def merge_up_down_doses(dos_up, dos_dn):
"""
Merge the up and down DOSs.
Args:
dos_up: Up DOS.
dos_dn: Down DOS
Return:
CompleteDos object
"""
warnings.warn("This function is not useful anymore. VasprunBSLoader deals \
with spin case.")
cdos = Dos(
dos_up.efermi,
dos_up.energies,
{
Spin.up: dos_up.densities[Spin.up],
Spin.down: dos_dn.densities[Spin.down]
},
)
if hasattr(dos_up, "pdos") and hasattr(dos_dn, "pdos"):
pdoss = {}
for site in dos_up.pdos:
pdoss.setdefault(site, {})
for orb in dos_up.pdos[site]:
pdoss[site].setdefault(orb, {})
pdoss[site][orb][Spin.up] = dos_up.pdos[site][orb][Spin.up]
pdoss[site][orb][Spin.down] = dos_dn.pdos[site][orb][Spin.down]
cdos = CompleteDos(dos_up.structure, total_dos=cdos, pdoss=pdoss)
return cdos
def get_dos_from_id(self, task_id):
"""
Overrides the get_dos_from_id for the MIT gridfs format.
"""
args = {'task_id': task_id}
fields = ['calculations']
structure = self.get_structure_from_id(task_id)
dosid = None
for r in self.query(fields, args):
dosid = r['calculations'][-1]['dos_fs_id']
if dosid != None:
self._fs = gridfs.GridFS(self.db, 'dos_fs')
with self._fs.get(dosid) as dosfile:
s = dosfile.read()
try:
d = json.loads(s)
except:
s = zlib.decompress(s)
d = json.loads(s)
tdos = Dos.from_dict(d)
pdoss = {}
for i in range(len(d['pdos'])):
ados = d['pdos'][i]
all_ados = {}
for j in range(len(ados)):
orb = Orbital.from_vasp_index(j)
odos = ados[str(orb)]
all_ados[orb] = {Spin.from_int(int(k)): v
for k, v
in odos['densities'].items()}
pdoss[structure[i]] = all_ados
return CompleteDos(structure, tdos, pdoss)
return None
def read_gap_from_doscar(self, doscar):
from pymatgen.electronic_structure.dos import Dos
fp = open(doscar)
lines = fp.readlines()
energies = []
dos = []
efermi = float(lines[5].split()[3])
if len(lines[6].split()) == 5: # spin up and dn
spin = 2
elif len(lines[6].split()) == 3:
spin = 0 # no spin
for i in range(6, len(lines)):
energies.append(float(lines[i].split()[0]))
if spin == 2:
dos.append(
float(lines[i].split()[1]) + float(lines[i].split()[2]))
else:
dos.append(float(lines[i].split()[1]))
return {
'efermi':
efermi,
'mygap':
np.array(
[Dos(efermi, energies, {
'spin': dos
}).get_gap(spin='spin')])
}
def get_dos(self, partial_dos=False, npts_mu=10000, T=None):
"""
Return a Dos object interpolating bands
Args:
partial_dos: if True, projections will be interpolated as well
and partial doses will be return. Projections must be available
in the loader.
npts_mu: number of energy points of the Dos
T: parameter used to smooth the Dos
"""
spin = self.data.spin if isinstance(self.data.spin, int) else 1
energies, densities, vvdos, cdos = BL.BTPDOS(self.eband,
self.vvband,
npts=npts_mu)
if T is not None:
densities = BL.smoothen_DOS(energies, densities, T)
tdos = Dos(self.efermi / units.eV, energies / units.eV,
{Spin(spin): densities})
if partial_dos:
tdos = self.get_partial_doses(tdos=tdos, npts_mu=npts_mu, T=T)
return tdos
def get_dos_from_id(self, task_id):
"""
Overrides the get_dos_from_id for the MIT gridfs format.
"""
args = {'task_id': task_id}
fields = ['calculations']
structure = self.get_structure_from_id(task_id)
dosid = None
for r in self.query(fields, args):
dosid = r['calculations'][-1]['dos_fs_id']
if dosid != None:
self._fs = gridfs.GridFS(self.db, 'dos_fs')
with self._fs.get(dosid) as dosfile:
s = dosfile.read()
try:
d = json.loads(s)
except:
s = zlib.decompress(s)
d = json.loads(s)
tdos = Dos.from_dict(d)
pdoss = {}
for i in range(len(d['pdos'])):
ados = d['pdos'][i]
all_ados = {}
for j in range(len(ados)):
orb = Orbital.from_vasp_index(j)
odos = ados[str(orb)]
all_ados[orb] = {
Spin.from_int(int(k)): v
for k, v in odos['densities'].items()
}
pdoss[structure[i]] = all_ados
return CompleteDos(structure, tdos, pdoss)
return None
def get_dos(db_file):
""" return density of states object from the database """
d1, d2, d3, d4 = get_collections(db_file)
db = get_db(db_file)
fs = gridfs.GridFS(db, 'dos_fs')
dos_fs_id = d4["calcs_reversed"][0]["dos_fs_id"]
dos_json = zlib.decompress(fs.get(dos_fs_id).read())
dos_dict = json.loads(dos_json.decode())
return Dos.from_dict(dos_dict)
def from_dict(data):
def _make_float_array(a):
res = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
for i in range(3):
for j in range(3):
res[i][j] = float(a[i][j])
return res
def _make_float_hall(a):
return [i for i in a[:27]]
return BoltztrapAnalyzer(
float(data['gap']), [float(d) for d in data['mu_steps']],
{int(d): [_make_float_array(v) for v in data['cond'][d]]
for d in data['cond']},
{int(d): [_make_float_array(v) for v in data['seebeck'][d]]
for d in data['seebeck']},
{int(d): [_make_float_array(v) for v in data['kappa'][d]]
for d in data['kappa']},
{int(d): [_make_float_hall(v) for v in data['hall'][d]]
for d in data['hall']},
{'p': [float(d) for d in data['doping']['p']],
'n': [float(d) for d in data['doping']['n']]},
{'p': {int(d): [float(v) for v in data['mu_doping']['p'][d]]
for d in data['mu_doping']['p']},
'n': {int(d): [float(v) for v in data['mu_doping']['n'][d]]
for d in data['mu_doping']['n']}},
{'p': {int(d): [_make_float_array(v)
for v in data['seebeck_doping']['p'][d]]
for d in data['seebeck_doping']['p']},
'n': {int(d): [_make_float_array(v)
for v in data['seebeck_doping']['n'][d]]
