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
from jar_functions import step_response
from jar_functions import JAR_eod
from jar_functions import base_eod


datasets = [(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)

    minimumt = min(len(t[0]), len(t[1]))
    # 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
    # interpolation
    f0 = np.interp(tnew, t[0], f[0])
    f1 = np.interp(tnew, t[1], f[1])

    #new array with frequencies of both loops as two lists put together
    frequency = np.array([f0, f1])

    #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)

    #appending data
    eodf.append(e)
    deltaf.append(d)
    stimulusf.append(s)
    amplitude.append(a)
    frequency_mean.append(mf)
    time.append(tnew)

"""
    for a in [0, 1, 2]:
        for b in [0, 1, 2]:
            r_step = step_response(t = ct_arr, a1 = a, a2 = b, tau1 = 30, tau2 = 60)
"""

for i in range(len(frequency_mean)):
    for n in [100, 500, 1000]:
        cf, ct = mean_noise_cut(frequency_mean[i], time[i], n=n)


        ct_arr = np.array(ct)
        cf_arr = np.array(cf)

        base = base_eod(cf_arr, ct_arr, onset_point = 0)
        ground = cf_arr - base
        jar = JAR_eod(ground, ct_arr, offset_point = 100)
        norm = ground / jar

        plt.plot(ct_arr, norm, label='n=%d' % n)


for n in [1480]:
    cf, ct = mean_noise_cut(frequency_mean[i], time[i], n=n)
    ct_arr = np.array(ct)
    cf_arr = np.array(cf)
    r_step = step_response(t=ct_arr + 10, a1=0.55, a2=0.89, tau1=11.2, tau2= 280)
    plt.plot(r_step, label='fit: n=%d' % n)

'plotting'
plt.xlim([-10,220])
#plt.ylim([400, 1000])
plt.xlabel('time [s]')
plt.ylabel('rel. JAR magnitude')
#plt.title('fit_function(a1=0)')
#plt.savefig('fit_function(a1=0)')
plt.legend()
plt.show()
embed()
# Zeitkonstante: von sec. 0 bis 63%? relative JAR