def process_gw_calculation(path: str, gaps: List[Gap]) -> dict:
"""
Process GW calculation results for VB and CB points specified in gaps
:param str path: Path to GW files
:param List[Gap] gaps: List of VB and CB points
:return: dict gw_results: Processed GW data for QP and KS energies for specified gaps
"""
print('Reading data from ', path)
gw_data = parse_gw_info(path)
qp_data = parse_gw_evalqp(path)
gw_results = {}
for gap in gaps:
results = process_gw_gap(gw_data, qp_data, gap.v, gap.c)
gap_label = gap.v_label + '_' + gap.c_label
try:
gw_results['E_qp_' + gap_label] = np.array(results['E_qp'])
gw_results['E_ks_' + gap_label] = np.array(results['E_ks'])
gw_results['delta_E_qp_' + gap_label] = np.array(results['E_qp'] - results['E_ks'])
except KeyError:
return gw_results
return gw_results
def parse_gw_results_two(gw_root: str, directories: list) -> dict:
"""
QP direct-gap (relative to the KS gap)
and self-energies of the band edges at Gamma.
Almost easier to just path full directive strings.
:return: dictionary containing the above.
"""
# Data to parse and return
delta_E_qp = np.empty(shape=(len(directories)))
re_self_energy_VBM = np.empty(shape=delta_E_qp.shape)
re_self_energy_CBm = np.empty(shape=delta_E_qp.shape)
# Directory extension
for ienergy, directory in enumerate(directories):
file_path = gw_root + '/' + directory
gw_data = parse_gw_info(file_path)
qp_data = parse_gw_evalqp(file_path)
print('Reading data from ', file_path)
results = process_gw_gamma_point(gw_data, qp_data)
delta_E_qp[ienergy] = results['E_qp'] - results['E_ks']
re_self_energy_VBM[ienergy] = results['re_sigma_VBM']
re_self_energy_CBm[ienergy] = results['re_sigma_CBm']
return {
'delta_E_qp': delta_E_qp,
're_self_energy_VBM': re_self_energy_VBM,
're_self_energy_CBm': re_self_energy_CBm
}
def process_gw_calculation(path: str) -> dict:
"""
:param str path: Path to GW files
:return: dict Processed GW data
"""
print('Reading data from ', path)
gw_data = parse_gw_info(path)
qp_data = parse_gw_evalqp(path)
results = process_gw_gamma_point(gw_data, qp_data)
if not results:
return {
'delta_E_qp': [],
're_self_energy_VBM': [],
're_self_energy_CBm': []
}
X_point = [0., 0.5, 0.5]
Gamma_point = [0., 0., 0.]
# Specific to the A1 system X (valence) -> Gamma (conduction)
results_x_gamma = process_gw_gap(gw_data, qp_data, X_point, Gamma_point)
results_x_x = process_gw_gap(gw_data, qp_data, X_point, X_point)
return {
'E_qp': np.array(results['E_qp']),
'E_ks': np.array(results['E_ks']),
'E_qp_X_Gamma': np.array(results_x_gamma['E_qp']),
'E_ks_X_Gamma': np.array(results_x_gamma['E_ks']),
'E_qp_X_X': np.array(results_x_x['E_qp']),
'E_ks_X_X': np.array(results_x_x['E_ks']),
'delta_E_qp': np.array(results['E_qp'] - results['E_ks']),
're_self_energy_VBM': np.array(results['re_sigma_VBM']),
're_self_energy_CBm': np.array(results['re_sigma_CBm'])
}
def process_gw_calculation(path: str, gaps: List[Gap]) -> dict:
"""
Process GW calculation results for VB and CB points specified in gaps
:param str path: Path to GW files
:param List[Gap] gaps: List of VB and CB points
:return: dict gw_results: Processed GW data for QP and KS energies for specified gaps
"""
print('Reading data from ', path)
gw_data = parse_gw_info(path)
qp_data = parse_gw_evalqp(path)
gw_results = {}
for gap in gaps:
results = process_gw_gap(gw_data, qp_data, gap.v, gap.c)
gap_label = gap.v_label + '_' + gap.c_label
try:
gw_results['E_qp_' + gap_label] = np.array(results['E_qp'])
gw_results['E_ks_' + gap_label] = np.array(results['E_ks'])
gw_results['delta_E_qp_' + gap_label] = np.array(results['E_qp'] -
results['E_ks'])
except KeyError:
return gw_results
# Haven't tested these, but assume same irrespective of k-point?:
#gw_results['re_self_energy_VBM_' + gap.v_label] = np.array(results['re_sigma_VBM'])
#gw_results['re_self_energy_CBm' + gap.c_label] = np.array(results['re_sigma_CBm'])
return gw_results
def parse_gw_results(root:str, settings:dict) -> dict:
"""
QP direct-gap (relative to the KS gap)
and self-energies of the band edges at Gamma.
