import pandas as pd
import matplotlib.ticker as mtick
import matplotlib.pyplot as plt
from pandas_datareader import wb
from datetime import date

data = wb.download(indicator='NY.GDP.MKTP.CD', country=['AM', '1W'], start=1990, end=date.today().year, errors='ignore')
ratio = data.loc['Armenia'] / data.loc['World']
ratio.index = pd.to_datetime(ratio.index)

# now get IMF data based on https://www.imf.org/en/Publications/WEO/weo-database/2023/October
# By Countries -> Middle East and Central Asia -> Armenia -> Gross domestic product, current prices U.S. DOLLARS -> prepare report
imf_data = pd.Series({
    '2022-01-01': 19.514 / 100135.361,
    '2023-01-01': 24.540 / 104476.432,
    '2024-01-01': 26.935 / 109734.270,
    '2025-01-01': 28.996 / 115598.839,
    '2026-01-01': 31.081 / 121580.994,
    '2027-01-01': 33.335 / 127499.667,
    '2028-01-01': 35.752 / 133782.888
})
imf_data.index = pd.to_datetime(imf_data.index)
print(imf_data)
# now align with WB data
imf_data2 = imf_data * ratio.iloc[0, 0] / imf_data.iloc[0]
print(imf_data2)
print(ratio)

ax = ratio.dropna().plot(legend=0, grid=1, lw=2);
imf_data2.plot(style='--', color='C0', grid=1, lw=2);
ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0, decimals=2))
ax.set_ylim(bottom = 0, top=0.0003);
ax.set_yticks([0, 0.0001, 0.0002, 0.0003]);
ax.xaxis.set_label_text("")
plt.tight_layout()
plt.savefig('հնա.svg')