load datasets bypassing query

[benjimin]

A simple way to convert datacube datasets to xarray datasets.

For experimenting with task apps (producing datasets that have not yet been indexed), this would get the benefits that xarray.open_dataset lack and avoid:

from unittest import mock
with mock.patch('datacube.api.core.Datacube.product_observations') as po:
    po.return_value = [dataset]
    n = dc.load(product='ls5_nbar_albers', measurements=['swir1','nir','green'])
with mock.patch('datacube.api.core.Datacube.product_observations') as po:
    po.return_value = result
    w = dc.load(product='wofs_albers')

[omad]

That sounds useful to me Ben. And it's a really nice small change.

Can you please update the formatting to make our overzealous pylint happy and I'll merge it in.

************* Module datacube.api.core
C:274, 0: Trailing whitespace (trailing-whitespace)
C:275, 0: Line too long (121/120) (line-too-long)

[coveralls]

Coverage remained the same at 80.152% when pulling 51bedef on benjimin:load-ds into ee75ff1 on data-cube:develop.

[v0lat1le]

Without context I'd say that product_sources and product_data should be used for that. load is meant as a higher level API where knowledge of existence of datasets is not required...

So, what's the context for this, why is this needed?

[omad]

If you have a datacube dataset from somewhere, and you want to load the associate data from it. You can do it with product_sources and product_data, but it requires some extra code messing around with their required parameters.

[benjimin]

Welcome back Greg. I think your concern that rarely-used optional arguments (in high-level API) impose undue cognitive burden on python users (to less emotively paraphrase "people's heads will explode") is misplaced; it is consistent with the high-level API style of popular python libraries, such as plot in matplotlib (e.g. the transform argument can bypass parts of its functionality; it is more convenient than replicating with the low-level API, and most users safely ignore it without needing to learn about the transforms which underlie matplotlib). The other good news is that the naming for product_sources, product_observations, etc has been fixed to describe to users what they do.