diff --git a/CURRENT.md b/CURRENT.md index 6e1f3f1..8a5b0c0 100644 --- a/CURRENT.md +++ b/CURRENT.md @@ -4,22 +4,22 @@ ## 最新结果 -Phase 4AG-r2：**FAIL-FIXED-CAL-REPRESENTATION**，已封存。 +Phase 4AH：**PASS-CONDITIONAL-KC-DRIVE-AUDIT**，已封存。只审计既有轨迹，未运行新脑实验、使用 GPU、调参或训练。 -作者 float64 LIF + 原 float32 graded APL 的数值门槛通过；8 个小型场景和两个完整全图 100 ms probe 与共享 APL 组件的 Brian 闭环对照精确一致。全图仍为 166,700 神经元、25,582,938 边，原始 contact sum 124,177,617。 +80 份记录的 KC 放电、计数及最终状态精确重建，8 个小型场景逐步状态一致；12 组重复统计一致。完整 CAL 未保存逐 KC 状态历史，不能把全脑哈希称为逐 KC 历史验证。 -固定 6 气味 × 5 seed × 2 模式的 60 次 CAL 均完整且有限，12 次重复精确通过。但 KC 平均活跃比例为 **1.2713% / 1.2336%**，低于原固定 3%–9% 门槛。其余原 CAL 条件通过，成对距离判据通过数为 **12/15、13/15**；这不是学习成功率或 held-out 泛化证明。 +关键发现：所有 KC 都收到过兴奋输入；从未放电的 KC 中约 93.35% 收到过 PN 兴奋输入，而且这些静默 KC 没有进入不应期。PN→KC 到达边事件拒绝率仅约 0.037%。约 89.8% 的 ALPN 放电来自当次非设计目标，持续 APL 写入与兴奋输入的累计比值约 3.05，但这不是瞬时 E/I 或已证明的失败原因。 -旧五组 deterministic-v2 参考精确通过；原 230,239 项及后续封存目录 checksum 全部通过。v1 的执行入口失败及 r2 修复记录均保留。原 raw/CSR/reference/runtime/APL 参数/eligibility-v2/14,232-edge mask 不改。 +原 Phase 4AG **FAIL-FIXED-CAL-REPRESENTATION** 不变：KC 平均活跃比例 1.2713% / 1.2336%，低于固定 3%–9%。没有学习候选，不能进入条件化、游戏/RL/decoder/Web。 -前序 Phase 4AF 的短时全图数值 PASS 保留。没有学习候选，没有训练，不进入游戏/RL/decoder/Web。 +原 230,239 项及后续冻结目录 checksum 全部通过。旧五组参考本阶段未重跑，上一轮封存 PASS 记录未变。base CSR、W_syn、sign、旧 APL 参数、eligibility-v2、14,232-edge mask 和既有阶段产物均不改。 ## 下一步 -先只读分析已有 CAL 记录：输入实际接受 → PN→KC 真实延迟投递 → 不应期/reset → APL 写入，解释低 KC 覆盖率。APL 是否为原因尚未证明；不调参、改门槛或增加刺激来追求通过。 +继续只读审计既有 CAL：重建 ALPN 响应，追踪非设计 PN 再招募的真实时序，并将 PN→APL 输入按设计/非设计来源分账。旧 APL 已明确是每步增量，不是“漏乘 dt”；不同时间算子的假设必须另立单位契约及解析匹配对照，不按 3.05 比值倒推 gain。 -后续仍需独立 seed/新气味表征、信用分配、隔离功能及全脑功能检查，不能由数值一致性推导学习可用。 +新的因果干预或动力学假设另立协议和目录，不能覆盖现有结果。仍需表征、独立 seed/新气味、信用分配、隔离功能及全脑功能检查后才能进入真正学习验证。 -完整新实验数组保存在服务器；Git 只同步代码、小型报告及校验记录，不是全量异机备份。诊断耗时包含逐步校验，不是实时性能基准。 +完整新审计数据约 81.4 MB 存在服务器；Git 发布代码、小报告和校验，不是全量异机备份。 -[最终解释](docs/progress/phase4ag-interpretation.md) · [正式实验报告](docs/progress/phase4ag-author-apl-r2.md) · [封存校验](registry/phase4ag-author-apl-r2/seal-verification.json) +[最新解释](docs/progress/phase4ah-interpretation.md) · [正式报告](docs/progress/phase4ah-kc-drive-audit.md) · [原 CAL 失败解释](docs/progress/phase4ag-interpretation.md) · [封存校验](registry/phase4ah-kc-drive-audit/seal-verification.json) diff --git a/docs/progress/phase4ah-interpretation.md b/docs/progress/phase4ah-interpretation.md new file mode 100644 index 0000000..65496d3 --- /dev/null +++ b/docs/progress/phase4ah-interpretation.md @@ -0,0 +1,75 @@ +# Phase 4AH：KC 输入记账把问题缩小到了哪里 + +更新：2026-09-30。正式状态：**PASS-CONDITIONAL-KC-DRIVE-AUDIT**。 + +这里的 PASS 仅表示对既有轨迹的条件重建和输入账目通过验证。Phase 4AG 的 `FAIL-FIXED-CAL-REPRESENTATION` 不变，仍没有学习候选。本阶段不运行新脑实验、不训练、不调参数、不使用 held-out，也未使用 GPU。 + +## 关键结果 + +全部 8 个小型场景、60 次 CAL 基础记录和 12 次重复，共 80 份记录通过重建。KC 放电由重建代码独立计算，未用原 KC 放电强制复位；80 份事件、计数及最终 V/g/ref 精确一致，8 个小型场景的逐 tick pre/post-APL 状态也精确一致。12 份重复的诊断统计相同。 + +完整 CAL 没有逐 KC 状态历史，只有全脑每 tick 哈希。因此不能声称完整 CAL 的每 tick KC V/g 都与独立保存的原历史逐位验证。记录中的上游放电和 APL h 是重建的条件输入，不是重新模拟出来的独立证据。 + +下表每列汇总 30 次 CAL：6 气味 × 5 seed。KC 比例按“神经元×试验”计数，时间比例按“神经元×试验×tick”计数，不是跨试验去重后的神经元数。 + +| 指标 | physiological | matched_energy | +| --- | ---: | ---: | +| 外部请求 / 接受 | 3,203 / 2,195 | 2,936 / 2,038 | +| 实际全部 ALPN 放电 | 154,514 | 153,049 | +| 当次非设计目标 ALPN 放电 | 138,679（89.75%） | 137,431（89.80%） | +| 静默 KC 中收到 PN 兴奋输入的比例 | 93.3455% | 93.3481% | +| 静默 KC 中收到任意兴奋输入的比例 | 100% | 100% | +| PN 占已接受兴奋增量总量的比例 | 89.1612% | 89.4183% | +| PN→KC 到达边事件拒绝率 | 0.03649% | 0.03731% | +| 从未放电 KC 的不应期 tick 数 | 0 | 0 | +| APL 累计写入量 / 已接受兴奋增量 | 3.0516 | 3.0539 | +| KC 末尾状态 g 为负的时间比例 | 91.8452% | 91.8721% | + +每个气味的设计目标为 33–55 个 ALPN；六气味设计目标并集为 76 个，实际放电 ALPN 并集为 556 个（两种模式均如此）。设计目标、实际请求、接受请求和实际放电是不同集合，均已分别保存。没有把实际输入事件数强行匹配，也没有把“未与请求同 tick 的放电”直接命名为噪声或独立自然活动。 + +## 哪些解释已被排除，哪些还没有 + +在这些已记录的 100 ms CAL 中，可排除字面意义的“KC 完全没收到兴奋输入”。也可排除“从未放电的 KC 因自身不应期拒收或不应期内 APL 累积，才不能首次放电”：它们没有进入过不应期。 + +不应期内 APL 写入仅占全体 APL 累计写入约 0.0193% / 0.0192%。这不支持把“屏蔽不应期 APL 写入”当成本轮覆盖率失败的直接主要解释；但没有做干预，不能排除已活跃细胞的时序或网络间接效应。 + +尚不能排除：兴奋输入过晚、不够同步、缺少气味特异结构、广泛反馈再招募，或它们与持续 APL 写入的相互作用。静默 KC 最接近阈值时的距离中位数约 6.697 / 6.703 mV；该值描述当前轨迹，不用于调整阈值。 + +APL 的约 3.05 倍是跨时间、神经元和试验累计状态增量的记账比值，不是瞬时 E/I 比、生物学抑制强度比例，也不是可据此倒算的新 gain。`g` 在该作者方程中以伏特表示，不是电导。源分账采用真实 availability/reset 条件；不能将其当作移除 APL 后的全脑反事实。 + +## 身份、浮点和证据范围 + +源类别从实际 schema 建立互斥分组：KC 4,064、APL 2、ALPN 686、MBON 97、解剖 DAN 340、仅 dopamine 标注的其他类别 52、other 160,143、unclassified 1,316。两枚 DPM 在 other 中明确标记，不因 dopamine 标注被归作 DAN；340 个 DAN 中有 2 个的当前 NT 字段不是 dopamine。没有依据名称前缀猜身份或删除未知项。 + +重建保留原始浮点位、18-step 延迟、22-step 不应期、严格阈值及逐边累加顺序。APL fast-edge 掩码原样保留在独立内存副本；磁盘原边权不改。按来源和符号分账不反馈到状态更新；舍入残差单独记录。 + +- 47 项合成测试在本地与服务器通过。 +- 实际 g 阶段账目的最大闭合残差为 2.17e-19 V，在记录的运算误差界内。 +- 来源状态分账最大残差为 6.11e-16 V；积分最大残差为 7.46e-14 V·ms。重建事件/最终状态自身仍要求逐位一致，没有放宽容差。 +- 原 230,239 项及全部后续已封存目录校验通过。五组旧参考本阶段未重新运行；引用的是上一轮已封存且本次校验未变的 PASS 记录。 +- CPU 条件记账合计约 133.34 s；这是审计耗时，不是脑模型实时性能。 +- 新模型 11 项、新实验 415 项已封存；封存前实验 payload 81,395,186 字节。完整分账表留在服务器，Git 只发布代码、小型证据及 SHA-256。 + +## 下一步，不从比值反推参数 + +优先检验可证伪的机制问题：**非设计目标 ALPN 的广泛再招募是否先于 KC 响应，并通过 PN→APL / PN→KC 形成跨气味共享输入？** + +先继续使用已有 60 次 CAL，做 ALPN 的局部条件重建、真实先后时序和设计/非设计来源的 PN→APL 分账。若时序或贡献不支持该假设，明确否定，不挑选支持案例。 + +旧 APL 的单位意图已核实：[原 4J 配置](../../models/male-cns-lif/phase4j.yaml) 明写 `gain_grid_mv_per_state_step`，[原 4J 实现](../../models/male-cns-lif/phase4j.py) 有意每 tick 把滤波后的抑制写入 g，4P 延续这一行为。所以不能称它为“遗漏 dt 的实现错误”，也不能偷偷把 .01 换解释成 tonic mV。后续可研究不同时间算子的结构假设，但需新单位契约和不依赖 CAL 的解析匹配对照，不能只是把累计抑制改小。[研究边界与来源](phase4ah-next-hypotheses.md) + +这仍不是学习训练或新的全脑动力学实验。任何因果干预或新的 APL 动力学语义必须另立独立分支协议，保留原结果，提前固定对照和判据，不做 gain 搜索，不改旧模型、输入、mask、eligibility 或阈值。 + +## 证据入口 + +- [正式审计报告](phase4ah-kc-drive-audit.md) +- [原始最终状态](../../registry/phase4ah-kc-drive-audit/evidence/audit/final-status.json) +- [全部基础 CAL 输入账目](../../registry/phase4ah-kc-drive-audit/evidence/audit/calibration-drive-summary.csv) +- [可重复计算的解释性统计及输入 SHA](../../registry/phase4ah-kc-drive-audit/interpretation-metrics.json) +- [解释性统计生成代码](../../tools/report_phase4ah.py) +- [冻结文件校验](../../registry/phase4ah-kc-drive-audit/evidence/regression/old-integrity-after.json) +- [新封存校验](../../registry/phase4ah-kc-drive-audit/seal-verification.json) + +模型 manifest SHA-256：`fa89cb0a6e2dc77462057b672ffbcfabbe74b893d8f70e4b41d8b5202de15530`。 + +实验 manifest SHA-256：`fb30ca09068d5d8dc1dc9972e00f674c5dc3aa49c7117d630607b5d9067927db`。 diff --git a/docs/progress/phase4ah-kc-drive-audit.md b/docs/progress/phase4ah-kc-drive-audit.md new file mode 100644 index 0000000..999addb --- /dev/null +++ b/docs/progress/phase4ah-kc-drive-audit.md @@ -0,0 +1,16 @@ +# Phase 4AH — conditional KC drive audit + +Status: `PASS-CONDITIONAL-KC-DRIVE-AUDIT`. + +80 existing records reconstructed; 8 fixture pre/post histories and 72 CAL event/final states exact. +12 repeated CAL records produced identical accounting. No new brain simulation, GPU use, tuning or learning. +Full prior frozen integrity passed. Five reference simulations were not rerun; the sealed prior PASS is referenced by SHA. +Previous CAL failure remains unchanged. Conditional replay is not an APL-removal counterfactual. + +|Mode|Mean active KC fraction|Requests|Accepted requests|ALPN accepted excitatory increment sum (V)|APL write sum (V)| +|---|---:|---:|---:|---:|---:| +|physiological|0.01271325|3203|2195|46379.7565|158735.25| +|matched_energy|0.01233596|2936|2038|45928.6416|156859.684| + +Increment sums span 30 trials × 4064 KC × time; not instantaneous conductance or causal percentages. +See audit/final-status.json, audit/calibration-drive-summary.csv and trials/*/per-kc.parquet for detailed evidence. diff --git a/docs/progress/phase4ah-next-hypotheses.md b/docs/progress/phase4ah-next-hypotheses.md new file mode 100644 index 0000000..ef5bbf4 --- /dev/null +++ b/docs/progress/phase4ah-next-hypotheses.md @@ -0,0 +1,45 @@ +# Phase 4AH 后的研究边界：先定位，再做独立干预 + +日期：2026-09-30。本页是研究备忘，不是已执行或已通过的新实验协议。Phase 4AH 的条件审计 PASS 和 Phase 4AG 的固定 CAL FAIL 均不改变。 + +## 立即优先：既有轨迹中的 ALPN 再招募 + +可证伪的问题是：非设计 ALPN 再招募的时序是否先于 KC 响应；这些源在当前 PN→APL / PN→KC 输入中是否占主要部分。先对全部 60 次原 CAL 做来源和时序分账，不能仅拿全程累计比例作为答案。仍使用既有输入/放电，不生成新全脑轨迹；零贡献、反向时序或跨气味不同的结果也完整报告。 + +## 值得研究但尚未执行：APL 时间算子 + +旧模型的设计不是缺少一个 dt 因子。[phase4j.yaml](../../models/male-cns-lif/phase4j.yaml) 使用 `gain_grid_mv_per_state_step`；[phase4j.py](../../models/male-cns-lif/phase4j.py) 每步将 20 ms 滤波状态产生的抑制加到 g；[phase4p_revalidate.py](../../models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py) 保持该语义。原 4J 文件 SHA-256 分别为 `b35e4332a91b8489f69348cedf5ac675e9c3902c7e8c7beec5d053773c6529a3` 和 `abb4574da85f00bd9ef61024c65993ef81d42651ff8d8d63c15dd69c6222471e`，与 frozen 子目录副本相同。 + +一个未来的独立结构假设可以比较“滤波后的逐步 g 增量”与“独立连续驱动状态”的时间响应，而不是把 gain 调小。两者变量的单位及调度不同，不能把旧 `.01 mV/state/step` 直接当作 `.01 mV/state`，再宣称只改变了算法实现。 + +根据已冻结的离散更新式，在无放电、无不应期、恒定每步抑制增量 H 的线性诊断条件下，由代码推导（不是文献实测）： + +```text +g_next = gd*g - H +V_next = c0 + cg*g + cv*V +g_steady = -H/(1-gd) +V_base = c0/(1-cv) +V_steady - V_base = -cg*H/((1-cv)*(1-gd)) +``` + +若以后选用 DC 匹配，应在新协议中预先固定这一匹配规则，使用冻结系数而不是重算常数，并先通过零输入、阶跃、脉冲、有限脉冲列、reset/ref22 边界的独立验证。不能看到 CAL 后再换成更有利的匹配标准。连续驱动可能使早期抑制更强，不能预设其改善编码。固定 trace 对照也不能代替闭环干预。 + +## 生物学依据能支持到哪里 + +Shiu 等人的 LIF 模型明确忽略非脉冲神经元和受体动力学。因此本项目的 graded APL 不是论文提供的“作者 APL 算子”，也不能由该论文直接获得 MaleCNS APL 参数。[Shiu et al., Nature 2024](https://pmc.ncbi.nlm.nih.gov/articles/PMC11446845/) + +Amin 等人的局部刺激与成像显示 APL 活动和对 KC 的抑制具有空间局部性，为研究局部 APL 模型提供依据，但不能直接指定当前 MaleCNS 的每条边强度。[Amin et al., eLife 2020](https://pmc.ncbi.nlm.nih.gov/articles/PMC7541083/) + +Prisco 等人结合 EM 和体内成像，研究了 calyx 中 APL 对 PN bouton 与 KC dendrite 的作用及区域化响应。这支持区分末梢与全神经元作用范围；不等于允许把 PN 整个细胞的输出统一抑制。[Prisco et al., eLife 2021](https://pmc.ncbi.nlm.nih.gov/articles/PMC8741211/) + +这些证据不提供本数据集逐边受体、局部时间常数和 mV 转换系数。缺项继续标记 unknown，不人工补齐。本次主代理核验了上述公开原始来源；eLife 直连出现浏览器验证时使用其 PMC 原文/作者摘要，不把搜索结果中的其他论文当作替代数据。 + +## 不重走的路 + +- 不因累计 APL/兴奋比值约 3.05 就将 gain 除以 3；那是结果导向调参。 +- 不因静默比例高就改 KC/MBON 阈值、提高输入或降低验收标准。 +- 不将 APL→PN 的解剖接触直接解释为 whole-PN 抑制或同 bouton 的有效抑制。 +- 不顺手恢复/改写 1,212 条未被 graded 替代的非 KC fast APL 边；若测试其作用，另立明确机制对照。 +- 不根据少放电、多放电或稀疏率单项选择候选。表征、泛化、信用分配和功能性记忆仍须分别验证。 + +本页没有新增边、模型参数、learned weights 或行为成功声明。 diff --git a/models/male-cns-lif/kc-drive-audit-v1/PROTOCOL.md b/models/male-cns-lif/kc-drive-audit-v1/PROTOCOL.md new file mode 100644 index 0000000..577ff20 --- /dev/null +++ b/models/male-cns-lif/kc-drive-audit-v1/PROTOCOL.md @@ -0,0 +1,71 @@ +# Phase 4AH — sealed-trajectory KC drive accounting + +This diagnostic was specified after Phase 4AG's fixed CAL failed. It is not a +new hypothesis trial, a preregistered discovery, a new brain simulation, a +parameter search, a held-out test, or training. All observations are conditional +on the existing logged trajectories. A priori here means before this accounting +is computed, not before the original CAL outcomes were known. + +## Scope and fixed inputs + +Read all eight Phase 4AG-r2 fixture r0 records and all 60 CAL r0 plus 12 first-seed +r1 records. No outcome-based subset choice. Read actual static canonical metadata, +all real incoming edges of the relevant KC, the exact frozen coefficients and SI +weights, requested and actual events, recorded float32 APL inhibition, and final +states. Keep all previous artifacts read-only. Do not import a GPU runtime or +advance a new whole-brain state. Use at most four CPU threads. No external data +download is required. + +## Exact target-state reconstruction + +Compute KC spikes independently, never force the logged KC spike/reset sequence. +Upstream arrivals use the logged whole-brain source events at exactly t-18. +At each tick decrement refractory, update available V/g in the exact separate +float64 operation order, apply accepted request override, strict threshold, +reset and ref22, then sequential canonical-order incoming edges only if post-reset +ref==0. Finally widen stored float32 h to float64, multiply by float64 .001 and +subtract unconditionally. In particular preserve the stored reset coefficient +bits; do not substitute hand-typed -0.052 or recompute exponentials. + +Verify all target spikes/counts and final V/g/ref byte-for-byte. For fixtures also +verify saved pre-APL and post-APL dense target histories at every tick. Full CAL +has whole-brain hashes only: these cannot verify each-tick KC subset states. +Never claim all-tick independently observed CAL state equivalence. On any failure, +save diagnostic failure and stop without adjusting tolerances or publishing a +mechanistic PASS. + +## Accounting (not counterfactual causality) + +Per KC and source class report actual due/accepted/rejected edges, original +contact counts, nominal signed SI increments (positive and negative separately), +and zeroed-APL arrivals. Keep actual anatomical population and dopamine annotation +distinct; unavailable metadata is unknown, not a guessed identity. + +At each tick ledger the actual g changes due to available-state decay, reset, +sequential arrivals and graded APL write. Sum these differences in extended +precision only as a non-feedback accounting device; report reconstruction residual +and an explicit roundoff bound. The actual replay always stays float64. +Nominal per-source increments do not exactly sum to applied floating increments; +report that rounding residual rather than altering the source labels or dynamics. + +Record APL writes during ref versus available state, pre-first-spike APL writes, +negative-g exposure, threshold margin, PN input acceptance, and active versus +silent KC summaries. KC-active/silent strata are descriptive and outcome-defined, +not an experimental comparison. Cumulative increments and state-time integrals +must retain distinct units and must not be called instantaneous conductance or +a causal percentage of spike production. + +## Gate and interpretation + +Gate: all 80 prescribed records reconstruct exactly under the stated observable +scope, inputs/checksums stay unchanged, all accounting is finite, and no missing +records are silently skipped. Repeated-record metrics must agree excluding wall +time. Pure adversarial tests are required before registration. + +Passing is PASS-CONDITIONAL-KC-DRIVE-AUDIT, not a learning/candidate gate. Do not +infer that removing APL would restore sparse/separable coding, that refractory +APL accumulation is the cause of CAL failure, or that a larger drive is justified. +An actual counterfactual would require a separately declared new-model hypothesis. +No gain/threshold/eta changes, new edges, mask expansion, held-out tuning, rewards, +games, decoder, visual input or Web. Preserve failed records. New output budget +2 GiB; source and experiment get SHA-256 manifests at completion. diff --git a/models/male-cns-lif/kc-drive-audit-v1/accounting.py b/models/male-cns-lif/kc-drive-audit-v1/accounting.py new file mode 100644 index 0000000..d6662ca --- /dev/null +++ b/models/male-cns-lif/kc-drive-audit-v1/accounting.py @@ -0,0 +1,197 @@ +"""Non-feedback drive ledger. None of these summaries affect the