def __init__(self): """ Initialize the default supports for nl solvers.""" super(NonLinearSolver, self).__init__() # What this solver supports self.supports = OptionsDictionary(read_only=True) self.supports.add_option('uses_derivatives', False)
def __init__(self): self.options = OptionsDictionary() self.options.add_option('record_metadata', True) self.options.add_option('record_unknowns', True) self.options.add_option('record_params', False) self.options.add_option('record_resids', False) self.options.add_option('record_derivs', True, desc='Set to True to record derivatives at the driver level') 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 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 linearize(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(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, lower=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, lower=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) # inheriting Drivers should override this setting and set it to False # if they don't use gradients. self.supports.add_option('gradients', 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 self.dv_conversions = {} self.fn_conversions = {}
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 = RecordingManager() self.local_meta = None
class NonLinearSolver(SolverBase): """ Base class for all nonlinear solvers. Inherit from this class to create a new custom nonlinear solver. Options ------- options['iprint'] : int(0) 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. """ def __init__(self): """ Initialize the default supports for nl solvers.""" super(NonLinearSolver, self).__init__() # What this solver supports self.supports = OptionsDictionary(read_only=True) self.supports.add_option('uses_derivatives', False) def add_recorder(self, recorder): """Appends the given recorder to this solver's list of recorders. Args ---- recorder: `BaseRecorder` A recorder object. """ self.recorders.append(recorder) def solve(self, params, unknowns, resids, system, metadata=None): """ Drive all residuals in self.system and all subsystems to zero. This includes all implicit components. This function must be defined when inheriting. Args ---- params : `VecWrapper` `VecWrapper` containing parameters. (p) unknowns : `VecWrapper` `VecWrapper` containing outputs and states. (u) resids : `VecWrapper` `VecWrapper` containing residuals. (r) system : `System` Parent `System` object. metadata : dict, optional Dictionary containing execution metadata (e.g. iteration coordinate). """ pass
class NonLinearSolver(SolverBase): """ Base class for all nonlinear solvers. Inherit from this class to create a new custom nonlinear solver. Options ------- options['iprint'] : int(0) Set to 0 to disable printing, set to 1 to print iteration totals to stdout, set to 2 to print the residual each iteration to stdout. """ def __init__(self): """ Initialize the default supports for nl solvers.""" super(NonLinearSolver, self).__init__() # What this solver supports self.supports = OptionsDictionary(read_only=True) self.supports.add_option('uses_derivatives', False) def add_recorder(self, recorder): """Appends the given recorder to this solver's list of recorders. Args ---- recorder: `BaseRecorder` A recorder object. """ self.recorders.append(recorder) def solve(self, params, unknowns, resids, system, metadata=None): """ Drive all residuals in self.system and all subsystems to zero. This includes all implicit components. This function must be defined when inheriting. Args ---- params : `VecWrapper` `VecWrapper` containing parameters. (p) unknowns : `VecWrapper` `VecWrapper` containing outputs and states. (u) resids : `VecWrapper` `VecWrapper` containing residuals. (r) system : `System` Parent `System` object. metadata : dict, optional Dictionary containing execution metadata (e.g. iteration coordinate). """ pass
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.options.add_option( 'err_on_maxiter', False, desc='If True, raise an AnalysisError if not converged at maxiter.' ) self.recorders = RecordingManager() self.local_meta = None
def __init__(self): self.iter_count = 0 self.options = OptionsDictionary() desc = "Set to 0 to print only failures, set to 1 to print iteration totals to" + \ "stdout, set to 2 to print the residual each iteration to stdout," + \ "or -1 to suppress all printing." self.options.add_option('iprint', 0, values=[-1, 0, 1, 2], desc=desc) self.options.add_option( 'err_on_maxiter', False, desc='If True, raise an AnalysisError if not converged at maxiter.' ) self.recorders = RecordingManager() self.local_meta = None