for d in data['seebeck_doping']['n']}},
{'p': {int(d): [_make_float_array(v)
for v in data['cond_doping']['p'][d]]
for d in data['cond_doping']['p']},
'n': {int(d): [_make_float_array(v)
for v in data['cond_doping']['n'][d]]
for d in data['cond_doping']['n']}},
{'p': {int(d): [_make_float_array(v)
for v in data['kappa_doping']['p'][d]]
for d in data['kappa_doping']['p']},
'n': {int(d): [_make_float_array(v)
for v in data['kappa_doping']['n'][d]]
for d in data['kappa_doping']['n']}},
{'p': {int(d): [_make_float_hall(v)
for v in data['hall_doping']['p'][d]]
for d in data['hall_doping']['p']},
'n': {int(d): [_make_float_hall(v)
for v in data['hall_doping']['n'][d]]
for d in data['hall_doping']['n']}},
Dos.from_dict(data['dos']), data['carrier_conc'], data['dos_partial'],
data['vol'], str(data['warning']))
def from_dict(data):
def _make_float_array(a):
res = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
for i in range(3):
for j in range(3):
res[i][j] = float(a[i][j])
return res
def _make_float_hall(a):
return [i for i in a[:27]]
return BoltztrapAnalyzer(
float(data['gap']), [float(d) for d in data['mu_steps']],
{int(d): [_make_float_array(v) for v in data['cond'][d]]
for d in data['cond']},
{int(d): [_make_float_array(v) for v in data['seebeck'][d]]
for d in data['seebeck']},
{int(d): [_make_float_array(v) for v in data['kappa'][d]]
for d in data['kappa']},
{int(d): [_make_float_hall(v) for v in data['hall'][d]]
for d in data['hall']},
{'p': [float(d) for d in data['doping']['p']],
'n': [float(d) for d in data['doping']['n']]},
{'p': {int(d): [float(v) for v in data['mu_doping']['p'][d]]
for d in data['mu_doping']['p']},
'n': {int(d): [float(v) for v in data['mu_doping']['n'][d]]
for d in data['mu_doping']['n']}},
{'p': {int(d): [_make_float_array(v)
for v in data['seebeck_doping']['p'][d]]
for d in data['seebeck_doping']['p']},
'n': {int(d): [_make_float_array(v)
for v in data['seebeck_doping']['n'][d]]
for d in data['seebeck_doping']['n']}},
{'p': {int(d): [_make_float_array(v)
for v in data['cond_doping']['p'][d]]
for d in data['cond_doping']['p']},
'n': {int(d): [_make_float_array(v)
for v in data['cond_doping']['n'][d]]
for d in data['cond_doping']['n']}},
{'p': {int(d): [_make_float_array(v)
for v in data['kappa_doping']['p'][d]]
for d in data['kappa_doping']['p']},
'n': {int(d): [_make_float_array(v)
for v in data['kappa_doping']['n'][d]]
for d in data['kappa_doping']['n']}},
{'p': {int(d): [_make_float_hall(v)
for v in data['hall_doping']['p'][d]]
for d in data['hall_doping']['p']},
'n': {int(d): [_make_float_hall(v)
for v in data['hall_doping']['n'][d]]
for d in data['hall_doping']['n']}},
Dos.from_dict(data['dos']), data['dos_partial'], data['carrier_conc'],
data['vol'], str(data['warning']))
def _get_tdos(filename):
energies, data, efermi, nsp = _read_dos_data(tdos_file,
check_tdos=True)
if nsp == 1:
# densities = np.array(data[0]).reshape((ne, 1))
densities = {Spin.up: data[0]}
elif nsp == 2:
#densities = np.concatenate((data[0], data[1]), axis=1)
densities = {Spin.up: data[0], Spin.down: data[1]}
else:
raise ValueError('There can\'t be {} spin channels, that makes '
'no sense!'.format(nsp))
return Dos(efermi, energies, densities)
def merge_up_down_doses(dos_up, dos_dn):
cdos = Dos(dos_up.efermi, dos_up.energies,
{Spin.up: dos_up.densities[Spin.up], Spin.down: dos_dn.densities[Spin.down]})
if hasattr(dos_up, 'pdos') and hasattr(dos_dn, 'pdos'):
pdoss = {}
for site in dos_up.pdos:
pdoss.setdefault(site, {})
for orb in dos_up.pdos[site]:
pdoss[site].setdefault(orb, {})
pdoss[site][orb][Spin.up] = dos_up.pdos[site][orb][Spin.up]
pdoss[site][orb][Spin.down] = dos_dn.pdos[site][orb][Spin.down]
cdos = CompleteDos(dos_up.structure, total_dos=cdos, pdoss=pdoss)
return cdos
def get_dos(self,
partial_dos=False,
npts_mu=10000,
T=None,
progress=False):
"""
Return a Dos object interpolating bands
Args:
partial_dos: if True, projections will be interpolated as well
and partial doses will be return. Projections must be available
in the loader.
npts_mu: number of energy points of the Dos
T: parameter used to smooth the Dos
progress: Default False, If True a progress bar is shown when
partial dos are computed.
"""
dos_dict = {}
enr = (self.eband.min(), self.eband.max())
if self.data.is_spin_polarized:
h = sum(np.array_split(self.accepted, 2)[0])
eband_ud = np.array_split(self.eband, [h], axis=0)
vvband_ud = np.array_split(self.vvband, [h], axis=0)
spins = [Spin.up, Spin.down]
else:
eband_ud = [self.eband]
vvband_ud = [self.vvband]
spins = [Spin.up]
for spin, eb, vvb in zip(spins, eband_ud, vvband_ud):
energies, densities, vvdos, cdos = BL.BTPDOS(eb,
vvb,
npts=npts_mu,
erange=enr)
if T:
densities = BL.smoothen_DOS(energies, densities, T)
dos_dict.setdefault(spin, densities)
tdos = Dos(self.efermi / units.eV, energies / units.eV, dos_dict)
if partial_dos:
tdos = self.get_partial_doses(tdos, eband_ud, spins, enr, npts_mu,
T, progress)
return tdos
def calculate_dos(self,
dos_estep: float = defaults["performance"]["dos_estep"],
dos_width: float = defaults["performance"]["dos_width"]):
"""
Args:
dos_estep: The DOS energy step, where smaller numbers give more
accuracy but are more expensive.
dos_width: The DOS gaussian smearing width in eV.
