add more motions

python/helpers.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import pathlib
-import re
 import subprocess
 from itertools import product
 

python/spectrum.py

@@ -1,8 +1,7 @@
-import numpy
 import numpy as np
-from matplotlib import pyplot
-
+import matplotlib.pyplot as plt
 
+from python.helpers import read_parameter_file
 
 # parameter for spectrum simulations
 lb = 2e3
@@ -41,30 +40,39 @@ def pulse_attn(freq: np.ndarray, t_pulse: float) -> np.ndarray:
     return np.pi * numerator / denominator / 2
 
 
-def post_process_spectrum(taus, apod, tpulse):
-    reduction_factor = np.zeros((taus.size, 5)) # hard-coded t_echo :(
+def post_process_spectrum(parameter_file, apod, tpulse):
+    parameter = read_parameter_file(parameter_file)
 
-    for i, tau in enumerate(taus):
-        try:
-            raw_data = np.loadtxt(f'fid_tau={tau:.6e}.dat')
-        except OSError:
-            continue
+    # 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]
 
-        t = raw_data[:, 0]
-        timesignal = raw_data[:, 1:]
+    # make evolution times
+    tevo = np.linspace(parameter['techo_start'], parameter['techo_stop'], num=int(parameter['techo_steps']))
 
-        timesignal *= dampening(t, apod)[:, None]
-        timesignal[0, :] /= 2
+    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'))
 
-        # FT to spectrum
-        freq = np.fft.fftshift(np.fft.fftfreq(t.size, d=1e-6))
-        spec = np.fft.fftshift(np.fft.fft(timesignal, axis=0), axes=0).real
-        spec *= pulse_attn(freq, t_pulse=tpulse)[:, None]
+    t = raw_data[:, 0]
+    timesignal = raw_data[:, 1:]
 
-        reduction_factor[i, :] = 2*timesignal[0, :]
+    timesignal *= dampening(t, apod)[:, None]
+    timesignal[0, :] /= 2
 
-        plt.plot(freq, spec)
-        plt.show()
+    # 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]
 
-    plt.semilogx(taus, reduction_factor, '.')
+    #
+    #
+    # 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()

python/ste.py

@@ -1,6 +1,5 @@
 import pathlib
 
-import numpy
 import numpy as np
 from matplotlib import pyplot as plt
 from scipy.optimize import curve_fit
@@ -74,7 +73,10 @@ def fit_ste(
     # make evolution times
     tevo = np.linspace(parameter['tevo_start'], parameter['tevo_stop'], num=int(parameter['tevo_steps']))
 
-    raw_data = np.loadtxt(parameter_file.with_name(f'{prefix}_{varied_string}.dat'))
+    if varied_string:
+        raw_data = np.loadtxt(parameter_file.with_name(f'{prefix}_{varied_string}.dat'))
+    else:
+        raw_data = np.loadtxt(parameter_file.with_name(f'{prefix}.dat'))
 
     t_mix = raw_data[:, 0]
     decay = raw_data[:, 1:]