analysis

code/analysis_rs.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from read_baseline_data import *
+from IPython import embed
+
+inch_factor = 2.54
+data_dir = '../data'
+dataset = '2018-11-09-ad-invivo-1'
+time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
+
+fig = plt.figure(figsize=(12/inch_factor, 8/inch_factor))
+ax = fig.add_subplot(111)
+ax.plot(time[:1000], eod[:1000])
+ax.set_xlabel('time [ms]', fontsize=12)
+ax.set_ylabel('voltage [mV]', fontsize=12)
+plt.xticks(fontsize = 8)
+plt.yticks(fontsize = 8)
+fig.tight_layout()
+plt.savefig('eod.pdf')
+
+#interspikeintervalhistogram, windowsize = 1 ms
+#plt.hist
+#coefficient of variation
+#embed()
+#exit()
+
+spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
+interspikeintervals = np.diff(spikes)
+fig = plt.figure()
+plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001))
+plt.show()
+
+mu = np.mean(interspikeintervals)
+sigma = np.std(interspikeintervals)
+cv = sigma/mu
+print(cv)
+
+# calculate zero crossings of the eod
+# plot mean of eod circles
+# plot std of eod circles
+# plot psth into the same plot
+# calculate vector strength
+
+threshold = 0;
+shift_eod = np.roll(eod, 1)
+eod_times = time[(eod >= threshold) & (shift_eod < threshold)]
+sampling_rate = 40000.0
+eod_idx = eod_times*sampling_rate
+
+fig = plt.figure()
+for i, idx in enumerate(eod_idx):
+	#embed()
+	#exit()
+	plt.plot(time[int(idx):int(eod_idx[i+1])], eod[int(idx):int(eod_idx[i+1])])
+plt.show()
+