11.6.5. astroML.stats.median_sigmaG¶
- astroML.stats.median_sigmaG(a, axis=None, overwrite_input=False, keepdims=False)¶
Compute median and rank-based estimate of the standard deviation
Parameters : a : array_like
Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.
axis : int, optional
Axis along which the means are computed. The default is to compute the mean of the flattened array.
overwrite_input : bool, optional
If True, then allow use of memory of input array a for calculations. The input array will be modified by the call to median. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default is False. Note that, if overwrite_input is True and the input is not already an array, an error will be raised.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original arr.
Returns : median : ndarray, see dtype parameter above
array containing the median values
sigmaG : ndarray, see dtype parameter above.
array containing the robust estimator of the standard deviation
See also
- mean_sigma
- non-robust version of this calculation
- sigmaG
- robust rank-based estimate of standard deviation
Notes
This routine uses a single call to np.percentile to find the quartiles along the given axis, and uses these to compute the median and sigmaG:
median = q50 sigmaG = (q75 - q25) * 0.7413
where 0.7413 ~ 1 / (2 sqrt(2) erf^-1(0.5))