#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 31 13:37:27 2025

@author: jdaniel
"""

import numpy as np
from matplotlib import pyplot as plt
import pickle

class otherParams:
    def __init__(self):
        self.prodemiss = 1-1/1.05   #0.05
   #     self.prodemiss = 1-1./1.15   #0.05
        self.prodemisssig = 1.
        self.solvent = .1#0.093
        self.solventsig = .5
        self.iyears = 200   #number of years to track values after production
        
from outputPresent import *
from appendix import *
from AltMarket import *

with open('pickledParams.pkl', 'rb') as f:   
    regions = pickle.load(f)
    paramvalsmc = pickle.load(f)
    emissvalsmc = pickle.load(f)
    emissvalsmcNEA = pickle.load(f)
    marketSegNoRefrigmc2008 = pickle.load(f)
    marketSegRefrigmc2008 = pickle.load(f)

val = np.zeros(len(paramvalsmc))
for i in range(len(val)):
    val[i] = paramvalsmc[i].solvent
index=5
indexcol = 0
plt.hist(emissvalsmc[:,index,indexcol],bins=100)
plt.show()
plt.hist(emissvalsmcNEA[:,index,indexcol],bins=100)
plt.show()
print(np.mean(emissvalsmc[:,index,indexcol]),np.std(emissvalsmc[:,index,indexcol]))
print(np.mean(emissvalsmcNEA[:,index,indexcol]),np.std(emissvalsmcNEA[:,index,indexcol]))
print(' ')

ireg = 0
imar = 0
plt.hist(marketSegNoRefrigmc2008[:,ireg,imar],bins=50)
marShareArr = []
meanActualArr = []
stdActualArr = []
for ireg in range(10):
    if (ireg != 0 and ireg != 5 and ireg !=2):
        print(regions[ireg])
        marShare = marketSegRefrigmc2008[0,ireg,:]
        marShareArr.append(marShare)
        iOrder = np.argsort(marShare)
        ix = np.where(marShare > 0.002)[0]
        print(np.shape(ix),ix)
        print(marShare)
        meanActual = np.mean(marketSegRefrigmc2008[:,ireg,:],axis=0)
        meanActualArr.append(meanActual)
        stdActual = np.std(marketSegRefrigmc2008[:,ireg,:],axis=0)
        stdActualArr.append(stdActual)

        if (ireg != 3):
            fig, axs = plt.subplots(3,4)
            plt.subplots_adjust(wspace=.6, hspace=.6)
            for j in range(12):
                plt.subplot(3,4,j+1)
                if (j == 0): 
                    plt.title(regions[ireg])
                #     plt.hist(marketSegNoRefrigmc2008[:,ireg,j])#,ax=axs[j])
                plt.hist(marketSegRefrigmc2008[:,ireg,j],bins=50)
                astring1 = f"{marShare[j]:.3f}"
                astring2 = f"{meanActual[j]:.3f}"
                plt.text(.55,340,astring2,fontsize=8)
                plt.text(.55,280,astring1,fontsize=8)
                plt.xlim(0,1)
                plt.plot([meanActual[j],meanActual[j]],[0,400])
                plt.ylim(0,400)
            plt.show()
        else:
            fig, axs = plt.subplots(2,3,figsize=(4,3))
            plt.subplots_adjust(wspace=.5,hspace=.5)
            tshort = ['Domest. Refrig.','Discont. Panels','Spray Foam','Refrig. Containers','Commercial Refrig.','Cont. Panels']
            iorder = [0,3,4,2,1,11]
            incr = 1
            for j in iorder:
                plt.subplot(2,3,incr)
                plt.hist(marketSegRefrigmc2008[:,ireg,j],bins=50)
                astring1 = f"{marShare[j]:.3f}"
                astring2 = f"{meanActual[j]:.3f}"
                plt.text(.55,280,astring1,fontsize=7)
                plt.text(.55,340,astring2,fontsize=7)
           #     axs.tick_params(axis="both", labelsize=7)
                if (incr == 1 or incr == 4):
                    plt.ylabel('Counts',fontsize=7)
                if (incr == 4 or incr == 5 or incr == 6):
                    plt.xlabel('Market Share',fontsize=7)
                plt.xticks(fontsize=7)
                plt.yticks(fontsize=7)
                plt.xlim(0,1)
                plt.plot([meanActual[j],meanActual[j]],[0,400])
                plt.ylim(0,400)
                plt.title(tshort[incr-1], fontsize=8)
                incr = incr + 1
            plt.savefig('histograms-LAC.png',dpi=300)
            plt.show()
                        
print(' ')
print(np.mean(marketSegNoRefrigmc2008[:,ireg,imar]))
print(np.min(emissvalsmc[:,index,indexcol]),np.max(emissvalsmc[:,index,indexcol]))
print(emissvalsmc[0:19,index,indexcol])

fig, ax = plt.subplots()
marShareArr = np.array(marShareArr)
meanActualArr = np.array(meanActualArr)
plt.plot(marShareArr[:,:], meanActualArr[:,:],'o',color='black')
plt.xlabel('Prescribed Mean Market Share')
plt.ylabel('Calculated Mean Market Share')
plt.plot([0,.5],[0,.5])
plt.tick_params(axis='both', direction='in', top=True, right=True)
plt.title('Market Share Bias')
plt.savefig('meanMarketbias.png',dpi=300)
plt.show()
plt.plot(meanActualArr,stdActualArr,'o',color='black')
plt.xlabel('Calculated Mean Market Share')
plt.ylabel('Standard Deviation')
plt.plot([0,.5],[0,.25])
plt.title('Distribution Bias')
plt.tick_params(axis='both', direction='in', top=True, right=True)
plt.savefig('meanMarketDistrBias1.png',dpi=300)
plt.show()
plt.plot(marShareArr,stdActualArr,'o',color='black')
plt.plot([0,.5],[0,.25])
plt.xlabel('Prescribed Mean Market Share')
plt.ylabel('Standard Deviation')
plt.title('Distribution Bias')
plt.tick_params(axis='both', direction='in', top=True, right=True)
plt.savefig('meanMarketDistrBias2.png',dpi=300)
plt.show()
#plt.hist(np.log(emissvalsmc[:,index,indexcol]),bins=100)
#plt.show()