def test_filter_phases_removes_disordered_phases_from_order_disorder():
"""Databases with order-disorder models should have the disordered phases be filtered if candidate_phases kwarg is not passed to filter_phases.
If candidate_phases kwarg is passed, disordered phases just are filtered if respective ordered phases are inactive"""
all_phases = set(ALNIPT_DBF.phases.keys())
filtered_phases = set(filter_phases(ALNIPT_DBF, unpack_components(ALNIPT_DBF, ['AL', 'NI', 'PT', 'VA'])))
assert all_phases.difference(filtered_phases) == {'FCC_A1'}
comps = unpack_components(ALCRNI_DBF, ['NI', 'AL', 'CR', 'VA'])
filtered_phases = set(filter_phases(ALCRNI_DBF, comps, ['FCC_A1', 'L12_FCC', 'LIQUID', 'BCC_A2']))
assert filtered_phases == {'L12_FCC', 'LIQUID', 'BCC_A2'}
filtered_phases = set(filter_phases(ALCRNI_DBF, comps, ['FCC_A1', 'LIQUID', 'BCC_A2']))
assert filtered_phases == {'FCC_A1', 'LIQUID', 'BCC_A2'}
filtered_phases = set(filter_phases(ALCRNI_DBF, comps, ['FCC_A1']))
assert filtered_phases == {'FCC_A1'}
def get_prop_samples(dbf, comps, phase_name, desired_data):
"""
Return data values and the conditions to calculate them by pycalphad
calculate from the datasets
Parameters
----------
dbf : pycalphad.Database
Database to consider
comps : list
List of active component names
phase_name : str
Name of the phase to consider from the Database
desired_data : list
List of dictionary datasets that contain the values to sample
Returns
-------
dict
Dictionary of condition kwargs for pycalphad's calculate and the expected values
"""
# TODO: assumes T, P as conditions
# sublattice constituents are Species objects, so we need to be doing intersections with those
species_comps = unpack_components(dbf, comps)
phase_constituents = dbf.phases[phase_name].constituents
# phase constituents must be filtered to only active:
phase_constituents = [[c.name for c in sorted(subl_constituents.intersection(set(species_comps)))] for subl_constituents in phase_constituents]
# calculate needs points, state variable lists, and values to compare to
calculate_dict = {
'P': np.array([]),
'T': np.array([]),
'points': np.atleast_2d([[]]).reshape(-1, sum([len(subl) for subl in phase_constituents])),
'values': np.array([]),
}
for datum in desired_data:
# extract the data we care about
datum_T = datum['conditions']['T']
datum_P = datum['conditions']['P']
configurations = datum['solver']['sublattice_configurations']
occupancies = datum['solver'].get('sublattice_occupancies')
values = np.array(datum['values'])
# broadcast and flatten the conditions arrays
P, T = ravel_conditions(values, datum_P, datum_T)
if occupancies is None:
occupancies = [None] * len(configurations)
# calculate the points arrays, should be 2d array of points arrays
points = np.array([calculate_points_array(phase_constituents, config, occup) for config, occup in zip(configurations, occupancies)])
# add everything to the calculate_dict
calculate_dict['P'] = np.concatenate([calculate_dict['P'], P])
calculate_dict['T'] = np.concatenate([calculate_dict['T'], T])
calculate_dict['points'] = np.concatenate([calculate_dict['points'], np.repeat(points, len(T)/points.shape[0], axis=0)], axis=0)
calculate_dict['values'] = np.concatenate([calculate_dict['values'], values.flatten()])
return calculate_dict
def test_filter_phases_removes_disordered_phases_from_order_disorder():
"""Databases with order-disorder models should have the disordered phases be filtered."""
all_phases = set(ALNIPT_DBF.phases.keys())
filtered_phases = set(
filter_phases(ALNIPT_DBF,
unpack_components(ALNIPT_DBF, ['AL', 'NI', 'PT', 'VA'])))
assert all_phases.difference(filtered_phases) == {'FCC_A1'}
def get_pure_elements(dbf, comps):
"""
Return a list of pure elements in the system
Parameters
----------
dbf : pycalphad.Database
A Database object
comps : list
A list of component names (species and pure elements)
Returns
-------
list
A list of pure elements in the Database
"""
comps = sorted(unpack_components(dbf, comps))
components = [x for x in comps]
desired_active_pure_elements = [
list(x.constituents.keys()) for x in components
]
desired_active_pure_elements = [
el.upper() for constituents in desired_active_pure_elements
for el in constituents
]
pure_elements = sorted(
set([x for x in desired_active_pure_elements if x != 'VA']))
return pure_elements
def test_filter_phases_removes_phases_with_inactive_sublattices():
"""Phases that have no active components in any sublattice should be filtered"""
all_phases = set(ALNIPT_DBF.phases.keys())
filtered_phases = set(
filter_phases(ALNIPT_DBF,
unpack_components(ALNIPT_DBF, ['AL', 'NI', 'VA'])))
assert all_phases.difference(filtered_phases) == {
'FCC_A1', 'PT8AL21', 'PT5AL21', 'PT2AL', 'PT2AL3', 'PT5AL3', 'ALPT2'
}
def order_disorder_dict(dbf, comps, phases):
"""Return a dictionary with the sublattice degrees of freedom and equivalent
sublattices for order/disorder phases
Parameters
----------
dbf : pycalphad.Database
comps : list[str]
List of active components to consider
phases : list[str]
List of active phases to consider
Returns
-------
dict
Notes
-----
Phases which should be checked for ordered/disordered configurations are
determined heuristically for this script.
The heuristic for a phase satisfies the following:
1. The phase is the ordered part of an order-disorder model
2. The equivalent sublattices have all the same number of elements
"""
species = unpack_components(dbf, comps)
ord_disord_phases = {}
for phase_name in phases:
phase_obj = dbf.phases[phase_name]
if phase_name == phase_obj.model_hints.get('ordered_phase', ''):
# This phase is active and modeled with an order/disorder model.
dof = generate_dof(dbf.phases[phase_name], species)[1]
# Define the symmetrically equivalent sublattices as any sublattices
# that have the same site ratio. Create a {site_ratio: [subl idx]} dict
site_ratio_idxs = defaultdict(lambda: [])
for subl_idx, site_ratio in enumerate(phase_obj.sublattices):
site_ratio_idxs[site_ratio].append(subl_idx)
equiv_sublattices = list(site_ratio_idxs.values())
ord_disord_phases[phase_name] = {
'subl_dof': dof,
'symmetric_subl_idx': equiv_sublattices,
'disordered_phase': phase_obj.model_hints['disordered_phase']
}
return ord_disord_phases
def sample_phase_points(dbf, comps, phase_name, conditions, calc_pdens, pdens):
"""Sample new points from a phase around the single phase equilibrium site fractions at the given conditions.
Parameters
----------
dbf :
comps :
phase_name :
conditions :
calc_pdens :
The point density passed to calculate for the nominal points added.
pdens : int
The number of points to add in the local sampling at each set of equilibrium site fractions.
Returns
-------
np.ndarray[:,:]
"""
_, subl_dof = generate_dof(dbf.phases[phase_name],
unpack_components(dbf, comps))
# subl_dof is number of species in each sublattice, e.g. (FE,NI,TI)(FE,NI)(FE,NI,TI) is [3, 2, 3]
eqgrid = equilibrium(dbf, comps, [phase_name], conditions)
all_eq_pts = eqgrid.Y.values[eqgrid.Phase.values == phase_name]
# sample points locally
additional_points = local_sample(all_eq_pts, subl_dof, pdens)
# get the grid between endmembers and random point sampling from calculate
pts_calc = calculate(dbf,
comps,
phase_name,
pdens=calc_pdens,
P=101325,
T=300,
N=1).Y.values.squeeze()
return np.concatenate([additional_points, pts_calc], axis=0)
def get_zpf_data(dbf: Database, comps: Sequence[str], phases: Sequence[str],
datasets: PickleableTinyDB, parameters: Dict[str, float]):
"""
Return the ZPF data used in the calculation of ZPF error
Parameters
----------
comps : list
List of active component names
phases : list
List of phases to consider
datasets : espei.utils.PickleableTinyDB
Datasets that contain single phase data
parameters : dict
Dictionary mapping symbols to optimize to their initial values
Returns
-------
list
List of data dictionaries with keys ``weight``, ``data_comps`` and
``phase_regions``. ``data_comps`` are the components for the data in
question. ``phase_regions`` are the ZPF phases, state variables and compositions.
"""
desired_data = datasets.search(
(tinydb.where('output') == 'ZPF')
& (tinydb.where('components').test(lambda x: set(x).issubset(comps)))
& (tinydb.where('phases').test(
lambda x: len(set(phases).intersection(x)) > 0)))
zpf_data = [] # 1:1 correspondence with each dataset
for data in desired_data:
data_comps = list(set(data['components']).union({'VA'}))
species = sorted(unpack_components(dbf, data_comps), key=str)
data_phases = filter_phases(dbf, species, candidate_phases=phases)
models = instantiate_models(dbf,
species,
data_phases,
parameters=parameters)
all_regions = data['values']
conditions = data['conditions']
phase_regions = []
# Each phase_region is one set of phases in equilibrium (on a tie-line),
# e.g. [["ALPHA", ["B"], [0.25]], ["BETA", ["B"], [0.5]]]
for idx, phase_region in enumerate(all_regions):
# We need to construct a PhaseRegion by matching up phases/compositions to the conditions
if len(phase_region) < 2:
# Skip single-phase regions for fitting purposes
continue
# Extract the conditions for entire phase region
region_potential_conds = extract_conditions(conditions, idx)
region_potential_conds[v.N] = region_potential_conds.get(
v.N) or 1.0 # Add v.N condition, if missing
# Extract all the phases and compositions from the tie-line points
region_phases, region_comp_conds, phase_flags = extract_phases_comps(
phase_region)
region_phase_records = [
build_phase_records(dbf,
species,
data_phases, {
**region_potential_conds,
**comp_conds
},
models,
parameters=parameters,
build_gradients=True,
build_hessians=True)
for comp_conds in region_comp_conds
]
phase_regions.append(
PhaseRegion(region_phases, region_potential_conds,
region_comp_conds, phase_flags, dbf, species,
data_phases, models, region_phase_records))
data_dict = {
'weight': data.get('weight', 1.0),
'data_comps': data_comps,
'phase_regions': phase_regions,
'dataset_reference': data['reference']
}
zpf_data.append(data_dict)
return zpf_data
def test_filter_phases_removes_phases_with_inactive_sublattices():
"""Phases that have no active components in any sublattice should be filtered"""
all_phases = set(ALNIPT_DBF.phases.keys())
filtered_phases = set(filter_phases(ALNIPT_DBF, unpack_components(ALNIPT_DBF, ['AL', 'NI', 'VA'])))
assert all_phases.difference(filtered_phases) == {'FCC_A1', 'PT8AL21', 'PT5AL21', 'PT2AL', 'PT2AL3', 'PT5AL3', 'ALPT2'}
def simulate_scheil_solidification(dbf,
comps,
phases,
composition,
start_temperature,
step_temperature=1.0,
liquid_phase_name='LIQUID',
eq_kwargs=None,
stop=0.0001,
verbose=False,
adaptive=True):
"""Perform a Scheil-Gulliver solidification simulation.
Parameters
----------
dbf : pycalphad.Database
Database object.
comps : list
List of components in the system.
phases : list
List of phases in the system.
composition : Dict[v.X, float]
Dictionary of independent `v.X` composition variables.
start_temperature : float
Starting temperature for simulation. Must be single phase liquid.
step_temperature : Optional[float]
Temperature step size. Defaults to 1.0.
liquid_phase_name : Optional[str]
Name of the phase treated as liquid (i.e. the phase with infinitely
fast diffusion). Defaults to 'LIQUID'.
eq_kwargs: Optional[Dict[str, Any]]
Keyword arguments for equilibrium
stop: Optional[float]
Stop when the phase fraction of liquid is below this amount.
adaptive: Optional[bool]
Whether to add additional points near the equilibrium points at each
step. Only takes effect if ``points`` is in the eq_kwargs dict.
Returns
-------
SolidificationResult
"""
eq_kwargs = eq_kwargs or dict()
STEP_SCALE_FACTOR = 1.2 # How much to try to adapt the temperature step by
MAXIMUM_STEP_SIZE_REDUCTION = 5.0
T_STEP_ORIG = step_temperature
phases = filter_phases(dbf, unpack_components(dbf, comps), phases)
models = instantiate_models(dbf, comps, phases)
if verbose:
print('building callables... ', end='')
cbs = build_callables(dbf,
comps,
phases,
models,
additional_statevars={v.P, v.T, v.N},
build_gradients=True,
build_hessians=True)
if verbose:
print('done')
solid_phases = sorted(set(phases) - {liquid_phase_name})
temp = start_temperature
independent_comps = sorted([str(comp)[2:] for comp in composition.keys()])
x_liquid = {comp: [composition[v.X(comp)]] for comp in independent_comps}
fraction_solid = [0.0]
temperatures = [temp]
phase_amounts = {ph: [0.0] for ph in solid_phases}
ord_disord_dict = order_disorder_dict(dbf, comps, phases)
if adaptive and ('points' in eq_kwargs.get('calc_opts', {})):
# Dynamically add points as the simulation runs
species = unpack_components(dbf, comps)
dof_dict = {
ph: generate_dof(dbf.phases[ph], species)[1]
for ph in phases
}
else:
adaptive = False
converged = False
phases_seen = {liquid_phase_name, ''}
liquid_comp = composition
while fraction_solid[-1] < 1:
conds = {v.T: temp, v.P: 101325.0, v.N: 1.0}
comp_conds = liquid_comp
fmt_comp_conds = ', '.join(
[f'{c}={val:0.2f}' for c, val in comp_conds.items()])
conds.update(comp_conds)
eq = equilibrium(dbf,
comps,
phases,
conds,
callables=cbs,
model=models,
**eq_kwargs)
if adaptive:
# Update the points dictionary with local samples around the equilibrium site fractions
points_dict = eq_kwargs['calc_opts']['points']
for vtx in range(eq.vertex.size):
masked = eq.isel(vertex=vtx)
ph = str(masked.Phase.values.squeeze())
pts = points_dict.get(ph)
if pts is not None:
if verbose:
print(f'Adding points to {ph}. ', end='')
dof = dof_dict[ph]
points_dict[ph] = np.concatenate([
pts,
local_sample(
masked.Y.values.squeeze()[:sum(dof)].reshape(
1, -1),
dof,
pdens=20)
],
axis=0)
eq_phases = order_disorder_eq_phases(eq, ord_disord_dict)
num_eq_phases = np.nansum(
np.array([str(ph) for ph in eq_phases]) != '')
new_phases_seen = set(eq_phases).difference(phases_seen)
if len(new_phases_seen) > 0:
if verbose:
print(f'New phases seen: {new_phases_seen}. ', end='')
phases_seen |= new_phases_seen
if liquid_phase_name not in eq["Phase"].values.squeeze():
found_ph = set(eq_phases) - {''}
if verbose:
print(
f'No liquid phase found at T={temp:0.3f}, {fmt_comp_conds}. (Found {found_ph}) ',
end='')
if len(found_ph) == 0:
# No phases found in equilibrium. Just continue on lowering the temperature without changing anything
if verbose:
print(f'(Convergence failure) ', end='')
if T_STEP_ORIG / step_temperature > MAXIMUM_STEP_SIZE_REDUCTION:
# Only found solid phases and the step size has already been reduced. Stop running without converging.
if verbose:
print('Maximum step size reduction exceeded. Stopping.')
converged = False
break
else:
# Only found solid phases. Try reducing the step size to zero-in on the correct phases
if verbose:
print(f'Stepping back and reducing step size.')
temp += step_temperature
step_temperature /= STEP_SCALE_FACTOR
continue
# TODO: Will break if there is a liquid miscibility gap
liquid_vertex = sorted(
np.nonzero(
eq["Phase"].values.squeeze().flat == liquid_phase_name))[0]
liquid_comp = {}
for comp in independent_comps:
x = float(eq["X"].isel(vertex=liquid_vertex).squeeze().sel(
component=comp).values)
x_liquid[comp].append(x)
liquid_comp[v.X(comp)] = x
np_liq = np.nansum(
eq.where(eq["Phase"] == liquid_phase_name).NP.values)
current_fraction_solid = float(fraction_solid[-1])
found_phase_amounts = [(liquid_phase_name, np_liq)
] # tuples of phase name, amount
for solid_phase in solid_phases:
if solid_phase not in eq_phases:
phase_amounts[solid_phase].append(0.0)
continue
np_tieline = np.nansum(
eq.isel(vertex=eq_phases.index(solid_phase))
["NP"].values.squeeze())
found_phase_amounts.append((solid_phase, np_tieline))
delta_fraction_solid = (1 - current_fraction_solid) * np_tieline
current_fraction_solid += delta_fraction_solid
phase_amounts[solid_phase].append(delta_fraction_solid)
fraction_solid.append(current_fraction_solid)
temperatures.append(temp)
NL = 1 - fraction_solid[-1]
if verbose:
phase_amnts = ' '.join(
[f'NP({ph})={amnt:0.3f}' for ph, amnt in found_phase_amounts])
if NL < 1.0e-3:
print(
f'T={temp:0.3f}, {fmt_comp_conds}, ΔT={step_temperature:0.3f}, NL: {NL:.2E}, {phase_amnts} ',
end='')
else:
print(
f'T={temp:0.3f}, {fmt_comp_conds}, ΔT={step_temperature:0.3f}, NL: {NL:0.3f}, {phase_amnts} ',
end='')
if NL < stop:
if verbose:
print(
f'Liquid fraction below criterion {stop} . Stopping at {fmt_comp_conds}'
)
converged = True
break
if verbose:
print() # add line break
temp -= step_temperature
if fraction_solid[-1] < 1:
for comp in independent_comps:
x_liquid[comp].append(np.nan)
fraction_solid.append(1.0)
temperatures.append(temp)
# set the final phase amount to the phase fractions in the eutectic
# this method gives the sum total phase amounts of 1.0 by construction
for solid_phase in solid_phases:
if solid_phase in eq_phases:
amount = np.nansum(
eq.isel(vertex=eq_phases.index(solid_phase))
["NP"].values.squeeze())
phase_amounts[solid_phase].append(
float(amount) * (1 - current_fraction_solid))
else:
phase_amounts[solid_phase].append(0.0)
return SolidificationResult(x_liquid, fraction_solid, temperatures,
phase_amounts, converged, "scheil")
def calculate(dbf, comps, phases, mode=None, output='GM', fake_points=False, broadcast=True, parameters=None, **kwargs):
"""
Sample the property surface of 'output' containing the specified
components and phases. Model parameters are taken from 'dbf' and any
state variables (T, P, etc.) can be specified as keyword arguments.
Parameters
----------
dbf : Database
Thermodynamic database containing the relevant parameters.
comps : str or sequence
Names of components to consider in the calculation.
phases : str or sequence
Names of phases to consider in the calculation.
mode : string, optional
See 'make_callable' docstring for details.
output : string, optional
Model attribute to sample.
fake_points : bool, optional (Default: False)
If True, the first few points of the output surface will be fictitious
points used to define an equilibrium hyperplane guaranteed to be above
all the other points. This is used for convex hull computations.
broadcast : bool, optional
If True, broadcast given state variable lists against each other to create a grid.
