Source code for openmdao.core.component

"""Define the Component class."""

from __future__ import division

from collections import OrderedDict, Counter, defaultdict
try:
    from collections.abc import Iterable
except ImportError:
    from collections import Iterable
from itertools import product
from six import string_types, iteritems, itervalues

import numpy as np
from numpy import ndarray, isscalar, atleast_1d, atleast_2d, promote_types
from scipy.sparse import issparse

from openmdao.core.system import System, _supported_methods, _DEFAULT_COLORING_META
from openmdao.approximation_schemes.complex_step import ComplexStep
from openmdao.approximation_schemes.finite_difference import FiniteDifference
from openmdao.jacobians.dictionary_jacobian import DictionaryJacobian
from openmdao.vectors.vector import INT_DTYPE
from openmdao.utils.units import valid_units
from openmdao.utils.name_maps import rel_key2abs_key, abs_key2rel_key, rel_name2abs_name
from openmdao.utils.mpi import MPI
from openmdao.utils.general_utils import format_as_float_or_array, ensure_compatible, \
    warn_deprecation, find_matches, simple_warning, make_set
import openmdao.utils.coloring as coloring_mod


# the following metadata will be accessible for vars on all procs
global_meta_names = {
    'input': ('units', 'shape', 'size', 'distributed', 'tags'),
    'output': ('units', 'shape', 'size',
               'ref', 'ref0', 'res_ref', 'distributed', 'lower', 'upper', 'tags'),
}

_full_slice = slice(None)
_forbidden_chars = ['.', '*', '?', '!', '[', ']']
_whitespace = set([' ', '\t', '\r', '\n'])


def _valid_var_name(name):
    """
    Determine if the proposed name is a valid variable name.

    Leading and trailing whitespace is illegal, and a specific list of characters
    are illegal anywhere in the string.

    Parameters
    ----------
    name : str
        Proposed name.

    Returns
    -------
    bool
        True if the proposed name is a valid variable name, else False.
    """
    global _forbidden_chars, _whitespace
    if not name:
        return False
    for char in _forbidden_chars:
        if char in name:
            return False
    return name[0] not in _whitespace and name[-1] not in _whitespace


[docs]class Component(System): """ Base Component class; not to be directly instantiated. Attributes ---------- _approx_schemes : OrderedDict A mapping of approximation types to the associated ApproximationScheme. _var_rel2meta : dict Dictionary mapping relative names to metadata. This is only needed while adding inputs and outputs. During setup, these are used to build the dictionaries of metadata. _static_var_rel2meta : dict Static version of above - stores data for variables added outside of setup. _var_rel_names : {'input': [str, ...], 'output': [str, ...]} List of relative names of owned variables existing on current proc. This is only needed while adding inputs and outputs. During setup, these are used to determine the list of absolute names. _static_var_rel_names : dict Static version of above - stores names of variables added outside of setup. _declared_partials : dict Cached storage of user-declared partials. _declared_partial_checks : list Cached storage of user-declared check partial options. """
[docs] def __init__(self, **kwargs): """ Initialize all attributes. Parameters ---------- **kwargs : dict of keyword arguments available here and in all descendants of this system. """ super(Component, self).__init__(**kwargs) self._var_rel_names = {'input': [], 'output': []} self._var_rel2meta = {} self._static_var_rel_names = {'input': [], 'output': []} self._static_var_rel2meta = {} self._declared_partials = defaultdict(dict) self._declared_partial_checks = []
def _declare_options(self): """ Declare options before kwargs are processed in the init method. """ super(Component, self)._declare_options() self.options.declare('distributed', types=bool, default=False, desc='True if the component has variables that are distributed ' 'across multiple processes.') @property def distributed(self): """ Provide 'distributed' property for backwards compatibility. Returns ------- bool reference to the 'distributed' option. """ warn_deprecation("The 'distributed' property provides backwards compatibility " "with OpenMDAO <= 2.4.0 ; use the 'distributed' option instead.") return self.options['distributed'] @distributed.setter def distributed(self, val): """ Provide for setting of the 'distributed' property for backwards compatibility. Parameters ---------- val : bool True if the component has variables that are distributed across multiple processes. """ warn_deprecation("The 'distributed' property provides backwards compatibility " "with OpenMDAO <= 2.4.0 ; use the 'distributed' option instead.") self.options['distributed'] = val
[docs] def setup(self): """ Declare inputs and outputs. Available attributes: name pathname comm options """ pass
