Audit APL PN scope and localize official synaptic contacts without changing frozen dynamics
This commit is contained in:
Digital Drosophila Archive
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# 当前研究状态 — Phase 4AA
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最新封存结果：**PASS-APL-PN-ANATOMICAL-LOCALIZATION / NO-DYNAMICS-CANDIDATE**。
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初始化 README 与原 registry 快照仍按原字节保留；它们描述初始化时的 Phase 4Z 截止状态。最新结论请从本文件进入。
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- [本轮完整报告](docs/progress/phase4aa-apl-pn-scope.md)
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- [本轮证据与最终结果](registry/phase4aa-apl-pn-scope/evidence/audit/final-summary.json)
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- [新模型/审计源码](models/male-cns-lif/apl-pn-scope-audit-v1/PROTOCOL.md)
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- [外部数据官方校验](registry/phase4aa-apl-pn-scope/evidence/external-resources/synaptic-partners/download-verification.json)
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新增事实：268 条 APL→PN 边、4,600 contacts；3,820 contacts 在官方 CA(L/R)，731 仍为 CentralBrain-unspecified。
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七个重点 PN 的 APL 输入集中在 calyx，而其局部 ALLN 回路主要位于触角叶。受体、末梢作用系数与功能因果仍未确认。
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5/5 frozen-v2 trials 事件/计数/V/g 精确一致；230,239 个旧科学文件哈希不变。
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没有训练，没有新动力学候选，顶层 gates 仍是 5/10（50%）。
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下一步是预先定义 compartment/terminal-specific upstream 假设和对照，不直接改冻结模型，不调学习率来制造通过。
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官方 syn-partners 附加数据为 6,777,179,098 bytes，在新实验 external-resources 目录；raw 不变，未下载 EM。
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Git 只保存代码、报告和 manifest；新增完整配对数据尚未异机备份。
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# Phase 4AA — APL→PN 作用范围与官方突触定位审计
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正式结论：**PASS-APL-PN-ANATOMICAL-LOCALIZATION / NO-DYNAMICS-CANDIDATE**。
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本轮从 Phase 4Z 接续，先完成只读 CPU 范围审计；随后用户明确授权下载官方 syn-partners 并继续定位，也确认 ComfyUI 空闲以便运行 frozen regression。
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原始静态审计 `audit/results.json` 中的空间 unknown/blocked 是追加数据前的状态，保留不覆盖；最终状态以 `audit/final-summary.json` 和本报告为准。
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本轮无训练、无参数修改、无新的正式动力学候选。顶层验收仍为 5/10（50%），不是生物学复现比例。
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## 核心新证据
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以实际 annotation 与原始 CSR 识别 2 个 APL、4,064 个 KC、686 个 ALPN，不通过 neurotransmitter 推断 DAN 身份。
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所有 166,700 个 neurons、25,582,938 edges、124,177,617 anatomical contacts 不变。
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| 已有 APL 实现范围 | 边数 | 原始 contacts |
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|---|---:|---:|
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| APL 全部出边 | 5,845 | 209,912 |
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| KC-only graded output 覆盖 | 4,633 | 196,200 |
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| 非 KC 出边，没有 graded 替代 | 1,212 | 13,712 |
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| 上述缺口中的 APL→PN | 268 | 4,600 |
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| APL 全部入边 | 6,185 | 234,025 |
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| 不被 KC/PN-only graded state 使用的入边 | 1,202 | 18,257 |
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268 条 APL→PN 边涉及 259 个 PN；247 对同时存在 PN→APL 反向连接。
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“graded state 未使用”不等于原图入边被删除：普通 LIF APL 仍有这些入边，但其快速出边被既有 overlay 置零。基于 KC/PN 计算的独立 graded state 没有使用其余来源。
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这属于既有模型作用范围，不能把每个未覆盖来源都自动补入；需要额外生理依据。
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## 官方附加数据与精确核对
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用户授权下载的 `syn-partners-male-cns-v1.0-minconf-0.5.feather`：
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- 6,777,179,098 bytes；官方 generation `1780494942562468`。
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- 官方 MD5：`58efcf712f8c4d4de5f2ad51e97def76`，验证通过。
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- SHA-256：`959d8ef4173b35382a3e6acfaf5167c795b6d10b877572d146af04e1b487bc07`。
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- 流式读取全部 311,833,243 条配对记录；实际字段和 dtype 已保存。
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- 来源：[MaleCNS 官方下载页](https://male-cns.janelia.org/download/)，许可证 CC BY 4.0。新增文件保存在本阶段 external-resources，不写 raw，不下载 EM。
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| 提取范围 | 配对记录总数 | canonical 配对记录 | canonical neuron pairs |
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|---|---:|---:|---:|
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| APL 所有入/出接触 | 500,348 | 443,915 | 12,027 |
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| APL 所连 PN→KC | 385,229 | 385,229 | 21,978 |
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| 七个重点 PN→ALLN | 22,599 | 22,599 | 823 |
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| ALLN→七个重点 PN | 19,024 | 19,024 | 886 |
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每组同时检查重复配对、完整 pair 集合及逐边 count：均与原始 CSR 完全一致。不存在只比较出现过的边而漏掉整条缺失边的情况。
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APL 配对中的非 canonical 部分保留来源记录，不加入 neuron-only graph。
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## 真实 ROI 定位
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4,600 个 APL→PN 接触都取得真实 pre/post 坐标：
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- `CA(L)` 2,063；`CA(R)` 1,757；合计 3,820（83.04%）。
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- `CentralBrain-unspecified` 731（15.89%）：坐标已知，但官方 ROI 未细分，绝不按邻近位置重标为 calyx。
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- 其余实际标注合计 49，分布于 LH、PED、SCL、SLP、a'L；不丢弃非 calyx 结果。
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CA 对应 calyx，官方作者 [Cell Type Explorer](https://reiserlab.github.io/celltype-explorer-drosophila-male-cns/types/KCg-d_R.html) 提供标签释义。
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七个重点 PN：
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| neuron_idx / body_id | type | APL→PN contacts | CA(R) | ROI 未细分 |
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|---|---|---:|---:|---:|
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| 30 / 10039 | VL2a_adPN | 50 | 47 | 3 |
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| 179 / 10198 | VA7l_adPN | 7 | 7 | 0 |
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| 484 / 10522 | VL2p_adPN | 50 | 49 | 1 |
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| 1644 / 11761 | VP1d+VP4_l2PN1 | 78 | 70 | 8 |
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| 6065 / 16610 | VA7m_lPN | 14 | 13 | 1 |
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| 6842 / 17484 | DC2_adPN | 13 | 13 | 0 |
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| 133666 / 527577 | VA6_adPN | 35 | 29 | 6 |
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合计 228/247（92.31%）在 CA(R)，19 个未细分。
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对同一 PN，APL postsynaptic sites 到最近 PN→KC presynaptic site 的距离，中位数范围为 0.545–0.861 µm；坐标按官方 8 nm voxel 转换。
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距离不是同 bouton 的证明，更不是受体或功能因果证明。保留全部分布和远距离样本，不设“最佳阈值”筛掉例外。
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259 个 APL 靶 PN 中，15 个在本 canonical 图中没有 PN→KC 出边，不能一概当作同一功能入口。
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七个 PN→ALLN 的 22,599 个接触全部位于 AL(L/R)；ALLN→七个 PN 的 19,024 个接触中，19,021 位于 AL(L/R)，另有 1 个 LAL(R)、2 个未细分。
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同一 PN 的 calyx 相关接触与触角叶局部回路接触必须区分，不能用单一 whole-neuron 抑制项自动代表两种位置。
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## 如何解释，不做什么
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已有原始研究支持 APL 的局部抑制与 PN bouton/KC dendrite 相互作用，但不提供当前 MaleCNS 每条边的受体或强度。
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见 [Prisco et al., eLife](https://doi.org/10.7554/eLife.74172)、[Amin et al., eLife](https://doi.org/10.7554/eLife.56954)。
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本轮数据加强了“空间作用范围缺失”这一机制假设，**没有证明补上它就能修复 KC 持续放电或刺激特异性记忆**。
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仍未知：具体 bouton 身份、GABA 受体、末梢释放变化、时间常数、量化作用系数及 whole-brain 干预效果。
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下一步应先设计独立的 compartment/terminal-specific input hypothesis 和预声明对照，不直接改变 frozen baseline；对未知系数不能用“学习效果更好”反向调参。
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优先处理 upstream encoding / 作用范围，而不是继续改 dopamine eta、扩大 plastic mask 或调 KC/MBON 阈值。
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## 回归、完整性与资源
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独立新目录重跑 deterministic-v2 的五组冻结试次（各一次）：
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| trial | biological time | wall time | 事件 / counts / V / g |
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|---|---:|---:|---|
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| auditory 10 Hz | 1 s | 2.690 s | 全部精确一致 |
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| auditory 100 Hz | 1 s | 4.915 s | 全部精确一致 |
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| olfactory 50 Hz | 1 s | 4.961 s | 全部精确一致 |
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| mechanosensory 50 Hz | 1 s | 4.982 s | 全部精确一致 |
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| gustatory 50 Hz | 1 s | 4.977 s | 全部精确一致 |
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均无 NaN/Inf，V/g 最大误差 0；4090 本进程 peak allocated VRAM 为 285,994,496 bytes（约 272.75 MiB），不包括 ComfyUI 已驻留显存。
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本轮不声称重做了 Phase 4P 的 20 次重复实验；旧 v1/v2 reference 均没有覆盖。
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GPU 编译加回归约 85.55 s；CPU 结构审计含两次旧文件哈希扫描约 86.92 s；下载加校验约 375.66 s；空间提取/分析约 37.98 s。
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230,239 个旧科学文件的 SHA-256 复核通过。初始 Git payload 的 681 项校验也通过；新阶段另做 manifest。
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未停止或卸载 ComfyUI，未更新任何环境包。
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## 异常与处理记录
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1. 初次建立实验目录遇到写权限限制，在新实验目录单独配置 owner；没有递归修改旧目录。
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2. GPU 首次启动 PATH 未包含原 venv 的 Ninja，未开始模拟；失败目录保留。仅补启动 PATH 后，在另一个新目录完成 5/5 回归。
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3. 下载首次校验选项分隔符错误，下载未开始；修正为 aria2 要求的语法后完成官方校验，没有坏文件被当作通过。
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4. 旧 NumPy→Torch 只读数组警告保留，未修改冻结加载器；文件哈希与数值回归通过。
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本轮到此结束，不生成 learning candidate，不进入游戏、RL、视觉或动作 decoder。
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# 授权追加：官方突触位置审计
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用户已明确授权下载约 6.78 GB 的官方 syn-partners v1.0 作为新增参考数据。
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本追加取代原 CPU 静态审计中“本轮不下载 bulk”的范围限制；不改变原审计结果、原 raw 或任何模型参数。
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原静态审计的 unknown 描述“追加数据前”的证据状态，最终结果另存而不覆盖。
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在查看位置结果前定义：
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1. 以官方 generation 和 MD5 校验下载，另生成本地 SHA-256；不下载 syn-points 全表或 EM。
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2. 检查实际 schema 后按原字段读取，不猜列名。坐标为 MaleCNS 8 nm voxel；距离乘 0.008 转 µm。
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3. 提取全部 APL 入/出突触对、APL 所连 PN→KC 对、七个既定观察 PN↔ALLN 对。
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4. 每个 canonical body pair 的实际突触对行数必须与 base CSR 原 synapse count 一致；有重复配对或 count 差异即停止机制解释。
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5. 依据真实 primary_post 字符串报告 ROI，不由神经元名称猜 compartment。
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6. 对 APL→PN postsynaptic point 与同一 PN 的 PN→KC presynaptic points 计算最近欧氏距离分布。距离只是空间邻近，不等于同 bouton、受体作用或功能因果。
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7. 分别报告 APL→PN、PN→KC、PN↔ALLN 的 ROI，判断 point-neuron 全局电流会不会混淆不同解剖区域。
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8. 无受体、时间常数、末梢释放系数的部分继续 unknown；不据此增删边、补抑制、改变阈值或训练。
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4090 仅追加五组已有 frozen-v2 reference regression。原五组输入、参数、初态和数值语义不变；所有新输出留在本阶段目录。
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# Phase 4AA — APL→PN scope audit（预先声明）
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接续封存 Phase 4Z，仅做 CPU 解剖/实现范围审计。不重新运行旧科学阶段，不使用 GPU，不影响 ComfyUI。
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不训练、不改参数/符号、不增加边、不调整 APL calibration、eligibility-v2 或 14,232 plastic mask。
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## 选择与检验
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- 直接从实际 annotation_type == APL、annotation_class == ALPN / Kenyon_Cell 识别；空 class 不靠名称猜测。
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- 审计所有 APL 入/出边，不只选择七个观察 PN。七个 PN 来自封存 Phase 4Z 的全部真实目标 KC 入源。
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- 从 CSR 独立抽取，再流式过滤原始 Feather，逐 body pair / 原始 count 严格核对。
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- 区分 frozen fast-current out-mask、KC-only graded output、KC/PN-only graded input；输入被 graded 计算忽略不等于原图边被删除。
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- 核对 PN↔APL 双向边、PN→KC 与 PN→ALLN 出边；这只证明解剖路径，不宣称空间/生理因果。
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- 检查真实 schema 是否含接触位置、ROI、靶位点受体；SWC centerline 不等同 synaptic partner 坐标。
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- 保存 Phase 4Z 所用运行时代码相关段落与 SHA-256，核实覆盖数量完全一致。
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## 未知与停止条件
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缺失的接触 ROI、末梢位置、受体和作用系数均记 unknown。
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有配对边和论文支持不等于这只 MaleCNS 中某条连接已经定位到 calyx bouton。
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不得据本审计自动恢复 APL→PN whole-cell negative current 或扩大 graded target matrix。
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官方 syn-partners/syn-points 只获取 HEAD 元数据；不自动下载数 GB 数据，不请求 EM。
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若继续定位需 neuPrint 授权或新的官方附加数据，输出具体需求，不猜测补齐。
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## 通过的含义
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audit PASS = 原始边核对通过、覆盖重建与 Phase 4Z 相符、旧文件哈希不变、未知被保留。
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不等于 learning PASS，不产生新的正式模型候选。全项目顶层 gate 保持 5/10。
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复现用新的 experiment_root；禁止覆盖任何已经封存的输出目录。
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## 证据
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- 官方 MaleCNS 下载定义：https://male-cns.janelia.org/download/
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- APL 局部化：https://doi.org/10.7554/eLife.56954
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- PN bouton/KC dendrite 研究：https://doi.org/10.7554/eLife.74172
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- Google Research 项目说明：https://www.research.google/blog/a-connectomics-milestone-mapping-the-complete-male-fruit-fly-brain/
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Google/Janelia 的解剖数据与论文用于约束来源和机制；不能替代受体/电导测量或学习任务验证。
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@@ -0,0 +1,111 @@
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# Phase 4AA — APL→PN 作用范围与官方突触定位审计
|
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正式结论：**PASS-APL-PN-ANATOMICAL-LOCALIZATION / NO-DYNAMICS-CANDIDATE**。
|
||||
|
||||
本轮从 Phase 4Z 接续，先完成只读 CPU 范围审计；随后用户明确授权下载官方 syn-partners 并继续定位，也确认 ComfyUI 空闲以便运行 frozen regression。
|
||||
原始静态审计 `audit/results.json` 中的空间 unknown/blocked 是追加数据前的状态，保留不覆盖；最终状态以 `audit/final-summary.json` 和本报告为准。
|
||||
本轮无训练、无参数修改、无新的正式动力学候选。顶层验收仍为 5/10（50%），不是生物学复现比例。
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## 核心新证据
|
||||
|
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以实际 annotation 与原始 CSR 识别 2 个 APL、4,064 个 KC、686 个 ALPN，不通过 neurotransmitter 推断 DAN 身份。
|
||||
所有 166,700 个 neurons、25,582,938 edges、124,177,617 anatomical contacts 不变。
|
||||
|
||||
| 已有 APL 实现范围 | 边数 | 原始 contacts |
|
||||
|---|---:|---:|
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||||
| APL 全部出边 | 5,845 | 209,912 |
|
||||
| KC-only graded output 覆盖 | 4,633 | 196,200 |
|
||||
| 非 KC 出边，没有 graded 替代 | 1,212 | 13,712 |
|
||||
| 上述缺口中的 APL→PN | 268 | 4,600 |
|
||||
| APL 全部入边 | 6,185 | 234,025 |
|
||||
| 不被 KC/PN-only graded state 使用的入边 | 1,202 | 18,257 |
|
||||
|
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268 条 APL→PN 边涉及 259 个 PN；247 对同时存在 PN→APL 反向连接。
|
||||
“graded state 未使用”不等于原图入边被删除：普通 LIF APL 仍有这些入边，但其快速出边被既有 overlay 置零。基于 KC/PN 计算的独立 graded state 没有使用其余来源。
|
||||
这属于既有模型作用范围，不能把每个未覆盖来源都自动补入；需要额外生理依据。
|
||||
|
||||
## 官方附加数据与精确核对
|
||||
|
||||
用户授权下载的 `syn-partners-male-cns-v1.0-minconf-0.5.feather`：
|
||||
|
||||
- 6,777,179,098 bytes；官方 generation `1780494942562468`。
|
||||
- 官方 MD5：`58efcf712f8c4d4de5f2ad51e97def76`，验证通过。
|
||||
- SHA-256：`959d8ef4173b35382a3e6acfaf5167c795b6d10b877572d146af04e1b487bc07`。
|
||||
- 流式读取全部 311,833,243 条配对记录；实际字段和 dtype 已保存。
|
||||
- 来源：[MaleCNS 官方下载页](https://male-cns.janelia.org/download/)，许可证 CC BY 4.0。新增文件保存在本阶段 external-resources，不写 raw，不下载 EM。
|
||||
|
||||
| 提取范围 | 配对记录总数 | canonical 配对记录 | canonical neuron pairs |
|
||||
|---|---:|---:|---:|
|
||||
| APL 所有入/出接触 | 500,348 | 443,915 | 12,027 |
|
||||
| APL 所连 PN→KC | 385,229 | 385,229 | 21,978 |
|
||||
| 七个重点 PN→ALLN | 22,599 | 22,599 | 823 |
|
||||
| ALLN→七个重点 PN | 19,024 | 19,024 | 886 |
|
||||
|
||||
每组同时检查重复配对、完整 pair 集合及逐边 count：均与原始 CSR 完全一致。不存在只比较出现过的边而漏掉整条缺失边的情况。
|
||||
APL 配对中的非 canonical 部分保留来源记录，不加入 neuron-only graph。
|
||||
|
||||
## 真实 ROI 定位
|
||||
|
||||
4,600 个 APL→PN 接触都取得真实 pre/post 坐标：
|
||||
|
||||
- `CA(L)` 2,063；`CA(R)` 1,757；合计 3,820（83.04%）。
|
||||
- `CentralBrain-unspecified` 731（15.89%）：坐标已知，但官方 ROI 未细分，绝不按邻近位置重标为 calyx。
|
||||
- 其余实际标注合计 49，分布于 LH、PED、SCL、SLP、a'L；不丢弃非 calyx 结果。
|
||||
|
||||
CA 对应 calyx，官方作者 [Cell Type Explorer](https://reiserlab.github.io/celltype-explorer-drosophila-male-cns/types/KCg-d_R.html) 提供标签释义。
|
||||
七个重点 PN：
|
||||
|
||||
| neuron_idx / body_id | type | APL→PN contacts | CA(R) | ROI 未细分 |
|
||||
|---|---|---:|---:|---:|
|
||||
| 30 / 10039 | VL2a_adPN | 50 | 47 | 3 |
|
||||
| 179 / 10198 | VA7l_adPN | 7 | 7 | 0 |
|
||||
| 484 / 10522 | VL2p_adPN | 50 | 49 | 1 |
|
||||
| 1644 / 11761 | VP1d+VP4_l2PN1 | 78 | 70 | 8 |
|
||||
| 6065 / 16610 | VA7m_lPN | 14 | 13 | 1 |
|
||||
| 6842 / 17484 | DC2_adPN | 13 | 13 | 0 |
|
||||
| 133666 / 527577 | VA6_adPN | 35 | 29 | 6 |
|
||||
|
||||
合计 228/247（92.31%）在 CA(R)，19 个未细分。
|
||||
对同一 PN，APL postsynaptic sites 到最近 PN→KC presynaptic site 的距离，中位数范围为 0.545–0.861 µm；坐标按官方 8 nm voxel 转换。
|
||||
距离不是同 bouton 的证明，更不是受体或功能因果证明。保留全部分布和远距离样本，不设“最佳阈值”筛掉例外。
|
||||
259 个 APL 靶 PN 中，15 个在本 canonical 图中没有 PN→KC 出边，不能一概当作同一功能入口。
|
||||
|
||||
七个 PN→ALLN 的 22,599 个接触全部位于 AL(L/R)；ALLN→七个 PN 的 19,024 个接触中，19,021 位于 AL(L/R)，另有 1 个 LAL(R)、2 个未细分。
|
||||
同一 PN 的 calyx 相关接触与触角叶局部回路接触必须区分，不能用单一 whole-neuron 抑制项自动代表两种位置。
|
||||
|
||||
## 如何解释，不做什么
|
||||
|
||||
已有原始研究支持 APL 的局部抑制与 PN bouton/KC dendrite 相互作用，但不提供当前 MaleCNS 每条边的受体或强度。
|
||||
见 [Prisco et al., eLife](https://doi.org/10.7554/eLife.74172)、[Amin et al., eLife](https://doi.org/10.7554/eLife.56954)。
|
||||
本轮数据加强了“空间作用范围缺失”这一机制假设，**没有证明补上它就能修复 KC 持续放电或刺激特异性记忆**。
|
||||
仍未知：具体 bouton 身份、GABA 受体、末梢释放变化、时间常数、量化作用系数及 whole-brain 干预效果。
|
||||
|
||||
下一步应先设计独立的 compartment/terminal-specific input hypothesis 和预声明对照，不直接改变 frozen baseline；对未知系数不能用“学习效果更好”反向调参。
|
||||
优先处理 upstream encoding / 作用范围，而不是继续改 dopamine eta、扩大 plastic mask 或调 KC/MBON 阈值。
|
||||
|
||||
## 回归、完整性与资源
|
||||
|
||||
独立新目录重跑 deterministic-v2 的五组冻结试次（各一次）：
|
||||
|
||||
| trial | biological time | wall time | 事件 / counts / V / g |
|
||||
|---|---:|---:|---|
|
||||
| auditory 10 Hz | 1 s | 2.690 s | 全部精确一致 |
|
||||
| auditory 100 Hz | 1 s | 4.915 s | 全部精确一致 |
|
||||
| olfactory 50 Hz | 1 s | 4.961 s | 全部精确一致 |
|
||||
| mechanosensory 50 Hz | 1 s | 4.982 s | 全部精确一致 |
|
||||
| gustatory 50 Hz | 1 s | 4.977 s | 全部精确一致 |
|
||||
|
||||
均无 NaN/Inf，V/g 最大误差 0；4090 本进程 peak allocated VRAM 为 285,994,496 bytes（约 272.75 MiB），不包括 ComfyUI 已驻留显存。
|
||||
本轮不声称重做了 Phase 4P 的 20 次重复实验；旧 v1/v2 reference 均没有覆盖。
|
||||
GPU 编译加回归约 85.55 s；CPU 结构审计含两次旧文件哈希扫描约 86.92 s；下载加校验约 375.66 s；空间提取/分析约 37.98 s。
|
||||
230,239 个旧科学文件的 SHA-256 复核通过。初始 Git payload 的 681 项校验也通过；新阶段另做 manifest。
|
||||
未停止或卸载 ComfyUI，未更新任何环境包。
|
||||
|
||||
## 异常与处理记录
|
||||
|
||||
1. 初次建立实验目录遇到写权限限制，在新实验目录单独配置 owner；没有递归修改旧目录。
|
||||
2. GPU 首次启动 PATH 未包含原 venv 的 Ninja，未开始模拟；失败目录保留。仅补启动 PATH 后，在另一个新目录完成 5/5 回归。
|
||||
3. 下载首次校验选项分隔符错误，下载未开始；修正为 aria2 要求的语法后完成官方校验，没有坏文件被当作通过。
|
||||
4. 旧 NumPy→Torch 只读数组警告保留，未修改冻结加载器；文件哈希与数值回归通过。
|
||||
|
||||
本轮到此结束，不生成 learning candidate，不进入游戏、RL、视觉或动作 decoder。
|
||||
@@ -0,0 +1,245 @@
|
||||
"""CPU-only, read-only APL/PN implementation scope audit. No scientific modules imported."""
