def plot(self, use_index=False, fontsize=8, **kwargs):
"""
Plot all arrays. Use multiple axes if datasets.
Args:
use_index: By default, the x-values are taken from the first column.
If use_index is False, the x-values are the row index.
fontsize: fontsize for title.
kwargs: options passed to ``ax.plot``.
Return: |matplotlib-figure|
"""
# build grid of plots.
num_plots, ncols, nrows = len(self.od), 1, 1
if num_plots > 1:
ncols = 2
nrows = (num_plots // ncols) + (num_plots % ncols)
ax_list, fig, plt = get_axarray_fig_plt(None, nrows=nrows, ncols=ncols,
sharex=False, sharey=False, squeeze=False)
ax_list = ax_list.ravel()
# Don't show the last ax if num_plots is odd.
if num_plots % ncols != 0: ax_list[-1].axis("off")
for ax, (key, arr) in zip(ax_list, self.od.items()):
ax.set_title(key, fontsize=fontsize)
ax.grid(True)
xs = arr[0] if not use_index else list(range(len(arr[0])))
for ys in arr[1:] if not use_index else arr:
ax.plot(xs, ys)
return fig
def plot(self, use_index=False, fontsize=8, colormap="viridis", **kwargs):
"""
Plot all arrays. Use multiple axes if datasets.
Args:
use_index: By default, the x-values are taken from the first column.
If use_index is False, the x-values are the row index.
fontsize: fontsize for title.
colormap: matplotlib color map.
kwargs: options passed to ``ax.plot``.
Return: |matplotlib-figure|
"""
if not self.odlist: return None
# Compute intersection of all keys.
# Here we loose the initial ordering in the dict but oh well!
klist = [list(d.keys()) for d in self.odlist]
keys = set(klist[0]).intersection(*klist)
if not keys:
print("Warning: cannot find common keys in files. Check input data")
return None
# Build grid of plots.
num_plots, ncols, nrows = len(keys), 1, 1
if num_plots > 1:
ncols = 2
nrows = (num_plots // ncols) + (num_plots % ncols)
ax_list, fig, plt = get_axarray_fig_plt(None, nrows=nrows, ncols=ncols,
sharex=False, sharey=False, squeeze=False)
ax_list = ax_list.ravel()
# Don't show the last ax if num_plots is odd.
if num_plots % ncols != 0: ax_list[-1].axis("off")
cmap = plt.get_cmap(colormap)
line_cycle = itertools.cycle(["-", ":", "--", "-.",])
# One ax for key, each ax may show multiple arrays
# so we need different line styles that are consistent with input data.
# Figure may be crowded but it's difficult to do better without metadata
# so I'm not gonna spend time to implement more complicated logic.
for ax, key in zip(ax_list, keys):
ax.set_title(key, fontsize=fontsize)
ax.grid(True)
for iod, (od, filepath) in enumerate(zip(self.odlist, self.filepaths)):
if key not in od: continue
arr = od[key]
color = cmap(iod / len(self.odlist))
xvals = arr[0] if not use_index else list(range(len(arr[0])))
arr_list = arr[1:] if not use_index else arr
for iarr, (ys, linestyle) in enumerate(zip(arr_list, line_cycle)):
ax.plot(xvals, ys, color=color, linestyle=linestyle,
label=os.path.relpath(filepath) if iarr == 0 else None)
ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
def plot(self, ax_list=None, fontsize=12, **kwargs):
"""
Uses matplotlib to plot the evolution of the SCF cycle.
Args:
ax_list: List of axes. If None a new figure is produced.
fontsize: legend fontsize.
kwargs: keyword arguments are passed to ax.plot
Returns: matplotlib figure
"""
# Build grid of plots.
num_plots, ncols, nrows = len(self), 1, 1
if num_plots > 1:
ncols = 2
nrows = num_plots // ncols + num_plots % ncols
ax_list, fig, plot = get_axarray_fig_plt(ax_list, nrows=nrows, ncols=ncols,
sharex=True, sharey=False, squeeze=False)
ax_list = np.array(ax_list).ravel()
iter_num = np.array(list(range(self.num_iterations))) + 1
label = kwargs.pop("label", None)
for i, ((key, values), ax) in enumerate(zip(self.items(), ax_list)):
ax.grid(True)
ax.set_xlabel('Iteration Step')
ax.set_xticks(iter_num, minor=False)
ax.set_ylabel(key)
xx, yy = iter_num, values
if self.num_iterations > 1:
