Fix ensure_positive_ndt for models with trial-to-trial ndt variability by EItanm1999 · Pull Request #1292 · lnccbrown/HSSM

EItanm1999 · 2026-08-31T20:33:05Z

ensure_positive_ndt floors the log-likelihood wherever rt - t <= 1e-15. That is the correct support edge for a fixed non-decision time, but not for any model carrying st: there t is drawn per trial from Uniform(t - st, t + st), so the fastest admissible response time is t - st and the band [t - st, t] carries real density. The guard replaces that entire band with LOGP_LB regardless of what the likelihood returned.

The comment above the call site states the assumption ("Assuming that the non-decision time parameter is always named t") but nothing checks whether the model also has st. Affects every *_st model, applied unconditionally at dist.py:607 and :617.

Measured on ddm_st: compiling HSSM's observed-RV logp for a model whose likelihood IS the exact ddm_st quadrature and comparing against the same quadrature called directly gave differences of -5.3 to -12586.8 nats, varying with theta. With the guard skipped the two agree to exactly 0.0000 on 7 of 8 parameter vectors (the 8th had a subject z outside its bound). So this guard accounts for the entire discrepancy while parameters stay in bounds.

p_outlier partially masks it: the floored value is wrapped in the lapse mixture, so affected trials emerge at log(0.05/20) = -5.99 rather than at LOGP_LB, which is why this presented as bad geometry rather than an obvious -inf.

Only st moves the response-time support edge. sz (starting point) and sv (drift) do not, and are deliberately not consulted; a regression test pins that down.

Summary by CodeRabbit

Bug Fixes
- Corrected response-time support boundaries for models with variable non-decision time.
- Preserved valid likelihood values within the supported response-time range.
- Ensured other variability parameters do not shift the boundary.
- Kept full-model boundary behavior unchanged.
Tests
- Added coverage for fixed and variable non-decision-time scenarios, including boundary values.
Documentation
- Documented the corrected support-boundary behavior and its impact on likelihood calculations.

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Walkthrough

ensure_positive_ndt now derives the response-time support boundary from t and optional st. Tests verify fixed non-decision time, st, sz, and sv behavior.

Changes

Non-decision time support boundary

Layer / File(s)	Summary
Boundary calculation and flooring `src/hssm/distribution_utils/dist.py`	`ensure_positive_ndt` uses `t - st` when `st` is present and `t` otherwise. Its annotations and documentation describe the updated boundary and `full_ddm` convention. Stale comments were removed.
Support boundary validation `tests/distribution_utils/test_distribution_utils.py`, `docs/changelog.md`	Tests verify sentinel replacement at or below the applicable boundary and preservation of valid values. The tests confirm that `sz` and `sv` do not shift the boundary. The changelog documents the correction.

Priority: ➖ Normal

Estimated code review effort: 2 (Simple) | ~10 minutes

Change: Bug fix · Severity of issue fixed: Medium

Suggested reviewers: krishnbera

Merge Risk: ⚪ Minimal · up to 444a2

The corrected support boundary and its regression coverage are merge-ready; no actionable risk remains.

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EItanm1999 · 2026-09-03T16:17:48Z

@AlexanderFengler

Ported from the LAN pipeline's canonical notebooks onto the registered models: `model="ddm_st"` / `"ddm_normalt"` with an explicit `loglik=` path (ST_NETWORKS_DIR, default /oscar/data/frankmj/st_networks) replaces the hand-built lanfactory likelihood, and the ensure_positive_ndt monkeypatches are gone because this branch carries the fix (lnccbrown#1292) and ddm_normalt ships ndt_edge_width=3.0. Section 2 of each notebook now says so. st_tutorial_helpers.py holds make_manual_prior / make_initvals (extracted from the pipeline's canonical settings file) plus summarize() and trace_plot(), which paper over arviz 1.x renaming summary's HDI columns and rebuilding plot_trace. Both notebooks re-executed end to end under arviz 1.3.0 (SLURM, 4 cores, ~30 min each). Convergence matches the 09-02 originals within sampler noise: worst r_hat 1.007, zero divergences, min ESS 623-2147 across all fits. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

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In `@docs/changelog.md`:
- Line 5: Revise the changelog entry’s initial `st` support claim to apply only
to LAN `*_st` models using the half-width convention, rather than every model
carrying `st`. Keep the `full_ddm` exception explicit, including its full-width
interpretation and support edge at `t - st / 2`, while preserving the existing
explanation of `sz` and `sv`.

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In `@tests/distribution_utils/test_distribution_utils.py`:
- Around line 312-338: Add a focused caller-path test for make_distribution that
uses a fake log-likelihood with an st parameter, invokes Dist.logp, and verifies
responses at or below t - st receive LOGP_LB while responses above the edge
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ensure_positive_ndt floors the log-likelihood wherever rt - t <= 1e-15. That is the correct support edge for a fixed non-decision time, but not for the LAN `*_st` models, which follow the half-width st convention of ssms: there t is drawn per trial from Uniform(t - st, t + st), so the fastest admissible response time is t - st and the band [t - st, t] carries real density. The guard replaced that entire band with LOGP_LB regardless of what the likelihood returned. Applied unconditionally at both call sites, affecting every LAN `*_st` model. Measured on ddm_st: compiling HSSM's observed-RV logp for a model whose likelihood IS the exact ddm_st quadrature and comparing against the same quadrature called directly gave differences of -5.3 to -12586.8 nats, varying with theta. With the guard skipped the two agree to exactly 0.0000 on 7 of 8 parameter vectors (the 8th had a subject z outside its bound). So this guard accounts for the entire discrepancy while parameters stay in bounds. p_outlier partially masks it: the floored value is wrapped in the lapse mixture, so affected trials emerge at log(0.05/20) = -5.99 rather than at LOGP_LB, which is why this presented as bad geometry rather than an obvious -inf. The t - st edge is stated as correct under the half-width convention of ssms/cssm, which every LAN *_st model follows, rather than as a universal law. full_ddm is the one bundled model on the other convention: its only likelihood is the blackbox wrapper around hddm_wfpt, which reads st as a full width and puts its own edge at t - st/2. No separate factor is needed, because that edge sits above t - st and hddm_wfpt already returns zero density across [t - st, t - st/2), which the wrapper maps to the same lower bound. Verified: across a 0.28-0.46 RT scan the raw likelihood's highest floored rt is exactly t - st/2, and the guard changes no value at any rt (max |delta| 0.0). Only st moves the response-time support edge. sz (starting point) and sv (drift) do not, and are deliberately not consulted; a regression test pins that down. Tests are one parametrized case per convention (fixed t, st moves the edge, sz/sv leave it) over an explicit RT vector that straddles all three edges, so the in-band assertion no longer depends on an unseeded draw. They compare against the imported LOGP_LB rather than a -66.1 literal: under PYTENSOR_FLAGS=floatX=float32 the bound is -66.0999984741211, and wrapping it in np.array defeats NumPy's weak promotion, so the literal form failed there. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

