Resize#

Functions for resizing N-dimensional data using standard spline interpolation, or projection-based antialiasing methods.

High-level helper#

The main entry point is resize(), which selects both the spline degrees and (optional) antialiasing behavior via a single method string.

splineops.resize.resize(data: ndarray[tuple[int, ...], dtype[_ScalarType_co]], *, zoom_factors: float | Sequence[float] | None = None, output: ndarray[tuple[int, ...], dtype[_ScalarType_co]] | dtype | None = None, output_size: Tuple[int, ...] | None = None, method: str = 'cubic') → ndarray[tuple[int, ...], dtype[_ScalarType_co]]#

Resize an N-dimensional array using spline interpolation or an antialiasing projection preset.

This entry point selects both the algorithm and the spline degrees via a single method string, and then delegates to resize_degrees().

Parameters:

  • data (ndarray) – Input array.

  • zoom_factors (float or sequence of float, optional) – Per-axis scale factors. Ignored if output_size is given.

  • output (ndarray or dtype, optional) – If an ndarray is supplied, the result is written in-place into that array and returned. If a dtype is supplied, a new array of that dtype is allocated and returned.

  • output_size (tuple of int, optional) – Desired shape (overrides zoom_factors).

  • method (str) –

    Preset selecting a specific (interp_degree, analy_degree, synthe_degree) triple.

    Interpolation (no anti-aliasing, analy = -1):

    • "fast" – degree 0 (nearest)

    • "linear" – degree 1

    • "quadratic" – degree 2

    • "cubic" – degree 3

    Antialiasing (projection-based, recommended for down-sampling):

    • "linear-antialiasing" – (interp=1, analy=0, synthe=1)

    • "quadratic-antialiasing" – (interp=2, analy=1, synthe=2)

    • "cubic-antialiasing" – (interp=3, analy=1, synthe=3)

Returns:

Resized data: either a new array or the one supplied via output.

Return type:

ndarray

Advanced degrees API#

For full control over the three spline degrees (interpolation, analysis, synthesis), use resize_degrees().

This exposes the underlying Muñoz/Unser projection framework directly

splineops.resize.resize_degrees(data: ndarray[tuple[int, ...], dtype[_ScalarType_co]], *, zoom_factors: float | Sequence[float] | None = None, output: ndarray[tuple[int, ...], dtype[_ScalarType_co]] | dtype | None = None, output_size: Tuple[int, ...] | None = None, interp_degree: int = 3, analy_degree: int = -1, synthe_degree: int | None = None, inversable: bool = False) → ndarray[tuple[int, ...], dtype[_ScalarType_co]]#

Resize an N-dimensional array using explicit spline degrees.

This is the most general entry point: it exposes the three degrees:

  • interp_degree : degree of the interpolation B-spline φ (0..3)

  • analy_degree : analysis spline degree (-1..3, -1 = no projection)

  • synthe_degree : synthesis spline degree (0..3)

Parameters:

  • data (ndarray) – Input array.

  • zoom_factors (float or sequence of float, optional) – Per-axis scale factors. Ignored if output_size is given.

  • output (ndarray or dtype, optional) – If an ndarray is supplied, the result is written in-place into that array and returned. If a dtype is supplied, a new array of that dtype is allocated and returned.

  • output_size (tuple of int, optional) – Desired shape (overrides zoom_factors).

  • interp_degree (int, default 3) – Degree of the interpolation B-spline φ (0..3).

  • analy_degree (int, default -1) –

    Degree of the analysis spline φ₁:

    • -1 → no projection (pure interpolation)

    • 0..3 → projection-based resizing (antialiasing, equal-degree projection, etc.)

  • synthe_degree (int, optional) – Degree of the synthesis spline φ₂ (output space). Defaults to interp_degree. Must be in [0..3] and <= interp_degree.

  • inversable (bool, default False) – If True, use a size policy that ensures invertible zoom along each axis.

Returns:

Resized data: either a new array or the one supplied via output.

Return type:

ndarray

See also#

TensorSpline

The base class used internally for spline interpolation.