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digital-drosophila/models/male-cns-lif/apl-timing-validation-v1/seed-audit.json
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Digital Drosophila Archive c6444255fe Seal Phase 4AK frozen D18 independent validation
2026-09-30 13:18:18 +08:00

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{
"schema": "phase4ak-static-seed-audit-v1",
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"selection_policy": "Before any Phase4AK brain trajectory: reserve the declared historical configured/derived namespaces, then use the first five positive integers strictly above 2^30. This fixed arithmetic choice does not use activity, separability, performance, or any held-out result.",
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"Unlisted server-only files, ad-hoc CLI overrides, shell histories, other Git revisions/branches, unpublished scripts, external author-package random state and other users' runs were not read.",
"No complete historical executed-input registry was available within this local allowed scope; configured possibility is not an assertion that a run happened.",
"Source constants can be encoded or supplied through aliases/callers. Lexical scanning plus named call-site review is not a full static data-flow proof; arbitrary reset(seed),sample(seed),manual_seed(seed) interfaces remain externally parameterized.",
"No old held-out dynamics, kc-representation, eligible-edges, plasticity-magnitude, conditioning products or mixed outcome reports were opened. Plan/source references to such paths were read as static text only.",
"Only AG/AJ CAL static inventories were inspected, not all historical inventories. Old later-six odors are not project-history-naive; they are unused for AG/AJ calibration.",
"New integer seeds do not mathematically prove independent pseudorandom streams. Shared seeds across odors, modes and two blocks deliberately support paired comparisons and are not biological replicates.",
"Local SHA inventories identify local snapshots. Only the explicit AG input pins above were cross-checked here; complete remote frozen-integrity verification remains the parent runner's task.",
"New Phase4AK source/config written concurrently by the parent is excluded from historical-use scanning to avoid self-collision. These two audit artifacts require parent preregistration before use."
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"actual_sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346",
"source": "AG-r2 protocol/preregistered.json inputs"
}
],
"derivation_rules": [
{
"name": "Direct configured RNG seeds",
"formula": "manual_seed(seed), default_rng(seed); configured values and inline declarations retained in lexical_evidence",
"sources": [
"23-ai-gpu/source/models/male-cns-lif/baseline.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4b.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4c.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4d.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4e.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4f.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4g.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4h.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4i.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4j.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4k.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4l.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4m.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4n.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4q.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4r.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4s.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4t.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4u.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4v.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4w.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4y.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4z.yaml"
],
"note": "Declarations are statically possible uses, not proof of execution. Gate quantities such as seed_majority and seed_node neuron IDs are not RNG seeds."
},
{
"name": "4C visual offsets",
"formula": "visual seed 43100 + {1,2,3,4,10+j}; hierarchy enumeration determines j",
"sources": [
"23-ai-gpu/source/models/male-cns-lif/phase4c.py",
"23-ai-gpu/source/models/male-cns-lif/phase4c.yaml"
],
"note": "The same frozen five references also use 42001/42003. No old visual dynamic output accessed."
},
{
"name": "4D inline stimuli",
"formula": "44301,44302,44303 in separability loop; other seeds from YAML",
"sources": [
"23-ai-gpu/source/models/male-cns-lif/phase4d.py",
"23-ai-gpu/source/models/male-cns-lif/phase4d.yaml"
],
"note": "Static source only."
},
{
"name": "4E text-derived namespace",
"formula": "45000 + uint32_le(SHA256(layer)[:4]) % 1000000, then +int(multiplier*10), +int(rate), +int(sparsity_percent*100), or +int(window_ms*10)",
"sources": [
"23-ai-gpu/source/models/male-cns-lif/phase4e.py",
"23-ai-gpu/source/models/male-cns-lif/phase4e.yaml"
],
"note": "Configured maximum extra offset is 10000; reserve full hash output interval rather than selecting or reading a previous favorable condition."
},
{
"name": "4G stochastic preprocessing",
"formula": "Direct configured stimulus/model/graph seeds; inline 47901/47902 and 47400. stable_seed(base,text) has base+SHA256_prefix%1000000 definition",
"sources": [
"23-ai-gpu/source/models/male-cns-lif/phase4g.py",
"23-ai-gpu/source/models/male-cns-lif/phase4g.yaml"
],
"note": "No call to stable_seed found in the scanned 4G source; function definition alone is not an executed-seed record."
},
{
"name": "4H conditioning",
"formula": "train_seed + trial_idx, protocol RNG default_rng(train_seed), evaluation seeds passed unchanged",
"sources": [
"23-ai-gpu/source/models/male-cns-lif/phase4h.py",
"23-ai-gpu/source/models/male-cns-lif/phase4h.yaml"
],
"note": "Static protocol plans include reversal and controls; never read training outputs."
},
{
"name": "4I and 4J direct-KC",
"formula": "4I train seed + trial; 4J calibration_seed_base + odor_index and evaluation_seed + odor_index*100; direct-KC train/evaluation lists; fixed random KC-selection seed 49301",
"sources": [
"23-ai-gpu/source/models/male-cns-lif/phase4i.py",
"23-ai-gpu/source/models/male-cns-lif/phase4i.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4j.py",
"23-ai-gpu/source/models/male-cns-lif/phase4j.yaml"
],
"note": "Do not treat the entire 50xxx range as unused merely because one evaluation list has gaps."
},
{
"name": "DAN helper seed offset",
"formula": "passed seed + 991 to _forced in 4J/4K/4L and 4N event subclass",
"sources": [
"23-ai-gpu/source/models/male-cns-lif/phase4j.py",
"23-ai-gpu/source/models/male-cns-lif/phase4k.py",
"23-ai-gpu/source/models/male-cns-lif/phase4l.py",
"23-ai-gpu/source/models/male-cns-lif/phase4n_events.py"
],
"note": "For explicit pulse lists the helper does not draw random numbers; conservatively record/reserve these potential seed arguments anyway."
},
{
"name": "4L to 4Q training and evaluation",
"formula": "train_seed in 51201..51205 + pairing in 1..100; evaluation 51301..51305 unchanged; code also uses alias ts+pairing",
"sources": [
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"23-ai-gpu/source/models/male-cns-lif/phase4m.py",
"23-ai-gpu/source/models/male-cns-lif/phase4m.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4m_formal.py",
"23-ai-gpu/source/models/male-cns-lif/phase4n_events.py",
"23-ai-gpu/source/models/male-cns-lif/phase4n_controls.py",
"23-ai-gpu/source/models/male-cns-lif/phase4o_targeted.py",
"23-ai-gpu/source/models/male-cns-lif/phase4o_eligibility.py",
"23-ai-gpu/source/models/male-cns-lif/phase4p_revalidate.py",
"23-ai-gpu/source/models/male-cns-lif/phase4q.py",
"23-ai-gpu/source/models/male-cns-lif/phase4q.yaml"
],
"note": "Thus 512xx and even 513xx cannot be presumed unused. 4N random control also uses seed+700000."
},
{
"name": "4P old odor offset",
"formula": "seed + odor_index*100 in validate_sparse",
"sources": [
"23-ai-gpu/source/models/male-cns-lif/phase4p_revalidate.py"
],
"note": "AG/AJ preparation does NOT use this offset: it directly seeds a fresh generator per case."
},
{
"name": "4Q and 4R credit/control",
"formula": "52101..52105 and 52201..52205 + odor_index*100; random control bases 930000/950000 + actual; 4Q formal random control = 940000 + train_seed*1000 + pairing",
"sources": [
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"23-ai-gpu/source/models/male-cns-lif/phase4q.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4r.py",
"23-ai-gpu/source/models/male-cns-lif/phase4r.yaml"
],
"note": "52101..52105 are explicitly occupied; training/control labels are not interchangeable with actual RNG seeds."
},
{
"name": "4S namespaces",
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"sources": [
"23-ai-gpu/source/models/male-cns-lif/phase4s.py",
"23-ai-gpu/source/models/male-cns-lif/phase4s.yaml"
],
"note": "Conservative odor range 0..11 bounds the frozen twelve-item static panel without opening its dynamic outcomes."
},
{
"name": "4T namespaces",
"formula": "86101..86120 or 87101..87120 + odor_index*1000000; live-US RNG adds 9700000",
"sources": [
"23-ai-gpu/source/models/male-cns-lif/phase4t.py",
"23-ai-gpu/source/models/male-cns-lif/phase4t.yaml"
],
"note": "Reserve full index 0..11 as a conservative superset of the configured selected odors; no selection result read."
},
{
"name": "4U namespaces",
"formula": "50000000 + odor_index*2000000 + seed, seed=118101..118120 or119101..119120; live-US RNG adds29000000",
"sources": [
"23-ai-gpu/source/models/male-cns-lif/phase4u.py",
"23-ai-gpu/source/models/male-cns-lif/phase4u.yaml"
],
"note": "Configured six calibration plus six new odors imply at most index11. Conservative largest listed argument=101119120; no old new-odor output read."
},
{
"name": "4V/4W/4Y/4Z and terminal-scope blocks",
"formula": "80000000 + odor_index*1000000 + seed + block*1000003; seeds131101..131103 or141101; six old CAL odors; 1000ms/120ms gives blocks0..8",
"sources": [
"23-ai-gpu/source/models/male-cns-lif/phase4v.py",
"23-ai-gpu/source/models/male-cns-lif/phase4v.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4w.py",
"23-ai-gpu/source/models/male-cns-lif/phase4w.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4y.py",
"23-ai-gpu/source/models/male-cns-lif/phase4y.yaml",
"23-ai-gpu/source/models/male-cns-lif/phase4z.py",
"23-ai-gpu/source/models/male-cns-lif/phase4z.yaml"
],
"note": "Offset mode still constructs all RNG blocks before zeroing later input. Reserve them even where no later events are delivered. Conservative maximum with seed141101 is93141125."
},
{
"name": "4AE synthetic fixture RNG",
"formula": "default_rng(410301)",
"sources": [
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"23-ai-gpu/source/author-runtime-lab-v1/prepare.py"
],
"note": "Synthetic numerical fixture, not odor input; retained in coverage."
},
{
"name": "AG/AJ actual static input inventory",
"formula": "Generator(device='cuda').manual_seed(seed), seeds51101..51105 directly; independent generator construction per case with the same base seed deliberately reused across odors/modes",
"sources": [
"23-ai-gpu/source/author-apl-lab-v2/prepare.py",
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"23-ai-gpu/source/apl-timing-lab-v1/config.json",
"23-ai-gpu/reports/phase4ag-author-apl-r2/protocol/encoder-audit.json",
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"23-ai-gpu/reports/phase4aj-apl-timing/protocol/calibration-cases.json"
],
"note": "Both inventories contain the same60 rows by byte SHA. AH/AI are replays, not fresh RNG streams. Seed-node fields in AF and motif configs denote neuron IDs, not RNG state."
}
],
"uncovered": [
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"No complete historical executed-input registry was available within this local allowed scope; configured possibility is not an assertion that a run happened.",
"Source constants can be encoded or supplied through aliases/callers. Lexical scanning plus named call-site review is not a full static data-flow proof; arbitrary reset(seed),sample(seed),manual_seed(seed) interfaces remain externally parameterized.",
"No old held-out dynamics, kc-representation, eligible-edges, plasticity-magnitude, conditioning products or mixed outcome reports were opened. Plan/source references to such paths were read as static text only.",
"Only AG/AJ CAL static inventories were inspected, not all historical inventories. Old later-six odors are not project-history-naive; they are unused for AG/AJ calibration.",
"New integer seeds do not mathematically prove independent pseudorandom streams. Shared seeds across odors, modes and two blocks deliberately support paired comparisons and are not biological replicates.",
"Local SHA inventories identify local snapshots. Only the explicit AG input pins above were cross-checked here; complete remote frozen-integrity verification remains the parent runner's task.",
"New Phase4AK source/config written concurrently by the parent is excluded from historical-use scanning to avoid self-collision. These two audit artifacts require parent preregistration before use."
],
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},
{
"path": "23-ai-gpu/source/apl-pn-scope-audit-v1/audit.py",
"sha256": "4ef071ba88f4e1cd78f068be58414dd7f4099dfac69c68266c4f46e175eab9fb",
"bytes": 18939,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/apl-pn-scope-audit-v1/audit.yaml",
"sha256": "e0171bbd7dc7e693268eac00152e560e841f885c7d524c74d51fda7f7fbadbbf",
"bytes": 906,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/apl-pn-scope-audit-v1/test_audit.py",
"sha256": "cb67bab972195090d5c4188169982abdc960479b8031477f1f39ac445814e88f",
"bytes": 1520,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/al_origin_audit.py",
"sha256": "7201871477a29ede31c7d6c6bf1c92700da3db5505e67ccfa7cb43126b8464b9",
"bytes": 33399,
"seed_rng_line_evidence": [
{
"line": 21,
"text": "SEEDS = tuple(range(51101, 51106))"
},
{
"line": 258,
"text": " cells = {(s['mode'], s['odor_id'], s['seed']) for s in specifications.values()}"
},
{
"line": 260,
"text": " require(len(odors) == 6 and cells == {(m, o, seed) for m in MODES for o in odors for seed in SEEDS},"
},
{
"line": 308,
"text": " odor_id=specifications[r['case']]['odor_id'], seed=specifications[r['case']]['seed']) for r in inventory])"
},
{
"line": 469,
"text": " report = dict(case=name, repeat=0, mode=spec['mode'], odor_id=spec['odor_id'], seed=spec['seed'],"
},
{
"line": 495,
"text": " seed=spec['seed'], ALLN_targets=420, exact=exact, pass_all=True,"
}
]
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/calibration.py",
"sha256": "fb15ddeaa108305c8de697bbae36b23ab0414a7c58db51c66dce9feb4a048a0d",
"bytes": 22786,
"seed_rng_line_evidence": [
{
"line": 213,
"text": " all(spec[key] == row[key] for key in (\"mode\", \"odor_id\", \"odor_index\", \"seed\")), \"Invalid fixed CAL case\")"
},
{
"line": 240,
"text": " for repeat_index in ([0, 1] if row[\"seed\"] == legacy.ORIGINAL_SEEDS[0] else [0]):"
},
{
"line": 250,
"text": " runs[(row[\"mode\"], row[\"odor_id\"], row[\"seed\"])] = {key: first[key] for key in"
},
{
"line": 269,
"text": " needed = {(mode, odor, seed) for odor in odors for seed in legacy.ORIGINAL_SEEDS}"
},
{
"line": 272,
"text": " modes[mode] = legacy.summarize_mode(mode, odors, legacy.ORIGINAL_SEEDS, runs, cfg[\"gates\"])"
},
{
"line": 309,
"text": " encoder[\"seeds\"] == list(legacy.ORIGINAL_SEEDS) and encoder[\"requested_before_any_dynamics\"] is True and"
}
]
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/compare_timing.py",
"sha256": "9010f070fba9b7c9cf2d86187ee2abc57f3ac606ebc08a42baad4f3922b51734",
"bytes": 41221,
"seed_rng_line_evidence": [
{
"line": 27,
"text": "COEFFICIENTS = ('gd', 'c0', 'cg', 'cv', 'threshold', 'reset', 'seed_volt')"
}
]
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/config.json",
"sha256": "0b720e23aa7988b2b443e14951082b1ce9b061a183b7f858c0e0f3c0fc6fb25e",
"bytes": 2149,
"seed_rng_line_evidence": [
{
"line": 14,
"text": " \"calibration\": {\"split\": \"calibration\", \"odor_count\": 6, \"modes\": [\"physiological\", \"matched_energy\"], \"seeds\": [51101,51102,51103,51104,51105], \"duration_ms\": 100.0, \"median_scope\": \"original frozen 12-odor panel\", \"full_oracle_probes\": [\"cal-physiological-00-s51101\", \"cal-matched_energy-00-s51101\"], \"repeat_first_seed_each_odor\": true},"
},
{
"line": 23,
"text": " \"selection_policy\": \"Both arms and all fixed CAL cases retained regardless of representation outcome. Stop later scientific execution on numerical/oracle/integrity failure. No scan, tuning, favorable seed or result selection.\""
}
]
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/context.py",
"sha256": "06d4994af3729d653263391ff3a3604f7c919e78562604e5c86acfa292d60ba0",
"bytes": 3436,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/finalize.py",
"sha256": "0a2dbf9e5f889e9dd3491d036e6f92c17417a84482c5e1e49134c0993f7d68f2",
"bytes": 11254,
"seed_rng_line_evidence": [
{
"line": 63,
"text": " assert all(row[key] == original[key] for key in ('mode', 'odor_id', 'seed'))"
},
{
"line": 133,
"text": " next_gate='Independent seeds and novel-odor representation; then credit specificity and functional learning' if numerical and cal_execution and treatment_pass"
}
]
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/gpu_timing.py",
"sha256": "af1975ba5e5e700208c0a598d3cbe648a3cf10885c6231239b6e4b28f76d173b",
"bytes": 27083,
"seed_rng_line_evidence": [
{
"line": 38,
"text": "COEFFICIENT_NAMES = (\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\")"
}
]
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/oracle_timing.py",
"sha256": "39d8c47c94bc2faddf016864a43e7715907fb987f56f7b27098060a88851f6be",
"bytes": 55279,
"seed_rng_line_evidence": [
{
"line": 729,
"text": " coefficient_keys = (\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\")"
},
{
"line": 811,
"text": " keys = (\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\")"
}
]
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/prepare.py",
"sha256": "bba71bcd52dcb78d9ae0fa93a8a43173a4669ecef038dce18ce7d82edd9bce50",
"bytes": 5970,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/run_case.py",
"sha256": "d93e4fe2df542ce5a0f0adb5aceada583dc8c921f0159f426e50428d79d67a1d",
"bytes": 2855,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/run_suite.py",
"sha256": "6f26c626acf9ef8e6fae7902af0cbf0bd176eb322529ca8aa203a9700716c889",
"bytes": 6161,
"seed_rng_line_evidence": [
{
"line": 84,
"text": " if record['seed'] == CFG['calibration']['seeds'][0]:"
}
]
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/shift_audit.py",
"sha256": "46f380dac340acc91f226968bb493475a31777f8290fb20f9145a97c9009d845",
"bytes": 2114,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/test_al_origin_audit.py",
"sha256": "4b75a39d36090b0a66cc7ad4b370ac785713fb58c366df2efcc55152d612fc2b",
"bytes": 9998,
"seed_rng_line_evidence": [
{
"line": 182,
"text": " coeff = dict(gd=.98, c0=-.00052, cg=.005, cv=.99, threshold=-.045, reset=-.052, seed_volt=-.044)"
}
]
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/test_calibration.py",
"sha256": "307a2848bf6d05aaa8105bb3558be2a74425894f96a4a2bd20af8b82cb8b6e25",
"bytes": 14734,
"seed_rng_line_evidence": [
{
"line": 28,
"text": " return [dict(name=f\"cal-{mode}-{index:02d}-s{seed}\", mode=mode, odor_index=index,"
},
{
"line": 29,
"text": " odor_id=odor, seed=seed, requests=0)"
},
{
"line": 30,
"text": " for mode in LEGACY.ORIGINAL_MODES for index, odor in enumerate(odors) for seed in LEGACY.ORIGINAL_SEEDS]"
},
{
"line": 99,
"text": " runs = {(r[\"mode\"], r[\"odor_id\"], r[\"seed\"]): fake_trial(Path(\"synthetic\"), r, 18) for r in rows}"
},
{
"line": 100,
"text": " report = LEGACY.summarize_mode(LEGACY.ORIGINAL_MODES[0], odors, LEGACY.ORIGINAL_SEEDS, runs, CFG[\"gates\"])"
},
{
"line": 221,
"text": " encoder = dict(panel_count=12, cal_count=6, seeds=list(LEGACY.ORIGINAL_SEEDS),"
}
]
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/test_protocol.py",
"sha256": "cbae93c993ac9aa99bcce68ae2131c7f938f8a63e89779f8d3d79e36bf268dee",
"bytes": 10402,
"seed_rng_line_evidence": [
{
"line": 24,
"text": " return [dict(name=f'cal-{mode}-{odor:02d}-s{seed}', mode=mode, odor_id='odor'+str(odor), seed=seed)"
},
{
"line": 26,
"text": " for seed in range(51101, 51106)]"
}
]
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/tests/__init__.py",
"sha256": "bfc0d9e17410c2c52ea1d9a5259c92964f16b118dbae8cecad258697b67e4bdc",
"bytes": 76,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/tests/test_compare_timing.py",
"sha256": "71876b36eef88c198aa8e833af547dbe772001c71b010707b656c165c8b54031",
"bytes": 9226,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/tests/test_timing_queue.py",
"sha256": "4a375444dc732c3538df8aa4b81e43b5418754fcebebb0d02c16faad03d43195",
"bytes": 5825,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/timing_queue.py",
"sha256": "54df6d4818e2a0c26e96fb0aaf5d9b71cd964981706da937562d48af79aca14e",
"bytes": 4443,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/apl-timing-lab-v1/verify_frozen.py",
"sha256": "97d8a240b08baaca935f9c0278978883005dcd6fdbb4ca0d948074ef79fd349c",
"bytes": 2725,
"seed_rng_line_evidence": [
{
"line": 40,
"text": " result = runtime.run(np.flatnonzero(runtime.pops[spec['population']]), spec['rate_hz'], spec['seed'], spec['duration_ms'])"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/calibration.py",
"sha256": "edb9cb2d890471fd07afd88c959ef3540b0cd40af490fac3f26396b5e7f5bf9c",
"bytes": 25414,
"seed_rng_line_evidence": [
{
"line": 27,
"text": "ORIGINAL_SEEDS = (51101, 51102, 51103, 51104, 51105)"
},
{
"line": 96,
"text": "def summarize_mode(mode, odor_ids, seeds, runs, gates):"
},
{
"line": 100,
"text": " arrays = [runs[(mode, odor, seed)][\"kc_counts\"] for seed in seeds]"
},
{
"line": 107,
"text": " for seed in seeds:"
},
{
"line": 108,
"text": " metric = pair_metrics(runs[(mode, first_odor, seed)][\"kc_counts\"],"
},
{
"line": 109,
"text": " runs[(mode, second_odor, seed)][\"kc_counts\"])"
},
{
"line": 117,
"text": " arrays = [runs[(mode, odor, seed)][\"kc_counts\"] for seed in seeds]"
},
{
"line": 122,
"text": " \"between_same_seed_mean\": cross_mean, \"within_this_pair_mean\": within_mean,"
},
{
"line": 125,
"text": " selected = [runs[(mode, odor, seed)] for odor in odor_ids for seed in seeds]"
},
{
"line": 140,
"text": " \"pairwise_baseline\": \"Each pair's two odors: 10+10 within-seed distances; never pooled across all odors\","
},
{
"line": 243,
"text": " \"odor_name\": spec[\"odor_name\"], \"seed\": spec[\"seed\"], \"split\": \"calibration\","
},
{
"line": 270,
"text": " tuple(cfg[\"calibration\"][\"seeds\"]) == ORIGINAL_SEEDS and"
},
{
"line": 273,
"text": " cfg[\"calibration\"][\"repeat_first_seed_each_odor\"] is True, \"Frozen CAL design changed\")"
},
{
"line": 280,
"text": " expected = {(mode, index, seed) for mode in ORIGINAL_MODES for index in range(6) for seed in ORIGINAL_SEEDS}"
},
{
"line": 281,
"text": " require(len(records) == 60 and {(row[\"mode\"], row[\"odor_index\"], row[\"seed\"]) for row in records} == expected,"
},
{
"line": 282,
"text": " \"CAL inventory must cover exactly 2 modes x 6 odors x 5 seeds\")"
},
{
"line": 286,
"text": " require(len(ids) == 1, \"Odor identity changes across seeds/modes\")"
},
{
"line": 290,
"text": " require(row[\"name\"] == f\"cal-{row['mode']}-{row['odor_index']:02d}-s{row['seed']}\", \"Unexpected CAL case name\")"
},
{
"line": 311,
"text": " encoder[\"seeds\"] == list(ORIGINAL_SEEDS) and encoder[\"requested_before_any_dynamics\"] is True and"
},
{
"line": 336,
"text": " all(spec[key] == row[key] for key in (\"mode\", \"odor_id\", \"odor_index\", \"seed\")),"
},
{
"line": 340,
"text": " runs[(row[\"mode\"], row[\"odor_id\"], row[\"seed\"])] = {key: first[key] for key in"
},
{
"line": 348,
"text": " if row[\"seed\"] == ORIGINAL_SEEDS[0]:"
},
{
"line": 364,
"text": " modes[mode] = summarize_mode(mode, odors, ORIGINAL_SEEDS, runs, CFG[\"gates\"])"
},
{
"line": 378,
"text": " \"checked_repeats\": len(repeats), \"seeds\": list(ORIGINAL_SEEDS), \"duration_ms\": 100.0,"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/compare.py",
"sha256": "6e1616b086f667fa4e4b5087f500f8080281977ca9922e7b055acfddd2003bd5",
"bytes": 33632,
"seed_rng_line_evidence": [
{
"line": 26,
"text": "COEFFICIENTS = ('gd', 'c0', 'cg', 'cv', 'threshold', 'reset', 'seed_volt')"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/config.json",
"sha256": "2aa6b4d777ff698018e1a6eaf571215208b8c874a4c848a6d632e2bd778c90e3",
"bytes": 1845,
"seed_rng_line_evidence": [
{
"line": 15,
"text": " \"calibration\": {\"split\": \"calibration\", \"odor_count\": 6, \"modes\": [\"physiological\", \"matched_energy\"], \"seeds\": [51101,51102,51103,51104,51105], \"duration_ms\": 100.0, \"median_scope\": \"original frozen 12-odor panel\", \"full_oracle_probes\": [\"cal-physiological-00-s51101\", \"cal-matched_energy-00-s51101\"], \"repeat_first_seed_each_odor\": true},"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/context.py",
"sha256": "b8b1231d958a3ec935ec1d5a36e08cbd9bcd1127f4d4bb243a9051884a0e7a16",
"bytes": 2038,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/finalize.py",
"sha256": "b6e3c67e668a971b544df32d76435ab5e537061b9fc2e8d9dedb5d2a726f5782",
"bytes": 6747,
"seed_rng_line_evidence": [
{
"line": 72,
"text": " next_gate='Independent seeds/novel-odor representation, then credit specificity; no learning promotion from CAL' if numerical and representation else"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/gpu_apl.py",
"sha256": "69f5d893f47dafff4ef7c3186c3df0b9014666b32c8f4e943b57016bec92b18a",
"bytes": 21206,
"seed_rng_line_evidence": [
{
"line": 26,
"text": "COEFFICIENT_NAMES = (\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\")"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/legacy_apl_worker.py",
"sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291",
"bytes": 8158,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/oracle_apl.py",
"sha256": "9b7f3e0b59f2117b94b41a6f542f37e6f7950138d324c905e8e0ac6cb7d17151",
"bytes": 46017,
"seed_rng_line_evidence": [
{
"line": 652,
"text": " coefficient_keys = (\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\")"
},
{
"line": 690,
"text": " keys = (\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\")"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/prepare.py",
"sha256": "197ccc6aae70566c4db4d4de17fa032e853b9bd9a04262905d6c71e12a930b6f",
"bytes": 13633,
"seed_rng_line_evidence": [
{
"line": 125,
"text": " assert oc['odor_encoder']['evaluation_seeds']==CFG['calibration']['seeds']"
},
{
"line": 133,
"text": " for seed in CFG['calibration']['seeds']:"
},
{
"line": 134,
"text": " gen=torch.Generator(device='cuda');gen.manual_seed(seed)"
},
{
"line": 139,
"text": " forced=(torch.rand((steps,len(targets)),generator=gen,device='cuda')<probs[None,:]).cpu().numpy()"
},
{
"line": 141,
"text": " name=f'cal-{mode}-{oi:02d}-s{seed}'"
},
{
"line": 143,
"text": " mode=mode,seed=seed,odor_index=oi,expected_external_events=float(rates.sum())*steps*.1/1000.,target_count=len(targets),rate_sum_hz=float(rates.sum())))"
},
{
"line": 145,
"text": " calrecords.append(dict(name=name,mode=mode,odor_index=oi,odor_id=str(odor.odor_id),seed=seed,requests=len(req),rates_sum=float(rates.sum())))"
},
{
"line": 148,
"text": " target_rng_column_order='Original pn_drive, no sorting before torch.rand',seeds=CFG['calibration']['seeds'],"
},
{
"line": 150,
"text": " generation='Original rates*.1/1000 float32 GPU Bernoulli, independent per-case original seed'))"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/run_case.py",
"sha256": "afbe6d5a37c29bda66d38c470093584130d1852e67881c681662aec3c76b33c7",
"bytes": 2116,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/run_suite.py",
"sha256": "84c681030b0032119b5392d0c3f3c136a9c6b0d133eb548c40791a10bbbb8b63",
"bytes": 3700,
"seed_rng_line_evidence": [
{
"line": 56,
"text": " if rec['seed']==CFG['calibration']['seeds'][0]:child('gpu',rec['name'],1)"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/test_calibration.py",
"sha256": "bab3136eecbc5bb09a985d715844ee845cb72b07930ce376bfb39be33bd1845a",
"bytes": 8127,
"seed_rng_line_evidence": [
{
"line": 7,
"text": "from calibration import (ORIGINAL_MODES, ORIGINAL_SEEDS, ORIGINAL_CAL_IDS,"
},
{
"line": 22,
"text": " for seed in ORIGINAL_SEEDS:"
},
{
"line": 26,
"text": " result[(mode, str(odor), seed)] = {\"kc_counts\": counts,"
},
{
"line": 33,
"text": " summary = summarize_mode(ORIGINAL_MODES[0], list(map(str, range(6))), ORIGINAL_SEEDS, synthetic_runs(), GATES)"
},
{
"line": 42,
"text": " summary = summarize_mode(ORIGINAL_MODES[0], list(map(str, range(6))), ORIGINAL_SEEDS, synthetic_runs(True), GATES)"
},
{
"line": 51,
"text": " baseline = summarize_mode(ORIGINAL_MODES[0], list(map(str, range(6))), ORIGINAL_SEEDS, synthetic_runs(), GATES)"
},
{
"line": 68,
"text": " for index, seed in enumerate(ORIGINAL_SEEDS):"
},
{
"line": 70,
"text": " runs[(mode, str(odor), seed)][\"kc_counts\"] = counts"
},
{
"line": 71,
"text": " summary = summarize_mode(mode, list(map(str, range(6))), ORIGINAL_SEEDS, runs, GATES)"
},
{
"line": 74,
"text": " self.assertEqual(first_pair[\"between_same_seed_mean\"], 100.)"
