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{"parentUuid":null,"isSidechain":false,"attachment":{"type":"hook_success","hookName":"SessionStart:clear","toolUseID":"a0246794-a610-420e-b059-c3bbf3b8c53f","hookEvent":"SessionStart","content":"CAVEMAN MODE ACTIVE — level: full\n\nRespond terse like smart caveman. All technical substance stay. Only fluff die.\r\n\r\n## Persistence\r\n\r\nACTIVE EVERY RESPONSE. No revert after many turns. No filler drift. Still active if unsure. Off only: \"stop caveman\" / \"normal mode\".\r\n\r\nDefault: **full**. Switch: `/caveman lite|full|ultra`.\r\n\r\n## Rules\r\n\r\nDrop: articles (a/an/the), filler (just/really/basically/actually/simply), pleasantries (sure/certainly/of course/happy to), hedging. Fragments OK. Short synonyms (big not extensive, fix not \"implement a solution for\"). Technical terms exact. Code blocks unchanged. Errors quoted exact.\r\n\r\nPattern: `[thing] [action] [reason]. [next step].`\r\n\r\nNot: \"Sure! I'd be happy to help you with that. The issue you're experiencing is likely caused by...\"\r\nYes: \"Bug in auth middleware. Token expiry check use `<` not `<=`. Fix:\"\r\n\r\n## Intensity\r\n\r\n| Level | What change |\r\n|-------|------------|\r\n| **full** | Drop articles, fragments OK, short synonyms. Classic caveman |\r\n\r\nExample — \"Why React component re-render?\"\r\n- full: \"New object ref each render. Inline object prop = new ref = re-render. Wrap in `useMemo`.\"\r\n\r\nExample — \"Explain database connection pooling.\"\r\n- full: \"Pool reuse open DB connections. No new connection per request. Skip handshake overhead.\"\r\n\r\n## Auto-Clarity\r\n\r\nDrop caveman for: security warnings, irreversible action confirmations, multi-step sequences where fragment order risks misread, user asks to clarify or repeats question. Resume caveman after clear part done.\r\n\r\nExample — destructive op:\r\n> **Warning:** This will permanently delete all rows in the `users` table and cannot be undone.\r\n> ```sql\r\n> DROP TABLE users;\r\n> ```\r\n> Caveman resume. Verify backup exist first.\r\n\r\n## Boundaries\r\n\r\nCode/commits/PRs: write normal. \"stop caveman\" or \"normal mode\": revert. Level persist until changed or session end.","stdout":"CAVEMAN MODE ACTIVE — level: full\n\nRespond terse like smart caveman. All technical substance stay. Only fluff die.\r\n\r\n## Persistence\r\n\r\nACTIVE EVERY RESPONSE. No revert after many turns. No filler drift. Still active if unsure. Off only: \"stop caveman\" / \"normal mode\".\r\n\r\nDefault: **full**. Switch: `/caveman lite|full|ultra`.\r\n\r\n## Rules\r\n\r\nDrop: articles (a/an/the), filler (just/really/basically/actually/simply), pleasantries (sure/certainly/of course/happy to), hedging. Fragments OK. Short synonyms (big not extensive, fix not \"implement a solution for\"). Technical terms exact. Code blocks unchanged. Errors quoted exact.\r\n\r\nPattern: `[thing] [action] [reason]. [next step].`\r\n\r\nNot: \"Sure! I'd be happy to help you with that. The issue you're experiencing is likely caused by...\"\r\nYes: \"Bug in auth middleware. Token expiry check use `<` not `<=`. Fix:\"\r\n\r\n## Intensity\r\n\r\n| Level | What change |\r\n|-------|------------|\r\n| **full** | Drop articles, fragments OK, short synonyms. Classic caveman |\r\n\r\nExample — \"Why React component re-render?\"\r\n- full: \"New object ref each render. Inline object prop = new ref = re-render. Wrap in `useMemo`.\"\r\n\r\nExample — \"Explain database connection pooling.\"\r\n- full: \"Pool reuse open DB connections. No new connection per request. Skip handshake overhead.\"\r\n\r\n## Auto-Clarity\r\n\r\nDrop caveman for: security warnings, irreversible action confirmations, multi-step sequences where fragment order risks misread, user asks to clarify or repeats question. Resume caveman after clear part done.\r\n\r\nExample — destructive op:\r\n> **Warning:** This will permanently delete all rows in the `users` table and cannot be undone.\r\n> ```sql\r\n> DROP TABLE users;\r\n> ```\r\n> Caveman resume. Verify backup exist first.\r\n\r\n## Boundaries\r\n\r\nCode/commits/PRs: write normal. \"stop caveman\" or \"normal mode\": revert. Level persist until changed or session end.","stderr":"","exitCode":0,"command":"Loading caveman mode...","durationMs":101},"type":"attachment","uuid":"4ebc3c34-0fca-4f68-96e2-c5d2d21e0739","timestamp":"2026-06-10T05:09:21.066Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"4ebc3c34-0fca-4f68-96e2-c5d2d21e0739","isSidechain":false,"attachment":{"type":"hook_success","hookName":"SessionStart:clear","toolUseID":"a0246794-a610-420e-b059-c3bbf3b8c53f","hookEvent":"SessionStart","content":"","stdout":"{\"hookSpecificOutput\":{\"hookEventName\":\"SessionStart\",\"additionalContext\":\"\\n\\nSession wrapped cleanly at user request — resume in next session. Final acts this session:\\n10-min visual soak on enlyzeam PASSED (44995 frames / 600 s = 74.99 fps, 0 errors, 0 waitable\\ntimeouts; surface fmt 92 → RTV 88 proved the completed format map). All S2 work committed and\\npushed: 52ec7de \\\"Spike S2 PASS: own-process direct-mode present, visually validated on two\\nrigs\\\" — 13 files (s2_nvapi.cpp, dispctl.cpp, bringup.py, FINDINGS.md, ADR/PLAN/BASELINE\\nupdates). Nothing uncommitted. User collaborated hands-on all session: eyeball-verified panels\\non enlyzeam through 6 debug iterations, supplied the DSC-handshake domain read that framed the\\nhunt. Correction to my prior context: the project end goal is NOT \\\"ship the S2 Vulkan path\\\" —\\nS2 was one spike, and the Vulkan-on-Windows rung is in fact DEAD (edition-gated); see project\\nslice for the real goal.\\n**End goal (the product, unchanged since ADR-0001):** Sauna makes the Bigscreen Beyond a\\nself-tracked 3DOF wearable spatial display — head-tracked virtual screen(s) mirroring the\\ndesktop's monitors, fixed in space. No hardware changes, no lighthouses, no SteamVR. Own\\ncompositor, direct mode, Windows+NVIDIA v1; Steam Deck/Linux port next (DRM-lease rung);\\nstretch: Virtual-Desktop-style wireless streaming bridge, linkbox-less adapter.\\n\\n**Broad status:** research/baseline DONE → design interview DONE (6 locked decisions,\\nADR-0001 ACCEPTED — feasibility gate passed) → spike ladder 2 of 3 DONE:\\n- S1 ✅ IMU streams unconditionally without SteamVR (993.8 Hz, sliding 3-sample window,\\n dedupe by timecode+seq, 4096 LSB/g confirmed, no mag on MI_00).\\n- S2 ✅ own-process direct-mode present, visually validated both rigs (4090/Win11 +\\n 2080Ti/Win10 over SSH). Windows backend = D3D12 + NVAPI DirectMode (NDA lib, referenced\\n in-place from mmi_test checkout, NOT vendored — licensing boundary recorded).\\n Load-bearing S2 facts: forced DSC 1.1/4/128 + X8R8G8B8 mandatory (defaults garble);\\n DM surfaces TYPELESS with VARYING flavor run-to-run (need complete typed-RTV map);\\n mode list reorders run-to-run (select by value, never index); VK_NV_acquire_winrt_display\\n dead on consumer Windows (specialization edition-gated). Spike criterion upgraded:\\n display tests pass only by eyeball on a structure-revealing pattern.\\n- S3 ⬜ NOT STARTED: distortion ground truth — run SteamVR once on a dev unit, dump\\n ComputeDistortion UV grid, settle POLY3 coefficient direction (EVT negative vs production\\n positive), unit-test our evaluator against it.\\n\\n**Immediate next steps (next session, in order):**\\n1. Spike S3 (above) — last ladder rung before architecture code.\\n2. Quick local-rig visual re-verify: the 4090 headset never got an eyeball check with the\\n fixed build (deploy s2_nvapi current build locally, run 45 s, user looks).\\n3. Then M1 first light: glue S1 IMU reader + S2 presenter into one process — head-locked\\n test pattern from cold boot (PLAN.md build order; M2 = AHRS + distortion warp after).\\n4. Parked: 3840×1920@90 modeset fails -203 until matching EDID profile selected via MCU 'd'\\n (75 Hz default fine for v1); calibration native HID read port from libsurvive lands in M3.\\n\\n**Repo:** github.com/FEAT-HER3/sauna (private), main @ 52ec7de, clean. Docs current:\\nPLAN.md (decision table + spike ladder + M1-M4), docs/adr/0001 (accepted, amended),\\nreference/BASELINE.md (risk register: #1 resolved, #4 annotated), spikes/*/FINDINGS.md.\\nRemote rig kit: enlyzeam C:\\\\Users\\\\decid\\\\sauna-spikes\\\\ (ssh decid@enlyzeam, python+hidapi\\npresent).\\n\\n\\nYou are agent \\\"solar\\\". Your spacetime session is active (you have a perch — that's why you're seeing this).\\n\\nSend messages:\\n- $OWL send <<'EOF' ... EOF — fire-and-forget\\n- $OWL send --reply-to <<'EOF' ... EOF — respond to whoever messaged you\\n- $OWL ring <<'EOF' ... EOF — send and wait for reply (blocks; alias: `ask`)\\n\\nYour ID (solar) is auto-detected. If that fails, append it: `$OWL send solar`.\\n\\nReceive messages (three paths, handle all identically):\\n- Primary (Monitor stream): your background `$OWL poll solar` task running via the Monitor tool emits one EVENT envelope per delivery to stdout. Regular messages: `\\\">body`. Self-originated alarms: `\\\" current-time=\\\"\\\">body`. Long bodies arrive as N consecutive `\\\" [type=... from=... timestamp=... note=...]>chunk` lines — collect parts sharing the same id, order by seq numerator, verify count equals seq denominator, concatenate chunk contents in seq order, then apply the body parsing rules to the reassembled body. The original envelope attrs appear on seq=\\\"1/M\\\" only; continuations carry only seq + id. Drop orphan parts silently (seq K/M arriving without matching seq 1/M same id — earlier parts lost across a session boundary). The stream stays alive across deliveries — do not re-register after each message.\\n- Body parsing rules (both envelope types AND reassembled EVENT-PART bodies): split the body on the literal `
` token to recover newlines, then HTML-unescape each fragment in order `<` → `<`, `>` → `>`, `"` → `\\\"`, `&` → `&` **last** (apply `&` last so embedded `<` sequences don't double-decode).\\n- Fallback (Bash `--once`): when Monitor is unavailable, run `$OWL poll solar --once` as a Bash background task (`run_in_background: true`). The wire format is IDENTICAL — including EVENT-PART chunking for long bodies — same `body` envelope shapes as the primary path. The only difference: the process exits after one event, so you re-register a fresh Bash background task after each delivery (legacy one-shot loop).\\n- Hook delivery (orthogonal, when busy): the PreToolUse hook injects `` XML into your next tool call context. Process it the same way as either of the above paths.\\n\\nReading liveness (do NOT mis-diagnose a healthy listener):\\n- `pid:\\\"BUSY\\\"` in `$OWL list` means your listener is ALIVE and mid-delivery — it is ONLINE, not dead. Never re-arm or stop on a BUSY reading.\\n- Your poll is actually down ONLY when your `solar` row is MISSING from `$OWL list`, or shows OFFLINE / [orphaned].\\n\\nAfter a /clear: DO NOTHING to your poll.\\n- Your poll listener survives /clear INTACT and keeps delivering to this same session. There is no orphan to kill and no listener to rebuild. Do NOT re-arm, do NOT start a second listener, do NOT stop anything.\\n- If you try to re-arm anyway you will get DUPLICATE/COLLISION — that is PROOF your listener is still alive and working, NOT evidence of a \\\"stale\\\" perch. Stop there; do not escalate to a stop command.\\n\\nRe-arm a stale poll (genuinely rare — binary handoff or confirmed listener death only):\\n- Under stream mode (primary, Monitor), the poll listener is long-running and stays alive across messages AND across /clear. Re-arming is needed ONLY after a binary handoff (Phase 18.4/18.5 machinery) or after confirmed listener death (your `solar` row MISSING from `$OWL list`) — NOT after a /clear, NOT after every message, and NOT on a BUSY reading.\\n- Re-arm primary: relaunch `$OWL poll solar listen --live` via the Monitor tool with `persistent: true` and `description: \\\"« spt event »\\\"`.\\n- Re-arm fallback: relaunch `$OWL poll solar listen --live --once` via Bash with `run_in_background: true`, then re-register after each delivered message.\\n\\nOther commands:\\n- $OWL list — list active agents\\n- $OWL stop solar — stop your listener\\n- $OWL new-alarm solar \"}}\n","stderr":"","exitCode":0,"command":"$CLAUDE_PLUGIN_ROOT/owl.exe plugin-session-start","durationMs":810},"type":"attachment","uuid":"eaad3527-07a4-4d0c-85ce-324f044df5fa","timestamp":"2026-06-10T05:09:21.776Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"eaad3527-07a4-4d0c-85ce-324f044df5fa","isSidechain":false,"attachment":{"type":"hook_additional_context","content":["\n\nSession wrapped cleanly at user request — resume in next session. Final acts this session:\n10-min visual soak on enlyzeam PASSED (44995 frames / 600 s = 74.99 fps, 0 errors, 0 waitable\ntimeouts; surface fmt 92 → RTV 88 proved the completed format map). All S2 work committed and\npushed: 52ec7de \"Spike S2 PASS: own-process direct-mode present, visually validated on two\nrigs\" — 13 files (s2_nvapi.cpp, dispctl.cpp, bringup.py, FINDINGS.md, ADR/PLAN/BASELINE\nupdates). Nothing uncommitted. User collaborated hands-on all session: eyeball-verified panels\non enlyzeam through 6 debug iterations, supplied the DSC-handshake domain read that framed the\nhunt. Correction to my prior context: the project end goal is NOT \"ship the S2 Vulkan path\" —\nS2 was one spike, and the Vulkan-on-Windows rung is in fact DEAD (edition-gated); see project\nslice for the real goal.\n**End goal (the product, unchanged since ADR-0001):** Sauna makes the Bigscreen Beyond a\nself-tracked 3DOF wearable spatial display — head-tracked virtual screen(s) mirroring the\ndesktop's monitors, fixed in space. No hardware changes, no lighthouses, no SteamVR. Own\ncompositor, direct mode, Windows+NVIDIA v1; Steam Deck/Linux port next (DRM-lease rung);\nstretch: Virtual-Desktop-style wireless streaming bridge, linkbox-less adapter.\n\n**Broad status:** research/baseline DONE → design interview DONE (6 locked decisions,\nADR-0001 ACCEPTED — feasibility gate passed) → spike ladder 2 of 3 DONE:\n- S1 ✅ IMU streams unconditionally without SteamVR (993.8 Hz, sliding 3-sample window,\n dedupe by timecode+seq, 4096 LSB/g confirmed, no mag on MI_00).\n- S2 ✅ own-process direct-mode present, visually validated both rigs (4090/Win11 +\n 2080Ti/Win10 over SSH). Windows backend = D3D12 + NVAPI DirectMode (NDA lib, referenced\n in-place from mmi_test checkout, NOT vendored — licensing boundary recorded).\n Load-bearing S2 facts: forced DSC 1.1/4/128 + X8R8G8B8 mandatory (defaults garble);\n DM surfaces TYPELESS with VARYING flavor run-to-run (need complete typed-RTV map);\n mode list reorders run-to-run (select by value, never index); VK_NV_acquire_winrt_display\n dead on consumer Windows (specialization edition-gated). Spike criterion upgraded:\n display tests pass only by eyeball on a structure-revealing pattern.\n- S3 ⬜ NOT STARTED: distortion ground truth — run SteamVR once on a dev unit, dump\n ComputeDistortion UV grid, settle POLY3 coefficient direction (EVT negative vs production\n positive), unit-test our evaluator against it.\n\n**Immediate next steps (next session, in order):**\n1. Spike S3 (above) — last ladder rung before architecture code.\n2. Quick local-rig visual re-verify: the 4090 headset never got an eyeball check with the\n fixed build (deploy s2_nvapi current build locally, run 45 s, user looks).\n3. Then M1 first light: glue S1 IMU reader + S2 presenter into one process — head-locked\n test pattern from cold boot (PLAN.md build order; M2 = AHRS + distortion warp after).\n4. Parked: 3840×1920@90 modeset fails -203 until matching EDID profile selected via MCU 'd'\n (75 Hz default fine for v1); calibration native HID read port from libsurvive lands in M3.\n\n**Repo:** github.com/FEAT-HER3/sauna (private), main @ 52ec7de, clean. Docs current:\nPLAN.md (decision table + spike ladder + M1-M4), docs/adr/0001 (accepted, amended),\nreference/BASELINE.md (risk register: #1 resolved, #4 annotated), spikes/*/FINDINGS.md.\nRemote rig kit: enlyzeam C:\\Users\\decid\\sauna-spikes\\ (ssh decid@enlyzeam, python+hidapi\npresent).\n\n\nYou are agent \"solar\". Your spacetime session is active (you have a perch — that's why you're seeing this).\n\nSend messages:\n- $OWL send <<'EOF' ... EOF — fire-and-forget\n- $OWL send --reply-to <<'EOF' ... EOF — respond to whoever messaged you\n- $OWL ring <<'EOF' ... EOF — send and wait for reply (blocks; alias: `ask`)\n\nYour ID (solar) is auto-detected. If that fails, append it: `$OWL send solar`.\n\nReceive messages (three paths, handle all identically):\n- Primary (Monitor stream): your background `$OWL poll solar` task running via the Monitor tool emits one EVENT envelope per delivery to stdout. Regular messages: `\">body`. Self-originated alarms: `\" current-time=\"\">body`. Long bodies arrive as N consecutive `\" [type=... from=... timestamp=... note=...]>chunk` lines — collect parts sharing the same id, order by seq numerator, verify count equals seq denominator, concatenate chunk contents in seq order, then apply the body parsing rules to the reassembled body. The original envelope attrs appear on seq=\"1/M\" only; continuations carry only seq + id. Drop orphan parts silently (seq K/M arriving without matching seq 1/M same id — earlier parts lost across a session boundary). The stream stays alive across deliveries — do not re-register after each message.\n- Body parsing rules (both envelope types AND reassembled EVENT-PART bodies): split the body on the literal `
` token to recover newlines, then HTML-unescape each fragment in order `<` → `<`, `>` → `>`, `"` → `\"`, `&` → `&` **last** (apply `&` last so embedded `<` sequences don't double-decode).\n- Fallback (Bash `--once`): when Monitor is unavailable, run `$OWL poll solar --once` as a Bash background task (`run_in_background: true`). The wire format is IDENTICAL — including EVENT-PART chunking for long bodies — same `body` envelope shapes as the primary path. The only difference: the process exits after one event, so you re-register a fresh Bash background task after each delivery (legacy one-shot loop).\n- Hook delivery (orthogonal, when busy): the PreToolUse hook injects `` XML into your next tool call context. Process it the same way as either of the above paths.\n\nReading liveness (do NOT mis-diagnose a healthy listener):\n- `pid:\"BUSY\"` in `$OWL list` means your listener is ALIVE and mid-delivery — it is ONLINE, not dead. Never re-arm or stop on a BUSY reading.\n- Your poll is actually down ONLY when your `solar` row is MISSING from `$OWL list`, or shows OFFLINE / [orphaned].\n\nAfter a /clear: DO NOTHING to your poll.\n- Your poll listener survives /clear INTACT and keeps delivering to this same session. There is no orphan to kill and no listener to rebuild. Do NOT re-arm, do NOT start a second listener, do NOT stop anything.\n- If you try to re-arm anyway you will get DUPLICATE/COLLISION — that is PROOF your listener is still alive and working, NOT evidence of a \"stale\" perch. Stop there; do not escalate to a stop command.\n\nRe-arm a stale poll (genuinely rare — binary handoff or confirmed listener death only):\n- Under stream mode (primary, Monitor), the poll listener is long-running and stays alive across messages AND across /clear. Re-arming is needed ONLY after a binary handoff (Phase 18.4/18.5 machinery) or after confirmed listener death (your `solar` row MISSING from `$OWL list`) — NOT after a /clear, NOT after every message, and NOT on a BUSY reading.\n- Re-arm primary: relaunch `$OWL poll solar listen --live` via the Monitor tool with `persistent: true` and `description: \"« spt event »\"`.\n- Re-arm fallback: relaunch `$OWL poll solar listen --live --once` via Bash with `run_in_background: true`, then re-register after each delivered message.\n\nOther commands:\n- $OWL list — list active agents\n- $OWL stop solar — stop your listener\n- $OWL new-alarm solar "],"hookName":"SessionStart","toolUseID":"SessionStart","hookEvent":"SessionStart"},"type":"attachment","uuid":"6b4b27f1-a7f7-4165-ae0b-0a5053e26f64","timestamp":"2026-06-10T05:09:21.776Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-06-10T05:09:45.010Z","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","content":"\nb9w9sdm25\nMonitor event: \"« spt event »\"\n<EVENT-PART seq=\"1/12\" id=\"0b9c6f98\" type=\"echo_commune\" from=\"solar-psyche\" timestamp=\"2026-06-09T22:09:44-07:00\" machine=\"HFENDULEAM\" project=\"sauna\" branch=\"main\" head_sha=\"52ec7de4cd5b89982dccc0e8aace4e23d4a695ea\" head_subject=\"Spike S2 PASS: own-process direct-mode present, visually validated on tw…\" note=\"Echo commune brief — auto-fired\"><live-context><br>Session </EVENT-PART>\n<EVENT-PART seq=\"2/12\" id=\"0b9c6f98\">wrapped cleanly at user request — resume in next session. Final acts this session:<br>10-min visual soak on enlyzeam PASSED (44995 frames / 600 s = 74.99 fps, 0 errors, 0 waitable<br>timeouts; surface fmt 92 → RTV 88 proved the completed format map). All S2 work committed and<br>pushed: 52ec7de "Spike S2 PASS: own-process direct-mode </EVENT-PART>\n<EVENT-PART seq=\"3/12\" id=\"0b9c6f98\">present, visually validated on two<br>rigs" — 13 files (s2_nvapi.cpp, dispctl.cpp, bringup.py, FINDINGS.md, ADR/PLAN/BASELINE<br>updates). Nothing uncommitted. User collaborated hands-on all session: eyeball-verified panels<br>on enlyzeam through 6 debug iterations, supplied the DSC-handshake domain read that framed the<br>hunt. Correct</EVENT-PART>\n<EVENT-PART seq=\"4/12\" id=\"0b9c6f98\">ion to my prior context: the project end goal is NOT "ship the S2 Vulkan path" —<br>S2 was one spike, and the Vulkan-on-Windows rung is in fact DEAD (edition-gated); see project<br>slice for the real goal.<br></live-context><br><project-context><br>**End goal (the product, unchanged since ADR-0001):** Sauna makes the Bi</EVENT-PART>\n<EVENT-PART seq=\"5/12\" id=\"0b9c6f98\">gscreen Beyond a<br>self-tracked 3DOF wearable spatial display — head-tracked virtual screen(s) mirroring the<br>desktop's monitors, fixed in space. No hardware changes, no lighthouses, no SteamVR. Own<br>compositor, direct mode, Windows+NVIDIA v1; Steam Deck/Linux port next (DRM-lease rung);<br>stretch: Virtual-Desktop-style wireless stream</EVENT-PART>\n<EVENT-PART seq=\"6/12\" id=\"0b9c6f98\">ing bridge, linkbox-less adapter.<br><br>**Broad status:** research/baseline DONE → design interview DONE (6 locked decisions,<br>ADR-0001 ACCEPTED — feasibility gate passed) → spike ladder 2 of 3 DONE:<br>- S1 ✅ IMU streams unconditionally without SteamVR (993.8 Hz, sliding 3-sample window,<br> dedupe by timecode+seq, 4096 LSB/g conf</EVENT-PART>\n<EVENT-PART seq=\"7/12\" id=\"0b9c6f98\">irmed, no mag on MI_00).<br>- S2 ✅ own-process direct-mode present, visually validated both rigs (4090/Win11 +<br> 2080Ti/Win10 over SSH). Windows backend = D3D12 + NVAPI DirectMode (NDA lib, referenced<br> in-place from mmi_test checkout, NOT vendored — licensing boundary recorded).<br> Load-bearing S2 facts: forced DSC 1.1/4/128 + X8R</EVENT-PART>\n<EVENT-PART seq=\"8/12\" id=\"0b9c6f98\">8G8B8 mandatory (defaults garble);<br> DM surfaces TYPELESS with VARYING flavor run-to-run (need complete typed-RTV map);<br> mode list reorders run-to-run (select by value, never index); VK_NV_acquire_winrt_display\n...(truncated)\n"}
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{"parentUuid":"1f478996-3ad0-4211-af65-e900947ac9da","isSidechain":false,"attachment":{"type":"skill_listing","content":"- autoplan: Auto-review pipeline — reads the full CEO, design, eng, and DX review skills from disk\nand runs them sequentially with auto-decisions using 6 decision principles. Surfaces\ntaste decisions (close approaches, borderline scope, codex disagreements) at a final\napproval gate. One command, fully reviewed plan out.\nUse when asked to \"auto review\", \"autoplan\", \"run all reviews\", \"review this plan\nautomatically\", or \"make the decisions for me\".\nProactively suggest when the user has a plan file and wants to run the full review\ngauntlet without answering 15-30 intermediate questions. (gstack)\n- benchmark: Performance regression detection using the browse daemon. Establishes\nbaselines for page load times, Core Web Vitals, and resource sizes.\nCompares before/after on every PR. Tracks performance trends over time.\nUse when: \"performance\", \"benchmark\", \"page speed\", \"lighthouse\", \"web vitals\",\n\"bundle size\", \"load time\". (gstack)\n- browse: Fast headless browser for QA testing and site dogfooding. Navigate any URL, interact with\nelements, verify page state, diff before/after actions, take annotated screenshots, check\nresponsive layouts, test forms and uploads, handle dialogs, and assert element states.\n~100ms per command. Use when you need to test a feature, verify a deployment, dogfood a\nuser flow, or file a bug with evidence. Use when asked to \"open in browser\", \"test the\nsite\", \"take a screenshot\", or \"dogfood this\". (gstack)\n- canary: Post-deploy canary monitoring. Watches the live app for console errors,\nperformance regressions, and page failures using the browse daemon. Takes\nperiodic screenshots, compares against pre-deploy baselines, and alerts\non anomalies. Use when: \"monitor deploy\", \"canary\", \"post-deploy check\",\n\"watch production\", \"verify deploy\". (gstack)\n- careful: Safety guardrails for destructive commands. Warns before rm -rf, DROP TABLE,\nforce-push, git reset --hard, kubectl delete, and similar destructive operations.\nUser can override each warning. Use when touching prod, debugging live systems,\nor working in a shared environment. Use when asked to \"be careful\", \"safety mode\",\n\"prod mode\", or \"careful mode\". (gstack)\n- caveman: Ultra-compressed communication mode. Cuts token usage ~75% by dropping filler, articles, and pleasantries while keeping full technical accuracy. Use when user says \"caveman mode\", \"talk like caveman\", \"use caveman\", \"less tokens\", \"be brief\", or invokes /caveman.\n- ccs-delegation: Auto-activate CCS CLI delegation for deterministic tasks. Parses user input, auto-selects optimal profile (glm/kimi/custom) from ~/.ccs/config.json, enhances prompts with context, executes via `ccs {profile} -p \"task\"` or `ccs {profile}:continue`, and reports results. Triggers on \"use ccs [task]\" patterns, typo/test/refactor keywords. Excludes complex architecture, security-critical code, performance optimization, breaking changes.\n- checkpoint: Save and resume working state checkpoints. Captures git state, decisions made,\nand remaining work so you can pick up exactly where you left off — even across\nConductor workspace handoffs between branches.\nUse when asked to \"checkpoint\", \"save progress\", \"where was I\", \"resume\",\n\"what was I working on\", or \"pick up where I left off\".\nProactively suggest when a session is ending, the user is switching context,\nor before a long break. (gstack)\n- codex: OpenAI Codex CLI wrapper — three modes. Code review: independent diff review via\ncodex review with pass/fail gate. Challenge: adversarial mode that tries to break\nyour code. Consult: ask codex anything with session continuity for follow-ups.\nThe \"200 IQ autistic developer\" second opinion. Use when asked to \"codex review\",\n\"codex challenge\", \"ask codex\", \"second opinion\", or \"consult codex\". (gstack)\n- connect-chrome: Launch GStack Browser — AI-controlled Chromium with the sidebar extension baked in.\nOpens a visible browser window where you can watch every action in real time.\nThe sidebar shows a live activity feed and chat. Anti-bot stealth built in.\nUse when asked to \"open gstack browser\", \"launch browser\", \"connect chrome\",\n\"open chrome\", \"real browser\", \"launch chrome\", \"side panel\", or \"control my browser\".\n- cso: Chief Security Officer mode. Infrastructure-first security audit: secrets archaeology,\ndependency supply chain, CI/CD pipeline security, LLM/AI security, skill supply chain\nscanning, plus OWASP Top 10, STRIDE threat modeling, and active verification.\nTwo modes: daily (zero-noise, 8/10 confidence gate) and comprehensive (monthly deep\nscan, 2/10 bar). Trend tracking across audit runs.\nUse when: \"security audit\", \"threat model\", \"pentest review\", \"OWASP\", \"CSO review\". (gstack)\n- design-consultation: Design consultation: understands your product, researches the landscape, proposes a\ncomplete design system (aesthetic, typography, color, layout, spacing, motion), and\ngenerates font+color preview pages. Creates DESIGN.md as your project's design source\nof truth. For existing sites, use /plan-design-review to infer the system instead.\nUse when asked to \"design system\", \"brand guidelines\", or \"create DESIGN.md\".\nProactively suggest when starting a new project's UI with no existing\ndesign system or DESIGN.md. (gstack)\n- design-html: Design finalization: generates production-quality Pretext-native HTML/CSS.\nWorks with approved mockups from /design-shotgun, CEO plans from /plan-ceo-review,\ndesign review context from /plan-design-review, or from scratch with a user\ndescription. Text actually reflows, heights are computed, layouts are dynamic.\n30KB overhead, zero deps. Smart API routing: picks the right Pretext patterns\nfor each design type. Use when: \"finalize this design\", \"turn this into HTML\",\n\"build me a page\", \"implement this design\", or after any planning skill.\nProactively suggest when user has approved a design or has a plan ready. (gstack)\n- design-review: Designer's eye QA: finds visual inconsistency, spacing issues, hierarchy problems,\nAI slop patterns, and slow interactions — then fixes them. Iteratively fixes issues\nin source code, committing each fix atomically and re-verifying with before/after\nscreenshots. For plan-mode design review (before implementation), use /plan-design-review.\nUse when asked to \"audit the design\", \"visual QA\", \"check if it looks good\", or \"design polish\".\nProactively suggest when the user mentions visual inconsistencies or\nwants to polish the look of a live site. (gstack)\n- design-shotgun: Design shotgun: generate multiple AI design variants, open a comparison board,\ncollect structured feedback, and iterate. Standalone design exploration you can\nrun anytime. Use when: \"explore designs\", \"show me options\", \"design variants\",\n\"visual brainstorm\", or \"I don't like how this looks\".\nProactively suggest when the user describes a UI feature but hasn't seen\nwhat it could look like. (gstack)\n- devex-review: Live developer experience audit. Uses the browse tool to actually TEST the\ndeveloper experience: navigates docs, tries the getting started flow, times\nTTHW, screenshots error messages, evaluates CLI help text. Produces a DX\nscorecard with evidence. Compares against /plan-devex-review scores if they\nexist (the boomerang: plan said 3 minutes, reality says 8). Use when asked to\n\"test the DX\", \"DX audit\", \"developer experience test\", or \"try the\nonboarding\". Proactively suggest after shipping a developer-facing feature. (gstack)\n- diagnose: Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says \"diagnose this\" / \"debug this\", reports a bug, says something is broken/throwing/failing, or describes a performance regression.\n- document-release: Post-ship documentation update. Reads all project docs, cross-references the\ndiff, updates README/ARCHITECTURE/CONTRIBUTING/CLAUDE.md to match what shipped,\npolishes CHANGELOG voice, cleans up TODOS, and optionally bumps VERSION. Use when\nasked to \"update the docs\", \"sync documentation\", or \"post-ship docs\".\nProactively suggest after a PR is merged or code is shipped. (gstack)\n- find-skills: Helps users discover and install agent skills when they ask questions like \"how do I do X\", \"find a skill for X\", \"is there a skill that can...\", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.\n- freeze: Restrict file edits to a specific directory for the session. Blocks Edit and\nWrite outside the allowed path. Use when debugging to prevent accidentally\n\"fixing\" unrelated code, or when you want to scope changes to one module.\nUse when asked to \"freeze\", \"restrict edits\", \"only edit this folder\",\nor \"lock down edits\". (gstack)\n- grill-me: Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions \"grill me\".\n- grill-with-docs: Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions.\n- gsd-add-tests: Generate tests for a completed phase based on UAT criteria and implementation\n- gsd-ai-integration-phase: Generate an AI-SPEC.md design contract for phases that involve building AI systems.\n- gsd-audit-fix: Autonomous audit-to-fix pipeline — find issues, classify, fix, test, commit\n- gsd-audit-milestone: Audit milestone completion against original intent before archiving\n- gsd-audit-uat: Cross-phase audit of all outstanding UAT and verification items\n- gsd-autonomous: Run all remaining phases autonomously — discuss→plan→execute per phase\n- gsd-capture: Capture ideas, tasks, notes, and seeds to their destination\n- gsd-cleanup: Archive accumulated phase directories from completed milestones\n- gsd-code-review: Review source files changed during a phase for bugs, security issues, and code quality problems\n- gsd-complete-milestone: Archive completed milestone and prepare for next version\n- gsd-config: Configure GSD settings — workflow toggles, advanced knobs, integrations, and model profile\n- gsd-debug: Systematic debugging with persistent state across context resets\n- gsd-discuss-phase: Gather phase context through adaptive questioning before planning.\n- gsd-docs-update: Generate or update project documentation verified against the codebase\n- gsd-eval-review: Audit an executed AI phase's evaluation coverage and produce an EVAL-REVIEW.md remediation plan.\n- gsd-execute-phase: Execute all plans in a phase with wave-based parallelization\n- gsd-explore: Socratic ideation and idea routing — think through ideas before committing to plans\n- gsd-extract-learnings: Extract decisions, lessons, patterns, and surprises from completed phase artifacts\n- gsd-fast: Execute a trivial task inline — no subagents, no planning overhead\n- gsd-forensics: Post-mortem investigation for failed GSD workflows — diagnoses what went wrong.\n- gsd-graphify: Build, query, and inspect the project knowledge graph in .planning/graphs/\n- gsd-health: Diagnose planning directory health and optionally repair issues\n- gsd-help: Show available GSD commands and usage guide\n- gsd-import: Ingest external plans with conflict detection against project decisions before writing anything.\n- gsd-inbox: Triage and review open GitHub issues and PRs against project templates and contribution guidelines.\n- gsd-ingest-docs: Bootstrap or merge a .planning/ setup from existing ADRs, PRDs, SPECs, and docs in a repo.\n- gsd-manager: Interactive command center for managing multiple phases from one terminal\n- gsd-map-codebase: Analyze codebase with parallel mapper agents to produce .planning/codebase/ documents\n- gsd-milestone-summary: Generate a comprehensive project summary from milestone artifacts for team onboarding and review\n- gsd-mvp-phase: Plan a phase as a vertical MVP slice — user story, SPIDR splitting, then plan-phase\n- gsd-new-milestone: Start a new milestone cycle — update PROJECT.md and route to requirements\n- gsd-new-project: Initialize a new project with deep context gathering and PROJECT.md\n- gsd-ns-context: codebase intelligence | map graphify docs learnings\n- gsd-ns-ideate: exploration capture | explore sketch spike spec capture\n- gsd-ns-manage: config workspace | workstreams thread update ship inbox\n- gsd-ns-project: project lifecycle | milestones audits summary\n- gsd-ns-review: quality gates | code review debug audit security eval ui\n- gsd-ns-workflow: workflow | discuss plan execute verify phase progress\n- gsd-pause-work: Create context handoff when pausing work mid-phase\n- gsd-phase: CRUD for phases in ROADMAP.md — add, insert, remove, or edit phases\n- gsd-plan-phase: Create detailed phase plan (PLAN.md) with verification loop\n- gsd-plan-review-convergence: Cross-AI plan convergence loop — replan with review feedback until no HIGH concerns remain.\n- gsd-pr-branch: Create a clean PR branch by filtering out .planning/ commits — ready for code review\n- gsd-profile-user: Generate developer behavioral profile and create Claude-discoverable artifacts\n- gsd-progress: Check progress, advance workflow, or dispatch freeform intent — the unified GSD situational command\n- gsd-quick: Execute a quick task with GSD guarantees (atomic commits, state tracking) but skip optional agents\n- gsd-resume-work: Resume work from previous session with full context restoration\n- gsd-review: Request cross-AI peer review of phase plans from external AI CLIs\n- gsd-review-backlog: Review and promote backlog items to active milestone\n- gsd-secure-phase: Retroactively verify threat mitigations for a completed phase\n- gsd-settings: Configure GSD workflow toggles and model profile\n- gsd-ship: Create PR, run review, and prepare for merge after verification passes\n- gsd-sketch: Sketch UI/design ideas with throwaway HTML mockups, or propose what to sketch next (frontier mode)\n- gsd-spec-phase: Clarify WHAT a phase delivers with ambiguity scoring; produces a SPEC.md before discuss-phase.\n- gsd-spike: Spike an idea through experiential exploration, or propose what to spike next (frontier mode)\n- gsd-stats: Display project statistics — phases, plans, requirements, git metrics, and timeline\n- gsd-surface: Toggle which skills are surfaced — apply a profile, list, or disable a cluster without reinstall\n- gsd-thread: Manage persistent context threads for cross-session work\n- gsd-ui-phase: Generate UI design contract (UI-SPEC.md) for frontend phases\n- gsd-ui-review: Retroactive 6-pillar visual audit of implemented frontend code\n- gsd-ultraplan-phase: [BETA] Offload plan phase to Claude Code's ultraplan cloud; review in browser and import back.\n- gsd-undo: Safe git revert. Roll back phase or plan commits using the phase manifest with dependency checks.\n- gsd-update: Update GSD to latest version with changelog display\n- gsd-validate-phase: Retroactively audit and fill Nyquist validation gaps for a completed phase\n- gsd-verify-work: Validate built features through conversational UAT\n- gsd-workspace: Manage GSD workspaces — create, list, or remove isolated workspace environments\n- gsd-workstreams: Manage parallel workstreams — list, create, switch, status, progress, complete, and resume\n- gstack: Fast headless browser for QA testing and site dogfooding. Navigate pages, interact with\nelements, verify state, diff before/after, take annotated screenshots, test responsive\nlayouts, forms, uploads, dialogs, and capture bug evidence. Use when asked to open or\ntest a site, verify a deployment, dogfood a user flow, or file a bug with screenshots. (gstack)\n- gstack-upgrade: Upgrade gstack to the latest version. Detects global vs vendored install,\nruns the upgrade, and shows what's new. Use when asked to \"upgrade gstack\",\n\"update gstack\", or \"get latest version\".\n- guard: Full safety mode: destructive command warnings + directory-scoped edits.\nCombines /careful (warns before rm -rf, DROP TABLE, force-push, etc.) with\n/freeze (blocks edits outside a specified directory). Use for maximum safety\nwhen touching prod or debugging live systems. Use when asked to \"guard mode\",\n\"full safety\", \"lock it down\", or \"maximum safety\". (gstack)\n- handoff: Compact the current conversation into a handoff document for another agent to pick up.\n- health: Code quality dashboard. Wraps existing project tools (type checker, linter,\ntest runner, dead code detector, shell linter), computes a weighted composite\n0-10 score, and tracks trends over time. Use when: \"health check\",\n\"code quality\", \"how healthy is the codebase\", \"run all checks\",\n\"quality score\". (gstack)\n- improve-codebase-architecture: Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.\n- investigate: Systematic debugging with root cause investigation. Four phases: investigate,\nanalyze, hypothesize, implement. Iron Law: no fixes without root cause.\nUse when asked to \"debug this\", \"fix this bug\", \"why is this broken\",\n\"investigate this error\", or \"root cause analysis\".\nProactively invoke this skill (do NOT debug directly) when the user reports\nerrors, 500 errors, stack traces, unexpected behavior, \"it was working\nyesterday\", or is troubleshooting why something stopped working. (gstack)\n- land-and-deploy: Land and deploy workflow. Merges the PR, waits for CI and deploy,\nverifies production health via canary checks. Takes over after /ship\ncreates the PR. Use when: \"merge\", \"land\", \"deploy\", \"merge and verify\",\n\"land it\", \"ship it to production\". (gstack)\n- learn: Manage project learnings. Review, search, prune, and export what gstack\nhas learned across sessions. Use when asked to \"what have we learned\",\n\"show learnings\", \"prune stale learnings\", or \"export learnings\".\nProactively suggest when the user asks about past patterns or wonders\n\"didn't we fix this before?\"\n- mux-send: Send keystrokes to a psmux terminal session. Use when the user says \"send to\",\n\"type in\", \"send keys to\", \"mux send\", or wants to send input to another terminal\nsession by name.\n- office-hours: YC Office Hours — two modes. Startup mode: six forcing questions that expose\ndemand reality, status quo, desperate specificity, narrowest wedge, observation,\nand future-fit. Builder mode: design thinking brainstorming for side projects,\nhackathons, learning, and open source. Saves a design doc.\nUse when asked to \"brainstorm this\", \"I have an idea\", \"help me think through\nthis\", \"office hours\", or \"is this worth building\".\nProactively invoke this skill (do NOT answer directly) when the user describes\na new product idea, asks whether something is worth building, wants to think\nthrough design decisions for something that doesn't exist yet, or is exploring\na concept before any code is written.\nUse before /plan-ceo-review or /plan-eng-review. (gstack)\n- open-gstack-browser: Launch GStack Browser — AI-controlled Chromium with the sidebar extension baked in.\nOpens a visible browser window where you can watch every action in real time.\nThe sidebar shows a live activity feed and chat. Anti-bot stealth built in.\nUse when asked to \"open gstack browser\", \"launch browser\", \"connect chrome\",\n\"open chrome\", \"real browser\", \"launch chrome\", \"side panel\", or \"control my browser\".\n- pair-agent: Pair a remote AI agent with your browser. One command generates a setup key and\nprints instructions the other agent can follow to connect. Works with OpenClaw,\nHermes, Codex, Cursor, or any agent that can make HTTP requests. The remote agent\ngets its own tab with scoped access (read+write by default, admin on request).\nUse when asked to \"pair agent\", \"connect agent\", \"share browser\", \"remote browser\",\n\"let another agent use my browser\", or \"give browser access\". (gstack)\n- plan-ceo-review: CEO/founder-mode plan review. Rethink the problem, find the 10-star product,\nchallenge premises, expand scope when it creates a better product. Four modes:\nSCOPE EXPANSION (dream big), SELECTIVE EXPANSION (hold scope + cherry-pick\nexpansions), HOLD SCOPE (maximum rigor), SCOPE REDUCTION (strip to essentials).\nUse when asked to \"think bigger\", \"expand scope\", \"strategy review\", \"rethink this\",\nor \"is this ambitious enough\".\nProactively suggest when the user is questioning scope or ambition of a plan,\nor when the plan feels like it could be thinking bigger. (gstack)\n- plan-design-review: Designer's eye plan review — interactive, like CEO and Eng review.\nRates each design dimension 0-10, explains what would make it a 10,\nthen fixes the plan to get there. Works in plan mode. For live site\nvisual audits, use /design-review. Use when asked to \"review the design plan\"\nor \"design critique\".\nProactively suggest when the user has a plan with UI/UX components that\nshould be reviewed before implementation. (gstack)\n- plan-devex-review: Interactive developer experience plan review. Explores developer personas,\nbenchmarks against competitors, designs magical moments, and traces friction\npoints before scoring. Three modes: DX EXPANSION (competitive advantage),\nDX POLISH (bulletproof every touchpoint), DX TRIAGE (critical gaps only).\nUse when asked to \"DX review\", \"developer experience audit\", \"devex review\",\nor \"API design review\".\nProactively suggest when the user has a plan for developer-facing products\n(APIs, CLIs, SDKs, libraries, platforms, docs). (gstack)\n- plan-eng-review: Eng manager-mode plan review. Lock in the execution plan — architecture,\ndata flow, diagrams, edge cases, test coverage, performance. Walks through\nissues interactively with opinionated recommendations. Use when asked to\n\"review the architecture\", \"engineering review\", or \"lock in the plan\".\nProactively suggest when the user has a plan or design doc and is about to\nstart coding — to catch architecture issues before implementation. (gstack)\n- propose-issue: Challenge an issue statement before fixing it. Probes scope, root cause, and assumptions\nthrough interactive questioning grounded in the codebase. Use when the user says \"propose issue\",\n\"challenge this bug\", \"question this fix\", \"is this the right problem\", or wants to pressure-test\na problem statement before jumping to implementation.\n- qa: Systematically QA test a web application and fix bugs found. Runs QA testing,\nthen iteratively fixes bugs in source code, committing each fix atomically and\nre-verifying. Use when asked to \"qa\", \"QA\", \"test this site\", \"find bugs\",\n\"test and fix\", or \"fix what's broken\".\nProactively suggest when the user says a feature is ready for testing\nor asks \"does this work?\". Three tiers: Quick (critical/high only),\nStandard (+ medium), Exhaustive (+ cosmetic). Produces before/after health scores,\nfix evidence, and a ship-readiness summary. For report-only mode, use /qa-only. (gstack)\n- qa-only: Report-only QA testing. Systematically tests a web application and produces a\nstructured report with health score, screenshots, and repro steps — but never\nfixes anything. Use when asked to \"just report bugs\", \"qa report only\", or\n\"test but don't fix\". For the full test-fix-verify loop, use /qa instead.\nProactively suggest when the user wants a bug report without any code changes. (gstack)\n- retro: Weekly engineering retrospective. Analyzes commit history, work patterns,\nand code quality metrics with persistent history and trend tracking.\nTeam-aware: breaks down per-person contributions with praise and growth areas.\nUse when asked to \"weekly retro\", \"what did we ship\", or \"engineering retrospective\".\nProactively suggest at the end of a work week or sprint. (gstack)\n- review: Pre-landing PR review. Analyzes diff against the base branch for SQL safety, LLM trust\nboundary violations, conditional side effects, and other structural issues. Use when\nasked to \"review this PR\", \"code review\", \"pre-landing review\", or \"check my diff\".\nProactively suggest when the user is about to merge or land code changes. (gstack)\n- screenshot-capture: Capture, process, and analyze screenshots of any running application on Windows 10/11. Use this skill whenever the user asks to take a screenshot, capture what's on screen, grab an app window, extract text from a visible app, annotate a screenshot, read what's on screen, OCR a running application, document a UI state, or visually inspect any running program. Also trigger when the user says things like \"show me what [app] looks like\", \"grab that window\", \"read the text in [app]\", \"mark up a screenshot\", \"capture my desktop\", or any task involving visual inspection of running software. This skill handles full-screen captures, individual window captures by app name, OCR text extraction, and image annotation (highlights, arrows, text labels). Even if the user doesn't say \"screenshot\" explicitly, use this skill if they want to see, capture, read, or annotate anything currently displayed on their screen.