Source code for aiida_quantumespresso.parsers.parse_raw.neb
# -*- coding: utf-8 -*-
"""A collection of function that are used to parse the output of Quantum Espresso Neb.
The function that needs to be called from outside is parse_raw_output_neb(). The functions mostly work without aiida
specific functionalities. The parsing will try to convert whatever it can in some dictionary, which by operative
decision doesn't have much structure encoded, [the values are simple ]
"""
from qe_tools import CONSTANTS
[docs]def parse_raw_output_neb(stdout):
"""Parses the output of a neb calculation Receives in input the paths to the output file.
:param stdout: the stdout content as a string
:return parameter_data: a dictionary with parsed parameters
:return iteration_data: a dictionary with arrays (for relax & md calcs.)
"""
import copy
parser_warnings = []
# parse the text output of the neb calculation
out_data, iteration_data = parse_neb_text_output(stdout)
# I add in the out_data all the last elements of iteration_data values.
# I leave the possibility to skip some large arrays (None for the time being).
skip_keys = []
tmp_iteration_data = copy.copy(iteration_data)
for k, v in tmp_iteration_data.items():
if k in skip_keys:
continue
out_data[k] = v[-1]
parameter_data = dict(list(out_data.items()) + [('parser_warnings', parser_warnings)])
return parameter_data, iteration_data
[docs]def parse_neb_text_output(data):
"""Parses the text output of QE Neb.
:param data: a string, the file as read by read()
:param input_dict: dictionary with the input parameters
:return parsed_data: dictionary with key values, referring to quantities
at the last step.
:return iteration_data: key,values referring to intermediate iterations.
Empty dictionary if no value is present.
:return critical_messages: a list with critical messages. If any is found in
parsed_data['warnings'], the calculation is FAILED!
"""
from collections import defaultdict
parsed_data = {}
parsed_data['warnings'] = []
iteration_data = defaultdict(list)
# set by default the calculation as not converged.
parsed_data['converged'] = [False, 0]
for count, line in enumerate(data.split('\n')):
if 'initial path length' in line:
initial_path_length = float(line.split('=')[1].split('bohr')[0])
parsed_data['initial_path_length'] = initial_path_length * CONSTANTS.bohr_to_ang
elif 'initial inter-image distance' in line:
initial_image_dist = float(line.split('=')[1].split('bohr')[0])
parsed_data['initial_image_dist'] = initial_image_dist * CONSTANTS.bohr_to_ang
elif 'string_method' in line:
parsed_data['string_method'] = line.split('=')[1].strip()
elif 'restart_mode' in line:
parsed_data['restart_mode'] = line.split('=')[1].strip()
elif 'opt_scheme' in line:
parsed_data['opt_scheme'] = line.split('=')[1].strip()
elif 'num_of_images' in line:
parsed_data['num_of_images'] = int(line.split('=')[1])
elif 'nstep_path' in line:
parsed_data['nstep_path'] = int(line.split('=')[1])
elif 'CI_scheme' in line:
parsed_data['ci_scheme'] = line.split('=')[1].strip()
elif 'first_last_opt' in line:
parsed_data['first_last_opt'] = True if line.split('=')[1] == 'T' else False
elif 'use_freezing' in line:
parsed_data['use_freezing'] = True if line.split('=')[1] == 'T' else False
elif ' ds ' in line:
parsed_data['ds_au'] = float(line.split('=')[1].split('a.u.')[0])
elif ' k_max' in line:
parsed_data['k_max'] = float(line.split('=')[1].split('a.u.')[0])
elif ' k_min_au' in line:
parsed_data['k_min_au'] = float(line.split('=')[1].split('a.u.')[0])
elif 'suggested k_max' in line:
parsed_data['suggested_k_max_au'] = float(line.split('=')[1].split('a.u.')[0])
elif 'suggested k_min' in line:
parsed_data['suggested_k_min_au'] = float(line.split('=')[1].split('a.u.')[0])
elif 'path_thr' in line:
parsed_data['path_thr'] = float(line.split('=')[1].split('eV')[0])
elif 'list of climbing images' in line:
parsed_data['climbing_images_manual'] = [int(_) for _ in line.split(':')[1].split(',')[:-1]]
elif 'neb: convergence achieved in' in line:
parsed_data['converged'] = [True, int(line.split('iteration')[0].split()[-1])]
num_images = parsed_data['num_of_images']
iteration_lines = data.split('-- iteration')[1:]
iteration_lines = [i.split('\n') for i in iteration_lines]
for iteration in iteration_lines:
for count, line in enumerate(iteration):
if 'activation energy (->)' in line:
activ_energy = float(line.split('=')[1].split('eV')[0])
iteration_data['forward_activation_energy'].append(activ_energy)
elif 'activation energy (<-)' in line:
activ_energy = float(line.split('=')[1].split('eV')[0])
iteration_data['backward_activation_energy'].append(activ_energy)
elif 'image energy (eV) error (eV/A) frozen' in line:
energies = []
forces = []
frozen = []
try:
for i in range(num_images):
split_line = iteration[count + 2 + i].split()[1:]
energies.append(float(split_line[0]))
forces.append(float(split_line[1]))
frozen.append(True if split_line[2] == 'T' else False)
iteration_data['image_energies'].append(energies)
iteration_data['image_forces'].append(forces)
iteration_data['image_frozen'].append(frozen)
except Exception:
parsed_data['warnings'].append('Error while parsing the image energies and forces.')
elif 'climbing image' in line:
iteration_data['climbing_image_auto'].append([int(_) for _ in line.split('=')[1].split(',')])
elif 'path length' in line:
path_length = float(line.split('=')[1].split('bohr')[0])
iteration_data['path_length'].append(path_length * CONSTANTS.bohr_to_ang)
elif 'inter-image distance' in line:
image_dist = float(line.split('=')[1].split('bohr')[0])
iteration_data['image_dist'].append(image_dist * CONSTANTS.bohr_to_ang)
return parsed_data, dict(iteration_data)
