#!/usr/bin/python3.6
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import numpy as np
import pylab

def g(x, s, m, n):
    return n*pylab.exp(-(x-m)**2/(2*s*s))

list_m=[]
list_n=[]
list_s=[]

for i in range(1, 6):
  file_in="data_"+str(i)+".txt"
  data = np.loadtxt(file_in)
  x = data[:, 0]
  y = data[:, 1]
  dy = np.sqrt(y)
  plt.figure()
  plt.errorbar(x, y, yerr = dy, capsize = 0, fmt = 'b', label='pomiar')
  # Uwaga! Ze powodu pustych binów, nie uwzględniamy niepewności pomiarowych!
  par, cov = curve_fit(g, x, y, p0=np.array([130, 1300, 30]))
  plt.plot(x, g(x, *par), "r", label="fit")
  file_out="wykres_"+str(i)+".pdf"
  plt.savefig(file_out, bbox_inches = 'tight')
  list_m.append(par[1])
  list_s.append(par[0])
  list_n.append(par[2])

if (len(list_m)==0) or (len(list_s)==0) or (len(list_n)==0):
  print("Pusta lista")
else:
  m_s=(sum(list_m))/5.
  print(m_s)
#plt.show()