def test_get_band_projection_kpt_9_band_8_aln_2d(file_path):
"""
Verify if the informations returned about projection
in the 11ª band of the 9º kpoint is correct for the 2d GeC.
"""
procar_filename = file_path("/aln-2d/PROCAR")
eigenval_filename = file_path("/aln-2d/EIGENVAL")
vasprun_filename = file_path("/aln-2d/vasprun.xml")
procar = Procar(procar_filename)
vasprun = Vasprun(vasprun_filename)
eigenval = Eigenvalues(eigenval_filename)
band_structure = BandStructure(eigenvalues=eigenval.eigenvalues,
fermi_energy=vasprun.fermi_energy,
atoms_map=vasprun.atoms_map,
num_bands=procar.num_bands,
band_projection=procar)
kpt_9_band_8_projection = {
"Al": [0.000, 0.000, 0.033, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000],
"N": [0.000, 0.000, 0.011, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000]
}
band_projection = band_structure.band_projection(9, 8)
for atom_index, projections in band_projection.items():
for index, element in enumerate(projections):
assert np.isclose(element,
kpt_9_band_8_projection[atom_index][index])
def band_character(kpoint: int, band: int, software: str,
base_path: str) -> None:
"""Uses output files from softwares that perform ab initio calculations to
read projections in a specific kpoint band and extract, in percentage, its
character corresponding to each orbital type (s, p, d, ... ). The
names of the files required for each software are listed below, it is
worth mentioning that their names cannot be modified.
VASP: PROCAR, EIGENVAL, vasprun.xml
"""
welcome_message("minushalf")
softwares = {"VASP": Vasp()}
factory = softwares[software.upper()]
eigenvalues = factory.get_eigenvalues(base_path=base_path)
fermi_energy = factory.get_fermi_energy(base_path=base_path)
atoms_map = factory.get_atoms_map(base_path=base_path)
num_bands = factory.get_number_of_bands(base_path=base_path)
band_projection_file = factory.get_band_projection_class(
base_path=base_path)
band_structure = BandStructure(eigenvalues, fermi_energy, atoms_map,
num_bands, band_projection_file)
band_projection = band_structure.band_projection(kpoint, band)
normalized_df = projection_to_df(band_projection)
click.echo(normalized_df.to_markdown())
end_message()
