def test_get_data_for_a_minimal_example():
"""Given a dataset and the congfiguration pertaining to that dataset, we should find the values."""
SAMPLE_DATASET = {
"components": ["CU", "MG", "VA"],
"phases": ["LAVES_C15"],
"solver": {
"mode":
"manual",
"sublattice_site_ratios": [2, 1],
"sublattice_configurations": [["CU", "MG"], ["MG", "CU"],
["MG", "MG"], ["CU", "CU"]]
},
"conditions": {
"P": 101325,
"T": 298.15
},
"output": "HM_FORM",
"values": [[[-15720, 34720, 7000, 15500]]]
}
datasets = PickleableTinyDB(storage=MemoryStorage)
datasets.insert(SAMPLE_DATASET)
comps = ['CU', 'MG', 'VA']
phase_name = 'LAVES_C15'
configuration = ('MG', 'CU')
symmetry = None
desired_props = ['HM_FORM']
# The following lines replace "get_data" in a more functional form
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)
assert len(desired_data) == 1
desired_data = desired_data[0]
assert desired_data['components'] == comps
assert desired_data['phases'][0] == phase_name
assert desired_data['solver']['sublattice_site_ratios'] == [2, 1]
assert desired_data['solver']['sublattice_configurations'] == (('MG',
'CU'), )
assert desired_data['conditions']['P'] == 101325
assert desired_data['conditions']['T'] == 298.15
assert desired_data['output'] == 'HM_FORM'
assert desired_data['values'] == np.array([[[34720.0]]])
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 fit_formation_energy(dbf, comps, phase_name, configuration, symmetry, datasets, ridge_alpha=None, aicc_phase_penalty=None, features=None):
"""
Find suitable linear model parameters for the given phase.
We do this by successively fitting heat capacities, entropies and
enthalpies of formation, and selecting against criteria to prevent
overfitting. The "best" set of parameters minimizes the error
without overfitting.
Parameters
----------
dbf : Database
pycalphad Database. Partially complete, so we know what degrees of freedom to fix.
comps : [str]
Names of the relevant components.
phase_name : str
Name of the desired phase for which the parameters will be found.
configuration : ndarray
Configuration of the sublattices for the fitting procedure.
symmetry : [[int]]
Symmetry of the sublattice configuration.
datasets : PickleableTinyDB
All the datasets desired to fit to.
ridge_alpha : float
Value of the :math:`\\alpha` hyperparameter used in ridge regression. Defaults to 1.0e-100, which should be degenerate
with ordinary least squares regression. For now, the parameter is applied to all features.
aicc_feature_factors : dict
Map of phase name to feature to a multiplication factor for the AICc's parameter penalty.
features : dict
Maps "property" to a list of features for the linear model.
These will be transformed from "GM" coefficients
e.g., {"CPM_FORM": (v.T*sympy.log(v.T), v.T**2, v.T**-1, v.T**3)} (Default value = None)
Returns
-------
dict
{feature: estimated_value}
"""
aicc_feature_factors = aicc_phase_penalty if aicc_phase_penalty is not None else {}
if interaction_test(configuration):
_log.debug('ENDMEMBERS FROM INTERACTION: %s', endmembers_from_interaction(configuration))
fitting_steps = (["CPM_FORM", "CPM_MIX"], ["SM_FORM", "SM_MIX"], ["HM_FORM", "HM_MIX"])
else:
# We are only fitting an endmember; no mixing data needed
fitting_steps = (["CPM_FORM"], ["SM_FORM"], ["HM_FORM"])
# create the candidate models and fitting steps
if features is None:
features = OrderedDict([("CPM_FORM", (v.T * sympy.log(v.T), v.T**2, v.T**-1, v.T**3)),
("SM_FORM", (v.T,)),
("HM_FORM", (sympy.S.One,)),
])
# dict of {feature, [candidate_models]}
candidate_models_features = build_candidate_models(configuration, features)
# All possible parameter values that could be taken on. This is some legacy
# code from before there were many candidate models built. For very large
# sets of candidate models, this could be quite slow.
# TODO: we might be able to remove this initialization for clarity, depends on fixed poritions
parameters = {}
for candidate_models in candidate_models_features.values():
for model in candidate_models:
for coef in model:
parameters[coef] = 0
# These is our previously fit partial model from previous steps
# Subtract out all of these contributions (zero out reference state because these are formation properties)
fixed_model = Model(dbf, comps, phase_name, parameters={'GHSER'+(c.upper()*2)[:2]: 0 for c in comps})
fixed_portions = [0]
for desired_props in fitting_steps:
feature_type = desired_props[0].split('_')[0] # HM_FORM -> HM
aicc_factor = aicc_feature_factors.get(feature_type, 1.0)
solver_qry = (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)
_log.trace('%s: datasets found: %s', desired_props, len(desired_data))
if len(desired_data) > 0:
config_tup = tuple(map(tuplify, configuration))
calculate_dict = get_prop_samples(desired_data, config_tup)
sample_condition_dicts = _get_sample_condition_dicts(calculate_dict, list(map(len, config_tup)))
weights = calculate_dict['weights']
assert len(sample_condition_dicts) == len(weights)