def _setup_procs(self, pathname, comm, mode, prob_options): """ Execute first phase of the setup process. Distribute processors, assign pathnames, and call setup on the component. Parameters ---------- pathname : str Global name of the system, including the path. comm : MPI.Comm or <FakeComm> MPI communicator object. mode : string Derivatives calculation mode, 'fwd' for forward, and 'rev' for reverse (adjoint). Default is 'rev'. prob_options : OptionsDictionary Problem level options. """ self.pathname = pathname self._problem_options = prob_options self.options._parent_name = self.msginfo self.recording_options._parent_name = self.msginfo orig_comm = comm if self._num_par_fd > 1: if comm.size > 1: comm = self._setup_par_fd_procs(comm) elif not MPI: msg = ("%s: MPI is not active but num_par_fd = %d. No parallel finite difference " "will be performed." % (self.msginfo, self._num_par_fd)) simple_warning(msg) self.comm = comm self._mode = mode self._subsystems_proc_range = [] self._first_call_to_linearize = True # Clear out old variable information so that we can call setup on the component. self._var_rel_names = {'input': [], 'output': []} self._var_rel2meta = {} self._design_vars = OrderedDict() self._responses = OrderedDict() self._static_mode = False self._var_rel2meta.update(self._static_var_rel2meta) for type_ in ['input', 'output']: self._var_rel_names[type_].extend(self._static_var_rel_names[type_]) self._design_vars.update(self._static_design_vars) self._responses.update(self._static_responses) self.setup() # check to make sure that if num_par_fd > 1 that this system is actually doing FD. # Unfortunately we have to do this check after system setup has been called because that's # when declare_partials generally happens, so we raise an exception here instead of just # resetting the value of num_par_fd (because the comm has already been split and possibly # used by the system setup). if self._num_par_fd > 1 and orig_comm.size > 1 and not (self._owns_approx_jac or self._approx_schemes): raise RuntimeError("%s: num_par_fd is > 1 but no FD is active." % self.msginfo) # check here if declare_coloring was called during setup but declare_partials # wasn't. If declare partials wasn't called, call it with of='*' and wrt='*' so we'll # have something to color. if self._coloring_info['coloring'] is not None: for key, meta in iteritems(self._declared_partials): if 'method' in meta and meta['method'] is not None: break else: method = self._coloring_info['method'] simple_warning("%s: declare_coloring or use_fixed_coloring was called but no approx" " partials were declared. Declaring all partials as approximated " "using default metadata and method='%s'." % (self.msginfo, method)) self.declare_partials('*', '*', method=method) self._static_mode = True if self.options['distributed']: if self._distributed_vector_class is not None: self._vector_class = self._distributed_vector_class else: simple_warning("The 'distributed' option is set to True for Component %s, " "but there is no distributed vector implementation (MPI/PETSc) " "available. The default non-distributed vectors will be used." % pathname) self._vector_class = self._local_vector_class else: self._vector_class = self._local_vector_class def _setup_var_data(self, recurse=True): """ Compute the list of abs var names, abs/prom name maps, and metadata dictionaries. Parameters ---------- recurse : bool Whether to call this method in subsystems. """ global global_meta_names super(Component, self)._setup_var_data() allprocs_abs_names = self._var_allprocs_abs_names allprocs_abs_names_discrete = self._var_allprocs_abs_names_discrete allprocs_prom2abs_list = self._var_allprocs_prom2abs_list abs2prom = self._var_abs2prom allprocs_abs2meta = self._var_allprocs_abs2meta abs2meta = self._var_abs2meta # Compute the prefix for turning rel/prom names into abs names prefix = self.pathname + '.' if self.pathname else '' for type_ in ['input', 'output']: for prom_name in self._var_rel_names[type_]: abs_name = prefix + prom_name metadata = self._var_rel2meta[prom_name] # Compute allprocs_abs_names allprocs_abs_names[type_].append(abs_name) # Compute allprocs_prom2abs_list, abs2prom allprocs_prom2abs_list[type_][prom_name] = [abs_name] abs2prom[type_][abs_name] = prom_name # Compute allprocs_abs2meta allprocs_abs2meta[abs_name] = { meta_name: metadata[meta_name] for meta_name in global_meta_names[type_] } # Compute abs2meta abs2meta[abs_name] = metadata for prom_name, val in iteritems(self._var_discrete[type_]): abs_name = prefix + prom_name # Compute allprocs_abs_names_discrete allprocs_abs_names_discrete[type_].append(abs_name) # Compute allprocs_prom2abs_list, abs2prom allprocs_prom2abs_list[type_][prom_name] = [abs_name] abs2prom[type_][abs_name] = prom_name # Compute allprocs_discrete (metadata for discrete vars) self._var_allprocs_discrete[type_][abs_name] = val self._var_allprocs_abs2prom = abs2prom self._var_abs_names = allprocs_abs_names self._var_abs_names_discrete = allprocs_abs_names_discrete if self._var_discrete['input'] or self._var_discrete['output']: self._discrete_inputs = _DictValues(self._var_discrete['input']) self._discrete_outputs = _DictValues(self._var_discrete['output']) else: self._discrete_inputs = self._discrete_outputs = () def _setup_var_sizes(self, recurse=True): """ Compute the arrays of local variable sizes for all variables/procs on this system. Parameters ---------- recurse : bool Whether to call this method in subsystems. """ super(Component, self)._setup_var_sizes() iproc = self.comm.rank nproc = self.comm.size sizes = self._var_sizes abs2meta = self._var_abs2meta if self._use_derivatives: vec_names = self._lin_rel_vec_name_list else: vec_names = self._vec_names # Initialize empty arrays for vec_name in vec_names: # at component level, _var_allprocs_* is the same as var_* since all vars exist in all # procs for a given component, so we don't have to mess with figuring out what vars are # local. if self._use_derivatives: relnames = self._var_allprocs_relevant_names[vec_name] else: relnames = self._var_allprocs_abs_names sizes[vec_name] = {} for type_ in ('input', 'output'): sizes[vec_name][type_] = sz = np.zeros((nproc, len(relnames[type_])), int) # Compute _var_sizes for idx, abs_name in