focal.focal_mean

focal.focal_mean(a: Array, *, window: int | tuple[int, ...] | list[int] | ndarray[tuple[int, ...], bool] | Window, fraction_accepted: float = 0.7, verbose: bool = False, reduce: bool = False, chunks: int | tuple[int, int] | None = None, error: Literal['bootstrap', 'parametric'] | None = None, bootstrap_config: BootstrapConfig | None = None, out: MeanResult | None = None) → MeanResult

Focal mean

Parameters:

• a (Array) – Input array to compute the focal mean on. Must be two-dimensional.

• window (int, array-like, or Window) –

  Window applied over the input array. It can be:

  • An integer (interpreted as a square window),

  • A sequence of integers (interpreted as a rectangular window),

  • A boolean array,

  • Or a pyspatialstats.windows.Window object.

• fraction_accepted (float, optional) –

  Fraction of valid (non-NaN) cells per window required for computation.

  • 0: all views are used if at least 1 value is present

  • 1: only fully valid views are used

  • Between 0 and 1: minimum fraction of valid cells required

  Default is 0.7.

• verbose (bool, optional) – If True, print progress message with timing. Default is False.

• reduce (bool, optional) – If True, uses each pixel exactly once without overlapping windows. The resulting array shape is a_shape / window_shape. Default is False.

• chunks (int or tuple of int, optional) – Shape of chunks to split the array into. If None, the array is not split into chunks, which is the default.

• error ({'parametric', 'bootstrap'}, optional) – Error type to compute. If None, no error is computed, which is the default.

• bootstrap_config (BootstrapConfig, optional) – Bootstrap configuration object.

• out (MeanResult, optional) – MeanResult object to update in-place

Returns:

Dataclass containing the focal mean array and (optionally) uncertainty measures.

Return type:

MeanResult