def get_starting_simplex(self):
"""
Calculate convex hull and find a suitable starting point.
Returns (DataFrame of phase compositions, ndarray of phase fractions)
"""
phase_compositions, phase_fracs, pots = \
lower_convex_hull(self.data, self.components, self.conditions)
if phase_compositions is None:
logger.error('Unable to find starting point for calculation')
raise EquilibriumError('Unable to find starting point for calculation')
logger.debug(self.data.iloc[phase_compositions])
independent_indices = \
check_degenerate_phases(self.data.iloc[phase_compositions],
mindist=0.1)
logger.debug('phase_fracs: %s', phase_fracs)
logger.debug('independent_indices: %s', independent_indices)
# renormalize phase fractions to 1 after eliminating redundant phases
phase_fracs = phase_fracs[independent_indices]
phase_fracs /= np.sum(phase_fracs)
return [self.data.iloc[phase_compositions[independent_indices]],
phase_fracs]
def lower_convex_hull(data, comps, conditions):
"""
Find the simplex on the lower convex hull satisfying the specified
conditions.
Parameters
----------
data : DataFrame
A sample of the energy surface of the system.
comps : list
All the components in the system.
conditions : dict
StateVariables and their corresponding value.
Returns
-------
A tuple containing:
(1) A numpy array of indices corresponding to vertices of the simplex.
(2) A numpy array corresponding to the phase fractions.
(3) A numpy array of chemical potentials in sorted(comps) order (no 'VA')
Note: This routine will not check if the simplex is degenerate.
Examples
--------
None yet.
"""
# determine column indices for degrees of freedom
comps = sorted(list(comps))
dof = ['X({0})'.format(c) for c in comps if c != 'VA']
dof_values = np.zeros(len(dof))
marked_dof_values = list(range(len(dof)))
for cond, value in conditions.items():
if not isinstance(cond, v.Composition):
continue
# ignore phase-specific composition conditions
if cond.phase_name is not None:
continue
if cond.species == 'VA':
continue
dof_values[dof.index('X({0})'.format(cond.species))] = value
marked_dof_values.remove(dof.index('X({0})'.format(cond.species)))
dof.append('GM')
if len(marked_dof_values) == 1:
dof_values[marked_dof_values[0]] = 1-sum(dof_values)
else:
logger.error('Not enough composition conditions specified')
raise ValueError('Not enough composition conditions specified.')
# convert DataFrame of independent columns to ndarray
dat = data[dof].values
temperature = data.at[0, 'T']
# Build a fictitious hyperplane which has an energy greater than the max
# energy in the system
# This guarantees our starting point is feasible but also makes it likely
# it won't be part of the solution
energy_ceiling = np.amax(dat[:, -1])
if np.isnan(energy_ceiling):
raise ValueError('Input energy surface contains one or more NaNs.')
if energy_ceiling < 0:
energy_ceiling *= 0.1
else:
energy_ceiling *= 10
start_matrix = np.empty([len(dof)-1, len(dof)])
start_matrix[:, :-1] = np.eye(len(dof)-1)
start_matrix[:, -1] = energy_ceiling # set energy
dat = np.concatenate([start_matrix, dat])
max_iterations = min(100, dat.shape[0])
# Need to choose a feasible starting point
# initialize simplex as first n points of fictitious hyperplane
candidate_simplex = np.array(range(len(dof)-1), dtype=np.int)
# Calculate chemical potentials
candidate_potentials = np.linalg.solve(dat[candidate_simplex, :-1],
dat[candidate_simplex, -1])
# Calculate driving forces for reducing our candidate potentials
driving_forces = np.dot(dat[:, :-1], candidate_potentials) - dat[:, -1]
# Mask points with negative (or nearly zero) driving force
point_mask = driving_forces/(8.3145*temperature) < 1e-4
#logger.debug(point_mask)
#logger.debug(np.array(range(dat.shape[0]), dtype=np.int)[~point_mask])
candidate_energy = np.dot(candidate_potentials, dof_values)
fractions = np.empty(len(dof_values))
iteration = 0
found_solution = False
index_array = np.array(range(dat.shape[0]), dtype=np.int)
while (found_solution == False) and (iteration < max_iterations):
iteration += 1
for new_point in index_array[~point_mask]:
found_point = False
# Need to successively replace columns with the new point
# The goal is to find positive phase fraction values
new_simplex = np.empty(dat.shape[1] - 1, dtype=np.int)
for col in range(dat.shape[1] - 1):
#print(candidate_simplex)
new_simplex[:] = candidate_simplex # [:] forces copy
new_simplex[col] = new_point
#print(new_simplex)
logger.debug('trial matrix: %s', dat[new_simplex, :-1].T)
try:
fractions = np.linalg.solve(dat[new_simplex, :-1].T,
dof_values)
except np.linalg.LinAlgError:
# singular matrix means the trial simplex is degenerate
# this usually happens due to collisions between points on
# the fictitious hyperplane and the endmembers
continue
logger.debug('fractions: %s', fractions)
if np.all(fractions > -1e-8):
