From b2cd81c7b9d1966af328d70f5cf0a160ae8adb80 Mon Sep 17 00:00:00 2001 From: Digital Drosophila Archive Date: Wed, 30 Sep 2026 10:45:48 +0800 Subject: [PATCH] Audit conditional PN recruitment and graded APL source timing --- CURRENT.md | 18 +- docs/progress/phase4ai-interpretation.md | 89 + docs/progress/phase4ai-next-hypotheses.md | 36 + .../pn-recruitment-audit-v1/README.md | 23 + .../pn-recruitment-audit-v1/apl_sources.py | 329 + .../pn-recruitment-audit-v1/checksums.sha256 | 17 + .../pn-recruitment-audit-v1/context.py | 86 + .../pn-recruitment-audit-v1/finalize.py | 258 + .../pn-recruitment-audit-v1/manifest.json | 85 + .../pn-recruitment-audit-v1/pn_observer.py | 315 + .../pn-recruitment-audit-v1/prepare.py | 93 + .../pn-recruitment-audit-v1/publication.py | 270 + .../pn-recruitment-audit-v1/run_audit.py | 283 + .../structure_metrics.py | 179 + .../test_apl_sources.py | 242 + .../pn-recruitment-audit-v1/test_harness.py | 36 + .../test_pn_observer.py | 152 + .../test_publication.py 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registry/phase4ai-pn-recruitment-audit/model/manifest.json create mode 100644 registry/phase4ai-pn-recruitment-audit/publication-receipt.json create mode 100644 registry/phase4ai-pn-recruitment-audit/seal-verification.json create mode 100644 tools/report_phase4ai.py diff --git a/CURRENT.md b/CURRENT.md index 8a5b0c0..7128b3e 100644 --- a/CURRENT.md +++ b/CURRENT.md @@ -4,22 +4,22 @@ ## 最新结果 -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。 +Phase 4AI：**PASS-CONDITIONAL-PN-APL-SOURCE-AUDIT**，已封存。74 项测试、80 份既有记录的 PN 事件/计数/最终状态重建、8 份小型逐步历史及 12 对重复检查全部通过。无新全脑实验、GPU、调参或训练。 -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。 +60/60 CAL 中，非设计目标 PN 的首次活动早于首个 KC。已激活非设计 PN 的首发前正增量约 66% 来自 ALLN、28% 来自 ALPN；这是正增量而非净去极化或唯一因果。考虑负项后，两类来源的净时间过滤 V 贡献排名不同。 -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。 +非设计 PN 贡献了约 74% 的整窗 graded-APL 累计 h；在全网首个 KC 前约为 62%–63%。这些是冻结工程算子的条件来源分账，不是已证实的失败原因。 -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。 +PN 活动具有很强共同背景，但仍有小的气味差异，且指标不一致；matched-energy 的 Euclidean between 未超过 within。不能把高相似性直接解释为零信息。 -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。 +原 Phase 4AG **FAIL-FIXED-CAL-REPRESENTATION** 不变；没有学习候选，不能进入条件化、游戏/RL/decoder/Web。原 230239 项及后续冻结目录校验全部通过。旧五组参考本轮未重跑，上一轮封存 PASS 记录未变。 ## 下一步 -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。 +先用既有记录核对最早非设计 PN 群、首个 KC 前窗口及 PN→ALLN 的真实先行边，补足当前尚未证明的动态闭环。再预声明独立 APL 时间算子对照：原 delay=0 与固定 18 步平移；先检验固定序列与末端记录完整性，不扫描最佳值，不调 gain、threshold、eta。18 步是工程对齐，不是文献确定的 MaleCNS 生理延迟。保留正负来源及 never-active 对照，不能把最大正增量来源直接当成最大净因果作用。 -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。 +旧 raw/CSR/W_syn/sign/APL 参数/eligibility-v2/14232-edge mask 与全部冻结产物保持不变。尚未启动上述后续实验；所有新模型假设需独立目录、协议和回归门，不能覆盖本轮结果。 -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。 +本轮完整新审计数据约 1.10 GB 在服务器；Git 发布小报告、代码和校验，不是全量异机备份。 -[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) +[最新解释](docs/progress/phase4ai-interpretation.md) · [正式封存状态](registry/phase4ai-pn-recruitment-audit/evidence/audit/final-status.json) · [下一步假设](docs/progress/phase4ai-next-hypotheses.md) · [封存校验](registry/phase4ai-pn-recruitment-audit/seal-verification.json) diff --git a/docs/progress/phase4ai-interpretation.md b/docs/progress/phase4ai-interpretation.md new file mode 100644 index 0000000..165b057 --- /dev/null +++ b/docs/progress/phase4ai-interpretation.md @@ -0,0 +1,89 @@ +# Phase 4AI：PN 再招募与 APL 来源审计 + +正式状态：`PASS-CONDITIONAL-PN-APL-SOURCE-AUDIT`。这是审计通过，不是表征或学习通过。 + +## 核验范围 + +74 项合成测试通过；固定 8 个小场景、60 个 CAL 主记录、12 个技术重复，共 80 份既有 Phase 4AG-r2 记录。PN 的完整放电事件、计数、最终 V/g/ref 逐位一致；8 个小场景的每步 pre/post 状态一致；12 对统计重复一致。CAL 没有独立保存逐 PN 的每步状态，不能把最终状态/事件验证称为完整逐步历史验证。 + +额外独立复核了每个首发前真实边事件的 source spike、18-step delay、真实 canonical edge、权重位值、到达时刻、接受状态、严格首发前条件。跨批唯一性与从原始 source events 重新推导的逐边期望数量均一致，无漏项或重复。 + +没有运行新全脑实验、使用 GPU、改参数、改权重、训练或访问 held-out 动态结果。全程只对既有记录做条件重建与非反馈分账；这不是独立全脑 oracle，也不是删掉某条通路的反事实。 + +## 1. 非设计 PN 的活动先于 KC + +“非设计”仅表示该 trial 静态刺激目标集合以外，不等于噪声、错误神经元或污染。 + +|指标|physiological|matched_energy| +|---|---:|---:| +|非设计 PN 首次活动早于首个 KC 的主记录|30/30|30/30| +|首个非设计 PN tick 范围|40–223|45–216| +|首个 KC tick 范围|111–322|126–294| +|首次活动前已有非设计 PN 活动的 active KC neuron-trials|1550/1550|1504/1504| +|首次活动前已有真实非设计 PN→KC 到达边事件|1539/1550|1496/1504| +|非设计 PN 放电 / 全部 PN 放电|138679/154514|137431/153049| + +tick 是保存记录的整数时间索引，dt=0.1 ms；这里给出的是记录内时间，不是相对每个外部刺激 onset 的延迟。上表多 trial 合计不能当作不同神经元数量。 + +## 2. 首发前 PN 输入来自哪里 + +限定对象为已经放电的非设计 PN：14967 / 14939 neuron-trials，两种模式各涉及 547 个不同 PN。 + +|首发前接受的正权重增量来源|physiological|matched_energy| +|---|---:|---:| +|ALLN|65.7040%|65.7683%| +|ALPN|27.7791%|27.8722%| +|其他已注释神经元|5.2174%|5.0328%| +|KC|0.00124%|0.00105%| + +ALLN 是 metadata 中精确的 `annotation_class == ALLN`，不是 ALIN；没有根据类型前缀补齐身份。来源表同时保留负权重与零权重。 + +这些占比是首发前**名义正增量的累计份额**，不是净电流、净去极化或因果贡献。非反馈 V 账本中，ALLN 在首发阈值检查前的平均正/负项约为 +8.93/−5.85 mV（净约 +3.08 mV），而 ALPN 约为 +4.43/−0.11 mV（净约 +4.32 mV）。不能因 ALLN 正增量占比最大就声称其净去极化最大。 + +这个统计窗口截至每个 PN 自身首发，不是全部截至全网首个 KC，不能拿 66% 直接解释最早一批 PN 的启动。最早非设计 PN 先于首个 KC，排除了此前 KC spikes 启动这一最早群体的解释；之后的再招募与反馈仍可存在。当前只直接覆盖 ALLN→PN 来边，不是 PN→ALLN→PN 动态闭环、必要性或唯一因果路径证明；后续必须保留负项、时间过滤、其他通路和从未激活 PN 对照。 + +## 3. graded APL 的来源 + +冻结 APL 输入矩阵是非负 raw-contact 归一化工程映射，不是实际 signed PN→APL LIF 电流，不能称为测得的 dopamine/电导或生理量。 + +|non-design ALPN 来源份额|physiological|matched_energy| +|---|---:|---:| +|整个记录累计 h 写入|73.9038%|73.9797%| +|每个 active KC 首发严格之前的 h 合计|66.5249%|66.7294%| +|全网首个 KC 严格之前、全部 KC 的 h 合计|62.8646%|62.4176%| + +全网首个 KC 之前的 KC 来源为 0，其余来自 design ALPN。在零初态、由 KC spikes 驱动的算子下，这是结构性结果，不能当成独立证明“KC 反馈无作用”。整窗 KC 来源仍占约 4.72% / 4.64%，每只 active KC 自身首发前占约 7.53% / 7.32%。这些均不证明 PN/APL 是 KC 稀疏度失败的原因。 + +三组 float64 合计与保存的 float32 drive/state/h 在预先推导的逐 tick 数值误差界内；这是数学来源分账，不是 bitwise CUDA APL replay。累计份额仍是描述统计，未构造累计来源份额的误差区间，不宣称“主导份额经严格误差界证明”。 + +## 4. 共同活动很强，但不能据此宣布气味信息为零 + +主比较固定全部 686 个 ALPN 维度；次比较固定六种 CAL 刺激目标并集 76 个 PN 之外的共同 610 维。每模式固定 60 within-seed-pair 与 75 between-odor/same-seed 比较；技术重复不充当新 seed。 + +|固定 686 维指标：within / between|physiological|matched_energy| +|---|---:|---:| +|Cosine distance|0.002812 / 0.003146|0.002936 / 0.003291| +|Pearson correlation|0.993240 / 0.992438|0.992964 / 0.992107| +|Active-set Jaccard|0.982743 / 0.980222|0.982590 / 0.979699| +|Euclidean distance|22.7359 / 26.6852|25.3697 / 25.2796| + +共同活动与小的气味差异可以并存。尤其 matched-energy 的 Euclidean between 不高于 within；不同指标并不一致。这不是无信息的统计证明，也不是学习可用性、held-out 泛化、分类器或显著性检验。 + +## 结论与下一步 + +原 Phase 4AG `FAIL-FIXED-CAL-REPRESENTATION` 完全不变，未产生学习候选。本轮减少了机制不确定性，没有把“出现差异”当作成功。 + +下一步先用固定 60 CAL 的已有记录，核对最早非设计 PN 群及首个 KC 前窗口，分开 design/non-design ALPN 与 ALLN 的正负输入，并查 PN→ALLN 方向的真实带时延先行边。不能挑出现所需结论的 cohort，也不能把 shortest path 当动态路径。 + +再按独立预声明协议检验一个时间算子假设：原 graded APL 当步末写入，与固定平移 18 步的工程对照。不能称为实测生理修正，不能调 gain、threshold 或 eta，不能扫描最佳 delay。先验证固定序列的按位平移与末端样本完整性，再决定是否进入闭环；详细边界见 [下一步假设](phase4ai-next-hypotheses.md)。 + +## 完整性和保存位置 + +- 原始登记 230239 个文件及后续 Phase 4AA–4AH 各冻结目录 checksum 全通过。旧五组 reference 本轮未重跑；上一轮封存 PASS 记录按 SHA 校验未变。 +- 新模型目录：`models/male-cns-lif/pn-recruitment-audit-v1/`，16 个 payload 文件，170860 bytes。 +- 新实验目录：`experiments/phase4ai-pn-recruitment-audit/`，734 个 payload 文件，1101594284 bytes，约 1.10 GB。纯 CPU 审计时间合计 726.84 秒，不是脑实时性能 benchmark。 +- 模型 manifest SHA-256：`a53443ac124e9bebc813867a5015d77276c2bd3edf89cd606a57d54756fe0dd7`。 +- 实验 manifest SHA-256：`12c6f2e9929b3f7084e8b39b90c8851185febbc0afbb7031f9eebb4c41d2731d`。 +- Git 仅发布代码、257 份小证据（约 5.85 MB）、文档与校验；完整新事件数据仍在服务器，不是异机全量备份。 + +解释指标由 `tools/report_phase4ai.py` 从已封存表只读导出，保存于 `registry/phase4ai-pn-recruitment-audit/interpretation-metrics.json` 并附独立 SHA；不修改原封存 manifest 或科学 gate。 diff --git a/docs/progress/phase4ai-next-hypotheses.md b/docs/progress/phase4ai-next-hypotheses.md new file mode 100644 index 0000000..cc421e0 --- /dev/null +++ b/docs/progress/phase4ai-next-hypotheses.md @@ -0,0 +1,36 @@ +# Phase 4AI 后的最小时间算子假设（尚未执行） + +这是后续独立分支的设计建议，不是已经注册或完成的实验，也不是生理正确性结论。Phase 1–4AI 的代码、数据、参数与结论不因此改变；学习门仍关闭。 + +先补一项窄范围只读时序检查：在固定 60 CAL 中保留同时首发的全部最早非设计 PN，窗口截至首个 KC，分开 design/non-design PN 与 ALLN 的正负来源；核实 PN→ALLN 方向的真实先行边，必要时仅条件重建 ALLN 的接受状态。Phase 4AI 已验证 ALLN→PN 输入，但未证明动态 PN→ALLN→PN 闭环。此检查不运行新全脑实验，也不用于选择“最佳延迟”。 + +## 当前可以由代码确认的时间契约 + +现有 graded APL 组件在 PN/KC 于 tick `s` 放电后，当步更新其状态并在 `s` 末写入 KC 的 `g`。这次写入最早影响 `s+1` 的 threshold。真实直接 PN→KC 边在 `s+18` 的 threshold 之后到达，最早影响 `s+19` 的 threshold。 + +这是两个算子的调度差异，不足以证明该差异就是失败原因。APL 原参数的单位是每状态每步增量，不是“漏乘 dt”；不能把原数值直接重新解释为 tonic 电位、也不能根据累计抑制/兴奋比反推 gain。 + +## 唯一建议的最小对照 + +在新的独立目录和预声明协议中，只比较原 `delay=0` 与固定 `delay=18 steps`： + +`h_apply[t] = 0 (t<18), otherwise h_generated[t-18]`。 + +其余全部保持原样：APL 状态递推、float32 位值、I/O 映射与归一化、gain、fast-APL mask、LIF 参数、connectome、sign、外部请求文件。不扫描“最佳延迟”，不选效果好的 seed，不训练。18 步来自现有解剖事件队列的工程对齐，不是测得的 MaleCNS APL 生理延迟。 + +注意：整条 `h` 延迟也会延迟 KC→APL 反馈，不是 PN-only 干预。解剖边到达仍受 refractory 保护，APL 写入仍不受该保护；时间对齐不等于两种作用完全等价。 + +## 两层验证不可混淆 + +1. 固定现有上游 spike/h 序列：按位平移 h，前补零并显式保留记录末端再延迟的 18 步。验证记录内每一个 h 样本无损、无漏、不循环移位，保持完整已生成序列的累计抑制量；不得把它称为未知未来的完整衰减尾部。 +2. 如继续全脑闭环：只能声明同一请求与同一算子参数。放电变化会改变后续 APL drive/state/h，不能声称两条件实际抑制总量仍相同，更不能事后重新归一化。报告末端状态和未排空事件，保留原固定 CAL 与后续学习门。 + +先完成零输入、单 PN 脉冲、有限脉冲列、末端脉冲及 reset/refractory 碰撞的 CPU/GPU oracle 检查。`delay=0` 必须重现原记录；任何差异均先停，不进入 CAL。若唯一 delay 改动后完整 CAL 仍失败，则“单独消除这项调度领先足以修复 CAL”的假设不成立；即使通过，也不等于生理正确、泛化通过或学习成功。 + +## 一手文献边界 + +- [Shiu et al., Nature 2024](https://www.nature.com/articles/s41586-024-07763-9) 使用固定 1.8 ms 的突触延迟，并讨论统一 spiking LIF 对 non-spiking 生理过程的限制。这不等于解剖上删除 APL，不能写成 MaleCNS/APL 实测值。 +- [Amin et al., eLife 2020](https://elifesciences.org/articles/56954.pdf) 支持 APL 非脉冲活动及局部 KC 抑制；约 5 Hz 成像不足以解析其推测的毫秒级传播，不能据此指定精确 MaleCNS 延迟。 +- [Prisco et al., eLife 2021](https://pmc.ncbi.nlm.nih.gov/articles/PMC8741211/) 支持 calyx 中 PN/APL/KC 的局部关系和响应归一化，不给出这些 MaleCNS 路径的毫秒延迟。 + +因此 0、1.8、3.6 ms 均不得宣称是由这些文献确定的 MaleCNS APL 生理参数。此建议只用于可证伪、单因素、可回归的工程动力学分支。 diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/README.md b/models/male-cns-lif/pn-recruitment-audit-v1/README.md new file mode 100644 index 0000000..d09c349 --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/README.md @@ -0,0 +1,23 @@ +# Phase 4AI — PN recruitment and graded-APL source audit + +Read-only, post-outcome conditional accounting of 80 already sealed Phase 4AG-r2 records. This is not a new whole-brain experiment, a learning trial, an independent brain oracle, or an APL-removal counterfactual. + +## Frozen contracts + +- Reuse sealed Phase 4AH `replay_core.py` without modification; reconstruct actual ALPN targets (686 in the full graph). +- Keep the full frozen fast-APL effective-edge zeroing, delay, refractory, reset, threshold, ordering and float64 LIF coefficients. PN graded inhibition is exactly zero because the existing overlay applied only to KC. +- Separate accepted timed incoming-edge antecedents, non-feedback voltage lineage, and the legacy nonnegative graded-APL input map. The latter is not signed PN-to-APL LIF current. +- APL source groups: trial design ALPN, other ALPN, KC. Reject unaccounted nonzero input support. Compare summed mathematical float64 traces with stored float32 using prior analytic per-tick bounds, including grouping roundoff. Accumulated source shares remain descriptive; their robust majority is not proven. +- Strict timing: direct PN arrival at s+18 must precede the KC threshold tick; APL write at s follows the threshold and can affect only later ticks. Never-fired cells remain explicitly censored. +- Primary odor statistics retain fixed 686 coordinates; secondary statistics retain the fixed complement of six-odor design union. Technical repeats are excluded from odor comparisons. + +## Execution + +Use the existing CPU environment, Python `-B`, and four-thread OMP/MKL/OpenBLAS/Arrow limits. No Torch import, GPU or parameter changes are needed. + +1. `prepare.py`: synthetic tests, source/input hashes, prior integrity, fixed inventory, metadata schema and preregistration. +2. `run_audit.py`: exact PN conditional replay, event-stream provenance, APL split, twelve repeat checks. +3. `finalize.py`: independently re-read saved evidence, validate timed edge events, aggregate all fixed records, check prior integrity, seal model and experiment. +4. `publication.py`: verify both seals, then exclusively publish small evidence to a new registry. Bulk arrays stay on the server; Git is not their off-host backup. + +Any failure is preserved. Do not edit a registered source or reuse a populated experiment directory. A new revision requires an explicit separate directory and record. Audit PASS never implies learning PASS, identified unique causality, or permission to tune frozen models. diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/apl_sources.py b/models/male-cns-lif/pn-recruitment-audit-v1/apl_sources.py new file mode 100644 index 0000000..ffe52be --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/apl_sources.py @@ -0,0 +1,329 @@ +"""Non-feedback float64 attribution of the frozen, recorded graded APL trace. + +No files, Torch, GPU, neuronal state, or counterfactual simulation are used. +The reference is mathematical source accounting, NOT bitwise CUDA replay. +Full bundle output is generated even for graph fixtures; the caller must not +interpret inhibition for absent fixture KCs as inhibition actually applied. +""" +from __future__ import annotations + +import math +import numpy as np + + +N = 166700 +GROUPS = ("design_ALPN", "non_design_ALPN", "KC") +DECAY32 = np.float32(math.exp(-.1 / 20.)) +GAIN32 = np.float32(.01) + + +def _require(ok, message): + if not ok: + raise ValueError(message) + + +def _ids(value, name): + value = np.asarray(value) + _require(value.dtype == np.int32 and value.ndim == 1, name + " must be an int32 vector") + _require(np.all((value >= 0) & (value < N)) and np.all(value[1:] > value[:-1]), + name + " must contain sorted unique global IDs") + return value + + +def _positive_array(value, shape, name): + value = np.asarray(value) + _require(value.dtype == np.float32 and value.shape == shape, name + " has the wrong shape/dtype") + _require(np.isfinite(value).all() and np.all(value >= 0), name + " must be finite and nonnegative") + return value + + +def _up(value): + return np.nextafter(np.asarray(value, np.float64), np.inf) + + +def _add(a, b): + return _up(np.asarray(a, np.float64) + np.asarray(b, np.float64)) + + +def _mul(a, b): + return _up(np.asarray(a, np.float64) * np.asarray(b, np.float64)) + + +def _gamma(count, unit_roundoff): + _require(count * unit_roundoff < 1, "Undefined gamma bound") + numerator = _mul(count, unit_roundoff) + denominator = np.nextafter(1. - numerator, -np.inf) + return _up(numerator / denominator) + + +def _sum_upper(values): + """Outward-rounded sum on axis zero; inputs are nonnegative.""" + result = np.zeros(values.shape[1:], np.float64) + for value in values: + result = _add(result, value) + return result + + +def _sum_groups(values): + # Explicit separate additions; never rely on a backend reduction over G. + return np.add(np.add(values[0], values[1]), values[2]) + + +def _positive_dot_upper(state, matrix): + result = np.zeros(matrix.shape[1], np.float64) + for index in range(len(state)): + result = _add(result, _mul(state[index], matrix[index])) + return result + + +def _normal(value, tiny): + return bool(np.isfinite(value).all() and np.all((value == 0) | (value >= tiny))) + + +def _metric(): + return dict(elements=0, sum_abs_error=0., max_abs_error=0., max_error_bound=0., + max_bound_utilization=0., bound_violations=0, zero_reference_nonzero_actual=0) + + +def _measure(metric, actual, reference, bound): + _require(np.isfinite(reference).all() and np.isfinite(bound).all(), "Nonfinite reference/bound") + error = np.abs(actual.astype(np.float64) - reference) + upper = np.where(error == 0, 0., _up(error)) + impossible_zero = (reference == 0) & (actual != 0) + bad = (upper > bound) | impossible_zero + usage = np.divide(upper, bound, out=np.zeros_like(upper), where=bound > 0) + metric['elements'] += error.size + metric['sum_abs_error'] += float(error.sum(dtype=np.float64)) + metric['max_abs_error'] = max(metric['max_abs_error'], float(error.max(initial=0))) + metric['max_error_bound'] = max(metric['max_error_bound'], float(bound.max(initial=0))) + metric['max_bound_utilization'] = max(metric['max_bound_utilization'], float(usage.max(initial=0))) + metric['bound_violations'] += int(bad.sum()) + metric['zero_reference_nonzero_actual'] += int(impossible_zero.sum()) + + +def decompose_apl_sources(I32, O32, pn_globals, kc_globals, design_pn_globals, + event_step, event_node, drive32, state32, h32, *, + decay32, gain32, standard_fp32, chunk_size=512, callback=None, + first_kc_tick=None): + """Attribute APL input/output to three exact anatomical source groups. + + I32 is the full (2,166700) normalized float32 input matrix; O32 is (2,K), + with columns in sorted kc_globals order. Traces are drive/state float32 + (T,2), inhibition float32 (T,K), including ALL bundle KC output columns. + Actual event vectors are int32 and sorted/unique by tick/global neuron. + Design IDs must be a subset of pn_globals; fixtures' design definition is + supplied explicitly by the caller, never inferred from response outcome. + + standard_fp32 must be True only after historical metadata verifies TF32 is + disabled and matmul precision is 'highest'. This function cannot inspect a + GPU environment. d/q must have the exact frozen float32 constant bits. + + Return summary (pure JSON values), tick_rows (T*3*2 JSON dictionaries), + first_kc_tick (int32[K], -1 for never fired), and per_kc cumulative arrays. + Optional first_kc_tick is checked against actual events, never overrides + them. No T*3*K history is retained; source cumulative storage is O(3*K). + callback(tick, dict) optionally receives copies of drive/state group arrays + (3,2), plus their totals, never a full inhibition history. + + Early means strictly tick < observed first KC spike; same-tick APL output + follows threshold and cannot cause that spike. Never-fired KC output and + output strictly before the first spike of ANY KC are reported separately. + """ + _require(standard_fp32 is True, "Historical standard FP32 arithmetic must be verified by caller") + _require(isinstance(decay32, np.float32) and isinstance(gain32, np.float32) and + decay32.view(np.uint32) == DECAY32.view(np.uint32) and + gain32.view(np.uint32) == GAIN32.view(np.uint32), "Frozen float32 decay/gain bits changed") + _require(isinstance(chunk_size, (int, np.integer)) and not isinstance(chunk_size, (bool, np.bool_)) and + chunk_size > 0, "chunk_size must be a positive integer") + _require(callback is None or callable(callback), "callback must be callable") + pn = _ids(pn_globals, 'pn_globals') + kc = _ids(kc_globals, 'kc_globals') + design = _ids(design_pn_globals, 'design_pn_globals') + _require(len(kc) > 0 and not np.intersect1d(pn, kc).size, "PN/KC identities overlap or KC set is empty") + _require(np.isin(design, pn).all(), "Design PN is outside the anatomical PN population") + k = len(kc) + I = _positive_array(I32, (2, N), 'I32').astype(np.float64) + O = _positive_array(O32, (2, k), 'O32').astype(np.float64) + state32 = np.asarray(state32) + _require(state32.ndim == 2 and len(state32) > 0, "Empty/invalid state trace") + steps = len(state32) + state32 = _positive_array(state32, (steps, 2), 'state32') + drive32 = _positive_array(drive32, (steps, 2), 'drive32') + h32 = np.asarray(h32) + _require(h32.dtype == np.float32 and h32.shape == (steps, k), "h32 has wrong shape/dtype") + # Validate without allocating a dense T*K temporary boolean array. + for start in range(0, k, chunk_size): + value = h32[:, start:start + chunk_size] + _require(np.isfinite(value).all() and np.all(value >= 0), "h32 must be finite and nonnegative") + labels = np.full(N, -1, np.int8) + labels[pn], labels[design], labels[kc] = 1, 0, 2 + support = np.any(I != 0, axis=0) + unknown = np.flatnonzero(support & (labels < 0)) + _require(not len(unknown), "Nonzero APL input outside design/non-design PN/KC: " + str(unknown[:10].tolist())) + ticks, nodes = np.asarray(event_step), np.asarray(event_node) + _require(ticks.dtype == nodes.dtype == np.int32 and ticks.ndim == nodes.ndim == 1 and + ticks.shape == nodes.shape, "Actual events must be matching int32 vectors") + _require(np.all((ticks >= 0) & (ticks < steps)) and np.all((nodes >= 0) & (nodes < N)), "Event out of range") + _require(np.all((ticks[1:] > ticks[:-1]) | ((ticks[1:] == ticks[:-1]) & (nodes[1:] > nodes[:-1]))), + "Actual events must be sorted and unique") + ptr = np.searchsorted(ticks, np.arange(steps + 1)) + lookup = np.full(N, -1, np.int32) + lookup[kc] = np.arange(k, dtype=np.int32) + first = np.full(k, steps, np.int32) + is_kc = lookup[nodes] >= 0 + np.minimum.at(first, lookup[nodes[is_kc]], ticks[is_kc]) + ever = first < steps + first_any = int(first.min()) if ever.any() else None + first[~ever] = -1 + if first_kc_tick is not None: + supplied = np.asarray(first_kc_tick) + _require(supplied.dtype == np.int32 and supplied.shape == (k,) and np.array_equal(supplied, first), + "Supplied first_kc_tick disagrees with observed actual KC events") + d, q = float(decay32), float(gain32) + u32, u64 = 2.**-24, 2.