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 parse_infodataset
from jar_functions import mean_loops
from jar_functions import mean_noise_cut
from jar_functions import norm_function
from jar_functions import step_response

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'))]
infodatasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\info.dat'))]


time = []
frequency_mean = []

constant_factors = []
time_constants = []

start = -10
stop = 200
timespan = 210
for infodataset in infodatasets:
    i= parse_infodataset(infodataset)
    identifier = i[0]

for dataset in datasets:
    #input of the function
    frequency, time, amplitude, eodf, deltaf, stimulusf, duration = parse_dataset(dataset)
    mf , tnew = mean_loops(start, stop, timespan, frequency, time)
    dm = np.mean(duration)
    frequency_mean.append(mf)
    time.append(tnew)

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

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

    norm = norm_function(cf_arr, ct_arr, onset_point = dm - dm, offset_point = dm) #dm-dm funktioniert nur wenn onset = 0 sec

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

    sv, sc = curve_fit(step_response, ct_arr[ct_arr < 100], norm[ct_arr < 100]) #step_values and step_cov
    a = sv[:2]
    tau = np.array(sorted(sv[2:], reverse=False))
    values = np.array([a, tau])
    values_flat = values.flatten()

    plt.plot(ct_arr [ct_arr < 100], step_response(ct_arr, *sv)[ct_arr < 100], 'r-',  label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values_flat))

    print('a1, a2, tau1, tau2', values_flat)
    constant_factors.append(a)
    time_constants.append(tau)

const_line = plt.axhline(y=0.632)
plt.xlim([-10,220])
plt.xlabel('time [s]')
plt.ylabel('rel. JAR magnitude')
plt.title('relative JAR')
plt.savefig('relative JAR')
plt.legend(loc = 'lower right')
plt.show()
embed()


# alle daten einlesen durch große for schleife (auch average über alle fische?)
# für einzelne fische fit kontrollieren

#Fragen: wie offset point wenn nicht start bei 0 sec?
#wie a1, tau1,.. ohne array? (funkt wegen dimensionen wenn ichs nochmal in liste appende)