def process_vasprun(self, dir_name, taskname, filename):
"""
Process a vasprun.xml file.
"""
vasprun_file = os.path.join(dir_name, filename)
if self.parse_projected_eigen and (self.parse_projected_eigen != 'final' or \
taskname == self.runs[-1]):
parse_projected_eigen = True
else:
parse_projected_eigen = False
r = Vasprun(vasprun_file, parse_projected_eigen=parse_projected_eigen)
d = r.as_dict()
d["dir_name"] = os.path.abspath(dir_name)
d["completed_at"] = \
str(datetime.datetime.fromtimestamp(os.path.getmtime(
vasprun_file)))
d["cif"] = str(CifWriter(r.final_structure))
d["density"] = r.final_structure.density
if self.parse_dos and (self.parse_dos != 'final' \
or taskname == self.runs[-1]):
try:
d["dos"] = r.complete_dos.as_dict()
except Exception:
logger.warning(
"No valid dos data exist in {}.\n Skipping dos".format(
dir_name))
if taskname == "relax1" or taskname == "relax2":
d["task"] = {"type": "aflow", "name": taskname}
else:
d["task"] = {"type": taskname, "name": taskname}
d["oxide_type"] = oxide_type(r.final_structure)
return d
def process_vasprun(self, dir_name, taskname, filename):
"""
Process a vasprun.xml file.
"""
vasprun_file = os.path.join(dir_name, filename)
r = Vasprun(vasprun_file)
d = r.as_dict()
d["dir_name"] = os.path.abspath(dir_name)
d["completed_at"] = \
str(datetime.datetime.fromtimestamp(os.path.getmtime(
vasprun_file)))
d["cif"] = str(CifWriter(r.final_structure))
d["density"] = r.final_structure.density
if self.parse_dos and (self.parse_dos != 'final' \
or taskname == self.runs[-1]):
try:
d["dos"] = r.complete_dos.as_dict()
except Exception:
logger.warn("No valid dos data exist in {}.\n Skipping dos"
.format(dir_name))
if taskname == "relax1" or taskname == "relax2":
d["task"] = {"type": "aflow", "name": taskname}
else:
d["task"] = {"type": taskname, "name": taskname}
return d
def process_vasprun(self, dir_name, taskname, filename):
"""
Adapted from matgendb.creator
Process a vasprun.xml file.
"""
vasprun_file = os.path.join(dir_name, filename)
if self.bandstructure_mode:
vrun = Vasprun(vasprun_file, parse_eigen=True, parse_projected_eigen=True)
else:
vrun = Vasprun(vasprun_file)
d = vrun.as_dict()
for k, v in {"formula_pretty": "pretty_formula",
"composition_reduced": "reduced_cell_formula",
"composition_unit_cell": "unit_cell_formula"}.items():
d[k] = d.pop(v)
for k in ["eigenvalues", "projected_eigenvalues"]: # large storage space breaks some docs
if k in d["output"]:
del d["output"][k]
comp = Composition(d["composition_unit_cell"])
d["formula_anonymous"] = comp.anonymized_formula
d["formula_reduced_abc"] = comp.reduced_composition.alphabetical_formula
d["dir_name"] = os.path.abspath(dir_name)
d["completed_at"] = str(datetime.datetime.fromtimestamp(os.path.getmtime(vasprun_file)))
d["density"] = vrun.final_structure.density
# replace 'crystal' with 'structure'
d["input"]["structure"] = d["input"].pop("crystal")
d["output"]["structure"] = d["output"].pop("crystal")
for k, v in {"energy": "final_energy", "energy_per_atom": "final_energy_per_atom"}.items():
d["output"][k] = d["output"].pop(v)
if self.parse_dos and self.parse_dos != 'final':
try:
d["dos"] = vrun.complete_dos.as_dict()
except:
raise ValueError("No valid dos data exist in {}.".format(dir_name))
if self.bandstructure_mode:
bs = vrun.get_band_structure(line_mode=(self.bandstructure_mode == "line"))
else:
bs = vrun.get_band_structure()
d["bandstructure"] = bs.as_dict()
d["output"]["vbm"] = bs.get_vbm()["energy"]
d["output"]["cbm"] = bs.get_cbm()["energy"]
bs_gap = bs.get_band_gap()
d["output"]["bandgap"] = bs_gap["energy"]
d["output"]["is_gap_direct"] = bs_gap["direct"]
d["output"]["is_metal"] = bs.is_metal()
d["task"] = {"type": taskname, "name": taskname}
# phonon-dfpt
if hasattr(vrun, "force_constants"):
d["output"]["force_constants"] = vrun.force_constants.tolist()
d["output"]["normalmode_eigenvals"] = vrun.normalmode_eigenvals.tolist()
d["output"]["normalmode_eigenvecs"] = vrun.normalmode_eigenvecs.tolist()
return d
def process_vasprun(self, dir_name, taskname, filename):
"""
Adapted from matgendb.creator
Process a vasprun.xml file.
