Simple support for selective netcdf loading.

[pp-mo]

🚀 Pull Request

Description

An initial heads-up on a really simple way of speeding up netcdf loads ...
... with files of many variables, as in #4134

This is actually much simpler than I imagined, but ..
Blockers to completion

• probably needs discussion
• not sure how to test this

Background...

Following #4135, ESMValTool devs are reporting that iris loading is still too slow, where you want only one a whole lot of diagnostics.

This example follows what we did for PP files in similar circumstances.
It's notable that, in creating a iris.fileformats.cf.CFReader, we are still doing a whole-file analysis, that includes the unwanted data-variables.
I actually don't think you can avoid that, as only context will distinguish a CF data-variable from an aux-coord.
However, the cost of this is not huge. I am finding <1sec for the testfile mentioned in #4134 (~250mB, 300 variables of content float[1,100]).

Some sample timings:

Using testfiles with many identical (small) variables:
n-vars : timings without // with fix
1 : 0.04 // 0.01 [loading 1 of N named variables]
10 : 0.14 // 0.02
30 : 0.45 // 0.03
100 : 1.70 // 0.07
300 : 8.19 // 0.40
(314) : 44.17 // 0.61

case (314) is based on the testfile mentioned in #4314
( I suspect it may be slower than the '300' because the variables data is larger?? WIP )
with the code like

cube = iris.load_cube(
    'Iris_multivar_data_file.nc',
    NameConstraint(long_name='Air Surface Temperature'))

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[pp-mo]

Wrong branch..