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()
