import sys, datetime, copy, os
from dateutil.relativedelta import relativedelta
import numpy as np; np.set_printoptions(threshold=sys.maxsize)
import matplotlib; matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.colors as clr
from mpl_toolkits.basemap import Basemap

import seaborn as sns
from matplotlib.colors import ListedColormap
from netCDF4 import Dataset

# by iterating over all properties of the box plot
def define_box_properties(plot_name, color_code, label, idx):
    for k, v in plot_name.items():
        plt.setp(plot_name.get(k), color=color_code)

    # use plot function to draw a small line to name the legend.
    plt.plot([], c=color_code, label=label)
    if idx == 0: plt.legend(loc='upper left',frameon=False,bbox_to_anchor=(0.675, 4.),ncols=2)
#

#nc   = Dataset('/work/users/hmo/remoclic/Phuong/PHD/BC_2023/Tan/NC1/T_R_season.nc','r') #FD_Char_1961-2020_1yrs_Daily_Reg.nc','r')
nc   = Dataset('/work/users/hmo/remoclic/Phuong/PHD/BC_2023/Tan/NC1/All_Cha_landpoints.nc','r')
#	cha = 4 ;
#	mem = 7 ;
#	point = 11717 ;

Model  = nc.variables['mem'][:]
Season = nc.variables['cha'][:]
month  = nc.variables['point'][:]

r_org  = nc.variables['aph'][:]
r_qm   = nc.variables['org'][:]
t_org  = nc.variables['qm'][:]
#t_qm   = nc.variables['tas_qm'][:]
xlabels = ['B1','B2','B3','B4','N1','N2','N3']
title = ["DJF","MAM","JJA","SON"]
#Amax = [ 7, 150, 4000, 85, 35, 35 ]
Amax = [ 20, 100, 100, 3 ]

nbox = len(Season)
#fig, ax = plt.subplots(nbox,figsize=(6,10),sharey=False)
fig, ax = plt.subplots(nbox,figsize=(8,10),sharey=False)
plt.subplots_adjust(hspace=0.4)
plt.subplots_adjust(top=1)
#plt.subplots_adjust(left=0.01)
#plt.subplots_adjust(right=0.02)

for k in range(len(Season)):
#    ymax = round(np.nanmax(data)); ymin = round(np.nanmin(data))
    ymax = Amax[k]; ymin = 0
    yticks = np.linspace(ymin,ymax,5)

    # https://www.geeksforgeeks.org/how-to-create-boxplots-by-group-in-matplotlib/
#    org = ax[k].boxplot(r_org[:,k,:], positions=np.array(np.arange(len(xlabels)))*2.0-0.35, sym='k+', widths=0.6)
#    qm  = ax[k].boxplot(r_qm[:,k,:], positions=np.array(np.arange(len(xlabels)))*2.0+0.35, sym='k+', widths=0.6)
    org = ax[k].boxplot(r_org[:,:,k], positions=np.array(np.arange(len(xlabels)))*2.0-0.35, sym='k+', widths=0.6)
    qm  = ax[k].boxplot(r_qm[:,:,k], positions=np.array(np.arange(len(xlabels)))*2.0+0.35, sym='k+', widths=0.6)

#    ax[k].set_title('var'+str(k+1))
    ax[k].set_title(title[k])
    ax[k].set_xticks(np.arange(0, len(xlabels) * 2, 2), xlabels)
    ax[k].set_xlim(-2, len(xlabels)*2)
    ax[k].set_ylim(ymin,ymax)
    #ax[k].set_yticks(yticks)
    define_box_properties(org, '#D7191C', 'R_org', k)
    define_box_properties(qm, '#2C7BB6', 'R_QM', k)

#plt.show()
#fig.savefig('box_model_new.pdf', bbox_inches="tight", pad_inches=.05, dpi=600)
fig.savefig('box_model_new.png', bbox_inches="tight", pad_inches=.05, dpi=600)