#!/usr/bin/env python

from matplotlib import pyplot as plt
import numpy as np
import sys


def moyal(x, μ, σ):
    N = (1)/(np.sqrt(2 * np.pi) * σ)
    return N * np.exp(- 0.5 * ((x - μ)/σ + np.exp( - (x - μ)/σ)))


# plt.figure()
plt.figure(figsize=(3.5, 2.5))
plt.rcParams['font.size'] = 8

# useful coordinates
# y_min = -0.0086     # y min axes
# y_max = 0.1895      # y max axes
# me = -0.22          # mode
# f_me = 0.1806       # f(mode)
# h_f_me = f_me/2     # falf f(mode)
# x_m = -1.5867       # x₋
# x_p = 2.4330        # x₊

# prepare plot
x, y = np.loadtxt(sys.stdin, unpack=True)
# plt.title('Landau distribution', loc='right')
# plt.xlim(-10, 10)
# plt.ylim(y_min, y_max)

# draw the lines
# plt.plot([-10, me],  [f_me, f_me],     color='gray')
# plt.plot([me, me],  [f_me, y_min],     color='gray')
# plt.plot([-10, x_p], [h_f_me, h_f_me], color='gray')
# plt.plot([x_m, x_m], [y_min, h_f_me],  color='gray')
# plt.plot([x_p, x_p], [y_min, h_f_me],  color='gray')

# draw the function
plt.plot(x, y, color='#92182b')

# draw the notes
# s = 0.012
# S = 0.2
# plt.annotate('$f(m_e)$',   [-10 + S, f_me - s])
# plt.annotate('$f(m_e)/2$', [-10 + S, h_f_me - s])
# plt.annotate('$x_-$',      [x_m + S, y_min + s/2])
# plt.annotate('$x_+$',      [x_p + S, y_min + s/2])
# plt.annotate('$m_e$',      [me + S, y_min + s/2])

# do Moyal plot
μ = -0.22278298
σ = 1.1191486
x = np.arange(-10,30, 0.01)
plt.plot(x, moyal(x, μ, σ), color='gray')

# save figure
plt.tight_layout()
plt.show()
# plt.savefig('notes/images/1-notes.pdf')
# plt.savefig('slides/images/both-pdf.pdf', transparent=True)