def process_task(self, path):
try:
#Override incorrect outcar subdocs for two step relaxations
if os.path.exists(os.path.join(path, "relax2")):
try:
run_stats = {}
for i in [1,2]:
outcar = Outcar(zpath(os.path.join(path,"relax"+str(i), "OUTCAR")))
m_key = "calculations."+str(i-1)+".output.outcar"
self.tasks.update({'dir_name_full': path}, {'$set': {m_key: outcar.as_dict()}})
run_stats["relax"+str(i)] = outcar.run_stats
except:
logger.error("Bad OUTCAR for {}.".format(path))
try:
overall_run_stats = {}
for key in ["Total CPU time used (sec)", "User time (sec)",
"System time (sec)", "Elapsed time (sec)"]:
overall_run_stats[key] = sum([v[key]
for v in run_stats.values()])
run_stats["overall"] = overall_run_stats
except:
logger.error("Bad run stats for {}.".format(path))
self.tasks.update({'dir_name_full': path}, {'$set': {"run_stats": run_stats}})
print 'FINISHED', path
else:
print 'SKIPPING', path
except:
print '-----'
print 'ENCOUNTERED AN EXCEPTION!!!', path
traceback.print_exc()
print '-----'
def check(self):
incar = Incar.from_file("INCAR")
is_npt = incar.get("MDALGO") == 3
if not is_npt:
return False
outcar = Outcar("OUTCAR")
patterns = {"MDALGO": "MDALGO\s+=\s+([\d]+)"}
outcar.read_pattern(patterns=patterns)
if outcar.data["MDALGO"] == [['3']]:
return False
else:
return True
def run_task(self, fw_spec):
wd = os.getcwd()
IrrepCaller(wd)
try:
raw_struct = Structure.from_file(wd + "/POSCAR")
formula = raw_struct.composition.formula
structure = raw_struct.as_dict()
outcar = Outcar(wd + "/OUTCAR")
efermi = outcar.efermi
nelect = outcar.nelect
except FileNotFoundError:
formula = None
structure = None
efermi = None
nelect = None
data = IrrepOutput(wd + "/outir.txt", efermi=efermi)
return FWAction(
update_spec={
"irrep_out": data.as_dict(),
"structure": structure,
"formula": formula,
"efermi": efermi,
"nelect": nelect,
})
def run_task(self, fw_spec):
wd = os.getcwd()
IRVSPCaller(wd)
try:
raw_struct = Structure.from_file(wd + "/POSCAR")
formula = raw_struct.composition.formula
structure = raw_struct.as_dict()
outcar = Outcar(wd + "/OUTCAR")
efermi = outcar.efermi
except:
formula = None
structure = None
efermi = None
data = IRVSPOutput(wd + "/outir.txt")
return FWAction(
update_spec={
"irvsp_out": data.as_dict(),
"structure": structure,
"formula": formula,
"efermi": efermi,
}
)
def test_make_perfect_band_edge_state_from_vasp(vasp_files):
procar = Procar(vasp_files / "MgO_2x2x2_perfect" / "PROCAR")
vasprun = Vasprun(vasp_files / "MgO_2x2x2_perfect" / "vasprun.xml")
outcar = Outcar(vasp_files / "MgO_2x2x2_perfect" / "OUTCAR")
actual = make_perfect_band_edge_state_from_vasp(procar, vasprun, outcar)
vbm_info = EdgeInfo(band_idx=127,
kpt_coord=(0.25, 0.25, 0.25),
orbital_info=OrbitalInfo(energy=2.7746,
orbitals={
'Mg':
[0.0, 0.0, 0.0, 0.0],
'O':
[0.0, 0.704, 0.0, 0.0]
},
occupation=1.0))
cbm_info = EdgeInfo(band_idx=128,
kpt_coord=(0.25, 0.25, 0.25),
orbital_info=OrbitalInfo(energy=8.2034,
orbitals={
'Mg':
[0.192, 0.0, 0.0, 0.0],
'O':
[0.224, 0.096, 0.0, 0.0]
},
occupation=0.0))
expected = PerfectBandEdgeState(vbm_info=vbm_info, cbm_info=cbm_info)
assert actual == expected
def test_band_edge_properties_from_vasp(test_data_files):
vasprun_file = str(test_data_files / "MnO_uniform_vasprun.xml")
vasprun = Vasprun(vasprun_file)
outcar_file = str(test_data_files / "MnO_uniform_OUTCAR")
outcar = Outcar(outcar_file)
band_edge = VaspBandEdgeProperties(vasprun, outcar)
assert pytest.approx(band_edge.band_gap) == 0.4702
def make_calc_results(args):
for d in args.dirs:
logger.info(f"Parsing data in {d} ...")