enumerate(relnames[type_]): sz[iproc, idx] = abs2meta[abs_name]['size'] if nproc > 1: for vec_name in vec_names: sizes = self._var_sizes[vec_name] if self.options['distributed']: for type_ in ['input', 'output']: sizes_in = sizes[type_][iproc, :].copy() self.comm.Allgather(sizes_in, sizes[type_]) else: # if component isn't distributed, we don't need to allgather sizes since # they'll all be the same. for type_ in ['input', 'output']: sizes[type_] = np.tile(sizes[type_][iproc], (nproc, 1)) if self._use_derivatives: self._var_sizes['nonlinear'] = self._var_sizes['linear'] # for a component, all vars are 'owned' self._owned_sizes = self._var_sizes['nonlinear']['output'] self._setup_global_shapes() def _setup_partials(self, recurse=True): """ Process all partials and approximations that the user declared. Parameters ---------- recurse : bool Whether to call this method in subsystems. """ self._subjacs_info = {} self._jacobian = DictionaryJacobian(system=self) for key, dct in iteritems(self._declared_partials): of, wrt = key self._declare_partials(of, wrt, dct) def _update_wrt_matches(self, info): """ Determine the list of wrt variables that match the wildcard(s) given in declare_coloring. Parameters ---------- info : dict Coloring metadata dict. """ ofs, allwrt = self._get_partials_varlists() wrt_patterns = info['wrt_patterns'] matches_prom = set() for w in wrt_patterns: matches_prom.update(find_matches(w, allwrt)) # error if nothing matched if not matches_prom: raise ValueError("{}: Invalid 'wrt' variable(s) specified for colored approx partial " "options: {}.".format(self.msginfo, wrt_patterns)) info['wrt_matches_prom'] = matches_prom info['wrt_matches'] = [rel_name2abs_name(self, n) for n in matches_prom] def _update_subjac_sparsity(self, sparsity): """ Update subjac sparsity info based on the given coloring. The sparsity of the partial derivatives in this component will be used when computing the sparsity of the total jacobian for the entire model. Without this, all of this component's partials would be treated as dense, resulting in an overly conservative coloring of the total jacobian. Parameters ---------- sparsity : dict A nested dict of the form dct[of][wrt] = (rows, cols, shape) """ # sparsity uses relative names, so we need to convert to absolute pathname = self.pathname for of, sub in iteritems(sparsity): of_abs = '.'.join((pathname, of)) if pathname else of for wrt, tup in iteritems(sub): wrt_abs = '.'.join((pathname, wrt)) if pathname else wrt abs_key = (of_abs, wrt_abs) if abs_key in self._subjacs_info: # add sparsity info to existing partial info self._subjacs_info[abs_key]['sparsity'] = tup
[docs] def add_input(self, name, val=1.0, shape=None, src_indices=None, flat_src_indices=None, units=None, desc='', tags=None): """ Add an input variable to the component. Parameters ---------- name : str name of the variable in this component's namespace. val : float or list or tuple or ndarray or Iterable The initial value of the variable being added in user-defined units. Default is 1.0. shape : int or tuple or list or None Shape of this variable, only required if src_indices not provided and val is not an array. Default is None. src_indices : int or list of ints or tuple of ints or int ndarray or Iterable or None The global indices of the source variable to transfer data from. A value of None implies this input depends on all entries of source. Default is None. The shapes of the target and src_indices must match, and form of the entries within is determined by the value of 'flat_src_indices'. flat_src_indices : bool If True, each entry of src_indices is assumed to be an index into the flattened source. Otherwise each entry must be a tuple or list of size equal to the number of dimensions of the source. units : str or None Units in which this input variable will be provided to the component during execution. Default is None, which means it is unitless. desc : str description of the variable tags : str or list of strs User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs. Returns ------- dict metadata for added variable """ if units == 'unitless': warn_deprecation("Input '%s' has units='unitless' but 'unitless' " "has been deprecated. Use " "units=None instead. Note that connecting a " "unitless variable to one with units is no longer " "an error, but will issue a warning instead." % name) units = None # First, type check all arguments if not isinstance(name, str): raise TypeError('%s: The name argument should be a string.' % self.msginfo) if not _valid_var_name(name): raise NameError("%s: '%s' is not a valid input name." % (self.msginfo, name)) if not isscalar(val) and not isinstance(val, (list, tuple, ndarray, Iterable)): raise TypeError('%s: The val argument should be a float, list, tuple, ndarray or ' 'Iterable' % self.msginfo) if shape is not None and not isinstance(shape, (int, tuple, list, np.integer)): raise TypeError("%s: The shape argument should be an int, tuple, or list but " "a '%s' was given" % (self.msginfo, type(shape))) if src_indices is not None and not isinstance(src_indices, (int, list, tuple, ndarray, Iterable)): raise TypeError('%s: The src_indices argument should be an int, list, ' 'tuple, ndarray or Iterable' % self.msginfo) if units is not None and not isinstance(units, str): raise TypeError('%s: The units argument should be a str or None' % self.msginfo) # Check that units are valid if units is not None and not valid_units(units): raise ValueError("%s: The units '%s' are invalid" % (self.msginfo, units)) if tags is not None and not isinstance(tags, (str, list)): raise