The `update_noise_dist` missing-return bug in petab.v2.petab1to2 (which
silently reverted every upgraded observable's noiseDistribution to
`normal`, corrupting iy_trafos/chi2 for non-linear observable
transformations) has been fixed upstream in
PEtab-dev/libpetab-python#502.

Remove the `_fix_petab1to2_noise_distribution_bug` shim and bump the v1
petab-suite CI job's libpetab pin from 44c8062 to 1b8599dd (the #502
merge commit) so the fix is present. Case 0016 now passes via the
upstream conversion; case 0007's xfail (inherent log10-normal ->
log-normal substitution) and its warning filter remain.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

…ution, and differentiability (#3207)

* Fix CI failures in JAX PEtab v1/v2 backend

Fixes a cluster of bugs surfaced by CI once the v1->v2 upgrade path
(from the two separate PRs merged into this branch) started actually
reaching previously-unreachable code:

* get_simulation_conditions_v2: a dynamic period may reference
  multiple condition ids (e.g. a synthetic preequilibration-indicator
  condition alongside the real experiment condition). Measurements are
  only ever queried by experiment id, so multiple condition-id rows
  per experiment produced duplicate/misaligned measurement arrays,
  causing vmap batch-size mismatches (`vmap got inconsistent sizes`).
  Collapse to one row per experiment.

* add_default_experiment_names_to_v2_problem: read condition ids from
  condition table elements instead of the long-format `condition_df`,
  which contributes zero rows for a condition with no changes (e.g.
  the default condition, or any no-op condition) -- exactly the
  "Experiment has no dynamic period with a condition id" case.

* _build_simulation_df_v2: the synthetic default experiment id was
  overwritten with NaN before being reused to query
  observableParameters/noiseParameters from the measurement table,
  silently matching nothing.

* _get_parameter_mappings: a condition table's target value can be a
  reference to another PEtab parameter id (not just a numeric
  literal), which crashed trying to cast the symbol straight to a jax
  array. Resolve it the same way `_map_experiment_model_parameter_value`
  already resolves other parameter references.

* import_petab_problem (legacy v1 path): snapshot the pristine v1
  problem before SBML/PySB compilation mutates it in place, so the
  later v1->v2 upgrade doesn't serialize an already-mutated (and
  potentially v1-lint-failing) problem.

* pytest.ini: ignore the benign, documented petab1to2 warning when
  falling back from a v1-only noise distribution (log10-normal) to
  log-normal for v2 -- was being promoted to a hard error by this
  repo's `filterwarnings = error` policy, exactly when the v1->v2
  upgrade path first became reachable.

* ExampleJaxPEtab.ipynb / test_petab_suite.py: two consumers still
  expected `dynamic_conditions` to hold bare condition-id strings;
  it now holds tuples (to support multi-condition periods). Updated to
  match.

Brings the official PEtab v1/v2 test suite (jax=True) from 28 failing
down to 18 (case 0007's chi2 mismatch is a likely-inherent consequence
of the log10-normal->log-normal fallback; the remaining ~8 cases
cluster around condition-table-driven state reinitialisation and are
tracked separately). jax=False path re-verified unaffected.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

* Ignore another benign petab1to2 warning-turned-error

Fixes a regression: petab1to2's "Parameter scales are not supported
in PEtab v2" warning (emitted whenever a v1 problem uses non-linear
parameterScale, e.g. the lotka_volterra test fixture) was being
promoted to a hard error by this repo's `filterwarnings = error`
policy, breaking test_preequilibration_failure/test_serialisation.

Verified benign via direct SUNDIALS simulation of the same converted
v2 problem (bypassing JAX) for petab test suite cases 0019/0020, which
also trip this warning: llh matches the ground truth solution exactly,
confirming the dropped parameterScale is purely estimation-scale
metadata that doesn't affect simulation values (petab v1's
nominalValue is always stored in linear units).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

* Fix state reinitialisation lookup and petab1to2 noise-distribution bug in JAX PEtab backend

Resolves the remaining 18 official PEtab test suite (jax=True) failures
(cases 0007, 0010, 0011, 0013, 0016-0020, both formats):

* JAXProblem._state_needs_reinitialisation/_state_reinitialisation_value/
  load_reinitialisation looked up species-level condition-table overrides
  via the old wide-format `condition_df.loc[condition, state_id]`, but the
  current petab.v2 API returns `condition_df` in long format
  (conditionId/targetId/targetValue columns), so the lookup always missed
  and every such override silently fell back to the SBML default. Rewired
  to reuse `_parameter_mappings["targets_map"]` (built from `c.changes`),
  which parameter-target lookups already relied on correctly. Fixes cases
  0010, 0011, 0013, 0017, 0018, 0019, 0020.

* Traced case 0007/0016's chi2-only mismatches (LLH and simulated values
  already matched) to an upstream libpetab-python bug: petab1to2's
  `update_noise_dist` computes the merged v1->v2 `noiseDistribution` (e.g.
  folding `observableTransformation=log` into `log-normal`) but never
  returns it, so every upgraded observable silently reverts to `normal`,
  discarding any log/log10 transform. This corrupted `iy_trafos` (and thus
  chi2) even though AMICI's own log-likelihood code generation is
  unaffected, since it's derived from the pristine v1 problem directly.
  Added a workaround in `import_petab_problem` that recomputes the correct
  value from the pristine v1 problem and patches it onto the upgraded v2
  observables. Fixes case 0016 outright.