# We assume all properties in the same fitting step have the same
# features (all CPM, all HM, etc., but different ref states).
# data quantities are the same for each candidate model and can be computed up front
data_qtys = get_data_quantities(feature_type, fixed_model, fixed_portions, desired_data, sample_condition_dicts)
# build the candidate model transformation matrix and response vector (A, b in Ax=b)
feature_matricies = []
data_quantities = []
for candidate_coefficients in candidate_models_features[desired_props[0]]:
# Map coeffiecients in G to coefficients in the feature_type (H, S, CP)
transformed_coefficients = list(map(feature_transforms[feature_type], candidate_coefficients))
if interaction_test(configuration, 3):
feature_matricies.append(_build_feature_matrix(sample_condition_dicts, transformed_coefficients))
else:
feature_matricies.append(_build_feature_matrix(sample_condition_dicts, transformed_coefficients))
data_quantities.append(data_qtys)
# provide candidate models and get back a selected model.
selected_model = select_model(zip(candidate_models_features[desired_props[0]], feature_matricies, data_quantities), ridge_alpha, weights=weights, aicc_factor=aicc_factor)
selected_features, selected_values = selected_model
parameters.update(zip(*(selected_features, selected_values)))
# Add these parameters to be fixed for the next fitting step
fixed_portion = np.array(selected_features, dtype=np.object_)
fixed_portion = np.dot(fixed_portion, selected_values)
fixed_portions.append(fixed_portion)
return parameters
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 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 plot_parameters(dbf, comps, phase_name, configuration, symmetry, datasets=None, fig=None, require_data=True):
"""
Plot parameters of interest compared with data in subplots of a single figure
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
configuration : tuple
Sublattice configuration to plot, such as ('CU', 'CU') or (('CU', 'MG'), 'CU')
symmetry : list
List of lists containing indices of symmetric sublattices e.g. [[0, 1], [2, 3]]
datasets : PickleableTinyDB
ESPEI datasets to compare against. If None, nothing is plotted.
fig : matplotlib.Figure
Figure to create with axes as subplots.
require_data : bool
If True, plot parameters that have data corresponding data. Defaults to
True. Will raise an error for non-interaction configurations.
Returns
-------
None
Examples
--------
>>> # plot the LAVES_C15 (Cu)(Mg) endmember
>>> plot_parameters(dbf, ['CU', 'MG'], 'LAVES_C15', ('CU', 'MG'), symmetry=None, datasets=datasets) # doctest: +SKIP
>>> # plot the mixing interaction in the first sublattice
>>> plot_parameters(dbf, ['CU', 'MG'], 'LAVES_C15', (('CU', 'MG'), 'MG'), symmetry=None, datasets=datasets) # doctest: +SKIP
"""
deprecation_msg = (
"`espei.plot.plot_parameters` is deprecated and will be removed in ESPEI 0.9. "
"Please use `plot_endmember` or `plot_interaction` instead."
)
warnings.warn(deprecation_msg, category=FutureWarning)
em_plots = [('T', 'CPM'), ('T', 'CPM_FORM'), ('T', 'SM'), ('T', 'SM_FORM'),
('T', 'HM'), ('T', 'HM_FORM')]
mix_plots = [ ('Z', 'HM_MIX'), ('Z', 'SM_MIX')]
comps = sorted(comps)
mod = Model(dbf, comps, phase_name)
mod.models['idmix'] = 0
# This is for computing properties of formation
mod_norefstate = Model(dbf, comps, phase_name, parameters={'GHSER'+(c.upper()*2)[:2]: 0 for c in comps})
# Is this an interaction parameter or endmember?
if any([isinstance(conf, list) or isinstance(conf, tuple) for conf in configuration]):
plots = mix_plots
else:
plots = em_plots
# filter which parameters to plot by the data that exists
if require_data and datasets is not None:
filtered_plots = []
for x_val, y_val in plots:
desired_props = [y_val.split('_')[0]+'_FORM', y_val] if y_val.endswith('_MIX') else [y_val]
solver_qry = (tinydb.where('solver').test(symmetry_filter, configuration, recursive_tuplify(symmetry) if symmetry else symmetry))
data = get_prop_data(comps, phase_name, desired_props, datasets, additional_query=solver_qry)
data = filter_configurations(data, configuration, symmetry)
data = filter_temperatures(data)
if len(data) > 0:
filtered_plots.append((x_val, y_val, data))
elif require_data:
raise ValueError('Plots require datasets, but no datasets were passed.')
elif plots == em_plots and not require_data:
# How we treat temperature dependence is ambiguous when there is no data, so we raise an error
raise ValueError('The "require_data=False" option is not supported for non-mixing configurations.')
elif datasets is not None:
filtered_plots = []
for x_val, y_val in plots:
desired_props = [y_val.split('_')[0]+'_FORM', y_val] if y_val.endswith('_MIX') else [y_val]
solver_qry = (tinydb.where('solver').test(symmetry_filter, configuration, recursive_tuplify(symmetry) if symmetry else symmetry))
data = get_prop_data(comps, phase_name, desired_props, datasets, additional_query=solver_qry)
data = filter_configurations(data, configuration, symmetry)
data = filter_temperatures(data)
filtered_plots.append((x_val, y_val, data))
else:
filtered_plots = [(x_val, y_val, []) for x_val, y_val in plots]
num_plots = len(filtered_plots)
if num_plots == 0:
return
if not fig:
fig = plt.figure(figsize=plt.figaspect(num_plots))
# plot them
for i, (x_val, y_val, data) in enumerate(filtered_plots):
if y_val.endswith('_FORM'):
ax = fig.add_subplot(num_plots, 1, i+1)
ax = _compare_data_to_parameters(dbf, comps, phase_name, data, mod_norefstate, configuration, x_val, y_val, ax=ax)
else:
ax = fig.add_subplot(num_plots, 1, i+1)
ax = _compare_data_to_parameters(dbf, comps, phase_name, data, mod, configuration, x_val, y_val, ax=ax)
