diff --git a/tests/topfarm/test_drivers.py b/tests/topfarm/test_drivers.py
index 8708d6308ce6ef67f1fbe9154157b06b01f9d9c6..38fdc0f325cee474b44adcda6fcf7bec5cfd1dfd 100644
--- a/tests/topfarm/test_drivers.py
+++ b/tests/topfarm/test_drivers.py
@@ -9,7 +9,7 @@ import numpy as np
import pytest
from topfarm.cost_models.dummy import DummyCost, DummyCostPlotComp
from topfarm.plotting import NoPlot
-from topfarm.easy_drivers import EasyScipyOptimizeDriver, EasySimpleGADriver,\
+from topfarm.easy_drivers import EasyScipyOptimizeDriver,\
EasyPyOptSparseSLSQP, EasyPyOptSparseIPOPT
@@ -28,53 +28,25 @@ def topfarm_generator():
return _topfarm_obj
-
-
-# # CONMIN-specific Settings
-# self.driver.itmax = 30
-# self.driver.fdch = 0.00001
-# self.driver.fdchm = 0.000001
-# self.driver.ctlmin = 0.01
-# self.driver.delfun = 0.001
-#
-# # NEWSUMT-specific Settings
-# #self.driver.itmax = 10
-#
-# # COBYLA-specific Settings
-# #self.driver.rhobeg = 1.0
-# #self.driver.rhoend = 1.0e-4
-# #self.driver.maxfun = 1000
-#
-# # SLSQP-specific Settings
-# #self.driver.accuracy = 1.0e-6
-# #self.driver.maxiter = 50
-#
-# # Genetic-specific Settings
-# #self.driver.population_size = 90
-# #self.driver.crossover_rate = 0.9
-# #self.driver.mutation_rate = 0.02
-# #self.selection_method = 'rank'
-
-
-@pytest.mark.parametrize('driver,tol',[(EasyScipyOptimizeDriver(), 1e-4),
- #(EasyScipyOptimizeDriver(tol=1e-3), 1e-2),
- #(EasyScipyOptimizeDriver(maxiter=13), 1e-1),
- (EasyScipyOptimizeDriver(optimizer='COBYLA', tol=1e-3), 1e-2),
- #(EasyPyOptSparseSLSQP(),1e-4),
- (EasyPyOptSparseIPOPT(),1e-4),
- #(EasySimpleGADriver(), 1e-4)
- ][:])
+@pytest.mark.parametrize('driver,tol', [(EasyScipyOptimizeDriver(), 1e-4),
+ (EasyScipyOptimizeDriver(tol=1e-3), 1e-2),
+ (EasyScipyOptimizeDriver(maxiter=13), 1e-1),
+ (EasyScipyOptimizeDriver(optimizer='COBYLA', tol=1e-3), 1e-2),
+ (EasyPyOptSparseSLSQP(), 1e-4),
+ (EasyPyOptSparseIPOPT(), 1e-4),
+ #(EasySimpleGADriver(), 1e-4)
+ ][:])
def test_optimizers(driver, tol, topfarm_generator):
- if isinstance(driver, str):
+ if driver.__class__.__name__=="PyOptSparseMissingDriver":
pytest.xfail("reason")
tf = topfarm_generator(driver)
tf.evaluate()
- print (driver.__class__.__name__)
+ print(driver.__class__.__name__)
tf.optimize()
tb_pos = tf.turbine_positions
- #tf.plot_comp.show()
+ # tf.plot_comp.show()
assert sum((tb_pos[2] - tb_pos[0])**2) > 2**2 - tol # check min spacing
- assert tb_pos[1][0]< 6 + tol # check within border
+ assert tb_pos[1][0] < 6 + tol # check within border
np.testing.assert_array_almost_equal(tb_pos, optimal, -int(np.log10(tol)))
#print (tb_pos - optimal)
diff --git a/topfarm/easy_drivers.py b/topfarm/easy_drivers.py
index 60d5756af5e0d8ae30df6a3e778bf4d5052e4801..4d61f43af774be0402a44783c8f3b190c3094704 100644
--- a/topfarm/easy_drivers.py
+++ b/topfarm/easy_drivers.py
@@ -1,18 +1,14 @@
from openmdao.drivers.scipy_optimizer import ScipyOptimizeDriver
-from openmdao.drivers.genetic_algorithm_driver import SimpleGADriver
class EasyScipyOptimizeDriver(ScipyOptimizeDriver):
def __init__(self, optimizer='SLSQP', maxiter=200, tol=1e-6, disp=True):
- """
+ """
Parameters
----------
- optimizer : {'Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B',
- 'TNC', 'COBYLA', 'SLSQP'}
- Inequality constraints are supported by COBYLA and SLSQP,
- but equality constraints are only supported by SLSQP. None of the other
- optimizers support constraints.
