class KSComp(Component):
"""Aggregates a number of functions to a single value via the
Kreisselmeier-Steinhauser Function."""
def __init__(self, n=2):
super(KS, self).__init__()
self.n = n
# Inputs
self.add_param('g',
np.zeros((n, )),
desc="Array of function values to be aggregated")
# Outputs
self.add_output('KS', 0.0, desc="Value of the aggregate KS function")
self.options = OptionsDictionary()
self.options.add_option(rho,
0.1,
desc="Hyperparameter for the KS function")
self._ks = KSfunction()
def solve_nonlinear(self, params, unknowns, resids):
""" Calculate output. """
unknowns['KS'] = self._ks.compute(params['g'], self.options['rho'])
def jacobian(self, params, unknowns, resids):
""" Calculate and save derivatives. (i.e., Jacobian) """
#use g_max, exponsnte, summation from last executed point
J = {}
J['KS', 'g'] = np.hstack(self._ks.derivatives())
def __init__(self):
super(Driver, self).__init__()
self.recorders = []
# What this driver supports
self.supports = OptionsDictionary(read_only=True)
self.supports.add_option('inequality_constraints', True)
self.supports.add_option('equality_constraints', True)
self.supports.add_option('linear_constraints', False)
self.supports.add_option('multiple_objectives', False)
self.supports.add_option('two_sided_constraints', False)
self.supports.add_option('integer_parameters', False)
# This driver's options
self.options = OptionsDictionary()
self._params = OrderedDict()
self._objs = OrderedDict()
self._cons = OrderedDict()
self._voi_sets = []
# We take root during setup
self.root = None
self.iter_count = 0
class KSComp(Component):
"""Aggregates a number of functions to a single value via the
Kreisselmeier-Steinhauser Function."""
def __init__(self, n=2):
super(KS, self).__init__()
self.n = n
# Inputs
self.add_param('g', np.zeros((n, )),
desc="Array of function values to be aggregated")
# Outputs
self.add_output('KS', 0.0,
desc="Value of the aggregate KS function")
self.options = OptionsDictionary()
self.options.add_option(rho, 0.1,
desc="Hyperparameter for the KS function")
self._ks = KSfunction()
def solve_nonlinear(self, params, unknowns, resids):
""" Calculate output. """
unknowns['KS'] = self._ks.compute(params['g'], self.options['rho'])
def jacobian(self, params, unknowns, resids):
""" Calculate and save derivatives. (i.e., Jacobian) """
#use g_max, exponsnte, summation from last executed point
J = {}
J['KS', 'g'] = np.hstack(self._ks.derivatives())
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = 'Set to 0 to disable printing, set to 1 to print the ' \
'residual to stdout each iteration, set to 2 to print ' \
'subiteration residuals as well.'
self.options.add_option('iprint', 0, values=[0, 1, 2], desc=desc)
self.recorders = []
self.local_meta = None
def __init__(self):
self.options = OptionsDictionary()
self.options.add_option(
'includes', ['*'],
desc='Patterns for variables to include in recording')
self.options.add_option(
'excludes', [],
desc='Patterns for variables to exclude from recording '
'(processed after includes)')
self.out = None
self._filtered = {}
def __init__(self):
super(MySimpleDriver, self).__init__()
# What we support
self.supports['inequality_constraints'] = True
self.supports['equality_constraints'] = False
self.supports['linear_constraints'] = False
self.supports['multiple_objectives'] = False
# My driver options
self.options = OptionsDictionary()
self.options.add_option('tol', 1e-4)
self.options.add_option('maxiter', 10)
self.alpha = .01
self.violated = []
def __init__(self):
super(Driver, self).__init__()
self.recorders = []
# What this driver supports
self.supports = OptionsDictionary(read_only=True)
self.supports.add_option("Inequality Constraints", True)
self.supports.add_option("Equality Constraints", True)
self.supports.add_option("Linear Constraints", False)
self.supports.add_option("Multiple Objectives", False)
self.supports.add_option("2-Sided Constraints", False)
self.supports.add_option("Integer Parameters", False)
# This driver's options
self.options = OptionsDictionary()
self._params = OrderedDict()
self._objs = OrderedDict()
self._cons = OrderedDict()
self._voi_sets = []
# We take root during setup
self.root = None
self.iter_count = 0
def __init__(self, n=2, h=.01):
super(RK4, self).__init__()
self.h = h
# Inputs
# All inputs are defined in subclasses.
# Options
self.options = opt = OptionsDictionary()
opt.add_option('state_var',
'',
desc="Name of the variable to be used for time "
"integration")
opt.add_option('init_state_var',
'',
desc="Name of the variable to be used for initial "
"conditions")
opt.add_option('external_vars', [],
desc="List of names of variables that are external "
"to the system but DO vary with time.")
opt.add_option('fixed_external_vars', [],
desc="List of names of variables that are "
"external to the system but DO NOT "
"vary with time.")
def __init__(self):
super(Driver, self).__init__()
self.recorders = []
# What this driver supports
self.supports = OptionsDictionary(read_only=True)
self.supports.add_option('inequality_constraints', True)
self.supports.add_option('equality_constraints', True)
self.supports.add_option('linear_constraints', False)
self.supports.add_option('multiple_objectives', False)
self.supports.add_option('two_sided_constraints', False)
self.supports.add_option('integer_parameters', False)
# This driver's options
self.options = OptionsDictionary()
self._params = OrderedDict()
self._objs = OrderedDict()
self._cons = OrderedDict()
self._voi_sets = []
# We take root during setup
self.root = None
self.iter_count = 0
def __init__(self):
super(ExternalCode, self).__init__()
self.STDOUT = STDOUT
self.DEV_NULL = DEV_NULL
# Input options for this Component
self.options = OptionsDictionary()
self.options.add_option('command', [], desc='command to be executed')
self.options.add_option('env_vars', {}, desc='Environment variables required by the command')
self.options.add_option('poll_delay', 0.0,
desc='Delay between polling for command completion. A value of zero will use an internally computed default')
self.options.add_option('timeout', 0.0, desc='Maximum time to wait for command completion. A value of zero implies an infinite wait')
self.options.add_option('check_external_outputs', True,
desc='Check that all input or output external files exist')
self.options.add_option( 'external_input_files', [],
desc='(optional) list of input file names to check the pressence of before solve_nonlinear')
self.options.add_option( 'external_output_files', [],
desc='(optional) list of input file names to check the pressence of after solve_nonlinear')
# Outputs of the run of the component or items that will not work with the OptionsDictionary
self.return_code = 0 # Return code from the command
self.timed_out = False # True if the command timed-out
self.stdin = self.DEV_NULL
self.stdout = None
self.stderr = "error.out"
def __init__(self):
super(Driver, self).__init__()
self.recorders = RecordingManager()
# What this driver supports
self.supports = OptionsDictionary(read_only=True)
self.supports.add_option("inequality_constraints", True)
self.supports.add_option("equality_constraints", True)
self.supports.add_option("linear_constraints", True)
self.supports.add_option("multiple_objectives", True)
self.supports.add_option("two_sided_constraints", True)
self.supports.add_option("integer_design_vars", True)
# This driver's options
self.options = OptionsDictionary()
self._desvars = OrderedDict()
self._objs = OrderedDict()
self._cons = OrderedDict()
self._voi_sets = []
self._vars_to_record = None
# We take root during setup
self.root = None
self.iter_count = 0
def __init__(self):
self.options = OptionsDictionary()
self.options.add_option('includes', ['*'], desc='Patterns for variables to include in recording')
self.options.add_option('excludes', [], desc='Patterns for variables to exclude from recording '
'(processed after includes)')
self.out = None
self._filtered = {}
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = 'Set to 0 to disable printing, set to 1 to print the ' \
'residual to stdout each iteration, set to 2 to print ' \
'subiteration residuals as well.'
self.options.add_option('iprint', 0, values=[0, 1, 2], desc=desc)
self.recorders = []
self.local_meta = None
def __init__(self, n=2):
super(KS, self).__init__()
self.n = n
# Inputs
self.add_param('g',
np.zeros((n, )),
desc="Array of function values to be aggregated")
# Outputs
self.add_output('KS', 0.0, desc="Value of the aggregate KS function")
self.options = OptionsDictionary()
self.options.add_option(rho,
0.1,
desc="Hyperparameter for the KS function")
self._ks = KSfunction()
def __init__(self):
super(ExternalCode, self).__init__()
self.STDOUT = STDOUT
self.DEV_NULL = DEV_NULL
# Input options for this Component
self.options = OptionsDictionary()
self.options.add_option('command', [], desc='command to be executed')
self.options.add_option(
'env_vars', {},
desc='Environment variables required by the command')
self.options.add_option(
'poll_delay',
0.0,
desc='''Delay between polling for command completion.
