diff --git a/NOTES.md b/NOTES.md index 1503ad4..bdc72f7 100644 --- a/NOTES.md +++ b/NOTES.md @@ -2631,6 +2631,131 @@ table gives per-column |residual|, the median over cameras, and the mean |camera - **Skyline check:** "none" −4.17 / 6.39, "precession" +1.80 / 3.21, "all" +0.32 / 2.79. The same 35 of 39 cameras move closer to the ridge. +## The coordinated re-run: refined peaks, the whole solved camera, one settings layer (2026-09-26) + +Every change that moves a README number was held for this one pass, so the numbers move once. +Tagged `results-2026-09-26`. + +**What changed in the code.** +- **`settings.py`.** All 13 FIGLIB_* switches are read in one place, with their defaults. A value + comes from the environment, then a profile (`configs/.toml`, chosen with + FIGLIB_PROFILE or by a module's `main()`), then the default. Nothing is written into + `os.environ` any more. So the import-order bug from 2026-09-25 (terrain_range set + FIGLIB_LENS when imported, and test_geolocation failed when run alone) cannot come back: + coverage, bias, fig_bearing and terrain_range now select `calibrated` in `__main__`. + `tests/test_settings.py` checks that importing them sets nothing. +- **The run log stores every setting as resolved,** defaults included, with where each value + came from and the profile's name and SHA-256. It used to store only the variables that were + set. `configs/` counts toward the dirty-tree check. +- **FIGLIB_REFINE_KM defaults to 0.01.** `solve` re-finds its peak at 10 m within two cells + of the grid's best, unless set to 0. `accumulate.posterior` and `bias` still report their + own grid argmax; they are separate estimators and were not part of this change. +- **FIGLIB_POSE_FULL is on in the calibrated profiles** (`calibrated`, `recent`). It was + measured first, with refinement on: + - Core corpus: bearings move a median 0.00° and at most 0.30° (sm-n at Steele). Seven fires + move by 0.03 km or less, and no count changes. + - Recent corpus: it removes the bm-lens miss (bm-w at Bernardo 249.70 → 246.72°, bm-e at + Junction 75.24 → 72.50°). + - The model is the camera the stars measured, so it is the calibrated one. + `compare_geolocation` gains the `_full` column, and `stars.fig_geolocation` draws it as + "after". +- **`rerun_results.sh`** runs every stage that reaches the README, in order, and refuses a + dirty tree. All of this pass ran from d469059, in `out/logs/rerun_d469059{,_recent}.log`. + The recent block was run a second time because a test docstring was edited while it ran, + which marked the first four entries dirty. Those entries stay in the log. The rerun's + outputs are identical. + +**Core corpus, center bearing, upper medians** (grid → refined): + +| | all 26 | confirmed 10 | probable 16 | ≤2 km (confirmed) | +|---|---|---|---|---| +| published | 2.58 → 2.80 km | 1.90 → 1.87 | 4.37 → 4.45 | 7 → 7 | +| fisheye lens | | 1.70 → 1.68 | | 8 → 6 | +| + star azimuth | | 2.00 → 2.02 | | 6 → 5 | +| + whole solved camera | | — → 2.02 | | — → 5 | + +- **These are the numbers the grid study predicted** (2026-09-25, "refined": 1.87 / 1.68 / 2.02). +- **The within-2 km counts are the fragile ones.** Clubfire (2.07), Roundfire (2.02) and + Creelman (2.09) sit within 0.1 km of the line under the calibrated model. +- **Fisheye loses Clubfire** (1.76 → 2.07). ResortFire.2 (1.87 → 0.70) and Roundfire + (3.39 → 2.05) are the lens's gains. +- **Kitchen:** 0.08 → 0.14 km (published), and 0.33 under the calibrated model. +- **Other fires:** Willow 0.30 → 0.21. ScissorsFire under the calibrated model is 0.07 + (0.05 with star azimuth alone). +- **PORTOLA:** WFIGS 24.03 → 23.86 km. CAL FIRE's point is now 1.24 km from the estimate + (was 1.36), still inside the 95% region (Δll 2.38 of 3.0). +- **LIBERTY:** 62.10 → 63.21 km, region 154 km². +- **The CAL FIRE agreement figures do not depend on the estimate** and are unchanged: 110 + fires, 0.65 km median, 72 within 1 km. +- **The Kitchen animation was re-rendered.** Three sites cross 1.22 km out at +240 s. The + lp-e bird enters at +600 s (37.5° off the official point) and holds the estimate at + 1.6–1.7 km until +1380 s. Four sites then sit near 1.3 km, and close to 0.39 at +2220 s + and 0.14 at +2340 s. The README caption had said +660 s and 39°; the bird is in the + +600 s frame. `docs/likelihood-over-time.md` quotes these numbers and is updated. +- **Bearing misses are unchanged:** outer half of the frame 4.4 → 3.5°, central half + 2.6 → 2.5°. + +**All of FIgLib, terrain range** (calibrated profile, so now with the whole solved camera): +- **Validate:** 74 bearings within 5° (was 73). 61 contain the truth at 100 px (median length + 0.91×, unchanged) and 57 at 30 px. The terrain row at the true distance is a median 0 px + from the box bottom (was +1). The extra bearing and the 1 px are both POSE_FULL's pitch + and roll; refinement does not touch validate. +- **Two-site confirmed median (17 fires):** 2.02 km before and after the term at cap, 100 + and 60 px (was 1.82). At 30 px it is 2.02 → 2.07, and within 2 km goes 8 → 7. +- **Rainbow, bearings alone: 1.42 → 2.17 km.** That is refinement, not the camera model: + refine=0 gives 1.42 under both FIGLIB_POSE_FULL settings, and refine=0.01 gives 2.21 + without it and 2.17 with it. The two bearings are within 1° of collinear, so the + likelihood peak is a long flat ridge, and the 0.4 km grid's best cell happened to sit + 0.75 km nearer the truth than the ridge's top. With terrain at 30 px: 2.11 km, and the region + 25.6 → 5.0 km² (7.5 at 60, 20.6 at 100; unchanged). The README had credited the precessed + re-solve with 5.82 → 1.42; part of that was the grid. +- **Palisades: 1.82 → 1.08 km, also refinement,** and the term changes nothing at any band. + The "peak next to dwpgm-s's tower" reading from 2026-09-25 was about the grid cell, and is + dropped from the README. +- **Grove:** 8.08 → 8.31 km. tp-w's interval is unchanged (14.7–28.9 km against 31.9). +- **Single-site figures:** confirmed miss 0.79 km / 4.42° and containment 17 of 19 ledger + and 13 of 18 nearest are all unchanged, because they do not solve. + +**Recent corpus (extra CDN cameras), center bearing, km, all sites** (grid values from +2026-09-25 in brackets where they moved by more than 0.1 km): + +| fire | published | fisheye | + star az | + whole camera | sites | +|---|---|---|---|---|---| +| Bernardo | 1.06 (1.19) | 0.94 (1.19) | 0.90 (0.63) | 1.00 (1.19) | 8 | +| Junction | 2.90 | 2.92 | 3.42 (3.54) | 3.42 (3.54) | 8 | +| Thorn | 2.68 (3.37) | 2.52 (2.99) | 1.08 | 1.12 | 4 | +| Creelman | 0.24 | 0.52 (0.65) | 1.22 | 1.21 | 9 | +| Rainbow | 1.20 | 1.14 | 1.47 (1.60) | 1.47 (1.60) | 8 | +| Beaver | 1.11 | 1.39 | 0.87 (0.77) | 0.90 (0.77) | 6 | +| Sorrento | 1.10 (1.22) | 0.97 | **0.70** (1.68) | **0.70** (1.68) | 2 | +| Church2 | 3.18 | 2.53 (2.15) | 1.50 | 1.51 | 4 | +| Chia | 1.30 (1.47) | 1.16 | 3.66 | 3.67 | 7 | +| Brengle | 0.76 | 0.83 | 1.07 (1.17) | 1.06 (1.17) | 10 | + +- **Bernardo's "whole camera costs 0.56 km" was mostly the grid.** Refined, the star-azimuth + and whole-camera estimates are 0.90 and 1.00. +- **Sorrento** is a two-site fire whose refined peak sits a kilometre from its grid cell. +- **The conclusion from 2026-09-25 stands.** Calibration fixes every large per-bearing error, + and loses at the fire level on Junction, Creelman, Rainbow, Chia and Brengle through + detection selection. + +**Coverage and the bias term** (`coverage`, `bias`, calibrated profile) are unchanged in +substance: +- **Best box:** cov95 27–41% across the six times; all boxes fall to 4% by 30 min. +- **With the pre-hoc flag, best box:** 48% flagged; of the rest, 75% covered (LOFO 74%), + 1.28 km, 25.7 km², 59% within 2 km. It was 47% / 74% (68%) / 1.28 / 25.7 / 58%. +- **With the pre-hoc flag, all boxes:** 70% flagged; 74% (74%), 1.03 km, 8.1 km², 74%. It + was 71% / 74% (72%) / 1.12 / 9.3 / 74%. + +**Not moved by this pass:** the quantization and edge figures (from `accumulate.posterior`, +not refined), the false-alarm sweep (no solve), and the skyline check (no solve). + +```sh +./rerun_results.sh # every stage above, from a committed tree +python -m src.figlib.settings # what each FIGLIB_* resolves to, and from where +FIGLIB_PROFILE=calibrated python -m src.figlib.geolocate +``` + ## Deliberately deferred Monochrome/NIR sequences (11 of them, paired with color views of the same fires) -- diff --git a/README.md b/README.md index 2eb2c8a..7db3b84 100644 --- a/README.md +++ b/README.md @@ -10,10 +10,10 @@ from t = −120 s to +2400 s and re-solved from each camera's best detection so far. Each camera's most confident smoke detection (right) casts a bearing from its tower; the bearings accumulate into a likelihood field whose peak (✗) is the estimate; the open circle is the official ignition -coordinate. One ray has no depth; by +300 s three sites cross 1.33 km out. From +660 s -lp-e-mobo-c's best box is a bird (apparently with very smoke-like feet), and its ray, 39° off, -drags the estimate to 1.66 km until a stronger box on the plume replaces it at +1380 s. Four -sites then close to **0.08 km**.* +coordinate. One ray has no depth; by +240 s three sites cross 1.22 km out. From +600 s +lp-e-mobo-c's best box is a bird (apparently with very smoke-like feet), and its ray, 38° off, +drags the estimate to 1.6–1.7 km until a stronger box on the plume replaces it at +1380 s. Four +sites then hold near 1.3 km until the last minutes' boxes close them to **0.14 km**.* Wildfire smoke detection is usually reported as mAP on a held-out split. That number does not tell an operator anything they can act on. This project measures three things that it @@ -30,17 +30,18 @@ interagency [WFIGS/IRWIN](https://data-nifc.opendata.arcgis.com/) incident feed. ## Results -### Geolocation: 1.90 km upper median where the ground truth is solid +### Geolocation: 1.87 km upper median where the ground truth is solid Bearings from two or more sites, accumulated into a likelihood field over the ground, scored -in kilometers against the official ignition coordinate. Medians in these tables are upper +in kilometers against the official ignition coordinate. The estimate is the likelihood peak, +re-found at 10 m around the best cell of a 0.4 km grid. Medians in these tables are upper medians: with an even count, the higher of the two middle values. | set | n | upper median error | max | ≤1 km | ≤2 km | ≤5 km | |---|---|---|---|---|---|---| -| all scoring fires | 26 | 2.58 km | 62.10 km | 6 | 12 | 19 | -| **name-confirmed truth** | 10 | **1.90 km** | **3.61 km** | | | | -| probable truth | 16 | 4.37 km | 62.10 km | | | | +| all scoring fires | 26 | 2.80 km | 63.21 km | 6 | 12 | 19 | +| **name-confirmed truth** | 10 | **1.87 km** | **3.74 km** | | | | +| probable truth | 16 | 4.45 km | 63.21 km | | | | ![Every scoring fire's estimate, re-centred on its official ignition point](docs/figures/geolocation_offsets.png) @@ -57,13 +58,13 @@ contour, and where the open circle is the official coordinate. The inset appears credible region is too small to see at the main scale.