def find_dirac_nodes():
"""
Look for band crossings near (within `tol` eV) the Fermi level.
Returns:
boolean. Whether or not a band crossing occurs at or near
the fermi level.
"""
vasprun = Vasprun('vasprun.xml')
dirac = False
if vasprun.get_band_structure().get_band_gap()['energy'] < 0.1:
efermi = vasprun.efermi
bsp = BSPlotter(vasprun.get_band_structure('KPOINTS', line_mode=True,
efermi=efermi))
bands = []
data = bsp.bs_plot_data(zero_to_efermi=True)
for d in range(len(data['distances'])):
for i in range(bsp._nb_bands):
x = data['distances'][d],
y = [data['energy'][d][str(Spin.up)][i][j]
for j in range(len(data['distances'][d]))]
band = [x, y]
bands.append(band)
considered = []
for i in range(len(bands)):
for j in range(len(bands)):
if i != j and (j, i) not in considered:
considered.append((j, i))
for k in range(len(bands[i][0])):
if ((-0.1 < bands[i][1][k] < 0.1) and
(-0.1 < bands[i][1][k] - bands[j][1][k] < 0.1)):
dirac = True
return dirac
def get_band_edges():
"""
Calculate the band edge locations relative to the vacuum level
for a semiconductor. If spin-polarized, returns all 4 band edges.
"""
# Vacuum level energy from LOCPOT.
locpot = Locpot.from_file("LOCPOT")
evac = max(locpot.get_average_along_axis(2))
vasprun = Vasprun("vasprun.xml")
efermi = vasprun.efermi - evac
if vasprun.get_band_structure().is_spin_polarized:
eigenvals = {Spin.up: [], Spin.down: []}
for band in vasprun.eigenvalues:
for eigenvalue in vasprun.eigenvalues[band]:
eigenvals[band[0]].append(eigenvalue)
up_cbm = min([e[0] for e in eigenvals[Spin.up] if not e[1]]) - evac
up_vbm = max([e[0] for e in eigenvals[Spin.up] if e[1]]) - evac
dn_cbm = min([e[0] for e in eigenvals[Spin.down] if not e[1]]) - evac
dn_vbm = max([e[0] for e in eigenvals[Spin.down] if e[1]]) - evac
edges = {"up_cbm": up_cbm, "up_vbm": up_vbm, "dn_cbm": dn_cbm, "dn_vbm": dn_vbm, "efermi": efermi}
else:
bs = vasprun.get_band_structure()
cbm = bs.get_cbm()["energy"] - evac
vbm = bs.get_vbm()["energy"] - evac
edges = {"cbm": cbm, "vbm": vbm, "efermi": efermi}
return edges
def find_dirac_nodes():
"""
Look for band crossings near (within `tol` eV) the Fermi level.
Returns:
boolean. Whether or not a band crossing occurs at or near
the fermi level.
"""
vasprun = Vasprun('vasprun.xml')
dirac = False
if vasprun.get_band_structure().get_band_gap()['energy'] < 0.1:
efermi = vasprun.efermi
bsp = BSPlotter(vasprun.get_band_structure('KPOINTS', line_mode=True,
efermi=efermi))
bands = []
data = bsp.bs_plot_data(zero_to_efermi=True)
for d in range(len(data['distances'])):
for i in range(bsp._nb_bands):
x = data['distances'][d],
y = [data['energy'][d][str(Spin.up)][i][j]
for j in range(len(data['distances'][d]))]
band = [x, y]
bands.append(band)
considered = []
for i in range(len(bands)):
for j in range(len(bands)):
if i != j and (j, i) not in considered:
considered.append((j, i))
for k in range(len(bands[i][0])):
if ((-0.1 < bands[i][1][k] < 0.1) and
(-0.1 < bands[i][1][k] - bands[j][1][k] < 0.1)):
dirac = True
return dirac
def get_bs(dos, vasprun_bands, kpts_bands):
## get BandStructureSymmLine object and "save" in hidden div in json format
bands = Vasprun(vasprun_bands, parse_projected_eigen = True)
if dos:
dos = CompleteDos.from_dict(json.loads(dos))
bs = bands.get_band_structure(kpts_bands, line_mode=True, efermi=dos.efermi)
else:
bs = bands.get_band_structure(kpts_bands, line_mode=True)
return json.dumps(bs.as_dict(), cls=MyEncoder)
def get_bandgap_from_aexx(self, structure, aexx, outdir=None):
vasprun_location = os.path.join(outdir, str(aexx).zfill(2), self.names[-1], 'vasprun.xml')
try:
vasprun = Vasprun(vasprun_location, parse_projected_eigen=False)
band_gap = vasprun.get_band_structure().get_band_gap()['energy']
except:
def set_aexx(vasp: Vasp, structure=None):
vasp.add_keyword('AEXX', aexx/100)
return vasp
for x in self.functionals: # Set nupdown
x.modifications.append(set_aexx)
super().__call__(structure, outdir=os.path.join(outdir, str(aexx).zfill(2)))
vasprun = Vasprun(vasprun_location, parse_projected_eigen=False)
band_gap = vasprun.get_band_structure().get_band_gap()['energy']
return band_gap
def boltz_run(vrun=""):
"""
Helper function to run and store Bolztrap
Please note high denske k-point mesh is needed
The automatic k-point convergene in JARVIS-DFT is generally good enough
Args:
vrun: path to vasprun.xml
Returns:
BoltztrapAnalyzer object
"""
if which("x_trans") is None or which("BoltzTraP") is None:
print("Please install BoltzTrap")
v = Vasprun(vrun)
kp = vrun.replace("vasprun.xml", "KPOINTS")
out = vrun.replace("vasprun.xml", "OUTCAR")
for line in open(out, "r"):
if "NELECT" in line:
nelect = float(line.split()[2])
bs = v.get_band_structure(kp, line_mode=False)
brun = BoltztrapRunner(bs, nelect)
path = vrun.split("vasprun.xml")[0]
folder = str(path) + str("/boltztrap")
out_trans = str(folder) + str("/boltztrap.outputtrans")
if not os.path.exists(out_trans):
brun.run(path_dir=path)
print("doesnt exist", out_trans)
bana = BoltztrapAnalyzer.from_files(folder)
return bana
def run_task(self, fw_spec):
potcar_spec = self.get("potcar_spec", False)
vasp_input_set_params = self.get("vasp_input_set_params") or {}
# update the bandgap based on output from the previous calculation,
# unless the user specified a bandgap via vasp_input_set_params
if vasp_input_set_params.get("bandgap") is None:
# First look for the gga_bandgap key in the FW spec, to save parsing time
if fw_spec.get("gga_bandgap") is not None:
vasp_input_set_params["bandgap"] = fw_spec.get("gga_bandgap")
# If not found, parse the files from the previous calc to find the bandgap
else:
parse_potcar_file = not potcar_spec
vasprun = Vasprun("vasprun.xml",
parse_potcar_file=parse_potcar_file)
bandgap = vasprun.get_band_structure(
efermi="smart").get_band_gap()["energy"]
vasp_input_set_params["bandgap"] = bandgap
# read the structure from the output of the previous calculation
structure = Structure.from_file("POSCAR")
vis = MPScanRelaxSet(structure, **vasp_input_set_params)
vis.write_input(".", potcar_spec=potcar_spec)
def read_vasprun(filename='vasprun.xml'):
"""Read a VASP vasprun.xml file to obtain the density of states
Pymatgen must be present on the system to use this method
Args:
filename (str): Path to vasprun.xml file
Returns:
data (pymatgen.electronic_structure.dos.Dos): A pymatgen Dos object
"""
try:
from pymatgen.io.vasp.outputs import Vasprun
from pymatgen.electronic_structure.core import Spin
except ImportError as e:
e.msg = "pymatgen package neccessary to load vasprun files"
raise
vr = Vasprun(filename)
band = vr.get_band_structure()
dos = vr.complete_dos
if band.is_metal():
zero_point = vr.efermi
else:
zero_point = band.get_vbm()['energy']
# Shift the energies so that the vbm is at 0 eV, also taking into account
# any gaussian broadening
dos.energies -= zero_point
if vr.parameters['ISMEAR'] == 0 or vr.parameters['ISMEAR'] == -1:
dos.energies -= vr.parameters['SIGMA']
return dos
def from_file(cls, filename):
"""
Loads a SolarCell instance from a vasprun.xml. # TODO extend
Args:
filename (str): vasprum.xml file from which to load the SolarCell object.
