Project update 3

My synthesis project of knowing the Gamut displays and plotting in CIE xy chromaticity space has come to a halting point. What to do? Let me enumerate my problems.

My power distribution versus wavelength, also known as spectroscopy data of my laptop display and Kindle display has come to a bump in the road. I need them to solve for the vertices of my Gamut polygon. 

Ma'am Jing suggested I use interpolation to reduce my data and I did. It worked. When I looked at my data, and used the function isnan() in Scilab, there were slots where it is true. 

So how can I reduce this NAN (which means not a number) in my data? Should I reduce my spectroscopy data using Excel? Or should I use the other interpolation methods like 'linear', 'spline', since I used 'nearest' in the interp1() function in Scilab.

Why should I use these methods? Will they yield fruitful results? I tried 'linear' and yes, there was an error: "Grid abscissae of dim 2 not in strict increasing order."

I think the 'nearest' method of interpolation yielded great results but it cannot plot the spectroscopy of the BLUE screen of my laptop display.

Here is the plot. The color of the graph corresponds to the red screen spectroscopy data and the green screen spectroscopy data.

I'm getting frustrated since I have to do the Kindle spectroscopy data, but once I solve this problem I can start my paper on this project.

Here is the color matching functions (left) and spectroscopy data of my laptop display (right):

It may look like they have the same data points but no, the spectroscopy data has a 1240x1 matrix for the red and blue screens, while 1238x1 matrix for the green screen. The color matching functions only have 471x1 matrix.

You can see from the data that the spectroscopy data and color matching functions are similar. Why? Because the color matching functions are like the sensitivity of the eye to color and they are based on standard observer data.