diff --git a/CHANGELOG.md b/CHANGELOG.md index cf78059..6fa7bf3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -128,6 +128,14 @@ this project uses [semantic versioning](https://semver.org/). One sequence and one window. [ADR-0023](docs/adr/0023-clone-against-a-measured-front-end.md) is accepted: the clone stays opt-in, with the inflation. +- A wider real-front-end measurement ([`docs/real_front_end.md`](docs/real_front_end.md)): six windows of five Machine + Hall sequences, the design committed before the run, the measured errors of each window replayed into the stochastic + clone. At the measured noise the clone meets the gate in two of six windows, at four times in all six, and the single + anchor in none. The first window was also run again with the run aligned to its frames (the earlier run began 1.08 s + after them, so the last second had no vision), which moved its figures slightly. `navkit euroc vo --wide` runs it. + [ADR-0024](docs/adr/0024-assumed-noise-for-the-opt-in-clone.md) is proposed; the decision section is for the + maintainer to write. + ### Changed - The implementation plan carries the same point-in-time banner as the other baseline records. P5-09 and P5-12 are diff --git a/CONSTRAINTS.md b/CONSTRAINTS.md index df8eb96..55c5a34 100644 --- a/CONSTRAINTS.md +++ b/CONSTRAINTS.md @@ -4,9 +4,9 @@ Invariants this project holds itself to. Every entry is either currently true or is a stated blocker; nothing here is aspirational. Ratchets are measured, not estimated, and the command to re-measure each one is given. -Last measured: 1302 tests collected, all of which pass except the deliberate +Last measured: 1313 tests collected, all of which pass except the deliberate xfails and the skips that `pytest -rs` lists, and 94.3% -line coverage (9819/10408 executable lines) on **CPython 3.13**, measured +line coverage (10009/10614 executable lines) on **CPython 3.13**, measured with `python scripts/coverage_report.py`. The figures come from `docs/data/metrics.json`, which `scripts/metrics.py` checks in CI. Coverage is quoted from that script alone: it is the project's dependency-free tracer (S1 forbids adding `pytest-cov`), and a number taken from a different tool is not comparable with the module figures @@ -83,7 +83,7 @@ These are floors, not goals. Each must not regress; raising one is welcome. | Ratchet | Floor | Current (CPython 3.13) | Re-measure with | |---|---:|---:|---| -| Tests collected | 405 | 1302 | `pytest`, `scripts/metrics.py --check` | +| Tests collected | 405 | 1313 | `pytest`, `scripts/metrics.py --check` | | Tests passing | 300 | all collected, except the deliberate xfails and the listed skips | `pytest -rs` | | Line coverage | 75% | 94.3% | `scripts/coverage_report.py`, `scripts/metrics.py --check-coverage` | | `io/trajectory.py` coverage | 85% | 95.0% | as above | @@ -104,7 +104,7 @@ These are floors, not goals. Each must not regress; raising one is welcome. | `outage_sweep.py` coverage | 80% | 98.4% | as above | | `fdir/nis_monitor.py` coverage | 80% | 98.8% | as above | | `degrade/` coverage | 50% | 78.0% (`config.py`) / 84.2% (`inject.py`) | as above | -| Docs | README + architecture + calibration + every ADR | 23 ADRs, all in the index | `scripts/metrics.py --check` | +| Docs | README + architecture + calibration + every ADR | 24 ADRs, all in the index | `scripts/metrics.py --check` | | Documented tables match the generated benchmark | exact | yes | `scripts/check_doc_tables.py` | | Open blockers documented | all | see ROADMAP | manual | diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 011c265..24ef397 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -70,7 +70,7 @@ mypy src --ignore-missing-imports `ruff format src tests scripts` applies formatting; the `--check` form is the gate. -The suite collects 1302 tests and all of them pass on a fresh clone, except the deliberate +The suite collects 1313 tests and all of them pass on a fresh clone, except the deliberate xfails and the skips. `pytest -rs` lists the skips: two need TUM VI reference data that is not vendored (see below), and the doc-table check runs once `python scripts/run_benchmark.py` has written `results/benchmark.json`. The count and the other numbers the docs quote are in diff --git a/ROADMAP.md b/ROADMAP.md index bbad97d..2f796cc 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -4,7 +4,7 @@ Sequenced by dependency, not by ambition. Each item names its exit test. Status markers: `[x]` done, `[~]` in progress, `[ ]` not started, `[!]` blocked. -Measured baseline: 1302 tests collected and 94.3% line coverage on CPython +Measured baseline: 1313 tests collected and 94.3% line coverage on CPython 3.13 (`docs/data/metrics.json`, from `scripts/metrics.py` and `scripts/coverage_report.py`). Every number below re-measures against that, not against a remembered value. @@ -260,9 +260,11 @@ this removes it. - [~] A visual front end with correlated errors, scale ambiguity and outliers. Outliers and scale drift are survived. Correlated errors are not, unless the assumed visual noise is inflated by a factor found by trial. A plain two-view front end was - then measured on real frames ([real front end](docs/real_front_end.md), ADR-0023): its errors are - correlated for about 0.25 s, and when they are replayed the clone stays inside the gate only with the assumed - noise inflated by four. One sequence and one window, so the default does not change. + then measured on real frames ([real front end](docs/real_front_end.md); [ADR-0023](docs/adr/0023-clone-against-a-measured-front-end.md) and + [ADR-0024](docs/adr/0024-assumed-noise-for-the-opt-in-clone.md), the second proposed): its + errors are correlated for 0.05 s to 0.5 s, and when they are replayed the clone stays inside the gate in all six + windows of five Machine Hall sequences only with the assumed noise inflated by four. One dataset and one + front end, so the default does not change. **Exit test:** NEES below 10 with vision enabled and GNSS denied, coverage above 90%, `vision_enabled = True` as the shipped default. Current value 286.2 for the diff --git a/docs/adr/0023-clone-against-a-measured-front-end.md b/docs/adr/0023-clone-against-a-measured-front-end.md index b549798..3e42839 100644 --- a/docs/adr/0023-clone-against-a-measured-front-end.md +++ b/docs/adr/0023-clone-against-a-measured-front-end.md @@ -30,3 +30,5 @@ configuration. The next step is more sequences and windows, so that the replay r The finding is limited to this front end, this window and a known translation scale. The factor of four is a trial value that held in one replay, not a derived constant. A front end with another error structure may need another factor. + +Update: the wider measurement ran, and its result is in [ADR-0024](0024-assumed-noise-for-the-opt-in-clone.md). diff --git a/docs/adr/0024-assumed-noise-for-the-opt-in-clone.md b/docs/adr/0024-assumed-noise-for-the-opt-in-clone.md new file mode 100644 index 0000000..38c65f2 --- /dev/null +++ b/docs/adr/0024-assumed-noise-for-the-opt-in-clone.md @@ -0,0 +1,35 @@ +# ADR-0024: The assumed visual noise of the opt-in clone + +- Status: proposed +- Date: 2026-10 +- Related: [ADR-0017](0017-stochastic-clone-for-the-visual-update.md), [ADR-0023](0023-clone-against-a-measured-front-end.md), [real front end](../real_front_end.md) + +## Context + +ADR-0023 kept the stochastic clone opt-in and asked for more windows and sequences before anything changed. The wider +measurement ran: six windows of five Machine Hall sequences, a plain two-view front end, the measured errors of each +window replayed into the filter. With the assumed visual noise at the measured size, the clone meets the gate (NEES below +10, 2-sigma coverage above 90%) in two of six windows. At twice the measured size, in two of six. At four times, in all +six. At eight times, in all six. The single anchor meets it in none. The tightest window at four times has a coverage of +91%. + +All of it is one dataset, one front end that is not a production one, and a translation with the true scale. + +## Options + +1. **Keep the clone opt-in and record four times as the supported assumption.** `vision_enabled` stays False. The docs + say that an opt-in user of a feature-tracking front end should assume four times the measured visual noise, and that + this holds on the windows measured so far. +2. **Add the factor to the configuration as a named setting.** A documented preset so that the opt-in path does not + depend on a number found in a page. Code and docs change; the default does not. +3. **Make the clone the default with four times the noise.** It needs a monocular scale, a second dataset and a front end + that is not mine first. + +## Decision + +To be written by the maintainer. + +## Consequences + +Whatever is chosen, the claim stays limited to the Machine Hall windows measured, this front end and a known scale. The +factor of four is an empirical value, not a derived constant. diff --git a/docs/adr/index.md b/docs/adr/index.md index 70ef55d..89c19f6 100644 --- a/docs/adr/index.md +++ b/docs/adr/index.md @@ -28,3 +28,4 @@ what it cost, and what was rejected. | [ADR-0021](0021-fault-classification-from-innovations.md) | Fault classification from the GNSS innovations | accepted | | [ADR-0022](0022-multipath-signature.md) | A multipath signature on the GNSS innovations | accepted | | [ADR-0023](0023-clone-against-a-measured-front-end.md) | The clone against a measured front end | accepted | +| [ADR-0024](0024-assumed-noise-for-the-opt-in-clone.md) | The assumed visual noise of the opt-in clone | proposed | diff --git a/docs/data/metrics.json b/docs/data/metrics.json index 40f9535..5e7c544 100644 --- a/docs/data/metrics.json +++ b/docs/data/metrics.json @@ -1,8 +1,8 @@ { - "adr_count": 23, - "coverage_lines_hit": 9819, - "coverage_lines_total": 10408, - "coverage_percent": 94.34, + "adr_count": 24, + "coverage_lines_hit": 10009, + "coverage_lines_total": 10614, + "coverage_percent": 94.3, "coverage_python": "3.13", - "tests_collected": 1302 + "tests_collected": 1313 } diff --git a/docs/data/vo_clone_check.csv b/docs/data/vo_clone_check.csv index 2d8bb8e..d0bc47e 100644 --- a/docs/data/vo_clone_check.csv +++ b/docs/data/vo_clone_check.csv @@ -24,18 +24,18 @@ IMU only,4,1.479515039799907,1.0,64.33376427977204,64.33376427977204,48.22398143 "clone, measured correlation, noise assumed x4",2,1.1598820006352226,1.0,0.3853541069446335,1.0644072764533532,0.6663882330282552 "clone, measured correlation, noise assumed x4",3,1.3663597214021872,1.0,0.8468382431054775,1.8443749062870949,0.6770825779385757 "clone, measured correlation, noise assumed x4",4,0.9313382116502497,1.0,0.3461272565693066,0.8911346077089927,0.6696683908514616 -"replay, anchor",0,162853.3177202277,0.045,238.5619630685604,238.5619630685604,0.2738550680289662 -"replay, 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x1,2,132807.5695084172,0.04899135446685879,198.64201836212678,198.64201836212678,0.2707061031481574 diff --git a/docs/real_front_end.md b/docs/real_front_end.md index d2f197a..fb6765e 100644 --- a/docs/real_front_end.md +++ b/docs/real_front_end.md @@ -54,12 +54,61 @@ standard deviations. | clone, independent errors | 5 | 2.0 | 100% | 0.30 | 0.27 | yes | | clone, measured correlation | 5 | 5.6 | 93% | 0.51 | 0.27 | yes | | clone, measured correlation, noise assumed x4 | 5 | 1.4 | 100% | 0.49 | 0.67 | yes | -| replay, anchor | 5 | 23497.9 | 5% | 93.64 | 0.27 | no | -| replay, clone | 5 | 27.0 | 55% | 0.25 | 0.27 | no | -| replay, clone, noise assumed x4 | 5 | 4.3 | 91% | 0.75 | 0.67 | yes | +| replay, anchor | 5 | 16102.0 | 6% | 72.40 | 0.27 | no | +| replay, clone | 5 | 27.2 | 54% | 0.25 | 0.27 | no | +| replay, clone, noise assumed x4 | 5 | 4.1 | 92% | 0.64 | 0.66 | yes | +## A wider measurement: six windows, five sequences + +The result above is one 30 s window of one sequence, and the replayed error series is one realisation. So the +measurement was widened, with a design fixed in code and committed before it ran +(`WIDE_SEQUENCES`, `WIDE_SCALES` and `pick_window` in `navkit.vo_error`): + +- **Windows.** For each Machine Hall sequence, the 30 s window with the highest median ground-truth speed, on a 5 s + grid, starting no earlier than 30 s. For MH_01 a second window that does not overlap the first. The rule picks windows + where the camera moves, so that a translation direction exists. +- **Front end.** The same front end, at gaps of one and four frames. +- **Clone.** Replayed with the errors of that window, with the assumed noise set to the window's measured sigmas times + 1, 2, 4 and 8, three seeds each, and the single anchor at 1 for comparison. + +The first window was run again with the run aligned to its frames. The first table of this page has five seeds and the +numbers below for that window have three, so they differ slightly. + + + +| Sequence | Window start s | Gap, frames | Pairs | Rotation RMS deg | Lateral RMS m | Outlier share | Rotation correlation time s | Lateral correlation time s | +|---|---:|---:|---:|---:|---:|---:|---:|---:| +| MH_01_easy | 60 | 1 | 590 | 0.08 | 0.008 | 2% | 0.20 | 0.25 | +| MH_01_easy | 60 | 4 | 590 | 0.14 | 0.018 | 8% | 0.10 | 0.20 | +| MH_01_easy | 90 | 1 | 595 | 0.09 | 0.006 | 5% | 0.20 | 0.40 | +| MH_01_easy | 90 | 4 | 596 | 0.13 | 0.010 | 6% | 0.10 | 0.20 | +| MH_02_easy | 105 | 1 | 596 | 0.07 | 0.005 | 8% | 0.25 | 0.20 | +| MH_02_easy | 105 | 4 | 596 | 0.09 | 0.007 | 7% | 0.25 | 0.15 | +| MH_03_medium | 80 | 1 | 596 | 0.12 | 0.013 | 6% | 0.05 | 0.05 | +| MH_03_medium | 80 | 4 | 590 | 0.38 | 0.036 | 10% | 0.05 | 0.20 | +| MH_04_difficult | 60 | 1 | 596 | 0.16 | 0.020 | 6% | 0.15 | 0.50 | +| MH_04_difficult | 60 | 4 | 596 | 0.44 | 0.061 | 8% | 0.05 | 0.30 | +| MH_05_difficult | 30 | 1 | 596 | 0.10 | 0.012 | 7% | 0.15 | 0.25 | +| MH_05_difficult | 30 | 4 | 596 | 0.29 | 0.039 | 11% | 0.20 | 0.10 | + + + + + +| Sequence | Window start s | clone x1: NEES / coverage | clone x2: NEES / coverage | clone x4: NEES / coverage | clone x8: NEES / coverage | anchor x1: NEES / coverage | +|---|---:|---|---|---|---|---| +| MH_01_easy | 60 | 31.5 / 52% | 12.6 / 67% | 4.49 / 91% ok | 1.88 / 100% ok | 1.13e+05 / 6% | +| MH_01_easy | 90 | 13.5 / 58% | 6.07 / 90% | 2.62 / 100% ok | 1.32 / 100% ok | 1.19e+05 / 5% | +| MH_02_easy | 105 | 4.28 / 100% ok | 3.7 / 100% ok | 1.94 / 100% ok | 1.27 / 100% ok | 1.56e+05 / 5% | +| MH_03_medium | 80 | 3.5 / 100% ok | 2.03 / 100% ok | 1.2 / 100% ok | 1.05 / 100% ok | 9.73e+04 / 7% | +| MH_04_difficult | 60 | 74.3 / 18% | 14.8 / 42% | 2.73 / 99% ok | 1.16 / 100% ok | 9.04e+04 / 9% | +| MH_05_difficult | 30 | 33.4 / 36% | 12.5 / 59% | 3.99 / 100% ok | 1.6 / 100% ok | 1.6e+05 / 6% | +| **Windows meeting the gate** | | 2 of 6 | 2 of 6 | 6 of 6 | 6 of 6 | 0 of 6 | + + + ## What it shows 1. **The front end's errors are smaller than the surrogate's.** At one frame the rotation error is 0.08° RMS and the @@ -72,20 +121,30 @@ standard deviations. times the baseline. 4. **Outliers grow with the gap.** 2% of one-frame pairs, 13% of ten-frame pairs. 5. **A Gaussian surrogate with the measured size and correlation time passes the gate.** NEES 5.6 and 93% coverage. -6. **The real errors do not.** Replayed at the measured size, the clone has NEES 27 and 55% coverage and fails the +6. **The real errors do not.** Replayed at the measured size, the clone has NEES 27 and 54% coverage and fails the gate. The surrogate misses what the real error series has beyond a size and a correlation time. -7. **Assuming four times the measured noise brings it back, narrowly.** NEES 4.3 and 91% coverage. That is the factor - ADR-0017 found by trial. The margin is thin. -8. **The single anchor fails badly on the same errors.** NEES in the tens of thousands and 5% coverage, as in the +7. **Assuming four times the measured noise brings it back.** NEES 4.1 and 92% coverage in this window. That is the + factor ADR-0017 found by trial. +8. **The single anchor fails badly on the same errors.** NEES in the tens of thousands and 6% coverage, as in the earlier studies. 9. **Vision is worth having.** Dead reckoning on the IMU alone ends the window 64 m off. The clone ends it within a metre. +10. **In every window the errors are small and short-lived.** One-frame rotation error is 0.07° to 0.16° RMS and + lateral error 5 mm to 20 mm. Correlation times run from 0.05 s to 0.5 s; the longest, 0.5 s on the lateral error in + the MH_04 window, sits at the edge of the range where ADR-0017 found the clone failing. +11. **At the measured noise the clone meets the gate in two of six windows.