**-53 + gd32, gd64 = _gamma(N - 1, u32), _gamma(N - 1, u64) + gs32, gs64 = _gamma(2, u32), _gamma(2, u64) + gh32, gh64, gg64 = _gamma(5, u32), _gamma(5, u64), _gamma(2, u64) + group_state = np.zeros((3, 2), np.float64) + e64 = np.zeros_like(group_state) + e32 = np.zeros(2, np.float64) + previous32 = np.zeros(2, np.float64) + statistics = {name: _metric() for name in ('drive', 'state', 'inhibition')} + tiny32, tiny64 = np.finfo(np.float32).tiny, np.finfo(np.float64).tiny + normal = _normal(I, tiny32) and _normal(O, tiny32) + sums = {name: dict(actual_spikes=0, input_supported_spikes=0, first_actual_spike_tick=None, + first_nonzero_drive_tick=None, cumulative_drive_by_apl=np.zeros(2), + cumulative_state_by_apl=np.zeros(2), h_sum_mV=0., h_before_first_KC_sum_mV=0., + h_never_fired_KC_sum_mV=0., h_before_first_any_KC_sum_mV=0.) for name in GROUPS} + rows = [] + per_kc = dict(neuron_idx=kc.copy(), first_spike_tick=first.copy(), ever_fired=ever.copy()) + for name in GROUPS: + for suffix in ('pre_first_h_sum_mV', 'never_fired_h_sum_mV', 'h_sum_mV'): + per_kc[name + '_' + suffix] = np.zeros(k, np.float64) + O_total, O_never = O.sum(axis=1), O[:, ~ever].sum(axis=1) + for tick in range(steps): + fired = nodes[ptr[tick]:ptr[tick + 1]] + group_drive = np.zeros_like(group_state) + spike_counts = np.zeros(3, np.int64) + for group, name in enumerate(GROUPS): + selected = fired[labels[fired] == group] + spike_counts[group] = len(selected) + group_drive[group] = I[:, selected].sum(axis=1, dtype=np.float64) + acc = sums[name] + acc['actual_spikes'] += len(selected) + acc['input_supported_spikes'] += int(support[selected].sum()) + if len(selected) and acc['first_actual_spike_tick'] is None: + acc['first_actual_spike_tick'] = tick + if np.any(group_drive[group] > 0) and acc['first_nonzero_drive_tick'] is None: + acc['first_nonzero_drive_tick'] = tick + # Each source-group positive reduction independently bounds its exact + # real sum. Outward sum across groups bounds the common exact drive. + Dupper = _up(group_drive / np.nextafter(1. - gd64, -np.inf)) + total_Dupper = _sum_upper(Dupper) + drive_ref = _sum_groups(group_drive) + drive_bound = _add(_mul(gd32, total_Dupper), + _add(_sum_upper(_mul(gd64, Dupper)), _mul(gg64, _sum_upper(group_drive)))) + e32 = _add(_mul(d, e32), _add(_mul(gs32, _add(_mul(d, previous32), total_Dupper)), + _mul(1. + u32, _mul(gd32, total_Dupper)))) + e64 = _add(_mul(d, e64), _add(_mul(gs64, _add(_mul(d, group_state), Dupper)), + _mul(1. + u64, _mul(gd64, Dupper)))) + normal &= _normal(d * previous32, tiny32) and _normal(d * group_state, tiny64) + group_state = np.add(np.multiply(d, group_state), group_drive) + state_ref = _sum_groups(group_state) + state_bound = _add(e32, _add(_sum_upper(e64), _mul(gg64, _sum_upper(group_state)))) + observed_state = state32[tick].astype(np.float64) + normal &= _normal(drive32[tick], tiny32) and _normal(observed_state, tiny32) + _measure(statistics['drive'], drive32[tick], drive_ref, drive_bound) + _measure(statistics['state'], state32[tick], state_ref, state_bound) + for start in range(0, k, chunk_size): + stop = min(k, start + chunk_size) + matrix = O[:, start:stop] + h_group = np.empty((3, stop - start), np.float64) + h_error = np.empty_like(h_group) + for group in range(3): + products = group_state[group, :, None] * matrix + dot = np.add(products[0], products[1]) + h_group[group] = np.multiply(q, dot) + normal &= _normal(products, tiny64) and _normal(h_group[group], tiny64) + h_error[group] = _add(_mul(q, _positive_dot_upper(e64[group], matrix)), + _mul(gh64, _mul(q, _positive_dot_upper(group_state[group], matrix)))) + name = GROUPS[group] + per_kc[name + '_h_sum_mV'][start:stop] += h_group[group] + early_mask = ever[start:stop] & (tick < first[start:stop]) + per_kc[name + '_pre_first_h_sum_mV'][start:stop] += h_group[group] * early_mask + per_kc[name + '_never_fired_h_sum_mV'][start:stop] += h_group[group] * ~ever[start:stop] + h_ref = _sum_groups(h_group) + h32_error = _add(_mul(q, _positive_dot_upper(e32, matrix)), + _mul(gh32, _mul(q, _positive_dot_upper(observed_state, matrix)))) + bound = _add(h32_error, _add(_sum_upper(h_error), _mul(gg64, _sum_upper(h_group)))) + actual_products = observed_state[:, None] * matrix + actual_dot = np.add(actual_products[0], actual_products[1]) + normal &= _normal(actual_products, tiny32) and _normal(q * actual_dot, tiny32) + normal &= _normal(h32[tick, start:stop], tiny32) + _measure(statistics['inhibition'], h32[tick, start:stop], h_ref, bound) + # Aggregate output per source and per APL, never T*G*K. This is linear + # f64 measurement; grouping/reduction is not claimed bitwise identical. + early = ever & (tick < first) + O_early = O[:, early].sum(axis=1) + before_any = first_any is not None and tick < first_any + for group, name in enumerate(GROUPS): + acc = sums[name] + acc['cumulative_drive_by_apl'] += group_drive[group] + acc['cumulative_state_by_apl'] += group_state[group] + for apl in range(2): + amplitude = q * group_state[group, apl] + h_sum = float(amplitude * O_total[apl]) + h_early = float(amplitude * O_early[apl]) + h_never = float(amplitude * O_never[apl]) + h_any = h_sum if before_any else 0. + row = dict(tick=tick, group=name, apl_index=apl, source_spikes=int(spike_counts[group]), + drive64=float(group_drive[group, apl]), state64=float(group_state[group, apl]), + h_sum_mV=h_sum, h_before_first_KC_sum_mV=h_early, + h_never_fired_KC_sum_mV=h_never, h_before_first_any_KC_sum_mV=h_any) + rows.append(row) + for key in ('h_sum_mV', 'h_before_first_KC_sum_mV', 'h_never_fired_KC_sum_mV', 'h_before_first_any_KC_sum_mV'): + acc[key] += row[key] + if callback is not None: + callback(tick, dict(drive_by_group=group_drive.copy(), state_by_group=group_state.copy(), + drive_total=drive_ref.copy(), state_total=state_ref.copy())) + previous32 = observed_state + total_h = sum(value['h_sum_mV'] for value in sums.values()) + total_drive = sum(float(value['cumulative_drive_by_apl'].sum()) for value in sums.values()) + for acc in sums.values(): + acc['generated_h_fraction'] = None if total_h == 0 else acc['h_sum_mV'] / total_h + acc['drive_fraction'] = None if total_drive == 0 else float(acc['cumulative_drive_by_apl'].sum()) / total_drive + for key in ('cumulative_drive_by_apl', 'cumulative_state_by_apl'): + acc[key] = acc[key].tolist() + for metric in statistics.values(): + metric['mean_abs_error'] = metric.pop('sum_abs_error') / metric['elements'] + finite_metrics = all(np.isfinite([row[key] for row in rows]).all() for key in + ('drive64', 'state64', 'h_sum_mV', 'h_before_first_KC_sum_mV', 'h_never_fired_KC_sum_mV', 'h_before_first_any_KC_sum_mV')) + _require(finite_metrics, "Nonfinite aggregate accounting") + _require(all(np.isfinite(value).all() for value in per_kc.values()), "Nonfinite per-KC accounting") + passed = bool(normal and all(metric['bound_violations'] == 0 for metric in statistics.values())) + summary = dict(pass_all=passed, steps=steps, neurons=N, apl=2, bundle_KC=k, + actual_events=len(nodes), groups=sums, group_order=list(GROUPS), + input_support_partition_exact=True, unknown_nonzero_input_columns=0, + source_population_counts={name: int(np.count_nonzero(labels == index)) for index, name in enumerate(GROUPS)}, + input_supported_population_counts={name: int(np.count_nonzero(support & (labels == index))) for index, name in enumerate(GROUPS)}, + first_any_KC_spike_tick=first_any, ever_fired_KC=int(ever.sum()), never_fired_KC=int((~ever).sum()), + decay_f32_bits=f'{int(decay32.view(np.uint32)):08x}', gain_f32_bits=f'{int(gain32.view(np.uint32)):08x}', + comparison=statistics, theoretical_bound=dict(applicable=bool(normal), + normal_or_zero_arithmetic=bool(normal), historical_standard_fp32_verified_by_caller=True, + gamma_definition='gamma_n=n*u/(1-n*u); u32=2^-24,u64=2^-53; outward rounded', + drive_terms_bound=N-1, state_operation_bound=2, output_operation_bound=5, group_sum_operation_bound=2, + state_recurrence='E_p<=d*E_p_prev+gamma_2,p*(d*abs(s_p_prev)+D_upper)+(1+u_p)*gamma_(N-1),p*D_upper', + grouped_f64_bound='Sum source-group error bounds, plus gamma_2,64*upward_sum(group_values) for per-tick compared drive/state/h totals only', + output_bound='q*upward_dot(E_p,O)+gamma_5,p*q*upward_dot(s_p,O); f32 error plus all f64 group/output/sum errors', + tolerance_fitted_to_observed_error=False, + scope='Per-tick summed-source drive/state/h trace comparisons only; not cumulative source fractions'), + early_definition='strict tick source_keys[:-1]) + source_time_keys = np.sort(expected['neuron_idx'].astype(np.int64) * spec['steps'] + expected['step']) + final_first = actual['first_spike'].copy() + assert np.array_equal(pn_table.first_spike_observed, pn_table.first_spike_tick >= 0) + for name in pn_table.select_dtypes(include='number'): + values = pn_table[name].to_numpy() + assert not np.isinf(values).any() + if np.isnan(values).any(): + assert name.startswith('first_') and np.array_equal(np.isnan(values), ~pn_table.first_spike_observed.to_numpy()) + assert np.isfinite(kc_table.select_dtypes(include='number').to_numpy()).all() + design = pn_table.is_design_target.to_numpy() + with np.load(AG / 'cases' / item['case'] / 'requests.npz') as req: + table = pd.read_csv(source / 'requests.csv') + assert np.array_equal(table.neuron_idx, req['neuron_idx']) and np.array_equal(table.tick, req['tick']) + requested = np.bincount(req['neuron_idx'], minlength=166700)[targets] + accepted = np.bincount(req['neuron_idx'][table.accepted.to_numpy().astype(bool)], minlength=166700)[targets] + assert np.array_equal(pn_table.requested, requested) and np.array_equal(pn_table.accepted_requests, accepted) + if item['kind'] == 'calibration': + with np.load(AG / 'cases' / item['case'] / 'pn-drive.npz') as pdz: + assert np.array_equal(design, np.isin(targets, pdz['targets'])) + totals = np.zeros(len(targets), np.int64) + edge_totals = np.zeros(len(incoming.edge_id), np.int64) + count = 0 + last_record_key = -1 + for batch in pq.ParquetFile(out / 'accepted-prefirst-edge-events.parquet').iter_batches(batch_size=65536): + columns = {name: batch.column(name).to_numpy(zero_copy_only=False) for name in batch.schema.names} + checked = validate_batch(columns, edge_ids=incoming.edge_id, srcglobals=incoming.src_global, + dstglobals=targets[incoming.dst_local], weights=incoming.weights, target_globals=targets, + target_first=final_first, source_event_keys=source_keys) + totals += checked['per_target_count'] + count += checked['count'] + pos = np.searchsorted(incoming.edge_id, columns['edge_id']) + keys = columns['arrival_tick'].astype(np.int64) * 25582938 + columns['edge_id'] + assert np.all(keys[1:] > keys[:-1]) and (not len(keys) or int(keys[0]) > last_record_key) + if len(keys): + last_record_key = int(keys[-1]) + np.add.at(edge_totals, pos, 1) + assert np.array_equal(columns['incoming_edge_row'], pos) + assert np.array_equal(columns['source_group'], np.asarray(GROUPS)[codes[pos]]) + assert np.array_equal(columns['target_is_design'], design[incoming.dst_local[pos]]) + assert count == result['observer']['first_arrival_record_count'] + expected_totals = sum((pn_table[g + '_accepted_nonzero_before_first_edges'].to_numpy() for g in GROUPS), start=np.zeros(len(targets), np.int64)) + assert np.array_equal(totals, expected_totals) + edge_summary = pd.read_parquet(out / 'prefirst-edge-summary.parquet') + assert int(edge_summary.accepted_event_count.sum()) == count + assert len(edge_summary) == result['observer']['edge_summary_count'] + selected_edges = np.flatnonzero(edge_totals) + assert np.array_equal(edge_summary.edge_id.to_numpy(), incoming.edge_id[selected_edges]) + assert np.array_equal(edge_summary.accepted_event_count.to_numpy(), edge_totals[selected_edges]) + # Independent completeness: initially ref=0 and no reset before the first spike. + # Every nonzero source event arriving strictly before that boundary must be present. + end = np.where(final_first[incoming.dst_local] >= 0, final_first[incoming.dst_local], spec['steps']) + source_start = incoming.src_global.astype(np.int64) * spec['steps'] + source_end = source_start + np.maximum(end - 18, 0) + independent_counts = np.searchsorted(source_time_keys, source_end) - np.searchsorted(source_time_keys, source_start) + independent_counts[incoming.weights == 0] = 0 + assert np.array_equal(edge_totals, independent_counts), 'Independent timed antecedent coverage mismatch' + # Validate aggregation receipts against per-neuron evidence instead of trusting JSON alone. + for stratum, mask in (('all_PN', np.ones(len(targets), bool)), ('design_PN', design), + ('non_design_PN', ~design), ('never_active_PN', final_first < 0)): + agg = result['observer']['aggregates'][stratum] + assert agg['neurons'] == int(mask.sum()) and agg['spikes'] == int(pn_table.spike_count.to_numpy()[mask].sum()) + for group, metrics in agg['source_groups'].items(): + for metric, value in metrics.items(): + summed = pn_table[group + '_' + metric].to_numpy()[mask].sum(dtype=np.longdouble) + if isinstance(value, int): + assert int(summed) == value + else: + # Serialization to per-neuron f64 and regrouping changes only final reporting arithmetic. + assert abs(float(summed) - value) <= 64 * np.finfo(np.float64).eps * max(1., abs(value)) + tick_table = pd.read_parquet(out / 'apl-per-tick.parquet') + assert len(tick_table) == spec['steps'] * 6 + assert not tick_table[['tick', 'group', 'apl_index']].duplicated().any() + assert set(tick_table.group.unique()) == {'design_ALPN', 'non_design_ALPN', 'KC'} + for group in ('design_ALPN', 'non_design_ALPN', 'KC'): + metric = group + '_h_sum_mV' + observed = float(kc_table[metric].sum()) + expected_sum = apl['groups'][group]['h_sum_mV'] + assert abs(observed - expected_sum) <= 128 * spec['steps'] * np.finfo(np.float64).eps * max(1., abs(expected_sum)) + return result, pn_table, kc_table, apl + + +def main(): + check() + assert not (EXP / 'audit/failure.json').exists(), 'Cannot promote a preserved failure' + inventory = read(EXP / 'protocol/inventory.json') + identities = [(r['case'], r['repeat'], r['kind']) for r in inventory] + assert identities == expected_inventory() and len(set(identities)) == 80 + completed = read(EXP / 'audit/completed.json') + assert completed['pass_all'] + ids = [(r['case'], r['repeat']) for r in completed['trials']] + assert len(ids) == len(set(ids)) == 80 and set(ids) == {(r[0], r[1]) for r in identities} + assert all(r['pass_all'] for r in completed['trials']) + repeats = completed['repeat_checks'] + assert len(repeats) == len({r['case'] for r in repeats}) == 12 + assert {r['case'] for r in repeats} == {r['case'] for r in inventory if r['repeat'] == 1} + assert all(r['pass_all'] and all(r['checks'].values()) for r in repeats) + reports, metrics, by_mode = [], [], {} + cal_rows = read(AG / 'protocol/calibration-cases.json') + odors = list(dict.fromkeys(r['odor_id'] for r in cal_rows)) + assert len(odors) == 6 + for item in inventory: + report, pn, kc, apl = verify_trial(item) + reports.append(report) + print('VERIFIED', item['case'], item['repeat'], flush=True) + if item['kind'] != 'calibration' or item['repeat'] != 0: + continue + metrics.append(dict(mode=report['mode'], odor_index=odors.index(report['odor_id']), seed=report['seed'], + counts=pn.spike_count.to_numpy(), design_mask=pn.is_design_target.to_numpy())) + by_mode.setdefault(report['mode'], []).append((report, pn, kc, apl)) + # Independently repeat the stored repeat gate without executing a trajectory. + for item in inventory: + if item['repeat'] != 1: + continue + a = EXP / 'trials' / (item['case'] + '-r0') + b = EXP / 'trials' / (item['case'] + '-r1') + for name in ('per-pn.parquet', 'prefirst-edge-summary.parquet', 'apl-per-tick.parquet', 'per-kc-apl-sources.parquet'): + assert pd.read_parquet(a / name).equals(pd.read_parquet(b / name)) + assert digest(a / 'accepted-prefirst-edge-events.parquet') == digest(b / 'accepted-prefirst-edge-events.parquet') + assert read(a / 'apl-source-summary.json') == read(b / 'apl-source-summary.json') + with np.load(a / 'replay-final.npz') as aa, np.load(b / 'replay-final.npz') as bb: + assert set(aa.files) == set(bb.files) and all(bit_equal(aa[k], bb[k]) for k in aa.files) + structure = aggregate_structure(metrics) + structure['odor_index_to_id'] = odors + structure['ALPN_column_neuron_idx'] = np.load(AG / 'apl-bundle/pn_globals.npy').tolist() + save(EXP / 'audit/PN-odor-structure.json', structure) + aggregate = {} + for mode, trials in by_mode.items(): + assert len(trials) == 30 + sources = {} + for group in ('design_ALPN', 'non_design_ALPN', 'KC'): + sources[group] = {metric: sum(a['groups'][group][metric] for _, _, _, a in trials) for metric in ( + 'h_sum_mV', 'h_before_first_KC_sum_mV', 'h_never_fired_KC_sum_mV', 'h_before_first_any_KC_sum_mV')} + sources[group]['drive_sum'] = sum(sum(a['groups'][group]['cumulative_drive_by_apl']) for _, _, _, a in trials) + for group in sources: + for key in ('h_sum_mV', 'h_before_first_KC_sum_mV', 'h_before_first_any_KC_sum_mV', 'drive_sum'): + total = sum(s[key] for s in sources.values()) + sources[group][key + '_fraction'] = None if total == 0 else sources[group][key] / total + source_inputs = {group: {metric: sum(r['observer']['aggregates']['non_design_PN']['source_groups'][group][metric] + for r, _, _, _ in trials) for metric in trials[0][0]['observer']['aggregates']['non_design_PN']['source_groups'][group]} + for group in GROUPS} + allpn = pd.concat([p for _, p, _, _ in trials], ignore_index=True) + allkc = pd.concat([k for _, _, k, _ in trials], ignore_index=True) + activated = allkc[allkc.first_spike_tick >= 0] + first_metrics = [r for r, _, _, _ in trials] + aggregate[mode] = dict(trials=30, + first_non_design_vs_first_KC=dict(Counter(r['first_non_design_vs_first_KC'] for r in first_metrics)), + first_non_design_PN_tick_range=[min(r['first_non_design_PN_tick'] for r in first_metrics), max(r['first_non_design_PN_tick'] for r in first_metrics)], + first_any_KC_tick_range=[min(r['first_any_KC_tick'] for r in first_metrics), max(r['first_any_KC_tick'] for r in first_metrics)], + each_active_KC_relation=dict(activated.first_non_design_relation.value_counts()), + each_active_KC_count=len(activated), + each_active_KC_has_strict_pre_first_nondesign_PN_edge=int((activated.non_design_ALPN_strict_pre_first_edge_events > 0).sum()), + PN_neuron_trials=len(allpn), PN_active_neuron_trials=int(allpn.first_spike_observed.sum()), + PN_spikes=int(allpn.spike_count.sum()), design_PN_spikes=int(allpn.loc[allpn.is_design_target, 'spike_count'].sum()), + nondesign_PN_spikes=int(allpn.loc[~allpn.is_design_target, 'spike_count'].sum()), + non_design_PN_input_sources=source_inputs, APL_sources=sources, + common_non_design_dimensions=structure['design']['common_non_design_count'], + counts_are_neuron_trials_not_distinct_neurons=True) + aggregate[mode]['each_active_KC_relation'] = {k: int(v) for k, v in aggregate[mode]['each_active_KC_relation'].items()} + after = integrity() + save(EXP / 'regression/old-integrity-after.json', after) + assert read(AG / 'regression/frozen-five.json')['pass_all'] + result = dict(status='PASS-CONDITIONAL-PN-APL-SOURCE-AUDIT', pass_all=True, + exact_PN_trials=80, fixture_dense_pre_post_trials=8, CAL_event_final_trials=72, repeat_accounting_pass=12, + modes=aggregate, max_PN_voltage_ledger_residual_SI=max(r['observer']['voltage_decomposition']['maximum_reconstruction_residual_SI'] for r in reports), + CPU_audit_wall_seconds=sum(r['CPU_audit_wall_seconds'] for r in reports), + old_integrity_pass=all(r['pass'] for r in after.values()), + prior_frozen_five_pass=True, frozen_five_reexecuted_this_audit=False, + prior_frozen_five_sha256=digest(AG / 'regression/frozen-five.json'), + new_brain_simulation=False, GPU_used=False, training=False, heldout_access=False, + candidate_generated=False, learning_achieved=False, prior_CAL_failure_unchanged=True, + source_fraction_robustness_proven=False, counterfactual_causality_tested=False, + limitations=['Logged upstream spikes are conditioning inputs, not an independent whole-brain oracle', + 'Real timed edges are accepted antecedents, not unique causes or necessity proofs', + 'APL uses the unchanged nonnegative engineering input map, not signed PN-to-APL LIF drive', + 'APL summed traces pass prior per-tick error bounds; accumulated shares remain descriptive', + 'Shared PN activity does not imply absent odor information; no learning gate applied', + 'No independently saved per-tick PN states for CAL; fixtures have full histories', + 'Frozen five simulation executions were not repeated; previous sealed PASS checked by hash']) + save(EXP / 'audit/final-status.json', result) + with (EXP / 'REPORT.md').open('x') as f: + f.write('# Phase 4AI — conditional PN recruitment and APL source audit\n\n' + 'Status: `PASS-CONDITIONAL-PN-APL-SOURCE-AUDIT`.\n\n' + '80 existing records; PN events/counts/final states exact; eight fixture histories exact; twelve repeats exact. ' + 'No new brain experiment, tuning, training, GPU, or learning candidate.