curryer.correction.results¶

Structured result models for the correction pipeline.

Provides CorrectionResult and ParameterSetResult, returned by run_correction().

Attributes¶

logger

Classes¶

`ParameterSetResult`	Results for a single parameter set in a correction sweep.
`CorrectionResult`	Structured result from `run_correction()`.

Functions¶

build_correction_result(→ CorrectionResult)

Build a CorrectionResult from raw loop() outputs.

Module Contents¶

curryer.correction.results.logger¶

class curryer.correction.results.ParameterSetResult(/, **data: Any)¶

Bases: pydantic.BaseModel

Results for a single parameter set in a correction sweep.

index¶

Zero-based index of this parameter set in the sweep.

Type:: int

parameter_values¶

Parameter name → sampled value for this set.

Type:: dict[str, float]

mean_rms_m¶

Mean RMS geolocation error across all GCP pairs (meters).

Type:: float

best_pair_rms_m¶

RMS error of the best-performing GCP pair (meters).

Type:: float

worst_pair_rms_m¶

RMS error of the worst-performing GCP pair (meters).

Type:: float

index: int¶

parameter_values: dict[str, float]¶

mean_rms_m: float¶

best_pair_rms_m: float¶

worst_pair_rms_m: float¶

class curryer.correction.results.CorrectionResult(/, **data: Any)¶

Bases: pydantic.BaseModel

Structured result from run_correction().

Provides the instrument engineer with a clear answer: what is the best parameter set, does it meet requirements, and what should they do next.

Serialisation¶

Most fields are JSON-serialisable via model_dump() / model_dump_json(). The results and netcdf_data fields are excluded from serialisation by default because they contain non-JSON-serialisable types (xr.Dataset, numpy arrays). Access them directly on the object when raw data is needed:

result = run_correction(setup, sweep, inputs, work_dir)
result.best_parameter_set   # dict[str, float] — use this
result.results              # raw per-iteration dicts — advanced use only
result.netcdf_data          # raw numpy arrays — advanced use only

best_parameter_set¶

Parameter values that produced the lowest aggregate RMS.

Type:: dict[str, float]

best_rms_m¶

Best aggregate RMS achieved across all parameter sets (meters).

Type:: float

best_index¶

Index of the best parameter set (for cross-referencing with NetCDF output).

Type:: int

worst_rms_m¶

Worst aggregate RMS across all parameter sets (meters).

Type:: float

mean_rms_m¶

Mean of all aggregate RMS values (meters).

Type:: float

n_parameter_sets¶

Number of parameter sets tested in the sweep.

Type:: int

n_gcp_pairs¶

Number of GCP pairs used.

Type:: int

all_parameter_sets¶

All tested parameter sets sorted by mean RMS (ascending).

Type:: list[ParameterSetResult]

met_threshold¶

Whether the best parameter set met the mission performance requirements.

Type:: bool

recommendation¶

Human-readable next-step guidance for the instrument engineer.

Type:: str

summary_table¶

Human-readable ASCII table of the top results.

Type:: str

netcdf_path¶

Path to the saved NetCDF output file.

Type:: Path or None

config_snapshot¶

Key config values used, for reproducibility records.

Type:: dict

elapsed_time_s¶

Total wall-clock processing time in seconds.

Type:: float

timestamp¶

UTC time when the run completed.

Type:: datetime

results¶

Raw per-iteration result dicts from loop(). Excluded from JSON serialisation.

Type:: list

netcdf_data¶

Raw NetCDF numpy arrays from loop(). Excluded from JSON serialisation.

Type:: dict

model_config¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

best_parameter_set: dict[str, float]¶

best_rms_m: float¶

best_index: int¶

worst_rms_m: float¶

mean_rms_m: float¶

n_parameter_sets: int¶

n_gcp_pairs: int¶

all_parameter_sets: list[ParameterSetResult]¶

met_threshold: bool¶

recommendation: str¶

summary_table: str¶

netcdf_path: pathlib.Path | None = None¶

config_snapshot: dict = None¶

elapsed_time_s: float = 0.0¶

timestamp: datetime.datetime = None¶

results: list = None¶

netcdf_data: dict = None¶

curryer.correction.results.build_correction_result(setup: curryer.correction.config.GeolocationSetup, sweep: curryer.correction.config.Sweep, netcdf_config: curryer.correction.config.NetCDFConfig, results: list, netcdf_data: dict, netcdf_path: pathlib.Path | None, elapsed_time_s: float) → CorrectionResult¶

Build a CorrectionResult from raw loop() outputs.

Parameters:

setup (GeolocationSetup) – The durable mission setup used for the run (requirements/thresholds).
sweep (Sweep) – The parameter-variation experiment (parameters, search strategy, seed).
netcdf_config (NetCDFConfig) – Resolved NetCDF metadata config (for parameter variable names).
results (list) – Per-iteration result dicts from loop().
netcdf_data (dict) – Raw NetCDF data dict from loop().
netcdf_path (Path or None) – Path to the saved NetCDF file, if any.
elapsed_time_s (float) – Total wall-clock time of the run in seconds.

Return type:

CorrectionResult