class_docstr: Distribution of Ansari-Bradley W-statistic under the null hypothesis.
docstr: Survival function.
docstr_tok: ["Survival","function","."]
code: def sf(self, k, n, m):
      """Survival function."""
      self._recalc(n, m)
      # Null distribution derived without considering ties is
      # approximate. Round down to avoid Type I error.
      ind = np.floor(k - self.astart).astype(int)
      return self.freqs[ind:].sum() / self.total
  
imports: from __future__ import annotations
  import math
  import warnings
  from collections import namedtuple
  import numpy
  from numpy import isscalar, r_, log, around, unique, asarray, zeros, arange, sort, amin, amax, atleast_1d, sqrt, array, compress, pi, exp, ravel, count_nonzero, sin, cos, arctan2, hypot
  from scipy import optimize
  from scipy import special
  from .None import statlib
  from .None import stats
  from .stats import find_repeats, _contains_nan, _normtest_finish
  from .contingency import chi2_contingency
  from .None import distributions
  from ._distn_infrastructure import rv_generic
  from ._hypotests import _get_wilcoxon_distr
label_desc: Use image node_id 5 for calling the _ABW obj's underlying member method code with example usage: obj.sf(k, n, m) and returns: unknown