Single-value RCR¶

When you have a 1-D dataset and want to recover the underlying central value (mu) and width (sigma) in the presence of outliers, single-value RCR is what you want.

The four rejection techniques¶

import rcrpy

# Pick one based on your data:
rcrpy.RejectionTech.LS_MODE_68    # Symmetric uncontaminated, one-sided contaminants
rcrpy.RejectionTech.LS_MODE_DL    # Mixed one-sided + two-sided contaminants
rcrpy.RejectionTech.SS_MEDIAN_DL  # Symmetric uncontaminated, two-sided contaminants
rcrpy.RejectionTech.ES_MODE_DL    # Mildly asymmetric / very small N

See section 3 of Maples et al. 2018 for a decision tree.

Unweighted, iterative rejection¶

import numpy as np
import rcrpy

# Some data: clean Gaussian + heavy one-sided contamination
rng = np.random.default_rng(42)
y = np.concatenate([
    rng.normal(0, 1, size=150),
    np.abs(rng.normal(0, 10, size=850)),
])

r = rcrpy.RCR(rcrpy.RejectionTech.LS_MODE_68)
r.perform_rejection(y.tolist())

print(f"mu       = {r.result.mu:.3f}")
print(f"sigma    = {r.result.sigma:.3f}")
print(f"stDev    = {r.result.st_dev:.3f}")
print(f"kept     = {int(r.result.flags.sum())} / {len(y)}")

Bulk rejection (faster on large N)¶

The bulk variant rejects many points in each pass instead of one at a time — substantially faster, with the same final result for well-behaved data:

r = rcrpy.RCR(rcrpy.RejectionTech.LS_MODE_68)
r.perform_bulk_rejection(y.tolist())
# Same r.result fields, plus rejectedY / originalY / stDevTotal which
# the bulk path populates via the C++'s setFinalVectors equivalent.

Weighted rejection¶

Pass per-point weights:

weights = np.ones(y.size)
weights[some_indices] = 0.5   # downweight some points

r = rcrpy.RCR(rcrpy.RejectionTech.LS_MODE_68)
r.perform_rejection(y.tolist(), w=weights.tolist())

Results¶

All four invocations populate r.result, which is an RCRResults dataclass with:

Field	What
`mu`	Recovered central value (mean / median / mode)
`sigma`	Recovered robust 68.3-percentile deviation
`st_dev`	Recovered standard deviation
`st_dev_below`, `st_dev_above`	Asymmetric width (when applicable)
`sigma_below`, `sigma_above`	Asymmetric robust width (ES_MODE_DL)
`st_dev_total`	Combined width (populated by `perform_bulk_rejection`)
`flags`	bool array, `True` for kept points
`indices`	int indices of kept points
`clean_y`, `clean_w`	Kept y / w
`rejected_y`, `rejected_w`, `original_y`, `original_w`	populated by bulk path

Performance¶

rcrpy runs single-value RCR at roughly 10× the C++ implementation’s runtime on large datasets (N=1000). Reproduce with ../benchmarks/diagnostics.py.