Fix HDF4 reading utility using dtype classes instead of instances

[djhoese]

I think we were getting lucky in previous versions of numpy and xarray where a dtype class could be used relatively interchangeably with a dytpe instance. Now in numpy 1.17 this isn't magic anymore and we have to fix some of our usage. For example, np.float32 is a class (a type) so things like np.float32.itemsize now return a property of a class not an instance.

This PR makes it so the HDF4 utility class uses instances of dtypes to avoid some errors I was getting.

• Tests added and test suite added to parent suite
• Tests passed
• Passes flake8 satpy

[djhoese]

I think the main issue here is with HDF4 (pyhdf's) object that we are adding a dtype to. Numpy is still fine with np.array([1, 2, 3], dtype=np.float32) and doing my_arr.dtype.itemsize works. I can't seem to get a combination of dask and numpy to not work with np.float32 style usage.

[mraspaud]

Good catch ! LGTM

[djhoese]

So I did a little more investigating. The var in from_sds is a pyhdf SDS object. We add a dtype and a shape and then dask seems to be able to treat it like a numpy array. One odd thing is that dask normally looks for an ndim attribute on objects, but since our shape is a list it doesn't need it. If I make shape a tuple then it complains about not having ndim. I...don't know. Going to leave it since it works and when it breaks we can look in to it more.

[coveralls]

Coverage decreased (-0.003%) to 84.641% when pulling 48591e5 on djhoese:bugfix-hdf4-dtype into b6e37ce on pytroll:master.

[codecov]

Codecov Report

Merging #891 into master will increase coverage by <.01%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master     #891      +/-   ##
==========================================
+ Coverage   84.64%   84.65%   +<.01%     
==========================================
  Files         171      171              
  Lines       25033    25034       +1     
==========================================
+ Hits        21189    21192       +3     
+ Misses       3844     3842       -2

Impacted Files	Coverage Δ
satpy/readers/hdf4_utils.py	92.72% <100%> (ø)	⬆️
satpy/tests/reader_tests/test_hdf4_utils.py	92.18% <100%> (+0.12%)	⬆️
satpy/readers/mersi2_l1b.py	97.59% <0%> (+1.2%)	⬆️
satpy/readers/virr_l1b.py	100% <0%> (+1.58%)	⬆️

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