EItanm1999 · 2026-09-22T21:05:12Z

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coderabbitai · 2026-09-22T21:05:19Z

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The Unreleased entries for this stack are written on main once the PRs are merged, so the PR carries no docs/changelog.md change. The entry also said full_ddm is "unaffected in value", while full_ddm is the registered model whose log-likelihood this fix changes. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

…rown#1292) full_ddm is the one registered model with st, so it is the model whose values this fix changes: hddm_wfpt reads st as the full width and has real density on (t - st / 2, t], which the old rt <= t guard replaced with LOGP_LB. The existing st tests use a fake likelihood and are slow-marked, so nothing in PR CI covered the registered model. Compares the blackbox distribution with the raw hddm_wfpt wrapper on a response-time grid from below t - st to above t. With the old guard 8 of the 21 values differ, by up to 63.7 nats. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

A likelihood whose support starts below the non-decision time can now say so with an ``ndt_edge_shift`` entry, ``{"param": <name>, "scale": <float>}``, meaning the edge sits at ``t - scale * <param>``. The key is read from a registry entry through ``Config.from_defaults`` and from a user ``ModelConfig`` through ``update_config`` (the user's wins, like rv and backend); ``validate`` rejects a parameter missing from ``list_params``, a model without ``t``, and a scale that is not a finite non-negative number. Nothing reads the field yet. Inferring the shift from a parameter name is not an option: "st" is a half-width for the ssms LAN models, the full width for full_ddm's hddm_wfpt likelihood and a standard deviation for a Normal kernel, so the likelihood has to declare its own edge (lnccbrown#1292). Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

hddm_wfpt reads st as the full width of the non-decision-time window, so its likelihood assigns real density down to t - st/2. Declaring the shift on the blackbox entry lets the guard floor only below that edge instead of at t, once it reads the declaration (lnccbrown#1292). Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

ensure_positive_ndt becomes a pure floor: it takes the lower edge of the response-time support and knows no parameter names. A new resolver, _ndt_lower_edge, is the one place that reads the "t" convention and a likelihood's ndt_edge_shift declaration, returning None (no floor) when there is no t, t when nothing is declared, and t - scale * param otherwise. make_distribution takes the declaration and applies the resolved edge at both call sites, lapse and non-lapse. This removes the "st" inference introduced earlier on this branch: a parameter called st shifts nothing unless a config declares it, so models without a declaration floor at t exactly as on main. The two existing st tests are adapted to the new contract; the full_ddm regression test stays red until the declaration is forwarded (lnccbrown#1292). Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

HSSM._make_model_distribution and make_distribution_for_supported_model now pass the config's declaration through, so a registry entry, a user model_config override and the supported-model helper all reach the guard. This is what makes full_ddm's declaration effective: its log-likelihood again equals raw logp_full_ddm everywhere, and the regression test pinned on this branch goes green (lnccbrown#1292). Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Covers the resolver (no t, t only, a declared parameter whether or not it is called st, trial-wise values), the guard with an explicit edge, make_distribution at both the lapse and non-lapse call sites, and the three routes a declaration takes to the compiled logp: a register_model entry, a user model_config override and make_distribution_for_supported_model. Each wiring test uses a flat stand-in likelihood on a fixed grid, where t = 0.5 and st = 0.1 under scale 1.0 versus 3.0 floor visibly different sets. Config tests pin the registry read (full_ddm carries its declaration as a deep copy), the override precedence and the three validation rules, with numpy scalars accepted as scales and bool refused (lnccbrown#1292). Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

register_model's likelihoods entry and the HSSM model_config field list now say what the key declares and which direction the override goes (lnccbrown#1292). Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

A declaration missing "param" or "scale" (or not a dict at all) raised a bare KeyError/TypeError from Config.validate; it now raises a ValueError that names the key and the expected shape, like the other three rules. Refs lnccbrown#1292. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

The regression test's docstring still named the t - st edge of the earlier name-keyed version; the declared edge is t - st / 2. The logp_full_ddm docstring now states the fact behind that scale (hddm_wfpt reads st as the full width), and the module docstring lists full_ddm. Refs lnccbrown#1292. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Brings in lnccbrown#1292 in its merged form, which replaced the st-aware floor the branch was built on with a per-likelihood `ndt_edge_shift` declaration (NDTEdgeShift in _types.py, a LoglikConfig key, Config/ModelConfig fields, validation in Config.validate and resolution in dist.py). Two conflicts: - src/hssm/_types.py: kept both sides. main's NDTEdgeShift class and the `ndt_edge_shift` LoglikConfig key stay where main put them; this branch's "ddm_uniform_st" SupportedModels entry and the `rv` LoglikConfig key are kept alongside. - docs/changelog.md: taken from main entirely. The branch's hunk (a new lnccbrown#1346 entry plus an edit of lnccbrown#1292's entry) is dropped: changelog entries are written on main after merge, not carried in PRs. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

ssms' ddm_st simulator draws the non-decision time as t + U(-st, st), so st is the half-width and the fastest admissible response time is t - st. Without an `ndt_edge_shift` declaration the guard from lnccbrown#1292 keeps its floor at t and replaces the likelihood on (t - st, t] with LOGP_LB, the band this kernel puts real density on. Declare {"param": "st", "scale": 1.0} (full_ddm's hddm_wfpt reads st as the full width and declares 0.5). The tests pin the registry entry and Config.from_defaults, and check the compiled logp against a flat stand-in likelihood: LOGP_LB at and below t - st, untouched above it. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Changelog entries for #1292, #1293, #1358 and docs for ndt_edge_shift

…st models HSSM (lnccbrown/HSSM#1292, #1358) reads where a model's response-time support starts from the per-model config, and ssms owns that fact: its own KDE label estimator needs it, and HSSM mirrors ssms configs for LAN models. ddm_st, full_ddm (and its alias full_ddm2) and full_ddm_rv draw the non-decision time as t + U(-st, st), so their RTs start at t - st. Every other model's support starts at t and declares nothing. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

The checks Config.validate ran on a likelihood's ndt_edge_shift declaration move to a module-level helper so the RL decision-process registry can apply the same rules, with the same messages, to the declaration it carries. Config's behaviour is unchanged. Refs lnccbrown#1292. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

The declaration that lnccbrown#1292 added to Config had no place in the RL config, so an RLSSM could not floor its log-likelihood at the edge its decision process declares. RLSSMConfig gains the field with the same meaning as Config.ndt_edge_shift, from_rlssm_dict reads it as an optional key, and validate() applies the shared checks against the sampled parameters. Refs lnccbrown#1292. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

