import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.colors as mcolors
import matplotlib.gridspec as gridspec
import math
from IPython import embed
from scipy import stats
import os
from IPython import embed
from params import *
import datetime
import itertools
import pandas as pd
import helper_functions as hf

inch = 2.45
save_path = '../../thesis/Figures/Methods/'

# color2 = ['#f39c12', '#d35400', '#f8c471', '#dc7633', '#c0392b', '#f1c40f']


# fig, ax = plt.subplots(1, 1, figsize=(16 / inch, 5 / inch))
# fig.subplots_adjust(left=0.1, bottom=0.30, right=0.95, top=0.95)
#
# ax.plot([0,0.5,1,2], [1,1,1,1], '*', color=color2[0], label='Fish 1', markersize=7)
# ax.plot([0,1,1.5,2], [2,2,2,2], '*', color=color2[1], label='Fish 2', markersize=7)
#
# ax.set_ylim(0.5,2.5)
# ax.set_xlim(-0.2,2.7)
# ax.set_xticks([0,1,2])
# ax.set_yticks([1,2])
# ax.set_yticklabels([700,720])
# ax.set_xlabel('Time', fontsize=fs)
# ax.set_ylabel('Frequency', fontsize=fs)
# ax.make_nice_ax()
# ax.legend(loc='upper right')
#
# # fig.savefig(save_path+'till_data_structure.png')
# fig.savefig(save_path + 'till_data_structure.pdf')
#
# plt.show()

x1 = [2.5, 5]
y1 = [1, 1]

x2 = [2.5, 4]
y2 = [1.2, 1.2]

x3 = [1,3]
y3 = [2.5,2.5]

x4 = [1,1.5]
y4 = [1.7,1.7]

#
fig1, [ax1,ax2,ax3] = plt.subplots(3, 1, figsize=(16 / inch, 10 / inch))
fig1.subplots_adjust(left=0.1, bottom=0.1, right=0.85, top=0.95)

ax2.plot(x1,y1, '-', color=color2[0], label='Fish 1', linewidth=2, markersize=7)
ax2.plot(x2,y2, '-', color=color2[1], label='ID 2', linewidth=2, markersize=7)
ax2.plot(x3,y3, '-', color=color2[4], label='ID 3', linewidth=2, markersize=7)
ax2.plot(x4,y4, '-', color='gray', label='ID 4', linewidth=2, markersize=7)

ax1.plot(x1,y1, '-', color=color2[0], linewidth=2, markersize=7)
ax1.plot(x2,y2, '-', color=color2[1], linewidth=2, markersize=7)
ax1.plot(x3,y3, '-', color=color2[4], linewidth=2, markersize=7)
ax1.plot(x4,y4, '-', color='gray', linewidth=2, markersize=7)

ax3.plot(x1,y1, '-', color=color2[0], linewidth=2, markersize=7)
ax3.plot(x2,y2, '-', color=color2[1], linewidth=2, markersize=7)
ax3.plot(x3,y3, '-', color='gray', linewidth=2, markersize=7)
ax3.plot([2.5, 4], y1, 'o', color=color2[0], linewidth=2, markersize=7)
ax3.plot(x2,y2, 'o', color=color2[1], linewidth=2, markersize=7)
ax3.plot([2.5, 3], y3, 'o', color='gray', linewidth=2, markersize=7)

ax1.vlines(2.2, ymin=0, ymax=5.5, color='k', linestyle='dashed')
ax1.vlines(5.3, ymin=0, ymax=5.5, color='k', linestyle='dashed')
ax1.plot([2.2,2.5], [0.8,0.8],'k', linestyle='dotted')
ax1.plot([5.0,5.3], [0.8,0.8],'k', linestyle='dotted')

ax2.axvspan(2.2, 2.8, color=color2[0], alpha=0.1)
ax2.axvspan(2.2, 2.8, color=color2[1], alpha=0.1)
ax2.axvspan(2.2, 2.8, color=color2[4], alpha=0.1)
ax2.axvspan(3.7, 4.3, color=color2[1], alpha=0.1)
ax2.axvspan(2.7, 3.3, color=color2[4], alpha=0.1)

ax3.hlines(0.6, xmin=0, xmax=5.5, color='k', linestyle='dashed')
ax3.hlines(1.4, xmin=0, xmax=5.5, color='k', linestyle='dashed')
# ax3.vlines(3.7, ymin=0.8, ymax=1.2, color='k')
# ax3.vlines(4.3, ymin=0.8, ymax=1.2, color='k')
# ax3.hlines(0.8, ymin=2.3, ymax=2.7, color='k')
# ax3.hlines(3.3, ymin=2.3, ymax=2.7, color='k')
# ax3.plot([2.2,2.8], [1.2,1.2],'k')
# ax3.plot([3.7,4.3], [1.2,1.2],'k')
# ax3.plot([2.7,3.3], [2.7,2.7],'k')
# ax3.plot([2.2,2.8], [0.8,0.8],'k')
# ax3.plot([3.7,4.3], [0.8,0.8],'k')
# ax3.plot([2.7,3.3], [2.3,2.3],'k')

for idx, ax in enumerate([ax1,ax2,ax3]):
    ax.text(-0.09, 1, chr(ord('A') + idx), transform=ax.transAxes, fontsize='large')
    ax.set_ylim(0,3.5)
    ax.set_xlim(0.5,5.5)
    ax.set_yticks([])
    ax.set_xticks([])
    # ax.set_xticklabels([])
    # ax.set_yticklabels([])
    ax.set_xlabel('Time', fontsize=fs)
    ax.set_ylabel('EODf', fontsize=fs)
    ax.make_nice_ax()

ax = plt.gca()
ax2.legend(loc='center right', bbox_to_anchor=(1.2, 0.5))

# fig1.savefig(save_path + 'connect_ID.png')
fig1.savefig(save_path + 'connect_ID.pdf')

plt.show()

exit()


# temperatur
t = []

for index, filename_idx in enumerate([1, 4, 6]):
    filename = sorted(os.listdir('../../../data/'))[filename_idx]

    temp = pd.read_csv('../../../data/' + filename + '/temperatures.csv', sep=';')
    t.append(np.array(temp.values.tolist())[:,1])


print(np.mean(np.hstack(t)))
print(np.min(np.hstack(t)))
print(np.max(np.hstack(t)))