* The geometry holds up in conditions that are not benign. `20201202_WillowFire` is a night -ignition seen against continuous city light from three sites, and lands **0.30 km** from the +ignition seen against continuous city light from three sites, and lands **0.21 km** from the assigned coordinate (probable-tier truth). ![Willow fire triangulation, at night against urban light](docs/figures/triangulate_willow_night.jpg) **The split between the tiers is the finding.** Every confirmed-tier fire lands within -3.61 km; the probable tier carries the entire tail. Where a probable-tier fire fails, the +3.74 km; the probable tier carries the entire tail. Where a probable-tier fire fails, the failure has a shape worth reading: bearings agreeing with each other to within a few km² while sitting 20–60 km from the assigned incident. That shape has two possible causes — a wrong record, or a weak crossing angle — and a second official source tells them apart. @@ -71,9 +72,9 @@ wrong record, or a weak crossing angle — and a second official source tells th ![PORTOLA: three bearings agree tightly 24 km from the assigned incident](docs/figures/triangulate_portola.jpg) *`20171010_FIRE` → PORTOLA: three bearings from two sites close on an 8 km² region, with the -WFIGS coordinate 24.03 km away and no ray passing near it. CAL FIRE records the same fire at +WFIGS coordinate 23.86 km away and no ray passing near it. CAL FIRE records the same fire at 33.50488, −117.02132 — Riverside County, De Portola Road east of Pauba Road, 23 acres — -**1.36 km from the estimate**, inside the 95% region. The WFIGS point falls outside it +**1.24 km from the estimate**, inside the 95% region. The WFIGS point falls outside it entirely, at zero relative posterior density; it reads 33°18′00″ 116°59′59″, rounded to the arcminute, and is filed in the wrong county.* @@ -85,8 +86,8 @@ records are usually right, and PORTOLA is an outlier rather than a tier-wide fai also the only one of the four disagreements over 5 km that has a solve to arbitrate it. The inverse case is the guard against reading too much into a large error. `20171207_FIRE.2` -→ LIBERTY has both sources agreeing to within 1.71 km while the estimate sits 62 km out, and -there the geometry really is what is wrong: two cameras 13° apart, a 155 km² credible region +→ LIBERTY has both sources agreeing to within 1.71 km while the estimate sits 63 km out, and +there the geometry really is what is wrong: two cameras 13° apart, a 154 km² credible region smeared along the line of sight. A large error indicts the record only when the credible region is small. @@ -122,15 +123,24 @@ Priced in kilometers, with the same detections and solver, on the name-confirmed | camera model | upper median error | ≤2 km | |---|---|---| -| published azimuth, rectilinear lens | 1.90 km | 7 of 10 | -| star-measured fisheye lens | **1.70 km** | **8 of 10** | -| + per-camera star azimuth from the ledger | 2.00 km | 6 of 10 | +| published azimuth, rectilinear lens | 1.87 km | 7 of 10 | +| star-measured fisheye lens | **1.68 km** | 6 of 10 | +| + per-camera star azimuth from the ledger | 2.02 km | 5 of 10 | +| + each camera's own solved lens, pitch and roll | 2.02 km | 5 of 10 | + +The ≤2 km counts are the fragile number: Clubfire (2.07 km), Roundfire (2.02) and Creelman +(2.09) all sit within 0.1 km of the line, so a tenth of a kilometre moves the count by three. +The last row is the calibrated model (`FIGLIB_PROFILE=calibrated`). On these fires it moves no +estimate by more than 0.03 km; it matters for the few cameras whose lens differs from the rest +(NOTES.md, 2026-09-25). ![Bearing rays and estimates before and after calibration](docs/figures/calibration_maps.png) -The lens is the clear gain: detections in the outer half of the frame go from a median bearing -miss of 4.4° to 3.5° ([every bearing](docs/figures/calibration_bearings.png)). The azimuth -ledger lands where detections are right: ScissorsFire goes from 0.58 km to **0.02 km**. It +The lens is the clear gain in bearing: detections in the outer half of the frame go from a +median miss of 4.4° to 3.5° ([every bearing](docs/figures/calibration_bearings.png)), and the +median falls to 1.68 km. ResortFire.2 (1.87 → 0.70 km) and Roundfire (3.39 → 2.05) gain most; +Clubfire (1.76 → 2.07) crosses the 2 km line the other way. The azimuth ledger lands where detections are right: +ScissorsFire goes from 0.62 km to **0.07 km**. It also makes JunctionFire worse, and that is informative. Correcting vo-n-mobo-c by 11.3° puts the ignition point outside that camera's field of view, and its low-confidence detection turns out to be a cumulus cloud at the frame edge. With the cameras calibrated, **detection @@ -158,9 +168,9 @@ can't be below the line, because smoke behind a crest can't be seen. It can't be it either, because a young plume's foot is on the terrain it rises from. Each camera with a star pose therefore gets one interval of distance along its bearing. -- **The interval contains the fire.** On 73 bearings within 5° of the official point, it - contains the official distance on 60, with a median length of 0.91× that distance. At the - official distance, the terrain row sits a median 1 px from the box bottom. +- **The interval contains the fire.** On 74 bearings within 5° of the official point, it + contains the official distance on 61, with a median length of 0.91× that distance. At the + official distance, the terrain row sits a median 0 px from the box bottom. - **One camera still says something.** Each of the 118 confirmed and probable fires seen from a single site gets its own figure: bearing, miss across the ray (a median of 0.79 km on confirmed fires), and the interval. The interval contains the official point on 30 of 37 @@ -169,22 +179,23 @@ star pose therefore gets one interval of distance along its bearing. The interval rarely moves a two-site estimate, but it can say how far along a shared line the fire is. `20260722_RainbowFire` is seen by two cameras looking straight at each other, so their bearings nearly coincide and give almost no distance: -- **Bearings alone:** 1.42 km, with a 95% region 25.6 km² long along the shared line. It - was 5.82 km before the ledger was re-solved with precession (and bearings made exact on - WGS84). The two bearings are within 1° of collinear, so a fifth of a degree slides the - crossing kilometres. Before the fresh star solves of 2026-09-12 to -14 it was 17 km, because - Boucher Hill West's published azimuth was 1.16° off. -- **With terrain, 30 px band:** still 1.42 km, but the region shrinks to 5.0 km² (7.5 km² at +- **Bearings alone:** 2.17 km, with a 95% region 25.6 km² long along the shared line. The two + bearings are within 1° of collinear, so the peak is a long flat ridge: the 0.4 km grid's best + cell had put it at 1.42 km, and a fifth of a degree slides it kilometres. It was 5.82 km + before the ledger was re-solved with precession (and bearings made exact on WGS84), and 17 km + before the fresh star solves of 2026-09-12 to -14, because Boucher Hill West's published + azimuth was 1.16° off. +- **With terrain, 30 px band:** 2.11 km, and the region shrinks to 5.0 km² (7.5 km² at 60 px, 20.6 at 100). ![Rainbow: two opposed cameras, bearings only vs bearings with terrain ranges](docs/figures/terrain_range_rainbow.jpg) **Why it is an opt-in term, not the default.** On the 17 name-confirmed two-site fires across -all of FIgLib, the median doesn't move at 100 px (1.82 km), and a 30 px band makes it worse -(2.00 km) while dropping 4 of the 73 validated truths. On the poses from before precession was -modelled, the 100 px term moved `20250107_PalisadesFire` from 1.82 to 0.32 km. On the -precessed ledger the bearings cross next to dwpgm-s-mobo-c's own tower, and a log(0.05) penalty -no longer outweighs the other bearing's miss, so it stays at 1.82 km. The interval is also only +all of FIgLib, the median doesn't move at 100 px (2.02 km), and a 30 px band makes it worse +(2.07 km) while dropping 4 of the 74 validated truths. On the poses from before precession was +modelled, the 100 px term moved `20250107_PalisadesFire` from 1.82 to 0.32 km. With the +precessed ledger and the refined peak, its bearings alone put it at 1.08 km, and the term +changes nothing at any band. The interval is also only as good as the pose under it. Toro Peak West was re-aimed between star solves: matching skylines puts it 2.4° from today's pose at `20240724_GroveFire`, and the interval drawn with the wrong pose excludes the fire (14.7–28.9 km against 31.9 km). @@ -280,7 +291,7 @@ Star calibration, optionally: ```sh python -m src.figlib.stars.nights 20260911 @cams.json # moonless-night frames from HPWREN's public CDN python -m src.figlib.stars.run_nights # track, star-solve, rebuild data/meta/pose_ledger.json -FIGLIB_LENS=fisheye FIGLIB_POSE_LEDGER=1 python -m src.figlib.geolocate +FIGLIB_PROFILE=calibrated python -m src.figlib.geolocate # star lens and whole solved camera python -m src.figlib.compare_geolocation # baseline vs calibrated, fire by fire ``` @@ -295,6 +306,9 @@ FIGLIB_CORPUS=all python -m src.figlib.fig_bearing # one figure pe FIGLIB_CORPUS=all python -m src.figlib.terrain_range figure 20260722_RainbowFire 30 ``` +Every number in this README comes from one pass of `./rerun_results.sh`, which runs each +stage above in order from a committed tree and logs it (see *Map of the code*). + The Core ML work is macOS-only and installs separately (`requirements-edge.txt`); see [docs/edge-m3.md](docs/edge-m3.md). @@ -307,7 +321,7 @@ likelihood field, and evidence accumulation end to end: ```sh pip install -r requirements-dev.txt -pytest # ~3 s; geolocates 20240701_Kitchenfire to 0.08 km +pytest # ~6 s; geolocates 20240701_Kitchenfire to 0.33 km (calibrated) ``` CI runs this on every push. `tests/test_geom.py` pins the bearing and projection maths @@ -331,15 +345,19 @@ Modules live in [src/figlib/](src/figlib/), with star calibration in [src/figlib | **Edge** | `bench_edge` `power` | | **Figures** | `viz` `viz_map` `viz_terrain` `animate` `animate_triangulate` `fig_peaks` `fig_pose` `fig_triangulate` `fig_bearing` `fig_offsets` · `stars.fig_solve_process` `stars.fig_moon` | -Two environment variables let a whole pipeline be re-scored against different inputs without -editing anything: `FIGLIB_DETS` points at an alternative detection directory (this is how -quantized variants are priced in kilometers) and `FIGLIB_CAMS` at an alternative camera table. -`FIGLIB_LENS=fisheye` and `FIGLIB_POSE_LEDGER=1` switch on the star-measured lens and the per-camera -azimuth corrections; `geolocate` writes those variants beside the baseline, never over it. -Two more select the data: `FIGLIB_CORPUS` scores separately fetched FIgLib archives in their -own directories, and `FIGLIB_TIER` restricts scoring to sequences the detector cannot have -trained on. Archive hashes and a per-run log live in `data/meta/` — see -[data/README.md](data/README.md#corpora-and-provenance). +Environment variables let a whole pipeline be re-scored against different inputs without +editing anything. All of them, with their defaults, are in +[settings.py](src/figlib/settings.py) (`python -m src.figlib.settings` lists them as resolved). +`FIGLIB_DETS` points at an alternative detection directory (this is how quantized variants are +priced in kilometers) and `FIGLIB_CAMS` at an alternative camera table. `FIGLIB_LENS=fisheye`, +`FIGLIB_POSE_LEDGER=1` and `FIGLIB_POSE_FULL=1` switch on the star-measured lens, the +per-camera star azimuths, and each camera's whole solved camera; `geolocate` writes those +variants beside the baseline, never over it. A named profile in [configs/](configs/) sets +several at once (`FIGLIB_PROFILE=calibrated`), and the environment overrides it. Two more +select the data: `FIGLIB_CORPUS` scores separately fetched FIgLib archives in their own +directories, and `FIGLIB_TIER` restricts scoring to sequences the detector cannot have trained +on. Archive hashes and a per-run log, which records every setting as resolved, live in +`data/meta/` — see [data/README.md](data/README.md#corpora-and-provenance). **[NOTES.md](NOTES.md) is the lab notebook** — running state, findings, and the reasoning behind each decision, including the predictions that were refuted and the claims that had to be diff --git a/configs/calibrated.toml b/configs/calibrated.toml new file mode 100644 index 0000000..32d1e27 --- /dev/null +++ b/configs/calibrated.toml @@ -0,0 +1,7 @@ +# The calibrated camera model: the star-measured fisheye lens, and each camera's star solve +# from data/meta/pose_ledger.json -- its azimuth, and (FIGLIB_POSE_FULL) its own lens, pitch +# and roll, read at the box's foot. coverage, bias, fig_bearing and terrain_range run under +# this profile unless FIGLIB_PROFILE names another; anything set in the environment wins. +FIGLIB_LENS = "fisheye" +FIGLIB_POSE_LEDGER = "1" +FIGLIB_POSE_FULL = "1" diff --git a/configs/recent.toml b/configs/recent.toml new file mode 100644 index 0000000..567f5ab --- /dev/null +++ b/configs/recent.toml @@ -0,0 +1,6 @@ +# The calibrated camera model on the extra CDN cameras (recent.py): the recent corpus's own +# pose ledger, which adds the star solves of cameras FIgLib never annotated. +FIGLIB_LENS = "fisheye" +FIGLIB_POSE_LEDGER = "1" +FIGLIB_POSE_FULL = "1" +FIGLIB_POSE_LEDGER_PATH = "out/recent/pose_ledger.json" diff --git a/data/README.md b/data/README.md index 53e2dde..17f0296 100644 --- a/data/README.md +++ b/data/README.md @@ -88,7 +88,8 @@ by epoch alone, with no plume-appearance offset and so no clock to score against **`meta/runs.jsonl`, `meta//runs.jsonl`** — one line per pipeline run: git commit and a hash of any uncommitted diff, package versions, the model pin, the manifest hashes it read, -parameters, and the SHA-256 of every file it wrote. A result whose hash is not in the log did +parameters, every FIGLIB_* setting as resolved (with its source, and the profile's hash; see +`src/figlib/settings.py`), and the SHA-256 of every file it wrote. A result whose hash is not in the log did not come from a recorded run. ## Attribution diff --git a/data/meta/all/calfire.json b/data/meta/all/calfire.json index 1eaab9f..1d16fd3 100644 --- a/data/meta/all/calfire.json +++ b/data/meta/all/calfire.json @@ -8696,9 +8696,9 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 12.68, - "area95_km2": 79.5, - "err_calfire_km": 11.48, + "err_wfigs_km": 12.92, + "area95_km2": 78.1, + "err_calfire_km": 11.72, "sources_apart_km": 2.51, "improvement_km": 1.2 }, @@ -8713,11 +8713,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 4.37, - "area95_km2": 7.4, - "err_calfire_km": 5.32, + "err_wfigs_km": 4.45, + "area95_km2": 8.0, + "err_calfire_km": 5.39, "sources_apart_km": 0.99, - "improvement_km": -0.95 + "improvement_km": -0.94 }, { "fire_id": "20160711_FIRE", @@ -8798,9 +8798,9 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 9.12, + "err_wfigs_km": 9.25, "area95_km2": 46.2, - "err_calfire_km": 8.9, + "err_calfire_km": 9.03, "sources_apart_km": 0.28, "improvement_km": 0.22 }, @@ -9053,11 +9053,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 6.0, + "err_wfigs_km": 5.95, "area95_km2": 6.1, - "err_calfire_km": 3.51, + "err_calfire_km": 3.49, "sources_apart_km": 2.74, - "improvement_km": 2.49 + "improvement_km": 2.46 }, { "fire_id": "20170713_FIRE", @@ -9189,11 +9189,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 23.63, - "area95_km2": 8.5, - "err_calfire_km": 1.02, + "err_wfigs_km": 23.86, + "area95_km2": 8.3, + "err_calfire_km": 1.24, "sources_apart_km": 22.87, - "improvement_km": 22.61 + "improvement_km": 22.62 }, { "fire_id": "20171016_FIRE", @@ -9257,9 +9257,9 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 17.53, + "err_wfigs_km": 17.49, "area95_km2": 1.8, - "err_calfire_km": 19.5, + "err_calfire_km": 19.46, "sources_apart_km": 2.26, "improvement_km": -1.97 }, @@ -9291,11 +9291,11 @@ "n_calfire_candidates": 2, "status": "matched", "corresponds": true, - "err_wfigs_km": 62.1, - "area95_km2": 155.4, - "err_calfire_km": 60.4, + "err_wfigs_km": 63.21, + "area95_km2": 154.1, + "err_calfire_km": 61.52, "sources_apart_km": 1.71, - "improvement_km": 1.7 + "improvement_km": 1.69 }, { "fire_id": "20171207_Lilac", @@ -9325,11 +9325,11 @@ "n_calfire_candidates": 3, "status": "matched", "corresponds": true, - "err_wfigs_km": 1.71, + "err_wfigs_km": 1.68, "area95_km2": 0.3, - "err_calfire_km": 5.11, + "err_calfire_km": 5.05, "sources_apart_km": 4.05, - "improvement_km": -3.4 + "improvement_km": -3.37 }, { "fire_id": "20180517_FIRE", @@ -9393,9 +9393,9 @@ "n_calfire_candidates": 2, "status": "matched", "corresponds": true, - "err_wfigs_km": 3.68, + "err_wfigs_km": 3.98, "area95_km2": 1.0, - "err_calfire_km": 3.66, + "err_calfire_km": 3.96, "sources_apart_km": 0.02, "improvement_km": 0.02 }, @@ -9903,11 +9903,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 7.98, + "err_wfigs_km": 7.89, "area95_km2": 6.1, - "err_calfire_km": 7.05, + "err_calfire_km": 6.98, "sources_apart_km": 1.29, - "improvement_km": 0.93 + "improvement_km": 0.91 }, { "fire_id": "20180813_FIRE", @@ -10889,8 +10889,8 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 8.1, - "area95_km2": 4.0, + "err_wfigs_km": 7.97, + "area95_km2": 3.8, "err_calfire_km": null, "sources_apart_km": null, "improvement_km": null @@ -10906,7 +10906,7 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 27.94, + "err_wfigs_km": 27.75, "area95_km2": 2.7, "err_calfire_km": null, "sources_apart_km": null, @@ -11246,8 +11246,8 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 1.07, - "area95_km2": 4.2, + "err_wfigs_km": 1.34, + "area95_km2": 4.3, "err_calfire_km": null, "sources_apart_km": null, "improvement_km": null @@ -11297,7 +11297,7 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 0.42, + "err_wfigs_km": 0.14, "area95_km2": 1.1, "err_calfire_km": null, "sources_apart_km": null, @@ -11331,7 +11331,7 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 2.58, + "err_wfigs_km": 2.8, "area95_km2": 0.8, "err_calfire_km": null, "sources_apart_km": null, @@ -11400,10 +11400,10 @@ "status": "matched", "corresponds": true, "err_wfigs_km": 1.9, - "area95_km2": 28.3, - "err_calfire_km": 1.54, + "area95_km2": 28.8, + "err_calfire_km": 1.55, "sources_apart_km": 0.39, - "improvement_km": 0.36 + "improvement_km": 0.35 }, { "fire_id": "20200813_SkylineFire", @@ -11501,8 +11501,8 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 1.4, - "area95_km2": 2.4, + "err_wfigs_km": 1.51, + "area95_km2": 2.6, "err_calfire_km": null, "sources_apart_km": null, "improvement_km": null @@ -11518,8 +11518,8 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 1.96, - "area95_km2": 11.0, + "err_wfigs_km": 1.97, + "area95_km2": 11.2, "err_calfire_km": null, "sources_apart_km": null, "improvement_km": null @@ -11535,9 +11535,9 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 3.61, - "area95_km2": 2.4, - "err_calfire_km": 3.85, + "err_wfigs_km": 3.74, + "area95_km2": 2.6, + "err_calfire_km": 3.98, "sources_apart_km": 0.35, "improvement_km": -0.24 }, @@ -11552,8 +11552,8 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 0.49, - "area95_km2": 3.5, + "err_wfigs_km": 0.36, + "area95_km2": 3.4, "err_calfire_km": null, "sources_apart_km": null, "improvement_km": null @@ -11654,8 +11654,8 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 3.59, - "area95_km2": 3.7, + "err_wfigs_km": 3.39, + "area95_km2": 3.8, "err_calfire_km": null, "sources_apart_km": null, "improvement_km": null @@ -11705,8 +11705,8 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 0.3, - "area95_km2": 2.1, + "err_wfigs_km": 0.21, + "area95_km2": 2.2, "err_calfire_km": null, "sources_apart_km": null, "improvement_km": null @@ -11858,8 +11858,8 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 5.21, - "area95_km2": 35.8, + "err_wfigs_km": 5.16, + "area95_km2": 36.3, "err_calfire_km": null, "sources_apart_km": null, "improvement_km": null @@ -12062,11 +12062,11 @@ "n_calfire_candidates": 2, "status": "matched", "corresponds": true, - "err_wfigs_km": 0.08, + "err_wfigs_km": 0.14, "area95_km2": 3.0, - "err_calfire_km": 0.57, + "err_calfire_km": 0.43, "sources_apart_km": 0.51, - "improvement_km": -0.49 + "improvement_km": -0.29 }, { "fire_id": "20240701_McCainfire", @@ -12181,11 +12181,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 6.09, - "area95_km2": 47.8, - "err_calfire_km": 6.08, + "err_wfigs_km": 6.12, + "area95_km2": 47.5, + "err_calfire_km": 6.12, "sources_apart_km": 0.0, - "improvement_km": 0.01 + "improvement_km": 0.0 }, { "fire_id": "20240727_Fire", @@ -12589,9 +12589,9 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 3.02, - "area95_km2": 