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 self.dv_conversions = {} self.fn_conversions = {}
def __init__(self, name, s, P, Cx, scaler=1.): super(FFDSpline, self).__init__() self._name = name opt = self.spline_options = OptionsDictionary() opt.add_option('spline_type', 'bezier', values=['pchip', 'bezier'],\ desc='spline type used in FFD') self.nC = Cx.shape[0] self.Cx = Cx self.s = s self.Pinit = P self._size = P.shape[0] self.scaler = scaler self.add_param(name + '_C', np.zeros(self.nC), desc='spline control points') self.add_output(name, np.zeros(self._size)) self.add_output(name + '_curv', np.zeros(self._size)) self._init_called = False self.spline = None self.set_spline(self.spline_options['spline_type'])
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.name = '' self.pathname = '' self._subsystems = OrderedDict() self._params_dict = OrderedDict() self._unknowns_dict = OrderedDict() # specify which variables are promoted up to the parent. Wildcards # are allowed. self._promotes = () self.comm = None # for those Systems that perform file I/O self.directory = '' # if True, create any directories needed by this System that don't exist self.create_dirs = False # create placeholders for all of the vectors self.unknowns = _PlaceholderVecWrapper('unknowns') self.resids = _PlaceholderVecWrapper('resids') self.params = _PlaceholderVecWrapper('params') self.dunknowns = _PlaceholderVecWrapper('dunknowns') self.dresids = _PlaceholderVecWrapper('dresids') opt = self.fd_options = OptionsDictionary() opt.add_option('force_fd', False, desc="Set to True to finite difference this system.") opt.add_option( 'form', 'forward', values=['forward', 'backward', 'central', 'complex_step'], desc="Finite difference mode. (forward, backward, central) " "You can also set to 'complex_step' to peform the complex " "step method if your components support it.") opt.add_option("step_size", 1.0e-6, lower=0.0, desc="Default finite difference stepsize") opt.add_option("step_type", 'absolute', values=['absolute', 'relative'], desc='Set to absolute, relative') self._impl = None self._num_par_fds = 1 # this will be >1 for ParallelFDGroup self._par_fd_id = 0 # for ParallelFDGroup, this will be >= 0 and # <= the number of parallel FDs self._reset() # initialize some attrs that are set during setup
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.options.add_option('err_on_maxiter', False, desc='If True, raise an AnalysisError if not converged at maxiter.') self.recorders = RecordingManager() self.local_meta = None
def __init__(self): self.iter_count = 0 self.options = OptionsDictionary() desc = "Set to 0 to print only failures, set to 1 to print iteration totals to" + \ "stdout, set to 2 to print the residual each iteration to stdout," + \ "or -1 to suppress all printing." self.options.add_option('iprint', 0, values=[-1, 0, 1, 2], desc=desc) self.options.add_option('err_on_maxiter', False, desc='If True, raise an AnalysisError if not converged at maxiter.') self.recorders = RecordingManager() self.local_meta = None
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, name, s, P, Cx, scaler=1.): super(FFDSpline, self).__init__() self._name = name opt = self.spline_options = OptionsDictionary() opt.add_option('spline_type', 'bezier', values=['linear', 'pchip', 'bezier'],\ desc='spline type used in FFD') self.nC = Cx.shape[0] self.Cx = Cx self.s = s self._iCx0 = 0 self._iCx1 = None if self.Cx[0] > 0.: self._iCx0 = np.where( np.abs(self.s - self.Cx[0]) == np.abs(self.s - self.Cx[0]).min())[0] if isinstance(self._iCx0, np.ndarray): self._iCx0 = self._iCx0[0] if self.Cx[-1] < 1.: self._iCx1 = np.where( np.abs(self.s - self.Cx[-1]) == np.abs(self.s - self.Cx[-1]).min())[0] + 1 if isinstance(self._iCx1, np.ndarray): self._iCx1 = self._iCx1[0] self.Pinit = P self._size = P.shape[0] self.scaler = scaler self.add_param(name + '_C', np.zeros(self.nC), desc='spline control points') self.add_output(name, np.zeros(self._size)) self.add_output(name + '_curv', np.zeros(self._size)) self._init_called = False self.spline = None self.set_spline(self.spline_options['spline_type'])
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.")
