Open emilbiju opened 4 years ago
@emilbiju - thanks for opening an issue here. You may want to take a look at the conversation in #1092.
Thanks @jhamman for sharing the link. Here are my thoughts on the same:
For use-cases similar to the one I have mentioned, I think it would be more meaningful to allow the tree structure (calling it Datatree
further) to exist as a separate data structure instead of residing within the Dataset. From what I understand, the xarray Dataset would enforce all its component variables to share the same coordinate set for a given dimension name. This would again result in memory wastage with nan
values when the value corresponding to a coordinate is unknown.
Besides, xarray only allows attribute access for getting (and not setting) values, but a separate data structure can allow attribute access for setting values as well. For example, the data structure that I have implemented would allow something like dt.weather = dt.weather.mean('time')
to alter all the data arrays under the weather
node.
I am currently using attribute-based access for accessing child nodes/data arrays in the Datatree
as it appears to reflect the tree structure better, but as @shoyer has pointed out, tuple-based access might be easier to use programmatically.
Instead of using netCDF4 groups for encoding the Datatree
, I am currently following a simple 3-step process:
Datatree
object into a dataset.xarray.Dataset.to_netcdf
method to store it in a netCDF file. Therefore, within the netCDF file, it would exist just as a Dataset. A specially implemented Datatree.open_datatree
method can open the dataset, detect this additional array and recreate the tree structure to instantiate the object. I would like to know if using netCDF4 groups instead provide any advantages over this approach?
Thanks for writing this up @emilbiju . These are very interesting ideas
The nice thing about using NetCDF groups (or HDF5?) is that it is a standard and your data files are readable using other software.
So far, xarray has been reluctant to add "groups" or this kind of hierarchical organization because of all the additional complexity involved (#1092)
That said, there is definitely interest in a package that provides a high-level object composed of multiple xarray datasets (again #1092). So I encourage you to post your code online so others can try it out and iterate.
a. For example, our friends over at Arviz have a InferenceData
structure composed of multiple Datasets that is represented on-disk using NetCDF groups: https://arviz-devs.github.io/arviz/notebooks/XarrayforArviZ.html
I would be open to exploring adding a hierarchical data structure into xarray (on an experimental basis, to start), but it would need someone with serious interest and time to make it happen. Certainly there are plenty of use cases across various fields.
The data model you sketch out here looks very similar to what we discussed in #1092. I agree that the semantics are well defined.
The main question in my mind is whether it would make more sense to make an entirely new data structure (e.g., xarray.TreeDataset
) or add in a new feature like groups
to the existing xarray.Dataset
.
Probably a new data structure would be easier at this point, because would keep Dataset
simpler and wouldn't break existing code that works on xarray.Dataset
.
@joshmoore - based on https://github.com/pangeo-forge/pangeo-forge/pull/27#issuecomment-755397835, you may be interested in this issue. One way to do multiscale datasets in Xarray would be to use hierarchical groups (one group per scale).
a. For example, our friends over at Arviz have a
InferenceData
structure composed of multiple Datasets that is represented on-disk using NetCDF groups: https://arviz-devs.github.io/arviz/notebooks/XarrayforArviZ.html
Just a note that this link has moved to: https://arviz-devs.github.io/arviz/getting_started/XarrayforArviZ.html
Thanks for the link, @jhamman. The most immediate issue I ran into when trying to use xarray with OME-Zarr data does seem similar. A rough representation of one multiscale image is:
image_pyramid:
|_ zyx_array_high_res
|_ zyx_array_mid_res
|_ zyx_array_low_res
but of course the x, y and z dimensions are of different sizes in each volume.
@jhamman @joshmoore a prototype to bring together XArray and OME-Zarr/NGFF with multiple groups: https://github.com/OpenImaging/miqa/blob/master/server/scripts/compress_encode.py
On today's Xarray dev call, we discussed pursuing another CZI grant to support this feature in Xarray. The image pyramid use case would provide a strong link to the bioimaging community. @alexamici and the B-open folks seem enthusiastic.
I had to leave the meeting early, so I didn't hear the end of the conversation. But did we decide who might serve as PI for such a proposal?
But did we decide who might serve as PI for such a proposal?
No.
@emilbiju are you interested in open-sourcing your work?