\n\n' + 'See audit/final-status.json and audit/PN-odor-structure.json. Accepted edges are timed antecedents, ' + 'not unique causes. Graded-APL cumulative source shares are descriptive, not causal or error-bounded majority claims. ' + 'Prior CAL failure remains unchanged. Frozen integrity passed; five reference simulations were not rerun.\n') + check() + assert sum(p.stat().st_size for p in EXP.rglob('*') if p.is_file()) < 2 * 1024**3 + seal(MODEL) + seal(EXP) + print(result['status'], flush=True) + + +if __name__ == '__main__': + main() diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/manifest.json b/models/male-cns-lif/pn-recruitment-audit-v1/manifest.json new file mode 100644 index 0000000..ae16b5d --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/manifest.json @@ -0,0 +1,85 @@ +{ + "root": "/data/digital-drosophila/models/male-cns-lif/pn-recruitment-audit-v1", + "files": [ + { + "path": "README.md", + "bytes": 2504, + "sha256": "58f2f78b94aa783cc30b4b5e79ab003cc83f8a09b66c195d963bca6ee691e44c" + }, + { + "path": "apl_sources.py", + "bytes": 19165, + "sha256": "61f4c35b464315d19bbffce534cfc037a0111725da0eb07ee53a41db81767fcb" + }, + { + "path": "context.py", + "bytes": 3182, + "sha256": "a166da3d964b49ca28a013d3a3cf33df1607c76d4a6a53a8ca8e2d955aa92cb0" + }, + { + "path": "finalize.py", + "bytes": 17774, + "sha256": "40e0f8a9e075db42ac63163de8362d808b9110f371e49f6d4088267eecf2eb4c" + }, + { + "path": "pn_observer.py", + "bytes": 23294, + "sha256": "0663c1f723f614ebcdb666b46eaa49288aec26125d6aceb18cd237330a705968" + }, + { + "path": "prepare.py", + "bytes": 7062, + "sha256": "9fbef01968c54637c9063bb1e2d7f45fdc97b886b3c25815a6146fc5f219164c" + }, + { + "path": "publication.py", + "bytes": 14239, + "sha256": "7a79c499098b74163e7bb16adabcbc54e7069dafa204d0b99c7f4c03d79f8016" + }, + { + "path": "run_audit.py", + "bytes": 16829, + "sha256": "a5a7861e8ced4739cd1a1f4314b8af7cff71168499f1ae6ed94ac71b481bcdd8" + }, + { + "path": "structure_metrics.py", + "bytes": 10327, + "sha256": "669e91a862efea5f243a054c883bc1a183e96b0bcf02fac5f6972c4a8272a4cb" + }, + { + "path": "test_apl_sources.py", + "bytes": 12733, + "sha256": "a7ac0f38877e0244a4bcc574264c72617ec487544a19dbc705e0c1e26c4be78d" + }, + { + "path": "test_harness.py", + "bytes": 1452, + "sha256": "c2e66adb18d25264d21bfce16462ef698a3ba09d178908bef1068881d46ac1e7" + }, + { + "path": "test_pn_observer.py", + "bytes": 8472, + "sha256": "fff3cb9da17d9d638e53a609a265d6531ed82824d10e06fcb3863edfeab7a267" + }, + { + "path": "test_publication.py", + "bytes": 10514, + "sha256": "a3516aefa6a753f8fca6c5a4c2109ae4d65ce0ab0278e4795d537148f04495ff" + }, + { + "path": "test_structure_metrics.py", + "bytes": 10003, + "sha256": "de1298a4a92facad97339e9a4cdb60ea2b7c30adc1a6f05274bd3be034239717" + }, + { + "path": "test_validate_events.py", + "bytes": 6426, + "sha256": "d687ceb171c7dad77dfe2eb53524091a7ee02ddc04d9bbafb3b4a59b0d1eb66c" + }, + { + "path": "validate_events.py", + "bytes": 6884, + "sha256": "32a33988c5c9619b2ff3553c2d508924a404ade17b526541a2ff113a61df68e4" + } + ] +} diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/pn_observer.py b/models/male-cns-lif/pn-recruitment-audit-v1/pn_observer.py new file mode 100644 index 0000000..d03e566 --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/pn_observer.py @@ -0,0 +1,315 @@ +"""CPU-only observer for the unchanged Phase 4AH replay_core callback. + +All input events are fixed observations, not interventions. This module writes +no files and never changes a callback array. Incoming records are antecedents, +not a selected cause, shortest path, necessity test, or whole-brain simulation. +""" +from __future__ import annotations + +import numpy as np + + +DELAY = 18 +COEFF_KEYS = ("gd", "c0", "cg", "cv", "threshold", "reset", "seed_volt") +FLOAT_KEYS = ("old_v", "old_g", "v_integrated", "g_decayed", "v_threshold", + "v_after_reset", "g_after_reset", "v_pre_apl", "g_pre_apl", "delta_si", "v", "g") +INT_KEYS = ("ref_before", "ref_after_reset", "ref_pre_apl", "ref") +BOOL_KEYS = ("available", "request", "request_accepted", "request_rejected", "fired") + + +def require(condition, message): + if not condition: + raise ValueError(message) + + +def bit_equal(a, 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)) + + +class PNObserver: + def __init__(self, target_globals, design_target_globals, incoming, edge_groups, + group_names, coeff, steps, *, max_first_event_rows, event_sink=None): + targets = np.asarray(target_globals) + require(targets.dtype == np.int32 and targets.ndim == 1 and len(targets) > 0 and + np.all((targets >= 0) & (targets < 166700)) and np.all(np.diff(targets) > 0), + "Targets must be sorted unique global int32 IDs") + design = np.asarray(design_target_globals) + require(design.dtype == np.int32 and design.ndim == 1 and len(np.unique(design)) == len(design) and + np.isin(design, targets).all(), "Design target IDs must be unique int32 subset of observed PNs") + require(type(steps) is int and steps > 0, "steps must be a positive integer") + require(type(max_first_event_rows) is int and max_first_event_rows > 0, "Explicit positive row budget required") + require(event_sink is None or callable(event_sink), "event_sink must be callable or None") + names = tuple(group_names) + require(names and all(isinstance(name, str) and name for name in names) and len(set(names)) == len(names), + "Source group names must be nonempty and unique") + codes = np.asarray(edge_groups) + e = len(incoming.edge_id) + require(codes.ndim == 1 and codes.shape == (e,) and codes.dtype.kind in "iu" and + np.all((codes >= 0) & (codes < len(names))), "Invalid source-group code per incoming edge") + require(incoming.target_count == len(targets) and incoming.weights.dtype == np.float64 and + np.isfinite(incoming.weights).all(), "Incoming index/weights mismatch") + require(all(name in coeff and np.isfinite(coeff[name]) for name in COEFF_KEYS), "Missing/nonfinite coefficient") + self.targets = targets.copy() + self.design = np.isin(targets, design) + self.incoming, self.groups, self.names = incoming, codes.astype(np.int64, copy=True), names + self.coeff = {name: np.float64(coeff[name]) for name in COEFF_KEYS} + self.steps, self.n = steps, len(targets) + self.max_rows, self.sink = max_first_event_rows, event_sink + self.callback_count, self.record_count = 0, 0 + self.first_arrival_records = [] + self.first = np.full(self.n, -1, np.int32) + self.first_request = np.full(self.n, -1, np.int32) + self.first_accepted_request = np.full(self.n, -1, np.int32) + self.first_positive_arrival = np.full(self.n, -1, np.int32) + self.counts = np.zeros(self.n, np.int32) + self.requested = np.zeros(self.n, np.int64) + self.request_accepted = np.zeros(self.n, np.int64) + self.request_rejected = np.zeros(self.n, np.int64) + self.first_request_coincident = np.zeros(self.n, bool) + self.first_state = {name: np.full(self.n, np.nan, np.float64) for name in + ("old_v", "old_g", "v_integrated", "v_threshold", "threshold_margin", "ref_before")} + shape = (len(names), self.n) + self.stats = {name: np.zeros(shape, np.int64) for name in + ("due_edges", "accepted_edges", "rejected_edges", "accepted_zero_edges", "rejected_zero_edges", + "accepted_positive_edges", "accepted_negative_edges", "rejected_positive_edges", "rejected_negative_edges", + "accepted_nonzero_before_first_edges", "accepted_positive_before_first_edges", "accepted_negative_before_first_edges")} + self.sums = {name: np.zeros(shape, np.longdouble) for name in + ("accepted_positive_V", "accepted_negative_abs_V", "rejected_positive_V", "rejected_negative_abs_V", + "accepted_positive_before_first_V", "accepted_negative_before_first_abs_V")} + self.edge_prefirst_count = np.zeros(e, np.int64) + self.edge_first_arrival = np.full(e, -1, np.int32) + self.edge_last_arrival = np.full(e, -1, np.int32) + # Non-feedback signed-source decomposition. Residual corrections are + # explicit lanes, never silently assigned to an anatomical population. + self.lane_names = [name + "/positive_weight" for name in names] + [name + "/negative_weight" for name in names] + self.lane_names += ["initial_and_affine_baseline", "request_or_reset", "roundoff_residual"] + self.baseline_lane, self.request_lane, self.round_lane = 2 * len(names), 2 * len(names) + 1, 2 * len(names) + 2 + self.lv = np.zeros((len(self.lane_names), self.n), np.longdouble) + self.lg = np.zeros_like(self.lv) + self.first_integrated_lanes = np.full_like(self.lv, np.nan) + self.first_threshold_lanes = np.full_like(self.lv, np.nan) + self.first_ledger_residual = np.full(self.n, np.nan, np.longdouble) + self.integration_roundoff_v = np.zeros(self.n, np.longdouble) + self.integration_roundoff_g = np.zeros(self.n, np.longdouble) + self.arrival_roundoff_g = np.zeros(self.n, np.longdouble) + self.max_ledger_residual = np.longdouble(0) + self.previous = None + self.finished = False + + def _validate_stage(self, tick, s): + require(not self.finished and type(tick) in (int, np.int32, np.int64) and + int(tick) == self.callback_count and tick < self.steps, "Missing, duplicate or out-of-order callback tick") + for names, dtype in ((FLOAT_KEYS, np.float64), (INT_KEYS, np.int32), (BOOL_KEYS, bool)): + for name in names: + require(name in s and isinstance(s[name], np.ndarray) and s[name].shape == (self.n,) and + s[name].dtype == dtype, "Missing/wrong callback array: " + name) + require(np.isfinite(s[name]).all(), "Nonfinite callback array: " + name) + require("h32" in s and s["h32"].dtype == np.float32 and s["h32"].shape == (self.n,) and + np.all(s["h32"] == 0) and np.all(s["delta_si"] == 0), "PN observer requires zero graded APL h/delta") + require(bit_equal(s["g"], s["g_pre_apl"]) and bit_equal(s["v"], s["v_pre_apl"]) and + bit_equal(s["ref"], s["ref_pre_apl"]), "Nonzero or unexpected post-APL PN state change") + require(np.all((s["ref_before"] >= 0) & (s["ref_before"] <= 22)) and + np.all((s["ref"] >= 0) & (s["ref"] <= 22)), "Refractory range mismatch") + require(np.array_equal(s["available"], s["ref_before"] <= 1), "Availability does not match pre-step refractory") + require(np.array_equal(s["request_accepted"], s["request"] & s["available"]) and + np.array_equal(s["request_rejected"], s["request"] & ~s["available"]), "Request accounting mismatch") + require(not np.any(s["request"] & ~self.design), "Observed request outside frozen odor design targets") + require(np.array_equal(s["fired"], s["available"] & (s["v_threshold"] > self.coeff["threshold"])), + "Callback firing does not match strict threshold") + if self.previous is not None: + require(all(bit_equal(s[key], self.previous[old]) for key, old in + (("old_v", "v"), ("old_g", "g"), ("ref_before", "ref"))), "Discontinuous callback state") + due = s.get("due_edge_indices") + require(isinstance(due, np.ndarray) and due.dtype == np.int64 and due.ndim == 1 and + np.all((due >= 0) & (due < len(self.incoming.edge_id))) and np.all(np.diff(due) > 0), + "Incoming queue must retain unique canonical edge rows") + require("due_edge_id" in s and bit_equal(s["due_edge_id"], self.incoming.edge_id[due]), "Incoming edge-ID mismatch") + accepted = s.get("arrival_accepted") + require(isinstance(accepted, np.ndarray) and accepted.dtype == bool and accepted.shape == due.shape and + np.array_equal(accepted, s["ref_after_reset"][self.incoming.dst_local[due]] == 0), "Arrival guard mismatch") + require(tick >= DELAY or not len(due), "Incoming event before the fixed 18-step delay") + return due, accepted + + def __call__(self, tick, s): + due, accepted = self._validate_stage(tick, s) + tick = int(tick) + dst, weights, groups = self.incoming.dst_local[due], self.incoming.weights[due], self.groups[due] + before = self.first < 0 + first_now = before & s["fired"] + # Arrivals happen AFTER this tick's threshold. Same-tick arrivals are + # never antecedents to a first spike, even if malformed data said accepted. + strictly_before = before[dst] & ~s["fired"][dst] + record_mask = accepted & (weights != 0) & strictly_before + new_records = int(np.count_nonzero(record_mask)) + require(self.record_count + new_records <= self.max_rows, + "First-arrival record resource limit exceeded; no truncation or event selection permitted") + if self.previous is None: + self.lv[self.baseline_lane] = s["old_v"].astype(np.longdouble) + self.lg[self.baseline_lane] = s["old_g"].astype(np.longdouble) + available = s["available"] + # Use old lane-g for V, as in the author update; do NOT use decayed g. + self.lv[:, available] = (np.longdouble(self.coeff["cv"]) * self.lv[:, available] + + np.longdouble(self.coeff["cg"]) * self.lg[:, available]) + self.lv[self.baseline_lane, available] += np.longdouble(self.coeff["c0"]) + self.lg[:, available] *= np.longdouble(self.coeff["gd"]) + rv = s["v_integrated"].astype(np.longdouble) - self.lv.sum(axis=0, dtype=np.longdouble) + rg = s["g_decayed"].astype(np.longdouble) - self.lg.sum(axis=0, dtype=np.longdouble) + self.integration_roundoff_v += rv + self.integration_roundoff_g += rg + self.lv[self.round_lane] += rv + self.lg[self.round_lane] += rg + self.first_integrated_lanes[:, first_now] = self.lv[:, first_now] + req = s["request_accepted"] + self.lv[:, req] = 0 + self.lv[self.request_lane, req] = np.longdouble(self.coeff["seed_volt"]) + self.first_threshold_lanes[:, first_now] = self.lv[:, first_now] + vres = s["v_threshold"].astype(np.longdouble) - self.lv.sum(axis=0, dtype=np.longdouble) + self.first_ledger_residual[first_now] = vres[first_now] + self.max_ledger_residual = max(self.max_ledger_residual, np.max(np.abs(vres), initial=np.longdouble(0))) + for name in self.first_state: + values = (self.coeff["threshold"] - s["v_threshold"]) if name == "threshold_margin" else s[name] + self.first_state[name][first_now] = values[first_now] + self.first_request_coincident[first_now] = req[first_now] + self.first[first_now] = tick + for array, selected in ((self.first_request, s["request"]), (self.first_accepted_request, req)): + array[(array < 0) & selected] = tick + self.requested += s["request"].astype(np.int64) + self.request_accepted += req.astype(np.int64) + self.request_rejected += s["request_rejected"].astype(np.int64) + self.counts += s["fired"].astype(np.int32) + fired = s["fired"] + self.lv[:, fired] = 0 + self.lv[self.request_lane, fired] = np.longdouble(self.coeff["reset"]) + self.lg[:, fired] = 0 + positive, negative, zero = weights > 0, weights < 0, weights == 0 + picks = {"due_edges": np.ones(len(due), bool), "accepted_edges": accepted, + "rejected_edges": ~accepted, "accepted_zero_edges": accepted & zero, + "rejected_zero_edges": ~accepted & zero, + "accepted_positive_edges": accepted & positive, "accepted_negative_edges": accepted & negative, + "rejected_positive_edges": ~accepted & positive, "rejected_negative_edges": ~accepted & negative, + "accepted_nonzero_before_first_edges": record_mask, + "accepted_positive_before_first_edges": record_mask & positive, + "accepted_negative_before_first_edges": record_mask & negative} + for name, mask in picks.items(): + np.add.at(self.stats[name], (groups[mask], dst[mask]), 1) + for name, mask, sign in ( + ("accepted_positive_V", accepted & positive, 1), ("accepted_negative_abs_V", accepted & negative, -1), + ("rejected_positive_V", ~accepted & positive, 1), ("rejected_negative_abs_V", ~accepted & negative, -1), + ("accepted_positive_before_first_V", record_mask & positive, 1), + ("accepted_negative_before_first_abs_V", record_mask & negative, -1)): + np.add.at(self.sums[name], (groups[mask], dst[mask]), sign * weights[mask].astype(np.longdouble)) + for offset, mask in ((0, accepted & positive), (len(self.names), accepted & negative)): + np.add.at(self.lg, (groups[mask] + offset, dst[mask]), weights[mask].astype(np.longdouble)) + ag = s["g_pre_apl"].astype(np.longdouble) - self.lg.sum(axis=0, dtype=np.longdouble) + self.arrival_roundoff_g += ag + self.lg[self.round_lane] += ag + gres = s["g"].astype(np.longdouble) - self.lg.sum(axis=0, dtype=np.longdouble) + self.max_ledger_residual = max(self.max_ledger_residual, np.max(np.abs(gres), initial=np.longdouble(0))) + require(np.isfinite(self.lv).all() and np.isfinite(self.lg).all(), "Nonfinite non-feedback lineage ledger") + positive_targets = np.unique(dst[accepted & positive]) + self.first_positive_arrival[positive_targets[self.first_positive_arrival[positive_targets] < 0]] = tick + selected_rows = due[record_mask] + np.add.at(self.edge_prefirst_count, selected_rows, 1) + self.edge_first_arrival[selected_rows[self.edge_first_arrival[selected_rows] < 0]] = tick + self.edge_last_arrival[selected_rows] = tick + rows = [] + for edge in selected_rows: + target = int(self.incoming.dst_local[edge]) + weight = float(self.incoming.weights[edge]) + rows.append({"edge_id": int(self.incoming.edge_id[edge]), "incoming_edge_row": int(edge), + "source_neuron_idx": int(self.incoming.src_global[edge]), "target_neuron_idx": int(self.targets[target]), + "source_group": self.names[int(self.groups[edge])], "source_spike_tick": tick - DELAY, + "arrival_tick": tick, "target_is_design": bool(self.design[target]), "accepted": True, + "weight_SI": weight, "weight_sign": 1 if weight > 0 else -1, + "ref_before": int(s["ref_before"][target]), "ref_at_arrival": int(s["ref_after_reset"][target]), + "available_at_integration": bool(s["available"][target]), + "old_v_SI": float(s["old_v"][target]), "old_g_SI": float(s["old_g"][target]), + "v_integrated_SI": float(s["v_integrated"][target]), "v_prethreshold_SI": float(s["v_threshold"][target]), + "threshold_margin_SI": float(self.coeff["threshold"] - s["v_threshold"][target]), + "g_before_arrivals_SI": float(s["g_after_reset"][target]), + "record_scope": "accepted nonzero input strictly before any observed first spike; join final first_spike_tick"}) + if rows: + if self.sink is None: + self.first_arrival_records.extend(rows) + else: + self.sink(rows) + self.record_count += len(rows) + self.previous = {name: s[name].copy() for name in ("v", "g", "ref")} + self.callback_count += 1 + + def edge_summary(self): + """All edges with stored pre-first records, including never-active PNs.""" + selected = np.flatnonzero(self.edge_prefirst_count) + dst = self.incoming.dst_local[selected] + return {"edge_id": self.incoming.edge_id[selected].copy(), + "source_neuron_idx": self.incoming.src_global[selected].copy(), "target_neuron_idx": self.targets[dst].copy(), + "source_group": np.asarray(self.names, dtype=str)[self.groups[selected]], + "weight_SI": self.incoming.weights[selected].copy(), + "accepted_event_count": self.edge_prefirst_count[selected].copy(), + "nominal_weight_times_event_count_SI": (self.incoming.weights[selected].astype(np.longdouble) * + self.edge_prefirst_count[selected]).astype(np.float64), + "first_arrival_tick": self.edge_first_arrival[selected].copy(), + "last_arrival_tick": self.edge_last_arrival[selected].copy(), + "target_first_spike_tick": self.first[dst].copy(), "target_is_design": self.design[dst].copy()} + + def finish(self, result): + """Return (per-PN arrays, JSON report); caller verifies original events/state. + + first_* floating fields are NaN when first_spike_observed is false. + They must not be imputed to zero or interpreted as observed voltages. + """ + require(not self.finished and self.callback_count == self.steps and self.previous is not None, "Observer did not cover every tick") + require(bit_equal(result["target_globals"], self.targets) and bit_equal(result["first_spike"], self.first) and + bit_equal(result["counts"], self.counts), "Replay result identity/count/first spike differs from observer") + require(all(bit_equal(result[name], self.previous[name]) for name in ("v", "g", "ref")), "Replay final state differs from last callback") + require(int(self.edge_prefirst_count.sum()) == self.record_count == + int(self.stats["accepted_nonzero_before_first_edges"].sum()), "Pre-first event coverage mismatch") + require(np.array_equal(self.stats["due_edges"], self.stats["accepted_edges"] + self.stats["rejected_edges"]), + "Due/accepted/rejected coverage mismatch") + data = {"neuron_idx": self.targets.copy(), "is_design_target": self.design.copy(), + "spike_count": self.counts.copy(), "first_spike_tick": self.first.copy(), "first_spike_observed": self.first >= 0, + "first_request_tick": self.first_request.copy(), "first_accepted_request_tick": self.first_accepted_request.copy(), + "first_accepted_positive_arrival_tick": self.first_positive_arrival.copy(), + "requested": self.requested.copy(), "accepted_requests": self.request_accepted.copy(), + "rejected_requests": self.request_rejected.copy(), "first_spike_coincident_with_accepted_request": self.first_request_coincident.copy(), + "first_voltage_ledger_residual_SI": self.first_ledger_residual.astype(np.float64), + "integration_roundoff_v_SI": self.integration_roundoff_v.astype(np.float64), + "integration_roundoff_g_SI": self.integration_roundoff_g.astype(np.float64), + "arrival_roundoff_g_SI": self.arrival_roundoff_g.astype(np.float64)} + data.update({"first_" + name + ("_SI" if name != "ref_before" else ""): value.copy() + for name, value in self.first_state.items()}) + for gi, name in enumerate(self.names): + for key, value in {**self.stats, **self.sums}.items(): + data[name + "_" + key] = value[gi].copy() if key in self.stats else