4.5, - "err_calfire_km": 2.8, + "err_wfigs_km": 2.91, + "area95_km2": 4.3, + "err_calfire_km": 2.69, "sources_apart_km": 0.23, "improvement_km": 0.22 }, @@ -12725,9 +12725,9 @@ "n_calfire_candidates": 2, "status": "matched", "corresponds": true, - "err_wfigs_km": 1.7, - "area95_km2": 5.3, - "err_calfire_km": 1.7, + "err_wfigs_km": 1.87, + "area95_km2": 5.4, + "err_calfire_km": 1.87, "sources_apart_km": 0.0, "improvement_km": 0.0 }, @@ -12861,11 +12861,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 1.03, + "err_wfigs_km": 1.12, "area95_km2": 0.5, - "err_calfire_km": 0.86, + "err_calfire_km": 0.97, "sources_apart_km": 0.66, - "improvement_km": 0.17 + "improvement_km": 0.15 }, { "fire_id": "20250120_TedWilliamsFire", @@ -13184,11 +13184,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 0.75, + "err_wfigs_km": 0.59, "area95_km2": 1.3, - "err_calfire_km": 0.51, + "err_calfire_km": 0.44, "sources_apart_km": 0.81, - "improvement_km": 0.24 + "improvement_km": 0.15 }, { "fire_id": "20250710_SpringsFire", @@ -13235,11 +13235,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 2.06, + "err_wfigs_km": 2.11, "area95_km2": 5.0, - "err_calfire_km": 2.06, + "err_calfire_km": 2.12, "sources_apart_km": 0.0, - "improvement_km": 0.0 + "improvement_km": -0.01 }, { "fire_id": "20250801_BernardoFire", @@ -13286,9 +13286,9 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": false, - "err_wfigs_km": 2.0, - "area95_km2": 0.2, - "err_calfire_km": 53.51, + "err_wfigs_km": 1.76, + "area95_km2": 0.3, + "err_calfire_km": 53.67, "sources_apart_km": 55.1, "improvement_km": null }, @@ -13405,11 +13405,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 0.82, - "area95_km2": 3.4, - "err_calfire_km": 1.27, + "err_wfigs_km": 0.79, + "area95_km2": 3.5, + "err_calfire_km": 1.21, "sources_apart_km": 0.78, - "improvement_km": -0.45 + "improvement_km": -0.42 }, { "fire_id": "20260128_Fire", @@ -13439,8 +13439,8 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 52.39, - "area95_km2": 134.4, + "err_wfigs_km": 53.19, + "area95_km2": 135.2, "err_calfire_km": null, "sources_apart_km": null, "improvement_km": null @@ -13558,9 +13558,9 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 2.21, + "err_wfigs_km": 2.07, "area95_km2": 1.1, - "err_calfire_km": 2.21, + "err_calfire_km": 2.07, "sources_apart_km": 0.0, "improvement_km": 0.0 }, @@ -13592,9 +13592,9 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": false, - "err_wfigs_km": 1.77, + "err_wfigs_km": 1.69, "area95_km2": 7.2, - "err_calfire_km": 46.14, + "err_calfire_km": 46.18, "sources_apart_km": 47.16, "improvement_km": null }, @@ -13609,11 +13609,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 2.98, + "err_wfigs_km": 3.81, "area95_km2": 25.8, - "err_calfire_km": 3.07, + "err_calfire_km": 3.89, "sources_apart_km": 0.18, - "improvement_km": -0.09 + "improvement_km": -0.08 }, { "fire_id": "20260807_BeaverFire", diff --git a/data/meta/all/runs.jsonl b/data/meta/all/runs.jsonl index a1b200f..75ef05c 100644 --- a/data/meta/all/runs.jsonl +++ b/data/meta/all/runs.jsonl @@ -63,3 +63,15 @@ {"argv": ["/home/rharnish/Desktop/GitHub/plume-triangulation/src/figlib/terrain_range.py", "figure", "20260722_RainbowFire", "100"], "code": {"branch": "terrain-los", "commit": "c841ee359aa43facce48be858490923b4fb4547e", "diff_sha256": null, "dirty": false, "untracked_src": ["src/figlib/stars/fig_lens_shape.py"]}, "corpus": "all", "env": {"FIGLIB_CORPUS": "all", "FIGLIB_LENS": "fisheye", "FIGLIB_POSE_LEDGER": "1"}, "finished_utc": "2026-09-26T03:09:50Z", "inputs": {"data/meta/all/fires.json": "d5fd9654ecfdd9370acb3a672bcca21d63108676480aaeb899f4a36531287b31", "data/meta/all/resolved.json": "a06f49c0b15486e91bcaf714d941e4f0610246e8c208c69f5334cf05a00d2077", "data/meta/all/sequences.json": "45f955e81c899b44205fa67f52224900c8cbd1f1621f8acda6dbb4e7b54ea5a7", "data/meta/manifests/core.json": "4912101682390769d6cba5d1854c822c698a8890d87b5aee58285985769fcb62", "data/meta/manifests/extra.json": "8a30e486ee4f99c3b0fc779ecea8324b652ae380476cb4e800f2f5e23f479b1e", "data/meta/pose_ledger.json": 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"improvement_km": 0.21 + "improvement_km": 0.22 }, { "fire_id": "20161113_FIRE.2", @@ -2439,11 +2439,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 6.0, + "err_wfigs_km": 5.95, "area95_km2": 6.1, - "err_calfire_km": 3.51, + "err_calfire_km": 3.49, "sources_apart_km": 2.74, - "improvement_km": 2.49 + "improvement_km": 2.46 }, { "fire_id": "20170722_FIRE", @@ -2473,11 +2473,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 24.03, + "err_wfigs_km": 23.86, "area95_km2": 8.3, - "err_calfire_km": 1.36, + "err_calfire_km": 1.24, "sources_apart_km": 22.87, - "improvement_km": 22.67 + "improvement_km": 22.62 }, { "fire_id": "20171207_FIRE.1", @@ -2507,11 +2507,11 @@ "n_calfire_candidates": 2, "status": "matched", "corresponds": true, - "err_wfigs_km": 62.1, + "err_wfigs_km": 63.21, "area95_km2": 154.1, - "err_calfire_km": 60.4, + "err_calfire_km": 61.52, "sources_apart_km": 1.71, - "improvement_km": 1.7 + "improvement_km": 1.69 }, { "fire_id": "20180504_FIRE", @@ -2524,11 +2524,11 @@ "n_calfire_candidates": 3, "status": "matched", "corresponds": true, - "err_wfigs_km": 1.71, + "err_wfigs_km": 1.68, "area95_km2": 0.3, - "err_calfire_km": 5.11, + "err_calfire_km": 5.05, "sources_apart_km": 4.05, - "improvement_km": -3.4 + "improvement_km": -3.37 }, { "fire_id": "20180602_FIRE", @@ -2541,9 +2541,9 @@ "n_calfire_candidates": 2, "status": "matched", "corresponds": true, - "err_wfigs_km": 3.68, + "err_wfigs_km": 3.98, "area95_km2": 1.0, - "err_calfire_km": 3.66, + "err_calfire_km": 3.96, "sources_apart_km": 0.02, "improvement_km": 0.02 }, @@ -2711,11 +2711,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 7.98, + "err_wfigs_km": 7.89, "area95_km2": 6.1, - "err_calfire_km": 7.05, + "err_calfire_km": 6.98, "sources_apart_km": 1.29, - "improvement_km": 0.93 + "improvement_km": 0.91 }, { "fire_id": "20180813_FIRE", @@ -2983,7 +2983,7 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 8.1, + "err_wfigs_km": 7.97, "area95_km2": 3.8, "err_calfire_km": null, "sources_apart_km": null, @@ -3000,7 +3000,7 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 27.94, + "err_wfigs_km": 27.75, "area95_km2": 2.7, "err_calfire_km": null, "sources_apart_km": null, @@ -3136,7 +3136,7 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 0.42, + "err_wfigs_km": 0.14, "area95_km2": 1.1, "err_calfire_km": null, "sources_apart_km": null, @@ -3153,7 +3153,7 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 2.58, + "err_wfigs_km": 2.8, "area95_km2": 0.8, "err_calfire_km": null, "sources_apart_km": null, @@ -3172,9 +3172,9 @@ "corresponds": true, "err_wfigs_km": 1.9, "area95_km2": 28.8, - "err_calfire_km": 1.54, + "err_calfire_km": 1.55, "sources_apart_km": 0.39, - "improvement_km": 0.36 + "improvement_km": 0.35 }, { "fire_id": "20200829_inside-Mexico", @@ -3187,7 +3187,7 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 1.96, + "err_wfigs_km": 1.97, "area95_km2": 11.2, "err_calfire_km": null, "sources_apart_km": null, @@ -3204,9 +3204,9 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 3.61, + "err_wfigs_km": 3.74, "area95_km2": 2.6, - "err_calfire_km": 3.85, + "err_calfire_km": 3.98, "sources_apart_km": 0.35, "improvement_km": -0.24 }, @@ -3221,7 +3221,7 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 0.49, + "err_wfigs_km": 0.36, "area95_km2": 3.4, "err_calfire_km": null, "sources_apart_km": null, @@ -3238,7 +3238,7 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 3.59, + "err_wfigs_km": 3.39, "area95_km2": 3.8, "err_calfire_km": null, "sources_apart_km": null, @@ -3255,7 +3255,7 @@ "n_calfire_candidates": 0, "status": "no-candidate", "corresponds": false, - "err_wfigs_km": 0.3, + "err_wfigs_km": 0.21, "area95_km2": 2.2, "err_calfire_km": null, "sources_apart_km": null, @@ -3289,11 +3289,11 @@ "n_calfire_candidates": 2, "status": "matched", "corresponds": true, - "err_wfigs_km": 0.08, + "err_wfigs_km": 0.14, "area95_km2": 3.0, - "err_calfire_km": 0.57, + "err_calfire_km": 0.43, "sources_apart_km": 0.51, - "improvement_km": -0.49 + "improvement_km": -0.29 }, { "fire_id": "20240728_Border65Fire", @@ -3340,9 +3340,9 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 3.02, + "err_wfigs_km": 2.91, "area95_km2": 4.3, - "err_calfire_km": 2.8, + "err_calfire_km": 2.69, "sources_apart_km": 0.23, "improvement_km": 0.22 }, @@ -3374,9 +3374,9 @@ "n_calfire_candidates": 2, "status": "matched", "corresponds": true, - "err_wfigs_km": 1.7, + "err_wfigs_km": 1.87, "area95_km2": 5.4, - "err_calfire_km": 1.7, + "err_calfire_km": 1.87, "sources_apart_km": 0.0, "improvement_km": 0.0 }, @@ -3391,11 +3391,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 0.93, + "err_wfigs_km": 0.59, "area95_km2": 1.3, - "err_calfire_km": 0.24, + "err_calfire_km": 0.44, "sources_apart_km": 0.81, - "improvement_km": 0.69 + "improvement_km": 0.15 }, { "fire_id": "20250823_Clubfire", @@ -3408,9 +3408,9 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": false, - "err_wfigs_km": 2.0, + "err_wfigs_km": 1.76, "area95_km2": 0.3, - "err_calfire_km": 53.51, + "err_calfire_km": 53.67, "sources_apart_km": 55.1, "improvement_km": null }, @@ -3425,11 +3425,11 @@ "n_calfire_candidates": 1, "status": "matched", "corresponds": true, - "err_wfigs_km": 0.82, + "err_wfigs_km": 0.79, "area95_km2": 3.5, - "err_calfire_km": 1.27, + "err_calfire_km": 1.21, "sources_apart_km": 0.78, - "improvement_km": -0.45 + "improvement_km": -0.42 }, { "fire_id": "20260629_JunctionFire", @@ 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"6a75b47ed2f56d49fcc7dfb1394eb3cc97852339a4e12c54ebe479cb3b6f7b51", "20241108_ResortFire_hp-e-mobo-c.json": "9da880a419336aae7c90d47f21b6d2b2bc4414fae24ee30b46e0d9edc6f3d431", "20241108_ResortFire_mg-n-mobo-c.json": "d4b1c53f185eb754de5190fc06976b56abd91f0994d3c614e678ab4798e46185", "20241108_ResortFire_rm-s-mobo-c.json": "9eedf31b0d321aec6ae47e1c09f8825aa9467185522c803e4c73c663b7cd5a28", "20250709_SteeleFire_lp-w-mobo-c.json": "228b4ce9add24cfd8c581c91f424e32c35809cd19c3e57922fcedbb5d77712de", "20250709_SteeleFire_om-n-mobo-c.json": "e0b0890ff962ad8a5e2859dda227747f5a0f63cc7cab1b08bd2a09a4553428bf", "20250709_SteeleFire_sm-n-mobo-c.json": "9343cf650bebc6c234e9f9b8da5a27aa4170a874303cd2d7c0c66546c04fbe6d", "20250823_Clubfire_starr-n-mobo-c.json": "2925ed1c18400660c38e9a34258f6875b47daab76cec8fa04edc486248254f96", "20250823_Clubfire_starr-n-mobo-m.json": "011a9b1cc5c9b6d5786870beb075cd17e49d7c6ba59c4b49eb9641d9376e63de", "20250823_Clubfire_stgo-s-mobo-c.json": 