"""
vasprun_file = os.path.join(dir_name, filename)
vrun = Vasprun(vasprun_file)
d = vrun.as_dict()
# rename formula keys
for k, v in {"formula_pretty": "pretty_formula",
"composition_reduced": "reduced_cell_formula",
"composition_unit_cell": "unit_cell_formula"}.items():
d[k] = d.pop(v)
for k in ["eigenvalues", "projected_eigenvalues"]: # large storage space breaks some docs
if k in d["output"]:
del d["output"][k]
comp = Composition(d["composition_unit_cell"])
d["formula_anonymous"] = comp.anonymized_formula
d["formula_reduced_abc"] = comp.reduced_composition.alphabetical_formula
d["dir_name"] = os.path.abspath(dir_name)
d["completed_at"] = str(datetime.datetime.fromtimestamp(os.path.getmtime(vasprun_file)))
d["density"] = vrun.final_structure.density
# replace 'crystal' with 'structure'
d["input"]["structure"] = d["input"].pop("crystal")
d["output"]["structure"] = d["output"].pop("crystal")
for k, v in {"energy": "final_energy", "energy_per_atom": "final_energy_per_atom"}.items():
d["output"][k] = d["output"].pop(v)
if self.parse_dos == True or (str(self.parse_dos).lower() == "auto" and vrun.incar.get("NSW", 1) == 0):
try:
d["dos"] = vrun.complete_dos.as_dict()
except:
raise ValueError("No valid dos data exist in {}.".format(dir_name))
# Band structure parsing logic
if str(self.bandstructure_mode).lower() == "auto":
# if line mode nscf
if vrun.incar.get("ICHARG", 0) > 10 and vrun.kpoints.num_kpts > 0:
bs_vrun = BSVasprun(vasprun_file, parse_projected_eigen=True)
bs = bs_vrun.get_band_structure(line_mode=True)
# else if nscf
elif vrun.incar.get("ICHARG", 0) > 10:
bs_vrun = BSVasprun(vasprun_file, parse_projected_eigen=True)
bs = bs_vrun.get_band_structure()
# else just regular calculation
else:
bs = vrun.get_band_structure()
# only save the bandstructure if not moving ions
if vrun.incar["NSW"] == 0:
d["bandstructure"] = bs.as_dict()
# legacy line/True behavior for bandstructure_mode
elif self.bandstructure_mode:
bs_vrun = BSVasprun(vasprun_file, parse_projected_eigen=True)
bs = bs_vrun.get_band_structure(line_mode=(str(self.bandstructure_mode).lower() == "line"))
d["bandstructure"] = bs.as_dict()
# parse bandstructure for vbm/cbm/bandgap but don't save
else:
bs = vrun.get_band_structure()
# Parse electronic information if possible.
# For certain optimizers this is broken and we don't get an efermi resulting in the bandstructure
try:
bs_gap = bs.get_band_gap()
d["output"]["vbm"] = bs.get_vbm()["energy"]
d["output"]["cbm"] = bs.get_cbm()["energy"]
d["output"]["bandgap"] = bs_gap["energy"]
d["output"]["is_gap_direct"] = bs_gap["direct"]
d["output"]["is_metal"] = bs.is_metal()
if not bs_gap["direct"]:
d["output"]["direct_gap"] = bs.get_direct_band_gap()
if isinstance(bs, BandStructureSymmLine):
d["output"]["transition"] = bs_gap["transition"]
except Exception:
if self.bandstructure_mode is True:
import traceback
logger.error(traceback.format_exc())
logger.error("Error in " + os.path.abspath(dir_name) + ".\n" + traceback.format_exc())
raise
logger.warning("Error in parsing bandstructure")
if vrun.incar["IBRION"] == 1:
logger.warning("Vasp doesn't properly output efermi for IBRION == 1")
d["task"] = {"type": taskname, "name": taskname}
d["output_file_paths"] = self.process_raw_data(dir_name, taskname=taskname)
if "locpot" in d["output_file_paths"] and self.parse_locpot:
locpot = Locpot.from_file(os.path.join(dir_name, d["output_file_paths"]["locpot"]))
d["output"]["locpot"] = {i: locpot.get_average_along_axis(i) for i in range(3)}
if hasattr(vrun, "force_constants"):
# phonon-dfpt
d["output"]["force_constants"] = vrun.force_constants.tolist()
d["output"]["normalmode_eigenvals"] = vrun.normalmode_eigenvals.tolist()
d["output"]["normalmode_eigenvecs"] = vrun.normalmode_eigenvecs.tolist()
return d
def process_vasprun(self, dir_name, taskname, filename):
"""
Adapted from matgendb.creator
Process a vasprun.xml file.