TypeError('The tags argument should be a str or list') metadata = {} # value, shape: based on args, making sure they are compatible metadata['value'], metadata['shape'], src_indices = ensure_compatible(name, val, shape, src_indices) metadata['size'] = np.prod(metadata['shape']) # src_indices: None or ndarray if src_indices is None: metadata['src_indices'] = None else: metadata['src_indices'] = np.asarray(src_indices, dtype=INT_DTYPE) metadata['flat_src_indices'] = flat_src_indices metadata['units'] = units metadata['desc'] = desc metadata['distributed'] = self.options['distributed'] metadata['tags'] = make_set(tags) # We may not know the pathname yet, so we have to use name for now, instead of abs_name. if self._static_mode: var_rel2meta = self._static_var_rel2meta var_rel_names = self._static_var_rel_names else: var_rel2meta = self._var_rel2meta var_rel_names = self._var_rel_names # Disallow dupes if name in var_rel2meta: raise ValueError("{}: Variable name '{}' already exists.".format(self.msginfo, name)) var_rel2meta[name] = metadata var_rel_names['input'].append(name) return metadata
[docs] def add_discrete_input(self, name, val, desc='', tags=None): """ Add a discrete input variable to the component. Parameters ---------- name : str name of the variable in this component's namespace. val : a picklable object The initial value of the variable being added. desc : str description of the variable tags : str or list of strs User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs. Returns ------- dict metadata for added variable """ # First, type check all arguments if not isinstance(name, str): raise TypeError('%s: The name argument should be a string.' % self.msginfo) if not _valid_var_name(name): raise NameError("%s: '%s' is not a valid input name." % (self.msginfo, name)) if tags is not None and not isinstance(tags, (str, list)): raise TypeError('%s: The tags argument should be a str or list' % self.msginfo) metadata = { 'value': val, 'type': type(val), 'desc': desc, 'tags': make_set(tags), } if self._static_mode: var_rel2meta = self._static_var_rel2meta else: var_rel2meta = self._var_rel2meta # Disallow dupes if name in var_rel2meta: raise ValueError("{}: Variable name '{}' already exists.".format(self.msginfo, name)) var_rel2meta[name] = self._var_discrete['input'][name] = metadata return metadata
[docs] def add_output(self, name, val=1.0, shape=None, units=None, res_units=None, desc='', lower=None, upper=None, ref=1.0, ref0=0.0, res_ref=1.0, tags=None): """ Add an output variable to the component. Parameters ---------- name : str name of the variable in this component's namespace. val : float or list or tuple or ndarray The initial value of the variable being added in user-defined units. Default is 1.0. shape : int or tuple or list or None Shape of this variable, only required if val is not an array. Default is None. units : str or None Units in which the output variables will be provided to the component during execution. Default is None, which means it has no units. res_units : str or None Units in which the residuals of this output will be given to the user when requested. Default is None, which means it has no units. desc : str description of the variable. lower : float or list or tuple or ndarray or Iterable or None lower bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no lower bound. Default is None. upper : float or list or tuple or ndarray or or Iterable None upper bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no upper bound. Default is None. ref : float or ndarray Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 1. Default is 1. ref0 : float or ndarray Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 0. Default is 0. res_ref : float or ndarray Scaling parameter. The value in the user-defined res_units of this output's residual when the scaled value is 1. Default is 1. tags : str or list of strs or set of strs User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs. Returns ------- dict metadata for added variable """ if units == 'unitless': warn_deprecation("Output '%s' has units='unitless' but 'unitless' " "has been deprecated. Use " "units=None instead. Note that connecting a " "unitless variable to one with units is no longer " "an error, but will issue a warning instead." % name) units = None if not isinstance(name, str): raise TypeError('%s: The name argument should be a string.' % self.msginfo) if not _valid_var_name(name): raise NameError("%s: '%s' is not a valid output name." % (self.msginfo, name)) if not isscalar(val) and not isinstance(val, (list, tuple, ndarray, Iterable)): msg = '%s: The val argument should be a float, list, tuple, ndarray or Iterable' raise TypeError(msg % self.msginfo) if not isscalar(ref) and not isinstance(val, (list, tuple, ndarray, Iterable)): msg = '%s: The ref argument should be a float, list, tuple, ndarray or Iterable' raise TypeError(msg % self.msginfo) if not isscalar(ref0) and not isinstance(val, (list, tuple, ndarray, Iterable)): msg = '%s: The ref0 argument should be a float, list, tuple, ndarray or Iterable' raise TypeError(msg % self.msginfo) if not isscalar(res_ref) and not isinstance(val, (list, tuple, ndarray, Iterable)): msg = '%s: The res_ref argument should be a float, list, tuple, ndarray or Iterable' raise TypeError(msg % self.msginfo) if shape is not None and not isinstance(shape, (int, tuple, list, np.integer)): raise TypeError("%s: The shape argument should be an int, tuple, or list but " "a '%s' was given" % (self.msginfo, type(shape))) if units is not None and not isinstance(units, str): raise