#!/usr/bin/env python3
"""Python module of file management functions for findTDE calculations."""
import os
import glob
from pathlib import Path, PurePosixPath
import importlib.resources as ilr
import numpy as np
import pandas as pd
base_path = Path.cwd()
bin_path, inp_path, perfect_path = base_path / 'bin', base_path / 'inp', base_path / 'perfect'
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def append_ftde_path(shell_config=".bashrc"):
"""
Function to append the findTDE path to the chosen shell configuration file.
"""
ftde_path = f"{ilr.files('findtde')}/"
ftde_alias_line = f"export PATH={ftde_path}:${{PATH}}\n"
# gather data from shell config file
with open(Path.home() / shell_config, 'r') as rcf:
config_data = rcf.readlines()
# append alias line to config data
config_data.append('\n# findTDE path\n')
config_data.append(ftde_alias_line)
# re-write shell config
with open(Path.home() / shell_config, 'w') as rcf:
rcf.write(''.join(config_data))
return ftde_path
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def find_contcars(tde_calc_dir=base_path, pseudo='*'):
"""
Function to create an array of post-CGM CONTCAR files for defect analysis.
"""
contcar_files = np.array(glob.glob(os.path.join(tde_calc_dir, pseudo, '*', 'cgm', 'CONTCAR'), recursive=True))
return contcar_files
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def find_dumpfiles(tde_calc_dir=base_path, pseudo='*'):
"""
Function to create an array of post-MD dump files for defect analysis.
"""
dump_files = np.array(glob.glob(os.path.join(tde_calc_dir, pseudo, '*', 'dump.final'), recursive=True))
return dump_files
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def pseudo_keys_to_file(pseudo_keys, pseudo_keyfile=os.path.join(base_path, 'pseudo_keys.csv')):
"""
Function to convert a pseudo keys dictionary to a CSV.
"""
pk_df = pd.DataFrame.from_dict(pseudo_keys, orient='index')
pk_df.to_csv(pseudo_keyfile)
return
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def pseudo_keys_from_file(pseudo_keyfile=(base_path / 'pseudo_keys.csv')):
"""
Function to convert a CSV file of pseudo keys to a dictionary.
"""
pk_df = pd.read_csv(pseudo_keyfile, index_col=0)
pks_dict = pk_df.to_dict('split')
pks_dict = dict(zip(pks_dict['index'], pks_dict['data']))
return pks_dict
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def write_sph_dirs(rpt, filepath=os.path.join(base_path, 'sph_directions.csv')):
"""
Function to write spherical directions to a text/csv file for multi_tde.py use.
Given a 2D array of spherical coordinates, writes array values to the file
represented by the filepath string.
"""
sph_f_header = '########################################\n' + \
'Spherical directions for multi_tde.py calculations\n' + \
'r, p, t\n' + \
'########################################\n'
sph_f = open(filepath, 'a+')
if os.stat(filepath).st_size == 0:
sph_f.write(sph_f_header)
sph_f.write('########################################\n')
for i in range(rpt.shape[0]):
sph_line = ', '.join([str(round(j, 2)) for j in rpt[i, :]])
sph_f.write(sph_line+'\n')
sph_f.close()
return
# function to gather data from find_tde runs
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def tde_data_gather(ofile='all_tde_data.csv', tde_calc_dir=base_path):
"""
Gathers all TDE data formatted as find_tde data.
"""
tde_data_header = 'Lattice Direction Pseudo, Atom Type, Atom Number, KE, Final Energy, Delta Energy\n'
data_filepaths = glob.glob(os.path.join(tde_calc_dir, '*', '*_data.csv'), recursive=True)
of = open(ofile, 'a+')
of.write(tde_data_header)
for file in data_filepaths:
data = np.genfromtxt(file, delimiter=', ', skip_header=1,
dtype=[('f0', '<U4'), ('f1', '<U4'), ('f2', '<i4'),
('f3', '<i4'), ('f4', '<f8'), ('f5', '<f8')]
)
np.savetxt(of, data, delimiter=', ', newline='\n',
fmt=['%s', '%s', '%d', '%d', '%f', '%f']
)
of.close()
# function to determine TDE from a single datafile
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def get_tde_from_datafile(data_filepath='*_data.csv', e_tol=1.0, ke_cut=45):
"""
Scans a findTDE *_data.csv file and returns the TDE value for the specified knockout type.
"""
# read in tde data into structured array
tde_data = np.genfromtxt(data_filepath, delimiter=', ', skip_header=1,
dtype=[('pseudo', '<U4'), ('atom_type', '<U4'), ('atom_num', '<i4'),
('ke', '<i4'), ('e_fin', '<f8'), ('dE', '<f8')]
)
# sort array from lowest KE to highest KE
tde_data = np.sort(tde_data, order='ke')
# find TDE value from energy difference
# first KE corresponding to a dE > e_tol is the TDE in the sorted array
tde_value = 0
for i in range(tde_data.shape[0]):
if np.isnan(tde_data[i]['dE']) == True:
continue
elif tde_data[i]['dE'] > e_tol:
if tde_data[i]['ke'] <= ke_cut:
tde_value = tde_data[i]['ke']
elif tde_data[i]['ke'] > ke_cut:
tde_value = ke_cut
break
else:
tde_value = ke_cut
return tde_value