"""
all_energies = np.vstack([self.energies[spin] for spin in self.spins])
all_energies /= units.eV # convert from Hartree to eV for DOS
# add a few multiples of dos_width to emin and emax to account for tails
pad = dos_width if dos_width else 0
dos_emin = np.min(all_energies) - pad * 5
dos_emax = np.max(all_energies) + pad * 5
npts = int(round((dos_emax - dos_emin) / dos_estep))
logger.debug("DOS parameters:")
log_list(["emin: {:.2f} eV".format(dos_emin),
"emax: {:.2f} eV".format(dos_emax),
"broadening width: {} eV".format(dos_width)])
emesh, densities = DOS(all_energies.T, erange=(dos_emin, dos_emax),
npts=npts)
if dos_width:
densities = gaussian_filter1d(densities, dos_width /
(emesh[1] - emesh[0]))
# integrate up to Fermi level to get number of electrons
efermi = self._efermi / units.eV
energy_mask = emesh <= efermi + pad
nelect = scipy.trapz(densities[energy_mask], emesh[energy_mask])
logger.debug("Intrinsic DOS Fermi level: {:.4f}".format(efermi))
logger.debug("DOS contains {:.3f} electrons".format(nelect))
dos = Dos(efermi, emesh, {Spin.up: densities})
self.dos_weight = 1 if self._soc or len(self.spins) == 2 else 2
self.dos = FermiDos(dos, structure=self.structure,
dos_weight=self.dos_weight)
def get_cdos():
for n in range(1, 74):
pdos = defaultdict(dict)
atom = ''
orb = ''
for a in atoms:
if os.path.exists(f'pwscf.pdos_atm#{n}({a})_wfc#{1}(s)'):
atom = Element(a)
if atom == '':
continue
for l, o in enumerate(orbital):
if os.path.exists(f'pwscf.pdos_atm#{n}({str(atom)})_wfc#{l + 1}({o})'):
orb = OrbitalType(l)
data = np.genfromtxt(f'pwscf.pdos_atm#{n}({str(atom)})_wfc#{l + 1}({orb.name})')
pdos[orb][Spin.up] = data[:, 1]
pdos[orb][Spin.down] = data[:, 2]
energies = data[:,0]
pdoss.append(pdos)
efermi = fermi_fromfile('report.scf')
total_density= six.moves.reduce(add_densities, [six.moves.reduce(add_densities, p.values()) for p in pdoss])
total_dos = Dos(efermi, energies, total_density)
s = Structure.from_file('123.cif')
return CompleteDos(s, total_dos, pdoss)
def get_dos_plot(vasprun_file: str,
cbm_vbm: list = None,
pdos_type: str = "element",
specific: list = None,
orbital: list = True,
xlim: list = None,
ymaxs: list = None,
zero_at_efermi: bool = True,
legend: bool = True,
crop_first_value: bool = True,
show_spg: bool = True,
symprec: float = SYMMETRY_TOLERANCE,
angle_tolerance: float = ANGLE_TOL):
"""
Args:
vasprun_file (str):
vasprun.xml-type file name
cbm_vbm (list):
List of [cbm, vbm]
pdos_type (str): Plot type of PDOS.
"element": PDOS grouped by element type
"site": PDOS grouped by equivalent sites
"none": PDOS are not grouped.
specific (list): Show specific PDOS. If list elements are integers,
PDOS at particular sites are shown. If elements are shown,
PDOS of particular elements are shown.
["1", "2"] --> At site 1 and 2 compatible with pdos_type = "none"
["Mg", "O"] --> Summed at Mg and O sites coompatible with
pdos_type = "element"
orbital (bool):
Whether to show orbital decomposed PDOS.
xlim (list):
Specifies the x-axis limits. Set to None for automatic determination.
ymaxs (list):
Specifies the maxima of absolute y-axis limits.
zero_at_efermi (bool):
Whether to show the plot in the absolute scale.
legend (bool):
Whether to show the figure legend.
crop_first_value (bool):
Whether to crop the fist DOS.
show_spg (bool):
Whether to show space group number in the title.
symprec (float):
Symprec for determining the equivalent sites.
"""
v = Vasprun(vasprun_file, ionic_step_skip=True, parse_eigen=False)
if v.converged_electronic is False:
logger.warning("SCF is not attained in the vasp calculation.")
complete_dos = v.complete_dos
# check cbm
if cbm_vbm is None:
if complete_dos.get_gap() > 0.1:
cbm_vbm = complete_dos.get_cbm_vbm()
structure = v.final_structure
dos = OrderedDict()
# The CompleteDos behaves as DOS for total dos.
dos["Total"] = complete_dos
if specific and specific[0].isdigit():
if pdos_type is not "none":
logger.warning(
"pdos_type is changed from {} to none".format(pdos_type))
pdos_type = "none"
elif specific and specific[0].isalpha():
if pdos_type is not "none":
logger.warning(
"pdos_type is changed from {} to element".format(pdos_type))
pdos_type = "element"
sga = None
grouped_indices = defaultdict(list)
if pdos_type == "element":
for indices, s in enumerate(structure):
grouped_indices[str(s.specie)].append(indices)
elif pdos_type == "site":
# equivalent_sites: Equivalent site indices from SpacegroupAnalyzer.
sga = SpacegroupAnalyzer(structure=structure,
symprec=symprec,
angle_tolerance=angle_tolerance)
symmetrized_structure = sga.get_symmetrized_structure()
# equiv_indices = [[0], [1], [2, 3], [4, 5]]
equiv_index_lists = symmetrized_structure.equivalent_indices
for l in equiv_index_lists:
name = str(structure[l[0]].specie) + " " \
+ sga.get_symmetry_dataset()["wyckoffs"][l[0]]
grouped_indices[name] = l
elif pdos_type == "none":
for indices, s in enumerate(structure):
grouped_indices[str(s.specie) + " site:" +
str(indices)].append(indices)
else:
raise KeyError("The given pdos_type is not supported.")
# TODO: Add specific handling
# if specific:
# tmp = defaultdict(list)
# for key, value in grouped_indices.items():
# if pdos_type == "element" and key in specific:
# tmp[key] = value
# else:
# # type(index) is str
# index = ''.join(c for c in key if c.isdigit())
# if index in specific:
# tmp[key] = value
# grouped_indices = tmp
# efermi is set to VBM if exists.
efermi = cbm_vbm[1] if cbm_vbm else complete_dos.efermi
complete_dos.efermi = efermi
energies = complete_dos.energies
for key, value in grouped_indices.items():
for indices in value:
site = structure[indices]
if orbital:
for orb, pdos in complete_dos.get_site_spd_dos(site).items():
# " " is used for grouping the plots.
if pdos_type == "none":
name = key + " " + str(orb)
else:
name = \
key + " #" + str(len(value)) + " " + str(orb)
density = divide_densities(pdos.densities, len(value))
if name in dos:
density = add_densities(dos[name].densities, density)
dos[name] = Dos(efermi, energies, density)
else:
dos[name] = Dos(efermi, energies, density)
else:
name = key + "(" + str(len(key)) + ")"
pdos = complete_dos.get_site_dos(site)
if name in dos:
dos[name] = add_densities(dos[name], pdos)
else:
