def dataplot(comps,
phases,
conds,
datasets,
ax=None,
plot_kwargs=None,
tieline_plot_kwargs=None):
"""
Plot datapoints corresponding to the components, phases, and conditions.
Parameters
----------
comps : list
Names of components to consider in the calculation.
phases : []
Names of phases to consider in the calculation.
conds : dict
Maps StateVariables to values and/or iterables of values.
datasets : PickleableTinyDB
ax : matplotlib.Axes
Default axes used if not specified.
plot_kwargs : dict
Additional keyword arguments to pass to the matplotlib plot function for points
tieline_plot_kwargs : dict
Additional keyword arguments to pass to the matplotlib plot function for tielines
Returns
-------
matplotlib.Axes
A plot of phase equilibria points as a figure
Examples
--------
>>> from espei.datasets import load_datasets, recursive_glob
>>> from espei.plot import dataplot
>>> datasets = load_datasets(recursive_glob('.', '*.json'))
>>> my_phases = ['BCC_A2', 'CUMG2', 'FCC_A1', 'LAVES_C15', 'LIQUID']
>>> my_components = ['CU', 'MG' 'VA']
>>> conditions = {v.P: 101325, v.T: (500, 1000, 10), v.X('MG'): (0, 1, 0.01)}
>>> dataplot(my_components, my_phases, conditions, datasets)
"""
indep_comps = [
key for key, value in conds.items()
if isinstance(key, v.Composition) and len(np.atleast_1d(value)) > 1
]
indep_pots = [
key for key, value in conds.items()
if ((key == v.T) or (key == v.P)) and len(np.atleast_1d(value)) > 1
]
plot_kwargs = plot_kwargs or {}
phases = sorted(phases)
# determine what the type of plot will be
if len(indep_comps) == 1 and len(indep_pots) == 1:
projection = None
elif len(indep_comps) == 2 and len(indep_pots) == 0:
projection = 'triangular'
else:
raise ValueError(
'The eqplot projection is not defined and cannot be autodetected. There are {} independent compositions and {} indepedent potentials.'
.format(len(indep_comps), len(indep_pots)))
if projection is None:
x = indep_comps[0].species
y = indep_pots[0]
elif projection == 'triangular':
x = indep_comps[0].species
y = indep_comps[1].species
# set up plot if not done already
if ax is None:
ax = plt.gca(projection=projection)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.tick_params(axis='both', which='major', labelsize=14)
ax.grid(True)
plot_title = '-'.join([
component.title() for component in sorted(comps)
if component != 'VA'
])
ax.set_title(plot_title, fontsize=20)
ax.set_xlabel('X({})'.format(x), labelpad=15, fontsize=20)
ax.set_xlim((0, 1))
if projection is None:
ax.set_ylabel(plot_mapping.get(str(y), y), fontsize=20)
elif projection == 'triangular':
ax.set_ylabel('X({})'.format(y), labelpad=15, fontsize=20)
ax.set_ylim((0, 1))
ax.yaxis.label.set_rotation(60)
# Here we adjust the x coordinate of the ylabel.
# We make it reasonably comparable to the position of the xlabel from the xaxis
# As the figure size gets very large, the label approaches ~0.55 on the yaxis
# 0.55*cos(60 deg)=0.275, so that is the xcoord we are approaching.
ax.yaxis.label.set_va('baseline')
fig_x_size = ax.figure.get_size_inches()[0]
y_label_offset = 1 / fig_x_size
ax.yaxis.set_label_coords(x=(0.275 - y_label_offset), y=0.5)
output = 'ZPF'
# TODO: used to include VA. Should this be added by default. Can't determine presence of VA in eq.
# Techincally, VA should not be present in any phase equilibria.
desired_data = datasets.search(
(tinydb.where('output') == output) & (tinydb.where('components').test(
lambda x: set(x).issubset(comps + ['VA'])))
& (tinydb.where('phases').test(
lambda x: len(set(phases).intersection(x)) > 0)))
# get all the possible references from the data and create the bibliography map
bib_reference_keys = sorted(
list({entry['reference']
for entry in desired_data}))
symbol_map = bib_marker_map(bib_reference_keys)
# The above handled the phases as in the equilibrium, but there may be
# phases that are in the datasets but not in the equilibrium diagram that
# we would like to plot point for (they need color maps).
# To keep consistent colors with the equilibrium diagram, we will append
# the new phases from the datasets to the existing phases in the equilibrium
# calculation.
data_phases = set()
for entry in desired_data:
data_phases.update(set(entry['phases']))
new_phases = sorted(list(data_phases.difference(set(phases))))
phases.extend(new_phases)
legend_handles, phase_color_map = phase_legend(phases)
if projection is None:
# TODO: There are lot of ways this could break in multi-component situations
# plot x vs. T
y = 'T'
# handle plotting kwargs
scatter_kwargs = {'markersize': 6, 'markeredgewidth': 1}
# raise warnings if any of the aliased versions of the default values are used
possible_aliases = [('markersize', 'ms'), ('markeredgewidth', 'mew')]
for actual_arg, aliased_arg in possible_aliases:
if aliased_arg in plot_kwargs:
warnings.warn(
"'{0}' passed as plotting keyword argument to dataplot, but the alias '{1}' is already set to '{2}'. Use the full version of the keyword argument '{1}' to override the default."
