xrexpr

[!WARNING] This is a work in progress, and I've had Claude (mostly Opus, some Fable) write the code for me. Because of that, it might look good (IDK), but it is certainly not complete or and has not been drive-tested in any meaningful sense of the word. Claims about functionality in this README should be considered probable at best, and aspirational at worst. Use at your own caution (whilst this warning is still up. I'll get rid of it once I'm confident in the codebase). P.S - This is not completely unread AI nonsense. I'm driving the AI pretty closely - but be warned that when you go this fast, things get missed and/or overlooked.

[!NOTE] This is not an xarray project. It isn't affiliated with, endorsed by, or supported by xarray or its maintainers — it just happens to plug into xarray via the accessor API. It also isn't really a package yet, despite looking like one: it's closer to an LLM-assisted, unusually deep proof of concept that I'm using to find out whether the idea holds up.

XREXPR: Xarray Expression Rewriter. Write the readable chain; run the fast one.

Imagine you have an xarray dataset that you want to do some analysis on. You might write something like this:

%%timeit
ds.mean(dim="lat").mean(dim="lon").isel(time=0).compute()

193 ms ± 49.6 ms per loop (mean ± std. dev. of 5 runs, 5 loops each)

However, it would be a lot faster if you instead wrote:

ds.isel(time=0).mean(dim="lat").mean(dim="lon").compute()

925 μs ± 401 μs per loop (mean ± std. dev. of 5 runs, 5 loops each)

In this instance, just reordering the operations makes a ~200x performance difference. We can see that these two expressions are equivalent, but unfortunately, xarray can't automatically reorder them for us (yet?).

from xarray.testing import assert_equal
assert_equal(
    ds.isel(time=0).mean(dim="lat").mean(dim="lon"),
    ds.mean(dim="lat").mean(dim="lon").isel(time=0),
)

# Does not raise an AssertionError

That's where xrexpr comes in. Importing it registers a .plan accessor on every Dataset. Chain your operations off ds.plan exactly as you would off ds — but instead of running eagerly, each call is recorded. Calling .collect() optimises the recorded plan (reordering and merging where it's provably safe) and replays it:

import xrexpr  # registers the ``.plan`` accessor

result = ds.plan.mean(dim="lat").mean(dim="lon").isel(time=0).collect()

(.compute() is a synonym for .collect(), if that's the terminal your fingers reach for.)

xrexpr pushes the isel in front of the reductions for you, so .collect() runs the fast ordering while you keep writing the readable one. The result is exactly what the eager chain would have produced:

assert_equal(result, ds.mean(dim="lat").mean(dim="lon").isel(time=0)).compute()

Seeing the rewrite

Use .explain() to see the optimised plan without running it:

>>> print(ds.plan.mean(dim="lat").mean(dim="lon").isel(time=0).explain())
plan (3 ops):
  1. Select  isel(time=0)
  2. Reduce  mean(dim='lat')  [consumes={lat}]
  3. Reduce  mean(dim='lon')  [consumes={lon}]

The isel has been hoisted to the front — that's the reorder that buys the speed-up.

Each line is one operation as xrexpr understands it: what kind it is, the calls it will replay as, and in brackets what the calls don't say — here, which dimensions each reduction removes. A bare .mean() shows consumes=every dim, and anything xrexpr does not model shows as Opaque ... [not modelled -- nothing crosses it], which is where to look when a rewrite you expected didn't happen.

Picking variables out of a dataset moves too, so the work is never done on variables you were about to discard:

>>> print(ds.plan.mean(dim="time")[["temperature"]].explain())
plan (2 ops):
  1. Project  [['temperature']]
  2. Reduce  mean(dim='time')  [consumes={time}]

Builder pairs like groupby(...).mean() are one operation, and selections move in front of them as well — the climatology case, where the grouping runs over one latitude instead of over all of them and then discarding the rest:

>>> print(ds.plan.groupby("time.month").mean().isel(lat=0).explain())
plan (2 ops):
  1. Select  isel(lat=0)
  2. GroupedReduce  groupby('time.month').mean()  [time -> month]

time -> month is the fact worth knowing about a grouped reduce: the result is indexed by a new month dimension and the original time is gone, so a selection on time after it means something quite different from one before it — and xrexpr leaves those where you put them.

Installing

pip install xrexpr

The only hard dependencies are xarray, frozendict and typing_extensions. Python 3.10+.

The whole idea, in three bullets

1. Nothing runs until you ask. ds.plan.<...> records calls instead of executing them; .collect() (or .compute()) is the only thing that touches data.
2. Rewrites are structural, not statistical. Between recording and replaying, xrexpr looks at dimensions and variable names only — never at the arrays — and applies rewrites that provably can't change the answer. There's no cost model and no guesswork.
3. When in doubt, it does nothing. Anything it can't prove safe is left exactly where you wrote it, so the worst realistic outcome is that you get the eager behaviour back.

What it rewrites today

And what it deliberately won't touch:

• Order-sensitive ops. A selection never hops over cumsum/cumprod/diff on the scanned dimension.
• Selections on a dimension an operation created. isel(month=0) after a groupby("time.month") is perfectly valid — it just can't move.
• Anything it doesn't recognise. An untabulated call (fillna, astype, ...) is a barrier: it replays verbatim, and rewrites don't cross it. explain() labels these Opaque.

It also catches one class of mistake early. A selection that indexes a dimension a reduction has already removed can never run, so xrexpr says so at .collect() (or .explain()) rather than letting it fail somewhere deep inside xarray:

>>> ds.plan.mean(dim="lon").isel(lon=0).collect()
InvalidExpressionError: isel() indexes ['lon'], which mean() has already reduced away

It can also make a chain stop failing

xrexpr computes only what your chain actually asks for, and that occasionally means not walking into an error eager evaluation walks straight into:

ds  # temperature(time, lat, lon) float, and station(lat, lon) -- strings, no time

ds.std("time")[["temperature"]]                  # TypeError, raised by `station`
ds.plan.std("time")[["temperature"]].collect()   # succeeds

The projection says outright that station isn't wanted. Eager computes its standard deviation anyway — purely because it happens to be in the Dataset — and falls over doing it, because numpy has no standard deviation for strings. The plan drops station before the reduction runs, so the failure never happens. weighted chains get the same treatment, and there the eager failure is even easier to hit: a weighted reduce refuses a variable lacking the reduced dim, where a plain .mean("time") merely wastes effort on it.

This isn't the optimiser playing fast and loose. The invariant, stated precisely:

optimize preserves the values of everything the plan asks for. It may additionally avoid an error raised by a computation whose result the plan discards. It may never change a value, nor introduce an error.

Under the hood

>>> print(ds.plan.chunk({"time": 100}).isel(time=0).explain())
plan (1 ops):
  1. Select  isel(time=0)

>>> print(ds.plan.rolling(time=5).mean().isel(lon=0).explain())
plan (2 ops):
  1. Select  isel(lon=0)
  2. WindowedReduce  rolling(time=5).mean()

Status

Early. The core invariant — ds.plan.<chain>.collect() equals the eager chain — is checked by a property-based test suite over generated datasets and generated chains, but the set of xarray operations it understands is small, and everything outside that set falls back to running your chain as written.

If it doesn't do anything for you, or does something surprising, please open an issue — the interesting bug reports are the chains where it should have found a rewrite and didn't.