If False, assume state variables are given as equal-length lists.
points : ndarray or a dict of phase names to ndarray, optional
Columns of ndarrays must be internal degrees of freedom (site fractions), sorted.
If this is not specified, points will be generated automatically.
pdens : int, a dict of phase names to int, or a seq of both, optional
Number of points to sample per degree of freedom.
Default: 2000; Default when called from equilibrium(): 500
model : Model, a dict of phase names to Model, or a seq of both, optional
Model class to use for each phase.
sampler : callable, a dict of phase names to callable, or a seq of both, optional
Function to sample phase constitution space.
Must have same signature as 'pycalphad.core.utils.point_sample'
grid_points : bool, a dict of phase names to bool, or a seq of both, optional (Default: True)
Whether to add evenly spaced points between end-members.
The density of points is determined by 'pdens'
parameters : dict, optional
Maps SymPy Symbol to numbers, for overriding the values of parameters in the Database.
Returns
-------
Dataset of the sampled attribute as a function of state variables
Examples
--------
None yet.
"""
# Here we check for any keyword arguments that are special, i.e.,
# there may be keyword arguments that aren't state variables
pdens_dict = unpack_kwarg(kwargs.pop('pdens', 2000), default_arg=2000)
points_dict = unpack_kwarg(kwargs.pop('points', None), default_arg=None)
callables = kwargs.pop('callables', {})
sampler_dict = unpack_kwarg(kwargs.pop('sampler', None), default_arg=None)
fixedgrid_dict = unpack_kwarg(kwargs.pop('grid_points', True), default_arg=True)
parameters = parameters or dict()
if isinstance(parameters, dict):
parameters = OrderedDict(sorted(parameters.items(), key=str))
if isinstance(phases, str):
phases = [phases]
if isinstance(comps, (str, v.Species)):
comps = [comps]
comps = sorted(unpack_components(dbf, comps))
if points_dict is None and broadcast is False:
raise ValueError('The \'points\' keyword argument must be specified if broadcast=False is also given.')
nonvacant_components = [x for x in sorted(comps) if x.number_of_atoms > 0]
all_phase_data = []
largest_energy = 1e10
# Consider only the active phases
list_of_possible_phases = filter_phases(dbf, comps)
active_phases = sorted(set(list_of_possible_phases).intersection(set(phases)))
active_phases = {name: dbf.phases[name] for name in active_phases}
if len(list_of_possible_phases) == 0:
raise ConditionError('There are no phases in the Database that can be active with components {0}'.format(comps))
if len(active_phases) == 0:
raise ConditionError('None of the passed phases ({0}) are active. List of possible phases: {1}.'
.format(phases, list_of_possible_phases))
models = instantiate_models(dbf, comps, list(active_phases.keys()), model=kwargs.pop('model', None), parameters=parameters)
if isinstance(output, (list, tuple, set)):
raise NotImplementedError('Only one property can be specified in calculate() at a time')
output = output if output is not None else 'GM'
# Implicitly add 'N' state variable as a string to keyword arguements if it's not passed
if kwargs.get('N') is None:
kwargs['N'] = 1
if np.any(np.array(kwargs['N']) != 1):
raise ConditionError('N!=1 is not yet supported, got N={}'.format(kwargs['N']))
# TODO: conditions dict of StateVariable instances should become part of the calculate API
statevar_strings = [sv for sv in kwargs.keys() if getattr(v, sv) is not None]
# If we don't do this, sympy will get confused during substitution
statevar_dict = dict((v.StateVariable(key), unpack_condition(value)) for key, value in kwargs.items() if key in statevar_strings)
# Sort after default state variable check to fix gh-116
statevar_dict = collections.OrderedDict(sorted(statevar_dict.items(), key=lambda x: str(x[0])))
phase_records = build_phase_records(dbf, comps, active_phases, statevar_dict,
models=models, parameters=parameters,
output=output, callables=callables,
verbose=kwargs.pop('verbose', False))
str_statevar_dict = collections.OrderedDict((str(key), unpack_condition(value)) \
for (key, value) in statevar_dict.items())
maximum_internal_dof = max(len(models[phase_name].site_fractions) for phase_name in active_phases)
for phase_name, phase_obj in sorted(active_phases.items()):
mod = models[phase_name]
phase_record = phase_records[phase_name]
points = points_dict[phase_name]
variables, sublattice_dof = generate_dof(phase_obj, mod.components)
if points is None:
points = _sample_phase_constitution(phase_name, phase_obj.constituents, sublattice_dof, comps,
tuple(variables), sampler_dict[phase_name] or point_sample,
fixedgrid_dict[phase_name], pdens_dict[phase_name])
points = np.atleast_2d(points)
fp = fake_points and (phase_name == sorted(active_phases.keys())[0])
phase_ds = _compute_phase_values(nonvacant_components, str_statevar_dict,
points, phase_record, output,
maximum_internal_dof, broadcast=broadcast,
largest_energy=float(largest_energy), fake_points=fp)
all_phase_data.append(phase_ds)
# speedup for single-phase case (found by profiling)
if len(all_phase_data) > 1:
final_ds = concat(all_phase_data, dim='points')
final_ds['points'].values = np.arange(len(final_ds['points']))
final_ds.coords['points'].values = np.arange(len(final_ds['points']))
else:
final_ds = all_phase_data[0]
return final_ds
def equilibrium(dbf, comps, phases, conditions, output=None, model=None,
verbose=False, broadcast=True, calc_opts=None,
scheduler='sync', parameters=None, solver=None, callables=None,
**kwargs):
"""
Calculate the equilibrium state of a system containing the specified
components and phases, under the specified conditions.
Parameters
----------
dbf : Database
Thermodynamic database containing the relevant parameters.
comps : list
Names of components to consider in the calculation.
phases : list or dict
Names of phases to consider in the calculation.
conditions : dict or (list of dict)
StateVariables and their corresponding value.
output : str or list of str, optional
Additional equilibrium model properties (e.g., CPM, HM, etc.) to compute.
These must be defined as attributes in the Model class of each phase.
model : Model, a dict of phase names to Model, or a seq of both, optional
Model class to use for each phase.
verbose : bool, optional
Print details of calculations. Useful for debugging.
broadcast : bool
If True, broadcast conditions against each other. This will compute all combinations.
If False, each condition should be an equal-length list (or single-valued).
Disabling broadcasting is useful for calculating equilibrium at selected conditions,
when those conditions don't comprise a grid.
calc_opts : dict, optional
Keyword arguments to pass to `calculate`, the energy/property calculation routine.
scheduler : Dask scheduler, optional
Job scheduler for performing the computation.
If None, return a Dask graph of the computation instead of actually doing it.
parameters : dict, optional
Maps SymPy Symbol to numbers, for overriding the values of parameters in the Database.
solver : pycalphad.core.solver.SolverBase
Instance of a solver that is used to calculate local equilibria.
Defaults to a pycalphad.core.solver.InteriorPointSolver.
callables : dict, optional
Pre-computed callable functions for equilibrium calculation.
Returns
-------
Structured equilibrium calculation, or Dask graph if scheduler=None.
Examples
--------
None yet.
"""
if not broadcast:
raise NotImplementedError('Broadcasting cannot yet be disabled')
comps = sorted(unpack_components(dbf, comps))
phases = unpack_phases(phases) or sorted(dbf.phases.keys())
# remove phases that cannot be active
list_of_possible_phases = filter_phases(dbf, comps)
active_phases = sorted(set(list_of_possible_phases).intersection(set(phases)))
if len(list_of_possible_phases) == 0:
raise ConditionError('There are no phases in the Database that can be active with components {0}'.format(comps))
if len(active_phases) == 0:
raise ConditionError('None of the passed phases ({0}) are active. List of possible phases: {1}.'.format(phases, list_of_possible_phases))
if isinstance(comps, (str, v.Species)):
comps = [comps]
if len(set(comps) - set(dbf.species)) > 0:
raise EquilibriumError('Components not found in database: {}'
.format(','.join([c.name for c in (set(comps) - set(dbf.species))])))
calc_opts = calc_opts if calc_opts is not None else dict()
solver = solver if solver is not None else InteriorPointSolver(verbose=verbose)
parameters = parameters if parameters is not None else dict()
if isinstance(parameters, dict):
parameters = OrderedDict(sorted(parameters.items(), key=str))
models = instantiate_models(dbf, comps, active_phases, model=model, parameters=parameters)
# Temporary solution until constraint system improves
if conditions.get(v.N) is None:
conditions[v.N] = 1
if np.any(np.array(conditions[v.N]) != 1):
raise ConditionError('N!=1 is not yet supported, got N={}'.format(conditions[v.N]))
# Modify conditions values to be within numerical limits, e.g., X(AL)=0
# Also wrap single-valued conditions with lists
conds = _adjust_conditions(conditions)
for cond in conds.keys():
if isinstance(cond, (v.Composition, v.ChemicalPotential)) and cond.species not in comps:
raise ConditionError('{} refers to non-existent component'.format(cond))
state_variables = sorted(get_state_variables(models=models, conds=conds), key=str)
str_conds = OrderedDict((str(key), value) for key, value in conds.items())
num_calcs = np.prod([len(i) for i in str_conds.values()])
components = [x for x in sorted(comps)]
desired_active_pure_elements = [list(x.constituents.keys()) for x in components]
desired_active_pure_elements = [el.upper() for constituents in desired_active_pure_elements for el in constituents]
pure_elements = sorted(set([x for x in desired_active_pure_elements if x != 'VA']))
if verbose:
print('Components:', ' '.join([str(x) for x in comps]))
print('Phases:', end=' ')
output = output if output is not None else 'GM'
output = output if isinstance(output, (list, tuple, set)) else [output]
output = set(output)
output |= {'GM'}
output = sorted(output)
need_hessians = any(type(c) in v.CONDITIONS_REQUIRING_HESSIANS for c in conds.keys())
phase_records = build_phase_records(dbf, comps, active_phases, conds, models,
output='GM', callables=callables,
parameters=parameters, verbose=verbose,
build_gradients=True, build_hessians=need_hessians)
if verbose:
print('[done]', end='\n')
# 'calculate' accepts conditions through its keyword arguments
grid_opts = calc_opts.copy()
statevar_strings = [str(x) for x in state_variables]
grid_opts.update({key: value for key, value in str_conds.items() if key in statevar_strings})
if 'pdens' not in grid_opts:
grid_opts['pdens'] = 500
grid = delayed(calculate, pure=False)(dbf, comps, active_phases,
model=models, fake_points=True,
callables=callables, output='GM',
parameters=parameters, **grid_opts)
coord_dict = str_conds.copy()
coord_dict['vertex'] = np.arange(
len(pure_elements) + 1) # +1 is to accommodate the degenerate degree of freedom at the invariant reactions
coord_dict['component'] = pure_elements
grid_shape = tuple(len(x) for x in conds.values()) + (len(pure_elements)+1,)
properties = delayed(starting_point, pure=False)(conds, state_variables, phase_records, grid)
conditions_per_chunk_per_axis = 2
if num_calcs > 1:
# Generate slices of 'properties'
slices = []
for val in grid_shape[:-1]:
idx_arr = list(range(val))
num_chunks = int(np.floor(val/conditions_per_chunk_per_axis))
if num_chunks > 0:
cond_slices = [x for x in np.array_split(np.asarray(idx_arr), num_chunks) if len(x) > 0]
else:
cond_slices = [idx_arr]
slices.append(cond_slices)
chunk_dims = [len(slc) for slc in slices]
chunk_grid = np.array(np.unravel_index(np.arange(np.prod(chunk_dims)), chunk_dims)).T
res = []
for chunk in chunk_grid:
prop_slice = properties[OrderedDict(list(zip(str_conds.keys(),
[np.atleast_1d(sl)[ch] for ch, sl in zip(chunk, slices)])))]
job = delayed(_solve_eq_at_conditions, pure=False)(comps, prop_slice, phase_records, grid,
list(str_conds.keys()), state_variables, verbose, solver=solver)
res.append(job)
properties = delayed(_merge_property_slices, pure=False)(properties, chunk_grid, slices, list(str_conds.keys()), res)
else:
# Single-process job; don't create child processes
properties = delayed(_solve_eq_at_conditions, pure=False)(comps, properties, phase_records, grid,
list(str_conds.keys()), state_variables, verbose, solver=solver)
# Compute equilibrium values of any additional user-specified properties
# We already computed these properties so don't recompute them
output = sorted(set(output) - {'GM', 'MU'})
for out in output:
if (out is None) or (len(out) == 0):
continue
# TODO: How do we know if a specified property should be per_phase or not?
# For now, we make a best guess
if (out == 'degree_of_ordering') or (out == 'DOO'):
per_phase = True
else:
per_phase = False
eqcal = delayed(_eqcalculate, pure=False)(dbf, comps, active_phases, conditions, out,
data=properties, per_phase=per_phase,
callables=callables,
parameters=parameters,
model=models, **calc_opts)
properties = delayed(properties.merge, pure=False)(eqcal, compat='equals')
if scheduler is not None:
properties = dask.compute(properties, scheduler=scheduler)[0]
properties.attrs['created'] = datetime.utcnow().isoformat()
if len(kwargs) > 0:
warnings.warn('The following equilibrium keyword arguments were passed, but unused:\n{}'.format(kwargs))
return properties
def equilibrium(dbf,
comps,
phases,
conditions,
output=None,
model=None,
verbose=False,
broadcast=True,
calc_opts=None,
scheduler='sync',
parameters=None,
solver=None,
callables=None,
**kwargs):
"""
Calculate the equilibrium state of a system containing the specified
components and phases, under the specified conditions.
Parameters
----------
dbf : Database
Thermodynamic database containing the relevant parameters.
comps : list
Names of components to consider in the calculation.
phases : list or dict
Names of phases to consider in the calculation.
conditions : dict or (list of dict)
StateVariables and their corresponding value.
output : str or list of str, optional
Additional equilibrium model properties (e.g., CPM, HM, etc.) to compute.
These must be defined as attributes in the Model class of each phase.
model : Model, a dict of phase names to Model, or a seq of both, optional
Model class to use for each phase.
verbose : bool, optional
Print details of calculations. Useful for debugging.
broadcast : bool
If True, broadcast conditions against each other. This will compute all combinations.
If False, each condition should be an equal-length list (or single-valued).
Disabling broadcasting is useful for calculating equilibrium at selected conditions,
when those conditions don't comprise a grid.
calc_opts : dict, optional
Keyword arguments to pass to `calculate`, the energy/property calculation routine.
scheduler : Dask scheduler, optional
Job scheduler for performing the computation.
If None, return a Dask graph of the computation instead of actually doing it.
parameters : dict, optional
Maps SymPy Symbol to numbers, for overriding the values of parameters in the Database.
solver : pycalphad.core.solver.SolverBase
Instance of a solver that is used to calculate local equilibria.
Defaults to a pycalphad.core.solver.InteriorPointSolver.
callables : dict, optional
Pre-computed callable functions for equilibrium calculation.
Returns
-------
Structured equilibrium calculation, or Dask graph if scheduler=None.
Examples
--------
None yet.
"""
if not broadcast:
raise NotImplementedError('Broadcasting cannot yet be disabled')
from pycalphad import __version__ as pycalphad_version
comps = sorted(unpack_components(dbf, comps))
phases = unpack_phases(phases) or sorted(dbf.phases.keys())
# remove phases that cannot be active
list_of_possible_phases = filter_phases(dbf, comps)
active_phases = sorted(
set(list_of_possible_phases).intersection(set(phases)))
if len(list_of_possible_phases) == 0:
raise ConditionError(
'There are no phases in the Database that can be active with components {0}'
.format(comps))
if len(active_phases) == 0:
raise ConditionError(
'None of the passed phases ({0}) are active. List of possible phases: {1}.'
.format(phases, list_of_possible_phases))
if isinstance(comps, (str, v.Species)):
comps = [comps]
if len(set(comps) - set(dbf.species)) > 0:
raise EquilibriumError('Components not found in database: {}'.format(
','.join([c.name for c in (set(comps) - set(dbf.species))])))
indep_vars = ['T', 'P']
calc_opts = calc_opts if calc_opts is not None else dict()
model = model if model is not None else Model
solver = solver if solver is not None else InteriorPointSolver(
verbose=verbose)
parameters = parameters if parameters is not None else dict()
if isinstance(parameters, dict):
parameters = OrderedDict(sorted(parameters.items(), key=str))
# Modify conditions values to be within numerical limits, e.g., X(AL)=0
# Also wrap single-valued conditions with lists
conds = _adjust_conditions(conditions)
for cond in conds.keys():
if isinstance(cond,
(v.Composition,
v.ChemicalPotential)) and cond.species not in comps:
raise ConditionError(
'{} refers to non-existent component'.format(cond))
str_conds = OrderedDict((str(key), value) for key, value in conds.items())
num_calcs = np.prod([len(i) for i in str_conds.values()])
components = [x for x in sorted(comps)]
desired_active_pure_elements = [
list(x.constituents.keys()) for x in components
]
desired_active_pure_elements = [
el.upper() for constituents in desired_active_pure_elements
for el in constituents
]
pure_elements = sorted(
set([x for x in desired_active_pure_elements if x != 'VA']))
other_output_callables = {}
if verbose:
print('Components:', ' '.join([str(x) for x in comps]))
print('Phases:', end=' ')
output = output if output is not None else 'GM'
output = output if isinstance(output, (list, tuple, set)) else [output]
output = set(output)
output |= {'GM'}
output = sorted(output)
for o in output:
if o == 'GM':
eq_callables = build_callables(dbf,
comps,
active_phases,
model=model,
parameters=parameters,
output=o,
build_gradients=True,
callables=callables,
verbose=verbose)
else:
other_output_callables[o] = build_callables(dbf,
comps,
active_phases,
model=model,
parameters=parameters,
output=o,
build_gradients=False,
verbose=False)
phase_records = eq_callables['phase_records']
models = eq_callables['model']
maximum_internal_dof = max(
len(mod.site_fractions) for mod in models.values())
if verbose:
print('[done]', end='\n')
# 'calculate' accepts conditions through its keyword arguments
grid_opts = calc_opts.copy()
grid_opts.update(
{key: value
for key, value in str_conds.items() if key in indep_vars})
if 'pdens' not in grid_opts:
grid_opts['pdens'] = 500
coord_dict = str_conds.copy()
coord_dict['vertex'] = np.arange(
len(pure_elements) + 1
) # +1 is to accommodate the degenerate degree of freedom at the invariant reactions
grid_shape = np.meshgrid(*coord_dict.values(), indexing='ij',
sparse=False)[0].shape
coord_dict['component'] = pure_elements
grid = delayed(calculate, pure=False)(dbf,
comps,
active_phases,
output='GM',
model=models,
fake_points=True,
callables=eq_callables,
parameters=parameters,
**grid_opts)
max_phase_name_len = max(len(name) for name in active_phases)
# Need to allow for '_FAKE_' psuedo-phase
max_phase_name_len = max(max_phase_name_len, 6)
properties = delayed(Dataset, pure=False)(
{
'NP': (list(str_conds.keys()) + ['vertex'], np.empty(grid_shape)),
'GM': (list(str_conds.keys()), np.empty(grid_shape[:-1])),
'MU': (list(str_conds.keys()) + ['component'],
np.empty(grid_shape[:-1] + (len(pure_elements), ))),
'X': (list(str_conds.keys()) + ['vertex', 'component'],
np.empty(grid_shape + (len(pure_elements), ))),
'Y': (list(str_conds.keys()) + ['vertex', 'internal_dof'],
np.empty(grid_shape + (maximum_internal_dof, ))),
'Phase': (list(str_conds.keys()) + ['vertex'],
np.empty(grid_shape, dtype='U%s' % max_phase_name_len)),
'points': (list(str_conds.keys()) + ['vertex'],
np.empty(grid_shape, dtype=np.int32))
},
coords=coord_dict,
attrs={
'engine': 'pycalphad %s' % pycalphad_version
},
)
# One last call to ensure 'properties' and 'grid' are consistent with one another
properties = delayed(lower_convex_hull, pure=False)(grid, properties)
conditions_per_chunk_per_axis = 2
if num_calcs > 1:
# Generate slices of 'properties'
slices = []
for val in grid_shape[:-1]:
idx_arr = list(range(val))
num_chunks = int(np.floor(val / conditions_per_chunk_per_axis))
if num_chunks > 0:
cond_slices = [
x for x in np.array_split(np.asarray(idx_arr), num_chunks)
if len(x) > 0
]
else:
cond_slices = [idx_arr]
slices.append(cond_slices)
chunk_dims = [len(slc) for slc in slices]
chunk_grid = np.array(
np.unravel_index(np.arange(np.prod(chunk_dims)), chunk_dims)).T
res = []
for chunk in chunk_grid:
prop_slice = properties[OrderedDict(
list(
zip(str_conds.keys(), [
np.atleast_1d(sl)[ch] for ch, sl in zip(chunk, slices)
])))]
job = delayed(_solve_eq_at_conditions,
pure=False)(comps,
prop_slice,
phase_records,
grid,
list(str_conds.keys()),
verbose,
solver=solver)
res.append(job)
properties = delayed(_merge_property_slices,
pure=False)(properties, chunk_grid, slices,
list(str_conds.keys()), res)
else:
# Single-process job; don't create child processes
properties = delayed(_solve_eq_at_conditions,
pure=False)(comps,
properties,
phase_records,
grid,
list(str_conds.keys()),
verbose,
solver=solver)
# Compute equilibrium values of any additional user-specified properties
# We already computed these properties so don't recompute them
output = sorted(set(output) - {'GM', 'MU'})
for out in output:
if (out is None) or (len(out) == 0):