A value of zero will use an internally computed default''')
self.options.add_option('timeout',
0.0,
desc='''Maximum time to wait for command
completion. A value of zero implies an infinite wait''')
self.options.add_option(
'check_external_outputs',
True,
desc='Check that all input or output external files exist')
self.options.add_option(
'external_input_files', [],
desc=
'(optional) list of input file names to check the pressence of before solve_nonlinear'
)
self.options.add_option(
'external_output_files', [],
desc=
'(optional) list of input file names to check the pressence of after solve_nonlinear'
)
# Outputs of the run of the component or items that will not work with the OptionsDictionary
self.return_code = 0 # Return code from the command
self.timed_out = False # True if the command timed-out
self.stdin = self.DEV_NULL
self.stdout = None
self.stderr = "error.out"
def __init__(self):
super(MySimpleDriver, self).__init__()
# What we support
self.supports["Inequality Constraints"] = True
self.supports["Equality Constraints"] = False
self.supports["Linear Constraints"] = False
self.supports["Multiple Objectives"] = False
# My driver options
self.options = OptionsDictionary()
self.options.add_option("tol", 1e-4)
self.options.add_option("maxiter", 10)
self.alpha = 0.01
self.violated = []
def __init__(self):
super(MySimpleDriver, self).__init__()
# What we support
self.supports['inequality_constraints'] = True
self.supports['equality_constraints'] = False
self.supports['linear_constraints'] = False
self.supports['multiple_objectives'] = False
# My driver options
self.options = OptionsDictionary()
self.options.add_option('tol', 1e-4)
self.options.add_option('maxiter', 10)
self.alpha = .01
self.violated = []
def __init__(self, n=2):
super(KS, self).__init__()
self.n = n
# Inputs
self.add_param('g', np.zeros((n, )),
desc="Array of function values to be aggregated")
# Outputs
self.add_output('KS', 0.0,
desc="Value of the aggregate KS function")
self.options = OptionsDictionary()
self.options.add_option(rho, 0.1,
desc="Hyperparameter for the KS function")
self._ks = KSfunction()
def __init__(self):
self.options = OptionsDictionary()
self.options.add_option('includes', ['*'],
desc='Patterns for variables to include in recording')
self.options.add_option('excludes', [],
desc='Patterns for variables to exclude from recording '
'(processed after includes)')
self.out = None
# This is for drivers to determine if a recorder supports
# real parallel recording (recording on each process), because
# if it doesn't, the driver figures out what variables must
# be gathered to rank 0 if running under MPI.
#
# By default, this is False, but it should be set to True
# if the recorder will record data on each process to avoid
# unnecessary gathering.
self._parallel = False
self._filtered = {}
class ExternalCode(Component):
"""Run an external code as a component
Default stdin is the 'null' device, default stdout is the console, and
default stderr is ``error.out``.
"""
def __init__(self):
super(ExternalCode, self).__init__()
self.STDOUT = STDOUT
self.DEV_NULL = DEV_NULL
# Input options for this Component
self.options = OptionsDictionary()
self.options.add_option('command', [], desc='command to be executed')
self.options.add_option(
'env_vars', {},
desc='Environment variables required by the command')
self.options.add_option(
'poll_delay',
0.0,
desc='''Delay between polling for command completion.
A value of zero will use an internally computed default''')
self.options.add_option('timeout',
0.0,
desc='''Maximum time to wait for command
completion. A value of zero implies an infinite wait''')
self.options.add_option(
'check_external_outputs',
True,
desc='Check that all input or output external files exist')
self.options.add_option(
'external_input_files', [],
desc=
'(optional) list of input file names to check the pressence of before solve_nonlinear'
)
self.options.add_option(
'external_output_files', [],
desc=
'(optional) list of input file names to check the pressence of after solve_nonlinear'
)
# Outputs of the run of the component or items that will not work with the OptionsDictionary
self.return_code = 0 # Return code from the command
self.timed_out = False # True if the command timed-out
self.stdin = self.DEV_NULL
self.stdout = None
self.stderr = "error.out"
def check_setup(self, out_stream=sys.stdout):
"""Write a report to the given stream indicating any potential problems found
with the current configuration of this ``Problem``.
Args
----
out_stream : a file-like object, optional
"""
# check for the command
if not self.options['command']:
out_stream.write("The command cannot be empty")
else:
if isinstance(self.options['command'], str):
program_to_execute = self.options['command']
else:
program_to_execute = self.options['command'][0]
command_full_path = find_executable(program_to_execute)
if not command_full_path:
msg = "The command to be executed, '%s', cannot be found" % program_to_execute
out_stream.write(msg)
# Check for missing input files
missing_files = self._check_for_files(input=True)
for iotype, path in missing_files:
msg = "The %s file %s is missing" % (iotype, path)
out_stream.write(msg)
def solve_nonlinear(self, params, unknowns, resids):
"""Runs the component
"""
self.return_code = -12345678
self.timed_out = False
if not self.options['command']:
raise ValueError('Empty command list')
# self.check_files(inputs=True)
return_code = None
error_msg = ''
try:
return_code, error_msg = self._execute_local()
if return_code is None:
# if self._stop:
# raise RuntimeError('Run stopped')
# else:
self.timed_out = True
raise RuntimeError('Timed out')
elif return_code:
if isinstance(self.stderr, str):
if os.path.exists(self.stderr):
stderrfile = open(self.stderr, 'r')
error_desc = stderrfile.read()
stderrfile.close()
err_fragment = "\nError Output:\n%s" % error_desc
else:
err_fragment = "\n[stderr %r missing]" % self.stderr
else:
err_fragment = error_msg
raise RuntimeError('return_code = %d%s' %
(return_code, err_fragment))
if self.options['check_external_outputs']:
missing_files = self._check_for_files(input=False)
msg = ""
for iotype, path in missing_files:
msg += "%s file %s is missing\n" % (iotype, path)
if msg:
raise RuntimeError("Missing files: %s" % msg)
# self.check_files(inputs=False)
finally:
self.return_code = -999999 if return_code is None else return_code
def _check_for_files(self, input=True):
"""
Check that all 'specific' input external files exist.
input: bool
If True, check inputs. Else check outputs
"""
missing_files = []
if input:
files = self.options['external_input_files']
else:
files = self.options['external_output_files']
for path in files:
if not os.path.exists(path):
missing_files.append(('input', path))
return missing_files
def _execute_local(self):
""" Run command. """
#self._logger.info('executing %s...', self.options['command'])
# start_time = time.time()
# check to make sure command exists
if isinstance(self.options['command'], str):
program_to_execute = self.options['command']
else:
program_to_execute = self.options['command'][0]
command_full_path = find_executable(program_to_execute)
if not command_full_path:
raise ValueError(
"The command to be executed, '%s', cannot be found" %
program_to_execute)
command_for_shell_proc = self.options['command']
if sys.platform == 'win32':
command_for_shell_proc = ['cmd.exe', '/c'] + command_for_shell_proc
self._process = \
ShellProc(command_for_shell_proc, self.stdin,
self.stdout, self.stderr, self.options['env_vars'])
#self._logger.debug('PID = %d', self._process.pid)
try:
return_code, error_msg = \
self._process.wait(self.options['poll_delay'], self.options['timeout'])
finally:
self._process.close_files()
self._process = None
# et = time.time() - start_time
#if et >= 60: #pragma no cover
#self._logger.info('elapsed time: %.1f sec.', et)
return (return_code, error_msg)
class ExternalCode(Component):
"""
Run an external code as a component
Default stdin is the 'null' device, default stdout is the console, and
default stderr is ``error.out``.
Options
-------
fd_options['force_fd'] : bool(False)
Set to True to finite difference this system.
fd_options['form'] : str('forward')
Finite difference mode. (forward, backward, central) You can also set to 'complex_step' to peform the complex step method if your components support it.
fd_options['step_size'] : float(1e-06)
Default finite difference stepsize
fd_options['step_type'] : str('absolute')
Set to absolute, relative
options['check_external_outputs'] : bool(True)
Check that all input or output external files exist
options['command'] : list([])
command to be executed
options['env_vars'] : dict({})
Environment variables required by the command
options['external_input_files'] : list([])
(optional) list of input file names to check the pressence of before solve_nonlinear
options['external_output_files'] : list([])
(optional) list of input file names to check the pressence of after solve_nonlinear
options['poll_delay'] : float(0.0)
Delay between polling for command completion. A value of zero will use an internally computed default
options['timeout'] : float(0.0)
Maximum time to wait for command completion. A value of zero implies an infinite wait
"""
def __init__(self):
super(ExternalCode, self).__init__()
self.STDOUT = STDOUT
self.DEV_NULL = DEV_NULL
# Input options for this Component
self.options = OptionsDictionary()
self.options.add_option('command', [], desc='command to be executed')
self.options.add_option('env_vars', {}, desc='Environment variables required by the command')
self.options.add_option('poll_delay', 0.0,
desc='Delay between polling for command completion. A value of zero will use an internally computed default')
self.options.add_option('timeout', 0.0, desc='Maximum time to wait for command completion. A value of zero implies an infinite wait')
self.options.add_option('check_external_outputs', True,
desc='Check that all input or output external files exist')
self.options.add_option( 'external_input_files', [],
desc='(optional) list of input file names to check the pressence of before solve_nonlinear')
self.options.add_option( 'external_output_files', [],
desc='(optional) list of input file names to check the pressence of after solve_nonlinear')
# Outputs of the run of the component or items that will not work with the OptionsDictionary
self.return_code = 0 # Return code from the command
self.timed_out = False # True if the command timed-out
self.stdin = self.DEV_NULL
self.stdout = None
self.stderr = "error.out"
def check_setup(self, out_stream=sys.stdout):
"""Write a report to the given stream indicating any potential problems found
with the current configuration of this ``Problem``.