TypeError('%s: The units argument should be a str or None' % self.msginfo) if res_units is not None and not isinstance(res_units, str): raise TypeError('%s: The res_units argument should be a str or None' % self.msginfo) # Check that units are valid if units is not None and not valid_units(units): raise ValueError("%s: The units '%s' are invalid" % (self.msginfo, units)) if tags is not None and not isinstance(tags, (str, set, list)): raise TypeError('The tags argument should be a str, set, or list') metadata = {} # value, shape: based on args, making sure they are compatible metadata['value'], metadata['shape'], _ = ensure_compatible(name, val, shape) metadata['size'] = np.prod(metadata['shape']) # units, res_units: taken as is metadata['units'] = units metadata['res_units'] = res_units # desc: taken as is metadata['desc'] = desc if lower is not None: lower = ensure_compatible(name, lower, metadata['shape'])[0] self._has_bounds = True if upper is not None: upper = ensure_compatible(name, upper, metadata['shape'])[0] self._has_bounds = True metadata['lower'] = lower metadata['upper'] = upper # All refs: check the shape if necessary for item, item_name in zip([ref, ref0, res_ref], ['ref', 'ref0', 'res_ref']): if not isscalar(item): it = atleast_1d(item) if it.shape != metadata['shape']: raise ValueError("{}: When adding output '{}', expected shape {} but got " "shape {} for argument '{}'.".format(self.msginfo, name, metadata['shape'], it.shape, item_name)) if isscalar(ref): self._has_output_scaling |= ref != 1.0 else: self._has_output_scaling |= np.any(ref != 1.0) if isscalar(ref0): self._has_output_scaling |= ref0 != 0.0 else: self._has_output_scaling |= np.any(ref0) if isscalar(res_ref): self._has_resid_scaling |= res_ref != 1.0 else: self._has_resid_scaling |= np.any(res_ref != 1.0) ref = format_as_float_or_array('ref', ref, flatten=True) ref0 = format_as_float_or_array('ref0', ref0, flatten=True) res_ref = format_as_float_or_array('res_ref', res_ref, flatten=True) metadata['ref'] = ref metadata['ref0'] = ref0 metadata['res_ref'] = res_ref metadata['distributed'] = self.options['distributed'] metadata['tags'] = make_set(tags) # We may not know the pathname yet, so we have to use name for now, instead of abs_name. if self._static_mode: var_rel2meta = self._static_var_rel2meta var_rel_names = self._static_var_rel_names else: var_rel2meta = self._var_rel2meta var_rel_names = self._var_rel_names # Disallow dupes if name in var_rel2meta: raise ValueError("{}: Variable name '{}' already exists.".format(self.msginfo, name)) var_rel2meta[name] = metadata var_rel_names['output'].append(name) return metadata
[docs] def add_discrete_output(self, name, val, desc='', tags=None): """ Add an output variable to the component. Parameters ---------- name : str name of the variable in this component's namespace. val : a picklable object The initial value of the variable being added. desc : str description of the variable. tags : str or list of strs or set of strs User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs. Returns ------- dict metadata for added variable """ if not isinstance(name, str): raise TypeError('%s: The name argument should be a string.' % self.msginfo) if not _valid_var_name(name): raise NameError("%s: '%s' is not a valid output name." % (self.msginfo, name)) if tags is not None and not isinstance(tags, (str, set, list)): raise TypeError('%s: The tags argument should be a str, set, or list' % self.msginfo) metadata = { 'value': val, 'type': type(val), 'desc': desc, 'tags': make_set(tags) } if self._static_mode: var_rel2meta = self._static_var_rel2meta else: var_rel2meta = self._var_rel2meta # Disallow dupes if name in var_rel2meta: raise ValueError("{}: Variable name '{}' already exists.".format(self.msginfo, name)) var_rel2meta[name] = self._var_discrete['output'][name] = metadata return metadata
def _approx_partials(self, of, wrt, method='fd', **kwargs): """ Inform the framework that the specified derivatives are to be approximated. Parameters ---------- of : str or list of str The name of the residual(s) that derivatives are being computed for. May also contain a glob pattern. wrt : str or list of str The name of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern. method : str The type of approximation that should be used. Valid options include: - 'fd': Finite Difference **kwargs : dict Keyword arguments for controlling the behavior of the approximation. """ pattern_matches = self._find_partial_matches(of, wrt) self._has_approx = True for of_bundle, wrt_bundle in product(*pattern_matches): of_pattern, of_matches = of_bundle wrt_pattern, wrt_matches = wrt_bundle if not of_matches: raise ValueError('{}: No matches were found for of="{}"'.format(self.msginfo, of_pattern)) if not wrt_matches: raise ValueError('{}: No matches were found for wrt="{}"'.format(self.msginfo, wrt_pattern)) info = self._subjacs_info for rel_key in product(of_matches, wrt_matches): abs_key = rel_key2abs_key(self, rel_key) meta = info[abs_key] meta['method'] = method meta.update(kwargs) info[abs_key] = meta
