transformer for OrdinationFormat --> metadata

[nbokulich]

This would be useful to use principal coordinates (or other ordination results) as input metadata. I am working on some methods that could employ this, e.g., to test whether samples change over PC1 before/after treatment.

The transformation of OrdinationFormat --> pd.DataFrame can be achieved with something like this (in a jupyter notebook, at least. I suppose the first line might be unnecessary in a transformer):

beta_div = beta_div.view(skbio.OrdinationResults)
beta_div = beta_div.samples.loc[:, 0:2]
beta_div.columns = ['unweighted-unifrac-pc1', 'unweighted-unifrac-pc2', 'unweighted-unifrac-pc3']

and then I assume the beta_div DataFrame can be converted to metadata with

qiime2.Metadata(beta_div)

I would find this extremely useful — any interest?

[ebolyen]

It would be qiime2.Metadata.from_artifact(beta_div) (if you wanted to preserve provenance), but yeah!

We're about to write a whole ton of transformers for FeatureData[...], I don't see why this couldn't be added to the list.

@ElDeveloper, does this kind of view make sense to you?

[thermokarst]

Duplicate of #122, moving discussion into there.