# Positive phase fractions
# Do I reduce the energy with this solution?
# Recalculate chemical potentials and energy
#logger.debug('new matrix: {0}'.format(dat[new_simplex, :-1]))
#logger.debug('new energies: {0}'.format(dat[new_simplex, -1]))
new_potentials = np.linalg.solve(dat[new_simplex, :-1],
dat[new_simplex, -1])
#logger.debug('new_potentials: {0}'.format(new_potentials))
new_energy = np.dot(new_potentials, dof_values)
# differences of less than 1mJ/mol are irrelevant
new_energy = np.around(new_energy, decimals=3)
if new_energy <= candidate_energy:
#logger.debug('New simplex {2} reduces energy from \
# {0} to {1}'.format(candidate_energy, new_energy, \
# new_simplex))
# [:] notation forces a copy
candidate_simplex[:] = new_simplex
candidate_potentials[:] = new_potentials
# np.array() forces a copy
candidate_energy = np.array(new_energy)
# Recalculate driving forces with new potentials
driving_forces[:] = np.dot(dat[:, :-1], \
candidate_potentials) - dat[:, -1]
#logger.debug('driving_forces: %s', driving_forces)
point_mask = driving_forces/(8.3145*temperature) < 1e-4
# Don't test points on the fictitious hyperplane
point_mask[list(range(len(dof)-1))] = True
found_point = True
break
#else:
# logger.debug('Trial simplex {2} increases energy from {0} to {1}'\
# .format(candidate_energy, new_energy, new_simplex))
# logger.debug('%s points with positive driving force remain',
# list(driving_forces >= 1e-4).count(True))
if found_point:
logger.debug('Found feasible simplex: moving to next iteration')
#logger.debug('%s points with positive driving force remain',
# list(driving_forces >= 1e-4).count(True))
break
# If there is no positive driving force, we have the solution
#print('Checking point mask')
#print(point_mask)
logger.debug('Iteration count: {0}'.format(iteration))
if np.all(point_mask) == True:
logger.debug('Unadjusted candidate_simplex: %s', candidate_simplex)
logger.debug(dat[candidate_simplex])
# Fix candidate simplex indices to remove fictitious points
candidate_simplex = candidate_simplex - (len(dof)-1)
logger.debug('Adjusted candidate_simplex: %s', candidate_simplex)
# Remove fictitious points from the candidate simplex
# These can inadvertently show up if we only calculate a phase with
# limited solubility
# Also remove points with very small estimated phase fractions
candidate_simplex, fractions = zip(*[(c, f) for c, f in
zip(candidate_simplex,
fractions)
if c >= 0 and f >= 1e-12])
candidate_simplex = np.array(candidate_simplex)
fractions = np.array(fractions)
fractions /= np.sum(fractions)
logger.debug('Final candidate_simplex: %s', candidate_simplex)
logger.debug('Final phase fractions: %s', fractions)
found_solution = True
logger.debug('Solution:')
logger.debug(candidate_potentials)
logger.debug(candidate_energy)
return candidate_simplex, fractions, candidate_potentials
logger.error('Iterations exceeded')
logger.debug('Positive driving force still exists for these points')
logger.debug(np.where(driving_forces/(8.3145*temperature) > 1e-4)[0])
return None, None, None
def lower_convex_hull(global_grid, result_array):
"""
Find the simplices on the lower convex hull satisfying the specified
conditions in the result array.
Parameters
----------
global_grid : Dataset
A sample of the energy surface of the system.
result_array : Dataset
This object will be modified!
Coordinates correspond to conditions axes.
Returns
-------
None. Results are written to result_array.
Notes
-----
This routine will not check if any simplex is degenerate.
Degenerate simplices will manifest with duplicate or NaN indices.
Examples
--------
None yet.