Returns:
SolarCell
"""
try:
vasprun = Vasprun(filename, parse_potcar_file=False)
diel_tensor = DielTensor.from_file(filename)
except ParseError:
raise IOError("Error while parsing the input file. Currently the "
"SolarCell class can only be constructed from "
"the vasprun.xml file. If you have provided this "
"file, check if the run has completed.")
# Extract the information on the direct and indirect band gap
bandstructure = vasprun.get_band_structure()
bandgaps = (bandstructure.get_band_gap()["energy"],
bandstructure.get_direct_band_gap())
return cls(diel_tensor, bandgaps)
def plot_band_structure(ylim=(-5, 5), draw_fermi=False, fmt='pdf'):
"""
Plot a standard band structure with no projections.
Args:
ylim (tuple): minimum and maximum potentials for the plot's y-axis.
draw_fermi (bool): whether or not to draw a dashed line at E_F.
fmt (str): matplotlib format style. Check the matplotlib docs
for options.
"""
vasprun = Vasprun('vasprun.xml')
efermi = vasprun.efermi
bsp = BSPlotter(
vasprun.get_band_structure('KPOINTS', line_mode=True, efermi=efermi))
if fmt == "None":
return bsp.bs_plot_data()
else:
plot = bsp.get_plot(ylim=ylim)
fig = plot.gcf()
ax = fig.gca()
ax.set_xticklabels(
[r'$\mathrm{%s}$' % t for t in ax.get_xticklabels()])
ax.set_yticklabels(
[r'$\mathrm{%s}$' % t for t in ax.get_yticklabels()])
if draw_fermi:
ax.plot([ax.get_xlim()[0], ax.get_xlim()[1]], [0, 0], 'k--')
plt.savefig('band_structure.{}'.format(fmt), transparent=True)
plt.close()
def plot_orb_projected_bands(orbitals, fmt='pdf', ylim=(-5, 5)):
"""
Plot a separate band structure for each orbital of each element in
orbitals.
Args:
orbitals (dict): dictionary of the form
{element: [orbitals]},
e.g. {'Mo': ['s', 'p', 'd'], 'S': ['p']}
ylim (tuple): minimum and maximum energies for the plot's
y-axis.
fmt (str): matplotlib format style. Check the matplotlib
docs for options.
"""
vasprun = Vasprun('vasprun.xml', parse_projected_eigen=True)
bs = vasprun.get_band_structure('KPOINTS', line_mode=True)
bspp = BSPlotterProjected(bs)
ax = bspp.get_projected_plots_dots(orbitals, ylim=ylim).gcf().gca()
ax.set_xticklabels([r'$\mathrm{%s}$' % t for t in ax.get_xticklabels()])
ax.set_yticklabels([r'$\mathrm{%s}$' % t for t in ax.get_yticklabels()])
if fmt == "None":
return ax
else:
plt.savefig('orb_projected_bands.{}'.format(fmt))
plt.close()
def test_get_band_structure(self):
filepath = os.path.join(test_dir, 'vasprun_Si_bands.xml')
vasprun = Vasprun(filepath,
parse_projected_eigen=True, parse_potcar_file=False)
bs = vasprun.get_band_structure(kpoints_filename=
os.path.join(test_dir,
'KPOINTS_Si_bands'))
cbm = bs.get_cbm()
vbm = bs.get_vbm()
self.assertEqual(cbm['kpoint_index'], [13], "wrong cbm kpoint index")
self.assertAlmostEqual(cbm['energy'], 6.2301, "wrong cbm energy")
self.assertEqual(cbm['band_index'], {Spin.up: [4], Spin.down: [4]},
"wrong cbm bands")
self.assertEqual(vbm['kpoint_index'], [0, 63, 64])
self.assertAlmostEqual(vbm['energy'], 5.6158, "wrong vbm energy")
self.assertEqual(vbm['band_index'], {Spin.up: [1, 2, 3],
Spin.down: [1, 2, 3]},
"wrong vbm bands")
self.assertEqual(vbm['kpoint'].label, "\Gamma", "wrong vbm label")
self.assertEqual(cbm['kpoint'].label, None, "wrong cbm label")
projected = bs.get_projection_on_elements()
self.assertAlmostEqual(projected[Spin.up][0][0]["Si"], 0.4238)
projected = bs.get_projections_on_elements_and_orbitals({"Si": ["s"]})
self.assertAlmostEqual(projected[Spin.up][0][0]["Si"]["s"], 0.4238)
def test_get_band_structure(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
filepath = os.path.join(test_dir, 'vasprun_Si_bands.xml')
vasprun = Vasprun(filepath,
parse_projected_eigen=True,
parse_potcar_file=False)
bs = vasprun.get_band_structure(
kpoints_filename=os.path.join(test_dir, 'KPOINTS_Si_bands'))
cbm = bs.get_cbm()
vbm = bs.get_vbm()
self.assertEqual(cbm['kpoint_index'], [13],
"wrong cbm kpoint index")
self.assertAlmostEqual(cbm['energy'], 6.2301, "wrong cbm energy")
self.assertEqual(cbm['band_index'], {
Spin.up: [4],
Spin.down: [4]
}, "wrong cbm bands")
self.assertEqual(vbm['kpoint_index'], [0, 63, 64])
self.assertAlmostEqual(vbm['energy'], 5.6158, "wrong vbm energy")
self.assertEqual(vbm['band_index'], {
Spin.up: [1, 2, 3],
Spin.down: [1, 2, 3]
}, "wrong vbm bands")
self.assertEqual(vbm['kpoint'].label, "\\Gamma", "wrong vbm label")
self.assertEqual(cbm['kpoint'].label, None, "wrong cbm label")
projected = bs.get_projection_on_elements()
self.assertAlmostEqual(projected[Spin.up][0][0]["Si"], 0.4238)
projected = bs.get_projections_on_elements_and_orbitals(
{"Si": ["s"]})
self.assertAlmostEqual(projected[Spin.up][0][0]["Si"]["s"], 0.4238)
def plot_orb_projected_bands(orbitals, fmt='pdf', ylim=(-5, 5)):
"""
Plot a separate band structure for each orbital of each element in
orbitals.
Args:
orbitals (dict): dictionary of the form
{element: [orbitals]},
e.g. {'Mo': ['s', 'p', 'd'], 'S': ['p']}
ylim (tuple): minimum and maximum energies for the plot's
y-axis.
fmt (str): matplotlib format style. Check the matplotlib
docs for options.