Args
----
out_stream : a file-like object, optional
"""
# check for the command
if not self.options['command']:
out_stream.write( "The command cannot be empty")
else:
if isinstance(self.options['command'], str):
program_to_execute = self.options['command']
else:
program_to_execute = self.options['command'][0]
command_full_path = find_executable( program_to_execute )
if not command_full_path:
msg = "The command to be executed, '%s', cannot be found" % program_to_execute
out_stream.write(msg)
# Check for missing input files
missing_files = self._check_for_files(input=True)
for iotype, path in missing_files:
msg = "The %s file %s is missing" % ( iotype, path )
out_stream.write(msg)
def solve_nonlinear(self, params, unknowns, resids):
"""Runs the component
"""
self.return_code = -12345678
self.timed_out = False
if not self.options['command']:
raise ValueError('Empty command list')
# self.check_files(inputs=True)
return_code = None
error_msg = ''
try:
return_code, error_msg = self._execute_local()
if return_code is None:
# if self._stop:
# raise RuntimeError('Run stopped')
# else:
self.timed_out = True
raise RuntimeError('Timed out')
elif return_code:
if isinstance(self.stderr, str):
if os.path.exists(self.stderr):
stderrfile = open(self.stderr, 'r')
error_desc = stderrfile.read()
stderrfile.close()
err_fragment = "\nError Output:\n%s" % error_desc
else:
err_fragment = "\n[stderr %r missing]" % self.stderr
else:
err_fragment = error_msg
raise RuntimeError('return_code = %d%s' % (return_code, err_fragment))
if self.options['check_external_outputs']:
missing_files = self._check_for_files(input=False)
msg = ""
for iotype, path in missing_files:
msg += "%s file %s is missing\n" % (iotype, path)
if msg:
raise RuntimeError( "Missing files: %s" % msg )
# self.check_files(inputs=False)
finally:
self.return_code = -999999 if return_code is None else return_code
def _check_for_files(self, input=True):
"""
Check that all 'specific' input external files exist.
input: bool
If True, check inputs. Else check outputs
"""
missing_files = []
if input:
files = self.options['external_input_files']
else:
files = self.options['external_output_files']
for path in files:
if not os.path.exists(path):
missing_files.append(('input', path))
return missing_files
def _execute_local(self):
""" Run command. """
#self._logger.info('executing %s...', self.options['command'])
# start_time = time.time()
# check to make sure command exists
if isinstance(self.options['command'], str):
program_to_execute = self.options['command']
else:
program_to_execute = self.options['command'][0]
command_full_path = find_executable( program_to_execute )
if not command_full_path:
raise ValueError("The command to be executed, '%s', cannot be found" % program_to_execute)
command_for_shell_proc = self.options['command']
if sys.platform == 'win32':
command_for_shell_proc = ['cmd.exe', '/c' ] + command_for_shell_proc
self._process = \
ShellProc(command_for_shell_proc, self.stdin,
self.stdout, self.stderr, self.options['env_vars'])
#self._logger.debug('PID = %d', self._process.pid)
try:
return_code, error_msg = \
self._process.wait(self.options['poll_delay'], self.options['timeout'])
finally:
self._process.close_files()
self._process = None
# et = time.time() - start_time
#if et >= 60: #pragma no cover
#self._logger.info('elapsed time: %.1f sec.', et)
return (return_code, error_msg)
def test_options_dictionary(self):
self.options = OptionsDictionary()
# Make sure we can't address keys we haven't added
with self.assertRaises(KeyError) as cm:
self.options['junk']
self.assertEqual('"Option \'{}\' has not been added"'.format('junk'), str(cm.exception))
# Type checking - don't set a float with an int
self.options.add_option('atol', 1e-6)
self.assertEqual(self.options['atol'], 1.0e-6)
with self.assertRaises(ValueError) as cm:
self.options['atol'] = 1
if PY2:
self.assertEqual("'atol' should be a '<type 'float'>'", str(cm.exception))
else:
self.assertEqual("'atol' should be a '<class 'float'>'", str(cm.exception))
# Check enum out of range
self.options.add_option('iprint', 0, values = [0, 1, 2, 3])
for value in [0,1,2,3]:
self.options['iprint'] = value
with self.assertRaises(ValueError) as cm:
self.options['iprint'] = 4
self.assertEqual("'iprint' must be one of the following values: '[0, 1, 2, 3]'", str(cm.exception))
# Type checking for boolean
self.options.add_option('conmin_diff', True)
self.options['conmin_diff'] = True
self.options['conmin_diff'] = False
with self.assertRaises(ValueError) as cm:
self.options['conmin_diff'] = "YES!"
if PY2:
self.assertEqual("'conmin_diff' should be a '<type 'bool'>'", str(cm.exception))
else:
self.assertEqual("'conmin_diff' should be a '<class 'bool'>'", str(cm.exception))
# Test Max and Min
self.options.add_option('maxiter', 10, low=0, high=10)
for value in range(0, 11):
self.options['maxiter'] = value
with self.assertRaises(ValueError) as cm:
self.options['maxiter'] = 15
self.assertEqual("maximum allowed value for 'maxiter' is '10'", str(cm.exception))
with self.assertRaises(ValueError) as cm:
self.options['maxiter'] = -1
self.assertEqual("minimum allowed value for 'maxiter' is '0'", str(cm.exception))
class BaseRecorder(object):
""" Base class for all case recorders. """
def __init__(self):
self.options = OptionsDictionary()
self.options.add_option('includes', ['*'], desc='Patterns for variables to include in recording')
self.options.add_option('excludes', [], desc='Patterns for variables to exclude from recording '
'(processed after includes)')
self.out = None
self._filtered = {}
# TODO: System specific includes/excludes
def startup(self, group):
""" Prepare for new run. """
# Compute the inclusion lists for recording
params = list(filter(self._check_path, group.params))
unknowns = list(filter(self._check_path, group.unknowns))
resids = list(filter(self._check_path, group.resids))
self._filtered[group.pathname] = (params, unknowns, resids)
def _check_path(self, path):
""" Return True if `path` should be recorded. """
includes = self.options['includes']
excludes = self.options['excludes']
# First see if it's included
for pattern in includes:
if fnmatch(path, pattern):
# We found a match. Check to see if it is excluded.
for ex_pattern in excludes:
if fnmatch(path, ex_pattern):
return False
return True
# Did not match anything in includes.
return False
def raw_record(self, params, unknowns, resids, metadata):
"""
This is the method that drivers and solvers will call during their
execution to record their current state. This method is responsible
for filtering the provided data to reflect the includes/excludes
provided by the user and then calling `self.record`.
Recorder subclasses should override `record`, altering this function
should not be necessary.
"""
# Coord will look like ['Driver', (1,), 'root', (1,), 'G1', (1,1), ...]
# So the pathname is every other entry, starting with the fifth.
pathname = '.'.join(metadata['coord'][4::2])
pnames, unames, rnames = self._filtered[pathname]
filtered_params = {key: params[key] for key in pnames}
filtered_unknowns = {key: unknowns[key] for key in unames}
filtered_resids = {key: resids[key] for key in rnames}
self.record(filtered_params, filtered_unknowns, filtered_resids, metadata)
def record(self, params, unknowns, resids, metadata):
raise NotImplementedError("record")
def close(self):
"""Closes `out` unless it's ``sys.stdout``, ``sys.stderr``, or StringIO.
Note that a closed recorder will do nothing in :meth:`record`."""
# Closing a StringIO deletes its contents.
if self.out not in (None, sys.stdout, sys.stderr):
if not isinstance(self.out, StringIO):
self.out.close()
self.out = None
class BaseRecorder(object):
""" Base class for all case recorders. """
def __init__(self):
self.options = OptionsDictionary()
self.options.add_option('includes', ['*'],
desc='Patterns for variables to include in recording')
self.options.add_option('excludes', [],
desc='Patterns for variables to exclude from recording '
'(processed after includes)')
self.out = None
# This is for drivers to determine if a recorder supports
# real parallel recording (recording on each process), because
# if it doesn't, the driver figures out what variables must
# be gathered to rank 0 if running under MPI.
#
# By default, this is False, but it should be set to True
# if the recorder will record data on each process to avoid
# unnecessary gathering.
self._parallel = False
self._filtered = {}
# TODO: System specific includes/excludes
def startup(self, group):
""" Prepare for a new run.
Args
----
group : `Group`
Group that owns this recorder.
"""
# Compute the inclusion lists for recording
params = list(filter(self._check_path, group.params))
unknowns = list(filter(self._check_path, group.unknowns))
resids = list(filter(self._check_path, group.resids))
self._filtered[group.pathname] = (params, unknowns, resids)
def _check_path(self, path):
""" Return True if `path` should be recorded. """
includes = self.options['includes']
excludes = self.options['excludes']
# First see if it's included
for pattern in includes:
if fnmatch(path, pattern):
# We found a match. Check to see if it is excluded.
for ex_pattern in excludes:
if fnmatch(path, ex_pattern):
return False
return True
# Did not match anything in includes.
return False
def _get_pathname(self, iteration_coordinate):
'''
Converts an iteration coordinate to key to index
`_filtered` to retrieve names of variables to be recorder
'''
return '.'.join(iteration_coordinate[4::2])
def _filter_vectors(self, params, unknowns, resids, iteration_coordinate):
'''
Returns subset of `params`, `unknowns` and `resids` to be recoder
'''
pathname = self._get_pathname(iteration_coordinate)
pnames, unames, rnames = self._filtered[pathname]
params = {key: params[key] for key in pnames}
unknowns = {key: unknowns[key] for key in unames}
resids = {key: resids[key] for key in rnames}
return params, unknowns, resids
def record(self, params, unknowns, resids, metadata):
""" Records the requested variables. This method must be defined in
all recorders.
Args
----
params : `VecWrapper`
`VecWrapper` containing parameters. (p)
unknowns : `VecWrapper`
`VecWrapper` containing outputs and states. (u)
resids : `VecWrapper`
`VecWrapper` containing residuals. (r)
metadata : dict
Dictionary containing execution metadata (e.g. iteration coordinate).
"""
raise NotImplementedError("record")
def close(self):
"""Closes `out` unless it's ``sys.stdout``, ``sys.stderr``, or StringIO.
Note that a closed recorder will do nothing in :meth:`record`."""