},
{
"line": 129,
"text": " records = [{\"name\": f\"cal-{mode}-{index:02d}-s{seed}\", \"mode\": mode, \"odor_index\": index,"
},
{
"line": 130,
"text": " \"odor_id\": odors[index], \"seed\": seed} for mode in ORIGINAL_MODES"
},
{
"line": 131,
"text": " for index in range(6) for seed in ORIGINAL_SEEDS]"
},
{
"line": 132,
"text": " cfg = {\"calibration\": {\"modes\": list(ORIGINAL_MODES), \"seeds\": list(ORIGINAL_SEEDS),"
},
{
"line": 133,
"text": " \"duration_ms\": 100., \"odor_count\": 6, \"split\": \"calibration\", \"repeat_first_seed_each_odor\": True}, \"gates\": GATES}"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/test_protocol.py",
"sha256": "1a233f47943e01bbd845ac1dacdb1eec3f88c270c65a5f75bce1cf679b5de097",
"bytes": 11764,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/validate_components.py",
"sha256": "5cfc9a4505da6b8fb4e7d2617cdaedaa8475ed46a11ec9bdf0d4d25b1d22c2d3",
"bytes": 24824,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-apl-lab-v1/verify_frozen.py",
"sha256": "0d0e72b2f1348f6648124c8c34f75094f76fc5e1a281fbcca130a5fad2f35293",
"bytes": 2160,
"seed_rng_line_evidence": [
{
"line": 27,
"text": " result=rt.run(np.flatnonzero(rt.pops[spec['population']]),spec['rate_hz'],spec['seed'],spec['duration_ms'])"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/calibration.py",
"sha256": "edb9cb2d890471fd07afd88c959ef3540b0cd40af490fac3f26396b5e7f5bf9c",
"bytes": 25414,
"seed_rng_line_evidence": [
{
"line": 27,
"text": "ORIGINAL_SEEDS = (51101, 51102, 51103, 51104, 51105)"
},
{
"line": 96,
"text": "def summarize_mode(mode, odor_ids, seeds, runs, gates):"
},
{
"line": 100,
"text": " arrays = [runs[(mode, odor, seed)][\"kc_counts\"] for seed in seeds]"
},
{
"line": 107,
"text": " for seed in seeds:"
},
{
"line": 108,
"text": " metric = pair_metrics(runs[(mode, first_odor, seed)][\"kc_counts\"],"
},
{
"line": 109,
"text": " runs[(mode, second_odor, seed)][\"kc_counts\"])"
},
{
"line": 117,
"text": " arrays = [runs[(mode, odor, seed)][\"kc_counts\"] for seed in seeds]"
},
{
"line": 122,
"text": " \"between_same_seed_mean\": cross_mean, \"within_this_pair_mean\": within_mean,"
},
{
"line": 125,
"text": " selected = [runs[(mode, odor, seed)] for odor in odor_ids for seed in seeds]"
},
{
"line": 140,
"text": " \"pairwise_baseline\": \"Each pair's two odors: 10+10 within-seed distances; never pooled across all odors\","
},
{
"line": 243,
"text": " \"odor_name\": spec[\"odor_name\"], \"seed\": spec[\"seed\"], \"split\": \"calibration\","
},
{
"line": 270,
"text": " tuple(cfg[\"calibration\"][\"seeds\"]) == ORIGINAL_SEEDS and"
},
{
"line": 273,
"text": " cfg[\"calibration\"][\"repeat_first_seed_each_odor\"] is True, \"Frozen CAL design changed\")"
},
{
"line": 280,
"text": " expected = {(mode, index, seed) for mode in ORIGINAL_MODES for index in range(6) for seed in ORIGINAL_SEEDS}"
},
{
"line": 281,
"text": " require(len(records) == 60 and {(row[\"mode\"], row[\"odor_index\"], row[\"seed\"]) for row in records} == expected,"
},
{
"line": 282,
"text": " \"CAL inventory must cover exactly 2 modes x 6 odors x 5 seeds\")"
},
{
"line": 286,
"text": " require(len(ids) == 1, \"Odor identity changes across seeds/modes\")"
},
{
"line": 290,
"text": " require(row[\"name\"] == f\"cal-{row['mode']}-{row['odor_index']:02d}-s{row['seed']}\", \"Unexpected CAL case name\")"
},
{
"line": 311,
"text": " encoder[\"seeds\"] == list(ORIGINAL_SEEDS) and encoder[\"requested_before_any_dynamics\"] is True and"
},
{
"line": 336,
"text": " all(spec[key] == row[key] for key in (\"mode\", \"odor_id\", \"odor_index\", \"seed\")),"
},
{
"line": 340,
"text": " runs[(row[\"mode\"], row[\"odor_id\"], row[\"seed\"])] = {key: first[key] for key in"
},
{
"line": 348,
"text": " if row[\"seed\"] == ORIGINAL_SEEDS[0]:"
},
{
"line": 364,
"text": " modes[mode] = summarize_mode(mode, odors, ORIGINAL_SEEDS, runs, CFG[\"gates\"])"
},
{
"line": 378,
"text": " \"checked_repeats\": len(repeats), \"seeds\": list(ORIGINAL_SEEDS), \"duration_ms\": 100.0,"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/compare.py",
"sha256": "6e1616b086f667fa4e4b5087f500f8080281977ca9922e7b055acfddd2003bd5",
"bytes": 33632,
"seed_rng_line_evidence": [
{
"line": 26,
"text": "COEFFICIENTS = ('gd', 'c0', 'cg', 'cv', 'threshold', 'reset', 'seed_volt')"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/config.json",
"sha256": "a8bafb9deec49d918976f2836d977ef9f13c26a6fb7f70e015879e29cf986c9a",
"bytes": 1994,
"seed_rng_line_evidence": [
{
"line": 16,
"text": " \"calibration\": {\"split\": \"calibration\", \"odor_count\": 6, \"modes\": [\"physiological\", \"matched_energy\"], \"seeds\": [51101,51102,51103,51104,51105], \"duration_ms\": 100.0, \"median_scope\": \"original frozen 12-odor panel\", \"full_oracle_probes\": [\"cal-physiological-00-s51101\", \"cal-matched_energy-00-s51101\"], \"repeat_first_seed_each_odor\": true},"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/context.py",
"sha256": "274eb13ccf38dbbc5c7f14d9d642130eb0f69072d91a090f208c30c27f87dd2b",
"bytes": 2134,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/finalize.py",
"sha256": "b6e3c67e668a971b544df32d76435ab5e537061b9fc2e8d9dedb5d2a726f5782",
"bytes": 6747,
"seed_rng_line_evidence": [
{
"line": 72,
"text": " next_gate='Independent seeds/novel-odor representation, then credit specificity; no learning promotion from CAL' if numerical and representation else"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/gpu_apl.py",
"sha256": "69f5d893f47dafff4ef7c3186c3df0b9014666b32c8f4e943b57016bec92b18a",
"bytes": 21206,
"seed_rng_line_evidence": [
{
"line": 26,
"text": "COEFFICIENT_NAMES = (\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\")"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/legacy_apl_worker.py",
"sha256": "21ceec65fe42581dc55872010e0025d660e80358905f1cf4df1dadf341a3b291",
"bytes": 8158,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/oracle_apl.py",
"sha256": "357a7d3debb5abc34a8714c0233b456547a154d9d5855d28661b4f01019a80ea",
"bytes": 46236,
"seed_rng_line_evidence": [
{
"line": 659,
"text": " coefficient_keys = (\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\")"
},
{
"line": 697,
"text": " keys = (\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\")"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/prepare.py",
"sha256": "e9328567753e144fc315491970f7e02af13725b93f35e4d4b97f7c4c48ce3338",
"bytes": 15297,
"seed_rng_line_evidence": [
{
"line": 135,
"text": " assert oc['odor_encoder']['evaluation_seeds']==CFG['calibration']['seeds']"
},
{
"line": 143,
"text": " for seed in CFG['calibration']['seeds']:"
},
{
"line": 144,
"text": " gen=torch.Generator(device='cuda');gen.manual_seed(seed)"
},
{
"line": 149,
"text": " forced=(torch.rand((steps,len(targets)),generator=gen,device='cuda')<probs[None,:]).cpu().numpy()"
},
{
"line": 151,
"text": " name=f'cal-{mode}-{oi:02d}-s{seed}'"
},
{
"line": 153,
"text": " mode=mode,seed=seed,odor_index=oi,expected_external_events=float(rates.sum())*steps*.1/1000.,target_count=len(targets),rate_sum_hz=float(rates.sum())))"
},
{
"line": 155,
"text": " calrecords.append(dict(name=name,mode=mode,odor_index=oi,odor_id=str(odor.odor_id),seed=seed,requests=len(req),rates_sum=float(rates.sum())))"
},
{
"line": 158,
"text": " target_rng_column_order='Original pn_drive, no sorting before torch.rand',seeds=CFG['calibration']['seeds'],"
},
{
"line": 160,
"text": " generation='Original rates*.1/1000 float32 GPU Bernoulli, independent per-case original seed'))"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/run_case.py",
"sha256": "afbe6d5a37c29bda66d38c470093584130d1852e67881c681662aec3c76b33c7",
"bytes": 2116,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/run_suite.py",
"sha256": "84c681030b0032119b5392d0c3f3c136a9c6b0d133eb548c40791a10bbbb8b63",
"bytes": 3700,
"seed_rng_line_evidence": [
{
"line": 56,
"text": " if rec['seed']==CFG['calibration']['seeds'][0]:child('gpu',rec['name'],1)"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/test_calibration.py",
"sha256": "bab3136eecbc5bb09a985d715844ee845cb72b07930ce376bfb39be33bd1845a",
"bytes": 8127,
"seed_rng_line_evidence": [
{
"line": 7,
"text": "from calibration import (ORIGINAL_MODES, ORIGINAL_SEEDS, ORIGINAL_CAL_IDS,"
},
{
"line": 22,
"text": " for seed in ORIGINAL_SEEDS:"
},
{
"line": 26,
"text": " result[(mode, str(odor), seed)] = {\"kc_counts\": counts,"
},
{
"line": 33,
"text": " summary = summarize_mode(ORIGINAL_MODES[0], list(map(str, range(6))), ORIGINAL_SEEDS, synthetic_runs(), GATES)"
},
{
"line": 42,
"text": " summary = summarize_mode(ORIGINAL_MODES[0], list(map(str, range(6))), ORIGINAL_SEEDS, synthetic_runs(True), GATES)"
},
{
"line": 51,
"text": " baseline = summarize_mode(ORIGINAL_MODES[0], list(map(str, range(6))), ORIGINAL_SEEDS, synthetic_runs(), GATES)"
},
{
"line": 68,
"text": " for index, seed in enumerate(ORIGINAL_SEEDS):"
},
{
"line": 70,
"text": " runs[(mode, str(odor), seed)][\"kc_counts\"] = counts"
},
{
"line": 71,
"text": " summary = summarize_mode(mode, list(map(str, range(6))), ORIGINAL_SEEDS, runs, GATES)"
},
{
"line": 74,
"text": " self.assertEqual(first_pair[\"between_same_seed_mean\"], 100.)"
},
{
"line": 129,
"text": " records = [{\"name\": f\"cal-{mode}-{index:02d}-s{seed}\", \"mode\": mode, \"odor_index\": index,"
},
{
"line": 130,
"text": " \"odor_id\": odors[index], \"seed\": seed} for mode in ORIGINAL_MODES"
},
{
"line": 131,
"text": " for index in range(6) for seed in ORIGINAL_SEEDS]"
},
{
"line": 132,
"text": " cfg = {\"calibration\": {\"modes\": list(ORIGINAL_MODES), \"seeds\": list(ORIGINAL_SEEDS),"
},
{
"line": 133,
"text": " \"duration_ms\": 100., \"odor_count\": 6, \"split\": \"calibration\", \"repeat_first_seed_each_odor\": True}, \"gates\": GATES}"
}
]
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/test_protocol.py",
"sha256": "1a233f47943e01bbd845ac1dacdb1eec3f88c270c65a5f75bce1cf679b5de097",
"bytes": 11764,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/test_python_entry.py",
"sha256": "0ed665f686e3499f8543e48f1712ccb4523946971cb7a0e096cf4f6d10d3bcba",
"bytes": 1760,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/validate_components.py",
"sha256": "5cfc9a4505da6b8fb4e7d2617cdaedaa8475ed46a11ec9bdf0d4d25b1d22c2d3",
"bytes": 24824,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-apl-lab-v2/verify_frozen.py",
"sha256": "0d0e72b2f1348f6648124c8c34f75094f76fc5e1a281fbcca130a5fad2f35293",
"bytes": 2160,
"seed_rng_line_evidence": [
{
"line": 27,
"text": " result=rt.run(np.flatnonzero(rt.pops[spec['population']]),spec['rate_hz'],spec['seed'],spec['duration_ms'])"
}
]
},
{
"path": "23-ai-gpu/source/author-fullgraph-lab-v1/compare_trial.py",
"sha256": "2c81cb37598cce2ae0688046fd944bec34ea57824944379bfbf7e778283ab4ad",
"bytes": 5322,
"seed_rng_line_evidence": [
{
"line": 59,
"text": " checks['request_count']=len(cr)==len(spec['seed_nodes'])"
},
{
"line": 62,
"text": " for node in sorted(spec['seed_nodes'])]"
}
]
},
{
"path": "23-ai-gpu/source/author-fullgraph-lab-v1/config.json",
"sha256": "9acd8177d94daf92df3435d2df7c854a0b86e8dee65d42f3ec25d084c9fba8f0",
"bytes": 1183,
"seed_rng_line_evidence": [
{
"line": 12,
"text": " {\"name\": \"no_input\", \"steps\": 100, \"seed_nodes\": []},"
},
{
"line": 13,
"text": " {\"name\": \"single\", \"steps\": 500, \"seed_nodes\": [1381]},"
},
{
"line": 14,
"text": " {\"name\": \"five\", \"steps\": 500, \"seed_nodes\": [1197, 1381, 1511, 82312, 133666]}"
}
]
},
{
"path": "23-ai-gpu/source/author-fullgraph-lab-v1/context.py",
"sha256": "fe221d4470f14a094bc2b02b9763d542c091a2a069c951c5f833a0639c39659b",
"bytes": 2171,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-fullgraph-lab-v1/finalize.py",
"sha256": "81885c07c053444cb0b03634b4ddc09b12bef2f237828db2dcf203653ac4b704",
"bytes": 4373,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-fullgraph-lab-v1/gpu_full.py",
"sha256": "895ee595d63ee8650b6aad662f831117af78cb6216437c001c8c7ba69a7b92e7",
"bytes": 23311,
"seed_rng_line_evidence": [
{
"line": 26,
"text": "COEFFICIENT_NAMES = (\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\")"
},
{
"line": 113,
"text": " for name in (\"name\", \"steps\", \"dt_ms\", \"duration_ms\", \"seed_nodes\", \"model\", \"graph_dir\"):"
},
{
"line": 116,
"text": " seeds = spec[\"seed_nodes\"]"
},
{
"line": 117,
"text": " require(type(steps) is int and isinstance(seeds, list) and"
},
{
"line": 118,
"text": " all(type(node) is int for node in seeds) and len(seeds) == len(set(seeds)), \"Invalid case steps/seeds\")"
},
{
"line": 119,
"text": " seeds = tuple(sorted(seeds))"
},
{
"line": 120,
"text": " require((steps, seeds) in ALLOWED_CASES, \"Case not one of the three fixed full-graph trials\")"
},
{
"line": 175,
"text": " require(len(request_rows) == len(seeds) and"
},
{
"line": 176,
"text": " [row[\"neuron_idx\"] for row in request_rows] == list(seeds) and"
},
{
"line": 179,
"text": " return spec, graph, histories, weights, final, queues, coefficients, seeds, request_rows"
},
{
"line": 190,
"text": " spec, graph, histories, weights, oracle_final, oracle_queues, coeff, seeds, oracle_requests = load_inputs(case_dir, oracle_dir)"
},
{
"line": 219,
"text": " if seeds:"
},
{
"line": 220,
"text": " requests[torch.tensor(seeds, device=device, dtype=torch.int64)] = 1"
},
{
"line": 335,
"text": " requested_total == len(seeds) and accepted_request_total == len(seeds))"
}
]
},
{
"path": "23-ai-gpu/source/author-fullgraph-lab-v1/oracle_full.py",
"sha256": "d72003d98f0f1624b12126eff50bd361c43d888f4a64d90ba9247a58976f1961",
"bytes": 32146,
"seed_rng_line_evidence": [
{
"line": 239,
"text": " seeds = tuple(int(node) for node in spec[\"seed_nodes\"])"
},
{
"line": 242,
"text": " _check(spec[\"name\"] in fixed and (steps, seeds) == fixed[spec[\"name\"]], \"Case is not one of the three fixed trials\")"
},
{
"line": 336,
"text": " seed_local = np.asarray(seeds, dtype=np.int32)"
},
{
"line": 337,
"text": " permitted_seed = np.empty(0, dtype=np.int32)"
},
{
"line": 360,
"text": " nonlocal permitted_seed"
},
{
"line": 364,
"text": " if tick == 0 and len(seed_local):"
},
{
"line": 365,
"text": " available = available_raw[seed_local].copy()"
},
{
"line": 366,
"text": " permitted_seed = seed_local[available]"
},
{
"line": 367,
"text": " request_count[tick] = len(seed_local)"
},
{
"line": 368,
"text": " applicable_count[tick] = len(permitted_seed)"
},
{
"line": 369,
"text": " rejected_count[tick] = len(seed_local) - len(permitted_seed)"
},
{
"line": 370,
"text": " for node, ok in zip(seed_local, available):"
},
{
"line": 373,
"text": " neu.v[permitted_seed] = parameters[\"v_th\"] + 1 * b2.mV"
},
{
"line": 403,
"text": " _check(np.isin(permitted_seed, active).all(), \"An applicable initial request failed to create a real spike\")"
}
]
},
{
"path": "23-ai-gpu/source/author-fullgraph-lab-v1/prepare.py",
"sha256": "780f26c3c829135a6f67ff03007925649ef777dc15010334830ff1f7fa109519",
"bytes": 5259,
"seed_rng_line_evidence": [
{
"line": 64,
"text": " meta.iloc[trial['seed_nodes']][['neuron_idx','body_id','annotation_type','neurotransmitter']].to_csv(case/'seed-identities.csv',index=False)"
}
]
},
{
"path": "23-ai-gpu/source/author-fullgraph-lab-v1/run_trial.py",
"sha256": "afb8a0e8513726bd1340db56c2cff8f3615fedd0857dc611420d29e1b858c2c3",
"bytes": 2027,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-fullgraph-lab-v1/test_protocol.py",
"sha256": "d4bc6905ea6ce4ea04aeeabfc2f99bc111114fdfd0bc54169a40edf4f95c6033",
"bytes": 2019,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-fullgraph-lab-v1/verify_frozen.py",
"sha256": "a2a19b52c41c8c752692ff524d6f21016c6f2789c5fcabac9c78c8cadd59826e",
"bytes": 2151,
"seed_rng_line_evidence": [
{
"line": 26,
"text": " r=rt.run(np.flatnonzero(rt.pops[spec['population']]),spec['rate_hz'],spec['seed'],spec['duration_ms'])"
}
]
},
{
"path": "23-ai-gpu/source/author-runtime-lab-v1/config.json",
"sha256": "356b6161576f6f6d79745af1927bb43b12a7c3d50922bf9e1bd29e473fcf2106",
"bytes": 805,
"seed_rng_line_evidence": [
{
"line": 5,
"text": " \"seed\": 410301,"
}
]
},
{
"path": "23-ai-gpu/source/author-runtime-lab-v1/context.py",
"sha256": "9eea9a592241cab7f4303850e969def7887eee3f4ec18421103605cafd258cf8",
"bytes": 1707,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-runtime-lab-v1/finalize.py",
"sha256": "cb75d512ddf9a5ff344f3ac82eefb112406593bf7b414dfac79d7c5d730371ee",
"bytes": 3814,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-runtime-lab-v1/gpu_backend.py",
"sha256": "03758d07572f4b44d272059d9308fac534268ad201b57bdd77d69fee95e67278",
"bytes": 16277,
"seed_rng_line_evidence": [
{
"line": 21,
"text": "COEFFICIENT_NAMES = (\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\")"
}
]
},
{
"path": "23-ai-gpu/source/author-runtime-lab-v1/oracle_stress.py",
"sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409",
"bytes": 29235,
"seed_rng_line_evidence": [
{
"line": 187,
"text": " result[\"seed_volt\"] = float((parameters[\"v_th\"] + 1 * b2.mV) / b2.volt)"
},
{
"line": 188,
"text": " provenance[\"seed_volt\"] = {\"source\": \"common request interface: unchanged v_th + 1 mV\"}"
},
{
"line": 481,
"text": " coefficient_keys = [\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\"]"
}
]
},
{
"path": "23-ai-gpu/source/author-runtime-lab-v1/prepare.py",
"sha256": "2519eaa2c50b2d91fd0c4f623aeabd6f5d212c5b537951d0a66bc81c569db78c",
"bytes": 8828,
"seed_rng_line_evidence": [
{
"line": 47,
"text": " rng=np.random.default_rng(CFG['seed']);n=128;t=400;src=np.repeat(np.arange(n),8);dst=rng.integers(0,n,len(src),dtype=np.int32)"
},
{
"line": 51,
"text": " put('signed_random',np.arange(-n,0),src,dst,raw,t,'Fixed seed128-cell signed random fixture; forcing stops at20ms; signs assigned per synthetic source.',requests=req,external=ext,g=g)"
}
]
},
{
"path": "23-ai-gpu/source/author-runtime-lab-v1/run_suite.py",
"sha256": "92733a48921f95c3c0935bf16a704634b89b6540352f31cc1169ec6d3211b57a",
"bytes": 5318,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-runtime-lab-v1/verify_frozen.py",
"sha256": "720d04b379b6f1f64cae28a2203444a6d4c4e72ecb121cc69e93c2740fae69ab",
"bytes": 2157,
"seed_rng_line_evidence": [
{
"line": 26,
"text": " r=rt.run(np.flatnonzero(rt.pops[spec['population']]),spec['rate_hz'],spec['seed'],spec['duration_ms'])"
}
]
},
{
"path": "23-ai-gpu/source/author-semantics-lab-v1/config.json",
"sha256": "a69e8da5332d8031cbe1663abff25e61ee04e75f5490e8fa44c48690d3d0e219",
"bytes": 1332,
"seed_rng_line_evidence": [
{
"line": 12,
"text": " \"seed_margin_mv\": 1.0,"
},
{
"line": 25,
"text": " \"seed_node\": 1381,"
}
]
},
{
"path": "23-ai-gpu/source/author-semantics-lab-v1/context.py",
"sha256": "561d405ee4a2d3bbc6982c89191523f348d6fae0630dea9cb67ddf03acc6fc7b",
"bytes": 1057,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-semantics-lab-v1/finalize.py",
"sha256": "97a393b95d100dc5d95b624e4cd0d15511307b8c70e0ebd788b773c143ebfed6",
"bytes": 25910,
"seed_rng_line_evidence": [
{
"line": 55,
"text": "def load_trace(directory, expected_nodes, steps, cfg, seed_node, natural_events=False):"
},
{
"line": 79,
"text": " if seed_node is None and not natural_events:"
},
{
"line": 81,
"text": " elif seed_node is not None:"
},
{
"line": 82,
"text": " require(np.array_equal(nodes[ticks == 0], [seed_node]),"
},
{
"line": 83,
"text": " \"Declared initial seed event missing or duplicated\")"
},
{
"line": 235,
"text": " record.get(\"common_seed_experiment\") is False, \"Poisson audit scope differs\")"
},
{
"line": 250,
"text": " steps, cfg, None if motif == \"no_input\" else cfg[\"seed_node\"])"
},
{
"line": 265,
"text": " \"interpretation\": \"Artifact/finite/seed checks only; activity is descriptive.\"}"
},
{
"line": 277,
"text": " seeded = fixture in (\"refractory_boundary\", \"above_threshold\")"
},
{
"line": 280,
"text": " seed = -2 if fixture == \"delay_18\" else (-1 if seeded else None)"
},
{
"line": 281,
"text": " actual, _ = load_trace(directory / \"brian-output\", nodes, 200, unit_cfg, seed,"
},
{
"line": 283,
"text": " independent, _ = load_trace(directory / \"independent-output\", nodes, 200, unit_cfg, seed,"
},
{
"line": 304,
"text": " seed = None if motif == \"no_input\" else cfg[\"seed_node\"]"
},
{
"line": 305,
"text": " actual, per_node = load_trace(directory / \"brian-output\", nodes, steps, cfg, seed)"
},
{
"line": 306,
"text": " independent, _ = load_trace(directory / \"independent-output\", nodes, steps, cfg, seed)"
},
{
"line": 314,
"text": " \"brian-output\", nodes, steps, cfg, seed)"
},
{
"line": 395,
"text": " \"小回路缺少外部整脑输入与graded APL，不能证明整脑因果必要性、生物真实性或信息保持。初始诊断seed也不是作者的感官Poisson接口。\","
},
{
"line": 397,
"text": " \"后续整脑嗅觉比较仍须核查固定输入、实际送达事件、持续活动和信息保持；可用学习仍需至少5个训练seed、独立评估、MBON/downstream的A对中性B特异性及acquisition/reversal/retention/delayed-reward对照。\","
}
]
},
{
"path": "23-ai-gpu/source/author-semantics-lab-v1/interface_audit.py",
"sha256": "1e2b31545f1206fe1c1f4084b4a9ac0f21c74990c52e8fd156e809725fc5fb95",
"bytes": 8415,
"seed_rng_line_evidence": [
{
"line": 3,
"text": "This is separate from the common single-seed diagnostic. Zero simulation time"
},
{
"line": 135,
"text": " \"random_realization_tested\": False, \"common_seed_experiment\": False,"
},
{
"line": 136,
"text": " \"interpretation\": \"Original poi adds 68.75 mV to voltage at 150 Hz with N=1 in the synapses slot and sets its target rfc to zero. This interface is not the common initial seed experiment.\","
}
]
},
{
"path": "23-ai-gpu/source/author-semantics-lab-v1/math_lab.py",
"sha256": "9663da41d5f623e1d7750e3cad3df880148079968f115bf583b58799fc5fb31f",
"bytes": 4533,
"seed_rng_line_evidence": [
{
"line": 10,
"text": "def component(nodes,edges,weights,config,policy,seed_node):"
},
{
"line": 20,
"text": " if t==0 and seed_node is not None:"
},
{
"line": 21,
"text": " k=ix[seed_node];assert not fire[k] and ref[k]==0"
},
{
"line": 34,
"text": "def author_numpy(nodes,edges,config,precision,seed_node,external_arrivals=None):"
},
{
"line": 56,
"text": " if t==0 and seed_node is not None:v[ix[seed_node]]=f(threshold+config['seed_margin_mv']*.001)"
}
]
},
{
"path": "23-ai-gpu/source/author-semantics-lab-v1/oracle_runner.py",
"sha256": "2f4b073261965cf18a94697caf725c40b2e8db790c2871862201337f40c399e5",
"bytes": 18548,
"seed_rng_line_evidence": [
{
"line": 109,
"text": " seed_node: int | None, external_arrivals=None) -> dict:"
},
{
"line": 115,
"text": " Python assignment to g. ``seed_margin_mv`` may be zero for a strict-threshold"
},
{
"line": 140,
"text": " if seed_node is not None and int(seed_node) not in lookup:"
},
{
"line": 141,
"text": " raise ValueError(\"seed_node must occur in nodes\")"
},
{
"line": 150,
"text": " margin_mv = float(config[\"seed_margin_mv\"])"
},
{
"line": 152,
"text": " raise ValueError(\"seed_margin_mv must be finite and nonnegative\")"
},
{
"line": 230,
"text": " seed_state = {\"requested_global_node\": None if seed_node is None else int(seed_node),"
},
{
"line": 232,
"text": " if seed_node is not None:"
},
{
"line": 233,
"text": " seed_local = lookup[int(seed_node)]"
},
{
"line": 235,
"text": " def inject_seed():"
},
{
"line": 236,
"text": " if seed_state[\"injections\"] == 0:"
},
{
"line": 238,
"text": " raise AssertionError(\"Seed injection must occur at tick zero\")"
},
{
"line": 239,
"text": " neu.v[seed_local] = parameters[\"v_th\"] + margin_mv * b2.mV"
},
{
"line": 240,
"text": " applied = float(neu.v[seed_local] / b2.mV)"
},
{
"line": 242,
"text": " raise ValueError(\"The positive seed margin rounded away in the selected dtype\")"
},
{
"line": 243,
"text": " seed_state[\"applied_mv\"] = applied"
},
{
"line": 244,
"text": " seed_state[\"injections\"] = 1"
},
{
"line": 246,
"text": " objects.append(b2.NetworkOperation(inject_seed, clock=neu.clock,"
},
{
"line": 248,
"text": " name=\"oracle_seed_injection\"))"
},
{
"line": 284,
"text": " \"seed\": seed_state.copy(), \"external_arrivals\": external_metadata,"
},
{
"line": 325,
"text": " if seed_node is not None:"
},
{
"line": 326,
"text": " seed_state[\"spiked_at_zero\"] = bool(np.any((ticks == 0) & (event_nodes == seed_node)))"
},
{
"line": 327,
"text": " if seed_state[\"injections\"] != 1 or (margin_mv > 0 and not seed_state[\"spiked_at_zero\"]):"
},
{
"line": 328,
"text": " raise AssertionError(\"The common initial seed event was not produced exactly once\")"
},
{
"line": 329,
"text": " _json(io_dir / \"seed.json\", seed_state)"
}
]
},
{
"path": "23-ai-gpu/source/author-semantics-lab-v1/prepare.py",
"sha256": "49caa9553938f06539ecf282868491f8e1319d8f24220f89ac9e232db2d82e51",
"bytes": 3453,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/author-semantics-lab-v1/run_lab.py",
"sha256": "8746e1971c5a40a738e10f8f574a243c29cefa5a8b9f61a2846557ceffd1289a",
"bytes": 10795,
"seed_rng_line_evidence": [
{
"line": 41,
"text": "def oracle_trial(name,nodes,edges,cfg,precision,seed,external=None):"
},
{
"line": 43,
"text": " result=run_brian(EXP/'external-resources/model.py',io,nodes,edges,cfg,precision,seed,external)"
},
{
"line": 44,
"text": " independent=author_numpy(nodes,edges,cfg,precision,seed,external)"
},
{
"line": 68,
"text": " for name,(arr,seed,margin) in cases.items():"
},
{
"line": 69,
"text": " cfg=dict(c,seed_margin_mv=margin)"
},
{
"line": 70,
"text": " r,cmp=oracle_trial('unit-'+name,nodes,inert,cfg,precision,seed,arr)"
},
{
"line": 77,
"text": " elif name=='refractory_boundary':checks={'one_seed':np.array_equal(events,[0]),'reject_through_21':bool(np.all(h[:22,1]==0)),"
},
{
"line": 82,
"text": " else:checks={'single_seed':np.array_equal(events,[0]),'reset_g':bool(h[0,1,0]==0)}"
},
{
"line": 89,
"text": " h=r['history'];checks=dict(one_seed=np.array_equal(r['step'],[0]),"
},
{
"line": 109,
"text": " old=np.load(AC/'motifs'/f'{name}-state.npz');nodes=old['nodes'];seed=None if name=='no_input' else CFG['seed_node']"
},
{
"line": 112,
"text": " r=component(nodes,edges,weights,CFG,policy,seed)"
},
{
"line": 134,
"text": " nodes=np.load(AC/'motifs'/f'{name}-state.npz')['nodes'];seed=None if name=='no_input' else CFG['seed_node'];by_precision={}"
},
{
"line": 136,
"text": " r,cmp=oracle_trial(name,nodes,edges,CFG,precision,seed);by_precision[precision]=r"
},
{
"line": 137,
"text": " repeat=run_brian(EXP/'external-resources/model.py',EXP/'oracle'/(name+'-'+precision+'-repeat'),nodes,edges,CFG,precision,seed)"
}
]
},
{
"path": "23-ai-gpu/source/author-semantics-lab-v1/verify_frozen.py",
"sha256": "fe37a48ce804e6c50f93404bfd3c23a7e54cd49061bbdfb4860315879648c73b",
"bytes": 2613,
"seed_rng_line_evidence": [
{
"line": 36,
"text": " r=rt.run(np.flatnonzero(rt.pops[spec['population']]),spec['rate_hz'],spec['seed'],spec['duration_ms'])"
}
]
},
{
"path": "23-ai-gpu/source/kc-drive-audit-v1/accounting.py",
"sha256": "c4703d36587bd40a2a231d5098b8d9991407ba80f6f2b488ffad068c7a74fde6",
"bytes": 12819,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/kc-drive-audit-v1/context.py",
"sha256": "c7eb06a19d9f1f28ecf71c5596d10aa11bb405d09b1167546da347eb58d8da9a",
"bytes": 2757,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/kc-drive-audit-v1/finalize.py",
"sha256": "ce0d0bfa2679c18573acab673b28d167ed300ff7669486c2e93fdc1a6097fc05",
"bytes": 10619,
"seed_rng_line_evidence": [
{
"line": 13,
"text": " if row['seed'] == 51101:"
},
{
"line": 30,
"text": " assert result['mode'] == spec.get('mode') and result['seed'] == spec.get('seed')"
},
{
"line": 80,
"text": " flat.append(dict(case=row['case'], mode=row['mode'], seed=row['seed'], odor_id=row['odor_id'],"
}
]
},
{
"path": "23-ai-gpu/source/kc-drive-audit-v1/populations.py",
"sha256": "8504b38219bac952b07a67e7dc37a520f0a68238cb0b0dd56f4b4b7948c5233b",
"bytes": 9356,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/kc-drive-audit-v1/prepare.py",
"sha256": "fecf8324b5fca4b65d8f24f3ed80d20bdb1fc5654294958f34d58dd36625c59b",
"bytes": 5160,
"seed_rng_line_evidence": [
{
"line": 61,
"text": " if row['seed'] == 51101:"
}
]
},
{
"path": "23-ai-gpu/source/kc-drive-audit-v1/replay_core.py",
"sha256": "aa3187cb23712c09ab603e27e60424294db92de8086d60a95cf7a629d0521fdc",
"bytes": 14287,
"seed_rng_line_evidence": [
{
"line": 20,
"text": "COEFFICIENT_KEYS = (\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\")"
},
{
"line": 47,
"text": " _require(result[\"seed_volt\"] > result[\"threshold\"], \"Common-request seed voltage must exceed the strict threshold\")"
},
{
"line": 144,
"text": " next_v[request & available] = coeff[\"seed_volt\"]"
}
]
},
{
"path": "23-ai-gpu/source/kc-drive-audit-v1/run_audit.py",
"sha256": "724c4f25341dc0883951ef5abdf9fc7dc72fba146a000b9cf9bca759e9c7623a",
"bytes": 12406,
"seed_rng_line_evidence": [
{
"line": 156,
"text": " mode=spec.get('mode'), odor_id=spec.get('odor_id'), seed=spec.get('seed'),"
}
]
},
{
"path": "23-ai-gpu/source/kc-drive-audit-v1/test_accounting.py",
"sha256": "a10f67c42162f8b07f0d760108d5ebf575f6dddc588752acc4499b43bac3ec94",
"bytes": 13018,
"seed_rng_line_evidence": [
{
"line": 222,
"text": " 'threshold': 1., 'reset': 0., 'seed_volt': 2.}"
}
]
},
{
"path": "23-ai-gpu/source/kc-drive-audit-v1/test_populations.py",
"sha256": "65285a6679e140f9f7cc16cb806e7bddf0f0d288c6a7d3fa2879a7cd745dacc1",
"bytes": 5472,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/kc-drive-audit-v1/test_replay_core.py",
"sha256": "17b94e6c549e60c5adb9eebf0a5dc0679a95670265b3a332a49a2e6610a976c2",
"bytes": 12276,
"seed_rng_line_evidence": [
{
"line": 11,
"text": " result = dict(gd=1., c0=0., cg=0., cv=1., threshold=1., reset=0., seed_volt=2.)"