+ optimizer : {'COBYLA', 'SLSQP'}
+ Gradients are only supported by SLSQP
maxiter : int
Maximum number of iterations.
tol : float
@@ -26,51 +22,60 @@ class EasyScipyOptimizeDriver(ScipyOptimizeDriver):
try:
from openmdao.drivers.pyoptsparse_driver import pyOptSparseDriver
- class EasyPyOptSparseSLSQP(pyOptSparseDriver):
- def __init__(self, maxit=200, acc=1e-6):
- pyOptSparseDriver.__init__(self)
- self.options.update({'optimizer': 'SLSQP'})
- #self.opt_settings.update({'maxit': maxit, 'acc': acc})
-
+
+# Not working:
+# capi_return is NULL
+# Call-back cb_slfunc_in_slsqp__user__routines failed.
+#
+# class EasyPyOptSparseSLSQP(pyOptSparseDriver):
+# def __init__(self, maxit=200, acc=1e-6):
+# pyOptSparseDriver.__init__(self)
+# self.options.update({'optimizer': 'SLSQP'})
+# self.opt_settings.update({'MAXIT': maxit, 'ACC': acc})
+
class EasyPyOptSparseIPOPT(pyOptSparseDriver):
def __init__(self, max_iter=200):
pyOptSparseDriver.__init__(self)
self.options.update({'optimizer': 'IPOPT'})
self.opt_settings.update({'linear_solver': 'ma27', 'max_iter': max_iter})
-
-
-except:
- EasyPyOptSparseSLSQP = "Importing PyOptSparse failed"
-class EasySimpleGADriver(SimpleGADriver):
- def __init__(self, elitism=True, max_gen=100):
- """Simple Genetic Algorithm Driver with argument
+except ModuleNotFoundError:
+ class PyOptSparseMissingDriver(object):
+ options = {}
- Parameters
- ----------
- bits : dict
- Number of bits of resolution. Default is an empty dict, where every unspecified variable is assumed to be integer, and the number of bits is calculated automatically. If you have a continuous var, you should set a bits value as a key in this dictionary.
- NotImplemented
- debug_print : list
- List of what type of Driver variables to print at each iteration. Valid items in list are 'desvars', 'ln_cons', 'nl_cons', 'objs', 'totals'
- NotImplemented
- elitism : bool
- If True, replace worst performing point with best from previous generation each iteration.
- max_gen : int
- Number of generations before termination.
- pop_size :
- Number of points in the GA. Set to 0 and it will be computed as four times the number of bits.
- NotImplemented
- procs_per_model : int
- Number of processors to give each model under MPI.
- NotImplemented
- run_parallel : bool
- Set to True to execute the points in a generation in parallel.
- NotImplemented
- """
- SimpleGADriver.__init__(self)
- self.options.update({'elitism': elitism, 'max_gen': max_gen})
+ EasyPyOptSparseSLSQP = PyOptSparseMissingDriver
+ EasyPyOptSparseIPOPT = PyOptSparseMissingDriver
+
+
+# class EasySimpleGADriver(SimpleGADriver):
+# def __init__(self, elitism=True, max_gen=100):
+# """Simple Genetic Algorithm Driver with argument
+#
+# Parameters
+# ----------
+# bits : dict
+# Number of bits of resolution. Default is an empty dict, where every unspecified variable is assumed to be integer, and the number of bits is calculated automatically. If you have a continuous var, you should set a bits value as a key in this dictionary.
+# NotImplemented
+# debug_print : list
+# List of what type of Driver variables to print at each iteration. Valid items in list are 'desvars', 'ln_cons', 'nl_cons', 'objs', 'totals'
+# NotImplemented
+# elitism : bool
+# If True, replace worst performing point with best from previous generation each iteration.
+# max_gen : int
+# Number of generations before termination.
+# pop_size :
+# Number of points in the GA. Set to 0 and it will be computed as four times the number of bits.
+# NotImplemented
+# procs_per_model : int
+# Number of processors to give each model under MPI.
+# NotImplemented
+# run_parallel : bool
+# Set to True to execute the points in a generation in parallel.
+# NotImplemented
+# """
+# SimpleGADriver.__init__(self)
+# self.options.update({'elitism': elitism, 'max_gen': max_gen})
#
# class COBYLADriverWrapper(CONMINdriver):