# Closing a StringIO deletes its contents.
if self.out not in (None, sys.stdout, sys.stderr):
if not isinstance(self.out, StringIO):
self.out.close()
self.out = None
class Driver(object):
""" Base class for drivers in OpenMDAO. Drivers can only be placed in a
Problem, and every problem has a Driver. Driver is the simplest driver that
runs (solves using solve_nonlinear) a problem once.
"""
def __init__(self):
super(Driver, self).__init__()
self.recorders = []
# What this driver supports
self.supports = OptionsDictionary(read_only=True)
self.supports.add_option('inequality_constraints', True)
self.supports.add_option('equality_constraints', True)
self.supports.add_option('linear_constraints', False)
self.supports.add_option('multiple_objectives', False)
self.supports.add_option('two_sided_constraints', False)
self.supports.add_option('integer_parameters', False)
# This driver's options
self.options = OptionsDictionary()
self._params = OrderedDict()
self._objs = OrderedDict()
self._cons = OrderedDict()
self._voi_sets = []
# We take root during setup
self.root = None
self.iter_count = 0
def _setup(self, root):
""" Updates metadata for params, constraints and objectives, and
check for errors.
"""
self.root = root
params = OrderedDict()
objs = OrderedDict()
cons = OrderedDict()
item_tups = [('Parameter', self._params, params),
('Objective', self._objs, objs),
('Constraint', self._cons, cons)]
for item_name, item, newitem in item_tups:
for name, meta in iteritems(item):
rootmeta = root.unknowns.metadata(name)
if MPI and 'src_indices' in rootmeta:
raise ValueError("'%s' is a distributed variable and may "
"not be used as a parameter, objective, "
"or constraint." % name)
# Check validity of variable
if name not in root.unknowns:
msg = "{} '{}' not found in unknowns."
msg = msg.format(item_name, name)
raise ValueError(msg)
if rootmeta.get('remote'):
continue
# Size is useful metadata to save
if 'indices' in meta:
meta['size'] = len(meta['indices'])
else:
meta['size'] = rootmeta['size']
newitem[name] = meta
self._params = params
self._objs = objs
self._cons = cons
def _map_voi_indices(self):
poi_indices = {}
qoi_indices = {}
for name, meta in chain(iteritems(self._cons), iteritems(self._objs)):
# set indices of interest
if 'indices' in meta:
qoi_indices[name] = meta['indices']
for name, meta in iteritems(self._params):
# set indices of interest
if 'indices' in meta:
poi_indices[name] = meta['indices']
return poi_indices, qoi_indices
def _of_interest(self, voi_list):
"""Return a list of tuples, with the given voi_list organized
into tuples based on the previously defined grouping of VOIs.
"""
vois = []
done_sets = set()
for v in voi_list:
for voi_set in self._voi_sets:
if voi_set in done_sets:
break
if v in voi_set:
vois.append(tuple([x for x in voi_set if x in voi_list]))
done_sets.add(voi_set)
break
else:
vois.append((v, ))
return vois
def params_of_interest(self):
"""
Returns
-------
list of tuples of str
The list of params, organized into tuples according to previously
defined VOI groups.
"""
return self._of_interest(self._params)
def outputs_of_interest(self):
"""
Returns
-------
list of tuples of str
The list of constraints and objectives, organized into tuples
according to previously defined VOI groups.
"""
return self._of_interest(list(chain(self._objs, self._cons)))
def parallel_derivs(self, vnames):
"""
Specifies that the named variables of interest are to be grouped
together so that their derivatives can be solved for concurrently.
Args
----
vnames : iter of str
The names of variables of interest that are to be grouped.
"""
for grp in self._voi_sets:
for vname in vnames:
if vname in grp:
msg = "'%s' cannot be added to VOI set %s because it " + \
"already exists in VOI set: %s"
raise RuntimeError(msg % (vname, tuple(vnames), grp))
param_intsect = set(vnames).intersection(self._params.keys())
if param_intsect and len(param_intsect) != len(vnames):
raise RuntimeError(
"%s cannot be grouped because %s are params and %s are not." %
(vnames, list(param_intsect),
list(set(vnames).difference(param_intsect))))
self._voi_sets.append(tuple(vnames))
def add_recorder(self, recorder):
"""
Adds a recorder to the driver.
Args
----
recorder : BaseRecorder
A recorder instance.
"""
self.recorders.append(recorder)
def add_param(self,
name,
low=None,
high=None,
indices=None,
adder=0.0,
scaler=1.0):
"""
Adds a parameter to this driver.
Args
----
name : string
Name of the paramcomp in the root system.
low : float or ndarray, optional
Lower boundary for the param
high : upper or ndarray, optional
Lower boundary for the param
indices : iter of int, optional
If a param is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if low is None:
low = -1e99
elif isinstance(low, np.ndarray):
low = low.flatten()
if high is None:
high = 1e99
elif isinstance(high, np.ndarray):
high = high.flatten()
if isinstance(adder, np.ndarray):
adder = adder.flatten()
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten()
# Scale the low and high values
low = (low + adder) * scaler
high = (high + adder) * scaler
param = {}
param['low'] = low
param['high'] = high
param['adder'] = adder
param['scaler'] = scaler
if indices:
param['indices'] = np.array(indices, dtype=int)
self._params[name] = param
def get_params(self):
""" Returns a dict of parameters.
Returns
-------
dict
Keys are the param object names, and the values are the param
values.
"""
uvec = self.root.unknowns
params = OrderedDict()
for key, meta in iteritems(self._params):
scaler = meta['scaler']
adder = meta['adder']
flatval = uvec.flat[key]
if 'indices' in meta:
# Make sure our indices are valid
try:
flatval = flatval[meta['indices']]
except IndexError:
msg = "Index for parameter '{}' is out of bounds. "
msg += "Requested index: {}, "
msg += "Parameter shape: {}."
raise IndexError(
msg.format(key, meta['indices'],
uvec.metadata(key)['shape']))
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) \
or scaler != 1.0 or adder != 0.0:
params[key] = (flatval + adder) * scaler
else:
params[key] = flatval
return params
def get_param_metadata(self):
""" Returns a dict of parameter metadata.
Returns
-------
dict
Keys are the param object names, and the values are the param
values.
"""
return self._params
def set_param(self, name, value):
""" Sets a parameter.
Args
----
name : string
Name of the paramcomp in the root system.
val : ndarray or float
value to set the parameter
"""
scaler = self._params[name]['scaler']
adder = self._params[name]['adder']
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) \
or scaler != 1.0 or adder != 0.0:
self.root.unknowns[name] = value / scaler - adder
else:
self.root.unknowns[name] = value
def add_objective(self, name, indices=None, adder=0.0, scaler=1.0):
""" Adds an objective to this driver.
Args
----
name : string
Promoted pathname of the output that will serve as the objective.
indices : iter of int, optional
If an objective is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if isinstance(adder, np.ndarray):
adder = adder.flatten()
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten()
obj = {}
obj['adder'] = adder
obj['scaler'] = scaler
if indices:
obj['indices'] = indices
if len(indices) > 1 and not self.supports['multiple_objectives']:
raise RuntimeError("Multiple objective indices specified for "
"variable '%s', but driver '%s' doesn't "
"support multiple objectives." %
(name, self.pathname))
self._objs[name] = obj
def get_objectives(self, return_type='dict'):
""" Gets all objectives of this driver.
Args
----
return_type : string
Set to 'dict' to return a dictionary, or set to 'array' to return a
flat ndarray.
Returns
-------
dict (for return_type 'dict')
Key is the objective name string, value is an ndarray with the values.
ndarray (for return_type 'array')
Array containing all objective values in the order they were added.
"""
uvec = self.root.unknowns
objs = OrderedDict()
for key, meta in iteritems(self._objs):
scaler = meta['scaler']
adder = meta['adder']
flatval = uvec.flat[key]
if 'indices' in meta:
# Make sure our indices are valid
try:
flatval = flatval[meta['indices']]
except IndexError:
msg = "Index for objective '{}' is out of bounds. "
msg += "Requested index: {}, "
msg += "Parameter shape: {}."
raise IndexError(
msg.format(key, meta['indices'],
uvec.metadata(key)['shape']))
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) \
or adder != 0.0 or scaler != 1.0:
objs[key] = (flatval + adder) * scaler
else:
objs[key] = flatval
return objs
def add_constraint(self,
name,
ctype='ineq',
linear=False,
jacs=None,
indices=None,
adder=0.0,
scaler=1.0):
""" Adds a constraint to this driver.
Args
----
name : string
Promoted pathname of the output that will serve as the objective.
ctype : string
Set to 'ineq' for inequality constraints, or 'eq' for equality
constraints. Make sure your driver supports the ctype of constraint
that you are adding.
linear : bool, optional
Set to True if this constraint is linear with respect to all params
so that it can be calculated once and cached.
jacs : dict of functions, optional
Dictionary of user-defined functions that return the flattened
Jacobian of this constraint with repsect to the params of
this driver, as indicated by the dictionary keys. Default is None
to let OpenMDAO calculate all derivatives. Note, this is currently
unsupported
indices : iter of int, optional
If a constraint is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if ctype == 'eq' and self.supports['equality_constraints'] is False:
msg = "Driver does not support equality constraint '{}'."
raise RuntimeError(msg.format(name))
if ctype == 'ineq' and self.supports['inequality_constraints'] is False:
msg = "Driver does not support inequality constraint '{}'."
raise RuntimeError(msg.format(name))
if isinstance(adder, np.ndarray):
adder = adder.flatten()
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten()
con = {}
con['linear'] = linear
con['ctype'] = ctype
con['adder'] = adder
con['scaler'] = scaler
con['jacs'] = jacs
if indices:
con['indices'] = indices
self._cons[name] = con
def get_constraints(self, ctype='all', lintype='all'):
""" Gets all constraints for this driver.
