scikit-tda / dreimac Goto Github PK

Hello 👋,

Thank you for this great implementation!

I would be interested in estimating the coordinates on a sample X and thenevaluating them at some new points X_new, that are in principle, close to the original ones. Is that feature available? Is it meaningful?

Looking at examples, I understood that what CircularCoordinates(X, n_landmarks).get_coordinates() returns is the evaluation of circular coordinates on X. Treating your code as a block-box, a first approach is to use nearest-neighbors to map X_new to X and then retrieve the coordinates. However, I'm thinking that one can certainly do better: if i understand well, you compute cohomology on landmarks L and you construct the coordinates on X. Is there an obstruction to using a different set of points, as long as it is contained in the offset of L? Would it be possible to obtain the function described in the output section?

If such a feature is not available, but you think it makes sense and it would be a good addition, I would happily contribute. Thank you for your help.

Incorporate Rann's cliquer code from before using Cython

Hi, first of all thanks for the well-written paper and great software! I love the detailed documentation with lots of examples, and how intuitive and easy to use it is.

I have a small comment regarding citing other software that you build upon. In particular, I think you should mention Ripser and Ripser.py as those are crucial to quickly computing persistent cohomology used to find circular coordinates. It might also be worth mentioning other software you use that has papers written on them (I could find papers on numba, matplotlib and numpy, you can decide which of these you want to include).

On a related note, my software package Ripserer also implements (a more limited form of) sparse circular coordinates. I understand that's easy to miss as I never advertised it and it is somewhat hidden in the docs. You could mention it alongside Dionysus in the statement of need.

Ref. #5791