def make_reference_bands_inline(wannier_bands, vasp_bands, efermi=None):
"""
Compare bandstructure results from wannier and vasp.
Takes two input array.bands nodes, stores them if they're not already
stored. Takes the relevant bands from the vasp bandstructure and stores and outputs
them in a node with linkname 'bandcmp'.
Also returns a parameter data node with linkname 'bandinfo' containing
fermi energy, bandgap etc of the reference bandstructure.
"""
import numpy as np
assert isinstance(wannier_bands, BANDS_CLS)
assert isinstance(vasp_bands, BANDS_CLS)
assert hasattr(wannier_bands, 'labels')
assert hasattr(vasp_bands, 'labels')
if vasp_bands.labels:
assert vasp_bands.labels == wannier_bands.labels
kpcomp = vasp_bands.get_kpoints() == wannier_bands.get_kpoints()
assert kpcomp.all(), 'kpoints may not differ'
owindow = get_outer_window(wannier_bands)
wbands = wannier_bands.get_bands()
vbands, vocc = vasp_bands.get_bands(also_occupations=True)
# throw away spin dimension if appropriate
if vbands.ndim == 3:
vbands = vbands[0]
vocc = vocc[0]
# grab the vbands within the outer_window
# find wich bands within the window match
# by searching for the best fit using the sum of square errors
vbands_window = np.empty(wbands.shape)
vocc_window = np.empty(wbands.shape)
w_nbands = wbands.shape[1]
ref_nbands = vbands.shape[1]
for band_idx in range(w_nbands):
errs = [band_error(wbands[:, band_idx], vbands[:, i]) for i in range(ref_nbands)]
minerr = np.argmin(errs)
vbands_window[:, band_idx] = vbands[:, minerr]
vocc_window[:, band_idx] = vocc[:, minerr]
# For the future:
# * find each band's index (s, px, py, ...)
# * store the legend with the comparison node
# find fermi energy from vasp_bands parent or work without it
if not efermi:
try:
efermi = vasp_bands.inp.bands.out.results.get_dict()['efermi']
except Exception: # pylint: disable=broad-except
pass
ref_gap_info = band_gap(vbands_window, vocc_window, efermi)
ref_info = DataFactory('parameter')()
ref_info.update_dict({'bandgap': ref_gap_info})
ref_info.update_dict({'efermi': efermi})
ref_info.update_dict({'outer_window': owindow})
ref_bands = DataFactory('array.bands')()
ref_bands.set_kpointsdata(wannier_bands)
ref_bands.set_bands(vbands_window, occupations=vocc_window)
return {'bands': ref_bands, 'info': ref_info}