Args
----
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
Returns
-------
dict
Key is the constraint name string, value is an ndarray with the values.
"""
uvec = self.root.unknowns
cons = OrderedDict()
for key, meta in iteritems(self._cons):
if lintype == 'linear' and meta['linear'] == False:
continue
if lintype == 'nonlinear' and meta['linear']:
continue
if ctype == 'eq' and meta['ctype'] == 'ineq':
continue
if ctype == 'ineq' and meta['ctype'] == 'eq':
continue
scaler = meta['scaler']
adder = meta['adder']
flatval = uvec.flat[key]
if 'indices' in meta:
# Make sure our indices are valid
try:
flatval = flatval[meta['indices']]
except IndexError:
msg = "Index for constraint '{}' is out of bounds. "
msg += "Requested index: {}, "
msg += "Parameter shape: {}."
raise IndexError(
msg.format(key, meta['indices'],
uvec.metadata(key)['shape']))
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) \
or adder != 0.0 or scaler != 1.0:
cons[key] = (flatval + adder) * scaler
else:
cons[key] = flatval
return cons
def get_constraint_metadata(self):
""" Returns a dict of constraint metadata.
Returns
-------
dict
Keys are the constraint object names, and the values are the param
values.
"""
return self._cons
def run(self, problem):
""" Runs the driver. This function should be overriden when inheriting.
Args
----
problem : `Problem`
Our parent `Problem`.
"""
system = problem.root
# Metadata Setup
self.iter_count += 1
metadata = create_local_meta(None, 'Driver')
system.ln_solver.local_meta = metadata
update_local_meta(metadata, (self.iter_count, ))
# Solve the system once and record results.
system.solve_nonlinear(metadata=metadata)
for recorder in self.recorders:
recorder.raw_record(system.params, system.unknowns, system.resids,
metadata)
class MySimpleDriver(Driver):
def __init__(self):
super(MySimpleDriver, self).__init__()
# What we support
self.supports['inequality_constraints'] = True
self.supports['equality_constraints'] = False
self.supports['linear_constraints'] = False
self.supports['multiple_objectives'] = False
# My driver options
self.options = OptionsDictionary()
self.options.add_option('tol', 1e-4)
self.options.add_option('maxiter', 10)
self.alpha = .01
self.violated = []
def run(self, problem):
""" Mimic a very simplistic unconstrained optimization."""
# Get dicts with pointers to our vectors
params = self.get_params()
objective = self.get_objectives()
constraints = self.get_constraints()
param_list = params.keys()
objective_names = list(objective.keys())
constraint_names = list(constraints.keys())
unknown_list = objective_names + constraint_names
itercount = 0
while itercount < self.options['maxiter']:
# Run the model
problem.root.solve_nonlinear()
#print('z1: %f, z2: %f, x1: %f, y1: %f, y2: %f' % (problem['z'][0],
#problem['z'][1],
#problem['x'],
#problem['y1'],
#problem['y2']))
#print('obj: %f, con1: %f, con2: %f' % (problem['obj'], problem['con1'],
#problem['con2']))
# Calculate gradient
J = problem.calc_gradient(param_list,
unknown_list,
return_format='dict')
objective = self.get_objectives()
constraints = self.get_constraints()
for key1 in objective_names:
for key2 in param_list:
grad = J[key1][key2] * objective[key1]
new_val = params[key2] - self.alpha * grad
# Set parameter
self.set_param(key2, new_val)
self.violated = []
for name, val in constraints.items():
if np.linalg.norm(val) > 0.0:
self.violated.append(name)
itercount += 1
class SolverBase(object):
""" Common base class for Linear and Nonlinear solver. Should not be used
by users. Always inherit from `LinearSolver` or `NonlinearSolver`."""
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = 'Set to 0 to disable printing, set to 1 to print the ' \
'residual to stdout each iteration, set to 2 to print ' \
'subiteration residuals as well.'
self.options.add_option('iprint', 0, values=[0, 1, 2], desc=desc)
self.recorders = []
self.local_meta = None
def setup(self, sub):
""" Solvers override to define post-setup initiailzation.
Args
----
sub: `System`
System that owns this solver.
"""
pass
def print_norm(self,
solver_string,
metadata,
iteration,
res,
res0,
msg=None,
indent=0,
solver='NL'):
""" Prints out the norm of the residual in a neat readable format.
Args
----
solver_string: string
Unique string to identify your solver type (e.g., 'LN_GS' or
'NEWTON').
metadata: dict
OpenMDAO execution metadata containing iteration info.
iteration: int
Current iteration number
res: float
Absolute residual value.
res0: float
Baseline initial residual for relative comparison.
msg: string, optional
Message that indicates convergence.
ident: int, optional
Additional indentation levels for subiterations.
solver: string, optional
Solver type if not LN or NL (mostly for line search operations.)
"""
name = metadata['name']
# Find indentation level
level = sum(
len(item) for item in metadata['coord']
if not isinstance(item, str))
# No indentation for driver; top solver is no indentation.
level = level + indent - 2
indent = ' ' * level
if msg is not None:
form = indent + '[%s] %s: %s %d | %s'
print(form % (name, solver, solver_string, iteration, msg))
return
form = indent + '[%s] %s: %s %d | %.9g %.9g'
print(form % (name, solver, solver_string, iteration, res, res / res0))
class SolverBase(object):
""" Common base class for Linear and Nonlinear solver. Should not be used
by users. Always inherit from `LinearSolver` or `NonlinearSolver`."""
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = 'Set to 0 to disable printing, set to 1 to print the ' \
'residual to stdout each iteration, set to 2 to print ' \
'subiteration residuals as well.'
self.options.add_option('iprint', 0, values=[0, 1, 2], desc=desc)
self.recorders = []
self.local_meta = None
def setup(self, sub):
""" Solvers override to define post-setup initiailzation.
Args
----
sub: `System`
System that owns this solver.
"""
pass
def print_norm(self, solver_string, metadata, iteration, res, res0,
msg=None, indent=0, solver='NL'):
""" Prints out the norm of the residual in a neat readable format.
Args
----
solver_string: string
Unique string to identify your solver type (e.g., 'LN_GS' or
'NEWTON').
metadata: dict
OpenMDAO execution metadata containing iteration info.
iteration: int
Current iteration number
res: float
Absolute residual value.
res0: float
Baseline initial residual for relative comparison.
msg: string, optional
Message that indicates convergence.
ident: int, optional
Additional indentation levels for subiterations.
solver: string, optional
Solver type if not LN or NL (mostly for line search operations.)
"""
name = metadata['name']
# Find indentation level
level = sum(len(item) for item in metadata['coord']
if not isinstance(item, str))
# No indentation for driver; top solver is no indentation.
level = level + indent - 2
indent = ' ' * level
if msg is not None:
form = indent + '[%s] %s: %s %d | %s'
print(form % (name, solver, solver_string, iteration, msg))
return
form = indent + '[%s] %s: %s %d | %.9g %.9g'
print(form % (name, solver, solver_string, iteration, res, res/res0))
class Driver(object):
""" Base class for drivers in OpenMDAO. Drivers can only be placed in a
Problem, and every problem has a Driver. Driver is the simplest driver that
runs (solves using solve_nonlinear) a problem once.
"""
def __init__(self):
super(Driver, self).__init__()
self.recorders = RecordingManager()
# What this driver supports
self.supports = OptionsDictionary(read_only=True)
self.supports.add_option("inequality_constraints", True)
self.supports.add_option("equality_constraints", True)
self.supports.add_option("linear_constraints", True)
self.supports.add_option("multiple_objectives", True)
self.supports.add_option("two_sided_constraints", True)
self.supports.add_option("integer_design_vars", True)
# This driver's options
self.options = OptionsDictionary()
self._desvars = OrderedDict()
self._objs = OrderedDict()
self._cons = OrderedDict()
self._voi_sets = []
self._vars_to_record = None
# We take root during setup
self.root = None
self.iter_count = 0
def _setup(self, root):
""" Updates metadata for params, constraints and objectives, and
check for errors. Also determines all variables that need to be
gathered for case recording.
"""
self.root = root
desvars = OrderedDict()
objs = OrderedDict()
cons = OrderedDict()
item_tups = [
("Parameter", self._desvars, desvars),
("Objective", self._objs, objs),
("Constraint", self._cons, cons),
]
for item_name, item, newitem in item_tups:
for name, meta in iteritems(item):
rootmeta = root.unknowns.metadata(name)
if MPI and "src_indices" in rootmeta: # pragma: no cover
raise ValueError(
"'%s' is a distributed variable and may "
"not be used as a design var, objective, "
"or constraint." % name
)
# Check validity of variable
if name not in root.unknowns:
msg = "{} '{}' not found in unknowns."
msg = msg.format(item_name, name)
raise ValueError(msg)
# Size is useful metadata to save
if "indices" in meta:
meta["size"] = len(meta["indices"])
else:
meta["size"] = rootmeta["size"]
newitem[name] = meta
self._desvars = desvars
self._objs = objs
self._cons = cons
def _map_voi_indices(self):
poi_indices = {}
qoi_indices = {}
for name, meta in chain(iteritems(self._cons), iteritems(self._objs)):
# set indices of interest
if "indices" in meta:
qoi_indices[name] = meta["indices"]
for name, meta in iteritems(self._desvars):
# set indices of interest
if "indices" in meta:
poi_indices[name] = meta["indices"]
return poi_indices, qoi_indices
def _of_interest(self, voi_list):
"""Return a list of tuples, with the given voi_list organized
into tuples based on the previously defined grouping of VOIs.