** In the other four the replayed errors make + it overconfident. Doubling the assumed noise does not change that count. +12. **At four times the measured noise it meets the gate in all six windows,** across five sequences including the two + marked difficult. The tightest window has a coverage of 91%, so the margin is thin there. Eight times is also + inside the gate everywhere, and costs a larger claimed uncertainty. +13. **The single anchor meets the gate in none of the six windows.** ## Limits -One sequence, one 30 s window, and one error series replayed: the five seeds change the simulated GNSS and not the -visual errors, so the replay result is one realisation and the 91% coverage has no spread behind it. The front end is -mine and plain. The translation has the true scale, which no monocular front end has. The ground truth is itself an -estimate. The conclusion that the clone needs about four times the measured noise holds for this front end on this -window, and it is not shown for another front end or sequence. `vision_enabled` stays False. +Six windows of five sequences, all from one dataset (the EuRoC Machine Hall) and one front end. In each window the +replayed errors are one realisation: the seeds change the simulated GNSS and not the visual errors, so a window's result +has no spread of its own. The front end is mine and plain. The translation has the true scale, which no monocular front +end has. The ground truth is itself an estimate. The conclusion that the clone needs about four times the measured noise +holds for this front end on these windows, and it is not shown for another front end or dataset. `vision_enabled` +stays False. See [ADR-0023](adr/0023-clone-against-a-measured-front-end.md). diff --git a/mkdocs.yml b/mkdocs.yml index c2dc48d..492c08c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -156,6 +156,7 @@ nav: - "0021 Fault classification": adr/0021-fault-classification-from-innovations.md - "0022 Multipath signature": adr/0022-multipath-signature.md - "0023 Clone against a measured front end": adr/0023-clone-against-a-measured-front-end.md + - "0024 Assumed noise of the opt-in clone": adr/0024-assumed-noise-for-the-opt-in-clone.md - Project: - Scope and responsible use: scope.md - Status and limits: status.md diff --git a/src/navkit/vo_error.py b/src/navkit/vo_error.py index ebf8d5d..8d5321d 100644 --- a/src/navkit/vo_error.py +++ b/src/navkit/vo_error.py @@ -32,6 +32,9 @@ FRAME_STEP_S = 0.05 # the camera is 20 Hz #: Below this true baseline the direction of the motion is undefined and the pair is skipped. MIN_TRUE_BASELINE_M = 1e-3 +#: Floors on the noise the filter is told to assume (0.01 degrees, 1 mm). Real windows are well above them. +MIN_ROT_SIGMA_DEG = 0.01 +MIN_TRANS_SIGMA_M = 0.001 MAX_LAG = 40 CSV_COLUMNS = ( "sequence", @@ -114,11 +117,18 @@ def pair_row( } -def measure(seq_dir: Path, gaps: tuple[int, ...] = GAPS, min_baseline_m: float = 0.0) -> list[dict[str, Any]]: +def measure( + seq_dir: Path, + gaps: tuple[int, ...] = GAPS, + min_baseline_m: float = 0.0, + window: tuple[float, float] | None = None, +) -> list[dict[str, Any]]: """Run the front end on every pair ``gap`` frames apart and return one row per pair that produced an estimate.""" seq = load_sequence(seq_dir.parent, seq_dir.name) cam = camera_from_yaml(seq_dir / "mav0/cam0/sensor.yaml") frames = frame_list(seq_dir) + if window is not None: + frames = [f for f in frames if window[0] <= f[0] <= window[1]] if len(frames) < max(gaps) + 2: raise ValueError(f"{seq_dir.name}: {len(frames)} frames are not enough for gaps up to {max(gaps)}") images: dict[int, np.ndarray] = {} @@ -132,8 +142,10 @@ def image(i: int) -> np.ndarray: rows: list[dict[str, Any]] = [] top = max(gaps) for i in range(0, len(frames) - top): - window = [image(i + k) for k in range(top + 1)] - found = relative_poses_along(window, gaps, cam, seed=i) + if frames[i + top][0] - frames[i][0] > top * FRAME_STEP_S * 1.5: + continue # the frames are not contiguous here: a gap between two fetched windows + stack = [image(i + k) for k in range(top + 1)] + found = relative_poses_along(stack, gaps, cam, seed=i) for gap in gaps: est = found[gap] (t0, _), (t1, _) = frames[i], frames[i + gap] @@ -148,6 +160,54 @@ def image(i: int) -> np.ndarray: # ------------------------------------------------------------------ statistics +def pick_window( + truth: Trajectory, + length_s: float = 30.0, + step_s: float = 5.0, + earliest_s: float = 30.0, + margin_s: float = 5.0, + avoid: tuple[tuple[float, float], ...] = (), +) -> float: + """The start of the window with the highest median speed, so that the camera moves and a direction exists. + + Starts are on a ``step_s`` grid from ``earliest_s``; the window must end ``margin_s`` before the recording does + and must not overlap any ``(start, stop)`` in ``avoid``. This is the rule used for the wider measurement, fixed + before it was run. + """ + t, p = truth.t, truth.positions + speed = np.linalg.norm(np.diff(p, axis=0), axis=1) / np.diff(t) + best: tuple[float, float] | None = None + start = earliest_s + while start + length_s <= float(t[-1]) - margin_s: + if not any(start < b and a < start + length_s for a, b in avoid): + m = (t[1:] >= start) & (t[1:] < start + length_s) + if m.any(): + med = float(np.median(speed[m])) + if best is None or med > best[0]: + best = (med, start) + start += step_s + if best is None: + raise ValueError("no window fits") + return best[1] + + +def windows(rows: list[dict[str, Any]], max_gap_s: float = 1.0) -> list[tuple[str, float, float]]: + """``(sequence, start, stop)`` of each run of contiguous pairs in the rows.""" + out: list[tuple[str, float, float]] = [] + for seq in sorted({r["sequence"] for r in rows}): + ts = sorted({float(r["t_s"]) for r in rows if r["sequence"] == seq and int(r["gap_frames"]) == 1}) + if not ts: + continue + start = prev = ts[0] + for t in ts[1:]: + if t - prev > max_gap_s: + out.append((seq, start, prev + FRAME_STEP_S)) + start = t + prev = t + out.append((seq, start, prev + FRAME_STEP_S)) + return out + + def autocorrelation(x: np.ndarray, max_lag: int = MAX_LAG) -> np.ndarray: """Normalised autocorrelation at lags 0 to ``max_lag`` of a series (mean removed).""" x = np.asarray(x, dtype=float) @@ -261,31 +321,52 @@ def replay_stream( } -def replay_rows( - root: str | Path, - sequence: str, +def _origin(seq: Any) -> float: + """The clock time where ``skip_s = 0`` starts a run: the later of the IMU's and the truth's first sample.""" + return max(float(seq.imu.t[0]), float(seq.truth.t[0])) + + +def _replay_window( + seq: Any, + cam: Camera, vo_rows: list[dict[str, Any]], - rot_sigma_deg: float, - trans_sigma_m: float, - seeds: int = 5, + start_s: float, + duration_s: float, + out_at_s: float, + scales: tuple[tuple[str, str, float], ...], + seeds: int, ) -> list[dict[str, Any]]: - """The clone fed the measured errors themselves. The filter assumes the measured sigmas times the scale.""" - seq = load_sequence(root, sequence) - cam = camera_from_yaml(Path(root) / sequence / "mav0/cam0/sensor.yaml") - skip, duration, out_at = CLONE_WINDOW - t_from = skip + float(seq.imu.t[0]) - stream = replay_stream(vo_rows, seq.truth, cam, t_from, t_from + duration) + """The clone on one window, fed the measured errors of that window. Scales are ``(name, model, scale)``. + + The run starts exactly at ``start_s`` on the clock the frames use. (``skip_s`` is counted from the later of the + IMU's and the ground truth's first sample, so the offset is taken off.) + """ + inside = [ + r + for r in vo_rows + if r["sequence"] == seq.name + and start_s <= float(r["t_s"]) + and float(r["t_s"]) + float(r["dt_s"]) <= start_s + duration_s + ] + one = [r for r in inside if int(r["gap_frames"]) == 1] + stream = replay_stream(one, seq.truth, cam, start_s, start_s + duration_s) + summ = summary(one, 1, FRAME_STEP_S) + # A floor, so that a window with (almost) no measured error does not hand the filter a zero measurement noise. + sigmas = ( + max(round(summ["rot_rms_deg"], 3), MIN_ROT_SIGMA_DEG), + max(round(summ["lat_rms_m"], 4), MIN_TRANS_SIGMA_M), + ) rows: list[dict[str, Any]] = [] - for name, (model, scale) in REPLAY_VARIANTS.items(): + for name, model, scale in scales: for seed in range(seeds): opts = RunOptions( seed=seed, - skip_s=skip, - duration_s=duration, - outages=((out_at, duration - out_at),), + skip_s=start_s - _origin(seq), + duration_s=duration_s, + outages=((out_at_s, duration_s - out_at_s),), vision=model, vision_noise_scale=scale, - vision_sigmas=(rot_sigma_deg, trans_sigma_m), + vision_sigmas=sigmas, **dict(CONFIGS["walk10"]), ) rec = run_sequence(seq, opts, vision_override=stream) @@ -305,6 +386,83 @@ def replay_rows( return rows +def replay_rows( + root: str | Path, + sequence: str, + vo_rows: list[dict[str, Any]], + seeds: int = 5, +) -> list[dict[str, Any]]: + """The clone fed the measured errors themselves. The filter assumes the window's measured sigmas times the scale.""" + seq = load_sequence(root, sequence) + cam = camera_from_yaml(Path(root) / sequence / "mav0/cam0/sensor.yaml") + start, duration, out_at = CLONE_WINDOW + scales = tuple((name, model, scale) for name, (model, scale) in REPLAY_VARIANTS.items()) + return _replay_window(seq, cam, vo_rows, start, duration, out_at, scales, seeds) + + +#: The wider measurement, fixed before it was run: the windows are chosen by speed (:func:`pick_window`), the front +#: end is run at gaps of one and four frames, and the clone is replayed at these assumed-noise scales. +WIDE_SEQUENCES = ("MH_01_easy", "MH_02_easy", "MH_03_medium", "MH_04_difficult", "MH_05_difficult") +WIDE_GAPS = (1, 4) +WIDE_LENGTH_S = 30.0 +WIDE_OUTAGE_AT_S = 10.0 +WIDE_SEEDS = 3 +WIDE_SCALES: tuple[tuple[str, str, float], ...] = ( + ("clone x1", "clone", 1.0), + ("clone x2", "clone", 2.0), + ("clone x4", "clone", 4.0), + ("clone x8", "clone", 8.0), + ("anchor x1", "anchor", 1.0), +) +WIDE_COLUMNS = ("sequence", "window_start_s", *CLONE_COLUMNS) + + +def wide_study( + root: str | Path, + sequences: tuple[str, ...] = WIDE_SEQUENCES, + existing: list[dict[str, Any]] | None = None, + seeds: int = WIDE_SEEDS, + fetch: bool = True, + earliest_s: float = 30.0, +) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: + """Choose a window per sequence, fetch its frames, measure the front end, and replay the clone. + + ``existing`` are the front-end rows already measured: their windows are avoided and kept. Returns the new + front-end rows and the replay rows, each replay row tagged with its sequence and window start. + """ + from .io.euroc_fetch import fetch_images + + existing = existing or [] + new_rows: list[dict[str, Any]] = [] + replay: list[dict[str, Any]] = [] + for name in sequences: + seq = load_sequence(root, name) + avoid = tuple((a, b) for s_, a, b in windows(existing, 1.0) if s_ == name) + start = pick_window(seq.truth, WIDE_LENGTH_S, earliest_s=earliest_s, avoid=avoid) + # Frames are counted from the first IMU sample, which is also the clock of the ground truth here. + if fetch: + fetch_images( + name, + Path(root), + start_s=start, + stop_s=start + WIDE_LENGTH_S, + full_download=True, + ) + print(f"{name}: window {start:.0f} s to {start + WIDE_LENGTH_S:.0f} s", file=sys.stderr, flush=True) + mine = measure(Path(root) / name, WIDE_GAPS, window=(start, start + WIDE_LENGTH_S)) + new_rows += mine + cam = camera_from_yaml(Path(root) / name / "mav0/cam0/sensor.yaml") + for r in _replay_window(seq, cam, mine, start, WIDE_LENGTH_S, WIDE_OUTAGE_AT_S, WIDE_SCALES, seeds): + replay.append({"sequence": name, "window_start_s": start, **r}) + # The window measured first is replayed again with the same scales, and with the run aligned to its frames. + for name, a, b in windows(existing, 1.0): + seq = load_sequence(root, name) + cam = camera_from_yaml(Path(root) / name / "mav0/cam0/sensor.yaml") + for r in _replay_window(seq, cam, existing, a, b - a, WIDE_OUTAGE_AT_S, WIDE_SCALES, seeds): + replay.append({"sequence": name, "window_start_s": a, **r}) + return new_rows, replay + + CLONE_WINDOW = (60.0, 30.0, 10.0) # skip, duration, and the start of the GNSS outage that runs to the end @@ -371,6 +529,51 @@ def clone_markdown(rows: list[dict[str, Any]]) -> str: return "\n".join(lines) +def write_wide_csv(path: str | Path, rows: list[dict[str, Any]]) -> None: + target = Path(path) + target.parent.mkdir(parents=True, exist_ok=True) + with target.open("w", newline="", encoding="utf-8") as fh: + writer = csv.DictWriter(fh, fieldnames=WIDE_COLUMNS) + writer.writeheader() + writer.writerows(rows) + + +def passes(nees: float, coverage: float) -> bool: + """The Track B gate: mean NEES below 10 and 2-sigma coverage above 90%.""" + return nees < 10.0 and coverage > 0.9 + + +def wide_markdown(rows: list[dict[str, Any]]) -> str: + """Per window and assumed-noise scale: the median over seeds, and whether it meets the gate.""" + names = [n for n, _, _ in WIDE_SCALES] + keys = sorted({(r["sequence"], float(r["window_start_s"])) for r in rows}) + lines = [ + "| Sequence | Window start s | " + " | ".join(f"{n}: NEES / coverage" for n in names) + " |", + "|---|---:|" + "---|" * len(names), + ] + verdict: dict[str, list[bool]] = {n: [] for n in names} + for seq, start in keys: + cells = [] + for n in names: + sel = [ + r for r in rows if r["sequence"] == seq and float(r["window_start_s"]) == start and r["variant"] == n + ] + if not sel: + cells.append("-") + continue + nees = float(np.median([float(r["nees_mean"]) for r in sel])) + cov = float(np.median([float(r["coverage_2sigma"]) for r in sel])) + ok = passes(nees, cov) + verdict[n].append(ok) + cells.append(f"{nees:.3g} / {100 * cov:.0f}%{' ok' if ok else ''}") + lines.append(f"| {seq} | {start:.0f} | " + " | ".join(cells) + " |") + total = len(keys) + lines.append( + "| **Windows meeting the gate** | | " + " | ".join(f"{sum(verdict[n])} of {total}" for n in names) + " |" + ) + return "\n".join(lines) + + def write_clone_csv(path: str | Path, rows: list[dict[str, Any]]) -> None: target = Path(path) target.parent.mkdir(parents=True, exist_ok=True) @@ -380,6 +583,55 @@ def write_clone_csv(path: str | Path, rows: list[dict[str, Any]]) -> None: writer.writerows(rows) +#: The window measured first (all five gaps), which the first tables of the page describe. +ORIGINAL_WINDOW = ("MH_01_easy", 60.0, 90.0) + + +def rows_in(rows: list[dict[str, Any]], sequence: str, start_s: float, stop_s: float) -> list[dict[str, Any]]: + """The rows of one sequence whose pair lies inside ``[start_s, stop_s]``.""" + return [ + r + for r in rows + if r["sequence"] == sequence and start_s <= float(r["t_s"]) and float(r["t_s"]) + float(r["dt_s"]) <= stop_s + ] + + +def rows_in_window(rows: list[dict[str, Any]], sequence: str, start_s: float, length_s: float) -> list[dict[str, Any]]: + """The rows of one sequence whose pair starts in ``[start_s, start_s + length_s - one frame)``. + + Two windows can touch (90.005 s ends where the next begins), so a window is defined by where its pairs start, and + a pair that straddles the boundary belongs to neither. + """ + return [ + r + for r in rows + if r["sequence"] == sequence and start_s - 1e-6 <= float(r["t_s"]) < start_s + length_s - FRAME_STEP_S - 1e-6 + ] + + +def windows_markdown( + rows: list[dict[str, Any]], wide_rows: list[dict[str, Any]], gaps: tuple[int, ...] = WIDE_GAPS +) -> str: + """The front end's errors in every window of the wider measurement, at the gaps it used.""" + lines = [ + "| Sequence | Window start s | Gap, frames | Pairs | Rotation RMS deg | Lateral RMS m | Outlier share " + "| Rotation correlation time s | Lateral correlation time s |", + "|---|---:|---:|---:|---:|---:|---:|---:|---:|", + ] + keys = sorted({(r["sequence"], float(r["window_start_s"])) for r in wide_rows}) + for seq, start in keys: + sel = rows_in_window(rows, seq, start, WIDE_LENGTH_S) + for gap in gaps: + if not any(int(r["gap_frames"]) == gap for r in sel): + continue + s = summary(sel, gap, FRAME_STEP_S) + lines.append( + f"| {seq} | {start:.0f} | {gap} | {s['pairs']:.0f} | {s['rot_rms_deg']:.2f} | {s['lat_rms_m']:.3f} | " + f"{100 * s['outlier_share']:.0f}% | {s['rot_corr_time_s']:.2f} | {s['lat_corr_time_s']:.2f} |" + ) + return "\n".join(lines) + + def write_csv(path: str | Path, rows: list[dict[str, Any]]) -> None: target = Path(path) target.parent.mkdir(parents=True, exist_ok=True) @@ -425,6 +677,13 @@ def build_parser() -> argparse.ArgumentParser: ) p.add_argument("--clone-csv", default=None, help="write (with --clone) or read the clone check's runs") p.add_argument("--page", default=None, help="fill the vo-errors and vo-clone blocks of this Markdown page") + p.add_argument( + "--wide", + action="store_true", + help="the wider measurement: pick a window per Machine Hall sequence, fetch its frames from the whole " + "archive on disk, measure, and replay the clone at several assumed-noise scales (an hour or more)", + ) + p.add_argument("--wide-csv", default="docs/data/vo_wide.csv", help="the replay runs of --wide") p.add_argument("--markdown", action="store_true", help="print the