The support-edge declaration is a property of the SSM backbone, so it travels with the decision-process spec: _build_ssm_spec_from_modelconfig copies it from the backbone's approx_differentiable entry, register_ssm accepts it (validated with the shared helper against the SSM's own parameters) and stores it, and get_rlssm_model_config and from_ssms_model hand it to RLSSMConfig. A declaration whose "t" or named parameter is computed by the learning process is rejected with an error that names the decision process and the parameter, since a per-trial computed parameter is not among the distribution's parameters and the edge cannot be resolved from it. Refs lnccbrown#1292. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

The RL distribution was built without the declaration, so the guard kept flooring every response time at or below t even when the decision process declares an earlier edge. The config's value now reaches make_distribution like the other RL-side arguments. Refs lnccbrown#1292. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Covers each hop of the declaration: the spec built from a backbone's modelconfig carries it (and None without one), register_ssm stores a copy and applies the shared rules, get_rlssm_model_config hands it to RLSSMConfig and rejects a declaration that leans on a parameter the learning process computes, RLSSMConfig round-trips it through from_rlssm_dict, validates it and reads it from the decision-process spec in from_ssms_model, and RLSSM forwards it to make_distribution. One end-to-end test evaluates the compiled logp on a small balanced panel and checks the floor sits at t - st rather than at t. Refs lnccbrown#1292. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

lnccbrown#1292 adds `ndt_edge_shift` to likelihood configs and ssms will carry the same key on its model registry. For LAN likelihoods the two must be equal, because the network learnt the support edge from that simulator's output; blackbox/analytical likelihoods keep their own convention and are left out, mirroring the existing bounds-vs-training-box guard. The guard skips until the installed ssms ships the key; a second test pins that it reports drift from either side once it runs. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

lnccbrown#1358 The maintainer writes the Unreleased entries on main after each merge wave; lnccbrown#1292, lnccbrown#1358 and lnccbrown#1360 merged without one, and lnccbrown#1293 is next in the queue. lnccbrown#1360 is test-only and the changelog has no internal section, so it gets no entry. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Brings in lnccbrown#1292, lnccbrown#1293, lnccbrown#1358 and lnccbrown#1361. The only conflict was docs/changelog.md: main gained an Unreleased section while this branch added its own entry at the top. Resolved by taking main's file entirely; PRs carry no changelog hunk, the entry is written on main later. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

AlexanderFengler · 2026-10-08T11:02:54Z

Thanks, Eitan. This is merged and goes out with HSSM 0.5.1. Here is what changed on the branch after your commit, and why.

What the fix fixes. More than the PR said: a live bug in full_ddm, the one registered model with st. I checked it at both call sites (lapse and non-lapse), with t/st as regression parameters, with the -999 missing-data path, and against analytic gradients. full_ddm's likelihood (hddm_wfpt) reads st as the full width, so it has real density in (t − st/2, t], and 0.5.0 floored all of it. On a 14-point RT grid (v=1, a=1, z=0.5, t=0.4, sz=0.1, sv=0.3, st=0.3), 8 values differed from raw logp_full_ddm by up to 63.7 nats, and in a 2000-trial profile over t the maximum sat at 0.36 instead of the true 0.40. Anyone with full_ddm fits whose fastest RTs fall within st/2 below t should refit; the changelog says so.

Two small commits first.

db60d734 dropped the changelog entry. None of the PRs in this stack carries a docs/changelog.md hunk (six PRs adding a block at the same spot conflict on every merge after the first); the ### Unreleased entries are on main via Changelog entries for #1292, #1293, #1358 and docs for ndt_edge_shift #1361, with credit to you for the fix and, in the second entry, for proposing the configurable edge in Add opt-in ndt_edge_width for unbounded ndt kernels #1344. The dropped entry also said full_ddm was "unaffected in value", which is the opposite of what changes.
a0e82b14 added a regression test for full_ddm at the end of the test file (not slow-marked, so PR CI runs it): the registered distribution's log-likelihood against raw logp_full_ddm over a 21-point RT grid from below t − st to above t. It failed on main at 8 of 21 points and passes now.

Then a design change. The PR (and #1344 on top of it) made ensure_positive_ndt behave differently depending on whether a parameter named st exists in list_params. I did not want a name-keyed rule in the core: st is a half-width for the ssms LANs (edge t − st), the full width for full_ddm's hddm_wfpt likelihood (edge t − st/2, which is why the fix was still off by st/2 for the one model it reached), and a standard deviation for ddm_normal_st (no hard edge at all). A model whose variability parameter had another name would get no edge, and the function's contract would rest on a naming convention nobody declared. So I replaced the rule with a declaration (fa0786e9 … c67a9e3f, eight commits):

A likelihood config can declare "ndt_edge_shift": {"param": "<name>", "scale": s} (TypedDict NDTEdgeShift in _types.py), meaning the support edge is t − s·<name>. full_ddm declares {"param": "st", "scale": 0.5}. Without a declaration the floor stays at t, as before; a model without t has no floor, as before. The one name convention left is t, which the whole repo already relies on.
ensure_positive_ndt(data, logp, lower_edge) is a pure floor and knows no parameter names; one resolver, _ndt_lower_edge, reads the declaration; make_distribution(..., ndt_edge_shift=) receives it from hssm.py and from make_distribution_for_supported_model (which in Add opt-in ndt_edge_width for unbounded ndt kernels #1344 did not forward the width).
It can be overridden per model, as in Add opt-in ndt_edge_width for unbounded ndt kernels #1344: hssm.HSSM(..., model_config={"ndt_edge_shift": {"param": "st", "scale": 2.5}}). Your finite, non-negative check is carried over, plus checks that t and the named parameter are in list_params. Tests cover all three routes (registry entry, model_config, convenience constructor) and pin that a model with st and no declaration floors at t.
Models without a declaration are bitwise identical to the previous main; full_ddm equals raw logp_full_ddm everywhere (your tests and mine both pass). The key is documented on the likelihoods explanation page (docs/explanations/likelihoods.md).

Sorry for reshaping the PR under you; the alternative was a review round-trip on the base of a four-PR stack. The same key now exists on the simulator side: ssm-simulators 0.15.0 declares it in the ddm_st, full_ddm and full_ddm_rv configs (lnccbrown/ssm-simulators#370), and a guard test in HSSM (#1360) checks that the LAN entries match the simulator's declaration.

For the stacked PRs. #1344 is superseded by this; the note there says so. #1346 already carries "ndt_edge_shift": {"param": "st", "scale": 1.0}, pushed as 6e8a3c82 on top of a merge of main; what remains is in its note. #1345 still sits on #1344's branch: it needs a rebase onto main that drops #1344's commits and {"param": "st", "scale": 3.0} in place of ndt_edge_width (the old key makes Config.from_defaults raise TypeError); details there. Since #1346 now carries commits of mine, please push further fixes there as new commits rather than amending.

Separately: full_ddm simulates with ssms' half-width st but scores with hddm_wfpt's full-width st, so the model's own predictive draws can fall below the likelihood's edge. The declaration is right for the scorer; the simulator side is a separate matter, not part of this PR.

