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kernels.pclip_combine_1d

kernels.pclip_combine_1d(
    values,
    *,
    mask=None,
    combine,
    frac=-0.5,
    sigma=3.0,
    nkeep=1,
    validate=True,
)

IRAF-style percentile clipping at each pixel (1-D output-only variant).

Parameters

values : (ndarray, shape(N))

One-dimensional value vector. Accepted dtypes are uint8, uint16, int16, int32, float32, and float64. Integer inputs are promoted to the package’s floating workspace when validate is True.

mask : ndarray of bool = None

Input mask; True means already masked. Must have shape (N,).

combine : {'mean', 'median'}

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

frac : float = -0.5

IRAF pclip value. If abs(frac) < 1, it is converted to an integer rank offset using half of the input image count. Positive values estimate sigma from the high side of the sorted median; negative values estimate sigma from the low side.

sigma : float or tuple of float = 3.0

User-supplied lower and upper clipping multipliers applied to the pclip-estimated spread. This maps to IRAF lsigma and hsigma.

nkeep : int = 1

Minimum number of unmasked samples to retain after pclip rejection.

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 : scalar

Mean or median of the surviving values after applying the input mask, finite-value filtering, and this rejection algorithm.

Notes

Sort the finite, unmasked samples and let m be their median. frac selects an offset rank r below (negative) or above (positive) the median; it is not a percentage of samples to reject. For abs(frac) < 1, scale by max(N // 2, 1) for the original image count N and truncate to an integer, with a minimum offset magnitude of one. Estimate::

d = sign(frac) * (x_sorted[r] - m)

Reject sorted endpoints while (m-x)/d >= sigma_lower or (x-m)/d >= sigma_upper, using the two sigma multipliers. Unlike sigma/CCD clipping, equality is rejected. The center and spread are computed once. If needed, restore the least-extreme rejected endpoints to satisfy nkeep, including IRAF’s residual-tie handling. With fewer than three usable samples, at most nkeep usable samples, or d == 0, no additional samples are rejected. In diagnostic calls, std is None and low/upp report retained extrema. The tutorial specifies the full rank convention. For derivations and worked examples, see the Rejection Methods tutorial.