Packing Qs

• If "the variable containing the packed data must be of type byte, short or int", how do we choose what size int?
• What to do if scale_factor and add_offset are not float or double? What if they are different types?
  • I assume issue a warning and continue?

Unpacking Qs

• Should the unpacked data just be np.find_common_type([data, add_offset, scale_factor], []), or should we then bump the type up by 1 level (float16->32, 32->64, 64->128, etc.) to cover overflow?

• From: https://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/ch08.html

This standard is more restrictive than the NUG with respect to the use of the scale_factor and add_offset attributes; ambiguities and precision problems related to data type conversions are resolved by these restrictions.

If the scale_factor and add_offset 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_factor and add_offset 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_factor 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.

Current algorithm

python def _choose_float_dtype(dtype, has_offset): """Return a float dtype that can losslessly represent `dtype` values.""" # Keep float32 as-is. Upcast half-precision to single-precision, # because float16 is "intended for storage but not computation" if dtype.itemsize <= 4 and np.issubdtype(dtype, np.floating): return np.float32 # float32 can exactly represent all integers up to 24 bits if dtype.itemsize <= 2 and np.issubdtype(dtype, np.integer): # A scale factor is entirely safe (vanishing into the mantissa), # but a large integer offset could lead to loss of precision. # Sensitivity analysis can be tricky, so we just use a float64 # if there's any offset at all - better unoptimised than wrong! if not has_offset: return np.float32 # For all other types and circumstances, we just use float64. # (safe because eg. complex numbers are not supported in NetCDF) return np.float64

Due to calling bug, has_offset is always None, so this can be simplified to:

python def _choose_float_dtype(dtype) if dtype.itemsize <= 4 and np.issubdtype(dtype, np.floating): return np.float32 if dtype.itemsize <= 2 and np.issubdtype(dtype, np.integer): return np.float32 return np.float64

Here I call the function twice, once with has_offset False, then True.

```python import numpy as np

def _choose_float_dtype(dtype, has_offset): if dtype.itemsize <= 4 and np.issubdtype(dtype, np.floating): return np.float32 if dtype.itemsize <= 2 and np.issubdtype(dtype, np.integer): if not has_offset: return np.float32 return np.float64

generic types

for dtype in [np.byte, np.ubyte, np.short, np.ushort, np.intc, np.uintc, np.int_, np.uint, np.longlong, np.ulonglong, np.half, np.float16, np.single, np.double, np.longdouble, np.csingle, np.cdouble, np.clongdouble, np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64, np.float16, np.float32, np.float64]: print("|", dtype, "|", _choose_float_dtype(np.dtype(dtype), False), "|", _choose_float_dtype(np.dtype(dtype), True), "|") ```

| Input | Output as called | Output as written | |-----------------------------|---------------------------|--------------------------| | <class 'numpy.int8'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.uint8'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.int16'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.uint16'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.int32'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.uint32'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.int64'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.uint64'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.longlong'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.ulonglong'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.float16'> | <class 'numpy.float32'> | <class 'numpy.float32'> | | <class 'numpy.float16'> | <class 'numpy.float32'> | <class 'numpy.float32'> | | <class 'numpy.float32'> | <class 'numpy.float32'> | <class 'numpy.float32'> | | <class 'numpy.float64'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.float128'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.complex64'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.complex128'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.complex256'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.int8'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.int16'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.int32'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.int64'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.uint8'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.uint16'> | <class 'numpy.float32'> | <class 'numpy.float64'> | | <class 'numpy.uint32'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.uint64'> | <class 'numpy.float64'> | <class 'numpy.float64'> | | <class 'numpy.float16'> | <class 'numpy.float32'> | <class 'numpy.float32'> | | <class 'numpy.float32'> | <class 'numpy.float32'> | <class 'numpy.float32'> | | <class 'numpy.float64'> | <class 'numpy.float64'> | <class 'numpy.float64'> |

This issue, based on its title and initial post, is fixed by PR #6851. The code to select dtype was already correct, but the outer function that called it had a bug in the call.

Per the CF spec,

the unpacked data should match the type of these attributes, which must both be of type float or both be of type double. An additional restriction in this case is that the variable containing the packed data must be of type byte, short or int. It is not advised to unpack an int into a float as there is a potential precision loss.

I find this is ambiguous. is float above referring to float16 or float32? Is double referring to float64? If so, then they do recommend float64, as requested by the OP, because the test data is short and the scale_factor is float64 (a.k.a double?)

The broader discussion here is about CF compliance. I find the spec ambiguous and xarray non-compliant. So many tests rely on the existing behavior, that I am unsure how best to proceed to improve compliance. I worry it may be a major refactor, and possibly break things relying on the existing behavior. I'd like to discuss architecture. Should this be in a new issue, if this closes with PR #6851? Should there be a new keyword for cf_strict or something?

I've run into this issue too, and the xarray decision to use float32 is causing problems. I recognize this is a generic floating-point representation issue, but it could be avoided with float64.

The data value is 1395. The scale is 0.0001.

python val = int(1395) scale = 0.0001 print(val*scale) # 0.1395 print( val * np.array(scale).astype(float) ) # 0.1395 print( val * np.array(scale).astype(np.float16) ) # 0.1395213... print( val * np.array(scale).astype(np.float32) ) # 0.13949999... print( val * np.array(scale).astype(np.float64) ) # 0.1395

Because we are using *1E3 * round(), the difference between 0.1395 and 0.1394999 (or 139.5 and 139.49) ends up being quite large in the downstream product.