"""
vasprun = Vasprun('vasprun.xml', parse_projected_eigen=True)
bs = vasprun.get_band_structure('KPOINTS', line_mode=True)
bspp = BSPlotterProjected(bs)
ax = bspp.get_projected_plots_dots(orbitals, ylim=ylim).gcf().gca()
ax.set_xticklabels([r'$\mathrm{%s}$' % t for t in ax.get_xticklabels()])
ax.set_yticklabels([r'$\mathrm{%s}$' % t for t in ax.get_yticklabels()])
if fmt == "None":
return ax
else:
plt.savefig('orb_projected_bands.{}'.format(fmt))
plt.close()
def plot_local_potential(axis=2, ylim=(-20, 0), fmt='pdf'):
"""
Plot data from the LOCPOT file along any of the 3 primary axes.
Useful for determining surface dipole moments and electric
potentials on the interior of the material.
Args:
axis (int): 0 = x, 1 = y, 2 = z
ylim (tuple): minimum and maximum potentials for the plot's y-axis.
fmt (str): matplotlib format style. Check the matplotlib docs
for options.
"""
ax = plt.figure(figsize=(16, 10)).gca()
locpot = Locpot.from_file('LOCPOT')
structure = Structure.from_file('CONTCAR')
vd = VolumetricData(structure, locpot.data)
abs_potentials = vd.get_average_along_axis(axis)
vacuum_level = max(abs_potentials)
vasprun = Vasprun('vasprun.xml')
bs = vasprun.get_band_structure()
if not bs.is_metal():
cbm = bs.get_cbm()['energy'] - vacuum_level
vbm = bs.get_vbm()['energy'] - vacuum_level
potentials = [potential - vacuum_level for potential in abs_potentials]
axis_length = structure.lattice._lengths[axis]
positions = np.arange(0, axis_length, axis_length / len(potentials))
ax.plot(positions, potentials, linewidth=2, color='k')
ax.set_xlim(0, axis_length)
ax.set_ylim(ylim[0], ylim[1])
ax.set_xticklabels(
[r'$\mathrm{%s}$' % tick for tick in ax.get_xticks()], size=20)
ax.set_yticklabels(
[r'$\mathrm{%s}$' % tick for tick in ax.get_yticks()], size=20)
ax.set_xlabel(r'$\mathrm{\AA}$', size=24)
ax.set_ylabel(r'$\mathrm{V\/(eV)}$', size=24)
if not bs.is_metal():
ax.text(ax.get_xlim()[1], cbm, r'$\mathrm{CBM}$',
horizontalalignment='right', verticalalignment='bottom',
size=20)
ax.text(ax.get_xlim()[1], vbm, r'$\mathrm{VBM}$',
horizontalalignment='right', verticalalignment='top', size=20)
ax.fill_between(ax.get_xlim(), cbm, ax.get_ylim()[1],
facecolor=plt.cm.jet(0.3), zorder=0, linewidth=0)
ax.fill_between(ax.get_xlim(), ax.get_ylim()[0], vbm,
facecolor=plt.cm.jet(0.7), zorder=0, linewidth=0)
if fmt == "None":
return ax
else:
plt.savefig('locpot.{}'.format(fmt))
plt.close()
def plot_local_potential(axis=2, ylim=(-20, 0), fmt='pdf'):
"""
Plot data from the LOCPOT file along any of the 3 primary axes.
Useful for determining surface dipole moments and electric
potentials on the interior of the material.
Args:
axis (int): 0 = x, 1 = y, 2 = z
ylim (tuple): minimum and maximum potentials for the plot's y-axis.
fmt (str): matplotlib format style. Check the matplotlib docs
for options.
"""
ax = plt.figure(figsize=(16, 10)).gca()
locpot = Locpot.from_file('LOCPOT')
structure = Structure.from_file('CONTCAR')
vd = VolumetricData(structure, locpot.data)
abs_potentials = vd.get_average_along_axis(axis)
vacuum_level = max(abs_potentials)
vasprun = Vasprun('vasprun.xml')
bs = vasprun.get_band_structure()
if not bs.is_metal():
cbm = bs.get_cbm()['energy'] - vacuum_level
vbm = bs.get_vbm()['energy'] - vacuum_level
potentials = [potential - vacuum_level for potential in abs_potentials]
axis_length = structure.lattice.lengths[axis]
positions = np.arange(0, axis_length, axis_length / len(potentials))
ax.plot(positions, potentials, linewidth=2, color='k')
ax.set_xlim(0, axis_length)
ax.set_ylim(ylim[0], ylim[1])
ax.set_xticklabels(
[r'$\mathrm{%s}$' % tick for tick in ax.get_xticks()], size=20)
ax.set_yticklabels(
[r'$\mathrm{%s}$' % tick for tick in ax.get_yticks()], size=20)
ax.set_xlabel(r'$\mathrm{\AA}$', size=24)
ax.set_ylabel(r'$\mathrm{V\/(eV)}$', size=24)
if not bs.is_metal():
ax.text(ax.get_xlim()[1], cbm, r'$\mathrm{CBM}$',
horizontalalignment='right', verticalalignment='bottom',
size=20)
ax.text(ax.get_xlim()[1], vbm, r'$\mathrm{VBM}$',
horizontalalignment='right', verticalalignment='top', size=20)
ax.fill_between(ax.get_xlim(), cbm, ax.get_ylim()[1],
facecolor=plt.cm.jet(0.3), zorder=0, linewidth=0)
ax.fill_between(ax.get_xlim(), ax.get_ylim()[0], vbm,
facecolor=plt.cm.jet(0.7), zorder=0, linewidth=0)
if fmt == "None":
return ax
else:
plt.savefig('locpot.{}'.format(fmt))
plt.close()
def plot_simple_smoothed_band_structure(ylim=[-1.5, 3.5], filename=None):
vasprun = Vasprun('./vasprun.xml')
bs = vasprun.get_band_structure(line_mode=True)
if filename is None:
# BSPlotter(bs).get_plot(smooth=True,ylim=ylim)
BSPlotter(bs).show(smooth=True, ylim=ylim)
else:
BSPlotter(bs).save_plot(filename)
def bandgap(directory):
# Load the vasprun.xml file
vasprun = Vasprun(os.path.join(directory, "vasprun.xml"))
# Extract the band gap from the band structure
band_gap = vasprun.get_band_structure().get_band_gap()
print("Band gap = " + str(band_gap) + " eV")
def fsplot(
filename: Optional[Union[Path, str]] = None,
interpolate_factor: int = 8,
mu: float = 0.0,
wigner_seitz: bool = True,
spin: Optional[Spin] = None,
plot_type: str = "plotly",
interactive: bool = False,
prefix: Optional[str] = None,
directory: Optional[Union[Path, str]] = None,
image_format: str = "png",
dpi: float = 400,
):
"""Plot Fermi surfaces from a vasprun.xml file.
Args:
filename: Path to input vasprun file.
interpolate_factor: The factor by which to interpolate the bands.
mu: The level above the Fermi energy at which the isosurfaces are to be plotted.
wigner_seitz: Controls whether the cell is the Wigner-Seitz cell or the
reciprocal unit cell parallelepiped.
spin: The spin channel to plot. By default plots both spin channels.
plot_type: Method used for plotting. Valid options are: "matplotlib", "plotly",
"mayavi".
interactive: Whether to enable interactive plots.
prefix: Prefix for file names.
directory: The directory in which to save files.
image_format: The image file format.
dpi: The dots-per-inch (pixel density) for the image.
Returns:
The filename written to disk.
