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pythonmatplotlibscalingcontour

Python Matplotlib nonlinear scaling in contour plot

I'm having some trouble visualizing a certain dataset that I have in a contour plot. The issue is that I have a bunch of datapoints (X,Y,Z) for which the Z values range from about 2 to 0, where a lot of the interesting features are located in the 0 to 0.3 range. Using a normal scaling, they are very difficult to see, as illustrated in this image:

https://i.sstatic.net/7O8hH.png

Now, I have thought about what else to do. Of course there is logarithmic scaling, but then I first need to think about some sort of mapping, and I am not 100% sure how one would do that. Inspired by this question one could think of a mapping of the type scaling(x) = Log(x/min)/Log(max/min) which worked reasonably well in that question.

Also interesting was the followup discussed here.

where they used some sort of ArcSinh scaling function. That seemed to enlarge the small features quite well, proportionally to the whole.

So my question is two fold in a way I suppose.

  1. How would one scale the data in my contour plot in such a way that the small amplitude features do not get blown away by the outliers?

  2. Would you do it using either of the methods mentioned above, or using something completely different?

I am rather new to python and I am constantly amazed by all the things that are already out there, so I am sure there might be a built in way that is better than anything I mentioned above.

For completeness I uploaded the datafile here (the upload site is robustfiles.com, which a quick google search told me is a trustworthy website to share things like these)

I plotted the above with 

data = np.load("D:\SavedData\ThreeQubitRess44SpecHighResNormalFreqs.npy")

fig, (ax1) = plt.subplots(1,figsize=(16,16))    
cs = ax1.contourf(X, Y, data, 210, alpha=1,cmap='jet')
fig.colorbar(cs, ax=ax1, shrink=0.9)
ax1.set_title("Freq vs B")
ax1.set_ylabel('Frequency (GHz)'); ax1.set_xlabel('B (arb.)')
Solution

  • Excellent question.

    Don't scale the data. You'll be looking for compromises in ranges with many scaling functions.

    Instead, use a custom colormap. That way, you won't have to remap your actual data and can easily customize the visualization of the regions you'd like to highlight. Another example can be found in the scipy cookbook and there's quite a few more on the internet.