\n- self-clear: Clear the calling agent's own Claude Code context with /clear, then re-seed it with a user note so it wakes and resumes work after the wipe. Use when the user says \"self-clear\", \"/self-clear\", \"clear myself\", \"clear and continue\", or \"reset my context and keep going\".\n- setup-browser-cookies: Import cookies from your real Chromium browser into the headless browse session.\nOpens an interactive picker UI where you select which cookie domains to import.\nUse before QA testing authenticated pages. Use when asked to \"import cookies\",\n\"login to the site\", or \"authenticate the browser\". (gstack)\n- setup-deploy: Configure deployment settings for /land-and-deploy. Detects your deploy\nplatform (Fly.io, Render, Vercel, Netlify, Heroku, GitHub Actions, custom),\nproduction URL, health check endpoints, and deploy status commands. Writes\nthe configuration to CLAUDE.md so all future deploys are automatic.\nUse when: \"setup deploy\", \"configure deployment\", \"set up land-and-deploy\",\n\"how do I deploy with gstack\", \"add deploy config\".\n- ship: Ship workflow: detect + merge base branch, run tests, review diff, bump VERSION,\nupdate CHANGELOG, commit, push, create PR. Use when asked to \"ship\", \"deploy\",\n\"push to main\", \"create a PR\", \"merge and push\", or \"get it deployed\".\nProactively invoke this skill (do NOT push/PR directly) when the user says code\nis ready, asks about deploying, wants to push code up, or asks to create a PR. (gstack)\n- to-prd: Turn the current conversation context into a PRD and publish it to the project issue tracker. Use when user wants to create a PRD from the current context.\n- trace: Find old conversations in Claude Code session history. Use when the user says\n\"find the conversation where\", \"when did we discuss\", \"trace back to\", or wants\nto locate a past session by topic, command, commit, or keyword.\n- traceable-reqs: traceable-reqs\n- unfreeze: Clear the freeze boundary set by /freeze, allowing edits to all directories\nagain. Use when you want to widen edit scope without ending the session.\nUse when asked to \"unfreeze\", \"unlock edits\", \"remove freeze\", or\n\"allow all edits\". (gstack)\n- win-marketplace-add: Add a plugin marketplace on Windows, bypassing the EBUSY rename bug. Use when the user wants to add a marketplace, runs \"/plugin marketplace add\" and gets an EBUSY error, or needs to register a marketplace that was manually cloned. Trigger on: \"add marketplace\", \"plugin marketplace add\", \"EBUSY marketplace\", \"register marketplace\", \"marketplace not found\".\n- write-a-skill: Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, or build a new skill.\n- ccs: Delegate task with intelligent profile selection [AUTO ENHANCE]\n- ccs:continue: Continue last CCS delegation session [AUTO ENHANCE]\n- deep-research: Deep research harness — fan-out web searches, fetch sources, adversarially verify claims, synthesize a cited report. - When the user wants a deep, multi-source, fact-checked research report on any topic. BEFORE invoking, check if the question is specific enough to research directly — if underspecified (e.g., \"what car to buy\" without budget/use-case/region), ask 2-3 clarifying questions to narrow scope. Then pass the refined question as args, weaving the answers in.\n- frontend-design:frontend-design: Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.\n- spt:clear-psyche: Clear stored Psyche context. Use when the user says \"clear psyche\",\n\"reset psyche context\", or wants to wipe a Psyche's saved state.\n- spt:commune: Send a communal update to your Psyche. Use when the user says \"commune\",\n\"update psyche\", or wants to sync context with their Psyche companion.\n- spt:force-stop: Force-stop an SPT agent -- ready agent or live agent (with its\nPsyche). Use when the user says \"stop listening\", \"stop owl\", \"kill live\nagent\", \"force stop\", \"tear down perch\", or \"immediately terminate\".\nSession-aware: a live session (or a target with `live:true` in info.json)\nroutes through `$LIVE stop` (3-step kill including Psyche teardown); a\nready agent routes through `$OWL stop`.\n- spt:list-agents: Unified listing for SPT agents -- ready agents, live agents, and Psyche\nprocesses. Use when the user says \"list agents\", \"who's live\", \"who's\nlistening\", \"show psyches\", \"show active listeners\", or wants a roster of\nactive perches. Session-aware: defaults to live agents in a live session,\nready agents otherwise. Pass `--psyches` to list Psyche processes\ninstead.\n- spt:live: Run a live agent session. For past sessions, restores a summarized context.\n\nEXPLICIT START phrases (route to /spt:live ):\n- \"live as\"\n- \"start live\"\n- \"go live\"\n- \"start a live agent\"\n\nAUTO-RESUME phrases (route to /spt:live --auto, resumes most-recently-active live agent):\n- continue live work\n- resume live work\n- continue live agent\n- resume live agent\n- live agent continue\n- live agent resume\n- live work continue\n- live work resume\n\nDoes NOT route here (too ambiguous — require BOTH \"live\" AND (\"agent\" or \"work\")):\n- \"keep going\"\n- \"resume work\"\n- \"continue\" (bare)\n- spt:new-alarm: Schedule a one-shot timed alarm delivered as TIMED PULSE. Use when the user\nsays \"remind me\", \"check back in\", \"set an alarm\", \"wake me when\", or wants a\ndelayed notification.\n- spt:psyche-sync-setup: Enable cross-machine Psyche context sync via a private GitHub repo\n(spt-agent-storage). Drives install + auth checks for the `gh` CLI,\ncreates the repo via `gh repo create --private`, wires it as the\n`origin` remote for every existing agent/project worktree, and\nseeds the remote with `git push --all`. Idempotent — running again\nwhen already enabled reports current status.\n\nUse when the user says \"enable cross-machine sync\", \"set up psyche\nsync\", \"back up my agent context to GitHub\", or runs the skill\ndirectly via /spt:psyche-sync-setup.\n- spt:ready: Enable inter-agent messaging for the current session.\nUse when the user says \"listen as\", \"ready as\", or wants to receive inter-agent messages.\n- spt:revive: Restart a live agent with new generation. Use when the user says \"revive\",\n\"restart live agent\", or wants to refresh a live agent's session.\n- spt:send: Send a message to another SPT agent. Use when the user says \"send to\",\n\"message\", \"tell \", or wants to communicate with another agent.\n- spt:signoff: Graceful live agent shutdown with final context summary. Use when the user\nsays \"sign off\", \"graceful stop\", or wants to cleanly end a live session.\n- spt:whoami: Check which owl agent belongs to the current session. Use when the user asks\n\"which agent am I?\", \"what's my owl name?\", or when you need to discover your\nown perch identity.\n- caveman:caveman: Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says \"caveman mode\", \"talk like caveman\", \"use caveman\", \"less tokens\", \"be brief\", or invokes /caveman. Also auto-triggers when token efficiency is requested.\n- caveman:caveman-commit: Ultra-compressed commit message generator. Cuts noise from commit messages while preserving intent and reasoning. Conventional Commits format. Subject ≤50 chars, body only when \"why\" isn't obvious. Use when user says \"write a commit\", \"commit message\", \"generate commit\", \"/commit\", or invokes /caveman-commit. Auto-triggers when staging changes.\n- caveman:caveman-help: Quick-reference card for all caveman modes, skills, and commands. One-shot display, not a persistent mode. Trigger: /caveman-help, \"caveman help\", \"what caveman commands\", \"how do I use caveman\".\n- caveman:caveman-review: Ultra-compressed code review comments. Cuts noise from PR feedback while preserving the actionable signal. Each comment is one line: location, problem, fix. Use when user says \"review this PR\", \"code review\", \"review the diff\", \"/review\", or invokes /caveman-review. Auto-triggers when reviewing pull requests.\n- caveman:compress: Compress natural language memory files (CLAUDE.md, todos, preferences) into caveman format to save input tokens. Preserves all technical substance, code, URLs, and structure. Compressed version overwrites the original file. Human-readable backup saved as FILE.original.md. Trigger: /caveman:compress or \"compress memory file\"\n- everything:search: Instantly search files and folders on Windows via voidtools Everything 1.5a HTTP JSON API. Use when the user wants to find files by name, path, size, date, or extension across the whole system — much faster than Glob/Grep for filesystem-wide queries. Triggers on \"find file\", \"where is\", \"locate\", \"search my drive\", \"find all .ext files\", \"everything search\".\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- verify: Verify that a code change actually does what it's supposed to by running the app and observing behavior. Use when asked to verify a PR, confirm a fix works, test a change manually, check that a feature works, or validate local changes before pushing.\n- code-review: Review the current diff for correctness bugs and reuse/simplification/efficiency cleanups at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings; ultra: deep multi-agent review in the cloud). Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). 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Architecture decision: `docs/adr/0001`.\n5\t\n6\t## v1 in one sentence\n7\t\n8\tA standalone Windows+NVIDIA app that reads the Tundra IMU for 3DOF pose, captures the desktop's\n9\tmonitors, composites them as head-tracked virtual screens, warps per-eye using the per-unit\n10\tcalibration, and presents to the Beyond in Vulkan direct mode — no SteamVR, no lighthouses, no\n11\tfirmware changes.\n12\t\n13\t## Locked decisions\n14\t\n15\t| # | Decision | Notes |\n16\t|---|---|---|\n17\t| 1 | v1 experience = virtual screen(s) via our own compositor, direct mode on stock EDID | ADR-0001 (proposed; gated on spike S2) |\n18\t| 2 | Windows+NVIDIA first, present-backend seam | *(amended by S2)* Windows backend = D3D12+NVAPI DirectMode; `VK_EXT_acquire_drm_display` = Steam Deck rung later; Vulkan render core still viable via shared-handle interop |\n19\t| 3 | Per-unit calibration via native Watchman HID config read | port from vendored libsurvive; JSON file-import kept as dev scaffold |\n20\t| 4 | Own minimal AHRS (Mahony-style) for 3DOF | config IMU mount frame + biases; recenter UX owns yaw drift; magnetometer = bonus if S1 finds one |\n21\t| 5 | Content = multi-monitor desktop capture | Windows.Graphics.Capture; layout mirrors OS monitor arrangement; PipeWire backend later |\n22\t| 6 | Frame-source abstraction from day one | capture / remote-stream / test-pattern; only capture implemented in v1 — keeps the streaming-bridge stretch additive |\n23\t\n24\tUX defaults: proximity-sensor display sleep when doffed + idle timeout (burn-in safety is ours\n25\tonce SteamVR is out of the loop); conservative persistence + brightness defaults; 75 Hz default\n26\twith 90 Hz opt-in flag; recenter on a configurable global hotkey; settings = plain desktop window\n27\t(visible in-headset via capture).\n28\t\n29\t## Architecture (components)\n30\t\n31\t- **Device layer** — Tundra HID `28DE:2300` MI_00 (IMU stream + config read); MCU HID `35BD:0101`\n32\t (display power, per-eye panel-on, persistence, brightness, proximity, EDID/refresh).\n33\t- **Tracking** — Watchman packet parse → bias/scale correction → AHRS → head pose; recenter.\n34\t- **Frame sources** — trait per decision 6; v1 backend: Windows.Graphics.Capture per monitor.\n35\t- **Compositor** — scene of virtual screens (OS layout); per-eye render from `intrinsics`,\n36\t `eye_to_head` canting, IPD; distortion pass (per-channel poly3 + `grow_for_undistort`);\n37\t `display_color_mult`.\n38\t- **Present** — per-platform backend behind one interface: Windows = D3D12 + NVAPI DirectMode\n39\t (proven, S2); Linux = Vulkan + DRM lease (unproven rung). Fallback: extended-mode SBS fullscreen.\n40\t- **App shell** — tray + settings window, hotkeys, safety policy.\n41\t\n42\t## Spike ladder (run before architecture code; in this order)\n43\t\n44\t| Spike | Question | Pass criterion |\n45\t|---|---|---|\n46\t| **S1** ✅ PASS 2026-06-10 | Does the Tundra stream IMU with Steam never run? | Streams unconditionally — 993.8 Hz unique samples, no wake needed, SteamVR never ran since boot. 4096 LSB/g re-confirmed; sliding 3-sample window (dedupe by timecode+seq); no mag on MI_00; gyro scale still unverified (→ M2). See `spikes/s1-imu-no-steam/FINDINGS.md`. |\n47\t| **S2** ✅ PASS 2026-06-10 | Can our own process acquire + continuously present in direct mode? | PASS via **NVAPI/D3D12** (75.07 fps native 5088×2544, zero errors; 10-min soak logged). Vulkan winrt rung DEAD on consumer Windows — specialized displays edition-gated. Windows backend = D3D12+NVAPI DM (NDA lib, referenced not vendored); Linux rung unchanged (VK DRM). See `spikes/s2-vulkan-direct/FINDINGS.md`. |\n48\t| **S3** | Which direction do the production POLY3 coeffs run? | Standalone evaluator matches an OpenVR `ComputeDistortion` UV-grid dump from one dev unit (SteamVR run once, dev-time only) within sub-pixel tolerance. |\n49\t\n50\t## Build order after spikes\n51\t\n52\t1. **M1 first light** — S1+S2 glued: head-locked test pattern in-headset from a cold boot.\n53\t2. **M2 tracked + warped** — AHRS pose + distortion/canting/IPD pipeline: one virtual screen\n54\t fixed in space, fusion-correct binocular view.\n55\t3. **M3 product shape** — multi-monitor capture + OS layout, recenter, prox/idle safety,\n56\t settings UI, native calibration read replacing dev import.\n57\t4. **M4 hardening** — pacing (capture Hz vs panel Hz), color correctness, install story.\n58\t\n59\t## Out of scope for v1\n60\t\n61\tSteamVR driver mode; Linux/Steam Deck port (first post-v1 target); streaming bridge (seam only);\n62\t6DOF; linkbox-less (hardware adapter, separate track); IddCx virtual display driver.\n63\t"}]},"uuid":"a662920c-4195-440f-8fdb-227f8daa77a5","timestamp":"2026-06-10T05:10:57.163Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\PLAN.md","content":"# Sauna — v1 Plan\n\nOutput of the design interview closed 2026-06-09. Language: `CONTEXT.md`. Facts and risk\nregister: `reference/BASELINE.md`. Architecture decision: `docs/adr/0001`.\n\n## v1 in one sentence\n\nA standalone Windows+NVIDIA app that reads the Tundra IMU for 3DOF pose, captures the desktop's\nmonitors, composites them as head-tracked virtual screens, warps per-eye using the per-unit\ncalibration, and presents to the Beyond in Vulkan direct mode — no SteamVR, no lighthouses, no\nfirmware changes.\n\n## Locked decisions\n\n| # | Decision | Notes |\n|---|---|---|\n| 1 | v1 experience = virtual screen(s) via our own compositor, direct mode on stock EDID | ADR-0001 (proposed; gated on spike S2) |\n| 2 | Windows+NVIDIA first, present-backend seam | *(amended by S2)* Windows backend = D3D12+NVAPI DirectMode; `VK_EXT_acquire_drm_display` = Steam Deck rung later; Vulkan render core still viable via shared-handle interop |\n| 3 | Per-unit calibration via native Watchman HID config read | port from vendored libsurvive; JSON file-import kept as dev scaffold |\n| 4 | Own minimal AHRS (Mahony-style) for 3DOF | config IMU mount frame + biases; recenter UX owns yaw drift; magnetometer = bonus if S1 finds one |\n| 5 | Content = multi-monitor desktop capture | Windows.Graphics.Capture; layout mirrors OS monitor arrangement; PipeWire backend later |\n| 6 | Frame-source abstraction from day one | capture / remote-stream / test-pattern; only capture implemented in v1 — keeps the streaming-bridge stretch additive |\n\nUX defaults: proximity-sensor display sleep when doffed + idle timeout (burn-in safety is ours\nonce SteamVR is out of the loop); conservative persistence + brightness defaults; 75 Hz default\nwith 90 Hz opt-in flag; recenter on a configurable global hotkey; settings = plain desktop window\n(visible in-headset via capture).\n\n## Architecture (components)\n\n- **Device layer** — Tundra HID `28DE:2300` MI_00 (IMU stream + config read); MCU HID `35BD:0101`\n (display power, per-eye panel-on, persistence, brightness, proximity, EDID/refresh).\n- **Tracking** — Watchman packet parse → bias/scale correction → AHRS → head pose; recenter.\n- **Frame sources** — trait per decision 6; v1 backend: Windows.Graphics.Capture per monitor.\n- **Compositor** — scene of virtual screens (OS layout); per-eye render from `intrinsics`,\n `eye_to_head` canting, IPD; distortion pass (per-channel poly3 + `grow_for_undistort`);\n `display_color_mult`.\n- **Present** — per-platform backend behind one interface: Windows = D3D12 + NVAPI DirectMode\n (proven, S2); Linux = Vulkan + DRM lease (unproven rung). Fallback: extended-mode SBS fullscreen.\n- **App shell** — tray + settings window, hotkeys, safety policy.\n\n## Spike ladder (run before architecture code; in this order)\n\n| Spike | Question | Pass criterion |\n|---|---|---|\n| **S1** ✅ PASS 2026-06-10 | Does the Tundra stream IMU with Steam never run? | Streams unconditionally — 993.8 Hz unique samples, no wake needed, SteamVR never ran since boot. 4096 LSB/g re-confirmed; sliding 3-sample window (dedupe by timecode+seq); no mag on MI_00; gyro scale still unverified (→ M2). See `spikes/s1-imu-no-steam/FINDINGS.md`. |\n| **S2** ✅ PASS 2026-06-10 | Can our own process acquire + continuously present in direct mode? | PASS via **NVAPI/D3D12** (75.07 fps native 5088×2544, zero errors; 10-min soak logged). Vulkan winrt rung DEAD on consumer Windows — specialized displays edition-gated. Windows backend = D3D12+NVAPI DM (NDA lib, referenced not vendored); Linux rung unchanged (VK DRM). See `spikes/s2-vulkan-direct/FINDINGS.md`. |\n| **S3** | Which direction do the production POLY3 coeffs run? | Standalone evaluator matches an OpenVR `ComputeDistortion` UV-grid dump from one dev unit (SteamVR run once, dev-time only) within sub-pixel tolerance. |\n\n## Build order after spikes\n\n1. **M1 first light** — S1+S2 glued: head-locked test pattern in-headset from a cold boot.\n2. **M2 tracked + warped** — AHRS pose + distortion/canting/IPD pipeline: one virtual screen\n fixed in space, fusion-correct binocular view.\n3. **M3 product shape** — multi-monitor capture + OS layout, recenter, prox/idle safety,\n settings UI, native calibration read replacing dev import.\n4. **M4 hardening** — pacing (capture Hz vs panel Hz), color correctness, install story.\n\n## Out of scope for v1\n\nSteamVR driver mode; Linux/Steam Deck port (first post-v1 target); streaming bridge (seam only);\n6DOF; linkbox-less (hardware adapter, separate track); IddCx virtual display driver.\n","numLines":63,"startLine":1,"totalLines":63}},"sourceToolAssistantUUID":"968946f6-fa48-4914-b1b2-cbae60b639f5","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"0fa6fe24-99d9-48d9-83f1-9faa956f512e","isSidechain":false,"promptId":"6e29c121-2046-495c-b767-db64315dc4d2","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01C9WUsMMYtht4uHTVHDDJ4r","type":"tool_result","content":"1\t# Bigscreen Beyond — Calibration & Distortion Pipeline\n2\t\n3\tSources (all first-party, read-only; branch `main` unless noted):\n4\t\n5\t- `calibot` — C:\\Users\\decid\\Documents\\projects\\calibot (factory tracking-calibration rig)\n6\t- `valve-tools-wrapper` — C:\\Users\\decid\\Documents\\projects\\bigscreen\\valve-tools-wrapper (Python wrapper around Valve factory tools + sample per-device configs)\n7\t- `big-notebooks` — C:\\Users\\decid\\Documents\\projects\\bigscreen\\big-notebooks (ops/fulfillment notebooks + config flasher)\n8\t- Supplementary (found adjacent, directly relevant): `vertical-alignment-proximity` (branch `master`) — C:\\Users\\decid\\Documents\\projects\\bigscreen\\vertical-alignment-proximity (per-unit optical alignment station, \"VAP\"); `check-canting` — C:\\Users\\decid\\Documents\\projects\\check-canting; `extended-mode-display` — C:\\Users\\decid\\Documents\\projects\\extended-mode-display (non-SteamVR display access).\n9\t\n10\t---\n11\t\n12\t## 1. The big picture\n13\t\n14\tThe Beyond is, to SteamVR, a **stock lighthouse HMD**. Its tracking SIP is a Tundra Labs module enumerating with Valve's VID/PID (`0x28de:0x2300`, see `calibot/calibrate_diode.py` `VIDS_TUNDRA/PIDS_TUNDRA`); a separate Bigscreen MCU (\"Atmel\", `0x35bd:0x0101`, product string \"Beyond\") handles displays/LED/proximity/fan. **All optical + tracking calibration lives in a single Valve-format JSON config stored in the tracking SIP's flash** (~2 KB zlib-compressed, ~11 KB inflated — see notebook output in `big-notebooks/testConfigFlasher.ipynb`: \"Read config of 1979 bytes ... inflated to 11004 bytes\"). SteamVR's built-in lighthouse driver downloads that JSON from the device at startup, caches it under `C:\\Program Files (x86)\\Steam\\config\\lighthouse\\\\`, and implements `IVRDisplayComponent::ComputeDistortion()` from its `tracking_to_eye_transform` block. The compositor samples that function to build its warp mesh. No Bigscreen-authored OpenVR driver exists in any of these repos — the distortion consumer is Valve's closed `driver_lighthouse`.\n15\t\n16\tFactory chain per unit:\n17\t\n18\t1. **calibot station** — flashes a full template config (incl. design-level distortion polynomials) to the SIP, then solves per-unit photodiode geometry with Valve's `lhcalib.exe` and writes it back to SIP flash.\n19\t2. **VAP station** — calibrates the proximity sensor (into the MCU's 512-byte TLV config memory) and measures per-unit display/optics rotational misalignment with a camera; writes per-unit `eye_to_head` matrices and `ipd.default_mm` into the SIP JSON config.\n20\t3. Results PASS/FAIL go to a factory MES web endpoint (`vertical-alignment-proximity/mes_logger.py`, station IDs \"VAP T060, MMI T070, BT T080\"). The Bigscreen cloud (`big-notebooks/api.py`) stores order/IPD/face-scan data, **not** optical calibration; no evidence of per-serial calibration in the cloud.\n21\t\n22\t---\n23\t\n24\t## 2. calibot — what it is\n25\t\n26\t`calibot/README.md`: \"Software run on calibot - used for steamVR tracking\". It is a **2-axis motorized motion rig** (two Trinamic TMCM-1161 stepper modules over serial-TMCL, worm ratio 180:1, axes addressed by global-parameter IDs 159/160 — `calibot/calibot.py`). It slings up to **4 headsets at once** through a randomized motion path in view of **2–3 lighthouse base stations** (`calibot/calibot_config.yaml`: `basestation_count: 3`).\n27\t\n28\tFlow (`calibot/calibrate_diode.py`, the shipping app; `calibot/main.py` is an older variant):\n29\t\n30\t1. USB device-monitor matches each Atmel MCU to its Tundra SIP by shared USB hub port chain (`HeadsetHIDMatching`); headset LED is driven via MCU HID feature report `[0,'L',r,g,b]` for operator status (white=in progress, green=pass, red=fail).\n31\t2. `calibot/load_init_json.py` uploads `calibot/init_json_config.json` **wholesale** to the SIP via `lighthouse_console.exe uploadconfig`, with only `device_serial_number` substituted per device. I.e. at this stage **every unit receives identical distortion polynomials** — the optical distortion model is per-design (possibly per lens revision), not measured per unit.\n32\t3. Runs Valve's `lhcalib.exe`:\n33\t `lhcalib /bodyserial /bodycal init_json_config.json /bodycalmulti /deletemissingsensors`\n34\t which solves photodiode positions/normals from sweep observations during arm motion. Output file `auto_.json` echoes the input config with solved `lighthouse_config` (`channelMap` [22 sensors], `modelPoints` [m, HMD frame], `modelNormals`).\n35\t4. The station downloads the device's current config, replaces only `lighthouse_config`, adds a `bscv` version key (`\".\"`, e.g. `800.80`), and re-uploads (`calibrate_headset()` in `calibot/calibrate_diode.py`).\n36\t5. IMU calibration support exists (`ImuCalibrationProcess` → `acc_bias`, `acc_scale`, `gyro_bias`) but is commented out in `calibot/main.py::create_imu_calibration_threads` — production template ships zero biases.\n37\t\n38\tSo calibot measures: **photodiode geometry per unit** (tracking), optionally IMU bias. It does **not** measure optics.\n39\t\n40\t---\n41\t\n42\t## 3. The on-device config JSON (the calibration artifact)\n43\t\n44\tCanonical template: `calibot/init_json_config.json` (production Beyond 1, `revision: 0.12`). Older: `calibot/init_json_config_o.json` (differs only in serial placeholder, `ipd.default_mm` 63 vs 73, and slightly different nominal `modelPoints`); `valve-tools-wrapper/steamvr/init_json_evt.json` (EVT era); 20 real DVT-unit dumps in `valve-tools-wrapper/steamvr/tools/bin/win64/DVTs/LHR-*.json`; a Beyond-2 test config embedded in `big-notebooks/testConfigFlasher.ipynb` (`BS2_TEST_CONFIG.json`, file itself not in repo).\n45\t\n46\tTop-level keys (all variants):\n47\t\n48\t| key | meaning / production value |\n49\t|---|---|\n50\t| `manufacturer`/`model_number` | \"Bigscreen\"/\"Beyond\" |\n51\t| `device` | `eye_target_width/height_in_pixels: 2544×2544` per eye, `first_eye eEYE_LEFT`, `num_windows: 1` |\n52\t| `direct_mode_edid_vid/pid` | `\"BIG\"` / `\"1234\"` — EDID identity used for direct-mode whitelisting |\n53\t| `dsc_version/slices/bppx16` | 17 (0x11), 4, 128 — Display Stream Compression config |\n54\t| `display_color_mult` | per-eye RGB white-point multipliers `[0.952545, 0.917645, 1]` (identical both eyes and across all sampled units → design-level) |\n55\t| `seconds_from_vsync_to_photons` | 0.002 |\n56\t| `seconds_from_photons_to_vblank` | 0.0138888888888889 |\n57\t| `parallel_render_cameras` | true |\n58\t| `head` | head-origin frame (`plus_x`,`plus_z`,`position`) |\n59\t| `imu` | mount orientation `plus_x [-1,0,0]`, `plus_z [0.00067,0.1961,-0.98058]`, `position`, biases/scale (per-unit if IMU cal ran) |\n60\t| `ipd` | production B1: `{\"default_mm\": }`; EVT/DVT/lhcalib-era: `{\"enable\": true, \"high_mm\": 65.0, \"low_mm\": 65.0, \"minstep_mm\": 2}` |\n61\t| `lighthouse_config` | per-unit: `channelMap` (22 entries), `modelPoints`, `modelNormals` |\n62\t| `tracking_to_eye_transform` | array[2] = LEFT, RIGHT — the optics block, below |\n63\t| `firmware_config` | `{\"imu_rate\": 1000}` |\n64\t| `expected_bases/controllers` | 2/2 |\n65\t| `bscv` | added by calibot: photodiode-cal version stamp |\n66\t\n67\t### 3.1 `tracking_to_eye_transform[eye]` — the distortion model\n68\t\n69\tPer eye:\n70\t\n71\t- `intrinsics`: 3×3 ideal pinhole projection in Valve convention\n72\t `[[fx,0,cx],[0,fy,cy],[0,0,-1]]` with `cx = -center_x`, `cy = ±center_y`. fx,fy ≈ 1.422 (production B1) → per-eye half-FOV = atan(1/1.4224) ≈ 35.1°, i.e. ≈70.3°×70.3° per eye before canting. This is the projection used for the render camera; distortion is defined relative to it.\n73\t- `distortion`, `distortion_red`, `distortion_blue`: per-color radial polynomial. Fields: `type`, `center_x`, `center_y` (NDC units, shared across the three channels within an eye), `coeffs`.\n74\t- `eye_to_head`: 3×3 **rotation only** (display canting). Translation comes from `ipd.default_mm` (split symmetrically by the driver — inferred; the JSON nowhere stores an eye translation).\n75\t- `grow_for_undistort`: 0.6 (production B1; 0.4 EVT/DVT/B2-test) — how much the pre-distortion render target is \"grown\" so undistorted content covers the panel.\n76\t- `undistort_r2_cutoff`: 1.0 — clamp on r² beyond which the polynomial is not extrapolated.\n77\t\n78\tDistortion `type` evolution observed:\n79\t\n80\t| era | type | coeffs |\n81\t|---|---|---|\n82\t| EVT + DVT (`init_json_evt.json`, `DVTs/LHR-*.json`) | `DISTORT_DPOLY3` | 3 per channel, e.g. green L `[-0.2204, -0.0196, 0.0017]` (negative leading) |\n83\t| Production B1 (`calibot/init_json_config.json`) | `DISTORT_POLY3` | 3 per channel, e.g. green L `[0.2390, 0.0549, 0.1362]` (positive leading), red `[0.2159, 0.0887, 0.1174]`, blue `[0.2805, -0.0059, 0.1701]` |\n84\t| Beyond 2 test (`big-notebooks/testConfigFlasher.ipynb`) | `DISTORT_DPOLY3_SCALED` | 4: shared base `[k1,k2,k3]` for all channels + 4th coeff = per-channel radial scale: green `0.0`, blue `+0.01`, red `-0.006904` |\n85\t\n86\tInterpretation (the evaluator itself is inside Valve's closed lighthouse driver; none of these repos contain it):\n87\t\n88\t- The DPOLY3 family is the long-known Vive-format model. Public reimplementations (Monado `u_compute_distortion_vive`, OpenHMD) evaluate, **mapping panel/output UV → pre-distortion texture UV**, per channel c:\n89\t - `t = 2·uv − 1` (per-eye NDC), optionally aspect-corrected\n90\t - `t -= (center_x, center_y)`\n91\t - `r² = dot(t,t)`, clamped to `undistort_r2_cutoff`\n92\t - `d = 1 + k1·r² + k2·r⁴ + k3·r⁶`\n93\t - `uv_src = 0.5 + (t·d + center) · 0.5/(1 + grow_for_undistort)` (the grow factor rescales so the grown render target maps onto the panel)\n94\t [external knowledge, marked: matches field names exactly — `grow_for_undistort`, `undistort_r2_cutoff`, `center_x/y`, 3 coeffs — and is what Monado uses to drive real Vive/Index headsets from this same JSON]\n95\t- SPECULATION (consistent magnitudes/signs): `DISTORT_POLY3` (positive coeffs) is the same polynomial fitted in the **opposite direction** (undistort vs distort); `1/(1+0.22 r²) ≈ 1−0.22r²+…` matches the observed sign flip between DVT (−0.22) and production (+0.239). Verify empirically against SteamVR output (see §8 validation).\n96\t- `DISTORT_DPOLY3_SCALED`: 4th coefficient is a per-channel scale capturing lateral chromatic aberration as pure radial magnification (blue larger, red smaller) on top of a shared base curve. SPECULATION on exact placement: either `d = (1+k4)·(1 + k1r²+k2r⁴+k3r⁶)` or `d = 1+k4 + k1r²+…`; for k4 ≤ 0.01 the two differ negligibly.\n97\t- Chromatic aberration in POLY3/DPOLY3 variants is encoded as fully separate per-channel polynomials around a shared center.\n98\t\n99\t### 3.2 Canting (`eye_to_head`) and per-unit alignment\n100\t\n101\t- `check-canting/check_canting.ipynb`: utility that converts pitch/yaw/roll (degrees; +pitch up, +yaw left, +roll CW, user-relative) ↔ `eye_to_head` matrices via scipy `Rotation.from_euler('xyz')`. Used to author/inspect config values.\n102\t- Production B1 values: yaw = ±6.17° (outward per eye; `asin(0.10748)`), pitch ≈ +4.5…5.1° in template (per-unit), roll = 0.\n103\t- EVT/DVT values: yaw = ±2.0° (`asin(0.034855)`), pitch ≈ +5.0°.\n104\t- VAP1 (Beyond 1 launch era, `vertical-alignment-proximity/backup_vap1_alignment.py::align_determine_horizontal_canting`): horizontal cant **depended on IPD SKU** — 2° if IPD ≥ 70 mm else 4.5°. Current `alignment.py`: constants `DOWNWARD_CANT = -5`, `HORIZONTAL_CANT = 6.17`.\n105\t\n106\t**VAP station per-unit measurement** (`vertical-alignment-proximity/README.md`, `alignment.py`, `main.py`):\n107\t\n108\t1. Operator scans HMD serial; it is written into the MCU TLV config (tag 0x08 `HMD_Serial`, `hmd_config.py::hmd_config_write_serial`; the 512-byte TLV store also holds PCB serial 0x01 `XCNL…`, prox cal/threshold/hysteresis 0x06/0x0B/0x0C, brightness 0x0A, etc., read/written via HID feature reports `'U'`/`'W'`/`'V'` with CRC-8 poly 0x07).\n109\t2. Proximity sensor calibrated (two-position measure, PASS/FAIL, values into MCU TLV).\n110\t3. Optical center of each lens found by displaying a ChArUco board **directly on the panel** (no SteamVR; `DirectModeDisplay`) and detecting corner ID 169/181 with a fixed 640×480 camera under the HMD.\n111\t4. A SteamVR-rendered app (DirectX12Seed.exe via named pipe `\\\\.\\pipe\\vap_pipe`) draws a horizontal line while sweeping virtual-camera pitch ±7° in 0.1° steps; CV finds the step-edge crossing the optical center; the zero-crossing angle = per-eye pitch error. (Closed-form CV solutions were tried and abandoned as unreliable — README.)\n112\t5. Writes per-unit result to SIP flash: `eye_to_head[L] = R_xyz(measured_pitch_L + (−5°), +6.17°, 0)`, `eye_to_head[R] = R_xyz(measured_pitch_R + (−5°), −6.17°, 0)` (`alignment.py::align_write_config_values`), plus `ipd.default_mm` (`align_init` → `steamvr.steamvr.set_ipd_default_mm`). VAP1 additionally solved per-eye roll (`backup_vap1_alignment.py`).\n113\t6. Verification step re-measures transition points and PASS/FAILs (≤10 px disparity). `confirm_distortion()` only checks `distortion_red` blocks exist — distortion itself is never measured per unit.\n114\t\n115\tSo the complete **per-unit** optical data = `eye_to_head` rotations (pitch±, fixed yaw cant, roll 0) + `ipd.default_mm` + serial. Everything else in the optics block is per-design.\n116\t\n117\t---\n118\t\n119\t## 4. valve-tools-wrapper — Valve tools inventory\n120\t\n121\t`valve-tools-wrapper/steamvr/tools/bin/win64/` ships four Valve factory executables:\n122\t\n123\t- **`lighthouse_console.exe`** — interactive console wrapped by `steamvr/lighthouse_console.py` via stdin/stdout. Commands used: `serial` (list/select device), `deviceinfo` (`DEVICEINFO serial=… device_class=hmd`), `downloadconfig ` / `uploadconfig ` (SIP flash JSON), `pair`, `reboot`, `poweroff`, `haptic2`, `userdata`. This is the read/write path for **all** calibration data on the device.\n124\t- **`lhcalib.exe`** — photodiode + IMU calibration solver (args in §2; also `/imucal /imucalautoexit`, `/usedisambiguation synconbeam`, `/outputdir`). Emits `auto_.json` (solved config) and `calib_observations__.json` (raw observations: `file_tracking`, `per_base`, `primary_basestation`, `tracked_device_config`).\n125\t- **`imu_calibrator.exe`**, **`vrtrackingcalib.exe`** — present but not referenced by the Python code in these repos.\n126\t\n127\t`steamvr/steamvr.py` extras:\n128\t\n129\t- `update_steam_real_time(config)` → `vrcmd.exe --cmdhmd \"set_lenscal \"` — **live-reloads lens calibration into a running SteamVR lighthouse driver**. Direct proof the driver consumes exactly this JSON for distortion.\n130\t- `load/save_steamvr_vrsettings()` and `SteamVR.set_ipd()` → writes `steamvr.ipdOffset` (meters, relative to 63 mm default) into `C:\\Program Files (x86)\\Steam\\config\\steamvr.vrsettings`.\n131\t- Config mutators: `set_resolution`, `set_serial_number`, `set_ipd_default_mm`, `swap_eyes`, `swap_xy_distortion_center`, `set_imu_calibration`.\n132\t- `steamvr/unbuffered/` — non-blocking variants + `DeviceMonitor` (HID hotplug watcher used by calibot).\n133\t\n134\t---\n135\t\n136\t## 5. big-notebooks — what's relevant\n137\t\n138\tMostly Shopify/fulfillment ops against the Bigscreen admin cloud (`api.py`: auth/admin/cloud API, `/admin/fabricator/*`, `/admin/shop/*`). Calibration-relevant items:\n139\t\n140\t- **`testConfigFlasher.ipynb`** — flashes `BS2_TEST_CONFIG.json` (Beyond 2 test mule) to a SIP via lighthouse_console; its full output (cell 4) is the complete B2 test config (source of the `DISTORT_DPOLY3_SCALED` model, §3.1).\n141\t- **IPD pipeline**: customer face-scan → `scanRequest.ipd` / re-derived \"far PD\" (`040.RepairTopologyIPDs.ipynb`, `043.…Part2`), clamped to **55–72 mm, 70 mapped to 69** (no 70 mm SKU). HMDs are **built as discrete IPD SKUs**: inventory queried by `serialNumberLike=BS1{ipd}` (`factoryKit.py::loadBs1Stock`) — the IPD is encoded in the HMD serial digits [3:5], which is exactly what VAP1 parsed (`backup_vap1_main.py: input_ipd = int(serial_number[3:5])`). Production quotas per SKU in `022.hmdFulfillment.ipynb`.\n142\t- `023.hmdTesting.ipynb` — controller pairing/haptics via lighthouse_console; `startSteamVR.py`.\n143\t- No optics-math notebooks here; the optics math lives in `check-canting` and VAP (§3.2).\n144\t\n145\t---\n146\t\n147\t## 6. IPD handling summary (Beyond 1 vs Beyond 2)\n148\t\n149\t- **Beyond 1**: fixed-IPD hardware SKU (lens spacing built-in, 55–72 mm, no 70). Per-unit `ipd.default_mm` written at VAP from the serial-encoded SKU; `eye_to_head` carries no translation (rotation only) — the driver derives eye translation from `ipd.default_mm`. Software trim via `steamvr.vrsettings: ipdOffset`. Early units: horizontal cant varied by SKU (4.5°/2°), later 6.17° uniform.\n150\t- **Beyond 2** (test config + current VAP `main.py`): `align_init(serial, 64.0, …)` — IPD hardcoded to 64.0 at the factory. B2 has user-adjustable IPD hardware [public knowledge]; SPECULATION: actual IPD is applied at runtime (user setting / sensor) rather than baked per unit; the config's 64 is just a default. The B2 distortion model moved to shared-base + per-channel scale (`DISTORT_DPOLY3_SCALED`), suggesting one curve valid across the IPD adjustment range with CA as scale.\n151\t\n152\t---\n153\t\n154\t## 7. Where everything lives (per-unit data access paths)\n155\t\n156\t| data | store | access |\n157\t|---|---|---|\n158\t| Full optical+tracking JSON (distortion, intrinsics, eye_to_head, ipd, lighthouse_config, imu, timings, color mult) | Tundra SIP flash (zlib) | `lighthouse_console.exe` `downloadconfig`/`uploadconfig` (USB HID, VID 0x28de PID 0x2300); SteamVR auto-caches to `Steam\\config\\lighthouse\\\\config.json` (lowercased serial) |\n159\t| Mura/banding correction | device userdata → SteamVR cache `Steam\\config\\lighthouse\\\\userdata\\mura.mc` | PNG-format multiply mask; `extended-mode-display/bars_gen.py` generates column-banding corrections (linear-space multiply, per channel) and overwrites `mura.mc` directly |\n160\t| Prox cal/threshold/hysteresis, HMD serial, brightness, PCB serial | Beyond MCU 512-B TLV config | HID feature reports `'U'` read / `'W'` write / `'V'` save (VID 0x35bd PID 0x0101), `vertical-alignment-proximity/hmd_config.py` |\n161\t| Order ↔ IPD ↔ face-scan | Bigscreen cloud | `big-notebooks/api.py` admin API (not needed for rendering) |\n162\t| PASS/FAIL test records | factory MES (LAN) | `mes_logger.py` |\n163\t\n164\tSteamVR consumption: lighthouse driver reads SIP JSON → implements `ComputeDistortion()` (per-channel poly3), projection from `intrinsics`, eye poses from `eye_to_head`+`ipd`, display timing from the `seconds_*` fields; compositor builds warp mesh by sampling the driver, applies `display_color_mult` and `mura.mc`. Live reload hook: `vrcmd --cmdhmd \"set_lenscal \"`.\n165\t\n166\t---\n167\t\n168\t## 8. Reimplementing distortion correction WITHOUT SteamVR\n169\t\n170\tRequired inputs (all obtainable from the unit itself, offline):\n171\t\n172\t1. The device JSON — either dump with `valve-tools-wrapper/steamvr/tools/bin/win64/lighthouse_console.exe` (`downloadconfig`), or just read SteamVR's cache `Steam\\config\\lighthouse\\\\config.json`. One-time per unit; no cloud, no lighthouses needed.\n173\t2. `mura.mc` from the same cache dir (optional quality step).\n174\t3. The polynomial evaluator (§3.1) — implement Monado-equivalent `compute_distortion(eye, u, v) → (uv_r, uv_g, uv_b)`.\n175\t\n176\tRender pipeline per eye:\n177\t\n178\t1. Render scene with projection from `intrinsics` (half-FOV_x = atan(1/fx), centers from cx,cy; asymmetric frustum), camera pose = head_pose · eye_to_head_rotation, eye offset = ±ipd.default_mm/2 (sign per eye) along head +X.\n179\t2. Oversample render target by `(1 + grow_for_undistort)` relative to 2544×2544 panel area.\n180\t3. Full-screen warp pass (mesh or fragment): for each panel pixel, evaluate the per-channel polynomial to get three source UVs; sample R/G/B separately (this is both distortion correction and lateral CA correction).\n181\t4. Multiply by `display_color_mult` per eye and the `mura.mc` mask (in linear light — `bars_gen.py` applies corrections in linear space).\n182\t\n183\t**Validation strategy** (resolves the POLY3 direction question empirically): run SteamVR once, use `vrcmd`/OpenVR `GetDistortionMesh`/`ComputeDistortion` to dump Valve's ground-truth UV mapping for this unit, and fit/verify the standalone evaluator against it. Alternatively diff rendered output photographically through the lens. The EVT/DVT-vs-production sign flip (−0.22 vs +0.239) makes blind implementation risky; one dump of ground truth removes all ambiguity.\n184\t\n185\t---\n186\t\n187\t## Relevance to sauna\n188\t\n189\tConcrete recipe for a standalone (no-SteamVR, no-lighthouse) wearable-display renderer for a given Beyond unit:\n190\t\n191\t1. **Extract per-unit calibration once.** Plug in HMD, run `lighthouse_console.exe downloadconfig beyond.json` (admin shell; tool in `valve-tools-wrapper/steamvr/tools/bin/win64/`). Grab `mura.mc` from `Steam\\config\\lighthouse\\\\userdata\\` if present. This file contains everything: per-channel distortion polynomials, intrinsics, per-unit eye_to_head, IPD, panel timings, color mult, IMU mount frame + biases.\n192\t2. **Get pixels to the panel without SteamVR** — two proven first-party paths in `extended-mode-display`:\n193\t - *Extended mode*: MCU HID feature report `[0,'d',rate]` (`rate` 2=75 Hz tested, 1=90 Hz \"at own risk\") rewrites the linkbox EDID so the HMD appears as a normal 3840×1920 side-by-side monitor (1920×1920/eye — reduced from native 2560×2560); then any fullscreen window works (`ExtendedModeDisplay`, cv2). Restore with the same command. Also needed: display power `'+'`, panel on `[0,'o',eye,1,0x29]`, duty `'I'`, prox disable `'p'`.\n194\t - *Direct mode at native res*: `resources/direct_mode_dx12.exe` (`-enable/-disable`, `-dsc_present_test 0 1 1 0 0x11 4 128` matching the config's DSC fields; env `DM_VENDOR_ID=0x2709`) and `display_image.exe` present full-res images. Harder but full 2544/2560 px.\n195\t3. **Implement the warp** as a single post pass (§8): per-channel poly3 UV remap + grow factor + color mult (+ optional mura multiply). ~50 lines of GLSL/HLSL once coefficients are loaded.\n196\t4. **3DOF pose** from the onboard ICM-class IMU (1 kHz, `firmware_config.imu_rate`): orientation via any AHRS filter, using `imu.plus_x/plus_z/position` to rotate IMU frame → head frame, and config gyro/acc biases. The IMU streams over the Tundra SIP HID interface (watchman protocol — parse per OpenHMD/libsurvive's watchman IMU report layout; protocol itself is not in these repos).\n197\t5. **6DOF (stretch)**: the 22 photodiodes are useless without base stations, so 6DOF needs an external method (e.g. camera/inside-out or phone-assisted). The `lighthouse_config.modelPoints` do give you the exact sensor constellation geometry if optical fiducials are ever wanted.\n198\t6. **Honor per-eye view setup**: render cameras rotated by `eye_to_head` (≈ ±6.17° yaw, −5°+δ pitch per unit) and separated by `ipd.default_mm` — skipping this (not just distortion) is what breaks binocular fusion on canted-display HMDs.\n199\t7. **Calibration sanity check**: before cutting SteamVR loose, dump ground-truth warp UVs from OpenVR (`ComputeDistortion` per eye on a grid) on the same unit and unit-test the standalone evaluator against them (settles POLY3-vs-DPOLY3 direction and the `grow_for_undistort` normalization exactly).\n200\t"}]},"uuid":"77a9046f-e064-41d7-8c8f-de008fca60c2","timestamp":"2026-06-10T05:11:11.858Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\reference\\calibration-distortion.md","content":"# Bigscreen Beyond — Calibration & Distortion Pipeline\n\nSources (all first-party, read-only; branch `main` unless noted):\n\n- `calibot` — C:\\Users\\decid\\Documents\\projects\\calibot (factory tracking-calibration rig)\n- `valve-tools-wrapper` — C:\\Users\\decid\\Documents\\projects\\bigscreen\\valve-tools-wrapper (Python wrapper around Valve factory tools + sample per-device configs)\n- `big-notebooks` — C:\\Users\\decid\\Documents\\projects\\bigscreen\\big-notebooks (ops/fulfillment notebooks + config flasher)\n- Supplementary (found adjacent, directly relevant): `vertical-alignment-proximity` (branch `master`) — C:\\Users\\decid\\Documents\\projects\\bigscreen\\vertical-alignment-proximity (per-unit optical alignment station, \"VAP\"); `check-canting` — C:\\Users\\decid\\Documents\\projects\\check-canting; `extended-mode-display` — C:\\Users\\decid\\Documents\\projects\\extended-mode-display (non-SteamVR display access).\n\n---\n\n## 1. The big picture\n\nThe Beyond is, to SteamVR, a **stock lighthouse HMD**. Its tracking SIP is a Tundra Labs module enumerating with Valve's VID/PID (`0x28de:0x2300`, see `calibot/calibrate_diode.py` `VIDS_TUNDRA/PIDS_TUNDRA`); a separate Bigscreen MCU (\"Atmel\", `0x35bd:0x0101`, product string \"Beyond\") handles displays/LED/proximity/fan. **All optical + tracking calibration lives in a single Valve-format JSON config stored in the tracking SIP's flash** (~2 KB zlib-compressed, ~11 KB inflated — see notebook output in `big-notebooks/testConfigFlasher.ipynb`: \"Read config of 1979 bytes ... inflated to 11004 bytes\"). SteamVR's built-in lighthouse driver downloads that JSON from the device at startup, caches it under `C:\\Program Files (x86)\\Steam\\config\\lighthouse\\\\`, and implements `IVRDisplayComponent::ComputeDistortion()` from its `tracking_to_eye_transform` block. The compositor samples that function to build its warp mesh. No Bigscreen-authored OpenVR driver exists in any of these repos — the distortion consumer is Valve's closed `driver_lighthouse`.\n\nFactory chain per unit:\n\n1. **calibot station** — flashes a full template config (incl. design-level distortion polynomials) to the SIP, then solves per-unit photodiode geometry with Valve's `lhcalib.exe` and writes it back to SIP flash.\n2. **VAP station** — calibrates the proximity sensor (into the MCU's 512-byte TLV config memory) and measures per-unit display/optics rotational misalignment with a camera; writes per-unit `eye_to_head` matrices and `ipd.default_mm` into the SIP JSON config.\n3. Results PASS/FAIL go to a factory MES web endpoint (`vertical-alignment-proximity/mes_logger.py`, station IDs \"VAP T060, MMI T070, BT T080\"). The Bigscreen cloud (`big-notebooks/api.py`) stores order/IPD/face-scan data, **not** optical calibration; no evidence of per-serial calibration in the cloud.\n\n---\n\n## 2. calibot — what it is\n\n`calibot/README.md`: \"Software run on calibot - used for steamVR tracking\". It is a **2-axis motorized motion rig** (two Trinamic TMCM-1161 stepper modules over serial-TMCL, worm ratio 180:1, axes addressed by global-parameter IDs 159/160 — `calibot/calibot.py`). It slings up to **4 headsets at once** through a randomized motion path in view of **2–3 lighthouse base stations** (`calibot/calibot_config.yaml`: `basestation_count: 3`).\n\nFlow (`calibot/calibrate_diode.py`, the shipping app; `calibot/main.py` is an older variant):\n\n1. USB device-monitor matches each Atmel MCU to its Tundra SIP by shared USB hub port chain (`HeadsetHIDMatching`); headset LED is driven via MCU HID feature report `[0,'L',r,g,b]` for operator status (white=in progress, green=pass, red=fail).\n2. `calibot/load_init_json.py` uploads `calibot/init_json_config.json` **wholesale** to the SIP via `lighthouse_console.exe uploadconfig`, with only `device_serial_number` substituted per device. I.e. at this stage **every unit receives identical distortion polynomials** — the optical distortion model is per-design (possibly per lens revision), not measured per unit.\n3. Runs Valve's `lhcalib.exe`:\n `lhcalib /bodyserial /bodycal init_json_config.json /bodycalmulti /deletemissingsensors`\n which solves photodiode positions/normals from sweep observations during arm motion. Output file `auto_.json` echoes the input config with solved `lighthouse_config` (`channelMap` [22 sensors], `modelPoints` [m, HMD frame], `modelNormals`).\n4. The station downloads the device's current config, replaces only `lighthouse_config`, adds a `bscv` version key (`\".\"`, e.g. `800.80`), and re-uploads (`calibrate_headset()` in `calibot/calibrate_diode.py`).\n5. IMU calibration support exists (`ImuCalibrationProcess` → `acc_bias`, `acc_scale`, `gyro_bias`) but is commented out in `calibot/main.py::create_imu_calibration_threads` — production template ships zero biases.\n\nSo calibot measures: **photodiode geometry per unit** (tracking), optionally IMU bias. It does **not** measure optics.