continue
# TODO: How do we know if a specified property should be per_phase or not?
# For now, we make a best guess
if (out == 'degree_of_ordering') or (out == 'DOO'):
per_phase = True
else:
per_phase = False
eqcal = delayed(_eqcalculate,
pure=False)(dbf,
comps,
active_phases,
conditions,
out,
data=properties,
per_phase=per_phase,
callables=other_output_callables[out],
parameters=parameters,
model=models,
**calc_opts)
properties = delayed(properties.merge, pure=False)(eqcal,
inplace=True,
compat='equals')
if scheduler is not None:
properties = dask.compute(properties, scheduler=scheduler)[0]
properties.attrs['created'] = datetime.utcnow().isoformat()
if len(kwargs) > 0:
warnings.warn(
'The following equilibrium keyword arguments were passed, but unused:\n{}'
.format(kwargs))
return properties
def build_phase_records(dbf,
comps,
phases,
state_variables,
models,
output='GM',
callables=None,
parameters=None,
verbose=False,
build_gradients=True,
build_hessians=True):
"""
Combine compiled callables and callables from conditions into PhaseRecords.
Parameters
----------
dbf : Database
A Database object
comps : List[Union[str, v.Species]]
List of active pure elements or species.
phases : list
List of phase names
state_variables : Iterable[v.StateVariable]
State variables used to produce the generated functions.
models : Mapping[str, Model]
Mapping of phase names to model instances
parameters : dict, optional
Maps SymEngine Symbol to numbers, for overriding the values of parameters in the Database.
callables : dict, optional
Pre-computed callables. If None are passed, they will be built.
Maps {'output' -> {'function' -> {'phase_name' -> AutowrapFunction()}}
output : str
Output property of the particular Model to sample
verbose : bool, optional
Print the name of the phase when its callables are built
build_gradients : bool
Whether or not to build gradient functions. Defaults to False. Only
takes effect if callables are not passed.
build_hessians : bool
Whether or not to build Hessian functions. Defaults to False. Only
takes effect if callables are not passed.
Returns
-------
dict
Dictionary mapping phase names to PhaseRecord instances.
Notes
-----
If callables are passed, don't rebuild them. This means that the callables
are not checked for incompatibility. Users of build_callables are
responsible for ensuring that the state variables, parameters and models
used to construct the callables are compatible with the ones used to
build the constraints and phase records.
"""
comps = sorted(unpack_components(dbf, comps))
parameters = parameters if parameters is not None else {}
callables = callables if callables is not None else {}
_constraints = {
'internal_cons_func': {},
'internal_cons_jac': {},
'internal_cons_hess': {},
}
phase_records = {}
state_variables = sorted(get_state_variables(models=models,
conds=state_variables),
key=str)
param_symbols, param_values = extract_parameters(parameters)
if callables.get(output) is None:
callables = build_callables(dbf,
comps,
phases,
models,
parameter_symbols=parameters.keys(),
output=output,
additional_statevars=state_variables,
build_gradients=False,
build_hessians=False)
# Temporary solution. PhaseRecord needs rework: https://github.com/pycalphad/pycalphad/pull/329#discussion_r634579356
formulacallables = build_callables(dbf,
comps,
phases,
models,
parameter_symbols=parameters.keys(),
output='G',
additional_statevars=state_variables,
build_gradients=build_gradients,
build_hessians=build_hessians)
# If a vector of parameters is specified, only pass the first row to the PhaseRecord
# Future callers of PhaseRecord.obj_parameters_2d() can pass the full param_values array as an argument
if len(param_values.shape) > 1:
param_values = param_values[0]
for name in phases:
mod = models[name]
site_fracs = mod.site_fractions
# build constraint functions
cfuncs = build_constraints(mod,
state_variables + site_fracs,
parameters=param_symbols)
_constraints['internal_cons_func'][name] = cfuncs.internal_cons_func
_constraints['internal_cons_jac'][name] = cfuncs.internal_cons_jac
_constraints['internal_cons_hess'][name] = cfuncs.internal_cons_hess
num_internal_cons = cfuncs.num_internal_cons
phase_records[name.upper()] = PhaseRecord(
comps, state_variables, site_fracs, param_values,
callables[output]['callables'][name],
formulacallables['G']['callables'][name],
formulacallables['G']['grad_callables'][name],
formulacallables['G']['hess_callables'][name],
callables[output]['massfuncs'][name],
formulacallables['G']['formulamolefuncs'][name],
formulacallables['G']['formulamolegradfuncs'][name],
formulacallables['G']['formulamolehessfuncs'][name],
_constraints['internal_cons_func'][name],
_constraints['internal_cons_jac'][name],
_constraints['internal_cons_hess'][name], num_internal_cons)
if verbose:
print(name + ' ')
return phase_records
def eq_callables_dict(dbf,
comps,
phases,
model=None,
param_symbols=None,
output='GM',
build_gradients=True):
"""
Create a dictionary of callable dictionaries for phases in equilibrium
Parameters
----------
dbf : pycalphad.Database
A pycalphad Database object
comps : list
List of component names
phases : list
List of phase names
model : dict or type
Dictionary of {phase_name: Model subclass} or a type corresponding to a
Model subclass. Defaults to ``Model``.
param_symbols : list
SymPy Symbol objects that will be preserved in the callable functions.
output : str
Output property of the particular Model to sample
build_gradients : bool
Whether or not to build gradient functions. Defaults to True.
Returns
-------
dict
Dictionary of keyword argument callables to pass to equilibrium.
Notes
-----
Based on the pycalphad equilibrium method for building phases as of commit 37ff75ce.
Examples
--------
>>> dbf = Database('AL-NI.tdb')
>>> comps = ['AL', 'NI', 'VA']
>>> phases = ['FCC_L12', 'BCC_B2', 'LIQUID', 'AL3NI5', 'AL3NI2', 'AL3NI']
>>> eq_callables = eq_callables_dict(dbf, comps, phases)
>>> equilibrium(dbf, comps, phases, conditions, **eq_callables)
"""
comps = sorted(unpack_components(dbf, comps))
pure_elements = get_pure_elements(dbf, comps)
eq_callables = {
'massfuncs': {},
'massgradfuncs': {},
'callables': {},
'grad_callables': {},
'hess_callables': {},
}
models = unpack_kwarg(model, default_arg=Model)
param_symbols = param_symbols if param_symbols is not None else []
# wrap param symbols in Symbols if they are strings
if all([isinstance(sym, string_types) for sym in param_symbols]):
param_symbols = [Symbol(sym) for sym in param_symbols]
param_values = np.zeros_like(param_symbols, dtype=np.float64)
phase_records = {}
# create models
# starting from pycalphad
for name in phases:
mod = models[name]
if isinstance(mod, type):
models[name] = mod = mod(dbf, comps, name)
site_fracs = mod.site_fractions
variables = sorted(site_fracs, key=str)
try:
out = getattr(mod, output)
except AttributeError:
raise AttributeError(
'Missing Model attribute {0} specified for {1}'.format(
output, mod.__class__))
# Build the callables of the output
# Only force undefineds to zero if we're not overriding them
undefs = list(
out.atoms(Symbol) - out.atoms(v.StateVariable) -
set(param_symbols))
for undef in undefs:
out = out.xreplace({undef: float(0)})
build_output = build_functions(out,
tuple([v.P, v.T] + site_fracs),
parameters=param_symbols,
include_grad=build_gradients)
if build_gradients:
cf, gf = build_output
else:
cf = build_output
gf = None
hf = None
eq_callables['callables'][name] = cf
eq_callables['grad_callables'][name] = gf
eq_callables['hess_callables'][name] = hf
# Build the callables for mass
# TODO: In principle, we should also check for undefs in mod.moles()
if build_gradients:
mcf, mgf = zip(*[
build_functions(mod.moles(el), [v.P, v.T] + variables,
include_obj=True,
include_grad=build_gradients,
parameters=param_symbols)
for el in pure_elements
])
else:
mcf = tuple([
build_functions(mod.moles(el), [v.P, v.T] + variables,
include_obj=True,
include_grad=build_gradients,
parameters=param_symbols)
for el in pure_elements
])
mgf = None
eq_callables['massfuncs'][name] = mcf
eq_callables['massgradfuncs'][name] = mgf
# creating the phase records triggers the compile
phase_records[name.upper()] = PhaseRecord_from_cython(
comps, variables,
np.array(dbf.phases[name].sublattices, dtype=np.float),
param_values, eq_callables['callables'][name],
eq_callables['grad_callables'][name],
eq_callables['hess_callables'][name],
eq_callables['massfuncs'][name],
eq_callables['massgradfuncs'][name])
# finally, add the models to the eq_callables
eq_callables['model'] = dict(models)
return eq_callables
def equilibrium(dbf, comps, phases, conditions, output=None, model=None,
verbose=False, broadcast=True, calc_opts=None,
scheduler=dask.local.get_sync,
parameters=None, **kwargs):
"""
Calculate the equilibrium state of a system containing the specified
components and phases, under the specified conditions.
Parameters
----------
dbf : Database
Thermodynamic database containing the relevant parameters.
comps : list
Names of components to consider in the calculation.
phases : list or dict
Names of phases to consider in the calculation.
conditions : dict or (list of dict)
StateVariables and their corresponding value.
output : str or list of str, optional
Additional equilibrium model properties (e.g., CPM, HM, etc.) to compute.
These must be defined as attributes in the Model class of each phase.
model : Model, a dict of phase names to Model, or a seq of both, optional
Model class to use for each phase.
verbose : bool, optional
Print details of calculations. Useful for debugging.
broadcast : bool
If True, broadcast conditions against each other. This will compute all combinations.
If False, each condition should be an equal-length list (or single-valued).
Disabling broadcasting is useful for calculating equilibrium at selected conditions,
when those conditions don't comprise a grid.
calc_opts : dict, optional
Keyword arguments to pass to `calculate`, the energy/property calculation routine.
scheduler : Dask scheduler, optional
Job scheduler for performing the computation.
If None, return a Dask graph of the computation instead of actually doing it.
parameters : dict, optional
Maps SymPy Symbol to numbers, for overriding the values of parameters in the Database.
Returns
-------
Structured equilibrium calculation, or Dask graph if scheduler=None.
Examples
--------
None yet.
"""
if not broadcast:
raise NotImplementedError('Broadcasting cannot yet be disabled')
from pycalphad import __version__ as pycalphad_version
comps = sorted(unpack_components(dbf, comps))
phases = unpack_phases(phases) or sorted(dbf.phases.keys())
# remove phases that cannot be active
list_of_possible_phases = filter_phases(dbf, comps)
active_phases = sorted(set(list_of_possible_phases).intersection(set(phases)))
if len(list_of_possible_phases) == 0:
raise ConditionError('There are no phases in the Database that can be active with components {0}'.format(comps))
if len(active_phases) == 0:
raise ConditionError('None of the passed phases ({0}) are active. List of possible phases: {1}.'.format(phases, list_of_possible_phases))
if isinstance(comps, (str, v.Species)):
comps = [comps]
if len(set(comps) - set(dbf.species)) > 0:
raise EquilibriumError('Components not found in database: {}'
.format(','.join([c.name for c in (set(comps) - set(dbf.species))])))
indep_vars = ['T', 'P']
calc_opts = calc_opts if calc_opts is not None else dict()
model = model if model is not None else Model
phase_records = dict()
diagnostic = kwargs.pop('_diagnostic', False)
callable_dict = kwargs.pop('callables', dict())
mass_dict = unpack_kwarg(kwargs.pop('massfuncs', None), default_arg=None)
mass_grad_dict = unpack_kwarg(kwargs.pop('massgradfuncs', None), default_arg=None)
grad_callable_dict = kwargs.pop('grad_callables', dict())
hess_callable_dict = kwargs.pop('hess_callables', dict())
parameters = parameters if parameters is not None else dict()
if isinstance(parameters, dict):
parameters = OrderedDict(sorted(parameters.items(), key=str))
param_symbols = tuple(parameters.keys())
param_values = np.atleast_1d(np.array(list(parameters.values()), dtype=np.float))
maximum_internal_dof = 0
# Modify conditions values to be within numerical limits, e.g., X(AL)=0
# Also wrap single-valued conditions with lists
conds = _adjust_conditions(conditions)
for cond in conds.keys():
if isinstance(cond, (v.Composition, v.ChemicalPotential)) and cond.species not in comps:
raise ConditionError('{} refers to non-existent component'.format(cond))
str_conds = OrderedDict((str(key), value) for key, value in conds.items())
num_calcs = np.prod([len(i) for i in str_conds.values()])
indep_vals = list([float(x) for x in np.atleast_1d(val)]
for key, val in str_conds.items() if key in indep_vars)
components = [x for x in sorted(comps)]
desired_active_pure_elements = [list(x.constituents.keys()) for x in components]
desired_active_pure_elements = [el.upper() for constituents in desired_active_pure_elements for el in constituents]
pure_elements = sorted(set([x for x in desired_active_pure_elements if x != 'VA']))
# Construct models for each phase; prioritize user models
models = unpack_kwarg(model, default_arg=Model)
if verbose:
print('Components:', ' '.join([str(x) for x in comps]))
print('Phases:', end=' ')
max_phase_name_len = max(len(name) for name in active_phases)
# Need to allow for '_FAKE_' psuedo-phase
max_phase_name_len = max(max_phase_name_len, 6)
for name in active_phases:
mod = models[name]
if isinstance(mod, type):
models[name] = mod = mod(dbf, comps, name, parameters=parameters)
site_fracs = mod.site_fractions
variables = sorted(site_fracs, key=str)
maximum_internal_dof = max(maximum_internal_dof, len(site_fracs))
out = models[name].energy
if (not callable_dict.get(name, False)) or not (grad_callable_dict.get(name, False)):
# Only force undefineds to zero if we're not overriding them
undefs = [x for x in out.free_symbols if (not isinstance(x, v.StateVariable)) and not (x in param_symbols)]
for undef in undefs:
out = out.xreplace({undef: float(0)})
cf, gf = build_functions(out, tuple([v.P, v.T] + site_fracs),
parameters=param_symbols)
hf = None
if callable_dict.get(name, None) is None:
callable_dict[name] = cf
if grad_callable_dict.get(name, None) is None:
grad_callable_dict[name] = gf
if hess_callable_dict.get(name, None) is None:
hess_callable_dict[name] = hf
if (mass_dict[name] is None) or (mass_grad_dict[name] is None):
# TODO: In principle, we should also check for undefs in mod.moles()
tup1, tup2 = zip(*[build_functions(mod.moles(el), [v.P, v.T] + variables,
include_obj=True, include_grad=True,
parameters=param_symbols)
for el in pure_elements])
if mass_dict[name] is None:
mass_dict[name] = tup1
if mass_grad_dict[name] is None:
mass_grad_dict[name] = tup2
phase_records[name.upper()] = PhaseRecord_from_cython(comps, variables,
np.array(dbf.phases[name].sublattices, dtype=np.float),
param_values, callable_dict[name],
grad_callable_dict[name], hess_callable_dict[name],
mass_dict[name], mass_grad_dict[name])
if verbose:
print(name, end=' ')
if verbose:
print('[done]', end='\n')
# 'calculate' accepts conditions through its keyword arguments
grid_opts = calc_opts.copy()
grid_opts.update({key: value for key, value in str_conds.items() if key in indep_vars})
if 'pdens' not in grid_opts:
grid_opts['pdens'] = 500
coord_dict = str_conds.copy()
coord_dict['vertex'] = np.arange(len(pure_elements))
grid_shape = np.meshgrid(*coord_dict.values(),
indexing='ij', sparse=False)[0].shape
coord_dict['component'] = pure_elements
grid = delayed(calculate, pure=False)(dbf, comps, active_phases, output='GM',
model=models, callables=callable_dict, massfuncs=mass_dict,
fake_points=True, parameters=parameters, **grid_opts)
properties = delayed(Dataset, pure=False)({'NP': (list(str_conds.keys()) + ['vertex'],
np.empty(grid_shape)),
'GM': (list(str_conds.keys()),
np.empty(grid_shape[:-1])),
'MU': (list(str_conds.keys()) + ['component'],
np.empty(grid_shape)),
'X': (list(str_conds.keys()) + ['vertex', 'component'],
np.empty(grid_shape + (grid_shape[-1],))),
'Y': (list(str_conds.keys()) + ['vertex', 'internal_dof'],
np.empty(grid_shape + (maximum_internal_dof,))),
'Phase': (list(str_conds.keys()) + ['vertex'],
np.empty(grid_shape, dtype='U%s' % max_phase_name_len)),
'points': (list(str_conds.keys()) + ['vertex'],
np.empty(grid_shape, dtype=np.int32))
},
coords=coord_dict,
attrs={'engine': 'pycalphad %s' % pycalphad_version},
)
# One last call to ensure 'properties' and 'grid' are consistent with one another
properties = delayed(lower_convex_hull, pure=False)(grid, properties)
conditions_per_chunk_per_axis = 2
if num_calcs > 1:
# Generate slices of 'properties'
slices = []
for val in grid_shape[:-1]:
idx_arr = list(range(val))
num_chunks = int(np.floor(val/conditions_per_chunk_per_axis))
if num_chunks > 0:
cond_slices = [x for x in np.array_split(np.asarray(idx_arr), num_chunks) if len(x) > 0]
else:
cond_slices = [idx_arr]
slices.append(cond_slices)
chunk_dims = [len(slc) for slc in slices]
chunk_grid = np.array(np.unravel_index(np.arange(np.prod(chunk_dims)), chunk_dims)).T
res = []
for chunk in chunk_grid:
prop_slice = properties[OrderedDict(list(zip(str_conds.keys(),
[np.atleast_1d(sl)[ch] for ch, sl in zip(chunk, slices)])))]
job = delayed(_solve_eq_at_conditions, pure=False)(comps, prop_slice, phase_records, grid,
list(str_conds.keys()), verbose)
res.append(job)
properties = delayed(_merge_property_slices, pure=False)(properties, chunk_grid, slices, list(str_conds.keys()), res)
else:
# Single-process job; don't create child processes
properties = delayed(_solve_eq_at_conditions, pure=False)(comps, properties, phase_records, grid,
list(str_conds.keys()), verbose)
# Compute equilibrium values of any additional user-specified properties
output = output if isinstance(output, (list, tuple, set)) else [output]
# We already computed these properties so don't recompute them
output = sorted(set(output) - {'GM', 'MU'})
for out in output:
if (out is None) or (len(out) == 0):
continue
# TODO: How do we know if a specified property should be per_phase or not?
# For now, we make a best guess
if (out == 'degree_of_ordering') or (out == 'DOO'):
per_phase = True
else:
per_phase = False
eqcal = delayed(_eqcalculate, pure=False)(dbf, comps, active_phases, conditions, out,
data=properties, per_phase=per_phase, model=models, **calc_opts)
properties = delayed(properties.merge, pure=False)(eqcal, inplace=True, compat='equals')
if scheduler is not None:
properties = dask.compute(properties, get=scheduler)[0]
properties.attrs['created'] = datetime.utcnow().isoformat()
if len(kwargs) > 0:
warnings.warn('The following equilibrium keyword arguments were passed, but unused:\n{}'.format(kwargs))
return properties
def build_callables(dbf,
comps,
phases,
model=None,
parameters=None,
callables=None,
output='GM',
build_gradients=True,
verbose=False):
"""
Create dictionaries of callable dictionaries and PhaseRecords.
