kernels.sigclip_combine

kernels.sigclip_combine(
    arr,
    *,
    mask=None,
    combine,
    sigma=(3.0, 3.0),
    maxiters=5,
    ddof=0,
    nkeep=1,
    maxrej=None,
    cenfunc='median',
    clip_cen=None,
    stdfunc='std',
    revert_on_nkeep=True,
    validate=True,
)

Sigma-clipping rejection (output-only variant).

Parameters

arr : (ndarray, shape(N, *spatial))

Image stack. Accepted dtypes are uint8, uint16, int16, int32, float32, and float64. Integer inputs are promoted to the package’s floating workspace when validate is True. Inputs with more than 3 dimensions are flattened internally; output shapes match the trailing spatial dimensions of the input.

mask : ndarray of bool = None

Input mask; True means already masked. Must have the same shape as arr before any internal flattening.

combine : str

Output combine method evaluated after rejection. Fused combine kernels support "mean", "average", "avg", "median", and "med".

sigma : float or tuple of float = (3.0, 3.0)

User-supplied clipping multiplier, not the measured data spread itself. Values are clipped when their residual from the clipping center exceeds sigma * spread. A scalar applies the same multiplier to both tails; a 2-tuple (sigma_lower, sigma_upper) clips asymmetrically. Thresholds must be finite and non-negative.

maxiters : int = 5

Maximum number of clipping iterations per pixel.

ddof : int = 0

Delta degrees of freedom for the per-pixel spread estimator.

nkeep : int = 1

Minimum number of unmasked values to preserve at each pixel. An iteration that would drop below this count is reverted in full. The default, 1, prevents clipping from rejecting every finite sample in a per-output stack. Set 0 only when all samples may be rejected.

maxrej : int or None = None

Maximum number of values that may be rejected at each pixel. None means no limit.

cenfunc : ('median', 'lmedian', 'mean') = "median"

Center estimator used each iteration. lmedian accepts the alias lmed and uses the lower of the two middle samples for even valid counts.

clip_cen : ('median', 'lmedian', 'mean') = "median"

Center used when computing the spread for iterative rejection. None uses the same center as cenfunc; lmedian also accepts lmed.

revert_on_nkeep : bool = True

If True, an iteration that would leave fewer than nkeep usable samples at a pixel is reverted in full for that pixel. Input masks, threshold masks, and non-finite samples remain excluded. The default is True. If False, clipping is strict and may leave fewer than nkeep samples.

stdfunc : ('std', 'mad') = "std"

Spread estimator used by sigma clipping. "std" uses the standard deviation. "mad" uses 1.4826 * median(abs(x - clip_cen)) as a robust sigma estimate; when ddof > 0, this estimate is multiplied by sqrt(nvalid / (nvalid - ddof)) and pixels with nvalid <= ddof return NaN spread diagnostics.

validate : bool = True

If True, check dimensionality and normalize dtype/contiguity before entering the Rust kernel. If False, callers must provide inputs that satisfy the compiled kernel assumptions.

Returns

combined : (ndarray, shape(*spatial))

Per-output mean or median after applying the input mask, finite-value filtering, and this rejection algorithm.