FWIW, a while ago I wrote a mock-up (and probably outdated) DatasetNode
class:
https://gist.github.com/benbovy/92e7c76220af1aaa4b3a0b65374e233a (nbviewer link)
This is related to some very recent work we have been doing at NSLS-II, primarily lead by @danielballan .
Not really sure if there is anything we can do from ArviZ to help with that, if there is let us know and we'll do our best cc @percygautam
@alexamici and I can write the technical part of the proposal.
Happy to provide assistance on the image pyramid (i.e. "multiscale") use case.
So we have:
We are just missing a PI, someone who is willing to put their name on top of the proposal and click submit. I have gone on record as committed to not leading any new proposals this year. And in any case, this is a good opportunity for someone else from the @pydata/xarray core dev team to try on a leadership role.
I volunteer to contribute writing to this from the condensed matter / sychrotron user facility perspective.
I can shoulder part of the load and help is definitely needed. LOI is due on Tuesday. I'll take a stab this evening and post a link.
Here are some biomedical papers that are using ArviZ and therefore xarray even if most don't cite xarray and some don't cite ArviZ either. Topics are quite disperse: covid, psychology, biomolecules, oncology...
I'm excited to see this coming together! I would be happy to advise as well...
Side note: at some point, this would probably be worth adding to Xarray's official roadmap.
We could also provide a use-case in remote sensing: it would be really useful in the interferometric processing for managing Sentinel-1 IW and EW SLC data, which has multiple tiles (burts) partially overlapping in one direction (azimuth).
This sounds like an interesting project - I'm also about to be able to work on xarray much more directly (thanks @rabernat ).
Should I add this as another xarray project board alongside explicit indexes and so on?
I wonder if this could find another domain use case in plasmapy as part of the overall plasma
object @StanczakDominik? At the very least this would allow you to store all the various equilibrium and diagnostics information that goes in an EFIT file.
Whoa, that sounds awesome! Thanks for the heads up :) Definitely could be quite handy, looking forward to seeing how this develops. @rocco8773 this should be interesting for you as well :)
For scientific imaging, i.e. biomicroscopy, medical imaging, where xarray compatibility is being considered in the NGFF, it would be helpful to avoid unnecessary divergence by ensuring the proposed hierarchical storage is compatible. This would mean:
xarray.Dataset
. “multiscales”: [
{
“datasets” : [
{"path": "0"},
{"path": "1"},
{"path": "2"},
{"path": "3"},
{"path": "4"}
]
“version” : “0.1”
}
]
}
Picking up on @dcherian's https://github.com/pydata/xarray/issues/4118#issuecomment-806954634 and @rabernat's https://github.com/ome/ngff/issues/48#issuecomment-833456889, Zarr was also accepted to the second round and certainly references this issue in case we want to sync up. (Apologies if I missed where that discussion moved.)
A simple comment/question:
In xarray.Dataset, why not just use the Unix-path notation into a "flat" dict model?
Actually, netCDF4 implements this Unix-like path access to groups and variables: /path/to/group/variable
.
All of the hierarchical stuff (e.g., getting a sub-Dataset from a random group) and conventions (e.g., dimensions scoping rule) would then be driven by the parsing of strings only. It's all about symbolic names (like in a file system right?) and there would be not any hierarchical data in memory anymore.
My question is then: Are there some tricky points for xarray.Dataset not to go this simple way?
Some related remarks:
open_dataset(filepath)
, but this function is unable to read the whole file in memory without getting help from a priori file content description, i.e., the names of the groups if you follow me. Considering xarray for simple tasks like geographical-selection-cropping, it seems to ignore the self-describing nature of netCDF4 format. As far as I can understand the situation, a "flat" model could be a good way to go.cc @d-v-b and https://github.com/JaneliaSciComp/xarray-multiscale
Flagging another possible use case, this time in Magnetic Confinement Fusion: representing the IMAS data model.
IMAS is currently closed-source (being part of the ITER project), but there is a big push to make it open-source and the standard data model for tokamak plasma data.
I'm not very familiar with IMAS (@smithsp and @orso82 are more so), but it is hierarchical. There is some more information in appendix A3 of this paper, which talks about "taking advantage of the homogeneity of grid sizes that is commonly found across arrays of structures", which sounds very closely related to the DataTree
proposal.
This might allow the xarray.DataTree
to do more of the heavy-lifting within OMAS (which already uses xarray, and is intended to be compatible with IMAS).
@martinitus raises a really interesting point about tags vs hierarchical structures over in https://github.com/pydata/xarray/issues/1092#issuecomment-868324949
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.
