petab1to2: fix conversion of logNormal, logLaplace and parameterScaleUniform priors

[FFroehlich]

Fixes three prior-conversion bugs in the PEtab v1 -> v2 converter (update_prior in v1v2_parameter_df). They turned up while verifying #535.

Mappings

v1 objectivePriorType	parameterScale	v1 parameters	before	now
logNormal / logLaplace	any	mu;sigma	logNormal / logLaplace verbatim → pydantic ValidationError when building v2.Parameter	log-normal / log-laplace, mu;sigma
parameterScaleUniform	log	a;b (ln-space)	log-uniform with a;b → prior pdf is NaN	log-uniform with exp(a);exp(b), clipped to the bounds
parameterScaleUniform	log10	a;b (log10-space)	NotImplementedError (log10-uniform)	log-uniform with 10^a;10^b, clipped to the bounds

Why these are correct:

1. logNormal / logLaplace. In v1 these are always based on the natural logarithm, whatever the parameterScale (Prior maps (LOG_NORMAL, _) to Normal(..., log=True)). That is the same convention as v2 log-normal / log-laplace, so only the name changes.
2. and 3. parameterScaleUniform on log/log10. If ln(x) (or log10(x)) is uniform on [a, b], then x is log-uniform on [e^a, e^b] (or [10^a, 10^b]). v2 log-uniform takes the bounds of x itself as parameters. Parameter.prior_dist notes this ("Mind the different interpretation of distribution parameters for Uniform(..., log=True) and LogUniform"). So the parameters are transformed back to linear scale. Like v1's Prior, the scaled interval [a, b] is first clipped to the scaled parameter bounds, and the linear bounds are used for clipped endpoints. Within the bounds this gives the same truncated density, and it avoids overflow for valid but wide intervals such as log10 -400;400.

Missing objectivePriorParameters for parameterScaleUniform stay missing, so update_prior_pars still fills in lowerBound;upperBound. In v1 they default to the scaled bounds, which is the same distribution.

Not changed: a missing objectivePriorType (v1 default parameterScaleUniform) still converts to uniform with lowerBound;upperBound on any scale. On log-scaled parameters the density differs from v1's log-uniform default. But this matches the v2 spec's default for parameters without a prior, and it keeps the optimum of v1 problems that have no priors.

Verification

For each case I compared petab.v1.priors.Prior.from_par_dict(row, type_="objective").pdf(x) with the converted v2.Parameter.prior_dist.pdf(x) on a grid spanning the bounds [1e-3, 1e2]. This included prior ranges inside the bounds, wider than the bounds (truncation) and partly outside them. All agree to rel. 1e-12.

Tests: test_petab1to2_parameter_scale_priors (from #535) is now test_petab1to2_priors. It is parametrized over prior parameters and adds the cases above: log/log10 parameterScaleUniform within bounds, exceeding bounds (including -400;400 / -1000;1000, which overflow when unscaled), and with missing parameters, plus logNormal/logLaplace on lin and log10 scale. 11 of the new cases fail on the previous converter code, and all pass with this change. pytest tests/v2 passes (141 passed), including test_benchmark_collection with benchmark_models_petab installed. pre-commit (ruff, ruff-format) is clean.

🤖 Generated with Claude Code

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 76.77%. Comparing base (37b7025) to head (fc0d2dd).

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #536      +/-   ##
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+ Coverage   76.69%   76.77%   +0.08%     
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  Files          67       67              
  Lines        7538     7547       +9     
  Branches     1344     1346       +2     
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+ Hits         5781     5794      +13     
+ Misses       1262     1259       -3     
+ Partials      495      494       -1     

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[Copilot]

🟡 Changes recommended

Valid wide log10-uniform prior intervals still overflow during conversion before truncation.

1 open finding

• Clip scaled prior bounds before unscaling endpoints · New

What changed in this PR

Corrects prior conversion from PEtab v1 to v2, alongside the prerequisite log10 noise and prior rescaling changes.

Changes:

• Maps log-normal/log-laplace prior names and converts scaled uniform bounds.
• Rescales log10 noise and normal/Laplace prior parameters.
• Adds regression tests comparing densities and likelihoods.

File	Description
tests/​v2/​test_conversion.py	Adds conversion equivalence tests.
petab/​v2/​petab1to2.py	Corrects prior mappings, bounds, and log10 rescaling.

🧠 Review effort: Balanced

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[FFroehlich]

Fixed in fc0d2dd: like v1's Prior, the scaled interval is now clipped to the scaled parameter bounds before unscaling, and the linear lowerBound / upperBound are used for clipped endpoints. Added regression cases for log10 -400;400 and log -1000;1000 to test_petab1to2_priors; both failed before (OverflowError / overflow in exp) and now match the v1 prior density.

[dweindl]

I think copilot raises a valid point. The rest looks good to me.