meancomb

sofia_redux.toolkit.stats.stats.meancomb(data, variance=None, mask=None, rms=False, axis=None, ignorenans=True, robust=0, returned=True, info=None)

(Robustly) averages arrays along arbitrary axes.

This routine will combine data using either a straight mean or weighted mean. If datavar are not given, then a straight mean, <x>, and square of the standard error of the mean, sigma_mu^2 = sigma^2/N are computed. If datavar are given, then a weighted mean and corresponding variance on the mean are computed.

Parameters:

dataarray_like of (int or float)

(shape1) input data array

maskarray_like of bool, optional

(shape1) An optional mask of the same shape as data identifying pixels to use in the combination (True=good, False=bad).

variancearray_like of float, optional

(shape1) The variances of the data points with the same shape as data. If given a weighted median is performed.

rmsbool, optional

Set to return the RMS error instead of the error on the mean.

axisint, optional

Specifies on which axis the mean operation should be performed.

ignorenansbool, optional

If True, NaNs will be ignored in all calculations

robustfloat, optional

Set to the sigma threshold to throw bad data out. A data point is identified as an outlier if abs(x_i - x_med)/MAD > thresh where x_med is the median and MAD is the median absolute deviation defined as 1.482*median(abs(x_i - x_med)). Set to a non-integer/non-float or 0 to skip outlier rejection.

returnedbool, optional

Flag indicating whether a tuple (mean, meanvar) should be returned as output (True), or just the mean (False). Default is True.

infodict, optional

If supplied will be updated with:

‘mask’ -> output mask as modified by mask, NaNs, and robust

Returns:

mean, [variance_of_mean](tuple of) float or numpy.ndarray

The mean along the specified axis. When returned is True, return a tuple with the average as the first element and the variance of the mean as the second element. The return type numpy.float64. If returned, variance_of_mean is numpy.float64. If keepdims then