"""
vois = []
remaining = set(voi_list)
for voi_set in self._voi_sets:
vois.append([])
for i, voi_set in enumerate(self._voi_sets):
for v in voi_list:
if v in voi_set:
vois[i].append(v)
remaining.remove(v)
vois = [tuple(x) for x in vois if x]
for v in voi_list:
if v in remaining:
vois.append((v,))
return vois
def desvars_of_interest(self):
"""
Returns
-------
list of tuples of str
The list of design vars, organized into tuples according to
previously defined VOI groups.
"""
return self._of_interest(self._desvars)
def outputs_of_interest(self):
"""
Returns
-------
list of tuples of str
The list of constraints and objectives, organized into tuples
according to previously defined VOI groups.
"""
return self._of_interest(list(chain(self._objs, self._cons)))
def parallel_derivs(self, vnames):
"""
Specifies that the named variables of interest are to be grouped
together so that their derivatives can be solved for concurrently.
Args
----
vnames : iter of str
The names of variables of interest that are to be grouped.
"""
# make sure all vnames are desvars, constraints, or objectives
found = set()
for n in vnames:
if not (n in self._desvars or n in self._objs or n in self._cons):
raise RuntimeError("'%s' is not a param, objective, or " "constraint" % n)
for grp in self._voi_sets:
for vname in vnames:
if vname in grp:
msg = "'%s' cannot be added to VOI set %s because it " + "already exists in VOI set: %s"
raise RuntimeError(msg % (vname, tuple(vnames), grp))
param_intsect = set(vnames).intersection(self._desvars.keys())
if param_intsect and len(param_intsect) != len(vnames):
raise RuntimeError(
"%s cannot be grouped because %s are design "
"vars and %s are not." % (vnames, list(param_intsect), list(set(vnames).difference(param_intsect)))
)
if MPI: # pragma: no cover
self._voi_sets.append(tuple(vnames))
else:
warnings.warn("parallel derivs %s specified but not running under MPI")
def add_recorder(self, recorder):
"""
Adds a recorder to the driver.
Args
----
recorder : BaseRecorder
A recorder instance.
"""
self.recorders.append(recorder)
def add_desvar(self, name, low=None, high=None, indices=None, adder=0.0, scaler=1.0):
"""
Adds a parameter to this driver.
Args
----
name : string
Name of the IndepVarComp in the root system.
low : float or ndarray, optional
Lower boundary for the param
high : upper or ndarray, optional
Lower boundary for the param
indices : iter of int, optional
If a param is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if low is None:
low = -1e99
elif isinstance(low, np.ndarray):
low = low.flatten()
if high is None:
high = 1e99
elif isinstance(high, np.ndarray):
high = high.flatten()
if isinstance(adder, np.ndarray):
adder = adder.flatten()
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten()
# Scale the low and high values
low = (low + adder) * scaler
high = (high + adder) * scaler
param = {}
param["low"] = low
param["high"] = high
param["adder"] = adder
param["scaler"] = scaler
if indices:
param["indices"] = np.array(indices, dtype=int)
self._desvars[name] = param
def add_param(self, name, low=None, high=None, indices=None, adder=0.0, scaler=1.0):
"""
Deprecated. Use ``add_desvar`` instead.
"""
warnings.simplefilter("always", DeprecationWarning)
warnings.warn("Driver.add_param() is deprecated. Use add_desvar() instead.", DeprecationWarning, stacklevel=2)
warnings.simplefilter("ignore", DeprecationWarning)
self.add_desvar(name, low=low, high=high, indices=indices, adder=adder, scaler=scaler)
def get_desvars(self):
""" Returns a dict of possibly distributed parameters.
Returns
-------
dict
Keys are the param object names, and the values are the param
values.
"""
uvec = self.root.unknowns
desvars = OrderedDict()
for key, meta in iteritems(self._desvars):
desvars[key] = self._get_distrib_var(key, meta, "design var")
return desvars
def _get_distrib_var(self, name, meta, voi_type):
uvec = self.root.unknowns
comm = self.root.comm
nproc = comm.size
iproc = comm.rank
if nproc > 1:
owner = self.root._owning_ranks[name]
if iproc == owner:
flatval = uvec.flat[name]
else:
flatval = None
else:
owner = 0
flatval = uvec.flat[name]
if "indices" in meta and not (nproc > 1 and owner != iproc):
# Make sure our indices are valid
try:
flatval = flatval[meta["indices"]]
except IndexError:
msg = "Index for {} '{}' is out of bounds. "
msg += "Requested index: {}, "
msg += "shape: {}."
raise IndexError(msg.format(voi_type, name, meta["indices"], uvec.metadata(name)["shape"]))
if nproc > 1:
flatval = comm.bcast(flatval, root=owner)
scaler = meta["scaler"]
adder = meta["adder"]
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) or scaler != 1.0 or adder != 0.0:
return (flatval + adder) * scaler
else:
return flatval
def get_desvar_metadata(self):
""" Returns a dict of parameter metadata.
Returns
-------
dict
Keys are the param object names, and the values are the param
values.
"""
return self._desvars
def set_desvar(self, name, value):
""" Sets a parameter.
Args
----
name : string
Name of the IndepVarComp in the root system.
val : ndarray or float
value to set the parameter
"""
if self.root.unknowns.flat[name].size == 0:
return
scaler = self._desvars[name]["scaler"]
adder = self._desvars[name]["adder"]
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) or scaler != 1.0 or adder != 0.0:
value = value / scaler - adder
else:
value = value
# Only set the indices we requested when we set the parameter.
idx = self._desvars[name].get("indices")
if idx is not None:
self.root.unknowns[name][idx] = value
else:
self.root.unknowns[name] = value
def add_objective(self, name, indices=None, adder=0.0, scaler=1.0):
""" Adds an objective to this driver.
Args
----
name : string
Promoted pathname of the output that will serve as the objective.
indices : iter of int, optional
If an objective is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if isinstance(adder, np.ndarray):
adder = adder.flatten()
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten()
obj = {}
obj["adder"] = adder
obj["scaler"] = scaler
if indices:
obj["indices"] = indices
if len(indices) > 1 and not self.supports["multiple_objectives"]:
raise RuntimeError(
"Multiple objective indices specified for "
"variable '%s', but driver '%s' doesn't "
"support multiple objectives." % (name, self.pathname)
)
self._objs[name] = obj
def get_objectives(self, return_type="dict"):
""" Gets all objectives of this driver.
Args
----
return_type : string
Set to 'dict' to return a dictionary, or set to 'array' to return a
flat ndarray.
Returns
-------
dict (for return_type 'dict')
Key is the objective name string, value is an ndarray with the values.
ndarray (for return_type 'array')
Array containing all objective values in the order they were added.
"""
uvec = self.root.unknowns
objs = OrderedDict()
for key, meta in iteritems(self._objs):
objs[key] = self._get_distrib_var(key, meta, "objective")
return objs
def add_constraint(
self, name, lower=None, upper=None, equals=None, linear=False, jacs=None, indices=None, adder=0.0, scaler=1.0
):
""" Adds a constraint to this driver. For inequality constraints,
`lower` or `upper` must be specified. For equality constraints, `equals`
must be specified.
Args
----
name : string
Promoted pathname of the output that will serve as the quantity to
constrain.
lower : float or ndarray, optional
Constrain the quantity to be greater than this value.
upper : float or ndarray, optional
Constrain the quantity to be less than this value.
equals : float or ndarray, optional
Constrain the quantity to be equal to this value.
linear : bool, optional
Set to True if this constraint is linear with respect to all design
variables so that it can be calculated once and cached.
jacs : dict of functions, optional
Dictionary of user-defined functions that return the flattened
Jacobian of this constraint with repsect to the design vars of
this driver, as indicated by the dictionary keys. Default is None
to let OpenMDAO calculate all derivatives. Note, this is currently
unsupported
indices : iter of int, optional
If a constraint is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if equals is not None and (lower is not None or upper is not None):
msg = "Constraint '{}' cannot be both equality and inequality."
raise RuntimeError(msg.format(name))
if equals is not None and self.supports["equality_constraints"] is False:
msg = "Driver does not support equality constraint '{}'."
raise RuntimeError(msg.format(name))
if equals is None and self.supports["inequality_constraints"] is False:
msg = "Driver does not support inequality constraint '{}'."
raise RuntimeError(msg.format(name))
if lower is not None and upper is not None and self.supports["two_sided_constraints"] is False:
msg = "Driver does not support 2-sided constraint '{}'."
raise RuntimeError(msg.format(name))
if lower is None and upper is None and equals is None:
msg = "Constraint '{}' needs to define lower, upper, or equals."
raise RuntimeError(msg.format(name))
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten()
if isinstance(adder, np.ndarray):
adder = adder.flatten()
if isinstance(lower, np.ndarray):
lower = lower.flatten()
if isinstance(upper, np.ndarray):
upper = upper.flatten()
if isinstance(equals, np.ndarray):
equals = equals.flatten()
con = {}
con["lower"] = lower
con["upper"] = upper
con["equals"] = equals
con["linear"] = linear
con["adder"] = adder
con["scaler"] = scaler
con["jacs"] = jacs
if indices:
con["indices"] = indices
self._cons[name] = con
def get_constraints(self, ctype="all", lintype="all"):
""" Gets all constraints for this driver.
Args
----
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
Returns
-------
dict
Key is the constraint name string, value is an ndarray with the values.
