issue_comments: 1201461626

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https://github.com/pydata/xarray/issues/2304#issuecomment-1201461626 https://api.github.com/repos/pydata/xarray/issues/2304 1201461626 IC_kwDOAMm_X85HnNl6 145117 2022-08-01T16:52:47Z 2022-08-01T16:52:47Z CONTRIBUTOR

This standard is more restrictive than the NUG with respect to the use of the scale<sub>factor</sub> and add<sub>offset</sub> attributes; ambiguities and precision problems related to data type conversions are resolved by these restrictions.

If the scale<sub>factor</sub> and add<sub>offset</sub> attributes are of the same data type as the associated variable, the unpacked data is assumed to be of the same data type as the packed data.

  • What if the result of the operation leads to overflow?

However, if the scale<sub>factor</sub> and add<sub>offset</sub> attributes are of a different data type from the variable (containing the packed data) then the unpacked data should match the type of these attributes, which must both be of type float or both be of type double.

  • What if they are not of the same type?

    • Presumably, use the largest of the three types.

  • Again, this may lead to loss of precision. what if packed data is type int64 and scale<sub>factor</sub> is type float16. Seems like the result should be float64, not float16.

An additional restriction in this case is that the variable containing the packed data must be of type byte, short or int.

  • What to do if packed data is type float or double?

It is not advised to unpack an int into a float as there is a potential precision loss.

I think this means double is advised? If so, this should be stated. Should be rephrased to advise what to do (if there is one or only a few choices) rather than what not to do, or at least include that if not replacing current wording.

 
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