replay.""" +import numpy as np + + +def bit_equal(a, b): + a, b = np.asarray(a), np.asarray(b) + return a.dtype == b.dtype and a.shape == b.shape and np.array_equal( + np.ascontiguousarray(a).view(np.uint8), np.ascontiguousarray(b).view(np.uint8)) + + +def fraction(numerator, denominator): + return None if denominator == 0 else float(numerator / denominator) + + +class DriveAccounting: + def __init__(self, target_globals, incoming, raw_contacts, source_groups, group_names, + expected_events, coeff, expected_counts, histories=None): + self.targets = target_globals + self.incoming = incoming + self.raw = raw_contacts + self.groups = source_groups + self.names = group_names + self.coeff = coeff + self.expected_counts = expected_counts + self.active = expected_counts > 0 + self.histories = histories or {} + self.expected = {} + for tick in np.unique(expected_events['step']): + self.expected[int(tick)] = expected_events['neuron_idx'][expected_events['step'] == tick] + n = len(target_globals) + self.n = n + self.first = np.full(n, -1, np.int32) + self.tick_rows = [] + self.group_stats = {name: {key: np.zeros(n, np.float64 if 'SI' in key else np.int64) + for key in ('due_edges', 'accepted_edges', 'rejected_edges', 'due_contacts', + 'accepted_contacts', 'rejected_contacts', 'accepted_exc_SI', + 'accepted_inh_abs_SI', 'rejected_exc_SI', 'rejected_inh_abs_SI')} + for name in group_names} + self.metrics = {key: np.zeros(n, np.float64) for key in ( + 'APL_write_SI', 'APL_write_ref_SI', 'APL_write_available_SI', 'APL_pre_first_spike_SI', + 'APL_same_tick_first_spike_SI', 'negative_g_ticks', 'refractory_ticks', + 'g_end_time_integral_V_ms', 'V_end_time_integral_V_ms', 'accepted_exc_before_first_SI', + 'accepted_inh_before_first_abs_SI', 'APL_zeroed_due_edges', 'APL_zeroed_due_contacts')} + self.min_g = np.full(n, np.inf) + self.max_v = np.full(n, -np.inf) + self.ledger = {key: np.zeros(n, np.longdouble) for key in ('decay', 'reset', 'arrivals', 'APL')} + self.ledger_abs = np.zeros(n, np.longdouble) + self.arrival_rounding = np.zeros(n, np.longdouble) + # Source/sign lanes are a conditional accounting, never fed to V/g. + self.shadow_names = [name + '_' + sign for name in group_names for sign in ('exc', 'inh')] + ['graded_APL'] + self.shadow = np.zeros((len(self.shadow_names), n), np.float64) + self.shadow_integral = np.zeros_like(self.shadow) + self.initial_shadow_integral = np.zeros(n, np.float64) + self.shadow_max_residual = 0.0 + self.callback_count = 0 + self.initial_g = None + + def __call__(self, tick, s): + fired_nodes = self.targets[s['fired']] + expected = self.expected.get(tick, np.empty(0, np.int32)) + if not bit_equal(fired_nodes, expected): + raise ValueError(f'KC spike divergence at tick {tick}: computed={fired_nodes.tolist()} expected={expected.tolist()}') + if self.initial_g is None: + self.initial_g = s['old_g'].copy() + # Fixture initial g is a separate lane, not attributed to any input class. + self.initial_shadow = s['old_g'].copy() + for name, history in self.histories.items(): + key = {'pre_v': 'v_pre_apl', 'pre_g': 'g_pre_apl', 'pre_ref': 'ref_pre_apl', + 'v': 'v', 'g': 'g', 'ref': 'ref'}[name] + if not bit_equal(history[tick], s[key]): + raise ValueError(f'Fixture dense {name} divergence at tick {tick}') + before_first = self.first < 0 + first_now = before_first & s['fired'] + self.first[first_now] = tick + self.max_v = np.maximum(self.max_v, s['v_threshold']) + self.min_g = np.minimum(self.min_g, s['g']) + ref_mask = s['ref'] > 0 + delta = s['delta_si'] + self.metrics['APL_write_SI'] += delta + self.metrics['APL_write_ref_SI'] += delta * ref_mask + self.metrics['APL_write_available_SI'] += delta * ~ref_mask + # Strictly prior ticks: arrival/APL at first-spike tick cannot cause that spike. + self.metrics['APL_pre_first_spike_SI'] += delta * (before_first & ~s['fired']) + self.metrics['APL_same_tick_first_spike_SI'] += delta * first_now + self.metrics['negative_g_ticks'] += s['g'] < 0 + self.metrics['refractory_ticks'] += ref_mask + self.metrics['g_end_time_integral_V_ms'] += s['g'] * .1 + self.metrics['V_end_time_integral_V_ms'] += s['v'] * .1 + stages = {'decay': ('g_decayed', 'old_g'), 'reset': ('g_after_reset', 'g_decayed'), + 'arrivals': ('g_pre_apl', 'g_after_reset'), 'APL': ('g', 'g_pre_apl')} + for label, (after, before) in stages.items(): + diff = s[after].astype(np.longdouble) - s[before].astype(np.longdouble) + self.ledger[label] += diff + self.ledger_abs += np.abs(diff) + due = s['due_edge_indices'] + dst = self.incoming.dst_local[due] + weights = self.incoming.weights[due] + groups = self.groups[due] + accepted = s['arrival_accepted'] + positive, negative = weights > 0, weights < 0 + nominal = np.zeros(self.n, np.longdouble) + np.add.at(nominal, dst[accepted], weights[accepted].astype(np.longdouble)) + actual = s['g_pre_apl'].astype(np.longdouble) - s['g_after_reset'].astype(np.longdouble) + self.arrival_rounding += actual - nominal + self.shadow[:, s['available']] *= self.coeff['gd'] + self.initial_shadow[s['available']] *= self.coeff['gd'] + self.shadow[:, s['fired']] = 0.0 + self.initial_shadow[s['fired']] = 0.0 + row = dict(tick=tick, time_ms=tick * .1, KC_spikes=int(s['fired'].sum()), + KC_negative_g=int(np.count_nonzero(s['g'] < 0)), KC_refractory=int(ref_mask.sum()), + APL_write_SI_sum=float(delta.sum()), APL_ref_write_SI_sum=float(delta[ref_mask].sum())) + for gi, name in enumerate(self.names): + mask = groups == gi + acc, rej = mask & accepted, mask & ~accepted + stat = self.group_stats[name] + for stem, selected in (('due', mask), ('accepted', acc), ('rejected', rej)): + np.add.at(stat[stem + '_edges'], dst[selected], 1) + np.add.at(stat[stem + '_contacts'], dst[selected], self.raw[due[selected]]) + for stem, selected in (('accepted', acc), ('rejected', rej)): + for suffix, signmask, sign in (('exc_SI', positive, 1), ('inh_abs_SI', negative, -1)): + pick = selected & signmask + np.add.at(stat[stem + '_' + suffix], dst[pick], sign * weights[pick]) + row[name + '_' + stem + '_' + suffix] = float((sign * weights[pick]).sum()) + for sign_index, signmask in ((0, positive), (1, negative)): + selected = acc & signmask + np.add.at(self.shadow[2 * gi + sign_index], dst[selected], weights[selected]) + self.shadow[-1] -= delta + self.shadow_integral += self.shadow * .1 + self.initial_shadow_integral += self.initial_shadow * .1 + self.shadow_max_residual = max(self.shadow_max_residual, + float(np.max(np.abs(self.shadow.sum(axis=0) + self.initial_shadow - s['g'])))) + no_first_yet = before_first[dst] & ~s['fired'][dst] + for metric, selected, sign in ( + ('accepted_exc_before_first_SI', accepted & positive & no_first_yet, 1), + ('accepted_inh_before_first_abs_SI', accepted & negative & no_first_yet, -1)): + np.add.at(self.metrics[metric], dst[selected], sign * weights[selected]) + apl_id = self.names.index('APL') + zeroed = (groups == apl_id) & (weights == 0) + np.add.at(self.metrics['APL_zeroed_due_edges'], dst[zeroed], 1) + np.add.at(self.metrics['APL_zeroed_due_contacts'], dst[zeroed], self.raw[due[zeroed]]) + for stratum, mask in (('ever_active', self.active), ('never_active', ~self.active)): + row[stratum + '_mean_g_V'] = None if not mask.any() else float(s['g'][mask].mean()) + row[stratum + '_mean_V_V'] = None if not mask.any() else float(s['v'][mask].mean()) + self.tick_rows.append(row) + self.callback_count += 1 + + def finish(self, final_g): + reconstructed = self.initial_g.astype(np.longdouble) + sum(self.ledger.values()) + residual = reconstructed - final_g.astype(np.longdouble) + bound = (64 * np.finfo(np.longdouble).eps * (self.callback_count + 1) * + (self.ledger_abs + np.abs(self.initial_g.astype(np.longdouble)) + np.abs(final_g))) + assert np.all(np.abs(residual) <= bound), 'g ledger residual exceeds stated arithmetic bound' + data = dict(neuron_idx=self.targets, spike_count=self.expected_counts, first_spike_tick=self.first, + minimum_end_g_V=self.min_g, maximum_prethreshold_V=self.max_v, + best_threshold_margin_mV=(self.coeff['threshold'] - self.max_v) * 1000, + g_ledger_residual_V=residual.astype(np.float64), + g_ledger_bound_V=bound.astype(np.float64), arrival_rounding_V=self.arrival_rounding.astype(np.float64), + initial_g_state_area_V_ms=self.initial_shadow_integral, + source_g_area_residual_V_ms=(self.shadow_integral.sum(axis=0) + self.initial_shadow_integral - + self.metrics['g_end_time_integral_V_ms']), + **self.metrics) + for name, values in self.group_stats.items(): + for metric, values2 in values.items(): + data[name + '_' + metric] = values2 + for name, value in self.ledger.items(): + data['actual_g_delta_' + name + '_V'] = value.astype(np.float64) + for index, name in enumerate(self.shadow_names): + data['source_g_area_' + name + '_V_ms'] = self.shadow_integral[index] + for key, value in data.items(): + assert np.isfinite(value).all(), 'Nonfinite accounting: ' + key + aggregates = {} + for label, mask in (('all', np.ones(self.n, bool)), ('ever_active', self.active), ('never_active', ~self.active)): + groups = {name: {key: float(value[mask].sum()) if 'SI' in key else int(value[mask].sum()) + for key, value in metrics.items()} for name, metrics in self.group_stats.items()} + exc = sum((self.group_stats[n]['accepted_exc_SI'] for n in self.names), np.zeros(self.n)) + alp = self.group_stats['ALPN']['accepted_exc_SI'] + aggregates[label] = dict(neurons=int(mask.sum()), source_groups=groups, + no_accepted_exc_neurons=int(np.count_nonzero(mask & (exc == 0))), + no_accepted_ALPN_exc_neurons=int(np.count_nonzero(mask & (alp == 0))), + has_exc_but_never_active=int(np.count_nonzero(mask & (exc > 0) & ~self.active)), + metrics_sum={k: float(v[mask].sum()) for k, v in self.metrics.items()}, + source_g_area_V_ms={name: float(self.shadow_integral[i, mask].sum()) for i, name in enumerate(self.shadow_names)}, + initial_g_state_area_V_ms=float(self.initial_shadow_integral[mask].sum()), + source_g_area_residual_V_ms=float(data['source_g_area_residual_V_ms'][mask].sum()), + maximum_prethreshold_V=None if not mask.any() else float(self.max_v[mask].max()), + minimum_end_g_V=None if not mask.any() else float(self.min_g[mask].min())) + return data, dict(strata=aggregates, ledger_max_abs_error_V=float(np.max(np.abs(residual))), + ledger_max_bound_V=float(bound.max()), ledger_roundoff_bound_pass=True, + ledger_accumulator_mantissa_bits=int(np.finfo(np.longdouble).nmant), + ledger_bound_formula='64 * eps(longdouble) * (ticks+1) * (sum_abs_actual_stage_deltas + abs(initial_g) + abs(final_g)) per KC', + source_shadow_max_residual_V=self.shadow_max_residual, + source_area_max_residual_V_ms=float(np.max(np.abs(data['source_g_area_residual_V_ms']))), + source_shadow_meaning='Signed source/sign g bookkeeping under observed availability/reset only; non-feedback, not counterfactual', + cumulative_increment_units='SI volts; sums of individual increments, not instantaneous conductance', + source_state_area_units='V*ms; post-APL source-state left samples times dt .1ms', + activity_strata_outcome_defined=True, APL_cause_of_CAL_failure_proven=False, + full_CAL_all_tick_subset_state_independently_verified=False) diff --git a/models/male-cns-lif/kc-drive-audit-v1/checksums.sha256 b/models/male-cns-lif/kc-drive-audit-v1/checksums.sha256 new file mode 100644 index 0000000..9fa1b1f --- /dev/null +++ b/models/male-cns-lif/kc-drive-audit-v1/checksums.sha256 @@ -0,0 +1,12 @@ +fc254f996eab81c8ad76c4eb3dad274823f0e23502e59c3da6b5cbbd68444e8e PROTOCOL.md +c4703d36587bd40a2a231d5098b8d9991407ba80f6f2b488ffad068c7a74fde6 accounting.py +c7eb06a19d9f1f28ecf71c5596d10aa11bb405d09b1167546da347eb58d8da9a context.py +ce0d0bfa2679c18573acab673b28d167ed300ff7669486c2e93fdc1a6097fc05 finalize.py +8504b38219bac952b07a67e7dc37a520f0a68238cb0b0dd56f4b4b7948c5233b populations.py +fecf8324b5fca4b65d8f24f3ed80d20bdb1fc5654294958f34d58dd36625c59b prepare.py +aa3187cb23712c09ab603e27e60424294db92de8086d60a95cf7a629d0521fdc replay_core.py +724c4f25341dc0883951ef5abdf9fc7dc72fba146a000b9cf9bca759e9c7623a run_audit.py +a10f67c42162f8b07f0d760108d5ebf575f6dddc588752acc4499b43bac3ec94 test_accounting.py +65285a6679e140f9f7cc16cb806e7bddf0f0d288c6a7d3fa2879a7cd745dacc1 test_populations.py +17b94e6c549e60c5adb9eebf0a5dc0679a95670265b3a332a49a2e6610a976c2 test_replay_core.py +fa89cb0a6e2dc77462057b672ffbcfabbe74b893d8f70e4b41d8b5202de15530 manifest.json diff --git a/models/male-cns-lif/kc-drive-audit-v1/context.py b/models/male-cns-lif/kc-drive-audit-v1/context.py new file mode 100644 index 0000000..9e88f14 --- /dev/null +++ b/models/male-cns-lif/kc-drive-audit-v1/context.py @@ -0,0 +1,73 @@ +"""Phase 4AH: read-only accounting of sealed Phase 4AG trajectories.""" +import hashlib +import importlib.util +import json +from pathlib import Path + +ROOT = Path('/data/digital-drosophila') +MODEL = Path(__file__).resolve().parent +EXP = ROOT / 'experiments/phase4ah-kc-drive-audit' +AG_MODEL = ROOT / 'models/male-cns-lif/author-apl-lab-v2' +AG = ROOT / 'experiments/phase4ag-author-apl-r2' +META = ROOT / 'datasets/male-cns-v1.0/processed/neuron-only/neuron-index/unified-metadata.parquet' +COEFF = ROOT / 'experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json' + + +def digest(path): + h = hashlib.sha256() + with Path(path).open('rb') as f: + for b in iter(lambda: f.read(8 << 20), b''): + h.update(b) + return h.hexdigest() + + +def save(path, value): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + with path.open('x', encoding='utf-8') as f: + json.dump(value, f, indent=2, ensure_ascii=False, allow_nan=False) + f.write('\n') + + +def sources(): + return {p.name: digest(p) for p in sorted(MODEL.iterdir()) if p.is_file() + and p.name not in ('manifest.json', 'checksums.sha256')} + + +def read(path): + return json.loads(Path(path).read_text(encoding='utf-8')) + + +def integrity(): + spec = importlib.util.spec_from_file_location('sealed_ag_context', AG_MODEL / 'context.py') + previous = importlib.util.module_from_spec(spec) + spec.loader.exec_module(previous) + result = previous.integrity() + import sys + sys.path.insert(0, str(ROOT / 'models/male-cns-lif/terminal-scope-diagnostic-v1')) + from diagnostic import manifest_check + for p in (AG_MODEL, AG): + result[str(p.relative_to(ROOT))] = manifest_check(p, digest) + assert all(r['pass'] for r in result.values()) + return result + + +def check(): + registered = read(EXP / 'protocol/preregistered.json') + assert registered['sources'] == sources(), 'Registered audit source changed' + for row in registered['inputs']: + assert digest(ROOT / row['path']) == row['sha256'], row['path'] + if (EXP / 'protocol/prepared-hashes.json').exists(): + for row in read(EXP / 'protocol/prepared-hashes.json'): + assert digest(EXP / row['path']) == row['sha256'], row['path'] + return registered + + +def seal(root): + rows = [dict(path=str(p.relative_to(root)), bytes=p.stat().st_size, sha256=digest(p)) + for p in sorted(root.rglob('*')) if p.is_file() and '__pycache__' not in p.parts] + save(root / 'manifest.json', dict(root=str(root), files=rows)) + with (root / 'checksums.sha256').open('x') as f: + for row in rows: + f.write(row['sha256'] + ' ' + row['path'] + '\n') + f.write(digest(root / 'manifest.json') + ' manifest.json\n') diff --git a/models/male-cns-lif/kc-drive-audit-v1/finalize.py b/models/male-cns-lif/kc-drive-audit-v1/finalize.py new file mode 100644 index 0000000..6f01d36 --- /dev/null +++ b/models/male-cns-lif/kc-drive-audit-v1/finalize.py @@ -0,0 +1,154 @@ +"""Aggregate and seal descriptive accounting without learning promotion.""" +import numpy as np +import pandas as pd +from context import ROOT, MODEL, EXP, AG, AG_MODEL, read, save, digest, check, integrity, seal +from accounting import bit_equal + + +def summarize(): + inventory = read(EXP / 'protocol/inventory.json') + expected_inventory = [(name, 0, 'fixture') for name in read(AG_MODEL / 'config.json')['fixtures']] + for row in read(AG / 'protocol/calibration-cases.json'): + expected_inventory.append((row['name'], 0, 'calibration')) + if row['seed'] == 51101: + expected_inventory.append((row['name'], 1, 'calibration')) + identities = [(r['case'], r['repeat'], r['kind']) for r in inventory] + assert len(identities) == len(set(identities)) == 80 and identities == expected_inventory + reports = [] + modes = {} + for item in inventory: + folder = EXP / 'trials' / (item['case'] + '-r' + str(item['repeat'])) + result = read(folder / 'summary.json') + assert result['pass_all'] and all(result['exact'].values()) + assert result['ledger_roundoff_bound_pass'] and result['steps'] > 0 + for name, sha in result['input_sha256'].items(): + assert digest(ROOT / item['source'] / name) == sha + # Re-read saved numeric evidence; a successful receipt alone is not proof. + spec = read(AG / 'cases' / item['case'] / 'spec.json') + assert (result['case'], result['repeat'], result['kind']) == (item['case'], item['repeat'], item['kind']) + assert result['steps'] == spec['steps'] and result['duration_ms'] == spec['duration_ms'] + assert result['mode'] == spec.get('mode') and result['seed'] == spec.get('seed') + assert result['odor_id'] == spec.get('odor_id') + nodes = np.load(spec['graph_dir'] + '/nodes.npy', mmap_mode='r') + kc = np.load(AG / 'apl-bundle/kc_globals.npy', mmap_mode='r') + targets, positions, _ = np.intersect1d(nodes, kc, assume_unique=True, return_indices=True) + table = pd.read_parquet(folder / 'per-kc.parquet') + with np.load(folder / 'replay-final.npz') as actual, np.load(ROOT / item['source'] / 'state.npz') as expected: + assert bit_equal(actual['target_globals'], targets) + for key in ('v', 'g', 'ref', 'counts'): + assert bit_equal(actual[key], expected[key][positions]), key + event_mask = np.isin(expected['neuron_idx'], targets) + for key in ('step', 'neuron_idx'): + assert bit_equal(actual[key], expected[key][event_mask]), key + assert bit_equal(table.neuron_idx.to_numpy(), targets) + assert bit_equal(table.spike_count.to_numpy(), actual['counts']) + assert bit_equal(table.first_spike_tick.to_numpy(), actual['first_spike']) + assert np.isfinite(table.select_dtypes(include='number').to_numpy()).all() + assert (table.g_ledger_residual_V.abs() <= table.g_ledger_bound_V).all() + reports.append(result) + if item['kind'] != 'calibration' or item['repeat'] != 0: + continue + modes.setdefault(result['mode'], []).append(result) + aggregate = {} + for mode, rows in modes.items(): + assert len(rows) == 30 + group_totals = {} + for group in rows[0]['strata']['all']['source_groups']: + group_totals[group] = {metric: sum(r['strata']['all']['source_groups'][group][metric] for r in rows) + for metric in rows[0]['strata']['all']['source_groups'][group]} + active = np.array([r['KC_active'] for r in rows], dtype=np.int64) + strata = {} + for stratum in ('all', 'ever_active', 'never_active'): + strata[stratum] = {metric: sum(r['strata'][stratum]['metrics_sum'][metric] for r in rows) + for metric in rows[0]['strata'][stratum]['metrics_sum']} + strata[stratum].update(neuron_trial_count=sum(r['strata'][stratum]['neurons'] for r in rows), + no_accepted_exc_neuron_trials=sum(r['strata'][stratum]['no_accepted_exc_neurons'] for r in rows), + no_accepted_ALPN_exc_neuron_trials=sum(r['strata'][stratum]['no_accepted_ALPN_exc_neurons'] for r in rows), + has_exc_but_never_active_neuron_trials=sum(r['strata'][stratum]['has_exc_but_never_active'] for r in rows)) + aggregate[mode] = dict(trials=30, KC_active_mean=float(active.mean()), + KC_active_min=int(active.min()), KC_active_max=int(active.max()), + KC_active_fraction_mean=float(active.mean() / 4064), source_groups=group_totals, + strata=strata, + input_totals={key: sum(r['input'][key] for r in rows) for key in + ('requests', 'accepted', 'rejected', 'design_target_PN_spikes', 'non_design_ALPN_spikes')}, + min_max_design_targets=[min(r['input']['odor_design_target_count'] for r in rows), + max(r['input']['odor_design_target_count'] for r in rows)], + neuron_trial_not_unique_neuron_counts=True) + flat = [] + for row in reports: + if row['kind'] == 'calibration' and row['repeat'] == 0: + flat.append(dict(case=row['case'], mode=row['mode'], seed=row['seed'], odor_id=row['odor_id'], + KC_active=row['KC_active'], KC_spikes=row['KC_spikes'], + requests=row['input']['requests'], accepted_requests=row['input']['accepted'], + ALPN_actual_spikes=row['input']['actual_spikes_by_source_group']['ALPN'], + no_exc_neurons=row['strata']['all']['no_accepted_exc_neurons'], + no_ALPN_exc_neurons=row['strata']['all']['no_accepted_ALPN_exc_neurons'], + APL_sum_SI=row['strata']['all']['metrics_sum']['APL_write_SI'], + APL_ref_sum_SI=row['strata']['all']['metrics_sum']['APL_write_ref_SI'], + maximum_ledger_error_V=row['ledger_max_abs_error_V'], + source_shadow_max_residual_V=row['source_shadow_max_residual_V'])) + assert len(reports) == 80 and len(flat) == 60 and len(aggregate) == 2 + return reports, aggregate, flat + + +def main(): + check() + assert not (EXP / 'audit/failure.json').exists(), 'Preserved failure cannot be promoted' + completed = read(EXP / 'audit/completed.json') + assert completed['pass_all'] and len(completed['trials']) == 80 and len(completed['repeat_checks']) == 12 + inventory = read(EXP / 'protocol/inventory.json') + expected_ids = {(r['case'], r['repeat']) for r in inventory} + actual_ids = [(r['case'], r['repeat']) for r in completed['trials']] + assert len(actual_ids) == len(set(actual_ids)) == 80 and set(actual_ids) == expected_ids + assert all(r['pass_all'] for r in completed['trials']) + repeat_ids = [r['case'] for r in completed['repeat_checks']] + assert len(repeat_ids) == len(set(repeat_ids)) == 12 + assert set(repeat_ids) == {r['case'] for r in inventory if r['repeat'] == 1} + assert all(r['pass_all'] and all(r['checks'].values()) for r in completed['repeat_checks']) + reports, aggregate, flat = summarize() + pd.DataFrame(flat).to_csv(EXP / 'audit/calibration-drive-summary.csv', index=False) + after = integrity() + save(EXP / 'regression/old-integrity-after.json', after) + prior_five = read(AG / 'regression/frozen-five.json') + assert prior_five['pass_all'] + result = dict(status='PASS-CONDITIONAL-KC-DRIVE-AUDIT', reconstructed_trials=80, + fixture_full_target_history_trials=8, CAL_final_and_event_trials=72, exact_accounting_repeats=12, + modes=aggregate, max_ledger_residual_V=max(r['ledger_max_abs_error_V'] for r in reports), + max_source_shadow_residual_V=max(r['source_shadow_max_residual_V'] for r in reports), + max_source_area_residual_V_ms=max(r['source_area_max_residual_V_ms'] for r in reports), + CPU_accounting_wall_sum_seconds=sum(r['CPU_audit_wall_seconds'] for r in reports), + old_integrity_pass=all(r['pass'] for r in after.values()), + frozen_five_reexecuted_this_audit=False, previous_sealed_five_reference_pass=True, + previous_five_reference_sha256=digest(AG / 'regression/frozen-five.json'), + previous_CAL_failure_not_changed=True, new_brain_simulation=False, GPU_used=False, + training=False, heldout_access=False, candidate_generated=False, learning_achieved=False, + APL_causal_failure_claim=False, counterfactual_tested=False, + limitations=['Logged upstream and APL are conditioning inputs, not independent components', + 'CAL has no independently saved per-tick KC state histories; only events and final states checked', + 'Source state lanes are non-feedback linear bookkeeping under observed reset/availability', + 'First-spike APL/ref mechanisms cannot establish whole-network counterfactual causality', + 'Counts pooled across trials are neuron-trial counts, not distinct neuron identities'], + payload_bytes_before_manifest=sum(p.stat().st_size for p in EXP.rglob('*') if p.is_file())) + assert result['payload_bytes_before_manifest'] < 2 * 1024 ** 3 + save(EXP / 'audit/final-status.json', result) + lines = ['# Phase 4AH — conditional KC drive audit', '', 'Status: `'+result['status']+'`.', '', + '80 existing records reconstructed; 8 fixture pre/post histories and 72 CAL event/final states exact.', + '12 repeated CAL records produced identical accounting. No new brain simulation, GPU use, tuning or learning.', + 'Full prior frozen integrity passed. Five reference simulations were not rerun; the sealed prior PASS is referenced by SHA.', + 'Previous CAL failure remains unchanged. Conditional replay is not an APL-removal counterfactual.', '', + '|Mode|Mean active KC fraction|Requests|Accepted requests|ALPN accepted excitatory increment sum (V)|APL write sum (V)|', + '|---|---:|---:|---:|---:|---:|'] + for mode, row in aggregate.items(): + lines.append(f"|{mode}|{row['KC_active_fraction_mean']:.8f}|{row['input_totals']['requests']}|{row['input_totals']['accepted']}|{row['source_groups']['ALPN']['accepted_exc_SI']:.9g}|{row['strata']['all']['APL_write_SI']:.9g}|") + lines += ['', 'Increment sums span 30 trials × 4064 KC × time; not instantaneous conductance or causal percentages.', + 'See audit/final-status.json, audit/calibration-drive-summary.csv and trials/*/per-kc.parquet for detailed evidence.'] + with (EXP / 'REPORT.md').open('x') as f: + f.write('\n'.join(lines) + '\n') + check() + seal(MODEL) + seal(EXP) + print(result['status'], flush=True) + + +if __name__ == '__main__': + main() diff --git a/models/male-cns-lif/kc-drive-audit-v1/manifest.json b/models/male-cns-lif/kc-drive-audit-v1/manifest.json new file mode 100644 index 0000000..b7585f3 --- /dev/null +++ b/models/male-cns-lif/kc-drive-audit-v1/manifest.json @@ -0,0 +1,60 @@ +{ + "root": "/data/digital-drosophila/models/male-cns-lif/kc-drive-audit-v1", + "files": [ + { + "path": "PROTOCOL.md", + "bytes": 4208, + "sha256": "fc254f996eab81c8ad76c4eb3dad274823f0e23502e59c3da6b5cbbd68444e8e" + }, + { + "path": "accounting.py", + "bytes": 12819, + "sha256": "c4703d36587bd40a2a231d5098b8d9991407ba80f6f2b488ffad068c7a74fde6" + }, + { + "path": "context.py", + "bytes": 2757, + "sha256": "c7eb06a19d9f1f28ecf71c5596d10aa11bb405d09b1167546da347eb58d8da9a" + }, + { + "path": "finalize.py", + "bytes": 10619, + "sha256": "ce0d0bfa2679c18573acab673b28d167ed300ff7669486c2e93fdc1a6097fc05" + }, + { + "path": "populations.py", + "bytes": 9356, + "sha256": "8504b38219bac952b07a67e7dc37a520f0a68238cb0b0dd56f4b4b7948c5233b" + }, + { + "path": "prepare.py", + "bytes": 5160, + "sha256": "fecf8324b5fca4b65d8f24f3ed80d20bdb1fc5654294958f34d58dd36625c59b" + }, + { + "path": "replay_core.py", + "bytes": 14287, + "sha256": "aa3187cb23712c09ab603e27e60424294db92de8086d60a95cf7a629d0521fdc" + }, + { + "path": "run_audit.py", + "bytes": 12406, + "sha256": "724c4f25341dc0883951ef5abdf9fc7dc72fba146a000b9cf9bca759e9c7623a" + }, + { + "path": "test_accounting.py", + "bytes": 13018, + "sha256": "a10f67c42162f8b07f0d760108d5ebf575f6dddc588752acc4499b43bac3ec94" + }, + { + "path": "test_populations.py", + "bytes": 5472, + "sha256": "65285a6679e140f9f7cc16cb806e7bddf0f0d288c6a7d3fa2879a7cd745dacc1" + }, + { + "path": "test_replay_core.py", + "bytes": 12276, + "sha256": "17b94e6c549e60c5adb9eebf0a5dc0679a95670265b3a332a49a2e6610a976c2" + } + ] +} diff --git a/models/male-cns-lif/kc-drive-audit-v1/populations.py b/models/male-cns-lif/kc-drive-audit-v1/populations.py new file mode 100644 index 0000000..f82507f --- /dev/null +++ b/models/male-cns-lif/kc-drive-audit-v1/populations.py @@ -0,0 +1,139 @@ +"""Pure, disjoint source labels from frozen anatomical metadata evidence. + +No files, trajectories, Torch, simulation, or parameters are touched. The +caller must verify context.META against its registered SHA-256 before loading. +Class membership follows Phase 4AG/4X; transmitter metadata is an orthogonal +diagnostic, never anatomical DAN identity or a reward/valence assertion. +""" +import numpy as np +import pandas as pd + + +N_NEURONS = 166700 +REQUIRED_COLUMNS = ("neuron_idx", "body_id", "annotation_class", "annotation_type", + "neurotransmitter", "nt_ground_truth") +GROUPS = ("KC", "APL", "ALPN", "MBON", "anatomical_DAN", + "dopamine_annotation_only", "other", "unclassified") +METADATA_SHA256 = "922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63" +FROZEN_4X_ROLES_SHA256 = "76ba0cae6c5db417ec030456797f7935216c6877bd339d5e435133a0061e3930" +FROZEN_4X_SIDECAR_SHA256 = "8b51d12d84d66cdca66aeb7d052a24e520b9c28f462bd6bf87f37dda85c33284" + + +def require(condition, message): + if not condition: + raise ValueError(message) + + +def _text_column(meta, name): + column = meta[name] + # Do not silently turn corrupt numeric metadata into a plausible label. + require(bool(column.map(lambda value: pd.isna(value) or isinstance(value, str)).all()), + "Expected nullable string metadata: " + name) + return column.fillna("").astype(str).to_numpy() + + +def classify(meta): + """Return (Unicode labels[N], JSON-serializable report), indexed by global ID. + + The full metadata must already be in exact neuron_idx order. Conflicting + exact identities and absent/explicitly unknown identities are retained as + unclassified instead of silently resolving them by assignment precedence. + DPM is reported explicitly but belongs to other (unless its annotations + conflict). A PAM/PPL1/DAN name prefix alone never creates a DAN identity. + """ + require(isinstance(meta, pd.DataFrame), "classify expects a pandas DataFrame") + missing = set(REQUIRED_COLUMNS) - set(meta.columns) + require(not missing, "Missing actual metadata schema fields: " + ", ".join(sorted(missing))) + require(meta.columns.is_unique, "Duplicate metadata columns") + require(len(meta) == N_NEURONS, "Expected the complete 166700-neuron metadata") + ids = meta["neuron_idx"].to_numpy() + bodies = meta["body_id"].to_numpy() + require(ids.dtype == np.int32 and np.array_equal(ids, np.arange(N_NEURONS, dtype=np.int32)), + "neuron_idx must be int32, complete, unique and in canonical row order") + require(bodies.dtype == np.uint64 and len(np.unique(bodies)) == N_NEURONS, + "body_id must be uint64 and unique") + cls, typ = _text_column(meta, "annotation_class"), _text_column(meta, "annotation_type") + nt_raw = _text_column(meta, "neurotransmitter") + ground_truth = _text_column(meta, "nt_ground_truth") + # Match frozen 4X normalization for the transmitter axis only. Anatomical + # class/type membership retains exact, case-sensitive original values. + nt = np.char.lower(np.char.strip(nt_raw.astype(str))) + dopamine = nt == "dopamine" + dpm = typ == "DPM" + masks = {"KC": cls == "Kenyon_Cell", "APL": typ == "APL", "ALPN": cls == "ALPN", + "MBON": cls == "MBON", "anatomical_DAN": cls == "DAN"} + hits = np.stack(list(masks.values())).sum(axis=0) + # Preserve the frozen 4X DAN/DPM conflict explicitly, and do not silently + # prefer an exact APL type over an incompatible exact anatomical class. + conflict = (hits > 1) | (dpm & (hits > 0)) + labels = np.full(N_NEURONS, "other", dtype=" 1, + "no_anatomical_annotation": no_anatomical_annotation, + "DAN_name_prefix_without_DAN_class": uncertain_dan_prefix, + "metadata_dopamine_not_annotation_DAN": dopamine & ~masks["anatomical_DAN"], + "annotation_DAN_not_metadata_dopamine": masks["anatomical_DAN"] & ~dopamine, + "has_raw_ground_truth": np.char.strip(ground_truth.astype(str)) != ""} + counts = {name: int(np.count_nonzero(labels == name)) for name in GROUPS} + require(sum(counts.values()) == N_NEURONS and set(np.unique(labels)).issubset(GROUPS), + "Output groups do not partition the complete graph") + require(not np.any((labels == "anatomical_DAN") & dpm), "DPM must never be assigned anatomical_DAN") + report = {"neurons": N_NEURONS, "groups": counts, "group_order": list(GROUPS), + "all_neurons_assigned_once": True, "labels_index": "global neuron_idx, identical to canonical row index", + "identity_conflicts_present": bool(np.any(conflict)), + "pass_all": bool(not np.any(conflict)), + "pass_all_scope": "schema, complete disjoint accounting and no conflicting exact identities; not biological validation", + "flags": {name: int(mask.sum()) for name, mask in flag_masks.items()}, + "flag_counts_by_group": {name: {group: int(np.count_nonzero(mask & (labels == group))) + for group in GROUPS} for name, mask in flag_masks.items()}, + "DPM_neuron_idx": ids[dpm].tolist(), "identity_conflict_neuron_idx": ids[conflict].tolist(), + "uncertain_DAN_prefix_neuron_idx": ids[uncertain_dan_prefix].tolist(), + "definitions": { + "KC": "annotation_class exactly Kenyon_Cell", + "APL": "annotation_type exactly APL", + "ALPN": "annotation_class exactly ALPN; annotation_class olfactory is NOT included", + "MBON": "annotation_class exactly MBON", + "anatomical_DAN": "annotation_class exactly DAN, no exact identity conflict; not inferred from NT or PAM/PPL1 prefix", + "dopamine_annotation_only": "normalized neurotransmitter=dopamine, no named exact group, no DPM; transmitter-evidence diagnostic, NOT DAN", + "other": "remaining annotated neurons, explicitly including non-conflicting annotation_type DPM", + "unclassified": "conflicting exact identity, no anatomical class/type evidence, or only uncertain DAN-name hint without class/NT evidence"}, + "uncertainty_policy": { + "explicit_unknown_tokens_casefolded": list(unknown_tokens), + "PAM_PPL1_DAN_prefix_never_assigns_identity": True, + "transmitter_role_and_anatomical_identity_are_separate": True, + "DPM_retained_as_other_with_explicit_flag": True, + "missing_ground_truth_is_not_filled_or_guessed": True, + "other_does_not_mean_known_function": True}, + "provenance": { + "metadata_path_contract": "context.META; caller must verify its preregistered SHA-256 before loading", + "metadata_expected_sha256_from_AG_r2": METADATA_SHA256, + "frozen_rules_path": "/data/digital-drosophila/models/male-cns-lif/transmitter-audit-v1/transmitter_roles_v1.py", + "frozen_rules_sha256": FROZEN_4X_ROLES_SHA256, + "frozen_sidecar_path": "/data/digital-drosophila/experiments/phase4x-transmitter-provenance/roles/neuron-evidence-axes.parquet", + "frozen_sidecar_sha256": FROZEN_4X_SIDECAR_SHA256, + "frozen_MB_DAN_mapping_path": "/data/digital-drosophila/experiments/phase4h-dopamine-plasticity/mappings/dan-to-mbon-confirmed.parquet", + "frozen_MB_DAN_mapping_used_to_assign_identity": False, + "file_hash_verified_by_this_pure_function": False, + "differences_from_4X_reporting": "ALPN separated from broader olfactory class; DPM grouped other with flag; absent/conflicting identity explicitly unclassified"}, + "source_graph_changed": False, "simulation_executed": False, + "heldout_dynamic_access": False, "reward_or_valence_inferred": False} + return labels, report diff --git a/models/male-cns-lif/kc-drive-audit-v1/prepare.py b/models/male-cns-lif/kc-drive-audit-v1/prepare.py new file mode 100644 index 0000000..e2bfbe9 --- /dev/null +++ b/models/male-cns-lif/kc-drive-audit-v1/prepare.py @@ -0,0 +1,75 @@ +"""Register conditional, CPU-only accounting; no new brain trajectories.""" +from datetime import datetime, timezone +import platform +import subprocess +import sys + +import numpy as np +import pandas as pd + +from context import ROOT, MODEL, EXP, AG, AG_MODEL, META, COEFF, read, save, digest, sources, integrity +from populations import classify, METADATA_SHA256, FROZEN_4X_ROLES_SHA256 + + +def main(): + assert not EXP.exists() or not any(EXP.iterdir()), 'Never reuse a populated audit directory' + prior = read(AG / 'audit/final-status.json') + assert prior['status'] == 'FAIL-FIXED-CAL-REPRESENTATION' and prior['numerical_gate_pass'] + for name in ('protocol', 'regression', 'trials', 'audit', 'logs'): + (EXP / name).mkdir(parents=True, exist_ok=False) + save(EXP / 'audit/preflight-directory.json', dict( + first_attempt='Parent experiments directory was not writable; failed before creating any audit directory/file or registration', + resolution='Administrator creates only the exact new empty audit directory owned by qishizhe; parent/frozen permissions unchanged', + empty_scaffold_allowed=True, scientific_accounting_started=False)) + command = [sys.executable, '-B', '-m', 'unittest', '-v', + 'test_replay_core', 'test_populations', 'test_accounting'] + completed = subprocess.run(command, cwd=MODEL, capture_output=True, text=True) + with (EXP / 'regression/unit-tests.log').open('x') as f: + f.write(completed.stdout + completed.stderr) + save(EXP / 'regression/unit-tests.json', dict(command=command, exit_code=completed.returncode, + pass_all=completed.returncode == 0, sha256=digest(EXP / 'regression/unit-tests.log'))) + assert completed.returncode == 0, 'Pure audit tests failed; preserve output and stop' + paths = [AG / 'manifest.json', AG / 'checksums.sha256', AG_MODEL / 'manifest.json', + AG_MODEL / 'checksums.sha256', META, COEFF, + ROOT / 'models/male-cns-lif/author-runtime-lab-v1/author_ext.cu', + ROOT / 'models/male-cns-lif/transmitter-audit-v1/transmitter_roles_v1.py', + ROOT / 'datasets/male-cns-v1.0/processed/neuron-only/gpu/csr-weights.int64.npy'] + assert digest(META) == METADATA_SHA256 + assert digest(ROOT / 'models/male-cns-lif/transmitter-audit-v1/transmitter_roles_v1.py') == FROZEN_4X_ROLES_SHA256 + save(EXP / 'protocol/preregistered.json', dict(timestamp_utc=datetime.now(timezone.utc).isoformat(), + sources=sources(), inputs=[dict(path=str(p.relative_to(ROOT)), sha256=digest(p)) for p in paths], + parent_commit=subprocess.check_output(['git', '-C', str(ROOT), 'rev-parse', 'HEAD'], text=True).strip(), + scope='Post-outcome conditional shadow accounting; no new brain simulation, no heldout, no tuning/training', + before_accounting=True, prior_CAL_result_known=True, output_budget_bytes=2 * 1024 ** 3, + CPU_only=True, max_threads=4, python=sys.version, numpy=np.__version__, pandas=pd.__version__, + machine=platform.machine(), longdouble_mantissa_bits=int(np.finfo(np.longdouble).nmant))) + save(EXP / 'regression/old-integrity-before.json', integrity()) + meta = pd.read_parquet(META).sort_values('neuron_idx').reset_index(drop=True) + assert len(meta) == 166700 and np.array_equal(meta.neuron_idx, np.arange(166700)) + labels, report = classify(meta) + assert report['pass_all'], 'Ambiguous/conflicting source identities: audit required' + for label, file in (('KC', 'kc_globals.npy'), ('APL', 'apl_globals.npy'), ('ALPN', 'pn_globals.npy')): + assert np.array_equal(np.flatnonzero(labels == label), np.load(AG / 'apl-bundle' / file)) + save(EXP / 'protocol/population-audit.json', report) + save(EXP / 'protocol/metadata-schema.json', {c: dict(dtype=str(meta[c].dtype), + nulls=int(meta[c].isna().sum())) for c in meta.columns}) + meta[['neuron_idx', 'body_id', 'annotation_class', 'annotation_type', 'neurotransmitter']].assign( + audit_source_group=labels).to_parquet(EXP / 'protocol/neuron-source-groups.parquet', index=False) + inventory = [dict(case=name, repeat=0, kind='fixture') for name in read(AG_MODEL / 'config.json')['fixtures']] + for row in read(AG / 'protocol/calibration-cases.json'): + inventory.append(dict(case=row['name'], repeat=0, kind='calibration')) + if row['seed'] == 51101: + inventory.append(dict(case=row['name'], repeat=1, kind='calibration')) + assert len(inventory) == 80 and len({(r['case'], r['repeat']) for r in inventory}) == 80 + for row in inventory: + source = AG / 'gpu' / (row['case'] + '-r' + str(row['repeat'])) + assert read(source / 'summary.json')['complete'] + row['source'] = str(source.relative_to(ROOT)) + save(EXP / 'protocol/inventory.json', inventory) + save(EXP / 'protocol/prepared-hashes.json', [dict(path=str(p.relative_to(EXP)), sha256=digest(p)) + for p in sorted((EXP / 'protocol').iterdir()) if p.is_file() and p.name != 'prepared-hashes.json']) + print('REGISTERED 8 fixtures + 60 CAL + 12 repeats; CPU-only existing-trajectory accounting', flush=True) + + +if __name__ == '__main__': + main() diff --git a/models/male-cns-lif/kc-drive-audit-v1/replay_core.py b/models/male-cns-lif/kc-drive-audit-v1/replay_core.py new file mode 100644 index 0000000..a740582 --- /dev/null +++ b/models/male-cns-lif/kc-drive-audit-v1/replay_core.py @@ -0,0 +1,253 @@ +"""CPU-only, target-local shadow of the frozen author LIF + legacy APL schedule. + +Targets generate their own threshold events. Recorded whole-graph events are +used ONLY as upstream, fixed-delay inputs; recorded target spikes are never +forced into the target state. APL h is an observed external component trace. +This is a conditional shadow reconstruction, not independent whole-brain or +independent APL validation. Nothing in this module imports Brian or Torch, +runs a scientific trial, reads a file, or writes a file. +""" +from __future__ import annotations + +from collections.abc import Mapping + +import numpy as np + + +N_GLOBAL = 166700 +DELAY_STEPS = 18 +REF_STEPS = 22 +COEFFICIENT_KEYS = ("gd", "c0", "cg", "cv", "threshold", "reset", "seed_volt") + + +def _require(condition, message): + if not condition: + raise ValueError(message) + + +def _vector(value, dtype, name, size=None, finite=False): + value = np.asarray(value) + _require(value.dtype == np.dtype(dtype) and value.ndim == 1, f"{name} must be a one-dimensional {dtype} array") + _require(size is None or len(value) == size, f"{name} has the wrong length") + if finite: + _require(np.isfinite(value).all(), f"{name} contains a nonfinite value") + return value + + +def _coefficients(coeff): + _require(isinstance(coeff, Mapping), "coeff must be a mapping") + result = {} + for name in COEFFICIENT_KEYS: + _require(name in coeff, f"Missing coefficient: {name}") + value = coeff[name] + _require(not isinstance(value, (bool, np.bool_)) and isinstance(value, (int, float, np.integer, np.float64)), + f"{name} must be a double-precision scalar, not a float32 or nonnumeric value") + result[name] = np.float64(value) + _require(np.isfinite(result[name]), f"Nonfinite coefficient: {name}") + _require(result["seed_volt"] > result["threshold"], "Common-request seed voltage must exceed the strict threshold") + return result + + +class IncomingIndex: + """Reusable immutable target-filtered anatomical edges in original order. + + src_global:int32, dst_local:int32, edge_id:int64, weights:float64 (SI volts). + All vectors have the same length. Sources and original global edge IDs + must retain canonical increasing source/edge order. Edges are NOT removed + because their weight is zero, nor reordered by target. The caller must + supply the already-audited effective weights (all fast APL source edges + zeroed in a separate copy); this object never edits its input arrays. + """ + + def __init__(self, src_global, dst_local, edge_id, weights, target_count): + _require(isinstance(target_count, (int, np.integer)) and not isinstance(target_count, (bool, np.bool_)) and + 0 < target_count <= N_GLOBAL, "target_count must be a positive in-range integer") + self.target_count = int(target_count) + source = _vector(src_global, "int32", "src_global") + count = len(source) + destination = _vector(dst_local, "int32", "dst_local", count) + ids = _vector(edge_id, "int64", "edge_id", count) + weight = _vector(weights, "float64", "weights", count, finite=True) + _require(np.all((source >= 0) & (source < N_GLOBAL)), "An incoming source is outside the global neuron range") + _require(np.all((destination >= 0) & (destination < target_count)), "Incoming destination is outside the target-local range") + _require(np.all(ids >= 0) and np.all(ids[1:] > ids[:-1]), "Original edge IDs must be unique and canonically increasing") + _require(np.all(source[1:] >= source[:-1]), "Incoming sources must remain in original canonical order") + self.src_global = np.array(source, dtype=np.int32, copy=True, order="C") + self.dst_local = np.array(destination, dtype=np.int32, copy=True, order="C") + self.edge_id = np.array(ids, dtype=np.int64, copy=True, order="C") + self.weights = np.array(weight, dtype=np.float64, copy=True, order="C") + self.sources, starts = np.unique(self.src_global, return_index=True) + self.starts = np.asarray(starts, dtype=np.int64) + self.stops = np.concatenate((self.starts[1:], np.array([count], np.int64))) if count else np.empty(0, np.int64) + for value in (self.src_global, self.dst_local, self.edge_id, self.weights, self.sources, self.starts, self.stops): + value.setflags(write=False) + + def due(self, global_spikes): + """Return local edge rows in original queue order; no target sort/sum.""" + fired = _vector(global_spikes, "int32", "global_spikes") + _require(np.all((fired >= 0) & (fired < N_GLOBAL)) and np.all(fired[1:] > fired[:-1]), + "Presynaptic spikes must be sorted, unique, in-range globals") + if