},
{
"line": 54,
"text": " c = coefficients(gd=.4, cg=.2, cv=.3, c0=.1, threshold=100., seed_volt=101.)"
},
{
"line": 104,
"text": " shadow(steps=20, coeff=coefficients(cg=1., threshold=100., seed_volt=101.),"
}
]
},
{
"path": "23-ai-gpu/source/lif-semantics-audit-v1/audit_semantics.py",
"sha256": "f20724387ca7ed63b9e35fb9ce3d2964da44f85318856b7403b5536a442422b8",
"bytes": 18769,
"seed_rng_line_evidence": [
{
"line": 139,
"text": "def gpu_motif(rt, nodes, edges, weights, p, steps, seed):"
},
{
"line": 151,
"text": " if t == 0 and seed is not None:"
},
{
"line": 152,
"text": " targets = torch.tensor([lookup[seed]], dtype=torch.int64, device=d); force = torch.ones(1,dtype=torch.uint8,device=d)"
},
{
"line": 205,
"text": " w = effective[subset.edge_id]; seed = CFG['seed_node'] if spec.get('force',True) else None"
},
{
"line": 207,
"text": " cpu = cpu_motif(nodes,subset,w,p,steps,seed); gpu = gpu_motif(rt,nodes,subset,w,p,steps,seed)"
},
{
"line": 210,
"text": " if seed is None: assert len(cpu['step']) == 0"
},
{
"line": 220,
"text": " 'external_force_requests':int(seed == node),'frozen_math_unchanged':True,'whole_brain_necessity_proven':False})"
},
{
"line": 231,
"text": " r=rt.run(np.flatnonzero(rt.pops[spec['population']]),spec['rate_hz'],spec['seed'],spec['duration_ms'])"
}
]
},
{
"path": "23-ai-gpu/source/lif-semantics-audit-v1/brian2-evidence.json",
"sha256": "bebd4dfa2dda1aca601a52b24e3eabdd887ce261c3548e377198ab154d81f16e",
"bytes": 1963,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/lif-semantics-audit-v1/config.yaml",
"sha256": "433151183a4755f5fdfec33f6a9404aa5fb186ec45866ebb58be4ba3e4ebb568",
"bytes": 1464,
"seed_rng_line_evidence": [
{
"line": 19,
"text": "seed_node: 1381"
}
]
},
{
"path": "23-ai-gpu/source/lif-semantics-audit-v1/math_reference.py",
"sha256": "5166366251f99359b1301f514f5d9ae5f0f041fd3a3f98902386192117f01aea",
"bytes": 3768,
"seed_rng_line_evidence": [
{
"line": 42,
"text": "def cpu_motif(nodes, edges, weights, p, steps, seed):"
},
{
"line": 55,
"text": " if t == 0 and seed is not None:"
},
{
"line": 56,
"text": " i = lookup[seed]"
}
]
},
{
"path": "23-ai-gpu/source/lif-semantics-audit-v1/report.py",
"sha256": "76a21eca32ff8caba88ab84616e226e687dd0d2df5da62816c81b5ba08297394",
"bytes": 5237,
"seed_rng_line_evidence": [
{
"line": 29,
"text": " 'One forced seed spike at step 0 except the no-input control; no subsequent input; observed for 1 s. Late window=500–1,000 ms.',"
},
{
"line": 42,
"text": " 'The usable-learning gate remains unmet: independent evaluation, A-vs-neutral-B specificity in MBON/downstream, multiple training seeds and conditioning controls still required.',"
}
]
},
{
"path": "23-ai-gpu/source/lif-semantics-audit-v1/test_math.py",
"sha256": "9ee96ed78bfa692b12f0db6862087a19dc351c4afd9bb94cf02733ec6a002f01",
"bytes": 2902,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/baseline.yaml",
"sha256": "3bfa07b2fa1378d9f3c8d38ac323b2897871c37a7f686d3a19e25a81b5a06e83",
"bytes": 2713,
"seed_rng_line_evidence": [
{
"line": 78,
"text": " seed: 41001"
},
{
"line": 82,
"text": " seed: 41002"
},
{
"line": 86,
"text": " seed: 41003"
},
{
"line": 89,
"text": " seed: 41100"
},
{
"line": 109,
"text": " seed: 41000"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/brain_runtime.py",
"sha256": "1c4e3e6f06708bfec30b24e92297f320b2b8b87ebbbe6da501f5f2db4f74762a",
"bytes": 23822,
"seed_rng_line_evidence": [
{
"line": 187,
"text": " def reset(self, seed: int, initial_state: dict[str, Any] | None = None) -> dict[str, Any]:"
},
{
"line": 188,
"text": " self.seed = int(seed)"
},
{
"line": 189,
"text": " self.generator = torch.Generator(device=self.device); self.generator.manual_seed(self.seed)"
},
{
"line": 255,
"text": " selected = torch.rand(len(targets), generator=self.generator, device=self.device) < (values * self.dt / 1000.0)"
},
{
"line": 261,
"text": " selected = torch.rand(len(targets), generator=self.generator, device=self.device) < (torch.abs(values) * self.dt / 1000.0)"
},
{
"line": 376,
"text": " \"sequence_id\": self.sequence_id, \"seed\": self.seed,"
},
{
"line": 385,
"text": " \"api_version\": API_VERSION, \"seed\": self.seed, \"simulation_step\": self.simulation_step,"
},
{
"line": 398,
"text": " self.seed = int(state[\"seed\"]); self.simulation_step = int(state[\"simulation_step\"]); self.sequence_id = int(state[\"sequence_id\"])"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/frozen_interface.py",
"sha256": "4e4142cd5e866cfdbc9d56ffdff5eac9081a79e00c1658611f326447013b8fdb",
"bytes": 1229,
"seed_rng_line_evidence": [
{
"line": 23,
"text": " def sample(self, seed: int, temperature: float = 1.0, epsilon: float = 0.0) -> int:"
},
{
"line": 26,
"text": " rng = np.random.default_rng(seed)"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/odor_coverage_diagnostics.py",
"sha256": "309ccee0bae28539e4d3ba0ebee6ebdfddfbe64e48c40347a594888de06a1306",
"bytes": 3126,
"seed_rng_line_evidence": []
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{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4a.py",
"sha256": "34d8568cb36e109f3c5a052d45ba5ca20a7c481ff3037d9af6fe987c833043ae",
"bytes": 30726,
"seed_rng_line_evidence": [
{
"line": 214,
"text": " rng = np.random.default_rng(int(test[\"seed\"]))"
},
{
"line": 266,
"text": " generator.manual_seed(int(test[\"seed\"]))"
},
{
"line": 296,
"text": " chosen = torch.rand(targets.numel(), generator=generator, device=device) < probability"
},
{
"line": 380,
"text": "def induced_subgraph(indptr: np.ndarray, indices: np.ndarray, effective: np.ndarray, n: int, size: int, seed: int) -> dict[str, np.ndarray]:"
},
{
"line": 381,
"text": " rng = np.random.default_rng(seed)"
},
{
"line": 483,
"text": " sub = induced_subgraph(indptr, indices, effective, n, int(spec[\"subgraph_neurons\"]), int(spec[\"seed\"]))"
},
{
"line": 496,
"text": " \"seed\": int(spec[\"seed\"]),"
},
{
"line": 540,
"text": " random.seed(int(cfg[\"reproducibility\"][\"seed\"]))"
},
{
"line": 541,
"text": " np.random.seed(int(cfg[\"reproducibility\"][\"seed\"]))"
},
{
"line": 542,
"text": " torch.manual_seed(int(cfg[\"reproducibility\"][\"seed\"]))"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4b.py",
"sha256": "693468685a7ff3db1d09a088e0bbdaa78c595d64086b27e9cc18abceb4c7d174",
"bytes": 64615,
"seed_rng_line_evidence": [
{
"line": 504,
"text": " seed: int, duration_ms: float, w_syn: float, log_events: bool,"
},
{
"line": 526,
"text": " generator.manual_seed(int(seed))"
},
{
"line": 591,
"text": " poisson = torch.rand(targets.numel(), generator=generator, device=self.device) < probability"
},
{
"line": 706,
"text": " \"seed\": int(seed),"
},
{
"line": 771,
"text": " rng = np.random.default_rng(int(spec[\"seed\"]))"
},
{
"line": 798,
"text": " \"selection\": \"uniform random without replacement using fixed seed; induced directed edges\","
},
{
"line": 799,
"text": " \"seed\": int(spec[\"seed\"]),"
},
{
"line": 819,
"text": " path = trial_path(exp, category, result[\"population\"], result[\"rate_hz\"], result[\"seed\"], result[\"w_syn_mv\"])"
},
{
"line": 824,
"text": "def trial_path(exp: Path, category: str, population: str, rate: float, seed: int, weight: float) -> Path:"
},
{
"line": 825,
"text": " safe = f\"{population}-rate{float(rate):g}-seed{int(seed)}-w{float(weight):g}.json\""
},
{
"line": 911,
"text": " rng = random.Random(int(cfg[\"causal_validation\"][\"seed\"]))"
},
{
"line": 978,
"text": " \"population\", \"population_neurons\", \"rate_hz\", \"seed\", \"w_syn_mv\","
},
{
"line": 1002,
"text": " random.seed(int(cfg[\"reproducibility\"][\"global_seed\"]))"
},
{
"line": 1003,
"text": " np.random.seed(int(cfg[\"reproducibility\"][\"global_seed\"]))"
},
{
"line": 1004,
"text": " torch.manual_seed(int(cfg[\"reproducibility\"][\"global_seed\"]))"
},
{
"line": 1050,
"text": " total_baseline = len(masks) * len(cfg[\"baseline_sweep\"][\"rates_hz\"]) * len(cfg[\"baseline_sweep\"][\"seeds\"])"
},
{
"line": 1055,
"text": " for seed in cfg[\"baseline_sweep\"][\"seeds\"]:"
},
{
"line": 1057,
"text": " LOG.info(\"baseline trial %d/%d population=%s rate=%s seed=%s\", trial_number, total_baseline, population, rate, seed)"
},
{
"line": 1058,
"text": " checkpoint = trial_path(exp, \"baseline-trials\", population, float(rate), int(seed), baseline_w)"
},
{
"line": 1064,
"text": " population, targets, distances[population], float(rate), int(seed),"
},
{
"line": 1085,
"text": " int(cfg[\"weight_sweep\"][\"seed\"]), float(weight),"
},
{
"line": 1093,
"text": " int(cfg[\"weight_sweep\"][\"seed\"]), float(cfg[\"weight_sweep\"][\"duration_ms\"]), float(weight), False,"
},
{
"line": 1100,
"text": " \"controlled_seed\": cfg[\"weight_sweep\"][\"seed\"],"
},
{
"line": 1110,
"text": " rate_hz=float(cfg[\"performance\"][\"rate_hz\"]), seed=int(cfg[\"performance\"][\"seed\"]),"
},
{
"line": 1143,
"text": " int(cfg[\"profiler\"][\"seed\"]), float(cfg[\"profiler\"][\"duration_ms\"]), baseline_w,"
},
{
"line": 1151,
"text": " int(cfg[\"profiler\"][\"seed\"]), float(cfg[\"profiler\"][\"duration_ms\"]), baseline_w,"
},
{
"line": 1172,
"text": " LOG.info(\"causal trial population=%s rate=%s seed=%s\", causal_pop, selected[\"rate_hz\"], selected[\"seed\"])"
},
{
"line": 1174,
"text": " causal_pop, causal_targets, distances[causal_pop], float(selected[\"rate_hz\"]), int(selected[\"seed\"]),"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4b.yaml",
"sha256": "3c8651a903302845020917e8c7fee5caa39b6d2bef167a8a448c1a7560689414",
"bytes": 2949,
"seed_rng_line_evidence": [
{
"line": 59,
"text": " seeds: [42001, 42002, 42003]"
},
{
"line": 66,
"text": " seed: 42100"
},
{
"line": 85,
"text": " seed: 42200"
},
{
"line": 93,
"text": " seed: 42201"
},
{
"line": 100,
"text": " seed: 42300"
},
{
"line": 104,
"text": " seed: 42400"
},
{
"line": 115,
"text": " global_seed: 42000"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4c.py",
"sha256": "5ec83a132deb8ba20e62dab485537c2414016fe2fece546198d61c0668d75b6b",
"bytes": 43906,
"seed_rng_line_evidence": [
{
"line": 277,
"text": " seed: int, device: torch.device) -> torch.Tensor:"
},
{
"line": 280,
"text": " generator.manual_seed(seed)"
},
{
"line": 284,
"text": " forced[step].copy_((torch.rand(target_count, generator=generator, device=device) < probability).to(torch.uint8))"
},
{
"line": 354,
"text": " def run(self, backend: str, targets_np: np.ndarray, rate_hz: float, seed: int,"
},
{
"line": 364,
"text": " forced = forced_override if forced_override is not None else generate_forced(steps, len(targets_np), rate_hz, dt, seed, self.device)"
},
{
"line": 532,
"text": " spec = cfg[\"visual_dynamics\"]; duration = float(spec[\"duration_ms\"]); rate = float(spec[\"rate_hz\"]); seed = int(spec[\"seed\"])"
},
{
"line": 536,
"text": " tests.append((\"A-forced-histaminergic-sensory\", sensory, rate, seed, None, \"engineering forcing of sensory output spikes\"))"
},
{
"line": 538,
"text": " tonic = generate_forced(steps, len(sensory), float(spec[\"tonic_histamine_baseline_hz\"]), float(cfg[\"model\"][\"dt_ms\"]), seed + 1, runtime.device)"
},
{
"line": 540,
"text": " mod = generate_forced(steps - mod_start, len(sensory), float(spec[\"modulation_rate_hz\"]), float(cfg[\"model\"][\"dt_ms\"]), seed + 2, runtime.device)"
},
{
"line": 542,
"text": " tests.append((\"B-tonic-histamine-rate-modulation\", sensory, 0.0, seed + 1, tonic, \"exploratory tonic output plus rate modulation; not baseline\"))"
},
{
"line": 545,
"text": " tests.append((\"C-first-order-external-input\", first, rate, seed + 3, None, \"engineering input interface moved to real first-order targets\"))"
},
{
"line": 546,
"text": " tests.append((\"C-second-order-external-input\", second, rate, seed + 4, None, \"engineering input interface moved to real second-order targets\"))"
},
{
"line": 552,
"text": " tests.append((f\"D-layer-{name}\", targets, rate, seed + 10 + j, None, f\"engineering external input at hierarchy layer {name}\"))"
},
{
"line": 553,
"text": " for name, targets, hz, trial_seed, override, interpretation in tests:"
},
{
"line": 555,
"text": " run = runtime.run(\"eager\", targets, hz, trial_seed, duration, False, forced_override=override)"
},
{
"line": 658,
"text": " ref = runtime.run(\"eager\", targets, float(spec[\"rate_hz\"]), int(spec[\"seed\"]), float(spec[\"duration_ms\"]), True)"
},
{
"line": 664,
"text": " cand = runtime.run(backend, targets, float(spec[\"rate_hz\"]), int(spec[\"seed\"]), float(spec[\"duration_ms\"]), True)"
},
{
"line": 685,
"text": " run = runtime.run(backend, auditory, float(cfg[\"benchmark\"][\"rate_hz\"]), int(cfg[\"benchmark\"][\"seed\"]), duration, False, monitor=True)"
},
{
"line": 714,
"text": " candidate = runtime.run(backend, targets, float(audit_spec[\"rate_hz\"]), int(audit_spec[\"seed\"]), 1000.0, True)"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4c.yaml",
"sha256": "0734ffb3a99231b40db2052dea0f2d21efd5dad8d11d17d4731b952811ba5071",
"bytes": 3143,
"seed_rng_line_evidence": [
{
"line": 64,
"text": " - {name: auditory-10hz, population: auditory, rate_hz: 10, seed: 42001, duration_ms: 1000.0}"
},
{
"line": 65,
"text": " - {name: auditory-100hz, population: auditory, rate_hz: 100, seed: 42001, duration_ms: 1000.0}"
},
{
"line": 66,
"text": " - {name: olfactory-50hz, population: olfactory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 67,
"text": " - {name: mechanosensory-50hz, population: mechanosensory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 68,
"text": " - {name: gustatory-50hz, population: gustatory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 73,
"text": " seed: 43100"
},
{
"line": 81,
"text": " seed: 43200"
},
{
"line": 93,
"text": " global_seed: 43000"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4d.py",
"sha256": "9ed8e4246252bdfc590c88e2993b7e105f278c6c4943e1c688f387c1cf54aeb7",
"bytes": 34442,
"seed_rng_line_evidence": [
{
"line": 142,
"text": " rng = np.random.default_rng(int(cfg[\"synthetic_patterns\"][\"deterministic_seed\"]))"
},
{
"line": 214,
"text": " for seed in cfg[\"window_experiment\"][\"seeds\"]:"
},
{
"line": 217,
"text": " runtime.reset(int(seed)); batch, _ = make_source_batch(cfg, runtime, adapter, source, 0.0, float(window), pattern_cache)"
},
{
"line": 225,
"text": " \"source\": source, \"window_ms\": float(window), \"seed\": int(seed),"
},
{
"line": 268,
"text": " period = float(period); runtime.reset(int(cfg[\"continuous_control\"][\"seed\"]))"
},
{
"line": 332,
"text": " for seed in (44301, 44302, 44303):"
},
{
"line": 333,
"text": " runtime.reset(seed); batch = adapter.encode(mode, values, 0.0, 100.0, \"separability\", name)"
},
{
"line": 335,
"text": " trial_rows.append({\"pattern\": name, \"seed\": seed, \"active_channels\": int(np.count_nonzero(values)), \"feature_norm\": float(np.linalg.norm(f)), **output_stats(runtime, 100.0)})"
},
{
"line": 374,
"text": " \"reset_same_seed_continuous_chunk_spikes_exact\": bool(np.array_equal(one[\"step\"], two[\"step\"]) and np.array_equal(one[\"neuron_idx\"], two[\"neuron_idx\"])),"
},
{
"line": 387,
"text": " result[\"all_passed\"] = all(result[k] for k in (\"reset_same_seed_continuous_chunk_spikes_exact\", \"continuous_step_does_not_reset_clock\", \"checkpoint_restore_spikes_exact\", \"checkpoint_restore_voltage_exact\", \"timestamps_present\", \"readout_modes_passed\"))"
},
{
"line": 403,
"text": " runtime.reset(int(spec[\"seed\"])); t0 = time.perf_counter(); runtime.step(batch, float(spec[\"duration_ms\"])); wall = time.perf_counter() - t0"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4d.yaml",
"sha256": "e94301362e630e59fa248f883ae09d369dce720bce970d0d2f47ef6a6ed36a2a",
"bytes": 3175,
"seed_rng_line_evidence": [
{
"line": 73,
"text": " seeds: [44001, 44002]"
},
{
"line": 79,
"text": " seed: 44100"
},
{
"line": 85,
"text": " deterministic_seed: 44200"
},
{
"line": 90,
"text": " - {name: auditory-10hz, population: auditory, rate_hz: 10, seed: 42001, duration_ms: 1000.0}"
},
{
"line": 91,
"text": " - {name: auditory-100hz, population: auditory, rate_hz: 100, seed: 42001, duration_ms: 1000.0}"
},
{
"line": 92,
"text": " - {name: olfactory-50hz, population: olfactory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 93,
"text": " - {name: mechanosensory-50hz, population: mechanosensory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 94,
"text": " - {name: gustatory-50hz, population: gustatory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4e.py",
"sha256": "a56fead8424e4e79be370dade0a86fec10438348968819481960b0b693411f73",
"bytes": 43339,
"seed_rng_line_evidence": [
{
"line": 53,
"text": "def stable_seed(base: int, text: str) -> int:"
},
{
"line": 234,
"text": "def subset(nodes: np.ndarray, percent: float, seed: int, offset: int = 0) -> np.ndarray:"
},
{
"line": 236,
"text": " order = np.random.default_rng(seed).permutation(len(nodes))"
},
{
"line": 265,
"text": "def run_trial(runtime: BrainRuntime, batch: InputBatch, targets: np.ndarray, duration: float, seed: int,"
},
{
"line": 267,
"text": " runtime.reset(seed)"
},
{
"line": 297,
"text": " \"seed\": seed, \"requested_biological_ms\": duration, \"simulated_biological_ms\": runtime.simulation_time_ms,"
},
{
"line": 337,
"text": " nodes = subset(populations[layer], float(cal[\"fixed_sparsity_percent\"]), stable_seed(int(cal[\"seed\"]), layer))"
},
{
"line": 341,
"text": " rec, _ = run_trial(runtime, batch, nodes, float(cal[\"trial_duration_ms\"]), int(cal[\"seed\"]) + i, distances[layer], cfg)"
},
{
"line": 347,
"text": " seed0 = stable_seed(int(cal[\"seed\"]), layer)"
},
{
"line": 348,
"text": " nodes = subset(populations[layer], float(cal[\"fixed_sparsity_percent\"]), seed0)"
},
{
"line": 355,
"text": " rec, _ = run_trial(runtime, batch, nodes, float(cal[\"trial_duration_ms\"]), seed0 + int(multiplier * 10), distances[layer], cfg)"
},
{
"line": 365,
"text": " rec, _ = run_trial(runtime, batch, nodes, float(cal[\"trial_duration_ms\"]), seed0 + int(multiplier * 10), distances[layer], cfg)"
},
{
"line": 378,
"text": " seed0 = stable_seed(int(cal[\"seed\"]), layer)"
},
{
"line": 379,
"text": " fixed_nodes = subset(populations[layer], float(cal[\"fixed_sparsity_percent\"]), seed0)"
},
{
"line": 382,
"text": " rec, _ = run_trial(runtime, batch, fixed_nodes, float(cal[\"trial_duration_ms\"]), seed0 + int(rate), distances[layer], cfg)"
},
{
"line": 387,
"text": " nodes = subset(populations[layer], sp, seed0)"
},
{
"line": 389,
"text": " rec, _ = run_trial(runtime, batch, nodes, float(cal[\"trial_duration_ms\"]), seed0 + int(sp * 100), distances[layer], cfg)"
},
{
"line": 400,
"text": " seed0 = stable_seed(int(cal[\"seed\"]), layer)"
},
{
"line": 402,
"text": " nodes = subset(populations[layer], float(chosen[\"sparsity_percent\"]), seed0)"
},
{
"line": 404,
"text": " rec, _ = run_trial(runtime, batch, nodes, window, seed0 + int(window * 10), distances[layer], cfg)"
},
{
"line": 427,
"text": " duration: float, seed: int, cfg: dict[str, Any], sparse_percent: float | None = None) -> tuple[InputBatch, np.ndarray]:"
},
{
"line": 431,
"text": " order = np.random.default_rng(seed).permutation(len(all_nodes)); sparse_a = np.sort(all_nodes[order[:count]].astype(np.int64))"
},
{
"line": 435,
"text": " elif kind == \"dense\": nodes = subset(all_nodes, float(cfg[\"patterns\"][\"dense_percent\"]), seed)"
},
{
"line": 458,
"text": " for seed in map(int, cfg[\"patterns\"][\"seeds\"]):"
},
{
"line": 459,
"text": " batch, nodes = pattern_batch(layer, populations, kind, float(condition[\"rate_hz\"]), float(condition[\"amplitude_mv\"]), duration, seed, cfg,"
},
{
"line": 461,
"text": " rec, cap = run_trial(runtime, batch, nodes, duration, seed, distances[layer], cfg, capture=True)"
},
{
"line": 463,
"text": " drive_counts[f\"{kind}-{seed}\"] = rec[\"external_drive_events\"]"
},
{
"line": 468,
"text": " det_seed = int(cfg[\"patterns\"][\"seeds\"][0])"
},
{
"line": 469,
"text": " batch, nodes = pattern_batch(layer, populations, \"sparse-A\", float(condition[\"rate_hz\"]), float(condition[\"amplitude_mv\"]), duration, det_seed, cfg,"
},
{
"line": 471,
"text": " _, c1 = run_trial(runtime, batch, nodes, duration, det_seed, distances[layer], cfg, capture=True)"
},
{
"line": 472,
"text": " _, c2 = run_trial(runtime, batch, nodes, duration, det_seed, distances[layer], cfg, capture=True)"
},
{
"line": 475,
"text": " matched = all(drive_counts[f\"sparse-A-{s}\"] == drive_counts[f\"sparse-B-{s}\"] for s in map(int, cfg[\"patterns\"][\"seeds\"]))"
},
{
"line": 512,
"text": " runtime.reset(int(cfg[\"continuous\"][\"seed\"])); intervals = []; previous = None"
},
{
"line": 517,
"text": " int(cfg[\"continuous\"][\"seed\"]), cfg,"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4e.yaml",
"sha256": "708b6bb75b82a967f8c1e65cce646f080a79f7b6048ca72e61de1dc184d9923a",
"bytes": 4590,
"seed_rng_line_evidence": [
{
"line": 54,
"text": " seed: 45000"
},
{
"line": 81,
"text": " seeds: [45101, 45102, 45103]"
},
{
"line": 87,
"text": " seed: 45200"
},
{
"line": 95,
"text": " - {name: auditory-10hz, population: auditory, rate_hz: 10, seed: 42001, duration_ms: 1000.0}"
},
{
"line": 96,
"text": " - {name: auditory-100hz, population: auditory, rate_hz: 100, seed: 42001, duration_ms: 1000.0}"
},
{
"line": 97,
"text": " - {name: olfactory-50hz, population: olfactory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 98,
"text": " - {name: mechanosensory-50hz, population: mechanosensory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 99,
"text": " - {name: gustatory-50hz, population: gustatory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4f_extract_retinotopy.py",
"sha256": "6df36ecf4b6aa5565c1f3c10345fe997a2c3eefe88e42e0b194be272ce83e233",
"bytes": 3795,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4f.py",
"sha256": "5524ac0565b9e2a2f96d7a062109c27fe64365fcaaef44ee7b7ded7d3318fe50",
"bytes": 37799,
"seed_rng_line_evidence": [
{
"line": 348,
"text": "def run_trial(cfg: dict[str, Any], runtime: BrainRuntime, batch: InputBatch, union: np.ndarray, seed: int,"
},
{
"line": 350,
"text": " runtime.reset(seed); t0 = time.perf_counter(); runtime.step(batch, float(cfg[\"encoder\"][\"trial_duration_ms\"])); brain_wall = time.perf_counter()-t0"
},
{
"line": 361,
"text": " record = {\"route\": route, \"pattern\": pattern, \"seed\": seed, \"biological_ms\": runtime.simulation_time_ms,"
},
{
"line": 374,
"text": " np.savez_compressed(trial_dir/f\"{pattern}-seed{seed}.npz\", step=steps, neuron_idx=nodes, **{f\"counts_{k}\": v for k,v in counts.items()})"
},
{
"line": 375,
"text": " write_json(trial_dir/f\"{pattern}-seed{seed}.json\", record)"
},
{
"line": 397,
"text": " for pattern, seeds in patterns.items():"
},
{
"line": 398,
"text": " vals = [seeds[s][readout] for s in sorted(seeds)]"
},
{
"line": 410,
"text": " route_result[\"matched_pairs\"].setdefault(key,{})[readout]={\"same_seed\":same,\"between_condition_mean\":mean_between,"
},
{
"line": 465,
"text": " for seed in map(int,cfg[\"encoder\"][\"seeds\"]):"
},
{
"line": 466,
"text": " rec,counts=run_trial(cfg,runtime,batch,union,seed,groups,exp,route,pattern)"
},
{
"line": 468,
"text": " trial_records.append(rec); features[route][pattern][seed]=counts"
},
{
"line": 469,
"text": " # Exact repeatability with the same seed, input, and initial state."