dos[name] = pdos
# use complete_dos.efermi for total dos.
plotter = ViseDosPlotter(zero_at_efermi=zero_at_efermi)
plotter.add_dos_dict(dos)
if xlim is None:
xlim = [-10, 10]
if ymaxs:
ylims = [[-y, y] for y in ymaxs] \
if v.incar.get("ISPIN", 1) == 2 else [[0, y] for y in ymaxs]
else:
energies = complete_dos.energies - efermi
tdos_max = max_density(complete_dos.densities, energies, xlim,
crop_first_value)
tdos_max *= 1.1
ylims = [[-tdos_max, tdos_max]] if v.incar.get("ISPIN", 1) == 2 \
else [[0, tdos_max]]
pdos_max = 0.0
for k, d in dos.items():
if k == "Total":
continue
pdos_max = \
max(max_density(d.densities, energies, xlim), pdos_max)
pdos_max *= 1.1
ylims.append([-pdos_max, pdos_max] if v.incar.get("ISPIN", 1) ==
2 else [0, pdos_max])
print("y-range", ylims)
if show_spg:
if sga is None:
sga = SpacegroupAnalyzer(structure, symprec=symprec)
sg_num_str = str(sga.get_space_group_number())
sg = f" {sga.get_space_group_symbol()} ({sg_num_str})"
print(f"Space group number: {sg}")
title = f"{structure.composition} SG: {sg}"
else:
title = str(structure.composition)
return plotter.get_plot(xlim=xlim,
ylims=ylims,
cbm_vbm=cbm_vbm,
legend=legend,
crop_first_value=crop_first_value,
title=title)
def _parse_doscar(self):
doscar = self._doscar
tdensities = {}
f = open(doscar)
natoms = int(f.readline().split()[0])
efermi = float([f.readline() for nn in range(4)][3].split()[17])
dos = []
orbitals = []
for atom in range(natoms + 1):
line = f.readline()
ndos = int(line.split()[2])
orbitals.append(line.split(';')[-1].split())
line = f.readline().split()
cdos = np.zeros((ndos, len(line)))
cdos[0] = np.array(line)
for nd in range(1, ndos):
line = f.readline().split()
cdos[nd] = np.array(line)
dos.append(cdos)
f.close()
doshere = np.array(dos[0])
energies = doshere[:, 0]
if not self._is_spin_polarized:
tdensities[Spin.up] = doshere[:, 1]
pdoss = []
spin = Spin.up
for atom in range(natoms):
pdos = defaultdict(dict)
data = dos[atom + 1]
_, ncol = data.shape
orbnumber = 0
for j in range(1, ncol):
orb = orbitals[atom + 1][orbnumber]
pdos[orb][spin] = data[:, j]
orbnumber = orbnumber + 1
pdoss.append(pdos)
else:
tdensities[Spin.up] = doshere[:, 1]
tdensities[Spin.down] = doshere[:, 2]
pdoss = []
for atom in range(natoms):
pdos = defaultdict(dict)
data = dos[atom + 1]
_, ncol = data.shape
orbnumber = 0
for j in range(1, ncol):
if j % 2 == 0:
spin = Spin.down
else:
spin = Spin.up
orb = orbitals[atom + 1][orbnumber]
pdos[orb][spin] = data[:, j]
if j % 2 == 0:
orbnumber = orbnumber + 1
pdoss.append(pdos)
self._efermi = efermi
self._pdos = pdoss
self._tdos = Dos(efermi, energies, tdensities)
self._energies = energies
self._tdensities = tdensities
final_struct = self._final_structure
pdossneu = {final_struct[i]: pdos for i, pdos in enumerate(self._pdos)}
self._completedos = LobsterCompleteDos(final_struct, self._tdos,
pdossneu)
def read_tdos(bands_file,
bin_width=0.01,
gaussian=None,
padding=None,
emin=None,
emax=None,
efermi_to_vbm=True):
"""Convert DOS data from CASTEP .bands file to Pymatgen/Sumo format
The data is binned into a regular series using np.histogram
Args:
bands_file (:obj:`str`): Path to CASTEP prefix.bands output file. The
k-point positions, weights and eigenvalues are read from this file.
bin_width (:obj:`float`, optional): Spacing for DOS energy axis
gaussian (:obj:`float` or None, optional): Width of Gaussian broadening
function
padding (:obj:`float`, optional): Energy range above and below occupied
region. (This is not used if xmin and xmax are set.)
emin (:obj:`float`, optional): Minimum energy value for output DOS)
emax (:obj:`float`, optional): Maximum energy value for output DOS
efermi_to_vbm (:obj:`bool`, optional):
If a bandgap is detected, modify the stored Fermi energy
so that it lies at the VBM.
Returns:
:obj:`pymatgen.electronic_structure.dos.Dos`
"""
header = _read_bands_header_verbose(bands_file)
logging.info("Reading band eigenvalues...")
_, weights, eigenvalues = read_bands_eigenvalues(bands_file, header)
calc_efermi = header['e_fermi'][0] * _ry_to_ev * 2
if efermi_to_vbm and not _is_metal(eigenvalues, calc_efermi):
logging.info("Setting energy zero to VBM")
efermi = _get_vbm(eigenvalues, calc_efermi)
else:
logging.info("Setting energy zero to Fermi energy")
efermi = calc_efermi
emin_data = min(eigenvalues[Spin.up].flatten())
emax_data = max(eigenvalues[Spin.up].flatten())
if Spin.down in eigenvalues:
emin_data = min(emin_data, min(eigenvalues[Spin.down].flatten()))
emax_data = max(emax_data, max(eigenvalues[Spin.down].flatten()))
if padding is None and gaussian:
padding = gaussian * 3
elif padding is None:
padding = 0.5
if emin is None:
emin = emin_data - padding
if emax is None:
emax = emax_data + padding
# Shift sampling window to account for zeroing at VBM/EFermi
emin += efermi
emax += efermi
bins = np.arange(emin, emax + bin_width, bin_width)
energies = (bins[1:] + bins[:-1]) / 2
# Add rows to weights for each band so they are aligned with eigenval data
weights = weights * np.ones([eigenvalues[Spin.up].shape[0], 1])
dos_data = {
spin: np.histogram(eigenvalue_set, bins=bins, weights=weights)[0]
for spin, eigenvalue_set in eigenvalues.items()
}
dos = Dos(efermi, energies, dos_data)
if gaussian:
dos.densities = dos.get_smeared_densities(gaussian)
return dos
def read_dos(
bands_file,
pdos_file=None,
cell_file=None,
bin_width=0.01,
gaussian=None,
padding=None,
emin=None,
emax=None,
efermi_to_vbm=True,
lm_orbitals=None,
elements=None,
atoms=None,
total_only=False,
):
"""Convert DOS data from CASTEP .bands file to Pymatgen/Sumo format
The data is binned into a regular series using np.histogram
Args:
bands_file (:obj:`str`): Path to CASTEP prefix.bands output file. The
k-point positions, weights and eigenvalues are read from this file.
bin_width (:obj:`float`, optional): Spacing for DOS energy axis
gaussian (:obj:`float` or None, optional): Width of Gaussian broadening
function
padding (:obj:`float`, optional): Energy range above and below occupied
region. (This is not used if xmin and xmax are set.)
emin (:obj:`float`, optional): Minimum energy value for output DOS)
emax (:obj:`float`, optional): Maximum energy value for output DOS
efermi_to_vbm (:obj:`bool`, optional):
If a bandgap is detected, modify the stored Fermi energy
so that it lies at the VBM.
elements (:obj:`dict`, optional): The elements and orbitals to extract
from the projected density of states. Should be provided as a
:obj:`dict` with the keys as the element names and corresponding
values as a :obj:`tuple` of orbitals. For example, the following
would extract the Bi s, px, py and d orbitals::
{'Bi': ('s', 'px', 'py', 'd')}
If an element is included with an empty :obj:`tuple`, all orbitals
for that species will be extracted. If ``elements`` is not set or
set to ``None``, all elements for all species will be extracted.