"""
conditions = [
x for x in result_array.coords.keys()
if x not in ['vertex', 'component']
]
indep_conds = sorted(
[x for x in sorted(result_array.coords.keys()) if x in ['T', 'P']])
indep_shape = tuple(len(result_array.coords[x]) for x in indep_conds)
comp_conds = sorted(
[x for x in sorted(result_array.coords.keys()) if x.startswith('X_')])
comp_shape = tuple(len(result_array.coords[x]) for x in comp_conds)
pot_conds = sorted(
[x for x in sorted(result_array.coords.keys()) if x.startswith('MU_')])
# force conditions to have particular ordering
conditions = indep_conds + pot_conds + comp_conds
trial_shape = (len(result_array.coords['component']), )
trial_points = None
_initialize_array(global_grid, result_array)
# Enforce ordering of shape
result_array['points'] = result_array['points'].transpose(*(conditions +
['vertex']))
result_array['GM'] = result_array['GM'].transpose(*(conditions))
result_array['NP'] = result_array['NP'].transpose(*(conditions +
['vertex']))
# Determine starting combinations of chemical potentials and compositions
# TODO: Check Gibbs phase rule compliance
if len(pot_conds) > 0:
raise NotImplementedError(
'Chemical potential conditions are not yet supported')
# FIRST CASE: Only composition conditions specified
# We only need to compute the dependent composition value directly
# Initialize trial points as lowest energy point in the system
if (len(comp_conds) > 0) and (len(pot_conds) == 0):
trial_points = np.empty(result_array['GM'].T.shape)
trial_points.fill(np.inf)
trial_points[...] = global_grid['GM'].argmin(dim='points').values.T
trial_points = trial_points.T
comp_values = cartesian(
[result_array.coords[cond] for cond in comp_conds])
# Insert dependent composition value
# TODO: Handle W(comp) as well as X(comp) here
specified_components = set([x[2:] for x in comp_conds])
dependent_component = set(
result_array.coords['component'].values) - specified_components
dependent_component = list(dependent_component)
if len(dependent_component) != 1:
raise ValueError(
'Number of dependent components is different from one')
insert_idx = sorted(result_array.coords['component'].values).index(
dependent_component[0])
comp_values = np.concatenate(
(comp_values[..., :insert_idx],
1 - np.sum(comp_values, keepdims=True, axis=-1),
comp_values[..., insert_idx:]),
axis=-1)
# SECOND CASE: Only chemical potential conditions specified
# TODO: Implementation of chemical potential
# THIRD CASE: Mixture of composition and chemical potential conditions
# TODO: Implementation of mixed conditions
if trial_points is None:
raise ValueError('Invalid conditions')
driving_forces = np.zeros(result_array.GM.values.shape +
(len(global_grid.points), ),
dtype=np.float)
max_iterations = 50
iterations = 0
while iterations < max_iterations:
iterations += 1
trial_simplices = np.empty(result_array['points'].values.shape + \
(result_array['points'].values.shape[-1],), dtype=np.int)
# Initialize trial simplices with values from best guess simplices
trial_simplices[..., :, :] = result_array['points'].values[
..., np.newaxis, :]
# Trial simplices will be the current simplex with each vertex
# replaced by the trial point
# Exactly one of those simplices will contain a given test point,
# excepting edge cases
trial_simplices.T[np.diag_indices(trial_shape[0])] = trial_points.T
#print('trial_simplices.shape', trial_simplices.shape)
#print('global_grid.X.values.shape', global_grid.X.values.shape)
flat_statevar_indices = np.unravel_index(
np.arange(np.multiply.reduce(result_array.MU.values.shape)),
result_array.MU.values.shape)[:len(indep_conds)]
#print('flat_statevar_indices', flat_statevar_indices)
trial_matrix = global_grid.X.values[np.index_exp[
flat_statevar_indices +
(trial_simplices.reshape(-1, trial_simplices.shape[-1]).T, )]]
trial_matrix = np.rollaxis(trial_matrix, 0, -1)
#print('trial_matrix', trial_matrix)
# Partially ravel the array to make indexing operations easier
trial_matrix.shape = (-1, ) + trial_matrix.shape[-2:]
# We have to filter out degenerate simplices before
# phase fraction computation
# This is because even one degenerate simplex causes the entire tensor
# to be singular
nondegenerate_indices = np.all(
np.linalg.svd(trial_matrix, compute_uv=False) > 1e-12,
axis=-1,
keepdims=True)
# Determine how many trial simplices remain for each target point.
# In principle this would always be one simplex per point, but once
# some target values reach equilibrium, trial_points starts
# to contain points already on our best guess simplex.
# This causes trial_simplices to create degenerate simplices.
# We can safely filter them out since those target values are
# already at equilibrium.
sum_array = np.sum(nondegenerate_indices, axis=-1, dtype=np.int)
index_array = np.repeat(np.arange(trial_matrix.shape[0], dtype=np.int),
sum_array)
comp_shape = trial_simplices.shape[:len(indep_conds)+len(pot_conds)] + \
(comp_values.shape[0], trial_simplices.shape[-2])
comp_indices = np.unravel_index(
index_array, comp_shape)[len(indep_conds) + len(pot_conds)]
fractions = np.linalg.solve(
np.swapaxes(trial_matrix[index_array], -2, -1),
comp_values[comp_indices])
# A simplex only contains a point if its barycentric coordinates
# (phase fractions) are positive.
bounding_indices = np.all(fractions >= 0, axis=-1)
index_array = np.atleast_1d(index_array[bounding_indices])
raveled_simplices = trial_simplices.reshape((-1, ) +
trial_simplices.shape[-1:])
candidate_simplices = raveled_simplices[index_array, :]
#print('candidate_simplices', candidate_simplices)
