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