calc_results = make_calc_results_from_vasp(
vasprun=Vasprun(d / defaults.vasprun),
outcar=Outcar(d / defaults.outcar))
calc_results.to_json_file(filename=Path(d) / "calc_results.json")
def calc_effective_mass(args: Namespace):
vasprun, outcar = Vasprun(args.vasprun), Outcar(args.outcar)
band_edge_prop = VaspBandEdgeProperties(vasprun, outcar)
effective_mass = make_effective_mass(vasprun, args.temperature,
args.concentrations,
band_edge_prop.band_gap)
print(effective_mass)
def get_magnetizations(mydir, ion_list):
data = []
max_row = 0
for (parent, subdirs, files) in os.walk(mydir):
for f in files:
if re.match(r"OUTCAR*", f):
try:
row = []
fullpath = os.path.join(parent, f)
outcar = Outcar(fullpath)
mags = outcar.magnetization
mags = [m["tot"] for m in mags]
all_ions = list(range(len(mags)))
row.append(fullpath.lstrip("./"))
if ion_list:
all_ions = ion_list
for ion in all_ions:
row.append(str(mags[ion]))
data.append(row)
if len(all_ions) > max_row:
max_row = len(all_ions)
except:
pass
for d in data:
if len(d) < max_row + 1:
d.extend([""] * (max_row + 1 - len(d)))
headers = ["Filename"]
for i in range(max_row):
headers.append(str(i))
print(tabulate(data, headers))
def postprocess(self):
"""
Postprocessing includes renaming and gzipping where necessary.
Also copies the magmom to the incar if necessary
"""
for f in VASP_OUTPUT_FILES + [self.output_file]:
if os.path.exists(f):
if self.final and self.suffix != "":
shutil.move(f, "{}{}".format(f, self.suffix))
elif self.suffix != "":
shutil.copy(f, "{}{}".format(f, self.suffix))
if self.copy_magmom and not self.final:
try:
outcar = Outcar("OUTCAR")
magmom = [m['tot'] for m in outcar.magnetization]
incar = Incar.from_file("INCAR")
incar['MAGMOM'] = magmom
incar.write_file("INCAR")
except:
logger.error('MAGMOM copy from OUTCAR to INCAR failed')
# Remove continuation so if a subsequent job is run in
# the same directory, will not restart this job.
if os.path.exists("continue.json"):
os.remove("continue.json")
def data_from_outcars_in_folder(folder):
initLogging()
if not os.path.exists(config['data_load_file']):
different_energies = []
different_structures = []
poscar = Poscar.from_file(folder + '/POSCAR')
logging.info('Assigned Spin values Energy Unique energy FilePath')
for outcar_file in sorted(glob.glob(folder + '/OUTCAR*')):
structure = poscar.structure.copy()
magnetic_element_index = [
n for n, e in enumerate(structure)
if e.species_string in config['magnetic_elements']
]
magnetic_substructure = Structure.from_sites([
e for e in structure
if e.species_string in config['magnetic_elements']
])
outcar = Outcar(outcar_file)
try:
magnetization = np.array([
entry['tot'] for entry in outcar.magnetization
])[magnetic_element_index]
except IndexError:
logging.info(f'Bad OUTCAR: {outcar_file}')
continue
spins = np.zeros_like(magnetization)
for i, m in enumerate(magnetization):
spins[i] = np.sign(m) * config['spin_values'][i]
#if abs(m) > config['spin_threshold']:
# spins[i] = config['spin_value'] * np.sign(m)
magnetic_substructure.add_spin_by_site(spins)
magnetic_substructure.vasp_energy = outcar.final_energy
magnetic_substructure.name = outcar_file.split('/')[-1]
isUnique = False
if is_unique(different_energies, magnetic_substructure.vasp_energy,
1e-15):
different_energies.append(magnetic_substructure.vasp_energy)
