Currently, the sorted data loses the original indexing formation and at first thought it looks hard to keep the indexing information on the fly. Keeping the original indexing information can be essential in any practical usage because each data point typically represents a different object in a N-d space. Examples are atoms in molecules, planets or stars in galaxies, or locations in a map. On the contrary, it might not be important in image vectorization and volumetric data vectorization. An option/way to keep the original indexing information is needed.
Thoughts:
- Skip the index column in the numpy array when doing sort. Technically, this enables skipping any columns.
- In implementation, should also provide an option to add an index column if not already there but required to keep the information to the original data.
Currently, the sorted data loses the original indexing formation and at first thought it looks hard to keep the indexing information on the fly. Keeping the original indexing information can be essential in any practical usage because each data point typically represents a different object in a N-d space. Examples are atoms in molecules, planets or stars in galaxies, or locations in a map. On the contrary, it might not be important in image vectorization and volumetric data vectorization. An option/way to keep the original indexing information is needed.
Thoughts: