def setup_solids():
with app.context():
aims_data, ds = app.get('solids')
aims_data_atoms = aims_data['atoms'].assign(ene=lambda x: x.data.apply(
lambda y: y['energy'][0]['value'][0]
if y else None, 1)).groupby('symbol').apply(
lambda x: x.iloc[x.reset_index()['ene'].idxmin()]['data'])
aims_data['solids'].sort_index(inplace=True)
for species, data in aims_data_atoms.items():
n = len(aims_data['solids'].loc(0)[:, :, species])
aims_data['solids'].loc[_I[:, :, species], 'data'] = n * [data]
pts = pd.concat(
dict(
aims_data['solids'].gridfile.loc[:, [1.], ['crystal']]
.apply(lambda x: pd.read_hdf(x))
),
names='label scale fragment i_point'.split()
)['i_atom part_weight rho rho_grad_norm kin_dens'.split()] \
.set_index('i_atom', append=True) \
.assign(kin_dens=lambda x: x.kin_dens/2)
alpha_vvs = pd.concat(
{
C_vv: pts.pipe(calc_vvpol, mbd.omega_grid[0], C=C_vv).groupby(
'label scale fragment i_atom'.split()).sum()
for C_vv in [0.0093, 0.0101]
},
names=['C_vv'])
return aims_data['solids'], pts, ds, alpha_vvs
def setup_C6_set():
coords = (pd.read_hdf(resource_filename(
'mbdvv', 'data/C6-data.h5'), 'coords').reset_index().rename(
{
'system': 'label',
'level_1': 'i_atom'
}, axis=1).assign(i_atom=lambda x: x.i_atom + 1).set_index(
'label i_atom'.split()))
with app.context():
aims_data = app.get('C6s')
hirsh = {
label: data['volumes'] /
data['free_atoms']['volumes'][data['free_atoms']['species'] - 1]
for (label, _, _), data, _ in aims_data.itertuples()
}
hirsh = pd.DataFrame([(label, i, val) for label, arr in hirsh.items()
for i, val in enumerate(arr)],
columns='label i_atom hirsh'.split()).set_index(
'label i_atom'.split()).hirsh
ref = (pd.read_hdf(resource_filename('mbdvv', 'data/C6-data.h5'),
'C6_ref').set_index('system1 system2'.split()))
pts = pd.concat(
dict(
aims_data['gridfile']
.loc[:, 1., 'main']
.apply(lambda x: pd.read_hdf(x))
),
names=('label', 'i_point')
)['i_atom part_weight rho rho_grad_norm kin_dens'.split()] \
.set_index('i_atom', append=True)
return coords, hirsh, ref, pts
def setup_x23():
with app.context():
aims_data, ds, alpha_vvs = app.get('x23')
alpha_vvs = pd.concat(
{
C: pd.read_hdf(alpha_vv['alpha.h5'].path)
for C, alpha_vv in alpha_vvs.items()
},
names=['C_vv'])
alpha_vvs.index = alpha_vvs.index.set_names('label', 1)
return aims_data, ds, alpha_vvs
def integrate_atomic_vv(dsname=None, C=None):
from pymbd import MBDCalc
import pandas as pd
from mbdvv.app import app
from mbdvv.functasks import evaluate_vv_batch
from multiprocessing import Pool
with app.context():
df = app.get(dsname)[0]
with MBDCalc(4) as mbd_calc:
freq = mbd_calc.omega_grid[0]
args_list = [(key, x, freq, C) for key, x in df.gridfile.groupby('label')]
with Pool() as pool:
pd.concat(dict(pool.starmap(evaluate_vv_batch,
args_list))).to_hdf('alpha.h5', 'alpha')
def setup_layered():
with app.context():
return app.get('layered')
def setup_surface():
with app.context():
return app.get('surface')