.format(aliased_arg, actual_arg,
scatter_kwargs.get(actual_arg)))
scatter_kwargs.update(plot_kwargs)
eq_dict = ravel_zpf_values(desired_data, [x])
# two phase
updated_tieline_plot_kwargs = {'linewidth': 1, 'color': 'k'}
if tieline_plot_kwargs is not None:
updated_tieline_plot_kwargs.update(tieline_plot_kwargs)
for eq in eq_dict.get(2, []): # list of things in equilibrium
# plot the scatter points for the right phases
x_points, y_points = [], []
for phase_name, comp_dict, ref_key in eq:
sym_ref = symbol_map[ref_key]
x_val, y_val = comp_dict[x], comp_dict[y]
if x_val is not None and y_val is not None:
ax.plot(x_val,
y_val,
label=sym_ref['formatted'],
fillstyle=sym_ref['markers']['fillstyle'],
marker=sym_ref['markers']['marker'],
linestyle='',
color=phase_color_map[phase_name],
**scatter_kwargs)
x_points.append(x_val)
y_points.append(y_val)
# plot the tielines
if all([
xx is not None and yy is not None
for xx, yy in zip(x_points, y_points)
]):
ax.plot(x_points, y_points, **updated_tieline_plot_kwargs)
elif projection == 'triangular':
scatter_kwargs = {'markersize': 4, 'markeredgewidth': 0.4}
# raise warnings if any of the aliased versions of the default values are used
possible_aliases = [('markersize', 'ms'), ('markeredgewidth', 'mew')]
for actual_arg, aliased_arg in possible_aliases:
if aliased_arg in plot_kwargs:
warnings.warn(
"'{0}' passed as plotting keyword argument to dataplot, but the alias '{1}' is already set to '{2}'. Use the full version of the keyword argument '{1}' to override the default."
.format(aliased_arg, actual_arg,
scatter_kwargs.get(actual_arg)))
scatter_kwargs.update(plot_kwargs)
eq_dict = ravel_zpf_values(desired_data, [x, y], {'T': conds[v.T]})
# two phase
updated_tieline_plot_kwargs = {'linewidth': 1, 'color': 'k'}
if tieline_plot_kwargs is not None:
updated_tieline_plot_kwargs.update(tieline_plot_kwargs)
for eq in eq_dict.get(2, []): # list of things in equilibrium
# plot the scatter points for the right phases
x_points, y_points = [], []
for phase_name, comp_dict, ref_key in eq:
sym_ref = symbol_map[ref_key]
x_val, y_val = comp_dict[x], comp_dict[y]
if x_val is not None and y_val is not None:
ax.plot(x_val,
y_val,
label=sym_ref['formatted'],
fillstyle=sym_ref['markers']['fillstyle'],
marker=sym_ref['markers']['marker'],
linestyle='',
color=phase_color_map[phase_name],
**scatter_kwargs)
x_points.append(x_val)
y_points.append(y_val)
# plot the tielines
if all([
xx is not None and yy is not None
for xx, yy in zip(x_points, y_points)
]):
ax.plot(x_points, y_points, **updated_tieline_plot_kwargs)
# three phase
updated_tieline_plot_kwargs = {'linewidth': 1, 'color': 'r'}
if tieline_plot_kwargs is not None:
updated_tieline_plot_kwargs.update(tieline_plot_kwargs)
for eq in eq_dict.get(3, []): # list of things in equilibrium
# plot the scatter points for the right phases
x_points, y_points = [], []
for phase_name, comp_dict, ref_key in eq:
x_val, y_val = comp_dict[x], comp_dict[y]
x_points.append(x_val)
y_points.append(y_val)
# Make sure the triangle completes
x_points.append(x_points[0])
y_points.append(y_points[0])
# plot
# check for None values
if all([
xx is not None and yy is not None
for xx, yy in zip(x_points, y_points)
]):
ax.plot(x_points, y_points, **updated_tieline_plot_kwargs)
# now we will add the symbols for the references to the legend handles
for ref_key in bib_reference_keys:
mark = symbol_map[ref_key]['markers']
# The legend marker edge width appears smaller than in the plot.
# We will add this small hack to increase the width in the legend only.
legend_kwargs = scatter_kwargs.copy()
legend_kwargs['markeredgewidth'] = 1
legend_kwargs['markersize'] = 6
legend_handles.append(
mlines.Line2D([], [],
linestyle='',
color='black',
markeredgecolor='black',
label=symbol_map[ref_key]['formatted'],
fillstyle=mark['fillstyle'],
marker=mark['marker'],
**legend_kwargs))
# finally, add the completed legend
ax.legend(handles=legend_handles,
loc='center left',
bbox_to_anchor=(1, 0.5))
return ax
# load the experimental and DFT datasets
datasets = load_datasets(recursive_glob(DATASETS_DIR, '*.json'))
# phases = ['LIQUID', 'BCC_A2', 'FCC_A1']
desired_data = datasets.search((tinydb.where('output') == 'ZPF') &
(tinydb.where('components').test(lambda x: set(x).issubset(comps + ['VA']))) #&
# (tinydb.where('phases').test(lambda x: len(set(phases).intersection(x)) > 0)))
)
raveled_dict = ravel_zpf_values(desired_data, [independent_component])
bib_reference_keys = sorted(list({entry['reference'] for entry in desired_data}))
symbol_map = bib_marker_map(bib_reference_keys)
# map matplotlib string markers to strings of markers for Thermo-Calc's POST
dataplot_symbols = ['S'+str(i) for i in range(1, 18)]
dataplot_marker_map = dict(zip([v['markers']['marker'] for v in symbol_map.values()], dataplot_symbols))
equilibria_to_plot = raveled_dict.get(2, [])
equilibria_lines = []
for eq in equilibria_to_plot:
x_points, y_points = [], []
for phase_name, comp_dict, ref_key in eq:
