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 = 14

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

# Global warming potentials to compare other gases with CO2 - from Wikipedia article
GWP_CH4 = 34
GWP_N2O = 298

#2019 data from https://unfccc.int/documents/223579 Turkey. 2021 Common Reporting Format (CRF) Table
#New data yearly so please update above url on 15 April 2022 with 2020 data
year = "2019"

# TABLE 1.A(a)  SECTORAL BACKGROUND DATA  FOR  ENERGY 1.A.1.a.i
# Electricity Generation Solid Fuels
# kt CO2
# Current spreadsheet cell G33 (G26 includes Combined Heat and Power)
Electricity_coal = 111831.754987763

# Electricity Generation Gaseous Fuels
# G34 (G27 includes Combined Heat and Power)
Electricity_gas = 18401.2644714952

# 1.A.3  b.  Road transportation
# G19 (cars have row G27 below for 2019 but there is no data in it - check 2020 data when it comes)
Road_transport = 75130.6078992

# 1.A.4 a.  Commercial/institutional energy G16
Work_buildings = 14609.5245130065

# 1.A.4  b.  Residential G30
Home_fuel = 41846.130574826

# 2A1 1. G10 (cement production)
Cement = 30423.1235019852

# 3A. F9 Enteric fermentation 1.   Cattle
Cattle_enteric_fermentation = 1074.09473919024 * GWP_CH4 # Convert methane to CO2eq

#3B.  J9 for CH3 & T10 for N2O on different sheets Manure management 1.    Cattle
Cattle_manure = (140.30660625 * GWP_CH4) + (6.30810021259532 * GWP_N2O) 

Cattle = Cattle_enteric_fermentation + Cattle_manure


# Total without LULUCF from National Inventory Report or elsewhere
total_Mt = 506.1
total = total_Mt * 1000

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, Cattle, Home_fuel, Cement, 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','Cattle','Home fuel','Cement','Electricity (gas)','Other']
 plt.xlabel('Source: Turkish Statistical Institute https://unfccc.int/documents/271541', fontsize = 'small', color = 'grey')
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',   'Sığırlar',  'Evler',    'Çimento','Doğalgazdan Elektriği', 'Diğer']
 plt.xlabel('Kaynak: Türkiye İstatistik Kurumu https://unfccc.int/documents/271541', fontsize = 'small', color = 'grey')
else:
 print("Unknown language " + lang)
 sys.exit()  

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_' + year + '.svg')
elif lang == "tr":
 plt.savefig('sera_gazlar_Türkiye_' + year + '.svg')
else:
 print("Unknown language " + lang)
 sys.exit()  

plt.show()