Parameters
----------
dbf : Database
A Database object
comps : list
List of component names
phases : list
List of phase names
model : dict or type
Dictionary of {phase_name: Model subclass} or a type corresponding to a
Model subclass. Defaults to ``Model``.
parameters : dict, optional
Maps SymPy Symbol to numbers, for overriding the values of parameters in the Database.
callables : dict, optional
Pre-computed callables
output : str
Output property of the particular Model to sample
build_gradients : bool
Whether or not to build gradient functions. Defaults to True.
verbose : bool
Print the name of the phase when its callables are built
Returns
-------
callables : dict
Dictionary of keyword argument callables to pass to equilibrium.
Example
-------
>>> dbf = Database('AL-NI.tdb')
>>> comps = ['AL', 'NI', 'VA']
>>> phases = ['FCC_L12', 'BCC_B2', 'LIQUID', 'AL3NI5', 'AL3NI2', 'AL3NI']
>>> callables = build_callables(dbf, comps, phases)
>>> equilibrium(dbf, comps, phases, conditions, **callables)
"""
parameters = parameters if parameters is not None else {}
if len(parameters) > 0:
param_symbols, param_values = zip(*[(key, val) for key, val in sorted(
parameters.items(), key=operator.itemgetter(0))])
param_values = np.asarray(param_values, dtype=np.float64)
else:
param_symbols = []
param_values = np.empty(0)
comps = sorted(unpack_components(dbf, comps))
pure_elements = get_pure_elements(dbf, comps)
callables = callables if callables is not None else {}
_callables = {
'massfuncs': {},
'massgradfuncs': {},
'callables': {},
'grad_callables': {}
}
models = unpack_kwarg(model, default_arg=Model)
param_symbols = [wrap_symbol(sym) for sym in param_symbols]
phase_records = {}
# create models
for name in phases:
mod = models[name]
if isinstance(mod, type):
models[name] = mod = mod(dbf,
comps,
name,
parameters=param_symbols)
site_fracs = mod.site_fractions
variables = sorted(site_fracs, key=str)
try:
out = getattr(mod, output)
except AttributeError:
raise AttributeError(
'Missing Model attribute {0} specified for {1}'.format(
output, mod.__class__))
if callables.get('callables', {}).get(name, False) and \
((not build_gradients) or callables.get('grad_callables',{}).get(name, False)):
_callables['callables'][name] = callables['callables'][name]
if build_gradients:
_callables['grad_callables'][name] = callables[
'grad_callables'][name]
else:
_callables['grad_callables'][name] = None
else:
# Build the callables of the output
# Only force undefineds to zero if we're not overriding them
undefs = {
x
for x in out.free_symbols
if not isinstance(x, v.StateVariable)
} - set(param_symbols)
undef_vals = repeat(0., len(undefs))
out = out.xreplace(dict(zip(undefs, undef_vals)))
build_output = build_functions(out,
tuple([v.P, v.T] + site_fracs),
parameters=param_symbols,
include_grad=build_gradients)
if build_gradients:
cf, gf = build_output
else:
cf = build_output
gf = None
_callables['callables'][name] = cf
_callables['grad_callables'][name] = gf
if callables.get('massfuncs', {}).get(name, False) and \
((not build_gradients) or callables.get('massgradfuncs', {}).get(name, False)):
_callables['massfuncs'][name] = callables['massfuncs'][name]
if build_gradients:
_callables['massgradfuncs'][name] = callables['massgradfuncs'][
name]
else:
_callables['massgradfuncs'][name] = None
else:
# Build the callables for mass
# TODO: In principle, we should also check for undefs in mod.moles()
if build_gradients:
mcf, mgf = zip(*[
build_functions(mod.moles(el), [v.P, v.T] + variables,
include_obj=True,
include_grad=build_gradients,
parameters=param_symbols)
for el in pure_elements
])
else:
mcf = tuple([
build_functions(mod.moles(el), [v.P, v.T] + variables,
include_obj=True,
include_grad=build_gradients,
parameters=param_symbols)
for el in pure_elements
])
mgf = None
_callables['massfuncs'][name] = mcf
_callables['massgradfuncs'][name] = mgf
if not callables.get('phase_records', {}).get(name, False):
pv = param_values
else:
# Copy parameter values from old PhaseRecord, if it exists
pv = callables['phase_records'][name].parameters
phase_records[name.upper()] = PhaseRecord_from_cython(
comps, variables,
np.array(dbf.phases[name].sublattices, dtype=np.float), pv,
_callables['callables'][name], _callables['grad_callables'][name],
_callables['massfuncs'][name], _callables['massgradfuncs'][name])
if verbose:
print(name + ' ')
# Update PhaseRecords with any user-specified parameter values, in case we skipped the build phase
# We assume here that users know what they are doing, and pass compatible combinations of callables and parameters
# See discussion in gh-192 for details
if len(param_values) > 0:
for prx_name in phase_records:
if len(phase_records[prx_name].parameters) != len(param_values):
raise ValueError(
'User-specified callables and parameters are incompatible')
phase_records[prx_name].parameters = param_values
# finally, add the models to the callables
_callables['model'] = dict(models)
_callables['phase_records'] = phase_records
return _callables
def build_callables(dbf, comps, phases, models, parameter_symbols=None,
output='GM', build_gradients=True, build_hessians=False,
additional_statevars=None):
"""
Create a compiled callables dictionary.
Parameters
----------
dbf : Database
A Database object
comps : list
List of component names
phases : list
List of phase names
models : dict
Dictionary of {phase_name: Model subclass}
parameter_symbols : list, optional
List of string or SymPy Symbols that will be overridden in the callables.
output : str, optional
Output property of the particular Model to sample. Defaults to 'GM'
build_gradients : bool, optional
Whether or not to build gradient functions. Defaults to True.
build_hessians : bool, optional
Whether or not to build Hessian functions. Defaults to False.
additional_statevars : set, optional
State variables to include in the callables that may not be in the models (e.g. from conditions)
verbose : bool, optional
Print the name of the phase when its callables are built
Returns
-------
callables : dict
Dictionary of keyword argument callables to pass to equilibrium.
Maps {'output' -> {'function' -> {'phase_name' -> AutowrapFunction()}}.
Notes
-----
*All* the state variables used in calculations must be specified.
If these are not specified as state variables of the models (e.g. often the
case for v.N), then it must be supplied by the additional_statevars keyword
argument.
Examples
--------
>>> from pycalphad import Database, equilibrium, variables as v
>>> from pycalphad.codegen.callables import build_callables
>>> from pycalphad.core.utils import instantiate_models
>>> dbf = Database('AL-NI.tdb')
>>> comps = ['AL', 'NI', 'VA']
>>> phases = ['LIQUID', 'AL3NI5', 'AL3NI2', 'AL3NI']
>>> models = instantiate_models(dbf, comps, phases)
>>> callables = build_callables(dbf, comps, phases, models, additional_statevars={v.P, v.T, v.N})
>>> 'GM' in callables.keys()
True
>>> 'massfuncs' in callables['GM']
True
>>> conditions = {v.P: 101325, v.T: 2500, v.X('AL'): 0.2}
>>> equilibrium(dbf, comps, phases, conditions, callables=callables)
"""
additional_statevars = set(additional_statevars) if additional_statevars is not None else set()
parameter_symbols = parameter_symbols if parameter_symbols is not None else []
parameter_symbols = sorted([wrap_symbol(x) for x in parameter_symbols], key=str)
comps = sorted(unpack_components(dbf, comps))
pure_elements = get_pure_elements(dbf, comps)
_callables = {
'massfuncs': {},
'massgradfuncs': {},
'masshessfuncs': {},
'callables': {},
'grad_callables': {},
'hess_callables': {},
'internal_cons': {},
'internal_jac': {},
'internal_cons_hess': {},
'mp_cons': {},
'mp_jac': {},
}
state_variables = get_state_variables(models=models)
state_variables |= additional_statevars
if state_variables != {v.T, v.P, v.N}:
warnings.warn("State variables in `build_callables` are not {{N, P, T}}, "
"but {}. Be sure you know what you are doing. "
"State variables can be added with the `additional_statevars` "
"argument.".format(state_variables))
state_variables = sorted(state_variables, key=str)
for name in phases:
mod = models[name]
site_fracs = mod.site_fractions
try:
out = getattr(mod, output)
except AttributeError:
raise AttributeError('Missing Model attribute {0} specified for {1}'
.format(output, mod.__class__))
# Build the callables of the output
# Only force undefineds to zero if we're not overriding them
undefs = {x for x in out.free_symbols if not isinstance(x, v.StateVariable)} - set(parameter_symbols)
undef_vals = repeat(0., len(undefs))
out = out.xreplace(dict(zip(undefs, undef_vals)))
build_output = build_functions(out, tuple(state_variables + site_fracs), parameters=parameter_symbols,
include_grad=build_gradients, include_hess=build_hessians)
cf, gf, hf = build_output.func, build_output.grad, build_output.hess
_callables['callables'][name] = cf
_callables['grad_callables'][name] = gf
_callables['hess_callables'][name] = hf
# Build the callables for mass
# TODO: In principle, we should also check for undefs in mod.moles()
mcf, mgf, mhf = zip(*[build_functions(mod.moles(el), state_variables + site_fracs,
include_obj=True,
include_grad=build_gradients,
include_hess=build_hessians,
parameters=parameter_symbols)
for el in pure_elements])
_callables['massfuncs'][name] = mcf
_callables['massgradfuncs'][name] = mgf
_callables['masshessfuncs'][name] = mhf
return {output: _callables}
def build_eqpropdata(
data: tinydb.database.Document,
dbf: Database,
parameters: Optional[Dict[str, float]] = None,
data_weight_dict: Optional[Dict[str, float]] = None) -> EqPropData:
"""
Build EqPropData for the calculations corresponding to a single dataset.
Parameters
----------
data : tinydb.database.Document
Document corresponding to a single ESPEI dataset.
dbf : Database
Database that should be used to construct the `Model` and `PhaseRecord` objects.
parameters : Optional[Dict[str, float]]
Mapping of parameter symbols to values.
data_weight_dict : Optional[Dict[str, float]]
Mapping of a data type (e.g. `HM` or `SM`) to a weight.
Returns
-------
EqPropData
"""
parameters = parameters if parameters is not None else {}
data_weight_dict = data_weight_dict if data_weight_dict is not None else {}
property_std_deviation = {
'HM': 500.0, # J/mol
'SM': 0.2, # J/K-mol
'CPM': 0.2, # J/K-mol
}
params_keys, _ = extract_parameters(parameters)
data_comps = list(set(data['components']).union({'VA'}))
species = sorted(unpack_components(dbf, data_comps), key=str)
data_phases = filter_phases(dbf, species, candidate_phases=data['phases'])
models = instantiate_models(dbf,
species,
data_phases,
parameters=parameters)
output = data['output']
property_output = output.split('_')[
0] # property without _FORM, _MIX, etc.
samples = np.array(data['values']).flatten()
reference = data.get('reference', '')
# Models are now modified in response to the data from this data
if 'reference_states' in data:
property_output = output[:-1] if output.endswith(
'R'
) else output # unreferenced model property so we can tell shift_reference_state what to build.
reference_states = []
for el, vals in data['reference_states'].items():
reference_states.append(
ReferenceState(
v.Species(el),
vals['phase'],
fixed_statevars=vals.get('fixed_state_variables')))
for mod in models.values():
mod.shift_reference_state(reference_states,
dbf,
output=(property_output, ))
data['conditions'].setdefault(
'N', 1.0
) # Add default for N. Nothing else is supported in pycalphad anyway.
pot_conds = OrderedDict([(getattr(v, key),
unpack_condition(data['conditions'][key]))
for key in sorted(data['conditions'].keys())
if not key.startswith('X_')])
comp_conds = OrderedDict([(v.X(key[2:]),
unpack_condition(data['conditions'][key]))
for key in sorted(data['conditions'].keys())
if key.startswith('X_')])
phase_records = build_phase_records(dbf,
species,
data_phases, {
**pot_conds,
**comp_conds
},
models,
parameters=parameters,
build_gradients=True,
build_hessians=True)
# Now we need to unravel the composition conditions
# (from Dict[v.X, Sequence[float]] to Sequence[Dict[v.X, float]]), since the
# composition conditions are only broadcast against the potentials, not
# each other. Each individual composition needs to be computed
# independently, since broadcasting over composition cannot be turned off
# in pycalphad.
rav_comp_conds = [
OrderedDict(zip(comp_conds.keys(), pt_comps))
for pt_comps in zip(*comp_conds.values())
]
# Build weights, should be the same size as the values
total_num_calculations = len(rav_comp_conds) * np.prod(
[len(vals) for vals in pot_conds.values()])
dataset_weights = np.array(data.get('weight',
1.0)) * np.ones(total_num_calculations)
weights = (property_std_deviation.get(property_output, 1.0) /
data_weight_dict.get(property_output, 1.0) /
dataset_weights).flatten()
return EqPropData(dbf, species, data_phases, pot_conds, rav_comp_conds,
models, params_keys, phase_records, output, samples,
weights, reference)
def __init__(self, dbe, comps, phase_name, parameters=None, build_reference=True):
self.components = set()
self.constituents = []
self.phase_name = phase_name.upper()
phase = dbe.phases[self.phase_name]
self.site_ratios = list(phase.sublattices)
for idx, sublattice in enumerate(phase.constituents):
subl_comps = set(sublattice).intersection(unpack_components(dbe, comps))
self.components |= subl_comps
# Support for variable site ratios in ionic liquid model
if phase.model_hints.get('ionic_liquid_2SL', False):
if idx == 0:
subl_idx = 1
elif idx == 1:
subl_idx = 0
else:
raise ValueError('Two-sublattice ionic liquid specified with more than two sublattices')
self.site_ratios[subl_idx] = Add(*[v.SiteFraction(self.phase_name, idx, spec) * abs(spec.charge) for spec in subl_comps])
if phase.model_hints.get('ionic_liquid_2SL', False):
# Special treatment of "neutral" vacancies in 2SL ionic liquid
# These are treated as having variable valence
for idx, sublattice in enumerate(phase.constituents):
subl_comps = set(sublattice).intersection(unpack_components(dbe, comps))
if v.Species('VA') in subl_comps:
if idx == 0:
subl_idx = 1
elif idx == 1:
subl_idx = 0
else:
raise ValueError('Two-sublattice ionic liquid specified with more than two sublattices')
self.site_ratios[subl_idx] += self.site_ratios[idx] * v.SiteFraction(self.phase_name, idx, v.Species('VA'))
self.site_ratios = tuple(self.site_ratios)
# Verify that this phase is still possible to build
for sublattice in phase.constituents:
if len(set(sublattice).intersection(self.components)) == 0:
# None of the components in a sublattice are active
# We cannot build a model of this phase
raise DofError(
'{0}: Sublattice {1} of {2} has no components in {3}' \
.format(self.phase_name, sublattice,
phase.constituents,
self.components))
self.constituents.append(set(sublattice).intersection(self.components))
self.components = sorted(self.components)
desired_active_pure_elements = [list(x.constituents.keys()) for x in self.components]
desired_active_pure_elements = [el.upper() for constituents in desired_active_pure_elements
for el in constituents]
self.pure_elements = sorted(set(desired_active_pure_elements))
self.nonvacant_elements = [x for x in self.pure_elements if x != 'VA']
# Convert string symbol names to sympy Symbol objects
# This makes xreplace work with the symbols dict
symbols = {Symbol(s): val for s, val in dbe.symbols.items()}
if parameters is not None:
self._parameters_arg = parameters
if isinstance(parameters, dict):
symbols.update([(wrap_symbol(s), val) for s, val in parameters.items()])
else:
# Lists of symbols that should remain symbolic
for s in parameters:
symbols.pop(wrap_symbol(s))
else:
self._parameters_arg = None
self._symbols = {wrap_symbol(key): value for key, value in symbols.items()}
self.models = OrderedDict()
self.build_phase(dbe)
# build reference model, this needs to be behind a flag to avoid recursion
if build_reference:
self.build_reference_model(dbe)
for name, value in self.models.items():
self.models[name] = self.symbol_replace(value, symbols)
self.site_fractions = sorted([x for x in self.variables if isinstance(x, v.SiteFraction)], key=str)
self.state_variables = sorted([x for x in self.variables if not isinstance(x, v.SiteFraction)], key=str)
def get_zpf_data(dbf: Database,
comps: Sequence[str],
phases: Sequence[str],
datasets: PickleableTinyDB,
parameters: Dict[str, float],
model: Optional[Dict[str, Type[Model]]] = None):
"""
Return the ZPF data used in the calculation of ZPF error
Parameters
----------
comps : list
List of active component names
phases : list
List of phases to consider
datasets : espei.utils.PickleableTinyDB
Datasets that contain single phase data
parameters : dict
Dictionary mapping symbols to optimize to their initial values
model : Optional[Dict[str, Type[Model]]]
Dictionary phase names to pycalphad Model classes.