# We need to convert the flat index arrays into multi-index tuples.
# These tuples will tell us which state variable combinations are relevant
# for the calculation. We can drop the last dimension, 'trial'.
#print('trial_simplices.shape[:-1]', trial_simplices.shape[:-1])
statevar_indices = np.unravel_index(
index_array,
trial_simplices.shape[:-1])[:len(indep_conds) + len(pot_conds)]
aligned_energies = global_grid.GM.values[statevar_indices +
(candidate_simplices.T, )].T
statevar_indices = tuple(x[..., np.newaxis] for x in statevar_indices)
#print('statevar_indices', statevar_indices)
aligned_compositions = global_grid.X.values[np.index_exp[
statevar_indices + (candidate_simplices, )]]
#print('aligned_compositions', aligned_compositions)
#print('aligned_energies', aligned_energies)
candidate_potentials = np.linalg.solve(
aligned_compositions.astype(np.float, copy=False),
aligned_energies.astype(np.float, copy=False))
#print('candidate_potentials', candidate_potentials)
logger.debug('candidate_simplices: %s', candidate_simplices)
comp_indices = np.unravel_index(
index_array, comp_shape)[len(indep_conds) + len(pot_conds)]
#print('comp_values[comp_indices]', comp_values[comp_indices])
candidate_energies = np.multiply(
candidate_potentials, comp_values[comp_indices]).sum(axis=-1)
#print('candidate_energies', candidate_energies)
# Generate a matrix of energies comparing our calculations for this iteration
# to each other.
# 'conditions' axis followed by a 'trial' axis
# Empty values are filled in with infinity
comparison_matrix = np.empty(
[trial_matrix.shape[0] / trial_shape[0], trial_shape[0]])
assert comparison_matrix.shape[0] == aligned_compositions.shape[0]
comparison_matrix.fill(np.inf)
comparison_matrix[
np.divide(index_array, trial_shape[0]).astype(np.int),
np.mod(index_array, trial_shape[0])] = candidate_energies
#print('comparison_matrix', comparison_matrix)
# If a condition point is all infinities, it means we did not calculate it
# We should filter those out from any comparisons
calculated_indices = ~np.all(comparison_matrix == np.inf, axis=-1)
# Extract indices for trials with the lowest energy for each target point
lowest_energy_indices = np.argmin(
comparison_matrix[calculated_indices], axis=-1)
# Filter conditions down to only those calculated this iteration
calculated_conditions_indices = np.arange(
comparison_matrix.shape[0])[calculated_indices]
#print('comparison_matrix[calculated_conditions_indices,lowest_energy_indices]',comparison_matrix[calculated_conditions_indices,
# lowest_energy_indices])
is_lower_energy = comparison_matrix[calculated_conditions_indices,
lowest_energy_indices] < \
result_array['GM'].values.flat[calculated_conditions_indices]
#print('is_lower_energy', is_lower_energy)
# These are the conditions we will update this iteration
final_indices = calculated_conditions_indices[is_lower_energy]
#print('final_indices', final_indices)
# Convert to multi-index form so we can index the result array
final_multi_indices = np.unravel_index(final_indices,
result_array['GM'].values.shape)
updated_potentials = candidate_potentials[is_lower_energy]
result_array['points'].values[
final_multi_indices] = candidate_simplices[is_lower_energy]
result_array['GM'].values[final_multi_indices] = candidate_energies[
is_lower_energy]
result_array['MU'].values[final_multi_indices] = updated_potentials
result_array['NP'].values[final_multi_indices] = \
fractions[bounding_indices][is_lower_energy]
#print('result_array.GM.values', result_array.GM.values)
# By profiling, it's faster to recompute all driving forces in-place
# versus doing fancy indexing to only update "changed" driving forces
# This avoids the creation of expensive temporaries
np.einsum('...i,...i',
result_array.MU.values[..., np.newaxis, :],
global_grid.X.values[np.index_exp[...] +
((np.newaxis, ) * len(comp_conds)) +
np.index_exp[:, :]],
out=driving_forces)
np.subtract(driving_forces,
global_grid.GM.values[np.index_exp[...] +
((np.newaxis, ) * len(comp_conds)) +
np.index_exp[:]],
out=driving_forces)
# Update trial points to choose points with largest remaining driving force
trial_points = np.argmax(driving_forces, axis=-1)
#print('trial_points', trial_points)
logger.debug('trial_points: %s', trial_points)
# If all driving force (within some tolerance) is consumed, we found equilibrium
if np.all(driving_forces <= DRIVING_FORCE_TOLERANCE):
return
#raise ValueError
print('Iterations exceeded. Remaining driving force: ',
driving_forces.max())
logger.error('Iterations exceeded')
def lower_convex_hull(global_grid, result_array):
"""
Find the simplices on the lower convex hull satisfying the specified
conditions in the result array.
