@djhoese not yet but hopefully soon! Most of the work on explicit indexes is currently happening in #5692, which once merged (probably after the next release) will provide all the infrastructure for custom indexes. This is quite a big internal refactoring (bigger than I initially thought) that we cannot avoid as we're changing Xarray's core data model. After that, we'll need to update some public API (Xarray object constructors, .set_index(), etc.) so that Xarray will accept custom index classes. This should take much less work than #5692, though.

@djhoese you're right, I thought it was better to do all the internal refactoring first but we maybe shouldn't wait too long before updating set_index and the DataArray / Dataset constructors so that you and others can start playing with custom indexes.

In your opinion will this type of CRSIndex/WCSIndex work need #5322? If so, will it also require (or benefit from) the additional internal xarray refactoring you mention in #5322?

Yes, CRSIndex/WCSIndex will need to provide an implementation for the query method added in #5322. However, this could be "as simple as" internally using PandasIndex for each 1-d coordinate in case of raster/grid data, maybe with an additional check that the values provided to .sel are in the same CRS (for example in the case of advanced indexing where xarray.DataArray or xarray.Variable objects are passed as arguments).

What will be probably more tricky is to find some common way to handle CRS for various indexes (e.g., regular gridded data vs. irregular data), probably via some class inheritance hierarchy or using mixins.

I can really see this becoming super easy for CRS-based dataset users where libraries like geoxarray (or xoak) "know" the common types of schemes/structures that might exist in the scientific field and have a simple .geo.set_index that figures out most of the parameters for .set_index by default.

In case we load such data from a file/store, thanks to the Xarray backend system, maybe we won't even need a .geo.set_index but we'll be able to build the right index(es) when opening the dataset!

We could also imagine

```python

returns a new dataset with both pixel and world (possibly lazy) coordinates

new_dataset = dataset.astro.append_world({'x': 'xw', 'y': 'yw', 'z': 'zw'})

so that we can directly select data either using the pixel coordinates...

new_dataset.sel(x=..., y=..., z=...)

...or using the world coordinates

new_dataset.sel(xw=..., yw=..., zw=...)

the WCS index would be attached to both pixel and world dataset coordinates

new_dataset <xarray.Dataset> Dimensions: (x: 100, y: 100, z: 100) Coordinates: * x (x) float64 ... * xw (x) float64 ... * y (y) float64 ... * yw (y) float64 ... * z (z) float64 ... * zw (z) float64 ... Data variables: field (x, y, z) float64 .... Indexes: x, y, z, zw, yw, zw WCSIndex ```

This looks like a nice use case for the forthcoming Xarray's custom index feature.

How I see CRS/WCS-aware Xarray datasets with custom indexes:

• A set of coordinate(s) and their attributes hold data or metadata relevant for public use and that could be easily (de)serialized

• A custom index (CRSIndex or WCSIndex) provides CRS/WCS-aware implementations of common Xarray operations such as alignment (merge/concat) and data selection (sel), via Xarray.Index's equals, union, intersection and query methods added in #5102 and #5322 (not yet ready for use outside of Xarray). Such custom index may also be used to hold some data that is tricky to propagate by other means, e.g., some internal information like "functional" coordinate parameters or a crs object. Xarray indexes should definitely provide more flexibility than coordinate data or attributes or accessor attributes for propagating this kind of information.

• Xarray accessors may be used to extend Dataset/DataArray public API. They could use the information stored in the CRSIndex/WCSIndex, e.g., add a crs read-only property that returns the crs object stored in CRSIndex, or add some some extract_crs_parameters method to extract the parameters and store them in Dataset/DataArray attributes similarly to what @djhoese suggests in his comment above.

For this use case a possible workflow would then be something like this:

```python

create or open an Xarray dataset with x, y, z "pixel" (possibly lazy) coordinates

and set a WCS index

dataset = ( xr.Dataset(...) .set_index(['x', 'y', 'z'], WCSIndex, wcs_params={...}) )

select data using pixel coordinates

dataset.sel(x=..., y=..., z=...)

select data using world coordinates (via the "astro" accessor,

which may access methods/attributes of the WCS index)

dataset.astro.sel_world(x=..., y=..., z=...)

return a new dataset where the x,y,z "pixel" coordnates are replaced by the "world" coordinates

(again using the WCS index, and propagating it to the returned dataset)

world_dataset = dataset.astro.pixel_to_world(['x', 'y', 'z'])

select data using world coordinates

world_dataset.sel(x=..., y=..., z=...)

select data using pixel coordinates (via the "astro" accessor)

world_dataset.astro.sel_pixel(x=..., y=..., z=...)

this could be reverted

pixel_dataset = world_dataset.astro.world_to_pixel(['x', 'y', 'z']) assert pixel_dataset.identical(dataset)

depending on the implementation in WCSIndex, would either raise an error

or implicitly convert to either pixel or world coordinates

xr.merge([world_dataset, another_pixel_dataset]) ```