edgebundle
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schochastics
migrate hammer bundle from python to c++
datashader is too big of a library to depend on. A re-implementation of the algorithm may be a good idea
code: https://datashader.org/_modules/datashader/bundling.html
original: https://gitlab.com/ianjcalvert/edgehammer
hammer bundle in the example codes never worked on my machine.
it is always
Error: C stack usage 17587454638400 is too close to the limit
the other two work fine. thank you for your fantastic job.
it’s exactly the codes in your blogpost. everything worked fine until it’s stuck at the hammer bundle.
20 jan. 2021 kl. 14:46 skrev David Schoch [email protected]:
could you share a reproducible example?
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hmm I think I cannot do much about that. The main lifting is done in a python library I have no influence on. Maybe this helps: https://stackoverflow.com/questions/14719349/error-c-stack-usage-is-too-close-to-the-limit
Just another motivation to implement it myself to be able to deal with these kind of problems.
I'm having some trouble with the internals of the hammer bundling. I can try to send you my graph object if that would be helpful.
Error in py_call_impl(callable, dots$args, dots$keywords) : ValueError: You are trying to merge on float64 and object columns. If you wish to proceed you should use pd.concat
Here's an RDS of my graph. I've been using the sfnetwork package to transform spatial data to a graph object and then casting (igraph::as.igraph()) when I call edge_bundle_hammer().
I am afraid that this is a problem of the python code that is called. So I cannot fix this issue. Yet another reason to implement it myself. I looked at the network a bit and the issue might be that they are all just paths. Did you try the other two edge bundling algorithms?
I did. Maybe because they're just paths, I could never make the force-directed look good. The stub failed too, but is unappealing to me.
From looking at the network a bit, I think any edge bundling will fail to produce something appealing. The structure seems too weird. (But I feel like the stub one always produces bad results irregardless of the input)