value[gi].astype(np.float64) + for index, name in enumerate(self.lane_names): + data["first_integrated_V_from_" + name] = self.first_integrated_lanes[index].astype(np.float64) + data["first_prethreshold_V_from_" + name] = self.first_threshold_lanes[index].astype(np.float64) + aggregates = {} + for name, mask in (("all_PN", np.ones(self.n, bool)), ("design_PN", self.design), + ("non_design_PN", ~self.design), ("never_active_PN", self.first < 0)): + aggregates[name] = {"neurons": int(mask.sum()), "active": int(np.count_nonzero(mask & (self.first >= 0))), + "spikes": int(self.counts[mask].sum()), "source_groups": { + group: {**{key: int(value[gi, mask].sum()) for key, value in self.stats.items()}, + **{key: float(value[gi, mask].sum(dtype=np.longdouble)) for key, value in self.sums.items()}} + for gi, group in enumerate(self.names)}} + report = {"pass_all": True, "scope": "Observer coverage/internal consistency only; caller must verify recorded PN events and final state exactly", + "callback_count": self.callback_count, "steps": self.steps, "PN_count": self.n, + "source_group_names": list(self.names), "first_arrival_record_count": self.record_count, + "first_arrival_record_limit": self.max_rows, "records_truncated": False, + "edge_summary_count": int(np.count_nonzero(self.edge_prefirst_count)), "aggregates": aggregates, + "time_contract": "source_spike_tick=arrival_tick-18; arrivals follow threshold; first-spike tick arrivals excluded", + "record_contract": "Every accepted nonzero edge event strictly before first spike for every PN; never-active PNs retain full-window records with final first=-1", + "sum_contract": "Observer nominal signed weight sums, not the sequentially rounded runtime g; weight-times-count edge summaries are descriptive", + "voltage_decomposition": {"nonfeedback": True, "precision": str(np.dtype(np.longdouble)), + "lanes": list(self.lane_names), "maximum_reconstruction_residual_SI": float(self.max_ledger_residual), + "roundoff_lane_explicit": True, "fitted_tolerance": False, + "reset_clears_prior_lanes": True, "request_override_has_separate_lane": True, + "first_integrated_is_before_request_override": True, + "never_spiked_first_fields_are_NaN": True, + "threshold_crossing_contributions_are_not_necessity_or_unique_cause": True}, + "graded_APL_to_PN": "Required h32=0 and delta_si=0 every tick; fast APL weights supplied already frozen-masked by caller", + "new_brain_run": False, "parameter_changes": False, "shortest_path_claim": False, + "unique_cause_claim": False, "whole_brain_intervention_claim": False} + self.finished = True + return data, report diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/prepare.py b/models/male-cns-lif/pn-recruitment-audit-v1/prepare.py new file mode 100644 index 0000000..f4f2a93 --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/prepare.py @@ -0,0 +1,93 @@ +"""Register fixed conditional audit before reading PN contribution results.""" +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, AH, AH_MODEL, AG, AG_MODEL, META, COEFF, + GROUPS, read, save, digest, sources, integrity, load_module) + + +def main(): + assert not EXP.exists() or not any(EXP.iterdir()), 'Populated audit directory must not be reused' + assert read(AH / 'audit/final-status.json')['status'] == 'PASS-CONDITIONAL-KC-DRIVE-AUDIT' + assert read(AG / 'audit/final-status.json')['numerical_gate_pass'] + for sub in ('protocol', 'regression', 'trials', 'audit', 'logs'): + (EXP / sub).mkdir(parents=True, exist_ok=False) + test_command = [sys.executable, '-B', '-m', 'unittest', '-v', + 'test_pn_observer', 'test_apl_sources', 'test_structure_metrics', 'test_harness', + 'test_validate_events', 'test_publication'] + tested = subprocess.run(test_command, cwd=MODEL, capture_output=True, text=True) + with (EXP / 'regression/unit-tests.log').open('x') as f: + f.write(tested.stdout + tested.stderr) + save(EXP / 'regression/unit-tests.json', dict(command=test_command, exit_code=tested.returncode, + pass_all=tested.returncode == 0, sha256=digest(EXP / 'regression/unit-tests.log'))) + assert tested.returncode == 0 + paths = [AH / 'manifest.json', AH / 'checksums.sha256', AH_MODEL / 'manifest.json', + AH_MODEL / 'checksums.sha256', AH_MODEL / 'replay_core.py', AH_MODEL / 'populations.py', + AG / 'manifest.json', AG / 'checksums.sha256', AG_MODEL / 'manifest.json', + AG_MODEL / 'checksums.sha256', META, COEFF, + ROOT / 'datasets/male-cns-v1.0/processed/neuron-only/gpu/csr-weights.int64.npy'] + 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 PN/APL source audit; no new whole-brain simulation or learning', + previous_KC_outcome_known=True, before_PN_contribution_accounting=True, + new_GPU_use=False, max_threads=4, output_budget_bytes=2 * 1024**3, + per_trial_first_arrival_row_limit=1000000, truncation_allowed=False, + exact_gate='All PN events/counts/final v,g,ref; fixture pre/post histories; 12 deterministic accounting repeats', + APL_gate='Source float64 mathematical accounting within prior standard-float32 analytic roundoff bounds; not CUDA bitwise replay', + hypotheses={ + 'temporal_precedence': 'For every trial and every KC first spike, non-design PN spike must be strictly earlier; equals/after/absent retained', + 'direct_PN_KC_arrival': 'PN source tick+18 strictly less than KC first spike; same tick arrival is after threshold', + 'APL_source_precedence': 'Group h write at tick s affects only KC threshold ticks greater than s', + 'APL_share': 'Cumulative group shares are descriptive float64; no accumulated source-share error bound or robust-majority claim', + 'odor_structure': 'Fixed 686-dimensional counts; secondary fixed outside-six-odor-design-union subset; no activity-fraction or learning gate'}, + attribution_limits=['Accepted edge antecedents are not proof of single-neuron necessity or intervention causality', + 'Non-design means not in static design targets; does not mean noise or biologically incorrect', + 'Source-V lanes are non-feedback accounting with initial/constant/request/reset/roundoff lanes', + 'APL input matrix is legacy nonnegative raw-contact-normalized engineering mapping, not signed LIF current', + 'No CAL per-tick target state was independently saved; final states and events only', + 'Fixtures generated h for 4064 KC but applied only to fixture KC'], + 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()) + population = load_module('frozen_population_ai', AH_MODEL / 'populations.py') + assert digest(META) == population.METADATA_SHA256 + meta = pd.read_parquet(META).sort_values('neuron_idx').reset_index(drop=True) + labels, report = population.classify(meta) + assert report['pass_all'] + for name, count in (('ALLN', 420), ('ALIN', 24), ('ALON', 14), ('olfactory', 2639)): + selected = (meta.annotation_class == name).fillna(False).to_numpy(dtype=bool) + assert int(selected.sum()) == count and np.all(labels[selected] == 'other') + labels[selected] = 'olfactory_sensory' if name == 'olfactory' else name + assert set(np.unique(labels)).issubset(GROUPS) + for label, fname, count in (('KC', 'kc_globals.npy', 4064), ('APL', 'apl_globals.npy', 2), ('ALPN', 'pn_globals.npy', 686)): + observed = np.flatnonzero(labels == label).astype(np.int32) + assert len(observed) == count and np.array_equal(observed, np.load(AG / 'apl-bundle' / fname)) + report.update(refined_exact_class_counts={label: int((labels == label).sum()) for label in GROUPS}, + refinement='AH anatomical classifier, then exact ALLN/ALIN/ALON/olfactory classes split only from other; no name-prefix guessing') + 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}) + 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) == 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 fixed 80 existing records; no new brain simulation', flush=True) + + +if __name__ == '__main__': + main() diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/publication.py b/models/male-cns-lif/pn-recruitment-audit-v1/publication.py new file mode 100644 index 0000000..ae1d03b --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/publication.py @@ -0,0 +1,270 @@ +"""Publish small evidence only after independent verification of both seals. + +No prior verification helper is imported or executed. Publishing verifies +bytes and declared completion; it does not rerun or certify a scientific gate. +Every destination is exclusive. An interrupted publication is retained, not +deleted, overwritten, or silently resumed. +""" +from __future__ import annotations + +import hashlib +import json +from pathlib import Path, PurePosixPath +import re +import shutil +import stat + + +ROOT = Path('/data/digital-drosophila') +MODEL_RELATIVE = Path('models/male-cns-lif/pn-recruitment-audit-v1') +EXPERIMENT_RELATIVE = Path('experiments/phase4ai-pn-recruitment-audit') +REGISTRY_RELATIVE = Path('registry/phase4ai-pn-recruitment-audit') +FINAL_STATUS = 'PASS-CONDITIONAL-PN-APL-SOURCE-AUDIT' +MAX_EVIDENCE_BYTES = 4 * 1024 ** 2 +EVIDENCE_SUFFIXES = frozenset(('.json', '.csv', '.md', '.log', '.sha256')) +CONTROLS = frozenset(('manifest.json', 'checksums.sha256')) +SHA256 = re.compile(r'^[0-9a-f]{64}$') + + +def _require(condition, message): + if not condition: + raise ValueError(message) + + +def _unique_object(pairs): + result = {} + for name, value in pairs: + _require(name not in result, 'Duplicate JSON object key: ' + name) + result[name] = value + return result + + +def _bad_constant(value): + raise ValueError('Nonfinite JSON constant: ' + value) + + +def _json_bytes(data): + return json.loads(data.decode('utf-8'), object_pairs_hook=_unique_object, + parse_constant=_bad_constant) + + +def _digest(path): + checksum = hashlib.sha256() + with Path(path).open('rb') as handle: + for block in iter(lambda: handle.read(8 << 20), b''): + checksum.update(block) + return checksum.hexdigest() + + +def _write_json(path, value): + with Path(path).open('x', encoding='utf-8') as handle: + json.dump(value, handle, indent=2, ensure_ascii=False, allow_nan=False) + handle.write('\n') + + +def _relative_name(value): + _require(isinstance(value, str) and bool(value) and '\\' not in value and + '\x00' not in value and '\n' not in value and '\r' not in value, + 'Invalid manifest/checksum relative path') + path = PurePosixPath(value) + _require(not path.is_absolute() and str(path) == value and + all(part not in ('', '.', '..') and ':' not in part for part in path.parts), + 'Noncanonical or escaping relative path: ' + value) + return path + + +def _regular_file(base, relative): + rel = _relative_name(relative) + target = base + for part in rel.parts: + target = target / part + _require(not target.is_symlink(), 'Symlink not allowed in sealed path: ' + str(target)) + _require(target.exists() and stat.S_ISREG(target.stat().st_mode), + 'Missing/nonregular sealed file: ' + str(target)) + _require(target.resolve().is_relative_to(base), 'Sealed file escapes its root') + return target + + +def verify_seal(directory): + """Independently verify every listed byte/hash and the checksum inventory. + + Unlisted files fail, apart from __pycache__ files explicitly excluded by + the producer's seal contract. Such caches are counted, never published. + The checksum file must describe exactly the manifest payload plus the + manifest itself. Symlinks, duplicate paths and path traversal fail. + """ + supplied = Path(directory) + _require(supplied.is_absolute() and supplied.is_dir() and not supplied.is_symlink(), + 'A sealed root must be an absolute real directory') + base = supplied.resolve(strict=True) + manifest_path = _regular_file(base, 'manifest.json') + checksums_path = _regular_file(base, 'checksums.sha256') + manifest_bytes = manifest_path.read_bytes() + manifest_sha = hashlib.sha256(manifest_bytes).hexdigest() + manifest = _json_bytes(manifest_bytes) + _require(isinstance(manifest, dict) and set(manifest) == {'root', 'files'}, 'Unexpected seal manifest schema') + _require(isinstance(manifest['root'], str) and Path(manifest['root']).is_absolute() and + Path(manifest['root']).resolve(strict=True) == base, 'Manifest names another root') + _require(isinstance(manifest['files'], list) and bool(manifest['files']), 'Empty/invalid manifest payload') + rows, expected = [], {} + for row in manifest['files']: + _require(isinstance(row, dict) and set(row) == {'path', 'bytes', 'sha256'}, 'Invalid manifest file row') + relative = row['path'] + parsed = _relative_name(relative) + _require(relative not in CONTROLS and '__pycache__' not in parsed.parts, + 'Control/cache file incorrectly listed as payload: ' + relative) + _require(relative not in expected, 'Duplicate manifest path: ' + relative) + _require(type(row['bytes']) is int and row['bytes'] >= 0 and isinstance(row['sha256'], str) and + SHA256.fullmatch(row['sha256']), 'Invalid byte count or SHA-256: ' + relative) + path = _regular_file(base, relative) + _require(path.stat().st_size == row['bytes'], 'Payload byte count mismatch: ' + relative) + _require(_digest(path) == row['sha256'], 'Payload SHA-256 mismatch: ' + relative) + _require(path.stat().st_size == row['bytes'], 'Payload changed during verification: ' + relative) + expected[relative] = row['sha256'] + rows.append(dict(row)) + observed, excluded_caches = set(), 0 + for path in base.rglob('*'): + _require(not path.is_symlink(), 'Symlink found in sealed tree: ' + str(path)) + if path.is_dir(): + continue + _require(stat.S_ISREG(path.stat().st_mode), 'Nonregular file in sealed tree: ' + str(path)) + relative = path.relative_to(base).as_posix() + if '__pycache__' in path.relative_to(base).parts: + excluded_caches += 1 + else: + observed.add(relative) + _require(observed == set(expected) | CONTROLS, + 'Sealed tree has unlisted/missing files: ' + repr(sorted(observed ^ (set(expected) | CONTROLS)))) + checksums_bytes = checksums_path.read_bytes() + checksum_rows = {} + for line in checksums_bytes.decode('utf-8').splitlines(): + _require(len(line) >= 67 and line[64:66] == ' ' and SHA256.fullmatch(line[:64]), + 'Malformed checksums.sha256 line') + relative = line[66:] + _relative_name(relative) + _require(relative not in checksum_rows, 'Duplicate checksum path: ' + relative) + checksum_rows[relative] = line[:64] + _require(checksum_rows == {**expected, 'manifest.json': manifest_sha}, + 'Checksum inventory or manifest self-checksum mismatch') + return dict(pass_all=True, root=str(base), payload_file_count=len(rows), + payload_bytes=sum(row['bytes'] for row in rows), manifest_sha256=manifest_sha, + checksums_sha256=hashlib.sha256(checksums_bytes).hexdigest(), + excluded_pycache_files=excluded_caches, files=sorted(rows, key=lambda row: row['path']), + verification_scope='All sealed payload bytes/SHA-256, exact checksum inventory, and manifest self-SHA; no scientific gate execution') + + +def _copy_verified(source, destination, byte_count, checksum): + _require(source.stat().st_size == byte_count, 'Source size changed before evidence copy') + destination.parent.mkdir(parents=True, exist_ok=True) + with source.open('rb') as src, destination.open('xb') as dst: + shutil.copyfileobj(src, dst, length=1 << 20) + _require(destination.stat().st_size == byte_count and _digest(destination) == checksum, + 'Copied evidence failed its sealed byte/hash check') + _require(source.stat().st_size == byte_count and _digest(source) == checksum, + 'Sealed source changed during evidence copy') + + +def _control_rows(root, verification): + return [dict(path='manifest.json', bytes=(root / 'manifest.json').stat().st_size, + sha256=verification['manifest_sha256']), + dict(path='checksums.sha256', bytes=(root / 'checksums.sha256').stat().st_size, + sha256=verification['checksums_sha256'])] + + +def publish(root=ROOT): + """Create the one new registry exclusively; call only after finalize seals. + + ``root`` exists for isolated synthetic tests. Production uses the fixed + default project root and fixed Phase 4AI relative directories above. + """ + root = Path(root) + _require(root.is_absolute() and root.is_dir() and not root.is_symlink(), 'Invalid project root') + root = root.resolve(strict=True) + model, experiment, registry = (root / part for part in + (MODEL_RELATIVE, EXPERIMENT_RELATIVE, REGISTRY_RELATIVE)) + _require(not registry.exists() and not registry.is_symlink(), 'Registry already exists; no overwrite/resume') + for path in (model, experiment, registry.parent): + current = root + for part in path.relative_to(root).parts: + current = current / part + _require(not current.is_symlink(), 'Symlink in publication root path: ' + str(current)) + verified = {'model': verify_seal(model), 'experiment': verify_seal(experiment)} + experiment_rows = {row['path']: row for row in verified['experiment']['files']} + _require('audit/final-status.json' in experiment_rows, + 'Final status must exist inside the sealed experiment payload') + status = _json_bytes((experiment / 'audit/final-status.json').read_bytes()) + _require(isinstance(status, dict) and status.get('status') == FINAL_STATUS and + status.get('pass_all') is True, 'Finalize completion status is missing or unexpected') + for flag in ('candidate_generated', 'learning_achieved', 'new_brain_simulation', 'GPU_used'): + _require(status.get(flag) is False, 'Unexpected finalized scope flag: ' + flag) + plans, omitted = [], [] + for row in verified['experiment']['files'] + _control_rows(experiment, verified['experiment']): + allowed = PurePosixPath(row['path']).suffix.lower() in EVIDENCE_SUFFIXES + if allowed and row['bytes'] <= MAX_EVIDENCE_BYTES: + plans.append((experiment, row, 'evidence/' + row['path'])) + else: + omitted.append(dict(path=row['path'], bytes=row['bytes'], + reason='exceeds_4_MiB' if row['bytes'] > MAX_EVIDENCE_BYTES else 'bulk_or_non_evidence_extension')) + for row in _control_rows(model, verified['model']): + _require(row['bytes'] <= MAX_EVIDENCE_BYTES, 'Model seal control exceeds small-evidence limit') + plans.append((model, row, 'model/' + row['path'])) + for control in ('manifest.json', 'checksums.sha256'): + _require(any(target == 'evidence/' + control for _, _, target in plans), + 'Experiment seal control exceeds small-evidence limit') + _require(any(target == 'evidence/audit/final-status.json' for _, _, target in plans), + 'Final status exceeds small-evidence limit') + registry.parent.mkdir(parents=True, exist_ok=True) + registry.mkdir(exist_ok=False) + try: + records = [] + for source_root, row, relative in plans: + source = _regular_file(source_root.resolve(), row['path']) + target = registry / relative + _copy_verified(source, target, row['bytes'], row['sha256']) + records.append(dict(source=source.relative_to(root).as_posix(), copy=relative, + bytes=row['bytes'], sha256=row['sha256'])) + # Revalidate after copying, including payloads deliberately not copied. + after = {'model': verify_seal(model), 'experiment': verify_seal(experiment)} + _require(after == verified, 'A sealed tree changed during publication') + proof = {name: {key: value for key, value in report.items() if key != 'files'} + for name, report in verified.items()} + _write_json(registry / 'seal-verification.json', proof) + _write_json(registry / 'evidence-index.json', records) + receipt_files = [] + for path in sorted(registry.rglob('*')): + if path.is_file(): + receipt_files.append(dict(path=path.relative_to(registry).as_posix(), + bytes=path.stat().st_size, sha256=_digest(path))) + receipt = dict(publication_complete=True, registry=registry.relative_to(root).as_posix(), + project_root=str(root), declared_audit_status=status['status'], + model_manifest_sha256=verified['model']['manifest_sha256'], + experiment_manifest_sha256=verified['experiment']['manifest_sha256'], + copied_evidence_files=len(records), copied_evidence_bytes=sum(row['bytes'] for row in records), + files=receipt_files, experiment_payload_not_copied=omitted, + model_payload_not_duplicated=True, model_payload_remains_at=MODEL_RELATIVE.as_posix(), + bulk_remains_at=EXPERIMENT_RELATIVE.as_posix(), + complete_bulk_backup=False, scientific_gate_executed_by_publisher=False, + scientific_gate_certified_by_publication=False, candidate_generated=False, + learning_achieved=False, existing_files_modified=False, + receipt_self_hash='Not self-referential; returned by publish() and printed by main()', + limits=dict(max_selected_evidence_file_bytes=MAX_EVIDENCE_BYTES, + selected_suffixes=sorted(EVIDENCE_SUFFIXES))) + receipt_path = registry / 'publication-receipt.json' + _write_json(receipt_path, receipt) + return dict(publication_complete=True, registry=str(registry), copied_evidence_files=len(records), + receipt_sha256=_digest(receipt_path), scientific_gate_executed_by_publisher=False) + except Exception as error: + failure = registry / 'publication-failure.json' + if not failure.exists(): + _write_json(failure, dict(publication_complete=False, type=type(error).