lm_orbitals (:obj:`dict`, optional): The orbitals to decompose into
their lm contributions (e.g. p -> px, py, pz). Should be provided
as a :obj:`dict`, with the elements names as keys and a
:obj:`tuple` of orbitals as the corresponding values. For example,
the following would be used to decompose the oxygen p and d
orbitals::
{'O': ('p', 'd')}
atoms (:obj:`dict`, optional): Which atomic sites to use when
calculating the projected density of states. Should be provided as
a :obj:`dict`, with the element names as keys and a :obj:`tuple` of
:obj:`int` specifying the atomic indices as the corresponding
values. The elemental projected density of states will be summed
only over the atom indices specified. If an element is included
with an empty :obj:`tuple`, then all sites for that element will
be included. The indices are 0 based for each element specified in
the POSCAR. For example, the following will calculate the density
of states for the first 4 Sn atoms and all O atoms in the
structure::
{'Sn': (1, 2, 3, 4), 'O': (, )}
If ``atoms`` is not set or set to ``None`` then all atomic sites
for all elements will be considered.
Returns:
(:obj:`pymatgen.electronic_structure.dos.Dos`, dict)
where the dict is either empty or contains a PDOS arranged::
{species: {orbital: Dos}}
"""
header = _read_bands_header_verbose(bands_file)
logging.info("Reading band eigenvalues...")
_, weights, eigenvalues = read_bands_eigenvalues(bands_file, header)
calc_efermi = header["e_fermi"][0] * _ry_to_ev * 2
if efermi_to_vbm and not _is_metal(eigenvalues, calc_efermi):
logging.info("Setting energy zero to VBM")
efermi = _get_vbm(eigenvalues, calc_efermi)
else:
logging.info("Setting energy zero to Fermi energy")
efermi = calc_efermi
emin_data = min(eigenvalues[Spin.up].flatten())
emax_data = max(eigenvalues[Spin.up].flatten())
if Spin.down in eigenvalues:
emin_data = min(emin_data, min(eigenvalues[Spin.down].flatten()))
emax_data = max(emax_data, max(eigenvalues[Spin.down].flatten()))
if padding is None and gaussian:
padding = gaussian * 3
elif padding is None:
padding = 0.5
if emin is None:
emin = emin_data - padding
if emax is None:
emax = emax_data + padding
# Shift sampling window to account for zeroing at VBM/EFermi
emin += efermi
emax += efermi
bins = np.arange(emin, emax + bin_width, bin_width)
energies = (bins[1:] + bins[:-1]) / 2
# Add rows to weights for each band so they are aligned with eigenval data
weights = weights * np.ones([eigenvalues[Spin.up].shape[0], 1])
dos_data = {
spin: np.histogram(eigenvalue_set, bins=bins, weights=weights)[0]
for spin, eigenvalue_set in eigenvalues.items()
}
dos = Dos(efermi, energies, dos_data)
if pdos_file is not None and not total_only:
if cell_file is None:
raise OSError(f"Cell file {cell_file} not found: this must be "
"provided for PDOS.")
pdos_raw = compute_pdos(pdos_file, eigenvalues, weights, bins)
# Also we, need to read the structure, but have it sorted with increasing
# atomic numbers
structure = CastepCell.from_file(
cell_file).structure.get_sorted_structure(
key=lambda x: x.species.elements[0].Z)
pdoss = {}
for isite, site in enumerate(structure.sites):
pdoss[site] = pdos_raw[isite]
# Get the pdos dictionary for potting
pdos = get_pdos(
CompleteDos(structure, dos, pdoss),
lm_orbitals=lm_orbitals,
elements=elements,
atoms=atoms,
)
# Smear the PDOS
for orbs in pdos.values():
for dtmp in orbs.values():
if gaussian:
dtmp.densities = dtmp.get_smeared_densities(gaussian)
else:
pdos = {}
if gaussian:
dos.densities = dos.get_smeared_densities(gaussian)
return dos, pdos
import pytest
from pymatgen.electronic_structure.dos import CompleteDos, Dos
from pymatgen.core import Site
from pymatgen.electronic_structure.core import Spin, Orbital
from pymatgen.io.vasp import Vasprun
from vise.analyzer.vasp.dos_data import DosDataFromVasp
from vise.analyzer.plot_dos import DosPlotter
efermi = 0.5
energies = [-1, 0, 1]
tdos = [3, 4, 5]
pdos = [1, 2, 3]
total_dos = Dos(efermi=efermi, energies=energies, densities={Spin.up: tdos})
pdoses = {
Site("H", [0, 0, 0]): {
Orbital.s: {
Spin.up: pdos
},
Orbital.px: {
Spin.up: pdos
},
Orbital.py: {
Spin.up: pdos
},
Orbital.pz: {
Spin.up: pdos
},
Orbital.dxy: {
def get_dos(
self,
kpoint_mesh: Union[float, int, List[int]],
energy_cutoff: Optional[float] = None,
scissor: Optional[float] = None,
bandgap: Optional[float] = None,
estep: float = 0.01,
width: float = 0.05,
symprec: float = 0.01,
fermi_dos: bool = False,
) -> Union[Dos, FermiDos]:
"""Calculates the density of states using the interpolated bands.
Args:
kpoint_mesh: The k-point mesh as a 1x3 array. E.g.,``[6, 6, 6]``.
Alternatively, if a single value is provided this will be
treated as a reciprocal density and the k-point mesh dimensions
generated automatically.
energy_cutoff: The energy cut-off to determine which bands are
included in the interpolation. If the energy of a band falls
within the cut-off at any k-point it will be included. For
metals the range is defined as the Fermi level ± energy_cutoff.
For gapped materials, the energy range is from the VBM -
energy_cutoff to the CBM + energy_cutoff.
scissor: The amount by which the band gap is scissored. Cannot
be used in conjunction with the ``bandgap`` option. Has no
effect for metallic systems.
bandgap: Automatically adjust the band gap to this value. Cannot
be used in conjunction with the ``scissor`` option. Has no
effect for metallic systems.
estep: The energy step, where smaller numbers give more
accuracy but are more expensive.
width: The gaussian smearing width in eV.
symprec: The symmetry tolerance used when determining the symmetry
inequivalent k-points on which to interpolate.
fermi_dos: Whether to return a FermiDos object, instead of a regular
Dos.
Returns:
The density of states.
