import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.gridspec as gridspec

from IPython import embed
import helper_functions as hf
from params import *
import os
import datetime

if __name__ == '__main__':
    ###################################################################################################################
    # parameter and variables
    # plot params
    inch = 2.45
    save_path = '../../thesis/Figures/Results/'
    kernel_size = 100

    fig = plt.figure(constrained_layout=True, figsize=[13 / inch, 15 / inch])
    gs = gridspec.GridSpec(ncols=1, nrows=2, figure=fig, hspace=0.05, wspace=0.0,
                           left=0.1, bottom=0.15, right=0.95, top=0.98)

    ax1 = fig.add_subplot(gs[0, 0])
    ax2 = fig.add_subplot(gs[1, 0])
    ###################################################################################################################
    # load all the data of one day

    for filename_idx in [0]:
        filename = sorted(os.listdir('../data/'))[filename_idx]

        all_max_ch_means = np.load('../data/' + filename + '/all_max_ch.npy', allow_pickle=True)
        all_xticks = np.load('../data/' + filename + '/all_xtickses.npy', allow_pickle=True)
        all_ipp = np.load('../data/' + filename + '/all_ipp.npy', allow_pickle=True)
        power_means = np.load('../data/' + filename + '/power_means.npy', allow_pickle=True)
        freq = np.load('../data/' + filename + '/fish_freq_q10.npy', allow_pickle=True)
        ###############################################################################################################
        # get fish
        for fish_number, ax, ax_idx in zip([5,47], [ax1,ax2], [0, 1]):
            if power_means[fish_number] >= -90.0:
                ipp = all_ipp[fish_number]
                x_tickses = all_xticks[fish_number]
                max_ch_mean = all_max_ch_means[fish_number]

                # smoothing of max channel mean
                kernel = np.ones(kernel_size) / kernel_size
                smooth_mcm = np.convolve(max_ch_mean, kernel, 'valid')

                try:
                    smooth_x = x_tickses[int(np.ceil(kernel_size/2)):-int(np.floor(kernel_size/2))]
                except:
                    embed()
                    quit()

                #####################################################################################################
                # plot traces
                ax.imshow(ipp[::20].T[::-1], vmin=-100, vmax=-50, aspect='auto', interpolation='gaussian',
                           extent=[x_tickses[0], x_tickses[-1], -0.5, 15.5])
                try:
                    ax.plot(smooth_x[::20], smooth_mcm[::20], '.', color=color2[4])
                except:
                    continue

                ax.beautimechannelaxis()
                ax.timeaxis()
                ax.text(-0.12, 0.95, chr(ord('A') + ax_idx), transform=ax.transAxes, fontsize='large')


    fig.savefig(save_path + 'trajectory_5_47.pdf')
    # fig.savefig('../../../goettingen2021_poster/pictures/trajectory_5_47.pdf')
    plt.show()