beverloo_fit — DRIVE `granular` op

• Data kinds: signal × signal × scalar × scalar → table

• Call: import fullseye as fs; fs.ledger.beverloo_fit(D0, W, d: 'float', bulk_density: 'float', k: 'float' = 1.4, g: 'float' = 9.80665) -> 'dict' (to call the implementation directly, import granular; granular.beverloo_fit(D0, W, d: 'float', bulk_density: 'float', k: 'float' = 1.4, g: 'float' = 9.80665) -> 'dict'; from the registry, opsdrive.get("beverloo_fit"))

Usage

> This operator's description has not been translated yet. The original text follows as it is.

排出率の列 `W_i(D0_i) に log W = log(C ρ_b √g) + n log(D₀ − k d) を最小二乗で当て、指数 n`

と係数 `C を返す(真値 n = 2.5、C ≈ 0.58)。返り: n, C, rms_log`(対数残差)。

Raises `ValueError: 点が 3 未満 / D0 ≤ k d` の点がある / W ≤ 0。

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_granular_heap_repose — py -3.11 examples/poc_granular_heap_repose.py

Ops the type connects to (they accept `table` as input)

course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same

Same category (`granular`)

heap_volume_cone · heap_mass · heap_volume_heightmap · beverloo_rate · discharge_synth · dispense_mass_from_video · hopper_discharge_rate · spoon_tilt_critical

*Provenance: granular.py — DRIVE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

beverloo_fit — DRIVE granular op