Serve replay playback from a cached analysis result
Replay re-ran the SDK chain on every seek; Part A's pass stores the flow samples once. Opening a recording now reuses a compatible result or runs the pass behind a progress/cancel surface, and one cursor with two modes (recorded CSV, analysed) feeds the charts, the 2D canvas and the 3D pane by frame id. A threshold, colour or idle-dot change redraws from the stored frames and writes nothing; a cancelled pass is reported and never played. Verified: apps/studio/tests 440 items / 0 failed / 18 skipped (15 new, no existing assertion changed); shared 417 / 0 / 1; autocal 358.
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# Replay panel contract
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The replay panel plays one recording — or one video — against a single cursor. Each
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capability states its requirement, data source and a falsifying check. The result format and
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playback semantics are owned by Part A ([replay-analysis.md](replay-analysis.md)) and Part B
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([replay-analysis-part-b.md](replay-analysis-part-b.md)).
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Status: Parts A and B shipped at `cf49b08`; the depth channel — result format and 2D view — is
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uncommitted; the cache-root and lifetime policy and the display-control separation (Part C)
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are decided below, not implemented.
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## 1. Load a video
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Selecting a video always gives a playable timeline, and the panel says what the file is.
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`load_video` calls a folder holding `forces.csv` beside the video a recording; anything else
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opens as a bare video (`VideoOnlySource`) and plays, with `playback.video_only_opened` on the
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rail and `playback.video_only_notice` in the notes. A file that cannot be decoded is refused
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with its reason.
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Not available for a bare video: force rows (the charts are empty by construction),
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`session.json` metadata, and an analysis pass — `recording_directory()` needs a
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`session.json` and reports `playback.analysis_no_source`; the only route to numbers is
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rerunning the acquisition pipeline over the video (`on_recompute_toggled`), forward only.
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Source: `record/playback.py`, `replay_controller.py`.
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Verify: `test_ui_smoke.py::test_a_bare_video_plays_even_though_it_is_not_a_recording`;
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`test_replay_controller.py::test_a_bare_video_is_opened_as_a_video_and_refused_outside_the_view`.
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## 2. Play and compute on the loaded video
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Opening a recording either serves a compatible stored result or runs one pass behind a
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progress-and-cancel surface. `on_analysis_requested` asks
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`AnalysisService.stored_result()` first — a file read, the surface showing
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`analysis.reused` — and otherwise starts `AnalysisWorker`, a plain thread whose `Progress` is
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coalesced at 0.1 s, with `AnalysisProgress` showing written/expected frames and elapsed time.
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The timeline stays in `recording` mode until the pass commits: a half-written result never
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reaches the screen.
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The pass is sequential and forward-only: `_pass_frames` walks segments and frames in order,
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and the chain is stateful (`Sensor.reset()` clears the tracker). Cancelling sets an event; the
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result keeps its `INCOMPLETE` marker and default readers refuse it (Part A §7). Measured:
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0.48–0.51 wall-seconds per video-second (Part A §8: 0.5036 s mean against 0.5052 s for the bare
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chain) — ten minutes is ≈4.8 minutes.
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Source: `analysis/service.py`, `analysis_worker.py`, `ui/analysis_progress.py`.
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Verify: `test_replay_analysis.py::test_opening_a_recording_with_a_stored_result_runs_no_pass`,
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`::test_opening_a_recording_without_a_result_starts_the_pass`,
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`::test_the_worker_reports_progress_and_a_cancelled_outcome`.
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## 3. Cursor playback
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The cursor is one position for the whole panel, on the recording's own signal time: `t_rel_s`
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from `frames.csv`, the axis `ReplayEngine.position()` and `AnalysisReader.frame_at()` index by.
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Container time is never derived from it or converted into it. Measured: 33 container frames at
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30 fps are 1.100 s of video and 1.920 s of signal at 16.664 fps (Part A §5).
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Frames are addressed by the recording's own frame id, never an index: `AnalysisSession.frame`
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refuses an id the result does not hold (`AnalysisNotFoundError`), and the evidence recording's
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ids run 20…52, so an index-based path would answer with another frame's measurement. A seek
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resolves to the stored frame at or before the position — the frame sequential playback passes —
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and re-runs no pass.
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Source: `replay.py`, `replay_analysis.py`.
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Verify: `test_replay_analysis.py::test_position_is_signal_time_and_resolves_to_a_stored_frame_id`,
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`::test_a_frame_id_the_recording_does_not_hold_is_refused`,
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`::test_a_seek_equals_playing_forward_to_the_same_frame`.
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## 4. Synchronised charts, 2D view and 3D view
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One cursor drives all three.
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| View | Data source |
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|---|---|
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| Charts | the recorded `forces.csv` (`RecordingCurveSource`); the result stores no force |
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| 2D canvas | the cached per-frame samples and depth, drawn at display time |
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| 3D pane | the packet built from the cached frame (`AnalysisSession.packet` → `Scene3DView.submit_packet`) |
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A seek equals playing forward to that frame: the cursor resolves to one stored frame id and all
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three views are fed from it. A derived view chosen while the timeline reads the video is drawn
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from the result at the same cursor (`available_result`). Not synchronised: the 3D pane's depth
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(`AnalysisSession.packet` sets `depth=None`, `depth_valid=False`); forces (the packet carries
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`forces_valid=False` and zero, so the pane's force readout is not a measurement here); the 3D
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pane with no result, which nothing feeds; and a recompute pass, where the pipeline owns the
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timeline (`on_cursor_moved`).
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Display settings are reads: the threshold re-derives activity from the stored `magnitude`, colour
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range and idle dots live in the draw call, and a grid-moving setting is refused by name
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(`GRID_KEYS`), the frame still drawn from the recorded grid.