"""
vasprun_file = os.path.join(dir_name, filename)
vrun = Vasprun(vasprun_file, parse_potcar_file=self.parse_potcar_file)
d = vrun.as_dict()
# rename formula keys
for k, v in {
"formula_pretty": "pretty_formula",
"composition_reduced": "reduced_cell_formula",
"composition_unit_cell": "unit_cell_formula"
}.items():
d[k] = d.pop(v)
for k in ["eigenvalues", "projected_eigenvalues"
]: # large storage space breaks some docs
if k in d["output"]:
del d["output"][k]
comp = Composition(d["composition_unit_cell"])
d["formula_anonymous"] = comp.anonymized_formula
d["formula_reduced_abc"] = comp.reduced_composition.alphabetical_formula
d["dir_name"] = os.path.abspath(dir_name)
d["completed_at"] = str(
datetime.datetime.fromtimestamp(os.path.getmtime(vasprun_file)))
d["density"] = vrun.final_structure.density
# replace 'crystal' with 'structure'
d["input"]["structure"] = d["input"].pop("crystal")
d["output"]["structure"] = d["output"].pop("crystal")
for k, v in {
"energy": "final_energy",
"energy_per_atom": "final_energy_per_atom"
}.items():
d["output"][k] = d["output"].pop(v)
# Process bandstructure and DOS
if self.bandstructure_mode != False:
bs = self.process_bandstructure(vrun)
if bs:
d["bandstructure"] = bs
if self.parse_dos != False:
dos = self.process_dos(vrun)
if dos:
d["dos"] = dos
# Parse electronic information if possible.
# For certain optimizers this is broken and we don't get an efermi resulting in the bandstructure
try:
bs = vrun.get_band_structure()
bs_gap = bs.get_band_gap()
d["output"]["vbm"] = bs.get_vbm()["energy"]
d["output"]["cbm"] = bs.get_cbm()["energy"]
d["output"]["bandgap"] = bs_gap["energy"]
d["output"]["is_gap_direct"] = bs_gap["direct"]
d["output"]["is_metal"] = bs.is_metal()
if not bs_gap["direct"]:
d["output"]["direct_gap"] = bs.get_direct_band_gap()
if isinstance(bs, BandStructureSymmLine):
d["output"]["transition"] = bs_gap["transition"]
except Exception:
logger.warning("Error in parsing bandstructure")
if vrun.incar["IBRION"] == 1:
logger.warning(
"Vasp doesn't properly output efermi for IBRION == 1")
if self.bandstructure_mode is True:
logger.error(traceback.format_exc())
logger.error("Error in " + os.path.abspath(dir_name) + ".\n" +
traceback.format_exc())
raise
# Should roughly agree with information from .get_band_structure() above, subject to tolerances
# If there is disagreement, it may be related to VASP incorrectly assigning the Fermi level
try:
band_props = vrun.eigenvalue_band_properties
d["output"]["eigenvalue_band_properties"] = {
"bandgap": band_props[0],
"cbm": band_props[1],
"vbm": band_props[2],
"is_gap_direct": band_props[3]
}
except Exception:
logger.warning("Error in parsing eigenvalue band properties")
