huhu

code/eod_freq_beat.py

@@ -2,38 +2,38 @@ from read_baseline_data import *
 from IPython import embed
 import matplotlib.pyplot as plt
 import numpy as np
+from IPython import embed
 
 ## beat
 
 data_dir = '../data'
 dataset = '2018-11-09-ad-invivo-1'
+inch_factor = 2.54
 
 time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
 eod_norm = eod - np.mean(eod)
+eod_norm = eod_norm[1000:2000]
 
-# calculate eod times and indices by zero crossings
+x = np.arange(0., len(eod_norm))
-threshold = 0
+y = np.sin(x*2*np.pi*550)
-shift_eod = np.roll(eod_norm, 1)
+ampl = eod_norm + y
-eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]
-
-#x = eod_times*40000
-x = np.arange(0., len(eod_times)-1)
-y = np.sin(x*2*np.pi*600)
-eod_freq_beat = 1/(np.diff(eod_times) + y)
 
-# glätten
+time_axis = np.arange(len(ampl))
-kernel = np.ones(7)/7
-smooth_eod_freq_beat = np.convolve(eod_freq_beat, kernel, mode = 'valid')
-time_axis = np.arange(len(smooth_eod_freq_beat))
 
-plt.plot(time_axis, smooth_eod_freq_beat)
-plt.show()
 
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
+plt.plot(time_axis, ampl)
+ax.set_xlabel("time [ms]", fontsize = 22)
+plt.xticks(fontsize = 18)
+ax.set_ylabel("eod amplitude [mV]", fontsize = 22)
+plt.yticks(fontsize = 18)
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+fig.tight_layout()
 
-#eod_freq_beat = eod_freq_normal + y
-#smooth_eod_freq_beat = np.convolve(eod_freq_beat, kernel, mode = 'valid')
-#fig = plt.plot(time_axis,smooth_eod_freq_beat)
-#plt.xlabel("time [ms]")
-#plt.ylabel("eod frequency [mV]")
 #plt.show()
+plt.savefig('beat.png')
+
+
+