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"4a56d9aa62e1601e5a8dfe4604ddda963fa709650b16632276cf2900c07fca5b", "20260629_JunctionFire_hp-s-mobo-c.json": "71c00eb867c8206ca205438bedb19207c8e266ee42388efefc175a7d6ab5b152", "20260629_JunctionFire_mg-e-mobo-c.json": "adeb90e4bbeaa094bf4c710f4621f4bae6b464dc535bb493f4d7146901f93f08", "20260629_JunctionFire_vo-n-mobo-c.json": "9e5bbbcf811ebf0d0290a7de293860e280784a9a563049b119ca6449a40968e6", "20260629_JunctionFire_vo-w-mobo-c.json": "6abbc8fc013ac2b267ffecbdf4d335314b3ebebe10b5d486d7e0749bc9e16d21", "20260722_CreelmanFire_bm-s-mobo-c.json": "88496f5398179d8b066327eb5c0c0f055534c9a0d7d52c501370c82de5f61d53", "20260722_CreelmanFire_cp-w-mobo-c.json": "2555c7a5be140d2f934230a7436942ed5d97222bc205c5af2259012046d4188f", "20260722_CreelmanFire_mg-s-mobo-c.json": "5fe455c6e2a101df5c1869d31304690bd73446435a7f48a2dfd2185ce36f9d84"}, "n_files": 189}}, "model": null, "outputs": {"out/triangulate/20171010_FIRE.png": "1625e5d20287df8bff85f4cc100019687db8d6f00e0e9e6915e037f82ad56287", "out/triangulate/20171207_FIRE.2.png": "9fbb1deba7d03095a48a0813cee9d8267b0706b5e3e72e1e8305afe15a6493e6", "out/triangulate/20201202_WillowFire-nightime-near-CDF-HQ.png": "8c36020081a75504c0d3bf021581c07041f28d88fb0407e3e6231a83b22fd70a", "out/triangulate/20240701_Kitchenfire.png": "79f91f71bc4988da5fb05aaa9f13c0f093864d309740a457d4f7a5ba4867fd5f", "out/triangulate/index.json": "2b12a439751f417f0cf46b97ff577ce0ea4c8ef4786be7a1986063aa2540437a"}, "params": {"fires": ["20171010_FIRE", "20240701_Kitchenfire", "20201202_WillowFire", "20171207_FIRE.2"]}, "platform": "Linux-6.8.0-138-generic-x86_64-with-glibc2.35", "settings": {"profile": null, "source": {"FIGLIB_ALLOW_UNPINNED_MODEL": "default", "FIGLIB_BAND_PX": "default", "FIGLIB_CAMS": "default", "FIGLIB_CORPUS": "default", "FIGLIB_DETS": "default", "FIGLIB_LENS": "default", "FIGLIB_POSE_FULL": "default", "FIGLIB_POSE_LEDGER": "default", "FIGLIB_POSE_LEDGER_PATH": "default", "FIGLIB_REFINE_KM": "default", "FIGLIB_TIER": "default", "FIGLIB_VARIANTS": "default", "FIGLIB_WIND_GATE": "default"}, "values": {"FIGLIB_ALLOW_UNPINNED_MODEL": "0", "FIGLIB_BAND_PX": "100", "FIGLIB_CAMS": null, "FIGLIB_CORPUS": "core", "FIGLIB_DETS": null, "FIGLIB_LENS": "rectilinear", "FIGLIB_POSE_FULL": "0", "FIGLIB_POSE_LEDGER": "0", "FIGLIB_POSE_LEDGER_PATH": null, "FIGLIB_REFINE_KM": "0.01", "FIGLIB_TIER": null, "FIGLIB_VARIANTS": null, "FIGLIB_WIND_GATE": "1"}}, "stage": "fig_triangulate", "started_utc": "2026-09-26T18:29:29Z", "tiers": null, "versions": {"cv2": "5.0.0", "matplotlib": "3.11.1", "numpy": "2.5.3", "python": "3.12.13"}} diff --git a/docs/figures/calibration_bearings.png b/docs/figures/calibration_bearings.png index 718a7f7..12f3850 100644 Binary files a/docs/figures/calibration_bearings.png and b/docs/figures/calibration_bearings.png differ diff --git a/docs/figures/calibration_maps.png b/docs/figures/calibration_maps.png index 4fe1261..da5b872 100644 Binary files a/docs/figures/calibration_maps.png and b/docs/figures/calibration_maps.png differ diff --git a/docs/figures/geolocation_offsets.png b/docs/figures/geolocation_offsets.png index f4d102b..31c90ed 100644 Binary files a/docs/figures/geolocation_offsets.png and b/docs/figures/geolocation_offsets.png differ diff --git a/docs/figures/terrain_range_rainbow.jpg b/docs/figures/terrain_range_rainbow.jpg index d5a31f0..86807ac 100644 Binary files a/docs/figures/terrain_range_rainbow.jpg and b/docs/figures/terrain_range_rainbow.jpg differ diff --git a/docs/figures/triangulate_kitchenfire.gif b/docs/figures/triangulate_kitchenfire.gif index 9d2bf05..2b341a7 100644 Binary files a/docs/figures/triangulate_kitchenfire.gif and b/docs/figures/triangulate_kitchenfire.gif differ diff --git a/docs/figures/triangulate_portola.jpg b/docs/figures/triangulate_portola.jpg index b35732a..57f5166 100644 Binary files a/docs/figures/triangulate_portola.jpg and b/docs/figures/triangulate_portola.jpg differ diff --git a/docs/figures/triangulate_willow_night.jpg b/docs/figures/triangulate_willow_night.jpg index ad20a6f..7e3dffa 100644 Binary files a/docs/figures/triangulate_willow_night.jpg and b/docs/figures/triangulate_willow_night.jpg differ diff --git a/docs/likelihood-over-time.md b/docs/likelihood-over-time.md index 227da8a..95082ed 100644 --- a/docs/likelihood-over-time.md +++ b/docs/likelihood-over-time.md @@ -50,8 +50,8 @@ That is this project's situation. Camera azimuths are nameplate values: `az` is 0/90/180/270 on 482 of 505 cameras, which is a per-station pointing bias. **The symptom is already visible.** In the Kitchen fire animation the 95% region holds at -about 2.6 km² — an equivalent radius near 0.9 km — while the error sits at 1.33 km for -most of the sequence. The official ignition point is outside the stated 95% region for +about 2.4–3.2 km² — an equivalent radius near 0.9–1.0 km — while the error sits at 1.2–1.7 km +for most of the sequence. The official ignition point is outside the stated 95% region for most of the fire's duration. The uncertainty is overconfident. Standard remedies: @@ -119,7 +119,7 @@ appearance. constraints (Sünderhauf & Protzel, 2012) — are the temporal relatives of the α = 0.25 uniform outlier term. They handle a transient false positive well: in the Kitchen fire animation, a bird in front of `lp-e-mobo-c` at 0.45 confidence moves the estimate from -1.33 km to 1.66 km error, and it recovers when a better detection replaces it. +1.22 km to 1.60 km error, and it recovers when a better detection replaces it. **Multi-target tracking** handles several fires, or a recurring false plume, at once: diff --git a/rerun_results.sh b/rerun_results.sh new file mode 100755 index 0000000..8cb6f61 --- /dev/null +++ b/rerun_results.sh @@ -0,0 +1,80 @@ +#!/usr/bin/env bash +# Every stage whose output reaches README.md or docs/, in dependency order, from one commit. +# +# Each stage appends a line to data/meta/{,all/,recent/}runs.jsonl with the commit, every +# FIGLIB_* setting as resolved (settings.py) and the hash of every output, so run it from a +# committed tree. Inputs are not re-made here: detections (run_detect.sh, `recent detect`), +# star solves and the pose ledgers, CDN frames, DEM tiles. +# +# ./rerun_results.sh # ~20 min on four cores +set -euo pipefail +cd "$(dirname "$0")" +PY=${PY:-.venv/bin/python} +export OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 + +if ! git diff --quiet HEAD -- src tests configs; then + echo "uncommitted changes under src/, tests/ or configs/: commit first" >&2 + exit 1 +fi +mkdir -p out/logs +LOG="out/logs/rerun_$(git rev-parse --short HEAD).log" +exec > >(tee "$LOG") 2>&1 +m() { echo; echo "== $*"; "$PY" -m "$@"; } + +# Resize a figure onto docs/figures/ (JPEG at 1600 px wide, or a straight PNG copy). +doc() { + "$PY" - "$1" "$2" <<'EOF' +import sys +from PIL import Image +src, dst = sys.argv[1:] +if dst.endswith(".png"): + Image.open(src).save(dst) +else: + im = Image.open(src).convert("RGB") + im.resize((1600, round(im.height * 1600 / im.width)), Image.LANCZOS).save(dst, quality=85) +print(f"{src} -> {dst}") +EOF +} + +# ---- core corpus: four camera models, same detections and solver +m src.figlib.geolocate +FIGLIB_LENS=fisheye m src.figlib.geolocate +FIGLIB_PROFILE=calibrated FIGLIB_POSE_FULL=0 m src.figlib.geolocate # star azimuths only +FIGLIB_PROFILE=calibrated m src.figlib.geolocate # the whole solved camera +m src.figlib.compare_geolocation +m src.figlib.calfire +m src.figlib.fig_triangulate 20171010_FIRE 20240701_Kitchenfire 20201202_WillowFire \ + 20171207_FIRE.2 +m src.figlib.animate_triangulate 20240701_Kitchenfire +m src.figlib.fig_offsets +m src.figlib.stars.fig_geolocation +m src.figlib.evolve +m src.figlib.coverage +m src.figlib.bias + +doc out/triangulate/20171010_FIRE.png docs/figures/triangulate_portola.jpg +doc out/triangulate/20201202_WillowFire-nightime-near-CDF-HQ.png docs/figures/triangulate_willow_night.jpg +cp out/videos/triangulate_20240701_Kitchenfire.gif docs/figures/triangulate_kitchenfire.gif +doc out/figures/offsets.png docs/figures/geolocation_offsets.png + +# ---- all of FIgLib: the calfire audit and the terrain range +FIGLIB_CORPUS=all m src.figlib.geolocate +FIGLIB_CORPUS=all m src.figlib.calfire +export FIGLIB_CORPUS=all +m src.figlib.terrain_range validate +for band in cap 100 60 30; do m src.figlib.terrain_range solve "$band"; done +m src.figlib.terrain_range solve 100 ledger +m src.figlib.terrain_range figure 20260722_RainbowFire 30 +m src.figlib.terrain_range figure 20260722_RainbowFire 100 +m src.figlib.terrain_range figure 20240724_GroveFire 100 +m src.figlib.fig_bearing +unset FIGLIB_CORPUS +doc out/all/terrain_range/20260722_RainbowFire_terrain_band30.png docs/figures/terrain_range_rainbow.jpg + +# ---- recent: the extra CDN cameras (recent.py sets its own corpus) +m src.figlib.recent score +FIGLIB_LENS=fisheye m src.figlib.recent score +FIGLIB_PROFILE=recent FIGLIB_POSE_FULL=0 m src.figlib.recent score +FIGLIB_PROFILE=recent m src.figlib.recent score + +echo; echo "log: $LOG" diff --git a/src/figlib/accumulate.py b/src/figlib/accumulate.py index 5a76b98..78f33e7 100644 --- a/src/figlib/accumulate.py +++ b/src/figlib/accumulate.py @@ -30,7 +30,6 @@ from __future__ import annotations import json -import os import math from pathlib import Path @@ -40,13 +39,14 @@ from .geom import bearing_grid, haversine_km, load_cams, offset_bearing_deg from . import corpus as C +from . import settings ROOT = Path(__file__).resolve().parents[2] META = C.current().meta # Which detection pass to score. Overridable so the Core ML variants run through this # exact pipeline rather than a parallel one -- the point of the quantization study is a # paired comparison, and a second implementation would be a second source of difference. -YOLO_DIR = Path(os.environ.get("FIGLIB_DETS", C.current().dets)) +YOLO_DIR = Path(settings.get("FIGLIB_DETS") or C.current().dets) SIGMA_DEG = 2.0 CONF_FLOOR = 0.10 # below this a detection is treated as pure noise diff --git a/src/figlib/bias.py b/src/figlib/bias.py index db0dbfa..06d74f9 100644 --- a/src/figlib/bias.py +++ b/src/figlib/bias.py @@ -25,17 +25,13 @@ """ from __future__ import annotations -import os - -os.environ.setdefault("FIGLIB_LENS", "fisheye") -os.environ.setdefault("FIGLIB_POSE_LEDGER", "1") - import itertools import json import math import numpy as np +from . import settings from .accumulate import PI_MAX from .coverage import (FINE_HALF_KM, FINE_STEP_KM, META, OUT, TIMES, best_calibrated, gather_calibrated, log, truth_cell) @@ -326,6 +322,7 @@ def figure(): if __name__ == "__main__": + settings.default_profile("calibrated") # configs/calibrated.toml unless FIGLIB_PROFILE is set import sys if sys.argv[1:] == ["figure"]: print(figure()) diff --git a/src/figlib/compare_geolocation.py b/src/figlib/compare_geolocation.py index 7fb69e2..00e15f4 100644 --- a/src/figlib/compare_geolocation.py +++ b/src/figlib/compare_geolocation.py @@ -1,11 +1,12 @@ """Compare geolocation outputs across calibration variants, fire by fire. Each variant is a file geolocate.py writes: the baseline `geolocation.json`, and beside it -`geolocation_fisheye.json` (FIGLIB_LENS=fisheye) and `geolocation_fisheye_ledger.json` -(plus FIGLIB_POSE_LEDGER=1). Same detections and solver in all of them, so differences are -the calibration alone. +`geolocation_fisheye.json` (FIGLIB_LENS=fisheye), `geolocation_fisheye_ledger.json` (plus +FIGLIB_POSE_LEDGER=1: star azimuths) and `geolocation_fisheye_ledger_full.json` (plus +FIGLIB_POSE_FULL=1: the whole solved camera; FIGLIB_PROFILE=calibrated). Same detections +and solver in all of them, so differences are the calibration alone. - python -m src.figlib.compare_geolocation [variant ...] # default: all three, center bearing + python -m src.figlib.compare_geolocation [variant ...] # default: all four, center bearing """ from __future__ import annotations @@ -16,7 +17,7 @@ from . import corpus as C -VARIANTS = ["", "_fisheye", "_fisheye_ledger"] +VARIANTS = ["", "_fisheye", "_fisheye_ledger", "_fisheye_ledger_full"] def load(variant: str) -> dict[str, dict] | None: diff --git a/src/figlib/corpus.py b/src/figlib/corpus.py index 539aea0..b8b1275 100644 --- a/src/figlib/corpus.py +++ b/src/figlib/corpus.py @@ -26,6 +26,8 @@ from dataclasses import dataclass from pathlib import Path +from . import settings + ROOT = Path(__file__).resolve().parents[2] SHARED_META = ROOT / "data" / "meta" @@ -65,7 +67,7 @@ class Corpus: def current() -> Corpus: - name = os.environ.get("FIGLIB_CORPUS", "core") + name = settings.get("FIGLIB_CORPUS") if name not in CORPORA: raise ValueError(f"FIGLIB_CORPUS={name!r}; expected one of {sorted(CORPORA)}") return CORPORA[name] @@ -107,7 +109,7 @@ def contamination(name: str) -> str: def tier_filter() -> tuple[str, ...] | None: """Tiers named in FIGLIB_TIER, or None to score everything.""" - raw = os.environ.get("FIGLIB_TIER") + raw = settings.get("FIGLIB_TIER") if not raw: return None tiers = tuple(t.strip() for t in raw.split(",") if t.strip()) diff --git a/src/figlib/coverage.py b/src/figlib/coverage.py index 7fd15e2..e93aafd 100644 --- a/src/figlib/coverage.py +++ b/src/figlib/coverage.py @@ -18,17 +18,13 @@ uniform on [0, 1]. Finally it finds the single log-likelihood temperature that would make the 95% region cover 95% -- how much wider the posterior needs to be. -Uses the calibrated camera model (fisheye lens + star pose ledger), the current best. +Uses the calibrated camera model (configs/calibrated.toml: fisheye lens + star pose ledger), +the current best, unless FIGLIB_PROFILE names another. python -m src.figlib.coverage # writes out/confidence/ """ from __future__ import annotations -import os - -os.environ.setdefault("FIGLIB_LENS", "fisheye") -os.environ.setdefault("FIGLIB_POSE_LEDGER", "1") - import json import math import time @@ -37,6 +33,7 @@ import numpy as np from . import corpus as C +from . import settings from . import pose_ledger from .accumulate import posterior as accum_posterior from .geolocate import FRAME_SIZES, YOLO_DIR, solve @@ -184,7 +181,7 @@ def main() -> None: resolved = [r for r in json.loads((META / "resolved.json").read_text()) if r["tier"] in ("confirmed", "probable") and r.get("triangulable")] log(f"coverage: {len(resolved)} scoring fires x {len(TIMES)} times x 2 estimators, " - f"lens={os.environ['FIGLIB_LENS']} ledger={os.environ['FIGLIB_POSE_LEDGER']}") + f"lens={settings.get('FIGLIB_LENS')} ledger={settings.get('FIGLIB_POSE_LEDGER')}") rows, grids = [], [] for k, r in enumerate(resolved, 1): @@ -301,6 +298,7 @@ def figure() -> Path: if __name__ == "__main__": + settings.default_profile("calibrated") # configs/calibrated.toml unless FIGLIB_PROFILE is set import sys if sys.argv[1:] == ["figure"]: print(figure()) diff --git a/src/figlib/falsealarm.py b/src/figlib/falsealarm.py index e3c4c5f..fe3b737 100644 --- a/src/figlib/falsealarm.py +++ b/src/figlib/falsealarm.py @@ -30,7 +30,6 @@ from __future__ import annotations import json -import os import math from collections import defaultdict from pathlib import Path @@ -38,6 +37,7 @@ import numpy as np from . import corpus as C +from . import settings from .geom import offset_bearing_deg @@ -60,7 +60,7 @@ def poisson_hi(n: int, cam_days: float) -> float | None: # Which detection pass to score. Overridable so the Core ML variants run through this # exact pipeline rather than a parallel one -- the point of the quantization study is a # paired comparison, and a second implementation would be a second source of difference. -YOLO_DIR = Path(os.environ.get("FIGLIB_DETS", CORPUS.dets)) +YOLO_DIR = Path(settings.get("FIGLIB_DETS") or CORPUS.dets) OUT = CORPUS.out # Restrict scoring to what the detector cannot have trained on (corpus.contamination). TIERS = C.tier_filter() diff --git a/src/figlib/fig_bearing.py b/src/figlib/fig_bearing.py index 1cffc82..7dec43e 100644 --- a/src/figlib/fig_bearing.py +++ b/src/figlib/fig_bearing.py @@ -27,12 +27,6 @@ from __future__ import annotations -import os - -# The calibrated camera model, as in coverage.py. Set before the geometry reads them. -os.environ.setdefault("FIGLIB_LENS", "fisheye") -os.environ.setdefault("FIGLIB_POSE_LEDGER", "1") - import json import math from pathlib import Path @@ -40,6 +34,7 @@ import numpy as np from . import corpus as C +from . import settings from . import provenance as P from . import terrain_range as TR from .fig_triangulate import PALETTE, _det_for, _hillshade, _plume_crop, _title @@ -425,6 +420,7 @@ def sheet(tile_w: int = 560, cols: int = 4) -> Path: if __name__ == "__main__": + settings.default_profile("calibrated") # configs/calibrated.toml unless FIGLIB_PROFILE is set import sys a = sys.argv[1:] if a and a[0] == "sheet": diff --git a/src/figlib/geolocate.py b/src/figlib/geolocate.py index 7cdea1c..ff222f8 100644 --- a/src/figlib/geolocate.py +++ b/src/figlib/geolocate.py @@ -22,7 +22,6 @@ from __future__ import annotations import json -import os import math from dataclasses import dataclass from pathlib import Path @@ -32,6 +31,7 @@ from .geom import (angdiff_deg, bearing_deg, bearing_grid, haversine_km, load_cams, offset_bearing_deg) from . import corpus as C +from . import settings from . import pose_ledger from .wind import upwind_x, wind_at @@ -40,7 +40,7 @@ # Which detection pass to score. Overridable so the Core ML variants run through this # exact pipeline rather than a parallel one -- the point of the quantization study is a # paired comparison, and a second implementation would be a second source of difference. -YOLO_DIR = Path(os.environ.get("FIGLIB_DETS", C.current().dets)) +YOLO_DIR = Path(settings.get("FIGLIB_DETS") or C.current().dets) # Frame width per archive (frame_sizes.py). The star-measured fisheye lens and the pose # ledger apply only to the frame format they were measured on, and the same camera name @@ -53,9 +53,14 @@ # source. Two degrees is deliberately generous: claiming less would shrink the # uncertainty region without earning it. SIGMA_DEG = 2.0 -# Opt-in: re-find the peak at this step within two cells of the grid's (`refine`). Off by -# default so every published number stays the grid's, until they are re-run together. -REFINE_KM = float(os.environ["FIGLIB_REFINE_KM"]) if os.environ.get("FIGLIB_REFINE_KM") else None + + +def refine_step() -> float | None: + """FIGLIB_REFINE_KM: the step at which `solve` re-finds its peak within two cells of the + grid's (`refine`), 0.01 km unless set; None when set to 0, for the bare grid node.""" + step = float(settings.get("FIGLIB_REFINE_KM") or 0) + return step if step > 0 else None + # Scored columns, in the order they are reported. The four box variants run by # default; everything past them takes the bearing from a pixel mask instead of a box @@ -67,8 +72,8 @@ "wedge_diff", "wedge_sam", "seq_diff", "seq_sam", "field_diff", "field_sam") ALL_VARIANTS = BOX_VARIANTS + MASK_VARIANTS -if os.environ.get("FIGLIB_VARIANTS"): - _keep = set(os.environ["FIGLIB_VARIANTS"].split(",")) +if settings.get("FIGLIB_VARIANTS"): + _keep = set(settings.get("FIGLIB_VARIANTS").split(",")) VARIANTS = tuple(v for v in ALL_VARIANTS if v in _keep) else: VARIANTS = BOX_VARIANTS @@ -203,7 +208,7 @@ def _fit_x(kind: str, method: str, seq_name: str, offset: int, det: dict, # two thirds of the sequences where the crosswind is decisive -- so it is not # obvious the constraint earns its rejections. FIGLIB_WIND_GATE=0 turns it off, to # measure that rather than argue about it. - if os.environ.get("FIGLIB_WIND_GATE") == "0": + if settings.get("FIGLIB_WIND_GATE") == "0": cross = None if kind == "seq": @@ -230,13 +235,14 @@ def _fit_x(kind: str, method: str, seq_name: str, offset: int, det: dict, def solve(bearings: list[Bearing], center: tuple[float, float], half_extent_km: float = 60.0, step_km: float = 0.4, - sigma_deg: float = SIGMA_DEG, refine_km: float | None = REFINE_KM): + sigma_deg: float = SIGMA_DEG, refine_km: float | None = None): """Log-likelihood surface over the ground, and its peak. The surface is returned at `step_km`. Without refinement the peak can only be a grid node. A 0.4 km cell then moves single fires by up to 0.5 km as the grid shifts under them, and can carry a median across the 2 km line (NOTES.md, 2026-09-25). - `refine_km` (FIGLIB_REFINE_KM) re-finds the peak off the grid; see `refine`.""" + `refine_km` re-finds the peak off the grid (see `refine`): None takes FIGLIB_REFINE_KM + (`refine_step`), and 0 keeps the grid node.""" lat0, lon0 = center dlat = step_km / 111.32 dlon = step_km / (111.32 * math.cos(math.radians(lat0))) @@ -261,8 +267,9 @@ def solve(bearings: list[Bearing], center: tuple[float, float], i, j = np.unravel_index(np.argmax(total), total.shape) la, lo = float(lats[i]), float(lons[j]) - if refine_km: - la, lo = refine(bearings, la, lo, step_km, sigma_deg=sigma_deg, fine_km=refine_km) + fine_km = refine_step() if refine_km is None else refine_km + if fine_km: + la, lo = refine(bearings, la, lo, step_km, sigma_deg=sigma_deg, fine_km=fine_km) return lats, lons, total, la, lo @@ -273,9 +280,9 @@ def refine(bearings: list[Bearing], lat: float, lon: float, step_km: float, `extra(lats, lons)`, if given, adds a term on the fine