Skip to main content
`Ctrl`+`K`
scipy.stats.

# sigmaclip

scipy.stats.sigmaclip(a, low=4.0, high=4.0, *, nan_policy='propagate')

  Perform iterative sigma-clipping of array elements.

  Starting from the full sample, all elements outside the critical range are removed, i.e. all elements of the input array c that satisfy either of the following conditions:

      c < mean(c) - std(c)*low
      c > mean(c) + std(c)*high

  The iteration continues with the updated sample until no elements are outside the (updated) range.

  Parameters:
    a array_like

      Data array, will be raveled if not 1-D.

    low float, optional

      Lower bound factor of sigma clipping. Default is 4.

    high float, optional

      Upper bound factor of sigma clipping. Default is 4.

    nan_policy{‘propagate’, ‘omit’, ‘raise’}

      Defines how to handle input NaNs.

      - `propagate`: if a NaN is present in the input, the clipped array will be empty, and the upper and lower thresholds will be NaN.

      - `omit`: NaNs will be omitted when performing the calculation.

      - `raise`: if a NaN is present, a `ValueError` will be raised.

  Returns:
    clipped ndarray

      Input array with clipped elements removed.

    lower float

      Lower threshold value use for clipping.

    upper float

      Upper threshold value use for clipping.

  Notes

  This function iteratively removes observations. Once observations are removed, they are not re-added in subsequent iterations. Consequently, although it is often the case that `clipped` is identical to `a[(a >= lower) & (a <= upper)]`, this property is not guaranteed to be satisfied; `clipped` may have fewer elements.

  Array API Standard Support

  `sigmaclip` has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable `SCIPY_ARRAY_API=1` and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

  Library | CPU | GPU
  --- | --- | ---
  NumPy | ✅ | n/a
  CuPy | n/a | ✅
  PyTorch | ✅ | ✅
  JAX | ⚠️ no JIT | ⚠️ no JIT
  Dask | ⛔ | n/a

  See Support for the array API standard for more information.

  Examples

      >>> import numpy as np
      >>> from scipy.stats import sigmaclip
      >>> a = np.concatenate((np.linspace(9.5, 10.5, 31),
      ...                     np.linspace(0, 20, 5)))
      >>> fact = 1.5
      >>> c, low, upp = sigmaclip(a, fact, fact)
      >>> c
      array([  9.96666667,  10.        ,  10.03333333,  10.        ])
      >>> c.var(), c.std()
      (0.00055555555555555165, 0.023570226039551501)
      >>> low, c.mean() - fact*c.std(), c.min()
      (9.9646446609406727, 9.9646446609406727, 9.9666666666666668)
      >>> upp, c.mean() + fact*c.std(), c.max()
      (10.035355339059327, 10.035355339059327, 10.033333333333333)

      >>> a = np.concatenate((np.linspace(9.5, 10.5, 11),
      ...                     np.linspace(-100, -50, 3)))
      >>> c, low, upp = sigmaclip(a, 1.8, 1.8)
      >>> (c == np.linspace(9.5, 10.5, 11)).all()
      True