class BaseRecorder(object): """ This is a base class for all case recorders and is not a functioning case recorder on its own. Options ------- options['record_metadata'] : bool(True) Tells recorder whether to record variable attribute metadata. options['record_unknowns'] : bool(True) Tells recorder whether to record the unknowns vector. options['record_params'] : bool(False) Tells recorder whether to record the params vector. options['record_resids'] : bool(False) Tells recorder whether to record the ressiduals vector. options['record_derivs'] : bool(True) Tells recorder whether to record derivatives that are requested by a `Driver`. options['includes'] : list of strings Patterns for variables to include in recording. options['excludes'] : list of strings Patterns for variables to exclude in recording (processed after includes). """ def __init__(self): self.options = OptionsDictionary() self.options.add_option('record_metadata', True) self.options.add_option('record_unknowns', True) self.options.add_option('record_params', False) self.options.add_option('record_resids', False) self.options.add_option('record_derivs', True, desc='Set to True to record derivatives at the driver level') 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. """ myparams = myunknowns = myresids = set() if MPI: rank = group.comm.rank owned = group._owning_ranks # Compute the inclusion lists for recording if self.options['record_params']: myparams = set(filter(self._check_path, group.params)) if self.options['record_unknowns']: myunknowns = set(filter(self._check_path, group.unknowns)) if self.options['record_resids']: myresids = set(filter(self._check_path, group.resids)) self._filtered[group.pathname] = { 'p': myparams, 'u': myunknowns, 'r': myresids } def _check_path(self, path): """ Return True if `path` should be recorded. """ excludes = self.options['excludes'] # First see if it's included for pattern in self.options['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 recorded. ''' return '.'.join(iteration_coordinate[5::2]) def _filter_vector(self, vecwrapper, key, iteration_coordinate): ''' Returns a dict that is a subset of the given vecwrapper to be recorded. ''' if not vecwrapper: return vecwrapper pathname = self._get_pathname(iteration_coordinate) filt = self._filtered[pathname][key] return {k: vecwrapper[k] for k in filt} def record_metadata(self, group): """Writes the metadata of the given group Args ---- group : `System` `System` containing vectors """ raise NotImplementedError() def record_iteration(self, params, unknowns, resids, metadata): """ Writes the provided data. Args ---- params : dict Dictionary containing parameters. (p) unknowns : dict Dictionary containing outputs and states. (u) resids : dict Dictionary containing residuals. (r) metadata : dict, optional Dictionary containing execution metadata (e.g. iteration coordinate). """ raise NotImplementedError() def record_derivatives(self, derivs, metadata): """Writes the metadata of the given group Args ---- derivs : dict Dictionary containing derivatives metadata : dict, optional Dictionary containing execution metadata (e.g. iteration coordinate). """ raise NotImplementedError() 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`, and closing a closed recorder also does nothing. """ # 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 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 = RecordingManager() 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 cleanup(self): """ Clean up resources prior to exit. """ self.recorders.close() def print_norm(self, solver_string, pathname, 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'). pathname: dict Parent system pathname. 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.) """ if pathname == '': name = 'root' else: name = 'root.' + pathname # Find indentation level level = pathname.count('.') # No indentation for driver; top solver is no indentation. level = level + indent 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)) def print_all_convergence(self): """ Turns on iprint for this solver and all subsolvers. Override if your solver has subsolvers.""" self.options['iprint'] = 1 def generate_docstring(self): """ Generates a numpy-style docstring for a user-created System class. Returns ------- docstring : str string that contains a basic numpy docstring. """ #start the docstring off docstring = ' \"\"\"\n' #Put options into docstring firstTime = 1 #for py3.4, items from vars must come out in same order. from collections import OrderedDict v = OrderedDict(sorted(vars(self).items())) for key, value in v.items(): if type(value) == OptionsDictionary: 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 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) # inheriting Drivers should override this setting and set it to False # if they don't use gradients. self.supports.add_option('gradients', 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 self.dv_conversions = {} self.fn_conversions = {} def _setup(self): """ Updates metadata for params, constraints and objectives, and check for errors. Also determines all variables that need to be gathered for case recording. """ root = self.root desvars = OrderedDict() objs = OrderedDict() cons = OrderedDict() if self.