grid, as it was added on the coarse one (terrain_range's penalty). The fine grid is centred on the coarse peak, so it moves with the peak and is not tied to the coarse grid's origin.""" - fine_km = fine_km or REFINE_KM or 0.01 + fine_km = fine_km or refine_step() or 0.01 lats, lons, ll, la, lo = solve(bearings, (lat, lon), half_extent_km=2 * step_km, - step_km=fine_km, sigma_deg=sigma_deg, refine_km=None) + step_km=fine_km, sigma_deg=sigma_deg, refine_km=0) if extra is not None: ll = ll + extra(lats, lons) i, j = np.unravel_index(np.argmax(ll), ll.shape) @@ -355,7 +362,7 @@ def main() -> None: _save_wind(wind_cache) # Calibration variants write beside the baseline, never over it. - variant = (("_fisheye" if os.environ.get("FIGLIB_LENS") == "fisheye" else "") + variant = (("_fisheye" if settings.get("FIGLIB_LENS") == "fisheye" else "") + ("_ledger" if pose_ledger.enabled() else "") + ("_full" if pose_ledger.enabled() and pose_ledger.full_enabled() else "")) dest = C.current().out / f"geolocation{C.tier_suffix(tiers)}{variant}.json" @@ -364,7 +371,7 @@ def main() -> None: from .wind import CACHE as WIND_CACHE P.record("geolocate", [dest, WIND_CACHE], started=started, params={"variants": list(VARIANTS), "sigma_deg": SIGMA_DEG, - "lens": os.environ.get("FIGLIB_LENS", "rectilinear"), + "lens": settings.get("FIGLIB_LENS"), "pose_ledger": str(pose_ledger.path()) if pose_ledger.enabled() else None, "pose_full": pose_ledger.enabled() and pose_ledger.full_enabled()}, extra_inputs=[META / "sequences.json", META / "fires.json", diff --git a/src/figlib/geom.py b/src/figlib/geom.py index e280cba..f61e42d 100644 --- a/src/figlib/geom.py +++ b/src/figlib/geom.py @@ -18,6 +18,8 @@ import math from pathlib import Path +from . import settings + EARTH_R_KM = 6371.0 @@ -27,8 +29,7 @@ def load_cams() -> dict: Set FIGLIB_CAMS to `data/meta/cams_refined.json` to run everything downstream against terrain-fitted azimuths instead of the published ones. """ - import os - p = os.environ.get("FIGLIB_CAMS") + p = settings.get("FIGLIB_CAMS") root = Path(__file__).resolve().parents[2] return json.loads(Path(p if p else root / "data" / "meta" / "cams.json").read_text()) @@ -186,8 +187,7 @@ def _fisheye(cam: dict) -> bool: Callers that know the frame width pass it as cam["frame_w"] (see frame_sizes.py); without it the lens is not applied. """ - import os - return (os.environ.get("FIGLIB_LENS") == "fisheye" and cam.get("fov") == 90 + return (settings.get("FIGLIB_LENS") == "fisheye" and cam.get("fov") == 90 and cam.get("frame_w") == 3072) diff --git a/src/figlib/pose_ledger.py b/src/figlib/pose_ledger.py index f52a642..3a0511e 100644 --- a/src/figlib/pose_ledger.py +++ b/src/figlib/pose_ledger.py @@ -34,10 +34,11 @@ from __future__ import annotations import json -import os import statistics from pathlib import Path +from . import settings + ROOT = Path(__file__).resolve().parents[2] LEDGER = ROOT / "data" / "meta" / "pose_ledger.json" SAME_DAYS = 3.0 @@ -49,17 +50,17 @@ def enabled() -> bool: - return os.environ.get("FIGLIB_POSE_LEDGER") == "1" + return settings.flag("FIGLIB_POSE_LEDGER") def full_enabled() -> bool: """FIGLIB_POSE_FULL=1: bearings go through the whole solved camera -- its own lens, pitch and roll, read at the box's foot -- instead of the shared lens along the middle row.""" - return os.environ.get("FIGLIB_POSE_FULL") == "1" + return settings.flag("FIGLIB_POSE_FULL") def path() -> Path: - return Path(os.environ.get("FIGLIB_POSE_LEDGER_PATH") or LEDGER) + return Path(settings.get("FIGLIB_POSE_LEDGER_PATH") or LEDGER) LEGACY_MODEL = "legacy: J2000, uncorrected" diff --git a/src/figlib/provenance.py b/src/figlib/provenance.py index 45c034b..778d6b6 100644 --- a/src/figlib/provenance.py +++ b/src/figlib/provenance.py @@ -9,7 +9,8 @@ 258-byte placeholder with no frames -- so the hash is what pins a result to its input. * **Run log** (`/runs.jsonl`). One line appended per pipeline stage: git commit and a hash of any uncommitted diff, package versions, the model pin, input - manifest hashes, parameters, and the SHA-256 of every output. A result file whose hash + manifest hashes, parameters, every FIGLIB_* setting as resolved (`settings.py`, with the + profile's hash), and the SHA-256 of every output. A result file whose hash is not in the log did not come from a recorded run. The detector weights are pinned here, and `check_model` refuses to run on anything else. @@ -31,6 +32,7 @@ from pathlib import Path from . import corpus as C +from . import settings ROOT = C.ROOT MANIFESTS = C.SHARED_META / "manifests" @@ -77,7 +79,7 @@ def check_model(path: Path | None = None) -> dict: path = path or ROOT / MODEL["file"] actual = sha256_file(path) if actual != MODEL["sha256"]: - if os.environ.get("FIGLIB_ALLOW_UNPINNED_MODEL") != "1": + if not settings.flag("FIGLIB_ALLOW_UNPINNED_MODEL"): raise RuntimeError( f"{rel(path)} has sha256 {actual}, not the pinned {MODEL['sha256']} " f"({MODEL['source']} @ {MODEL['revision'][:8]}). Refetch it per " @@ -97,8 +99,9 @@ def _git(*args: str) -> str | None: def code_state() -> dict: - diff = _git("diff", "HEAD", "--", "src", "tests", "run_detect.sh", "data/fetch.sh") - untracked = _git("ls-files", "--others", "--exclude-standard", "--", "src") + diff = _git("diff", "HEAD", "--", "src", "tests", "configs", "run_detect.sh", + "data/fetch.sh") + untracked = _git("ls-files", "--others", "--exclude-standard", "--", "src", "configs") return { "commit": (_git("rev-parse", "HEAD") or "").strip() or None, "branch": (_git("rev-parse", "--abbrev-ref", "HEAD") or "").strip() or None, @@ -152,6 +155,8 @@ def record(stage: str, outputs, params: dict | None = None, model: dict | None = "finished_utc": utc_now(), "argv": sys.argv, "env": {k: v for k, v in sorted(os.environ.items()) if k.startswith("FIGLIB_")}, + # Every FIGLIB_* setting as the run saw it, defaults included, and the profile's hash. + "settings": settings.resolved(), "code": code_state(), "versions": _versions(), "platform": platform.platform(), diff --git a/src/figlib/recent.py b/src/figlib/recent.py index 6debae9..adc032c 100644 --- a/src/figlib/recent.py +++ b/src/figlib/recent.py @@ -19,7 +19,7 @@ data/hpwren_recent///*.jpg frames (gitignored) data/meta/recent/manifest.json source URL, SHA-256, Last-Modified per frame out/recent/yolo/_.json detections, in detect_yolo's shape - out/recent/geolocation{_fisheye}{_ledger}.json scores + out/recent/geolocation{_fisheye}{_ledger}{_full}.json scores Fires, sequences and ground truth come from the `all` corpus, so no FIgLib result moves. Only the box-center bearings are scored (`center`, `early`), which need no wind lookup. @@ -48,6 +48,7 @@ from zoneinfo import ZoneInfo from . import corpus as C +from . import settings ROOT = Path(__file__).resolve().parents[2] FRAMES = ROOT / "data" / "hpwren_recent" @@ -378,7 +379,7 @@ def score() -> Path: row[tag] = out rows.append(row) - variant = (("_fisheye" if os.environ.get("FIGLIB_LENS") == "fisheye" else "") + variant = (("_fisheye" if settings.get("FIGLIB_LENS") == "fisheye" else "") + ("_ledger" if pose_ledger.enabled() else "") + ("_full" if pose_ledger.enabled() and pose_ledger.full_enabled() else "")) dest = CORPUS.out / f"geolocation{variant}.json" @@ -386,7 +387,7 @@ def score() -> Path: dest.write_text(json.dumps(rows, indent=1) + "\n") P.record("recent-score", [dest], started=started, params={"variants": [v[0] for v in VARIANTS], "use_wind": False, - "lens": os.environ.get("FIGLIB_LENS", "rectilinear"), + "lens": settings.get("FIGLIB_LENS"), "pose_ledger": str(pose_ledger.path()) if pose_ledger.enabled() else None, "pose_full": pose_ledger.enabled() and pose_ledger.full_enabled()}, extra_inputs=[meta / "sequences.json", meta / "fires.json", meta / "resolved.json", diff --git a/src/figlib/settings.py b/src/figlib/settings.py new file mode 100644 index 0000000..3877d95 --- /dev/null +++ b/src/figlib/settings.py @@ -0,0 +1,122 @@ +"""Every FIGLIB_* switch in one place: its default, and where its value came from. + +A value comes from, in order: + +1. the environment (`FIGLIB_LENS=fisheye python -m ...`); +2. a named profile, a committed file `configs/.toml` selected with `FIGLIB_PROFILE`; +3. a module's own default profile, named by that module's `main()` (`default_profile`); +4. the default below. + +Values are read when they are asked for, never written back into `os.environ`, so importing +a module cannot change what another one sees. The run log (`provenance.record`) stores the +whole resolved set, defaults included, with the profile's name and hash. +""" + +from __future__ import annotations + +import hashlib +import os +from pathlib import Path + +import tomllib + +ROOT = Path(__file__).resolve().parents[2] +CONFIGS = ROOT / "configs" + +# name -> (default, what it does). None means unset. +SETTINGS: dict[str, tuple[str | None, str]] = { + "FIGLIB_CORPUS": ("core", "which archives and outputs: core, extra, all, recent"), + "FIGLIB_TIER": (None, "restrict scoring to contamination tiers, comma-separated"), + "FIGLIB_DETS": (None, "alternative detection directory (default: the corpus's own)"), + "FIGLIB_CAMS": (None, "alternative camera table (default: data/meta/cams.json)"), + "FIGLIB_LENS": ("rectilinear", "rectilinear, or fisheye for the star-measured lens"), + "FIGLIB_POSE_LEDGER": ("0", "1: per-camera star azimuths from the pose ledger"), + "FIGLIB_POSE_LEDGER_PATH": (None, "alternative pose ledger (default: data/meta/pose_ledger.json)"), + "FIGLIB_POSE_FULL": ("0", "1: bearings through the whole solved camera (lens, pitch, roll)"), + "FIGLIB_REFINE_KM": ("0.01", "re-find the likelihood peak at this step off the grid; 0 for grid nodes"), + "FIGLIB_VARIANTS": (None, "bearing variants to score, comma-separated (default: the box variants)"), + "FIGLIB_WIND_GATE": ("1", "0: mask fits may lean against the wind"), + "FIGLIB_BAND_PX": ("100", "terrain_range: allowed rows above the terrain line"), + "FIGLIB_ALLOW_UNPINNED_MODEL": ("0", "1: run detection on weights other than the pinned ones"), +} + +_module_default: str | None = None + + +def default_profile(name: str) -> None: + """The profile a module's `main()` runs under when FIGLIB_PROFILE is not set. + + Call it from `main()`, never at import: it applies to the whole process.""" + global _module_default + _module_default = name + + +def _profile() -> tuple[str, str, dict[str, str]] | None: + """(name, source, values) of the active profile, or None.""" + name, source = os.environ.get("FIGLIB_PROFILE") or None, "env" + if name is None and _module_default: + name, source = _module_default, "module default" + if