[docs] def declare_partials(self, of, wrt, dependent=True, rows=None, cols=None, val=None, method='exact', step=None, form=None, step_calc=None): """ Declare information about this component's subjacobians. Parameters ---------- of : str or list of str The name of the residual(s) that derivatives are being computed for. May also contain a glob pattern. wrt : str or list of str The name of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern. dependent : bool(True) If False, specifies no dependence between the output(s) and the input(s). This is only necessary in the case of a sparse global jacobian, because if 'dependent=False' is not specified and declare_partials is not called for a given pair, then a dense matrix of zeros will be allocated in the sparse global jacobian for that pair. In the case of a dense global jacobian it doesn't matter because the space for a dense subjac will always be allocated for every pair. rows : ndarray of int or None Row indices for each nonzero entry. For sparse subjacobians only. cols : ndarray of int or None Column indices for each nonzero entry. For sparse subjacobians only. val : float or ndarray of float or scipy.sparse Value of subjacobian. If rows and cols are not None, this will contain the values found at each (row, col) location in the subjac. method : str The type of approximation that should be used. Valid options include: 'fd': Finite Difference, 'cs': Complex Step, 'exact': use the component defined analytic derivatives. Default is 'exact'. step : float Step size for approximation. Defaults to None, in which case the approximation method provides its default value. form : string Form for finite difference, can be 'forward', 'backward', or 'central'. Defaults to None, in which case the approximation method provides its default value. step_calc : string Step type for finite difference, can be 'abs' for absolute', or 'rel' for relative. Defaults to None, in which case the approximation method provides its default value. Returns ------- dict Metadata dict for the specified partial(s). """ try: method_func = _supported_methods[method] except KeyError: msg = '{}: d({})/d({}): method "{}" is not supported, method must be one of {}' raise ValueError(msg.format(self.msginfo, of, wrt, method, sorted(_supported_methods))) if isinstance(of, list): of = tuple(of) if isinstance(wrt, list): wrt = tuple(wrt) meta = self._declared_partials[of, wrt] meta['dependent'] = dependent # If only one of rows/cols is specified if (rows is None) ^ (cols is None): raise ValueError('{}: d({})/d({}): If one of rows/cols is specified, then ' 'both must be specified.'.format(self.msginfo, of, wrt)) if dependent: meta['value'] = val if rows is not None: meta['rows'] = rows meta['cols'] = cols # First, check the length of rows and cols to catch this easy mistake and give a # clear message. if len(cols) != len(rows): raise RuntimeError("{}: d({})/d({}): declare_partials has been called " "with rows and cols, which should be arrays of equal length," " but rows is length {} while cols is length " "{}.".format(self.msginfo, of, wrt, len(rows), len(cols))) # Check for repeated rows/cols indices. idxset = set(zip(rows, cols)) if len(rows) - len(idxset) > 0: dups = [n for n, val in iteritems(Counter(zip(rows, cols))) if val > 1] raise RuntimeError("{}: d({})/d({}): declare_partials has been called " "with rows and cols that specify the following duplicate " "subjacobian entries: {}.".format(self.msginfo, of, wrt, sorted(dups))) if method_func is not None: # we're doing approximations self._has_approx = True meta['method'] = method self._get_approx_scheme(method) default_opts = method_func.DEFAULT_OPTIONS # If rows/cols is specified if rows is not None or cols is not None: raise ValueError("{}: d({})/d({}): Sparse FD specification not supported " "yet.".format(self.msginfo, of, wrt)) else: default_opts = () if step: if 'step' in default_opts: meta['step'] = step else: raise RuntimeError("{}: d({})/d({}): 'step' is not a valid option for " "'{}'".format(self.msginfo, of, wrt, method)) if form: if 'form' in default_opts: meta['form'] = form else: raise RuntimeError("{}: d({})/d({}): 'form' is not a valid option for " "'{}'".format(self.msginfo, of, wrt, method)) if step_calc: if 'step_calc' in default_opts: meta['step_calc'] = step_calc else: raise RuntimeError("{}: d({})/d({}): 'step_calc' is not a valid option " "for '{}'".format(self.msginfo, of, wrt, method)) return meta
[docs] def declare_coloring(self, wrt=_DEFAULT_COLORING_META['wrt_patterns'], method=_DEFAULT_COLORING_META['method'], form=None, step=None, per_instance=_DEFAULT_COLORING_META['per_instance'], num_full_jacs=_DEFAULT_COLORING_META['num_full_jacs'], tol=_DEFAULT_COLORING_META['tol'], orders=_DEFAULT_COLORING_META['orders'], perturb_size=_DEFAULT_COLORING_META['perturb_size'], min_improve_pct=_DEFAULT_COLORING_META['min_improve_pct'], show_summary=_DEFAULT_COLORING_META['show_summary'], show_sparsity=_DEFAULT_COLORING_META['show_sparsity']): """ Set options for deriv coloring of a set of wrt vars matching the given pattern(s). Parameters ---------- wrt : str or list of str The name or names of the variables that derivatives are taken with respect to. This can contain input names, output names, or glob patterns. method : str Method used to compute derivative: "fd" for finite difference, "cs" for complex step. form : str Finite difference form, can be "forward", "central", or "backward". Leave undeclared to keep unchanged from previous or default value. step : float Step size for finite difference. Leave undeclared to keep unchanged from previous or default value. per_instance : bool If True, a separate coloring will be generated for each instance of a given class. Otherwise, only one coloring for a given class will be generated and all instances of that class will use it. num_full_jacs : int Number of times to repeat partial jacobian computation when computing sparsity. tol : float Tolerance used to determine if an array entry is nonzero during sparsity determination. orders : int Number of orders above and below the tolerance to check during the tolerance sweep. perturb_size : float Size of input/output perturbation during generation of sparsity. min_improve_pct : float If coloring does not improve (decrease) the number of solves more than the given percentage, coloring will not be used. show_summary : bool If True, display summary information after generating coloring. show_sparsity : bool If True, display sparsity with coloring info after generating coloring. """ super(Component, self).declare_coloring(wrt, method, form, step, per_instance, num_full_jacs, tol, orders, perturb_size, min_improve_pct, show_summary, show_sparsity) # create approx partials for all matches meta = self.declare_partials('*', wrt, method=method, step=step, form=form) meta['coloring'] = True