Returns
-------
list
List of data dictionaries with keys ``weight``, ``phase_regions`` and ``dataset_references``.
"""
desired_data = datasets.search(
(tinydb.where('output') == 'ZPF')
& (tinydb.where('components').test(lambda x: set(x).issubset(comps)))
& (tinydb.where('phases').test(
lambda x: len(set(phases).intersection(x)) > 0)))
zpf_data = [] # 1:1 correspondence with each dataset
for data in desired_data:
data_comps = list(set(data['components']).union({'VA'}))
species = sorted(unpack_components(dbf, data_comps), key=str)
data_phases = filter_phases(dbf, species, candidate_phases=phases)
models = instantiate_models(dbf,
species,
data_phases,
model=model,
parameters=parameters)
# assumed N, P, T state variables
phase_recs = build_phase_records(dbf,
species,
data_phases, {v.N, v.P, v.T},
models,
parameters=parameters,
build_gradients=True,
build_hessians=True)
all_phase_points = {
phase_name: _sample_phase_constitution(models[phase_name],
point_sample, True, 50)
for phase_name in data_phases
}
all_regions = data['values']
conditions = data['conditions']
phase_regions = []
# Each phase_region is one set of phases in equilibrium (on a tie-line),
# e.g. [["ALPHA", ["B"], [0.25]], ["BETA", ["B"], [0.5]]]
for idx, phase_region in enumerate(all_regions):
# Extract the conditions for entire phase region
pot_conds = _extract_pot_conds(conditions, idx)
pot_conds.setdefault(v.N, 1.0) # Add v.N condition, if missing
# Extract all the phases and compositions from the tie-line points
vertices = []
for vertex in phase_region:
phase_name, comp_conds, disordered_flag = _extract_phases_comps(
vertex)
# Construct single-phase points satisfying the conditions for each phase in the region
mod = models[phase_name]
composition = _compute_vertex_composition(
data_comps, comp_conds)
if np.any(np.isnan(composition)):
# We can't construct points because we don't have a known composition
has_missing_comp_cond = True
phase_points = None
elif _phase_is_stoichiometric(mod):
has_missing_comp_cond = False
phase_points = None
else:
has_missing_comp_cond = False
# Only sample points that have an average mass residual within tol
tol = 0.02
phase_points = _subsample_phase_points(
phase_recs[phase_name], all_phase_points[phase_name],
composition, tol)
assert phase_points.shape[
0] > 0, f"phase {phase_name} must have at least one set of points within the target tolerance {pot_conds} {comp_conds}"
vtx = RegionVertex(phase_name, composition, comp_conds,
phase_points, phase_recs, disordered_flag,
has_missing_comp_cond)
vertices.append(vtx)
region = PhaseRegion(vertices, pot_conds, species, data_phases,
models)
phase_regions.append(region)
data_dict = {
'weight': data.get('weight', 1.0),
'phase_regions': phase_regions,
'dataset_reference': data['reference']
}
zpf_data.append(data_dict)
return zpf_data
def calculate(dbf,
comps,
phases,
mode=None,
output='GM',
fake_points=False,
broadcast=True,
parameters=None,
to_xarray=True,
**kwargs):
"""
Sample the property surface of 'output' containing the specified
components and phases. Model parameters are taken from 'dbf' and any
state variables (T, P, etc.) can be specified as keyword arguments.
Parameters
----------
dbf : Database
Thermodynamic database containing the relevant parameters.
comps : str or sequence
Names of components to consider in the calculation.
phases : str or sequence
Names of phases to consider in the calculation.
mode : string, optional
See 'make_callable' docstring for details.
output : string, optional
Model attribute to sample.
fake_points : bool, optional (Default: False)
If True, the first few points of the output surface will be fictitious
points used to define an equilibrium hyperplane guaranteed to be above
all the other points. This is used for convex hull computations.
broadcast : bool, optional
If True, broadcast given state variable lists against each other to create a grid.
If False, assume state variables are given as equal-length lists.
points : ndarray or a dict of phase names to ndarray, optional
Columns of ndarrays must be internal degrees of freedom (site fractions), sorted.
If this is not specified, points will be generated automatically.
pdens : int, a dict of phase names to int, or a seq of both, optional
Number of points to sample per degree of freedom.
Default: 2000; Default when called from equilibrium(): 500
model : Model, a dict of phase names to Model, or a seq of both, optional
Model class to use for each phase.
sampler : callable, a dict of phase names to callable, or a seq of both, optional
Function to sample phase constitution space.
Must have same signature as 'pycalphad.core.utils.point_sample'
grid_points : bool, a dict of phase names to bool, or a seq of both, optional (Default: True)
Whether to add evenly spaced points between end-members.
The density of points is determined by 'pdens'
parameters : dict, optional
Maps SymPy Symbol to numbers, for overriding the values of parameters in the Database.
Returns
-------
Dataset of the sampled attribute as a function of state variables
Examples
--------
None yet.
"""
# Here we check for any keyword arguments that are special, i.e.,
# there may be keyword arguments that aren't state variables
pdens_dict = unpack_kwarg(kwargs.pop('pdens', 2000), default_arg=2000)
points_dict = unpack_kwarg(kwargs.pop('points', None), default_arg=None)
callables = kwargs.pop('callables', {})
sampler_dict = unpack_kwarg(kwargs.pop('sampler', None), default_arg=None)
fixedgrid_dict = unpack_kwarg(kwargs.pop('grid_points', True),
default_arg=True)
parameters = parameters or dict()
if isinstance(parameters, dict):
parameters = OrderedDict(sorted(parameters.items(), key=str))
if isinstance(phases, str):
phases = [phases]
if isinstance(comps, (str, v.Species)):
comps = [comps]
comps = sorted(unpack_components(dbf, comps))
if points_dict is None and broadcast is False:
raise ValueError(
'The \'points\' keyword argument must be specified if broadcast=False is also given.'
)
nonvacant_components = [x for x in sorted(comps) if x.number_of_atoms > 0]
all_phase_data = []
largest_energy = 1e10
# Consider only the active phases
list_of_possible_phases = filter_phases(dbf, comps)
if len(list_of_possible_phases) == 0:
raise ConditionError(
'There are no phases in the Database that can be active with components {0}'
.format(comps))
active_phases = {
name: dbf.phases[name]
for name in filter_phases(dbf, comps, phases)
}
if len(active_phases) == 0:
raise ConditionError(
'None of the passed phases ({0}) are active. List of possible phases: {1}.'
.format(phases, list_of_possible_phases))
models = instantiate_models(dbf,
comps,
list(active_phases.keys()),
model=kwargs.pop('model', None),
parameters=parameters)
if isinstance(output, (list, tuple, set)):
raise NotImplementedError(
'Only one property can be specified in calculate() at a time')
output = output if output is not None else 'GM'
# Implicitly add 'N' state variable as a string to keyword arguements if it's not passed
if kwargs.get('N') is None:
kwargs['N'] = 1
if np.any(np.array(kwargs['N']) != 1):
raise ConditionError('N!=1 is not yet supported, got N={}'.format(
kwargs['N']))
# TODO: conditions dict of StateVariable instances should become part of the calculate API
statevar_strings = [
sv for sv in kwargs.keys() if getattr(v, sv) is not None
]
# If we don't do this, sympy will get confused during substitution
statevar_dict = dict((v.StateVariable(key), unpack_condition(value))
for key, value in kwargs.items()
if key in statevar_strings)
# Sort after default state variable check to fix gh-116
statevar_dict = collections.OrderedDict(
sorted(statevar_dict.items(), key=lambda x: str(x[0])))
phase_records = build_phase_records(dbf,
comps,
active_phases,
statevar_dict,
models=models,
parameters=parameters,
output=output,
callables=callables,
build_gradients=False,
build_hessians=False,
verbose=kwargs.pop('verbose', False))
str_statevar_dict = collections.OrderedDict((str(key), unpack_condition(value)) \
for (key, value) in statevar_dict.items())
maximum_internal_dof = max(
len(models[phase_name].site_fractions) for phase_name in active_phases)
for phase_name, phase_obj in sorted(active_phases.items()):
mod = models[phase_name]
phase_record = phase_records[phase_name]
points = points_dict[phase_name]
variables, sublattice_dof = generate_dof(phase_obj, mod.components)
if points is None:
points = _sample_phase_constitution(
phase_name, phase_obj.constituents, sublattice_dof, comps,
tuple(variables), sampler_dict[phase_name] or point_sample,
fixedgrid_dict[phase_name], pdens_dict[phase_name])
points = np.atleast_2d(points)
fp = fake_points and (phase_name == sorted(active_phases.keys())[0])
phase_ds = _compute_phase_values(nonvacant_components,
str_statevar_dict,
points,
phase_record,
output,
maximum_internal_dof,
broadcast=broadcast,
largest_energy=float(largest_energy),
fake_points=fp)
all_phase_data.append(phase_ds)
# speedup for single-phase case (found by profiling)
if len(all_phase_data) > 1:
concatenated_coords = all_phase_data[0].coords
data_vars = all_phase_data[0].data_vars
concatenated_data_vars = {}
for var in data_vars.keys():
data_coords = data_vars[var][0]
points_idx = data_coords.index('points') # concatenation axis
arrs = []
for phase_data in all_phase_data:
arrs.append(getattr(phase_data, var))
concat_data = np.concatenate(arrs, axis=points_idx)
concatenated_data_vars[var] = (data_coords, concat_data)
final_ds = LightDataset(data_vars=concatenated_data_vars,
coords=concatenated_coords)
else:
final_ds = all_phase_data[0]
if to_xarray:
return final_ds.get_dataset()
else:
return final_ds
def get_thermochemical_data(dbf,
comps,
phases,
datasets,
weight_dict=None,
symbols_to_fit=None):
"""
Parameters
----------
dbf : pycalphad.Database
Database to consider
comps : list
List of active component names
phases : list
List of phases to consider
datasets : espei.utils.PickleableTinyDB
Datasets that contain single phase data
weight_dict : dict
Dictionary of weights for each data type, e.g. {'HM': 200, 'SM': 2}
symbols_to_fit : list
Parameters to fit. Used to build the models and PhaseRecords.
Returns
-------
list
List of data dictionaries to iterate over
"""
# phase by phase, then property by property, then by model exclusions
if weight_dict is None:
weight_dict = {}
if symbols_to_fit is not None:
symbols_to_fit = sorted(symbols_to_fit)
else:
symbols_to_fit = database_symbols_to_fit(dbf)
# estimated from NIST TRC uncertainties
property_std_deviation = {
'HM': 500.0 / weight_dict.get('HM', 1.0), # J/mol
'SM': 0.2 / weight_dict.get('SM', 1.0), # J/K-mol
'CPM': 0.2 / weight_dict.get('CPM', 1.0), # J/K-mol
}
properties = [
'HM_FORM', 'SM_FORM', 'CPM_FORM', 'HM_MIX', 'SM_MIX', 'CPM_MIX'
]
ref_states = []
for el in get_pure_elements(dbf, comps):
ref_state = ReferenceState(el, dbf.refstates[el]['phase'])
ref_states.append(ref_state)
all_data_dicts = []
for phase_name in phases:
for prop in properties:
desired_data = get_prop_data(
comps,
phase_name,
prop,
datasets,
additional_query=(where('solver').exists()))
if len(desired_data) == 0:
continue
unique_exclusions = set([
tuple(sorted(d.get('excluded_model_contributions', [])))
for d in desired_data
])
for exclusion in unique_exclusions:
data_dict = {
'phase_name': phase_name,
'prop': prop,
# needs the following keys to be added:
# species, calculate_dict, phase_records, model, output, weights
}
# get all the data with these model exclusions
if exclusion == tuple([]):
exc_search = (
~where('excluded_model_contributions').exists()) & (
where('solver').exists())
else:
exc_search = (where('excluded_model_contributions').test(
lambda x: tuple(sorted(x)) == exclusion)) & (
where('solver').exists())
curr_data = get_prop_data(comps,
phase_name,
prop,
datasets,
additional_query=exc_search)
calculate_dict = get_prop_samples(dbf, comps, phase_name,
curr_data)
mod = Model(dbf, comps, phase_name, parameters=symbols_to_fit)
if prop.endswith('_FORM'):
output = ''.join(prop.split('_')[:-1]) + 'R'
mod.shift_reference_state(
ref_states,
dbf,
contrib_mods={e: sympy.S.Zero
for e in exclusion})
else:
output = prop
for contrib in exclusion:
mod.models[contrib] = sympy.S.Zero
mod.reference_model.models[contrib] = sympy.S.Zero
species = sorted(unpack_components(dbf, comps), key=str)
data_dict['species'] = species
model = {phase_name: mod}
statevar_dict = {
getattr(v, c, None): vals
for c, vals in calculate_dict.items()
if isinstance(getattr(v, c, None), v.StateVariable)
}
statevar_dict = OrderedDict(
sorted(statevar_dict.items(), key=lambda x: str(x[0])))
str_statevar_dict = OrderedDict(
(str(k), vals) for k, vals in statevar_dict.items())
phase_records = build_phase_records(
dbf,
species, [phase_name],
statevar_dict,
model,
output=output,
parameters={s: 0
for s in symbols_to_fit},
build_gradients=False,
build_hessians=False)
data_dict['str_statevar_dict'] = str_statevar_dict
data_dict['phase_records'] = phase_records
data_dict['calculate_dict'] = calculate_dict
data_dict['model'] = model
data_dict['output'] = output
data_dict['weights'] = np.array(
property_std_deviation[prop.split('_')[0]]) / np.array(
calculate_dict.pop('weights'))
all_data_dicts.append(data_dict)
return all_data_dicts
def plot_interaction(dbf, comps, phase_name, configuration, output, datasets=None, symmetry=None, ax=None, plot_kwargs=None, dataplot_kwargs=None) -> plt.Axes:
"""
Return one set of plotted Axes with data compared to calculated parameters
Parameters
----------
dbf : Database
pycalphad thermodynamic database containing the relevant parameters.
comps : Sequence[str]
Names of components to consider in the calculation.
phase_name : str
Name of the considered phase phase
configuration : Tuple[Tuple[str]]
ESPEI-style configuration
output : str
Model property to plot on the y-axis e.g. ``'HM_MIX'``, or ``'SM_MIX'``.
Must be a ``'_MIX'`` property.
datasets : tinydb.TinyDB
symmetry : list
List of lists containing indices of symmetric sublattices e.g. [[0, 1], [2, 3]]
ax : plt.Axes
Default axes used if not specified.
plot_kwargs : Optional[Dict[str, Any]]
Keyword arguments to ``ax.plot`` for the predicted data.
dataplot_kwargs : Optional[Dict[str, Any]]
Keyword arguments to ``ax.plot`` the observed data.
Returns
-------
plt.Axes
"""
if not output.endswith('_MIX'):
raise ValueError("`plot_interaction` only supports HM_MIX, SM_MIX, or CPM_MIX outputs.")
if not plot_kwargs:
plot_kwargs = {}
if not dataplot_kwargs:
dataplot_kwargs = {}
if not ax:
ax = plt.subplot()
# Plot predicted values from the database
grid, predicted_values = _get_interaction_predicted_values(dbf, comps, phase_name, configuration, output)
plot_kwargs.setdefault('label', 'This work')
plot_kwargs.setdefault('color', 'k')
ax.plot(grid, predicted_values, **plot_kwargs)
# Plot the observed values from the datasets
# TODO: model exclusions handling
# TODO: better reference state handling
mod_srf = Model(dbf, comps, phase_name, parameters={'GHSER'+c.upper(): 0 for c in comps})
mod_srf.models = {'ref': mod_srf.models['ref']}
# _MIX assumption
prop = output.split('_MIX')[0]
desired_props = (f"{prop}_MIX", f"{prop}_FORM")
if datasets is not None:
solver_qry = (tinydb.where('solver').test(symmetry_filter, configuration, recursive_tuplify(symmetry) if symmetry else symmetry))
desired_data = get_prop_data(comps, phase_name, desired_props, datasets, additional_query=solver_qry)
desired_data = filter_configurations(desired_data, configuration, symmetry)
desired_data = filter_temperatures(desired_data)
else:
desired_data = []
species = unpack_components(dbf, comps)
# phase constituents are Species objects, so we need to be doing intersections with those
phase_constituents = dbf.phases[phase_name].constituents
# phase constituents must be filtered to only active
constituents = [[sp.name for sp in sorted(subl_constituents.intersection(species))] for subl_constituents in phase_constituents]
subl_dof = list(map(len, constituents))
calculate_dict = get_prop_samples(desired_data, constituents)
sample_condition_dicts = _get_sample_condition_dicts(calculate_dict, subl_dof)
interacting_subls = [c for c in recursive_tuplify(configuration) if isinstance(c, tuple)]
if (len(set(interacting_subls)) == 1) and (len(interacting_subls[0]) == 2):
# This configuration describes all sublattices with the same two elements interacting
# In general this is a high-dimensional space; just plot the diagonal to see the disordered mixing
endpoints = endmembers_from_interaction(configuration)
endpoints = [endpoints[0], endpoints[-1]]
disordered_config = True
else:
disordered_config = False
bib_reference_keys = sorted({entry.get('reference', '') for entry in desired_data})
symbol_map = bib_marker_map(bib_reference_keys)
for data in desired_data:
indep_var_data = None
response_data = np.zeros_like(data['values'], dtype=np.float_)
if disordered_config:
# Take the second element of the first interacting sublattice as the coordinate
# Because it's disordered all sublattices should be equivalent
# TODO: Fix this to filter because we need to guarantee the plot points are disordered
occ = data['solver']['sublattice_occupancies']
subl_idx = np.nonzero([isinstance(c, (list, tuple)) for c in occ[0]])[0]
if len(subl_idx) > 1:
subl_idx = int(subl_idx[0])
else:
subl_idx = int(subl_idx)
indep_var_data = [c[subl_idx][1] for c in occ]
else:
interactions = np.array([cond_dict[Symbol('YS')] for cond_dict in sample_condition_dicts])
indep_var_data = 1 - (interactions+1)/2
if data['output'].endswith('_FORM'):
# All the _FORM data we have still has the lattice stability contribution
# Need to zero it out to shift formation data to mixing
temps = data['conditions'].get('T', 298.15)
pressures = data['conditions'].get('P', 101325)
points = build_sitefractions(phase_name, data['solver']['sublattice_configurations'],
data['solver']['sublattice_occupancies'])
for point_idx in range(len(points)):
missing_variables = mod_srf.ast.atoms(v.SiteFraction) - set(points[point_idx].keys())
# Set unoccupied values to zero
points[point_idx].update({key: 0 for key in missing_variables})
# Change entry to a sorted array of site fractions
points[point_idx] = list(OrderedDict(sorted(points[point_idx].items(), key=str)).values())
points = np.array(points, dtype=np.float_)
# TODO: Real temperature support
points = points[None, None]
stability = calculate(dbf, comps, [phase_name], output=data['output'][:-5],
T=temps, P=pressures, points=points,
model=mod_srf)
response_data -= stability[data['output'][:-5]].values.squeeze()
response_data += np.array(data['values'], dtype=np.float_)
response_data = response_data.flatten()
ref = data.get('reference', '')
dataplot_kwargs.setdefault('markersize', 8)
dataplot_kwargs.setdefault('linestyle', 'none')
dataplot_kwargs.setdefault('clip_on', False)
# Cannot use setdefault because it won't overwrite previous iterations
dataplot_kwargs['label'] = symbol_map[ref]['formatted']
dataplot_kwargs['marker'] = symbol_map[ref]['markers']['marker']
dataplot_kwargs['fillstyle'] = symbol_map[ref]['markers']['fillstyle']
ax.plot(indep_var_data, response_data, **dataplot_kwargs)
ax.set_xlim((0, 1))
ax.set_xlabel(str(':'.join(endpoints[0])) + ' to ' + str(':'.join(endpoints[1])))
ax.set_ylabel(plot_mapping.get(output, output))
leg = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) # legend outside
leg.get_frame().set_edgecolor('black')
return ax
def simulate_equilibrium_solidification(dbf,
comps,
phases,
composition,
start_temperature,
step_temperature=1.0,
liquid_phase_name='LIQUID',
adaptive=True,
eq_kwargs=None,
binary_search_tol=0.1,
verbose=False):
"""
Compute the equilibrium solidification path.