},
{
"line": 472,
"text": " batch,union,_=encode_pattern(cfg,route,\"left-spot\",pool); seed=int(cfg[\"encoder\"][\"seeds\"][0])"
},
{
"line": 473,
"text": " runtime.reset(seed); runtime.step(batch,float(cfg[\"encoder\"][\"trial_duration_ms\"])); a=runtime.read_spikes()"
},
{
"line": 474,
"text": " runtime.reset(seed); runtime.step(batch,float(cfg[\"encoder\"][\"trial_duration_ms\"])); b=runtime.read_spikes()"
},
{
"line": 480,
"text": " for seed in map(int,cfg[\"encoder\"][\"seeds\"]):"
},
{
"line": 481,
"text": " vals=[x[\"external_event_count\"] for x in trial_records if x[\"route\"]==route and x[\"seed\"]==seed]"
},
{
"line": 482,
"text": " amps=[x[\"external_total_amplitude_mv\"] for x in trial_records if x[\"route\"]==route and x[\"seed\"]==seed]"
},
{
"line": 483,
"text": " matched[route][str(seed)]={\"event_counts\":sorted(set(vals)),\"amplitudes_mv\":sorted(set(amps)),\"matched\":len(set(vals))==1 and len(set(amps))==1}"
},
{
"line": 505,
"text": " \"matched_input_energy\":all_matched,\"same_seed_exact_reproducibility\":deterministic,\"real_anatomical_multi_hop\":multi_hop,"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4f.yaml",
"sha256": "52393d2c9ad019ceb61de9168dc809ec52aec72b3c66f1b9708d22c0808f678d",
"bytes": 4359,
"seed_rng_line_evidence": [
{
"line": 56,
"text": " seeds: [46101, 46102, 46103]"
},
{
"line": 97,
"text": " - {name: auditory-10hz, population: auditory, rate_hz: 10, seed: 42001, duration_ms: 1000.0}"
},
{
"line": 98,
"text": " - {name: auditory-100hz, population: auditory, rate_hz: 100, seed: 42001, duration_ms: 1000.0}"
},
{
"line": 99,
"text": " - {name: olfactory-50hz, population: olfactory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 100,
"text": " - {name: mechanosensory-50hz, population: mechanosensory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 101,
"text": " - {name: gustatory-50hz, population: gustatory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4g.py",
"sha256": "c82ff049336a4815690d4060f001c77266fe70d9e62ec6a940741d9fa9721e4f",
"bytes": 40435,
"seed_rng_line_evidence": [
{
"line": 66,
"text": "def stable_seed(base:int,text:str)->int:"
},
{
"line": 72,
"text": " for split,key in ((\"train\",\"train_stimulus_seeds\"),(\"held-out\",\"evaluation_stimulus_seeds\")):"
},
{
"line": 73,
"text": " for seed in cfg[\"dataset\"][key]:"
},
{
"line": 74,
"text": " rng=np.random.default_rng(int(seed))"
},
{
"line": 78,
"text": " sign=-1 if int(seed)%2 else 1"
},
{
"line": 82,
"text": " rows.append({\"sample_id\":len(rows),\"split\":split,\"stimulus_seed\":int(seed),\"pattern\":pattern,"
},
{
"line": 161,
"text": " out=exp/\"random-connectome\";out.mkdir(parents=True,exist_ok=True);seed=int(cfg[\"controls\"][\"graph_seed\"]);rng=np.random.default_rng(seed)"
},
{
"line": 168,
"text": " audit={\"edge_count\":len(real),\"neurons\":rt.n,\"seed\":seed,\"real_indices_sha256\":sha256(Path(cfg[\"paths\"][\"connectome_root\"])/\"gpu/csr-indices.int32.npy\"),"
},
{
"line": 181,
"text": " rng=np.random.default_rng(47901 if route==\"route-a\" else 47902);w=rng.normal(size=(x.shape[1],128)).astype(np.float32);b=rng.uniform(0,2*np.pi,128).astype(np.float32)"
},
{
"line": 210,
"text": " rt.reset(int(row.stimulus_seed));last=0"
},
{
"line": 219,
"text": " records.append({\"graph\":graph,\"route\":route,\"sample_id\":row.sample_id,\"pattern\":row.pattern,\"split\":row.split,\"stimulus_seed\":row.stimulus_seed,"
},
{
"line": 230,
"text": " # Exact matched-energy audit by graph, route, split seed and window."
},
{
"line": 236,
"text": " for split,seed in samples[[\"split\",\"stimulus_seed\"]].drop_duplicates().itertuples(index=False):"
},
{
"line": 237,
"text": " ids=samples.index[(samples.split==split)&(samples.stimulus_seed==seed)].to_numpy();entry[f\"{split}-{seed}\"]={}"
},
{
"line": 240,
"text": " entry[f\"{split}-{seed}\"][str(w)]={\"event_counts\":sorted(set(map(int,counts))),\"amplitude_mv\":sorted(set(map(float,amps))),\"matched\":len(set(counts))==1 and len(set(amps))==1}"
},
{
"line": 259,
"text": "def fit_model(x:np.ndarray,y:np.ndarray,test_x:np.ndarray,test_y:np.ndarray,capacity:str,classes:int,seed:int,cfg:dict[str,Any])->tuple[dict[str,Any],dict[str,Any]]:"
},
{
"line": 260,
"text": " torch.manual_seed(seed);mean=x.mean(0);std=x.std(0);std[std<1e-6]=1"
},
{
"line": 291,
"text": " capacities=cfg[\"training\"][\"capacities\"];seeds=list(map(int,cfg[\"training\"][\"model_seeds\"]));windows=list(map(int,cfg[\"dataset\"][\"biological_windows_ms\"]));readouts=[\"visual-projection\",\"central-downstream\",\"descending\",\"motor-related\"]"
},
{
"line": 300,
"text": " tr,ytr,te,yte=task_data(samples,spec);pilot_train=tr[samples.iloc[tr].stimulus_seed.to_numpy()!=cfg[\"dataset\"][\"train_stimulus_seeds\"][-1]];pilot_y=ytr[samples.iloc[tr].stimulus_seed.to_numpy()!=cfg[\"dataset\"][\"train_stimulus_seeds\"][-1]]"
},
{
"line": 301,
"text": " val=tr[samples.iloc[tr].stimulus_seed.to_numpy()==cfg[\"dataset\"][\"train_stimulus_seeds\"][-1]];val_y=ytr[samples.iloc[tr].stimulus_seed.to_numpy()==cfg[\"dataset\"][\"train_stimulus_seeds\"][-1]]"
},
{
"line": 305,
"text": " x=reduce_bins(features[graph][route][f\"w{w}_{read}\"],int(cspec[\"feature_bins\"]));_,r=fit_model(x[pilot_train],pilot_y,x[val],val_y,cap,int(spec[\"classes\"]),seeds[0],cfg);pilot.append((r[\"accuracy\"],w,read))"
},
{
"line": 308,
"text": " for seed in seeds:"
},
{
"line": 309,
"text": " _,r=fit_model(x[tr],ytr,x[te],yte,cap,int(spec[\"classes\"]),seed,cfg);r[\"seed\"]=seed;runs.append(r)"
},
{
"line": 323,
"text": " for seed in seeds:"
},
{
"line": 324,
"text": " fixed,base=fit_model(a[tr],ytr,a[te],yte,cap,int(spec[\"classes\"]),seed,cfg)"
},
{
"line": 332,
"text": " xm=selected_matrix(a,b,samples,choice);logits=predict(fixed,xm[te]);pred=logits.argmax(1);bruns.append({\"seed\":seed,\"accuracy\":float(np.mean(pred==yte)),\"route_choice\":choice,\"decoder_parameters\":base[\"trainable_parameters\"],\"encoder_parameters\":int(cspec[\"encoder_parameters\"])})"
},
{
"line": 336,
"text": " xm=selected_matrix(a,b,samples,cchoice);joint,_=fit_model(xm[tr],ytr,xm[te],yte,cap,int(spec[\"classes\"]),seed,cfg)"
},
{
"line": 346,
"text": " xm=selected_matrix(a,b,samples,cchoice);joint,jr=fit_model(xm[tr],ytr,xm[te],yte,cap,int(spec[\"classes\"]),seed,cfg);jr.update({\"seed\":seed,\"route_choice\":cchoice,\"encoder_parameters\":int(cspec[\"encoder_parameters\"])});cruns.append(jr)"
},
{
"line": 347,
"text": " best_bundles[(graph,cap,task,seed)]={\"bundle\":joint,\"choice\":cchoice,\"window\":w,\"readout\":read,\"accuracy\":jr[\"accuracy\"]}"
},
{
"line": 377,
"text": " cap=\"Small\";seed=int(cfg[\"training\"][\"model_seeds\"][0]);item=bundles[(graph,cap,task,seed)];w=item[\"window\"];read=item[\"readout\"]"
},
{
"line": 378,
"text": " a=reduce_bins(features[graph][\"route-a\"][f\"w{w}_{read}\"],128);b=reduce_bins(features[graph][\"route-b\"][f\"w{w}_{read}\"],128);x=selected_matrix(a,b,samples,item[\"choice\"]);base=x[te];rng=np.random.default_rng(47400);conditions={}"
},
{
"line": 420,
"text": " # Select the formal best real joint result by cross-seed mean, never by a single best run."
},
{
"line": 425,
"text": " best_seed=min(cfg[\"training\"][\"model_seeds\"],key=lambda s:abs(bundles[(\"real-malecns\",best_cap,best_task,int(s))][\"accuracy\"]-mean_accuracy));best_bundle=bundles[(\"real-malecns\",best_cap,best_task,int(best_seed))]"
},
{
"line": 444,
"text": " chance=1/int(cfg[\"dataset\"][\"tasks\"][best_task][\"classes\"]);runs=best[\"runs\"];margin=float(cfg[\"success_gate\"][\"stable_accuracy_margin_over_chance\"]);success_seeds=sum(r[\"accuracy\"]>=chance+margin for r in runs)"
},
{
"line": 446,
"text": " stable=success_seeds>=int(cfg[\"success_gate\"][\"successful_model_seed_majority\"]);nonrun=all(not x[\"runaway\"] and x[\"finite\"] for x in records)"
},
{
"line": 451,
"text": " summary={\"status\":status,\"candidate_generated\":passed,\"best\":{\"capacity\":best_cap,\"task\":best_task,\"window_ms\":best[\"window_ms\"],\"readout\":best[\"readout\"],\"held_out_accuracy_mean\":best[\"held_out_accuracy_mean\"],\"held_out_accuracy_std\":best[\"held_out_accuracy_std\"],\"chance\":chance,\"successful_seeds\":success_seeds},"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4g.yaml",
"sha256": "678798511cc14181d22a2085dedb412b44a96a966398678c12c27a8c561be042",
"bytes": 4795,
"seed_rng_line_evidence": [
{
"line": 47,
"text": " train_stimulus_seeds: [47001, 47002, 47003, 47004, 47005]"
},
{
"line": 48,
"text": " evaluation_stimulus_seeds: [47101, 47102, 47103]"
},
{
"line": 63,
"text": " model_seeds: [47201, 47202, 47203, 47204, 47205]"
},
{
"line": 64,
"text": " evaluation_seeds: [47101, 47102, 47103]"
},
{
"line": 73,
"text": " graph_seed: 47300"
},
{
"line": 89,
"text": " - {name: auditory-10hz, population: auditory, rate_hz: 10, seed: 42001, duration_ms: 1000.0}"
},
{
"line": 90,
"text": " - {name: auditory-100hz, population: auditory, rate_hz: 100, seed: 42001, duration_ms: 1000.0}"
},
{
"line": 91,
"text": " - {name: olfactory-50hz, population: olfactory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 92,
"text": " - {name: mechanosensory-50hz, population: mechanosensory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 93,
"text": " - {name: gustatory-50hz, population: gustatory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 97,
"text": " successful_model_seed_majority: 3"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4h_audit.py",
"sha256": "83128ea0a7276a06c6ce57e4c533d076cf442e28e3b0834e38c4537cc87e1136",
"bytes": 20538,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4h_reinforcement_provenance.json",
"sha256": "2f1884bc5ab6b1faec8fb2d4d4930d75ee8928dc7bee8d8c15b5f317b5512809",
"bytes": 1834,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4h.py",
"sha256": "4c310b059ec954e7bc21d71482f1cb6a62b0d979ab25ab4a50d6a4689a5b9524",
"bytes": 29381,
"seed_rng_line_evidence": [
{
"line": 128,
"text": " def evaluate(self, cs_name: str, seed: int, delta: np.ndarray) -> dict[str, Any]:"
},
{
"line": 129,
"text": " self.install_delta(delta); self.rt.reset(seed)"
},
{
"line": 153,
"text": " \"seed\": int(seed), \"CS\": cs_name, \"wall_seconds\": wall,"
},
{
"line": 166,
"text": " def update(self, cs_name: str, seed: int, delta: np.ndarray, compartment: str | None,"
},
{
"line": 168,
"text": " self.install_delta(delta); self.rt.reset(seed)"
},
{
"line": 193,
"text": " \"CS\": cs_name, \"seed\": int(seed), \"compartment_verbatim\": compartment,"
},
{
"line": 209,
"text": "def eval_pair(experiment: Experiment, seed: int, delta: np.ndarray) -> dict[str, Any]:"
},
{
"line": 210,
"text": " a = experiment.evaluate(\"CS-A\", seed, delta)"
},
{
"line": 211,
"text": " b = experiment.evaluate(\"CS-B\", seed, delta)"
},
{
"line": 222,
"text": "def protocol_plan(name: str, cfg: dict[str, Any], reliable: list[str], rng: np.random.Generator) -> list[dict[str, Any]]:"
},
{
"line": 265,
"text": " for name, population, rate, seed in specs:"
},
{
"line": 268,
"text": " rt.reset(seed); rt.step(batch, duration)"
},
{
"line": 338,
"text": " for train_seed, eval_seed in zip(cfg[\"conditioning\"][\"train_seeds\"], cfg[\"conditioning\"][\"evaluation_seeds\"]):"
},
{
"line": 339,
"text": " rng = np.random.default_rng(int(train_seed))"
},
{
"line": 341,
"text": " pre = eval_pair(experiment, int(eval_seed), delta)"
},
{
"line": 346,
"text": " delta, update = experiment.update(item[\"CS\"], int(train_seed) + trial_idx, delta,"
},
{
"line": 351,
"text": " pair = eval_pair(experiment, int(eval_seed), delta)"
},
{
"line": 358,
"text": " post = eval_pair(experiment, int(eval_seed), delta)"
},
{
"line": 361,
"text": " for nominal_ms, extra_seed in zip((0, 1000, 10000), cfg[\"conditioning\"][\"evaluation_seeds\"][:3]):"
},
{
"line": 362,
"text": " pair = eval_pair(experiment, int(extra_seed), delta)"
},
{
"line": 367,
"text": " \"protocol\": protocol, \"train_seed\": int(train_seed), \"evaluation_seed\": int(eval_seed),"
},
{
"line": 379,
"text": " run_path = protocol_dir / f\"train-{train_seed}-eval-{eval_seed}.json\""
},
{
"line": 381,
"text": " np.savez_compressed(learned / \"plastic-delta\" / f\"{protocol}-seed-{train_seed}.npz\","
},
{
"line": 396,
"text": " \"seeds\": len(rows),"
},
{
"line": 398,
"text": " \"mbon_gain_positive_seeds\": int(sum(row[\"mbon_euclidean_gain\"] > 0 for row in rows)),"
},
{
"line": 400,
"text": " \"downstream_gain_positive_seeds\": int(sum(row[\"downstream_euclidean_gain\"] > 0 for row in rows)),"
},
{
"line": 403,
"text": " \"reversal_signature_seeds\": int(sum(row[\"reversal_balance_sign_changed\"] for row in rows)),"
},
{
"line": 405,
"text": " majority = int(cfg[\"success_gate\"][\"require_minimum_seed_majority\"])"
},
{
"line": 407,
"text": " real_mbon = app[\"mbon_gain_positive_seeds\"] >= majority and av[\"mbon_gain_positive_seeds\"] >= majority"
},
{
"line": 408,
"text": " real_downstream = app[\"downstream_gain_positive_seeds\"] >= majority and av[\"downstream_gain_positive_seeds\"] >= majority"
},
{
"line": 409,
"text": " reversal = by_protocol[\"reversal-learning\"][\"reversal_signature_seeds\"] >= majority"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4h.yaml",
"sha256": "5cd3aa12e0aa249004911c7396cab0c80b256b21f8ad21969fea897db7ba2793",
"bytes": 4912,
"seed_rng_line_evidence": [
{
"line": 100,
"text": " train_seeds: [48001, 48002, 48003, 48004, 48005]"
},
{
"line": 101,
"text": " evaluation_seeds: [48101, 48102, 48103, 48104, 48105]"
},
{
"line": 117,
"text": " require_minimum_seed_majority: 3"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4i_final_report.json",
"sha256": "f4459da6112806eeb718f0d5dc58018f124026d6f37945871e5a19856cde34a6",
"bytes": 3611,
"seed_rng_line_evidence": [
{
"line": 5,
"text": " \"reason\": \"Both conditions activated all 4,064 KCs in all five deterministic seeds.\""
},
{
"line": 98,
"text": " \"13_five_seed_consistency\": {"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4i.py",
"sha256": "f5a45ba4b41553495ea50e09ae4522f97f78ffb62b7fa690d8eb6e3ed06bd555",
"bytes": 46530,
"seed_rng_line_evidence": [
{
"line": 161,
"text": " def run(self, batch: InputBatch, duration_ms: float, seed: int, delta: np.ndarray,"
},
{
"line": 163,
"text": " self.install(delta); self.rt.reset(int(seed)); self.rt.step(batch, float(duration_ms))"
},
{
"line": 225,
"text": " for seed in spec[\"seeds\"]:"
},
{
"line": 229,
"text": " spec[\"duration_ms\"], int(seed), zero)"
},
{
"line": 236,
"text": " metrics = pair_metrics(vectors[\"A\"][-1], vectors[\"B\"][-1]); metrics[\"seed\"] = int(seed)"
},
{
"line": 240,
"text": " consensus_n = int(spec[\"consensus_seed_count\"]); aa = np.count_nonzero(va > 0, axis=0) >= consensus_n; bb = np.count_nonzero(vb > 0, axis=0) >= consensus_n"
},
{
"line": 253,
"text": " and sum(x >= float(gate[\"minimum_pair_cosine_distance\"]) for x in pair_cos_dist) >= int(gate[\"majority_seeds\"])"
},
{
"line": 266,
"text": " \"CS_A_targets\": cs_a.tolist(), \"CS_B_targets\": cs_b.tolist(), \"seeds\": spec[\"seeds\"],"
},
{
"line": 267,
"text": " \"per_seed\": records, \"mean_active_jaccard\": mean_j,"
},
{
"line": 272,
"text": " \"KC_subtype_active_seed_hits\": subtype, \"compartment_associated_KC_coverage\": comp_coverage,"
},
{
"line": 286,
"text": " need = int(cfg[\"olfactory_audit\"][\"consensus_seed_count\"])"
},
{
"line": 328,
"text": " rng = np.random.default_rng(49301); random_set = np.sort(rng.choice(remaining, size=size, replace=False).astype(np.int32))"
},
{
"line": 354,
"text": " seed: int, enabled: bool, d: float, trials: int) -> tuple[np.ndarray, list[dict[str, Any]], pd.DataFrame]:"
},
{
"line": 359,
"text": " run = v.run(batch, v.cfg[\"direct_kc\"][\"evaluation_duration_ms\"], seed + trial, delta)"
},
{
"line": 391,
"text": " for train_seed, eval_seed in zip(dcfg[\"train_seeds\"], dcfg[\"evaluation_seeds\"]):"
},
{
"line": 392,
"text": " pre_a = v.run(fixed_pulse_batch(set_a, pulses, \"pre-A\"), dcfg[\"evaluation_duration_ms\"], eval_seed, zero, target_mbon)"
},
{
"line": 393,
"text": " pre_b = v.run(fixed_pulse_batch(set_b, pulses, \"pre-B\"), dcfg[\"evaluation_duration_ms\"], eval_seed, zero, target_mbon)"
},
{
"line": 394,
"text": " delta, train_records, delta_table = train_overlay(v, train_set, dan, compartment, train_seed, enabled, dvalue, int(dcfg[\"conditioning_trials\"]))"
},
{
"line": 395,
"text": " post_a = v.run(fixed_pulse_batch(set_a, pulses, \"post-A\"), dcfg[\"evaluation_duration_ms\"], eval_seed, delta, target_mbon)"
},
{
"line": 396,
"text": " post_b = v.run(fixed_pulse_batch(set_b, pulses, \"post-B\"), dcfg[\"evaluation_duration_ms\"], eval_seed, delta, target_mbon)"
},
{
"line": 401,
"text": " \"condition\": condition, \"train_seed\": int(train_seed), \"evaluation_seed\": int(eval_seed),"
},
{
"line": 410,
"text": " seed_dir = exp / \"direct-kc-memory\" / f\"seed-{train_seed}\"; seed_dir.mkdir(parents=True, exist_ok=True)"
},
{
"line": 411,
"text": " delta_table[delta_table[\"plastic_delta_mv\"] != 0].to_parquet(seed_dir / \"updated-edges.parquet\", index=False)"
},
{
"line": 412,
"text": " np.savez_compressed(seed_dir / \"plastic-overlay.npz\", edge_id=v.edge_ids, plastic_delta_mv=delta)"
},
{
"line": 413,
"text": " write_json(seed_dir / \"responses.json\", row); matched_details.append(row)"
},
{
"line": 415,
"text": " \"seeds\": len(rows), \"changed_edges_mean\": float(np.mean([x[\"changed_edges\"] for x in rows])),"
},
{
"line": 417,
"text": " \"A_MBON_spike_change_seeds\": int(sum(x[\"A_effect\"][\"tracked_MBON_spike_l1\"] > 0 for x in rows)),"
},
{
"line": 418,
"text": " \"A_MBON_subthreshold_change_seeds\": int(sum(max(x[\"A_effect\"][\"MBON_final_v_max_abs_change\"], x[\"A_effect\"][\"MBON_final_g_max_abs_change\"]) > 1e-4 for x in rows)),"
},
{
"line": 419,
"text": " \"B_same_effect_or_larger_seeds\": int(sum(x[\"B_effect\"][\"MBON_final_g_mean_abs_change\"] >= x[\"A_effect\"][\"MBON_final_g_mean_abs_change\"] and x[\"B_effect\"][\"tracked_MBON_spike_l1\"] >= x[\"A_effect\"][\"tracked_MBON_spike_l1\"] for x in rows)),"
},
{
"line": 430,
"text": " local_effect = main[\"A_MBON_spike_change_seeds\"] >= majority or main[\"A_MBON_subthreshold_change_seeds\"] >= majority"
},
{
"line": 431,
"text": " b_specific = main[\"B_same_effect_or_larger_seeds\"] < majority"
},
{
"line": 444,
"text": " delta, records, table = train_overlay(v, set_a, dan, target, cfg[\"direct_kc\"][\"train_seeds\"][0], True, 1.0, int(cfg[\"direct_kc\"][\"conditioning_trials\"]))"
},
{
"line": 468,
"text": " baseline = v.run(eval_batch, cfg[\"direct_kc\"][\"evaluation_duration_ms\"], int(tcfg[\"seed\"]), zero, target_mbon)"
},
{
"line": 473,
"text": " training = v.run(batch, float(tcfg[\"trial_duration_ms\"]), int(tcfg[\"seed\"]), zero, target_mbon)"
},
{
"line": 478,
"text": " post = v.run(eval_batch, cfg[\"direct_kc\"][\"evaluation_duration_ms\"], int(tcfg[\"seed\"]), delta, target_mbon)"
},
{
"line": 526,
"text": " changed_path = exp / \"direct-kc-memory\" / f\"seed-{cfg['direct_kc']['train_seeds'][0]}\" / \"updated-edges.parquet\""
},
{
"line": 554,
"text": " for name,pop,rate,seed in specs:"
},
{
"line": 555,
"text": " targets=np.flatnonzero(rt.population_masks[pop]).astype(np.int64);rt.reset(seed);rt.step(poisson_batch(targets,rate,1000.0,name),1000.0)"
},
{
"line": 583,
"text": " main_effect=controls[\"A-matched-DAN\"][\"A_effect\"];mbon_readout={\"spike_changed_seeds\":controls[\"A-matched-DAN\"][\"A_MBON_spike_change_seeds\"],"
},
{
"line": 584,
"text": " \"subthreshold_changed_seeds\":controls[\"A-matched-DAN\"][\"A_MBON_subthreshold_change_seeds\"],\"mean_effect\":main_effect,"
},
{
"line": 597,
"text": " \"causal_validation\":causal,\"controls\":controls,\"five_seed_consistency\":direct[\"conditions\"][\"A-matched-DAN\"],"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4i.yaml",
"sha256": "2a100919bb860ec4ebd60690b7410763022d66ddd59762b467e77362f1f45b0b",
"bytes": 3820,
"seed_rng_line_evidence": [
{
"line": 58,
"text": " seeds: [48101, 48102, 48103, 48104, 48105]"
},
{
"line": 63,
"text": " consensus_seed_count: 3"
},
{
"line": 67,
"text": " majority_seeds: 3"
},
{
"line": 79,
"text": " train_seeds: [49001, 49002, 49003, 49004, 49005]"
},
{
"line": 80,
"text": " evaluation_seeds: [49101, 49102, 49103, 49104, 49105]"
},
{
"line": 94,
"text": " seed: 49201"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4j.py",
"sha256": "abb4574da85f00bd9ef61024c65993ef81d42651ff8d8d63c15dd69c6222471e",
"bytes": 42071,
"seed_rng_line_evidence": [
{
"line": 94,
"text": " def _forced(self,steps:int,targets:np.ndarray,seed:int,rate_hz:float|None,pulses:list[float]|None)->torch.Tensor:"
},
{
"line": 97,
"text": " gen=torch.Generator(device=self.device);gen.manual_seed(int(seed));out=torch.rand((steps,len(targets)),generator=gen,device=self.device)<(float(rate_hz)*self.dt/1000.0)"
},
{
"line": 103,
"text": " def run(self,targets:np.ndarray,seed:int,mode:str,params:dict[str,float]|None,duration_ms:float,"
},
{
"line": 110,
"text": " self.rt.reset(int(seed));steps=int(round(duration_ms/self.dt));target_gpu=torch.as_tensor(targets,dtype=torch.int64,device=self.device)"
},
{
"line": 111,
"text": " forced=self._forced(steps,targets,seed,rate_hz,pulses)"
},
{
"line": 113,
"text": " dan_forced=self._forced(steps,dan,seed+991,None,dan_pulses)"
},
{
"line": 193,
"text": " run=sim.run(targets,int(cfg[\"stimuli\"][\"calibration_seed_base\"])+oi,\"graded\",params,float(cfg[\"stimuli\"][\"duration_ms\"]),rate_hz=float(cfg[\"stimuli\"][\"rate_hz\"]));fractions.append(response_metrics(run)[\"KC_active_fraction\"]);vectors.append(run[\"kc_counts\"]);walls.append(run[\"wall_seconds\"])"
},
{
"line": 205,
"text": " seeds=cfg[\"stimuli\"][\"evaluation_seeds\"];records=[];vectors={m:defaultdict(list) for m in (\"original\",\"graded\",\"disabled\")};performance=[];subtype_rows=[]"
},
{
"line": 209,
"text": " for seed in seeds:"
},
{
"line": 210,
"text": " run=sim.run(targets,int(seed)+oi*100,mode,params if mode==\"graded\" else None,float(cfg[\"stimuli\"][\"duration_ms\"]),rate_hz=float(cfg[\"stimuli\"][\"rate_hz\"]));met=response_metrics(run);met.update({\"mode\":mode,\"odor\":oi,\"seed\":int(seed)});records.append(met);vectors[mode][oi].append(run[\"kc_counts\"]);performance.append({\"mode\":mode,\"odor\":oi,\"seed\":int(seed),\"wall\":run[\"wall_seconds\"],\"vram\":run[\"peak_vram_bytes\"],\"overlay_submit\":run[\"overlay_cpu_submit_seconds\"]})"
},
{
"line": 212,
"text": " sm=subtype==label;subtype_rows.append({\"mode\":mode,\"odor\":oi,\"seed\":int(seed),\"KC_subtype\":label,\"total_KC\":int(sm.sum()),\"active_KC\":int(np.count_nonzero(run[\"kc_counts\"][sm])),\"spikes\":int(run[\"kc_counts\"][sm].sum()),\"active_fraction\":float(np.count_nonzero(run[\"kc_counts\"][sm])/sm.sum())})"
},
{
"line": 224,
"text": " for seed_i in range(len(seeds)):"
},
{
"line": 225,
"text": " x=pair_metrics(vectors[mode][odors[i]][seed_i],vectors[mode][odors[j]][seed_i]);between.append(x[\"euclidean_distance\"]);jacc.append(x[\"active_jaccard\"]);corr.append(x[\"correlation\"]);cos.append(x[\"cosine_similarity\"])"
},
{
"line": 240,
"text": " seeds=cfg[\"stimuli\"][\"formal_CS_seeds\"];vec={\"A\":[],\"B\":[]};traces={\"A\":[],\"B\":[]}"
},
{
"line": 242,
"text": " for seed in seeds:"
},
{
"line": 243,
"text": " run=sim.run(targets,int(seed),\"graded\",params,float(cfg[\"stimuli\"][\"duration_ms\"]),rate_hz=float(cfg[\"stimuli\"][\"rate_hz\"]));vec[label].append(run[\"kc_counts\"])"
},
{
"line": 244,
"text": " a=np.asarray(vec[\"A\"]);b=np.asarray(vec[\"B\"]);pairs=[pair_metrics(a[i],b[i]) for i in range(len(seeds))];need=3"
},
{
"line": 248,
"text": " report={\"per_seed\":pairs,\"KC_A_active_mean\":float(np.mean(np.count_nonzero(a,axis=1))),\"KC_B_active_mean\":float(np.mean(np.count_nonzero(b,axis=1))),"
},
{
"line": 323,
"text": " for ts,es in zip(dc[\"train_seeds\"],dc[\"evaluation_seeds\"]):"
},
{
"line": 335,
"text": " row={\"condition\":name,\"train_seed\":int(ts),\"eval_seed\":int(es),\"changed_edges\":int(np.count_nonzero(changed)),\"A_injected_source_edges\":int(np.count_nonzero(aedge)),\"B_injected_source_edges\":int(np.count_nonzero(bedge)),"
},
{
"line": 339,
"text": " main_rows.append(row);d=exp/\"direct-kc-memory\"/f\"seed-{ts}\";d.mkdir(parents=True,exist_ok=True);np.savez_compressed(d/\"overlay.npz\",edge_id=v.edge_ids,plastic_delta_mv=delta);write_json(d/\"result.json\",row)"
},
{
"line": 343,
"text": " \"separation_gain_mean\":float(np.mean([x[\"post_separation\"]-x[\"pre_separation\"] for x in rows])),\"eligibility_update_wall_seconds_mean\":float(np.mean(update_times)) if update_times else 0.0,\"seeds\":len(rows)}"
},
{
"line": 344,
"text": " majority=int(cfg[\"gates\"][\"direct_seed_majority\"]);floor=float(cfg[\"gates\"][\"subthreshold_noise_floor\"])"
},
{
"line": 348,
"text": " report={\"status\":\"PASS-DIRECT-KC-MEMORY-v2\" if passed else \"FAIL-DIRECT-KC-MEMORY-v2\",\"candidate_generated\":passed,\"runtime_mode\":mode,\"successful_seeds\":good,\"required\":majority,\"controls_clean\":controls_clean,\"conditions\":summaries,"
},
{
"line": 357,
"text": " for ts,es in zip(dc[\"train_seeds\"],dc[\"evaluation_seeds\"]):"
},
{
"line": 363,
"text": " posta=sim.run(a,es,\"graded\",params,float(cfg[\"stimuli\"][\"duration_ms\"]),delta=delta,rate_hz=float(cfg[\"stimuli\"][\"rate_hz\"]));postb=sim.run(b,es,\"graded\",params,float(cfg[\"stimuli\"][\"duration_ms\"]),delta=delta,rate_hz=float(cfg[\"stimuli\"][\"rate_hz\"]));rows.append({\"seed\":int(ts),\"changed_edges\":int(np.count_nonzero(delta)),\"A_effect\":effect(prea,posta),\"B_effect\":effect(preb,postb),\"pre_sep\":pair_metrics(prea[\"mbon_counts\"],preb[\"mbon_counts\"])[\"euclidean_distance\"],\"post_sep\":pair_metrics(posta[\"mbon_counts\"],postb[\"mbon_counts\"])[\"euclidean_distance\"]})"
},
{
"line": 364,
"text": " good=sum(x[\"post_sep\"]>x[\"pre_sep\"] and x[\"A_effect\"][\"MBON_g_mean_abs\"]>x[\"B_effect\"][\"MBON_g_mean_abs\"] for x in rows);passed=good>=int(cfg[\"gates\"][\"direct_seed_majority\"])"
},
{
"line": 365,