Parameters
----------
global_grid : Dataset
A sample of the energy surface of the system.
result_array : Dataset
This object will be modified!
Coordinates correspond to conditions axes.
Returns
-------
None. Results are written to result_array.
Notes
-----
This routine will not check if any simplex is degenerate.
Degenerate simplices will manifest with duplicate or NaN indices.
Examples
--------
None yet.
"""
conditions = [x for x in result_array.coords.keys() if x not in ['vertex',
'component']]
indep_conds = sorted([x for x in sorted(result_array.coords.keys()) if x in ['T', 'P']])
indep_shape = tuple(len(result_array.coords[x]) for x in indep_conds)
comp_conds = sorted([x for x in sorted(result_array.coords.keys()) if x.startswith('X_')])
comp_shape = tuple(len(result_array.coords[x]) for x in comp_conds)
pot_conds = sorted([x for x in sorted(result_array.coords.keys()) if x.startswith('MU_')])
# force conditions to have particular ordering
conditions = indep_conds + pot_conds + comp_conds
trial_shape = (len(result_array.coords['component']),)
trial_points = None
_initialize_array(global_grid, result_array)
# Enforce ordering of shape
result_array['points'] = result_array['points'].transpose(*(conditions + ['vertex']))
result_array['GM'] = result_array['GM'].transpose(*(conditions))
result_array['NP'] = result_array['NP'].transpose(*(conditions + ['vertex']))
# Determine starting combinations of chemical potentials and compositions
# TODO: Check Gibbs phase rule compliance
if len(pot_conds) > 0:
raise NotImplementedError('Chemical potential conditions are not yet supported')
# FIRST CASE: Only composition conditions specified
# We only need to compute the dependent composition value directly
# Initialize trial points as lowest energy point in the system
if (len(comp_conds) > 0) and (len(pot_conds) == 0):
trial_points = np.empty(result_array['GM'].T.shape)
trial_points.fill(np.inf)
trial_points[...] = global_grid['GM'].argmin(dim='points').values.T
trial_points = trial_points.T
comp_values = cartesian([result_array.coords[cond] for cond in comp_conds])
# Insert dependent composition value
# TODO: Handle W(comp) as well as X(comp) here
specified_components = set([x[2:] for x in comp_conds])
dependent_component = set(result_array.coords['component'].values) - specified_components
dependent_component = list(dependent_component)
if len(dependent_component) != 1:
raise ValueError('Number of dependent components is different from one')
insert_idx = sorted(result_array.coords['component'].values).index(dependent_component[0])
comp_values = np.concatenate((comp_values[..., :insert_idx],
1 - np.sum(comp_values, keepdims=True, axis=-1),
comp_values[..., insert_idx:]),
axis=-1)
# SECOND CASE: Only chemical potential conditions specified
# TODO: Implementation of chemical potential
# THIRD CASE: Mixture of composition and chemical potential conditions
# TODO: Implementation of mixed conditions
if trial_points is None:
raise ValueError('Invalid conditions')
driving_forces = np.zeros(result_array.GM.values.shape + (len(global_grid.points),),
dtype=np.float)
max_iterations = 50
iterations = 0
while iterations < max_iterations:
iterations += 1
trial_simplices = np.empty(result_array['points'].values.shape + \
(result_array['points'].values.shape[-1],), dtype=np.int)
# Initialize trial simplices with values from best guess simplices
trial_simplices[..., :, :] = result_array['points'].values[..., np.newaxis, :]
# Trial simplices will be the current simplex with each vertex
# replaced by the trial point
# Exactly one of those simplices will contain a given test point,
# excepting edge cases
trial_simplices.T[np.diag_indices(trial_shape[0])] = trial_points.T
#print('trial_simplices.shape', trial_simplices.shape)
#print('global_grid.X.values.shape', global_grid.X.values.shape)
flat_statevar_indices = np.unravel_index(np.arange(np.multiply.reduce(result_array.MU.values.shape)),
result_array.MU.values.shape)[:len(indep_conds)]
#print('flat_statevar_indices', flat_statevar_indices)
trial_matrix = global_grid.X.values[np.index_exp[flat_statevar_indices +
(trial_simplices.reshape(-1, trial_simplices.shape[-1]).T,)]]
trial_matrix = np.rollaxis(trial_matrix, 0, -1)
#print('trial_matrix', trial_matrix)
# Partially ravel the array to make indexing operations easier
trial_matrix.shape = (-1,) + trial_matrix.shape[-2:]
# We have to filter out degenerate simplices before
# phase fraction computation
# This is because even one degenerate simplex causes the entire tensor
# to be singular
nondegenerate_indices = np.all(np.linalg.svd(trial_matrix,
compute_uv=False) > 1e-12,
axis=-1, keepdims=True)