|
||||
import argparse
|
||||
import ast
|
||||
import concurrent.futures as cf
|
||||
from datetime import datetime, timezone
|
||||
import gzip
|
||||
import hashlib
|
||||
import json
|
||||
from pathlib import Path
|
||||
import resource
|
||||
import time
|
||||
import urllib.request
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pyarrow as pa
|
||||
import pyarrow.compute as pc
|
||||
import yaml
|
||||
|
||||
|
||||
def digest(path):
|
||||
h=hashlib.sha256()
|
||||
with path.open('rb') as f:
|
||||
for b in iter(lambda:f.read(8<<20),b''):h.update(b)
|
||||
return h.hexdigest()
|
||||
|
||||
|
||||
def save(path,value):
|
||||
path.parent.mkdir(parents=True,exist_ok=True)
|
||||
with path.open('x',encoding='utf-8') as f:
|
||||
json.dump(value,f,ensure_ascii=False,indent=2,sort_keys=True,allow_nan=False)
|
||||
f.write('\n')
|
||||
|
||||
|
||||
def immutable_check(root,snapshot,workers):
|
||||
summary=json.loads((snapshot/'summary.json').read_text())
|
||||
assert digest(snapshot/'files.jsonl.gz')==summary['files_jsonl_gz_sha256']
|
||||
with gzip.open(snapshot/'files.jsonl.gz','rt') as f:rows=[json.loads(x) for x in f]
|
||||
def check(r):
|
||||
p=root/r['path']
|
||||
if r['kind']=='symlink':
|
||||
ok=p.is_symlink() and str(p.readlink())==r['target']
|
||||
else:
|
||||
ok=p.is_file() and p.stat().st_size==r['bytes'] and digest(p)==r['sha256']
|
||||
return None if ok else r['path']
|
||||
with cf.ThreadPoolExecutor(max_workers=workers) as pool:bad=[x for x in pool.map(check,rows) if x]
|
||||
return {'pass':not bad,'checked':len(rows),'mismatches':bad,'reference_sha256':summary['files_jsonl_gz_sha256'],
|
||||
'exclusions':summary['excluded_directory_names'],'new_stage_additions_allowed':True,
|
||||
'meaning':'All original listed bytes checked; only newly named stage outputs are additions.'}
|
||||
|
||||
|
||||
def population(meta):
|
||||
out=meta.annotation_class.fillna('UNKNOWN_CLASS').astype(str).to_numpy(object)
|
||||
for typ in ('APL','DPM'):out[meta.annotation_type.eq(typ).to_numpy()]=typ
|
||||
return out
|
||||
|
||||
|
||||
def edge_frame(ids,sources,targets,weights,meta,pops):
|
||||
src=sources[ids];dst=targets[ids]
|
||||
f=pd.DataFrame({'edge_id':ids,'source_neuron_idx':src,'target_neuron_idx':dst,'raw_synapse_count':weights[ids]})
|
||||
for pref,arr in [('source',src),('target',dst)]:
|
||||
f[pref+'_population']=pops[arr]
|
||||
for col in ('body_id','annotation_class','annotation_type','annotation_instance','annotation_somaSide','neurotransmitter','nt_ground_truth','annotation_receptorType'):
|
||||
f[pref+'_'+col]=meta[col].to_numpy()[arr]
|
||||
return f
|
||||
|
||||
|
||||
def raw_incident(path,bodyids):
|
||||
tables=[]
|
||||
with pa.memory_map(str(path),'r') as f:
|
||||
reader=pa.ipc.open_file(f)
|
||||
assert reader.schema.names==['body_pre','body_post','weight'],str(reader.schema)
|
||||
ids=pa.array(bodyids.astype(np.int64))
|
||||
for i in range(reader.num_record_batches):
|
||||
batch=reader.get_batch(i)
|
||||
keep=pc.or_(pc.is_in(batch.column(0),value_set=ids),pc.is_in(batch.column(1),value_set=ids))
|
||||
selected=batch.filter(keep)
|
||||
if len(selected):tables.append(pa.Table.from_batches([selected]))
|
||||
return pa.concat_tables(tables).to_pandas()
|
||||
|
||||
|
||||
def official_head(timeout):
|
||||
base='https://storage.googleapis.com/flyem-male-cns/v1.0/connectome-data/flat-connectome/'
|
||||
out=[]
|
||||
for name in ('syn-partners-male-cns-v1.0-minconf-0.5.feather','syn-points-male-cns-v1.0-minconf-0.5.feather'):
|
||||
row={'url':base+name,'retrieved_utc':datetime.now(timezone.utc).isoformat(),'method':'HEAD','body_downloaded':False}
|
||||
try:
|
||||
with urllib.request.urlopen(urllib.request.Request(base+name,method='HEAD'),timeout=timeout) as r:
|
||||
row.update(status=r.status,content_length=int(r.headers['Content-Length']),etag=r.headers.get('ETag'),
|
||||
generation=r.headers.get('x-goog-generation'),upstream_hash_header=r.headers.get('x-goog-hash'),
|
||||
last_modified=r.headers.get('Last-Modified'),accept_ranges=r.headers.get('Accept-Ranges'))
|
||||
except Exception as e:row.update(status='UNAVAILABLE',error=type(e).__name__+': '+str(e))
|
||||
out.append(row)
|
||||
return out
|
||||
|
||||
|
||||
def seal(root):
|
||||
paths=sorted(p for p in root.rglob('*') if p.is_file() and p.name not in ('manifest.json','checksums.sha256'))
|
||||
rows=[{'path':p.relative_to(root).as_posix(),'bytes':p.stat().st_size,'sha256':digest(p)} for p in paths]
|
||||
save(root/'manifest.json',{'files':rows,'created_utc':datetime.now(timezone.utc).isoformat()})
|
||||
with (root/'checksums.sha256').open('x') as f:
|
||||
for r in rows:f.write(r['sha256']+' '+r['path']+'\n')
|
||||
return len(rows)
|
||||
|
||||
|
||||
def run(cfg):
|
||||
pa.set_cpu_count(4);pa.set_io_thread_count(4)
|
||||
start=time.perf_counter();root=Path(cfg['root']);exp=Path(cfg['experiment_root']);model=Path(cfg['model_root'])
|
||||
assert exp.is_relative_to(root/'experiments') and model.is_relative_to(root/'models/male-cns-lif')
|
||||
if exp.exists() and any(exp.iterdir()):raise RuntimeError('Refusing nonempty experiment directory')
|
||||
exp.mkdir(parents=True,exist_ok=True)
|
||||
for d in ('protocol','audit','connectivity','metadata','spatial','external-resources','regression','performance'):(exp/d).mkdir()
|
||||
save(exp/'protocol/preregistered.json',{'config':cfg,'source_hashes':{p.name:digest(p) for p in model.iterdir() if p.is_file()},
|
||||
'created_utc':datetime.now(timezone.utc).isoformat(),'before_analysis':True})
|
||||
pre=immutable_check(root,root/cfg['snapshot'],cfg['scope']['snapshot_workers']);save(exp/'regression/preflight.json',pre)
|
||||
assert pre['pass'],'original bytes differ'
|
||||
canonical=root/cfg['canonical'];prior=root/cfg['prior'];expect=cfg['expected']
|
||||
meta=pd.read_parquet(canonical/'neuron-index/unified-metadata.parquet').sort_values('neuron_idx').reset_index(drop=True)
|
||||
ptr=np.load(canonical/'gpu/csr-indptr.int64.npy',mmap_mode='r');dst=np.load(canonical/'gpu/csr-indices.int32.npy',mmap_mode='r');w=np.load(canonical/'gpu/csr-weights.int64.npy',mmap_mode='r')
|
||||
assert len(meta)==expect['neurons'] and np.array_equal(meta.neuron_idx,np.arange(len(meta)))
|
||||
assert str(meta.neuron_idx.dtype)=='int32' and str(meta.body_id.dtype)=='uint64' and meta.body_id.is_unique
|
||||
assert len(ptr)==len(meta)+1 and ptr[0]==0 and ptr[-1]==len(w)==len(dst)==expect['edges']
|
||||
assert ptr.dtype==np.int64 and dst.dtype==np.int32 and w.dtype==np.int64
|
||||
assert int(w.sum())==expect['synapse_count_sum'] and np.all(np.diff(ptr)>=0) and w.min()>0
|
||||
src=np.repeat(np.arange(len(meta),dtype=np.int32),np.diff(ptr));pops=population(meta)
|
||||
apl=np.flatnonzero(meta.annotation_type.eq('APL'));kc=np.flatnonzero(meta.annotation_class.eq('Kenyon_Cell'));pn=np.flatnonzero(meta.annotation_class.eq('ALPN'))
|
||||
assert len(apl)==expect['APL'] and len(kc)==expect['KC']
|
||||
oi=np.flatnonzero(np.isin(src,apl));ii=np.flatnonzero(np.isin(dst,apl))
|
||||
outgoing=edge_frame(oi,src,dst,w,meta,pops);incoming=edge_frame(ii,src,dst,w,meta,pops)
|
||||
outgoing['fast_current_masked']=True;outgoing['graded_output_covered']=np.isin(dst[oi],kc)
|
||||
incoming['graded_input_used']=np.isin(src[ii],np.r_[kc,pn])
|
||||
# This describes the graded-state input only. Fast synapses into APL remain in CSR/runtime.
|
||||
watched=json.loads((prior/'protocol/watched.json').read_text())['all_incoming_ALPNs']
|
||||
focus=outgoing[outgoing.target_neuron_idx.isin(watched)].copy()
|
||||
old=pd.read_parquet(prior/'audit/APL-target-coverage.parquet')
|
||||
fields=['edge_id','source_neuron_idx','target_neuron_idx','raw_synapse_count']
|
||||
assert np.array_equal(outgoing[fields].sort_values('edge_id').to_numpy(np.int64),old[fields].sort_values('edge_id').to_numpy(np.int64))
|
||||
nonkc=outgoing[~outgoing.graded_output_covered]
|
||||
assert (len(outgoing),int(outgoing.raw_synapse_count.sum()),len(nonkc),int(nonkc.raw_synapse_count.sum()),len(focus),int(focus.raw_synapse_count.sum()))==(expect['APL_outgoing_edges'],expect['APL_outgoing_contacts'],expect['non_KC_edges'],expect['non_KC_contacts'],expect['watched_PN_edges'],expect['watched_PN_contacts'])
|
||||
print('CSR and Phase4Z coverage exact',flush=True)
|
||||
raw_path=root/'datasets/male-cns-v1.0/raw/connectivity/connectome-weights-male-cns-v1.0-minconf-0.5.feather'
|
||||
raw=raw_incident(raw_path,meta.iloc[apl].body_id.to_numpy())
|
||||
valid=raw.body_pre.isin(meta.body_id)&raw.body_post.isin(meta.body_id)
|
||||
actual=raw.loc[valid].sort_values(['body_pre','body_post']).reset_index(drop=True)
|
||||
assert not actual.duplicated(['body_pre','body_post']).any()
|
||||
incident=pd.concat([outgoing,incoming]).drop_duplicates('edge_id')
|
||||
expected=incident[['source_body_id','target_body_id','raw_synapse_count']].rename(columns={'source_body_id':'body_pre','target_body_id':'body_post','raw_synapse_count':'weight'}).sort_values(['body_pre','body_post']).reset_index(drop=True)
|
||||
assert np.array_equal(actual.to_numpy(np.int64),expected.to_numpy(np.int64))
|
||||
raw.to_parquet(exp/'connectivity/raw-APL-incident-including-noncanonical.parquet',index=False)
|
||||
print('Raw Feather APL incident edges exact',len(actual),flush=True)
|
||||
outgoing.to_parquet(exp/'connectivity/APL-outgoing.parquet',index=False);incoming.to_parquet(exp/'connectivity/APL-incoming.parquet',index=False)
|
||||
for label,frame,col in [('output',outgoing,'target_population'),('input',incoming,'source_population')]:
|
||||
grouped=frame.groupby(col,dropna=False).agg(edges=('edge_id','size'),contacts=('raw_synapse_count','sum')).reset_index()
|
||||
grouped.to_parquet(exp/'connectivity'/f'APL-{label}-population-summary.parquet',index=False)
|
||||
grouped.to_csv(exp/'connectivity'/f'APL-{label}-population-summary.csv',index=False)
|
||||
ignored=incoming[~incoming.graded_input_used]
|
||||
ignored.to_parquet(exp/'connectivity/graded-input-not-used.parquet',index=False)
|
||||
pairs=incoming[['source_neuron_idx','target_neuron_idx','raw_synapse_count','edge_id']].rename(columns={'source_neuron_idx':'target_neuron_idx','target_neuron_idx':'source_neuron_idx','raw_synapse_count':'reverse_contacts','edge_id':'reverse_edge_id'})
|
||||
pn_out=outgoing[outgoing.target_neuron_idx.isin(pn)].merge(pairs,on=['source_neuron_idx','target_neuron_idx'],how='left',validate='one_to_one')
|
||||
pn_out['has_PN_to_APL']=pn_out.reverse_edge_id.notna()
|
||||
pn_out['watched_in_Phase4Z']=pn_out.target_neuron_idx.isin(watched)
|
||||
pn_out.to_parquet(exp/'connectivity/APL-PN-reciprocity.parquet',index=False)
|
||||
pn_out.to_csv(exp/'connectivity/APL-PN-reciprocity.csv',index=False)
|
||||
downstream=[]
|
||||
for node in pn_out.target_neuron_idx.unique():
|
||||
ids=np.arange(ptr[node],ptr[node+1]);cl=pops[dst[ids]]
|
||||
for pop in ('Kenyon_Cell','ALLN','APL'):
|
||||
selected=ids[cl==pop]
|
||||
downstream.append({'PN_neuron_idx':int(node),'PN_body_id':int(meta.iloc[node].body_id),'PN_type':meta.iloc[node].annotation_type,
|
||||
'target_population':pop,'edges':len(selected),'contacts':int(w[selected].sum())})
|
||||
pd.DataFrame(downstream).to_parquet(exp/'connectivity/APL-target-PN-downstream.parquet',index=False)
|
||||
focus=pn_out[pn_out.watched_in_Phase4Z].copy()
|
||||
focus[['source_neuron_idx','source_body_id','source_annotation_instance','target_neuron_idx','target_body_id','target_annotation_type','raw_synapse_count','reverse_contacts','reverse_edge_id']].to_csv(exp/'connectivity/watched-PN-reciprocity.csv',index=False)
|
||||
# Reconstruct the exact frozen matrices on CPU; do not simulate or apply any weights.
|
||||
inp=np.zeros((len(apl),len(meta)),np.float32);output=np.zeros((len(apl),len(kc)),np.float32)
|
||||
kmap={int(n):i for i,n in enumerate(kc)};amap={int(n):i for i,n in enumerate(apl)}
|
||||
for r in incoming[incoming.graded_input_used].itertuples():inp[amap[r.target_neuron_idx],r.source_neuron_idx]+=r.raw_synapse_count
|
||||
for r in outgoing[outgoing.graded_output_covered].itertuples():output[amap[r.source_neuron_idx],kmap[r.target_neuron_idx]]+=r.raw_synapse_count
|
||||
input_scale=float(inp[inp>0].sum()/np.count_nonzero(np.any(inp>0,axis=0)));output_scale=float(output[output>0].mean())
|
||||
coverage={'input_scale':input_scale,'output_scale':output_scale,'input_matrix_shape':list(inp.shape),'output_matrix_shape':list(output.shape),
|
||||
'KC_targets_with_graded_input':int(np.count_nonzero(np.any(output>0,axis=0))),
|
||||
'normalized_input_matrix_sha256':hashlib.sha256((inp/input_scale).tobytes()).hexdigest(),
|
||||
'normalized_output_matrix_sha256':hashlib.sha256((output/output_scale).tobytes()).hexdigest()}
|
||||
save(exp/'audit/frozen-matrix-reconstruction.json',coverage)
|
||||
frozen=json.loads((prior/'audit/APL-target-coverage.json').read_text());source=Path(frozen['source'])
|
||||
assert digest(source)==frozen['source_sha256']
|
||||
body=source.read_text();tree=ast.parse(body)
|
||||
cls=next(n for n in tree.body if isinstance(n,ast.ClassDef) and n.name=='DeterministicAPLSim')
|
||||
with (exp/'audit/frozen-APL-class-source.txt').open('x') as f:f.write(ast.get_source_segment(body,cls)+'\n')
|
||||
save(exp/'audit/frozen-implementation-provenance.json',{'path':str(source),'sha256':digest(source),'same_as_Phase4Z':True,'models_imported':False})
|
||||
ids=np.unique(np.r_[apl,pn_out.target_neuron_idx]);meta.iloc[ids].to_parquet(exp/'metadata/APL-and-target-PNs.parquet',index=False)
|
||||
spatial=pn_out[['edge_id','source_neuron_idx','source_body_id','target_neuron_idx','target_body_id','target_annotation_type','raw_synapse_count']].copy()
|
||||
for col in ('synapse_location','synapse_ROI','PN_subcellular_target','postsynaptic_GABA_receptor','effective_inhibition_coefficient'):spatial[col]='unknown'
|
||||
spatial['reason']='Local body-pair counts and SWC centerlines do not contain synapse partner locations or target receptor measurements'
|
||||
spatial.to_parquet(exp/'spatial/APL-PN-unknowns.parquet',index=False)
|
||||
swc=[]
|
||||
for node in np.r_[apl,watched]:
|
||||
p=Path(meta.iloc[node].swc_path)
|
||||
a=np.loadtxt(p,comments='#',ndmin=2)
|
||||
assert a.shape[1]==7 and np.all(np.isfinite(a))
|
||||
swc.append({'neuron_idx':int(node),'body_id':int(meta.iloc[node].body_id),'path':str(p),'sha256':digest(p),
|
||||
'nodes':len(a),'columns':int(a.shape[1]),'synaptic_partner_identity_in_standard_SWC':False,
|
||||
'coordinates_used_to_infer_synapse_locations':False})
|
||||
save(exp/'spatial/watched-SWC-audit.json',swc)
|
||||
schemas=[]
|
||||
for p in sorted((root/'datasets/male-cns-v1.0/raw').glob('*/*.feather')):
|
||||
with pa.memory_map(str(p),'r') as f:sc=pa.ipc.open_file(f).schema
|
||||
schemas.append({'path':str(p),'sha256':digest(p),'fields':[{'name':x.name,'dtype':str(x.type)} for x in sc]})
|
||||
save(exp/'spatial/local-schemas.json',schemas)
|
||||
save(exp/'external-resources/official-data-access.json',official_head(cfg['scope']['official_network_timeout_s']))
|
||||
evidence={
|
||||
'official_definition':{'url':'https://male-cns.janelia.org/download/','dataset':'male-cns:v1.0','license':'CC-BY-4.0',
|
||||
'license_url':'https://creativecommons.org/licenses/by/4.0/','observation':'Official page specifies paired synapse coordinates/primary_post in syn-partners; local flat weights contain none.',
|
||||
'required_subset':'All outgoing APL partner records; exact body IDs from metadata; minimum confidence 0.5; include pre/post x/y/z, body IDs and primary_post.',
|
||||
'no_EM_needed':True,'coordinate_units':'synapse coordinates and original SWC in 8nm voxels; precomputed skeletons have different units'},
|
||||
'primary_physiology':[
|
||||
{'doi':'10.7554/eLife.74172','url':'https://elifesciences.org/articles/74172','claim':'Local APL inhibition at PN boutons and KC dendritic claws was studied; no edge-specific MaleCNS coefficient follows.'},
|
||||
{'doi':'10.7554/eLife.56954','url':'https://pmc.ncbi.nlm.nih.gov/articles/PMC7541083/','claim':'APL activity/inhibition can be spatially localized; global point-neuron inhibition is a modeling assumption.'}],
|
||||
'google_research':{'url':'https://www.research.google/blog/a-connectomics-milestone-mapping-the-complete-male-fruit-fly-brain/',
|
||||
'role':'Primary project provenance, not evidence of associative-learning performance'},
|
||||
'access_choices':['Authorized neuPrint male-cns:v1.0 targeted query, requiring user-configured API token',
|
||||
'Explicitly approved official syn-partners bulk download into a NEW external-reference directory'],
|
||||
'not_attempted':['bulk synapse download','EM download','credential discovery','unauthenticated query bypass','new dynamics','GPU regression']}
|
||||
save(exp/'external-resources/evidence-and-limitations.json',evidence)
|
||||
results={'phase':'4AA','status':'PASS-STATIC-SCOPE-AUDIT / BLOCKED-SPATIAL-PHYSIOLOGY','candidate':False,
|
||||
'neurons':len(meta),'graph_edges':len(w),'synapse_count_sum':int(w.sum()),'APL_neurons':len(apl),'KC_neurons':len(kc),'ALPN_neurons':len(pn),
|
||||
'APL_outgoing_edges':len(outgoing),'APL_outgoing_contacts':int(outgoing.raw_synapse_count.sum()),
|
||||
'non_KC_uncovered_edges':len(nonkc),'non_KC_uncovered_contacts':int(nonkc.raw_synapse_count.sum()),'non_KC_uncovered_targets':int(nonkc.target_neuron_idx.nunique()),
|
||||
'APL_to_PN_edges':len(pn_out),'APL_to_PN_contacts':int(pn_out.raw_synapse_count.sum()),'APL_target_PNs':int(pn_out.target_neuron_idx.nunique()),
|
||||
'APL_PN_pairs_with_reverse_edge':int(pn_out.has_PN_to_APL.sum()),'watched_PN_contacts':int(focus.raw_synapse_count.sum()),
|
||||
'APL_incoming_edges':len(incoming),'APL_incoming_contacts':int(incoming.raw_synapse_count.sum()),
|
||||
'incoming_edges_not_used_by_graded_state':len(ignored),'incoming_contacts_not_used_by_graded_state':int(ignored.raw_synapse_count.sum()),
|
||||
'raw_APL_incident_canonical_edges_checked':len(actual),'raw_APL_incident_noncanonical_rows':int((~valid).sum()),
|
||||
'raw_to_CSR_exact':True,'Phase4Z_static_coverage_exact':True,'APL_PN_pairs_with_confirmed_local_synapse_ROI':0,
|
||||
'APL_PN_pairs_with_confirmed_subcellular_target':0,'whole_cell_inhibition_repair_authorized_by_evidence':False,
|
||||
'new_simulation':False,'new_scientific_regression_run':False,'old_reference_files_rehashed':True,
|
||||
'weights_changed':False,'gpu_used':False,'ComfyUI_models_unloaded':False,'top_level_gates_passed':5,'top_level_gates_total':10}
|
||||
post=immutable_check(root,root/cfg['snapshot'],cfg['scope']['snapshot_workers']);save(exp/'regression/postflight.json',post);assert post['pass']
|
||||
results['frozen_integrity_pass']=post['pass'];save(exp/'audit/results.json',results)
|
||||
save(exp/'performance/resources.json',{'wall_seconds':time.perf_counter()-start,'max_RSS_KiB':resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,'gpu_used':False,'includes_two_full_integrity_scans':True})
|
||||
print(json.dumps(results,indent=2),flush=True)
|
||||
|
||||
|
||||
if __name__=='__main__':
|
||||
ap=argparse.ArgumentParser();ap.add_argument('--config',type=Path,required=True);args=ap.parse_args()
|
||||
run(yaml.safe_load(args.config.read_text()))
|
||||
@@ -0,0 +1,31 @@
|
||||
phase: 4AA
|
||||
name: APL-PN-SCOPE-AUDIT
|
||||
root: /data/digital-drosophila
|
||||
model_root: /data/digital-drosophila/models/male-cns-lif/apl-pn-scope-audit-v1
|
||||
experiment_root: /data/digital-drosophila/experiments/phase4aa-apl-pn-scope
|
||||
canonical: datasets/male-cns-v1.0/processed/neuron-only
|
||||
prior: experiments/phase4z-pn-feedback-audit
|
||||
snapshot: registry/snapshots/bootstrap-20260929
|
||||
expected:
|
||||
neurons: 166700
|
||||
edges: 25582938
|
||||
synapse_count_sum: 124177617
|
||||
APL: 2
|
||||
KC: 4064
|
||||
APL_outgoing_edges: 5845
|
||||
APL_outgoing_contacts: 209912
|
||||
non_KC_edges: 1212
|
||||
non_KC_contacts: 13712
|
||||
watched_PN_edges: 7
|
||||
watched_PN_contacts: 247
|
||||
scope:
|
||||
cpu_only: true
|
||||
new_simulation: false
|
||||
conditioning: false
|
||||
parameter_changes: false
|
||||
sign_changes: false
|
||||
learned_candidate: false
|
||||
bulk_data_download: false
|
||||
new_spatial_positions_inferred_from_skeletons: false
|
||||
snapshot_workers: 4
|
||||
official_network_timeout_s: 30
|
||||
@@ -0,0 +1,12 @@
|
||||
b87a601d869f0a4cf7f88ccb2b7f5592b22da25d11d3e5bfb1be4bba05c079c0 LOCALIZATION-PROTOCOL.md
|
||||
e88f765be0fd91178eed1d862b1de8d8b307ba5ec3cc0ae0d0d93202f264acce PROTOCOL.md
|
||||
fa0bfd5a163a436e061c94e37cf6fe84ca75fdfa94d6f20ce85eda5058ca9ce7 REPORT.md
|
||||
4ef071ba88f4e1cd78f068be58414dd7f4099dfac69c68266c4f46e175eab9fb audit.py
|
||||
e0171bbd7dc7e693268eac00152e560e841f885c7d524c74d51fda7f7fbadbbf audit.yaml
|
||||
23db504ac8f31c62a434b24506cbe8456e2993e923bae4c89040cc6a1d8d06f7 fetch_syn_partners.py
|
||||
ae203b77c011d01ab8fb1cc76fcf4b0223a71e309d6995445fc7f33ce22a2f06 finalize.py
|
||||
aca6c69399538a41bc9148f2c837f0546e8a39d6b2b833585b9c55e53fa8414b localize.py
|
||||
554e9378e068fb13abb4f08dbf9ea710228fa535da2442edb913261fa22fcb20 regression.py
|
||||
238506a18fb81d544480c815d2517589e5b9d8c1932e86a5fe163a1b2955f712 regression_retry.py
|
||||
cb67bab972195090d5c4188169982abdc960479b8031477f1f39ac445814e88f test_audit.py
|
||||
732dcfe1a9c807356f7664393f6678743c61e3fe38a915ca0ce3f00ee7b17d9a validate_localization.py
|
||||
@@ -0,0 +1,49 @@
|
||||
"""User-authorized official additional reference download. Never writes raw."""