"""
kpoints, weights = get_kpoints(kpoint_mesh,
self._band_structure.structure,
symprec=symprec)
energies, efermi = self.get_energies(
kpoints,
scissor=scissor,
bandgap=bandgap,
energy_cutoff=energy_cutoff,
atomic_units=False,
return_efermi=True,
)
emin = np.min([np.min(spin_eners) for spin_eners in energies.values()])
emin -= width * 5 if width else 0.1
emax = np.max([np.max(spin_eners) for spin_eners in energies.values()])
emax += width * 5 if width else 0.1
epoints = int(round((emax - emin) / estep))
dos = {}
emesh = None
for spin in self._spins:
kpoint_weights = np.tile(weights / np.sum(weights),
(len(energies[spin]), 1))
emesh, dos[spin] = get_dos(
energies[spin].T,
erange=(emin, emax),
npts=epoints,
weights=kpoint_weights.T,
)
if width:
dos[spin] = gaussian_filter1d(dos[spin],
width / (emesh[1] - emesh[0]))
if fermi_dos:
return FermiDos(efermi,
emesh,
dos,
self._band_structure.structure,
atomic_units=False)
else:
return Dos(efermi, emesh, dos)
def from_file(filename1='feff.inp', filename2='ldos'):
""""
Creates FeffLdos object from raw Feff ldos files by
by assuming they are numbered consequetively, i.e. ldos01.dat
ldos02.dat...
Args:
filename1: input file of run to obtain structure
filename2: output ldos file of run to obtain dos info, etc.
"""
ldos_filename = filename2
header_str = Header.header_string_from_file(filename1)
header = Header.from_string(header_str)
structure = header.struct
nsites = structure.num_sites
pot_string = FeffPot.pot_string_from_file(filename1)
dicts = FeffPot.pot_dict_from_string(pot_string)
pot_dict = dicts[0]
with zopen(ldos_filename + "00.dat", "r") as fobject:
f = fobject.readlines()
efermi = float(f[0].split()[4])
dos_energies = []
ldos = {}
for i in range(1, len(pot_dict) + 1):
if len(str(i)) == 1:
ldos[i] = np.loadtxt("{}0{}.dat".format(ldos_filename, i))
else:
ldos[i] = np.loadtxt("{}{}.dat".format(ldos_filename, i))
for i in range(0, len(ldos[1])):
dos_energies.append(ldos[1][i][0])
all_pdos = []
vorb = {
"s": Orbital.s,
"p": Orbital.py,
"d": Orbital.dxy,
"f": Orbital.f0
}
forb = {"s": 0, "p": 1, "d": 2, "f": 3}
dlength = len(ldos[1])
for i in range(nsites):
pot_index = pot_dict[structure.species[i].symbol]
all_pdos.append(defaultdict(dict))
for k, v in vorb.items():
density = [
ldos[pot_index][j][forb[k] + 1] for j in range(dlength)
]
updos = density
downdos = None
if downdos:
all_pdos[-1][v] = {Spin.up: updos, Spin.down: downdos}
else:
all_pdos[-1][v] = {Spin.up: updos}
pdos = all_pdos
vorb2 = {0: Orbital.s, 1: Orbital.py, 2: Orbital.dxy, 3: Orbital.f0}
pdoss = {
structure[i]: {v: pdos[i][v]
for v in vorb2.values()}
for i in range(len(pdos))
}
forb = {"s": 0, "p": 1, "d": 2, "f": 3}
tdos = [0] * dlength
for i in range(nsites):
pot_index = pot_dict[structure.species[i].symbol]
for v in forb.values():
density = [ldos[pot_index][j][v + 1] for j in range(dlength)]
for j in range(dlength):
tdos[j] = tdos[j] + density[j]
tdos = {Spin.up: tdos}
dos = Dos(efermi, dos_energies, tdos)
complete_dos = CompleteDos(structure, dos, pdoss)
charge_transfer = FeffLdos.charge_transfer_from_file(
filename1, filename2)
return FeffLdos(complete_dos, charge_transfer)
def get_dos(self,
kpoint_mesh: Union[float, int, List[int]],
energy_cutoff: Optional[float] = None,
scissor: Optional[float] = None,
bandgap: Optional[float] = None,
estep: float = 0.01,
width: float = 0.05,
symprec: float = 0.01
) -> Dos:
"""Calculates the density of states using the interpolated bands.
Args:
kpoint_mesh: The k-point mesh as a 1x3 array. E.g.,``[6, 6, 6]``.
Alternatively, if a single value is provided this will be
treated as a reciprocal density and the k-point mesh dimensions
generated automatically.
energy_cutoff: The energy cut-off to determine which bands are
included in the interpolation. If the energy of a band falls
within the cut-off at any k-point it will be included. For
metals the range is defined as the Fermi level ± energy_cutoff.
For gapped materials, the energy range is from the VBM -
energy_cutoff to the CBM + energy_cutoff.
scissor: The amount by which the band gap is scissored. Cannot
be used in conjunction with the ``bandgap`` option. Has no
effect for metallic systems.
bandgap: Automatically adjust the band gap to this value. Cannot
be used in conjunction with the ``scissor`` option. Has no
effect for metallic systems.
estep: The energy step, where smaller numbers give more
accuracy but are more expensive.
width: The gaussian smearing width in eV.
symprec: The symmetry tolerance used when determining the symmetry
inequivalent k-points on which to interpolate.
Returns:
The density of states.
"""
kpoints, weights = _get_kpoints(
kpoint_mesh, self._band_structure.structure, symprec=symprec)
energies = self.get_energies(
kpoints, scissor=scissor, bandgap=bandgap,
energy_cutoff=energy_cutoff, atomic_units=False)
energies = np.vstack([energies[spin] for spin in self._spins])
nbands = energies.shape[0]
nkpts = energies.shape[1]
emin = np.min(energies) - width * 5
emax = np.max(energies) + width * 5
epoints = int(round((emax - emin) / estep))
# BoltzTraP DOS kpoint_weights don't work as you'd expect so we include
# the degeneracy manually
all_energies = np.array([[energies[nb][nk] for nb in range(nbands)]
for nk in range(nkpts)
for _ in range(weights[nk])])
emesh, dos = DOS(all_energies, erange=(emin, emax), npts=epoints)
dos = gaussian_filter1d(dos, width / (emesh[1] - emesh[0]))
dos *= 1 if self._soc or len(self._spins) == 2 else 1
return Dos(self._band_structure.efermi, emesh, dos)
def setUp(self):
with open(os.path.join(test_dir, "complete_dos.json"), "r") as f:
self.dos = Dos.from_dict(json.load(f))
from pymatgen import Site
from pymatgen.electronic_structure.dos import Dos
import os
dsp = DosPlotter()
vrun = Vasprun('vasprun.xml')
cdos = vrun.complete_dos
atoms = []
i: Site
dos = {
'd_in':
Dos(cdos.efermi, cdos.energies, {Spin.up: np.zeros_like(cdos.energies)}),
'd_out':
Dos(cdos.efermi, cdos.energies, {Spin.up: np.zeros_like(cdos.energies)})
}
for i in vrun.final_structure.sites:
if i.species_string == 'V':
atoms.append(i)
for j in atoms:
for n, k in dos.items():
if n == 'd_in':
dos[n] = k.__add__(cdos.get_site_orbital_dos(j, Orbital.dxy))
else:
dos[n] = k.__add__(cdos.get_site_orbital_dos(j, Orbital.dyz))
dos[n] = k.__add__(cdos.get_site_orbital_dos(j, Orbital.dxz))
def from_file(feff_inp_file="feff.inp", ldos_file="ldos"):
"""
Creates LDos object from raw Feff ldos files by
by assuming they are numbered consecutively, i.e. ldos01.dat
ldos02.dat...