# Determine how many trial simplices remain for each target point.
# In principle this would always be one simplex per point, but once
# some target values reach equilibrium, trial_points starts
# to contain points already on our best guess simplex.
# This causes trial_simplices to create degenerate simplices.
# We can safely filter them out since those target values are
# already at equilibrium.
sum_array = np.sum(nondegenerate_indices, axis=-1, dtype=np.int)
index_array = np.repeat(np.arange(trial_matrix.shape[0], dtype=np.int),
sum_array)
comp_shape = trial_simplices.shape[:len(indep_conds)+len(pot_conds)] + \
(comp_values.shape[0], trial_simplices.shape[-2])
comp_indices = np.unravel_index(index_array, comp_shape)[len(indep_conds)+len(pot_conds)]
fractions = np.linalg.solve(np.swapaxes(trial_matrix[index_array], -2, -1),
comp_values[comp_indices])
# A simplex only contains a point if its barycentric coordinates
# (phase fractions) are positive.
bounding_indices = np.all(fractions >= 0, axis=-1)
index_array = np.atleast_1d(index_array[bounding_indices])
raveled_simplices = trial_simplices.reshape((-1,) + trial_simplices.shape[-1:])
candidate_simplices = raveled_simplices[index_array, :]
#print('candidate_simplices', candidate_simplices)
# We need to convert the flat index arrays into multi-index tuples.
# These tuples will tell us which state variable combinations are relevant
# for the calculation. We can drop the last dimension, 'trial'.
#print('trial_simplices.shape[:-1]', trial_simplices.shape[:-1])
statevar_indices = np.unravel_index(index_array, trial_simplices.shape[:-1]
)[:len(indep_conds)+len(pot_conds)]
aligned_energies = global_grid.GM.values[statevar_indices + (candidate_simplices.T,)].T
statevar_indices = tuple(x[..., np.newaxis] for x in statevar_indices)
#print('statevar_indices', statevar_indices)
aligned_compositions = global_grid.X.values[np.index_exp[statevar_indices + (candidate_simplices,)]]
#print('aligned_compositions', aligned_compositions)
#print('aligned_energies', aligned_energies)
candidate_potentials = np.linalg.solve(aligned_compositions.astype(np.float, copy=False),
aligned_energies.astype(np.float, copy=False))
#print('candidate_potentials', candidate_potentials)
logger.debug('candidate_simplices: %s', candidate_simplices)
comp_indices = np.unravel_index(index_array, comp_shape)[len(indep_conds)+len(pot_conds)]
#print('comp_values[comp_indices]', comp_values[comp_indices])
candidate_energies = np.multiply(candidate_potentials,
comp_values[comp_indices]).sum(axis=-1)
#print('candidate_energies', candidate_energies)
# Generate a matrix of energies comparing our calculations for this iteration
# to each other.
# 'conditions' axis followed by a 'trial' axis
# Empty values are filled in with infinity
comparison_matrix = np.empty([trial_matrix.shape[0] / trial_shape[0],
trial_shape[0]])
assert comparison_matrix.shape[0] == aligned_compositions.shape[0]
comparison_matrix.fill(np.inf)
comparison_matrix[np.divide(index_array, trial_shape[0]).astype(np.int),
np.mod(index_array, trial_shape[0])] = candidate_energies
#print('comparison_matrix', comparison_matrix)
# If a condition point is all infinities, it means we did not calculate it
# We should filter those out from any comparisons
calculated_indices = ~np.all(comparison_matrix == np.inf, axis=-1)
# Extract indices for trials with the lowest energy for each target point
lowest_energy_indices = np.argmin(comparison_matrix[calculated_indices],
axis=-1)
# Filter conditions down to only those calculated this iteration
calculated_conditions_indices = np.arange(comparison_matrix.shape[0])[calculated_indices]
#print('comparison_matrix[calculated_conditions_indices,lowest_energy_indices]',comparison_matrix[calculated_conditions_indices,
# lowest_energy_indices])
is_lower_energy = comparison_matrix[calculated_conditions_indices,
lowest_energy_indices] < \
result_array['GM'].values.flat[calculated_conditions_indices]
#print('is_lower_energy', is_lower_energy)
# These are the conditions we will update this iteration
final_indices = calculated_conditions_indices[is_lower_energy]
#print('final_indices', final_indices)
# Convert to multi-index form so we can index the result array
final_multi_indices = np.unravel_index(final_indices,
result_array['GM'].values.shape)
updated_potentials = candidate_potentials[is_lower_energy]