not len(fired) or not len(self.sources): + return np.empty(0, np.int64) + positions = np.searchsorted(self.sources, fired) + present = positions < len(self.sources) + present[present] &= self.sources[positions[present]] == fired[present] + positions = positions[present] + if not len(positions): + return np.empty(0, np.int64) + # Each presynaptic group occupies a contiguous source-ordered block in + # this filtered graph. Concatenation preserves every target's original + # per-edge accumulation order, including zero-weight events. + return np.concatenate([np.arange(self.starts[pos], self.stops[pos], dtype=np.int64) for pos in positions]) + + +class _EventIndex: + def __init__(self, record, steps, name): + _require(isinstance(record, Mapping), f"{name} must be a mapping") + time_keys = [key for key in ("tick", "step") if key in record] + _require(len(time_keys) == 1 and "neuron_idx" in record, f"{name} needs exactly one tick/step vector and neuron_idx") + ticks = _vector(record[time_keys[0]], "int32", name + ".tick") + nodes = _vector(record["neuron_idx"], "int32", name + ".neuron_idx", len(ticks)) + _require(np.all((ticks >= 0) & (ticks < steps)), f"{name} has an event outside the trial") + _require(np.all((nodes >= 0) & (nodes < N_GLOBAL)), f"{name} contains an out-of-range global neuron") + _require(np.all((ticks[1:] > ticks[:-1]) | ((ticks[1:] == ticks[:-1]) & (nodes[1:] > nodes[:-1]))), + f"{name} must be sorted and unique by tick/global neuron") + self.nodes = np.array(nodes, copy=True, order="C") + counts = np.bincount(ticks, minlength=steps) + self.pointer = np.concatenate((np.zeros(1, np.int64), np.cumsum(counts, dtype=np.int64))) + self.nodes.setflags(write=False) + self.pointer.setflags(write=False) + + def at(self, tick): + if tick < 0: + return np.empty(0, np.int32) + return self.nodes[self.pointer[tick]:self.pointer[tick + 1]] + + +def _state_step(v, g, ref, request, coeff, capture): + """Separate NumPy array operations correspond to explicit CUDA RN ops.""" + next_ref = np.maximum(ref - np.int32(1), np.int32(0)) + available = next_ref == 0 + next_v, next_g = v.copy(), g.copy() + oldg, oldv = g[available], v[available] + next_g[available] = np.multiply(coeff["gd"], oldg) + driven = np.multiply(coeff["cg"], oldg) + leaked = np.multiply(coeff["cv"], oldv) + combined = np.add(driven, leaked) + next_v[available] = np.add(coeff["c0"], combined) + result = {} + if capture: + result.update(old_v=v.copy(), old_g=g.copy(), ref_before=ref.copy(), + available=available.copy(), request=request.copy(), + request_accepted=(request & available), request_rejected=(request & ~available), + v_integrated=next_v.copy(), g_decayed=next_g.copy()) + next_v[request & available] = coeff["seed_volt"] + if capture: + result["v_threshold"] = next_v.copy() + fired = available & (next_v > coeff["threshold"]) + next_v[fired] = coeff["reset"] + next_g[fired] = np.float64(0.0) + next_ref[fired] = np.int32(REF_STEPS) + _require(np.isfinite(next_v).all() and np.isfinite(next_g).all(), "State step produced a nonfinite value") + result.update(v=next_v, g=next_g, ref=next_ref, fired=fired) + if capture: + result.update(v_after_reset=next_v.copy(), g_after_reset=next_g.copy(), ref_after_reset=next_ref.copy()) + return result + + +def state_step(v, g, ref, request, coeff, *, capture=True): + """One group/request/threshold/reset step; no arrivals or APL yet. + + Inputs are never modified. v/g are float64 SI, ref is int32 in [0,22], + request is a bool vector. With capture=True this returns the corresponding + pre-threshold/intermediate callback fields as well as v/g/ref/fired. + """ + voltage = _vector(v, "float64", "v", finite=True) + n = len(voltage) + conductance = _vector(g, "float64", "g", n, finite=True) + refractory = _vector(ref, "int32", "ref", n) + requested = _vector(request, "bool", "request", n) + _require(n > 0 and np.all((refractory >= 0) & (refractory <= REF_STEPS)), "Invalid target count/refractory state") + _require(isinstance(capture, bool), "capture must be Boolean") + return _state_step(voltage, conductance, refractory, requested, _coefficients(coeff), capture) + + +def replay(initial_v, initial_g, coeff, steps, target_globals, requests, events, + incoming: IncomingIndex, h32, callback=None): + """Reconstruct targets with bounded working state and sparse output events. + + initial_v/initial_g: float64 SI vectors, initially nonrefractory. + target_globals: sorted unique int32 target IDs. + requests/events: mappings with tick (or step) and neuron_idx int32 vectors; + requests outside the target subset are ignored, not reassigned. + h32: float32 (steps, target_count), ALREADY mapped to target_globals, in mV. + Full 4064-KC input is valid when the target list is those same 4064 KCs. + callback(tick, stage): optional copies of old states, available/request + masks, integrated/threshold voltages, reset state, canonical due edges, + accepted arrivals, pre-APL states, h32/delta_si, and final v/g/ref. It may + raise to stop at the first caller-detected mismatch. Modifying callback + arrays cannot affect this replay or its input arrays. + + No dense history is stored. Memory is O(incoming edges + logged events + + steps + targets + current due edges + predicted sparse events), excluding + caller-owned h32 and anything the callback elects to retain. + """ + _require(isinstance(steps, (int, np.integer)) and not isinstance(steps, (bool, np.bool_)) and + 0 < steps <= np.iinfo(np.int32).max, "steps must be a positive int32-range integer") + steps = int(steps) + targets = _vector(target_globals, "int32", "target_globals") + n = len(targets) + _require(0 < n <= N_GLOBAL and np.all((targets >= 0) & (targets < N_GLOBAL)) and np.all(targets[1:] > targets[:-1]), + "Targets must be sorted unique in-range globals") + targets = targets.copy() + v = _vector(initial_v, "float64", "initial_v", n, finite=True).copy() + g = _vector(initial_g, "float64", "initial_g", n, finite=True).copy() + constants = _coefficients(coeff) + _require(isinstance(incoming, IncomingIndex) and incoming.target_count == n, "Incoming index target count mismatch") + _require(callback is None or callable(callback), "callback must be callable or None") + inhibition = np.asarray(h32) + _require(inhibition.dtype == np.dtype("float32") and inhibition.shape == (steps, n), "h32 must be float32 (steps, targets), already target-mapped") + for tick in range(steps): + _require(np.isfinite(inhibition[tick]).all() and np.all(inhibition[tick] >= 0), "APL inhibition must be finite and nonnegative") + request_index = _EventIndex(requests, steps, "requests") + event_index = _EventIndex(events, steps, "events") + ref = np.zeros(n, np.int32) + counts = np.zeros(n, np.int32) + first_spike = np.full(n, -1, np.int32) + predicted_steps, predicted_nodes = [], [] + capture = callback is not None + for tick in range(steps): + requested_globals = request_index.at(tick) + local = np.searchsorted(targets, requested_globals) + present = local < n + present[present] &= targets[local[present]] == requested_globals[present] + request = np.zeros(n, dtype=bool) + request[local[present]] = True + stage = _state_step(v, g, ref, request, constants, capture) + v, g, ref, fired = stage["v"], stage["g"], stage["ref"], stage["fired"] + counts += fired.astype(np.int32) + first_spike[fired & (first_spike < 0)] = np.int32(tick) + fired_local = np.flatnonzero(fired) + if len(fired_local): + predicted_steps.append(np.full(len(fired_local), tick, np.int32)) + predicted_nodes.append(targets[fired_local].copy()) + due = incoming.due(event_index.at(tick - DELAY_STEPS)) + accepted = ref[incoming.dst_local[due]] == 0 + if len(due): + accepted_edges = due[accepted] + # add.at is deliberately unbuffered: start at existing g and add + # each accepted edge in original order, NEVER sum weights first. + np.add.at(g, incoming.dst_local[accepted_edges], incoming.weights[accepted_edges]) + if capture: + stage.update(due_edge_indices=due.copy(), due_edge_id=incoming.edge_id[due].copy(), + arrival_accepted=accepted.copy(), v_pre_apl=v.copy(), g_pre_apl=g.copy(), ref_pre_apl=ref.copy()) + delta_si = inhibition[tick].astype(np.float64) * np.float64(.001) + g = np.subtract(g, delta_si) + _require(np.isfinite(v).all() and np.isfinite(g).all(), "Arrival/APL stage produced a nonfinite value") + if capture: + stage.update(h32=inhibition[tick].copy(), delta_si=delta_si.copy(), v=v.copy(), g=g.copy(), ref=ref.copy(), fired=fired.copy()) + callback(tick, stage) + return {"v": v.copy(), "g": g.copy(), "ref": ref.copy(), "counts": counts, + "first_spike": first_spike, "target_globals": targets, + "step": np.concatenate(predicted_steps) if predicted_steps else np.empty(0, np.int32), + "neuron_idx": np.concatenate(predicted_nodes) if predicted_nodes else np.empty(0, np.int32)} diff --git a/models/male-cns-lif/kc-drive-audit-v1/run_audit.py b/models/male-cns-lif/kc-drive-audit-v1/run_audit.py new file mode 100644 index 0000000..b1e7312 --- /dev/null +++ b/models/male-cns-lif/kc-drive-audit-v1/run_audit.py @@ -0,0 +1,205 @@ +"""Reconstruct existing KC targets; never launch a brain simulation or CUDA.""" +from functools import lru_cache +import time +import traceback + +import numpy as np +import pandas as pd + +from context import ROOT, EXP, AG, META, COEFF, read, save, digest, check +from replay_core import IncomingIndex, replay +from accounting import DriveAccounting, bit_equal +from populations import GROUPS + + +@lru_cache(maxsize=2) +def graph_view(graph_name): + folder = AG / 'graphs' / graph_name + nodes = np.load(folder / 'nodes.npy', mmap_mode='r') + all_kc = np.load(AG / 'apl-bundle/kc_globals.npy', mmap_mode='r') + apl = np.load(AG / 'apl-bundle/apl_globals.npy', mmap_mode='r') + targets, node_positions, apl_columns = np.intersect1d(nodes, all_kc, assume_unique=True, return_indices=True) + lookup = np.full(len(nodes), -1, np.int32) + lookup[node_positions] = np.arange(len(targets), dtype=np.int32) + destination = np.load(folder / 'dst.npy', mmap_mode='r') + selected = np.flatnonzero(lookup[destination] >= 0) + src_local = np.load(folder / 'src.npy', mmap_mode='r')[selected] + src = nodes[src_local] + dst = lookup[destination[selected]] + ids = np.load(folder / 'edge_id.npy', mmap_mode='r')[selected] + weights = np.load(folder / 'weights.npy', mmap_mode='r')[selected].copy() + weights[np.isin(src, apl)] = 0.0 + original = np.load(ROOT / 'datasets/male-cns-v1.0/processed/neuron-only/gpu/csr-weights.int64.npy', mmap_mode='r') + raw = original[ids] + assert raw.dtype == np.int64 and np.all(raw > 0) + incoming = IncomingIndex(src, dst, ids, weights, len(targets)) + group_frame = pd.read_parquet(EXP / 'protocol/neuron-source-groups.parquet') + labels = group_frame.audit_source_group.to_numpy() + group_codes = np.array([GROUPS.index(x) for x in labels], np.int32)[src] + audit = dict(graph_name=graph_name, graph_nodes=len(nodes), graph_edges=len(destination), + KC_targets=len(targets), incoming_edges=len(ids), incoming_contacts=int(raw.sum()), + zeroed_APL_incoming_edges=int(np.isin(src, apl).sum()), + groups={name: dict(edges=int(np.count_nonzero(group_codes == gi)), + contacts=int(raw[group_codes == gi].sum())) for gi, name in enumerate(GROUPS)}, + source_graph=str(folder), conditional_target_filter_only=True, source_graph_modified=False) + return incoming, raw, group_codes, targets, node_positions, apl_columns, audit + + +def input_audit(case, source, spec, state, population): + with np.load(case / 'requests.npz', allow_pickle=False) as data: + req = {k: data[k].copy() for k in data.files} + table = pd.read_csv(source / 'requests.csv') + assert len(table) == len(req['tick']) + assert np.array_equal(table.tick.to_numpy(), req['tick']) + assert np.array_equal(table.neuron_idx.to_numpy(), req['neuron_idx']) + assert (table.attempted == 1).all() and table.accepted.isin([0, 1]).all() + assert np.array_equal(table.rejected.to_numpy(), 1 - table.accepted.to_numpy()) + request_keys = req['tick'].astype(np.int64) * 166700 + req['neuron_idx'] + event_keys = state['step'].astype(np.int64) * 166700 + state['neuron_idx'] + accepted = np.isin(request_keys, event_keys) + assert np.array_equal(accepted, table.accepted.to_numpy().astype(bool)) + labels = population.audit_source_group.to_numpy() + all_pn = np.flatnonzero(labels == 'ALPN').astype(np.int32) + observed_count = np.bincount(state['neuron_idx'], minlength=166700) + requested_count = np.bincount(req['neuron_idx'], minlength=166700) + accepted_count = np.bincount(req['neuron_idx'][accepted], minlength=166700) + targets = np.empty(0, np.int32) + rates = np.empty(0, np.float32) + if spec['kind'] == 'calibration': + with np.load(case / 'pn-drive.npz', allow_pickle=False) as data: + targets, rates = data['targets'].copy(), data['rates_hz'].copy() + assert targets.dtype == np.int32 and rates.dtype == np.float32 + assert targets.shape == rates.shape == (spec['target_count'],) + assert len(np.unique(targets)) == len(targets) and np.isfinite(rates).all() and (rates > 0).all() + assert np.isin(targets, all_pn).all() and np.isin(req['neuron_idx'], targets).all() + rate_map = dict(zip(targets.tolist(), rates.tolist())) + pn_table = population.iloc[all_pn][['neuron_idx', 'body_id', 'annotation_type']].copy() + pn_table['design_target'] = np.isin(all_pn, targets) + pn_table['design_rate_hz'] = [rate_map.get(int(i), 0.0) for i in all_pn] + pn_table['requests'] = requested_count[all_pn] + pn_table['accepted_requests'] = accepted_count[all_pn] + pn_table['rejected_requests'] = requested_count[all_pn] - accepted_count[all_pn] + pn_table['actual_spikes'] = observed_count[all_pn] + pn_table['spikes_not_coincident_with_accepted_request'] = observed_count[all_pn] - accepted_count[all_pn] + assert (pn_table.spikes_not_coincident_with_accepted_request >= 0).all() + info = dict(requests=len(request_keys), accepted=int(accepted.sum()), rejected=int((~accepted).sum()), + actual_spikes_by_source_group={name: int(observed_count[labels == name].sum()) for name in GROUPS}, + all_ALPN=len(all_pn), odor_design_target_count=len(targets), PN_ever_requested=int(np.count_nonzero(requested_count[all_pn])), + PN_ever_accepted_request=int(np.count_nonzero(accepted_count[all_pn])), + PN_actual_active=int(np.count_nonzero(observed_count[all_pn])), + design_target_PN_spikes=int(observed_count[targets].sum()), + non_design_ALPN_spikes=int(observed_count[np.setdiff1d(all_pn, targets)].sum()), + accepted_request_is_not_proof_spike_was_caused_exclusively_by_request=True, + actual_requested_events_not_forced_to_equal_across_odors=True) + return req, info, pn_table + + +def audit_trial(row, population): + start = time.perf_counter() + name = row['case'] + '-r' + str(row['repeat']) + out = EXP / 'trials' / name + out.mkdir(exist_ok=False) + source = ROOT / row['source'] + case = AG / 'cases' / row['case'] + spec = read(case / 'spec.json') + graph_name = 'fixtures' if row['kind'] == 'fixture' else 'full' + incoming, raw, groups, targets, positions, apl_columns, graph_audit = graph_view(graph_name) + with np.load(source / 'state.npz', allow_pickle=False) as data: + state = {k: data[k].copy() for k in data.files} + assert set(state) == {'step', 'neuron_idx', 'counts', 'v', 'g', 'ref'} + assert all(np.isfinite(v).all() for v in state.values()) + req, input_report, pn_table = input_audit(case, source, spec, state, population) + event_mask = np.isin(state['neuron_idx'], targets) + expected = {key: state[key][event_mask] for key in ('step', 'neuron_idx')} + initial_v = np.full(len(targets), spec['model']['resting_potential_mv'], np.float64) * np.float64(.001) + initial_g = np.zeros(len(targets), np.float64) + if (case / 'arrays.npz').exists(): + with np.load(case / 'arrays.npz', allow_pickle=False) as data: + initial_v = data['initial_v_mv'][positions] * np.float64(.001) + initial_g = data['initial_g_mv'][positions] * np.float64(.001) + histories = {} + if row['kind'] == 'fixture': + for key, suffix in (('v', 'float64'), ('g', 'float64'), ('ref', 'int32')): + histories[key] = np.load(source / f'{key}-history.