Open question

Can the quadrature ddm_st likelihood behind the -5.3 … -12586.8 nats figure in the PR body be shared, e.g. as a gist, or as hddm_wfpt with st' = 2*st as a test helper?

ddm_normal_st draws the non-decision time as Normal(t, st): st is the kernel SD and the kernel has no hard edge. Without an `ndt_edge_shift` declaration the guard from lnccbrown#1292 keeps its floor at t and replaces the likelihood below t, where this kernel puts nearly half its mass, with LOGP_LB. Declare {"param": "st", "scale": 3.0}: 3.0 is a practical ~3-sigma edge for the untruncated Normal kernel, chosen by us, not a property of the model or the simulator. ssm-simulators 0.15.0 declares no edge for ddm_normal_st. The tests pin the registry entry and Config.from_defaults, and check the compiled logp against a flat stand-in likelihood: LOGP_LB at and below t - 3 * st, untouched above it. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

…st models HSSM (lnccbrown/HSSM#1292, #1358) reads where a model's response-time support starts from the per-model config, and ssms owns that fact: its own KDE label estimator needs it, and HSSM mirrors ssms configs for LAN models. ddm_st, full_ddm (and its alias full_ddm2) and full_ddm_rv draw the non-decision time as t + U(-st, st), so their RTs start at t - st. Every other model's support starts at t and declares nothing. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

