issues

xarray version 0.16.2/ Python 3.8.5

When reading a dataset containing complex variables using Dataset.open_zarr method the following warning appears: __/home/.../python3.8/site-packages/xarray/coding/variables.py:218: ComplexWarning: Casting complex values to real discards the imaginary part_ And the imaginary part is effectively discarded which is not what I expected. After a slightly more in-depth analysis I came across the function (xarray/coding/variables.py:226) 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 For me, this behavior is strange, I find more natural to use the stored type rather than to make a systematic transformation into a float. To test, I have modified the decode method (xarray/coding/variables.py:265) ```python def decode(self, variable, name=None): dims, data, attrs, encoding = unpack_for_decoding(variable)

    if "scale_factor" in attrs or "add_offset" in attrs:
        scale_factor = pop_to(attrs, encoding, "scale_factor", name=name)
        add_offset = pop_to(attrs, encoding, "add_offset", name=name)
        # my change
        # dtype = _choose_float_dtype(data.dtype, "add_offset" in attrs)
        dtype = data.dtype
        if np.ndim(scale_factor) > 0:
            scale_factor = scale_factor.item()
        if np.ndim(add_offset) > 0:
            add_offset = add_offset.item()
        transform = partial(
            _scale_offset_decoding,
            scale_factor=scale_factor,
            add_offset=add_offset,
            dtype=dtype,
        )
        data = lazy_elemwise_func(data, transform, dtype)

    return Variable(dims, data, attrs, encoding)

``` and it is working as I expected. If there is a good reason to keep things as they are, can you explain me how to deal with complex data without creating a new variable?

Thank you for your great job, xarray is awesome.