Read HDF5 data with numpy axis order with Julia HDF5

Question

I have an HDF5 file containing arrays that are saved with Python/numpy. When I read them into Julia using HDF5.jl, the axes are in the reverse of the order in which they appear in Python. To reduce the mental gymnastics involved in moving between the Python and Julia codebases, I reverse the axis order when I read the data into Julia. I have written my own function to do this:

function reversedims(ary::Array)
  permutedims(ary, [ ndims(ary):-1:1 ])
end

data = HDF5.read(someh5file, somekey) |> reversedims

This is not ideal because (1) I always have to import reversedims to use this; (2) I have to remember to do this for each Array I read. I am wondering if it is possible to either:

• instruct HDF5.jl to read in the arrays with a numpy-style axis order, either through a keyword argument or some kind of global configuration parameter
• use a builtin single argument function to reverse the axes

Answer 1

The best approach would be to create a H5py.jl package, modeled on MAT.jl (which reads and writes .mat files created by Matlab). See also https://github.com/timholy/HDF5.jl/issues/180.

Answer 2

It looks to me like permutedims! does what you're looking for, however it does do an array copy. If you can rewrite the hdf5 files in python, numpy.asfortranarray claims to return your data stored in column-major format, though the numpy internals docs seem to suggest that the data isn't altered, simply the stride is, so I don't know if the hdf5 file output would be any different

Edit: Sorry, I just saw you are already using permutedims in your function. I couldn't find anything else on the Julia side, but I would still try the numpy.asfortranarray and see if that helps.