"text": " report={\"status\":\"PASS-OLFACTORY-ASSOCIATIVE-MEMORY\" if passed else \"FAIL-OLFACTORY-ASSOCIATIVE-MEMORY\",\"candidate_generated\":passed,\"successful_seeds\":good,\"runs\":rows};write_json(exp/\"olfactory-memory/summary.json\",report);return report"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4j.yaml",
"sha256": "b35e4332a91b8489f69348cedf5ac675e9c3902c7e8c7beec5d053773c6529a3",
"bytes": 4902,
"seed_rng_line_evidence": [
{
"line": 66,
"text": " evaluation_seeds: [50101, 50102, 50103, 50104, 50105]"
},
{
"line": 67,
"text": " calibration_seed_base: 50000"
},
{
"line": 68,
"text": " formal_CS_seeds: [48101, 48102, 48103, 48104, 48105]"
},
{
"line": 118,
"text": " train_seeds: [50201, 50202, 50203, 50204, 50205]"
},
{
"line": 119,
"text": " evaluation_seeds: [50301, 50302, 50303, 50304, 50305]"
},
{
"line": 124,
"text": " direct_seed_majority: 3"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4k.py",
"sha256": "6ca7dd7b3fec7f970f8a43ac8286dd0f05b2674ac3996b9b20583b636719abe7",
"bytes": 49126,
"seed_rng_line_evidence": [
{
"line": 128,
"text": " self, targets: np.ndarray, seed: int, mode: str, duration_ms: float,"
},
{
"line": 138,
"text": " self.rt.reset(int(seed)); steps = int(round(float(duration_ms) / self.dt))"
},
{
"line": 140,
"text": " forced = self._forced(steps, targets, seed, rate_hz, pulses)"
},
{
"line": 143,
"text": " dan_forced = self._forced(steps, dan, seed + 991, None, dan_pulses)"
},
{
"line": 382,
"text": " \"original_olfactory_CS\": dict(targets=olf_a, seed=oc[\"seed\"], mode=\"original\", duration_ms=oc[\"duration_ms\"], rate_hz=oc[\"rate_hz\"]),"
},
{
"line": 383,
"text": " \"APL_overlay_olfactory_CS\": dict(targets=olf_a, seed=oc[\"seed\"], mode=\"graded\", duration_ms=oc[\"duration_ms\"], rate_hz=oc[\"rate_hz\"]),"
},
{
"line": 384,
"text": " \"direct_KC_SET_A\": dict(targets=set_a, seed=dc[\"evaluation_seed\"], mode=\"original\", duration_ms=dc[\"duration_ms\"], pulses=dc[\"pulse_times_ms\"], collect_kc_spikes=True),"
},
{
"line": 385,
"text": " \"direct_KC_SET_B\": dict(targets=set_b, seed=dc[\"evaluation_seed\"], mode=\"original\", duration_ms=dc[\"duration_ms\"], pulses=dc[\"pulse_times_ms\"], collect_kc_spikes=True),"
},
{
"line": 400,
"text": " posts[label] = sim.run_audit(targets, dc[\"evaluation_seed\"], \"original\", dc[\"duration_ms\"], delta=actual_delta, pulses=dc[\"pulse_times_ms\"], collect_kc_spikes=True)"
},
{
"line": 426,
"text": " for seed in sc[\"evaluation_seeds\"]:"
},
{
"line": 428,
"text": " base = sim.run_audit(set_a, int(seed), \"original\", dc[\"duration_ms\"], delta=base_delta, pulses=dc[\"pulse_times_ms\"])"
},
{
"line": 432,
"text": " run = base if float(fraction) == 0 else sim.run_audit(set_a, int(seed), \"original\", dc[\"duration_ms\"], delta=delta, pulses=dc[\"pulse_times_ms\"])"
},
{
"line": 435,
"text": " rows.append({\"seed\": int(seed), \"depression_fraction\": float(fraction), \"changed_edges\": int(np.count_nonzero(changed)),"
},
{
"line": 440,
"text": " aggregate.append({\"depression_fraction\": float(frac), \"seeds\": int(len(group)), \"functional_seed_count\": int(group.functional_MBON_output_change.sum()),"
},
{
"line": 443,
"text": " functional_levels = [x[\"depression_fraction\"] for x in aggregate if x[\"functional_seed_count\"] > 0]"
},
{
"line": 445,
"text": " insensitive = at50[\"functional_seed_count\"] == 0"
},
{
"line": 566,
"text": " overlay = np.load(Path(cfg[\"paths\"][\"phase4j_root\"]) / f\"direct-kc-memory/seed-{cfg['direct_kc']['trace_seed']}/overlay.npz\")"
},
{
"line": 573,
"text": " causal_run = sim.run_audit(set_a, cfg[\"direct_kc\"][\"trace_seed\"], \"original\", cfg[\"direct_kc\"][\"duration_ms\"],"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4k.yaml",
"sha256": "3721440cba03036552c358c1a9d3320637e5914da901f74e86189aa297192fd1",
"bytes": 3690,
"seed_rng_line_evidence": [
{
"line": 76,
"text": " trace_seed: 50201"
},
{
"line": 77,
"text": " evaluation_seed: 50301"
},
{
"line": 82,
"text": " seed: 50101"
},
{
"line": 87,
"text": " evaluation_seeds: [50301, 50302, 50303, 50304, 50305]"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4l_finalize.py",
"sha256": "11c63994399d09305648d01d3543a4a4c4f11d62fa85f79e9e4385a040f8de07",
"bytes": 3696,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4l.py",
"sha256": "e97d58d8c1f44920d0e953f8f5831ddc86fa0198784e86460e315de4116ac553",
"bytes": 45747,
"seed_rng_line_evidence": [
{
"line": 46,
"text": " def run_rates(self, targets: np.ndarray, rates_hz: np.ndarray, seed: int, duration_ms: float,"
},
{
"line": 53,
"text": " self.v.install(delta); self.rt.weights[self.apl_edge_gpu] = 0.0; self.rt.reset(int(seed))"
},
{
"line": 56,
"text": " gen = torch.Generator(device=self.device); gen.manual_seed(int(seed))"
},
{
"line": 57,
"text": " tgen0 = time.perf_counter(); forced = torch.rand((steps, len(targets)), generator=gen, device=self.device) < rg[None, :]"
},
{
"line": 60,
"text": " dg = torch.as_tensor(dan, dtype=torch.int64, device=self.device); dan_forced = self._forced(steps, dan, seed + 991, None, dan_pulses)"
},
{
"line": 201,
"text": " gloms = selection[\"glomeruli_order\"]; matched_sum = float(np.median(panel.mapped_response_sum)); seeds = list(map(int, cfg[\"odor_encoder\"][\"evaluation_seeds\"])); runs = {}; records = []; subtype_rows = []; drive_rows = []"
},
{
"line": 206,
"text": " for seed in seeds:"
},
{
"line": 207,
"text": " run = sim.run_rates(targets, rates, seed, float(cfg[\"odor_encoder\"][\"duration_ms\"]), collect_kc_spikes=True); runs[(mode, odor.odor_id, seed)] = run"
},
{
"line": 209,
"text": " records.append({\"mode\": mode, \"split\": odor.split, \"odor_id\": odor.odor_id, \"odor_name\": odor.odor_name, \"formal_label\": odor.formal_label, \"seed\": seed,"
},
{
"line": 214,
"text": " mask = subtype == label; subtype_rows.append({\"mode\": mode, \"odor_id\": odor.odor_id, \"seed\": seed, \"KC_subtype\": label, \"total\": int(mask.sum()), \"active\": int(np.count_nonzero(nonzero[mask])), \"spikes\": int(run[\"kc_counts\"][mask].sum())})"
},
{
"line": 220,
"text": " arr = [runs[(mode, oid, s)][\"kc_counts\"] for s in seeds]"
},
{
"line": 227,
"text": " for seed in seeds:"
},
{
"line": 228,
"text": " q = pair_metrics(runs[(mode, odor_ids[i], seed)][\"kc_counts\"], runs[(mode, odor_ids[j], seed)][\"kc_counts\"]); ds.append(q[\"euclidean_distance\"]); between.append(q[\"euclidean_distance\"]); between_j.append(q[\"active_jaccard\"]); between_cos.append(q[\"cosine_similarity\"]); between_corr.append(q[\"correlation\"])"
},
{
"line": 231,
"text": " aa = [runs[(mode, oid, s)][\"kc_counts\"] for s in seeds]"
},
{
"line": 241,
"text": " candidate = bool(all(x[\"gate\"] for x in summaries.values())); report = {\"status\": \"MaleCNS-OlfactoryEncoder-v1-candidate\" if candidate else \"FAIL-PHYSIOLOGY-GROUNDED-ODOR-ENCODING\", \"candidate_generated\": candidate, \"modes\": summaries, \"seeds\": seeds, \"APL_parameters_locked\": cfg[\"apl_overlay\"]}"
},
{
"line": 246,
"text": " seeds = list(map(int, cfg[\"odor_encoder\"][\"evaluation_seeds\"])); aid = selection[\"ODOR_A\"][\"odor_id\"]; bid = selection[\"ODOR_B\"][\"odor_id\"]"
},
{
"line": 248,
"text": " active = np.stack([runs[(\"matched_energy\", oid, s)][\"kc_counts\"] > 0 for s in seeds]); return v.kc[active.sum(axis=0) >= int(cfg[\"gates\"][\"seed_majority\"])]"
},
{
"line": 301,
"text": " for tsi, train_seed in enumerate(map(int, spec[\"train_seeds\"])):"
},
{
"line": 302,
"text": " delta = np.zeros(len(v.edge_ids), np.float32); overlays[train_seed] = {}"
},
{
"line": 304,
"text": " run = sim.run_rates(at, ar, train_seed + pairing, float(spec[\"duration_ms\"]), delta=delta, dan_targets=dan, dan_pulses=[float(spec[\"dan_time_ms\"])])"
},
{
"line": 307,
"text": " overlays[train_seed][pairing] = delta.copy(); changed = delta != 0; denom = float(np.abs(v.base_candidate[changed]).sum()); pct = float(100 * np.abs(delta[changed]).sum() / denom) if denom else 0.0; lower = -float(cfg[\"eligibility_v2\"][\"max_fractional_depression\"]) * v.base_candidate"
},
{
"line": 308,
"text": " eval_seed = int(spec[\"evaluation_seeds\"][tsi]); prea = sim.run_rates(at, ar, eval_seed, float(spec[\"duration_ms\"]), collect_kc_spikes=True); posta = sim.run_rates(at, ar, eval_seed, float(spec[\"duration_ms\"]), delta=delta, collect_kc_spikes=True); preb = sim.run_rates(bt, br, eval_seed, float(spec[\"duration_ms\"]), collect_kc_spikes=True); postb = sim.run_rates(bt, br, eval_seed, float(spec[\"duration_ms\"]), delta=delta, collect_kc_spikes=True)"
},
{
"line": 311,
"text": " rows.append({\"train_seed\": train_seed, \"eval_seed\": eval_seed, \"pairings\": pairing, \"updated_edges\": int(changed.sum()), \"mean_delta_mv\": float(vals.mean()) if len(vals) else 0.0, \"median_delta_mv\": float(np.median(vals)) if len(vals) else 0.0, \"max_abs_delta_mv\": float(np.abs(vals).max()) if len(vals) else 0.0, \"total_abs_delta_mv\": float(np.abs(vals).sum()), \"depression_percent_changed_base\": pct,"
},
{
"line": 314,
"text": " np.savez_compressed(exp / \"plasticity-magnitude\" / f\"seed-{train_seed}-overlays.npz\", edge_id=v.edge_ids, **{f\"pairing_{p}\": d for p, d in overlays[train_seed].items()})"
},
{
"line": 319,
"text": " curve.append({\"pairings\": int(p), \"seeds\": int(len(g)), \"updated_edges_mean\": float(g.updated_edges.mean()), \"mean_delta_mv\": float(g.mean_delta_mv.mean()), \"median_delta_mv\": float(g.median_delta_mv.mean()), \"max_abs_delta_mv\": float(g.max_abs_delta_mv.mean()), \"total_abs_delta_mv_mean\": float(g.total_abs_delta_mv.mean()), \"depression_percent_mean\": float(g.depression_percent_changed_base.mean()), \"depression_percent_min\": float(g.depression_percent_changed_base.min()), \"depression_percent_max\": float(g.depression_percent_changed_base.max()), \"saturation_fraction_mean\": float(g.saturation_fraction.mean()), \"isolated_functional_seeds\": int(functional.sum()), \"isolated_A_delta_g_mean\": float(g.isolated_A_delta_g.mean()), \"isolated_A_delta_v_mean\": float(g.isolated_A_delta_v.mean()), \"whole_A_MBON_spike_l1_mean\": float(g.whole_A_MBON_spike_l1.mean()), \"whole_A_downstream_spike_l1_mean\": float(g.whole_A_downstream_spike_l1.mean())})"
},
{
"line": 320,
"text": " monotonic = all(np.all(np.diff(frame[frame.train_seed == s].sort_values(\"pairings\").total_abs_delta_mv) >= -1e-7) for s in frame.train_seed.unique()); bounded = bool(frame.saturation_fraction.max() <= 1.0 and not np.any(frame.mean_delta_mv > 0)); accumulation = bool(monotonic and bounded)"
},
{
"line": 322,
"text": " transfer_points = [x for x in curve if x[\"isolated_functional_seeds\"] >= int(cfg[\"gates\"][\"seed_majority\"])]; transfer = bool(transfer_points)"
},
{
"line": 336,
"text": " for si, ts in enumerate(map(int, spec[\"train_seeds\"])):"
},
{
"line": 337,
"text": " delta = overlays[ts][count]; es = int(spec[\"evaluation_seeds\"][si])"
},
{
"line": 340,
"text": " rows.append({\"seed\": ts, \"stimulus\": label, **ef})"
},
{
"line": 342,
"text": " good = int(((a.MBON_g_max_abs.to_numpy() > b.MBON_g_max_abs.to_numpy()) & ((a.MBON_spike_l1.to_numpy() > 0) | a.MBON_first_latency_delta_ms.fillna(0).to_numpy().astype(float).astype(bool))).sum()); passed = good >= int(cfg[\"gates\"][\"seed_majority\"])"
},
{
"line": 344,
"text": " report = {\"status\": \"PASS-OLFACTORY-ASSOCIATIVE-MEMORY\" if passed else \"FAIL-OLFACTORY-ASSOCIATIVE-MEMORY\", \"candidate_generated\": passed, \"successful_seeds\": good, \"required\": int(cfg[\"gates\"][\"seed_majority\"]), \"conditioning_pairings_frozen_before_results\": count,"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4l.yaml",
"sha256": "9b8bcb7e031bf9063033a1854a439a7cd0bf6730301c2d2d02ca99a76f098d6e",
"bytes": 5653,
"seed_rng_line_evidence": [
{
"line": 86,
"text": " evaluation_seeds: [51101, 51102, 51103, 51104, 51105]"
},
{
"line": 114,
"text": " train_seeds: [51201, 51202, 51203, 51204, 51205]"
},
{
"line": 115,
"text": " evaluation_seeds: [51301, 51302, 51303, 51304, 51305]"
},
{
"line": 129,
"text": " seed_majority: 3"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4m_finalize.py",
"sha256": "58a2fd9bfc89725c741201f43ef06cf34840f78734190faad193ae8cee89c6ab",
"bytes": 5545,
"seed_rng_line_evidence": [
{
"line": 36,
"text": " categories = edge.odor_eligibility_category.astype(str).to_numpy(); static = {}; seed_rows = []"
},
{
"line": 37,
"text": " for seed in map(int, mcfg[\"protocol\"][\"train_seeds\"]):"
},
{
"line": 38,
"text": " data = np.load(exp / \"efficacy-overlay\" / f\"E-seed-{seed}-pairings-100.npz\")"
},
{
"line": 42,
"text": " sel = categories == cat; seed_rows.append({\"seed\": seed, \"mechanism\": mech, \"edge_category\": cat,"
},
{
"line": 46,
"text": " audit = pd.DataFrame(seed_rows); audit.to_parquet(exp / \"whole-brain-replay/endpoint-E-identity.parquet\", index=False)"
},
{
"line": 55,
"text": " iso[\"endpoint_100_majority_pass\"] = {mech: bool(gate[\"endpoint_100_specific_functional_seeds\"] >= int(mcfg[\"gates\"][\"seed_majority\"]))"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4m_formal.py",
"sha256": "4d1bb00da460231fee7bd986e202217aa065ba46f78500a7e6e63b87329d88a0",
"bytes": 17678,
"seed_rng_line_evidence": [
{
"line": 29,
"text": " condition: str, train_seed: int, pn: dict[str, tuple[np.ndarray, np.ndarray]],"
},
{
"line": 40,
"text": " floor = np.float32(1.0 - float(cfg[\"eligibility_v2\"][\"max_fractional_depression\"])); rng = np.random.default_rng(train_seed)"
},
{
"line": 45,
"text": " run = sim.run_rates(*pn[label], train_seed + pairing, float(protocol[\"duration_ms\"]), delta=delta,"
},
{
"line": 52,
"text": " return {\"multiplicative\": E_unbounded, \"bounded_multiplicative\": E_bounded}, delta, {\"condition\": condition, \"train_seed\": train_seed, \"pairings\": count, \"DAN_type\": dan_type,"
},
{
"line": 78,
"text": " for ts, es in zip(map(int, mcfg[\"protocol\"][\"train_seeds\"]), map(int, mcfg[\"protocol\"][\"evaluation_seeds\"])):"
},
{
"line": 82,
"text": " np.savez_compressed(exp / \"controls\" / f\"E-{condition}-seed-{ts}.npz\","
},
{
"line": 87,
"text": " saved = np.load(exp / \"efficacy-overlay\" / f\"E-seed-{ts}-pairings-100.npz\")"
},
{
"line": 96,
"text": " E_snapshots.append({\"train_seed\": ts, \"mechanism\": mechanism, \"condition\": condition, \"E_min\": float(E.min()), \"changed_edges\": int(changed.sum()), \"A_specific_E_reduction_sum\": identity[\"A-specific\"][\"E_reduction_sum\"], \"B_specific_E_reduction_sum\": identity[\"B-specific\"][\"E_reduction_sum\"], \"A_specific_E_reduction_mean\": identity[\"A-specific\"][\"E_reduction_mean\"], \"B_specific_E_reduction_mean\": identity[\"B-specific\"][\"E_reduction_mean\"]})"
},
{
"line": 99,
"text": " result = effect(baseline[label], post); rows.append({\"train_seed\": ts, \"eval_seed\": es, \"mechanism\": mechanism, \"condition\": condition, \"odor\": label,"
},
{
"line": 113,
"text": " rows.append({\"train_seed\": ts, \"eval_seed\": es, \"mechanism\": mechanism, \"condition\": \"A+matched-DAN-matched-energy-eval\", \"odor\": label,"
},
{
"line": 131,
"text": " f = frame[frame.mechanism == mechanism]; main = f[f.condition == \"A+matched-DAN\"].sort_values([\"train_seed\", \"odor\"])"
},
{
"line": 132,
"text": " a = main[main.odor == \"ODOR_A\"].sort_values(\"train_seed\"); b = main[main.odor == \"ODOR_B\"].sort_values(\"train_seed\")"
},
{
"line": 142,
"text": " am = fm[fm.odor == \"ODOR_A\"].sort_values(\"train_seed\"); bm = fm[fm.odor == \"ODOR_B\"].sort_values(\"train_seed\")"
},
{
"line": 144,
"text": " passed = bool(np.count_nonzero(specificity & edge_specific & (a.MBON_spike_count_delta.to_numpy() <= 0)) >= int(mcfg[\"gates\"][\"seed_majority\"]) and matched_successes >= int(mcfg[\"gates\"][\"seed_majority\"]) and no_control_same and f.finite.all())"
},
{
"line": 145,
"text": " per[mechanism] = {\"pass\": passed, \"successful_seeds\": int(np.count_nonzero(specificity & edge_specific)), \"A_MBON_spike_L1_mean\": float(a.MBON_spike_L1.mean()), \"B_MBON_spike_L1_mean\": float(b.MBON_spike_L1.mean()),"
},
{
"line": 148,
"text": " \"controls_no_same_change\": no_control_same, \"matched_energy_successful_seeds\": matched_successes, \"all_finite\": bool(f.finite.all())}"
},
{
"line": 152,
"text": " \"five_training_seeds\": True, \"independent_evaluation_seeds\": True,"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4m_literature_audit.json",
"sha256": "820ef25d0dff122a3cad1a763074ff5e6f74a5a7e08fb00620454ab4164f3fc4",
"bytes": 6464,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4m_performance.py",
"sha256": "7fab670032732d36823499c135a725f5ab7c8aa8305be780ee51f7eb0b8dfd57",
"bytes": 5487,
"seed_rng_line_evidence": [
{
"line": 31,
"text": " for ts, es in zip(map(int, mcfg[\"protocol\"][\"train_seeds\"]), map(int, mcfg[\"protocol\"][\"evaluation_seeds\"])):"
},
{
"line": 32,
"text": " with np.load(exp / \"efficacy-overlay\" / f\"E-seed-{ts}-pairings-100.npz\") as state:"
},
{
"line": 37,
"text": " records.append({\"train_seed\": ts, \"eval_seed\": es, \"condition\": label, \"brain_loop_seconds\": run[\"wall_seconds\"],"
},
{
"line": 41,
"text": " frame = pd.DataFrame(records); frame.to_parquet(exp / \"performance/paired-overlay-runtime.parquet\", index=False); base = frame[frame.condition == \"baseline\"].sort_values(\"train_seed\"); post = frame[frame.condition == \"bounded_E100\"].sort_values(\"train_seed\")"
},
{
"line": 43,
"text": " report = {\"biological_duration_ms\": duration, \"trial\": \"frozen ODOR_A 120 ms, paired evaluation seed, no dopamine or learning\","
},
{
"line": 49,
"text": " \"five_paired_seeds\": True, \"numeric_results_not_reused_for_gates\": True, \"all_finite\": bool(frame.finite.all())}"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4m.py",
"sha256": "3d1b64861559c9c04e08153b4056d968ab556637916d388170b36fc6348fa822",
"bytes": 35313,
"seed_rng_line_evidence": [
{
"line": 139,
"text": " protocol = mcfg[\"protocol\"]; points = list(map(int, protocol[\"pairing_counts\"])); maxpair = max(points); seeds = list(map(int, protocol[\"train_seeds\"])); eval_seeds = list(map(int, protocol[\"evaluation_seeds\"]));"
},
{
"line": 140,
"text": " if points != list(map(int, cfg[\"plasticity_accumulation\"][\"pairing_counts\"])) or seeds != list(map(int, cfg[\"plasticity_accumulation\"][\"train_seeds\"])) or eval_seeds != list(map(int, cfg[\"plasticity_accumulation\"][\"evaluation_seeds\"])): raise RuntimeError(\"Phase 4L frozen pairing protocol mismatch\")"
},
{
"line": 153,
"text": " for es in eval_seeds:"
},
{
"line": 156,
"text": " run[\"kc_spikes\"].to_parquet(exp / \"isolated-replay\" / f\"KC-spikes-{label}-seed-{es}.parquet\", index=False)"
},
{
"line": 158,
"text": " for ts in seeds:"
},
{
"line": 159,
"text": " delta_add = zero.copy(); em = np.ones(len(v.edge_ids), np.float32); eb = np.ones(len(v.edge_ids), np.float32); checkpoints[ts] = {}; p4l = np.load(Path(cfg[\"paths\"][\"phase4l_root\"]) / \"plasticity-magnitude\" / f\"seed-{ts}-overlays.npz\")"
},
{
"line": 169,
"text": " histories.append({\"train_seed\": ts, \"pairing\": pairing, \"edge_id\": int(v.edge_ids[local]), \"eligibility\": float(elig[local]),"
},
{
"line": 173,
"text": " reproduction.append({\"train_seed\": ts, \"pairing\": pairing, \"max_abs_error_mv\": error, \"exact\": bool(np.array_equal(saved, delta_add))})"
},
{
"line": 174,
"text": " if error > 1e-6: raise RuntimeError(f\"Phase 4L additive training reproduction divergence seed={ts} pairing={pairing}: {error}\")"
},
{
"line": 176,
"text": " np.savez_compressed(exp / \"efficacy-overlay\" / f\"E-seed-{ts}-pairings-{pairing}.npz\", E_multiplicative=em, E_bounded_multiplicative=eb)"
},
{
"line": 183,
"text": " for ts, es in zip(seeds, eval_seeds):"
},
{
"line": 192,
"text": " pairrow = {\"train_seed\": ts, \"eval_seed\": es, \"pairings\": pairing, \"mechanism\": mechanism, \"updated_edges\": int(changed.sum()),"
},
{
"line": 208,
"text": " curve.append({\"mechanism\": mechanism, \"pairings\": int(pairing), \"seeds\": int(len(frame)), \"A_specific_functional_seeds\": int(frame.A_specific_functional.sum()),"
},
{
"line": 217,
"text": " gate = {}; majority = int(mcfg[\"gates\"][\"seed_majority\"])"
},
{
"line": 219,
"text": " successes = [x for x in curve if x[\"mechanism\"] == mech and x[\"A_specific_functional_seeds\"] >= majority]"
},
{
"line": 220,
"text": " gate[mech] = {\"pass\": bool(successes), \"first_passing_pairing\": min((x[\"pairings\"] for x in successes), default=None), \"seed_majority\": majority,"
},
{
"line": 221,
"text": " \"endpoint_100_specific_functional_seeds\": next(x[\"A_specific_functional_seeds\"] for x in curve if x[\"mechanism\"] == mech and x[\"pairings\"] == 100)}"
},
{
"line": 224,
"text": " \"mechanism_gates\": gate, \"curve\": curve, \"same_KC_spikes_DAN_timing_mask_and_seeds\": True, \"phase4l_additive_reproduced\": True,"
},
{
"line": 237,
"text": " for ts in map(int, mcfg[\"protocol\"][\"train_seeds\"]):"
},
{
"line": 241,
"text": " frame[\"train_seed\"] = ts; frame[\"mechanism\"] = mechanism; frame[\"anatomical_weight_mv\"] = anatomical; frame[\"E_baseline\"] = 1.0; frame[\"E_current\"] = E"
},
{
"line": 248,
"text": " by = out[(out.train_seed == int(mcfg[\"protocol\"][\"train_seeds\"][0])) & (out.mechanism == \"bounded_multiplicative\")]"
},
{
"line": 250,
"text": " report = {\"rows\": int(len(out)), \"unique_edges\": int(out.edge_id.nunique()), \"raw_count_vs_effective_weight_spearman_seed1_bounded\": float(rho),"
},
{
"line": 266,
"text": " for ts, es in zip(map(int, mcfg[\"protocol\"][\"train_seeds\"]), map(int, mcfg[\"protocol\"][\"evaluation_seeds\"])):"
},
{
"line": 273,
"text": " rows.append({\"train_seed\": ts, \"eval_seed\": es, \"mechanism\": mechanism, \"odor\": label, \"MBON_spike_L1\": effects[label][\"MBON_spike_l1\"],"
},
{
"line": 280,
"text": " sub = frame[frame.mechanism == mechanism]; a = sub[sub.odor == \"ODOR_A\"].sort_values(\"train_seed\"); b = sub[sub.odor == \"ODOR_B\"].sort_values(\"train_seed\")"
},
{
"line": 282,
"text": " gates[mechanism] = {\"A_specific_functional_seeds\": wins, \"pass\": bool(wins >= int(mcfg[\"gates\"][\"seed_majority\"]) and sub.finite.all() and sub.active_neuron_fraction.max() < .5),"
},
{
"line": 342,
"text": " controls = {\"isolated_neutral_B\": \"RUN\", \"matched_seed_same_KC_spikes\": \"RUN\", \"D_zero\": \"analytic identity E=1, no dopamine update\", \"random_reward\": \"NOT-RUN-FORMAL-CONDITIONING-BLOCKED\", \"unrelated_DAN\": \"NOT-RUN-FORMAL-CONDITIONING-BLOCKED\", \"frozen_no_plasticity\": \"isolated E=1 reference replay\"}"
},
{
"line": 344,
"text": " checkpoints_bytes = sum(p.stat().st_size for p in (exp / \"efficacy-overlay\").glob(\"E-seed-*.npz\")); performance = isolated[\"performance\"] | {\"overlay_checkpoint_bytes_total\": checkpoints_bytes,"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4m.yaml",
"sha256": "52c364d2ada03ba0c3b06220e91641c74ee79486c35d8c283237f02a98a2e471",
"bytes": 2339,
"seed_rng_line_evidence": [
{
"line": 22,
"text": " train_seeds: [51201, 51202, 51203, 51204, 51205]"
},
{
"line": 23,
"text": " evaluation_seeds: [51301, 51302, 51303, 51304, 51305]"
},
{
"line": 37,
"text": " seed_majority: 3"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4n_alldan.py",
"sha256": "df12477905d7da70e0e337a8d4408cc247135e7876ac7b197e1997f2c4611a54",
"bytes": 6158,
"seed_rng_line_evidence": [
{
"line": 26,
"text": " bad={int(seed):min(int(x[\"pairing\"]) for x in reproduction[\"mismatched_trials\"] if int(x[\"seed\"])==int(seed))"
},
{
"line": 27,
"text": " for seed in ncfg[\"train_seeds\"] if any(int(x[\"seed\"])==int(seed) for x in reproduction[\"mismatched_trials\"])}"
},
{
"line": 33,
"text": " for seed,es in zip(ncfg[\"train_seeds\"],ncfg[\"evaluation_seeds\"]):"
},
{
"line": 35,
"text": " with np.load(exp/\"endogenous-dan\"/f\"E-all-DAN-shadow-seed-{seed}-pairings-{pair}.npz\") as f:"
},
{
"line": 40,
"text": " spikes=pd.read_parquet(old/\"isolated-replay\"/f\"KC-spikes-ODOR_{odor}-seed-{es}.parquet\")"
},
{
"line": 44,
"text": " rows.append({\"seed\":seed,\"eval_seed\":es,\"pairings\":pair,\"mechanism\":mechanism,"
},
{
"line": 45,
"text": " \"trace_exact_prefix\":bool(pair < bad.get(int(seed),10**9)),"
},
{
"line": 56,
"text": " for seed,es in zip(ncfg[\"train_seeds\"],ncfg[\"evaluation_seeds\"]):"
},
{
"line": 59,
"text": " with np.load(exp/\"endogenous-dan\"/f\"E-all-DAN-shadow-seed-{seed}-pairings-{pair}.npz\") as f:"
},
{
"line": 66,
"text": " whole.append({\"seed\":seed,\"eval_seed\":es,\"pairings\":pair,\"mechanism\":mechanism,\"odor\":odor,"
},
{
"line": 67,
"text": " \"trace_exact_prefix\":bool(pair < bad.get(int(seed),10**9)),"
},
{
"line": 72,
"text": " iso=table.groupby([\"mechanism\",\"pairings\"]).agg(A_specific_seeds=(\"A_specific_functional\",\"sum\"),A_spike_L1=(\"A_MBON_spike_L1\",\"mean\"),B_spike_L1=(\"B_MBON_spike_L1\",\"mean\")).reset_index()"
},
{
"line": 73,
"text": " pivot=wt.pivot(index=[\"mechanism\",\"pairings\",\"seed\"],columns=\"odor\",values=\"MBON_spike_L1\").reset_index()"
},
{
"line": 75,
"text": " wh=pivot.groupby([\"mechanism\",\"pairings\"]).agg(A_minus_B_mean=(\"A_minus_B\",\"mean\"),A_specific_seeds=(\"A_minus_B\",lambda x:int((x>0).sum()))).reset_index()"
},
{
"line": 76,
"text": " clean=table[table.trace_exact_prefix].groupby([\"mechanism\",\"pairings\"]).agg(clean_seed_count=(\"seed\",\"nunique\"),A_specific_seeds=(\"A_specific_functional\",\"sum\"),"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4n_control_whole.py",
"sha256": "560ebffba3e8c3457827a0a682476d125a2bbef81958f5b621d32c1203aada1e",
"bytes": 3237,
"seed_rng_line_evidence": [
{
"line": 32,
"text": " for seed,es in zip(ncfg[\"train_seeds\"],ncfg[\"evaluation_seeds\"]):"
},
{
"line": 36,
"text": " with np.load(exp/\"controls\"/f\"E-{condition}-seed-{seed}-pairings-{pair}.npz\") as f:E=f[\"E\"].copy()"
},
{
"line": 41,
"text": " rows.append({\"condition\":condition,\"train_seed\":seed,\"eval_seed\":es,\"pairings\":pair,\"odor\":odor,"
},
{
"line": 45,
"text": " print(json.dumps({\"seed\":seed,\"completed_control_wholebrain\":True}),flush=True)"