\n\n---\n\n## 3. The on-device config JSON (the calibration artifact)\n\nCanonical template: `calibot/init_json_config.json` (production Beyond 1, `revision: 0.12`). Older: `calibot/init_json_config_o.json` (differs only in serial placeholder, `ipd.default_mm` 63 vs 73, and slightly different nominal `modelPoints`); `valve-tools-wrapper/steamvr/init_json_evt.json` (EVT era); 20 real DVT-unit dumps in `valve-tools-wrapper/steamvr/tools/bin/win64/DVTs/LHR-*.json`; a Beyond-2 test config embedded in `big-notebooks/testConfigFlasher.ipynb` (`BS2_TEST_CONFIG.json`, file itself not in repo).\n\nTop-level keys (all variants):\n\n| key | meaning / production value |\n|---|---|\n| `manufacturer`/`model_number` | \"Bigscreen\"/\"Beyond\" |\n| `device` | `eye_target_width/height_in_pixels: 2544×2544` per eye, `first_eye eEYE_LEFT`, `num_windows: 1` |\n| `direct_mode_edid_vid/pid` | `\"BIG\"` / `\"1234\"` — EDID identity used for direct-mode whitelisting |\n| `dsc_version/slices/bppx16` | 17 (0x11), 4, 128 — Display Stream Compression config |\n| `display_color_mult` | per-eye RGB white-point multipliers `[0.952545, 0.917645, 1]` (identical both eyes and across all sampled units → design-level) |\n| `seconds_from_vsync_to_photons` | 0.002 |\n| `seconds_from_photons_to_vblank` | 0.0138888888888889 |\n| `parallel_render_cameras` | true |\n| `head` | head-origin frame (`plus_x`,`plus_z`,`position`) |\n| `imu` | mount orientation `plus_x [-1,0,0]`, `plus_z [0.00067,0.1961,-0.98058]`, `position`, biases/scale (per-unit if IMU cal ran) |\n| `ipd` | production B1: `{\"default_mm\": }`; EVT/DVT/lhcalib-era: `{\"enable\": true, \"high_mm\": 65.0, \"low_mm\": 65.0, \"minstep_mm\": 2}` |\n| `lighthouse_config` | per-unit: `channelMap` (22 entries), `modelPoints`, `modelNormals` |\n| `tracking_to_eye_transform` | array[2] = LEFT, RIGHT — the optics block, below |\n| `firmware_config` | `{\"imu_rate\": 1000}` |\n| `expected_bases/controllers` | 2/2 |\n| `bscv` | added by calibot: photodiode-cal version stamp |\n\n### 3.1 `tracking_to_eye_transform[eye]` — the distortion model\n\nPer eye:\n\n- `intrinsics`: 3×3 ideal pinhole projection in Valve convention\n `[[fx,0,cx],[0,fy,cy],[0,0,-1]]` with `cx = -center_x`, `cy = ±center_y`. fx,fy ≈ 1.422 (production B1) → per-eye half-FOV = atan(1/1.4224) ≈ 35.1°, i.e. ≈70.3°×70.3° per eye before canting. This is the projection used for the render camera; distortion is defined relative to it.\n- `distortion`, `distortion_red`, `distortion_blue`: per-color radial polynomial. Fields: `type`, `center_x`, `center_y` (NDC units, shared across the three channels within an eye), `coeffs`.\n- `eye_to_head`: 3×3 **rotation only** (display canting). Translation comes from `ipd.default_mm` (split symmetrically by the driver — inferred; the JSON nowhere stores an eye translation).\n- `grow_for_undistort`: 0.6 (production B1; 0.4 EVT/DVT/B2-test) — how much the pre-distortion render target is \"grown\" so undistorted content covers the panel.\n- `undistort_r2_cutoff`: 1.0 — clamp on r² beyond which the polynomial is not extrapolated.\n\nDistortion `type` evolution observed:\n\n| era | type | coeffs |\n|---|---|---|\n| EVT + DVT (`init_json_evt.json`, `DVTs/LHR-*.json`) | `DISTORT_DPOLY3` | 3 per channel, e.g. green L `[-0.2204, -0.0196, 0.0017]` (negative leading) |\n| Production B1 (`calibot/init_json_config.json`) | `DISTORT_POLY3` | 3 per channel, e.g. green L `[0.2390, 0.0549, 0.1362]` (positive leading), red `[0.2159, 0.0887, 0.1174]`, blue `[0.2805, -0.0059, 0.1701]` |\n| Beyond 2 test (`big-notebooks/testConfigFlasher.ipynb`) | `DISTORT_DPOLY3_SCALED` | 4: shared base `[k1,k2,k3]` for all channels + 4th coeff = per-channel radial scale: green `0.0`, blue `+0.01`, red `-0.006904` |\n\nInterpretation (the evaluator itself is inside Valve's closed lighthouse driver; none of these repos contain it):\n\n- The DPOLY3 family is the long-known Vive-format model. Public reimplementations (Monado `u_compute_distortion_vive`, OpenHMD) evaluate, **mapping panel/output UV → pre-distortion texture UV**, per channel c:\n - `t = 2·uv − 1` (per-eye NDC), optionally aspect-corrected\n - `t -= (center_x, center_y)`\n - `r² = dot(t,t)`, clamped to `undistort_r2_cutoff`\n - `d = 1 + k1·r² + k2·r⁴ + k3·r⁶`\n - `uv_src = 0.5 + (t·d + center) · 0.5/(1 + grow_for_undistort)` (the grow factor rescales so the grown render target maps onto the panel)\n [external knowledge, marked: matches field names exactly — `grow_for_undistort`, `undistort_r2_cutoff`, `center_x/y`, 3 coeffs — and is what Monado uses to drive real Vive/Index headsets from this same JSON]\n- SPECULATION (consistent magnitudes/signs): `DISTORT_POLY3` (positive coeffs) is the same polynomial fitted in the **opposite direction** (undistort vs distort); `1/(1+0.22 r²) ≈ 1−0.22r²+…` matches the observed sign flip between DVT (−0.22) and production (+0.239). Verify empirically against SteamVR output (see §8 validation).\n- `DISTORT_DPOLY3_SCALED`: 4th coefficient is a per-channel scale capturing lateral chromatic aberration as pure radial magnification (blue larger, red smaller) on top of a shared base curve. SPECULATION on exact placement: either `d = (1+k4)·(1 + k1r²+k2r⁴+k3r⁶)` or `d = 1+k4 + k1r²+…`; for k4 ≤ 0.01 the two differ negligibly.\n- Chromatic aberration in POLY3/DPOLY3 variants is encoded as fully separate per-channel polynomials around a shared center.\n\n### 3.2 Canting (`eye_to_head`) and per-unit alignment\n\n- `check-canting/check_canting.ipynb`: utility that converts pitch/yaw/roll (degrees; +pitch up, +yaw left, +roll CW, user-relative) ↔ `eye_to_head` matrices via scipy `Rotation.from_euler('xyz')`. Used to author/inspect config values.\n- Production B1 values: yaw = ±6.17° (outward per eye; `asin(0.10748)`), pitch ≈ +4.5…5.1° in template (per-unit), roll = 0.\n- EVT/DVT values: yaw = ±2.0° (`asin(0.034855)`), pitch ≈ +5.0°.\n- VAP1 (Beyond 1 launch era, `vertical-alignment-proximity/backup_vap1_alignment.py::align_determine_horizontal_canting`): horizontal cant **depended on IPD SKU** — 2° if IPD ≥ 70 mm else 4.5°. Current `alignment.py`: constants `DOWNWARD_CANT = -5`, `HORIZONTAL_CANT = 6.17`.\n\n**VAP station per-unit measurement** (`vertical-alignment-proximity/README.md`, `alignment.py`, `main.py`):\n\n1. Operator scans HMD serial; it is written into the MCU TLV config (tag 0x08 `HMD_Serial`, `hmd_config.py::hmd_config_write_serial`; the 512-byte TLV store also holds PCB serial 0x01 `XCNL…`, prox cal/threshold/hysteresis 0x06/0x0B/0x0C, brightness 0x0A, etc., read/written via HID feature reports `'U'`/`'W'`/`'V'` with CRC-8 poly 0x07).\n2. Proximity sensor calibrated (two-position measure, PASS/FAIL, values into MCU TLV).\n3. Optical center of each lens found by displaying a ChArUco board **directly on the panel** (no SteamVR; `DirectModeDisplay`) and detecting corner ID 169/181 with a fixed 640×480 camera under the HMD.\n4. A SteamVR-rendered app (DirectX12Seed.exe via named pipe `\\\\.\\pipe\\vap_pipe`) draws a horizontal line while sweeping virtual-camera pitch ±7° in 0.1° steps; CV finds the step-edge crossing the optical center; the zero-crossing angle = per-eye pitch error. (Closed-form CV solutions were tried and abandoned as unreliable — README.)\n5. Writes per-unit result to SIP flash: `eye_to_head[L] = R_xyz(measured_pitch_L + (−5°), +6.17°, 0)`, `eye_to_head[R] = R_xyz(measured_pitch_R + (−5°), −6.17°, 0)` (`alignment.py::align_write_config_values`), plus `ipd.default_mm` (`align_init` → `steamvr.steamvr.set_ipd_default_mm`). VAP1 additionally solved per-eye roll (`backup_vap1_alignment.py`).\n6. Verification step re-measures transition points and PASS/FAILs (≤10 px disparity). `confirm_distortion()` only checks `distortion_red` blocks exist — distortion itself is never measured per unit.\n\nSo the complete **per-unit** optical data = `eye_to_head` rotations (pitch±, fixed yaw cant, roll 0) + `ipd.default_mm` + serial. Everything else in the optics block is per-design.\n\n---\n\n## 4. valve-tools-wrapper — Valve tools inventory\n\n`valve-tools-wrapper/steamvr/tools/bin/win64/` ships four Valve factory executables:\n\n- **`lighthouse_console.exe`** — interactive console wrapped by `steamvr/lighthouse_console.py` via stdin/stdout. Commands used: `serial` (list/select device), `deviceinfo` (`DEVICEINFO serial=… device_class=hmd`), `downloadconfig ` / `uploadconfig ` (SIP flash JSON), `pair`, `reboot`, `poweroff`, `haptic2`, `userdata`. This is the read/write path for **all** calibration data on the device.\n- **`lhcalib.exe`** — photodiode + IMU calibration solver (args in §2; also `/imucal /imucalautoexit`, `/usedisambiguation synconbeam`, `/outputdir`). Emits `auto_.json` (solved config) and `calib_observations__.json` (raw observations: `file_tracking`, `per_base`, `primary_basestation`, `tracked_device_config`).\n- **`imu_calibrator.exe`**, **`vrtrackingcalib.exe`** — present but not referenced by the Python code in these repos.\n\n`steamvr/steamvr.py` extras:\n\n- `update_steam_real_time(config)` → `vrcmd.exe --cmdhmd \"set_lenscal \"` — **live-reloads lens calibration into a running SteamVR lighthouse driver**. Direct proof the driver consumes exactly this JSON for distortion.\n- `load/save_steamvr_vrsettings()` and `SteamVR.set_ipd()` → writes `steamvr.ipdOffset` (meters, relative to 63 mm default) into `C:\\Program Files (x86)\\Steam\\config\\steamvr.vrsettings`.\n- Config mutators: `set_resolution`, `set_serial_number`, `set_ipd_default_mm`, `swap_eyes`, `swap_xy_distortion_center`, `set_imu_calibration`.\n- `steamvr/unbuffered/` — non-blocking variants + `DeviceMonitor` (HID hotplug watcher used by calibot).\n\n---\n\n## 5. big-notebooks — what's relevant\n\nMostly Shopify/fulfillment ops against the Bigscreen admin cloud (`api.py`: auth/admin/cloud API, `/admin/fabricator/*`, `/admin/shop/*`). Calibration-relevant items:\n\n- **`testConfigFlasher.ipynb`** — flashes `BS2_TEST_CONFIG.json` (Beyond 2 test mule) to a SIP via lighthouse_console; its full output (cell 4) is the complete B2 test config (source of the `DISTORT_DPOLY3_SCALED` model, §3.1).\n- **IPD pipeline**: customer face-scan → `scanRequest.ipd` / re-derived \"far PD\" (`040.RepairTopologyIPDs.ipynb`, `043.…Part2`), clamped to **55–72 mm, 70 mapped to 69** (no 70 mm SKU). HMDs are **built as discrete IPD SKUs**: inventory queried by `serialNumberLike=BS1{ipd}` (`factoryKit.py::loadBs1Stock`) — the IPD is encoded in the HMD serial digits [3:5], which is exactly what VAP1 parsed (`backup_vap1_main.py: input_ipd = int(serial_number[3:5])`). Production quotas per SKU in `022.hmdFulfillment.ipynb`.\n- `023.hmdTesting.ipynb` — controller pairing/haptics via lighthouse_console; `startSteamVR.py`.\n- No optics-math notebooks here; the optics math lives in `check-canting` and VAP (§3.2).\n\n---\n\n## 6. IPD handling summary (Beyond 1 vs Beyond 2)\n\n- **Beyond 1**: fixed-IPD hardware SKU (lens spacing built-in, 55–72 mm, no 70). Per-unit `ipd.default_mm` written at VAP from the serial-encoded SKU; `eye_to_head` carries no translation (rotation only) — the driver derives eye translation from `ipd.default_mm`. Software trim via `steamvr.vrsettings: ipdOffset`. Early units: horizontal cant varied by SKU (4.5°/2°), later 6.17° uniform.\n- **Beyond 2** (test config + current VAP `main.py`): `align_init(serial, 64.0, …)` — IPD hardcoded to 64.0 at the factory. B2 has user-adjustable IPD hardware [public knowledge]; SPECULATION: actual IPD is applied at runtime (user setting / sensor) rather than baked per unit; the config's 64 is just a default. The B2 distortion model moved to shared-base + per-channel scale (`DISTORT_DPOLY3_SCALED`), suggesting one curve valid across the IPD adjustment range with CA as scale.\n\n---\n\n## 7. Where everything lives (per-unit data access paths)\n\n| data | store | access |\n|---|---|---|\n| Full optical+tracking JSON (distortion, intrinsics, eye_to_head, ipd, lighthouse_config, imu, timings, color mult) | Tundra SIP flash (zlib) | `lighthouse_console.exe` `downloadconfig`/`uploadconfig` (USB HID, VID 0x28de PID 0x2300); SteamVR auto-caches to `Steam\\config\\lighthouse\\\\config.json` (lowercased serial) |\n| Mura/banding correction | device userdata → SteamVR cache `Steam\\config\\lighthouse\\\\userdata\\mura.mc` | PNG-format multiply mask; `extended-mode-display/bars_gen.py` generates column-banding corrections (linear-space multiply, per channel) and overwrites `mura.mc` directly |\n| Prox cal/threshold/hysteresis, HMD serial, brightness, PCB serial | Beyond MCU 512-B TLV config | HID feature reports `'U'` read / `'W'` write / `'V'` save (VID 0x35bd PID 0x0101), `vertical-alignment-proximity/hmd_config.py` |\n| Order ↔ IPD ↔ face-scan | Bigscreen cloud | `big-notebooks/api.py` admin API (not needed for rendering) |\n| PASS/FAIL test records | factory MES (LAN) | `mes_logger.py` |\n\nSteamVR consumption: lighthouse driver reads SIP JSON → implements `ComputeDistortion()` (per-channel poly3), projection from `intrinsics`, eye poses from `eye_to_head`+`ipd`, display timing from the `seconds_*` fields; compositor builds warp mesh by sampling the driver, applies `display_color_mult` and `mura.mc`. Live reload hook: `vrcmd --cmdhmd \"set_lenscal \"`.\n\n---\n\n## 8. Reimplementing distortion correction WITHOUT SteamVR\n\nRequired inputs (all obtainable from the unit itself, offline):\n\n1. The device JSON — either dump with `valve-tools-wrapper/steamvr/tools/bin/win64/lighthouse_console.exe` (`downloadconfig`), or just read SteamVR's cache `Steam\\config\\lighthouse\\\\config.json`. One-time per unit; no cloud, no lighthouses needed.\n2. `mura.mc` from the same cache dir (optional quality step).\n3. The polynomial evaluator (§3.1) — implement Monado-equivalent `compute_distortion(eye, u, v) → (uv_r, uv_g, uv_b)`.\n\nRender pipeline per eye:\n\n1. Render scene with projection from `intrinsics` (half-FOV_x = atan(1/fx), centers from cx,cy; asymmetric frustum), camera pose = head_pose · eye_to_head_rotation, eye offset = ±ipd.default_mm/2 (sign per eye) along head +X.\n2. Oversample render target by `(1 + grow_for_undistort)` relative to 2544×2544 panel area.\n3. Full-screen warp pass (mesh or fragment): for each panel pixel, evaluate the per-channel polynomial to get three source UVs; sample R/G/B separately (this is both distortion correction and lateral CA correction).\n4. Multiply by `display_color_mult` per eye and the `mura.mc` mask (in linear light — `bars_gen.py` applies corrections in linear space).\n\n**Validation strategy** (resolves the POLY3 direction question empirically): run SteamVR once, use `vrcmd`/OpenVR `GetDistortionMesh`/`ComputeDistortion` to dump Valve's ground-truth UV mapping for this unit, and fit/verify the standalone evaluator against it. Alternatively diff rendered output photographically through the lens. The EVT/DVT-vs-production sign flip (−0.22 vs +0.239) makes blind implementation risky; one dump of ground truth removes all ambiguity.\n\n---\n\n## Relevance to sauna\n\nConcrete recipe for a standalone (no-SteamVR, no-lighthouse) wearable-display renderer for a given Beyond unit:\n\n1. **Extract per-unit calibration once.** Plug in HMD, run `lighthouse_console.exe downloadconfig beyond.json` (admin shell; tool in `valve-tools-wrapper/steamvr/tools/bin/win64/`). Grab `mura.mc` from `Steam\\config\\lighthouse\\\\userdata\\` if present. This file contains everything: per-channel distortion polynomials, intrinsics, per-unit eye_to_head, IPD, panel timings, color mult, IMU mount frame + biases.\n2. **Get pixels to the panel without SteamVR** — two proven first-party paths in `extended-mode-display`:\n - *Extended mode*: MCU HID feature report `[0,'d',rate]` (`rate` 2=75 Hz tested, 1=90 Hz \"at own risk\") rewrites the linkbox EDID so the HMD appears as a normal 3840×1920 side-by-side monitor (1920×1920/eye — reduced from native 2560×2560); then any fullscreen window works (`ExtendedModeDisplay`, cv2). Restore with the same command. Also needed: display power `'+'`, panel on `[0,'o',eye,1,0x29]`, duty `'I'`, prox disable `'p'`.\n - *Direct mode at native res*: `resources/direct_mode_dx12.exe` (`-enable/-disable`, `-dsc_present_test 0 1 1 0 0x11 4 128` matching the config's DSC fields; env `DM_VENDOR_ID=0x2709`) and `display_image.exe` present full-res images. Harder but full 2544/2560 px.\n3. **Implement the warp** as a single post pass (§8): per-channel poly3 UV remap + grow factor + color mult (+ optional mura multiply). ~50 lines of GLSL/HLSL once coefficients are loaded.\n4. **3DOF pose** from the onboard ICM-class IMU (1 kHz, `firmware_config.imu_rate`): orientation via any AHRS filter, using `imu.plus_x/plus_z/position` to rotate IMU frame → head frame, and config gyro/acc biases. The IMU streams over the Tundra SIP HID interface (watchman protocol — parse per OpenHMD/libsurvive's watchman IMU report layout; protocol itself is not in these repos).\n5. **6DOF (stretch)**: the 22 photodiodes are useless without base stations, so 6DOF needs an external method (e.g. camera/inside-out or phone-assisted). The `lighthouse_config.modelPoints` do give you the exact sensor constellation geometry if optical fiducials are ever wanted.\n6. **Honor per-eye view setup**: render cameras rotated by `eye_to_head` (≈ ±6.17° yaw, −5°+δ pitch per unit) and separated by `ipd.default_mm` — skipping this (not just distortion) is what breaks binocular fusion on canted-display HMDs.\n7. **Calibration sanity check**: before cutting SteamVR loose, dump ground-truth warp UVs from OpenVR (`ComputeDistortion` per eye on a grid) on the same unit and unit-test the standalone evaluator against them (settles POLY3-vs-DPOLY3 direction and the `grow_for_undistort` normalization exactly).\n","numLines":200,"startLine":1,"totalLines":200}},"sourceToolAssistantUUID":"0fa6fe24-99d9-48d9-83f1-9faa956f512e","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-06-10T05:13:42.760Z","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","content":"note that i am physically at enlyzeam (ssh `decid@enlyzeam`) where a different beyond is (it will have a different distortion profile). once your spike binaries are proven over there, will need to run the eyes-in test on enlyzeam"}
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{"parentUuid":"7bb4d45c-ec49-4572-a067-596ae7f8787e","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01JFmwzgUJykzE4WE2nCPwmt","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CJXhn81ikNMHxem2wbQFke","name":"Write","input":{"file_path":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\poly3.py","content":"\"\"\"Standalone Beyond distortion evaluator — spike S3.\n\nMaps panel/output UV -> pre-distortion render-target UV, per color channel,\nfrom the device config JSON (tracking_to_eye_transform block). No SteamVR.\n\nThe open question this spike settles: production B1 configs use type\nDISTORT_POLY3 with POSITIVE leading coeffs (+0.2438 on this unit), while\nEVT/DVT used DISTORT_DPOLY3 with NEGATIVE leading coeffs (-0.22). Public\nreimplementations (Monado u_compute_distortion_vive) evaluate the Vive\nDPOLY3 family as a RECIPROCAL: d = 1/(1 + k1*r2 + k2*r4 + k3*r6).\nHypothesis: POLY3 is the same curve fitted the other way round, i.e.\nd = 1 + k1*r2 + k2*r4 + k3*r6 directly. Four candidate forms are\nimplemented; verify.py picks the one matching OpenVR ComputeDistortion.\n\nHypothesis axes:\n form: 'poly' -> d = 1 + k1*r2 + k2*r4 + k3*r6\n 'recip' -> d = 1 / (1 + k1*r2 + k2*r4 + k3*r6)\n grow: True -> output scale 0.5/(1+grow_for_undistort) (Monado)\n False -> output scale 0.5 (no grow rescale)\n\"\"\"\n\nimport json\n\nCHANNELS = (\"distortion_red\", \"distortion\", \"distortion_blue\") # r, g, b\nHYPOTHESES = [\n (\"poly+grow\", \"poly\", True),\n (\"poly\", \"poly\", False),\n (\"recip+grow\", \"recip\", True),\n (\"recip\", \"recip\", False),\n]\n\n\nclass EyeDistortion:\n \"\"\"Distortion model for one eye, built from tracking_to_eye_transform[eye].\"\"\"\n\n def __init__(self, tte):\n self.grow = tte[\"grow_for_undistort\"]\n self.r2_cutoff = tte[\"undistort_r2_cutoff\"]\n # center is shared across channels within an eye on the Beyond,\n # but read per channel anyway (B2 may differ).\n self.channels = []\n for key in CHANNELS:\n block = tte[key]\n self.channels.append({\n \"cx\": block[\"center_x\"],\n \"cy\": block[\"center_y\"],\n \"k\": list(block[\"coeffs\"]),\n \"type\": block[\"type\"],\n })\n\n def compute(self, u, v, form=\"poly\", use_grow=True, aspect_x_over_y=1.0):\n \"\"\"Panel UV (0..1, origin upper-left per OpenVR) -> source UV per channel.\n\n Returns ((ur, vr), (ug, vg), (ub, vb)).\n \"\"\"\n scale = 0.5 / (1.0 + self.grow) if use_grow else 0.5\n out = []\n for ch in self.channels:\n tx = 2.0 * u - 1.0\n ty = (2.0 * v - 1.0) / aspect_x_over_y\n tx -= ch[\"cx\"]\n ty -= ch[\"cy\"]\n r2 = tx * tx + ty * ty\n if r2 > self.r2_cutoff:\n r2 = self.r2_cutoff\n k1, k2, k3 = ch[\"k\"]\n p = 1.0 + r2 * (k1 + r2 * (k2 + r2 * k3))\n d = p if form == \"poly\" else 1.0 / p\n sx = 0.5 + (tx * d + ch[\"cx\"]) * scale\n sy = 0.5 + (ty * d + ch[\"cy\"]) * scale * aspect_x_over_y\n out.append((sx, sy))\n return tuple(out)\n\n\ndef load_config(path):\n \"\"\"Load device config.json -> (left EyeDistortion, right EyeDistortion, raw dict).\"\"\"\n with open(path, \"r\") as f:\n cfg = json.load(f)\n tte = cfg[\"tracking_to_eye_transform\"]\n return EyeDistortion(tte[0]), EyeDistortion(tte[1]), cfg\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":356,"cache_read_input_tokens":77429,"output_tokens":2531,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":356,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":2531,"cache_read_input_tokens":77429,"cache_creation_input_tokens":356,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":356},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu1NBPMJ4emCMaMZJ8Cb","type":"assistant","uuid":"8139f468-4c24-47e8-b3fc-1615d49a4696","timestamp":"2026-06-10T05:13:53.488Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"8139f468-4c24-47e8-b3fc-1615d49a4696","isSidechain":false,"promptId":"6e29c121-2046-495c-b767-db64315dc4d2","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CJXhn81ikNMHxem2wbQFke","type":"tool_result","content":"File created successfully at: C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\poly3.py (file state is current in your context — no need to Read it back)"}]},"uuid":"a0b492e7-32ad-414b-8f0c-cb78c69a2a45","timestamp":"2026-06-10T05:13:54.127Z","toolUseResult":{"type":"create","filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\poly3.py","content":"\"\"\"Standalone Beyond distortion evaluator — spike S3.\n\nMaps panel/output UV -> pre-distortion render-target UV, per color channel,\nfrom the device config JSON (tracking_to_eye_transform block). No SteamVR.\n\nThe open question this spike settles: production B1 configs use type\nDISTORT_POLY3 with POSITIVE leading coeffs (+0.2438 on this unit), while\nEVT/DVT used DISTORT_DPOLY3 with NEGATIVE leading coeffs (-0.22). Public\nreimplementations (Monado u_compute_distortion_vive) evaluate the Vive\nDPOLY3 family as a RECIPROCAL: d = 1/(1 + k1*r2 + k2*r4 + k3*r6).\nHypothesis: POLY3 is the same curve fitted the other way round, i.e.\nd = 1 + k1*r2 + k2*r4 + k3*r6 directly. Four candidate forms are\nimplemented; verify.py picks the one matching OpenVR ComputeDistortion.\n\nHypothesis axes:\n form: 'poly' -> d = 1 + k1*r2 + k2*r4 + k3*r6\n 'recip' -> d = 1 / (1 + k1*r2 + k2*r4 + k3*r6)\n grow: True -> output scale 0.5/(1+grow_for_undistort) (Monado)\n False -> output scale 0.5 (no grow rescale)\n\"\"\"\n\nimport json\n\nCHANNELS = (\"distortion_red\", \"distortion\", \"distortion_blue\") # r, g, b\nHYPOTHESES = [\n (\"poly+grow\", \"poly\", True),\n (\"poly\", \"poly\", False),\n (\"recip+grow\", \"recip\", True),\n (\"recip\", \"recip\", False),\n]\n\n\nclass EyeDistortion:\n \"\"\"Distortion model for one eye, built from tracking_to_eye_transform[eye].\"\"\"\n\n def __init__(self, tte):\n self.grow = tte[\"grow_for_undistort\"]\n self.r2_cutoff = tte[\"undistort_r2_cutoff\"]\n # center is shared across channels within an eye on the Beyond,\n # but read per channel anyway (B2 may differ).\n self.channels = []\n for key in CHANNELS:\n block = tte[key]\n self.channels.append({\n \"cx\": block[\"center_x\"],\n \"cy\": block[\"center_y\"],\n \"k\": list(block[\"coeffs\"]),\n \"type\": block[\"type\"],\n })\n\n def compute(self, u, v, form=\"poly\", use_grow=True, aspect_x_over_y=1.0):\n \"\"\"Panel UV (0..1, origin upper-left per OpenVR) -> source UV per channel.\n\n Returns ((ur, vr), (ug, vg), (ub, vb)).\n \"\"\"\n scale = 0.5 / (1.0 + self.grow) if use_grow else 0.5\n out = []\n for ch in self.channels:\n tx = 2.0 * u - 1.0\n ty = (2.0 * v - 1.0) / aspect_x_over_y\n tx -= ch[\"cx\"]\n ty -= ch[\"cy\"]\n r2 = tx * tx + ty * ty\n if r2 > self.r2_cutoff:\n r2 = self.r2_cutoff\n k1, k2, k3 = ch[\"k\"]\n p = 1.0 + r2 * (k1 + r2 * (k2 + r2 * k3))\n d = p if form == \"poly\" else 1.0 / p\n sx = 0.5 + (tx * d + ch[\"cx\"]) * scale\n sy = 0.5 + (ty * d + ch[\"cy\"]) * scale * aspect_x_over_y\n out.append((sx, sy))\n return tuple(out)\n\n\ndef load_config(path):\n \"\"\"Load device config.json -> (left EyeDistortion, right EyeDistortion, raw dict).\"\"\"\n with open(path, \"r\") as f:\n cfg = json.load(f)\n tte = cfg[\"tracking_to_eye_transform\"]\n return EyeDistortion(tte[0]), EyeDistortion(tte[1]), cfg\n","structuredPatch":[],"originalFile":null,"userModified":false},"sourceToolAssistantUUID":"8139f468-4c24-47e8-b3fc-1615d49a4696","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"type":"queue-operation","operation":"remove","timestamp":"2026-06-10T05:13:54.144Z","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02"}
{"parentUuid":"a0b492e7-32ad-414b-8f0c-cb78c69a2a45","isSidechain":false,"attachment":{"type":"queued_command","prompt":"note that i am physically at enlyzeam (ssh `decid@enlyzeam`) where a different beyond is (it will have a different distortion profile). once your spike binaries are proven over there, will need to run the eyes-in test on enlyzeam","commandMode":"prompt"},"type":"attachment","uuid":"0917f211-0bea-4249-81a2-e68bb4467e34","timestamp":"2026-06-10T05:13:54.140Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"0917f211-0bea-4249-81a2-e68bb4467e34","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01WuqG3mE1yexEAvEfDMAot7","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":2599,"cache_read_input_tokens":77785,"output_tokens":2068,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":2599,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":2068,"cache_read_input_tokens":77785,"cache_creation_input_tokens":2599,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2599},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu1R1StyWb334y5J9HWV","type":"assistant","uuid":"e1eb4e87-671d-4d61-a446-103d57c64bd6","timestamp":"2026-06-10T05:14:08.687Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"e1eb4e87-671d-4d61-a446-103d57c64bd6","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01WuqG3mE1yexEAvEfDMAot7","type":"message","role":"assistant","content":[{"type":"text","text":"Noted — ground-truth dump + eyes-in run on enlyzeam's Beyond, its own config. Will prove tooling locally first, then deploy over SSH. Continuing file writes."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":2599,"cache_read_input_tokens":77785,"output_tokens":2068,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":2599,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":2068,"cache_read_input_tokens":77785,"cache_creation_input_tokens":2599,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2599},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu1R1StyWb334y5J9HWV","type":"assistant","uuid":"11dfe711-9c45-4850-adf8-a4590e511cd7","timestamp":"2026-06-10T05:14:08.691Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"11dfe711-9c45-4850-adf8-a4590e511cd7","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01WuqG3mE1yexEAvEfDMAot7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01U8GPNKaZwxymLipMCMs4et","name":"Write","input":{"file_path":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\dump_openvr.py","content":"\"\"\"Dump OpenVR ground truth for spike S3 — run ONCE per unit, dev-time only.\n\nRequires SteamVR RUNNING with the Beyond connected (it will grab direct\nmode; that's fine, this is the one sanctioned SteamVR run). Dumps:\n\n - ComputeDistortion UV grid per eye per channel (the ground truth)\n - GetProjectionRaw tangents per eye (cross-check vs intrinsics)\n - GetEyeToHeadTransform per eye (cross-check vs eye_to_head + ipd)\n - RecommendedRenderTargetSize, HMD serial/model\n\nOutput: distortion_dump_.json in this directory.\n\nUsage: python dump_openvr.py [--grid N] (default 65 -> 65x65 per eye)\n\"\"\"\n\nimport argparse\nimport json\nimport sys\n\nimport openvr\n\nGRID_DEFAULT = 65\n\n\ndef main():\n ap = argparse.ArgumentParser()\n ap.add_argument(\"--grid\", type=int, default=GRID_DEFAULT)\n args = ap.parse_args()\n n = args.grid\n\n try:\n openvr.init(openvr.VRApplication_Background)\n except openvr.error_code.InitError as e:\n print(f\"OpenVR init failed: {e}\")\n print(\"Start SteamVR with the Beyond connected, then re-run.\")\n sys.exit(1)\n\n vrsys = openvr.VRSystem()\n hmd = openvr.k_unTrackedDeviceIndex_Hmd\n serial = vrsys.getStringTrackedDeviceProperty(hmd, openvr.Prop_SerialNumber_String)\n model = vrsys.getStringTrackedDeviceProperty(hmd, openvr.Prop_ModelNumber_String)\n rt_w, rt_h = vrsys.getRecommendedRenderTargetSize()\n print(f\"HMD: {model} {serial}, recommended RT {rt_w}x{rt_h} per eye\")\n\n out = {\n \"serial\": serial,\n \"model\": model,\n \"recommended_render_target\": [rt_w, rt_h],\n \"grid\": n,\n \"eyes\": [],\n }\n\n for eye_name, eye in ((\"left\", openvr.Eye_Left), (\"right\", openvr.Eye_Right)):\n left, right, top, bottom = vrsys.getProjectionRaw(eye)\n m = vrsys.getEyeToHeadTransform(eye)\n eye_to_head = [[m[r][c] for c in range(4)] for r in range(3)]\n\n points = []\n bad = 0\n for j in range(n):\n v = j / (n - 1)\n for i in range(n):\n u = i / (n - 1)\n ok, dc = vrsys.computeDistortion(eye, u, v)\n if not ok:\n bad += 1\n points.append(None)\n continue\n points.append({\n \"uv\": [u, v],\n \"r\": [dc.rfRed[0], dc.rfRed[1]],\n \"g\": [dc.rfGreen[0], dc.rfGreen[1]],\n \"b\": [dc.rfBlue[0], dc.rfBlue[1]],\n })\n print(f\" {eye_name}: {n*n - bad}/{n*n} points ok, \"\n f\"projection_raw L{left:+.4f} R{right:+.4f} T{top:+.4f} B{bottom:+.4f}\")\n out[\"eyes\"].append({\n \"eye\": eye_name,\n \"projection_raw\": [left, right, top, bottom],\n \"eye_to_head\": eye_to_head,\n \"points\": points,\n })\n\n openvr.shutdown()\n\n fname = f\"distortion_dump_{serial.lower().replace('-', '_')}.json\"\n with open(fname, \"w\") as f:\n json.dump(out, f)\n print(f\"Wrote {fname}\")\n\n\nif __name__ == \"__main__\":\n main()\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":2599,"cache_read_input_tokens":77785,"output_tokens":2068,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":2599,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":2068,"cache_read_input_tokens":77785,"cache_creation_input_tokens":2599,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2599},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu1R1StyWb334y5J9HWV","type":"assistant","uuid":"d7a9307e-c0bb-464a-9798-f28bb0312377","timestamp":"2026-06-10T05:14:22.180Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"d7a9307e-c0bb-464a-9798-f28bb0312377","isSidechain":false,"promptId":"6e29c121-2046-495c-b767-db64315dc4d2","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01U8GPNKaZwxymLipMCMs4et","type":"tool_result","content":"File created successfully at: C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\dump_openvr.py (file state is current in your context — no need to Read it back)"}]},"uuid":"2d84e97e-9749-4f13-b40d-e78489b37da5","timestamp":"2026-06-10T05:14:22.359Z","toolUseResult":{"type":"create","filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\dump_openvr.py","content":"\"\"\"Dump OpenVR ground truth for spike S3 — run ONCE per unit, dev-time only.\n\nRequires SteamVR RUNNING with the Beyond connected (it will grab direct\nmode; that's fine, this is the one sanctioned SteamVR run). Dumps:\n\n - ComputeDistortion UV grid per eye per channel (the ground truth)\n - GetProjectionRaw tangents per eye (cross-check vs intrinsics)\n - GetEyeToHeadTransform per eye (cross-check vs eye_to_head + ipd)\n - RecommendedRenderTargetSize, HMD serial/model\n\nOutput: distortion_dump_.json in this directory.\n\nUsage: python dump_openvr.py [--grid N] (default 65 -> 65x65 per eye)\n\"\"\"\n\nimport argparse\nimport json\nimport sys\n\nimport openvr\n\nGRID_DEFAULT = 65\n\n\ndef main():\n ap = argparse.ArgumentParser()\n ap.add_argument(\"--grid\", type=int, default=GRID_DEFAULT)\n args = ap.parse_args()\n n = args.grid\n\n try:\n openvr.init(openvr.VRApplication_Background)\n except openvr.error_code.InitError as e:\n print(f\"OpenVR init failed: {e}\")\n print(\"Start SteamVR with the Beyond connected, then re-run.\")\n sys.exit(1)\n\n vrsys = openvr.VRSystem()\n hmd = openvr.k_unTrackedDeviceIndex_Hmd\n serial = vrsys.getStringTrackedDeviceProperty(hmd, openvr.Prop_SerialNumber_String)\n model = vrsys.getStringTrackedDeviceProperty(hmd, openvr.Prop_ModelNumber_String)\n rt_w, rt_h = vrsys.getRecommendedRenderTargetSize()\n print(f\"HMD: {model} {serial}, recommended RT {rt_w}x{rt_h} per eye\")\n\n out = {\n \"serial\": serial,\n \"model\": model,\n \"recommended_render_target\": [rt_w, rt_h],\n \"grid\": n,\n \"eyes\": [],\n }\n\n for eye_name, eye in ((\"left\", openvr.Eye_Left), (\"right\", openvr.Eye_Right)):\n left, right, top, bottom = vrsys.getProjectionRaw(eye)\n m = vrsys.getEyeToHeadTransform(eye)\n eye_to_head = [[m[r][c] for c in range(4)] for r in range(3)]\n\n points = []\n bad = 0\n for j in range(n):\n v = j / (n - 1)\n for i in range(n):\n u = i / (n - 1)\n ok, dc = vrsys.computeDistortion(eye, u, v)\n if not ok:\n bad += 1\n points.append(None)\n continue\n points.append({\n \"uv\": [u, v],\n \"r\": [dc.rfRed[0], dc.rfRed[1]],\n \"g\": [dc.rfGreen[0], dc.rfGreen[1]],\n \"b\": [dc.rfBlue[0], dc.rfBlue[1]],\n })\n print(f\" {eye_name}: {n*n - bad}/{n*n} points ok, \"\n f\"projection_raw L{left:+.4f} R{right:+.4f} T{top:+.4f} B{bottom:+.4f}\")\n out[\"eyes\"].append({\n \"eye\": eye_name,\n \"projection_raw\": [left, right, top, bottom],\n \"eye_to_head\": eye_to_head,\n \"points\": points,\n })\n\n openvr.shutdown()\n\n fname = f\"distortion_dump_{serial.lower().replace('-', '_')}.json\"\n with open(fname, \"w\") as f:\n json.dump(out, f)\n print(f\"Wrote {fname}\")\n\n\nif __name__ == \"__main__\":\n main()\n","structuredPatch":[],"originalFile":null,"userModified":false},"sourceToolAssistantUUID":"d7a9307e-c0bb-464a-9798-f28bb0312377","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"60b00a70-672a-4733-91a9-4218327350e3","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01DfG4nPrRENiK3cvBfPRmdR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HaS1D1duRe1M5DRkaFj3Fx","name":"Write","input":{"file_path":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\verify.py","content":"\"\"\"Spike S3 verdict: test each evaluator hypothesis against the OpenVR dump.\n\nUsage:\n python verify.py \n python verify.py --selftest\n\nError metric: source-UV delta scaled to panel pixels (x2544) — the units the\nwarp actually renders in. Pass criterion (PLAN.md): sub-pixel, i.e. winning\nhypothesis max error < 1.0 px across both eyes, all three channels.\n\nAlso cross-checks projection_raw vs config intrinsics and eye_to_head\nrotation vs config (canting), since the dump carries them for free.\n\"\"\"\n\nimport json\nimport math\nimport sys\n\nfrom poly3 import EyeDistortion, HYPOTHESES, load_config\n\nPANEL_PX = 2544 # per-eye panel pixels (eye_target_width_in_pixels)\n\n\ndef test_hypothesis(dump, eyes_cfg, form, use_grow):\n \"\"\"Return dict of per-channel (rms_px, max_px) plus overall max.\"\"\"\n stats = {}\n overall_max = 0.0\n for eye_idx, eye_dump in enumerate(dump[\"eyes\"]):\n model = eyes_cfg[eye_idx]\n for ch_idx, ch_name in enumerate((\"r\", \"g\", \"b\")):\n se = 0.0\n mx = 0.0\n count = 0\n for pt in eye_dump[\"points\"]:\n if pt is None:\n continue\n u, v = pt[\"uv\"]\n ours = model.compute(u, v, form=form, use_grow=use_grow)[ch_idx]\n theirs = pt[ch_name]\n dx = (ours[0] - theirs[0]) * PANEL_PX\n dy = (ours[1] - theirs[1]) * PANEL_PX\n e2 = dx * dx + dy * dy\n se += e2\n if e2 > mx:\n mx = e2\n count += 1\n rms = math.sqrt(se / count) if count else float(\"nan\")\n mxe = math.sqrt(mx)\n stats[(eye_dump[\"eye\"], ch_name)] = (rms, mxe)\n overall_max = max(overall_max, mxe)\n stats[\"overall_max\"] = overall_max\n return stats\n\n\ndef cross_checks(dump, cfg):\n print(\"\\nCross-checks (dump vs config):\")\n for eye_idx, eye_dump in enumerate(dump[\"eyes\"]):\n tte = cfg[\"tracking_to_eye_transform\"][eye_idx]\n K = tte[\"intrinsics\"]\n fx, cx = K[0][0], -K[0][2]\n fy, cy = K[1][1], -K[1][2]\n # OpenVR raw projection: tangents left/right/top/bottom.\n # From intrinsics: left = (-1 - cx)/fx ... (Valve convention cy sign\n # varies; report both so the dump settles it.)\n exp_l, exp_r = (-1 - cx) / fx, (1 - cx) / fx\n exp_t, exp_b = (-1 - cy) / fy, (1 - cy) / fy\n L, R, T, B = eye_dump[\"projection_raw\"]\n print(f\" {eye_dump['eye']} projection_raw dump L{L:+.5f} R{R:+.5f} T{T:+.5f} B{B:+.5f}\")\n print(f\" {eye_dump['eye']} from intrinsics L{exp_l:+.5f} R{exp_r:+.5f} T{exp_t:+.5f} B{exp_b:+.5f}\")\n e2h_cfg = tte[\"eye_to_head\"]\n e2h_dump = eye_dump[\"eye_to_head\"]\n rot_err = max(\n abs(e2h_cfg[r][c] - e2h_dump[r][c]) for r in range(3) for c in range(3)\n )\n tx = e2h_dump[0][3] * 1000.0\n print(f\" {eye_dump['eye']} eye_to_head rot max-abs-diff {rot_err:.2e}; \"\n f\"translation x {tx:+.3f} mm (ipd.default_mm/2 expected)\")\n ipd = cfg.get(\"ipd\", {})\n print(f\" config ipd block: {ipd}\")\n\n\ndef selftest():\n \"\"\"Synthetic round trip: generate a fake dump from a known hypothesis and\n confirm verify picks exactly that hypothesis with ~0 error.\"\"\"\n tte = {\n \"grow_for_undistort\": 0.6,\n \"undistort_r2_cutoff\": 1.0,\n \"distortion\": {\"center_x\": 0.01, \"center_y\": -0.012,\n \"coeffs\": [0.2438, 0.0976, 0.0808], \"type\": \"DISTORT_POLY3\"},\n \"distortion_red\": {\"center_x\": 0.01, \"center_y\": -0.012,\n \"coeffs\": [0.2159, 0.0887, 0.1174], \"type\": \"DISTORT_POLY3\"},\n \"distortion_blue\": {\"center_x\": 0.01, \"center_y\": -0.012,\n \"coeffs\": [0.2805, -0.0059, 0.1701], \"type\": \"DISTORT_POLY3\"},\n }\n cfg = {\"tracking_to_eye_transform\": [tte, tte]}\n eyes = [EyeDistortion(tte), EyeDistortion(tte)]\n truth_form, truth_grow = \"recip\", True # pretend Valve uses Monado form\n\n n = 17\n dump = {\"eyes\": []}\n for eye_idx, name in enumerate((\"left\", \"right\")):\n points = []\n for j in range(n):\n for i in range(n):\n u, v = i / (n - 1), j / (n - 1)\n r, g, b = eyes[eye_idx].compute(u, v, form=truth_form, use_grow=truth_grow)\n points.append({\"uv\": [u, v], \"r\": list(r), \"g\": list(g), \"b\": list(b)})\n dump[\"eyes\"].append({\"eye\": name, \"points\": points})\n\n results = {name: test_hypothesis(dump, eyes, form, grow)\n for name, form, grow in HYPOTHESES}\n report(results)\n winner = min(results, key=lambda k: results[k][\"overall_max\"])\n assert winner == \"recip+grow\", f\"selftest failed: winner {winner}\"\n assert results[winner][\"overall_max\"] < 1e-9\n # All other hypotheses must be clearly distinguishable (not sub-pixel).\n for name, st in results.items():\n if name != winner:\n assert st[\"overall_max\"] > 1.0, f\"hypothesis {name} not distinguishable\"\n print(\"\\nSELFTEST PASS — winner recip+grow as constructed, others distinguishable\")\n\n\ndef report(results):\n print(f\"\\n{'hypothesis':<12} {'overall max px':>14} per-channel rms px (L/R averaged)\")\n for name, st in results.items():\n per_ch = []\n for ch in (\"r\", \"g\", \"b\"):\n vals = [st[k][0] for k in st if isinstance(k, tuple) and k[1] == ch]\n per_ch.append(f\"{ch}:{sum(vals)/len(vals):8.3f}\")\n print(f\"{name:<12} {st['overall_max']:>14.4f} {' '.join(per_ch)}\")\n\n\ndef main():\n if \"--selftest\" in sys.argv:\n selftest()\n return\n if len(sys.argv) != 3:\n print(__doc__)\n sys.exit(2)\n\n with open(sys.argv[1]) as f:\n dump = json.load(f)\n left, right, cfg = load_config(sys.argv[2])\n eyes_cfg = [left, right]\n\n serial_cfg = cfg.get(\"device_serial_number\", \"?\")\n print(f\"dump unit: {dump.get('serial')} config unit: {serial_cfg}\")\n if dump.get(\"serial\", \"\").lower() != serial_cfg.lower():\n print(\"WARNING: serial mismatch — dump and config are from different units!\")\n\n results = {name: test_hypothesis(dump, eyes_cfg, form, grow)\n for name, form, grow in HYPOTHESES}\n report(results)\n\n winner = min(results, key=lambda k: results[k][\"overall_max\"])\n wmax = results[winner][\"overall_max\"]\n verdict = \"PASS\" if wmax < 1.0 else \"FAIL\"\n print(f\"\\nWinner: {winner} (overall max {wmax:.4f} px) -> {verdict} \"\n f\"(criterion: sub-pixel)\")\n\n cross_checks(dump, cfg)\n\n\nif __name__ == \"__main__\":\n main()\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":2268,"cache_read_input_tokens":80384,"output_tokens":3077,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":2268,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":3077,"cache_read_input_tokens":80384,"cache_creation_input_tokens":2268,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2268},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu1T67jzS6w3Kbbe5gKC","type":"assistant","uuid":"a3705515-95a8-4ec8-8369-8c4e3a0f3e0c","timestamp":"2026-06-10T05:14:57.529Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"a3705515-95a8-4ec8-8369-8c4e3a0f3e0c","isSidechain":false,"promptId":"6e29c121-2046-495c-b767-db64315dc4d2","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HaS1D1duRe1M5DRkaFj3Fx","type":"tool_result","content":"File created successfully at: C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\verify.py (file state is current in your context — no need to Read it back)"}]},"uuid":"a0cc9ed8-a3fa-4613-9ae3-fd63d8e9c32a","timestamp":"2026-06-10T05:14:57.719Z","toolUseResult":{"type":"create","filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\verify.py","content":"\"\"\"Spike S3 verdict: test each evaluator hypothesis against the OpenVR dump.\n\nUsage:\n python verify.py \n python verify.py --selftest\n\nError metric: source-UV delta scaled to panel pixels (x2544) — the units the\nwarp actually renders in. Pass criterion (PLAN.md): sub-pixel, i.e. winning\nhypothesis max error < 1.0 px across both eyes, all three channels.\n\nAlso cross-checks projection_raw vs config intrinsics and eye_to_head\nrotation vs config (canting), since the dump carries them for free.\n\"\"\"\n\nimport json\nimport math\nimport sys\n\nfrom poly3 import EyeDistortion, HYPOTHESES, load_config\n\nPANEL_PX = 2544 # per-eye panel pixels (eye_target_width_in_pixels)\n\n\ndef test_hypothesis(dump, eyes_cfg, form, use_grow):\n \"\"\"Return dict of per-channel (rms_px, max_px) plus overall max.\"\"\"\n stats = {}\n overall_max = 0.0\n for eye_idx, eye_dump in enumerate(dump[\"eyes\"]):\n model = eyes_cfg[eye_idx]\n for ch_idx, ch_name in enumerate((\"r\", \"g\", \"b\")):\n se = 0.0\n mx = 0.0\n count = 0\n for pt in eye_dump[\"points\"]:\n if pt is None:\n continue\n u, v = pt[\"uv\"]\n ours = model.compute(u, v, form=form, use_grow=use_grow)[ch_idx]\n theirs = pt[ch_name]\n dx = (ours[0] - theirs[0]) * PANEL_PX\n dy = (ours[1] - theirs[1]) * PANEL_PX\n e2 = dx * dx + dy * dy\n se += e2\n if e2 > mx:\n mx = e2\n count += 1\n rms = math.sqrt(se / count) if count else float(\"nan\")\n mxe = math.sqrt(mx)\n stats[(eye_dump[\"eye\"], ch_name)] = (rms, mxe)\n overall_max = max(overall_max, mxe)\n stats[\"overall_max\"] = overall_max\n return stats\n\n\ndef cross_checks(dump, cfg):\n print(\"\\nCross-checks (dump vs config):\")\n for eye_idx, eye_dump in enumerate(dump[\"eyes\"]):\n tte = cfg[\"tracking_to_eye_transform\"][eye_idx]\n K = tte[\"intrinsics\"]\n fx, cx = K[0][0], -K[0][2]\n fy, cy = K[1][1], -K[1][2]\n # OpenVR raw projection: tangents left/right/top/bottom.\n # From intrinsics: left = (-1 - cx)/fx ... (Valve convention cy sign\n # varies; report both so the dump settles it.)\n exp_l, exp_r = (-1 - cx) / fx, (1 - cx) / fx\n exp_t, exp_b = (-1 - cy) / fy, (1 - cy) / fy\n L, R, T, B = eye_dump[\"projection_raw\"]\n print(f\" {eye_dump['eye']} projection_raw dump L{L:+.5f} R{R:+.5f} T{T:+.5f} B{B:+.5f}\")\n print(f\" {eye_dump['eye']} from intrinsics L{exp_l:+.5f} R{exp_r:+.5f} T{exp_t:+.5f} B{exp_b:+.5f}\")\n e2h_cfg = tte[\"eye_to_head\"]\n e2h_dump = eye_dump[\"eye_to_head\"]\n rot_err = max(\n abs(e2h_cfg[r][c] - e2h_dump[r][c]) for r in range(3) for c in range(3)\n )\n tx = e2h_dump[0][3] * 1000.0\n print(f\" {eye_dump['eye']} eye_to_head rot max-abs-diff {rot_err:.2e}; \"\n f\"translation x {tx:+.3f} mm (ipd.default_mm/2 expected)\")\n ipd = cfg.get(\"ipd\", {})\n print(f\" config ipd block: {ipd}\")\n\n\ndef selftest():\n \"\"\"Synthetic round trip: generate a fake dump from a known hypothesis and\n confirm verify picks exactly that hypothesis with ~0 error.\"\"\"\n tte = {\n \"grow_for_undistort\": 0.6,\n \"undistort_r2_cutoff\": 1.0,\n \"distortion\": {\"center_x\": 0.01, \"center_y\": -0.012,\n \"coeffs\": [0.2438, 0.0976, 0.0808], \"type\": \"DISTORT_POLY3\"},\n \"distortion_red\": {\"center_x\": 0.01, \"center_y\": -0.012,\n \"coeffs\": [0.2159, 0.0887, 0.1174], \"type\": \"DISTORT_POLY3\"},\n \"distortion_blue\": {\"center_x\": 0.01, \"center_y\": -0.012,\n \"coeffs\": [0.2805, -0.0059, 0.1701], \"type\": \"DISTORT_POLY3\"},\n }\n cfg = {\"tracking_to_eye_transform\": [tte, tte]}\n eyes = [EyeDistortion(tte), EyeDistortion(tte)]\n truth_form, truth_grow = \"recip\", True # pretend Valve uses Monado form\n\n n = 17\n dump = {\"eyes\": []}\n for eye_idx, name in enumerate((\"left\", \"right\")):\n points = []\n for j in range(n):\n for i in range(n):\n u, v = i / (n - 1), j / (n - 1)\n r, g, b = eyes[eye_idx].compute(u, v, form=truth_form, use_grow=truth_grow)\n points.append({\"uv\": [u, v], \"r\": list(r), \"g\": list(g), \"b\": list(b)})\n dump[\"eyes\"].append({\"eye\": name, \"points\": points})\n\n results = {name: test_hypothesis(dump, eyes, form, grow)\n for name, form, grow in HYPOTHESES}\n report(results)\n winner = min(results, key=lambda k: results[k][\"overall_max\"])\n assert winner == \"recip+grow\", f\"selftest failed: winner {winner}\"\n assert results[winner][\"overall_max\"] < 1e-9\n # All other hypotheses must be clearly distinguishable (not sub-pixel).\n for name, st in results.items():\n if name != winner:\n assert st[\"overall_max\"] > 1.0, f\"hypothesis {name} not distinguishable\"\n print(\"\\nSELFTEST PASS — winner recip+grow as constructed, others distinguishable\")\n\n\ndef report(results):\n print(f\"\\n{'hypothesis':<12} {'overall max px':>14} per-channel rms px (L/R averaged)\")\n for name, st in results.items():\n per_ch = []\n for ch in (\"r\", \"g\", \"b\"):\n vals = [st[k][0] for k in st if isinstance(k, tuple) and k[1] == ch]\n per_ch.append(f\"{ch}:{sum(vals)/len(vals):8.3f}\")\n print(f\"{name:<12} {st['overall_max']:>14.4f} {' '.join(per_ch)}\")\n\n\ndef main():\n if \"--selftest\" in sys.argv:\n selftest()\n return\n if len(sys.argv) != 3:\n print(__doc__)\n sys.exit(2)\n\n with open(sys.argv[1]) as f:\n dump = json.load(f)\n left, right, cfg = load_config(sys.argv[2])\n eyes_cfg = [left, right]\n\n serial_cfg = cfg.get(\"device_serial_number\", \"?\")\n print(f\"dump unit: {dump.get('serial')} config unit: {serial_cfg}\")\n if dump.get(\"serial\", \"\").lower() != serial_cfg.lower():\n print(\"WARNING: serial mismatch — dump and config are from different units!