"""
from ifermi.fermi_surface import FermiSurface
from ifermi.interpolator import Interpolater
from ifermi.plotter import FSPlotter
if not filename:
filename = find_vasprun_file()
vr = Vasprun(filename)
bs = vr.get_band_structure()
interpolater = Interpolater(bs)
interp_bs, kpoint_dim = interpolater.interpolate_bands(interpolate_factor)
fs = FermiSurface.from_band_structure(
interp_bs, kpoint_dim, mu=mu, wigner_seitz=wigner_seitz, spin=spin,
)
plotter = FSPlotter(fs)
directory = directory if directory else "."
prefix = "{}_".format(prefix) if prefix else ""
output_filename = "{}fermi_surface.{}".format(prefix, image_format)
output_filename = Path(directory) / output_filename
plotter.plot(plot_type=plot_type, interactive=interactive, filename=output_filename)
def get_band_edges():
"""
Calculate the band edge locations relative to the vacuum level
for a semiconductor.
Returns:
edges (dict): {'up_cbm': , 'up_vbm': , 'dn_cbm': , 'dn_vbm': , 'efermi'}
"""
# Vacuum level energy from LOCPOT.
locpot = Locpot.from_file('LOCPOT')
evac = max(locpot.get_average_along_axis(2))
vasprun = Vasprun('vasprun.xml')
bs = vasprun.get_band_structure()
eigenvals = vasprun.eigenvalues
efermi = vasprun.efermi - evac
if bs.is_spin_polarized:
print(eigenvals[Spin.up])
print([e[0] - evac for e in eigenvals[Spin.up][0]])
up_cbm = min([
min([e[0] for e in eigenvals[Spin.up][i] if not e[1]])
for i in range(len(eigenvals[Spin.up]))
]) - evac
up_vbm = max([
max([e[0] for e in eigenvals[Spin.up][i] if e[1]])
for i in range(len(eigenvals[Spin.up]))
]) - evac
dn_cbm = min([
min([e[0] for e in eigenvals[Spin.down][i] if not e[1]])
for i in range(len(eigenvals[Spin.down]))
]) - evac
dn_vbm = max([
max([e[0] for e in eigenvals[Spin.down][i] if e[1]])
for i in range(len(eigenvals[Spin.down]))
]) - evac
edges = {
'up_cbm': up_cbm,
'up_vbm': up_vbm,
'dn_cbm': dn_cbm,
'dn_vbm': dn_vbm,
'efermi': efermi
}
else:
cbm = bs.get_cbm()['energy'] - evac
vbm = bs.get_vbm()['energy'] - evac
edges = {
'up_cbm': cbm,
'up_vbm': vbm,
'dn_cbm': cbm,
'dn_vbm': vbm,
'efermi': efermi
}
return edges
def get_dir_indir_gap(run=""):
"""
Get direct and indirect bandgaps for a vasprun.xml
"""
v = Vasprun(run)
bandstructure = v.get_band_structure()
dir_gap = bandstructure.get_direct_band_gap()
indir_gap = bandstructure.get_band_gap()["energy"]
return dir_gap, indir_gap
def banddos(pref='',storedir=None):
ru=str("vasprun.xml")
kpfile=str("KPOINTS")
run = Vasprun(ru, parse_projected_eigen = True)
bands = run.get_band_structure(kpfile, line_mode = True, efermi = run.efermi)
bsp = BSPlotter(bands)
zero_to_efermi=True
bandgap=str(round(bands.get_band_gap()['energy'],3))
print "bg=",bandgap
data=bsp.bs_plot_data(zero_to_efermi)
plt = get_publication_quality_plot(12, 8)
band_linewidth = 3
x_max = data['distances'][-1][-1]
print (x_max)
for d in range(len(data['distances'])):
for i in range(bsp._nb_bands):
plt.plot(data['distances'][d],
[data['energy'][d]['1'][i][j]
for j in range(len(data['distances'][d]))], 'b-',
linewidth=band_linewidth)
if bsp._bs.is_spin_polarized:
plt.plot(data['distances'][d],
[data['energy'][d]['-1'][i][j]
for j in range(len(data['distances'][d]))],
'r--', linewidth=band_linewidth)
bsp._maketicks(plt)
if bsp._bs.is_metal():
e_min = -10
e_max = 10
band_linewidth = 3
for cbm in data['cbm']:
plt.scatter(cbm[0], cbm[1], color='r', marker='o',
s=100)
for vbm in data['vbm']:
plt.scatter(vbm[0], vbm[1], color='g', marker='o',
s=100)
plt.xlabel(r'$\mathrm{Wave\ Vector}$', fontsize=30)
ylabel = r'$\mathrm{E\ -\ E_f\ (eV)}$' if zero_to_efermi \
else r'$\mathrm{Energy\ (eV)}$'
plt.ylabel(ylabel, fontsize=30)
plt.ylim(-4,4)
plt.xlim(0,x_max)
plt.tight_layout()
plt.savefig('BAND.png',img_format="png")
plt.close()
def get_band_edges():
"""
Calculate the band edge locations relative to the vacuum level
for a semiconductor. If spin-polarized, returns all 4 band edges.
"""
# Vacuum level energy from LOCPOT.
locpot = Locpot.from_file('LOCPOT')
evac = max(locpot.get_average_along_axis(2))
vasprun = Vasprun('vasprun.xml')
efermi = vasprun.efermi - evac
if vasprun.get_band_structure().is_spin_polarized:
eigenvals = {Spin.up: [], Spin.down: []}
for band in vasprun.eigenvalues:
for eigenvalue in vasprun.eigenvalues[band]:
eigenvals[band[0]].append(eigenvalue)
up_cbm = min([e[0] for e in eigenvals[Spin.up] if not e[1]]) - evac
up_vbm = max([e[0] for e in eigenvals[Spin.up] if e[1]]) - evac
dn_cbm = min([e[0] for e in eigenvals[Spin.down] if not e[1]]) - evac
dn_vbm = max([e[0] for e in eigenvals[Spin.down] if e[1]]) - evac
edges = {
'up_cbm': up_cbm,
'up_vbm': up_vbm,
'dn_cbm': dn_cbm,
'dn_vbm': dn_vbm,
'efermi': efermi
}
else:
bs = vasprun.get_band_structure()
cbm = bs.get_cbm()['energy'] - evac
vbm = bs.get_vbm()['energy'] - evac
edges = {'cbm': cbm, 'vbm': vbm, 'efermi': efermi}
return edges
def test_get_band_structure(self):
filepath = os.path.join(test_dir, "vasprun_Si_bands.xml")
vasprun = Vasprun(filepath, parse_potcar_file=False)
bs = vasprun.get_band_structure(kpoints_filename=os.path.join(test_dir, "KPOINTS_Si_bands"))
cbm = bs.get_cbm()
vbm = bs.get_vbm()
self.assertEqual(cbm["kpoint_index"], [13], "wrong cbm kpoint index")
self.assertAlmostEqual(cbm["energy"], 6.2301, "wrong cbm energy")
self.assertEqual(cbm["band_index"], {Spin.up: [4], Spin.down: [4]}, "wrong cbm bands")
self.assertEqual(vbm["kpoint_index"], [0, 63, 64])
self.assertAlmostEqual(vbm["energy"], 5.6158, "wrong vbm energy")
self.assertEqual(vbm["band_index"], {Spin.up: [1, 2, 3], Spin.down: [1, 2, 3]}, "wrong vbm bands")
self.assertEqual(vbm["kpoint"].label, "\Gamma", "wrong vbm label")
self.assertEqual(cbm["kpoint"].label, None, "wrong cbm label")
def plot_elt_projected_bands(ylim=(-5, 5), fmt="pdf"):
"""
Plot separate band structures for each element where the size of the
markers indicates the elemental character of the eigenvalue.
