Validator of algorithm results¶

This module validates the results returned by an algorithm.

class encore.validators.algorithm_results.AnalysisOutput(*, enabled: bool = False, success: bool, results: ResultsBlock, answer: Dict[str, Any], engine_time: float = 0.0, algorithm_time: float = 0.0, update_params: Dict[str, Any] | None = {}, **extra_data: Any)¶

algorithm_time: float¶

answer: Dict[str, Any]¶

enabled: bool¶

engine_time: float¶

model_config: ClassVar[ConfigDict] = {'extra': 'allow'}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

results: ResultsBlock¶

success: bool¶

update_params: Dict[str, Any] | None¶

validate_shapes_with_context(info)¶

Validate result matrix shapes using contextual neuron and timepoint information.

Checks that results.neus_in_ens and results.timecourse match the expected shapes derived from the number of ensembles and the validation context (neurons and timepoints).

class encore.validators.algorithm_results.ResultsBlock(*, ensembles_cant: int, timecourse: ndarray, neus_in_ens: ndarray)¶

ensembles_cant: int¶

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

neus_in_ens: ndarray¶

timecourse: ndarray¶

classmethod validate_binary_matrix(v: ndarray)¶

Validate that the input is a 2D binary NumPy array.

Ensures the array is a NumPy ndarray, has exactly two dimensions, and contains only binary values (0 or 1).

encore.validators.algorithm_results.validate_analysis_output(output: dict, neurons: int, timepoints: int) → AnalysisOutput¶

Validate and parse an analysis output dictionary.

Uses contextual information about neuron and timepoint counts to perform shape validation and returns a fully validated AnalysisOutput model.

Parameters:

output (dict) – Raw analysis output dictionary.
neurons (int) – Expected number of neurons.
timepoints (int) – Expected number of timepoints.

Raises:

RuntimeError – If validation fails.

Returns:

Validated analysis output model.

Return type:

AnalysisOutput