# Don't show the first iteration since it's not very useful.
xx, yy = xx[1:], values[1:]
if not kwargs and label is None:
ax.plot(xx, yy, "-o", lw=2.0)
else:
ax.plot(xx, yy, label=label if i == 0 else None, **kwargs)
if key in _VARS_SUPPORTING_LOGSCALE and np.all(yy > 1e-22):
ax.set_yscale("log")
if key in _VARS_WITH_YRANGE:
ymin, ymax = _VARS_WITH_YRANGE[key]
val_min, val_max = np.min(yy), np.max(yy)
if abs(val_max - val_min) > abs(ymax - ymin):
ax.set_ylim(ymin, ymax)
if label is not None:
ax.legend(loc="best", fontsize=fontsize, shadow=True)
# Get around a bug in matplotlib.
if num_plots % ncols != 0:
ax_list[-1].plot(xx, yy, lw=0.0)
ax_list[-1].axis('off')
return fig
def plot(self, ax_list=None, fontsize=12, **kwargs):
"""
Plot relaxation history i.e. the results of the last iteration of each SCF cycle.
Args:
ax_list: List of axes. If None a new figure is produced.
fontsize: legend fontsize.
kwargs: keyword arguments are passed to ax.plot
Returns: matplotlib figure
"""
history = self.history
# Build grid of plots.
num_plots, ncols, nrows = len(history), 1, 1
if num_plots > 1:
ncols = 2
nrows = num_plots // ncols + num_plots % ncols
ax_list, fig, plot = get_axarray_fig_plt(ax_list, nrows=nrows, ncols=ncols,
sharex=True, sharey=False, squeeze=False)
ax_list = np.array(ax_list).ravel()
iter_num = np.array(list(range(self.num_iterations))) + 1
label = kwargs.pop("label", None)
for i, ((key, values), ax) in enumerate(zip(history.items(), ax_list)):
ax.grid(True)
ax.set_xlabel('Relaxation Step')
ax.set_xticks(iter_num, minor=False)
ax.set_ylabel(key)
xx, yy = iter_num, values
if not kwargs and label is None:
ax.plot(xx, yy, "-o", lw=2.0)
else:
ax.plot(xx, yy, label=label if i == 0 else None, **kwargs)
if key in _VARS_SUPPORTING_LOGSCALE and np.all(yy > 1e-22):
ax.set_yscale("log")
if key in _VARS_WITH_YRANGE:
ymin, ymax = _VARS_WITH_YRANGE[key]
val_min, val_max = np.min(yy), np.max(yy)
if abs(val_max - val_min) > abs(ymax - ymin):
ax.set_ylim(ymin, ymax)
if label is not None:
ax.legend(loc="best", fontsize=fontsize, shadow=True)
# Get around a bug in matplotlib.
if num_plots % ncols != 0:
ax_list[-1].plot(xx, yy, lw=0.0)
ax_list[-1].axis('off')
return fig
def plot(self, use_index=False, fontsize=8, **kwargs):
"""
Plot all arrays. Use multiple axes if datasets.
Args:
use_index: By default, the x-values are taken from the first column.
If use_index is False, the x-values are the row index.
fontsize: fontsize for title.
kwargs: options passed to ``ax.plot``.
Return: |matplotlib-figure|
"""
# build grid of plots.
num_plots, ncols, nrows = len(self.od), 1, 1
if num_plots > 1:
ncols = 2
nrows = (num_plots // ncols) + (num_plots % ncols)
ax_list, fig, plt = get_axarray_fig_plt(None,
nrows=nrows,
ncols=ncols,
sharex=False,
sharey=False,
squeeze=False)
ax_list = ax_list.ravel()
# Don't show the last ax if num_plots is odd.
if num_plots % ncols != 0: ax_list[-1].axis("off")
for ax, (key, arr) in zip(ax_list, self.od.items()):
ax.set_title(key, fontsize=fontsize)
ax.grid(True)
xs = arr[0] if not use_index else list(range(len(arr[0])))
for ys in arr[1:] if not use_index else arr:
ax.plot(xs, ys)
return fig
#%%
from matplotlib import pyplot as plt
import numpy as np
from pymatgen.electronic_structure.plotter import DosPlotter
from pymatgen.util.plotting import get_axarray_fig_plt, pretty_plot
import os
os.chdir('/home/jinho93/oxides/perobskite/lanthanum-aluminate/slab/14')
arr = np.genfromtxt('lsmo.dat')
num = len(arr[0])
num -= 1
ax_array, fig, plt = get_axarray_fig_plt(None, ncols=num, sharey=True)
plt = pretty_plot(12, 12, plt=plt)
for i in range(1, num):
fig.add_subplot(1, num, i)
plt.plot(arr[:, i], arr[:, 0])
plt.ylim((-5, 3))
plt.xlim(0, 3)
plt.xticks([])
plt.yticks([])
plt.axis('off')
plt.subplots_adjust(wspace=0)
plt.savefig('figure.png')
plt.show()
# %%
def plot(self, use_index=False, fontsize=8, colormap="viridis", **kwargs):
"""
Plot all arrays. Use multiple axes if datasets.