Args:
feff_inp_file (str): input file of run to obtain structure
ldos_file (str): output ldos file of run to obtain dos info, etc.
"""
header_str = Header.header_string_from_file(feff_inp_file)
header = Header.from_string(header_str)
structure = header.struct
nsites = structure.num_sites
parameters = Tags.from_file(feff_inp_file)
if "RECIPROCAL" in parameters:
pot_dict = {}
pot_readstart = re.compile(".*iz.*lmaxsc.*xnatph.*xion.*folp.*")
pot_readend = re.compile(".*ExternalPot.*switch.*")
pot_inp = re.sub(r"feff.inp", r"pot.inp", feff_inp_file)
dos_index = 1
begin = 0
with zopen(pot_inp, "r") as potfile:
for line in potfile:
if len(pot_readend.findall(line)) > 0:
break
if begin == 1:
begin += 1
continue
if begin == 2:
z_number = int(line.strip().split()[0])
ele_name = Element.from_Z(z_number).name
if ele_name not in pot_dict:
pot_dict[ele_name] = dos_index
else:
pot_dict[ele_name] = min(dos_index,
pot_dict[ele_name])
dos_index += 1
if len(pot_readstart.findall(line)) > 0:
begin = 1
else:
pot_string = Potential.pot_string_from_file(feff_inp_file)
dicts = Potential.pot_dict_from_string(pot_string)
pot_dict = dicts[0]
with zopen(ldos_file + "00.dat", "r") as fobject:
f = fobject.readlines()
efermi = float(f[0].split()[4])
dos_energies = []
ldos = {}
for i in range(1, len(pot_dict) + 1):
if len(str(i)) == 1:
ldos[i] = np.loadtxt(f"{ldos_file}0{i}.dat")
else:
ldos[i] = np.loadtxt(f"{ldos_file}{i}.dat")
for i in range(0, len(ldos[1])):
dos_energies.append(ldos[1][i][0])
all_pdos = []
vorb = {
"s": Orbital.s,
"p": Orbital.py,
"d": Orbital.dxy,
"f": Orbital.f0
}
forb = {"s": 0, "p": 1, "d": 2, "f": 3}
dlength = len(ldos[1])
for i in range(nsites):
pot_index = pot_dict[structure.species[i].symbol]
all_pdos.append(defaultdict(dict))
for k, v in vorb.items():
density = [
ldos[pot_index][j][forb[k] + 1] for j in range(dlength)
]
updos = density
downdos = None
if downdos:
all_pdos[-1][v] = {Spin.up: updos, Spin.down: downdos}
else:
all_pdos[-1][v] = {Spin.up: updos}
pdos = all_pdos
vorb2 = {0: Orbital.s, 1: Orbital.py, 2: Orbital.dxy, 3: Orbital.f0}
pdoss = {
structure[i]: {v: pdos[i][v]
for v in vorb2.values()}
for i in range(len(pdos))
}
forb = {"s": 0, "p": 1, "d": 2, "f": 3}
tdos = [0] * dlength
for i in range(nsites):
pot_index = pot_dict[structure.species[i].symbol]
for v in forb.values():
density = [ldos[pot_index][j][v + 1] for j in range(dlength)]
for j in range(dlength):
tdos[j] = tdos[j] + density[j]
tdos = {Spin.up: tdos}
dos = Dos(efermi, dos_energies, tdos)
complete_dos = CompleteDos(structure, dos, pdoss)
charge_transfer = LDos.charge_transfer_from_file(
feff_inp_file, ldos_file)
return LDos(complete_dos, charge_transfer)
def read_dos(bands_file,
pdos_file=None,
cell_file=None,
bin_width=0.01,
gaussian=None,
padding=None,
emin=None,
emax=None,
efermi_to_vbm=True,
lm_orbitals=None,
elements=None,
atoms=None,
total_only=False):
"""Convert DOS data from CASTEP .bands file to Pymatgen/Sumo format
The data is binned into a regular series using np.histogram
Args:
bands_file (:obj:`str`): Path to CASTEP prefix.bands output file. The
k-point positions, weights and eigenvalues are read from this file.
pdos_bin (:obj:`str`): Path to CASTEP prefix.pdos_bin output file. The
weights of projected density of states are read from this file.
bin_width (:obj:`float`, optional): Spacing for DOS energy axis
gaussian (:obj:`float` or None, optional): Width of Gaussian broadening
function
padding (:obj:`float`, optional): Energy range above and below occupied
region. (This is not used if xmin and xmax are set.)
emin (:obj:`float`, optional): Minimum energy value for output DOS)
emax (:obj:`float`, optional): Maximum energy value for output DOS
efermi_to_vbm (:obj:`bool`, optional):
If a bandgap is detected, modify the stored Fermi energy
so that it lies at the VBM.
Returns:
(:obj:`pymatgen.electronic_structure.dos.Dos`, dict)
where the dict is either empty or contains a PDOS arranged::
{species: {orbital: Dos}}
"""
header = _read_bands_header_verbose(bands_file)
logging.info("Reading band eigenvalues...")
_, weights, eigenvalues = read_bands_eigenvalues(bands_file, header)
calc_efermi = header['e_fermi'][0] * _ry_to_ev * 2
if efermi_to_vbm and not _is_metal(eigenvalues, calc_efermi):
logging.info("Setting energy zero to VBM")
efermi = _get_vbm(eigenvalues, calc_efermi)
else:
logging.info("Setting energy zero to Fermi energy")
efermi = calc_efermi
emin_data = min(eigenvalues[Spin.up].flatten())
emax_data = max(eigenvalues[Spin.up].flatten())
if Spin.down in eigenvalues:
emin_data = min(emin_data, min(eigenvalues[Spin.down].flatten()))
emax_data = max(emax_data, max(eigenvalues[Spin.down].flatten()))
if padding is None and gaussian:
padding = gaussian * 3
elif padding is None:
padding = 0.5
if emin is None:
emin = emin_data - padding
if emax is None:
emax = emax_data + padding
# Shift sampling window to account for zeroing at VBM/EFermi
emin += efermi
emax += efermi
bins = np.arange(emin, emax + bin_width, bin_width)
energies = (bins[1:] + bins[:-1]) / 2
# Add rows to weights for each band so they are aligned with eigenval data
weights = weights * np.ones([eigenvalues[Spin.up].shape[0], 1])
dos_data = {
spin: np.histogram(eigenvalue_set, bins=bins, weights=weights)[0]
for spin, eigenvalue_set in eigenvalues.items()
}
dos = Dos(efermi, energies, dos_data)
if pdos_file is not None and not total_only:
if cell_file is None:
raise OSError(f'Cell file {cell_file} not found: this must be '
'provided for PDOS.')