|
||||
import base64
|
||||
import hashlib
|
||||
import json
|
||||
from pathlib import Path
|
||||
import subprocess
|
||||
import time
|
||||
import urllib.parse
|
||||
import urllib.request
|
||||
from audit import save
|
||||
|
||||
ROOT=Path('/data/digital-drosophila')
|
||||
OUT=ROOT/'experiments/phase4aa-apl-pn-scope/external-resources/synaptic-partners'
|
||||
NAME='syn-partners-male-cns-v1.0-minconf-0.5.feather'
|
||||
OBJECT='v1.0/connectome-data/flat-connectome/'+NAME
|
||||
META='https://storage.googleapis.com/storage/v1/b/flyem-male-cns/o/'+urllib.parse.quote(OBJECT,safe='')
|
||||
|
||||
def main():
|
||||
OUT.mkdir(parents=True,exist_ok=True)
|
||||
with urllib.request.urlopen(META,timeout=30) as r:meta=json.load(r)
|
||||
assert meta['name']==OBJECT and int(meta['size'])==6777179098
|
||||
assert meta['generation']=='1780494942562468','Stop on an unexpected upstream revision'
|
||||
md5=base64.b64decode(meta['md5Hash']).hex()
|
||||
if (OUT/'upstream-object.json').exists():
|
||||
prior=json.loads((OUT/'upstream-object.json').read_text())
|
||||
assert all(prior[k]==meta[k] for k in ('name','generation','size','md5Hash'))
|
||||
else:save(OUT/'upstream-object.json',meta)
|
||||
url='https://storage.googleapis.com/flyem-male-cns/'+OBJECT+'?generation='+meta['generation']
|
||||
if not (OUT/'authorization-and-source.json').exists():
|
||||
save(OUT/'authorization-and-source.json',{'user_authorized_additional_download':True,'new_reference_only':True,
|
||||
'source_page':'https://male-cns.janelia.org/download/','url':url,'metadata_url':META,
|
||||
'dataset':'male-cns:v1.0','license':'CC-BY-4.0','EM_downloaded':False,
|
||||
'raw_modified':False,'retrieved_utc':time.strftime('%Y-%m-%dT%H:%M:%SZ',time.gmtime())})
|
||||
cmd=['aria2c','--continue=true','--max-connection-per-server=8','--split=8','--min-split-size=16M',
|
||||
'--max-tries=8','--retry-wait=5','--connect-timeout=30','--timeout=60','--auto-file-renaming=false',
|
||||
'--allow-overwrite=false','--check-integrity=true','--file-allocation=none','--summary-interval=20',
|
||||
'--enable-color=false','--console-log-level=warn','--checksum=md5='+md5,'--dir='+str(OUT),'--out='+NAME,url]
|
||||
begin=time.perf_counter();subprocess.run(cmd,check=True)
|
||||
file=OUT/NAME;hs=hashlib.sha256();hm=hashlib.md5()
|
||||
with file.open('rb') as f:
|
||||
for b in iter(lambda:f.read(8<<20),b''):hs.update(b);hm.update(b)
|
||||
result={'bytes':file.stat().st_size,'sha256':hs.hexdigest(),'md5':hm.hexdigest(),'official_md5':md5,
|
||||
'generation':meta['generation'],'download_and_verification_seconds':time.perf_counter()-begin,
|
||||
'pass':file.stat().st_size==int(meta['size']) and hm.hexdigest()==md5,'path':str(file)}
|
||||
assert result['pass'],'Official MD5/size mismatch; retain file and stop'
|
||||
if not (OUT/'download-verification.json').exists():save(OUT/'download-verification.json',result)
|
||||
print(json.dumps(result),flush=True)
|
||||
|
||||
if __name__=='__main__':main()
|
||||
@@ -0,0 +1,51 @@
|
||||
"""Seal only this newly created audit and copy small evidence into Git registry."""
|
||||
import json
|
||||
from pathlib import Path
|
||||
import shutil
|
||||
import time
|
||||
from audit import save,digest,seal,immutable_check
|
||||
|
||||
ROOT=Path('/data/digital-drosophila');MODEL=ROOT/'models/male-cns-lif/apl-pn-scope-audit-v1';EXP=ROOT/'experiments/phase4aa-apl-pn-scope'
|
||||
def main():
|
||||
assert not (EXP/'manifest.json').exists() and not (MODEL/'manifest.json').exists(),'Already sealed'
|
||||
static=json.loads((EXP/'audit/results.json').read_text())
|
||||
local=json.loads((EXP/'localization/summary.json').read_text())
|
||||
pairs=json.loads((EXP/'localization/complete-pair-set-validation.json').read_text())
|
||||
gpu=json.loads((EXP/'retry-with-correct-PATH/gpu-frozen-regression/summary.json').read_text())
|
||||
external=json.loads((EXP/'external-resources/synaptic-partners/download-verification.json').read_text())
|
||||
assert static['frozen_integrity_pass'] and local['pass'] and pairs['pass'] and gpu['pass'] and external['pass']
|
||||
post=immutable_check(ROOT,ROOT/'registry/snapshots/bootstrap-20260929',4)
|
||||
save(EXP/'regression/final-old-files-integrity.json',post);assert post['pass']
|
||||
result={'phase':'4AA','status':'PASS-APL-PN-ANATOMICAL-LOCALIZATION / NO-DYNAMICS-CANDIDATE',
|
||||
'candidate':False,'previous_static_spatial_block_resolved_by_user_authorized_reference_data':True,
|
||||
'old_static_audit_retained_without_overwrite':True,'canonical_neurons':166700,'canonical_edges':25582938,
|
||||
'APL_PN_edges':268,'APL_target_PNs':259,'APL_PN_contacts':4600,'calyx_contacts':3820,
|
||||
'calyx_fraction':3820/4600,'ROI_unspecified_contacts':731,'other_ROI_contacts':49,
|
||||
'watched_PN_contacts':247,'watched_calyx_contacts':228,'watched_unspecified_contacts':19,
|
||||
'complete_pair_checks':pairs,'frozen_trials_pass':gpu['pass'],'frozen_trials_count':len(gpu['trials']),
|
||||
'old_scientific_files_verified':post['checked'],'old_file_integrity_pass':post['pass'],
|
||||
'external_MD5_pass':external['pass'],'external_SHA256':external['sha256'],
|
||||
'brain_dynamics_changed':False,'training':False,'receptor_or_terminal_gain_calibrated':False,
|
||||
'bouton_identity_confirmed':False,'ComfyUI_stopped_or_unloaded':False,'top_level_gate_fraction':'5/10',
|
||||
'failures_preserved':['initial experiment-directory permission before any output','first GPU build lookup of Ninja failed before simulation','first aria2 checksum argument invalid before download'],
|
||||
'finalized_utc':time.strftime('%Y-%m-%dT%H:%M:%SZ',time.gmtime())}
|
||||
save(EXP/'audit/final-summary.json',result)
|
||||
with (MODEL/'REPORT.md').open('rb') as src,(EXP/'REPORT.md').open('xb') as dst:shutil.copyfileobj(src,dst)
|
||||
model_count=seal(MODEL);experiment_count=seal(EXP)
|
||||
verified=[]
|
||||
for base in (MODEL,EXP):
|
||||
manifest=json.loads((base/'manifest.json').read_text());bad=[r['path'] for r in manifest['files'] if digest(base/r['path'])!=r['sha256']]
|
||||
assert not bad;verified.append({'path':str(base),'files':len(manifest['files']),'pass':not bad})
|
||||
registry=ROOT/'registry/phase4aa-apl-pn-scope';registry.mkdir(exist_ok=False)
|
||||
records=[]
|
||||
for p in sorted(EXP.rglob('*')):
|
||||
if not p.is_file() or p.suffix not in ('.json','.csv','.md','.sha256') or p.stat().st_size>2*1024**2:continue
|
||||
dst=registry/'evidence'/p.relative_to(EXP);dst.parent.mkdir(parents=True,exist_ok=True)
|
||||
with p.open('rb') as src,dst.open('xb') as out:shutil.copyfileobj(src,out)
|
||||
records.append({'original_path':str(p.relative_to(ROOT)),'copy_path':str(dst.relative_to(registry)),'sha256':digest(p),'bytes':p.stat().st_size})
|
||||
save(registry/'evidence-index.json',records);save(registry/'seal-verification.json',verified)
|
||||
doc=ROOT/'docs/progress/phase4aa-apl-pn-scope.md';doc.parent.mkdir(parents=True,exist_ok=True)
|
||||
with (MODEL/'REPORT.md').open('rb') as src,doc.open('xb') as dst:shutil.copyfileobj(src,dst)
|
||||
print(json.dumps({'result':result['status'],'model_files':model_count,'experiment_files':experiment_count,'evidence_copies':len(records),'all_seals_pass':True}),flush=True)
|
||||
|
||||
if __name__=='__main__':main()
|
||||
@@ -0,0 +1,112 @@
|
||||
"""Official paired-synapse localization; numerical counts must reconcile to CSR."""
|
||||
from pathlib import Path
|
||||
import hashlib
|
||||
import json
|
||||
import time
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pyarrow as pa
|
||||
import pyarrow.compute as pc
|
||||
from scipy.spatial import cKDTree
|
||||
from audit import save,digest
|
||||
|
||||
ROOT=Path('/data/digital-drosophila');EXP=ROOT/'experiments/phase4aa-apl-pn-scope'
|
||||
|
||||
def main():
|
||||
pa.set_cpu_count(4);pa.set_io_thread_count(4);start=time.perf_counter()
|
||||
out=EXP/'localization';out.mkdir(exist_ok=False)
|
||||
official=EXP/'external-resources/synaptic-partners'
|
||||
verified=json.loads((official/'download-verification.json').read_text());assert verified['pass']
|
||||
path=Path(verified['path']);assert path.stat().st_size==verified['bytes']
|
||||
source_hash=digest(path);assert source_hash==verified['sha256']
|
||||
save(out/'protocol.json',{'source_sha256':source_hash,'source_generation':verified['generation'],
|
||||
'source_script_sha256':digest(Path(__file__)),'protocol_sha256':digest(Path(__file__).with_name('LOCALIZATION-PROTOCOL.md')),
|
||||
'coordinate_unit_nm':8,'distance_to_microns_multiplier':0.008,
|
||||
'nearest_neighbor_is_not_bouton_or_functional_identity':True,'before_analysis':True})
|
||||
canonical=ROOT/'datasets/male-cns-v1.0/processed/neuron-only'
|
||||
m=pd.read_parquet(canonical/'neuron-index/unified-metadata.parquet').sort_values('neuron_idx').reset_index(drop=True)
|
||||
ptr=np.load(canonical/'gpu/csr-indptr.int64.npy',mmap_mode='r');dst=np.load(canonical/'gpu/csr-indices.int32.npy',mmap_mode='r');w=np.load(canonical/'gpu/csr-weights.int64.npy',mmap_mode='r')
|
||||
body=m.body_id.to_numpy();apl=body[m.annotation_type.eq('APL')];kc=body[m.annotation_class.eq('Kenyon_Cell')];alln=body[m.annotation_class.eq('ALLN')]
|
||||
pnpairs=pd.read_parquet(EXP/'connectivity/APL-PN-reciprocity.parquet');pn=pnpairs.target_body_id.unique()
|
||||
watchidx=json.loads((ROOT/'experiments/phase4z-pn-feedback-audit/protocol/watched.json').read_text())['all_incoming_ALPNs']
|
||||
watch=body[watchidx]
|
||||
groups={k:[] for k in ('APL-incident','APL-target-PN-to-KC','watched-PN-to-ALLN','ALLN-to-watched-PN')}
|
||||
sets={name:pa.array(arr.astype(np.int64)) for name,arr in [('APL',apl),('KC',kc),('ALLN',alln),('PN',pn),('watched',watch)]}
|
||||
total=0
|
||||
with pa.memory_map(str(path),'r') as f:
|
||||
reader=pa.ipc.open_file(f);names=reader.schema.names
|
||||
required=['x_pre','y_pre','z_pre','body_pre','conf_pre','x_post','y_post','z_post','body_post','conf_post','primary_post']
|
||||
save(out/'actual-schema.json',{'fields':[{'name':x.name,'dtype':str(x.type)} for x in reader.schema],'record_batches':reader.num_record_batches})
|
||||
assert set(required).issubset(names),names
|
||||
for i in range(reader.num_record_batches):
|
||||
batch=reader.get_batch(i);total+=len(batch)
|
||||
pre=batch.column(names.index('body_pre'));post=batch.column(names.index('body_post'))
|
||||
masks={
|
||||
'APL-incident':pc.or_(pc.is_in(pre,value_set=sets['APL']),pc.is_in(post,value_set=sets['APL'])),
|
||||
'APL-target-PN-to-KC':pc.and_(pc.is_in(pre,value_set=sets['PN']),pc.is_in(post,value_set=sets['KC'])),
|
||||
'watched-PN-to-ALLN':pc.and_(pc.is_in(pre,value_set=sets['watched']),pc.is_in(post,value_set=sets['ALLN'])),
|
||||
'ALLN-to-watched-PN':pc.and_(pc.is_in(pre,value_set=sets['ALLN']),pc.is_in(post,value_set=sets['watched']))}
|
||||
for name,mask in masks.items():
|
||||
sub=batch.filter(mask)
|
||||
if len(sub):groups[name].append(pa.Table.from_batches([sub]))
|
||||
if (i+1)%500==0:print('streamed_batches',i+1,'rows',total,flush=True)
|
||||
lookup=pd.Series(m.neuron_idx.to_numpy(),index=body)
|
||||
dfs={};checks=[]
|
||||
keys=['body_pre','x_pre','y_pre','z_pre','body_post','x_post','y_post','z_post']
|
||||
for name,tables in groups.items():
|
||||
a=pa.concat_tables(tables).to_pandas();assert not a.duplicated(keys).any(),name+' duplicate partner row'
|
||||
a['source_neuron_idx']=a.body_pre.map(lookup);a['target_neuron_idx']=a.body_post.map(lookup)
|
||||
canonical_rows=a.source_neuron_idx.notna()&a.target_neuron_idx.notna()
|
||||
pairs=a.loc[canonical_rows].groupby(['source_neuron_idx','target_neuron_idx']).size()
|
||||
problems=[]
|
||||
for (s,t),count in pairs.items():
|
||||
s,t=int(s),int(t);eid=np.arange(ptr[s],ptr[s+1]);found=eid[dst[eid]==t]
|
||||
if len(found)!=1 or int(w[found[0]])!=count:problems.append({'source':s,'target':t,'actual_partner_count':int(count),'CSR_weight':int(w[found[0]]) if len(found)==1 else None})
|
||||
result={'group':name,'rows':len(a),'canonical_rows':int(canonical_rows.sum()),'canonical_pairs':len(pairs),'duplicate_partner_rows':0,'mismatches':problems,'pass':not problems}
|
||||
checks.append(result);save(out/(name+'-count-check.json'),result);assert result['pass'],name+' count mismatch'
|
||||
a.to_parquet(out/(name+'.parquet'),index=False);dfs[name]=a
|
||||
print(json.dumps(result),flush=True)
|
||||
a=dfs['APL-incident'];forward=a[a.body_pre.isin(apl)&a.body_post.isin(pn)].copy();reverse=a[a.body_pre.isin(pn)&a.body_post.isin(apl)].copy()
|
||||
assert len(forward)==int(pnpairs.raw_synapse_count.sum())==4600
|
||||
frames={'APL-to-PN':forward,'PN-to-APL':reverse,'PN-to-KC':dfs['APL-target-PN-to-KC'],
|
||||
'watched-PN-to-ALLN':dfs['watched-PN-to-ALLN'],'ALLN-to-watched-PN':dfs['ALLN-to-watched-PN']}
|
||||
roi_rows=[];pair_rows=[]
|
||||
for name,frame in frames.items():
|
||||
for roi,g in frame.groupby('primary_post',dropna=False,observed=True):
|
||||
roi_rows.append({'group':name,'primary_post':str(roi),'contacts':len(g),'fraction':len(g)/len(frame),'source_bodies':g.body_pre.nunique(),'target_bodies':g.body_post.nunique()})
|
||||
for (pre,post,roi),g in frame.groupby(['body_pre','body_post','primary_post'],dropna=False,observed=True):
|
||||
pair_rows.append({'group':name,'body_pre':int(pre),'body_post':int(post),'primary_post':str(roi),'contacts':len(g)})
|
||||
rois=pd.DataFrame(roi_rows);rois.to_csv(out/'population-ROI-summary.csv',index=False);rois.to_parquet(out/'population-ROI-summary.parquet',index=False)
|
||||
pd.DataFrame(pair_rows).to_parquet(out/'pair-ROI-counts.parquet',index=False)
|
||||
# Neuropil annotation is a location label, not evidence for receptor or inhibitory coefficient.
|
||||
distances=[];pnkc=dfs['APL-target-PN-to-KC']
|
||||
for b,g in forward.groupby('body_post'):
|
||||
q=pnkc[pnkc.body_pre==b]
|
||||
if len(q):
|
||||
sites=q[['x_pre','y_pre','z_pre']].drop_duplicates().to_numpy(np.float64)
|
||||
query=g[['x_post','y_post','z_post']].to_numpy(np.float64)
|
||||
dist=cKDTree(sites).query(query,workers=1)[0]*0.008
|
||||
distances.append({'PN_body_id':int(b),'PN_neuron_idx':int(lookup.loc[b]),'APL_postsynaptic_contacts':len(g),'PN_to_KC_unique_presynaptic_sites':len(sites),
|
||||
'nearest_distance_microns_min':float(dist.min()),'nearest_distance_microns_median':float(np.median(dist)),
|
||||
'nearest_distance_microns_p95':float(np.quantile(dist,.95)),'nearest_distance_microns_max':float(dist.max()),
|
||||
'bouton_identity_confirmed':False,'watched':int(lookup.loc[b]) in watchidx})
|
||||
else:distances.append({'PN_body_id':int(b),'PN_neuron_idx':int(lookup.loc[b]),'APL_postsynaptic_contacts':len(g),'PN_to_KC_unique_presynaptic_sites':0,
|
||||
'bouton_identity_confirmed':False,'watched':int(lookup.loc[b]) in watchidx})
|
||||
pd.DataFrame(distances).to_csv(out/'spatial-neighborhoods.csv',index=False)
|
||||
watched_rows=[]
|
||||
for node in watchidx:
|
||||
b=body[node];row={'neuron_idx':node,'body_id':int(b),'type':str(m.iloc[node].annotation_type)}
|
||||
for name,frame in frames.items():
|
||||
sub=frame[frame.body_post==b] if name in ('APL-to-PN','ALLN-to-watched-PN') else frame[frame.body_pre==b]
|
||||
row[name]={str(k):int(v) for k,v in sub.groupby('primary_post',observed=True).size().items()}
|
||||
watched_rows.append(row)
|
||||
save(out/'watched-PN-ROI.json',watched_rows)
|
||||
summary={'pass':all(x['pass'] for x in checks),'source_sha256':source_hash,'full_source_rows_streamed':total,
|
||||
'groups':checks,'APL_to_PN_contacts_localized':len(forward),'APL_to_PN_pairs_localized':len(pnpairs),
|
||||
'APL_target_PNs_localized':int(forward.body_post.nunique()),'ROI_counts':rois.to_dict('records'),
|
||||
'spatial_location_from_official_partners':True,'bouton_identity_confirmed':False,'target_receptor_confirmed':False,
|
||||
'terminal_inhibition_coefficient_known':False,'new_dynamics':False,'whole_brain_functional_intervention_run':False,
|
||||
'wall_seconds':time.perf_counter()-start}
|
||||
save(out/'summary.json',summary);print(json.dumps(summary),flush=True)
|
||||
|
||||
if __name__=='__main__':main()
|
||||
@@ -0,0 +1,65 @@
|
||||
{
|
||||
"created_utc": "2026-09-29T15:56:36.952596+00:00",
|
||||
"files": [
|
||||
{
|
||||
"bytes": 1607,
|
||||
"path": "LOCALIZATION-PROTOCOL.md",
|
||||
"sha256": "b87a601d869f0a4cf7f88ccb2b7f5592b22da25d11d3e5bfb1be4bba05c079c0"
|
||||
},
|
||||
{
|
||||
"bytes": 2468,
|
||||
"path": "PROTOCOL.md",
|
||||
"sha256": "e88f765be0fd91178eed1d862b1de8d8b307ba5ec3cc0ae0d0d93202f264acce"
|
||||
},
|
||||
{
|
||||
"bytes": 7290,
|
||||
"path": "REPORT.md",
|
||||
"sha256": "fa0bfd5a163a436e061c94e37cf6fe84ca75fdfa94d6f20ce85eda5058ca9ce7"
|
||||
},
|
||||
{
|
||||
"bytes": 18939,
|
||||
"path": "audit.py",
|
||||
"sha256": "4ef071ba88f4e1cd78f068be58414dd7f4099dfac69c68266c4f46e175eab9fb"
|
||||
},
|
||||
{
|
||||
"bytes": 906,
|
||||
"path": "audit.yaml",
|
||||
"sha256": "e0171bbd7dc7e693268eac00152e560e841f885c7d524c74d51fda7f7fbadbbf"
|
||||
},
|
||||
{
|
||||
"bytes": 2939,
|
||||
"path": "fetch_syn_partners.py",
|
||||
"sha256": "23db504ac8f31c62a434b24506cbe8456e2993e923bae4c89040cc6a1d8d06f7"
|
||||
},
|
||||
{
|
||||
"bytes": 4144,
|
||||
"path": "finalize.py",
|
||||
"sha256": "ae203b77c011d01ab8fb1cc76fcf4b0223a71e309d6995445fc7f33ce22a2f06"
|
||||
},
|
||||
{
|
||||
"bytes": 8867,
|
||||
"path": "localize.py",
|
||||
"sha256": "aca6c69399538a41bc9148f2c837f0546e8a39d6b2b833585b9c55e53fa8414b"
|
||||
},
|
||||
{
|
||||
"bytes": 4455,
|
||||
"path": "regression.py",
|
||||
"sha256": "554e9378e068fb13abb4f08dbf9ea710228fa535da2442edb913261fa22fcb20"
|
||||
},
|
||||
{
|
||||
"bytes": 394,
|
||||
"path": "regression_retry.py",
|
||||
"sha256": "238506a18fb81d544480c815d2517589e5b9d8c1932e86a5fe163a1b2955f712"
|
||||
},
|
||||
{
|
||||
"bytes": 1520,
|
||||
"path": "test_audit.py",
|
||||
"sha256": "cb67bab972195090d5c4188169982abdc960479b8031477f1f39ac445814e88f"
|
||||
},
|
||||
{
|
||||
"bytes": 2229,
|
||||
"path": "validate_localization.py",
|
||||
"sha256": "732dcfe1a9c807356f7664393f6678743c61e3fe38a915ca0ce3f00ee7b17d9a"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,72 @@
|
||||
"""Supplement: existing five frozen trials on 4090, new output paths only."""
