grabowski
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Phase 2a of docs/RUST_REWRITE.md. rust/forecast (ping-forecast) ports the serving half of src/ml: the HII mirror gap fill, the hourly grid, every feature (pandas' Kahan rolling sums included), the Mae Ngat dam frame, HistGradientBoosting tree inference, the heuristic fallback, stale-threshold handling and the P.1 flood-stage probabilities. Hand-over from Python (src/ml/export.py): - models/flood_<station>.json.gz next to each joblib bundle; train_all() writes it, retrain.sh promotes it, `python -m src.ml.export` exports existing ones. - rust/forecast/flood_config.json: thresholds, stages, leads and notify constants, compiled in. tests/test_model_export.py fails while it differs from the Python source. Parity (rust/forecast/tests/parity.rs, fixtures from testdata/gen_forecast_fixtures.py on the production code path): four scenarios incl. the 2024 flood with RID lagging behind HII. Merged readings, all features and all levels identical; with all 15 local models 377/384 probabilities bit-identical, the rest within 2 ulp (exp). Python refactor without behaviour change: predict.forecasts_from_long, and get_latest_forecasts/load_latest/serving_frame take now/rain for reproducible comparisons; the HII query orders its rows.
28 lines
858 B
Rust
28 lines
858 B
Rust
//! Catchment rain series (`src/ml/rain.py`: `catchment_mean`).
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use chrono::NaiveDateTime;
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use std::collections::BTreeMap;
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/// Hourly catchment-average precipitation (mm); NaN = no value.
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pub type RainSeries = BTreeMap<NaiveDateTime, f64>;
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/// pandas `mean(axis=1)` over the catchment points: the points that have a
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/// value, summed in column order, divided by their count.
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pub fn catchment_mean(points: &[Option<f64>]) -> Option<f64> {
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let mut sum = 0.0;
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let mut n = 0usize;
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for v in points.iter().flatten() {
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sum += *v;
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n += 1;
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}
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(n > 0).then(|| sum / n as f64)
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}
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/// The series on the given hours (`Series.reindex`), NaN where absent.
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pub fn reindex(rain: &RainSeries, times: &[NaiveDateTime]) -> Vec<f64> {
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times
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.iter()
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.map(|t| rain.get(t).copied().unwrap_or(f64::NAN))
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.collect()
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}
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