# store run name and location ,e.g. relax1, relax2, etc.
d["task"] = {"type": taskname, "name": taskname}
# include output file names
d["output_file_paths"] = self.process_raw_data(dir_name,
taskname=taskname)
# parse axially averaged locpot
if "locpot" in d["output_file_paths"] and self.parse_locpot:
locpot = Locpot.from_file(
os.path.join(dir_name, d["output_file_paths"]["locpot"]))
d["output"]["locpot"] = {
i: locpot.get_average_along_axis(i)
for i in range(3)
}
if self.store_volumetric_data:
for file in self.store_volumetric_data:
if file in d["output_file_paths"]:
try:
# assume volumetric data is all in CHGCAR format
data = Chgcar.from_file(
os.path.join(dir_name,
d["output_file_paths"][file]))
d[file] = data.as_dict()
except:
raise ValueError("Failed to parse {} at {}.".format(
file, d["output_file_paths"][file]))
# parse force constants
if hasattr(vrun, "force_constants"):
d["output"]["force_constants"] = vrun.force_constants.tolist()
d["output"][
"normalmode_eigenvals"] = vrun.normalmode_eigenvals.tolist()
d["output"][
"normalmode_eigenvecs"] = vrun.normalmode_eigenvecs.tolist()
# perform Bader analysis using Henkelman bader
if self.parse_bader and "chgcar" in d["output_file_paths"]:
suffix = '' if taskname == 'standard' else ".{}".format(taskname)
bader = bader_analysis_from_path(dir_name, suffix=suffix)
d["bader"] = bader
return d
"""
Import from my results
"""
file_path = '%03d_%s/2nd/' % (idx + 1.0, formula)
try:
oszicar = Oszicar(file_path + 'OSZICAR')
tot_E = oszicar.ionic_steps[-1]['F']
tot_mag = oszicar.ionic_steps[-1]['mag']
df_bulk.total_E[idx+1] = tot_E
df_bulk.tot_mag[idx+1] = tot_mag
v = Vasprun(file_path + 'vasprun.xml')
volume = v.as_dict()['output']['crystal']['lattice']['volume']
df_bulk.Volume[idx+1] = volume
df_bulk.Err_V_percent[idx+1] = (volume_ref - volume) / volume_ref * 100
el = v.as_dict()['elements']
el.remove('O')
fE = tot_E - ref_dict[el[0]] - ref_dict[el[1]] - 1.5*ref_dict['O2_corr']
if el[0] or el[1] in U_corr_dict:
for x in range(len(el)):
if el[x] in U_corr_dict:
fE = fE - U_corr_dict[el[x]]
fE = fE/5.0 # eV/atom
def process_vasprun(self, dir_name, taskname, filename):
"""
Adapted from matgendb.creator
Process a vasprun.xml file.
"""
vasprun_file = os.path.join(dir_name, filename)
vrun = Vasprun(vasprun_file)
d = vrun.as_dict()
# rename formula keys
for k, v in {"formula_pretty": "pretty_formula",
"composition_reduced": "reduced_cell_formula",
"composition_unit_cell": "unit_cell_formula"}.items():
d[k] = d.pop(v)
for k in ["eigenvalues", "projected_eigenvalues"]: # large storage space breaks some docs
if k in d["output"]:
del d["output"][k]
comp = Composition(d["composition_unit_cell"])
d["formula_anonymous"] = comp.anonymized_formula
d["formula_reduced_abc"] = comp.reduced_composition.alphabetical_formula
d["dir_name"] = os.path.abspath(dir_name)
d["completed_at"] = str(datetime.datetime.fromtimestamp(os.path.getmtime(vasprun_file)))
d["density"] = vrun.final_structure.density
# replace 'crystal' with 'structure'
d["input"]["structure"] = d["input"].pop("crystal")
d["output"]["structure"] = d["output"].pop("crystal")
for k, v in {"energy": "final_energy", "energy_per_atom": "final_energy_per_atom"}.items():
d["output"][k] = d["output"].pop(v)
# Process bandstructure and DOS
if self.bandstructure_mode != False:
bs = self.process_bandstructure(vrun)
if bs:
d["bandstructure"] = bs
if self.parse_dos != False:
dos = self.process_dos(vrun)
if dos:
d["dos"] = dos
# Parse electronic information if possible.
# For certain optimizers this is broken and we don't get an efermi resulting in the bandstructure
try:
bs = vrun.get_band_structure()
bs_gap = bs.get_band_gap()
d["output"]["vbm"] = bs.get_vbm()["energy"]
d["output"]["cbm"] = bs.get_cbm()["energy"]
d["output"]["bandgap"] = bs_gap["energy"]
d["output"]["is_gap_direct"] = bs_gap["direct"]
d["output"]["is_metal"] = bs.is_metal()
if not bs_gap["direct"]:
d["output"]["direct_gap"] = bs.get_direct_band_gap()
if isinstance(bs, BandStructureSymmLine):
d["output"]["transition"] = bs_gap["transition"]
except Exception:
logger.warning("Error in parsing bandstructure")
if vrun.incar["IBRION"] == 1:
logger.warning("Vasp doesn't properly output efermi for IBRION == 1")
if self.bandstructure_mode is True:
logger.error(traceback.format_exc())
logger.error("Error in " + os.path.abspath(dir_name) + ".\n" + traceback.format_exc())