Args:
ylim (tuple): minimum and maximum energies for the plot's
y-axis.
fmt (str): matplotlib format style. Check the matplotlib
docs for options.
"""
vasprun = Vasprun("vasprun.xml", parse_projected_eigen=True)
bs = vasprun.get_band_structure("KPOINTS", line_mode=True)
bspp = BSPlotterProjected(bs)
bspp.get_elt_projected_plots(ylim=ylim).savefig("elt_projected_bands.{}".format(fmt))
plt.close()
def plot_elt_projected_bands(ylim=(-5, 5), fmt='pdf'):
"""
Plot separate band structures for each element where the size of the
markers indicates the elemental character of the eigenvalue.
Args:
ylim (tuple): minimum and maximum energies for the plot's
y-axis.
fmt (str): matplotlib format style. Check the matplotlib
docs for options.
"""
vasprun = Vasprun('vasprun.xml', parse_projected_eigen=True)
bs = vasprun.get_band_structure('KPOINTS', line_mode=True)
bspp = BSPlotterProjected(bs)
bspp.get_elt_projected_plots(ylim=ylim).savefig(
'elt_projected_bands.{}'.format(fmt))
plt.close()
def get_band_edges():
"""
Calculate the band edge locations relative to the vacuum level
for a semiconductor. For a metal, returns the fermi level.
Returns:
edges (dict): {'up_cbm': , 'up_vbm': , 'dn_cbm': , 'dn_vbm': , 'efermi'}
"""
# Vacuum level energy from LOCPOT.
locpot = Locpot.from_file('LOCPOT')
evac = max(locpot.get_average_along_axis(2))
vasprun = Vasprun('vasprun.xml')
bs = vasprun.get_band_structure()
eigenvals = vasprun.eigenvalues
efermi = vasprun.efermi - evac
if bs.is_metal():
edges = {'up_cbm': None, 'up_vbm': None, 'dn_cbm': None, 'dn_vbm': None,
'efermi': efermi}
elif bs.is_spin_polarized:
up_cbm = min(
[min([e[0] for e in eigenvals[Spin.up][i] if not e[1]])
for i in range(len(eigenvals[Spin.up]))]) - evac
up_vbm = max(
[max([e[0] for e in eigenvals[Spin.up][i] if e[1]])
for i in range(len(eigenvals[Spin.up]))]) - evac
dn_cbm = min(
[min([e[0] for e in eigenvals[Spin.down][i] if not e[1]])
for i in range(len(eigenvals[Spin.down]))]) - evac
dn_vbm = max(
[max([e[0] for e in eigenvals[Spin.down][i] if e[1]])
for i in range(len(eigenvals[Spin.down]))]) - evac
edges = {'up_cbm': up_cbm, 'up_vbm': up_vbm, 'dn_cbm': dn_cbm,
'dn_vbm': dn_vbm, 'efermi': efermi}
else:
cbm = bs.get_cbm()['energy'] - evac
vbm = bs.get_vbm()['energy'] - evac
edges = {'up_cbm': cbm, 'up_vbm': vbm, 'dn_cbm': cbm, 'dn_vbm': vbm,
'efermi': efermi}
return edges
def test_get_band_structure(self):
filepath = os.path.join(test_dir, 'vasprun_Si_bands.xml')
vasprun = Vasprun(filepath, parse_potcar_file=False)
bs = vasprun.get_band_structure(kpoints_filename=
os.path.join(test_dir,
'KPOINTS_Si_bands'))
cbm = bs.get_cbm()
vbm = bs.get_vbm()
self.assertEqual(cbm['kpoint_index'], [13], "wrong cbm kpoint index")
self.assertAlmostEqual(cbm['energy'], 6.2301, "wrong cbm energy")
self.assertEqual(cbm['band_index'], {Spin.up: [4], Spin.down: [4]},
"wrong cbm bands")
self.assertEqual(vbm['kpoint_index'], [0, 63, 64])
self.assertAlmostEqual(vbm['energy'], 5.6158, "wrong vbm energy")
self.assertEqual(vbm['band_index'], {Spin.up: [1, 2, 3],
Spin.down: [1, 2, 3]},
"wrong vbm bands")
self.assertEqual(vbm['kpoint'].label, "\Gamma", "wrong vbm label")
self.assertEqual(cbm['kpoint'].label, None, "wrong cbm label")
def read_convergence_data(self, data_dir):
results = {}
if 'G0W0' in data_dir or 'GW0' in data_dir or 'scGW0' in data_dir:
run = os.path.join(data_dir, 'vasprun.xml')
kpoints = os.path.join(data_dir, 'IBZKPT')
if os.path.isfile(run):
try:
logger.debug(run)
print(run)
data = Vasprun(run, ionic_step_skip=1)
parameters = data.incar.as_dict()
bandstructure = data.get_band_structure(kpoints)
results = {'ecuteps': parameters['ENCUTGW'],
'nbands': parameters['NBANDS'],
'nomega': parameters['NOMEGA'],
'gwgap': bandstructure.get_band_gap()['energy']}
print(results)
except (IOError, OSError, IndexError, KeyError):
pass
return results
def plot_orb_projected_bands(orbitals, fmt="pdf", ylim=(-5, 5)):
"""
Plot a separate band structure for each orbital of each element in
orbitals.
Args:
orbitals (dict): dictionary of the form
{element: [orbitals]},
e.g. {'Mo': ['s', 'p', 'd'], 'S': ['p']}
ylim (tuple): minimum and maximum energies for the plot's
y-axis.
fmt (str): matplotlib format style. Check the matplotlib
docs for options.
"""
vasprun = Vasprun("vasprun.xml", parse_projected_eigen=True)
bs = vasprun.get_band_structure("KPOINTS", line_mode=True)
bspp = BSPlotterProjected(bs)
bspp.get_projected_plots_dots(orbitals, ylim=ylim).savefig("orb_projected_bands.{}".format(fmt))
plt.close()
def __init__(self, waveder_file_ip, outcar_file_ip, vasprun_file_static):
"""
waveder and outcar files from the linear optical independent particle approximation
vasprun file from the associated static calculation
"""
waveder = Waveder(waveder_file_ip)
outcar = Outcar(outcar_file_ip)
vasprun = Vasprun(vasprun_file_static)
M_norm = norm(waveder.cder_data, axis=-1) * (1e-10 / e)
self.M_squared = M_norm * M_norm
nelect = round(outcar.nelect)
self.vbm_band = nelect // 2 - 1
self.cbm_band = nelect // 2
self.bs = vasprun.get_band_structure()
self.nbands = self.bs.nb_bands
self.kpoints = self.bs.kpoints
self.nkpoints = waveder.nkpoints
def plot_color_projected_bands(ylim=(-5, 5), fmt="pdf"):
"""
Plot a single band structure where the color of the band indicates
the elemental character of the eigenvalue.
Args:
ylim (tuple): minimum and maximum energies for the plot's
y-axis.
fmt (str): matplotlib format style. Check the matplotlib
docs for options.
"""
vasprun = Vasprun("vasprun.xml", parse_projected_eigen=True)
bs = vasprun.get_band_structure("KPOINTS", line_mode=True)
bspp = BSPlotterProjected(bs)
plot = bspp.get_elt_projected_plots_color()
fig = plot.gcf()
ax = fig.gca()
ax.set_xticklabels([r"$\mathrm{%s}$" % t for t in ax.get_xticklabels()])
ax.set_ylim(ylim)
fig.savefig("color_projected_bands.{}".format(fmt))
plt.close()
def plot_color_projected_bands(ylim=(-5, 5), fmt='pdf'):
"""
Plot a single band structure where the color of the band indicates
the elemental character of the eigenvalue.