I think using tags is a really interesting alternative to hierarchies. I don't have a clear sense of the overall tradeoffs, though.
I think using tags is a really interesting alternative to hierarchies. I don't have a clear sense of the overall tradeoffs, though.
That is interesting. I think there is an argument for using a hierarchical model to map onto the full netCDF data model with groups, but perhaps methods to select elements via tags could be included too, for the best of both?
@shoyer if you used tags wouldn't you lose the ability to round-trip a netCDF file with groups? When you read in the groups from the file you would be throwing information away by going from a hierarchy A/B to simply tags A&B, and there wouldn't be a way to restore that before calling .to_netcdf()
would there?
if you used tags wouldn't you lose the ability to round-trip a netCDF file with groups?
That sounds right to me -- a downside of tags is that they can't be (uniquely) expressed in a hierarchical arrangement like those found in HDF5/netCDF4 files.
But if this is a better way to organize data in memory, we could consider how to make an adapter layer for on disk storage.
Some other thoughts about tags:
1) Does the definition of tags include variable names of DataArrays? I think it should.
2) As @martinitus mentioned, a DataTree
containing only leaves with only 1 tag each is effectively a Dataset. I wonder if Dataset could be refactored to be a special case of a more general DataTree, possibly as a subclass?
3) Selecting via tags would need to allow a distinction between "get me all leaves with these exact tags" and "get me all leaves whose tags include these ones". Maybe dt.choose_only(tags)
and dt.choose_all(tags)
?
4) The latter type of tag-based access would make plotting different leaves against one another easier too - given a multi-resolution (or multi-model) datatree like this:
dt
|-- high_res
| |-- temperature
| |-- CO2
|-- medium_res
| |-- temperature
| |-- CO2
|-- low_res
| |-- temperature
| |-- CO2
then assuming that the definition of tags included the DataArray variable names, then
dt.choose_all('temperature').plot.line(x='time')
would select all leaves with a tempature tag, check that the temperature DataArrays had the same dimensions (but no need for any time
coordinates to share size or values), and then plot them against one another on the same axes. This would be so useful - I would say this use case is 90% of the reason users iterate over dictionaries of datasets currently.
5) With a tag-based system you can create cycles of tags, like A&B, B&C, C&A, which you can't really do with hierarchical trees. I don't think that actually causes any problems though...
That sounds right to me -- a downside of tags is that they can't be (uniquely) expressed in a hierarchical arrangement like those found in HDF5/netCDF4 files.
hdf5 allows for internal links so a datasets and groups can appear in multiple places in the tree. You can even make cycles where groups are in them selves (or their children). The NeXuS format (the xray/neutron one) makes heavy use of this to let data appear both where it "makes sense" from a science point of view from an instrumentation point of view.
I think it is reasonable to expect that netcdf -> xarray -> netcdf always , however I think it is unreasonable to ask that xarray -> netcdf -> xarray will always work. I think it is OK if xarray can express more complex relationship and structures that you can in netcdf (or hdf5 or any existing at-rest format). In an extreme case, consider an interface to a database that returns xarrays 😈 .
As a user who (so far) does not use any netCDF or HDF5 features of xarray I obviously would not like to have a otherwise potentially useful feature blocked by restrictions imposed by netCDF or HDF5 ;-).
That said - I think @tacaswell comment about round trips is very reasonable and such invariants should be maintained! It would be extremely confusing for users if netcdf -> xarray-> netcdf is not a "no-op". The same obviously holds true for any other storage format. As a user I would generally expect something like the following:
a1= xarray.load("foo.myformat")
xarray.save( a1, "bar.myformat")
a2= xarray.load("bar.myformat")
assert a1 == a2, "Why should they not be exactly equal?!?"
I think that xarray's current use of both dict-like access and attribute-like access for variables makes representing a general netCDF file in a single DataTree
incompatible with the nice syntax that @emilbiju originally suggested.
Consider a tree with a node structure for a hypothetical DataTree
object dt
that looks something like
DataTree("root")
|-- DatasetNode("weather")
| |-- DatasetNode("temperature")
| | |-- DataArrayNode("sea_surface_temperature")
| | |-- DataArrayNode("dew_point_temperature")
| |-- DataArrayNode("wind_speed")
|-- DataArrayNode("population")
We ideally want to be able to seamlessly access both subtrees and individual variables via chains of keys, e.g.
weather_subtree = dt['weather']
, and wind_speed_da = dt['weather']['wind_speed']
. (We want that so that each subtree behaves as much like an xarray.Dataset
as possible, with respect to mapping functions over all its child nodes and so on.)