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
import sys
|
||||
import time
|
||||
import urllib.request
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import yaml
|
||||
|
||||
ROOT=Path('/data/digital-drosophila')
|
||||
MODEL=ROOT/'models/male-cns-lif/apl-pn-scope-audit-v1'
|
||||
EXP=ROOT/'experiments/phase4aa-apl-pn-scope'
|
||||
sys.path.insert(0,str(MODEL))
|
||||
from audit import save,digest,immutable_check
|
||||
|
||||
def queues():
|
||||
result=[]
|
||||
for port in (8188,8189,8190):
|
||||
with urllib.request.urlopen(f'http://192.168.10.51:{port}/queue',timeout=5) as r:q=json.load(r)
|
||||
result.append({'port':port,'running':len(q.get('queue_running',[])),'pending':len(q.get('queue_pending',[]))})
|
||||
return result
|
||||
|
||||
def main():
|
||||
out=EXP/'gpu-frozen-regression';out.mkdir(exist_ok=False)
|
||||
q=queues();assert all(x['running']==0 and x['pending']==0 for x in q),'ComfyUI busy; defer'
|
||||
runtime=ROOT/'models/male-cns-lif/deterministic-runtime-v2'
|
||||
cfg=yaml.safe_load((runtime/'phase4p.yaml').read_text())
|
||||
os.environ['CUDA_VISIBLE_DEVICES']=cfg['device']['visible_gpu_uuid']
|
||||
os.environ['TORCH_CUDA_ARCH_LIST']='8.9'
|
||||
os.environ['CUDA_HOME']='/usr/local/cuda'
|
||||
os.environ['MAX_JOBS']='2'
|
||||
os.environ['PYTHONDONTWRITEBYTECODE']='1'
|
||||
sys.path.insert(0,str(ROOT/'models/male-cns-lif'))
|
||||
sys.path.insert(0,str(runtime))
|
||||
import torch
|
||||
import phase4p
|
||||
free,total=torch.cuda.mem_get_info();assert free>2*1024**3,'Insufficient free VRAM; no service eviction'
|
||||
save(out/'protocol-addendum.json',{'scope':'Five frozen reference v2 trials only; supplement to CPU structural audit',
|
||||
'reason':'User confirmed ComfyUI is not in use; baseline behavior check before any future mechanism intervention',
|
||||
'new_mechanism_or_training':False,'ComfyUI_queues':q,'available_vram_bytes':free,
|
||||
'reference':'experiments/phase4p-deterministic-runtime/phase4c-v2/reference',
|
||||
'historical_files_overwritten':False,'source_sha256':digest(Path(__file__)),
|
||||
'runtime_sha256':digest(runtime/'phase4p.py'),'config_sha256':digest(runtime/'phase4p.yaml')})
|
||||
torch.cuda.reset_peak_memory_stats();start=time.perf_counter();rt=phase4p.CanonicalRuntime(cfg,runtime,out)
|
||||
rows=[]
|
||||
for spec in cfg['reference_trials']:
|
||||
q=queues();assert all(x['running']==0 and x['pending']==0 for x in q),'ComfyUI work arrived; stop between trials'
|
||||
ref=ROOT/'experiments/phase4p-deterministic-runtime/phase4c-v2/reference'/spec['name']
|
||||
events=pd.read_parquet(ref/'spike-events.parquet')
|
||||
expected={'step':events.step.to_numpy(),'neuron_idx':events.neuron_idx.to_numpy(),
|
||||
'counts':np.load(ref/'per-neuron-spike-counts.int32.npy'),'v':np.load(ref/'final-voltage.float32.npy'),'g':np.load(ref/'final-g.float32.npy')}
|
||||
result=rt.run(np.flatnonzero(rt.pops[spec['population']]),spec['rate_hz'],spec['seed'],spec['duration_ms'])
|
||||
exact={k:bool(np.array_equal(result[k],v)) for k,v in expected.items()}
|
||||
finite=bool(np.isfinite(result['v']).all() and np.isfinite(result['g']).all())
|
||||
row={'trial':spec['name'],'exact':exact,'finite':finite,'pass':all(exact.values()) and finite,
|
||||
'wall_seconds':result['metrics']['wall_seconds'],'events':len(result['step']),
|
||||
'events_sha256':phase4p.sha_bytes(result['step'],result['neuron_idx']),
|
||||
'max_abs_V_error':float(np.max(np.abs(result['v'].astype(float)-expected['v']))),
|
||||
'max_abs_g_error':float(np.max(np.abs(result['g'].astype(float)-expected['g'])))}
|
||||
save(out/(spec['name']+'.json'),row);rows.append(row);print(json.dumps(row),flush=True)
|
||||
assert row['pass'],'Frozen regression diverged'
|
||||
save(out/'summary.json',{'pass':all(r['pass'] for r in rows),'trials':rows,'total_wall_seconds_including_build':time.perf_counter()-start,
|
||||
'peak_allocated_VRAM_bytes':int(torch.cuda.max_memory_allocated()),'device':torch.cuda.get_device_name(),
|
||||
'ComfyUI_models_unloaded':False,'new_dynamics':False,'original_reference_v1_not_overwritten':True,
|
||||
'oracle':'deterministic v2 / original v1 preserved; not claiming v1 atomic runtime is deterministic'})
|
||||
# No old scientific artifact may change even after imports/build/run.
|
||||
post=immutable_check(ROOT,ROOT/'registry/snapshots/bootstrap-20260929',4)
|
||||
save(out/'post-regression-integrity.json',post);assert post['pass']
|
||||
|
||||
if __name__=='__main__':main()
|
||||
@@ -0,0 +1,10 @@
|
||||
"""Retry regression with the existing venv/toolkit executables on PATH; no install."""
|
||||
from pathlib import Path
|
||||
import os
|
||||
import regression
|
||||
|
||||
root=Path('/data/digital-drosophila')
|
||||
os.environ['PATH']=str(root/'models/male-cns-lif/.venv/bin')+':/usr/local/cuda/bin:'+os.environ['PATH']
|
||||
regression.EXP=regression.EXP/'retry-with-correct-PATH'
|
||||
regression.EXP.mkdir(exist_ok=False)
|
||||
regression.main()
|
||||
@@ -0,0 +1,29 @@
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
import unittest
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pyarrow as pa
|
||||
import pyarrow.feather as feather
|
||||
from audit import raw_incident,population,save
|
||||
|
||||
class AuditTests(unittest.TestCase):
|
||||
def test_raw_incident_union_keeps_self_loop_once(self):
|
||||
with tempfile.TemporaryDirectory(prefix='apl-scope-test-') as d:
|
||||
p=Path(d)/'raw.feather'
|
||||
frame=pd.DataFrame({'body_pre':[10,10,20,40],'body_post':[10,30,10,50],'weight':[1,2,3,4]},dtype=np.int64)
|
||||
feather.write_feather(pa.Table.from_pandas(frame,preserve_index=False),p,chunksize=2)
|
||||
result=raw_incident(p,np.array([10],np.uint64))
|
||||
self.assertTrue(np.array_equal(result.to_numpy(),frame.iloc[:3].to_numpy()))
|
||||
with self.assertRaises(AssertionError):
|
||||
feather.write_feather(pa.table({'body_pre':[10],'wrong':[30],'weight':[1]}),p)
|
||||
raw_incident(p,np.array([10],np.uint64))
|
||||
def test_population_does_not_equate_dopamine_to_DAN(self):
|
||||
m=pd.DataFrame({'annotation_class':[None,None,'ALPN','DAN'], 'annotation_type':['APL','DPM','VA6_adPN','PAM01']})
|
||||
self.assertEqual(list(population(m)),['APL','DPM','ALPN','DAN'])
|
||||
def test_exclusive_output(self):
|
||||
with tempfile.TemporaryDirectory(prefix='apl-scope-test-') as d:
|
||||
p=Path(d)/'x.json';save(p,{'original':True})
|
||||
with self.assertRaises(FileExistsError):save(p,{'original':False})
|
||||
|
||||
if __name__=='__main__':unittest.main()
|
||||
@@ -0,0 +1,34 @@
|
||||
"""Independent complete-pair-set and weight check (including entirely missing pairs)."""
|
||||
from pathlib import Path
|
||||
import json
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from audit import save
|
||||
|
||||
ROOT=Path('/data/digital-drosophila');EXP=ROOT/'experiments/phase4aa-apl-pn-scope'
|
||||
c=ROOT/'datasets/male-cns-v1.0/processed/neuron-only'
|
||||
m=pd.read_parquet(c/'neuron-index/unified-metadata.parquet').sort_values('neuron_idx').reset_index(drop=True)
|
||||
ptr=np.load(c/'gpu/csr-indptr.int64.npy',mmap_mode='r');dst=np.load(c/'gpu/csr-indices.int32.npy',mmap_mode='r');w=np.load(c/'gpu/csr-weights.int64.npy',mmap_mode='r')
|
||||
src=np.repeat(np.arange(len(m),dtype=np.int32),np.diff(ptr))
|
||||
apl=np.flatnonzero(m.annotation_type.eq('APL'));kc=np.flatnonzero(m.annotation_class.eq('Kenyon_Cell'));alln=np.flatnonzero(m.annotation_class.eq('ALLN'))
|
||||
pn=pd.read_parquet(EXP/'connectivity/APL-PN-reciprocity.parquet').target_neuron_idx.unique()
|
||||
watch=json.loads((ROOT/'experiments/phase4z-pn-feedback-audit/protocol/watched.json').read_text())['all_incoming_ALPNs']
|
||||
selectors={
|
||||
'APL-incident':np.isin(src,apl)|np.isin(dst,apl),
|
||||
'APL-target-PN-to-KC':np.isin(src,pn)&np.isin(dst,kc),
|
||||
'watched-PN-to-ALLN':np.isin(src,watch)&np.isin(dst,alln),
|
||||
'ALLN-to-watched-PN':np.isin(src,alln)&np.isin(dst,watch)}
|
||||
results=[]
|
||||
for name,mask in selectors.items():
|
||||
frame=pd.read_parquet(EXP/'localization'/(name+'.parquet'))
|
||||
frame=frame[frame.source_neuron_idx.notna()&frame.target_neuron_idx.notna()]
|
||||
counts=frame.groupby(['source_neuron_idx','target_neuron_idx']).size().reset_index(name='weight')
|
||||
actual=counts.to_numpy(np.int64)
|
||||
ids=np.flatnonzero(mask)
|
||||
expected=np.column_stack((src[ids],dst[ids],w[ids])).astype(np.int64)
|
||||
actual=actual[np.lexsort((actual[:,1],actual[:,0]))];expected=expected[np.lexsort((expected[:,1],expected[:,0]))]
|
||||
passed=np.array_equal(actual,expected)
|
||||
results.append({'group':name,'expected_CSR_pairs':len(expected),'observed_pairs':len(actual),'complete_pair_set_and_weight_exact':passed})
|
||||
assert passed,name
|
||||
save(EXP/'localization/complete-pair-set-validation.json',{'pass':all(r['complete_pair_set_and_weight_exact'] for r in results),'groups':results})
|
||||
print(json.dumps(results,indent=2))
|
||||
@@ -0,0 +1,266 @@
|
||||
[
|
||||
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|
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|
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|
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|
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|
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|
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|
||||
@@ -0,0 +1,111 @@
|
||||
# Phase 4AA — APL→PN 作用范围与官方突触定位审计
|
||||
|
||||
正式结论：**PASS-APL-PN-ANATOMICAL-LOCALIZATION / NO-DYNAMICS-CANDIDATE**。
|
||||
|
||||
本轮从 Phase 4Z 接续，先完成只读 CPU 范围审计；随后用户明确授权下载官方 syn-partners 并继续定位，也确认 ComfyUI 空闲以便运行 frozen regression。
|
||||
原始静态审计 `audit/results.json` 中的空间 unknown/blocked 是追加数据前的状态，保留不覆盖；最终状态以 `audit/final-summary.json` 和本报告为准。
|
||||
本轮无训练、无参数修改、无新的正式动力学候选。顶层验收仍为 5/10（50%），不是生物学复现比例。
|
||||
|
||||
## 核心新证据
|
||||
|
||||
以实际 annotation 与原始 CSR 识别 2 个 APL、4,064 个 KC、686 个 ALPN，不通过 neurotransmitter 推断 DAN 身份。
|
||||
所有 166,700 个 neurons、25,582,938 edges、124,177,617 anatomical contacts 不变。
|
||||
|
||||
| 已有 APL 实现范围 | 边数 | 原始 contacts |
|
||||
|---|---:|---:|
|
||||
| APL 全部出边 | 5,845 | 209,912 |
|
||||
| KC-only graded output 覆盖 | 4,633 | 196,200 |
|
||||
| 非 KC 出边，没有 graded 替代 | 1,212 | 13,712 |
|
||||
| 上述缺口中的 APL→PN | 268 | 4,600 |
|
||||
| APL 全部入边 | 6,185 | 234,025 |
|
||||
| 不被 KC/PN-only graded state 使用的入边 | 1,202 | 18,257 |
|
||||
|
||||
268 条 APL→PN 边涉及 259 个 PN；247 对同时存在 PN→APL 反向连接。
|
||||
“graded state 未使用”不等于原图入边被删除：普通 LIF APL 仍有这些入边，但其快速出边被既有 overlay 置零。基于 KC/PN 计算的独立 graded state 没有使用其余来源。
|
||||
这属于既有模型作用范围，不能把每个未覆盖来源都自动补入；需要额外生理依据。
|
||||
|
||||
## 官方附加数据与精确核对
|
||||
|
||||
用户授权下载的 `syn-partners-male-cns-v1.0-minconf-0.5.feather`：
|
||||
|
||||
- 6,777,179,098 bytes；官方 generation `1780494942562468`。
|
||||
- 官方 MD5：`58efcf712f8c4d4de5f2ad51e97def76`，验证通过。
|
||||
- SHA-256：`959d8ef4173b35382a3e6acfaf5167c795b6d10b877572d146af04e1b487bc07`。
|
||||
- 流式读取全部 311,833,243 条配对记录；实际字段和 dtype 已保存。
|
||||
- 来源：[MaleCNS 官方下载页](https://male-cns.janelia.org/download/)，许可证 CC BY 4.0。新增文件保存在本阶段 external-resources，不写 raw，不下载 EM。
|
||||
|
||||
| 提取范围 | 配对记录总数 | canonical 配对记录 | canonical neuron pairs |
|
||||
|---|---:|---:|---:|
|
||||
| APL 所有入/出接触 | 500,348 | 443,915 | 12,027 |
|
||||
| APL 所连 PN→KC | 385,229 | 385,229 | 21,978 |
|
||||
| 七个重点 PN→ALLN | 22,599 | 22,599 | 823 |
|
||||
| ALLN→七个重点 PN | 19,024 | 19,024 | 886 |
|
||||
|
||||
每组同时检查重复配对、完整 pair 集合及逐边 count：均与原始 CSR 完全一致。不存在只比较出现过的边而漏掉整条缺失边的情况。
|
||||
APL 配对中的非 canonical 部分保留来源记录，不加入 neuron-only graph。
|
||||
|
||||
## 真实 ROI 定位
|
||||
|
||||
4,600 个 APL→PN 接触都取得真实 pre/post 坐标：
|
||||
|
||||
- `CA(L)` 2,063；`CA(R)` 1,757；合计 3,820（83.04%）。
|
||||
- `CentralBrain-unspecified` 731（15.89%）：坐标已知，但官方 ROI 未细分，绝不按邻近位置重标为 calyx。
|
||||
- 其余实际标注合计 49，分布于 LH、PED、SCL、SLP、a'L；不丢弃非 calyx 结果。
|
||||
|
||||
CA 对应 calyx，官方作者 [Cell Type Explorer](https://reiserlab.github.io/celltype-explorer-drosophila-male-cns/types/KCg-d_R.html) 提供标签释义。
|
||||
七个重点 PN：
|
||||
|
||||
| neuron_idx / body_id | type | APL→PN contacts | CA(R) | ROI 未细分 |
|
||||
|---|---|---:|---:|---:|
|
||||
| 30 / 10039 | VL2a_adPN | 50 | 47 | 3 |
|
||||
| 179 / 10198 | VA7l_adPN | 7 | 7 | 0 |
|
||||
| 484 / 10522 | VL2p_adPN | 50 | 49 | 1 |
|
||||
| 1644 / 11761 | VP1d+VP4_l2PN1 | 78 | 70 | 8 |
|
||||
| 6065 / 16610 | VA7m_lPN | 14 | 13 | 1 |
|
||||
| 6842 / 17484 | DC2_adPN | 13 | 13 | 0 |
|
||||
| 133666 / 527577 | VA6_adPN | 35 | 29 | 6 |
|
||||
|
||||
合计 228/247（92.31%）在 CA(R)，19 个未细分。
|
||||
对同一 PN，APL postsynaptic sites 到最近 PN→KC presynaptic site 的距离，中位数范围为 0.545–0.861 µm；坐标按官方 8 nm voxel 转换。
|
||||
距离不是同 bouton 的证明，更不是受体或功能因果证明。保留全部分布和远距离样本，不设“最佳阈值”筛掉例外。
|
||||
259 个 APL 靶 PN 中，15 个在本 canonical 图中没有 PN→KC 出边，不能一概当作同一功能入口。
|
||||
|
||||
七个 PN→ALLN 的 22,599 个接触全部位于 AL(L/R)；ALLN→七个 PN 的 19,024 个接触中，19,021 位于 AL(L/R)，另有 1 个 LAL(R)、2 个未细分。
|
||||
同一 PN 的 calyx 相关接触与触角叶局部回路接触必须区分，不能用单一 whole-neuron 抑制项自动代表两种位置。
|
||||
|
||||
## 如何解释，不做什么
|
||||
|
||||
已有原始研究支持 APL 的局部抑制与 PN bouton/KC dendrite 相互作用，但不提供当前 MaleCNS 每条边的受体或强度。
|
||||
见 [Prisco et al., eLife](https://doi.org/10.7554/eLife.74172)、[Amin et al., eLife](https://doi.org/10.7554/eLife.56954)。
|
||||
本轮数据加强了“空间作用范围缺失”这一机制假设，**没有证明补上它就能修复 KC 持续放电或刺激特异性记忆**。
|
||||
仍未知：具体 bouton 身份、GABA 受体、末梢释放变化、时间常数、量化作用系数及 whole-brain 干预效果。
|
||||
|
||||
下一步应先设计独立的 compartment/terminal-specific input hypothesis 和预声明对照，不直接改变 frozen baseline；对未知系数不能用“学习效果更好”反向调参。
|
||||
优先处理 upstream encoding / 作用范围，而不是继续改 dopamine eta、扩大 plastic mask 或调 KC/MBON 阈值。
|
||||
|
||||
## 回归、完整性与资源
|
||||
|
||||
独立新目录重跑 deterministic-v2 的五组冻结试次（各一次）：
|
||||
|
||||
| trial | biological time | wall time | 事件 / counts / V / g |
|
||||
|---|---:|---:|---|
|
||||
| auditory 10 Hz | 1 s | 2.690 s | 全部精确一致 |
|
||||
| auditory 100 Hz | 1 s | 4.915 s | 全部精确一致 |
|
||||
| olfactory 50 Hz | 1 s | 4.961 s | 全部精确一致 |
|
||||
| mechanosensory 50 Hz | 1 s | 4.982 s | 全部精确一致 |
|
||||
| gustatory 50 Hz | 1 s | 4.977 s | 全部精确一致 |
|
||||
|
||||
均无 NaN/Inf，V/g 最大误差 0；4090 本进程 peak allocated VRAM 为 285,994,496 bytes（约 272.75 MiB），不包括 ComfyUI 已驻留显存。
|
||||
本轮不声称重做了 Phase 4P 的 20 次重复实验；旧 v1/v2 reference 均没有覆盖。
|
||||
GPU 编译加回归约 85.55 s；CPU 结构审计含两次旧文件哈希扫描约 86.92 s；下载加校验约 375.66 s；空间提取/分析约 37.98 s。
|
||||
230,239 个旧科学文件的 SHA-256 复核通过。初始 Git payload 的 681 项校验也通过；新阶段另做 manifest。
|
||||
未停止或卸载 ComfyUI，未更新任何环境包。
|
||||
|
||||
## 异常与处理记录
|
||||
|
||||
1. 初次建立实验目录遇到写权限限制，在新实验目录单独配置 owner；没有递归修改旧目录。
|
||||
2. GPU 首次启动 PATH 未包含原 venv 的 Ninja，未开始模拟；失败目录保留。仅补启动 PATH 后，在另一个新目录完成 5/5 回归。
|
||||
3. 下载首次校验选项分隔符错误，下载未开始；修正为 aria2 要求的语法后完成官方校验，没有坏文件被当作通过。
|
||||
4. 旧 NumPy→Torch 只读数组警告保留，未修改冻结加载器；文件哈希与数值回归通过。
|
||||
|
||||
本轮到此结束，不生成 learning candidate，不进入游戏、RL、视觉或动作 decoder。
|
||||
@@ -0,0 +1,66 @@
|
||||
{
|
||||
"APL_PN_contacts": 4600,
|
||||
"APL_PN_edges": 268,
|
||||
"APL_target_PNs": 259,
|
||||
"ComfyUI_stopped_or_unloaded": false,
|
||||
"ROI_unspecified_contacts": 731,
|
||||
"bouton_identity_confirmed": false,
|
||||
"brain_dynamics_changed": false,
|
||||
"calyx_contacts": 3820,
|
||||
"calyx_fraction": 0.8304347826086956,
|
||||
"candidate": false,
|
||||
"canonical_edges": 25582938,
|
||||
"canonical_neurons": 166700,
|
||||
"complete_pair_checks": {
|
||||
"groups": [
|
||||
{
|
||||
"complete_pair_set_and_weight_exact": true,
|
||||
"expected_CSR_pairs": 12027,
|
||||
"group": "APL-incident",
|
||||
"observed_pairs": 12027
|
||||
},
|
||||
{
|
||||
"complete_pair_set_and_weight_exact": true,
|
||||
"expected_CSR_pairs": 21978,
|
||||
"group": "APL-target-PN-to-KC",
|
||||
"observed_pairs": 21978
|
||||
},
|
||||
{
|
||||
"complete_pair_set_and_weight_exact": true,
|
||||
"expected_CSR_pairs": 823,
|
||||
"group": "watched-PN-to-ALLN",
|
||||
"observed_pairs": 823
|
||||
},
|
||||
{
|
||||
"complete_pair_set_and_weight_exact": true,
|
||||
"expected_CSR_pairs": 886,
|
||||
"group": "ALLN-to-watched-PN",
|
||||
"observed_pairs": 886
|
||||
}
|
||||
],
|
||||
"pass": true
|
||||
},
|
||||
"external_MD5_pass": true,
|
||||
"external_SHA256": "959d8ef4173b35382a3e6acfaf5167c795b6d10b877572d146af04e1b487bc07",
|
||||
"failures_preserved": [
|
||||
"initial experiment-directory permission before any output",
|
||||
"first GPU build lookup of Ninja failed before simulation",
|
||||
"first aria2 checksum argument invalid before download"
|
||||
],
|
||||
"finalized_utc": "2026-09-29T15:56:36Z",
|
||||
"frozen_trials_count": 5,
|
||||
"frozen_trials_pass": true,
|
||||
"old_file_integrity_pass": true,
|
||||
"old_scientific_files_verified": 230239,
|
||||
"old_static_audit_retained_without_overwrite": true,
|
||||
"other_ROI_contacts": 49,
|
||||
"phase": "4AA",
|
||||
"previous_static_spatial_block_resolved_by_user_authorized_reference_data": true,
|
||||
"receptor_or_terminal_gain_calibrated": false,
|
||||
"status": "PASS-APL-PN-ANATOMICAL-LOCALIZATION / NO-DYNAMICS-CANDIDATE",
|
||||
"top_level_gate_fraction": "5/10",
|
||||
"training": false,
|
||||
"watched_PN_contacts": 247,
|
||||
"watched_calyx_contacts": 228,
|
||||
"watched_unspecified_contacts": 19
|
||||
}
|
||||
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"models_imported": false,
|
||||
"path": "/data/digital-drosophila/models/male-cns-lif/deterministic-runtime-v2/phase4p_revalidate.py",
|
||||
"same_as_Phase4Z": true,
|
||||
"sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346"
|
||||
}
|
||||
@@ -0,0 +1,15 @@
|
||||
{
|
||||
"KC_targets_with_graded_input": 4063,
|
||||
"input_matrix_shape": [
|
||||
2,
|
||||
166700
|
||||
],
|
||||
"input_scale": 49.65891647338867,
|
||||
"normalized_input_matrix_sha256": "528a5f6f5ffe424f323c5a44695db4a7cf32570709832eff2f37f49e85c92c3e",
|
||||
"normalized_output_matrix_sha256": "0dcf02cdb7836ab72da32ac47f5682ca33e25f63a8a662a8452d4ea9d46babb9",
|
||||
"output_matrix_shape": [
|
||||
2,
|
||||
4064
|
||||
],
|
||||
"output_scale": 42.34836959838867
|
||||
}
|
||||
@@ -0,0 +1,41 @@
|
||||
{
|
||||
"ALPN_neurons": 686,
|
||||
"APL_PN_pairs_with_confirmed_local_synapse_ROI": 0,
|
||||
"APL_PN_pairs_with_confirmed_subcellular_target": 0,
|
||||
"APL_PN_pairs_with_reverse_edge": 247,
|
||||
"APL_incoming_contacts": 234025,
|
||||
"APL_incoming_edges": 6185,
|
||||
"APL_neurons": 2,
|
||||
"APL_outgoing_contacts": 209912,
|
||||
"APL_outgoing_edges": 5845,
|
||||
"APL_target_PNs": 259,
|
||||
"APL_to_PN_contacts": 4600,
|
||||
"APL_to_PN_edges": 268,
|
||||
"ComfyUI_models_unloaded": false,
|
||||
"KC_neurons": 4064,
|
||||
"Phase4Z_static_coverage_exact": true,
|
||||
"candidate": false,
|
||||
"frozen_integrity_pass": true,
|
||||
"gpu_used": false,
|
||||
"graph_edges": 25582938,
|
||||
"incoming_contacts_not_used_by_graded_state": 18257,
|
||||
"incoming_edges_not_used_by_graded_state": 1202,
|
||||
"neurons": 166700,
|
||||
"new_scientific_regression_run": false,
|
||||
"new_simulation": false,
|
||||
"non_KC_uncovered_contacts": 13712,
|
||||
"non_KC_uncovered_edges": 1212,
|
||||
"non_KC_uncovered_targets": 999,
|
||||
"old_reference_files_rehashed": true,
|
||||
"phase": "4AA",
|
||||
"raw_APL_incident_canonical_edges_checked": 12027,
|
||||
"raw_APL_incident_noncanonical_rows": 50386,
|
||||
"raw_to_CSR_exact": true,
|
||||
"status": "PASS-STATIC-SCOPE-AUDIT / BLOCKED-SPATIAL-PHYSIOLOGY",
|
||||
"synapse_count_sum": 124177617,
|
||||
"top_level_gates_passed": 5,
|
||||
"top_level_gates_total": 10,
|
||||
"watched_PN_contacts": 247,
|
||||
"weights_changed": false,
|
||||
"whole_cell_inhibition_repair_authorized_by_evidence": false
|
||||
}
|
||||
@@ -0,0 +1,68 @@
|
||||
fa0bfd5a163a436e061c94e37cf6fe84ca75fdfa94d6f20ce85eda5058ca9ce7 REPORT.md
|
||||
69a381512dcb4a80a974a36ce85d78b06842f67b1ba347f0bdddb325ea3b9b1d audit/final-summary.json
|
||||
46a4e4bc1475f121b142e90ddc9d9996856cf91c35a12314c1d9ec36d226cf8e audit/frozen-APL-class-source.txt
|
||||
c960b11f471de5ab1edff2ed8adf6a5b38a3d9ba523fed2ff1304f9b00827001 audit/frozen-implementation-provenance.json
|
||||
b7cae9e2557d872c305c1168ebe2748031c0452d2679f1ade11f9ff95e52cbe9 audit/frozen-matrix-reconstruction.json
|
||||
206c35bee8bfae6daf87b154854f77e649bebe2655b26b6d22dff04fbd64e160 audit/results.json
|
||||
da4ddcaf4527a1c4bac5055f9865fe10dc36e79daa15685e3be4545f5284b164 connectivity/APL-PN-reciprocity.csv
|
||||
08b9b6c4ce67f87c88fe645c44589ecde4e2669827f5b83692a255c45f4b9512 connectivity/APL-PN-reciprocity.parquet
|
||||
140f2d53910d92c7485e176901fea8309deb752f763413c086a622e02d0895f6 connectivity/APL-incoming.parquet
|
||||
219ed78d1eae79c41958185740e6745c62015450a6d1f143ec93c068bcc90340 connectivity/APL-input-population-summary.csv
|
||||
1dc1befba0e3a5310a248631019327cb122048fc866d62acc29edd55d1345291 connectivity/APL-input-population-summary.parquet
|
||||
77d6cc8d8034df77e656153f3f136b7e91ec6ca31eee8107fdbc40966b0162be connectivity/APL-outgoing.parquet
|
||||
1977d1fd85d659de0490928213ae11b11e3e67fa01f37692f555538661fcf5e2 connectivity/APL-output-population-summary.csv
|
||||
4f714086b9b8b470c6db2652b71a00f297050f8b734e4033622c4a33eb7a4fee connectivity/APL-output-population-summary.parquet
|
||||
477724007b55e65d07440288624790e16e5dc6340cf04dd687a09f02f82739eb connectivity/APL-target-PN-downstream.parquet
|
||||
77516b0b0b3049f2c1e3571c6f0fa48cbb0e7b65379a5508bfbc92bc6e1fdf0f connectivity/graded-input-not-used.parquet
|
||||
6a0e2e328bd86401507bba8c58934621b0f204e597be0b2acf27b0e41bbbc4b7 connectivity/raw-APL-incident-including-noncanonical.parquet
|
||||
cae347c669207eca2304de672cf5309a7b9f24e8b2f5722e6fd779a51a94ea30 connectivity/watched-PN-reciprocity.csv
|
