def pdos_from_files(return_plt=False, **kwargs):
"""Read input data, process for PDOS before plotting and/or writing
Args:
return_plt (bool): If True, return the pyplot object instead of writing
or displaying plot output.
**kwargs: See command reference for full argument list
"""
pdos_plotting_data = galore.process_pdos(**kwargs)
# For plotting and writing, "None" means "write to screen"
# while False means "do nothing"
if kwargs['plot'] or kwargs['plot'] is None:
if 'style' in kwargs and kwargs['style'] is not None:
import matplotlib.pyplot
matplotlib.pyplot.style.use(kwargs['style'])
plt = galore.plot.plot_pdos(pdos_plotting_data,
**kwargs) # flipx is included in kwargs
if kwargs['overlay'] is not None:
plt = galore.plot.add_overlay(
plt,
kwargs['overlay'],
overlay_offset=kwargs['overlay_offset'],
overlay_scale=kwargs['overlay_scale'],
overlay_style=kwargs['overlay_style'],
overlay_label=kwargs['overlay_label'])
plt.legend(loc='best')
xlabel = galore.plot.guess_xlabel(units=kwargs['units'],
flipx=kwargs['flipx'],
energy_label=None)
plt.xlabel(xlabel)
if kwargs['ylabel'] is not None:
plt.ylabel(kwargs['ylabel'])
if return_plt:
return plt
elif kwargs['plot']:
plt.savefig(kwargs['plot'])
elif kwargs['plot'] is None:
plt.show()
if kwargs['csv'] or kwargs['csv'] is None:
galore.formats.write_pdos(pdos_plotting_data,
filename=kwargs['csv'],
filetype='csv',
flipx=kwargs['flipx'])
if kwargs['txt'] or kwargs['txt'] is None:
galore.formats.write_pdos(pdos_plotting_data,
filename=kwargs['txt'],
filetype='txt',
flipx=kwargs['flipx'])
import galore
import galore.plot
vasprun = 'test/SnO2/vasprun.xml.gz'
xmin, xmax = (-10, 4)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
weightings = ('He2', 'Alka', 'Yeh_HAXPES')
for i, weighting in enumerate(weightings):
plotting_data = galore.process_pdos(input=[vasprun],
gaussian=0.3,
lorentzian=0.2,
xmin=xmin,
xmax=xmax,
weighting=weighting)
galore.plot.plot_pdos(plotting_data,
ax=ax,
show_orbitals=False,
units='eV',
xmin=-xmin,
xmax=-xmax,
flipx=True)
line = ax.lines[-1]
line.set_label(weighting)
line.set_color(cmap(i / len(weightings)))
ymax = max(line.get_ydata())
#! /usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
import galore
import galore.plot
vasprun = './test/MgO/vasprun.xml.gz'
xmin, xmax = (-10, 2)
fig = plt.figure()
for i, l in enumerate(np.arange(0.05, 0.50, 0.05)):
ax = fig.add_subplot(3, 3, i + 1)
ax.set_title("$\gamma = {0:4.2f}$".format(l))
plotting_data = galore.process_pdos(input=[vasprun], lorentzian=l,
xmin=xmin, xmax=xmax)
galore.plot.plot_pdos(plotting_data, ax=ax)
ax.legend().set_visible(False)
fig.tight_layout()
plt.show()
ax3.set_xlim(xlim)
ax3.legend().remove()
## Fix consistent colors
for line in ax3.lines:
line.set_color(line_colors[line.get_label()])
line.set_linestyle('-')
# Summed PDOS
ax4 = fig.add_subplot(1, 5, 4, sharey=ax3)
galore.plot.plot_pdos(weighted_data, ax=ax4, total=True, show_orbitals=False)
ax4.set_title("Sum")
ax4.set_xlim(xlim)
ax4.set_ylim(new_ylim)
ax4.legend().remove()
# Broadened output
ax5 = fig.add_subplot(1, 5, 5)
broadened_data = galore.process_pdos(input='test/MgO/vasprun.xml.gz',
gaussian=0.5,
weighting=weighting)
galore.plot.plot_pdos(broadened_data, ax=ax5, total=True, show_orbitals=False)
ax5.set_title("Broaden")
ax5.set_xlim(xlim)
ax5.set_ylim(final_ylim)
ax5.legend().remove()
fig.subplots_adjust(left=0.03, right=0.98, top=0.78)
fig.savefig('docs/source/figures/pe_schematic.pdf')