def test_get_specie_kwargs(self):
# Tests function does not fail when species' name not present in dict
in_dict = {'val': 1}
self.assertDictEqual(pmutt._get_specie_kwargs('test', **in_dict),
in_dict)
# Tests function correctly reads value when species' name is present
in_dict = {'val1': 1, 'test_kwargs': {'val2': 2}}
out_dict = {'val1': 1, 'val2': 2}
self.assertDictEqual(pmutt._get_specie_kwargs('test', **in_dict),
out_dict)
# Tests function correctly reads value when species' name is present
# and ignores other names
in_dict = {
'val1': 1,
'test_kwargs': {
'val2': 2
},
'other_kwargs': {
'val3': 3
}
}
out_dict = {'val1': 1, 'val2': 2}
self.assertDictEqual(pmutt._get_specie_kwargs('test', **in_dict),
out_dict)
# Tests function overwrites default values if species' name is present
in_dict = {'val1': 1, 'test_kwargs': {'val1': 2}}
out_dict = {'val1': 2}
self.assertDictEqual(pmutt._get_specie_kwargs('test', **in_dict),
out_dict)
def _get_mix_quantity(misc_models,
method_name,
raise_error=True,
raise_warning=True,
default_value=0.,
**kwargs):
"""Calculate contribution from mixing models to desired quantity
Parameters
----------
misc_models : list (length N) of ``pmutt.mixture`` objects
Mix models to calculate the property
method_name : str
Name of method to use to calculate quantity. Calculates any
quantity as long as the relevant objects have the same method
name
raise_error : bool, optional
If True, raises an error if any of the modes do not have the
quantity of interest. Default is True
raise_warning : bool, optional
Only relevant if raise_error is False. Raises a warning if any
of the modes do not have the quantity of interest. Default is
True
default_value : float, optional
Default value if the object does not contain the method. Default is
0
kwargs : key-word arguments
Arguments to calculate mixture model properties, if any
Returns
-------
mix_quantity : (N,) `numpy.ndarray`_
Mixing quantity of interest. If verbose is True, each element
corresponds to the contribution of each mix_model
.. _`numpy.ndarray`: https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html
"""
# Return default value if no mixture models exist
if misc_models is None:
return np.array([default_value])
# Calculate contribution from mixing models if any
mix_quantity = np.full_like(a=misc_models,
fill_value=default_value,
dtype=np.double)
for i, mix_model in enumerate(misc_models):
if mix_model is None:
continue
try:
specie_kwargs = _get_specie_kwargs(mix_model.name_j, **kwargs)
except AttributeError:
specie_kwargs = kwargs.copy()
mix_quantity[i] = _get_mode_quantity(mode=mix_model,
method_name=method_name,
raise_error=raise_error,
raise_warning=raise_warning,
default_value=default_value,
**specie_kwargs)
return mix_quantity
def get_state_quantity(species, stoich, method_name, **kwargs):
if method_name == 'get_q':
state_quantity = 1.
else:
state_quantity = 0.
for specie, coeff in zip(species, stoich):
# Process the inputs and methods for each specie
specie_kwargs = _get_specie_kwargs(specie.name, **kwargs)
method = getattr(specie, method_name)
if method_name == 'get_q':
state_quantity *= \
_force_pass_arguments(method, **specie_kwargs)**coeff
else:
state_quantity += \
_force_pass_arguments(method, **specie_kwargs)*coeff
return state_quantity
def get_quantity(self,
method_name,
raise_error=True,
raise_warning=True,
operation='sum',
verbose=False,
use_references=True,
**kwargs):
"""Generic method to get any quantity from modes.
Parameters
----------
method_name : str
Name of method to use to calculate quantity. Calculates any
quantity as long as the relevant objects have the same method
name
raise_error : bool, optional
If True, raises an error if any of the modes do not have the
quantity of interest. Default is True
raise_warning : bool, optional
Only relevant if raise_error is False. Raises a warning if any
of the modes do not have the quantity of interest. Default is
True
operation : str, optional
Operation to apply when combining the modes. Supported options
include:
- sum (Default)
- prod
use_references : bool, optional
If True, adds contribution from references. Default is True
verbose : bool, optional
If False, returns the total Gibbs energy. If True, returns
contribution of each mode.
kwargs : key-word arguments
Parameters passed to each mode
Returns
-------
quantity : float or (N+6,) `numpy.ndarray`_
Desired quantity. N represents the number of misc models. If
verbose is True, contribution to each mode are as follows:
[trans, vib, rot, elec, nucl, references, misc_models (if any)]
.. _`numpy.ndarray`: https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html
"""
# Get the default value
operation = operation.lower()
if operation == 'sum':
default_value = 0.
elif operation == 'prod':
default_value = 1.
else:
err_msg = 'Operation: {} not supported'.format(operation)
raise ValueError(err_msg)
# Calculate the quantity for each mode
specie_kwargs = _get_specie_kwargs(specie_name=self.name, **kwargs)
quantity = np.array([
_get_mode_quantity(mode=self.trans_model,
method_name=method_name,
raise_error=raise_error,
raise_warning=raise_warning,
default_value=default_value,
**specie_kwargs),
_get_mode_quantity(mode=self.vib_model,
method_name=method_name,
raise_error=raise_error,
raise_warning=raise_warning,
default_value=default_value,
**specie_kwargs),
_get_mode_quantity(mode=self.rot_model,
method_name=method_name,
raise_error=raise_error,
raise_warning=raise_warning,
default_value=default_value,
**specie_kwargs),
_get_mode_quantity(mode=self.elec_model,
method_name=method_name,
raise_error=raise_error,
raise_warning=raise_warning,
default_value=default_value,
**specie_kwargs),
_get_mode_quantity(mode=self.nucl_model,
method_name=method_name,
raise_error=raise_error,
raise_warning=raise_warning,
default_value=default_value,
**specie_kwargs)
])
if use_references and self.references is not None:
ref_kwargs = copy(specie_kwargs)
ref_kwargs['descriptors'] = getattr(self,
self.references.descriptor)
refs_quantity = np.atleast_1d(
_get_mode_quantity(mode=self.references,
method_name=method_name,
raise_error=raise_error,
raise_warning=raise_warning,
default_value=default_value,
**ref_kwargs))
else:
refs_quantity = np.atleast_1d(default_value)
# Calculate contribution from misc models if any
misc_quantity = _get_mix_quantity(misc_models=self.misc_models,
method_name=method_name,
raise_error=raise_error,
raise_warning=raise_warning,
default_value=default_value,
verbose=verbose,
**kwargs)
# Add misc quantities onto quantity
quantity = np.concatenate([quantity, refs_quantity, misc_quantity])
quantity = _apply_numpy_operation(quantity,
verbose=verbose,
operation=operation)
return quantity