* Add forward race simulation utilities * Address race simulation review feedback * Fix race simulator boundary validation * test: define generic observation result contract * feat: validate native observation results * docs: specify structured observation results * fix: validate categorical labels against observation dtype * docs: require representable categorical labels * test: define every legacy singleton-axis projection * feat: normalize legacy results without inference * docs: migrate consumers without changing defaults * test: expose legacy omission authority * fix: make legacy omission authority explicit * docs: require explicit legacy omission authority * Simplify legacy observation projection * test: focus legacy projection evidence * test: preserve projection boundary parity * docs: tighten legacy projection guidance * test: preserve normalized result invariants * build(docs): consolidate simulator documentation dependencies * build(docs): add the standard simulator docs entry point * ci(docs): adopt shared artifact deployment * docs: update simulator documentation ownership * ci(docs): vendor the reusable workflow locally * fix(docs): repair rendered tutorial links * docs: align the site legal notice * docs: consolidate the HSSM RLSSM handoff * docs: align guides with their documentation genres * docs: complete the public API reference * docs: preserve API reference compatibility * docs: align RLSSM handoff initialization * chore: ignore generated .cursor/ and .agents/ directories HSSMSpine's sync_agent_config.py materializes these into every ecosystem repo on each launch, but only the spine gitignored them. That left every repo one 'git add -A' away from committing generated editor config -- which just happened on lnccbrown/HSSM#1248, where the swept-in files also leaked local worktree paths. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> * feat: add angle_extended, an angle variant with drift on (-6, 6) A collaborator needs `angle` fitted to data whose drift runs past the current +/-3 box. The simulator already handles it -- `cssm.ddm_flexbound` with the `angle` boundary does not care how large v is -- so this is a config, not new machinery. It is a separate model rather than wider bounds on `angle` because the published angle.onnx was trained on (-3, 3). Widening the declared bounds without retraining would let a sampler explore where the network never learned, and that fails silently: no error, just a wrong likelihood. A separate model earns a separate network and leaves `angle` and its artifact untouched. Naming follows `shrink_spot_extended`, the registry's only other bounds-only variant. Not `angle_drift_ext`: in this codebase `drift` names a drift FUNCTION -- base.py carries a drift_config registry, and `gamma_drift` is called that because its drift_name IS gamma_drift -- so a name of that shape would read as "angle with a custom drift function", which is exactly what this is not. The tests assert the RELATIONSHIP rather than just the shape, because two configs silently diverging is the failure mode a bounds-only variant actually has: only `name` and the bounds may differ, only v's bounds may have moved, and only outward. They also pin `name` == the registry key, which `get_shrink_spot_extended_config` gets wrong -- it names itself "shrink_spot" -- so the precedent's bug is not inherited. And they check the new range does something rather than merely not crashing: past +/-3 the choice follows the sign of v and RTs stay short, which is the region worth having a network for. Measured while validating, and relevant to whoever trains that network: 55.3% of uniform draws from this box produce one-sided data, against 32.3% for `angle`, and RT dispersion falls from sd 0.250 at v=1 to 0.059 at v=6. Validation per the add-ssm-model skill: config loads, counts consistent, bounds ordered, defaults inside, simulator runs on defaults and on random draws from the full box. 1189 passed, 123 skipped. * docs: say which registry APIs are for reading and which path is for authoring CodeRabbit reviewed the angle_extended config and asked for the boundary to be resolved through `get_boundary_registry()` and the model registered through `get_model_registry()`, citing this file's Config Registry System section. That would have been wrong -- zero of the 24 modules under _modelconfig/ use those APIs, 22 of 24 name `bf.<fn>` directly, and model_registry.py:357-367 builds the registry FROM the static configs dict at import, with a comment saying so. Registering through the registry would bypass the source it derives from, and `ssms.config.model_config` reads from `get_model_config()`, so the model would be missing there. But the section invited the reading. It lists five ways to reach a config under a heading about registration, without saying they are for consumers -- so a reviewer generalising from it lands exactly where CodeRabbit landed, and would land there again on the next model. One paragraph now separates reading a config from authoring one, and points at the add-ssm-model skill. No code change; angle_extended is already reachable through get_model_registry() and present in ssms.config.model_config, which is the outcome the review was asking for. * fix: mask omissions on rts, not on choices The simulator marks a trial that ran past its deadline by writing -999.0 into `rts` and leaving the latent choice alone. Two of the auxiliary labels masked on `choices` instead, where the sentinel never appears, so the mask matched nothing: omission_p = P(choices == SENTINEL) -> 0.0, always choice_p_no_omission masked on choices -> excluded nothing Measured before the fix, on the shipped deadline models at deadline 0.8: model true omission rate omission_p as recorded ddm_deadline 0.3511 0.0000 angle_deadline 0.3511 0.0000 gamma_drift_deadline 0.5429 0.0000 gamma_drift_angle_deadline 0.5429 0.0000 The consequence is worth stating plainly, because it is not a cosmetic mislabel: an OPN is trained on `omission_p`, so a deadline data-generation campaign -- around 6,500 core-hours per model -- would have produced a network trained against a constant zero. Nothing downstream would have failed loudly; the network would simply have been useless. `nogo_p` was already correct because it tested `rts` as well as `choices`, and that inconsistency between neighbouring lines is what made the defect visible. The three now share one `omitted` mask so they cannot drift apart again. Tests cover the premise (the simulator marks rts and not choices -- a regression here means the mask must be revisited), that omission_p tracks the real rate on three deadline models, that choice_p_no_omission now excludes what it claims to and differs from the all-trials version, and that nogo_p equals P(wrong choice, responded) + P(omitted) -- the identity the derived-network work depends on. 1197 passed, 123 skipped. * Prepare ssm-simulators 0.14.0 release * Refresh uv.lock for the 0.14.0 version bump The lockfile records the project's own version, and docs CI syncs with --locked; first release since that workflow exists, so #305 never hit this. One-line diff: ssm-simulators 0.13.2 -> 0.14.0, no dependency changes. * build(deps): bump pypa/cibuildwheel from 4.1.0 to 4.2.0 Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 4.1.0 to 4.2.0. - [Release notes](https://github.com/pypa/cibuildwheel/releases) - [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md) - [Commits](pypa/cibuildwheel@v4.1.0...v4.2.0) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-version: 4.2.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> * Filter non-positive RTs before KDE bandwidth estimation A genuinely negative or zero RT - real and expected for models whose non-decision-time kernel has unbounded support (e.g. a Normal(t, st) kernel), and distinct from the omission sentinel - produces log(non-positive) = NaN in the log-RT array. A single NaN poisons np.std() over the entire choice group inside bandwidth_silverman, which then fails the 'bandwidth > 0' check and silently discards the whole group as 'no_base_data'. Measured directly: 1 negative RT out of 198,865 samples collapsed its entire choice group, flattening that group's density to a theta-independent floor in the training labels. Extend the validity mask to require rt > 0 and finite log(rt), applied to the final (possibly displace_t-shifted) RTs. - The mask stays after the log rather than at it: with displace_t, measured 10 of 200 strictly-positive input RTs become non-positive only after the shift, so an earlier-only mask misses them. The RuntimeWarning from the earlier log is left unsuppressed - it reports real data. - All three mask terms are load-bearing, measured over 2M draws at the displace_t boundary: 3 cases where rt - t > 0 but the log path underflows to -inf (caught only by isfinite) and 1 in the reverse direction (caught only by rt > 0); rt = +inf is caught only by isfinite. != filter_rts is kept because filter_rts is a documented parameter and rt > 0 subsumes it only for negative sentinels. - No input existed for which the old code produced a finite bandwidth that this changes: contamination always forced no_base_data. Verified byte-identical (float hex) bandwidths and log_rts for clean data across ddm, ddm_st, full_ddm and ddm_sdv, at n=200 and n=5000. - Filtering can now leave a choice group with a single sample, where bandwidth_silverman returns its std_n_1 fallback of ~10.59 instead of collapsing. That is an improvement on a flat fallback but not a meaningful bandwidth, so it is documented on the class and pinned by a test. Raising the len(log_rts) == 0 floor to < 2 would also change pre-existing n=1 behavior and is left as a separate change. - Retention contract, the pre-filter basis of choice_proportions, and the n=1 limitation are now in the LogKDE class docstring, which is what the API docs render; the stale caller-precondition sentence in the kde_class tutorial is corrected. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * test: cover the post-shift mask and the isfinite term of the KDE RT filter The filter's two design choices were untested: all three tests used displace_t=False with a single negative RT, so applying the mask before the displace_t shift, or dropping the isfinite term, left them green. Parametrize the choice-group test over displace_t (t = 0.4 puts 11 of the 199 positive RTs at or below t, so they turn non-positive only after the shift), over rt in {-0.5, 0, +inf} and over rts-only and log_rts-only input, and pin the number of retained samples. Masking before the shift now fails the 6 displace_t cases and dropping isfinite fails the 4 rt = +inf cases; all 12 fail on main's kde_class.py. The test is renamed because +inf is not a non-positive RT. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * docs: say that LogKDE's pre-filter choice_proportions are intentional The class docstring states that choice_proportions still count the samples the RT filter drops, but not whether that is by design. It is: omitted trials keep their choice label and have always been counted the same way, in the proportion but not in the density. Saying so keeps the next reader from "fixing" the basis in passing; it changes the mass of the KDE training labels and should only be changed on purpose. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * Pin the trial-to-trial variability draws to random_state - set_seed seeds C's srand and a module-level NumPy Generator, but the *_dist simulator param mappings (e.g. ddm_st's t_dist = partial(sps.uniform.rvs, ...)) are scipy calls with no explicit random_state, so scipy drew them from NumPy's legacy global RNG, which nothing seeded. Two simulator() calls with the same random_state returned different data for every model carrying sv, sz or st, while plain ddm - which has no *_dist - stayed reproducible, hiding the gap. Any "same data across arms" comparison relying on random_state alone was not actually holding the data fixed. - Fix: bind those partials to a Generator derived from random_state, after adapt_parameters, so the draws are pinned without touching process-global state. Verified that np.random's stream position and its next draws are unchanged across seeded simulator() calls, and that reproducibility holds while the global stream is being consumed between calls. - Only integer seeds participate; any other random_state object passes through and fails where it already failed (the Cython layer requires an integer). The modulo keeps the validated negative-seed range inside default_rng's domain without changing that pre-existing failure point. - Regression test folded into the existing random_state block in tests/test_simulator.py, over ddm, ddm_st, full_ddm_rv and ddm_sdv. It fails for the three variability models without the fix and passes with it, and it also asserts that a call leaves NumPy's global RNG state byte-identical, which a global reseed or a global draw would not - so it pins where the draws come from, not only that they repeat. full_ddm is deliberately not used: it has no *_dist mappings and draws sz/sv/st internally from the generator set_seed already seeds, so it cannot exercise this path. - Alternatives considered: reseeding np.random globally inside simulator() (rejected - mutates process-global state for callers who did not ask for it), and adding the legacy seed inside set_seed itself (one line, and broader since sequential_models.pyx has the same bare np.random.