issues

As @Hoeze notes in https://github.com/pydata/xarray/issues/5179, calling stack() can be "incredibly slow and memory-demanding, since it creates a MultiIndex of every possible coordinate in the array."

This is true with how stack() works currently, but I'm not sure this is necessary. I suspect it's a vestigial design choice from copying pandas, back from before Xarray had optional indexes. One benefit is that it's convenient for making unstack() the inverse of stack(), but isn't always required.

Regardless of how we define the semantics for boolean indexing (https://github.com/pydata/xarray/issues/1887), it seems like it could be a good idea to allow stack to skip creating a MultiIndex for the new dimension, via a new keyword argument such as ds.stack(index=False). This would be equivalent to calling reset_index() after stack() but would be cheaper because the MultiIndex is never created in the first place.

E.g., see https://github.com/pydata/xarray/issues/1151 where stalebot closed an issue even after another comment.

Is this something we need to reconfigure or just a bug?

cc @pydata/xarray

Our current policy is that xarray supports "the minor version (X.Y) initially published no more than N months ago" where N is:

• Python: 42 months (NEP 29)
• numpy: 24 months (NEP 29)
• pandas: 12 months
• scipy: 12 months
• sparse, pint and other libraries that rely on NEP-18 for integration: very latest available versions only,
• all other libraries: 6 months

I think this policy is too aggressive, particularly for pandas, SciPy and other libraries. Some of these projects can go 6+ months between minor releases. For example, version 2.3 of zarr is currently more than 6 months old. So if zarr released 2.4 today and xarray issued a new release tomorrow, and then our policy would dictate that we could ask users to upgrade to the new version.

In https://github.com/pydata/xarray/pull/4178, I misinterpreted our policy as supporting "the most recent minor version (X.Y) initially published more than N months ago". This version makes a bit more sense to me: users only need to upgrade dependencies at least every N months to use the latest xarray release.

I understand that NEP-29 chose its language intentionally, so that distributors know ahead of time when they can drop support for a Python or NumPy version. But this seems like a (very) poor fit for projects without regular releases. At the very least we should adjust the specific time windows.

I'll see if I can gain some understanding of the motivation for this particular language over on the NumPy tracker...

Proposal

Given that a major purpose for plotting an array is to look at data or attributes, I wonder if we should expand these sections by default? - I worry that clicking on icons to expand sections may not be easy to discover - This would also be consistent with the text repr, which shows these sections by default (the Dataset repr is already consistent by default between text and HTML already)

Context

Currently the HTML repr for DataArray/Variable looks like this:

To see array data, you have to click on the icon:

(thanks to @max-sixty for making this a little bit more manageably sized in https://github.com/pydata/xarray/pull/3905!)

There's also a really nice repr for nested dask arrays:

xref https://github.com/astropy/astropy/pull/8445

xref #702 - [ ] Serialization to NetCDF - [x] Better repr, showing level names/dtypes? - [x] Indexing a scalar at a particular level should drop that level from the MultiIndex (#767) - [x] Make levels accessible as coordinate variables (e.g., ds['time'] can pull out the 'time' level of a multi-index) - [x] Support indexing with levels, e.g., ds.sel(time='2000-01'). - [x] ~~Make isel_points/sel_points return objects with a MultiIndex? (probably after the previous TODO, so we can preserve basic backwards compatibility)~~ (differed until we figure out #974) - [x] Add set_index/reset_index/swaplevel to make it easier to create and manipulate multi-indexes