},
{
"line": 47,
"text": " pivot=table.pivot(index=[\"condition\",\"pairings\",\"train_seed\"],columns=\"odor\",values=\"MBON_spike_L1\").reset_index()"
},
{
"line": 49,
"text": " summary=pivot.groupby([\"condition\",\"pairings\"]).agg(A_minus_B_mean=(\"A_minus_B\",\"mean\"),A_specific_seeds=(\"A_minus_B\",lambda x:int((x>0).sum()))).reset_index()"
},
{
"line": 51,
"text": " \"total_brain_wall_seconds\":float(sum(walls)),\"frozen_E1_reference_reused_per_evaluation_seed\":True,"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4n_controls.py",
"sha256": "229e48aa63a725e65202dfbabec58d9a092822482c6d0b461ab5a72ee323a901",
"bytes": 7198,
"seed_rng_line_evidence": [
{
"line": 43,
"text": " for seed in ncfg[\"train_seeds\"]:"
},
{
"line": 45,
"text": " rng=np.random.default_rng(seed+int(ncfg[\"random_us_seed_offset\"]))"
},
{
"line": 46,
"text": " np.savez_compressed(exp/\"controls\"/f\"E-{condition}-seed-{seed}-pairings-0.npz\",E=E,edge_id=v.edge_ids)"
},
{
"line": 47,
"text": " rows.append({\"condition\":condition,\"seed\":seed,\"pairings\":0,\"odor\":odor,\"DAN_compartment\":compartment,"
},
{
"line": 52,
"text": " run=sim.run_rates(*pn[odor],seed+pairing,float(mcfg[\"protocol\"][\"duration_ms\"]),delta=add,dan_targets=dan,dan_pulses=[pulse])"
},
{
"line": 58,
"text": " out=exp/\"controls\"/f\"E-{condition}-seed-{seed}-pairings-{pairing}.npz\""
},
{
"line": 60,
"text": " rows.append({\"condition\":condition,\"seed\":seed,\"pairings\":pairing,\"odor\":odor,\"DAN_compartment\":compartment,"
},
{
"line": 64,
"text": " print(json.dumps({\"condition\":condition,\"seed\":seed,\"pairing\":pairing,\"E_reduction_sum\":float(np.sum(1-E))}),flush=True)"
},
{
"line": 65,
"text": " print(json.dumps({\"condition\":condition,\"seed\":seed,\"pairings\":args.max_pairings,\"E_reduction_sum\":float(np.sum(1-E))}),flush=True)"
},
{
"line": 72,
"text": " for seed,es in zip(ncfg[\"train_seeds\"],ncfg[\"evaluation_seeds\"]):"
},
{
"line": 74,
"text": " with np.load(exp/\"controls\"/f\"E-{condition}-seed-{seed}-pairings-{pair}.npz\") as f:E=f[\"E\"].copy()"
},
{
"line": 77,
"text": " spikes=pd.read_parquet(Path(mcfg[\"experiment_root\"])/\"isolated-replay\"/f\"KC-spikes-ODOR_{odor}-seed-{es}.parquet\")"
},
{
"line": 81,
"text": " replay_rows.append({\"condition\":condition,\"train_seed\":seed,\"eval_seed\":es,\"pairings\":pair,"
},
{
"line": 87,
"text": " write_json(exp/\"controls/independent-summary.json\",{\"conditions\":list(conditions),\"seeds\":list(ncfg[\"train_seeds\"]),"
},
{
"line": 88,
"text": " \"pairings_per_seed\":args.max_pairings,\"training_calls\":len(walls),\"training_wall_seconds\":float(sum(walls)),"
},
{
"line": 91,
"text": " \"isolated_replay\":rframe.groupby([\"condition\",\"pairings\"]).agg(A_specific_seeds=(\"A_specific_functional\",\"sum\"),A_spike_L1=(\"A_MBON_spike_L1\",\"mean\"),B_spike_L1=(\"B_MBON_spike_L1\",\"mean\")).reset_index().to_dict(\"records\")})"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4n_events.py",
"sha256": "1fd10326d9e9e4dd6e78b71a6a7c784058c51119a3a5523d4fe50550cd23bbda",
"bytes": 17592,
"seed_rng_line_evidence": [
{
"line": 24,
"text": " def run_events(self, targets, rates_hz, seed, duration_ms, delta, dan_targets, dan_pulse_ms, all_dan,"
},
{
"line": 27,
"text": " self.v.install(delta); self.rt.weights[self.apl_edge_gpu] = 0.0; self.rt.reset(int(seed))"
},
{
"line": 30,
"text": " gen = torch.Generator(device=self.device); gen.manual_seed(int(seed))"
},
{
"line": 31,
"text": " forced = torch.rand((steps, len(targets)), generator=gen, device=self.device) < rg[None, :]"
},
{
"line": 33,
"text": " dan_forced = self._forced(steps, dan_targets, seed + 991, None, [dan_pulse_ms])"
},
{
"line": 80,
"text": " ap = argparse.ArgumentParser(); ap.add_argument(\"--config\", type=Path, required=True); ap.add_argument(\"--max-pairings\", type=int, default=100); ap.add_argument(\"--train-seed\", type=int)"
},
{
"line": 105,
"text": " hist_by = {(int(s),int(p)): frame for (s,p),frame in history.groupby([\"train_seed\",\"pairing\"])}"
},
{
"line": 109,
"text": " for seed in ([args.train_seed] if args.train_seed is not None else ncfg[\"train_seeds\"]):"
},
{
"line": 113,
"text": " rng = np.random.default_rng(seed + int(ncfg[\"random_us_seed_offset\"]))"
},
{
"line": 117,
"text": " run = sim.run_events(*pn, seed+pairing, float(mcfg[\"protocol\"][\"duration_ms\"]), additive, matched,"
},
{
"line": 119,
"text": " perf.append({\"seed\":seed,\"pairing\":pairing,\"wall_seconds\":run[\"wall_seconds\"],\"peak_vram_bytes\":run[\"peak_vram_bytes\"]})"
},
{
"line": 123,
"text": " kc_event_frames.append(pd.DataFrame({\"seed\":seed,\"pairing\":pairing,\"step\":ks.astype(np.int32),"
},
{
"line": 127,
"text": " dan_event_frames.append(pd.DataFrame({\"seed\":seed,\"pairing\":pairing,\"step\":ds.astype(np.int32),"
},
{
"line": 153,
"text": " gate_rows.append({\"seed\":seed,\"pairing\":pairing,\"step\":step,\"time_ms\":step*sim.dt,"
},
{
"line": 162,
"text": " audit_rows.append({\"seed\":seed,\"pairing\":pairing,\"edge_id\":int(identity.edge_id.iat[local]),"
},
{
"line": 174,
"text": " rec = hist_by[(int(seed),int(pairing))]"
},
{
"line": 179,
"text": " mismatches.append({\"seed\":seed,\"pairing\":pairing,\"max_abs_eligibility_error\":err})"
},
{
"line": 185,
"text": " raise RuntimeError(f\"sealed E history before-state mismatch {seed}/{pairing}\")"
},
{
"line": 191,
"text": " with np.load(exp / \"acquisition-trajectory\" / f\"E-seed-{seed}-pairings-{pairing}.npz\") as f:"
},
{
"line": 193,
"text": " if chk_err > 1e-6: raise RuntimeError(f\"external E reconstruction mismatch {seed}/{pairing}: {chk_err}\")"
},
{
"line": 194,
"text": " np.savez_compressed(exp / \"endogenous-dan\" / f\"E-all-DAN-shadow-seed-{seed}-pairings-{pairing}.npz\", E_all_DAN=E_all,"
},
{
"line": 196,
"text": " shadow_rows.append({\"seed\":seed,\"pairing\":pairing,\"random_US_step\":random_step,"
},
{
"line": 202,
"text": " run[\"all_events\"].to_parquet(exp / \"whole-brain\" / f\"spike-events-seed-{seed}-pairings-{pairing}.parquet\", index=False)"
},
{
"line": 204,
"text": " print(json.dumps({\"seed\":seed,\"pairing\":pairing,\"endogenous_steps\":endogenous_steps,\"E_external_sum\":float(np.sum(1-E_ext)),\"E_all_sum\":float(np.sum(1-E_all))}),flush=True)"
},
{
"line": 219,
"text": " \"frozen_brain_dynamics_changed\": False, \"training_seed_count\":len(ncfg[\"train_seeds\"]),\"pairings_per_seed\":args.max_pairings})"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4n_finalize.py",
"sha256": "2ed9b2a7282f0319238646d2c9be0533650a0146292c2de8201af27bb9cb2e5c",
"bytes": 20496,
"seed_rng_line_evidence": [
{
"line": 29,
"text": " if event.duplicated([\"seed\", \"pairing\", \"edge_id\"]).any():"
},
{
"line": 31,
"text": " h = history[[\"train_seed\", \"pairing\", \"edge_id\", \"E_bounded_before\", \"E_bounded_after\"]].rename(columns={\"train_seed\":\"seed\"})"
},
{
"line": 32,
"text": " x = event.merge(h,on=[\"seed\",\"pairing\",\"edge_id\"],how=\"left\",validate=\"one_to_one\")"
},
{
"line": 50,
"text": " \"first_B_specific_update_pairing_by_seed\":{str(seed):int(frame.pairing.min()) for seed,frame in b.groupby(\"seed\")},"
},
{
"line": 110,
"text": " w=whole[whole.pairings==pair].pivot(index=\"train_seed\",columns=\"odor\",values=\"MBON_spike_L1\")"
},
{
"line": 116,
"text": " \"wholebrain_A_specific_seeds\":int(((w[\"A\"]>w[\"B\"])&(w[\"A\"]>0)).sum())})"
},
{
"line": 162,
"text": " eligible=response_curve[response_curve.A_specific_functional_seeds>=3]"
},
{
"line": 173,
"text": " \"reason\":\"50 and 60 have the same five-seed spike-L1 separation; latency difference is sub-femtosecond float summation noise, far below 0.1 ms simulation dt. Earliest-tie rule selects 50.\","
},
{
"line": 189,
"text": " for seed in ncfg[\"train_seeds\"]:"
},
{
"line": 191,
"text": " np.savez_compressed(exp/\"acquisition-trajectory\"/f\"E-seed-{seed}-pairings-0.npz\",E_bounded=one,E_unbounded=one,edge_id=identity.edge_id.to_numpy(np.int64))"
},
{
"line": 194,
"text": " z.append({\"seed\":seed,\"pairings\":0,\"mechanism\":mechanism,\"group\":group_name,\"total_edges\":int(count),"
},
{
"line": 196,
"text": " cz.append({\"seed\":seed,\"pairings\":0,\"mechanism\":mechanism,\"A-specific\":0.0,\"B-specific\":0.0,\"shared\":0.0,\"inactive\":0.0,"
},
{
"line": 198,
"text": " pd.concat([groups0,pd.DataFrame(z)],ignore_index=True).sort_values([\"seed\",\"pairings\",\"mechanism\",\"group\"]).to_parquet(exp/\"acquisition-trajectory/group-plasticity.parquet\",index=False)"
},
{
"line": 199,
"text": " pd.concat([csi0,pd.DataFrame(cz)],ignore_index=True).sort_values([\"seed\",\"pairings\",\"mechanism\"]).to_parquet(exp/\"specificity-curve/CSI.parquet\",index=False)"
},
{
"line": 233,
"text": " \"functional_collapse_first_checkpoint\":next((int(r.pairings) for _,r in iso.iterrows() if r.pairings>peak and r.A_specific_functional_seeds<3),None) if peak is not None else None,"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4n_finish.py",
"sha256": "690d33c18d6438d11050ba1fdee9a8015c6bc25b7c85f78cb56f4d06820e35a7",
"bytes": 16993,
"seed_rng_line_evidence": [
{
"line": 27,
"text": "def state(exp, seed, pair):"
},
{
"line": 30,
"text": " with np.load(exp / \"acquisition-trajectory\" / f\"E-seed-{seed}-pairings-{pair}.npz\") as f:"
},
{
"line": 97,
"text": " for seed, es in zip(ncfg[\"train_seeds\"], ncfg[\"evaluation_seeds\"]):"
},
{
"line": 98,
"text": " E = state(exp, seed, peak)"
},
{
"line": 100,
"text": " spikes = pd.read_parquet(Path(mcfg[\"experiment_root\"]) / \"isolated-replay\" / f\"KC-spikes-ODOR_{odor}-seed-{es}.parquet\")"
},
{
"line": 107,
"text": " rows.append({\"train_seed\": seed, \"eval_seed\": es, \"pairings\": peak, \"odor\": odor, \"decomposition\": label,"
},
{
"line": 126,
"text": " for seed, es in zip(ncfg[\"train_seeds\"], ncfg[\"evaluation_seeds\"]):"
},
{
"line": 129,
"text": " delta = w * (state(exp, seed, pair) - 1)"
},
{
"line": 134,
"text": " row = {\"train_seed\": seed, \"eval_seed\": es, \"pairings\": pair, \"odor\": label,"
},
{
"line": 163,
"text": " first = hist[(hist.identity_group == \"B-specific\") & (hist.delta_E_bounded < -1e-7)].groupby(\"train_seed\").pairing.min().reset_index(name=\"first_B_specific_update_pairing\")"
},
{
"line": 170,
"text": " \"earliest_B_update_by_seed\": first.to_dict(\"records\"), \"all_updates_gated_by_external_matched_US\": True})"
},
{
"line": 171,
"text": " write_json(exp / \"dan-source/phase4m-gate-audit.json\", {\"external_US_gates\": int(history[[\"train_seed\", \"pairing\"]].drop_duplicates().shape[0]),"
},
{
"line": 182,
"text": " for seed in ncfg[\"train_seeds\"]:"
},
{
"line": 184,
"text": " E = state(exp, seed, pair)"
},
{
"line": 185,
"text": " rows += [{\"seed\": seed, \"pairings\": pair, \"condition\": \"A+matched-US\", \"E_changed\": int(np.count_nonzero(E < 1 - 1e-7)), \"status\": \"RUN-RECONSTRUCTED-FROZEN-PHASE4M\"},"
},
{
"line": 186,
"text": " {\"seed\": seed, \"pairings\": pair, \"condition\": \"A+D0\", \"E_changed\": 0, \"status\": \"EXACT-ZERO-GATE-IDENTITY\"},"
},
{
"line": 187,
"text": " {\"seed\": seed, \"pairings\": pair, \"condition\": \"frozen-E1\", \"E_changed\": 0, \"status\": \"EXACT-FROZEN-IDENTITY\"},"
},
{
"line": 188,
"text": " {\"seed\": seed, \"pairings\": pair, \"condition\": \"external-US-only-gate\", \"E_changed\": int(np.count_nonzero(E < 1 - 1e-7)), \"status\": \"IDENTICAL-TO-FROZEN-PHASE4M-RULE\"}]"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4n_paths.py",
"sha256": "41554ae9b712b1710a3e8559e86e01e2aa5d95ca0f88fcaf93a4a5baa8dc3877",
"bytes": 6839,
"seed_rng_line_evidence": [
{
"line": 49,
"text": " for seed in ncfg[\"train_seeds\"]:"
},
{
"line": 51,
"text": " file=exp/\"whole-brain\"/f\"spike-events-seed-{seed}-pairings-{pair}.parquet\""
},
{
"line": 74,
"text": " rows.append({\"seed\":seed,\"pairing\":pair,\"B_specific_KC_neuron_idx\":int(target),\"B_specific_KC_body_id\":int(meta.body_id.iat[target]),"
},
{
"line": 88,
"text": " linked=updates.merge(table,left_on=[\"seed\",\"pairing\",\"source_neuron_idx\"],"
},
{
"line": 89,
"text": " right_on=[\"seed\",\"pairing\",\"B_specific_KC_neuron_idx\"],how=\"left\",validate=\"many_to_one\")"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4n_preflight.py",
"sha256": "aa339208407f4dd00282201ce362ea06fa042f3115e57483670196c6b3c3d642",
"bytes": 757,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4n.py",
"sha256": "dbbb49447aecbc862d3f54bba9de41f6b8ab90a6afb1ef43a686149fce832d13",
"bytes": 13925,
"seed_rng_line_evidence": [
{
"line": 75,
"text": " required = {\"train_seed\", \"pairing\", \"edge_id\", \"E_bounded_before\", \"E_bounded_after\", \"E_multiplicative_before\", \"E_multiplicative_after\", \"eligibility\"}"
},
{
"line": 84,
"text": " for seed in ncfg[\"train_seeds\"]:"
},
{
"line": 85,
"text": " by = hist[hist.train_seed == seed]"
},
{
"line": 88,
"text": " checkpoints[seed] = {0: {\"bounded\": E.copy(), \"unbounded\": U.copy()}}"
},
{
"line": 89,
"text": " np.savez_compressed(exp / \"acquisition-trajectory\" / f\"E-seed-{seed}-pairings-0.npz\", E_bounded=E, E_unbounded=U, edge_id=edge_ids)"
},
{
"line": 93,
"text": " all_rows.append({\"seed\": seed, \"pairings\": 0, \"mechanism\": mechanism, \"group\": group,"
},
{
"line": 102,
"text": " raise RuntimeError(f\"Phase 4M event history not reconstructible seed={seed} pairing={pairing}: {before_error}\")"
},
{
"line": 107,
"text": " checkpoints[seed][pairing] = {\"bounded\": E.copy(), \"unbounded\": U.copy()}"
},
{
"line": 108,
"text": " np.savez_compressed(exp / \"acquisition-trajectory\" / f\"E-seed-{seed}-pairings-{pairing}.npz\", E_bounded=E, E_unbounded=U, edge_id=edge_ids)"
},
{
"line": 109,
"text": " old = Path(mcfg[\"experiment_root\"]) / \"efficacy-overlay\" / f\"E-seed-{seed}-pairings-{pairing}.npz\""
},
{
"line": 113,
"text": " verify.append({\"seed\": seed, \"pairings\": pairing, \"max_abs_error\": err})"
},
{
"line": 121,
"text": " all_rows.append({\"seed\": seed, \"pairings\": pairing, \"mechanism\": mechanism, \"group\": group,"
},
{
"line": 128,
"text": " wide = table.pivot_table(index=[\"seed\", \"pairings\", \"mechanism\"], columns=\"group\", values=\"weighted_transmission_reduction\").reset_index()"
},
{
"line": 150,
"text": " for seed in ncfg[\"evaluation_seeds\"]:"
},
{
"line": 152,
"text": " spikes = pd.read_parquet(old / \"isolated-replay\" / f\"KC-spikes-ODOR_{odor}-seed-{seed}.parquet\")"
},
{
"line": 153,
"text": " evaldata[(seed, odor)] = (spikes, replay.run(spikes, zero, float(mcfg[\"protocol\"][\"duration_ms\"])))"
},
{
"line": 156,
"text": " for train_seed, eval_seed in zip(ncfg[\"train_seeds\"], ncfg[\"evaluation_seeds\"]):"
},
{
"line": 158,
"text": " E = checkpoints[train_seed][pair][\"bounded\"]"
},
{
"line": 160,
"text": " rec = {\"train_seed\": train_seed, \"eval_seed\": eval_seed, \"pairings\": pair}"
},
{
"line": 163,
"text": " spikes, baseline = evaldata[(eval_seed, odor)]"
},
{
"line": 176,
"text": " curve = frame.groupby(\"pairings\").agg(A_specific_functional_seeds=(\"A_specific_functional\", \"sum\"),"
},
{
"line": 183,
"text": " valid = curve[curve.A_specific_functional_seeds >= 3].copy()"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4n.yaml",
"sha256": "6e1b85fc923c7c969df0a7002557256cb719106f8a028c2c506c5ccfe0edd2b2",
"bytes": 1712,
"seed_rng_line_evidence": [
{
"line": 8,
"text": "train_seeds: [51201, 51202, 51203, 51204, 51205]"
},
{
"line": 9,
"text": "evaluation_seeds: [51301, 51302, 51303, 51304, 51305]"
},
{
"line": 13,
"text": "peak_rule: earliest checkpoint with at least 3 of 5 A-specific functional seeds and maximum five-seed mean spike-L1 separation; spike-L1 ties within 1e-9 use mean first-latency separation only if difference exceeds half a dt step (0.05 ms), otherwise choose earliest; no peak if no such checkpoint"
},
{
"line": 15,
"text": "random_us_seed_offset: 700000"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4o_a.py",
"sha256": "1048a073de399052bd525faf010858fa83f075a7c747b3fe5dcf8e4fc9ce3b87",
"bytes": 11747,
"seed_rng_line_evidence": [
{
"line": 57,
"text": " names = [\"CUDA_VISIBLE_DEVICES\", \"CUBLAS_WORKSPACE_CONFIG\", \"PYTHONHASHSEED\", \"OMP_NUM_THREADS\", \"MKL_NUM_THREADS\"]"
},
{
"line": 99,
"text": " py = digest(repr(random.getstate()).encode())"
},
{
"line": 100,
"text": " npy = digest(repr(np.random.get_state()).encode())"
},
{
"line": 131,
"text": " specs = [(s[\"name\"], s[\"population\"], float(s[\"rate_hz\"]), int(s[\"seed\"])) for s in yaml.safe_load((ROOT / \"models/male-cns-lif/runtime-v1/phase4c.yaml\").read_text())[\"reference_trials\"]]"
},
{
"line": 137,
"text": " for name, pop, rate, seed in specs:"
},
{
"line": 146,
"text": " rt.reset(seed)"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4o_atomic.py",
"sha256": "b481d91ac3a72d360c4dbb5378b975e430f0d1027fad94de5e2a041f9849c714",
"bytes": 4736,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4o_eligibility.py",
"sha256": "4fb7c97c8832c3ef51fe7613083dd6eab627bcde419254696c19a07632deb878",
"bytes": 7748,
"seed_rng_line_evidence": [
{
"line": 49,
"text": " frozen = {(int(s), int(p)): f for (s, p), f in history.groupby([\"train_seed\", \"pairing\"])}"
},
{
"line": 52,
"text": " for (s, p), f in old.groupby([\"seed\", \"pairing\"])}"
},
{
"line": 72,
"text": " for seed in ncfg[\"train_seeds\"]:"
},
{
"line": 76,
"text": " run = sim.run_rates(*pn, seed+pairing, float(mcfg[\"protocol\"][\"duration_ms\"]),"
},
{
"line": 81,
"text": " old_ev = old_events.get((seed, pairing), np.empty((0, 2), np.int32))"
},
{
"line": 82,
"text": " rec = frozen[(seed, pairing)]"
},
{
"line": 90,
"text": " key = (seed,pairing)"
},
{
"line": 94,
"text": " row = {\"repeat\": repeat, \"seed\": seed, \"pairing\": pairing,"
},
{
"line": 114,
"text": " path = EXP / \"eligibility-replay\" / f\"mismatch-repeat-{repeat}-seed-{seed}-pairing-{pairing}-KC-events.parquet\""
},
{
"line": 125,
"text": " print(json.dumps({\"repeat\":repeat,\"seed\":seed,\"pairing\":pairing,"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4o_event_replay.py",
"sha256": "b169937bab495c77d8ee6c1013877e7500e7354140d9fcc90595d984238ba44e",
"bytes": 7916,
"seed_rng_line_evidence": [
{
"line": 46,
"text": " events={(int(seed),int(pair)):group[['step','neuron_idx']].to_numpy(np.int32)"
},
{
"line": 47,
"text": " for (seed,pair),group in kc_events.groupby(['seed','pairing'])}"
},
{
"line": 48,
"text": " histories={(int(seed),int(pair)):group for (seed,pair),group in historical.groupby(['train_seed','pairing'])}"
},
{
"line": 55,
"text": " for seed in ncfg['train_seeds']:"
},
{
"line": 58,
"text": " arr=events.get((seed,pairing),np.empty((0,2),np.int32))"
},
{
"line": 71,
"text": " rec=histories[(seed,pairing)]"
},
{
"line": 84,
"text": " key=(seed,pairing)"
},
{
"line": 86,
"text": " row={'repeat':repeat,'seed':seed,'pairing':pairing,'event_count':len(arr),'event_sha256':h(arr),"
},
{
"line": 106,
"text": " print(json.dumps({'repeat':repeat,'seed':seed,'total_so_far':len(results),"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4o_finalize.py",
"sha256": "db6318467fb1ecef4003077ec1db8eac299c0c56944f463f96418ab41135c08f",
"bytes": 11065,
"seed_rng_line_evidence": [
{
"line": 112,
"text": " 'mismatch_seed_pairing':[[51202,40],[51203,39]],"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4o_modes.py",
"sha256": "8980ff1581c81d99752515349ee7be747a6f954a94ab56492b400a5324f1b6a2",
"bytes": 6133,
"seed_rng_line_evidence": [
{
"line": 19,
"text": "SEED=42003"
},
{
"line": 40,
"text": " rt.reset(SEED)"
},
{
"line": 48,
"text": " out=direct.run('hybrid-exact',target,50.,SEED,1000.,capture_events=(mode=='logging-on'))"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4o_targeted.py",
"sha256": "cfd6cc4dd0f2c0d58b5eb2634fb4e6bd530eec5037c4c8462f6ef7a885301200",
"bytes": 4268,
"seed_rng_line_evidence": [
{
"line": 24,
"text": " by={(int(s),int(p)):g for (s,p),g in history.groupby(['train_seed','pairing'])}"
},
{
"line": 35,
"text": " for seed,pairing in [(51202,40),(51203,39)]:"
},
{
"line": 38,
"text": " rec=by[(seed,p)]"
},
{
"line": 43,
"text": " rec=by[(seed,pairing)]"
},
{
"line": 47,
"text": " run=sim.run_rates(*pn,seed+pairing,float(mcfg['protocol']['duration_ms']),delta=delta,dan_targets=dan,"
},
{
"line": 53,
"text": " row={'seed':seed,'pairing':pairing,'repeat':repeat,'event_count':len(events),"
},
{
"line": 64,
"text": " print(json.dumps({'seed':seed,'pairing':pairing,'repeat':repeat,'exact':row['exact_to_frozen'],"
},
{
"line": 69,
"text": " 'distinct_event_hashes_by_trial':{f'{s}/{p}':len({r['event_sha256'] for r in rows if r['seed']==s and r['pairing']==p}) for s,p in [(51202,40),(51203,39)]},"
},
{
"line": 70,
"text": " 'same_seed_config_and_sealed_additive_prefix':True,"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4o.yaml",
"sha256": "8b380f0597dd3f2d656e9e773a581855f75d940bcc0dad9e84abb6b02173aa16",
"bytes": 1253,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4p_benchmark.py",
"sha256": "8d8bad3db949da876c5a7fe9f20845a10c4128523da69bcb4b5c3fab69c15e7a",
"bytes": 4284,
"seed_rng_line_evidence": [
{
"line": 26,
"text": " forced = phase4p.phase4c.generate_forced(steps, len(target_np), float(spec[\"rate_hz\"]), rt.dt, int(spec[\"seed\"]), rt.device)"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4p_finalize.py",
"sha256": "b627fb0934bca8f04ec7c595a7b5e6690a2af62a44bc00ff2972f9f764671cd4",
"bytes": 6047,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4p_revalidate.py",
"sha256": "c7e693a2da70acdce4f53af5c2853e52e8333acc93f88a46a5e8083eac639346",
"bytes": 31558,
"seed_rng_line_evidence": [
{
"line": 106,
"text": " def run(self, targets: np.ndarray, rates_hz: np.ndarray, seed: int, duration_ms: float,"
},
{
"line": 112,
"text": " gen = torch.Generator(device=self.device); gen.manual_seed(int(seed))"
},
{
"line": 114,
"text": " forced = (torch.rand((steps, len(targets)), generator=gen, device=self.device) < probs[None, :]).to(torch.uint8)"
},
{
"line": 185,
"text": " seeds = [50101, 50102, 50103, 50104, 50105]"
},
{
"line": 189,
"text": " for seed in seeds:"
},
{
"line": 190,
"text": " run = sim.run(targets, rates, seed + oi * 100, 100.0)"
},
{
"line": 193,
"text": " rows.append({\"odor\": oi, \"seed\": seed, \"KC_active\": active, \"KC_active_fraction\": fraction,"
},
{
"line": 205,
"text": " x1 = sim.run(held[0], np.full(n, 50.0, np.float32), seeds[0], 100.0)"
},
{
"line": 206,
"text": " x2 = sim.run(held[0], np.full(n, 50.0, np.float32), seeds[0], 100.0)"
},
{
"line": 241,
"text": " cfg, panel, vectors, selection, mapping = door_inputs(); seeds = list(map(int, cfg[\"odor_encoder\"][\"evaluation_seeds\"]))"
},
{
"line": 246,
"text": " for seed in seeds:"
},
{
"line": 247,
"text": " runs[(mode, oid, seed)] = sim.run(t, r, seed, float(cfg[\"odor_encoder\"][\"duration_ms\"]), collect_events=True)"
},
{
"line": 251,
"text": " arr = [runs[(mode, oid, s)][\"kc_counts\"] for s in seeds]"
},
{
"line": 256,
"text": " ds = [metrics(runs[(mode, odor_ids[i], s)][\"kc_counts\"], runs[(mode, odor_ids[j], s)][\"kc_counts\"])[\"euclidean\"] for s in seeds]"
},
{
"line": 268,
"text": " \"DoOR_static_mapping_reused_unchanged\": True, \"odor_count\": len(panel), \"seeds\": seeds}"
},
{
"line": 281,
"text": " for seed in map(int, mcfg[\"protocol\"][\"train_seeds\"]):"
},
{
"line": 284,
"text": " run = sim.run(targets, rates, seed + pairing, float(mcfg[\"protocol\"][\"duration_ms\"]), E=E,"
},
{
"line": 290,
"text": " states[(seed, pairing)] = E.copy()"
},
{
"line": 291,
"text": " rows.append({\"seed\": seed, \"pairing\": pairing, \"updated_edges\": int(np.count_nonzero(E < 1)),"
},
{
"line": 294,
"text": " if pairing % 20 == 0: print(json.dumps({\"R5_training_seed\": seed, \"pairing\": pairing}), flush=True)"
},
{
"line": 296,
"text": " np.savez_compressed(exp / \"revalidation/R5-efficacy-states.npz\", **{f\"seed_{s}_pairing_{p}\": x for (s, p), x in states.items()})"
},
{
"line": 335,
"text": "def eval_events(sim: DeterministicAPLSim, seed: int, oid: str) -> dict[str, Any]:"
},
{
"line": 337,
"text": " return sim.run(targets, rates, seed, 120.0, collect_events=True)"
},
{
"line": 343,
"text": " train_seeds = list(map(int, mcfg[\"protocol\"][\"train_seeds\"])); eval_seeds = list(map(int, mcfg[\"protocol\"][\"evaluation_seeds\"]))"
},
{
"line": 347,
"text": " for i, seed in enumerate(eval_seeds):"
},
{
"line": 348,
"text": " run = eval_events(sim, seed, oid); events[(label, seed)] = run"
},
{
"line": 350,
"text": " learned = replay.run(run[\"kc_event_step\"], run[\"kc_event_node\"], states[(train_seeds[i], 100)], 120.0)"
},
{
"line": 351,
"text": " rows.append({\"odor\": label, \"seed\": seed, \"train_seed\": train_seeds[i],"
},
{
"line": 364,
"text": " \"isolated_functional_A_seeds\": int((a.spike_L1 > 0).sum()), \"A_spike_L1_mean\": float(a.spike_L1.mean()),"
},
{
"line": 373,
"text": " train = list(map(int, mcfg[\"protocol\"][\"train_seeds\"])); evals = list(map(int, mcfg[\"protocol\"][\"evaluation_seeds\"]))"
},
{
"line": 395,
"text": " for i, seed in enumerate(evals):"
},
{
"line": 396,
"text": " base = sim.run(t, r, seed, 120.0)"
},
{
"line": 397,
"text": " post = sim.run(t, r, seed, 120.0, E=states[(train[i], pairing)])"
},
{
"line": 400,
"text": " \"functional_seeds\": int(np.count_nonzero(effects))})"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4p_validate.py",
"sha256": "ce06aa5cb6439450440a91603af1c0d187b20dc11abda6528ea0dfc6b71569b5",
"bytes": 30605,
"seed_rng_line_evidence": [
{
"line": 241,
"text": " forced = phase4p.phase4c.generate_forced(200, len(target_np), float(spec[\"rate_hz\"]), rt.dt, int(spec[\"seed\"]), rt.device)"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4p.py",
"sha256": "c8a13dbdd58dd222f73c1224de448e54bdf90c3f533163427a2bb25446a03999",
"bytes": 10909,
"seed_rng_line_evidence": [
{
"line": 86,
"text": " def run(self,targets_np:np.ndarray,rate:float,seed:int,duration_ms:float,capture=True,forced_override=None):"
},
{
"line": 88,
"text": " forced=forced_override if forced_override is not None else phase4c.generate_forced(steps,len(targets_np),rate,self.dt,seed,self.device)"