||||
f171fe0e12ae50da6947f7b636a9f554f7c661942e4c076f54a3dc314f61b0cc external-resources/evidence-and-limitations.json
|
||||
11bd6336e51bb3e85a61dfa3deef294767e43362b0731b40cb25384a0cf1ced9 external-resources/official-data-access.json
|
||||
6e2213f296aef1a6ff7d0acb245f69785346daba1bd38eba9a600224c60b791f external-resources/synaptic-partners/authorization-and-source.json
|
||||
4fd796f88f05a08d5f7f3c3ff2c63c939f330e9acc4519a5272c8b3b97fcf4ac external-resources/synaptic-partners/download-verification.json
|
||||
959d8ef4173b35382a3e6acfaf5167c795b6d10b877572d146af04e1b487bc07 external-resources/synaptic-partners/syn-partners-male-cns-v1.0-minconf-0.5.feather
|
||||
36c941740074f2bd7b9e382b01d7743791f03f9376763a2704ca3d8d7f1888cf external-resources/synaptic-partners/upstream-object.json
|
||||
e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 gpu-frozen-regression/gpu-reference/extension-build/lock
|
||||
5598a6867d6f7f22a5e6472f00c763a9e94850bda18f10df992bba71ef99e982 gpu-frozen-regression/protocol-addendum.json
|
||||
4513ec607f278684b4825c5893db18e4e20d660afa44dccd6e0305bc5efa2286 localization/ALLN-to-watched-PN-count-check.json
|
||||
b3ef49f06d5fed9d807aab2cb6adb52091a2fad298f9c9889167b1890d45f837 localization/ALLN-to-watched-PN.parquet
|
||||
aa56cb7c98c9186c69c5ef06f5b80ecc7e13fba7f1d0316c298687e2c9c191af localization/APL-incident-count-check.json
|
||||
442b0fa2640cd1bfaf97e558ae912a8601dc3289b420021526353bc3bcfba849 localization/APL-incident.parquet
|
||||
2fe50c10f0d497ec522782bfbd7b7a5ecada77e62960737b608d78485638a695 localization/APL-target-PN-to-KC-count-check.json
|
||||
310827739c293b633d6c392d2b59ced6820a0b39606fdb29ff263ce3f5b4847e localization/APL-target-PN-to-KC.parquet
|
||||
40e14fb1c63a890c6dc3ff126f7429fa9c577a29dad6e2b3dd6c4fe9b06a2ce6 localization/actual-schema.json
|
||||
30bc6ca4a8b92188f52ca2d9d1da9179dfe7de11e9ae83ec6592c4d84a757b8b localization/complete-pair-set-validation.json
|
||||
16246a4061d7d7115d15d1a9f683ead9311ceedcccda94c3a7b94b1eb73ef405 localization/pair-ROI-counts.parquet
|
||||
c3f8fe2bc18fb2cea7906c8b6ea98fcbe542c19c9f0d835c2b1b5bc3266edb83 localization/population-ROI-summary.csv
|
||||
c9e3b4a5d7858e1237dd4d88a9e6c712059b690d58dec0355a4e61aefb71cdd7 localization/population-ROI-summary.parquet
|
||||
56d78bcee99d4bd84f0942dac330a12686a02641acbf993ed72b83db93fca941 localization/protocol.json
|
||||
271d2a5a857842335e905d7e7665e6484c9492df23e350a1b3f3c7b27df4424f localization/spatial-neighborhoods.csv
|
||||
b53b5a714d9ba400db1ce5e7ed35e386522ba6c7a47266d2e7fec314aa1e4d1d localization/summary.json
|
||||
e5640846514bac50d667c814197227feaa770e254e333004364c749b406a2153 localization/watched-PN-ROI.json
|
||||
b43a48b9ae400172ca8cd5e6d1e327073f29e4b0d18db8402b222ada62113b6d localization/watched-PN-to-ALLN-count-check.json
|
||||
1ae1d00e18c88e84a8e1bdc75b3dd0beb596b93de4b18e23dbc5243ce6008ded localization/watched-PN-to-ALLN.parquet
|
||||
bfbb2ca509a5f140bb7a42bf07735a1ca4693006d3104796ea3a7970539fc631 metadata/APL-and-target-PNs.parquet
|
||||
eec1917188ab67a73d1c487642fa2eb5d2468ff51d344cf6f37bb93810dcb2c6 performance/resources.json
|
||||
ff9892351ed16d9ef45817c5c104dca9a23547fe97ce2a4adf45b2270595673f protocol/preregistered.json
|
||||
1431b9be6dc067f48699f3c3708486718a79380c04a8b342ad247def222cd24c regression/final-old-files-integrity.json
|
||||
1431b9be6dc067f48699f3c3708486718a79380c04a8b342ad247def222cd24c regression/postflight.json
|
||||
1431b9be6dc067f48699f3c3708486718a79380c04a8b342ad247def222cd24c regression/preflight.json
|
||||
41309fea8a1e292e007c6e1cf7646836a83b47e9aef3e0bcb4a0c0caba621493 retry-with-correct-PATH/gpu-frozen-regression/auditory-100hz.json
|
||||
2b76a9c17aa3771c8602da15a823e35c06022d5f92d55a2ef1a23174679b31d9 retry-with-correct-PATH/gpu-frozen-regression/auditory-10hz.json
|
||||
c08864962bfb25ce2103f4c5e792f000cd045e7cc42152f38a68c238e4f86c44 retry-with-correct-PATH/gpu-frozen-regression/gpu-reference/extension-build/.ninja_deps
|
||||
2e76199498ca4b3dff488d7cc6a5577bf26fd0f88c19fbb301d2e4bc30ecb892 retry-with-correct-PATH/gpu-frozen-regression/gpu-reference/extension-build/.ninja_log
|
||||
c570f72b9a2f486e104ed3bf5b1b9f2ccc729ecab02046cd62389d629fb4f48f retry-with-correct-PATH/gpu-frozen-regression/gpu-reference/extension-build/build.ninja
|
||||
e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 retry-with-correct-PATH/gpu-frozen-regression/gpu-reference/extension-build/lock
|
||||
587b2bd64186e62b628800536613ac1245937405236e51143dedc159cdd9d235 retry-with-correct-PATH/gpu-frozen-regression/gpu-reference/extension-build/male_cns_lif_deterministic_v2_ext.so
|
||||
bc256cf493b979dc8c00ed54c133c371f7704ffaf491f118a0f5cfe860cc903b retry-with-correct-PATH/gpu-frozen-regression/gpu-reference/extension-build/phase4p_ext.cuda.o
|
||||
27d284f6a369221a931251b5a03ce133cc8231a97942567d4efafbfb45483811 retry-with-correct-PATH/gpu-frozen-regression/gpu-reference/extension-build/phase4p_ext.o
|
||||
58797c63494551cab358315feff51a83ad6ea8a854f5e919b1fd5fbae36c7fa0 retry-with-correct-PATH/gpu-frozen-regression/gpu-reference/extension.json
|
||||
6777d390ce499d85a91833d46e645cf2a2604c3e96c809c815d397addfa510b6 retry-with-correct-PATH/gpu-frozen-regression/gustatory-50hz.json
|
||||
bd87a77937afd185497f9a538047aa3e5b4960211687524cbac1886cba1ddb26 retry-with-correct-PATH/gpu-frozen-regression/mechanosensory-50hz.json
|
||||
5b9284784f76d27517b6dc6c7df56de76bdd8e2b09d1f08a781a42e10ac1ddf6 retry-with-correct-PATH/gpu-frozen-regression/olfactory-50hz.json
|
||||
1431b9be6dc067f48699f3c3708486718a79380c04a8b342ad247def222cd24c retry-with-correct-PATH/gpu-frozen-regression/post-regression-integrity.json
|
||||
5598a6867d6f7f22a5e6472f00c763a9e94850bda18f10df992bba71ef99e982 retry-with-correct-PATH/gpu-frozen-regression/protocol-addendum.json
|
||||
fb92144b782fb907d3f9090b44b9713b7b32559335a6558ecf72798f4926bc81 retry-with-correct-PATH/gpu-frozen-regression/summary.json
|
||||
70fd8e78eedc9ddacbaa8627caa3d662efad90727b63cb352d2ebb412f89f3c8 spatial/APL-PN-unknowns.parquet
|
||||
784f2105af57bf8d45b9220402360dc3dfa53148f4fa1f89052af5dd4ddb6522 spatial/local-schemas.json
|
||||
22f6f472a221b3f933ded4430e9bde5d554db29507dc18b8f8064c87d3344dae spatial/watched-SWC-audit.json
|
||||
@@ -0,0 +1,269 @@
|
||||
edge_id,source_neuron_idx,target_neuron_idx,raw_synapse_count,source_population,source_body_id,source_annotation_class,source_annotation_type,source_annotation_instance,source_annotation_somaSide,source_neurotransmitter,source_nt_ground_truth,source_annotation_receptorType,target_population,target_body_id,target_annotation_class,target_annotation_type,target_annotation_instance,target_annotation_somaSide,target_neurotransmitter,target_nt_ground_truth,target_annotation_receptorType,fast_current_masked,graded_output_covered,reverse_contacts,reverse_edge_id,has_PN_to_APL,watched_in_Phase4Z
|
||||
762232,502,30,50,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10039,ALPN,VL2a_adPN,VL2a_adPN_R,R,acetylcholine,acetylcholine,,True,False,70.0,46118.0,True,True
|
||||
762234,502,71,73,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10084,ALPN,DP1m_adPN,DP1m_adPN_R,R,acetylcholine,acetylcholine,,True,False,36.0,150788.0,True,False
|
||||
762237,502,159,131,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10176,ALPN,DM1_lPN,DM1_lPN_R,R,acetylcholine,acetylcholine,,True,False,44.0,315078.0,True,False
|
||||
762239,502,179,7,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10198,ALPN,VA7l_adPN,VA7l_adPN_R,R,acetylcholine,acetylcholine,,True,False,52.0,347211.0,True,True
|
||||
762241,502,241,6,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10262,ALPN,V_ilPN,V_ilPN_L,L,acetylcholine,acetylcholine,,True,False,14.0,438131.0,True,False
|
||||
762248,502,360,54,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10390,ALPN,VA2_adPN,VA2_adPN_R,R,acetylcholine,acetylcholine,,True,False,46.0,604101.0,True,False
|
||||
762250,502,390,79,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10423,ALPN,DP1l_adPN,DP1l_adPN_R,R,acetylcholine,acetylcholine,,True,False,40.0,631717.0,True,False
|
||||
762252,502,484,50,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10522,ALPN,VL2p_adPN,VL2p_adPN_R,R,acetylcholine,acetylcholine,,True,False,86.0,748443.0,True,True
|
||||
762253,502,492,2,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10530,ALPN,VL1_ilPN,VL1_ilPN_L,L,acetylcholine,acetylcholine,,True,False,7.0,753875.0,True,False
|
||||
762256,502,530,32,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10569,ALPN,DC1_adPN,DC1_adPN_R,R,acetylcholine,acetylcholine,,True,False,73.0,789558.0,True,False
|
||||
762257,502,571,44,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10613,ALPN,DM4_adPN,DM4_adPN_R,R,acetylcholine,acetylcholine,,True,False,33.0,827996.0,True,False
|
||||
762261,502,700,18,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10756,ALPN,VC2_lPN,VC2_lPN_R,R,acetylcholine,acetylcholine,,True,False,23.0,939821.0,True,False
|
||||
762262,502,728,11,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10786,ALPN,VC5_lvPN,VC5_lvPN_R,R,acetylcholine,acetylcholine,,True,False,67.0,956945.0,True,False
|
||||
762264,502,835,29,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10904,ALPN,VC1_lPN,VC1_lPN_R,R,acetylcholine,acetylcholine,,True,False,45.0,1060367.0,True,False
|
||||
762265,502,837,24,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10906,ALPN,VM3_adPN,VM3_adPN_R,R,acetylcholine,acetylcholine,,True,False,26.0,1061532.0,True,False
|
||||
762267,502,900,11,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10973,ALPN,VP1m+VP5_ilPN,VP1m+VP5_ilPN_L,L,acetylcholine,acetylcholine,,True,False,2.0,1115270.0,True,False
|
||||
762269,502,921,21,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,10996,ALPN,VA4_lPN,VA4_lPN_R,R,acetylcholine,acetylcholine,,True,False,20.0,1131006.0,True,False
|
||||
762275,502,1138,6,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,11229,ALPN,V_ilPN,V_ilPN_R,R,acetylcholine,acetylcholine,,True,False,19.0,1301252.0,True,False
|
||||
762278,502,1262,11,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,11355,ALPN,VA1d_adPN,VA1d_adPN_R,R,acetylcholine,acetylcholine,,True,False,17.0,1385980.0,True,False
|
||||
762279,502,1294,4,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,11390,ALPN,VL1_ilPN,VL1_ilPN_R,R,acetylcholine,acetylcholine,,True,False,7.0,1408888.0,True,False
|
||||
762281,502,1372,9,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,11469,ALPN,DM3_adPN,DM3_adPN_R,R,acetylcholine,acetylcholine,,True,False,43.0,1459577.0,True,False
|
||||
762282,502,1413,11,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,11514,ALPN,DL4_adPN,DL4_adPN_R,R,acetylcholine,acetylcholine,,True,False,54.0,1489039.0,True,False
|
||||
762283,502,1420,40,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,11521,ALPN,VP1m_l2PN,VP1m_l2PN_R,R,acetylcholine,,,True,False,46.0,1492690.0,True,False
|
||||
762289,502,1514,12,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,11624,ALPN,DC2_adPN,DC2_adPN_R,R,acetylcholine,acetylcholine,,True,False,25.0,1560733.0,True,False
|
||||
762296,502,1644,78,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,11761,ALPN,VP1d+VP4_l2PN1,VP1d+VP4_l2PN1_R,R,acetylcholine,,,True,False,21.0,1645422.0,True,True
|
||||
762298,502,1661,7,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,11780,ALPN,DA1_lPN,DA1_lPN_R,R,acetylcholine,acetylcholine,,True,False,20.0,1654484.0,True,False
|
||||
762301,502,1694,5,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,11816,ALPN,DA1_lPN,DA1_lPN_R,R,acetylcholine,acetylcholine,,True,False,10.0,1674757.0,True,False
|
||||
762305,502,1866,8,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,11996,ALPN,DA1_lPN,DA1_lPN_R,R,acetylcholine,acetylcholine,,True,False,48.0,1772954.0,True,False
|
||||
762308,502,1982,9,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,12121,ALPN,VC5_lvPN,VC5_lvPN_R,R,acetylcholine,acetylcholine,,True,False,19.0,1836818.0,True,False
|
||||
762309,502,1983,20,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,12122,ALPN,DA1_lPN,DA1_lPN_R,R,acetylcholine,acetylcholine,,True,False,32.0,1837355.0,True,False
|
||||
762310,502,2018,3,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,12160,ALPN,DC4_adPN,DC4_adPN_R,R,acetylcholine,acetylcholine,,True,False,4.0,1861378.0,True,False
|
||||
762311,502,2021,15,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,12163,ALPN,VA5_lPN,VA5_lPN_R,R,acetylcholine,acetylcholine,,True,False,20.0,1864414.0,True,False
|
||||
762312,502,2022,17,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,12164,ALPN,VM3_adPN,VM3_adPN_R,R,acetylcholine,acetylcholine,,True,False,38.0,1864924.0,True,False
|
||||
762313,502,2029,30,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,12171,ALPN,DM2_lPN,DM2_lPN_R,R,acetylcholine,acetylcholine,,True,False,26.0,1867527.0,True,False
|
||||
762314,502,2157,24,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,12309,ALPN,DC3_adPN,DC3_adPN_R,R,acetylcholine,acetylcholine,,True,False,36.0,1939728.0,True,False
|
||||
762315,502,2186,6,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,12340,ALPN,DA1_lPN,DA1_lPN_R,R,acetylcholine,acetylcholine,,True,False,37.0,1952315.0,True,False
|
||||
762316,502,2231,13,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,12387,ALPN,D_adPN,D_adPN_R,R,acetylcholine,acetylcholine,,True,False,8.0,1972169.0,True,False
|
||||
762321,502,2493,2,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,12667,ALPN,VA1v_adPN,VA1v_adPN_R,R,acetylcholine,acetylcholine,,True,False,1.0,2111273.0,True,False
|
||||
762324,502,2552,23,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,12728,ALPN,DC3_adPN,DC3_adPN_R,R,acetylcholine,acetylcholine,,True,False,23.0,2148041.0,True,False
|
||||
762327,502,2601,34,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,12782,ALPN,DL1_adPN,DL1_adPN_R,R,acetylcholine,acetylcholine,,True,False,58.0,2170055.0,True,False
|
||||
762331,502,2867,6,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,13064,ALPN,DA1_lPN,DA1_lPN_R,R,acetylcholine,acetylcholine,,True,False,18.0,2290216.0,True,False
|
||||
762334,502,2994,5,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,13198,ALPN,VA1v_adPN,VA1v_adPN_R,R,acetylcholine,acetylcholine,,True,False,,,False,False
|
||||
762335,502,3016,7,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,13225,ALPN,VM7d_adPN,VM7d_adPN_R,R,acetylcholine,acetylcholine,,True,False,2.0,2357578.0,True,False
|
||||
762336,502,3073,22,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,13283,ALPN,VM1_lPN,VM1_lPN_R,R,acetylcholine,acetylcholine,,True,False,53.0,2382965.0,True,False
|
||||
762338,502,3120,7,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,13334,ALPN,VP1m+VP5_ilPN,VP1m+VP5_ilPN_R,R,acetylcholine,acetylcholine,,True,False,1.0,2405914.0,True,False
|
||||
762339,502,3161,8,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,13379,ALPN,VA1v_adPN,VA1v_adPN_R,R,acetylcholine,acetylcholine,,True,False,3.0,2425091.0,True,False
|
||||
762340,502,3168,14,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,13386,ALPN,VM7v_adPN,VM7v_adPN_R,R,acetylcholine,acetylcholine,,True,False,24.0,2428472.0,True,False
|
||||
762341,502,3171,2,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,13389,ALPN,M_vPNml50,M_vPNml50_R,R,gaba,gaba,,True,False,27.0,2430462.0,True,False
|
||||
762342,502,3253,15,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,13480,ALPN,VC3_adPN,VC3_adPN_R,R,acetylcholine,acetylcholine,,True,False,8.0,2465839.0,True,False
|
||||
762346,502,3369,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,13610,ALPN,M_l2PNm16,M_l2PNm16_R,R,acetylcholine,,,True,False,,,False,False
|
||||
762348,502,3474,9,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,13725,ALPN,VA1d_adPN,VA1d_adPN_R,R,acetylcholine,acetylcholine,,True,False,10.0,2573017.0,True,False
|
||||
762361,502,4072,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,14391,ALPN,M_ilPNm90,M_ilPNm90(M_ilPN8t91)_L,L,acetylcholine,acetylcholine,,True,False,6.0,2833650.0,True,False
|
||||
762362,502,4113,13,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,14437,ALPN,VP3+VP1l_ivPN,VP3+VP1l_ivPN_R,R,acetylcholine,,,True,False,2.0,2846214.0,True,False
|
||||
762363,502,4173,9,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,14510,ALPN,VA1v_adPN,VA1v_adPN_R,R,acetylcholine,acetylcholine,,True,False,1.0,2871710.0,True,False
|
||||
762364,502,4192,5,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,14530,ALPN,DA4l_adPN,DA4l_adPN_R,R,acetylcholine,acetylcholine,,True,False,33.0,2879645.0,True,False
|
||||
762366,502,4441,8,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,14802,ALPN,VA7m_lPN,VA7m_lPN_R,R,acetylcholine,acetylcholine,,True,False,3.0,2969669.0,True,False
|
||||
762367,502,4482,4,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,14849,ALPN,DM5_lPN,DM5_lPN_R,R,acetylcholine,acetylcholine,,True,False,10.0,2981676.0,True,False
|
||||
762369,502,4584,3,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,14962,ALPN,VC5_lvPN,VC5_lvPN_R,R,acetylcholine,acetylcholine,,True,False,16.0,3022866.0,True,False
|
||||
762370,502,4605,10,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,14985,ALPN,VA5_lPN,VA5_lPN_R,R,acetylcholine,acetylcholine,,True,False,17.0,3031454.0,True,False
|
||||
762371,502,4624,14,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,15005,ALPN,DM6_adPN,DM6_adPN_R,R,acetylcholine,acetylcholine,,True,False,27.0,3038213.0,True,False
|
||||
762378,502,4768,21,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,15164,ALPN,DC3_adPN,DC3_adPN_R,R,acetylcholine,acetylcholine,,True,False,33.0,3097663.0,True,False
|
||||
762379,502,4905,10,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,15313,ALPN,VA1d_adPN,VA1d_adPN_R,R,acetylcholine,acetylcholine,,True,False,11.0,3149154.0,True,False
|
||||
762382,502,5004,7,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,15431,ALPN,VM5d_adPN,VM5d_adPN_R,R,acetylcholine,acetylcholine,,True,False,10.0,3184454.0,True,False
|
||||
762388,502,5519,12,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,16005,ALPN,D_adPN,D_adPN_R,R,acetylcholine,acetylcholine,,True,False,5.0,3360042.0,True,False
|
||||
762390,502,5584,8,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,16083,ALPN,VM2_adPN,VM2_adPN_R,R,acetylcholine,acetylcholine,,True,False,31.0,3385292.0,True,False
|
||||
762391,502,5634,17,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,16141,ALPN,VM4_adPN,VM4_adPN_R,R,acetylcholine,acetylcholine,,True,False,34.0,3403164.0,True,False
|
||||
762392,502,5890,11,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,16426,ALPN,VM5v_adPN,VM5v_adPN_R,R,acetylcholine,acetylcholine,,True,False,4.0,3492698.0,True,False
|
||||
762395,502,6065,14,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,16610,ALPN,VA7m_lPN,VA7m_lPN_R,R,acetylcholine,acetylcholine,,True,False,6.0,3559819.0,True,True
|
||||
762397,502,6099,5,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,16648,ALPN,VP1d_il2PN,VP1d_il2PN_L,L,acetylcholine,,,True,False,3.0,3570929.0,True,False
|
||||
762399,502,6842,13,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,17484,ALPN,DC2_adPN,DC2_adPN_R,R,acetylcholine,acetylcholine,,True,False,45.0,3810910.0,True,True
|
||||
762401,502,6994,27,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,17650,ALPN,VC3_adPN,VC3_adPN_R,R,acetylcholine,acetylcholine,,True,False,12.0,3864914.0,True,False
|
||||
762406,502,7354,3,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,18049,ALPN,VP1d_il2PN,VP1d_il2PN_R,R,acetylcholine,,,True,False,5.0,3976343.0,True,False
|
||||
762407,502,7385,9,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,18084,ALPN,VM7d_adPN,VM7d_adPN_R,R,acetylcholine,acetylcholine,,True,False,8.0,3985239.0,True,False
|
||||
762408,502,7680,13,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,18417,ALPN,DM6_adPN,DM6_adPN_R,R,acetylcholine,acetylcholine,,True,False,20.0,4079640.0,True,False
|
||||
762409,502,7746,7,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,18493,ALPN,DM5_lPN,DM5_lPN_R,R,acetylcholine,acetylcholine,,True,False,25.0,4099088.0,True,False
|
||||
762410,502,7781,4,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,18536,ALPN,D_adPN,D_adPN_R,R,acetylcholine,acetylcholine,,True,False,3.0,4108591.0,True,False
|
||||
762418,502,8955,10,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,19845,ALPN,VP3+VP1l_ivPN,VP3+VP1l_ivPN_L,L,acetylcholine,,,True,False,4.0,4451808.0,True,False
|
||||
762420,502,9052,13,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,19953,ALPN,DM6_adPN,DM6_adPN_R,R,acetylcholine,acetylcholine,,True,False,21.0,4479205.0,True,False
|
||||
762421,502,9096,12,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,20001,ALPN,VM5v_adPN,VM5v_adPN_R,R,acetylcholine,acetylcholine,,True,False,19.0,4492802.0,True,False
|
||||
762423,502,9370,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,20311,ALPN,DA2_lPN,DA2_lPN_R,R,acetylcholine,acetylcholine,,True,False,2.0,4570000.0,True,False
|
||||
762425,502,9484,10,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,20445,ALPN,VM5v_adPN,VM5v_adPN_R,R,acetylcholine,acetylcholine,,True,False,7.0,4603215.0,True,False
|
||||
762428,502,9752,29,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,20739,ALPN,DL2v_adPN,DL2v_adPN_R,R,acetylcholine,acetylcholine,,True,False,28.0,4679429.0,True,False
|
||||
762438,502,10769,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,21876,ALPN,DA2_lPN,DA2_lPN_R,R,acetylcholine,acetylcholine,,True,False,6.0,4955209.0,True,False