{suffix}.npy', mmap_mode='r')[:, positions] + histories['pre_' + key] = np.load(source / f'preAPL-{key}.{suffix}.npy', mmap_mode='r')[:, positions] + h = np.load(source / 'apl-inhibition.float32.npy', mmap_mode='r') + assert h.shape == (spec['steps'], 4064) + if not np.array_equal(apl_columns, np.arange(4064)): + h = h[:, apl_columns] + coeff = read(COEFF) + accounting = DriveAccounting(targets, incoming, raw, groups, GROUPS, expected, + coeff, state['counts'][positions], histories) + result = replay(initial_v, initial_g, coeff, spec['steps'], targets, req, + dict(step=state['step'], neuron_idx=state['neuron_idx']), incoming, h, accounting) + exact = {key: bit_equal(result[key], state[key][positions]) for key in ('v', 'g', 'ref', 'counts')} + exact.update({key: bit_equal(result[key], expected[key]) for key in ('step', 'neuron_idx')}) + assert all(exact.values()), 'Reconstruction diverged: ' + str(exact) + assert accounting.callback_count == spec['steps'] + data, report = accounting.finish(result['g']) + assert bit_equal(data['first_spike_tick'], result['first_spike']) + per_kc = pd.DataFrame(data) + per_kc.insert(1, 'body_id', population.iloc[targets].body_id.to_numpy()) + per_kc.to_parquet(out / 'per-kc.parquet', index=False) + pn_table.to_parquet(out / 'per-pn.parquet', index=False) + pd.DataFrame(accounting.tick_rows).to_parquet(out / 'per-tick.parquet', index=False) + with (out / 'replay-final.npz').open('xb') as f: + np.savez_compressed(f, **result) + late_counts = np.bincount(state['neuron_idx'][state['step'] >= spec['steps'] - 18], minlength=166700) + pending = late_counts[incoming.src_global] + report.update(case=row['case'], repeat=row['repeat'], kind=row['kind'], pass_all=True, + exact=exact, full_KC_sparse_events_and_final_state_exact=True, + every_tick_fixture_pre_post_state_exact=bool(histories), + graph=graph_audit, input=input_report, steps=spec['steps'], duration_ms=spec['duration_ms'], + KC_active=int(np.count_nonzero(result['counts'])), KC_spikes=int(result['counts'].sum()), + pending_after_record_window=dict(edge_events=int(pending.sum()), contacts=int(np.dot(pending, raw)), + nominal_abs_SI=float(np.dot(pending, np.abs(incoming.weights)))), + mode=spec.get('mode'), odor_id=spec.get('odor_id'), seed=spec.get('seed'), + CPU_audit_wall_seconds=time.perf_counter() - start, + original_brain_time_and_wall_not_rebenchmarked=True, training=False, new_brain_simulation=False, + candidate_generated=False, input_sha256={filename: digest(source / filename) + for filename in ('state.npz', 'apl-inhibition.float32.npy', 'requests.csv', 'summary.json')}) + save(out / 'summary.json', report) + return report + + +def main(): + check() + prepared = read(EXP / 'protocol/prepared-hashes.json') + for row in prepared: + assert digest(EXP / row['path']) == row['sha256'] + population = pd.read_parquet(EXP / 'protocol/neuron-source-groups.parquet') + inventory = read(EXP / 'protocol/inventory.json') + reports = [] + for row in inventory: + try: + report = audit_trial(row, population) + reports.append(dict(case=row['case'], repeat=row['repeat'], pass_all=report['pass_all'])) + assert sum(p.stat().st_size for p in EXP.rglob('*') if p.is_file()) < 2 * 1024 ** 3 + print(row['case'], row['repeat'], 'EXACT', report['KC_active'], + 'KC active; CPU audit seconds', round(report['CPU_audit_wall_seconds'], 3), flush=True) + except Exception as error: + save(EXP / 'audit/failure.json', dict(case=row, error=type(error).__name__, message=str(error), + traceback=traceback.format_exc(), completed=reports, new_brain_simulation=False)) + raise + repeat_checks = [] + for row in inventory: + if row['repeat'] != 1: + continue + first = EXP / 'trials' / (row['case'] + '-r0') + repeat = EXP / 'trials' / (row['case'] + '-r1') + checks = {} + for filename in ('per-kc.parquet', 'per-pn.parquet', 'per-tick.parquet'): + a, b = pd.read_parquet(first / filename), pd.read_parquet(repeat / filename) + checks[filename] = a.equals(b) + with np.load(first / 'replay-final.npz') as a, np.load(repeat / 'replay-final.npz') as b: + checks['replay-final'] = set(a.files) == set(b.files) and all(bit_equal(a[k], b[k]) for k in a.files) + assert all(checks.values()), 'Repeat accounting differs' + repeat_checks.append(dict(case=row['case'], pass_all=True, checks=checks)) + assert len(reports) == 80 and len(repeat_checks) == 12 + save(EXP / 'audit/completed.json', dict(pass_all=True, trials=reports, repeat_checks=repeat_checks, + meaning='Exact conditional KC reconstruction and reproducible accounting only', candidate_generated=False)) + print('PASS 80/80 conditional reconstructions; 12/12 accounting repeats', flush=True) + + +if __name__ == '__main__': + main() diff --git a/models/male-cns-lif/kc-drive-audit-v1/test_accounting.py b/models/male-cns-lif/kc-drive-audit-v1/test_accounting.py new file mode 100644 index 0000000..fef72ff --- /dev/null +++ b/models/male-cns-lif/kc-drive-audit-v1/test_accounting.py @@ -0,0 +1,246 @@ +import unittest +from types import SimpleNamespace +import numpy as np +from accounting import DriveAccounting, bit_equal, fraction + + +GROUPS = ('KC', 'APL', 'ALPN', 'MBON', 'anatomical_DAN', + 'dopamine_annotation_only', 'other', 'unclassified') + + +def _events(ticks=(), nodes=()): + return {'step': np.asarray(ticks, np.int32), + 'neuron_idx': np.asarray(nodes, np.int32)} + + +def _account(targets, destinations=(), weights=(), raw=(), groups=(), + expected=None, counts=None, gd=.5, histories=None): + targets = np.asarray(targets, np.int32) + incoming = SimpleNamespace(dst_local=np.asarray(destinations, np.int32), + weights=np.asarray(weights, np.float64)) + coeff = {'gd': np.float64(gd), 'threshold': np.float64(-.045)} + return DriveAccounting(targets, incoming, np.asarray(raw, np.int64), + np.asarray(groups, np.int32), GROUPS, + _events() if expected is None else expected, coeff, + np.zeros(len(targets), np.int32) if counts is None else np.asarray(counts, np.int32), + histories=histories) + + +def _stage(old_g, g_decayed, g_after_reset, g_pre_apl, delta, *, + available, fired, ref, due=(), accepted=(), v_threshold=None): + """Manually specified accounting callback; no runtime or replay dependency.""" + old_g = np.asarray(old_g, np.float64) + n = len(old_g) + delta = np.asarray(delta, np.float64) + pre = np.asarray(g_pre_apl, np.float64) + ref = np.asarray(ref, np.int32) + threshold_v = np.full(n, -.05, np.float64) if v_threshold is None else np.asarray(v_threshold, np.float64) + return {'old_g': old_g, + 'g_decayed': np.asarray(g_decayed, np.float64), + 'g_after_reset': np.asarray(g_after_reset, np.float64), + 'g_pre_apl': pre, + 'delta_si': delta, + 'g': np.subtract(pre, delta), + 'available': np.asarray(available, bool), + 'fired': np.asarray(fired, bool), + 'ref': ref, + 'ref_pre_apl': ref.copy(), + 'v': np.full(n, -.052, np.float64), + 'v_pre_apl': np.full(n, -.052, np.float64), + 'v_threshold': threshold_v, + 'due_edge_indices': np.asarray(due, np.int64), + 'arrival_accepted': np.asarray(accepted, bool)} + + +def _two_tick_fixture(): + # KC 10 fires immediately, KC 20 first fires one tick later, KC 30 never + # fires. All contacts are positive anatomical counts; signed effective + # SI increments are an independent vector, including a zeroed APL edge. + account = _account([10, 20, 30], [0, 1, 2, 2, 2, 0], + [1., 2., 3., -1., 0., -2.], [4, 8, 12, 4, 16, 8], + [2, 2, 0, 3, 1, 3], _events([0, 1], [10, 20]), [1, 1, 0]) + first = _stage([2., 4., 8.], [1., 2., 4.], [0., 2., 4.], + [0., 4., 6.], [.25, .5, 1.], available=[1, 1, 1], + fired=[1, 0, 0], ref=[22, 0, 0], due=np.arange(6), + accepted=[0, 1, 1, 1, 1, 0], v_threshold=[-.044, -.049, -.05]) + second = _stage([-.25, 3.5, 5.], [-.25, 1.75, 2.5], + [-.25, 0., 2.5], [-.25, 0., 2.5], [.25, .5, 1.], + available=[0, 1, 1], fired=[0, 1, 0], ref=[21, 22, 0], + v_threshold=[-.052, -.044, -.05]) + return account, first, second + + +class AccountingTests(unittest.TestCase): + def test_bitwise_signed_zero(self): + self.assertFalse(bit_equal(np.array([0.0]), np.array([-0.0]))) + + def test_dtype_is_part_of_evidence(self): + self.assertFalse(bit_equal(np.array([1], np.int32), np.array([1], np.int64))) + + def test_shape_is_part_of_evidence(self): + self.assertFalse(bit_equal(np.array([1.0]), np.array([[1.0]]))) + + def test_equal(self): + self.assertTrue(bit_equal(np.array([1.0, -2.0]), np.array([1.0, -2.0]))) + + def test_zero_denominator_is_unknown(self): + self.assertIsNone(fraction(1, 0)) + self.assertEqual(fraction(1, 2), .5) + + def test_actual_stage_ledger_telescopes_without_source_feedback(self): + account, first, second = _two_tick_fixture() + account(0, first) + account(1, second) + data, report = account.finish(second['g']) + for name, expected in { + 'decay': [-1., -3.75, -6.5], 'reset': [-1., -1.75, 0.], + 'arrivals': [0., 2., 2.], 'APL': [-.5, -1., -2.]}.items(): + np.testing.assert_array_equal(data['actual_g_delta_' + name + '_V'], expected) + np.testing.assert_array_equal(data['g_ledger_residual_V'], np.zeros(3)) + np.testing.assert_array_equal(data['initial_g_state_area_V_ms'], [0., .2, .4 + .2]) + self.assertTrue(report['ledger_roundoff_bound_pass']) + self.assertFalse(report['APL_cause_of_CAL_failure_proven']) + self.assertFalse(report['full_CAL_all_tick_subset_state_independently_verified']) + + def test_ref_write_includes_new_spike_and_already_refractory(self): + account, first, second = _two_tick_fixture() + account(0, first) + account(1, second) + data, _ = account.finish(second['g']) + np.testing.assert_array_equal(data['APL_write_ref_SI'], [.5, .5, 0.]) + np.testing.assert_array_equal(data['APL_write_available_SI'], [0., .5, 2.]) + np.testing.assert_array_equal(data['APL_write_SI'], + data['APL_write_ref_SI'] + data['APL_write_available_SI']) + np.testing.assert_array_equal(data['refractory_ticks'], [2., 1., 0.]) + + def test_first_spike_tick_arrival_and_apl_are_not_prior_causes(self): + account, first, second = _two_tick_fixture() + account(0, first) + account(1, second) + data, _ = account.finish(second['g']) + np.testing.assert_array_equal(data['first_spike_tick'], [0, 1, -1]) + np.testing.assert_array_equal(data['APL_pre_first_spike_SI'], [0., .5, 2.]) + np.testing.assert_array_equal(data['APL_same_tick_first_spike_SI'], [.25, .5, 0.]) + np.testing.assert_array_equal(data['accepted_exc_before_first_SI'], [0., 2., 3.]) + np.testing.assert_array_equal(data['accepted_inh_before_first_abs_SI'], [0., 0., 1.]) + + def test_group_contact_sign_and_rejected_edge_accounting(self): + account, first, second = _two_tick_fixture() + account(0, first) + account(1, second) + data, report = account.finish(second['g']) + for name in GROUPS: + np.testing.assert_array_equal(data[name + '_due_edges'], + data[name + '_accepted_edges'] + data[name + '_rejected_edges']) + np.testing.assert_array_equal(data[name + '_due_contacts'], + data[name + '_accepted_contacts'] + data[name + '_rejected_contacts']) + np.testing.assert_array_equal(data['ALPN_accepted_exc_SI'], [0., 2., 0.]) + np.testing.assert_array_equal(data['ALPN_rejected_exc_SI'], [1., 0., 0.]) + np.testing.assert_array_equal(data['MBON_accepted_inh_abs_SI'], [0., 0., 1.]) + np.testing.assert_array_equal(data['MBON_rejected_inh_abs_SI'], [2., 0., 0.]) + self.assertEqual(report['strata']['never_active']['neurons'], 1) + self.assertEqual(report['strata']['never_active']['no_accepted_ALPN_exc_neurons'], 1) + + def test_zeroed_apl_keeps_anatomical_edge_and_contact_counts(self): + account, first, second = _two_tick_fixture() + account(0, first) + account(1, second) + data, _ = account.finish(second['g']) + np.testing.assert_array_equal(data['APL_zeroed_due_edges'], [0., 0., 1.]) + np.testing.assert_array_equal(data['APL_zeroed_due_contacts'], [0., 0., 16.]) + np.testing.assert_array_equal(data['APL_accepted_edges'], [0, 0, 1]) + np.testing.assert_array_equal(data['APL_accepted_exc_SI'], np.zeros(3)) + np.testing.assert_array_equal(data['APL_accepted_inh_abs_SI'], np.zeros(3)) + + def test_observer_never_modifies_supplied_callback_arrays(self): + account, first, _ = _two_tick_fixture() + original = {name: value.copy() for name, value in first.items()} + account(0, first) + for name, value in original.items(): + self.assertTrue(bit_equal(first[name], value), name) + + def test_independent_spike_disagreement_stops_before_accounting(self): + account, first, _ = _two_tick_fixture() + first['fired'][0] = False + with self.assertRaisesRegex(ValueError, 'KC spike divergence at tick 0'): + account(0, first) + self.assertEqual(account.callback_count, 0) + + def test_dense_fixture_history_disagreement_is_not_tolerated(self): + account, first, _ = _two_tick_fixture() + account.histories = {'pre_g': first['g_pre_apl'][None, :].copy()} + account.histories['pre_g'][0, 1] = np.nextafter(account.histories['pre_g'][0, 1], np.inf) + with self.assertRaisesRegex(ValueError, 'Fixture dense pre_g divergence'): + account(0, first) + + def test_cancellation_reports_roundoff_instead_of_forcing_source_sum(self): + account = _account([10], [0, 0], [2. ** 53, -(2. ** 53)], + [1, 1], [2, 3], gd=1.) + stage = _stage([1.], [1.], [1.], [0.], [0.], + available=[1], fired=[0], ref=[0], due=[0, 1], accepted=[1, 1]) + account(0, stage) + data, report = account.finish(stage['g']) + np.testing.assert_array_equal(data['arrival_rounding_V'], [-1.]) + self.assertEqual(report['source_shadow_max_residual_V'], 1.) + np.testing.assert_array_equal(data['initial_g_state_area_V_ms'], [.1]) + np.testing.assert_array_equal(data['source_g_area_residual_V_ms'], [.1]) + self.assertEqual(report['source_area_max_residual_V_ms'], .1) + np.testing.assert_array_equal(data['g_ledger_residual_V'], [0.]) + + def test_initial_g_is_not_misattributed_to_a_source_group(self): + account = _account([10]) + stage = _stage([2.], [1.], [1.], [1.], [0.], + available=[1], fired=[0], ref=[0]) + account(0, stage) + data, report = account.finish(stage['g']) + for key, value in data.items(): + if key.startswith('source_g_area_'): + np.testing.assert_array_equal(value, [0.]) + self.assertEqual(report['source_shadow_max_residual_V'], 0.) + np.testing.assert_array_equal(data['actual_g_delta_decay_V'], [-1.]) + np.testing.assert_array_equal(data['initial_g_state_area_V_ms'], [.1]) + np.testing.assert_array_equal(data['source_g_area_residual_V_ms'], [0.]) + for stratum in ('all', 'never_active'): + self.assertEqual(report['strata'][stratum]['initial_g_state_area_V_ms'], .1) + self.assertEqual(report['strata'][stratum]['source_g_area_residual_V_ms'], 0.) + self.assertEqual(report['strata']['ever_active']['initial_g_state_area_V_ms'], 0.) + self.assertEqual(report['ledger_accumulator_mantissa_bits'], np.finfo(np.longdouble).nmant) + self.assertIn('eps(longdouble)', report['ledger_bound_formula']) + + def test_wrong_final_g_fails_ledger_closure(self): + account, first, second = _two_tick_fixture() + account(0, first) + account(1, second) + with self.assertRaisesRegex(AssertionError, 'g ledger residual'): + account.finish(second['g'] + 1.) + + def test_synthetic_replay_accounting_integration_keeps_delay_and_stage_order(self): + from replay_core import IncomingIndex, replay + targets = np.asarray([10, 20], np.int32) + expected = _events([0, 19], [10, 20]) + coeff = {'gd': .5, 'c0': 0., 'cg': 1., 'cv': 1., + 'threshold': 1., 'reset': 0., 'seed_volt': 2.