result_array['points'].values[final_multi_indices] = candidate_simplices[is_lower_energy]
result_array['GM'].values[final_multi_indices] = candidate_energies[is_lower_energy]
result_array['MU'].values[final_multi_indices] = updated_potentials
result_array['NP'].values[final_multi_indices] = \
fractions[bounding_indices][is_lower_energy]
#print('result_array.GM.values', result_array.GM.values)
# By profiling, it's faster to recompute all driving forces in-place
# versus doing fancy indexing to only update "changed" driving forces
# This avoids the creation of expensive temporaries
np.einsum('...i,...i',
result_array.MU.values[..., np.newaxis, :],
global_grid.X.values[np.index_exp[...] + ((np.newaxis,) * len(comp_conds)) + np.index_exp[:, :]],
out=driving_forces)
np.subtract(driving_forces,
global_grid.GM.values[np.index_exp[...] + ((np.newaxis,) * len(comp_conds)) + np.index_exp[:]],
out=driving_forces)
# Update trial points to choose points with largest remaining driving force
trial_points = np.argmax(driving_forces, axis=-1)
#print('trial_points', trial_points)
logger.debug('trial_points: %s', trial_points)
# If all driving force (within some tolerance) is consumed, we found equilibrium
if np.all(driving_forces <= DRIVING_FORCE_TOLERANCE):
return
#raise ValueError
print('Iterations exceeded. Remaining driving force: ', driving_forces.max())
logger.error('Iterations exceeded')
def minimize(self, simplex, phase_fractions=None):
"""
Accept a list of simplex vertices and return the values of the
variables that minimize the energy under the constraints.
"""
# Generate phase fraction variables
# Track the multiplicity of phases with a Counter object
composition_sets = Counter()
all_variables = []
# starting point
x_0 = []
# scaling factor -- set to minimum energy of starting simplex
# Scaling the objective to be of order '10' seems to result in
# sufficient precision (at least 5 significant figures).
scaling_factor = abs(simplex['GM'].min()) / 10.0
# a list of tuples for where each phase's variable indices
# start and end
index_ranges = []
#print(list(enumerate(simplex.iterrows())))
#print((simplex.iterrows()))
#print('END')
for m_idx, vertex in enumerate(simplex.iterrows()):
vertex = vertex[1]
# increase multiplicity by one
composition_sets[vertex['Phase']] += 1
# create new phase fraction variable
all_variables.append(
v.PhaseFraction(vertex['Phase'],
composition_sets[vertex['Phase']])
)
start = len(x_0)
# default position is centroid of the simplex
if phase_fractions is None:
x_0.append(1.0/len(list(simplex.iterrows())))
else:
# use the provided guess for the phase fraction
x_0.append(phase_fractions[m_idx])
# add site fraction variables
all_variables.extend(self._variables[vertex['Phase']])
# add starting point for variable
for varname in self._variables[vertex['Phase']]:
x_0.append(vertex[str(varname)])
index_ranges.append([start, len(x_0)])
# Create master objective function
def obj(input_x):
"Objective function. Takes x vector as input. Returns scalar."
objective = 0.0
for idx, vertex in enumerate(simplex.iterrows()):
vertex = vertex[1]
cur_x = input_x[index_ranges[idx][0]+1:index_ranges[idx][1]]
#print('Phase: '+vertex['Phase']+' '+str(cur_x))
# phase fraction times value of objective for that phase
objective += input_x[index_ranges[idx][0]] * \
self._phase_callables[vertex['Phase']](
*list(cur_x))
return objective / scaling_factor
# Create master gradient function
def gradient(input_x):
"Accepts input vector and returns gradient vector."
gradient = np.zeros(len(input_x))
for idx, vertex in enumerate(simplex.iterrows()):
vertex = vertex[1]
cur_x = input_x[index_ranges[idx][0]+1:index_ranges[idx][1]]
#print('grad cur_x: '+str(cur_x))
# phase fraction derivative is just the phase energy
gradient[index_ranges[idx][0]] = \
self._phase_callables[vertex['Phase']](
*list(cur_x))
# gradient for particular phase's variables
# NOTE: We assume all phase d.o.f are independent here,
# and we handle any coupling through the constraints
for g_idx, grad in \
enumerate(self._gradient_callables[vertex['Phase']]):
gradient[index_ranges[idx][0]+1+g_idx] = \
input_x[index_ranges[idx][0]] * \
grad(*list(cur_x))
#print('grad: '+str(gradient / scaling_factor))
return gradient / scaling_factor
# Generate constraint sequence
constraints = []
# phase fraction constraint
def phasefrac_cons(input_x):
"Accepts input vector and returns phase fraction constraint."
