import numpy	# modul pro matematické funkce a práci s poli
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

lines = numpy.loadtxt("cv11/upf_cv11_linear.txt")	# nacti soubor do pole N x 3

x = lines[:,0]
y = lines[:,1]
sigma = lines[:,2]
N = numpy.size(x)

plt.errorbar(x,y,sigma,marker='o',ls='None')

############ fitovani obecne funkce numericky: ##############################

def fit_func(x,a,b):
	return a * numpy.exp(b*x)

M = 2	# pocet parametru
p = curve_fit(fit_func, x, y, sigma=sigma)

print("Parametry:")
print(p[0])

yModel = numpy.empty(N)
for i in range(0,N):
	yModel[i] = fit_func(x[i], p[0][0], p[0][1])
plt.plot(x,yModel)

print("\nMatice kovariance:")
print(p[1]);

string = ""
for i in range(0,M):
	string = string + " " + str(round(numpy.sqrt(p[1][i][i]),3))
print("\nSigmas: " + string)

plt.show()