import matplotlib.pyplot as plt
import os
import glob
import IPython
import numpy as np
from IPython import embed
from jar_functions import parse_dataset


datasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ab\\beats-eod.dat'))]
#           (os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))]

eodf = []
deltaf = []
stimulusf = []

time = []
frequency = []
amplitude = []


for dataset in datasets:
    t, f, a, e, d, s= parse_dataset(dataset)

    time.append(t)
    frequency.append(f)
    amplitude.append(a)
    eodf.append(e)
    deltaf.append(d)
    stimulusf.append(s)

mean = np.mean(frequency, axis=0)

#embed()

#evtl. normiert darstellen (frequency / baseline frequency?)?
#Zeitkonstante: von sec. 0 bis 63%? relative JAR

plt.plot(time, frequency)

plt.xlabel('time [s]')
plt.ylabel('frequency [Hz]')
plt.xlim([-10,200])
plt.title('second try because first try was sold out')
plt.show()