freq_baseline

code/eod_freq_normal.py

@@ -0,0 +1,56 @@
+from read_baseline_data import *
+from IPython import embed
+import matplotlib.pyplot as plt
+import numpy as np
+
+data_dir = '../data'
+dataset = '2018-11-09-ad-invivo-1'
+
+# read eod and time of baseline
+time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
+
+eod_norm = eod - np.mean(eod)
+
+# calculate eod times and indices by zero crossings
+threshold = 0
+shift_eod = np.roll(eod_norm, 1)
+eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]
+
+
+## normal
+eod_freq_normal = 1/np.diff(eod_times)
+kernel = np.ones(7)/7
+
+smooth_eod_freq_normal = np.convolve(eod_freq_normal, kernel, mode = 'valid')
+time_axis = np.arange(len(smooth_eod_freq_normal))
+#print(eod_freq)
+#print(smooth_eod_freq)
+
+fig = plt.plot(time_axis,smooth_eod_freq_normal, linewidth = 2.0)
+plt.xlabel("time [ms]", fontsize = 14)
+plt.xticks(fontsize = 12)
+plt.ylabel("eod frequency [mV]", fontsize = 14)
+plt.yticks(fontsize = 12)
+plt.axis([0, len(time_axis), 0, 1000])
+
+plt.show()
+
+
+
+
+
+
+
+## beat
+#x = np.arange(0, len(time_axis, 0.000001)  # Start, Stop, Step
+#y = np.sin(x * 2* np.pi * 600)
+#plt.plot(x, y)
+#plt.show()
+
+
+#eod_freq_beat = eod_freq_normal + np.sin(x * 2* np.pi * 600)
+#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()