This is a coding/data exercise to be done in class today. Work together on this but only 1 person should be writing the actual code.

This exercise should take 45 minutes or 30.

Procedure:

1. copy the data filed called data.txt on ~dkmatter/sciprog/data.txt on ACISS
   
   
2. The data is two columns with 1000 events. Smooth the data using any procedure that you want - you need to balance your smooth width with reduced noise vs loss of feature resolution. Your smoothed data set should look like this:
   
   
   
   
3. Write a function fitter: The simplest one is a sine wave + linear equation and sum the two together. Obviously you will need more than one linear equation because of the slope change. Alternatively you might be able to fit a higher order curve to whatever you think the baseline is, but the only way to fit the harmonics is with a sin wave.
   
   
4. You will want to write code which will immediately pass through your parameter changes (line slopes, periods, amplitude, etc) and draw the function through the data; an example will be shown in class.
   
   
   
   
5. You will need to program in the residuals (difference between the model and the data) and calculate the minimum of the sum using chi² technique:
   
   
   
   f_o = data; f_e = model values; don't worry about normalization for this statistic, the above form is good enough to determine a local minimum (best fit)
   
   
6. When you have a best fit, predict the value for x=1200; we will compare these values and then I will tell you what the data is.
   
   
7. Upload your plot with fits to http://homework.uoregon.edu/upload/what