Decreases temperature until no liquid is found, performing a binary search to get the soildus temperature.
dbf : pycalphad.Database
Database object.
comps : list
List of components in the system.
phases : list
List of phases in the system.
composition : Dict[v.X, float]
Dictionary of independent `v.X` composition variables.
start_temperature : float
Starting temperature for simulation. Should be single phase liquid.
step_temperature : Optional[float]
Temperature step size. Defaults to 1.0.
liquid_phase_name : Optional[str]
Name of the phase treated as liquid (i.e. the phase with infinitely
fast diffusion). Defaults to 'LIQUID'.
eq_kwargs: Optional[Dict[str, Any]]
Keyword arguments for equilibrium
binary_search_tol : float
Stop the binary search when the difference between temperatures is less than this amount.
adaptive: Optional[bool]
Whether to add additional points near the equilibrium points at each
step. Only takes effect if ``points`` is in the eq_kwargs dict.
"""
eq_kwargs = eq_kwargs or dict()
phases = filter_phases(dbf, unpack_components(dbf, comps), phases)
ord_disord_dict = order_disorder_dict(dbf, comps, phases)
solid_phases = sorted(set(phases) - {liquid_phase_name})
independent_comps = sorted([str(comp)[2:] for comp in composition.keys()])
models = instantiate_models(dbf, comps, phases)
if verbose:
print('building callables... ', end='')
cbs = build_callables(dbf,
comps,
phases,
models,
additional_statevars={v.P, v.T, v.N},
build_gradients=True,
build_hessians=True)
if verbose:
print('done')
conds = {v.P: 101325, v.N: 1.0}
conds.update(composition)
if adaptive and ('points' in eq_kwargs.get('calc_opts', {})):
# Dynamically add points as the simulation runs
species = unpack_components(dbf, comps)
dof_dict = {
ph: generate_dof(dbf.phases[ph], species)[1]
for ph in phases
}
else:
adaptive = False
temperatures = []
x_liquid = {comp: [] for comp in independent_comps}
fraction_solid = []
phase_amounts = {ph: []
for ph in solid_phases} # instantaneous phase amounts
cum_phase_amounts = {ph: [] for ph in solid_phases}
converged = False
current_T = start_temperature
if verbose:
print('T=')
while fraction_solid[-1] < 1 if len(fraction_solid) > 0 else True:
sys.stdout.flush()
conds[v.T] = current_T
if verbose:
print(f'{current_T} ', end='')
eq = equilibrium(dbf,
comps,
phases,
conds,
callables=cbs,
model=models,
to_xarray=False,
**eq_kwargs)
if not is_converged(eq):
if verbose:
comp_conds = {
cond: val
for cond, val in conds.items() if isinstance(cond, v.X)
}
print(f"Convergence failure at T={conds[v.T]} X={comp_conds} ")
if adaptive:
# Update the points dictionary with local samples around the equilibrium site fractions
update_points(eq, eq_kwargs['calc_opts']['points'], dof_dict)
if liquid_phase_name in eq.Phase:
# Add the liquid phase composition
# TODO: will break in a liquid miscibility gap
liquid_vertex = np.nonzero(eq.Phase == liquid_phase_name)[-1][0]
for comp in independent_comps:
x_liquid[comp].append(
float(eq.X[..., liquid_vertex,
eq.component.index(comp)]))
temperatures.append(current_T)
current_T -= step_temperature
else:
# binary search to find the solidus
T_high = current_T + step_temperature # High temperature, liquid
T_low = current_T # Low temperature, solids only
found_ph = set(eq.Phase[eq.Phase != ''].tolist())
if verbose:
print(
f'Found phases {found_ph}. Starting binary search between T={(T_low, T_high)} ',
end='')
while (T_high - T_low) > binary_search_tol:
bin_search_T = (T_high - T_low) * 0.5 + T_low
conds[v.T] = bin_search_T
eq = equilibrium(dbf,
comps,
phases,
conds,
callables=cbs,
model=models,
to_xarray=False,
**eq_kwargs)
if adaptive:
# Update the points dictionary with local samples around the equilibrium site fractions
update_points(eq, eq_kwargs['calc_opts']['points'],
dof_dict)
if not is_converged(eq):
if verbose:
comp_conds = {
cond: val
for cond, val in conds.items()
if isinstance(cond, v.X)
}
print(
f"Convergence failure at T={conds[v.T]} X={comp_conds} "
)
if liquid_phase_name in eq.Phase:
T_high = bin_search_T
else:
T_low = bin_search_T
conds[v.T] = T_low
temperatures.append(T_low)
eq = equilibrium(dbf,
comps,
phases,
conds,
callables=cbs,
model=models,
to_xarray=False,
**eq_kwargs)
if not is_converged(eq):
if verbose:
comp_conds = {
cond: val
for cond, val in conds.items()
if isinstance(cond, v.X)
}
print(
f"Convergence failure at T={conds[v.T]} X={comp_conds} "
)
if verbose:
found_phases = set(eq.Phase[eq.Phase != ''].tolist())
print(
f"Finshed binary search at T={conds[v.T]} with phases={found_phases} and NP={eq.NP.squeeze()[:len(found_phases)]}"
)
if adaptive:
# Update the points dictionary with local samples around the equilibrium site fractions
update_points(eq, eq_kwargs['calc_opts']['points'], dof_dict)
# Set the liquid phase composition to NaN
for comp in independent_comps:
x_liquid[comp].append(float(np.nan))
# Calculate fraction of solid and solid phase amounts
current_fraction_solid = 0.0
current_cum_phase_amnts = get_phase_amounts(
order_disorder_eq_phases(eq.get_dataset(), ord_disord_dict),
eq.NP.squeeze(), solid_phases)
for solid_phase, amount in current_cum_phase_amnts.items():
# Since the equilibrium calculations always give the "cumulative" phase amount,
# we need to take the difference to get the instantaneous.
cum_phase_amounts[solid_phase].append(amount)
if len(phase_amounts[solid_phase]) == 0:
phase_amounts[solid_phase].append(amount)
else:
phase_amounts[solid_phase].append(
amount - cum_phase_amounts[solid_phase][-2])
current_fraction_solid += amount
fraction_solid.append(current_fraction_solid)
converged = True if np.isclose(fraction_solid[-1], 1.0) else False
return SolidificationResult(x_liquid, fraction_solid, temperatures,
phase_amounts, converged, "equilibrium")
def map_binary(
dbf,
comps,
phases,
conds,
eq_kwargs=None,
calc_kwargs=None,
boundary_sets=None,
verbose=False,
summary=False,
):
"""
Map a binary T-X phase diagram
Parameters
----------
dbf : Database
comps : list of str
phases : list of str
List of phases to consider in mapping
conds : dict
Dictionary of conditions
eq_kwargs : dict
Dictionary of keyword arguments to pass to equilibrium
verbose : bool
Print verbose output for mapping
boundary_sets : ZPFBoundarySets
Existing ZPFBoundarySets
Returns
-------
ZPFBoundarySets
Notes
-----
Assumes conditions in T and X.
Simple algorithm to map a binary phase diagram in T-X. More or less follows
the algorithm described in Figure 2 by Snider et al. [1] with the small
algorithmic improvement of constructing a convex hull to find the next
potential two phase region.
For each temperature, proceed along increasing composition, skipping two
over two phase regions, once calculated.
[1] J. Snider, I. Griva, X. Sun, M. Emelianenko, Set based framework for
Gibbs energy minimization, Calphad. 48 (2015) 18-26.
doi: 10.1016/j.calphad.2014.09.005
"""
eq_kwargs = eq_kwargs or {}
calc_kwargs = calc_kwargs or {}
# implicitly add v.N to conditions
if v.N not in conds:
conds[v.N] = [1.0]
if 'pdens' not in calc_kwargs:
calc_kwargs['pdens'] = 2000
species = unpack_components(dbf, comps)
phases = filter_phases(dbf, species, phases)
parameters = eq_kwargs.get('parameters', {})
models = eq_kwargs.get('model')
statevars = get_state_variables(models=models, conds=conds)
if models is None:
models = instantiate_models(dbf,
comps,
phases,
model=eq_kwargs.get('model'),
parameters=parameters,
symbols_only=True)
prxs = build_phase_records(dbf,
species,
phases,
conds,
models,
output='GM',
parameters=parameters,
build_gradients=True,
build_hessians=True)
indep_comp = [
key for key, value in conds.items()
if isinstance(key, v.MoleFraction) and len(np.atleast_1d(value)) > 1
]
indep_pot = [
key for key, value in conds.items()
if (type(key) is v.StateVariable) and len(np.atleast_1d(value)) > 1
]
if (len(indep_comp) != 1) or (len(indep_pot) != 1):
raise ValueError(
'Binary map requires exactly one composition and one potential coordinate'
)
if indep_pot[0] != v.T:
raise ValueError(
'Binary map requires that a temperature grid must be defined')
# binary assumption, only one composition specified.
comp_cond = [k for k in conds.keys() if isinstance(k, v.X)][0]
indep_comp = comp_cond.name[2:]
indep_comp_idx = sorted(get_pure_elements(dbf, comps)).index(indep_comp)
composition_grid = unpack_condition(conds[comp_cond])
dX = composition_grid[1] - composition_grid[0]
Xmax = composition_grid.max()
temperature_grid = unpack_condition(conds[v.T])
dT = temperature_grid[1] - temperature_grid[0]
boundary_sets = boundary_sets or ZPFBoundarySets(comps, comp_cond)
equilibria_calculated = 0
equilibrium_time = 0
convex_hulls_calculated = 0
convex_hull_time = 0
curr_conds = {key: unpack_condition(val) for key, val in conds.items()}
str_conds = sorted([str(k) for k in curr_conds.keys()])
grid_conds = _adjust_conditions(curr_conds)
for T_idx in range(temperature_grid.size):
T = temperature_grid[T_idx]
iter_equilibria = 0
if verbose:
print("=== T = {} ===".format(float(T)))
curr_conds[v.T] = [float(T)]
eq_conds = deepcopy(curr_conds)
Xmax_visited = 0.0
hull_time = time.time()
grid = calculate(dbf,
comps,
phases,
fake_points=True,
output='GM',
T=T,
P=grid_conds[v.P],
N=1,
model=models,
parameters=parameters,
to_xarray=False,
**calc_kwargs)
hull = starting_point(eq_conds, statevars, prxs, grid)
convex_hull_time += time.time() - hull_time
convex_hulls_calculated += 1
while Xmax_visited < Xmax:
hull_compsets = find_two_phase_region_compsets(
hull,
T,
indep_comp,
indep_comp_idx,
minimum_composition=Xmax_visited,
misc_gap_tol=2 * dX)
if hull_compsets is None:
if verbose:
print(
"== Convex hull: max visited = {} - no multiphase phase compsets found =="
.format(Xmax_visited, hull_compsets))
break
Xeq = hull_compsets.mean_composition
eq_conds[comp_cond] = [float(Xeq)]
eq_time = time.time()
start_point = starting_point(eq_conds, statevars, prxs, grid)
eq_ds = _solve_eq_at_conditions(species, start_point, prxs, grid,
str_conds, statevars, False)
equilibrium_time += time.time() - eq_time
equilibria_calculated += 1
iter_equilibria += 1
# composition sets in the plane of the calculation:
# even for isopleths, this should always be two.
compsets = get_compsets(eq_ds, indep_comp, indep_comp_idx)
if verbose:
print(
"== Convex hull: max visited = {:0.4f} - hull compsets: {} equilibrium compsets: {} =="
.format(Xmax_visited, hull_compsets, compsets))
if compsets is None:
# equilibrium calculation, didn't find a valid multiphase composition set
# we need to find the next feasible one from the convex hull.
Xmax_visited += dX
continue
else:
boundary_sets.add_compsets(compsets, Xtol=0.10, Ttol=2 * dT)
if compsets.max_composition > Xmax_visited:
Xmax_visited = compsets.max_composition
# this seems kind of sloppy, but captures the effect that we want to
# keep doing equilibrium calculations, if possible.
while Xmax_visited < Xmax and compsets is not None:
eq_conds[comp_cond] = [float(Xmax_visited + dX)]
eq_time = time.time()
# TODO: starting point could be improved by basing it off the previous calculation
start_point = starting_point(eq_conds, statevars, prxs, grid)
eq_ds = _solve_eq_at_conditions(species, start_point, prxs,
grid, str_conds, statevars,
False)
equilibrium_time += time.time() - eq_time
equilibria_calculated += 1
compsets = get_compsets(eq_ds, indep_comp, indep_comp_idx)
if compsets is not None:
Xmax_visited = compsets.max_composition
boundary_sets.add_compsets(compsets,
Xtol=0.10,
Ttol=2 * dT)
else:
Xmax_visited += dX
if verbose:
print("Equilibrium: at X = {:0.4f}, found compsets {}".
format(Xmax_visited, compsets))
if verbose:
print(iter_equilibria, 'equilibria calculated in this iteration.')
if verbose or summary:
print("{} Convex hulls calculated ({:0.1f}s)".format(
convex_hulls_calculated, convex_hull_time))
print("{} Equilbria calculated ({:0.1f}s)".format(
equilibria_calculated, equilibrium_time))
print("{:0.0f}% of brute force calculations skipped".format(
100 * (1 - equilibria_calculated /
(composition_grid.size * temperature_grid.size))))
return boundary_sets
def plot_endmember(dbf, comps, phase_name, configuration, output, datasets=None, symmetry=None, x='T', ax=None, plot_kwargs=None, dataplot_kwargs=None) -> plt.Axes:
"""
Return one set of plotted Axes with data compared to calculated parameters
Parameters
----------
dbf : Database
pycalphad thermodynamic database containing the relevant parameters.
comps : Sequence[str]
Names of components to consider in the calculation.
phase_name : str
Name of the considered phase phase
configuration : Tuple[Tuple[str]]
ESPEI-style configuration
output : str
Model property to plot on the y-axis e.g. ``'HM_MIX'``, or ``'SM_MIX'``.
Must be a ``'_MIX'`` property.
datasets : tinydb.TinyDB
symmetry : list
List of lists containing indices of symmetric sublattices e.g. [[0, 1], [2, 3]]
ax : plt.Axes
Default axes used if not specified.
plot_kwargs : Optional[Dict[str, Any]]
Keyword arguments to ``ax.plot`` for the predicted data.
dataplot_kwargs : Optional[Dict[str, Any]]
Keyword arguments to ``ax.plot`` the observed data.
Returns
-------
plt.Axes
"""
if output.endswith('_MIX'):
raise ValueError("`plot_interaction` only supports HM, HM_FORM, SM, SM_FORM or CPM, CPM_FORM outputs.")
if x not in ('T',):
raise ValueError(f'`x` passed to `plot_endmember` must be "T" got {x}')
if not plot_kwargs:
plot_kwargs = {}
if not dataplot_kwargs:
dataplot_kwargs = {}
if not ax:
ax = plt.subplot()
if datasets is not None:
solver_qry = (tinydb.where('solver').test(symmetry_filter, configuration, recursive_tuplify(symmetry) if symmetry else symmetry))
desired_data = get_prop_data(comps, phase_name, output, datasets, additional_query=solver_qry)
desired_data = filter_configurations(desired_data, configuration, symmetry)
desired_data = filter_temperatures(desired_data)
else:
desired_data = []
# Plot predicted values from the database
endpoints = endmembers_from_interaction(configuration)
if len(endpoints) != 1:
raise ValueError(f"The configuration passed to `plot_endmember` must be an endmebmer configuration. Got {configuration}")
if output.endswith('_FORM'):
# TODO: better reference state handling
mod = Model(dbf, comps, phase_name, parameters={'GHSER'+(c.upper()*2)[:2]: 0 for c in comps})
prop = output[:-5]
else:
mod = Model(dbf, comps, phase_name)
prop = output
endmember = _translate_endmember_to_array(endpoints[0], mod.ast.atoms(v.SiteFraction))[None, None]
# Set up the domain of the calculation
species = unpack_components(dbf, comps)
# phase constituents are Species objects, so we need to be doing intersections with those
phase_constituents = dbf.phases[phase_name].constituents
# phase constituents must be filtered to only active
constituents = [[sp.name for sp in sorted(subl_constituents.intersection(species))] for subl_constituents in phase_constituents]
calculate_dict = get_prop_samples(desired_data, constituents)
potential_values = np.asarray(calculate_dict[x] if len(calculate_dict[x]) > 0 else 298.15)
potential_grid = np.linspace(max(potential_values.min()-1, 0), potential_values.max()+1, num=100)
predicted_values = calculate(dbf, comps, [phase_name], output=prop, T=potential_grid, P=101325, points=endmember, model=mod)[prop].values.flatten()
ax.plot(potential_grid, predicted_values, **plot_kwargs)
# Plot observed values
# TODO: model exclusions handling
bib_reference_keys = sorted({entry.get('reference', '') for entry in desired_data})
symbol_map = bib_marker_map(bib_reference_keys)
for data in desired_data:
indep_var_data = None
response_data = np.zeros_like(data['values'], dtype=np.float_)
indep_var_data = np.array(data['conditions'][x], dtype=np.float_).flatten()
response_data += np.array(data['values'], dtype=np.float_)
response_data = response_data.flatten()
ref = data.get('reference', '')
dataplot_kwargs.setdefault('markersize', 8)
dataplot_kwargs.setdefault('linestyle', 'none')
dataplot_kwargs.setdefault('clip_on', False)
# Cannot use setdefault because it won't overwrite previous iterations
dataplot_kwargs['label'] = symbol_map[ref]['formatted']
dataplot_kwargs['marker'] = symbol_map[ref]['markers']['marker']
dataplot_kwargs['fillstyle'] = symbol_map[ref]['markers']['fillstyle']
ax.plot(indep_var_data, response_data, **dataplot_kwargs)
ax.set_xlabel(plot_mapping.get(x, x))
ax.set_ylabel(plot_mapping.get(output, output))
leg = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) # legend outside
leg.get_frame().set_edgecolor('black')
return ax
def setup_context(dbf,
datasets,
symbols_to_fit=None,
data_weights=None,
phase_models=None,
make_callables=True):
"""
Set up a context dictionary for calculating error.