This particular example is fine, and would correspond to a netCDF file with groups "root", "root/weather", and "root/weather/temperature", plus the four stored DataArray variables.
However, if one of the variables has the same name as one of the groups (which I think is permitted in the netCDF format), then there is no easy way to access all the elements whilst retaining the nice syntax. For example consider
DataTree("root")
|-- DatasetNode("A")
| |-- DatasetNode("B")
| | |-- DataArrayNode("foo")
| | |-- DataArrayNode("bar")
| |-- DataArrayNode("B")
|-- DataArrayNode("C")
Now we have a key collision between the group named "B" and the DataArray named "B", i.e. dt['A']['B']
is ambiguous.
We can't just forbid this type of tree because then there would be netCDF files that we couldn't represent as a DataTree
, so we would not have the property netCDF -> xarray.DataTree -> netCDF
in general.
We can't use different types of access (e.g. subtree = dt.A.B
for the subtree and da = dt.A['B']
for the variable, because we've already given up the .B
namespace to also point to the variable (i.e. same location as ['B']
). If we break that convention it's going to be very confusing for users who are expecting the root of the DataTree
to behave like xarray.Dataset
currently does.
(We could divide access through __call__
like ds['A']('B')
but that wouldn't be very pythonic).
The only way I can see around this is to hide a node's data variables behind a .ds
property (i.e. da = dt['A'].ds['B']
), or get groups via a dedicated method (i.e. subtree = dt.get_child('A')
), but those are so much more ugly and less intuitive that it feels like a shame to have to do that.
It sounds like @emilbiju avoided this by not satisfying netCDF -> xarray.DataTree -> netCDF
:
(Instead of using netCDF4 groups for encoding the Datatree ... within the netCDF file, it would exist just as a Dataset)
so I'm wondering if anyone else has other suggestions or thoughts?
However, if one of the variables has the same name as one of the groups (which I think is permitted in the netCDF format), then there is no easy way to access all the elements whilst retaining the nice syntax.
NetCDF does not allow variables and groups with the same name, e..g,
import netCDF4
nc = netCDF4.Dataset('testing.nc', 'w')
nc.createVariable('foo', float)
nc.createGroup('foo')
# RuntimeError: NetCDF: String match to name in use
I'm pretty sure this is also prohibited for all HDF5 files, just like how you can't have a directory and file with the same name on most filesystems.
Oh excellent, thanks for the clarification Stephan!
On Thu, 19 Aug 2021, 00:23 Stephan Hoyer, @.***> wrote:
However, if one of the variables has the same name as one of the groups (which I think is permitted in the netCDF format), then there is no easy way to access all the elements whilst retaining the nice syntax.
NetCDF does not allow variables and groups with the same name, e..g,
import netCDF4 nc = netCDF4.Dataset('testing.nc', 'w')nc.createVariable('foo', float)nc.createGroup('foo')# RuntimeError: NetCDF: String match to name in use
I'm pretty sure this is also prohibited for all HDF5 files, just like how you can't have a directory and file with the same name on most filesystems.
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So I had a crack at making a full DataTree
class - you can find it in this repo.
It's based on @benbovy's DatasetNode
example - the basic idea is that each tree node wraps a single Dataset
. The differences are that this effort:
xarray.Dataset
's API over every node in the tree (such as .isel
or __add__
),Some limitations of the approach I used are:
dt.sel(time=50)
would require each Dataset in that subtree to have it's own coordinate called 'time'
. (That's normally useful though because then
'time'
can be a different resolution on each ds),('folder1', 'folder2')
points to a different node than ('folder2', 'folder1')
,You can create a DataTree
object in 3 ways:
1) Load from a netCDF file that has groups via open_datatree()
,
2) Using the init method of DataTree
, which accepts a nested dictionary of Datasets,
3) Manually create individual nodes with DataNode()
and specify their relationships to each other, either by setting .parent
and .children
attributes, or through __get/setitem__
access, e.g.
dt['path/to/node'] = DataNode('node_name', data=xr.Dataset())
.
