def _add_variable(self, name, val, var_type, **kwargs):
""" Code common to all add functions goes here."""
shape = kwargs.get('shape')
self._check_val(name, var_type, val, shape)
self._check_name(name)
args = kwargs.copy()
args['promoted_name'] = name
args['val'] = val = self._get_initial_val(val, shape)
if is_differentiable(val) and not args.get('pass_by_obj'):
if isinstance(val, np.ndarray):
args['size'] = val.size
args['shape'] = val.shape
else:
args['size'] = 1
args['shape'] = 1
else:
args['size'] = 0
args['pass_by_obj'] = True
if isinstance(shape, int) and shape > 1:
args['shape'] = (shape,)
return args
def _add_variable(self, name, val, var_type, **kwargs):
""" Contruct metadata for new variable.
Args
----
name : string
Name of the variable.
val : float or ndarray or object
Initial value for the variable.
var_type : 'param' or 'output'
Type of variable.
**kwargs
Arbitrary keyword arguments to be added to metadata.
Raises
------
RuntimeError
If name is already in use or if setup has already been performed.
NameError
If name is not valid.
ValueError
If a valid value or shape is not specified.
"""
shape = kwargs.get('shape')
self._check_val(name, var_type, val, shape)
self._check_name(name)
meta = kwargs.copy()
meta['promoted_name'] = name
meta['val'] = val = self._get_initial_val(val, shape)
if is_differentiable(val) and not meta.get('pass_by_obj'):
if isinstance(val, np.ndarray):
meta['size'] = val.size
meta['shape'] = val.shape
else:
meta['size'] = 1
meta['shape'] = 1
else:
meta['size'] = 0
meta['pass_by_obj'] = True
if isinstance(shape, int) and shape > 1:
meta['shape'] = (shape,)
return meta
def _setup_var_meta(self, name, meta):
"""
Populate the metadata dict for the named variable.
Args
----
name : str
The name of the variable to add.
meta : dict
Starting metadata for the variable, collected from components
in an earlier stage of setup.
"""
vmeta = meta.copy()
val = meta['val']
if not is_differentiable(val) or meta.get('pass_by_obj'):
vmeta['val'] = _ByObjWrapper(val)
return vmeta