} + incoming = IncomingIndex(np.asarray([10], np.int32), np.asarray([1], np.int32), + np.asarray([123], np.int64), np.asarray([2.], np.float64), 2) + account = DriveAccounting(targets, incoming, np.asarray([5], np.int64), + np.asarray([0], np.int32), GROUPS, expected, coeff, np.asarray([1, 1], np.int32)) + result = replay(np.zeros(2, np.float64), np.zeros(2, np.float64), coeff, 24, + targets, _events([0], [10]), expected, incoming, + np.zeros((24, 2), np.float32), callback=account) + data, report = account.finish(result['g']) + np.testing.assert_array_equal(result['step'], [0, 19]) + np.testing.assert_array_equal(result['neuron_idx'], targets) + np.testing.assert_array_equal(result['counts'], [1, 1]) + np.testing.assert_array_equal(result['ref'], [0, 18]) + np.testing.assert_array_equal(data['first_spike_tick'], [0, 19]) + np.testing.assert_array_equal(data['KC_accepted_contacts'], [0, 5]) + np.testing.assert_array_equal(data['actual_g_delta_arrivals_V'], [0., 2.]) + np.testing.assert_array_equal(data['actual_g_delta_decay_V'], [0., -1.]) + np.testing.assert_array_equal(data['actual_g_delta_reset_V'], [0., -1.]) + np.testing.assert_array_equal(data['source_g_area_KC_exc_V_ms'], [0., .2]) + self.assertEqual(account.callback_count, 24) + self.assertTrue(report['ledger_roundoff_bound_pass']) + + +if __name__ == '__main__': + unittest.main() diff --git a/models/male-cns-lif/kc-drive-audit-v1/test_populations.py b/models/male-cns-lif/kc-drive-audit-v1/test_populations.py new file mode 100644 index 0000000..77a2fb0 --- /dev/null +++ b/models/male-cns-lif/kc-drive-audit-v1/test_populations.py @@ -0,0 +1,105 @@ +"""Synthetic CPU-only classification tests; no scientific files are read.""" +import unittest + +import numpy as np +import pandas as pd + +from populations import N_NEURONS, GROUPS, classify + + +def synthetic_metadata(): + return pd.DataFrame({"neuron_idx": np.arange(N_NEURONS, dtype=np.int32), + "body_id": np.arange(1, N_NEURONS + 1, dtype=np.uint64), + "annotation_class": np.full(N_NEURONS, "ALLN", dtype=object), + "annotation_type": np.full(N_NEURONS, "unresolved_function", dtype=object), + "neurotransmitter": np.full(N_NEURONS, "acetylcholine", dtype=object), + "nt_ground_truth": np.full(N_NEURONS, None, dtype=object)}) + + +class PopulationTests(unittest.TestCase): + def test_exact_groups_are_disjoint_and_full(self): + meta = synthetic_metadata() + classes = ["Kenyon_Cell", None, "ALPN", "MBON", "DAN", None, "olfactory", None] + types = ["KCg", "APL", "VA6_adPN", "MBON01", "PAM15", None, "ORN", None] + meta.loc[:7, "annotation_class"] = classes + meta.loc[:7, "annotation_type"] = types + meta.loc[5, "neurotransmitter"] = " DOPAMINE " + before = meta.copy(deep=True) + labels, report = classify(meta) + self.assertEqual(labels[:8].tolist(), list(GROUPS)) + self.assertEqual(labels.dtype.kind, "U") + self.assertEqual(sum(report["groups"].values()), N_NEURONS) + self.assertTrue(report["all_neurons_assigned_once"]) + self.assertTrue(report["pass_all"]) + pd.testing.assert_frame_equal(meta, before) + + def test_dopamine_does_not_prove_DAN_and_DPM_is_other(self): + meta = synthetic_metadata() + meta.loc[:3, "annotation_class"] = [None, None, "DAN", "ALPN"] + meta.loc[:3, "annotation_type"] = ["DPM", "PAM-not-proof", "PAM15", "VA6_adPN"] + meta.loc[:3, "neurotransmitter"] = ["dopamine", "dopamine", "gaba", "dopamine"] + labels, report = classify(meta) + self.assertEqual(labels[:4].tolist(), ["other", "dopamine_annotation_only", "anatomical_DAN", "ALPN"]) + self.assertEqual(report["DPM_neuron_idx"], [0]) + self.assertEqual(report["flags"]["annotation_DAN_not_metadata_dopamine"], 1) + self.assertEqual(report["uncertain_DAN_prefix_neuron_idx"], [1]) + self.assertFalse(report["reward_or_valence_inferred"]) + + def test_DPM_and_exact_identity_conflicts_are_unclassified(self): + meta = synthetic_metadata() + meta.loc[:2, "annotation_class"] = ["DAN", "Kenyon_Cell", "MBON"] + meta.loc[:2, "annotation_type"] = ["DPM", "APL", "DPM"] + labels, report = classify(meta) + self.assertEqual(labels[:3].tolist(), ["unclassified"] * 3) + self.assertEqual(report["identity_conflict_neuron_idx"], [0, 1, 2]) + self.assertFalse(report["pass_all"]) + self.assertTrue(report["all_neurons_assigned_once"]) + + def test_prefix_and_missing_evidence_are_not_guessed(self): + meta = synthetic_metadata() + meta.loc[:6, "annotation_class"] = [None, "", "unknown", None, None, None, "olfactory"] + meta.loc[:6, "annotation_type"] = ["PAM15", "PPL1-01", "DAN-like", None, "APL", "DPM", "ALPN-like"] + labels, report = classify(meta) + self.assertEqual(labels[:7].tolist(), ["unclassified"] * 4 + ["APL", "other", "other"]) + self.assertEqual(report["uncertain_DAN_prefix_neuron_idx"], [0, 1, 2]) + self.assertEqual(report["groups"]["ALPN"], 0) + + def test_case_sensitive_anatomical_values_not_normalized_into_identity(self): + meta = synthetic_metadata() + meta.loc[:2, "annotation_class"] = ["dan", "ALPN ", "kenyon_cell"] + meta.loc[:2, "annotation_type"] = ["type", "type", "type"] + labels, _ = classify(meta) + self.assertEqual(labels[:3].tolist(), ["other"] * 3) + + def test_missing_schema_and_numeric_annotations_fail_closed(self): + meta = synthetic_metadata() + with self.assertRaises(ValueError): + classify(meta.drop(columns="nt_ground_truth")) + meta["annotation_type"] = meta["annotation_type"].astype(object) + meta.loc[0, "annotation_type"] = 123 + with self.assertRaises(ValueError): + classify(meta) + + def test_index_dtype_order_and_duplicate_body_fail_closed(self): + for failure in ("short", "reordered", "index_dtype", "duplicate_body", "body_dtype"): + meta = synthetic_metadata() + if failure == "short": meta = meta.iloc[:-1] + if failure == "reordered": meta = meta.iloc[::-1] + if failure == "index_dtype": meta["neuron_idx"] = meta.neuron_idx.astype(np.int64) + if failure == "duplicate_body": meta.loc[1, "body_id"] = meta.loc[0, "body_id"] + if failure == "body_dtype": meta["body_id"] = meta.body_id.astype(np.int64) + with self.subTest(failure=failure), self.assertRaises(ValueError): + classify(meta) + + def test_raw_ground_truth_is_only_an_evidence_flag(self): + meta = synthetic_metadata() + meta.loc[0, "nt_ground_truth"] = "dopamine" + labels, report = classify(meta) + self.assertEqual(labels[0], "other") + self.assertEqual(report["flags"]["has_raw_ground_truth"], 1) + self.assertEqual(report["groups"]["anatomical_DAN"], 0) + self.assertFalse(report["provenance"]["file_hash_verified_by_this_pure_function"]) + + +if __name__ == "__main__": + unittest.main() diff --git a/models/male-cns-lif/kc-drive-audit-v1/test_replay_core.py b/models/male-cns-lif/kc-drive-audit-v1/test_replay_core.py new file mode 100644 index 0000000..8de9925 --- /dev/null +++ b/models/male-cns-lif/kc-drive-audit-v1/test_replay_core.py @@ -0,0 +1,237 @@ +"""Synthetic CPU-only contract tests; no real trajectories, GPU, or file writes.""" +import sys +import unittest + +import numpy as np + +from replay_core import IncomingIndex, replay, state_step + + +def coefficients(**changes): + result = dict(gd=1., c0=0., cg=0., cv=1., threshold=1., reset=0., seed_volt=2.) + result.update(changes) + return result + + +def record(rows=(), key="tick"): + values = np.array(rows, np.int32).reshape(-1, 2) + return {key: values[:, 0], "neuron_idx": values[:, 1]} + + +def empty_index(count=1): + return IncomingIndex(np.empty(0, np.int32), np.empty(0, np.int32), + np.empty(0, np.int64), np.empty(0, np.float64), count) + + +def one_edge(weight=2.): + return IncomingIndex(np.array([10], np.int32), np.array([0], np.int32), + np.array([81], np.int64), np.array([weight], np.float64), 1) + + +def shadow(steps=25, *, initial_v=None, initial_g=None, coeff=None, requests=None, + events=None, incoming=None, h32=None, callback=None, targets=None): + targets = np.array([100], np.int32) if targets is None else targets + n = len(targets) + return replay(np.zeros(n, np.float64) if initial_v is None else initial_v, + np.zeros(n, np.float64) if initial_g is None else initial_g, + coefficients() if coeff is None else coeff, steps, targets, + record() if requests is None else requests, + record() if events is None else events, + empty_index(n) if incoming is None else incoming, + np.zeros((steps, n), np.float32) if h32 is None else h32, callback) + + +class StepTests(unittest.TestCase): + def test_strict_threshold_and_positive_zero_reset(self): + v = np.array([1., np.nextafter(1., np.inf)], np.float64) + result = state_step(v, np.array([3., -3.]), np.zeros(2, np.int32), np.zeros(2, bool), coefficients()) + np.testing.assert_array_equal(result["fired"], [False, True]) + np.testing.assert_array_equal(result["ref"], [0, 22]) + self.assertEqual(result["g"].view(np.uint64)[1], 0) + self.assertEqual(result["v_threshold"][1], v[1]) + + def test_voltage_uses_old_g_and_separate_expression_order(self): + c = coefficients(gd=.4, cg=.2, cv=.3, c0=.1, threshold=100., seed_volt=101.) + result = state_step(np.array([3.]), np.array([2.]), np.array([0], np.int32), np.array([False]), c) + expected = np.float64(c["c0"]) + ((np.float64(c["cg"]) * np.float64(2.)) + + (np.float64(c["cv"]) * np.float64(3.))) + self.assertEqual(result["v"][0].hex(), expected.hex()) + self.assertEqual(result["g"][0].hex(), (np.float64(.4) * np.float64(2.)).hex()) + self.assertNotEqual(result["v"][0], .1 + (.2 * .8 + .3 * 3.)) + + def test_negative_g_and_voltage_freeze_when_refractory(self): + result = state_step(np.array([-.052]), np.array([-.123]), np.array([22], np.int32), + np.array([True]), coefficients(gd=.5, cg=1., c0=.3)) + self.assertEqual(result["v"][0], -.052) + self.assertEqual(result["g"][0], -.123) + self.assertEqual(result["ref"][0], 21) + self.assertFalse(result["fired"][0]) + self.assertTrue(result["request_rejected"][0]) + + def test_ref_one_releases_before_request_and_reset(self): + result = state_step(np.array([-.052]), np.array([-.123]), np.array([1], np.int32), + np.array([True]), coefficients()) + self.assertTrue(result["available"][0]) + self.assertTrue(result["request_accepted"][0]) + self.assertEqual(result["v_threshold"][0], 2.) + self.assertTrue(result["fired"][0]) + self.assertEqual(result["ref"][0], 22) + self.assertEqual(result["g"][0], 0.) + + def test_state_inputs_unchanged(self): + v, g, ref, request = np.array([2.]), np.array([5.]), np.array([0], np.int32), np.array([False]) + inputs = [x.copy() for x in (v, g, ref, request)] + state_step(v, g, ref, request, coefficients()) + for actual, expected in zip((v, g, ref, request), inputs): + np.testing.assert_array_equal(actual, expected) + + +class ReplayTests(unittest.TestCase): + def test_logged_target_spikes_are_not_forced(self): + result = shadow(events=record([(0, 100), (22, 100)])) + self.assertEqual(len(result["step"]), 0) + np.testing.assert_array_equal(result["first_spike"], [-1]) + + def test_predicted_events_exist_without_logged_target_spikes(self): + result = shadow(requests=record([(0, 100), (1, 100), (21, 100), (22, 100), (23, 100)])) + np.testing.assert_array_equal(result["step"], [0, 22]) + np.testing.assert_array_equal(result["neuron_idx"], [100, 100]) + np.testing.assert_array_equal(result["counts"], [2]) + np.testing.assert_array_equal(result["first_spike"], [0]) + + def test_anatomical_arrival_is_tick18_then_voltage_tick19(self): + history = [] + shadow(steps=20, coeff=coefficients(cg=1., threshold=100., seed_volt=101.), + events=record([(0, 10)]), incoming=one_edge(), callback=lambda t, s: history.append(s)) + self.assertEqual(history[17]["g"][0], 0.) + self.assertEqual(history[18]["g"][0], 2.) + self.assertEqual(history[18]["v"][0], 0.) + self.assertEqual(history[19]["v"][0], 2.) + np.testing.assert_array_equal(history[18]["due_edge_id"], [81]) + + def test_arrival_rejected_on_same_tick_target_fire(self): + history = [] + result = shadow(steps=19, events=record([(0, 10)]), incoming=one_edge(), + requests=record([(18, 100)]), callback=lambda t, s: history.append(s)) + np.testing.assert_array_equal(result["step"], [18]) + self.assertFalse(history[18]["arrival_accepted"][0]) + self.assertEqual(history[18]["g_pre_apl"][0], 0.) + + def test_refractory_rejects18_and_release22_accepts(self): + history = [] + shadow(steps=23, events=record([(0, 10), (4, 10)]), incoming=one_edge(), + requests=record([(0, 100)]), callback=lambda t, s: history.append(s)) + self.assertFalse(history[18]["arrival_accepted"][0]) + self.assertTrue(history[22]["arrival_accepted"][0]) + self.assertEqual(history[22]["ref"][0], 0) + self.assertEqual(history[22]["g_pre_apl"][0], 2.) + + def test_apl_unconditional_during_freeze_and_release_decay(self): + history = [] + h = np.ones((23, 1), np.float32) + shadow(steps=23, coeff=coefficients(gd=.5), requests=record([(0, 100)]), h32=h, + callback=lambda t, s: history.append(s)) + self.assertEqual(history[0]["g_after_reset"][0], 0.) + self.assertEqual(history[0]["g"][0], -.001) + for tick in range(1, 