\")\n\n results = {name: test_hypothesis(dump, eyes_cfg, form, grow)\n for name, form, grow in HYPOTHESES}\n report(results)\n\n winner = min(results, key=lambda k: results[k][\"overall_max\"])\n wmax = results[winner][\"overall_max\"]\n verdict = \"PASS\" if wmax < 1.0 else \"FAIL\"\n print(f\"\\nWinner: {winner} (overall max {wmax:.4f} px) -> {verdict} \"\n f\"(criterion: sub-pixel)\")\n\n cross_checks(dump, cfg)\n\n\nif __name__ == \"__main__\":\n main()\n","structuredPatch":[],"originalFile":null,"userModified":false},"sourceToolAssistantUUID":"a3705515-95a8-4ec8-8369-8c4e3a0f3e0c","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"a0cc9ed8-a3fa-4613-9ae3-fd63d8e9c32a","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01YHkrLfx7AwhEancNDYq5y2","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01V6mSgahreauJoF9ca8jabA","name":"PowerShell","input":{"command":"cd C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth; python verify.py --selftest","description":"Run evaluator selftest"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3273,"cache_read_input_tokens":82652,"output_tokens":122,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":3273,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":122,"cache_read_input_tokens":82652,"cache_creation_input_tokens":3273,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":3273},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu1VhAwgYi8n45nYyefi","type":"assistant","uuid":"a85ff898-3b8b-4d58-8b1c-de7f307307cb","timestamp":"2026-06-10T05:15:03.335Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"a85ff898-3b8b-4d58-8b1c-de7f307307cb","isSidechain":false,"promptId":"6e29c121-2046-495c-b767-db64315dc4d2","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01V6mSgahreauJoF9ca8jabA","type":"tool_result","content":"hypothesis overall max px per-channel rms px (L/R averaged)\r\npoly+grow 855.3592 r: 421.789 g: 424.695 b: 442.856\r\npoly 1833.2245 r: 969.135 g: 974.094 b: 998.962\r\nrecip+grow 0.0000 r: 0.000 g: 0.000 b: 0.000\r\nrecip 476.5902 r: 313.650 g: 312.419 b: 309.176\r\n\r\nSELFTEST PASS � winner recip+grow as constructed, others distinguishable","is_error":false}]},"uuid":"87ccfe5c-81db-4dd6-b8f0-dc82ae9f9e5b","timestamp":"2026-06-10T05:15:04.093Z","toolUseResult":{"stdout":"hypothesis overall max px per-channel rms px (L/R averaged)\r\npoly+grow 855.3592 r: 421.789 g: 424.695 b: 442.856\r\npoly 1833.2245 r: 969.135 g: 974.094 b: 998.962\r\nrecip+grow 0.0000 r: 0.000 g: 0.000 b: 0.000\r\nrecip 476.5902 r: 313.650 g: 312.419 b: 309.176\r\n\r\nSELFTEST PASS � winner recip+grow as constructed, others distinguishable","stderr":"","interrupted":false,"isImage":false},"sourceToolAssistantUUID":"a85ff898-3b8b-4d58-8b1c-de7f307307cb","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"29470b4a-df73-414e-8cdc-bf3d626cd274","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01LVmroEqaq6YUZkbs8XM9Ue","type":"message","role":"assistant","content":[{"type":"text","text":"Selftest PASS — hypotheses distinguishable by hundreds of px, winner exact. Writing README + FINDINGS skeleton, then checking enlyzeam."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":305,"cache_read_input_tokens":85925,"output_tokens":1113,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":305,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1113,"cache_read_input_tokens":85925,"cache_creation_input_tokens":305,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":305},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu1WAUWQudn9tDyPMDLs","type":"assistant","uuid":"594bd69f-c64e-45cb-9d3d-04fcf91f6894","timestamp":"2026-06-10T05:15:12.317Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"594bd69f-c64e-45cb-9d3d-04fcf91f6894","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01LVmroEqaq6YUZkbs8XM9Ue","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SEFmoSWmGvYK9UBhtPpYd6","name":"Write","input":{"file_path":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\README.md","content":"# Spike S3 — distortion ground truth\n\n**Question (PLAN.md):** which direction do the production `DISTORT_POLY3` coeffs run?\nEVT/DVT configs used `DISTORT_DPOLY3` with negative leading coeffs (−0.22); production B1\nuses `DISTORT_POLY3` with positive ones (+0.24). Public reimplementations (Monado) evaluate\nthe DPOLY3 family as `d = 1/(1 + k1·r² + k2·r⁴ + k3·r⁶)`. Is POLY3 the same curve fitted the\nother way (`d = 1 + …` direct), and is the `0.5/(1+grow_for_undistort)` output rescale right?\n\n**Pass criterion:** standalone evaluator matches an OpenVR `ComputeDistortion` UV-grid dump\nfrom one dev unit within sub-pixel tolerance (max error < 1 px at 2544 px/eye).\n\n## Files\n\n| file | role |\n|---|---|\n| `poly3.py` | standalone evaluator, 4 hypothesis variants (form × grow-rescale) |\n| `dump_openvr.py` | ground-truth dump — needs SteamVR running with the Beyond (one sanctioned dev-time run) |\n| `verify.py` | tests all hypotheses against a dump; `--selftest` for synthetic round-trip |\n\n## Run order (on the rig with the headset)\n\n```\npip install openvr\n# 1. Start SteamVR with the Beyond connected (it takes direct mode — fine).\npython dump_openvr.py # writes distortion_dump_.json\n# 2. Quit SteamVR.\npython verify.py distortion_dump_.json \"C:\\Program Files (x86)\\Steam\\config\\lighthouse\\\\config.json\"\n```\n\nThe config path is SteamVR's cache of the unit's SIP flash JSON (refreshed by the same\nSteamVR run). `verify.py` warns if dump and config serials differ.\n\nBonus ground truth captured in the same dump: `GetProjectionRaw` (settles intrinsics→frustum\nconvention incl. cy sign), `GetEyeToHeadTransform` (settles canting matrix + which side gets\n+ipd/2) — both cross-checked by `verify.py` for M2.\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":305,"cache_read_input_tokens":85925,"output_tokens":1113,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":305,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1113,"cache_read_input_tokens":85925,"cache_creation_input_tokens":305,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":305},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu1WAUWQudn9tDyPMDLs","type":"assistant","uuid":"6f394cb9-cb79-40e0-a289-d276e1162614","timestamp":"2026-06-10T05:15:22.532Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"e387f556-fed8-4304-b193-5f4a29712106","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01AaF76Vot9b883MLfFQ6zCC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PsiEeYMpNmGcVJDPB2MPis","name":"PowerShell","input":{"command":"Start-Sleep -Seconds 8; ssh decid@enlyzeam \"cd C:\\Users\\decid\\sauna-spikes\\s3-distortion-truth; Get-Content dump_log.txt; Get-ChildItem distortion_dump_*.json | Select-Object Name, Length\"","description":"Check dump task output","timeout":60000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":648,"cache_read_input_tokens":93156,"output_tokens":198,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":648,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":198,"cache_read_input_tokens":93156,"cache_creation_input_tokens":648,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":648},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu2epZZFkRcp9tt6t6UX","type":"assistant","uuid":"26acabda-66bd-42d9-aa4e-d4ac81a9655d","timestamp":"2026-06-10T05:30:14.664Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"26acabda-66bd-42d9-aa4e-d4ac81a9655d","isSidechain":false,"promptId":"b79efa4d-9c11-4f46-975f-363733b93c3f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PsiEeYMpNmGcVJDPB2MPis","type":"tool_result","content":"HMD: Beyond LHR-599F3B91, recommended RT 3104x3104 per eye\r\n left: 4225/4225 points ok, projection_raw L-1.4000 R+0.9130 T-1.3798 B+0.9233\r\n right: 4225/4225 points ok, projection_raw L-0.9044 R+1.4020 T-1.3411 B+0.9411\r\nWrote distortion_dump_lhr_599f3b91.json\r\n\r\nName Length\r\n---- ------\r\ndistortion_dump_lhr_599f3b91.json 1440515","is_error":false}]},"uuid":"b750e548-ff47-4bf0-9309-d6b5bcab99f7","timestamp":"2026-06-10T05:30:25.170Z","toolUseResult":{"stdout":"HMD: Beyond LHR-599F3B91, recommended RT 3104x3104 per eye\r\n left: 4225/4225 points ok, projection_raw L-1.4000 R+0.9130 T-1.3798 B+0.9233\r\n right: 4225/4225 points ok, projection_raw L-0.9044 R+1.4020 T-1.3411 B+0.9411\r\nWrote distortion_dump_lhr_599f3b91.json\r\n\r\nName Length\r\n---- ------\r\ndistortion_dump_lhr_599f3b91.json 1440515","stderr":"","interrupted":false,"isImage":false},"sourceToolAssistantUUID":"26acabda-66bd-42d9-aa4e-d4ac81a9655d","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"2e408beb-9be6-4445-89a8-42c5437b9220","isSidechain":false,"promptId":"b79efa4d-9c11-4f46-975f-363733b93c3f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SpF5Fx8gF8hSqK51rtM4V7","type":"tool_result","content":"dump unit: LHR-599F3B91 config unit: LHR-599F3B91\r\n\r\nhypothesis overall max px per-channel rms px (L/R averaged)\r\npoly+grow 1395.7371 r: 150.535 g: 157.060 b: 168.829\r\npoly 686.7507 r: 451.670 g: 452.940 b: 455.401\r\nrecip+grow 2202.6093 r: 472.659 g: 484.309 b: 505.258\r\nrecip 1728.7205 r: 267.943 g: 278.061 b: 296.869\r\n\r\nWinner: poly (overall max 686.7507 px) -> FAIL (criterion: sub-pixel)\r\n\r\nCross-checks (dump vs config):\r\n left projection_raw dump L-1.39998 R+0.91304 T-1.37976 B+0.92332\r\n left from intrinsics L-0.69784 R+0.71157 T-0.68506 B+0.72266\r\n left eye_to_head rot max-abs-diff 1.07e-01; translation x -31.000 mm (ipd.default_mm/2 expected)\r\n right projection_raw dump L-0.90444 R+1.40201 T-1.34110 B+0.94106\r\n right from intrinsics L-0.70222 R+0.70144 T-0.68771 B+0.71415\r\n right eye_to_head rot max-abs-diff 1.07e-01; translation x +31.000 mm (ipd.default_mm/2 expected)\r\n config ipd block: {'default_mm': 69}","is_error":false}]},"uuid":"40f274e2-1cea-4c28-87c5-cfbbe55aef5d","timestamp":"2026-06-10T05:30:40.028Z","toolUseResult":{"stdout":"dump unit: LHR-599F3B91 config unit: LHR-599F3B91\r\n\r\nhypothesis overall max px per-channel rms px (L/R averaged)\r\npoly+grow 1395.7371 r: 150.535 g: 157.060 b: 168.829\r\npoly 686.7507 r: 451.670 g: 452.940 b: 455.401\r\nrecip+grow 2202.6093 r: 472.659 g: 484.309 b: 505.258\r\nrecip 1728.7205 r: 267.943 g: 278.061 b: 296.869\r\n\r\nWinner: poly (overall max 686.7507 px) -> FAIL (criterion: sub-pixel)\r\n\r\nCross-checks (dump vs config):\r\n left projection_raw dump L-1.39998 R+0.91304 T-1.37976 B+0.92332\r\n left from intrinsics L-0.69784 R+0.71157 T-0.68506 B+0.72266\r\n left eye_to_head rot max-abs-diff 1.07e-01; translation x -31.000 mm (ipd.default_mm/2 expected)\r\n right projection_raw dump L-0.90444 R+1.40201 T-1.34110 B+0.94106\r\n right from intrinsics L-0.70222 R+0.70144 T-0.68771 B+0.71415\r\n right eye_to_head rot max-abs-diff 1.07e-01; translation x +31.000 mm (ipd.default_mm/2 expected)\r\n config ipd block: {'default_mm': 69}","stderr":"","interrupted":false,"isImage":false},"sourceToolAssistantUUID":"2e408beb-9be6-4445-89a8-42c5437b9220","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"beyond connected + steamvr up on enlyzeam","leafUuid":"216fdbbc-7c25-46ab-a9db-29d8283aadb6","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02"}
{"type":"ai-title","aiTitle":"Set up Beyond Connected and SteamVR","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02"}
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{"parentUuid":"bba1b1c7-e8c0-4009-9fde-527a60568ff3","isSidechain":false,"promptId":"b79efa4d-9c11-4f46-975f-363733b93c3f","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nLEFT\r\n intrinsics [[1.4190387502680653, 0.0, 0.0097386872668569], [0.0, 1.420734589979133, 0.026714069319401], [0.0, 0.0, -1.0]]\r\n eye_to_head [[0.9942073760240819, 0.0, 0.10747880469799379], [-0.011514392898854297, 0.9942448326331053, 0.10651118034525311], [-0.10686024618856305, -0.10713175431387381, 0.9884855461776623]]\r\n grow 0.6 cutoff 1\r\n distortion DISTORT_POLY3 center -0.00974 0.02671 coeffs [0.20891, 0.13323, 0.05599]\r\n distortion_red DISTORT_POLY3 center -0.00974 0.02671 coeffs [0.18585, 0.16702, 0.03718]\r\n distortion_blue DISTORT_POLY3 center -0.00974 0.02671 coeffs [0.25042, 0.07241, 0.08984]\r\nRIGHT\r\n intrinsics [[1.4248367292861137, 0.0, -0.0005557552421618], [0.0, 1.426677521485123, 0.0188615443189732], [0.0, 0.0, -1.0]]\r\n eye_to_head [[0.9942073760240819, 0.0, -0.10747880469799379], [0.010021268493629042, 0.9956437105068295, 0.09269938460405774], [0.1070105959103494, -0.09323948588550035, 0.9898763208778758]]\r\n grow 0.6 cutoff 1\r\n distortion DISTORT_POLY3 center 0.00056 0.01886 coeffs [0.20536, 0.14656, 0.04697]\r\n distortion_red DISTORT_POLY3 center 0.00056 0.01886 coeffs [0.1823, 0.18034, 0.02817]\r\n distortion_blue DISTORT_POLY3 center 0.00056 0.01886 coeffs [0.24687, 0.08574, 0.08082]\r\nipd {'default_mm': 69}\r\n\u001b[31;1mTraceback (most recent call last):\u001b[0m\r\n\u001b[31;1m File \"\", line 1, in \u001b[0m\r\n\u001b[31;1mModuleNotFoundError: No module named 'scipy'\u001b[0m","is_error":true,"tool_use_id":"toolu_01CwdmxVqbtG3S7XZke7jAmg"}]},"uuid":"f5fa27a9-4d5f-45a9-850b-a2f4ae9200f6","timestamp":"2026-06-10T05:32:19.412Z","toolUseResult":"Error: Exit code 1\nLEFT\r\n intrinsics [[1.4190387502680653, 0.0, 0.0097386872668569], [0.0, 1.420734589979133, 0.026714069319401], [0.0, 0.0, -1.0]]\r\n eye_to_head [[0.9942073760240819, 0.0, 0.10747880469799379], [-0.011514392898854297, 0.9942448326331053, 0.10651118034525311], [-0.10686024618856305, -0.10713175431387381, 0.9884855461776623]]\r\n grow 0.6 cutoff 1\r\n distortion DISTORT_POLY3 center -0.00974 0.02671 coeffs [0.20891, 0.13323, 0.05599]\r\n distortion_red DISTORT_POLY3 center -0.00974 0.02671 coeffs [0.18585, 0.16702, 0.03718]\r\n distortion_blue DISTORT_POLY3 center -0.00974 0.02671 coeffs [0.25042, 0.07241, 0.08984]\r\nRIGHT\r\n intrinsics [[1.4248367292861137, 0.0, -0.0005557552421618], [0.0, 1.426677521485123, 0.0188615443189732], [0.0, 0.0, -1.0]]\r\n eye_to_head [[0.9942073760240819, 0.0, -0.10747880469799379], [0.010021268493629042, 0.9956437105068295, 0.09269938460405774], [0.1070105959103494, -0.09323948588550035, 0.9898763208778758]]\r\n grow 0.6 cutoff 1\r\n distortion DISTORT_POLY3 center 0.00056 0.01886 coeffs [0.20536, 0.14656, 0.04697]\r\n distortion_red DISTORT_POLY3 center 0.00056 0.01886 coeffs [0.1823, 0.18034, 0.02817]\r\n distortion_blue DISTORT_POLY3 center 0.00056 0.01886 coeffs [0.24687, 0.08574, 0.08082]\r\nipd {'default_mm': 69}\r\n\u001b[31;1mTraceback (most recent call last):\u001b[0m\r\n\u001b[31;1m File \"\", line 1, in \u001b[0m\r\n\u001b[31;1mModuleNotFoundError: No module named 'scipy'\u001b[0m","sourceToolAssistantUUID":"bba1b1c7-e8c0-4009-9fde-527a60568ff3","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"af695d6d-0fc8-4846-b5b7-f6ab893f6d54","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_011eJZkeVH2fjUj4RsUUkjzR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BDxjq4eqXMAPfgFVZSNwov","name":"Write","input":{"file_path":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\analyze.py","content":"\"\"\"Spike S3 empirical analysis: discover Valve's actual POLY3 evaluation.\n\nThe 4 naive hypotheses all failed because the source-UV space is the\nGetProjectionRaw frustum, not a grow-rescaled NDC. This script fits the\ngeneral radial model per eye/channel:\n\n t = (2u-1, 2v-1) panel NDC (v down, OpenVR viewport)\n r2 = |t - c|^2\n tan = a + s * (t - c) * d(r2) (s, a 2-vector; tan = raw-projection\n tangent space, y-down: tan = T+v*(B-T))\n\nwith d either poly: 1 + k1 r2 + k2 r2^2 + k3 r2^3\n or recip: 1 / (1 + k1 r2 + k2 r2^2 + k3 r2^3)\n\nFree params per channel: c(2) a(2) s(2) k(3) = 9. Ground truth: 4225 pts.\nThen compares fitted c,k against the config block and fitted a,s against\nintrinsics/projection_raw to recover the closed form.\n\nUsage: python analyze.py distortion_dump_*.json config_*.json\n\"\"\"\n\nimport json\nimport sys\n\nimport numpy as np\nfrom scipy.optimize import least_squares\n\nCHANNELS = {\"r\": \"distortion_red\", \"g\": \"distortion\", \"b\": \"distortion_blue\"}\n\n\ndef load_eye_points(eye_dump):\n uv, tgt = [], {\"r\": [], \"g\": [], \"b\": []}\n for pt in eye_dump[\"points\"]:\n if pt is None:\n continue\n uv.append(pt[\"uv\"])\n for ch in (\"r\", \"g\", \"b\"):\n tgt[ch].append(pt[ch])\n uv = np.array(uv)\n L, R, T, B = eye_dump[\"projection_raw\"]\n tans = {}\n for ch in (\"r\", \"g\", \"b\"):\n suv = np.array(tgt[ch])\n # source UV -> tangent space of the raw projection (v=0 top -> T)\n tans[ch] = np.stack([L + suv[:, 0] * (R - L), T + suv[:, 1] * (B - T)], axis=1)\n return uv, tans\n\n\ndef model(params, t, form):\n cx, cy, ax, ay, sx, sy, k1, k2, k3 = params\n d0 = t - np.array([cx, cy])\n r2 = (d0 ** 2).sum(axis=1)\n p = 1 + r2 * (k1 + r2 * (k2 + r2 * k3))\n d = p if form == \"poly\" else 1.0 / p\n return np.array([ax, ay]) + np.array([sx, sy]) * d0 * d[:, None]\n\n\ndef fit(uv, tan, form, k0):\n t = 2 * uv - 1\n x0 = np.array([0.0, 0.0, 0.0, 0.0, 0.7, 0.7] + list(k0))\n\n def resid(params):\n return (model(params, t, form) - tan).ravel()\n\n return least_squares(resid, x0, method=\"lm\", max_nfev=20000)\n\n\ndef main():\n dump = json.load(open(sys.argv[1]))\n cfg = json.load(open(sys.argv[2]))\n rt = dump[\"recommended_render_target\"][0]\n\n for eye_idx, eye_dump in enumerate(dump[\"eyes\"]):\n tte = cfg[\"tracking_to_eye_transform\"][eye_idx]\n K = tte[\"intrinsics\"]\n L, R, T, B = eye_dump[\"projection_raw\"]\n print(f\"\\n=== {eye_dump['eye'].upper()} ===\")\n print(f\"projection_raw L{L:+.5f} R{R:+.5f} T{T:+.5f} B{B:+.5f}\")\n print(f\"intrinsics fx {K[0][0]:.5f} fy {K[1][1]:.5f} K02 {K[0][2]:+.5f} K12 {K[1][2]:+.5f}\")\n print(\"config eye_to_head:\")\n for row in tte[\"eye_to_head\"]:\n print(\" \", [round(x, 6) for x in row])\n print(\"dump eye_to_head:\")\n for row in eye_dump[\"eye_to_head\"]:\n print(\" \", [round(x, 6) for x in row])\n\n uv, tans = load_eye_points(eye_dump)\n for ch in (\"g\", \"r\", \"b\"):\n blk = tte[CHANNELS[ch]]\n kcfg = blk[\"coeffs\"]\n for form in (\"poly\", \"recip\"):\n res = fit(uv, tans[ch], form, kcfg)\n cx, cy, ax, ay, sx, sy, k1, k2, k3 = res.x\n # residual in source pixels\n err = res.fun.reshape(-1, 2)\n err_px = np.linalg.norm(err, axis=1) * rt / abs(R - L)\n print(f\" {ch}/{form:5s} rms {err_px.std() + err_px.mean():7.3f}px max {err_px.max():8.3f}px | \"\n f\"c ({cx:+.5f},{cy:+.5f}) a ({ax:+.5f},{ay:+.5f}) s ({sx:+.5f},{sy:+.5f})\")\n print(f\" k_fit [{k1:+.5f}, {k2:+.5f}, {k3:+.5f}]\")\n print(f\" k_cfg [{kcfg[0]:+.5f}, {kcfg[1]:+.5f}, {kcfg[2]:+.5f}] \"\n f\"center_cfg ({blk['center_x']:+.5f},{blk['center_y']:+.5f}) \"\n f\"1/fx {1/K[0][0]:.5f} 1/fy {1/K[1][1]:.5f}\")\n\n\nif __name__ == \"__main__\":\n main()\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1448,"cache_read_input_tokens":101797,"output_tokens":2103,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1448,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":2103,"cache_read_input_tokens":101797,"cache_creation_input_tokens":1448,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1448},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu2sYmQKLL1fWcZDBhfQ","type":"assistant","uuid":"8b3a02c2-bf2f-4b8e-8237-9fcde29db8b6","timestamp":"2026-06-10T05:33:29.642Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"8b3a02c2-bf2f-4b8e-8237-9fcde29db8b6","isSidechain":false,"promptId":"b79efa4d-9c11-4f46-975f-363733b93c3f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BDxjq4eqXMAPfgFVZSNwov","type":"tool_result","content":"File created successfully at: C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\analyze.py (file state is current in your context — no need to Read it back)"}]},"uuid":"67ac5543-4c2d-4839-ae14-b2b693236a04","timestamp":"2026-06-10T05:33:29.829Z","toolUseResult":{"type":"create","filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\analyze.py","content":"\"\"\"Spike S3 empirical analysis: discover Valve's actual POLY3 evaluation.\n\nThe 4 naive hypotheses all failed because the source-UV space is the\nGetProjectionRaw frustum, not a grow-rescaled NDC. This script fits the\ngeneral radial model per eye/channel:\n\n t = (2u-1, 2v-1) panel NDC (v down, OpenVR viewport)\n r2 = |t - c|^2\n tan = a + s * (t - c) * d(r2) (s, a 2-vector; tan = raw-projection\n tangent space, y-down: tan = T+v*(B-T))\n\nwith d either poly: 1 + k1 r2 + k2 r2^2 + k3 r2^3\n or recip: 1 / (1 + k1 r2 + k2 r2^2 + k3 r2^3)\n\nFree params per channel: c(2) a(2) s(2) k(3) = 9. Ground truth: 4225 pts.\nThen compares fitted c,k against the config block and fitted a,s against\nintrinsics/projection_raw to recover the closed form.\n\nUsage: python analyze.py distortion_dump_*.json config_*.json\n\"\"\"\n\nimport json\nimport sys\n\nimport numpy as np\nfrom scipy.optimize import least_squares\n\nCHANNELS = {\"r\": \"distortion_red\", \"g\": \"distortion\", \"b\": \"distortion_blue\"}\n\n\ndef load_eye_points(eye_dump):\n uv, tgt = [], {\"r\": [], \"g\": [], \"b\": []}\n for pt in eye_dump[\"points\"]:\n if pt is None:\n continue\n uv.append(pt[\"uv\"])\n for ch in (\"r\", \"g\", \"b\"):\n tgt[ch].append(pt[ch])\n uv = np.array(uv)\n L, R, T, B = eye_dump[\"projection_raw\"]\n tans = {}\n for ch in (\"r\", \"g\", \"b\"):\n suv = np.array(tgt[ch])\n # source UV -> tangent space of the raw projection (v=0 top -> T)\n tans[ch] = np.stack([L + suv[:, 0] * (R - L), T + suv[:, 1] * (B - T)], axis=1)\n return uv, tans\n\n\ndef model(params, t, form):\n cx, cy, ax, ay, sx, sy, k1, k2, k3 = params\n d0 = t - np.array([cx, cy])\n r2 = (d0 ** 2).sum(axis=1)\n p = 1 + r2 * (k1 + r2 * (k2 + r2 * k3))\n d = p if form == \"poly\" else 1.0 / p\n return np.array([ax, ay]) + np.array([sx, sy]) * d0 * d[:, None]\n\n\ndef fit(uv, tan, form, k0):\n t = 2 * uv - 1\n x0 = np.array([0.0, 0.0, 0.0, 0.0, 0.7, 0.7] + list(k0))\n\n def resid(params):\n return (model(params, t, form) - tan).ravel()\n\n return least_squares(resid, x0, method=\"lm\", max_nfev=20000)\n\n\ndef main():\n dump = json.load(open(sys.argv[1]))\n cfg = json.load(open(sys.argv[2]))\n rt = dump[\"recommended_render_target\"][0]\n\n for eye_idx, eye_dump in enumerate(dump[\"eyes\"]):\n tte = cfg[\"tracking_to_eye_transform\"][eye_idx]\n K = tte[\"intrinsics\"]\n L, R, T, B = eye_dump[\"projection_raw\"]\n print(f\"\\n=== {eye_dump['eye'].upper()} ===\")\n print(f\"projection_raw L{L:+.5f} R{R:+.5f} T{T:+.5f} B{B:+.5f}\")\n print(f\"intrinsics fx {K[0][0]:.5f} fy {K[1][1]:.5f} K02 {K[0][2]:+.5f} K12 {K[1][2]:+.5f}\")\n print(\"config eye_to_head:\")\n for row in tte[\"eye_to_head\"]:\n print(\" \", [round(x, 6) for x in row])\n print(\"dump eye_to_head:\")\n for row in eye_dump[\"eye_to_head\"]:\n print(\" \", [round(x, 6) for x in row])\n\n uv, tans = load_eye_points(eye_dump)\n for ch in (\"g\", \"r\", \"b\"):\n blk = tte[CHANNELS[ch]]\n kcfg = blk[\"coeffs\"]\n for form in (\"poly\", \"recip\"):\n res = fit(uv, tans[ch], form, kcfg)\n cx, cy, ax, ay, sx, sy, k1, k2, k3 = res.x\n # residual in source pixels\n err = res.fun.reshape(-1, 2)\n err_px = np.linalg.norm(err, axis=1) * rt / abs(R - L)\n print(f\" {ch}/{form:5s} rms {err_px.std() + err_px.mean():7.3f}px max {err_px.max():8.3f}px | \"\n f\"c ({cx:+.5f},{cy:+.5f}) a ({ax:+.5f},{ay:+.5f}) s ({sx:+.5f},{sy:+.5f})\")\n print(f\" k_fit [{k1:+.5f}, {k2:+.5f}, {k3:+.5f}]\")\n print(f\" k_cfg [{kcfg[0]:+.5f}, {kcfg[1]:+.5f}, {kcfg[2]:+.5f}] \"\n f\"center_cfg ({blk['center_x']:+.5f},{blk['center_y']:+.5f}) \"\n f\"1/fx {1/K[0][0]:.5f} 1/fy {1/K[1][1]:.5f}\")\n\n\nif __name__ == \"__main__\":\n main()\n","structuredPatch":[],"originalFile":null,"userModified":false},"sourceToolAssistantUUID":"8b3a02c2-bf2f-4b8e-8237-9fcde29db8b6","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"f8d1a6aa-b5c2-4330-8b00-3a7c666a1059","isSidechain":false,"promptId":"b79efa4d-9c11-4f46-975f-363733b93c3f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011AehnLiQWCLZ97Mdf532B9","type":"tool_result","content":"=== LEFT ===\r\nprojection_raw L-1.39998 R+0.91304 T-1.37976 B+0.92332\r\nintrinsics fx 1.41904 fy 1.42073 K02 +0.00974 K12 +0.02671\r\nconfig eye_to_head:\r\n [0.994207, 0.0, 0.107479]\r\n [-0.011514, 0.994245, 0.106511]\r\n [-0.10686, -0.107132, 0.988486]\r\ndump eye_to_head:\r\n [1.0, 0.0, 0.0, -0.031]\r\n [0.0, 1.0, 0.0, 0.0]\r\n [0.0, 0.0, 1.0, 0.0]\r\n g/poly rms 0.000px max 0.000px | c (-0.00974,+0.02671) a (-0.25051,-0.20900) s (+0.72282,+0.71971)\r\n k_fit [+0.20891, +0.13323, +0.05599]\r\n k_cfg [+0.20891, +0.13323, +0.05599] center_cfg (-0.00974,+0.02671) 1/fx 0.70470 1/fy 0.70386\r\n g/recip rms 1.003px max 16.833px | c (-0.00976,+0.02676) a (-0.25053,-0.20895) s (+0.72114,+0.71803)\r\n k_fit [-0.21189, -0.10899, +0.03457]\r\n k_cfg [+0.20891, +0.13323, +0.05599] center_cfg (-0.00974,+0.02671) 1/fx 0.70470 1/fy 0.70386\r\n r/poly rms 0.000px max 0.000px | c (-0.00974,+0.02671) a (-0.25051,-0.20900) s (+0.72282,+0.71971)\r\n k_fit [+0.18585, +0.16702, +0.03718]\r\n k_cfg [+0.18585, +0.16702, +0.03718] center_cfg (-0.00974,+0.02671) 1/fx 0.70470 1/fy 0.70386\r\n r/recip rms 1.501px max 21.851px | c (-0.00977,+0.02678) a (-0.25054,-0.20893) s (+0.71801,+0.71492)\r\n k_fit [-0.21341, -0.10665, +0.03496]\r\n k_cfg [+0.18585, +0.16702, +0.03718] center_cfg (-0.00974,+0.02671) 1/fx 0.70470 1/fy 0.70386\r\n b/poly rms 0.000px max 0.000px | c (-0.00974,+0.02671) a (-0.25051,-0.20900) s (+0.72282,+0.71971)\r\n k_fit [+0.25042, +0.07241, +0.08984]\r\n k_cfg [+0.25042, +0.07241, +0.08984] center_cfg (-0.00974,+0.02671) 1/fx 0.70470 1/fy 0.70386\r\n b/recip rms 0.641px max 7.471px | c (-0.00975,+0.02673) a (-0.25052,-0.20898) s (+0.72698,+0.72385)\r\n k_fit [-0.20787, -0.11496, +0.03458]\r\n k_cfg [+0.25042, +0.07241, +0.08984] center_cfg (-0.00974,+0.02671) 1/fx 0.70470 1/fy 0.70386\r\n\r\n=== RIGHT ===\r\nprojection_raw L-0.90444 R+1.40201 T-1.34110 B+0.94106\r\nintrinsics fx 1.42484 fy 1.42668 K02 -0.00056 K12 +0.01886\r\nconfig eye_to_head:\r\n [0.994207, 0.0, -0.107479]\r\n [0.010021, 0.995644, 0.092699]\r\n [0.107011, -0.093239, 0.989876]\r\ndump eye_to_head:\r\n [1.0, 0.0, 0.0, 0.031]\r\n [0.0, 1.0, 0.0, 0.0]\r\n [0.0, 0.0, 1.0, 0.0]\r\n g/poly rms 0.000px max 0.000px | c (+0.00056,+0.01886) a (+0.24919,-0.18657) s (+0.72076,+0.71318)\r\n k_fit [+0.20536, +0.14656, +0.04697]\r\n k_cfg [+0.20536, +0.14656, +0.04697] center_cfg (+0.00056,+0.01886) 1/fx 0.70183 1/fy 0.70093\r\n g/recip rms 1.121px max 14.450px | c (+0.00056,+0.01891) a (+0.24919,-0.18652) s (+0.71787,+0.71031)\r\n k_fit [-0.21878, -0.10272, +0.03367]\r\n k_cfg [+0.20536, +0.14656, +0.04697] center_cfg (+0.00056,+0.01886) 1/fx 0.70183 1/fy 0.70093\r\n r/poly rms 0.000px max 0.000px | c (+0.00056,+0.01886) a (+0.24919,-0.18657) s (+0.72076,+0.71318)\r\n k_fit [+0.18230, +0.18034, +0.02817]\r\n k_cfg [+0.18230, +0.18034, +0.02817] center_cfg (+0.00056,+0.01886) 1/fx 0.70183 1/fy 0.70093\r\n r/recip rms 1.623px max 18.610px | c (+0.00056,+0.01892) a (+0.24919,-0.18651) s (+0.71484,+0.70732)\r\n k_fit [-0.21979, -0.10106, +0.03432]\r\n k_cfg [+0.18230, +0.18034, +0.02817] center_cfg (+0.00056,+0.01886) 1/fx 0.70183 1/fy 0.70093\r\n b/poly rms 0.000px max 0.000px | c (+0.00056,+0.01886) a (+0.24919,-0.18657) s (+0.72076,+0.71318)\r\n k_fit [+0.24687, +0.08574, +0.08082]\r\n k_cfg [+0.24687, +0.08574, +0.08082] center_cfg (+0.00056,+0.01886) 1/fx 0.70183 1/fy 0.70093\r\n b/recip rms 0.529px max 6.647px | c (+0.00056,+0.01888) a (+0.24919,-0.18655) s (+0.72353,+0.71592)\r\n k_fit [-0.21563, -0.10752, +0.03325]\r\n k_cfg [+0.24687, +0.08574, +0.08082] center_cfg (+0.00056,+0.01886) 1/fx 0.70183 1/fy 0.70093","is_error":false}]},"uuid":"4a2dd7ed-798b-4cdd-9d4f-16c25ddd12f9","timestamp":"2026-06-10T05:33:40.867Z","toolUseResult":{"stdout":"=== LEFT ===\r\nprojection_raw L-1.39998 R+0.91304 T-1.37976 B+0.92332\r\nintrinsics fx 1.41904 fy 1.42073 K02 +0.00974 K12 +0.02671\r\nconfig eye_to_head:\r\n [0.994207, 0.0, 0.107479]\r\n [-0.011514, 0.994245, 0.106511]\r\n [-0.10686, -0.107132, 0.988486]\r\ndump eye_to_head:\r\n [1.0, 0.0, 0.0, -0.031]\r\n [0.0, 1.0, 0.0, 0.0]\r\n [0.0, 0.0, 1.0, 0.0]\r\n g/poly rms 0.000px max 0.000px | c (-0.00974,+0.02671) a (-0.25051,-0.20900) s (+0.72282,+0.71971)\r\n k_fit [+0.20891, +0.13323, +0.05599]\r\n k_cfg [+0.20891, +0.13323, +0.05599] center_cfg (-0.00974,+0.02671) 1/fx 0.70470 1/fy 0.70386\r\n g/recip rms 1.003px max 16.833px | c (-0.00976,+0.02676) a (-0.25053,-0.20895) s (+0.72114,+0.71803)\r\n k_fit [-0.21189, -0.10899, +0.03457]\r\n k_cfg [+0.20891, +0.13323, +0.05599] center_cfg (-0.00974,+0.02671) 1/fx 0.70470 1/fy 0.70386\r\n r/poly rms 0.000px max 0.000px | c (-0.00974,+0.02671) a (-0.25051,-0.20900) s (+0.72282,+0.71971)\r\n k_fit [+0.18585, +0.16702, +0.03718]\r\n k_cfg [+0.18585, +0.16702, +0.03718] center_cfg (-0.00974,+0.02671) 1/fx 0.70470 1/fy 0.70386\r\n r/recip rms 1.501px max 21.851px | c (-0.00977,+0.02678) a (-0.25054,-0.20893) s (+0.71801,+0.71492)\r\n k_fit [-0.21341, -0.10665, +0.03496]\r\n k_cfg [+0.18585, +0.16702, +0.03718] center_cfg (-0.00974,+0.02671) 1/fx 0.70470 1/fy 0.70386\r\n b/poly rms 0.000px max 0.000px | c (-0.00974,+0.02671) a (-0.25051,-0.20900) s (+0.72282,+0.71971)\r\n k_fit [+0.25042, +0.07241, +0.08984]\r\n k_cfg [+0.25042, +0.07241, +0.08984] center_cfg (-0.00974,+0.02671) 1/fx 0.70470 1/fy 0.70386\r\n b/recip rms 0.641px max 7.471px | c (-0.00975,+0.02673) a (-0.25052,-0.20898) s (+0.72698,+0.72385)\r\n k_fit [-0.20787, -0.11496, +0.03458]\r\n k_cfg [+0.25042, +0.07241, +0.08984] center_cfg (-0.00974,+0.02671) 1/fx 0.70470 1/fy 0.70386\r\n\r\n=== RIGHT ===\r\nprojection_raw L-0.90444 R+1.40201 T-1.34110 B+0.94106\r\nintrinsics fx 1.42484 fy 1.42668 K02 -0.00056 K12 +0.01886\r\nconfig eye_to_head:\r\n [0.994207, 0.0, -0.107479]\r\n [0.010021, 0.995644, 0.092699]\r\n [0.107011, -0.093239, 0.989876]\r\ndump eye_to_head:\r\n [1.0, 0.0, 0.0, 0.031]\r\n [0.0, 1.0, 0.0, 0.0]\r\n [0.0, 0.0, 1.0, 0.0]\r\n g/poly rms 0.000px max 0.000px | c (+0.00056,+0.01886) a (+0.24919,-0.18657) s (+0.72076,+0.71318)\r\n k_fit [+0.20536, +0.14656, +0.04697]\r\n k_cfg [+0.20536, +0.14656, +0.04697] center_cfg (+0.00056,+0.01886) 1/fx 0.70183 1/fy 0.70093\r\n g/recip rms 1.121px max 14.450px | c (+0.00056,+0.01891) a (+0.24919,-0.18652) s (+0.71787,+0.71031)\r\n k_fit [-0.21878, -0.10272, +0.03367]\r\n k_cfg [+0.20536, +0.14656, +0.04697] center_cfg (+0.00056,+0.01886) 1/fx 0.70183 1/fy 0.70093\r\n r/poly rms 0.000px max 0.000px | c (+0.00056,+0.01886) a (+0.24919,-0.18657) s (+0.72076,+0.71318)\r\n k_fit [+0.18230, +0.18034, +0.02817]\r\n k_cfg [+0.18230, +0.18034, +0.02817] center_cfg (+0.00056,+0.01886) 1/fx 0.70183 1/fy 0.70093\r\n r/recip rms 1.623px max 18.610px | c (+0.00056,+0.01892) a (+0.24919,-0.18651) s (+0.71484,+0.70732)\r\n k_fit [-0.21979, -0.10106, +0.03432]\r\n k_cfg [+0.18230, +0.18034, +0.02817] center_cfg (+0.00056,+0.01886) 1/fx 0.70183 1/fy 0.70093\r\n b/poly rms 0.000px max 0.000px | c (+0.00056,+0.01886) a (+0.24919,-0.18657) s (+0.72076,+0.71318)\r\n k_fit [+0.24687, +0.08574, +0.08082]\r\n k_cfg [+0.24687, +0.08574, +0.08082] center_cfg (+0.00056,+0.01886) 1/fx 0.70183 1/fy 0.70093\r\n b/recip rms 0.529px max 6.647px | c (+0.00056,+0.01888) a (+0.24919,-0.18655) s (+0.72353,+0.71592)\r\n k_fit [-0.21563, -0.10752, +0.03325]\r\n k_cfg [+0.24687, +0.08574, +0.08082] center_cfg (+0.00056,+0.01886) 1/fx 0.70183 1/fy 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{"type":"ai-title","aiTitle":"Set up Beyond Connected and SteamVR","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02"}
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{"parentUuid":"00b27979-2564-43d9-b8a7-4b82058d6d41","isSidechain":false,"promptId":"b79efa4d-9c11-4f46-975f-363733b93c3f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019AKgznm8PKdAwQApmvcZCU","type":"tool_result","content":"The file C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\poly3.py has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"8746c8dd-40ee-4258-9d96-1096d61c7ffc","timestamp":"2026-06-10T05:35:34.891Z","toolUseResult":{"filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\poly3.py","oldString":" r2 = tx * tx + ty * ty\n if r2 > self.r2_cutoff:\n r2 = self.r2_cutoff","newString":" # NOTE (S3 finding): ComputeDistortion does NOT clamp r2 at\n # undistort_r2_cutoff — ground truth fits exactly with no clamp\n # across the full panel (corner r2 ~ 2.1 > cutoff 1.0). The\n # cutoff presumably gates mesh/hidden-area logic elsewhere.\n r2 = tx * tx + ty * ty","originalFile":"\"\"\"Standalone Beyond distortion evaluator — spike S3.\n\nMaps panel/output UV -> pre-distortion render-target UV, per color channel,\nfrom the device config JSON (tracking_to_eye_transform block). No SteamVR.\n\nThe open question this spike settles: production B1 configs use type\nDISTORT_POLY3 with POSITIVE leading coeffs (+0.2438 on this unit), while\nEVT/DVT used DISTORT_DPOLY3 with NEGATIVE leading coeffs (-0.22). Public\nreimplementations (Monado u_compute_distortion_vive) evaluate the Vive\nDPOLY3 family as a RECIPROCAL: d = 1/(1 + k1*r2 + k2*r4 + k3*r6).\nHypothesis: POLY3 is the same curve fitted the other way round, i.e.\nd = 1 + k1*r2 + k2*r4 + k3*r6 directly. Four candidate forms are\nimplemented; verify.py picks the one matching OpenVR ComputeDistortion.\n\nHypothesis axes:\n form: 'poly' -> d = 1 + k1*r2 + k2*r4 + k3*r6\n 'recip' -> d = 1 / (1 + k1*r2 + k2*r4 + k3*r6)\n grow: True -> output scale 0.5/(1+grow_for_undistort) (Monado)\n False -> output scale 0.5 (no grow rescale)\n\"\"\"\n\nimport json\n\nCHANNELS = (\"distortion_red\", \"distortion\", \"distortion_blue\") # r, g, b\nHYPOTHESES = [\n (\"poly+grow\", \"poly\", True),\n (\"poly\", \"poly\", False),\n (\"recip+grow\", \"recip\", True),\n (\"recip\", \"recip\", False),\n]\n\n\nclass EyeDistortion:\n \"\"\"Distortion model for one eye, built from tracking_to_eye_transform[eye].\"\"\"\n\n def __init__(self, tte):\n self.grow = tte[\"grow_for_undistort\"]\n self.r2_cutoff = tte[\"undistort_r2_cutoff\"]\n # center is shared across channels within an eye on the Beyond,\n # but read per channel anyway (B2 may differ).\n self.channels = []\n for key in CHANNELS:\n block = tte[key]\n self.channels.append({\n \"cx\": block[\"center_x\"],\n \"cy\": block[\"center_y\"],\n \"k\": list(block[\"coeffs\"]),\n \"type\": block[\"type\"],\n })\n\n def compute(self, u, v, form=\"poly\", use_grow=True, aspect_x_over_y=1.0):\n \"\"\"Panel UV (0..1, origin upper-left per OpenVR) -> source UV per channel.\n\n Returns ((ur, vr), (ug, vg), (ub, vb)).\n \"\"\"\n scale = 0.5 / (1.0 + self.grow) if use_grow else 0.5\n out = []\n for ch in self.channels:\n tx = 2.0 * u - 1.0\n ty = (2.0 * v - 1.0) / aspect_x_over_y\n tx -= ch[\"cx\"]\n ty -= ch[\"cy\"]\n r2 = tx * tx + ty * ty\n if r2 > self.r2_cutoff:\n r2 = self.r2_cutoff\n k1, k2, k3 = ch[\"k\"]\n p = 1.0 + r2 * (k1 + r2 * (k2 + r2 * k3))\n d = p if form == \"poly\" else 1.0 / p\n sx = 0.5 + (tx * d + ch[\"cx\"]) * scale\n sy = 0.5 + (ty * d + ch[\"cy\"]) * scale * aspect_x_over_y\n out.append((sx, sy))\n return tuple(out)\n\n\ndef load_config(path):\n \"\"\"Load device config.json -> (left EyeDistortion, right EyeDistortion, raw dict).\"\"\"\n with open(path, \"r\") as f:\n cfg = json.load(f)\n tte = cfg[\"tracking_to_eye_transform\"]\n return EyeDistortion(tte[0]), EyeDistortion(tte[1]), cfg\n","structuredPatch":[{"oldStart":60,"oldLines":9,"newStart":60,"newLines":11,"lines":[" ty = (2.0 * v - 1.0) / aspect_x_over_y"," tx -= ch[\"cx\"]"," ty -= ch[\"cy\"]","+ # NOTE (S3 finding): ComputeDistortion does NOT clamp r2 at","+ # undistort_r2_cutoff — ground truth fits exactly with no clamp","+ # across the full panel (corner r2 ~ 2.1 > cutoff 1.0). The","+ # cutoff presumably gates mesh/hidden-area logic elsewhere."," r2 = tx * tx + ty * ty","- if r2 > self.r2_cutoff:","- r2 = self.r2_cutoff"," k1, k2, k3 = ch[\"k\"]"," p = 1.0 + r2 * (k1 + r2 * (k2 + r2 * k3))"," d = p if form == \"poly\" else 1.0 / p"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"00b27979-2564-43d9-b8a7-4b82058d6d41","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"60320f8f-b4d9-4ece-b82e-f3bfe2b2aaa4","isSidechain":false,"promptId":"b79efa4d-9c11-4f46-975f-363733b93c3f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QupUtZUmfE7gNAtYqjCxAc","type":"tool_result","content":"dump unit: LHR-599F3B91 config unit: LHR-599F3B91\r\n\r\nhypothesis overall max px per-channel rms px (L/R averaged)\r\npoly+grow 0.0002 r: 0.000 g: 0.000 b: 0.000\r\npoly 1795.4725 r: 569.161 g: 574.258 b: 583.472\r\nrecip+grow 2578.0168 r: 519.908 g: 532.533 b: 555.124\r\nrecip 2329.3700 r: 349.681 g: 361.978 b: 384.304\r\n\r\nWinner: poly+grow (overall max 0.0002 px) -> PASS (criterion: sub-pixel)\r\n\r\nCross-checks (dump vs config):\r\n left projection_raw dump L-1.39998 R+0.91304 T-1.37976 B+0.92332\r\n left from intrinsics L-0.69784 R+0.71157 T-0.68506 B+0.72266\r\n left eye_to_head rot max-abs-diff 1.07e-01; translation x -31.000 mm (ipd.default_mm/2 expected)\r\n right projection_raw dump L-0.90444 R+1.40201 T-1.34110 B+0.94106\r\n right from intrinsics L-0.70222 R+0.70144 T-0.68771 B+0.71415\r\n right eye_to_head rot max-abs-diff 1.07e-01; translation x +31.000 mm (ipd.default_mm/2 expected)\r\n config ipd block: {'default_mm': 69}","is_error":false}]},"uuid":"c2547098-22c7-484d-b8ec-5247f4bf1279","timestamp":"2026-06-10T05:35:43.388Z","toolUseResult":{"stdout":"dump unit: LHR-599F3B91 config unit: LHR-599F3B91\r\n\r\nhypothesis overall max px per-channel rms px (L/R averaged)\r\npoly+grow 0.0002 r: 0.000 g: 0.000 b: 0.000\r\npoly 1795.4725 r: 569.161 g: 574.258 b: 583.472\r\nrecip+grow 2578.0168 r: 519.908 g: 532.533 b: 555.124\r\nrecip 2329.3700 r: 349.681 g: 361.978 b: 384.304\r\n\r\nWinner: poly+grow (overall max 0.0002 px) -> PASS (criterion: sub-pixel)\r\n\r\nCross-checks (dump vs config):\r\n left projection_raw dump L-1.39998 R+0.91304 T-1.37976 B+0.92332\r\n left from intrinsics L-0.69784 R+0.71157 T-0.68506 B+0.72266\r\n left eye_to_head rot max-abs-diff 1.07e-01; translation x -31.000 mm (ipd.default_mm/2 expected)\r\n right projection_raw dump L-0.90444 R+1.40201 T-1.34110 B+0.94106\r\n right from intrinsics L-0.70222 R+0.70144 T-0.68771 B+0.71415\r\n right eye_to_head rot max-abs-diff 1.07e-01; translation x +31.000 mm (ipd.default_mm/2 expected)\r\n config ipd block: {'default_mm': 69}","stderr":"","interrupted":false,"isImage":false},"sourceToolAssistantUUID":"60320f8f-b4d9-4ece-b82e-f3bfe2b2aaa4","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"6301533e-ce60-4824-947c-d16949b3b9b9","isSidechain":false,"promptId":"b79efa4d-9c11-4f46-975f-363733b93c3f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KDWB8Qtkz8vTCTpXfCHS9b","type":"tool_result","content":"{\r\n \"DesktopUI\" : {\r\n \"controllerbinding_desktop\" : \"313,216,825,1216,0\",\r\n \"debugcommands_desktop\" : \"-2116,609,600,800,0\",\r\n \"pairing\" : \"888,451,800,600,0\",\r\n \"settings_desktop\" : \"317,539,800,600,0\"\r\n },\r\n \"DismissedWarnings\" : {\r\n \"DismissableWarning_UnknownHardwareDetected\" : true,\r\n \"DismissableWarning_WiredIndexControllerActivity_58553692_LastDismissedTime\" : \"1777602673\",\r\n \"DismissableWarning_WiredIndexControllerActivity_58553692_LastShownTime\" : \"1777523363\",\r\n \"DismissableWarning_WiredIndexControllerActivity_LastDismissedTime\" : \"1760134692\",\r\n \"DismissableWarning_WiredIndexControllerActivity_LastShownTime\" : \"1760134309\"\r\n },\r\n \"GpuSpeed\" : {\r\n \"gpuSpeed0\" : 1568,\r\n \"gpuSpeed1\" : 1551,\r\n \"gpuSpeed2\" : 1550,\r\n \"gpuSpeed3\" : 1551,\r\n \"gpuSpeed4\" : 1566,\r\n \"gpuSpeed5\" : 1561,\r\n \"gpuSpeed6\" : 1557,\r\n \"gpuSpeed7\" : 1562,\r\n \"gpuSpeed8\" : 1560,\r\n \"gpuSpeed9\" : 1560,\r\n \"gpuSpeedCount\" : 10,\r\n \"gpuSpeedDriver\" : \"32.0.15.7602\",\r\n \"gpuSpeedHorsepower\" : 1558,\r\n \"gpuSpeedRenderTargetScale\" : 0.81999999284744263,\r\n \"gpuSpeedVendor\" : \"NVIDIA GeForce RTX 2080 Ti\",\r\n \"gpuSpeedVersion\" : 2\r\n },\r\n \"LastKnown\" : {\r\n \"ActualHMDDriver\" : \"lighthouse\",\r\n \"HMDManufacturer\" : \"Bigscreen\",\r\n \"HMDModel\" : \"Beyond\",\r\n \"HMDSerialNumber\" : \"LHR-599F3B91\"\r\n },\r\n \"TrackingOverrides\" : {\r\n \"/devices/nodof/PNP0000\" : \"/user/head\"\r\n },\r\n \"audio\" : {\r\n \"enablePlaybackDeviceOverride_Bigscreen_Beyond\" : true,\r\n \"enablePlaybackMirrorIndependentVolume\" : true,\r\n \"lastHmdPlaybackDeviceId_Valve_Index\" : \"{0.0.0.00000000}.{a30261a7-0352-4316-b758-d2bba1032b0b}\",\r\n \"playbackDeviceOverrideName_Bigscreen_Beyond\" : \"Speakers (Beyond Audio Strap)\",\r\n \"playbackDeviceOverride_Bigscreen_Beyond\" : \"{0.0.0.00000000}.{2c9ca4c4-890b-4e0a-a094-8881b91cc56d}\",\r\n \"playbackMirrorDeviceName\" : \"Speakers (Realtek(R) Audio)\"\r\n },\r\n \"collisionBounds\" : {\r\n \"CollisionBoundsFadeDistance\" : 0.40000000596046448,\r\n \"CollisionBoundsStyle\" : 4\r\n },\r\n \"dashboard\" : {\r\n \"autoShowGameTheater\" : false,\r\n \"desktopScale1\" : 1,\r\n \"desktopScale2\" : 1,\r\n \"lastAccessedExternalOverlayKey\" : \"elvissteinjr.DesktopPlusDashboard\",\r\n \"showEscapeHatch\" : true,\r\n \"showPowerOptions\" : false\r\n },\r\n \"direct_mode\" : {\r\n \"count\" : 4,\r\n \"edidPid3\" : 4660,\r\n \"edidVid3\" : 9993\r\n },\r\n \"driver_00vrinputemulator\" : {\r\n \"enable\" : false\r\n },\r\n \"driver_BeyondSteamVR\" : {\r\n \"ipd_mm\" : 62.000003814697266\r\n },\r\n \"driver_VirtualDesktop\" : 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{"parentUuid":"3511d950-d1b5-4979-856c-1270916bbfd1","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_014AnjDiqvaKRvT6oXP9en48","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UjJXtfW3U8wojzYYasxFdC","name":"Write","input":{"file_path":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\FINDINGS.md","content":"# Spike S3 — FINDINGS: distortion ground truth\n\n**Status: PASS, 2026-06-09.** Standalone evaluator matches OpenVR `ComputeDistortion`\nto **0.0002 px max error** (float32 noise floor) across 65×65 grid × 3 channels × 2 eyes\non unit LHR-599F3B91 (enlyzeam rig). Criterion was sub-pixel; beaten by ~4 orders.\n\n## The verified formula (per eye, per color channel)\n\nInput: panel/viewport UV `(u,v)`, origin top-left of the eye's viewport (OpenVR\n`ComputeDistortion` convention). Output: source UV into the eye's render target.\n\n```\nt = (2u−1, 2v−1) # per-eye NDC, y down\nt -= (center_x, center_y) # shared across channels within an eye\nr² = t·t # NO clamp (see below)\nd = 1 + k1·r² + k2·r⁴ + k3·r⁶ # DIRECT polynomial — NOT reciprocal\nuv_src = 0.5 + (t·d + center) · 0.5/(1 + grow_for_undistort)\n```\n\n`(k1,k2,k3)` = the `coeffs` of `distortion_red` / `distortion` / `distortion_blue`\nverbatim from the device config JSON. Implementation: `poly3.py` (hypothesis\n`poly+grow` in `verify.py`).\n\n### Answers to the open questions\n\n1. **POLY3 direction:** production `DISTORT_POLY3` (positive leading coeffs) is the\n **direct** polynomial `d = 1 + k1·r² + …` applied panel→source. The Monado/OpenHMD\n Vive evaluator's reciprocal form `d = 1/(1+…)` is **wrong for POLY3** — it loses by\n 2578 px max. The historical EVT/DVT `DISTORT_DPOLY3` negative coeffs are explained:\n a reciprocal re-fit of the dump lands near −0.21 leading (vs +0.209 config), i.e.\n DPOLY3(−k) ≈ 1/POLY3(+k) — same optics, opposite fit direction. Only POLY3 is\n validated here; all production Beyonds are POLY3, so DPOLY3 handling is moot.\n2. **`grow_for_undistort` normalization:** exactly `0.5/(1+grow)` on the output, exactly\n as Monado does it. Confirmed numerically: fitted scale/offset reproduce\n `0.31250 = 0.5/1.6` and `0.5 + center·0.3125` to 5 decimals.\n3. **`undistort_r2_cutoff` is NOT applied** by `ComputeDistortion`: panel corners reach\n r² ≈ 2.1 > cutoff 1.0 and still follow the polynomial exactly. Clamping at the cutoff\n was the sole reason the otherwise-correct hypothesis initially failed (1395 px corner\n error). The cutoff presumably gates something else (mesh/hidden-area). Our warp must\n not clamp within the panel.\n\n## Bonus ground truth captured (load-bearing for M2)\n\n- **`GetEyeToHeadTransform` = identity rotation + (∓31 mm, 0, 0)** — the lighthouse\n driver does NOT expose the config `eye_to_head` canting rotation. Config has\n `parallel_render_cameras: true`: SteamVR renders parallel cameras and folds the\n ±6.17° yaw / ~−5° pitch cant into the **asymmetric projection** instead.\n- **`GetProjectionRaw` ≠ intrinsics frustum.** Dump (left eye): L −1.400 R +0.913\n T −1.380 B +0.923 vs intrinsics-derived ±0.70. The raw projection = grow-expanded,\n cant-folded window; the `uv_src` formula above lands in exactly this window\n (`tan = L + u_src·(R−L)`, y-down, v=0 ↦ T). M2 must either replicate this fold\n (derive closed form config → frustum; the dump + fitted per-eye offset/scale\n `a=(−0.2505,−0.2090)L /(+0.2492,−0.1866)R`, `s=(0.7228,0.7197)L /(0.7208,0.7132)R`\n are the ground truth to derive against) or render genuinely canted cameras with its\n own consistent warp math — NOT mix the two.\n- **IPD translation comes from a user setting, not the config:** dump says 62.0 mm,\n config `ipd.default_mm` = 69. Source: `steamvr.vrsettings →\n driver_BeyondSteamVR.ipd_mm = 62.000003814697266` (Bigscreen's SteamVR driver\n utility setting; `steamvr.ipdOffset` ≈ 0 here). v1: default to config\n `ipd.default_mm`, expose a user override exactly like Bigscreen does.\n- **Intrinsics sign convention settled:** distortion `center_x = −K[0][2]`,\n `center_y = +K[1][2]` (confirmed on both eyes of this unit; matches the B1\n production template pattern).\n- `steamvr.vrsettings` also confirms the direct-mode EDID whitelist entry\n (`direct_mode: edidVid3 9993 = 0x2709, edidPid3 4660 = 0x1234`) and that users can\n carry per-channel color-gain overrides (`hmdDisplayColorGain* = 0.8/0.85/1.2` on this\n rig — remember when judging eyes-in color).\n\n## Method\n\n1. `dump_openvr.py` — pyopenvr `VRApplication_Background` against a running SteamVR\n with the Beyond connected (the one sanctioned dev-time SteamVR run). 65×65 UV grid\n per eye, all 4225 points ok both eyes; plus `GetProjectionRaw`,\n `GetEyeToHeadTransform`, recommended RT (3104×3104 @ supersampleScale 0.76).\n2. `verify.py` — scores 4 closed-form hypotheses {poly, recip} × {grow rescale on/off}\n in panel pixels (2544/eye). Result: poly+grow 0.0002 px; next best 1795 px. PASS.\n3. `analyze.py` — the step that found the formula after all 4 original hypotheses\n failed: 9-param free fit (center, offset, scale, k1..3) per eye/channel in\n `GetProjectionRaw` tangent space. Direct-poly fit converged to **0.000 px with\n fitted center and coeffs equal to the config values to 5 decimals** — proving the\n config values are used verbatim and isolating the normalization algebra, which then\n reduced exactly to the formula above.\n4. Ops note: OpenVR IPC refuses connections from SSH logon sessions. Workaround that\n works: `schtasks /create … /it` + `schtasks /run` to execute the dump inside the\n interactive session (see git history for the exact incantation).\n\nArtifacts in this directory: `distortion_dump_lhr_599f3b91.json` (ground truth, 1.4 MB),\n`config_lhr_599f3b91.json` (unit config snapshot), `poly3.py` (the evaluator to port to\nthe compositor shader in M2), `verify.py`, `analyze.py`, `dump_openvr.py`.\n\n## Eyes-in follow-up (pending)\n\nNumeric criterion passed. Visual close-out planned: bake the winning warp into the S2\npresenter path and eyeball a structure-revealing grid through the lens on enlyzeam\n(straight lines must look straight). 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{"parentUuid":"4909d3e0-d642-432e-b4bf-1db8b41020f0","isSidechain":false,"promptId":"b79efa4d-9c11-4f46-975f-363733b93c3f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UjJXtfW3U8wojzYYasxFdC","type":"tool_result","content":"File created successfully at: C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\FINDINGS.md (file state is current in your context — no need to Read it back)"}]},"uuid":"9b5323f8-bde6-4627-8067-4e8712423936","timestamp":"2026-06-10T05:37:01.163Z","toolUseResult":{"type":"create","filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\FINDINGS.md","content":"# Spike S3 — FINDINGS: distortion ground truth\n\n**Status: PASS, 2026-06-09.** Standalone evaluator matches OpenVR `ComputeDistortion`\nto **0.0002 px max error** (float32 noise floor) across 65×65 grid × 3 channels × 2 eyes\non unit LHR-599F3B91 (enlyzeam rig). Criterion was sub-pixel; beaten by ~4 orders.