# -*- coding: utf-8 -*-
from scipy import interpolate
import numpy as np
import matplotlib.pyplot as plt

x = np.arange(0, 20)
y = np.sin(x)

print x

f = interpolate.interp1d(x, y)
g = interpolate.interp1d(x, y, kind = 'cubic')
h = interpolate.interp1d(x, y, kind = 'nearest')
i = interpolate.interp1d(x, y, kind = 'zero')

# wyniki różnych metod przybliżeń:
x1 = np.arange(0, 19, 0.1)
yf = f(x1)
yg = g(x1)
yh = h(x1)
yi = i(x1)


def linear(x,y):
    def result(tab):
        mtab = np.copy(tab)
        for index in range(len(mtab)):
            mindex = sum(map(lambda x: x <= mtab[index], x))-1
            print "{} < {} < {}".format(x[mindex], mtab[index], x[mindex+1])
            a = (y[mindex+1]-y[mindex])/(x[mindex+1]-x[mindex])
            b = y[mindex] - a*x[mindex]
            mtab[index] = a*mtab[index] + b
        return mtab
    return result

j = linear(x,y)
yj = j(x1)

a = plt.plot(x1, yj, label='linear', linestyle='-', linewidth=1.5, color='red')
b = plt.plot(x1, yf, label='linear', linestyle='-', linewidth=1.5, color='blue')

plt.legend((a[0], b[0]),
           ('$\Theta = 0.2$', '$\Theta = 0.7$'),
           loc = (0.01, 0.1),  handlelength = -0.1)

plt.show()