import matplotlib.pyplot as plt
import numpy as np
import os
import nix_helpers as nh
from IPython import embed
#from tqdm import tqdm
from jar_functions import parse_stimuli_dat
from jar_functions import norm_function_eigen
from jar_functions import mean_noise_cut_eigen
from jar_functions import get_time_zeros

base_path = 'D:\\jar_project\\JAR\\eigenmannia\\2015eigen16'

datasets = ['2020-07-08-aa', '2020-07-08-as']

response = []
deltaf = []

for dataset in os.listdir(base_path):
    datapath = os.path.join(base_path, dataset, '%s.nix' % dataset)
    print(datapath)
    stimuli_dat = os.path.join(base_path, dataset, 'manualjar-eod.dat')

    df, duration = parse_stimuli_dat(stimuli_dat)
    dur = int(duration[0][0:2])
    print(df)

    time, eod = nh.read_eod(datapath, duration = 2000)

    zeropoints = get_time_zeros(time, eod, threshold = np.max(eod)*0.1)

    frequencies = 1 / np.diff(zeropoints)

    # norm, base, jar = norm_function_eigen(frequencies, zeropoints[:-1], onset_point=(dur - dur)+10, offset_point=dur+10)  # dm-dm funktioniert nur wenn onset = 0 sec

    # cf, ct = mean_noise_cut_eigen(frequencies, zeropoints[:-1], n=200)

    window = np.ones(101) / 101
    freq = np.convolve(frequencies, window, mode='same')

    ''' # plt.plot(ct, cf)
    plt.plot(zeropoints[:-1], freq)
    plt.ylabel('EOD_frequency [Hz]')
    plt.xlabel('time [s]')
    plt.xlim(1, 140)
    plt.ylim(np.median(freq) - 10, np.median(freq) + 10)
    plt.title('JAR_deltaf_%s' % deltaf)
    plt.show()
    '''
    j = []
    for idx, i in enumerate(zeropoints):
        if i > 20 and i < 80:
            j.append(freq[idx])

    b = []
    for idx, i in enumerate(zeropoints):
        if i < 20:
            b.append(freq[idx])

    r = np.median(j) - np.median(b)
    response.append(r)
    deltaf.append(df[0])

res_df1 = sorted(zip(deltaf[:32],response[:32]))
res_df2 = sorted(zip(deltaf[33:],response[33:]))
res_df = sorted(zip(deltaf,response))

np.save('res_df1', res_df1)
np.save('res_df2', res_df2)
np.save('res_df', res_df)