|
||||
762443,502,11316,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,22473,ALPN,VM4_lvPN,VM4_lvPN_R,R,acetylcholine,acetylcholine,,True,False,,,False,False
|
||||
762445,502,11598,11,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,22787,ALPN,DL2d_adPN,DL2d_adPN_R,R,acetylcholine,acetylcholine,,True,False,25.0,5168666.0,True,False
|
||||
762446,502,11770,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,22977,ALPN,VL2a_vPN,VL2a_vPN_R,R,gaba,gaba,,True,False,,,False,False
|
||||
762450,502,11961,8,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,23200,ALPN,DL2d_adPN,DL2d_adPN_R,R,acetylcholine,acetylcholine,,True,False,7.0,5260104.0,True,False
|
||||
762452,502,12644,2,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,23958,ALPN,DA2_lPN,DA2_lPN_R,R,acetylcholine,acetylcholine,,True,False,,,False,False
|
||||
762456,502,13262,2,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,24660,ALPN,DL3_lPN,DL3_lPN_R,R,acetylcholine,acetylcholine,,True,False,3.0,5589353.0,True,False
|
||||
762462,502,13715,7,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,25166,ALPN,VC4_adPN,VC4_adPN_R,R,acetylcholine,acetylcholine,,True,False,4.0,5697043.0,True,False
|
||||
762464,502,13955,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,25433,ALPN,CB2004,CB2004_R,R,gaba,gaba,,True,False,,,False,False
|
||||
762468,502,14519,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,26071,ALPN,M_lPNm11D,M_lPNm11D_R,R,acetylcholine,acetylcholine,,True,False,3.0,5880048.0,True,False
|
||||
762469,502,14547,23,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,26102,ALPN,DL2v_adPN,DL2v_adPN_R,R,acetylcholine,acetylcholine,,True,False,21.0,5887059.0,True,False
|
||||
762470,502,14655,5,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,26223,ALPN,M_lPNm11D,M_lPNm11D_R,R,acetylcholine,acetylcholine,,True,False,14.0,5911780.0,True,False
|
||||
762471,502,14805,10,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,26387,ALPN,VC4_adPN,VC4_adPN_R,R,acetylcholine,acetylcholine,,True,False,4.0,5949622.0,True,False
|
||||
762475,502,15369,8,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,27024,ALPN,VM5d_adPN,VM5d_adPN_R,R,acetylcholine,acetylcholine,,True,False,4.0,6067738.0,True,False
|
||||
762478,502,15687,2,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,27379,ALPN,M_lPNm11D,M_lPNm11D_R,R,acetylcholine,acetylcholine,,True,False,4.0,6133873.0,True,False
|
||||
762483,502,16042,13,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,27775,ALPN,VM5d_adPN,VM5d_adPN_R,R,acetylcholine,acetylcholine,,True,False,6.0,6208174.0,True,False
|
||||
762487,502,17293,9,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,29176,ALPN,DL2v_adPN,DL2v_adPN_R,R,acetylcholine,acetylcholine,,True,False,4.0,6475484.0,True,False
|
||||
762491,502,17809,11,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,29750,ALPN,VM4_lvPN,VM4_lvPN_R,R,acetylcholine,acetylcholine,,True,False,57.0,6580854.0,True,False
|
||||
762498,502,19004,5,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,31117,ALPN,M_lPNm11D,M_lPNm11D_R,R,acetylcholine,acetylcholine,,True,False,3.0,6823261.0,True,False
|
||||
762512,502,21524,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,33987,ALPN,VP1m+_lvPN,VP1m+_lvPN_R,R,glutamate,,,True,False,1.0,7300739.0,True,False
|
||||
762514,502,21802,2,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,34301,ALPN,DA2_lPN,DA2_lPN_R,R,acetylcholine,acetylcholine,,True,False,23.0,7350146.0,True,False
|
||||
762515,502,21875,13,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,34385,ALPN,VM5d_adPN,VM5d_adPN_R,R,acetylcholine,acetylcholine,,True,False,24.0,7364609.0,True,False
|
||||
762551,502,25520,6,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,38510,ALPN,VM5d_adPN,VM5d_adPN_R,R,acetylcholine,acetylcholine,,True,False,5.0,8015926.0,True,False
|
||||
762620,502,29804,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,43363,ALPN,M_lvPNm30,M_lvPNm30_R,R,acetylcholine,acetylcholine,,True,False,,,False,False
|
||||
762625,502,29917,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,43489,ALPN,M_vPNml67,M_vPNml67_R,R,gaba,gaba,,True,False,,,False,False
|
||||
762653,502,31238,5,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,44995,ALPN,M_lPNm11D,M_lPNm11D_R,R,acetylcholine,acetylcholine,,True,False,4.0,9001958.0,True,False
|
||||
762833,502,37369,4,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,52099,ALPN,M_lPNm11D,M_lPNm11D_R,R,acetylcholine,acetylcholine,,True,False,6.0,10005648.0,True,False
|
||||
762839,502,37468,3,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,52223,ALPN,M_adPNm4,M_adPNm4_R,R,acetylcholine,acetylcholine,,True,False,2.0,10020743.0,True,False
|
||||
764005,502,70280,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,92909,ALPN,M_vPNml67,M_vPNml67_R,R,gaba,gaba,,True,False,,,False,False
|
||||
764076,502,72419,2,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,95720,ALPN,M_adPNm5,M_adPNm5_R,R,acetylcholine,acetylcholine,,True,False,2.0,14809507.0,True,False
|
||||
764552,502,101754,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,140108,ALPN,CB2703,CB2703_R,R,gaba,gaba,,True,False,1.0,17560365.0,True,False
|
||||
764930,502,125773,7,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,511807,ALPN,VM7d_adPN,VM7d_adPN_R,R,acetylcholine,acetylcholine,,True,False,9.0,19313862.0,True,False
|
||||
764934,502,126350,7,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,512883,ALPN,DL2d_adPN,DL2d_adPN_R,R,acetylcholine,acetylcholine,,True,False,15.0,19455428.0,True,False
|
||||
764939,502,126688,30,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,513513,ALPN,VC3_adPN,VC3_adPN_R,R,acetylcholine,acetylcholine,,True,False,12.0,19530810.0,True,False
|
||||
764947,502,128185,1,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,516097,ALPN,M_ilPNm90,M_ilPNm90(M_ilPN8t91)_R,R,acetylcholine,acetylcholine,,True,False,,,False,False
|
||||
764948,502,128229,11,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,516162,ALPN,VC4_adPN,VC4_adPN_R,R,acetylcholine,acetylcholine,,True,False,4.0,19835828.0,True,False
|
||||
764950,502,129486,5,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,518294,ALPN,VA5_lPN,VA5_lPN_R,R,acetylcholine,acetylcholine,,True,False,11.0,20057404.0,True,False
|
||||
764960,502,130257,12,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,519572,ALPN,DM6_adPN,DM6_adPN_R,R,acetylcholine,acetylcholine,,True,False,22.0,20205074.0,True,False
|
||||
764963,502,130400,6,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,519850,ALPN,DA4m_adPN,DA4m_adPN_R,R,acetylcholine,acetylcholine,,True,False,3.0,20242668.0,True,False
|
||||
764966,502,130472,25,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,519970,ALPN,VA3_adPN,VA3_adPN_R,R,acetylcholine,acetylcholine,,True,False,16.0,20265963.0,True,False
|
||||
764967,502,130497,39,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,520003,ALPN,DL1_adPN,DL1_adPN_R,R,acetylcholine,acetylcholine,,True,False,39.0,20274074.0,True,False
|
||||
764975,502,130753,17,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,520466,ALPN,DM2_lPN,DM2_lPN_R,R,acetylcholine,acetylcholine,,True,False,28.0,20342295.0,True,False
|
||||
764977,502,130758,2,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,520475,ALPN,DM5_lPN,DM5_lPN_R,R,acetylcholine,acetylcholine,,True,False,13.0,20345290.0,True,False
|
||||
764981,502,130990,19,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,520916,ALPN,VM6_adPN,VM6_adPN_R,R,acetylcholine,acetylcholine,,True,False,32.0,20398820.0,True,False
|
||||
764985,502,131226,35,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,521437,ALPN,VA3_adPN,VA3_adPN_R,R,acetylcholine,acetylcholine,,True,False,25.0,20462077.0,True,False
|
||||
764987,502,131311,17,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,521575,ALPN,DL2v_adPN,DL2v_adPN_R,R,acetylcholine,acetylcholine,,True,False,8.0,20480513.0,True,False
|
||||
764995,502,131777,21,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,523351,ALPN,DL2d_adPN,DL2d_adPN_R,R,acetylcholine,acetylcholine,,True,False,12.0,20611553.0,True,False
|
||||
765005,502,132182,14,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,524207,ALPN,VA7m_lPN,VA7m_lPN_R,R,acetylcholine,acetylcholine,,True,False,7.0,20721529.0,True,False
|
||||
765009,502,132659,26,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,525105,ALPN,DL5_adPN,DL5_adPN_R,R,acetylcholine,acetylcholine,,True,False,27.0,20822655.0,True,False
|
||||
765019,502,133646,8,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,527523,ALPN,VM7d_adPN,VM7d_adPN_R,R,acetylcholine,acetylcholine,,True,False,13.0,20988951.0,True,False
|
||||
765020,502,133647,8,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,527524,ALPN,VM5d_adPN,VM5d_adPN_R,R,acetylcholine,acetylcholine,,True,False,6.0,20989417.0,True,False
|
||||
765021,502,133666,35,APL,10540,,APL,APL_R,R,gaba,gaba,,ALPN,527577,ALPN,VA6_adPN,VA6_adPN_R,R,acetylcholine,acetylcholine,,True,False,45.0,20992269.0,True,True
|
||||
1119569,904,40,14,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10049,ALPN,VA7m_lPN,VA7m_lPN_L,L,acetylcholine,acetylcholine,,True,False,14.0,69565.0,True,False
|
||||
1119571,904,62,13,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10075,ALPN,DA1_lPN,DA1_lPN_L,L,acetylcholine,acetylcholine,,True,False,30.0,138529.0,True,False
|
||||
1119573,904,97,21,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10111,ALPN,DM3_adPN,DM3_adPN_L,L,acetylcholine,acetylcholine,,True,False,41.0,226812.0,True,False
|
||||
1119574,904,112,97,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10126,ALPN,DP1m_adPN,DP1m_adPN_L,L,acetylcholine,acetylcholine,,True,False,61.0,248380.0,True,False
|
||||
1119575,904,120,58,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10134,ALPN,VL2a_adPN,VL2a_adPN_L,L,acetylcholine,acetylcholine,,True,False,90.0,263380.0,True,False
|
||||
1119577,904,189,242,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10208,ALPN,DM1_lPN,DM1_lPN_L,L,acetylcholine,acetylcholine,,True,False,58.0,358462.0,True,False
|
||||
1119579,904,241,12,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10262,ALPN,V_ilPN,V_ilPN_L,L,acetylcholine,acetylcholine,,True,False,18.0,438143.0,True,False
|
||||
1119582,904,296,59,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10321,ALPN,VL2p_adPN,VL2p_adPN_L,L,acetylcholine,acetylcholine,,True,False,84.0,532837.0,True,False
|
||||
1119584,904,331,51,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10359,ALPN,DL1_adPN,DL1_adPN_L,L,acetylcholine,acetylcholine,,True,False,57.0,581952.0,True,False
|
||||
1119586,904,394,22,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10429,ALPN,VM6_adPN,VM6_adPN_L,L,acetylcholine,acetylcholine,,True,False,31.0,641333.0,True,False
|
||||
1119587,904,434,72,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10470,ALPN,DC1_adPN,DC1_adPN_L,L,acetylcholine,acetylcholine,,True,False,107.0,695994.0,True,False
|
||||
1119590,904,486,73,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10524,ALPN,DP1l_adPN,DP1l_adPN_L,L,acetylcholine,acetylcholine,,True,False,19.0,749793.0,True,False
|
||||
1119591,904,492,2,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10530,ALPN,VL1_ilPN,VL1_ilPN_L,L,acetylcholine,acetylcholine,,True,False,5.0,753887.0,True,False
|
||||
1119593,904,522,72,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10561,ALPN,VA2_adPN,VA2_adPN_L,L,acetylcholine,acetylcholine,,True,False,66.0,783800.0,True,False
|
||||
1119599,904,607,17,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10653,ALPN,VA5_lPN,VA5_lPN_L,L,acetylcholine,acetylcholine,,True,False,21.0,855614.0,True,False
|
||||
1119600,904,622,77,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10670,ALPN,DM4_adPN,DM4_adPN_L,L,acetylcholine,acetylcholine,,True,False,55.0,867234.0,True,False
|
||||
1119602,904,666,26,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10716,ALPN,DC3_adPN,DC3_adPN_L,L,acetylcholine,acetylcholine,,True,False,40.0,914984.0,True,False
|
||||
1119603,904,683,2,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10736,ALPN,VP2_adPN,VP2_adPN_L,L,acetylcholine,acetylcholine,,True,False,7.0,929722.0,True,False
|
||||
1119605,904,710,35,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10767,ALPN,DL5_adPN,DL5_adPN_L,L,acetylcholine,acetylcholine,,True,False,43.0,944572.0,True,False
|
||||
1119606,904,749,25,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10808,ALPN,VC1_lPN,VC1_lPN_L,L,acetylcholine,acetylcholine,,True,False,32.0,976737.0,True,False
|
||||
1119610,904,813,59,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10877,ALPN,VA6_adPN,VA6_adPN_L,L,acetylcholine,acetylcholine,,True,False,79.0,1041596.0,True,False
|
||||
1119611,904,900,9,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,10973,ALPN,VP1m+VP5_ilPN,VP1m+VP5_ilPN_L,L,acetylcholine,acetylcholine,,True,False,7.0,1115279.0,True,False
|
||||
1119614,904,972,75,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11050,ALPN,VP1d+VP4_l2PN1,VP1d+VP4_l2PN1_L,L,acetylcholine,,,True,False,12.0,1179458.0,True,False
|
||||
1119615,904,999,47,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11080,ALPN,DM2_lPN,DM2_lPN_L,L,acetylcholine,acetylcholine,,True,False,47.0,1200299.0,True,False
|
||||
1119616,904,1011,43,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11092,ALPN,DM2_lPN,DM2_lPN_L,L,acetylcholine,acetylcholine,,True,False,42.0,1209511.0,True,False
|
||||
1119617,904,1022,29,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11105,ALPN,VC2_lPN,VC2_lPN_L,L,acetylcholine,acetylcholine,,True,False,21.0,1216205.0,True,False
|
||||
1119618,904,1050,9,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11135,ALPN,DA4m_adPN,DA4m_adPN_L,L,acetylcholine,acetylcholine,,True,False,16.0,1235742.0,True,False
|
||||
1119619,904,1056,44,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11143,ALPN,VP1m_l2PN,VP1m_l2PN_L,L,acetylcholine,,,True,False,14.0,1238708.0,True,False
|
||||
1119622,904,1138,7,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11229,ALPN,V_ilPN,V_ilPN_R,R,acetylcholine,acetylcholine,,True,False,8.0,1301268.0,True,False
|
||||
1119625,904,1191,16,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11283,ALPN,VA1v_adPN,VA1v_adPN_L,L,acetylcholine,acetylcholine,,True,False,2.0,1334630.0,True,False
|
||||
1119630,904,1294,2,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11390,ALPN,VL1_ilPN,VL1_ilPN_R,R,acetylcholine,acetylcholine,,True,False,3.0,1408899.0,True,False
|
||||
1119633,904,1370,29,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11467,ALPN,VA4_lPN,VA4_lPN_L,L,acetylcholine,acetylcholine,,True,False,21.0,1458653.0,True,False
|
||||
1119634,904,1374,24,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11471,ALPN,VA1d_adPN,VA1d_adPN_L,L,acetylcholine,acetylcholine,,True,False,35.0,1461033.0,True,False
|
||||
1119635,904,1458,13,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11564,ALPN,DA1_lPN,DA1_lPN_L,L,acetylcholine,acetylcholine,,True,False,18.0,1521135.0,True,False
|
||||
1119638,904,1590,32,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11703,ALPN,VM1_lPN,VM1_lPN_L,L,acetylcholine,acetylcholine,,True,False,101.0,1614877.0,True,False
|
||||
1119639,904,1607,12,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11721,ALPN,DA1_lPN,DA1_lPN_L,L,acetylcholine,acetylcholine,,True,False,22.0,1625269.0,True,False
|
||||
1119643,904,1695,15,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11817,ALPN,VA5_lPN,VA5_lPN_L,L,acetylcholine,acetylcholine,,True,False,13.0,1675160.0,True,False
|
||||
1119644,904,1707,5,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,11829,ALPN,DL4_adPN,DL4_adPN_L,L,acetylcholine,acetylcholine,,True,False,14.0,1680304.0,True,False
|
||||
1119650,904,1890,28,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12023,ALPN,VA3_adPN,VA3_adPN_L,L,acetylcholine,acetylcholine,,True,False,19.0,1787710.0,True,False
|
||||
1119651,904,1898,2,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12031,ALPN,DC4_adPN,DC4_adPN_L,L,acetylcholine,acetylcholine,,True,False,2.0,1790881.0,True,False
|
||||
1119652,904,1916,6,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12050,ALPN,VA1d_adPN,VA1d_adPN_L,L,acetylcholine,acetylcholine,,True,False,6.0,1801068.0,True,False
|
||||
1119653,904,1923,37,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12057,ALPN,VM3_adPN,VM3_adPN_L,L,acetylcholine,acetylcholine,,True,False,51.0,1804289.0,True,False
|
||||
1119654,904,1927,36,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12061,ALPN,DL1_adPN,DL1_adPN_L,L,acetylcholine,acetylcholine,,True,False,40.0,1805735.0,True,False
|
||||
1119656,904,2010,26,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12152,ALPN,DC2_adPN,DC2_adPN_L,L,acetylcholine,acetylcholine,,True,False,61.0,1854856.0,True,False
|
||||
1119659,904,2160,30,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12312,ALPN,DC3_adPN,DC3_adPN_L,L,acetylcholine,acetylcholine,,True,False,59.0,1941085.0,True,False
|
||||
1119660,904,2162,4,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12314,ALPN,DA1_lPN,DA1_lPN_L,L,acetylcholine,acetylcholine,,True,False,24.0,1942170.0,True,False
|
||||
1119661,904,2212,27,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12367,ALPN,VM3_adPN,VM3_adPN_L,L,acetylcholine,acetylcholine,,True,False,34.0,1964547.0,True,False
|
||||
1119663,904,2302,11,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12460,ALPN,VA1d_adPN,VA1d_adPN_L,L,acetylcholine,acetylcholine,,True,False,7.0,2008167.0,True,False
|
||||
1119664,904,2347,35,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12509,ALPN,VA3_adPN,VA3_adPN_L,L,acetylcholine,acetylcholine,,True,False,23.0,2029356.0,True,False
|
||||
1119669,904,2481,14,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12653,ALPN,DA1_lPN,DA1_lPN_L,L,acetylcholine,acetylcholine,,True,False,18.0,2106838.0,True,False
|
||||
1119672,904,2577,16,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12756,ALPN,VA5_lPN,VA5_lPN_L,L,acetylcholine,acetylcholine,,True,False,16.0,2158032.0,True,False
|
||||
1119674,904,2769,15,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,12958,ALPN,DL2d_adPN,DL2d_adPN_L,L,acetylcholine,acetylcholine,,True,False,28.0,2242156.0,True,False
|
||||
1119676,904,2834,13,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13027,ALPN,VC5_lvPN,VC5_lvPN_L,L,acetylcholine,acetylcholine,,True,False,24.0,2273529.0,True,False
|
||||
1119677,904,2943,8,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13143,ALPN,VA1v_adPN,VA1v_adPN_L,L,acetylcholine,acetylcholine,,True,False,1.0,2327343.0,True,False
|
||||
1119679,904,3021,10,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13230,ALPN,VC5_lvPN,VC5_lvPN_L,L,acetylcholine,acetylcholine,,True,False,19.0,2359583.0,True,False
|
||||
1119681,904,3120,7,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13334,ALPN,VP1m+VP5_ilPN,VP1m+VP5_ilPN_R,R,acetylcholine,acetylcholine,,True,False,4.0,2405920.0,True,False
|
||||
1119682,904,3139,26,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13356,ALPN,VM4_adPN,VM4_adPN_L,L,acetylcholine,acetylcholine,,True,False,52.0,2414973.0,True,False
|
||||
1119684,904,3202,17,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13423,ALPN,D_adPN,D_adPN_L,L,acetylcholine,acetylcholine,,True,False,15.0,2442555.0,True,False
|
||||
1119686,904,3244,34,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13470,ALPN,DC3_adPN,DC3_adPN_L,L,acetylcholine,acetylcholine,,True,False,50.0,2462759.0,True,False
|
||||
1119687,904,3252,24,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13479,ALPN,DC2_adPN,DC2_adPN_L,L,acetylcholine,acetylcholine,,True,False,59.0,2465265.0,True,False
|
||||
1119688,904,3256,12,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13483,ALPN,DA1_lPN,DA1_lPN_L,L,acetylcholine,acetylcholine,,True,False,26.0,2467881.0,True,False
|
||||
1119689,904,3345,9,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13582,ALPN,VA1v_adPN,VA1v_adPN_L,L,acetylcholine,acetylcholine,,True,False,4.0,2509268.0,True,False
|
||||
1119690,904,3346,1,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13583,ALPN,DA1_lPN,DA1_lPN_L,L,acetylcholine,acetylcholine,,True,False,7.0,2509711.0,True,False
|
||||
1119692,904,3435,1,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13683,ALPN,M_vPNml55,M_vPNml55_L,L,gaba,gaba,,True,False,,,False,False
|
||||
1119693,904,3436,16,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13684,ALPN,VC3_adPN,VC3_adPN_L,L,acetylcholine,acetylcholine,,True,False,2.0,2559482.0,True,False
|
||||
1119699,904,3682,2,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13951,ALPN,M_vPNml50,M_vPNml50_L,L,gaba,gaba,,True,False,37.0,2668047.0,True,False
|
||||
1119700,904,3702,8,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,13972,ALPN,VM7v_adPN,VM7v_adPN_L,L,acetylcholine,acetylcholine,,True,False,3.0,2679241.0,True,False
|
||||
1119704,904,4026,12,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,14339,ALPN,VA1v_adPN,VA1v_adPN_L,L,acetylcholine,acetylcholine,,True,False,1.0,2814675.0,True,False
|
||||
1119705,904,4044,1,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,14360,ALPN,M_smPNm1,M_smPNm1_R,R,gaba,,,True,False,,,False,False
|
||||
1119706,904,4104,16,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,14427,ALPN,VM5v_adPN,VM5v_adPN_L,L,acetylcholine,acetylcholine,,True,False,7.0,2843574.0,True,False
|
||||
1119707,904,4107,8,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,14430,ALPN,VM7d_adPN,VM7d_adPN_L,L,acetylcholine,acetylcholine,,True,False,3.0,2844234.0,True,False