Almost easier to just path full directive strings.
:return: dictionary containing the above.
"""
# Basis settings
rgkmax = settings['rgkmax']
l_max_values = settings['l_max_values']
max_energy_exts = settings['max_energy_cutoffs']
# GW settings
n_empty_ext = settings['n_empty_ext']
q_grid = settings['q_grid']
n_img_freq = settings['n_img_freq']
# Data to parse and return
delta_E_qp = np.empty(shape=(len(max_energy_exts), len(l_max_values)))
re_self_energy_VBM = np.empty(shape=delta_E_qp.shape)
re_self_energy_CBm = np.empty(shape=delta_E_qp.shape)
q_str = "".join(str(q) for q in q_grid)
# Lmax in LO basis
for i, l_max in enumerate(l_max_values):
basis_root = root + '/' + directory_string(l_max) + 'rgkmax' + str(rgkmax)
gw_root = basis_root + "/gw_q" + q_str + "_omeg" + str(n_img_freq) + "_nempty" + str(n_empty_ext[i])
# Max energy cut-off of LOs in each l-channel
for ienergy, energy in enumerate(max_energy_exts):
file_path = gw_root + '/max_energy_' + str(energy)
gw_data = parse_gw_info(file_path)
qp_data = parse_gw_evalqp(file_path)
print('Reading data from ', file_path)
results = process_gw_gamma_point(gw_data, qp_data)
delta_E_qp[ienergy, i] = results['E_qp'] - results['E_ks']
re_self_energy_VBM[ienergy, i] = results['re_sigma_VBM']
re_self_energy_CBm[ienergy, i] = results['re_sigma_CBm']
return {'delta_E_qp': delta_E_qp,
're_self_energy_VBM': re_self_energy_VBM,
're_self_energy_CBm': re_self_energy_CBm
}
def process_gw_calculation(path: str) -> dict:
"""
This could be a more generic routine
# TODO(Alex) Check parse_gw_evalqp parser is valid (and replace with one from exciting tools)
:param str path: Path to GW files
:return: dict Processed GW data
"""
print('Reading data from ', path)
gw_data = parse_gw_info(path)
qp_data = parse_gw_evalqp(path)
results = process_gw_gamma_point(gw_data, qp_data)
if not results:
return {
'delta_E_qp': [],
're_self_energy_VBM': [],
're_self_energy_CBm': []
}
X_point = [0., 0.5, 0.5]
Gamma_point = [0., 0., 0.]
# Specific to the A1 system X (valence) -> Gamma (conduction)
results_x_gamma = process_gw_gap(gw_data, qp_data, X_point, Gamma_point)
results_x_x = process_gw_gap(gw_data, qp_data, X_point, X_point)
return {
'E_qp': np.array(results['E_qp']),
'E_ks': np.array(results['E_ks']),
'E_qp_X_Gamma': np.array(results_x_gamma['E_qp']),
'E_ks_X_Gamma': np.array(results_x_gamma['E_ks']),
'E_qp_X_X': np.array(results_x_x['E_qp']),
'E_ks_X_X': np.array(results_x_x['E_ks']),
'delta_E_qp': np.array(results['E_qp'] - results['E_ks']),
're_self_energy_VBM': np.array(results['re_sigma_VBM']),
're_self_energy_CBm': np.array(results['re_sigma_CBm'])
}
from parse.parse_gw import parse_gw_evalqp, parse_gw_info, parse_gw_timings
gw_data = parse_gw_info(file_path="./parse")
qp_data = parse_gw_evalqp("./parse", nempty=600, nkpts=3)
gw_timings = parse_gw_timings(file_path="./parse")
# qp - ks at gamma
process_gw_gamma_point(gw_data, qp_data)
# TODOs
# ⁃ parse lorecommendations.
# ⁃ The main thing to do with avoiding low n states, is to know the nodal structure associated with the functions of a given l, already in the basis
# ⁃ See question to Andris ==> CORRECTLY SET UP OPTIMISED BASIS
# ⁃ Label basis not with trial energies but with number of los per l-channel
# ⁃ See the downloaded paper and then ask
# ⁃ Generate slurm or pbs input
# ⁃ Write my own or do with Aiida?
# ⁃ GW input from ground state
# ⁃ Parse ground state, change ground state fromscratch to fromfile, add GW options
# ⁃ Some post-processing to get the data formats for plotting