petab1to2: fix conversion of logNormal, logLaplace and parameterScaleUniform priors

* v1 `logNormal` / `logLaplace` were passed through verbatim, but v2 only
  knows `log-normal` / `log-laplace`. Both are based on the natural
  logarithm, so the parameters are kept.
* `parameterScaleUniform` on `log` scale became `log-uniform` with the
  ln-space parameters, but v2 `log-uniform` takes the bounds of the
  parameter itself, so the prior density was NaN. Use exp(a); exp(b).
* `parameterScaleUniform` on `log10` scale raised NotImplementedError.
  Convert to `log-uniform` with 10^a; 10^b.

Missing parameterScaleUniform parameters still default to the parameter
bounds.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

Author: FFroehlich

petab/v2/petab1to2.py

@@ -17,6 +17,7 @@
 
 from .. import v1, v2
 from ..v1.math import sympify_petab
+from ..v1.parameters import unscale
 from ..v1.yaml import get_path_prefix, load_yaml, validate
 from ..versions import get_major_version
 from .models import MODEL_TYPE_SBML
@@ -496,7 +497,25 @@ def update_prior(row) -> tuple[str, str | None]:
             pscale = v1.C.LIN
 
         if prior_type not in v1.C.PARAMETER_SCALE_PRIOR_TYPES:
-            return prior_type, prior_pars
+            # v1 logNormal / logLaplace are based on the natural logarithm,
+            #  just like v2 log-normal / log-laplace
+            return {
+                v1.C.LOG_NORMAL: v2.C.LOG_NORMAL,
+                v1.C.LOG_LAPLACE: v2.C.LOG_LAPLACE,
+            }.get(prior_type, prior_type), prior_pars
+
+        if prior_type == v1.C.PARAMETER_SCALE_UNIFORM and pscale != v1.C.LIN:
+            # A uniform distribution of ln(x) (log10(x)) on [a, b] is a
+            #  log-uniform distribution of x on [e^a, e^b] ([10^a, 10^b]).
+            #  v2 log-uniform takes the bounds of x as parameters.
+            #  Missing parameters default to the parameter bounds (see
+            #  `update_prior_pars` below), as in v1.
+            if not pd.isna(prior_pars):
+                prior_pars = v2.C.PARAMETER_SEPARATOR.join(
+                    str(float(unscale(float(p), pscale)))
+                    for p in str(prior_pars).split(v1.C.PARAMETER_SEPARATOR)
+                )
+            return v2.C.LOG_UNIFORM, prior_pars
 
         if pscale == v1.C.LOG10 and prior_type in (
             v1.C.PARAMETER_SCALE_NORMAL,

tests/v2/test_conversion.py

@@ -195,24 +195,38 @@ def simulate(measurement_df):
     "ignore:.*Parameter scales are not supported in PEtab v2.*:UserWarning"
 )
 @pytest.mark.parametrize(
-    "parameter_scale, prior_type",
+    "parameter_scale, prior_type, prior_parameters",
     [
-        (v1.C.LOG10, v1.C.PARAMETER_SCALE_NORMAL),
-        (v1.C.LOG10, v1.C.PARAMETER_SCALE_LAPLACE),
-        (v1.C.LOG, v1.C.PARAMETER_SCALE_NORMAL),
-        (v1.C.LOG, v1.C.PARAMETER_SCALE_LAPLACE),
-        (v1.C.LIN, v1.C.PARAMETER_SCALE_NORMAL),
+        (v1.C.LOG10, v1.C.PARAMETER_SCALE_NORMAL, "-0.5;0.7"),
+        (v1.C.LOG10, v1.C.PARAMETER_SCALE_LAPLACE, "-0.5;0.7"),
+        (v1.C.LOG, v1.C.PARAMETER_SCALE_NORMAL, "-0.5;0.7"),
+        (v1.C.LOG, v1.C.PARAMETER_SCALE_LAPLACE, "-0.5;0.7"),
+        (v1.C.LIN, v1.C.PARAMETER_SCALE_NORMAL, "-0.5;0.7"),
+        # parameterScaleUniform within the bounds, exceeding the bounds,
+        #  and defaulting to the bounds
+        (v1.C.LOG10, v1.C.PARAMETER_SCALE_UNIFORM, "-0.5;0.7"),
+        (v1.C.LOG10, v1.C.PARAMETER_SCALE_UNIFORM, "-5;0.7"),
+        (v1.C.LOG10, v1.C.PARAMETER_SCALE_UNIFORM, np.nan),
+        (v1.C.LOG, v1.C.PARAMETER_SCALE_UNIFORM, "-0.5;0.7"),
+        (v1.C.LOG, v1.C.PARAMETER_SCALE_UNIFORM, "-10;0.7"),
+        (v1.C.LOG, v1.C.PARAMETER_SCALE_UNIFORM, np.nan),
+        (v1.C.LIN, v1.C.PARAMETER_SCALE_UNIFORM, "0.1;0.7"),
+        # logNormal / logLaplace do not depend on the parameter scale
+        (v1.C.LOG10, v1.C.LOG_NORMAL, "-0.5;0.7"),
+        (v1.C.LOG10, v1.C.LOG_LAPLACE, "-0.5;0.7"),
+        (v1.C.LIN, v1.C.LOG_NORMAL, "-0.5;0.7"),
+        (v1.C.LIN, v1.C.LOG_LAPLACE, "-0.5;0.7"),
     ],
 )
-def test_petab1to2_parameter_scale_priors(
-    tmp_path, parameter_scale, prior_type
+def test_petab1to2_priors(
+    tmp_path, parameter_scale, prior_type, prior_parameters
 ):
-    """Test that parameterScale{Normal,Laplace} priors are converted to
-    priors with the same density."""
+    """Test that objective priors are converted to priors with the same
+    density."""
     k1 = _K1 | {
         v1.C.PARAMETER_SCALE: parameter_scale,
         v1.C.OBJECTIVE_PRIOR_TYPE: prior_type,
-        v1.C.OBJECTIVE_PRIOR_PARAMETERS: "-0.5;0.7",
+        v1.C.OBJECTIVE_PRIOR_PARAMETERS: prior_parameters,
     }
     yaml_file = _write_v1_problem(
         tmp_path,