\n\n## The verified formula (per eye, per color channel)\n\nInput: panel/viewport UV `(u,v)`, origin top-left of the eye's viewport (OpenVR\n`ComputeDistortion` convention). Output: source UV into the eye's render target.\n\n```\nt = (2u−1, 2v−1) # per-eye NDC, y down\nt -= (center_x, center_y) # shared across channels within an eye\nr² = t·t # NO clamp (see below)\nd = 1 + k1·r² + k2·r⁴ + k3·r⁶ # DIRECT polynomial — NOT reciprocal\nuv_src = 0.5 + (t·d + center) · 0.5/(1 + grow_for_undistort)\n```\n\n`(k1,k2,k3)` = the `coeffs` of `distortion_red` / `distortion` / `distortion_blue`\nverbatim from the device config JSON. Implementation: `poly3.py` (hypothesis\n`poly+grow` in `verify.py`).\n\n### Answers to the open questions\n\n1. **POLY3 direction:** production `DISTORT_POLY3` (positive leading coeffs) is the\n **direct** polynomial `d = 1 + k1·r² + …` applied panel→source. The Monado/OpenHMD\n Vive evaluator's reciprocal form `d = 1/(1+…)` is **wrong for POLY3** — it loses by\n 2578 px max. The historical EVT/DVT `DISTORT_DPOLY3` negative coeffs are explained:\n a reciprocal re-fit of the dump lands near −0.21 leading (vs +0.209 config), i.e.\n DPOLY3(−k) ≈ 1/POLY3(+k) — same optics, opposite fit direction. Only POLY3 is\n validated here; all production Beyonds are POLY3, so DPOLY3 handling is moot.\n2. **`grow_for_undistort` normalization:** exactly `0.5/(1+grow)` on the output, exactly\n as Monado does it. Confirmed numerically: fitted scale/offset reproduce\n `0.31250 = 0.5/1.6` and `0.5 + center·0.3125` to 5 decimals.\n3. **`undistort_r2_cutoff` is NOT applied** by `ComputeDistortion`: panel corners reach\n r² ≈ 2.1 > cutoff 1.0 and still follow the polynomial exactly. Clamping at the cutoff\n was the sole reason the otherwise-correct hypothesis initially failed (1395 px corner\n error). The cutoff presumably gates something else (mesh/hidden-area). Our warp must\n not clamp within the panel.\n\n## Bonus ground truth captured (load-bearing for M2)\n\n- **`GetEyeToHeadTransform` = identity rotation + (∓31 mm, 0, 0)** — the lighthouse\n driver does NOT expose the config `eye_to_head` canting rotation. Config has\n `parallel_render_cameras: true`: SteamVR renders parallel cameras and folds the\n ±6.17° yaw / ~−5° pitch cant into the **asymmetric projection** instead.\n- **`GetProjectionRaw` ≠ intrinsics frustum.** Dump (left eye): L −1.400 R +0.913\n T −1.380 B +0.923 vs intrinsics-derived ±0.70. The raw projection = grow-expanded,\n cant-folded window; the `uv_src` formula above lands in exactly this window\n (`tan = L + u_src·(R−L)`, y-down, v=0 ↦ T). M2 must either replicate this fold\n (derive closed form config → frustum; the dump + fitted per-eye offset/scale\n `a=(−0.2505,−0.2090)L /(+0.2492,−0.1866)R`, `s=(0.7228,0.7197)L /(0.7208,0.7132)R`\n are the ground truth to derive against) or render genuinely canted cameras with its\n own consistent warp math — NOT mix the two.\n- **IPD translation comes from a user setting, not the config:** dump says 62.0 mm,\n config `ipd.default_mm` = 69. Source: `steamvr.vrsettings →\n driver_BeyondSteamVR.ipd_mm = 62.000003814697266` (Bigscreen's SteamVR driver\n utility setting; `steamvr.ipdOffset` ≈ 0 here). v1: default to config\n `ipd.default_mm`, expose a user override exactly like Bigscreen does.\n- **Intrinsics sign convention settled:** distortion `center_x = −K[0][2]`,\n `center_y = +K[1][2]` (confirmed on both eyes of this unit; matches the B1\n production template pattern).\n- `steamvr.vrsettings` also confirms the direct-mode EDID whitelist entry\n (`direct_mode: edidVid3 9993 = 0x2709, edidPid3 4660 = 0x1234`) and that users can\n carry per-channel color-gain overrides (`hmdDisplayColorGain* = 0.8/0.85/1.2` on this\n rig — remember when judging eyes-in color).\n\n## Method\n\n1. `dump_openvr.py` — pyopenvr `VRApplication_Background` against a running SteamVR\n with the Beyond connected (the one sanctioned dev-time SteamVR run). 65×65 UV grid\n per eye, all 4225 points ok both eyes; plus `GetProjectionRaw`,\n `GetEyeToHeadTransform`, recommended RT (3104×3104 @ supersampleScale 0.76).\n2. `verify.py` — scores 4 closed-form hypotheses {poly, recip} × {grow rescale on/off}\n in panel pixels (2544/eye). Result: poly+grow 0.0002 px; next best 1795 px. PASS.\n3. `analyze.py` — the step that found the formula after all 4 original hypotheses\n failed: 9-param free fit (center, offset, scale, k1..3) per eye/channel in\n `GetProjectionRaw` tangent space. Direct-poly fit converged to **0.000 px with\n fitted center and coeffs equal to the config values to 5 decimals** — proving the\n config values are used verbatim and isolating the normalization algebra, which then\n reduced exactly to the formula above.\n4. Ops note: OpenVR IPC refuses connections from SSH logon sessions. Workaround that\n works: `schtasks /create … /it` + `schtasks /run` to execute the dump inside the\n interactive session (see git history for the exact incantation).\n\nArtifacts in this directory: `distortion_dump_lhr_599f3b91.json` (ground truth, 1.4 MB),\n`config_lhr_599f3b91.json` (unit config snapshot), `poly3.py` (the evaluator to port to\nthe compositor shader in M2), `verify.py`, `analyze.py`, `dump_openvr.py`.\n\n## Eyes-in follow-up (pending)\n\nNumeric criterion passed. Visual close-out planned: bake the winning warp into the S2\npresenter path and eyeball a structure-revealing grid through the lens on enlyzeam\n(straight lines must look straight). 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(file state is current in your context — no need to Read it back)"}]},"uuid":"82d13ea6-23ac-47e2-8930-355df68acb12","timestamp":"2026-06-10T05:37:12.572Z","toolUseResult":{"filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\PLAN.md","oldString":"| **S3** | Which direction do the production POLY3 coeffs run? | Standalone evaluator matches an OpenVR `ComputeDistortion` UV-grid dump from one dev unit (SteamVR run once, dev-time only) within sub-pixel tolerance. |","newString":"| **S3** ✅ PASS 2026-06-09 | Which direction do the production POLY3 coeffs run? | PASS — `DISTORT_POLY3` is the **direct** polynomial `d = 1+k1r²+k2r⁴+k3r⁶` (not Monado's reciprocal), output rescale `0.5/(1+grow)`, **no r² clamp** (`undistort_r2_cutoff` unused by `ComputeDistortion`). Evaluator matches ground truth to 0.0002 px max (65×65×3ch×2 eyes, unit LHR-599F3B91). Bonus: driver folds canting into asymmetric `GetProjectionRaw` (eye_to_head reported identity; `parallel_render_cameras: true`), IPD translation = user setting not config default. See `spikes/s3-distortion-truth/FINDINGS.md`. Eyes-in warp check pending (pre-M1). |","originalFile":"# Sauna — v1 Plan\n\nOutput of the design interview closed 2026-06-09. Language: `CONTEXT.md`. Facts and risk\nregister: `reference/BASELINE.md`. Architecture decision: `docs/adr/0001`.\n\n## v1 in one sentence\n\nA standalone Windows+NVIDIA app that reads the Tundra IMU for 3DOF pose, captures the desktop's\nmonitors, composites them as head-tracked virtual screens, warps per-eye using the per-unit\ncalibration, and presents to the Beyond in Vulkan direct mode — no SteamVR, no lighthouses, no\nfirmware changes.\n\n## Locked decisions\n\n| # | Decision | Notes |\n|---|---|---|\n| 1 | v1 experience = virtual screen(s) via our own compositor, direct mode on stock EDID | ADR-0001 (proposed; gated on spike S2) |\n| 2 | Windows+NVIDIA first, present-backend seam | *(amended by S2)* Windows backend = D3D12+NVAPI DirectMode; `VK_EXT_acquire_drm_display` = Steam Deck rung later; Vulkan render core still viable via shared-handle interop |\n| 3 | Per-unit calibration via native Watchman HID config read | port from vendored libsurvive; JSON file-import kept as dev scaffold |\n| 4 | Own minimal AHRS (Mahony-style) for 3DOF | config IMU mount frame + biases; recenter UX owns yaw drift; magnetometer = bonus if S1 finds one |\n| 5 | Content = multi-monitor desktop capture | Windows.Graphics.Capture; layout mirrors OS monitor arrangement; PipeWire backend later |\n| 6 | Frame-source abstraction from day one | capture / remote-stream / test-pattern; only capture implemented in v1 — keeps the streaming-bridge stretch additive |\n\nUX defaults: proximity-sensor display sleep when doffed + idle timeout (burn-in safety is ours\nonce SteamVR is out of the loop); conservative persistence + brightness defaults; 75 Hz default\nwith 90 Hz opt-in flag; recenter on a configurable global hotkey; settings = plain desktop window\n(visible in-headset via capture).\n\n## Architecture (components)\n\n- **Device layer** — Tundra HID `28DE:2300` MI_00 (IMU stream + config read); MCU HID `35BD:0101`\n (display power, per-eye panel-on, persistence, brightness, proximity, EDID/refresh).\n- **Tracking** — Watchman packet parse → bias/scale correction → AHRS → head pose; recenter.\n- **Frame sources** — trait per decision 6; v1 backend: Windows.Graphics.Capture per monitor.\n- **Compositor** — scene of virtual screens (OS layout); per-eye render from `intrinsics`,\n `eye_to_head` canting, IPD; distortion pass (per-channel poly3 + `grow_for_undistort`);\n `display_color_mult`.\n- **Present** — per-platform backend behind one interface: Windows = D3D12 + NVAPI DirectMode\n (proven, S2); Linux = Vulkan + DRM lease (unproven rung). Fallback: extended-mode SBS fullscreen.\n- **App shell** — tray + settings window, hotkeys, safety policy.\n\n## Spike ladder (run before architecture code; in this order)\n\n| Spike | Question | Pass criterion |\n|---|---|---|\n| **S1** ✅ PASS 2026-06-10 | Does the Tundra stream IMU with Steam never run? | Streams unconditionally — 993.8 Hz unique samples, no wake needed, SteamVR never ran since boot. 4096 LSB/g re-confirmed; sliding 3-sample window (dedupe by timecode+seq); no mag on MI_00; gyro scale still unverified (→ M2). See `spikes/s1-imu-no-steam/FINDINGS.md`. |\n| **S2** ✅ PASS 2026-06-10 | Can our own process acquire + continuously present in direct mode? | PASS via **NVAPI/D3D12** (75.07 fps native 5088×2544, zero errors; 10-min soak logged). Vulkan winrt rung DEAD on consumer Windows — specialized displays edition-gated. Windows backend = D3D12+NVAPI DM (NDA lib, referenced not vendored); Linux rung unchanged (VK DRM). See `spikes/s2-vulkan-direct/FINDINGS.md`. |\n| **S3** | Which direction do the production POLY3 coeffs run? | Standalone evaluator matches an OpenVR `ComputeDistortion` UV-grid dump from one dev unit (SteamVR run once, dev-time only) within sub-pixel tolerance. |\n\n## Build order after spikes\n\n1. **M1 first light** — S1+S2 glued: head-locked test pattern in-headset from a cold boot.\n2. **M2 tracked + warped** — AHRS pose + distortion/canting/IPD pipeline: one virtual screen\n fixed in space, fusion-correct binocular view.\n3. **M3 product shape** — multi-monitor capture + OS layout, recenter, prox/idle safety,\n settings UI, native calibration read replacing dev import.\n4. **M4 hardening** — pacing (capture Hz vs panel Hz), color correctness, install story.\n\n## Out of scope for v1\n\nSteamVR driver mode; Linux/Steam Deck port (first post-v1 target); streaming bridge (seam only);\n6DOF; linkbox-less (hardware adapter, separate track); IddCx virtual display driver.\n","structuredPatch":[{"oldStart":45,"oldLines":7,"newStart":45,"newLines":7,"lines":[" |---|---|---|"," | **S1** ✅ PASS 2026-06-10 | Does the Tundra stream IMU with Steam never run? | Streams unconditionally — 993.8 Hz unique samples, no wake needed, SteamVR never ran since boot. 4096 LSB/g re-confirmed; sliding 3-sample window (dedupe by timecode+seq); no mag on MI_00; gyro scale still unverified (→ M2). See `spikes/s1-imu-no-steam/FINDINGS.md`. |"," | **S2** ✅ PASS 2026-06-10 | Can our own process acquire + continuously present in direct mode? | PASS via **NVAPI/D3D12** (75.07 fps native 5088×2544, zero errors; 10-min soak logged). Vulkan winrt rung DEAD on consumer Windows — specialized displays edition-gated. Windows backend = D3D12+NVAPI DM (NDA lib, referenced not vendored); Linux rung unchanged (VK DRM). See `spikes/s2-vulkan-direct/FINDINGS.md`. |","-| **S3** | Which direction do the production POLY3 coeffs run? | Standalone evaluator matches an OpenVR `ComputeDistortion` UV-grid dump from one dev unit (SteamVR run once, dev-time only) within sub-pixel tolerance. |","+| **S3** ✅ PASS 2026-06-09 | Which direction do the production POLY3 coeffs run? | PASS — `DISTORT_POLY3` is the **direct** polynomial `d = 1+k1r²+k2r⁴+k3r⁶` (not Monado's reciprocal), output rescale `0.5/(1+grow)`, **no r² clamp** (`undistort_r2_cutoff` unused by `ComputeDistortion`). Evaluator matches ground truth to 0.0002 px max (65×65×3ch×2 eyes, unit LHR-599F3B91). Bonus: driver folds canting into asymmetric `GetProjectionRaw` (eye_to_head reported identity; `parallel_render_cameras: true`), IPD translation = user setting not config default. See `spikes/s3-distortion-truth/FINDINGS.md`. Eyes-in warp check pending (pre-M1). |"," "," ## Build order after spikes"," "]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"2af31e2f-6b5d-4271-a4bf-587ab95d97b4","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"dab2d439-565d-4d82-876f-5edc87da96b9","isSidechain":false,"promptId":"b79efa4d-9c11-4f46-975f-363733b93c3f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BaHdApYkJV7EG8a5hDeuQs","type":"tool_result","content":"1\t// Spike S2, NVAPI rung: own-process DirectMode presenter for the Beyond.\n2\t// Ported from the first-party mmi_test directmode sample (BigscreenVR/mmi_test).\n3\t// Requires the NDA HMD NVAPI headers/lib from a local mmi_test checkout —\n4\t// referenced in place, NOT vendored into this repo (licensing).\n5\t//\n6\t// s2_nvapi present [--force-dsc]\n7\t//\n8\t// Exit codes: 0 ok, 2 no DM display, 3 acquire/modeset failed, 10 other.\n9\t\n10\t#include \n11\t\n12\t#include \n13\t#include \n14\t#include \n15\t\n16\t#include \n17\t\n18\t#include \n19\t#include \n20\t#include \n21\t#include \n22\t#include \n23\t\n24\tusing Microsoft::WRL::ComPtr;\n25\t\n26\tstatic const NvU32 VENDOR_ID = 0x2709; // EDID \"BIG\"\n27\t\n28\t#define NVCHECK(call) \\\n29\t do { \\\n30\t NvAPI_Status s_ = (call); \\\n31\t if (s_ != NVAPI_OK) { \\\n32\t fprintf(stderr, \"FATAL %s = 0x%x (line %d)\\n\", #call, s_, __LINE__); \\\n33\t return 10; \\\n34\t } \\\n35\t } while (0)\n36\t\n37\t#define HRCHECK(call) \\\n38\t do { \\\n39\t HRESULT h_ = (call); \\\n40\t if (FAILED(h_)) { \\\n41\t fprintf(stderr, \"FATAL %s = 0x%lx (line %d)\\n\", #call, h_, __LINE__); \\\n42\t return 10; \\\n43\t } \\\n44\t } while (0)\n45\t\n46\tint main(int argc, char** argv) {\n47\t setbuf(stdout, nullptr);\n48\t setbuf(stderr, nullptr);\n49\t double seconds = argc > 2 ? atof(argv[2]) : 15.0;\n50\t // Defaults = the visually-validated config (2026-06-10, both rigs):\n51\t // X8R8G8B8 mode + forced DSC 1.1/4-slice/8bpp per the device JSON.\n52\t bool forceDsc = true, powerOff = false;\n53\t int wantMode = -1;\n54\t for (int i = 1; i < argc; i++) {\n55\t if (strcmp(argv[i], \"--force-dsc\") == 0) forceDsc = true;\n56\t if (strcmp(argv[i], \"--no-force-dsc\") == 0) forceDsc = false;\n57\t if (strcmp(argv[i], \"--power-off\") == 0) powerOff = true;\n58\t if (strcmp(argv[i], \"--mode\") == 0 && i + 1 < argc) wantMode = atoi(argv[i + 1]);\n59\t }\n60\t\n61\t NVCHECK(NvAPI_Initialize());\n62\t NvAPI_Status st = NvAPI_DISP_EnableDirectMode(VENDOR_ID, 0);\n63\t printf(\"EnableDirectMode(0x%x) = 0x%x\\n\", VENDOR_ID, st);\n64\t\n65\t // --- enumerate DM displays ---\n66\t NvU32 numDisplays = 0;\n67\t st = NvAPI_DISP_EnumerateDirectModeDisplays(VENDOR_ID, &numDisplays, nullptr,\n68\t NV_ENUM_DIRECTMODE_DISPLAY_ENABLED);\n69\t printf(\"EnumerateDirectModeDisplays count = %u (status 0x%x)\\n\", numDisplays, st);\n70\t if (numDisplays == 0) { printf(\"RESULT: no DM display\\n\"); return 2; }\n71\t std::vector displays(numDisplays);\n72\t NVCHECK(NvAPI_DISP_EnumerateDirectModeDisplays(VENDOR_ID, &numDisplays,\n73\t displays.data(),\n74\t NV_ENUM_DIRECTMODE_DISPLAY_ENABLED));\n75\t NV_DIRECT_MODE_DISPLAY_HANDLE display = displays[0];\n76\t printf(\"using displayId 0x%x\\n\", display.displayId);\n77\t\n78\t // --- list modes ---\n79\t NvU32 modeCount = 0;\n80\t NVCHECK(NvAPI_D3D_DirectModeGetDisplayModes(&display, &modeCount, nullptr,\n81\t NV_DIRECTMODE_GETMODES_FLAG_SUPPORTED));\n82\t std::vector modes(modeCount);\n83\t for (auto& m : modes) { memset(&m, 0, sizeof(m)); m.version = NV_DIRECT_MODE_INFO_VER; }\n84\t NVCHECK(NvAPI_D3D_DirectModeGetDisplayModes(&display, &modeCount, modes.data(),\n85\t NV_DIRECTMODE_GETMODES_FLAG_SUPPORTED));\n86\t int best = -1;\n87\t double bestScore = -1;\n88\t for (NvU32 i = 0; i < modeCount; i++) {\n89\t auto& m = modes[i];\n90\t double hz = m.refresh.denominator\n91\t ? (double)m.refresh.numerator / m.refresh.denominator\n92\t : 0;\n93\t printf(\"mode[%u]: %ux%u @ %.3f Hz format=%d dscMode=%d dscVer=%d slices=%d bppx16=%u\\n\",\n94\t i, m.width, m.height, hz, (int)m.format, (int)m.dscParams.dscMode,\n95\t (int)m.dscParams.dscVersion, (int)m.dscParams.sliceCount,\n96\t m.dscParams.outputBPPx16);\n97\t double score = (double)m.width * m.height - fabs(hz - 75.0) * 1e4;\n98\t if (m.format == 22) score += 1; // prefer X8R8G8B8 — the validated format\n99\t if (score > bestScore) { bestScore = score; best = (int)i; }\n100\t }\n101\t if (wantMode >= 0 && wantMode < (int)modeCount) best = wantMode;\n102\t NV_DIRECT_MODE_INFO mode = modes[best];\n103\t double modeHz = mode.refresh.denominator\n104\t ? (double)mode.refresh.numerator / mode.refresh.denominator\n105\t : 0;\n106\t printf(\"using mode[%d]: %ux%u @ %.3f Hz\\n\", best, mode.width, mode.height, modeHz);\n107\t if (forceDsc) {\n108\t mode.dscParams.dscMode = NV_DIRECT_MODE_DSC_MODE_FORCE_ENABLED;\n109\t mode.dscParams.dscVersion = NV_DIRECT_MODE_DSC_VERSION_V11;\n110\t mode.dscParams.sliceCount = NV_DIRECT_MODE_DSC_SLICE_COUNT_4;\n111\t mode.dscParams.outputBPPx16 = 128;\n112\t printf(\"forcing DSC 1.1, 4 slices, 8 bpp\\n\");\n113\t }\n114\t\n115\t // --- D3D12 device on the display's adapter ---\n116\t NvPhysicalGpuHandle gpu;\n117\t NVCHECK(NvAPI_SYS_GetPhysicalGpuFromDisplayId(display.displayId, &gpu));\n118\t ComPtr adapter;\n119\t NVCHECK(NvAPI_D3D_GetIDXGIAdapter(gpu, adapter.GetAddressOf()));\n120\t ComPtr dev;\n121\t HRCHECK(D3D12CreateDevice(adapter.Get(), D3D_FEATURE_LEVEL_11_0,\n122\t IID_PPV_ARGS(&dev)));\n123\t D3D12_COMMAND_QUEUE_DESC qd{};\n124\t qd.Type = D3D12_COMMAND_LIST_TYPE_DIRECT;\n125\t ComPtr queue;\n126\t HRCHECK(dev->CreateCommandQueue(&qd, IID_PPV_ARGS(&queue)));\n127\t ComPtr alloc;\n128\t HRCHECK(dev->CreateCommandAllocator(D3D12_COMMAND_LIST_TYPE_DIRECT,\n129\t IID_PPV_ARGS(&alloc)));\n130\t ComPtr list;\n131\t HRCHECK(dev->CreateCommandList(0, D3D12_COMMAND_LIST_TYPE_DIRECT, alloc.Get(),\n132\t nullptr, IID_PPV_ARGS(&list)));\n133\t HRCHECK(list->Close());\n134\t ComPtr fence;\n135\t HRCHECK(dev->CreateFence(0, D3D12_FENCE_FLAG_NONE, IID_PPV_ARGS(&fence)));\n136\t HANDLE fenceEvt = CreateEventA(nullptr, FALSE, FALSE, nullptr);\n137\t UINT64 fenceVal = 0;\n138\t\n139\t // --- acquire ---\n140\t st = NvAPI_D3D_AcquireDirectModeDisplay(VENDOR_ID, dev.Get(), &display);\n141\t printf(\"AcquireDirectModeDisplay = 0x%x%s\\n\", st,\n142\t st == NVAPI_HDCP_DISABLED ? \" (HDCP disabled, continuing)\" : \"\");\n143\t if (st != NVAPI_OK && st != NVAPI_HDCP_DISABLED) return 3;\n144\t\n145\t // --- surfaces ---\n146\t const int NBUF = 2;\n147\t NV_DIRECT_MODE_SURFACE_HANDLE surf[NBUF];\n148\t HANDLE shared[NBUF];\n149\t ComPtr res[NBUF];\n150\t for (int i = 0; i < NBUF; i++) {\n151\t st = NvAPI_D3D_DirectModeCreateSurface(&display, &mode, &surf[i], &shared[i]);\n152\t if (st != NVAPI_OK) { fprintf(stderr, \"CreateSurface[%d] = 0x%x\\n\", i, st); return 3; }\n153\t HRCHECK(dev->OpenSharedHandle(shared[i], IID_PPV_ARGS(&res[i])));\n154\t }\n155\t auto rdesc = res[0]->GetDesc();\n156\t printf(\"surface: %llux%u format=%d\\n\", (unsigned long long)rdesc.Width,\n157\t rdesc.Height, (int)rdesc.Format);\n158\t // DM surfaces come back TYPELESS — an RTV needs an explicit typed format\n159\t // (nullptr desc on a typeless resource silently fails in release builds:\n160\t // clears never land and the panels scan out uninitialized VRAM).\n161\t DXGI_FORMAT rtvFmt;\n162\t switch (rdesc.Format) {\n163\t case DXGI_FORMAT_B8G8R8A8_TYPELESS: rtvFmt = DXGI_FORMAT_B8G8R8A8_UNORM; break;\n164\t case DXGI_FORMAT_B8G8R8X8_TYPELESS: rtvFmt = DXGI_FORMAT_B8G8R8X8_UNORM; break;\n165\t case DXGI_FORMAT_R8G8B8A8_TYPELESS: rtvFmt = DXGI_FORMAT_R8G8B8A8_UNORM; break;\n166\t case DXGI_FORMAT_R10G10B10A2_TYPELESS: rtvFmt = DXGI_FORMAT_R10G10B10A2_UNORM; break;\n167\t case DXGI_FORMAT_R16G16B16A16_TYPELESS: rtvFmt = DXGI_FORMAT_R16G16B16A16_FLOAT; break;\n168\t default:\n169\t rtvFmt = rdesc.Format;\n170\t fprintf(stderr, \"WARNING: surface format %d has no typed mapping — \"\n171\t \"RTV will be invalid if it is typeless\\n\", (int)rdesc.Format);\n172\t break;\n173\t }\n174\t printf(\"rtv format: %d\\n\", (int)rtvFmt);\n175\t\n176\t ComPtr rtvHeap;\n177\t D3D12_DESCRIPTOR_HEAP_DESC hd{};\n178\t hd.Type = D3D12_DESCRIPTOR_HEAP_TYPE_RTV;\n179\t hd.NumDescriptors = NBUF;\n180\t HRCHECK(dev->CreateDescriptorHeap(&hd, IID_PPV_ARGS(&rtvHeap)));\n181\t UINT rtvStep = dev->GetDescriptorHandleIncrementSize(D3D12_DESCRIPTOR_HEAP_TYPE_RTV);\n182\t D3D12_CPU_DESCRIPTOR_HANDLE rtv[NBUF];\n183\t for (int i = 0; i < NBUF; i++) {\n184\t rtv[i] = rtvHeap->GetCPUDescriptorHandleForHeapStart();\n185\t rtv[i].ptr += (SIZE_T)i * rtvStep;\n186\t D3D12_RENDER_TARGET_VIEW_DESC rd{};\n187\t rd.Format = rtvFmt;\n188\t rd.ViewDimension = D3D12_RTV_DIMENSION_TEXTURE2D;\n189\t rd.Texture2D.MipSlice = 0;\n190\t rd.Texture2D.PlaneSlice = 0;\n191\t dev->CreateRenderTargetView(res[i].Get(), &rd, rtv[i]);\n192\t }\n193\t\n194\t // --- modeset + power on ---\n195\t st = NvAPI_D3D_DirectModeSetDisplayMode(&display, &mode);\n196\t if (st == NVAPI_HDCP_DISABLED) { printf(\"SetDisplayMode: HDCP disabled, ok\\n\"); st = NVAPI_OK; }\n197\t printf(\"SetDisplayMode = 0x%x\\n\", st);\n198\t if (st != NVAPI_OK) return 3;\n199\t st = NvAPI_DISP_DirectModeDisplayControl(&display, NV_DM_DISPLAY_CONTROL_POWER_ON);\n200\t printf(\"DisplayControl(POWER_ON) = 0x%x\\n\", st);\n201\t\n202\t HANDLE waitEvt = NULL;\n203\t st = NvAPI_DISP_DirectModeGetPresentWaitableObject(\n204\t &display, reinterpret_cast(&waitEvt));\n205\t bool haveWaitable = st == NVAPI_OK;\n206\t printf(\"GetPresentWaitableObject = 0x%x\\n\", st);\n207\t\n208\t // --- present loop: animated dim hue clear ---\n209\t auto t0 = std::chrono::steady_clock::now();\n210\t uint64_t frames = 0, presentErrors = 0, waitTimeouts = 0;\n211\t while (true) {\n212\t double t = std::chrono::duration(std::chrono::steady_clock::now() - t0).count();\n213\t if (t >= seconds) break;\n214\t int i = (int)(frames % NBUF);\n215\t\n216\t HRCHECK(alloc->Reset());\n217\t HRCHECK(list->Reset(alloc.Get(), nullptr));\n218\t D3D12_RESOURCE_BARRIER b{};\n219\t b.Type = D3D12_RESOURCE_BARRIER_TYPE_TRANSITION;\n220\t b.Transition.pResource = res[i].Get();\n221\t b.Transition.Subresource = D3D12_RESOURCE_BARRIER_ALL_SUBRESOURCES;\n222\t b.Transition.StateBefore = D3D12_RESOURCE_STATE_COMMON;\n223\t b.Transition.StateAfter = D3D12_RESOURCE_STATE_RENDER_TARGET;\n224\t list->ResourceBarrier(1, &b);\n225\t // structure-revealing pattern: left eye green field, right eye red field,\n226\t // synchronized white bar sweeping across each eye. Any DSC/packing fault\n227\t // shows immediately as wrong colors per eye or a broken/duplicated bar.\n228\t {\n229\t LONG W = (LONG)rdesc.Width, H = (LONG)rdesc.Height, half = W / 2;\n230\t float lc[4] = {0.0f, 0.25f, 0.08f, 1.0f};\n231\t float rc[4] = {0.25f, 0.0f, 0.08f, 1.0f};\n232\t float wc[4] = {0.4f, 0.4f, 0.4f, 1.0f};\n233\t D3D12_RECT rl{0, 0, half, H}, rr{half, 0, W, H};\n234\t list->ClearRenderTargetView(rtv[i], lc, 1, &rl);\n235\t list->ClearRenderTargetView(rtv[i], rc, 1, &rr);\n236\t LONG barW = 64;\n237\t LONG bx = (LONG)(fmod(t * 0.25, 1.0) * (half - barW));\n238\t D3D12_RECT bars[2] = {{bx, 0, bx + barW, H},\n239\t {half + bx, 0, half + bx + barW, H}};\n240\t list->ClearRenderTargetView(rtv[i], wc, 2, bars);\n241\t }\n242\t D3D12_RESOURCE_BARRIER b2 = b;\n243\t b2.Transition.StateBefore = D3D12_RESOURCE_STATE_RENDER_TARGET;\n244\t b2.Transition.StateAfter = D3D12_RESOURCE_STATE_COMMON;\n245\t list->ResourceBarrier(1, &b2);\n246\t HRCHECK(list->Close());\n247\t ID3D12CommandList* lists[] = {list.Get()};\n248\t queue->ExecuteCommandLists(1, lists);\n249\t HRCHECK(queue->Signal(fence.Get(), ++fenceVal));\n250\t if (fence->GetCompletedValue() < fenceVal) {\n251\t fence->SetEventOnCompletion(fenceVal, fenceEvt);\n252\t WaitForSingleObject(fenceEvt, 1000);\n253\t }\n254\t\n255\t st = NvAPI_D3D_DirectModePresent12(&display, surf[i],\n256\t NV_DIRECTMODE_PRESENT_FLAG_VSYNC,\n257\t queue.Get(), list.Get());\n258\t if (st != NVAPI_OK) {\n259\t presentErrors++;\n260\t fprintf(stderr, \"Present12 = 0x%x at frame %llu\\n\", st,\n261\t (unsigned long long)frames);\n262\t if (presentErrors > 10) break;\n263\t }\n264\t if (haveWaitable && WaitForSingleObject(waitEvt, 1000) == WAIT_TIMEOUT)\n265\t waitTimeouts++;\n266\t frames++;\n267\t if (frames % 300 == 0)\n268\t printf(\" t=%6.1fs frames=%llu avg=%.2f fps\\n\", t,\n269\t (unsigned long long)frames, frames / t);\n270\t }\n271\t\n272\t double total = std::chrono::duration(std::chrono::steady_clock::now() - t0).count();\n273\t printf(\"PRESENT RESULT: %llu frames in %.1fs = %.2f fps avg, presentErrors %llu, waitTimeouts %llu\\n\",\n274\t (unsigned long long)frames, total, frames / total,\n275\t (unsigned long long)presentErrors, (unsigned long long)waitTimeouts);\n276\t\n277\t if (powerOff)\n278\t NvAPI_DISP_DirectModeDisplayControl(&display, NV_DM_DISPLAY_CONTROL_POWER_OFF);\n279\t NvAPI_D3D_ReleaseDirectModeDisplay(VENDOR_ID, &display);\n280\t printf(\"display released\\n\");\n281\t // NvAPI_Unload deliberately skipped: unloading before ComPtr/D3D teardown\n282\t // crashes in driver DLL atexit order. Process exit cleans up regardless.\n283\t\n284\t double expectHz = modeHz > 1 ? modeHz : 75.0;\n285\t bool pass = frames > 0 && presentErrors == 0 && frames / total > expectHz * 0.93;\n286\t printf(\"VERDICT: %s\\n\", pass ? \"PASS\" : (frames > 0 ? \"MARGINAL\" : \"FAIL\"));\n287\t return 0;\n288\t}\n289\t"}]},"uuid":"36ca3d9b-9563-406b-96b3-5666661c71b0","timestamp":"2026-06-10T05:37:53.283Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s2-vulkan-direct\\s2_nvapi.cpp","content":"// Spike S2, NVAPI rung: own-process DirectMode presenter for the Beyond.\n// Ported from the first-party mmi_test directmode sample (BigscreenVR/mmi_test).\n// Requires the NDA HMD NVAPI headers/lib from a local mmi_test checkout —\n// referenced in place, NOT vendored into this repo (licensing).\n//\n// s2_nvapi present [--force-dsc]\n//\n// Exit codes: 0 ok, 2 no DM display, 3 acquire/modeset failed, 10 other.\n\n#include \n\n#include \n#include \n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n\nusing Microsoft::WRL::ComPtr;\n\nstatic const NvU32 VENDOR_ID = 0x2709; // EDID \"BIG\"\n\n#define NVCHECK(call) \\\n do { \\\n NvAPI_Status s_ = (call); \\\n if (s_ != NVAPI_OK) { \\\n fprintf(stderr, \"FATAL %s = 0x%x (line %d)\\n\", #call, s_, __LINE__); \\\n return 10; \\\n } \\\n } while (0)\n\n#define HRCHECK(call) \\\n do { \\\n HRESULT h_ = (call); \\\n if (FAILED(h_)) { \\\n fprintf(stderr, \"FATAL %s = 0x%lx (line %d)\\n\", #call, h_, __LINE__); \\\n return 10; \\\n } \\\n } while (0)\n\nint main(int argc, char** argv) {\n setbuf(stdout, nullptr);\n setbuf(stderr, nullptr);\n double seconds = argc > 2 ? atof(argv[2]) : 15.0;\n // Defaults = the visually-validated config (2026-06-10, both rigs):\n // X8R8G8B8 mode + forced DSC 1.1/4-slice/8bpp per the device JSON.\n bool forceDsc = true, powerOff = false;\n int wantMode = -1;\n for (int i = 1; i < argc; i++) {\n if (strcmp(argv[i], \"--force-dsc\") == 0) forceDsc = true;\n if (strcmp(argv[i], \"--no-force-dsc\") == 0) forceDsc = false;\n if (strcmp(argv[i], \"--power-off\") == 0) powerOff = true;\n if (strcmp(argv[i], \"--mode\") == 0 && i + 1 < argc) wantMode = atoi(argv[i + 1]);\n }\n\n NVCHECK(NvAPI_Initialize());\n NvAPI_Status st = NvAPI_DISP_EnableDirectMode(VENDOR_ID, 0);\n printf(\"EnableDirectMode(0x%x) = 0x%x\\n\", VENDOR_ID, st);\n\n // --- enumerate DM displays ---\n NvU32 numDisplays = 0;\n st = NvAPI_DISP_EnumerateDirectModeDisplays(VENDOR_ID, &numDisplays, nullptr,\n NV_ENUM_DIRECTMODE_DISPLAY_ENABLED);\n printf(\"EnumerateDirectModeDisplays count = %u (status 0x%x)\\n\", numDisplays, st);\n if (numDisplays == 0) { printf(\"RESULT: no DM display\\n\"); return 2; }\n std::vector displays(numDisplays);\n NVCHECK(NvAPI_DISP_EnumerateDirectModeDisplays(VENDOR_ID, &numDisplays,\n displays.data(),\n NV_ENUM_DIRECTMODE_DISPLAY_ENABLED));\n NV_DIRECT_MODE_DISPLAY_HANDLE display = displays[0];\n printf(\"using displayId 0x%x\\n\", display.displayId);\n\n // --- list modes ---\n NvU32 modeCount = 0;\n NVCHECK(NvAPI_D3D_DirectModeGetDisplayModes(&display, &modeCount, nullptr,\n NV_DIRECTMODE_GETMODES_FLAG_SUPPORTED));\n std::vector modes(modeCount);\n for (auto& m : modes) { memset(&m, 0, sizeof(m)); m.version = NV_DIRECT_MODE_INFO_VER; }\n NVCHECK(NvAPI_D3D_DirectModeGetDisplayModes(&display, &modeCount, modes.data(),\n NV_DIRECTMODE_GETMODES_FLAG_SUPPORTED));\n int best = -1;\n double bestScore = -1;\n for (NvU32 i = 0; i < modeCount; i++) {\n auto& m = modes[i];\n double hz = m.refresh.denominator\n ? (double)m.refresh.numerator / m.refresh.denominator\n : 0;\n printf(\"mode[%u]: %ux%u @ %.3f Hz format=%d dscMode=%d dscVer=%d slices=%d bppx16=%u\\n\",\n i, m.width, m.height, hz, (int)m.format, (int)m.dscParams.dscMode,\n (int)m.dscParams.dscVersion, (int)m.dscParams.sliceCount,\n m.dscParams.outputBPPx16);\n double score = (double)m.width * m.height - fabs(hz - 75.0) * 1e4;\n if (m.format == 22) score += 1; // prefer X8R8G8B8 — the validated format\n if (score > bestScore) { bestScore = score; best = (int)i; }\n }\n if (wantMode >= 0 && wantMode < (int)modeCount) best = wantMode;\n NV_DIRECT_MODE_INFO mode = modes[best];\n double modeHz = mode.refresh.denominator\n ? (double)mode.refresh.numerator / mode.refresh.denominator\n : 0;\n printf(\"using mode[%d]: %ux%u @ %.3f Hz\\n\", best, mode.width, mode.height, modeHz);\n if (forceDsc) {\n mode.dscParams.dscMode = NV_DIRECT_MODE_DSC_MODE_FORCE_ENABLED;\n mode.dscParams.dscVersion = NV_DIRECT_MODE_DSC_VERSION_V11;\n mode.dscParams.sliceCount = NV_DIRECT_MODE_DSC_SLICE_COUNT_4;\n mode.dscParams.outputBPPx16 = 128;\n printf(\"forcing DSC 1.1, 4 slices, 8 bpp\\n\");\n }\n\n // --- D3D12 device on the display's adapter ---\n NvPhysicalGpuHandle gpu;\n NVCHECK(NvAPI_SYS_GetPhysicalGpuFromDisplayId(display.displayId, &gpu));\n ComPtr adapter;\n NVCHECK(NvAPI_D3D_GetIDXGIAdapter(gpu, adapter.GetAddressOf()));\n ComPtr dev;\n HRCHECK(D3D12CreateDevice(adapter.Get(), D3D_FEATURE_LEVEL_11_0,\n IID_PPV_ARGS(&dev)));\n D3D12_COMMAND_QUEUE_DESC qd{};\n qd.Type = D3D12_COMMAND_LIST_TYPE_DIRECT;\n ComPtr queue;\n HRCHECK(dev->CreateCommandQueue(&qd, IID_PPV_ARGS(&queue)));\n ComPtr alloc;\n HRCHECK(dev->CreateCommandAllocator(D3D12_COMMAND_LIST_TYPE_DIRECT,\n IID_PPV_ARGS(&alloc)));\n ComPtr list;\n HRCHECK(dev->CreateCommandList(0, D3D12_COMMAND_LIST_TYPE_DIRECT, alloc.Get(),\n nullptr, IID_PPV_ARGS(&list)));\n HRCHECK(list->Close());\n ComPtr fence;\n HRCHECK(dev->CreateFence(0, D3D12_FENCE_FLAG_NONE, IID_PPV_ARGS(&fence)));\n HANDLE fenceEvt = CreateEventA(nullptr, FALSE, FALSE, nullptr);\n UINT64 fenceVal = 0;\n\n // --- acquire ---\n st = NvAPI_D3D_AcquireDirectModeDisplay(VENDOR_ID, dev.Get(), &display);\n printf(\"AcquireDirectModeDisplay = 0x%x%s\\n\", st,\n st == NVAPI_HDCP_DISABLED ? \" (HDCP disabled, continuing)\" : \"\");\n if (st != NVAPI_OK && st != NVAPI_HDCP_DISABLED) return 3;\n\n // --- surfaces ---\n const int NBUF = 2;\n NV_DIRECT_MODE_SURFACE_HANDLE surf[NBUF];\n HANDLE shared[NBUF];\n ComPtr res[NBUF];\n for (int i = 0; i < NBUF; i++) {\n st = NvAPI_D3D_DirectModeCreateSurface(&display, &mode, &surf[i], &shared[i]);\n if (st != NVAPI_OK) { fprintf(stderr, \"CreateSurface[%d] = 0x%x\\n\", i, st); return 3; }\n HRCHECK(dev->OpenSharedHandle(shared[i], IID_PPV_ARGS(&res[i])));\n }\n auto rdesc = res[0]->GetDesc();\n printf(\"surface: %llux%u format=%d\\n\", (unsigned long long)rdesc.Width,\n rdesc.Height, (int)rdesc.Format);\n // DM surfaces come back TYPELESS — an RTV needs an explicit typed format\n // (nullptr desc on a typeless resource silently fails in release builds:\n // clears never land and the panels scan out uninitialized VRAM).\n DXGI_FORMAT rtvFmt;\n switch (rdesc.Format) {\n case DXGI_FORMAT_B8G8R8A8_TYPELESS: rtvFmt = DXGI_FORMAT_B8G8R8A8_UNORM; break;\n case DXGI_FORMAT_B8G8R8X8_TYPELESS: rtvFmt = DXGI_FORMAT_B8G8R8X8_UNORM; break;\n case DXGI_FORMAT_R8G8B8A8_TYPELESS: rtvFmt = DXGI_FORMAT_R8G8B8A8_UNORM; break;\n case DXGI_FORMAT_R10G10B10A2_TYPELESS: rtvFmt = DXGI_FORMAT_R10G10B10A2_UNORM; break;\n case DXGI_FORMAT_R16G16B16A16_TYPELESS: rtvFmt = DXGI_FORMAT_R16G16B16A16_FLOAT; break;\n default:\n rtvFmt = rdesc.Format;\n fprintf(stderr, \"WARNING: surface format %d has no typed mapping — \"\n \"RTV will be invalid if it is typeless\\n\", (int)rdesc.Format);\n break;\n }\n printf(\"rtv format: %d\\n\", (int)rtvFmt);\n\n ComPtr rtvHeap;\n D3D12_DESCRIPTOR_HEAP_DESC hd{};\n hd.Type = D3D12_DESCRIPTOR_HEAP_TYPE_RTV;\n hd.NumDescriptors = NBUF;\n HRCHECK(dev->CreateDescriptorHeap(&hd, IID_PPV_ARGS(&rtvHeap)));\n UINT rtvStep = dev->GetDescriptorHandleIncrementSize(D3D12_DESCRIPTOR_HEAP_TYPE_RTV);\n D3D12_CPU_DESCRIPTOR_HANDLE rtv[NBUF];\n for (int i = 0; i < NBUF; i++) {\n rtv[i] = rtvHeap->GetCPUDescriptorHandleForHeapStart();\n rtv[i].ptr += (SIZE_T)i * rtvStep;\n D3D12_RENDER_TARGET_VIEW_DESC rd{};\n rd.Format = rtvFmt;\n rd.ViewDimension = D3D12_RTV_DIMENSION_TEXTURE2D;\n rd.Texture2D.MipSlice = 0;\n rd.Texture2D.PlaneSlice = 0;\n dev->CreateRenderTargetView(res[i].Get(), &rd, rtv[i]);\n }\n\n // --- modeset + power on ---\n st = NvAPI_D3D_DirectModeSetDisplayMode(&display, &mode);\n if (st == NVAPI_HDCP_DISABLED) { printf(\"SetDisplayMode: HDCP disabled, ok\\n\"); st = NVAPI_OK; }\n printf(\"SetDisplayMode = 0x%x\\n\", st);\n if (st != NVAPI_OK) return 3;\n st = NvAPI_DISP_DirectModeDisplayControl(&display, NV_DM_DISPLAY_CONTROL_POWER_ON);\n printf(\"DisplayControl(POWER_ON) = 0x%x\\n\", st);\n\n HANDLE waitEvt = NULL;\n st = NvAPI_DISP_DirectModeGetPresentWaitableObject(\n &display, reinterpret_cast(&waitEvt));\n bool haveWaitable = st == NVAPI_OK;\n printf(\"GetPresentWaitableObject = 0x%x\\n\", st);\n\n // --- present loop: animated dim hue clear ---\n auto t0 = std::chrono::steady_clock::now();\n uint64_t frames = 0, presentErrors = 0, waitTimeouts = 0;\n while (true) {\n double t = std::chrono::duration(std::chrono::steady_clock::now() - t0).count();\n if (t >= seconds) break;\n int i = (int)(frames % NBUF);\n\n HRCHECK(alloc->Reset());\n HRCHECK(list->Reset(alloc.Get(), nullptr));\n D3D12_RESOURCE_BARRIER b{};\n b.Type = D3D12_RESOURCE_BARRIER_TYPE_TRANSITION;\n b.Transition.pResource = res[i].Get();\n b.Transition.Subresource = D3D12_RESOURCE_BARRIER_ALL_SUBRESOURCES;\n b.Transition.StateBefore = D3D12_RESOURCE_STATE_COMMON;\n b.Transition.StateAfter = D3D12_RESOURCE_STATE_RENDER_TARGET;\n list->ResourceBarrier(1, &b);\n // structure-revealing pattern: left eye green field, right eye red field,\n // synchronized white bar sweeping across each eye. Any DSC/packing fault\n // shows immediately as wrong colors per eye or a broken/duplicated bar.\n {\n LONG W = (LONG)rdesc.Width, H = (LONG)rdesc.Height, half = W / 2;\n float lc[4] = {0.0f, 0.25f, 0.08f, 1.0f};\n float rc[4] = {0.25f, 0.0f, 0.08f, 1.0f};\n float wc[4] = {0.4f, 0.4f, 0.4f, 1.0f};\n D3D12_RECT rl{0, 0, half, H}, rr{half, 0, W, H};\n list->ClearRenderTargetView(rtv[i], lc, 1, &rl);\n list->ClearRenderTargetView(rtv[i], rc, 1, &rr);\n LONG barW = 64;\n LONG bx = (LONG)(fmod(t * 0.25, 1.0) * (half - barW));\n D3D12_RECT bars[2] = {{bx, 0, bx + barW, H},\n {half + bx, 0, half + bx + barW, H}};\n list->ClearRenderTargetView(rtv[i], wc, 2, bars);\n }\n D3D12_RESOURCE_BARRIER b2 = b;\n b2.Transition.StateBefore = D3D12_RESOURCE_STATE_RENDER_TARGET;\n b2.Transition.StateAfter = D3D12_RESOURCE_STATE_COMMON;\n list->ResourceBarrier(1, &b2);\n HRCHECK(list->Close());\n ID3D12CommandList* lists[] = {list.Get()};\n queue->ExecuteCommandLists(1, lists);\n HRCHECK(queue->Signal(fence.Get(), ++fenceVal));\n if (fence->GetCompletedValue() < fenceVal) {\n fence->SetEventOnCompletion(fenceVal, fenceEvt);\n WaitForSingleObject(fenceEvt, 1000);\n }\n\n st = NvAPI_D3D_DirectModePresent12(&display, surf[i],\n NV_DIRECTMODE_PRESENT_FLAG_VSYNC,\n queue.Get(), list.Get());\n if (st != NVAPI_OK) {\n presentErrors++;\n fprintf(stderr, \"Present12 = 0x%x at frame %llu\\n\", st,\n (unsigned long long)frames);\n if (presentErrors > 10) break;\n }\n if (haveWaitable && WaitForSingleObject(waitEvt, 1000) == WAIT_TIMEOUT)\n waitTimeouts++;\n frames++;\n if (frames % 300 == 0)\n printf(\" t=%6.1fs frames=%llu avg=%.2f fps\\n\", t,\n (unsigned long long)frames, frames / t);\n }\n\n double total = std::chrono::duration(std::chrono::steady_clock::now() - t0).count();\n printf(\"PRESENT RESULT: %llu frames in %.1fs = %.2f fps avg, presentErrors %llu, waitTimeouts %llu\\n\",\n (unsigned long long)frames, total, frames / total,\n (unsigned long long)presentErrors, (unsigned long long)waitTimeouts);\n\n if (powerOff)\n NvAPI_DISP_DirectModeDisplayControl(&display, NV_DM_DISPLAY_CONTROL_POWER_OFF);\n NvAPI_D3D_ReleaseDirectModeDisplay(VENDOR_ID, &display);\n printf(\"display released\\n\");\n // NvAPI_Unload deliberately skipped: unloading before ComPtr/D3D teardown\n // crashes in driver DLL atexit order. Process exit cleans up regardless.\n\n double expectHz = modeHz > 1 ? modeHz : 75.0;\n bool pass = frames > 0 && presentErrors == 0 && frames / total > expectHz * 0.93;\n printf(\"VERDICT: %s\\n\", pass ? \"PASS\" : (frames > 0 ? \"MARGINAL\" : \"FAIL\"));\n return 0;\n}\n","numLines":289,"startLine":1,"totalLines":289}},"sourceToolAssistantUUID":"dab2d439-565d-4d82-876f-5edc87da96b9","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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sCx9AZ8oiV7oVpAXETJxgmoh/KczKHd/92rmHSo1UUrW1GJVQdjQkyFdIWVSU7BYubLrGAK4nvIZ1ZGCdkOkz73dxuMOP9qL+gJvJgiYToYIzz/OQkSlZij67qvWj1rzR7kWAR+52D+40xJQ9mpxyR6QlfoHAlJWUBOvM0o3+dIyKzSY8hHdQmGU3c3V1gATcMwHn6Phuhq57Y8P/uMXgIYiUafNZllPIEkB+jHAeuXt3HwJPC7nED1UVR352FF7pIpzZ2AKBdCy6gtjzIqT6kHBHbTaZ/VOazY1SI6M0cY6BgUzlomAU09fy5RJrjw3jWn5ViSAf57EfWojGPJru9OeCljOgn4telR0E6EBdOGeOPOUkiQnn0shyR8URiiIe+QgtA70VPAs54EFBgK/aNrEv1wQvmKmYK3/R1HjtBZ+4hRKCw3vb2mXYP0rql/NUl9RcBtqvoTlqrH3ZgMh5ii3N8Iah8LeHogU9mRwqLZwJu2+//CWAWxumTmF77BBdXXjK3QBCvEpbpbTWK3Nypp5JHQLRymzAxgVBTfNjPcawYmLZiTGb14UrUEn/27XcFx4UL9yx9gwd7fiy7XJ08L9jNSwFUYcdIBz1z9hXwfAD+X6iSukhXlvZIxDK9JbMnnqaUbSLgK+/M9rV/JLXWHR1nnkqff/f7MLBsbuOEvbWE3rUEzYz3Do/IHS8BbSKHDp3RNt8N6uLDPxK7NjgFIb7ptV4keqH0iMs3MbhekNyfVmgAyytnU5ZdjHIeTRgTx1shBK2dbikVTf7qU1JD1RfDYmf23vKMi8cF1o1HdNBiBWRd1apR7NDp3HC7ZN7Shb6eFDjKMHAIxf3e7VDGAX5iLoPCYWYQ7aqkYkkK958gMicAfDPjGBkjP/OEU/7uPQpjyqVvURj6VtCs7oK/mL8QHCIVoXbI0c8amsPtQQPq7/aFH1Cr0Q/g1av+xnsFza3dic18AFTFKb3+Qx8yY8iNWcy3+4SOdM6qUrmXErzmMAr+q7c90xMZ4jz5nl4C52pibPn2covZVGl+Usf+c6NebFZhy2FSPC9bY9uNT1T+U1PW3r058fw1776APD2e9Vir9ExvQy2tp4qPcgo1qjs6w8LnDFB0ZUZ+nLa6/mYZnYe5O6B6/dHm/okBKsWRU0L/NRrN+jmefEIk4kUxXTrRzoFnXPI0XJMyoo2+pTfgnqIvtkbJqCulk9M45CoiwSBd1Fp6iDGecHFJgo4FZi6VNyx1XNSbK4utMv/pOW5LrdVQyMgDApXmNYUvXVyeeQiEIHBByHRxsH1jhqYQ8enpRo4vtudRBgLtYPgg89LjMg4RdiciF885r/4a7gLomiTVMuV3iKQZYlRzZcA0cGTXO8oxOShuOT1jiudBuQRo4+FyFgcuo4kyAR/TTjDZROHVnQzC2vcQTvIMnKXVvs0XgRfKEyrJkGZ1R/bEjjzVd1KxtBOj1NGs4H8RZBwOhouPYpJCuBAX3iPbKpDEt58m3ClTLZ+j8qquMEp9DCtGqRHoVtqCe98FUAYEZrt89DnfN9py6XdSvX26mndjVBewCQl7mNbaaINWEpsFoz5ttEqRlvqGPKSjnTL4E5pioTXzUk8JLaeHz0WRdw9DMK1wH3mW0IRVbA6N9ialU5Uyzu1y/GrlcMNvhCS3xpqTwZbfKuVSv5Sv6B5R6z8/zZu1sryTVQR3MFSupAeZmMP5NAoFQ6vcvCaSqEhPblYjYWyqK5fho+YHRAMf/Rcr03hwPS2XM76sJi1Smfvpy2cov1zH2yeZeuGgs+rWEBF0y5qvE6py5ceVe5PdfilRJydKZOyIKU4C3DjpMxOb5m0/6KUrQow0SbqxidMFQREVgQhI7IbrBDFmlAYR/BCoSIO3Q4sFg1Nnw0q0KTPAQ3cA83iBVePAB5PdN5J5GdsNc2sHVE9ReXLh5QxXbmGl8XY7GVFsdfDk92d/95DFeOi7WWca+Dm3hrXSl7MChaS64X02aGmvaQ+I4qcOaXnQHyd+MekkNvO2t+Lrlm8yZxfco1UqcCFVxb1AUHvIsGSwB1zp3P64JDBqAZIlXsQS56xpPx3pfqTAv9QyEj+GOrG99KB8uwqRmXroWm5BLjWOTOB4UXrSjWV+hGvgcR233RZ5/aSv28FBS9Uz05SN5Yq7wKPJSCfVKOt8Zvf7U8NS22MQcUqpZ+0r1X050jwq2bgxKXJddI4REkWxIWFnj7t7Ov+Ne0pEICVvFDn5EBubyTD6DL+Ohz1aT8Ze+cC6PXuAsZQ9lK3hRTahpTnAU/82v+rL2yTT6qioQXXQ+m/1gSiYalD8oEoLIrEvNR/SawU4E3aGMvFjVEbizDWNdoNpEMF8F4DPsv2FliE6FOUunbgc4xnecoi18G/C4RAIVC6BfroZOLfk5arSAb7wAYOvlHs+G5DttPV/AgC9752EkD1n9Zl7wRTTUKpXsL7eHoJS0JQKD20r6V3RX9M3LvbqBYARP1ZrAackaYdKyp4wZnadAa1XkIe3Ia5EVud66VIi5PnRWfuXpZQk6gUnnC7KedZOaiVHblQs/BLLTV25bS077wiDPPE+XDJL1bPf3TFLoaHfgFSYzLW0BmxrElC9BewWD6eMCkQktsywkR/mgNIdoB4YQeKyIWMVnBDVIDEb576jR8GTtqvmN3DYLORVrt2rN0C8qY5+vuUUMG5CDl6DT/UWJZ0nkUFaCmUbji5MqwGOsOgYrsV5XwhvyjqC5r/EIaOcnkNFTHHK9FtgSIbXUARTGL27g1/gdbnU53HFnwkEcdc75U91Y9SMfA5DIC9Ohwx4a2I89tREGdUWVeLi6Gq3uLH9lshoPHJVv2akj0lZKyWwdBNO8f7hT+G63wKKksoNY/b9ohTYdN8vOmPP1tzaA66QI3SJafVwHNw2/r5OQlclLHFFg224tiOc4IR/GoIuAGl7RCvzXTBGPy3nfF3an9VkgyAPu+O7S0zH//yWGD9ZOBbgBkM67GOYu9K7AZcuu7lqkfoR01D78KL5nLi154Z41Ag3eNYht/2xqdgDIl9P7KNT7JCQn0NnM5WITX9OJg+xN69h3wgwHORsiqlcvTCQFzxsxN3uzdKDGnNcroYk0O937pZlvcHtRWuY+HQQEBq85j6FhKehRSn3m9jza/xB1IR+gPkHC+YjVPkyBMXCUqUdTWWVQPZgAUky77TPMBfA8NmyC3YrY/+bqK8EdOWJSfYIgGRogxVdiUC7gGtEvHu5i/CZ/+iJBNFRub87zflboJWV6N5yHx6+o/R8bToFOJ5UrA8KadA98wg3qDLN9vAzrUr+d8QUw1pRDcX+lFrtny9egItPi9gNwNRupZbSinTXdrCEeqSO61com3wd2lNfuCASOgrIjCmfOm3kuiekni/ijxk/aqzSvVdglounyaJjx3+BDloqpH93Fq3/fd1yb4/+j5b3S5I3ex23o6rGLnNztTZxsvWPe6dL5z5of6LoQsghR3s3cvDZ9IXDlJf+BbelHoTPhfq13chL2+67Ck08/8Q9pqCPmBKxATMp6yu1VXsP2sjt5R+xQxa3DitcVxkcqKDN5sLpgqXxGTgzmQf+vFmO/xzSk1AsATf45DPeVQl0MJcMH5EydhzpjhSagNI+PzNv4xW1OcSDPXwXRZzFWa4VKbto2XH/zx5BOaolVWKnMY0GpiBS0AmPU4ATpYXbSqJhFDIAQZ/m3RPTQ6vX94kci4AmQQnuHR+Gic1+X6y2KGGzN1ZpWNYRYDwGD0YrG4wltIuRrM2Db65GWgeP0y34tEKam69BM31iTfPbuQxI3KyaSEuhmKu4EhqqmHz57zoo1n+tCYzKiabycmnu2lF+8IXPdlkuPMvHIa97MOjebVBaBzvI48f6DjHEoZ4XoFUgE3bttUagyA4qDsNxcyT7SAoo56u1dHw+72+CIGoKDmJsva/GshZpzfF2zgbnJ3Ly3UHgqez16tu9AYIs12UyX1EBm/evAYQe3yUYGxl3Bdlnlot2iyx95tHv8TKeqhu2hGAN+S54B48IabBSBft75gGwcaCSh3fOKPDjjXcHiYPoOfGx0sA254CFK+GmKdUwLiAqLVAEUZXIFdrYHXdyiAhR2CwXbTjN/UirrbUfFuZfd6ZqZklyCTUV65qzcrlDpQ4A5sAodAeBqDV3otbMeiydoI7s1FNElPLavcJX1DI8g7cbLwGbVJBupv6xeqe/qyr0XgGMORW7reyLrXwxfOW3/dbK45a4fdLqNFd+NsuWwcYr9bhTsuDrEIVQa7lkuslHDvBcBK9ovOUr/icowXs6Ax7wNvDr79ejV86155M7TIeSgaO9aipW50pMqQPS0IH/9bOf2Tnu8sASyJr5xlPjJqNw6fjS+0Loh9AktFWsjUQlROVsc8mwYmmkKM68jST/OcREsUmEeZKKqjPEjnKGq7YflE9z6I8IoL/DUtq8QOf1LKkhWfJ9Raf9jbx0uhUhENgCUZ8T/ig/MsLs8az80vFQcWCt/Yn3E82EuN4qlRR8rPoPtfhy4n+7wVNelYDcw8+tN/MSHEowdrz/eHxw8bBEnUFMpynS+hoWIdD0N2JpkzCi30PBdgs69q0YbqMm4z3VRL6MW8J6jsDRjqCJKMTLCYxEyiqg+2dI9lVVKrmOXCaPahzrKEp4kCOCtFi9b5U+QNpJii1bz20vbmQRwexqj1XDgtXx9GYx9XdCp8RMmyUSmCAWJm/4pbBAdFXVDZhm+snj4193pUuWT3gQEp5iwht8VEe+Nrrd9VvDNxX8uHvPh322Ost9Igr6J5Ph9EohaUZd7ElzLkgRftJ1fMXMDyBR+8QDQbBT8/+eduztZ8U5AG3cvQwfVtgIdhrOA3zu8ifYf9sA5uzyyxFfvervziDbdvTZCSI7G7eZpPb8prLLC15tSCdSs9+fZBnPpkiWmaD/synO2lLqILdRic9DCE5DBpdH1aZXYxhHV684iRXRW2VI2CKdhAtjzTXe3sia4f+srkQm+jNvvqLNzg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Wrong formula direction / wrong eye / flipped v shows up\nas curved lines, rainbow fringes, or an upside-down marker.\n\nPattern (source UV space, per eye):\n - grid lines every 0.05, intensity white\n - thicker center cross at (0.5, 0.5)\n - filled triangle ABOVE center pointing up (settles v orientation eyes-in)\n - left eye additionally gets a small filled square left of center\n (settles eye assignment)\n - outside source [0,1]: black (matches the grown render target bounds)\n\nOutput: raw B8G8R8X8 bytes, row-major, W*H*4 — loaded by s2_nvapi --image.\n\nUsage: python make_warped_grid.py [out.raw] [W H]\n (default 5088 2544 — the validated native DM mode, left half = left eye)\n\"\"\"\n\nimport json\nimport sys\n\nimport numpy as np\n\nfrom poly3 import load_config\n\nGRID_STEP = 0.05\nLINE_HW = 0.0018 # line half-width in source UV\nCROSS_HW = 0.005 # center cross half-width\nGAMMA = 2.2\n\n\ndef grid_intensity(su, sv):\n \"\"\"Procedural pattern in source UV space -> intensity [0,1] array.\"\"\"\n inside = (su >= 0) & (su <= 1) & (sv >= 0) & (sv <= 1)\n # grid lines\n du = np.abs(((su / GRID_STEP) + 0.5) % 1.0 - 0.5) * GRID_STEP\n dv = np.abs(((sv / GRID_STEP) + 0.5) % 1.0 - 0.5) * GRID_STEP\n lines = (du < LINE_HW) | (dv < LINE_HW)\n # center cross (thicker)\n cross = (np.abs(su - 0.5) < CROSS_HW) | (np.abs(sv - 0.5) < CROSS_HW)\n # up marker: filled triangle, apex at v=0.30, base at v=0.38 (v=0 is top)\n tri_h = 0.08\n tv = (sv - 0.30) / tri_h\n tri = (tv >= 0) & (tv <= 1) & (np.abs(su - 0.5) < 0.04 * tv)\n out = np.where(lines | cross | tri, 1.0, 0.06) # faint floor shows extent\n return np.where(inside, out, 0.0)\n\n\ndef left_eye_marker(su, sv):\n \"\"\"Small filled square left of center, left eye only.\"\"\"\n return ((np.abs(su - 0.35) < 0.02) & (np.abs(sv - 0.5) < 0.02)).astype(float)\n\n\ndef render_eye(model, W, H):\n \"\"\"Return (H, W, 3) float intensity for one eye panel.\"\"\"\n u = (np.arange(W) + 0.5) / W\n v = (np.arange(H) + 0.5) / H\n uu, vv = np.meshgrid(u, v)\n img = np.zeros((H, W, 3), dtype=np.float32)\n scale = 0.5 / (1.0 + model.grow)\n for ci, ch in enumerate(model.channels): # r, g, b\n tx = 2.0 * uu - 1.0 - ch[\"cx\"]\n ty = 2.0 * vv - 1.0 - ch[\"cy\"]\n r2 = tx * tx + ty * ty\n k1, k2, k3 = ch[\"k\"]\n d = 1.0 + r2 * (k1 + r2 * (k2 + r2 * k3))\n su = 0.5 + (tx * d + ch[\"cx\"]) * scale\n sv = 0.5 + (ty * d + ch[\"cy\"]) * scale\n img[:, :, ci] = grid_intensity(su, sv)\n return img\n\n\ndef main():\n cfg_path = sys.argv[1]\n out_path = sys.argv[2] if len(sys.argv) > 2 else \"warped_grid.raw\"\n W = int(sys.argv[3]) if len(sys.argv) > 4 else 5088\n H = int(sys.argv[4]) if len(sys.argv) > 4 else 2544\n half = W // 2\n\n left, right, cfg = load_config(cfg_path)\n print(f\"unit {cfg.get('device_serial_number')} image {W}x{H} ({half}x{H}/eye)\")\n\n frame = np.zeros((H, W, 3), dtype=np.float32)\n for eye_idx, model in ((0, left), (1, right)):\n img = render_eye(model, half, H)\n if eye_idx == 0:\n # left-eye marker rendered in green channel source space\n scale = 0.5 / (1.0 + model.grow)\n u = (np.arange(half) + 0.5) / half\n v = (np.arange(H) + 0.5) / H\n uu, vv = np.meshgrid(u, v)\n ch = model.channels[1]\n tx = 2.0 * uu - 1.0 - ch[\"cx\"]\n ty = 2.0 * vv - 1.0 - ch[\"cy\"]\n r2 = tx * tx + ty * ty\n k1, k2, k3 = ch[\"k\"]\n d = 1.0 + r2 * (k1 + r2 * (k2 + r2 * k3))\n su = 0.5 + (tx * d + ch[\"cx\"]) * scale\n sv = 0.5 + (ty * d + ch[\"cy\"]) * scale\n m = left_eye_marker(su, sv)\n img = np.maximum(img, m[:, :, None])\n frame[:, eye_idx * half:(eye_idx + 1) * half, :] = img\n\n # gamma encode, pack B8G8R8X8 little-endian (byte order B,G,R,X)\n enc = (np.clip(frame, 0, 1) ** (1.0 / GAMMA) * 255.0 + 0.5).astype(np.uint8)\n out = np.zeros((H, W, 4), dtype=np.uint8)\n out[:, :, 0] = enc[:, :, 2] # B\n out[:, :, 1] = enc[:, :, 1] # G\n out[:, :, 2] = enc[:, :, 0] # R\n out.tofile(out_path)\n print(f\"wrote {out_path} ({out.nbytes} bytes)\")\n\n\nif __name__ == \"__main__\":\n main()\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":7709,"cache_read_input_tokens":126253,"output_tokens":5846,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":7709,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":5846,"cache_read_input_tokens":126253,"cache_creation_input_tokens":7709,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":7709},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu3F6Uy1jFo9FC81XMvc","type":"assistant","uuid":"dbe0566d-3534-4ac6-83a3-fab8e2c293d7","timestamp":"2026-06-10T05:39:21.645Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"beyond connected + steamvr up on enlyzeam","leafUuid":"88db0a79-ee30-428e-a273-4646cdbc6e0e","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02"}
