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

#nicht: 19-aa, 22-ae, 22-ad (?)
datasets = [#(os.path.join('D:\\jar_project\\JAR\\2020-06-19-aa\\beats-eod.dat')),   #-5Hz delta f, horrible fit
            #(os.path.join('D:\\jar_project\\JAR\\2020-06-19-ab\\beats-eod.dat')),   #-5Hz delta f, bad fit
            #(os.path.join('D:\\jar_project\\JAR\\2020-06-22-aa\\beats-eod.dat')),   #-5Hz delta f, bad fit
            #(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ab\\beats-eod.dat')),   #-5Hz delta f, bad fit
            (os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat')),   #-15Hz delta f, good fit
            #(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ad\\beats-eod.dat')),   #-15Hz delta f, horrible fit
            #(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ae\\beats-eod.dat')),   #-15Hz delta f, maxfev way to high so horrible
            (os.path.join('D:\\jar_project\\JAR\\2020-06-22-af\\beats-eod.dat'))]   #-15Hz delta f, good fit

#np.array(sorted(glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')))

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


time_all = []
freq_all = []

constant_factors = []
time_constants = []

ID = []

for infodataset in infodatasets:
    i = parse_infodataset(infodataset)
    identifier = i[0]
    ID.append(identifier)


for dataset in datasets:
    #input of the function
    frequency, time, amplitude, eodf, deltaf, stimulusf, duration, pause = parse_dataset(dataset)
    dm = np.mean(duration)
    pm = np.mean(pause)
    timespan = dm + pm
    start = -10
    stop = 200
    mf , tnew = mean_loops(start, stop, timespan, frequency, time)

    #for i in range(len(mf)):

    cf, ct = mean_noise_cut(mf, tnew, n=1250)

    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

    freq_all.append(norm.tolist())
    time_all.append(ct_arr)

    plt.plot(ct_arr, norm) #, label='fish=%s' % ID)

    sv, sc = curve_fit(step_response, ct_arr[ct_arr < dm], norm[ct_arr < dm], maxfev = 2000) #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], 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)
fr = []
for j in freq_all:
    fr.append(freq_all[j])
    embed()
    minimumf_all = min(len(freq_all[j]))
    f_all = freq_all[j][:minimumf_all]
    print(freq_all[0])
print(len((freq_all[j])))

#f_all_arr = np.array([f0_all], [f1_all])
#f_mean_all = np.mean(freq_all, axis = 0)
#t_mean_all = np.mean(time_all, axis = 0)

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? über zeitdatenpunkt?
# wie zip ich ID liste mit plot (für eine for schleife) zusammen?