Hey Folks, I stumbled over this discussion having a similar use case as described in some comments above: A DataSet with a bunch of arrays called count_a, test_count_a, train_count_a, count_b, ... , controlled_test_mean, controlled_train_mean, ... controlled_test_sigma, ... Obviously a hierarchical structure would help to arrange this.

However, one point I didn't see in the discussion is the following:

Hierarchical structures often force a user to come up with some arbitrary order of hierarchy levels. The classical example is document filing: do you put your health insurance documents under /insurance/health/2021, 2021/health/insurance,....?

One solution to that is a tagging of documents instead of putting them into a hierarchy. This would give the full flexibility to retrieve any flat DataSet out of a TaggedDataSet by specifying the set of tags that the individual DataArrays must be listed under.

Back to the above example, one could think of stuff like:

```python

get a flat view (DataSet-like object) on all arrays of tagged that have the 'count' tag

ds: DataSet(View) = tagged.tag_select("count") bar1 = ds.mean(dim="foo")

get a flat view (DataSet-like object) on all arrays of tagged that have the "train and "controlled" tag

bar2 = tagged.tag_select("train", "controlled").mean(dim="foo") # order of arguments to tag_select is irrelevant! ``` I hope it is clear what I mean, I know that there is e.g. some awesome file system plugins (he has incredibly nice high level documentation on the topic) that use such a data model.

Just wanted to add that aspect to the discussion even if it might collide with the hierarchical approach!

One side note: If every array in the tagged container has exactly one tag, and tags do not repeat, then the whole thing should be semantically identical to a DataSet because every tag_select will yield a single DataArray - I.e. it might be possible to integrate such functionality directly into DataSet !?!

Regards,

Martin

I did a ctrl-f for zarr in this issue, found nothing, so here's my two cents: it should be possible to write a Datagroup with either zarr or netcdf. I wonder if @emilbiju (posted https://github.com/pydata/xarray/issues/4118 ) has any of that code laying around, could be a starting point. In general, a tree structure to which I can broadcast operations in the same dimensions to different datasets that do not necessary share dimension lengths would solve my use case . This corresponds to bullet point number 3 in https://github.com/pydata/xarray/issues/1092#issuecomment-290159834 . My use case is a set of experiments, that have: the same parameter variables, with different values; the same dimensions with different lengths for their data. The parameters and data would benefit of having a hierarchical naming structure. Currently I build a master dataset containing experiment datasets, with a coordinate for each parameter. Then I map functions over it.

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I'm late to the discussion and may be repeating some things essentially already said, but I'd still like to add a further voice.

@shoyer said on 8 Nov 2016:

I am reluctant to add the additional complexity of groups directly into the xarray.Dataset data model. For example, how do groups get updated when you slice, aggregate or concatenate datasets? The rules for coordinates are already pretty complex.

If you prepend the paths to all the names (of dimensions, coordinate variables, and variables) and use the resulting strings as names, don't you just get a collection that would fit right in a xarray.Dataset? (Perhaps I'm just repeating what @lamorton said on 28 Mar 2017.) My feeling is that the only thing missing would be coordinate variables defined in a group closer to the root of the hierarchy, but that needs to be dealt with anyway if you want to read from netCDF4 files correctly (see my #1888). My guess would be that having groups inherit all the dimensions/coordinates of their parent that they do not redefine should be the way to go.

My use case is data from a single metmast over time. There are various instruments measuring all kinds of variables of which 10-minute statistics are recorded. I use groups to keep an overview. (I use something like /[wind|air|prec]/<device>/<variable>, /<device>/<variable>, or /<device>/<variable>/statistic as a hierarchy.) Slicing along the time axis for the whole hierarchy would make perfect sense.

@shoyer said on 30 Mar 2017:

We would possibly want to make another Dataset subclass for the sub-datasets to ensure that their variables are linked to the parent, e.g., xarray.LinkedDataset. This would enable ds['flux']['poloidal'] = subset.

