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
from jar_functions import mean_noise_cut


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

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

time = []
frequency_mean= []
amplitude = []

start = -10
stop = 200
timespan = 210

for dataset in datasets:
    #input of the function
    t, f, a, e, d, s= parse_dataset(dataset)

    'times'
    # same for time in both loops
    minimumt = min(len(t[0]), len(t[1]))
    t0 = t[0][:minimumt]
    t1 = t[1][:minimumt]

    # new time with wished timespan because it varies for different loops
    tnew = np.arange(start, stop, timespan / minimumt)  # 3rd input is stepspacing:
    # in case complete measuring time devided by total number of datapoints

    'frequencies'
    # minimum datapoint lenght of both loops of frequencies
    minimumf = min(len(f[0]), len(f[1]))
    # new frequencies to minimum for both loops
    f0 = f[0][:minimumf]
    # interpolation
    f0new = np.interp(tnew, t0, f0)

    f1 = f[1] #[:minimumf]
    # interpolation
    f1new = np.interp(tnew, t1, f1)

    #new array with frequencies of both loops as two lists put together
    frequency = np.array([f0new, f1new])

    #making a mean over both loops with the axis 0 (=averaged in y direction, axis=1 would be over x axis)
    mf = np.mean(frequency, axis=0)           #.T as transition (1,0) -> (0,1)

    #appending data
    eodf.append(e)
    deltaf.append(d)
    stimulusf.append(s)
    amplitude.append(a)

    frequency_mean.append(mf)
    time.append(tnew)

for i in range(len(frequency_mean)):
    for n in [10, 50, 100, 1000, 10000, 20000, 30000]:
        cf, ct = mean_noise_cut(frequency_mean[i], time[i], n=n)
        plt.plot(ct, cf, label='n=%d' % n)



'plotting'
plt.xlim([-10,200])
#plt.ylim([400, 1000])
plt.xlabel('time [s]')
plt.ylabel('frequency [Hz]')
#plt.title('noise_cut_n=100')
#plt.savefig('noise_cut_n=100')
plt.legend()
plt.show()


def double_exp(t, a1, a2, tau1, tau2):
    return a1*np.exp(-t/tau1)
# plotten mit manual values for a1, ...
# auch mal a1 oder a2 auf Null setzen.

#evtl. normiert darstellen (frequency / baseline frequency?)?
#Zeitkonstante: von sec. 0 bis 63%? relative JAR
'''
'controll of interpolation'
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.plot(tnew, mf, c = 'r', marker = 'o', ls = 'solid', label = 'new')
ax.plot(t0, f0, c = 'b', marker = '+', ls = '-', label = 'loop_0')
ax.plot(t1, f1, c= 'g', marker = '+', ls = '-', label = 'loop_1')
plt.legend(loc = 'best')
#plt.plot(tnew, mf, marker = 'r-o', label = new, t0, f0, marker = 'b-+', label = loop_0, t1, f1, marker = 'g-+', label = loop_1)
plt.show()
'''