sift_client.util.test_results.bounds ¶

FUNCTION	DESCRIPTION
`all_within_bounds`	Return True when every element of `arr` is within `bounds`.
`assign_value_to_measurement`	Resolve value type from a given value and assign it to a measurement.
`evaluate_measurement_bounds`	Update a measurement with the resolved bounds type and result of evaluating the given value against those bounds.
`out_of_bounds_mask`	Return a boolean mask selecting elements of `arr` that violate `bounds`.
`to_numpy_array`	Normalize a list / ndarray / pandas Series into a numpy array.
`value_passes_bounds`	Evaluate a value against bounds without recording a measurement.

all_within_bounds ¶

all_within_bounds(
    arr: NDArray[float64],
    bounds: dict[str, float] | NumericBounds,
) -> bool

Return True when every element of arr is within bounds.

assign_value_to_measurement ¶

assign_value_to_measurement(
    measurement: TestMeasurement
    | TestMeasurementCreate
    | TestMeasurementUpdate,
    value: float | str | bool,
) -> None

Resolve value type from a given value and assign it to a measurement.

PARAMETER	DESCRIPTION
`measurement`	The measurement to assign the value to. TYPE: `TestMeasurement \| TestMeasurementCreate \| TestMeasurementUpdate`
`value`	The value to resolve and assign to the measurement. TYPE: `float \| str \| bool`

evaluate_measurement_bounds ¶

evaluate_measurement_bounds(
    measurement: TestMeasurement
    | TestMeasurementCreate
    | TestMeasurementUpdate,
    value: float | str | bool,
    bounds: dict[str, float]
    | NumericBounds
    | str
    | bool
    | None,
) -> bool

Update a measurement with the resolved bounds type and result of evaluating the given value against those bounds.

PARAMETER	DESCRIPTION
`measurement`	The measurement to update. TYPE: `TestMeasurement \| TestMeasurementCreate \| TestMeasurementUpdate`
`value`	The value to evaluate the bounds of. TYPE: `float \| str \| bool`
`bounds`	The bounds to evaluate the value against. Either a dictionary with "min" and "max" keys, a NumericBounds object, a string, a boolean, or None. TYPE: `dict[str, float] \| NumericBounds \| str \| bool \| None`

RETURNS	DESCRIPTION
`bool`	True if the value is within the bounds, False otherwise.

out_of_bounds_mask ¶

out_of_bounds_mask(
    arr: NDArray[float64],
    bounds: dict[str, float] | NumericBounds,
) -> NDArray[bool_]

Return a boolean mask selecting elements of arr that violate bounds.

Raises ValueError when bounds has neither min nor max set.

to_numpy_array ¶

to_numpy_array(
    values: list[float | int] | NDArray[float64] | Series,
) -> NDArray[float64]

Normalize a list / ndarray / pandas Series into a numpy array.

Shared by measure_avg and measure_all in both the real and stub step implementations so the accepted input types stay in sync.

value_passes_bounds ¶

value_passes_bounds(
    value: float | str | bool,
    bounds: dict[str, float]
    | NumericBounds
    | str
    | bool
    | None,
) -> bool

Evaluate a value against bounds without recording a measurement.

Used by consumers that need pass/fail semantics matching the real plugin but do not transmit a measurement (e.g. --sift-disabled mode in the pytest plugin).