pdos_raw = compute_pdos(pdos_file, eigenvalues, weights, bins)
# Also we, need to read the structure, but have it sorted with increasing
# atomic numbers
structure = CastepCell.from_file(
cell_file).structure.get_sorted_structure(
key=lambda x: x.species.elements[0].Z)
pdoss = {}
for isite, site in enumerate(structure.sites):
pdoss[site] = pdos_raw[isite]
# Get the pdos dictionary for potting
pdos = get_pdos(CompleteDos(structure, dos, pdoss),
lm_orbitals=lm_orbitals,
elements=elements,
atoms=atoms)
# Smear the PDOS
for orbs in pdos.values():
for dtmp in orbs.values():
if gaussian:
dtmp.densities = dtmp.get_smeared_densities(gaussian)
else:
pdos = {}
if gaussian:
dos.densities = dos.get_smeared_densities(gaussian)
return dos, pdos
def from_dict(data):
def _make_float_array(a):
res = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
for i in range(3):
for j in range(3):
res[i][j] = float(a[i][j])
return res
def _make_float_hall(a):
return [i for i in a[:27]]
return BoltztrapAnalyzer(
float(data["gap"]),
[float(d) for d in data["mu_steps"]],
{int(d): [_make_float_array(v) for v in data["cond"][d]] for d in data["cond"]},
{int(d): [_make_float_array(v) for v in data["seebeck"][d]] for d in data["seebeck"]},
{int(d): [_make_float_array(v) for v in data["kappa"][d]] for d in data["kappa"]},
{int(d): [_make_float_hall(v) for v in data["hall"][d]] for d in data["hall"]},
{"p": [float(d) for d in data["doping"]["p"]], "n": [float(d) for d in data["doping"]["n"]]},
{
"p": {int(d): [float(v) for v in data["mu_doping"]["p"][d]] for d in data["mu_doping"]["p"]},
"n": {int(d): [float(v) for v in data["mu_doping"]["n"][d]] for d in data["mu_doping"]["n"]},
},
{
"p": {
int(d): [_make_float_array(v) for v in data["seebeck_doping"]["p"][d]]
for d in data["seebeck_doping"]["p"]
},
"n": {
int(d): [_make_float_array(v) for v in data["seebeck_doping"]["n"][d]]
for d in data["seebeck_doping"]["n"]
},
},
{
"p": {
int(d): [_make_float_array(v) for v in data["cond_doping"]["p"][d]]
for d in data["cond_doping"]["p"]
},
"n": {
int(d): [_make_float_array(v) for v in data["cond_doping"]["n"][d]]
for d in data["cond_doping"]["n"]
},
},
{
"p": {
int(d): [_make_float_array(v) for v in data["kappa_doping"]["p"][d]]
for d in data["kappa_doping"]["p"]
},
"n": {
int(d): [_make_float_array(v) for v in data["kappa_doping"]["n"][d]]
for d in data["kappa_doping"]["n"]
},
},
{
"p": {
int(d): [_make_float_hall(v) for v in data["hall_doping"]["p"][d]] for d in data["hall_doping"]["p"]
},
"n": {
int(d): [_make_float_hall(v) for v in data["hall_doping"]["n"][d]] for d in data["hall_doping"]["n"]
},
},
Dos.from_dict(data["dos"]),
data["carrier_conc"],
data["dos_partial"],
data["vol"],
str(data["warning"]),
)
def _make_boltztrap_analyzer_from_data(
data_full, data_hall, data_dos, temperature_steps, mu_steps,
efermi, gap, doping, data_doping_full, data_doping_hall, vol,
warning=False):
"""
Make a BoltztrapAnalyzer object from raw data typically parse from
files.
"""
cond = {t: [] for t in temperature_steps}
seebeck = {t: [] for t in temperature_steps}
kappa = {t: [] for t in temperature_steps}
hall = {t: [] for t in temperature_steps}
carrier_conc = {t: [] for t in temperature_steps}
dos_full = {'energy': [], 'density': []}
warning = warning
new_doping = {'p': [], 'n': []}
for d in doping:
if d > 0:
new_doping['p'].append(d)
else:
new_doping['n'].append(-d)
mu_doping = {'p': {t: [] for t in temperature_steps},
'n': {t: [] for t in temperature_steps}}
seebeck_doping = {'p': {t: [] for t in temperature_steps},
'n': {t: [] for t in temperature_steps}}
cond_doping = {'p': {t: [] for t in temperature_steps},
'n': {t: [] for t in temperature_steps}}
kappa_doping = {'p': {t: [] for t in temperature_steps},
'n': {t: [] for t in temperature_steps}}
hall_doping = {'p': {t: [] for t in temperature_steps},
'n': {t: [] for t in temperature_steps}}
for d in data_full:
carrier_conc[d[1]].append(d[2])
tens_cond = [[d[3], d[4], d[5]],
[d[6], d[7], d[8]],
[d[9], d[10], d[11]]]
cond[d[1]].append(tens_cond)
tens_seebeck = [[d[12], d[13], d[14]],
[d[15], d[16], d[17]],
[d[18], d[19], d[20]]]
seebeck[d[1]].append(tens_seebeck)
tens_kappa = [[d[21], d[22], d[23]],
[d[24], d[25], d[26]],
[d[27], d[28], d[29]]]
kappa[d[1]].append(tens_kappa)
for d in data_hall:
hall_tens = d[3:]
hall[d[1]].append(hall_tens)
for d in data_doping_full:
tens_cond = [[d[2], d[3], d[4]],
[d[5], d[6], d[7]],
[d[8], d[9], d[10]]]
tens_seebeck = [[d[11], d[12], d[13]],
[d[14], d[15], d[16]],
[d[17], d[18], d[19]]]
tens_kappa = [[d[20], d[21], d[22]],
[d[23], d[24], d[25]],
[d[26], d[27], d[28]]]
if d[1] < 0:
mu_doping['n'][d[0]].append(Energy(d[-1], "Ry").to("eV"))
cond_doping['n'][d[0]].append(tens_cond)
seebeck_doping['n'][d[0]].append(tens_seebeck)
kappa_doping['n'][d[0]].append(tens_kappa)
else:
mu_doping['p'][d[0]].append(Energy(d[-1], "Ry").to("eV"))
cond_doping['p'][d[0]].append(tens_cond)
seebeck_doping['p'][d[0]].append(tens_seebeck)
kappa_doping['p'][d[0]].append(tens_kappa)
for i in range(len(data_doping_hall)):
hall_tens = [data_hall[i][j] for j in range(3, len(data_hall[i]))]
if data_doping_hall[i][1] < 0:
hall_doping['n'][data_doping_hall[i][0]].append(hall_tens)
else:
hall_doping['p'][data_doping_hall[i][0]].append(hall_tens)
for t in data_dos['total']:
dos_full['energy'].append(t[0])
dos_full['density'].append(t[1])
dos = Dos(efermi, dos_full['energy'], {Spin.up: dos_full['density']})
dos_partial = data_dos['partial']
return BoltztrapAnalyzer(
gap, mu_steps, cond, seebeck, kappa, hall, new_doping, mu_doping,
seebeck_doping, cond_doping, kappa_doping, hall_doping, dos, dos_partial, carrier_conc,
vol, warning)