But I'm also not convinced this is actually worth the trouble given how easy it is to write ds['flux', 'poloidal'].

I would prefer the former option, as it more clearly shows the hierarchical nature. If also copying the netCDF4-path-separator-convention, then ds['flux/poloidal'] is shorter than ds['flux', 'poloidal']. (Allowing Dataset or DataArray to have names that include '/' would be dangerous anyway in view of netCDF4 serialization.)

@shoyer I see your point about the string manipulation. On the other hand, this is exactly how h5py and netCDF4-python implement the group/subgroup access syntax: just like a filepath.

I'm also having thoughts about the attribute access: if ds['flux']['poloidal'] = subset does not work, then neither does ds.flux.poloidal = subset, correct? If so, it is almost pointless to have the attribute access in the first place. I suppose that is the price to pay for merely making it appear as though there is attribute-access.

For my own understanding, I tried to translate between xarray and netCDF4-python : - nc.Variable <--> xr.Variable - nc.????? <--> xr.DataArray (netCDF doesn't distinguish vars/coords, so no analog is possible) - nc.Group <--> xr.NestableDataset - nc.Dataset <--> xr.NestableDataset

From netCDF4-python

Groups define a hierarchical namespace within a netCDF file. They are analogous to directories in a unix filesystem. Each Group behaves like a Dataset within a Dataset, and can contain it's own variables, dimensions and attributes (and other Groups). Group inherits from Dataset, so all the Dataset class methods and variables are available to a Group instance (except the close method).

It appears that the only things special about a nc.Dataset as compared to an nc.Group are: 1. The file access is tied to the nc.Dataset. 2. The nc.Dataset group has children but no parent.

A big difference between xarray and netCDF4-python datasets is that the children datasets in xarray can go have a life of their own, independent of their parent & the file it represents. It makes sense to me to have just a single xarray type (modified version of xarray.Dataset) to deal with both of these cases.

The nc.Group instances have an attribute groups that lists all the subgroups. So one option I suppose would be to follow that route and actually have Datasets that contain other datasets alongside everything else.

As an aside, it seems that ragged arrays are now supported in netCDF4-python:VLen.

@darothen: Hmm, are your coordinate grids identical for each simulation (ie, any(ds1.x != ds2.x) evaluates as false)?
- If so, then it really does make sense to do what you described and create new dimensions for the experimental factors, on top of the spatial dimensions of the simulations.
- If not, but the length of all the dimensions is the same, one could still keep all the simulations in the same dataset, one would just need to index the coordinates with the experimental factors as well. - Finally, if the shape of the coordinate arrays varies with the experimental factor (for instance, doing convergence studies with finer meshes), that violates the xarray data model for having a single set of dimensions, each of which has a fixed length throughout the dataset, in order to enable smart broadcasting by dimension name. If (and only if) the dimensions are changing length, it would be better to keep a collection of datasets in some other type of data structure.

It might work for my case to convert my 'tags' to indexes for new dimensions (ie, ds.sel(quantity='flux',direction='poloidal',variation='perturbed'). However, there are two issues: 1. The background flux is defined to be uniform in some coordinates, so it is lower-dimensionality than the total flux. It doesn't make sense to turn a 1-D variable into a 3-D variable just to match the others so I can put it into an array. This goes especially for scalars and metadata that really should not be turned into arrays, but do belong with the subsets. 2. During my processing sequence, I may want to add something like ds.flux.helical.background. In order to do this, however, I'd be forced to define the 'perturbed' and 'total' helical fluxes at that time. But often I don't want or need to compute these.

There is still a good reason to have a flexible data model for lumping more heterogeneous collections together under some headings, with the potential for recursion. I suppose my question is, what is the most natural data model & corresponding access syntax?