|
||||
1119708,904,4113,8,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,14437,ALPN,VP3+VP1l_ivPN,VP3+VP1l_ivPN_R,R,acetylcholine,,,True,False,3.0,2846227.0,True,False
|
||||
1119709,904,4117,33,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,14442,ALPN,VC3_adPN,VC3_adPN_L,L,acetylcholine,acetylcholine,,True,False,18.0,2848285.0,True,False
|
||||
1119711,904,4260,8,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,14607,ALPN,VM5v_adPN,VM5v_adPN_L,L,acetylcholine,acetylcholine,,True,False,7.0,2904642.0,True,False
|
||||
1119713,904,4311,19,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,14661,ALPN,D_adPN,D_adPN_L,L,acetylcholine,acetylcholine,,True,False,11.0,2922497.0,True,False
|
||||
1119714,904,4315,8,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,14665,ALPN,VM2_adPN,VM2_adPN_L,L,acetylcholine,acetylcholine,,True,False,20.0,2924017.0,True,False
|
||||
1119715,904,4334,23,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,14685,ALPN,VC3_adPN,VC3_adPN_L,L,acetylcholine,acetylcholine,,True,False,12.0,2931447.0,True,False
|
||||
1119716,904,4505,13,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,14874,ALPN,D_adPN,D_adPN_L,L,acetylcholine,acetylcholine,,True,False,9.0,2991016.0,True,False
|
||||
1119718,904,4523,7,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,14895,ALPN,VM7d_adPN,VM7d_adPN_L,L,acetylcholine,acetylcholine,,True,False,9.0,3000058.0,True,False
|
||||
1119722,904,4786,22,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,15184,ALPN,DM6_adPN,DM6_adPN_L,L,acetylcholine,acetylcholine,,True,False,43.0,3104063.0,True,False
|
||||
1119727,904,5359,3,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,15823,ALPN,DM5_lPN,DM5_lPN_L,L,acetylcholine,acetylcholine,,True,False,18.0,3304571.0,True,False
|
||||
1119728,904,5507,11,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,15992,ALPN,VM2_adPN,VM2_adPN_L,L,acetylcholine,acetylcholine,,True,False,8.0,3355471.0,True,False
|
||||
1119729,904,5692,19,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,16206,ALPN,VA7m_lPN,VA7m_lPN_L,L,acetylcholine,acetylcholine,,True,False,8.0,3423393.0,True,False
|
||||
1119730,904,5693,24,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,16207,ALPN,VC5_lvPN,VC5_lvPN_L,L,acetylcholine,acetylcholine,,True,False,70.0,3423774.0,True,False
|
||||
1119731,904,5712,3,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,16228,ALPN,DL3_lPN,DL3_lPN_L,L,acetylcholine,acetylcholine,,True,False,12.0,3431466.0,True,False
|
||||
1119732,904,5897,13,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,16433,ALPN,VC4_adPN,VC4_adPN_L,L,acetylcholine,acetylcholine,,True,False,3.0,3495778.0,True,False
|
||||
1119733,904,5984,15,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,16524,ALPN,VM7v_adPN,VM7v_adPN_L,L,acetylcholine,acetylcholine,,True,False,12.0,3528885.0,True,False
|
||||
1119735,904,6099,2,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,16648,ALPN,VP1d_il2PN,VP1d_il2PN_L,L,acetylcholine,,,True,False,,,False,False
|
||||
1119736,904,6435,28,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,17027,ALPN,DL2v_adPN,DL2v_adPN_L,L,acetylcholine,acetylcholine,,True,False,7.0,3678385.0,True,False
|
||||
1119737,904,6455,1,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,17052,ALPN,DM5_lPN,DM5_lPN_L,L,acetylcholine,acetylcholine,,True,False,24.0,3684536.0,True,False
|
||||
1119739,904,6721,20,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,17350,ALPN,DM6_adPN,DM6_adPN_L,L,acetylcholine,acetylcholine,,True,False,39.0,3776761.0,True,False
|
||||
1119741,904,6892,26,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,17540,ALPN,DL2v_adPN,DL2v_adPN_L,L,acetylcholine,acetylcholine,,True,False,10.0,3830161.0,True,False
|
||||
1119742,904,6919,26,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,17569,ALPN,VC3_adPN,VC3_adPN_L,L,acetylcholine,acetylcholine,,True,False,8.0,3839610.0,True,False
|
||||
1119743,904,6920,11,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,17570,ALPN,VM5d_adPN,VM5d_adPN_L,L,acetylcholine,acetylcholine,,True,False,8.0,3840021.0,True,False
|
||||
1119745,904,7322,20,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,18015,ALPN,DM6_adPN,DM6_adPN_L,L,acetylcholine,acetylcholine,,True,False,32.0,3965997.0,True,False
|
||||
1119751,904,8001,1,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,18776,ALPN,DA2_lPN,DA2_lPN_L,L,acetylcholine,acetylcholine,,True,False,5.0,4180219.0,True,False
|
||||
1119752,904,8055,4,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,18834,ALPN,VM7d_adPN,VM7d_adPN_L,L,acetylcholine,acetylcholine,,True,False,2.0,4195262.0,True,False
|
||||
1119753,904,8083,19,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,18867,ALPN,VM5d_adPN,VM5d_adPN_L,L,acetylcholine,acetylcholine,,True,False,9.0,4204580.0,True,False
|
||||
1119757,904,8504,4,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,19339,ALPN,DA2_lPN,DA2_lPN_L,L,acetylcholine,acetylcholine,,True,False,7.0,4329600.0,True,False
|
||||
1119759,904,8717,15,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,19579,ALPN,VC4_adPN,VC4_adPN_L,L,acetylcholine,acetylcholine,,True,False,8.0,4386039.0,True,False
|
||||
1119760,904,8866,15,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,19743,ALPN,VA7m_lPN,VA7m_lPN_L,L,acetylcholine,acetylcholine,,True,False,4.0,4428297.0,True,False
|
||||
1119762,904,8955,3,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,19845,ALPN,VP3+VP1l_ivPN,VP3+VP1l_ivPN_L,L,acetylcholine,,,True,False,,,False,False
|
||||
1119763,904,9188,1,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,20105,ALPN,DA2_lPN,DA2_lPN_L,L,acetylcholine,acetylcholine,,True,False,1.0,4518534.0,True,False
|
||||
1119765,904,9904,8,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,20910,ALPN,M_lPNm11D,M_lPNm11D_L,L,acetylcholine,acetylcholine,,True,False,11.0,4716582.0,True,False
|
||||
1119766,904,10097,14,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,21132,ALPN,VC4_adPN,VC4_adPN_L,L,acetylcholine,acetylcholine,,True,False,8.0,4770061.0,True,False
|
||||
1119774,904,11334,10,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,22494,ALPN,VM5d_adPN,VM5d_adPN_L,L,acetylcholine,acetylcholine,,True,False,7.0,5100754.0,True,False
|
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1119775,904,11398,16,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,22569,ALPN,VM5d_adPN,VM5d_adPN_L,L,acetylcholine,acetylcholine,,True,False,18.0,5119826.0,True,False
|
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1119776,904,11533,2,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,22717,ALPN,M_lPNm11D,M_lPNm11D_L,L,acetylcholine,acetylcholine,,True,False,4.0,5153282.0,True,False
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1119779,904,12088,8,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,23339,ALPN,VM5d_adPN,VM5d_adPN_L,L,acetylcholine,acetylcholine,,True,False,11.0,5289769.0,True,False
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1119780,904,12423,1,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,23718,ALPN,DM5_lPN,DM5_lPN_L,L,acetylcholine,acetylcholine,,True,False,13.0,5374786.0,True,False
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1119782,904,12997,2,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,24352,ALPN,DM5_lPN,DM5_lPN_L,L,acetylcholine,acetylcholine,,True,False,15.0,5523195.0,True,False
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1119784,904,13128,6,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,24502,ALPN,M_lPNm11D,M_lPNm11D_L,L,acetylcholine,acetylcholine,,True,False,,,False,False
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1119785,904,13423,6,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,24841,ALPN,DL2v_adPN,DL2v_adPN_L,L,acetylcholine,acetylcholine,,True,False,7.0,5625430.0,True,False
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1119788,904,14395,1,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,25927,ALPN,VL2a_vPN,VL2a_vPN_L,L,gaba,gaba,,True,False,,,False,False
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1119790,904,14839,6,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,26423,ALPN,DA2_lPN,DA2_lPN_L,L,acetylcholine,acetylcholine,,True,False,11.0,5956881.0,True,False
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1119792,904,15212,11,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,26845,ALPN,DL2v_adPN,DL2v_adPN_L,L,acetylcholine,acetylcholine,,True,False,7.0,6037034.0,True,False
|
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1119797,904,16855,19,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,28680,ALPN,VM5d_adPN,VM5d_adPN_L,L,acetylcholine,acetylcholine,,True,False,12.0,6385014.0,True,False
|
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1119800,904,17327,8,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,29217,ALPN,DL2d_adPN,DL2d_adPN_L,L,acetylcholine,acetylcholine,,True,False,14.0,6482455.0,True,False
|
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1119803,904,17874,16,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,29824,ALPN,M_adPNm4,M_adPNm4_L,L,acetylcholine,acetylcholine,,True,False,5.0,6593613.0,True,False
|
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1119806,904,19196,1,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,31348,ALPN,M_lPNm11D,M_lPNm11D_L,L,acetylcholine,acetylcholine,,True,False,8.0,6859919.0,True,False
|
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1119858,904,26772,2,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,39922,ALPN,M_lPNm11D,M_lPNm11D_L,L,acetylcholine,acetylcholine,,True,False,6.0,8237794.0,True,False
|
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1120076,904,39352,2,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,54383,ALPN,M_lvPNm30,M_lvPNm30_L,L,acetylcholine,acetylcholine,,True,False,,,False,False
|
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1120537,904,58326,1,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,77438,ALPN,M_vPNml80,M_vPNml80_L,L,gaba,gaba,,True,False,,,False,False
|
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1120806,904,66580,6,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,87970,ALPN,M_adPNm5,M_adPNm5_L,L,acetylcholine,acetylcholine,,True,False,3.0,14158527.0,True,False
|
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1120845,904,68010,10,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,89871,ALPN,M_adPNm5,M_adPNm5_L,L,acetylcholine,acetylcholine,,True,False,6.0,14323074.0,True,False
|
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1120968,904,73384,10,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,97057,ALPN,M_adPNm5,M_adPNm5_L,L,acetylcholine,acetylcholine,,True,False,1.0,14910286.0,True,False
|
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|
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1122336,904,128299,1,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,516322,ALPN,VL1_vPN,VL1_vPN_L,L,gaba,gaba,,True,False,,,False,False
|
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1122337,904,128946,11,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,517351,ALPN,DL2d_adPN,DL2d_adPN_L,L,acetylcholine,acetylcholine,,True,False,19.0,19961681.0,True,False
|
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|
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1122409,904,137761,1,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,538094,ALPN,M_adPNm8,M_adPNm8_L,L,acetylcholine,acetylcholine,,True,False,,,False,False
|
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1122434,904,140635,8,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,547655,ALPN,DL2d_adPN,DL2d_adPN_L,L,acetylcholine,acetylcholine,,True,False,6.0,21963162.0,True,False
|
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1122461,904,143201,3,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,563873,ALPN,M_lPNm11D,M_lPNm11D_L,L,acetylcholine,acetylcholine,,True,False,2.0,22241944.0,True,False
|
||||
1122462,904,143218,35,APL,10977,,APL,APL_L,L,gaba,gaba,,ALPN,564174,ALPN,DL2d_adPN,DL2d_adPN_L,L,acetylcholine,acetylcholine,,True,False,47.0,22243770.0,True,False
|
||||
|
@@ -0,0 +1,9 @@
|
||||
source_population,edges,contacts
|
||||
ALPN,290,5416
|
||||
APL,3,22
|
||||
CX,9,12
|
||||
DAN,621,8099
|
||||
DPM,4,3521
|
||||
Kenyon_Cell,4693,210352
|
||||
MBON,79,2383
|
||||
UNKNOWN_CLASS,486,4220
|
||||
|
@@ -0,0 +1,9 @@
|
||||
target_population,edges,contacts
|
||||
ALPN,268,4600
|
||||
APL,3,22
|
||||
CX,6,7
|
||||
DAN,451,1747
|
||||
DPM,4,1595
|
||||
Kenyon_Cell,4633,196200
|
||||
MBON,120,4466
|
||||
UNKNOWN_CLASS,360,1275
|
||||
|
@@ -0,0 +1,8 @@
|
||||
source_neuron_idx,source_body_id,source_annotation_instance,target_neuron_idx,target_body_id,target_annotation_type,raw_synapse_count,reverse_contacts,reverse_edge_id
|
||||
502,10540,APL_R,30,10039,VL2a_adPN,50,70.0,46118.0
|
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502,10540,APL_R,179,10198,VA7l_adPN,7,52.0,347211.0
|
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502,10540,APL_R,484,10522,VL2p_adPN,50,86.0,748443.0
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502,10540,APL_R,1644,11761,VP1d+VP4_l2PN1,78,21.0,1645422.0
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502,10540,APL_R,6065,16610,VA7m_lPN,14,6.0,3559819.0
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502,10540,APL_R,6842,17484,DC2_adPN,13,45.0,3810910.0
|
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502,10540,APL_R,133666,527577,VA6_adPN,35,45.0,20992269.0
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||||
"dtype": "int32",
|
||||
"name": "x_post"
|
||||
},
|
||||
{
|
||||
"dtype": "int32",
|
||||
"name": "y_post"
|
||||
},
|
||||
{
|
||||
"dtype": "int32",
|
||||
"name": "z_post"
|
||||
},
|
||||
{
|
||||
"dtype": "int64",
|
||||
"name": "body_post"
|
||||
},
|
||||
{
|
||||
"dtype": "float",
|
||||
"name": "conf_post"
|
||||
},
|
||||
{
|
||||
"dtype": "dictionary<values=string, indices=int16, ordered=1>",
|
||||
"name": "primary_post"
|
||||
}
|
||||
],
|
||||
"record_batches": 4759
|
||||
}
|
||||
+29
@@ -0,0 +1,29 @@
|
||||
{
|
||||
"groups": [
|
||||
{
|
||||
"complete_pair_set_and_weight_exact": true,
|
||||
"expected_CSR_pairs": 12027,
|
||||
"group": "APL-incident",
|
||||
"observed_pairs": 12027
|
||||
},
|
||||
{
|
||||
"complete_pair_set_and_weight_exact": true,
|
||||
"expected_CSR_pairs": 21978,
|
||||
"group": "APL-target-PN-to-KC",
|
||||
"observed_pairs": 21978
|
||||
},
|
||||
{
|
||||
"complete_pair_set_and_weight_exact": true,
|
||||
"expected_CSR_pairs": 823,
|
||||
"group": "watched-PN-to-ALLN",
|
||||
"observed_pairs": 823
|
||||
},
|
||||
{
|
||||
"complete_pair_set_and_weight_exact": true,
|
||||
"expected_CSR_pairs": 886,
|
||||
"group": "ALLN-to-watched-PN",
|
||||
"observed_pairs": 886
|
||||
}
|
||||
],
|
||||
"pass": true
|
||||
}
|
||||
@@ -0,0 +1,39 @@
|
||||
group,primary_post,contacts,fraction,source_bodies,target_bodies
|
||||
APL-to-PN,CA(L),2063,0.4484782608695652,1,118
|
||||
APL-to-PN,CA(R),1757,0.3819565217391304,1,122
|
||||
APL-to-PN,LH(L),2,0.0004347826086956522,1,2
|
||||
APL-to-PN,PED(R),2,0.0004347826086956522,1,1
|
||||
APL-to-PN,SCL(L),34,0.007391304347826087,1,22
|
||||
APL-to-PN,SLP(R),8,0.0017391304347826088,1,8
|
||||
APL-to-PN,a'L(L),3,0.0006521739130434783,1,2
|
||||
APL-to-PN,CentralBrain-unspecified,731,0.15891304347826088,2,163
|
||||
PN-to-APL,CA(L),2272,0.43449990437942243,117,1
|
||||
PN-to-APL,CA(R),2204,0.4214955058328552,119,1
|
||||
PN-to-APL,CRE(L),6,0.0011474469305794606,1,1
|
||||
PN-to-APL,CRE(R),4,0.0007649646203863071,1,1
|
||||
PN-to-APL,LH(L),6,0.0011474469305794606,3,1
|
||||
PN-to-APL,LH(R),12,0.002294893861158921,2,1
|
||||
PN-to-APL,PED(R),3,0.0005737234652897303,1,1
|
||||
PN-to-APL,SCL(L),43,0.008223369669152802,18,1
|
||||
PN-to-APL,SIP(L),12,0.002294893861158921,1,1
|
||||
PN-to-APL,SIP(R),7,0.0013386880856760374,1,1
|
||||
PN-to-APL,SLP(R),8,0.0015299292407726143,3,1
|
||||
PN-to-APL,SMP(L),4,0.0007649646203863071,1,1
|
||||
PN-to-APL,a'L(L),10,0.0019124115509657677,1,1
|
||||
PN-to-APL,aL(L),2,0.00038248231019315357,1,1
|
||||
PN-to-APL,CentralBrain-unspecified,636,0.12162937464142283,130,2
|
||||
PN-to-KC,CA(L),153554,0.3986044664342508,123,1870
|
||||
PN-to-KC,CA(R),184483,0.4788917760604731,123,1890
|
||||
PN-to-KC,LH(L),15,3.893787850862734e-05,2,1
|
||||
PN-to-KC,PLP(L),14,3.634201994138552e-05,3,1
|
||||
PN-to-KC,SCL(L),792,0.0020559199852555232,25,62
|
||||
PN-to-KC,SCL(R),140,0.00036342019941385515,9,20
|
||||
PN-to-KC,SLP(R),57,0.00014796393833278388,11,18
|
||||
PN-to-KC,SMP(L),15,3.893787850862734e-05,1,9
|
||||
PN-to-KC,CentralBrain-unspecified,46159,0.11982223560531528,189,2519
|
||||
watched-PN-to-ALLN,AL(L),857,0.037922031948316295,1,100
|
||||
watched-PN-to-ALLN,AL(R),21742,0.9620779680516837,7,197
|
||||
ALLN-to-watched-PN,AL(L),943,0.04956896551724138,132,1
|
||||
ALLN-to-watched-PN,AL(R),18078,0.950273338940286,220,7
|
||||
ALLN-to-watched-PN,LAL(R),1,5.256518082422203e-05,1,1
|
||||
ALLN-to-watched-PN,CentralBrain-unspecified,2,0.00010513036164844407,2,1
|
||||
|
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"before_analysis": true,
|
||||
"coordinate_unit_nm": 8,
|
||||
"distance_to_microns_multiplier": 0.008,
|
||||
"nearest_neighbor_is_not_bouton_or_functional_identity": true,
|
||||
"protocol_sha256": "b87a601d869f0a4cf7f88ccb2b7f5592b22da25d11d3e5bfb1be4bba05c079c0",
|
||||
"source_generation": "1780494942562468",
|
||||
"source_script_sha256": "aca6c69399538a41bc9148f2c837f0546e8a39d6b2b833585b9c55e53fa8414b",
|
||||
"source_sha256": "959d8ef4173b35382a3e6acfaf5167c795b6d10b877572d146af04e1b487bc07"
|
||||
}
|
||||
@@ -0,0 +1,260 @@
|
||||
PN_body_id,PN_neuron_idx,APL_postsynaptic_contacts,PN_to_KC_unique_presynaptic_sites,nearest_distance_microns_min,nearest_distance_microns_median,nearest_distance_microns_p95,nearest_distance_microns_max,bouton_identity_confirmed,watched
|
||||
10039,30,50,760,0.32614107377023216,0.6377925913705558,1.0157144370388704,1.038183028179521,False,True
|
||||
10049,40,14,245,0.29869047524151154,0.5070181997019045,0.6956626167431111,0.7455467792164352,False,False
|
||||
10075,62,13,160,0.4121358999165203,0.7135712998712883,5.210429213570748,6.738840256305235,False,False
|
||||
10084,71,73,1490,0.1643411086733931,0.6488389630717317,1.235898708508112,6.909022506838432,False,False
|
||||
10111,97,21,705,0.19563230817019975,0.5261786768769712,1.1529440576194494,1.31835048450706,False,False
|
||||
10126,112,97,1954,0.318898102847916,0.6312400494265237,1.067649862983311,2.611727397719754,False,False
|
||||
10134,120,58,791,0.2573713270743266,0.5799221993097676,0.8263424135347435,26.008841573588008,False,False
|
||||
10176,159,131,1187,0.28833314065504156,0.6899507228780908,1.2866005144428363,5.110127982741724,False,False
|
||||
10198,179,7,436,0.2692805228753094,0.5452925820144632,0.9529021783619328,0.9987832597716083,False,True
|
||||
10208,189,242,1815,0.19249935064825544,0.7015405674292241,1.3275406085019374,29.84357619321116,False,False
|
||||
10262,241,18,679,0.28599300690751167,0.6585069777901823,7.330699538507631,10.971768863770327,False,False
|
||||
10321,296,59,910,0.28855502074994294,0.6648187722981355,0.9567395499684019,1.0458604113360443,False,False
|
||||
10359,331,51,487,0.2819361629872975,0.6230762393158641,0.9828316447841321,1.208185416233783,False,False
|
||||
10390,360,54,963,0.24851559307214507,0.6404856063390175,1.1311501585139474,5.960300663557167,False,False
|
||||
10423,390,79,949,0.30473595127585457,0.6720952313474632,1.1543230357208136,2.147281071494833,False,False
|
||||
10429,394,22,615,0.37710475998056564,0.7223503097087192,1.9780408516324008,2.3923076725204058,False,False
|
||||
10470,434,72,1544,0.261686835740738,0.5755012228571407,1.3499778960888393,3.1809633760859306,False,False
|
||||
10522,484,50,922,0.18948350851723217,0.6133988644854391,1.0063114755667468,1.1817546276617663,False,True
|
||||
10524,486,73,1033,0.26868568997994663,0.6228707731142954,1.208067778367167,13.04661182069889,False,False
|
||||
10530,492,4,128,0.417612260356422,0.6297676320484469,0.7068760993035946,0.7166142616498782,False,False
|
||||
10561,522,72,1154,0.2892196397204035,0.5594334554511577,0.9075554799533935,8.317765084444257,False,False
|
||||
10569,530,32,1622,0.34344140693865094,0.5911886331789541,1.007924544778516,1.166629332735981,False,False
|
||||
10613,571,44,1026,0.2845347079004598,0.7007599396676927,1.3020943384042432,3.6613527554716714,False,False
|
||||
10653,607,17,228,0.35072496346852755,0.6557072517518775,7.569693664215983,8.24450750499992,False,False
|
||||
10670,622,77,1234,0.21150886506243657,0.624256357596781,0.991618336822589,12.156275745474023,False,False
|
||||
10716,666,26,264,0.41630757860024603,0.6325346181862803,2.9666127297868234,36.74516523299358,False,False
|
||||
10736,683,2,564,0.7581451048447125,0.8036758967332555,0.8446536094329441,0.8492066886217984,False,False
|
||||
10756,700,18,509,0.4395634197701169,0.6719841756401636,0.9931510483404092,1.0864400581716416,False,False
|
||||
10767,710,35,456,0.32819506394825626,0.5933498125052371,0.7820672190543125,2.0333224043422136,False,False
|
||||
10786,728,11,295,0.35290792000180443,0.5934037411408862,1.0593272677927135,1.3445951063424262,False,False
|
||||
10808,749,25,391,0.46606437323614425,0.6743292964123685,1.0249367606869804,1.1931538040001382,False,False
|
||||
10877,813,59,921,0.2007984063681781,0.5932419405267972,0.9891269815447413,12.505842474619612,False,False