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Verify: `test_replay_analysis.py::test_the_3d_pane_is_fed_the_same_frame_the_canvas_draws`,
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`::test_a_display_change_that_moves_the_grid_is_refused_not_served`.
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## 5. What the 2D view can show
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The modes come from the frame's own fields and from whether a result exists (`plan_image`,
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`result_available`): `arrows_pure` (Arrows), `arrows_stitched` (Arrows / Captured), `depth`
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(Depth map), `recorded` (Recorded image). With a result all four are offered, even on the
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recording's timeline, defaulting to `recorded`; with none, `recorded` alone plus
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`panels.replay_raw_note`.
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| Mode | Drawn from |
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|---|---|
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| recorded | the decoded `raw_*.mp4` frame (`RecordingReader.video_frame_at`) |
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| arrows / arrows-captured | the cached samples: the grid keyframe's locations plus this frame's float32 `dx`/`dy`/`magnitude` and bit-packed `active` (4,034 B/frame before the depth channel; 4,048 B/frame now) |
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| depth | a per-frame depth flag, a non-zero count and min/max; only a non-empty map adds a uint8 plane quantised over that frame's range and zlib'd at level 6 (island restriction rejected: 418 B vs 486 B on the worst frame) |
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Measured: the depth map is exactly all zeros on 241 of 264 frames of the eight-times clip
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(≈91 %; 32 of 33 on the box recording) and never exceeded 336 of 84,000 pixels (0.4 %). A blank
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depth view is therefore usually correct and must be explainable: `panels.depth_empty` says the
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frame's depth is all zeros, `panels.depth_unavailable` says the frame has no depth channel at
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all — different facts, neither drawing the processed image.
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A mode needing derived data is served from the result, never faked: with none, that view is not
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offered and `panels.replay_raw_note` names the action that produces a result.
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Verify: `test_image_backend.py` (the plan), `test_replay_analysis.py::test_a_derived_mode_is_served_from_the_result_even_on_the_recording_timeline`,
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`::test_the_empty_depth_note_is_not_the_missing_depth_note`, `test_analysis_depth.py::test_an_empty_map_costs_a_header_and_states_its_shape`.
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## Cache and export policies
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No rendered 2D images are cached. PNGs of the arrow and depth views would cost 180–540 MB per
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ten minutes and bake display settings into pixels, destroying the property that a threshold,
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colour or idle-dot change is a read. Drawing a frame from the cached numbers costs ≈1.4 ms, the
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idle-dot pass 37 µs (owner's bench, not reproduced here). Check:
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`::test_a_display_change_is_a_read_and_writes_no_result`, `::test_a_mode_change_writes_no_analysis`.
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Derived views cannot be computed from raw data during a scrub: the chain is stateful and
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forward-only, so a backward seek would need a full re-run at 0.48–0.51 s per video-second; the
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panel scrubs stored numbers and offers that run separately.
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Cache location. Results belong under an OS-correct derived-data root,
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`%LOCALAPPDATA%\OrisysStudio\analysis-cache\` — never the user's recording folder or the app's
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`outputs/`, which in the packaged build is the EXE's own directory and holds the recordings
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(`paths.recordings_dir()` → `outputs/recordings`). The key is path-independent
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(`recording_identity` ignores the path, includes session facts and segment sizes), so a copied
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recording finds its result. Today `ResultCache.for_recording` roots the cache at
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`<recording>/analysis`; the OS root is decided, not implemented.
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Cache lifetime. A size cap with LRU eviction (1 GiB, `DEFAULT_MAX_BYTES`), a startup sweep of
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stale and incomplete results, a manual "clear analysis cache" action, and wipe-on-exit only as
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an explicit preference that is off by default — because wiping on exit forces a full re-analysis
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(≈4.8 minutes per ten minutes) on reopen and does not protect against a crash. Implemented
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today: `evict()` removes whole results, incomplete first then oldest, protecting the caller's
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`keep` — with no call site in the app, so there is no sweep, no clear action and no preference.
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Export. The deliberate way to keep a result is to export it: ≈70 MB per ten minutes, portable
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because the key ignores where the file sits. A rendered arrow or depth video is a later
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possibility, belonging to export rather than the cache. Not implemented.
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## Owners and status
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| Part | Owner | Status |
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|---|---|---|
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| Format, cache, reader, pass | `orisys_app_shared.analysis` | A/B shipped; depth channel uncommitted |
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| Timeline, cursor, policy, pass thread, progress surface | `replay_analysis.py`, `replay.py`, `replay_controller.py`, `analysis_worker.py`, `ui/analysis_progress.py` | shipped; the depth view and the result-aware mode plan are uncommitted |
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| Mode plan, drawing, window wiring | `image_backend.py`, `acquisition/arrows.py`, `app_window.py` | shipped (Part B §12 names the seams); the result-aware plan is uncommitted |
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| Cache root and lifetime | `analysis/cache.py`, app preferences | decided, not implemented |
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| Display-control separation: rail reset as the measurement reference, a separate display baseline, the F8 view reset | `toolbar.py`, `display_prefs.py`, `app_window.py` | not started; Part B §12 records these as unchanged. The pass accepts a `reference_frame` but `_analysis_service` passes none |
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## Not confirmed in code
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* The render cost (≈1.4 ms, idle dots 37 µs) and the PNG estimate (180–540 MB per ten minutes)
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are the owner's bench, not measurements here; Part B §14's single-frame snapshots (4.2–4.9 kB)
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suggest the estimate is high, so it should be re-measured.
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* The OS cache root, startup sweep, clear action, wipe-on-exit preference and result export
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have no code in `orisys-app`; they are decisions.
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* The status lines describe the working tree, not `cf49b08`: the depth work is uncommitted.
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