cpp/python/spectrum.py
2024-12-15 18:34:05 +01:00

79 lines
2.3 KiB
Python

import numpy as np
import matplotlib.pyplot as plt
from python.helpers import read_parameter_file
# parameter for spectrum simulations
lb = 2e3
pulse_length = 2e-6
def dampening(x: np.ndarray, apod: float) -> np.ndarray:
"""
Calculate additional dampening to account e.g. for field inhomogeneities.
:param x: Time axis in seconds
:param apod: Dampening factor in 1/seconds
:return: Exponential dampening
"""
return np.exp(-apod * x)
def pulse_attn(freq: np.ndarray, t_pulse: float) -> np.ndarray:
"""
Calculate attenuation of signal to account for finite pulse lengths.
See Schmitt-Rohr/Spieß, eq. 2.126 for more information.
:param freq: Frequency axis in Hz
:param t_pulse: Assumed pulse length in s
:return: Attenuation factor.
"""
# cf. Schmitt-Rohr/Spieß eq. 2.126; omega_1 * t_p = pi/2
pi_half_squared = np.pi**2 / 4
omega = 2 * np.pi * freq
numerator = np.sin(np.sqrt(pi_half_squared + omega**2 * t_pulse**2 / 2))
denominator = np.sqrt(pi_half_squared + omega**2 * t_pulse**2 / 4)
return np.pi * numerator / denominator / 2
def post_process_spectrum(parameter_file, apod, tpulse):
parameter = read_parameter_file(parameter_file)
# files have form ste_arg=0.000000e+01_parameter.txt, first remove ste part then parameter.txt to get variables
varied_string = str(parameter_file).partition('_')[-1].rpartition('_')[0]
# make evolution times
tevo = np.linspace(parameter['techo_start'], parameter['techo_stop'], num=int(parameter['techo_steps']))
if varied_string:
raw_data = np.loadtxt(parameter_file.with_name(f'timesignal_{varied_string}.dat'))
else:
raw_data = np.loadtxt(parameter_file.with_name(f'timesignal.dat'))
t = raw_data[:, 0]
timesignal = raw_data[:, 1:]
timesignal *= dampening(t, apod)[:, None]
timesignal[0, :] /= 2
# FT to spectrum
freq = np.fft.fftshift(np.fft.fftfreq(t.size, d=parameter['dwell_time']))
spec = np.fft.fftshift(np.fft.fft(timesignal, axis=0), axes=0).real
spec *= pulse_attn(freq, t_pulse=tpulse)[:, None]
#
#
# reduction_factor[i, :] = 2*timesignal[0, :]
plt.plot(freq, spec)
plt.gca().set_title(varied_string)
plt.show()
#
# plt.semilogx(taus, reduction_factor, '.')
# plt.show()