Updates to FCI reader to include CT, CTTH, GII and the latest filenam…

[ColinDuff]

Draft PR for the updates need for the FCI L2 PF to read the CT, CTTH, GII products and to use the latest product filename format

ASR to follow

[codecov]

Codecov Report

Merging #1465 (104db01) into master (a18b6e2) will increase coverage by 1.08%.
The diff coverage is 99.46%.

@@            Coverage Diff             @@
##           master    #1465      +/-   ##
==========================================
+ Coverage   90.52%   91.60%   +1.08%     
==========================================
  Files         238      246       +8     
  Lines       34153    35964    +1811     
==========================================
+ Hits        30916    32944    +2028     
+ Misses       3237     3020     -217     

Flag	Coverage Δ
behaviourtests	4.38% <0.00%> (-0.15%)	⬇️
unittests	92.07% <99.46%> (+1.06%)	⬆️

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Impacted Files	Coverage Δ
satpy/readers/fci_l2_nc.py	92.80% <98.27%> (+13.90%)	⬆️
satpy/tests/reader_tests/test_fci_l2_nc.py	100.00% <100.00%> (+4.70%)	⬆️
satpy/readers/seviri_l1b_hrit.py	90.93% <0.00%> (-1.25%)	⬇️
satpy/readers/_geos_area.py	100.00% <0.00%> (ø)
satpy/tests/test_file_handlers.py	100.00% <0.00%> (ø)
satpy/tests/reader_tests/test_glm_l2.py	100.00% <0.00%> (ø)
satpy/tests/reader_tests/test_abi_l1b.py	100.00% <0.00%> (ø)
satpy/tests/reader_tests/test_olci_nc.py	100.00% <0.00%> (ø)
satpy/tests/reader_tests/test_eum_base.py	100.00% <0.00%> (ø)
satpy/tests/reader_tests/test_geos_area.py	100.00% <0.00%> (ø)
... and 41 more

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[ghost]

DeepCode's analysis on #104db0 found:

• ℹ️ 2 minor issues. 👇
• ✔️ 1 issue was fixed.

Top issues

Description	Example fixes
Access to a protected member _start_time of a client class Occurrences: test_fci_l2_nc.py:105 test_fci_l2_nc.py:109 test_fci_l2_nc.py:113	🔧 Example fixes
Imports from package unittest are not grouped Occurrences: test_fci_l2_nc.py:25	🔧 Example fixes

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[ColinDuff]

For the ASR data, it will return a 4 dimensional area for the product parameters.

So eg scn.show('bt_mean') would not work , the user would have to do
data=scn['bt_mean'].values[:,:,1,1]
to visualise the data.
The returned xarray dataset has the dimensions marked accordingly so the user knows what the correspond to

[mraspaud]

I'm good with this PR, just a few suggestions and comments. Thanks for adding aaaaaaaall the new datasets!!!