gpf.pheno.prepare package

Submodules

gpf.pheno.prepare.measure_classifier module

class gpf.pheno.prepare.measure_classifier.Convertible(*values)

Bases: Enum

nan = 0

non_numeric = 2

numeric = 1

class gpf.pheno.prepare.measure_classifier.InferenceReport(*, value_type: type, histogram_type: type[NullHistogram | CategoricalHistogram | NumberHistogram], min_individuals: int, count_total: int, count_with_values: int, count_without_values: int, count_unique_values: int, min_value: float | int | None, max_value: float | int | None, values_domain: str)

Bases: BaseModel

Inference results report.

count_total: int

count_unique_values: int

count_with_values: int

count_without_values: int

histogram_type: type[NullHistogram | CategoricalHistogram | NumberHistogram]

max_value: float | int | None

min_individuals: int

min_value: float | int | None

model_config = {}

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

value_type: type

values_domain: str

gpf.pheno.prepare.measure_classifier.convert_to_float(value: int | float | str | None) → float | None

gpf.pheno.prepare.measure_classifier.convert_to_int(value: int | float | str | None) → int | None

gpf.pheno.prepare.measure_classifier.convert_to_numeric(val: Any) → float | float64

Convert passed value to float.

gpf.pheno.prepare.measure_classifier.convert_to_string(val: Any) → str | None

Convert passed value to string.

gpf.pheno.prepare.measure_classifier.determine_histogram_type(report: InferenceReport, config: InferenceConfig) → type[NullHistogram | CategoricalHistogram | NumberHistogram]

Given an inference report and a configuration, return histogram type.

gpf.pheno.prepare.measure_classifier.force_type_inference(values: list[str | None], config: InferenceConfig) → tuple[list[float | None] | list[int | None] | list[str | None], InferenceReport]

Perform type inference when a type is forced.

gpf.pheno.prepare.measure_classifier.inference_reference_impl(values: list[str | None], config: InferenceConfig) → tuple[list[float | None] | list[int | None] | list[str | None], InferenceReport]

Infer value and histogram type for a list of values.

gpf.pheno.prepare.measure_classifier.is_convertible_to_numeric(val: Any) → Convertible

Check if the passed string is convertible to number.

gpf.pheno.prepare.measure_classifier.is_nan(val: Any) → bool

Check if the passed value is a NaN.

Module contents