Args:
ylim (tuple): minimum and maximum energies for the plot's
y-axis.
fmt (str): matplotlib format style. Check the matplotlib
docs for options.
"""
vasprun = Vasprun('vasprun.xml', parse_projected_eigen=True)
bs = vasprun.get_band_structure('KPOINTS', line_mode=True)
bspp = BSPlotterProjected(bs)
plot = bspp.get_elt_projected_plots_color()
fig = plot.gcf()
ax = fig.gca()
ax.set_xticklabels([r'$\mathrm{%s}$' % t for t in ax.get_xticklabels()])
ax.set_ylim(ylim)
fig.savefig('color_projected_bands.{}'.format(fmt))
plt.close()
def read_convergence_data(self, data_dir):
results = {}
if 'G0W0' in data_dir or 'GW0' in data_dir or 'scGW0' in data_dir:
run = os.path.join(data_dir, 'vasprun.xml')
kpoints = os.path.join(data_dir, 'IBZKPT')
if os.path.isfile(run):
try:
logger.debug(run)
print(run)
data = Vasprun(run, ionic_step_skip=1)
parameters = data.incar.as_dict()
bandstructure = data.get_band_structure(kpoints)
results = {
'ecuteps': parameters['ENCUTGW'],
'nbands': parameters['NBANDS'],
'nomega': parameters['NOMEGA'],
'gwgap': bandstructure.get_band_gap()['energy']
}
print(results)
except (IOError, OSError, IndexError, KeyError):
pass
return results
def get_fermi_velocities():
"""
Calculates the fermi velocity of each band that crosses the fermi
level, according to v_F = dE/(h_bar*dk).
Returns:
fermi_velocities (list). The absolute values of the
adjusted slopes of each band, in Angstroms/s.
"""
vr = Vasprun('vasprun.xml')
eigenvalues = vr.eigenvalues
bs = vr.get_band_structure()
bands = bs.bands
kpoints = bs.kpoints
efermi = bs.efermi
h_bar = 6.582e-16 # eV*s
fermi_bands = []
for spin in bands:
for i in range(len(bands[spin])):
if max(bands[spin][i]) > efermi > min(bands[spin][i]):
fermi_bands.append(bands[spin][i])
fermi_velocities = []
for band in fermi_bands:
for i in range(len(band) - 1):
if (band[i] < efermi
and band[i + 1] > efermi) or (band[i] > efermi
and band[i + 1] < efermi):
dk = np.sqrt((kpoints[i + 1].cart_coords[0] -
kpoints[i].cart_coords[0])**2 +
(kpoints[i + 1].cart_coords[1] -
kpoints[i].cart_coords[1])**2)
v_f = abs((band[i + 1] - band[i]) / (h_bar * dk))
fermi_velocities.append(v_f)
return fermi_velocities # Values are in Angst./s
def plot_elt_projected_bands(ylim=(-5, 5), fmt='pdf'):
"""
Plot separate band structures for each element where the size of the
markers indicates the elemental character of the eigenvalue.
Args:
ylim (tuple): minimum and maximum energies for the plot's
y-axis.
fmt (str): matplotlib format style. Check the matplotlib
docs for options.
"""
vasprun = Vasprun('vasprun.xml', parse_projected_eigen=True)
bs = vasprun.get_band_structure('KPOINTS', line_mode=True)
bspp = BSPlotterProjected(bs)
ax = bspp.get_elt_projected_plots(ylim=ylim).gcf().gca()
ax.set_xticklabels([r'$\mathrm{%s}$' % t for t in ax.get_xticklabels()])
ax.set_yticklabels([r'$\mathrm{%s}$' % t for t in ax.get_yticklabels()])
if fmt == "None":
return ax
else:
plt.savefig('elt_projected_bands.{}'.format(fmt))
plt.close()
def get_fermi_velocities():
"""
Calculates the fermi velocity of each band that crosses the fermi
level, according to v_F = dE/(h_bar*dk).
Returns:
fermi_velocities (list). The absolute values of the
adjusted slopes of each band, in Angstroms/s.
"""
vr = Vasprun('vasprun.xml')
# eigenvalues = vr.eigenvalues
bs = vr.get_band_structure()
bands = bs.bands
kpoints = bs.kpoints
efermi = bs.efermi
h_bar = 6.582e-16 # eV*s
fermi_bands = []
for spin in bands:
for i in range(len(bands[spin])):
if max(bands[spin][i]) > efermi > min(bands[spin][i]):
fermi_bands.append(bands[spin][i])
fermi_velocities = []
for band in fermi_bands:
for i in range(len(band)-1):
if (band[i] < efermi < band[i+1]) or (band[i] > efermi > band[i+1]):
dk = np.sqrt((kpoints[i+1].cart_coords[0]
- kpoints[i].cart_coords[0])**2
+ (kpoints[i+1].cart_coords[1]
- kpoints[i].cart_coords[1])**2)
v_f = abs((band[i+1] - band[i]) / (h_bar * dk))
fermi_velocities.append(v_f)
return fermi_velocities # Values are in Angst./s
def plot_band_structure(ylim=(-5, 5), draw_fermi=False, fmt="pdf"):
"""
Plot a standard band structure with no projections.
Args:
ylim (tuple): minimum and maximum potentials for the plot's
y-axis.
draw_fermi (bool): whether or not to draw a dashed line at
E_F.
fmt (str): matplotlib format style. Check the matplotlib
docs for options.
"""
vasprun = Vasprun("vasprun.xml")
efermi = vasprun.efermi
bsp = BSPlotter(vasprun.get_band_structure("KPOINTS", line_mode=True, efermi=efermi))
plot = bsp.get_plot(ylim=ylim)
fig = plot.gcf()
ax = fig.gca()
ax.set_xticklabels([r"$\mathrm{%s}$" % t for t in ax.get_xticklabels()])
if draw_fermi:
ax.plot([ax.get_xlim()[0], ax.get_xlim()[1]], [0, 0], "k--")
fig.savefig("band_structure.{}".format(fmt), transparent=True)
plt.close()
nseg = len(k)-1
r = [0.5*(red[i]+red[i+1]) for i in range(nseg)]
g = [0.5*(green[i]+green[i+1]) for i in range(nseg)]
b = [0.5*(blue[i]+blue[i+1]) for i in range(nseg)]
a = np.ones(nseg, np.float)*alpha
lc = LineCollection(seg, colors=zip(r,g,b,a), linewidth = 2)
ax.add_collection(lc)
if __name__ == "__main__":
# --------------------------------------------------------------
# read data
# --------------------------------------------------------------
# readin bandstructure and density of states from vasprun.xml file
run = Vasprun("vasprun.xml", parse_projected_eigen = True)
bands = run.get_band_structure("KPOINTS", line_mode = True, efermi = run.efermi)
complete_dos = run.complete_dos
print 'cbm and vbm ', complete_dos.get_cbm_vbm()
print 'gap = ', complete_dos.get_gap()
