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Question: What is the best way to interpolate and plot a large set of scattered data?

I have a set of scattered data with about 55,000 points. I want to make a map of these points similar to the answer provided here: Python : 2d contour plot from 3 lists : x, y and rho?.

  • I implemented the code provided in that link, however I do not think that rbf interpolation is right for the job. If it is right for the job I don't know how to only include nearby points in the interpolation.
  • I also tried to implement some other techniques such as SmoothBivariateSpline but I could not get the code to work. I also tried to use RectBivariateSpline (suggested here: SciPy interpolation of large matrix) but was unable to order my data points correctly.
  • I have also read about IDW (from this answer: Inverse Distance Weighted (IDW) Interpolation with Python) but am unsure if it suits my needs.

I am sure there is a better method out there to interpolate the data, but I am not knowledgable about the subject. I also am not sure how to graphically represent the 55,000 points without the plot looking crazy. I have a plot to show what I am dealing with but I cannot post an image yet since I am new to the stack.

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thegreatyonini
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  • Do you need to interpolate or only to plot ? In the latter case you could try *tricontour* http://matplotlib.org/dev/api/axes_api.html?highlight=tricontou#matplotlib.axes.Axes.tricontour – GBy May 13 '14 at 11:26
  • As this stands, this question is too broad to be answerable and will probably get closed as 'off-topic - too broad' or 'off-topic - opinion based'. If you can make simple example we might be able to help you with your second bullet point. I would also try just binning your data down to a grid (the value of each gridbox is the average of the points that fall into it). – tacaswell May 13 '14 at 12:23

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