__class__ is Driver: has_gradients = False else: has_gradients = self.supports['gradients'] 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): # Check validity of variable if name not in root.unknowns: msg = "{} '{}' not found in unknowns." msg = msg.format(item_name, name) raise ValueError(msg) rootmeta = root.unknowns.metadata(name) if name in self._desvars: rootmeta['is_desvar'] = True if name in self._objs: rootmeta['is_objective'] = True if name in self._cons: rootmeta['is_constraint'] = True if MPI and 'src_indices' in rootmeta: raise ValueError("'%s' is a distributed variable and may " "not be used as a design var, objective, " "or constraint." % name) if has_gradients and rootmeta.get('pass_by_obj'): if 'optimizer' in self.options: oname = self.options['optimizer'] else: oname = self.__class__.__name__ raise RuntimeError("%s '%s' is a 'pass_by_obj' variable " "and can't be used with a gradient " "based driver of type '%s'." % (item_name, name, oname)) # 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 # Cache scalers for derivative calculation self.dv_conversions = OrderedDict() for name, meta in iteritems(desvars): scaler = meta.get('scaler') if isinstance(scaler, np.ndarray): if all(scaler == 1.0): continue elif scaler == 1.0: continue self.dv_conversions[name] = np.reciprocal(scaler) self.fn_conversions = OrderedDict() for name, meta in chain(iteritems(objs), iteritems(cons)): scaler = meta.get('scaler') if isinstance(scaler, np.ndarray): if all(scaler == 1.0): continue elif scaler == 1.0: continue self.fn_conversions[name] = scaler def _setup_communicators(self, comm, parent_dir): """ Assign a communicator to the root `System`. Args ---- comm : an MPI communicator (real or fake) The communicator being offered by the Problem. parent_dir : str Absolute directory of the Problem. """ self.root._setup_communicators(comm, parent_dir) def get_req_procs(self): """ Returns ------- tuple A tuple of the form (min_procs, max_procs), indicating the min and max processors usable by this `Driver`. """ return self.root.get_req_procs() def cleanup(self): """ Clean up resources prior to exit. """ self.recorders.close() def _map_voi_indices(self): poi_indices = OrderedDict() qoi_indices = OrderedDict() 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 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: 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, lower=None, upper=None, low=None, high=None, indices=None, adder=0.0, scaler=1.0): """ Adds a design variable to this driver. Args ---- name : string Name of the design variable in the root system. lower : float or ndarray, optional Lower boundary for the param upper : upper or ndarray, optional Upper 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 name in self._desvars: msg = "Desvar '{}' already exists." raise RuntimeError(msg.format(name)) if low is not None or high is not None: warnings.simplefilter('always', DeprecationWarning) warnings.warn("'low' and 'high' are deprecated. " "Use 'lower' and 'upper' instead.", DeprecationWarning,stacklevel=2) warnings.simplefilter('ignore', DeprecationWarning) if low is not None and lower is None: lower = low if high is not None and upper is None: upper = high if isinstance(lower, np.ndarray): lower = lower.flatten() elif lower is None or lower == -float('inf'): lower = -sys.float_info.max if isinstance(upper, np.ndarray): upper = upper.flatten() elif upper is None or upper == float('inf'): upper = sys.float_info.max if isinstance(adder, np.ndarray): adder = adder.flatten().astype('float') else: adder = float(adder) if isinstance(scaler, np.ndarray): scaler = scaler.flatten().astype('float') else: scaler = float(scaler) # Scale the lower and upper values lower = (lower + adder)*scaler upper = (upper + adder)*scaler param = OrderedDict() param['lower'] = lower param['upper'] = upper param['adder'] = adder param['scaler'] = scaler if indices: param['indices'] = np.array(indices, dtype=int) self._desvars[name] = param def add_param(self, name, lower=None, upper=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, lower=lower, upper=upper, indices=indices, adder=adder, scaler=scaler) def get_desvars(self): """ Returns a dict of possibly distributed design variables. Returns ------- dict Keys are the param object names, and the values are the param values. """ 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._dat[name].val else: flatval = None else: owner = 0 flatval = uvec._dat[name].val 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: # TODO: use Bcast for improved performance if trace: debug("%s.driver._get_distrib_var bcast: val=%s" % (self.root.pathname, flatval)) flatval = comm.bcast(flatval, root=owner) if trace: debug("%s.driver._get_distrib_var bcast DONE" % self.root.pathname) 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 design variable 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 design variable. Args ---- name : string Name of the design variable in the root system. val : ndarray or float value to assign to the design variable. """ val = self.root.unknowns._dat[name].val if not isinstance(val, _ByObjWrapper) and \ self.root.unknowns._dat[name].val.size == 0: return meta = self._desvars[name] scaler = meta['scaler'] adder = meta['adder'] if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) \ or scaler != 1.0 or adder != 0.0: value = value/scaler - adder # Only set the indices we requested when we set the design variable. idx = meta.