What
- record: one packet->row conversion (record/sample.py). A failed compute chain
records no force value at all, a newton value is written only for an axis that
really has a calibration curve, and mono_t (not the wall clock) orders history
with a per-row calibration hash.
- record lifecycle: explicit starting/recording/stopping/stopped/failed states
owned by the writer thread; stop() reports completed=False on timeout and does
not write ended_at; consecutive write failures stop acceptance immediately.
- record capacity: byte budget for the video queue (512 MiB default); a full
queue sheds the *frame* and parks the tiny data row, so a video overflow no
longer deletes CSV rows. Lost rows leave a dropped=1 index row and are counted
as dropped_gaps / unrecorded_gaps.
- calibration identity: sha256 content hash instead of a path, minted from the
calibration the sensor actually loaded (reported before the first frame) and
confirmed by the acquisition thread; events are timestamped when they happen.
- calibration diagnostics: the fit summary, residual and validation error now
come from the model that export/apply would use (piecewise_linear); validation
points are held out of the build; repeated reference loads are merged with an
n-weighted mean and pooled std.
- calibration record: every point keeps its full three-axis sample series
(frame id, time, raw, newtons, valid) plus diagnostics, written to an
independent calibration_session_*.json that can be reopened for inspection;
save record / export coefficients / apply to device are three separate actions.
- depth replay: the cursor's frame is bound to the depth result by request
sequence and frame id, so a moved cursor never shows another frame's map; the
SDK is stepped from its current position instead of re-reading from the start;
the live chain (box deformation+contact, finger unroll+tactile) is replayed
with the exact resolved config path; algorithm state is rebuilt from a reset
marker or from the recorded pre-roll, and says so when it cannot be.
Why
The review (D:/orisys_ws/_review_phase12/review.md) showed the app writing raw
counts into the newton columns, recording a stale value after a failed compute,
losing CSV rows when the video queue filled, reporting success while a writer was
still running, diagnosing a model nobody applies, applying a calibration before
the device confirmed it, dropping the samples behind a calibration point, showing
a stale depth map under a moved cursor and recomputing depth of the wrong frame.
Verification
- D:/orisys_ws/_review_phase12/reproduce.py: all ten acceptance lines flipped
(log saved as _review_phase12/reproduce_after.log). Including 25/25 rows kept
with 8 video frames shed, frames [2]->[3], depth 8.0/8.0, fit 2/2 points.
- apps/studio/tools/ui_check.py: 249 checks, 0 failures (twice).
- shared suites: 91 tests, 0 failures (new: test_record_semantics,
test_record_lifecycle, test_calibration_model, test_calibration_record,
test_depth_replay).
- apps/studio/tests/test_ui_smoke.py: 23/30; the 7 remaining are harness
artifacts of the sandbox (4 tmp_path PermissionError, 3 parametrized tests the
shim cannot drive), unchanged from before this change.
- compileall clean; locale parity zh/en + t() key coverage: 0 failures.
Not in this change
- P2#10 disk-based browsing of a long recording and range CSV export (next
round, per your choice).
- Stability-triggered calibration sampling, replay fixed colour scale, PyOpenGL
three-dimensional view.
- Hardware verification: no camera attached, so the SDK's real processing chain
and the pre-roll's effect on a live tracker were exercised with fakes only.
ykcheungab
22cc59c402