|
||||
10904,835,29,484,0.31414646265714974,0.66332495807108,0.9225660722748511,1.0092056282046786,False,False
|
||||
10906,837,24,366,0.30630703550522637,0.5997045290661167,1.798333495903597,2.2306806136244606,False,False
|
||||
10973,900,20,313,0.5832323722153975,0.9738278971635564,1.3573624290600375,1.4574498276098564,False,False
|
||||
10996,921,21,703,0.13096564434995922,0.6802352534234021,1.1897697256192057,4.319770364267064,False,False
|
||||
11050,972,75,580,0.3089077532209252,0.7907464827617003,3.66819676789212,11.449786024201499,False,False
|
||||
11080,999,47,533,0.23879698490558882,0.6376958522681483,1.3184664869397695,14.67713105480768,False,False
|
||||
11092,1011,43,638,0.3283900120283807,0.6287511431401139,1.7176082059335904,8.594688592380763,False,False
|
||||
11105,1022,29,644,0.264,0.5513039089286416,4.199204667204444,7.757875997977797,False,False
|
||||
11135,1050,9,137,0.3740374312819507,0.6630354439997911,1.747743054697715,2.2039528125620116,False,False
|
||||
11143,1056,44,483,0.2745760368276882,0.6900834677187604,1.8152050919471376,6.851349648062052,False,False
|
||||
11229,1138,13,639,0.379810479055015,0.6812224306348111,1.731704442588357,2.6925913169287314,False,False
|
||||
11283,1191,16,116,0.38690567325899994,0.856325302116866,6.500469046082351,16.003751560181133,False,False
|
||||
11355,1262,11,322,0.4608166663652694,0.646615805559994,0.8102247777993361,0.8573820618604054,False,False
|
||||
11390,1294,6,125,0.5452338947644396,0.6921425091298001,0.8236643705374138,0.8253968742368728,False,False
|
||||
11467,1370,29,791,0.4004797123450825,0.6110450065257059,3.2373904688166864,4.612705930362351,False,False
|
||||
11469,1372,9,616,0.21346662502602134,0.5479270024373685,0.6600577648809263,0.6716665839536756,False,False
|
||||
11471,1374,24,391,0.2433105012119288,0.5968700246024764,0.9242375461303489,3.9988958476059366,False,False
|
||||
11514,1413,11,188,0.03752332607858744,0.5996799146211251,1.047557367064608,1.1092628182716664,False,False
|
||||
11521,1420,40,434,0.3015692291995322,0.7886809052834882,1.9885164239505988,10.202340123716715,False,False
|
||||
11564,1458,13,162,0.34185376990754396,0.6055212630453204,3.469547371838683,7.118768432811957,False,False
|
||||
11624,1514,12,387,0.49198373956869756,0.7084754807733484,0.9886078724779322,1.0037370173506603,False,False
|
||||
11703,1590,32,451,0.28385911998736274,0.6247977041659403,1.124298660328737,1.322155815325864,False,False
|
||||
11721,1607,12,188,0.48166378315169184,0.6680994997077767,0.9803144047290633,1.048518955479585,False,False
|
||||
11761,1644,78,621,0.266172876153826,0.8609996955826638,2.8232245881500204,5.2193486183622575,False,True
|
||||
11780,1661,7,187,0.29131426329653,0.5675491168172143,1.223570895379603,1.3624918348379191,False,False
|
||||
11816,1694,5,158,0.513186126079028,0.5871694814957603,0.7837760630111454,0.8197950963503013,False,False
|
||||
11817,1695,15,212,0.19216659439142902,0.504,0.7864841691009086,0.8051832089655124,False,False
|
||||
11829,1707,5,184,0.39686773615399884,0.7209327291779727,3.6690674457928947,4.3511322664336465,False,False
|
||||
11996,1866,8,145,0.2921917178839948,0.7676937679415607,27.277332948710583,41.49681587784778,False,False
|
||||
12023,1890,28,381,0.29869047524151154,0.5200568172261075,0.8341553215412327,0.9759672125640287,False,False
|
||||
12031,1898,2,284,0.3180943256331367,0.5136737269653924,0.6896951881644224,0.709253128297648,False,False
|
||||
12050,1916,6,198,0.6502184248389152,0.7103485838339838,1.6589125897071337,1.7610905712086473,False,False
|
||||
12057,1923,37,480,0.24383601046605075,0.5811436999572481,0.8946469221044793,4.539187592510361,False,False
|
||||
12061,1927,36,427,0.29697137909232935,0.6187552781122321,1.0834408318251099,4.276821249479571,False,False
|
||||
12121,1982,9,203,0.3307204257375102,0.5797102724637542,0.7273878574986796,0.7723211767134189,False,False
|
||||
12122,1983,20,192,0.4176888794306116,0.6005565733348903,1.1961133273759836,1.457098486719412,False,False
|
||||
12152,2010,26,384,0.13023056476879766,0.6078182273914833,2.159064439048579,3.528226750082823,False,False
|
||||
12160,2018,3,252,0.2827295527531567,0.6568287448033924,1.355789474727001,1.4334517780518465,False,False
|
||||
12163,2021,15,282,0.3299454500368205,0.5829030794222998,1.265568037608132,1.5102847413650182,False,False
|
||||
12164,2022,17,405,0.2581162528784269,0.5542562584220408,0.7955910056481913,0.9386287871144802,False,False
|
||||
12171,2029,30,615,0.29208218021645893,0.6688763423187132,3.543990151781294,4.919941463066405,False,False
|
||||
12309,2157,24,304,0.3766802357437937,0.8219136756235398,6.1256513607654455,20.381145797035064,False,False
|
||||
12312,2160,30,317,0.33466401061363027,0.5238238027968327,1.0423692977830707,2.065580789995879,False,False
|
||||
12314,2162,4,155,0.45663114217057077,0.5811944314729927,1.074252900627574,1.154053724919252,False,False
|
||||
12340,2186,6,186,0.5072908435996061,0.6166014333140507,1.847429967736642,2.1971472413108777,False,False
|
||||
12367,2212,27,379,0.3365709434874021,0.6161038873436849,0.9180164599393732,2.0235256361113887,False,False
|
||||
12387,2231,13,226,0.3712896443479134,0.7878680092502804,1.1248211628712794,1.4648959007383426,False,False
|
||||
12460,2302,11,215,0.41523005671555135,0.5838355932966061,0.6560632988814886,0.687255411037265,False,False
|
||||
12509,2347,35,470,0.3257483691440373,0.6239487158412941,0.9168967699182743,1.7971533045347021,False,False
|
||||
12653,2481,14,186,0.29686360504447157,0.6668383067701869,1.2592524435973163,1.7961024469667648,False,False
|
||||
12667,2493,2,116,0.78097375115941,2.2100868316139977,3.496288604023127,3.6391999120685856,False,False
|
||||
12728,2552,23,307,0.3407286310247497,0.7341280542248743,1.3381074128537256,13.739754874087092,False,False
|
||||
12756,2577,16,261,0.34834465691323585,0.6190715352783277,1.3601982044558092,2.04455961028286,False,False
|
||||
12782,2601,34,440,0.1848675201326615,0.6427815214276122,1.8864652439762222,2.7969926707090247,False,False
|
||||
12958,2769,15,186,0.3678260458423248,0.6775012915116841,0.8132295773227166,0.848490424224104,False,False
|
||||
13027,2834,13,298,0.28102668912400475,0.4785310857196218,0.8814884521506193,1.1695606012515982,False,False
|
||||
13064,2867,6,207,0.604675119382301,0.6724499884806354,17.244028143678836,22.69306149465074,False,False
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||||
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22977,11770,1,0,,,,,False,False
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24502,13128,6,0,,,,,False,False
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25433,13955,1,0,,,,,False,False
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25927,14395,1,0,,,,,False,False
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26845,15212,11,113,0.23919866220361688,0.7725697379525036,9.144603734119638,13.287255849120992,False,False
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27775,16042,13,199,0.3226887044815793,0.5832323722153975,5.743631709563589,13.233315533153437,False,False
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28680,16855,19,180,0.4488563244513772,7.079606768740762,33.296315845558894,38.256225846259326,False,False
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29176,17293,9,128,0.3410102637751538,0.5452925820144632,4.496604934294457,4.5205167846165555,False,False
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29217,17327,8,93,0.3794733192202055,0.7988114731436029,67.24376940287789,67.97712415217343,False,False
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29824,17874,16,112,0.4023133107417651,4.849410006905966,36.703932503508454,36.73136120537871,False,False
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31117,19004,5,28,0.3286822173467862,0.4978674522400515,4.064437160856841,4.342961201760844,False,False
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31348,19196,1,14,0.5452338947644396,0.5452338947644396,0.5452338947644396,0.5452338947644396,False,False
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33987,21524,1,1,0.1732512626216617,0.1732512626216617,0.1732512626216617,0.1732512626216617,False,False
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34301,21802,2,102,0.6251271870587617,0.6390478963679707,0.6515765347462587,0.6529686056771795,False,False
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34385,21875,13,168,0.3426018096858217,0.6206061553030231,1.407714419242509,2.1950927087483114,False,False
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38510,25520,6,125,0.39879819457966453,0.5570715217642417,6.6850951708542246,8.573682522697,False,False
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39922,26772,2,46,0.3287795614085523,0.5008180791573046,0.6556527451311817,0.6728565969060569,False,False
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43363,29804,1,0,,,,,False,False
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43489,29917,1,0,,,,,False,False
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44995,31238,5,24,0.3688685402687521,0.581418953939412,1.9946418707315496,2.2240143884426646,False,False
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52099,37369,4,16,0.1470782104868019,0.6014202214289611,4.257956130003902,4.889551308658086,False,False
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52223,37468,3,78,0.318898102847916,2.4198776828591977,12.563241133102341,13.690281516462692,False,False
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54383,39352,2,0,,,,,False,False
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77438,58326,1,0,,,,,False,False
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87970,66580,6,12,0.5443528267585279,17.261144311009964,37.018028485976764,38.3107588021955,False,False
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89871,68010,10,32,0.2583640841912823,4.798347125417276,46.6074945655719,46.76536496168933,False,False
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92909,70280,1,0,,,,,False,False
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95720,72419,2,11,0.5970594610254493,10.512318841317784,19.436052283580885,20.42757822161012,False,False
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97057,73384,10,3,0.9848451654955717,29.791203086187352,38.59962633030649,39.990717322898824,False,False
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140108,101754,1,0,,,,,False,False
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511807,125773,7,156,0.4279813080030482,0.5526373132534573,0.628847793076934,0.6484442921331022,False,False
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||||
512883,126350,7,118,0.630986529174752,1.0737299474262605,1.9429483680333606,2.2373984893174486,False,False
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||||
513513,126688,30,288,0.2976172038038124,0.6582979886793796,6.286241844673976,12.77225994098147,False,False
|
||||
515440,127785,1,0,,,,,False,False
|
||||
516097,128185,1,10,8.032880429833373,8.032880429833373,8.032880429833373,8.032880429833373,False,False
|
||||
516162,128229,11,213,0.4773929199307422,0.8118324950382314,4.652621054210245,5.035992057181981,False,False
|
||||
516322,128299,1,0,,,,,False,False
|
||||
517351,128946,11,133,0.36318590281011737,0.7301068414965032,20.41046665011151,35.61922896414239,False,False
|
||||
518294,129486,5,200,0.42835499296728174,0.7207107602915334,13.64557938863755,16.192351774834933,False,False
|
||||
519572,130257,12,243,0.4107359248957899,0.6399896128347567,17.17223333138889,25.57381035356288,False,False
|
||||
519850,130400,6,155,0.38937899275641463,0.6860508519851847,0.8587043874263335,0.8621090418270766,False,False
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||||
519970,130472,25,420,0.261686835740738,0.6014382761348,0.8964241177210769,1.0453095235383632,False,False
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||||
520003,130497,39,510,0.24488364584022346,0.6913870117379991,1.0493931052332905,1.8535328429785107,False,False
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||||
520466,130753,17,574,0.2902137143554729,0.6767924349458998,4.239893907283367,16.721069822233265,False,False
|
||||
520475,130758,2,213,0.14444376068214232,0.41083079930216404,0.6505791340601836,0.6772178379221858,False,False
|
||||
520916,130990,19,589,0.3403527581789224,0.6434904816700865,1.4966526803993225,1.6595710289107846,False,False
|
||||
521437,131226,35,475,0.3850818094898797,0.6405997190133633,1.686069575556858,19.195529479542884,False,False
|
||||
521575,131311,17,190,0.41523005671555135,0.6961838837548597,8.861712960683988,14.063638220602805,False,False
|
||||
523351,131777,21,213,0.5445879176037602,1.0626758677978907,11.597089289989967,15.740963121740677,False,False
|
||||
524207,132182,14,213,0.3855800824731485,0.6310360908570465,1.431911259528012,2.3861600952157422,False,False
|
||||
525105,132659,26,435,0.34770102099361166,0.723303499565019,3.6806594944390745,8.314709856633604,False,False
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||||
527523,133646,8,194,0.1670449041425688,0.7109879102488461,4.88861310969004,6.946257697494385,False,False
|
||||
527524,133647,8,177,0.4608166663652694,0.7883505242132749,3.4457338073328163,4.4332705759969135,False,False
|
||||
527577,133666,35,836,0.28955137713366175,0.6166749548992565,0.9691512753099297,2.8537582238164463,False,True
|
||||
529665,134090,23,462,0.3407286310247497,0.7365650005260906,1.1474987180891179,3.9528096336656535,False,False
|
||||
538094,137761,1,13,41.03361899711016,41.03361899711016,41.03361899711016,41.03361899711016,False,False
|
||||
547655,140635,8,66,0.3883091551843711,0.6145628340339906,6.248013503990884,9.046231480566922,False,False
|
||||
563873,143201,3,16,2.0108823933785884,2.668848440807383,6.498084484599408,6.923555156131855,False,False
|
||||
564174,143218,35,290,0.27011108825814617,0.6163635290962631,2.242550067586304,12.19257478960043,False,False
|
||||
|
@@ -0,0 +1,359 @@
|
||||
{
|
||||
"APL_target_PNs_localized": 259,
|
||||
"APL_to_PN_contacts_localized": 4600,
|
||||
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|
||||
"ROI_counts": [
|
||||
{
|
||||
"contacts": 2063,
|
||||
"fraction": 0.4484782608695652,
|
||||
"group": "APL-to-PN",
|
||||
"primary_post": "CA(L)",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"contacts": 1757,
|
||||
"fraction": 0.3819565217391304,
|
||||
"group": "APL-to-PN",
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"contacts": 2,
|
||||
"fraction": 0.0004347826086956522,
|
||||
"group": "APL-to-PN",
|
||||
"primary_post": "LH(L)",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"contacts": 2,
|
||||
"fraction": 0.0004347826086956522,
|
||||
"group": "APL-to-PN",
|
||||
"primary_post": "PED(R)",
|
||||
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||||
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||||
},
|
||||
{
|
||||
"contacts": 34,
|
||||
"fraction": 0.007391304347826087,
|
||||
"group": "APL-to-PN",
|
||||
"primary_post": "SCL(L)",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"contacts": 8,
|
||||
"fraction": 0.0017391304347826088,
|
||||
"group": "APL-to-PN",
|
||||
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|
||||
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||||
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|
||||
},
|
||||
{
|
||||
"contacts": 3,
|
||||
"fraction": 0.0006521739130434783,
|
||||
"group": "APL-to-PN",
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"contacts": 731,
|
||||
"fraction": 0.15891304347826088,
|
||||
"group": "APL-to-PN",
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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||||
"fraction": 0.43449990437942243,
|
||||
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|
||||
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|
||||
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||||
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|
||||
},
|
||||
{
|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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},
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{
|
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|
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|
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|
||||
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|
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},
|
||||
{
|
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|
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|
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|
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|
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},
|
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{
|
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|
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|
||||
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|
||||
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},
|
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{
|
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|
||||
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},
|
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{
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|
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|
||||
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|
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|
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},
|
||||
{
|
||||
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|
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|
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|
||||
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|
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},
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{
|
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
"group": "PN-to-APL",
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
"fraction": 0.00038248231019315357,
|
||||
"group": "PN-to-APL",
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"contacts": 636,
|
||||
"fraction": 0.12162937464142283,
|
||||
"group": "PN-to-APL",
|
||||
"primary_post": "CentralBrain-unspecified",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"contacts": 153554,
|
||||
"fraction": 0.3986044664342508,
|
||||
"group": "PN-to-KC",
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
"primary_post": "SMP(L)",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"contacts": 46159,
|
||||
"fraction": 0.11982223560531528,
|
||||
"group": "PN-to-KC",
|
||||
"primary_post": "CentralBrain-unspecified",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"contacts": 857,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
"group": "ALLN-to-watched-PN",
|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
],
|
||||
"bouton_identity_confirmed": false,
|
||||
"full_source_rows_streamed": 311833243,
|
||||
"groups": [
|
||||
{
|
||||
"canonical_pairs": 12027,
|
||||
"canonical_rows": 443915,
|
||||
"duplicate_partner_rows": 0,
|
||||
"group": "APL-incident",
|
||||
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|
||||
"pass": true,
|
||||
"rows": 500348
|
||||
},
|
||||
{
|
||||
"canonical_pairs": 21978,
|
||||
"canonical_rows": 385229,
|
||||
"duplicate_partner_rows": 0,
|
||||
"group": "APL-target-PN-to-KC",
|
||||
"mismatches": [],
|
||||
"pass": true,
|
||||
"rows": 385229
|
||||
},
|
||||
{
|
||||
"canonical_pairs": 823,
|
||||
"canonical_rows": 22599,
|
||||
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|
||||
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|
||||
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|
||||
"pass": true,
|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
"group": "ALLN-to-watched-PN",
|
||||
"mismatches": [],
|
||||
"pass": true,
|
||||
"rows": 19024
|
||||
}
|
||||
],
|
||||
"new_dynamics": false,
|
||||
"pass": true,
|
||||
"source_sha256": "959d8ef4173b35382a3e6acfaf5167c795b6d10b877572d146af04e1b487bc07",
|
||||
"spatial_location_from_official_partners": true,
|
||||
"target_receptor_confirmed": false,
|
||||
"terminal_inhibition_coefficient_known": false,
|
||||
"wall_seconds": 37.98379981498874,
|
||||
"whole_brain_functional_intervention_run": false
|
||||
}
|
||||
@@ -0,0 +1,164 @@
|
||||
[
|
||||
{
|
||||
"ALLN-to-watched-PN": {
|
||||
"AL(R)": 3277
|
||||
},
|
||||
"APL-to-PN": {
|
||||
"CA(R)": 47,
|
||||
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|
||||
},
|
||||
"PN-to-APL": {
|
||||
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|
||||
"CentralBrain-unspecified": 4
|
||||
},
|
||||
"PN-to-KC": {
|
||||
"CA(R)": 3168,
|
||||
"CentralBrain-unspecified": 342
|
||||
},
|
||||
"body_id": 10039,
|
||||
"neuron_idx": 30,
|
||||
"type": "VL2a_adPN",
|
||||
"watched-PN-to-ALLN": {
|
||||
"AL(R)": 4771
|
||||
}
|
||||
},
|
||||
{
|
||||
"ALLN-to-watched-PN": {
|
||||
"AL(R)": 1703
|
||||
},
|
||||
"APL-to-PN": {
|
||||
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|
||||
},
|
||||
"PN-to-APL": {
|
||||
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|
||||
},
|
||||
"PN-to-KC": {
|
||||
"CA(R)": 1654,
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||||
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||||
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||||
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||||
Reference in New Issue
Block a user