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 len(self._objs) > 0 and not self.supports["multiple_objectives"]: raise RuntimeError("Attempted to add multiple objectives to a " "driver that does not support multiple " "objectives.") if name in self._objs: msg = "Objective '{}' already exists." raise RuntimeError(msg.format(name)) if isinstance(adder, np.ndarray): adder = adder.flatten().astype('float') else: adder = float(adder) if isinstance(scaler, np.ndarray): scaler = scaler.flatten().astype('float') else: scaler = float(scaler) obj = OrderedDict() 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. """ 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 or equal to this value. upper : float or ndarray, optional Constrain the quantity to be less than or equal to 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 name in self._cons: msg = "Constraint '{}' already exists." raise RuntimeError(msg.format(name)) 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().astype('float') else: scaler = float(scaler) if isinstance(adder, np.ndarray): adder = adder.flatten().astype('float') else: adder = float(adder) if isinstance(lower, np.ndarray): lower = lower.flatten() if isinstance(upper, np.ndarray): upper = upper.flatten() if isinstance(equals, np.ndarray): equals = equals.flatten() # Scale the lower and upper values if lower is not None: lower = (lower + adder)*scaler if upper is not None: upper = (upper + adder)*scaler if equals is not None: equals = (equals + adder)*scaler con = OrderedDict() 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. """ cons = OrderedDict() for key, meta in iteritems(self._cons): if lintype == 'linear' and meta['linear'] is 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 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 overridden when inheriting. Args ---- problem : `Problem` Our parent `Problem`. """ self.run_once(problem) def run_once(self, problem): """ Runs root's solve_nonlinear one time Args ---- problem : `Problem` Our parent `Problem`. """ system = problem.root # Metadata Setup self.iter_count += 1 metadata = self.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. with system._dircontext: system.solve_nonlinear(metadata=metadata) self.recorders.record_iteration(system, metadata) def calc_gradient(self, indep_list, unknown_list, mode='auto', return_format='array', sparsity=None): """ Returns the scaled gradient for the system that is contained in self.root, scaled by all scalers that were specified when the desvars and constraints were added. Args ---- indep_list : list of strings List of independent variable names that derivatives are to be calculated with respect to. All params must have a IndepVarComp. unknown_list : list of strings List of output or state names that derivatives are to be calculated for. All must be valid unknowns in OpenMDAO. mode : string, optional Deriviative direction, can be 'fwd', 'rev', 'fd', or 'auto'. Default is 'auto', which uses mode specified on the linear solver in root. return_format : string, optional Format for the derivatives, can be 'array' or 'dict'. sparsity : dict, optional Dictionary that gives the relevant design variables for each constraint. This option is only supported in the `dict` return format. Returns ------- ndarray or dict Jacobian of unknowns with respect to params. """ J = self._problem.calc_gradient(indep_list, unknown_list, mode=mode, return_format=return_format, dv_scale=self.dv_conversions, cn_scale=self.fn_conversions, sparsity=sparsity) self.recorders.record_derivatives(J, self.metadata) return J 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 firstTime = 1 for key, value in sorted(vars(self).items()): if type(value)==OptionsDictionary: if key == "supports": continue if firstTime: #start of Options docstring docstring += '\n Options\n -------\n' firstTime = 0 docstring += value._generate_docstring(key) #finish up docstring docstring += '\n \"\"\"\n' return docstring
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 print only failures, set to 1 to print iteration totals to" + \ "stdout, set to 2 to print the residual each iteration to stdout," + \ "or -1 to suppress all printing." self.options.add_option('iprint', 0, values=[-1, 0, 1, 2], desc=desc) self.options.add_option( 'err_on_maxiter', False, desc='If True, raise an AnalysisError if not converged at maxiter.' ) self.recorders = RecordingManager() 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 cleanup(self): """ Clean up resources prior to exit. """ self.recorders.close() def print_norm(self, solver_string, system, iteration, res, res0, msg=None, indent=0, solver='NL', u_norm=None): """ 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'). system: system Parent system, which contains pathname and the preconditioning flag. iteration: int Current iteration number res: float Norm of the