These are both entirely lazy operations:

```

%time res = ds.groupby('time.month').mean('time') CPU times: user 142 ms, sys: 20.3 ms, total: 162 ms Wall time: 159 ms %time res = ds.groupby('time.month').apply(lambda x: x - x.mean()) CPU times: user 46.1 s, sys: 4.9 s, total: 51 s Wall time: 50.4 s ```

I suspect the issue (in part) is that _interleaved_concat_slow indexes out single elements from each dask array along the grouped axis prior to concatenating them together (unit tests for interleaved_concat can be found here). So we end up creating way too many small dask arrays.

Profiling results on slightly smaller data are in this gist.

It would be great if we could figure out a way to make this faster, because these sort of operations are a really nice show case for xray + dask.

CC @mrocklin in case you have any ideas.

This slows things down unnecessarily.

See here for the rendered version: https://github.com/shoyer/xarray/blob/4dd4463d5f79c07eb3a01f67b7329f2f4a60dd31/doc/roadmap.rst

Fixes #548

In particular, it would be helpful if someone else could review the new documentation section to see if it makes sense.

Apparently CDAT has a number of useful modules for working with weather and climate data, especially for things like computing climatologies (related: #112). There's no point in duplicating that work in xray, of course (also, climatologies may be too domain specific for xray), so we should make it possible to use both xray and CDAT interchangeably.

Unfortunately, I haven't used CDAT, so it not obvious to me what the right interface is. Also, CDAT seems to be somewhat difficult (impossible?) to install as a Python library, so it may be hard to setup automated testing.

CC @DamienIrving

Dask is a library for out of core computation somewhat similar to biggus in conception, but with slightly grander aspirations. For examples of how Dask could be applied to weather data, see this blog post by @mrocklin: http://matthewrocklin.com/blog/work/2015/02/13/Towards-OOC-Slicing-and-Stacking/

It would be interesting to explore using dask internally in xray, so that we can implement lazy/out-of-core aggregations, concat and groupby to complement the existing lazy indexing. This functionality would be quite useful for xray, and even more so than merely supporting datasets-on-disk (#199).

A related issue is #79: we can easily imagine using Dask with groupby/apply to power out-of-core and multi-threaded computation.

Todos for xray: - [x] refactor Variable.concat to make use of functions like concatenate and stack instead of in-place array modification (Dask arrays do not support mutation, for good reasons) - [x] refactor reindex_variables to not make direct use of mutation (e.g., by using da.insert below) - [x] add some sort of internal abstraction to represent "computable" arrays that are not necessarily numpy.ndarray objects (done: this is the data attribute) - [x] expose reblock in the public API - [x] load datasets into dask arrays from disk - [x] load dataset from multiple files into dask - [x] ~~some sort of API for user controlled lazy apply on dask arrays (using groupby, mostly likely)~~ (not necessary for initial release) - [x] save from dask arrays - [x] an API for lazy ufuncs like sin and sqrt - [x] robustly handle indexing along orthogonal dimensions if dask can't handle it directly.

Todos for dask (to be clear, none of these are blockers for a proof of concept): - [x] support for NaN skipping aggregations - [x] ~~support for interleaved concatenation (necessary for transformations by group, which are quite common)~~ (turns out to be a one-liner with concatenate and take, see below) - [x] ~~support for something like take_nd from pandas: like np.take, but with -1 as a sentinel value for "missing" (necessary for many alignment operations)~~ da.insert, modeled after np.insert would solve this problem. - [x] ~~support "orthogonal" MATLAB-like array-based indexing along multiple dimensions~~ (taking along one axis at a time is close enough) - [x] broadcast_to: see https://github.com/numpy/numpy/pull/5371

This change is rebased on master and should let us pick up from #175. CC @akleeman

Restructured Backends to make CF convention application more consistent.

Amongst other things this includes: - EncodedDataStores which can wrap other stores and allow for modular encoding/decoding. - Trivial indices ds['x'] = ('x', np.arange(10)) are no longer stored on disk and are only created when accessed. - AbstractDataStore API change. Shouldn't effect external users. - missing_value attributes now function like _FillValue

All current tests are passing (though it could use more new ones).

Post rebase notes (shoyer, Oct 2, 2014): Most tests are passing, though a couple are broken: - test_roundtrip_mask_and_scale (because this change needs a fix to not break the current API) - test_roundtrip_strings_with_fill_value on TestCFEncodedDataStore (I don't entirely understand why, let's come back to it later)