22): + self.assertEqual(history[tick]["g_decayed"][0].hex(), history[tick]["old_g"][0].hex()) + self.assertEqual(history[tick]["g"][0].hex(), (history[tick]["old_g"][0] - np.float64(.001)).hex()) + self.assertEqual(history[22]["g_decayed"][0].hex(), (.5 * history[22]["old_g"][0]).hex()) + + def test_si_promotion_order_is_not_early_float32_scaling(self): + h = np.array([[.01]], np.float32) + result = shadow(steps=1, h32=h) + expected = -(np.float64(h[0, 0]) * np.float64(.001)) + wrong = -np.float64(h[0, 0] * np.float32(.001)) + self.assertEqual(result["g"][0].hex(), expected.hex()) + self.assertNotEqual(result["g"][0].hex(), wrong.hex()) + + def test_arrivals_start_at_existing_g_in_original_edge_order(self): + incoming = IncomingIndex(np.array([10, 11], np.int32), np.array([0, 0], np.int32), + np.array([2, 81], np.int64), np.array([-1e16, 1.], np.float64), 1) + result = shadow(steps=19, initial_g=np.array([1e16]), incoming=incoming, events=record([(0, 10), (0, 11)])) + self.assertEqual(result["g"][0], 1.) + self.assertNotEqual(result["g"][0], np.float64(1e16) + np.sum(incoming.weights)) + + def test_zero_weight_edges_still_present_in_due_trace(self): + history = [] + shadow(steps=19, events=record([(0, 10)]), incoming=one_edge(0.), callback=lambda t, s: history.append(s)) + np.testing.assert_array_equal(history[18]["due_edge_id"], [81]) + np.testing.assert_array_equal(history[18]["arrival_accepted"], [True]) + + def test_non_target_requests_ignored_and_step_alias_supported(self): + result = shadow(requests=record([(0, 10), (0, 100)], key="step"), events=record(key="step")) + np.testing.assert_array_equal(result["step"], [0]) + np.testing.assert_array_equal(result["neuron_idx"], [100]) + + def test_callback_mutation_cannot_change_replay(self): + args = dict(steps=23, requests=record([(0, 100)]), h32=np.ones((23, 1), np.float32)) + expected = shadow(**args) + + def mutate(tick, stage): + for value in stage.values(): + value[...] = 0 + + actual = shadow(**args, callback=mutate) + for name in expected: + np.testing.assert_array_equal(actual[name].view(np.uint8), expected[name].view(np.uint8)) + + def test_callback_can_fail_fast_without_later_ticks(self): + visited = [] + + def stop(tick, stage): + visited.append(tick) + if tick == 2: + raise RuntimeError("intentional mismatch") + + with self.assertRaisesRegex(RuntimeError, "intentional mismatch"): + shadow(callback=stop) + self.assertEqual(visited, [0, 1, 2]) + + def test_no_dense_history_and_exact_output_dtypes(self): + result = shadow() + self.assertFalse(any(value.ndim > 1 for value in result.values())) + for name in ("v", "g"): + self.assertEqual(result[name].dtype, np.float64) + for name in ("ref", "counts", "step", "neuron_idx", "first_spike", "target_globals"): + self.assertEqual(result[name].dtype, np.int32) + + +class ValidationTests(unittest.TestCase): + def test_index_copies_inputs_and_reuses_source_lookup(self): + src, dst, ids, w = np.array([3, 3, 8], np.int32), np.array([1, 0, 1], np.int32), np.array([8, 20, 99], np.int64), np.array([1., 0., -2.]) + index = IncomingIndex(src, dst, ids, w, 2) + w[:] = 99 + np.testing.assert_array_equal(index.weights, [1., 0., -2.]) + np.testing.assert_array_equal(index.due(np.array([3, 7, 8], np.int32)), [0, 1, 2]) + self.assertFalse(index.weights.flags.writeable) + + def test_rejects_bad_index_dtype_range_order_and_nonfinite(self): + base = dict(src_global=np.array([10, 11], np.int32), dst_local=np.array([0, 0], np.int32), + edge_id=np.array([3, 8], np.int64), weights=np.array([1., 2.]), target_count=1) + changes = [dict(src_global=np.array([10, 11], np.int64)), dict(src_global=np.array([11, 10], np.int32)), + dict(src_global=np.array([10, 166700], np.int32)), dict(dst_local=np.array([0, 1], np.int32)), + dict(edge_id=np.array([3, 3], np.int64)), dict(weights=np.array([1., np.nan])), + dict(weights=np.array([1., 2.], np.float32))] + for change in changes: + with self.subTest(change=change), self.assertRaises(ValueError): + IncomingIndex(**(base | change)) + + def test_rejects_invalid_replay_arrays(self): + variants = [dict(initial_v=np.array([0.], np.float32)), dict(initial_g=np.array([np.inf])), + dict(targets=np.array([100], np.int64)), dict(targets=np.array([100, 100], np.int32)), + dict(h32=np.zeros((25, 1), np.float64)), dict(h32=np.full((25, 1), np.nan, np.float32)), + dict(h32=np.full((25, 1), -1, np.float32)), dict(coeff=coefficients(gd=np.float32(.9))), + dict(requests=record([(0, 100), (0, 100)])), dict(events=record([(25, 10)])), + dict(events=record([(0, 166700)])), dict(steps=0)] + 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existing records reconstructed; 8 fixture pre/post histories and 72 CAL event/final states exact. +12 repeated CAL records produced identical accounting. No new brain simulation, GPU use, tuning or learning. +Full prior frozen integrity passed. Five reference simulations were not rerun; the sealed prior PASS is referenced by SHA. +Previous CAL failure remains unchanged. Conditional replay is not an APL-removal counterfactual. + +|Mode|Mean active KC fraction|Requests|Accepted requests|ALPN accepted excitatory increment sum (V)|APL write sum (V)| +|---|---:|---:|---:|---:|---:| +|physiological|0.01271325|3203|2195|46379.7565|158735.25| +|matched_energy|0.01233596|2936|2038|45928.6416|156859.684| + +Increment sums span 30 trials × 4064 KC × time; not instantaneous conductance or causal percentages. +See audit/final-status.json, audit/calibration-drive-summary.csv and trials/*/per-kc.parquet for detailed evidence. diff --git a/registry/phase4ah-kc-drive-audit/evidence/audit/calibration-drive-summary.csv b/registry/phase4ah-kc-drive-audit/evidence/audit/calibration-drive-summary.csv new file mode 100644 index 0000000..4ede732 --- /dev/null +++ b/registry/phase4ah-kc-drive-audit/evidence/audit/calibration-drive-summary.csv @@ -0,0 +1,61 @@ 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"64cc5a962c272af31551c9e35b5327d4840c4a76ecd690cfcb7d4a2da7a62de9" + } + ], + "source_script_sha256": "1e336c348a05cfab86d02e2934c197abf9a795168955b5759b08e8fdba82a364", + "sealed_model_manifest_sha256": "fa89cb0a6e2dc77462057b672ffbcfabbe74b893d8f70e4b41d8b5202de15530", + "sealed_experiment_manifest_sha256": "fb30ca09068d5d8dc1dc9972e00f674c5dc3aa49c7117d630607b5d9067927db" +} diff --git a/registry/phase4ah-kc-drive-audit/interpretation-metrics.sha256 b/registry/phase4ah-kc-drive-audit/interpretation-metrics.sha256 new file mode 100644 index 0000000..2684c78 --- /dev/null +++ b/registry/phase4ah-kc-drive-audit/interpretation-metrics.sha256 @@ -0,0 +1 @@ +0d124becbe9fc2e4239d89d378356c39ac638a44bfef6effcbb9b779225d5604 interpretation-metrics.json diff --git a/registry/phase4ah-kc-drive-audit/seal-verification.json b/registry/phase4ah-kc-drive-audit/seal-verification.json new file mode 100644 index 0000000..c2ddb9b --- /dev/null +++ b/registry/phase4ah-kc-drive-audit/seal-verification.json @@ -0,0 +1,16 @@ +{ + "models/male-cns-lif/kc-drive-audit-v1": { + "pass": true, + "checked": 11, + "mismatches": [], + "manifest_sha256": "fa89cb0a6e2dc77462057b672ffbcfabbe74b893d8f70e4b41d8b5202de15530", + "checksums_sha256": "ed082e56978ac841c1fd92810eb611b63d6c2238d0406c92c281bb5023bc9f70" + }, + "experiments/phase4ah-kc-drive-audit": { + "pass": true, + "checked": 415, + "mismatches": [], + "manifest_sha256": "fb30ca09068d5d8dc1dc9972e00f674c5dc3aa49c7117d630607b5d9067927db", + "checksums_sha256": "567a3eb5758763c86146235835984a352dd860bf69489d1cf2b048d93fba05c9" + } +} diff --git a/tools/publish_phase4ah.py b/tools/publish_phase4ah.py new file mode 100644 index 0000000..4069331 --- /dev/null +++ b/tools/publish_phase4ah.py @@ -0,0 +1,42 @@ +"""Publish only small sealed conditional-audit evidence, not bulk backup.""" +import json +from pathlib import Path +import shutil +import sys + +ROOT = Path('/data/digital-drosophila') +MODEL = ROOT / 'models/male-cns-lif/kc-drive-audit-v1' +EXP = ROOT / 'experiments/phase4ah-kc-drive-audit' +sys.path.insert(0, str(MODEL)) +from context import check, digest, save +sys.path.insert(0, str(ROOT / 'models/male-cns-lif/terminal-scope-diagnostic-v1')) +from diagnostic import manifest_check + + +def main(): + check() + verification = {str(p.relative_to(ROOT)): manifest_check(p, digest) for p in (MODEL, EXP)} + assert all(v['pass'] for v in verification.values()) + registry = ROOT / 'registry/phase4ah-kc-drive-audit' + registry.mkdir(exist_ok=False) + records = [] + manifest = json.loads((EXP / 'manifest.json').read_text()) + for p in [EXP / r['path'] for r in manifest['files']] + [EXP / 'manifest.json', EXP / 'checksums.sha256']: + if p.stat().st_size > 4 * 1024**2 or p.suffix not in ('.json', '.csv', '.md', '.sha256'): + continue + target = registry / 'evidence' / p.relative_to(EXP) + target.parent.mkdir(parents=True, exist_ok=True) + with p.open('rb') as src, target.open('xb') as dst: + shutil.copyfileobj(src, dst) + assert digest(target) == digest(p) + records.append(dict(source=str(p.relative_to(ROOT)), copy=str(target.relative_to(registry)), + bytes=p.stat().st_size, sha256=digest(p))) + save(registry / 'evidence-index.json', records) + save(registry / 'seal-verification.json', verification) + with (EXP / 'REPORT.md').open('rb') as src, (ROOT / 'docs/progress/phase4ah-kc-drive-audit.md').open('xb') as dst: + shutil.copyfileobj(src, dst) + print(json.dumps(dict(copied=len(records), verification=verification)), flush=True) + + +if __name__ == '__main__': + main() diff --git a/tools/report_phase4ah.py b/tools/report_phase4ah.py new file mode 100644 index 0000000..c80805e --- /dev/null +++ b/tools/report_phase4ah.py @@ -0,0 +1,100 @@ +"""Descriptive post-audit metrics from sealed outputs, without new trajectories.""" +from pathlib import Path +import json +import sys +import numpy as np +import pandas as pd + +ROOT = Path('/data/digital-drosophila') +EXP = ROOT / 'experiments/phase4ah-kc-drive-audit' +MODEL = ROOT / 'models/male-cns-lif/kc-drive-audit-v1' +sys.path.insert(0, str(MODEL)) +from context import read, save, digest +from populations import GROUPS + + +def ratio(a, b): + return None if b == 0 else float(a / b) + + +def main(): + final = read(EXP / 'audit/final-status.json') + assert final['status'] == 'PASS-CONDITIONAL-KC-DRIVE-AUDIT' + manifest = {r['path']: r for r in read(EXP / 'manifest.json')['files']} + grouped = {} + inputs = [] + for item in read(EXP / 'protocol/inventory.json'): + if item['kind'] != 'calibration' or item['repeat'] != 0: + continue + name = item['case'] + '-r0' + folder = EXP / 'trials' / name + for filename in ('summary.json', 'per-kc.parquet', 'per-pn.parquet'): + p = folder / filename + rel = str(p.relative_to(EXP)) + assert digest(p) == manifest[rel]['sha256'] + inputs.append(dict(path=rel, sha256=manifest[rel]['sha256'])) + summary = read(folder / 'summary.json') + kc = pd.read_parquet(folder / 'per-kc.parquet') + pn = pd.read_parquet(folder / 'per-pn.parquet') + grouped.setdefault(summary['mode'], []).append((summary, kc, pn)) + output = {} + for mode, rows in grouped.items(): + assert len(rows) == 30 + allkc = pd.concat([x[1] for x in rows], ignore_index=True) + assert len(allkc) == 30 * 4064 + strata = {} + for label, data in (('all', allkc), ('ever_active', allkc[allkc.spike_count > 0]), + ('never_active', allkc[allkc.spike_count == 0])): + exc = data[[g + '_accepted_exc_SI' for g in GROUPS]].sum(axis=1) + inh = data[[g + '_accepted_inh_abs_SI' for g in GROUPS]].sum(axis=1) + pnexc = data.ALPN_accepted_exc_SI + margin = data.best_threshold_margin_mV + strata[label] = dict(neuron_trials=len(data), + with_ALPN_exc_neuron_trials=int((pnexc > 0).sum()), + with_any_exc_neuron_trials=int((exc > 0).sum()), + with_ALPN_exc_fraction=ratio(int((pnexc > 0).sum()), len(data)), + with_any_exc_fraction=ratio(int((exc > 0).sum()), len(data)), + accepted_exc_increment_sum_V=float(exc.sum()), + accepted_inh_increment_abs_sum_V=float(inh.sum()), + ALPN_exc_increment_sum_V=float(pnexc.sum()), + APL_write_increment_sum_V=float(data.APL_write_SI.sum()), + APL_write_ref_sum_V=float(data.APL_write_ref_SI.sum()), + APL_pre_first_spike_increment_sum_V=float(data.APL_pre_first_spike_SI.sum()), + APL_write_to_exc_increment_ratio=ratio(data.APL_write_SI.sum(), exc.sum()), + refractory_target_ticks=int(data.refractory_ticks.sum()), + negative_g_target_tick_fraction=ratio(data.negative_g_ticks.sum(), len(data) * 1000), + best_threshold_margin_mV_quantiles={str(q): float(margin.quantile(q)) for q in (0, .25, .5, .75, 1)}, + source_g_area_V_ms={g: float(data['source_g_area_' + g + '_V_ms'].sum()) + for g in [n + '_' + sign for n in GROUPS for sign in ('exc', 'inh')] + ['graded_APL']}, + source_area_max_abs_residual_V_ms=float(data.source_g_area_residual_V_ms.abs().max())) + accepted = sum(x[0]['input']['accepted'] for x in rows) + requested = sum(x[0]['input']['requests'] for x in rows) + source = final['modes'][mode]['source_groups'] + source_exc = {g: source[g]['accepted_exc_SI'] for g in GROUPS} + output[mode] = dict(trials=30, input_requests=requested, input_accepted=accepted, + input_acceptance_fraction=ratio(accepted, requested), + PN_actual_spikes=sum(x[0]['input']['actual_spikes_by_source_group']['ALPN'] for x in rows), + PN_design_targets_distinct=sorted({int(i) for _, _, pn in rows for i in pn.loc[pn.design_target, 'neuron_idx']}), + PN_ever_active_distinct=sorted({int(i) for _, _, pn in rows for i in pn.loc[pn.actual_spikes > 0, 'neuron_idx']}), + source_accepted_exc_increment_sum_V=source_exc, + ALPN_fraction_of_accepted_exc_increment=ratio(source_exc['ALPN'], sum(source_exc.values())), + ALPN_due_edges=source['ALPN']['due_edges'], ALPN_accepted_edges=source['ALPN']['accepted_edges'], + ALPN_rejected_edges=source['ALPN']['rejected_edges'], + ALPN_rejection_fraction=ratio(source['ALPN']['rejected_edges'], source['ALPN']['due_edges']), + strata=strata) + result = dict(scope='Post-outcome descriptive conditional audit, no causal intervention, no learning', modes=output, + target_tick_and_neuron_trial_counts_not_unique_cells=True, + ratios_are_cumulative_increment_bookkeeping_not_spike_causality=True, + inputs=inputs, source_script_sha256=digest(Path(__file__)), + sealed_model_manifest_sha256=digest(MODEL / 'manifest.json'), + sealed_experiment_manifest_sha256=digest(EXP / 'manifest.json')) + destination = ROOT / 'registry/phase4ah-kc-drive-audit/interpretation-metrics.json' + save(destination, result) + with destination.with_suffix('.sha256').open('x') as f: + f.write(digest(destination) + ' ' + destination.name + '\n') + print(json.dumps({mode: {k: v for k, v in data.items() if k not in ('PN_design_targets_distinct', 'PN_ever_active_distinct')} + for mode, data in output.items()}, indent=2), flush=True) + + +if __name__ == '__main__': + main()