import matplotlib.pyplot as plt
import os
import glob
import IPython
import numpy as np
from IPython import embed
from scipy.optimize import curve_fit
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
from jar_functions import mean_loops
from jar_functions import norm_function


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)
    mf , tnew = mean_loops(start, stop, timespan, f, t)
embed()

for i in range(len(mf)):
    for n in [500, 1000, 1500]:
        cf, ct = mean_noise_cut(mf[i], time[i], n=n)

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

        norm = norm_function(cf_arr, ct_arr, onset_point = 0, offset_point = 100)

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

        #r_step = step_response(t=ct_arr, a1=0.58, a2=0.47, tau1=11.7, tau2=60)

        #plt.plot(ct_arr[ct_arr < 100], r_step[ct_arr < 100], label='fit: n=%d' % n)

        step_values, step_cov = curve_fit(step_response, ct_arr[ct_arr < 100], norm [ct_arr < 100])

        plt.plot(ct_arr [ct_arr < 100], step_response(ct_arr, *step_values)[ct_arr < 100], 'r-',  label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(step_values))
        print(step_values)
const_line = plt.axhline(y=0.632)

'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(loc = 'lower right')
plt.show()
embed()

# noch mehr in funktionen reinhauen (quasi nur noch plotting und funktionen einlesen)
# zeitkonstanten nach groß und klein sortieren
# onset dauer auslesen
# ID aus info.dat auslesen
# alle daten einlesen durch große for schleife (auch average über alle fische?)
# für einzelne fische fit kontrollieren