# coding: utf-8


#
import numpy as np
import matplotlib.pyplot as plt

n = np.array([1., 5., 10., 50., 100., 500., 1000., 5000., 10000., 60000.])
tguix = np.array([2.871, 3.006, 2.987, 2.892, 2.991, 3.038, 3.088, 3.315, 3.565, 5.634])
tgit  = np.array([0.013, 0.006, 0.013, 0.016, 0.014, 0.029, 0.047, 0.12, 0.233, 1.283])

normalize = lambda x: x - x[0]
logify    = lambda x: np.log10(x[3:])

x = logify(n)
y = logify(normalize(tguix))
z = logify(normalize(tgit))

X = np.concatenate((x.reshape((len(x), 1)), np.ones((len(x), 1))), axis=1)

guix, res, rank, s = np.linalg.lstsq(X, y,  rcond=None)
git,  res, rank, s = np.linalg.lstsq(X, z, rcond=None)

predict = lambda sol: sol[0] * x + sol[1]

plt.plot(x, y, 'bo', label='guix')
plt.plot(x, z, 'ro', label='git')
plt.plot(x, predict(guix), 'bx-')
plt.plot(x, predict(git),  'rx-')
plt.legend(loc='lower right')
plt.xlabel("log(N) where N in `| head -n N`")
plt.ylabel("log(real time)")
plt.show()