[docs] def set_check_partial_options(self, wrt, method='fd', form=None, step=None, step_calc=None, directional=False): """ Set options that will be used for checking partial derivatives. Parameters ---------- wrt : str or list of str The name or names of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern. method : str Method for check: "fd" for finite difference, "cs" for complex step. form : str Finite difference form for check, can be "forward", "central", or "backward". Leave undeclared to keep unchanged from previous or default value. step : float Step size for finite difference check. Leave undeclared to keep unchanged from previous or default value. step_calc : str Type of step calculation for check, can be "abs" for absolute (default) or "rel" for relative. Leave undeclared to keep unchanged from previous or default value. directional : bool Set to True to perform a single directional derivative for each vector variable in the pattern named in wrt. """ supported_methods = ('fd', 'cs') if method not in supported_methods: msg = "{}: Method '{}' is not supported, method must be one of {}" raise ValueError(msg.format(self.msginfo, method, supported_methods)) if step and not isinstance(step, (int, float)): msg = "{}: The value of 'step' must be numeric, but '{}' was specified." raise ValueError(msg.format(self.msginfo, step)) supported_step_calc = ('abs', 'rel') if step_calc and step_calc not in supported_step_calc: msg = "{}: The value of 'step_calc' must be one of {}, but '{}' was specified." raise ValueError(msg.format(self.msginfo, supported_step_calc, step_calc)) if not isinstance(wrt, (string_types, list, tuple)): msg = "{}: The value of 'wrt' must be a string or list of strings, but a type " \ "of '{}' was provided." raise ValueError(msg.format(self.msginfo, type(wrt).__name__)) if not isinstance(directional, bool): msg = "{}: The value of 'directional' must be True or False, but a type " \ "of '{}' was provided." raise ValueError(msg.format(self.msginfo, type(directional).__name__)) wrt_list = [wrt] if isinstance(wrt, string_types) else wrt self._declared_partial_checks.append((wrt_list, method, form, step, step_calc, directional))
def _get_check_partial_options(self): """ Return dictionary of partial options with pattern matches processed. This is called by check_partials. Returns ------- dict(wrt : (options)) Dictionary keyed by name with tuples of options (method, form, step, step_calc) """ opts = {} of, wrt = self._get_potential_partials_lists() invalid_wrt = [] for wrt_list, method, form, step, step_calc, directional in self._declared_partial_checks: for pattern in wrt_list: matches = find_matches(pattern, wrt) # if a non-wildcard var name was specified and not found, save for later Exception if len(matches) == 0 and _valid_var_name(pattern): invalid_wrt.append(pattern) for match in matches: if match in opts: opt = opts[match] # New assignments take precedence keynames = ['method', 'form', 'step', 'step_calc', 'directional'] for name, value in zip(keynames, [method, form, step, step_calc, directional]): if value is not None: opt[name] = value else: opts[match] = {'method': method, 'form': form, 'step': step, 'step_calc': step_calc, 'directional': directional} if invalid_wrt: msg = "{}: Invalid 'wrt' variables specified for check_partial options: {}." raise ValueError(msg.format(self.msginfo, invalid_wrt)) return opts def _declare_partials(self, of, wrt, dct): """ Store subjacobian metadata for later use. Parameters ---------- of : tuple of str The names of the residuals that derivatives are being computed for. May also contain glob patterns. wrt : tuple of str The names of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain glob patterns. dct : dict Metadata dict specifying shape, and/or approx properties. """ val = dct['value'] if 'value' in dct else None is_scalar = isscalar(val) dependent = dct['dependent'] if dependent: if 'rows' in dct and dct['rows'] is not None: # sparse list format rows = dct['rows'] cols = dct['cols'] rows = np.array(rows, dtype=INT_DTYPE, copy=False) cols = np.array(cols, dtype=INT_DTYPE, copy=False) if rows.shape != cols.shape: raise ValueError('{}: d({})/d({}): rows and cols must have the same shape,' ' rows: {}, cols: {}'.format(self.msginfo, of, wrt, rows.shape, cols.shape)) if is_scalar: val = np.full(rows.size, val, dtype=float) is_scalar = False elif val is not None: # np.promote_types will choose the smallest dtype that can contain # both arguments val = atleast_1d(val) safe_dtype = promote_types(val.dtype, float) val = val.astype(safe_dtype, copy=False) if rows.shape != val.shape: raise ValueError('{}: d({})/d({}): If rows and cols are specified, val ' 'must be a scalar or have the same shape, val: {}, ' 'rows/cols: {}'.format(self.msginfo, of, wrt, val.shape, rows.shape)) else: val = np.zeros_like(rows, dtype=float) if rows.size > 0: if rows.min() < 0: msg = '{}: d({})/d({}): row indices must be non-negative' raise ValueError(msg.format(self.msginfo, of, wrt)) if cols.min() < 0: msg = '{}: d({})/d({}): col indices must be non-negative' raise ValueError(msg.format(self.msginfo, of, wrt)) rows_max = rows.max() cols_max = cols.max() else: rows_max = cols_max = 0 else: if val is not None and not is_scalar and not issparse(val): val = atleast_2d(val) val = val.astype(promote_types(val.dtype, float), copy=False) rows_max = cols_max = 0 rows = None cols = None pattern_matches = self._find_partial_matches(of, wrt) abs2meta = self._var_abs2meta is_array = isinstance(val, ndarray) patmeta = dict(dct) patmeta_not_none = {k: v for k, v in dct.items() if v is not None} for of_bundle, wrt_bundle in product(*pattern_matches): of_pattern, of_matches = of_bundle wrt_pattern, wrt_matches = wrt_bundle if not of_matches: raise ValueError('{}: No matches were found for of="{}"'.format(self.msginfo, of_pattern)) if not wrt_matches: raise ValueError('{}: No matches were found for wrt="{}"'.format(self.msginfo, wrt_pattern)) for rel_key in product(of_matches, wrt_matches): abs_key = rel_key2abs_key(self, rel_key) if not dependent: if abs_key in self._subjacs_info: del self._subjacs_info[abs_key] continue if abs_key in self._subjacs_info: meta = self._subjacs_info[abs_key] meta.update(patmeta_not_none) else: meta = patmeta.copy() meta['rows'] = rows meta['cols'] = cols meta['shape'] = shape = (abs2meta[abs_key[0]]['size'], abs2meta[abs_key[1]]['size']) if val is None: # we can only get here if rows is None (we're not sparse list format) meta['value'] = np.zeros(shape) elif is_array: if rows is None and val.shape != shape and val.size == shape[0] * shape[1]: meta['value'] = val = val.copy().reshape(shape) else: meta['value'] = val.copy() elif is_scalar: meta['value'] = np.full(shape, val, dtype=float) else: meta['value'] = val if rows_max >= shape[0] or cols_max >= shape[1]: of, wrt = rel_key msg = '{}: d({})/d({}): Expected {}x{} but declared at least {}x{}' raise ValueError(msg.format(self.msginfo, of, wrt, shape[0], shape[1], rows_max + 1, cols_max + 1)) self._check_partials_meta(abs_key, meta['value'], shape if rows is None else (rows.shape[0], 1)) self._subjacs_info[abs_key] = meta def _find_partial_matches(self, of, wrt): """ Find all partial derivative matches from of and wrt. Parameters ---------- of : str or list of str The relative name of the residual(s) that derivatives are being computed for. May also contain a glob pattern. wrt : str or list of str The relative name of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern. Returns ------- tuple(list, list) Pair of lists containing pattern matches (if any). Returns (of_matches, wrt_matches) where of_matches is a list of tuples (pattern, matches) and wrt_matches is a list of tuples (pattern, output_matches, input_matches). """ of_list = [of] if isinstance(of, string_types) else of wrt_list = [wrt] if isinstance(wrt, string_types) else wrt of, wrt = self._get_potential_partials_lists() of_pattern_matches = [(pattern, find_matches(pattern, of)) for pattern in of_list] wrt_pattern_matches = [(pattern, find_matches(pattern, wrt)) for pattern in wrt_list] return of_pattern_matches, wrt_pattern_matches def _check_partials_meta(self, abs_key, val, shape): """ Check a given partial derivative and metadata for the correct shapes. Parameters ---------- abs_key : tuple(str, str) The of/wrt pair (given absolute names) defining the partial derivative. val : ndarray Subjac value. shape : tuple Expected shape of val. """ out_size, in_size = shape if in_size == 0 and self.comm.rank != 0: # 'inactive' component return if val is not None: val_shape = val.shape if len(val_shape) == 1: val_out, val_in = val_shape[0], 1 else: val_out, val_in = val.shape if val_out > out_size or val_in > in_size: of, wrt = abs_key2rel_key(self, abs_key) msg = '{}: d({})/d({}): Expected {}x{} but val is {}x{}' raise ValueError(msg.format(self.msginfo, of, wrt, out_size, in_size, val_out, val_in)) def _set_approx_partials_meta(self): """ Add approximations for those partials registered with method=fd or method=cs. """ self._get_static_wrt_matches() subjacs = self._subjacs_info for key in self._approx_subjac_keys_iter(): meta = subjacs[key] self._approx_schemes[meta['method']].add_approximation(key, self, meta) def _guess_nonlinear(self): """ Provide initial guess for states. Does nothing on any non-implicit component. """ pass def _clear_iprint(self): """ Clear out the iprint stack from the solvers. Components don't have nested solvers, so do nothing to prevent errors. """ pass def _check_first_linearize(self): if self._first_call_to_linearize: self._first_call_to_linearize = False # only do this once if coloring_mod._use_partial_sparsity: coloring = self._get_coloring() if coloring is not None: if not self._coloring_info['dynamic']: coloring._check_config_partial(self) self._update_subjac_sparsity(coloring.get_subjac_sparsity())
class _DictValues(object): """ A dict-like wrapper for a dict of metadata, where getitem returns 'value' from metadata. """ def __init__(self, dct): self._dict = dct def __getitem__(self, key): return self._dict[key]['value'] def __setitem__(self, key, value): self._dict[key]['value'] = value def __contains__(self, key): return key in self._dict def __len__(self): return len(self._dict) def items(self): return [(key, self._dict[key]['value']) for key in self._dict] def iteritems(self): for key, val in self._dict.iteritems(): yield key, val['value']