absolute residual value. res0: float Norm of the 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.) u_norm: float, optional Norm of the u vector, if applicable. """ pathname = system.pathname if pathname == '': name = 'root' else: name = 'root.' + pathname # Find indentation level level = name.count('.') # No indentation for driver; top solver is no indentation. level = level + indent indent = ' ' * level if system._probdata.precon_level > 0: solver_string = 'PRECON:' + solver_string indent += ' ' * system._probdata.precon_level if msg is not None: form = indent + '[%s] %s: %s %d | %s' if u_norm: form += ' (%s)' % u_norm print(form % (name, solver, solver_string, iteration, msg)) return form = indent + '[%s] %s: %s %d | %.9g %.9g' if u_norm: form += ' (%s)' % u_norm print(form % (name, solver, solver_string, iteration, res, res / res0)) def print_all_convergence(self, level=2): """ Turns on iprint for this solver and all subsolvers. Override if your solver has subsolvers. Args ---- level : int(2) iprint level. Set to 2 to print residuals each iteration; set to 1 to print just the iteration totals. """ self.options['iprint'] = level def generate_docstring(self): """ Generates a numpy-style docstring for a user-created System class. Returns ------- docstring : str string that contains a basic numpy docstring. """ #start the docstring off docstrings = [' \"\"\"'] #Put options into docstring firstTime = 1 for key, value in sorted(vars(self).items()): if type(value) == OptionsDictionary: if firstTime: #start of Options docstring docstrings.extend(['', ' Options', ' -------']) firstTime = 0 docstrings.append(value._generate_docstring(key)) #finish up docstring docstrings.extend([' \"\"\"', '']) return '\n'.join(docstrings)
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, lower=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, lower=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] # suppress message from find_executable function, we'll handle it numpy.distutils.log.set_verbosity(-1) 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 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 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 ------- deriv_options['type'] : str('user') Derivative calculation type ('user', 'fd', 'cs') Default is 'user', where derivative is calculated from user-supplied derivatives. Set to 'fd' to finite difference this system. Set to 'cs' to perform the complex step if your components support it. deriv_options['form'] : str('forward') Finite difference mode. (forward, backward, central) deriv_options['step_size'] : float(1e-06) Default finite difference stepsize deriv_options['step_calc'] : str('absolute') Set to absolute, relative deriv_options['check_type'] : str('fd') Type of derivative check for check_partial_derivatives. Set to 'fd' to finite difference this system. Set to 'cs' to perform the complex step method if your components support it. deriv_options['check_form'] : str('forward') Finite difference mode: ("forward", "backward", "central") During check_partial_derivatives, the difference form that is used for the check. deriv_options['check_step_calc'] : str('absolute',) Set to 'absolute' or 'relative'. Default finite difference step calculation for the finite difference check in check_partial_derivatives. deriv_options['check_step_size'] : float(1e-06) Default finite difference stepsize for the finite difference check in check_partial_derivatives" deriv_options['linearize'] : bool(False) Set to True if you want linearize to be called even though you are using FD. options['command'] : list([]) Command to be executed. Command must be a list of command line args. options['env_vars'] : dict({}) Environment variables required by the command options['external_input_files'] : list([]) (optional) list of input file names to check the existence of before solve_nonlinear options['external_output_files'] : list([]) (optional) list of input file names to check the existence 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 in seconds to wait for command completion. A value of zero implies an infinite wait. If the timeout interval is exceeded, an AnalysisError will be raised. options['fail_hard'] : bool(True) Behavior on error returned from code, either raise a 'hard' error (RuntimeError) if True or a 'soft' error (AnalysisError) if False. """ 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, lower=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, lower=0.0, desc='Maximum time to wait for command completion. A value of zero implies an infinite wait') self.options.add_option( 'external_input_files', [], desc='(optional) list of input file names to check the existence of before solve_nonlinear') self.options.add_option( 'external_output_files', [], desc='(optional) list of input file names to check the existence of after solve_nonlinear') self.options.add_option('fail_hard', True, desc="If True, external code errors raise a 'hard' exception (RuntimeError). Otherwise raise a 'soft' exception (AnalysisError).") # 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.