isUnique = True
different_structures.append(magnetic_substructure)
#energy_checked = np.round(magnetic_substructure.vasp_energy, decimals=3)
#if not np.in1d(energy_checked, different_energies).any():
# different_energies.append(energy_checked)
# isUnique = True
# different_structures.append(magnetic_substructure)
logging.info((spins, magnetic_substructure.vasp_energy, isUnique,
outcar_file))
logging.info(
f'Data reading complete, saving to {config["data_load_file"]}')
with open(config['data_load_file'], 'wb') as f:
pickle.dump(different_structures, f)
logging.info('Data load complete. Rerun script')
import sys
sys.exit()
else:
with open(config['data_load_file'], 'rb') as f:
different_structures = pickle.load(f)
for s in different_structures:
logging.info(s.vasp_energy)
return different_structures
def plot_absorption(args: Namespace):
diele_func_data = make_diele_func(Vasprun(args.vasprun),
Outcar(args.outcar),
use_vasp_real=not args.calc_kk,
ita=args.ita)
plotter = AbsorptionCoeffMplPlotter(diele_func_data, yranges=args.y_ranges)
plotter.construct_plot()
plotter.plt.savefig(args.filename, format="pdf")
def test_make_diele_func_2(test_data_files):
v = Vasprun(test_data_files / "MgSe_absorption_vasprun_gamma.xml")
o = Outcar(test_data_files / "MgSe_absorption_OUTCAR_gamma")
actual = make_shifted_diele_func(make_diele_func(v, o),
original_band_gap=1.997,
shift=1.0)
assert isinstance(actual.diele_func_imag[0], list)
assert isinstance(actual.diele_func_imag[0][0], float)
def test_band_edge_properties_from_vasp_non_mag(test_data_files):
vasprun_file = str(test_data_files / "MgO_band_vasprun.xml")
vasprun = Vasprun(vasprun_file)
outcar_file = str(test_data_files / "MgO_band_OUTCAR")
outcar = Outcar(outcar_file)
band_edge = VaspBandEdgeProperties(vasprun, outcar)
assert pytest.approx(band_edge.band_gap) == 4.6597
def make_perfect_band_edge_state(args):
procar = Procar(args.dir / defaults.procar)
vasprun = Vasprun(args.dir / defaults.vasprun)
outcar = Outcar(args.dir / defaults.outcar)
perfect_band_edge_state = \
make_perfect_band_edge_state_from_vasp(procar, vasprun, outcar)
perfect_band_edge_state.to_json_file(args.dir /
"perfect_band_edge_state.json")
def process_task(self, path):
try:
#Override incorrect outcar subdocs for two step relaxations
if os.path.exists(os.path.join(path, "relax2")):
try:
run_stats = {}
for i in [1, 2]:
outcar = Outcar(
zpath(
os.path.join(path, "relax" + str(i),
"OUTCAR")))
m_key = "calculations." + str(i - 1) + ".output.outcar"
self.tasks.update({'dir_name_full': path},
{'$set': {
m_key: outcar.as_dict()
}})
run_stats["relax" + str(i)] = outcar.run_stats
except:
logger.error("Bad OUTCAR for {}.".format(path))
try:
overall_run_stats = {}
for key in [
"Total CPU time used (sec)", "User time (sec)",
"System time (sec)", "Elapsed time (sec)"
]:
overall_run_stats[key] = sum(
[v[key] for v in run_stats.values()])
run_stats["overall"] = overall_run_stats
except:
logger.error("Bad run stats for {}.".format(path))
self.tasks.update({'dir_name_full': path},
{'$set': {
"run_stats": run_stats
}})
print 'FINISHED', path
else:
print 'SKIPPING', path
except:
print '-----'
print 'ENCOUNTERED AN EXCEPTION!!!', path
traceback.print_exc()
print '-----'
def magnetic_moments(self, outcar):
"""
Extracts magnetic moments.
