def generate_entry_label(entry):
"""
Generates a label for the pourbaix plotter
Args:
entry (PourbaixEntry or MultiEntry): entry to get a label for
"""
if isinstance(entry, MultiEntry):
return " + ".join([latexify_ion(latexify(e.name)) for e in entry.entry_list])
return latexify_ion(latexify(entry.name))
def _get_xaxis_title(self, latex: bool = True) -> str:
"""Returns the formatted title of the x axis (using either html/latex)"""
if latex:
f1 = latexify(self.c1.reduced_formula)
f2 = latexify(self.c2.reduced_formula)
title = f"$x$ in $x${f1} + $(1-x)${f2}"
else:
f1 = htmlify(self.c1.reduced_formula)
f2 = htmlify(self.c2.reduced_formula)
title = f"<i>x</i> in <i>x</i>{f1} + (1-<i>x</i>){f2}"
return title
def _get_plot(self, label_stable=True, label_unstable=False):
"""
Plot convex hull of Pourbaix Diagram entries
"""
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as p3
from matplotlib.font_manager import FontProperties
fig = plt.figure()
ax = p3.Axes3D(fig)
font = FontProperties()
font.set_weight("bold")
font.set_size(14)
(lines, labels, unstable) = self.pourbaix_hull_plot_data
count = 1
newlabels = list()
for x, y, z in lines:
ax.plot(x,
y,
z,
"bo-",
linewidth=3,
markeredgecolor="b",
markerfacecolor="r",
markersize=10)
for coords in sorted(labels.keys()):
entry = labels[coords]
label = self.print_name(entry)
if label_stable:
ax.text(coords[0], coords[1], coords[2], str(count))
newlabels.append("{} : {}".format(
count, latexify_ion(latexify(label))))
count += 1
if label_unstable:
for entry in unstable.keys():
label = self.print_name(entry)
coords = unstable[entry]
ax.plot([coords[0], coords[0]], [coords[1], coords[1]],
[coords[2], coords[2]],
"bo",
markerfacecolor="g",
markersize=10)
ax.text(coords[0], coords[1], coords[2], str(count))
newlabels.append("{} : {}".format(
count, latexify_ion(latexify(label))))
count += 1
plt.figtext(0.01, 0.01, "\n".join(newlabels))
plt.xlabel("pH")
plt.ylabel("V")
return plt
def print_name(self, entry):
"""
Print entry name if single, else print multientry
"""
str_name = ""
if isinstance(entry, MultiEntry):
if len(entry.entrylist) > 2:
return str(self._pd.qhull_entries.index(entry))
for e in entry.entrylist:
str_name += latexify_ion(latexify(e.name)) + " + "
str_name = str_name[:-3]
return str_name
else:
return latexify_ion(latexify(entry.name))
def print_name(self, entry):
"""
Print entry name if single, else print multientry
"""
str_name = ""
if isinstance(entry, MultiEntry):
if len(entry.entrylist) > 2:
return str(self._pd.qhull_entries.index(entry))
for e in entry.entrylist:
str_name += latexify_ion(latexify(e.name)) + " + "
str_name = str_name[:-3]
return str_name
else:
return latexify_ion(latexify(entry.name))
def _get_3d_plot(self, label_stable=True):
"""
Shows the plot using pylab. Usually I won"t do imports in methods,
but since plotting is a fairly expensive library to load and not all
machines have matplotlib installed, I have done it this way.
"""
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as p3
from matplotlib.font_manager import FontProperties
fig = plt.figure()
ax = p3.Axes3D(fig)
font = FontProperties()
font.set_weight("bold")
font.set_size(20)
(lines, labels, unstable) = self.pd_plot_data
count = 1
newlabels = list()
for x, y, z in lines:
ax.plot(x, y, z, "bo-", linewidth=3, markeredgecolor="b",
markerfacecolor="r", markersize=10)
for coords in sorted(labels.keys()):
entry = labels[coords]
label = entry.name
if label_stable:
if len(entry.composition.elements) == 1:
ax.text(coords[0], coords[1], coords[2], label)
else:
ax.text(coords[0], coords[1], coords[2], str(count))
newlabels.append("{} : {}".format(count, latexify(label)))
count += 1
plt.figtext(0.01, 0.01, "\n".join(newlabels))
ax.axis("off")
return plt
def plot_planes(self):
"""
Plot the free energy facets as a function of pH and V
"""
if self.show_unstable:
entries = self._pd._all_entries
else:
entries = self._pd.stable_entries
num_plots = len(entries)
import matplotlib.pyplot as plt
colormap = plt.cm.gist_ncar
fig = plt.figure().gca(projection='3d')
color_array = [colormap(i) for i in np.linspace(0, 0.9, num_plots)]
labels = []
color_index = -1
for entry in entries:
normal = np.array([-PREFAC * entry.npH, -entry.nPhi, +1])
d = entry.g0
color_index += 1
pH, V = np.meshgrid(np.linspace(-10, 28, 100),
np.linspace(-3, 3, 100))
g = (-normal[0] * pH - normal[1] * V + d) / normal[2]
lbl = latexify_ion(
latexify(entry._entry.composition.reduced_formula))
labels.append(lbl)
fig.plot_surface(pH, V, g, color=color_array[color_index],
label=lbl)
plt.legend(labels)
plt.xlabel("pH")
plt.ylabel("E (V)")
plt.show()
def plot_planes(self):
"""
Plot the free energy facets as a function of pH and V
"""
if self.show_unstable:
entries = self._pd._all_entries
else:
entries = self._pd.stable_entries
num_plots = len(entries)
import matplotlib.pyplot as plt
colormap = plt.cm.gist_ncar
fig = plt.figure().gca(projection='3d')
color_array = [colormap(i) for i in np.linspace(0, 0.9, num_plots)]
labels = []
color_index = -1
for entry in entries:
normal = np.array([-PREFAC * entry.npH, -entry.nPhi, +1])
d = entry.g0
color_index += 1
pH, V = np.meshgrid(np.linspace(-10, 28, 100),
np.linspace(-3, 3, 100))
g = (-normal[0] * pH - normal[1] * V + d) / normal[2]
lbl = latexify_ion(
latexify(entry._entry.composition.reduced_formula))
labels.append(lbl)
fig.plot_surface(pH, V, g, color=color_array[color_index],
label=lbl)
plt.legend(labels)
plt.xlabel("pH")
plt.ylabel("E (V)")
plt.show()
def _plot(self):
for composition, position in self.info.comp_centers.items():
self.draw_simplex(composition)
self._ax.text(*position,
latexify(composition.reduced_formula),
color=self._text_color(composition),
**self._mpl_defaults.label,
**self._text_kwargs)
def _get_plot(self, label_stable=True, label_unstable=False):
"""
Plot convex hull of Pourbaix Diagram entries
"""
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as p3
from matplotlib.font_manager import FontProperties
fig = plt.figure()
ax = p3.Axes3D(fig)
font = FontProperties()
font.set_weight("bold")
font.set_size(14)
(lines, labels, unstable) = self.pourbaix_hull_plot_data
count = 1
newlabels = list()
for x, y, z in lines:
ax.plot(x, y, z, "bo-", linewidth=3, markeredgecolor="b",
markerfacecolor="r", markersize=10)
for coords in sorted(labels.keys()):
entry = labels[coords]
label = self.print_name(entry)
if label_stable:
ax.text(coords[0], coords[1], coords[2], str(count))
newlabels.append("{} : {}".format(
count, latexify_ion(latexify(label))))
count += 1
if label_unstable:
for entry in unstable.keys():
label = self.print_name(entry)
coords = unstable[entry]
ax.plot([coords[0], coords[0]], [coords[1], coords[1]],
[coords[2], coords[2]], "bo", markerfacecolor="g",
markersize=10)
ax.text(coords[0], coords[1], coords[2], str(count))
newlabels.append("{} : {}".format(
count, latexify_ion(latexify(label))))
count += 1
plt.figtext(0.01, 0.01, "\n".join(newlabels))
plt.xlabel("pH")
plt.ylabel("V")
return plt
def generate_entry_label(entry):
"""
Generates a label for the pourbaix plotter
Args:
entry (PourbaixEntry or MultiEntry): entry to get a label for
"""
if isinstance(entry, MultiEntry):
return " + ".join([latexify_ion(e.name) for e in entry.entry_list])
else:
return latexify_ion(latexify(entry.name))
def get_all_component_descriptions(self) -> str:
"""Gets the descriptions of all components in the structure.
Returns:
A description of all components in the structure.
