def run_test():
dbf = Database()
dbf.elements = frozenset(["A"])
dbf.add_phase("TEST", {}, [1])
dbf.add_phase_constituents("TEST", [["A"]])
# add THETA parameters here
dbf.add_parameter("THETA", "TEST", [["A"]], 0, 334.0)
conds = {v.T: np.arange(1.0, 800.0, 1), v.P: 101325}
res = calculate(dbf, ["A"], "TEST", T=conds[v.T], P=conds[v.P], model=EinsteinModel, output="testprop")
# res_TE = calculate(dbf, ['A'], 'TEST', T=conds[v.T], P=conds[v.P],
# model=EinsteinModel, output='einstein_temperature')
import matplotlib.pyplot as plt
plt.scatter(res["T"], res["testprop"])
plt.xlabel("Temperature (K)")
plt.ylabel("Molar Heat Capacity (J/mol-K)")
plt.savefig("einstein.png")
def run_test():
dbf = Database()
dbf.elements = frozenset(['A'])
dbf.add_phase('TEST', {}, [1])
dbf.add_phase_constituents('TEST', [['A']])
# add THETA parameters here
dbf.add_parameter('THETA', 'TEST', [['A']], 0, 334.)
conds = {v.T: np.arange(1.,800.,1), v.P: 101325}
res = calculate(dbf, ['A'], 'TEST', T=conds[v.T], P=conds[v.P],
model=EinsteinModel, output='testprop')
#res_TE = calculate(dbf, ['A'], 'TEST', T=conds[v.T], P=conds[v.P],
# model=EinsteinModel, output='einstein_temperature')
import matplotlib.pyplot as plt
plt.scatter(res['T'], res['testprop'])
plt.xlabel('Temperature (K)')
plt.ylabel('Molar Heat Capacity (J/mol-K)')
plt.savefig('einstein.png')
print(dbf.to_string(fmt='tdb'))
def run_test():
dbf = Database()
dbf.elements = frozenset(['A'])
dbf.add_phase('TEST', {}, [1])
dbf.add_phase_constituents('TEST', [['A']])
# add THETA parameters here
dbf.add_parameter('THETA', 'TEST', [['A']], 0, 334.)
conds = {v.T: np.arange(1., 800., 1), v.P: 101325}
res = calculate(dbf, ['A'],
'TEST',
T=conds[v.T],
P=conds[v.P],
model=EinsteinModel,
output='testprop')
#res_TE = calculate(dbf, ['A'], 'TEST', T=conds[v.T], P=conds[v.P],
# model=EinsteinModel, output='einstein_temperature')
import matplotlib.pyplot as plt
plt.scatter(res['T'], res['testprop'])
plt.xlabel('Temperature (K)')
plt.ylabel('Molar Heat Capacity (J/mol-K)')
plt.savefig('einstein.png')
print(dbf.to_string(fmt='tdb'))
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