# get orbital projected DOS.
spd_dos = complete_dos.get_spd_dos()
# kpoints labels, must conform with the label in the KPOINTS file
labels = [ r"$G$", r"$X$", r"$M$", r"$G$"]
# --------------------------------------------------------------
# compute a dictionary of projections on elements and specific orbitals
#A dictionary of Elements and Orbitals for which we want
#to have projections on. It is given as: {Element:[orbitals]},
#e.g., {'Cu':['d','s']}
# --------------------------------------------------------------
pbands = bands.get_projections_on_elts_and_orbitals({"Fe": ["s", "p", "d"]})
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
from pymatgen.electronic_structure.core import Spin
from pymatgen.io.vasp.outputs import Vasprun
if __name__ == "__main__":
vasprun_dirctory = '/mnt/c/Users/a/OneDrive/Calculation_Data/Mg2C_Graphene/Paper_results/Bands/data/'
vasprun_file = 'vasprun.xml'
kpoints_file = 'KPOINTS'
saving_dictory = '/mnt/c/Users/a/OneDrive/Calculation_Data/Mg2C_Graphene/Paper_results/Picture/'
saving_file = '{}'.format('TotBand')
title = '{}'.format(r"$Mg_2C$" + '-Gr' + ' TotBand')
vasprun = Vasprun("{}".format(vasprun_dirctory + vasprun_file))
bands = vasprun.get_band_structure("{}".format(vasprun_dirctory +
kpoints_file),
line_mode=True,
efermi=vasprun.efermi)
energy_min = 0
energy_max = 0.12
# 高对称点设置
labels = [r"$M$", r"$\Gamma$", r"$K$", r"$M$"]
labels_position = list()
font = {'family': 'sans-serif', 'size': 24}
# 开始画图
fig, ax1 = plt.subplots(figsize=(16, 10))
# 设置刻度向内
ax1.tick_params(direction='in')
# 设置能量区间
ax1.set_ylim(energy_min, energy_max)
# 设置x轴区间
# !/usr/bin/env python
# -*- coding: utf-8 -*-
from pymatgen.io.vasp.outputs import Vasprun
from pymatgen.electronic_structure.plotter import BSPlotter
vasprun = Vasprun("vasprun.xml")
bss = vasprun.get_band_structure(kpoints_filename="KPOINTS", line_mode=True)
plotter = BSPlotter(bss)
#plotter.save_plot("bandStructure.svg", img_format="svg")
#plotter.save_plot("bandStructure.png", img_format="png")
#plotter.save_plot("lim_bandStructure.svg", img_format="svg", ylim=(-.2, 1.4))
plotter.save_plot("MAPbI3-primitive.png", img_format="png", ylim=(-5, 5))
plotter.plot_brillouin()
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
from matplotlib.pyplot import figure
import matplotlib
matplotlib.rcParams.update({'font.size': 22})
#
from pymatgen.io.vasp.outputs import Vasprun
# modified version of pymatgen plotter
from my_functions.pmg_electronic_structure_plotter import BSPlotter
vaspout = Vasprun("./vasprun.xml")
bandstr = vaspout.get_band_structure(line_mode=True)
# give range for y axis
ymin = -7
ymax = 6
BSPlotter(bandstr).get_plot(ylim=[ymin, ymax], get_subplot=True)
# path for location of DOS file
dos_path = '..'
dos_path += '/1-DOS/'
# give elements in list
type_elements = ['Na', 'Nb', 'O']
# give a list of orbitals you want to plot for each element : 'element_orbital'
def _get_fermi_surface(
filename,
interpolation_factor,
properties,
mu,
decimate_factor,
smooth,
wigner_seitz,
calculate_dimensionality,
):
"""Common helper method to get Fermi surface"""
import numpy as np
from pymatgen.electronic_structure.core import Spin
from pymatgen.io.vasp.outputs import Vasprun
from ifermi.interpolate import FourierInterpolator
from ifermi.kpoints import kpoints_from_bandstructure
from ifermi.surface import FermiSurface
if not filename:
filename = find_vasprun_file()
parse_projections = properties == "spin"
vr = Vasprun(filename, parse_projected_eigen=parse_projections)
bs = vr.get_band_structure()
interpolator = FourierInterpolator(bs)
interp_bs, velocities = interpolator.interpolate_bands(
interpolation_factor, return_velocities=True
)
property_data = None
property_kpoints = None
if properties == "velocity":
property_data = velocities
property_kpoints = kpoints_from_bandstructure(interp_bs)
elif properties == "spin":
if vr.projected_magnetisation is not None:
# transpose so shape is (nbands, nkpoints, natoms, norbitals, 3)
property_data = vr.projected_magnetisation.transpose(1, 0, 2, 3, 4)
# sum across all atoms and orbitals
property_data = property_data.sum(axis=(2, 3))
property_data /= np.linalg.norm(property_data, axis=-1)[..., None]
property_data = {Spin.up: property_data}
property_kpoints = kpoints_from_bandstructure(bs)
else:
click.echo(
"ERROR: Band structure does not include spin properties.\n"
"Ensure calculation was run with LSORBIT or LNONCOLLINEAR = True "
"and LSORBIT = 11."
)
sys.exit()
return (
FermiSurface.from_band_structure(
interp_bs,
mu=mu,
wigner_seitz=wigner_seitz,
decimate_factor=decimate_factor,
smooth=smooth,
property_data=property_data,
property_kpoints=property_kpoints,
calculate_dimensionality=calculate_dimensionality,
),
bs,
)
yaxis=dosyaxis,
font=dict(
family="Arial",
size=22
)
)
dosfig = go.Figure(data=dosdata, layout=doslayout)
plot_url = iplot(dosfig, filename="DOS")
# In[9]:
run = Vasprun("./vasprun.xml", parse_projected_eigen = True)
bands = run.get_band_structure("./KPOINTS", line_mode=True, efermi=dosrun.efermi)
# In[10]:
# Look for the boundaries of the band diagram in order to set up y axes range.
emin = 1e100
emax = -1e100
for spin in bands.bands.keys():
for band in range(bands.nb_bands):
emin = min(emin, min(bands.bands[spin][band]))
emax = max(emax, max(bands.bands[spin][band]))
emin = emin - bands.efermi - 1
emax = emax - bands.efermi + 1
if __name__ == "__main__":
# read data
# ---------
# kpoints labels
labels = [r"$L$", r"$\Gamma$", r"$X$", r"$U,K$", r"$\Gamma$"]
# density of states
dosrun = Vasprun("./DOS/vasprun.xml")
spd_dos = dosrun.complete_dos.get_spd_dos()
# bands
run = Vasprun("./Bandes/vasprun.xml", parse_projected_eigen=True)
bands = run.get_band_structure("./Bandes/KPOINTS",
line_mode=True,
efermi=dosrun.efermi)
# set up matplotlib plot
# ----------------------
# general options for plot
font = {'family': 'serif', 'size': 24}
plt.rc('font', **font)
# set up 2 graph with aspec ration 2/1
# plot 1: bands diagram
# plot 2: Density of States
gs = GridSpec(1, 2, width_ratios=[2, 1])
fig = plt.figure(figsize=(11.69, 8.27))
fig.suptitle("Bands diagram of copper")
from pathlib import Path
from monty.serialization import dumpfn
from ifermi.fermi_surface import FermiSurface
from ifermi.interpolator import Interpolater
from pymatgen.io.vasp.outputs import Vasprun
if __name__ == "__main__":
example_dir = Path("../../examples")
vr = Vasprun(example_dir / "MgB2/vasprun.xml")
bs = vr.get_band_structure()
dumpfn(bs.structure, "structure.json.gz")
interpolater = Interpolater(bs)
new_bs, kpoint_dim = interpolater.interpolate_bands(1)
bs_data = {"bs": new_bs, "dim": kpoint_dim, "structure": bs.structure}
dumpfn(bs_data, "bs_BaFe2As2.json.gz")
fs = FermiSurface.from_band_structure(new_bs, kpoint_dim, wigner_seitz=True)
dumpfn(fs, "fs_BaFe2As2_wigner.json.gz")
dumpfn(fs.reciprocal_space, "rs_wigner.json.gz")
fs = FermiSurface.from_band_structure(new_bs, kpoint_dim, wigner_seitz=False)
dumpfn(fs, "fs_BaFe2As2_reciprocal.json.gz")
dumpfn(fs.reciprocal_space, "rs_reciprocal.json.gz")
def assimilate(self, path, launches_coll=None):
"""
Parses vasp runs. Then insert the result into the db. and return the
task_id or doc of the insertion.