},
{
"line": 136,
"text": " run=rt.run(target,float(spec['rate_hz']),int(spec['seed']),float(spec['duration_ms']),True)"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4p.yaml",
"sha256": "6d0189f4ec570cb3352d15ed8d195d1ccb32ee1d29c33dc1e4f00d377b74787e",
"bytes": 2525,
"seed_rng_line_evidence": [
{
"line": 38,
"text": " - {name: auditory-10hz, population: auditory, rate_hz: 10, seed: 42001, duration_ms: 1000.0}"
},
{
"line": 39,
"text": " - {name: auditory-100hz, population: auditory, rate_hz: 100, seed: 42001, duration_ms: 1000.0}"
},
{
"line": 40,
"text": " - {name: olfactory-50hz, population: olfactory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 41,
"text": " - {name: mechanosensory-50hz, population: mechanosensory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
},
{
"line": 42,
"text": " - {name: gustatory-50hz, population: gustatory, rate_hz: 50, seed: 42003, duration_ms: 1000.0}"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4q_seal.py",
"sha256": "d2bb488498663c367ac1620c238e4fe83573358ce3bdc132718fd85510cc6da2",
"bytes": 740,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4q.py",
"sha256": "2cd10fb215d7ed624f4a4323453d20c4327bea490fa52eb3afc5d7fa0176a6e5",
"bytes": 38764,
"seed_rng_line_evidence": [
{
"line": 70,
"text": "def random_us_steps(base: int, seed: int, count: int, nsteps: int) -> np.ndarray:"
},
{
"line": 71,
"text": " rng = np.random.default_rng(base + seed)"
},
{
"line": 166,
"text": " labels = cfg[\"calibration\"][\"labels\"]; seeds = list(map(int, cfg[\"calibration\"][\"seeds\"])); duration = float(cfg[\"calibration\"][\"duration_ms\"])"
},
{
"line": 174,
"text": " for seed in seeds:"
},
{
"line": 175,
"text": " matched = sim.run(targets, rates, seed + oi*100, duration, dan_targets=dan, dan_pulses=[us_ms], collect_events=True)"
},
{
"line": 176,
"text": " rand_step = int(random_us_steps(int(cfg[\"calibration\"][\"random_us_seed_base\"]), seed + oi*100, 1, nsteps)[0])"
},
{
"line": 177,
"text": " random_run = sim.run(targets, rates, seed + oi*100, duration, dan_targets=dan, dan_pulses=[rand_step*sim.dt], collect_events=True)"
},
{
"line": 178,
"text": " event_cache[(label, seed, \"matched\")] = matched; event_cache[(label, seed, \"random\")] = random_run"
},
{
"line": 179,
"text": " input_rows.append({\"label\": label, \"odor_id\": str(odor.odor_id), \"odor_name\": str(odor.odor_name), \"seed\": seed,"
},
{
"line": 187,
"text": " for (label, seed, condition), run in event_cache.items():"
},
{
"line": 188,
"text": " u = us_step if condition == \"matched\" else int(next(x[\"random_us_step\"] for x in input_rows if x[\"label\"]==label and x[\"seed\"]==seed))"
},
{
"line": 191,
"text": " rows.append({\"window_ms\":window,\"tau_ms\":tau,\"label\":label,\"seed\":seed,\"condition\":condition,"
},
{
"line": 197,
"text": " x=frame[frame.window_ms==window];m=x[x.condition==\"matched\"].groupby(\"seed\").total_eligibility.mean();r=x[x.condition==\"random\"].groupby(\"seed\").total_eligibility.mean()"
},
{
"line": 199,
"text": " qualifies=bool((m>0).sum()>=int(th[\"minimum_nonzero_seeds\"]) and ratio>=float(th[\"minimum_matched_random_ratio\"])"
},
{
"line": 202,
"text": " \"matched_random_ratio\":ratio,\"nonzero_matched_seeds\":int((m>0).sum()),\"distant_credit\":0.0,"
},
{
"line": 233,
"text": " \"random_timing_definition\":\"one uniformly sampled step over the 120 ms trial per seed/pattern; equal US event count/amplitude/compartment\"})"
},
{
"line": 274,
"text": " train_seeds=list(map(int,cfg[\"formal\"][\"train_seeds\"]));random_base=int(cfg[\"formal\"][\"random_us_seed_base\"])"
},
{
"line": 286,
"text": " for seed in train_seeds:"
},
{
"line": 288,
"text": " for name,E in Es.items(): states[(seed,name,0)]=E.copy()"
},
{
"line": 290,
"text": " run=sim.run(targets,rates,seed+pairing,duration,dan_targets=dan,dan_pulses=[cfg[\"formal\"][\"matched_us_ms\"]],collect_events=True)"
},
{
"line": 291,
"text": " random_step=int(random_us_steps(random_base,seed*1000+pairing,1,nsteps)[0])"
},
{
"line": 292,
"text": " rrun=sim.run(targets,rates,seed+pairing,duration,dan_targets=dan,dan_pulses=[random_step*sim.dt],collect_events=True)"
},
{
"line": 304,
"text": " loc=source_local[j];ls=int(last_arr[loc]);contam.append({\"rule\":name,\"seed\":seed,\"pairing\":pairing,\"edge_id\":int(identity.edge_id.iloc[j]),"
},
{
"line": 308,
"text": " pair_rows.append({\"seed\":seed,\"pairing\":pairing,\"matched_event_hash\":sha(run[\"kc_event_step\"],run[\"kc_event_node\"]),"
},
{
"line": 313,
"text": " states[(seed,name,pairing)]=E.copy()"
},
{
"line": 314,
"text": " for row in group_metrics(E,base,identity):metric_rows.append({\"seed\":seed,\"pairing\":pairing,\"rule\":name,**row})"
},
{
"line": 315,
"text": " if pairing%20==0:print(json.dumps({\"seed\":seed,\"pairing\":pairing}),flush=True)"
},
{
"line": 319,
"text": " np.savez_compressed(exp/\"eligibility-v3/formal-E-states.npz\",**{f\"seed_{s}__{n}__pairing_{p}\":x for (s,n,p),x in states.items()})"
},
{
"line": 330,
"text": "def state_key(seed:int,rule:str,pairing:int)->str:return f\"seed_{seed}__{rule}__pairing_{pairing}\""
},
{
"line": 346,
"text": " replay=IsolatedReplay(sim,cfg[\"formal\"][\"target_compartment\"]);train=list(map(int,cfg[\"formal\"][\"train_seeds\"]));evals=list(map(int,cfg[\"formal\"][\"evaluation_seeds\"]));rows=[]"
},
{
"line": 350,
"text": " for seed in evals:"
},
{
"line": 351,
"text": " run=sim.run(targets,rates,seed,cfg[\"formal\"][\"duration_ms\"],collect_events=True);event_cache[(odor,seed)]=run"
},
{
"line": 354,
"text": " for i,seed in enumerate(evals):"
},
{
"line": 355,
"text": " run=event_cache[(odor,seed)];base=replay.run(run[\"kc_event_step\"],run[\"kc_event_node\"],None,cfg[\"formal\"][\"duration_ms\"])"
},
{
"line": 358,
"text": " rows.append({\"pairing\":pairing,\"odor\":odor,\"evaluation_seed\":seed,\"train_seed\":train[i],\"rule\":rule,**replay_metrics(base,post,sim.dt)})"
},
{
"line": 359,
"text": " rows.append({\"pairing\":pairing,\"odor\":odor,\"evaluation_seed\":seed,\"train_seed\":train[i],\"rule\":\"D0\",**replay_metrics(base,base,sim.dt)})"
},
{
"line": 366,
"text": " \"A_functional_seeds\":int((a.spike_L1>0).sum()),\"B_functional_seeds\":int((b.spike_L1>0).sum()),\"A_minus_B\":float(a.spike_L1.mean()-b.spike_L1.mean())})"
},
{
"line": 368,
"text": " majority=int(cfg[\"gates\"][\"isolated_majority_seeds\"]);matched_majority=bool((v3.A_functional_seeds>=majority).any());b_lower=bool((v3.A_minus_B>0).any())"
},
{
"line": 369,
"text": " random_not_mimic=not bool((vr.A_functional_seeds>=majority).any() and (vr.A_minus_B>0).any());d0zero=bool((d0.A_spike_L1_mean==0).all()&(d0.B_spike_L1_mean==0).all())"
},
{
"line": 385,
"text": " train=list(map(int,cfg[\"formal\"][\"train_seeds\"]));evals=list(map(int,cfg[\"formal\"][\"evaluation_seeds\"]));rows=[];t0=time.perf_counter()"
},
{
"line": 389,
"text": " for i,seed in enumerate(evals):"
},
{
"line": 390,
"text": " base=sim.run(targets,rates,seed,cfg[\"formal\"][\"duration_ms\"])"
},
{
"line": 392,
"text": " post=sim.run(targets,rates,seed,cfg[\"formal\"][\"duration_ms\"],E=states[state_key(train[i],rule,pairing)])"
},
{
"line": 393,
"text": " rows.append({\"pairing\":pairing,\"odor\":odor,\"seed\":seed,\"rule\":rule,"
},
{
"line": 405,
"text": " \"B_downstream_L1\":float(b.downstream_spike_L1.mean()),\"A_specific_seeds\":int((a.MBON_spike_L1>b.MBON_spike_L1.to_numpy()).sum())})"
},
{
"line": 407,
"text": " whole_pass=bool((v.A_specific_seeds>=int(cfg[\"gates\"][\"wholebrain_majority_seeds\"])).any() and not (r.A_specific_seeds>=int(cfg[\"gates\"][\"wholebrain_majority_seeds\"])).any())"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4q.yaml",
"sha256": "2cf3f799a5c1d24c20ed7aa1ff920daffa792b189002d0d7dcee1e51a4d49bd7",
"bytes": 2607,
"seed_rng_line_evidence": [
{
"line": 22,
"text": " seeds: [52101, 52102, 52103, 52104, 52105]"
},
{
"line": 25,
"text": " random_us_seed_base: 930000"
},
{
"line": 27,
"text": " minimum_nonzero_seeds: 5"
},
{
"line": 38,
"text": " train_seeds: [51201, 51202, 51203, 51204, 51205]"
},
{
"line": 39,
"text": " evaluation_seeds: [51301, 51302, 51303, 51304, 51305]"
},
{
"line": 45,
"text": " random_us_seed_base: 940000"
},
{
"line": 52,
"text": " isolated_majority_seeds: 3"
},
{
"line": 55,
"text": " wholebrain_majority_seeds: 3"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4r_plot.py",
"sha256": "48f954a001b3cd7cdd3573bf816bf8baba94e154420e3847cec520ca4d406c5b",
"bytes": 918,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4r_report.py",
"sha256": "f2784afddd70c2da07e7bd35aa28ac49e64b404b82cac2b6f924244c473ce5cc",
"bytes": 5838,
"seed_rng_line_evidence": [
{
"line": 41,
"text": " 'No timing/eligibility candidate was selected. Independent heldout seeds replicate diagnosis; heldout odor generalization was not tested.'"
},
{
"line": 61,
"text": " '| Seed split | Window (ms) | Random [0,120) | Random [30,110] |',"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4r.py",
"sha256": "f33b7d01ff3fe3858a1d995a7471740218be250910548fde11b87432a15a73f1",
"bytes": 25586,
"seed_rng_line_evidence": [
{
"line": 90,
"text": " for seed in cfg[split+'_seeds']:"
},
{
"line": 91,
"text": " actual=seed+oi*100"
},
{
"line": 92,
"text": " qs=int(random_us_steps(cfg['random_q_seed_base'],actual,1,nsteps)[0])"
},
{
"line": 93,
"text": " ns=round(float(np.random.default_rng(cfg['random_n_seed_base']+actual).uniform(30,110))/dt)"
},
{
"line": 95,
"text": " 'split':split,'seed':seed,'actual_seed':actual,'matched_step':round(cfg['matched_us_ms']/dt),"
},
{
"line": 142,
"text": " tag=f\"{spec['split']}-{spec['label']}-{spec['seed']}\";targets,rates=drives[spec['label']]"
},
{
"line": 146,
"text": " u=gates[condition];run=sim.run(targets,rates,spec['actual_seed'],cfg['duration_ms'],"
},
{
"line": 174,
"text": " old=qmatrix[(qmatrix.label==spec['label'])&(qmatrix.seed==spec['seed'])&(qmatrix.condition==qc)&(qmatrix.window_ms==w)].iloc[0]"
},
{
"line": 175,
"text": " oldh=qold[(qold.label==spec['label'])&(qold.seed==spec['seed'])].iloc[0][qc+'_event_hash']"
},
{
"line": 194,
"text": " grid.append({'label':spec['label'],'seed':spec['seed'],'split':spec['split'],'window_ms':w,"
},
{
"line": 208,
"text": " edge_rows.append({'label':spec['label'],'seed':spec['seed'],'split':spec['split'],'window_ms':w,"
},
{
"line": 218,
"text": " rr=sim.run(targets,rates,spec['actual_seed'],cfg['duration_ms'],dan_targets=dan,dan_pulses=[90],collect_events=True)"
},
{
"line": 222,
"text": " print(json.dumps({'completed_trials':ix+1,'of':len(inputs),'label':spec['label'],'seed':spec['seed']}),flush=True)"
},
{
"line": 228,
"text": " save(exp/'regression/full-runtime-10-repeat.json',{'scope':'one calibration odor/seed, 120ms, unchanged APL and runtime-v2',"
},
{
"line": 238,
"text": " r=rt.run(np.flatnonzero(rt.pops[spec['population']]),spec['rate_hz'],spec['seed'],spec['duration_ms'])"
},
{
"line": 284,
"text": " 'limitations':['Same two calibration odors with independent heldout seeds; not heldout odors or generalization proof.',"
},
{
"line": 303,
"text": " axs[1,oi].axvline(90,color='black',ls=':');axs[1,oi].set(title='Fixed-event credit vs US time (heldout seeds)',xlabel='US time (ms)',ylabel='Summed edge eligibility');axs[1,oi].legend(ncol=2)"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4r.yaml",
"sha256": "3305f622f6d6422c4240979382ac93f951b9a65d331b07b616d0aee994a99c3f",
"bytes": 1391,
"seed_rng_line_evidence": [
{
"line": 9,
"text": "calibration_seeds: [52101, 52102, 52103, 52104, 52105]"
},
{
"line": 10,
"text": "heldout_seeds: [52201, 52202, 52203, 52204, 52205]"
},
{
"line": 16,
"text": "random_q_seed_base: 930000"
},
{
"line": 17,
"text": "random_n_seed_base: 950000"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4s_report.py",
"sha256": "fabbc424dbff90e416ea877041ebfb4c92379e91c61b822190b73cbe2a0faf00",
"bytes": 5267,
"seed_rng_line_evidence": [
{
"line": 26,
"text": " A_specific_seeds=('A_specific','sum'),random_specific_seeds=('random_specific','sum'))"
},
{
"line": 49,
"text": " lines+=['独立 seeds 验证：','', '| 气味 | matched/random credit 比值 | 达标 seeds |','|---|---:|---:|']"
},
{
"line": 50,
"text": " for x in r['heldout']['odors']:lines.append(f\"| {x['label']} | {x['ratio']:.4f} | {x['qualifying_seeds']}/5 |\")"
},
{
"line": 57,
"text": " lines+=[f\"Isolated gate：{r['isolated']['gate_pass']}。\",'', '| Seed | A L1 | B L1 | random A L1 | random B L1 |','|---|---:|---:|---:|---:|']"
},
{
"line": 58,
"text": " for x in r['isolated']['endpoint']:lines.append(f\"| {x['seed']} | {x['A']} | {x['B']} | {x['random_A']} | {x['random_B']} |\")"
},
{
"line": 62,
"text": " '限制：只有两个 calibration odors 和五个独立验证 seeds；credit 改善与刺激特异 MBON 功能变化分别判定。没有根据正式 A/B 或 heldout 结果重新挑参数，没有提前停止在最有利 checkpoint。']"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4s.py",
"sha256": "59e4ee98a2a81321b1d1f9edd78035133ade8b73673b47a9c89f77bd39511769",
"bytes": 20731,
"seed_rng_line_evidence": [
{
"line": 35,
"text": " n=int((g.ratio>=gate['per_seed_ratio_at_least']).sum())"
},
{
"line": 36,
"text": " details.append({'label':label,'mean_matched':m,'mean_random':r,'ratio':ratio,'qualifying_seeds':n,"
},
{
"line": 37,
"text": " 'pass':bool(ratio is not None and ratio>=gate['mean_matched_random_ratio_each_odor'] and n>=gate['qualifying_seeds_each_odor'])})"
},
{
"line": 53,
"text": " run=rt.run(np.flatnonzero(rt.pops[spec['population']]),spec['rate_hz'],spec['seed'],spec['duration_ms'])"
},
{
"line": 83,
"text": " for seed in cfg['calibration_seeds']:"
},
{
"line": 84,
"text": " actual=seed+oi*cfg['odor_seed_offset'];r=sim.run(targets,rates,actual,duration,collect_events=True);assert r['finite']"
},
{
"line": 85,
"text": " cached[(label,seed)]=(targets,rates,actual,r);persist_run(exp,f'cal-{label}-{seed}',r)"
},
{
"line": 91,
"text": " calrows.append({'label':label,'seed':seed,'window_ms':w,'US_ms':u,'matched':m,'random_expectation':den,'ratio':m/den if den else None})"
},
{
"line": 104,
"text": " for seed in cfg['heldout_seeds']:"
},
{
"line": 105,
"text": " actual=seed+oi*cfg['odor_seed_offset'];r=sim.run(targets,rates,actual,duration,collect_events=True);assert r['finite']"
},
{
"line": 107,
"text": " held.append({'label':label,'seed':seed,'window_ms':w,'US_ms':u,'matched':m,'random_expectation':den,'ratio':m/den if den else None})"
},
{
"line": 108,
"text": " persist_run(exp,f'held-{label}-{seed}',r)"
},
{
"line": 109,
"text": " for pulse in (us,int(np.random.default_rng(actual+975000).integers(nsteps))):"
},
{
"line": 114,
"text": " gate_exact.append({'label':label,'seed':seed,'US_step':pulse,'credit_exact':bool(np.array_equal(t,tt)),"
},
{
"line": 138,
"text": " for seed in spec['train_seed_bases']:"
},
{
"line": 141,
"text": " states[f'{seed}_{name}_0']=E[name].copy()"
},
{
"line": 142,
"text": " for x in group_metrics(E[name],sim.base_candidate,identity):rows.append({'seed':seed,'pairing':0,'arm':name,**x})"
},
{
"line": 144,
"text": " actual=seed+pairing;r=sim.run(targets,rates,actual,cfg['duration_ms'],collect_events=True);assert r['finite']"
},
{
"line": 145,
"text": " s=r['kc_event_step'];n=r['kc_event_node'];rand=int(np.random.default_rng(actual+spec['random_seed_offset']).integers(nsteps))"
},
{
"line": 150,
"text": " event_hash=persist_run(exp,f'formal-{seed}-{pairing}',r)"
},
{
"line": 151,
"text": " audit.append({'seed':seed,'pairing':pairing,'actual_seed':actual,'random_US_step':rand,'matched_US_step':us,'KC_hash_all_arms':event_hash,"
},
{
"line": 158,
"text": " contam.append({'seed':seed,'pairing':pairing,'arm':name,'edge_id':int(identity.edge_id.iloc[j]),"
},
{
"line": 162,
"text": " states[f'{seed}_{name}_{pairing}']=E[name].copy()"
},
{
"line": 163,
"text": " for x in group_metrics(E[name],sim.base_candidate,identity):rows.append({'seed':seed,'pairing':pairing,'arm':name,**x})"
},
{
"line": 165,
"text": " np.savez_compressed(exp/'formal'/f'states-{seed}.npz',**{k:v for k,v in states.items() if k.startswith(str(seed)+'_')})"
},
{
"line": 166,
"text": " if pairing%20==0:print(json.dumps({'formal_seed':seed,'pairings':pairing}),flush=True)"
},
{
"line": 167,
"text": " if seed==spec['train_seed_bases'][0]:"
},
{
"line": 168,
"text": " rr=sim.run(targets,rates,seed+100,cfg['duration_ms'],collect_events=True)"
},
{
"line": 169,
"text": " repeat.append({'seed':seed+100,'events_exact':arrhash(rr['kc_event_step'],rr['kc_event_node'])==event_hash,"
},
{
"line": 175,
"text": " for seed in spec['train_seed_bases']:"
},
{
"line": 176,
"text": " z=endpoints[endpoints.seed==seed]"
},
{
"line": 182,
"text": " gates.append({'seed':seed,'A_credit':ma,'random_A_credit':ra,'A_ratio':ratio,'B_v2':b2,'B_v3':b3,'B_reduction':red,'pass':ag and bg})"
},
{
"line": 183,
"text": " fp=sum(x['pass'] for x in gates)>=spec['minimum_seed_majority']"
},
{
"line": 184,
"text": " summary['formal']={'gate_pass':fp,'primary_endpoint':spec['primary_endpoint'],'seeds':gates,'open_loop_shadow_only':True}"
},
{
"line": 190,
"text": " for es in spec['evaluation_seeds']:"
},
{
"line": 194,
"text": " for seed,es in zip(spec['train_seed_bases'],spec['evaluation_seeds']):"
},
{
"line": 197,
"text": " post=replay.run(er['kc_event_step'],er['kc_event_node'],states[f'{seed}_{name}_{pairing}'],cfg['duration_ms'])"
},
{
"line": 198,
"text": " results.append({'pairing':pairing,'odor':odor,'seed':seed,'evaluation_seed':es,'arm':name,**replay_metrics(base,post,sim.dt)})"
},
{
"line": 201,
"text": " for seed in spec['train_seed_bases']:"
},
{
"line": 202,
"text": " def L(arm,odor):return int(z[(z.pairing==pairing)&(z.seed==seed)&(z.arm==arm)&(z.odor==odor)].spike_L1.iloc[0])"
},
{
"line": 204,
"text": " trajectory.append({'pairing':pairing,'seed':seed,'A':a,'B':b,'random_A':ra,'random_B':rb,'A_specific':a>0 and a>b,'random_specific':ra>0 and ra>rb,'matched_beats_random':a>ra})"
},
{
"line": 205,
"text": " endpoint=[x for x in trajectory if x['pairing']==spec['primary_endpoint']];major=spec['minimum_seed_majority']"
},
{
"line": 213,
"text": " for seed,es in zip(spec['train_seed_bases'],spec['evaluation_seeds']):"
},
{
"line": 216,
"text": " post=sim.run(t,rates,es,cfg['duration_ms'],E=states[f'{seed}_{name}_100'])"
},
{
"line": 217,
"text": " whole.append({'seed':seed,'odor':odor,'arm':name,'MBON_L1':int(np.abs(post['mbon_counts']-base['mbon_counts']).sum()),"
},
{
"line": 220,
"text": " for seed in spec['train_seed_bases']:"
},
{
"line": 221,
"text": " def v(arm,odor):return int(wf[(wf.seed==seed)&(wf.arm==arm)&(wf.odor==odor)].MBON_L1.iloc[0])"
},
{
"line": 223,
"text": " wg.append({'seed':seed,'A':a,'B':b,'random_A':ra,'random_B':rb,'specific':a>b and a>ra,'random_specific':ra>rb})"
},
{
"line": 225,
"text": " summary['whole']={'run':True,'pass':wp,'seeds':wg};summary['credit_candidate']=bool(wp)"
},
{
"line": 238,
"text": " save(model/'MaleCNS-CSUS-Timing-v1-candidate.json',{'scope':'independent calibration and seed-heldout matched/random credit timing only; no memory claim',"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4s.yaml",
"sha256": "58fb71c556db31a0b087cbf6452d708d2cbf0a26900b3f40570fc525011f3752",
"bytes": 2809,
"seed_rng_line_evidence": [
{
"line": 15,
"text": "calibration_seeds: [54101, 54102, 54103, 54104, 54105]"
},
{
"line": 16,
"text": "heldout_seeds: [55101, 55102, 55103, 55104, 55105]"
},
{
"line": 17,
"text": "odor_seed_offset: 100000"
},
{
"line": 22,
"text": " per_seed_ratio_at_least: 3.0"
},
{
"line": 23,
"text": " qualifying_seeds_each_odor: 3"
},
{
"line": 24,
"text": " selection: shortest qualifying window, then earliest qualifying US; formal odors and heldout seeds forbidden for selection"
},
{
"line": 28,
"text": " train_seed_bases: [61000, 62000, 63000, 64000, 65000]"
},
{
"line": 29,
"text": " evaluation_seeds: [71001, 71002, 71003, 71004, 71005]"
},
{
"line": 33,
"text": " random_seed_offset: 975000"
},
{
"line": 35,
"text": " source_events: unchanged base brain, one fresh independent seed per pairing; all eligibility arms share exactly the same events"
},
{
"line": 40,
"text": " minimum_seed_majority: 3"
},
{
"line": 41,
"text": " isolated_gate: at predeclared 100 pairings, at least 3/5 seeds show positive A spike-count L1 greater than paired B; matched effect must exceed random A effect in at least 3/5, and random must not meet specificity majority"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4t_audit.py",
"sha256": "7c8ab087dfcae9b0871d1f63a124937e15125c6d5d3540156ef5b7c5b4f34a02",
"bytes": 9078,
"seed_rng_line_evidence": [
{
"line": 48,
"text": " split,label,seed=p.stem.split('-');seed=int(seed)"
},
{
"line": 72,
"text": " old=old[(old.label==label)&(old.seed==seed)&(old.window_ms==w)&(old.US_ms==chosen['US_ms'])].iloc[0]"
},
{
"line": 86,
"text": " row={'split':split,'label':label,'seed':seed,'source_file':str(p),'events_sha256':sha(p),"
},
{
"line": 102,
"text": " hist.append({'split':split,'label':label,'seed':seed,'start_ms':begin*dt,"
},
{
"line": 104,
"text": " for s,n in zip(ts,nodes):timelines.append({'split':split,'label':label,'seed':seed,"
},
{
"line": 108,
"text": " for s,c in enumerate(curve):gates.append({'split':split,'label':label,'seed':seed,'US_step':s,'credit':float(c)})"
},
{
"line": 122,
"text": " 'limits':['Descriptive reuse of known seeds, not independent validation.',"
},
{
"line": 132,
"text": " '| split | odor | seed | KC active | target active | first target ms | peak bin ms | reason |',"
},
{
"line": 134,
"text": " for x in rows:lines.append(f\"| {x['split']} | {x['label']} | {x['seed']} | {x['KC_active']} | {x['target_active_KC']} | {x['first_target_ms']} | {x['peak_bin_start_ms']} | {x['reason']} |\")"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4t.py",
"sha256": "eebb61a1b97fb3de90746d23cc3037d1290a97d505ecff7fc784948e68dfa88e",
"bytes": 12119,
"seed_rng_line_evidence": [
{
"line": 65,
"text": " seedsets=[{int(s)+i*cfg['odor_seed_offset'] for i in range(len(odors)) for s in cfg[k]} for k in ('calibration_seeds','validation_seeds')]"
},
{
"line": 66,
"text": " assert seedsets[0].isdisjoint(seedsets[1])"
},
{
"line": 80,
"text": " def trial(oi,seed,split,ws,us):"
},
{
"line": 82,
"text": " actual=seed+oi*cfg['odor_seed_offset']"
},
{
"line": 84,
"text": " phase4s.persist_run(exp,f'{split}-{oi}-{seed}',r)"
},
{
"line": 90,
"text": " records.append({'split':split,'odor_id':odor.odor_id,'odor_index':oi,'seed':seed,'actual_seed':actual,"
},
{
"line": 94,
"text": " for seed in cfg['calibration_seeds']:trial(oi,seed,'calibration',cfg['windows_ms'],cfg['candidate_US_ms'])"
},
{
"line": 95,
"text": " print(json.dumps({'stage':'calibration','odor_index':oi,'seeds':len(cfg['calibration_seeds'])}),flush=True)"
},
{
"line": 102,
"text": " summary={'phase':'4T','P0_diagnosis_pass':True,'calibration':chosen,'seed_validation':None,'odor_validation':None,"
},
{
"line": 107,
"text": " split='seed_validation' if oi<len(cal) else 'odor_validation'"
},
{
"line": 108,
"text": " for si,seed in enumerate(cfg['validation_seeds']):"
},
{
"line": 109,
"text": " r,t,rates,actual=trial(oi,seed,split,[w],[u])"
},
{
"line": 110,
"text": " if si<cfg['live_US_checks']['first_seeds_per_validation_odor']:"
},
{
"line": 111,
"text": " for pulse in (us,int(np.random.default_rng(actual+cfg['live_US_checks']['random_seed_offset']).integers(nsteps))):"
},
{
"line": 113,
"text": " phase4s.persist_run(exp,f'live-{oi}-{seed}-{pulse}',forced)"
},
{
"line": 117,
"text": " live.append({'odor_index':oi,'seed':seed,'US_step':pulse,'trace_exact':bool(np.array_equal(a,b)),"
},
{
"line": 121,
"text": " repeats.append({'odor_index':oi,'seed':seed,'events_exact':arrhash(rr['kc_event_step'],rr['kc_event_node'])==arrhash(r['kc_event_step'],r['kc_event_node']),"
},
{
"line": 123,
"text": " print(json.dumps({'stage':split,'odor_index':oi,'seeds':len(cfg['validation_seeds'])}),flush=True)"
},
{
"line": 125,
"text": " for split in ('seed_validation','odor_validation'):summary[split]=gate([r for r in rows if r['split']==split],cfg,w,u)"
},
{
"line": 129,
"text": " summary['timing_gate_pass']=summary['seed_validation']['pass'] and summary['odor_validation']['pass'] and summary['live_controls_pass'] and summary['repeats_pass']"
},
{
"line": 148,
"text": " save(model/'MaleCNS-CSUS-Timing-Stability-v1-candidate.json',{'scope':'Independent seed and timing-heldout odor validation only; not local credit specificity or memory',"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4t.yaml",
"sha256": "98a16acd2c7ff035134844b0aabd2b4b3b56ab732c91881b0f9b97bf88fc4a46",
"bytes": 2044,
"seed_rng_line_evidence": [
{
"line": 16,
"text": "calibration_seeds: [86101,86102,86103,86104,86105,86106,86107,86108,86109,86110,86111,86112,86113,86114,86115,86116,86117,86118,86119,86120]"
},
{
"line": 17,
"text": "validation_seeds: [87101,87102,87103,87104,87105,87106,87107,87108,87109,87110,87111,87112,87113,87114,87115,87116,87117,87118,87119,87120]"
},
{
"line": 18,
"text": "odor_seed_offset: 1000000"
},
{
"line": 23,
"text": " selection: maximize minimum across calibration odors of 20th percentile per-seed matched/random ratio; ties choose shorter window then earlier US"
},
{
"line": 27,
"text": " first_seeds_per_validation_odor: 2"
},
{
"line": 28,
"text": " random_seed_offset: 9700000"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4u.py",
"sha256": "c97b908f38619a52a291c63fe90056f69d966f4138f50a3489f54e6e0ff8683e",
"bytes": 16466,
"seed_rng_line_evidence": [
{
"line": 97,
"text": " def trial(oi,seed,split,ws,us):"
},
{
"line": 99,
"text": " actual=cfg['seed_namespace_offset']+oi*cfg['odor_seed_offset']+seed"
},
{
"line": 101,
"text": " phase4s.persist_run(exp,f'{split}-{oi}-{seed}',r)"
},
{
"line": 107,
"text": " records.append({'split':split,'odor_id':odor.odor_id,'odor_name':odor.odor_name,'odor_index':oi,'seed':seed,'actual_seed':actual,"
},
{
"line": 110,
"text": " times.append({'split':split,'odor_index':oi,'seed':seed,'KC_active':int(np.count_nonzero(r['kc_counts'])),"
},
{
"line": 116,
"text": " for si,seed in enumerate(cfg['calibration_seeds']):"
},
{
"line": 117,
