def generate_parameters(phase_models, datasets, ref_state, excess_model):
"""Generate parameters from given phase models and datasets
Parameters
----------
phase_models : dict
Dictionary of components and phases to fit.
datasets : PickleableTinyDB
database of single- and multi-phase to fit.
ref_state : str
String of the reference data to use, e.g. 'SGTE91' or 'SR2016'
excess_model : str
String of the type of excess model to fit to, e.g. 'linear'
Returns
-------
pycalphad.Database
"""
logging.info('Generating parameters.')
dbf = Database()
dbf.elements = set(phase_models['components'])
for el in dbf.elements:
if Species is not None: # TODO: drop this on release of pycalphad 0.7
dbf.species.add(Species(el, {el: 1}, 0))
# Write reference state to Database
refdata = getattr(espei.refdata, ref_state)
stabledata = getattr(espei.refdata, ref_state + 'Stable')
for key, element in refdata.items():
if isinstance(element, sympy.Piecewise):
newargs = element.args + ((0, True), )
refdata[key] = sympy.Piecewise(*newargs)
for key, element in stabledata.items():
if isinstance(element, sympy.Piecewise):
newargs = element.args + ((0, True), )
stabledata[key] = sympy.Piecewise(*newargs)
comp_refs = {
c.upper(): stabledata[c.upper()]
for c in dbf.elements if c.upper() != 'VA'
}
comp_refs['VA'] = 0
# note that the `c.upper()*2)[:2]` returns 'AL' for c.upper()=='AL' and 'VV' for c.upper()=='V'
dbf.symbols.update(
{'GHSER' + (c.upper() * 2)[:2]: data
for c, data in comp_refs.items()})
for phase_name, phase_obj in sorted(phase_models['phases'].items(),
key=operator.itemgetter(0)):
# Perform parameter selection and single-phase fitting based on input
# TODO: Need to pass particular models to include: magnetic, order-disorder, etc.
symmetry = phase_obj.get('equivalent_sublattices', None)
aliases = phase_obj.get('aliases', None)
# TODO: More advanced phase data searching
site_ratios = phase_obj['sublattice_site_ratios']
subl_model = phase_obj['sublattice_model']
dbf.add_phase(phase_name, dict(), site_ratios)
dbf.add_phase_constituents(phase_name, subl_model)
dbf.add_structure_entry(phase_name, phase_name)
phase_fit(dbf,
phase_name,
symmetry,
subl_model,
site_ratios,
datasets,
refdata,
aliases=aliases)
logging.info('Finished generating parameters.')
return dbf
def generate_parameters(phase_models,
datasets,
ref_state,
excess_model,
ridge_alpha=None,
aicc_penalty_factor=None,
dbf=None):
"""Generate parameters from given phase models and datasets
Parameters
----------
phase_models : dict
Dictionary of components and phases to fit.
datasets : PickleableTinyDB
database of single- and multi-phase to fit.
ref_state : str
String of the reference data to use, e.g. 'SGTE91' or 'SR2016'
excess_model : str
String of the type of excess model to fit to, e.g. 'linear'
ridge_alpha : float
Value of the $alpha$ hyperparameter used in ridge regression. Defaults
to None, which falls back to ordinary least squares regression.
For now, the parameter is applied to all features.
aicc_penalty_factor : dict
Map of phase name to feature to a multiplication factor for the AICc's parameter penalty.
dbf : Database
Initial pycalphad Database that can have parameters that would not be fit by ESPEI
Returns
-------
pycalphad.Database
"""
logging.info('Generating parameters.')
logging.log(
TRACE,
f'Found the following user reference states: {espei.refdata.INSERTED_USER_REFERENCE_STATES}'
)
phases = sorted(map(lambda x: x.upper(), phase_models['phases'].keys()))
dbf = dbf or Database()
dbf.elements.update(set(phase_models['components']))
for el in dbf.elements:
dbf.species.add(Species(el, {el: 1}, 0))
# Add the SER reference data
dbf.refstates[el] = espei.refdata.ser_dict[el]
# update the refdata for this element with the reference phase
if el not in espei.refdata.pure_element_phases.keys():
# Probably VA, /- or something else
continue
refdata_phase = espei.refdata.pure_element_phases[el]
if refdata_phase in phases:
dbf.refstates[el]['phase'] = refdata_phase
else:
# Check all the aliases and set the one that matches
for phase_name, phase_obj in phase_models['phases'].items():
for alias in phase_obj.get('aliases', []):
if alias == refdata_phase:
dbf.refstates[el]['phase'] = phase_name
# Write reference state to Database
refdata = getattr(espei.refdata, ref_state)
stabledata = getattr(espei.refdata, ref_state + 'Stable')
for key, element in refdata.items():
if isinstance(element, sympy.Piecewise):
newargs = element.args + ((0, True), )
refdata[key] = sympy.Piecewise(*newargs)
for key, element in stabledata.items():
if isinstance(element, sympy.Piecewise):
newargs = element.args + ((0, True), )
stabledata[key] = sympy.Piecewise(*newargs)
comp_refs = {
c.upper(): stabledata[c.upper()]
for c in dbf.elements if c.upper() != 'VA'
}
comp_refs['VA'] = 0
# note that the `c.upper()*2)[:2]` returns 'AL' for c.upper()=='AL' and 'VV' for c.upper()=='V'
dbf.symbols.update(
{'GHSER' + (c.upper() * 2)[:2]: data
for c, data in comp_refs.items()})
for phase_name, phase_obj in sorted(phase_models['phases'].items(),
key=operator.itemgetter(0)):
# Perform parameter selection and single-phase fitting based on input
# TODO: Need to pass particular models to include: magnetic, order-disorder, etc.
symmetry = phase_obj.get('equivalent_sublattices', None)
aliases = phase_obj.get('aliases', None)
# TODO: More advanced phase data searching
site_ratios = phase_obj['sublattice_site_ratios']
subl_model = phase_obj['sublattice_model']
if phase_name not in dbf.phases.keys():
dbf.add_phase(phase_name, dict(), site_ratios)
dbf.add_phase_constituents(phase_name, subl_model)
dbf.add_structure_entry(phase_name, phase_name)
phase_fit(dbf,
phase_name,
symmetry,
subl_model,
site_ratios,
datasets,
refdata,
ridge_alpha,
aicc_penalty=aicc_penalty_factor,
aliases=aliases)
logging.info('Finished generating parameters.')
return dbf