array.to_hdf5(compute=False) doesn't return delayed value

[nirizr]

array.to_hdf5 accepts a compute keyword implicitly by passing it along to store (which handles it correctly), but array.to_hdf5 does not handle that correctly. It should accept it explicitly and return a single delayed() object in that case.

[nirizr]

On a side note, all other hdf functions are called hdf, this is the only place the function is called hdf5

[shoyer]

Pandas calls everything just "HDF", but I think it's actually useful to say "HDF5" for the array stuff, because there still folks who use earlier versions of the file format.

[GenevieveBuckley]

Should this issue be closed now #1286 is merged?

[mrocklin]

It looks like #1286 solved this problem for Dask Dataframe, but not yet for dask array. Looking briefly at the following code, it looks like da.to_hdf5 always returns None, so this isn't yet done.

dask/dask/array/core.py

Lines 4332 to 4379 in 539d1e2

	def to_hdf5(filename, *args, **kwargs):
	""" Store arrays in HDF5 file
	This saves several dask arrays into several datapaths in an HDF5 file.
	It creates the necessary datasets and handles clean file opening/closing.
	>>> da.to_hdf5('myfile.hdf5', '/x', x) # doctest: +SKIP
	or
	>>> da.to_hdf5('myfile.hdf5', {'/x': x, '/y': y}) # doctest: +SKIP
	Optionally provide arguments as though to ``h5py.File.create_dataset``
	>>> da.to_hdf5('myfile.hdf5', '/x', x, compression='lzf', shuffle=True) # doctest: +SKIP
	This can also be used as a method on a single Array
	>>> x.to_hdf5('myfile.hdf5', '/x') # doctest: +SKIP
	See Also
	--------
	da.store
	h5py.File.create_dataset
	"""
	if len(args) == 1 and isinstance(args[0], dict):
	data = args[0]
	elif len(args) == 2 and isinstance(args[0], str) and isinstance(args[1], Array):
	data = {args[0]: args[1]}
	else:
	raise ValueError("Please provide {'/data/path': array} dictionary")
	chunks = kwargs.pop("chunks", True)
	import h5py
	with h5py.File(filename, mode="a") as f:
	dsets = [
	f.require_dataset(
	dp,
	shape=x.shape,
	dtype=x.dtype,
	chunks=tuple([c[0] for c in x.chunks]) if chunks is True else chunks,
	**kwargs
	)
	for dp, x in data.items()
	]
	store(list(data.values()), dsets)