Source code for pycalphad.core.lower_convex_hull

The lower_convex_hull module handles geometric calculations associated with
equilibrium calculation.
from __future__ import print_function
from pycalphad.core.cartesian import cartesian
from pycalphad.core.constants import MIN_SITE_FRACTION
from .hyperplane import hyperplane
import numpy as np

# The energetic difference, in J/mol-atom, below which is considered 'zero'

[docs]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. """ indep_conds = sorted([x for x in sorted(result_array.coords.keys()) if x in ['T', 'P']]) comp_conds = sorted([x for x in sorted(result_array.coords.keys()) if x.startswith('X_')]) pot_conds = sorted([x for x in sorted(result_array.coords.keys()) if x.startswith('MU_')]) # 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): 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 = {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) # Prevent compositions near an edge from going negative comp_values[np.nonzero(comp_values < MIN_SITE_FRACTION)] = MIN_SITE_FRACTION*10 # TODO: Assumes N=1 comp_values /= comp_values.sum(axis=-1, keepdims=True) #print(comp_values) # 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 # factored out via profiling result_array_GM_values = result_array.GM.values result_array_points_values = result_array.points.values result_array_MU_values = result_array.MU.values result_array_NP_values = result_array.NP.values result_array_X_values = result_array.X.values result_array_Y_values = result_array.Y.values result_array_Phase_values = result_array.Phase.values global_grid_GM_values = global_grid.GM.values global_grid_X_values = global_grid.X.values num_comps = result_array.dims['component'] it = np.nditer(result_array_GM_values, flags=['multi_index']) comp_coord_shape = tuple(len(result_array.coords[cond]) for cond in comp_conds) while not it.finished: indep_idx = it.multi_index[:len(indep_conds)] if len(comp_conds) > 0: comp_idx = np.ravel_multi_index(it.multi_index[len(indep_conds):], comp_coord_shape) idx_comp_values = comp_values[comp_idx] else: idx_comp_values = np.atleast_1d(1.) idx_global_grid_X_values = global_grid_X_values[indep_idx] idx_global_grid_GM_values = global_grid_GM_values[indep_idx] idx_result_array_MU_values = result_array_MU_values[it.multi_index] idx_result_array_NP_values = result_array_NP_values[it.multi_index] idx_result_array_GM_values = result_array_GM_values[it.multi_index] idx_result_array_points_values = result_array_points_values[it.multi_index] result_array_GM_values[it.multi_index] = \ hyperplane(idx_global_grid_X_values, idx_global_grid_GM_values, idx_comp_values, idx_result_array_MU_values, idx_result_array_NP_values, idx_result_array_points_values) # Copy phase values out points = result_array_points_values[it.multi_index] result_array_Phase_values[it.multi_index][:num_comps] = global_grid.Phase.values[indep_idx].take(points, axis=0)[:num_comps] result_array_X_values[it.multi_index][:num_comps] = global_grid.X.values[indep_idx].take(points, axis=0)[:num_comps] result_array_Y_values[it.multi_index][:num_comps] = global_grid.Y.values[indep_idx].take(points, axis=0)[:num_comps] # Special case: Sometimes fictitious points slip into the result # This can happen when we calculate stoichimetric phases by themselves if '_FAKE_' in result_array_Phase_values[it.multi_index]: # Chemical potentials are meaningless in this case idx_result_array_MU_values[...] = 0 new_energy = 0. molesum = 0. for idx in range(len(result_array_Phase_values[it.multi_index])): midx = it.multi_index + (idx,) if result_array_Phase_values[midx] == '_FAKE_': result_array_Phase_values[midx] = '' result_array_X_values[midx] = np.nan result_array_Y_values[midx] = np.nan idx_result_array_NP_values[idx] = np.nan else: new_energy += idx_result_array_NP_values[idx] * global_grid.GM.values[np.index_exp[indep_idx + (points[idx],)]] molesum += idx_result_array_NP_values[idx] result_array_GM_values[it.multi_index] = new_energy / molesum it.iternext() del result_array['points'] return result_array