#So can save on ipad
import console 

import logging 
logging.basicConfig(level=logging.INFO) # can be commented out when not debugging

import matplotlib.pyplot as plt
import sys

lang = "en"
#lang = "tr"

#If text changed in future default size may chop off part of text so may need to set size
#fig = plt.figure(figsize=(width, height))
#fig = plt.figure(figsize=(6, 4))
fig = plt.figure()

# Set font size as default was a bit small to read
fontsize = 13

#Get current axes object
ax = fig.gca()

# Global warming potentials (GWP 100) to compare other gases with CO2 -
# from https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter_07_Supplementary_Material.pdf
# 7.SM.6 Tables of greenhouse gas lifetimes, radiative efficiencies and metrics

GWP_CH4 = 27.9
GWP_N2O = 273

data_source = 'https://unfccc.int/documents/656426'
#2024 data from  Turkey. 2026 Common Reporting Format (CRF) Table
#New data yearly so please update above url on 15 April 2027 from https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-convention/greenhouse-gas-inventories-annex-i-parties/national-inventory-submissions-2027
year = "2024"

# Column letters below are ipad Numbers - on excel they may be the previous letter. 
# TABLE 1.A(a)  SECTORAL BACKGROUND DATA FOR ENERGY sheet 1
# Electricity Generation Solid Fuels
# kt CO2
# Current spreadsheet cell H33 (H26 includes Combined Heat and Power)
Electricity_coal = 121071.12

# Electricity Generation Gaseous Fuels
# H34 (H27 includes Combined Heat and Power)
Electricity_gas = 22975.56

# sheet 3  Road transportation
# H20 (cars and lorries have rows below but there is no data in them - check next year to see if can split)
# If not maybe best to continue splitting by fuel below
# Road_transport = 95112.06

# H21 Gasoline
Petrol = 14685.46
# Actually for 2024 non-residential buildings are very slightly more but I think within margin of error - put this here as it seems certain from #Climate Trace that petrol will be more in 2025

# H22
Diesel = 69954.14

# sheet 4  Residential H37
Home_fuel = 50956.33

# TABLE 2(I).A-H sheet 1  SECTORAL BACKGROUND DATA FOR  INDUSTRIAL PROCESSES AND PRODUCT USE
# H11 (cement production)
Cement = 44781.64

# TABLE 3.A SECTORAL BACKGROUND DATA FOR AGRICULTURE G10 Enteric fermentation 1.   Cattle
Cattle_enteric_fermentation = 1067.20 * GWP_CH4 # Convert methane to CO2eq

# TABLE 3.B(a) SECTORAL BACKGROUND DATA FOR AGRICULTURE
# K10 for CH4
# TABLE 3.B(b) SECTORAL BACKGROUND DATA FOR AGRICULTURE
# X10 for N2O
# Manure management 1.    Cattle
Cattle_manure = (183.33 * GWP_CH4) + (6.19 * GWP_N2O) 

Cattle = Cattle_enteric_fermentation + Cattle_manure

# Total without LULUCF from National Inventory Report table ES 1 or elsewhere
total_Mt = 584.5
total = total_Mt * 1000

logging.info('%s Electricity_coal', Electricity_coal)
logging.info('%s Diesel', Diesel)
#logging.info('%s Road_transport', Road_transport)
logging.info('%s Home_fuel', Home_fuel)
logging.info('%s Cement', Cement)
logging.info('%s Cattle', Cattle)
logging.info('%s Electricity_gas', Electricity_gas)
logging.info('%s Petrol', Petrol)

#Other = total - (Electricity_coal + Road_transport + Cattle + Home_fuel + Cement + Electricity_gas)
Other = total - (Electricity_coal + Diesel + Petrol + Cattle + Home_fuel + Cement + Electricity_gas)

#Put in order of size to make easier to compare
percents = [Electricity_coal, Diesel, Home_fuel, Cement, Cattle, Electricity_gas, Petrol, Other]

colors = [
'dimgrey',
'grey',
'darkgrey',
'silver',
'lightgrey',
'gainsboro',
'whitesmoke',
'white']

# Or from https://learnui.design/tools/data-color-picker.html#palette can pick e.g.
#colors = ['#6d5d42',
#'#796748',
#'#86714d',
#'#937c53',
#'#a08658',
#'#ae915e',
#'#bb9c64',
#'whitesmoke']
#colors = ['#4f4c4c', '#7e5853', '#a86452', '#cd7548', '#eb8a34', '#ffa600', 'whitesmoke']

if lang == "en":
 plt.title ("Greenhouse gases largest sources in Turkey " + year, fontsize = fontsize)
 # % sign after number in English
 custom_autopct = lambda pct: ('%1.0f%%' % pct) if pct < 30 else '' # Kludge to avoid printing percent for the 'other' white slice
 labels = ['Electricity (coal)','Diesel','Home fuel','Cement','Cattle','Electricity (gas)','Petrol','']
 data_source = 'Source: Turkish Statistical Institute ' + data_source
elif lang == "tr":
 plt.title (year + " Türkiye'de sera gazı emisyon kaynakları",  fontsize = fontsize)
 # % sign before number in Turkish
 custom_autopct = lambda pct: ('%%%1.0f' % pct) if pct < 30 else '' # Kludge to avoid printing percent for the 'other' white slice
 labels = ['Kömürden Elektrik', 'Mazot' , 'Konut Isıtma', 'Çimento','İnekler','Doğalgazdan Elektrik', 'Benzin', '']
 data_source = 'Kaynak: Türkiye İstatistik Kurumu ' + data_source
else:
 print("Unknown language " + lang)
 sys.exit()
plt.xlabel(data_source, fontsize = 'small', color = 'grey')

#ax.pie(percents, labels=labels, textprops={'fontsize': fontsize}, colors=colors, counterclock=False, startangle=90)
ax.pie(percents, labels=labels, textprops={'fontsize': 10}, colors=colors, autopct=custom_autopct, startangle=90)

if lang == "en":
 plt.savefig('ghg_pie_chart_Turkey.svg')
 console.open_in('ghg_pie_chart_Turkey.svg')  # Opens the Share Sheet for the file on ipad
elif lang == "tr":
 plt.savefig('sera_gazlar_dairesel_grafik_Türkiye.svg')
 console.open_in('sera_gazlar_dairesel_grafik_Türkiye.svg')  # Opens the Share Sheet for the file on ipad
else:
 print("Unknown language " + lang)
 sys.exit()  

#plt.show()