# coding: utf-8
#!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np

imf_data = np.array([
    [1980, 1.272 ],
    [1981, 0.110 ],
    [1982, -0.788],
    [1983, 1.555 ],
    [1984, 2.826 ],
    [1985, 2.192 ],
    [1986, 2.417 ],
    [1987, 1.469 ],
    [1988, 3.736 ],
    [1989, 3.913 ],
    [1990, 5.723 ],
    [1991, 5.011 ],
    [1992, 1.508 ],
    [1993, -0.979],
    [1994, 2.527 ],
    [1995, 1.815 ],
    [1996, 0.859 ],
    [1997, 1.902 ],
    [1998, 1.776 ],
    [1999, 1.845 ],
    [2000, 3.194 ],
    [2001, 1.843 ],
    [2002, 0.016 ],
    [2003, -0.733],
    [2004, 0.703 ],
    [2005, 0.878 ],
    [2006, 3.879 ],
    [2007, 3.383 ],
    [2008, 0.805 ],
    [2009, -5.565],
    [2010, 3.945 ],
    [2011, 3.718 ],
    [2012, 0.613 ],
    [2013, 0.406 ],
    [2014, 1.580 ]])

imf_estimates = np.array([
    [2014, 1.580 ],
    [2015, 1.509 ],
    [2016, 1.573 ],
    [2017, 1.510 ],
    [2018, 1.295 ],
    [2019, 1.295 ],
    [2020, 1.269 ]])

def running_mean(x, N):
    window = np.ones(N) / N
    mean = np.convolve(x, window)
    return mean[(N-1):]

# Prepare axis
fig = plt.figure(figsize=(8, 4))
ax = fig.add_subplot(111)
 
# Plot data
years = imf_data[:, 0]
growth = imf_data[:, 1]
growth_mean = running_mean(growth, 5)

years_est = imf_estimates[:, 0]
growth_est = imf_estimates[:, 1]
ax.plot(years, growth, 'k')
ax.plot(years, growth_mean, 'r')
ax.plot(years_est, growth_est, 'k--')

plt.grid()
plt.title(u'Wirtschaftswachstum Deutschlands 1980-2020')
plt.xlabel('Jahr')
plt.ylabel(u'Reales jährliches prozentuales\n Wirtschaftswachstum')
plt.savefig('wirtschaftswachstum-deutschland.svg', transparent=True)