[dweindl]

PEtab v2 has no parameterScale. Converting a v1 parameterScaleNormal(μ, σ) prior on a log10-scaled parameter to log-normal(μ ln10, σ ln10) keeps the prior distribution, but adds a Jacobian term to the v2 MAP objective, which moves the optimum by −σ² ln10 decades. No v2 prior over the unscaled parameter can preserve both the distribution and the optimum.

petab1to2 and petab_files_1to2 get a reparametrize argument, which uses petab.v2.reparametrize_log (#560) to estimate log10_<id> (or log_<id>) instead of <id>:

• "none" (default): current behaviour, now with a warning for converted parameterScale* priors
• "priors": log-scaled parameters with parameterScale* priors
• "all": all estimated log-scaled parameters without priors on linear scale (i.e., without logNormal, uniform, ...); this also preserves the v1 default prior (uniform on parameter scale)

Log-scaled parameters sharing a placeholder with selected parameters are added. With "all", parameters without parameter-scale priors are skipped (with a warning) if their placeholder can't be log-transformed (e.g., Bachmann has 0 offsets). Parameters with lowerBound = 0 (allowed in v1 for log-scaled parameters with an initialization prior) get lowerBound = -inf on log scale (PEtab-dev/PEtab#682). They are skipped with a warning if their prior on log scale would be an improper uniform prior, i.e., the v1 default prior if other estimated parameters have priors.

Also fixes petab1to2 adding default uniform priors to non-estimated parameters, and to all parameters if the v1 parameter table has an objectivePriorType column, but no estimated parameter has a prior. Priors of non-estimated parameters are converted as before.

Non-estimated parameters are only reparametrized if they share a placeholder with selected parameters. A nominal value of 0 then becomes -inf, assuming PEtab-dev/PEtab#685 is accepted. Their priors are dropped, since they are ignored anyway (PEtab-dev/PEtab#686).

Checked on the benchmark collection: all 35 problems convert and pass v2 linting with "all" (now also tested in CI); with "priors", 44 parameters are reparametrized in Bachmann, Isensee, Raimundez and Schwen.

Checked with AMICI (v1 importer vs. v2 PetabImporter, forward sensitivities) on 30 benchmark problems converted with "all" (5 large ones excluded), at the nominal parameters and at a shifted point: log-likelihoods agree to a relative difference of ≤ 2e-8, and gradients agree to ≤ 4e-4 of the largest gradient entry. The remaining deviations are due to integration tolerances. Log-priors are identical for Isensee and Raimundez. For Bachmann, they differ by a constant (6 log-scaled parameters with the v1 default prior that can't be reparametrized become linear uniform), so the MAP is preserved.

The default is "none" for now, to keep petab1to2 uninvasive, i.e., to not change the parametrization unless requested. Open to change.

References Benchmarking-Initiative/Benchmark-Models-PEtab#333, PEtab-dev/PEtab#680, PEtab-dev/PEtab#683

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Codecov Report

❌ Patch coverage is 97.11538% with 3 lines in your changes missing coverage. Please review.
✅ Project coverage is 79.12%. Comparing base (68be6d1) to head (54362c3).

Files with missing lines	Patch %	Lines
petab/v2/petab1to2.py	97.11%	1 Missing and 2 partials ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #539      +/-   ##
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+ Coverage   78.85%   79.12%   +0.26%     
==========================================
  Files          69       69              
  Lines        8043     8142      +99     
  Branches     1445     1475      +30     
==========================================
+ Hits         6342     6442     +100     
+ Misses       1214     1213       -1     
  Partials      487      487              

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