"""
if len(self._da.components) == 1:
return self.get_component_description(
self._da.get_component_groups()[0].components[0].index,
single_component=True,
)
else:
component_groups = self._da.get_component_groups()
component_descriptions = []
for group in component_groups:
for component in group.components:
if group.molecule_name:
# don't describe known molecules
continue
formula = group.formula
group_count = group.count
component_count = component.count
shape = dimensionality_to_shape[group.dimensionality]
if self.fmt == "latex":
formula = latexify(formula)
elif self.fmt == "unicode":
formula = unicodeify(formula)
elif self.fmt == "html":
formula = htmlify(formula)
if group_count == component_count:
s_filler = "the" if group_count == 1 else "each"
else:
s_filler = "{} of the".format(
en.number_to_words(component_count)
)
shape = en.plural(shape)
desc = f"In {s_filler} {formula} {shape}, "
desc += self.get_component_description(component.index)
component_descriptions.append(desc)
return " ".join(component_descriptions)
def _get_matplotlib_figure(self) -> plt.Figure:
"""Returns a matplotlib figure of reaction kinks diagram"""
pretty_plot(8, 5)
plt.xlim([-0.05, 1.05]) # plot boundary is 5% wider on each side
kinks = list(zip(*self.get_kinks())) # type: ignore
_, x, energy, reactions, _ = kinks
plt.plot(x, energy, "o-", markersize=8, c="navy", zorder=1)
plt.scatter(self.minimum[0],
self.minimum[1],
marker="*",
c="red",
s=400,
zorder=2)
for x_coord, y_coord, rxn in zip(x, energy, reactions):
products = ", ".join([
latexify(p.reduced_formula)
for p in rxn.products # type: ignore
if not np.isclose(rxn.get_coeff(p), 0) # type: ignore
])
plt.annotate(
products,
xy=(x_coord, y_coord),
xytext=(10, -30),
textcoords="offset points",
ha="right",
va="bottom",
arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=0"),
)
if self.norm:
plt.ylabel("Energy (eV/atom)")
else:
plt.ylabel("Energy (eV/f.u.)")
plt.xlabel(self._get_xaxis_title())
plt.ylim(self.minimum[1] +
0.05 * self.minimum[1]) # plot boundary is 5% lower
fig = plt.gcf()
plt.close(fig)
return fig
def make_defect_formation_energy(args):
title = latexify(
args.perfect_calc_results.structure.composition.reduced_formula)
chem_pot_diag = ChemPotDiag.from_yaml(args.chem_pot_diag)
abs_chem_pot = chem_pot_diag.abs_chem_pot_dict(args.label)
single_energies = []
for d in args.dirs:
if args.skip_shallow and loadfn(
d / "band_edge_states.json").is_shallow:
continue
single_energies.append(
make_single_defect_energy(args.perfect_calc_results,
loadfn(d / "calc_results.json"),
loadfn(d / "defect_entry.json"),
abs_chem_pot,
loadfn(d / "correction.json")))
defect_energies = make_defect_energies(single_energies)
if args.print:
print(" charge E_f correction ")
for e in defect_energies:
print(e)
print("")
print("-- cross points -- ")
for e in defect_energies:
print(e.name)
print(e.cross_points(args.unitcell.vbm, args.unitcell.cbm))
print("")
return
plotter = DefectEnergyMplPlotter(
title=title,
defect_energies=defect_energies,
vbm=args.unitcell.vbm,
cbm=args.unitcell.cbm,
supercell_vbm=args.perfect_calc_results.vbm,
supercell_cbm=args.perfect_calc_results.cbm,
y_range=args.y_range)
plotter.construct_plot()
plotter.plt.savefig(f"energy_{args.label}.pdf")
def defect_energy_plotters():
va_o = DefectEnergy(name="Va_O1",
charges=[0, 1, 2],
energies=[5, 2, -5],
corrections=[1, 1, 1])
va_mg = DefectEnergy(name="Va_Mg1",
charges=[-2, -1, 0],
energies=[5, 2, 0],
corrections=[-1, -1, -1])
mg_i = DefectEnergy(name="Mg_i1",
charges=[1],
energies=[4],
corrections=[1])
d = dict(title=latexify("MgAl2O4"),
defect_energies=[va_o, va_mg, mg_i],
vbm=1.5,
cbm=5.5,
supercell_vbm=1.0,
supercell_cbm=6.0)
return DefectEnergyMplPlotter(**d), DefectEnergyPlotlyPlotter(**d)
def get_mineral_description(self) -> str:
"""Gets the mineral name and space group description.
If the structure is a perfect match for a known prototype (e.g.
the distance parameter is -1, the mineral name is the prototype name.
If a structure is not a perfect match but similar to a known mineral,
"-like" will be added to the mineral name. If the structure is a good
match to a mineral but contains a different number of element types than
the mineral prototype, "-derived" will be added to the mineral name.
Returns:
The description of the mineral name.
"""
spg_symbol = self._da.spg_symbol
formula = self._da.formula
if self.fmt == "latex":
spg_symbol = latexify_spacegroup(self._da.spg_symbol)
formula = latexify(formula)
elif self.fmt == "unicode":
spg_symbol = unicodeify_spacegroup(self._da.spg_symbol)
formula = unicodeify(formula)
elif self.fmt == "html":
spg_symbol = htmlify_spacegroup(self._da.spg_symbol)
formula = htmlify(formula)
mineral_name = get_mineral_name(self._da.mineral)
if mineral_name:
desc = f"{formula} is {mineral_name} structured and"
else:
desc = f"{formula}"
desc += " crystallizes in the {} {} space group.".format(
self._da.crystal_system, spg_symbol
)
return desc
def get_pourbaix_plot_colorfill_by_domain_name(self, limits=None, title="",
label_domains=True, label_color='k', domain_color=None, domain_fontsize=None,
domain_edge_lw=0.5, bold_domains=None, cluster_domains=(),
add_h2o_stablity_line=True, add_center_line=False, h2o_lw=0.5):
"""
Color domains by the colors specific by the domain_color dict
Args:
limits: 2D list containing limits of the Pourbaix diagram
of the form [[xlo, xhi], [ylo, yhi]]
lable_domains (Bool): whether add the text lable for domains
label_color (str): color of domain lables, defaults to be black
domain_color (dict): colors of each domain e.g {"Al(s)": "#FF1100"}. If set
to None default color set will be used.
domain_fontsize (int): Font size used in domain text labels.
domain_edge_lw (int): line width for the boundaries between domains.
bold_domains (list): List of domain names to use bold text style for domain
lables.
cluster_domains (list): List of domain names in cluster phase
add_h2o_stablity_line (Bool): whether plot H2O stability line
add_center_line (Bool): whether plot lines shows the center coordinate
h2o_lw (int): line width for H2O stability line and center lines
"""
# helper functions
def len_elts(entry):
comp = Composition(entry[:-3]) if "(s)" in entry else Ion.from_formula(entry)
return len(set(comp.elements) - {Element("H"), Element("O")})
def special_lines(xlim, ylim):
h_line = np.transpose([[xlim[0], -xlim[0] * PREFAC],
[xlim[1], -xlim[1] * PREFAC]])
o_line = np.transpose([[xlim[0], -xlim[0] * PREFAC + 1.23],
[xlim[1], -xlim[1] * PREFAC + 1.23]])
neutral_line = np.transpose([[7, ylim[0]], [7, ylim[1]]])
V0_line = np.transpose([[xlim[0], 0], [xlim[1], 0]])
return h_line, o_line, neutral_line, V0_line
from matplotlib.patches import Polygon
from pymatgen import Composition, Element
from pymatgen.core.ion import Ion
default_domain_font_size = 12
default_solid_phase_color = '#b8f9e7' # this slighly darker than the MP scheme, to
default_cluster_phase_color = '#d0fbef' # avoid making the cluster phase too light
plt = pretty_plot(8, dpi=300)
(stable, unstable) = self.pourbaix_plot_data(limits)
num_of_overlaps = {key: 0 for key in stable.keys()}
entry_dict_of_multientries = collections.defaultdict(list)
for entry in stable:
if isinstance(entry, MultiEntry):
for e in entry.entrylist:
entry_dict_of_multientries[e.name].append(entry)
num_of_overlaps[entry] += 1
else:
entry_dict_of_multientries[entry.name].append(entry)
xlim, ylim = limits[:2] if limits else self._analyzer.chempot_limits[:2]
h_line, o_line, neutral_line, V0_line = special_lines(xlim, ylim)
ax = plt.gca()
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax.tick_params(direction='out')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
sorted_entry = list(entry_dict_of_multientries.keys())
sorted_entry.sort(key=len_elts)
if domain_fontsize is None:
domain_fontsize = {en: default_domain_font_size for en in sorted_entry}
if domain_color is None:
domain_color = {en: default_solid_phase_color if '(s)' in en else
(default_cluster_phase_color if en in cluster_domains else 'w')
for i, en in enumerate(sorted_entry)}
if bold_domains is None:
bold_domains = [en for en in sorted_entry if '(s)' not in en]
for entry in sorted_entry:
x_coord, y_coord, npts = 0.0, 0.0, 0
for e in entry_dict_of_multientries[entry]:
xy = self.domain_vertices(e)
if add_h2o_stablity_line:
c = self.get_distribution_corrected_center(stable[e], h_line, o_line, 0.3)
else:
c = self.get_distribution_corrected_center(stable[e])
x_coord += c[0]
y_coord += c[1]
npts += 1
patch = Polygon(xy, facecolor=domain_color[entry],
closed=True, lw=domain_edge_lw, fill=True, antialiased=True)
ax.add_patch(patch)
xy_center = (x_coord / npts, y_coord / npts)
if label_domains:
if platform.system() == 'Darwin':
# Have to hack to the hard coded font path to get current font On Mac OS X
if entry in bold_domains:
font = FontProperties(fname='/Library/Fonts/Times New Roman Bold.ttf',
size=domain_fontsize[entry])
else:
font = FontProperties(fname='/Library/Fonts/Times New Roman.ttf',
size=domain_fontsize[entry])
else:
if entry in bold_domains:
font = FontProperties(family='Times New Roman',
weight='bold',
size=domain_fontsize[entry])
else:
font = FontProperties(family='Times New Roman',
weight='regular',
size=domain_fontsize[entry])
plt.text(*xy_center, s=latexify_ion(latexify(entry)), fontproperties=font,
horizontalalignment="center", verticalalignment="center",
multialignment="center", color=label_color)
if add_h2o_stablity_line:
dashes = (3, 1.5)
line, = plt.plot(h_line[0], h_line[1], "k--", linewidth=h2o_lw, antialiased=True)
line.set_dashes(dashes)
line, = plt.plot(o_line[0], o_line[1], "k--", linewidth=h2o_lw, antialiased=True)
line.set_dashes(dashes)
if add_center_line:
plt.plot(neutral_line[0], neutral_line[1], "k-.", linewidth=h2o_lw, antialiased=False)
plt.plot(V0_line[0], V0_line[1], "k-.", linewidth=h2o_lw, antialiased=False)
plt.xlabel("pH", fontname="Times New Roman", fontsize=18)
plt.ylabel("E (V)", fontname="Times New Roman", fontsize=18)
plt.xticks(fontname="Times New Roman", fontsize=16)
plt.yticks(fontname="Times New Roman", fontsize=16)
plt.title(title, fontsize=20, fontweight='bold', fontname="Times New Roman")
return plt
def _get_2d_plot(self, label_stable=True, label_unstable=True,
ordering=None, energy_colormap=None, vmin_mev=-60.0,
vmax_mev=60.0, show_colorbar=True,
process_attributes=False):
"""
Shows the plot using pylab. Usually I won't do imports in methods,
but since plotting is a fairly expensive library to load and not all
machines have matplotlib installed, I have done it this way.
