def __setitem__(self, name, value):
"""
Set the unscaled variable value in true units.
Parameters
----------
name : str
Promoted or relative variable name in the owning system's namespace.
value : float or list or tuple or ndarray
variable value to set (not scaled, not dimensionless)
"""
abs_name = name2abs_name(self._system, name, self._names, self._typ)
if abs_name is not None:
if self._icol is None:
slc = _full_slice
else:
slc = (_full_slice, self._icol)
value, _ = ensure_compatible(name, value,
self._views[abs_name][slc].shape)
if self._vector_info._under_complex_step:
# setitem overwrites anything you may have done with numpy indexing
try:
del self._complex_view_cache[abs_name]
except KeyError:
pass
self._views[abs_name][slc] = value.real
self._imag_views[abs_name][slc] = value.imag
else:
self._views[abs_name][slc] = value
else:
msg = 'Variable name "{}" not found.'
raise KeyError(msg.format(name))
def add_output(self, name, val=1.0, units=None):
"""
Add an independent variable to this component.
This should never be called by a user, as it skips all checks.
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.
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.
Returns
-------
dict
Metadata for added variable.
"""
# Add the output quickly.
# We don't need to check for errors because we get the value straight from a
# source, and ivc metadata is minimal.
value, shape = ensure_compatible(name, val, None)
metadata = {
'val': value,
'shape': shape,
'size': shape_to_len(shape),
'units': units,
'res_units': None,
'desc': '',
'distributed': False,
'tags': set(),
'ref': 1.0,
'ref0': 0.0,
'res_ref': 1.0,
'lower': None,
'upper': None,
'shape_by_conn': False,
'copy_shape': None
}
self._static_var_rel2meta[name] = metadata
self._static_var_rel_names['output'].append(name)
self._var_added(name)
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, var_set=0):
"""
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.
var_set : hashable object
For advanced users only. ID or color for this variable, relevant for reconfigurability.
Default is 0.
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
# First, type check all arguments
if not isinstance(name, str):
raise TypeError('The name argument should be a string')
if not np.isscalar(val) and not isinstance(val, (list, tuple, np.ndarray, Iterable)):
raise TypeError('The val argument should be a float, list, tuple, or ndarray')
if not np.isscalar(ref) and not isinstance(val, (list, tuple, np.ndarray, Iterable)):
raise TypeError('The ref argument should be a float, list, tuple, or ndarray')
if not np.isscalar(ref0) and not isinstance(val, (list, tuple, np.ndarray, Iterable)):
raise TypeError('The ref0 argument should be a float, list, tuple, or ndarray')
if not np.isscalar(res_ref) and not isinstance(val, (list, tuple, np.ndarray, Iterable)):
raise TypeError('The res_ref argument should be a float, list, tuple, or ndarray')
if shape is not None and not isinstance(shape, (int, tuple, list, np.integer)):
raise TypeError("The shape argument should be an int, tuple, or list but "
"a '%s' was given" % type(shape))
if units is not None and not isinstance(units, str):
raise TypeError('The units argument should be a str or None')
if res_units is not None and not isinstance(res_units, str):
raise TypeError('The res_units argument should be a str or None')
# Check that units are valid
if units is not None and not valid_units(units):
raise ValueError("The units '%s' are invalid" % units)
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]
if upper is not None:
upper = ensure_compatible(name, upper, metadata['shape'])[0]
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 np.isscalar(item):
if np.atleast_1d(item).shape != metadata['shape']:
raise ValueError('The %s argument has the wrong shape' % item_name)
if np.isscalar(ref):
self._has_output_scaling |= ref != 1.0
else:
self._has_output_scaling |= np.any(ref != 1.0)
if np.isscalar(ref0):
self._has_output_scaling |= ref0 != 0.0
else:
self._has_output_scaling |= np.any(ref0)
if np.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)
# ref, ref0, res_ref: taken as is
metadata['ref'] = ref
metadata['ref0'] = ref0
metadata['res_ref'] = res_ref
# var_set: taken as is
metadata['var_set'] = var_set
metadata['distributed'] = self.distributed
# We may not know the pathname yet, so we have to use name for now, instead of abs_name.
if self._static_mode:
var_rel2data_io = self._static_var_rel2data_io
var_rel_names = self._static_var_rel_names
else:
var_rel2data_io = self._var_rel2data_io
var_rel_names = self._var_rel_names
# Disallow dupes
if name in var_rel2data_io:
msg = "Variable name '{}' already exists.".format(name)
raise ValueError(msg)
var_rel2data_io[name] = {
'prom': name, 'rel': name,
'my_idx': len(self._var_rel_names['output']),
'type': 'output', 'metadata': metadata}
var_rel_names['output'].append(name)
return metadata
def add_input(self, name, val=1.0, shape=None, src_indices=None, flat_src_indices=None,
units=None, desc='', var_set=0):
"""
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
var_set : hashable object
For advanced users only. ID or color for this variable, relevant for
reconfigurability. Default is 0.
