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


max_cut = int(np.max(np.diff(eod_idx)))
eod_cuts = np.zeros([len(eod_idx)-1, max_cut])
# eods 15 + 16 are to short
relative_times = []

for i, idx in enumerate(eod_idx[:-1]):
	eod_cut = eod[int(idx):int(eod_idx[i+1])]
	eod_cuts[i, :len(eod_cut)] = eod_cut
	eod_cuts[i, len(eod_cut):] = np.nan
	time_cut = time[int(idx):int(eod_idx[i+1])]
	spike_cut = spikes[(spikes > time_cut[0]) & (spikes < time_cut[-1])]
	relative_time  = spike_cut - time_cut[0]
	if len(relative_time) > 0:
		relative_times.append(relative_time[:][0]*1000)


mu_eod = np.nanmean(eod_cuts, axis=0)
std_eod = np.nanstd(eod_cuts, axis=0)*3

time_axis = np.arange(max_cut)/sampling_rate*1000

#fig = plt.figure(figsize=(12/inch_factor, 8/inch_factor))
fig, ax1 = plt.subplots(figsize=(12/inch_factor, 8/inch_factor))
ax1.hist(relative_times, color='crimson')
ax1.set_xlabel('time [ms]', fontsize=12)
ax1.set_ylabel('number', fontsize=12)
ax1.tick_params(axis='y', labelcolor='crimson')
plt.yticks(fontsize = 8)
ax1.spines['top'].set_visible(False)

ax2 = ax1.twinx()

ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='dodgerblue', alpha=0.5)
ax2.plot(time_axis, mu_eod, color='black', lw=2)
ax2.set_ylabel('voltage [mV]', fontsize=12)
ax2.tick_params(axis='y', labelcolor='dodgerblue')

plt.xticks(fontsize = 8)
plt.yticks(fontsize = 8)
fig.tight_layout()
plt.show()