name is None: + return None + return name, source, profile_values(name) + + +def profile_path(name: str) -> Path: + p = CONFIGS / f"{name}.toml" + if not p.exists(): + have = sorted(q.stem for q in CONFIGS.glob("*.toml")) + raise ValueError(f"FIGLIB_PROFILE={name!r}: no {p}; profiles are {have}") + return p + + +def profile_values(name: str) -> dict[str, str]: + raw = tomllib.loads(profile_path(name).read_text()) + bad = sorted(set(raw) - set(SETTINGS)) + if bad: + raise ValueError(f"configs/{name}.toml sets unknown {bad}; known are {sorted(SETTINGS)}") + return {k: ("1" if v is True else "0" if v is False else str(v)) for k, v in raw.items()} + + +def get(name: str) -> str | None: + """The value of one FIGLIB_* setting: environment, then profile, then default.""" + if name not in SETTINGS: + raise KeyError(f"{name} is not a setting; add it to settings.SETTINGS") + v = os.environ.get(name) + if v: + return v + prof = _profile() + if prof and name in prof[2]: + return prof[2][name] + return SETTINGS[name][0] + + +def flag(name: str) -> bool: + return get(name) == "1" + + +def resolved() -> dict: + """Every setting's value and its source, and the profile: what the run log stores.""" + prof = _profile() + values, source = {}, {} + for name, (default, _) in SETTINGS.items(): + values[name] = get(name) + source[name] = ("env" if os.environ.get(name) else + "profile" if prof and name in prof[2] else "default") + out = {"values": values, "source": source, "profile": None} + if prof: + p = profile_path(prof[0]) + out["profile"] = {"name": prof[0], "selected_by": prof[1], "file": str(p.relative_to(ROOT)), + "sha256": hashlib.sha256(p.read_bytes()).hexdigest()} + return out + + +def main() -> None: + r = resolved() + if r["profile"]: + print(f"profile {r['profile']['name']} ({r['profile']['file']}, {r['profile']['sha256'][:12]})") + for name, (default, doc) in SETTINGS.items(): + print(f" {name:28s} {str(r['values'][name]):12s} {r['source'][name]:8s} {doc}") + + +if __name__ == "__main__": + main() diff --git a/src/figlib/stars/fig_geolocation.py b/src/figlib/stars/fig_geolocation.py index c85ff06..d2da41b 100644 --- a/src/figlib/stars/fig_geolocation.py +++ b/src/figlib/stars/fig_geolocation.py @@ -1,8 +1,9 @@ """Geolocation before and after the star calibration, from geolocate.py's own outputs. Before: `geolocation.json` (published azimuth, rectilinear nameplate lens). After: -`geolocation_fisheye_ledger.json` (FIGLIB_LENS=fisheye, FIGLIB_POSE_LEDGER=1). Same -detections and solver, so every difference is the calibration. +`geolocation_fisheye_ledger_full.json` (FIGLIB_PROFILE=calibrated: the star-measured lens and +each camera's whole star solve). Same detections and solver, so every difference is the +calibration. docs/figures/calibration_maps.png bearing rays and estimates for a few fires docs/figures/calibration_bearings.png every confirmed-fire bearing's miss vs frame position @@ -146,7 +147,7 @@ def med(ps, i): def main(argv): - before, after = _load(""), _load("_fisheye_ledger") + before, after = _load(""), _load("_fisheye_ledger_full") cams = geom.load_cams() fires = argv if not fires: diff --git a/src/figlib/terrain_range.py b/src/figlib/terrain_range.py index fcb4770..dbbf31f 100644 --- a/src/figlib/terrain_range.py +++ b/src/figlib/terrain_range.py @@ -26,12 +26,6 @@ from __future__ import annotations -import os - -# The calibrated camera model, as in coverage.py. Set before the geometry reads them. -os.environ.setdefault("FIGLIB_LENS", "fisheye") -os.environ.setdefault("FIGLIB_POSE_LEDGER", "1") - import json import math import sys @@ -39,12 +33,13 @@ import numpy as np from . import corpus as C +from . import settings from . import pose_ledger from . import provenance as P from . import terrain as T from .fig_triangulate import _det_for -from .geolocate import (FRAME_SIZES, META, REFINE_KM, YOLO_DIR, bearings_for_fire, credible_area_km2, - refine, solve) +from .geolocate import (FRAME_SIZES, META, YOLO_DIR, bearings_for_fire, credible_area_km2, + refine, refine_step, solve) from .geom import angdiff_deg, bearing_deg, enu_grid, haversine_km, load_cams, ray_latlon from .stars.fisheye import initial_k, project_fisheye @@ -57,7 +52,7 @@ # The near-side bound: the terrain line's row may lie at most this far below the early box # bottom. 100 px lost no truth the cap kept on 73 validated bearings (validate, 2026-09-14); # narrower bands start dropping truths. -BAND_PX = float(os.environ.get("FIGLIB_BAND_PX", 100)) +BAND_PX = float(settings.get("FIGLIB_BAND_PX")) BAND_GRID = (30, 60, 100, 150, 200, 300) STEP_KM = 0.4 # geolocate.solve's default grid, which the penalty is added on NEAR_SAMPLES = 10 # truth is "contained" within this many samples (0.3 km): the official @@ -329,7 +324,7 @@ def solve_compare(band_px: float | None = BAND_PX, ledger_only: bool = False) -> ll2 = ll + terrain_loglik(lats, lons, terr, band_px) i, j = np.unravel_index(np.argmax(ll2), ll2.shape) alat, alon = float(lats[i]), float(lons[j]) - if REFINE_KM: + if refine_step(): alat, alon = refine(bs, alat, alon, STEP_KM, extra=lambda la, lo: terrain_loglik(la, lo, terr, band_px)) rec["after_km"] = round(haversine_km(alat, alon, t["lat"], t["lon"]), 2) @@ -387,7 +382,7 @@ def before_after(fire_id: str, band_px: float = BAND_PX): ll1 = ll + terrain_loglik(lats, lons, terr, band_px) i, j = np.unravel_index(np.argmax(ll1), ll1.shape) e1lat, e1lon = float(lats[i]), float(lons[j]) - if REFINE_KM: + if refine_step(): e1lat, e1lon = refine(bs, e1lat, e1lon, STEP_KM, extra=lambda la, lo: terrain_loglik(la, lo, terr, band_px)) err1 = haversine_km(e1lat, e1lon, t["lat"], t["lon"]); area1 = credible_area_km2(lats, lons, ll1) @@ -563,6 +558,7 @@ def draw_map(ax, surface, est, err, area, title, with_terrain): if __name__ == "__main__": + settings.default_profile("calibrated") # configs/calibrated.toml unless FIGLIB_PROFILE is set a = sys.argv[1:] if a and a[0] == "validate": validate() diff --git a/tests/test_geolocation.py b/tests/test_geolocation.py index 4b5e2fd..5431811 100644 --- a/tests/test_geolocation.py +++ b/tests/test_geolocation.py @@ -1,9 +1,10 @@ """End-to-end geolocation on one fire's real detections. This is the smoke test: if bearings, the projection, and the likelihood surface all agree, -`20240701_Kitchenfire` lands within a few hundred meters of its official coordinate. The -full corpus run reports 0.08 km here; the assertion leaves generous margin so detector -noise or a grid-resolution change does not make it flaky. +`20240701_Kitchenfire` lands within a few hundred meters of its official coordinate. Under +the calibrated model pinned below it lands 0.33 km out (0.14 km on the published table); the +assertion leaves generous margin so detector noise or a grid-resolution change does not make +it flaky. """ from __future__ import annotations @@ -21,8 +22,8 @@ def calibrated(monkeypatch): """The calibrated camera model the corpus figures use: star-measured fisheye and ledger poses. - Pinned here rather than inherited: coverage, bias, fig_bearing and terrain_range set these - at import, so without the pin the result depended on which test module was collected first. + Pinned here: configs/calibrated.toml is only the default of those modules' `main()`, so an + imported module scores whatever the environment says. """ monkeypatch.setenv("FIGLIB_LENS", "fisheye") monkeypatch.setenv("FIGLIB_POSE_LEDGER", "1") diff --git a/tests/test_settings.py b/tests/test_settings.py new file mode 100644 index 0000000..44bfb75 --- /dev/null +++ b/tests/test_settings.py @@ -0,0 +1,86 @@ +"""The FIGLIB_* settings: environment over profile over default, and nothing set at import. + +Before settings.py, four modules ran `os.environ.setdefault` when imported, so a result +depended on which of them had been imported first (NOTES.md, 2026-09-25). +""" + +from __future__ import annotations + +import importlib +import os + +import pytest + +from src.figlib import settings as S + + +@pytest.fixture(autouse=True) +def clean(monkeypatch): + for k in list(os.environ): + if k.startswith("FIGLIB_"): + monkeypatch.delenv(k) + monkeypatch.setattr(S, "_module_default", None) + + +def test_defaults_with_nothing_set(): + assert S.get("FIGLIB_CORPUS") == "core" + assert S.get("FIGLIB_LENS") == "rectilinear" + assert not S.flag("FIGLIB_POSE_LEDGER") + assert S.get("FIGLIB_DETS") is None + r = S.resolved() + assert r["profile"] is None + assert set(r["values"]) == set(S.SETTINGS) + assert set(r["source"].values()) == {"default"} + + +def test_profile_then_environment(monkeypatch): + monkeypatch.setenv("FIGLIB_PROFILE", "calibrated") + assert S.get("FIGLIB_LENS") == "fisheye" and S.flag("FIGLIB_POSE_LEDGER") + monkeypatch.setenv("FIGLIB_LENS", "rectilinear") + r = S.resolved() + assert r["values"]["FIGLIB_LENS"] == "rectilinear" and r["source"]["FIGLIB_LENS"] == "env" + assert r["source"]["FIGLIB_POSE_LEDGER"] == "profile" + assert r["profile"]["name"] == "calibrated" and r["profile"]["selected_by"] == "env" + assert len(r["profile"]["sha256"]) == 64 + + +def test_module_default_yields_to_named_profile(monkeypatch): + S.default_profile("calibrated") + assert S.get("FIGLIB_LENS") == "fisheye" + assert S.resolved()["profile"]["selected_by"] == "module default" + monkeypatch.setenv("FIGLIB_PROFILE", "recent") + assert S.get("FIGLIB_POSE_LEDGER_PATH") == "out/recent/pose_ledger.json" + + +def test_bad_profiles_fail_loudly(monkeypatch, tmp_path): + monkeypatch.setenv("FIGLIB_PROFILE", "no-such-profile") + with pytest.raises(ValueError, match="no-such-profile"): + S.get("FIGLIB_LENS") + (tmp_path / "typo.toml").write_text('FIGLIB_LENZ = "fisheye"\n') + monkeypatch.setattr(S, "CONFIGS", tmp_path) + monkeypatch.setenv("FIGLIB_PROFILE", "typo") + with pytest.raises(ValueError, match="FIGLIB_LENZ"): + S.get("FIGLIB_LENS") + with pytest.raises(KeyError): + S.get("FIGLIB_LENZ") + + +def test_every_profile_parses(): + for p in S.CONFIGS.glob("*.toml"): + assert S.profile_values(p.stem) + + +@pytest.mark.parametrize("name", ["coverage", "bias", "fig_bearing", "terrain_range"]) +def test_importing_sets_nothing(name): + importlib.reload(importlib.import_module(f"src.figlib.{name}")) + assert not [k for k in os.environ if k.startswith("FIGLIB_")] + assert S.get("FIGLIB_LENS") == "rectilinear" + + +def test_refinement_is_on_unless_set_to_zero(monkeypatch): + from src.figlib.geolocate import refine_step + assert refine_step() == 0.01 + monkeypatch.setenv("FIGLIB_REFINE_KM", "0") + assert refine_step() is None + monkeypatch.setenv("FIGLIB_REFINE_KM", "0.05") + assert refine_step() == 0.05