* pattern, but it needs a Cython rebuild and widens the contract for every direct cssm caller). The sequential_models.pyx gap is left as a separate issue. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> - Binding is restricted to callables that accept random_state: a model config may map a parameter to any callable, and one that does not take the keyword would otherwise raise on the simulator call. scipy's rvs qualifies through **kwds. - The regression test snapshots and restores NumPy's global RNG state, so its deliberate consumption of that stream cannot make later tests order-dependent. - A positional-only random_state does not count as accepting the keyword: the value would be diverted into **kwargs while the parameter kept its default, or raise. A test pins both the exclusion and that divert-and-keep-default behaviour. - A distribution that already binds its own random_state is left alone rather than rebound, so a caller's deliberate per-distribution seeding survives; the docstring says so, since such draws follow that generator, not the seed. * fix: draw trial-to-trial variability from a child stream of the seed The variability generator was default_rng(seed), and the Cython layer seeds its own generator with default_rng(seed) as well, so both replayed one PCG64 stream: the sv/sz/st draws reused the words behind the diffusion noise. Spawn a child of the seed instead, which is independent of the C-level generator. default_rng([seed, 1]) is avoided because it aliases default_rng(seed + 2**32). The comment no longer calls the modulo load-bearing: it only keeps negative seeds inside SeedSequence's domain. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix: pin variability draws in Simulator.simulate as well Simulator.simulate duplicates simulator()'s preprocessing but had no binding step, so same-seed calls through the class API still drew sv/sz/st from NumPy's global RNG. That is the recommended entry point, and the one the training-data pipeline seeds. Move the binding into one helper shared by both entry points. Each calls it once theta is final: in the class that is after the custom adaptations, which may still replace a *_dist entry. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test: route the pre-bound random_state test through simulator() test_bound_random_state_is_not_overridden only inspected a functools.partial and never called simulator(), so it kept passing with the "already bound" check removed. Feed a pre-bound t_dist through simulator() and assert that the caller's generator was the one consumed. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test: cover the unreadable-signature branch of _accepts_random_state codecov/patch flagged the `except (TypeError, ValueError)` branch as never exercised. A callable whose __signature__ is not a Signature makes inspect.signature raise, on every supported Python, and must be left unbound. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test: pin the binding to run after the custom adaptations Simulator.simulate has to bind once theta is final. A parameter adaptation may still put a *_dist entry into theta, and with the binding ahead of the custom adaptations that entry would draw from NumPy's global RNG again, while the per-model regression test keeps passing. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * feat: add ddm_normal_st, a DDM with a Normal non-decision-time kernel This is the config part of #361 (890c8e9), split off so that the model can be released without #359, which that branch is stacked on and which is not ready. ddm_normal_st draws ndt ~ Normal(t, st), so st is a standard deviation, against the uniform half-width that ddm_st means by the same parameter. Parameters, order, bounds and simulator machinery are ddm_st's; only the kernel differs. The model joins KDE_NO_DISPLACE_T like the other ndt-variability models. The kernel is unbounded, so it emits rt <= 0 by design (about 9% of samples at the box corner t=0.25, st=0.25, a=0.3). KDE label generation therefore needs the non-positive-RT filter of #349, which this commit sits on. Left out of 890c8e9: - The Normal branch of _recover_admissibility_boundary in kde_class.py and its three tests. The helper only exists after #359, so they belong with that PR. - The ddm_uniform_st alias of ddm_st (config function, registry entry, KDE_NO_DISPLACE_T entry and test). The name of the uniform model is being settled separately, and an alias can be added later without breaking anything. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test: LogKDE keeps both choice groups of ddm_normal_st at the box corner At t=0.25, st=0.25, a=0.3 about 9% of ddm_normal_st's RTs are non-positive. Without the non-positive-RT filter of #349, one such RT turns its whole choice group into "no_base_data" and its labels into a constant, on the displace_t=False path the generator takes for this model. The test fails when kde_class.py is restored to main's version, so it pins the model's dependency on that filter. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * feat(config): declare the response-time support edge for the uniform-st models HSSM (lnccbrown/HSSM#1292, #1358) reads where a model's response-time support starts from the per-model config, and ssms owns that fact: its own KDE label estimator needs it, and HSSM mirrors ssms configs for LAN models. ddm_st, full_ddm (and its alias full_ddm2) and full_ddm_rv draw the non-decision time as t + U(-st, st), so their RTs start at t - st. Every other model's support starts at t and declares nothing. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * feat(config): validate and carry ndt_edge_shift through the builder A malformed declaration would silently mislead HSSM's likelihood floor and the KDE estimator, so check its shape once (a dict with exactly param and scale, both param and t in params, a finite non-negative scale) in one helper shared by ModelConfigBuilder.validate_config and the import-time registry check, and let from_scratch keep the key like the other optional fields. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(config): cover the ndt_edge_shift declaration and its plumbing Check the declarations against the simulators (min(rt) >= t - scale * param on several parameter sets, and RTs below t at the corner so the declaration is not vacuous), that undeclared models start at t, that declaring models stay in KDE_NO_DISPLACE_T, and that the builder and validators carry and police the key. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * docs: document the ndt_edge_shift model-config key Add the key to the places that list the model-config fields for users: the configs tutorial, the contributing guide's Level 1 example notes, and the add-ssm-model skill. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(config): pin the declared edge scale and check it is tight min(rt) >= t - scale * st holds for any scale that is too large, so a declaration twice the true shift passed every test. Pin the shipped value for each declaring model, and check at the a-lower corner that the fastest RTs fall below t - scale * st / 2, which they only do (by about 0.1 s) when the scale is right. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(config): cover the ndt_edge_shift registry hook get_invalid_ndt_edge_shift_configs was tested on its own, but the call from _validate_configs() could be dropped without a test noticing. Swap in a one-config registry whose only fault is the declaration and check the import-time validator raises on it. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(config): prove non-vacuity at the a-lower corner only At the plain corner (t at its lower bound, st at its upper bound, the rest mid-box) only a handful of the 3000 simulated RTs fall below t, and the count depends on the RNG stream, so the assertion could flake. The a-lower corner puts hundreds below t and is the one that matters. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * docs: add the ndt_edge_shift checkbox to the model-validation checklist The skill's field table documents the key; its checklist did not remind an author to declare it when the non-decision time varies per trial. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * docs: say when not to declare ndt_edge_shift "Absent means RTs start at t" was wrong for kernels with no lower edge (ddm_normal_st, now on main) and for models without a t parameter; absence only means that no finite edge below t is declared. The docs now say so, list the cases where the key must be left out, state the scale convention (1.0 for a half-width), and tell a model author to add declaring and unbounded models to KDE_NO_DISPLACE_T. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * docs: say when not to declare ndt_edge_shift, all remaining sites The previous commit reached only the validator docstring. This one covers the builder docstrings, the add-ssm-model skill and its checklist, the contributing guide, the config tutorial and the test-file docstrings: absence means no finite edge below t is declared; leave the key out for models without a t parameter, for kernels with no lower edge (ddm_normal_st) and for edges not of the form t - scale * param; and add declaring and unbounded models to KDE_NO_DISPLACE_T. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * Prepare ssm-simulators 0.15.0 release Minor bump: ddm_normal_st is a new model, and the seeded variability streams (#348) change seeded outputs for the full_ddm_rv-based models. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * Refresh uv.lock for the 0.15.0 version bump The lockfile records the project's own version, and docs CI syncs with --locked. Plain `uv lock` on 273c5f9 moved nothing else: one-line diff, ssm-simulators 0.14.0 -> 0.15.0, no dependency changes. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * build(deps): bump pypa/cibuildwheel from 4.2.0 to 4.2.1 Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 4.2.0 to 4.2.1. - [Release notes](https://github.com/pypa/cibuildwheel/releases) - [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md) - [Commits](pypa/cibuildwheel@v4.2.0...v4.2.1) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-version: 4.2.1 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> * build(deps): bump actions/deploy-pages from 5.0.0 to 5.0.1 Bumps [actions/deploy-pages](https://github.com/actions/deploy-pages) from 5.0.0 to 5.0.1. - [Release notes](https://github.com/actions/deploy-pages/releases) - [Commits](actions/deploy-pages@cd2ce8f...368f825) --- updated-dependencies: - dependency-name: actions/deploy-pages dependency-version: 5.0.1 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> * Stop tracking uv.lock A lockfile never reaches users of a published library; it only pinned the dev/CI environment, and CI did not enforce it (uv sync without --frozen re-resolves whenever pyproject.toml changes). HSSM and LANfactory already resolve fresh, which is what surfaces upstream breakage early. Dropping it removes the lock-refresh commit from every version bump. Also: scripts/docs.sh no longer passes --locked, the docs workflow no longer triggers on the lockfile, and the uv rule and CLAUDE.md say that pyproject.toml is the source of truth. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(rl): build the non-numeric frames explicitly for pandas 3 Two data-validation tests assigned a string into an int64/float64 column and relied on pandas upcasting it to object. pandas 3 raises LossySetitemError instead, so the tests failed before reaching the validator. Cast the column to object first; the validator's own dtype report is what the tests check. The lockfile had pinned pandas 2.3.3; resolving fresh is what surfaced this. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(kde): refuse displace_t for models whose RTs start below t LogKDE(displace_t=True) shifts every RT by t and floors the labels of anything that lands at or below zero. For the models in KDE_NO_DISPLACE_T and those declaring ndt_edge_shift that puts the cutoff inside the response-time support: a uniform st kernel starts at t - st, and the Normal, truncated-Normal and Rayleigh kernels have no edge at t at all. TrainingDataGenerator already overrides displace_t to False for them, so the pipeline was never affected; a direct LogKDE caller got flat labels in the band below t without a word. LogKDE now resolves the model from metadata["model"] (a _deadline suffix stripped, as lan_mlp does) and raises a ValueError naming the model when its config declares ndt_edge_shift or its name is in KDE_NO_DISPLACE_T. An unregistered name is refused when the metadata carries a non-zero st. Every other model keeps the shift by t unchanged. This replaces #359 by EItanm1999, which recovered the boundary from the simulator metadata instead: t - st for the uniform kernels, which was right, and t + t_dist.keywords["loc"] otherwise, which for ddm_truncnormt is the kernel's centre rather than its edge at 0 and collapsed the labels. Since the generator never displaces these models, the honest behaviour for direct callers is an error pointing at displace_t=False. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(kde): cover the displace_t guard Every KDE_NO_DISPLACE_T model (plus full_ddm2, whose config name differs from its key) is refused at displace_t=True with the model named and still builds at displace_t=False. ddm, ddm_sdv and angle keep the plain shift by the metadata t, pinned against the label formula. An unregistered model is judged by its st, and a _deadline name is looked up by its base model. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * Give each generated file its own theta block and pin the sampling order Two independent defects made multi-file LAN training sets far smaller than their nominal size. Both were introduced together in 57389be ("big refactor of training data generators to improve modularity", PR #247, 2026-01-02) and are present in every release from v0.12.0-beta.1 through v0.14.0. 1. `AbstractParameterSampler._topological_sort` walked `self._dependency_graph`, a dict whose insertion order was seeded at `_build_dependency_graph` by `all_params = set(self.param_space.keys())`. Python randomises string hashing per interpreter, so the sampling order -- and therefore which parameter receives which draw from the sequentially consumed RNG in `sample()` -- differed between processes. Same seed, same numbers, different parameters. Verified: for a full_ddm-shaped space the order is `v,a,sv,t,st,z,sz` at PYTHONHASHSEED=0 and `a,v,sv,t,st,z,sz` at PYTHONHASHSEED=2. Fixed by traversing `param_space` insertion order first, then any dependency-only names sorted, and by sorting the adjacency sets. Topological validity is unchanged. 2. `SimulationPipeline.generate_for_parameter_set` used the theta INDEX directly as the parameter-RNG seed, and every caller supplied `range(0, n_parameter_sets)`. Two consequences: - `ssms generate --n-files N` loops `generate_data_training(save=True)` N times in ONE process (ssms/cli/generate.py), so the N files carry N identical theta sets. Reproduced: two calls on a 4-theta full_ddm config share 4 of 4 theta rows. - one SLURM array task per output file gives files that are coordinate permutations of a single theta set, by defect 1. Fixed with a `parameter_sampler_index_offset` read from `generator_config` (set it to `task_id * n_parameter_sets` for array jobs) plus a per-generator cursor in `TrainingDataGenerator` that advances by `n_parameter_sets` on every call, so repeated in-process calls consume disjoint index blocks. Together the two mean that files which look distinct recycle one pool of rows, each file assigning that pool's per-index numbers to a different parameter -- so the design acquires cross-axis structure it was never meant to have, while every margin stays uniform and nothing errors. Measured effect on a 10-file x 5000-theta ddm_sz set generated with the pre-fix code: ~20k distinct values per axis out of 50k rows, and a union centred L2 discrepancy of 1.7e-4 against 2.2e-5 for 50k i.i.d. points -- the union is 5000 distinct unit points, not 50,000. On a 10-file x 5000-theta ddm_sdv set, ten files nominally drew 250,000 unit values and delivered 25,408; file 1's `v` column is file 0's `a` column, and files 2 and 7 share a permutation exactly. Regression coverage: tests/test_parameter_sampling_determinism.py asserts sampling-order stability across PYTHONHASHSEED values in separate subprocesses, identical thetas for equal offsets, disjoint thetas (row-wise and per-axis) for distinct offsets, and disjoint thetas across two `generate_data_training()` calls. All four need more than one process or more than one call, which is why the existing single-process tests passed. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(datagen): apply the theta-index offset once, in the generator The offset introduced by the previous commit could only come from the YAML, and LAN_pipeline_minimal runs one YAML for every SLURM array task. With the sampling order now pinned and the offset defaulting to 0, every array task would have written the SAME theta block -- the KDE path going from permuted copies of one block to exact copies. Two further problems sat next to it: each concrete pipeline read the offset itself, so a custom DataGenerationPipelineProtocol got the cursor but never the offset, and the documented array formula task_id * n_parameter_sets collides as soon as a task writes a second file. TrainingDataGenerator now owns the whole index line. When the config gives no parameter_sampler_index_offset it draws a 62-bit base from OS entropy once per instance (wide enough that independently drawn blocks never overlap, narrow enough that base + block survives YAML, JSON, MLflow and int64 unchanged); an explicit offset is used as given. Base and cursor are added before the index is handed to the pipeline, so pipelines -- custom ones included -- receive final indices and the per-pipeline reads are gone. Every output file records its own first index under generator_config["pipeline"]["parameter_sampler_index_offset"], so a run started without an offset can be audited and any single file regenerated by passing the recorded value back. The config helper returns None for "absent" (an explicit 0 must stay 0) and rejects negative offsets, which cannot seed an RNG. The documented array formula is task_id * n_files * n_parameter_sets. Tests adapted to the design: two offset-free generators draw different bases and share no row on any axis; a generator built from a file's recorded config reproduces the file in a fresh interpreter under another PYTHONHASHSEED; offset 0 called twice equals offsets 0 and n; an explicit offset is honoured from either config placement and reaches a custom pipeline; neither concrete pipeline shifts an index itself. The dependency-order test was vacuous -- no model it listed has a dependent bound -- and now covers full_ddm_rv plus a synthetic space with a two-dependent parameter, with the edge counts asserted. One subprocess per hash seed prints every space's order instead of one per (seed, model): the file runs in 16 s instead of 47 s. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(datagen): prove the recorded offset is what reproduces a file The regeneration test passed on the previous source as well: with no base drawn and a default offset of 0, any offset-free config reproduces any other, so the check could not tell a recorded value from a fixed default. The worker now regenerates from both the recorded config and the offset-free one it came from, in one interpreter, and only the first may match. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(sampler): sample in the declared parameter order The first pin put the sampling order at the exact reverse of the declaration -- the depth-first search prepends -- and visited "dependency-only" names that cannot exist, since _build_dependency_graph rejects any bound naming a parameter outside param_space. The order changes for every stored training set in this release regardless, so pin the one that needs no explanation: param_space insertion order, a dependency pulled ahead only when it is declared after a parameter that needs it. For every registered model that is `params` itself, so the parameter <-> draw assignment of a file is readable from its model config. Topological validity and cycle detection are unchanged; the dependency graph is still built, for its validation. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(datagen): keep the PyDDM legacy seed inside the 32-bit range PyDDMPipeline still seeds the legacy global RNG with the theta index for any downstream code that draws from it, and np.random.seed rejects anything above 2**32 - 1. With the generator now drawing a 62-bit base from entropy, every PyDDM run raised "Seed must be between 0 and 2**32 - 1" from inside the worker pool; the KDE path was unaffected because default_rng takes any non-negative integer. Reduce the legacy seed modulo 2**32 and pin both pipelines on a 62-bit index. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * docs(race): cite the validation reference and record why the inline RNG stays The module docstring pointed at a race_model.pdf that is not in the repo. Describe the model directly and cite the preprint whose Appendix B the Volterra validation reference (#365) follows. Also state why this kernel keeps the xoshiro/Box-Muller generator copied from addm_models.pyx rather than the shared GSL RNG: the GSL path is only live on GSL builds and only for n_threads > 1 (PyPI wheels build without GSL and the stub returns 0.0), whereas the inline generator needs no GSL and gives n_threads-independent results. Consolidation of the two kernels is tracked in #376. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * refactor(race): mechanical review items, no behaviour change - stage_changed is a bint assigned True/False - the per-accumulator stage-count memoryview is n_stages, not d (the public d_array keyword is unchanged) - shape validation names the offending array instead of one combined condition, and the node-monotonicity check is a single vectorised numpy expression over the active transitions that reports the row and accumulator - nondecision_time / deadline resolution uses plain if blocks, and the two length checks are separate with distinct messages Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(race): add simulator behaviour tests Taken unchanged from the closed #364 branch (6114f34), where they were written for this simulator but never merged: deterministic single-accumulator crossing, fastest accumulator wins, native stage switches are used, the omission sentinel, and seeded output independent of n_threads. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(race): reject negative nondecision_time/deadline and deadline < ndt A negative nondecision_time silently produced negative response times and a deadline below the nondecision time omitted every trial without saying so. Validate both after they are resolved to per-trial arrays. The three tests are the ones proposed in review on #342. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(race): cover return_option and the input contract return_option: minimal omits x_final, full reports it per (sample, trial, accumulator), anything else raises. Input contract through the public function: per-array shape mismatch, d_array range and shape, decreasing active nodes (and decreasing nodes in padded stages being ignored), x0 shape, the 32-accumulator cap, non-3D mu, zero samples, and nondecision_time / deadline of the wrong length. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * refactor: move race_math to notebooks/ as notebook-only reference code The three analytical one-accumulator quantities (first-passage density, CDF, and surviving-position density) are only used by the two race notebooks, so they move out of ssms.basic_simulators as suggested in review, and their validation-only test goes with them. The notebooks now import the module from their own directory. While moving: describe each function and the shared arguments in the docstrings instead of citing an unnamed "equation (14)", and split the dense density expression into a normaliser and an exponent. The maths is unchanged. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: “mariama” <“maria_ma@brown.edu”> Co-authored-by: Alexander Fengler <alexanderfengler@gmx.de> Co-authored-by: Claude Opus 5 <noreply@anthropic.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: eitan_perlin@brown.edu <eperlin@node2333.oscar.ccv.brown.edu>