__name__, + message=str(error), partial_outputs_preserved=True, + existing_files_modified=False)) + raise + + +def main(): + print(json.dumps(publish(), ensure_ascii=False, allow_nan=False), flush=True) + + +if __name__ == '__main__': + main() diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/run_audit.py b/models/male-cns-lif/pn-recruitment-audit-v1/run_audit.py new file mode 100644 index 0000000..4cd80a0 --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/run_audit.py @@ -0,0 +1,283 @@ +"""Read existing records and replay PN targets only; no GPU or counterfactual.""" +from functools import lru_cache +import time +import traceback +import numpy as np +import pandas as pd +import pyarrow as pa +import pyarrow.parquet as pq +from context import (ROOT, EXP, AH_MODEL, AG, COEFF, GROUPS, read, save, digest, + check, bit_equal, load_module) +from pn_observer import PNObserver +from apl_sources import decompose_apl_sources + + +def first_ticks(event_tick, event_node, targets): + first = np.full(166700, np.iinfo(np.int32).max, np.int32) + np.minimum.at(first, event_node, event_tick) + first[first == np.iinfo(np.int32).max] = -1 + return first[targets] + + +def temporal_relation(source_tick, target_tick): + if source_tick < 0 or target_tick < 0: + return 'unobserved_source' if source_tick < 0 and target_tick >= 0 else ( + 'unobserved_target' if source_tick >= 0 else 'both_unobserved') + return 'before' if source_tick < target_tick else ('same_tick' if source_tick == target_tick else 'after') + + +def min_observed(values): + values = np.asarray(values) + values = values[values >= 0] + return int(values.min()) if len(values) else -1 + + +class EventSink: + """Streaming accepted antecedents, never silently truncate.""" + def __init__(self, path): + ints64 = ('edge_id', 'incoming_edge_row') + ints32 = ('source_neuron_idx', 'target_neuron_idx', 'source_spike_tick', 'arrival_tick', + 'weight_sign', 'ref_before', 'ref_at_arrival') + bools = ('target_is_design', 'accepted', 'available_at_integration') + floats = ('weight_SI', 'old_v_SI', 'old_g_SI', 'v_integrated_SI', 'v_prethreshold_SI', + 'threshold_margin_SI', 'g_before_arrivals_SI') + self.schema = pa.schema([(k, t) for names, t in ((ints64, pa.int64()), (ints32, pa.int32()), + (bools, pa.bool_()), (floats, pa.float64()), (('source_group', 'record_scope'), pa.string())) for k in names]) + assert not path.exists() + self.writer = pq.ParquetWriter(path, self.schema, compression='zstd') + self.rows = 0 + + def __call__(self, rows): + self.writer.write_table(pa.Table.from_pylist(rows, schema=self.schema)) + self.rows += len(rows) + + def close(self): + self.writer.close() + + +@lru_cache(maxsize=2) +def graph_view(graph_name): + core = load_module('sealed_ah_replay_ai_' + graph_name, AH_MODEL / 'replay_core.py') + folder = AG / 'graphs' / graph_name + nodes = np.load(folder / 'nodes.npy', mmap_mode='r') + pn = np.load(AG / 'apl-bundle/pn_globals.npy') + apl = np.load(AG / 'apl-bundle/apl_globals.npy') + targets, positions, _ = np.intersect1d(nodes, pn, assume_unique=True, return_indices=True) + assert len(targets) > 0, 'Empty PN fixture subset: stop and report nonapplicable; never fabricate targets' + lookup = np.full(len(nodes), -1, np.int32) + lookup[positions] = np.arange(len(targets), dtype=np.int32) + dst = np.load(folder / 'dst.npy', mmap_mode='r') + selected = np.flatnonzero(lookup[dst] >= 0) + src = nodes[np.load(folder / 'src.npy', mmap_mode='r')[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 + raw = np.load(ROOT / 'datasets/male-cns-v1.0/processed/neuron-only/gpu/csr-weights.int64.npy', mmap_mode='r')[ids] + assert raw.dtype == np.int64 and np.all(raw > 0) + incoming = core.IncomingIndex(src, lookup[dst[selected]], ids, weights, len(targets)) + groups = pd.read_parquet(EXP / 'protocol/neuron-source-groups.parquet').audit_source_group.to_numpy() + codes = np.array([GROUPS.index(x) for x in groups], np.int32)[src] + audit = dict(graph=graph_name, PN_targets=len(targets), incoming_edges=len(ids), contacts=int(raw.sum()), + zeroed_fast_APL_edges=int(np.isin(src, apl).sum()), graded_APL_to_PN=False, + groups={g: dict(edges=int((codes == i).sum()), contacts=int(raw[codes == i].sum())) for i, g in enumerate(GROUPS)}, + source_graph_unchanged=True, effective_fast_APL_zero_mask_preserved=True) + return core, incoming, raw, codes, targets, positions, audit + + +@lru_cache(maxsize=2) +def pn_kc_edges(graph_name): + folder = AG / 'graphs' / graph_name + nodes = np.load(folder / 'nodes.npy', mmap_mode='r') + pn = np.load(AG / 'apl-bundle/pn_globals.npy') + kc = np.load(AG / 'apl-bundle/kc_globals.npy') + src = nodes[np.load(folder / 'src.npy', mmap_mode='r')] + dst = nodes[np.load(folder / 'dst.npy', mmap_mode='r')] + picked = np.flatnonzero(np.isin(src, pn) & np.isin(dst, kc)) + return src[picked], np.searchsorted(kc, dst[picked]), np.load(folder / 'weights.npy', mmap_mode='r')[picked] + + +def pn_kc_before_first(graph_name, state, kc_first, design): + """All PN edge arrivals strictly before first KC spike; no refractory yet.""" + src, dst, weights = pn_kc_edges(graph_name) + rows = {'neuron_idx': np.load(AG / 'apl-bundle/kc_globals.npy').copy(), 'first_spike_tick': kc_first.copy()} + for name in ('design_ALPN', 'non_design_ALPN'): + rows[name + '_strict_pre_first_edge_events'] = np.zeros(len(kc_first), np.int64) + rows[name + '_strict_pre_first_positive_V'] = np.zeros(len(kc_first), np.float64) + rows[name + '_strict_pre_first_negative_abs_V'] = np.zeros(len(kc_first), np.float64) + # Fixed graph edges only. Before the first spike, initially zero ref has never been reset. + for source in np.unique(src): + edges = np.flatnonzero((src == source) & (kc_first[dst] >= 0)) + spikes = state['step'][state['neuron_idx'] == source] + count = np.searchsorted(spikes, kc_first[dst[edges]] - 18, side='left') + label = 'design_ALPN' if source in design else 'non_design_ALPN' + np.add.at(rows[label + '_strict_pre_first_edge_events'], dst[edges], count) + np.add.at(rows[label + '_strict_pre_first_positive_V'], dst[edges], count * np.maximum(weights[edges], 0)) + np.add.at(rows[label + '_strict_pre_first_negative_abs_V'], dst[edges], count * np.maximum(-weights[edges], 0)) + return rows + + +def audit_trial(row, population): + start = time.perf_counter() + out = EXP / 'trials' / (row['case'] + '-r' + str(row['repeat'])) + out.mkdir(exist_ok=False) + source, case = ROOT / row['source'], AG / 'cases' / row['case'] + spec = read(case / 'spec.json') + graph_name = 'fixtures' if row['kind'] == 'fixture' else 'full' + core, incoming, raw, codes, targets, positions, 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'} + with np.load(case / 'requests.npz', allow_pickle=False) as data: + req = {k: data[k].copy() for k in data.files} + rt = pd.read_csv(source / 'requests.csv') + assert np.array_equal(req['tick'], rt.tick) and np.array_equal(req['neuron_idx'], rt.neuron_idx) + assert (rt.attempted == 1).all() and rt.accepted.isin([0, 1]).all() + assert np.array_equal(rt.rejected, 1 - rt.accepted) + if row['kind'] == 'calibration': + with np.load(case / 'pn-drive.npz', allow_pickle=False) as data: + design = np.sort(data['targets'].copy()) + assert design.dtype == np.int32 and data['rates_hz'].dtype == np.float32 + assert len(design) == spec['target_count'] and (data['rates_hz'] > 0).all() + else: + design = np.intersect1d(req['neuron_idx'], targets) + assert np.isin(design, targets).all() and len(np.unique(design)) == len(design) + expected_mask = np.isin(state['neuron_idx'], targets) + expected = {k: state[k][expected_mask] for k 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, dtype in (('v', 'float64'), ('g', 'float64'), ('ref', 'int32')): + histories[key] = np.load(source / f'{key}-history.{dtype}.npy', mmap_mode='r')[:, positions] + histories['pre_' + key] = np.load(source / f'preAPL-{key}.{dtype}.npy', mmap_mode='r')[:, positions] + sink = EventSink(out / 'accepted-prefirst-edge-events.parquet') + observer = PNObserver(targets, design, incoming, codes, GROUPS, read(COEFF), spec['steps'], + max_first_event_rows=1000000, event_sink=sink) + request_lookup = np.full(166700, -1, np.int32) + request_lookup[targets] = np.arange(len(targets), dtype=np.int32) + def callback(tick, stage): + for key in ('v', 'g', 'ref'): + if histories: + assert bit_equal(stage[key], histories[key][tick]), 'Fixture post state mismatch' + assert bit_equal(stage[key + '_pre_apl'], histories['pre_' + key][tick]), 'Fixture pre state mismatch' + indices = np.flatnonzero((req['tick'] == tick) & np.isin(req['neuron_idx'], targets)) + if len(indices): + local = request_lookup[req['neuron_idx'][indices]] + assert np.array_equal(stage['request_accepted'][local], rt.accepted.to_numpy()[indices].astype(bool)) + observer(tick, stage) + try: + result = core.replay(initial_v, initial_g, read(COEFF), spec['steps'], targets, req, + dict(step=state['step'], neuron_idx=state['neuron_idx']), incoming, + np.zeros((spec['steps'], len(targets)), np.float32), callback) + finally: + sink.close() + exact = {k: bit_equal(result[k], state[k][positions]) for k in ('v', 'g', 'ref', 'counts')} + exact.update({k: bit_equal(result[k], expected[k]) for k in ('step', 'neuron_idx')}) + assert all(exact.values()), 'PN replay diverged: ' + str(exact) + data, observer_report = observer.finish(result) + assert sink.rows == observer_report['first_arrival_record_count'] + table = pd.DataFrame(data) + table.insert(1, 'body_id', population.iloc[targets].body_id.to_numpy()) + table.insert(2, 'annotation_type', population.iloc[targets].annotation_type.to_numpy()) + table.to_parquet(out / 'per-pn.parquet', index=False) + edges = pd.DataFrame(observer.edge_summary()) + if len(edges): + edge_positions = np.searchsorted(incoming.edge_id, edges.edge_id.to_numpy()) + assert np.array_equal(incoming.edge_id[edge_positions], edges.edge_id.to_numpy()) + edges['raw_contact_count'] = raw[edge_positions] + for prefix in ('source', 'target'): + global_ids = edges[prefix + '_neuron_idx'].to_numpy() + edges[prefix + '_body_id'] = population.iloc[global_ids].body_id.to_numpy() + edges[prefix + '_type'] = population.iloc[global_ids].annotation_type.to_numpy() + edges.to_parquet(out / 'prefirst-edge-summary.parquet', index=False) + with (out / 'replay-final.npz').open('xb') as f: + np.savez_compressed(f, **result) + kc = np.load(AG / 'apl-bundle/kc_globals.npy') + pn = np.load(AG / 'apl-bundle/pn_globals.npy') + first_kc = first_ticks(state['step'], state['neuron_idx'], kc) + first_pn = first_ticks(state['step'], state['neuron_idx'], pn) + first_nondesign = min_observed(first_pn[~np.isin(pn, design)]) + first_anykc = min_observed(first_kc) + component = read(source / 'apl-component.json') + env = component['environment'] + assert env['cuda_matmul_allow_tf32'] is False and env['float32_matmul_precision'] == 'highest' + d32 = np.array([int(component['decay_f32_bits'], 16)], np.uint32).view(np.float32)[0] + q32 = np.array([int(component['gain_f32_bits'], 16)], np.uint32).view(np.float32)[0] + apl = decompose_apl_sources(np.load(AG / 'apl-bundle/input_matrix.npy', mmap_mode='r'), + np.load(AG / 'apl-bundle/output_matrix.npy', mmap_mode='r'), pn, kc, design, + state['step'], state['neuron_idx'], np.load(source / 'apl-drive.float32.npy', mmap_mode='r'), + np.load(source / 'apl-state.float32.npy', mmap_mode='r'), + np.load(source / 'apl-inhibition.float32.npy', mmap_mode='r'), + decay32=d32, gain32=q32, standard_fp32=True, first_kc_tick=first_kc) + assert apl['summary']['pass_all'] + save(out / 'apl-source-summary.json', apl['summary']) + pd.DataFrame(apl['tick_rows']).to_parquet(out / 'apl-per-tick.parquet', index=False) + per_kc = pd.DataFrame(apl['per_kc']) + per_kc.insert(1, 'body_id', population.iloc[kc].body_id.to_numpy()) + per_kc['KC_present_in_source_graph'] = np.isin(kc, np.load(spec['graph_dir'] + '/nodes.npy')) + per_kc['first_non_design_PN_tick'] = first_nondesign + per_kc['first_non_design_relation'] = [temporal_relation(first_nondesign, int(t)) for t in first_kc] + for key, value in pn_kc_before_first(graph_name, state, first_kc, design).items(): + if key not in ('neuron_idx', 'first_spike_tick'): + per_kc[key] = value + per_kc.to_parquet(out / 'per-kc-apl-sources.parquet', index=False) + report = dict(case=row['case'], repeat=row['repeat'], kind=row['kind'], pass_all=True, exact=exact, + every_tick_fixture_pre_post_exact=bool(histories), CAL_per_tick_independent_state_available=False, + steps=spec['steps'], duration_ms=spec['duration_ms'], mode=spec.get('mode'), seed=spec.get('seed'), + odor_id=spec.get('odor_id'), graph=graph_audit, observer=observer_report, + first_non_design_PN_tick=first_nondesign, first_any_KC_tick=first_anykc, + first_non_design_vs_first_KC=temporal_relation(first_nondesign, first_anykc), + fixture_design_definition='Unique requested PN IDs for numerical fixture only; not odor design' if histories else None, + APL_source_bound_pass=apl['summary']['pass_all'], candidate_generated=False, new_brain_simulation=False, + CPU_audit_wall_seconds=time.perf_counter() - start, + input_sha256={f: digest(source / f) for f in ('state.npz', 'requests.csv', 'summary.json', + 'apl-component.json', 'apl-drive.float32.npy', 'apl-state.float32.npy', 'apl-inhibition.float32.npy')}) + save(out / 'summary.json', report) + save(out / 'output-hashes.json', [dict(path=p.name, sha256=digest(p), bytes=p.stat().st_size) + for p in sorted(out.iterdir()) if p.is_file()]) + return report + + +def main(): + check() + population = pd.read_parquet(EXP / 'protocol/neuron-source-groups.parquet') + inventory = read(EXP / 'protocol/inventory.json') + receipts = [] + for row in inventory: + try: + report = audit_trial(row, population) + receipts.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 PN / APL BOUND PASS', + round(report['CPU_audit_wall_seconds'], 3), 'CPU sec', 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=receipts, new_brain_simulation=False)) + raise + repeats = [] + for row in inventory: + if row['repeat'] != 1: + continue + a = EXP / 'trials' / (row['case'] + '-r0') + b = EXP / 'trials' / (row['case'] + '-r1') + checks = {} + for f in ('per-pn.parquet', 'prefirst-edge-summary.parquet', 'apl-per-tick.parquet', 'per-kc-apl-sources.parquet'): + checks[f] = pd.read_parquet(a / f).equals(pd.read_parquet(b / f)) + checks['antecedent_events'] = digest(a / 'accepted-prefirst-edge-events.parquet') == digest(b / 'accepted-prefirst-edge-events.parquet') + checks['apl-summary'] = read(a / 'apl-source-summary.json') == read(b / 'apl-source-summary.json') + with np.load(a / 'replay-final.npz') as aa, np.load(b / 'replay-final.npz') as bb: + checks['replay'] = set(aa.files) == set(bb.files) and all(bit_equal(aa[k], bb[k]) for k in aa.files) + assert all(checks.values()), 'Repeated accounting differs' + repeats.append(dict(case=row['case'], pass_all=True, checks=checks)) + assert len(receipts) == 80 and len(repeats) == 12 + save(EXP / 'audit/completed.json', dict(pass_all=True, trials=receipts, repeat_checks=repeats, + meaning='Conditional PN exactness and bounded APL attribution only; not a learning or causal gate')) + print('PASS 80 conditional audits and 12 repeats', flush=True) + + +if __name__ == '__main__': + main() diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/structure_metrics.py b/models/male-cns-lif/pn-recruitment-audit-v1/structure_metrics.py new file mode 100644 index 0000000..69c954b --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/structure_metrics.py @@ -0,0 +1,179 @@ +"""Fixed descriptive ALPN-count comparisons; pure NumPy, no files or tuning. + +Column positions refer to the caller's fixed canonical 686-ALPN order. This +module cannot assign body IDs or infer anatomical identities. Repeated runs +are integrity evidence, never additional independent seeds in these metrics. +""" +from __future__ import annotations + +import itertools + +import numpy as np + + +MODES = ('physiological', 'matched_energy') +ODORS = tuple(range(6)) +SEEDS = tuple(range(51101, 51106)) +N_ALPN = 686 +FIELDS = frozenset(('mode', 'odor_index', 'seed', 'counts', 'design_mask')) +METRICS = ('cosine_distance', 'pearson_correlation', 'jaccard', 'euclidean_distance') + + +def _require(condition, message): + if not condition: + raise ValueError(message) + + +def _integer(value, name): + _require(isinstance(value, (int, np.integer)) and not isinstance(value, (bool, np.bool_)), + name + ' must be an integer, not a Boolean') + return int(value) + + +def _array(value, dtype, name): + _require(isinstance(value, np.ndarray) and not np.ma.isMaskedArray(value), + name + ' must be an unmasked NumPy array') + _require(value.dtype == np.dtype(dtype) and value.shape == (N_ALPN,), + name + ' must have dtype ' + np.dtype(dtype).name + ' and shape (686,)') + return np.array(value, copy=True, order='C') + + +def _pair_metrics(first, second): + # Cast BEFORE subtraction/norm so int32 extrema do not overflow. + x, y = first.astype(np.float64), second.astype(np.float64) + active_x, active_y = first > 0, second > 0 + intersection = int(np.count_nonzero(active_x & active_y)) + union = int(np.count_nonzero(active_x | active_y)) + result = dict(active_a=int(active_x.sum()), active_b=int(active_y.sum()), + active_intersection=intersection, active_union=union) + if not len(first): + result.update({name: None for name in METRICS}) + return result + norm_x, norm_y = float(np.linalg.norm(x)), float(np.linalg.norm(y)) + cosine = None if norm_x == 0 or norm_y == 0 else float(np.clip( + np.dot(x, y) / (norm_x * norm_y), -1., 1.)) + # A constant or one-channel vector has undefined Pearson correlation; + # equality does not turn it into evidence of shared structure. + correlation = None if bool(np.all(x == x[0]) or np.all(y == y[0])) else float( + np.clip(np.corrcoef(x, y)[0, 1], -1., 1.)) + result.update(cosine_distance=None if cosine is None else float(1. - cosine), + pearson_correlation=correlation, + jaccard=None if union == 0 else float(intersection / union), + euclidean_distance=float(np.linalg.norm(x - y))) + _require(all(value is None or np.isfinite(value) for value in result.values()), + 'A structure metric became nonfinite') + return result + + +def _summary(rows): + result = {} + for name in METRICS: + values = np.asarray([row[name] for row in rows if row[name] is not None], np.float64) + result[name] = dict(mean=None if not len(values) else float(values.mean()), + variance_ddof0=None if not len(values) else float(values.var(ddof=0)), + valid_comparisons=int(len(values)), undefined_comparisons=int(len(rows) - len(values))) + return result + + +def _view(mode, records, positions): + arrays = {(odor, seed): records[(mode, odor, seed)]['counts'][positions] + for odor in ODORS for seed in SEEDS} + within, between, pairs = [], [], [] + for odor in ODORS: + for first_seed, second_seed in itertools.combinations(SEEDS, 2): + within.append(dict(odor_index=odor, seed_a=first_seed, seed_b=second_seed, + **_pair_metrics(arrays[(odor, first_seed)], arrays[(odor, second_seed)]))) + for first_odor, second_odor in itertools.combinations(ODORS, 2): + pair_between = [] + for seed in SEEDS: + row = dict(odor_index_a=first_odor, odor_index_b=second_odor, seed=seed, + **_pair_metrics(arrays[(first_odor, seed)], arrays[(second_odor, seed)])) + pair_between.append(row) + between.append(row) + # Original pair-specific comparison: 10 within-seed pairs for each + # member odor, not a baseline pooled across all six odors. + pair_within = [row for row in within if row['odor_index'] in (first_odor, second_odor)] + within_summary, between_summary = _summary(pair_within), _summary(pair_between) + differences = {name: None if (within_summary[name]['mean'] is None or + between_summary[name]['mean'] is None) else + float(between_summary[name]['mean'] - within_summary[name]['mean']) + for name in METRICS} + pairs.append(dict(odor_index_a=first_odor, odor_index_b=second_odor, + within_comparison_count=len(pair_within), between_comparison_count=len(pair_between), + within=within_summary, between=between_summary, + mean_between_minus_within=differences, + within_comparisons=pair_within, between_comparisons=pair_between)) + _require(len(within) == 60 and len(between) == 75 and len(pairs) == 15 and + all(row['within_comparison_count'] == 20 and row['between_comparison_count'] == 5 + for row in pairs), 'Internal fixed comparison inventory changed') + return dict(dimensions=int(len(positions)), applicable=bool(len(positions)), + column_positions=[int(value) for value in positions], + within_comparison_count=len(within), between_comparison_count=len(between), pair_count=len(pairs), + within=_summary(within), between=_summary(between), + within_comparisons=within, between_comparisons=between, pairs=pairs) + + +def aggregate_structure(records): + """Describe precisely 60 fixed CAL records, without selecting results. + + Each record has exactly mode, odor_index, seed, counts:int32[686] and + design_mask:bool[686]. There are two named modes, six odors (0..5), five + seeds (51101..51105), and no repeat records. Every odor's design mask must + be identical across seeds AND modes. No caller-owned array is modified. + + Undefined cosine (zero norm), Pearson (constant vector), or Jaccard (empty + active union) is null with explicit valid/undefined counts. An empty + common-nondesign population is nonapplicable, with every metric null. + """ + _require(isinstance(records, list) and