"""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
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.
"""
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
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
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
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
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
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
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
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
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']