Conflicts, all from main's ndt_edge_shift (#1292) landing next to the new n_choices/has_deadline arguments: - dist.py, hssm.py: keep ndt_edge_shift and append n_choices and has_deadline after it. - changelog.md: H1's entry moves from its own 0.5.1 section to item 17 of main's unreleased 0.5.1. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

EItanm1999 force-pushed the fix/ensure-positive-ndt-st-aware branch from 3834b46 to 25a487f Compare September 2, 2026 20:58

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Add opt-in ndt_edge_width for unbounded ndt kernels #1344

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Register ddm_normal_st (Normal non-decision-time variability) #1345

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Register ddm_st (uniform non-decision-time variability) #1346

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EItanm1999 force-pushed the fix/ensure-positive-ndt-st-aware branch from c7ee6a5 to 527e026 Compare September 22, 2026 19:00

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Comment thread tests/distribution_utils/test_distribution_utils.py Resolved

EItanm1999 force-pushed the fix/ensure-positive-ndt-st-aware branch from 527e026 to 444a211 Compare September 22, 2026 20:09

AlexanderFengler and others added 10 commits October 6, 2026 01:05

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AlexanderFengler merged commit 32b7e23 into lnccbrown:main Oct 6, 2026
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Changelog entries for #1292, #1293, #1358 and docs for ndt_edge_shift #1361

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Merge pull request #1361 from lnccbrown/docs/unreleased-ndt-edge-shift

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Changelog entries for #1292, #1293, #1358 and docs for ndt_edge_shift

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Prepare HSSM 0.5.1 release #1362

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Native analytical DDM decision process for RLSSM #1420

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Discussion: should default priors be declared in the model configs? #1486

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Default priors for t put most of their mass above the fastest RT, and differ by likelihood #1487

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Docs: how-to for fits of ddm_st and ddm_normal_st that won't converge #1496

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docs: how-to notebook for fits of ddm_st and ddm_normal_st that won't converge #1497

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Fix ensure_positive_ndt for models with trial-to-trial ndt variability - #1292

Fix ensure_positive_ndt for models with trial-to-trial ndt variability#1292
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