tables") return p @@ -446,6 +705,18 @@ def write_block(path: str | Path, name: str, block: str) -> None: def main(argv: list[str] | None = None) -> int: args = build_parser().parse_args(argv) + if args.wide: + existing = read_csv(args.csv) if args.csv and Path(args.csv).is_file() else [] + try: + new_rows, replay = wide_study(args.root, WIDE_SEQUENCES, existing) + except (OSError, ValueError) as exc: + print(f"error: {exc}", file=sys.stderr) + return 1 + if args.csv: + write_csv(args.csv, existing + new_rows) + write_wide_csv(args.wide_csv, replay) + print(wide_markdown(replay)) + return 0 if args.from_csv: rows = read_csv(args.from_csv) else: @@ -458,13 +729,14 @@ def main(argv: list[str] | None = None) -> int: if args.csv: write_csv(args.csv, rows) print(f"wrote {args.csv}", file=sys.stderr) + first = rows_in(rows, *ORIGINAL_WINDOW) clone = None if args.clone: - one = summary(rows, 1, FRAME_STEP_S) + one = summary(first, 1, FRAME_STEP_S) sigmas = (round(one["rot_rms_deg"], 3), round(one["lat_rms_m"], 4)) corr = round(one["lat_corr_time_s"], 2) root, seq = args.root, args.sequence - clone = clone_rows(root, seq, *sigmas, corr_s=corr) + replay_rows(root, seq, rows, *sigmas) + clone = clone_rows(root, seq, *sigmas, corr_s=corr) + replay_rows(root, seq, first) if args.clone_csv: write_clone_csv(args.clone_csv, clone) print(f"wrote {args.clone_csv}", file=sys.stderr) @@ -472,14 +744,18 @@ def main(argv: list[str] | None = None) -> int: clone = read_clone_csv(args.clone_csv) if args.page: try: - write_block(args.page, "vo-errors", summary_markdown(rows)) + write_block(args.page, "vo-errors", summary_markdown(first)) if clone is not None: write_block(args.page, "vo-clone", clone_markdown(clone)) + if Path(args.wide_csv).is_file(): + wide = read_csv(args.wide_csv) + write_block(args.page, "vo-wide-errors", windows_markdown(rows, wide)) + write_block(args.page, "vo-wide-clone", wide_markdown(wide)) except ValueError as exc: print(f"error: {exc}", file=sys.stderr) return 1 if args.markdown or not (args.csv or args.page): - print(summary_markdown(rows)) + print(summary_markdown(first)) if clone is not None: print("\n" + clone_markdown(clone)) return 0 diff --git a/tests/test_vo_page.py b/tests/test_vo_page.py index 942fed8..38f4e2b 100644 --- a/tests/test_vo_page.py +++ b/tests/test_vo_page.py @@ -25,6 +25,17 @@ def clone(): return ve.read_csv(ROOT / "docs" / "data" / "vo_clone_check.csv") +@pytest.fixture(scope="module") +def wide(): + return ve.read_csv(ROOT / "docs" / "data" / "vo_wide.csv") + + +@pytest.fixture(scope="module") +def first(rows): + """The window measured first, at all five gaps.""" + return ve.rows_in(rows, *ve.ORIGINAL_WINDOW) + + def _variant(clone, name): sel = [r for r in clone if r["variant"] == name] return ( @@ -33,22 +44,24 @@ def _variant(clone, name): ) -def test_blocks_match_the_csvs(rows, clone): +def test_blocks_match_the_csvs(rows, clone, wide, first): text = PAGE.read_text(encoding="utf-8") - assert ve.summary_markdown(rows) in text + assert ve.summary_markdown(first) in text assert ve.clone_markdown(clone) in text + assert ve.windows_markdown(rows, wide) in text + assert ve.wide_markdown(wide) in text -def test_the_one_frame_errors_are_small_and_shorter_lived_than_the_clone_needs(rows): - s = ve.summary(rows, 1, ve.FRAME_STEP_S) +def test_the_one_frame_errors_are_small_and_shorter_lived_than_the_clone_needs(first): + s = ve.summary(first, 1, ve.FRAME_STEP_S) assert 0.05 < s["rot_rms_deg"] < 0.12 and 0.005 < s["lat_rms_m"] < 0.012 assert s["rot_rms_deg"] < 0.35 and s["lat_rms_m"] < 0.05 # the surrogate's defaults assert 0.1 <= s["rot_corr_time_s"] <= 0.3 and 0.15 <= s["lat_corr_time_s"] <= 0.3 assert max(s["rot_corr_time_s"], s["lat_corr_time_s"]) < 0.5 -def test_a_longer_baseline_gives_a_better_direction_and_more_outliers(rows): - by = {g: ve.summary(rows, g, ve.FRAME_STEP_S) for g in (1, 2, 4, 6, 10)} +def test_a_longer_baseline_gives_a_better_direction_and_more_outliers(first): + by = {g: ve.summary(first, g, ve.FRAME_STEP_S) for g in (1, 2, 4, 6, 10)} direction = [by[g]["dir_median_deg"] for g in (1, 2, 4, 6, 10)] outliers = [by[g]["outlier_share"] for g in (1, 2, 4, 6, 10)] lateral = [by[g]["lat_rms_m"] for g in (1, 2, 4, 6, 10)] @@ -130,7 +143,7 @@ def test_clone_and_replay_runs_on_a_synthetic_sequence(tmp_path, monkeypatch): ) monkeypatch.setattr(ve, "REPLAY_VARIANTS", {"replay, clone": ve.REPLAY_VARIANTS["replay, clone"]}) sim = ve.clone_rows(tmp_path, "MH_01_easy", 0.08, 0.008, 0.25, seeds=1) - rep = ve.replay_rows(tmp_path, "MH_01_easy", rows, 0.08, 0.008, seeds=1) + rep = ve.replay_rows(tmp_path, "MH_01_easy", rows, seeds=1) assert {r["variant"] for r in sim} == set(ve.CLONE_VARIANTS) assert {r["variant"] for r in rep} == set(ve.REPLAY_VARIANTS) imu = next(r for r in sim if r["variant"] == "IMU only") @@ -144,17 +157,153 @@ def test_clone_and_replay_runs_on_a_synthetic_sequence(tmp_path, monkeypatch): def test_cli_fills_a_page_and_refuses_one_without_markers(rows, clone, tmp_path): page = tmp_path / "p.md" - page.write_text( - "\n\n\n\n" - ) + names = ("vo-errors", "vo-clone", "vo-wide-errors", "vo-wide-clone") + page.write_text("".join(f"\n\n" for n in names)) args = [ "--from-csv", str(ROOT / "docs/data/vo_errors.csv"), "--clone-csv", str(ROOT / "docs/data/vo_clone_check.csv"), + "--wide-csv", + str(ROOT / "docs/data/vo_wide.csv"), ] assert ve.main([*args, "--page", str(page)]) == 0 - assert "Rotation RMS" in page.read_text() and "replay, clone" in page.read_text() + text = page.read_text() + assert "Rotation RMS" in text and "replay, clone" in text and "clone x4" in text bare = tmp_path / "b.md" bare.write_text("none\n") assert ve.main([*args, "--page", str(bare)]) == 1 + + +# ------------------------------------------------------------------ the wider measurement + + +def _windows(wide): + return sorted({(r["sequence"], float(r["window_start_s"])) for r in wide}) + + +def _passing(wide, variant): + ok = 0 + for key in _windows(wide): + sel = [r for r in wide if (r["sequence"], float(r["window_start_s"])) == key and r["variant"] == variant] + nees = float(np.median([float(r["nees_mean"]) for r in sel])) + cov = float(np.median([float(r["coverage_2sigma"]) for r in sel])) + ok += ve.passes(nees, cov) + return ok + + +def test_the_wider_measurement_has_six_windows_across_five_sequences(wide): + keys = _windows(wide) + assert len(keys) == 6 and {k[0] for k in keys} == set(ve.WIDE_SEQUENCES) + assert sum(k[0] == "MH_01_easy" for k in keys) == 2 + assert {r["variant"] for r in wide} == {n for n, _, _ in ve.WIDE_SCALES} + + +def test_the_design_was_fixed_before_the_run(): + assert ve.WIDE_GAPS == (1, 4) and ve.WIDE_LENGTH_S == 30.0 and ve.WIDE_OUTAGE_AT_S == 10.0 and ve.WIDE_SEEDS == 3 + assert [(n, s) for n, _, s in ve.WIDE_SCALES] == [ + ("clone x1", 1.0), + ("clone x2", 2.0), + ("clone x4", 4.0), + ("clone x8", 8.0), + ("anchor x1", 1.0), + ] + + +def test_the_measured_noise_meets_the_gate_in_two_of_six_windows_and_four_times_in_all(wide): + assert _passing(wide, "clone x1") == 2 and _passing(wide, "clone x2") == 2 + assert _passing(wide, "clone x4") == 6 and _passing(wide, "clone x8") == 6 + assert _passing(wide, "anchor x1") == 0 + + +def test_four_times_is_tightest_on_the_first_window(wide): + sel = [r for r in wide if r["variant"] == "clone x4"] + covs = sorted( + float(np.median([float(r["coverage_2sigma"]) for r in sel if (r["sequence"], r["window_start_s"]) == k])) + for k in {(r["sequence"], r["window_start_s"]) for r in sel} + ) + assert 0.9 < covs[0] < 0.93 and covs[1] > 0.98 + + +def test_the_errors_are_small_in_every_window_and_the_longest_correlation_sits_at_half_a_second(rows, wide): + worst_corr = 0.0 + for seq, start in _windows(wide): + sel = ve.rows_in_window(rows, seq, start, ve.WIDE_LENGTH_S) + s = ve.summary(sel, 1, ve.FRAME_STEP_S) + assert 0.05 < s["rot_rms_deg"] < 0.2 and 0.004 < s["lat_rms_m"] < 0.025 + worst_corr = max(worst_corr, s["rot_corr_time_s"], s["lat_corr_time_s"]) + assert worst_corr == pytest.approx(0.5) + + +def test_windows_are_defined_by_where_their_pairs_start(): + def row(t): + return {"sequence": "s", "t_s": t, "dt_s": 0.05, "gap_frames": 1} + + rows = [row(89.9), row(89.955), row(90.005), row(119.9), row(119.955)] + first = ve.rows_in_window(rows, "s", 60.005, 30.0) + second = ve.rows_in_window(rows, "s", 90.005, 30.0) + assert [r["t_s"] for r in first] == [89.9] # the pair that straddles 90.005 belongs to neither + assert [r["t_s"] for r in second] == [90.005, 119.9] + assert ve.rows_in(rows, "s", 90.0, 120.0)[0]["t_s"] == 90.005 + assert ve.rows_in(rows, "other", 0.0, 500.0) == [] + + +def test_contiguous_runs_of_pairs_are_found_as_windows(): + def row(t): + return {"sequence": "s", "t_s": t, "dt_s": 0.05, "gap_frames": 1} + + rows = [row(10 + 0.05 * k) for k in range(40)] + [row(50 + 0.05 * k) for k in range(40)] + got = ve.windows(rows) + assert [(a, round(b - a)) for _, a, b in got] == [(10.0, 2), (50.0, 2)] + assert ve.windows([]) == [] + + +def test_pick_window_prefers_the_fastest_stretch_and_avoids_what_it_is_told(): + from navkit.types import Trajectory + + t = np.arange(0.0, 200.0, 1.0) + x = np.where(t < 100.0, 0.1 * t, 10.0 + 2.0 * (t - 100.0)) # slow, then fast + poses = np.tile(np.eye(4), (len(t), 1, 1)) + poses[:, 0, 3] = x + truth = Trajectory(t=t, poses=poses) + best = ve.pick_window(truth, 30.0, step_s=5.0, earliest_s=30.0, margin_s=5.0) + assert best >= 85.0 # the median of a window is fast once more than half of it is + other = ve.pick_window(truth, 30.0, step_s=5.0, earliest_s=30.0, margin_s=5.0, avoid=((best, best + 30.0),)) + assert other + 30.0 <= best or other >= best + 30.0 + with pytest.raises(ValueError): + ve.pick_window(truth, 500.0) + + +def test_the_gate_is_nees_below_ten_and_coverage_above_ninety_percent(): + assert ve.passes(9.9, 0.91) and not ve.passes(10.0, 0.95) and not ve.passes(2.0, 0.9) + + +def test_wide_study_on_a_synthetic_sequence(tmp_path, monkeypatch): + from test_vo_error import _write_cam + + ee.write_fixture(tmp_path, "MH_01_easy", duration_s=45.0, seed=2) + d = tmp_path / "MH_01_easy" + _write_cam(d, 5.0, 40.0) + _exact_frontend(monkeypatch, d) + monkeypatch.setattr(ve, "WIDE_LENGTH_S", 25.0) + monkeypatch.setattr(ve, "WIDE_SCALES", (("clone x4", "clone", 4.0),)) + new_rows, replay = ve.wide_study(tmp_path, ("MH_01_easy",), existing=None, seeds=1, fetch=False, earliest_s=5.0) + assert new_rows and replay + assert {r["variant"] for r in replay} == {"clone x4"} and {r["sequence"] for r in replay} == {"MH_01_easy"} + out = tmp_path / "w.csv" + ve.write_wide_csv(out, replay) + assert "clone x4" in ve.wide_markdown(ve.read_csv(out)) + assert ve.windows_markdown(new_rows, replay).count("MH_01_easy") >= 1 + + +def test_the_wide_flag_runs_the_study_and_reports_a_failure(tmp_path, monkeypatch, capsys): + monkeypatch.setattr(ve, "wide_study", lambda *a, **k: ([], [])) + assert ve.main(["--wide", "--wide-csv", str(tmp_path / "w.csv")]) == 0 + assert (tmp_path / "w.csv").is_file() + + def boom(*a, **k): + raise ValueError("no window fits") + + monkeypatch.setattr(ve, "wide_study", boom) + assert ve.main(["--wide", "--wide-csv", str(tmp_path / "x.csv")]) == 1 + assert "no window fits" in capsys.readouterr().err