Returns:
list
"""
mag = Outcar(outcar).magnetization
return [[0, 0, ion['tot']] if isinstance(ion['tot'], float) else
ion['tot'].moment.tolist() for ion in mag]
def make_unitcell_from_vasp(vasprun_band: Vasprun,
outcar_band: Outcar,
outcar_dielectric_clamped: Outcar,
outcar_dielectric_ionic: Outcar,
system_name: str = None) -> Unitcell:
name = (system_name
or vasprun_band.final_structure.composition.reduced_formula)
outcar_dielectric_clamped.read_lepsilon()
outcar_dielectric_ionic.read_lepsilon_ionic()
band_edge_properties = VaspBandEdgeProperties(vasprun_band, outcar_band)
vbm, cbm = band_edge_properties.vbm_cbm
return Unitcell(
system=name,
# vbm and cbm are <class 'numpy.float64'>.
vbm=float(vbm),
cbm=float(cbm),
ele_dielectric_const=outcar_dielectric_clamped.dielectric_tensor,
ion_dielectric_const=outcar_dielectric_ionic.dielectric_ionic_tensor)
def test_make_calc_results_from_vasp_results(vasp_files):
vasprun = Vasprun(vasp_files / "MgO_conv_Va_O_0" / "vasprun.xml")
outcar = Outcar(vasp_files / "MgO_conv_Va_O_0" / "OUTCAR")
results = make_calc_results_from_vasp(vasprun, outcar)
expected_structure = \
Structure.from_file(vasp_files / "MgO_conv_Va_O_0" / "CONTCAR")
assert results.structure == expected_structure
assert results.energy == -34.91084360
assert results.magnetization == 1.03e-05
assert results.potentials ==\
[35.9483, 36.066, 35.948, 35.9478, 69.799, 69.7994, 69.7995]
def pot_al(outcar, ref_outcar, tol=0.5):
"""
given a reference outcar, and a outcar of interest, will calculate a potential alignment:
args:
outcar: outcar from defect calculation
ref_outcar: outcar from stoichiometric calculation
tol: tolerance factor for electrostatic potentials considered 'far' from the defect
"""
avg_defect = []
avg_ref = []
for i, j in zip(
Outcar.read_avg_core_poten(ref_outcar)[-1],
Outcar.read_avg_core_poten(outcar)[-1]):
diff = i - j
if diff < tol and diff > -tol:
avg_defect.append(j)
avg_ref.append(i)
plt.plot(avg_defect)
plt.plot(avg_ref)
pot_al = np.mean(np.array(avg_defect)) - np.mean(np.array(avg_ref))
return pot_al
def make_composition_energies(args):
if args.yaml_file:
composition_energies = CompositionEnergies.from_yaml(args.yaml_file)
else:
composition_energies = CompositionEnergies()
for d in args.dirs:
outcar = Outcar(d / defaults.outcar)
composition = Structure.from_file(d / defaults.contcar).composition
energy = float(outcar.final_energy) # original type is FloatWithUnit
composition_energies[composition] = CompositionEnergy(energy, "local")
composition_energies.to_yaml_file()
def calc_effective_mass(args: Namespace):
vasprun, outcar = Vasprun(args.vasprun), Outcar(args.outcar)
band_edge_prop = VaspBandEdgeProperties(vasprun, outcar)
try:
vbm, cbm = band_edge_prop.vbm_cbm
except TypeError:
logger.warning("Band gap does not exist, so not suited for effective"
"mass calculation.")
return
effective_mass = make_effective_mass(vasprun, args.temperature,
args.concentrations, vbm, cbm)
print(effective_mass)
def test_runs_assimilate(self):
drone = VaspDrone(runs=["relax1", "relax2"])
doc = drone.assimilate(self.relax2)
oszicar2 = Oszicar(os.path.join(self.relax2, "OSZICAR.relax2.gz"))
outcar1 = Outcar(os.path.join(self.relax2, "OUTCAR.relax1.gz"))
outcar2 = Outcar(os.path.join(self.relax2, "OUTCAR.relax2.gz"))
outcar1 = outcar1.as_dict()
outcar2 = outcar2.as_dict()
run_stats1 = outcar1.pop("run_stats")
run_stats2 = outcar2.pop("run_stats")
self.assertEqual(len(doc["calcs_reversed"]), 2)
self.assertEqual(doc["composition_reduced"], {"Si": 1.0})
self.assertEqual(doc["composition_unit_cell"], {"Si": 2.0})
self.assertAlmostEqual(doc["output"]["energy"], oszicar2.ionic_steps[-1]["E0"])
self.assertEqual(doc["formula_pretty"], "Si")
self.assertEqual(doc["formula_anonymous"], "A")
self.assertEqual(list(doc["calcs_reversed"][0]["input"].keys()), list(doc["calcs_reversed"][1]["input"].keys()))
self.assertEqual(
list(doc["calcs_reversed"][0]["output"].keys()), list(doc["calcs_reversed"][1]["output"].keys())
)
self.assertEqual(doc["calcs_reversed"][0]["output"]["energy"], doc["output"]["energy"])
self.assertEqual(doc["run_stats"][doc["calcs_reversed"][0]["task"]["name"]], run_stats2)
self.assertEqual(doc["run_stats"][doc["calcs_reversed"][1]["task"]["name"]], run_stats1)
self.assertEqual(doc["calcs_reversed"][0]["output"]["outcar"], outcar2)
self.assertEqual(doc["calcs_reversed"][1]["output"]["outcar"], outcar1)
def make_edge_characters(args):
for d in args.dirs:
logger.info(f"Parsing data in {d} ...")