"""
plt = pretty_plot(8, 6)
from matplotlib.font_manager import FontProperties
if ordering is None:
(lines, labels, unstable) = self.pd_plot_data
else:
(_lines, _labels, _unstable) = self.pd_plot_data
(lines, labels, unstable) = order_phase_diagram(
_lines, _labels, _unstable, ordering)
if energy_colormap is None:
if process_attributes:
for x, y in lines:
plt.plot(x, y, "k-", linewidth=3, markeredgecolor="k")
# One should think about a clever way to have "complex"
# attributes with complex processing options but with a clear
# logic. At this moment, I just use the attributes to know
# whether an entry is a new compound or an existing (from the
# ICSD or from the MP) one.
for x, y in labels.keys():
if labels[(x, y)].attribute is None or \
labels[(x, y)].attribute == "existing":
plt.plot(x, y, "ko", linewidth=3, markeredgecolor="k",
markerfacecolor="b", markersize=12)
else:
plt.plot(x, y, "k*", linewidth=3, markeredgecolor="k",
markerfacecolor="g", markersize=18)
else:
for x, y in lines:
plt.plot(x, y, "ko-", linewidth=3, markeredgecolor="k",
markerfacecolor="b", markersize=15)
else:
from matplotlib.colors import Normalize, LinearSegmentedColormap
from matplotlib.cm import ScalarMappable
pda = PDAnalyzer(self._pd)
for x, y in lines:
plt.plot(x, y, "k-", linewidth=3, markeredgecolor="k")
vmin = vmin_mev / 1000.0
vmax = vmax_mev / 1000.0
if energy_colormap == 'default':
mid = - vmin / (vmax - vmin)
cmap = LinearSegmentedColormap.from_list(
'my_colormap', [(0.0, '#005500'), (mid, '#55FF55'),
(mid, '#FFAAAA'), (1.0, '#FF0000')])
else:
cmap = energy_colormap
norm = Normalize(vmin=vmin, vmax=vmax)
_map = ScalarMappable(norm=norm, cmap=cmap)
_energies = [pda.get_equilibrium_reaction_energy(entry)
for coord, entry in labels.items()]
energies = [en if en < 0.0 else -0.00000001 for en in _energies]
vals_stable = _map.to_rgba(energies)
ii = 0
if process_attributes:
for x, y in labels.keys():
if labels[(x, y)].attribute is None or \
labels[(x, y)].attribute == "existing":
plt.plot(x, y, "o", markerfacecolor=vals_stable[ii],
markersize=12)
else:
plt.plot(x, y, "*", markerfacecolor=vals_stable[ii],
markersize=18)
ii += 1
else:
for x, y in labels.keys():
plt.plot(x, y, "o", markerfacecolor=vals_stable[ii],
markersize=15)
ii += 1
font = FontProperties()
font.set_weight("bold")
font.set_size(24)
# Sets a nice layout depending on the type of PD. Also defines a
# "center" for the PD, which then allows the annotations to be spread
# out in a nice manner.
if len(self._pd.elements) == 3:
plt.axis("equal")
plt.xlim((-0.1, 1.2))
plt.ylim((-0.1, 1.0))
plt.axis("off")
center = (0.5, math.sqrt(3) / 6)
else:
all_coords = labels.keys()
miny = min([c[1] for c in all_coords])
ybuffer = max(abs(miny) * 0.1, 0.1)
plt.xlim((-0.1, 1.1))
plt.ylim((miny - ybuffer, ybuffer))
center = (0.5, miny / 2)
plt.xlabel("Fraction", fontsize=28, fontweight='bold')
plt.ylabel("Formation energy (eV/fu)", fontsize=28,
fontweight='bold')
for coords in sorted(labels.keys(), key=lambda x: -x[1]):
entry = labels[coords]
label = entry.name
# The follow defines an offset for the annotation text emanating
# from the center of the PD. Results in fairly nice layouts for the
# most part.
vec = (np.array(coords) - center)
vec = vec / np.linalg.norm(vec) * 10 if np.linalg.norm(vec) != 0 \
else vec
valign = "bottom" if vec[1] > 0 else "top"
if vec[0] < -0.01:
halign = "right"
elif vec[0] > 0.01:
halign = "left"
else:
halign = "center"
if label_stable:
if process_attributes and entry.attribute == 'new':
plt.annotate(latexify(label), coords, xytext=vec,
textcoords="offset points",
horizontalalignment=halign,
verticalalignment=valign,
fontproperties=font,
color='g')
else:
plt.annotate(latexify(label), coords, xytext=vec,
textcoords="offset points",
horizontalalignment=halign,
verticalalignment=valign,
fontproperties=font)
if self.show_unstable:
font = FontProperties()
font.set_size(16)
pda = PDAnalyzer(self._pd)
energies_unstable = [pda.get_e_above_hull(entry)
for entry, coord in unstable.items()]
if energy_colormap is not None:
energies.extend(energies_unstable)
vals_unstable = _map.to_rgba(energies_unstable)
ii = 0
for entry, coords in unstable.items():
ehull = pda.get_e_above_hull(entry)
if ehull < self.show_unstable:
vec = (np.array(coords) - center)
vec = vec / np.linalg.norm(vec) * 10 \
if np.linalg.norm(vec) != 0 else vec
label = entry.name
if energy_colormap is None:
plt.plot(coords[0], coords[1], "ks", linewidth=3,
markeredgecolor="k", markerfacecolor="r",
markersize=8)
else:
plt.plot(coords[0], coords[1], "s", linewidth=3,
markeredgecolor="k",
markerfacecolor=vals_unstable[ii],
markersize=8)
if label_unstable:
plt.annotate(latexify(label), coords, xytext=vec,
textcoords="offset points",
horizontalalignment=halign, color="b",
verticalalignment=valign,
fontproperties=font)
ii += 1
if energy_colormap is not None and show_colorbar:
_map.set_array(energies)
cbar = plt.colorbar(_map)
cbar.set_label(
'Energy [meV/at] above hull (in red)\nInverse energy ['
'meV/at] above hull (in green)',
rotation=-90, ha='left', va='center')
ticks = cbar.ax.get_yticklabels()
# cbar.ax.set_yticklabels(['${v}$'.format(
# v=float(t.get_text().strip('$'))*1000.0) for t in ticks])
f = plt.gcf()
f.set_size_inches((8, 6))
plt.subplots_adjust(left=0.09, right=0.98, top=0.98, bottom=0.07)
return plt
def get_pourbaix_plot_colorfill_by_domain_name(self,
limits=None,
title="",
label_domains=True,
label_color='k',
domain_color=None,
domain_fontsize=None,
domain_edge_lw=0.5,
bold_domains=None,
cluster_domains=(),
add_h2o_stablity_line=True,
add_center_line=False,
h2o_lw=0.5):
"""
Color domains by the colors specific by the domain_color dict
Args:
limits: 2D list containing limits of the Pourbaix diagram
of the form [[xlo, xhi], [ylo, yhi]]
lable_domains (Bool): whether add the text lable for domains
label_color (str): color of domain lables, defaults to be black
domain_color (dict): colors of each domain e.g {"Al(s)": "#FF1100"}. If set
to None default color set will be used.
