import argparse
import numpy as np
from ..thy import gen_theory_data
[docs]def broadening():
description = '''
broadening: voigt broaden theoritical calc spectrum
type ``TRXASprefitpack_info gen_theory_data'' to see
detailed information
'''
parser = argparse.ArgumentParser(description=description)
parser.add_argument('peak',
help='filename for calculated line shape spectrum')
parser.add_argument('e_min', type=float,
help='minimum energy')
parser.add_argument('e_max', type=float,
help='maximum energy')
parser.add_argument('A', type=float,
help='scale factor')
parser.add_argument('fwhm_G', type=float,
help='Full Width at Half Maximum of gaussian shape')
parser.add_argument('fwhm_L', type=float,
help='Full Width at Half Maximum of lorenzian shape')
parser.add_argument('peak_shift', type=float,
help='discrepancy of peak position between theory ' +
'and experiment')
parser.add_argument('-o', '--out', default='out',
help='prefix for output files')
args = parser.parse_args()
peak = np.genfromtxt(args.peak)
out = args.out
e_min = args.e_min
e_max = args.e_max
A = args.A
fwhm_G = args.fwhm_G
fwhm_L = args.fwhm_L
peak_shift = args.peak_shift
e = np.linspace(e_min, e_max, int((e_max-e_min)*100)+1)
gen_theory_data(e, peak, A, fwhm_G, fwhm_L, peak_shift,
out=out)
return