vasprun = Vasprun(d / defaults.vasprun)
procar = Procar(d / defaults.procar)
outcar = Outcar(d / defaults.outcar)
calc_results = loadfn(d / "calc_results.json")
structure_analyzer = DefectStructureAnalyzer(
calc_results.structure, args.perfect_calc_results.structure)
edge_characters = MakeEdgeCharacters(
procar, vasprun, outcar,
structure_analyzer.neighboring_atom_indices).edge_characters
edge_characters.to_json_file(d / "edge_characters.json")
def correct(self):
backup(VASP_BACKUP_FILES | {self.output_filename})
actions = []
vi = VaspInput.from_directory(".")
if "lrf_comm" in self.errors:
if Outcar(zpath(os.path.join(
os.getcwd(), "OUTCAR"))).is_stopped is False:
if not vi["INCAR"].get("LPEAD"):
actions.append({"dict": "INCAR",
"action": {"_set": {"LPEAD": True}}})
VaspModder(vi=vi).apply_actions(actions)
return {"errors": list(self.errors), "actions": actions}
def check(self):
incar = Incar.from_file("INCAR")
if incar.get("EDIFFG", 0.1) >= 0 or incar.get("NSW",0) == 0:
# Only activate when force relaxing and ionic steps
# NSW check prevents accidental effects when running DFPT
return False
if not self.max_drift:
self.max_drift = incar["EDIFFG"] * -1
outcar = Outcar("OUTCAR")
if len(outcar.data.get('drift', [])) < self.to_average:
# Ensure enough steps to get average drift
return False
else:
curr_drift = outcar.data.get("drift", [])[::-1][:self.to_average]
curr_drift = np.average([np.linalg.norm(d) for d in curr_drift])
return curr_drift > self.max_drift
def make_energy_yaml():
dirs = [f.name for f in os.scandir(".") if f.is_dir()]
for e in list(Element):
e = str(e)
if e not in dirs or is_target_element(e) is False:
continue
try:
v = Vasprun(Path(e) / "vasprun.xml")
if v.converged_electronic is False:
logger.warning(f"Calculation for {e} is not converged.")
continue
except ParseError:
logger.warning(f"Parsing vasprun.xml for {e} failed.")
continue
outcar = Outcar(Path(e) / "OUTCAR")
print(f"{e + ':':<3} {outcar.final_energy:11.8f}")
def postprocess(self):
"""
Postprocessing includes renaming and gzipping where necessary.
Also copies the magmom to the incar if necessary
"""
for f in VASP_OUTPUT_FILES + [self.output_file]:
if os.path.exists(f):
if self.final and self.suffix != "":
shutil.move(f, "{}{}".format(f, self.suffix))
elif self.suffix != "":
shutil.copy(f, "{}{}".format(f, self.suffix))
if self.copy_magmom and not self.final:
try:
outcar = Outcar("OUTCAR")
magmom = [m['tot'] for m in outcar.magnetization]
incar = Incar.from_file("INCAR")
incar['MAGMOM'] = magmom
incar.write_file("INCAR")
except:
logging.error('MAGMOM copy from OUTCAR to INCAR failed')
def correct(self):
backup(VASP_BACKUP_FILES | {self.output_filename})
actions = []
vi = VaspInput.from_directory(".")
