Meta Data Preservation #8
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By @ matthew.brett on July 17, 2021, 10:03pm Thanks for starting this. The first thing to say is that it does not seem that that the The second was to ask about your (Brendan's) understanding of the rows = df.loc['first':'fourth'] and I assumed that's what they mean by indexing - selecting values by label rather than integer position. What do you think? |
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By @ moloney on July 16, 2021, 6:38pm
I wanted to kick off a conversation about the meta data preservation aspect of this project. I had floated the idea of using xarray instead of the code in dcmstack to provide a nice API (and serialization format) to the meta data that we parse from the DICOM files and then summarize with respect to the axis of the nD array. The main concern with this approach seems to revolve around how "heavy" of a dependency xarray is (i.e. pulling in Pandas), with a potential solution being some future "xarray-lite" package.
However, my understanding of the proposed xarray-lite package (see: here) is that it will not be able to support anything beyond labeled dimensions which will be of quite limited utility. It can't even keep track of per-volume parameter values, never mind more complicated cases like per-slice acquisition times. So it seems like we might be stuck having the full xarray package at least as an optional dependency, or taking some other approach.
Thoughts?
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