@shoyer thanks for the clarity

@OriolAbril would you mind if we changed this issue to "apply_ufunc should preemptively broadcast"

I'm trying to think whether there would be any performance cost there - i.e. are there any arrays where preemptive broadcasting would be both expensive and unnecessary?

We should probably make apply_ufunc() explicitly broadcast arrays first.

To confirm, so that we have something like this?

```python xr.apply_ufunc(func, a, c)

Out

(7, 3, 5, 6)

(7, 3, 5, 6)

```

Because func receives unlabelled arrays, apply_ufunc aligns the axes order. So if they share a dimension:

```python In [8]: import xarray as xr ...: import numpy as np ...: ...: a = xr.DataArray(data=np.random.normal(size=(7, 3)), dims=["dim1", "dim2"]) ...: c = xr.DataArray(data=np.random.normal(size=(7, 6)), dims=["dim1", "dim4"]) # <- change here ...: ...: def func(x,y): ...: print(x.shape) ...: print(y.shape) ...: return x ...:

In [9]: xr.apply_ufunc(func, a, c) (7, 3, 1) (7, 1, 6) ```

...otherwise func wouldn't know how to align.

Another option would be for your original example to put lengths of 1 in all axes, rather than only 'forward filling', e.g.

``` xr.apply_ufunc(func, a, c)

Out

(7, 3, 1, 1)

(1, 1, 5, 6) # <- change here

```

I think it operates without that step because functions 'in the wild' generally will handle that themselves, but that's a guess and needs someone who knows this better to weight in

Thanks for the issue & code sample @OriolAbril

IIUC, func needs to do this broadcasting itself; from the apply_ufunc docstring:

func : callable Function to call like ``func(*args, **kwargs)`` on unlabeled arrays (``.data``) that returns an array or tuple of arrays. If multiple arguments with non-matching dimensions are supplied, this function is expected to vectorize (broadcast) over axes of positional arguments in the style of NumPy universal functions [1]_ (if this is not the case, set ``vectorize=True``). If this function returns multiple outputs, you must set ``output_core_dims`` as well.