if "lrf_comm" in self.errors:
if self.error_count['lrf_comm'] == 0:
if Outcar(zpath(os.path.join(os.getcwd(),
"OUTCAR"))).is_stopped is False:
# simply rerun the job and increment
# error count for next time
actions.append({
"dict": "INCAR",
"action": {
"_set": {
"ISTART": 1
}
}
})
self.error_count['lrf_comm'] += 1
if "kpoints_trans" in self.errors:
if self.error_count["kpoints_trans"] == 0:
m = reduce(operator.mul, vi["KPOINTS"].kpts[0])
m = max(int(round(m**(1 / 3))), 1)
if vi["KPOINTS"].style.name.lower().startswith("m"):
m += m % 2
actions.append({
"dict": "KPOINTS",
"action": {
"_set": {
"kpoints": [[m] * 3]
}
}
})
self.error_count['kpoints_trans'] += 1
VaspModder(vi=vi).apply_actions(actions)
return {"errors": list(self.errors), "actions": actions}
def prior_info_from_calc_dir(prev_dir_path: Path,
vasprun: str = "vasprun.xml",
outcar: str = "OUTCAR",
potcar: str = "POTCAR"):
vasprun = Vasprun(str(prev_dir_path / vasprun))
outcar = Outcar(str(prev_dir_path / outcar))
potcar = Potcar.from_file(str(prev_dir_path / potcar))
charge = get_net_charge_from_vasp(vasprun.final_structure,
vasprun.parameters["NELECT"], potcar)
structure = vasprun.final_structure.copy()
energy_per_atom = outcar.final_energy / len(structure)
band_edge_property = VaspBandEdgeProperties(vasprun, outcar)
total_magnetization = outcar.total_mag
return PriorInfo(structure=structure,
charge=charge,
energy_per_atom=energy_per_atom,
band_gap=band_edge_property.band_gap,
vbm_cbm=band_edge_property.vbm_cbm,
total_magnetization=total_magnetization)
def main():
df = pd.read_csv('calc_data.csv')
converged = df['converged'] == True
few_steps = df['ionic_steps'] < 10
to_run = list(df[converged & few_steps].iloc[:, 0])
converged_calculations = []
for converged_calculation in tqdm(to_run):
if os.path.exists(
f'mkdir {converged_calculation}/../run.final') == False:
os.system(f'mkdir {converged_calculation}/../run.final')
outcar = Outcar(f'{converged_calculation}/OUTCAR')
mags = [i['tot'] for i in outcar.magnetization]
structure = Poscar.from_file(
f'{converged_calculation}/CONTCAR').structure
structure.add_site_property('magmom', mags)
incar = Incar.from_file(f'{converged_calculation}/INCAR')
incar.update(static_params)
try:
del incar['MAGMOM']
except:
None
potcar = Potcar.from_file(f'{converged_calculation}/POTCAR')
calculation = DictSet(structure, {
'INCAR': incar,
'POTCAR': potcar
})
calculation.incar.write_file(
f'{converged_calculation}/../run.final/INCAR')
calculation.poscar.write_file(
f'{converged_calculation}/../run.final/POSCAR')
os.system(
f'cp {converged_calculation}/POTCAR {converged_calculation}/../run.final ; cp {converged_calculation}/job.sh {converged_calculation}/../run.final'
)
os.system(
f'touch {converged_calculation}/../run.final/vasp_out ; touch {converged_calculation}/../run.final/vasprun.xml'
)
def plot_dos(args: Namespace):
vasprun = Vasprun(args.vasprun)
outcar = Outcar(args.outcar)
band_edge = VaspBandEdgeProperties(vasprun, outcar)
if band_edge.band_gap:
vertical_lines = [band_edge.vbm_info.energy, band_edge.cbm_info.energy]
else:
vertical_lines = [vasprun.efermi]
if args.base_energy is None:
base = vertical_lines[0]
else:
base = args.base_energy
dos_data_from_vasp = \
DosDataFromVasp(vasprun, vertical_lines, base, args.crop_first_value)
dos_data = dos_data_from_vasp.make_dos_data()
ylim_set = None
if args.y_max_ranges:
if dos_data.spin:
ylim_set = [[-y_max, y_max] for y_max in args.y_max_ranges]
else:
ylim_set = [[0, y_max] for y_max in args.y_max_ranges]
structure = vasprun.final_structure
grouped_atom_indices = args.type.grouped_atom_indices(
structure, args.target)
logger.info(f"Grouped atom indices: {grouped_atom_indices}")