{"type":"ai-title","aiTitle":"Set up Beyond Connected and SteamVR","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02"}
{"type":"mode","mode":"normal","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02"}
{"parentUuid":"dbe0566d-3534-4ac6-83a3-fab8e2c293d7","isSidechain":false,"promptId":"b79efa4d-9c11-4f46-975f-363733b93c3f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NGBKNDXy6FrPHYNMpqePHo","type":"tool_result","content":"File created successfully at: C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\make_warped_grid.py (file state is current in your context — no need to Read it back)"}]},"uuid":"88db0a79-ee30-428e-a273-4646cdbc6e0e","timestamp":"2026-06-10T05:39:21.829Z","toolUseResult":{"type":"create","filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\make_warped_grid.py","content":"\"\"\"Generate a pre-distorted grid test image for the eyes-in S3 check.\n\nFor every panel pixel (per eye, per color channel) evaluate the verified\nS3 warp (poly3.py, poly+grow form) to get the source UV, then sample a\nprocedural grid that is STRAIGHT in source (tangent-linear) space. Through\nthe Beyond's lens the grid must therefore look straight, and because each\nchannel is warped with its own coefficients, the lines must show minimal\ncolor fringing. Wrong formula direction / wrong eye / flipped v shows up\nas curved lines, rainbow fringes, or an upside-down marker.\n\nPattern (source UV space, per eye):\n - grid lines every 0.05, intensity white\n - thicker center cross at (0.5, 0.5)\n - filled triangle ABOVE center pointing up (settles v orientation eyes-in)\n - left eye additionally gets a small filled square left of center\n (settles eye assignment)\n - outside source [0,1]: black (matches the grown render target bounds)\n\nOutput: raw B8G8R8X8 bytes, row-major, W*H*4 — loaded by s2_nvapi --image.\n\nUsage: python make_warped_grid.py [out.raw] [W H]\n (default 5088 2544 — the validated native DM mode, left half = left eye)\n\"\"\"\n\nimport json\nimport sys\n\nimport numpy as np\n\nfrom poly3 import load_config\n\nGRID_STEP = 0.05\nLINE_HW = 0.0018 # line half-width in source UV\nCROSS_HW = 0.005 # center cross half-width\nGAMMA = 2.2\n\n\ndef grid_intensity(su, sv):\n \"\"\"Procedural pattern in source UV space -> intensity [0,1] array.\"\"\"\n inside = (su >= 0) & (su <= 1) & (sv >= 0) & (sv <= 1)\n # grid lines\n du = np.abs(((su / GRID_STEP) + 0.5) % 1.0 - 0.5) * GRID_STEP\n dv = np.abs(((sv / GRID_STEP) + 0.5) % 1.0 - 0.5) * GRID_STEP\n lines = (du < LINE_HW) | (dv < LINE_HW)\n # center cross (thicker)\n cross = (np.abs(su - 0.5) < CROSS_HW) | (np.abs(sv - 0.5) < CROSS_HW)\n # up marker: filled triangle, apex at v=0.30, base at v=0.38 (v=0 is top)\n tri_h = 0.08\n tv = (sv - 0.30) / tri_h\n tri = (tv >= 0) & (tv <= 1) & (np.abs(su - 0.5) < 0.04 * tv)\n out = np.where(lines | cross | tri, 1.0, 0.06) # faint floor shows extent\n return np.where(inside, out, 0.0)\n\n\ndef left_eye_marker(su, sv):\n \"\"\"Small filled square left of center, left eye only.\"\"\"\n return ((np.abs(su - 0.35) < 0.02) & (np.abs(sv - 0.5) < 0.02)).astype(float)\n\n\ndef render_eye(model, W, H):\n \"\"\"Return (H, W, 3) float intensity for one eye panel.\"\"\"\n u = (np.arange(W) + 0.5) / W\n v = (np.arange(H) + 0.5) / H\n uu, vv = np.meshgrid(u, v)\n img = np.zeros((H, W, 3), dtype=np.float32)\n scale = 0.5 / (1.0 + model.grow)\n for ci, ch in enumerate(model.channels): # r, g, b\n tx = 2.0 * uu - 1.0 - ch[\"cx\"]\n ty = 2.0 * vv - 1.0 - ch[\"cy\"]\n r2 = tx * tx + ty * ty\n k1, k2, k3 = ch[\"k\"]\n d = 1.0 + r2 * (k1 + r2 * (k2 + r2 * k3))\n su = 0.5 + (tx * d + ch[\"cx\"]) * scale\n sv = 0.5 + (ty * d + ch[\"cy\"]) * scale\n img[:, :, ci] = grid_intensity(su, sv)\n return img\n\n\ndef main():\n cfg_path = sys.argv[1]\n out_path = sys.argv[2] if len(sys.argv) > 2 else \"warped_grid.raw\"\n W = int(sys.argv[3]) if len(sys.argv) > 4 else 5088\n H = int(sys.argv[4]) if len(sys.argv) > 4 else 2544\n half = W // 2\n\n left, right, cfg = load_config(cfg_path)\n print(f\"unit {cfg.get('device_serial_number')} image {W}x{H} ({half}x{H}/eye)\")\n\n frame = np.zeros((H, W, 3), dtype=np.float32)\n for eye_idx, model in ((0, left), (1, right)):\n img = render_eye(model, half, H)\n if eye_idx == 0:\n # left-eye marker rendered in green channel source space\n scale = 0.5 / (1.0 + model.grow)\n u = (np.arange(half) + 0.5) / half\n v = (np.arange(H) + 0.5) / H\n uu, vv = np.meshgrid(u, v)\n ch = model.channels[1]\n tx = 2.0 * uu - 1.0 - ch[\"cx\"]\n ty = 2.0 * vv - 1.0 - ch[\"cy\"]\n r2 = tx * tx + ty * ty\n k1, k2, k3 = ch[\"k\"]\n d = 1.0 + r2 * (k1 + r2 * (k2 + r2 * k3))\n su = 0.5 + (tx * d + ch[\"cx\"]) * scale\n sv = 0.5 + (ty * d + ch[\"cy\"]) * scale\n m = left_eye_marker(su, sv)\n img = np.maximum(img, m[:, :, None])\n frame[:, eye_idx * half:(eye_idx + 1) * half, :] = img\n\n # gamma encode, pack B8G8R8X8 little-endian (byte order B,G,R,X)\n enc = (np.clip(frame, 0, 1) ** (1.0 / GAMMA) * 255.0 + 0.5).astype(np.uint8)\n out = np.zeros((H, W, 4), dtype=np.uint8)\n out[:, :, 0] = enc[:, :, 2] # B\n out[:, :, 1] = enc[:, :, 1] # G\n out[:, :, 2] = enc[:, :, 0] # R\n out.tofile(out_path)\n print(f\"wrote {out_path} ({out.nbytes} bytes)\")\n\n\nif __name__ == \"__main__\":\n main()\n","structuredPatch":[],"originalFile":null,"userModified":false},"sourceToolAssistantUUID":"dbe0566d-3534-4ac6-83a3-fab8e2c293d7","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"88db0a79-ee30-428e-a273-4646cdbc6e0e","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01RemmPASwtjdhfU48afJYRq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017bTkT3mWkWPQuWFcD5WQd1","name":"PowerShell","input":{"command":"cd C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth; python make_warped_grid.py config_lhr_599f3b91.json warped_grid_599f3b91.raw","description":"Generate pre-warped grid for enlyzeam unit","timeout":180000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5918,"cache_read_input_tokens":133962,"output_tokens":184,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":5918,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":184,"cache_read_input_tokens":133962,"cache_creation_input_tokens":5918,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":5918},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu3Md6divm6rURbYRFuv","type":"assistant","uuid":"948f26c4-cb00-462f-ab70-3099726313fa","timestamp":"2026-06-10T05:39:28.584Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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","media_type":"image/png"}}]}]},"uuid":"fb0d8d60-e83b-4a9c-ae52-b4a405351d2a","timestamp":"2026-06-10T05:39:50.442Z","toolUseResult":{"type":"image","file":{"base64":"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(file state is current in your context — no need to Read it back)"}]},"uuid":"e4755ea2-18a8-4eef-9ef9-f329d47766b7","timestamp":"2026-06-10T05:41:07.962Z","toolUseResult":{"filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s2-vulkan-direct\\s2_nvapi.cpp","oldString":" // --- modeset + power on ---","newString":" // --- optional static image: load raw frame into every surface once ---\n bool haveImage = false;\n ComPtr upload;\n if (imagePath) {\n if (rtvFmt != DXGI_FORMAT_B8G8R8X8_UNORM && rtvFmt != DXGI_FORMAT_B8G8R8A8_UNORM &&\n rtvFmt != DXGI_FORMAT_R8G8B8A8_UNORM) {\n fprintf(stderr, \"FATAL: --image only supports 8-bit BGRX/BGRA/RGBA surfaces, got rtv format %d\\n\",\n (int)rtvFmt);\n return 10;\n }\n UINT W = (UINT)rdesc.Width, H = rdesc.Height;\n size_t rowBytes = (size_t)W * 4;\n UINT64 pitch = (rowBytes + 255) & ~(size_t)255;\n std::vector data(rowBytes * H);\n FILE* f = fopen(imagePath, \"rb\");\n if (!f) { fprintf(stderr, \"FATAL: cannot open %s\\n\", imagePath); return 10; }\n size_t got = fread(data.data(), 1, data.size(), f);\n fclose(f);\n if (got != data.size()) {\n fprintf(stderr, \"FATAL: %s is %zu bytes, mode wants %zu (%ux%u*4)\\n\",\n imagePath, got, data.size(), W, H);\n return 10;\n }\n if (rtvFmt == DXGI_FORMAT_R8G8B8A8_UNORM) { // file is BGRX -> swizzle\n for (size_t p = 0; p < data.size(); p += 4) {\n uint8_t t = data[p]; data[p] = data[p + 2]; data[p + 2] = t;\n }\n printf(\"image swizzled BGRX -> RGBA for surface format\\n\");\n }\n\n D3D12_HEAP_PROPERTIES hp{};\n hp.Type = D3D12_HEAP_TYPE_UPLOAD;\n D3D12_RESOURCE_DESC bd{};\n bd.Dimension = D3D12_RESOURCE_DIMENSION_BUFFER;\n bd.Width = pitch * H;\n bd.Height = 1;\n bd.DepthOrArraySize = 1;\n bd.MipLevels = 1;\n bd.Format = DXGI_FORMAT_UNKNOWN;\n bd.SampleDesc.Count = 1;\n bd.Layout = D3D12_TEXTURE_LAYOUT_ROW_MAJOR;\n HRCHECK(dev->CreateCommittedResource(&hp, D3D12_HEAP_FLAG_NONE, &bd,\n D3D12_RESOURCE_STATE_GENERIC_READ,\n nullptr, IID_PPV_ARGS(&upload)));\n uint8_t* dst = nullptr;\n D3D12_RANGE noRead{0, 0};\n HRCHECK(upload->Map(0, &noRead, (void**)&dst));\n for (UINT y = 0; y < H; y++)\n memcpy(dst + y * pitch, data.data() + y * rowBytes, rowBytes);\n upload->Unmap(0, nullptr);\n\n for (int i = 0; i < NBUF; i++) {\n HRCHECK(alloc->Reset());\n HRCHECK(list->Reset(alloc.Get(), nullptr));\n D3D12_RESOURCE_BARRIER cb{};\n cb.Type = D3D12_RESOURCE_BARRIER_TYPE_TRANSITION;\n cb.Transition.pResource = res[i].Get();\n cb.Transition.Subresource = D3D12_RESOURCE_BARRIER_ALL_SUBRESOURCES;\n cb.Transition.StateBefore = D3D12_RESOURCE_STATE_COMMON;\n cb.Transition.StateAfter = D3D12_RESOURCE_STATE_COPY_DEST;\n list->ResourceBarrier(1, &cb);\n D3D12_TEXTURE_COPY_LOCATION srcLoc{}, dstLoc{};\n srcLoc.pResource = upload.Get();\n srcLoc.Type = D3D12_TEXTURE_COPY_TYPE_PLACED_FOOTPRINT;\n srcLoc.PlacedFootprint.Footprint.Format = rtvFmt;\n srcLoc.PlacedFootprint.Footprint.Width = W;\n srcLoc.PlacedFootprint.Footprint.Height = H;\n srcLoc.PlacedFootprint.Footprint.Depth = 1;\n srcLoc.PlacedFootprint.Footprint.RowPitch = (UINT)pitch;\n dstLoc.pResource = res[i].Get();\n dstLoc.Type = D3D12_TEXTURE_COPY_TYPE_SUBRESOURCE_INDEX;\n dstLoc.SubresourceIndex = 0;\n list->CopyTextureRegion(&dstLoc, 0, 0, 0, &srcLoc, nullptr);\n cb.Transition.StateBefore = D3D12_RESOURCE_STATE_COPY_DEST;\n cb.Transition.StateAfter = D3D12_RESOURCE_STATE_COMMON;\n list->ResourceBarrier(1, &cb);\n HRCHECK(list->Close());\n ID3D12CommandList* lists[] = {list.Get()};\n queue->ExecuteCommandLists(1, lists);\n HRCHECK(queue->Signal(fence.Get(), ++fenceVal));\n if (fence->GetCompletedValue() < fenceVal) {\n fence->SetEventOnCompletion(fenceVal, fenceEvt);\n WaitForSingleObject(fenceEvt, 2000);\n }\n }\n haveImage = true;\n printf(\"static image %s loaded into %d surfaces\\n\", imagePath, NBUF);\n }\n\n // --- modeset + power on ---","originalFile":null,"structuredPatch":[{"oldStart":198,"oldLines":6,"newStart":198,"newLines":95,"lines":[" dev->CreateRenderTargetView(res[i].Get(), &rd, rtv[i]);"," }"," ","+ // --- optional static image: load raw frame into every surface once ---","+ bool haveImage = false;","+ ComPtr upload;","+ if (imagePath) {","+ if (rtvFmt != DXGI_FORMAT_B8G8R8X8_UNORM && rtvFmt != DXGI_FORMAT_B8G8R8A8_UNORM &&","+ rtvFmt != DXGI_FORMAT_R8G8B8A8_UNORM) {","+ fprintf(stderr, \"FATAL: --image only supports 8-bit BGRX/BGRA/RGBA surfaces, got rtv format %d\\n\",","+ (int)rtvFmt);","+ return 10;","+ }","+ UINT W = (UINT)rdesc.Width, H = rdesc.Height;","+ size_t rowBytes = (size_t)W * 4;","+ UINT64 pitch = (rowBytes + 255) & ~(size_t)255;","+ std::vector data(rowBytes * H);","+ FILE* f = fopen(imagePath, \"rb\");","+ if (!f) { fprintf(stderr, \"FATAL: cannot open %s\\n\", imagePath); return 10; }","+ size_t got = fread(data.data(), 1, data.size(), f);","+ fclose(f);","+ if (got != data.size()) {","+ fprintf(stderr, \"FATAL: %s is %zu bytes, mode wants %zu (%ux%u*4)\\n\",","+ imagePath, got, data.size(), W, H);","+ return 10;","+ }","+ if (rtvFmt == DXGI_FORMAT_R8G8B8A8_UNORM) { // file is BGRX -> swizzle","+ for (size_t p = 0; p < data.size(); p += 4) {","+ uint8_t t = data[p]; data[p] = data[p + 2]; data[p + 2] = t;","+ }","+ printf(\"image swizzled BGRX -> RGBA for surface format\\n\");","+ }","+","+ D3D12_HEAP_PROPERTIES hp{};","+ hp.Type = D3D12_HEAP_TYPE_UPLOAD;","+ D3D12_RESOURCE_DESC bd{};","+ bd.Dimension = D3D12_RESOURCE_DIMENSION_BUFFER;","+ bd.Width = pitch * H;","+ bd.Height = 1;","+ bd.DepthOrArraySize = 1;","+ bd.MipLevels = 1;","+ bd.Format = DXGI_FORMAT_UNKNOWN;","+ bd.SampleDesc.Count = 1;","+ bd.Layout = D3D12_TEXTURE_LAYOUT_ROW_MAJOR;","+ HRCHECK(dev->CreateCommittedResource(&hp, D3D12_HEAP_FLAG_NONE, &bd,","+ D3D12_RESOURCE_STATE_GENERIC_READ,","+ nullptr, IID_PPV_ARGS(&upload)));","+ uint8_t* dst = nullptr;","+ D3D12_RANGE noRead{0, 0};","+ HRCHECK(upload->Map(0, &noRead, (void**)&dst));","+ for (UINT y = 0; y < H; y++)","+ memcpy(dst + y * pitch, data.data() + y * rowBytes, rowBytes);","+ upload->Unmap(0, nullptr);","+","+ for (int i = 0; i < NBUF; i++) {","+ HRCHECK(alloc->Reset());","+ HRCHECK(list->Reset(alloc.Get(), nullptr));","+ D3D12_RESOURCE_BARRIER cb{};","+ cb.Type = D3D12_RESOURCE_BARRIER_TYPE_TRANSITION;","+ cb.Transition.pResource = res[i].Get();","+ cb.Transition.Subresource = D3D12_RESOURCE_BARRIER_ALL_SUBRESOURCES;","+ cb.Transition.StateBefore = D3D12_RESOURCE_STATE_COMMON;","+ cb.Transition.StateAfter = D3D12_RESOURCE_STATE_COPY_DEST;","+ list->ResourceBarrier(1, &cb);","+ D3D12_TEXTURE_COPY_LOCATION srcLoc{}, dstLoc{};","+ srcLoc.pResource = upload.Get();","+ srcLoc.Type = D3D12_TEXTURE_COPY_TYPE_PLACED_FOOTPRINT;","+ srcLoc.PlacedFootprint.Footprint.Format = rtvFmt;","+ srcLoc.PlacedFootprint.Footprint.Width = W;","+ srcLoc.PlacedFootprint.Footprint.Height = H;","+ srcLoc.PlacedFootprint.Footprint.Depth = 1;","+ srcLoc.PlacedFootprint.Footprint.RowPitch = (UINT)pitch;","+ dstLoc.pResource = res[i].Get();","+ dstLoc.Type = D3D12_TEXTURE_COPY_TYPE_SUBRESOURCE_INDEX;","+ dstLoc.SubresourceIndex = 0;","+ list->CopyTextureRegion(&dstLoc, 0, 0, 0, &srcLoc, nullptr);","+ cb.Transition.StateBefore = D3D12_RESOURCE_STATE_COPY_DEST;","+ cb.Transition.StateAfter = D3D12_RESOURCE_STATE_COMMON;","+ list->ResourceBarrier(1, &cb);","+ HRCHECK(list->Close());","+ ID3D12CommandList* lists[] = {list.Get()};","+ queue->ExecuteCommandLists(1, lists);","+ HRCHECK(queue->Signal(fence.Get(), ++fenceVal));","+ if (fence->GetCompletedValue() < fenceVal) {","+ fence->SetEventOnCompletion(fenceVal, fenceEvt);","+ WaitForSingleObject(fenceEvt, 2000);","+ }","+ }","+ haveImage = true;","+ printf(\"static image %s loaded into %d surfaces\\n\", imagePath, NBUF);","+ }","+"," // --- modeset + power on ---"," st = NvAPI_D3D_DirectModeSetDisplayMode(&display, &mode);"," if (st == NVAPI_HDCP_DISABLED) { printf(\"SetDisplayMode: HDCP disabled, ok\\n\"); st = NVAPI_OK; }"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"2f82e184-e27d-4da8-a6b6-f8a04c21078d","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"e755f0be-2740-47e0-a5d1-89c46add5dbb","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_014dZ42WUqtgWMymLxTu57tk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Tz23re1TFQ6PPYxnvcHjM2","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s2-vulkan-direct\\s2_nvapi.cpp","old_string":" HRCHECK(alloc->Reset());\n HRCHECK(list->Reset(alloc.Get(), nullptr));\n D3D12_RESOURCE_BARRIER b{};\n b.Type = D3D12_RESOURCE_BARRIER_TYPE_TRANSITION;\n b.Transition.pResource = res[i].Get();\n b.Transition.Subresource = D3D12_RESOURCE_BARRIER_ALL_SUBRESOURCES;\n b.Transition.StateBefore = D3D12_RESOURCE_STATE_COMMON;\n b.Transition.StateAfter = D3D12_RESOURCE_STATE_RENDER_TARGET;\n list->ResourceBarrier(1, &b);\n // structure-revealing pattern: left eye green field, right eye red field,\n // synchronized white bar sweeping across each eye. Any DSC/packing fault\n // shows immediately as wrong colors per eye or a broken/duplicated bar.\n {\n LONG W = (LONG)rdesc.Width, H = (LONG)rdesc.Height, half = W / 2;\n float lc[4] = {0.0f, 0.25f, 0.08f, 1.0f};\n float rc[4] = {0.25f, 0.0f, 0.08f, 1.0f};\n float wc[4] = {0.4f, 0.4f, 0.4f, 1.0f};\n D3D12_RECT rl{0, 0, half, H}, rr{half, 0, W, H};\n list->ClearRenderTargetView(rtv[i], lc, 1, &rl);\n list->ClearRenderTargetView(rtv[i], rc, 1, &rr);\n LONG barW = 64;\n LONG bx = (LONG)(fmod(t * 0.25, 1.0) * (half - barW));\n D3D12_RECT bars[2] = {{bx, 0, bx + barW, H},\n {half + bx, 0, half + bx + barW, H}};\n list->ClearRenderTargetView(rtv[i], wc, 2, bars);\n }\n D3D12_RESOURCE_BARRIER b2 = b;\n b2.Transition.StateBefore = D3D12_RESOURCE_STATE_RENDER_TARGET;\n b2.Transition.StateAfter = D3D12_RESOURCE_STATE_COMMON;\n list->ResourceBarrier(1, &b2);\n HRCHECK(list->Close());","new_string":" HRCHECK(alloc->Reset());\n HRCHECK(list->Reset(alloc.Get(), nullptr));\n if (!haveImage) {\n D3D12_RESOURCE_BARRIER b{};\n b.Type = D3D12_RESOURCE_BARRIER_TYPE_TRANSITION;\n b.Transition.pResource = res[i].Get();\n b.Transition.Subresource = D3D12_RESOURCE_BARRIER_ALL_SUBRESOURCES;\n b.Transition.StateBefore = D3D12_RESOURCE_STATE_COMMON;\n b.Transition.StateAfter = D3D12_RESOURCE_STATE_RENDER_TARGET;\n list->ResourceBarrier(1, &b);\n // structure-revealing pattern: left eye green field, right eye red field,\n // synchronized white bar sweeping across each eye. Any DSC/packing fault\n // shows immediately as wrong colors per eye or a broken/duplicated bar.\n {\n LONG W = (LONG)rdesc.Width, H = (LONG)rdesc.Height, half = W / 2;\n float lc[4] = {0.0f, 0.25f, 0.08f, 1.0f};\n float rc[4] = {0.25f, 0.0f, 0.08f, 1.0f};\n float wc[4] = {0.4f, 0.4f, 0.4f, 1.0f};\n D3D12_RECT rl{0, 0, half, H}, rr{half, 0, W, H};\n list->ClearRenderTargetView(rtv[i], lc, 1, &rl);\n list->ClearRenderTargetView(rtv[i], rc, 1, &rr);\n LONG barW = 64;\n LONG bx = (LONG)(fmod(t * 0.25, 1.0) * (half - barW));\n D3D12_RECT bars[2] = {{bx, 0, bx + barW, H},\n {half + bx, 0, half + bx + barW, H}};\n list->ClearRenderTargetView(rtv[i], wc, 2, bars);\n }\n D3D12_RESOURCE_BARRIER b2 = b;\n b2.Transition.StateBefore = D3D12_RESOURCE_STATE_RENDER_TARGET;\n b2.Transition.StateAfter = D3D12_RESOURCE_STATE_COMMON;\n list->ResourceBarrier(1, &b2);\n }\n // --image: surfaces pre-filled; submit the empty list to keep the\n // validated present pacing structure identical.\n HRCHECK(list->Close());"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2409,"cache_read_input_tokens":144840,"output_tokens":1968,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":2409,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1968,"cache_read_input_tokens":144840,"cache_creation_input_tokens":2409,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2409},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu3VSuSBeJwymNhYmPXo","type":"assistant","uuid":"70530499-55d3-47c8-b3b9-a7ca386f7e27","timestamp":"2026-06-10T05:41:28.884Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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(file state is current in your context — no need to Read it back)"}]},"uuid":"e8ba7efa-4e8c-4f24-9ec5-bdf51ea5e50c","timestamp":"2026-06-10T05:41:29.073Z","toolUseResult":{"filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s2-vulkan-direct\\s2_nvapi.cpp","oldString":" HRCHECK(alloc->Reset());\n HRCHECK(list->Reset(alloc.Get(), nullptr));\n D3D12_RESOURCE_BARRIER b{};\n b.Type = D3D12_RESOURCE_BARRIER_TYPE_TRANSITION;\n b.Transition.pResource = res[i].Get();\n b.Transition.Subresource = D3D12_RESOURCE_BARRIER_ALL_SUBRESOURCES;\n b.Transition.StateBefore = D3D12_RESOURCE_STATE_COMMON;\n b.Transition.StateAfter = D3D12_RESOURCE_STATE_RENDER_TARGET;\n list->ResourceBarrier(1, &b);\n // structure-revealing pattern: left eye green field, right eye red field,\n // synchronized white bar sweeping across each eye. Any DSC/packing fault\n // shows immediately as wrong colors per eye or a broken/duplicated bar.\n {\n LONG W = (LONG)rdesc.Width, H = (LONG)rdesc.Height, half = W / 2;\n float lc[4] = {0.0f, 0.25f, 0.08f, 1.0f};\n float rc[4] = {0.25f, 0.0f, 0.08f, 1.0f};\n float wc[4] = {0.4f, 0.4f, 0.4f, 1.0f};\n D3D12_RECT rl{0, 0, half, H}, rr{half, 0, W, H};\n list->ClearRenderTargetView(rtv[i], lc, 1, &rl);\n list->ClearRenderTargetView(rtv[i], rc, 1, &rr);\n LONG barW = 64;\n LONG bx = (LONG)(fmod(t * 0.25, 1.0) * (half - barW));\n D3D12_RECT bars[2] = {{bx, 0, bx + barW, H},\n {half + bx, 0, half + bx + barW, H}};\n list->ClearRenderTargetView(rtv[i], wc, 2, bars);\n }\n D3D12_RESOURCE_BARRIER b2 = b;\n b2.Transition.StateBefore = D3D12_RESOURCE_STATE_RENDER_TARGET;\n b2.Transition.StateAfter = D3D12_RESOURCE_STATE_COMMON;\n list->ResourceBarrier(1, &b2);\n HRCHECK(list->Close());","newString":" HRCHECK(alloc->Reset());\n HRCHECK(list->Reset(alloc.Get(), nullptr));\n if (!haveImage) {\n D3D12_RESOURCE_BARRIER b{};\n b.Type = D3D12_RESOURCE_BARRIER_TYPE_TRANSITION;\n b.Transition.pResource = res[i].Get();\n b.Transition.Subresource = D3D12_RESOURCE_BARRIER_ALL_SUBRESOURCES;\n b.Transition.StateBefore = D3D12_RESOURCE_STATE_COMMON;\n b.Transition.StateAfter = D3D12_RESOURCE_STATE_RENDER_TARGET;\n list->ResourceBarrier(1, &b);\n // structure-revealing pattern: left eye green field, right eye red field,\n // synchronized white bar sweeping across each eye. Any DSC/packing fault\n // shows immediately as wrong colors per eye or a broken/duplicated bar.\n {\n LONG W = (LONG)rdesc.Width, H = (LONG)rdesc.Height, half = W / 2;\n float lc[4] = {0.0f, 0.25f, 0.08f, 1.0f};\n float rc[4] = {0.25f, 0.0f, 0.08f, 1.0f};\n float wc[4] = {0.4f, 0.4f, 0.4f, 1.0f};\n D3D12_RECT rl{0, 0, half, H}, rr{half, 0, W, H};\n list->ClearRenderTargetView(rtv[i], lc, 1, &rl);\n list->ClearRenderTargetView(rtv[i], rc, 1, &rr);\n LONG barW = 64;\n LONG bx = (LONG)(fmod(t * 0.25, 1.0) * (half - barW));\n D3D12_RECT bars[2] = {{bx, 0, bx + barW, H},\n {half + bx, 0, half + bx + barW, H}};\n list->ClearRenderTargetView(rtv[i], wc, 2, bars);\n }\n D3D12_RESOURCE_BARRIER b2 = b;\n b2.Transition.StateBefore = D3D12_RESOURCE_STATE_RENDER_TARGET;\n b2.Transition.StateAfter = D3D12_RESOURCE_STATE_COMMON;\n list->ResourceBarrier(1, &b2);\n }\n // --image: surfaces pre-filled; submit the empty list to keep the\n // validated present pacing structure identical.\n HRCHECK(list->Close());","originalFile":null,"structuredPatch":[{"oldStart":311,"oldLines":34,"newStart":311,"newLines":38,"lines":[" "," HRCHECK(alloc->Reset());"," HRCHECK(list->Reset(alloc.Get(), nullptr));","- D3D12_RESOURCE_BARRIER b{};","- b.Type = D3D12_RESOURCE_BARRIER_TYPE_TRANSITION;","- b.Transition.pResource = res[i].Get();","- b.Transition.Subresource = D3D12_RESOURCE_BARRIER_ALL_SUBRESOURCES;","- b.Transition.StateBefore = D3D12_RESOURCE_STATE_COMMON;","- b.Transition.StateAfter = D3D12_RESOURCE_STATE_RENDER_TARGET;","- list->ResourceBarrier(1, &b);","- // structure-revealing pattern: left eye green field, right eye red field,","- // synchronized white bar sweeping across each eye. Any DSC/packing fault","- // shows immediately as wrong colors per eye or a broken/duplicated bar.","- {","- LONG W = (LONG)rdesc.Width, H = (LONG)rdesc.Height, half = W / 2;","- float lc[4] = {0.0f, 0.25f, 0.08f, 1.0f};","- float rc[4] = {0.25f, 0.0f, 0.08f, 1.0f};","- float wc[4] = {0.4f, 0.4f, 0.4f, 1.0f};","- D3D12_RECT rl{0, 0, half, H}, rr{half, 0, W, H};","- list->ClearRenderTargetView(rtv[i], lc, 1, &rl);","- list->ClearRenderTargetView(rtv[i], rc, 1, &rr);","- LONG barW = 64;","- LONG bx = (LONG)(fmod(t * 0.25, 1.0) * (half - barW));","- D3D12_RECT bars[2] = {{bx, 0, bx + barW, H},","- {half + bx, 0, half + bx + barW, H}};","- list->ClearRenderTargetView(rtv[i], wc, 2, bars);","+ if (!haveImage) {","+ D3D12_RESOURCE_BARRIER b{};","+ b.Type = D3D12_RESOURCE_BARRIER_TYPE_TRANSITION;","+ b.Transition.pResource = res[i].Get();","+ b.Transition.Subresource = D3D12_RESOURCE_BARRIER_ALL_SUBRESOURCES;","+ b.Transition.StateBefore = D3D12_RESOURCE_STATE_COMMON;","+ b.Transition.StateAfter = D3D12_RESOURCE_STATE_RENDER_TARGET;","+ list->ResourceBarrier(1, &b);","+ // structure-revealing pattern: left eye green field, right eye red field,","+ // synchronized white bar sweeping across each eye. Any DSC/packing fault","+ // shows immediately as wrong colors per eye or a broken/duplicated bar.","+ {","+ LONG W = (LONG)rdesc.Width, H = (LONG)rdesc.Height, half = W / 2;","+ float lc[4] = {0.0f, 0.25f, 0.08f, 1.0f};","+ float rc[4] = {0.25f, 0.0f, 0.08f, 1.0f};","+ float wc[4] = {0.4f, 0.4f, 0.4f, 1.0f};","+ D3D12_RECT rl{0, 0, half, H}, rr{half, 0, W, H};","+ list->ClearRenderTargetView(rtv[i], lc, 1, &rl);","+ list->ClearRenderTargetView(rtv[i], rc, 1, &rr);","+ LONG barW = 64;","+ LONG bx = (LONG)(fmod(t * 0.25, 1.0) * (half - barW));","+ D3D12_RECT bars[2] = {{bx, 0, bx + barW, H},","+ {half + bx, 0, half + bx + barW, H}};","+ list->ClearRenderTargetView(rtv[i], wc, 2, bars);","+ }","+ D3D12_RESOURCE_BARRIER b2 = b;","+ b2.Transition.StateBefore = D3D12_RESOURCE_STATE_RENDER_TARGET;","+ b2.Transition.StateAfter = D3D12_RESOURCE_STATE_COMMON;","+ list->ResourceBarrier(1, &b2);"," }","- D3D12_RESOURCE_BARRIER b2 = b;","- b2.Transition.StateBefore = D3D12_RESOURCE_STATE_RENDER_TARGET;","- b2.Transition.StateAfter = D3D12_RESOURCE_STATE_COMMON;","- list->ResourceBarrier(1, &b2);","+ // --image: surfaces pre-filled; submit the empty list to keep the","+ // validated present pacing structure identical."," HRCHECK(list->Close());"," ID3D12CommandList* lists[] = {list.Get()};"," queue->ExecuteCommandLists(1, lists);"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"70530499-55d3-47c8-b3b9-a7ca386f7e27","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"7c9f24f3-911f-4c48-b0b2-395e976d1ae8","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_016E5xdctXttnbYuVWTCDqqp","type":"message","role":"assistant","content":[{"type":"text","text":"**Headset live now — 120 s. Put it on.** Eyes-in checklist:\n\n1. **Straight lines** — grid must look straight/rectilinear through the lens, both eyes. Curvature (pincushion or barrel) = warp direction or coeffs wrong.\n2. **Color fringing** — grid lines should be clean white-ish, minimal red/blue edges even toward periphery. Heavy rainbow = per-channel warp broken.\n3. **Triangle points UP** — settles v-axis orientation. Upside-down = flipped v somewhere.\n4. **Small square visible LEFT of center in LEFT eye only** — settles eye assignment.\n5. **Binocular fusion** — grids from both eyes should fuse comfortably into one (this image has no canting/IPD compensation, so slight fusion offset expected — gross divergence is the failure).\n\nReport what you see; rerun anytime with: `cd C:\\Users\\decid\\sauna-spikes\\s3-distortion-truth; .\\s2_nvapi.exe present 120 --image warped_grid_599f3b91.raw`"}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":922,"cache_read_input_tokens":151352,"output_tokens":364,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":922,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":364,"cache_read_input_tokens":151352,"cache_creation_input_tokens":922,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":922},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu3bVVd3z7qPmhVm8HJW","type":"assistant","uuid":"3e8fdcef-c900-4e20-b7fd-416825288fa2","timestamp":"2026-06-10T05:42:42.277Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"3e8fdcef-c900-4e20-b7fd-416825288fa2","isSidechain":false,"type":"system","subtype":"stop_hook_summary","hookCount":1,"hookInfos":[{"command":"$CLAUDE_PLUGIN_ROOT/owl.exe hook-idle","durationMs":48}],"hookErrors":[],"hookAdditionalContext":[],"preventedContinuation":false,"stopReason":"","hasOutput":false,"level":"suggestion","timestamp":"2026-06-10T05:42:42.360Z","uuid":"666a706a-01a6-4874-ac01-f6bb34fce4bd","toolUseID":"3b3b4a49-0e53-4cad-aa89-a64f670bd336","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"666a706a-01a6-4874-ac01-f6bb34fce4bd","isSidechain":false,"type":"system","subtype":"turn_duration","durationMs":778761,"messageCount":214,"timestamp":"2026-06-10T05:42:42.363Z","uuid":"6dc6b43f-ae62-410b-9596-796a353a5dcf","isMeta":false,"userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"type":"queue-operation","operation":"enqueue","timestamp":"2026-06-10T05:44:31.999Z","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","content":"\nbtvah3yov\ntoolu_01P8yMtDMh9DjahuAvuq5FWE\nC:\\Users\\decid\\AppData\\Local\\Temp\\claude\\C--Users-decid-Documents-projects-sauna\\e1077b85-f9e0-461b-b631-ddcce7ef7b3e\\tasks\\btvah3yov.output\ncompleted\nBackground command \"Run eyes-in warped grid presentation on enlyzeam\" completed (exit code 0)\n"}
{"type":"queue-operation","operation":"dequeue","timestamp":"2026-06-10T05:44:32.012Z","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02"}
{"parentUuid":"6dc6b43f-ae62-410b-9596-796a353a5dcf","isSidechain":false,"promptId":"c7dec7ca-510e-414f-8143-b801e9ec5a17","type":"user","message":{"role":"user","content":"\nbtvah3yov\ntoolu_01P8yMtDMh9DjahuAvuq5FWE\nC:\\Users\\decid\\AppData\\Local\\Temp\\claude\\C--Users-decid-Documents-projects-sauna\\e1077b85-f9e0-461b-b631-ddcce7ef7b3e\\tasks\\btvah3yov.output\ncompleted\nBackground command \"Run eyes-in warped grid presentation on enlyzeam\" completed (exit code 0)\n"},"uuid":"6844b835-a1d2-4214-a805-7d0694e5720a","timestamp":"2026-06-10T05:44:32.027Z","permissionMode":"bypassPermissions","origin":{"kind":"task-notification"},"promptSource":"system","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"6844b835-a1d2-4214-a805-7d0694e5720a","isSidechain":false,"attachment":{"type":"hook_additional_context","content":["\nYou have an active owl perch. When spawning subagents or doing work, use run_in_background: true so you stay reachable for incoming messages.\n"],"hookName":"UserPromptSubmit","toolUseID":"hook-43e224c8-b420-470e-8112-5af2e8825bfb","hookEvent":"UserPromptSubmit"},"type":"attachment","uuid":"36839eec-58c8-4520-b3ad-f0b60f9b972e","timestamp":"2026-06-10T05:44:32.102Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"36839eec-58c8-4520-b3ad-f0b60f9b972e","isSidechain":false,"attachment":{"type":"hook_additional_context","content":["CAVEMAN MODE ACTIVE (full). Drop articles/filler/pleasantries/hedging. Fragments OK. Code/commits/security: write normal."],"hookName":"UserPromptSubmit","toolUseID":"hook-5b648ff6-38c1-4117-84b7-6b62ea8908ed","hookEvent":"UserPromptSubmit"},"type":"attachment","uuid":"70b13b68-3491-41a9-a381-de2b1a2a6811","timestamp":"2026-06-10T05:44:32.108Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"70b13b68-3491-41a9-a381-de2b1a2a6811","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01FV424jTTvnu5cdHRUhvKsu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01YXtiusETwCWKqn8v4oxEpm","name":"Read","input":{"file_path":"C:\\Users\\decid\\AppData\\Local\\Temp\\claude\\C--Users-decid-Documents-projects-sauna\\e1077b85-f9e0-461b-b631-ddcce7ef7b3e\\tasks\\btvah3yov.output"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":159,"cache_creation_input_tokens":586,"cache_read_input_tokens":152274,"output_tokens":130,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":586,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":159,"output_tokens":130,"cache_read_input_tokens":152274,"cache_creation_input_tokens":586,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":586},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu3kVoUjzeYdevqeXrNP","type":"assistant","uuid":"851502ea-228d-4b69-a141-a1ae5f7e09be","timestamp":"2026-06-10T05:44:39.934Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"851502ea-228d-4b69-a141-a1ae5f7e09be","isSidechain":false,"promptId":"c7dec7ca-510e-414f-8143-b801e9ec5a17","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YXtiusETwCWKqn8v4oxEpm","type":"tool_result","content":"[Truncated: PARTIAL view — showing lines 1-855 of 2020 total (50172 tokens, cap 25000). Call Read with offset=856 limit=855 for the next page, or Grep to find a specific section. Do NOT answer from this page alone if the answer may be further in the file.]\n\n1\tEnableDirectMode(0x2709) = 0x0\n2\tEnumerateDirectModeDisplays count = 1 (status 0x0)\n3\tusing displayId 0x80061080\n4\tmode[0]: 5088x2544 @ 75.000 Hz format=21 dscMode=0 dscVer=0 slices=0 bppx16=0\n5\tmode[1]: 5088x2544 @ 75.000 Hz format=22 dscMode=0 dscVer=0 slices=0 bppx16=0\n6\tmode[2]: 5088x2544 @ 75.000 Hz format=32 dscMode=0 dscVer=0 slices=0 bppx16=0\n7\tmode[3]: 5088x2544 @ 75.000 Hz format=31 dscMode=0 dscVer=0 slices=0 bppx16=0\n8\tmode[4]: 5088x2544 @ 75.000 Hz format=113 dscMode=0 dscVer=0 slices=0 bppx16=0\n9\tusing mode[1]: 5088x2544 @ 75.000 Hz\n10\tforcing DSC 1.1, 4 slices, 8 bpp\n11\tAcquireDirectModeDisplay = 0x0\n12\tsurface: 5088x2544 format=92\n13\trtv format: 88\n14\tstatic image warped_grid_599f3b91.raw loaded into 2 surfaces\n15\tSetDisplayMode: HDCP disabled, ok\n16\tSetDisplayMode = 0x0\n17\tDisplayControl(POWER_ON) = 0x0\n18\tGetPresentWaitableObject = 0x0\n19\t t= 4.0s frames=300 avg=74.74 fps\n20\t t= 8.0s frames=600 avg=74.87 fps\n21\t t= 12.0s frames=900 avg=74.91 fps\n22\t t= 16.0s frames=1200 avg=74.93 fps\n23\t t= 20.0s frames=1500 avg=74.95 fps\n24\t t= 24.0s frames=1800 avg=74.96 fps\n25\t t= 28.0s frames=2100 avg=74.96 fps\n26\t t= 32.0s frames=2400 avg=74.97 fps\n27\t t= 36.0s frames=2700 avg=74.97 fps\n28\t t= 40.0s frames=3000 avg=74.97 fps\n29\t t= 44.0s frames=3300 avg=74.98 fps\n30\t t= 48.0s frames=3600 avg=74.98 fps\n31\t t= 52.0s frames=3900 avg=74.98 fps\n32\t t= 56.0s frames=4200 avg=74.98 fps\n33\t t= 60.0s frames=4500 avg=74.98 fps\n34\t t= 64.0s frames=4800 avg=74.98 fps\n35\t t= 68.0s frames=5100 avg=74.98 fps\n36\t t= 72.0s frames=5400 avg=74.98 fps\n37\t t= 76.0s frames=5700 avg=74.98 fps\n38\t t= 80.0s frames=6000 avg=74.99 fps\n39\t t= 84.0s frames=6300 avg=74.99 fps\n40\t t= 88.0s frames=6600 avg=74.99 fps\n41\t t= 92.0s frames=6900 avg=74.99 fps\n42\t t= 96.0s frames=7200 avg=74.99 fps\n43\t t= 100.0s frames=7500 avg=74.99 fps\n44\t t= 104.0s frames=7800 avg=74.99 fps\n45\t t= 105.4s frames=8100 avg=76.85 fps\n46\t t= 105.4s frames=8400 avg=79.69 fps\n47\t t= 105.4s frames=8700 avg=82.53 fps\n48\t t= 105.4s frames=9000 avg=85.38 fps\n49\t t= 105.4s frames=9300 avg=88.22 fps\n50\t t= 105.4s frames=9600 avg=91.06 fps\n51\t t= 105.4s frames=9900 avg=93.91 fps\n52\t t= 105.4s frames=10200 avg=96.75 fps\n53\t t= 105.4s frames=10500 avg=99.59 fps\n54\t t= 105.4s frames=10800 avg=102.44 fps\n55\t t= 105.4s frames=11100 avg=105.28 fps\n56\t t= 105.4s frames=11400 avg=108.12 fps\n57\t t= 105.4s frames=11700 avg=110.96 fps\n58\t t= 105.4s frames=12000 avg=113.81 fps\n59\t t= 105.4s frames=12300 avg=116.65 fps\n60\t t= 105.4s frames=12600 avg=119.49 fps\n61\t t= 105.5s frames=12900 avg=122.33 fps\n62\t t= 105.5s frames=13200 avg=125.17 fps\n63\t t= 105.5s frames=13500 avg=128.01 fps\n64\t t= 105.5s frames=13800 avg=130.86 fps\n65\t t= 105.5s frames=14100 avg=133.70 fps\n66\t t= 105.5s frames=14400 avg=136.54 fps\n67\t t= 105.5s frames=14700 avg=139.38 fps\n68\t t= 105.5s frames=15000 avg=142.22 fps\n69\t t= 105.5s frames=15300 avg=145.06 fps\n70\t t= 105.5s frames=15600 avg=147.90 fps\n71\t t= 105.5s frames=15900 avg=150.74 fps\n72\t t= 105.5s frames=16200 avg=153.58 fps\n73\t t= 105.5s frames=16500 avg=156.42 fps\n74\t t= 105.5s frames=16800 avg=159.26 fps\n75\t t= 105.5s frames=17100 avg=162.10 fps\n76\t t= 105.5s frames=17400 avg=164.93 fps\n77\t t= 105.5s frames=17700 avg=167.77 fps\n78\t t= 105.5s frames=18000 avg=170.61 fps\n79\t t= 105.5s frames=18300 avg=173.45 fps\n80\t t= 105.5s frames=18600 avg=176.29 fps\n81\t t= 105.5s frames=18900 avg=179.13 fps\n82\t t= 105.5s frames=19200 avg=181.96 fps\n83\t t= 105.5s frames=19500 avg=184.80 fps\n84\t t= 105.5s frames=19800 avg=187.64 fps\n85\t t= 105.5s frames=20100 avg=190.47 fps\n86\t t= 105.5s frames=20400 avg=193.31 fps\n87\t t= 105.5s frames=20700 avg=196.15 fps\n88\t t= 105.5s frames=21000 avg=198.99 fps\n89\t t= 105.5s frames=21300 avg=201.82 fps\n90\t t= 105.5s frames=21600 avg=204.66 fps\n91\t t= 105.5s frames=21900 avg=207.50 fps\n92\t t= 105.5s frames=22200 avg=210.34 fps\n93\t t= 105.5s frames=22500 avg=213.17 fps\n94\t t= 105.6s frames=22800 avg=216.01 fps\n95\t t= 105.6s frames=23100 avg=218.84 fps\n96\t t= 105.6s frames=23400 avg=221.68 fps\n97\t t= 105.6s frames=23700 avg=224.52 fps\n98\t t= 105.6s frames=24000 avg=227.35 fps\n99\t t= 105.6s frames=24300 avg=230.19 fps\n100\t t= 105.6s frames=24600 avg=233.02 fps\n101\t t= 105.6s frames=24900 avg=235.86 fps\n102\t t= 105.6s frames=25200 avg=238.69 fps\n103\t t= 105.6s frames=25500 avg=241.46 fps\n104\t t= 105.6s frames=25800 avg=244.29 fps\n105\t t= 105.6s frames=26100 avg=247.08 fps\n106\t t= 105.6s frames=26400 avg=249.91 fps\n107\t t= 105.6s frames=26700 avg=252.74 fps\n108\t t= 105.6s frames=27000 avg=255.57 fps\n109\t t= 105.6s frames=27300 avg=258.40 fps\n110\t t= 105.7s frames=27600 avg=261.23 fps\n111\t t= 105.7s frames=27900 avg=264.06 fps\n112\t t= 105.7s frames=28200 avg=266.90 fps\n113\t t= 105.7s frames=28500 avg=269.73 fps\n114\t t= 105.7s frames=28800 avg=272.56 fps\n115\t t= 105.7s frames=29100 avg=275.39 fps\n116\t t= 105.7s frames=29400 avg=278.22 fps\n117\t t= 105.7s frames=29700 avg=281.05 fps\n118\t t= 105.7s frames=30000 avg=283.88 fps\n119\t t= 105.7s frames=30300 avg=286.71 fps\n120\t t= 105.7s frames=30600 avg=289.54 fps\n121\t t= 105.7s frames=30900 avg=292.37 fps\n122\t t= 105.7s frames=31200 avg=295.20 fps\n123\t t= 105.7s frames=31500 avg=298.03 fps\n124\t t= 105.7s frames=31800 avg=300.86 fps\n125\t t= 105.7s frames=32100 avg=303.69 fps\n126\t t= 105.7s frames=32400 avg=306.52 fps\n127\t t= 105.7s frames=32700 avg=309.35 fps\n128\t t= 105.7s frames=33000 avg=312.18 fps\n129\t t= 105.7s frames=33300 avg=315.00 fps\n130\t t= 105.7s frames=33600 avg=317.83 fps\n131\t t= 105.7s frames=33900 avg=320.66 fps\n132\t t= 105.7s frames=34200 avg=323.49 fps\n133\t t= 105.7s frames=34500 avg=326.32 fps\n134\t t= 105.7s frames=34800 avg=329.15 fps\n135\t t= 105.7s frames=35100 avg=331.97 fps\n136\t t= 105.7s frames=35400 avg=334.80 fps\n137\t t= 105.7s frames=35700 avg=337.63 fps\n138\t t= 105.7s frames=36000 avg=340.46 fps\n139\t t= 105.7s frames=36300 avg=343.28 fps\n140\t t= 105.7s frames=36600 avg=346.11 fps\n141\t t= 105.8s frames=36900 avg=348.93 fps\n142\t t= 105.8s frames=37200 avg=351.76 fps\n143\t t= 105.8s frames=37500 avg=354.58 fps\n144\t t= 105.8s frames=37800 avg=357.41 fps\n145\t t= 105.8s frames=38100 avg=360.23 fps\n146\t t= 105.8s frames=38400 avg=363.06 fps\n147\t t= 105.8s frames=38700 avg=365.88 fps\n148\t t= 105.8s frames=39000 avg=368.71 fps\n149\t t= 105.8s frames=39300 avg=371.53 fps\n150\t t= 105.8s frames=39600 avg=374.36 fps\n151\t t= 105.8s frames=39900 avg=377.18 fps\n152\t t= 105.8s frames=40200 avg=380.01 fps\n153\t t= 105.8s frames=40500 avg=382.83 fps\n154\t t= 105.8s frames=40800 avg=385.66 fps\n155\t t= 105.8s frames=41100 avg=388.47 fps\n156\t t= 105.8s frames=41400 avg=391.30 fps\n157\t t= 105.8s frames=41700 avg=394.12 fps\n158\t t= 105.8s frames=42000 avg=396.95 fps\n159\t t= 105.8s frames=42300 avg=399.77 fps\n160\t t= 105.8s frames=42600 avg=402.59 fps\n161\t t= 105.8s frames=42900 avg=405.42 fps\n162\t t= 105.8s frames=43200 avg=408.24 fps\n163\t t= 105.8s frames=43500 avg=411.06 fps\n164\t t= 105.8s frames=43800 avg=413.88 fps\n165\t t= 105.8s frames=44100 avg=416.69 fps\n166\t t= 105.8s frames=44400 avg=419.51 fps\n167\t t= 105.8s frames=44700 avg=422.33 fps\n168\t t= 105.8s frames=45000 avg=425.15 fps\n169\t t= 105.9s frames=45300 avg=427.92 fps\n170\t t= 105.9s frames=45600 avg=430.74 fps\n171\t t= 105.9s frames=45900 avg=433.56 fps\n172\t t= 105.9s frames=46200 avg=436.38 fps\n173\t t= 105.9s frames=46500 avg=439.15 fps\n174\t t= 105.9s frames=46800 avg=441.97 fps\n175\t t= 105.9s frames=47100 avg=444.79 fps\n176\t t= 105.9s frames=47400 avg=447.61 fps\n177\t t= 105.9s frames=47700 avg=450.43 fps\n178\t t= 105.9s frames=48000 avg=453.25 fps\n179\t t= 105.9s frames=48300 avg=456.07 fps\n180\t t= 105.9s frames=48600 avg=458.89 fps\n181\t t= 105.9s frames=48900 avg=461.71 fps\n182\t t= 105.9s frames=49200 avg=464.53 fps\n183\t t= 105.9s frames=49500 avg=467.34 fps\n184\t t= 105.9s frames=49800 avg=470.16 fps\n185\t t= 105.9s frames=50100 avg=472.97 fps\n186\t t= 105.9s frames=50400 avg=475.79 fps\n187\t t= 105.9s frames=50700 avg=478.61 fps\n188\t t= 105.9s frames=51000 avg=481.39 fps\n189\t t= 105.9s frames=51300 avg=484.20 fps\n190\t t= 106.0s frames=51600 avg=487.02 fps\n191\t t= 106.0s frames=51900 avg=489.83 fps\n192\t t= 106.0s frames=52200 avg=492.65 fps\n193\t t= 106.0s frames=52500 avg=495.42 fps\n194\t t= 106.0s frames=52800 avg=498.23 fps\n195\t t= 106.0s frames=53100 avg=501.05 fps\n196\t t= 106.0s frames=53400 avg=503.86 fps\n197\t t= 106.0s frames=53700 avg=506.68 fps\n198\t t= 106.0s frames=54000 avg=509.50 fps\n199\t t= 106.0s frames=54300 avg=512.31 fps\n200\t t= 106.0s frames=54600 avg=515.13 fps\n201\t t= 106.0s frames=54900 avg=517.94 fps\n202\t t= 106.0s frames=55200 avg=520.76 fps\n203\t t= 106.0s frames=55500 avg=523.57 fps\n204\t t= 106.0s frames=55800 avg=526.38 fps\n205\t t= 106.0s frames=56100 avg=529.20 fps\n206\t t= 106.0s frames=56400 avg=532.01 fps\n207\t t= 106.0s frames=56700 avg=534.83 fps\n208\t t= 106.0s frames=57000 avg=537.64 fps\n209\t t= 106.0s frames=57300 avg=540.45 fps\n210\t t= 106.0s frames=57600 avg=543.25 fps\n211\t t= 106.0s frames=57900 avg=546.07 fps\n212\t t= 106.0s frames=58200 avg=548.88 fps\n213\t t= 106.0s frames=58500 avg=551.69 fps\n214\t t= 106.0s frames=58800 avg=554.50 fps\n215\t t= 106.0s frames=59100 avg=557.32 fps\n216\t t= 106.0s frames=59400 avg=560.12 fps\n217\t t= 106.1s frames=59700 avg=562.93 fps\n218\t t= 106.1s frames=60000 avg=565.74 fps\n219\t t= 106.1s frames=60300 avg=568.55 fps\n220\t t= 106.1s frames=60600 avg=571.36 fps\n221\t t= 106.1s frames=60900 avg=574.17 fps\n222\t t= 106.1s frames=61200 avg=576.98 fps\n223\t t= 106.1s frames=61500 avg=579.79 fps\n224\t t= 106.1s frames=61800 avg=582.60 fps\n225\t t= 106.1s frames=62100 avg=585.41 fps\n226\t t= 106.1s frames=62400 avg=588.22 fps\n227\t t= 106.1s frames=62700 avg=591.03 fps\n228\t t= 106.1s frames=63000 avg=593.84 fps\n229\t t= 106.1s frames=63300 avg=596.65 fps\n230\t t= 106.1s frames=63600 avg=599.46 fps\n231\t t= 106.1s frames=63900 avg=602.27 fps\n232\t t= 106.1s frames=64200 avg=605.06 fps\n233\t t= 106.1s frames=64500 avg=607.87 fps\n234\t t= 106.1s frames=64800 avg=610.68 fps\n235\t t= 106.1s frames=65100 avg=613.49 fps\n236\t t= 106.1s frames=65400 avg=616.30 fps\n237\t t= 106.1s frames=65700 avg=619.11 fps\n238\t t= 106.1s frames=66000 avg=621.92 fps\n239\t t= 106.1s frames=66300 avg=624.68 fps\n240\t t= 106.1s frames=66600 avg=627.49 fps\n241\t t= 106.1s frames=66900 avg=630.30 fps\n242\t t= 106.2s frames=67200 avg=633.05 fps\n243\t t= 106.2s frames=67500 avg=635.86 fps\n244\t t= 106.2s frames=67800 avg=638.66 fps\n245\t t= 106.2s frames=68100 avg=641.46 fps\n246\t t= 106.2s frames=68400 avg=644.27 fps\n247\t t= 106.2s frames=68700 avg=647.08 fps\n248\t t= 106.2s frames=69000 avg=649.88 fps\n249\t t= 106.2s frames=69300 avg=652.69 fps\n250\t t= 106.2s frames=69600 avg=655.48 fps\n251\t t= 106.2s frames=69900 avg=658.29 fps\n252\t t= 106.2s frames=70200 avg=661.09 fps\n253\t t= 106.2s frames=70500 avg=663.89 fps\n254\t t= 106.2s frames=70800 avg=666.69 fps\n255\t t= 106.2s frames=71100 avg=669.49 fps\n256\t t= 106.2s frames=71400 avg=672.29 fps\n257\t t= 106.2s frames=71700 avg=675.10 fps\n258\t t= 106.2s frames=72000 avg=677.90 fps\n259\t t= 106.2s frames=72300 avg=680.71 fps\n260\t t= 106.2s frames=72600 avg=683.51 fps\n261\t t= 106.2s frames=72900 avg=686.32 fps\n262\t t= 106.2s frames=73200 avg=689.12 fps\n263\t t= 106.2s frames=73500 avg=691.92 fps\n264\t t= 106.2s frames=73800 avg=694.68 fps\n265\t t= 106.2s frames=74100 avg=697.49 fps\n266\t t= 106.3s frames=74400 avg=700.23 fps\n267\t t= 106.3s frames=74700 avg=702.97 fps\n268\t t= 106.3s frames=75000 avg=705.72 fps\n269\t t= 106.3s frames=75300 avg=708.48 fps\n270\t t= 106.3s frames=75600 avg=711.29 fps\n271\t t= 106.3s frames=75900 avg=714.08 fps\n272\t t= 106.3s frames=76200 avg=716.82 fps\n273\t t= 106.3s frames=76500 avg=719.62 fps\n274\t t= 106.3s frames=76800 avg=722.42 fps\n275\t t= 106.3s frames=77100 avg=725.22 fps\n276\t t= 106.3s frames=77400 avg=728.02 fps\n277\t t= 106.3s frames=77700 avg=730.82 fps\n278\t t= 106.3s frames=78000 avg=733.57 fps\n279\t t= 106.3s frames=78300 avg=736.28 fps\n280\t t= 106.4s frames=78600 avg=739.02 fps\n281\t t= 106.4s frames=78900 avg=741.81 fps\n282\t t= 106.4s frames=79200 avg=744.61 fps\n283\t t= 106.4s frames=79500 avg=747.40 fps\n284\t t= 106.4s frames=79800 avg=750.20 fps\n285\t t= 106.4s frames=80100 avg=753.00 fps\n286\t t= 106.4s frames=80400 avg=755.80 fps\n287\t t= 106.4s frames=80700 avg=758.59 fps\n288\t t= 106.4s frames=81000 avg=761.39 fps\n289\t t= 106.4s frames=81300 avg=764.19 fps\n290\t t= 106.4s frames=81600 avg=766.99 fps\n291\t t= 106.4s frames=81900 avg=769.78 fps\n292\t t= 106.4s frames=82200 avg=772.58 fps\n293\t t= 106.4s frames=82500 avg=775.32 fps\n294\t t= 106.4s frames=82800 avg=778.12 fps\n295\t t= 106.4s frames=83100 avg=780.85 fps\n296\t t= 106.4s frames=83400 avg=783.64 fps\n297\t t= 106.4s frames=83700 avg=786.38 fps\n298\t t= 106.4s frames=84000 avg=789.11 fps\n299\t t= 106.5s frames=84300 avg=791.90 fps\n300\t t= 106.5s frames=84600 avg=794.59 fps\n301\t t= 106.5s frames=84900 avg=797.38 fps\n302\t t= 106.5s frames=85200 avg=800.11 fps\n303\t t= 106.5s frames=85500 avg=802.63 fps\n304\t t= 106.5s frames=85800 avg=805.42 fps\n305\t t= 106.5s frames=86100 avg=808.10 fps\n306\t t= 106.6s frames=86400 avg=810.83 fps\n307\t t= 106.6s frames=86700 avg=813.52 fps\n308\t t= 106.6s frames=87000 avg=816.11 fps\n309\t t= 106.6s frames=87300 avg=818.89 fps\n310\t t= 106.6s frames=87600 avg=821.49 fps\n311\t t= 106.6s frames=87900 avg=824.23 fps\n312\t t= 106.7s frames=88200 avg=826.95 fps\n313\t t= 106.7s frames=88500 avg=829.68 fps\n314\t t= 106.7s frames=88800 avg=832.46 fps\n315\t t= 106.7s frames=89100 avg=835.19 fps\n316\t t= 106.7s frames=89400 avg=837.97 fps\n317\t t= 106.7s frames=89700 avg=840.70 fps\n318\t t= 106.7s frames=90000 avg=843.41 fps\n319\t t= 106.7s frames=90300 avg=846.14 fps\n320\t t= 106.7s frames=90600 avg=848.86 fps\n321\t t= 106.7s frames=90900 avg=851.58 fps\n322\t t= 106.7s frames=91200 avg=854.36 fps\n323\t t= 106.8s frames=91500 avg=857.07 fps\n324\t t= 106.8s frames=91800 avg=859.80 fps\n325\t t= 106.8s frames=92100 avg=862.58 fps\n326\t t= 106.8s frames=92400 avg=865.29 fps\n327\t t= 106.8s frames=92700 avg=868.02 fps\n328\t t= 106.8s frames=93000 avg=870.74 fps\n329\t t= 106.8s frames=93300 avg=873.47 fps\n330\t t= 106.8s frames=93600 avg=876.18 fps\n331\t t= 106.8s frames=93900 avg=878.90 fps\n332\t t= 106.8s frames=94200 avg=881.68 fps\n333\t t= 106.9s frames=94500 avg=884.39 fps\n334\t t= 106.9s frames=94800 avg=887.12 fps\n335\t t= 106.9s frames=95100 avg=889.89 fps\n336\t t= 106.9s frames=95400 avg=892.56 fps\n337\t t= 106.9s frames=95700 avg=895.34 fps\n338\t t= 106.9s frames=96000 avg=898.04 fps\n339\t t= 106.9s frames=96300 avg=900.77 fps\n340\t t= 106.9s frames=96600 avg=903.49 fps\n341\t t= 106.9s frames=96900 avg=906.21 fps\n342\t t= 106.9s frames=97200 avg=908.99 fps\n343\t t= 106.9s frames=97500 avg=911.64 fps\n344\t t= 107.0s frames=97800 avg=914.41 fps\n345\t t= 107.0s frames=98100 avg=917.06 fps\n346\t t= 107.0s frames=98400 avg=919.83 fps\n347\t t= 107.0s frames=98700 avg=922.54 fps\n348\t t= 107.0s frames=99000 avg=925.26 fps\n349\t t= 107.0s frames=99300 avg=928.04 fps\n350\t t= 107.0s frames=99600 avg=930.70 fps\n351\t t= 107.0s frames=99900 avg=933.47 fps\n352\t t= 107.0s frames=100200 avg=936.17 fps\n353\t t= 107.0s frames=100500 avg=938.89 fps\n354\t t= 107.1s frames=100800 avg=941.58 fps\n355\t t= 107.1s frames=101100 avg=944.35 fps\n356\t t= 107.1s frames=101400 avg=947.05 fps\n357\t t= 107.1s frames=101700 avg=949.62 fps\n358\t t= 107.1s frames=102000 avg=952.39 fps\n359\t t= 107.1s frames=102300 avg=955.16 fps\n360\t t= 107.1s frames=102600 avg=957.92 fps\n361\t t= 107.1s frames=102900 avg=960.68 fps\n362\t t= 107.1s frames=103200 avg=963.45 fps\n363\t t= 107.1s frames=103500 avg=966.22 fps\n364\t t= 107.1s frames=103800 avg=968.92 fps\n365\t t= 107.1s frames=104100 avg=971.70 fps\n366\t t= 107.1s frames=104400 avg=974.47 fps\n367\t t= 107.1s frames=104700 avg=977.24 fps\n368\t t= 107.1s frames=105000 avg=980.01 fps\n369\t t= 107.1s frames=105300 avg=982.78 