Args:
use_index: By default, the x-values are taken from the first column.
If use_index is False, the x-values are the row index.
fontsize: fontsize for title.
colormap: matplotlib color map.
kwargs: options passed to ``ax.plot``.
Return: |matplotlib-figure|
"""
if not self.odlist: return None
# Compute intersection of all keys.
# Here we loose the initial ordering in the dict but oh well!
klist = [list(d.keys()) for d in self.odlist]
keys = set(klist[0]).intersection(*klist)
if not keys:
print(
"Warning: cannot find common keys in files. Check input data")
return None
# Build grid of plots.
num_plots, ncols, nrows = len(keys), 1, 1
if num_plots > 1:
ncols = 2
nrows = (num_plots // ncols) + (num_plots % ncols)
ax_list, fig, plt = get_axarray_fig_plt(None,
nrows=nrows,
ncols=ncols,
sharex=False,
sharey=False,
squeeze=False)
ax_list = ax_list.ravel()
# Don't show the last ax if num_plots is odd.
if num_plots % ncols != 0: ax_list[-1].axis("off")
cmap = plt.get_cmap(colormap)
line_cycle = itertools.cycle([
"-",
":",
"--",
"-.",
])
# One ax for key, each ax may show multiple arrays
# so we need different line styles that are consistent with input data.
# Figure may be crowded but it's difficult to do better without metadata
# so I'm not gonna spend time to implement more complicated logic.
for ax, key in zip(ax_list, keys):
ax.set_title(key, fontsize=fontsize)
ax.grid(True)
for iod, (od,
filepath) in enumerate(zip(self.odlist, self.filepaths)):
if key not in od: continue
arr = od[key]
color = cmap(iod / len(self.odlist))
xvals = arr[0] if not use_index else list(range(len(arr[0])))
arr_list = arr[1:] if not use_index else arr
for iarr, (ys,
linestyle) in enumerate(zip(arr_list, line_cycle)):
ax.plot(
xvals,
ys,
color=color,
linestyle=linestyle,
label=os.path.relpath(filepath) if iarr == 0 else None)
ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
def plot_xy_with_hue(data,
x,
y,
hue,
decimals=None,
ax=None,
xlims=None,
ylims=None,
fontsize=12,
**kwargs):
"""
Plot y = f(x) relation for different values of `hue`.
Useful for convergence tests done wrt to two parameters.
Args:
data: |pandas-DataFrame| containing columns `x`, `y`, and `hue`.
x: Name of the column used as x-value
y: Name of the column(s) used as y-value
hue: Variable that define subsets of the data, which will be drawn on separate lines
decimals: Number of decimal places to round `hue` columns. Ignore if None
ax: |matplotlib-Axes| or None if a new figure should be created.
xlims ylims: Set the data limits for the x(y)-axis. Accept tuple e.g. `(left, right)`
or scalar e.g. `left`. If left (right) is None, default values are used
fontsize: Legend fontsize.
kwargs: Keywork arguments are passed to ax.plot method.
Returns: |matplotlib-Figure|
"""
if isinstance(y, (list, tuple)):
# Recursive call for each ax in ax_list.
num_plots, ncols, nrows = len(y), 1, 1
if num_plots > 1:
ncols = 2
nrows = (num_plots // ncols) + (num_plots % ncols)
ax_list, fig, plt = get_axarray_fig_plt(None,
nrows=nrows,
ncols=ncols,
sharex=False,
sharey=False,
squeeze=False)
ax_list = ax_list.ravel()
if num_plots % ncols != 0: ax_list[-1].axis('off')
for yname, ax in zip(y, ax_list):
plot_xy_with_hue(data,
x,
str(yname),
hue,
decimals=decimals,
ax=ax,
xlims=xlims,
ylims=ylims,
fontsize=fontsize,
show=False,
**kwargs)
return fig
# Check here because pandas error messages are a bit criptic.
miss = [k for k in (x, y, hue) if k not in data]
if miss:
raise ValueError(
"Cannot find `%s` in dataframe.\nAvailable keys are: %s" %
(str(miss), str(data.keys())))
