cobre-io

Case directory loading, validation, and result writing for the Cobre power systems ecosystem.

This crate provides two top-level entry points for all I/O in the Cobre ecosystem. load_case reads a case directory of JSON and Parquet files, executes a five-layer validation pipeline (structural, schema, referential integrity, dimensional consistency, and semantic), and produces a fully-validated System ready for the solver. write_results accepts aggregate result types and writes all output artifacts — Parquet tables, FlatBuffers policy checkpoints, and JSON manifests — to a specified root directory.

When to Use

Depend on cobre-io when you need to load a case directory from disk or write solver outputs to a result directory. If you are building a new subcommand or integration that reads case files and hands a System to an algorithm, this crate is the boundary between the filesystem and cobre-core types. Do not depend on it from pure algorithm crates — pass the System value instead.

Key Types

• load_case — Reads and validates a case directory, returning a System or a LoadError
• write_results — Writes all output artifacts (Parquet, FlatBuffers, JSON) to a result directory
• Config — Deserialized run configuration loaded from config.json in the case directory
• LoadError — Typed error enum covering I/O, parse, schema, and constraint failures
• ValidationContext — Collects all validation diagnostics across all pipeline layers before failing

Validation pipeline

load_case runs five layers in sequence; earlier layers gate later ones (a file missing in Layer 1 is never parsed in Layer 2), and every layer collects all of its diagnostics into a shared ValidationContext before the pipeline decides whether to fail — a ConstraintError reports every problem found in one pass, not just the first.

1. Structural — do the required files exist on disk? Missing required files fail; missing optional files are only noted in the file manifest.
2. Schema — parse every present file; check required fields, types, and value ranges. JSON parsing uses #[serde(deny_unknown_fields)], so both missing fields and unrecognized keys surface as hard errors.
3. Referential integrity — every cross-entity ID reference (bus_id, source_hydro_id, bound/penalty override rows, etc.) must resolve to a known entity.
4. Dimensional consistency — optional per-entity files must cover every entity that needs them (e.g. inflow statistics must exist for every hydro; load seasonal statistics must cover every bus for every stage).
5. Semantic — domain business rules: acyclic hydro cascade, penalty ordering (lower tiers may not exceed upper), PAR model stationarity, stage count consistency, and estimation prerequisites (see below).

After all five layers pass, load_case resolves the three-tier penalty/bound cascade, assembles the scenario models (running the estimation pipeline first when inflow_history.parquet is present without inflow_seasonal_stats.parquet), and calls SystemBuilder::build() to construct the immutable System.

Error handling (LoadError)

LoadError::io(path, source) is the constructor to use instead of a From<std::io::Error> impl — the latter would lose the path context every diagnostic needs.

Config struct (config.json)

Config (src/config/mod.rs) has seven sections, all but training defaulted:

training.forward_passes and training.stopping_rules (must include at least one iteration_limit rule) have no defaults; parse_config returns LoadError::SchemaError if either is absent.

training.stopping_rules accepts four internally-tagged ("type") rule variants — iteration_limit { limit }, time_limit { seconds }, bound_stalling { iterations, tolerance }, and simulation { replications, period, bound_window, distance_tol, bound_tol } — combined via training.stopping_mode: "any" (default, OR) or "all" (AND).

policy.mode is one of PolicyMode::Fresh (default, start from scratch), WarmStart (load existing cuts/states from policy.path), or Resume (continue an interrupted run from the last checkpoint); the latter two trigger the PolicyIncompatible compatibility checks above.

Three-tier penalty/bound resolution

Penalty and bound values follow global → entity → stage precedence. Tiers 1 and 2 (penalties.json and per-entity JSON fields) resolve during Layer-2 parsing, so each entity struct already holds its tier-2 value. The dedicated resolution step (after all validation layers pass) applies the sparse tier-3 constraints/penalty_overrides_*.parquet / constraints/*_bounds.parquet overrides — a Parquet row only needs to exist for stages where the value differs from tier 2 — and expands the result into dense [n_entities × n_stages] ResolvedPenalties / ResolvedBounds arrays on System for O(1), branch-free solver lookup.

Estimation pipeline

When scenarios/inflow_history.parquet is present and scenarios/inflow_seasonal_stats.parquet is absent, load_case derives seasonal statistics and AR coefficients from the historical series instead of reading pre-computed Parquet files. config.estimation controls the fit:

The estimation path additionally requires season_definitions in stages.json (to group observations by season) and at least one history observation per hydro plant; groups below min_observations_per_season produce a ModelQuality warning rather than a hard failure. When explicit stats files are provided instead, inflow_history.parquet (if present) is still loaded and stored on ScenarioData.inflow_history but does not influence model assembly.

Links

Status

Alpha — API is functional but not yet stable.

License

Apache-2.0