Returns:
If in simulate_mode, the entire doc is returned for debugging
purposes. Else, only the task_id of the inserted doc is returned.
"""
d = self.get_task_doc(path)
if self.additional_fields:
d.update(self.additional_fields) # always add additional fields, even for failed jobs
try:
d["dir_name_full"] = d["dir_name"].split(":")[1]
d["dir_name"] = get_block_part(d["dir_name_full"])
d["stored_data"] = {}
except:
print 'COULD NOT GET DIR NAME'
pprint.pprint(d)
print traceback.format_exc()
raise ValueError('IMPROPER PARSING OF {}'.format(path))
if not self.simulate:
# Perform actual insertion into db. Because db connections cannot
# be pickled, every insertion needs to create a new connection
# to the db.
conn = MongoClient(self.host, self.port)
db = conn[self.database]
if self.user:
db.authenticate(self.user, self.password)
coll = db[self.collection]
# Insert dos data into gridfs and then remove it from the dict.
# DOS data tends to be above the 4Mb limit for mongo docs. A ref
# to the dos file is in the dos_fs_id.
result = coll.find_one({"dir_name": d["dir_name"]})
if result is None or self.update_duplicates:
if self.parse_dos and "calculations" in d:
for calc in d["calculations"]:
if "dos" in calc:
dos = json.dumps(calc["dos"], cls=MontyEncoder)
fs = gridfs.GridFS(db, "dos_fs")
dosid = fs.put(dos)
calc["dos_fs_id"] = dosid
del calc["dos"]
d["last_updated"] = datetime.datetime.today()
if result is None:
if ("task_id" not in d) or (not d["task_id"]):
d["task_id"] = "mp-{}".format(
db.counter.find_one_and_update(
{"_id": "taskid"}, {"$inc": {"c": 1}}
)["c"])
logger.info("Inserting {} with taskid = {}"
.format(d["dir_name"], d["task_id"]))
elif self.update_duplicates:
d["task_id"] = result["task_id"]
logger.info("Updating {} with taskid = {}"
.format(d["dir_name"], d["task_id"]))
#Fireworks processing
self.process_fw(path, d)
try:
#Add oxide_type
struct=Structure.from_dict(d["output"]["crystal"])
d["oxide_type"]=oxide_type(struct)
except:
logger.error("can't get oxide_type for {}".format(d["task_id"]))
d["oxide_type"] = None
#Override incorrect outcar subdocs for two step relaxations
if "optimize structure" in d['task_type'] and \
os.path.exists(os.path.join(path, "relax2")):
try:
run_stats = {}
for i in [1,2]:
o_path = os.path.join(path,"relax"+str(i),"OUTCAR")
o_path = o_path if os.path.exists(o_path) else o_path+".gz"
outcar = Outcar(o_path)
d["calculations"][i-1]["output"]["outcar"] = outcar.as_dict()
run_stats["relax"+str(i)] = outcar.run_stats
except:
logger.error("Bad OUTCAR for {}.".format(path))
try:
overall_run_stats = {}
for key in ["Total CPU time used (sec)", "User time (sec)",
"System time (sec)", "Elapsed time (sec)"]:
overall_run_stats[key] = sum([v[key]
for v in run_stats.values()])
run_stats["overall"] = overall_run_stats
except:
logger.error("Bad run stats for {}.".format(path))
d["run_stats"] = run_stats
# add is_compatible
mpc = MaterialsProjectCompatibility("Advanced")
try:
func = d["pseudo_potential"]["functional"]
labels = d["pseudo_potential"]["labels"]
symbols = ["{} {}".format(func, label) for label in labels]
parameters = {"run_type": d["run_type"],
"is_hubbard": d["is_hubbard"],
"hubbards": d["hubbards"],
"potcar_symbols": symbols}
entry = ComputedEntry(Composition(d["unit_cell_formula"]),
0.0, 0.0, parameters=parameters,
entry_id=d["task_id"])
d['is_compatible'] = bool(mpc.process_entry(entry))
except:
traceback.print_exc()
print 'ERROR in getting compatibility'
d['is_compatible'] = None
#task_type dependent processing
if 'static' in d['task_type']:
launch_doc = launches_coll.find_one({"fw_id": d['fw_id'], "launch_dir": {"$regex": d["dir_name"]}}, {"action.stored_data": 1})
for i in ["conventional_standard_structure", "symmetry_operations",
"symmetry_dataset", "refined_structure"]:
try:
d['stored_data'][i] = launch_doc['action']['stored_data'][i]
except:
pass
#parse band structure if necessary
if ('band structure' in d['task_type'] or "Uniform" in d['task_type'])\
and d['state'] == 'successful':
launch_doc = launches_coll.find_one({"fw_id": d['fw_id'], "launch_dir": {"$regex": d["dir_name"]}},
{"action.stored_data": 1})
vasp_run = Vasprun(zpath(os.path.join(path, "vasprun.xml")), parse_projected_eigen=False)
if 'band structure' in d['task_type']:
def string_to_numlist(stringlist):
g=re.search('([0-9\-\.eE]+)\s+([0-9\-\.eE]+)\s+([0-9\-\.eE]+)', stringlist)
return [float(g.group(i)) for i in range(1,4)]
for i in ["kpath_name", "kpath"]:
d['stored_data'][i] = launch_doc['action']['stored_data'][i]
kpoints_doc = d['stored_data']['kpath']['kpoints']
for i in kpoints_doc:
kpoints_doc[i]=string_to_numlist(kpoints_doc[i])
bs=vasp_run.get_band_structure(efermi=d['calculations'][0]['output']['outcar']['efermi'],
line_mode=True)
else:
bs=vasp_run.get_band_structure(efermi=d['calculations'][0]['output']['outcar']['efermi'],
line_mode=False)
bs_json = json.dumps(bs.as_dict(), cls=MontyEncoder)
fs = gridfs.GridFS(db, "band_structure_fs")
bs_id = fs.put(bs_json)
d['calculations'][0]["band_structure_fs_id"] = bs_id
# also override band gap in task doc
gap = bs.get_band_gap()
vbm = bs.get_vbm()
cbm = bs.get_cbm()
update_doc = {'bandgap': gap['energy'], 'vbm': vbm['energy'], 'cbm': cbm['energy'], 'is_gap_direct': gap['direct']}
d['analysis'].update(update_doc)
d['calculations'][0]['output'].update(update_doc)
coll.update_one({"dir_name": d["dir_name"]}, {'$set': d}, upsert=True)
return d["task_id"], d
else:
logger.info("Skipping duplicate {}".format(d["dir_name"]))
return result["task_id"], result
else:
d["task_id"] = 0
logger.info("Simulated insert into database for {} with task_id {}"
.format(d["dir_name"], d["task_id"]))
return 0, d