* With the above fixed, case 0007 has one genuinely inherent residual:
  PEtab v2 has no `log10-normal` distribution, so `log-normal` is
  substituted (with a warning); recomputing chi2 with log10 in place of
  log reproduces the expected ground-truth value exactly, confirming this
  is a real v1->v2 upgrade limitation, not an AMICI bug. Marked as an
  explicit, documented `pytest.xfail` rather than silently skipped.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

* Resolve JAX PEtab reinitialisation values live so gradients flow

The previous commit resolved condition-table state initial values through
`_parameter_mappings["targets_map"]`, which `_get_parameter_mappings` builds
once at construction time. For a value referencing an estimated parameter,
that captured `self.parameters[i]` as a constant in a separate (frozen)
pytree leaf. Consequences:

* `update_parameters(...)` only replaces `.parameters`, never the cached
  `targets_map`, so the initial value stayed pinned at the nominal parameter
  value -- re-simulating after an update silently ignored it.
* Differentiating via the documented `eqx.filter_grad(run_simulations)`
  idiom put the sensitivity into `grad._parameter_mappings[...]` rather than
  `grad.parameters`, so `grad.parameters` (what callers read) was 0 for any
  parameter used as an initial value.

Verified on petab test suite case 0020 (initial_A estimated, entering the
likelihood only through A(0)): pre-fix, updating initial_A left llh
unchanged and grad.parameters[initial_A] was 0 while -8.70 leaked into the
cache.

Fix: resolve the reinitialisation value live from the raw condition-table
change (`_condition_reinit_target_value` + `_resolve_condition_target_value`)
inside `_state_reinitialisation_value`, which runs within the traced region
via `_prepare_experiments`. Reading `self.parameters` there keeps the value
a function of the current parameters. After the fix, autodiff matches
central finite differences to ~1e-10 with zero gradient leaking into the
cache, for estimated initial values (cases 0020, 0019), parameter-referenced
initial values (case 0013) and ordinary rate parameters alike.

Adds `test_condition_table_initial_value_is_differentiable` (the petab test
suite skips derivative checks for jax, so this bug was uncaught).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* Resolve condition-table parameter overrides live too

Same construction-time freezing bug as the preceding commit, on the
parameter-mapping path: `_map_experiment_model_parameter_value` read the
override value from `_parameter_mappings["targets_map"]`, which caches
`self.parameters[i]` at construction time. So a model parameter mapped by
the condition table to an estimated parameter -- the standard PEtab pattern
for condition-specific estimated parameters -- was frozen at the nominal
value: `update_parameters` had no effect and its gradient leaked into the
cache instead of `grad.parameters`.

Verified: a condition setting model parameter `k1` to estimated `k1_c0`
previously left llh unchanged under `update_parameters(k1_c0)` with
`grad.parameters[k1_c0] == 0` (and -0.40 leaking into the cache); after the
fix, forward responds and autodiff matches central finite differences.

Fix: build the override lookup from the raw condition-table changes and
resolve it live via `_resolve_condition_target_value` (which reads the live
`self.parameters` inside the traced region), mirroring the reinitialisation
fix. Adds `test_condition_table_parameter_override_is_differentiable`.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* Apply suggestions from code review

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

* Drop petab1to2 noise-distribution workaround; rely on upstream fix

The `update_noise_dist` missing-return bug in petab.v2.petab1to2 (which
silently reverted every upgraded observable's noiseDistribution to
`normal`, corrupting iy_trafos/chi2 for non-linear observable
transformations) has been fixed upstream in
PEtab-dev/libpetab-python#502.

Remove the `_fix_petab1to2_noise_distribution_bug` shim and bump the v1
petab-suite CI job's libpetab pin from 44c8062 to 1b8599dd (the #502
merge commit) so the fix is present. Case 0016 now passes via the
upstream conversion; case 0007's xfail (inherent log10-normal ->
log-normal substitution) and its warning filter remain.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* Revert unnecessary _petab_import.py changes

The pristine-v1-problem snapshot (deepcopy before compilation, used for
the v1->v2 upgrade instead of the possibly-mutated problem) is not needed:
the full v1.0.0 jax petab test suite -- including the placeholder cases
(0003/0004/0005/0009) that trigger `_workaround_observable_parameters`'s
in-place mutation -- passes upgrading `petab_problem` directly, as on main.
Restore the file to match main so this PR leaves it untouched.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* Fix _build_simulation_df_v2: consistent masking, drop fragile sc[0] lookup

Two issues in the PEtab v2 simulation-DataFrame builder:

* Length inconsistency (reviewer comment): the DataFrame mixed
  masked/valid-only arrays (obs, t[mask], y[mask]) with unmasked lengths
  (`len(t)`, `index=_petab_measurement_indices[ic, :]`). Since
  `_get_measurements` pads every experiment to a common length, any
  problem whose experiments have differing numbers of timepoints would
  raise `ValueError: arrays must all be same length` or leak
  padded/duplicated indices. Apply the per-experiment `_ts_masks[ic]`
  mask consistently to the index and every column.

* Fragile zero-indexing: the experiment id was recovered by reverse-
  mapping `dyn_conditions`' first condition id (`sc[0]`) through
  `_conditions_to_experiment_map`. `run_simulations` already builds these
  per experiment, so thread the experiment ids through directly and drop
  both the `sc[0]` lookup and the now-unused `_conditions_to_experiment_map`.

No PEtab test-suite case exercises ragged (differing-timepoint)
experiments, so add `test_petab_simulate_ragged_experiments` as a
regression test.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

---------

Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>