"""
uvec = self.root.unknowns
cons = OrderedDict()
for key, meta in iteritems(self._cons):
if lintype == "linear" and meta["linear"] == False:
continue
if lintype == "nonlinear" and meta["linear"]:
continue
if ctype == "eq" and meta["equals"] is None:
continue
if ctype == "ineq" and meta["equals"] is not None:
continue
scaler = meta["scaler"]
adder = meta["adder"]
cons[key] = self._get_distrib_var(key, meta, "constraint")
return cons
def get_constraint_metadata(self):
""" Returns a dict of constraint metadata.
Returns
-------
dict
Keys are the constraint object names, and the values are the param
values.
"""
return self._cons
def run(self, problem):
""" Runs the driver. This function should be overriden when inheriting.
Args
----
problem : `Problem`
Our parent `Problem`.
"""
system = problem.root
# Metadata Setup
self.iter_count += 1
metadata = create_local_meta(None, "Driver")
system.ln_solver.local_meta = metadata
update_local_meta(metadata, (self.iter_count,))
# Solve the system once and record results.
system.solve_nonlinear(metadata=metadata)
self.recorders.record(system, metadata)
def generate_docstring(self):
"""
Generates a numpy-style docstring for a user-created Driver class.
Returns
-------
docstring : str
string that contains a basic numpy docstring.
"""
# start the docstring off
docstring = ' """\n'
# Put options into docstring
from openmdao.core.options import OptionsDictionary
firstTime = 1
# for py3.4, items from vars must come out in same order.
v = OrderedDict(sorted(vars(self).items()))
for key, value in v.items():
if type(value) == OptionsDictionary:
if key == "supports":
continue
if firstTime: # start of Options docstring
docstring += "\n Options\n -------\n"
firstTime = 0
for (name, val) in sorted(value.items()):
docstring += " " + key + "['"
docstring += name + "']"
docstring += " : " + type(val).__name__
docstring += "("
if type(val).__name__ == "str":
docstring += "'"
docstring += str(val)
if type(val).__name__ == "str":
docstring += "'"
docstring += ")\n"
desc = value._options[name]["desc"]
if desc:
docstring += " " + desc + "\n"
# finish up docstring
docstring += '\n """\n'
return docstring
class MySimpleDriver(Driver):
def __init__(self):
super(MySimpleDriver, self).__init__()
# What we support
self.supports['inequality_constraints'] = True
self.supports['equality_constraints'] = False
self.supports['linear_constraints'] = False
self.supports['multiple_objectives'] = False
# My driver options
self.options = OptionsDictionary()
self.options.add_option('tol', 1e-4)
self.options.add_option('maxiter', 10)
self.alpha = .01
self.violated = []
def run(self, problem):
""" Mimic a very simplistic unconstrained optimization."""
# Get dicts with pointers to our vectors
params = self.get_desvars()
objective = self.get_objectives()
constraints = self.get_constraints()
indep_list = params.keys()
objective_names = list(objective.keys())
constraint_names = list(constraints.keys())
unknown_list = objective_names + constraint_names
itercount = 0
while itercount < self.options['maxiter']:
# Run the model
problem.root.solve_nonlinear()
#print('z1: %f, z2: %f, x1: %f, y1: %f, y2: %f' % (problem['z'][0],
#problem['z'][1],
#problem['x'],
#problem['y1'],
#problem['y2']))
#print('obj: %f, con1: %f, con2: %f' % (problem['obj'], problem['con1'],
#problem['con2']))
# Calculate gradient
J = problem.calc_gradient(indep_list, unknown_list, return_format='dict')
objective = self.get_objectives()
constraints = self.get_constraints()
for key1 in objective_names:
for key2 in indep_list:
grad = J[key1][key2] * objective[key1]
new_val = params[key2] - self.alpha*grad
# Set parameter
self.set_desvar(key2, new_val)
self.violated = []
for name, val in constraints.items():
if np.linalg.norm(val) > 0.0:
self.violated.append(name)
itercount += 1
class Driver(object):
""" Base class for drivers in OpenMDAO. Drivers can only be placed in a
Problem, and every problem has a Driver. Driver is the simplest driver that
runs (solves using solve_nonlinear) a problem once.
"""
def __init__(self):
super(Driver, self).__init__()
self.recorders = []
# What this driver supports
self.supports = OptionsDictionary(read_only=True)
self.supports.add_option('inequality_constraints', True)
self.supports.add_option('equality_constraints', True)
self.supports.add_option('linear_constraints', False)
self.supports.add_option('multiple_objectives', False)
self.supports.add_option('two_sided_constraints', False)
self.supports.add_option('integer_parameters', False)
# This driver's options
self.options = OptionsDictionary()
self._params = OrderedDict()
self._objs = OrderedDict()
self._cons = OrderedDict()
self._voi_sets = []
# We take root during setup
self.root = None
self.iter_count = 0
def _setup(self, root):
""" Prepares some things we need."""
self.root = root
item_names = ['Parameter', 'Objective', 'Constraint']
items = [self._params, self._objs, self._cons]
for item, item_name in zip(items, item_names):
for name, meta in item.items():
# Check validity of variable
if name not in root.unknowns:
msg = "{} '{}' not found in unknowns."
msg = msg.format(item_name, name)
raise ValueError(msg)
# Size is useful metadata to save
if 'indices' in meta:
meta['size'] = len(meta['indices'])
else:
meta['size'] = root.unknowns.metadata(name)['size']
def _map_voi_indices(self):
poi_indices = {}
qoi_indices = {}
for name, meta in chain(self._cons.items(), self._objs.items()):
# set indices of interest
if 'indices' in meta:
qoi_indices[name] = meta['indices']
for name, meta in self._params.items():
# set indices of interest
if 'indices' in meta:
poi_indices[name] = meta['indices']
return poi_indices, qoi_indices
def _of_interest(self, voi_list):
"""Return a list of tuples, with the given voi_list organized
into tuples based on the previously defined grouping of VOIs.
"""
vois = []
done_sets = set()
for v in voi_list:
for voi_set in self._voi_sets:
if voi_set in done_sets:
break
if v in voi_set:
vois.append(tuple([x for x in voi_set
if x in voi_list]))
done_sets.add(voi_set)
break
else:
vois.append((v,))
return vois
def params_of_interest(self):
"""
Returns
-------
list of tuples of str
The list of params, organized into tuples according to previously
defined VOI groups.
"""
return self._of_interest(self._params)
def outputs_of_interest(self):
"""
Returns
-------
list of tuples of str
The list of constraints and objectives, organized into tuples
according to previously defined VOI groups.
"""
return self._of_interest(list(chain(self._objs, self._cons)))
def parallel_derivs(self, vnames):
"""
Specifies that the named variables of interest are to be grouped
together so that their derivatives can be solved for concurrently.
Args
----
vnames : iter of str
The names of variables of interest that are to be grouped.
"""
for grp in self._voi_sets:
for vname in vnames:
if vname in grp:
msg = "'%s' cannot be added to VOI set %s because it " + \
"already exists in VOI set: %s"
raise RuntimeError(msg % (vname, tuple(vnames), grp))
param_intsect = set(vnames).intersection(self._params.keys())
if param_intsect and len(param_intsect) != len(vnames):
raise RuntimeError("%s cannot be grouped because %s are params and %s are not." %
(vnames, list(param_intsect),
list(set(vnames).difference(param_intsect))))
self._voi_sets.append(tuple(vnames))
def add_recorder(self, recorder):
"""
Adds a recorder to the driver.
Args
----
recorder : BaseRecorder
A recorder instance.
"""
self.recorders.append(recorder)
def add_param(self, name, low=None, high=None, indices=None, adder=0.0, scaler=1.0):
"""
Adds a parameter to this driver.
Args
----
name : string
Name of the paramcomp in the root system.
low : float or ndarray, optional
Lower boundary for the param
high : upper or ndarray, optional
Lower boundary for the param
indices : iter of int, optional
If a param is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if low is None:
low = -1e99
elif isinstance(low, np.ndarray):
low = low.flatten()
if high is None:
high = 1e99
elif isinstance(high, np.ndarray):
high = high.flatten()
if isinstance(adder, np.ndarray):
adder = adder.flatten()
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten()
# Scale the low and high values
low = (low + adder)*scaler
high = (high + adder)*scaler
param = {}
param['low'] = low
param['high'] = high
param['adder'] = adder
param['scaler'] = scaler
if indices:
param['indices'] = np.array(indices, dtype=int)
self._params[name] = param
def get_params(self):
""" Returns a dict of parameters.
Returns
-------
dict
Keys are the param object names, and the values are the param
values.
"""
uvec = self.root.unknowns
params = OrderedDict()
for key, meta in self._params.items():
scaler = meta['scaler']
adder = meta['adder']
flatval = uvec.flat[key]
if 'indices' in meta:
# Make sure our indices are valid
try:
flatval = flatval[meta['indices']]
except IndexError:
msg = "Index for parameter '{}' is out of bounds. "
msg += "Requested index: {}, "
msg += "Parameter shape: {}."
raise IndexError(msg.format(key, meta['indices'],
uvec.metadata(key)['shape']))
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) \
or scaler != 1.0 or adder != 0.0:
params[key] = (flatval + adder)*scaler
else:
params[key] = flatval
return params
def get_param_metadata(self):
""" Returns a dict of parameter metadata.
Returns
-------
dict
Keys are the param object names, and the values are the param
values.
"""
return self._params
def set_param(self, name, value):
""" Sets a parameter.
Args
----
name : string
Name of the paramcomp in the root system.
val : ndarray or float
value to set the parameter
"""
scaler = self._params[name]['scaler']
adder = self._params[name]['adder']
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) \
or scaler != 0.0 or adder != 1.0:
self.root.unknowns[name] = value/scaler - adder
else:
self.root.unknowns[name] = value
def add_objective(self, name, indices=None, adder=0.0, scaler=1.0):
""" Adds an objective to this driver.
