Context:

- Restart from the code used yesterday (session 1) that read values from an input file
- Use the classes from ROOT that you need for the exercices described below
- use the corresponding #include commands in our file
- use the proper compilation line in order to link the ROOT libraries

Exercices:

- Create an histogram filled with the values read from the input file
- change the color (blue), the width (3), write meaningful axis titles

- Print the histogram on a canvas
- Save the canvas on a eps and then a png file
- Save also it on a ROOT file
- Fit the histogram using
- a 1st order polynomial
- a gaussian function
- a double gaussian function

- Retrieve the parameters of the fits and dump then in a text file
- Compute the p-value from the Chi2 results for each fit
- Produce a plot which is the ratio between the data and the fit (take the uncertainty properly).
- On the same canvas
- put on the top the histogram with the fitted function (in red)
- write on top of this histogram the results on the fit
- put on the bottom a plot corresponding to the ratio histo/fit with a y-scale ranged from 0.5 to 1.5

- Create 2 new histograms selecting respectively the odd and even entries from the and check if the histogram are compatible using a kolmogorov test
- Dump on the text file the values of the mean, mean error, rms, rms error of both histograms
- Normalise both distributions to unity and superpose them in a new canvas
- Extension:
- create an histogram filled with random value from a gaussian distribution with parameter mu = 50 and rms = 5
- Fit the histogram with a gaussian
- Compare the results with the input parameters