output = 1.0 - sum([input_x[i[0]] for i in index_ranges])#** 2
return output
def phasefrac_jac(input_x):
"Accepts input vector and returns Jacobian of constraint."
output_x = np.zeros(len(input_x))
for idx_range in index_ranges:
output_x[idx_range[0]] = -1.0 #\
# -2.0*sum([input_x[i[0]] for i in index_ranges])
return output_x
phasefrac_dict = dict()
phasefrac_dict['type'] = 'eq'
phasefrac_dict['fun'] = phasefrac_cons
phasefrac_dict['jac'] = phasefrac_jac
constraints.append(phasefrac_dict)
# bounds constraint
def nonneg_cons(input_x, idx):
"Accepts input vector and returns non-negativity constraint."
output = input_x[idx]
#print('nonneg_cons: '+str(output))
return output
def nonneg_jac(input_x, idx):
"Accepts input vector and returns Jacobian of constraint."
output_x = np.zeros(len(input_x))
output_x[idx] = 1.0
return output_x
for idx in range(len(all_variables)):
nonneg_dict = dict()
nonneg_dict['type'] = 'ineq'
nonneg_dict['fun'] = nonneg_cons
nonneg_dict['jac'] = nonneg_jac
nonneg_dict['args'] = [idx]
constraints.append(nonneg_dict)
# Generate all site fraction constraints
for idx_range in index_ranges:
# need to generate constraint for each sublattice
dofs = self._sublattice_dof[all_variables[idx_range[0]].phase_name]
cur_idx = idx_range[0]+1
for dof in dofs:
sitefrac_dict = dict()
sitefrac_dict['type'] = 'eq'
sitefrac_dict['fun'] = sitefrac_cons
sitefrac_dict['jac'] = sitefrac_jac
sitefrac_dict['args'] = [[cur_idx, cur_idx+dof]]
cur_idx += dof
if dof > 0:
constraints.append(sitefrac_dict)
# All other constraints, e.g., mass balance
def molefrac_cons(input_x, species, fix_val, all_variables, phases):
"""
Accept input vector, species and fixed value.
Returns constraint.
"""
output = -fix_val
for idx, vertex in enumerate(simplex.iterrows()):
vertex = vertex[1]
cur_x = input_x[index_ranges[idx][0]+1:index_ranges[idx][1]]
res = self._molefrac_callables[vertex['Phase']][species](*cur_x)
output += input_x[index_ranges[idx][0]] * res
#print('molefrac_cons: '+str(output))
return output
def molefrac_jac(input_x, species, fix_val, all_variables, phases):
"Accepts input vector and returns Jacobian vector."
output_x = np.zeros(len(input_x))
for idx, vertex in enumerate(simplex.iterrows()):
vertex = vertex[1]
cur_x = input_x[index_ranges[idx][0]+1:index_ranges[idx][1]]
output_x[index_ranges[idx][0]] = \
self._molefrac_callables[vertex['Phase']][species](*cur_x)
for g_idx, grad in \
enumerate(self._molefrac_jac_callables[vertex['Phase']][species]):
output_x[index_ranges[idx][0]+1+g_idx] = \
input_x[index_ranges[idx][0]] * \
grad(*list(cur_x))
#print('molefrac_jac '+str(output_x))
return output_x
eqs = len([x for x in constraints if x['type'] == 'eq'])
if eqs < len(x_0):
for condition, value in self.conditions.items():
if isinstance(condition, v.Composition):
# mass balance constraint for mole fraction
molefrac_dict = dict()
molefrac_dict['type'] = 'eq'
molefrac_dict['fun'] = molefrac_cons
molefrac_dict['jac'] = molefrac_jac
molefrac_dict['args'] = \
[condition.species, value, all_variables,
self._phases]
constraints.append(molefrac_dict)
else:
logger.warning("""Dropping mass balance constraints due to
zero internal degrees of freedom""")
# Run optimization
res = scipy.optimize.minimize(obj, x_0, method='slsqp', jac=gradient,\
constraints=constraints, options={'maxiter': 1000})
# rescale final values back to original
res['raw_fun'] = copy.deepcopy(res['fun'])
res['raw_jac'] = copy.deepcopy(res['jac'])
res['raw_x'] = copy.deepcopy(res['x'])
res['fun'] *= scaling_factor
res['jac'] *= scaling_factor
# force tiny numerical values to be positive
res['x'] = np.maximum(res['x'], np.zeros(1, dtype=np.float64))
logger.debug(res)
if not res['success']:
logger.error('Energy minimization failed')
return None
# Build result object
eq_res = EquilibriumResult(self._phases, self.components,
self.statevars, res['fun'],
zip(all_variables, res['x']))
return eq_res