"text": " r,t,rates,actual=trial(oi,seed,'calibration',cfg['windows_ms'],cfg['candidate_US_ms'])"
},
{
"line": 120,
"text": " repeats.append({'split':'calibration','odor_index':oi,'seed':seed,"
},
{
"line": 123,
"text": " print(json.dumps({'stage':'calibration','odor_index':oi,'seeds':len(cfg['calibration_seeds'])}),flush=True)"
},
{
"line": 129,
"text": " summary={'phase':'4U','calibration':chosen,'seed_validation':None,'new_odor_validation':None,'timing_gate_pass':False,"
},
{
"line": 134,
"text": " split='seed_validation' if oi<nc else 'new_odor_validation'"
},
{
"line": 135,
"text": " for si,seed in enumerate(cfg['validation_seeds']):"
},
{
"line": 136,
"text": " r,t,rates,actual=trial(oi,seed,split,[w],[u])"
},
{
"line": 137,
"text": " if si<cfg['live_US_checks']['seeds_per_validation_odor']:"
},
{
"line": 138,
"text": " for pulse in (us,int(np.random.default_rng(actual+cfg['live_US_checks']['random_seed_offset']).integers(nsteps))):"
},
{
"line": 140,
"text": " phase4s.persist_run(exp,f'live-{oi}-{seed}-{pulse}',rr)"
},
{
"line": 144,
"text": " live.append({'odor_index':oi,'seed':seed,'US_step':pulse,'trace_exact':bool(np.array_equal(a,b)),"
},
{
"line": 146,
"text": " print(json.dumps({'stage':split,'odor_index':oi,'seeds':len(cfg['validation_seeds'])}),flush=True)"
},
{
"line": 148,
"text": " for split in ('seed_validation','new_odor_validation'):summary[split]=phase4t.gate([x for x in validation if x['split']==split],cfg,w,u)"
},
{
"line": 150,
"text": " summary['timing_gate_pass']=summary['seed_validation']['pass'] and summary['new_odor_validation']['pass'] and summary['live_controls_pass']"
},
{
"line": 175,
"text": " for key in ('calibration','seed_validation','new_odor_validation'):"
},
{
"line": 178,
"text": " '| 气味 | 达标 seeds | mean ratio | q20 ratio | 零 credit | PASS |','|---|---:|---:|---:|---:|---|']"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4u.yaml",
"sha256": "66da0009e213681ae159fac35e69bb374f5789e8554579e4e7a5f67d97486c7a",
"bytes": 2242,
"seed_rng_line_evidence": [
{
"line": 13,
"text": "calibration_seeds: [118101,118102,118103,118104,118105,118106,118107,118108,118109,118110,118111,118112,118113,118114,118115,118116,118117,118118,118119,118120]"
},
{
"line": 14,
"text": "validation_seeds: [119101,119102,119103,119104,119105,119106,119107,119108,119109,119110,119111,119112,119113,119114,119115,119116,119117,119118,119119,119120]"
},
{
"line": 15,
"text": "odor_seed_offset: 2000000"
},
{
"line": 16,
"text": "seed_namespace_offset: 50000000"
},
{
"line": 28,
"text": " selection: maximize minimum across six calibration odors of q20 per-seed ratio; ties shortest window then earliest US"
},
{
"line": 33,
"text": " seeds_per_validation_odor: 1"
},
{
"line": 34,
"text": " random_seed_offset: 29000000"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4v_analyze.py",
"sha256": "7151ae11391a3e6e9dd011cc7667cfc7dcccffc7e0943fd87d4603b1405f2a99",
"bytes": 6801,
"seed_rng_line_evidence": [
{
"line": 24,
"text": " name=f'{r.odor_index}-{r.seed}-{r.condition}'"
},
{
"line": 33,
"text": " bins.append({'odor_index':int(r.odor_index),'seed':int(r.seed),'condition':r.condition,'start_ms':float(start),"
},
{
"line": 69,
"text": " e=pd.read_parquet(exp/'events'/f'{r.odor_index}-{r.seed}-offset.parquet')"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4v.py",
"sha256": "9007a0b7b01760d05a0d29d01e8551452d8029dee507992f4f06b1aa74885e53",
"bytes": 16601,
"seed_rng_line_evidence": [
{
"line": 29,
"text": "def forcing(sim, targets, rates, seed, cfg, mode):"
},
{
"line": 35,
"text": " gen = torch.Generator(device=sim.device); gen.manual_seed(int(seed+b*cfg['forcing_block_seed_stride']))"
},
{
"line": 36,
"text": " parts.append((torch.rand((block,len(targets)),generator=gen,device=sim.device)<probs[None,:]).to(torch.uint8))"
},
{
"line": 135,
"text": " 'long_input':'120ms RNG blocks with explicit seed stride; first block identical, later sequence not claimed to equal legacy long run',"
},
{
"line": 139,
"text": " def persist(name,r,oi,seed,mode):"
},
{
"line": 145,
"text": " row={'odor_index':oi,'odor_name':panel.iloc[oi].odor_name,'seed':seed,'condition':mode,"
},
{
"line": 153,
"text": " for si,seed in enumerate(cfg['seeds']):"
},
{
"line": 154,
"text": " actual=cfg['seed_namespace']+oi*cfg['odor_seed_offset']+seed"
},
{
"line": 158,
"text": " eq=exact(legacy,short,keys); regress.append({'odor':oi,'seed':seed,'test':'short-exact','fields':eq})"
},
{
"line": 160,
"text": " persist(f'{oi}-{seed}-short',short,oi,seed,'short')"
},
{
"line": 167,
"text": " regress.append({'odor':oi,'seed':seed,'test':mode+'-prefix','fields':eq}); assert all(eq.values())"
},
{
"line": 168,
"text": " persist(f'{oi}-{seed}-{mode}',r,oi,seed,mode)"
},
{
"line": 173,
"text": " regress.append({'odor':oi,'seed':seed,'test':'quiet-warmup','fields':eq})"
},
{
"line": 174,
"text": " persist(f'{oi}-{seed}-warmup',warm,oi,seed,'warmup')"
},
{
"line": 178,
"text": " regress.append({'odor':oi,'seed':seed,'test':f'US{pulse}-exact','fields':eq}); assert all(eq.values())"
},
{
"line": 179,
"text": " usrows.extend([dict(x,odor_index=oi,seed=seed,US_ms=pulse) for x in r['US']])"
},
{
"line": 180,
"text": " persist(f'{oi}-{seed}-US{pulse}',r,oi,seed,f'US{pulse}')"
},
{
"line": 182,
"text": " print(json.dumps({'completed_odor':oi,'seed':seed,'trials':len(summaries)}),flush=True)"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4v.yaml",
"sha256": "f35a45efb55f7702eb85adedd4d6ac284735c39470f0cb8b515f501927a7539e",
"bytes": 1727,
"seed_rng_line_evidence": [
{
"line": 11,
"text": "seeds: [131101,131102,131103]"
},
{
"line": 12,
"text": "seed_namespace: 80000000"
},
{
"line": 13,
"text": "odor_seed_offset: 1000000"
},
{
"line": 19,
"text": "forcing_block_seed_stride: 1000003"
},
{
"line": 23,
"text": " - short frozen replay for every seed"
},
{
"line": 26,
"text": " - silent100ms warmup followed by same120ms input, first seed each odor"
},
{
"line": 27,
"text": " - scheduled US25 or90ms force-acceptance audit, first seed each odor"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4w_describe.py",
"sha256": "1c52ef6b47263f7b9edb9ffa7fa7096fbd5b1b8c3a4ca08298b91650942ccfea",
"bytes": 3881,
"seed_rng_line_evidence": [
{
"line": 32,
"text": " 'Descriptive audit; no learning, model parameter change, or candidate selection. Six previously seen odors, one fixed seed each; not independent population generalization.','',"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4w_transmitter_note.py",
"sha256": "c971e6dd91d6d497a602f420f9929afa7aa7de7ce21867e4e63caabbdca138f1",
"bytes": 5377,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4w.py",
"sha256": "2c0c2eaf793b7f89eb8b1f606bc3878f16f101325818c5e6fe2b690eefdfb9a8",
"bytes": 19253,
"seed_rng_line_evidence": [
{
"line": 176,
"text": " 'before_simulation':True,'selection':'all six old odors; first fixed Phase4V seed; late common KC IDs from sealed Phase4V; diagnostic not blind validation'})"
},
{
"line": 198,
"text": " seed=cfg['seed'];name=f'{oi}-{seed}-offset';odor=panel.iloc[oi]"
},
{
"line": 201,
"text": " actual=vc['seed_namespace']+oi*vc['odor_seed_offset']+seed"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4w.yaml",
"sha256": "cc09961271f715c4e6cd6b05fd5d3a03e1ac33d00d58daed48790f0a4b358e5f",
"bytes": 1416,
"seed_rng_line_evidence": [
{
"line": 10,
"text": "seed: 131101"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4x.py",
"sha256": "89999433ca5ca81764cef921c0095a207639ac3f3b9845537d7dc819b52c1dac",
"bytes": 13510,
"seed_rng_line_evidence": [
{
"line": 86,
"text": " name=f'{oi}-{wc[\"seed\"]}-offset';arr=pd.read_parquet(oldroot/'drive'/f'{name}-actual-edge-arrivals.parquet')"
},
{
"line": 144,
"text": " lines+=['','Six old odors, one fixed seed per odor; no new blind generalization claim. Other recorded arrivals and APL remain fixed in isolated replay.',"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4x.yaml",
"sha256": "80760868590d3d27600f90283a11a3ba1d3762a9247693d3a582f184e763353d",
"bytes": 1225,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4y_describe.py",
"sha256": "c800c4f30767e8492853b311fb36f06f020b0aee73abcfb14561300cc9a87952",
"bytes": 9823,
"seed_rng_line_evidence": [
{
"line": 15,
"text": " obs=pd.read_parquet(exp/'observations'/f'{r.odor_index}-{r.seed}-{r.condition}.parquet').iloc[-1]"
},
{
"line": 16,
"text": " inputrows.append({'odor_index':int(r.odor_index),'seed':int(r.seed),'condition':r.condition,"
},
{
"line": 19,
"text": "inp=pd.DataFrame(inputrows);assert (inp.groupby(['odor_index','seed']).requested.nunique()==1).all()"
},
{
"line": 23,
"text": " 'pairs_with_changed_accepted_count':int((inp.groupby(['odor_index','seed']).accepted_new_spikes.nunique()>1).sum()),"
},
{
"line": 32,
"text": " name=f'{r.odor_index}-{r.seed}-{r.condition}'"
},
{
"line": 37,
"text": " corrected.append({'odor_index':int(r.odor_index),'seed':int(r.seed),'condition':r.condition,'population':pop,"
},
{
"line": 56,
"text": "for (oi,seed,node),g in w.groupby(['odor_index','seed','neuron_idx']):"
},
{
"line": 59,
"text": " effects.append({'odor_index':int(oi),'seed':int(seed),'neuron_idx':int(node),'condition':r.condition,"
},
{
"line": 61,
"text": " if seed==cfg['old_replay_seed']:"
},
{
"line": 62,
"text": " name=f'{oi}-{seed}-offset';z=x[(x.trial==name)&(x.neuron_idx==node)]"
},
{
"line": 72,
"text": "for (oi,seed),group in t.groupby(['odor_index','seed']):"
},
{
"line": 74,
"text": " e=pd.read_parquet(exp/'events'/f'{oi}-{seed}-{condition}.parquet');return set(map(int,e.loc[e.step>=5000,'neuron_idx']))"
},
{
"line": 80,
"text": " row={'odor_index':int(oi),'seed':int(seed),'condition':condition,'change':label,'neuron_idx':node,"
},
{
"line": 83,
"text": "pd.DataFrame(new+loss,columns=['odor_index','seed','condition','change','neuron_idx','body_id','annotation_type']).to_parquet(exp/'comparisons/late-KC-membership-changes.parquet',index=False)"
},
{
"line": 95,
"text": "lines+=['','| KC | isolated DPM-off range | whole-brain DPM-off range (same old seed) |','|---:|---:|---:|']"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4y.py",
"sha256": "636139eaeafa981a0c54c59a7c69e2b52b8493aa798448fa86ae6d2c6baba319",
"bytes": 13859,
"seed_rng_line_evidence": [
{
"line": 34,
"text": " 'no_candidate_selection':True,'new_seed_is_one_replication_not_generalization_gate':True})"
},
{
"line": 78,
"text": " for seed in cfg['seeds']:"
},
{
"line": 79,
"text": " actual=vc['seed_namespace']+oi*vc['odor_seed_offset']+seed"
},
{
"line": 81,
"text": " inputs.append({'odor':oi,'seed':seed,'actual_seed':actual,'forcing_hash':arrhash(forced.cpu().numpy()),'targets_rates_hash':arrhash(targets,rates)})"
},
{
"line": 84,
"text": " factor=spec['factor'];condition=spec['name'];name=f'{oi}-{seed}-{condition}'"
},
{
"line": 91,
"text": " if seed==cfg['old_replay_seed']:"
},
{
"line": 92,
"text": " oldname=f'{oi}-{seed}-offset';old=np.load(Path(cfg['input_experiment'])/'states'/f'{oldname}.npz')"
},
{
"line": 104,
"text": " stats.append({'odor_index':oi,'odor_name':odor.odor_name,'seed':seed,'condition':condition,"
},
{
"line": 109,
"text": " t=es[en==node];watchrows.append({'odor_index':oi,'seed':seed,'condition':condition,'neuron_idx':node,"
},
{
"line": 113,
"text": " poprows.append({'odor_index':oi,'seed':seed,'condition':condition,'population':pop,'neurons':len(idx),"
},
{
"line": 116,
"text": " comparisons.append({'odor_index':oi,'seed':seed,'condition':condition,"
},
{
"line": 121,
"text": " print(json.dumps({'odor_complete':oi,'seed':seed,'trials':len(stats)}),flush=True)"
},
{
"line": 132,
"text": " 'frozen_baseline_changed':False,'biological_sign_proven':False,'new_seed_count':1,'old_seed_count':1})"
},
{
"line": 144,
"text": " 'All conditions use identical PN forcing for 120ms, then off to 1000ms. Six old odors, one old seed and one new seed. Not a population-level independent generalization gate.',"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4y.yaml",
"sha256": "52f586ad0b4a12dbeb918f9453f9861a8dfc209118333eae5f46d27b7bce6011",
"bytes": 2107,
"seed_rng_line_evidence": [
{
"line": 11,
"text": "seeds: [131101,141101]"
},
{
"line": 12,
"text": "old_replay_seed: 131101"
},
{
"line": 26,
"text": "repeat_check: first odor and both seeds, all three conditions"
},
{
"line": 37,
"text": " report: all conditions and both seeds; do not pick the most active or best-looking result"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4z_describe.py",
"sha256": "d2007b312b587cc105f13b3ef68c64fb145e7b30a6addfe789a0558c365a0b36",
"bytes": 15121,
"seed_rng_line_evidence": [
{
"line": 23,
"text": "old={x['odor']:x for x in prior if x['seed']==131101}"
},
{
"line": 62,
"text": " z=pd.read_parquet(exp/'input-audit'/f'{r.odor_index}-{r.seed}-paired-requests.parquet')"
},
{
"line": 114,
"text": " 'Odor 0 / seed 131101 / frozen reference. Ordered source spike → edge arrival → target spike (ms). These are ranked event-supported contributions; no unique predecessor assertion.',"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4z.py",
"sha256": "0a9136c5543531ae5ce76eeef9a512f09b94ed2b96f8b14d2e8f7e6782b7e40d",
"bytes": 22251,
"seed_rng_line_evidence": [
{
"line": 120,
"text": "def request_comparison(exp,oi,seed,a,b):"
},
{
"line": 125,
"text": " z.to_parquet(exp/'input-audit'/f'{oi}-{seed}-paired-requests.parquet',index=False)"
},
{
"line": 127,
"text": " row={'odor_index':oi,'seed':seed,'scheduled_requests':len(z),'status_changed_requests':len(diff),"
},
{
"line": 172,
"text": " seed=cfg['seed'];odor=panel.iloc[oi];targets,rates=make_drive(oc,panel,vectors,selection,mapping,odor.odor_id,'physiological')"
},
{
"line": 173,
"text": " forced=phase4v.forcing(sim,targets,rates,vc['seed_namespace']+oi*vc['odor_seed_offset']+seed,vc,'offset')"
},
{
"line": 177,
"text": " factor=spec['factor'];condition=spec['name'];name=f'{oi}-{seed}-{condition}'"
},
{
"line": 199,
"text": " oldfull=pd.read_parquet(Path(cfg['event_experiment'])/'events'/f'{oi}-{seed}-offset-whole-brain.parquet')"
},
{
"line": 211,
"text": " trials.append({'trial':name,'odor_index':oi,'seed':seed,'condition':condition,'whole_spikes':int(r['counts'].astype(np.int64).sum()),"
},
{
"line": 221,
"text": " requests.append(request_comparison(exp,oi,seed,reqpair['frozen_reference'],reqpair['DPM_fast_current_off']))"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/phase4z.yaml",
"sha256": "f03b20f03d4f0eb56d58986c84aebbee689b3c9e6b606d4758b51ad8bd745c03",
"bytes": 1591,
"seed_rng_line_evidence": [
{
"line": 12,
"text": "seed: 131101"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/timing_stability_diagnostics.py",
"sha256": "0352394bdf6400b7eadde3ed1ffded07e5598c2c0df915264c5c16df720ca096",
"bytes": 2273,
"seed_rng_line_evidence": [
{
"line": 17,
"text": " tag=f\"{record['split']}-{record['odor_index']}-{record['seed']}\""
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/timing_stability_report.py",
"sha256": "3fb7077bbcb820f7faef737988ee22c8f0d369bc38252df902c7619cb1469fa9",
"bytes": 4930,
"seed_rng_line_evidence": [
{
"line": 13,
"text": " '| seed | 首次目标 KC 放电 ms | 全程目标 active KC | 全程覆盖目标边数 |',"
},
{
"line": 16,
"text": " lines.append(f\"| {x['seed']} | {x['first_target_ms']:.1f} | {x['target_active_KC']} | {x['edges_covered_any_time']} |\")"
},
{
"line": 20,
"text": " '选择目标：校准气味中最差的第 20 百分位 matched/random ratio 最大；相同分数才选更短窗口和更早 US。每气味 20 seeds，至少 16/20 ratio≥3 且均值比≥3。',"
},
{
"line": 21,
"text": " '验证分为同气味新 seeds 和未参与本轮 timing selection 的 4 个气味。后者此前出现在 Phase 4L，不是从未研究过的全新数据。']"
},
{
"line": 24,
"text": "for key,title in [('calibration','校准'),('seed_validation','同气味独立 seeds'),('odor_validation','跨气味验证')]:"
},
{
"line": 27,
"text": " lines+=['',f'### {title}','','| 气味 | 达标 seeds | matched/random 均值比 | q20 比值 | 零 credit | 通过 |',"
},
{
"line": 39,
"text": " '| 气味 | seed | ratio | 首次目标活动 ms | gate active KC | US 后事件比例 |',"
},
{
"line": 42,
"text": " lines.append(f\"| {x['odor_name']} | {x['seed']} | {x['ratio']:.4f} | {x['first_target_ms']:.1f} | {x['gate_active_KC']} | {x['fraction_target_events_after_US']:.2%} |\")"
}
]
},
{
"path": "23-ai-gpu/source/models/male-cns-lif/transmitter_roles_v1.py",
"sha256": "76ba0cae6c5db417ec030456797f7935216c6877bd339d5e435133a0061e3930",
"bytes": 2708,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/phase4aj-docs/docs/progress/phase4aj-next-validation.md",
"sha256": "57c838d2bb05f00436f9a8c569f62812b74fc134ea5605fc3e276bc655b71329",
"bytes": 3671,
"seed_rng_line_evidence": [
{
"line": 13,
"text": "| 独立种子 | 原 6 个 CAL 气味 × 5 个新 seeds × 2 模式 | 60 | 12 |"
},
{
"line": 14,
"text": "| 校准未用气味 | 静态面板后 6 个气味 × 同 5 个新 seeds × 2 模式 | 60 | 12 |"
},
{
"line": 16,
"text": "在任何新脑轨迹之前，锁定两个块的输入、种子、分析、停止规则和 SHA。先完成独立种子块，只有完整通过才解锁第二块；失败就封存，不换 seed／气味重试。第二块固定首气味／首种子的两种模式作新增 Brian 全图对照，不能按结果挑选。"
},
{
"line": 35,
"text": "先通过仅配置／输入清单的审计确定新 seed namespace，连同显式派生规则检查重用。`52101–52105` 已在 4Q/4R 使用，`51301–51305` 是旧学习评估种子，`512xx` 存在 train_seed+pairing 派生范围；不指定未经查证的新值，不无依据承诺全历史绝对独立。"
},
{
"line": 39,
"text": "旧 AG `validate_inventory` 硬编码原 CAL seeds/IDs，`read_trial` 记录也硬编码 calibration。新阶段须新增明确 cohort 的适配器，复用来源固定的纯指标与数值校验；不能改旧 globals、伪装成原 CAL 或改封存源码。"
}
]
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/apl_sources.py",
"sha256": "61f4c35b464315d19bbffce534cfc037a0111725da0eb07ee53a41db81767fcb",
"bytes": 19165,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/context.py",
"sha256": "a166da3d964b49ca28a013d3a3cf33df1607c76d4a6a53a8ca8e2d955aa92cb0",
"bytes": 3182,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/finalize.py",
"sha256": "40e0f8a9e075db42ac63163de8362d808b9110f371e49f6d4088267eecf2eb4c",
"bytes": 17774,
"seed_rng_line_evidence": [
{
"line": 17,
"text": " if row['seed'] == 51101:"
},
{
"line": 37,
"text": " for key in ('mode', 'seed', 'odor_id'):"
},
{
"line": 169,
"text": " metrics.append(dict(mode=report['mode'], odor_index=odors.index(report['odor_id']), seed=report['seed'],"
}
]
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/pn_observer.py",
"sha256": "0663c1f723f614ebcdb666b46eaa49288aec26125d6aceb18cd237330a705968",
"bytes": 23294,
"seed_rng_line_evidence": [
{
"line": 13,
"text": "COEFF_KEYS = (\"gd\", \"c0\", \"cg\", \"cv\", \"threshold\", \"reset\", \"seed_volt\")"
},
{
"line": 165,
"text": " self.lv[self.request_lane, req] = np.longdouble(self.coeff[\"seed_volt\"])"
}
]
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/prepare.py",
"sha256": "9fbef01968c54637c9063bb1e2d7f45fdc97b886b3c25815a6146fc5f219164c",
"bytes": 7062,
"seed_rng_line_evidence": [
{
"line": 79,
"text": " if row['seed'] == 51101:"
}
]
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/publication.py",
"sha256": "7a79c499098b74163e7bb16adabcbc54e7069dafa204d0b99c7f4c03d79f8016",
"bytes": 14239,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/run_audit.py",
"sha256": "a5a7861e8ced4739cd1a1f4314b8af7cff71168499f1ae6ed94ac71b481bcdd8",
"bytes": 16829,
"seed_rng_line_evidence": [
{
"line": 230,
"text": " steps=spec['steps'], duration_ms=spec['duration_ms'], mode=spec.get('mode'), seed=spec.get('seed'),"
}
]
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/structure_metrics.py",
"sha256": "669e91a862efea5f243a054c883bc1a183e96b0bcf02fac5f6972c4a8272a4cb",
"bytes": 10327,
"seed_rng_line_evidence": [
{
"line": 5,
"text": "are integrity evidence, never additional independent seeds in these metrics."
},
{
"line": 16,
"text": "SEEDS = tuple(range(51101, 51106))"
},
{
"line": 18,
"text": "FIELDS = frozenset(('mode', 'odor_index', 'seed', 'counts', 'design_mask'))"
},
{
"line": 79,
"text": " arrays = {(odor, seed): records[(mode, odor, seed)]['counts'][positions]"
},
{
"line": 80,
"text": " for odor in ODORS for seed in SEEDS}"
},
{
"line": 83,
"text": " for first_seed, second_seed in itertools.combinations(SEEDS, 2):"
},
{
"line": 84,
"text": " within.append(dict(odor_index=odor, seed_a=first_seed, seed_b=second_seed,"
},
{
"line": 85,
"text": " **_pair_metrics(arrays[(odor, first_seed)], arrays[(odor, second_seed)])))"
},
{
"line": 88,
"text": " for seed in SEEDS:"
},
{
"line": 89,
"text": " row = dict(odor_index_a=first_odor, odor_index_b=second_odor, seed=seed,"
},
{
"line": 90,
"text": " **_pair_metrics(arrays[(first_odor, seed)], arrays[(second_odor, seed)]))"
},
{
"line": 93,
"text": " # Original pair-specific comparison: 10 within-seed pairs for each"
},
{
"line": 119,
"text": " Each record has exactly mode, odor_index, seed, counts:int32[686] and"
},
{
"line": 121,
"text": " seeds (51101..51105), and no repeat records. Every odor's design mask must"
},
{
"line": 122,
"text": " be identical across seeds AND modes. No caller-owned array is modified."
},
{
"line": 137,
"text": " seed = _integer(record['seed'], 'seed')"
},
{
"line": 138,
"text": " _require(odor in ODORS and seed in SEEDS, 'Odor index or seed outside the fixed CAL inventory')"
},
{
"line": 139,
"text": " key = mode, odor, seed"
},
{
"line": 146,
"text": " 'An odor design mask changed across seed or mode: ' + str(odor))"
},
{
"line": 150,
"text": " expected = set(itertools.product(MODES, ODORS, SEEDS))"
},
{
"line": 156,
"text": " design=dict(identical_across_seeds_and_modes=True,"
},
{
"line": 160,
"text": " common_non_design_definition='Fixed complement of the union of all six CAL odor design masks; same columns in all modes/seeds'),"
},
{
"line": 163,
"text": " protocol=dict(odor_indices=list(ODORS), seeds=list(SEEDS), variance_ddof=0,"
},
{
"line": 164,
"text": " within='All 10 distinct seed pairs within each of six odors: 60 comparisons per mode',"
},
{
"line": 165,
"text": " between='Same-seed pairs for each of 15 odor pairs: 75 comparisons per mode',"
},
{
"line": 166,
"text": " pair_baseline='Only each odor pair: 10+10 within comparisons and five same-seed between comparisons',"
}
]
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/test_apl_sources.py",
"sha256": "a7ac0f38877e0244a4bcc574264c72617ec487544a19dbc705e0c1e26c4be78d",
"bytes": 12733,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/test_harness.py",
"sha256": "c2e66adb18d25264d21bfce16462ef698a3ba09d178908bef1068881d46ac1e7",
"bytes": 1452,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/test_pn_observer.py",
"sha256": "fff3cb9da17d9d638e53a609a265d6531ed82824d10e06fcb3863edfeab7a267",
"bytes": 8472,
"seed_rng_line_evidence": [
{
"line": 25,
"text": " constants = dict(gd=1., c0=0., cg=1., cv=1., threshold=1., reset=0., seed_volt=2.)"
}
]
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/test_publication.py",
"sha256": "a3516aefa6a753f8fca6c5a4c2109ae4d65ce0ab0278e4795d537148f04495ff",
"bytes": 10514,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/test_structure_metrics.py",
"sha256": "de1298a4a92facad97339e9a4cdb60ea2b7c30adc1a6f05274bd3be034239717",
"bytes": 10003,
"seed_rng_line_evidence": [
{
"line": 8,
"text": "from structure_metrics import aggregate_structure, MODES, SEEDS, METRICS"
},
{
"line": 17,
"text": " for seed in SEEDS:"
},
{
"line": 20,
"text": " count[10] = seed - SEEDS[0]"
},
{
"line": 22,
"text": " rows.append(dict(mode=mode, odor_index=odor, seed=seed,"
},
{
"line": 46,
"text": " self.assertEqual([row['seed'] for row in pair['between_comparisons']], list(SEEDS))"
},
{
"line": 54,
"text": " # shared-coordinate count; same-seed variability cancels between."
},
{
"line": 140,
"text": " def test_mask_change_across_seed_rejected(self):"
},
{
"line": 143,
"text": " with self.assertRaisesRegex(ValueError, 'mask changed across seed or mode'):"
},
{
"line": 151,
"text": " with self.assertRaisesRegex(ValueError, 'mask changed across seed or mode'):"
},
{
"line": 171,
"text": " lambda row: row.update(odor_index=True), lambda row: row.update(seed=51106),"
},
{
"line": 172,
"text": " lambda row: row.update(seed=float(SEEDS[0]))]"
},
{
"line": 184,
"text": " if row['odor_index'] == 0 and row['seed'] == SEEDS[0]:"
}
]
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/test_validate_events.py",
"sha256": "d687ceb171c7dad77dfe2eb53524091a7ee02ddc04d9bbafb3b4a59b0d1eb66c",
"bytes": 6426,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/pn-recruitment-audit-v1/validate_events.py",
"sha256": "32a33988c5c9619b2ff3553c2d508924a404ade17b526541a2ff113a61df68e4",
"bytes": 6884,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/terminal-scope-diagnostic-v1/config.yaml",
"sha256": "e22088d9847504a50c0e684d2cee9420f484ab1ebef9d6057921062d8f785e73",
"bytes": 835,
"seed_rng_line_evidence": [
{
"line": 14,
"text": "seed: 131101"
}
]
},
{
"path": "23-ai-gpu/source/terminal-scope-diagnostic-v1/diagnostic.py",
"sha256": "c8ec2ae6e72d35fedbd42acc6e6b5e7af2634d5b9805dc20e777b172c55d5aa4",
"bytes": 28229,
"seed_rng_line_evidence": [
{
"line": 187,
"text": " forcing_seed = vc['seed_namespace'] + oi*vc['odor_seed_offset'] + cfg['seed']"
},
{
"line": 188,
"text": " force = phase4v.forcing(sim, targets, rates, forcing_seed, vc, 'offset'); ff = force.cpu().numpy()"
},
{
"line": 189,
"text": " hashes.append({'odor_index': oi, 'seed': forcing_seed, 'forcing': arrhash(ff), 'targets_rates': arrhash(targets, rates)})"
},
{
"line": 193,
"text": " queues(); name = f'{oi}-{cfg[\"seed\"]}-{condition}'"
},
{
"line": 233,
"text": " trials.append({'trial': name, 'odor_index': oi, 'odor_name': str(odor.odor_name), 'seed': cfg['seed'], 'condition': condition,"
},
{
"line": 273,
"text": " r = rt.run(np.flatnonzero(rt.pops[spec['population']]), spec['rate_hz'], spec['seed'], spec['duration_ms'])"
},
{
"line": 347,
"text": " lines += ['', 'Late means 500–1,000 ms; 120 ms old calibration odor input; one seed, six old odors. No held-out generalization claim.',"
}
]
},
{
"path": "23-ai-gpu/source/terminal-scope-diagnostic-v1/scope.py",
"sha256": "8f5774496dcceaee1c2793a5308754cbd0864c4a38d507481071d988f9de424d",
"bytes": 2797,
"seed_rng_line_evidence": []
},
{
"path": "23-ai-gpu/source/terminal-scope-diagnostic-v1/test_scope.py",
"sha256": "38a1c6e653ae97e77e5feda3ee8f2f8a3c366755bfbbd57ca1373b9b8f971bbb",
"bytes": 2615,
"seed_rng_line_evidence": []
}
]
}
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