- Attribute-style access is convenient and idiomatic; it implies a tree-like structure. This probably makes the most sense. - An alternative data model would be sets with subsets, which could be accessed by something similar to ds.sel but accepting set names as *args rather than **kwargs. Then requesting members of some set could return a dataset with those members, and the new dataset would lack the membership flag for variables, much the way slicing reduces dimensionality. In fact, one could even keep a record of the applied set requests much like point axes. A variable's key in data_vars would essentially just be a list/tuple of sets of which it is a member. Assignment would be tricky because it could create new sets, and the membership of existing elements in a new set would probably require user intervention to clarify...

@shoyer: Your approach is quite clever, and 'smells' much better than parsing strings. I do have two quibbles though. - Accessing via ds['flux','poloidal'] is a bit confusing because ds[] is (I think) a dictionary, but supplying multiple names is suggestive of either array indexing or getting a list with two things inside, flux and poloidal. That is, the syntax doesn't reflect the semantics very well. - If I am at the console, and I start typing ds.flux and use the tab-completion, does that end up creating a new dataset just so I can see what is inside ds.flux? Is that an expensive operation?

[Edited for formatting]

Ah, thanks for the heads-up @benbovy! I see the difference now, and I agree both approaches could co-exist. I may play around with building some of your proposed DatasetNode functionality into my Experiment tool.

Would the domain for this just be to simulate the tree-like structure that NetCDF permits, or could it extend to multiple datasets on disk? One of the ideas that we had during the aospy hackathon involved some sort of idiom based on xarray for packing multiple, similar datasets together. For instance, it's very common in climate science to re-run a model multiple times nearly identically, but changing a parameter or boundary condition. So you end up with large archives of data on disk which are identical in shape and metadata, and you want to be able to quickly analyze across them.

As an example, I built a helper tool during my dissertation to automate much of this, allowing you to dump your processed output in some sort of directory structure and consistent naming scheme, and then easily ingest what you need for a given analysis. It's actually working great for a much larger, Monte Carlo set of model simulations right now (3 factor levels with 3-5 values at each level, for a total of 1500 years of simulation). My tool works by concatenating each experimental factor as a new dimension, which lets you use xarray's selection tools to perform analyses across the ensemble. You can pre-process things before concatenating too, if the data ends up being too big to fit in memory (e.g. for every simulation in the experiment, compute time-zonal averages before concatenation).

Going back to @shoyer's comment, it still seems as though there is room to build some sort of collection of Datasets, in the same way that a Dataset is a collection of DataArrays. Maybe this is different than @lamorton's grouping example, but it would be really, really cool if you could use the same sort of syntactic sugar to select across multiple Datasets with like-dimensions just as you could slice into groups inside a Dataset as proposed here. It would certainly make things much more manageable than concatenating huge combinations of Datasets in memory!

One important reason to keep the tree-like structure within a dataset is that it provides some assurance to the recipient of the dataset that all the variables 'belong' in the same coordinate space. Constructing a tree (from a nested dictionary, say) whose leaves are datasets or dataArrays doesn't guarantee that the coordinates/dimensions in all the leaves are compatible, whereas a tree within the dataset does make a guarantee about the leaves.

As far as motivation for making trees, I find myself with several dozen variable names such as ds.fluxPoloidalPerturbation and ds.fieldToroidalBackground and various permutations, so it would be logical to be able to write ds.flux.poloidal and get a sub-dataset that contains dataArrays named perturbation and background.

As far as implementation, the DataGroup could really just be syntactic sugar around a flat dataset that is hidden from the user, and has keys like 'flux.poloidal.perturbed,' so that dg.flux.poloidal.perturbed would be an alias to dg.__hiddenDataset__['flux.poloidal.perturbed'], and dg.flux.poloidal would be an alias to dg.__hiddenDataset__[['flux.poloidal.perturbed','flux.poloidal.background']]. Seems like it would require mucking with dg.__getattr__, dg.__setattr__, and dg.__dir__ at a minimum to get it off the ground, but by making the tree virtual, one avoids the difficulties with slicing, etc. The return type of dg.__getattr__ should be another DataGroup as long as there are branches in the output, but it should fall back to a Dataset when there are only leaves.