def MAPI_soc_data_object_with_DOSCAR(
MAPI_settings_object
): # MAPI spin orbit coupling calculation data with doscar loaded
data = inputs.DataVasp(os.path.join(os.path.dirname(__file__),
'data_vasp/MAPI_soc_OUTCAR'),
os.path.join(os.path.dirname(__file__),
'data_vasp/MAPI_soc_PROCAR'),
ignore=216)
data.parse_DOSCAR(
os.path.join(os.path.dirname(__file__), 'data_vasp/MAPI_soc_DOSCAR'))
return data
def toy_data_object():
data_object = inputs.DataVasp(os.path.join(os.path.dirname(__file__),
'data_vasp/MAPI_soc_OUTCAR'),
os.path.join(os.path.dirname(__file__),
'data_vasp/MAPI_soc_PROCAR'),
ignore=0)
data_object.number_of_kpoints = 5
data_object.number_of_bands = 2
data_object.number_of_ions = 1
data_object.kpoints = np.array([[0, 0, 0], [0.25, 0, 0], [0.5, 0, 0],
[0.5, 0.25, 0], [0.5, 0.5, 0]])
data_object.energies = np.array([[1, 0.5], [2, 1], [1, 0.49124914462],
[2, 1.04], [1, 0.4649965785], [2, 1.07],
[1, 0.48], [2, 1.04], [1, 0.46],
[2, 1.0005]])[:, 1:].reshape(
data_object.number_of_kpoints,
data_object.number_of_bands).T
data_object.occupancy = np.array([[1, 1], [2, 0], [1, 1], [2, 0], [1, 1],
[2, 0], [1, 1], [2, 0], [1, 1],
[2, 0]])[:, 1:].reshape(
data_object.number_of_kpoints,
data_object.number_of_bands).T
data_object.reciprocal_lattice = np.array([[1, 0, 0], [0, 1, 0], [0, 0,
1]])
data_object.CBM = 1
data_object.VBM = 0.5
data_object.fermi_energy = 0.75
data_object.dos = np.array([[0.42, 0], [0.46, 7], [0.47, 3], [0.48, 5],
[0.49, 3], [0.5, 24], [0.6, 0], [0.7, 0],
[0.8, 0], [0.9, 0], [1.0, 3.1], [1.02, 0.2],
[1.04, 3.20], [1.06, 1.215]])
data_object.integrated_dos = np.array([[0.42, 3], [0.46, 3.02],
[0.47, 3.04], [0.48, 3.08],
[0.49, 3.08], [0.5, 3.16],
[0.6, 3.16], [0.7,
3.16], [0.8, 3.16],
[0.9, 3.16], [1.0, 3.17],
[1.02, 3.19], [1.04, 3.20],
[1.06, 3.215]])
return data_object
def cli():
print("Welcome to effmass 2.0.0 \U0001F388")
ignore, seedname, fermi_level = None, None, None
random_int = randint(10000, 99999)
DFT_code = questionary.select(
"Which DFT code have you used to generate the bandstructure?",
choices=['Vasp', 'FHI-aims', 'Castep']).ask()
pathname = questionary.path(
"What's the path to your {} output files?".format(DFT_code),
default="./",
only_directories=True).ask()
if DFT_code == 'Vasp':
ignore = questionary.text(
"How many k-points should I ignore at the start of the file? (useful for hybrid calculations)",
default="0").ask()
if DFT_code == 'Castep':
seedname = questionary.text(
"What's the seedname of your Castep files? (e.g. Si.bands and Si.castep have the seedname Si)",
).ask()
fermi_level = questionary.text(
"I will infer the position of the CBM and VBM from the calculated Fermi level."
+
" If you know this value to be incorrect, please input a more accurate value:",
).ask()
extrema_search_depth = questionary.text(
"How far (in eV) from the CBM (VBM) would you like me to search for minima (maxima)?",
default="0.05").ask()
energy_range = questionary.text(
"What would you like the energy range (in eV) of each segment to be?",
default="0.5").ask()
which_values = questionary.checkbox(
"Which values would you like me to calculate?",
choices=[
questionary.Choice("parabolic m* (least squares)", checked=True),
questionary.Choice("parabolic m* (finite difference)",
checked=True)
]).ask() # need to select oe
save_plot = questionary.confirm(
"Would you like me to save a plot of the band segments?",
default=True,
auto_enter=False).ask()
save_summary = questionary.confirm(
"Would you like me to save a summary file?",
default=True,
auto_enter=False).ask()
settings = inputs.Settings(
extrema_search_depth=float(extrema_search_depth),
energy_range=float(energy_range))
print("Reading in data...")
if DFT_code == "Vasp":
data = inputs.DataVasp(pathname + "/OUTCAR",
pathname + "/PROCAR",
ignore=int(ignore))
elif DFT_code == "FHI-aims":
data = inputs.DataAims(pathname)
else:
data = inputs.DataCastep(pathname + "/", seedname)
if fermi_level:
data.fermi_level = fermi_level
data.find_cbm_vbm()
print("Finding extrema...")
print("Generating segments...")
segments = extrema.generate_segments(settings, data)
print("Calculating effective masses...")
table = outputs.make_table(segments, which_values)
outputs.print_terminal_table(table)
if save_plot:
print("Plotting segments...")
outputs.plot_segments(data,
settings,
segments,
savefig=True,
random_int=random_int)
print("Plot of segments saved to effmass_{}.png".format(random_int))
if save_summary:
print("Writing summary file...")
outputs.print_summary_file(random_int, DFT_code, pathname, ignore,
seedname, fermi_level, extrema_search_depth,
energy_range, table)
print("Summary file saved as effmass_{}.txt".format(random_int))
def MAPI_cl_data_object(): # MAPI cl calculation data
return inputs.DataVasp(os.path.join(os.path.dirname(__file__),
'data_vasp/MAPI_cl_OUTCAR'),
os.path.join(os.path.dirname(__file__),
'data_vasp/MAPI_cl_PROCAR'),
ignore=216)
def Ge_SP_data_object(): # Ge spin-polarised calculation from Adam Jackson
return inputs.DataVasp(
os.path.join(os.path.dirname(__file__), 'data_vasp/Ge_SP_OUTCAR'),
os.path.join(os.path.dirname(__file__), 'data_vasp/Ge_SP_PROCAR'))