domain_fontsize (int): Font size used in domain text labels.
domain_edge_lw (int): line width for the boundaries between domains.
bold_domains (list): List of domain names to use bold text style for domain
lables.
cluster_domains (list): List of domain names in cluster phase
add_h2o_stablity_line (Bool): whether plot H2O stability line
add_center_line (Bool): whether plot lines shows the center coordinate
h2o_lw (int): line width for H2O stability line and center lines
"""
# helper functions
def len_elts(entry):
comp = Composition(
entry[:-3]) if "(s)" in entry else Ion.from_formula(entry)
return len(set(comp.elements) - {Element("H"), Element("O")})
def special_lines(xlim, ylim):
h_line = np.transpose([[xlim[0], -xlim[0] * PREFAC],
[xlim[1], -xlim[1] * PREFAC]])
o_line = np.transpose([[xlim[0], -xlim[0] * PREFAC + 1.23],
[xlim[1], -xlim[1] * PREFAC + 1.23]])
neutral_line = np.transpose([[7, ylim[0]], [7, ylim[1]]])
V0_line = np.transpose([[xlim[0], 0], [xlim[1], 0]])
return h_line, o_line, neutral_line, V0_line
from matplotlib.patches import Polygon
from pymatgen import Composition, Element
from pymatgen.core.ion import Ion
default_domain_font_size = 12
default_solid_phase_color = '#b8f9e7' # this slighly darker than the MP scheme, to
default_cluster_phase_color = '#d0fbef' # avoid making the cluster phase too light
plt = pretty_plot(8, dpi=300)
(stable, unstable) = self.pourbaix_plot_data(limits)
num_of_overlaps = {key: 0 for key in stable.keys()}
entry_dict_of_multientries = collections.defaultdict(list)
for entry in stable:
if isinstance(entry, MultiEntry):
for e in entry.entrylist:
entry_dict_of_multientries[e.name].append(entry)
num_of_overlaps[entry] += 1
else:
entry_dict_of_multientries[entry.name].append(entry)
xlim, ylim = limits[:2] if limits else self._analyzer.chempot_limits[:2]
h_line, o_line, neutral_line, V0_line = special_lines(xlim, ylim)
ax = plt.gca()
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax.tick_params(direction='out')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
sorted_entry = list(entry_dict_of_multientries.keys())
sorted_entry.sort(key=len_elts)
if domain_fontsize is None:
domain_fontsize = {
en: default_domain_font_size
for en in sorted_entry
}
if domain_color is None:
domain_color = {
en: default_solid_phase_color if '(s)' in en else
(default_cluster_phase_color if en in cluster_domains else 'w')
for i, en in enumerate(sorted_entry)
}
if bold_domains is None:
bold_domains = [en for en in sorted_entry if '(s)' not in en]
for entry in sorted_entry:
x_coord, y_coord, npts = 0.0, 0.0, 0
for e in entry_dict_of_multientries[entry]:
xy = self.domain_vertices(e)
if add_h2o_stablity_line:
c = self.get_distribution_corrected_center(
stable[e], h_line, o_line, 0.3)
else:
c = self.get_distribution_corrected_center(stable[e])
x_coord += c[0]
y_coord += c[1]
npts += 1
patch = Polygon(xy,
facecolor=domain_color[entry],
closed=True,
lw=domain_edge_lw,
fill=True,
antialiased=True)
ax.add_patch(patch)
xy_center = (x_coord / npts, y_coord / npts)
if label_domains:
if platform.system() == 'Darwin':
# Have to hack to the hard coded font path to get current font On Mac OS X
if entry in bold_domains:
font = FontProperties(
fname='/Library/Fonts/Times New Roman Bold.ttf',
size=domain_fontsize[entry])
else:
font = FontProperties(
fname='/Library/Fonts/Times New Roman.ttf',
size=domain_fontsize[entry])
else:
if entry in bold_domains:
font = FontProperties(family='Times New Roman',
weight='bold',
size=domain_fontsize[entry])
else:
font = FontProperties(family='Times New Roman',
weight='regular',
size=domain_fontsize[entry])
plt.text(*xy_center,
s=latexify_ion(latexify(entry)),
fontproperties=font,
horizontalalignment="center",
verticalalignment="center",
multialignment="center",
color=label_color)
if add_h2o_stablity_line:
dashes = (3, 1.5)
line, = plt.plot(h_line[0],
h_line[1],
"k--",
linewidth=h2o_lw,
antialiased=True)
line.set_dashes(dashes)
line, = plt.plot(o_line[0],
o_line[1],
"k--",
linewidth=h2o_lw,
antialiased=True)
line.set_dashes(dashes)
if add_center_line:
plt.plot(neutral_line[0],
neutral_line[1],
"k-.",
linewidth=h2o_lw,
antialiased=False)
plt.plot(V0_line[0],
V0_line[1],
"k-.",
linewidth=h2o_lw,
antialiased=False)
plt.xlabel("pH", fontname="Times New Roman", fontsize=18)
plt.ylabel("E (V)", fontname="Times New Roman", fontsize=18)
plt.xticks(fontname="Times New Roman", fontsize=16)
plt.yticks(fontname="Times New Roman", fontsize=16)
plt.title(title,
fontsize=20,
fontweight='bold',
fontname="Times New Roman")
return plt
def make_defect_formation_energy(args):
formula = args.perfect_calc_results.structure.composition.reduced_formula
chem_pot_diag = ChemPotDiag.from_yaml(args.cpd_yaml)
pcr = args.perfect_calc_results
defects, defect_entries, corrections, edge_states = [], [], [], []
for d in args.dirs:
if args.skip_shallow:
edge_states = BandEdgeStates.from_yaml(d / "band_edge_states.yaml")
if edge_states.is_shallow:
continue
defects.append(loadfn(d / "calc_results.json"))
defect_entries.append(loadfn(d / "defect_entry.json"))
corrections.append(loadfn(d / "correction.json"))
if args.web_gui:
from crystal_toolkit.settings import SETTINGS
import dash_html_components as html
from crystal_toolkit.helpers.layouts import Column
import crystal_toolkit.components as ctc
import dash
edge_states = []
for d in args.dirs:
edge_states.append(
BandEdgeStates.from_yaml(d / "band_edge_states.yaml"))
app = dash.Dash(__name__,
suppress_callback_exceptions=True,
assets_folder=SETTINGS.ASSETS_PATH,
external_stylesheets=[
'https://codepen.io/chriddyp/pen/bWLwgP.css'
])
cpd_plot_info = CpdPlotInfo(chem_pot_diag)
cpd_e_component = CpdEnergyComponent(cpd_plot_info, pcr, defects,
defect_entries, corrections,
args.unitcell.vbm,
args.unitcell.cbm, edge_states)
my_layout = html.Div([Column(cpd_e_component.layout)])
ctc.register_crystal_toolkit(app=app, layout=my_layout, cache=None)
app.run_server(port=args.port)
return
abs_chem_pot = chem_pot_diag.abs_chem_pot_dict(args.label)
title = " ".join([latexify(formula), "point", args.label])
defect_energies = make_energies(pcr, defects, defect_entries, corrections,
abs_chem_pot)
if args.print:
defect_energies = slide_energy(defect_energies, args.unitcell.vbm)
print(" charge E_f correction ")
for e in defect_energies:
print(e)
print("")
print("-- cross points -- ")
for e in defect_energies:
print(e.name)
print(
e.cross_points(ef_min=args.unitcell.vbm,
ef_max=args.unitcell.cbm,
base_ef=args.unitcell.vbm))
print("")
return
plotter = DefectEnergyMplPlotter(title=title,
defect_energies=defect_energies,
vbm=args.unitcell.vbm,
cbm=args.unitcell.cbm,
supercell_vbm=pcr.vbm,
supercell_cbm=pcr.cbm,
y_range=args.y_range,
supercell_edge=args.supercell_edge,
label_line=args.label_line,
add_charges=args.add_charges)
plotter.construct_plot()
plotter.plt.savefig(f"energy_{args.label}.pdf")
def _title(self):
return latexify(self._composition.reduced_formula)
def test_latexify(self):
self.assertEqual(latexify("Li3Fe2(PO4)3"),
"Li$_{3}$Fe$_{2}$(PO$_{4}$)$_{3}$")
self.assertEqual(latexify("Li0.2Na0.8Cl"), "Li$_{0.2}$Na$_{0.8}$Cl")
def get_pourbaix_plot_colorfill_by_element(self, limits=None, title="",
label_domains=True, element=None):
"""
Color domains by element
"""
