TODO.md

Incomplete implementations/bigger jobs

Grids/rest of cosmomc scripts

Lots of batchjob stuff (hasConvergeBetterThan,wantCheckpointContinue etc) now broken

containers

cosmetic/consistency/speed

version attribute should be in all components not just theory (samplers can have versions) [done for samplers; missing: likelihoods]

post: use MPI, and TODO's in code

In the docs "Bases" (and UML diagram) not hyperlinked correctly (not sure how to fix)

Make numba a requirement?

dump log info along with each chain file if saving to file (currently in stdout)

Faster Collections for MCMC: numpy cache for merging OnePoint into Collection, out_update method would take care of flushing into the Pandas table.

PolyChord: check overhead

PolyChord: lower dimension of tests?

Enhancements/Refactorings

Way to have parameters with different speeds within the same component without splitting into separate theories or sub-HelpTheories, or specify a sub-blocking for a component's parameters

some way to change default options, e.g. always use cobaya-run -f by default. Also argument to run() function.

Support "parameterization" option of theory .yaml to specify parameter yaml variants?/generalize !defaults

Let classes do all defaults combining; allow separate like instantiation + use equivalent to loading in cobaya

check_conflicts theory method or similar (so likelihoods can raise error when used in combination with other variant likelihoods using non-independent data)

If non-linear lensing on, model the non-linear correction via limber for faster semi-slow parameters

minimize:

• unbounded parameters with flat prior (this would make it safe to rotate the unbounded ones in minimize) [JT: not very much in favour, since that would break a bunch of other stuff. Maybe let's explore an alternative solution? e.g. auto-extend uniform priors.]
• add MINUIT

mcmc:

• finish removing .checkpoint in favour of updated.yaml and .progress
• For learning checks, X should perhaps ideally also depend slightly on the speed of the cycles, e.g. if either check becomes slow compared to a fast cycle.
• Update output thin factor once chains get over a given size, so that asymptotically the memory size of the chains doesn't grow indefinitely (and convergence checking time also doesn't grow correspondingly), just the thinning factor increases.
• more clever learning of covmat when only a few parameters missing: update only the row/columns of missing params, shrinkage estimator etc.

CLASS: make it non-agnostic

Test installed only (would need more clever pytest marking?)

auto-covmats:

• separate parameter matching into slow ones and fast ones, and prefer missing some fast parameters than missing slow ones.
• refactor to more be general and don't hard code Planck etc in main source (e.g. so external likelihood distributions can provide own covmat databases)

doc, install, model may be better documented generally rather than only in cosmo sections.

parameterization: there should be no need for "drop" if there are no agnostic components.

Dependencies system

• Maybe remove distinction between input parameters and requirements, so that calculate/logp takes both of them, which would be prepared by check_cache_and_compute. This would simplify the code a bit (in particular the part about input parameters that can be requirements, e.g. YHe) and makes all likelihood automatically callable outside a Model feeding requirements by hand. Problem: to prepare requirements we need arguments (e.g. units, ell_factor for Cl's) which are not passed to must_compute.
• AL: We should perhaps allow all requirements to also be arrays of Requirement or (key, option) tuples (this is already done internally for combining requirements, but currently no component can individually return two requests with the same key but different options because dicts have unique keys).
• Provider: it should be possible to save retrieving methods at initialisation so that everything (params, results, methods) can be retrieved with Provider.get(**args). Maybe it is interesting?

Add MPI tests for convergence and resuming (see pytest-mpi)