#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 25 12:08:05 2024

@author: jdaniel
"""

from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from scipy.stats import norm
plt.rcParams["font.family"] = "Arial"

def outputAppendix(years,markets,regions,shapes,consump_bymarket,outkernel):
    
    nyears = len(years)
    dims = np.shape(shapes)
    ii = np.where(regions == 'Japan')
    shapeuse = shapes[:,ii[0],:]
    shapeuse = np.reshape(shapeuse,[dims[0],dims[2]])
  #  print(np.shape(shapeuse)) 
  #  print(np.shape(shapes))
    nmarkets = len(shapes[0,0,:])
    x = np.zeros(1000)
    xbin = np.zeros(51)
    for i in range(len(xbin)):
        xbin[i] = i/len(xbin)
    for i in range(len(x)):
        x[i] = i/len(x)
    for i in range(0,nmarkets):
        tmp = norm(loc = shapeuse[0,i], scale = .4 * shapeuse[0,i])
        out = tmp.pdf(x)*10000/50
       # print(i,np.mean(shapeuse[1::,i]),shapeuse[0,i])
        m = np.max(out)
        plt.plot(x,out/m)
        plt.hist(shapeuse[:,i],bins=xbin, histtype = 'step', weights = 1/m*np.ones_like(shapeuse[:,i]))
    plt.ylim(0,2.1)
#    plt.yscale('log')
#    plt.xscale('log')
    plt.xlim(.0,1)
    plt.show()
    
    index = [5,0,7,4,3,11]  #markets to show range of
    index = [0,1,2,3,4,5,6,7,8,9,10,11]  #markets to show range of
    s = np.shape(consump_bymarket)
    percentile = .165
    itop = int((1-percentile)*s[0])
    ibot = int(percentile*s[0])
    low, high = np.zeros(nyears), np.zeros(nyears)
    consump_avg_overregion = np.mean(consump_bymarket,axis=2)
    consump_total = np.sum(consump_avg_overregion[0,:,:],axis=1)
    for i in index:
        fig, ax = plt.subplots()
        print(i,markets[i])
        for j in range(0,nyears-1):
            vals = np.sort(consump_avg_overregion[1::,j,i])
            low[j] = vals[ibot] / consump_total[j]
            high[j] = vals[itop] / consump_total[j]
#        plt.plot(years,low,color='blue')
#        plt.plot(years,high,color='blue')
        plt.fill_between(years,low,high,color='black',alpha=.2)
        plt.plot(years[0:-1],consump_avg_overregion[0,0:-1,i]/consump_total[0:-1],color='black')
        plt.title(markets[i]+' (Global)')
        plt.xlim(1990,2021)
        plt.xlabel('Year'); plt.ylabel('Global Fraction')
        ax.tick_params(axis='y', which='both', direction='in', right=True)
        ax.tick_params(axis='x', direction='in', top=True)
        plt.show()

    s = np.shape(outkernel.activebank)
    for i in range(0,s[0]):
        plt.plot(outkernel.activebank[i,:])
        plt.title(markets[i])
        plt.show()
#    return(consump_avg_overregion, consump_total)