from matplotlib.patches import Polygon
entry_dict_of_multientries = collections.defaultdict(list)
plt = pretty_plot(16)
optim_colors = ['#0000FF', '#FF0000', '#00FF00', '#FFFF00', '#FF00FF',
'#FF8080', '#DCDCDC', '#800000', '#FF8000']
optim_font_color = ['#FFFFA0', '#00FFFF', '#FF00FF', '#0000FF', '#00FF00',
'#007F7F', '#232323', '#7FFFFF', '#007FFF']
hatch = ['/', '\\', '|', '-', '+', 'o', '*']
(stable, unstable) = self.pourbaix_plot_data(limits)
num_of_overlaps = {key: 0 for key in stable.keys()}
for entry in stable:
if isinstance(entry, MultiEntry):
for e in entry.entrylist:
if element in e.composition.elements:
entry_dict_of_multientries[e.name].append(entry)
num_of_overlaps[entry] += 1
else:
entry_dict_of_multientries[entry.name].append(entry)
if limits:
xlim = limits[0]
ylim = limits[1]
else:
xlim = self._analyzer.chempot_limits[0]
ylim = self._analyzer.chempot_limits[1]
h_line = np.transpose([[xlim[0], -xlim[0] * PREFAC],
[xlim[1], -xlim[1] * PREFAC]])
o_line = np.transpose([[xlim[0], -xlim[0] * PREFAC + 1.23],
[xlim[1], -xlim[1] * PREFAC + 1.23]])
neutral_line = np.transpose([[7, ylim[0]], [7, ylim[1]]])
V0_line = np.transpose([[xlim[0], 0], [xlim[1], 0]])
ax = plt.gca()
ax.set_xlim(xlim)
ax.set_ylim(ylim)
from pymatgen import Composition, Element
from pymatgen.core.ion import Ion
def len_elts(entry):
if "(s)" in entry:
comp = Composition(entry[:-3])
else:
comp = Ion.from_formula(entry)
return len([el for el in comp.elements if el not in
[Element("H"), Element("O")]])
sorted_entry = entry_dict_of_multientries.keys()
sorted_entry.sort(key=len_elts)
i = -1
label_chr = map(chr, list(range(65, 91)))
for entry in sorted_entry:
color_indx = 0
x_coord = 0.0
y_coord = 0.0
npts = 0
i += 1
for e in entry_dict_of_multientries[entry]:
hc = 0
fc = 0
bc = 0
xy = self.domain_vertices(e)
c = self.get_center(stable[e])
x_coord += c[0]
y_coord += c[1]
npts += 1
color_indx = i
if "(s)" in entry:
comp = Composition(entry[:-3])
else:
comp = Ion.from_formula(entry)
if len([el for el in comp.elements if el not in
[Element("H"), Element("O")]]) == 1:
if color_indx >= len(optim_colors):
color_indx = color_indx -\
int(color_indx / len(optim_colors)) * len(optim_colors)
patch = Polygon(xy, facecolor=optim_colors[color_indx],
closed=True, lw=3.0, fill=True)
bc = optim_colors[color_indx]
else:
if color_indx >= len(hatch):
color_indx = color_indx - int(color_indx / len(hatch)) * len(hatch)
patch = Polygon(xy, hatch=hatch[color_indx], closed=True, lw=3.0, fill=False)
hc = hatch[color_indx]
ax.add_patch(patch)
xy_center = (x_coord / npts, y_coord / npts)
if label_domains:
if color_indx >= len(optim_colors):
color_indx = color_indx -\
int(color_indx / len(optim_colors)) * len(optim_colors)
fc = optim_font_color[color_indx]
if bc and not hc:
bbox = dict(boxstyle="round", fc=fc)
if hc and not bc:
bc = 'k'
fc = 'w'
bbox = dict(boxstyle="round", hatch=hc, fill=False)
if bc and hc:
bbox = dict(boxstyle="round", hatch=hc, fc=fc)
# bbox.set_path_effects([PathEffects.withSimplePatchShadow()])
plt.annotate(latexify_ion(latexify(entry)), xy_center,
color=bc, fontsize=30, bbox=bbox)
# plt.annotate(label_chr[i], xy_center,
# color=bc, fontsize=30, bbox=bbox)
lw = 3
plt.plot(h_line[0], h_line[1], "r--", linewidth=lw)
plt.plot(o_line[0], o_line[1], "r--", linewidth=lw)
plt.plot(neutral_line[0], neutral_line[1], "k-.", linewidth=lw)
plt.plot(V0_line[0], V0_line[1], "k-.", linewidth=lw)
plt.xlabel("pH")
plt.ylabel("E (V)")
plt.title(title, fontsize=20, fontweight='bold')
return plt
def test_latexify(self):
self.assertEqual(latexify("Li3Fe2(PO4)3"),
"Li$_{3}$Fe$_{2}$(PO$_{4}$)$_{3}$")
self.assertEqual(latexify("Li0.2Na0.8Cl"),
"Li$_{0.2}$Na$_{0.8}$Cl")
def _get_poly_site_description(self, site_index: int):
"""Gets a description of a connected polyhedral site.
If the site likeness (order parameter) is less than ``distorted_tol``,
"distorted" will be added to the geometry description.
Args:
site_index: An inequivalent site index.
Returns:
A description the a polyhedral site, including connectivity.
"""
site = self._da.sites[site_index]
nnn_details = self._da.get_next_nearest_neighbor_details(
site_index, group=not self.describe_symmetry_labels
)
from_element = get_formatted_el(
site["element"],
self._da.sym_labels[site_index],
use_oxi_state=self.describe_oxidation_state,
use_sym_label=self.describe_symmetry_labels,
fmt=self.fmt,
)
from_poly_formula = site["poly_formula"]
if self.fmt == "latex":
from_poly_formula = latexify(from_poly_formula)
elif self.fmt == "unicode":
from_poly_formula = unicodeify(from_poly_formula)
elif self.fmt == "html":
from_poly_formula = htmlify(from_poly_formula)
s_from_poly_formula = get_el(site["element"]) + from_poly_formula
if site["geometry"]["likeness"] < self.distorted_tol:
s_distorted = "distorted "
else:
s_distorted = ""
s_polyhedra = geometry_to_polyhedra[site["geometry"]["type"]]
s_polyhedra = polyhedra_plurals[s_polyhedra]
nn_desc = self._get_nearest_neighbor_description(site_index)
desc = f"{from_element} is bonded to {nn_desc} to form "
# handle the case we were are connected to the same type of polyhedra
if (
nnn_details[0].element == site["element"]
and len(
{(nnn_site.element, nnn_site.poly_formula) for nnn_site in nnn_details}
)
) == 1:
connectivities = list({nnn_site.connectivity for nnn_site in nnn_details})
s_mixture = "a mixture of " if len(connectivities) != 1 else ""
s_connectivities = en.join(connectivities)
desc += "{}{}{}-sharing {} {}".format(
s_mixture,
s_distorted,
s_connectivities,
s_from_poly_formula,
s_polyhedra,
)
return desc
# otherwise loop through nnn connectivities and describe individually
desc += "{}{} {} that share ".format(
s_distorted, s_from_poly_formula, s_polyhedra
)
nnn_descriptions = []
for nnn_site in nnn_details:
to_element = get_formatted_el(
nnn_site.element,
nnn_site.sym_label,
use_oxi_state=False,
use_sym_label=self.describe_symmetry_labels,
)
to_poly_formula = nnn_site.poly_formula
if self.fmt == "latex":
to_poly_formula = latexify(to_poly_formula)
elif self.fmt == "unicode":
to_poly_formula = unicodeify(to_poly_formula)
elif self.fmt == "html":
to_poly_formula = htmlify(to_poly_formula)
to_poly_formula = to_element + to_poly_formula
to_shape = geometry_to_polyhedra[nnn_site.geometry]
if len(nnn_site.sites) == 1 and nnn_site.count != 1:
s_equivalent = " equivalent "
else:
s_equivalent = " "
if nnn_site.count == 1:
s_an = f" {en.an(nnn_site.connectivity)}"
else:
s_an = ""
to_shape = polyhedra_plurals[to_shape]
nnn_descriptions.append(
"{}{} with {}{}{} {}".format(
s_an,
en.plural(nnn_site.connectivity, nnn_site.count),
en.number_to_words(nnn_site.count),
s_equivalent,
to_poly_formula,
to_shape,
)
)
return desc + en.join(nnn_descriptions)
def get_chempot_range_map_plot(self, elements,referenced=True):
"""
Returns a plot of the chemical potential range _map. Currently works
only for 3-component PDs.
Args:
elements: Sequence of elements to be considered as independent
variables. E.g., if you want to show the stability ranges of
all Li-Co-O phases wrt to uLi and uO, you will supply
[Element("Li"), Element("O")]
referenced: if True, gives the results with a reference being the
energy of the elemental phase. If False, gives absolute values.
Returns:
A matplotlib plot object.