Parameters
----------
dbf : Database
A pycalphad Database that will be fit
datasets : PickleableTinyDB
A database of single- and multi-phase data to fit
symbols_to_fit : list of str
List of symbols in the Database that will be fit. If None (default) are
passed, then all parameters prefixed with `VV` followed by a number,
e.g. VV0001 will be fit.
Returns
-------
Notes
-----
A copy of the Database is made and used in the context. To commit changes
back to the original database, the dbf.symbols.update method should be used.
"""
dbf = copy.deepcopy(dbf)
if phase_models is not None:
comps = sorted(phase_models['components'])
else:
comps = sorted([sp for sp in dbf.elements])
if symbols_to_fit is None:
symbols_to_fit = database_symbols_to_fit(dbf)
else:
symbols_to_fit = sorted(symbols_to_fit)
data_weights = data_weights if data_weights is not None else {}
if len(symbols_to_fit) == 0:
raise ValueError(
'No degrees of freedom. Database must contain symbols starting with \'V\' or \'VV\', followed by a number.'
)
else:
_log.info('Fitting %s degrees of freedom.', len(symbols_to_fit))
for x in symbols_to_fit:
if isinstance(dbf.symbols[x], symengine.Piecewise):
_log.debug('Replacing %s in database', x)
dbf.symbols[x] = dbf.symbols[x].args[0]
# construct the models for each phase, substituting in the SymEngine symbol to fit.
if phase_models is not None:
model_dict = get_model_dict(phase_models)
else:
model_dict = {}
_log.trace('Building phase models (this may take some time)')
import time
t1 = time.time()
phases = sorted(
filter_phases(dbf, unpack_components(dbf, comps), dbf.phases.keys()))
parameters = dict(zip(symbols_to_fit, [0] * len(symbols_to_fit)))
models = instantiate_models(dbf,
comps,
phases,
model=model_dict,
parameters=parameters)
if make_callables:
eq_callables = build_callables(dbf,
comps,
phases,
models,
parameter_symbols=symbols_to_fit,
output='GM',
build_gradients=True,
build_hessians=True,
additional_statevars={v.N, v.P, v.T})
else:
eq_callables = None
t2 = time.time()
_log.trace('Finished building phase models (%0.2fs)', t2 - t1)
_log.trace(
'Getting non-equilibrium thermochemical data (this may take some time)'
)
t1 = time.time()
thermochemical_data = get_thermochemical_data(
dbf,
comps,
phases,
datasets,
model=model_dict,
weight_dict=data_weights,
symbols_to_fit=symbols_to_fit)
t2 = time.time()
_log.trace('Finished getting non-equilibrium thermochemical data (%0.2fs)',
t2 - t1)
_log.trace(
'Getting equilibrium thermochemical data (this may take some time)')
t1 = time.time()
eq_thermochemical_data = get_equilibrium_thermochemical_data(
dbf,
comps,
phases,
datasets,
model=model_dict,
parameters=parameters,
data_weight_dict=data_weights)
t2 = time.time()
_log.trace('Finished getting equilibrium thermochemical data (%0.2fs)',
t2 - t1)
_log.trace('Getting ZPF data (this may take some time)')
t1 = time.time()
zpf_data = get_zpf_data(dbf,
comps,
phases,
datasets,
model=model_dict,
parameters=parameters)
t2 = time.time()
_log.trace('Finished getting ZPF data (%0.2fs)', t2 - t1)
# context for the log probability function
# for all cases, parameters argument addressed in MCMC loop
error_context = {
'symbols_to_fit': symbols_to_fit,
'zpf_kwargs': {
'zpf_data': zpf_data,
'data_weight': data_weights.get('ZPF', 1.0),
},
'equilibrium_thermochemical_kwargs': {
'eq_thermochemical_data': eq_thermochemical_data,
},
'thermochemical_kwargs': {
'thermochemical_data': thermochemical_data,
},
'activity_kwargs': {
'dbf': dbf,
'comps': comps,
'phases': phases,
'datasets': datasets,
'phase_models': models,
'callables': eq_callables,
'data_weight': data_weights.get('ACR', 1.0),
},
}
return error_context
def _compare_data_to_parameters(dbf, comps, phase_name, desired_data, mod, configuration, x, y, ax=None):
"""
Return one set of plotted Axes with data compared to calculated parameters
Parameters
----------
dbf : Database
pycalphad thermodynamic database containing the relevant parameters.
comps : list
Names of components to consider in the calculation.
phase_name : str
Name of the considered phase phase
desired_data :
mod : Model
A pycalphad Model. The Model may or may not have the reference state zeroed out for formation properties.
configuration :
x : str
Model property to plot on the x-axis e.g. 'T', 'HM_MIX', 'SM_FORM'
y : str
Model property to plot on the y-axis e.g. 'T', 'HM_MIX', 'SM_FORM'
ax : matplotlib.Axes
Default axes used if not specified.
Returns
-------
matplotlib.Axes
"""
species = unpack_components(dbf, comps)
# phase constituents are Species objects, so we need to be doing intersections with those
phase_constituents = dbf.phases[phase_name].constituents
# phase constituents must be filtered to only active:
constituents = [[sp.name for sp in sorted(subl_constituents.intersection(species))] for subl_constituents in phase_constituents]
subl_dof = list(map(len, constituents))
calculate_dict = get_prop_samples(desired_data, constituents)
sample_condition_dicts = _get_sample_condition_dicts(calculate_dict, subl_dof)
endpoints = endmembers_from_interaction(configuration)
interacting_subls = [c for c in recursive_tuplify(configuration) if isinstance(c, tuple)]
disordered_config = False
if (len(set(interacting_subls)) == 1) and (len(interacting_subls[0]) == 2):
# This configuration describes all sublattices with the same two elements interacting
# In general this is a high-dimensional space; just plot the diagonal to see the disordered mixing
endpoints = [endpoints[0], endpoints[-1]]
disordered_config = True
if not ax:
ax = plt.subplot()
bar_chart = False
bar_labels = []
bar_data = []
if y.endswith('_FORM'):
# We were passed a Model object with zeroed out reference states
yattr = y[:-5]
else:
yattr = y
if len(endpoints) == 1:
# This is an endmember so we can just compute T-dependent stuff
Ts = calculate_dict['T']
temperatures = np.asarray(Ts if len(Ts) > 0 else 298.15)
if temperatures.min() != temperatures.max():
temperatures = np.linspace(temperatures.min(), temperatures.max(), num=100)
else:
# We only have one temperature: let's do a bar chart instead
bar_chart = True
temperatures = temperatures.min()
endmember = _translate_endmember_to_array(endpoints[0], mod.ast.atoms(v.SiteFraction))[None, None]
predicted_quantities = calculate(dbf, comps, [phase_name], output=yattr,
T=temperatures, P=101325, points=endmember, model=mod, mode='numpy')
if y == 'HM' and x == 'T':
# Shift enthalpy data so that value at minimum T is zero
predicted_quantities[yattr] -= predicted_quantities[yattr].sel(T=temperatures[0]).values.flatten()
response_data = predicted_quantities[yattr].values.flatten()
if not bar_chart:
extra_kwargs = {}
if len(response_data) < 10:
extra_kwargs['markersize'] = 20
extra_kwargs['marker'] = '.'
extra_kwargs['linestyle'] = 'none'
extra_kwargs['clip_on'] = False
ax.plot(temperatures, response_data,
label='This work', color='k', **extra_kwargs)
ax.set_xlabel(plot_mapping.get(x, x))
ax.set_ylabel(plot_mapping.get(y, y))
else:
bar_labels.append('This work')
bar_data.append(response_data[0])
elif len(endpoints) == 2:
# Binary interaction parameter
first_endpoint = _translate_endmember_to_array(endpoints[0], mod.ast.atoms(v.SiteFraction))
second_endpoint = _translate_endmember_to_array(endpoints[1], mod.ast.atoms(v.SiteFraction))
point_matrix = np.linspace(0, 1, num=100)[None].T * second_endpoint + \
(1 - np.linspace(0, 1, num=100))[None].T * first_endpoint
# TODO: Real temperature support
point_matrix = point_matrix[None, None]
predicted_quantities = calculate(dbf, comps, [phase_name], output=yattr,
T=300, P=101325, points=point_matrix, model=mod, mode='numpy')
response_data = predicted_quantities[yattr].values.flatten()
if not bar_chart:
extra_kwargs = {}
if len(response_data) < 10:
extra_kwargs['markersize'] = 20
extra_kwargs['marker'] = '.'
extra_kwargs['linestyle'] = 'none'
extra_kwargs['clip_on'] = False
ax.plot(np.linspace(0, 1, num=100), response_data, label='This work', color='k', **extra_kwargs)
ax.set_xlim((0, 1))
ax.set_xlabel(str(':'.join(endpoints[0])) + ' to ' + str(':'.join(endpoints[1])))
ax.set_ylabel(plot_mapping.get(y, y))
else:
bar_labels.append('This work')
bar_data.append(response_data[0])
else:
raise NotImplementedError('No support for plotting configuration {}'.format(configuration))
bib_reference_keys = sorted({entry.get('reference', '') for entry in desired_data})
symbol_map = bib_marker_map(bib_reference_keys)
for data in desired_data:
indep_var_data = None
response_data = np.zeros_like(data['values'], dtype=np.float_)
if x == 'T' or x == 'P':
indep_var_data = np.array(data['conditions'][x], dtype=np.float_).flatten()
elif x == 'Z':
if disordered_config:
# Take the second element of the first interacting sublattice as the coordinate
# Because it's disordered all sublattices should be equivalent
# TODO: Fix this to filter because we need to guarantee the plot points are disordered
occ = data['solver']['sublattice_occupancies']
subl_idx = np.nonzero([isinstance(c, (list, tuple)) for c in occ[0]])[0]
if len(subl_idx) > 1:
subl_idx = int(subl_idx[0])
else:
subl_idx = int(subl_idx)
indep_var_data = [c[subl_idx][1] for c in occ]
else:
interactions = np.array([cond_dict[Symbol('YS')] for cond_dict in sample_condition_dicts])
indep_var_data = 1 - (interactions+1)/2
if y.endswith('_MIX') and data['output'].endswith('_FORM'):
# All the _FORM data we have still has the lattice stability contribution
# Need to zero it out to shift formation data to mixing
mod_latticeonly = Model(dbf, comps, phase_name, parameters={'GHSER'+c.upper(): 0 for c in comps})
mod_latticeonly.models = {key: value for key, value in mod_latticeonly.models.items()
if key == 'ref'}
temps = data['conditions'].get('T', 300)
pressures = data['conditions'].get('P', 101325)
points = build_sitefractions(phase_name, data['solver']['sublattice_configurations'],
data['solver']['sublattice_occupancies'])
for point_idx in range(len(points)):
missing_variables = mod_latticeonly.ast.atoms(v.SiteFraction) - set(points[point_idx].keys())
# Set unoccupied values to zero
points[point_idx].update({key: 0 for key in missing_variables})
# Change entry to a sorted array of site fractions
points[point_idx] = list(OrderedDict(sorted(points[point_idx].items(), key=str)).values())
points = np.array(points, dtype=np.float_)
# TODO: Real temperature support
points = points[None, None]
stability = calculate(dbf, comps, [phase_name], output=data['output'][:-5],
T=temps, P=pressures, points=points,
model=mod_latticeonly, mode='numpy')
response_data -= stability[data['output'][:-5]].values.squeeze()
response_data += np.array(data['values'], dtype=np.float_)
response_data = response_data.flatten()
if not bar_chart:
extra_kwargs = {}
extra_kwargs['markersize'] = 8
extra_kwargs['linestyle'] = 'none'
extra_kwargs['clip_on'] = False
ref = data.get('reference', '')
mark = symbol_map[ref]['markers']
ax.plot(indep_var_data, response_data,
label=symbol_map[ref]['formatted'],
marker=mark['marker'],
fillstyle=mark['fillstyle'],
**extra_kwargs)
else:
bar_labels.append(data.get('reference', None))
bar_data.append(response_data[0])
if bar_chart:
ax.barh(0.02 * np.arange(len(bar_data)), bar_data,
color='k', height=0.01)
endmember_title = ' to '.join([':'.join(i) for i in endpoints])
ax.get_figure().suptitle('{} (T = {} K)'.format(endmember_title, temperatures), fontsize=20)
ax.set_yticks(0.02 * np.arange(len(bar_data)))
ax.set_yticklabels(bar_labels, fontsize=20)
# This bar chart is rotated 90 degrees, so "y" is now x
ax.set_xlabel(plot_mapping.get(y, y))
else:
ax.set_frame_on(False)
leg = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) # legend outside
leg.get_frame().set_edgecolor('black')
return ax
def build_callables(dbf,
comps,
phases,
models,
parameter_symbols=None,
output='GM',
build_gradients=True,
build_hessians=False,
additional_statevars=None):
"""
Create a compiled callables dictionary.
Parameters
----------
dbf : Database
A Database object
comps : list
List of component names
phases : list
List of phase names
models : dict
Dictionary of {phase_name: Model subclass}
parameter_symbols : list, optional
List of string or SymPy Symbols that will be overridden in the callables.
output : str, optional
Output property of the particular Model to sample. Defaults to 'GM'
build_gradients : bool, optional
Whether or not to build gradient functions. Defaults to True.
build_hessians : bool, optional
Whether or not to build Hessian functions. Defaults to False.
additional_statevars : set, optional
State variables to include in the callables that may not be in the models (e.g. from conditions)
verbose : bool, optional
Print the name of the phase when its callables are built
Returns
-------
callables : dict
Dictionary of keyword argument callables to pass to equilibrium.
Maps {'output' -> {'function' -> {'phase_name' -> AutowrapFunction()}}.
Notes
-----
*All* the state variables used in calculations must be specified.
If these are not specified as state variables of the models (e.g. often the
case for v.N), then it must be supplied by the additional_statevars keyword
argument.
Examples
--------
>>> from pycalphad import Database, equilibrium, variables as v
>>> from pycalphad.codegen.callables import build_callables
>>> from pycalphad.core.utils import instantiate_models
>>> dbf = Database('AL-NI.tdb')
>>> comps = ['AL', 'NI', 'VA']
>>> phases = ['LIQUID', 'AL3NI5', 'AL3NI2', 'AL3NI']
>>> models = instantiate_models(dbf, comps, phases)
>>> callables = build_callables(dbf, comps, phases, models, additional_statevars={v.P, v.T, v.N})
>>> 'GM' in callables.keys()
True
>>> 'massfuncs' in callables['GM']
True
>>> conditions = {v.P: 101325, v.T: 2500, v.X('AL'): 0.2}
>>> equilibrium(dbf, comps, phases, conditions, callables=callables)
"""
additional_statevars = set(
additional_statevars) if additional_statevars is not None else set()
parameter_symbols = parameter_symbols if parameter_symbols is not None else []
parameter_symbols = sorted([wrap_symbol(x) for x in parameter_symbols],
key=str)
comps = sorted(unpack_components(dbf, comps))
pure_elements = get_pure_elements(dbf, comps)
_callables = {
'massfuncs': {},
'massgradfuncs': {},
'masshessfuncs': {},
'callables': {},
'grad_callables': {},
'hess_callables': {},
'internal_cons_func': {},
'internal_cons_jac': {},
'internal_cons_hess': {},
'multiphase_cons_func': {},
'multiphase_cons_jac': {},
'multiphase_cons_hess': {}
}
state_variables = get_state_variables(models=models)
state_variables |= additional_statevars
if state_variables != {v.T, v.P, v.N}:
warnings.warn(
"State variables in `build_callables` are not {{N, P, T}}, but {}. This can lead to incorrectly "
"calculated values if the state variables used to call the generated functions do not match the "
"state variables used to create them. State variables can be added with the "
"`additional_statevars` argument.".format(state_variables))
state_variables = sorted(state_variables, key=str)
for name in phases:
mod = models[name]
site_fracs = mod.site_fractions
try:
out = getattr(mod, output)
except AttributeError:
raise AttributeError(
'Missing Model attribute {0} specified for {1}'.format(
output, mod.__class__))
# Build the callables of the output
# Only force undefineds to zero if we're not overriding them
undefs = {
x
for x in out.free_symbols if not isinstance(x, v.StateVariable)
} - set(parameter_symbols)
undef_vals = repeat(0., len(undefs))
out = out.xreplace(dict(zip(undefs, undef_vals)))
build_output = build_functions(out,
tuple(state_variables + site_fracs),
parameters=parameter_symbols,
include_grad=build_gradients,
include_hess=build_hessians)
cf, gf, hf = build_output.func, build_output.grad, build_output.hess
_callables['callables'][name] = cf
_callables['grad_callables'][name] = gf
_callables['hess_callables'][name] = hf
# Build the callables for mass
# TODO: In principle, we should also check for undefs in mod.moles()
mcf, mgf, mhf = zip(*[
build_functions(mod.moles(el),
state_variables + site_fracs,
include_obj=True,
include_grad=build_gradients,
include_hess=build_hessians,
parameters=parameter_symbols)
for el in pure_elements
])
_callables['massfuncs'][name] = mcf
_callables['massgradfuncs'][name] = mgf
_callables['masshessfuncs'][name] = mhf
return {output: _callables}
def __init__(self, dbe, comps, phase_name, parameters=None):
self.components = set()
self.constituents = []
self.phase_name = phase_name.upper()
phase = dbe.phases[self.phase_name]
self.site_ratios = list(phase.sublattices)
for idx, sublattice in enumerate(phase.constituents):
subl_comps = set(sublattice).intersection(unpack_components(dbe, comps))
self.components |= subl_comps
# Support for variable site ratios in ionic liquid model
if phase.model_hints.get('ionic_liquid_2SL', False):
if idx == 0:
subl_idx = 1
elif idx == 1:
subl_idx = 0
else:
raise ValueError('Two-sublattice ionic liquid specified with more than two sublattices')
self.site_ratios[subl_idx] = Add(*[v.SiteFraction(self.phase_name, idx, spec) * abs(spec.charge) for spec in subl_comps])
if phase.model_hints.get('ionic_liquid_2SL', False):
# Special treatment of "neutral" vacancies in 2SL ionic liquid
# These are treated as having variable valence
for idx, sublattice in enumerate(phase.constituents):
subl_comps = set(sublattice).intersection(unpack_components(dbe, comps))
if v.Species('VA') in subl_comps:
if idx == 0:
subl_idx = 1
elif idx == 1:
subl_idx = 0
else:
raise ValueError('Two-sublattice ionic liquid specified with more than two sublattices')
self.site_ratios[subl_idx] += self.site_ratios[idx] * v.SiteFraction(self.phase_name, idx, v.Species('VA'))
self.site_ratios = tuple(self.site_ratios)
# Verify that this phase is still possible to build
for sublattice in phase.constituents:
if len(set(sublattice).intersection(self.components)) == 0:
# None of the components in a sublattice are active
# We cannot build a model of this phase
raise DofError(
'{0}: Sublattice {1} of {2} has no components in {3}' \
.format(self.phase_name, sublattice,
phase.constituents,
self.components))
self.constituents.append(set(sublattice).intersection(self.components))
self.components = sorted(self.components)
# Convert string symbol names to sympy Symbol objects
# This makes xreplace work with the symbols dict
symbols = {Symbol(s): val for s, val in dbe.symbols.items()}
def wrap_symbol(obj):
if isinstance(obj, Symbol):
return obj
else:
return Symbol(obj)
if parameters is not None:
symbols.update([(wrap_symbol(s), val) for s, val in parameters.items()])
self._symbols = {wrap_symbol(key): value for key, value in symbols.items()}
self.models = OrderedDict()
self.build_phase(dbe)
self.site_fractions = sorted([x for x in self.ast.free_symbols if isinstance(x, v.SiteFraction)], key=str)
for name, value in self.models.items():
self.models[name] = self.symbol_replace(value, symbols)
def test_filter_phases_removes_disordered_phases_from_order_disorder():
"""Databases with order-disorder models should have the disordered phases be filtered."""
all_phases = set(ALNIPT_DBF.phases.keys())
filtered_phases = set(filter_phases(ALNIPT_DBF, unpack_components(ALNIPT_DBF, ['AL', 'NI', 'PT', 'VA'])))
assert all_phases.difference(filtered_phases) == {'FCC_A1'}
def equilibrium(dbf,
comps,
phases,
conditions,
output=None,
model=None,
verbose=False,
broadcast=True,
calc_opts=None,
to_xarray=True,
scheduler='sync',
parameters=None,
solver=None,
callables=None,
**kwargs):
"""
Calculate the equilibrium state of a system containing the specified
components and phases, under the specified conditions.