plot_data = dos_data.dos_plot_data(grouped_atom_indices,
xlim=args.x_range,
ylim_set=ylim_set)
plot_data.to_json_file()
plotter = DosPlotter(plot_data, args.legend)
plotter.construct_plot()
plotter.plt.savefig(args.filename, format="pdf")
def main():
df = pd.read_csv('calc_data.csv')
converged = df['converged'] == True
few_steps = df['ionic_steps'] < 10
to_scrape = list(df[converged & few_steps].iloc[:, 0])
converged_calculations = []
for converged_calculation in tqdm(to_scrape):
vr = Vasprun(f'{converged_calculation}/vasprun.xml',
parse_potcar_file=False)
entry = vr.get_computed_entry()
entry.entry_id = converged_calculation
entry_dict = entry.as_dict()
if vr.parameters['LORBIT'] == 11:
outcar = Outcar(f'{converged_calculation}/OUTCAR')
entry_dict.update({'MAGMOMS': outcar.magnetization})
converged_calculations.append(entry_dict)
with open('calculation_data.json', 'w') as calc_data:
json.dump(converged_calculations, calc_data)
def post_process(self, dir_name, d):
"""
Simple post-processing for various files other than the vasprun.xml.
Called by generate_task_doc. Modify this if your runs have other
kinds of processing requirements.
Args:
dir_name:
The dir_name.
d:
Current doc generated.
"""
logger.info("Post-processing dir:{}".format(dir_name))
fullpath = os.path.abspath(dir_name)
# VASP input generated by pymatgen's alchemy has a
# transformations.json file that keeps track of the origin of a
# particular structure. This is extremely useful for tracing back a
# result. If such a file is found, it is inserted into the task doc
# as d["transformations"]
transformations = {}
filenames = glob.glob(os.path.join(fullpath, "transformations.json*"))
if len(filenames) >= 1:
with zopen(filenames[0], "rt") as f:
transformations = json.load(f)
try:
m = re.match("(\d+)-ICSD",
transformations["history"][0]["source"])
if m:
d["icsd_id"] = int(m.group(1))
except Exception as ex:
logger.warning("Cannot parse ICSD from transformations "
"file.")
pass
else:
logger.warning("Transformations file does not exist.")
other_parameters = transformations.get("other_parameters")
new_tags = None
if other_parameters:
# We don't want to leave tags or authors in the
# transformations file because they'd be copied into
# every structure generated after this one.
new_tags = other_parameters.pop("tags", None)
new_author = other_parameters.pop("author", None)
if new_author:
d["author"] = new_author
if not other_parameters: # if dict is now empty remove it
transformations.pop("other_parameters")
d["transformations"] = transformations
# Calculations done using custodian has a custodian.json,
# which tracks the jobs performed and any errors detected and fixed.
# This is useful for tracking what has actually be done to get a
# result. If such a file is found, it is inserted into the task doc
# as d["custodian"]
filenames = glob.glob(os.path.join(fullpath, "custodian.json*"))
if len(filenames) >= 1:
with zopen(filenames[0], "rt") as f:
d["custodian"] = json.load(f)
# Parse OUTCAR for additional information and run stats that are
# generally not in vasprun.xml.
try:
run_stats = {}
for filename in glob.glob(os.path.join(fullpath, "OUTCAR*")):
outcar = Outcar(filename)
i = 1 if re.search("relax2", filename) else 0
taskname = "relax2" if re.search("relax2", filename) else \
"relax1"
d["calculations"][i]["output"]["outcar"] = outcar.as_dict()
run_stats[taskname] = outcar.run_stats
except:
logger.error("Bad OUTCAR for {}.".format(fullpath))
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(fullpath))
d["run_stats"] = run_stats
#Convert to full uri path.
if self.use_full_uri:
d["dir_name"] = get_uri(dir_name)
if new_tags:
d["tags"] = new_tags
logger.info("Post-processed " + fullpath)