"""
plt = pretty_plot(12, 8)
analyzer = PDAnalyzer(self._pd)
chempot_ranges = analyzer.get_chempot_range_map(
elements, referenced=referenced)
missing_lines = {}
excluded_region = []
for entry, lines in chempot_ranges.items():
comp = entry.composition
center_x = 0
center_y = 0
coords = []
contain_zero = any([comp.get_atomic_fraction(el) == 0
for el in elements])
is_boundary = (not contain_zero) and \
sum([comp.get_atomic_fraction(el) for el in elements]) == 1
for line in lines:
(x, y) = line.coords.transpose()
plt.plot(x, y, "k-")
for coord in line.coords:
if not in_coord_list(coords, coord):
coords.append(coord.tolist())
center_x += coord[0]
center_y += coord[1]
if is_boundary:
excluded_region.extend(line.coords)
if coords and contain_zero:
missing_lines[entry] = coords
else:
xy = (center_x / len(coords), center_y / len(coords))
plt.annotate(latexify(entry.name), xy, fontsize=22)
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()
#Shade the forbidden chemical potential regions.
excluded_region.append([xlim[1], ylim[1]])
excluded_region = sorted(excluded_region, key=lambda c: c[0])
(x, y) = np.transpose(excluded_region)
plt.fill(x, y, "0.80")
#The hull does not generate the missing horizontal and vertical lines.
#The following code fixes this.
el0 = elements[0]
el1 = elements[1]
for entry, coords in missing_lines.items():
center_x = sum([c[0] for c in coords])
center_y = sum([c[1] for c in coords])
comp = entry.composition
is_x = comp.get_atomic_fraction(el0) < 0.01
is_y = comp.get_atomic_fraction(el1) < 0.01
n = len(coords)
if not (is_x and is_y):
if is_x:
coords = sorted(coords, key=lambda c: c[1])
for i in [0, -1]:
x = [min(xlim), coords[i][0]]
y = [coords[i][1], coords[i][1]]
plt.plot(x, y, "k")
center_x += min(xlim)
center_y += coords[i][1]
elif is_y:
coords = sorted(coords, key=lambda c: c[0])
for i in [0, -1]:
x = [coords[i][0], coords[i][0]]
y = [coords[i][1], min(ylim)]
plt.plot(x, y, "k")
center_x += coords[i][0]
center_y += min(ylim)
xy = (center_x / (n + 2), center_y / (n + 2))
else:
center_x = sum(coord[0] for coord in coords) + xlim[0]
center_y = sum(coord[1] for coord in coords) + ylim[0]
xy = (center_x / (n + 1), center_y / (n + 1))
plt.annotate(latexify(entry.name), xy,
horizontalalignment="center",
verticalalignment="center", fontsize=22)
plt.xlabel("$\\mu_{{{0}}} - \\mu_{{{0}}}^0$ (eV)"
.format(el0.symbol))
plt.ylabel("$\\mu_{{{0}}} - \\mu_{{{0}}}^0$ (eV)"
.format(el1.symbol))
plt.tight_layout()
return plt
def get_component_makeup_summary(self) -> str:
"""Gets a summary of the makeup of components in a structure.
Returns:
A description of the number of components and their dimensionalities
and orientations.
"""
component_groups = self._da.get_component_groups()
if (
len(component_groups) == 1
and component_groups[0].count == 1
and component_groups[0].dimensionality == 3
):
desc = ""
else:
if self._da.dimensionality == 3:
desc = "The structure consists of "
else:
desc = "The structure is {}-dimensional and consists of " "".format(
en.number_to_words(self._da.dimensionality)
)
component_makeup_summaries = []
nframeworks = len(
[
c
for g in component_groups
for c in g.components
if c.dimensionality == 3
]
)
for component_group in component_groups:
if nframeworks == 1 and component_group.dimensionality == 3:
s_count = "a"
else:
s_count = en.number_to_words(component_group.count)
dimensionality = component_group.dimensionality
if component_group.molecule_name:
if component_group.nsites == 1:
shape = "atom"
else:
shape = "molecule"
shape = en.plural(shape, s_count)
formula = component_group.molecule_name
else:
shape = en.plural(dimensionality_to_shape[dimensionality], s_count)
formula = component_group.formula
if self.fmt == "latex":
formula = latexify(formula)
elif self.fmt == "unicode":
formula = unicodeify(formula)
print(formula)
elif self.fmt == "html":
formula = htmlify(formula)
comp_desc = f"{s_count} {formula} {shape}"
if component_group.dimensionality in [1, 2]:
orientations = list(
{c.orientation for c in component_group.components}
)
s_direction = en.plural("direction", len(orientations))
comp_desc += " oriented in the {} {}".format(
en.join(orientations), s_direction
)
component_makeup_summaries.append(comp_desc)
if nframeworks == 1 and len(component_makeup_summaries) > 1:
# when there is a single framework, make the description read
# "... and 8 Sn atoms inside a SnO2 framework" instead of
# "..., 8 Sn atoms and one SnO2 framework"
# This works because the component summaries are sorted by
# dimensionality
desc += en.join(component_makeup_summaries[:-1])
desc += f" inside {component_makeup_summaries[-1]}."
else:
desc += en.join(component_makeup_summaries) + "."
return desc
def optplot(modes=('absorption', ),
filenames=None,
prefix=None,
directory=None,
gaussian=None,
band_gaps=None,
labels=None,
average=True,
height=6,
width=6,
xmin=0,
xmax=None,
ymin=0,
ymax=1e5,
colours=None,
style=None,
no_base_style=None,
image_format='pdf',
dpi=400,
plt=None,
fonts=None):
"""A script to plot optical absorption spectra from VASP calculations.
Args:
modes (:obj:`list` or :obj:`tuple`):
Ordered list of :obj:`str` determining properties to plot.
Accepted options are 'absorption' (default), 'eps', 'eps-real',
'eps-im', 'n', 'n-real', 'n-im', 'loss' (equivalent to n-im).
filenames (:obj:`str` or :obj:`list`, optional): Path to vasprun.xml
file (can be gzipped). Alternatively, a list of paths can be
provided, in which case the absorption spectra for each will be
plotted concurrently.
prefix (:obj:`str`, optional): Prefix for file names.
directory (:obj:`str`, optional): The directory in which to save files.
gaussian (:obj:`float`): Standard deviation for gaussian broadening.
band_gaps (:obj:`float` or :obj:`list`, optional): The band gap as a
:obj:`float`, plotted as a dashed line. If plotting multiple
spectra then a :obj:`list` of band gaps can be provided.
labels (:obj:`str` or :obj:`list`): A label to identify the spectra.
If plotting multiple spectra then a :obj:`list` of labels can
be provided.
average (:obj:`bool`, optional): Average the dielectric response across
all lattice directions. Defaults to ``True``.
height (:obj:`float`, optional): The height of the plot.
width (:obj:`float`, optional): The width of the plot.
xmin (:obj:`float`, optional): The minimum energy on the x-axis.
xmax (:obj:`float`, optional): The maximum energy on the x-axis.
ymin (:obj:`float`, optional): The minimum absorption intensity on the
y-axis.
ymax (:obj:`float`, optional): The maximum absorption intensity on the
y-axis.
colours (:obj:`list`, optional): A :obj:`list` of colours to use in the
plot. The colours can be specified as a hex code, set of rgb
values, or any other format supported by matplotlib.
style (:obj:`list` or :obj:`str`, optional): (List of) matplotlib style
specifications, to be composed on top of Sumo base style.
no_base_style (:obj:`bool`, optional): Prevent use of sumo base style.
This can make alternative styles behave more predictably.
image_format (:obj:`str`, optional): The image file format. Can be any
format supported by matplotlib, including: png, jpg, pdf, and svg.
Defaults to pdf.
dpi (:obj:`int`, optional): The dots-per-inch (pixel density) for
the image.
plt (:obj:`matplotlib.pyplot`, optional): A
:obj:`matplotlib.pyplot` object to use for plotting.
fonts (:obj:`list`, optional): Fonts to use in the plot. Can be a
a single font, specified as a :obj:`str`, or several fonts,
specified as a :obj:`list` of :obj:`str`.
Returns:
A matplotlib pyplot object.