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('The name argument should be a string')
if not np.isscalar(val) and not isinstance(val, (list, tuple, np.ndarray, Iterable)):
raise TypeError('The val argument should be a float, list, tuple, ndarray or Iterable')
if shape is not None and not isinstance(shape, (int, tuple, list, np.integer)):
raise TypeError("The shape argument should be an int, tuple, or list but "
"a '%s' was given" % type(shape))
if src_indices is not None and not isinstance(src_indices, (int, list, tuple,
np.ndarray, Iterable)):
raise TypeError('The src_indices argument should be an int, list, '
'tuple, ndarray or Iterable')
if units is not None and not isinstance(units, str):
raise TypeError('The units argument should be a str or None')
# Check that units are valid
if units is not None and not valid_units(units):
raise ValueError("The units '%s' are invalid" % units)
metadata = {}
# value, shape: based on args, making sure they are compatible
metadata['value'], metadata['shape'] = 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.atleast_1d(src_indices)
metadata['flat_src_indices'] = flat_src_indices
# units: taken as is
metadata['units'] = units
# desc: taken as is
metadata['desc'] = desc
# var_set: taken as is
metadata['var_set'] = var_set
# We may not know the pathname yet, so we have to use name for now, instead of abs_name.
if self._static_mode:
var_rel2data_io = self._static_var_rel2data_io
var_rel_names = self._static_var_rel_names
else:
var_rel2data_io = self._var_rel2data_io
var_rel_names = self._var_rel_names
# Disallow dupes
if name in var_rel2data_io:
msg = "Variable name '{}' already exists.".format(name)
raise ValueError(msg)
var_rel2data_io[name] = {
'prom': name, 'rel': name,
'my_idx': len(self._var_rel_names['input']),
'type': 'input', 'metadata': metadata}
var_rel_names['input'].append(name)
return metadata
def add_output(self,
name,
val=1.0,
shape=None,
units=None,
res_units=None,
desc='',
lower=None,
upper=None,
ref=None,
ref0=None,
res_ref=None,
tags=None,
shape_by_conn=False,
copy_shape=None):
"""
Add an independent variable to this component.
This should never be called by a user, as it skips all checks.
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 : None
This argument is deprecated because it was unused.
desc : str
description of the variable
lower : None
This argument is deprecated because it was unused.
upper : None
This argument is deprecated because it was unused.
ref : None
This argument is deprecated because it was unused.
ref0 : None
This argument is deprecated because it was unused.
res_ref : None
This argument is deprecated because it was unused.
tags : str or list of strs
User defined tags that can be used to filter what gets listed when calling
list_outputs.
shape_by_conn : bool
If True, shape this output to match its connected input(s).
copy_shape : str or None
If a str, that str is the name of a variable. Shape this output to match that of
the named variable.
"""
# Add the output quickly.
# We don't need to check for errors because we get the value straight from a
# source, and ivc metadata is minimal.
value, shape, _ = ensure_compatible(name, val, None)
metadata = {
'value': value,
'shape': shape,
'size': shape_to_len(shape),
'units': units,
'res_units': None,
'desc': '',
'distributed': False,
'tags': set(),
'ref': 1.0,
'ref0': 0.0,
'res_ref': 1.0,
'lower': None,
'upper': None,
'shape_by_conn': False,
'copy_shape': None
}
self._static_var_rel2meta[name] = metadata
self._static_var_rel_names['output'].append(name)
self._var_added(name)
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):
"""
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.
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('The name argument should be a string')
if not _valid_var_name(name):
raise NameError("'%s' is not a valid output name." % name)
if not isscalar(val) and not isinstance(val, (list, tuple, ndarray, Iterable)):
msg = 'The val argument should be a float, list, tuple, ndarray or Iterable'
raise TypeError(msg)
if not isscalar(ref) and not isinstance(val, (list, tuple, ndarray, Iterable)):
msg = 'The ref argument should be a float, list, tuple, ndarray or Iterable'
raise TypeError(msg)
if not isscalar(ref0) and not isinstance(val, (list, tuple, ndarray, Iterable)):
msg = 'The ref0 argument should be a float, list, tuple, ndarray or Iterable'
raise TypeError(msg)
if not isscalar(res_ref) and not isinstance(val, (list, tuple, ndarray, Iterable)):
msg = 'The res_ref argument should be a float, list, tuple, ndarray or Iterable'
raise TypeError(msg)
if shape is not None and not isinstance(shape, (int, tuple, list, np.integer)):
raise TypeError("The shape argument should be an int, tuple, or list but "
"a '%s' was given" % type(shape))
if units is not None and not isinstance(units, str):
raise TypeError('The units argument should be a str or None')
if res_units is not None and not isinstance(res_units, str):
raise TypeError('The res_units argument should be a str or None')
# Check that units are valid
if units is not None and not valid_units(units):
raise ValueError("The units '%s' are invalid" % units)
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]
if upper is not None:
upper = ensure_compatible(name, upper, metadata['shape'])[0]
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.name, 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']
# 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:
msg = "Variable name '{}' already exists.".format(name)
raise ValueError(msg)
var_rel2meta[name] = metadata
var_rel_names['output'].append(name)
return metadata
def add_input(self, name, val=1.0, shape=None, src_indices=None, flat_src_indices=None,
units=None, desc=''):
"""
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
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('The name argument should be a string')
if not _valid_var_name(name):
raise NameError("'%s' is not a valid input name." % name)
if not isscalar(val) and not isinstance(val, (list, tuple, ndarray, Iterable)):
raise TypeError('The val argument should be a float, list, tuple, ndarray or Iterable')
if shape is not None and not isinstance(shape, (int, tuple, list, np.integer)):
raise TypeError("The shape argument should be an int, tuple, or list but "
"a '%s' was given" % type(shape))
if src_indices is not None and not isinstance(src_indices, (int, list, tuple,
ndarray, Iterable)):
raise TypeError('The src_indices argument should be an int, list, '
'tuple, ndarray or Iterable')
if units is not None and not isinstance(units, str):
raise TypeError('The units argument should be a str or None')
# Check that units are valid
if units is not None and not valid_units(units):
raise ValueError("The units '%s' are invalid" % units)
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']
# 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:
msg = "Variable name '{}' already exists.".format(name)
raise ValueError(msg)
var_rel2meta[name] = metadata
var_rel_names['input'].append(name)
return metadata
