import numpy as np
from collections import namedtuple
from pycalphad.core.constants import MIN_SITE_FRACTION
from pycalphad.core.minimizer import find_solution
SolverResult = namedtuple('SolverResult', ['converged', 'x', 'chemical_potentials'])
[docs]class SolverBase(object):
""""Base class for solvers."""
ignore_convergence = False
[docs] def solve(self, composition_sets, conditions):
"""
*Implement this method.*
Minimize the energy under the specified conditions using the given candidate composition sets.
Parameters
----------
composition_sets : List[pycalphad.core.composition_set.CompositionSet]
List of CompositionSet objects in the starting point. Modified in place.
conditions : OrderedDict[str, float]
Conditions to satisfy.
Returns
-------
pycalphad.core.solver.SolverResult
"""
raise NotImplementedError("A subclass of Solver must be implemented.")
[docs]class Solver(SolverBase):
def __init__(self, verbose=False, **options):
self.verbose = verbose
[docs] def solve(self, composition_sets, conditions):
"""
Minimize the energy under the specified conditions using the given candidate composition sets.
Parameters
----------
composition_sets : List[pycalphad.core.composition_set.CompositionSet]
List of CompositionSet objects in the starting point. Modified in place.
conditions : OrderedDict[str, float]
Conditions to satisfy.
Returns
-------
SolverResult
"""
compsets = composition_sets
state_variables = compsets[0].phase_record.state_variables
nonvacant_elements = compsets[0].phase_record.nonvacant_elements
num_statevars = len(state_variables)
num_components = len(nonvacant_elements)
chemical_potentials = np.zeros(num_components)
prescribed_elemental_amounts = []
prescribed_element_indices = []
for cond, value in conditions.items():
if str(cond).startswith('X_'):
el = str(cond)[2:]
el_idx = list(nonvacant_elements).index(el)
prescribed_elemental_amounts.append(float(value))
prescribed_element_indices.append(el_idx)
prescribed_element_indices = np.array(prescribed_element_indices, dtype=np.int32)
prescribed_elemental_amounts = np.array(prescribed_elemental_amounts)
prescribed_system_amount = conditions.get('N', 1.0)
fixed_chemical_potential_indices = np.array([nonvacant_elements.index(key[3:]) for key in conditions.keys() if key.startswith('MU_')], dtype=np.int32)
free_chemical_potential_indices = np.array(sorted(set(range(num_components)) - set(fixed_chemical_potential_indices)), dtype=np.int32)
for fixed_chempot_index in fixed_chemical_potential_indices:
el = nonvacant_elements[fixed_chempot_index]
chemical_potentials[fixed_chempot_index] = conditions.get('MU_' + str(el))
fixed_statevar_indices = []
for statevar_idx, statevar in enumerate(state_variables):
if str(statevar) in [str(k) for k in conditions.keys()]:
fixed_statevar_indices.append(statevar_idx)
free_statevar_indices = np.array(sorted(set(range(num_statevars)) - set(fixed_statevar_indices)), dtype=np.int32)
fixed_statevar_indices = np.array(fixed_statevar_indices, dtype=np.int32)
converged, x, chemical_potentials = \
find_solution(compsets, num_statevars, num_components, prescribed_system_amount,
chemical_potentials, free_chemical_potential_indices, fixed_chemical_potential_indices,
prescribed_element_indices, prescribed_elemental_amounts,
free_statevar_indices, fixed_statevar_indices)
if self.verbose:
print('Chemical Potentials', chemical_potentials)
print(np.asarray(x))
return SolverResult(converged=converged, x=x, chemical_potentials=chemical_potentials)