It's about 70% working, but some things I could do with some help with are:
1) ~Fundamental design questions about the class structure, such as whether DataTree
should be a subclass of Dataset
?~
2) ~Getting arithmetic and ufuncs to act properly on the whole tree~,
3) ~Saving a tree to a single netCDF file~, (thanks Joe!)
4) ~Setting up CI and all that jazz~, (thanks Joe again!)
5) ~Setting up basic docs.~
There will definitely be many bugs, but any thoughts or input appreciated!
Thanks @TomNicholas! I've just been starting to look into this. I'm going to give it a spin and would be happy to help with your numbers 3 and 4.
Thanks @jhamman - expect things to break as I keep realizing certain methods have to be defined differently from in Dataset for things to work.
Help with 3 would be especially appreciated, as at the moment whilst I can open and alter a file with groups, I can't save my resulting tree :sweat_smile:
Great work @TomNicholas!
For rich/html reprs, I think that we could take much inspiration from some of the dask reprs shown in this blog post.
I haven't looked at your repository in detail yet, but I have one general question about the design: what is the rationale of having two separate classes DataTree
and DataNode
? Could those classes be merged somehow? To me it seems that any node could be the root of a (sub)tree. h5py
has a separate File
object that does double duty as the HDF5 root group, and serves as entry point into the file, but in the case of Xarray maybe this is less relevant since Xarray object are abstract data containers? Zarr (abstract data store) has no such separate class and uses a regular zarr.hierarchy.Group
as the root.
Thanks @benbovy !
For rich/html reprs, I think that we could take much inspiration from some of the dask reprs shown in this blog post.
I don't know much about HTML, but graphs where you can mouseover nodes to see node information sound awesome!
what is the rationale of having two separate classes DataTree and DataNode?
They aren't separate: DataNode
is merely a (perhaps badly-named) pointer to a second init method for the same DataTree
class.
The idea was that creating a single node of a tree by specifying only its (name, dataset, parent, children)
attributes was conceptually different to "I have loads of datasets, and I want to arrange them all into one big tree using path-like addresses", so I made two different init methods on DataTree
to cover that. The idea was from the xarray.Dataset._construct_direct()
classmethod, which creates a new instance of a Dataset by directly setting attributes like (variables, coord_names, dims, attrs)
. That is an internal classmethod though, and isn't externally exposed like DataNode()
.
We could just merge the two signatures into one __init__
method though, or use a less confusing name (I just didn't want DataTree._init_single_node(name, data, parent)
everywhere in my tests.) Also internally it's nice to have a separate ._init_single_node()
method because that's (a) closer to the super().__init__()
defined by TreeNode
, and (b) doesn't require calling the fairly complex getting and setting methods.
Could those classes be merged somehow?
They were originally separate (I had DataTree
and DatasetNode
, where the former was a subclass of the latter), but then I merged them together in Condense DatasetNode and DataTree into a single DataTree class #11.
Zarr (abstract data store) has no such separate class and uses a regular zarr.hierarchy.Group as the root.
Good to know that other nested structures took a similar approach. I think that as we want to be able to save and load any subtree even after changing parents etc. then we ideally don't want to treat any one node as special.
We would like some opinions from the community on two different possible models for a tree-like structure in xarray.
A tree contains many groups, but the question is what constraints should be imposed on the contents of those groups.
Option (1) - Each group is a Dataset
.isel
)..attrs
) are arguably most useful when set at this levelOption (2) - Variables within groups are unconstrained
.sizes['x']=10
and the other have .sizes['x']=20
..mean()
)This is by no means the only question, and we have various choices to make within these options.
The questions for the potential users here are:
(@alexamici , @shoyer, @jhamman, @aurghs please edit this comment to add anything I've missed)
Thanks for launching this discussion @TomNicholas ! I'm a core dev of pytroll/satpy which handles earth observing satellite data. I got interested in DataTree because we have data from the same instruments available at mulitple resolution, hence not fitting into a single Dataset. For use Option 1 is probably feeling better. Even when having data at multiple resolutions, it is still a limited number of resolutions and hence splitting them in groups is the natural way of going I would say. We do not use the features you mention in Zarr or GRIB, as a majority of the satellite data we use is provided in netcdf nowadays. Don't hesitate to ask if you want to know more or if something is unclear, we are really interested in these developments, so if we can help that way...
@TomNicholas (cc @mraspaud)
Do you have use cases which one of these designs could handle but the other couldn't?
