import numpy as np

import os 

import numpy as np
import matplotlib.pyplot as plt 
from thunderfish.powerspectrum import decibel

from IPython import embed
from pandas import read_csv
from modules.logger import makeLogger
from modules.plotstyle import PlotStyle
from modules.behaviour_handling import Behavior, correct_chasing_events

ps = PlotStyle()

logger = makeLogger(__name__)


def main(datapath: str):
    
    foldernames = [datapath + x + '/'  for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
    for foldername in foldernames:
        if foldername == '../data/mount_data/2020-05-12-10_00/':
            continue
        # behabvior is pandas dataframe with all the data
        bh = Behavior(foldername)

        category = bh.behavior
        timestamps = bh.start_s
        # Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
        # Get rid of tracking faults (two onsets or two offsets after another)
        category, timestamps = correct_chasing_events(category, timestamps)

        # split categories
        chasing_onset = (timestamps[category == 0]/ 60) /60
        chasing_offset = (timestamps[category == 1]/ 60) /60
        physical_contact = (timestamps[category == 2] / 60) /60

        all_fish_ids = np.unique(bh.chirps_ids)
        fish1_id = all_fish_ids[0]
        fish2_id = all_fish_ids[1]
        # Associate chirps to inidividual fish
        fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) /60
        fish2 = (bh.chirps[bh.chirps_ids == fish2_id] / 60) /60
        fish1_color = ps.red
        fish2_color = ps.orange

        fig, ax = plt.subplots(4, 1, figsize=(10, 5), height_ratios=[0.5, 0.5, 0.5, 6], sharex=True)
        # marker size 
        s = 200
        ax[0].scatter(physical_contact, np.ones(len(physical_contact)), color='firebrick', marker='|', s=s)
        ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='green', marker='|', s=s )
        ax[2].scatter(fish1, np.ones(len(fish1))-0.25, color=fish1_color, marker='|', s=s)
        ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, color=fish2_color, marker='|', s=s)
    

        freq_temp = bh.freq[bh.ident==fish1_id]
        time_temp = bh.time[bh.idx[bh.ident==fish1_id]]
        ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish1_color)

        freq_temp = bh.freq[bh.ident==fish2_id]
        time_temp = bh.time[bh.idx[bh.ident==fish2_id]]
        ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish2_color)

        #ax[3].imshow(decibel(bh.spec), extent=[bh.time[0]/60/60, bh.time[-1]/60/60, 0, 2000], aspect='auto', origin='lower')

            # Hide grid lines
        ax[0].grid(False)
        ax[0].set_frame_on(False)
        ax[0].set_xticks([])
        ax[0].set_yticks([])
        ps.hide_ax(ax[0])


        ax[1].grid(False)
        ax[1].set_frame_on(False)
        ax[1].set_xticks([])
        ax[1].set_yticks([])
        ps.hide_ax(ax[1])

        ax[2].grid(False)
        ax[2].set_frame_on(False)
        ax[2].set_yticks([])
        ax[2].set_xticks([])
        ps.hide_ax(ax[2])



        ax[3].axvspan(3, 6, 0, 5, facecolor='grey', alpha=0.5)
        ax[3].set_xticks(np.arange(0, 6.1, 0.5))

        labelpad = 40
        ax[0].set_ylabel('Physical contact', rotation=0, labelpad=labelpad)
        ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad)
        ax[2].set_ylabel('Chirps', rotation=0, labelpad=labelpad)
        ax[3].set_ylabel('EODf')

        ax[3].set_xlabel('Time [h]')
        ax[0].set_title(foldername.split('/')[-2])
        # 2020-03-31-9_59
        plt.show()
        embed()

    # plot chirps


if __name__ == '__main__':
    # Path to the data
    datapath = '../data/mount_data/'
    main(datapath)