import numpy as np
import matplotlib.pyplot as plt
from read_baseline_data import *
from IPython import embed
from NixFrame import *

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()
eod_cuts = [];
#for i, idx in enumerate(eod_idx)-1:
	#eod_cuts.append(eod[int(idx):int(eod_idx[i+1])])
	#time_cut = time[int(idx):int(eod_idx[i+1])]
	#plt.plot(time[int(idx):int(eod_idx[i+1])], eod[int(idx):int(eod_idx[i+1])])
#plt.show()
data = NixToFrame(data_dir)


embed()
exit()