issue_comments: 438372589

construct method does not allocate that large array in memory. It uses the strided trick and therefore consumers only the order of 1000x1000x1000.

Ah. I didn't realize that. Good to know.

What I'm actually looking to do is a rolling weighted regression. I have three DataArrays: - observations, dims=('date', 'dim1', 'dim2') - variables, dims=('date', 'dim1', 'dim2', 'var') - weights, dims=('date', 'dim1', 'dim2')

I want to calculate a regression_coefficients DataArray with dims=('date', 'var'), where for each date it has the weighted regression coefficients calculated over the trailing N dates (over 'dim1' and 'dim2'). One way would be to put the three DataArrays in a Dataset, and then use a newly-defined Dataset.rolling().apply(). Another way would be to use an enhanced version of apply_ufunc() that can take Rolling objects. But now that I know that DataArrayRolling.construct() won't kill my machine, I'll try apply_ufunc() with the three DataArrayRolling.construct() objects. I'd welcome other suggestions.

OK, I seem to have got my problem working using: apply_ufunc(lambda a, b, w: ..., variables.rolling(date=N).construct('temp_date'), observations.rolling(date=N).construct('temp_date'), weights.rolling(date=N).construct('temp_date'), input_core_dims=[['temp_date', 'dim1', 'dim2', 'var'], ['temp_date', 'dim1', 'dim2'], ['temp_date', 'dim1', 'dim2']], output_core_dims=[['var']], vectorize=True) Still, I wonder if there isn't a more "natural" way of accomplishing this.