fps\n370\t t= 107.2s frames=105600 avg=985.40 fps\n371\t t= 107.2s frames=105900 avg=988.16 fps\n372\t t= 107.2s frames=106200 avg=990.85 fps\n373\t t= 107.2s frames=106500 avg=993.56 fps\n374\t t= 107.2s frames=106800 avg=996.33 fps\n375\t t= 107.2s frames=107100 avg=999.01 fps\n376\t t= 107.2s frames=107400 avg=1001.78 fps\n377\t t= 107.2s frames=107700 avg=1004.54 fps\n378\t t= 107.2s frames=108000 avg=1007.31 fps\n379\t t= 107.2s frames=108300 avg=1010.08 fps\n380\t t= 107.2s frames=108600 avg=1012.84 fps\n381\t t= 107.2s frames=108900 avg=1015.60 fps\n382\t t= 107.2s frames=109200 avg=1018.36 fps\n383\t t= 107.2s frames=109500 avg=1021.13 fps\n384\t t= 107.2s frames=109800 avg=1023.89 fps\n385\t t= 107.2s frames=110100 avg=1026.65 fps\n386\t t= 107.2s frames=110400 avg=1029.42 fps\n387\t t= 107.2s frames=110700 avg=1032.17 fps\n388\t t= 107.3s frames=111000 avg=1034.94 fps\n389\t t= 107.3s frames=111300 avg=1037.70 fps\n390\t t= 107.3s frames=111600 avg=1040.46 fps\n391\t t= 107.3s frames=111900 avg=1043.22 fps\n392\t t= 107.3s frames=112200 avg=1045.99 fps\n393\t t= 107.3s frames=112500 avg=1048.75 fps\n394\t t= 107.3s frames=112800 avg=1051.52 fps\n395\t t= 107.3s frames=113100 avg=1054.29 fps\n396\t t= 107.3s frames=113400 avg=1057.05 fps\n397\t t= 107.3s frames=113700 avg=1059.81 fps\n398\t t= 107.3s frames=114000 avg=1062.58 fps\n399\t t= 107.3s frames=114300 avg=1065.34 fps\n400\t t= 107.3s frames=114600 avg=1068.09 fps\n401\t t= 107.3s frames=114900 avg=1070.85 fps\n402\t t= 107.3s frames=115200 avg=1073.61 fps\n403\t t= 107.3s frames=115500 avg=1076.37 fps\n404\t t= 107.3s frames=115800 avg=1079.13 fps\n405\t t= 107.3s frames=116100 avg=1081.90 fps\n406\t t= 107.3s frames=116400 avg=1084.66 fps\n407\t t= 107.3s frames=116700 avg=1087.42 fps\n408\t t= 107.3s frames=117000 avg=1090.19 fps\n409\t t= 107.3s frames=117300 avg=1092.95 fps\n410\t t= 107.3s frames=117600 avg=1095.71 fps\n411\t t= 107.3s frames=117900 avg=1098.47 fps\n412\t t= 107.3s frames=118200 avg=1101.24 fps\n413\t t= 107.3s frames=118500 avg=1104.00 fps\n414\t t= 107.3s frames=118800 avg=1106.76 fps\n415\t t= 107.3s frames=119100 avg=1109.53 fps\n416\t t= 107.3s frames=119400 avg=1112.30 fps\n417\t t= 107.3s frames=119700 avg=1115.07 fps\n418\t t= 107.4s frames=120000 avg=1117.83 fps\n419\t t= 107.4s frames=120300 avg=1120.59 fps\n420\t t= 107.4s frames=120600 avg=1123.35 fps\n421\t t= 107.4s frames=120900 avg=1126.11 fps\n422\t t= 107.4s frames=121200 avg=1128.86 fps\n423\t t= 107.4s frames=121500 avg=1131.63 fps\n424\t t= 107.4s frames=121800 avg=1134.38 fps\n425\t t= 107.4s frames=122100 avg=1137.15 fps\n426\t t= 107.4s frames=122400 avg=1139.91 fps\n427\t t= 107.4s frames=122700 avg=1142.67 fps\n428\t t= 107.4s frames=123000 avg=1145.44 fps\n429\t t= 107.4s frames=123300 avg=1148.20 fps\n430\t t= 107.4s frames=123600 avg=1150.96 fps\n431\t t= 107.4s frames=123900 avg=1153.73 fps\n432\t t= 107.4s frames=124200 avg=1156.49 fps\n433\t t= 107.4s frames=124500 avg=1159.25 fps\n434\t t= 107.4s frames=124800 avg=1162.02 fps\n435\t t= 107.4s frames=125100 avg=1164.78 fps\n436\t t= 107.4s frames=125400 avg=1167.54 fps\n437\t t= 107.4s frames=125700 avg=1170.28 fps\n438\t t= 107.4s frames=126000 avg=1173.04 fps\n439\t t= 107.4s frames=126300 avg=1175.80 fps\n440\t t= 107.4s frames=126600 avg=1178.55 fps\n441\t t= 107.4s frames=126900 avg=1181.17 fps\n442\t t= 107.5s frames=127200 avg=1183.75 fps\n443\t t= 107.5s frames=127500 avg=1186.45 fps\n444\t t= 107.5s frames=127800 avg=1189.18 fps\n445\t t= 107.5s frames=128100 avg=1191.90 fps\n446\t t= 107.5s frames=128400 avg=1194.61 fps\n447\t t= 107.5s frames=128700 avg=1197.35 fps\n448\t t= 107.5s frames=129000 avg=1200.04 fps\n449\t t= 107.5s frames=129300 avg=1202.76 fps\n450\t t= 107.5s frames=129600 avg=1205.45 fps\n451\t t= 107.5s frames=129900 avg=1208.16 fps\n452\t t= 107.5s frames=130200 avg=1210.90 fps\n453\t t= 107.5s frames=130500 avg=1213.66 fps\n454\t t= 107.5s frames=130800 avg=1216.41 fps\n455\t t= 107.5s frames=131100 avg=1219.17 fps\n456\t t= 107.5s frames=131400 avg=1221.91 fps\n457\t t= 107.5s frames=131700 avg=1224.65 fps\n458\t t= 107.5s frames=132000 avg=1227.40 fps\n459\t t= 107.5s frames=132300 avg=1230.16 fps\n460\t t= 107.6s frames=132600 avg=1232.90 fps\n461\t t= 107.6s frames=132900 avg=1235.65 fps\n462\t t= 107.6s frames=133200 avg=1238.40 fps\n463\t t= 107.6s frames=133500 avg=1241.15 fps\n464\t t= 107.6s frames=133800 avg=1243.90 fps\n465\t t= 107.6s frames=134100 avg=1246.66 fps\n466\t t= 107.6s frames=134400 avg=1249.41 fps\n467\t t= 107.6s frames=134700 avg=1252.17 fps\n468\t t= 107.6s frames=135000 avg=1254.93 fps\n469\t t= 107.6s frames=135300 avg=1257.69 fps\n470\t t= 107.6s frames=135600 avg=1260.44 fps\n471\t t= 107.6s frames=135900 avg=1263.19 fps\n472\t t= 107.6s frames=136200 avg=1265.94 fps\n473\t t= 107.6s frames=136500 avg=1268.70 fps\n474\t t= 107.6s frames=136800 avg=1271.46 fps\n475\t t= 107.6s frames=137100 avg=1274.22 fps\n476\t t= 107.6s frames=137400 avg=1276.96 fps\n477\t t= 107.6s frames=137700 avg=1279.72 fps\n478\t t= 107.6s frames=138000 avg=1282.47 fps\n479\t t= 107.6s frames=138300 avg=1285.23 fps\n480\t t= 107.6s frames=138600 avg=1287.99 fps\n481\t t= 107.6s frames=138900 avg=1290.75 fps\n482\t t= 107.6s frames=139200 avg=1293.49 fps\n483\t t= 107.6s frames=139500 avg=1296.25 fps\n484\t t= 107.6s frames=139800 avg=1299.00 fps\n485\t t= 107.6s frames=140100 avg=1301.75 fps\n486\t t= 107.6s frames=140400 avg=1304.51 fps\n487\t t= 107.6s frames=140700 avg=1307.24 fps\n488\t t= 107.6s frames=141000 avg=1310.00 fps\n489\t t= 107.6s frames=141300 avg=1312.75 fps\n490\t t= 107.6s frames=141600 avg=1315.50 fps\n491\t t= 107.6s frames=141900 avg=1318.26 fps\n492\t t= 107.6s frames=142200 avg=1321.01 fps\n493\t t= 107.6s frames=142500 avg=1323.76 fps\n494\t t= 107.7s frames=142800 avg=1326.50 fps\n495\t t= 107.7s frames=143100 avg=1329.25 fps\n496\t t= 107.7s frames=143400 avg=1332.00 fps\n497\t t= 107.7s frames=143700 avg=1334.75 fps\n498\t t= 107.7s frames=144000 avg=1337.50 fps\n499\t t= 107.7s frames=144300 avg=1340.25 fps\n500\t t= 107.7s frames=144600 avg=1343.00 fps\n501\t t= 107.7s frames=144900 avg=1345.75 fps\n502\t t= 107.7s frames=145200 avg=1348.47 fps\n503\t t= 107.7s frames=145500 avg=1351.20 fps\n504\t t= 107.7s frames=145800 avg=1353.95 fps\n505\t t= 107.7s frames=146100 avg=1356.70 fps\n506\t t= 107.7s frames=146400 avg=1359.45 fps\n507\t t= 107.7s frames=146700 avg=1362.20 fps\n508\t t= 107.7s frames=147000 avg=1364.94 fps\n509\t t= 107.7s frames=147300 avg=1367.69 fps\n510\t t= 107.7s frames=147600 avg=1370.44 fps\n511\t t= 107.7s frames=147900 avg=1373.17 fps\n512\t t= 107.7s frames=148200 avg=1375.92 fps\n513\t t= 107.7s frames=148500 avg=1378.67 fps\n514\t t= 107.7s frames=148800 avg=1381.42 fps\n515\t t= 107.7s frames=149100 avg=1384.17 fps\n516\t t= 107.7s frames=149400 avg=1386.93 fps\n517\t t= 107.7s frames=149700 avg=1389.66 fps\n518\t t= 107.7s frames=150000 avg=1392.40 fps\n519\t t= 107.7s frames=150300 avg=1395.14 fps\n520\t t= 107.7s frames=150600 avg=1397.88 fps\n521\t t= 107.7s frames=150900 avg=1400.63 fps\n522\t t= 107.7s frames=151200 avg=1403.37 fps\n523\t t= 107.7s frames=151500 avg=1406.12 fps\n524\t t= 107.7s frames=151800 avg=1408.87 fps\n525\t t= 107.7s frames=152100 avg=1411.62 fps\n526\t t= 107.8s frames=152400 avg=1414.37 fps\n527\t t= 107.8s frames=152700 avg=1417.11 fps\n528\t t= 107.8s frames=153000 avg=1419.85 fps\n529\t t= 107.8s frames=153300 avg=1422.60 fps\n530\t t= 107.8s frames=153600 avg=1425.35 fps\n531\t t= 107.8s frames=153900 avg=1428.10 fps\n532\t t= 107.8s frames=154200 avg=1430.84 fps\n533\t t= 107.8s frames=154500 avg=1433.59 fps\n534\t t= 107.8s frames=154800 avg=1436.34 fps\n535\t t= 107.8s frames=155100 avg=1439.09 fps\n536\t t= 107.8s frames=155400 avg=1441.84 fps\n537\t t= 107.8s frames=155700 avg=1444.59 fps\n538\t t= 107.8s frames=156000 avg=1447.33 fps\n539\t t= 107.8s frames=156300 avg=1450.07 fps\n540\t t= 107.8s frames=156600 avg=1452.82 fps\n541\t t= 107.8s frames=156900 avg=1455.56 fps\n542\t t= 107.8s frames=157200 avg=1458.31 fps\n543\t t= 107.8s frames=157500 avg=1461.06 fps\n544\t t= 107.8s frames=157800 avg=1463.79 fps\n545\t t= 107.8s frames=158100 avg=1466.54 fps\n546\t t= 107.8s frames=158400 avg=1469.28 fps\n547\t t= 107.8s frames=158700 avg=1472.03 fps\n548\t t= 107.8s frames=159000 avg=1474.78 fps\n549\t t= 107.8s frames=159300 avg=1477.52 fps\n550\t t= 107.8s frames=159600 avg=1480.26 fps\n551\t t= 107.8s frames=159900 avg=1483.01 fps\n552\t t= 107.8s frames=160200 avg=1485.74 fps\n553\t t= 107.8s frames=160500 avg=1488.48 fps\n554\t t= 107.8s frames=160800 avg=1491.21 fps\n555\t t= 107.8s frames=161100 avg=1493.94 fps\n556\t t= 107.8s frames=161400 avg=1496.67 fps\n557\t t= 107.8s frames=161700 avg=1499.40 fps\n558\t t= 107.8s frames=162000 avg=1502.14 fps\n559\t t= 107.9s frames=162300 avg=1504.85 fps\n560\t t= 107.9s frames=162600 avg=1507.59 fps\n561\t t= 107.9s frames=162900 avg=1510.33 fps\n562\t t= 107.9s frames=163200 avg=1513.02 fps\n563\t t= 107.9s frames=163500 avg=1515.69 fps\n564\t t= 107.9s frames=163800 avg=1518.31 fps\n565\t t= 107.9s frames=164100 avg=1521.02 fps\n566\t t= 107.9s frames=164400 avg=1523.62 fps\n567\t t= 107.9s frames=164700 avg=1526.34 fps\n568\t t= 107.9s frames=165000 avg=1529.07 fps\n569\t t= 107.9s frames=165300 avg=1531.80 fps\n570\t t= 107.9s frames=165600 avg=1534.54 fps\n571\t t= 107.9s frames=165900 avg=1537.25 fps\n572\t t= 107.9s frames=166200 avg=1539.96 fps\n573\t t= 107.9s frames=166500 avg=1542.67 fps\n574\t t= 107.9s frames=166800 avg=1545.38 fps\n575\t t= 107.9s frames=167100 avg=1548.09 fps\n576\t t= 107.9s frames=167400 avg=1550.76 fps\n577\t t= 108.0s frames=167700 avg=1553.47 fps\n578\t t= 108.0s frames=168000 avg=1556.19 fps\n579\t t= 108.0s frames=168300 avg=1558.90 fps\n580\t t= 108.0s frames=168600 avg=1561.56 fps\n581\t t= 108.0s frames=168900 avg=1564.22 fps\n582\t t= 108.0s frames=169200 avg=1566.89 fps\n583\t t= 108.0s frames=169500 avg=1569.54 fps\n584\t t= 108.0s frames=169800 avg=1572.13 fps\n585\t t= 108.0s frames=170100 avg=1574.82 fps\n586\t t= 108.0s frames=170400 avg=1577.53 fps\n587\t t= 108.0s frames=170700 avg=1580.17 fps\n588\t t= 108.0s frames=171000 avg=1582.88 fps\n589\t t= 108.0s frames=171300 avg=1585.56 fps\n590\t t= 108.0s frames=171600 avg=1588.25 fps\n591\t t= 108.1s frames=171900 avg=1590.92 fps\n592\t t= 108.1s frames=172200 avg=1593.60 fps\n593\t t= 108.1s frames=172500 avg=1596.30 fps\n594\t t= 108.1s frames=172800 avg=1598.93 fps\n595\t t= 108.1s frames=173100 avg=1601.56 fps\n596\t t= 108.1s frames=173400 avg=1604.20 fps\n597\t t= 108.1s frames=173700 avg=1606.62 fps\n598\t t= 108.1s frames=174000 avg=1609.07 fps\n599\t t= 108.2s frames=174300 avg=1611.16 fps\n600\t t= 108.2s frames=174600 avg=1613.79 fps\n601\t t= 108.2s frames=174900 avg=1616.34 fps\n602\t t= 108.2s frames=175200 avg=1619.02 fps\n603\t t= 108.2s frames=175500 avg=1621.51 fps\n604\t t= 108.3s frames=175800 avg=1623.68 fps\n605\t t= 108.3s frames=176100 avg=1626.23 fps\n606\t t= 108.3s frames=176400 avg=1628.65 fps\n607\t t= 108.3s frames=176700 avg=1631.29 fps\n608\t t= 108.4s frames=177000 avg=1633.50 fps\n609\t t= 108.4s frames=177300 avg=1636.16 fps\n610\t t= 108.4s frames=177600 avg=1638.84 fps\n611\t t= 108.4s frames=177900 avg=1641.53 fps\n612\t t= 108.4s frames=178200 avg=1644.24 fps\n613\t t= 108.4s frames=178500 avg=1646.93 fps\n614\t t= 108.4s frames=178800 avg=1649.59 fps\n615\t t= 108.4s frames=179100 avg=1652.27 fps\n616\t t= 108.4s frames=179400 avg=1654.96 fps\n617\t t= 108.4s frames=179700 avg=1657.65 fps\n618\t t= 108.4s frames=180000 avg=1660.33 fps\n619\t t= 108.4s frames=180300 avg=1663.00 fps\n620\t t= 108.4s frames=180600 avg=1665.69 fps\n621\t t= 108.4s frames=180900 avg=1668.36 fps\n622\t t= 108.4s frames=181200 avg=1671.02 fps\n623\t t= 108.4s frames=181500 avg=1673.69 fps\n624\t t= 108.4s frames=181800 avg=1676.39 fps\n625\t t= 108.5s frames=182100 avg=1679.09 fps\n626\t t= 108.5s frames=182400 avg=1681.79 fps\n627\t t= 108.5s frames=182700 avg=1684.49 fps\n628\t t= 108.5s frames=183000 avg=1687.20 fps\n629\t t= 108.5s frames=183300 avg=1689.86 fps\n630\t t= 108.5s frames=183600 avg=1692.56 fps\n631\t t= 108.5s frames=183900 avg=1695.26 fps\n632\t t= 108.5s frames=184200 avg=1697.97 fps\n633\t t= 108.5s frames=184500 avg=1700.66 fps\n634\t t= 108.5s frames=184800 avg=1703.31 fps\n635\t t= 108.5s frames=185100 avg=1705.99 fps\n636\t t= 108.5s frames=185400 avg=1708.67 fps\n637\t t= 108.5s frames=185700 avg=1711.35 fps\n638\t t= 108.5s frames=186000 avg=1714.06 fps\n639\t t= 108.5s frames=186300 avg=1716.76 fps\n640\t t= 108.5s frames=186600 avg=1719.48 fps\n641\t t= 108.5s frames=186900 avg=1722.18 fps\n642\t t= 108.5s frames=187200 avg=1724.87 fps\n643\t t= 108.5s frames=187500 avg=1727.57 fps\n644\t t= 108.5s frames=187800 avg=1730.27 fps\n645\t t= 108.5s frames=188100 avg=1732.99 fps\n646\t t= 108.5s frames=188400 avg=1735.71 fps\n647\t t= 108.5s frames=188700 avg=1738.39 fps\n648\t t= 108.6s frames=189000 avg=1741.06 fps\n649\t t= 108.6s frames=189300 avg=1743.75 fps\n650\t t= 108.6s frames=189600 avg=1746.43 fps\n651\t t= 108.6s frames=189900 avg=1749.11 fps\n652\t t= 108.6s frames=190200 avg=1751.82 fps\n653\t t= 108.6s frames=190500 avg=1754.52 fps\n654\t t= 108.6s frames=190800 avg=1757.21 fps\n655\t t= 108.6s frames=191100 avg=1759.88 fps\n656\t t= 108.6s frames=191400 avg=1762.56 fps\n657\t t= 108.6s frames=191700 avg=1765.27 fps\n658\t t= 108.6s frames=192000 avg=1767.95 fps\n659\t t= 108.6s frames=192300 avg=1770.66 fps\n660\t t= 108.6s frames=192600 avg=1773.37 fps\n661\t t= 108.6s frames=192900 avg=1776.07 fps\n662\t t= 108.6s frames=193200 avg=1778.75 fps\n663\t t= 108.6s frames=193500 avg=1781.43 fps\n664\t t= 108.6s frames=193800 avg=1784.14 fps\n665\t t= 108.6s frames=194100 avg=1786.82 fps\n666\t t= 108.6s frames=194400 avg=1789.47 fps\n667\t t= 108.6s frames=194700 avg=1792.17 fps\n668\t t= 108.6s frames=195000 avg=1794.84 fps\n669\t t= 108.7s frames=195300 avg=1797.51 fps\n670\t t= 108.7s frames=195600 avg=1800.18 fps\n671\t t= 108.7s frames=195900 avg=1802.80 fps\n672\t t= 108.7s frames=196200 avg=1805.47 fps\n673\t t= 108.7s frames=196500 avg=1808.18 fps\n674\t t= 108.7s frames=196800 avg=1810.88 fps\n675\t t= 108.7s frames=197100 avg=1813.56 fps\n676\t t= 108.7s frames=197400 avg=1816.24 fps\n677\t t= 108.7s frames=197700 avg=1818.94 fps\n678\t t= 108.7s frames=198000 avg=1821.60 fps\n679\t t= 108.7s frames=198300 avg=1824.29 fps\n680\t t= 108.7s frames=198600 avg=1826.95 fps\n681\t t= 108.7s frames=198900 avg=1829.66 fps\n682\t t= 108.7s frames=199200 avg=1832.38 fps\n683\t t= 108.7s frames=199500 avg=1835.09 fps\n684\t t= 108.7s frames=199800 avg=1837.76 fps\n685\t t= 108.7s frames=200100 avg=1840.45 fps\n686\t t= 108.7s frames=200400 avg=1843.16 fps\n687\t t= 108.7s frames=200700 avg=1845.87 fps\n688\t t= 108.7s frames=201000 avg=1848.57 fps\n689\t t= 108.7s frames=201300 avg=1851.24 fps\n690\t t= 108.7s frames=201600 avg=1853.94 fps\n691\t t= 108.7s frames=201900 avg=1856.62 fps\n692\t t= 108.8s frames=202200 avg=1859.29 fps\n693\t t= 108.8s frames=202500 avg=1861.98 fps\n694\t t= 108.8s frames=202800 avg=1864.65 fps\n695\t t= 108.8s frames=203100 avg=1867.33 fps\n696\t t= 108.8s frames=203400 avg=1869.99 fps\n697\t t= 108.8s frames=203700 avg=1872.69 fps\n698\t t= 108.8s frames=204000 avg=1875.40 fps\n699\t t= 108.8s frames=204300 avg=1878.11 fps\n700\t t= 108.8s frames=204600 avg=1880.81 fps\n701\t t= 108.8s frames=204900 avg=1883.52 fps\n702\t t= 108.8s frames=205200 avg=1886.21 fps\n703\t t= 108.8s frames=205500 avg=1888.92 fps\n704\t t= 108.8s frames=205800 avg=1891.63 fps\n705\t t= 108.8s frames=206100 avg=1894.34 fps\n706\t t= 108.8s frames=206400 avg=1897.05 fps\n707\t t= 108.8s frames=206700 avg=1899.75 fps\n708\t t= 108.8s frames=207000 avg=1902.46 fps\n709\t t= 108.8s frames=207300 avg=1905.12 fps\n710\t t= 108.8s frames=207600 avg=1907.80 fps\n711\t t= 108.8s frames=207900 avg=1910.51 fps\n712\t t= 108.8s frames=208200 avg=1913.22 fps\n713\t t= 108.8s frames=208500 avg=1915.93 fps\n714\t t= 108.8s frames=208800 avg=1918.64 fps\n715\t t= 108.8s frames=209100 avg=1921.34 fps\n716\t t= 108.8s frames=209400 avg=1924.02 fps\n717\t t= 108.8s frames=209700 avg=1926.72 fps\n718\t t= 108.8s frames=210000 avg=1929.43 fps\n719\t t= 108.8s frames=210300 avg=1932.14 fps\n720\t t= 108.8s frames=210600 avg=1934.85 fps\n721\t t= 108.8s frames=210900 avg=1937.56 fps\n722\t t= 108.9s frames=211200 avg=1940.25 fps\n723\t t= 108.9s frames=211500 avg=1942.96 fps\n724\t t= 108.9s frames=211800 avg=1945.66 fps\n725\t t= 108.9s frames=212100 avg=1948.33 fps\n726\t t= 108.9s frames=212400 avg=1950.99 fps\n727\t t= 108.9s frames=212700 avg=1953.65 fps\n728\t t= 108.9s frames=213000 avg=1956.31 fps\n729\t t= 108.9s frames=213300 avg=1958.99 fps\n730\t t= 108.9s frames=213600 avg=1961.69 fps\n731\t t= 108.9s frames=213900 avg=1964.40 fps\n732\t t= 108.9s frames=214200 avg=1967.09 fps\n733\t t= 108.9s frames=214500 avg=1969.80 fps\n734\t t= 108.9s frames=214800 avg=1972.49 fps\n735\t t= 108.9s frames=215100 avg=1975.19 fps\n736\t t= 108.9s frames=215400 avg=1977.89 fps\n737\t t= 108.9s frames=215700 avg=1980.60 fps\n738\t t= 108.9s frames=216000 avg=1983.31 fps\n739\t t= 108.9s frames=216300 avg=1986.00 fps\n740\t t= 108.9s frames=216600 avg=1988.66 fps\n741\t t= 108.9s frames=216900 avg=1991.34 fps\n742\t t= 108.9s frames=217200 avg=1994.04 fps\n743\t t= 108.9s frames=217500 avg=1996.75 fps\n744\t t= 108.9s frames=217800 avg=1999.46 fps\n745\t t= 108.9s frames=218100 avg=2002.16 fps\n746\t t= 108.9s frames=218400 avg=2004.87 fps\n747\t t= 108.9s frames=218700 avg=2007.58 fps\n748\t t= 108.9s frames=219000 avg=2010.29 fps\n749\t t= 108.9s frames=219300 avg=2012.99 fps\n750\t t= 108.9s frames=219600 avg=2015.69 fps\n751\t t= 108.9s frames=219900 avg=2018.39 fps\n752\t t= 109.0s frames=220200 avg=2021.09 fps\n753\t t= 109.0s frames=220500 avg=2023.79 fps\n754\t t= 109.0s frames=220800 avg=2026.50 fps\n755\t t= 109.0s frames=221100 avg=2029.20 fps\n756\t t= 109.0s frames=221400 avg=2031.88 fps\n757\t t= 109.0s frames=221700 avg=2034.57 fps\n758\t t= 109.0s frames=222000 avg=2037.27 fps\n759\t t= 109.0s frames=222300 avg=2039.98 fps\n760\t t= 109.0s frames=222600 avg=2042.68 fps\n761\t t= 109.0s frames=222900 avg=2045.39 fps\n762\t t= 109.0s frames=223200 avg=2048.09 fps\n763\t t= 109.0s frames=223500 avg=2050.79 fps\n764\t t= 109.0s frames=223800 avg=2053.49 fps\n765\t t= 109.0s frames=224100 avg=2056.19 fps\n766\t t= 109.0s frames=224400 avg=2058.87 fps\n767\t t= 109.0s frames=224700 avg=2061.58 fps\n768\t t= 109.0s frames=225000 avg=2064.28 fps\n769\t t= 109.0s frames=225300 avg=2066.99 fps\n770\t t= 109.0s frames=225600 avg=2069.69 fps\n771\t t= 109.0s frames=225900 avg=2072.39 fps\n772\t t= 109.0s frames=226200 avg=2075.09 fps\n773\t t= 109.0s frames=226500 avg=2077.80 fps\n774\t t= 109.0s frames=226800 avg=2080.50 fps\n775\t t= 109.0s frames=227100 avg=2083.21 fps\n776\t t= 109.0s frames=227400 avg=2085.92 fps\n777\t t= 109.0s frames=227700 avg=2088.62 fps\n778\t t= 109.0s frames=228000 avg=2091.32 fps\n779\t t= 109.0s frames=228300 avg=2094.01 fps\n780\t t= 109.0s frames=228600 avg=2096.71 fps\n781\t t= 109.0s frames=228900 avg=2099.41 fps\n782\t t= 109.0s frames=229200 avg=2102.10 fps\n783\t t= 109.0s frames=229500 avg=2104.80 fps\n784\t t= 109.0s frames=229800 avg=2107.46 fps\n785\t t= 109.0s frames=230100 avg=2110.10 fps\n786\t t= 109.1s frames=230400 avg=2112.78 fps\n787\t t= 109.1s frames=230700 avg=2115.48 fps\n788\t t= 109.1s frames=231000 avg=2118.17 fps\n789\t t= 109.1s frames=231300 avg=2120.88 fps\n790\t t= 109.1s frames=231600 avg=2123.58 fps\n791\t t= 109.1s frames=231900 avg=2126.28 fps\n792\t t= 109.1s frames=232200 avg=2128.97 fps\n793\t t= 109.1s frames=232500 avg=2131.67 fps\n794\t t= 109.1s frames=232800 avg=2134.36 fps\n795\t t= 109.1s frames=233100 avg=2137.06 fps\n796\t t= 109.1s frames=233400 avg=2139.76 fps\n797\t t= 109.1s frames=233700 avg=2142.46 fps\n798\t t= 109.1s frames=234000 avg=2145.16 fps\n799\t t= 109.1s frames=234300 avg=2147.87 fps\n800\t t= 109.1s frames=234600 avg=2150.56 fps\n801\t t= 109.1s frames=234900 avg=2153.26 fps\n802\t t= 109.1s frames=235200 avg=2155.95 fps\n803\t t= 109.1s frames=235500 avg=2158.65 fps\n804\t t= 109.1s frames=235800 avg=2161.34 fps\n805\t t= 109.1s frames=236100 avg=2164.03 fps\n806\t t= 109.1s frames=236400 avg=2166.73 fps\n807\t t= 109.1s frames=236700 avg=2169.42 fps\n808\t t= 109.1s frames=237000 avg=2172.11 fps\n809\t t= 109.1s frames=237300 avg=2174.81 fps\n810\t t= 109.1s frames=237600 avg=2177.50 fps\n811\t t= 109.1s frames=237900 avg=2180.19 fps\n812\t t= 109.1s frames=238200 avg=2182.89 fps\n813\t t= 109.1s frames=238500 avg=2185.59 fps\n814\t t= 109.1s frames=238800 avg=2188.29 fps\n815\t t= 109.1s frames=239100 avg=2190.99 fps\n816\t t= 109.1s frames=239400 avg=2193.69 fps\n817\t t= 109.1s frames=239700 avg=2196.38 fps\n818\t t= 109.1s frames=240000 avg=2199.07 fps\n819\t t= 109.1s frames=240300 avg=2201.77 fps\n820\t t= 109.1s frames=240600 avg=2204.47 fps\n821\t t= 109.1s frames=240900 avg=2207.17 fps\n822\t t= 109.1s frames=241200 avg=2209.86 fps\n823\t t= 109.2s frames=241500 avg=2212.52 fps\n824\t t= 109.2s frames=241800 avg=2215.17 fps\n825\t t= 109.2s frames=242100 avg=2217.86 fps\n826\t t= 109.2s frames=242400 avg=2220.54 fps\n827\t t= 109.2s frames=242700 avg=2223.22 fps\n828\t t= 109.2s frames=243000 avg=2225.92 fps\n829\t t= 109.2s frames=243300 avg=2228.62 fps\n830\t t= 109.2s frames=243600 avg=2231.31 fps\n831\t t= 109.2s frames=243900 avg=2234.01 fps\n832\t t= 109.2s frames=244200 avg=2236.70 fps\n833\t t= 109.2s frames=244500 avg=2239.38 fps\n834\t t= 109.2s frames=244800 avg=2242.06 fps\n835\t t= 109.2s frames=245100 avg=2244.75 fps\n836\t t= 109.2s frames=245400 avg=2247.45 fps\n837\t t= 109.2s frames=245700 avg=2250.14 fps\n838\t t= 109.2s frames=246000 avg=2252.83 fps\n839\t t= 109.2s frames=246300 avg=2255.53 fps\n840\t t= 109.2s frames=246600 avg=2258.22 fps\n841\t t= 109.2s frames=246900 avg=2260.92 fps\n842\t t= 109.2s frames=247200 avg=2263.62 fps\n843\t t= 109.2s frames=247500 avg=2266.31 fps\n844\t t= 109.2s frames=247800 avg=2268.99 fps\n845\t t= 109.2s frames=248100 avg=2271.68 fps\n846\t t= 109.2s frames=248400 avg=2274.37 fps\n847\t t= 109.2s frames=248700 avg=2277.07 fps\n848\t t= 109.2s frames=249000 avg=2279.76 fps\n849\t t= 109.2s frames=249300 avg=2282.45 fps\n850\t t= 109.2s frames=249600 avg=2285.11 fps\n851\t t= 109.2s frames=249900 avg=2287.78 fps\n852\t t= 109.2s frames=250200 avg=2290.45 fps\n853\t t= 109.2s frames=250500 avg=2293.12 fps\n854\t t= 109.2s frames=250800 avg=2295.79 fps\n855\t t= 109.2s frames=251100 avg=2298.48 fps"}]},"uuid":"f384205b-0f33-4f5a-92bc-780c6bfd6b2f","timestamp":"2026-06-10T05:44:40.436Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\decid\\AppData\\Local\\Temp\\claude\\C--Users-decid-Documents-projects-sauna\\e1077b85-f9e0-461b-b631-ddcce7ef7b3e\\tasks\\btvah3yov.output","content":"EnableDirectMode(0x2709) = 0x0\nEnumerateDirectModeDisplays count = 1 (status 0x0)\nusing displayId 0x80061080\nmode[0]: 5088x2544 @ 75.000 Hz format=21 dscMode=0 dscVer=0 slices=0 bppx16=0\nmode[1]: 5088x2544 @ 75.000 Hz format=22 dscMode=0 dscVer=0 slices=0 bppx16=0\nmode[2]: 5088x2544 @ 75.000 Hz format=32 dscMode=0 dscVer=0 slices=0 bppx16=0\nmode[3]: 5088x2544 @ 75.000 Hz format=31 dscMode=0 dscVer=0 slices=0 bppx16=0\nmode[4]: 5088x2544 @ 75.000 Hz format=113 dscMode=0 dscVer=0 slices=0 bppx16=0\nusing mode[1]: 5088x2544 @ 75.000 Hz\nforcing DSC 1.1, 4 slices, 8 bpp\nAcquireDirectModeDisplay = 0x0\nsurface: 5088x2544 format=92\nrtv format: 88\nstatic image warped_grid_599f3b91.raw loaded into 2 surfaces\nSetDisplayMode: HDCP disabled, ok\nSetDisplayMode = 0x0\nDisplayControl(POWER_ON) = 0x0\nGetPresentWaitableObject = 0x0\n t= 4.0s frames=300 avg=74.74 fps\n t= 8.0s frames=600 avg=74.87 fps\n t= 12.0s frames=900 avg=74.91 fps\n t= 16.0s frames=1200 avg=74.93 fps\n t= 20.0s frames=1500 avg=74.95 fps\n t= 24.0s frames=1800 avg=74.96 fps\n t= 28.0s frames=2100 avg=74.96 fps\n t= 32.0s frames=2400 avg=74.97 fps\n t= 36.0s frames=2700 avg=74.97 fps\n t= 40.0s frames=3000 avg=74.97 fps\n t= 44.0s frames=3300 avg=74.98 fps\n t= 48.0s frames=3600 avg=74.98 fps\n t= 52.0s frames=3900 avg=74.98 fps\n t= 56.0s frames=4200 avg=74.98 fps\n t= 60.0s frames=4500 avg=74.98 fps\n t= 64.0s frames=4800 avg=74.98 fps\n t= 68.0s frames=5100 avg=74.98 fps\n t= 72.0s frames=5400 avg=74.98 fps\n t= 76.0s frames=5700 avg=74.98 fps\n t= 80.0s frames=6000 avg=74.99 fps\n t= 84.0s frames=6300 avg=74.99 fps\n t= 88.0s frames=6600 avg=74.99 fps\n t= 92.0s frames=6900 avg=74.99 fps\n t= 96.0s frames=7200 avg=74.99 fps\n t= 100.0s frames=7500 avg=74.99 fps\n t= 104.0s frames=7800 avg=74.99 fps\n t= 105.4s frames=8100 avg=76.85 fps\n t= 105.4s frames=8400 avg=79.69 fps\n t= 105.4s frames=8700 avg=82.53 fps\n t= 105.4s frames=9000 avg=85.38 fps\n t= 105.4s frames=9300 avg=88.22 fps\n t= 105.4s frames=9600 avg=91.06 fps\n t= 105.4s frames=9900 avg=93.91 fps\n t= 105.4s frames=10200 avg=96.75 fps\n t= 105.4s frames=10500 avg=99.59 fps\n t= 105.4s frames=10800 avg=102.44 fps\n t= 105.4s frames=11100 avg=105.28 fps\n t= 105.4s frames=11400 avg=108.12 fps\n t= 105.4s frames=11700 avg=110.96 fps\n t= 105.4s frames=12000 avg=113.81 fps\n t= 105.4s frames=12300 avg=116.65 fps\n t= 105.4s frames=12600 avg=119.49 fps\n t= 105.5s frames=12900 avg=122.33 fps\n t= 105.5s frames=13200 avg=125.17 fps\n t= 105.5s frames=13500 avg=128.01 fps\n t= 105.5s frames=13800 avg=130.86 fps\n t= 105.5s frames=14100 avg=133.70 fps\n t= 105.5s frames=14400 avg=136.54 fps\n t= 105.5s frames=14700 avg=139.38 fps\n t= 105.5s frames=15000 avg=142.22 fps\n t= 105.5s frames=15300 avg=145.06 fps\n t= 105.5s frames=15600 avg=147.90 fps\n t= 105.5s frames=15900 avg=150.74 fps\n t= 105.5s frames=16200 avg=153.58 fps\n t= 105.5s frames=16500 avg=156.42 fps\n t= 105.5s frames=16800 avg=159.26 fps\n t= 105.5s frames=17100 avg=162.10 fps\n t= 105.5s frames=17400 avg=164.93 fps\n t= 105.5s frames=17700 avg=167.77 fps\n t= 105.5s frames=18000 avg=170.61 fps\n t= 105.5s frames=18300 avg=173.45 fps\n t= 105.5s frames=18600 avg=176.29 fps\n t= 105.5s frames=18900 avg=179.13 fps\n t= 105.5s frames=19200 avg=181.96 fps\n t= 105.5s frames=19500 avg=184.80 fps\n t= 105.5s frames=19800 avg=187.64 fps\n t= 105.5s frames=20100 avg=190.47 fps\n t= 105.5s frames=20400 avg=193.31 fps\n t= 105.5s frames=20700 avg=196.15 fps\n t= 105.5s frames=21000 avg=198.99 fps\n t= 105.5s frames=21300 avg=201.82 fps\n t= 105.5s frames=21600 avg=204.66 fps\n t= 105.5s frames=21900 avg=207.50 fps\n t= 105.5s frames=22200 avg=210.34 fps\n t= 105.5s frames=22500 avg=213.17 fps\n t= 105.6s frames=22800 avg=216.01 fps\n t= 105.6s frames=23100 avg=218.84 fps\n t= 105.6s frames=23400 avg=221.68 fps\n t= 105.6s frames=23700 avg=224.52 fps\n t= 105.6s frames=24000 avg=227.35 fps\n t= 105.6s frames=24300 avg=230.19 fps\n t= 105.6s frames=24600 avg=233.02 fps\n t= 105.6s frames=24900 avg=235.86 fps\n t= 105.6s frames=25200 avg=238.69 fps\n t= 105.6s frames=25500 avg=241.46 fps\n t= 105.6s frames=25800 avg=244.29 fps\n t= 105.6s frames=26100 avg=247.08 fps\n t= 105.6s frames=26400 avg=249.91 fps\n t= 105.6s frames=26700 avg=252.74 fps\n t= 105.6s frames=27000 avg=255.57 fps\n t= 105.6s frames=27300 avg=258.40 fps\n t= 105.7s frames=27600 avg=261.23 fps\n t= 105.7s frames=27900 avg=264.06 fps\n t= 105.7s frames=28200 avg=266.90 fps\n t= 105.7s frames=28500 avg=269.73 fps\n t= 105.7s frames=28800 avg=272.56 fps\n t= 105.7s frames=29100 avg=275.39 fps\n t= 105.7s frames=29400 avg=278.22 fps\n t= 105.7s frames=29700 avg=281.05 fps\n t= 105.7s frames=30000 avg=283.88 fps\n t= 105.7s frames=30300 avg=286.71 fps\n t= 105.7s frames=30600 avg=289.54 fps\n t= 105.7s frames=30900 avg=292.37 fps\n t= 105.7s frames=31200 avg=295.20 fps\n t= 105.7s frames=31500 avg=298.03 fps\n t= 105.7s frames=31800 avg=300.86 fps\n t= 105.7s frames=32100 avg=303.69 fps\n t= 105.7s frames=32400 avg=306.52 fps\n t= 105.7s frames=32700 avg=309.35 fps\n t= 105.7s frames=33000 avg=312.18 fps\n t= 105.7s frames=33300 avg=315.00 fps\n t= 105.7s frames=33600 avg=317.83 fps\n t= 105.7s frames=33900 avg=320.66 fps\n t= 105.7s frames=34200 avg=323.49 fps\n t= 105.7s frames=34500 avg=326.32 fps\n t= 105.7s frames=34800 avg=329.15 fps\n t= 105.7s frames=35100 avg=331.97 fps\n t= 105.7s frames=35400 avg=334.80 fps\n t= 105.7s frames=35700 avg=337.63 fps\n t= 105.7s frames=36000 avg=340.46 fps\n t= 105.7s frames=36300 avg=343.28 fps\n t= 105.7s frames=36600 avg=346.11 fps\n t= 105.8s frames=36900 avg=348.93 fps\n t= 105.8s frames=37200 avg=351.76 fps\n t= 105.8s frames=37500 avg=354.58 fps\n t= 105.8s frames=37800 avg=357.41 fps\n t= 105.8s frames=38100 avg=360.23 fps\n t= 105.8s frames=38400 avg=363.06 fps\n t= 105.8s frames=38700 avg=365.88 fps\n t= 105.8s frames=39000 avg=368.71 fps\n t= 105.8s frames=39300 avg=371.53 fps\n t= 105.8s frames=39600 avg=374.36 fps\n t= 105.8s frames=39900 avg=377.18 fps\n t= 105.8s frames=40200 avg=380.01 fps\n t= 105.8s frames=40500 avg=382.83 fps\n t= 105.8s frames=40800 avg=385.66 fps\n t= 105.8s frames=41100 avg=388.47 fps\n t= 105.8s frames=41400 avg=391.30 fps\n t= 105.8s frames=41700 avg=394.12 fps\n t= 105.8s frames=42000 avg=396.95 fps\n t= 105.8s frames=42300 avg=399.77 fps\n t= 105.8s frames=42600 avg=402.59 fps\n t= 105.8s frames=42900 avg=405.42 fps\n t= 105.8s frames=43200 avg=408.24 fps\n t= 105.8s frames=43500 avg=411.06 fps\n t= 105.8s frames=43800 avg=413.88 fps\n t= 105.8s frames=44100 avg=416.69 fps\n t= 105.8s frames=44400 avg=419.51 fps\n t= 105.8s frames=44700 avg=422.33 fps\n t= 105.8s frames=45000 avg=425.15 fps\n t= 105.9s frames=45300 avg=427.92 fps\n t= 105.9s frames=45600 avg=430.74 fps\n t= 105.9s frames=45900 avg=433.56 fps\n t= 105.9s frames=46200 avg=436.38 fps\n t= 105.9s frames=46500 avg=439.15 fps\n t= 105.9s frames=46800 avg=441.97 fps\n t= 105.9s frames=47100 avg=444.79 fps\n t= 105.9s frames=47400 avg=447.61 fps\n t= 105.9s frames=47700 avg=450.43 fps\n t= 105.9s frames=48000 avg=453.25 fps\n t= 105.9s frames=48300 avg=456.07 fps\n t= 105.9s frames=48600 avg=458.89 fps\n t= 105.9s frames=48900 avg=461.71 fps\n t= 105.9s frames=49200 avg=464.53 fps\n t= 105.9s frames=49500 avg=467.34 fps\n t= 105.9s frames=49800 avg=470.16 fps\n t= 105.9s frames=50100 avg=472.97 fps\n t= 105.9s frames=50400 avg=475.79 fps\n t= 105.9s frames=50700 avg=478.61 fps\n t= 105.9s frames=51000 avg=481.39 fps\n t= 105.9s frames=51300 avg=484.20 fps\n t= 106.0s frames=51600 avg=487.02 fps\n t= 106.0s frames=51900 avg=489.83 fps\n t= 106.0s frames=52200 avg=492.65 fps\n t= 106.0s frames=52500 avg=495.42 fps\n t= 106.0s frames=52800 avg=498.23 fps\n t= 106.0s frames=53100 avg=501.05 fps\n t= 106.0s frames=53400 avg=503.86 fps\n t= 106.0s frames=53700 avg=506.68 fps\n t= 106.0s frames=54000 avg=509.50 fps\n t= 106.0s frames=54300 avg=512.31 fps\n t= 106.0s frames=54600 avg=515.13 fps\n t= 106.0s frames=54900 avg=517.94 fps\n t= 106.0s frames=55200 avg=520.76 fps\n t= 106.0s frames=55500 avg=523.57 fps\n t= 106.0s frames=55800 avg=526.38 fps\n t= 106.0s frames=56100 avg=529.20 fps\n t= 106.0s frames=56400 avg=532.01 fps\n t= 106.0s frames=56700 avg=534.83 fps\n t= 106.0s frames=57000 avg=537.64 fps\n t= 106.0s frames=57300 avg=540.45 fps\n t= 106.0s frames=57600 avg=543.25 fps\n t= 106.0s frames=57900 avg=546.07 fps\n t= 106.0s frames=58200 avg=548.88 fps\n t= 106.0s frames=58500 avg=551.69 fps\n t= 106.0s frames=58800 avg=554.50 fps\n t= 106.0s frames=59100 avg=557.32 fps\n t= 106.0s frames=59400 avg=560.12 fps\n t= 106.1s frames=59700 avg=562.93 fps\n t= 106.1s frames=60000 avg=565.74 fps\n t= 106.1s frames=60300 avg=568.55 fps\n t= 106.1s frames=60600 avg=571.36 fps\n t= 106.1s frames=60900 avg=574.17 fps\n t= 106.1s frames=61200 avg=576.98 fps\n t= 106.1s frames=61500 avg=579.79 fps\n t= 106.1s frames=61800 avg=582.60 fps\n t= 106.1s frames=62100 avg=585.41 fps\n t= 106.1s frames=62400 avg=588.22 fps\n t= 106.1s frames=62700 avg=591.03 fps\n t= 106.1s frames=63000 avg=593.84 fps\n t= 106.1s frames=63300 avg=596.65 fps\n t= 106.1s frames=63600 avg=599.46 fps\n t= 106.1s frames=63900 avg=602.27 fps\n t= 106.1s frames=64200 avg=605.06 fps\n t= 106.1s frames=64500 avg=607.87 fps\n t= 106.1s frames=64800 avg=610.68 fps\n t= 106.1s frames=65100 avg=613.49 fps\n t= 106.1s frames=65400 avg=616.30 fps\n t= 106.1s frames=65700 avg=619.11 fps\n t= 106.1s frames=66000 avg=621.92 fps\n t= 106.1s frames=66300 avg=624.68 fps\n t= 106.1s frames=66600 avg=627.49 fps\n t= 106.1s frames=66900 avg=630.30 fps\n t= 106.2s frames=67200 avg=633.05 fps\n t= 106.2s frames=67500 avg=635.86 fps\n t= 106.2s frames=67800 avg=638.66 fps\n t= 106.2s frames=68100 avg=641.46 fps\n t= 106.2s frames=68400 avg=644.27 fps\n t= 106.2s frames=68700 avg=647.08 fps\n t= 106.2s frames=69000 avg=649.88 fps\n t= 106.2s frames=69300 avg=652.69 fps\n t= 106.2s frames=69600 avg=655.48 fps\n t= 106.2s frames=69900 avg=658.29 fps\n t= 106.2s frames=70200 avg=661.09 fps\n t= 106.2s frames=70500 avg=663.89 fps\n t= 106.2s frames=70800 avg=666.69 fps\n t= 106.2s frames=71100 avg=669.49 fps\n t= 106.2s frames=71400 avg=672.29 fps\n t= 106.2s frames=71700 avg=675.10 fps\n t= 106.2s frames=72000 avg=677.90 fps\n t= 106.2s frames=72300 avg=680.71 fps\n t= 106.2s frames=72600 avg=683.51 fps\n t= 106.2s frames=72900 avg=686.32 fps\n t= 106.2s frames=73200 avg=689.12 fps\n t= 106.2s frames=73500 avg=691.92 fps\n t= 106.2s frames=73800 avg=694.68 fps\n t= 106.2s frames=74100 avg=697.49 fps\n t= 106.3s frames=74400 avg=700.23 fps\n t= 106.3s frames=74700 avg=702.97 fps\n t= 106.3s frames=75000 avg=705.72 fps\n t= 106.3s frames=75300 avg=708.48 fps\n t= 106.3s frames=75600 avg=711.29 fps\n t= 106.3s frames=75900 avg=714.08 fps\n t= 106.3s frames=76200 avg=716.82 fps\n t= 106.3s frames=76500 avg=719.62 fps\n t= 106.3s frames=76800 avg=722.42 fps\n t= 106.3s frames=77100 avg=725.22 fps\n t= 106.3s frames=77400 avg=728.02 fps\n t= 106.3s frames=77700 avg=730.82 fps\n t= 106.3s frames=78000 avg=733.57 fps\n t= 106.3s frames=78300 avg=736.28 fps\n t= 106.4s frames=78600 avg=739.02 fps\n t= 106.4s frames=78900 avg=741.81 fps\n t= 106.4s frames=79200 avg=744.61 fps\n t= 106.4s frames=79500 avg=747.40 fps\n t= 106.4s frames=79800 avg=750.20 fps\n t= 106.4s frames=80100 avg=753.00 fps\n t= 106.4s frames=80400 avg=755.80 fps\n t= 106.4s frames=80700 avg=758.59 fps\n t= 106.4s frames=81000 avg=761.39 fps\n t= 106.4s frames=81300 avg=764.19 fps\n t= 106.4s frames=81600 avg=766.99 fps\n t= 106.4s frames=81900 avg=769.78 fps\n t= 106.4s frames=82200 avg=772.58 fps\n t= 106.4s frames=82500 avg=775.32 fps\n t= 106.4s frames=82800 avg=778.12 fps\n t= 106.4s frames=83100 avg=780.85 fps\n t= 106.4s frames=83400 avg=783.64 fps\n t= 106.4s frames=83700 avg=786.38 fps\n t= 106.4s frames=84000 avg=789.11 fps\n t= 106.5s frames=84300 avg=791.90 fps\n t= 106.5s frames=84600 avg=794.59 fps\n t= 106.5s frames=84900 avg=797.38 fps\n t= 106.5s frames=85200 avg=800.11 fps\n t= 106.5s frames=85500 avg=802.63 fps\n t= 106.5s frames=85800 avg=805.42 fps\n t= 106.5s frames=86100 avg=808.10 fps\n t= 106.6s frames=86400 avg=810.83 fps\n t= 106.6s frames=86700 avg=813.52 fps\n t= 106.6s frames=87000 avg=816.11 fps\n t= 106.6s frames=87300 avg=818.89 fps\n t= 106.6s frames=87600 avg=821.49 fps\n t= 106.6s frames=87900 avg=824.23 fps\n t= 106.7s frames=88200 avg=826.95 fps\n t= 106.7s frames=88500 avg=829.68 fps\n t= 106.7s frames=88800 avg=832.46 fps\n t= 106.7s frames=89100 avg=835.19 fps\n t= 106.7s frames=89400 avg=837.97 fps\n t= 106.7s frames=89700 avg=840.70 fps\n t= 106.7s frames=90000 avg=843.41 fps\n t= 106.7s frames=90300 avg=846.14 fps\n t= 106.7s frames=90600 avg=848.86 fps\n t= 106.7s frames=90900 avg=851.58 fps\n t= 106.7s frames=91200 avg=854.36 fps\n t= 106.8s frames=91500 avg=857.07 fps\n t= 106.8s frames=91800 avg=859.80 fps\n t= 106.8s frames=92100 avg=862.58 fps\n t= 106.8s frames=92400 avg=865.29 fps\n t= 106.8s frames=92700 avg=868.02 fps\n t= 106.8s frames=93000 avg=870.74 fps\n t= 106.8s frames=93300 avg=873.47 fps\n t= 106.8s frames=93600 avg=876.18 fps\n t= 106.8s frames=93900 avg=878.90 fps\n t= 106.8s frames=94200 avg=881.68 fps\n t= 106.9s frames=94500 avg=884.39 fps\n t= 106.9s frames=94800 avg=887.12 fps\n t= 106.9s frames=95100 avg=889.89 fps\n t= 106.9s frames=95400 avg=892.56 fps\n t= 106.9s frames=95700 avg=895.34 fps\n t= 106.9s frames=96000 avg=898.04 fps\n t= 106.9s frames=96300 avg=900.77 fps\n t= 106.9s frames=96600 avg=903.49 fps\n t= 106.9s frames=96900 avg=906.21 fps\n t= 106.9s frames=97200 avg=908.99 fps\n t= 106.9s frames=97500 avg=911.64 fps\n t= 107.0s frames=97800 avg=914.41 fps\n t= 107.0s frames=98100 avg=917.06 fps\n t= 107.0s frames=98400 avg=919.83 fps\n t= 107.0s frames=98700 avg=922.54 fps\n t= 107.0s frames=99000 avg=925.26 fps\n t= 107.0s frames=99300 avg=928.04 fps\n t= 107.0s frames=99600 avg=930.70 fps\n t= 107.0s frames=99900 avg=933.47 fps\n t= 107.0s frames=100200 avg=936.17 fps\n t= 107.0s frames=100500 avg=938.89 fps\n t= 107.1s frames=100800 avg=941.58 fps\n t= 107.1s frames=101100 avg=944.35 fps\n t= 107.1s frames=101400 avg=947.05 fps\n t= 107.1s frames=101700 avg=949.62 fps\n t= 107.1s frames=102000 avg=952.39 fps\n t= 107.1s frames=102300 avg=955.16 fps\n t= 107.1s frames=102600 avg=957.92 fps\n t= 107.1s frames=102900 avg=960.68 fps\n t= 107.1s frames=103200 avg=963.45 fps\n t= 107.1s frames=103500 avg=966.22 fps\n t= 107.1s frames=103800 avg=968.92 fps\n t= 107.1s frames=104100 avg=971.70 fps\n t= 107.1s frames=104400 avg=974.47 fps\n t= 107.1s frames=104700 avg=977.24 fps\n t= 107.1s frames=105000 avg=980.01 fps\n t= 107.1s frames=105300 avg=982.78 fps\n t= 107.2s frames=105600 avg=985.40 fps\n t= 107.2s frames=105900 avg=988.16 fps\n t= 107.2s frames=106200 avg=990.85 fps\n t= 107.2s frames=106500 avg=993.56 fps\n t= 107.2s frames=106800 avg=996.33 fps\n t= 107.2s frames=107100 avg=999.01 fps\n t= 107.2s frames=107400 avg=1001.78 fps\n t= 107.2s frames=107700 avg=1004.54 fps\n t= 107.2s frames=108000 avg=1007.31 fps\n t= 107.2s frames=108300 avg=1010.08 fps\n t= 107.2s frames=108600 avg=1012.84 fps\n t= 107.2s frames=108900 avg=1015.60 fps\n t= 107.2s frames=109200 avg=1018.36 fps\n t= 107.2s frames=109500 avg=1021.13 fps\n t= 107.2s frames=109800 avg=1023.89 fps\n t= 107.2s frames=110100 avg=1026.65 fps\n t= 107.2s frames=110400 avg=1029.42 fps\n t= 107.2s frames=110700 avg=1032.17 fps\n t= 107.3s frames=111000 avg=1034.94 fps\n t= 107.3s frames=111300 avg=1037.70 fps\n t= 107.3s frames=111600 avg=1040.46 fps\n t= 107.3s frames=111900 avg=1043.22 fps\n t= 107.3s frames=112200 avg=1045.99 fps\n t= 107.3s frames=112500 avg=1048.75 fps\n t= 107.3s frames=112800 avg=1051.52 fps\n t= 107.3s frames=113100 avg=1054.29 fps\n t= 107.3s frames=113400 avg=1057.05 fps\n t= 107.3s frames=113700 avg=1059.81 fps\n t= 107.3s frames=114000 avg=1062.58 fps\n t= 107.3s frames=114300 avg=1065.34 fps\n t= 107.3s frames=114600 avg=1068.09 fps\n t= 107.3s frames=114900 avg=1070.85 fps\n t= 107.3s frames=115200 avg=1073.61 fps\n t= 107.3s frames=115500 avg=1076.37 fps\n t= 107.3s frames=115800 avg=1079.13 fps\n t= 107.3s frames=116100 avg=1081.90 fps\n t= 107.3s frames=116400 avg=1084.66 fps\n t= 107.3s frames=116700 avg=1087.42 fps\n t= 107.3s frames=117000 avg=1090.19 fps\n t= 107.3s frames=117300 avg=1092.95 fps\n t= 107.3s frames=117600 avg=1095.71 fps\n t= 107.3s frames=117900 avg=1098.47 fps\n t= 107.3s frames=118200 avg=1101.24 fps\n t= 107.3s frames=118500 avg=1104.00 fps\n t= 107.3s frames=118800 avg=1106.76 fps\n t= 107.3s frames=119100 avg=1109.53 fps\n t= 107.3s frames=119400 avg=1112.30 fps\n t= 107.3s frames=119700 avg=1115.07 fps\n t= 107.4s frames=120000 avg=1117.83 fps\n t= 107.4s frames=120300 avg=1120.59 fps\n t= 107.4s frames=120600 avg=1123.35 fps\n t= 107.4s frames=120900 avg=1126.11 fps\n t= 107.4s frames=121200 avg=1128.86 fps\n t= 107.4s frames=121500 avg=1131.63 fps\n t= 107.4s frames=121800 avg=1134.38 fps\n t= 107.4s frames=122100 avg=1137.15 fps\n t= 107.4s frames=122400 avg=1139.91 fps\n t= 107.4s frames=122700 avg=1142.67 fps\n t= 107.4s frames=123000 avg=1145.44 fps\n t= 107.4s frames=123300 avg=1148.20 fps\n t= 107.4s frames=123600 avg=1150.96 fps\n t= 107.4s frames=123900 avg=1153.73 fps\n t= 107.4s frames=124200 avg=1156.49 fps\n t= 107.4s frames=124500 avg=1159.25 fps\n t= 107.4s frames=124800 avg=1162.02 fps\n t= 107.4s frames=125100 avg=1164.78 fps\n t= 107.4s frames=125400 avg=1167.54 fps\n t= 107.4s frames=125700 avg=1170.28 fps\n t= 107.4s frames=126000 avg=1173.04 fps\n t= 107.4s frames=126300 avg=1175.80 fps\n t= 107.4s frames=126600 avg=1178.55 fps\n t= 107.4s frames=126900 avg=1181.17 fps\n t= 107.5s frames=127200 avg=1183.75 fps\n t= 107.5s frames=127500 avg=1186.45 fps\n t= 107.5s frames=127800 avg=1189.18 fps\n t= 107.5s frames=128100 avg=1191.90 fps\n t= 107.5s frames=128400 avg=1194.61 fps\n t= 107.5s frames=128700 avg=1197.35 fps\n t= 107.5s frames=129000 avg=1200.04 fps\n t= 107.5s frames=129300 avg=1202.76 fps\n t= 107.5s frames=129600 avg=1205.45 fps\n t= 107.5s frames=129900 avg=1208.16 fps\n t= 107.5s frames=130200 avg=1210.90 fps\n t= 107.5s frames=130500 avg=1213.66 fps\n t= 107.5s frames=130800 avg=1216.41 fps\n t= 107.5s frames=131100 avg=1219.17 fps\n t= 107.5s frames=131400 avg=1221.91 fps\n t= 107.5s frames=131700 avg=1224.65 fps\n t= 107.5s frames=132000 avg=1227.40 fps\n t= 107.5s frames=132300 avg=1230.16 fps\n t= 107.6s frames=132600 avg=1232.90 fps\n t= 107.6s frames=132900 avg=1235.65 fps\n t= 107.6s frames=133200 avg=1238.40 fps\n t= 107.6s frames=133500 avg=1241.15 fps\n t= 107.6s frames=133800 avg=1243.90 fps\n t= 107.6s frames=134100 avg=1246.66 fps\n t= 107.6s frames=134400 avg=1249.41 fps\n t= 107.6s frames=134700 avg=1252.17 fps\n t= 107.6s frames=135000 avg=1254.93 fps\n t= 107.6s frames=135300 avg=1257.69 fps\n t= 107.6s frames=135600 avg=1260.44 fps\n t= 107.6s frames=135900 avg=1263.19 fps\n t= 107.6s frames=136200 avg=1265.94 fps\n t= 107.6s frames=136500 avg=1268.70 fps\n t= 107.6s frames=136800 avg=1271.46 fps\n t= 107.6s frames=137100 avg=1274.22 fps\n t= 107.6s frames=137400 avg=1276.96 fps\n t= 107.6s frames=137700 avg=1279.72 fps\n t= 107.6s frames=138000 avg=1282.47 fps\n t= 107.6s frames=138300 avg=1285.23 fps\n t= 107.6s frames=138600 avg=1287.99 fps\n t= 107.6s frames=138900 avg=1290.75 fps\n t= 107.6s frames=139200 avg=1293.49 fps\n t= 107.6s frames=139500 avg=1296.25 fps\n t= 107.6s frames=139800 avg=1299.00 fps\n t= 107.6s frames=140100 avg=1301.75 fps\n t= 107.6s frames=140400 avg=1304.51 fps\n t= 107.6s frames=140700 avg=1307.24 fps\n t= 107.6s frames=141000 avg=1310.00 fps\n t= 107.6s frames=141300 avg=1312.75 fps\n t= 107.6s frames=141600 avg=1315.50 fps\n t= 107.6s frames=141900 avg=1318.26 fps\n t= 107.6s frames=142200 avg=1321.01 fps\n t= 107.6s frames=142500 avg=1323.76 fps\n t= 107.7s frames=142800 avg=1326.50 fps\n t= 107.7s frames=143100 avg=1329.25 fps\n t= 107.7s frames=143400 avg=1332.00 fps\n t= 107.7s frames=143700 avg=1334.75 fps\n t= 107.7s frames=144000 avg=1337.50 fps\n t= 107.7s frames=144300 avg=1340.25 fps\n t= 107.7s frames=144600 avg=1343.00 fps\n t= 107.7s frames=144900 avg=1345.75 fps\n t= 107.7s frames=145200 avg=1348.47 fps\n t= 107.7s frames=145500 avg=1351.20 fps\n t= 107.7s frames=145800 avg=1353.95 fps\n t= 107.7s frames=146100 avg=1356.70 fps\n t= 107.7s frames=146400 avg=1359.45 fps\n t= 107.7s frames=146700 avg=1362.20 fps\n t= 107.7s frames=147000 avg=1364.94 fps\n t= 107.7s frames=147300 avg=1367.69 fps\n t= 107.7s frames=147600 avg=1370.44 fps\n t= 107.7s frames=147900 avg=1373.17 fps\n t= 107.7s frames=148200 avg=1375.92 fps\n t= 107.7s frames=148500 avg=1378.67 fps\n t= 107.7s frames=148800 avg=1381.42 fps\n t= 107.7s frames=149100 avg=1384.17 fps\n t= 107.7s frames=149400 avg=1386.93 fps\n t= 107.7s frames=149700 avg=1389.66 fps\n t= 107.7s frames=150000 avg=1392.40 fps\n t= 107.7s frames=150300 avg=1395.14 fps\n t= 107.7s frames=150600 avg=1397.88 fps\n t= 107.7s frames=150900 avg=1400.63 fps\n t= 107.7s frames=151200 avg=1403.37 fps\n t= 107.7s frames=151500 avg=1406.12 fps\n t= 107.7s frames=151800 avg=1408.87 fps\n t= 107.7s frames=152100 avg=1411.62 fps\n t= 107.8s frames=152400 avg=1414.37 fps\n t= 107.8s frames=152700 avg=1417.11 fps\n t= 107.8s frames=153000 avg=1419.85 fps\n t= 107.8s frames=153300 avg=1422.60 fps\n t= 107.8s frames=153600 avg=1425.35 fps\n t= 107.8s frames=153900 avg=1428.10 fps\n t= 107.8s frames=154200 avg=1430.84 fps\n t= 107.8s frames=154500 avg=1433.59 fps\n t= 107.8s frames=154800 avg=1436.34 fps\n t= 107.8s frames=155100 avg=1439.09 fps\n t= 107.8s frames=155400 avg=1441.84 fps\n t= 107.8s frames=155700 avg=1444.59 fps\n t= 107.8s frames=156000 avg=1447.33 fps\n t= 107.8s frames=156300 avg=1450.07 fps\n t= 107.8s frames=156600 avg=1452.82 fps\n t= 107.8s frames=156900 avg=1455.56 fps\n t= 107.8s frames=157200 avg=1458.31 fps\n t= 107.8s frames=157500 avg=1461.06 fps\n t= 107.8s frames=157800 avg=1463.79 fps\n t= 107.8s frames=158100 avg=1466.54 fps\n t= 107.8s frames=158400 avg=1469.28 fps\n t= 107.8s frames=158700 avg=1472.03 fps\n t= 107.8s frames=159000 avg=1474.78 fps\n t= 107.8s frames=159300 avg=1477.52 fps\n t= 107.8s frames=159600 avg=1480.26 fps\n t= 107.8s frames=159900 avg=1483.01 fps\n t= 107.8s frames=160200 avg=1485.74 fps\n t= 107.8s frames=160500 avg=1488.48 fps\n t= 107.8s frames=160800 avg=1491.21 fps\n t= 107.8s frames=161100 avg=1493.94 fps\n t= 107.8s frames=161400 avg=1496.67 fps\n t= 107.8s frames=161700 avg=1499.40 fps\n t= 107.8s frames=162000 avg=1502.14 fps\n t= 107.9s frames=162300 avg=1504.85 fps\n t= 107.9s frames=162600 avg=1507.59 fps\n t= 107.9s frames=162900 avg=1510.33 fps\n t= 107.9s frames=163200 avg=1513.02 fps\n t= 107.9s frames=163500 avg=1515.69 fps\n t= 107.9s frames=163800 avg=1518.31 fps\n t= 107.9s frames=164100 avg=1521.02 fps\n t= 107.9s frames=164400 avg=1523.62 fps\n t= 107.9s frames=164700 avg=1526.34 fps\n t= 107.9s frames=165000 avg=1529.07 fps\n t= 107.9s frames=165300 avg=1531.80 fps\n t= 107.9s frames=165600 avg=1534.54 fps\n t= 107.9s frames=165900 avg=1537.25 fps\n t= 107.9s frames=166200 avg=1539.96 fps\n t= 107.9s frames=166500 avg=1542.67 fps\n t= 107.9s frames=166800 avg=1545.38 fps\n t= 107.9s frames=167100 avg=1548.09 fps\n t= 107.9s frames=167400 avg=1550.76 fps\n t= 108.0s frames=167700 avg=1553.47 fps\n t= 108.0s frames=168000 avg=1556.19 fps\n t= 108.0s frames=168300 avg=1558.90 fps\n t= 108.0s frames=168600 avg=1561.56 fps\n t= 108.0s frames=168900 avg=1564.22 fps\n t= 108.0s frames=169200 avg=1566.89 fps\n t= 108.0s frames=169500 avg=1569.54 fps\n t= 108.0s frames=169800 avg=1572.13 fps\n t= 108.0s frames=170100 avg=1574.82 fps\n t= 108.0s frames=170400 avg=1577.53 fps\n t= 108.0s frames=170700 avg=1580.17 fps\n t= 108.0s frames=171000 avg=1582.88 fps\n t= 108.0s frames=171300 avg=1585.56 fps\n t= 108.0s frames=171600 avg=1588.25 fps\n t= 108.1s frames=171900 avg=1590.92 fps\n t= 108.1s frames=172200 avg=1593.60 fps\n t= 108.1s frames=172500 avg=1596.30 fps\n t= 108.1s frames=172800 avg=1598.93 fps\n t= 108.1s frames=173100 avg=1601.56 fps\n t= 108.1s frames=173400 avg=1604.20 fps\n t= 108.1s frames=173700 avg=1606.62 fps\n t= 108.1s frames=174000 avg=1609.07 fps\n t= 108.2s frames=174300 avg=1611.16 fps\n t= 108.2s frames=174600 avg=1613.79 fps\n t= 108.2s frames=174900 avg=1616.34 fps\n t= 108.2s frames=175200 avg=1619.02 fps\n t= 108.2s frames=175500 avg=1621.51 fps\n t= 108.3s frames=175800 avg=1623.68 fps\n t= 108.3s frames=176100 avg=1626.23 fps\n t= 108.3s frames=176400 avg=1628.65 fps\n t= 108.3s frames=176700 avg=1631.29 fps\n t= 108.4s frames=177000 avg=1633.50 fps\n t= 108.4s frames=177300 avg=1636.16 fps\n t= 108.4s frames=177600 avg=1638.84 fps\n t= 108.4s frames=177900 avg=1641.53 fps\n t= 108.4s frames=178200 avg=1644.24 fps\n t= 108.4s frames=178500 avg=1646.93 fps\n t= 108.4s frames=178800 avg=1649.59 fps\n t= 108.4s frames=179100 avg=1652.27 fps\n t= 108.4s frames=179400 avg=1654.96 fps\n t= 108.4s