"""
if not filenames:
if os.path.exists('vasprun.xml'):
filenames = ['vasprun.xml']
elif os.path.exists('vasprun.xml.gz'):
filenames = ['vasprun.xml.gz']
else:
logging.error('ERROR: No vasprun.xml found!')
sys.exit()
elif isinstance(filenames, str):
filenames = [filenames]
vrs = [Vasprun(f) for f in filenames]
dielectrics = [vr.dielectric for vr in vrs]
if gaussian:
dielectrics = [broaden_eps(d, gaussian) for d in dielectrics]
# initialize spectrum data ready to append from each dataset
abs_data = OrderedDict()
for mode in modes:
abs_data.update({mode: []})
# for each calculation, get all required properties and append to data
for d in dielectrics:
for mode, spectrum in calculate_dielectric_properties(
d, set(modes), average=average).items():
abs_data[mode].append(spectrum)
if isinstance(band_gaps, list) and not band_gaps:
# empty list therefore get bandgap from vasprun files
band_gaps = [
vr.get_band_structure().get_band_gap()['energy'] for vr in vrs
]
elif isinstance(band_gaps, list) and 'vasprun' in band_gaps[0]:
# band_gaps contains list of vasprun files
bg_vrs = [Vasprun(f) for f in band_gaps]
band_gaps = [
vr.get_band_structure().get_band_gap()['energy'] for vr in bg_vrs
]
elif isinstance(band_gaps, list):
# band_gaps is non empty list w. no vaspruns; presume floats
band_gaps = [float(i) for i in band_gaps]
save_files = False if plt else True
if len(abs_data) > 1 and not labels:
labels = [
latexify(vr.final_structure.composition.reduced_formula).replace(
'$_', '$_\mathregular') for vr in vrs
]
plotter = SOpticsPlotter(abs_data, band_gap=band_gaps, label=labels)
plt = plotter.get_plot(width=width,
height=height,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
colours=colours,
dpi=dpi,
plt=plt,
fonts=fonts,
style=style,
no_base_style=no_base_style)
if save_files:
basename = 'absorption'
if prefix:
basename = '{}_{}'.format(prefix, basename)
image_filename = '{}.{}'.format(basename, image_format)
if directory:
image_filename = os.path.join(directory, image_filename)
plt.savefig(image_filename, format=image_format, dpi=dpi)
for mode, data in abs_data.items():
basename = 'absorption' if mode == 'abs' else mode
write_files(data,
basename=basename,
prefix=prefix,
directory=directory)
else:
return plt
def get_pourbaix_plot_colorfill_by_element(self, limits=None, title="",
label_domains=True, element=None):
"""
Color domains by element
"""
from matplotlib.patches import Polygon
entry_dict_of_multientries = collections.defaultdict(list)
plt = pretty_plot(16)
optim_colors = ['#0000FF', '#FF0000', '#00FF00', '#FFFF00', '#FF00FF',
'#FF8080', '#DCDCDC', '#800000', '#FF8000']
optim_font_color = ['#FFFFA0', '#00FFFF', '#FF00FF', '#0000FF', '#00FF00',
'#007F7F', '#232323', '#7FFFFF', '#007FFF']
hatch = ['/', '\\', '|', '-', '+', 'o', '*']
(stable, unstable) = self.pourbaix_plot_data(limits)
num_of_overlaps = {key: 0 for key in stable.keys()}
for entry in stable:
if isinstance(entry, MultiEntry):
for e in entry.entrylist:
if element in e.composition.elements:
entry_dict_of_multientries[e.name].append(entry)
num_of_overlaps[entry] += 1
else:
entry_dict_of_multientries[entry.name].append(entry)
if limits:
xlim = limits[0]
ylim = limits[1]
else:
xlim = self._analyzer.chempot_limits[0]
ylim = self._analyzer.chempot_limits[1]
h_line = np.transpose([[xlim[0], -xlim[0] * PREFAC],
[xlim[1], -xlim[1] * PREFAC]])
o_line = np.transpose([[xlim[0], -xlim[0] * PREFAC + 1.23],
[xlim[1], -xlim[1] * PREFAC + 1.23]])
neutral_line = np.transpose([[7, ylim[0]], [7, ylim[1]]])
V0_line = np.transpose([[xlim[0], 0], [xlim[1], 0]])
ax = plt.gca()
ax.set_xlim(xlim)
ax.set_ylim(ylim)
from pymatgen import Composition, Element
from pymatgen.core.ion import Ion
def len_elts(entry):
if "(s)" in entry:
comp = Composition(entry[:-3])
else:
comp = Ion.from_formula(entry)
return len([el for el in comp.elements if el not in
[Element("H"), Element("O")]])
sorted_entry = entry_dict_of_multientries.keys()
sorted_entry.sort(key=len_elts)
i = -1
label_chr = map(chr, list(range(65, 91)))
for entry in sorted_entry:
color_indx = 0
x_coord = 0.0
y_coord = 0.0
npts = 0
i += 1
for e in entry_dict_of_multientries[entry]:
hc = 0
fc = 0
bc = 0
xy = self.domain_vertices(e)
c = self.get_center(stable[e])
x_coord += c[0]
y_coord += c[1]
npts += 1
color_indx = i
if "(s)" in entry:
comp = Composition(entry[:-3])
else:
comp = Ion.from_formula(entry)
if len([el for el in comp.elements if el not in
[Element("H"), Element("O")]]) == 1:
if color_indx >= len(optim_colors):
color_indx = color_indx -\
int(color_indx / len(optim_colors)) * len(optim_colors)
patch = Polygon(xy, facecolor=optim_colors[color_indx],
closed=True, lw=3.0, fill=True)
bc = optim_colors[color_indx]
else:
if color_indx >= len(hatch):
color_indx = color_indx - int(color_indx / len(hatch)) * len(hatch)
patch = Polygon(xy, hatch=hatch[color_indx], closed=True, lw=3.0, fill=False)
hc = hatch[color_indx]
ax.add_patch(patch)
xy_center = (x_coord / npts, y_coord / npts)
if label_domains:
if color_indx >= len(optim_colors):
color_indx = color_indx -\
int(color_indx / len(optim_colors)) * len(optim_colors)
fc = optim_font_color[color_indx]
if bc and not hc:
bbox = dict(boxstyle="round", fc=fc)
if hc and not bc:
bc = 'k'
fc = 'w'
bbox = dict(boxstyle="round", hatch=hc, fill=False)
if bc and hc:
bbox = dict(boxstyle="round", hatch=hc, fc=fc)
# bbox.set_path_effects([PathEffects.withSimplePatchShadow()])
plt.annotate(latexify_ion(latexify(entry)), xy_center,
color=bc, fontsize=30, bbox=bbox)
# plt.annotate(label_chr[i], xy_center,
# color=bc, fontsize=30, bbox=bbox)
lw = 3
plt.plot(h_line[0], h_line[1], "r--", linewidth=lw)
plt.plot(o_line[0], o_line[1], "r--", linewidth=lw)
plt.plot(neutral_line[0], neutral_line[1], "k-.", linewidth=lw)
plt.plot(V0_line[0], V0_line[1], "k-.", linewidth=lw)
plt.xlabel("pH")
plt.ylabel("E (V)")
plt.title(title, fontsize=20, fontweight='bold')
return plt
def optplot(
modes=("absorption", ),
filenames=None,
codes="vasp",
prefix=None,
directory=None,
gaussian=None,
band_gaps=None,
labels=None,
average=True,
height=6,
width=6,
xmin=0,
xmax=None,
ymin=0,
ymax=1e5,
colours=None,
style=None,
no_base_style=None,
image_format="pdf",
dpi=400,
plt=None,
fonts=None,
units="eV",
):
"""A script to plot optical absorption spectra from VASP calculations.
Args:
modes (:obj:`list` or :obj:`tuple`):
Ordered list of :obj:`str` determining properties to plot.
Accepted options are 'absorption' (default), 'eps', 'eps-real',
'eps-im', 'n', 'n-real', 'n-im', 'loss' (equivalent to n-im).
filenames (:obj:`str` or :obj:`list`, optional): Path to data file.
For VASP this is a *vasprun.xml* file (can be gzipped); for
Questaal the *opt.ext* file from *lmf* or *eps_BSE.out* from
*bethesalpeter* may be used.
Alternatively, a list of paths can be
provided, in which case the absorption spectra for each will be
plotted concurrently.
codes (:obj:`str` or :obj:`list`, optional): Original
calculator. Accepted values are 'vasp' and 'questaal'. Items should
correspond to filenames.
prefix (:obj:`str`, optional): Prefix for file names.
directory (:obj:`str`, optional): The directory in which to save files.
gaussian (:obj:`float`): Standard deviation for gaussian broadening.
band_gaps (:obj:`float`, :obj:`str` or :obj:`list`, optional): The band
gap as a :obj:`float`, in eV, plotted as a dashed line. If plotting
multiple spectra then a :obj:`list` of band gaps can be provided.
Band gaps can be provided as a floating-point number or as a path
to a *vasprun.xml* file. To skip over a line, set its bandgap to
zero or a negative number to place it outside the visible range.
labels (:obj:`str` or :obj:`list`): A label to identify the spectra.
If plotting multiple spectra then a :obj:`list` of labels can
be provided.
average (:obj:`bool`, optional): Average the dielectric response across
all lattice directions. Defaults to ``True``.
height (:obj:`float`, optional): The height of the plot.
width (:obj:`float`, optional): The width of the plot.
xmin (:obj:`float`, optional): The minimum energy on the x-axis.
xmax (:obj:`float`, optional): The maximum energy on the x-axis.
ymin (:obj:`float`, optional): The minimum absorption intensity on the
y-axis.
ymax (:obj:`float`, optional): The maximum absorption intensity on the
y-axis.
colours (:obj:`list`, optional): A :obj:`list` of colours to use in the
plot. The colours can be specified as a hex code, set of rgb
values, or any other format supported by matplotlib.
style (:obj:`list` or :obj:`str`, optional): (List of) matplotlib style
specifications, to be composed on top of Sumo base style.
no_base_style (:obj:`bool`, optional): Prevent use of sumo base style.
This can make alternative styles behave more predictably.
image_format (:obj:`str`, optional): The image file format. Can be any
format supported by matplotlib, including: png, jpg, pdf, and svg.
