# import  the toolbox and call it B
import LT.box as B
import numpy as np

# the data
t = np.array([ 0. ,  0.2,  0.4,  0.6,  0.8,  1. ,
                     1.2,  1.4,  1.6,  1.8,  2. ])

counts = np.array([ 1235.,   665.,   369.,   221.,
                           165.,   108.,    79.,    77.,
                            67.,    56.,    46.])

sigma = np.array([ 34.64101615,  24.92931125,
                         18.70989477,  14.77306954,
                         12.26884392,  10.62021419,
                         9.46275219,   8.58402083,
                         7.8676807 ,   7.25221607,
                         6.7053333 ])

# now the parameters
a1 = B.Parameter( 1000., 'A1')
a2 = B.Parameter( 100., 'A2')
lam1 = B.Parameter( 3., 'lambda1')
lam2 = B.Parameter( 1., 'lambda2')

# now the fit function, not the indentation
def f(x):
    return ( a1() * np.exp(-lam1()*x) + a2() * np.exp(-lam2()*x) ) 
# end of the fit function

# carry out the fit
F = B.genfit(f, [a1, lam1, a2, lam2], x = t, y = counts, y_err = sigma)

B.plot_exp(t, counts, sigma, logy = 'True',
           x_label = 't (s)',
           y_label = 'Counts',
           plot_title = 'Radioactive Decay with 2 time constants')  # use a logarithmic scale !

B.plot_line(F.xpl, F.ypl)

# show everything
B.pl.show()