Efficient Visualization Techniques for Large Datasets: A Scientific Inquiry

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Discussion Overview

The discussion revolves around techniques for visualizing a large dataset consisting of x, y, z coordinates and a function value f(x,y,z). Participants explore various methods and tools to effectively represent this data in a 3D format, focusing on issues related to memory constraints and data representation strategies.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant expresses the need for a visualization method that uses 3D spheres with opacity based on f(x,y,z) values, but has encountered limitations with existing software like VisIt and ParaView.
  • Another participant suggests using an octree for visibility determination due to the large number of data points.
  • A third participant points to a resource related to point cloud representation and suggests that streaming data might be necessary since the entire dataset may not fit in memory.
  • One participant proposes setting a threshold for f(x,y,z) to reduce the number of points generated, focusing only on those with larger values to manage memory usage.
  • Another participant recommends taking the logarithm of f(x,y,z) to better visualize the data, and suggests sampling a fraction of the data points to identify clusters for more meaningful representation.
  • A brief mention of Mayavi as a potential tool for visualization is made.

Areas of Agreement / Disagreement

Participants present multiple competing views and suggestions regarding visualization techniques and tools, with no consensus reached on a single approach or solution.

Contextual Notes

Participants note limitations related to memory constraints and the need for effective data representation strategies, but do not resolve these issues.

Who May Find This Useful

This discussion may be useful for researchers and practitioners dealing with large datasets in fields such as data science, computational physics, and engineering, particularly those interested in visualization techniques and tools.

lylos
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First of all, let me apologize if this is the wrong section to post to.

I am in need of ideas on how to visualize a large dataset. I have a dataset (~4GB) of x,y,z,f(x,y,z) values. To best be able to draw conclusions from my data, it must be plotted in such a way that at each x,y,z there is a 3d sphere that has opacity scaled by f(x,y,z).

I have tried a couple plotting programs: VisIt, and ParaView. I was not able to get either to provide the functionality I need, although they are very close!

I then wrote a python script that created a VTK file with each individual point defined. Yet, the vtk file turned out to be pretty large and there was no viewer capable of displaying it.

Any suggestion would be greatly appreciated. I am just looking for some ideas here, I've about ran out.
 
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With that many points you need something with visibility determination. The usual approach for this kind of data is an octree.
 
Take a look at this:
http://pointclouds.org/

This is based on VTK though.. It sounds to me like the problem you have is that the data itself needs to be streamed in as it won't all fit in memory.

Is there some way you could aggregate the datapoints so that it would still give you meaningful data?
 
The idea of a point cloud representation is similar to what I had in mind.

The f(x,y,z) spans many magnitudes in value. I'm only interested in those points that have a larger value of f(x,y,z). As such, perhaps I could have a lower threshold, below which a point will not even be generated. This should lower the memory footprint, still giving me meaningful data...
 
Since your f spans many orders of magnitudes almost nothing will display that meaningfully, so take the log of the f before trying to display.

Since you have a vast supply of data, more than almost anything could visualize meaningfully, try sampling your data. Randomly select 1/16 of your data points, repeat the process, display both of those side by side and see if you see any substantial difference.

If you see large differences then perhaps you might have success identifying clusters within the data and then displaying one representative for each cluster. There are lots of papers describing how to identify clusters, but you will need a program that can cope with that much data.
 
Mayavi?
 

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