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/627773'
#2021 data from  Turkey. 2023 Common Reporting Format (CRF) Table
#New data yearly so please update above url on 15 April 2024 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-2024
year = "2021"

# TABLE 1.A(a)  SECTORAL BACKGROUND DATA FOR ENERGY sheet 1
# Electricity Generation Solid Fuels
# kt CO2
# Current spreadsheet cell G33 (G26 includes Combined Heat and Power)
Electricity_coal = 101558.02

# Electricity Generation Gaseous Fuels
# G34 (G27 includes Combined Heat and Power)
Electricity_gas = 38456.40

# sheet 3  Road transportation
# G19 (cars have row G27 below but there is no data in it - check next year data when it comes)
Road_transport = 84698.63

# sheet 4  Commercial/institutional energy G16
Work_buildings = 13895.30

# sheet 4  Residential G30
Home_fuel = 48408.50

# TABLE 2(I).A-H sheet 1  SECTORAL BACKGROUND DATA FOR  INDUSTRIAL PROCESSES AND PRODUCT USE
# G10 (cement production)
Cement = 44226.79

# TABLE 3.A SECTORAL BACKGROUND DATA FOR AGRICULTURE F9 Enteric fermentation 1.   Cattle
Cattle_enteric_fermentation = 1091.60 * GWP_CH4 # Convert methane to CO2eq

# TABLE 3.B(a) SECTORAL BACKGROUND DATA FOR AGRICULTURE
# J9 for CH4
# TABLE 3.B(b) SECTORAL BACKGROUND DATA FOR AGRICULTURE
# T10 for N2O
# Manure management 1.    Cattle
Cattle_manure = (144.97 * GWP_CH4) + (6.47 * GWP_N2O) 

Cattle = Cattle_enteric_fermentation + Cattle_manure

# Total without LULUCF from National Inventory Report or elsewhere
total_Mt = 564.4
total = total_Mt * 1000

logging.info('%s Electricity_coal', Electricity_coal)
logging.info('%s Road_transport', Road_transport)
logging.info('%s Home_fuel', Home_fuel)
logging.info('%s Cement', Cement)
logging.info('%s Cattle', Cattle)

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

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

# From https://learnui.design/tools/data-color-picker.html#palette
colors = ['#4f4c4c', '#7e5853', '#a86452', '#cd7548', '#eb8a34', '#ffa600', 'whitesmoke']
# Or you can use color names like:
#colors = ['peru'               ,'grey'      ,'pink'       ,'lightgrey','orange','khaki'          ,'whitesmoke']


if lang == "en":
 plt.title ("Greenhouse gases largest sources in Turkey " + year, fontsize = fontsize)
 autopct='%1.0f%%' # % sign after number in English
 labels = ['Electricity (coal)','Road Transport','Home fuel','Cement','Cattle','Electricity (gas)','Other']
 data_source = 'Source: Turkish Statistical Institute ' + data_source
elif lang == "tr":
 plt.title (year + " Türkiye'de sera gazı emisyon kaynakları",  fontsize = fontsize)
 autopct='%%%1.0f' # % sign before number in Turkish
 labels = ['Kömürden Elektriği', 'Karayolu Ulaşim',  'Evler',    'Çimento','Sığırlar','Doğalgazdan Elektriği', 'Diğer']
 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)
# Or if you want percents use below but that also puts % on the "other" slice
#ax.pie(percents, labels=labels, textprops={'fontsize': 10}, colors=colors, autopct=autopct, counterclock=False, startangle=90)

if lang == "en":
 plt.savefig('ghg_pie_chart_Turkey.svg')
elif lang == "tr":
 plt.savefig('sera_gazlar_dairesel_grafik_Türkiye.svg')
else:
 print("Unknown language " + lang)
 sys.exit()  

plt.show()