import numpy as np
import matplotlib
import matplotlib.pyplot as plt
 
bars =    ('Emissions (mostly fossil fuels)', 'Absorption (mostly forests)')

Total_excluding_LULUCF = 526 # From Executive Summary in National Inventory Report (NIR) on UNFCCC website and rounded to nearest megatonne
LULUCF = -100                # From Table ES3 in NIR Executive Summary rounded to nearest megatonne

height =  (Total_excluding_LULUCF, LULUCF)

LULUCF_error_percent = 51.14 # From NIR Table 6.16 Uncertainty calculation results for the whole LULUCF sector
LULUCF_error         =  LULUCF_error_percent * np.abs(LULUCF)/100

Total_excluding_LULUCF_error_percent = 5.3 # From NIR Introduction 1.6. General Uncertainty Evaluation
Total_excluding_LULUCF_error = Total_excluding_LULUCF_error_percent * Total_excluding_LULUCF/100

errorbars   = (Total_excluding_LULUCF_error, LULUCF_error)

color   = ('grey',  'green')

ax = plt.subplot() # Defines ax variable by creating an empty plot
ax.tick_params(axis="x", labelsize=12)  # Size of the text below
ax.tick_params(axis="y", labelsize=14)  # Size of the numbers on left

y_pos = np.arange(len(bars))# the x locations for the bars
 
plt.bar(y_pos,  height=height, yerr=errorbars, color=color) # Create bars
 
plt.xticks(y_pos, bars) # Create names on the x-axis

plt.ylabel('Megatonnes')

#plt.title ('Greenhouse gases emitted and absorbed in Turkey in 2017')

plt.savefig('ghg_bar_chart_Turkey.svg')

# Show graphic
plt.show()