The two main classes of on-disk formats that, I know of, which cannot be always represented in the "group is a Dataset" approach are:
I don't have an example at hand, but my impression is that satellite products that use HDF5 file format also place arrays with inconsistent dimensions / coordinates in the same group.
One thing that came up in our discussion about this in the developer meeting today is that we could also pretty easily expose a "low level" API for IO using dictionaries of xarray.Variable objects. This intermediate representation could be useful for cleaning up data into a form suitable for conversion into Dataset objects.
On Wed, Feb 16, 2022 at 11:39 PM Alessandro Amici @.***> wrote:
@TomNicholas https://github.com/TomNicholas (cc @mraspaud https://github.com/mraspaud)
Do you have use cases which one of these designs could handle but the other couldn't?
The two main classes of on-disk formats that, I know of, which cannot be always represented in the "group is a Dataset" approach are:
- in netCDF following the CF conventions for groups https://cfconventions.org/Data/cf-conventions/cf-conventions-1.9/cf-conventions.html#groups, it is legal for an array to refer to a dimension or a coordinate in a different group and so arrays in the same group may have dimensions with the same name, but different size / coordinate values,
- the current spec for the Next-generation file formats (NGFF) https://ngff.openmicroscopy.org for bio-imaging has all scales of the same 5D data in the same group.
I don't have an example at hand, but my impression is that satellite products that use HDF5 file format also place arrays with inconsistent dimensions / coordinates in the same group.
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@TomNicholas I also have a few comments on the comparison:
Option (1) - Each group is a Dataset
- Model maps more directly onto netCDF (though still not exactly, because netCDF has dimensions as separate objects)
This is only true for flat netCDF files, once you introduce groups in a netCDF AND accept CF conventions the DataGroup approach can map 100% of the files, while the DataTree approach fails on a (admittedly small) class of them.
- Enforcing consistency between variables guarantees certain operations are always well-defined (in particular selection via an integer index like in
.isel
).- Guarantees that all valid operations on a Dataset are also valid operations on a single group of a DataTree - so API can be essentially identical to Dataset.
Both points are only true for the DataArray in a single group, once you broadcast any operation to subgroups the two implementations would share the same limitations (dimensions in subgroups can be inconsistent in both cases).
In my opinion the advantage for the DataTree is minimal.
- Metadata (i.e.
.attrs
) are arguably most useful when set at this level
The two approach are identical in this respect, group attributes are mapped in the same way to DataTree and DataGroup
I share your views on all other points.
@alexamici
- in netCDF following the CF conventions for groups, it is legal for an array to refer to a dimension or a coordinate in a different group and so arrays in the same group may have dimensions with the same name, but different size / coordinate values, (this was the orginal motivation to explore the DataGroup approach)
I'm having difficulties to understand your above point wrt to the scoping rules from the above CF document. Shouldn't it be impossible to create two arrays (in the same group) having dimensions with exactly the same name from different groups? Looking at the example here https://github.com/alexamici/xarray-datagroup there are coordinates with name "/lat" vs "lat". Aren't that two different names? Maybe I'm missing something essential here.
I am using xarray for processing geospatial data and have encountered two major challenges with existing data structures in xarray:
Data arrays stored in an xarray Dataset cannot be grouped into hierarchical levels/logical subsets to reflect the internal organisation of the data. This makes it difficult to identify and process a subset of the data variables that pertain to a specific problem.
When two data arrays having a shared dimension but different coordinate values along the dimension are merged into a Dataset, the union of coordinate values from the 2 data arrays becomes the new coordinate set corresponding to that dimension. Consequently, when the value of a variable in the dataset corresponding to a coordinate value is unknown,
nan
is used as a substitute which results in memory wastage.I would like to suggest a tree-based data structure for xarray in which the leaves store individual data arrays and the other nodes store the hierarchical information. Since data arrays are stored independently, each dimension only needs to be associated with coordinate values that are valid for that data array.
To meet these requirements, I have implemented a data structure that also supports the below capabilities:
dt
) with child nodes:weather
,satellite image
andpopulation
. Each of these nodes has data arrays/subtrees under it.The mean over time of all data variables associated with weather can be obtained using
dt.weather.mean('time')
which applies the function tosea_surface_temperature
,dew_point_temperature
,wind_speed
andpressure
.I would like to know of the possibility of introducing such a data structure in xarray and the challenges involved in the same.