Create sleep_data2.py

Here is a summary :

1)	plot_comp : compares the bsl and rec eeg data for two subjects.
2)	eeg_epochs  : I used for pblm set 4. It turns raw eeg data into an array of power spectral densities for 30 sec epochs. It takes some time to calculate (I would call it a pregnant pause) To calculate the FFT, I used the welch method in the scipy signal module. 
3)	get_NREM stages : returns stages with just stage 1,2,3 from all the NREM stages. In short, I get rid of stages 0,5,6,7. Then I put stages 3 and 4 into one group called stage 3. 
4)	get_NREM_psd: returns the psd from the eeg epochs into into a psd with just the NREM stages (corresponding to the get_NREM stages)
5)	norm_Pxx: returns a normalized version of Pxx
6)	get_mean: takes the mean of the 4 psd channels at each frequency
7)	prepare_data: This function takes the 4 channel psd from 0-5 and 10-15, takes the average and normalizes it.
8)	Prepare_data2: does the same thing as prepare_data but for the entire frequency range
9)	Get_rand_indices: randomly returns indices for each of the sleep stages
10)	Divide_data: divides the data into train and test sets : WARNING: I choose the same number of sleep stages for a given fraction so that the tester works well. As a result if you try to do a learning curve based on number of points, this scheme produces errors.