Source code for bands_inspect.eigenvals

# -*- coding: utf-8 -*-

# (c) 2017-2019, ETH Zurich, Institut fuer Theoretische Physik
# Author: Dominik Gresch <>
Defines the data container for eigenvalue data (bandstructures).

import types

import numpy as np
from fsc.export import export
from fsc.hdf5_io import HDF5Enabled, subscribe_hdf5

from .kpoints import KpointsExplicit, KpointsBase
from .io import from_hdf5

[docs]@export @subscribe_hdf5( 'bands_inspect.eigenvals_data', extra_tags=('eigenvals_data', ) ) class EigenvalsData(HDF5Enabled, types.SimpleNamespace): """ Data container for the eigenvalues at a given set of k-points. The eigenvalues are automatically sorted by value. :param kpoints: List of k-points where the eigenvalues are given. :type kpoints: list :param eigenvals: Eigenvalues at each k-point. The outer axis corresponds to the different k-points, and the inner axis corresponds to the different eigenvalues at a given k-point. :type eigenvals: 2D array """ def __init__(self, *, kpoints, eigenvals): if not isinstance(kpoints, KpointsBase): kpoints = KpointsExplicit(kpoints) eigenvals = np.sort(eigenvals) if len(kpoints.kpoints_explicit) != len(eigenvals): raise ValueError( "Number of kpoints ({}) does not match the number of eigenvalue lists ({})" .format(len(kpoints.kpoints_explicit), len(eigenvals)) ) self.kpoints = kpoints self.eigenvals = eigenvals
[docs] def slice_bands(self, band_idx): """ Returns a new instance which contains only the bands given in the index. :param band_idx: Indices for the bands in the new instance. :type band_idx: list """ new_eigenvals = self.eigenvals.T[sorted(band_idx)].T return type(self)(kpoints=self.kpoints, eigenvals=new_eigenvals)
[docs] @classmethod def from_eigenval_function( cls, *, kpoints, eigenval_function, listable=False ): """ Create an instance using a function that calculates the eigenvalues. :param kpoints: k-points for which the eigenvalues are to be calculated. :type kpoints: KpointsBase :param eigenval_function: Function which calculates the eigenvalues. :param listable: Flag showing whether the function can handle a list of k-points (``True``) or only single k-points (``False``). :type listable: bool """ if listable: eigenvals = eigenval_function(kpoints.kpoints_explicit) else: eigenvals = [ eigenval_function(k) for k in kpoints.kpoints_explicit ] return cls(kpoints=kpoints, eigenvals=eigenvals)
[docs] def to_hdf5(self, hdf5_handle): hdf5_handle.create_group('kpoints_obj') self.kpoints.to_hdf5(hdf5_handle['kpoints_obj']) hdf5_handle['eigenvals'] = self.eigenvals
[docs] @classmethod def from_hdf5(cls, hdf5_handle): kpoints = from_hdf5(hdf5_handle['kpoints_obj']) eigenvals = hdf5_handle['eigenvals'][()] return cls(kpoints=kpoints, eigenvals=eigenvals)
[docs] def shift(self, value): """ Returns an instance with eigenvalues shifted by the given value. :param value: The value by which the eigenvalues are shifted. :type value: float """ new_eigenvals = self.eigenvals + value return type(self)(kpoints=self.kpoints, eigenvals=new_eigenvals)