Defaults to pdf.
dpi (:obj:`int`, optional): The dots-per-inch (pixel density) for
the image.
plt (:obj:`matplotlib.pyplot`, optional): A
:obj:`matplotlib.pyplot` object to use for plotting.
fonts (:obj:`list`, optional): Fonts to use in the plot. Can be a
a single font, specified as a :obj:`str`, or several fonts,
specified as a :obj:`list` of :obj:`str`.
units (:obj:`str`, optional): X-axis units for the plot. 'eV' for
energy in electronvolts or 'nm' for wavelength in nanometers.
Defaults to 'eV'.
Returns:
A matplotlib pyplot object.
"""
# Don't write files if this is being done to manipulate existing plt
save_files = False if plt else True
# BUILD LIST OF FILES AUTOMATICALLY IF NECESSARY
if codes == "vasp":
if not filenames:
if os.path.exists("vasprun.xml"):
filenames = ["vasprun.xml"]
elif os.path.exists("vasprun.xml.gz"):
filenames = ["vasprun.xml.gz"]
else:
logging.error("ERROR: No vasprun.xml found!")
sys.exit()
elif codes == "questaal":
if not filenames:
if len(glob("opt.*")) > 0:
filenames = glob("opt.*")
if len(filenames) == 1:
logging.info("Found optics file: " + filenames[0])
else:
logging.info("Found optics files: " + ", ".join(filenames))
if isinstance(filenames, str):
filenames = [filenames]
if isinstance(codes, str):
codes = [codes] * len(filenames)
elif len(codes) == 1:
codes = list(codes) * len(filenames)
# ITERATE OVER FILES READING DIELECTRIC DATA
dielectrics = []
auto_labels = []
auto_band_gaps = []
for i, (filename, code) in enumerate(zip(filenames, codes)):
if code == "vasp":
vr = Vasprun(filename)
dielectrics.append(vr.dielectric)
auto_labels.append(
latexify(
vr.final_structure.composition.reduced_formula).replace(
"$_", r"$_\mathregular"))
if isinstance(band_gaps, list) and not band_gaps:
# band_gaps = [], auto band gap requested
auto_band_gaps.append(
vr.get_band_structure().get_band_gap()["energy"])
else:
auto_band_gaps.append(None)
elif code == "questaal":
if not save_files:
out_filename = None
elif len(filenames) == 1:
out_filename = "dielectric.dat"
else:
out_filename = f"dielectric_{i + 1}.dat"
dielectrics.append(
questaal.dielectric_from_file(filename, out_filename))
auto_band_gaps.append(None)
auto_labels.append(filename.split(".")[-1])
if isinstance(band_gaps, list) and not band_gaps:
logging.info("Bandgap requested but not supported for Questaal"
" file {}: skipping...".format(filename))
else:
raise Exception(f'Code selection "{code}" not recognised')
if not labels and len(filenames) > 1:
labels = auto_labels
# PROCESS DIELECTRIC DATA: BROADENING AND DERIVED PROPERTIES
if gaussian:
dielectrics = [broaden_eps(d, gaussian) for d in dielectrics]
# initialize spectrum data ready to append from each dataset
abs_data = OrderedDict()
for mode in modes:
abs_data.update({mode: []})
# for each calculation, get all required properties and append to data
for d in dielectrics:
# TODO: add support for other eigs and full modes
energies, properties = calculate_dielectric_properties(
d, set(modes), mode="average" if average else "trace")
for mode, spectrum in properties.items():
abs_data[mode].append((energies, spectrum))
if isinstance(band_gaps, list) and not band_gaps:
# empty list therefore use bandgaps collected from vasprun files
band_gaps = auto_band_gaps
elif isinstance(band_gaps, list):
# list containing filenames and/or values: mutate the list in-place
for i, item in enumerate(band_gaps):
if item is None:
pass
elif _floatable(item):
band_gaps[i] = float(item)
elif "vasprun" in item:
band_gaps[i] = (Vasprun(
item).get_band_structure().get_band_gap()["energy"])
else:
raise ValueError(
f"Format not recognised for auto bandgap: {item}.")
plotter = SOpticsPlotter(abs_data, band_gap=band_gaps, label=labels)
plt = plotter.get_plot(
width=width,
height=height,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
colours=colours,
dpi=dpi,
plt=plt,
fonts=fonts,
style=style,
no_base_style=no_base_style,
units=units,
)
if save_files:
basename = "absorption"
if prefix:
basename = f"{prefix}_{basename}"
image_filename = f"{basename}.{image_format}"
if directory:
image_filename = os.path.join(directory, image_filename)
plt.savefig(image_filename, format=image_format, dpi=dpi)
for mode, data in abs_data.items():
basename = "absorption" if mode == "abs" else mode
write_files(data,
basename=basename,
prefix=prefix,
directory=directory)
else:
return plt
def optplot(filenames=None,
prefix=None,
directory=None,
gaussian=None,
band_gaps=None,
labels=None,
average=True,
height=6,
width=6,
xmin=0,
xmax=None,
ymin=0,
ymax=1e5,
colours=None,
image_format='pdf',
dpi=400,
plt=None,
fonts=None):
"""A script to plot optical absorption spectra from VASP calculations.
Args:
filenames (:obj:`str` or :obj:`list`, optional): Path to vasprun.xml
file (can be gzipped). Alternatively, a list of paths can be
provided, in which case the absorption spectra for each will be
plotted concurrently.
prefix (:obj:`str`, optional): Prefix for file names.
directory (:obj:`str`, optional): The directory in which to save files.
gaussian (:obj:`float`): Standard deviation for gaussian broadening.
band_gaps (:obj:`float` or :obj:`list`, optional): The band gap as a
:obj:`float`, plotted as a dashed line. If plotting multiple
spectra then a :obj:`list` of band gaps can be provided.
labels (:obj:`str` or :obj:`list`): A label to identify the spectra.
If plotting multiple spectra then a :obj:`list` of labels can
be provided.
average (:obj:`bool`, optional): Average the dielectric response across
all lattice directions. Defaults to ``True``.
height (:obj:`float`, optional): The height of the plot.
width (:obj:`float`, optional): The width of the plot.
xmin (:obj:`float`, optional): The minimum energy on the x-axis.
xmax (:obj:`float`, optional): The maximum energy on the x-axis.
ymin (:obj:`float`, optional): The minimum absorption intensity on the
y-axis.
ymax (:obj:`float`, optional): The maximum absorption intensity on the
y-axis.
colours (:obj:`list`, optional): A :obj:`list` of colours to use in the
plot. The colours can be specified as a hex code, set of rgb
values, or any other format supported by matplotlib.
image_format (:obj:`str`, optional): The image file format. Can be any
format supported by matplotlib, including: png, jpg, pdf, and svg.
Defaults to pdf.
dpi (:obj:`int`, optional): The dots-per-inch (pixel density) for
the image.
plt (:obj:`matplotlib.pyplot`, optional): A
:obj:`matplotlib.pyplot` object to use for plotting.
fonts (:obj:`list`, optional): Fonts to use in the plot. Can be a
a single font, specified as a :obj:`str`, or several fonts,
specified as a :obj:`list` of :obj:`str`.
Returns:
A matplotlib pyplot object.
"""
if not filenames:
if os.path.exists('vasprun.xml'):
filenames = ['vasprun.xml']
elif os.path.exists('vasprun.xml.gz'):
filenames = ['vasprun.xml.gz']
else:
logging.error('ERROR: No vasprun.xml found!')
sys.exit()
elif type(filenames) is str:
filenames = [filenames]
vrs = [Vasprun(f) for f in filenames]
dielectrics = [vr.dielectric for vr in vrs]
if gaussian:
dielectrics = [broaden_eps(d, gaussian) for d in dielectrics]
abs_data = [calculate_alpha(d, average=average) for d in dielectrics]
if type(band_gaps) is list and not band_gaps:
# empty list therefore get bandgap from vasprun files
band_gaps = [
vr.get_band_structure().get_band_gap()['energy'] for vr in vrs
]
elif type(band_gaps) is list and 'vasprun' in band_gaps[0]:
# band_gaps contains list of vasprun files
bg_vrs = [Vasprun(f) for f in band_gaps]
band_gaps = [
vr.get_band_structure().get_band_gap()['energy'] for vr in bg_vrs
]
elif type(band_gaps) is list:
# band_gaps is non empty list w. no vaspruns; presume floats
band_gaps = [float(i) for i in band_gaps]
save_files = False if plt else True
if len(abs_data) > 1 and not labels:
labels = [
latexify(vr.final_structure.composition.reduced_formula).replace(
'$_', '$_\mathregular') for vr in vrs
]
plotter = SOpticsPlotter(abs_data, band_gap=band_gaps, label=labels)
plt = plotter.get_plot(width=width,
height=height,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
colours=colours,
dpi=dpi,
plt=plt,
fonts=fonts)
if save_files:
basename = 'absorption.{}'.format(image_format)
filename = '{}_{}'.format(prefix, basename) if prefix else basename
if directory:
filename = os.path.join(directory, filename)
plt.savefig(filename, format=image_format, dpi=dpi)
write_files(abs_data, prefix=prefix, directory=directory)
else:
return plt
