi / 2.)
self.driver.add_parameter("%s.CP_P_comm" % name, low=0., high=25.)
self.driver.add_parameter("%s.iSOC[0]" % name, low=0.2, high=1.)
# add constraints
self.driver.add_constraint("%s.ConCh <= 0" % name)
self.driver.add_constraint("%s.ConDs <= 0" % name)
self.driver.add_constraint("%s.ConS0 <= 0" % name)
self.driver.add_constraint("%s.ConS1 <= 0" % name)
self.driver.add_constraint(
"%s.SOC[0][0] = %s.SOC[0][-1]" % (name, name))
# add parameter groups
cell_param = ["%s.cellInstd" % name for name in names]
self.driver.add_parameter(cell_param, low=0, high=1)
finangles = ["%s.finAngle" % name for name in names]
self.driver.add_parameter(finangles, low=0, high=np.pi / 2.)
antangles = ["%s.antAngle" % name for name in names]
self.driver.add_parameter(antangles, low=-np.pi / 4, high=np.pi / 4)
# add objective
obj = ''.join(["-%s.Data[0][-1]" % name for name in names])
self.driver.add_objective(obj)