raise
# store run name and location ,e.g. relax1, relax2, etc.
d["task"] = {"type": taskname, "name": taskname}
# include output file names
d["output_file_paths"] = self.process_raw_data(dir_name, taskname=taskname)
# parse axially averaged locpot
if "locpot" in d["output_file_paths"] and self.parse_locpot:
locpot = Locpot.from_file(os.path.join(dir_name, d["output_file_paths"]["locpot"]))
d["output"]["locpot"] = {i: locpot.get_average_along_axis(i) for i in range(3)}
if self.parse_chgcar != False:
# parse CHGCAR file only for static calculations
# TODO require static run later
# if self.parse_chgcar == True and vrun.incar.get("NSW", 0) < 1:
try:
chgcar = self.process_chgcar(os.path.join(dir_name, d["output_file_paths"]["chgcar"]))
except:
raise ValueError("No valid charge data exist")
d["chgcar"] = chgcar
if self.parse_aeccar != False:
try:
chgcar = self.process_chgcar(os.path.join(dir_name, d["output_file_paths"]["aeccar0"]))
except:
raise ValueError("No valid charge data exist")
d["aeccar0"] = chgcar
try:
chgcar = self.process_chgcar(os.path.join(dir_name, d["output_file_paths"]["aeccar2"]))
except:
raise ValueError("No valid charge data exist")
d["aeccar2"] = chgcar
# parse force constants
if hasattr(vrun, "force_constants"):
d["output"]["force_constants"] = vrun.force_constants.tolist()
d["output"]["normalmode_eigenvals"] = vrun.normalmode_eigenvals.tolist()
d["output"]["normalmode_eigenvecs"] = vrun.normalmode_eigenvecs.tolist()
# Try and perform bader
if self.parse_bader:
try:
bader = bader_analysis_from_path(dir_name, suffix=".{}".format(taskname))
except Exception as e:
bader = "Bader analysis failed: {}".format(e)
d["bader"] = bader
return d
def process_vasprun(self, dir_name, taskname, filename):
"""
Adapted from matgendb.creator
Process a vasprun.xml file.
"""
vasprun_file = os.path.join(dir_name, filename)
if self.bandstructure_mode:
vrun = Vasprun(vasprun_file,
parse_eigen=True,
parse_projected_eigen=True)
else:
vrun = Vasprun(vasprun_file)
d = vrun.as_dict()
for k, v in {
"formula_pretty": "pretty_formula",
"composition_reduced": "reduced_cell_formula",
"composition_unit_cell": "unit_cell_formula"
}.items():
d[k] = d.pop(v)
for k in ["eigenvalues", "projected_eigenvalues"
]: # large storage space breaks some docs
if k in d["output"]:
del d["output"][k]
comp = Composition(d["composition_unit_cell"])
d["formula_anonymous"] = comp.anonymized_formula
d["formula_reduced_abc"] = comp.reduced_composition.alphabetical_formula
d["dir_name"] = os.path.abspath(dir_name)
d["completed_at"] = str(
datetime.datetime.fromtimestamp(os.path.getmtime(vasprun_file)))
d["density"] = vrun.final_structure.density
# replace 'crystal' with 'structure'
d["input"]["structure"] = d["input"].pop("crystal")
d["output"]["structure"] = d["output"].pop("crystal")
for k, v in {
"energy": "final_energy",
"energy_per_atom": "final_energy_per_atom"
}.items():
d["output"][k] = d["output"].pop(v)
if self.parse_dos and self.parse_dos != 'final':
try:
d["dos"] = vrun.complete_dos.as_dict()
except:
raise ValueError(
"No valid dos data exist in {}.".format(dir_name))
if self.bandstructure_mode:
bs = vrun.get_band_structure(
line_mode=(self.bandstructure_mode == "line"))
else:
bs = vrun.get_band_structure()
d["bandstructure"] = bs.as_dict()
d["output"]["vbm"] = bs.get_vbm()["energy"]
d["output"]["cbm"] = bs.get_cbm()["energy"]
bs_gap = bs.get_band_gap()
d["output"]["bandgap"] = bs_gap["energy"]
d["output"]["is_gap_direct"] = bs_gap["direct"]
d["output"]["is_metal"] = bs.is_metal()
d["task"] = {"type": taskname, "name": taskname}
# phonon-dfpt
if hasattr(vrun, "force_constants"):
d["output"]["force_constants"] = vrun.force_constants.tolist()
d["output"][
"normalmode_eigenvals"] = vrun.normalmode_eigenvals.tolist()
d["output"][
"normalmode_eigenvecs"] = vrun.normalmode_eigenvecs.tolist()
return d