Parameters
----------
dbf : Database
Thermodynamic database containing the relevant parameters.
comps : list
Names of components to consider in the calculation.
phases : list or dict
Names of phases to consider in the calculation.
conditions : dict or (list of dict)
StateVariables and their corresponding value.
output : str or list of str, optional
Additional equilibrium model properties (e.g., CPM, HM, etc.) to compute.
These must be defined as attributes in the Model class of each phase.
model : Model, a dict of phase names to Model, or a seq of both, optional
Model class to use for each phase.
verbose : bool, optional
Print details of calculations. Useful for debugging.
broadcast : bool
If True, broadcast conditions against each other. This will compute all combinations.
If False, each condition should be an equal-length list (or single-valued).
Disabling broadcasting is useful for calculating equilibrium at selected conditions,
when those conditions don't comprise a grid.
calc_opts : dict, optional
Keyword arguments to pass to `calculate`, the energy/property calculation routine.
to_xarray : bool
Whether to return an xarray Dataset (True, default) or an EquilibriumResult.
scheduler : Dask scheduler, optional
Job scheduler for performing the computation.
If None, return a Dask graph of the computation instead of actually doing it.
parameters : dict, optional
Maps SymPy Symbol to numbers, for overriding the values of parameters in the Database.
solver : pycalphad.core.solver.SolverBase
Instance of a solver that is used to calculate local equilibria.
Defaults to a pycalphad.core.solver.InteriorPointSolver.
callables : dict, optional
Pre-computed callable functions for equilibrium calculation.
Returns
-------
Structured equilibrium calculation, or Dask graph if scheduler=None.
Examples
--------
None yet.
"""
if not broadcast:
raise NotImplementedError('Broadcasting cannot yet be disabled')
comps = sorted(unpack_components(dbf, comps))
phases = unpack_phases(phases) or sorted(dbf.phases.keys())
list_of_possible_phases = filter_phases(dbf, comps)
if len(list_of_possible_phases) == 0:
raise ConditionError(
'There are no phases in the Database that can be active with components {0}'
.format(comps))
active_phases = {
name: dbf.phases[name]
for name in filter_phases(dbf, comps, phases)
}
if len(active_phases) == 0:
raise ConditionError(
'None of the passed phases ({0}) are active. List of possible phases: {1}.'
.format(phases, list_of_possible_phases))
if isinstance(comps, (str, v.Species)):
comps = [comps]
if len(set(comps) - set(dbf.species)) > 0:
raise EquilibriumError('Components not found in database: {}'.format(
','.join([c.name for c in (set(comps) - set(dbf.species))])))
calc_opts = calc_opts if calc_opts is not None else dict()
solver = solver if solver is not None else InteriorPointSolver(
verbose=verbose)
parameters = parameters if parameters is not None else dict()
if isinstance(parameters, dict):
parameters = OrderedDict(sorted(parameters.items(), key=str))
models = instantiate_models(dbf,
comps,
active_phases,
model=model,
parameters=parameters)
# Temporary solution until constraint system improves
if conditions.get(v.N) is None:
conditions[v.N] = 1
if np.any(np.array(conditions[v.N]) != 1):
raise ConditionError('N!=1 is not yet supported, got N={}'.format(
conditions[v.N]))
# Modify conditions values to be within numerical limits, e.g., X(AL)=0
# Also wrap single-valued conditions with lists
conds = _adjust_conditions(conditions)
for cond in conds.keys():
if isinstance(cond,
(v.Composition,
v.ChemicalPotential)) and cond.species not in comps:
raise ConditionError(
'{} refers to non-existent component'.format(cond))
state_variables = sorted(get_state_variables(models=models, conds=conds),
key=str)
str_conds = OrderedDict((str(key), value) for key, value in conds.items())
components = [x for x in sorted(comps)]
desired_active_pure_elements = [
list(x.constituents.keys()) for x in components
]
desired_active_pure_elements = [
el.upper() for constituents in desired_active_pure_elements
for el in constituents
]
pure_elements = sorted(
set([x for x in desired_active_pure_elements if x != 'VA']))
if verbose:
print('Components:', ' '.join([str(x) for x in comps]))
print('Phases:', end=' ')
output = output if output is not None else 'GM'
output = output if isinstance(output, (list, tuple, set)) else [output]
output = set(output)
output |= {'GM'}
output = sorted(output)
phase_records = build_phase_records(dbf,
comps,
active_phases,
conds,
models,
output='GM',
callables=callables,
parameters=parameters,
verbose=verbose,
build_gradients=True,
build_hessians=True)
if verbose:
print('[done]', end='\n')
# 'calculate' accepts conditions through its keyword arguments
grid_opts = calc_opts.copy()
statevar_strings = [str(x) for x in state_variables]
grid_opts.update({
key: value
for key, value in str_conds.items() if key in statevar_strings
})
if 'pdens' not in grid_opts:
grid_opts['pdens'] = 500
grid = calculate(dbf,
comps,
active_phases,
model=models,
fake_points=True,
callables=callables,
output='GM',
parameters=parameters,
to_xarray=False,
**grid_opts)
coord_dict = str_conds.copy()
coord_dict['vertex'] = np.arange(
len(pure_elements) + 1
) # +1 is to accommodate the degenerate degree of freedom at the invariant reactions
coord_dict['component'] = pure_elements
properties = starting_point(conds, state_variables, phase_records, grid)
properties = _solve_eq_at_conditions(comps,
properties,
phase_records,
grid,
list(str_conds.keys()),
state_variables,
verbose,
solver=solver)
# Compute equilibrium values of any additional user-specified properties
# We already computed these properties so don't recompute them
output = sorted(set(output) - {'GM', 'MU'})
for out in output:
if (out is None) or (len(out) == 0):
continue
# TODO: How do we know if a specified property should be per_phase or not?
# For now, we make a best guess
if (out == 'degree_of_ordering') or (out == 'DOO'):
per_phase = True
else:
per_phase = False
eqcal = _eqcalculate(dbf,
comps,
active_phases,
conditions,
out,
data=properties,
per_phase=per_phase,
model=models,
callables=callables,
parameters=parameters,
**calc_opts)
properties = properties.merge(eqcal, inplace=True, compat='equals')
if to_xarray:
properties = properties.get_dataset()
properties.attrs['created'] = datetime.utcnow().isoformat()
if len(kwargs) > 0:
warnings.warn(
'The following equilibrium keyword arguments were passed, but unused:\n{}'
.format(kwargs))
return properties
def calculate(dbf, comps, phases, mode=None, output='GM', fake_points=False, broadcast=True, parameters=None, **kwargs):
"""
Sample the property surface of 'output' containing the specified
components and phases. Model parameters are taken from 'dbf' and any
state variables (T, P, etc.) can be specified as keyword arguments.
Parameters
----------
dbf : Database
Thermodynamic database containing the relevant parameters.
comps : str or sequence
Names of components to consider in the calculation.
phases : str or sequence
Names of phases to consider in the calculation.
mode : string, optional
See 'make_callable' docstring for details.
output : string, optional
Model attribute to sample.
fake_points : bool, optional (Default: False)
If True, the first few points of the output surface will be fictitious
points used to define an equilibrium hyperplane guaranteed to be above
all the other points. This is used for convex hull computations.
broadcast : bool, optional
If True, broadcast given state variable lists against each other to create a grid.
If False, assume state variables are given as equal-length lists.
points : ndarray or a dict of phase names to ndarray, optional
Columns of ndarrays must be internal degrees of freedom (site fractions), sorted.
If this is not specified, points will be generated automatically.
pdens : int, a dict of phase names to int, or a seq of both, optional
Number of points to sample per degree of freedom.
Default: 2000; Default when called from equilibrium(): 500
model : Model, a dict of phase names to Model, or a seq of both, optional
Model class to use for each phase.
sampler : callable, a dict of phase names to callable, or a seq of both, optional
Function to sample phase constitution space.
Must have same signature as 'pycalphad.core.utils.point_sample'
grid_points : bool, a dict of phase names to bool, or a seq of both, optional (Default: True)
Whether to add evenly spaced points between end-members.
The density of points is determined by 'pdens'
parameters : dict, optional
Maps SymPy Symbol to numbers, for overriding the values of parameters in the Database.
Returns
-------
Dataset of the sampled attribute as a function of state variables
Examples
--------
None yet.
"""
# Here we check for any keyword arguments that are special, i.e.,
# there may be keyword arguments that aren't state variables
pdens_dict = unpack_kwarg(kwargs.pop('pdens', 2000), default_arg=2000)
points_dict = unpack_kwarg(kwargs.pop('points', None), default_arg=None)
model_dict = unpack_kwarg(kwargs.pop('model', Model), default_arg=Model)
callable_dict = unpack_kwarg(kwargs.pop('callables', None), default_arg=None)
mass_dict = unpack_kwarg(kwargs.pop('massfuncs', None), default_arg=None)
sampler_dict = unpack_kwarg(kwargs.pop('sampler', None), default_arg=None)
fixedgrid_dict = unpack_kwarg(kwargs.pop('grid_points', True), default_arg=True)
parameters = parameters or dict()
if isinstance(parameters, dict):
parameters = OrderedDict(sorted(parameters.items(), key=str))
param_symbols = tuple(parameters.keys())
param_values = np.atleast_1d(np.array(list(parameters.values()), dtype=np.float))
if isinstance(phases, str):
phases = [phases]
if isinstance(comps, (str, v.Species)):
comps = [comps]
comps = sorted(unpack_components(dbf, comps))
if points_dict is None and broadcast is False:
raise ValueError('The \'points\' keyword argument must be specified if broadcast=False is also given.')
nonvacant_components = [x for x in sorted(comps) if x.number_of_atoms > 0]
# Convert keyword strings to proper state variable objects
# If we don't do this, sympy will get confused during substitution
statevar_dict = dict((v.StateVariable(key), unpack_condition(value)) for (key, value) in kwargs.items())
# XXX: CompiledModel assumes P, T are the only state variables
if statevar_dict.get(v.P, None) is None:
statevar_dict[v.P] = 101325
if statevar_dict.get(v.T, None) is None:
statevar_dict[v.T] = 300
# Sort after default state variable check to fix gh-116
statevar_dict = collections.OrderedDict(sorted(statevar_dict.items(), key=lambda x: str(x[0])))
str_statevar_dict = collections.OrderedDict((str(key), unpack_condition(value)) \
for (key, value) in statevar_dict.items())
all_phase_data = []
comp_sets = {}
largest_energy = 1e30
maximum_internal_dof = 0
# Consider only the active phases
active_phases = dict((name.upper(), dbf.phases[name.upper()]) \
for name in unpack_phases(phases))
for phase_name, phase_obj in sorted(active_phases.items()):
# Build the symbolic representation of the energy
mod = model_dict[phase_name]
# if this is an object type, we need to construct it
if isinstance(mod, type):
try:
model_dict[phase_name] = mod = mod(dbf, comps, phase_name, parameters=parameters)
except DofError:
# we can't build the specified phase because the
# specified components aren't found in every sublattice
# we'll just skip it
warnings.warn("""Suspending specified phase {} due to
some sublattices containing only unspecified components""".format(phase_name))
continue
if points_dict[phase_name] is None:
maximum_internal_dof = max(maximum_internal_dof, sum(len(x) for x in mod.constituents))
else:
maximum_internal_dof = max(maximum_internal_dof, np.asarray(points_dict[phase_name]).shape[-1])
for phase_name, phase_obj in sorted(active_phases.items()):
try:
mod = model_dict[phase_name]
except KeyError:
continue
# this is a phase model we couldn't construct for whatever reason; skip it
if isinstance(mod, type):
continue
# Construct an ordered list of the variables
variables, sublattice_dof = generate_dof(phase_obj, mod.components)
# Build the "fast" representation of that model
if callable_dict[phase_name] is None:
try:
out = getattr(mod, output)
except AttributeError:
raise AttributeError('Missing Model attribute {0} specified for {1}'
.format(output, mod.__class__))
# As a last resort, treat undefined symbols as zero
# But warn the user when we do this
# This is consistent with TC's behavior
undefs = list(out.atoms(Symbol) - out.atoms(v.StateVariable))
for undef in undefs:
out = out.xreplace({undef: float(0)})
warnings.warn('Setting undefined symbol {0} for phase {1} to zero'.format(undef, phase_name))
comp_sets[phase_name] = build_functions(out, list(statevar_dict.keys()) + variables,
include_obj=True, include_grad=False,
parameters=param_symbols)
else:
comp_sets[phase_name] = callable_dict[phase_name]
if mass_dict[phase_name] is None:
pure_elements = [spec for spec in nonvacant_components
if (len(spec.constituents.keys()) == 1 and
list(spec.constituents.keys())[0] == spec.name)
]
# TODO: In principle, we should also check for undefs in mod.moles()
mass_dict[phase_name] = [build_functions(mod.moles(el), list(statevar_dict.keys()) + variables,
include_obj=True, include_grad=False,
parameters=param_symbols)
for el in pure_elements]
phase_record = PhaseRecord_from_cython(comps, list(statevar_dict.keys()) + variables,
np.array(dbf.phases[phase_name].sublattices, dtype=np.float),
param_values, comp_sets[phase_name], None, None, mass_dict[phase_name], None)
points = points_dict[phase_name]
if points is None:
points = _sample_phase_constitution(phase_name, phase_obj.constituents, sublattice_dof, comps,
tuple(variables), sampler_dict[phase_name] or point_sample,
fixedgrid_dict[phase_name], pdens_dict[phase_name])
points = np.atleast_2d(points)
fp = fake_points and (phase_name == sorted(active_phases.keys())[0])
phase_ds = _compute_phase_values(nonvacant_components, str_statevar_dict,
points, phase_record, output,
maximum_internal_dof, broadcast=broadcast,
largest_energy=float(largest_energy), fake_points=fp)
all_phase_data.append(phase_ds)
# speedup for single-phase case (found by profiling)
if len(all_phase_data) > 1:
final_ds = concat(all_phase_data, dim='points')
final_ds['points'].values = np.arange(len(final_ds['points']))
final_ds.coords['points'].values = np.arange(len(final_ds['points']))
else:
final_ds = all_phase_data[0]
return final_ds
def calculate_activity_error(dbf,
comps,
phases,
datasets,
parameters=None,
phase_models=None,
callables=None,
data_weight=1.0):
"""
Return the sum of square error from activity data
Parameters
----------
dbf : pycalphad.Database
Database to consider
comps : list
List of active component names
phases : list
List of phases to consider
datasets : espei.utils.PickleableTinyDB
Datasets that contain single phase data
parameters : dict
Dictionary of symbols that will be overridden in pycalphad.equilibrium
phase_models : dict
Phase models to pass to pycalphad calculations
callables : dict
Callables to pass to pycalphad
data_weight : float
Weight for standard deviation of activity measurements, dimensionless.
Corresponds to the standard deviation of differences in chemical
potential in typical measurements of activity, in J/mol.
Returns
-------
float
A single float of the sum of square errors
Notes
-----
General procedure:
1. Get the datasets
2. For each dataset
a. Calculate reference state equilibrium
b. Calculate current chemical potentials
c. Find the target chemical potentials
d. Calculate error due to chemical potentials
"""
std_dev = 500 # J/mol
if parameters is None:
parameters = {}
activity_datasets = datasets.search(
(tinydb.where('output').test(lambda x: 'ACR' in x))
& (tinydb.where('components').test(lambda x: set(x).issubset(comps))))
error = 0
if len(activity_datasets) == 0:
return error
for ds in activity_datasets:
acr_component = ds['output'].split('_')[1] # the component of interest
# calculate the reference state equilibrium
ref = ds['reference_state']
# data_comps and data_phases ensures that we only do calculations on
# the subsystem of the system defining the data.
data_comps = ds['components']
data_phases = filter_phases(dbf,
unpack_components(dbf, data_comps),
candidate_phases=phases)
ref_conditions = {
_map_coord_to_variable(coord): val
for coord, val in ref['conditions'].items()
}
ref_result = equilibrium(dbf,
data_comps,
ref['phases'],
ref_conditions,
model=phase_models,
parameters=parameters,
callables=callables)
# calculate current chemical potentials
# get the conditions
conditions = {}
# first make sure the conditions are paired
# only get the compositions, P and T are special cased
conds_list = [(cond, value)
for cond, value in ds['conditions'].items()
if cond not in ('P', 'T')]
# ravel the conditions
# we will ravel each composition individually, since they all must have the same shape
for comp_name, comp_x in conds_list:
P, T, X = ravel_conditions(ds['values'], ds['conditions']['P'],
ds['conditions']['T'], comp_x)
conditions[v.P] = P
conditions[v.T] = T
conditions[_map_coord_to_variable(comp_name)] = X
# do the calculations
# we cannot currently turn broadcasting off, so we have to do equilibrium one by one
# invert the conditions dicts to make a list of condition dicts rather than a condition dict of lists
# assume now that the ravelled conditions all have the same size
conditions_list = [{c: conditions[c][i]
for c in conditions.keys()}
for i in range(len(conditions[v.T]))]
current_chempots = []
for conds in conditions_list:
sample_eq_res = equilibrium(dbf,
data_comps,
data_phases,
conds,
model=phase_models,
parameters=parameters,
callables=callables)
current_chempots.append(
sample_eq_res.MU.sel(
component=acr_component).values.flatten()[0])
current_chempots = np.array(current_chempots)
# calculate target chempots
samples = np.array(ds['values']).flatten()
target_chempots = target_chempots_from_activity(
acr_component, samples, conditions[v.T], ref_result)
# calculate the error
weight = ds.get('weight', 1.0)
pe = chempot_error(current_chempots,
target_chempots,
std_dev=std_dev / data_weight / weight)
error += np.sum(pe)
_log.debug(
'Data: %s, chemical potential difference: %s, probability: %s, reference: %s',
samples, current_chempots - target_chempots, pe, ds["reference"])
# TODO: write a test for this
if np.any(np.isnan(np.array([
error
], dtype=np.float64))): # must coerce sympy.core.numbers.Float to float64
return -np.inf
return error