len(records) == 60, + 'Exactly 60 base CAL records are required; technical repeats must be excluded') + indexed, masks = {}, {} + for position, record in enumerate(records): + _require(isinstance(record, dict) and set(record) == FIELDS, + 'Record schema must contain exactly ' + ', '.join(sorted(FIELDS))) + mode = record['mode'] + _require(isinstance(mode, str) and mode in MODES, 'Unexpected CAL mode') + odor = _integer(record['odor_index'], 'odor_index') + seed = _integer(record['seed'], 'seed') + _require(odor in ODORS and seed in SEEDS, 'Odor index or seed outside the fixed CAL inventory') + key = mode, odor, seed + _require(key not in indexed, 'Duplicate CAL identity: ' + str(key)) + counts = _array(record['counts'], np.int32, 'counts') + design = _array(record['design_mask'], np.bool_, 'design_mask') + _require(bool(np.all(counts >= 0)), 'Spike counts must be nonnegative') + if odor in masks: + _require(np.array_equal(design, masks[odor]), + 'An odor design mask changed across seed or mode: ' + str(odor)) + else: + masks[odor] = design.copy() + indexed[key] = dict(counts=counts, design_mask=design) + expected = set(itertools.product(MODES, ODORS, SEEDS)) + _require(set(indexed) == expected, 'Incomplete fixed CAL identity set') + union = np.logical_or.reduce([masks[odor] for odor in ODORS]) + primary = np.arange(N_ALPN, dtype=np.int32) + common = np.flatnonzero(~union).astype(np.int32) + return dict(schema='phase4ai-alpn-structure-v1', record_count=60, ALPN_dimensions=N_ALPN, + design=dict(identical_across_seeds_and_modes=True, + design_neuron_count_by_odor={str(odor): int(masks[odor].sum()) for odor in ODORS}, + design_union_count=int(union.sum()), common_non_design_count=int(len(common)), + common_non_design_column_positions=[int(value) for value in common], + common_non_design_definition='Fixed complement of the union of all six CAL odor design masks; same columns in all modes/seeds'), + modes={mode: dict(trials=30, fixed_686=_view(mode, indexed, primary), + common_non_design=_view(mode, indexed, common)) for mode in MODES}, + protocol=dict(odor_indices=list(ODORS), seeds=list(SEEDS), variance_ddof=0, + within='All 10 distinct seed pairs within each of six odors: 60 comparisons per mode', + between='Same-seed pairs for each of 15 odor pairs: 75 comparisons per mode', + pair_baseline='Only each odor pair: 10+10 within comparisons and five same-seed between comparisons', + counts='Original nonnegative int32 spike counts; no normalization, fitting or resampling', + undefined_cosine='null if either vector has zero norm', + undefined_Pearson='null if either vector is constant, including dimension one', + undefined_Jaccard='null if active union is empty', + empty_population='All metrics null, applicable=false; no fabricated zero distance', + clipping='Only cosine/correlation numerical roundoff is clipped to their mathematical [-1,1] range', + technical_repeats_included=False, population_columns='Caller-supplied fixed canonical ALPN order; positions are not neuron_idx'), + decision_gate_applied=False, learning_claimed=False, absence_of_encoding_claimed=False, + limitations=['Descriptive CAL structure only, not a held-out or learning result', + 'Shared activity can coexist with odor-specific information', + 'Active-set Jaccard can saturate even when spike-count patterns differ', + 'Valid metric counts vary when a metric is undefined; null is not zero similarity', + 'No KC sparsity gate is transferred to the ALPN population']) diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/test_apl_sources.py b/models/male-cns-lif/pn-recruitment-audit-v1/test_apl_sources.py new file mode 100644 index 0000000..80ad909 --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/test_apl_sources.py @@ -0,0 +1,242 @@ +"""Synthetic CPU-only tests. No real records, files, Torch, GPU, or model runs.""" +import json +import sys +import unittest + +import numpy as np + +from apl_sources import N, GROUPS, DECAY32, GAIN32, decompose_apl_sources + + +def fixture(events=((0, 1), (1, 4), (2, 10), (4, 20)), steps=6): + I = np.zeros((2, N), np.float32) + I[:, 1] = [1., .3] + I[:, 4] = [.5, 2.] + I[:, 10] = [1.1, .2] + I[:, 20] = [.7, 1.5] + O = np.array([[1., .7, .2, 0.], [.3, .5, 1., 2.]], np.float32) + pairs = np.asarray(events, np.int32).reshape(-1, 2) + tick, node = pairs[:, 0], pairs[:, 1] + # This is merely one legal ordinary f32 reduction implementation for + # exercising the mathematical bound, not a CUDA-equality oracle. + state = np.zeros(2, np.float32) + drive_log, state_log, h_log = [], [], [] + for t in range(steps): + drive = I[:, node[tick == t]].sum(axis=1, dtype=np.float32) + state = np.add(np.multiply(DECAY32, state), drive) + h = np.multiply(np.matmul(state, O), GAIN32) + drive_log.append(drive) + state_log.append(state.copy()) + h_log.append(h) + return dict(I32=I, O32=O, pn_globals=np.array([1, 4, 6], np.int32), + kc_globals=np.array([10, 20, 30, 40], np.int32), design_pn_globals=np.array([1], np.int32), + event_step=tick, event_node=node, drive32=np.asarray(drive_log), + state32=np.asarray(state_log), h32=np.asarray(h_log), + decay32=DECAY32, gain32=GAIN32, standard_fp32=True) + + +class AttributionTests(unittest.TestCase): + def test_three_groups_cover_drive_and_pass_prior_bound(self): + f = fixture() + calls = [] + out = decompose_apl_sources(**f, callback=lambda t, s: calls.append(s)) + summary = out['summary'] + self.assertTrue(summary['pass_all']) + self.assertTrue(summary['input_support_partition_exact']) + np.testing.assert_array_equal(out['first_kc_tick'], [2, 4, -1, -1]) + self.assertEqual(len(out['tick_rows']), 6 * 3 * 2) + self.assertEqual([summary['groups'][g]['actual_spikes'] for g in GROUPS], [1, 1, 2]) + for s in calls: + np.testing.assert_array_equal(s['drive_total'], (s['drive_by_group'][0] + s['drive_by_group'][1]) + s['drive_by_group'][2]) + np.testing.assert_array_equal(s['state_total'], (s['state_by_group'][0] + s['state_by_group'][1]) + s['state_by_group'][2]) + self.assertFalse(summary['CUDA_float32_bitwise_replay']) + self.assertFalse(summary['independent_brain_oracle']) + self.assertFalse(summary['theoretical_bound']['tolerance_fitted_to_observed_error']) + self.assertFalse(summary['cumulative_source_fraction_error_bounds_verified']) + self.assertFalse(summary['source_majority_robustness_proven']) + json.dumps(summary, allow_nan=False) + json.dumps(out['tick_rows'], allow_nan=False) + + def test_early_excludes_same_tick_and_never_fired_columns(self): + f = fixture(events=((0, 1), (2, 10))) + out = decompose_apl_sources(**f) + rows = [r for r in out['tick_rows'] if r['group'] == 'design_ALPN' and r['apl_index'] == 0] + d, q = float(DECAY32), float(GAIN32) + self.assertAlmostEqual(rows[0]['h_before_first_KC_sum_mV'], q * 1.) + self.assertAlmostEqual(rows[1]['h_before_first_KC_sum_mV'], q * d) + self.assertEqual(rows[2]['h_before_first_KC_sum_mV'], 0.) + self.assertGreater(rows[2]['h_never_fired_KC_sum_mV'], 0.) + self.assertGreater(rows[1]['h_before_first_any_KC_sum_mV'], 0.) + self.assertEqual(rows[2]['h_before_first_any_KC_sum_mV'], 0.) + + def test_first_kc_at_zero_has_no_early_output(self): + out = decompose_apl_sources(**fixture(events=((0, 1), (0, 10)))) + self.assertEqual(out['summary']['first_any_KC_spike_tick'], 0) + self.assertTrue(all(r['h_before_first_any_KC_sum_mV'] == 0 for r in out['tick_rows'])) + self.assertTrue(all(r['h_before_first_KC_sum_mV'] == 0 for r in out['tick_rows'])) + + def test_no_kc_spikes_keep_never_output_separate(self): + out = decompose_apl_sources(**fixture(events=((0, 1),))) + self.assertIsNone(out['summary']['first_any_KC_spike_tick']) + np.testing.assert_array_equal(out['first_kc_tick'], [-1] * 4) + for row in out['tick_rows']: + self.assertEqual(row['h_sum_mV'], row['h_never_fired_KC_sum_mV']) + self.assertEqual(row['h_before_first_KC_sum_mV'], 0.) + self.assertEqual(row['h_before_first_any_KC_sum_mV'], 0.) + + def test_zero_input_exact_zeros_and_undefined_fractions(self): + out = decompose_apl_sources(**fixture(events=())) + self.assertTrue(out['summary']['pass_all']) + for group in out['summary']['groups'].values(): + self.assertEqual(group['h_sum_mV'], 0.) + self.assertIsNone(group['generated_h_fraction']) + self.assertIsNone(group['drive_fraction']) + + def test_source_first_spike_is_not_first_nonzero_drive(self): + out = decompose_apl_sources(**fixture(events=((0, 6), (2, 4), (3, 100)))) + group = out['summary']['groups']['non_design_ALPN'] + self.assertEqual(group['actual_spikes'], 2) + self.assertEqual(group['input_supported_spikes'], 1) + self.assertEqual(group['first_actual_spike_tick'], 0) + self.assertEqual(group['first_nonzero_drive_tick'], 2) + self.assertEqual(out['summary']['actual_events'], 3) + self.assertTrue(out['summary']['pass_all']) + + def test_empty_design_set_reclassifies_pn_not_drops_them(self): + f = fixture() + f['design_pn_globals'] = np.empty(0, np.int32) + out = decompose_apl_sources(**f) + self.assertTrue(out['summary']['pass_all']) + self.assertEqual(out['summary']['groups']['design_ALPN']['actual_spikes'], 0) + self.assertEqual(out['summary']['groups']['non_design_ALPN']['actual_spikes'], 2) + + def test_chunk_size_does_not_change_source_accounting(self): + f = fixture() + a = decompose_apl_sources(**f, chunk_size=1) + b = decompose_apl_sources(**f, chunk_size=7) + self.assertTrue(a['summary']['pass_all'] and b['summary']['pass_all']) + self.assertEqual(a['tick_rows'], b['tick_rows']) + self.assertEqual(a['summary']['groups'], b['summary']['groups']) + for name in ('drive', 'state', 'inhibition'): + for key in ('max_abs_error', 'max_error_bound', 'max_bound_utilization', 'bound_violations', 'elements'): + self.assertEqual(a['summary']['comparison'][name][key], b['summary']['comparison'][name][key]) + + def test_callback_and_core_never_modify_inputs_or_running_state(self): + f = fixture() + inputs = {k: v.copy() for k, v in f.items() if isinstance(v, np.ndarray)} + expected = decompose_apl_sources(**f) + def mutate(t, s): + for value in s.values(): + value[:] = 100. + actual = decompose_apl_sources(**f, callback=mutate) + self.assertEqual(actual['summary'], expected['summary']) + self.assertEqual(actual['tick_rows'], expected['tick_rows']) + for name, before in inputs.items(): + np.testing.assert_array_equal(np.ascontiguousarray(f[name]).view(np.uint8), + np.ascontiguousarray(before).view(np.uint8)) + + def test_per_kc_strict_pre_first_and_never_sums(self): + f = fixture(events=((0, 1), (2, 10))) + out = decompose_apl_sources(**f, first_kc_tick=np.array([2, -1, -1, -1], np.int32)) + per = out['per_kc'] + d, q = float(DECAY32), float(GAIN32) + first_two = q * (1. + d) * (float(f['I32'][0, 1]) * float(f['O32'][0, 0]) + + float(f['I32'][1, 1]) * float(f['O32'][1, 0])) + self.assertAlmostEqual(per['design_ALPN_pre_first_h_sum_mV'][0], first_two) + np.testing.assert_array_equal(per['design_ALPN_pre_first_h_sum_mV'][1:], [0., 0., 0.]) + self.assertEqual(per['design_ALPN_never_fired_h_sum_mV'][0], 0.) + np.testing.assert_array_equal(per['design_ALPN_never_fired_h_sum_mV'][1:], per['design_ALPN_h_sum_mV'][1:]) + self.assertEqual(per['KC_pre_first_h_sum_mV'][0], 0.) + self.assertTrue(out['summary']['fixture_absent_KC_output_is_not_claimed_applied']) + + def test_supplied_first_times_must_match_actual_logs(self): + for first in (np.array([3, 4, -1, -1], np.int32), np.array([2, 4, -1, -1], np.int64)): + with self.subTest(first=first), self.assertRaises(ValueError): + decompose_apl_sources(**fixture(), first_kc_tick=first) + + def test_corrupted_positive_trace_violates_unfitted_bound(self): + for field in ('drive32', 'state32', 'h32'): + with self.subTest(field=field): + f = fixture() + f[field][1, 0] += np.float32(100.) + out = decompose_apl_sources(**f) + self.assertFalse(out['summary']['pass_all']) + target = {'drive32': 'drive', 'state32': 'state', 'h32': 'inhibition'}[field] + self.assertGreater(out['summary']['comparison'][target]['bound_violations'], 0) + + def test_zero_reference_cannot_accept_nonzero_actual(self): + f = fixture(events=()) + f['h32'][0, 0] = np.finfo(np.float32).tiny + out = decompose_apl_sources(**f) + self.assertFalse(out['summary']['pass_all']) + self.assertEqual(out['summary']['comparison']['inhibition']['zero_reference_nonzero_actual'], 1) + + def test_subnormal_disables_theoretical_pass(self): + f = fixture(events=()) + f['I32'][0, 1] = np.float32(np.finfo(np.float32).tiny / 2) + out = decompose_apl_sources(**f) + self.assertFalse(out['summary']['pass_all']) + self.assertFalse(out['summary']['theoretical_bound']['applicable']) + + +class ValidationTests(unittest.TestCase): + def test_nonzero_unknown_source_is_rejected(self): + f = fixture() + f['I32'][0, 100] = 1. + with self.assertRaisesRegex(ValueError, 'outside design/non-design'): + decompose_apl_sources(**f) + + def test_bad_identity_and_nonpartition_are_rejected(self): + for key, value in [('design_pn_globals', np.array([10], np.int32)), + ('design_pn_globals', np.array([1, 1], np.int32)), + ('pn_globals', np.array([1, 4, 10], np.int32)), + ('kc_globals', np.array([10, 20, 40, 30], np.int32)), + ('pn_globals', np.array([1, 4, 6], np.int64))]: + with self.subTest(key=key): + f = fixture() + f[key] = value + with self.assertRaises(ValueError): + decompose_apl_sources(**f) + + def test_invalid_events_dtype_order_duplicate_range(self): + changes = [('event_step', np.array([0, 1, 2, 4], np.int64)), + ('event_step', np.array([1, 0, 2, 4], np.int32)), + ('event_step', np.array([0, 1, 2, 6], np.int32)), + ('event_node', np.array([1, 4, 10, N], np.int32))] + for key, value in changes: + with self.subTest(key=key): + f = fixture() + f[key] = value + with self.assertRaises(ValueError): + decompose_apl_sources(**f) + f = fixture(events=((0, 1), (0, 1))) + with self.assertRaises(ValueError): + decompose_apl_sources(**f) + + def test_invalid_trace_matrix_precision_constants_or_environment(self): + variants = [dict(standard_fp32=False), dict(standard_fp32=1), dict(chunk_size=0), + dict(decay32=float(DECAY32)), dict(gain32=np.nextafter(GAIN32, np.float32(np.inf))), + dict(callback=3)] + for change in variants: + with self.subTest(change=change), self.assertRaises(ValueError): + decompose_apl_sources(**(fixture() | change)) + for name in ('I32', 'O32', 'drive32', 'state32', 'h32'): + for bad in ('dtype', 'negative', 'nonfinite'): + with self.subTest(name=name, bad=bad): + f = fixture() + if bad == 'dtype': + f[name] = f[name].astype(np.float64) + else: + f[name].flat[0] = -1. if bad == 'negative' else np.nan + with self.assertRaises(ValueError): + decompose_apl_sources(**f) + + def test_no_dense_source_history_or_scientific_runtime(self): + out = decompose_apl_sources(**fixture()) + self.assertEqual(set(out), {'summary', 'tick_rows', 'first_kc_tick', 'per_kc'}) + self.assertNotIn('torch', sys.modules) + self.assertNotIn('brian2', sys.modules) + + +if __name__ == '__main__': + unittest.main(verbosity=2) diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/test_harness.py b/models/male-cns-lif/pn-recruitment-audit-v1/test_harness.py new file mode 100644 index 0000000..c1f67e7 --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/test_harness.py @@ -0,0 +1,36 @@ +import tempfile +import unittest +from pathlib import Path +import numpy as np +import pyarrow.parquet as pq +from run_audit import first_ticks, temporal_relation, min_observed, EventSink + + +class HarnessTests(unittest.TestCase): + def test_first_tick_uses_minimum_not_order(self): + self.assertEqual(first_ticks(np.array([4, 2, 3], np.int32), np.array([7, 7, 8], np.int32), + np.array([7, 8, 9], np.int32)).tolist(), [2, 3, -1]) + + def test_strict_temporal_boundary(self): + self.assertEqual(temporal_relation(17, 18), 'before') + self.assertEqual(temporal_relation(18, 18), 'same_tick') + self.assertEqual(temporal_relation(19, 18), 'after') + self.assertEqual(temporal_relation(-1, 18), 'unobserved_source') + self.assertEqual(temporal_relation(18, -1), 'unobserved_target') + self.assertEqual(temporal_relation(-1, -1), 'both_unobserved') + + def test_min_observed(self): + self.assertEqual(min_observed([-1, 0, 3]), 0) + self.assertEqual(min_observed([-1, -1]), -1) + + def test_empty_event_sink_has_schema(self): + with tempfile.TemporaryDirectory() as tmp: + path = Path(tmp) / 'events.parquet' + sink = EventSink(path) + sink.close() + self.assertEqual(pq.read_table(path).num_rows, 0) + self.assertIn('source_spike_tick', pq.read_schema(path).names) + + +if __name__ == '__main__': + unittest.main() diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/test_pn_observer.py b/models/male-cns-lif/pn-recruitment-audit-v1/test_pn_observer.py new file mode 100644 index 0000000..1ff4e27 --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/test_pn_observer.py @@ -0,0 +1,152 @@ +"""Synthetic CPU-only tests, including the untouched sibling AH callback.""" +import importlib.util +from pathlib import Path +import unittest + +import numpy as np + +from pn_observer import PNObserver + + +path = Path(__file__).resolve().parent.parent / "kc-drive-audit-v1/replay_core.py" +spec = importlib.util.spec_from_file_location("frozen_ah_observer_test_core", path) +core = importlib.util.module_from_spec(spec) +spec.loader.exec_module(core) + + +def event(rows=()): + values = np.array(rows, np.int32).reshape(-1, 2) + return {"step": values[:, 0], "neuron_idx": values[:, 1]} + + +def run(*, edges=(), events=(), requests=(), n=1, design=(), steps=25, coeff=None, + initial_v=None, initial_g=None, sink=None, cap=1000, h=None, hook=None): + targets = np.arange(100, 100 + n, dtype=np.int32) + constants = dict(gd=1., c0=0., cg=1., cv=1., threshold=1., reset=0., seed_volt=2.) + if coeff: constants.update(coeff) + arrays = np.asarray(edges).reshape(-1, 4) + incoming = core.IncomingIndex(arrays[:, 0].astype(np.int32), arrays[:, 1].astype(np.int32), + np.arange(len(arrays), dtype=np.int64) + 81, arrays[:, 2].astype(np.float64), n) + groups = arrays[:, 3].astype(np.int16) + observer = PNObserver(targets, np.array(design, np.int32), incoming, groups, ("ALPN", "other"), constants, + steps, max_first_event_rows=cap, event_sink=sink) + def callback(tick, state): + if hook: hook(tick, state, observer) + observer(tick, state) + result = core.replay(np.zeros(n, np.float64) if initial_v is None else np.array(initial_v, np.float64), + np.zeros(n, np.float64) if initial_g is None else np.array(initial_g, np.float64), constants, steps, targets, + event(requests), event(events), incoming, np.zeros((steps, n), np.float32) if h is None else h, callback) + data, report = observer.finish(result) + return observer, result, data, report + + +class PNObserverTests(unittest.TestCase): + def test_18_step_arrival_then_19_step_first_spike(self): + observer, result, data, report = run(edges=[(10, 0, 2., 0)], events=[(0, 10)]) + self.assertEqual(data["first_spike_tick"].tolist(), [19]) + self.assertEqual(len(observer.first_arrival_records), 1) + row = observer.first_arrival_records[0] + self.assertEqual((row["source_spike_tick"], row["arrival_tick"]), (0, 18)) + self.assertEqual(row["v_prethreshold_SI"], 0.) + self.assertEqual(row["threshold_margin_SI"], 1.) + self.assertEqual(data["first_integrated_V_from_ALPN/positive_weight"].tolist(), [2.]) + self.assertEqual(observer.edge_summary()["target_first_spike_tick"].tolist(), [19]) + self.assertTrue(report["pass_all"]) + + def test_same_tick_first_spike_arrival_is_rejected_not_antecedent(self): + observer, _, data, _ = run(edges=[(10, 0, 2., 0)], events=[(0, 10)], requests=[(18, 100)], design=[100]) + self.assertEqual(data["first_spike_tick"].tolist(), [18]) + self.assertEqual(observer.first_arrival_records, []) + self.assertEqual(data["ALPN_rejected_positive_edges"].tolist(), [1]) + self.assertEqual(data["ALPN_accepted_positive_before_first_edges"].tolist(), [0]) + self.assertEqual(data["first_prethreshold_V_from_request_or_reset"].tolist(), [2.]) + + def test_all_positive_and_inhibitory_events_are_retained(self): + observer, _, data, _ = run(edges=[(10, 0, 2., 0), (11, 0, -.5, 1), (12, 0, 0., 1)], + events=[(0, 10), (0, 11), (0, 12)]) + self.assertEqual([r["weight_sign"] for r in observer.first_arrival_records], [1, -1]) + self.assertEqual(data["first_spike_tick"].tolist(), [19]) + self.assertEqual(data["other_accepted_zero_edges"].tolist(), [1]) + self.assertEqual(data["other_accepted_negative_before_first_abs_V"].tolist(), [.5]) + self.assertEqual(data["first_integrated_V_from_other/negative_weight"].tolist(), [-.5]) + + def test_never_active_targets_retain_all_prefirst_records(self): + observer, _, data, report = run(edges=[(10, 0, .25, 0)], events=[(0, 10), (1, 10)], + coeff={"cg": 0.