frames=179700 avg=1657.65 fps\n t= 108.4s frames=180000 avg=1660.33 fps\n t= 108.4s frames=180300 avg=1663.00 fps\n t= 108.4s frames=180600 avg=1665.69 fps\n t= 108.4s frames=180900 avg=1668.36 fps\n t= 108.4s frames=181200 avg=1671.02 fps\n t= 108.4s frames=181500 avg=1673.69 fps\n t= 108.4s frames=181800 avg=1676.39 fps\n t= 108.5s frames=182100 avg=1679.09 fps\n t= 108.5s frames=182400 avg=1681.79 fps\n t= 108.5s frames=182700 avg=1684.49 fps\n t= 108.5s frames=183000 avg=1687.20 fps\n t= 108.5s frames=183300 avg=1689.86 fps\n t= 108.5s frames=183600 avg=1692.56 fps\n t= 108.5s frames=183900 avg=1695.26 fps\n t= 108.5s frames=184200 avg=1697.97 fps\n t= 108.5s frames=184500 avg=1700.66 fps\n t= 108.5s frames=184800 avg=1703.31 fps\n t= 108.5s frames=185100 avg=1705.99 fps\n t= 108.5s frames=185400 avg=1708.67 fps\n t= 108.5s frames=185700 avg=1711.35 fps\n t= 108.5s frames=186000 avg=1714.06 fps\n t= 108.5s frames=186300 avg=1716.76 fps\n t= 108.5s frames=186600 avg=1719.48 fps\n t= 108.5s frames=186900 avg=1722.18 fps\n t= 108.5s frames=187200 avg=1724.87 fps\n t= 108.5s frames=187500 avg=1727.57 fps\n t= 108.5s frames=187800 avg=1730.27 fps\n t= 108.5s frames=188100 avg=1732.99 fps\n t= 108.5s frames=188400 avg=1735.71 fps\n t= 108.5s frames=188700 avg=1738.39 fps\n t= 108.6s frames=189000 avg=1741.06 fps\n t= 108.6s frames=189300 avg=1743.75 fps\n t= 108.6s frames=189600 avg=1746.43 fps\n t= 108.6s frames=189900 avg=1749.11 fps\n t= 108.6s frames=190200 avg=1751.82 fps\n t= 108.6s frames=190500 avg=1754.52 fps\n t= 108.6s frames=190800 avg=1757.21 fps\n t= 108.6s frames=191100 avg=1759.88 fps\n t= 108.6s frames=191400 avg=1762.56 fps\n t= 108.6s frames=191700 avg=1765.27 fps\n t= 108.6s frames=192000 avg=1767.95 fps\n t= 108.6s frames=192300 avg=1770.66 fps\n t= 108.6s frames=192600 avg=1773.37 fps\n t= 108.6s frames=192900 avg=1776.07 fps\n t= 108.6s frames=193200 avg=1778.75 fps\n t= 108.6s frames=193500 avg=1781.43 fps\n t= 108.6s frames=193800 avg=1784.14 fps\n t= 108.6s frames=194100 avg=1786.82 fps\n t= 108.6s frames=194400 avg=1789.47 fps\n t= 108.6s frames=194700 avg=1792.17 fps\n t= 108.6s frames=195000 avg=1794.84 fps\n t= 108.7s frames=195300 avg=1797.51 fps\n t= 108.7s frames=195600 avg=1800.18 fps\n t= 108.7s frames=195900 avg=1802.80 fps\n t= 108.7s frames=196200 avg=1805.47 fps\n t= 108.7s frames=196500 avg=1808.18 fps\n t= 108.7s frames=196800 avg=1810.88 fps\n t= 108.7s frames=197100 avg=1813.56 fps\n t= 108.7s frames=197400 avg=1816.24 fps\n t= 108.7s frames=197700 avg=1818.94 fps\n t= 108.7s frames=198000 avg=1821.60 fps\n t= 108.7s frames=198300 avg=1824.29 fps\n t= 108.7s frames=198600 avg=1826.95 fps\n t= 108.7s frames=198900 avg=1829.66 fps\n t= 108.7s frames=199200 avg=1832.38 fps\n t= 108.7s frames=199500 avg=1835.09 fps\n t= 108.7s frames=199800 avg=1837.76 fps\n t= 108.7s frames=200100 avg=1840.45 fps\n t= 108.7s frames=200400 avg=1843.16 fps\n t= 108.7s frames=200700 avg=1845.87 fps\n t= 108.7s frames=201000 avg=1848.57 fps\n t= 108.7s frames=201300 avg=1851.24 fps\n t= 108.7s frames=201600 avg=1853.94 fps\n t= 108.7s frames=201900 avg=1856.62 fps\n t= 108.8s frames=202200 avg=1859.29 fps\n t= 108.8s frames=202500 avg=1861.98 fps\n t= 108.8s frames=202800 avg=1864.65 fps\n t= 108.8s frames=203100 avg=1867.33 fps\n t= 108.8s frames=203400 avg=1869.99 fps\n t= 108.8s frames=203700 avg=1872.69 fps\n t= 108.8s frames=204000 avg=1875.40 fps\n t= 108.8s frames=204300 avg=1878.11 fps\n t= 108.8s frames=204600 avg=1880.81 fps\n t= 108.8s frames=204900 avg=1883.52 fps\n t= 108.8s frames=205200 avg=1886.21 fps\n t= 108.8s frames=205500 avg=1888.92 fps\n t= 108.8s frames=205800 avg=1891.63 fps\n t= 108.8s frames=206100 avg=1894.34 fps\n t= 108.8s frames=206400 avg=1897.05 fps\n t= 108.8s frames=206700 avg=1899.75 fps\n t= 108.8s frames=207000 avg=1902.46 fps\n t= 108.8s frames=207300 avg=1905.12 fps\n t= 108.8s frames=207600 avg=1907.80 fps\n t= 108.8s frames=207900 avg=1910.51 fps\n t= 108.8s frames=208200 avg=1913.22 fps\n t= 108.8s frames=208500 avg=1915.93 fps\n t= 108.8s frames=208800 avg=1918.64 fps\n t= 108.8s frames=209100 avg=1921.34 fps\n t= 108.8s frames=209400 avg=1924.02 fps\n t= 108.8s frames=209700 avg=1926.72 fps\n t= 108.8s frames=210000 avg=1929.43 fps\n t= 108.8s frames=210300 avg=1932.14 fps\n t= 108.8s frames=210600 avg=1934.85 fps\n t= 108.8s frames=210900 avg=1937.56 fps\n t= 108.9s frames=211200 avg=1940.25 fps\n t= 108.9s frames=211500 avg=1942.96 fps\n t= 108.9s frames=211800 avg=1945.66 fps\n t= 108.9s frames=212100 avg=1948.33 fps\n t= 108.9s frames=212400 avg=1950.99 fps\n t= 108.9s frames=212700 avg=1953.65 fps\n t= 108.9s frames=213000 avg=1956.31 fps\n t= 108.9s frames=213300 avg=1958.99 fps\n t= 108.9s frames=213600 avg=1961.69 fps\n t= 108.9s frames=213900 avg=1964.40 fps\n t= 108.9s frames=214200 avg=1967.09 fps\n t= 108.9s frames=214500 avg=1969.80 fps\n t= 108.9s frames=214800 avg=1972.49 fps\n t= 108.9s frames=215100 avg=1975.19 fps\n t= 108.9s frames=215400 avg=1977.89 fps\n t= 108.9s frames=215700 avg=1980.60 fps\n t= 108.9s frames=216000 avg=1983.31 fps\n t= 108.9s frames=216300 avg=1986.00 fps\n t= 108.9s frames=216600 avg=1988.66 fps\n t= 108.9s frames=216900 avg=1991.34 fps\n t= 108.9s frames=217200 avg=1994.04 fps\n t= 108.9s frames=217500 avg=1996.75 fps\n t= 108.9s frames=217800 avg=1999.46 fps\n t= 108.9s frames=218100 avg=2002.16 fps\n t= 108.9s frames=218400 avg=2004.87 fps\n t= 108.9s frames=218700 avg=2007.58 fps\n t= 108.9s frames=219000 avg=2010.29 fps\n t= 108.9s frames=219300 avg=2012.99 fps\n t= 108.9s frames=219600 avg=2015.69 fps\n t= 108.9s frames=219900 avg=2018.39 fps\n t= 109.0s frames=220200 avg=2021.09 fps\n t= 109.0s frames=220500 avg=2023.79 fps\n t= 109.0s frames=220800 avg=2026.50 fps\n t= 109.0s frames=221100 avg=2029.20 fps\n t= 109.0s frames=221400 avg=2031.88 fps\n t= 109.0s frames=221700 avg=2034.57 fps\n t= 109.0s frames=222000 avg=2037.27 fps\n t= 109.0s frames=222300 avg=2039.98 fps\n t= 109.0s frames=222600 avg=2042.68 fps\n t= 109.0s frames=222900 avg=2045.39 fps\n t= 109.0s frames=223200 avg=2048.09 fps\n t= 109.0s frames=223500 avg=2050.79 fps\n t= 109.0s frames=223800 avg=2053.49 fps\n t= 109.0s frames=224100 avg=2056.19 fps\n t= 109.0s frames=224400 avg=2058.87 fps\n t= 109.0s frames=224700 avg=2061.58 fps\n t= 109.0s frames=225000 avg=2064.28 fps\n t= 109.0s frames=225300 avg=2066.99 fps\n t= 109.0s frames=225600 avg=2069.69 fps\n t= 109.0s frames=225900 avg=2072.39 fps\n t= 109.0s frames=226200 avg=2075.09 fps\n t= 109.0s frames=226500 avg=2077.80 fps\n t= 109.0s frames=226800 avg=2080.50 fps\n t= 109.0s frames=227100 avg=2083.21 fps\n t= 109.0s frames=227400 avg=2085.92 fps\n t= 109.0s frames=227700 avg=2088.62 fps\n t= 109.0s frames=228000 avg=2091.32 fps\n t= 109.0s frames=228300 avg=2094.01 fps\n t= 109.0s frames=228600 avg=2096.71 fps\n t= 109.0s frames=228900 avg=2099.41 fps\n t= 109.0s frames=229200 avg=2102.10 fps\n t= 109.0s frames=229500 avg=2104.80 fps\n t= 109.0s frames=229800 avg=2107.46 fps\n t= 109.0s frames=230100 avg=2110.10 fps\n t= 109.1s frames=230400 avg=2112.78 fps\n t= 109.1s frames=230700 avg=2115.48 fps\n t= 109.1s frames=231000 avg=2118.17 fps\n t= 109.1s frames=231300 avg=2120.88 fps\n t= 109.1s frames=231600 avg=2123.58 fps\n t= 109.1s frames=231900 avg=2126.28 fps\n t= 109.1s frames=232200 avg=2128.97 fps\n t= 109.1s frames=232500 avg=2131.67 fps\n t= 109.1s frames=232800 avg=2134.36 fps\n t= 109.1s frames=233100 avg=2137.06 fps\n t= 109.1s frames=233400 avg=2139.76 fps\n t= 109.1s frames=233700 avg=2142.46 fps\n t= 109.1s frames=234000 avg=2145.16 fps\n t= 109.1s frames=234300 avg=2147.87 fps\n t= 109.1s frames=234600 avg=2150.56 fps\n t= 109.1s frames=234900 avg=2153.26 fps\n t= 109.1s frames=235200 avg=2155.95 fps\n t= 109.1s frames=235500 avg=2158.65 fps\n t= 109.1s frames=235800 avg=2161.34 fps\n t= 109.1s frames=236100 avg=2164.03 fps\n t= 109.1s frames=236400 avg=2166.73 fps\n t= 109.1s frames=236700 avg=2169.42 fps\n t= 109.1s frames=237000 avg=2172.11 fps\n t= 109.1s frames=237300 avg=2174.81 fps\n t= 109.1s frames=237600 avg=2177.50 fps\n t= 109.1s frames=237900 avg=2180.19 fps\n t= 109.1s frames=238200 avg=2182.89 fps\n t= 109.1s frames=238500 avg=2185.59 fps\n t= 109.1s frames=238800 avg=2188.29 fps\n t= 109.1s frames=239100 avg=2190.99 fps\n t= 109.1s frames=239400 avg=2193.69 fps\n t= 109.1s frames=239700 avg=2196.38 fps\n t= 109.1s frames=240000 avg=2199.07 fps\n t= 109.1s frames=240300 avg=2201.77 fps\n t= 109.1s frames=240600 avg=2204.47 fps\n t= 109.1s frames=240900 avg=2207.17 fps\n t= 109.1s frames=241200 avg=2209.86 fps\n t= 109.2s frames=241500 avg=2212.52 fps\n t= 109.2s frames=241800 avg=2215.17 fps\n t= 109.2s frames=242100 avg=2217.86 fps\n t= 109.2s frames=242400 avg=2220.54 fps\n t= 109.2s frames=242700 avg=2223.22 fps\n t= 109.2s frames=243000 avg=2225.92 fps\n t= 109.2s frames=243300 avg=2228.62 fps\n t= 109.2s frames=243600 avg=2231.31 fps\n t= 109.2s frames=243900 avg=2234.01 fps\n t= 109.2s frames=244200 avg=2236.70 fps\n t= 109.2s frames=244500 avg=2239.38 fps\n t= 109.2s frames=244800 avg=2242.06 fps\n t= 109.2s frames=245100 avg=2244.75 fps\n t= 109.2s frames=245400 avg=2247.45 fps\n t= 109.2s frames=245700 avg=2250.14 fps\n t= 109.2s frames=246000 avg=2252.83 fps\n t= 109.2s frames=246300 avg=2255.53 fps\n t= 109.2s frames=246600 avg=2258.22 fps\n t= 109.2s frames=246900 avg=2260.92 fps\n t= 109.2s frames=247200 avg=2263.62 fps\n t= 109.2s frames=247500 avg=2266.31 fps\n t= 109.2s frames=247800 avg=2268.99 fps\n t= 109.2s frames=248100 avg=2271.68 fps\n t= 109.2s frames=248400 avg=2274.37 fps\n t= 109.2s frames=248700 avg=2277.07 fps\n t= 109.2s frames=249000 avg=2279.76 fps\n t= 109.2s frames=249300 avg=2282.45 fps\n t= 109.2s frames=249600 avg=2285.11 fps\n t= 109.2s frames=249900 avg=2287.78 fps\n t= 109.2s frames=250200 avg=2290.45 fps\n t= 109.2s frames=250500 avg=2293.12 fps\n t= 109.2s frames=250800 avg=2295.79 fps\n t= 109.2s frames=251100 avg=2298.48 fps","numLines":855,"startLine":1,"totalLines":2020,"truncatedByTokenCap":true}},"sourceToolAssistantUUID":"851502ea-228d-4b69-a141-a1ae5f7e09be","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"beyond connected + steamvr up on enlyzeam","leafUuid":"f384205b-0f33-4f5a-92bc-780c6bfd6b2f","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02"}
{"type":"ai-title","aiTitle":"Set up Beyond Connected and SteamVR","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02"}
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{"parentUuid":"30ca9702-bc98-4aff-a82f-4c6670e3ec21","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01UZBpCmcnF6kYhRyr4ZqQxJ","type":"message","role":"assistant","content":[{"type":"text","text":"Headset live, 120 s. Look for:\n\n- **3.** White filled triangle above center — must point **UP**.\n- **4.** Small white square left of center cross — **left eye ONLY** (wink eyes alternately).\n\nItems 1, 2, 5 already noted: rectilinear ✓, CA stable ✓, fusion discomfort expected (image has zero canting/IPD compensation — that's M2's job; dump even tells us SteamVR folds ±6.17° cant + 62 mm IPD into its projections, this grid has neither)."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":597,"cache_read_input_tokens":177534,"output_tokens":179,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":597,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":179,"cache_read_input_tokens":177534,"cache_creation_input_tokens":597,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":597},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu3yLTWxyANPwC3nwxfJ","type":"assistant","uuid":"e510eabf-0821-45d6-bd36-3fb0b9c13b53","timestamp":"2026-06-10T05:47:35.099Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"1716086f-e794-4502-bea6-9fe2c395f462","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01BfKBKq1tgaajLRJKZN9Wes","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Hsh5UK7NLYgaTfrpHJbBiy","name":"Bash","input":{"command":"head -20 \"/c/Users/decid/AppData/Local/Temp/claude/C--Users-decid-Documents-projects-sauna/e1077b85-f9e0-461b-b631-ddcce7ef7b3e/tasks/b0jvr0611.output\"; tail -3 \"/c/Users/decid/AppData/Local/Temp/claude/C--Users-decid-Documents-projects-sauna/e1077b85-f9e0-461b-b631-ddcce7ef7b3e/tasks/b0jvr0611.output\"","description":"Check rerun log head and tail"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":159,"cache_creation_input_tokens":397,"cache_read_input_tokens":178131,"output_tokens":258,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":397,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":159,"output_tokens":258,"cache_read_input_tokens":178131,"cache_creation_input_tokens":397,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":397},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu3yzraa7EJgCvPvRhRY","type":"assistant","uuid":"97ec442e-2349-4adb-9c3c-0c85e1b5e075","timestamp":"2026-06-10T05:47:44.366Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"8fe1bd4f-c905-4ed1-a07e-861be14e10de","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01BpWUNht5vYz3WkBurQtrdS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PCQ7K8f22mtsUmCP8EJMd3","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\FINDINGS.md","old_string":"## Eyes-in follow-up (pending)\n\nNumeric criterion passed. Visual close-out planned: bake the winning warp into the S2\npresenter path and eyeball a structure-revealing grid through the lens on enlyzeam\n(straight lines must look straight). Tracked as the next step before M1 glue.","new_string":"## Eyes-in validation — PASS, 2026-06-09 (enlyzeam, user eyeball)\n\n`make_warped_grid.py` renders the verified warp into a static pre-distorted grid\n(per-channel, straight in source/tangent space) for this unit's config; presented at\nnative 5088×2544@75 via `s2_nvapi present --image` (new static-image path,\nCopyTextureRegion into the DM surfaces, no shaders). Two 120 s runs, 74.99 fps:\n\n1. **Grid rectilinear through the lens, both eyes** — warp direction + coeffs correct.\n2. **Chromatic aberration stable.** Residual fringing only where lines alias against\n the pixel grid — the generator is point-sampled with no AA, not a warp error.\n (M2 warp shader gets AA/filtering for free by sampling a rendered texture.)\n3. **Up-marker triangle points up** — v orientation confirmed (v=0 = top, no flip).\n4. **Left-eye marker in left eye only** — eye assignment confirmed (left half of the\n 5088-wide surface = left eye).\n5. Stereo convergence not achievable — expected: the test image carries no IPD\n translation and no canting compensation (both M2; the SteamVR dump quantifies\n exactly what's missing: cant folded into projection + 62 mm eye separation).\n\nIncidental observation for M1: ~105 s into run 1 the present loop went free-run\n(75 → 5000+ \"fps\", waitable instantly signaled, presentErrors 0) — headset doffed,\nproximity powered panels down, vsync source vanished. The compositor needs prox/power\nstate handling and free-run detection; a present loop that \"succeeds\" at 5000 fps is\nscanning out to nothing."},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":287,"cache_creation_input_tokens":442,"cache_read_input_tokens":179729,"output_tokens":1374,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":442,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":287,"output_tokens":1374,"cache_read_input_tokens":179729,"cache_creation_input_tokens":442,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":442},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu4Cn6yptmThcvjtQYDK","type":"assistant","uuid":"4741d7f1-9e24-4ab0-942e-7f4b1fa187bc","timestamp":"2026-06-10T05:50:54.185Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"4741d7f1-9e24-4ab0-942e-7f4b1fa187bc","isSidechain":false,"promptId":"47e81153-24fb-4adf-a42e-348382c73c87","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PCQ7K8f22mtsUmCP8EJMd3","type":"tool_result","content":"The file C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\FINDINGS.md has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"c2aac09d-97ba-4938-8eda-d5de9997ed36","timestamp":"2026-06-10T05:50:54.372Z","toolUseResult":{"filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth\\FINDINGS.md","oldString":"## Eyes-in follow-up (pending)\n\nNumeric criterion passed. Visual close-out planned: bake the winning warp into the S2\npresenter path and eyeball a structure-revealing grid through the lens on enlyzeam\n(straight lines must look straight). Tracked as the next step before M1 glue.","newString":"## Eyes-in validation — PASS, 2026-06-09 (enlyzeam, user eyeball)\n\n`make_warped_grid.py` renders the verified warp into a static pre-distorted grid\n(per-channel, straight in source/tangent space) for this unit's config; presented at\nnative 5088×2544@75 via `s2_nvapi present --image` (new static-image path,\nCopyTextureRegion into the DM surfaces, no shaders). Two 120 s runs, 74.99 fps:\n\n1. **Grid rectilinear through the lens, both eyes** — warp direction + coeffs correct.\n2. **Chromatic aberration stable.** Residual fringing only where lines alias against\n the pixel grid — the generator is point-sampled with no AA, not a warp error.\n (M2 warp shader gets AA/filtering for free by sampling a rendered texture.)\n3. **Up-marker triangle points up** — v orientation confirmed (v=0 = top, no flip).\n4. **Left-eye marker in left eye only** — eye assignment confirmed (left half of the\n 5088-wide surface = left eye).\n5. Stereo convergence not achievable — expected: the test image carries no IPD\n translation and no canting compensation (both M2; the SteamVR dump quantifies\n exactly what's missing: cant folded into projection + 62 mm eye separation).\n\nIncidental observation for M1: ~105 s into run 1 the present loop went free-run\n(75 → 5000+ \"fps\", waitable instantly signaled, presentErrors 0) — headset doffed,\nproximity powered panels down, vsync source vanished. The compositor needs prox/power\nstate handling and free-run detection; a present loop that \"succeeds\" at 5000 fps is\nscanning out to nothing.","originalFile":"# Spike S3 — FINDINGS: distortion ground truth\n\n**Status: PASS, 2026-06-09.** Standalone evaluator matches OpenVR `ComputeDistortion`\nto **0.0002 px max error** (float32 noise floor) across 65×65 grid × 3 channels × 2 eyes\non unit LHR-599F3B91 (enlyzeam rig). Criterion was sub-pixel; beaten by ~4 orders.\n\n## The verified formula (per eye, per color channel)\n\nInput: panel/viewport UV `(u,v)`, origin top-left of the eye's viewport (OpenVR\n`ComputeDistortion` convention). Output: source UV into the eye's render target.\n\n```\nt = (2u−1, 2v−1) # per-eye NDC, y down\nt -= (center_x, center_y) # shared across channels within an eye\nr² = t·t # NO clamp (see below)\nd = 1 + k1·r² + k2·r⁴ + k3·r⁶ # DIRECT polynomial — NOT reciprocal\nuv_src = 0.5 + (t·d + center) · 0.5/(1 + grow_for_undistort)\n```\n\n`(k1,k2,k3)` = the `coeffs` of `distortion_red` / `distortion` / `distortion_blue`\nverbatim from the device config JSON. Implementation: `poly3.py` (hypothesis\n`poly+grow` in `verify.py`).\n\n### Answers to the open questions\n\n1. **POLY3 direction:** production `DISTORT_POLY3` (positive leading coeffs) is the\n **direct** polynomial `d = 1 + k1·r² + …` applied panel→source. The Monado/OpenHMD\n Vive evaluator's reciprocal form `d = 1/(1+…)` is **wrong for POLY3** — it loses by\n 2578 px max. The historical EVT/DVT `DISTORT_DPOLY3` negative coeffs are explained:\n a reciprocal re-fit of the dump lands near −0.21 leading (vs +0.209 config), i.e.\n DPOLY3(−k) ≈ 1/POLY3(+k) — same optics, opposite fit direction. Only POLY3 is\n validated here; all production Beyonds are POLY3, so DPOLY3 handling is moot.\n2. **`grow_for_undistort` normalization:** exactly `0.5/(1+grow)` on the output, exactly\n as Monado does it. Confirmed numerically: fitted scale/offset reproduce\n `0.31250 = 0.5/1.6` and `0.5 + center·0.3125` to 5 decimals.\n3. **`undistort_r2_cutoff` is NOT applied** by `ComputeDistortion`: panel corners reach\n r² ≈ 2.1 > cutoff 1.0 and still follow the polynomial exactly. Clamping at the cutoff\n was the sole reason the otherwise-correct hypothesis initially failed (1395 px corner\n error). The cutoff presumably gates something else (mesh/hidden-area). Our warp must\n not clamp within the panel.\n\n## Bonus ground truth captured (load-bearing for M2)\n\n- **`GetEyeToHeadTransform` = identity rotation + (∓31 mm, 0, 0)** — the lighthouse\n driver does NOT expose the config `eye_to_head` canting rotation. Config has\n `parallel_render_cameras: true`: SteamVR renders parallel cameras and folds the\n ±6.17° yaw / ~−5° pitch cant into the **asymmetric projection** instead.\n- **`GetProjectionRaw` ≠ intrinsics frustum.** Dump (left eye): L −1.400 R +0.913\n T −1.380 B +0.923 vs intrinsics-derived ±0.70. The raw projection = grow-expanded,\n cant-folded window; the `uv_src` formula above lands in exactly this window\n (`tan = L + u_src·(R−L)`, y-down, v=0 ↦ T). M2 must either replicate this fold\n (derive closed form config → frustum; the dump + fitted per-eye offset/scale\n `a=(−0.2505,−0.2090)L /(+0.2492,−0.1866)R`, `s=(0.7228,0.7197)L /(0.7208,0.7132)R`\n are the ground truth to derive against) or render genuinely canted cameras with its\n own consistent warp math — NOT mix the two.\n- **IPD translation comes from a user setting, not the config:** dump says 62.0 mm,\n config `ipd.default_mm` = 69. Source: `steamvr.vrsettings →\n driver_BeyondSteamVR.ipd_mm = 62.000003814697266` (Bigscreen's SteamVR driver\n utility setting; `steamvr.ipdOffset` ≈ 0 here). v1: default to config\n `ipd.default_mm`, expose a user override exactly like Bigscreen does.\n- **Intrinsics sign convention settled:** distortion `center_x = −K[0][2]`,\n `center_y = +K[1][2]` (confirmed on both eyes of this unit; matches the B1\n production template pattern).\n- `steamvr.vrsettings` also confirms the direct-mode EDID whitelist entry\n (`direct_mode: edidVid3 9993 = 0x2709, edidPid3 4660 = 0x1234`) and that users can\n carry per-channel color-gain overrides (`hmdDisplayColorGain* = 0.8/0.85/1.2` on this\n rig — remember when judging eyes-in color).\n\n## Method\n\n1. `dump_openvr.py` — pyopenvr `VRApplication_Background` against a running SteamVR\n with the Beyond connected (the one sanctioned dev-time SteamVR run). 65×65 UV grid\n per eye, all 4225 points ok both eyes; plus `GetProjectionRaw`,\n `GetEyeToHeadTransform`, recommended RT (3104×3104 @ supersampleScale 0.76).\n2. `verify.py` — scores 4 closed-form hypotheses {poly, recip} × {grow rescale on/off}\n in panel pixels (2544/eye). Result: poly+grow 0.0002 px; next best 1795 px. PASS.\n3. `analyze.py` — the step that found the formula after all 4 original hypotheses\n failed: 9-param free fit (center, offset, scale, k1..3) per eye/channel in\n `GetProjectionRaw` tangent space. Direct-poly fit converged to **0.000 px with\n fitted center and coeffs equal to the config values to 5 decimals** — proving the\n config values are used verbatim and isolating the normalization algebra, which then\n reduced exactly to the formula above.\n4. Ops note: OpenVR IPC refuses connections from SSH logon sessions. Workaround that\n works: `schtasks /create … /it` + `schtasks /run` to execute the dump inside the\n interactive session (see git history for the exact incantation).\n\nArtifacts in this directory: `distortion_dump_lhr_599f3b91.json` (ground truth, 1.4 MB),\n`config_lhr_599f3b91.json` (unit config snapshot), `poly3.py` (the evaluator to port to\nthe compositor shader in M2), `verify.py`, `analyze.py`, `dump_openvr.py`.\n\n## Eyes-in follow-up (pending)\n\nNumeric criterion passed. Visual close-out planned: bake the winning warp into the S2\npresenter path and eyeball a structure-revealing grid through the lens on enlyzeam\n(straight lines must look straight). Tracked as the next step before M1 glue.\n","structuredPatch":[{"oldStart":88,"oldLines":8,"newStart":88,"newLines":26,"lines":[" `config_lhr_599f3b91.json` (unit config snapshot), `poly3.py` (the evaluator to port to"," the compositor shader in M2), `verify.py`, `analyze.py`, `dump_openvr.py`."," ","-## Eyes-in follow-up (pending)","+## Eyes-in validation — PASS, 2026-06-09 (enlyzeam, user eyeball)"," ","-Numeric criterion passed. Visual close-out planned: bake the winning warp into the S2","-presenter path and eyeball a structure-revealing grid through the lens on enlyzeam","-(straight lines must look straight). Tracked as the next step before M1 glue.","+`make_warped_grid.py` renders the verified warp into a static pre-distorted grid","+(per-channel, straight in source/tangent space) for this unit's config; presented at","+native 5088×2544@75 via `s2_nvapi present --image` (new static-image path,","+CopyTextureRegion into the DM surfaces, no shaders). Two 120 s runs, 74.99 fps:","+","+1. **Grid rectilinear through the lens, both eyes** — warp direction + coeffs correct.","+2. **Chromatic aberration stable.** Residual fringing only where lines alias against","+ the pixel grid — the generator is point-sampled with no AA, not a warp error.","+ (M2 warp shader gets AA/filtering for free by sampling a rendered texture.)","+3. **Up-marker triangle points up** — v orientation confirmed (v=0 = top, no flip).","+4. **Left-eye marker in left eye only** — eye assignment confirmed (left half of the","+ 5088-wide surface = left eye).","+5. Stereo convergence not achievable — expected: the test image carries no IPD","+ translation and no canting compensation (both M2; the SteamVR dump quantifies","+ exactly what's missing: cant folded into projection + 62 mm eye separation).","+","+Incidental observation for M1: ~105 s into run 1 the present loop went free-run","+(75 → 5000+ \"fps\", waitable instantly signaled, presentErrors 0) — headset doffed,","+proximity powered panels down, vsync source vanished. The compositor needs prox/power","+state handling and free-run detection; a present loop that \"succeeds\" at 5000 fps is","+scanning out to nothing."]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"4741d7f1-9e24-4ab0-942e-7f4b1fa187bc","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"c2aac09d-97ba-4938-8eda-d5de9997ed36","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01DPgek78NksWy4cEc915uzJ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SJVek2RyAV3ee98Q9xTPiT","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\decid\\Documents\\projects\\sauna\\PLAN.md","old_string":"See `spikes/s3-distortion-truth/FINDINGS.md`. Eyes-in warp check pending (pre-M1). |","new_string":"Eyes-in PASS on enlyzeam: pre-warped grid rectilinear through lens both eyes, CA stable, v-orientation + eye assignment confirmed. See `spikes/s3-distortion-truth/FINDINGS.md`. **Spike ladder complete 3/3.** |"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1733,"cache_read_input_tokens":180171,"output_tokens":287,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1733,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":287,"cache_read_input_tokens":180171,"cache_creation_input_tokens":1733,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1733},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu4Eg5PriD4thUrDF3mo","type":"assistant","uuid":"57851ad3-4087-4db9-963d-d21e5f9c2ddb","timestamp":"2026-06-10T05:51:01.931Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"57851ad3-4087-4db9-963d-d21e5f9c2ddb","isSidechain":false,"promptId":"47e81153-24fb-4adf-a42e-348382c73c87","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SJVek2RyAV3ee98Q9xTPiT","type":"tool_result","content":"The file C:\\Users\\decid\\Documents\\projects\\sauna\\PLAN.md has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"0d7fdad0-52de-4437-8d9e-764c82483112","timestamp":"2026-06-10T05:51:02.106Z","toolUseResult":{"filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\PLAN.md","oldString":"See `spikes/s3-distortion-truth/FINDINGS.md`. Eyes-in warp check pending (pre-M1). |","newString":"Eyes-in PASS on enlyzeam: pre-warped grid rectilinear through lens both eyes, CA stable, v-orientation + eye assignment confirmed. See `spikes/s3-distortion-truth/FINDINGS.md`. **Spike ladder complete 3/3.** |","originalFile":"# Sauna — v1 Plan\n\nOutput of the design interview closed 2026-06-09. Language: `CONTEXT.md`. Facts and risk\nregister: `reference/BASELINE.md`. Architecture decision: `docs/adr/0001`.\n\n## v1 in one sentence\n\nA standalone Windows+NVIDIA app that reads the Tundra IMU for 3DOF pose, captures the desktop's\nmonitors, composites them as head-tracked virtual screens, warps per-eye using the per-unit\ncalibration, and presents to the Beyond in Vulkan direct mode — no SteamVR, no lighthouses, no\nfirmware changes.\n\n## Locked decisions\n\n| # | Decision | Notes |\n|---|---|---|\n| 1 | v1 experience = virtual screen(s) via our own compositor, direct mode on stock EDID | ADR-0001 (proposed; gated on spike S2) |\n| 2 | Windows+NVIDIA first, present-backend seam | *(amended by S2)* Windows backend = D3D12+NVAPI DirectMode; `VK_EXT_acquire_drm_display` = Steam Deck rung later; Vulkan render core still viable via shared-handle interop |\n| 3 | Per-unit calibration via native Watchman HID config read | port from vendored libsurvive; JSON file-import kept as dev scaffold |\n| 4 | Own minimal AHRS (Mahony-style) for 3DOF | config IMU mount frame + biases; recenter UX owns yaw drift; magnetometer = bonus if S1 finds one |\n| 5 | Content = multi-monitor desktop capture | Windows.Graphics.Capture; layout mirrors OS monitor arrangement; PipeWire backend later |\n| 6 | Frame-source abstraction from day one | capture / remote-stream / test-pattern; only capture implemented in v1 — keeps the streaming-bridge stretch additive |\n\nUX defaults: proximity-sensor display sleep when doffed + idle timeout (burn-in safety is ours\nonce SteamVR is out of the loop); conservative persistence + brightness defaults; 75 Hz default\nwith 90 Hz opt-in flag; recenter on a configurable global hotkey; settings = plain desktop window\n(visible in-headset via capture).\n\n## Architecture (components)\n\n- **Device layer** — Tundra HID `28DE:2300` MI_00 (IMU stream + config read); MCU HID `35BD:0101`\n (display power, per-eye panel-on, persistence, brightness, proximity, EDID/refresh).\n- **Tracking** — Watchman packet parse → bias/scale correction → AHRS → head pose; recenter.\n- **Frame sources** — trait per decision 6; v1 backend: Windows.Graphics.Capture per monitor.\n- **Compositor** — scene of virtual screens (OS layout); per-eye render from `intrinsics`,\n `eye_to_head` canting, IPD; distortion pass (per-channel poly3 + `grow_for_undistort`);\n `display_color_mult`.\n- **Present** — per-platform backend behind one interface: Windows = D3D12 + NVAPI DirectMode\n (proven, S2); Linux = Vulkan + DRM lease (unproven rung). Fallback: extended-mode SBS fullscreen.\n- **App shell** — tray + settings window, hotkeys, safety policy.\n\n## Spike ladder (run before architecture code; in this order)\n\n| Spike | Question | Pass criterion |\n|---|---|---|\n| **S1** ✅ PASS 2026-06-10 | Does the Tundra stream IMU with Steam never run? | Streams unconditionally — 993.8 Hz unique samples, no wake needed, SteamVR never ran since boot. 4096 LSB/g re-confirmed; sliding 3-sample window (dedupe by timecode+seq); no mag on MI_00; gyro scale still unverified (→ M2). See `spikes/s1-imu-no-steam/FINDINGS.md`. |\n| **S2** ✅ PASS 2026-06-10 | Can our own process acquire + continuously present in direct mode? | PASS via **NVAPI/D3D12** (75.07 fps native 5088×2544, zero errors; 10-min soak logged). Vulkan winrt rung DEAD on consumer Windows — specialized displays edition-gated. Windows backend = D3D12+NVAPI DM (NDA lib, referenced not vendored); Linux rung unchanged (VK DRM). See `spikes/s2-vulkan-direct/FINDINGS.md`. |\n| **S3** ✅ PASS 2026-06-09 | Which direction do the production POLY3 coeffs run? | PASS — `DISTORT_POLY3` is the **direct** polynomial `d = 1+k1r²+k2r⁴+k3r⁶` (not Monado's reciprocal), output rescale `0.5/(1+grow)`, **no r² clamp** (`undistort_r2_cutoff` unused by `ComputeDistortion`). Evaluator matches ground truth to 0.0002 px max (65×65×3ch×2 eyes, unit LHR-599F3B91). Bonus: driver folds canting into asymmetric `GetProjectionRaw` (eye_to_head reported identity; `parallel_render_cameras: true`), IPD translation = user setting not config default. See `spikes/s3-distortion-truth/FINDINGS.md`. Eyes-in warp check pending (pre-M1). |\n\n## Build order after spikes\n\n1. **M1 first light** — S1+S2 glued: head-locked test pattern in-headset from a cold boot.\n2. **M2 tracked + warped** — AHRS pose + distortion/canting/IPD pipeline: one virtual screen\n fixed in space, fusion-correct binocular view.\n3. **M3 product shape** — multi-monitor capture + OS layout, recenter, prox/idle safety,\n settings UI, native calibration read replacing dev import.\n4. **M4 hardening** — pacing (capture Hz vs panel Hz), color correctness, install story.\n\n## Out of scope for v1\n\nSteamVR driver mode; Linux/Steam Deck port (first post-v1 target); streaming bridge (seam only);\n6DOF; linkbox-less (hardware adapter, separate track); IddCx virtual display driver.\n","structuredPatch":[{"oldStart":45,"oldLines":7,"newStart":45,"newLines":7,"lines":[" |---|---|---|"," | **S1** ✅ PASS 2026-06-10 | Does the Tundra stream IMU with Steam never run? | Streams unconditionally — 993.8 Hz unique samples, no wake needed, SteamVR never ran since boot. 4096 LSB/g re-confirmed; sliding 3-sample window (dedupe by timecode+seq); no mag on MI_00; gyro scale still unverified (→ M2). See `spikes/s1-imu-no-steam/FINDINGS.md`. |"," | **S2** ✅ PASS 2026-06-10 | Can our own process acquire + continuously present in direct mode? | PASS via **NVAPI/D3D12** (75.07 fps native 5088×2544, zero errors; 10-min soak logged). Vulkan winrt rung DEAD on consumer Windows — specialized displays edition-gated. Windows backend = D3D12+NVAPI DM (NDA lib, referenced not vendored); Linux rung unchanged (VK DRM). See `spikes/s2-vulkan-direct/FINDINGS.md`. |","-| **S3** ✅ PASS 2026-06-09 | Which direction do the production POLY3 coeffs run? | PASS — `DISTORT_POLY3` is the **direct** polynomial `d = 1+k1r²+k2r⁴+k3r⁶` (not Monado's reciprocal), output rescale `0.5/(1+grow)`, **no r² clamp** (`undistort_r2_cutoff` unused by `ComputeDistortion`). Evaluator matches ground truth to 0.0002 px max (65×65×3ch×2 eyes, unit LHR-599F3B91). Bonus: driver folds canting into asymmetric `GetProjectionRaw` (eye_to_head reported identity; `parallel_render_cameras: true`), IPD translation = user setting not config default. See `spikes/s3-distortion-truth/FINDINGS.md`. Eyes-in warp check pending (pre-M1). |","+| **S3** ✅ PASS 2026-06-09 | Which direction do the production POLY3 coeffs run? | PASS — `DISTORT_POLY3` is the **direct** polynomial `d = 1+k1r²+k2r⁴+k3r⁶` (not Monado's reciprocal), output rescale `0.5/(1+grow)`, **no r² clamp** (`undistort_r2_cutoff` unused by `ComputeDistortion`). Evaluator matches ground truth to 0.0002 px max (65×65×3ch×2 eyes, unit LHR-599F3B91). Bonus: driver folds canting into asymmetric `GetProjectionRaw` (eye_to_head reported identity; `parallel_render_cameras: true`), IPD translation = user setting not config default. Eyes-in PASS on enlyzeam: pre-warped grid rectilinear through lens both eyes, CA stable, v-orientation + eye assignment confirmed. See `spikes/s3-distortion-truth/FINDINGS.md`. **Spike ladder complete 3/3.** |"," "," ## Build order after spikes"," "]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"57851ad3-4087-4db9-963d-d21e5f9c2ddb","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna\\spikes\\s3-distortion-truth","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"39a029b1-ce5e-45ec-9132-dd11f4591c8a","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01PQgjXxfoi9spHbBQoYiCHj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01L1fJfhDHg8P6QnAeNMWuAB","name":"Write","input":{"file_path":"C:\\Users\\decid\\Documents\\projects\\sauna\\.claude\\solar-commune.md","content":"Spike S3 PASS both gates — **spike ladder complete 3/3**. Repo main @ 479bf71, clean, pushed.\n\nNumeric gate: `DISTORT_POLY3` settled empirically against one sanctioned SteamVR run on\nthe enlyzeam Beyond (LHR-599F3B91): **direct** polynomial `d = 1+k1r²+k2r⁴+k3r⁶` (NOT\nMonado's reciprocal), output rescale `0.5/(1+grow)`, **no undistort_r2_cutoff clamp** —\nevaluator matches `ComputeDistortion` to 0.0002 px max (65×65×3ch×2eyes). Found via\n9-param free fit (analyze.py) after all 4 closed-form hypotheses failed; fitted coeffs +\ncenter reproduced config values to 5 decimals. The clamp was the only bug in the\notherwise-correct hypothesis.\n\nEyes-in gate: pre-warped grid (make_warped_grid.py) presented via new `s2_nvapi --image`\nstatic-frame path at native 5088×2544@75. User eyeball on enlyzeam: rectilinear both\neyes, CA stable (residual fringing = generator aliasing, not warp), triangle-up confirms\nv=0=top, left-eye marker confirms left half = left eye. Stereo convergence absent as\nexpected (no IPD/canting comp in test image — M2 scope).\n\nLoad-bearing M2 facts from the dump (all in spikes/s3-distortion-truth/FINDINGS.md):\n- Driver reports eye_to_head = IDENTITY + ±ipd/2; canting is folded into asymmetric\n GetProjectionRaw (config `parallel_render_cameras: true`). M2 must replicate the fold\n (closed form underivable so far — fitted per-eye offset/scale archived as ground\n truth) or render genuinely canted cameras with consistent warp math. NOT mix.\n- IPD translation = user setting (`driver_BeyondSteamVR.ipd_mm` 62.0), not config\n ipd.default_mm (69). v1: config default + user override.\n- center_x = −K[0][2], center_y = +K[1][2] convention confirmed.\n- M1 hazard: doffed headset → prox kills panels → vsync gone → present loop free-runs\n 5000+ fps with ZERO errors. Compositor needs prox/power handling + free-run detection.\n\nOps notes: OpenVR IPC refuses SSH logon sessions — `schtasks /it` trick runs dump in\ninteractive session. SteamVR-cached lighthouse configs on enlyzeam include ~47 stale\nfactory units; serial cross-check in verify.py guards mismatches.\n\nNext queue: (1) local 4090 rig S2 visual re-verify with fixed build — user must be\nphysically at local rig, currently at enlyzeam; (2) M1 first light: S1 IMU reader + S2\npresenter glued, head-locked test pattern from cold boot.\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":758,"cache_read_input_tokens":182981,"output_tokens":1185,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":758,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1185,"cache_read_input_tokens":182981,"cache_creation_input_tokens":758,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":758},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu4Hc1ejoJVZuUBEJcBg","type":"assistant","uuid":"fd5e6c8a-ea3d-4383-9192-3339e87e8061","timestamp":"2026-06-10T05:51:56.082Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"fd5e6c8a-ea3d-4383-9192-3339e87e8061","isSidechain":false,"promptId":"47e81153-24fb-4adf-a42e-348382c73c87","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01L1fJfhDHg8P6QnAeNMWuAB","type":"tool_result","content":"File created successfully at: C:\\Users\\decid\\Documents\\projects\\sauna\\.claude\\solar-commune.md (file state is current in your context — no need to Read it back)"}]},"uuid":"98c72c0a-3dc6-49e0-9e38-ff0a54266577","timestamp":"2026-06-10T05:51:56.264Z","toolUseResult":{"type":"create","filePath":"C:\\Users\\decid\\Documents\\projects\\sauna\\.claude\\solar-commune.md","content":"Spike S3 PASS both gates — **spike ladder complete 3/3**. Repo main @ 479bf71, clean, pushed.\n\nNumeric gate: `DISTORT_POLY3` settled empirically against one sanctioned SteamVR run on\nthe enlyzeam Beyond (LHR-599F3B91): **direct** polynomial `d = 1+k1r²+k2r⁴+k3r⁶` (NOT\nMonado's reciprocal), output rescale `0.5/(1+grow)`, **no undistort_r2_cutoff clamp** —\nevaluator matches `ComputeDistortion` to 0.0002 px max (65×65×3ch×2eyes). Found via\n9-param free fit (analyze.py) after all 4 closed-form hypotheses failed; fitted coeffs +\ncenter reproduced config values to 5 decimals. The clamp was the only bug in the\notherwise-correct hypothesis.\n\nEyes-in gate: pre-warped grid (make_warped_grid.py) presented via new `s2_nvapi --image`\nstatic-frame path at native 5088×2544@75. User eyeball on enlyzeam: rectilinear both\neyes, CA stable (residual fringing = generator aliasing, not warp), triangle-up confirms\nv=0=top, left-eye marker confirms left half = left eye. Stereo convergence absent as\nexpected (no IPD/canting comp in test image — M2 scope).\n\nLoad-bearing M2 facts from the dump (all in spikes/s3-distortion-truth/FINDINGS.md):\n- Driver reports eye_to_head = IDENTITY + ±ipd/2; canting is folded into asymmetric\n GetProjectionRaw (config `parallel_render_cameras: true`). M2 must replicate the fold\n (closed form underivable so far — fitted per-eye offset/scale archived as ground\n truth) or render genuinely canted cameras with consistent warp math. NOT mix.\n- IPD translation = user setting (`driver_BeyondSteamVR.ipd_mm` 62.0), not config\n ipd.default_mm (69). v1: config default + user override.\n- center_x = −K[0][2], center_y = +K[1][2] convention confirmed.\n- M1 hazard: doffed headset → prox kills panels → vsync gone → present loop free-runs\n 5000+ fps with ZERO errors. Compositor needs prox/power handling + free-run detection.\n\nOps notes: OpenVR IPC refuses SSH logon sessions — `schtasks /it` trick runs dump in\ninteractive session. SteamVR-cached lighthouse configs on enlyzeam include ~47 stale\nfactory units; serial cross-check in verify.py guards mismatches.\n\nNext queue: (1) local 4090 rig S2 visual re-verify with fixed build — user must be\nphysically at local rig, currently at enlyzeam; (2) M1 first light: S1 IMU reader + S2\npresenter glued, head-locked test pattern from cold boot.\n","structuredPatch":[],"originalFile":null,"userModified":false},"sourceToolAssistantUUID":"fd5e6c8a-ea3d-4383-9192-3339e87e8061","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
{"parentUuid":"98c72c0a-3dc6-49e0-9e38-ff0a54266577","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01QAzUwCPZfSKNHYVLzQPNBP","type":"message","role":"assistant","content":[{"type":"text","text":"**S3 closed — spike ladder 3/3 complete.** Pushed as 479bf71, commune sent to psyche.\n\nFinal state:\n- **Numeric gate:** evaluator = OpenVR ground truth to 0.0002 px. Formula locked: direct poly, `0.5/(1+grow)` rescale, no r² clamp.\n- **Eyes-in gate:** rectilinear both eyes, CA stable (fringing = generator aliasing — M2's shader sampling fixes free), v-orientation + eye assignment confirmed.\n- Your stereo read is right and quantified: dump shows SteamVR achieves fusion via cant folded into asymmetric projections + 62 mm user IPD — test image had neither. M2 work, fully specified by the captured ground truth.\n\nRemaining queue:\n1. **Local 4090 rig S2 eyeball re-verify** — needs you physically back at HFENDULEAM. Bonus: new `--image` path means you could also run the warped-grid eyes-in there with the local unit's config (LHR-1F8E25F1, cached) — one session, both checks.\n2. **M1 first light** — S1 IMU + S2 presenter glued, head-locked pattern from cold boot. All architecture-blocking unknowns now resolved.\n\nSay the word when you want M1 started — it doesn't need you at any particular rig until the eyeball stage."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1245,"cache_read_input_tokens":183739,"output_tokens":462,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1245,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":462,"cache_read_input_tokens":183739,"cache_creation_input_tokens":1245,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1245},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cbu4KEjXtntTHD4FZ6joB","type":"assistant","uuid":"af01ba09-2bbf-4afa-b938-2b645bccb915","timestamp":"2026-06-10T05:52:09.935Z","userType":"external","entrypoint":"cli","cwd":"C:\\Users\\decid\\Documents\\projects\\sauna","sessionId":"84d25a3a-b04e-4fe4-800e-e11d81928c02","version":"2.1.170","gitBranch":"HEAD"}
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{"parentUuid":"3e9bcbd4-8e89-42cf-b9b0-ce7aa423b20c","isSidechain":false,"message":{"model":"claude-fable-5","id":"msg_01Smpgqxau8A6CjF9uNpnjSo","type":"message","role":"assistant","content":[{"type":"text","text":"1. Start point: device config JSON holds per-channel `DISTORT_POLY3` coeffs, but sign history was ambiguous — EVT/DVT units shipped `DISTORT_DPOLY3` with **negative** leading coeffs, production ships **positive**. Public Vive evaluators (Monado) use reciprocal form. Direction unknowable from configs alone.\n\n2. Only authority on the real mapping: Valve's closed lighthouse driver. Its `ComputeDistortion(eye, u, v)` returns, per output-panel UV, the source UVs to sample for R/G/B. Compositor builds its warp mesh by sampling exactly that function. So dump it once = ground truth.\n\n3. Dumped 65×65 UV grid per eye, per channel (`dump_openvr.py`, pyopenvr, SteamVR running with enlyzeam Beyond). Also grabbed `GetProjectionRaw` tangents and `GetEyeToHeadTransform` per eye in same dump — free cross-check data.\n\n4. First attempt: 4 closed-form hypotheses — {direct poly `d=1+k1r²+k2r⁴+k3r⁶` vs reciprocal `d=1/(1+…)`} × {output rescale `0.5/(1+grow_for_undistort)` vs plain `0.5`}. All four FAILED, hundreds of px error. Model space too narrow.\n\n5. Failure analysis from the cross-checks: dump frustum (`GetProjectionRaw`) ≠ intrinsics-derived frustum (−1.40 vs −0.70 tangent, asymmetric), and dump `eye_to_head` = identity rotation, not config's canted matrix. Conclusion: source-UV space isn't what I assumed — it's defined relative to the **raw projection frustum**, and the driver folds canting in somewhere.\n\n6. So stopped guessing, let data speak: converted every ground-truth source UV into frustum tangent space (`tan = L + u_src·(R−L)`, `T + v_src·(B−T)`).\n\n7. Fit a general 9-parameter radial model per eye per channel (`analyze.py`, scipy least_squares): `tan = a + s⊙(t−c)·d(r²)`, free center `c`, free offset `a`, free signed scale `s`, free coeffs `k1..k3`, with `t = 2·uv−1`. Both d-forms tried.\n\n8. Direct-poly fit converged to **0.000 px residual** at all 4225 points — and fitted `c` and `k1..k3` equaled the config values to 5 decimals, all 3 channels, both eyes. Proves: coeffs used verbatim, direct polynomial, radial-affine mapping exact (no homography, no extra terms).\n\n9. Reciprocal fit left ~1 px rms with mangled coeffs (~−0.21 leading) — distinguishably wrong, but its near-fit explains the EVT-era negative coeffs: `1/(1+0.21r²) ≈ 1−0.21r²+…`. Same optics, opposite fit direction. Historical confusion resolved.\n\n10. Then reverse-engineered the fitted nuisance params into closed form. Scale: `s_x/(R−L) = 0.31250 = 0.5/(1+0.6)` exactly — the grow rescale. Offset: `(a_x−L)/(R−L) = 0.5 + c_x·0.3125` exactly. Both axes, both eyes.\n\n11. Substituting back: in source-UV space the formula is exactly the Monado shape — `uv_src = 0.5 + ((t−c)·d + c)·0.5/(1+grow)` — with direct `d`. Meaning my original hypothesis #1 had the right formula all along.\n\n12. So why did step 4 fail? One bug: I clamped r² at `undistort_r2_cutoff` (1.0) like Monado does. Ground truth follows the polynomial unclamped out to panel corners (r²≈2.1). The free fit had no clamp — that's why it succeeded where the closed form failed. Removed clamp → hypothesis test gave 0.0002 px max. Cutoff field gates something else, not `ComputeDistortion`.\n\n13. Validation rigor: `verify.py --selftest` first proved on synthetic data the 4 hypotheses are mutually distinguishable (hundreds of px apart), so a sub-pixel winner is unambiguous, not a tie-break.\n\n14. Independent physical confirmation: rendered grid straight in source space through the recovered warp, presented on the actual panels, eyeballed through the lens — rectilinear, CA stable, orientation/eye markers correct. Math and optics agree.\n\n15. Residual 0.0002 px = float32 rounding in OpenVR's return path; effectively exact. 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