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 cmd = [c for c in self.options['command'] if c.strip()] if not cmd: out_stream.write( "The command cannot be empty") else: program_to_execute = self.options['command'][0] command_full_path = find_executable( program_to_execute ) if not command_full_path: out_stream.write("The command to be executed, '%s', " "cannot be found" % program_to_execute) # Check for missing input files missing = self._check_for_files(self.options['external_input_files']) if missing: out_stream.write("The following input files are missing at setup " " time: %s" % missing) def solve_nonlinear(self, params, unknowns, resids): """Runs the component """ self.return_code = -12345678 if not self.options['command']: raise ValueError('Empty command list') if self.options['fail_hard']: err_class = RuntimeError else: err_class = AnalysisError return_code = None try: missing = self._check_for_files(self.options['external_input_files']) if missing: raise err_class("The following input files are missing: %s" % sorted(missing)) return_code, error_msg = self._execute_local() if return_code is None: raise AnalysisError('Timed out after %s sec.' % self.options['timeout']) 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 err_class('return_code = %d%s' % (return_code, err_fragment)) missing = self._check_for_files(self.options['external_output_files']) if missing: raise err_class("The following output files are missing: %s" % sorted(missing)) finally: self.return_code = -999999 if return_code is None else return_code def _check_for_files(self, files): """ Check that specified files exist. """ return [path for path in files if not os.path.exists(path)] def _execute_local(self): """ Run command. """ # 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] # suppress message from find_executable function, we'll handle it numpy.distutils.log.set_verbosity(-1) 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']) try: return_code, error_msg = \ self._process.wait(self.options['poll_delay'], self.options['timeout']) finally: self._process.close_files() self._process = None return (return_code, error_msg)
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.options.add_option('err_on_maxiter', False, desc='If True, raise an AnalysisError if not converged at maxiter.') self.recorders = RecordingManager() 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 cleanup(self): """ Clean up resources prior to exit. """ self.recorders.close() def print_norm(self, solver_string, pathname, iteration, res, res0, msg=None, indent=0, solver='NL', u_norm=None): """ 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'). pathname: dict Parent system pathname. iteration: int Current iteration number res: float Norm of the absolute residual value. res0: float Norm of the 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.) u_norm: float, optional Norm of the u vector, if applicable. """ if pathname=='': name = 'root' else: name = 'root.' + pathname # Find indentation level level = pathname.count('.') # No indentation for driver; top solver is no indentation. level = level + indent indent = ' ' * level if msg is not None: form = indent + '[%s] %s: %s %d | %s' if u_norm: form += ' (%s)' % u_norm print(form % (name, solver, solver_string, iteration, msg)) return form = indent + '[%s] %s: %s %d | %.9g %.9g' if u_norm: form += ' (%s)' % u_norm print(form % (name, solver, solver_string, iteration, res, res/res0)) def print_all_convergence(self): """ Turns on iprint for this solver and all subsolvers. Override if your solver has subsolvers.""" self.options['iprint'] = 1 def generate_docstring(self): """ Generates a numpy-style docstring for a user-created System class. Returns ------- docstring : str string that contains a basic numpy docstring. """ #start the docstring off docstrings = [' \"\"\"'] #Put options into docstring firstTime = 1 for key, value in sorted(vars(self).items()): if type(value)==OptionsDictionary: if firstTime: #start of Options docstring docstrings.extend(['',' Options',' -------']) firstTime = 0 docstrings.append(value._generate_docstring(key)) #finish up docstring docstrings.extend([' \"\"\"','']) return '\n'.join(docstrings)
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 fd_options['extra_check_partials_form'] : None or str Finite difference mode: ("forward", "backward", "central", "complex_step") During check_partial_derivatives, you can optionally do a second finite difference with a different mode. fd_options['linearize'] : bool(False) Set to True if you want linearize to be called even though you are using FD. options['command'] : list([]) Command to be executed. Command must be a list of command line args. options['env_vars'] : dict({}) Environment variables required by the command options['external_input_files'] : list([]) (optional) list of input file names to check the existence of before solve_nonlinear options['external_output_files'] : list([]) (optional) list of input file names to check the existence 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 in seconds to wait for command completion. A value of zero implies an infinite wait. If the timeout interval is exceeded, an AnalysisError will be raised. options['fail_hard'] : bool(True) Behavior on error returned from code, either raise a 'hard' error (RuntimeError) if True or a 'soft' error (AnalysisError) if False. """ 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, lower=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, lower=0.0, desc= 'Maximum time to wait for command completion. A value of zero implies an infinite wait' ) self.options.add_option( 'external_input_files', [], desc= '(optional) list of input file names to check the existence of before solve_nonlinear' ) self.options.add_option( 'external_output_files', [], desc= '(optional) list of input file names to check the existence of after solve_nonlinear' ) self.options.add_option( 'fail_hard', True, desc= "If True, external code errors raise a 'hard' exception (RuntimeError). Otherwise raise a 'soft' exception (AnalysisError)." ) # 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.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 cmd = [c for c in self.options['command'] if c.strip()] if not cmd: out_stream.write("The command cannot be empty") else: program_to_execute = self.options['command'][0] command_full_path = find_executable(program_to_execute) if not command_full_path: out_stream.write("The command to be executed, '%s', " "cannot be found" % program_to_execute) # Check for missing input files missing = self._check_for_files(self.options['external_input_files']) if missing: out_stream.write("The following input files are missing at setup " " time: %s" % missing) def solve_nonlinear(self, params, unknowns, resids): """Runs the component """ self.return_code = -12345678 if not self.options['command']: raise ValueError('Empty command list') if self.options['fail_hard']: err_class = RuntimeError else: err_class = AnalysisError return_code = None try: missing = self._check_for_files( self.options['external_input_files']) if missing: raise err_class("The following input files are missing: %s" % sorted(missing)) return_code, error_msg = self._execute_local() if return_code is None: raise AnalysisError('Timed out after %s sec.' % self.options['timeout']) 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 err_class('return_code = %d%s' % (return_code, err_fragment)) missing = self._check_for_files( self.options['external_output_files']) if missing: raise err_class("The following output files are missing: %s" % sorted(missing)) finally: self.return_code = -999999 if return_code is None else return_code def _check_for_files(self, files): """ Check that specified files exist. """ return [path for path in files if not os.path.exists(path)] def _execute_local(self): """ Run command. """ # 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] # suppress message from find_executable function, we'll handle it numpy.distutils.log.set_verbosity(-1) 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']) try: return_code, error_msg = \ self._process.wait(self.options['poll_delay'], self.options['timeout']) finally: self._process.close_files() self._process = None return (return_code, error_msg)
class BaseRecorder(object): """ Base class for all case recorders. """ def __init__(self): self.options = OptionsDictionary() self.options.add_option('record_metadata', True) self.options.add_option('record_unknowns', True) self.options.add_option('record_params', False) self.options.add_option('record_resids', False) 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_iteration(self, params, unknowns, resids, metadata): """ Writes the provided data. Args ---- params : dict Dictionary containing parameters. (p) unknowns : dict Dictionary containing outputs and states. (u) resids : dict Dictionary containing residuals. (r) metadata : dict, optional Dictionary containing execution metadata (e.g. iteration coordinate). """ raise NotImplementedError() def record_metadata(self, group): """Writes the metadata of the given group Args ---- group : `System` `System` containing vectors """ raise NotImplementedError() 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 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 = RecordingManager() 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, pathname, 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'). pathname: dict Parent system pathname. 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.) """ if pathname=='': name = 'root' else: name = 'root.' + pathname # Find indentation level level = pathname.count('.') # No indentation for driver; top solver is no indentation. level = level + indent 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)) def print_all_convergence(self): """ Turns on iprint for this solver and all subsolvers. Override if your solver has subsolvers.""" self.options['iprint'] = 1 def generate_docstring(self): """ Generates a numpy-style docstring for a user-created System class. Returns ------- docstring : str string that contains a basic numpy docstring. """ #start the docstring off docstring = ' \"\"\"\n' #Put options into docstring firstTime = 1 #for py3.4, items from vars must come out in same order. from collections import OrderedDict v = OrderedDict(sorted(vars(self).items())) for key, value in v.items(): if type(value)==OptionsDictionary: 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