Args
----
name : string
Promoted pathname of the output that will serve as the objective.
indices : iter of int, optional
If an objective is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if isinstance(adder, np.ndarray):
adder = adder.flatten()
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten()
obj = {}
obj['adder'] = adder
obj['scaler'] = scaler
if indices:
obj['indices'] = indices
if len(indices) > 1 and not self.supports['multiple_objectives']:
raise RuntimeError("Multiple objective indices specified for "
"variable '%s', but driver '%s' doesn't "
"support multiple objectives." %
(name, self.pathname))
self._objs[name] = obj
def get_objectives(self, return_type='dict'):
""" Gets all objectives of this driver.
Args
----
return_type : string
Set to 'dict' to return a dictionary, or set to 'array' to return a
flat ndarray.
Returns
-------
dict (for return_type 'dict')
Key is the objective name string, value is an ndarray with the values.
ndarray (for return_type 'array')
Array containing all objective values in the order they were added.
"""
uvec = self.root.unknowns
objs = OrderedDict()
for key, meta in self._objs.items():
scaler = meta['scaler']
adder = meta['adder']
flatval = uvec.flat[key]
if 'indices' in meta:
# Make sure our indices are valid
try:
flatval = flatval[meta['indices']]
except IndexError:
msg = "Index for objective '{}' is out of bounds. "
msg += "Requested index: {}, "
msg += "Parameter shape: {}."
raise IndexError(msg.format(key, meta['indices'],
uvec.metadata(key)['shape']))
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) \
or adder != 0.0 or scaler != 1.0:
objs[key] = (flatval + adder)*scaler
else:
objs[key] = flatval
return objs
def add_constraint(self, name, ctype='ineq', linear=False, jacs=None,
indices=None, adder=0.0, scaler=1.0):
""" Adds a constraint to this driver.
Args
----
name : string
Promoted pathname of the output that will serve as the objective.
ctype : string
Set to 'ineq' for inequality constraints, or 'eq' for equality
constraints. Make sure your driver supports the ctype of constraint
that you are adding.
linear : bool, optional
Set to True if this constraint is linear with respect to all params
so that it can be calculated once and cached.
jacs : dict of functions, optional
Dictionary of user-defined functions that return the flattened
Jacobian of this constraint with repsect to the params of
this driver, as indicated by the dictionary keys. Default is None
to let OpenMDAO calculate all derivatives. Note, this is currently
unsupported
indices : iter of int, optional
If a constraint is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if ctype == 'eq' and self.supports['equality_constraints'] is False:
msg = "Driver does not support equality constraint '{}'."
raise RuntimeError(msg.format(name))
if ctype == 'ineq' and self.supports['inequality_constraints'] is False:
msg = "Driver does not support inequality constraint '{}'."
raise RuntimeError(msg.format(name))
if isinstance(adder, np.ndarray):
adder = adder.flatten()
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten()
con = {}
con['linear'] = linear
con['ctype'] = ctype
con['adder'] = adder
con['scaler'] = scaler
con['jacs'] = jacs
if indices:
con['indices'] = indices
self._cons[name] = con
def get_constraints(self, ctype='all', lintype='all'):
""" Gets all constraints for this driver.
Args
----
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
Returns
-------
dict
Key is the constraint name string, value is an ndarray with the values.
"""
uvec = self.root.unknowns
cons = OrderedDict()
for key, meta in self._cons.items():
if lintype == 'linear' and meta['linear'] == False:
continue
if lintype == 'nonlinear' and meta['linear'] == True:
continue
if ctype == 'eq' and meta['ctype'] == 'ineq':
continue
if ctype == 'ineq' and meta['ctype'] == 'eq':
continue
scaler = meta['scaler']
adder = meta['adder']
flatval = uvec.flat[key]
if 'indices' in meta:
# Make sure our indices are valid
try:
flatval = flatval[meta['indices']]
except IndexError:
msg = "Index for constraint '{}' is out of bounds. "
msg += "Requested index: {}, "
msg += "Parameter shape: {}."
raise IndexError(msg.format(key, meta['indices'],
uvec.metadata(key)['shape']))
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) \
or adder != 0.0 or scaler != 1.0:
cons[key] = (flatval + adder)*scaler
else:
cons[key] = flatval
return cons
def get_constraint_metadata(self):
""" Returns a dict of constraint metadata.
Returns
-------
dict
Keys are the constraint object names, and the values are the param
values.
"""
return self._cons
def run(self, problem):
""" Runs the driver. This function should be overriden when inheriting.
Args
----
problem : `Problem`
Our parent `Problem`.
"""
system = problem.root
# Metadata Setup
self.iter_count += 1
metadata = create_local_meta(None, 'Driver')
system.ln_solver.local_meta = metadata
update_local_meta(metadata, (self.iter_count,))
# Solve the system once and record results.
system.solve_nonlinear(metadata=metadata)
for recorder in self.recorders:
recorder.raw_record(system.params, system.unknowns, system.resids, metadata)
class SolverBase(object):
""" Common base class for Linear and Nonlinear solver. Should not be used
by users. Always inherit from one of the subclasses."""
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = (
"Set to 0 to disable printing, set to 1 to print the "
"residual to stdout each iteration, set to 2 to print "
"subiteration residuals as well."
)
self.options.add_option("iprint", 0, values=[0, 1, 2], desc=desc)
self.recorders = []
self.local_meta = None
def print_norm(self, solver_string, metadata, iteration, res, res0, msg=None, indent=0, solver="NL"):
""" Prints out the norm of the residual in a neat readable format.
Args
----
solver_string: string
Unique string to identify your solver type (e.g., 'LN_GS' or
'NEWTON').
metadata: dict
OpenMDAO execution metadata containing iteration info.
iteration: int
Current iteration number
res: float
Absolute residual value.
res0: float
Baseline initial residual for relative comparison.
msg: string, optional
Message that indicates convergence.
ident: int
Additional indentation levels for subiterations.
solver: string
Solver type if not LN or NL (mostly for line search operations.)
"""
name = metadata["name"]
# Find indentation level
level = sum(len(item) for item in metadata["coord"] if not isinstance(item, str))
# No indentation for driver; top solver is no indentation.
level = level + indent - 2
indent = " " * level
if msg is not None:
form = indent + "[%s] %s: %s %d | %s"
print(form % (name, solver, solver_string, iteration, msg))
return
form = indent + "[%s] %s: %s %d | %.9g %.9g"
print(form % (name, solver, solver_string, iteration, res, res / res0))
class BaseRecorder(object):
""" Base class for all case recorders. """
def __init__(self):
self.options = OptionsDictionary()
self.options.add_option(
'includes', ['*'],
desc='Patterns for variables to include in recording')
self.options.add_option(
'excludes', [],
desc='Patterns for variables to exclude from recording '
'(processed after includes)')
self.out = None
self._filtered = {}
# TODO: System specific includes/excludes
def startup(self, group):
""" Prepare for a new run.
Args
----
group : `Group`
Group that owns this recorder.
"""
# Compute the inclusion lists for recording
params = list(filter(self._check_path, group.params))
unknowns = list(filter(self._check_path, group.unknowns))
resids = list(filter(self._check_path, group.resids))
self._filtered[group.pathname] = (params, unknowns, resids)
def _check_path(self, path):
""" Return True if `path` should be recorded. """
includes = self.options['includes']
excludes = self.options['excludes']
# First see if it's included
for pattern in includes:
if fnmatch(path, pattern):
# We found a match. Check to see if it is excluded.
for ex_pattern in excludes:
if fnmatch(path, ex_pattern):
return False
return True
# Did not match anything in includes.
return False
def raw_record(self, params, unknowns, resids, metadata):
"""
This is the method that drivers and solvers will call during their
execution to record their current state. This method is responsible
for filtering the provided data to reflect the includes/excludes
provided by the user and then calling `self.record`.
Recorder subclasses should override `record`, altering this function
should not be necessary.
Args
----
params : `VecWrapper`
`VecWrapper` containing parameters. (p)
unknowns : `VecWrapper`
`VecWrapper` containing outputs and states. (u)
resids : `VecWrapper`
`VecWrapper` containing residuals. (r)
metadata : dict
Dictionary containing execution metadata (e.g. iteration coordinate).
"""
# Coord will look like ['Driver', (1,), 'root', (1,), 'G1', (1,1), ...]
# So the pathname is every other entry, starting with the fifth.
pathname = '.'.join(metadata['coord'][4::2])
pnames, unames, rnames = self._filtered[pathname]
filtered_params = {key: params[key] for key in pnames}
filtered_unknowns = {key: unknowns[key] for key in unames}
filtered_resids = {key: resids[key] for key in rnames}
self.record(filtered_params, filtered_unknowns, filtered_resids,
metadata)
def record(self, params, unknowns, resids, metadata):
""" Records the requested variables. This method must be defined in
all recorders.
Args
----
params : `VecWrapper`
`VecWrapper` containing parameters. (p)
unknowns : `VecWrapper`
`VecWrapper` containing outputs and states. (u)
resids : `VecWrapper`
`VecWrapper` containing residuals. (r)
metadata : dict
Dictionary containing execution metadata (e.g. iteration coordinate).
"""
raise NotImplementedError("record")
def close(self):
"""Closes `out` unless it's ``sys.stdout``, ``sys.stderr``, or StringIO.
Note that a closed recorder will do nothing in :meth:`record`."""
# Closing a StringIO deletes its contents.
if self.out not in (None, sys.stdout, sys.stderr):
if not isinstance(self.out, StringIO):
self.out.close()
self.out = None