}) + self.assertEqual(data["first_spike_tick"].tolist(), [-1]) + self.assertTrue(np.isnan(data["first_v_threshold_SI"][0])) + self.assertEqual(observer.edge_summary()["accepted_event_count"].tolist(), [2]) + self.assertEqual(observer.edge_summary()["target_first_spike_tick"].tolist(), [-1]) + self.assertEqual(report["first_arrival_record_count"], 2) + + def test_multiple_targets_independent_first_times_and_design(self): + observer, _, data, _ = run(n=2, edges=[(10, 0, 2., 0), (10, 1, .25, 0)], + events=[(0, 10)], requests=[(23, 101)], design=[101]) + self.assertEqual(data["first_spike_tick"].tolist(), [19, 23]) + self.assertEqual([r["target_is_design"] for r in observer.first_arrival_records], [False, True]) + self.assertEqual(data["first_spike_coincident_with_accepted_request"].tolist(), [False, True]) + + def test_old_g_not_decayed_g_used_for_first_voltage(self): + _, _, data, _ = run(edges=[(10, 0, 4., 0)], events=[(0, 10)], + coeff={"gd": .1, "cg": .5, "cv": 0.}) + self.assertEqual(data["first_integrated_V_from_ALPN/positive_weight"].tolist(), [2.]) + self.assertEqual(data["first_spike_tick"].tolist(), [19]) + + def test_initial_state_is_not_assigned_to_source_class(self): + _, _, data, _ = run(initial_g=[2.]) + self.assertEqual(data["first_spike_tick"].tolist(), [0]) + self.assertEqual(data["first_integrated_V_from_initial_and_affine_baseline"].tolist(), [2.]) + self.assertEqual(data["first_integrated_V_from_ALPN/positive_weight"].tolist(), [0.]) + + def test_streaming_sink_and_row_budget_fail_closed(self): + received = [] + observer, _, _, report = run(edges=[(10, 0, .2, 0)], events=[(0, 10)], + sink=lambda rows: received.extend(rows)) + self.assertEqual(observer.first_arrival_records, []) + self.assertEqual(len(received), report["first_arrival_record_count"]) + with self.assertRaisesRegex(ValueError, "resource limit"): + run(edges=[(10, 0, .2, 0), (11, 0, .2, 1)], events=[(0, 10), (0, 11)], cap=1) + + def test_nonzero_PN_APL_and_non_design_request_are_rejected(self): + h = np.zeros((25, 1), np.float32); h[0, 0] = 1 + with self.assertRaisesRegex(ValueError, "zero graded APL"): + run(h=h) + with self.assertRaisesRegex(ValueError, "outside frozen odor design"): + run(requests=[(0, 100)]) + + def test_callback_arrays_not_modified(self): + snapshots = [] + def hook(tick, state, observer): + if tick == 0: + before = {key: value.copy() for key, value in state.items() if isinstance(value, np.ndarray)} + observer(tick, state) + for key, value in before.items(): + self.assertEqual(state[key].tobytes(), value.tobytes()) + snapshots.append(True) + # Already consumed this tick; stop this synthetic test directly. + raise RuntimeError("checked immutability") + with self.assertRaisesRegex(RuntimeError, "checked immutability"): + run(hook=hook) + self.assertEqual(snapshots, [True]) + + def test_bad_queue_guard_and_tick_fail_closed(self): + def wrong_guard(tick, state, observer): + if tick == 18: state["arrival_accepted"][:] = False + with self.assertRaisesRegex(ValueError, "Arrival guard"): + run(edges=[(10, 0, 2., 0)], events=[(0, 10)], hook=wrong_guard) + def wrong_tick(tick, state, observer): + observer(tick + 1, state) + with self.assertRaisesRegex(ValueError, "out-of-order"): + run(hook=wrong_tick) + + def test_sequential_arrival_roundoff_has_own_lane(self): + _, _, data, report = run(edges=[(10, 0, 1e16, 0), (11, 0, 1., 0), (12, 0, -1e16, 1)], + events=[(0, 10), (0, 11), (0, 12)], coeff={"cg": 0.}) + if np.finfo(np.longdouble).nmant > np.finfo(np.float64).nmant: + self.assertEqual(data["arrival_roundoff_g_SI"].tolist(), [-1.]) + self.assertTrue(report["voltage_decomposition"]["roundoff_lane_explicit"]) + self.assertFalse(report["unique_cause_claim"]) + + +if __name__ == "__main__": + unittest.main() diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/test_publication.py b/models/male-cns-lif/pn-recruitment-audit-v1/test_publication.py new file mode 100644 index 0000000..8fa69c9 --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/test_publication.py @@ -0,0 +1,205 @@ +"""Only temporary synthetic seals are used; production publication is never run.""" +import hashlib +import json +from pathlib import Path +import tempfile +import unittest +from unittest import mock + +import publication + + +def _sha(path): + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def _write(path, value): + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(value), encoding='utf-8') + + +def _seal(root): + rows = [dict(path=path.relative_to(root).as_posix(), bytes=path.stat().st_size, sha256=_sha(path)) + for path in sorted(root.rglob('*')) if path.is_file() and + path.name not in ('manifest.json', 'checksums.sha256') and '__pycache__' not in path.parts] + _write(root / 'manifest.json', dict(root=str(root), files=rows)) + lines = [row['sha256'] + ' ' + row['path'] for row in rows] + lines.append(_sha(root / 'manifest.json') + ' manifest.json') + (root / 'checksums.sha256').write_text('\n'.join(lines) + '\n', encoding='utf-8') + + +class PublicationTests(unittest.TestCase): + def setUp(self): + self.temporary = tempfile.TemporaryDirectory(prefix='phase4ai-publication-unit-') + self.addCleanup(self.temporary.cleanup) + self.root = Path(self.temporary.name).resolve() + self.model = self.root / publication.MODEL_RELATIVE + self.experiment = self.root / publication.EXPERIMENT_RELATIVE + self.registry = self.root / publication.REGISTRY_RELATIVE + self.model.mkdir(parents=True) + self.experiment.mkdir(parents=True) + (self.model / 'module.py').write_text('# synthetic\n', encoding='utf-8') + status = dict(status=publication.FINAL_STATUS, pass_all=True, candidate_generated=False, + learning_achieved=False, new_brain_simulation=False, GPU_used=False) + _write(self.experiment / 'audit/final-status.json', status) + _write(self.experiment / 'trials/example/summary.json', dict(synthetic=True)) + _write(self.experiment / 'trials/example/apl-summary.json', dict(synthetic=True)) + (self.experiment / 'REPORT.md').write_text('Synthetic evidence.\n', encoding='utf-8') + (self.experiment / 'unit-tests.log').write_text('Synthetic tests passed.\n', encoding='utf-8') + (self.experiment / 'counts.npy').write_bytes(b'not-a-real-array') + _seal(self.model) + _seal(self.experiment) + + def test_all_payload_and_checksum_self_hash_are_verified(self): + result = publication.verify_seal(self.experiment) + self.assertTrue(result['pass_all']) + self.assertEqual(result['payload_file_count'], 6) + self.assertEqual(result['manifest_sha256'], _sha(self.experiment / 'manifest.json')) + + def test_publication_only_copies_small_evidence_and_leaves_sources_unchanged(self): + before = {str(path): _sha(path) for base in (self.model, self.experiment) + for path in base.rglob('*') if path.is_file()} + outcome = publication.publish(self.root) + self.assertTrue(outcome['publication_complete']) + self.assertFalse(outcome['scientific_gate_executed_by_publisher']) + self.assertTrue((self.registry / 'evidence/trials/example/apl-summary.json').is_file()) + self.assertTrue((self.registry / 'evidence/unit-tests.log').is_file()) + self.assertFalse((self.registry / 'evidence/counts.npy').exists()) + self.assertFalse((self.registry / 'model/module.py').exists()) + self.assertTrue((self.registry / 'model/manifest.json').is_file()) + receipt = json.loads((self.registry / 'publication-receipt.json').read_text()) + self.assertFalse(receipt['complete_bulk_backup']) + self.assertFalse(receipt['scientific_gate_certified_by_publication']) + for row in receipt['files']: + path = self.registry / row['path'] + self.assertEqual(path.stat().st_size, row['bytes']) + self.assertEqual(_sha(path), row['sha256']) + for name, checksum in before.items(): + self.assertEqual(_sha(Path(name)), checksum) + + def test_existing_registry_is_never_overwritten_or_resumed(self): + self.registry.mkdir(parents=True) + sentinel = self.registry / 'keep.txt' + sentinel.write_text('keep', encoding='utf-8') + with self.assertRaisesRegex(ValueError, 'Registry already exists'): + publication.publish(self.root) + self.assertEqual(sentinel.read_text(), 'keep') + self.assertEqual(list(self.registry.iterdir()), [sentinel]) + + def test_bulk_payload_corruption_blocks_before_registry_creation(self): + (self.experiment / 'counts.npy').write_bytes(b'corrupt') + with self.assertRaisesRegex(ValueError, 'Payload byte count mismatch'): + publication.publish(self.root) + self.assertFalse(self.registry.exists()) + + def test_same_size_payload_hash_corruption_is_detected(self): + path = self.experiment / 'counts.npy' + path.write_bytes(b'x' * path.stat().st_size) + with self.assertRaisesRegex(ValueError, 'Payload SHA-256 mismatch'): + publication.verify_seal(self.experiment) + + def test_missing_payload_or_unlisted_file_is_detected(self): + (self.experiment / 'extra.txt').write_text('unlisted', encoding='utf-8') + with self.assertRaisesRegex(ValueError, 'unlisted/missing'): + publication.verify_seal(self.experiment) + + def test_wrong_manifest_self_checksum_is_detected(self): + path = self.experiment / 'checksums.sha256' + lines = path.read_text().splitlines() + lines[-1] = '0' * 64 + ' manifest.json' + path.write_text('\n'.join(lines) + '\n') + with self.assertRaisesRegex(ValueError, 'manifest self-checksum'): + publication.verify_seal(self.experiment) + + def test_duplicate_checksum_path_is_detected(self): + path = self.experiment / 'checksums.sha256' + text = path.read_text() + path.write_text(text + text.splitlines()[0] + '\n') + with self.assertRaisesRegex(ValueError, 'Duplicate checksum'): + publication.verify_seal(self.experiment) + + def test_manifest_root_mismatch_is_detected(self): + path = self.experiment / 'manifest.json' + value = json.loads(path.read_text()) + value['root'] = str(self.model) + _write(path, value) + with self.assertRaisesRegex(ValueError, 'another root'): + publication.verify_seal(self.experiment) + + def test_manifest_duplicate_path_and_traversal_are_rejected(self): + manifest = self.experiment / 'manifest.json' + original = json.loads(manifest.read_text()) + for bad_path in ('../escape.txt', '/absolute', 'a/../escape', 'a\\escape', 'C:/escape'): + with self.subTest(path=bad_path): + value = json.loads(json.dumps(original)) + value['files'][0]['path'] = bad_path + _write(manifest, value) + with self.assertRaises(ValueError): + publication.verify_seal(self.experiment) + original['files'].append(original['files'][0].copy()) + _write(manifest, original) + with self.assertRaisesRegex(ValueError, 'Duplicate manifest path'): + publication.verify_seal(self.experiment) + + def test_explicitly_excluded_pycache_is_not_published(self): + cache = self.experiment / '__pycache__/unused.pyc' + cache.parent.mkdir() + cache.write_bytes(b'cache') + self.assertEqual(publication.verify_seal(self.experiment)['excluded_pycache_files'], 1) + publication.publish(self.root) + self.assertFalse((self.registry / 'evidence/__pycache__').exists()) + + def test_file_size_limit_skips_large_json_without_omitting_verification(self): + huge = self.experiment / 'huge.json' + huge.write_bytes(b' ' * (publication.MAX_EVIDENCE_BYTES + 1)) + _seal(self.experiment) + publication.publish(self.root) + self.assertFalse((self.registry / 'evidence/huge.json').exists()) + receipt = json.loads((self.registry / 'publication-receipt.json').read_text()) + omitted = {row['path']: row for row in receipt['experiment_payload_not_copied']} + self.assertEqual(omitted['huge.json']['reason'], 'exceeds_4_MiB') + + def test_unexpected_final_status_or_scope_flag_blocks_publication(self): + path = self.experiment / 'audit/final-status.json' + original = json.loads(path.read_text()) + for field, value in [('status', 'OTHER-PASS'), ('pass_all', False), ('learning_achieved', True), + ('GPU_used', True), ('new_brain_simulation', True), ('candidate_generated', True)]: + with self.subTest(field=field): + bad = dict(original) + bad[field] = value + _write(path, bad) + _seal(self.experiment) + with self.assertRaises(ValueError): + publication.publish(self.root) + self.assertFalse(self.registry.exists()) + + def test_copy_failure_is_retained_without_success_receipt(self): + with mock.patch.object(publication, '_copy_verified', side_effect=OSError('synthetic interruption')): + with self.assertRaisesRegex(OSError, 'synthetic interruption'): + publication.publish(self.root) + self.assertTrue(self.registry.is_dir()) + self.assertTrue((self.registry / 'publication-failure.json').is_file()) + self.assertFalse((self.registry / 'publication-receipt.json').exists()) + with self.assertRaisesRegex(ValueError, 'Registry already exists'): + publication.publish(self.root) + + def test_source_change_during_copy_prevents_success(self): + original = publication._copy_verified + changed = False + + def copy_then_modify(source, target, byte_count, checksum): + nonlocal changed + original(source, target, byte_count, checksum) + if not changed: + changed = True + (self.experiment / 'counts.npy').write_bytes(b'deliberately-changed-after-initial-verification') + + with mock.patch.object(publication, '_copy_verified', side_effect=copy_then_modify): + with self.assertRaisesRegex(ValueError, 'Payload byte count mismatch'): + publication.publish(self.root) + self.assertFalse((self.registry / 'publication-receipt.json').exists()) + self.assertTrue((self.registry / 'publication-failure.json').exists()) + + +if __name__ == '__main__': + unittest.main() diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/test_structure_metrics.py b/models/male-cns-lif/pn-recruitment-audit-v1/test_structure_metrics.py new file mode 100644 index 0000000..567fc4a --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/test_structure_metrics.py @@ -0,0 +1,204 @@ +"""Synthetic tests only; no saved scientific results are accessed.""" +import copy +import json +import unittest + +import numpy as np + +from structure_metrics import aggregate_structure, MODES, SEEDS, METRICS + + +def records(): + rows = [] + for mode in MODES: + for odor in range(6): + design = np.zeros(686, bool) + design[odor] = True + for seed in SEEDS: + count = np.zeros(686, np.int32) + count[odor] = 10 + count[10] = seed - SEEDS[0] + count[11] = odor + rows.append(dict(mode=mode, odor_index=odor, seed=seed, + counts=count, design_mask=design.copy())) + return rows + + +class StructureMetricsTests(unittest.TestCase): + def test_fixed_complete_inventory_and_pair_specific_baseline(self): + result = aggregate_structure(records()) + self.assertEqual(result['record_count'], 60) + self.assertEqual(result['design']['design_union_count'], 6) + self.assertEqual(result['design']['common_non_design_count'], 680) + self.assertEqual(result['design']['common_non_design_column_positions'], list(range(6, 686))) + for mode in MODES: + self.assertEqual(result['modes'][mode]['trials'], 30) + for view_name in ('fixed_686', 'common_non_design'): + view = result['modes'][mode][view_name] + self.assertEqual(len(view['within_comparisons']), 60) + self.assertEqual(len(view['between_comparisons']), 75) + self.assertEqual(len(view['pairs']), 15) + for pair in view['pairs']: + self.assertEqual(len(pair['within_comparisons']), 20) + self.assertEqual(len(pair['between_comparisons']), 5) + self.assertEqual({row['odor_index'] for row in pair['within_comparisons']}, + {pair['odor_index_a'], pair['odor_index_b']}) + self.assertEqual([row['seed'] for row in pair['between_comparisons']], list(SEEDS)) + json.dumps(result, allow_nan=False) + + def test_primary_retains_design_columns_and_common_view_is_fixed(self): + result = aggregate_structure(records())['modes'][MODES[0]] + pair = result['fixed_686']['pairs'][0] + common_pair = result['common_non_design']['pairs'][0] + # Odors zero and one differ by two ten-count design axes plus one + # shared-coordinate count; same-seed variability cancels between. + self.assertEqual(pair['between']['euclidean_distance']['mean'], np.sqrt(201.)) + self.assertEqual(common_pair['between']['euclidean_distance']['mean'], 1.) + self.assertEqual(pair['within']['euclidean_distance']['mean'], 2.) + self.assertEqual(common_pair['within']['euclidean_distance']['mean'], 2.) + + def test_input_order_does_not_change_result(self): + rows = records() + expected = aggregate_structure(rows) + actual = aggregate_structure(list(reversed(rows))) + self.assertEqual(expected, actual) + + def test_inputs_unchanged_and_readonly_arrays_accepted(self): + rows = records() + before = copy.deepcopy(rows) + for row in rows: + row['counts'].setflags(write=False) + row['design_mask'].setflags(write=False) + aggregate_structure(rows) + for old, new in zip(before, rows): + np.testing.assert_array_equal(old['counts'], new['counts']) + np.testing.assert_array_equal(old['design_mask'], new['design_mask']) + self.assertFalse(new['counts'].flags.writeable) + + def test_zero_vectors_do_not_become_similarity_evidence(self): + rows = records() + for row in rows: + row['counts'].fill(0) + result = aggregate_structure(rows) + for mode in MODES: + for name in ('fixed_686', 'common_non_design'): + view = result['modes'][mode][name] + for metric in ('cosine_distance', 'pearson_correlation', 'jaccard'): + self.assertIsNone(view['within'][metric]['mean']) + self.assertEqual(view['within'][metric]['valid_comparisons'], 0) + self.assertEqual(view['between'][metric]['undefined_comparisons'], 75) + self.assertEqual(view['between']['euclidean_distance']['mean'], 0.) + json.dumps(result, allow_nan=False) + + def test_constant_nonzero_vectors_have_undefined_pearson(self): + rows = records() + for row in rows: + row['counts'].fill(1) + view = aggregate_structure(rows)['modes'][MODES[0]]['fixed_686'] + self.assertIsNone(view['within']['pearson_correlation']['mean']) + self.assertEqual(view['within']['jaccard']['mean'], 1.) + self.assertAlmostEqual(view['within']['cosine_distance']['mean'], 0., places=14) + + def test_empty_common_population_is_nonapplicable(self): + rows = records() + for row in rows: + row['design_mask'].fill(True) + result = aggregate_structure(rows) + self.assertEqual(result['design']['common_non_design_count'], 0) + for mode in MODES: + view = result['modes'][mode]['common_non_design'] + self.assertFalse(view['applicable']) + self.assertEqual(view['dimensions'], 0) + for metric in METRICS: + self.assertIsNone(view['between'][metric]['mean']) + self.assertEqual(view['within'][metric]['undefined_comparisons'], 60) + + def test_int32_extrema_are_promoted_before_subtraction_and_norm(self): + rows = records() + maximum = np.iinfo(np.int32).max + for row in rows: + row['counts'].fill(0) + row['counts'][row['odor_index']] = maximum + result = aggregate_structure(rows)['modes'][MODES[0]]['fixed_686'] + distance = result['pairs'][0]['between']['euclidean_distance']['mean'] + self.assertAlmostEqual(distance / float(maximum), np.sqrt(2.)) + self.assertEqual(result['between']['cosine_distance']['mean'], 1.) + json.dumps(result, allow_nan=False) + + def test_missing_record_or_extra_repeat_rejected(self): + rows = records() + for bad in (rows[:-1], rows + [copy.deepcopy(rows[0])]): + with self.assertRaisesRegex(ValueError, 'Exactly 60'): + aggregate_structure(bad) + + def test_duplicate_identity_rejected_even_with_sixty_rows(self): + rows = records() + rows[-1] = copy.deepcopy(rows[0]) + with self.assertRaisesRegex(ValueError, 'Duplicate CAL identity'): + aggregate_structure(rows) + + def test_mask_change_across_seed_rejected(self): + rows = records() + rows[1]['design_mask'][100] = True + with self.assertRaisesRegex(ValueError, 'mask changed across seed or mode'): + aggregate_structure(rows) + + def test_mask_change_across_mode_rejected(self): + rows = records() + for row in rows: + if row['mode'] == MODES[1] and row['odor_index'] == 0: + row['design_mask'][100] = True + with self.assertRaisesRegex(ValueError, 'mask changed across seed or mode'): + aggregate_structure(rows) + + def test_negative_counts_and_incorrect_dtype_or_shape_rejected(self): + changes = [('negative', lambda row: row['counts'].