# Truncate values in hue column so that we can group.
if decimals is not None:
data = data.round({hue: decimals})
ax, fig, plt = get_ax_fig_plt(ax=ax)
for key, grp in data.groupby(hue):
# Sort xs and rearrange ys
xy = np.array(sorted(zip(grp[x], grp[y]), key=lambda t: t[0]))
xvals, yvals = xy[:, 0], xy[:, 1]
#label = "{} = {}".format(hue, key)
label = "%s" % (str(key))
if not kwargs:
ax.plot(xvals, yvals, 'o-', label=label)
else:
ax.plot(xvals, yvals, label=label, **kwargs)
ax.grid(True)
ax.set_xlabel(x)
ax.set_ylabel(y)
set_axlims(ax, xlims, "x")
set_axlims(ax, ylims, "y")
ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
def plot_xy_with_hue(data, x, y, hue, decimals=None, ax=None,
xlims=None, ylims=None, fontsize=12, **kwargs):
"""
Plot y = f(x) relation for different values of `hue`.
Useful for convergence tests done wrt to two parameters.
Args:
data: |pandas-DataFrame| containing columns `x`, `y`, and `hue`.
x: Name of the column used as x-value
y: Name of the column(s) used as y-value
hue: Variable that define subsets of the data, which will be drawn on separate lines
decimals: Number of decimal places to round `hue` columns. Ignore if None
ax: |matplotlib-Axes| or None if a new figure should be created.
xlims ylims: Set the data limits for the x(y)-axis. Accept tuple e.g. `(left, right)`
or scalar e.g. `left`. If left (right) is None, default values are used
fontsize: Legend fontsize.
kwargs: Keywork arguments are passed to ax.plot method.
Returns: |matplotlib-Figure|
"""
if isinstance(y, (list, tuple)):
# Recursive call for each ax in ax_list.
num_plots, ncols, nrows = len(y), 1, 1
if num_plots > 1:
ncols = 2
nrows = (num_plots // ncols) + (num_plots % ncols)
ax_list, fig, plt = get_axarray_fig_plt(None, nrows=nrows, ncols=ncols,
sharex=False, sharey=False, squeeze=False)
ax_list = ax_list.ravel()
if num_plots % ncols != 0: ax_list[-1].axis('off')
for yname, ax in zip(y, ax_list):
plot_xy_with_hue(data, x, str(yname), hue, decimals=decimals, ax=ax,
xlims=xlims, ylims=ylims, fontsize=fontsize, show=False, **kwargs)
return fig
# Check here because pandas error messages are a bit criptic.
miss = [k for k in (x, y, hue) if k not in data]
if miss:
raise ValueError("Cannot find `%s` in dataframe.\nAvailable keys are: %s" % (str(miss), str(data.keys())))
# Truncate values in hue column so that we can group.
if decimals is not None:
data = data.round({hue: decimals})
ax, fig, plt = get_ax_fig_plt(ax=ax)
for key, grp in data.groupby(hue):
# Sort xs and rearrange ys
xy = np.array(sorted(zip(grp[x], grp[y]), key=lambda t: t[0]))
xvals, yvals = xy[:, 0], xy[:, 1]
#label = "{} = {}".format(hue, key)
label = "%s" % (str(key))
if not kwargs:
ax.plot(xvals, yvals, 'o-', label=label)
else:
ax.plot(xvals, yvals, label=label, **kwargs)
ax.grid(True)
ax.set_xlabel(x)
ax.set_ylabel(y)
set_axlims(ax, xlims, "x")
set_axlims(ax, ylims, "y")
ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
os.chdir(
'/home/ksrc5/FTJ/1.bfo/111-dir/junction/sto/vasp/vac/conf3/4.node03/dense_k_dos/again'
)
vrun = Vasprun('vasprun.xml')
s: Structure = vrun.final_structure
cdos = vrun.complete_dos
pdos = cdos.pdos
doss = dict()
num = 16
arr = np.linspace(0, 1, num, endpoint=False)
darr = arr[1] - arr[0]
for j in arr:
densities = []
for i in s.sites:
if j + darr > i.c >= j:
densities.append(cdos.get_site_dos(i).get_densities())
densities = np.sum(densities, axis=0)
doss[f'{j:.2f}'] = Dos(cdos.efermi, cdos.energies, {Spin.up: densities})
dsp = DosPlotter(sigma=0.1)
ax_array, fig, plt = get_axarray_fig_plt(None, nrows=num, sharex=True)
plt = pretty_plot(12, 6, plt=plt)
for i in range(num):
dsp.__init__(sigma=0.05)
a = doss.popitem()
dsp.add_dos(*a)
fig.add_subplot(num, 1, i + 1)
subplt = dsp.get_plot(xlim=(-1, 2), plt=plt)
plt.savefig('figure.png')
plt.show()
