issues: 199188476

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
199188476 MDU6SXNzdWUxOTkxODg0NzY= 1194 Use masked arrays while preserving int 500246 open 0     9 2017-01-06T12:40:22Z 2020-03-29T20:37:29Z   CONTRIBUTOR      

A great beauty of numpys masked arrays is that it works with any dtype, since it does not use nan. Unfortunately, when I try to put my data into an xarray.Dataset, it converts ints to float, as shown below:

``` In [137]: x = arange(30, dtype="i1").reshape(3, 10)

In [138]: xr.Dataset({"count": (["x", "y"], ma.masked_where(x%5>3, x))}, coords={"x": range(3), "y": ...: range(10)}) Out[138]: <xarray.Dataset> Dimensions: (x: 3, y: 10) Coordinates: * y (y) int64 0 1 2 3 4 5 6 7 8 9 * x (x) int64 0 1 2 Data variables: count (x, y) float64 0.0 1.0 2.0 3.0 nan 5.0 6.0 7.0 8.0 nan 10.0 ... ```

This happens in the function _maybe_promote.

Such type “promotion” is unaffordable for me; the memory consumption of my multi-gigabyte arrays would explode by a factor 4. Secondly, many of my integer-dtype fields are bit arrays, for which floating point representation is not desirable.

It would greatly benefit xarray if it could use masking while preserving the dtype of input data.

(See also: Stackoverflow question)

 
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