Enhancement to the perturbative modelling

[tilmantroester]

This PR contains enhancement to the perturbative modelling in CCL to make its integration in firecrown easier.

• Allow skipping of calling ptc.update_pk in get_pt_pk2d

[coveralls]

Pull Request Test Coverage Report for Build 3296719831

• 0 of 0 changed or added relevant lines in 0 files are covered.
• 1 unchanged line in 1 file lost coverage.
• Overall coverage increased (+0.0005%) to 97.503%

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Files with Coverage Reduction	New Missed Lines	%
pyccl/nl_pt/power.py	1	98.74%

Totals
Change from base Build 2917968053:	0.0005%
Covered Lines:	4841
Relevant Lines:	4965

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💛 - Coveralls

[damonge]

I'm happy to implement this fix for now, but the fact that we have to do it seems like a design flaw to me.

So looking forward, since we'll be able to break API with v3, how does the following scheme sound?
a) We make the PTCalculator also take in an array of scale factors as input (right now we fix the k sampling, so fixing the a sampling would seem ok to me too).
b) Then, we make the update_pk method depend on a cosmology and an input pk. It will then call the necessary fastpt functions, as it now does, and will evaluate and store the growth factor at the internal set of as.
c) We then remove any cosmology dependence from the get_pt_pk2d function, which will now only depend on tracers and a PTCalculator.

[tilmantroester]

That sounds good to me. One thing I noticed was that for nonlin_pk_type == 'nonlinear' and requesting a matter-matter power spectrum, get_pt_pk2d evaluates the existing non-linear power spectrum at the ptc k sampling and then recreates a new Pk2D object (with is_logp=False). Since the ptc k sampling differs from the internal P(k) splines, the extrapolation (or lack thereof) then is different for the Pk2D object you get back than the CCL-internal power spectrum, even though they both use the same underlying P(k).

[tilmantroester]

Could we merge this for now? This is currently holding up the firecrown PR.

[marcpaterno]

To close the Firecrown PR in question, we will need a release on conda forge with this addition. When is such a release feasible?

[damonge]

@marcpaterno let me try to tag a release and see if we can get it up within the next couple of days

[marcpaterno]

Thanks David!