Support for I/O of standard formats about stdlib HOT 10 OPEN

It could be good to have them for comparison, but it could be the occasion to make a clean-room design.
Maybe decided on the style for the signature of IO procedures, this could already enable focusing on individual implementations that should respect the decided signature.

I had in mind something like this:

subroutine matrix_<save/load>_<format>( matrix_type , file_name , ...optionals... )
end subroutine

from stdlib.

For reference, the SciPy IO API: https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.mmwrite.html#scipy.io.mmwrite
the TACO library also offers an API of the sorts for C++ and Python: http://tensor-compiler.org/docs/scientific_computing.html

from stdlib.

I've added #486 which is also relevant. I would be keen to have the SciPy .npz format supported, which is also used to pass sparse matrices.

from stdlib.

There is also a naive text format that MATLAB uses, described under

• spconvert
• Importing Sparse Matrices

I've used it in the past, to quickly output a matrix and import it into MATLAB for inspection (spy) and calculating the spectral radius.

from stdlib.

Can we use the MatrixMarket routines from NIST (potentially, wrapped in a nicer interface)? Or should we do a clean-room design and implementation?

from stdlib.

Can we use the MatrixMarket routines from NIST (potentially, wrapped in a nicer interface)?

If their licenses permit it, I would say yes. It might be good to contact them, before using their codes (e.g., like it was done for the hash and some sort procedures).

from stdlib.

Thanks, @perazz, for opening this issue and all of you for your input and adding the references. I'll take care of it over the next few weeks.

from stdlib.

the TACO library also offers an API of the sorts for C++ and Python: http://tensor-compiler.org/docs/scientific_computing.html

The formats supported by TACO are:

• Matrix Market (Coordinate) Format (.mtx)
• Rutherford-Boeing Format (.rb)
• FROSTT Format (.tns)

from stdlib.

I've also used the same format locally to then use matplotlib's spy https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.spy.html
So basically take whichever sparse format > transform to COO > dump a file <name>.dat

from stdlib.

I will ask @minhqdao if he plans to take care of this issue.

from stdlib.