__setitem__(0, -1)), + ('counts64', lambda row: row.update(counts=row['counts'].astype(np.int64))), + ('counts_float', lambda row: row.update(counts=row['counts'].astype(np.float64))), + ('shape', lambda row: row.update(counts=np.zeros(685, np.int32))), + ('mask_dtype', lambda row: row.update(design_mask=row['design_mask'].astype(np.uint8))), + ('mask_shape', lambda row: row.update(design_mask=np.zeros(685, bool)))] + for label, change in changes: + with self.subTest(label=label): + rows = records() + change(rows[0]) + with self.assertRaises(ValueError): + aggregate_structure(rows) + + def test_strict_record_schema_and_id_ranges(self): + changes = [lambda row: row.update(extra=1), lambda row: row.pop('counts'), + lambda row: row.update(mode='new_mode'), lambda row: row.update(odor_index=6), + lambda row: row.update(odor_index=True), lambda row: row.update(seed=51106), + lambda row: row.update(seed=float(SEEDS[0]))] + for change in changes: + rows = records() + change(rows[0]) + with self.assertRaises(ValueError): + aggregate_structure(rows) + + def test_partial_undefined_metrics_report_counts_without_imputation(self): + rows = records() + # One zero-count record per mode. Four of the 60 within comparisons + # and five of the 75 between comparisons then have undefined cosine. + for row in rows: + if row['odor_index'] == 0 and row['seed'] == SEEDS[0]: + row['counts'].fill(0) + view = aggregate_structure(rows)['modes'][MODES[0]]['fixed_686'] + self.assertEqual(view['within']['cosine_distance']['undefined_comparisons'], 4) + self.assertEqual(view['between']['cosine_distance']['undefined_comparisons'], 5) + self.assertEqual(view['within']['cosine_distance']['valid_comparisons'], 56) + self.assertEqual(view['between']['cosine_distance']['valid_comparisons'], 70) + + def test_one_common_channel_pearson_is_undefined(self): + rows = records() + for row in rows: + row['design_mask'][:685] = True + row['counts'][685] = row['odor_index'] + 1 + view = aggregate_structure(rows)['modes'][MODES[0]]['common_non_design'] + self.assertEqual(view['dimensions'], 1) + self.assertIsNone(view['between']['pearson_correlation']['mean']) + self.assertEqual(view['between']['cosine_distance']['mean'], 0.) + + +if __name__ == '__main__': + unittest.main() diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/test_validate_events.py b/models/male-cns-lif/pn-recruitment-audit-v1/test_validate_events.py new file mode 100644 index 0000000..fb924d6 --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/test_validate_events.py @@ -0,0 +1,134 @@ +"""Pure synthetic checks for stateless pre-first event validation.""" +import unittest + +import numpy as np + +from validate_events import COLUMN_DTYPES, N_GLOBAL, validate_batch + + +def fixture(): + reference = dict(edge_ids=np.array([7, 20, 99], np.int64), + srcglobals=np.array([3, 4, 3], np.int32), dstglobals=np.array([10, 10, 20], np.int32), + weights=np.array([.001, -.002, .003], np.float64), + target_globals=np.array([10, 20, 30], np.int32), target_first=np.array([30, -1, 0], np.int32), + source_event_keys=np.array([3, 4, 4 * N_GLOBAL + 3], np.int64)) + # Arbitrary order, repeated edge at a later tick, negative and positive. + values = dict(edge_id=[99, 20, 7, 99], source_neuron_idx=[3, 4, 3, 3], + target_neuron_idx=[20, 10, 10, 20], source_spike_tick=[4, 0, 0, 0], + arrival_tick=[22, 18, 18, 18], weight_sign=[1, -1, 1, 1], + ref_at_arrival=[0] * 4, weight_SI=[.003, -.002, .001, .003], accepted=[True] * 4) + columns = {name: np.array(values[name], dtype) for name, dtype in COLUMN_DTYPES.items()} + return columns, reference + + +class EventValidationTests(unittest.TestCase): + def test_unordered_rows_real_edges_signed_weights_and_never_first(self): + columns, ref = fixture() + original = {k: v.copy() for k, v in columns.items()} + result = validate_batch(columns, **ref) + self.assertEqual(result['count'], 4) + np.testing.assert_array_equal(result['per_target_count'], [2, 2, 0]) + self.assertEqual(result['per_target_count'].dtype, np.int64) + self.assertFalse(result['unique_cause_claim']) + self.assertFalse(result['cross_batch_completeness_checked']) + for key in columns: + np.testing.assert_array_equal(columns[key], original[key]) + + def test_empty_valid_but_missing_wrong_dtype_or_shape_still_rejected(self): + columns, ref = fixture() + empty = {name: value[:0] for name, value in columns.items()} + result = validate_batch(empty, **ref) + self.assertEqual(result['count'], 0) + np.testing.assert_array_equal(result['per_target_count'], [0, 0, 0]) + bad = empty.copy() + bad['arrival_tick'] = np.empty(0, np.int64) + with self.assertRaises(ValueError): + validate_batch(bad, **ref) + bad = empty.copy() + del bad['accepted'] + with self.assertRaises(ValueError): + validate_batch(bad, **ref) + bad = empty.copy() + bad['weight_sign'] = np.empty((0, 1), np.int32) + with self.assertRaises(ValueError): + validate_batch(bad, **ref) + + def test_every_edge_field_and_weight_bit_is_checked(self): + for field, value in [('edge_id', 8), ('source_neuron_idx', 4), ('target_neuron_idx', 10), + ('weight_sign', -1), ('weight_SI', np.nextafter(.003, np.inf))]: + with self.subTest(field=field): + columns, ref = fixture() + columns[field][0] = value + with self.assertRaises(ValueError): + validate_batch(columns, **ref) + columns, ref = fixture() + ref['weights'][2] = 0. + columns['weight_SI'][[0, 3]] = -0. + with self.assertRaisesRegex(ValueError, 'bits mismatch'): + validate_batch(columns, **ref) + columns['weight_SI'][[0, 3]] = 0. + with self.assertRaisesRegex(ValueError, 'zero-weight'): + validate_batch(columns, **ref) + + def test_guard_strict_prefirst_and_source_delay(self): + changes = [('accepted', False), ('ref_at_arrival', 1), ('source_spike_tick', -1), + ('source_spike_tick', 3), ('arrival_tick', 21)] + for field, value in changes: + with self.subTest(field=field): + columns, ref = fixture() + columns[field][0] = value + with self.assertRaises(ValueError): + validate_batch(columns, **ref) + for first in (22, 21): + columns, ref = fixture() + ref['target_first'][1] = first + with self.assertRaisesRegex(ValueError, 'strictly before'): + validate_batch(columns, **ref) + + def test_source_event_membership_and_target_population(self): + columns, ref = fixture() + ref['source_event_keys'] = ref['source_event_keys'][:2] + with self.assertRaisesRegex(ValueError, 'source spike'): + validate_batch(columns, **ref) + columns, ref = fixture() + ref['target_globals'] = np.array([10, 30], np.int32) + ref['target_first'] = np.array([30, 0], np.int32) + with self.assertRaisesRegex(ValueError, 'target global'): + validate_batch(columns, **ref) + + def test_duplicate_rows_rejected_without_sort_assumption(self): + columns, ref = fixture() + for value in columns.values(): + value[0] = value[3] + with self.assertRaisesRegex(ValueError, 'Duplicate edge event'): + validate_batch(columns, **ref) + + def test_invalid_reference_schema_order_duplicate_range_nonfinite(self): + variants = [dict(edge_ids=np.array([7, 20, 99], np.int32)), + dict(edge_ids=np.array([7, 99, 20], np.int64)), + dict(edge_ids=np.array([7, 20, 20], np.int64)), + dict(srcglobals=np.array([3, 4, N_GLOBAL], np.int32)), + dict(weights=np.array([.001, -.002, np.inf])), + dict(target_first=np.array([30, -2, 0], np.int32)), + dict(source_event_keys=np.array([3, 3, 4], np.int64)), + dict(source_event_keys=np.array([4, 3], np.int64)), + dict(source_event_keys=np.array([-1, 3], np.int64))] + for change in variants: + with self.subTest(change=change): + columns, ref = fixture() + with self.assertRaises(ValueError): + validate_batch(columns, **(ref | change)) + + def test_optional_design_and_local_row_fields_are_not_assumed(self): + columns, ref = fixture() + columns['incoming_edge_row'] = np.array([1000, 2, 1, 1000], np.int64) + columns['target_is_design'] = np.array([True, False, True, False]) + self.assertEqual(validate_batch(columns, **ref)['count'], 4) + bad = columns.copy() + bad['accepted'] = columns['accepted'].astype(np.uint8) + with self.assertRaises(ValueError): + validate_batch(bad, **ref) + + +if __name__ == '__main__': + unittest.main(verbosity=2) diff --git a/models/male-cns-lif/pn-recruitment-audit-v1/validate_events.py b/models/male-cns-lif/pn-recruitment-audit-v1/validate_events.py new file mode 100644 index 0000000..2769165 --- /dev/null +++ b/models/male-cns-lif/pn-recruitment-audit-v1/validate_events.py @@ -0,0 +1,123 @@ +"""Stateless independent checks for accepted pre-first anatomical event rows. + +Pure NumPy: no files or brain execution. Passing establishes row consistency +with the supplied graph and recorded source events, not necessity/unique cause. +The caller owns provenance, design labels, trial bounds, cross-batch duplicate +detection, expected totals, and completeness of the full record collection. +""" +from collections.abc import Mapping + +import numpy as np + + +N_GLOBAL = 166700 +DELAY_STEPS = 18 +COLUMN_DTYPES = { + 'edge_id': np.int64, + 'source_neuron_idx': np.int32, + 'target_neuron_idx': np.int32, + 'source_spike_tick': np.int32, + 'arrival_tick': np.int32, + 'weight_sign': np.int32, + 'ref_at_arrival': np.int32, + 'weight_SI': np.float64, + 'accepted': np.bool_, +} + + +def _require(condition, message): + if not condition: + raise ValueError(message) + + +def _vector(value, dtype, name, length=None): + _require(isinstance(value, np.ndarray), name + ' must be a NumPy array') + _require(value.dtype == np.dtype(dtype) and value.ndim == 1, name + ' has the wrong dtype/shape') + _require(length is None or len(value) == length, name + ' has the wrong length') + return value + + +def _globals(value, name, length=None, sorted_unique=False): + value = _vector(value, np.int32, name, length) + _require(np.all((value >= 0) & (value < N_GLOBAL)), name + ' contains an out-of-range global ID') + if sorted_unique: + _require(np.all(value[1:] > value[:-1]), name + ' must be sorted and unique') + return value + + +def _lookup(sorted_reference, wanted, name): + positions = np.searchsorted(sorted_reference, wanted) + _require(np.all(positions < len(sorted_reference)), name + ' is absent from the reference') + _require(np.array_equal(sorted_reference[positions], wanted), name + ' is absent from the reference') + return positions + + +def validate_batch(columns, *, edge_ids, srcglobals, dstglobals, weights, + target_globals, target_first, source_event_keys): + """Validate arbitrary-order rows; return count and target-indexed int64 counts. + + Required columns and exact dtypes are COLUMN_DTYPES. Additional columns are + intentionally not interpreted: in particular incoming_edge_row has a + caller-specific coordinate system, and design/source labels are separate. + References: sorted unique nonnegative edge_ids:int64[E]; source/destination + globals:int32[E]; finite effective SI weights:float64[E]; sorted unique + target_globals:int32[K]; target_first:int32[K] (-1 means never fired); + source_event_keys:int64 sorted unique, encoding tick*166700+global ID. + + Every row must reference a real nonzero edge, its exact weight bits/sign, + accepted=True/ref_at_arrival=0, a recorded source event exactly 18 ticks + earlier, and arrival strictly before the target's first spike, if any. + Duplicate (arrival_tick,edge_id) rows within this batch are rejected without + assuming any input row ordering. Cross-batch duplicates require caller state. + Empty batches still require valid complete schema/references and return zero. + No supplied array is modified or sorted in place. + """ + _require(isinstance(columns, Mapping), 'columns must be a mapping') + _require(set(COLUMN_DTYPES).issubset(columns), 'Missing required event columns') + ids = _vector(edge_ids, np.int64, 'edge_ids') + e = len(ids) + _require(np.all(ids >= 0) and np.all(ids[1:] > ids[:-1]), 'edge_ids must be sorted unique nonnegative IDs') + sources = _globals(srcglobals, 'srcglobals', e) + destinations = _globals(dstglobals, 'dstglobals', e) + weight = _vector(weights, np.float64, 'weights', e) + _require(np.isfinite(weight).all(), 'Nonfinite reference weight') + targets = _globals(target_globals, 'target_globals', sorted_unique=True) + first = _vector(target_first, np.int32, 'target_first', len(targets)) + _require(np.all(first >= -1), 'target_first must be -1 or a nonnegative tick') + keys = _vector(source_event_keys, np.int64, 'source_event_keys') + largest_key = int(np.iinfo(np.int32).max) * N_GLOBAL + N_GLOBAL - 1 + _require(np.all((keys >= 0) & (keys <= largest_key)) and np.all(keys[1:] > keys[:-1]), + 'source_event_keys must be sorted unique valid int32-tick/global encodings') + count = len(_vector(columns['edge_id'], np.int64, 'columns.edge_id')) + data = {name: _vector(columns[name], dtype, 'columns.' + name, count) + for name, dtype in COLUMN_DTYPES.items()} + _require(np.isfinite(data['weight_SI']).all(), 'Nonfinite recorded weight') + _require(np.all(data['accepted']) and np.all(data['ref_at_arrival'] == 0), + 'Recorded arrival was not accepted at ref=0') + row_edge = _lookup(ids, data['edge_id'], 'Recorded edge ID') + _require(np.array_equal(data['source_neuron_idx'], sources[row_edge]), 'Recorded edge source mismatch') + _require(np.array_equal(data['target_neuron_idx'], destinations[row_edge]), 'Recorded edge target mismatch') + actual_weight = weight[row_edge] + _require(np.array_equal(np.ascontiguousarray(data['weight_SI']).view(np.uint64), + np.ascontiguousarray(actual_weight).view(np.uint64)), 'Recorded weight bits mismatch') + _require(np.all(actual_weight != 0), 'Pre-first nonzero-event table contains a zero-weight edge') + _require(np.array_equal(data['weight_sign'], np.sign(actual_weight).astype(np.int32)), 'Recorded weight sign mismatch') + local_targets = _lookup(targets, data['target_neuron_idx'], 'Recorded target global') + arrival = data['arrival_tick'].astype(np.int64) + source_tick = data['source_spike_tick'].astype(np.int64) + _require(np.all(source_tick >= 0) and np.array_equal(source_tick, arrival - DELAY_STEPS), + 'Source/arrival delay is not exactly 18 nonnegative ticks') + wanted_keys = source_tick * np.int64(N_GLOBAL) + data['source_neuron_idx'].astype(np.int64) + _lookup(keys, wanted_keys, 'Recorded source spike') + target_first_rows = first[local_targets].astype(np.int64) + _require(np.all((target_first_rows == -1) | (arrival < target_first_rows)), + 'Arrival is not strictly before the observed first target spike') + if count > 1: + order = np.lexsort((data['edge_id'], arrival)) + sorted_arrival, sorted_edge = arrival[order], data['edge_id'][order] + _require(not np.any((sorted_arrival[1:] == sorted_arrival[:-1]) & + (sorted_edge[1:] == sorted_edge[:-1])), 'Duplicate edge event within batch') + per_target = np.bincount(local_targets, minlength=len(targets)).astype(np.int64, copy=False) + _require(int(per_target.sum()) == count, 'Target counts do not cover the batch') + return dict(pass_all=True, count=count, per_target_count=per_target, + unique_cause_claim=False, cross_batch_completeness_checked=False) diff --git a/registry/phase4ai-pn-recruitment-audit/evidence-index.json b/registry/phase4ai-pn-recruitment-audit/evidence-index.json new file mode 100644 index 0000000..22e95d7 --- /dev/null +++ b/registry/phase4ai-pn-recruitment-audit/evidence-index.json @@ -0,0 +1,1544 @@ +[ + { + "source": 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4AI — conditional PN recruitment and APL source audit + +Status: `PASS-CONDITIONAL-PN-APL-SOURCE-AUDIT`. + +80 existing records; PN events/counts/final states exact; eight fixture histories exact; twelve repeats exact. No new brain experiment, tuning, training, GPU, or learning candidate. + +See audit/final-status.json and audit/PN-odor-structure.json. Accepted edges are timed antecedents, not unique causes. Graded-APL cumulative source shares are descriptive, not causal or error-bounded majority claims. Prior CAL failure remains unchanged. Frozen integrity passed; five reference simulations were not rerun. diff --git a/registry/phase4ai-pn-recruitment-audit/evidence/audit/PN-odor-structure.json b/registry/phase4ai-pn-recruitment-audit/evidence/audit/PN-odor-structure.json new file mode 100644 index 0000000..e0243da --- /dev/null +++ b/registry/phase4ai-pn-recruitment-audit/evidence/audit/PN-odor-structure.json @@ -0,0 +1,34834 @@ +{ + "schema": "phase4ai-alpn-structure-v1", + "record_count": 60, + "ALPN_dimensions": 686, + "design": { + "identical_across_seeds_and_modes": true, + "design_neuron_count_by_odor": { + "0": 48, + "1": 55, + "2": 33, + "3": 44, + "4": 48, + "5": 48 + }, + "design_union_count": 76, + "common_non_design_count": 610, + "common_non_design_column_positions": [ + 0, + 1, + 2, + 3, + 5, + 6, + 8, + 10, + 11, + 14, + 15, + 16, + 17, + 19, + 20, + 21, + 22, + 24, + 27, + 29, + 30, + 31, + 32, + 34, + 35, + 37, + 38, + 39, + 40, + 41, + 42, + 45, + 46, + 47, + 48, + 49, + 50, + 52, + 53, + 54, + 55, + 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a/registry/phase4ai-pn-recruitment-audit/evidence/regression/unit-tests.json b/registry/phase4ai-pn-recruitment-audit/evidence/regression/unit-tests.json new file mode 100644 index 0000000..a0b7dd0 --- /dev/null +++ b/registry/phase4ai-pn-recruitment-audit/evidence/regression/unit-tests.json @@ -0,0 +1,18 @@ +{ + "command": [ + "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python", + "-B", + "-m", + "unittest", + "-v", + "test_pn_observer", + "test_apl_sources", + "test_structure_metrics", + "test_harness", + "test_validate_events", + "test_publication" + ], + "exit_code": 0, + "pass_all": true, + "sha256": "c3fa21aa9b3dd8e82c1f6daee6c8bdfe3ad5750daf62c57d45634fc609a170f3" +} diff --git a/registry/phase4ai-pn-recruitment-audit/evidence/trials/apl_only-r0/apl-source-summary.json b/registry/phase4ai-pn-recruitment-audit/evidence/trials/apl_only-r0/apl-source-summary.json new file mode 100644 index 0000000..13a5515 --- /dev/null +++ b/registry/phase4ai-pn-recruitment-audit/evidence/trials/apl_only-r0/apl-source-summary.json @@ -0,0 +1,153 @@ +{ + "pass_all": true, + "steps": 100, + "neurons": 166700, + "apl": 2, + "bundle_KC": 4064, + "actual_events": 2, + "groups": { + "design_ALPN": { + "actual_spikes": 0, + "input_supported_spikes": 0, + "first_actual_spike_tick": null, + "first_nonzero_drive_tick": null, + "cumulative_drive_by_apl": [ + 0.0, + 0.0 + ], + "cumulative_state_by_apl": [ + 0.0, + 0.0 + ], + "h_sum_mV": 0.0, + "h_before_first_KC_sum_mV": 0.0, + "h_never_fired_KC_sum_mV": 0.0, + "h_before_first_any_KC_sum_mV": 0.0, + "generated_h_fraction": null, + "drive_fraction": null + }, + "non_design_ALPN": { + "actual_spikes": 0, + "input_supported_spikes": 0, + "first_actual_spike_tick": null, + "first_nonzero_drive_tick": null, + "cumulative_drive_by_apl": [ + 0.0, + 0.0 + ], + "cumulative_state_by_apl": [ + 0.0, + 0.0 + ], + "h_sum_mV": 0.0, + "h_before_first_KC_sum_mV": 0.0, + "h_never_fired_KC_sum_mV": 0.0, + "h_before_first_any_KC_sum_mV": 0.0, + "generated_h_fraction": null, + "drive_fraction": null + }, + "KC": { + "actual_spikes": 0, + "input_supported_spikes": 0, + "first_actual_spike_tick": null, + "first_nonzero_drive_tick": null, + "cumulative_drive_by_apl": [ + 0.0, + 0.0 + ], + "cumulative_state_by_apl": [ + 0.0, + 0.0 + ], + "h_sum_mV": 0.0, + "h_before_first_KC_sum_mV": 0.0, + "h_never_fired_KC_sum_mV": 0.0, + "h_before_first_any_KC_sum_mV": 0.0, + "generated_h_fraction": null, + "drive_fraction": null + } + }, + "group_order": [ + "design_ALPN", + "non_design_ALPN", + "KC" + ], + "input_support_partition_exact": true, + "unknown_nonzero_input_columns": 0, + "source_population_counts": { + "design_ALPN": 0, + "non_design_ALPN": 686, + "KC": 4064 + }, + "input_supported_population_counts": { + "design_ALPN": 0, + "non_design_ALPN": 282, + "KC": 4063 + }, + "first_any_KC_spike_tick": null, + "ever_fired_KC": 0, + "never_fired_KC": 4064, + "decay_f32_bits": "3f7eb923", + "gain_f32_bits": "3c23d70a", + "comparison": { + "drive": { + "elements": 200, + "max_abs_error": 0.0, + "max_error_bound": 5e-323, + "max_bound_utilization": 0.0, + "bound_violations": 0, + "zero_reference_nonzero_actual": 0, + "mean_abs_error": 0.0 + }, + "state": { + "elements": 200, + "max_abs_error": 0.0, + "max_error_bound": 9.397e-321, + "max_bound_utilization": 0.0, + "bound_violations": 0, + "zero_reference_nonzero_actual": 0, + "mean_abs_error": 0.0 + }, + "inhibition": { + "elements": 406400, + "max_abs_error": 0.0, + "max_error_bound": 5.43e-322, + "max_bound_utilization": 0.0, + "bound_violations": 0, + "zero_reference_nonzero_actual": 0, + "mean_abs_error": 0.0 + } + }, + "theoretical_bound": { + "applicable": true, + "normal_or_zero_arithmetic": true, + "historical_standard_fp32_verified_by_caller": true, + "gamma_definition": "gamma_n=n*u/(1-n*u); u32=2^-24,u64=2^-53; outward rounded", + "drive_terms_bound": 166699, + "state_operation_bound": 2, + "output_operation_bound": 5, + "group_sum_operation_bound": 2, + "state_recurrence": "E_p<=d*E_p_prev+gamma_2,p*(d*abs(s_p_prev)+D_upper)+(1+u_p)*gamma_(N-1),p*D_upper", + "grouped_f64_bound": "Sum source-group error bounds, plus gamma_2,64*upward_sum(group_values) for per-tick compared drive/state/h totals only", + "output_bound": "q*upward_dot(E_p,O)+gamma_5,p*q*upward_dot(s_p,O); f32 error plus all f64 group/output/sum errors", + "tolerance_fitted_to_observed_error": false, + "scope": "Per-tick summed-source drive/state/h trace comparisons only; not cumulative source fractions" + }, + "early_definition": "strict tick 0 for p in view['pairs'])) for m, v in view['within'].items()}) + output[mode] = dict(frozen_aggregate=aggregate, + active_non_design_PN_neuron_trials=len(chosen), unique_active_non_design_PN=int(chosen.neuron_idx.nunique()), + active_non_design_PN_prefirst_source_accounting=source, + source_shares_are_nominal_positive_increment_sums_not_necessity_or_instantaneous_current=True, + odor_structure_descriptive=view_metrics) + result = dict(status=status['status'], modes=output, + exact_PN_trials=status['exact_PN_trials'], repeat_accounting_pass=status['repeat_accounting_pass'], + source_script_sha256=digest(Path(__file__)), + model_manifest_sha256=verified['model']['manifest_sha256'], + experiment_manifest_sha256=verified['experiment']['manifest_sha256'], + descriptive_postseal_interpretation=True, new_scientific_gate=False, learning_achieved=False, + original_CAL_failure_unchanged=True) + path = REGISTRY / 'interpretation-metrics.json' + save(path, result) + with (REGISTRY / 'interpretation-metrics.sha256').open('x') as f: + f.write(digest(path) + ' interpretation-metrics.json\n') + print(json.dumps(result, ensure_ascii=False, allow_nan=False), flush=True) + + +if __name__ == '__main__': + main()