DataArray read from netcdf with unexpected type

[mraspaud]

Code Sample, a copy-pastable example if possible

import numpy as np
import h5netcdf

filename = "mask_and_scale_float32.nc"

with h5netcdf.File(filename, 'w') as f:
    f.dimensions = {'x': 5}
    v = f.create_variable('hello', ('x',), dtype=np.uint16)
    v[:] = np.ones(5, dtype=np.uint16)
    v[0] = np.uint16(65535)
    v.attrs['_FillValue'] = np.uint16(65535)
    v.attrs['scale_factor'] = np.float32(2)
    v.attrs['add_offset'] = np.float32(0.5)


import xarray as xr

v = xr.open_dataset(filename, mask_and_scale=True)['hello']
print(v.dtype)

Problem description

The scale_factor and add_offset being float32, I would expect the result from loading to be a float32 array. However, we get a float64 array instead. A float32 array for a very large dataset is better for faster computations.

Expected Output

float32

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 2.7.5.final.0 python-bits: 64 OS: Linux OS-release: 3.10.0-693.11.6.el7.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: None.None

xarray: 0.10.0
pandas: 0.21.0
numpy: 1.13.3
scipy: 0.18.1
netCDF4: 1.1.8
h5netcdf: 0.4.2
Nio: None
bottleneck: None
cyordereddict: None
dask: 0.16.0+37.g1fef002
matplotlib: 2.1.0
cartopy: None
seaborn: None
setuptools: 38.2.4
pip: 9.0.1
conda: None
pytest: 3.1.3
IPython: 5.5.0
sphinx: 1.3.6

[fmaussion]

Seems like this issue is a good use case for #1840 ;)