Speed up cviirs tiepoint interpolation

[mraspaud]

This PR by factorizes expand_array (renamed to expand_arrays) to handle similar arrays at the same time.

• Closes Loading of DNB data from VIIRS compact SDR is slow #940
• Tests added and test suite added to parent suite
• Tests passed
• Passes flake8 satpy
• Fully documented

[coveralls]

Coverage increased (+0.4%) to 86.614% when pulling a861637 on mraspaud:feature-faster-cviirs-dnb into 2442ed2 on pytroll:master.

[codecov]

Codecov Report

Merging #941 into master will increase coverage by 0.54%.
The diff coverage is 67.3%.

@@            Coverage Diff             @@
##           master     #941      +/-   ##
==========================================
+ Coverage   86.19%   86.73%   +0.54%     
==========================================
  Files         174      175       +1     
  Lines       26714    27178     +464     
==========================================
+ Hits        23026    23574     +548     
+ Misses       3688     3604      -84

Impacted Files	Coverage Δ
satpy/tests/reader_tests/test_viirs_compact.py	94.33% <ø> (ø)
satpy/tests/reader_tests/__init__.py	98.24% <100%> (+0.03%)	⬆️
satpy/readers/utils.py	75.23% <100%> (ø)	⬆️
satpy/readers/viirs_compact.py	64.56% <66.66%> (+49.52%)	⬆️
satpy/config.py	82.38% <0%> (+0.4%)	⬆️
satpy/tests/test_writers.py	98.33% <0%> (+0.52%)	⬆️
satpy/readers/file_handlers.py	97.01% <0%> (+2.98%)	⬆️
satpy/writers/__init__.py	88.28% <0%> (+3.07%)	⬆️

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[pnuu]

Seems to make loading significantly faster. Timings for single DNB granule in Compact SDR format, master branch vs. this PR:

• scn.load(['DNB']): 7.1 s -> 4.5 s
• scn.load(['hncc_dnb']): 22.6 s -> 14.6 s

[mraspaud]

I think it could go even faster if the code between navigation and angles was also factorized

[pnuu]

With the latest updates the performance is looking pretty spectacular vs. master branch. Timings for one DNB granule:

• scn.load(['DNB']): 7.1 s -> 0.6 s
• scn.load(['hncc_dnb']): 22.6 s -> 0.8 s

For hncc_dnb that's a speedup of 26-27x.

[mraspaud]

Just to clarify, it is just creating the datasets that we discuss here, not any computation of the data