design_space

design_space ¤

Multivariate design-space qualification and monitoring.

In process development the design space is the multidimensional region of input parameters that has been demonstrated -- through DOE or process characterisation runs -- to deliver acceptable output quality. ICH Q8 formalises the concept for pharma; it is equally useful in any process industry where multiple critical process parameters (CPPs) interact.

This detector fits a design space to qualification data and emits events when commercial-operation data exits the qualified region. Two fitting modes are supported: per-axis quantile boxes (cheap, conservative) and a scipy convex hull (tight, captures correlation between factors).

DesignSpaceEvents ¤

DesignSpaceEvents(
    dataframe: DataFrame,
    cpp_uuids: list[str],
    *,
    event_uuid: str = "dev:design_space",
    value_column: str = "value_double",
    time_column: str = "systime"
)

Bases: Base

Fit and monitor a multivariate qualified operating window.

Workflow::

ds = DesignSpaceEvents(qualification_df, cpp_uuids=[...])
ds.fit_box()                    # or ds.fit_hull()
excursions = ds.detect_excursions(operation_df)
near = ds.boundary_proximity(operation_df, warn_margin=0.1)

fit_box ¤

fit_box(
    quantiles: tuple[float, float] = (0.05, 0.95)
) -> DesignSpaceEvents

Fit per-axis bounds from the qualification data.

Parameters:

Name	Type	Description	Default
`quantiles`	`tuple[float, float]`	`(low, high)` quantile pair used to set each axis's bounds. `(0.0, 1.0)` reduces to absolute min/max.	`(0.05, 0.95)`

Returns:

Type	Description
`DesignSpaceEvents`	`self` to allow `DesignSpaceEvents(...).fit_box()` chaining.

fit_hull ¤

fit_hull() -> DesignSpaceEvents

Fit a convex hull around the qualification data.

Requires :mod:scipy.spatial.ConvexHull. The hull captures correlation between CPPs that a per-axis box cannot, at the cost of needing len(cpps) + 1 non-degenerate qualification points.

Returns:

Type	Description
`DesignSpaceEvents`	`self` for chaining.

detect_excursions ¤

detect_excursions(operation_df: DataFrame) -> pd.DataFrame

Find contiguous intervals where operation leaves the design space.

Parameters:

Name	Type	Description	Default
`operation_df`	`DataFrame`	Long-form signal DataFrame containing all CPP signals; only the configured `cpp_uuids` are used.	required

Returns:

Type	Description
`DataFrame`	Interval-shape DataFrame with columns: `start`, `end`,
`DataFrame`	`duration_seconds`, `uuid`, `excursion_mode`
`DataFrame`	(`"box"` / `"hull"`).

boundary_proximity ¤

boundary_proximity(
    operation_df: DataFrame, warn_margin: float = 0.1
) -> pd.DataFrame

Emit point events for samples within warn_margin of the boundary.

Only supported for the fit_box mode -- a convex hull's boundary distance is more expensive to compute per facet and is not in scope for this detector. Call :meth:detect_excursions for hull-fitted spaces.

Parameters:

Name	Type	Description	Default
`operation_df`	`DataFrame`	Long-form signal DataFrame.	required
`warn_margin`	`float`	Normalised distance threshold (fraction of the axis span). A sample is reported when its closest axis margin is below this value while still inside the box.	`0.1`

Returns:

Type	Description
`DataFrame`	Point-shape DataFrame: `systime`, `uuid`,
`DataFrame`	`signed_margin`, `closest_axis`.